From zaheduzzaman.sarker@ericsson.com Sun Nov 3 08:23:58 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id F3CC711E817C for ; Sun, 3 Nov 2013 08:23:57 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.598 X-Spam-Level: X-Spam-Status: No, score=-2.598 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, HTML_MESSAGE=0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id oewUETxiguaY for ; Sun, 3 Nov 2013 08:23:53 -0800 (PST) Received: from sesbmg20.ericsson.net (sesbmg20.ericsson.net [193.180.251.56]) by ietfa.amsl.com (Postfix) with ESMTP id 864D211E8162 for ; Sun, 3 Nov 2013 08:23:51 -0800 (PST) X-AuditID: c1b4fb38-b7f2c8e000006d25-ea-527678969165 Received: from ESESSHC018.ericsson.se (Unknown_Domain [153.88.253.124]) by sesbmg20.ericsson.net (Symantec Mail Security) with SMTP id 44.E3.27941.69876725; Sun, 3 Nov 2013 17:23:50 +0100 (CET) Received: from ESESSMB307.ericsson.se ([169.254.7.88]) by ESESSHC018.ericsson.se ([153.88.183.72]) with mapi id 14.02.0328.009; Sun, 3 Nov 2013 17:23:49 +0100 From: Zaheduzzaman Sarker To: "Xiaoqing Zhu (xiaoqzhu)" , "Mo Zanaty (mzanaty)" Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFA= Date: Sun, 3 Nov 2013 16:23:48 +0000 Message-ID: References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [153.88.183.150] Content-Type: multipart/alternative; boundary="_000_E0F7A68B07B53F4FBD12DABD61CBA90E17DC1BESESSMB307ericsso_" MIME-Version: 1.0 X-Brightmail-Tracker: H4sIAAAAAAAAA+NgFmphkeLIzCtJLcpLzFFi42KZGfG3RndaRVmQwaa/QhbLX55gtPhxdier xYsHc5gsVt/8wGaxt8nf4nXLMiYHNo8pvzeyeky7f5/NY+esu+weS5b8ZPJYvfohcwBrFJdN SmpOZllqkb5dAlfG0iPzGAv2lVbc2v6ItYGxL7WLkZNDQsBEYue1TUwQtpjEhXvr2boYuTiE BI4wStxoucwGkhASWMQocXGjTRcjBwebgI3E48V+IKaIQLxE/5xikHJmgV5GidVdT9hA4sIC SRIvHwqAdIoIJEucfLOAHaLcSmL3xHwQk0VARWLfOWGQCl4Bb4llG2YxQyztZ5Q4cbyBHSTB KeArca31F9hljECXfT+1BsxmFhCXuPVkPtTFAhJL9pxnhrBFJV4+/scKYStJrNh+iRGiPl9i wtteVohlghInZz5hmcAoOgvJqFlIymYhKYOI60gs2P2JDcLWlli28DUzjH3mwGMmZPEFjOyr GDmKU4uTctONDDYxAmPy4JbfFjsYL/+1OcQozcGiJM778a1zkJBAemJJanZqakFqUXxRaU5q 8SFGJg5OqQZG6V4n2Rz9fScVE1fKShs2s0jEat+USL83VVf8G7OLp9P5+RnFmcGnjGZP0alf yRSQudIiu8q4ZOM1veJj+zdZKb+a1HpGlOlmmknS/Oi3xlrnt319cYdH1ulZD6vtfanJefcm vk73vNu17D3XyjscNY/2PJ957n3JpcsihzMux9ulq4rdfuqsxFKckWioxVxUnAgAaaYe+JcC AAA= Cc: rmcat WG , Colin Perkins , Michael Welzl , Varun Singh Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 03 Nov 2013 16:23:58 -0000 --_000_E0F7A68B07B53F4FBD12DABD61CBA90E17DC1BESESSMB307ericsso_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi Xiaoging, Clarification question. >>a) delayed response to a rate change request (e.g., > 0.5s) Where is this delay coming from? Fixed delay or delay based of some assumpt= ion lack of resource to process the request? Are you thinking of particular= device, implementation? BR Zahed From: rmcat-bounces@ietf.org [mailto:rmcat-bounces@ietf.org] On Behalf Of X= iaoqing Zhu (xiaoqzhu) Sent: den 28 oktober 2013 13:15 To: Mo Zanaty (mzanaty) Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouve= r (03 Nov, 1300-1500) Agree with Colin and Mo. For a statistical video traffic model, I would propose to break down the 2n= d point (encoder rate control) in Mo's list further, to three sub-component= s: a) delayed response to a rate change request (e.g., > 0.5s) b) deviations from the requested rate (maybe as randomly fluctuating around= the target?) c) the different frame types or coding modes inside the encoder will yield = some encoded frames much larger than others. This will lead to burstiness on time scale = of frame intervals (typically 33ms), and later smoothed out by the pacing/shaper. Also, if we go for the approach of using uncompressed test video sequence f= or testing the combination of video codec and congestion control, we should= also recommend a reference codec and default configurations. So that focus= of the evaluations remains on the congestion control module. What do folk= s think of x264 (http://www.videolan.org/developers/x264.html)? Best, Xiaoqing On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) > wrote: I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small set of fixed bitrates like DASH/HLS. A more formal approach to the problem would be to decompose the factors contributing to the final rate placed onto the network. 1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the encoded frame size using constant quantization, i.e. no encoder rate control. 2. Video encoder rate control behavior, which modulates the source statistics to fit into network constraints over rather large time intervals (>0.5s). 3. Network transmission pacing / shaper, which smooths out bursts in encoder output to fit into network constraints over fine time intervals (<0.5s). It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors). Mo On 10/28/13, 1:49 PM, Colin Perkins > wrote: On 27 Oct 2013, at 11:50, Michael Welzl > wrote: On 27. okt. 2013, at 12:33, Varun Singh > wrote: Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that the network can always follow it - Do you mean preparing the same video sequence at multiple rates and the congestion controller picking the appropriate media rate that fits its estimate? ie rate switch between different media rates of the same sequence? I think that could be a very good idea! I'd be more interested in a standard set of *uncompressed* video test sequences, that we can use to test the combination of video codec and congestion control. If we're building something for interactive video congestion control we should assume live encoding, with the codec potentially having the flexibility to react to the network, and our testbed ought to reflect that. -- Colin Perkins http://csperkins.org/ --_000_E0F7A68B07B53F4FBD12DABD61CBA90E17DC1BESESSMB307ericsso_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi Xiaoging,

 <= /p>

Clarification question.

 <= /p>

>>a) delayed response to a rate change request= (e.g., > 0.5s)

 <= /p>

 <= /p>

Where is this delay comin= g from? Fixed delay or delay based of some assumption lack of resource to p= rocess the request? Are you thinking of particular device, implementation?

 <= /p>

BR

Zahed

 <= /p>

From: rmcat-bo= unces@ietf.org [mailto:rmcat-bounces@ietf.org] On Behalf Of Xiaoqing Zhu (xiaoqzhu)
Sent: den 28 oktober 2013 13:15
To: Mo Zanaty (mzanaty)
Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh
Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in V= ancouver (03 Nov, 1300-1500)

 

Agree with Colin and Mo. 

 

For a statistical video traffic model, I would propo= se to break down the 2nd point (encoder rate control) in Mo's list further,= to three sub-components:

 

a) delayed response to a rate change request (e.g., = > 0.5s)

b) deviations from the requested rate (maybe as rand= omly fluctuating around the target?) 

c) the different frame types or coding modes inside = the encoder will yield some encoded

frames much larger than others. This will lead to bu= rstiness on time scale of frame intervals

(typically 33ms), and later smoothed out by the paci= ng/shaper. 

 

Also, if we go for the approach of using uncompresse= d test video sequence for testing the combination of video codec and c= ongestion control, we should also recommend a reference codec and defa= ult configurations. So that focus of the evaluations remains on the congestion control module.  What do folks think of x26= 4 (http://www.vide= olan.org/developers/x264.html)? 

 

Best,

Xiaoqing

 

 

 

On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <= ;mzanaty@cisco.com> wrote:=



I agree with Colin. We should be focused on live enc= oding where the
encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small
set of fixed bitrates like DASH/HLS.

A more formal approach to the problem would be to decompose the factors
contributing to the final rate placed onto the network.
1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the
encoded frame size using constant quantization, i.e. no encoder rate
control.
2. Video encoder rate control behavior, which modulates the source
statistics to fit into network constraints over rather large time
intervals (>0.5s).
3. Network transmission pacing / shaper, which smooths out bursts in
encoder output to fit into network constraints over fine time intervals
(<0.5s).

It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any
real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors).

Mo



On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:

On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio.no> wrote:

On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@gmail.com> wrote:

Now, next, what if we'd have a set of such sequences= , and then stack
them over a codec that produces quite arbitrary bitrates, assuming that
the network can always follow it -


Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?


I think that could be a very good idea!


I'd be more interested in a standard set of *uncompressed* video test
sequences, that we can use to test the combination of video codec and
congestion control. If we're building something for interactive video
congestion control we should assume live encoding, with the codec
potentially having the flexibility to react to the network, and our
testbed ought to reflect that.

--
Colin Perkins
http://csperkins.org/



 

--_000_E0F7A68B07B53F4FBD12DABD61CBA90E17DC1BESESSMB307ericsso_-- From harald@alvestrand.no Sun Nov 3 09:38:50 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 3E15421E80AC for ; Sun, 3 Nov 2013 09:38:47 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -110.598 X-Spam-Level: X-Spam-Status: No, score=-110.598 tagged_above=-999 required=5 tests=[AWL=-0.000, BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8, USER_IN_WHITELIST=-100] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id Ez1HM56ww81T for ; Sun, 3 Nov 2013 09:38:32 -0800 (PST) Received: from eikenes.alvestrand.no (eikenes.alvestrand.no [158.38.152.233]) by ietfa.amsl.com (Postfix) with ESMTP id 2724D11E82C6 for ; Sun, 3 Nov 2013 09:37:36 -0800 (PST) Received: from localhost (localhost [127.0.0.1]) by eikenes.alvestrand.no (Postfix) with ESMTP id 534A639E176 for ; Sun, 3 Nov 2013 18:36:37 +0100 (CET) X-Virus-Scanned: Debian amavisd-new at eikenes.alvestrand.no Received: from eikenes.alvestrand.no ([127.0.0.1]) by localhost (eikenes.alvestrand.no [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id m9geleXdly9N for ; Sun, 3 Nov 2013 18:36:33 +0100 (CET) Received: from [172.19.244.5] (unknown [216.239.45.130]) by eikenes.alvestrand.no (Postfix) with ESMTPSA id DCAF839E091 for ; Sun, 3 Nov 2013 18:36:31 +0100 (CET) Message-ID: <5276899C.9090503@alvestrand.no> Date: Sun, 03 Nov 2013 18:36:28 +0100 From: Harald Alvestrand User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:24.0) Gecko/20100101 Thunderbird/24.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <3879D71E758A7E4AA99A35DD8D41D3D91E124D97@xmb-rcd-x14.cisco.com> <8372589D-DFAE-4560-BE06-E0D21DAFF47C@ifi.uio.no> <73EAAE7B-3DF8-49EB-9359-4643F9CA655B@csperkins.org> <20A1EBF7-6D36-4E1B-A5C7-6E692B189933@ifi.uio.no> In-Reply-To: <20A1EBF7-6D36-4E1B-A5C7-6E692B189933@ifi.uio.no> X-Enigmail-Version: 1.5.2 Content-Type: multipart/alternative; boundary="------------010305030003080209030204" Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 03 Nov 2013 17:38:51 -0000 This is a multi-part message in MIME format. --------------010305030003080209030204 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: 8bit On 10/29/2013 11:32 AM, Michael Welzl wrote: > Okay, > > I'm not convinced, but I will not argue the point any longer. I raised > my concerns but maybe I'm wrong, no problem. I think we need to do both.... Arguing by analogy (always dangerous): in another debate, evaluating the performance of a codec was likened to evaluating the performance of a car; by taking out the engine and mounting it on a bench, you get very precise information about how much force the engine is putting out, what its power/consumption curves are and so on. But if the question is "how well does the car drive in a race?", there's no substitute for mounting the engine in the car and driving a few laps around the track. I agree with Michael that when we model congestion, we have to make the offered load fit some simple model. Otherwise, we have no idea whether a simple tweak to the congestion control's constants was actually beneficial, but happened to randomly interact with some property of the load. And I agree with Colin that in order to evaluate the whole system, we have to run the whole system. One should be done, and the other not be neglected. Expensive, but necessary! > > Cheers, > Michael > > > On 29. okt. 2013, at 10:31, Colin Perkins wrote: > >> Michael, >> >> Your concern is why I think we need to test the codec and the >> congestion control together. The dynamics of how, and importantly >> when, the codec can change its rate will be critical to the dynamics >> of the congestion control. This is especially true if we want to keep >> the latency low by avoiding sender-side buffering to smooth the codec >> output to match the desired sending rate. Testing congestion control >> without a codec will give us something that doesn't respect the QoE >> concerns, and runs the risk of producing an algorithm that is safe >> and responsive, but not useful for interactive video. >> >> Colin >> >> >> On 29 Oct 2013, at 08:30, Michael Welzl > > wrote: >>> So - looking at this thread, it seems that consensus is against me :-) >>> >>> Hence I'd like to clarify - my concern was that we're making the >>> system too complex by involving a codec that reacts to the >>> congestion controller **when evaluating congestion control >>> mechanisms**. I mean, once I've done a test, I'd like to know why I >>> see the effects that I see, and which mechanism is to blame for >>> them. This will get harder with this setup. >>> >>> Let me give an example to explain my concern: >>> >>> Say, we create a test with a reactive codec over a congestion >>> controller, and in some network scenarios, we see oscillations: the >>> congestion controller increased its rate, allowing the codec to >>> switch to a higher encoding rate; this higher rate was too much, and >>> so quite fast the congestion controller reduces its rate again, >>> hence the codec reacts and switches to a lower rate, and so forth. >>> >>> Now, if that is the test setup for evaluating a congestion >>> controller, a conclusion from such a test could be that the >>> congestion controller has to be stabilized. I would, however, argue >>> that a congestion controller should be as quick as it possibly can >>> at seeking out available bandwidth, and hence such fluctuations are >>> natural (*), and it's rather the job of the codec to dampen its rate >>> switching. >>> >>> Either way, in this example test, we face a bad result, and two >>> screws to tune to fix the problem - one on the side of the >>> congestion control, and one on the side of the codec. >>> >>> This is why I would prefer to evaluate congestion controls (again, >>> not the whole system, to me that's a different evaluation, and of >>> course a better congestion control should lead to a better total >>> system) with a static traffic profile. >>> >>> Is my concern unfounded? Is it a non-problem I'm envisioning? (if >>> so, see below for 2 more concerns :-) ) >>> >>> Cheers, >>> Michael >>> >>> PS: Concern #2: we can expect a static traffic profile feeding into >>> a congestion controller to be relatively quickly done, but creating >>> a test setup with a reactive codec is going to be much harder and >>> take more time. Will we ever see it? When? Will it be easy to use? >>> PPS: Concern #3: the parameter space for tests might expand quite >>> significantly when we involve reactive codecs... this will make any >>> evaluation harder, making the results harder to compare, resulting >>> in more time necessary to come to a conclusion about which >>> controller does a better job. >>> >>> >>> (*) TFRC is an argument to the contrary, but I haven't seen a single >>> evaluation result that shows that its smooth rate is indeed >>> beneficial. On the contrary, we now seem to have some agreement that >>> TFRC isn't the best mechanism. >>> >>> >>> On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote: >>> >>>> Agreed. The base layer has no flexibility just like a flat >>>> non-scalable stream. >>>> >>>> >>>> On 10/28/13, 7:45 PM, Bernard Aboba >>> > wrote: >>>> >>>> You're right that there is flexibility in enhancement layers. But >>>> because of the layering structure, the base is not flexible - since >>>> the enhancement layers depend on it, it is typically protected via >>>> FEC or RTX. This affects the viability of "slow start" schemes as >>>> well other aspects - no point sending video at all if you cannot >>>> support the base layer, at least. >>>> >>>> On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mzanaty)" >>>> > wrote: >>>> >>>>> This is true for simulcast layers, but not necessarily scalable >>>>> layers. For the latter, arbitrary rates can be achieved within a >>>>> limited range (like down to half the bitrate) by selectively >>>>> dropping some packets of the highest enhancement layer with >>>>> minimal visual quality impact. If the stream is highly scalable >>>>> (many layers, which is rare), the limited range expands (like down >>>>> to maybe a third, quarter, or less of the bitrate). If the stream >>>>> is coarsely scalable (e.g. two layers, which is common), dropping >>>>> below the limited range incurs changing encoding parameters, at >>>>> which point arbitrary rates are again possible. >>>>> >>>>> Mo >>>>> >>>>> >>>>> On 10/28/13, 5:58 PM, Bernard Aboba >>>> > wrote: >>>>> >>>>> Colin said: >>>>> >>>>> “Do you mean preparing the same video sequence at multiple rates >>>>> and the >>>>> congestion controller picking the appropriate media rate that fits its >>>>> estimate? ie rate switch between different media rates of the same >>>>> sequence? >>>>> ” >>>>> >>>>> [BA] I like this approach, because it somewhat approximates the >>>>> situation of simulcast/layered coding. Previous work has >>>>> indicated a number of considerations that arise in those >>>>> implementations (such as the need for rapid rampup, so as to be >>>>> able to send at least one video stream as well as issues with >>>>> TFRFC) that would be likely to be missed in a model in which an >>>>> encoder has fully smooth rate control. >>>>> >>>>> >>>>> >>>>> I agree with Colin. We should be focused on live encoding where the >>>>> encoder rate control is highly granular (often fully smooth) and >>>>> coupled >>>>> to the network congestion control, not pre-encoded streaming of a >>>>> small >>>>> set of fixed bitrates like DASH/HLS. >>>>> >>>>> A more formal approach to the problem would be to decompose the >>>>> factors >>>>> contributing to the final rate placed onto the network. >>>>> 1. Video content / source statistics, using a large set of >>>>> representative >>>>> sequences. An estimate of the entropy per frame could simply be the >>>>> encoded frame size using constant quantization, i.e. no encoder rate >>>>> control. >>>>> 2. Video encoder rate control behavior, which modulates the source >>>>> statistics to fit into network constraints over rather large time >>>>> intervals (>0.5s). >>>>> 3. Network transmission pacing / shaper, which smooths out bursts in >>>>> encoder output to fit into network constraints over fine time >>>>> intervals >>>>> (<0.5s). >>>>> >>>>> It should be possible to model all three in an abstract way to >>>>> arrive at >>>>> some statistical model that can then be used for all tests without any >>>>> real video content or encoding (to avoid introducing noise in >>>>> comparisons >>>>> if there was any variability in any of the factors). >>>>> >>>>> Mo >>>>> >>>>> >>>>> >>>>> On 10/28/13, 1:49 PM, Colin Perkins >>>> > wrote: >>>>> >>>>> On 27 Oct 2013, at 11:50, Michael Welzl >>>> > wrote: >>>>> >>>>> On 27. okt. 2013, at 12:33, Varun Singh >>>> > wrote: >>>>> >>>>>> Now, next, what if we'd have a set of such sequences, and then stack >>>>>> them over a codec that produces quite arbitrary bitrates, >>>>>> assuming that >>>>>> the network can always follow it - >>>>> >>>>> Do you mean preparing the same video sequence at multiple rates >>>>> and the >>>>> congestion controller picking the appropriate media rate that fits its >>>>> estimate? ie rate switch between different media rates of the same >>>>> sequence? >>>>> >>>>> I think that could be a very good idea! >>>>> >>>>> >>>>> I'd be more interested in a standard set of *uncompressed* video test >>>>> sequences, that we can use to test the combination of video codec and >>>>> congestion control. If we're building something for interactive video >>>>> congestion control we should assume live encoding, with the codec >>>>> potentially having the flexibility to react to the network, and our >>>>> testbed ought to reflect that. >>>>> >>>>> -- >>>>> Colin Perkins >>>>> http://csperkins.org/ >>>>> >> > -- Surveillance is pervasive. Go Dark. --------------010305030003080209030204 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: 8bit
On 10/29/2013 11:32 AM, Michael Welzl wrote:
Okay,

I'm not convinced, but I will not argue the point any longer. I raised my concerns but maybe I'm wrong, no problem.

I think we need to do both....

Arguing by analogy (always dangerous):

in another debate, evaluating the performance of a codec was likened to evaluating the performance of a car; by taking out the engine and mounting it on a bench, you get very precise information about how much force the engine is putting out, what its power/consumption curves are and so on.

But if the question is "how well does the car drive in a race?", there's no substitute for mounting the engine in the car and driving a few laps around the track.

I agree with Michael that when we model congestion, we have to make the offered load fit some simple model. Otherwise, we have no idea whether a simple tweak to the congestion control's constants was actually beneficial, but happened to randomly interact with some property of the load.

And I agree with Colin that in order to evaluate the whole system, we have to run the whole system.

One should be done, and the other not be neglected.
Expensive, but necessary!



Cheers,
Michael

 
On 29. okt. 2013, at 10:31, Colin Perkins wrote:

Michael,

Your concern is why I think we need to test the codec and the congestion control together. The dynamics of how, and importantly when, the codec can change its rate will be critical to the dynamics of the congestion control. This is especially true if we want to keep the latency low by avoiding sender-side buffering to smooth the codec output to match the desired sending rate. Testing congestion control without a codec will give us something that doesn't respect the QoE concerns, and runs the risk of producing an algorithm that is safe and responsive, but not useful for interactive video.

Colin


On 29 Oct 2013, at 08:30, Michael Welzl <michawe@ifi.uio.no> wrote:
So - looking at this thread, it seems that consensus is against me  :-)

Hence I'd like to clarify - my concern was that we're making the system too complex by involving a codec that reacts to the congestion controller **when evaluating congestion control mechanisms**. I mean, once I've done a test, I'd like to know why I see the effects that I see, and which mechanism is to blame for them. This will get harder with this setup.

Let me give an example to explain my concern:

Say, we create a test with a reactive codec over a congestion controller, and in some network scenarios, we see oscillations: the congestion controller increased its rate, allowing the codec to switch to a higher encoding rate; this higher rate was too much, and so quite fast the congestion controller reduces its rate again, hence the codec reacts and switches to a lower rate, and so forth.

Now, if that is the test setup for evaluating a congestion controller, a conclusion from such a test could be that the congestion controller has to be stabilized. I would, however, argue that a congestion controller should be as quick as it possibly can at seeking out available bandwidth, and hence such fluctuations are natural (*), and it's rather the job of the codec to dampen its rate switching.

Either way, in this example test, we face a bad result, and two screws to tune to fix the problem - one on the side of the congestion control, and one on the side of the codec.

This is why I would prefer to evaluate congestion controls (again, not the whole system, to me that's a different evaluation, and of course a better congestion control should lead to a better total system) with a static traffic profile.

Is my concern unfounded? Is it a non-problem I'm envisioning?   (if so, see below for 2 more concerns  :-)   )

Cheers,
Michael

PS:  Concern #2: we can expect a static traffic profile feeding into a congestion controller to be relatively quickly done, but creating a test setup with a reactive codec is going to be much harder and take more time. Will we ever see it? When? Will it be easy to use?
PPS: Concern #3: the parameter space for tests might expand quite significantly when we involve reactive codecs... this will make any evaluation harder, making the results harder to compare, resulting in more time necessary to come to a conclusion about which controller does a better job.


(*) TFRC is an argument to the contrary, but I haven't seen a single evaluation result that shows that its smooth rate is indeed beneficial. On the contrary, we now seem to have some agreement that TFRC isn't the best mechanism.


On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote:

Agreed. The base layer has no flexibility just like a flat non-scalable stream.


On 10/28/13, 7:45 PM, Bernard Aboba <bernard_aboba@hotmail.com> wrote:

You're right that there is flexibility in enhancement layers. But because of the layering structure, the base is not flexible - since the enhancement layers depend on it, it is typically protected via FEC or RTX. This affects the viability of "slow start" schemes as well other aspects - no point sending video at all if you cannot support the base layer, at least.

On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mzanaty)" <mzanaty@cisco.com> wrote:

This is true for simulcast layers, but not necessarily scalable layers. For the latter, arbitrary rates can be achieved within a limited range (like down to half the bitrate) by selectively dropping some packets of the highest enhancement layer with minimal visual quality impact. If the stream is highly scalable (many layers, which is rare), the limited range expands (like down to maybe a third, quarter, or less of the bitrate). If the stream is coarsely scalable (e.g. two layers, which is common), dropping below the limited range incurs changing encoding parameters, at which point arbitrary rates are again possible.

Mo


On 10/28/13, 5:58 PM, Bernard Aboba <bernard_aboba@hotmail.com> wrote:

Colin said:
 
“Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?
”
 
[BA] I like this approach, because it somewhat approximates the situation of simulcast/layered coding.  Previous work has indicated a number of considerations that arise in those implementations (such as the need for rapid rampup, so as to be able to send at least one video stream as well as issues with TFRFC) that would be likely to be missed in a model in which an encoder has fully smooth rate control.   

 

I agree with Colin. We should be focused on live encoding where the
encoder rate control is highly granular (often fully smooth) and coupled
to the network congestion control, not pre-encoded streaming of a small
set of fixed bitrates like DASH/HLS.

A more formal approach to the problem would be to decompose the factors
contributing to the final rate placed onto the network.
1. Video content / source statistics, using a large set of representative
sequences. An estimate of the entropy per frame could simply be the
encoded frame size using constant quantization, i.e. no encoder rate
control.
2. Video encoder rate control behavior, which modulates the source
statistics to fit into network constraints over rather large time
intervals (>0.5s).
3. Network transmission pacing / shaper, which smooths out bursts in
encoder output to fit into network constraints over fine time intervals
(<0.5s).

It should be possible to model all three in an abstract way to arrive at
some statistical model that can then be used for all tests without any
real video content or encoding (to avoid introducing noise in comparisons
if there was any variability in any of the factors).

Mo



On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:

On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio.no> wrote:

On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@gmail.com> wrote:

Now, next, what if we'd have a set of such sequences, and then stack
them over a codec that produces quite arbitrary bitrates, assuming that
the network can always follow it -

Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?

I think that could be a very good idea!


I'd be more interested in a standard set of *uncompressed* video test
sequences, that we can use to test the combination of video codec and
congestion control. If we're building something for interactive video
congestion control we should assume live encoding, with the codec
potentially having the flexibility to react to the network, and our
testbed ought to reflect that.

--
Colin Perkins
http://csperkins.org/





-- 
Surveillance is pervasive. Go Dark.
--------------010305030003080209030204-- From dave.taht@gmail.com Sun Nov 3 12:45:28 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 71ADA11E8169 for ; Sun, 3 Nov 2013 12:45:28 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.362 X-Spam-Level: X-Spam-Status: No, score=-2.362 tagged_above=-999 required=5 tests=[AWL=0.237, BAYES_00=-2.599, HTML_MESSAGE=0.001, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 5LmW2EqxCf5j for ; Sun, 3 Nov 2013 12:45:26 -0800 (PST) Received: from mail-wi0-x232.google.com (mail-wi0-x232.google.com [IPv6:2a00:1450:400c:c05::232]) by ietfa.amsl.com (Postfix) with ESMTP id 7E7A021E80AF for ; Sun, 3 Nov 2013 12:45:24 -0800 (PST) Received: by mail-wi0-f178.google.com with SMTP id hn9so2987007wib.5 for ; Sun, 03 Nov 2013 12:45:23 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :cc:content-type; bh=k+b0FBAnsXeOIz9A+SpD2QSu+E5K6RKAQpbku4EEBPI=; b=FY8m4ZxT3Qcykt0TzZSZ4cl0FVtzavfrlMeak3AiANkS4mxlA02ZtRcGNRxYLzaezX qEvZ2EiAbqrawl80oOj7LozF162jfZU35NYZT8kahHHJgb2Lon/npY4Aw8LxsW09vD+d cOIieqpNJfH85bSdy/Inm6zh4POKcu2YbODYJD1ivkHme+9dAuRXFa5MjjMoTGECWW6E HfhgnHldroiIyrgvVH6TN59u/gpGjiQVDk6Ey64FOTKvVJ7vPRQ+JZ3b8ZO6BMYlbhaO /BHt3ZK7af43XIIzcG3XgVWX1p3SQdC1fyGq1HHlvXpSotgguWH4Wz/nYyN1Hozdelig jY0g== MIME-Version: 1.0 X-Received: by 10.180.37.67 with SMTP id w3mr9870542wij.56.1383511523614; Sun, 03 Nov 2013 12:45:23 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sun, 3 Nov 2013 12:45:23 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sun, 3 Nov 2013 12:45:23 -0800 (PST) In-Reply-To: <5276899C.9090503@alvestrand.no> References: <3879D71E758A7E4AA99A35DD8D41D3D91E124D97@xmb-rcd-x14.cisco.com> <8372589D-DFAE-4560-BE06-E0D21DAFF47C@ifi.uio.no> <73EAAE7B-3DF8-49EB-9359-4643F9CA655B@csperkins.org> <20A1EBF7-6D36-4E1B-A5C7-6E692B189933@ifi.uio.no> <5276899C.9090503@alvestrand.no> Date: Sun, 3 Nov 2013 12:45:23 -0800 Message-ID: From: Dave Taht To: Harald Tveit Alvestrand Content-Type: multipart/alternative; boundary=e89a8f502cfae8684504ea4be03f Cc: rmcat@ietf.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 03 Nov 2013 20:45:28 -0000 --e89a8f502cfae8684504ea4be03f Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable On Nov 3, 2013 9:38 AM, "Harald Alvestrand" wrote: > > On 10/29/2013 11:32 AM, Michael Welzl wrote: >> >> Okay, >> >> I'm not convinced, but I will not argue the point any longer. I raised my concerns but maybe I'm wrong, no problem. > > > I think we need to do both.... > > Arguing by analogy (always dangerous): > > in another debate, evaluating the performance of a codec was likened to evaluating the performance of a car; by taking out the engine and mounting it on a bench, you get very precise information about how much force the engine is putting out, what its power/consumption curves are and so on. > > But if the question is "how well does the car drive in a race?", there's no substitute for mounting the engine in the car and driving a few laps around the track. > > I agree with Michael that when we model congestion, we have to make the offered load fit some simple model. Otherwise, we have no idea whether a simple tweak to the congestion control's constants was actually beneficial, but happened to randomly interact with some property of the load. > > And I agree with Colin that in order to evaluate the whole system, we have to run the whole system. > > One should be done, and the other not be neglected. > Expensive, but necessary! +100! I am hauling enough gear along to be able to evaluate the main aqm technologies we're fiddling with against some webrtc loads... I tend to argue that experiments tend to uncover variables you didn't even know existed. > > > >> >> Cheers, >> Michael >> >> >> On 29. okt. 2013, at 10:31, Colin Perkins wrote: >> >>> Michael, >>> >>> Your concern is why I think we need to test the codec and the congestion control together. The dynamics of how, and importantly when, the codec can change its rate will be critical to the dynamics of the congestion control. This is especially true if we want to keep the latency low by avoiding sender-side buffering to smooth the codec output to match the desired sending rate. Testing congestion control without a codec will give us something that doesn't respect the QoE concerns, and runs the risk of producing an algorithm that is safe and responsive, but not useful for interactive video. >>> >>> Colin >>> >>> >>> On 29 Oct 2013, at 08:30, Michael Welzl wrote: >>>> >>>> So - looking at this thread, it seems that consensus is against me :-= ) >>>> >>>> Hence I'd like to clarify - my concern was that we're making the system too complex by involving a codec that reacts to the congestion controller **when evaluating congestion control mechanisms**. I mean, once I've done a test, I'd like to know why I see the effects that I see, and which mechanism is to blame for them. This will get harder with this setup. >>>> >>>> Let me give an example to explain my concern: >>>> >>>> Say, we create a test with a reactive codec over a congestion controller, and in some network scenarios, we see oscillations: the congestion controller increased its rate, allowing the codec to switch to a higher encoding rate; this higher rate was too much, and so quite fast the congestion controller reduces its rate again, hence the codec reacts and switches to a lower rate, and so forth. >>>> >>>> Now, if that is the test setup for evaluating a congestion controller, a conclusion from such a test could be that the congestion controller has to be stabilized. I would, however, argue that a congestion controller should be as quick as it possibly can at seeking out available bandwidth, and hence such fluctuations are natural (*), and it's rather the job of the codec to dampen its rate switching. >>>> >>>> Either way, in this example test, we face a bad result, and two screws to tune to fix the problem - one on the side of the congestion control, and one on the side of the codec. >>>> >>>> This is why I would prefer to evaluate congestion controls (again, not the whole system, to me that's a different evaluation, and of course a better congestion control should lead to a better total system) with a static traffic profile. >>>> >>>> Is my concern unfounded? Is it a non-problem I'm envisioning? (if so, see below for 2 more concerns :-) ) >>>> >>>> Cheers, >>>> Michael >>>> >>>> PS: Concern #2: we can expect a static traffic profile feeding into a congestion controller to be relatively quickly done, but creating a test setup with a reactive codec is going to be much harder and take more time. Will we ever see it? When? Will it be easy to use? >>>> PPS: Concern #3: the parameter space for tests might expand quite significantly when we involve reactive codecs... this will make any evaluation harder, making the results harder to compare, resulting in more time necessary to come to a conclusion about which controller does a better job. >>>> >>>> >>>> (*) TFRC is an argument to the contrary, but I haven't seen a single evaluation result that shows that its smooth rate is indeed beneficial. On the contrary, we now seem to have some agreement that TFRC isn't the best mechanism. >>>> >>>> >>>> On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote: >>>> >>>>> Agreed. The base layer has no flexibility just like a flat non-scalable stream. >>>>> >>>>> >>>>> On 10/28/13, 7:45 PM, Bernard Aboba wrote= : >>>>> >>>>> You're right that there is flexibility in enhancement layers. But because of the layering structure, the base is not flexible - since the enhancement layers depend on it, it is typically protected via FEC or RTX. This affects the viability of "slow start" schemes as well other aspects - no point sending video at all if you cannot support the base layer, at least. >>>>> >>>>> On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mzanaty)" wrote: >>>>> >>>>>> This is true for simulcast layers, but not necessarily scalable layers. For the latter, arbitrary rates can be achieved within a limited range (like down to half the bitrate) by selectively dropping some packets of the highest enhancement layer with minimal visual quality impact. If the stream is highly scalable (many layers, which is rare), the limited range expands (like down to maybe a third, quarter, or less of the bitrate). If the stream is coarsely scalable (e.g. two layers, which is common), dropping below the limited range incurs changing encoding parameters, at which point arbitrary rates are again possible. >>>>>> >>>>>> Mo >>>>>> >>>>>> >>>>>> On 10/28/13, 5:58 PM, Bernard Aboba wrote: >>>>>> >>>>>> Colin said: >>>>>> >>>>>> =93Do you mean preparing the same video sequence at multiple rates a= nd the >>>>>> congestion controller picking the appropriate media rate that fits its >>>>>> estimate? ie rate switch between different media rates of the same >>>>>> sequence? >>>>>> =94 >>>>>> >>>>>> [BA] I like this approach, because it somewhat approximates the situation of simulcast/layered coding. Previous work has indicated a number of considerations that arise in those implementations (such as the need for rapid rampup, so as to be able to send at least one video stream as well as issues with TFRFC) that would be likely to be missed in a model in which an encoder has fully smooth rate control. >>>>>> >>>>>> >>>>>> >>>>>> I agree with Colin. We should be focused on live encoding where the >>>>>> encoder rate control is highly granular (often fully smooth) and coupled >>>>>> to the network congestion control, not pre-encoded streaming of a small >>>>>> set of fixed bitrates like DASH/HLS. >>>>>> >>>>>> A more formal approach to the problem would be to decompose the factors >>>>>> contributing to the final rate placed onto the network. >>>>>> 1. Video content / source statistics, using a large set of representative >>>>>> sequences. An estimate of the entropy per frame could simply be the >>>>>> encoded frame size using constant quantization, i.e. no encoder rate >>>>>> control. >>>>>> 2. Video encoder rate control behavior, which modulates the source >>>>>> statistics to fit into network constraints over rather large time >>>>>> intervals (>0.5s). >>>>>> 3. Network transmission pacing / shaper, which smooths out bursts in >>>>>> encoder output to fit into network constraints over fine time intervals >>>>>> (<0.5s). >>>>>> >>>>>> It should be possible to model all three in an abstract way to arrive at >>>>>> some statistical model that can then be used for all tests without any >>>>>> real video content or encoding (to avoid introducing noise in comparisons >>>>>> if there was any variability in any of the factors). >>>>>> >>>>>> Mo >>>>>> >>>>>> >>>>>> >>>>>> On 10/28/13, 1:49 PM, Colin Perkins wrote: >>>>>> >>>>>> On 27 Oct 2013, at 11:50, Michael Welzl wrote: >>>>>> >>>>>> On 27. okt. 2013, at 12:33, Varun Singh wrote: >>>>>>> >>>>>>> Now, next, what if we'd have a set of such sequences, and then stac= k >>>>>>> them over a codec that produces quite arbitrary bitrates, assuming that >>>>>>> the network can always follow it - >>>>>> >>>>>> >>>>>> Do you mean preparing the same video sequence at multiple rates and the >>>>>> congestion controller picking the appropriate media rate that fits its >>>>>> estimate? ie rate switch between different media rates of the same >>>>>> sequence? >>>>>> >>>>>> I think that could be a very good idea! >>>>>> >>>>>> >>>>>> I'd be more interested in a standard set of *uncompressed* video tes= t >>>>>> sequences, that we can use to test the combination of video codec an= d >>>>>> congestion control. If we're building something for interactive vide= o >>>>>> congestion control we should assume live encoding, with the codec >>>>>> potentially having the flexibility to react to the network, and our >>>>>> testbed ought to reflect that. >>>>>> >>>>>> -- >>>>>> Colin Perkins >>>>>> http://csperkins.org/ >>> >>> >> > > > -- > Surveillance is pervasive. Go Dark. --e89a8f502cfae8684504ea4be03f Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable


On Nov 3, 2013 9:38 AM, "Harald Alvestrand" <harald@alvestrand.no> wrote:
>
> On 10/29/2013 11:32 AM, Michael Welzl wrote:
>>
>> Okay,
>>
>> I'm not convinced, but I will not argue the point any longer. = I raised my concerns but maybe I'm wrong, no problem.
>
>
> I think we need to do both....
>
> Arguing by analogy (always dangerous):
>
> in another debate, evaluating the performance of a codec was likened t= o evaluating the performance of a car; by taking out the engine and mountin= g it on a bench, you get very precise information about how much force the = engine is putting out, what its power/consumption curves are and so on.
>
> But if the question is "how well does the car drive in a race?&qu= ot;, there's no substitute for mounting the engine in the car and drivi= ng a few laps around the track.
>
> I agree with Michael that when we model congestion, we have to make th= e offered load fit some simple model. Otherwise, we have no idea whether a = simple tweak to the congestion control's constants was actually benefic= ial, but happened to randomly interact with some property of the load.
>
> And I agree with Colin that in order to evaluate the whole system, we = have to run the whole system.
>
> One should be done, and the other not be neglected.
> Expensive, but necessary!

+100!

I am hauling enough gear along to be able to evaluate the ma= in aqm technologies we're fiddling with against some webrtc loads...

I tend to argue that experiments tend to uncover variables y= ou didn't even know existed.
>
>
>
>>
>> Cheers,
>> Michael
>>
>> =A0
>> On 29. okt. 2013, at 10:31, Colin Perkins wrote:
>>
>>> Michael,
>>>
>>> Your concern is why I think we need to test the codec and the = congestion control together. The dynamics of how, and importantly when, the= codec can change its rate will be critical to the dynamics of the congesti= on control. This is especially true if we want to keep the latency low by a= voiding sender-side buffering to smooth the codec output to match the desir= ed sending rate. Testing congestion control without a codec will give us so= mething that doesn't respect the QoE concerns, and runs the risk of pro= ducing an algorithm that is safe and responsive, but not useful for interac= tive video.
>>>
>>> Colin
>>>
>>>
>>> On 29 Oct 2013, at 08:30, Michael Welzl <michawe@ifi.uio.no> wrote:
>>>>
>>>> So - looking at this thread, it seems that consensus is ag= ainst me =A0:-)
>>>>
>>>> Hence I'd like to clarify - my concern was that we'= ;re making the system too complex by involving a codec that reacts to the c= ongestion controller **when evaluating congestion control mechanisms**. I m= ean, once I've done a test, I'd like to know why I see the effects = that I see, and which mechanism is to blame for them.=A0This will get harde= r with this setup.
>>>>
>>>> Let me give an example to explain my concern:
>>>>
>>>> Say, we create a test with a reactive codec over a congest= ion controller, and in some network scenarios, we see oscillations: the con= gestion controller increased its rate, allowing the codec to switch to a hi= gher encoding rate; this higher rate was too much, and so quite fast the co= ngestion controller reduces its rate again, hence the codec reacts and swit= ches to a lower rate, and so forth.
>>>>
>>>> Now, if that is the test setup for evaluating a congestion= controller, a conclusion from such a test could be that the congestion con= troller has to be stabilized. I would, however, argue that a congestion=A0c= ontroller should be as quick as it possibly can at seeking out available ba= ndwidth, and hence such fluctuations are natural (*), and it's rather t= he job of the codec to dampen its rate switching.
>>>>
>>>> Either way, in this example test, we face a bad result, an= d two screws to tune to fix the problem - one on the side of the congestion= control, and one on the side of the codec.
>>>>
>>>> This is why I would prefer to evaluate congestion controls= (again, not the whole system, to me that's a different evaluation, and= of course a better congestion control should lead to a better total system= ) with a static traffic profile.
>>>>
>>>> Is my concern unfounded? Is it a non-problem I'm envis= ioning? =A0 (if so, see below for 2 more concerns =A0:-) =A0 )
>>>>
>>>> Cheers,
>>>> Michael
>>>>
>>>> PS: =A0Concern #2:=A0we can expect=A0a static traffic prof= ile feeding into a congestion controller to be relatively quickly done, but= creating a test setup with a reactive codec is going to be much harder and= take more time. Will we ever see it? When? Will it be easy to use?
>>>> PPS: Concern #3: the parameter space for tests might expan= d quite significantly when we involve reactive codecs... this will make any= evaluation harder, making the results harder to compare, resulting in more= time necessary to come to a conclusion about which controller does a bette= r job.
>>>>
>>>>
>>>> (*) TFRC is an argument to the contrary, but I haven't= seen a single evaluation result that shows that its smooth rate is indeed = beneficial. On the contrary, we now seem to have some agreement that TFRC i= sn't the best mechanism.
>>>>
>>>>
>>>> On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote:
>>>>
>>>>> Agreed. The base layer has no flexibility just like a = flat non-scalable stream.
>>>>>
>>>>>
>>>>> On 10/28/13, 7:45 PM, Bernard Aboba <bernard_aboba@hotmail.com> wrote:
>>>>>
>>>>> You're right that there is flexibility in enhancem= ent layers. But because of the layering structure, the base is not flexible= - since the enhancement layers depend on it, it is typically protected via= FEC or RTX. This affects the viability of "slow start" schemes a= s well other aspects - no point sending video at all if you cannot support = the base layer, at least.
>>>>>
>>>>> On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mzanaty)= " <mzanaty@cisco.com> w= rote:
>>>>>
>>>>>> This is true for simulcast layers, but not necessa= rily scalable layers. For the latter, arbitrary rates can be achieved withi= n a limited range (like down to half the bitrate) by selectively dropping s= ome packets of the highest enhancement layer with minimal visual quality im= pact. If the stream is highly scalable (many layers, which is rare), the li= mited range expands (like down to maybe a third, quarter, or less of the bi= trate). If the stream is coarsely scalable (e.g. two layers, which is commo= n), dropping below the limited range incurs changing encoding parameters, a= t which point arbitrary rates are again possible.
>>>>>>
>>>>>> Mo
>>>>>>
>>>>>>
>>>>>> On 10/28/13, 5:58 PM, Bernard Aboba <bernard_aboba@hotmail.com> wrote:=
>>>>>>
>>>>>> Colin said:
>>>>>> =A0
>>>>>> =93Do you mean preparing the same video sequence a= t multiple rates and the
>>>>>> congestion controller picking the appropriate medi= a rate that fits its
>>>>>> estimate? ie rate switch between different media r= ates of the same
>>>>>> sequence?
>>>>>> =94
>>>>>> =A0
>>>>>> [BA] I like this approach, because it somewhat app= roximates the situation of simulcast/layered coding.=A0 Previous work has i= ndicated a number of considerations that arise in those implementations (su= ch as the need for rapid rampup, so as to be able to send at least one vide= o stream as well as issues with TFRFC) that would be likely to be missed in= a model in which an encoder has fully smooth rate control. =A0=A0
>>>>>>
>>>>>> =A0
>>>>>>
>>>>>> I agree with Colin. We should be focused on live e= ncoding where the
>>>>>> encoder rate control is highly granular (often ful= ly smooth) and coupled
>>>>>> to the network congestion control, not pre-encoded= streaming of a small
>>>>>> set of fixed bitrates like DASH/HLS.
>>>>>>
>>>>>> A more formal approach to the problem would be to = decompose the factors
>>>>>> contributing to the final rate placed onto the net= work.
>>>>>> 1. Video content / source statistics, using a larg= e set of representative
>>>>>> sequences. An estimate of the entropy per frame co= uld simply be the
>>>>>> encoded frame size using constant quantization, i.= e. no encoder rate
>>>>>> control.
>>>>>> 2. Video encoder rate control behavior, which modu= lates the source
>>>>>> statistics to fit into network constraints over ra= ther large time
>>>>>> intervals (>0.5s).
>>>>>> 3. Network transmission pacing / shaper, which smo= oths out bursts in
>>>>>> encoder output to fit into network constraints ove= r fine time intervals
>>>>>> (<0.5s).
>>>>>>
>>>>>> It should be possible to model all three in an abs= tract way to arrive at
>>>>>> some statistical model that can then be used for a= ll tests without any
>>>>>> real video content or encoding (to avoid introduci= ng noise in comparisons
>>>>>> if there was any variability in any of the factors= ).
>>>>>>
>>>>>> Mo
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:
>>>>>>
>>>>>> On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio.no> wrote:
>>>>>>
>>>>>> On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@gmail.com> wrote:
>>>>>>>
>>>>>>> Now, next, what if we'd have a set of such= sequences, and then stack
>>>>>>> them over a codec that produces quite arbitrar= y bitrates, assuming that
>>>>>>> the network can always follow it -
>>>>>>
>>>>>>
>>>>>> Do you mean preparing the same video sequence at m= ultiple rates and the
>>>>>> congestion controller picking the appropriate medi= a rate that fits its
>>>>>> estimate? ie rate switch between different media r= ates of the same
>>>>>> sequence?
>>>>>>
>>>>>> I think that could be a very good idea!
>>>>>>
>>>>>>
>>>>>> I'd be more interested in a standard set of *u= ncompressed* video test
>>>>>> sequences, that we can use to test the combination= of video codec and
>>>>>> congestion control. If we're building somethin= g for interactive video
>>>>>> congestion control we should assume live encoding,= with the codec
>>>>>> potentially having the flexibility to react to the= network, and our
>>>>>> testbed ought to reflect that.
>>>>>>
>>>>>> --
>>>>>> Colin Perkins
>>>>>> http://csperkins= .org/
>>>
>>>
>>
>
>
> --
> Surveillance is pervasive. Go Dark.

--e89a8f502cfae8684504ea4be03f-- From dave.taht@gmail.com Sun Nov 3 12:55:02 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B860C11E813F for ; Sun, 3 Nov 2013 12:55:02 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.392 X-Spam-Level: X-Spam-Status: No, score=-2.392 tagged_above=-999 required=5 tests=[AWL=0.208, BAYES_00=-2.599, HTML_MESSAGE=0.001, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id V4IkJZvvzQa1 for ; Sun, 3 Nov 2013 12:55:01 -0800 (PST) Received: from mail-wg0-x230.google.com (mail-wg0-x230.google.com [IPv6:2a00:1450:400c:c00::230]) by ietfa.amsl.com (Postfix) with ESMTP id 66CE211E80FA for ; Sun, 3 Nov 2013 12:55:00 -0800 (PST) Received: by mail-wg0-f48.google.com with SMTP id b13so1355446wgh.3 for ; Sun, 03 Nov 2013 12:54:59 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:date:message-id:subject:from:to:cc:content-type; bh=KSokqWR43HkL90CqMiyo1SFbNpT/AgrU/VqBTTM6DAQ=; b=fu0juLvKSzd5I9quWLZ6SARngsmHkt7PeOLsy3lVzvsOoCW6GwxQbuCrbUar1gLWfl +5GGymER7UVsBXFcmZne2IoCJ7iJ9OWRXTxFiQfw4OBsY8NUFym6l7e8M6vpXOJtstzI 1OII67hoXF0FDkeAJPhQ8Xbt8q5GOLnvNKpm3ssyDsR7x1cGKurqzmdD8KlgS3jb2Qqg QXcYdhJ5Dfwv9Qs7JDb/ML9n+ICimSLUlFcORH/RL0kGSbG7oC/0XivZ6ccCB2NUTD1e dHMXzuVuq4UOmj0ezqlesiFtveJaqoVdPiUQU+G8OSV71M8QJ6m0FLFkCd/Gb5LBkU9u KmaQ== MIME-Version: 1.0 X-Received: by 10.180.79.230 with SMTP id m6mr9763635wix.19.1383512099499; Sun, 03 Nov 2013 12:54:59 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sun, 3 Nov 2013 12:54:59 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sun, 3 Nov 2013 12:54:59 -0800 (PST) Date: Sun, 3 Nov 2013 12:54:59 -0800 Message-ID: From: Dave Taht To: Harald Tveit Alvestrand Content-Type: multipart/alternative; boundary=f46d044403783bb77c04ea4c033d Cc: rmcat@ietf.org Subject: [rmcat] Where? Re: [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 03 Nov 2013 20:55:02 -0000 --f46d044403783bb77c04ea4c033d Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable So did this ever get a room? On Nov 3, 2013 12:45 PM, "Dave Taht" wrote: > > > On Nov 3, 2013 9:38 AM, "Harald Alvestrand" wrote: > > > > On 10/29/2013 11:32 AM, Michael Welzl wrote: > >> > >> Okay, > >> > >> I'm not convinced, but I will not argue the point any longer. I raised my concerns but maybe I'm wrong, no problem. > > > > > > I think we need to do both.... > > > > Arguing by analogy (always dangerous): > > > > in another debate, evaluating the performance of a codec was likened to evaluating the performance of a car; by taking out the engine and mounting it on a bench, you get very precise information about how much force the engine is putting out, what its power/consumption curves are and so on. > > > > But if the question is "how well does the car drive in a race?", there's no substitute for mounting the engine in the car and driving a few laps around the track. > > > > I agree with Michael that when we model congestion, we have to make the offered load fit some simple model. Otherwise, we have no idea whether a simple tweak to the congestion control's constants was actually beneficial, but happened to randomly interact with some property of the load. > > > > And I agree with Colin that in order to evaluate the whole system, we have to run the whole system. > > > > One should be done, and the other not be neglected. > > Expensive, but necessary! > > +100! > > I am hauling enough gear along to be able to evaluate the main aqm technologies we're fiddling with against some webrtc loads... > > I tend to argue that experiments tend to uncover variables you didn't even know existed. > > > > > > > > >> > >> Cheers, > >> Michael > >> > >> > >> On 29. okt. 2013, at 10:31, Colin Perkins wrote: > >> > >>> Michael, > >>> > >>> Your concern is why I think we need to test the codec and the congestion control together. The dynamics of how, and importantly when, the codec can change its rate will be critical to the dynamics of the congestion control. This is especially true if we want to keep the latency low by avoiding sender-side buffering to smooth the codec output to match the desired sending rate. Testing congestion control without a codec will give us something that doesn't respect the QoE concerns, and runs the risk of producing an algorithm that is safe and responsive, but not useful for interactive video. > >>> > >>> Colin > >>> > >>> > >>> On 29 Oct 2013, at 08:30, Michael Welzl wrote: > >>>> > >>>> So - looking at this thread, it seems that consensus is against me :-) > >>>> > >>>> Hence I'd like to clarify - my concern was that we're making the system too complex by involving a codec that reacts to the congestion controller **when evaluating congestion control mechanisms**. I mean, once I've done a test, I'd like to know why I see the effects that I see, and which mechanism is to blame for them. This will get harder with this setup. > >>>> > >>>> Let me give an example to explain my concern: > >>>> > >>>> Say, we create a test with a reactive codec over a congestion controller, and in some network scenarios, we see oscillations: the congestion controller increased its rate, allowing the codec to switch to a higher encoding rate; this higher rate was too much, and so quite fast the congestion controller reduces its rate again, hence the codec reacts and switches to a lower rate, and so forth. > >>>> > >>>> Now, if that is the test setup for evaluating a congestion controller, a conclusion from such a test could be that the congestion controller has to be stabilized. I would, however, argue that a congestion controller should be as quick as it possibly can at seeking out available bandwidth, and hence such fluctuations are natural (*), and it's rather the job of the codec to dampen its rate switching. > >>>> > >>>> Either way, in this example test, we face a bad result, and two screws to tune to fix the problem - one on the side of the congestion control, and one on the side of the codec. > >>>> > >>>> This is why I would prefer to evaluate congestion controls (again, not the whole system, to me that's a different evaluation, and of course a better congestion control should lead to a better total system) with a static traffic profile. > >>>> > >>>> Is my concern unfounded? Is it a non-problem I'm envisioning? (if so, see below for 2 more concerns :-) ) > >>>> > >>>> Cheers, > >>>> Michael > >>>> > >>>> PS: Concern #2: we can expect a static traffic profile feeding into a congestion controller to be relatively quickly done, but creating a test setup with a reactive codec is going to be much harder and take more time. Will we ever see it? When? Will it be easy to use? > >>>> PPS: Concern #3: the parameter space for tests might expand quite significantly when we involve reactive codecs... this will make any evaluation harder, making the results harder to compare, resulting in more time necessary to come to a conclusion about which controller does a better job. > >>>> > >>>> > >>>> (*) TFRC is an argument to the contrary, but I haven't seen a single evaluation result that shows that its smooth rate is indeed beneficial. On the contrary, we now seem to have some agreement that TFRC isn't the best mechanism. > >>>> > >>>> > >>>> On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote: > >>>> > >>>>> Agreed. The base layer has no flexibility just like a flat non-scalable stream. > >>>>> > >>>>> > >>>>> On 10/28/13, 7:45 PM, Bernard Aboba wrote: > >>>>> > >>>>> You're right that there is flexibility in enhancement layers. But because of the layering structure, the base is not flexible - since the enhancement layers depend on it, it is typically protected via FEC or RTX. This affects the viability of "slow start" schemes as well other aspects - no point sending video at all if you cannot support the base layer, at least. > >>>>> > >>>>> On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mzanaty)" < mzanaty@cisco.com> wrote: > >>>>> > >>>>>> This is true for simulcast layers, but not necessarily scalable layers. For the latter, arbitrary rates can be achieved within a limited range (like down to half the bitrate) by selectively dropping some packets of the highest enhancement layer with minimal visual quality impact. If the stream is highly scalable (many layers, which is rare), the limited range expands (like down to maybe a third, quarter, or less of the bitrate). If the stream is coarsely scalable (e.g. two layers, which is common), dropping below the limited range incurs changing encoding parameters, at which point arbitrary rates are again possible. > >>>>>> > >>>>>> Mo > >>>>>> > >>>>>> > >>>>>> On 10/28/13, 5:58 PM, Bernard Aboba wrote: > >>>>>> > >>>>>> Colin said: > >>>>>> > >>>>>> =93Do you mean preparing the same video sequence at multiple rates and the > >>>>>> congestion controller picking the appropriate media rate that fits its > >>>>>> estimate? ie rate switch between different media rates of the same > >>>>>> sequence? > >>>>>> =94 > >>>>>> > >>>>>> [BA] I like this approach, because it somewhat approximates the situation of simulcast/layered coding. Previous work has indicated a number of considerations that arise in those implementations (such as the need for rapid rampup, so as to be able to send at least one video stream as well as issues with TFRFC) that would be likely to be missed in a model in which an encoder has fully smooth rate control. > >>>>>> > >>>>>> > >>>>>> > >>>>>> I agree with Colin. We should be focused on live encoding where th= e > >>>>>> encoder rate control is highly granular (often fully smooth) and coupled > >>>>>> to the network congestion control, not pre-encoded streaming of a small > >>>>>> set of fixed bitrates like DASH/HLS. > >>>>>> > >>>>>> A more formal approach to the problem would be to decompose the factors > >>>>>> contributing to the final rate placed onto the network. > >>>>>> 1. Video content / source statistics, using a large set of representative > >>>>>> sequences. An estimate of the entropy per frame could simply be th= e > >>>>>> encoded frame size using constant quantization, i.e. no encoder rate > >>>>>> control. > >>>>>> 2. Video encoder rate control behavior, which modulates the source > >>>>>> statistics to fit into network constraints over rather large time > >>>>>> intervals (>0.5s). > >>>>>> 3. Network transmission pacing / shaper, which smooths out bursts in > >>>>>> encoder output to fit into network constraints over fine time intervals > >>>>>> (<0.5s). > >>>>>> > >>>>>> It should be possible to model all three in an abstract way to arrive at > >>>>>> some statistical model that can then be used for all tests without any > >>>>>> real video content or encoding (to avoid introducing noise in comparisons > >>>>>> if there was any variability in any of the factors). > >>>>>> > >>>>>> Mo > >>>>>> > >>>>>> > >>>>>> > >>>>>> On 10/28/13, 1:49 PM, Colin Perkins wrote: > >>>>>> > >>>>>> On 27 Oct 2013, at 11:50, Michael Welzl wrote= : > >>>>>> > >>>>>> On 27. okt. 2013, at 12:33, Varun Singh wrote: > >>>>>>> > >>>>>>> Now, next, what if we'd have a set of such sequences, and then stack > >>>>>>> them over a codec that produces quite arbitrary bitrates, assuming that > >>>>>>> the network can always follow it - > >>>>>> > >>>>>> > >>>>>> Do you mean preparing the same video sequence at multiple rates and the > >>>>>> congestion controller picking the appropriate media rate that fits its > >>>>>> estimate? ie rate switch between different media rates of the same > >>>>>> sequence? > >>>>>> > >>>>>> I think that could be a very good idea! > >>>>>> > >>>>>> > >>>>>> I'd be more interested in a standard set of *uncompressed* video test > >>>>>> sequences, that we can use to test the combination of video codec and > >>>>>> congestion control. If we're building something for interactive video > >>>>>> congestion control we should assume live encoding, with the codec > >>>>>> potentially having the flexibility to react to the network, and ou= r > >>>>>> testbed ought to reflect that. > >>>>>> > >>>>>> -- > >>>>>> Colin Perkins > >>>>>> http://csperkins.org/ > >>> > >>> > >> > > > > > > -- > > Surveillance is pervasive. Go Dark. --f46d044403783bb77c04ea4c033d Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable


So did this ever get a room?

On Nov 3, 2013 12:45 PM, "Dave Taht" <dave.taht@gmail.com> wrote:
>
>
> On Nov 3, 2013 9:38 AM, "Harald Alvestrand" <harald@alvestrand.no> wrote:
> >
> > On 10/29/2013 11:32 AM, Michael Welzl wrote:
> >>
> >> Okay,
> >>
> >> I'm not convinced, but I will not argue the point any lon= ger. I raised my concerns but maybe I'm wrong, no problem.
> >
> >
> > I think we need to do both....
> >
> > Arguing by analogy (always dangerous):
> >
> > in another debate, evaluating the performance of a codec was like= ned to evaluating the performance of a car; by taking out the engine and mo= unting it on a bench, you get very precise information about how much force= the engine is putting out, what its power/consumption curves are and so on= .
> >
> > But if the question is "how well does the car drive in a rac= e?", there's no substitute for mounting the engine in the car and = driving a few laps around the track.
> >
> > I agree with Michael that when we model congestion, we have to ma= ke the offered load fit some simple model. Otherwise, we have no idea wheth= er a simple tweak to the congestion control's constants was actually be= neficial, but happened to randomly interact with some property of the load.=
> >
> > And I agree with Colin that in order to evaluate the whole system= , we have to run the whole system.
> >
> > One should be done, and the other not be neglected.
> > Expensive, but necessary!
>
> +100!
>
> I am hauling enough gear along to be able to evaluate the main aqm tec= hnologies we're fiddling with against some webrtc loads...
>
> I tend to argue that experiments tend to uncover variables you didn= 9;t even know existed.
>
> >
> >
> >
> >>
> >> Cheers,
> >> Michael
> >>
> >> =A0
> >> On 29. okt. 2013, at 10:31, Colin Perkins wrote:
> >>
> >>> Michael,
> >>>
> >>> Your concern is why I think we need to test the codec and= the congestion control together. The dynamics of how, and importantly when= , the codec can change its rate will be critical to the dynamics of the con= gestion control. This is especially true if we want to keep the latency low= by avoiding sender-side buffering to smooth the codec output to match the = desired sending rate. Testing congestion control without a codec will give = us something that doesn't respect the QoE concerns, and runs the risk o= f producing an algorithm that is safe and responsive, but not useful for in= teractive video.
> >>>
> >>> Colin
> >>>
> >>>
> >>> On 29 Oct 2013, at 08:30, Michael Welzl <michawe@ifi.uio.no> wrote:
> >>>>
> >>>> So - looking at this thread, it seems that consensus = is against me =A0:-)
> >>>>
> >>>> Hence I'd like to clarify - my concern was that w= e're making the system too complex by involving a codec that reacts to = the congestion controller **when evaluating congestion control mechanisms**= . I mean, once I've done a test, I'd like to know why I see the eff= ects that I see, and which mechanism is to blame for them.=A0This will get = harder with this setup.
> >>>>
> >>>> Let me give an example to explain my concern:
> >>>>
> >>>> Say, we create a test with a reactive codec over a co= ngestion controller, and in some network scenarios, we see oscillations: th= e congestion controller increased its rate, allowing the codec to switch to= a higher encoding rate; this higher rate was too much, and so quite fast t= he congestion controller reduces its rate again, hence the codec reacts and= switches to a lower rate, and so forth.
> >>>>
> >>>> Now, if that is the test setup for evaluating a conge= stion controller, a conclusion from such a test could be that the congestio= n controller has to be stabilized. I would, however, argue that a congestio= n=A0controller should be as quick as it possibly can at seeking out availab= le bandwidth, and hence such fluctuations are natural (*), and it's rat= her the job of the codec to dampen its rate switching.
> >>>>
> >>>> Either way, in this example test, we face a bad resul= t, and two screws to tune to fix the problem - one on the side of the conge= stion control, and one on the side of the codec.
> >>>>
> >>>> This is why I would prefer to evaluate congestion con= trols (again, not the whole system, to me that's a different evaluation= , and of course a better congestion control should lead to a better total s= ystem) with a static traffic profile.
> >>>>
> >>>> Is my concern unfounded? Is it a non-problem I'm = envisioning? =A0 (if so, see below for 2 more concerns =A0:-) =A0 )
> >>>>
> >>>> Cheers,
> >>>> Michael
> >>>>
> >>>> PS: =A0Concern #2:=A0we can expect=A0a static traffic= profile feeding into a congestion controller to be relatively quickly done= , but creating a test setup with a reactive codec is going to be much harde= r and take more time. Will we ever see it? When? Will it be easy to use? > >>>> PPS: Concern #3: the parameter space for tests might = expand quite significantly when we involve reactive codecs... this will mak= e any evaluation harder, making the results harder to compare, resulting in= more time necessary to come to a conclusion about which controller does a = better job.
> >>>>
> >>>>
> >>>> (*) TFRC is an argument to the contrary, but I haven&= #39;t seen a single evaluation result that shows that its smooth rate is in= deed beneficial. On the contrary, we now seem to have some agreement that T= FRC isn't the best mechanism.
> >>>>
> >>>>
> >>>> On 29. okt. 2013, at 01:53, Mo Zanaty (mzanaty) wrote= :
> >>>>
> >>>>> Agreed. The base layer has no flexibility just li= ke a flat non-scalable stream.
> >>>>>
> >>>>>
> >>>>> On 10/28/13, 7:45 PM, Bernard Aboba <bernard_aboba@hotmail.com> wro= te:
> >>>>>
> >>>>> You're right that there is flexibility in enh= ancement layers. But because of the layering structure, the base is not fle= xible - since the enhancement layers depend on it, it is typically protecte= d via FEC or RTX. This affects the viability of "slow start" sche= mes as well other aspects - no point sending video at all if you cannot sup= port the base layer, at least.
> >>>>>
> >>>>> On Oct 28, 2013, at 3:10 PM, "Mo Zanaty (mza= naty)" <mzanaty@cisco.com&= gt; wrote:
> >>>>>
> >>>>>> This is true for simulcast layers, but not ne= cessarily scalable layers. For the latter, arbitrary rates can be achieved = within a limited range (like down to half the bitrate) by selectively dropp= ing some packets of the highest enhancement layer with minimal visual quali= ty impact. If the stream is highly scalable (many layers, which is rare), t= he limited range expands (like down to maybe a third, quarter, or less of t= he bitrate). If the stream is coarsely scalable (e.g. two layers, which is = common), dropping below the limited range incurs changing encoding paramete= rs, at which point arbitrary rates are again possible.
> >>>>>>
> >>>>>> Mo
> >>>>>>
> >>>>>>
> >>>>>> On 10/28/13, 5:58 PM, Bernard Aboba <bernard_aboba@hotmail.com> w= rote:
> >>>>>>
> >>>>>> Colin said:
> >>>>>> =A0
> >>>>>> =93Do you mean preparing the same video seque= nce at multiple rates and the
> >>>>>> congestion controller picking the appropriate= media rate that fits its
> >>>>>> estimate? ie rate switch between different me= dia rates of the same
> >>>>>> sequence?
> >>>>>> =94
> >>>>>> =A0
> >>>>>> [BA] I like this approach, because it somewha= t approximates the situation of simulcast/layered coding.=A0 Previous work = has indicated a number of considerations that arise in those implementation= s (such as the need for rapid rampup, so as to be able to send at least one= video stream as well as issues with TFRFC) that would be likely to be miss= ed in a model in which an encoder has fully smooth rate control. =A0=A0
> >>>>>>
> >>>>>> =A0
> >>>>>>
> >>>>>> I agree with Colin. We should be focused on l= ive encoding where the
> >>>>>> encoder rate control is highly granular (ofte= n fully smooth) and coupled
> >>>>>> to the network congestion control, not pre-en= coded streaming of a small
> >>>>>> set of fixed bitrates like DASH/HLS.
> >>>>>>
> >>>>>> A more formal approach to the problem would b= e to decompose the factors
> >>>>>> contributing to the final rate placed onto th= e network.
> >>>>>> 1. Video content / source statistics, using a= large set of representative
> >>>>>> sequences. An estimate of the entropy per fra= me could simply be the
> >>>>>> encoded frame size using constant quantizatio= n, i.e. no encoder rate
> >>>>>> control.
> >>>>>> 2. Video encoder rate control behavior, which= modulates the source
> >>>>>> statistics to fit into network constraints ov= er rather large time
> >>>>>> intervals (>0.5s).
> >>>>>> 3. Network transmission pacing / shaper, whic= h smooths out bursts in
> >>>>>> encoder output to fit into network constraint= s over fine time intervals
> >>>>>> (<0.5s).
> >>>>>>
> >>>>>> It should be possible to model all three in a= n abstract way to arrive at
> >>>>>> some statistical model that can then be used = for all tests without any
> >>>>>> real video content or encoding (to avoid intr= oducing noise in comparisons
> >>>>>> if there was any variability in any of the fa= ctors).
> >>>>>>
> >>>>>> Mo
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:
> >>>>>>
> >>>>>> On 27 Oct 2013, at 11:50, Michael Welzl <<= a href=3D"mailto:michawe@ifi.uio.no">michawe@ifi.uio.no> wrote:
> >>>>>>
> >>>>>> On 27. okt. 2013, at 12:33, Varun Singh <<= a href=3D"mailto:vsingh.ietf@gmail.com">vsingh.ietf@gmail.com> wrote= :
> >>>>>>>
> >>>>>>> Now, next, what if we'd have a set of= such sequences, and then stack
> >>>>>>> them over a codec that produces quite arb= itrary bitrates, assuming that
> >>>>>>> the network can always follow it -
> >>>>>>
> >>>>>>
> >>>>>> Do you mean preparing the same video sequence= at multiple rates and the
> >>>>>> congestion controller picking the appropriate= media rate that fits its
> >>>>>> estimate? ie rate switch between different me= dia rates of the same
> >>>>>> sequence?
> >>>>>>
> >>>>>> I think that could be a very good idea!
> >>>>>>
> >>>>>>
> >>>>>> I'd be more interested in a standard set = of *uncompressed* video test
> >>>>>> sequences, that we can use to test the combin= ation of video codec and
> >>>>>> congestion control. If we're building som= ething for interactive video
> >>>>>> congestion control we should assume live enco= ding, with the codec
> >>>>>> potentially having the flexibility to react t= o the network, and our
> >>>>>> testbed ought to reflect that.
> >>>>>>
> >>>>>> --
> >>>>>> Colin Perkins
> >>>>>> http://cspe= rkins.org/
> >>>
> >>>
> >>
> >
> >
> > --
> > Surveillance is pervasive. Go Dark.

--f46d044403783bb77c04ea4c033d-- From xiaoqzhu@cisco.com Sun Nov 3 13:33:06 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 100D711E8242 for ; Sun, 3 Nov 2013 13:33:06 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.598 X-Spam-Level: X-Spam-Status: No, score=-10.598 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id evi4StT6YhHZ for ; Sun, 3 Nov 2013 13:33:01 -0800 (PST) Received: from rcdn-iport-6.cisco.com (rcdn-iport-6.cisco.com [173.37.86.77]) by ietfa.amsl.com (Postfix) with ESMTP id 21E2311E823D for ; Sun, 3 Nov 2013 13:33:00 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=21282; q=dns/txt; s=iport; t=1383514380; x=1384723980; h=from:to:cc:subject:date:message-id:references: in-reply-to:mime-version; bh=HY5Gymi39S/59sjK5aSWKzncIZSIRRN0iYjp5J3bVjk=; b=mmen3VOsyr51zNWcWwamrA2qYRO0JbcQ5LQRCPkMdvxnTXh+J2HB1vNk qSOBvKekdnEGb+t004F9RH9vIuxaHa8traRiDGAjBHzSfZu1/5DDf5Bqe W71+wT33e2nHlARxG2IJJw22KBeyPSDiAUaUAAQauqkH0XLCBGZuNLel+ 4=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AmoFACzAdlKtJV2Z/2dsb2JhbABYgkMjIThTrSKSLYEbFnSCJQEBAQR5EAIBCBEEAQEoByERFAkIAgQOBQmHZgMPAQyzRw2Ja4xognAGAQmDF4EOA5QrgXSBa4EviyOFN4FogT6CKg X-IronPort-AV: E=Sophos;i="4.93,627,1378857600"; d="scan'208,217";a="280152444" Received: from rcdn-core-2.cisco.com ([173.37.93.153]) by rcdn-iport-6.cisco.com with ESMTP; 03 Nov 2013 21:32:59 +0000 Received: from xhc-aln-x03.cisco.com (xhc-aln-x03.cisco.com [173.36.12.77]) by rcdn-core-2.cisco.com (8.14.5/8.14.5) with ESMTP id rA3LWxNp019608 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Sun, 3 Nov 2013 21:32:59 GMT Received: from xmb-aln-x13.cisco.com ([fe80::5404:b599:9f57:834b]) by xhc-aln-x03.cisco.com ([173.36.12.77]) with mapi id 14.03.0123.003; Sun, 3 Nov 2013 15:32:58 -0600 From: "Xiaoqing Zhu (xiaoqzhu)" To: Zaheduzzaman Sarker Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFCAAL5TAA== Date: Sun, 3 Nov 2013 21:32:58 +0000 Message-ID: <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.21.146.171] Content-Type: multipart/alternative; boundary="_000_1F38856F160F4EE0AFEA7F636784E1FBciscocom_" MIME-Version: 1.0 Cc: rmcat WG , Varun Singh , "Mo Zanaty \(mzanaty\)" , Michael Welzl , Colin Perkins Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 03 Nov 2013 21:33:06 -0000 --_000_1F38856F160F4EE0AFEA7F636784E1FBciscocom_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Hi Zahed, We proposed such a delay term based on our understanding of the behavior of= live encoder rate control: it takes time for the encoder output rate to converge on the = newly specified rate. As such, it also means that the encoder cannot conform to arbitrarily frequ= ent rate change requests. Unlike CBR traffic, there is an inherent sluggishness in live enc= oder response. So, strictly speaking, maybe we should call that term "sluggishness in resp= onse", or "convergence time of encoder rate control=94? The aforementioned value (~0.5s) is based on our observations of encoder ou= tput traces from one commercial video conferencing system. Other encoders may act diffe= rently and we have yet to test them more extensively to confirm our conjecture. However, = our guess is that the order of magnitude of that value (say between 0.1s and 2s) will stay th= e same. And, to clarify your question, we are not making any specific assumptions o= n why the encoder is slow in response. Rate control of live video encoding is an active rese= arch topic all by its own, as the encoder needs to figure out what quantization parameters to cho= ose for an incoming, unknown frame, so as to meet the specified target rate. Different encoders= may achieve different tradeoff in terms of responsiveness, accuracy, and computational = complexity (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are proba= bly better off by treating that as a black box. Best, Xiaoqing On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker > wrote: Hi Xiaoging, Clarification question. >>a) delayed response to a rate change request (e.g., > 0.5s) Where is this delay coming from? Fixed delay or delay based of some assumpt= ion lack of resource to process the request? Are you thinking of particular= device, implementation? BR Zahed From: rmcat-bounces@ietf.org [mailto:rmcat-b= ounces@ietf.org] On Behalf Of Xiaoqing Zhu (xiaoqzhu) Sent: den 28 oktober 2013 13:15 To: Mo Zanaty (mzanaty) Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouve= r (03 Nov, 1300-1500) Agree with Colin and Mo. For a statistical video traffic model, I would propose to break down the 2n= d point (encoder rate control) in Mo's list further, to three sub-component= s: a) delayed response to a rate change request (e.g., > 0.5s) b) deviations from the requested rate (maybe as randomly fluctuating around= the target?) c) the different frame types or coding modes inside the encoder will yield = some encoded frames much larger than others. This will lead to burstiness on time scale = of frame intervals (typically 33ms), and later smoothed out by the pacing/shaper. Also, if we go for the approach of using uncompressed test video sequence f= or testing the combination of video codec and congestion control, we should= also recommend a reference codec and default configurations. So that focus= of the evaluations remains on the congestion control module. What do folk= s think of x264 (http://www.videolan.org/developers/x264.html)? Best, Xiaoqing On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) > wrote: I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small set of fixed bitrates like DASH/HLS. A more formal approach to the problem would be to decompose the factors contributing to the final rate placed onto the network. 1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the encoded frame size using constant quantization, i.e. no encoder rate control. 2. Video encoder rate control behavior, which modulates the source statistics to fit into network constraints over rather large time intervals (>0.5s). 3. Network transmission pacing / shaper, which smooths out bursts in encoder output to fit into network constraints over fine time intervals (<0.5s). It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors). Mo On 10/28/13, 1:49 PM, Colin Perkins > wrote: On 27 Oct 2013, at 11:50, Michael Welzl > wrote: On 27. okt. 2013, at 12:33, Varun Singh > wrote: Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that the network can always follow it - Do you mean preparing the same video sequence at multiple rates and the congestion controller picking the appropriate media rate that fits its estimate? ie rate switch between different media rates of the same sequence? I think that could be a very good idea! I'd be more interested in a standard set of *uncompressed* video test sequences, that we can use to test the combination of video codec and congestion control. If we're building something for interactive video congestion control we should assume live encoding, with the codec potentially having the flexibility to react to the network, and our testbed ought to reflect that. -- Colin Perkins http://csperkins.org/ --_000_1F38856F160F4EE0AFEA7F636784E1FBciscocom_ Content-Type: text/html; charset="Windows-1252" Content-ID: <404F5588F8C49243B639B81023E9489F@emea.cisco.com> Content-Transfer-Encoding: quoted-printable Hi Zahed, 

We proposed such a delay term based on our understanding of the behavi= or of live encoder
rate control: it takes time for the encoder output rate to converge on= the newly specified rate.
As such, it also means that the encoder cannot conform to arbitrarily = frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in liv= e encoder response.  

So, strictly speaking, maybe we should call that term "sluggishne= ss in response", or "convergence
time of encoder rate control=94?  

The aforementioned value (~0.5s) is based on our observations of encod= er output traces
from one commercial video conferencing system. Other encoders may act = differently and we
have yet to test them more extensively to confirm our conjecture. Howe= ver, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will st= ay the same.   

And, to clarify your question, we are not making any specific assumpti= ons on why the encoder
is slow in response.  Rate control of live video encoding is an a= ctive research topic all by its
own, as the encoder needs to figure out what quantization parameters t= o choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Differen= t encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computati= onal complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we= are probably better off by
treating that as a black box. 

Best,
Xiaoqing


On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker <zaheduzzaman.sarker@ericsson.com>= wrote:

Hi Xiaoging,
 
Clarification question.
 
>>a) delayed response to a rate change request (e.g., > 0.5s)=
 
 
Where is this delay coming from? Fixed delay or delay base= d of some assumption lack of resource to process the request? Are you think= ing of particular device, implementation?
 
BR

Zahed
 
 
Agree with Colin and Mo. 
 
For a statistical video traffic model, I would propose to break down the 2n= d point (encoder rate control) in Mo's list further, to three sub-component= s:
 
a) delayed response to a rate change request (e.g., > 0.5s)
b) deviations from the requested rate (maybe as randomly fluctuating around= the target?) 
c) the different frame types or coding modes inside the encoder will yield = some encoded
frames much larger than others. This will lead to burstiness on time scale = of frame intervals
(typically 33ms), and later smoothed out by the pacing/shaper. 
 
 
Best,
Xiaoqing
 
 
 
On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <mzanaty= @cisco.com> wrote:


I agree with Colin. We should be focused on live encoding where the
encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small
set of fixed bitrates like DASH/HLS.

A more formal approach to the problem would be to decompose the factors
contributing to the final rate placed onto the network.
1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the
encoded frame size using constant quantization, i.e. no encoder rate
control.
2. Video encoder rate control behavior, which modulates the source
statistics to fit into network constraints over rather large time
intervals (>0.5s).
3. Network transmission pacing / shaper, which smooths out bursts in
encoder output to fit into network constraints over fine time intervals
(<0.5s).

It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any
real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors).

Mo



On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:

On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio= .no> wrote:

On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@= gmail.com> wrote:

Now, next, what if we'd have a set of such sequences, and then stack
them over a codec that produces quite arbitrary bitrates, assuming that
the network can always follow it -

Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?

I think that could be a very good idea!


I'd be more interested in a standard set of *uncompressed* video test
sequences, that we can use to test the combination of video codec and
congestion control. If we're building something for interactive video
congestion control we should assume live encoding, with the codec
potentially having the flexibility to react to the network, and our
testbed ought to reflect that.

-- 
Colin Perkins
http://csperkins.org/


--_000_1F38856F160F4EE0AFEA7F636784E1FBciscocom_-- From randell-ietf@jesup.org Sun Nov 3 21:35:35 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 013CF11E82A1 for ; Sun, 3 Nov 2013 21:35:35 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[AWL=0.001, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id LNzcGKPt68xu for ; Sun, 3 Nov 2013 21:35:29 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 22C7611E818D for ; Sun, 3 Nov 2013 21:35:28 -0800 (PST) Received: from [64.114.24.114] (port=52431 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdCoy-000Cdd-Fp for rmcat@ietf.org; Sun, 03 Nov 2013 23:35:28 -0600 Message-ID: <5277321F.1050104@jesup.org> Date: Sun, 03 Nov 2013 21:35:27 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> In-Reply-To: <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 05:35:35 -0000 On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: > H > > 1) Whilst not explicitly laid out in the draft, from your presentation in RMCAT@IETF86, d_n = t_{r,n} - t_{s,n} - which I understood to mean that it was calculating the absolute one-way delay for each packet - which would imply that one would need synchronised clocks on the sender and receiver? Is that correct or was there an additional difference factor that I missed? > >> Your understanding is correct. For now, we are using one-way delay as the baseline for congestion signal. We are aware of all the drawbacks and limitations associated with one-way delay measurements, and are planning to address those issues once we start playing around with a testbed setup. One tentative plan is to subtract out a minimum OWD and work with relative OWD instead, another option is to move the per-packet statistical handling to the sender instead, and use RTT in lieu of OWD. Yet another measure could be to employ a separate mechanism for clock synchronization between sender and receiver. We are, of course, also open to suggestions on that matter. >> > Ok. Yes it would be nice to have a scheme that doesn't rely on absolute OWD, though there are some environments where one could expect synchronised clocks. I think it's more than nice to not rely on synchronized clocks; I think we absolutely have to work with non-synchronized clocks as the default, or include within this scheme a way to (sufficiently) synchronize the clocks - or prove that non-synchronized clocks don't cause a problem, even if they're sub-optimal. >>> 2) On a related issue in Section "6.3 Reference rate calculator" in equation (2) it seems that the selection of x_ref is dependent upon knowledge of the absolute one-way delay? >> The rationale of choose x_ref to be the value of absolute one-way delay (basically, propagation delay along the path) is that ideally, we would want the video stream to reach the highest possible rate when the queue stays is empty, e.g., when bottleneck link rate exceeds r_max of video. In practice, the stream can simply set x_ref as the minimum OWD it has observed so far. >> > ok - though it might also be useful to have some sort of mechanism to accommodate path changes in the setting of x_ref. Right - the problem is that accommodating path changes means in some manner aging-out the minimum OWD observed, and that can lead to delay-pumping (we start seeing delay we're inducing as part of OWD). I believe this was shown in simulation at least for LEDBAT. -- Randell Jesup randell-ietf@jesup.org From randell-ietf@jesup.org Sun Nov 3 21:48:47 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B2DF121E811D for ; Sun, 3 Nov 2013 21:48:47 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[AWL=0.000, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id bx9+Q+zRcz6z for ; Sun, 3 Nov 2013 21:48:41 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 621FA21E8121 for ; Sun, 3 Nov 2013 21:48:26 -0800 (PST) Received: from [64.114.24.114] (port=52761 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdD1V-000Fo1-CY for rmcat@ietf.org; Sun, 03 Nov 2013 23:48:25 -0600 Message-ID: <52773528.2030102@jesup.org> Date: Sun, 03 Nov 2013 21:48:24 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <9B69754F-101D-4F62-B77D-2A4A2A6A7388@ifi.uio.no> <-1283924527815223035@unknownmsgid> <36A884CD-80DE-4AAC-87DD-8550896675F2@csperkins.org> In-Reply-To: <36A884CD-80DE-4AAC-87DD-8550896675F2@csperkins.org> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 05:48:47 -0000 On 10/28/2013 10:49 AM, Colin Perkins wrote: > I'd be more interested in a standard set of *uncompressed* video test sequences, that we can use to test the combination of video codec and congestion control. If we're building something for interactive video congestion control we should assume live encoding, with the codec potentially having the flexibility to react to the network, and our testbed ought to reflect that. [catching up on pre-IETF mail) God yes. Switching between files encoded at different rates is nothing like what the applications are doing here - realtime encoding. Either use a dummy source that (with small delays in response to account for encoding time) follows the rate recommendations on the nose with dummy data (which is not *that* far off from reality, BTW) or use a real encoder with real video. I'll note that a real encoder may steal a few bits for a frame and give them back the next frame, etc - this gets hidden in practice because the data goes into jitter buffers on reception, so isn't played out as it comes in. (Or more to the point, the audio data is run through a jitter buffer, and the video data is queued in a buffer to be decoded in sync with the audio - the effect is the same; there is smoothing over the approximate jitterbuffer-length. However, modeling the encoder as perfectly following the signals from the CC should produce a fine result.) -- Randell Jesup randell-ietf@jesup.org From randell-ietf@jesup.org Sun Nov 3 21:50:50 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id EBDEF21E80CB for ; Sun, 3 Nov 2013 21:50:50 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[AWL=0.000, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id svg1481Cba27 for ; Sun, 3 Nov 2013 21:50:45 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 8608E21E8094 for ; Sun, 3 Nov 2013 21:50:42 -0800 (PST) Received: from [64.114.24.114] (port=52820 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdD3i-000GJH-3M for rmcat@ietf.org; Sun, 03 Nov 2013 23:50:42 -0600 Message-ID: <527735B1.9000005@jesup.org> Date: Sun, 03 Nov 2013 21:50:41 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> In-Reply-To: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 05:50:51 -0000 On 10/28/2013 11:39 AM, Mo Zanaty (mzanaty) wrote: > 2. Video encoder rate control behavior, which modulates the source > statistics to fit into network constraints over rather large time > intervals (>0.5s). 0.5 seconds to respond to rate-control changes? Where? Please fix that system! ;-) -- Randell Jesup randell-ietf@jesup.org From randell-ietf@jesup.org Sun Nov 3 22:06:21 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id BF66A21E8137 for ; Sun, 3 Nov 2013 22:06:21 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.598 X-Spam-Level: X-Spam-Status: No, score=-2.598 tagged_above=-999 required=5 tests=[AWL=-0.000, BAYES_00=-2.599, HTML_MESSAGE=0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 6sn8mkemIwVr for ; Sun, 3 Nov 2013 22:06:15 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 96AD421E8128 for ; Sun, 3 Nov 2013 22:06:15 -0800 (PST) Received: from [64.114.24.114] (port=53207 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdDIk-0003ji-OU for rmcat@ietf.org; Mon, 04 Nov 2013 00:06:14 -0600 Message-ID: <52773955.7010802@jesup.org> Date: Sun, 03 Nov 2013 22:06:13 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <3879D71E758A7E4AA99A35DD8D41D3D91E124D97@xmb-rcd-x14.cisco.com> <8372589D-DFAE-4560-BE06-E0D21DAFF47C@ifi.uio.no> <73EAAE7B-3DF8-49EB-9359-4643F9CA655B@csperkins.org> <20A1EBF7-6D36-4E1B-A5C7-6E692B189933@ifi.uio.no> In-Reply-To: Content-Type: multipart/alternative; boundary="------------050807040704020304000702" X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 06:06:21 -0000 This is a multi-part message in MIME format. --------------050807040704020304000702 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit On 10/29/2013 6:32 AM, Michael Ramalho (mramalho) wrote: > e codec to "shut up" (stop producing data for a while) or to quickly > slowdown in order to have a stable transport layer -- then we need it > to shut up or slow down. This goes to the "upper layer API" issue that > Mirja brought up a few weeks ago. Right - we're telling the rest of the system what's possible. If the actual data sent exceeds the amount the CC says is possible, bad things *may* happen. If the encoder has "too much" delay in responding to a rate change (down), "bad things" may happen. News at 11: the sun came up in the east. :-) > Once we know what is possible, then codec designers will differentiate > themselves on how best to render what we (i.e., RMCAT or similar) can > provide them. Good codec designs are NOT STATIC, but rather are > improved as the constraints presented to them are known. For example, > if I frame bursts are "too big", then you find codec manufacturers > building gradual I-frame refresh capabilities. Designing the rmcat > transport assuming that codec capabilities are known and are forever > static is like TCP having been designed with the assumption that hosts > would forever have sub 200 MHz processors in them. > Right. Rate controllers are an art and not part of any codec spec; they're implementation and constantly improved upon (witness the kerfluffle over VP8 poking down the QP every so often (i.e. somewhat more bits) so that following frames (less bits) have a better reference to use, while keeping the same overall bitrate. So long as this is hidden by the jitter buffer (not spread over too many frames) this is a win. There are H.264 encoders with great rate controllers, and ones with sucky controllers. > There is only so much the rmcat layer can do regarding QoE. Codec > manufactures may desire, for example, not to change to a higher > resolution until they are "very sure" that they will not have to > change downward anytime soon -- because changing resolutions too often > does not yield a better QoE. Let them design algorithms on when and > how to shift based on whatever information the "rmcat layer" provides > them. > +1 - though I'll note that the resolution switching is often not controlled at the codec/rate-controller layer, but at the control logic that integrates CC inputs to control the codec rate controller knobs (and to decide when to drop framerates). This *can* be done in the rate controller, but it may not have all the relevant information, and the issues may be application and usage-specific. -- Randell Jesup randell-ietf@jesup.org --------------050807040704020304000702 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit
On 10/29/2013 6:32 AM, Michael Ramalho (mramalho) wrote:
e codec to “shut up” (stop producing data for a while) or to quickly slowdown in order to have a stable transport layer – then we need it to shut up or slow down. This goes to the “upper layer API” issue that Mirja brought up a few weeks ago.

Right - we're telling the rest of the system what's possible.  If the actual data sent exceeds the amount the CC says is possible, bad things *may* happen. If the encoder has "too much" delay in responding to a rate change (down), "bad things" may happen.  News at 11: the sun came up in the east.  :-)

Once we know what is possible, then codec designers will differentiate themselves on how best to render what we (i.e., RMCAT or similar) can provide them. Good codec designs are NOT STATIC, but rather are improved as the constraints presented to them are known. For example, if I frame bursts are “too big”, then you find codec manufacturers building gradual I-frame refresh capabilities. Designing the rmcat transport assuming that codec capabilities are known and are forever static is like TCP having been designed with the assumption that hosts would forever have sub 200 MHz processors in them.


Right.  Rate controllers are an art and not part of any codec spec; they're implementation and constantly improved upon (witness the kerfluffle over VP8 poking down the QP every so often (i.e. somewhat more bits) so that following frames (less bits) have a better reference to use, while keeping the same overall bitrate.  So long as this is hidden by the jitter buffer (not spread over too many frames) this is a win.  There are H.264 encoders with great rate controllers, and ones with sucky controllers.

 

There is only so much the rmcat layer can do regarding QoE. Codec manufactures may desire, for example, not to change to a higher resolution until they are “very sure” that they will not have to change downward anytime soon – because changing resolutions too often does not yield a better QoE. Let them design algorithms on when and how to shift based on whatever information the “rmcat layer” provides them.


+1 - though I'll note that the resolution switching is often not controlled at the codec/rate-controller layer, but at the control logic that integrates CC inputs to control the codec rate controller knobs (and to decide when to drop framerates).  This *can* be done in the rate controller, but it may not have all the relevant information, and the issues may be application and usage-specific.


-- 
Randell Jesup
randell-ietf@jesup.org
--------------050807040704020304000702-- From randell-ietf@jesup.org Sun Nov 3 22:19:23 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E1A3511E82D8 for ; Sun, 3 Nov 2013 22:19:23 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.598 X-Spam-Level: X-Spam-Status: No, score=-2.598 tagged_above=-999 required=5 tests=[AWL=-0.000, BAYES_00=-2.599, HTML_MESSAGE=0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 1CcGRbmis5cg for ; Sun, 3 Nov 2013 22:19:18 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 5EB8311E82DB for ; Sun, 3 Nov 2013 22:19:11 -0800 (PST) Received: from [64.114.24.114] (port=53527 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdDVC-00075M-U8 for rmcat@ietf.org; Mon, 04 Nov 2013 00:19:07 -0600 Message-ID: <52773C5A.1060800@jesup.org> Date: Sun, 03 Nov 2013 22:19:06 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> In-Reply-To: <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> Content-Type: multipart/alternative; boundary="------------020403030004060900080708" X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 06:19:24 -0000 This is a multi-part message in MIME format. --------------020403030004060900080708 Content-Type: text/plain; charset=windows-1252; format=flowed Content-Transfer-Encoding: 8bit On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu) wrote: > Hi Zahed, > > We proposed such a delay term based on our understanding of the > behavior of live encoder > rate control: it takes time for the encoder output rate to converge on > the newly specified rate. > As such, it also means that the encoder cannot conform to arbitrarily > frequent rate change > requests. Unlike CBR traffic, there is an inherent sluggishness in > live encoder response. > > So, strictly speaking, maybe we should call that term "sluggishness in > response", or "convergence > time of encoder rate control”? > > The aforementioned value (~0.5s) is based on our observations of > encoder output traces > from one commercial video conferencing system. Other encoders may act > differently and we > have yet to test them more extensively to confirm our conjecture. > However, our guess is that > the order of magnitude of that value (say between 0.1s and 2s) will > stay the same. Honestly, I'd love to know what systems have such long rate control delays. The ones I've worked on have typically 1 or 2 frame times of delay - I haven't measured the VP8 implementation in Mozilla, but I'd be shocked if it was much more than that (in fact, I've simply assumed that it's fast, and that if it wasn't things would be horribly broken). If your rate controller takes 1/2 *second* to hit a new requested rate - it's simply broken for realtime work, and likely the systems using it are usually used either well below congestion levels, or on dedicated links (which might be true for high-buck teleconference systems). PLEASE do not optimize for rate controllers that take multiple hundreds of ms to respond. (I'll verify with some codec people, just for sanity, before the session). > > And, to clarify your question, we are not making any specific > assumptions on why the encoder > is slow in response. Rate control of live video encoding is an active > research topic all by its > own, as the encoder needs to figure out what quantization parameters > to choose for an incoming, > unknown frame, so as to meet the specified target rate. Different > encoders may achieve > different tradeoff in terms of responsiveness, accuracy, and > computational complexity > (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are > probably better off by > treating that as a black box. Two-pass, in realtime work? Seems unlikely... Maybe I just haven't played with enough high-bucks equipment on dedicated lines. -- Randell Jesup randell-ietf@jesup.org --------------020403030004060900080708 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: 8bit
On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu) wrote:
Hi Zahed, 

We proposed such a delay term based on our understanding of the behavior of live encoder
rate control: it takes time for the encoder output rate to converge on the newly specified rate.
As such, it also means that the encoder cannot conform to arbitrarily frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in live encoder response.  

So, strictly speaking, maybe we should call that term "sluggishness in response", or "convergence
time of encoder rate control”?  

The aforementioned value (~0.5s) is based on our observations of encoder output traces
from one commercial video conferencing system. Other encoders may act differently and we
have yet to test them more extensively to confirm our conjecture. However, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will stay the same.  

Honestly, I'd love to know what systems have such long rate control delays.  The ones I've worked on have typically 1 or 2 frame times of delay -  I haven't measured the VP8 implementation in Mozilla, but I'd be shocked if it was much more than that (in fact, I've simply assumed that it's fast, and that if it wasn't things would be horribly broken).  If your rate controller takes 1/2 *second* to hit a new requested rate - it's simply broken for realtime work, and likely the systems using it are usually used either well below congestion levels, or on dedicated links (which might be true for high-buck teleconference systems).

PLEASE do not optimize for rate controllers that take multiple hundreds of ms to respond.

(I'll verify with some codec people, just for sanity, before the session).


And, to clarify your question, we are not making any specific assumptions on why the encoder
is slow in response.  Rate control of live video encoding is an active research topic all by its
own, as the encoder needs to figure out what quantization parameters to choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Different encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computational complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we are probably better off by
treating that as a black box.

Two-pass, in realtime work?   Seems unlikely...  Maybe I just haven't played with enough high-bucks equipment on dedicated lines.

-- 
Randell Jesup
randell-ietf@jesup.org
--------------020403030004060900080708-- From randell-ietf@jesup.org Sun Nov 3 22:22:02 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 529D411E82D2 for ; Sun, 3 Nov 2013 22:22:02 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[AWL=0.000, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id yqLQbvN6rrvc for ; Sun, 3 Nov 2013 22:21:56 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 9648011E819B for ; Sun, 3 Nov 2013 22:21:53 -0800 (PST) Received: from [64.114.24.114] (port=53598 helo=[142.131.18.239]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VdDXt-0007kZ-0d for rmcat@ietf.org; Mon, 04 Nov 2013 00:21:53 -0600 Message-ID: <52773D00.5000408@jesup.org> Date: Sun, 03 Nov 2013 22:21:52 -0800 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> In-Reply-To: Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 06:22:02 -0000 On 10/29/2013 11:29 AM, Dave Taht wrote: > I would be very happy if also along the way, while exploring e2e cc > techniques for webrtc, that folk also tried the various aqm and packet > scheduling techniques being developed and deployed, other than drop > tail, notably wred, fq_codel, sqf & pie. That's a two way street as > I'd also very much like the aqm developers paying attention here > also... (I did some early work with webrtc + fq_codel earlier this > year but it was too buggy to get anything publishable.) In fact, one of the requirements is that the algorithm work when exposed to AQM (which may largely involve switching to a loss-reactive mode when it see no added delay at increased BW but increased loss). AQM is out there (if not prevalent), and AQM one assumes will become more common. -- Randell Jesup randell-ietf@jesup.org From dave.taht@gmail.com Sun Nov 3 22:33:32 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 1193611E8181 for ; Sun, 3 Nov 2013 22:33:32 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.434 X-Spam-Level: X-Spam-Status: No, score=-2.434 tagged_above=-999 required=5 tests=[AWL=0.166, BAYES_00=-2.599, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id xG-+5h3Xcq2N for ; Sun, 3 Nov 2013 22:33:31 -0800 (PST) Received: from mail-wg0-x236.google.com (mail-wg0-x236.google.com [IPv6:2a00:1450:400c:c00::236]) by ietfa.amsl.com (Postfix) with ESMTP id 8F95011E81A0 for ; Sun, 3 Nov 2013 22:33:30 -0800 (PST) Received: by mail-wg0-f54.google.com with SMTP id c11so1700529wgh.9 for ; Sun, 03 Nov 2013 22:33:29 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :cc:content-type:content-transfer-encoding; bh=5y3FIqfYbNA9w4J+XnZ8tN7+KDMElH9CPCP8W9Mc1cc=; b=GseD/djKku92I7hJPthptuiNwNQhJLHoiHyLkdhmY4VQHkzkN/5SFy3QtX+KROsyea 0/thWQh4mzhJMBE7zw9MOMX9dUAMSBToT92nYWKPrr6auWDmeP/xWu6VRJvIlgH16HXm um7FxnEIPXEoJ+GeHRcQqmb7tH6MnD3C0vIs/uCbQxr+idXoEOlocor5lQLJhJ733ydN kJZP3CfF3RUKJ9574/MwsJNa4x2jnZFybKcQQ32gR1HJmUPE2VJYIfxAFngIRKaiW3yB rmD7nFlbF/AyUIM+Lpb9G4qRGn1gPvMjPI/uf7ZhMGvCnqE6spKnOQ0rd//5TYWkuXhn CE9A== MIME-Version: 1.0 X-Received: by 10.180.189.132 with SMTP id gi4mr11158593wic.19.1383546809696; Sun, 03 Nov 2013 22:33:29 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sun, 3 Nov 2013 22:33:29 -0800 (PST) In-Reply-To: <52773D00.5000408@jesup.org> References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <52773D00.5000408@jesup.org> Date: Sun, 3 Nov 2013 22:33:29 -0800 Message-ID: From: Dave Taht To: Randell Jesup Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Cc: "rmcat@ietf.org" Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 06:33:32 -0000 On Sun, Nov 3, 2013 at 10:21 PM, Randell Jesup wro= te: > On 10/29/2013 11:29 AM, Dave Taht wrote: >> >> I would be very happy if also along the way, while exploring e2e cc >> techniques for webrtc, that folk also tried the various aqm and packet >> scheduling techniques being developed and deployed, other than drop tail= , >> notably wred, fq_codel, sqf & pie. That's a two way street as I'd also v= ery >> much like the aqm developers paying attention here also... (I did some e= arly >> work with webrtc + fq_codel earlier this year but it was too buggy to ge= t >> anything publishable.) > > > In fact, one of the requirements is that the algorithm work when exposed = to > AQM (which may largely involve switching to a loss-reactive mode when it = see > no added delay at increased BW but increased loss). > > AQM is out there (if not prevalent), and AQM one assumes will become more > common. Based on the preliminary results from multiple cc papers I think the success of webrtc and complex forms of videoconferencing is inescapably conjoined with the need for and delivery of better packet scheduling and aqm techniques in our bottleneck technologies (DSL/cable/wifi/wireless) --=20 Dave T=E4ht From xiaoqzhu@cisco.com Sun Nov 3 22:40:29 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DFB0011E82EB for ; Sun, 3 Nov 2013 22:40:28 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.561 X-Spam-Level: X-Spam-Status: No, score=-10.561 tagged_above=-999 required=5 tests=[AWL=0.037, BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 3HnUwyzipHC7 for ; Sun, 3 Nov 2013 22:40:23 -0800 (PST) Received: from rcdn-iport-3.cisco.com (rcdn-iport-3.cisco.com [173.37.86.74]) by ietfa.amsl.com (Postfix) with ESMTP id CD47711E82EE for ; Sun, 3 Nov 2013 22:40:19 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=9856; q=dns/txt; s=iport; t=1383547219; x=1384756819; h=from:to:cc:subject:date:message-id:references: in-reply-to:mime-version; bh=LFEcDpFpJZ9bjbDsaY3SuetA5KVIGhJbOj5GOVcJiD8=; b=ExuANCNGPmIbKvjDmfCKSPSy18JtB6mOrWApBoBPYMmk3I/o6d/KXp2R BTFXPWUF/IZiQvjs+Mlh2nfAVtR6bDKDgVWWkHcqeMsaguVcKzwH7914L ATWv4l+1bisVfeRMaaWuGlsT/SIdWYqF5RXLFUlx4Qs2zlsvv1b7CzECR E=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AgMFAOY/d1KtJXG+/2dsb2JhbABZgmYhgQu/O4EgFnSCJgEBBHkQAgEIPwcyFBECBA4FiAEBvWmPWAeDIIEOA5Qrg1+SCYFogT6CKg X-IronPort-AV: E=Sophos;i="4.93,630,1378857600"; d="scan'208,217";a="280268427" Received: from rcdn-core2-3.cisco.com ([173.37.113.190]) by rcdn-iport-3.cisco.com with ESMTP; 04 Nov 2013 06:40:19 +0000 Received: from xhc-rcd-x04.cisco.com (xhc-rcd-x04.cisco.com [173.37.183.78]) by rcdn-core2-3.cisco.com (8.14.5/8.14.5) with ESMTP id rA46eJTb017906 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Mon, 4 Nov 2013 06:40:19 GMT Received: from xmb-aln-x13.cisco.com ([fe80::5404:b599:9f57:834b]) by xhc-rcd-x04.cisco.com ([fe80::200:5efe:173.37.183.34%12]) with mapi id 14.03.0123.003; Mon, 4 Nov 2013 00:40:19 -0600 From: "Xiaoqing Zhu (xiaoqzhu)" To: Randell Jesup Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFCAAL5TAIAAkv4AgAAF7AA= Date: Mon, 4 Nov 2013 06:40:18 +0000 Message-ID: References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> <52773C5A.1060800@jesup.org> In-Reply-To: <52773C5A.1060800@jesup.org> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.21.78.24] Content-Type: multipart/alternative; boundary="_000_FF6B2CF52EA04B58BF473EA1B0064DCFciscocom_" MIME-Version: 1.0 Cc: rmcat WG Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 06:40:29 -0000 --_000_FF6B2CF52EA04B58BF473EA1B0064DCFciscocom_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Randell: Just wanted to clarify: I do agree with you that the encoder will react/res= pond almost immediately to a new rate change request. However, I find myself ha= rd pressed to believe that: a) It can do so on a very frequent basis, say, to conform to new rate chang= es once per 0.1 second; b) It can converge to the newly specified target rate within a very short w= indow, say, within two frames. If, indeed, a) and b) are true, it would be wonderful if you could show us = some traces from those live encoders that hit a new target rate precisely within= a couple of frames, every so often. To be a bit more specific: if the target rate is= changed to 2.4Mbps for a 30 fps stream, then all ensuing encoded frames will come o= ut magically at a size of 10KB (=3D 2400000/8/30) within a couple of frames? R= eally? I also wonder how that trick can be achieved, given that in live encoding, = the encoder can only do one-pass rate control (as you already noted, due to com= plexity and latency constraints), and can at best guess at the scene complexity of = an incoming frame. Curious to find out more ... Thanks, Xiaoqing On Nov 4, 2013, at 12:19 AM, Randell Jesup > wrote: On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu) wrote: Hi Zahed, We proposed such a delay term based on our understanding of the behavior of= live encoder rate control: it takes time for the encoder output rate to converge on the = newly specified rate. As such, it also means that the encoder cannot conform to arbitrarily frequ= ent rate change requests. Unlike CBR traffic, there is an inherent sluggishness in live enc= oder response. So, strictly speaking, maybe we should call that term "sluggishness in resp= onse", or "convergence time of encoder rate control=94? The aforementioned value (~0.5s) is based on our observations of encoder ou= tput traces from one commercial video conferencing system. Other encoders may act diffe= rently and we have yet to test them more extensively to confirm our conjecture. However, = our guess is that the order of magnitude of that value (say between 0.1s and 2s) will stay th= e same. Honestly, I'd love to know what systems have such long rate control delays.= The ones I've worked on have typically 1 or 2 frame times of delay - I h= aven't measured the VP8 implementation in Mozilla, but I'd be shocked if it= was much more than that (in fact, I've simply assumed that it's fast, and = that if it wasn't things would be horribly broken). If your rate controlle= r takes 1/2 *second* to hit a new requested rate - it's simply broken for r= ealtime work, and likely the systems using it are usually used either well = below congestion levels, or on dedicated links (which might be true for hig= h-buck teleconference systems). PLEASE do not optimize for rate controllers that take multiple hundreds of = ms to respond. (I'll verify with some codec people, just for sanity, before the session). And, to clarify your question, we are not making any specific assumptions o= n why the encoder is slow in response. Rate control of live video encoding is an active rese= arch topic all by its own, as the encoder needs to figure out what quantization parameters to cho= ose for an incoming, unknown frame, so as to meet the specified target rate. Different encoders= may achieve different tradeoff in terms of responsiveness, accuracy, and computational = complexity (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are proba= bly better off by treating that as a black box. Two-pass, in realtime work? Seems unlikely... Maybe I just haven't playe= d with enough high-bucks equipment on dedicated lines. -- Randell Jesup randell-ietf@jesup.org --_000_FF6B2CF52EA04B58BF473EA1B0064DCFciscocom_ Content-Type: text/html; charset="Windows-1252" Content-ID: Content-Transfer-Encoding: quoted-printable
Randell: 

Just wanted to clarify: I do agree with you that the encoder will reac= t/respond
almost immediately to a new rate change request.  However, I find= myself hard
pressed to believe that: 

a) It can do so on a very frequent basis, say, to conform to new rate = changes once
per 0.1 second; 

b) It can converge to the newly specified target rate within a very sh= ort window,
say, within two frames. 

If, indeed, a) and b) are true, it would be wonderful if you could sho= w us some
traces from those live encoders that hit a new target rate precisely w= ithin a couple
of frames, every so often. To be a bit more specific: if the target ra= te is changed
to 2.4Mbps for a 30 fps stream, then all ensuing encoded frames will c= ome out
magically at a size of 10KB (=3D 2400000/8/30) within a couple of fram= es? Really? 

I also wonder how that trick can be achieved, given that in live encod= ing, the
encoder can only do one-pass rate control (as you already noted, due t= o complexity
and latency constraints), and can at best guess at the scene complexit= y of an
incoming frame. 

Curious to find out more ...

Thanks,
Xiaoqing


On Nov 4, 2013, at 12:19 AM, Randell Jesup <randell-ietf@jesup.org> wrote:

On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu= ) wrote:
Hi Zahed, 

We proposed such a delay term based on our understanding of the behavi= or of live encoder
rate control: it takes time for the encoder output rate to converge on= the newly specified rate.
As such, it also means that the encoder cannot conform to arbitrarily = frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in liv= e encoder response.  

So, strictly speaking, maybe we should call that term "sluggishne= ss in response", or "convergence
time of encoder rate control=94?  

The aforementioned value (~0.5s) is based on our observations of encod= er output traces
from one commercial video conferencing system. Other encoders may act = differently and we
have yet to test them more extensively to confirm our conjecture. Howe= ver, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will st= ay the same.  

Honestly, I'd love to know what systems have such long rate control delays.=   The ones I've worked on have typically 1 or 2 frame times of delay -=   I haven't measured the VP8 implementation in Mozilla, but I'd be sho= cked if it was much more than that (in fact, I've simply assumed that it's fast, and that if it wasn't things would be = horribly broken).  If your rate controller takes 1/2 *second* to hit a= new requested rate - it's simply broken for realtime work, and likely the = systems using it are usually used either well below congestion levels, or on dedicated links (which might be true f= or high-buck teleconference systems).

PLEASE do not optimize for rate controllers that take multiple hundreds of = ms to respond.

(I'll verify with some codec people, just for sanity, before the session).<= br>

And, to clarify your question, we are not making any specific assumpti= ons on why the encoder
is slow in response.  Rate control of live video encoding is an a= ctive research topic all by its
own, as the encoder needs to figure out what quantization parameters t= o choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Differen= t encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computati= onal complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we= are probably better off by
treating that as a black box.

Two-pass, in realtime work?   Seems unlikely...  Maybe I jus= t haven't played with enough high-bucks equipment on dedicated lines.

--=20
Randell Jesup
randell-ietf@jesup.org

--_000_FF6B2CF52EA04B58BF473EA1B0064DCFciscocom_-- From michawe@ifi.uio.no Mon Nov 4 05:42:58 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id AD2F411E81DB for ; Mon, 4 Nov 2013 05:42:58 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -102.599 X-Spam-Level: X-Spam-Status: No, score=-102.599 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, USER_IN_WHITELIST=-100] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id eEh7upgDnegy for ; Mon, 4 Nov 2013 05:42:53 -0800 (PST) Received: from mail-out1.uio.no (mail-out1.uio.no [IPv6:2001:700:100:10::57]) by ietfa.amsl.com (Postfix) with ESMTP id 42E9C11E81C3 for ; Mon, 4 Nov 2013 05:42:52 -0800 (PST) Received: from mail-mx4.uio.no ([129.240.10.45]) by mail-out1.uio.no with esmtp (Exim 4.75) (envelope-from ) id 1VdKQb-0008Tq-D3; Mon, 04 Nov 2013 14:42:49 +0100 Received: from dhcp-8c4f.meeting.ietf.org ([31.133.140.79]) by mail-mx4.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80) (envelope-from ) id 1VdKQa-0007Nx-N4; Mon, 04 Nov 2013 14:42:49 +0100 Content-Type: text/plain; charset=iso-8859-1 Mime-Version: 1.0 (Mac OS X Mail 6.6 \(1510\)) From: Michael Welzl In-Reply-To: <5277321F.1050104@jesup.org> Date: Mon, 4 Nov 2013 05:42:44 -0800 Content-Transfer-Encoding: quoted-printable Message-Id: <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> To: Randell Jesup X-Mailer: Apple Mail (2.1510) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 6 msgs/h 3 sum rcpts/h 8 sum msgs/h 4 total rcpts 9323 max rcpts/h 40 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-5.0, required=5.0, autolearn=disabled, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: EA12DA06F07B92B946A77769E752F0A6C2BB7775 X-UiO-SPAM-Test: remote_host: 31.133.140.79 spam_score: -49 maxlevel 80 minaction 2 bait 0 mail/h: 3 total 20 max/h 3 blacklist 0 greylist 0 ratelimit 0 Cc: rmcat@ietf.org Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 13:42:58 -0000 On 3. nov. 2013, at 21:35, Randell Jesup wrote: > On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: >> H >>=20 >> 1) Whilst not explicitly laid out in the draft, from your = presentation in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I = understood to mean that it was calculating the absolute one-way delay = for each packet - which would imply that one would need synchronised = clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed? >>=20 >>> Your understanding is correct. For now, we are using one-way delay = as the baseline for congestion signal. We are aware of all the drawbacks = and limitations associated with one-way delay measurements, and are = planning to address those issues once we start playing around with a = testbed setup. One tentative plan is to subtract out a minimum OWD and = work with relative OWD instead, another option is to move the per-packet = statistical handling to the sender instead, and use RTT in lieu of OWD. = Yet another measure could be to employ a separate mechanism for clock = synchronization between sender and receiver. We are, of course, also = open to suggestions on that matter. >>>=20 >> Ok. Yes it would be nice to have a scheme that doesn't rely on = absolute OWD, though there are some environments where one could expect = synchronised clocks. >=20 > I think it's more than nice to not rely on synchronized clocks; I = think we absolutely have to work with non-synchronized clocks as the = default, or include within this scheme a way to (sufficiently) = synchronize the clocks - or prove that non-synchronized clocks don't = cause a problem, even if they're sub-optimal. I don't get that. Any congestion control scheme will only use OWD as a = congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone = cares. This is how LEDBAT works with OWD; clock drift is another issue, = but perhaps not such a big deal; IIRC it's also addressed in LEDBAT in = some way. >>>> 2) On a related issue in Section "6.3 Reference rate calculator" in = equation (2) it seems that the selection of x_ref is dependent upon = knowledge of the absolute one-way delay? >>> The rationale of choose x_ref to be the value of absolute one-way = delay (basically, propagation delay along the path) is that ideally, we = would want the video stream to reach the highest possible rate when the = queue stays is empty, e.g., when bottleneck link rate exceeds r_max of = video. In practice, the stream can simply set x_ref as the minimum OWD = it has observed so far. >>>=20 >> ok - though it might also be useful to have some sort of mechanism to = accommodate path changes in the setting of x_ref. >=20 > Right - the problem is that accommodating path changes means in some = manner aging-out the minimum OWD observed, and that can lead to = delay-pumping (we start seeing delay we're inducing as part of OWD). I = believe this was shown in simulation at least for LEDBAT. Both in simulation and with real-life tests. Here: = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=3D&arnumber=3D6496997= &queryText=3Dassessing+ledbat's+delay+impact But this would be easy to fix: just don't send a lot while you update = your base delay. LEDBAT keeps sending, and so it keeps measuring itself. Summarizing, I don't see a problem with using OWD. Cheers, Michael From p.ohanlon@gmail.com Mon Nov 4 06:25:26 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 42FB711E82ED for ; Mon, 4 Nov 2013 06:25:26 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.549 X-Spam-Level: X-Spam-Status: No, score=-2.549 tagged_above=-999 required=5 tests=[AWL=0.050, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 0mGy9LfBIqn9 for ; Mon, 4 Nov 2013 06:25:25 -0800 (PST) Received: from mail-we0-x22a.google.com (mail-we0-x22a.google.com [IPv6:2a00:1450:400c:c03::22a]) by ietfa.amsl.com (Postfix) with ESMTP id 0E5F111E81FA for ; Mon, 4 Nov 2013 06:25:22 -0800 (PST) Received: by mail-we0-f170.google.com with SMTP id u57so2136387wes.15 for ; Mon, 04 Nov 2013 06:25:22 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=subject:mime-version:content-type:from:in-reply-to:date:cc :content-transfer-encoding:message-id:references:to; bh=UaYsiLnTztB7plj6jWTdL+SeCBqpLRIutz9YEUds2CI=; b=eh91XX+ISCyve5CJ0BRYOL+70+UxgrPiw0WFAUGpFLL4Aia0OwS8wL9vj/S2Fz+mEa tJcri0A+m+f9g9xQl5Lw2YhZVndocYgwRAIzDZmFAeAEg5jsZabW8c0brDz5vSfdQVeK +6rJ41MHxn83ufn630V2q8FGjcwiXdmklEkI6LuMemsreTswrCEjEEKn/DeN3ElLiaAy s2+9hXix4Pgzow3GQ2HX6D3qvBMrfa4Jk8szLdyCPDaRcd3YRH+bmwCWV0MPCsaHVggc 0+x6fclFk9hDLmgZ7JFXfukG/1rl+81pbydOCrzDedKRqHpGwcqohsYjf++0++Wy59dP hhcg== X-Received: by 10.194.243.170 with SMTP id wz10mr322903wjc.74.1383575122118; Mon, 04 Nov 2013 06:25:22 -0800 (PST) Received: from black.lan ([149.241.129.101]) by mx.google.com with ESMTPSA id b7sm4368685wiz.8.2013.11.04.06.25.20 for (version=TLSv1 cipher=ECDHE-RSA-RC4-SHA bits=128/128); Mon, 04 Nov 2013 06:25:21 -0800 (PST) Mime-Version: 1.0 (Apple Message framework v1283) Content-Type: text/plain; charset=iso-8859-1 From: Piers O'Hanlon In-Reply-To: <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> Date: Mon, 4 Nov 2013 14:25:01 +0000 Content-Transfer-Encoding: quoted-printable Message-Id: References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> To: Michael Welzl X-Mailer: Apple Mail (2.1283) Cc: Randell Jesup , rmcat@ietf.org Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 14:25:26 -0000 On 4 Nov 2013, at 13:42, Michael Welzl wrote: >=20 > On 3. nov. 2013, at 21:35, Randell Jesup = wrote: >=20 >> On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: >>> H >>>=20 >>> 1) Whilst not explicitly laid out in the draft, from your = presentation in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I = understood to mean that it was calculating the absolute one-way delay = for each packet - which would imply that one would need synchronised = clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed? >>>=20 >>>> Your understanding is correct. For now, we are using one-way delay = as the baseline for congestion signal. We are aware of all the drawbacks = and limitations associated with one-way delay measurements, and are = planning to address those issues once we start playing around with a = testbed setup. One tentative plan is to subtract out a minimum OWD and = work with relative OWD instead, another option is to move the per-packet = statistical handling to the sender instead, and use RTT in lieu of OWD. = Yet another measure could be to employ a separate mechanism for clock = synchronization between sender and receiver. We are, of course, also = open to suggestions on that matter. >>>>=20 >>> Ok. Yes it would be nice to have a scheme that doesn't rely on = absolute OWD, though there are some environments where one could expect = synchronised clocks. >>=20 >> I think it's more than nice to not rely on synchronized clocks; I = think we absolutely have to work with non-synchronized clocks as the = default, or include within this scheme a way to (sufficiently) = synchronize the clocks - or prove that non-synchronized clocks don't = cause a problem, even if they're sub-optimal. >=20 > I don't get that. Any congestion control scheme will only use OWD as a = congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone = cares. This is how LEDBAT works with OWD; clock drift is another issue, = but perhaps not such a big deal; IIRC it's also addressed in LEDBAT in = some way. >=20 The issue with NADA is that it does not use the OWD difference - it uses = the absolute OWD. I think LEDBAT essentially references the NTP RFC which says that clock = drift isn't a big issue in the time scales that LEDBAT would be = operating. Though that does depend on speed at which LEDBAT is running - = Not sure how well it'll work at 10Gb/s? >=20 >>>>> 2) On a related issue in Section "6.3 Reference rate calculator" = in equation (2) it seems that the selection of x_ref is dependent upon = knowledge of the absolute one-way delay? >>>> The rationale of choose x_ref to be the value of absolute one-way = delay (basically, propagation delay along the path) is that ideally, we = would want the video stream to reach the highest possible rate when the = queue stays is empty, e.g., when bottleneck link rate exceeds r_max of = video. In practice, the stream can simply set x_ref as the minimum OWD = it has observed so far. >>>>=20 >>> ok - though it might also be useful to have some sort of mechanism = to accommodate path changes in the setting of x_ref. >>=20 >> Right - the problem is that accommodating path changes means in some = manner aging-out the minimum OWD observed, and that can lead to = delay-pumping (we start seeing delay we're inducing as part of OWD). I = believe this was shown in simulation at least for LEDBAT. >=20 > Both in simulation and with real-life tests. Here: > = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=3D&arnumber=3D6496997= &queryText=3Dassessing+ledbat's+delay+impact >=20 > But this would be easy to fix: just don't send a lot while you update = your base delay. LEDBAT keeps sending, and so it keeps measuring itself. >=20 > Summarizing, I don't see a problem with using OWD. Agree - If it used as the basis for calculating the OWD difference. Piers > Cheers, > Michael >=20 From michawe@ifi.uio.no Mon Nov 4 06:28:49 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 979F311E81FD for ; Mon, 4 Nov 2013 06:28:49 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -102.599 X-Spam-Level: X-Spam-Status: No, score=-102.599 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, USER_IN_WHITELIST=-100] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id TCy0Q4UJXCHi for ; Mon, 4 Nov 2013 06:28:43 -0800 (PST) Received: from mail-out5.uio.no (mail-out5.uio.no [IPv6:2001:700:100:10::17]) by ietfa.amsl.com (Postfix) with ESMTP id 812B511E8294 for ; Mon, 4 Nov 2013 06:28:41 -0800 (PST) Received: from mail-mx6.uio.no ([129.240.10.40]) by mail-out5.uio.no with esmtp (Exim 4.80.1) (envelope-from ) id 1VdL8x-0003sB-Mc; Mon, 04 Nov 2013 15:28:39 +0100 Received: from dhcp-8c4f.meeting.ietf.org ([31.133.140.79]) by mail-mx6.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80.1) (envelope-from ) id 1VdL8w-0004mE-SM; Mon, 04 Nov 2013 15:28:39 +0100 Content-Type: text/plain; charset=iso-8859-1 Mime-Version: 1.0 (Mac OS X Mail 6.6 \(1510\)) From: Michael Welzl In-Reply-To: Date: Mon, 4 Nov 2013 06:28:35 -0800 Content-Transfer-Encoding: quoted-printable Message-Id: <2A9116E7-E6F6-4559-9030-4A5150B57A51@ifi.uio.no> References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> To: Piers O'Hanlon X-Mailer: Apple Mail (2.1510) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 11 msgs/h 6 sum rcpts/h 14 sum msgs/h 8 total rcpts 9335 max rcpts/h 40 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-5.0, required=5.0, autolearn=disabled, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: 286828866E541327F64E46247373936BCC30F973 X-UiO-SPAM-Test: remote_host: 31.133.140.79 spam_score: -49 maxlevel 80 minaction 2 bait 0 mail/h: 6 total 27 max/h 6 blacklist 0 greylist 0 ratelimit 0 Cc: Randell Jesup , rmcat@ietf.org Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 14:28:49 -0000 On 4. nov. 2013, at 06:25, Piers O'Hanlon wrote: >=20 > On 4 Nov 2013, at 13:42, Michael Welzl wrote: >=20 >>=20 >> On 3. nov. 2013, at 21:35, Randell Jesup = wrote: >>=20 >>> On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: >>>> H >>>>=20 >>>> 1) Whilst not explicitly laid out in the draft, from your = presentation in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I = understood to mean that it was calculating the absolute one-way delay = for each packet - which would imply that one would need synchronised = clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed? >>>>=20 >>>>> Your understanding is correct. For now, we are using one-way = delay as the baseline for congestion signal. We are aware of all the = drawbacks and limitations associated with one-way delay measurements, = and are planning to address those issues once we start playing around = with a testbed setup. One tentative plan is to subtract out a minimum = OWD and work with relative OWD instead, another option is to move the = per-packet statistical handling to the sender instead, and use RTT in = lieu of OWD. Yet another measure could be to employ a separate = mechanism for clock synchronization between sender and receiver. We are, = of course, also open to suggestions on that matter. >>>>>=20 >>>> Ok. Yes it would be nice to have a scheme that doesn't rely on = absolute OWD, though there are some environments where one could expect = synchronised clocks. >>>=20 >>> I think it's more than nice to not rely on synchronized clocks; I = think we absolutely have to work with non-synchronized clocks as the = default, or include within this scheme a way to (sufficiently) = synchronize the clocks - or prove that non-synchronized clocks don't = cause a problem, even if they're sub-optimal. >>=20 >> I don't get that. Any congestion control scheme will only use OWD as = a congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone = cares. This is how LEDBAT works with OWD; clock drift is another issue, = but perhaps not such a big deal; IIRC it's also addressed in LEDBAT in = some way. >>=20 > The issue with NADA is that it does not use the OWD difference - it = uses the absolute OWD. ...but that obviously makes no sense and should just be fixed. And it = shouldn't be too hard to fix. Cheers, Michael From p.ohanlon@gmail.com Mon Nov 4 06:34:30 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B87B921E8191 for ; Mon, 4 Nov 2013 06:34:30 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.562 X-Spam-Level: X-Spam-Status: No, score=-2.562 tagged_above=-999 required=5 tests=[AWL=0.038, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id BJkW+jSA+tzg for ; Mon, 4 Nov 2013 06:34:30 -0800 (PST) Received: from mail-wi0-x234.google.com (mail-wi0-x234.google.com [IPv6:2a00:1450:400c:c05::234]) by ietfa.amsl.com (Postfix) with ESMTP id 878D721E818E for ; Mon, 4 Nov 2013 06:34:26 -0800 (PST) Received: by mail-wi0-f180.google.com with SMTP id ey11so593766wid.7 for ; Mon, 04 Nov 2013 06:34:25 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=subject:mime-version:content-type:from:in-reply-to:date:cc :content-transfer-encoding:message-id:references:to; bh=IdMqWjjfdPL9ZWs2IfgG2kdkORjQjvehkZzkiTTP1is=; b=yz8S4Qy5otkLvu58ySJ2DpGDnt3t8K99CoQrkk1lzD8jjwX9uI32v1oD5bS/2u7GI0 MFLOKygG0xJj/jWQpPIr/Td/Z3//wY2NSRcsu0YJ7Abvptm73v65+rjGfPQqH6x+cHoi iQuBRglD2aPGWqFrd7at/1uOE09i42sKnS6t9qL78Xl9f+lv08v9oIodk7SgbN1JMRDU NtdRpuny4xWghlpHV+esjemT3WzVYpQnBEg7KYUs7bDrr7sAwB5ExUerHweWvUB/1daC NRJWeRoSWW4/3UEoKep8LBBQNrxe1LXxx593rHJivBVWslIgcsFZ2IbCZ7X1xpMzP6cx lj5A== X-Received: by 10.194.174.36 with SMTP id bp4mr12960780wjc.7.1383575665439; Mon, 04 Nov 2013 06:34:25 -0800 (PST) Received: from black.lan ([149.241.129.101]) by mx.google.com with ESMTPSA id ma3sm4426690wic.1.2013.11.04.06.34.23 for (version=TLSv1 cipher=ECDHE-RSA-RC4-SHA bits=128/128); Mon, 04 Nov 2013 06:34:24 -0800 (PST) Mime-Version: 1.0 (Apple Message framework v1283) Content-Type: text/plain; charset=iso-8859-1 From: Piers O'Hanlon In-Reply-To: <2A9116E7-E6F6-4559-9030-4A5150B57A51@ifi.uio.no> Date: Mon, 4 Nov 2013 14:34:22 +0000 Content-Transfer-Encoding: quoted-printable Message-Id: <1580151C-DBD7-459E-A273-2365702AD8D4@gmail.com> References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> <2A9116E7-E6F6-4559-9030-4A5150B57A51@ifi.uio.no> To: Michael Welzl X-Mailer: Apple Mail (2.1283) Cc: Randell Jesup , rmcat@ietf.org Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 14:34:30 -0000 On 4 Nov 2013, at 14:28, Michael Welzl wrote: >=20 > On 4. nov. 2013, at 06:25, Piers O'Hanlon wrote: >=20 >>=20 >> On 4 Nov 2013, at 13:42, Michael Welzl wrote: >>=20 >>>=20 >>> On 3. nov. 2013, at 21:35, Randell Jesup = wrote: >>>=20 >>>> On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: >>>>> H >>>>>=20 >>>>> 1) Whilst not explicitly laid out in the draft, from your = presentation in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I = understood to mean that it was calculating the absolute one-way delay = for each packet - which would imply that one would need synchronised = clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed? >>>>>=20 >>>>>> Your understanding is correct. For now, we are using one-way = delay as the baseline for congestion signal. We are aware of all the = drawbacks and limitations associated with one-way delay measurements, = and are planning to address those issues once we start playing around = with a testbed setup. One tentative plan is to subtract out a minimum = OWD and work with relative OWD instead, another option is to move the = per-packet statistical handling to the sender instead, and use RTT in = lieu of OWD. Yet another measure could be to employ a separate = mechanism for clock synchronization between sender and receiver. We are, = of course, also open to suggestions on that matter. >>>>>>=20 >>>>> Ok. Yes it would be nice to have a scheme that doesn't rely on = absolute OWD, though there are some environments where one could expect = synchronised clocks. >>>>=20 >>>> I think it's more than nice to not rely on synchronized clocks; I = think we absolutely have to work with non-synchronized clocks as the = default, or include within this scheme a way to (sufficiently) = synchronize the clocks - or prove that non-synchronized clocks don't = cause a problem, even if they're sub-optimal. >>>=20 >>> I don't get that. Any congestion control scheme will only use OWD as = a congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone = cares. This is how LEDBAT works with OWD; clock drift is another issue, = but perhaps not such a big deal; IIRC it's also addressed in LEDBAT in = some way. >>>=20 >> The issue with NADA is that it does not use the OWD difference - it = uses the absolute OWD. >=20 > ...but that obviously makes no sense and should just be fixed. And it = shouldn't be too hard to fix. >=20 Sure - though the current algorithm would need to be largely rewritten = for it operate using OWD difference. This is why the question first = occurred to me when I looked over the draft in more detail. I presume they have use cases for NADA in some controlled = environments... Cheers, Piers. > Cheers, > Michael >=20 From xiaoqzhu@cisco.com Mon Nov 4 08:04:19 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 9944D21F9FAB for ; Mon, 4 Nov 2013 08:04:19 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.598 X-Spam-Level: X-Spam-Status: No, score=-10.598 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 6+jpUkRTyHrK for ; Mon, 4 Nov 2013 08:04:14 -0800 (PST) Received: from rcdn-iport-8.cisco.com (rcdn-iport-8.cisco.com [173.37.86.79]) by ietfa.amsl.com (Postfix) with ESMTP id ADAE611E8221 for ; Mon, 4 Nov 2013 08:04:12 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=9035; q=dns/txt; s=iport; t=1383581053; x=1384790653; h=from:to:cc:subject:date:message-id:references: in-reply-to:mime-version; bh=9OdfDQUfdE218Ga0Rtb+zkgvwB7+AQrII+t9cSbCVZE=; b=fZYi/0J1P60RGTWSqp6odXbR+uUuJDIzngg7fQRe/AjVfbB33DyNaysA 6u4qweHjLT0gIzbtSpD30UCz1bCMeqyQIuiWMcL9kE3HWG16oqIQF4dXn 2ocshZHOWi+9JB2/24HURpqmSKuzFqHcceJDVObFpPu9nM3jJdzMgXtjs w=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AiAFAFPEd1KtJXG+/2dsb2JhbABZgkMjIYELvz2BKBZ0giYBAQR5EAIBCD8HIREUEQIEDgUbBIdQAw8BtHMNiWuMaIEmgUoHgyCBDgOWH4FrjFKFN4Mmgio X-IronPort-AV: E=Sophos;i="4.93,633,1378857600"; d="scan'208,217";a="280431406" Received: from rcdn-core2-3.cisco.com ([173.37.113.190]) by rcdn-iport-8.cisco.com with ESMTP; 04 Nov 2013 16:04:10 +0000 Received: from xhc-aln-x04.cisco.com (xhc-aln-x04.cisco.com [173.36.12.78]) by rcdn-core2-3.cisco.com (8.14.5/8.14.5) with ESMTP id rA4G49PV018246 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Mon, 4 Nov 2013 16:04:09 GMT Received: from xmb-aln-x13.cisco.com ([fe80::5404:b599:9f57:834b]) by xhc-aln-x04.cisco.com ([173.36.12.78]) with mapi id 14.03.0123.003; Mon, 4 Nov 2013 10:04:09 -0600 From: "Xiaoqing Zhu (xiaoqzhu)" To: "Piers O'Hanlon" Thread-Topic: [rmcat] NADA questions Thread-Index: AQHOz/r5x9IK43P2gkikJ3I+aBD/nJoDPQmAgAEm8gCAEJ/5gIAAiCUAgAAL0YCAAAD/gIAAAZ0AgAAZFQA= Date: Mon, 4 Nov 2013 16:04:08 +0000 Message-ID: <39928D07-596D-4437-BF8B-40BEC2E1D906@cisco.com> References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> <2A9116E7-E6F6-4559-9030-4A5150B57A51@ifi.uio.no> <1580151C-DBD7-459E-A273-2365702AD8D4@gmail.com> In-Reply-To: <1580151C-DBD7-459E-A273-2365702AD8D4@gmail.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.152.12.125] Content-Type: multipart/alternative; boundary="_000_39928D07596D4437BF8B40BEC2E1D906ciscocom_" MIME-Version: 1.0 Cc: Randell Jesup , rmcat WG , Michael Welzl Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 16:04:19 -0000 --_000_39928D07596D4437BF8B40BEC2E1D906ciscocom_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Folks: We hear you about the potential issues regarding using absolute OWD as a congestion metric. Agree. Special thanks to Piers who first pointed this out. I also tend to think along with Michael Welzl that it should be relatively easy to fix by using a relative OWD instead. Just that we are deferring this change until we can try it out in a real test-bed. This is one aspect of a CC solution where simulation-based studies won=92t help much. So, yes, please stay tuned for an update ... Best, Xiaoqing On Nov 4, 2013, at 8:34 AM, Piers O'Hanlon > wrote: On 4 Nov 2013, at 14:28, Michael Welzl wrote: On 4. nov. 2013, at 06:25, Piers O'Hanlon > wrote: On 4 Nov 2013, at 13:42, Michael Welzl wrote: On 3. nov. 2013, at 21:35, Randell Jesup > wrote: On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: H 1) Whilst not explicitly laid out in the draft, from your presentation in R= MCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I understood to mean that it= was calculating the absolute one-way delay for each packet - which would i= mply that one would need synchronised clocks on the sender and receiver? Is= that correct or was there an additional difference factor that I missed? Your understanding is correct. For now, we are using one-way delay as the = baseline for congestion signal. We are aware of all the drawbacks and limit= ations associated with one-way delay measurements, and are planning to addr= ess those issues once we start playing around with a testbed setup. One ten= tative plan is to subtract out a minimum OWD and work with relative OWD ins= tead, another option is to move the per-packet statistical handling to the = sender instead, and use RTT in lieu of OWD. Yet another measure could be t= o employ a separate mechanism for clock synchronization between sender and = receiver. We are, of course, also open to suggestions on that matter. Ok. Yes it would be nice to have a scheme that doesn't rely on absolute OWD= , though there are some environments where one could expect synchronised cl= ocks. I think it's more than nice to not rely on synchronized clocks; I think we = absolutely have to work with non-synchronized clocks as the default, or inc= lude within this scheme a way to (sufficiently) synchronize the clocks - or= prove that non-synchronized clocks don't cause a problem, even if they're = sub-optimal. I don't get that. Any congestion control scheme will only use OWD as a cong= estion indicator, which means that the absolute OWD value isn't really what= counts - it's about changes in the OWD. This, again, means that non-synchr= onized clocks produce a wrong absolute value, but noone cares. This is how = LEDBAT works with OWD; clock drift is another issue, but perhaps not such a= big deal; IIRC it's also addressed in LEDBAT in some way. The issue with NADA is that it does not use the OWD difference - it uses th= e absolute OWD. ...but that obviously makes no sense and should just be fixed. And it shoul= dn't be too hard to fix. Sure - though the current algorithm would need to be largely rewritten for = it operate using OWD difference. This is why the question first occurred to= me when I looked over the draft in more detail. I presume they have use cases for NADA in some controlled environments... Cheers, Piers. Cheers, Michael --_000_39928D07596D4437BF8B40BEC2E1D906ciscocom_ Content-Type: text/html; charset="Windows-1252" Content-ID: <4EAF0AA98641E64EB21E7346D2F0C41A@emea.cisco.com> Content-Transfer-Encoding: quoted-printable Folks:  

We hear you about the potential issues regarding using absolute
OWD as a congestion metric. Agree.  Special thanks to Piers who
first pointed this out.  

I also tend to think along with Michael Welzl that it should be
relatively easy to fix by using a relative OWD instead.  Just tha= t
we are deferring this change until we can try it out in a real test-be= d.
This is one aspect of a CC solution where simulation-based studies
won=92t help much. 

So, yes, please stay tuned for an update ...

Best,
Xiaoqing


 
On Nov 4, 2013, at 8:34 AM, Piers O'Hanlon <p.ohanlon@gmail.com> wrote:


On 4 Nov 2013, at 14:28, Michael Welzl wrote:


On 4. nov. 2013, at 06:25, Piers O'Hanlon <p.ohanlon@gmail.com> wrote:


On 4 Nov 2013, at 13:42, Michael Welzl wrote:


On 3. nov. 2013, at 21:35, Randell Jesup <randell-ietf@jesup.org> wrote:

On 10/24/2013 9:42 AM, Piers O'Hanlon wrote:
H

1) Whilst not explicitly laid out in the draft, from your presentation in R= MCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I understood to mean that it= was calculating the absolute one-way delay for each packet - which would i= mply that one would need synchronised clocks on the sender and receiver? Is that correct or was there an additio= nal difference factor that I missed?

Your understanding is correct.  For now, we = are using one-way delay as the baseline for congestion signal. We are aware= of all the drawbacks and limitations associated with one-way delay measure= ments, and are planning to address those issues once we start playing around with a testbed setup. One tentative pl= an is to subtract out a minimum OWD and work with relative OWD instead, ano= ther option is to move the per-packet statistical handling to the sender in= stead, and use RTT in lieu of OWD.  Yet another measure could be to employ a separate mechanism for cloc= k synchronization between sender and receiver. We are, of course, also open= to suggestions on that matter.

Ok. Yes it would be nice to have a scheme that doesn't rely on absolute OWD= , though there are some environments where one could expect synchronised cl= ocks.

I think it's more than nice to not rely on synchronized clocks; I think we = absolutely have to work with non-synchronized clocks as the default, or inc= lude within this scheme a way to (sufficiently) synchronize the clocks - or= prove that non-synchronized clocks don't cause a problem, even if they're sub-optimal.

I don't get that. Any congestion control scheme will only use OWD as a cong= estion indicator, which means that the absolute OWD value isn't really what= counts - it's about changes in the OWD. This, again, means that non-synchr= onized clocks produce a wrong absolute value, but noone cares. This is how LEDBAT works with OWD; clock drift is = another issue, but perhaps not such a big deal; IIRC it's also addressed in= LEDBAT in some way.

The issue with NADA is that it does not use the OWD difference - it uses th= e absolute OWD.

...but that obviously makes no sense and should just be fixed. And it shoul= dn't be too hard to fix.

Sure - though the current algorithm would need to be largely rewritten for = it operate using OWD difference. This is why the question first occurred to= me when I looked over the draft in more detail.

I presume they have use cases for NADA in some controlled environments...
Cheers,

Piers.

Cheers,
Michael

--_000_39928D07596D4437BF8B40BEC2E1D906ciscocom_-- From zaheduzzaman.sarker@ericsson.com Mon Nov 4 09:55:54 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C9B6811E8337 for ; Mon, 4 Nov 2013 09:55:52 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.423 X-Spam-Level: X-Spam-Status: No, score=-4.423 tagged_above=-999 required=5 tests=[AWL=1.825, BAYES_00=-2.599, HELO_EQ_SE=0.35, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-4] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id l1Mz9YDHyebL for ; Mon, 4 Nov 2013 09:55:46 -0800 (PST) Received: from mailgw7.ericsson.se (mailgw7.ericsson.se [193.180.251.48]) by ietfa.amsl.com (Postfix) with ESMTP id 2730411E82BA for ; Mon, 4 Nov 2013 09:54:52 -0800 (PST) X-AuditID: c1b4fb30-b7eff8e000006d14-a9-5277df674ce0 Received: from ESESSHC013.ericsson.se (Unknown_Domain [153.88.253.124]) by mailgw7.ericsson.se (Symantec Mail Security) with SMTP id D2.81.27924.76FD7725; Mon, 4 Nov 2013 18:54:48 +0100 (CET) Received: from ESESSMB307.ericsson.se ([169.254.7.88]) by ESESSHC013.ericsson.se ([153.88.183.57]) with mapi id 14.02.0328.009; Mon, 4 Nov 2013 18:54:47 +0100 From: Zaheduzzaman Sarker To: "Xiaoqing Zhu (xiaoqzhu)" Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFCAAL5TAIAA66cw Date: Mon, 4 Nov 2013 17:54:46 +0000 Message-ID: References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> In-Reply-To: <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [153.88.183.154] Content-Type: multipart/alternative; boundary="_000_E0F7A68B07B53F4FBD12DABD61CBA90E17E960ESESSMB307ericsso_" MIME-Version: 1.0 X-Brightmail-Tracker: H4sIAAAAAAAAA+NgFupgkeLIzCtJLcpLzFFi42KZGfG3RjfjfnmQwbp3PBbLX55gtPhxdier xYsHc5gsVt/8wGaxt8nf4nXLMiYHNo8pvzeyeky7f5/NY+esu+weS5b8ZPJYvfohcwBrFJdN SmpOZllqkb5dAlfGm11TmAreTWes6P4W08B4sIGxi5GTQ0LAROLGlD5WCFtM4sK99WxdjFwc QgKHGCXWzb3PCuEsYpRY/+omUxcjBwebgI3E48V+IA0iAkYSU+a/YASpYRbYxSjRv+I9C0iN sECSxMuHAhA1yRIn3yxgh7DdJCae62EBsVkEVCRuTr8DtphXwFviS+dCJohdLUwSh2+/Aivi FLCVmHytAayIEei676fWMIHYzALiEreezGeCuFpAYsme88wQtqjEy8f/oL5Rklh0+zNUfb7E xL6LbBDLBCVOznzCMoFRdBaSUbOQlM1CUgYR15FYsPsTG4StLbFs4WtmGPvMgcdMyOILGNlX MbLnJmbmpJebb2IExubBLb8NdjBuui92iFGag0VJnPfDW+cgIYH0xJLU7NTUgtSi+KLSnNTi Q4xMHJxSDYxrku6xv98VPb23aZGl4veD9gceeptmS0xU7p7/lHHfmQNNNzomzzys8mPJa+1W E+ZNuuwHp1WwX9vLn3Hog/KdL8sd9XfaqJ78k3bHj63brdH9/p9H14PmPE/11F+btrfoV9qz pDsTsi5v5Hi/LrLJ+5XjhaupAt83z2zL4xC5y5n2cVnvO+YdSizFGYmGWsxFxYkAz6/FF5sC AAA= Cc: rmcat WG , Varun Singh , "Mo Zanaty \(mzanaty\)" , Michael Welzl , Colin Perkins Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 17:55:54 -0000 --_000_E0F7A68B07B53F4FBD12DABD61CBA90E17E960ESESSMB307ericsso_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable We proposed such a delay term based on our understanding of the behavior of= live encoder rate control: it takes time for the encoder output rate to converge on the = newly specified rate. [Zaheduzzaman Sarker] Theoretically this can be done in next frame. Questio= n is that do we need to take care of optimization features of codec impleme= ntation in congestion control design? As such, it also means that the encoder cannot conform to arbitrarily frequ= ent rate change requests. Unlike CBR traffic, there is an inherent sluggishness in live enc= oder response. [Zaheduzzaman Sarker] I don't know why this has to be true for CBR and not = for VBR. The down switch on rate change should be fine with both CBR and VB= R. Up switching is always dangerous and all my experience says you will per= haps not jump to the requested up switched bitrate directly but like to gra= dually go there. Hence you have enough time to reach to the higher bitrate.= Anyway if the rate request is too frequent that's a sign of instable syste= m to me. So, strictly speaking, maybe we should call that term "sluggishness in resp= onse", or "convergence time of encoder rate control"? [Zaheduzzaman Sarker] I will regard that kind of sluggishness is a byproduc= t of optimization in coded and they will differ from implementation to impl= ementation. The aforementioned value (~0.5s) is based on our observations of encoder ou= tput traces from one commercial video conferencing system. Other encoders may act diffe= rently and we have yet to test them more extensively to confirm our conjecture. However, = our guess is that the order of magnitude of that value (say between 0.1s and 2s) will stay th= e same. [Zaheduzzaman Sarker] will there be any chance of making the survey availa= ble where we can look at the sluggishness in response in numerical values? And, to clarify your question, we are not making any specific assumptions o= n why the encoder is slow in response. Rate control of live video encoding is an active rese= arch topic all by its own, as the encoder needs to figure out what quantization parameters to cho= ose for an incoming, unknown frame, so as to meet the specified target rate. Different encoders= may achieve different tradeoff in terms of responsiveness, accuracy, and computational = complexity (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are proba= bly better off by treating that as a black box. [Zaheduzzaman Sarker] so we don't care about sluggishness in response , ri= ght? Best, Xiaoqing On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker > wrote: Hi Xiaoging, Clarification question. >>a) delayed response to a rate change request (e.g., > 0.5s) Where is this delay coming from? Fixed delay or delay based of some assumpt= ion lack of resource to process the request? Are you thinking of particular= device, implementation? BR Zahed From: rmcat-bounces@ietf.org [mailto:rmcat-b= ounces@ietf.org] On Behalf Of Xiaoqing Zhu (xiaoqzhu) Sent: den 28 oktober 2013 13:15 To: Mo Zanaty (mzanaty) Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouve= r (03 Nov, 1300-1500) Agree with Colin and Mo. For a statistical video traffic model, I would propose to break down the 2n= d point (encoder rate control) in Mo's list further, to three sub-component= s: a) delayed response to a rate change request (e.g., > 0.5s) b) deviations from the requested rate (maybe as randomly fluctuating around= the target?) c) the different frame types or coding modes inside the encoder will yield = some encoded frames much larger than others. This will lead to burstiness on time scale = of frame intervals (typically 33ms), and later smoothed out by the pacing/shaper. Also, if we go for the approach of using uncompressed test video sequence f= or testing the combination of video codec and congestion control, we should= also recommend a reference codec and default configurations. So that focus= of the evaluations remains on the congestion control module. What do folk= s think of x264 (http://www.videolan.org/developers/x264.html)? Best, Xiaoqing On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) > wrote: I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small set of fixed bitrates like DASH/HLS. A more formal approach to the problem would be to decompose the factors contributing to the final rate placed onto the network. 1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the encoded frame size using constant quantization, i.e. no encoder rate control. 2. Video encoder rate control behavior, which modulates the source statistics to fit into network constraints over rather large time intervals (>0.5s). 3. Network transmission pacing / shaper, which smooths out bursts in encoder output to fit into network constraints over fine time intervals (<0.5s). It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors). Mo On 10/28/13, 1:49 PM, Colin Perkins > wrote: On 27 Oct 2013, at 11:50, Michael Welzl > wrote: On 27. okt. 2013, at 12:33, Varun Singh > wrote: Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that the network can always follow it - Do you mean preparing the same video sequence at multiple rates and the congestion controller picking the appropriate media rate that fits its estimate? ie rate switch between different media rates of the same sequence? I think that could be a very good idea! I'd be more interested in a standard set of *uncompressed* video test sequences, that we can use to test the combination of video codec and congestion control. If we're building something for interactive video congestion control we should assume live encoding, with the codec potentially having the flexibility to react to the network, and our testbed ought to reflect that. -- Colin Perkins http://csperkins.org/ --_000_E0F7A68B07B53F4FBD12DABD61CBA90E17E960ESESSMB307ericsso_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable
 
 
We proposed such a delay term based on our understanding of the behavi= or of live encoder
rate control: it takes time for the encoder output rate to converge on= the newly specified rate.
[Zaheduzzaman Sarker] Theoretically this can be done in next frame. Question is that do we need to tak= e care of optimization features of codec implementation in congestion control design?
As such, it also means that the encoder cannot conform to arbitrarily = frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in liv= e encoder response.  
[Zaheduzzaman Sarker] I don’t know = why this has to be true for CBR and not for VBR. The down switch on rate ch= ange should be fine with both CBR and VBR. Up switching is always dangerous and all my exp= erience says you will perhaps not jump to the requested up switche= d bitrate directly but like to gradually go there. Hence you have enough= time to reach to the higher bitrate. Anyway if the rate request is too frequent that= 217;s a sign of instable system to me. 
 
So, strictly speaking, maybe we should call that term "sluggishne= ss in response", or "convergence
time of encoder rate control”?  
[Zaheduzzaman Sarker] I will regard that = kind of sluggishness is a byproduct of optimiza= tion in coded and they will differ from implementation to imp= lementation.
 
The aforementioned value (~0.5s) is based on our observations of encod= er output traces
from one commercial video conferencing system. Other encoders may act = differently and we
have yet to test them more extensively to confirm our conjecture. Howe= ver, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will st= ay the same.   
[Zaheduzzaman Sarker] will = there be any <= /b>chance of making the survey available where we can look at the sluggishness in response in numerical values?=
 
 
And, to clarify your question, we are not making any specific assumpti= ons on why the encoder
is slow in response.  Rate control of live video encoding is an a= ctive research topic all by its
own, as the encoder needs to figure out what quantization parameters t= o choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Differen= t encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computati= onal complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we= are probably better off by
treating that as a black box. 
[Zaheduzzaman Sarker] so we  donR= 17;t care about sluggishness in response , right?
 
Best,
Xiaoqing
 
 
On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker <zaheduzzaman.sar= ker@ericsson.com> wrote:

Hi Xiaoging,
 
Clarification question.
 
>>a) delayed response to a rate change request (e.g., > 0.5s)=
 
 
Where is this delay coming from? Fixed delay or delay based o= f some assumption lack of resource to process the request? Are you thinking= of particular device, implementation?
 
BR

Zahed
 
 
Agree with Colin and Mo. 
 
For a statistical video traffic model, I would propose to break down t= he 2nd point (encoder rate control) in Mo's list further, to three sub-comp= onents:
 
a) delayed response to a rate change request (e.g., > 0.5s)
b) deviations from the requested rate (maybe as randomly fluctuating a= round the target?) 
c) the different frame types or coding modes inside the encoder will y= ield some encoded
frames much larger than others. This will lead to burstiness on time s= cale of frame intervals
(typically 33ms), and later smoothed out by the pacing/shaper. 
 
 
Best,
Xiaoqing
 
 
 
On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <mzanaty@cisco.com<= /a>> wrote:


I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small
set of fixed bitrates like DASH/HLS.

A more formal approach to the problem would be to decompose the factors
contributing to the final rate placed onto the network.
1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the
encoded frame size using constant quantization, i.e. no encoder rate
control.
2. Video encoder rate control behavior, which modulates the source
statistics to fit into network constraints over rather large time
intervals (>0.5s).
3. Network transmission pacing / shaper, which smooths out bursts in
encoder output to fit into network constraints over fine time intervals
(<0.5s).

It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any
real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors).

Mo



On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:

On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio.no> wrot= e:

On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@gmail.com> wrote:

Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that
the network can always follow it -

Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?

I think that could be a very good idea!
 
 
 --_000_E0F7A68B07B53F4FBD12DABD61CBA90E17E960ESESSMB307ericsso_-- From vsingh.ietf@gmail.com Mon Nov 4 10:07:02 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2B51511E8117; Mon, 4 Nov 2013 10:07:02 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.008 X-Spam-Level: X-Spam-Status: No, score=-2.008 tagged_above=-999 required=5 tests=[AWL=0.592, BAYES_00=-2.599, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id qw9MiKPBSkM1; Mon, 4 Nov 2013 10:06:59 -0800 (PST) Received: from mail-ie0-x234.google.com (mail-ie0-x234.google.com [IPv6:2607:f8b0:4001:c03::234]) by ietfa.amsl.com (Postfix) with ESMTP id 1700C11E812B; Mon, 4 Nov 2013 10:06:56 -0800 (PST) Received: by mail-ie0-f180.google.com with SMTP id e14so12652268iej.39 for ; Mon, 04 Nov 2013 10:06:54 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :content-type; bh=qn3hlVoSN94zfRmOH4bDPTixPJF0CA4SbpCBAvhvEeo=; b=JAV5cx6mCjQZPz0bnk4g4YFx7bZ9H25/6CEz61ri12m98rZovljen5H4TFVRXjp9Kq XKf8CM9iDTTtN8LImODnWLQlIqQXKoGSmAT5we+94k4u0yc4ztG+NjiMaa9pzUeDlmJn KdBQMGI8KEEDhnfBpNv+zA5ReHY7IJTR+cMeznDa28KUrbRR4vZu1zNZX4e4UGHjRhqz IiRxDFLfTYMmF8XYCj/fVANBuR1iHxhUtUxxzO97B8kxMjIshLB1J0CfKvNFCKgD+BrQ UrkGNj+mKz5SzXq6g2pF5bZzgbAaB9am+q2ysXJux5D8M0ZOzITICtKDIWhoJd8tQ7VK y2eQ== X-Received: by 10.50.39.51 with SMTP id m19mr12679666igk.51.1383588414417; Mon, 04 Nov 2013 10:06:54 -0800 (PST) MIME-Version: 1.0 Received: by 10.50.102.8 with HTTP; Mon, 4 Nov 2013 10:06:34 -0800 (PST) In-Reply-To: References: From: Varun Singh Date: Mon, 4 Nov 2013 10:06:34 -0800 Message-ID: To: "rtcweb@ietf.org" , "avt@ietf.org" , rmcat WG Content-Type: text/plain; charset=ISO-8859-1 Subject: Re: [rmcat] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 18:07:02 -0000 Hi, Slideset from yesterday's meeting are available at: https://speakerdeck.com/vr000m/media-traffic-generator-discussion Note takers, if you could send the notes to me, I can then process and post them to the mailing lists. Cheers, Varun On Wed, Oct 30, 2013 at 6:49 AM, Varun Singh wrote: > Hi all, > > Practical details for the meeting on **RMCAT Traffic models** > > Date: 03.11. 2013 > Time: 12:30 to 14:30. > Assigned Room: Regency A > > Cheers, > Varun > > On Sun, Oct 27, 2013 at 12:57 PM, Varun Singh wrote: >> Hi all, >> >> In RMCAT WG there has been discussion surrounding the use >> of a media traffic generator and to facilitate discussion, >> RMCAT is hosting a side meeting on Sunday, 03 November >> at 1300-1500. I suppose the room details will be announced >> shortly. >> >> Below is a list of questions that will help design or choose >> an appropriate traffic generator for Interactive multimedia traffic. >> The list is an initial set and mainly to foster discussion both >> on the mailing list and at the meeting. >> >> Mirja and Lars are not available that day, hence on their behalf, >> the meeting is hosted by Colin and I . >> >> Cheers, >> Varun >> --- >> >> Traffic generator: to model the intrinsic variability of video: >> >> - what target bit rates can be achieved? (max?, min?) >> - how much variability in media bit rate for a given target bit rate >> - how to model motion? capturing high motion leads to bursty video >> (higher than target bit rate), but by how much? can this even be done? >> >> Traffic generator: on responsiveness to a new target bitrate: >> >> - how quickly can the codec generate a lower bit rate? >> - immediately? does this depend on the new or requested bit rate? >> - what is immediately? is it next frame or a few frames later, until then >> does it generates at the current rate? >> - if not immediately, what bitrates (and duration) will it generate before >> meeting the target bitrate. >> >> >> - how quickly can the codec generate a higher bit rate? >> - immediately in the next frame? does this also depend on the new >> or requested bit rate? >> - not immediately but in steps of intermediate bitrates, i.e., intermediate >> bitrate for a short duration then an increase in step, repeating until >> reaching the target bit rate. >> >> - If the target bit rate is achieved in steps, can they be announced to the >> congestion controller, i.e., the congestion controller knows the ramp up >> curve to meet the target bit rate. >> >> - If the media bit rate has not yet reached the requested target bit rate and >> the congestion control requests a new target bit rate, how does the codec >> respond? >> >> Application design related: >> - should the congestion control be able to change the frame rate (video) or >> packetization time (audio) or is this something that the application controls. >> - should the congestion control be able to change video resolution? >> - should error-repair: NACKs, FEC be considered part of congestion control or >> outside it, i.e., it is something that the application does and not >> congestion control >> - If FEC is controlled by the congestion control, how quickly can the sending >> rate be adapted by reducing/adding redundancy (FEC)? is something link this >> done? Does this makes sense at all? >> - Apart from loss and RTT, what other congestion cues are currently used by >> congestion control algorithms? e.g., Decoding rate/goodput, application >> decode error rate, ECN, PCN, etc. >> - If the codec is data limited, i.e., producing a sending rate smaller than the >> one calculated by the congestion control, can the congestion control probe >> the bandwidth by sending additional data packets in a certain pattern (e.g., >> PathChirp)? would somehting link this help? >> - application input on prioritization: audio vs video (vs data) >> >> >> >> >> >> -- >> http://www.netlab.tkk.fi/~varun/ > > > > -- > http://www.netlab.tkk.fi/~varun/ -- http://www.netlab.tkk.fi/~varun/ From xiaoqzhu@cisco.com Mon Nov 4 10:54:24 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 7744521E8179 for ; Mon, 4 Nov 2013 10:54:24 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.568 X-Spam-Level: X-Spam-Status: No, score=-10.568 tagged_above=-999 required=5 tests=[AWL=0.030, BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id B5EgG+oy59jV for ; Mon, 4 Nov 2013 10:54:19 -0800 (PST) Received: from rcdn-iport-1.cisco.com (rcdn-iport-1.cisco.com [173.37.86.72]) by ietfa.amsl.com (Postfix) with ESMTP id 50FAE21E8164 for ; Mon, 4 Nov 2013 10:54:19 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=30879; q=dns/txt; s=iport; t=1383591259; x=1384800859; h=from:to:cc:subject:date:message-id:references: in-reply-to:mime-version; bh=2T5pt3jG9Y4tFis5EiI/CGXHrje5BUYbU0aL/bLJI/0=; b=MPmJJ7Ix+PG/fV63CWpt4qckVR4lswirPh6inQYouqC4sZ6E8/oNbzoo JWa5TahQiOG9CxVZYmSATG5M489bmUVxA4IwYGPW0y+ZEonnT+AXBkT3r bTyIcClf50beCFyPyze74/UWEXLZqG/tBLm7giXuqH3HxV8QElO14NtsF M=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AisFAG/sd1KtJXG+/2dsb2JhbABZgkMjIThTrQ+SL4ErFnSCJQEBAQQYYRACAQgRBAEBIQcHIREUCQgCBA4FCRKHVAMPAQywZINyDYlrjGiCbAQGAQmDF4EOA5QrgXSBa4EviyOFN4FogT6CKg X-IronPort-AV: E=Sophos;i="4.93,634,1378857600"; d="scan'208,217";a="280308762" Received: from rcdn-core2-3.cisco.com ([173.37.113.190]) by rcdn-iport-1.cisco.com with ESMTP; 04 Nov 2013 18:54:18 +0000 Received: from xhc-rcd-x07.cisco.com (xhc-rcd-x07.cisco.com [173.37.183.81]) by rcdn-core2-3.cisco.com (8.14.5/8.14.5) with ESMTP id rA4IsIdh012785 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Mon, 4 Nov 2013 18:54:18 GMT Received: from xmb-aln-x13.cisco.com ([fe80::5404:b599:9f57:834b]) by xhc-rcd-x07.cisco.com ([173.37.183.81]) with mapi id 14.03.0123.003; Mon, 4 Nov 2013 12:54:17 -0600 From: "Xiaoqing Zhu (xiaoqzhu)" To: Zaheduzzaman Sarker Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFCAAL5TAIAA66cwgAB6VoA= Date: Mon, 4 Nov 2013 18:54:17 +0000 Message-ID: <153150C5-3CD9-4123-A6F9-1C74E6115F9F@cisco.com> References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.152.12.125] Content-Type: multipart/alternative; boundary="_000_153150C53CD94123A6F91C74E6115F9Fciscocom_" MIME-Version: 1.0 Cc: rmcat WG , Varun Singh , "Mo Zanaty \(mzanaty\)" , Michael Welzl , Colin Perkins Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 04 Nov 2013 18:54:24 -0000 --_000_153150C53CD94123A6F91C74E6115F9Fciscocom_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Hi Zahed, Please see my detailed replies inline. To highlight my point, agree that we don't want to take care of codec imple= mentation issues in the design of a rmcat solution. However, we may want to capture t= he imperfections in live video encoder rate control when evaluating the rmcat = solution. Now, looks like we are having very different assumptions about what a live encoder can do =97 how fast and how perfectly it can conform to rate update requests. How about collecting some more data on this before making a call= ? What popular codecs do people care about, for this purpose? Cheers, Xiaoqing On Nov 4, 2013, at 11:54 AM, Zaheduzzaman Sarker > wrote: We proposed such a delay term based on our understanding of the behavior of= live encoder rate control: it takes time for the encoder output rate to converge on the = newly specified rate. [Zaheduzzaman Sarker] Theoretically this can be done in next frame. Questio= n is that do we need to take care of optimization features of codec impleme= ntation in congestion control design? Yes, the encoder will react in the next frame. But it may not be able to ge= nerate a frame with the perfectly fitting size (corresponding to the newly specified= rate). The encoder will need to guess at the new quantization parameter (QP) when = encoding an incoming frame with unknown scene complexity, and it takes time for the = size of the newly generated frames to converge around the new target. We don=92t need to take care of optimization features of codec implementati= on in the design of congestion control scheme. But, I thought we were discussi= ng about how to evaluate a solution using a video traffic model. Then, the behavior of the video traffic model had better capture the transient charac= teristics of a live encoder output. As such, it also means that the encoder cannot conform to arbitrarily frequ= ent rate change requests. Unlike CBR traffic, there is an inherent sluggishness in live enc= oder response. [Zaheduzzaman Sarker] I don=92t know why this has to be true for CBR and no= t for VBR. The down switch on rate change should be fine with both CBR and = VBR. Up switching is always dangerous and all my experience says you will p= erhaps not jump to the requested up switched bitrate directly but like to g= radually go there. Hence you have enough time to reach to the higher bitrat= e. Anyway if the rate request is too frequent that=92s a sign of instable s= ystem to me. By CBR I was referring to an ideal video source, which reacts promptly and perfectly to rate update requests from the congestion control solution. Looks like we =E5re agreeing on not having too frequent rate requests from the congestion control solution. The question now is how often is considere= d =93too frequent=94? Obviously it cannot be more frequent than the frame in= tervals (e.g., 33ms). Is an update once every 100ms (3 frames) acceptable? So, strictly speaking, maybe we should call that term "sluggishness in resp= onse", or "convergence time of encoder rate control=94? [Zaheduzzaman Sarker] I will regard that kind of sluggishness is a byproduc= t of optimization in coded and they will differ from implementation to impl= ementation. Yes. That=92s why we may want to capture a wide range of behaviors using a = statistical model, with a tunable parameter to represent different level of sluggishnes= s in different codec implementations. The aforementioned value (~0.5s) is based on our observations of encoder ou= tput traces from one commercial video conferencing system. Other encoders may act diffe= rently and we have yet to test them more extensively to confirm our conjecture. However, = our guess is that the order of magnitude of that value (say between 0.1s and 2s) will stay th= e same. [Zaheduzzaman Sarker] will there be any chance of making the survey availab= le where we can look at the sluggishness in response in numerical values? Alas, we don=92t have an extensive survey yet. Although, looking at the dis= cussions here, there is a real need for it. Or maybe the codec folks already have such inf= o? What popular codecs do we care about? The data we have collected so far should only be considered as an example. = Here is link to one graph as an illustration (Michael will present it on Wednesday = as well). The graph shows a rate upshift from 1Mbps to 1.5Mbps at time t=3D5 sec. https://www.dropbox.com/s/utg5jkgsvtkikvb/sample_trace_rate_upshift.png And, to clarify your question, we are not making any specific assumptions o= n why the encoder is slow in response. Rate control of live video encoding is an active rese= arch topic all by its own, as the encoder needs to figure out what quantization parameters to cho= ose for an incoming, unknown frame, so as to meet the specified target rate. Different encoders= may achieve different tradeoff in terms of responsiveness, accuracy, and computational = complexity (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are proba= bly better off by treating that as a black box. [Zaheduzzaman Sarker] so we don=92t care about sluggishness in response , = right? IMHO: no for design, yes for evaluations. Best, Xiaoqing On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker > wrote: Hi Xiaoging, Clarification question. >>a) delayed response to a rate change request (e.g., > 0.5s) Where is this delay coming from? Fixed delay or delay based of some assumpt= ion lack of resource to process the request? Are you thinking of particular= device, implementation? BR Zahed From: rmcat-bounces@ietf.org [mailto:rmcat-b= ounces@ietf.org] On Behalf Of Xiaoqing Zhu (xiaoqzhu) Sent: den 28 oktober 2013 13:15 To: Mo Zanaty (mzanaty) Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouve= r (03 Nov, 1300-1500) Agree with Colin and Mo. For a statistical video traffic model, I would propose to break down the 2n= d point (encoder rate control) in Mo's list further, to three sub-component= s: a) delayed response to a rate change request (e.g., > 0.5s) b) deviations from the requested rate (maybe as randomly fluctuating around= the target?) c) the different frame types or coding modes inside the encoder will yield = some encoded frames much larger than others. This will lead to burstiness on time scale = of frame intervals (typically 33ms), and later smoothed out by the pacing/shaper. Also, if we go for the approach of using uncompressed test video sequence f= or testing the combination of video codec and congestion control, we should= also recommend a reference codec and default configurations. So that focus= of the evaluations remains on the congestion control module. What do folk= s think of x264 (http://www.videolan.org/developers/x264.html)? Best, Xiaoqing On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) > wrote: I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small set of fixed bitrates like DASH/HLS. A more formal approach to the problem would be to decompose the factors contributing to the final rate placed onto the network. 1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the encoded frame size using constant quantization, i.e. no encoder rate control. 2. Video encoder rate control behavior, which modulates the source statistics to fit into network constraints over rather large time intervals (>0.5s). 3. Network transmission pacing / shaper, which smooths out bursts in encoder output to fit into network constraints over fine time intervals (<0.5s). It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors). Mo On 10/28/13, 1:49 PM, Colin Perkins > wrote: On 27 Oct 2013, at 11:50, Michael Welzl > wrote: On 27. okt. 2013, at 12:33, Varun Singh > wrote: Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that the network can always follow it - Do you mean preparing the same video sequence at multiple rates and the congestion controller picking the appropriate media rate that fits its estimate? ie rate switch between different media rates of the same sequence? I think that could be a very good idea! I'd be more interested in a standard set of *uncompressed* video test sequences, that we can use to test the combination of video codec and congestion control. If we're building something for interactive video congestion control we should assume live encoding, with the codec potentially having the flexibility to react to the network, and our testbed ought to reflect that. -- Colin Perkins http://csperkins.org/ --_000_153150C53CD94123A6F91C74E6115F9Fciscocom_ Content-Type: text/html; charset="Windows-1252" Content-ID: <20423347DB6A514F89E5F3D93B0C72AB@emea.cisco.com> Content-Transfer-Encoding: quoted-printable Hi Zahed, 

Please see my detailed replies inline. 

To highlight my point, agree that we don't want to take care of codec = implementation
issues in the design of a rmcat solution. However, we may want to capt= ure the
imperfections in live video encoder rate control when evaluating the r= mcat solution.  

Now, looks like we are having very different assumptions about what a = live
encoder can do =97 how fast and how perfectly it can conform to rate u= pdate
requests.  How about collecting some more data on this before mak= ing a call? 
What popular codecs do people care about, for this purpose? 

Cheers,
Xiaoqing

  
On Nov 4, 2013, at 11:54 AM, Zaheduzzaman Sarker <zaheduzzaman.sarker@ericsson.com>= wrote:

 
 
We proposed such a delay term based on our understanding of the behavi= or of live encoder
rate control: it takes time for the encoder output rate to converge on= the newly specified rate.
[Zaheduzzaman Sarker] Theoretically this can be done in next frame. Question is that do we need to take care of optimization featu= res of code= c implementation in congestion control design?

Yes, the encoder will react in the next frame. But it may not be able = to generate a
frame with the perfectly fitting size (corresponding to the newly spec= ified rate).
The encoder will need to guess at the new quantization parameter (QP) = when encoding
an incoming frame with unknown scene complexity, and it takes time for= the size
of the newly generated frames to converge around the new target. =

We don=92t need to take care of optimization features of codec impleme= ntation
in the design of congestion control scheme. But, I thought we were dis= cussing
about how to evaluate a solution using a video traffic model. Then, th= e
behavior of the video traffic model had better capture the transient c= haracteristics
of a live encoder output. 
  

As such, it also means that the encoder cannot conform to arbitrarily = frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in liv= e encoder response.  
[Zaheduzzaman Sarker] I don=92t know why this has to be true for = CBR and not for VBR. The down switch on rate change should be fine with both CBR and VBR. Up switching is always dangerous and all my experience says you will perhaps not jump to the requested up switched bitrate directly but like to gradually go there. Hence you have enough time to reach to the higher bitrate. = Anyway if the rate request is too frequent&= nbsp;that=92s a sign of instable system to me.<= /b> 

By CBR I was referring to an ideal video source, which reacts promptly= and
perfectly to rate update requests from the congestion control solution= . 

Looks like we =E5re agreeing on not having too frequent rate requests = from 
the congestion control solution. The question now is how often is cons= idered
=93too frequent=94?  Obviously it cannot be more frequent than th= e frame intervals
(e.g., 33ms). Is an update once every 100ms (3 frames) acceptable? &nb= sp;

 
So, strictly speaking, maybe we should call that term "sluggishne= ss in response", or "convergence
time of encoder rate control=94?  
[Zaheduzzaman Sarker] I will regard that kind of sluggishness is = a byproduct=  of optimization = in coded= and they will differ = from implementation to implementation.
 

Yes. That=92s why we may want to capture a wide range of behaviors usi= ng a statistical
model, with a tunable parameter to represent different level of sluggi= shness in different
codec implementations. 



The aforementioned value (~0.5s) is based on our observations of encod= er output traces
from one commercial video conferencing system. Other encoders may act = differently and we
have yet to test them more extensively to confirm our conjecture. Howe= ver, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will st= ay the same.   
[Zaheduzzaman Sarker] will there be <= /i>any chance of making the survey available = where we can look = at th= e sluggishness in = response in numerical values?
 

Alas, we don=92t have an extensive survey yet. Although, looking at th= e discussions here, 
there is a real need for it. Or maybe the codec folks already have suc= h info? What popular
codecs do we care about?

The data we have collected so far should only be considered as an exam= ple. Here is
link to one graph as an illustration (Michael will present it on Wedne= sday as well).
The graph shows a rate upshift from 1Mbps to 1.5Mbps at time t=3D5 sec= .  



 
And, to clarify your question, we are not making any specific assumpti= ons on why the encoder
is slow in response.  Rate control of live video encoding is an a= ctive research topic all by its
own, as the encoder needs to figure out what quantization parameters t= o choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Differen= t encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computati= onal complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we= are probably better off by
treating that as a black box. 
[Zaheduzzaman Sarker] so we  don=92t care about sluggishness= in response , right?

IMHO: no for design, yes for evaluations. 

 
Best,
Xiaoqing
 
 
On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker <zaheduzzaman.sar= ker@ericsson.com> wrote:

Hi Xiaoging,
 
Clarification question.
 
>>a) delayed response to a rate change request (e.g., > 0.5s)=
 
 
Where is this delay coming from? Fixed delay or delay based = of some assumption lack of resource to process the request? Are you thinkin= g of particular device, implementation?
 
BR

Zahed
 
F= rom: rmcat-bounces@ietf.org [mailto:rmcat-bounces@ietf.org] On Behalf Of Xiaoqing Zhu (xiaoqzhu)
Sent: den 28 oktober 2013 13:15
To: Mo Zanaty (mzanaty)
Cc: rmcat WG; Colin Perkins; Michael Welzl; Varun Singh
Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model= in Vancouver (03 Nov, 1300-1500)
 
Agree with Colin and Mo. 
 
For a statistical video traffic model, I would propose to break down t= he 2nd point (encoder rate control) in Mo's list further, to three sub-comp= onents:
 
a) delayed response to a rate change request (e.g., > 0.5s)
b) deviations from the requested rate (maybe as randomly fluctuating a= round the target?) 
c) the different frame types or coding modes inside the encoder will y= ield some encoded
frames much larger than others. This will lead to burstiness on time s= cale of frame intervals
(typically 33ms), and later smoothed out by the pacing/shaper. 
 
Also, if we go for the approach of using uncompressed test video seque= nce for testing the combination of video codec and congestion control,= we should also recommend a reference codec and default configurations= . So that focus of the evaluations remains on the congestion control module.  What do folks think of x264 (= http://www.videolan.org/developers/x264.html)? 
 
Best,
Xiaoqing
 
 
 
On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <mzanaty@cisco.com<= /a>> wrote:


I agree with Colin. We should be focused on live encoding where the encoder rate control is highly granular (often fully smooth) and coupled to the network congestion control, not pre-encoded streaming of a small
set of fixed bitrates like DASH/HLS.

A more formal approach to the problem would be to decompose the factors
contributing to the final rate placed onto the network.
1. Video content / source statistics, using a large set of representative sequences. An estimate of the entropy per frame could simply be the
encoded frame size using constant quantization, i.e. no encoder rate
control.
2. Video encoder rate control behavior, which modulates the source
statistics to fit into network constraints over rather large time
intervals (>0.5s).
3. Network transmission pacing / shaper, which smooths out bursts in
encoder output to fit into network constraints over fine time intervals
(<0.5s).

It should be possible to model all three in an abstract way to arrive at some statistical model that can then be used for all tests without any
real video content or encoding (to avoid introducing noise in comparisons if there was any variability in any of the factors).

Mo



On 10/28/13, 1:49 PM, Colin Perkins <csp@csperkins.org> wrote:

On 27 Oct 2013, at 11:50, Michael Welzl <michawe@ifi.uio.no> wrot= e:

On 27. okt. 2013, at 12:33, Varun Singh <vsingh.ietf@gmail.com> wrote:

Now, next, what if we'd have a set of such sequences, and then stack them over a codec that produces quite arbitrary bitrates, assuming that
the network can always follow it -

Do you mean preparing the same video sequence at multiple rates and the
congestion controller picking the appropriate media rate that fits its
estimate? ie rate switch between different media rates of the same
sequence?

I think that could be a very good idea!

--_000_153150C53CD94123A6F91C74E6115F9Fciscocom_-- From lars@netapp.com Mon Nov 4 16:53:45 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 7B3DC21E818C for ; Mon, 4 Nov 2013 16:53:45 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.716 X-Spam-Level: X-Spam-Status: No, score=-4.716 tagged_above=-999 required=5 tests=[AWL=-2.117, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id TcmjlyelqA6i for ; Mon, 4 Nov 2013 16:53:41 -0800 (PST) Received: from mx11.netapp.com (mx11.netapp.com [216.240.18.76]) by ietfa.amsl.com (Postfix) with ESMTP id 3753D11E825E for ; Mon, 4 Nov 2013 16:53:41 -0800 (PST) X-IronPort-AV: E=Sophos;i="4.93,636,1378882800"; d="asc'?scan'208";a="69547366" Received: from vmwexceht01-prd.hq.netapp.com ([10.106.76.239]) by mx11-out.netapp.com with ESMTP; 04 Nov 2013 16:53:41 -0800 Received: from SACEXCMBX01-PRD.hq.netapp.com ([169.254.2.51]) by vmwexceht01-prd.hq.netapp.com ([10.106.76.239]) with mapi id 14.03.0123.003; Mon, 4 Nov 2013 16:53:40 -0800 From: "Eggert, Lars" To: rmcat WG Thread-Topic: please send slides now Thread-Index: AQHO2cF34XUt5laDRUmsZBm6yeegQQ== Date: Tue, 5 Nov 2013 00:53:39 +0000 Message-ID: References: <526E86E5.6010604@meetecho.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [10.104.60.117] Content-Type: multipart/signed; boundary="Apple-Mail=_4A47325E-C5B8-4B72-AE7F-D4C1F5038E45"; protocol="application/pgp-signature"; micalg=pgp-sha1 MIME-Version: 1.0 Subject: [rmcat] please send slides now X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list Reply-To: "" List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 00:53:45 -0000 --Apple-Mail=_4A47325E-C5B8-4B72-AE7F-D4C1F5038E45 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Hi, RMCAT presenters, please email your final slides to us chairs. Thanks, Lars On Oct 29, 2013, at 2:32, "Eggert, Lars" wrote: > FYI, we're being webcast. >=20 > This means that we'll need FINAL slides from all presenters uploaded = 24h before the meeting, because they need to be preprocessed. >=20 > Lars >=20 > Begin forwarded message: >=20 >> From: Meetecho IETF support >> Subject: Meetecho support at IETF-88 >> Date: October 28, 2013 23:46:45 GMT+08:00 >> To: >>=20 >> Dear chair(s), >>=20 >> this email is to confirm Meetecho support for your WG/BOF meeting = session at IETF-88. >>=20 >> The agenda of supported sessions is available at: >> http://ietf88.conf.meetecho.com. >>=20 >> If you plan to have remote presenters, you're kindly requested to = inform us in proper advance, since this needs special set-up and a = preliminary test with the remote speaker. >>=20 >> *The deadline for requesting remote presentation support is November = 1.* >>=20 >> Thanks, >> the Meetecho team >>=20 >> --=20 >> Meetecho s.r.l. >> www.meetecho.com >=20 --Apple-Mail=_4A47325E-C5B8-4B72-AE7F-D4C1F5038E45 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="signature.asc" Content-Type: application/pgp-signature; name="signature.asc" Content-Description: Message signed with OpenPGP using GPGMail -----BEGIN PGP SIGNATURE----- iQCVAwUBUnhBk9ZcnpRveo1xAQJQSwQApRACLj24w2bFfaKZJKEsSdcM7kY7quyW VBpEKVkMu3ChUh+feaAqmbEyuo8/4UrAHUvJczbWJ8ww69onrwxBSm0yitcVMl7t biBmTGSQGfgf1bo0C3FeKx4BlvO6qIHF9N70BNe0AeZY24SGsRZqVEqZSDHNEOnM 7bgh77Gpb8w= =5BqZ -----END PGP SIGNATURE----- --Apple-Mail=_4A47325E-C5B8-4B72-AE7F-D4C1F5038E45-- From zaheduzzaman.sarker@ericsson.com Mon Nov 4 22:02:13 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E0AB011E8189 for ; Mon, 4 Nov 2013 22:02:13 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -3.511 X-Spam-Level: X-Spam-Status: No, score=-3.511 tagged_above=-999 required=5 tests=[AWL=-0.912, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id TnEjUCmeAlai for ; Mon, 4 Nov 2013 22:02:09 -0800 (PST) Received: from sesbmg20.ericsson.net (sesbmg20.ericsson.net [193.180.251.56]) by ietfa.amsl.com (Postfix) with ESMTP id 6CF8C11E8182 for ; Mon, 4 Nov 2013 22:01:57 -0800 (PST) X-AuditID: c1b4fb38-b7f2c8e000006d25-69-527889d32fc9 Received: from ESESSHC018.ericsson.se (Unknown_Domain [153.88.253.124]) by sesbmg20.ericsson.net (Symantec Mail Security) with SMTP id F3.2E.27941.3D988725; Tue, 5 Nov 2013 07:01:55 +0100 (CET) Received: from [153.88.63.128] (153.88.183.153) by smtp.internal.ericsson.com (153.88.183.74) with Microsoft SMTP Server id 14.2.328.9; Tue, 5 Nov 2013 07:01:54 +0100 Message-ID: <527889D0.2050808@ericsson.com> Date: Mon, 4 Nov 2013 22:01:52 -0800 From: Zaheduzzaman Sarker Organization: Ericsson AB User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:24.0) Gecko/20100101 Thunderbird/24.1.0 MIME-Version: 1.0 To: "Xiaoqing Zhu (xiaoqzhu)" References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> <153150C5-3CD9-4123-A6F9-1C74E6115F9F@cisco.com> In-Reply-To: <153150C5-3CD9-4123-A6F9-1C74E6115F9F@cisco.com> Content-Type: text/plain; charset="windows-1252"; format=flowed Content-Transfer-Encoding: 8bit X-Brightmail-Tracker: H4sIAAAAAAAAA+NgFtrILMWRmVeSWpSXmKPExsUyM+Jvje7lzooggxW9FhbLX55gtPhxdier xYsHc5gsVt/8wGaxt8nf4nXLMiYHNo8pvzeyeky7f5/NY+esu+weS5b8ZPJYvfohcwBrFJdN SmpOZllqkb5dAlfG42/P2Qvexlfs3/yUtYHxh28XIyeHhICJxOdNm9khbDGJC/fWs3UxcnEI CRxhlPh69hEjhLOKUaLj9lVGkCpeAW2JhQsms4DYLAIqEnsOP2bqYuTgYBOwkXi82A8kzC8g KbGhYTcziC0qECXxcPY0ZohWQYmTM5+AtYoIGElMmf8CbD6zwA5GiYtfzrGCJIQFkiT2b/nH BmILCbxnklixKArE5hSwlfhz8AbYLmYBe4kHW8tAwswC8hLNW2czg4SFBHQlul7GTWAUmoVk 2yyEhllIGhYwMq9i5ChOLU7KTTcy2MQIDPaDW35b7GC8/NfmEKM0B4uSOO/Ht85BQgLpiSWp 2ampBalF8UWlOanFhxiZODilGhh3X9WsnO+n3fyj5wm/QswRweCblw+LJ8YWND5vUu+s8Iwq OFTddICzVvS8nKO2erfie4N5yW/SK7mCNJ597kvjOS1a9OOodk+XufuJy7sfBH9Y83Z//d+U FQXJr+bOrpTqYqs+5r1bh/8fL09M38S5f99PZTv+PuPY6t7p2awutseYD6QdtVJiKc5INNRi LipOBABRJaPORAIAAA== Cc: rmcat WG , Varun Singh , "Mo Zanaty \(mzanaty\)" , Michael Welzl , Colin Perkins Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 06:02:14 -0000 Hi Xiaoqing, Thanks for your detailed reply. Please see inline. BR Zahed On 2013-11-04 10:54, Xiaoqing Zhu (xiaoqzhu) wrote: > Hi Zahed, > > Please see my detailed replies inline. > > To highlight my point, agree that we don't want to take care of codec > implementation > issues in the design of a rmcat solution. However, we may want to > capture the > imperfections in live video encoder rate control when evaluating the > rmcat solution. I guess you are calling it "imperfections" from congestion control designers point of view :-). I think the "imperfections" are there for particular and valid reasons. I hope rmcat will able to list the possible imperfections if we choose to go in that path. > > Now, looks like we are having very different assumptions about what a live > encoder can do — how fast and how perfectly it can conform to rate update > requests. How about collecting some more data on this before making a > call? that would be nice > What popular codecs do people care about, for this purpose? that is a very controversial question and I guess you asked that question to the WG. > > Cheers, > Xiaoqing > > > On Nov 4, 2013, at 11:54 AM, Zaheduzzaman Sarker > > wrote: > >> We proposed such a delay term based on our understanding of the >> behavior of live encoder >> rate control: it takes time for the encoder output rate to converge on >> the newly specified rate. >> */[Zaheduzzaman Sarker]/**/Theoretically/**/this can be done in next >> frame. Question is that do we need to take care of optimization >> features of/**/codec implementation in congestion control design?/* > > Yes, the encoder will react in the next frame. But it may not be able to > generate a > frame with the perfectly fitting size (corresponding to the newly > specified rate). > The encoder will need to guess at the new quantization parameter (QP) > when encoding > an incoming frame with unknown scene complexity, and it takes time for > the size > of the newly generated frames to converge around the new target. > > We don’t need to take care of optimization features of codec implementation > in the design of congestion control scheme. But, I thought we were > discussing > about how to evaluate a solution using a video traffic model. Then, the > behavior of the video traffic model had better capture the transient > characteristics > of a live encoder output. I guess then the “capturing transient characteristics of a live encoder output” need to be a part of the RMCAT requirements draft, right? The evaluation process is there to make sure requirements are fulfilled. > >> As such, it also means that the encoder cannot conform to arbitrarily >> frequent rate change >> requests. Unlike CBR traffic, there is an inherent sluggishness in >> live encoder response. >> */[Zaheduzzaman Sarker]/**/I don’t know why this has to be true for >> CBR and not for VBR. The down switch on rate change should be fine >> with both CBR and VBR./**/Up switching is always/**/dangerous/**/and >> all my experience says you will perhaps not jum/**/p/**/to >> the/**/request/**/ed/**/up/**/switched/**/bitrate directly but like to >> gradually go there. Hence you have/**/enough/**/time to reach to the >> highe/**/r bitrate./**//**/Anyway if the rate request is too >> frequent/**/that’s/**//**/a sign of instable system to me./**//* > > By CBR I was referring to an ideal video source, which reacts promptly and > perfectly to rate update requests from the congestion control solution. > > Looks like we åre agreeing on not having too frequent rate requests from > the congestion control solution. The question now is how often is considered > “too frequent”? Obviously it cannot be more frequent than the frame > intervals > (e.g., 33ms). Is an update once every 100ms (3 frames) acceptable? I was referring to consecutive up switching and down switching of rate when talking about too frequent rate requests and instability not the rate request in the same direction. are we talking about same thing? However, I dont think we need to settle down to a hard coded value. In some networks things might change slowly and in some the change is quick. To me a better congestion control algorithm need to address the network change in such a way that it does not show ping-pong pattern in rate change requests between up switching and down switching of bitrate. > >> *//* >> So, strictly speaking, maybe we should call that term "sluggishness in >> response", or "convergence >> time of encoder rate control”? >> */[Zaheduzzaman Sarker]/**/I will regard that kind of sluggishness is >> a/**/byproduct/**/of optimization/**/in/**/coded and they >> will/**/differ/**/from implementation to implementation./* > > Yes. That’s why we may want to capture a wide range of behaviors using a > statistical > model, with a tunable parameter to represent different level of > sluggishness in different > codec implementations. > > > >> The aforementioned value (~0.5s) is based on our observations of >> encoder output traces >> from one commercial video conferencing system. Other encoders may act >> differently and we >> have yet to test them more extensively to confirm our conjecture. >> However, our guess is that >> the order of magnitude of that value (say between 0.1s and 2s) will >> stay the same. >> */[Zaheduzzaman >> Sarker]/**/will/**/there/**/be/**/a/**/ny/**//**/chance of making >> the/**/survey available/**/where/**/we can look/**/at/**/the >> sluggishness in/**/response in numerical values?/* > > Alas, we don’t have an extensive survey yet. Although, looking at the > discussions here, > there is a real need for it. Or maybe the codec folks already have such > info? What popular > codecs do we care about? > > The data we have collected so far should only be considered as an > example. Here is > link to one graph as an illustration (Michael will present it on > Wednesday as well). > The graph shows a rate upshift from 1Mbps to 1.5Mbps at time t=5 sec. > > https://www.dropbox.com/s/utg5jkgsvtkikvb/sample_trace_rate_upshift.png > > >> And, to clarify your question, we are not making any specific >> assumptions on why the encoder >> is slow in response. Rate control of live video encoding is an active >> research topic all by its >> own, as the encoder needs to figure out what quantization parameters >> to choose for an incoming, >> unknown frame, so as to meet the specified target rate. Different >> encoders may achieve >> different tradeoff in terms of responsiveness, accuracy, and >> computational complexity >> (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are >> probably better off by >> treating that as a black box. >> */[Zaheduzzaman Sarker]/**/so we don’t care about sluggishness in >> response , right?/* > > IMHO: no for design, yes for evaluations. I am bit confused if the solution is not designed to take some properties of the system into account, why should one evaluate it against such properties? if you like to evaluate a solution against some properties, please put them in them in the requirements. > >> Best, >> Xiaoqing >> On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker >> <_zaheduzzaman.sarker@ericsson.com_ >> > wrote: >> >> Hi Xiaoging, >> Clarification question. >> >>a) delayed response to a rate change request (e.g., > 0.5s) >> Where is this delay coming from? Fixed delay or delay based of some >> assumption lack of resource to process the request? Are you thinking >> of particular device, implementation? >> BR >> >> Zahed >> *From:* _rmcat-bounces@ietf.org_ >> [_mailto:rmcat-bounces@ietf.org_] *On >> Behalf Of***Xiaoqing Zhu (xiaoqzhu) >> *Sent:* den 28 oktober 2013 13:15 >> *To:* Mo Zanaty (mzanaty) >> *Cc:* rmcat WG; Colin Perkins; Michael Welzl; Varun Singh >> *Subject:* Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in >> Vancouver (03 Nov, 1300-1500) >> Agree with Colin and Mo. >> For a statistical video traffic model, I would propose to break down >> the 2nd point (encoder rate control) in Mo's list further, to three >> sub-components: >> a) delayed response to a rate change request (e.g., > 0.5s) >> b) deviations from the requested rate (maybe as randomly fluctuating >> around the target?) >> c) the different frame types or coding modes inside the encoder will >> yield some encoded >> frames much larger than others. This will lead to burstiness on time >> scale of frame intervals >> (typically 33ms), and later smoothed out by the pacing/shaper. >> Also, if we go for the approach of using uncompressed test video >> sequence for testing the combination of video codec and congestion >> control, we should also recommend a reference codec and default >> configurations. So that focus of the evaluations remains on the >> congestion control module. What do folks think of x264 >> (_http://www.videolan.org/developers/x264.html_)? >> Best, >> Xiaoqing >> On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <_mzanaty@cisco.com_ >> > wrote: >> >> >> I agree with Colin. We should be focused on live encoding where the >> encoder rate control is highly granular (often fully smooth) and coupled >> to the network congestion control, not pre-encoded streaming of a small >> set of fixed bitrates like DASH/HLS. >> >> A more formal approach to the problem would be to decompose the factors >> contributing to the final rate placed onto the network. >> 1. Video content / source statistics, using a large set of representative >> sequences. An estimate of the entropy per frame could simply be the >> encoded frame size using constant quantization, i.e. no encoder rate >> control. >> 2. Video encoder rate control behavior, which modulates the source >> statistics to fit into network constraints over rather large time >> intervals (>0.5s). >> 3. Network transmission pacing / shaper, which smooths out bursts in >> encoder output to fit into network constraints over fine time intervals >> (<0.5s). >> >> It should be possible to model all three in an abstract way to arrive at >> some statistical model that can then be used for all tests without any >> real video content or encoding (to avoid introducing noise in comparisons >> if there was any variability in any of the factors). >> >> Mo >> >> >> >> On 10/28/13, 1:49 PM, Colin Perkins <_csp@csperkins.org_ >> > wrote: >> >> On 27 Oct 2013, at 11:50, Michael Welzl <_michawe@ifi.uio.no_ >> > wrote: >> >> On 27. okt. 2013, at 12:33, Varun Singh <_vsingh.ietf@gmail.com_ >> > wrote: >> >> Now, next, what if we'd have a set of such sequences, and then stack >> them over a codec that produces quite arbitrary bitrates, assuming that >> the network can always follow it - >> >> Do you mean preparing the same video sequence at multiple rates and the >> congestion controller picking the appropriate media rate that fits its >> estimate? ie rate switch between different media rates of the same >> sequence? >> >> I think that could be a very good idea! >> >> I'd be more interested in a standard set of *uncompressed* video test >> sequences, that we can use to test the combination of video codec and >> congestion control. If we're building something for interactive video >> congestion control we should assume live encoding, with the codec >> potentially having the flexibility to react to the network, and our >> testbed ought to reflect that. >> >> -- >> Colin Perkins >> _http://csperkins.org/_ > -- Zahed ================================================== ANM ZAHEDUZZAMAN SARKER Ericsson AB Multimedia Technologies Laboratoriegränd 11 97128 Luleå, Sweden Phone +46 10 717 37 43 Fax +46 920 996 21 SMS/MMS +46 76 115 37 43 zaheduzzaman.sarker@ericsson.com www.ericsson.com ================================================== From mirja.kuehlewind@ikr.uni-stuttgart.de Tue Nov 5 02:58:10 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 25F4521E821E for ; Tue, 5 Nov 2013 02:58:10 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.908 X-Spam-Level: X-Spam-Status: No, score=-1.908 tagged_above=-999 required=5 tests=[AWL=0.041, BAYES_00=-2.599, HELO_EQ_DE=0.35, MIME_8BIT_HEADER=0.3] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id rEUagmZtXYhh for ; Tue, 5 Nov 2013 02:58:04 -0800 (PST) Received: from mailsrv.ikr.uni-stuttgart.de (mailsrv.ikr.uni-stuttgart.de [129.69.170.2]) by ietfa.amsl.com (Postfix) with ESMTP id 068D521E8219 for ; Tue, 5 Nov 2013 02:58:03 -0800 (PST) Received: from netsrv1.ikr.uni-stuttgart.de (netsrv1-c [10.11.12.12]) by mailsrv.ikr.uni-stuttgart.de (Postfix) with ESMTP id 62A2B6010B; Tue, 5 Nov 2013 11:58:02 +0100 (CET) Received: from vpn-2-cl195 (vpn-2-cl195 [10.41.21.195]) by netsrv1.ikr.uni-stuttgart.de (Postfix) with ESMTP id 5C23D6010A; Tue, 5 Nov 2013 11:58:02 +0100 (CET) From: Mirja =?iso-8859-1?q?K=FChlewind?= To: rmcat@ietf.org Date: Tue, 5 Nov 2013 11:58:01 +0100 User-Agent: KMail/1.9.10 (enterprise35 0.20101217.1207316) MIME-Version: 1.0 Content-Type: Text/Plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit Content-Disposition: inline Message-Id: <201311051158.01614.mirja.kuehlewind@ikr.uni-stuttgart.de> Cc: Zaheduzzaman Sarker Subject: [rmcat] Presentation on Sprout @ ICCRG X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 10:58:10 -0000 Hi working group, please be aware that there will a presentation on the evaluation of Sprout at the ICCRG meeting: 1610-1840 Tuesday Afternoon Session III (Regency B) Congestion control issues in Real-time Communication - "Sprout" as an example by Zaheduzzaman Sarker As Sprout was already discussed on the rmact list, this presentation was initially requested for rmcat. But as Sprout is not a directly possible candidate and our agenda is quite full, we decided to redirect this talk to ICCRG. Anyway there are intersting finding which might be relevant for rmcat design as well, thus I would like to point the group to this talk and encourage people to go there. Mirja From xiaoqzhu@cisco.com Tue Nov 5 08:40:01 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C328D21E8137 for ; Tue, 5 Nov 2013 08:39:58 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.599 X-Spam-Level: X-Spam-Status: No, score=-10.599 tagged_above=-999 required=5 tests=[AWL=0.000, BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id WUN1jhz7Tw3Q for ; Tue, 5 Nov 2013 08:39:51 -0800 (PST) Received: from rcdn-iport-2.cisco.com (rcdn-iport-2.cisco.com [173.37.86.73]) by ietfa.amsl.com (Postfix) with ESMTP id ABC1011E815A for ; Tue, 5 Nov 2013 08:38:39 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=14066; q=dns/txt; s=iport; t=1383669519; x=1384879119; h=from:to:cc:subject:date:message-id:references: in-reply-to:content-id:content-transfer-encoding: mime-version; bh=6qfYhd4RrCANLG/1zVtqX6K815U+/V8QoJvit/UIO94=; b=L3mRL66tkbHSOZLCpNgG8mmimE04etxvu0415LIxX9j1YW/uv/Cjw+pP ngW5cIm0eEpWSgp1n6Gt6pnS8x5otdGjeOl4JVbYQbTjQWUAf5wuqZRdt 9O+fPNc4KTOfGraqipOeLzLyc2j5/yZ4Rj9GqjlOpUxWAX2Qd/rM3pyB6 M=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: As8JAH4eeVKtJV2c/2dsb2JhbABZgmYhOFOqdJRDgSwWdIIlAQEBAwEYMTAFCQICAQgRBAEBARUSBxsGERQJCAIEDgUJEodUAwkGAQywb4QGDYlrBIxjgj8zAgUKgxaBDwOUK4F0gWuBL4sjhTeBaIE+gio X-IronPort-AV: E=Sophos;i="4.93,640,1378857600"; d="scan'208";a="280929974" Received: from rcdn-core-5.cisco.com ([173.37.93.156]) by rcdn-iport-2.cisco.com with ESMTP; 05 Nov 2013 16:38:38 +0000 Received: from xhc-aln-x12.cisco.com (xhc-aln-x12.cisco.com [173.36.12.86]) by rcdn-core-5.cisco.com (8.14.5/8.14.5) with ESMTP id rA5GccbP021644 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Tue, 5 Nov 2013 16:38:38 GMT Received: from xmb-aln-x13.cisco.com ([fe80::5404:b599:9f57:834b]) by xhc-aln-x12.cisco.com ([173.36.12.86]) with mapi id 14.03.0123.003; Tue, 5 Nov 2013 10:38:37 -0600 From: "Xiaoqing Zhu (xiaoqzhu)" To: Zaheduzzaman Sarker Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO1A0MgwVs6QKB+UiBda2MQkwFlZoK4IGAgAjWMFCAAL5TAIAA66cwgAB6VoCAALqFAIAAseaA Date: Tue, 5 Nov 2013 16:38:36 +0000 Message-ID: <05B59943-0DCE-406A-AA23-B2AB93167C2B@cisco.com> References: <3879D71E758A7E4AA99A35DD8D41D3D91E124C57@xmb-rcd-x14.cisco.com> <1F38856F-160F-4EE0-AFEA-7F636784E1FB@cisco.com> <153150C5-3CD9-4123-A6F9-1C74E6115F9F@cisco.com> <527889D0.2050808@ericsson.com> In-Reply-To: <527889D0.2050808@ericsson.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.21.115.63] Content-Type: text/plain; charset="Windows-1252" Content-ID: <8629023070A82A49A296C3405382C540@emea.cisco.com> Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Cc: rmcat WG , Varun Singh , "Mo Zanaty \(mzanaty\)" , Michael Welzl , Colin Perkins Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 16:40:01 -0000 Zahed,=20 I mostly agree with what you=92ve said, esp. that:=20 * the notion of =93imperfections=94 is from congestion control point of vid= eo, live video encoder rate control have good reasons to act that way, and the design of a good rmcat solution should be mindful about that.=20 So, ok, I should revise my last statement as =93yes for design, and yes for evaluations=94. =20 Just that I don=92t yet see how we can state that crisply as a requirement.= =20 Can we say something more concrete than =93works well with a real-world video codec=94, which seems to be too obvious anyway? =20 Maybe, we could say =93no undesirable ping-pong patterns=94 in rate request change, and then specify what we mean by an undesirable ping-pong pattern. Then we inevitably need to pick a value (or a range of value, depending on which network we operate at) as undesirably rate change frequency.=20 Thanks, Xiaoqing On Nov 5, 2013, at 12:01 AM, Zaheduzzaman Sarker wrote: > Hi Xiaoqing, >=20 > Thanks for your detailed reply. Please see inline. >=20 > BR >=20 > Zahed >=20 > On 2013-11-04 10:54, Xiaoqing Zhu (xiaoqzhu) wrote: >> Hi Zahed, >>=20 >> Please see my detailed replies inline. >>=20 >> To highlight my point, agree that we don't want to take care of codec >> implementation >> issues in the design of a rmcat solution. However, we may want to >> capture the >> imperfections in live video encoder rate control when evaluating the >> rmcat solution. > I guess you are calling it "imperfections" from congestion control design= ers point of view :-). I think the "imperfections" are there for particular= and valid reasons. I hope rmcat will able to list the possible imperfectio= ns if we choose to go in that path. >>=20 >> Now, looks like we are having very different assumptions about what a li= ve >> encoder can do =97 how fast and how perfectly it can conform to rate upd= ate >> requests. How about collecting some more data on this before making a >> call? > that would be nice >> What popular codecs do people care about, for this purpose? > that is a very controversial question and I guess you asked that question= to the WG. >>=20 >> Cheers, >> Xiaoqing >>=20 >>=20 >> On Nov 4, 2013, at 11:54 AM, Zaheduzzaman Sarker >> > > wrote: >>=20 >>> We proposed such a delay term based on our understanding of the >>> behavior of live encoder >>> rate control: it takes time for the encoder output rate to converge on >>> the newly specified rate. >>> */[Zaheduzzaman Sarker]/**/Theoretically/**/this can be done in next >>> frame. Question is that do we need to take care of optimization >>> features of/**/codec implementation in congestion control design?/* >>=20 >> Yes, the encoder will react in the next frame. But it may not be able to >> generate a >> frame with the perfectly fitting size (corresponding to the newly >> specified rate). >> The encoder will need to guess at the new quantization parameter (QP) >> when encoding >> an incoming frame with unknown scene complexity, and it takes time for >> the size >> of the newly generated frames to converge around the new target. >>=20 >> We don=92t need to take care of optimization features of codec implement= ation >> in the design of congestion control scheme. But, I thought we were >> discussing >> about how to evaluate a solution using a video traffic model. Then, the >> behavior of the video traffic model had better capture the transient >> characteristics >> of a live encoder output. > I guess then the =93capturing transient characteristics of a live encoder= output=94 need to be a part of the RMCAT requirements draft, right? The ev= aluation process is there to make sure requirements are fulfilled. >>=20 >>> As such, it also means that the encoder cannot conform to arbitrarily >>> frequent rate change >>> requests. Unlike CBR traffic, there is an inherent sluggishness in >>> live encoder response. >>> */[Zaheduzzaman Sarker]/**/I don=92t know why this has to be true for >>> CBR and not for VBR. The down switch on rate change should be fine >>> with both CBR and VBR./**/Up switching is always/**/dangerous/**/and >>> all my experience says you will perhaps not jum/**/p/**/to >>> the/**/request/**/ed/**/up/**/switched/**/bitrate directly but like to >>> gradually go there. Hence you have/**/enough/**/time to reach to the >>> highe/**/r bitrate./**//**/Anyway if the rate request is too >>> frequent/**/that=92s/**//**/a sign of instable system to me./**//* >>=20 >> By CBR I was referring to an ideal video source, which reacts promptly a= nd >> perfectly to rate update requests from the congestion control solution. >>=20 >> Looks like we =E5re agreeing on not having too frequent rate requests fr= om >> the congestion control solution. The question now is how often is consid= ered >> =93too frequent=94? Obviously it cannot be more frequent than the frame >> intervals >> (e.g., 33ms). Is an update once every 100ms (3 frames) acceptable? > I was referring to consecutive up switching and down switching of rate wh= en talking about too frequent rate requests and instability not the rate re= quest in the same direction. are we talking about same thing? >=20 > However, I dont think we need to settle down to a hard coded value. In so= me networks things might change slowly and in some the change is quick. To = me a better congestion control algorithm need to address the network change= in such a way that it does not show ping-pong pattern in rate change reque= sts between up switching and down switching of bitrate. >>=20 >>> *//* >>> So, strictly speaking, maybe we should call that term "sluggishness in >>> response", or "convergence >>> time of encoder rate control=94? >>> */[Zaheduzzaman Sarker]/**/I will regard that kind of sluggishness is >>> a/**/byproduct/**/of optimization/**/in/**/coded and they >>> will/**/differ/**/from implementation to implementation./* >>=20 >> Yes. That=92s why we may want to capture a wide range of behaviors using= a >> statistical >> model, with a tunable parameter to represent different level of >> sluggishness in different >> codec implementations. >>=20 >>=20 >>=20 >>> The aforementioned value (~0.5s) is based on our observations of >>> encoder output traces >>> from one commercial video conferencing system. Other encoders may act >>> differently and we >>> have yet to test them more extensively to confirm our conjecture. >>> However, our guess is that >>> the order of magnitude of that value (say between 0.1s and 2s) will >>> stay the same. >>> */[Zaheduzzaman >>> Sarker]/**/will/**/there/**/be/**/a/**/ny/**//**/chance of making >>> the/**/survey available/**/where/**/we can look/**/at/**/the >>> sluggishness in/**/response in numerical values?/* >>=20 >> Alas, we don=92t have an extensive survey yet. Although, looking at the >> discussions here, >> there is a real need for it. Or maybe the codec folks already have such >> info? What popular >> codecs do we care about? >>=20 >> The data we have collected so far should only be considered as an >> example. Here is >> link to one graph as an illustration (Michael will present it on >> Wednesday as well). >> The graph shows a rate upshift from 1Mbps to 1.5Mbps at time t=3D5 sec. >>=20 >> https://www.dropbox.com/s/utg5jkgsvtkikvb/sample_trace_rate_upshift.png >>=20 >>=20 >>> And, to clarify your question, we are not making any specific >>> assumptions on why the encoder >>> is slow in response. Rate control of live video encoding is an active >>> research topic all by its >>> own, as the encoder needs to figure out what quantization parameters >>> to choose for an incoming, >>> unknown frame, so as to meet the specified target rate. Different >>> encoders may achieve >>> different tradeoff in terms of responsiveness, accuracy, and >>> computational complexity >>> (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are >>> probably better off by >>> treating that as a black box. >>> */[Zaheduzzaman Sarker]/**/so we don=92t care about sluggishness in >>> response , right?/* >>=20 >> IMHO: no for design, yes for evaluations. > I am bit confused if the solution is not designed to take some properties= of the system into account, why should one evaluate it against such proper= ties? if you like to evaluate a solution against some properties, please pu= t them in them in the requirements. >>=20 >>> Best, >>> Xiaoqing >>> On Nov 3, 2013, at 10:23 AM, Zaheduzzaman Sarker >>> <_zaheduzzaman.sarker@ericsson.com_ >>> > wrote: >>>=20 >>> Hi Xiaoging, >>> Clarification question. >>> >>a) delayed response to a rate change request (e.g., > 0.5s) >>> Where is this delay coming from? Fixed delay or delay based of some >>> assumption lack of resource to process the request? Are you thinking >>> of particular device, implementation? >>> BR >>>=20 >>> Zahed >>> *From:* _rmcat-bounces@ietf.org_ >>> [_mailto:rmcat-bounces@ietf.org_] *On >>> Behalf Of***Xiaoqing Zhu (xiaoqzhu) >>> *Sent:* den 28 oktober 2013 13:15 >>> *To:* Mo Zanaty (mzanaty) >>> *Cc:* rmcat WG; Colin Perkins; Michael Welzl; Varun Singh >>> *Subject:* Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in >>> Vancouver (03 Nov, 1300-1500) >>> Agree with Colin and Mo. >>> For a statistical video traffic model, I would propose to break down >>> the 2nd point (encoder rate control) in Mo's list further, to three >>> sub-components: >>> a) delayed response to a rate change request (e.g., > 0.5s) >>> b) deviations from the requested rate (maybe as randomly fluctuating >>> around the target?) >>> c) the different frame types or coding modes inside the encoder will >>> yield some encoded >>> frames much larger than others. This will lead to burstiness on time >>> scale of frame intervals >>> (typically 33ms), and later smoothed out by the pacing/shaper. >>> Also, if we go for the approach of using uncompressed test video >>> sequence for testing the combination of video codec and congestion >>> control, we should also recommend a reference codec and default >>> configurations. So that focus of the evaluations remains on the >>> congestion control module. What do folks think of x264 >>> (_http://www.videolan.org/developers/x264.html_)? >>> Best, >>> Xiaoqing >>> On Oct 28, 2013, at 1:39 PM, Mo Zanaty (mzanaty) <_mzanaty@cisco.com_ >>> > wrote: >>>=20 >>>=20 >>> I agree with Colin. We should be focused on live encoding where the >>> encoder rate control is highly granular (often fully smooth) and couple= d >>> to the network congestion control, not pre-encoded streaming of a small >>> set of fixed bitrates like DASH/HLS. >>>=20 >>> A more formal approach to the problem would be to decompose the factors >>> contributing to the final rate placed onto the network. >>> 1. Video content / source statistics, using a large set of representati= ve >>> sequences. An estimate of the entropy per frame could simply be the >>> encoded frame size using constant quantization, i.e. no encoder rate >>> control. >>> 2. Video encoder rate control behavior, which modulates the source >>> statistics to fit into network constraints over rather large time >>> intervals (>0.5s). >>> 3. Network transmission pacing / shaper, which smooths out bursts in >>> encoder output to fit into network constraints over fine time intervals >>> (<0.5s). >>>=20 >>> It should be possible to model all three in an abstract way to arrive a= t >>> some statistical model that can then be used for all tests without any >>> real video content or encoding (to avoid introducing noise in compariso= ns >>> if there was any variability in any of the factors). >>>=20 >>> Mo >>>=20 >>>=20 >>>=20 >>> On 10/28/13, 1:49 PM, Colin Perkins <_csp@csperkins.org_ >>> > wrote: >>>=20 >>> On 27 Oct 2013, at 11:50, Michael Welzl <_michawe@ifi.uio.no_ >>> > wrote: >>>=20 >>> On 27. okt. 2013, at 12:33, Varun Singh <_vsingh.ietf@gmail.com_ >>> > wrote: >>>=20 >>> Now, next, what if we'd have a set of such sequences, and then stack >>> them over a codec that produces quite arbitrary bitrates, assuming that >>> the network can always follow it - >>>=20 >>> Do you mean preparing the same video sequence at multiple rates and the >>> congestion controller picking the appropriate media rate that fits its >>> estimate? ie rate switch between different media rates of the same >>> sequence? >>>=20 >>> I think that could be a very good idea! >>>=20 >>> I'd be more interested in a standard set of *uncompressed* video test >>> sequences, that we can use to test the combination of video codec and >>> congestion control. If we're building something for interactive video >>> congestion control we should assume live encoding, with the codec >>> potentially having the flexibility to react to the network, and our >>> testbed ought to reflect that. >>>=20 >>> -- >>> Colin Perkins >>> _http://csperkins.org/_ >>=20 >=20 > --=20 >=20 > Zahed >=20 > =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D > ANM ZAHEDUZZAMAN SARKER >=20 >=20 > Ericsson AB > Multimedia Technologies > Laboratoriegr=E4nd 11 > 97128 Lule=E5, Sweden > Phone +46 10 717 37 43 > Fax +46 920 996 21 > SMS/MMS +46 76 115 37 43 > zaheduzzaman.sarker@ericsson.com > www.ericsson.com >=20 > =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D From michawe@ifi.uio.no Tue Nov 5 10:46:11 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 49F8E21E809E for ; Tue, 5 Nov 2013 10:46:00 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -102.283 X-Spam-Level: X-Spam-Status: No, score=-102.283 tagged_above=-999 required=5 tests=[AWL=0.315, BAYES_00=-2.599, HTML_MESSAGE=0.001, USER_IN_WHITELIST=-100] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id YiXTcB9XdrUi for ; Tue, 5 Nov 2013 10:45:39 -0800 (PST) Received: from mail-out4.uio.no (mail-out4.uio.no [IPv6:2001:700:100:10::15]) by ietfa.amsl.com (Postfix) with ESMTP id 9812521E80BA for ; Tue, 5 Nov 2013 10:44:44 -0800 (PST) Received: from mail-mx4.uio.no ([129.240.10.45]) by mail-out4.uio.no with esmtp (Exim 4.80.1) (envelope-from ) id 1VdlcG-0005EN-AW; Tue, 05 Nov 2013 19:44:40 +0100 Received: from dhcp-8c4f.meeting.ietf.org ([31.133.140.79]) by mail-mx4.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80) (envelope-from ) id 1VdlcF-0000sj-1x; Tue, 05 Nov 2013 19:44:40 +0100 Content-Type: multipart/alternative; boundary="Apple-Mail=_5E93FFD0-C7BA-48B3-A23C-A3820FADAC01" Mime-Version: 1.0 (Mac OS X Mail 6.6 \(1510\)) From: Michael Welzl In-Reply-To: <39928D07-596D-4437-BF8B-40BEC2E1D906@cisco.com> Date: Tue, 5 Nov 2013 10:44:35 -0800 Message-Id: <245CC4C8-0E0A-43CB-B1E5-9D8E39A21BD4@ifi.uio.no> References: <51FAA413.8060403@jesup.org> <14641FD2-C995-4E3A-BC21-A98781D1DB02@gmail.com> <2398BFCD-D4DA-4792-A117-A8D105848702@gmail.com> <5277321F.1050104@jesup.org> <02DC8AEA-8BAE-4D3D-801C-5AAE3A46648C@ifi.uio.no> <2A9116E7-E6F6-4559-9030-4A5150B57A51@ifi.uio.no> <1580151C-DBD7-459E-A273-2365702AD8D4@gmail.com> <39928D07-596D-4437-BF8B-40BEC2E1D906@cisco.com> To: Xiaoqing Zhu (xiaoqzhu) X-Mailer: Apple Mail (2.1510) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 13 msgs/h 6 sum rcpts/h 20 sum msgs/h 10 total rcpts 9423 max rcpts/h 40 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-5.0, required=5.0, autolearn=disabled, HTML_MESSAGE=0.001, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: E2B362C6A3AEF385CB39971136366C9799C7E015 X-UiO-SPAM-Test: remote_host: 31.133.140.79 spam_score: -49 maxlevel 80 minaction 2 bait 0 mail/h: 6 total 47 max/h 7 blacklist 0 greylist 0 ratelimit 0 Cc: Randell Jesup , rmcat WG , Piers O'Hanlon Subject: Re: [rmcat] NADA questions X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 18:46:11 -0000 --Apple-Mail=_5E93FFD0-C7BA-48B3-A23C-A3820FADAC01 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 As a follow-up to this, I'd just like to say that I think RFC6297 (*) should be required reading = for folks dealing with delay-based congestion control. The focus is on = Lower-than-Best-Effort schemes, but naturally, many of them are = delay-based, and many of these typical problems have been resolved in = such schemes long ago. Similarly, there's a ton of work on delay-based = TCP, e.g. CxTCP, CDG-TCP, but also much older yet elaborate ones like = e.g. FAST, but also Compound TCP, TCP-Illinois, TCP-Yeah used delay. = Does anyone have a good survey of delay-based TCP's? Surely there is one = somewhere. Else I suppose the related work section of e.g. the paper on = CxTCP should have useful pointers. The goals of both these things are not the same as RMCAT's, but the = problems experienced with delay-based congestion control are close = enough. Why repeat history? Cheers, Michael (*) or the paper we later made out of it: = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=3D&arnumber=3D6226797= &contentType=3DEarly+Access+Articles&searchField=3DSearch_All&queryText=3D= Less-than-Best-Effort+Service%3A+A+Survey+of+End-to-End+Approaches On 4. nov. 2013, at 08:04, Xiaoqing Zhu (xiaoqzhu) = wrote: > Folks: =20 >=20 > We hear you about the potential issues regarding using absolute > OWD as a congestion metric. Agree. Special thanks to Piers who > first pointed this out. =20 >=20 > I also tend to think along with Michael Welzl that it should be > relatively easy to fix by using a relative OWD instead. Just that > we are deferring this change until we can try it out in a real = test-bed. > This is one aspect of a CC solution where simulation-based studies > won=92t help much.=20 >=20 > So, yes, please stay tuned for an update ... >=20 > Best, > Xiaoqing >=20 >=20 > =20 > On Nov 4, 2013, at 8:34 AM, Piers O'Hanlon = wrote: >=20 >>=20 >> On 4 Nov 2013, at 14:28, Michael Welzl wrote: >>=20 >>>=20 >>> On 4. nov. 2013, at 06:25, Piers O'Hanlon = wrote: >>>=20 >>>>=20 >>>> On 4 Nov 2013, at 13:42, Michael Welzl wrote: >>>>=20 >>>>>=20 >>>>> On 3. nov. 2013, at 21:35, Randell Jesup = wrote: >>>>>=20 >>>>>> On 10/24/2013 9:42 AM, Piers O'Hanlon wrote: >>>>>>> H >>>>>>>=20 >>>>>>> 1) Whilst not explicitly laid out in the draft, from your = presentation in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I = understood to mean that it was calculating the absolute one-way delay = for each packet - which would imply that one would need synchronised = clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed? >>>>>>>=20 >>>>>>>> Your understanding is correct. For now, we are using one-way = delay as the baseline for congestion signal. We are aware of all the = drawbacks and limitations associated with one-way delay measurements, = and are planning to address those issues once we start playing around = with a testbed setup. One tentative plan is to subtract out a minimum = OWD and work with relative OWD instead, another option is to move the = per-packet statistical handling to the sender instead, and use RTT in = lieu of OWD. Yet another measure could be to employ a separate = mechanism for clock synchronization between sender and receiver. We are, = of course, also open to suggestions on that matter. >>>>>>>>=20 >>>>>>> Ok. Yes it would be nice to have a scheme that doesn't rely on = absolute OWD, though there are some environments where one could expect = synchronised clocks. >>>>>>=20 >>>>>> I think it's more than nice to not rely on synchronized clocks; I = think we absolutely have to work with non-synchronized clocks as the = default, or include within this scheme a way to (sufficiently) = synchronize the clocks - or prove that non-synchronized clocks don't = cause a problem, even if they're sub-optimal. >>>>>=20 >>>>> I don't get that. Any congestion control scheme will only use OWD = as a congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone = cares. This is how LEDBAT works with OWD; clock drift is another issue, = but perhaps not such a big deal; IIRC it's also addressed in LEDBAT in = some way. >>>>>=20 >>>> The issue with NADA is that it does not use the OWD difference - it = uses the absolute OWD. >>>=20 >>> ...but that obviously makes no sense and should just be fixed. And = it shouldn't be too hard to fix. >>>=20 >> Sure - though the current algorithm would need to be largely = rewritten for it operate using OWD difference. This is why the question = first occurred to me when I looked over the draft in more detail. >>=20 >> I presume they have use cases for NADA in some controlled = environments... >>=20 >> Cheers, >>=20 >> Piers. >>=20 >>> Cheers, >>> Michael >=20 --Apple-Mail=_5E93FFD0-C7BA-48B3-A23C-A3820FADAC01 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 As a = follow-up to this,

I'd just like to say that I think = RFC6297 (*) should be required reading for folks dealing with = delay-based congestion control. The focus is on Lower-than-Best-Effort = schemes, but naturally, many of them are delay-based, and many of these = typical problems have been resolved in such schemes long ago. =  Similarly, there's a ton of work on delay-based TCP, e.g. CxTCP, = CDG-TCP, but also much older yet elaborate ones like e.g. FAST, but = also Compound TCP, TCP-Illinois, TCP-Yeah used delay. Does anyone have a = good survey of delay-based TCP's? Surely there is one somewhere. Else I = suppose the related work section of e.g. the paper on CxTCP should have = useful pointers.

The goals of both these things = are not the same as RMCAT's, but the problems experienced with = delay-based congestion control are close enough. Why repeat = history?

Cheers,
Michael

=

(*) or the paper we later made out of = it:


On = 4. nov. 2013, at 08:04, Xiaoqing Zhu (xiaoqzhu) <xiaoqzhu@cisco.com> = wrote:

Folks:  

We hear you about the potential issues regarding using = absolute
OWD as a congestion metric. Agree.  Special thanks to Piers = who
first pointed this out.  

I also tend to think along with Michael Welzl that it should = be
relatively easy to fix by using a relative OWD instead.  Just = that
we are deferring this change until we can try it out in a real = test-bed.
This is one aspect of a CC solution where simulation-based = studies
won=92t help much. 

So, yes, please stay tuned for an update ...

Best,
Xiaoqing


 
On Nov 4, 2013, at 8:34 AM, Piers O'Hanlon <p.ohanlon@gmail.com> = wrote:


On 4 Nov 2013, at 14:28, Michael Welzl wrote:


On 4. nov. 2013, at 06:25, Piers O'Hanlon <p.ohanlon@gmail.com> = wrote:


On 4 Nov 2013, at 13:42, Michael Welzl wrote:


On 3. nov. 2013, at 21:35, Randell Jesup <randell-ietf@jesup.org> = wrote:

On 10/24/2013 9:42 AM, Piers O'Hanlon = wrote:
H

1) Whilst not explicitly laid out in the draft, from your presentation = in RMCAT@IETF86, d_n =3D t_{r,n} - t_{s,n} - which I understood to mean = that it was calculating the absolute one-way delay for each packet - = which would imply that one would need synchronised clocks on the sender and receiver? Is that correct or was there an = additional difference factor that I missed?

Your understanding is correct.  For now, = we are using one-way delay as the baseline for congestion signal. We are = aware of all the drawbacks and limitations associated with one-way delay = measurements, and are planning to address those issues once we start playing around with a testbed setup. One tentative = plan is to subtract out a minimum OWD and work with relative OWD = instead, another option is to move the per-packet statistical handling = to the sender instead, and use RTT in lieu of OWD.  Yet another measure could be to employ a separate mechanism for = clock synchronization between sender and receiver. We are, of course, = also open to suggestions on that matter.

Ok. Yes it would be nice to have a scheme that doesn't rely on absolute = OWD, though there are some environments where one could expect = synchronised clocks.

I think it's more than nice to not rely on synchronized clocks; I think = we absolutely have to work with non-synchronized clocks as the default, = or include within this scheme a way to (sufficiently) synchronize the = clocks - or prove that non-synchronized clocks don't cause a problem, even if they're sub-optimal.

I don't get that. Any congestion control scheme will only use OWD as a = congestion indicator, which means that the absolute OWD value isn't = really what counts - it's about changes in the OWD. This, again, means = that non-synchronized clocks produce a wrong absolute value, but noone cares. This is how LEDBAT works with OWD; clock drift = is another issue, but perhaps not such a big deal; IIRC it's also = addressed in LEDBAT in some way.

The issue with NADA is that it does not use the OWD difference - it uses = the absolute OWD.

...but that obviously makes no sense and should just be fixed. And it = shouldn't be too hard to fix.

Sure - though the current algorithm would need to be largely rewritten = for it operate using OWD difference. This is why the question first = occurred to me when I looked over the draft in more detail.

I presume they have use cases for NADA in some controlled = environments...

Cheers,

Piers.

Cheers,
Michael


= --Apple-Mail=_5E93FFD0-C7BA-48B3-A23C-A3820FADAC01-- From lars@netapp.com Tue Nov 5 11:15:43 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2663411E8110 for ; Tue, 5 Nov 2013 11:15:43 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -8.785 X-Spam-Level: X-Spam-Status: No, score=-8.785 tagged_above=-999 required=5 tests=[AWL=1.814, BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 6iDRKa2BFVAJ for ; Tue, 5 Nov 2013 11:15:38 -0800 (PST) Received: from mx12.netapp.com (mx12.netapp.com [216.240.18.77]) by ietfa.amsl.com (Postfix) with ESMTP id B5F0321F9FE9 for ; Tue, 5 Nov 2013 11:15:38 -0800 (PST) X-IronPort-AV: E=Sophos;i="4.93,640,1378882800"; d="asc'?scan'208";a="110462430" Received: from vmwexceht03-prd.hq.netapp.com ([10.106.76.241]) by mx12-out.netapp.com with ESMTP; 05 Nov 2013 11:15:38 -0800 Received: from SACEXCMBX01-PRD.hq.netapp.com ([169.254.2.51]) by vmwexceht03-prd.hq.netapp.com ([10.106.76.241]) with mapi id 14.03.0123.003; Tue, 5 Nov 2013 11:15:38 -0800 From: "Eggert, Lars" To: rmcat WG Thread-Topic: wanted: minute taker and jabber scribe Thread-Index: AQHO2ltolQ+l1FhB8UO6Bq+jffkjmw== Date: Tue, 5 Nov 2013 19:15:37 +0000 Message-ID: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [10.104.60.115] Content-Type: multipart/signed; boundary="Apple-Mail=_F17A835B-DD08-469A-A8AD-47E0ED154CE5"; protocol="application/pgp-signature"; micalg=pgp-sha1 MIME-Version: 1.0 Subject: [rmcat] wanted: minute taker and jabber scribe X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 05 Nov 2013 19:15:43 -0000 --Apple-Mail=_F17A835B-DD08-469A-A8AD-47E0ED154CE5 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Hi, I'd like to ask for volunteers to act as minute takers and jabber = scribes for the meeting this week. Please volunteer! For the minutes, I'd suggest we use the Etherpad at = http://tools.ietf.org/wg/rmcat/minutes for collaborative editing. This = has the advantage that people can contribute to it during the session = and after, in a way that leaves us with one consolidated set of minutes = in the end. I'd still like one identified minute taker, however, others = are free in helping out by adding to and correcting the Etherpad. Lars --Apple-Mail=_F17A835B-DD08-469A-A8AD-47E0ED154CE5 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="signature.asc" Content-Type: application/pgp-signature; name="signature.asc" Content-Description: Message signed with OpenPGP using GPGMail -----BEGIN PGP SIGNATURE----- iQCVAwUBUnlD0tZcnpRveo1xAQK1lwQAkfUSyHf8HqMy58pjqEjb36b9sybO24B8 NSOZZBTvbt+hDx0KA8Vwba4R+UzlmCra50xhpNJRBpNHLPQcdFWO1d8p2CGDunha T+YlCJ9lT2ArHf0sLRU7tdF8aK9D96hvang8utcgl1dda9Idoet6X3QrH3/tS+qf 1fDBmJAw2rc= =ap/B -----END PGP SIGNATURE----- --Apple-Mail=_F17A835B-DD08-469A-A8AD-47E0ED154CE5-- From mzanaty@cisco.com Wed Nov 6 10:09:17 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 69F0911E81C7 for ; Wed, 6 Nov 2013 10:09:17 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.437 X-Spam-Level: X-Spam-Status: No, score=-10.437 tagged_above=-999 required=5 tests=[AWL=0.162, BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id wdW9ZqJlnjHK for ; Wed, 6 Nov 2013 10:09:11 -0800 (PST) Received: from rcdn-iport-1.cisco.com (rcdn-iport-1.cisco.com [173.37.86.72]) by ietfa.amsl.com (Postfix) with ESMTP id B6B5B11E81CC for ; Wed, 6 Nov 2013 10:09:10 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=1154; q=dns/txt; s=iport; t=1383761350; x=1384970950; h=from:to:subject:date:message-id:in-reply-to:content-id: content-transfer-encoding:mime-version; bh=otzLZOz9z8b26C5DMWIT5fnkfr3K2X2UKE5RZ7gyWNk=; b=mkm4e02ylk+0Gn/rVueLWf1uzpdNBGklzSLeFaZKJ60CYBaXe8Rxltdt OIl/se3DC1KSh27N8oEO159POhXoEAbFRl5vDnxcyB2NYmLniKUZLvOq+ vyou/9WXL8em5BbtLNhXoB6onRwXAT0enZGUM//yBR+5Hm8EdyBEKU61W 0=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AhwFAJeEelKtJXG9/2dsb2JhbABagweBC782gSUWdIIlAQEBBDpRAQgYHkIlAgQBEogBvy6PYIQwA5gMkgqBaIE+gio X-IronPort-AV: E=Sophos;i="4.93,647,1378857600"; d="scan'208";a="281324043" Received: from rcdn-core2-2.cisco.com ([173.37.113.189]) by rcdn-iport-1.cisco.com with ESMTP; 06 Nov 2013 18:09:09 +0000 Received: from xhc-aln-x15.cisco.com (xhc-aln-x15.cisco.com [173.36.12.89]) by rcdn-core2-2.cisco.com (8.14.5/8.14.5) with ESMTP id rA6I98On026633 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Wed, 6 Nov 2013 18:09:08 GMT Received: from xmb-rcd-x14.cisco.com ([169.254.4.50]) by xhc-aln-x15.cisco.com ([173.36.12.89]) with mapi id 14.03.0123.003; Wed, 6 Nov 2013 12:09:08 -0600 From: "Mo Zanaty (mzanaty)" To: Randell Jesup , "rmcat@ietf.org" Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO2xtIgwVs6QKB+UiBda2MQkwFlQ== Date: Wed, 6 Nov 2013 18:09:06 +0000 Message-ID: In-Reply-To: <527735B1.9000005@jesup.org> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: user-agent: Microsoft-MacOutlook/14.3.8.130913 x-originating-ip: [10.82.211.178] Content-Type: text/plain; charset="us-ascii" Content-ID: Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 06 Nov 2013 18:09:17 -0000 I should have used clearer wording. This is not about response time or rate changes. It is the window over which the encoder will enforce the average bit rate when there is no rate change. (VBV in MPEG terms.) Typically a few frames, up to maybe tens of frames, assuming semi-network-friendly real-time encoding. Like you said, it will steal bits from some frames and give them back later, where later can be a few or tens of frames. So the network will observe theft and generosity over short timescales (if no shaper), which net to zero over longer timescales. Adding a shaper gives the encoder more freedom to exploit content statistics without agitating the network, but at the expense of potential latency. Mo On 11/4/13, 12:50 AM, Randell Jesup wrote: On 10/28/2013 11:39 AM, Mo Zanaty (mzanaty) wrote: > 2. Video encoder rate control behavior, which modulates the source > statistics to fit into network constraints over rather large time > intervals (>0.5s). 0.5 seconds to respond to rate-control changes? Where? Please fix that system! ;-) --=20 Randell Jesup randell-ietf@jesup.org From mzanaty@cisco.com Wed Nov 6 10:17:02 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 6298711E8164 for ; Wed, 6 Nov 2013 10:17:02 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.456 X-Spam-Level: X-Spam-Status: No, score=-10.456 tagged_above=-999 required=5 tests=[AWL=0.142, BAYES_00=-2.599, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id wgPB-H18P9G3 for ; Wed, 6 Nov 2013 10:16:56 -0800 (PST) Received: from rcdn-iport-4.cisco.com (rcdn-iport-4.cisco.com [173.37.86.75]) by ietfa.amsl.com (Postfix) with ESMTP id 8A3AF11E80F9 for ; Wed, 6 Nov 2013 10:16:56 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=12111; q=dns/txt; s=iport; t=1383761816; x=1384971416; h=from:to:cc:subject:date:message-id:in-reply-to: mime-version; bh=XbO06GXiIDhHmF4AG90T492nJM83McqXGHG+Yy6IgdE=; b=LvyzYoIL3HlPSsrjlzL2lw6P5IAUIjJaweWuQE3rqOAEB0IET+Py7JO5 2ufKv1+RjYsN4Hk9CBYoz9iAnPizUM6Rc6DwaatDEsXlMxwvtRjddy2s0 Ly2hvl7kD1/go+wu5tG3hYXqkrkOyEvN6v5GCKjC/Zua5Ox9Jb/JyVxXl o=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AkUFAEKGelKtJV2d/2dsb2JhbABagkNEgQu/NoElFnSCLHkSAQg/ORQRAgQBDQWIAb8oj1kHhDADlCyDYJIKgWiBPoIq X-IronPort-AV: E=Sophos;i="4.93,647,1378857600"; d="scan'208,217";a="281606409" Received: from rcdn-core-6.cisco.com ([173.37.93.157]) by rcdn-iport-4.cisco.com with ESMTP; 06 Nov 2013 18:16:56 +0000 Received: from xhc-aln-x01.cisco.com (xhc-aln-x01.cisco.com [173.36.12.75]) by rcdn-core-6.cisco.com (8.14.5/8.14.5) with ESMTP id rA6IGtOh011456 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Wed, 6 Nov 2013 18:16:55 GMT Received: from xmb-rcd-x14.cisco.com ([169.254.4.50]) by xhc-aln-x01.cisco.com ([173.36.12.75]) with mapi id 14.03.0123.003; Wed, 6 Nov 2013 12:16:55 -0600 From: "Mo Zanaty (mzanaty)" To: "Xiaoqing Zhu (xiaoqzhu)" , Randell Jesup Thread-Topic: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) Thread-Index: AQHO2xxfgwVs6QKB+UiBda2MQkwFlQ== Date: Wed, 6 Nov 2013 18:16:54 +0000 Message-ID: In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: user-agent: Microsoft-MacOutlook/14.3.8.130913 x-originating-ip: [10.82.211.178] Content-Type: multipart/alternative; boundary="_000_CE9FF0101C2F6mzanatyciscocom_" MIME-Version: 1.0 Cc: rmcat WG Subject: Re: [rmcat] [rtcweb] Discussion on RMCAT traffic model in Vancouver (03 Nov, 1300-1500) X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 06 Nov 2013 18:17:02 -0000 --_000_CE9FF0101C2F6mzanatyciscocom_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable I think you are talking about different things. Most live encoders can acce= pt changes in target rate almost immediately, which is Randell=92s point. B= ut they also have leaky bucket rate control algorithms that have time windo= ws of several frames. When the target rate changes (assume instantly), that= interacts with the leaky bucket in some way. A naive encoder which doesn= =92t expect getting hammered with frequent rate changes (like every frame) = may do something dumb like reset all leaky bucket parameters upon target ra= te changes. Or it may alter fill rate but not bucket size or current occupa= ncy level. I think that=92s what Xiaoqing is trying to model. Maybe we don= =92t need to model such dynamics and make simpler assumptions. Or maybe use= the MPEG VBV model and assume encoders conform to that. Mo On 11/4/13, 1:40 AM, Xiaoqing Zhu (xiaoqzhu) > wrote: Randell: Just wanted to clarify: I do agree with you that the encoder will react/res= pond almost immediately to a new rate change request. However, I find myself ha= rd pressed to believe that: a) It can do so on a very frequent basis, say, to conform to new rate chang= es once per 0.1 second; b) It can converge to the newly specified target rate within a very short w= indow, say, within two frames. If, indeed, a) and b) are true, it would be wonderful if you could show us = some traces from those live encoders that hit a new target rate precisely within= a couple of frames, every so often. To be a bit more specific: if the target rate is= changed to 2.4Mbps for a 30 fps stream, then all ensuing encoded frames will come o= ut magically at a size of 10KB (=3D 2400000/8/30) within a couple of frames? R= eally? I also wonder how that trick can be achieved, given that in live encoding, = the encoder can only do one-pass rate control (as you already noted, due to com= plexity and latency constraints), and can at best guess at the scene complexity of = an incoming frame. Curious to find out more ... Thanks, Xiaoqing On Nov 4, 2013, at 12:19 AM, Randell Jesup > wrote: On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu) wrote: Hi Zahed, We proposed such a delay term based on our understanding of the behavior of= live encoder rate control: it takes time for the encoder output rate to converge on the = newly specified rate. As such, it also means that the encoder cannot conform to arbitrarily frequ= ent rate change requests. Unlike CBR traffic, there is an inherent sluggishness in live enc= oder response. So, strictly speaking, maybe we should call that term "sluggishness in resp= onse", or "convergence time of encoder rate control=94? The aforementioned value (~0.5s) is based on our observations of encoder ou= tput traces from one commercial video conferencing system. Other encoders may act diffe= rently and we have yet to test them more extensively to confirm our conjecture. However, = our guess is that the order of magnitude of that value (say between 0.1s and 2s) will stay th= e same. Honestly, I'd love to know what systems have such long rate control delays.= The ones I've worked on have typically 1 or 2 frame times of delay - I h= aven't measured the VP8 implementation in Mozilla, but I'd be shocked if it= was much more than that (in fact, I've simply assumed that it's fast, and = that if it wasn't things would be horribly broken). If your rate controlle= r takes 1/2 *second* to hit a new requested rate - it's simply broken for r= ealtime work, and likely the systems using it are usually used either well = below congestion levels, or on dedicated links (which might be true for hig= h-buck teleconference systems). PLEASE do not optimize for rate controllers that take multiple hundreds of = ms to respond. (I'll verify with some codec people, just for sanity, before the session). And, to clarify your question, we are not making any specific assumptions o= n why the encoder is slow in response. Rate control of live video encoding is an active rese= arch topic all by its own, as the encoder needs to figure out what quantization parameters to cho= ose for an incoming, unknown frame, so as to meet the specified target rate. Different encoders= may achieve different tradeoff in terms of responsiveness, accuracy, and computational = complexity (one-pass vs. two-pass, etc.). For the purpose of RMCAT work, we are proba= bly better off by treating that as a black box. Two-pass, in realtime work? Seems unlikely... Maybe I just haven't playe= d with enough high-bucks equipment on dedicated lines. -- Randell Jesup randell-ietf@jesup.org --_000_CE9FF0101C2F6mzanatyciscocom_ Content-Type: text/html; charset="Windows-1252" Content-ID: <5BB974D2E5D1F44995275DB658DB7877@emea.cisco.com> Content-Transfer-Encoding: quoted-printable
I think you are talking about different things. Most live encoders can= accept changes in target rate almost immediately, which is Randell=92s poi= nt. But they also have leaky bucket rate control algorithms that have time = windows of several frames. When the target rate changes (assume instantly), that interacts with the leaky buck= et in some way. A naive encoder which doesn=92t expect getting hammered wit= h frequent rate changes (like every frame) may do something dumb like reset= all leaky bucket parameters upon target rate changes. Or it may alter fill rate but not bucket size or curr= ent occupancy level. I think that=92s what Xiaoqing is trying to model. May= be we don=92t need to model such dynamics and make simpler assumptions. Or = maybe use the MPEG VBV model and assume encoders conform to that.

Mo


On 11/4/13, 1:40 AM, Xiaoqing Zhu (xiaoqzhu) <xiaoqzhu@cisco.com> wrote:

Randell: 

Just wanted to clarify: I do agree with you that the encoder will reac= t/respond
almost immediately to a new rate change request.  However, I find= myself hard
pressed to believe that: 

a) It can do so on a very frequent basis, say, to conform to new rate = changes once
per 0.1 second; 

b) It can converge to the newly specified target rate within a very sh= ort window,
say, within two frames. 

If, indeed, a) and b) are true, it would be wonderful if you could sho= w us some
traces from those live encoders that hit a new target rate precisely w= ithin a couple
of frames, every so often. To be a bit more specific: if the target ra= te is changed
to 2.4Mbps for a 30 fps stream, then all ensuing encoded frames will c= ome out
magically at a size of 10KB (=3D 2400000/8/30) within a couple of fram= es? Really? 

I also wonder how that trick can be achieved, given that in live encod= ing, the
encoder can only do one-pass rate control (as you already noted, due t= o complexity
and latency constraints), and can at best guess at the scene complexit= y of an
incoming frame. 

Curious to find out more ...

Thanks,
Xiaoqing


On Nov 4, 2013, at 12:19 AM, Randell Jesup <randell-ietf@jesup.org> wrote:

On 11/3/2013 1:32 PM, Xiaoqing Zhu (xiaoqzhu= ) wrote:
Hi Zahed, 

We proposed such a delay term based on our understanding of the behavi= or of live encoder
rate control: it takes time for the encoder output rate to converge on= the newly specified rate.
As such, it also means that the encoder cannot conform to arbitrarily = frequent rate change
requests. Unlike CBR traffic, there is an inherent sluggishness in liv= e encoder response.  

So, strictly speaking, maybe we should call that term "sluggishne= ss in response", or "convergence
time of encoder rate control=94?  

The aforementioned value (~0.5s) is based on our observations of encod= er output traces
from one commercial video conferencing system. Other encoders may act = differently and we
have yet to test them more extensively to confirm our conjecture. Howe= ver, our guess is that
the order of magnitude of that value (say between 0.1s and 2s) will st= ay the same.  

Honestly, I'd love to know what systems have such long rate control delays.=   The ones I've worked on have typically 1 or 2 frame times of delay -=   I haven't measured the VP8 implementation in Mozilla, but I'd be sho= cked if it was much more than that (in fact, I've simply assumed that it's fast, and that if it wasn't things would be = horribly broken).  If your rate controller takes 1/2 *second* to hit a= new requested rate - it's simply broken for realtime work, and likely the = systems using it are usually used either well below congestion levels, or on dedicated links (which might be true f= or high-buck teleconference systems).

PLEASE do not optimize for rate controllers that take multiple hundreds of = ms to respond.

(I'll verify with some codec people, just for sanity, before the session).<= br>

And, to clarify your question, we are not making any specific assumpti= ons on why the encoder
is slow in response.  Rate control of live video encoding is an a= ctive research topic all by its
own, as the encoder needs to figure out what quantization parameters t= o choose for an incoming, 
unknown frame, so as to meet the specified target rate.  Differen= t encoders may achieve
different tradeoff in terms of responsiveness, accuracy, and computati= onal complexity
(one-pass vs. two-pass, etc.).  For the purpose of RMCAT work, we= are probably better off by
treating that as a black box.

Two-pass, in realtime work?   Seems unlikely...  Maybe I jus= t haven't played with enough high-bucks equipment on dedicated lines.

--=20
Randell Jesup
randell-ietf@jesup.org

 --_000_CE9FF0101C2F6mzanatyciscocom_-- From lars@netapp.com Wed Nov 6 18:16:58 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 31E3F11E821D for ; Wed, 6 Nov 2013 18:16:58 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -8.811 X-Spam-Level: X-Spam-Status: No, score=-8.811 tagged_above=-999 required=5 tests=[AWL=1.788, BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id yfoOIU-6Onvh for ; Wed, 6 Nov 2013 18:16:54 -0800 (PST) Received: from mx12.netapp.com (mx12.netapp.com [216.240.18.77]) by ietfa.amsl.com (Postfix) with ESMTP id 17ABE11E81A7 for ; Wed, 6 Nov 2013 18:16:54 -0800 (PST) X-IronPort-AV: E=Sophos;i="4.93,648,1378882800"; d="asc'?scan'208";a="111173217" Received: from vmwexceht04-prd.hq.netapp.com ([10.106.77.34]) by mx12-out.netapp.com with ESMTP; 06 Nov 2013 18:16:52 -0800 Received: from SACEXCMBX01-PRD.hq.netapp.com ([169.254.2.51]) by vmwexceht04-prd.hq.netapp.com ([10.106.77.34]) with mapi id 14.03.0123.003; Wed, 6 Nov 2013 18:16:52 -0800 From: "Eggert, Lars" To: rmcat WG Thread-Topic: check the preliminary minutes Thread-Index: AQHO219sCrp3x9p0WkGE77S/npK+EA== Date: Thu, 7 Nov 2013 02:16:51 +0000 Message-ID: References: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> In-Reply-To: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [10.104.60.117] Content-Type: multipart/signed; boundary="Apple-Mail=_6B12BD86-87E9-4452-A33C-2CFA11E923C8"; protocol="application/pgp-signature"; micalg=pgp-sha1 MIME-Version: 1.0 Subject: [rmcat] check the preliminary minutes X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 07 Nov 2013 02:16:58 -0000 --Apple-Mail=_6B12BD86-87E9-4452-A33C-2CFA11E923C8 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Hi, thank you Gorry! for taking minutes today. Everyone: please go to the Etherpad at = http://tools.ietf.org/wg/rmcat/minutes and check and correct the = minutes. Especially if you presented or spoke at a microphone. Thanks, Lars --Apple-Mail=_6B12BD86-87E9-4452-A33C-2CFA11E923C8 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="signature.asc" Content-Type: application/pgp-signature; name="signature.asc" Content-Description: Message signed with OpenPGP using GPGMail -----BEGIN PGP SIGNATURE----- iQCVAwUBUnr4E9ZcnpRveo1xAQLutwQAgNoj08KmEflrEUbtQM10cmHpWNaLvAcC 9UKgUFEZvJNxyc5Xxqb8tx4Ddc90TQQroxmyBmIo48ivVVTQBVxRA58zqOeMKvmZ 8cz6KU4whTUUh57wkEzgevvS2ZEuy3VXpjfuDWAf8YI6mMDUywnEj//7rg09jQ10 BVrhTbxAG0o= =b7Ub -----END PGP SIGNATURE----- --Apple-Mail=_6B12BD86-87E9-4452-A33C-2CFA11E923C8-- From vsingh.ietf@gmail.com Fri Nov 8 19:00:47 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 3FBD311E810E for ; Fri, 8 Nov 2013 19:00:47 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.965 X-Spam-Level: X-Spam-Status: No, score=-1.965 tagged_above=-999 required=5 tests=[AWL=0.635, BAYES_00=-2.599, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 8IkPVke++6o4 for ; Fri, 8 Nov 2013 19:00:44 -0800 (PST) Received: from mail-ie0-x234.google.com (mail-ie0-x234.google.com [IPv6:2607:f8b0:4001:c03::234]) by ietfa.amsl.com (Postfix) with ESMTP id 9A8C311E80FB for ; Fri, 8 Nov 2013 19:00:29 -0800 (PST) Received: by mail-ie0-f180.google.com with SMTP id e14so4472896iej.25 for ; Fri, 08 Nov 2013 19:00:19 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc:content-type; bh=GsIYvHpnXmpRy9EWJIXw9ra+tVrxW4IEsNdGaOXVBK4=; b=cNCUYx02YxIMbYXvpMccURK6hXvB1SobKpHccFGZQlwSpbZFM+yDnSeTsrN8D3THbO dQpdTcd+7GKtkzP2Be/ZWHx7sZLkrbdUAAUnVh0ap5aUX0u8T0ZdwUg7mKzLNn3VPBYy kZw9PJV3njoROzIO+XLCwlpbTK1NnkoJHhyo/umWdUXHbEZXtZkTP8WM9/VcjtYL4gR3 gynuioyoUIj59gAtWjIea2BPFCu44O6m1vMltEEoJFE0gwqqf+SXcV7QgPGX7P5MYqjk tLljim9waEhHLBBQZ/vMxyz/r1TiWJcFxjhwg4rSbPGFATrJ9X9rtK+Cbpwzi4LExWtV TFEQ== X-Received: by 10.42.100.209 with SMTP id b17mr6711607ico.42.1383966019584; Fri, 08 Nov 2013 19:00:19 -0800 (PST) MIME-Version: 1.0 Received: by 10.50.102.8 with HTTP; Fri, 8 Nov 2013 18:59:59 -0800 (PST) In-Reply-To: References: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> From: Varun Singh Date: Fri, 8 Nov 2013 18:59:59 -0800 Message-ID: To: "Eggert, Lars" Content-Type: multipart/mixed; boundary=90e6ba614f44fb3edc04eab5b290 Cc: rmcat WG Subject: Re: [rmcat] check the preliminary minutes X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sat, 09 Nov 2013 03:00:47 -0000 --90e6ba614f44fb3edc04eab5b290 Content-Type: text/plain; charset=ISO-8859-1 Hi all, Meeting notes from the traffic model session are attached within. Thank you Colin for the taking the notes. if something is not documented or incorrect, please send me the corrections. Cheers, Varun On Wed, Nov 6, 2013 at 6:16 PM, Eggert, Lars wrote: > Hi, > > thank you Gorry! for taking minutes today. > > Everyone: please go to the Etherpad at http://tools.ietf.org/wg/rmcat/minutes and check and correct the minutes. Especially if you presented or spoke at a microphone. > > Thanks, > Lars -- http://www.netlab.tkk.fi/~varun/ --90e6ba614f44fb3edc04eab5b290 Content-Type: text/plain; charset=US-ASCII; name="88-notes-traffic-model.txt" Content-Disposition: attachment; filename="88-notes-traffic-model.txt" Content-Transfer-Encoding: base64 X-Attachment-Id: f_hns9it3e1 Uk1DQVQgTWVkaWEgVHJhZmZpYyBHZW5lcmF0b3IgTWVldGluZwoKU2hvdWxkIHN3aXRjaGVkIE1D VXMgYmUgaW4gc2NvcGU/ICBIb3cgZG8gdGhleSByZWxhdGUgdG8gc2ltdWxjYXN0PwoKTW9kZWxs aW5nOiB2YXJpYWJpbGl0eQotIGRvIHdlIG5lZWQgdG8gbW9kZWwgYXVkaW8gc291cmNlcyB0b28/ IGhhcyBkaWZmZXJlbnQgY2hhcmFjdGVyaXN0aWNzIGFuZAogcmVxdWlyZW1lbnRzCi0gbmVlZCB0 byBtb2RlbCAicS1wIHRvZ2dsaW5nIiBmb3IgbWFjcm9ibG9ja3M/IAotIG92ZXIgd2hhdCB0aW1l IHBlcmlvZCBkbyB3ZSBuZWVkIHRvIGtlZXAgdG8gdGhlIHRhcmdldCBiaXQgcmF0ZT8gaXQncwog IGF2ZXJhZ2VkIG92ZXIgc29tZSBudW1iZXIgb2YgZnJhbWVzL3NvbWUgdGltZSBwZXJpb2QKLS0g aGFsZiBhIHNlY29uZD8KLS0gaS5lLiwgdGhlIG1vZGVsIHNob3VsZCBzYXkgImF0IHRpbWUgZnJh bWUgcmF0ZSwgZ2VuZXJhdGUgdHJhZmZpYyB0aGF0CiAgIGhpdHMgdGhpcyByYXRlIGF2ZXJhZ2Vk IG92ZXIgeCBmcmFtZXMiCi0gRnJhbWUgc2l6ZXMKLS0gcWNpZiwgMzYwcCwgNzIwcCwgMTA4MHAK LS0gdmFyaWFibGUgZnJhbWUgcmF0ZXM6IDcuNS8xNS8zMC82MAotLSBtYXggZnJhbWUgcmF0ZSBz aG91bGQgYmUgNHggbWluIGZyYW1lIHJhdGUKLSBpLWZyYW1lcyBub3Qgc2VudCB1bmxlc3Mgc29t ZXRoaW5nIGNhdXNlcyBhIGxvc3Mgb2Ygc3luYyAoaGlnaCBsb3NzCiAgcmF0ZXMvc291cmNlIHN3 aXRjaCBvbiBNQ1UvY2FtZXJhIGNoYW5nZS9ldGMpCi0tIGFzc3VtZSBpLWZyYW1lIGlzIHVwdG8g MTB4IGF2ZXJhZ2UgZnJhbWUgc2l6ZQotLSB3ZSdsbCBuZWVkIHRvIG1vZGVsIHdoYXQgbG9zcyBw YXR0ZXJucyB0cmlnZ2VyIGFuIGktZnJhbWUgKGJ1cnN0CiAgIGxlbmd0aCB0byB0cmlnZ2VyKQot LSBtaWdodCBhbHNvIHdhbnQgdG8gbW9kZWwgY29kZWNzIHRoYXQgc2VuZCBwZXJpb2RpYyBpLWZy YW1lcwotIHdlJ2xsIG5lZWQgYW4gZW50cm9weSBtb2RlbCBmb3IgdmlkZW8KLS0gbWF5YmUgZ2Vu ZXJhdGUgYnkgZW5jb2RpbmcgYSBidW5jaCBvZiBzZXF1ZW5jZXMgdXNpbmcgYSBidW5jaCBvZgog ICBjb2RlY3MsIHRoZW4gYXZlcmFnZSBpdCBvdXQ/IHdoYXQgZG9lcyB0aGUgZGlzdHJpYnV0aW9u IGxvb2sgbGlrZT8KCk1vZGVsbGluZzogcmVzcG9uc2l2ZW5lc3MKLSBubyBHT1A7IAotIGNhbiBj aGFuZ2UgYml0cmF0ZSBvbiB0aGUgbmV4dCBmcmFtZSBmb3IgYm90aCBpbmNyZWFzZSBhbmQgZGVj cmVhc2UgaW4gcmF0ZQotLSBzZWVtcyB0byBiZSBzb21lIGRpc2N1c3Npb246IGNvZGVjcyBtYXkg bm90IGxpa2UgaW5jcmVhc2luZyByYXRlIG9uIHRoZQogICBuZXh0IGZyYW1lOyB0aGV5IGluY3Jl YXNlIGF2ZXJhZ2VkIG92ZXIgc2V2ZXJhbCBmcmFtZXM/Ci0gc3dpdGNoaW5nIGJldHdlZW4gZnJh bWUgc2l6ZXMgZ2VuZXJhdGVzIGFuIGktZnJhbWUKLSBzbWFsbCByYXRlIGNoYW5nZXMgKDEwJT8p IGNhbiBiZSBkb25lIHF1aWNrbHk7IGxhcmdlciBjaGFuZ2VzIHRoYXQgbmVlZAogIGEgY2hhbmdl IGluIGZyYW1lIHNpemUgd2lsbCBjYXVzZSBhIGktZnJhbWUgYnVyc3QKLS0gc2VuZCBpLWZyYW1l IHNsb3dseSBpbiBwYXJhbGxlbCB3aXRoIGxvd2VyIHJhdGUgY29kaW5nIHdoZW4gdXAtcmF0aW5n Ci0gY29kZWMgaXNzdWU6IGNoYW5naW5nIHJlc29sdXRpb24gdG9vIG9mdGVuIGlzIGEgcHJvYmxl bTsgbmVlZCBhdCBsZWFzdCBhCiAgY291cGxlIG9mIHNlY29uZHMgYmV0d2VlbiByZXNvbHV0aW9u IHN3aXRjaGVzOyBtb3JlIHdvdWxkIGJlIGJldHRlcgotLSBpZiB0aGUgc2VuZGVyIGNoYW5nZXMg ZnJhbWUgcmF0ZSBvZnRlbiwgZG9lcyBpdCBkZWdyYWRlIG9yIGltcGFjdCB0aGUgdXNlcj8KLSBh dCBhIHBhcnRpY3VsYXIgcmF0ZSwgY2FuIGxpa2VseSBnbyAwLjV4LTIuMHggdGFyZ2V0IGJpdCBy YXRlOyB0aGVyZSdzIGEgCiAgc2lnbmlmaWNhbnQgb3ZlcmxhcCBiZXR3ZWVuIHRoZSByYXRlcyB0 aGF0IGNhbiBiZSBlbmNvZGVkIGZvciBhZGphY2VudAogIHJlc29sdXRpb25zCgp3aG8gaXMgaW4g Y29udHJvbD8KLSB3aGF0J3MgdGhlIGludGVyZmFjZSBiZXR3ZWVuIGNvZGVjIGFuZCBjb25nZXN0 aW9uIGNvbnRyb2w/Ci0tIGRvZXMgdGhlIGNvbmdlc3Rpb24gY29udHJvbCBqdXN0IGdpdmUgYSB0 YXJnZXQgYml0IHJhdGU/Ci0tIG9yIGlzIHRoZXJlIG1vcmUgaW5mb3JtYXRpb24gYWJvdXQgZnJh bWUgc2l6ZXMvcmF0ZXMgcGFzc2VkIGFyb3VuZD8KLS0gbm8gcmVhbCBhZ3JlZW1lbnQKLS0tIGEg bmFycm93IGludGVyZmFjZSBpcyBjbGVhcmx5IGFjY2VwdGFibGUgdG8gdGhlIGdyb3VwCi0tLSB3 aWRlciBBUElzIChnaXZpbmcgY29kZWMgaW5mb3JtYXRpb24pIGhhdmUgcG90ZW50aWFsLCBidXQg bWF5YmUgZm9yIGEKICAgIGxhdGVyIHZlcnNpb24= --90e6ba614f44fb3edc04eab5b290-- From v.srinivasa.somayazulu@intel.com Mon Nov 11 21:28:20 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 37C3011E81CF for ; Mon, 11 Nov 2013 21:28:19 -0800 (PST) X-Quarantine-ID: X-Amavis-Modified: Mail body modified (defanged) by ietfa.amsl.com X-Virus-Scanned: amavisd-new at amsl.com X-Amavis-Alert: BANNED, message contains part: multipart/mixed | application/vnd.openxmlformats-officedocument.presentationml.presentation, .zip, IETF Rmcat VQ Eval Criteria-r2.pptx | .dat,ppt/media/image2.emf X-Spam-Flag: NO X-Spam-Score: 0 X-Spam-Level: X-Spam-Status: No, score=x tagged_above=-999 required=5 tests=[] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id K31WieJ8OpIi for ; Mon, 11 Nov 2013 21:28:18 -0800 (PST) Content-Type: multipart/mixed; boundary="----------=_1384234099-3409-0" Content-Transfer-Encoding: binary MIME-Version: 1.0 Received: from mga03.intel.com (mga03.intel.com [143.182.124.21]) by ietfa.amsl.com (Postfix) with ESMTP id D9E7C11E810E for ; Mon, 11 Nov 2013 21:24:11 -0800 (PST) Received: from azsmga001.ch.intel.com ([10.2.17.19]) by azsmga101.ch.intel.com with ESMTP; 11 Nov 2013 14:07:55 -0800 Received: from orsmsx106.amr.corp.intel.com ([10.22.225.133]) by azsmga001.ch.intel.com with ESMTP; 11 Nov 2013 14:07:50 -0800 Received: from orsmsx103.amr.corp.intel.com ([169.254.2.216]) by ORSMSX106.amr.corp.intel.com ([169.254.5.158]) with mapi id 14.03.0123.003; Mon, 11 Nov 2013 14:07:49 -0800 From: "Somayazulu, V Srinivasa" To: "rmcat@ietf.org" Date: Mon, 11 Nov 2013 22:07:49 +0000 Message-ID: <42CD495267BEF24CACD045A8B3747BA72DC8AC45@ORSMSX103.amr.corp.intel.com> Subject: [rmcat] Video Quality Evaluation - Objective metrics X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 12 Nov 2013 05:28:21 -0000 This is a multi-part message in MIME format... ------------=_1384234099-3409-0 Content-Type: text/plain; charset="iso-8859-1" Content-Disposition: inline Content-Transfer-Encoding: 7bit WARNING: contains banned part ------------=_1384234099-3409-0 Content-Type: message/rfc822; x-spam-type=original; name="message" Content-Disposition: attachment; filename="message" Content-Transfer-Encoding: 7bit Content-Description: Original message Return-Path: Received: from mga03.intel.com (mga03.intel.com [143.182.124.21]) by ietfa.amsl.com (Postfix) with ESMTP id D9E7C11E810E for ; Mon, 11 Nov 2013 21:24:11 -0800 (PST) Received: from azsmga001.ch.intel.com ([10.2.17.19]) by azsmga101.ch.intel.com with ESMTP; 11 Nov 2013 14:07:55 -0800 X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="4.93,535,1378882800"; d="xml'?pptx'72,48,150?emf'72,48,150?scan'72,48,150,208,72,48,145,150,217?rels'72,48,150,208,72,48,145,150,217?jpeg'72,48,150,208,72,48,145,150,217,145?png'72,48,150,208,72,48,145,150,217,145,150";a="387902482" Received: from orsmsx106.amr.corp.intel.com ([10.22.225.133]) by azsmga001.ch.intel.com with ESMTP; 11 Nov 2013 14:07:50 -0800 Received: from orsmsx103.amr.corp.intel.com ([169.254.2.216]) by ORSMSX106.amr.corp.intel.com ([169.254.5.158]) with mapi id 14.03.0123.003; Mon, 11 Nov 2013 14:07:49 -0800 From: "Somayazulu, V Srinivasa" To: "rmcat@ietf.org" Subject: [rmcat] Video Quality Evaluation - Objective metrics Thread-Topic: [rmcat] Video Quality Evaluation - Objective metrics Thread-Index: Ac7fKXM/wGi3ndNtSi6LuMSs3DmdkQ== Date: Mon, 11 Nov 2013 22:07:49 +0000 Message-ID: <42CD495267BEF24CACD045A8B3747BA72DC8AC45@ORSMSX103.amr.corp.intel.com> Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [10.22.254.139] Content-Type: multipart/mixed; boundary="_004_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_" MIME-Version: 1.0 --_004_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_ Content-Type: multipart/alternative; boundary="_000_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_" --_000_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hi, I'm attaching a presentation with some ideas on defining and using= objective video quality metrics we believe would be suited for the RMCAT p= roposal evaluation. We ran short of time in the session to present to the = group, though my colleague Hassnaa had a chance to discuss in person with s= ome of the folks. We'd welcome any further thoughts on this direction. Th= anks! Regards, Zulu V. S. Somayazulu Intel Labs, Hillsboro, OR --_000_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable

Hi,

    &= nbsp;    I’m attaching a presentation with some ideas = on defining and using objective video quality metrics we believe would be s= uited for the RMCAT proposal evaluation.  We ran short of time in the session to present to the gr= oup, though my colleague Hassnaa had a chance to discuss in person with som= e of the folks.   We’d welcome any further thoughts on this= direction. Thanks!

 <= /p>

Regards,

Zulu

 <= /p>

V. S. Somayazulu

Intel Labs, Hillsboro, OR=

--_000_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_-- --_004_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_ Content-Type: application/vnd.openxmlformats-officedocument.presentationml.presentation; name="IETF Rmcat VQ Eval Criteria-r2.pptx" Content-Description: IETF Rmcat VQ Eval Criteria-r2.pptx Content-Disposition: attachment; filename="IETF Rmcat VQ Eval Criteria-r2.pptx"; size=1028005; creation-date="Mon, 04 Nov 2013 18:24:21 GMT"; modification-date="Tue, 05 Nov 2013 20:11:40 GMT" Content-Transfer-Encoding: base64 UEsDBBQABgAIAAAAIQBRqQWgFAIAAOAWAAATAAgCW0NvbnRlbnRfVHlwZXNdLnhtbCCiBAIooAAC AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADE mMtu2zAQRfcF+g8Ct4VE023TNLCcRdKu+gjQ5ANYaWSzFR8gadf++1KSEziBHCdhiNkI4OPOHELE HZKz841sszVYJ7QqCSsmJANV6VqoRUlurr/mpyRznquat1pBSbbgyPn87ZvZ9daAy4JauZIsvTdn lLpqCZK7QhtQYaTRVnIfmnZBDa/+8gXQ6WRyQiutPCif+y4Gmc8uoeGr1mdfNqF7IDFqQbKLYV6X qiRCdvqun44qQDajik3ejYxr/hgYT9MPjGsstO6BhhvTior7ME7Xqn6w/ny39iIo+zluKYx7FyYc yNCNHE6w0/0MP82KGrIrbv0PLsMsaoynxoILun5u8XikEVTdNKKCWlcrGSTFfjDZ3msWkgt1u4hD MK4Nnd+582GD7TfYa5PtxX4S044mDcdzCKboBO/RCT6gE3xEJzhBJ/iETnCKTvAZnYBN8BHwXZHh 2yLD90WGb4wM3xkZvjUyfG9k+ObI8N1xiu+OUxx37DRXVhuX4kzfBz5GsBbwLwnBXeBjBD7cLmH4 xv+EPszRjPx3C7/8toVXX/Ve6Cdtv298q1d+twmHRpqdOMR+KVOawh3HlKaSxzGlKe1xTGlqfRxT muIfx5TmNBDHlOZ4EMeU5rwQx5TofhUJheXkQd5XXlppC89nuH3Y7NS5CYHAevF4PbvLGEJHLxq6 N9Ma6pHctH+fnv8HAAD//wMAUEsDBBQABgAIAAAAIQBo+HShAwEAAOICAAALAAgCX3JlbHMvLnJl bHMgogQCKKAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAArJLbSgMxEIbvBd8hzH032yoi0mxvROidyPoAYzK7G90cSKbSvr2h4GFhLYK9zMw/ H98kWW/2bhTvlLINXsGyqkGQ18FY3yt4bh8WtyAyozc4Bk8KDpRh01xerJ9oRC5DebAxi0LxWcHA HO+kzHogh7kKkXzpdCE55HJMvYyo37AnuarrG5l+MqCZMMXWKEhbcwWiPUT6H1s6YjTIKHVItIip TCe2ZRfRYuqJFZigH0s5HxNVIYOcF1qdV4iHnXvxaMcZla9e9Rqp/01o+Xeh0HVW033QO0ee57ym iW+nGFnGRLkUj+lTN3R9TiHaM3lD5vSjYYyfRnLyM5sPAAAA//8DAFBLAwQUAAYACAAAACEAS/U9 7L0AAAA3AQAAIAAAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGU3LnhtbC5yZWxzjM+9CsIwEAfwXfAd wu0m1UFEmrqIIDiJPsCRXNtgm4RcFPv2ZrTg4Hhfvz9XH97jIF6U2AWvYS0rEORNsM53Gu6302oH gjN6i0PwpGEihkOzXNRXGjCXI+5dZFEUzxr6nONeKTY9jcgyRPJl0oY0Yi5l6lRE88CO1Kaqtip9 G9DMTHG2GtLZrkHcpkj/2KFtnaFjMM+RfP4RoXhwli44hWcuLKaOsgYpv/uzpY0sEaCaWs3ebT4A AAD//wMAUEsDBBQABgAIAAAAIQBskBhtwAAAADcBAAAgAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlk ZTkueG1sLnJlbHOMz71qwzAQB/C90HcQt1eyUyilWM5SCoZMJX2AQzrbIrIkdHKI374abejQ8b5+ f647PxYv7pTZxaChlQ0ICiZaFyYNP9evl3cQXDBY9DGQho0Yzv3zU/dNHks94tklFlUJrGEuJX0o xWamBVnGRKFOxpgXLLXMk0pobjiROjXNm8p7A/qDKQarIQ+2BXHdEv3HjuPoDH1Gsy4Uyh8Rir2z dMEtrqWymCcqGqTc9w9Lr7JGgOo7dXi3/wUAAP//AwBQSwMEFAAGAAgAAAAhAGhPt1XWAAAAvgEA ACAAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlNi54bWwucmVsc6yQPWvDMBCG90L/g7i9OsdDKSVy llIIdCrpDziksy1ifaBTSv3vq9IlhgwdOt7X8z7c/vAVFvXJRXyKBna6A8XRJufjZODj9PrwBEoq RUdLimxgZYHDcH+3f+eFajuS2WdRjRLFwFxrfkYUO3Mg0SlzbJMxlUC1lWXCTPZME2PfdY9Yrhkw bJjq6AyUo+tBndbMf2GncfSWX5K9BI71RgT60LIbkMrE1YDWGNh5+u33msMIeFtj958asnjHb7Sm S93IXPU3S71uET9muPn68A0AAP//AwBQSwMEFAAGAAgAAAAhAEv1Pey9AAAANwEAACAAAABwcHQv c2xpZGVzL19yZWxzL3NsaWRlOC54bWwucmVsc4zPvQrCMBAH8F3wHcLtJtVBRJq6iCA4iT7AkVzb YJuEXBT79ma04OB4X78/Vx/e4yBelNgFr2EtKxDkTbDOdxrut9NqB4IzeotD8KRhIoZDs1zUVxow lyPuXWRRFM8a+pzjXik2PY3IMkTyZdKGNGIuZepURPPAjtSmqrYqfRvQzExxthrS2a5B3KZI/9ih bZ2hYzDPkXz+EaF4cJYuOIVnLiymjrIGKb/7s6WNLBGgmlrN3m0+AAAA//8DAFBLAwQUAAYACAAA ACEAS/U97L0AAAA3AQAAIAAAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGU0LnhtbC5yZWxzjM+9CsIw EAfwXfAdwu0m1UFEmrqIIDiJPsCRXNtgm4RcFPv2ZrTg4Hhfvz9XH97jIF6U2AWvYS0rEORNsM53 Gu6302oHgjN6i0PwpGEihkOzXNRXGjCXI+5dZFEUzxr6nONeKTY9jcgyRPJl0oY0Yi5l6lRE88CO 1Kaqtip9G9DMTHG2GtLZrkHcpkj/2KFtnaFjMM+RfP4RoXhwli44hWcuLKaOsgYpv/uzpY0sEaCa Ws3ebT4AAAD//wMAUEsDBBQABgAIAAAAIQBL9T3svQAAADcBAAAhAAAAcHB0L3NsaWRlcy9fcmVs cy9zbGlkZTEwLnhtbC5yZWxzjM+9CsIwEAfwXfAdwu0m1UFEmrqIIDiJPsCRXNtgm4RcFPv2ZrTg 4Hhfvz9XH97jIF6U2AWvYS0rEORNsM53Gu6302oHgjN6i0PwpGEihkOzXNRXGjCXI+5dZFEUzxr6 nONeKTY9jcgyRPJl0oY0Yi5l6lRE88CO1Kaqtip9G9DMTHG2GtLZrkHcpkj/2KFtnaFjMM+RfP4R oXhwli44hWcuLKaOsgYpv/uzpY0sEaCaWs3ebT4AAAD//wMAUEsDBBQABgAIAAAAIQBL9T3svQAA ADcBAAAgAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTMueG1sLnJlbHOMz70KwjAQB/Bd8B3C7SbV QUSauoggOIk+wJFc22CbhFwU+/ZmtODgeF+/P1cf3uMgXpTYBa9hLSsQ5E2wznca7rfTageCM3qL Q/CkYSKGQ7Nc1FcaMJcj7l1kURTPGvqc414pNj2NyDJE8mXShjRiLmXqVETzwI7Upqq2Kn0b0MxM cbYa0tmuQdymSP/YoW2doWMwz5F8/hGheHCWLjiFZy4spo6yBim/+7OljSwRoJpazd5tPgAAAP// AwBQSwMEFAAGAAgAAAAhAEv1Pey9AAAANwEAACAAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMi54 bWwucmVsc4zPvQrCMBAH8F3wHcLtJtVBRJq6iCA4iT7AkVzbYJuEXBT79ma04OB4X78/Vx/e4yBe lNgFr2EtKxDkTbDOdxrut9NqB4IzeotD8KRhIoZDs1zUVxowlyPuXWRRFM8a+pzjXik2PY3IMkTy ZdKGNGIuZepURPPAjtSmqrYqfRvQzExxthrS2a5B3KZI/9ihbZ2hYzDPkXz+EaF4cJYuOIVnLiym jrIGKb/7s6WNLBGgmlrN3m0+AAAA//8DAFBLAwQUAAYACAAAACEAY1wjtMAAAAA3AQAAIAAAAHBw dC9zbGlkZXMvX3JlbHMvc2xpZGUxLnhtbC5yZWxzjM+9asMwEAfwPdB3ELdXsjuEECxlKQVDp5A+ wCGdbVFbEjq5xG8fjTF0yHhfvz/XXe7LLP4os49BQysbEBRsdD6MGn5uX+8nEFwwOJxjIA0bMVzM 26G70oylHvHkE4uqBNYwlZLOSrGdaEGWMVGokyHmBUst86gS2l8cSX00zVHlZwPMzhS905B714K4 bYleseMweEuf0a4LhfJPhOLZO/rGLa6lsphHKhqkfO7vllpZI0CZTu3eNQ8AAAD//wMAUEsDBBQA BgAIAAAAIQBUKrbG1gAAAL4BAAAgAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTUueG1sLnJlbHOs kD1rwzAQhvdC/4O4vZLtoZQSOUspBDKV9Acc0lkWsT7QKaH+91XpEkOGDh3v63kfbrf/Cou4UmGf ooZediAommR9dBo+T+9PLyC4YrS4pEgaVmLYj48Puw9asLYjnn1m0SiRNcy15lel2MwUkGXKFNtk SiVgbWVxKqM5oyM1dN2zKrcMGDdMcbAaysEOIE5rpr+w0zR5Q2/JXALFeidC+dCyGxCLo6pBShXI evzt9zJHB+q+Rv+fGrx4S0dc06VuZG76m6VBtogfM7X5+vgNAAD//wMAUEsDBBQABgAIAAAAIQDu DIdh1wAAAL4BAAAhAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTEyLnhtbC5yZWxzrJC7agMxEEX7 QP5BTG9pveAQgrVuQsDgKjgfMEizWpHVA40cvH8fhTRecJEi5bzOPcz+cA2z+KLCPkUNW9mBoGiS 9dFp+Di/bZ5BcMVocU6RNCzEcBgeH/bvNGNtRzz5zKJRImuYas0vSrGZKCDLlCm2yZhKwNrK4lRG 84mOVN91T6rcMmBYMcXRaihH24M4L5n+wk7j6A29JnMJFOudCOVDy25ALI6qBilVIOvxt7+TOTpQ 9zW2/6nBs7d0wiVd6krmpr9a6mWL+DFTq68P3wAAAP//AwBQSwMEFAAGAAgAAAAhAAsogYmIAQAA UQ4AAB8ACAFwcHQvX3JlbHMvcHJlc2VudGF0aW9uLnhtbC5yZWxzIKIEASigAAEAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAvJfPasMwDMbvg71D8H117P4fTXsZgx4GY+sewEvUNCyxg+1169vP tCOkpYgejI5WbPnH98kSWax+mzrZg3WV0RkTg5QloHNTVLrM2Mfm+WHGEueVLlRtNGTsAI6tlvd3 izeolQ+H3K5qXRKyaJexnfftI+cu30Gj3MC0oMOXrbGN8mFpS96q/EuVwGWaTrjt52DLs5zJusiY XRfh/s2hhVtym+22yuHJ5N8NaH/lCu7qqoCQUNkSfMaOy//odBCyMX4dQgyJKIREMajEEKgachIT w6vPGt79oQ6V1cH0ghgJlS2oK1JQUaQYxpSIYoJWqKSqUIFiUIkhUDXkOOpDCWd7GMflKYhqQeYI akjUloFRjFEtUiqMOapGdIoX5TzYC5ZT8GwHahKVR6hFgqqbCrSbylFMjH0FP6/WtL0J14Uwiqj9 A9FihDpCRSFQDBl12LcW3IUjXQgVg6qD4A1kTmXJDMOI+kYQiiEGQSUFqoSgkkKgWkiqYS+7QcLP fgSXfwAAAP//AwBQSwMEFAAGAAgAAAAhAEv1Pey9AAAANwEAACEAAABwcHQvc2xpZGVzL19yZWxz L3NsaWRlMjEueG1sLnJlbHOMz70KwjAQB/Bd8B3C7SbVQUSauoggOIk+wJFc22CbhFwU+/ZmtODg eF+/P1cf3uMgXpTYBa9hLSsQ5E2wznca7rfTageCM3qLQ/CkYSKGQ7Nc1FcaMJcj7l1kURTPGvqc 414pNj2NyDJE8mXShjRiLmXqVETzwI7Upqq2Kn0b0MxMcbYa0tmuQdymSP/YoW2doWMwz5F8/hGh eHCWLjiFZy4spo6yBim/+7OljSwRoJpazd5tPgAAAP//AwBQSwMEFAAGAAgAAAAhAEv1Pey9AAAA NwEAACEAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMjAueG1sLnJlbHOMz70KwjAQB/Bd8B3C7SbV QUSauoggOIk+wJFc22CbhFwU+/ZmtODgeF+/P1cf3uMgXpTYBa9hLSsQ5E2wznca7rfTageCM3qL Q/CkYSKGQ7Nc1FcaMJcj7l1kURTPGvqc414pNj2NyDJE8mXShjRiLmXqVETzwI7Upqq2Kn0b0MxM cbYa0tmuQdymSP/YoW2doWMwz5F8/hGheHCWLjiFZy4spo6yBim/+7OljSwRoJpazd5tPgAAAP// AwBQSwMEFAAGAAgAAAAhAEv1Pey9AAAANwEAACEAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMTku eG1sLnJlbHOMz70KwjAQB/Bd8B3C7SbVQUSauoggOIk+wJFc22CbhFwU+/ZmtODgeF+/P1cf3uMg XpTYBa9hLSsQ5E2wznca7rfTageCM3qLQ/CkYSKGQ7Nc1FcaMJcj7l1kURTPGvqc414pNj2NyDJE 8mXShjRiLmXqVETzwI7Upqq2Kn0b0MxMcbYa0tmuQdymSP/YoW2doWMwz5F8/hGheHCWLjiFZy4s po6yBim/+7OljSwRoJpazd5tPgAAAP//AwBQSwMEFAAGAAgAAAAhABunAFzAAAAANwEAACEAAABw cHQvc2xpZGVzL19yZWxzL3NsaWRlMTgueG1sLnJlbHOMz71qwzAQB/C90HcQt1eyQymlWM5SCoZM JX2AQzrbIrIkdHKI374abejQ8b5+f647PxYv7pTZxaChlQ0ICiZaFyYNP9evl3cQXDBY9DGQho0Y zv3zU/dNHks94tklFlUJrGEuJX0oxWamBVnGRKFOxpgXLLXMk0pobjiROjXNm8p7A/qDKQarIQ+2 BXHdEv3HjuPoDH1Gsy4Uyh8Rir2zdMEtrqWymCcqGqTc9w9Lr7JGgOo7dXi3/wUAAP//AwBQSwME FAAGAAgAAAAhAF2b1XLXAAAAvwEAACEAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMTcueG1sLnJl bHOskD1rwzAQhvdC/4O4vZLtoZQSOUspBDKV9Acc0lkWsT7QKaH+91XpEkOGDh3v63kfbrf/Cou4 UmGfooZediAommR9dBo+T+9PLyC4YrS4pEgaVmLYj48Puw9asLYjnn1m0SiRNcy15lel2MwUkGXK FNtkSiVgbWVxKqM5oyM1dN2zKrcMGDdMcbAaysEOIE5rpr+w0zR5Q2/JXALFeidC+dCyGxCLo6pB ShXIevzt973M0YG679H/pwcv3tIR13SpG5ub/mZpkC3ix0xt3j5+AwAA//8DAFBLAwQUAAYACAAA ACEAt6+1HN8AAABGAgAAIQAAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGUxNi54bWwucmVsc7yRy2rD MBBF94X8g5h9JNuF0pbI2YRAoKuQfsAgjWUR64GklPrvo9JNDKF0Ubqc17kHZrP9dBP7oJRt8BJa 3gAjr4K23kh4P+3Xz8ByQa9xCp4kzJRh268eNkeasNSjPNqYWaX4LGEsJb4KkdVIDjMPkXydDCE5 LLVMRkRUZzQkuqZ5EumWAf2CyQ5aQjroR2CnOdJv2GEYrKJdUBdHvtyJENbV7ArEZKhI4Fw40ha/ +23Dozcg7nt0/+bx8pNG+5caebKa3nAOl7KQuekvljpeI77MxOL7/RUAAP//AwBQSwMEFAAGAAgA AAAhAO+r0ZfWAAAAvgEAACEAAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMTUueG1sLnJlbHOskLtq AzEQRfuA/0FMH816i2CCtW5CwJDKOB8wSLNakdUDSQ7Zv7dMGi+4SJFyXuceZn/48bP45lxcDAq2 sgPBQUfjglXweX5/3oEolYKhOQZWsHCBw7B52p94ptqOyuRSEY0SioKp1vSKWPTEnoqMiUObjDF7 qq3MFhPpL7KMfde9YL5nwLBiiqNRkI+mB3FeEv+FHcfRaX6L+uI51AcR6HzLbkDKlqsCKdGzcfTb 38kULOBjje1/apTZGf6gJV7qSuauv1rqZYu4meHq68MVAAD//wMAUEsDBBQABgAIAAAAIQD5qhCW 3gAAAEUCAAAhAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTE0LnhtbC5yZWxzvJHPSgMxEIfvgu8Q 5m6yXaGKNNuLCAVPUh9gSGazwc0kJKm4b2/ESxeKeBCP8+/7fTC7/UeYxTvl4iNr2MgOBLGJ1rPT 8Hp8urkHUSqyxTkyaViowH64vtq90Iy1HZXJpyIahYuGqdb0oFQxEwUsMibiNhljDlhbmZ1KaN7Q keq7bqvyOQOGFVMcrIZ8sLcgjkui37DjOHpDj9GcAnG9EKF8aNkNiNlR1SClCmQ9fvfvZGIH6rJG /28a2580Nn+pUWZv6RmXeKormbP+aqmXLeLLTK2eP3wCAAD//wMAUEsDBBQABgAIAAAAIQBL9T3s vQAAADcBAAAhAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTEzLnhtbC5yZWxzjM+9CsIwEAfwXfAd wu0m1UFEmrqIIDiJPsCRXNtgm4RcFPv2ZrTg4Hhfvz9XH97jIF6U2AWvYS0rEORNsM53Gu6302oH gjN6i0PwpGEihkOzXNRXGjCXI+5dZFEUzxr6nONeKTY9jcgyRPJl0oY0Yi5l6lRE88CO1Kaqtip9 G9DMTHG2GtLZrkHcpkj/2KFtnaFjMM+RfP4RoXhwli44hWcuLKaOsgYpv/uzpY0sEaCaWs3ebT4A AAD//wMAUEsDBBQABgAIAAAAIQBZlQOr3gAAAEUCAAAhAAAAcHB0L3NsaWRlcy9fcmVscy9zbGlk ZTExLnhtbC5yZWxzvJFNSwMxEIbvgv8hzN1kuxURabYXEQqepP6AIZnNBjcfZFJx/70RL10o4kE8 ztfzPjC7/UeYxTsV9ilq2MgOBEWTrI9Ow+vx6eYeBFeMFucUScNCDPvh+mr3QjPWdsSTzywaJbKG qdb8oBSbiQKyTJlim4ypBKytLE5lNG/oSPVdd6fKOQOGFVMcrIZysFsQxyXTb9hpHL2hx2ROgWK9 EKF8aNkNiMVR1SClCmQ9fvdvZY4O1GWN/t80tj9pbP5Sg2dv6RmXdKormbP+aqmXLeLLTK2eP3wC AAD//wMAUEsDBBQABgAIAAAAIQDU4WQXQQMAAIYQAAAUAAAAcHB0L3ByZXNlbnRhdGlvbi54bWzs l91u4ygUx+9X2newuF2lNv52VGeUzkxWK3WkatJ5AGqT1BoMFpBO2tW8+xyInZDQlUba21wF+J9z gJ/JgXP7Yd+z4IVK1QleI3wToYDyRrQd39bo2+NqVqJAacJbwgSnNXqlCn1Y/PnH7TAfJFWUa6LB NYAwXM1JjZ61HuZhqJpn2hN1IwbKQdsI2RMNXbkNW0l+QPiehXEU5WFPOo5Gf/k7/mKz6Rr6STS7 HqY/BJGU2XWo525QU7Thd6K5uzhfkiIvdL17UlSvBNcK6KAFbFux9gtRmsp/2nulL0aCrq1RjNMi LZM8BXZybkZAwShc3Ib/4X7ePgTJcsc7Nt7nshs88eQyc+TUl1NHzjw5d+Xck4vEkQtfdr1Lf+WV I1eeXBUutch3P9Mt1vOtudywD64883+H3NlXewedu3zss6siV/fhVdjVfXpV7Oo+vsqFj9/h59KP fX6VezJin191du4sv8OxdQ/p+i1o9jWqcJpGEWy4ea1RXmal7ejXATKFaiSlPN2PhLnQVI1uR0vj NsWwVi3dkB3Tj3Sv1/qV0cUtMWMPD3JsfX2QASMmOVE++7a2q3NN2AvDA9j0RN7XCKYgbAuJjaEA bB7J0/ptmhF2qZk1oeSe38nv5g8emDTCxy5IzzAV5KqHHW/0IQEcV6EgEi5NnO9UmtwJqcPqSrCu XXWM2Y7JPPQjk8ELgdn0/pAHLqzsrJbbhjTA7q+ez5g2lmROyYVAyUFo1IXQqBOOrwZHeOQxoolP aNKsMAu+8rFQRj7Jic8E4crHQBn5pCc+OClwfgU0URkBZQ6gEq6SK6CJyggoPwGK4aq2t8AVkKEy AiocQEWaXHP0kcoIqDwBMnSuSfpIZQRUOYDyrLgm6SMV+5L1n5jDHNrj2xZawU52Nfr382q5uouT ZBblyWqWxnfZrIRLb1Z9WiWrDN8tcbT8aSpCnJkX8d+7rqUQZKo9ceZVn33XSKHERt80oh/L2HAQ P6gcRGcrWRwfas9D1K0JaR/l8EmE7KBMhZhCvqFgEMrUmblhbUwbZr++ktunI+VlukyWtn4Jjya2 ZeNeThFPQeHS+h9BbdvBYZED1Ol3GnLL7sUvAAAA//8DAFBLAwQUAAYACAAAACEAfEtjYccHAACu MAAAFgAAAHBwdC9zbGlkZXMvc2xpZGUyMS54bWzsWt1u27gSvj/AeQdCVz2LtS3LkiwHmyxsJ04L tKnXTrvXtETFQihSh6Ty06LAAvsIe3Xe5TzNvsC+wg5JyXbspPWmaZCk8YVFUeRw5uNwRI6+n36+ yCk6I0JmnO067abrIMJinmTsZNd5dzxqRA6SCrMEU87IrnNJpPPz3r//9VOxI2mCoDeTO3jXmStV 7LRaMp6THMsmLwiDZykXOVZwK05aicDnIDWnLc91w1aOM+ZU/cU2/XmaZjHZ53GZE6asEEEoVqC5 nGeFrKUV20grBJEgxvS+otIeWBZPaaKvsjgWhOgSOzsUxbQYC/P46GwsUJYAXg5iOAdYnFb1oGpm btmZKbTWup/URbxzkYpcX8E2dLHrAPiX+r+l68iFQrGtjJe18fztNW3j+cE1rVv1AK2VQbVVVrlN c7zanONMUYLaC6tqfWXxmsenEjEO9mjzrXmLFtZmfS3mSF0WIEppUVU7+9AUlspUYKmLAU8u9SAz uJpKvEOlmqpLSsxNof+MGgL0pVh7KGGNd1MHJZlQxmokczWkBLMFNGpv3Gx7rodekIsmGjePBv03 0/9obJRByAgkLBljgSc3yrVoFkb5WtNWDeXNgHZqQIecKXA3NKY4JnNOEyKQ93XwZsnFsskNyF7j Y52o7brWedrdwAvdNXfzvCg0DbQbeV2/49kWq85kRW/OmS4xWGT9UvE0UygFq6cxpgBA6AVaKGXT Ip6QpIz1woMBXD2WFb+Use20yw8gwddyP+8AOD4lCr0kGGCXCIIZWpv/7cUTIcyyv2aYWaakEgTn txZ+jVCUsaJUiDN6eUWs9ca7QOeIKxgEvDMhCcyYQBD9SYzIGaalCY/3Na4FT6LzTM2RzE5YBgEf w6IRWBGEE1zYcP2N9HmfJYSjMcFCcgYgiPrlghBPkdvstSOjpg0nzTZ60Ys8JHFeUCK3DSjX6PLZ 0HIicDHP4pGAKFK/h5Y16+EmXMRvPIP4Hay8la52wlcE3xR0Noeqww8Ei9dSbYSezQ5rwceL2j1j /U3RJ4jCru+2bfSBlhFgXSlUi6o0XynuY4VRKbJb7EKUBsoxDjCj1QXATDMh1YSfm6U+g4BRlU0L 3cWEp1fJ3sdg6HmB7/cb3YODsOF3fK8xcP2oEQWD/WFvtN8edvqfjHOsdjMVeqRqyEORJdagLBly ivRgfhT6bmThWa3v9nqdTrhR7wVROwy6lTutyFQCzSHsd1wdyE1FjHS/aaHdv2Ortnv5QthHmJ5A t1gJq8HNS669xZLDNEtgcSfomEiFRjhWXMh/sJTWxlgupVWTVGy3YbpU2T9/Q8QJWczodtY/VUX0 xTripqf8A62+yhMmJOY57OuTzQj/TYC/L7vql8WTs8j7thY9BJ/sl0nG0WDydObuqP9sy2PyQLMl 3fo0YQXOsCQ006maLx4tnpJru003QH/+9gcK0ZtZcQ97iPsy7B3TuQKpyuTqAfBJOnx1UqdcyvUj +mOcQpPTucxNJizFMazJX+HsoXOrslJSVEeAvb9+//+zzz7SrWtjVqYpETCtD2oK9Wktx+K1URwK E1PIYG6YMkV7lKOgBUmP8WwKWvXavtFLKGrTXfg1G4hTcw7W2by+6YJLxR2ksyOsegxN52AhQDAu WazqA41J99nMZjyOFTrDIFbnHxdJv9UWA5LWbZW0betmV572U/WZdtXTWTmk4vjCwDErpx8WxRGY sbg54swiBsdzm9HAO4DGpIa9yqE+7OW/7pXPjvRgHelbzup3sGOdEMlp+bRSBC/fH/Z/RL/Y/+Gr 0aOy7Ht/+duPBvUX80c1ddsvu5dNL/Rb/ffDRZ/nreqj9NbR5M/f/mc/gJ6SS5Tq7zTm69oTOi+b j0/2k2HQ6Ljo5YfbfC/cYqDDt2NECTtR83X07vCF5zXa7uOanO8+hSG44jGnTyhE7o8b2mWTZz98 hH6oP+wrBCFekeSHp+OUx8PH6JQPaitiaQLmukKkWL2vmINXKSg3s926Nf1kQmIFelKCDE9hwWDb YEV+gasWhIEbeB1DFwl6rheGhjO3pIt03G7QbfuWLgI38LvCVQMkhFSHhOdIF3YdAZqZhYjPKgLL sslnqW3oHGDYdRgc821ep9Akt1FWybgdd21t7oxcTrNklFFDRtGaDamwmYkZxfFpnZ1YtlrNIv2A XsSYoRlBJayMH9GsVCjHlyYCQKWAXYdmodwX+9Gv/WHEuQLXXiU/du6CW5oqYYH8b4kFjOBUpMgv sCK/nm96zep9xRShaMhZmum8WIa33AHcEtugxnYKnkDQUZnP1hD27wJhSRMQfS3IZineDcgpTYxR H8PBwPW60ajh7YdBw+/53UYv6A4bbtSPooN+MDjo9z85C9000qDdtlPkrdAwUs3wvs2kmEtNC7c8 uKpkKWgfB4Ne6A2jQWPQ9kcNf7/XbfRHYM0o6Pj+cBD1h52DT5pm3vZ3YkEM4eVVzaSHyg0WW57F gkueqmbM84oG3yr4OREFzwwTvu1WdHqbFm133DDsdAKzxlpGt/pqtNVzXjHcYyre4OLtmfEPGAzm d2iqioydVO6xbAJgZDk8MCVWWV5g65rHrKbEJ6XYdXR6OM1YpogDgUcq8B0In+RMOxDjCTm27PB8 ArGh0tNI0ohb0bpUDadBB4X/BgAA//8DAFBLAwQUAAYACAAAACEATSpTdBkGAAACGAAAFgAAAHBw dC9zbGlkZXMvc2xpZGUxMS54bWzsWOtu2zYU/j9g70D4dxXdqJvRtLBluyiQtlmctr8ZiY6JSKRG 0ZdsGNAH2V6uT7JDSvItTpaiWbEBcYCQpsjDc/nOOZ/18vW6LNCSypoJftpzT5weojwTOePXp72P lxMr7qFaEZ6TQnB62rulde/1q59/eln16yJHcJrXfXLamytV9W27zua0JPWJqCiHZzMhS6Lgq7y2 c0lWILUsbM9xQrskjPfa8/Ix58VsxjI6EtmipFw1QiQtiALN6zmr6k5a9RhplaQ1iDGn91R6BZZl 0yLXY11dSkr1jC/fyGpanUvz+P3yXCKWg796iJMS3NKz2wftNvOVL83EPjh+3U1Jfz2TpR7BNrQ+ 7YHzb/V/W6/RtUJZs5htV7P5hyN7s/n4yG67u8DeuVRb1Sh31xyvM+eSqYIid2NVp29dnYnspkZc gD3a/Ma8zY7GZj1Wc6RuKxCltKh2X/PQTLbKtM5S66HIb/UlVzA213EI2GChxIwpNBNcTTNSgMjE gU9r4HZzUaupui2ocUil/5llCbYVRKOZcuvjtIdyJpXxEKpLlRaU8I0b1atPLKcC/bIgBVO36BOR zCAErWz0nqqVkDdI36rM3Y38hy9p5abfduqYaoLnzGB9q9aBUMrzcyLJxUPK2MY39tbfdgeI+2ER bmABmByKNQr2gIG0qHWLhn3U70b5CN5xEEHiGSAHcRjhJqpb6Mch/OkNGtJ+mPi+t4drsEXW6g0V JdKT056kmerpdbI8q1VrbrulAdSEFUWjXaPTXeChlSSA7frXBZG0h6QqUlFo7zUJoPE4Ya3sb0cf M0XjwUBPxUJmtI8urDconVN+jahCpDh5gb5++fMdJfVCUl0CEVRlNOCkuK1ZjcQMTW8g41AD4UtJ dL188fXLX2hw/jmdIvB0fPJIxBxo+SBuKpa1YWfZnYLiBJsSCY8VaI78DXjaA0SL6OpKOgc96KCu IJJNfe2WpBSrOSV5vVd3Npfuwm6zeFWwSkfcRBfmSPZpeUVBM/k290xEAWsAlQ51Cwmm/+7FA8dJ vKGVBk5qYScaW4MER1bkjAGkOHZTN/1Dn3Zxf1FT0J0Uo4p13dDFdzpQyTIpajFTJ5ko21bWdURo Pq7T9sMlMVBrHA4KdaNRUQMOjDA4lNkFeMggplaSqmyupzOwtV23dx7Yu55okI+uVu9EDmEhgGfj iIPc9KIkDkKTm24YhX6S7Ocm9twgwbjNTS+KndjseILkhGzi+9nardDZDIQ0p4+HLnGScTyOsYW9 cAyhG42swSTFVjhxo2Dkj9J05Hahm7M8p1zf8f2RM4EQBcs7uJnzNC1kE1OSZZCyDXD3dtr7mmwD v2eV62Fn6CXWJIwjC09wYCWRE1uOmwyT0MEJHk32rTpjnD4BHlfQbQMv+Efz1PqIaaRfMkWhtLAS arlu2m2N02k85nlT7wgrmvmOJ7T2xz0xmAROhP3YiqLAt7A/dqxhPEmtQeqGYTQepsPxQXzHBjP1 04R4A0CTKQuwbjrPV1ApNcD9IPGasqnTp7EXCvc1FPdMSWglQn1maj6dE02M2oay78er667DbYQ3 ftjeu+Om1rTjxaLrcPamRN/f4ePDDh89UYffqSKB4yW62+9XETcOA6/r8E4En/97h69/A0dpKnOn 11MpzcqRnn8+fX9RiLp+FE28/4YjkoE4S9gBSJGaM/CWxV4BeVixXM11drLmd9Dh7fexg3vv/x6e sPnt0fEE/F/hCf4zT3iAJ2Achn60JQp3SDz2kjD0tkQhCZ4sx5+JwjNReCYKP5IouM4hUzCk/wmY QkcPdB3BSYBxHO/XEd8HbIfPVOGHUYWKZDdUIX0XkkTRf5EdmKF719oB08zabB5C9fLSeGgNXTyx 8CiJoFqHgTUJfIzTYTxI/bHO5gogn0lq6Mzb7vV09dhUrsSKykow83p5v9dGgQNcNcDm1ZdtVOvG Lou6t8ZZId+R6sPSeAnugvRMzVKly0SzdbsFfAEEjF+bGW8Nr0iTd5e8e82cLwAQjOd0xjjUSoAi rRWRAHNOl1Rq/pPTy+aNa3khhGr1NJK0wxvRetZep30OCv8NAAD//wMAUEsDBBQABgAIAAAAIQCa 42afMgUAAPYUAAAWAAAAcHB0L3NsaWRlcy9zbGlkZTEwLnhtbMRYW0/jOBR+X2n/g5VnSnqjQDVl REsZIcEMaju7z8ZxGy+O7bWd0s5o//ue4yQUaBh12WH6UruOfa7fudgfPq4ySZbcOqHVIGodNiPC FdOJUItB9HV22TiJiPNUJVRqxQfRmrvo49nvv30wfScTAqeV69NBlHpv+nHsWMoz6g614Qq+zbXN qIe/dhEnlj4A1UzG7WazF2dUqKg8b3c5r+dzwfiFZnnGlS+IWC6pB8ldKoyrqJldqBnLHZAJp5+J dAaasalMcHRmZjnHmVp+smZqbm34/Hl5a4lIwF4RUTQDs0Rx+aHcFv6qZZjEL44vqintr+Y2wxF0 I6tBBMZf42+Ma3zlCSsW2WaVpV9q9rJ0XLM7rhjET5iiVoVw2+q0K3VmwktOWo9aVfI6c63ZvSNK gz6ofqHe445CZxxNSvzaACmPpMp9xccw2QhTa4le87TXrDXHSbtdfEI1W61up9ncUrYgavp+NdTJ Gk/fwRicQfvS+alfSx7+GPwJAliwgaSIeq4aX6cRSYT1gT1xmR9JTtWjLP5sSNn9wupcJcjYB/aB DFfJLbV08iq1QlQT5KzkCyL/2DWdyjUjrTwAl9xKyniqZcItaf8/R4lktdmyu4+6R8ft0kdP/PCK p47aR8cnu3sKZwrC9Tz3ei48mYPWU0YlGOC4iZyIVFPDJjzJGYYwYDcsF+Q3NLadDQwJlQs48Vfu fCFxPQDcN6DaQV4bKHBrQ9TXQGKSMepfoGF3wjUEyZ+fiHAk4VRC3iQPwqeEabXgDjXGqbdagmks uQJMWMU9Sain+MUJwAWeEviFgo2WnBgN/xpeN8KEWCDc8CLjJMsljomgxHG7hDTriIaiQCaz22ca Fch9YsmM2usgv1AJoDJMn1g3ODT/DJWjhH3hldeCZAfDbEnwdl/W+fCt/ivP879zYTnWJxf8UuMu kFdbcGXmCLUcvDsXiidPffZji8ulDAj8D3qHeHkp6rV+AOaSrsk786uz85RnojHhUtA7qDO7G71W k4B5UAUgbte/xniXVEC4OUe8JtqnEClzqR/ce5uy5H6eUFN0Lsgfwv5B2/s3W7HOP4DGRITGapfw rwpBpd/PywVb6r9v+N9wbwV7PXSfxCsY/o7vIXIuMGQPiE+h+VikJvcHJBNKZOIbZntvqXKZcNjN E0s95nHHhCy75APM+VgLhF+jBmG7wG6CL0POgj6fGGhtoJJI7RwvVhLhGLXJfrHQwu7hV9SF0tDj n1nKl1TmRcCOIPdDlqc7lIeUQs2+41ztoUjUKXG+1ALvhWS0EXukAVvG7SMSpp7eCQlI3gPvm9Ar zSzFO+l+dLc+N2TIASRC230kIiy3jpOxWgoLd2jMH3sQ4w9q14jJWwrd8Sil2OtCgAA62T7EmYT8 iplYIzzK7DousmvVqNvc7FBb30W+x8Yl3CamUDcktZCUINnvEc5XGRQdT0J63K8k4xVcrrF6htYO rj/xZDZ6fgd6W8uyfdsPQ/W+BHfma+fLGcmtGETfh8PTXnt0MmwMW93LRvfi9Lhxftk7alwedbrd 0fDkfNQZ/4PvVa1un0FhR0xdVU9ysLj1DJYJBubVc3/IdFa+p8VGP3AbLoRxu9lqlu9yULDgAt/r 9LrNdu+ofBoA0aoxCIsPCuVLGZP2hpovy2Af4AUoH4UlA8FQvj1stoAtAHlqEWaqVNzQ4sljpqqn tSSHyzY2EFD9oGpG0L04THyDSEG/YiOidMJnxStTNtE6ACAuKaHBC9I4K9mhzUHgfwEAAP//AwBQ SwMEFAAGAAgAAAAhADpLGCeEAgAAPAUAABUAAABwcHQvc2xpZGVzL3NsaWRlOS54bWyMVF1P2zAU fZ+0/2D5vSRp0g8iAqKFTpPYqGj5AcZxiTXHtmxT0qH99107Dt2AB17iG/t+nHN8r88uulagPTOW K1nh7CTFiEmqai4fK3y/XY3mGFlHZE2EkqzCB2bxxfnXL2e6tKJGEC1tSSrcOKfLJLG0YS2xJ0oz CWc7ZVri4Nc8JrUhz5C1Fck4TadJS7jEMd58Jl7tdpyyK0WfWiZdn8QwQRwgtw3XdsimP5NNG2Yh TYj+D9I5MKMbUfvV6q1hzFty/83ojV6bcPxzvzaI16AXRpK0IAtO4kF0C79yH4zkTfjjYJKy25nW r8ANdRUG8Q/+m/g91jlE+0163KXN7Qe+tLn+wDsZCiT/FPWsenDv6eQDna0vvhaEskaJmhk0fiU4 QLf6RtFfFkkF1LwSPdNXj56+X3WD3EFD1gdVHzAU6o7uvU8wjvA+1GaS50Xakx5neT5N38g0m83G wcHTzyZpms1nb0XoU+vSdQuA4qM9pHBJpBTWbdxBsPCj/SfAMKCNIH4amBzdb2AaflcYCkGlh9AA NTcuKI5s65aCEfl6Le78UkrWeQwuIAkZmazXxJC7TyfuWehAYYAe2MRlaFKQ4sa6aKEnwyv8slic TsfL+WK0yIrVqLg6nY0uV9PJaDXJi2K5mF8u8+s/vumzoqSGhXn4Psw1bL6bpZZTo6zauROq2jiU iVbPzGjFw1xmaRzuPRFwWWmWz6cFlItXDtiGNaD1TRPnjQrzg+jbfZAJijlmlmFLw8MR++voAmLw Fg6CJSNzTfqe3sphQOsneF64rNmOS+4YRjD5jhhXYcng4YOWVzXbhgZ17Z1SLuIMmbzifWpvxXJe dAD8FwAA//8DAFBLAwQUAAYACAAAACEAjSUxgGAGAACEFgAAFQAAAHBwdC9zbGlkZXMvc2xpZGU4 LnhtbNRY224bNxB9L9B/IPahaAHrZku2rEY2bPmCoE7iyErTV4o70jLmkgzJlaUWBQr0K/rWb+sP 9Bc65O5aF9ux48hG6gcvtZwdzhyemeHwxf40FWQCxnIlu1GjWo8ISKZiLsfd6N3gpNKOiHVUxlQo Cd1oBjba3/v2mxe6Y0VM8GtpO7QbJc7pTq1mWQIptVWlQeLcSJmUOvxpxrXY0CvUmoraZr2+XUsp l1HxvXnI92o04gyOFMtSkC5XYkBQh5bbhGtbatMP0aYNWFQTvl4yaQ89Yxci9k+rBwbAj+Tk1OgL fW7C9OvJuSE8RrwiImmKsES1YqIQCz/lJAxqK5+PyyHtTEcm9U/0jUy7EYI/8/9r/h1MHWH5SzZ/ y5I3t8iy5PgW6Vq5QG1hUe9VbtxNdzZLdwbcCSCNa69Ke60+U+zSEqnQH+9+7t61RO6zf+qEuJlG Vc6rKuTyyTCYG1OA5aaHKp75RYb4DC9pR1h34WYCwg/t/wUzDNorqGcoyMq7i4jE3LjgNbGp6wmg 8hoat3fELcusJ7hHxAVcghqQ8Tk1tH+nthxDHUwu7auVAN4N41YJY09JhyQj54IySJSIwZDNLwOV x9O5yB143sKszc32dj2nTKPR3KrXV0jWbrbam17Ak6e1vdVu5xKLFMpV39wpP5IYWgeZUyPuyAi9 vmBUIACtll+JCHmhWR/ijPlwQ2vqhQG1RR03NxsXJFSM8YsPmXW5xY8gwLlRWlkgThEax2TCY1Dk Y0YFdzMCEyqykAZICs5wZtEBQxgmFZQz+cxwRla4c58ZYExID7eY008ZdZ+p7hY15P1plZAjsNzQ IUbrCKjLMKsRNSIugWtvbKIyzNRcMpHF0FlaOGf3AtpiIoLZa0D9VKkYYTRljiZX3CXEZsMPgDSY wMo+aDAMtBd8Ngt7Kh1yCSg0GoHxgaoxHSBuWA49WbRR3kZCJaLnYIxkgJgIpS4JdYX566PFW3W8 BlLwVFO2zK6nhLAfNncCYkYsLi1CkGHsswzfAxlSi4jh3sfUUTIyKiUS3JUyl17cy/jivQziU5r7 MgYqxGxjbpjOhoLbBH/kpxwT32fOGsy4mAfBLWmIW5KgHR5JPKlQyX/1FIwJTIFlDjYIWA2Me0eQ l4T6XIWhPszC1wuKdIJePrkzGEUpLqauXbolo2JKWor2Z9vwY2o5Fl2EMsMCgGjpUAuoWLDSbjwf AU8yISoGQrphQL6jqf6RvFZy/q6D2hxmIJLSGRliGlcGMGtyn3p8Yeq/6h0MFjH+vA2+98yzavVT UObl4F1lQPrAqqfVRn2nXoTj6z75+e01Z2KsbGNZeJ2zB4meo4R9BKFaC87yHXy2/TuIY+5XzLOI SzBWYwVW/vPHX44Y+Jhx3C2mUt9eeJfWVx6G3FlngKbrKBLSZxDvByYTx6od4rFRBgt0ipM6c5ZQ dCTh44QIwPSOctVxFR02KhsnKLCBtZJdgsNyaC3xlTFX9ZXScRhg/XRSVkgn3Fyk1gggHqJ7ecUa o886FDEYcennlw+Hc36WybykMLX+ROF8+rsrYB9ocGEjwy0Cw2l5RB2DDSpDBVBivovLEfEFqOyT R9m7pCo0ObM0NIQj7IS60XtE0V8xYM+uqcSzOYr5VmHlD2e5Y8kJTbmYhfaUYWW04Pw4J4PJewe3 9++ff5NeZvJj3I3CdzfT7sHg/8TgEyTFKDN4+F/pXzqkF3oAzK5YUWCqhQr1BNuaT5Jof62F8QEe nCvfL+PRJhAcWxZltPI+YNAthxiX+W1OODBJrO/9Qe+c/NI/PHvT+wkzMX7m7Cp5bziQUnPWjZqt ndD7lu5wGaMVwcYFz0LDm2Gt9g2q15Qz7ysOh+/XlQTOqD+QhKj64XFhdRvv1wD+I4Pv5nVOeJSX fTB1Z9YVI5IZ3o1+Ozzc3d7stQ8rh43mSaV5tLtTOTjZblVOWlvNZu+wfdDbOv7dXx42mh2GZdoT 82V5P4ovb9xJppwZZdXIVfG4UFxu1rS6AmQ8D/ebjXpxSYqlA3d4t91o7LZ3dlvF5Q/aVj6Dtf7K qLi3ZMK8ovrNJGCEi+Hu9cIrjSQrbpfmIggGT3EijGThOdatoGkgy4vOOMOzid8dXwQdRBhk2DIZ 3CqJJwQTEaliGOR3fmlfqZALaoUmj3iu2o+K5TzoaPB/AAAA//8DAFBLAwQUAAYACAAAACEAww3T /MMEAADjEAAAFQAAAHBwdC9zbGlkZXMvc2xpZGU3LnhtbLxXzW7bOBC+L7DvMNApBWrLdmzHNuoE sdu0BdptEDu7Z4YaW0QpUiFpx0bR077IvuG+wg4pyWlSp8iPvTpI/NPMNzMfyZk3J6tMwhKNFVoN o2a9EQEqrhOh5sPocnpW60VgHVMJk1rhMFqjjU6Of//tTT6wMgH6W9kBG0apc/kgji1PMWO2rnNU NDfTJmOOumYeJ4bdkNRMxq1GoxtnTKio/N885n89mwmObzVfZKhcIcSgZI6Q21TktpKWP0ZabtCS mPD3HUjHZBmfyMR/bT41iL6llu9NPsnPTZj+Y3luQCTkrwgUy8gtUVxOlMtCVy1DI773+7xqssFq ZjL/JdtgNYzI+Wv/jv0YrhzwYpDfjvL0y5a1PH23ZXVcKYh/UOqtKsD9bE6rMmcqnERobqyq8Nr8 k+ZfLShN9njzC/M2Kwqb/TdPwa1zEuW8qHJdMRkat2BKZ7nVSCdrr+SKvoU6RQE7XTg9Ew5mWrkJ Z5JE9hv0lAbeLpbWTdxaYnBI7l9h2JBtknk2o6pdTiJIhHHBQ2AzN5bI1MaN7nhMXBIJmoJVoBUs LIKewcfpZW0K7+vNxlHD63VBe9CAKjlnhl08qKhAmgfLKzPjKg4PR+OwigaBcsRVOJeMY6ol4YPW y2IjktXtkgfCsoWg7c4R7ZTAvGa30fDtO1zttVr9rl/gOdjutDr97uF9JhainxTwXscHHKSa5PwC kwX30SG67o8HlxR1XDK5CEQAy1ExI/SBfQVOg1nQkMgWxeHj6eGYmSP5QKs52jDIllokTHEEJufa CJdmcENvYDAXS1TgaCEsiWwaLF4v0C89cIZCDDMh8dUdlhX8qV7elKUsA/hMnkuJ3EGhjwzIGf9K BjBjBJkNTmRoX1ejUlvru8wRVOXpRxcCGORIlpi9Ap3g3B/3kDDHQChyvk21cb6JhpBaDz7AhQOs z+vQIYgUhsSe7NeDZ9oAMp6SugLhvVNhu0gR7oxfChaPEvRLEYNHWB4OkOdxh0nuqY8le68EsYh6 /j4ljlhLFzRReIUJzAwdYOBn/ydEjPjI5vgjcYN6T90n8HUXUHI0tYobhaOuF0wKt94ZU54nCI0J I1sE/vkEgVfMohQ+Jay1iuP5USqud6Rii+jXcH/rPHQzP0Hwzg/eDT80URFcSveMcsJUm6m6Cva7 hT9TN5zhS7rVwiXl77DU72Hnm99ewqz9kGm3/NlQZn903ME5/n2vJPgg5ilRMKu4IPUNdTeMeAH8 kI6us1ABzCjBGEZ/UeXna0oq0nKmtKWhhvfhvYdmhePpGcuEXId6hKfMEClLOz2YIstzx//+/Q9c oaM0IGwlJuXdY/bZWfoDHn5Jmh8+VS1JyfIn68oWLAyx69to1O+2xr1RbdRsn9Xab/tHtdOzbqd2 1jlst8ej3un48N13X5s22wNuMOSdH6vymwZ/KnkzwY22eubqXGdl7RznPsK5FqF8bjbKGpyyKCo2 uv0e5evNRr8sCghb9Q1ofSlRlsVcms8s/7IMfiVlFIJxGMopwmXVcbuEnCEymggtVVqes6LYmaqq jk4WtM0FJZczoYTDiK5rS1k1RV4hhZdqIZ3gtCgpswutXYkzSPIeL0T7VqnOO50A/wcAAP//AwBQ SwMEFAAGAAgAAAAhAFn3oYlmBQAAnA0AABYAAABwcHQvc2xpZGVzL3NsaWRlMTIueG1srFfrbts2 FP4/YO9A6HcV3S3JaFrYsl0USNoiTrvfDEVHRCWSo+hLMAzog2wv1yfZISX5krhdtjYOLJqXw3P5 zneOXr7eNTXaUNUywS+d4MJ3EOVElIzfXzofbxdu5qBWY17iWnB66TzQ1nn96tdfXspxW5cITvN2 jC+dSms59ryWVLTB7YWQlMPaSqgGa/ip7r1S4S1IbWov9P2R12DGnf68es55sVoxQmeCrBvKdSdE 0Rpr0LytmGwHafI50qSiLYixp09UegWWkWVdmmcrbxWlZsQ3b5Rcyg/KLr/bfFCIleAvB3HcgFsc r1/ot9mffGMH3qPj98MQj3cr1Zgn2IZ2lw44/8F8e2aO7jQi3SQ5zJLq/Zm9pJqf2e0NF3hHlxqr OuWemhMO5twyXVMU7K0a9G3llSCfW8QF2GPM78zb7+hsNk9ZIf0gQZQ2ovp93aIdHJTpnaV3U1E+ mEvu4NldxyFgk7UWK6bRSnC9JLgGkbkPf72Bh811q5f6oabWIdJ82WkFttXYoJly9+PSQSVT2noI tY0uaor53o361dtGYqKRWKHbSon1fSXXGn3CilmgIPj/xEoqUCH4iirIFEA0mkhZM9IB0eikrWb2 dsrLD1jhm28q0VkhrVcGF3hDjCQjvU8ZeQI9PxwN4YJlvVYUhfuA9QewETGErKhAAzppJSW6g+4w pZTYVhSX7UlI95ce43g/eVczuWB1bSMGY6TGtLmjoJl6W4ZOj+CrVg9YXit26fwRZhPfz8OpWyR+ 4cZ+OncneZy6qT9PYz/OgiIo/jSng3i8binojuuZZAPRBPGT5G4YUaIVK31BRNOzxEA2kNeB31PN BtcHh4NCw9OqaJAERliMK3IDHrKAaLWimlRmuAJb+3nvaME79kQHZ3S3vRYlhAUDdK0jHqV5EkWx H9j8DZIw7sF8yPg0ygOY7TI5TvwkCpOTfIawqla/oaJBZgAuB8XsTXgD5vSY6rd0iWQUtJfU/GTC G2boagVCutPnQ5f7+TybZ7Ebh6M5hG42cyeLInZHiyBNZtGsKGbBELqKlSXl5o4fj5wNhKhZOcDN nqdFrbqYYkKAyTvgnuz0TjU5BP7EqgAiMA1zdzHKUjdexImbp37m+kE+zUd+nMezxalVV4zTn4DH LRBZAoH9N/P07oxpeNwwTYFUWHPpZIYPewyZNJ7zsqMzzOpufOQJo/15T0wWiZ/GUeamaRK5cTT3 3Wm2KNxJEYxG6XxaTOeP4ju3mGl/Toj3ALSZsgbrllW5BaY0AI+SPAwsbUKVSjt7Ea7vgbuJVg5S Qv/GdLWssKk5fufUUz/e3Vt69I6Ed3443Hvkpt6082TR5bkd9BT97Zqa7GsqHJ+KHYpPqioypL/r efeUao9L5BkWiZM0NF4AjkiyUWoI44RFshF8BhYZxaMoGnD0oySyt/9p1UZbhaExaH9fY0UhLLou hKXdrnswxXzBetn/vXQzW7a+W8CXYq0IHaOrCzSj6FE1fp5UqpSdOSO9YISI/yX0jDBENUD44gX6 +uWv5Wdolvre4oa2EjoJtqGcti3SAk2h/96yUleHVuTF1y9/o0lxjd69Xy5nk08IsJBdPLP7eKTc d3oQ+xia4AH/dtSTxhRIMiyyqTsN4oUbz/IUisIocRdJFMfFNJsU0dyQhoTMIopa3d8O7w3yuYwh xZYqKZjt+08reuBHURKlo3zoXLpsPWhrmpm+nye1usby/cY6By4DGijslDR01G09bAFnsAYW7Ij3 lkvc5fctH14AyjXAhfGSrhgHTgbQU3hhUpBQnMKLlWmzSnrb9cLNjRC619NKMh7vRJtRf51xOij8 DwAAAP//AwBQSwMEFAAGAAgAAAAhABA5OCEGBAAAFwwAABYAAABwcHQvc2xpZGVzL3NsaWRlMTMu eG1stFZfTyM3EH+v1O8w2qdWImwSAg3RhRPJQYV0FEpyfTf2JOue17Zs7yZR1e/esXc3lLujosDl IfZ6xjPzm3+ed++3pYIanZdGT7PBYT8D1NwIqdfT7NPysjfOwAemBVNG4zTboc/en/34wzs78UoA 3dZ+wqZZEYKd5LnnBZbMHxqLmmgr40oW6NOtc+HYhqSWKh/2+yd5yaTO2vvuOffNaiU5fjC8KlGH RohDxQJZ7gtpfSfNPkeadehJTLr9yKQzQsYXSsTV26VDjDtd/+rswt66RP6tvnUgBfkrA81KckuW t4SWLX3qOm3yL66vuy2bbFeujCthg+00I+fv4n8ez3AbgDeH/OGUFzff4OXFxTe4805B/i+lEVVj 3Ndwhh2cpQwKYbBH1dnr7UfDP3vQhvBE+A28PUeDOa62gLCzJCpEUS1fQ0ybB2NaZ4XtzIhdVHJP a6NOU8DOq2BWMsDK6LDgTJHI0z79WoAPzMqHRdgpTA6x8S8dO8KmWMxm1L1PiwyEdCF5CHwZ5gqZ 3rsxnP0hBRr4vWJKhh1c1ExVKUUmcKWDM6Li8SsqDkl9UoFa3DLH7p7U1JhqE/QOZ94F4ulwHHXh mBN2Sla4VYxjYZRAB8PXBUeK7QPLG8VlfBzjAkovLL/D1lek5W3C1Uaoi41ZwRdheEG4L7YWnaSG h49kNcHq/qLEWrXeepGaZYFgqMkypf6n0a0A3NsJgWRx6njUBR0UzMOmQA2Mc/Se2usbOIWaPVQv kNVet87UVEXiDSyJcgx4dDV1fv90iF4gmtJIB7naRZiPiv67JsI1fcKNlZoKAxbcOISfrm8WPz9D aar3FyldVPd/ItVijWoHgl7xCTjkrqLiZbB2prKxmCwaSz0/GNiwwAsieQyR0ASBK3phIabGmgQR VccElBx8QkHXkNGtyPZdwcS2Q4+4pI6zRyPQyzXVgFobJ0NR+mSOD5IYo6nkYbhnnlKSvM4LatU8 kO3EwH1EWKKQBDc4ZOUBaAwb4z4fkNiYdgeAgR8+TubXJt/jV+bK+wr9f2h4g8RrNHJCGHvyQRvU btLLg2PaN/uOlo5K6eNg2J0J6a1izymRNsCveRnT0s1fNAx99KHdQeXkNPtrNjs9Gc7Hs95sMLrs jT6c/tI7vzw57l0eH41G89n4fH508Xec5wajSYf8qhtZ6fCrMbGU3BlvVuGQm7KdN3NrNuiskWnk HPTbuZUGA3qfh6PheHw8GCa0ebKtW5O18fVtR0mu3DWzN3WKHSmjBJynIxv937A+sJAzZEmEtNMt csua+WCpu9lTVDQ5Sy1wRR0lYEZ1TbO6C9NMIz03ND4YgctmDCvvjAmtnUlS9HgjOu5addHpZPA/ AAAA//8DAFBLAwQUAAYACAAAACEANMcshMEFAACuFgAAFgAAAHBwdC9zbGlkZXMvc2xpZGUxNC54 bWzsWFtv2zYUfh+w/yDoqXtQdLcko25hy/ZQoF2zJu2eaYmOuFEiR9K3DvvvO6QkXxKnzdq02IAk gEXxci7fOfxInecvtzW11lhIwpqR7V94toWbgpWkuRnZ76/nTmpbUqGmRJQ1eGTvsLRfvvjxh+d8 KGlpwepGDtHIrpTiQ9eVRYVrJC8Yxw2MLZmokYJXceOWAm1Aak3dwPMGbo1IY3frxUPWs+WSFHjK ilWNG9UKEZgiBZbLinDZS+MPkcYFliDGrD4x6QV4VlzRUj8lvxYY61az/lnwK34pzPAv60thkRLw sq0G1QCL7XYD3TTz2qxNw721/KZvouF2KWr9BN+s7cgG8Hf619V9eKusou0sDr1F9fbM3KKanZnt 9grcI6Xaq9a4u+4EvTvXRFFs+Xuvenslf82KP6TVMPBHu9+6t5/R+qyfvLLUjoMopUV189pB0zgY 04GlthNW7rSSBTxNJxpSqa7UjmLzwvWPMUOAvRTpDMWN8/7KtkoilPHakrXKKUbNHhr14gMpMbN+ XSFK1M56JeUKS0uDowxERiJuyksk0Lt7BbdwcmN9b6rbY3k/omGPaM4aBflmXVJU4IrREgsr+Dp8 Sbk9TPk30OpWA3thvFJsSVTr2mHooaDLj5Axnuc9CP4FBcdIg6W0nvUE89NJEFp4H1PjSohOY4G4 WsHLjft9dFMmJSTZMyVQI2siNbt+W42/Y9HDe6x0769bEskp2n1bK5ZAlx+1tlvb6+GisRCGVs+o EHixWi6xDuMXiz8j9vtEaex+sOSuKSwu2ILi+p4wnaq/n5fOaP0kQ3FSdOxBijus78VeT1MwDBsF H1FTtwBpET055RXYgseS40K1h2DfJQTbVBiV8oS89kqPT8R954ISPieUGoKCtiWGuF5gsEy8KgO7 OwtfS9WfirCTR/ZfQTr2vCyYOHns5U7kJTNnnEWJk3izJPKi1M/9/G+92o+GK4nBdkSnnPRXFj+6 c02oSSGYZEt1UbC6u2/01xa4Ifhed2lZI3oAHAzqn8ZETaXghGFzUbwDhEwGSCWwKirdXIKvXb97 NOAeI9Gyt7XYvGElhAUBUxsgbl0Y/DRIU50FkAFhNggi79blIYDxNE7bS4GfJnEatnE5iOJCqp8x qy3dAMzBMqMKrcGfLqm6Ke3BoS1sT+fmpMPtezDs0kKj0ZtyJ3aZl83SWRo5UTCYQeymU2c8zyNn MPeTeBpO83zq97GrSFniRuv4+tCZSDBKyj7fzHqcU9EGFRUFHNI9Qkcz3VNLDpE/8cqHAEyCzJkP 0sSJ5lHsZImXOp6fTbKBF2XRdH7q1Wug7EdIyM3IzuIg/qx7anvGNTSsiYLbCCX1yIZk0qxiAqf3 8awpWwJDhLbtIyS09eeRGM9jL4nC1EmSOHSicOY5k3SeO+PcHwyS2SSfzG7Fd2ZyRj5OiPcJaLbK Cry7qsoNUKVO8DDOAt/wJmyPpPXXQvQG2LpQwrYEU78RVV1VSF9fvRbUUxwXN4Yf3SPhLQ4HvUcw da6dZ4t2o5tGx9Gfoer9J0dP1eF/harDJ6r+BFVHURafUrVB/Ymqn6j6iar/p1R9/3d/tK+kwPIJ 2x6xtJ5s6Qt6//V+yrXHX+9naCQIB0HQ0UicRnEaG/8ONBKF8K8naBoJB1kY9gB8LYvsAbhbUbA2 AvGRLf9cIYEhLormzBBvW9TQhYb5lxcaPvlhNTkUFtq/tbzoWocKwCNWe8yjLwv2uWFa3YaaAIEE eTpxJn40d6JplgBhDmJnHodRlE/ScR7O9IbikHWFwKYC+aqvpPKH7ibONlhwRkwl9PS4C5M0zbIs SJLuWG8z+WCtPum7CmdBxRvE364NLKAMtkhuurjequ3UwxQAg9QwYFpN5zlHbe5fN31JtFzBfiVN iZekAb6CfMBSIQG51uA1FvoOUuLrtjpYv2NMdXYaSRrxVrRudeo06GDwPwAAAP//AwBQSwMEFAAG AAgAAAAhAGMAvqOlBAAALg8AABYAAABwcHQvc2xpZGVzL3NsaWRlMTkueG1svFdfTxs5EH8/6b7D aJ9aqWQTCBRoQwWhqdDRa0TSvhvvLGvhtX22NyQ9ndQPcvfl+klu7N0NJVAuHC15iGf9Zzzz8/x9 /WZeSpihdUKrQdLrdBNAxXUm1MUg+Tgdbewm4DxTGZNa4SBZoEveHPz6y2uz72QGdFq5fTZICu/N fpo6XmDJXEcbVLSWa1syT5/2Is0suyKupUw3u92dtGRCJc15u855neeC47HmVYnK10wsSuZJclcI 41puZh1uxqIjNvH0DZEOSDM+kVkYnZlaxECp2TtrJmZs4/Lvs7EFkRFeCShWEixJ2iw02+KnmkUi XTl+0ZJsf57bMoykG8wHCYG/CP9pmMO5B15P8utZXny4Yy8v3t6xO20vSL+5NGhVC3dbnc1Wnanw EqG31KqV15lTzS8dKE36BPVr9ZY7ap3DaArwC0OsfGDV7KsXI3EtTAOWnx/pbBEuOacxTrJ96fzE LyTGDxP+ohiW5JUsWCiqjY+TBDJhfdQaXOmHEplaQuMPxp3eZnfemcMnkaGGk9IwYYMNwXudoQww +QhW5I0qGzPLzr57RQ2siXq0Qqctqt/HdqvFdqiVD5ePJeNYaJmhhc3HIS2y+fWWh4AcKEVecVh5 nQsPOck24UySmC+79EtAqonhZ5hVPHgKmUicrjG45rHuO7nPxGE38P0WzuUjbcKhMVYzXtx4lBru 9i/wnMlG33sv6t910TE6caGAQYneCk4qW8AZkxWFAnUBf1RMCr8AocAZ5IJiDtEhAnJ8BaKDnRdw Mp5+Aod2RvHI/TxJp4VwsGKd/8VDxJi0yiloHDRhysvFAzmu8gJO4dZbCpUOfIFA73UusXwBlCPA FbqirFCyS4yLTssq2A2UFS+g1BYhR+YEnVgDt+gV90oYTXRVwt+UvlIwi84ecxnaVy0BuvKm8sBm TEj2BGLwgimFkkznOuwY5j1a9YC3vXnBXUHuboXWCGerzG5g8D+i7UhXFkImbV4gE85rG5M0MM51 RdEvC173WMe5V4qhLkOGD1XNejCvvt4hiZwz7tfx73sN5Fb8qcg19AOFArR2CYc/kNq59fz4lve2 xd0PQ79hPJGCh/j5s3EbM36JHk4JAaBMypHJ6FPPxqfD56BzIGyCh4Vdj47NqzKMqBr8/CRang7B oqEKIdxGj0+1t64c5JZKCAdXwhfEhxS90DqrZ+HZ1y9/562AcYe7FMbQ19cv/zz/cd520xTP8LzK c7SrJrW2QT4RlFind9aARVFIyIa2yNHELEWULsmcKK3fhpMyxxJRSKk6EErFtQJRPgzix9SYcWjb EuoRTp1vKKgspf8/j472djaHu0cbR73+aKN/vPdy43C0s70x2t7q94dHu4fDrbd/hTan19/nNuCi 1UnbydHkre6pFNxqp3PfIXSaNiw1+gqt0SJ2Yr1u085RHUWV7k5/d6fb297baSpSkq0do7Shjm06 LC7te2Y+zOKT0mWUF4dxKqDclLzXWwgMUdJCpFSjuWF1pT1VbUuWVWShQmWYCyU8JvSuVMBZP0gU UqtLhTiVAtO6OynPtPaNnJFTQLxmHajmugA6CfwvAAAA//8DAFBLAwQUAAYACAAAACEAbhFwlmAJ AADRRQAAFgAAAHBwdC9zbGlkZXMvc2xpZGUxOC54bWzsHNtyozj2fav2H1R+mp1qx1wNZCY9FSdx urv64omdfscgYrYBMUJ2kt2aqv2H/cP5kjmSAN8wvubS1e6HNgFx7jcdSfz620McoQmmWUiSs4Z6 ojQQTjzih8ndWeN20G3aDZQxN/HdiCT4rPGIs8Zvb//5j1/T0yzyEbydZKfuWWPEWHraamXeCMdu dkJSnMCzgNDYZfAnvWv51L0HqHHU0hSl3YrdMGnk79NN3idBEHr4knjjGCdMAqE4chlQno3CNCug pZtASynOAIx4e46kt8CZ1498/pulA4oxv0om1zTtpz0qHn+e9CgKfZBXAyVuDGJptPIH+TDxZzIR F62F1++KS/f0IaAx/wXe0MNZA4T/yP9v8Xv4gSFP3vSmd73Rl4qx3uiqYnSrQNCaQcq5ksQts6MV 7AxCFmGkllwV9GbpR+J9y1BCgB/OvmSvHCF55r/pCLHHFEAxDiofJx+KiykxubDYQ4f4jxzJEH4l ugQUdj5mJAgZCkjC+p4bAUhHgX85g9PBUcb67DHCQiAp/0/cpsBb5HJrxknztt9AfkiZkBDKYnYR YTcpxcjevh/cNgcI9a9VDX0mzRscYAr+gNGX4b+xx8IJRn3hDdTPOHomiBCIcOL3XOrerMQnCU6F AApuW4U6apSiFFq5ABGAzaJe5Hp4RCIfU6Q6+ykp+w94ixsFDcD1MB28QlMVNqvbKtcGN0bNsCxd sefNV1c1Q+MDuFnCU1NztEXjlKBX2UBuAbvqm3Oo2pyCWs2DcHlAwD4iFJ2nKSUTuPbzcJOhbETu ExQmaEHt65AORZSoRT0EfX6bAyvt5BBsDVx6hzlXwFIUejJani7wMNwZBdfIRmKo97re4CvKMJ1A gM9+AcNNmq7vpsLdMkaxG0PikPdDUBF1hSc+kcR6J6qmqCcP6K///R/F44iFMfZDd1u9T3FgSoUN VOD6nVztDLYC3C+LRL60YoUstVyWk9DH5JnEuCoYb8b5oX0PggkjHomyUySM+N1g0EM/3Qx6b9Cg 3yQJur2ES8y8k39VGHVVuhDpvKYwsYqccU3JOEXtw9QnhmKojm3LYG9CGjYE4GmwNzRHacNNEew1 x9JM01ioW1Rbs4XoYISqqQ7PDQs1zDwUUzXaZbrfrp7RCynckHHigyJuIIeDIqG2ETmoTIpL4liX 9HRTq2SiEERxcyULoFmasWtMYsQvzhqUk8jpa/CH7uRjlme8YpxI7SQK/W4YReIPXt3ii4iiiRtB ofWgiVejcfyJ+PKeVlRK4jbXgLhtzxRQJRT51xTBTF7OeJ4VQBKoiGSlIJEt0OB6HiRLVT4auT6W t82V+ARAPjoAnCXsHEA1bAkmHy9kHgQgtvJlZf3L5RsCM1RV5cuQaAitAhBNMcvxUkBSMMuVC6Is uiCRiAxu4o0IxC6P0VwVS5ULyBm50V0yM2hFBNuxiDRWe4K+lyesdOejJxw94Uk9Ye+JlbnaJ0TS 2tknDMiMGs9fR584+sRryQ5FzVTvE3bZAQJb7ZAHZM15AuJAizbBVj5htp02GPrq0tE2DYc/5y4B xZ+uFxJd5RFrnCEh3FAldav6C+ieuulZA0py3JjRiWyi8LZDd5O2Q4V6dug8dUK22eRt1ylbLfa+ mGU/U0fLWTQy0TI6gJEZqsWj6Wojc0xbNawf18h6rvcNv6SdvcMQ9ulztU7VsnVaWNp8t3R3S7M0 Q3faeduzytJURXVMjv9HNbVL7BEoql7Q1r7yftNzWVq5dLJcTKrz6yhb9xp4M8RShanpmt7WLZGT Z0zN1AzRguemZlp6WxNTuoMWk/RuWBY7l9aV0bmU70bpyC2qONURPsCrODn8WDO+bM0YTcTwGg8W jUw++VjhSUuGwC2k5ECsWxRKmFGzQDTXTFe3aPvOk7M3BT/tirmy4bw3OdGQ7kzQQQn5eb7V/Iwq kc/Wrk8cEOkmixkHNrvc8DX0yuxv9Grtr3bJ5jDamaLYMcvXLCioe64ozGZ5224v9Yx0xeCNmZks L5pUxyx/zPKvI8trxyx/zPLfV5avl/VSB73w5Hov0FAME190QGfYlpAndoZtyXmykmNbQg5acmyI fO+So2blVt1v6RZKDsOw5TKV4dja0tY9A8oMxzZlyWFolr2wc+9YcvzYJcdmXULhTMYucWbWNrrd WcXMDpMSkqYGBLssTMTW48D1wGs+4ZA+EnT7Pn+1DAYfToYhm/wRv/lwEsdNECUONo9SC+y8IBtv tkgzr1EJ36/gt9hq+Tqo3qQ+ej3y/fr71XXzw9U1evc4pKGPepTwHfjbZPB5Znaj/xApvF2k8I84 YKhDgXGkmtvnbkQJJFmTN/sVyU+xuO5oSps3J/iGE92wDQF8mshNXdedMo/LzZW1iTwCQgWdqxP5 mpy7QV6sTqgb5MTnTqjT2u673PunlpuBi1VQdbofeM9dHbwlpckFd9XSFF3Pt/sWlmdZUESq+eGP tqPr6yrIV7MKusvhoSQds2c7FlSuOHYJYZjOnQrSDnJ0KwB7FJH0j7FLAUVxRmjNIaHK41wrjH9F KqqSLcMRuiBJEPrgzaEbPZecy55vH5ICRp/H8XBR2vOt3x2lnUU+wK4UuHSagwg8iHzB1n/bnY4C U7puU7tsm03DMaymY1oXTcU+t+2rc7NzdX7+Z6OkjUsdqNtUXao9VU/AD1M+oYLKQ5gVu/wF1IM0 5Q1TVy1r8byDafFNlHl8M0weC48z5OMM+Sma8vu1I7VjO/LYjtx/LlNOZYpScn4as8eGOr7imZ8j gRlMW3EW98yrJj9pkk9iFEVV122Z32O3/PCuQnw8wG2yzb5K7vzNFypZhfUI0dYWrz9vESAWoNWc zNxqIWw9kU0UkXs0DBmiLps/AFzfgakHvUlnZD1xCD2JCLeKV5uIcBTejVbJsDYEzQOvP2sw/XQG ODD4X36FxjSE4rPTcdrahd1pdlSj2zQuHat53oUytGvqhnHRsc8v9Ks/+ac4VOPUo1icWn9ffG0E bi594SMOPUoyErATj8T5p0JaKbnHNCWh+FqIquSfHJFHX6C0Mx3DEfuBW4K04lcQy2v1/CMgXkQ/ uemXiZAP4IK6/ELcSsPkLi/rp0NAFiE/rS6ukpzx1JVhdJAUXw3xx6DvEKrVIExCxp0eZwxqfogB eMIL/wSS9kB+QCO+gQleTqeAxAUuQfOrHB2XORD8NwAAAP//AwBQSwMEFAAGAAgAAAAhAK7BBK1k BQAAFw4AABYAAABwcHQvc2xpZGVzL3NsaWRlMTcueG1srFfbjts2EH0v0H8g9JQAq0iyJUsy4gS2 bAcpkux2vWmfuRK9JiKRLEnfUrTf3iEpeW3HSYNk/WBRvAznHM6coV6+3jU12hCpKGcjL3oReoiw kleUPYy8j3dzP/OQ0phVuOaMjLw9Ud7rV7/+8lIMVV0hWM3UEI+8ldZiGASqXJEGqxdcEAZjSy4b rOFVPgSVxFuw2tRBLwwHQYMp89r18nvW8+WSlmTKy3VDmHZGJKmxBs/VigrVWRPfY01IosCMXX3i 0itAVi7qyjyVuJOEmBbbvJFiIW6kHf6wuZGIVsCXhxhugBYvaAfaafaVbWwjOFv+0DXxcLeUjXkC NrQbeUD+3vwHpo/sNCpdZ/nYW66uL8wtV7MLs4Nug+BoU4PKOfclnF4H547qmqDogKrzV4l3vPyk EOOAx8B38A4zHGbzFCuk9wJMaWOqnecGbePRmZYsvZvwam82uYen7cTDWumF3tfEvgjzZ92Q4G+N TYQS5n9ceKiiUlvUSDW6qAlmB2r0q5vZH78bLrRlxBogrLrBEt9+1Y5jT1hnO8+CjjpByxYqLc8p 7Idp0rEIw3otCeofeGwXYGOiY7JYgQdkrAQptYuorktKvl0RXKkTpg+bHofXofO+pmJO69oSCW0k h6S5J+CZfFv1vDaw3indhdha0pH3dy8bh2Hem/hFEhZ+HKYzf5zHqZ+GszQO4ywqouIfszqKh2tF wHdcTwXt8j+Kv8i5hpaSK77UL0retMnbaQCkWxS2CrDB9SPh4FD3tC4GDoQNPVneAkP2TJWWRJcr 01wC1rY/OBoIjplwUYbut+95BceC15pbIs6yr5dHYRbbtIqiuB+GZ4mYDcI8TAcuwfp5lETJ4CTN 4Fil0m8Ib5BpAOXgmN0JbwBOG1PtFNPNuHHQRTo76Qi6HrJcghG3+vLR5WE+y2ZZ7Me9wQyObjr1 x/Mi9gfzKE2m/WlRTKPu6Fa0qggze/z8ydmD4DWtunCz60lRS3emuCxBYF3gnswMTj15PPgTVFEv Die93J8PstSP53Hi52mY+WGUT/JBGOfxdH6K6h1l5AnicTvy8qSX/C88vbsADQ8bqgmICm0gXkLz czFk0njGKqdImNaufcSE8f4yE+N5EqZxP/PTNOn7cX8W+pNsXvjjIhoM0tmkmMzOzndmY0Y9zREf AtBmyhrQLVbVFpTSBHg/yXuRlU1In9ThRbh+APkttfSQ5PpPqleLFTalIHSknvJ4/9BreTwYdzw8 7ntEUwvtsli4PLeNVqK/XuoGh1IHyyd8h5KTYoeM6O9a3T2V2uPK9W0VSaIsSvL8TEWyBKa0KpLl vSTp8P+siBzwf1lM0VZiqNfqrzWWBI5F1wW3suuK+hgUcU5b227Fk1Xf67UWa41MEUbvrxdIwkqI MrSUgDBCz+5x9fzfBD0ju5LUNSjG85Na7arwj28/hTjl0tzy0FtW0RLDmxqis/vAZcPqMwTAwIT0 t7eAS+j+Ck0kfVhpRpS6QgVnWmKlr9DN4sPtFfqNyE/UDU3qtTT/UP3bnrmEQETqE1xhEVy0gRlC PhMYuCON4BLXaFxquqF6b4d/1PXWW/WU0AXcn8E/EBFRkx14+IT3LPvo7t9djttWK4wTKAS9Ipv4 kyie+/E0T6HwDRJ/nvTjuJhk46I/M8IoQD1KSexV/233ySK+VxUF3xIpOLWfHKe3ln4/hCvAII4d Dutb9+wUqfuUKGv5HovrjaUFNgOpK2yXMJLrpj5OATJoAwO2xVrkAjsNu2Pdt0e1Bt2lrCJLyqDu QGIT+FaTIBqMwDeduUpW5M5dw5tbznXrp7VkGHemTavdzpAODv8HAAD//wMAUEsDBBQABgAIAAAA IQDYkWG4dggAAKMzAAAWAAAAcHB0L3NsaWRlcy9zbGlkZTE2LnhtbOxbzW4juRG+B8g7NHSaBaan //+ElRdSS5oMMM4OLC9yCHKguik3d/pv2ZQtb5AgpyC3bHIJkiDAPsgmhzxKniCPkCLZlKwfe2Vb 9s5mZEBuis0mq776qlgSS59+tihy7RLThlRlr2O9MjsaLpMqJeVFr/PF+VgPO1rDUJmivCpxr3ON m85nJz/9yad1t8lTDZ4umy7qdTLG6q5hNEmGC9S8qmpcwr1ZRQvE4C29MFKKrmDWIjds0/SNApGy 0z5P93m+ms1IgodVMi9wyeQkFOeIgeRNRupGzVbvM1tNcQPTiKfXRDoBzZJJnvJrU59TjHmrvHxN 60n9jorbP798RzWSAl4drUQFwNIx2hvtMPG2vBQNY+PxC9VE3cWMFvwKummLXgfAv+b/Dd6HF0xL ZGey6k2yz3eMTbLRjtGGWsC4sSjXSgq3rY6t1DknLMeatdRKydvUb6vkfaOVFejD1ZfqLUdInfm1 zjR2XcNUjE/VjpM3RWMlTAsWWwyq9JovMoWr6ETdvGETdp1j8abm/4QYFOTNEWcoLvUvJh0tJZQJ rbWmYHGOUbmEhp2cTvTJ5M0ph4MJUMQcuEzfIYrObp1KAlgLeZVwhkKvJkmrLUk2UXTNKFRAwm02 p1izl1C2DyA+hQIzzkAC3G9qnDBJKtVFaXWVYZQ2a2AvF73JsGXnNCf1mOS5wBLaGu3iYopBMvom tTstt942TLFsTkmv82s77JtmZA/02DNj3TWDkd6P3EAPzFHgmm5oxVb8G/605XbnDQbZUT6siQoB lrvldgVJaNVUM/YqqYrWf1UYAI+zzDYIXKJ8BTgIpK5CREMqIdhHkzNASJi1YRSzJOPNGeja9hs3 bhg3kZBE06ZXp1UKZkFzVgkgNhzQttwwdIVn2WHk+eaGL4ZB4AeWL33MdtwgsIVll54GZqUNe42r QuMNgBwEEyuhS1Cn5VQ7hHeXFRdQkr1c6zBUD57NYBL59G7TRWY0Ckehq7u2PwLTDYd6fxy7uj+2 Am/oDON4aCnTZSRNccnXeLzlhCGqnKSKbuJ5HOdU2hQlCcRYSdy1kca6JCvDr2ll2a45sCN97IeB 7o5dT48CM9RNKxpEvulG7nC8rtVbUuID8PGq14k82/te9dhih2qoWxCGIaiQAvhi8j/JIe7GozKV QQmRXLZvIMGl341Ef+yZgeuEehB4ju46I1MfhONY78eW7wejQTwYbdh3JDjTHMbESwIKT5mDdpMs vYJIyQnueJFtibAJHhFIfTWUX0AEThjtaLRivyAsm2SI7wamBHUdx+mFcqLl5BKH1bo3YGpV2x0s pJ+LhgjRRdLt5zBniRiOq5IBHVtMimSfNKFA9P281gGiGlKFKckJuxbJgko2HgUtSBdnFdzVzvBX cwIpCfiM5Yoc5PZd2lWbCw96sEvATu2s7dRbycfNzXZH1PNDL+RWg5hmmb7P22tRL3Aiy+UDeNSz rMD27egxUW9ppe1dX7uiCBKL5qs5olhype5DsB6TdhY5bHduIFXnTIFFim51iljG93hl8YdmmXAz 49ZazcglKLpfJlrRFQzmYQ7T17Sa1wKYraHi7cHk2J0ENV+DBwo7EZ4tCIyAtKVIxGYoAcbEqJhS gjQuTWtBKgVkJ//9x59+B6/fr17ffMf7uDbctcSyRTcV/+RD4OC5NMajhdFqVFYNjxC26Zqe6cDL NX24wnu4S2BPH6OC5JyCkF4lGaINZqu8QehhrGQyllLifSCbirzrgcD9+a8bID2JgV7ec5HHqfS3 tdUMASPH9MnU622o10zmtbr+KOi2EnQvxj0IpF9ugzRV1x8LSK2gTwcSRK11h5TshcUlWE8WPjcX Feu1Sj/Zwi+eNSzcN9I9YrUfMt49ka0++dUtvFSR7ujMbeqJ9rbc37/7AH1ddPDvjlb7WInotbq2 1s2oSiD/84c/8kU+eIuvhF/hWyhgpvQ96IPyss/4tyNtJixuPokN/vIcLtuzdlp66bTPSDCxniT3 MTc65kYPZdY3P0S8XPfVY250zI2OudEz5Eb/fBZfv5dM3xcKdqROx+3uuN09eOf59rjdHbe743b3 UWx3//o/2O6WX7XLLwzUWdL6qVJ7IirO3m6rZFlptk9Ni7E8jBTnkmOU51OUvJdnVr3OY88k16uH zqqNOpebpS+n1SVum2e4IV+rN6OUsHcVKZkohoGOfvrlvGE/Q2Wa85PTW+tmlt3iEPpclCWJ3nO8 YHzWWyuZPtADVFkSI2o4dh2vbxX9CFPsVTOzmvnOY9r7FGdtFrW03wexk39/u1aSdRcxFRvlu80T /bup6S+L2sAcg2qheWvE1Phyi5YB97J6GARR5MhiITsKwO7rVrdt17fNQFrd8SPHOVSt0N4H6Bpl eVyJ6qp7nqU/oM7u7bwgJSoTrG2U2u2eS4Tbu2fMXyxeatef7Fm5tzHhnfV7t9Ml2KSLfyC6OJ7r OG2U2M0X04rEgI+EL9x7KWrYweiSvHjQTJhS0bNjxsXL60MJ97wsXhagKhYHB2KxF7kOL4u8I+qZ kdgMPxIWTxidyxrfZj/6HZR4B6HcY8nmbZLNPRTZHEekTSCc5zvhVl7lBZYn6g0/Eq5B6koSlPOq yTlutClmVxiXmvkq/K31UsvIRYbBIuIGZEgHtL64qN9CqGJL0WorVAeDyLfjcKAPLHesu8Mo0Ptj 39PHnuO68SDsx86IV6jW8KEloVj87OKN+vlIvW8NZV1dYVrzjwBb5eMQi6zI911PBDpDyKauqjRU /awjyekpqj+/FLDAYoBVLLpqXqAph66GABgEEpsL0SpbzWskfeC8VL8DSefgzKRM8YyUhHFi4IYh CqQr8SWm/ONG2n72YMVZVbFWTjETR1xOzVvtchx0EPh/AAAA//8DAFBLAwQUAAYACAAAACEAQeSY ACMGAAA6GAAAFgAAAHBwdC9zbGlkZXMvc2xpZGUxNS54bWzcWM1uGzcQvhfoOxB7aoEqu/q1LUQO LCVOA8SOITnomdqlvIy55IbkylKKFi2KFj22xx4K9DF67qP0CfoIHf7pz3Irp0lj+LDL/+HM8JsZ Dh8+mhUMTYlUVPBeVH+QRIjwVGSUX/Sil+fHtf0IKY15hpngpBfNiYoeHX780cOyq1iGYDVXXdyL cq3LbhyrNCcFVg9ESTiMTYQssIamvIgzia+AasHiRpJ04gJTHvn1cpf1YjKhKXks0qogXDsikjCs gXOV01IFauUu1EpJFJCxq9dYOgTJ0hHLTKnKc0mIqfHpU1mOyjNph0+nZxLRDPQVIY4LUEsU+wE/ zTb51FbijeUXoYq7s4ksTAmyoVkvAuXPzT82fWSmUeo602Vvmr/YMjfNn2yZHYcN4pVNjVSOuevi NII451QzguoLqQK/qnwu0kuFuAB5jPhOvMUMJ7MpyxzpeQmktCHl57lBW1ky45WlZ32Rzc0mYyht J+4ypUd6zohtlOZn2ZDAL8MGoYTXXo4ilFGprdRIFXrACOYL1ejDs9HpEH1yRvAlGtELjllNi9qp oIqgoTn/T42itFWXpU54doYlHt64iVNtaSUJbMdBrzdrtxm0OxBcA/bQGcMpyQXLiESN/6Zrms2W Uz6Mmk+EAgiKohCczdGGTv+FnidRKZLdcuU2TgqiJU3fjgUtUEGwqiRBOifodYUZ1XMkJkiSFByN llVqfIbpYUKp20q6jV9QmnFHxgWDAjOSqjWaDmvhB5QLLJ/3olZ7D7xWhNiUWUdEeQagMmTt6Van 4K89Uh2gdsf1B9rLU7daeQs1ns9LmmIG4JtiVhGFxkRfEcJRM/m6naCs/xnK6UUOxkbtmCbyHVp+ kXaPGJDkWJNg4C4eFekuAQkUfVmVNYMFcEpjamBnw1IIa7jeukanoKkUSkz0A1jnI2QItLC4noSY BtwNcgGjaEheVxTQ1ouAoI12N3us1iIeQDDqixlqrnkpZFQQ/M56uFv1O1sCXaORHOwbQIFWm/VG y4BrLeg19zqterLngtlB46Czt78W0eAQpNJPiSiQqfQisE5twYinz5X25+SnmG4ujiljjj3H1HVf iK4kBk/LAcwRkpoNBAsQV+VRpYGEp+zmb3edztOCcruwcdEVJ1jnBlUBDW9714HB3JzkkqLhoOi+ SlHRBcT3ohTmEvlUiqq0yro21TbfGR/bTVS9geNtmbMdW0Ol1mGsmqtVG2Cc2xvCBGIghERcjCXF yDDoD1o6nvXhX7/+/B18P8D3LXzfG8GM0VoO/j82evXkzx9/2dh8UvE0lG5iqiVz0HgfPKESc6Gg N2kkraSdNOFrJR0ooQ2jVKf5MS4oA7Opw9U9zbFUxHpqZ194B2njpRTxqmiTU3AGpqJG1TiU91Tq pWhWYoc016HgPuAtLtiZHdlN9o3YtQsKdxTAAJOJizWExp5/5Q7sfdrKLbisJ5tMWv682h3qAtbs L7O/ewo1L5cVdGJ/99WTOLl4ZQMjHLW5Ntny/joRL1pwInfB+hrt9g0+wp7HXeHyuosw9+6Am3iB I8hE7hDbcEH5xl1Wfvp9UwLLqbGDoHPTt1J3l4lwbVu/wJld3JXy5jRlKewuCUu8yAlsenAMmdMY p5fuetiL3lVqsP6UMRTavZqJQQ6skyNVwsXdd52IKfHVIVH0TWg8yag+E5Rr5TuOsleV0p9jnjGT yKzSk1Jc5QRn692jHJfk3L5H2V7DtKF647PKnc1jxoyWJpNBUugvqM6tZItEHAaR7JJiTOCI5LPM viqB/rUk4N1MdQJrh7CHo7wYMDmNp/yPKdJtnovW0y5vIybf/uO3Le8b2wEaUOlamwm2nxoeaUHp oDNfQ5UEY/+y3z/oNAb7/Vq/3jqutR4f7NWOjjvt2nG72WoN+vtHg+aTrwzWwbhSSex78LPwrl3u mnKX4orI0kB0Jes2zxBgKJ29pHXQbnucAWehtLwa6PnX5pTJE1y+mFotwVYg68B2lSabd1OXU0Bd tIABW+Ne7hI7Mz3n4Xk6q2QvMs81E8qpNnktURpLQBonUyKNMWTeMnQxFEJ7Pi0lcyaOtKn57YzK geG/AQAA//8DAFBLAwQUAAYACAAAACEAMNcJhe0FAAAOEwAAFQAAAHBwdC9zbGlkZXMvc2xpZGU2 LnhtbLxYS28bNxC+F+h/GAhIT3pbkmU1TmE7sZsgTlxbyaXogebOeglzSZbkylKKAvkPvbZ/Lr+k Q+6u/FJc2bF72eWS3Hl88+AMn/80zyXM0Dqh1Xaj1+42ABXXiVBn240P0/3WuAHOM5UwqRVuNxbo Gj+9+P6752biZAL0t3ITtt3IvDeTTsfxDHPm2tqgorVU25x5+rRnncSyC6Kay06/2x11ciZUo/rf rvO/TlPB8aXmRY7Kl0QsSuZJcpcJ42pqZh1qxqIjMvHvayK9IM34iUzC25mpRQwjNTuw5sQc2bj8 bnZkQSSEVwMUywmWRqdaqLbFTzWLg86N38/qIZvMU5uHN+kG8+0Ggb8Iz06Yw7kHXk7yy1mevV+x l2evVuzu1Aw6V5iaiRG8kk7wmwoNaoVozRcWYWOpWbWbhf/fan7uQOm9jKkz3HEGuQ9olNpebr6O wXLyVAqzL6QM1MIY7ATzUyT+9nXSL1Vy3qLnWRimtPWYOJQqLRc6VwkFY63EdNgb90bdEq1ef2s4 6N7Atz8ajsZhQ8BtPBhtlBuW4JHC1vkD1DmEAclIojTCPJu9dZVQ9ZYoVSlJZ4m0MyUSt/2nX8M9 FV4iRADjeu0gztRQkwNdQ7gmFgCOVs3ALwyR8oFUta9cjIN6fxAucvHzXZ0sognoHSfZRDp/4hcS 44cJjyiGJXklCykBVevDSQMSYX10M3C535PI1NIX/YvX0w+tKRy0e93NLnwUCWr4pWBS+AUcoreC B8R8xC1SR5UcMcuOv8qkwjhqUosdcb4b3Y0a3T2tPAU7HEnGMdMyQQvRzR6OtUjml1vuA3MYKUpE O4XXqfCQkmwnnEkSc9QfBj+U6sTwY0wKHpITOUm3u3TJSxqPYqnSOL9XxhEOSBBeUE7FBJibwDVD lSaoH4HPTFYYPIj5RYaUYG74wmo6IqbaS2pobZxZ5Xz3IHjKHEoRjrVWgL67JovyeFyLz50AgMXq GHJBFMFhds0ezAGDtFDRD0Cn1fKp8JYsBHQoQ2rJwSF8fqup7gHxy6eH+MjIj48LcIJndJZfoliD bPVp4bxCR1sKBK/BMH6OHqSmqYDxY3vo2rrdpLlKw28HiaLeZ3hN6+heQsGzJ43/aUamCXZQdGQJ JLApHVxJQIWjKKMIcCQWmS0Vc5o0LHi8p4KVLEplXRLyOkUHxSTyeAI2KwOX1V4TznFRhYmgU8DO mGxGs5a7EuGMZAtw4tM6IVQVJw/QtjoOjdWBsSsHoepmVH8zWdAUSQw/t/ujQRM+HuwsFegRAlyT xLc1iYpstfs0wbNal2YEIwl5IlqSkgv6C0QFVPvA+alx8OXzX9CHQxo2bxnewQ/S/wjDZzcSDMln qQYaUhxDalz7SdEK9UA081VsclJLRp9QC9DktLb2jQCPiI0AXOiC+hKFBADF8imSm1gxw6AJSX89 9jEQjy3A15V5gPD7hY3CoaLymGNoV1wQJoRZmeejJlGnRKQpHYPkxNGDyR61R1bdSrBmWb3U/s7J wlQBtyFjMyIYDItzI7XFhBbaZ+3Jk4btr/dMOXdkwjfanesLwT99exZrApVJW7/dNx/+X8K9aZPJ gAL2y+e/X61wi/dGqHDIHy4dI5ZmrSlKNJkmf98xRgpedrtfPv/TfNixtCrUrCZfGj7oTN+Ipend 9H32aIfdW1JfwU5OEc2ZgneU1rQ9DxmBKvwYRhzXBOZORsa0YbzZ2hqsk+SqSPrPJuYmo691NPFV 3ztQk0o9ZjWCwhJKf+zubo36e+Pd1m5vsN8avNzabO3sj4at/eHGYLC3O97Z23j1Z7jH6A0m3GJ0 mdf1VQ1N3roeyQW32unUt7nOq3uWjtEXaI0W8aql163uayhhUks97m5udrvDQdX+kGj1Owobmqbq BoVLe8jM+1k0AvGijL4Xp8jfz6r+6nILYSFyWogjVSlOJ36kNFX1lUtSUNAKSoUpBY3HBpV5zjNL rbnCGVrq+iiKpmUznB9r7Ss5I6UAeEk6jCp2AXMS+F8AAAD//wMAUEsDBBQABgAIAAAAIQAotj01 DAYAAHASAAAVAAAAcHB0L3NsaWRlcy9zbGlkZTUueG1sxFjbbhs3EH0v0H8g9NQ+rPd+E+IE0koK AjiJGzntM71LeYnukluSkuUWBfoP/cN+SYfkri62kjix0b54aV6GM2dmzgz14tW2bdCGCEk5Ox/5 Z94IEVbyirKb89HHq4WTjZBUmFW44Yycj+6IHL16+f13L7qxbCoEp5kc4/NRrVQ3dl1Z1qTF8ox3 hMHaiosWK/hX3LiVwLcgtW3cwPMSt8WUjfrz4jHn+WpFSzLj5bolTFkhgjRYgeaypp0cpHWPkdYJ IkGMOX2k0kuwrFw2lf7K7koQokds81p0y+5SmOV3m0uBaAV4jRDDLcAycvuFfpv5l23MwL13/GYY 4vF2JVr9BdvQ9nwE4N/pv66eI1uFSjtZ7mfL+v2JvWU9P7HbHS5wDy7VVlnlHpoTDOZcUdUQ5O+s GvSV3QUvf5WIcbBHm2/N2+2wNutvVyN114EopUX1++yiGeyVOYlE6EWZZ00MkjyPzU17TLI4jCK9 rm31/Sj0vAcWW8ndWG2nvLrTp6/ha29jEAmTteIrqtCKM7UscQO65t5ezn5zI9VS3TXEaNDpP2Za AGgN1mlCmPNxOUIVFcpAj2SrioZgtvOPevkzrQhHBWcrIiDBIBGQvkWZu6y8LwglQpiAOyH8Jz7/ SmEnhKB//vobvbn66Fyh12e+l3r3RBJWXWKBP3xSqMWtM+APoBs/fD7ooiHoABwFKYkuG1ySmjcV ESh8WgjSarvf8onoMxfcDxKDyUPHw3bUYnFhMKSsAnX10JxbvwOC7DGwqn6LE54V9I6WPSC0fJDr XuwNyMOyWguCgh3a/QGsRQx4FzVoQCayI6Wy1DdMCcFva4IreeSP3aWHPLibvG5ot6BNY7CDMRJj 0l4T0Ey8qQIDKWT7hVRD3q8FPR/9EWQTz8uDqVPEXuFEXjp3JnmUOqk3TyMgDb/wiz/1aT8aryUB 3XEz6+hQqPzoQXFoaSm45Ct1VvK2rzJDsYK64Ht9qdrgZg84KDR8jYqaMMAIE6Ci/AAIGYdKJYgq az1cga39vHuw4B4iYQMSXd++5RW4BQNDGSDukaMf5mHih4Yd/cDPA0tae3pM/Njzs9TSY5QnWegn R/QIfhVSvSa8RXoAmINm5iq8AXv6oOq3WMLUGtqsYEcT7jBDVisQYk+f9l3u5fNsnkVOFCRz8N1s 5kwWReQkCz+NZ+GsKGb+4LuaVpBd+o6nu854gje0GuLNnCdFI6xTcVlCItvIPdrpHmuy9/yRVX4Q edMgdxZJljrRIoqdPPUyx/PzaZ54UR7NFsdWXVBGniEgb6FgxUH8RfPU9oRpeNxSBQTb0BbKqa57 fQzpPJ6zyvIRpo0dHyChtT+NxGQRe2kUZk6axqEThXPPmWaLwpkUfpKk82kxnd/z79zEjHweF+8C 0KTKGqxb1tUtUKUO8DDOA9/wJlBfau1FuLkB8i2VGCHB1S9U1csa66bFUvo9HK9vDD+6B8ItDvt7 D2DqTTvNFkN34u44+tP1Md41ZXB8yrcoOqqJSLP+UOWOufYLPVaUpcnQZPlRlOrxEY0EfhgmQ5cV JnkYDgA8lUU+056hW4Ghrsvf1lgQ8ItqCm6I1xZ/3bUtaC/7mXu0/6H5Se47N34m54aZb0L8TlcD 4KPACD7soL0wSWPr2ziLIGX/M98yaJSe3bPyd4DJROuhT7QToeryFt6MlXnsHbW4YBvvuCQSYabZ gAvggBapGitEpKLwXIS1je7duxq0RhCVDVV38GgQw/goUmwMfKOifIXItiOCwgOBoB8e1dKfkDa8 FL7ycfBpvX50B7NBQ0nEhj6resunqoc62MmIkGcnfPEwH81neNgPlGxGfR2bQt0OimzqTP1o4USz PIU+JYmdhX51FtNsUoRzXcc6IPtSEBNWb4bfQrrHFrGO3xLRcWp+yzjuMoMMWro0CbK876ZtAdlr qxvs/jeKshFvcfd+Y1CDy6AyFWaq0xXSbt1vATBoCwtmxHrLO2xZ6YoNP2pUa3CRft+sKIM2AZIV sgELoAFGNkTo1r8iV/Z9337gXPV6GkkacStaj/rrNOig8L8AAAD//wMAUEsDBBQABgAIAAAAIQC8 U70+5AsAAFxKAAAVAAAAcHB0L3NsaWRlcy9zbGlkZTQueG1s7FxZb9tIEn5fYP9DQ0+7QGTxPoxx BpaPjBeTjCdy8t4iWxLXFJtptmR7BgPMf9jX3T83v2SrL1K35EjxJAMlgMWju7qOr6urqkl+9/3j OEdTwqqMFmct+8RqIVIkNM2K4Vnrw911O2qhiuMixTktyFnriVSt71///W/fladVniLoXVSn+Kw1 4rw87XSqZETGuDqhJSng3oCyMeZwyoadlOEHoDrOO45lBZ0xzoqW7s926U8HgywhlzSZjEnBFRFG csyB82qUlZWhVu5CrWSkAjKy9xxLr0GypJen4rcq7xgh4qiYvmFlr7xl8va76S1DWQr6aqECj0Et rY6+oZvJ02IqDzoL3YfmEJ8+DthY/IJs6PGsBcp/En874hp55ChRF5PmajL6aUXbZHS1onXHDNCZ GVRIpZhbFscx4txlPCfIrqUy/FbljzS5r1BBQR4hvhKvbqFkFr/lCPGnEkhxQUq3UzflQcOMVhZ/ 7NL0SQzSh181XAEGO59wOsg4GtCC9xKcA8nYgn9awKZxXvEef8qJVEgp/sjLDGTLsUAzKdofei2U ZoxLDaFqzC9ygotajfz1T/1/k4RnU4LeEs6ypEJ//P4f1M2G6DZL+IQR9DEjD2JgLoeXQ5AivcUM v187kmK1lKIbOTvGEOvN4RtzXIDsAFZ0m+OEjGieEoa8/YyTpY9NkzV2WYFQ27eD0JXQs/3Ys61w HqyeZ8eBpUHo+Y4fB+4iFBXpZ1k8cHxBNC96ZfKepJNETFvA615AqH4BCpGguxESXZzcDxmdFOmc 2ZVBzR9Be5prjW4c0Ns6YIPBscZgSqYkByeWIk7ROBtnCRpNxrhAJWEJKYUyEB2gaZYS+gqRk+HJ K8Dtf3NK7xHmiI8IYiQhQDI1jcCjo39P0iFBI/qAhpSmCFQuelR//P6/F5L0Mqs4ZdKBoz7hD4QU qKITkEnyJ1lFCdyFAwa8YyFVHybBPerTR+BTNkuzFDDPER4MQG9aaaCzKhsWOwjibBVEgGyzIG+E /mB5QZOin+EKWCVTnE/k6iIsI5dUWGTBODw5+QLa1Xy8o0WbkQEoqwAddpCcK8BNcw1DH7GQTnKO Skb7ORmjrACIZBUakoII1S24t71MnIA21su7xUNrGteTPJ8Ra1qdgL1nBf3489t9dbpqzEZtp4DC cYnB/Yu5NCa4mgg8KoA+ZHwkr1OWDbMC54AC0RwiDIEE2WhfHC6yd9t79/4Vettr93o3b1+h26uP Px9aA3NYOoWZhvOnX5QCGrnpBOadVk5W0b3n20YsXYFjQ7uhs6YjF0aaZ+l1lufyhA37FzlDMD/P WtfXMyvITDNBXC0moGrbsZznDar53Xm2P8v0N3cf2nfozQmsvdbuNl8Xo+wwjT8ndHFN6HJNKYdo ZTZykdLuG1YOOFPMf5pgBiO0dESzJaRZGWpKBe0cNSzj8gaisxxBkDaAeVHwDOe7BYifqVvP6LYH eCXo3WTcX9CwDLn21TCkd0B6pZKlBQ+j5AEkkUKoX4Nu13LC6LrtXAZ+24u9sB374UXbis6j6Orc 716dn//WqnkTmgbudjWR11hkIHK7fYN2mU5tSgzrCfAGQscSKZXtnyH6TmBbQEwG4LHla+/VBOCu 71iR46sAPPAC19UOoE4cfduKAkMiBIKGRJNEriHy7HwyqPNJYK9LH5E/B0wk1GrSkHllbElD1spg 1BCBTwudtQKAXVnF3xA6RuLgrAXhMZdgwtMfK65Nr5uovEQsDArDxfKaIuoMpF5VcJKAGzAjzi8r oveGHAg9MAzzE8Ib0kKM5xc0r1exUiRG15nmblO+I3SL8yFMgATc5Ocutz0ZiS+4sv4zCQlOd1ky VzHwcSly+nK5drgI1eBAUA1Dz4+CTVCNXD9cP9eOUN0JKeSBzOfnf12oRotQlTWYA0A1sP3IDeL1 ULUd17UAn38lrBZU15teCqszRY+DAiZ5LExIciEOl0ISq44bOcPZcMTROWP0QQSuBdiJMhTXQKop gKI5nJjlXMZ/MryU7Jo7kYkMm7hyhodZCM5cXre2x64TRJ5EoWOFfhhIV9yg0HH8INIOc77OvYS/ Sktai2hvQqOCIMdZflWkOtLEQkV6kAWQVQIdqt97MjAK2Apc2VqGvgDeuqN2nJs66vZSFbLU9ZzO dQ85Mi2azuOsoGwVAf5Yj6zaK+mV1MLIBnBbkVfvK6xFnsrb1kPPAGwZeuEBoRe6sS1z4XXQc33P E2v5EXpfBfRMHrJLRuYtZGTegTIyL4xjT6ciju/bdcJVb4nYsWNHkQKNE8X+TMKlUzIv8EKT1UF/ dyZpMynZOirP3+Or9yxN+OBIQgeIH+bEiCCFD+SCMhPqhm7gBsdQd5/w4YcTJ5ipZrx4pHsltzJe KNR1mh1pA9b5Tek98jKYtb4dbgBrHAaRewTrtwzWS/KSYK1XmBqs86XuPepdFiA0jCRYAYqwFCws MnHghhsKdkew7gCWm1v0jw+Xt//8E/F6i5N7wg+bF24AbL2TUAN2fudgD8A6figfrQD+fD9w/HjB u9q2G4fHWGA/xCq4/Il4/ZFWVedfGeeEvRRm64eiaszOPwm1R/krCKM4sGvMLpe/PMeLhBc+YnZH zBLG5EOSq7B7vwjcz8AfEgD8E/EPyX1CcC4eSH0p/C/tqTmH2lQLYsexXOWzVwYZtue5YXwMiffy 2d3JYLDkLV8Ss29xgYfk4JDdVgR06m2LtUVAp9nIWFkEFKULWTfSz8vMVAFF7K3rUQeoQFuRG5mJ 4MW+CGTmSzpw19bbILbnevqpk2Mt8KsuQwuLbkPglh2QGmYrEOgdEoF+EESeDkWcyPdVtXJmDySw IpguqhoYhpYvp84RgV85ArdvhKgldQMCDcyWd0JEaHywrRBY/0Nf5GcCgVHkh4EMM2YQGEE4rCsO R/f3LYCveW5wLfia9H81+AzCzHo4Az6zOWyeDXw2+NAgz8qPxngrduRWO0If/s86wuiIxW8Ai3UW sx6LTVqzGosGcCuw6HwZLAYeJEVqx2BlWBh60ELvDh/Dwm8Fi95yWDiDwqWAcCmr3gShHxYgtHaj t06xfdt2fe3PYP213S3+LM8gp92KGnHlElcjpboUjo6g2Q809S76CtB4zYb6M0Ezt/KFga2rka7r WLG/VI0JXAvweYTK1/IIyoZXNurY6z3wgYthTpA3v9fy7Gqda1uheVhz1ZsAB39Yc9NrVOKf6puX I6yuBrGlHi0R5TrVfLF0Z0BXFwJ3w5mzyeLy1ginRF0Wiqm5MD02QHEHDL80FPNm5GUorqh2NhVO hItkRNlMxXKnsubz31eSP+ZjCYBAAJA+QhOWnbV+7XbjwLmIuu2u7V23vcs4bJ9fB3772nc976Ib nV+4V7+Jjy/Y3mnCiHxz9sZ8XwIuLn3TYZwljFZ0wE8SOtYfh+iU9IGwkmby+xC2pT8yIZXoxlFs h3bkm6AQeDO/klsxp/V3H5KcvcXlT1M572AwTtiFvFRmxVCHlE0TUEYGthvKo0JLXmLlAu5USArT MJ2AHbIiJYOsyLgoRJOKYwYTryBT8W5VQVNyp76ZMH5PqZiLQGCU5emdoVqRT+J96GTCGEBKfX8C +D9PIJgk5F710EM6K4ZshhCa6ZFPK8ZY4NxtIYF30D/NU9m8EgFrqlsDNylKSY6f5kZSnk3cI1Pg kxZdMhSm0C3FPBHKUqapw3Ror6E9O8Im7rzduTN1ud2JQ7YrPxPCby6lqtXJRY6rSkwJ8QaiutSb 9FU8bs2xg2BluEnl1UbxeABoupJOYG9+YWh5sTtSODSMB9r09l7aERa5K6Shhvwq1x9EGXJUlXpN U13qu5hz9g5WOU3enL6WnupkmlVZP8sz/iQ61TdnT+phjUCcKqbZR5wrrEgq9QdFxH3BtNCD6Dej HcN9pzbrgnXDfa3b2FS8Cf2FTRq9iEnVtzq+FZN+kdtwl5Hpokq1F7uFW4tOrFYkkBrKsKm+OOvT FqgKt71mkHdw6zMHmaMqtfhpFz3Uq1Y/NwzB0W0z0Wc8GS6ycXfYFJlNF0FGL4NiYFhI/w8AAP// AwBQSwMEFAAGAAgAAAAhAJaAqjMVAwAAnAcAABUAAABwcHQvc2xpZGVzL3NsaWRlMS54bWy0Vdtu GjEQfa/Uf7D2KX2A5ZoQFJICgTRSaFIg6bPj9QarXtuyvRto1X/v2OuFpkkk1KYI4WFsz5wzN5+c rTOOCqoNk2IQNeuNCFFBZMLEwyC6XU5rvQgZi0WCuRR0EG2oic5O3787UX3DEwS3henjQbSyVvXj 2JAVzbCpS0UF7KVSZ9jCX/0QJxo/gtWMx61G4zDOMBNRuK/3uS/TlBF6LkmeUWFLI5pybAG5WTFl KmtqH2tKUwNm/O0nkE6BGVnwxK1GLTWlThLFhVYLdaP99ufiRiOWQLwiJHAGYYnisBGO+b+i8EL8 x/WHSsT9daoztwI3tB5EEPyN+42djq4tIqWS7LRkdf3CWbKavHA6rhzEvzl1rEpwz+m0KjpLZjlF zS2rCq9RV5J8M0hI4OPol/S2J0rOblUrZDcKTBGrvbVwtNz3wg5PiJddj2SycX7uYS09CsjZMLcy ZRalUtgFwRysHjfgEzjuDnNjF3bDqY+Jcj9erYEex66gqajdLiKUMG19kJDJ7JhTLLaRtKeXk+UU zWfj4RJ9veijO5ZQib7kmDO7QTNqNSMGnTNDcuOaBlBpNCkwz30tobFmlmqGHTLr8XkMVCQ3WOP5 q1BKLsrHpgpEXCXr9ZS1q5Qt8nvrs9Z6i6yZ/L7MGjhZ7668VfZaXcge4mKhyJwmOXGRg/L7f0m9 q6MFfGWGN/h7znN0UNSNZoIV2OC62eo/MmEprxOZfXiSwDI1+/kPLj9hYwTGaCZzGKApRgd/lMRf 0FiVNutZsLkX3H8pPb9UQxAm0pWxQUK5ZoPox2h0fNga90a1UbMzrXXOj49qw+lhtzbttjud8ag3 HLcnP91QbXb6RFPfI5fVuwHKZ7M6Y0RLI1PrWIWhHyv5SLWSzM/9ZiM8HtB0UDS9bvOo02gftUON ArZq9WhdpYd5TrieYXVd+MiDM2jUsVcpeJhCU+yOQDBYBhteEoG5wmUDLkX1ACQ5PF9MJDSFcrLQ MfCyWKztIBIUHlboT5nQpe8qm82ltAGnt+QiXpp2UnDngg6AfwEAAP//AwBQSwMEFAAGAAgAAAAh AFDXmLLbAwAAAQwAABYAAABwcHQvc2xpZGVzL3NsaWRlMjAueG1svFbbbuM2EH0v0H8g+FC0QCTZ 8t1dZ2E58W6AbNa78uadkaiYKEWyJO3YLRboj/Tn+iUdUpK9l6TIepO+mCNeZuacIY/nxcttydGG asOkmOB22MKIikzmTNxO8IflPBhiZCwROeFS0AneUYNfnv74wws1NjxHcFqYMZnglbVqHEUmW9GS mFAqKmCtkLokFj71bZRrcgdeSx7FrVY/KgkTuD6vH3NeFgXL6JnM1iUVtnKiKScWMjcrpkzjTT3G m9LUgBt/+rOUTgFZlvLcjUYtNaXOEptXWqVqof3y1WahEcuBL4wEKYEWHNUL9Tb/KTbeiL44ftuY ZLwtdOlGwIa2Ewzk79xv5Obo1qKsmswOs9nq7T17s9X5PbujJkD0SVCHqkruazhxA2fJLKeovUfV 5GvUpcx+M0hIwOPgV/D2OyrMblQrZHcKXFnnqt5XLXrjkExNlt0mMt+5IDcw+kky5samdsep/1Du x6ehIV9O3A2lIviQYpQzbT1qZEo745SIPTX2dBG241aMfqbbEC3Cq2T6Jv3FcWM9Q94hFfmCaPL+ Qb8Vm8on32QaNVQ+TGinIXQmhYXrhhacZHQleU41ir+PXpZvD1u+hVlnCXgK07WVBbMVtMPSk3C+ vJOIKMVZ5l8YIpoSg8af0V4R+r11DdsnmMs74BNetORrH66UOf3nr7/H6OGAysXa8JrCoxJIobQI YC0gj3bY/g90+2C+5kcFYyENT9C72cU8eHf9ahq8hp8TAGos3yFQNjBvGDza5TUCtUapBcZL0Nvn oDw+QXjFblf3k/6snF8yQYlGN1qSPCPGOrw/kVL9iq5ZTiWSIshB80X+vNVILeMcrYV7x6ld57v/ SVC6jaDMpbQQ+1M96TyFXBdWA+A/Jvj3NdEQAdc6c4zQfJOE30PyBWgmRyCdBZRWWEb441g+ktte w23KIR66Wpc3XzDcfQqGoW0C1/eS7O/j05BcQHPmQP3ZT5JWPBjOg/is3wu6o+4gGPUGs6A1nA6H 59Necj6dfsT73BzTkN1jSxS3DiUpXNN0TFH80HRa0PZcGltbaK0ZYEiSUT+eDZMgaXfnQfdsNAim c0Az73W63VkynM465x9d59bujjPQPSdHF01zCpNfNYQly7Q0srBhJsu6s4yU+xdRkvnmst2qO9QN AaHodEZxfxB3BoO6QpBbM/psXc3rpjHj+g1Rbzf+fkAwqO/MTylQ4/p6HLYAGczJtLdEjVyR6mou RdNl5mvokRnITcEEsxQ73bVwdyZY0I27QALEd1k1XOV70IY6T+/JMV65dlYdzpEOCf8LAAD//wMA UEsDBBQABgAIAAAAIQCzhPSzZQQAAM8MAAAVAAAAcHB0L3NsaWRlcy9zbGlkZTMueG1s1FZbTyM3 FH6v1P9wNE8gSIZAwpJow4oE2FZi2ZSE7rPjOcm467Fd25OLqv73HntmoMuCSoE+NA9jx5dz+c7F 3/sPm0LCCq0TWg2TTvsgAVRcZ0Ith8nt7LJ1koDzTGVMaoXDZIsu+XD64w/vzcDJDOi2cgM2THLv zSBNHc+xYK6tDSraW2hbME9/7TLNLFuT1EKmhwcHx2nBhErq+/Y59/ViITiea14WqHwlxKJknix3 uTCukWaeI81YdCQm3v7GpFPyjE9lFkZnZhYxzNTqozVTM7Fx+3o1sSAywisBxQqCJUnrjfpY/KtW cZI+uL5spmywWdgijOQbbIYJgb8N3zSs4cYDrxb5/SrPPz9ylucXj5xOGwXp35QGryrjvnfnsHFn JrxE6Nx51djrzJXmXx0oTf4E9yv37k5UPofR5OC3hkT5IKo+V23Gyb0xNVh+M9LZNiiZ01ipUxSw s9LrhfCw0MpPOZMksn9Av9rB+8PS+anfSoyAmPCJy5Z8kyxkM6rW7TSBTFgfEQJX+LFEpu5g9Kc7 P4llDle4QrkL07IomN2CXsDFiskyJgtMOSpmhYZz4XjpQuE4EApuPo3PZvDlY7DKR9uiflTZhFl2 86QZlR8m4tKAkDZRejpWR02sxgQMZTJMJOOYa5mhhcPXRU5km/sjbxS0k14IGkg1NfwGs5IHMEnL fxfLcWltAGYHV1quqPXshvMYFYNDLBx4DXOEVhNeBE2tkEkJBm3sForjPog2tvfhV5GhTs/KTOg9 uEa/1vYrEPhLdFEiJ1+tlsDkUlvh8+KbRKhC/ApndGmoJCkV12wbDReKyzJDWAW7gOeUZNyjFWQN d4OnlVPsgNL6iuroXafXCzFZydjKhMoIr2HS6nW6R7QR41pekl+xlheUX8Nkr/itJX1l1rwMgb4u i7rWyYS54BMyQmdNXlcBfYHHtw6Bgbcs9H0odIYSfM6ozTHjS2rf4V3ylbsBF583WDyowH/SjdZG 9x+xwenSctyzlBr/Uugjwt7QrjrX9lzODCX2i1vO/ywlJpra7VxuoaTcKImmFOEZd5jVgXf4e0ns hVJjTQVI2SPFCis+Q01xZw/CcW7FnG449J6gc7tA5CYU78M6frtohY7w+vRpP6ekexS/0GYfxi3K eIHSLwFHpA/hx4yxmvF8H9YILtcl8b8MF0KFOi3oXizDqjoJWRCFoY4EBGkozaazvnWJ/qIv3hrb 1z3acWh4I5G4K+frGRAuw+SP0ah/fDg+GbVGne5lq3vef9c6uzzutS57R93ueHRyNj66+DPw0E53 wC1G1vFzQ7Vp8Tt6WwhutdML36aKqHlyavQardEiUuXOQc236Z0jIPv0GPePe/3miSfbmjFaG4hB TYG5tJ+Y+byKsJAyemHGcSn0nZpD3B8hMERBG3Gmas8Nq6jLTDWcOSspnCFFKXmExwSoLD2zlK+K mJclZkMFO6voY3GjdewtaS0pIF6JDrNaXQCdDP4LAAD//wMAUEsDBBQABgAIAAAAIQBA4ZyUDQUA AMAQAAAVAAAAcHB0L3NsaWRlcy9zbGlkZTIueG1stFdtc+I2EP7emf6HHX8uMQZCgLnkJnBJpjNp jkty7WchC6xGlhRJJtBO/3tXsh0IgbxxN8NgWZJXu8++PKtPnxe5gDkzlit5HCUHzQiYpCrlcnYc fb89b/QisI7IlAgl2XG0ZDb6fPLrL5/0wIoU8GtpB+Q4ypzTgzi2NGM5sQdKM4lrU2Vy4vDVzOLU kAeUmou41Wx245xwGVXfm7d8r6ZTTtkXRYucSVcKMUwQh5rbjGtbS9NvkaYNsygmfP1EpRO0jN6I 1D+tvjWM+ZGcXxh9o8cmLF/NxwZ4inhFIEmOsERxtVBtC69yHgbxxuezekgGi6nJ/RNtg8VxhOAv /X/s59jCAS0n6WqWZl+37KXZ2ZbdcX1AvHaot6pU7rk5rdqcW+4Eg+TRqlpfqy8VvbMgFdrjzS/N e9xR2uyfOgO31CjKeVHVvnIxDFbKVGC5xVClS3/IBJ9hkgyEdTduKVh40f4vqGFQX0F8hDLZ+H4T QcqNC1aDzd1IMCIfoXEnY6MmguVwg95mPnY8MC7AE6QxmY6JIdc7hZZQ6qB5rWZc47gbzXaN5khJ h8fCWBDKMiVSZqC1H7Y8Xay27IB1S4B1Do8w0kPkJK3+Yae5EWu9Vqvf9Rt8DLV7SbNZ7liPpFL0 c4f5kcQMOy2cmnJXfrVaeqsr7T8Yh14xmIT84uF/3RfeeX/ylCn4VhDB3RLGzFCmfSrDhm9fO2NT LrdADXecEgFYLrAWpo3Cor8+KheYMZWnvIBv6mxPDZ9qUsZl/eelzMXjcW9XckvW3CrNKWTEwoQx iXstLaxlKXAJf138BpPCYagCVZKKwrMHLJkDfHA/4fkDXIZw8r194jIGht0X3ITstYB+8XNsTkQR CjjkzBlObXCZX0KtZsyGJRw6o8THHbgFGyJmCqMky+0bS8l243Y5D1A+Hvd3Yd0PcOQVcw/K3MXO EGm1Mg4Ca6Ina9SIQcQKYxBcsYSKGb3jNlD2mcEMJwHmH5QQ1/vK2WZyTslmkd8Lwm3xtIqBMhdS hengYFoIxJAS7QpE1QPIc01oyIx5KFo/w+A9yso2cQcv15icmMvwKZcpRoofBhoorrBFrOK6LPov 5UMSiGanHj/p1JZntNdOfR/Pdx67JqTRoVpA+wm3gxdVs/XTpvA1tu4nze5RYOtOv9ntd0PTsMbW zaOSzj1bJ62k71/W2RqNMdZdMJWDHxxHhlEXUCPzS1tRdL2l5O9zLkSp3i6ehwdDsD+x9wXWjQiM EyMlam9Y7en//OP0X5L+y/1cYbSyDNS0ZJj1Vn6wmV67YmHjnM2A+7ByyQFgv+kzHSaE3s2MKmS6 Sv77qmOpS6/LCPqSSMiY0H5VOj5dPtaM6bbKo8rC/I5qsqn3uyrJGxF8MX8/DGfrANt2nheesIDU XYhHA38kVWXThzgRrY3SSE64rwYXGWwLe+0ZIM9rQ3jUF0XMTcysagSFwfb13+EQU3fUGzaGSee8 0fnSP2qcnncPG+eH7U5nNOydjtpn//mLZ9IZUMOCur/Xd2ucfHafzTk1yqqpO6Aqry7GsVYPDBOD h7tx0qwu2Gg/dvKddvcwabc6verGgLrVz6Ctv2dUd14qzB9Ef50HcPAwxGwUpjS2dNWVZLUFweA5 LoSRrCzXpKyQt7K+JKcFBpyv21Mu0QtYNTCmicGKJNmcGSygKmW35X0xv1Yq9D1xJckjXor2o+o4 Dzoq/D8AAAD//wMAUEsDBBQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAcHB0L3NsaWRlTGF5b3V0 cy9fcmVscy9zbGlkZUxheW91dDIueG1sLnJlbHOMz70KwjAQB/Bd8B3C7Satg4g0dRHBwUX0AY7k 2gbbJOSi6Nub0YKD4339/lyzf02jeFJiF7yGWlYgyJtgne813K7H1RYEZ/QWx+BJw5sY9u1y0Vxo xFyOeHCRRVE8axhyjjul2Aw0IcsQyZdJF9KEuZSpVxHNHXtS66raqPRtQDszxclqSCdbg7i+I/1j h65zhg7BPCby+UeE4tFZOiNnSoXF1FPWIOV3f7ZUyxIBqm3U7N32AwAA//8DAFBLAwQUAAYACAAA ACEA1dGS8bwAAAA3AQAALQAAAHBwdC9zbGlkZUxheW91dHMvX3JlbHMvc2xpZGVMYXlvdXQxMS54 bWwucmVsc4zPvQrCMBAH8F3wHcLtJq2DiDR1EcHBRfQBjuTaBtsk5KLo25vRgoPjff3+XLN/TaN4 UmIXvIZaViDIm2Cd7zXcrsfVFgRn9BbH4EnDmxj27XLRXGjEXI54cJFFUTxrGHKOO6XYDDQhyxDJ l0kX0oS5lKlXEc0de1Lrqtqo9G1AOzPFyWpIJ1uDuL4j/WOHrnOGDsE8JvL5R4Ti0Vk6I2dKhcXU U9Yg5Xd/tlTLEgGqbdTs3fYDAAD//wMAUEsDBBQABgAIAAAAIQDV0ZLxvAAAADcBAAAtAAAAcHB0 L3NsaWRlTGF5b3V0cy9fcmVscy9zbGlkZUxheW91dDEwLnhtbC5yZWxzjM+9CsIwEAfwXfAdwu0m rYOINHURwcFF9AGO5NoG2yTkoujbm9GCg+N9/f5cs39No3hSYhe8hlpWIMibYJ3vNdyux9UWBGf0 FsfgScObGPbtctFcaMRcjnhwkUVRPGsYco47pdgMNCHLEMmXSRfShLmUqVcRzR17Uuuq2qj0bUA7 M8XJakgnW4O4viP9Y4euc4YOwTwm8vlHhOLRWTojZ0qFxdRT1iDld3+2VMsSAapt1Ozd9gMAAP// AwBQSwMEFAAGAAgAAAAhANXRkvG8AAAANwEAACwAAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3Ns aWRlTGF5b3V0OS54bWwucmVsc4zPvQrCMBAH8F3wHcLtJq2DiDR1EcHBRfQBjuTaBtsk5KLo25vR goPjff3+XLN/TaN4UmIXvIZaViDIm2Cd7zXcrsfVFgRn9BbH4EnDmxj27XLRXGjEXI54cJFFUTxr GHKOO6XYDDQhyxDJl0kX0oS5lKlXEc0de1Lrqtqo9G1AOzPFyWpIJ1uDuL4j/WOHrnOGDsE8JvL5 R4Ti0Vk6I2dKhcXUU9Yg5Xd/tlTLEgGqbdTs3fYDAAD//wMAUEsDBBQABgAIAAAAIQDV0ZLxvAAA ADcBAAAsAAAAcHB0L3NsaWRlTGF5b3V0cy9fcmVscy9zbGlkZUxheW91dDgueG1sLnJlbHOMz70K wjAQB/Bd8B3C7Satg4g0dRHBwUX0AY7k2gbbJOSi6Nub0YKD4339/lyzf02jeFJiF7yGWlYgyJtg ne813K7H1RYEZ/QWx+BJw5sY9u1y0VxoxFyOeHCRRVE8axhyjjul2Aw0IcsQyZdJF9KEuZSpVxHN HXtS66raqPRtQDszxclqSCdbg7i+I/1jh65zhg7BPCby+UeE4tFZOiNnSoXF1FPWIOV3f7ZUyxIB qm3U7N32AwAA//8DAFBLAwQUAAYACAAAACEA1dGS8bwAAAA3AQAALAAAAHBwdC9zbGlkZUxheW91 dHMvX3JlbHMvc2xpZGVMYXlvdXQ3LnhtbC5yZWxzjM+9CsIwEAfwXfAdwu0mrYOINHURwcFF9AGO 5NoG2yTkoujbm9GCg+N9/f5cs39No3hSYhe8hlpWIMibYJ3vNdyux9UWBGf0FsfgScObGPbtctFc aMRcjnhwkUVRPGsYco47pdgMNCHLEMmXSRfShLmUqVcRzR17Uuuq2qj0bUA7M8XJakgnW4O4viP9 Y4euc4YOwTwm8vlHhOLRWTojZ0qFxdRT1iDld3+2VMsSAapt1Ozd9gMAAP//AwBQSwMEFAAGAAgA AAAhANXRkvG8AAAANwEAACwAAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0Ni54 bWwucmVsc4zPvQrCMBAH8F3wHcLtJq2DiDR1EcHBRfQBjuTaBtsk5KLo25vRgoPjff3+XLN/TaN4 UmIXvIZaViDIm2Cd7zXcrsfVFgRn9BbH4EnDmxj27XLRXGjEXI54cJFFUTxrGHKOO6XYDDQhyxDJ l0kX0oS5lKlXEc0de1Lrqtqo9G1AOzPFyWpIJ1uDuL4j/WOHrnOGDsE8JvL5R4Ti0Vk6I2dKhcXU U9Yg5Xd/tlTLEgGqbdTs3fYDAAD//wMAUEsDBBQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAcHB0 L3NsaWRlTGF5b3V0cy9fcmVscy9zbGlkZUxheW91dDUueG1sLnJlbHOMz70KwjAQB/Bd8B3C7Sat g4g0dRHBwUX0AY7k2gbbJOSi6Nub0YKD4339/lyzf02jeFJiF7yGWlYgyJtgne813K7H1RYEZ/QW x+BJw5sY9u1y0VxoxFyOeHCRRVE8axhyjjul2Aw0IcsQyZdJF9KEuZSpVxHNHXtS66raqPRtQDsz xclqSCdbg7i+I/1jh65zhg7BPCby+UeE4tFZOiNnSoXF1FPWIOV3f7ZUyxIBqm3U7N32AwAA//8D AFBLAwQUAAYACAAAACEA1dGS8bwAAAA3AQAALAAAAHBwdC9zbGlkZUxheW91dHMvX3JlbHMvc2xp ZGVMYXlvdXQ0LnhtbC5yZWxzjM+9CsIwEAfwXfAdwu0mrYOINHURwcFF9AGO5NoG2yTkoujbm9GC g+N9/f5cs39No3hSYhe8hlpWIMibYJ3vNdyux9UWBGf0FsfgScObGPbtctFcaMRcjnhwkUVRPGsY co47pdgMNCHLEMmXSRfShLmUqVcRzR17Uuuq2qj0bUA7M8XJakgnW4O4viP9Y4euc4YOwTwm8vlH hOLRWTojZ0qFxdRT1iDld3+2VMsSAapt1Ozd9gMAAP//AwBQSwMEFAAGAAgAAAAhANXRkvG8AAAA NwEAACwAAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0My54bWwucmVsc4zPvQrC MBAH8F3wHcLtJq2DiDR1EcHBRfQBjuTaBtsk5KLo25vRgoPjff3+XLN/TaN4UmIXvIZaViDIm2Cd 7zXcrsfVFgRn9BbH4EnDmxj27XLRXGjEXI54cJFFUTxrGHKOO6XYDDQhyxDJl0kX0oS5lKlXEc0d e1Lrqtqo9G1AOzPFyWpIJ1uDuL4j/WOHrnOGDsE8JvL5R4Ti0Vk6I2dKhcXUU9Yg5Xd/tlTLEgGq bdTs3fYDAAD//wMAUEsDBBQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAcHB0L3NsaWRlTGF5b3V0 cy9fcmVscy9zbGlkZUxheW91dDEueG1sLnJlbHOMz70KwjAQB/Bd8B3C7Satg4g0dRHBwUX0AY7k 2gbbJOSi6Nub0YKD4339/lyzf02jeFJiF7yGWlYgyJtgne813K7H1RYEZ/QWx+BJw5sY9u1y0Vxo xFyOeHCRRVE8axhyjjul2Aw0IcsQyZdJF9KEuZSpVxHNHXtS66raqPRtQDszxclqSCdbg7i+I/1j h65zhg7BPCby+UeE4tFZOiNnSoXF1FPWIOV3f7ZUyxIBqm3U7N32AwAA//8DAFBLAwQUAAYACAAA ACEALxtMVSoHAADaLwAAIQAAAHBwdC9zbGlkZU1hc3RlcnMvc2xpZGVNYXN0ZXIxLnhtbOxaXW7j NhB+L9A7COpj4bUlUbJsrFPETtwukG6DJj0ALdG2GopSKdqbbFFg79Ab9BZt33qUPUmHI9KWEzub NFkgSQ0EFj0cDYfzzQ85zutvLnPuLJmsskIMXO9Vx3WYSIo0E7OB+9P5uBW7TqWoSCkvBBu4V6xy vzn48ovXZb/i6fe0Ukw6IENUfTpw50qV/Xa7SuYsp9WromQC5qaFzKmCr3LWTiV9B7Jz3vY7naid 00y45n15l/eL6TRL2FGRLHImVC1EMk4V6F/Ns7Ky0sq7SCslq0AMvr2h0gHsLznjqX5OZvXnj2zq ZOklWKnT8YCD9lEyG3HpLCkfuJOZ57YPXrcNsxnpl6vyXDKmR2L5rSzPylOJK7xdnkqQCSJdR9Ac 7KsF4IRhw69iiYP2tddndkj7l1OZ6yeYxwENAcUr/dnWNHapnKQmJmtqMv9hC28yP97C3bYLtBuL 6l3Vyt3cjm+3c54pzpxTThM2L3gKvoImQl6re1WeFMlF5YgC9qZNUW91xVHvXz/LuaOuShCrtFjD V0/iYK3YVquQsAsI43b9LomCeNM+se/3Ij2v9+15JOh0NndP+6Ws1LesyB09GLiSJQodgS5PKlWz WhZUqVak7KvLYZFeac4JPMFIEHHw/ryQ712HvxHVwO15hMDaCr+gpq4jmzOTjRnFRwVHlKhIQM7A TZREXQT49+FCFdPMaFQvqad4pc7UFWe47VJ/IFmCQpzqgGei9dMZBHyuRpxRsXILdTDiWXLhqMJh aaYcE/cIA6QHEKkXUrgcimQiPaWS/nhNsjER2sbaBM10uzsFK3fSWDW9yX8Mb9IGck1oP8SpPPAe Pd7tVST0w14UPH2vurcjlRrpJb6LxAc6lrYe+lW14Vi181xfEkG7x5JnLClE6nC2ZPwO4tHH7iH+ fJ7Ju0tHZ7iH9HGxkGp+Z/HkvuKz6Vbpjx3SxIb0EVWbBQIN8tCQThXs7j3EAuVTE9oI438N7SgI 4e9aaPteEKxCO4hCzw+fWb3A7dhgxvGSe9p3KJ+BV3BUNmVTDbo2p6ftgZAUPEvHGedbjkHqsj4d qUyomtIN16V0xVx/W8tp25VwaBSpxw0F0bunPEUn+nVIRlHYCY5bvejYa5EOGbYOx9G4RcbB8TAO x8eEHP3mWp8AT1NZzsbZbCHZD4sairsEhee1QzgcesE6JKb6bPjYQRHaoBgXhU6DzbDAQH5oWEwB cwTylwWVsIIJjbog3Sc0As8nt8dG3AtfdGzY49bTi47H9cnI+uQZ6MKct4t8cs0zEdiHeiZcJkH0 NudEx7+Xc0ZhGPy/E/dTdc1V4h5FnaM4DkjraESOWiQ+7rQgX3dbvW4YdAJ/ODo8HK4Sd6U9T4B3 3DVff/zw51cfP/z1CNkaH/buDu4D6JuRs5CZrkDDXuSP4mFr6BGoO0e9rq5AYWscBoSMhvHhKDj+ TbcTPNJPJMNOw5vU9ig8cqNLkWeJLKpiql4lRW7aHe2yeMdkWWTY8fA6pm2CEAVB2PM6IVRBEyeg m32itjrwTCcj4fJ7WjqTmQe1XXlg30sYpRcwmsx8TfM1zdc0GNEkYUIBhxlYim8pK57AUgJLIZZC LCW0lNBSIkuBHDPnmbgAY+iH60wL/l1NsKM6x0CWOKFXxUK9SQ0SDUrdefBIl8RBRHoQO31NkW9S 03PYxRvqOLO85ka5k9dr8Jqj6k5ev8Fr6vdO3qDBazLqTl7S4I0+wRs2eLuf4I0avNgcuYW32+Dt fYI3bmKBUXoL8wZwtnTcBF5dYmqpcKz7ELfUaQfy0zmdnL03ObbOq5hUGT0RQ3mBHTjdRRTmK0zN IUVkYna6EInS83VlS4a6s4ej08SkyVWKXM1OFm8LUV+PG1m4XvqCSd1qvWtGNqKbXKgoJscplOGB +3X+c4srU+PotQlGTWuvujaRVEb21uy9adUS69kNE+dUnkAKIn5PbywTkKbBVC1LsLeIz21/MOW6 TDUwGBdQyNabPpQZBWXKTCXzMc0zrqs/xFIyp7JialVAJosRUJA8cD9++KOmNnCsTxefA0exC0ex C0dxO4449NdYdQEane9WWPlQdjXhZWD1+w2s/PgZYKUBMlgFa6xsD7oBlh/jufWlBpb/2RLkI4Kl ETJgkQZYpvn7UsHaElnom08cLI2QAStcg+V3wi4C8CLB+ufv54mVBshgFTWwCj2C0Px/suBzAEsj ZMDqNsDqdT0suHuwnhJYGiEDVnz93L4H64mBpREyYPUaYMVx9JLPF88ULI1Q/Y8+6/tx2S/UnMnV bRneOK0hNbu72Xxcs2xerT8LvM2+8HO4HW2/ydpfZ/f22X57tEbY22fHhS3o6jvb3kC7Lkle7Meo /d5AO24mWID3Btp9G+iSulW6N9COEziou0/St516o7C7T9KbJ83m4RJ/sbY/hNW/k9X/QnnwLwAA AP//AwBQSwMEFAAGAAgAAAAhAAmJLw/QAwAArwwAACIAAABwcHQvc2xpZGVMYXlvdXRzL3NsaWRl TGF5b3V0MTEueG1stFfbjts2EH0v0H8g1Get7rJsxBtYsl0U2GQXtdN3RqLWQiiRJWnHThEgv9V+ Tr6kQ0qys14ntbHui0hRw8OZc2ZI6tXrbU3RhghZsWZseTeuhUiTs6JqHsfWu+XcTiwkFW4KTFlD xtaOSOv17c8/veIjSYs7vGNrhQCjkSM8tlZK8ZHjyHxFaixvGCcNfCuZqLGCV/HoFAJ/BOyaOr7r xk6Nq8bq5otz5rOyrHIyZfm6Jo1qQQShWIH/clVx2aPxc9C4IBJgzOynLqkdh2iBGLWsFCWTplhu LWTsxQa+eNYtUJAvaIEaXMPAH2Ba5ZgiY4+AMbQkW2XMJF8KQnSv2fwq+II/CDP77eZBoKrQaB2K 5XQfOjPz2mxMxzma/th38Whbilq3wA7aji0Qcaefjh4DJ1DeDuaH0Xx1f8I2X81OWDv9As43i+qo Wueeh+P34RyR4u3D6x2X/I7lHyRqGASmeWjj3Fu0weuWrzpNlIayEBMVKNdK1M1qTU3n4ONJguLY H4ZuG7o/COMgecqV70YD811zECWRF/nRMRMtNB+pbcqKnZ79HlpgQHs0tgjWwbewVKqF2lFiXrh+ GKcEGFOsC4009rsFFFqtMkpws9dD3Wa0yj8gxRApKoXeYKmIQIYCKEuA1C4p45iBJE3xgAX+/Qi5 dZ0bv3t/TQg/1jF4rqNm6IHinKwYLcAV/xqSauKOFIX1t4fJ5ysbRgP/B8LGrjdM/k9huWZ+Q/cK vlBo7bfRWT4RuhXzeEnD1gVLLkjOYJuiZEPoGfBG6gvgl6tKnI8eXIg+Z2uhVmfDh5fCV+VJ9GuX WNiX2BQr8qSyDCEvrawCKkl+gqMQ07KvqXZP/25RmVWPc/872V7C8aej+CsNszhyg5k9jGeeHbph ak/m8dwO58EsTaL5LAynn/tTtYBQVVWTefW4FuR+rQ/J81TxPCeCU9oLDpqAC9dXJepVmTOm6/Bb XUwmvVSXUolWmD/XWMAKvTb/seFdos11GYl7Rha0Kgh6u67fH/Fi9tGX8gJ3SoA+SY3Zfq6ctlns TpMkCO1pFk7tMJm5NmTrwB4OosAN/DSbTNJ92kodeQPenZutX7/8/cvXL/9cIVdN098h4Uy4k6rr obWodP2lw9jPktROvRCqbjoc6PqL7HkUhGGWJpMsmH3Wd1EvHOWCmAvvb0V/VfbCZ5flusoFk6xU Nzmru1u3w9lHIjirzMXbc7ur8gbD/hp7CdR/EA07lcC1vjXOat0XOnxoqXiD+f3G5EhtDrrMDHH4 MehS5GCiQ+9/NG7/BQAA//8DAFBLAwQUAAYACAAAACEApKlLTO0CAABqBwAAIQAAAHBwdC9zbGlk ZUxheW91dHMvc2xpZGVMYXlvdXQ3LnhtbKxVS27bMBDdF+gdBHWtSLLoL2IHkWwXBdLEqJMDMBJl C6FIlqRdu0WAXKs9Tk7SISU6aZICWXgjkqPhcN57w+Hp2a6m3pZIVXE29uOTyPcIy3lRsdXYv7me BwPfUxqzAlPOyNjfE+WfTT5+OBUjRYsLvOcb7UEMpkZ47K+1FqMwVPma1FidcEEY/Cu5rLGGpVyF hcQ/IHZNw04U9cIaV8xv98v37OdlWeVkyvNNTZhugkhCsYb81boSykUT74kmJFEQxu7+NyW9F4D2 lmJ253vWTW7BEPsTQJ4vaeExXIMhtR7GqMS1JMTM2PazFEuxkNb3cruQXlWYve0eP2x/tG52ybZ2 Er7YvnJTPNqVsjYjUODtxj4otTff0NjITnt5Y8yfrPn66g3ffD17wzt0B4TPDjWomuRew+k4OFOs ibegOCdrTgsivfgA0KWuxAXP75THOEAzTDRIDx4NfDOKdUt9oaHwfoKImJY+HAjpxk2iztlOnvJs edS7lBd7c+gtjNaIR1Tppd5TYhfCfEpQ0KD4laKs142SWTDszeIARSgNzue9eYDmySwddOczhKb3 rh4KgKqrmsyr1UaSq432TSwJjEAZwIUhLLhZQt61zijB7EC5nsRx2IX6ihNDtLZ0QwpWOlYssMTf XgRpJBEWpsMUOj3+r0riVJlzrkGL57p0jqFLqWUjzPcNlnCC08btPYI2x2UEOUaWtCqId7mpb1/w khyDF+iGEPpNaizvRy7brBdNB4MEBdMMTQM0mEUBVGs/GPa7SZR00uz8PD2UrTLIGWT33mp9fPj9 6fHhzxFq1Q6uMUKXulC6nXkbWZn7lw57nWyQBmmM4NZNh31z/7rBvJsglKWD8yyZ3ZsGG6NRLolt 1V8K1+Rj9KrN11UuueKlPsl53b4XoeA/iBS8sk9GHLVNfosp3JgoQsMEaOu3MkFubrTZGuGXBj+M VH7F4mpriwQOA5EzaxLwprU18uRisLs3cvIXAAD//wMAUEsDBBQABgAIAAAAIQB5Q1xm1wQAAMUS AAAhAAAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDgueG1szFjdcuI2GL3vTN/B4147tvwP s8lOMNDpTDbJlOwDKLbA7tqWKwsW2tmZfa32cfZJ+km2CDgkmISL3uAPcXSk7+/I6MPHdZFrK8Lq jJaXOrqwdI2UMU2ycnGpf36YGqGu1RyXCc5pSS71Dan1j1c///ShGtZ5coM3dMk14CjrIb7UU86r oWnWcUoKXF/QipTw25yyAnP4yhZmwvBX4C5y07Ys3yxwVurtfNZnPp3Ps5iMabwsSMkbEkZyzGH/ dZpVtWKr+rBVjNRAI2fvb4lvKvCWPv7xsNY1CWMrGED6FXgez/JEK3EBAxEtOTBoXzOeahGuBJPE 1NUDI0RY5epXVs2qeyan3q7umZYlgqql0M32hxYmv5YraZid6Qtl4uF6zgrxhIho60sdErcRn6YY I2uuxc1g/DQap3cHsHE6OYA21QLmzqLCq2Zzz92xlTsPGc+JhrZeqf3W1Q2Nv9RaScEf4X7j3hbR +CyeVdqGnwuqFtf8KI2nzRyMhOsFkEjpoh04lteJiWNZoYOcxleEfLtF7HrcMFdDvh7RZCNmP8IT PMVlnFIo1MeGM6/5jG9yIu1VjioByRfQSbkuxhIy/x2G6r9gK5bY06NyfItv7B2eSnxIvxhMzbFo RFIan2fQiAWPcoLLbe74VZRn8ReNU40kGdc+4ZoTpsm4QdsCo2Dncg1JScrkHjMsNrXL3Oyokr4r n2UYXs+5o3KuuuA+xzFJaZ7AJuz3VUCWrJ8g/ZPveIEnEvpS9j2EUCB+F9n3Qs9BUAo9s/9SyjuZ dkT1dXIsTfs51g53sQoApnMA6+5iFQBM9wBWVNsWqwBgesewCgCmfwyrAGAGx7AKAGZ4DKsAYA6O YRvAoR4SzQiAbbO8s6dEBcmWqvd6qumb7pKycE9YckZiWiZaTlYk70Eve+sE+oc0Y/3ZZUOcwD6l SwanX19691T6bH6Q/dxq5m5PMJHqXSmTAXnvYSY0RJcFnOJ8rjcCJxP51tMNuY6Hml544Xhz/QGy /HcLnFZgdiPfD7IyAZ0Xppy1vIWXQjlrpz/Rnk519K+lUl704tvT045GtnwD5IpV+/Ht6UdHR1s+ 5ATI70s4eEVrFV9oh0LqT+fr6HHLZ9uhL18oTubraLbiC1x5bJ3O19H1lk+Q9U7IHl9H+xWf7wVv y8f/43w4TYk8pURjzMmeEkntfK8SJfyZDqEmYi8KkVz1VbmQX2Rc5/DnSHjx98iNfM9yJsbAnyDD tdyRcT31p4Y7dSaj0JtOXHf8Tf3VSsBVnhVkmi2WjNwtuUxwn3QgZHrw1w05T3GHLZz/fPBVVqaU iozv5sU7R17mnDWJ+XOJGaygcnPkLfiU3Jw3IoGKyCzPEqLdLovHTlz8c8SlzhOgPhiaI+fnm8o2 8q1xGDquMY7cseGGE8uAag2MQeA5lmOPouvr0bZsa+F5CbvrW60/vv/zy4/v/56hVuVDXTKA+tzU vLW0JctE/40Gvh2FI2OEXOi68SAQ/ecZU89x3WgUXkfO5Ju4rEDuMGZE3oL8lqj7E+Q+u0EpspjR ms75RUyL9irGrOhXwiqaydsYZLX3JyssXlM9z4ODeeDJMjDl3tRT7lYkfib8h2fOPuHqbiWLpJCa GsmhKisXbY08QYTv6vrp6j8AAAD//wMAUEsDBBQABgAIAAAAIQDVrkEYngQAADQSAAAhAAAAcHB0 L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDkueG1svJjZbts4FIbvB5h3EDTXikSLWmw0KWLZHgyQ JkGdPgAj0bZQbUPRjt1Bgb7WzOP0SeYcarGVra4j9MaiaPIjz/aL0rv32zTRNlyUcZ6d6+TM0jWe hXkUZ8tz/dPdzPB1rZQsi1iSZ/xc3/FSf3/x+2/vilGZRFdsl6+lBoysHLFzfSVlMTLNMlzxlJVn ecEz+G+Ri5RJuBVLMxLsAdhpYg4syzVTFmd6PV8cMz9fLOKQT/JwnfJMVhDBEyZh/+UqLsqGVhxD KwQvAaNmd7ckdwVYW8Th3VbX1DCxgQ6iX4Dl4TyJtIyl0HEbh3ItuPYQy5UWsAJJakxZ3AnOsZVt /hTFvLgVaur15lZocYSoGqGb9R/1MHWbbVTDfDR92TTZaLsQKV7BI9r2XIfA7fDXxD6+lVpYdYb7 3nB188zYcDV9ZrTZLGAeLIpWVZt7as6gMeculgnXSGtVs9+yuMrDz6WW5WAPml+Z146obMZrsard LxFVj6v+VI39Zp71BPGGg4HvKxupDyG1HnnFob5Lrdpax3U9239scoUuRnI7zqMdTr6HK5jKsnCV Q6beV8iklHO5S7hqbxJS4JBkCaWU6NgX8cVH6Cq/gIMsXPK+sbwdX7UPOAX+KMMETE0YViLPjE9z qMRUBglnWRs8eREkcfhZk7nGo1hqH1gpudCU46BugYh0qdZQSJ5Ft0ww3NQhudpRoWxvbFZueD3o dhP0pgxuExbyVZ5EsIlBHykAFajDUhjWNyWCSwae57ySB5QQTJZjE+HF6KdMXKlSirMIpAWbatb6 GvRTzTrICXvQrthmg2oO9ijqeDjqKN5gb0ENqXn2njckVBl9FA9HtjyE1Dy65xHbI1hixwGxCFog UmqgcwD0IWinAZFSA909EJLAVcX380Ck1EDvAOhRFbkTgEipgf4eiLTjg9IBIqUGDg+AruOdGBSk PK9J/WoHbR8YWI+HwmH3IRxYproyb8WSRa0hSpJO1hDHhkdF9ax4QUR8C+6qRX6ZhpBOjb5dQ0hH k96uIaSTXT1oyLBnCenwelCQDq8HAenwetCPDq8H+ejwXlYPpMOA9ujyxhMO1p864JSdE84pSuQ0 SjRhsnuEoX0oUSSf6BCpPPaiEKlVX5ULdaP8uoB3EbTinzENXMeyp8bQnRKDWnRsXM7cmUFn9nTs O7MppZOvzZtNBKbKOOWzeAnntpu1VAE+JhyEmA68KRF773fYQv/PB7eJyizPMeKHcVEHubfGZSFF FZi/10zACk1sfnDQ/JnY9OsRr/HIPIkjrl2v0/tHfnH78Au81wP6Wdf84Pl5UtoGrjXxfZsak4BO DOpPLQOy1TOGnmNb9mAcXF6O27Qt0fIMdndstn7/9u8f37/910OuqkvzTg/qc1XKuqWtRYz1Nx66 g8AfG2NCoeomQw/rzzFmjk1pMPYvA3v6Fb8NEDoKBVcfHf6Kms8VhD75YJHGocjLfCHPwjytv3yY Rf7ARZHH6uMHserPFRsGwmr7LvHgPDKsXmfV3pqr2i0Gfo72wzURH1hxs1FJkipNDVRXEWfLOkf2 Q9D25mvPxf8AAAD//wMAUEsDBBQABgAIAAAAIQDfRwwQmwMAANALAAAiAAAAcHB0L3NsaWRlTGF5 b3V0cy9zbGlkZUxheW91dDEwLnhtbKxW3Y6bOBS+X6nvYNFrBgiQSaJmqoGE1UrTzmiT7r0LZrBq sNd2aNKqUl9r93H6JHtsIOn8VEp2coPBHH8+5/vOsc+bt9uaoZZIRXkzd4IL30GkyXlBm/u582Gd uRMHKY2bAjPekLmzI8p5e/Xqtzdiplhxg3d8oxFgNGqG506ltZh5nsorUmN1wQVp4F/JZY01fMp7 r5D4M2DXzBv5/tirMW2cfr08Zj0vS5qTBc83NWl0ByIJwxr8VxUVakATx6AJSRTA2NUPXdI7AdEC MXq9dZC1ky3MBM4VhJ6vWIEaXMPEmmpGEBCE/gJjmmOG1mSrrZkSa0mIeWva36VYiTtpV79v7ySi hUHrURyv/9Gb2c+mtS/eo+X3wyuebUtZmxFYQdu5A+LtzNMzc+AEyrvJ/DCbV7fP2ObV8hlrb9jA +2lTE1Xn3NNwRkM4HSnBPqrBXyVueP5JoYZDPCb8Lry9RRezGUXVS6ANVG/X/bQvB2d6svQ24cXO bPIRRjuJZ0zpld4xYj+EeVg3JPjLsMlw0rgfVpDhtU4Zwc2eEH2VMpp/QpojUlCN3mGliUTWGagH gDTsaMuRhSRNcYcl/vMRcseisE4PHnoDhb8mMhyIfJBT6I7hnFScFeDK6BzkGqocxCWFIuiy3YH9 t4fFpzBujhFAIdg43dH4lH9hCGrZnugX6mFYsXKoB3p0nD/e0gZ1wpYrknOoa0Zawo6At4qcAL+u qDwePTwRPeMbqauj4aNT4Wn5LPq5KyEaKmGBNXlQAJaQlxZAAQmvvsBVgVk5pH539p3ltCnhmjBR fE2idBz74dKdjpeBG/lR4l5n48yNsnCZTOJsGUWLb8OtU0ComtYko/cbSW435jI5TpUg8GK4xYLw oAm4cH5V4kGVjHNThz/rYjPppbqUWnbC/L3BEnYYtPk/59IvtDkvI+OBkRWjBUHvN/XHR7zE5+AF ei6AfpYae/ycOW3Tsb+YTMLIXaTRwo0mS9+FbL10p5dx6IejJL2+TvZpq0zkDXh3bLb++P7P6x/f /z1Drtph6LXgTrhRun9DG0lN/SXT8SidJG4SRFB1i+mlqb/YzeIwitJkcp2Gy2+mZwuiWS6JbQj/ KIZWMoieNJM1zSVXvNQXOa/7rtQT/DORglPbmAZ+30q22Nw9wSj2L4NoOu1lAt+G0XprhF+Z+GFk 8h0Wt61NktredKmdEtA59zlyMDGxD5341X8AAAD//wMAUEsDBBQABgAIAAAAIQBpol8hFQEAAMcH AAAsAAAAcHB0L3NsaWRlTWFzdGVycy9fcmVscy9zbGlkZU1hc3RlcjEueG1sLnJlbHPE1U1qwzAQ BeB9oXcws48lO4mTlMjZhEKgq5IeQFjjH2pLRlJKffuKlkIMYWghoI3AkvXm4220P3wOffKB1nVG C8hSDgnqyqhONwLezs+LLSTOS61kbzQKmNDBoXx82L9iL3245NpudElI0U5A6/34xJirWhykS82I OpzUxg7Sh0/bsFFW77JBlnNeMHudAeUsMzkpAfakwvzzNOJfsk1ddxUeTXUZUPsbI5jrO4UvcjIX H2KlbdALSNPr/dlP2zSMAHZbtowpW1KyTUzZhpJl+T1pPtzFGep752fNKMddGf9tKCcbiikjOyti ygqys7ilka2tY9LWZGs8amucsq1i0laUbBdTtvuVsdnzW34BAAD//wMAUEsDBBQABgAIAAAAIQA/ wfV+HAMAALsIAAAhAAAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDYueG1srFbdbtMwFL5H 4h2scJ0laZz+iQ4taYuQNjbR7QFM4q4Rjm1st7SgSbwWPM6ehGMn7sYY0i5yEzv28fH5vu+cnLx9 t28Y2lGla8FnQXISB4jyUlQ1v50FN9fLcBwgbQivCBOczoID1cG709ev3sqpZtU5OYitQeCD6ymZ BRtj5DSKdLmhDdEnQlIOe2uhGmLgVd1GlSLfwHfDokEcD6OG1DzozquXnBfrdV3SuSi3DeWmdaIo Iwbi15taau9NvsSbVFSDG3f675DMQQJaUxtGLzk7BMiZqh0sJsEpoC9XrEKcNLBwba2QM7M7Wl4r Su2M794ruZJXyh34uLtSqK6sg+5gEHUbnZl75Ts3iZ4cv/VTMt2vVWNH4ALtZwFIdrDPyK7RvUFl u1g+rJaby2dsy83iGevIXxA9utSiaoP7F87Aw2l5SI6ofLxanovyi0ZcAB4Lv4V3tGgx21FuHhPf 2bWbbvIQTEeW2eeiOthLPsPoFsmUabMyB0bdi7QPF4aCeBmxeU15eLOCvG5MwSjhR0LMacHq8gsy AtGqNuiCaEMVcsFAFYBLy45xHDmXlFdXRJFPTzy3LEoXtI8w8hT+n8jUEzknhqIrRkq6EayCCAZ9 cFoZgPwdyoKwdQAXgu5Jq3gvHK+hHiyKHzkuhlmcLsLJcJGEOMZ5eLYcLkO8TBf5OFsuMJ7f+Qqr AKqpG7qsb7eKXm5N8FKpkiTKoGKT9EETCKF/VbBXZSmEzYbHuqR96LI2qhXm65YouMFr48/2oE2/ jGSekRWrK4o+bpvPT3jBffAC/QVcP0uNq4ee07YYxvPxOMXhvMDzEI8XcQjZOgonoyyN00FenJ3l x7TVFjmH6F6arfc/f725//m7h1x1g+8w8Lk/16aboa2qbf3lk+GgGOdhnmCouvlkZOsvC5dZinGR j8+KdHFnO1WCp6Wirvl9qHzbTPA/jbOpSyW0WJuTUjRdB46k+EaVFLVrwknctc0dYSBPNhwleJh5 kSE0P7pgre4rCx9Gpi6IvNy5HGnc57ZwSxJ+EroUeTCx0P1Px+kfAAAA//8DAFBLAwQUAAYACAAA ACEA9cjSaRoEAABrEgAAIQAAAHBwdC9zbGlkZUxheW91dHMvc2xpZGVMYXlvdXQ0LnhtbOxY3Y6j NhS+r9R3QPSaAYIhJNrMakKSqtLszqjJPoAHnISuwdR2MslWK+1rtY+zT9Jjg/M32Sbp5HJu8MF8 /nz+Ab97vyqotSRc5Kzs2f6NZ1ukTFmWl7Oe/WkycmLbEhKXGaasJD17TYT9/vbnn95VXUGze7xm C2kBRym6uGfPpay6rivSOSmwuGEVKeHZlPECS7jlMzfj+Bm4C+q2PC9yC5yXdrOen7OeTad5SgYs XRSklDUJJxRL0F/M80oYtuoctooTATR69b5Kcl2BtfKZPTz9YVsax5cw49u3YHo6pplV4gImJs/M SlgpgUY/EtWEE6Kkcvkrr8bVI9crPi4fuZVniqFZabvNgwamb8ulFtyD5TMj4u5qygs1giesVc+G gK3V1VVzZCWttJ5Mt7Pp/OEINp0Pj6Bds4G7s6myqlbupTktY84kl5RY/sYqo6+o7ln6WVglA3uU +bV5G0RtsxqruXG7ompw9UMtbJVpnCVXfZat1SZPMOpJ3KVCjuWaEn1TqYtWg4O+FKusJqXzaQxZ XciEElxuHCJvE5qnny3JLJLl0vqAhSTc0spADQCl8o7UPtKUpMweMce/HzDXXqy00kZD17jwx44M jCObbLIeKU7JnNEMlGi9zq3iC1QDplMbdlptwT/w7ZEsQ2EbikOnjx95npL3Eg55QRwpgEokFLbC ThQcplNNfSJqWl5Sv1EjI1PlXqV/K643dfcAILaOYNEu1gBADI5gvV2sAYCIXmL9PR0MAMTwFNYA QIxOYQ0AxPYprAGAGJ/CGgCInVPYGqDkncDoaqpUui/ppmxeWV0qg3Rxib3qqivocEuduBdsOSYp KzOLkiWhZ9DrKruAfjLP+fnsuiAuYB+xBZfzs+nRpfT59Cj7tfsa+q++pn1ytb6m43dZX4tQ/NbY 3hrbW2N7a2yXNrbQNLYBlmSvq2mNX/sRnEn7xXdbncdX+Siewh+MsuKvPkqi0AuGTica+g7yUN+5 G0UjB42CYT8OR0OEBl/ND1EGpsq8IKN8tuDkYaH+ec6Liu+7Ifxg+cE2JqDC9aMSmaiMGFN1uBuX 8BpxmUpeB+bPBeawg4nNiY/qS2JzXY+0jUfGNM+I9XFRPB34JbqGXwTNgPqoa068l/9X2iaRN4jj ADmDBA0cFA89B7K17XTaYeAFrX5yd9ffpK1Qlpeg3bnZ+v3b3798//bPFXJVD+ZIAN4J90I2krXg uaq/fidqJXHf6fsIqm7Qaav6C51RGCCU9OO7JBh+VUcLPuqmnOizit8yc8rhoxfnHEWecibYVN6k rGgOTNyKPRNesVyfmfhec8qxxOrlECCv3Q6jWKeBq3Uzo9ZWBX6s7IeR8g+4eljqJCn0my7RU1Ve zpoc2UKU7eaQ6PZfAAAA//8DAFBLAwQUAAYACAAAACEAfTih0gwFAAA6HAAAIQAAAHBwdC9zbGlk ZUxheW91dHMvc2xpZGVMYXlvdXQ1LnhtbOxZ3XKjNhS+70zfgaHXBATix55NdmJsdzqTTTJr7wMo IMd0QaJCdux2dmZfq32cfZJKAoLtYIc4vuhMfRNkcfTp/H46QR8+rrJUW2JWJJRc6uDC0jVMIhon 5PFS/zIdG4GuFRyRGKWU4Et9jQv949XPP33I+0Ua36A1XXBNYJCijy71Oed53zSLaI4zVFzQHBPx bkZZhrj4yR7NmKEngZ2lpm1ZnpmhhOjVetZlPZ3NkggPabTIMOElCMMp4kL/Yp7kRY2Wd0HLGS4E jFq9rRJf58Ja/kSnq+kTvXv4XdeUMFuKaaBfCfujSRprBGViIqRZjlhSUKLeFPmUYSxHZPkryyf5 PVMLbpf3TEtiCVAt1M3qRSWmfpKlGpg7yx/rIeqvZiyTT+ENbXWpi6Ct5V9TzuEV16JyMmpmo/ld i2w0H7VIm/UG5sam0qpSuZfm2LU504SnWAPPVtX6FvkNjb4WGqHCHml+ad6zRGmzfObz2vUSqpIr X6pBo0zlLL4a0HgtN3kQTzWJ+mnBJ3ydYjVepqBSI8azz6VrN6bleEM8l3+UNBPWpUjWASbGl4mo g4yHKUbk2X38KkyT6KvGqYbjhGufUMEx05TqomoEokTnag8FiUl8jxj6vINcapQrE2t7zNrh+93u PLtdxvw+RRGe0zQWGtiniID0py42WjXiewLRkpLQ9UU1qVwDruMC4GxnJ7SgBYKgzDrP6fme0nkz 9UrklxHWEInmVLDFQwnZEmwtQ+xGJXVCYlHgcqgAFreCxdSqMhe04k+RvlBq+lCbuZUyYmg3gLVV nVCtl6gSqkJ1GtQegEqDLqggeIkqoSpU2KACxwdeZ1gluQ0rsSpYdwM2sAOlw7GwEquC9RpY2w48 5bBjYSVWBetvwPrQ6RyxNliJVcEGDazE7B6yFliJVcH2NmA9139XyCRWO6PJTYTAM3W9k+FkGSuC K7YY7hgWgzWLhZRwYfUWkSnWOJ7IpJ/mKJ1VNFZSzJE0ZgMfBr57gMacngtEcXTlsddPqoad9vFS G+fsY5s2JtnHIW25to8YDsruVPtB2Z0SPii7U5cHZXeK7aDsf6OCdrdUWf6GLSc4oiTWUrzEaQd4 VRdvgJ/OE9YdvTr5O6OP6YLxeWd4+Fb4ZNaKfuruzN3bnSmNT9OdyQT+Y4GYSKmK45S338ZxHnQt 2z3YqwFfMN+5Vzv3aude7f/cq3mHejXVGr2vV9umMsWTR1PZvn6tobJzv3bu18792rlfK7nNr7lt iDjeIjbvFP1azMtubePfUVDm8Tu+b6ofyr2zNFZW/DWAoedazsjoeSNgCL4bGNdjb2zAsTMaBO54 BOHwW/19Oxam8iTD4+RxwfDdgqszrEtUADBd07aA08REqHD6qAR1VMaUyjrcjIt/irjMOGtro8Er nzzfEpvTeqRXe2SSJjHWbhfZw45f1In2Xr8UaSygW13zymeUo9I29KxhEDjQGIZwaMBgZBkiW32j 57uO5diD8Pp68Jy2hbScCO26ZuuP73//8uP7PyfIVfWob3fEmXBT8GqkLVgi62/Q8+wwGBgDAEXV DXu+rD/XGLsOhOEguA6d0Td5SwRgP2JYXT39FteXVgC+uLbKkojRgs74RUSz6v7LzOkTZjlN1BUY sKpLqyWSZw/oeeKIdGzV+phKt/qptJWBn0j7xTNln1B+t1RJkqmTLlRTeUIeqxxpRKTt9Z3f1b8A AAD//wMAUEsDBBQABgAIAAAAIQBbOWPKQQQAAAcRAAAhAAAAcHB0L3NsaWRlTGF5b3V0cy9zbGlk ZUxheW91dDEueG1szJjrbts2FID/D9g7CNpvRTfqYqNOEcn2MCBNgjp9AEaibaEUqVG0a28o0Nfa HqdPskOKspw027zNA/zHoqjDw+/cePGbt7uaWlsi2oqzie1febZFWMHLiq0m9ofHuZPaVisxKzHl jEzsPWntt9fff/emGbe0vMV7vpEW6GDtGE/stZTN2HXbYk1q3F7xhjD4tuSixhJexcotBf4Eumvq Bp4XuzWumG3Gi1PG8+WyKsiUF5uaMNkpEYRiCfztumraXltzirZGkBbU6NHPkeS+AWtlJSmxLS0m ttDh29dgebGgpcVwDR2PSsJa0Kok+lPbPApCVIttfxTNonkQesTd9kFYVak0mJG2az4YMf3Ktrrh vhi+6pt4vFuKWj3BEdZuYkO89urXVX1kJ62i6yyG3mJ9/4pssZ69Iu32E7hHkyqrOrhvzQl6czpH +Aeret62ueXFx9ZiHOxR5nfmHSQ6m9WzWRuvF1JobUa0+64bA8+rzojTKPU6KwM/9FAQPfdLkiQB UgLKXh8lntdJHFvdqW7Gcpfxcq9GP8FTRwWPaSsXck+JfmnUj8YQ4AyKVcUQ5nxYQMXUMqcEs4O3 5XVOq+KjJblFykpa73AribB0fkF9gUoFITWKVklY+YAFfv9CcwfbaNKeUEP/dZTCPkqLzVM3Z3CO QLWbpy5QMMluGHJ6wPww8WMTsTBNYyjA5xGLIVw6pDpiSRQo6X8TMd3eUh9krRqLW532FSuh+nUT 0xXTmWdrBZs7WO20gpIs3xsHcajyeUWpflGLCsmpsLaYwkKxUysDRLBisutJIu+AehDu3gY97qDf PfAZ1GBARVGiPHOBvArS8IYD78hHuswuj1dBGl408B7S8PKAFaUBjo6A0yDVZXF5wIrSAMcDcBCk sdJ2gcCK0gAnR8AJCi+05hSlAU4HYEV7oUWnKA3w6Ag4jpILLTpF2bWPdo8zbPdtv/v+/zs+6nf8 KZbEeqC4IGtOS4AIz7HzlxKs/gWO2Jgu+92/8/Ofbv961hNPVUs4Xysrfs1QHkdeOHNG8cx3kIcy 52Yezx00D2dZGs1nCE0/96f1EkyVVU3m1WojyP1G6mQ4JVq+70Zw+vfDISaAcP6oRH1U5pyrhDiO CzpHXJZQOjowP2+wgBn62PzN0eyfxOa8HokPJ1N1hbLuNvXTC7/oE/p/PqnSElS/6hp9Ej5z2uax N03TEDnTHE0dlM48B7I1cUZJFHphkOU3N9khbVtlOQO6U7P165fffvj65fcz5Kp+9BdWOHPfttK0 rI2oVP1lozjI08zJfARVNx0lqv4iZx6FCOVZepOHs8/q4uujcSGIvkj/VPZXcB99cwmvq0Lwli/l VcFrc5t3G/6JiIZX+kLve+YKrhdwFMRR5MPNwEQJ0PqnhlVxXyjz4UnFO9zcb3WO1HrFzXVXU7GV SZFBRJne/4Fx/QcAAAD//wMAUEsDBBQABgAIAAAAIQCNWR2NgwQAAFwRAAAhAAAAcHB0L3NsaWRl TGF5b3V0cy9zbGlkZUxheW91dDMueG1szJjrbts2FID/D9g7CNpvVaJEXWw0KeLbNiBNgjl9AEai baGUqFG0a28o0NfaHqdPsnMoyXLStHMbI8gfi6LIw+/ceEi/frMthLXhqs5leWaTV55t8TKVWV4u z+x3tzMnsa1aszJjQpb8zN7x2n5z/vNPr6thLbJLtpNrbYGMsh6yM3uldTV03Tpd8YLVr2TFS/i2 kKpgGl7V0s0U+wCyC+H6nhe5BctLu52vjpkvF4s85ROZrgte6kaI4oJp4K9XeVV30qpjpFWK1yDG zL6PpHcVaFvz9DfOMtsyA9UGuoh9Drqnc5FZJSugY85TnG7hQK7M17q6VZxjq9z8qqp5daPMpKvN jbLyDIW0k223/dAOM6/lxjTcB9OXXZMNtwtV4BOsYW3PbHDaDn9d7ONbbaVNZ9r3pqvrR8amq+kj o91uAfdgUdSqgftSHb9T5zbXgltkr1XHW1eXMn1fW6UEfVD9Rr39iEZnfFar1vQaRbXjmo+m0cM8 aonY9wMSGBUp9aKB98AocRz71GuVJUHke3H4UOVGdDXU25HMdjj7Dp6gKivTlYQo1Y1MUeu53glu 2htBKhwilpBGwsa+jC/+gK76L2DxcM074/iUgQWYEO2y7cymfSCxwh+jogIhgmE+8tJ5N4d8LPRY cFbu3ajPxyJP31taWjzLtfWW1Zory5gQshckonRt1jAieZndMMUQ71ByQ1QZK3TaG4N82/3B3v1o 5hvBUr6SApLB8k8RCWh9Gxba9sN/KCD8gRfF0P5GQISeR5L4uwPi7usBUTB1abIrLzPYabBpBKyv YDs1sw7CxMcwMVaSIs9muRDmBfcvPhbK2jAB0bfFLQjcmZe66YlDnNdA7wc3b70ct1vpftSZpt+T 0jD20RzH4JLkGXGRscUNetwBocZ7R+FGz4iLjC0u7XFJEBuK43hRs+fiRciWNzzgTfwEnfzyeBGy 5Y16Xt9PIrPVvjhehGx54wPemAbHp9tz8iJky5v0vAh7fL49Jy9CtryDA94ojF9mviHk4zUf6WHA vrg/8QyAhc4cAep7Z4AfqfO0q/MTpvm9Om+K6lPrfKZt44cVE4uu3jeG/WrBN6s+LMv3arF5MXZd wIkdtfh7RMdR6AVTZxBNiUM9OnIuZtHMobNgOkrC2ZTSycfuBpCBqjov+CxfrhW/Xmvj/WPcQYgb wo2CBL3dAeH0p6+w88pMSvT4oV/oKfyy0KpxzJ9rpmCFzjf/cxj7Ht+c1iJRZ5E55B+3rtbF3QO7 mDP/U+0C918Q/ahpzLn3xGE7jrxJkgTUmYzpxKHJ1HMgWmNnEIeBF/ij8cXFaB+2NWpeAt2x0fr5 0z+/fP707wli1Ty6+y/sPpe1blvWWuWYf6NB5I+TkTMiFLJuMogx/0JnFgaUjkfJxTiYfsR7NKHD VHFzOf896671hH5xsS/yVMlaLvSrVBbtPwRuJT9wVcnc/ElAvPZab3Zs4pFBTIjnm/RwDVv3NLTo +DnqD0+h3rLqemOCpDB76th0VXm5bGOkH4K6d/+KnP8HAAD//wMAUEsDBBQABgAIAAAAIQCUqRLj egMAAJkLAAAhAAAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDIueG1srFZdbts4EH5foHcg tM+KJIt2bKNOEcn2YoG0Cer0AIxERWopkUvSqt2iQK+1e5yepENKtJs0CziIXsQfDT/O982QnNdv djVDLZWq4s3Ci85CD9Em43nV3C+8D7drf+ohpUmTE8YbuvD2VHlvLl798VrMFcuvyJ5vNQKMRs3J wiu1FvMgUFlJa6LOuKAN/Cu4rImGobwPckk+A3bNglEYToKaVI3Xr5enrOdFUWV0ybNtTRvdgUjK iAb/VVkJ5dDEKWhCUgUwdvVDl/ReAFt+99FD1ki2MIy8C+CdbViOGlLDxG2lGUWgDkp5owHJGihx Kyk1vab9S4qNuJF23bv2RqIqNzj9ei/of/Rmdti0thM8Wn7vumS+K2RtWhAD7RYexGxvvoGZozuN sm4yO85m5fUTtlm5esI6cBsEv2xqWHXO/U5n5Oh0ckQHVs5fJa549kmhhgMfQ7+jd7DoOJtWlL3y 2kD1dt1P2zk604uldwnP92aTO2jtJJkzpTd6z6gdCPOxbkjwlxGT2LTxP2wgsWudMkqagyD6ImVV 9glpjmheafSWKE0lss7AMQBIo462GllI2uQ3RJL3j5A7FYV12nkYOAn/X8jYCdlnE7phJKMlZzk4 MXqZrFW+O5oMoKgwlFt2kO6FCpu0tQKrBwp3Kj7e0tJ4xpYbmnE4o4y2lJ0Ab5V+BvxtWcnT0eNn oq/5VuryZHj8XPiqeBJ96NzGLreXRNMHiW0Feel9kWtg9wXufMIKr0/27jYbJNsLuPINi68JTifj MF75s8kq8nGIE/9yPVn7eB2vkul4vcJ4+c09HzlQ1VVN19X9VtLrrXkeTotKFAVjeI6i+BgTcGH4 qIxdVNacm3P4a1xsJr00LoWWXWD+2RIJO7jYDHgTDavIxCmyYVVO0bttffdIl/EQukDxBNBPSmOv n4HTNp2Ey+k0xv4yxUsfT1ehD9l67s/Ox3EYj5L08jI5pK0yzBvw7tRs/fH93z9/fP9vgFy1jaue 4E24Urrvoa2szPlLZpNROk38JMJw6pazc3P+xv56HGOcJtPLNF59M1VYhOeZpLay+zt3NWGEf6sK 6yqTXPFCn2W87svLQPDPVApe2QozCvuasCXm7YmjEJ/jeObCBL651nprAr8x/KFl8i0R161Nktq+ dKmdElAC9zlyNDHcXUl98RMAAP//AwBQSwMEFAAGAAgAAAAhALl/7nPtBQAAsBsAABQAAABwcHQv dGhlbWUvdGhlbWUxLnhtbOxZTW8TRxi+V+p/GO0d/BHbJBEOih0bWghEiaHiON4d7w6e3VnNjBN8 q+BYqVJVWvVSqbceqrZIIPVCf01aqpZK/IW+M7te79jjYkiqIkEO8czs835/+J315Sv3Y4aOiZCU J22vdrHqIZL4PKBJ2PZuD/oXNj0kFU4CzHhC2t6USO/KzocfXMbbKiIxQUCfyG3c9iKl0u1KRfpw jOVFnpIEno24iLGCrQgrgcAnwDdmlXq12qrEmCYeSnAMbG+NRtQnaKBZejsz5j0G/xIl9YHPxJFm TSwKgw3GNf0hp7LLBDrGrO2BnICfDMh95SGGpYIHba9q/rzKzuVKQcTUCtoSXd/85XQ5QTCuGzoR DgvCWr+xdWmv4G8ATC3jer1et1cr+BkA9n2wNNOljG30N2udGc8SKFsu8+5Wm9WGjS/x31jCb3U6 neaWhTegbNlYwm9WW43duoU3oGzZXNa/s9vttiy8AWXL1hK+f2mr1bDxBhQxmoyX0DqeRWQKyIiz a074JsA3ZwkwR1VK2ZXRJ2pVrsX4Hhd9AJjgYkUTpKYpGWEfcF3M6FBQLQBvE1x6kh35culIy0LS FzRVbe/jFENFzCEvn/348tkT9PLZ49MHT08f/HL68OHpg58dhNdwEpYJX3z/xd/ffor+evLdi0df ufGyjP/9p89++/VLN1CVgc+/fvzH08fPv/n8zx8eOeC7Ag/L8AGNiUQ3yQk65DHY5hBAhuL1KAYR pmWK3SSUOMGaxoHuqchC35xihh24DrE9eEdAF3ABr07uWQofRWKi8pBbwOtRbAH3OWcdLpw2Xdey yl6YJKFbuJiUcYcYH7tkdxfi25ukkM7UxbIbEUvNAwYhxyFJiEL6GR8T4iC7S6nl133qCy75SKG7 FHUwdbpkQIdWNs2JrtEY4jJ1KQjxtnyzfwd1OHOx3yPHNhKqAjMXS8IsN17FE4Vjp8Y4ZmXkDawi l5JHU+FbDpcKIh0SxlEvIFK6aG6JqaXudege7rDvs2lsI4WiYxfyBua8jNzj426E49SpM02iMvYj OYYUxeiAK6cS3K4QvYc44GRluO9QYoX71bV9m4aWSvME0U8mwlUShNv1OGUjTAzzykK7jmnyvnev 3bt3BXUWz2LHXoVb7NNdLgL69rfpPTxJDghUxvsu/b5Lv4tdelU9n39vnrdjM47Phm7DJl45gY8o Y0dqysgNaRq5BPOCPhyajSEqBv40gmUuzsKFAps1Elx9QlV0FOEUxNSMhFDmrEOJUi7hmmGOnbzN XZWCzeasObtgAhqrfR5kxxvli2fBxuxCc7mdCdrQDNYVtnHpbMJqGXBNaTWj2rK0wmSnNPORexPq BmH9WqHWqmeiIVEwI4H2e8ZgFpZzD5GMcEDyGGm7lw2pGb+t4TZ9iVxf2pZmewZp6wSpLK6xQtws emeJ0ozBPEq6bhfKkSX2Dp2AVs1600M+TtveCOYuWMYp8JO6VWEWJm3PV7kpryzmRYPdaVmrrjTY EpEKqfawjDIq82j2XiaZ619vNrQfzscARzdaT4uNzdr/qIX5KIeWjEbEVytO5tv8GZ8oIo6i4AQN 2UQcYtBbpyrYE1AJXxUm1/RGQIWaJ7CzKz+vgsX3P3l1YJZGOO9JukRnFmZwsy50MLuSesVuQfc3 NMWU/DmZUk7jd8wUnbkw4G4E5voFY4DASOdo2+NCRRy6UBpRvy9gcDCyQC8EZaFVQky/zda6kuN5 38p4ZE0ujNQhDZGg0OlUJAg5ULmdr2BWy7tiXhk5o7zPFOrKNPsckmPCBrp6W9p+D0WzbpI7wuAW g2bvc2cMQ12ob+vkk6XN644Hc0EZ/brCSk2/9FWwdTYVXvOrNutYS+LqzbW/alO4piD9Dxo3FT6b z7cDfgjRR8VEiSARL2SDB9KlmK2GoHN2mEnTrP7bMWoegkLufzh8lpxdjEsLzv53cW/u7Hxl+bqc Rw5XV5ZLVI9Hs4uM2S39qsWH90D2HtyPJkzJ7N3TfbiUdme/RwCfTKIh3fkHAAD//wMAUEsDBAoA AAAAAAAAIQAkqahuMrMCADKzAgAUAAAAcHB0L21lZGlhL2ltYWdlMS5wbmeJUE5HDQoaCgAAAA1J SERSAAAFYwAABFoIAgAAAKY09/YAAAABc1JHQgCuzhzpAAD/yklEQVR4XuydB1wUx9vHlwvxVSkS LHCoQQHBgiAlGhuCKKjYAtGoKBg0yt8Sg7FEYzQq0URQYiyxkoDBDrERFQURRQOhBMXCUcSo3KFC gDtACR7v7O7dcfR2IOU3n/sk3u7M8zzzndnh5tmZZ5RKSkooJBAAARAAARAAARAAARAAARAAARAA ARBgCHDAAQRAAARAAARAAARAAARAAARAAARAAARkBOApQGcAARAAARAAARAAARAAARAAARAAARAo JQBPAXoDCIAACIAACIAACIAACIAACIAACIAAPAXoAyAAAiAAAiAAAiAAAiAAAiAAAiAAApURwJoC 9AsQAAEQAAEQAAEQAAEQAAEQAAEQAAGsKUAfAAEQAAEQAAEQAAEQAAEQAAEQAAEQwJoC9AEQAAEQ AAEQAAEQAAEQAAEQAAEQAIHqCWD3AXoICIAACIAACIAACIAACIAACIAACIBAKQF4CtAbQAAEQAAE QAAEQAAEQAAEQAAEQAAE4ClAHwABEAABEAABEAABEAABEAABEAABEKiMANYUoF+AAAiAAAiAAAiA AAiAAAiAAAiAAAhgTQH6AAiAAAiAAAiAAAiAAAiAAAiAAAiAANYUoA+AAAiAAAiAAAiAAAiAAAiA AAiAAAhUTwC7D9BDQAAEQAAEQAAEQAAEQAAEQAAEQAAESgnAU4DeAAIgAAIgAAIgAAIgAAIgAAIg AAIgAE8B+gAIgAAIgAAIgAAIgAAIgAAIgAAIgEBlBLCmAP0CBEAABEAABEAABEAABEAABEAABEAA awrQB0AABEAABEAABEAABEAABEAABEAABCojoFRSUqJAMkpKSgqUBlEgAAJNRkCxQ0GTmQ1FIAAC IAACIAACIAACIAACCieA3QcKRwqBIAACIAACIAACIAACIAACIAACINCCCTTKmgK8nGzBPQKmtz0C 7FIgPLZtr+VRYxAAARAAARAAARAAARConADWFKBngAAIgAAIgAAIgAAIgAAIgAAIgAAIlBKApwC9 AQRAAARAAARAAARAAARAAARAAARAAJ4C9AEQAAEQAAEQAAEQAAEQAAEQAAEQAIHKCGBNAfoFCIAA CIAACIAACIAACIAACIAACIAA1hSgD4AACIAACIAACIAACIAACIAACIAACGBNAfoACIAACIAACIAA CIAACIAACIAACIBA9QSw+wA9BARAAARAAARAAARAAARAAARAAARAoJQAPAXoDSAAAiAAAiAAAiAA AiAAAiAAAiAAAvAUoA+AAAiAAAiAAAiAAAiAAAiAAAiAAAhURkCppKREgWSUlJSINMXKVKB5DRSV kZHz5En28+eilJSXeXmvGygNxVsQAXPz7sTafv20DQy6tSCza2lq635sawlBMdkKC6mkJCotjcrI oP75RzEyIaVFEDA2plRVKT09ysiI6tChRZgMI0EABEAABEAABECgGgLwFNTcPQoL/7t9O9XfPyEx Mbfm3MjRqgloa//f1KkGTk6D3ntPpdVUFJ4CBTQlj0edPk19/bUCREFESyfw3XfUxx9ThoYtvR6w HwRAAARAAARAoC0TgKeghta/e/fpmjWhAgFWELTlx6SSum/Y8OGkSSatAwo8BQ1qx+xs6vvvKS+v BglB4dZHYOVK6quvKE3N1lcz1AgEQAAEQAAEQKAtEICnoLpWPnQoct++e22hH6CO9SBgZdXtu+8c OnR4tx5lm1UReArq3xx//00tWED99Vf9JaBkKybwwQeUjw81fHgrriKqBgIgAAIgAAIg0FoJwFNQ ZctWdBPYju9pY2ekZ6Cl0YpWnrfWnq3Yer169d+jlMz4vx5fvpDyIrN0gUnrcBbAU1DP3hIZSY0Y UaaswxTqs0VU3/5U9x71lIliLZdACo96nE4d3EsFny1TiZs34Sxoua0Ky0EABEAABECgzRKAp6Dy pi/nJiA+Ame3odpcjTbbUVBxlgBxGVw+n7B/Z5wMSCtwFsBTUJ/uTQITkNh1smT+AfWDD/Uh3h7X h2VrK/NnJLXag4qTW2kSH08NGtTaqon6gAAIgAAIgAAItGoCnFZdu3pWLioqTX7Twex5A778ejzc BPWk2bqKtW//7pRplp4+Y7pq/R9bs4iI52fPJrSuWqI2NREgZxzMnl2ayXU+dek63AQ1UWsz94nD 6MwlivQKWSJbVEifQQIBEAABEAABEACBlkMAnoLybUVOOti8+YbsKnETzPoU7wlbTo9uEkvNP9D7 aqOtTJW3d1xKyvMm0QwlzYPAqVOlsQnIhNDrJxyM1zwaptlY8Z4m3StkzgISyeLw4WZjHAwBARAA ARAAARAAgZoJwFNQntHVqw9kJx2QTQdwE9Tcidpkjn7GPTzWfiir+vnziW0SQ5usNHk57OpaWvM1 G+AmaJP9oKZKd+hAkb5BtqWwaelSihyTgQQCIAACIAACIAACLYQAPAXlG2r//njZpXmLrFpIO8LM t0Bg7AQTw/6dWMUBASn//pv/FoyAyqYncKN0zREVEIjghU3fAi1GIwls+eVXpdZeuNBiLIehIAAC IAACIAACbZ4APAVlugBZQy5bUED2HeCMgzb/gNQAYJqzqSxHfPwT4GoTBMjWA1kaad0mqoxK1pvA 2PGlywqCguotBgVBAARAAARAAARAoIkJwFNQBviDBwLZd7MPdJu4MaCuxREYYNJTZnNCQkaLsx8G 14fAoUOSUl+spMh2dCQQqIYA2YMwy0Vy/+xZbEBAZwEBEAABEAABEGgpBOApKNNSKSkvZd/JRvSW 0oqw820RIKtOZBsQ8vJevy0zoLfpCJDDEWXpg9JAFU1nADS1OAIGhqUmvyz9E9Pi6gGDQQAEQAAE QAAE2hQBeArKNLdssic7A69N9QZUth4Eeuqqs6XOn8fug3rwa8lFeum1ZOthe1MR0O1Vqik9vam0 Qg8IgAAIgAAIgAAINIgAPAWV4zMx79YgrigMAiDQ6gl07Njqq4gKKpiASKRggRAHAiAAAiAAAiAA Ao1DAJ6CxuEKqSAAAiAAAiAAAiAAAiAAAiAAAiDQMgnAU9Ay2w1WgwAIgAAIgAAIgAAIgAAIgAAI gEDjEICnoHG4QioIgAAIgAAIgAAIgAAIgAAIgAAItEwC8BS0zHaD1SAAAiAAAiAAAiAAAiAAAiAA AiDQOATgKWgcrpAKAiAAAiAAAiAAAiAAAiAAAiAAAi2TADwFLbPdYDUIgAAIgAAIgAAIgAAIgAAI gAAINA4BeAoah2stpQqClnZQ0irzcQ8WFDOlizPPupfeGuCdUEyJBVe22OuYzPdLEYkraBAl+NjI 8g/xiWWlvOUkV8HGN6lZEnjLDQD1LZ+AIMhVTUmpzMc9SDpK8M+5l94y8SaPPRkl1k7Q0Vng9zC/ wihRHOttIhNl4x3fok7sk+NgsFMyvlVX2Zbf8qgBCIAACIAACIAACLxFAvAUvEX4FKXtuOvFXR9n Y8YI7rAtUWmF+xy0lZmvylpT9mXmP/AZw9Vy/jUyarmpcvGLqMCdEfzMgF9DeAUV7FY19bj2LNKr 11utUHnl2o477xy2rrNJ4vw4v6NxdZ3GNEsCda47CoBAWQLajn6Cu4dnSkYJa88ooXCfo3SU4E7e V5L74LAtlzvz1we3l1soF2dGB269wecf+/VCxVFC2WLFHWGMZ92fyObQJtqOv8QftitjSVWVzYn9 +Ujz8JU2B3CwAQRAAARAAARAAATqQwCegvpQU2QZVeMZqz2YX+78W7vOxOeVfQ0oykilVh75cY6B Kmkp5a5DnJZZEcfBXDvDjoq0oTFlcdQ0utVRvlgQunPN0Zw6lkJ2EGi1BFSM567yYCbJ/PDdZ6Ir jBJJ1MrzPnP6qtCjhNZgpzUjieNg7sSWM0rUsuE4qhraZbJWWtki/vWdK37OrKVMZAMBEAABEAAB EAABEKiUADwFb79jcPpMXvqlBTMLOB947Ymcq+BV6u8B2fMdB9JuAjpxtMeuvZxx55Ar4zhonYks J/7e1XVnRFHrrB5qBQL1IsAxmLzagxklBOcDwsuMErwzAS/dHM1oN4FklNjyR0bGAVfGcdDKExkS y1aWuAm2zpr4bXgrrzeqBwIgAAIgAAIgAAKNTqD1/5RsdIQKUKBp5ug8jJaTePRQ6COZq0B05/IN 07m2PZlGkg9bYLO3dGV+UWZc0N75A+kIBfZrL9zlVzBHnJ98WZJBz3VvGC+/NEdearjvZnsdJrqB /Wa/sNRKwh/IcssbQIIpZKeG+SzVI0p9EiSl8iRXOiiZzPe5npxXNRhi84Wj6+wlURXsV5+I4zOV LsqM2bNsmB3ZYUFR4RuHq2l1kNW0mlrUSEABLQQRIPC2CWgO/sjZmhklfH1DU2SjRP6dczdNF0lH CbmwBXJhCMT82HPern1IhAIdm/UnE7IquuHEopTL3gsG0iEP+rh6X+PJ7fzJ4133XT1Bh4mGYL/a n4wgFYOkyLORy9/H1Sfm8bP4I6s/ZuKmyEVJYAINPAlaYCAJsrAgSDZyiQWxZ/1X20jiMtivDohm R4cKqbh8ZcX86N2fWbBugkcrLd9TUjL5LihQLo6DGstEplcW7uFtty30gwAIgAAIgAAIgEDzIwBP QXNoE47KoPHTxnBpU64GXf47h7FJLIy5EDnSTrqggIQt8Axklx6UJrKff4/7cKeNYaN8U19nXlzc JS0xvXyGnbNNxm184hbyojDBR22fg9O6s4+ZH955KX4eH41fFzfuTFrhm7TIcXHuth99caSyWIms RHkDhI+j7lH9h/brw6Uikp7SnoKcBJ9Zwxy8ni6NSstP36rsO91+bXBGpesCigRnl48dvi/H8URm 4b8hWxyoiG2fD192PPkVRbXTsly4zmcho856Q6Qws/DaInNVgiI/rqpa1EigObQvbACBhhPgqJqO n2PLjBKhQecSJKNEXsyF8BF20gUFytzJnqHs0oPSlBO76zNL55VX7AKf5maELuqZEvOo3DAiit85 yWzcyqduMYLCp9vVdkx2WnZOMko89Pewnrgu2v6MUPhGGDEuerGttceRSmIlSiTmxO6cZUTyD/HL yH2dcUjvnE2vHlYu23hvmCHEYkVIoEup7p6O3gdWld1OIOYHfTZy0o//Op2n1e2wpkK2uU+d+9vD ynwFFSrL4Q5e4rl7pj6tobdXzL8lJXe+dnTa9mCPG0ONa7fn5+VmZDzp6bjvwmH7ZYG8n6ThHhre OpAAAiAAAiAAAiAAAq2NADwFzaNFOXqj5ztq0bYE7wuKE5L/i59EBL6eNVZProXaq3VWK2Ou+En4 Ad9b5NLo0ZY67ShO9w+njO0ln0PMO7vBi2ToNc5qgGr7bv3N+0mXLYgzru7Z6JtJGY0d1V+F4qiY WI3VozID1nwdyKv6jaHMgKSsTv30tc0dFzn2ch5qpMoRJwdtWRtMsdI4Wn2H9qP4Qf5X0ioRJU4L OxSUKammqrZeD8beuNv3X1TZElXXglCqgUDzaF5YAQIKIMDRs3dzZCa9wTt+j6MX7YifXA167VZ2 lOhUdpQQ88P27ibPpv5Y+w+6c8gOpqFO9kZlhxHeyY1e4SSHvZWpSntuf/OB0mULYv5Vr82+fMrI waq/KsVRHWjl0JviH1tDJtmVjRJiUfwvK9YRXSPmOI/kctpxh08k+atLHTt17SB//1XKleO+AjYW A6XK1X2fvskPuXm/iqgD7ctVtjJd6n1nr9/tQuJB8kM2+4ezIR5EWe986jGV204BjQIRIAACIAAC IAACINBKCcBT0Ewatp22jeM0ZhKQuX3f+eRX4tTQo53srcj8v5okSo0LSST3ew3Q7VxZNnHqrbNX yape/cF63ciBCpLgglcvRaWKXvwVcpRe79tVQ405akG5m+5Q8i6OH36jmim7TIeleV8NsgRAe8ru KDpoQtGjW5fITIN4CnR12hNZqhqatKjTt0p3UsiKcnQGfzxei+Lqd65trIWqa/GKqolAM2ldmAEC iiDQrru14xzmJTzfZ9/plFfitFDfTvZjqpvxikW8mIsCUqLnwJ7vVWqDOO3WiVB6lBjemxkl2KiB oZdupoky/wrxpct21ZSOEnof0qNEFVP3gqSIc/Q4oG3Qq1v9JuHtdD50cCHq+3Tt9I4igLEyOD3t 5rtZk38Ifv3hDPFxvOKdv99zJLurCwkEQAAEQAAEQAAEQKByAvix1Gx6hrr55KUOjDU3z97684Zf tIPrsLJLCCqYWpDzvGJcArlcYmF2Ov019eSM9+mgAL2dTtJfX+QIRYK0JCajZid2DiBLt9MExU+C 5xtIggjQIQyUtOaTVQDVpGJRNrsiYL9b73e1OrxrMmM//S0tu7K4B+oGrofvFKbvG/X87IavD9wu txC6Ei1V16KYqolAs2ldGAICiiCgbj59iWSUOHHrz1D/aEeXYerVCRbn52ZXO0iQbU7Zqcwo4e/8 PhOnwMmfGSWyhaKMR5JRQkO17CjxZ1oGiTtQPv37+N4T+loHzU4d6/eXhaNq6OqXXPLG1+pp4ObV e8P+UQQz2vsh2bjBDzl+MV5w5+Yb88Hq9bNQMQZBCgiAAAiAAAiAAAg0fwL4tdR82khj4ERHa9oc fri7zdKXdqP1yft5xaReW2KeFZZkSj5k838Xbb2yK5CLnz++zcwXhuppK/d0OJQizcyUOsTujKgx scEFpIrurTAtO79gy4sF0SfWTTIhoQqmrFwwtPrVyWU0VqgF2XKMBAJtioCGmYMje1xiyGIb1yw7 ez2FjRL6njH/CUtKJJ9rK8y66PQuP0qk/cmMEh/q6VT2aCugJUjwxYDVtn1IqILx3ywazew+UETi GH7sMZdes3XDd8f6M+8MG4CxQxFYIQMEQAAEQAAEQKA1E4CnoBm1bulxiZTFtBlW2jU2Trf+Q5k4 iOlhCWz8MVkqzMotJNsADMwnMjsa5DOIBYl3BeKu/Qdb03eSHmeQUIKyxLUe2b9rnZF01DMbyrgS kq7ffSo1pCgzITGz4m5mUfQ+16mfbw/R+nL5HHNVUU52jdqqrkURVROBGoUjAwi0LAKlxyVSFnOm W5HQA9UmZa3+gxnPQtKV0meTLSF8kUc/+8r65k7MjobUawmyyCJklIgXiGVl0/hlRgm7Ef0rcx12 NR5hTgt6lPL4ZSVLDuhbXXQHVucbZIIvum8L1577zaeWHUU59NYHxSSOuuXU5SPJaJh49LnBUMW5 VxRjHaSAAAiAAAiAAAiAQPMjUONktPmZ3Jot0jQdY0f/BB+zaJZVlwoVfSXMomMdkp/4WcxPfEoW B5E9MUGUeHKvXzqTI3O77RhyMJn6iM98FtMCr65bsfEKPW8X8f7M6DxAux2nj+Nacu4AmT9cv59P Dhe4E3EljaKsVq51Mqy6T8gMyM4Vys8EOGpWC7e60jHDwt2XfR/+TEwEJscJtAy1OGRtc85zxiTW eUGJnj6gD0GkMhPSMzNS8jQG9CpTT+XOPQ2YK6SOBfnJfyYIiqiqa1EzgdbcW1C3tklA09LWjnYA 2i6aN7KSUSJXOkqwjgCOnu1CNp4fWXifXyziBf60O5wBF7tt4jT61ED1EZ9vX0wLDF23cPMV+kjC fF4Ev7MpGSUMHLd40qNEcMR9ESUW3Y0IJruFRq7cUvko0d5g7Aw32ukQeTbkoUj8OGjZ3JXldhdx VDT1aFWMVyLv4dG9O9gMz3OEtFdR+PjeQ/qrICVd8DghR8OmrFtBLPUdFGTlFtD5XpWrLEW9pzug JzPcZOcW5PBuxJeesKgyaKY7iQdpscpjcjVjXNvsUqg1CIAACIAACIAACFQkAE9Bs+oVZMrt8tUY i1nzbXuXb5nizLPrnLbHsj/xd4+ftjeOHAxOYgpu+T14x3Ru8Mbh/T/6nmcx27mfs9eh44F+kRm3 PSyUSzPwb22zMzFw3RtNmZpzGdkaph5HbwWv7LFriF6Hd/SGXzLfF3orcJlplXEG5Q045WEynjFA mji6Dt6BJ7e4aFEhO8f3GDR/ZwzVn8w0KEHQMpN54UwuifOi25BPVpF3nNxhpm9SBJ1NJ81dT7sY Uq+fP309mY7mrjzIed82Iid2t9vK33J1BxIh1dWiRgLNqn1hDAg0nABHfaSLp62Fm5utQYVRgn9u na2PZJSQOAI4uo7bAkM8Xbg3lltqO2x+2M91yVQXz4DAgOCY5Msr6FMD23WfvCX83A4XbX64t52O kat3NGVpJhklLJYdTTq38v3dQ9TU3lGzujR4T2jSqWUWKpX/4eBwp+68eMlrJuW/eKDaxP3U0h1e 5VYQcAyne+9eRd7th84zmvhdVD+Hz9kM5Kt7EJ/SGuo8x5p8HdmbSs7WNp84x5NxYVw7G3CNJxIE fWo2L4TJzvexNd/555OKlaVUzT7x3DHTmBJsnb0iIKfPALYaTGLiQfa2szcj8VaRQAAEQAAEQAAE QAAEaiCgVFJSokBISkpKRJpiZSrQvBpFbdx48fx5OiiX7fieX349vsb8yAAC27+7GHqRCeRGUTEx C1oikJb+2DYpcx6PMpLu3o9PogwMm1R7y1JWHOttbkkvK+jtFRO3gngu33LKC/c5r73MuW9TO8hT eJSZtM8EBlKOjm+ZA9SDAAiAAAiAAAiAQC0INPVPplqYhCwgAAIgAAIgoAgCZBPE4oFKU315YnFe fLLmkF74m6cIrJABAiAAAiAAAiDQ+gngV1Prb2PUEARAAATeAoGC3Bd0bBI2akDF6KZNYpEoNSok kQqNTsx8dO1p/48MFHZURJNYDyUgAAIgAAIgAAIg8NYIwFPw1tBDMQiAAAi0VgLF8d4G3W23sacX CLbadl8UJKjiQIRGRaBu+ek3blxq/5INl7i2H6g3qi4IBwEQAAEQAAEQAIFWRKCZximwtDzwdiEj TsHb5d+CtMvHKXiLZjckREKLjFPAhER5ywlxCt5yA7QQ9fJxCt6iyQqNSfQW6wHVIAACIAACIAAC TUMAawqahjO0gAAIgAAIgAAIgAAIgAAIgAAIgEDLIABPQctoJ1gJAiAAAiAAAiAAAiAAAiAAAiAA Ak1DAJ6CpuEMLSAAAiAAAiAAAiAAAiAAAiAAAiDQMgi0gDgFf9xsujPqZXvOEaegZfTfZmClfJyC puyrpOoTRpSG82jTcQqEJU3XEeT3nCNOQdNxb8ma5PtMQCA12bHpKqMmF84DcQqajjs0gQAIgAAI gEBrIIA1Ba2hFVEHEAABEAABEAABEAABEAABEAABEFAUAXgKFEUSckAABEAABEAABEAABEAABEAA BECgNRCAp6A1tGJLqENRZvjGjzoMXOqXmN8SzIWNIAACIAACIAACIAACIAACINBmCcBT0GabvvEr Loo+6BdfLNGTGfPbkVtU4sltl1KklxrfAmgAARBoOQTyo3/yl40YLcdsWAoCIAACIAACIAACrZEA PAWtsVWbQ53Ez256fXMoWyy1Rcty9pxhlPH0VeMMlJuDfbABBECgOREQPwvz/uanf2UjRnOyDbaA AAiAAAiAAAiAQNsjAE9B22vzJqgxcRNsdHPaFiKnqp2W9YbfC+/ucjVWaQIDoAIEQKAFESBugs1u tt7yI0YLsh6mggAIgAAIgAAIgEArJABPQXNsVLEg9pLf6o86KGnRH/vNv0Vnyt60iXjXfVxNyHX7 tcHJqTf9LmfSNRDnJ1/eO3+gVgedj9YFpWaEH73whFwtjvMeIhFiszdORAmClkq+Giw9S2egimP3 DmC1KGnNP50qEaKkpee6P4ZfLLiyxV6HuTtw6a5I2oYy+Q/ePLuWNdJk/p54QRGLUiyI3L9gHOsm SF9r2b2D0hCfE2fmG0i0DPBOkO0+EPFuyqppv/poOE8awkCU4GMjyd9h3tHwy0fX2dNfGaukJPJS w3yW6jF8zt4XhB+9JMCuhubYmWFT4xEojvc2UFNSoj8Grv5/BK23Z/490HV3pOw5YR7J2LP+q20k Oe1XB0RL7z4JWmDAFFFSWhDEz+dd2emqo6ZjszNaJHmWqyooit1pIymo5h6Ucp8pKFVd/CxsI2uJ ks6CPdHSkYEYIkq57L1gIH2rj6v3NZ5US6SP+zjWTZC6zvJdNSWDnbHMw1xp/mL+OfdS1YJs3jUf 1z5KShN8YvPJ2CAromOzPojHD/cNZgY6JBAAARAAARAAARAAgToSgKegjsAaP7s4I+jLYZP2Zzv9 VvgmLXLHMCpk92dTPz/ykJkhv0oNXDd9beFXdwozLy7VubRp0Q1mci3mnV3+6cZMj1v5GYFLtK6u //r2G/qysvkX544vLDVZe+qWi2u05KugbLHodugSLnMp4OCJZ8buBy74uhpTfP/1I3W6917On3U+ 8vhiLRJfYNWyAxEvqTL5T0d0WhxYmBu5z40KWDLOdU+CiLaRoz18oefG6YzIXltinhWWRHl8MnXH AYkWmXZR4tEvnJzc742N/Dez8N+Qcfc8xjt9JYl3qGrqcSrwSwsmb/Ttx92neAbQX4lV03fdyqO1 iJNPr3Xwer7hAb/w/ILu175zi2BqjAQCbYiAstnyuAtrmMc31f/4XxqLzpcI7h6eSfmvmTZrl3S2 zw/6bOSkH/91Oi98I4zYYU2FbHOfOvc3djzp6eh9YJU2Q+xN2u04sbHVICOKH37vqZA8YtUVVLVY dirUg31Cz+0KeGa89OBfAYu5VKL/mhE67/WY/WzWvdhTbtoU/9iSqXtv5tHZxKL4nZPMxq186hYj KHy6XW3HZKdl5x4TM8iI4bFpowsjS98z5j9hScoyC+Uq8ytzJ3tKVQvTou9R/YYO7MOlbiQ9ForJ SHhyxacrn3sk5WaELtIK3vD1dYwLbeiBQFVBAARAAARAAAQUSQCeAkXSVISsV4+uHD/K59/adSY+ j1LR0e1BC+WH37j/gv7Hi4c34ijqwe1bKfkcrtniTVtN29OXn9+/fZVP3YuMSskjP7s/27TG+J1K beF0UNfsUO5Ox06d2UvOC+dZd+dwOvcw6sJ8N56+79j3rha9evZi7uc8zy2k/y/LP+aTT6y6cyh1 A3uHUeR6hNeWQF6Vm4xlpSTaiwSh+74PSKT0rIebaFCUxoBR1r2IP8J909nkV0yW9mqd1Zh/DB9n 31dF9pWfnVtAr214cT86nOI/uBGVJlLWsvzsax+LymusiCaBDBBorgQ4HdU1O9LGce1mfGKt3Y5S 6Tt+4nB6xFjnfTKFPEqvUq4c9xXww3efic6jVLm679OZ+SE37zNrkejHuSv7+Edka5gack0dl7pY u4wwVKuxINW+k+QJnbx0oQ2X006rZy/GEoo789cwnzn99Xr3YyTzBTm0O5PM4Td6hRNfgL2VqUp7 bn/zgVSir29oSlVDRnX5ZaqTXnTqZ6htPtPdUX/m0P5qHDISXg+lR8KbzEi4bOOaQRgXmmvfhV0g AAIgAAIgAALNnAA8Bc2tgdppD3WYTt4S9ulKfvdWSF37jjSn6Bn1wKHzfa6nvuewdAK9RqBb/6Fj uBTf18N0+FKfy4/UJyyc2LPBFWOn6FUnLQ1V1sIuuv306BmB1J1RG82Zsecv0XMVPU1WiLKO3mD6 /3G3WZdIDUm5a//B1hSVGTB3uMmne8PS1aZ8Nk4bkRJrwob7rZZAR20NFfZJ6trTQJ95lK7Tj1I7 nQ8dXMiqgT5dO1U/Z7ax7KfOoTi6jnuu+bkYq9a+YHmk+mMnju7L2iKXxGm3TpA5PKU/vHc38qBy VDXopQyhl26msZ7B8ql2+S2HGGmQOnafvDvlgCuttFv/UbZcSuA7z2K4687LKeoTPBwaPhK22k6D ioEACIAACIAACIBANQTgKWhu3YOjYui6K62E72fFD9y8eXfY0zIGttefs/PSNhfiHcgMWD7dZNrm sw/p93WcvjMOntrkbEw7EdaOG+a0LjiZWfDbbFPx88e3U2nrZO4Giamp0WnPaxNvgNNn9k83dtMu Fb7/Rgfr2SQ6A7P3AQkEQECOAEfV0NUvueSNr9XTwM2r94b9U1s69S5YuQKxMJt54FP9nd9n4hQ4 +dNfX2QLK3/c65pfopXTd+6+Uztm0iOh/7pxRtPWBaU075Gwts2BfCAAAiAAAiAAAiDQ1ATgKWhq 4jXrE/MTflvt1JuEKhi/fMloZveB/C9/rtnSg1ci/ZdYkVlyyO4Z69nl+nR0gAMhIQdXDSNfIra6 LT+d2pwnzsrddIcyLz6lqTgjLZr+t/5gPfqVYy1SOy3LxTtvRf30pR3xFtzavmRtNXsfaiEOWUCg dRIQ82MDVtv2IaEKxn+zaDSz+6B2qd4FqxXPRiIokXyurTBTrd6auuanox7sC4nZt8qayL2x1WnF 6ar3RNWOA3KBAAiAAAiAAAiAQJskAE9Bc2v2nISdn9l9tu0Wd+6X8yw7CHOelzHw5a3fLgvE7bTM 53wTeGYD7SxgluvnRZ648FjM4ZrO3vobHQSRoq5GP6TfzSt37mnQq7Gr+PLxgzSig2s9sn/X2upi t1FQ1O20skcWmA+tlQyxMOLEpYwijvbgTzxPhGxxqOPeh9paiXwg0NIIFL94ksK8vTcfRT9KObG7 PrN03xauPfebTy07inIEta1PvQtWrkBZ39yJCZ2Yei0hTerEFAsSZWemlCtW1/yS4nmRfsH0SGjh vPU8Hb6RbHCITqzVKqXackE+EAABEAABEAABEGgjBOApaG4NLXx67yFtEz/lH8Hje7kaw+kQALLU XuXFjv+tZ1bad9TS7d2Z4o4b94EW1bF91uoF39E7ETgdtHV7cSktVzsL5t08R02zF1066frdp2JR 4sm9fun014LnOfSRYsw/c7OYSIVUZg6zfv+VMIsEPicpO5deGCwuzMtm7ssVYQsG7D9AaywSRIVd J1+tVq51MpT0J0nkAqowK7dQxLsVxxfLtBRmC+mQhO31nVbQno608Mg7OWQ+c+96eDrFHbZlxZQ+ TIzG8maUs4rTQfXJhk+/ZTZZdNTS66VFGc+aZFFrP4UcUfwTBFoDgVT/fT/TK+3Fz25fjqTddp4r phuQR0n4mB1PBCnpgscJORo2vcvWtiD3Bfv4P88hRwfIpZoKUq9ypQNFjogeKArysguYgYJEMKQl lZOsPuLz7eRwBDJ1X7dw8xX6mMZ8XgS/symJwkhSF92BjGEFWbkF+bzweL64uvzlVMtZ3bH9izUL 1px7KKI4HbV19bQprovdkFquUmoN3QB1AAEQAAEQAAEQAAGFEYCnQGEoFSRIy3L2HHpRgFVvipfd zXzi9C3kkEKKCjsbGMYjIQk4fVbvXtQj5vvxWiq93B5N8r28xUGH/NT+Pz2vXZ/1/GuHvQ5Xf0n6 rMDfvadqM23L6fPxluNrhpFwg+6jnb6PN5r0cS/6Mvk6cdnZJ1Rx7N6htrtJoDGSrs4btsA32Gea 0/ZY5vspD5MvzpzdOmb8ViZMurSIrJ7OrpNpjb1MZ+yhnHec3D3PVBLhkCxlGOjkRwcRyNy+bO0v L/QHFV9cvkCihb/VaeiOBDKzUB28KDD85JbO+4a/p9XhPbtLA3wuhv/mMZiJoShKKGPGwr0+X8pb FUzWIbzT3/Pwwh5R333U4f9MZyRPO37Kc4ouerOCOiHEtDgC+i4LJutGfWfTqRcJAODi+cv++ZbM sn6toc5zrMn/R/amkrO1zSfO8WSm69fOBlzjiagnQSsWbGOXGYTOMxrkHVsaNKD6gqLYndNsfSQD xTyzL06c22o+cSszkPBDFk/8NPCE9wjbUsnuQXw67uCW8HM7XLT54d52Okau3tGUpRlX8swqD3T2 3U0iL/J9li395YWRKbleVf5i/rl1cqrHe8eL5Frr//ps2/V5z782T9B5x3BJ2szA8G1TyQEtSCAA AiAAAiAAAiAAAnUloFRSUlLXMtXkV1JSIncbLtPS8oBMyx83FyjQwupFbf/uYujFJySP7fieX349 vsn0tiRFxLlgarmRbDdwDrxzyJH2YrTtJOszBENT9lWibsKI0sckJqb+j4miHtsm7QjMUCNJQkUO YjXUIoVHmRlJ8sQnUQaGTVrrCsqK4737Wq1MpfRdAq75TUac/7fbGlVol+8zAYHUZMems1JN7jFR 6N/6pqsCNIEACIAACIAACLwlAnjb8pbAQy0IgAAIgAAIgAAIgAAIgAAIgAAINEsC8BQ0y2ZpzkbJ Ig48eSzAwYTNuaVgW+snUBod4J9HfPlV+K2/6qghCIAACIAACIAACIBAYxKAp6Ax6bY+2fJxDSKW 23W13RuH6Unra2bUqGUQKI7fIYsOEL5uiJqJfKCBllEFWAkCIAACIAACIAACINA8CcBT0Dzbpbla pWyx6F5JZqHsc22ReQ3HoTfXmsAuEGjxBJTNVqQIS0pknzsrLOgDT5BAAARAAARAAARAAARAoKEE 4CloKEGUBwEQAAEQAAEQAAEQAAEQAAEQAIHWRACegspbkz0BAQkEak+gq9b/1T4zcrYGAo/TW0Mt UIemJNBLrym1QRcIgAAIgAAIgAAI1JsAPAVl0HF11GXfc/7NrzdWFGw7BO7EPWcra2Lere3Uuu3W tHOX0rpnCtouB9S89gTuJ5bm7dix9uWQEwRAAARAAARAAATeIgF4CsrA19XrLPuelpL5FhsGqlsE AQE/50Xma9ZUA8OuLcJmGNkgAu9pUuYfSCScC2qQKBRuIwRCLpZW1MCwjVQa1QQBEAABEAABEGjp BOApKNOCA0x6yr6fO32npbcu7G9sAmGX7slUGPbTbmx1kN8sCEyaKjEj+CyVwmsWJsGIZkvg32zK 75DEOtf5zdZMGAYCIAACIAACIAAC5QjAU1AGiMZ7KoOHS9aQR0c+f5D4FD0GBKoiQPan/HZY4ikg QQr6GfcAqzZBYMSo0mrKJoFtouaoZN0J7Pi+tMzsuXUvjxIgAAIgAAIgAAIg8HYIwFNQnvvkj01k l77fEPrq1X9vp2WgtdkTOLw3Qmbj7Hlmzd5eGKggAh8OpxymSGT96EWFhihILsS0OgJ/RlKkh7CJ bFoxNW91NUSFQAAEQAAEQAAEWi0BeArKN635B3q24yV7EMgW9O83BMNZ0Gq7fwMqdvSXSNkBGWRB wcjR/RogDEVbGoEvVpZaPNWeuvN3S6sA7G18AqRXjB1Rqma9J9WhQ+NrhQYQAAEQAAEQAAEQUAwB eAoq4ejsNlR24h3Zg0CcBSRwnWJ4Q0rLJ0A8Rwd2hcn2HZAKfbXRtn37d1t+zVCDWhMgywo2fFea e7gZRd4eI4GAjABZaUJ6hSwR15KtHfCAAAiAAAiAAAiAQAsiAE9BJY2lzdVY9tVI2Q3iLHCbdvLK H3dwbmIL6tmNZGrcX2lffR505kSKTP7CZeaIUNBItJu12MUelHyAOvL2+JtVCHDYrJusaYwjQS5J TyArTWSJ7FVZu7FplEMLCIAACIAACIAACCiKgFJJSYmiZBE5SkpK5L8Nl2lpeUBm1R83FyjQwtqL InPCdR5Xy+UnGxNMzLp37qqq2Vm19qKQs6UTSH4oeJEpvHwhRXYmIluj2fMGzPp0+Nuq3YQRpY9J TEz9HxNFPbZNyoEZaiRJqMhBrA61KCykVn5eGtmeLUncB8NGUlralG6vOohC1pZO4HE6lSmgyMGZ 5EQM+UT6w5oNVPe3FO5UTe4xUejf+pbeXLAfBEAABEAABECgRgLwFFSHKD3t+YaVF8tNDmtkigxt hMDX31kNH9X3LVYWnoK37Ckg6omzYMuG0qh1b7E3QHUzJEA2HZDVBG8xPAE8Bc2wV8AkEAABEAAB EGghBLD7oLqG6qXXbX/ALLK8vIW0JsxsIgJkaYnvqelv103QRFWFmuoJkEng5m3UlZulpyGAGAgQ AmTHAekVpG+8RTcBGgIEQAAEQAAEQAAEGkAAawpqBY9EKLh358mVPx6SmAW1KoBMrZGAYf9Og4f2 GD1uAIlk0RzqhzUFklZ4W7sPynUCEtTw5nVq49fNoW/AhrdDgByFOGkqNWIURWJeNoeENQXNoRVg AwiAAAiAAAi0TALwFNS53ciWhDqXQYGWT0Bb573mdsABPAXNy1Mg6+TPnlKFBS2/y6MGdSTQuQv1 nmYdyzRydngKGhkwxIMACIAACIBAKyYAT0ErblxUrZUTgKegmXoKWnm/Q/VaDgF4ClpOW8FSEAAB EAABEGhuBBCnoLm1COwBARAAARAAARAAARAAARAAARAAgbdJAJ6Ct0kfukEABEAABEAABEAABEAA BEAABECguRGAp6C5tQjsAQEQAAEQAAEQAAEQAAEQAAEQAIG3SQCegrdJH7pBAARAAARAAARAAARA AARAAARAoLkRgKegubUI7AEBEAABEAABEAABEAABEAABEACBt0kAnoK3SR+6QQAEQAAEQAAEQAAE QAAEQAAEQKC5EYCnoLm1COwBARAAARAAARAAARAAARAAARAAgbdJAJ6Ct0kfukEABEAABEAABEAA BEAABEAABECguRGAp6C5tQjsAQEQAAEQAAEQAAEQAAEQAAEQAIG3SQCegrdJH7pBAARAAARAAARA AARAAARAAARAoLkRgKegubUI7AEBEAABEAABEAABEAABEAABEACBt0mgNXoKimP3DlDS6lDtZ4B3 QnHTcRcLrmyx1zGZ75ciEjdYa3HmWXdp7Wz2xokaLBACQKCNESiO9TZRUlKr5uMeJKjHACGK3Wkj E2uwM7YeIt5OS5QBYuMdX+OoUqeaFvPPuUux1Eb422EArSAAAiAAAiAAAiAAAvIEWqOngNSvg8uG 4KS0wpLMQmHIFmu2wr22xDyjr+TeOr5mWJP2guIXUYE7I/iZAb+G8AoarFlZa8qPt/ZNq4WcnIQ9 R5rSIVILk5AFBJoBAWWLFXdK3vAC3bRZYxYGJv9XIiyhP4KkEE8Xbj1tVLVYdu2/CC/92hYXi+L9 fGueltdWXP3z0UCEMZ6SobIWcupUU2Xu5B+T9tRmyKqFZmQBARAAARAAARAAARBoEgKt0lPA6bx8 jftoQ5XKCarrT1n17SKtJsHLKlHuOsRpmRVXy3munWFHRehVVtXQrElOUWb4zm/3ZtaUDfdBoI0S 4HTV7dehQt1VDMcu23nes2cTQBELQrd8fTS7CTS9fRXKajUPWW/fSlgAAiAAAiAAAiAAAiAgI9Aa PQXKZp/M6lttxTRMF88xVW66bsDRHrv2csadQ64Gqk0DnLgJtrqP//ZW01URmkCg5RMQhAXRb/g1 TK1M2zVybciOpHVurltvFDWyHogHARAAARAAARAAARAAgfoQaJqJa30sa6oyealhPkv16KAGJvN9 rifnMXrlYwG4BwuyJXnsfRJEeQk+NtIwAe7Byfev+7ia0DERBi7dFZlZ/OzmBnv2rsn8PfECMg2o JKxAcZz3EEkYBYOlfpdv+q3+SCahNI5BUWbchaPrJNK07FefiOPXKsiBmB+/67OxrJsgbaWdmpLW gO+CT8lCGxDb2OgGT4LnGzCmugc//bM0ssP8gzfPrmXskVVB1hSVsmqqhoIeEGhcAuK8pITHjApl s4kTtWlXYnG8t4EknIGBq//lMP/VNvTXga67I+WexiJ+fJD3goH0VvwJawPv8msys4gfs+fTkXZb b5Cc4Sut1JTUZLv383jXfVdP0GF29duv9g/j5df00Ofzrux01WGt2nlZml8+jsA83+uXfdfb0zL7 uPrEyBku5see83btQ8rq2Kw/mZBVo9uixpqKRSmXJSj6uHpf41UX8IBoDz4orSwx4EgsPWBSlCDI VS6EBBPuQRbpwMD13JOa8OI+CIAACIAACIAACICAAgi0cU9BToLPrGEOXk+XRqXlp29V9p1uvzY4 g/xaJbEAPAO/tGAACx9H3aP6D+3Xh0tFJD0VdTT1OCW9de7Qb8/6LjsYcnyxFpV4ctUIE7Uei57N ikg4NYtLZQYscd19U1hGlKTBlM2XX724htn/kHr9hkDb6Tt/+iuRsOxAxEsmU5Hg7PKxw/flOJ7I LPw3ZIsDFbHt8+HLjie/qrnNOVyzpZ5bnZm90npeIcKSzHtfO0zbFrnPjdHItd73s7u5KkX1dDhw wWfcMt/Unxx6fLjodugSdnN2wOmITosDC3NJfipgyTjXPQmSKIxVsarZIuQAgeZPgOwF+P6HS2/K GqpstjzuwhrmyUi9clOg4/TdWfprov+aZT/eYB9VEmtgzywrp5XXRgXyXpdcWNz1UWJqDbVtx7Vc +P32hUwua68IYYnw2goz8kjmPfT3sJ64Ltr+jFD4RhgxLnqxrbXHkYfVOAvyo72nWduty1oe8e8b 3kbl3eOsV515RvsWSByBU6Ee7AgWff1x9+mbAuivAv/lM3eF57Heh5zYXZ9ZOq+8Yhf4NDcjdFHP lJhH1RpeY01Jhp2TzMatfOoWIyh8ul1tx2SnZeceV+7qED8OWmpnuUc0/dTTEkGU10gq3MfF0v03 HsmtPXVP7K8ubAgJ28N/LLVQpthIB25uAaG/TG6KjSHNv7vCQhAAARAAARAAARBobAJt2lMgTg7a sjaYoozGjuqvwtHqO7QfxQ/yv5LG/LRtr9ZZjaGflNWpn762ueMix17OQ43o7QOyW5Pn/89Gi9Ou S89e7H5nLedfg36cY6TXuw/zPVOQwwQwlOWXtSang7omk0V/1KTRBqrK0q85z3ML6cvitLBDQZlU 8L6gOCGlqq3XgykZd/v+i/p2CHWDOeu3uhpTFD98o/8tdqogynpnnsd4HWaddcdOndk6jPnkE6vu HErdwN5hFPka4bUlkP71Xi2r+hqFciDQLAjsd+rz7jt9yEv+ii/VOR3VNZngIvpjJ47uq6Is/Zoj kDyqT0IO+YaT+zajh3LbUZzuVpPH1jqiYZnKi/lXvTb78ikjB6v+qhRHdaCVQ2+Kf2zNMuYBrCy9 4gV6ryQLE3pb2wzU4Gj1Hz6Ay/c/fjmN9Se27yQZwYZPseurKvsqyM4poOWJ+WF7d5PRT3+s/Qfk gedoD3WyN6quNcQ11VTMO7nRi6DQt7cyVWnP7W8+kEr09Q1Nqcx6cVrofv9E6kbAyZhsSoWr14Nh HBqd+JycF8FRNZy5Zfti2kETuvewxCMjynhn+qaJum36L1azeFhgBAiAAAiAAAiAQFsh0JZ/d716 dOsS/ROfMtLVaU/WETBhAvnhp289KvPT1tK8rwZFtdOesjuqhkAD7LRfEUg5OoM/Hq9FcfU7K0Qc 05s5Pa0XuNGHPvB/3fU7mXu8Sj13v7tVz/LmamlIVHbR7adH5w6/QfwTtWTVVh4b1LN1ESBnH7wW xgeuGln36ASi1KiQREJDf4Bu1wZBKc78K8RXQER01VRjYqgod9P7kPgc+CE371cemFScfjPoJp3z Qz0dUoKjotGNzLdvnriVXtOOBVJGLOLFXKTV9RzY871aGV5TTcVpt06Ekv0U+sN7d6PNUdWglwWE Xrop8VyUUcLhDv5kJnFcandVrzRgTLvuti7LRxJfQew2n3NktBKnhMb3sCAeDSQQAAEQAAEQAAEQ AIGmIdCWf3kVi7LZV/T73Xq/q9XhXZMZ++lvadmS5fZN0wKVa1E3cD18pzB936jnZzd8feB29auC a2koR2XQ+GljyI9vfvjRi3cFd6LemJup17IDNGdWtaw+soFANQQ4qgZTv1497p26QirIEdDz7aqS bIM9iQXAftyDBOS1ecX0KuNREnNVU0O17OT5z7SM4idBCwykEhg5C4L44vzsNCYmwjEnJk7B+07H yL4Hfuq/NcY2IGXE+bnZNQZUKGNlDTWlxMJsZttFqr/z+0xMBCd/+uuLbGFl9VUxdjtwtyT3oE3G qbXrd0c+rXB8rIrJ5BkjaAGhQecSHsXfemNlWeOBL3VtPOQHARAAARAAARAAARCokkAtJ4qtm6D1 hkhhZmGJ5HNvRVMei1AVWbEg+sS6SSYkVMGUlQuG9lZMA3AMJ305l45WEOG7b92Zd4YNqOIgyWq0 NUdWioEDKW2dAEfdyFRXAqHo2aU/4iud0deNEtlgv69EWCL32efIxEqskNrr9C67+L/4edqfzNSb XjLQ0/FAShk5BxzZuCJ06u0V82+pipRlZGP/20z6njH/lVaZDcFQMZHgiEdWT7QgoQqcVi8azu4+ KJPaG051X0UvSwjesdvz5DvmZir4a/U2mxW6QQAEQAAEQAAE2hqBtvzbq6Oe2VAmyF/S9btPpet1 izITEjNrsXi3cTuKKHqf69TPt4dofbl8jrmqKEdRZ65z1CynuluRKUZiUKaBpT7Zc1F1evn4QRq5 y7Ue2b8r1YxZNW5LQHpbIqA92pGZ1ooF1/fc+q/yGX05Ht36j7Kl5+yp1xLYACeyVJCVW+FFeTUw lbX6D7ZjhqM0vnzgUq7diP7MMFUhKeuaT2ViFj6KTkiXFiFHn9ypzSEppequlI5+rArhi7zKIqfW VFNlfXMnJgyhPAqxIJE5AqZcYoMjumy7obNq9RyLDoU5zytDpW4+fYkDKck/KTQa0qst/61qSw8h 6goCIAACIAACINBcCLT6X1+vhFlCFnZhFhuCTJY4alYLJUH+3Jd9H05Chovzk+MEWoZaNBVZwezc 8qtny90SF+ZlM5ILnucw634LcrNYTZk5zEYGWX5hluQneLkiFSSInj6IoJcGZyakZ2ak5GkM6FXG 8mKp70AmsFx/eq/HACZCeGG2sCAnNSK+1PehOoiEZtSiLJZ8OVm/0sYP2H/g7MN8cvhCVNh1IsFq 5VonQxLvrFpWzaU3ww4QqD0B8YvHDyQjQnaOSLp+gDm6b42b68nOPZi4A+KCvGxmFlsgoJ/ucl/J dnw9ezfm9T69SD6Hyk/0/9mPPfuA72NrTp/wV2lS7trTgAl8SKblBaKUP8kBgRwDxy2eZGKcFBxx X0TiCNyNCCa7jkau3EI/gJWmLtb/W+tGT85Pzfv8hzB6Qp7HixRoG3OZ/K9yJUMfW7tyX4nhtgtd 6BCnIccvxucXi3iBP+0OZ9TEbps4zTu+wvmGNdZUfcTnkjCE6xZuvkK7K/J5EfzOptok+kOxUOLu ZN0QYuGTJEZZxt+Pnz1LzNKwqjSYoobZdDdSQa6H+8cG1bo1a9/qyAkCIAACIAACIAACIFBLAiUK TazShou0sNgv+5TuC5BtEKjVP/67c5w9h0w+6U8//k8ZgS+STm5xYV/ZaTnvOHknl7lbrqz8knth yBZrOYkL9x/37FX6XX+6//ENdCBAaXI+frmMGbSoZ5FeZYps8ZwuL4FYmP80cBV5v8gd9uUBv8gM fn66L31sAWthVtl6ld0OICXDf3RzkzNbZPelR6/LVDkzdInemsDMN2UuCmMkZjsHhET6L6HXHcgD YbZmVM5KumujVo2CzIokIP+YNOShU9Rj2xAb6lyWjDOyT5nl/fJL/av4978xXjVs6OHa7XnwRljy X4SX3EEG+i6eni5yj6pLwD/MjoDcpHM7mFP9uNYegfcubDWe6XUqIPBMRAaRUHbrgdzX3IyorS70 Y6btsuOaLKdMFLlht2pPaJLgTZUSaOFvhPGXvOjQgCQZu3hekuYXxnjKD1MuXvKGUySC43+0WEFS iCdjA9EVEBXqOdXFMyAwIDgm+XUVSmusqZz92i5e55KEtJH/ZQTID8X0wZBvkkPW0AELSR3PE3Vv eIGMy0O+CiyrF6EeDqsuvKgWQi1avK49pNXkl39M6vyMoQAIgAAIgAAIgECbJqBEal9uMt2Qr0pK SqR4w2VaWh6QmfHHzQUNMQllyxPIC99/Tvuz2X3LvKgsjt1rarmRbDdwDrxziCw6QGoBBCaMKH1M YmLq/5go6rFtUmTMUCNJQkUOYk1aCyirgcDLsN2hPRZ9UtWyCvCrgYCa3GOi0L/1IA8CIAACIAAC INDqCbT63QetvgVrV0Hx42D3gVqTfFPFYmFcsgY2/dYOG3KBAAg0OYGiZ+eW6KhN9015ReUlJrxn aoA/U03eBlAIAiAAAiAAAiAAAvgJ1jb6gCg1jpz6fjX6oeBR5NP+E/pU2PQri63w5LGgGZwS2TZa BbUEARCoSCAvKeZPPhV3/f6zZzcyzSZhPQE6CQiAAAiAAAiAAAi8BQLwFLwF6G9BpbrlzA1uWtT+ NesvaY35QK2cBWTrwVDb3ezp6hHL7bra7o2rEM/sLRgNlSAAAm2QgObg2UtdtFP9v9x0UsvKSh1/ pNpgH0CVQQAEQAAEQAAE3j4BxCl4+20AC0CgfgQQp0DCDXEK6teBUKrVE0CcglbfxKggCIAACIAA CDQaAbyuaTS0EAwCIAACIAACIAACIAACIAACIAACLZAAPAUtsNFgMgiAAAiAAAiAAAiAAAiAAAiA AAg0GgF4ChoNLQSDAAiAAAiAAAiAAAiAAAiAAAiAQAskAE9BC2w0mAwCIAACIAACIAACIAACIAAC IAACjUYAnoJGQwvBIAACIAACIAACIAACIAACIAACINACCcBT0AIbDSaDAAiAAAiAAAiAAAiAAAiA AAiAQKMRaI2eguLYvQOUtDpU+xngnVDcaFArChY/u7nBXktv3lFeXoO1FmeedZfWzmZvnKhOAovj vIdIyBgsPfukTmVbQuaizPCNH3UYuNQvMb/O5jakbJ2VocDbJCAIclVTUirzcQ8SsCNCMf+ce+kt E+/YYkosuLJ2go7OAr+H+eIKZotid9rI8hvsJNmbW2r+FjY3YrAHBEAABEAABEAABECAao2eAtKs HVw2BCelFZZkFgpDtliz7dxrS8wz+krureNrhjVx0z+POrEthOL77gxObvBEQllryo+39k2rXw2U zZdfvbhGq36FG7eUOD/O72gdHR8VLMqM+e3ILSrx5LZLKTWDLqexTmUblwWkNy4BbUc/wd3DM40Z LVxrzyihcJ+jtjLzVZk7eV9J7oPDtlzuzF8f3F5uoVycGR249Qaff+zXC7yCCoapWiy79l+El37j WtwQ6c3fwobUDmVBAARAAARAAARAAAQahUCr9BRwOi9f4z7aUKVyYur6U1Z9u6hpJ8vdhnyyyo7i ui1z6MNORxqWlFU1NOsrgdNBXbNDfQs3XjmxIHTnmqM5DVWgZTl7zjDKePqqcQY1ga6gsQ5lG2om yr91AirGc1d52NFm8MN3n4nOK7tYQJSRRK087zOnrwoZIZW1BjutGUkcB3MnGnZ864bDABAAARAA ARAAARAAARBoAgKt0VOgbPbJrL7VVkzDdPEc05pmkoqkz+k+YuPlzLTDswzVFSm2tcgiq7u/d3Xd GVHU4Aq107Le8Hvh3V2uxlX4iSQaKtNY27INNhICmgUBjsHk1R4WtCmC8wHhT+RcBa94ZwJeujma 0W4COnG0x275IyPjgCvjOEACARAAARAAARAAARAAgdZPAD9881LDfJbq0UENTOb7XE9m4wjIxwJw DxZkS/LY+ySI8hJ8bKRhAtyDk+9f93E1oXf+D1y6KzKzmIlHwAQCMJm/J17ATH0FQUulQROG+NDb mMsEC/C7fNNv9UcyCaXzlaLMuAtH10mkadmvPhHHr7hJuuoempca7rvZXocxxn6zX1iqqELpN88S fpOqLrOxvxrVT4LnG0iqPz8oU8Rjqq/zkU80ExegepvF+clh0hrpfLTO9yZNuygzZs+yYXY7I/gU Fb5xuJpWB1nwBZL/8t75A2l1eq57w3iMimqaJr3UNkkcCpkEoi4oNSP86AUSmqFSjfcrlGXQinjS 1lEiTXA0nLWBvi5pd/u1wcmpN/0uZ7b+saL11VBz8EfO1nS1En19Q1Nkz0f+nXM3TRfZ9mQGR/mw BTbe8bKwIEX8+CDvBQPpCAUT1gbeJb23XBKLUi5LMvRx9b7Gkwsokse77rt6gg4T3cB+tT/p2dU8 2UROmO96eyazjs1637AUWayTam7RD2MDLKxecuvrCagRCIAACIAACIAACIBAeQJt3FOQk+Aza5iD 19OlUWn56VuVfaeTiV8Gmd6TWACegV8y7xsp4eOoe1T/of36cKmIpKeijqYep6S3zh367VnfZQdD ji/WInvjV40wUeux6NmsiIRTs7hUZsAS1903hUSA9tQtZUMDyAULSL1+Q6Dt9J0/nYFIWHYg4iWj tEhwdvnY4ftyHE9kFv4bssWBitj2+fBlx5Nf1a4LM/Uavy5u3BlSL1/XjN3us9cG8spOR1JP7r9N TZWqdp+7I7w2qns67DiwhMtYUZwWEyfuO2qQPsW/de+pqAabSVCAnbNNbD3+GhfyojDhuGPq9nlO y0+nittpWS5c57OQkWi9IVKYWXhtkbkqRbH5x2184kbn91Hb5+C07uxjcXVN06PUNhaTmHd2+acb Mz1u5WcELtG6uv7r22/I1Uo19i9flmQURe91snZyvzc28l9+auCs5G0e49edpZvgVWrguulrC7+6 U5h5canOpU2LbtQ9fmLtGhK5GpMAR9V0/BxbpjeHBp1LyGE7TV7MhfARdtIFBSRsgWcou/SgNIlF 8XtmWTmtvDYqkPe65MLiro8SU8tn2DnJbNzKp24xgsKn29V2THZado70XpLyHvp7WE9cF21/Rih8 I4wYF73Y1trjSGWxEmljRPFEju28mHG0nADHJJ95titOM0+yWMQ7sng8e+tNieCMQ8w62/EevpKY qQ2xsBqljdkakA0CIAACIAACIAACINCcCLRpT4E4OWjL2mCKMho7qr8KR6vv0H4UP8j/Shrzg769 Wmc1pqWSsjr109c2d1zk2Mt5qJEqISa7NXn+/2y0OO269OzF7vzXcv416Mc5Rnq9+zDfMwU5TAC0 iqEBZFf0R00abaCqLI0dkPM8t5AuIU4LOxSUSQXvC4oTUqraej0Y8XG377+oTecpWy+uxfhRWlRn HY1y0Qn0py/62LQeqjt26sxKisjuNMhQa5DjfFfr6SMNVau3mUzaN3jdInElx1kNUG3f7YPRZIKm 1V2jyj0C5fL3N+9HJR49FPqIbpuqm0ZmG4vp+f3bV/nUvciolDyO9vDPNq0xfqdqfuXK0u4A741k pYOe9XATDY62xRg7Y4rbrRO9/vzFwxtxFPXg9q2UfA7XbPGmrabta9MuyNPsCHD07N0cGVdB8I7f 4+iX9eInV4Neu43VkxsZ23eSDAVS88VPQg75hpNvNqOHcttRnO5Wk8eWiWgo5p3c6EUy6Ntbmaq0 5/Y3HyhdtiDmX/Xa7MunjBys+qtSHNWBVg69Kf6xNcvKO/IYXeXkfDB6vDbFZZ8aYsNOb38Bq4JD qfS3sTeiBL7zVjF+hAZYWJ3SZtd+MAgEQAAEQAAEQAAEQKCxCLRlT8GrR7cu0T/3KSNdHTLTY8ME 8sNP32Kmo7Jkad5Xg7yI1p6yO+qQqwHtKagqsdN+RSDl6Az+eLwWxdXvXA9xsnp11VAjwRhoy+8U 3vWZolsry2qverRlH3UOxdF12HeNjgtQbUFx6q2zZNJOcXtp0lNtjo6jT1rJHRJvvgqbpPn1B+t1 I3XgqGl0I9SvXopKlV9VUVPTdOs/dAyXHDnhYTp8qc/lR+oTFk7sWdsnSZwedfomnVlPk24Dupp3 M9N2O+i0o6iufUeakyXrJ90HDiU7VlLfc1g6oWkjZNa2EshXE4F23a0d52jTufg++06nvBKnhfp2 sh9D5v/VJFFqVEgiua8/QLdrZdnEabdOhJLerj+8N9N7VTVoDaGXbqaJMv8K8RWQL1016WeTjDrd 9D4kTgZ+yM37FTewSOVw9d9jnhqu4+Hkkow9jt3Jl+exZ/2JDZJbxH2m09uIFhganfi8mKq/hYQA a3xlSmuiifsgAAIgAAIgAAIgAAKthkCtJo+tprZlK1IsymZf0e936/2uVod3TWbsp7+lZVfc0d/k BNQNXA/fKUzfN+r52Q1fH7j9qC4GyOr1IkdY81GBFSTXW3V1BcXC7HRaEz89u7o92TJjpPlTT854 n45T0NvpJH2vjjXi9J1x8NQmZ3IYXuLJteOGOa0LlsShqAVOcX5OGrP5vJL+0F5/zs5L21yIdyAz YPl0k2mbzz7E9oNaMG2WWdTNpy9xYCy7eeLWn6H+0Y4uw2qIO1qQI6Bn+1Um0nuZzQip/s7v0/EF +jj5M703WyjKeJTEFNPUUC0bUvXPtIwKD6tUDj/13/JPTTE/LZKW01Fbo1yUxaQ0/iuq/hYWV6O0 WbYfjAIBEAABEAABEAABEGgUAm3ZUyADym6PL5F87q1o0mMRqmhWsSD6xLpJJiRUwZSVC4b2rlfj P3nw5N96FKy36toUTL/3OKsuNvXaEvNM1jSSEAZ1KE82HSw8EBJycNUwUihiqxsdGaEOxemsaSlP X1aYw5FNB0sPXon0X2JFlq6H7J6xnolfgNQSCWiYOTiyxyWGLLZxzbKz11PYXhJ9z5j/hCUlks+1 FWZdJG/+ZZyKn6f9yXgVPtTTqfo0ltR7j8vtO1Lm6g0vA/uV1AdhpMetg/0VLCQhQiSpotKW2Lqw GQRAAARAAARAAARAoH4E2rKnoKOe2VBm0XjS9btPpfPHosyExMy6Tibrx76aUqLofa5TP98eovXl 8jnmqqKc7LpokNUr9frFvwR1rUu9VVdbUNnAfCIbBzEsLIaOGVlDkuVPD0tgA8GRJBYk3mWPk6hl yos8ceGxmMM1nb31t8gdtLPgavRDsja7NklZ1+QjNo5d5NWoZ2Upvrz122UBicVoPuebwDMbaGdB bUNI1EYz8jQxgdLjEimLOdOt6LX91adu/UcxcRBTryWwQU1kqSArl4QmUdY3d2J2NMhnIL03XiDW 6j+Y8Uowb/5LE9duRP+KG1hkcqhrYbf5ZXu+xIbUyEdlO7TtYGOy46H+FhZVp7QmMLgPAiAAAiAA AiAAAiDQagjU+KO4pdf0lTCLPn+ApMIsNlqgLHHUrBZudSVL0/nh7su+DyezQXKoXpxAy1CLpiIr mJ1bfg1/uVviwrxsRnLB8xxmkXBBbharKTOH2cggyyCzoVyRChJETx/QpwZSmQnpmRkpeRoDepWx vFjqOxBm5VV8lS2rF5Xpt+G7I4n0wnjR/eCzCcwK+apUp0anMVOOGlRXrJ3UsuoLqo/4zIecEEFg 71mz/lgKzSUv5eyFu/Q/lDv3NGAqSKpTkJ/8ZwJxB8jyX123YuMV2ncj4v2Z0XmANtlAXnXTyMgX ZgsLxFTH9lmrF3xHbw3gdNDW7UViKLraWdA7xyvTWK4s1WXYorXkDAuyc+Gox6bjdEh5cT7vj2A6 Qn57lRc7/rc+iD54sqOWbu/OFHfcuA8QqaDljhWalrZ2dFPbLpo3skuFarzKlYwhwhfs4yaLg8ie mJCf6P+zH3v2Ad/H1nxnbLH6iM+3L6YFhq5buPkKfbppPi+C39lUux3HwHGLJ9nskBQccV9Ezi+4 GxFMthaNXLnFybCSsVgmR7BnyYZjzPkIeQ/PXYgn/+AYTt+w0po4Iy5HJJCv+fevXSb7Ghy8NjjS ghpgIXn0JMZXVNpyWxiWgwAIgAAIgAAIgAAI1JVAiUITq73hIi0s9ss+pfsCSlehS3cKVHflvzvH 2bP35JP+9OP/lBH4IunkFnrDOUlazjtO3sll7pYrK789QRiyhfw+l6WF+4979ir9qj/d//gGPbn7 zoF3HgVOl7tAltP/E+lVpsgWT7kMjIX5TwNXkVeP3GFfHvCLzODTJx0SjwZrYVbZepXdOiEDkp8h WXJPilmt8rmYlMbcetYg1QTOP77OclHe9bxChNK2qM5mlurrO5Ll+sQmuyX7Qm+9eCNpi/wMdts/ xXXZdCODL6lF7q3gHdPZlQhclw3BbBWqaZoKtv171y8wKUGilMAMrFpj5fXiP4r66UvmHTDdFocD Jd1DePVUaFyq9JbVGl/J9dp0S0XmkX9MGvLQKeqxbYgNdS5LxhnZp3SFv2ypfx3/kfvgsK2FW0D6 m/Ki/ssIkB9GrL0ihMxugtykcztc6IUDXGuPwHsXthrP9DoVEHgmIkMqQZaBnJPq4nUuSVgqWe4W ZbdqT2gSOeawyiq8zojwXzWSfQzKZX4jjL/kNZMeGRgzDofG58rJaYiF1SitI9iGNw0kNISA/GNS 52cMBUAABEAABEAABNo0ASVS+zJT6YZ9UVJSIgIaLtPS8oDMkD9uLmiYUSgNAq2TwIQRpY9JTEz9 HxNFPbZNSpkZaiRJqMhBrElrAWUg0KgE1OQeE4X+rW9UqyEcBEAABEAABECgORBo9bsPmgNk2AAC IAACIAACIAACIAACIAACIAACLYYAPAUtpqlgKAiAAAiAAAiAAAiAAAiAAAiAAAg0AQF4CpoAMlSA AAiAAAiAAAiAAAiAAAiAAAiAQIshAE9Bi2kqGAoCIAACIAACIAACIAACIAACIAACTUAAnoImgAwV IAACIAACIAACIAACIAACIAACINBiCMBT0GKaCoaCAAiAAAiAAAiAAAiAAAiAAAiAQBMQgKegCSBD BQiAAAiAAAiAAAiAAAiAAAiAAAi0GALwFLSYpoKhIAACIAACIAACIAACIAACIAACINAEBOApaALI UAECIAACIAACIAACIAACIAACIAACLYYAPAUtpqlgKAiAAAiAAAiAAAiAAAiAAAiAAAg0AQF4CmSQ nwTPN9DqoCT9WG4Of1m2AcT5cT4flWZQGuITW9zwJhIlHp0/UMt+401BUcOF1VnC29VeZ3NRAASa CYEifvwfQf6rbdSUlOiP/Wr/00HnTvvuPssT19VCqag+a8Py6ly4rsqQHwRAAARAAARAAARAAARq QwCeAhmlng6H7iccX6wluZDzPLewDEFRjN8ar1vsJatvAx+9jvKwUK4N42rzFPMu7QxIpCKOnIjK lMuYl+L3/YnkVw0WX4OAKrQ3tlrIB4GWS0AsSrnsvcBCx2r+rhSjb+JzS4QlJcJT8zTidjlP2/Km h17dxtRi/rnPdawcnBZvS5o5xlK9boVrgpj30P97v5RGH0ZqMgP3QQAEQAAEQAAEQAAEWh4Bxf4w bXn1L2uxOD/nufx8Xe5uTsLBTRsj+MwVrvWsT4Zpt1NIbZUNxy1zNqas5nwyROqjoIibwMPxdDdL /fYKUVGNkMq0N7ZOyAeBlkugiH998ySzcSuPUS57Qs5vchttoM5URt1w8vr9exY6DdKto/dQmTtx TaCLMUVZzLE1YWUpKBE3gcfooG5D9Rp9GFGQwRADAiAAAiAAAiAAAiDQjAjAUyDXGOL0qDM6GzZP ZS6lRqc9l24uKBKcXbfgnpG7FZe5NWLKsF4KA6dqPOvQ3czLG0YwrgexIPrEumnD3X0p017dFKaj 6g5XVnsz6pgwBQSaHQGxKH7PrInfhhNnocvGLbONVctY2F5v4MSRRhp1tVqcFrrfP5HStrM306xr 2aryk2HEb/20fot9KZNe2k0wjCjKbsgBARAAARAAARAAARBoNgTwK7K0KcSpt4IHjLfrI3u3L7kl zrjww6Gu2xdZZLNrCsaMG1LmbX9eaviJvSTWAB3CwH7zb9GZsr3GIt7Ns6ePrrPXmuSbKhbnJ19m sk3cG5cjES3NYLIuTEiJheFrB/Ue8vn2EHI38+jVBMmmZVIw/LyPqwktX+ejdUcSZBENxPyEC0H0 LSI/j4l3QCuq2LnyUsN8luqxxQ8Gnz19M7mAzlRGO/FSPDw6SUcuUoOSyXy/FJFMHKlmEF0Xpppb wp9hR3WzeYphSJMQyI/Z97UXcRNQ2ot3r5vYvcLYqWw2caK2bElBHu/6Ce8FAyVRDAKi+fIPTD4v jI1xMOHLHf4nyAPPLbf1gGQ4d3D1BB26+ISNYaVBTMjeh/DTO1116OAIOjbrg3j55R5Ecd71tT36 DJnrQw8j/GNXY/JekZAKpIjB4iDmoSXLIjbaqA2cd+4x/U3Mjw0OogX2WR0U8wdjsI7NxitlrKVo pdKgDBXvNgl8KAEBEAABEAABEAABEGhaAvAUyHgXv7if1HeMRa+eBr2Ya+n3HmfRv6QfX9wU2Gfj UtO8xOv0Za71x8N6y7CJHgavmzZsvG/O7Ev8/HRf14zdn837/jzzE7w4du8QI6cZ0zy237X++MNu KUe+sh+3kYQkoIL9rqfSqxXywjabsBmypo0xUaM4ataekRfX0I6KMYdvpWwZQW9azks9u262ySyi 4Er+64TjjqnbXeZ8e0FAFJCJ/RQLu2lO89de6fex3tNtcz2I8LTs0qm9pGYkEOPhFQ5eT5dGpRU+ /c21c9ze8Fy1dhW0E8dBRuo9dnsFk6x2HPlxjoEqXVWxIHL//OEf/cbvt+T3O8cXUlTI8atJ+U3b U6ENBN4qAXFezJkdN+gHhDvTcQy32s1H+Q+D1k8zmuib7XzpTW56oEvGNvd56y4wwwJJ+Ym+Hk62 i/8eeuHpf4eHPzhGpvTyWw+K+DF7XAc5+f3T/4sLUYEz9akb5y8n5TEl83jn1k0ym7Una0q4oDDj wkLKZ9c5HuP1K00c9VGeDy+soZc/2R5OStrUL/wLEgdh2jr/VP+QKGaZlDj3RRKV6HshNlP80NfR wnKGE7nL7/MOpTFsgfMkLsUP9z54MU0a3UDMj979qeH4Xx73/yI0OdCF3P3t2oMKo8xbbRooBwEQ AAEQAAEQAAEQUDwBeApkTHOS/6LM+2pw1DR7lXLOSznic3XoGldzccLVECaEgdzWA/Hj4BXT3Lbf HbZl8zLr7hyOSqcu/0dRibfSXtJTAmUL9zOHrZkik3STf/bnLPv7ns8Y8gPeYqIZs5lZffQ3twKn k39w7WzM2YXH2YwWmTOC7HpY+9GMrbesVn670laLo6zSid7InHkjjV62wOk763yUr7M+RQ0ZRV3/ cXssETRsvpVB+X3SYtGTpFsUP/XeA76IUukz9Ytt8wZ0U66oXZyZnihzFHAX+/6y2JRxE1Ci6H2u 09Y/cTvy4//Mur2IuRhJqsC4NpBAoO0QyI4JDWGej5oCCogfB62a5uRz19pz89pR9LCg0ZkeFq4/ YoYF8bMw7y/nHcti7yp31e3XgVy1HCLZtsBscLD57p8lh/csGa6V+VfwtVTpxgSyFsBnofPWJJfd v33raNgh/aLP/vCRk20MO1ZoA9ZUrp3jMAOOMnfyXmHEDmYgSkrjk/l/u+6TPc97TrUb0V+L09ft DOOMoCxWrV7uaKDRUV2TEfciW8huvcqJ3fXZkDVZy0/u9LDszI8Ou1Jx+UPb6QKoKQiAAAiAAAiA AAi0JQLwFEhbO+/OtXtGfbspc9Q0urHXMrMEMYe/vm21eo6xCrl7lEzFKUpv8IDebIQwsTBi/xq/ RIrrOP+TQSr0hZf/JGTIdR5x/rOUB+S7Vbd/4l85fuWsl/nn2av0D3ipX0AsfBhD1ilozRpjysY8 l2gxGjWwB/097+ZBjz2ZlPGsRY4D6Ul70fPHKWUCLorzczMLKKvuWckqXz56nVmY8bvHYNqSMkm5 a//B1qQ2AXMdvziSIqLUTM2YKAvltBe/SCqckpCbWfhvyJZlG057Ouiwb03ZUI6dZy2aqscPP7p+ gZsfNX3fr8utu7SlxwR1bfMExC/T7zBPdw0BBcR5N/YvoeMOOC6dPogOZCAryAwaooSTm71DZHfF 6QlXHpGX/4ONifOOJHaDA11WL+O675p5S3wpt8PBX40m40PezR/nkxAJ0zw9JtAbH+hJfkbJHx4W KhXG8Lw7l4+RwcpoLDuMUBzVgVYOvck/hC/ypCsF3pu2xcmQGWSSo0qdEbJW7qqpRuwhbotfVqwL 5rq4zdR/Hub/zWznPdTMX8NWWys08mKb71oAAAIgAAIgAAIgAALNkgA8BWyziIVx4Q+nMtsKuuj2 02Ou3fv1290FS7+dqM0hd6+eYt4nan1krse+tBfzzm//lczbtWY5WjGTahLmgHYEyJYMSBYIUFQy ZfrJRwaqRY9uXQqX9wtIMsjez0u1cIeaGJAXe69Sf9+3m8jjTnKy6clM7dOjTt+Ut4HVqEUV93ac zwZErDRx+szeyZz+mBmwZuXBGOmuAXb9gky7stbExbMM2wvObv1Nz8PdXEPCJTloy9pg8kb06Aw9 A3u/HPNlIY9id7kaV/BHNMveDaNAQFEExPnZacwQ0EGzU8eqh00x77TPr/RTK92hIE67dSKUHhbo YxHET0IO+ZJBQHr3VcqtSyGSl/9E9CteoPdKssFBsMfJsKt1QPYHi89nJB12MyQT81e8M/u2CYhP YdyIGs4yEOfFXz1CcmoPNdeXLjfgqGjqkdVMOQL65Fcy//8jYeAExsUgySyNkvAq7W50KjFE26BX t3ZkiDu5kY7LwPd36tHdye9f0y8iMp4ecO1b0TehKMiQAwIgAAIgAAIgAAIg0GwIwFPANgWZNj93 KHeiAf+9sZ8vZmbgBWnxt5mX+XKr7p/fv834BaTr8F+xjgDKynmyJbOVQLJAwHiWz8rxxJUgmeeX ShAnn9tFtgyU33ogW2Lw4uGNOCJGtuJA6olwcHc0Z1b+v7zlt5fW2NtuzCCNantUO+0pW4L2uWlR /Ftrvc8m0+8VK2inr+XH7fsuzenbKbrSbkFiN0TTKqiFvo/+y0zzW+Q4wVRBx0M2m0cAhoBALQgo 65pPtaDzFWbnFlQdzfP5/euMX0B65CHrC6Cokc7TybAgSo0KIZFKpHfz75w7Tnx/Iz6RjDwvEm/S jzw1MzBDWJJxYMXHDhZcyaMoepqSRu7o25jq1TBmS3ZJlAmRSHZAdJN6DfLjT941mm7GjhhsZqk9 4vSbQcQert03LtZkFYOkLvouAf+UCO/6LZs1xUxqTi2AIQsIgAAIgAAIgAAIgECLJgBPAdN8xY/v JA+WnGig3E13KNm4yx22Zb0r+2pd/PReWBL9j9JZvazR1Tqr05sRxBl/7N54iqIcNmz9lNneL1sg IFkRIJnnEwmG2QnJJD6Z1LMw2rIP5xF9ReJZIK4Eo/yIyHTpZKRD5070RmYSWHG7T7i8VZL8Du4L Rld9EFpx5oWjN+kzFNQNXH/Y+yUz1aFTBe10Fc6sm8Mbx+6kkGYTpLEVl3uPKn5y80ICwhm26Mce xtedgObgj5ytSTHBnbhUNr5gaRILYi/Eyx8XoNaVHRb4f/ywmR4WvL77lLzDFz9P/5u87afYu3kP A/fTIRJtxw1TSYknpxsUP0/7k36jz9XWkD2DYv6Ns3dyiH8iR0D+SxVk5coCGIr5f4bL4g7KbJFs PSCTfyPhjciyJ6EUCHL+iQ5MMnUyk5zvyGaW7Kcg+yb81xE3x8iV7MaEYn4aCUlCURrazAjEpKJn 1y/Hlz9toe4sUQIEQAAEQAAEQAAEQKDZE4CngP45H//zT37vyX6dd+ikraHluvvnZeyefxLU0Ot7 evkARZFZPRtQgKRu/YfS4QmFWWTrryjx+PoNRymXTTcOStfts2v7ZSsCpDNz6nHElQwtffXSrQTU gyuhIoM+6pKAgtwBndMCw1QH9OJ07TvSnOgpzCIrhokNm9aQAAHbTu2TWCX1RIxxtK9uQUFO8p97 tm87k0pilZOgBi9fS454lG1kkGqnwxZ+uuRo2h63hb/RBzrG+XxEn+ZYpGc2lD6LgX/+xO/3iXdA LPjzxNH0PhNMsfug2T/aMFCxBDiqZvN+3uPGpYJXTl/mfY0nYsWT4wzP+P0aq2LNvm/v1n+ULT0s 0BEB8hN/3bDBl3LZce3gcuYdPkdVQ5suQ6IAkEEn5v6/2fSwUnTnwl9v+pClOtJlC/xjAad5xBlB DkE4cSRFb5IJKdvVeAQ9GpTeiv/jvEDXqsJOBIkzQntA10eBl1UH6JcZ4Pkhi7dGSPYdEGHSfQpU Xk5+kYh3ZOn8rfyR34b6LmZjHyjrmzvR5sYeCQh+SLwD5BCEgNPJRjZm2H2g2J4FaSAAAiAAAiAA AiDQLAm0dU9BcZz3EBWdcav80/2cTFXcgwUk4nd7Na1JW9eT8ATFmWfdtTp0Gu7uK4kjGOBk0oHN Q371G07Z8csG59e7x3fV6vpl6sitIbcOLrSUrs4lixR+J0HFZHsNOKq6A4aRFQGznD5xsmKWAHTo 1L0zRRmPMrUeO4mZeKto6NAnm1HvmEybQa9laK/v5Hlyiwu13dagQ9+VyR9uiwzZuXS4lqTFyp2S UEXnEgs5dvu9xuQcdeqhpTLxktHGkIOzmclDWe30IQ7zNkYw3pCrl6JSs1Oun7tFPXzwJF/Naqnf NhctKvGk+0A9+9XHH2pOnD1SakOz7NEwCgQai4B6X5efYyPOH54pWDnZSE1NSUltoOuheMr4IzeH vpK39BzD6d6/eM18vW1iVyXtL5NGbI25cdBDNixoj1lD+xoeXA++msEdPKR3dy5l7DJhmvNEU6Z4 F+tFO3fMNKYEvvMs+tqs/+2BxnjXkXT4QjIaGM7eGbXVhSu7pTai8r0AKho9GG/EO8bT5kq2GMhw cK0917uXXmS3Hhi7fTM6a8MHahbe1JLAGN81o2Xbi9RHfHFst4s2cU/M7ac9fjUxZ+oMa2w+aqze BbkgAAIgAAIgAAIg0LwIKJWUlCjQIiUlJSKt4TItLQ/IrPrj5gIFWghRlRAoEOW3V1XhkEMZl9vN uG57/Mxqyst0xtMNkb8tkoY2BLdmSGDCiNLHJCam/o+Joh7bJkXEDDWSJFTkINaktWgiZU+CFtgs eXfbX7scWccDnfLCVlvYbuuzI+bUskoOUGgiw6CmkQmoyT0mCv1b38h2QzwIgAAIgAAIgMDbJ9DW 1xS8/RZoDhZ0JG4CYgcd+PD3YLdiDwPTGY+WHPeWhGloDhbCBhAAgboRKHp2bonOBJ9YeuNAfs67 n5/fNrXUTSA79cDStA92E9QNLHKDAAiAAAiAAAiAQJsgAE9Bm2jmWldSXX+0x660kszCy99M6Ytg BLXmhowg0NwIZEZduMS/4bs3NOXJjft637jLLxwQC0K//4E+zREJBEAABEAABEAABEAABColAE8B OgYIgAAItD4CWkOd51hTWWkxUc+NJsrFFyjmn3N/p4/dVnLsAgmR6GNrvjOWibyCBAIgAAIgAAIg AAIgAAKlBBCnAL0BBFoqAcQpkLQc4hS01C4MuxuZAOIUNDJgiAcBEAABEACBVkwAawpaceOiaiAA AiAAAiAAAiAAAiAAAiAAAiBQZwLwFNQZGQqAAAiAAAiAAAiAAAiAAAiAAAiAQCsmAE9BK25cVA0E QAAEQAAEQAAEQAAEQAAEQAAE6kwAnoI6I0MBEAABEAABEAABEAABEAABEAABEGjFBOApaMWNi6qB AAiAAAiAAAiAAAiAAAiAAAiAQJ0JwFNQZ2QoAAIgAAIgAAIgAAIgAAIgAAIgAAKtmAA8BbLGfRI8 30Crg5L0Y7k5/GXZhhfnx/l8VJpBaYiPIs4hFyUenT9Qy37jTUFRa+hnIt7Ns0f3zh++9OyTelan lQGpJwUUa34EimO9TZSU1OQ+C4L4EjNf8fynS2+5BwmKRDw/1z46Nhuv8MWVVUTMjw0+7bveXmd9 WF5jVJTIP+ft2oc2VWeB38P8So1oDMWQCQIgAAIgAAIgAAIg0EoIwFMga8ieDofuJxxfrCW5kPM8 t7BMI4ti/NZ43WIvWX0b+Oh1lIeFcoO7QTHv0s6ARCriyImoTDlheSl+359IftVg8U0s4EnwFxOc ZjhvDOg+tH/XuugurW8VQOoiDHlBoDEIKFus+PvBYVsuI9vYLSD9zQFH9gtFtTd02RvqMczawz8m +SdH7aKki7/6C/jh3oG3nxdXsOVJkPtIyxnT5vlkzbE1UVe4qeLHQUvtLJ39qG/u5sYfHnjs1wu8 AoUrqbtAsYh3ZG3AQzgt6o4OJUAABEAABEAABEDgLRCAp0Aeujg/57n8fF3uXk7CwU0bI9g3iFzr WZ8M026nkOZSNhy3zNmYsprzyRCpj4Ii02YPx9PdLPXbK0RFEwrp6bDvxAY9ihozbkgdjC9T38qA NGENoAoEqiHAUdHo1pG5P9xhcHe50ZNMg0PjjPaf3zTHgh4ZOhqNn+uizbVe4TS0W0V3Ys+pG7e5 aVOUtp29maaieefE7lrs5J9l7Xl4j4uxKpGuPUBPQYNVA0yl3QSLHc4bDOmFPzkNwIiiIAACIAAC IAACINB0BPCzTY61OD3qjM6GzVOZS6nRabK3gUWCs+sW3DNyt2LfII6YMkxxv3dVjWcdupt5ecMI 5te8WBB9Yt204e6+lGmvbi2wccSPEq6nca0/Hta7dsZXUt+yQJruUYAmEKgnATINDth8UtN5JjMz pxNH1dDVLznj2oax3EoehFcpV477CijuzDGW6rV7TmprWBH/+s4V64LJGDXHwYQYwzFwu5y825Gr GLdmba0ol49shQhYM8lirj9l0KvbW7WknhVAMRAAARAAARAAARBoiwQU+zu1ZRMUp94KHjDero/s 3b6kOuKMCz8c6rp9kUU2u6ag/AvzvNTwE3tJrAE6hIH95t+iM2Xra+lN+6ePrrPXmuSbKhbnJ19m sk3cG5cjES3NYLIuTEiJheFrB/Ue8vn2EHI38+jVhDxWECkYft7H1YSWr/PRuiMJsogGYn7ChSD6 FpGfx8Q7oBVVaIXqs9E2HNxsr0PbLwuXIOJdl2i03+x3OvhoWGrpGuqq60u9enTrUjhlNGpgD0nH KlNBUpWHRycRRTqf+JFFyJXVt1x+SVWq0kiTCaaNN2AE5qWG+SzVI7XwSRBhjXPLfhibu/Xamhod JX1czD+zYo/K52tlTgGxKCU86NzB1ROGr74ui3VCLoaRwARKajo267cfPn6ToizKbj3I410PYjKQ 4AL2a68/K+3B+bywM5KgA0oTVvvFVx76gDxcvlN76Uz8Npxmd2re+E1h0gGE2HN6p6sOLZloPxIr N4DEBgfRt6b68vISfRcMVCL/EMvsN7D3fygW86N3k7I6NjujRURw/sMg2siB8849LvOM0e6A1TZE xQTvK+dW07rYIA55YauNdCzdt9FWCUIux2c395aFfSAAAiAAAiAAAiAAAgwBeApkHaH4xf2kvmMs evU06MVcS7/3OIuepz++uCmwz8alpnmJ1+nLZV+Yix4Gr5s2bLxvzuxL/Px0X9eM3Z/N+/488xu6 OHbvECOnGdM8tt+1/vjDbilHvrIft5GEJKCC/a4z8+68sM0mbIasaWNM1CiOmrVn5MU1tKNizOFb KVtG0O8b81LPrpttMosouJL/OuG4Y+p2lznfXhAQBWTWPcXCbprT/LVX+n2s93TbXA8iPC27/By5 umxFmTF7lpo4nfin/4KLUb7O+lTE+WsPSYQ1stVi+fS1We6R/2YWnpqlEXcoLEeVXUNdTX1pVulR p29S3KEmBswK7fIVpIgv5uxVMn3QMdbtwqlYXyq8LJAaNBbH7RhtYuM2Y8HuCPMpwzTT/Dw+clh+ koiPOBfZLDZmY4xpdQTo/UnMnv8Omp0YT4FYcOXbn/O/2DJVuhVBnHd9naGZjZPzgm3Jo6SbC5i1 9+Nt58UMC03m/WbJP3KDL7/1QCyI9Fkw3DqAb7zo94yAhRQV8mtoEj0tp8ikPWj1NGvbA/muN16/ 4QW6JW+bO/27M/zKop9y+rqdPu/Vm5SyWHXhRUnyltHMAMI7t26S2aw9ObNjc18/DXBM8nGZtPkC 7YcgngVHC8sZTtPWXRnoqPfYe+68Y/QAkh27YxBr/w3zTz58N3zz3CFr/PkUP/zeU2F+oq/HNCcf 4spM9L0QW7pRi1x3t7N0v+cQkSX87p0tztv4FNduRH96KFMf/UNs6Cqy1YKadjj+5g+jurS6PoEK tToCJHapvlK1acrmoJh/Cursjy6O/aG3TG73/x26l1VnEW8Htih22wg5w3+5l/Vf9YbUpabF/KCF UuFm/zsQ9rAm4W+HAbSCAAiAQJskAE+BrNlzkv+izPtqcNQ0e5V2hbyUIz5Xh65xNRcnXA1hfhnL bT0QPw5eMc1t+91hWzYvs+7O4ah06vJ/5Df0rbSX9J9/ZQv3M4etmSKTdJN/9ucs+/uezxiyf8Fi opkuPe9WH/3NrcDp5B9cOxtzdrtyNqNF5owgux7WfjRj6y2rld+utNXiKKt0osOfZd5Io5ctcPrO Os9M76kho6jrP26PJYKGzbcyKLctusps5CiHPe4jv3u69PD3S4d3Ffx1NSxVaonw6b2HFPX4wd2n +ZS6/pQVmxZZ0PEJq68vuc84ArRmjTFl11Srj15z2ouGKakgu+Kg6vpWBFKtRmXzJUf3TaMVjRn1 /hP/Y5RH5B0GuMxV0SYfaVS6EQmI87PTmIVFeppqpI/nR+9Y/2DKaue+KrKBlKM+alMsPdsv3Vwg FoRu+WKNv8DB67tlo7VVtXr2oh1pNpb92MeECHGbtvyp23mf/w3u9uL25UjZcgOyWmHZeCcyY2cK tiNBEuh5tuBeWhXnpIjTE648oo0z6s5ugyh6dm6ttfPW8JErvVfYcjnKaswAwr+RRrsaiWfhTFTg THoAGUtd3+JDDyDW86yMP1jxMMKLXKVsR/V8eCnRYVuIhwX90I7QevJLILXiTCj9ldIfoCuJWUoC KK6aOe8YiYyw3t1MU5Wr+z6tesQnki1a4rz4q0cERNq4EXotLvBKI3YliG6+BEjs0tSSN08D6WAi dJq8KfCvx/lvSkgSJoV8P0OLUlaql/XKFqsf/RfzQ69aFhaLYg//FNgc3AmqFqtu/hfzfW0Np3/+ 1L6mylzHnUmHP64lFGQDARAAARBoSgLwFEhp5925ds+obzdljppGN/ZaZpYg5vDXt61WzzFWIXeP kl/S5Ef44AG92d+7YmHE/jV+iRTXcf4ng1ToCy//SciQazxx/rOUB+S7Vbd/4l85fuWsl/kn/Ua9 1C8gFj6MIesUSqfWEi3S1ft5Nw967MmkjGctchyoSlqq6PnjlDIBF8X5uZkFlFX3rGSVLx+9zizM +N1jMG1JuVRpNvYoB9p4PUG473efLjlKuflc+opZyNC170hz4vI46T7zK7/EfEpzoLkup+b6so4A C2Z9BJtePb4bnS6roOjO5aNk0XXV9SUqygCpkbCIn5xG+0cGZN7JGLfc1eA544ko5dmUTxJ0tSkC 3TTUChN9f4gw3+RuUeomYBHkPIiJkdtckBN/YufWG3yui9tMUw3yUKTdjaZ9cuwrdyon9tCmlTc6 u7lP7cMP9/12gZM/5bLn12/od+8vw3/eQoczkBSkxM/T/yZT7irTq5Rbl+idS7I5ed7Nn77cwyfH NLg7mtFGFgkep0iPdWTHMGaJxMjuL1JUvkl+XSLMuLZssColLsjLLiBeg/6Zie9MXGKYdfUYGfoG GqcERw5f5qb9+DL91cJpEOPuJKseEoJ2+SdS2o5Lpw8i/olifhpxdcj5BbJjQkOIUn0bUz38tWlT z0gLryxHS7cfG71UPqkajl39c7CXac+u5K1A4yYx/+qmpb7J/75uXDXNQrqymobCY7s2i4rBCBAA ARBo6QTw241tQbEwLvzhVCYOXxfdfiR6P0n3fv12d8HSbydqc8jdq6eYn9haH5nrsS/txbzz238l 83atWY5WOkwwQsnSeumSAckCAYpKpkw/+chAtYh9oy43j2VXEMim1lItklfir1J/37ebKOVOcrLp ySx0Ztb2y9kgeYdPFfd2nM8GRKw0VZbtVWqgN32UA3+Pm37Xj37LHrT0/J2Uw7MM2SPb2uvP8fJ1 NWacBV/5sVEVaqyvxDxLsi5DYobkCltB2eERVdWXFCoLpEaNEscKPzVnkOMsY5Uy6lr6gwn7mzeB N7yLPiep2QvIq/7yhubdoefS0nMNxPywvbtJiEGLOdOt6B0K4vSbQeQplrxyF6cEraUDECb6Out1 Gu+Xbb4sJjnWjzmzQJxy7gf6Pb+0ICV1BGgPNdevOIORSebaOQ4zoMeLV7wz+7YRz4L2JGdryQDC qKa4U837sLP8tFsnQvlcqtjgo/mlFRE/uXryPBkaknIHTR/fU8SuCKAyciwXLjNTly4QWDRvJLOP QPwk5JAvGda4Mx3H0HETpX4QiQ1k9wNDo9Sz0LybFdaBQDUE+GFBkSn/ZKmYkkWAnRUcjLScWjE/ 5Cun2V63CyoesoomAgEQAAEQAIEmIwBPAYuazFGfO5Q70YD/3tjPFzMz8IK0+NvMy3y5F+bP79+m t9zLrkiX1ls5T7ZkvOOSeazxLJ+V44krocI8Vpx8bhfZMlB+64HMlfDi4Y04IkbmWZB6IhzcHc2Z l/Yvb/ntJb/Rqd52YwZpVN1jKs0mEU45B94pLLlzaMWkiRZa8n2Bo+vgfcyHHN9IBW/cEERHSayp vhLzxgwm6zIYY8iKAP/vJWsoNPLjzl/P6kS/RK2yvlR5IDVolDlWFm9dT7w5Uk9Nqfwme4igqM0Q ePn4Lr28n8TCSKamr3CTeNbkqy+Zn0vPNSARCmIu0tN19kBE8gb+4pFQvvSVe3Hm/Wh6FQC1MDD5 v5JkvxVTJzCHLJIkvSU7SVHiYuBaL5k6uNJJiigj6R4ZkYzGSuKJvki8SQ8gshMWWL8ARTks/8ic 8Qi+DPffS2vvbTeBXuwgSeK00P30GoHFu9dN7M6RrAjgumzcNJEsLGK/ypwRJHBJalQIib2iP9ay Dy0z79bhzacq2XrQKOdBtpleh4o2CwLivAcJaSJhIb20fuKk/ro9SKSS4thtssAD9qv2/bZjyYfd yJb7LvbL/W4+LSiRPlP8v45t/Ji+oTR41tbzaTUEJyjiR/nMNLX3uv2Cov7e95lFl3fM/ncojglp QMaTkN1LrJhd/V3MZn196Cov9z+pmsop5fEubZ3G6O5i//meS0lsfrk4At3tV+/5bcfiD+ksevZL D998LjO8iB9DDDei75jN2XqBV2NUBXENNSX2X/phZj/Wmv/tCIrj51Yd8IBoD/xp0UhJCAMz52+O R72gY0M8CXLuVRpFgg33wA90Zi/Zf3fpn/xm0V9gBAiAAAi0CgLwFLA/yx/fSR48RJ/ZVqDcTXco 2aXLHbZlvas58wNa/PReWBL9j0pmoWqd1elS4ow/dm8kP5EdNmz91JTeKSCbx0pWBEgm0kSCYXZC MokaKPUsjLbsw3lEX5F4FojrwSg/IjJd+je5Q+dOHWgFjy9u9wmXt0qS38GdvNesphkrzVb8/PHt VCJVS1tD9nZSnHHjUkIO+TN8ye+GkNwjpxXu2LmEPReyNFVVX7Y68uEe2QUCxNNhYyAI9LuvrU8l Sb4+iqSPdShf3wpAJEqr0iiTz67pkBSn1WXeTcXZB61ieGqulbBbc2jT3ErcBLIX+2TmrM9JSeDl v5Is+GcjIObfP/kzeQNPZtoWKomJfPGrjEfMwCJ3kgJ5Sx8WnMCEM2SSJHRi0bMLu9eReT6JODDf UnoWozweaTiACisOOnbuRD/j4sdnfEgoQi4TTYAZ1iRv+x2Wzx8tDcdIrkpWLkjWCEhXBEgWREi+ ymIQUMWpcYH0ioOeA3u+RyQ+PBNAL0DoPdi0c1LYHTKYSB0NM8eY50eHp71qrg0Ku0CgBgJiQdj3 357lZ+SUmZcrW6xKuLqCPS4p9l56+zHfBR8jX7NCfL7cG/S3gOnwoiivaQ6zvo37YHPwo/yI7cOK 7/xTva523CFLf9w3j8k0yP1g7Ms38T/PN+/MEYse/vLZSPulkaa+sZlFwuAZ/xz8bIbH1rN3q3YW 5MT+8LHR+O0pTr6xz5P3c0OWzP5qV1ga8QQoW6xMCF3FGJ4R+3dG+7HfBtNfH4Xs3rb3aIygiNSS REn4adoHs74N77/5Ukp+zPfD8lNrMLyGmhKBOx2Mxn/10HZ/aMLdn/XPfLl6/aErCf+IKnN1FD0L XDrog+0PPtjxsig75vux1N9HPWd+5X3hYW5JT8f95w/MMGL4WK/yWTqtf2cO1+lI0kG7aV6X9i+z f7+SLZjo3yAAAiAAAvUjAE8B+YPIj//5J7/3NKR/Xjp00tbQct398zJ2zz8JauhFvxsniczqZW/z uvUfSocnFGblvaJEicfXbzhKuWy6cdCddS5Itx5IVwRIp8HU44grGVr66qVbCagHV0JFBn3UxZnp ifRegwGd0wLDVAf04rDBAqjCrNxC2oZNa/yo6dtO7ZNYJfVEjHG0r25BQRXZlHVNPqLDkmUeDTjP I24LcgjCiVMpenbk1WJe8l8bPX88+5C45cWinGzZ5L/6+kpWTJAICzqFyeGX4vhiaWSHzO2u/wvQ dJr0Thwb6EFwI+zfXgPVOeXrS0n3VkiBUNVrLHU0MGERpFszaPnPNHvTzhokEGgMAsZuAQc8R8lN ruWUSN/bd6BSQkIKdA1V2kn235IIiBQ/NuZh9nP6QJWih2FRVA8up2OfQUNpX5zgvN+Z+8Q7IBb8 6Xcsvd94U1VKWav/YDv6+c/OLSgW8Y6t/XIPNXN3lO/iCmERWPWlc3JLyRjV1XgEPYAUZOUWkDn8 b5uWkAgIW08dXUoiEdAPjHQfgeNkuQUF0s0R7AmOMu+DZEGEpEjvwcbt711gDmtU5uoNp6UJX+QV iFLu3/83mx4oezy9doMabKJBQrek3yGhW4zHdn5w6Mq7loho2Bj9ETIbncC59U4f9Na333ojt6Di C/yOGl1pXz5ZXzjMerB+p06Sr1lZwrz/3tBRRS/77ogkqwMGfmjZr1vH9txRH03tVS+LxU8uf+91 /Dml86H5YN333lUdMGayIZX1xw/bjl5Oz6t0XYGYd3rVV1eII+9DC1Pdzj0GjuxHZd04Fxb/LI8Y xunYSZMxXMdixIjB+p07Sb6KsoT5/xWXEJfl5V2H6JgjFpaW/bQ7vtN91CfjqzW8ppqKeSfXbosg +j40MdTp3tfU0oRKCT7/V9LTzEqsF6dd2nvmOXX71JXbSS/f1enDhEmleH8nPc4tFFOqA+d5f+dK B5QKP3zm6j3aIyMWZfzfLI+PR+qq1i/YZL2aBIVAAARAoPUTaOsTquI47yEqOuNW+af7OZmquAcL yK7A9mpak5gF7cWZZ921OnQa7u4riSMY4GTSgc1D/s4aTtnxywbn17vHd9Xq+mXqyK0htw4utORK gJJFCr/Tu3Ol4f04qroDhpEVAbOcPnGyYpYAdOjUvTP5DT3K1HrsJFPaJaGiocO8wH/HZNoM2t3Q Xt/J8+QWF2q7rUGHviuTP9wWGbJz6XDpHoFypyRU1VOrytZl2JKdm8jmAr6vh2nfj9b9ltxp/Cfs NuqX/2d7abtNzi+zOyhxR5zt43P+pzl9aXurr6+kOuH79h6OoczHmXM5nG76o8nmBbslB8/s2zBW 6x3Vzn1I9eymTZo1hd01Xb6+FYBUq7E4Je4C7ViRHRuh9r75wDLyW//Dixo2NYFiPr/DnmM7J5N1 +FUkFY0eJF66tvkgq4lTyVSZTKVtlxyeaUzdu37ujwyd4R/okadee+xkxxnMXY76yKVntrpwqUT/ xQPVJqz+NUnTyXkkO4hwDD7edW6HC7XVtvu7al/cH/VjTOy+xYOrCkdS/DjuDBPUgJ7hs6m9oZNn iKcL5WPbSa3v/1I+/Dki5Jclw6UjVIV9BEwZiadDslmgvPeBwzUaS2rX48WDFzoTzBhJ2qM/93Qg ryWjQy9EU1xdiiwlNnaxHz/ZdiDjj+igQY9yZFAb5DzborKlEE3dgtAHAnUnQM4+iHqQcHrFCPWO 79Z1HpqX9Oefz4lKHR1uT2b9Yb1TZkyQH1mC1Lm7ulo7ZfLwtdcx6EsL++tBUmo22RNRIb1KuREc Rl99X6fHex3I0SfvkYcx669rMfHPK32NX1aAKOXPS/SKJx09nZ4qVUZBkitTQ03FKbdOhJAVRzrG Op1VO7YnMQzpkyX+ir7zSPCS9lyUTRyd4TPsulGde3ZSa9+ukuGW09126fKxpEzWsd/P3kjLfcML uv2ucS/tjnVtn3o3BwqCAAiAQNsgoETO/VFgTck2MSKt4TItLQ/IrPrj5gIFWghRINBqCEwYUfqY xMTU/zFR1GPbpGCZoUaShIocxJq0FgpSJk7xHW82L0R7TWis5+jGDbWmIIshpmkIqMk9Jgr9W980 5r9NLcWx3kaWK8npOvQpid+4jhuk8eeuA/9ZT7I0Nur8bqlhJFRBH8vV6cRN7Xnx4BfjdB6W+fp+ dpDzSKejjykd94Mhnm4DOnNk+dkrmuFzdD4+WipOx27ToYNf2r57aZGO02Fm98FhTzey9YCOLNDH 8qt0is1g/35HMT/IXZrnwNL39g/4mOSXpcmbLm4ZfWfhiNX0soAySWqJuKxAnQdeZeTn/u7MGKbj 7hviOXtA53elBpReKSuXhA+orqYur37px+Iskwa579++zGlop+tf6DiRP2fs15F9JYRJqILzp/xC /s5L/MX/FrFFWncmxDPv0Fijz8KozoPcf/jpY6V4kaXzhIGd6+zKeZtdDLpBAARAoPkTaOtrCpp/ C8FCEAABEKiMQNGzc0t0+qwNyytgzkc0dtu+0BpuAvQVEGgUAhz1fqZ6qmrMin0SNyT49uNnTJTB hiWuUwB5tVKanl3+Zvz7JKZJhaSsYzCszMVXGSkPmQtkyUCXXk6HyogpOfvNOF3pEgY22IH0/rOf 5xOHRcOsbkhpHff91x7IzIn/ecFoqV+gjFQx/8+Dn4/RIaEKzD9dMZFZPVE2cQynfr3ChKwq+PuU 33f+gt4DdDXhJmhIw6AsCIAACFRG4C3+vUCDgAAIgAAI1JtAZtSFS2QLzhvBjV+P35QeT1BvaSgI AiBQLQHuaMfhBu93flfMv77n2otXueIONf+A6mo82owWmvEw+V5mmcVPr4Wi3Fd1OARRa8Do0WRr fkZihkBYKLdc/4N+RvpsxIFyqWMfy+FMzMInsclp2UK2SBE/Lub+C9npBlXXV6KOBDxMupddJv/r XGFuUYX9AlQNNaX6mH/M7K/MiE19lp3DohDzE+PSMrPoEw3KJjo44uQFu250njljzri+ynnZlRmq OXjGHDpOStb9bG3Dvl0RoQDPLwiAAAgonkDNf+gUrxMSQQAEQAAEGkpAa6jzHGvBVjuLHW+cz8Tu cqw8ymJDtaA8CLRFAuLMxw8K2IrniQpf/8fGMhTzY8/s+cJx9pGi/5TadaRf2hfkvGCDBPwrzHtd XFLuK9XeYNxsJvbe3ZBrUanZLx/+8uP2dCZ/VoDHNE9/csJfJXSVu+r26UVfL8gVCf99cON6Ar9A yfCTbSvJxDjjz7jox//+J7p39RyPoj50/5+TbS/1yrbnc9StF+1xJWcEZP21bdvGIxHP6NioUbx3 uvfqQrbziwty2egGr/8V5b/+703ZryUUR2/coqm04X9dv3aTl/3vnV+8f5EYfmzZtE3H7mWVO9+w ppo+6bd09yImNsFh771nb9zhFwh5Ef+00+6kptlRLCTRk9n65r8qIm4I4dM7dBhIKisl48mjewLl PrqVUOKomk3zIBXsPG7WlKG6nd5pi90UdQYBEACBRiaAOAWNDBjiQaDRCCBOgQRtm49T0GhdDIJb OAHEKahHA5ZGKKiqcOcPVv3444JJQ3VTyOZ+OkgBm+zmf97h8k9nn0i/MpEL3u8g4l30+Xr5+tM8 atDsLevHFa89JZhra9JZz3KKjXnXKmLwiflR3l9MWn3yRWe7JdvXfzNtaDd6S4K4VBTZnz9z4WI3 12mj+nSqZtW9KOmSzzqX9afJtLuz3dKNHotn2xqS/LKgA4ypOnbuUzrs+/msxG5ZOAAR79Ker13W nn5BdK1YPzl/7fHcubb9O+sNmWJj2rWSLRJy5lVe0zzelQMb5qw8TmJEd7ZzX7vws5m2A7kqL4MW M0EK2MSEKvhI/5mX6xivxEEzP5v7ybRZtu9FLBn/sV+SfBWYzOK8sDUmv71/9Lt5w7gNihdZj26C IiAAAiDQFgjAU9AWWhl1bJ0E4CmQtCs8Ba2zg6NWDSYAT0GDEUJAMyZAPAX7fvnPdq6dUSecetCM 2wmmgQAItFwC2H3QctsOloMACIAACIAACIBAGyIgfhY4V6vr4NWnk3NfxMS901dfu9LdF22ICKoK AiAAAo1GAGsKGg0tBINAIxPAmgKsKWjkLgbxLZyAdE1BKkXtX7lSVpn3339fR0eH/SoSiRITEyu9 lZGR8c8//9R4y9jYeOLEiZqammxOHo8nL9Da2rqBtyIjIzMzyYJ1SZIX2Ni3HB0dZXrL6Wr4raCg IJlwLS2t4cPp8HxsatRbpL0MDQ0r1dXwW9nZ2eHh4TLh8gIVcsva2kr5by9D222Zdp7nv+519c93 7QyVXzHBHuQ7RjldDe+E9evVte+fDe9O1fRP+e5EQMnravitpuy68roU0p2q6Z9sH5bvObJejX+A QNsiUPZknYZ+Y9k1VEpJiYXFftkns7AEHxAAgYoE5B+Thjx0inpsG2JDncuScUb2EZaU4AMCIFCR gPQZudzIv2uSkpJkj3BgYKC8tobfWinn4yCS5QU29i35camcrobfkqdEhMsLbNRbpIGq0tXwW6R1 5I2XF6iQW3TrC+8cmEEiNfa2W/zz94vsO0v1yXeMcroa3gnr16tr3z8b3p2q6Z/lnv6qWr/cr/da lmrKriuvSyHdqZr+yVZfvufU+VcKCoBAqyCA3QeN/AMK4kEABEAABEAABEAABBRCQHXgZ8celpSk 7fp89Fd7L2cpRCaEgAAIgAAIVEYAngL0CxAAARAAARAAARAAARAAgbZOYP78+WStAZu6dOnS1nGg /m2eQNvwFOSFbdabfjT5lQKaWxC0tIOSFvMZ4hNbXKlE8bObG+y19OYd5eUpQGMtRIgFV7bY65jM 90sRke16T4LnG7AWag3wTiAm1sbmWmipKUtRZvjGjzoMXOqXmF9TVtwHgZZIoDje20BNSUn+Y/Jd UKC73BUb73iRYqsmFsQG7XTVoZUOdPVPVLD0Btqan+i7YKDShI1hgqIGSmqS4nm862dPE5gLgvhN og9KQAAEQAAEWhaBQ4cOkYAIJJoDSbIYKy2rCrAWBBRIoC1ENHyV6ucyzP2U1pehkZ6j1RoMrzjO e/jwlekU1WtLTKSHhXJFgWRm3tvpZDUZ6mGDKPpg4LufuppVoo4qzjy7xGTGfhJ7ZUPk+UXmqpT4 4dEpoz2u8ik9r5CEFabKlDjZd6bJvHDFmlS+FsRDYeMWkCpTWo9aokidCCCioQRXE56SKOYHfWbl 5Csgmu3WXPD1HNWdQ70MWz/ONsb28OLp48dbcBXqfZWoo9wOB28d8ecSI+/BMXErKht06tRxFJaZ uE76Wq1MpfRdAq75Te5Za7nk3PWALX994OncV6G0qtdfzD+3RMeZjJNcuz1hF12aUnWtwSg8ozSi YTZFxVwuDVbw3nvvderUidX2+vVrPr/UcyJ/Kzc3999//5UZJbsVEhKydOlS2fXLly+PHDmyQ4cO 7BUSaezly5eyuz179mzgradPnxYUFFQqsLFvyUf+K6er4bdIkDxZpTp27NijRw/Z10a9RV6Tys9/ 5HU1/FZhYeGTJ09kFZEXqJBb8t2pnMBa3lJ4/6xGYO37Z8O7UzX9U76JSdPI62r4rabsuvK6FNKd quqf5Sql8IEZAkGgBRFoA54C2bSZuybwb88R6g39aVqzp4CsKdjo5nSkh88ln1mG6groDYzALzW2 VO6YoCiypuB7V9fjPbcG/TjHQJVUsMKkvTGcF+UrRtYUbHUff7rHvmPfuxqrKKDaEFEDAXgKJICa 0FNA5YWttrDdJqC4LoF/7XLsTvFjjx2/pjbefXJfVYV32Pxo72lTV94w94r4bYVZe56/i3XUDFpp Q8cwxRlK1hR4zJz39ONQ3zWjtdvVTi5xExxZ7BA86qK/m0H72hVRVC5R7M5Jluu6Ho5vetWKqkId 5Ug9BXSxEhIDVDGJxEt3cnKSySJBv+TnHorRASkgAAIgAAIgAAJvm0Dz+cnZSCTE+X9fPEXerpPE Px947QlzmE4jJ073ERsvZ6YdVqCbwGlbSDVGc7THrr2cceeQK+MmeFupnZb1ht8L7+6Cm+BttQD0 NjoBcV781SP0ggL9sfYfdKeehW3ZFWE0Y3ljuAnEz8K8v1l5g0/ZOk421aCo9oYuJzP2NCc3AcGg Yux24G7JHxtq6yYQ82MD1kyymOtPGfTqVkvPguIaVfw04VoSZTtuhF4TeygUVwVIAgEQAAEQaDQC ZIUIWWchS2TlQqOpgmAQaBkE3uLEsmkAZcefFTjtWaZFa0s8eij0UVlXAVkgMEQSd8Bg6dnE1DCf pXr0Dn+T+XviBSJm4z2z4V/PdX8Mv7yX4c3jhN9WMxkGLvW5nErHCKgqKIA4P/ny3vkDWVF7w3iS nfzFsXsHSKIeaM0/eDPcd7O9jlQ7ve9XLIjcv2Ac6yZIX2vZvZLgCGTrgbskKkEHm71xtdrFLFdr pkhpIAMCgV03KErwsZGKdQ9Ovn/dx9WEremuyMxiJhADQ4YBRUytEByhjIR5R8MvH13HFClPsigz 7oLkFrlrv/pEnIRzmabxu3zTT4qaGFDaEgRsmLS4zkfrfG8my2JDVMG8afodtLROAtkxoSGM39F8 VN+isM1L/Ez+52FZ6YYDsSglPMh/tQ0dX0DHZuMVvriIH/8H2SRvsDjoGd2Bi/jXN9qoDZx37nFF 96U4xde+Uw9bb8Y/GDrP+tsw0q1JwIKz5456L7C097+fx/Nz7aNj7/9QWpYIP7J6go6S2kTvaydW 9yFK3YMETByVfF7YudO+6+2VpvryiA0xe1zJ3Qk+sfmkaB7v3FpioY7EJLk2o0sdZASSzFWEIZBK 1llPm0cMpKtMlzJgDMvjXfOR00W0ha020rF03xZOj5Mhl+PJing2VWTF8In/I+iMt2uf6b4pzx/6 z9NRI//Ik1z8mNSFKRjvU46hWBDtRypLmO88G7TeUknNwPWcZC20OO3WiVC+vo2pXqv/u9c6nz7U CgRAAAQal0B4eLiRXJLfStO4iiEdBJotAcWe9chWs+Ey5Q+Kr3iMfO2v8FMDXVaFpmSGLuGypjls iPy3bPE3KRfXMH4EirL6NvDRa1JkliQz2dDq5hP3t6+rMX2XbF7IfEPKPov06sVk13LefenR68z8 dDaDlmtgQn5JZmGpQBLI4FkhfSUtcscwWv6OkBeFCccXa1HGs46n8+lbJZky24iuhFzZVxJVIYXN 8ChwOqNOKo25WObzIvBLCyYLiVMgZG794+usT18gcQqElQr5787xhXJFZDbrTz/+j1S4TCx32KqQ O/mvGcslScv514iEUywoiamyisiUFsokGE/fdzdN9lVKMrOQlck2yr8hWxwY6dN87hQyNpSSJOoi X/wnbSmLJRdfsBkqgKWoMYdvMa1QHfPyACsibRlX5B+Thjx0bKM2RMJbKCs9KJ6sqC6peIx8I115 FrpKm4GlPWvVUjt6VPAIza2oKzcjaqsLV9tlx7WMN8mBLnT+5Yd/cpM+PQsDk/8rEb5+GrCYfoBm BmZUZu1/EV70M6y9JvTZG1LBN/GHaX3MA3L42nnWDH3PmP/osrkP9rhxKa61Z5RQEOU1knksbQ8n 5ZaU/Bvj1ZstRTbn//30wrfWEiHEhiymFJtYk4go8nmdcW23i7axy9abGbn/BM4kVlisuvCiPGQZ CuldicGshfGZcsKtvSKEkuKSUiRDoeRKJaxIlfOT9kyTmGZ7IOrcGsZOC5eZE6UMWQlvci8wtyQM 3whjf3UhZEbuiBFkxXhKxhOprkJGZmV1aaTe0hzEyj8mintKqzlbXnFKIAkEQAAEmpoABremJg59 zZ5A63638urRlRB1GxM19WGzNrC/O4P3BcUJpT82mf9zOqhrMoGY9Kcvchuh3Y7TTbcPG5iJTN1J oIF++j2MmFNS+Nm5BWVe/nUY8OFAsjWX09NqxiQyhc70Ox6WSo5XkAmUqhHzzm7wukWm+uOsBqi2 79bfvJ/86oaOnTqz6kY72JKgBtKvmYKc0ghOZQyu+KW9WucGBmqsYDOtRCZ28vz/2Whx2nXp2Yu1 lMzbSUAEI73eLCiJqbKKlBookzB8nH1fFZlAGUlxWtihoExJo6hq67HxnOJu339RrmlGTRptoKos bamc57nMerByYD8YbcultLpr0CESqmdeA0/cBoFKCci2HnDt/jew+BT9wp9/7GpMXrk1ATmxuz4b siZr+cmdHpad+dFhV8hYMtPh408PCiN2WNOCk9L4ZKBo132y53nPqXYj+ssccHJaX6XdjU4l3wcY 9GC2FHEM3C6zTgdbUypiHwmUQPye7lb6ylTRs3NrRy/25Y9c6T3fUlWFq9ejI+0XcBxmQMopW6yI Y50FIz7umXzy/uif2Vm3rYXu05O/UJ//xc60exvodmGDpZK39Htm2Xz3z5LDe5YM18r8K/haKqVt Z2+mWZ6I+ugfbrBOEMldZbMlf7DTe9tRvZ74/0J5PGS9G9pDzfWJSbRwyd6N0vX/lbIaY6ne0dDl aEYA8WZy7WzehHn9Sq/jGOn8uc/vUoZpSc/I+imO+kgXT1t9lqGYf2aZw1x/gYPXd59aqKjr9GbG E5kucfrNoJuV1wX9HQRAAARAAARAAARAoAKBVu0pEN25fHvoLCsyz2/fe9g4a6bymUevJpT/ZV9F v2Cn7jUn2TRbNsUtU0aceussHShBf7BeN/JjnKOm0Y3cv3opinYrtMSkz8zbFdFzODqDPx6vRXH1 O9dHnBQst5emCrGGo+Pok1ZyZ5+jNodqdcxbYj9pfTbLth6M+GSys8NMZiFPmVX09GRYFP/LinXB XBe3mfrPw/y/me28h5r5a9hqa3WKozrQyoGetAtf5Emf/fembXEyrORZYqe1sgk/w1IsyiGBFK37 5yUVLMsg6wWEF1aYaVD5fx/bR878M3ZzdzQjz0Hx87Q/iYdhxCfDeknEFuS+II61kd0e3es4fWG/ jGtk9wQRknLyT/NvXLonMZspuFPN+7COgvyYfV97hWs7Lp2ul3Hdd828Jb70sQtfja4kEKw4LymG cYKQiT2rSvQ0JY1csO6fGZcx7hsXA8GtS8SbIpdBAlC6/r8aVkRasTCHbE8YaJz1qGD1X2/I+/k/ PCxUlKUMM/5+/JL20HC69DKbvnAs2U+QE3/Sl5xJQZOnwzq8yniUJA+Q3XogZ0zr65+oEQiAAAiA AAiAAAgokoAi5nuKtEeBssTCmAuRQ4f0ZqrI0bd1YTcR8H/d9TuzxbWpkliYnU7rSj054316oz5z gCJFvcgRMruI23RSN3A9fKcwfd+o52c3fH3g9qM6wZCC5adn01uu5ROY14kkMteKQN6dy8di6Zz0 a+rulrZ2zJL42COhd2SxMchilpMbvcLJMOPv1KO7k9+/pl9EZDw94NqX9mWRYUhFU48UyhHQi2LI PPmPhIETLNhb5TowM60tM+GnXqXQE++O1EuD6R620tAIRc9C/XeQqIfak5ytexJB4vSEK+QxKn1p X/QsPIgOwZhMWTqN6U6cp3QV+Em5Qz5fNFhVUqNpni7DGJ/oK16gNx1DUbDHybCrdUD2B4vPZyQd dqvcYcpO+y3m2JpI/KkSaUT4oJkzjVUlzo6KGSycBunSfonqWUmWABS91Ju+ij6KUpqUu+l9SDZE 8FP/JU89YXj6qtnCqdx2Yn7Y3t3BZHPBnOlWdG42eGGpx4Slpz/Wsk9tvL+16g/IBAIgAAIg0LoI GBsbkw0IskSOUWxd9UNtQKDOBFqxpyA74eofV937cdmQhCq93PwSGTz88NO3ysU1rDO2ygtodOsk OVC60vtlAw1cW2Su+FPVFFSRphNDwo+dWDfJZPi+nCkrFwyV7Kiuq/r0e4+zqigD5nWFifxVEJDb ekAv7Oeom42ZwwQLKLMB4fn96/QMX98l4J8S4V2/ZbOmmMnFO+SoaHRj1+GTt/fxJ+8aTSeLAipJ YtGzlLvkeu/Bpr2kUfrzbh3efIqiOuhNtKPXDkgSea//J70y38ayH/1i/2W4/94Q+ZUI4rTLvsyK g+0rp3KVJYv/tRfvXjexO6fiXoAXiTfjaMHMtv+MAys+drCoNFojySJOOfeDT6zcYn6pNIlwin2H X1kG6V6G6liJ8274r6NJDphiP7DsQPme7oCepQwjDZdO1CWriES8mIv0jgzLIUYEqbR4ua0HJA5l /67o4SAAAiAAAiBQKQFy4KujXNLUrLDzDuBAoI0RaLWeAnHyuZ+pbZKggOViB14Nuvx3juIaujjr SUo6EWflPNmykjFF2cB8IvPyMT0sQRblXCxIvEsfGfC2knLnnga93pZymV5R9D7XqZ9vD9H6cvkc c1URvdi4DkkGlgoLi8koA7NZMq9D1ZC1+RGQ23rALuxXN7GvsAGhmJ8WSZuuoV3qNCx6dv1yfJlV LwWCnH+iA5NMncyqcBZKdJVuCpDt8B/ptsiWXjsgScWP487Qyxz0B+h2pWfLwX70kgGjsQM7xF9P JPv4pdN1dsWBVOxMxzFcckIh+1Ua0YBIkexcoLjaTLAPJon5N87eqThasq/oGQ/FO49iU8iiinLC JRm4M22Mnt/l0dWXZRhjnh8dnvaqWlYS26yXuNjRpsonZbX36Nl+wb1rZ8Kp8YtGM76MguS/b9Me EzbgQv790wHnaYeNjXGXhBsEvoQDzkdsfs8VLAIBEAABEAABEGi2BFqlp4Acjxf03ZITwx0Hlwn0 pz7iMx82en/wxjXbgiXH6YkL87KZ+HgFz3OYn/MFuVns+amZOcy5h6+EWWwMxOzcsvsF0i9F3CM5 RA9Dz5PZgd2yNdMH0tvtZQKpwiwm8p5M79V1KzZeoU/4E/H+zOg8gERDrKiuvHaK6qLbT4/OSEsT 8W5JDxGU61IyC4VZ7P5ncX5uJhMPsTBbyERhFAtznjMFJCbR4RI0e9EXkq7ffSoWJZ7c65cuD4H+ d7mK1wRKZrlUaQUJFUiKnj6IoH/eZyakZ2ak5GkMYEySpXIaKxggA8vfs2b9sRS6tfJSzl64S/5R PfMyWvAFBGokQE403LOZvEIvk5Q7dWYPQojd9sOeMMbxp6xv7kRfiz0SEPyQXh3Pjw44nWxkI7cK gNzlhyzeGlHFvgNanmQZv9y6fdmsfsb4MqIkS/GpgqzcgvxH9x/yBfR79Xb8iFiOZX9VyYYFaaSA cmIlX43G9ufEX4qlzyZV1jWfSgdf4B8LOM0jk39ypOKJIyl6k0w0ygOS7CyguNSDC6EiIwP18jZL M1CCG5efaRqQRRDil+l3MijKeGznB4euvGup1746VhLbRsxxMKngTFFW0yAOWeOxI8aNK13y0F6n txFtZCGJO5vDe8DLfk6WGXF7ZkaGvzPQTKWI9WvQ8REKeWHBTGWRQAAEQAAEQAAEQAAEqieg2NMZ WF0Nl9mAUxKFIVusS6ssO7FPGLOBmW/LJ7I0/R/pkYfMdf3p/sfLZHM+7CsvjVro++i/Z5E7xnzp H5KaHnJwFX32IX0WgJdvZIbk1EPpoYasIunq99xbwTumsyeScV02BCelscscylnl7LmBPd2QTc6B d+hsr+/c2M2UNZ6+7eYd+ghA+Y/svEO2DDko8Z7kiET2AiGQLDlnsbxJx9cw9nOHfen/x/Fve0m0 sgcllsVILdx/3FOageSrCGrzennLaezlJLhs2EIHSpcmmmRm/tPAVSQ4OjHggB8BKDtv0nnHyTu5 stMomSL607d4sqdFSr5KTnN8fSfSf4kVS9Zuyb7QWy/okyyZTxXMW8sRiaSOOCWRPiKxsU9JLD1o sLT7uQTEhHqwR5OWdmn27EPmiEH2ot2qPaFJAvqMQ+mHPXSQOc6w6iP0yp2PSJeteLigpPgbyVkA I1cdvvAggzlbgTtzR0h8Ll0q98FhchyI5FxA6YGC0mMXSwR3D88koVvsVu2LYuIj0p83yTd30BdJ 4lp7HA5l5VRyDGR6oAvJxp6kSGcob3Pu09AV5NG2WxXwQFLT3PJFqmYlNZU96LG89v8yju+kj58s d11yPCTDPPk2fVSktovXuSRGu/RAytIrlVWqORxqqHAbcEpiw3+OQAIIgECbIZCVlZUklwoKCtpM 1VFREKicgBK5XPb3boO+KSkpkfINl2lpeUBmxx83FzTIJhQGgVZKYMKI0sckJqb+j4miHtsmxcwM NZIkVOQg1pi1eBK0wGbJu9v+2uUoF6OvnMJXPH8Xo8WnuB6hDzeNRvi9xmyONiBbTe4xUdzf+qCg ICcnJxk+8ruabO5tAzRRRRAAgVZOAINbK29gVK/uBFrl7oO6Y0AJEAABEGgUAkXPzi3RmeATS29G yM959/Pz26ZW4iYQPw5aPFBnfVieJOb/4t3/GwE3QaM0CISCAAiAAAiAAAiAAAjUggA8BbWAhCwg AAIgUE8CmVEXLvFv+O4NTXly477eN+6VHotIPY89608OZ3mVcePEkdDOzFEF5SL51VM9ioEACIAA CIAACIAACIBAPQjAU1APaCgCAiAAArUkoDXUeY41lZUWE/XcaKI1G8e0Yuo2xHWFHd/Hod8PBXMj Qg5OJif/IYEACIAACIAACIAACIDAWyOAOAVvDT0Ug0ADCSBOgQRgi4lT0MAGR3EQqCMBxCmoIzBk BwEQaMsEeDxeYiJZ3ydJ1tbWmpqVHH/elhGh7m2NAF5ctbUWR31BAARAAARAAARAAARAAATKECDB WR3lEtwE6B8gAE8B+gAIgAAIgAAIgAAIgAAIgAAIgAAIgEApAXgK0BtAAARAAARAAARAAARAAARA AARAAATgKUAfAAEQAAEQAAEQAAEQAAEQAAGGQHZ2NglVIEuFhYUAAwJtnADWFLTxDoDqgwAIgAAI gAAIgAAIgEBbJxAeHm4kl548edLWiaD+bZ4APAVtvgsAAAiAAAiAAAiAAAiAAAiAAAiAAAjIEYCn AN0BBEAABGpBoDjW20RJSU3usyCILyn3iuc/XXrLPUhQJOL5ufbRsdl4hS+uTLKYHxt82ne9vc76 sLxaaK5nlvJaxDVYVVs1ebzrZ0/vdNUprX5tS9Yrn6LMrpdyFAIBEAABEAABEACBtkoAnoK22vKo NwiAQJ0IKFus+PvBYVsuU8jYLSD9zQFH9gtFtTd02RvqMczawz8m+SdH7aKki7/6C/jh3oG3nxdX UPIkyH2k5Yxp83yy5tiaqNfJhjpkrqiloFqraim6mH9uldHEqdPWXRk4or9WLQs1KJtCzG6QBSgM AiAAAiAAAiAAAm2QADwFbbDRUWUQAIF6EeCoaHTryJQc7jC4u9zoSd57h8YZ7T+/aY6FdjuK6mg0 fq6LNtd6hdPQbsoVNPWcunGbmzZFadvZm2nWy47aFKqopXqraiOT5FHmTvaO8bSmqBGfDOvVJH8/ FGJ2LWuHbCAAAiAAAm2XgLGxcaBc6tKlS9tlgZqDAEOgSX7pgTUIgAAItFoCxE0QsPmkpvNMY1VJ HTmqhq5+yRnXNozlVjLEvkq5ctxXQHFnjrFUb7wRuKKW6q2qdfOInyZcS6Jsx43Qa1/rMg3JqCCz G2ICyoIACIAACLQBAoaGho5ySVOz8bz5bYAmqtgqCDTe79RWgQeVAAEQAIGKBLQ1NTpKBk8x/8yK PSqfr5U5BcSilPCgcwdXTxi++vpLaVFyMYwEJlBS07FZv/3w8ZsUZVF26wHZ/B/EZCBxEOzXXn9W Gt8gnxd2xtu1DxMfYcJqv/jKQx8wiqrRUtGqPN41H0YsMelg0LnTYSkFtAhB7NlzR70XDNRZfzpa kqGMPeK0WydC+fo2pnqyvx4kIELAahvaPO8r51brkH8wIQxoUcRykyVB/CJG8pW1E3R0FgfJ1U1G lph3mShl6+h77vSR66nMto1yZsvHg2CA9Jnny5ML9UBY0eR1GDkbwwS0XiQQAAEQAAEQAAEQAIF6 EICnoB7QUAQEQKBNEhDn5zynp9NUB81OjKeAzH6//Tn/iy1TpVsRxHnX1xma2Tg5L9iWPEq6uYAs OjiyeLztvJhhocm83yz5R27w5bceiAWRPguGWwfwjRf9nhGwkKJCfg1NEknmyUGrp1nbHsh3vfH6 DS/QLXnb3OnfnWEm3hVSNVoqWiUWxR9eONnrnyVRQuHT8y6do/aF56i2E6f4ju9jOdXZeeWxLCOK ozHY2XWmBbFnq8/lFInr4lXKrUshlIXTIF3Jtor8RF93O0v3ew4RWcLv3tnivI1Pce1ICANB0KdE 1JyV/o8unf0rk7a2RJiZzOf7h0RVjN2QH7Pv809XPnWLEbwpOfWpRkzA1dx2ylRFs0VPU9LkKu7g dXK7myEb6qGIH7PHdZCT3z/9v7gQFThTn7px/nJSI8aLbJMPACoNAiAAAiAAAiDQhgjAU9CGGhtV BQEQaBABcX52GnPcgZ6mGhk786N3rH8wZbVzXxXZQMpRH7Uplp7tl24uEAtCt3yxxl/g4PXdstHa qlo9e9GhDmws+7FbD4gQt2nLn7qd9/nf4G4vbl+OlC03IKsVlo132nbDnCnYjgRJoHdMCu6lVfaq vFotFa0SC58khVP8pHsPnuZTqgZTv/5+nmk3ZY6B2+XkQBeiRXvuNyumGqp07NRZjTYyLVvIegrE 6TeDbpa6OcSPg1bNnHcsy9pzvbuZpipX9306ExPCQNvRTxDlNZLEfEyNfET7BjjcqTtP7rC2HWxc MXaD8Old4j1JTkx4JqJU+jqu+O5z466kRHmY4pfpdzKkLUiCSu5ZbqbBmiWK3zPL5rt/lhzes2S4 VuZfwddSGzkMRIP6EQqDAAiAAAg0QwLZ2dk8uVRYWNgMjYRJINCUBOApaEra0AUCINAqCHTTUCtM 9P0hwnyTu0Wpm4CtWs6DmBi5zQU58Sd2br3B57q4zTQl09pXaXejU9m37kzm2EObVt7o7OY+tQ8/ 3PfbBU7+lMueX78ZRXwCL8N/3kKHM5AUpMTP0/8WVEWvei0VrVLW6j/YjqL4x+aO9jjyMJ9SNzHT ZxZJ5CXFXJFzc0j0sZ4RcpvZeiCNsCAWJQTt8k+ktB2XTh9EYjQU89OIn6M0hIFKfxt7I3KhICuX WYlBktacDY6GFf/sdOs/ihwqIfCd5+BB7yZQ728hCYJQFubztGzHEKHwjTBip4vn4Z2TdSWSyJKE r73CaTP0Mq77rpm3xJdyOxz81ehGDAPRKroxKgECIAACICBHIDw83EguPXnyBHhAoI0TgKegjXcA VB8EQKDuBN7wLvqcpGYvIK/6yxfOu3P5WKzshbaYH7Z3dzDtOJhuRe9QYN/JSw8OEKcErV1H7ib6 Out1Gu+Xbb4sJjnWz4WOjChOOfeDT2xpQYpd9k/e9g8112fPXyhN1Wuh85W1ilzgGMz2DVhM3vjz j63536EYZrMDSdkxoSF8WQwFNnghcRyY9NKm/1awNuiPtexDr/gXPwk55BtOuxUcx3AJB6kTxHGY geQPi7Lae2RpAMUX5OST/+XHn7xrNF2yCqBsBTh95/68mz4PgjgLvjgcmy+N0lDObO0JK1yMO5LA EL/22LJ0sDR+5CteoPdKsiRBsMfJsKt1QPYHi89nJB2W7kqoe+OiBAiAAAiAAAiAAAiAAM4+QB8A ARAAgdoSePn47iMmb0QyNX1FZXPRV7wz+7aVnmtAYgfEXCQLASQHIpKX8BePhPKlb92LM+9H05N/ amFg8n8lyX4rpk5gDlkkSXpLdpKixMXAtV4ydXD5V+XVa6Fn+GWtYqvbrvvkLWF73LgUP3yd98mU V/S1cm4OZgUBRU3zdBnGuAZYN4f5qP70/J8SpUaFJFIyx0HercObT5U9PVFZTUMWODonNjDJ1MlM Or0vj5zemxD8qwtxFtzwWhvIY1wFlZlNNmtsePpZaWAIku1F4s04OvvMwAxhScaBFR87WFR25ERt Gxn5QAAEQAAEQAAEQAAECAGsKUA3AAEQAIE6EbBbc2jTXEkgvbIFJXNp8tZdn5OSwMt/JXicQgc2 YCMg5t8/+TN5Cc+1c7RQSUzki19lPKLf2FNyJymQF/VhwQnSN/yy0IlFzy7sXkcm7SNXes+3rDDZ LqpWi2yGL7OqiB98NCyPTMbV+7r88JsHiVnIJnFe/NUjpW6Ol+H+e0MorrXniukG9IGI7NYD2eaC 4tS4QHo3RM+BPd8jPoaHZwJIWar3YNPOSWF3cspweZ6VEfNHwsAJFXZqMLkEl33pQyLIaYhzdh1a Q5Y5SM1hHRMys8UUHRZh9YOJU8zkd3wUP0/7M5UU4WprqMiK8m+cLWdDnVoYmUEABEAABEAABECg zROAp6DNdwEAAAEQqAMBEkjvgOco6VkHZQtK5tJUByolJKRA11ClneSlOtnnT/FjYx5mP88iJYoe hkVRPbicjn0GDaUnxoLzfmfuE++AWPCn37H0fuNNVSlJHAGqMDu3oFjEO7b2yz3UzN1Rvosrm2xL X91XrkU6wy+1Ku9B1J7N3md4ZJE/Oc0h67V08s9uPaCorByhOO+h/+rZPhnWK/yOStb5S7Y/0Ocj FvLCgmNfaOkNp6svfJFXIEq5f//fbLpsj6fXblCDTTTKgAndsCLSoPJ9B3RkhOvrftgRlELOKSgW 5pL/MgERZY4JObNjdy128g/3dV75K1kBkR/tPcHAZme0SFnXfCrt7OAfCzhNn5hIDkE4cSRFb1I5 G+rQxMgKAiAAAiDQFglYW1snyaWePXu2RQqoMwjIEYCnAN0BBEAABGpFoJjP77DnWGkgvYqFVDR6 kPXz2uaDrCZOpWeqylzbJYdnGlP3rp/7I0Nn+Ad63TtT2mMnO85g7nLURy49s9WFSyX6Lx6oNmH1 r0maTs4j2ZXzHIOPd53b4UJtte3+rtoX90f9GBO7b/HgimER6LzVa6GoclaJhZyx+3+2zTk8rYdS p4lnDTfG7JtNRxmUbD1Y6On4fK1Rp36bi5cHnD/6zVjpSn5y9kI3+iSD3T/9GE0NJiv8dUZ/7ulA URnRoReiKa4u9R9FGbvYj59sO7D8qofKl0Kw+IpecBzCfhyT4z9NSe3/PrjQa3fEzrnMEoayZnd8 dm7dJDqmA0k3T9xKL+JF7LuRGn7vqZDqYr1o5w4CmcQ4sOhrs/63BxrjXUdW7sqpVTMjEwiAAAiA QJskoKmpaSiXOnTo0CYxoNIgUEpAqaSkRIE8lJSUiLSGy7S0PCCz6o+bCxRoIUSBQKshMGFE6WMS E1P/x0RRj22TgmWGGkkSKnIQa9JaNCNl4rzr6/pO/NXI88z5ZbJggQ20r5h/bonOl8qBETsc6ZCH DUmvRPnKqirKYn7QZ1ZOF+0C/1pHrTVc8o8irW2Iec21rJrcY6K4v/VBQUFOTk6yOpM3cOSndXNF ALtAAARAAARAAATqSQBrCuoJDsVAAARAoBURYLce6AwepFdV0MFaVpZM5uf1megdn0PvJsjp5HXS s8FuAqK5PXETkP/RgQ8vXlr+34YexE3gsXt/JVEbamkmsoEACIAACIAACIAACFRHAJ4C9A8QAAEQ aOMEivjX92ymD2VseCrO/CvEVxC841DYk6d/RvX+fHmlxyLWXw9H1cB+xYG7JcKMa5scDeVDG9Zf JkqCAAiAAAiAAMXj8ciaKVnKzs4GFBBo4wTgKWjjHQDVBwEQaOMEngQt6K8z8dtwGkPstonTvONL z16oOxplrcFOa0Zy+Y+iwl4YOSNeQN0JogQIgAAIgMBbIZCYmEi2VsnSy5fkXB4kEGjTBOApaNPN j8qDAAi0eQI9HQ+klAhLpJ9rK8watP+Aoz12yx8ZJX/84GomDYbY5hEDAAiAAAiAAAiAAAi0OALw FLS4JoPBIAACIAACIAACIAACIAACIAACINCIBOApaES4EA0CIAACIAACIAACIAACIAACIAACLY4A PAUtrslgMAiAAAiAAAiAAAiAAAiAgCIJWFtbk2NfZalnz56KlA5ZINACCcBT0AIbDSaDAAiAAAiA AAiAAAiAAAgojoCmpqahXOrQoYPiZEMSCLRIAvAUtMhmg9EgAAIgAAIgAAIgAAIgAAIgAAIg0EgE 4CloJLAQCwIgAAIgAAIgAAIgAAIgAAIgAAItkgA8BS2y2WA0CIBA/QkUx3qbKCmpVfqxX+1/IVZQ VCpc/Cxso71Sn3m+vLz6a0RJEAABEAABEACB5k2Ax+MFyaXs7OzmbS+sA4FGJwBPQaMjhgIQAIHm RUDZYsWdkje8QDdtxq7eXjH/lpTkZsTsW2VNhWxbPMnSbWuYzFnwPMrPO4QS+G65mFws5z8Qxfv5 xosap145sT8fiZVT1jhaIBUEQAAEQAAEQKCUQGJiopNcevnyJeiAQBsnAE9BG+8AqD4ItFECnK66 /eRjFXG4Fs4rv/GwoHHc2P9DSJqYBdNtiOsKO0rbbe34PspSVGJB6JavjzbOu4Yi/vWdK37ObKOt gmqDAAiAAAiAAAiAAAg0DwLwFDSPdoAVIAACb5+ARj9LS8YKfuq/+RJPAaf76A2XS5IPuxmqswaK BVfWubluvSG3Q0FhlhM3wdZZE78NV5hACAIBEAABEAABEAABEACB+hCAp6A+1FAGBECgNRLIeRAT w9TLeFTvLvTgKAhylYYzMNhJNgQU8WP2fDrSbusNPkWFr7RSU1Kz8a64B6G0lIHruceilMveCwYq qU30js9hPA382OCDqyfoMIESdGzWH5GERRDzo3d/ZsG6CR6ttHxPScnEW7IHIZ93ZaerDp1/oOvO yzypE6M1NgHqBAIgAAIgAAIgAAIg0CwIwFPQLJoBRoAACLxlAmQCH+C12SeWouxW7TvsOVGXHhy1 p+66sIZbalk7ruXC77cvZC5Ye0UIS4TXVpiplrdcrlTxo79iqL42A3Qp6uHdJ0JK/DhoqZ3lHtH0 U09LBFFeI6lwHxdL9994ZAEDhzt4iefumfq0NDZ0wp0VFmTDQ3609zRru3VZyyP+fcPbqLx7nPWq M88kCx7eMjOoBwEQAAEQAIFWQ8Da2jpJLvXs2bPVVA0VAYH6EYCnoH7cUAoEQKC1EGBf4HfSsXTf Fq7t4hWw5YuZg7mSoZHTUV2zY50rKiuVei1L09xA12y6m1vvUaP6dhKnhe73T6RuBJyMyaZUuHo9 GNmh0YnPqwpg+IoX6L2SLGHobW0zUIOj1X/4AC7f//jltFd1NgoFQAAEQAAEQAAEqiagqalpKJc6 dJCPZgRwINAWCcBT0BZbHXUGARAoJcC+wBeS4w+ehs4WrHS21DFy9YnhK+K1vf5Yyz4kvAGH63j4 Dh3pgKwb+GSmMVmr0FVdFh6x2qYQp98Muknn+FBPh5TgqGh0I86FmydupSvCPPQCEAABEAABEAAB EAABEKicADwF6BkgAAIgwBDgdLd2/sSO/EPgv3zmrvC8RpiMqxi7HbhbknvQJuPU2vW7I58W1IBe nJ+dRmIiUNQxJyZOwftOx1LLBFxE04EACIAACIAACIAACIBAIxCAp6ARoEIkCIBAyyTAUdXQZi0X ZOcUNIKngMREjD+yeqIFCVXgtHrRcHb3QW2SbOEDWfsgLElZRkcwQAIBEAABEAABEFAUAR6PFySX srMb5zRkRZkLOSDQ+ATgKWh8xtAAAiDQMgiI85JirrCmamtqdFT48CgWxe+ZZeWy7YbOqtVzLDoU 5jyvaU2Bsq75VAvankfRCenS2ARifvwdhWyOaBmtAitBAARAAARAoAkIJCYmOsmlly9fNoFSqACB 5kxA4T+Fm3NlYRsIgAAISAiIXzx+UMj8uzA7l14+QE5A/Hnp/K3MWn+7NYcWW6uT4VFckJfNzuYL snKZfyh37WnAnE8gfJFXIEr5U3LGYRmuslIFghz5Iw3FwidJ4XTOjL8fP3uWmKVhZVS2Pd7THcBE WqZNyuHdiOeLu1j/b60bvc7h1LzPfwgTFFH/z955wEVxfHF8ObHQ1QPhwEq1IKhgxYKi2JWAvWHs UdRYsOdviMaCKFGxKwnGbiAaOwpiwQrYsICABeFABOko4vGf3TuOo0iR47jymw+fhN2defPed+bw 5u2bN1R6ZHCCnnlBykWMJwiAAAiAAAiAAAiAAAhUAwF4CqoBKkSCAAhIM4G8UA8LpVqmTt4JjJYJ 6+0Mailp1NXv7XIwwX7JDt+AByfX9TKg/zgmnJo7hO87oLiedh22hpIjCpQtxx9ZP4lDhbpPc92X 2qy9Xp3itha24vrPaWXKtGKKsm4np+U9yKmLbdtRsQmNOjqMnct4AYJPHw+IpF0K6u1Hr91CUh4m rJ+w+HCqSRviD2BxHLZeuLiJ3Lzxq52JlfPW21QHS/gJpHl+QTcQAAEQAAEQAAEQkAMCSvn5+WI0 Q0lJiUirukxr671Crc7fnCFGDSEKBOSGwKDuhR+TkJDv/5iI62MrUbDMnxpByRDnHzGJWoHOQKBa CWiIfEzE92892cZL4nOFipPTx8mxYtVqB4SDAAiAgAQI4I+bBCCjC9kigJgC2RovaAsCIAACIAAC IAACIAACICBmAra2tsT1KSxNmjD7AVFAQIEJwFOgwIMP00EABEAABEAABEAABEAABCiqYcOGJEJK WFRUVEAFBBScADwFCj4BYD4IgAAIgAAIgAAIgAAIgAAIgAAIFCEATwEmBAiAAAiAAAiAAAiAAAiA AAiAAAiAQCEBeAowG0AABEAABEAABEAABEAABBSaQGRkJElqKCwpKSkKjQPGgwBFwVOAWQACIAAC IAACIAACIAACIKDQBMLDw8nBLsLy4cMHhcYB40EAngLMARAAARAAARAAARAAARAAARAAARAAAVEC iCnAfAABEAABEAABEAABEAABEAABEAABECgkAE8BZgMIgAAIgAAIgAAIgAAIgAAIgAAIgAA8BZgD IAACIAACIAACIAACIAACIMAQcHR0zBcppqamAAMCCk4AMQUKPgFgPggoHoG8UA8LJSWNMn56ezzI VDwusBgEQAAEQAAEQAAEQAAEBATgKcBUAAEQUDACylaLH+fnxwUs0eMbbrvpekZ+Rj79kxARsGOJ rTzzSA3d9XdonjxbCNtAAARAAARAAARAAASqTgCegqozhAQQAAEZJKCqpaNSQm010z6T1hw6bFtL Bg2qgMq53GtbF+9KrEBNVAEBEAABEAABEAABEFBoAvAUKPTww3gQAAERApmh/wVyqToGnXo0k0Mu xE2wftyQX4Pk0DSYBAIgAAIgAAJVJRAZGeknUlJSUqoqEe1BQMYJwFMg4wMI9UEABMRFIOvZ1ZAM WpheH8f26lSCn7Mgl4Gx839vMqMuecxoq6QxxONBKtNhLvfBWe//9RfkOxi01OcBl8c8yHvgYVzY MDzyqqezCZ0TQX/GjnsJmdxrbr35T02cPUMETZh26SI1PS9HpZduVtla8bih5/YtHaTPaKXf+39/ hybk0nJ43Hte0634boJXrtYNlJQsPAR7ELIiL2911qfrt3Xeeikyi28ECgiAAAiAAAgoFoHw8HAn kfLhwwfFsh/WgkAJAvAUYFKAAAiAAEVlvfDb+ItrwtdCFnoO288u5/AX/6/uh1Ate7choQYvnsQS b0Ju3H8LrXruTvnheH7Gx5C1g6kb7pN7zv8r6hOprNx+YZigYfTB3b7vWs3587rvFD2Ke9Sls4mG /rS3E+8/9J1kTiUcXDh2e1A6f2WeGrp1nNmwTW9d7makvfaq7W0/cIUfl1nkFytlaMV74zfX3npH 5qiT7/IT7m7qQQV5TrKedSiS9MDidHJZ6zXWiBbWYlPIx/z8x4utlInV9zxG2tqvSl54/ePXSDdl rwG2S07FwVeADwQIgAAIgAAIgAAIKDwBeAoUfgoAAAgoOoEg154aSnqtnDz9i5JgqWo2VKVvRV9N btjBuFn7UVOmtOjVq6UWxYu55O3Hpc5t+TcsnVLXb9GYaRh27VkS84uwodGkWVP66NVh6TRrxc+J oDflwDnPKS2Nmplq05cJKanZ9LqcF+W3YtU5ijIb3LO1OkvXvHMrEtGw53JMaWv2b2rFiwnYczCc unH4REgKpcYxbMzoHnAv/P23Ehh+ivT1cL3BpVrY9m5bn6Xb2qYNh3vw2KUY2t+BAgIgAAIgAAIg AAIgoMgE4ClQ5NGH7SAAAoQAffbB15d3/1pg/w0cRv2sTTTpF/OOBx4fmGKqSbH0uzkO5FAcM7Z6 5f6G9h48kDQvXj5F3brIeCnMDDn1SFCCRv2GFMX197sVVdbr/eJakbiB0WPNiTdCR5NEC1Sg8F7f 9LtJ1+tiqE9asNTqNyLOhZvHb71GVEEF8KEKCIAACIAACIAACMgzgcp9y5VnErANBEBAgQmw9Do5 L12zSa+Chx5otpx0ID7j9ZGe70+4rfzjzquqkcvL+MgPRtjjZFJbSaO2/vg99FVMSkalluxq5lP2 PslP29c7/uSK/3kFv8suRyteVkoMl65z1InJU9DU6Wg08VBEf0SugqqNJ1qDAAiAAAjIIAFHR8d8 kWJqaiqDRkBlEBAnAXgKxEkTskAABGSYgFrr3tYaFdSfl3DP539D9UmqgmGuP3dpUcFW5VWjoxvy M/IFP/xUApUoJMni30uHWJFUBU5LZ9vwdx9UpPAzFxT0GzW/kt1WpAvUAQEQAAEQAAEQAAEQkCkC 8BTI1HBBWRAAgWokoG41rA8/hWE5JevelikOkz39OQsWzmivnpFaxYOUVE3adWX6jbj85F1BGEEu 93G46NEI5enEy3ywY1zPSe439JcsnWilkpP6vryYAuVmHRysaLGv7j16XZCbgMd98Lgy3ZanFp6D AAiAAAiAAAiAAAjIIgF4CmRx1KAzCIBAlQlkpyXl8IVkJKWXmsOPl52ewqy2sxNSiwbkZ7x7QhIB kkj9x6/juVGp9dswhwoIS4mGwr7epzIbCj6lJTPHMVIpqZkk3SBLs8dML3IaAslNMGf+qmvk8AFe ZlRYfCNTTil/ob+lFS8jNiKIlhn/8E1cXHhy/Z5mRRk1aNamCX0nJyUtOzXyBjnUUdv2pxXkUAaK Ojl13sZA+jzF9MjgBD3z0rqtMm8IAAEQAAEQAAEQAAEQkCEC8BTI0GBBVRAAAXEQyAv1sFBSMrBz T+BLC3UfoqOkMcsvoegZAQmn5g5Zz+zjJwv4VqZbQwsfN+rsvJikP+TYWnyNSGBbD5nsTq/zoy+f /edyVHregy0dChsO+dH3uEf3gr4CpprN8vbbOtLOM5Tp+uTU9j/T/bKaObr7+q+dxKH81w9p3HjG 1ttUayu9OqVY+02tlHU7OS3vQUIT2rajYhMadXQYO5fxAgSfPh4QSTs61NuPXruFpDxMWD9h8eFU kzbEH8DiOGy9cHETuXnjVzsTK2fSbQdL+AnEMckgAwRAAARAQMYIREZG+omUlJQqBgzKmPlQFwRK ElAimTvEyEVJSYlIq7pMa+u9Qq3O35whRg0hCgTkhsCg7oUfk5CQ7/+YiOtjK1GwzJ8aQckQ5x8x iVqBzkCgWgloiHxMxPdvPfki7eTkJFQ8IiICeb+qdRghHARAQDIE8MdNMpzRiwwRQEyBDA0WVAUB EAABEAABEAABEAABEAABEACBaicAT0G1I0YHIAACIAACIAACIAACIAACIAACICBDBOApkKHBgqog AAIgAAIgAAIgAAIgAAIgAAIgUO0E4CmodsToAARAAARAAARAAARAAARAQJoJODo6klRrwoIMLNI8 WNBNMgTgKZAMZ/QCAiAAAiAAAiAAAiAAAiAAAiAAArJBAJ4C2RgnaAkCIAACIAACIAACIAACIAAC IAACkiEAT4FkOKMXEAABEAABEAABEAABEAABEAABEJANAvAUyMY4QUsQAAEQAAEQAAEQAAEQAIFq IhAZGeknUlJSUqqpI4gFAVkhAE+BrIwU9AQBEAABEAABEAABEAABEKgWAuHh4U4i5cOHD9XSDYSC gOwQgKdAdsYKmoIACIAACIAACIAACIAACIAACIBA9ROAp6D6GaMHEAAB6SIQ6zfDWElDqcRPW+et R04/4PKqpm3eAw9jgfDeHg8yqybsG60T/JwL9DfeGppXLX1AKAiAAAiAAAiAAAiAgOISgKdAccce loOAohJo4rg3Kj8uYIkeH8BM35df8hOe+y7QP7hqvENP++n/vamKs0C5/c83Ds+sGlte5gMf7zK8 DHqOfz44YF+1PiTYujxzJKgKugIBEAABEAABEAABEKgIAXgKKkIJdUAABOSOgKqWjoqIUWotHWfP nETfCPdetCcovSq+gqqy4iUErFt5pOxMSiz1+gJHR1V7q/b2FTGn2pVAByAAAiAAAiBQJgFHR8d8 kWJqagpgIKDgBOApUPAJAPNBAAQKCGg3a9uC+T0hJTW7xjwFvITLq6Y4r7+RKx8DI2fmyMegwAoQ AAEQAAEQAAEQKJcAPAXlIkIFEAABhSDAe/3o8ivGUr2G9VX5fxtzuQ/Oev+vvyCjwaClPkWyGPAy owILnur3/p93YFR6qaREMhcYO/8Xy9QhbS95zGhLSzZx9rgayeQzyOWG7Pixh/36G1yKCnLtqaGk UalMB2XoI+yO6OkXyQ3yPldMjWL3Re3I4/43qyCnwyy/hJTIq57OJkpKgzxDs/j+lG9RKsOcdIEQ DSX9GZ6Xv8FNIaYdjAQBEAABEAABEAABqSQAT4FUDguUAgEQkDCBrBenDh73pzs1n7J5pq0m+duY G/ffQqueu1N+OJ6f8TFk7WDqhvvknvP/ivokWOo/2Dq0vd3UkAEhCTnvDjtGeE61W/xPZGmxCMrt F4adXc7psdz3QZjPsCa0m4BuO8D13RS67WaNLcOc5tPJEepwrGdu2MzPcWC76XpGfsbVxe3VK0aC lxn595yBfH2+5iecGhyyym7gAu9IxnnBizyx+EfX9wsi0uIDZuueW73y2le+Ed+4X6RLZc6wtQEL rJh7GTH3nlKturY14VA3It5kEGvLoPQtc1JDt44zG7bprcvdjLTXXrW97Qeu8OPKSQxFxQYLtUAA BEAABEAABEBA2gnAUyDtIwT9QAAEqpnAHieT2kp6rZw84yetPXzquv++Yc3ov4y8mEveflzq3JZ/ w9Ipdf0WjRk1wq49SxKssd02BVGUUf+elmr1OB37DNSjOAb11UrTlWzU3+CZ7XXgV0djzVLatu7Q liRH8A6IqsqOB16s/1aPgwl8fViUWuve/c2oBO+pSxjnxftn1wK41NPgm1HpLD2b+W7L29ViFP3W /eJW1NNiazD3IpK0WpnqdRg7y9FobNfWGqxyKJVKI8pvxapzFGU2uGdrdZaueedWVILfnssxVbG+ mmcIxIMACIAACMg/geDg4CUi5d27d/JvMywEgTIJwFOACQICIKDgBJizDzLy8zOe+MwfN7w9R/Bn kaXfzXEgh+KYsdVL/qHkxdw6TtbeFMeoAVmXUyyO44GX+fE7HA2KV+VH4O8z9tjgyKnDB13Q1sim RSNl0pafmzDg4s0YfrTCd5X3oacPhgv1oah6+i3MaEEB98Lf51GNWvey49COAysb562XojQHLRhM Qhuob97/pgrWnc3qU1Qdg2FeUXudWxLTy6RUmphPUbcuMrEbZoacehSlrFG/IUVx/f1uVclR8l3M 0AgEQAAEQAAEhAQSExM3iZTs7GzAAQEFJwBPgYJPAJgPAiDwLQKaLScdiM94faTn+xNuK/+4w89h ICi8jJRo+ldu9EfBZv3SpHx4sGuqVW+Xgwk3/z73mMlEQJeCttEHxzdl8hQ4HaRvJ6Vk5JUmRDRN gBKTL4AkCyheM48bE0w3VtWrT3suREpEDPcTxWo5effJLWPNyckOB1cNMBu5yo+fGuBb9ysxKcqi VKo5GR+ZoAyKCeXQqK0/fg99FZNCb2VAAQEQAAEQAAEQAAEQkA4C8BRIxzhACxAAAekjwEu45/O/ ofokVcEw15+78M9FKF6in77hL3xLLfWG/O8USXBAcYNW/bb7QWqxOkZrQ77QsQz8n2+lJCBpAnYX 1OHX3O2oR2IRihRljqFNkRuf4l9FMDf4r+4psulgwW7/kN1LbMnFjfVOBSkVvnW/4qNREUqlSeMn Yigw//Fiq+I2VVwF1AQBEAABEAABEAABEBAzAXgKxAwU4kAABOSEQNa9LVMcJnv6cxYsnNFePSM1 RdQuZaMOTvSeAYq6Gnj7m9n4tFs1Mek0d0/AYnuKOuc6ahU/b5+wbfTVR8Ld+byE8AcJVcjqx99f QEUHvyKbDUSKXSdzssUhPdjn3Bsei2M1fv2Z61toZwF/V8K37ld8CMukVJoYVZN2XYmiJOXB5Sfv CsIIcrmPw7mIKag4dtQEARAAARAAARAAgWomAE9BNQOGeBAAAekkkJ2WlMPXLCU1s7Sw/4x3T+jT Cinu49fx3KjU+m2MRA3R7D5v8xx6xZuww2X10Rf0FoT0F/+dfVBkL0JGUjqJ/Dfos3jNph4kTcAO p6m/0mH/wrYBq2auuUyvkLMir3PZlnokkYGyThNjpiPSNjsz6k7oN9wHvMzUBEaf7OQ0eicly3TU aldb4iq4dP0R0SHr2dVLJKZg8KbVjqbkz7xqvaTlM5b/9yKTYqnqNTMkyRcn2XcmHoRv3S8+ZJ/S kjNKZ1U2pVLMydPsMdNrEtkHwfWfM3/VtTgefWBkWHwj04L8ENI5XaAVCIAACICAnBNwdHTMFymm pqZybjDMA4HyCMBTUB4hPAcBEJA3ArF+M4yVDOzc+Utt6uTU9ipKFh6hxdwFjTo707EAHFuLrxEJ bOshk93p9W305bP/XKY3+ZOsfltCrx9c0oPDPTq5ld7ApQdDWHaD2qux8h780YO/954KdR8y0uNB KjfA25VxOtBh/+2X+CWwDIatC/pvyyQ9bpCHvb6Zs8c9yrogk6Ky5fgj6ydxSNtprvtSm7Wn3Qcl SoLfj+2nMnkBKa6nXYetRHeWevv5Zx5c3NTY21qvlpKewznrtQEPjixuX5+pVdfEffu8JvfXDNKv ZeoSM9Y3yN2BSb74rfuiPZJECavsPEMLWA30eCBMuUByIpZNiSrFHFYzR3df/7XERv/1Qxo3nrH1 NtXaqlQz5W3iwR4QAAEQAAEQAAEQkBkCSsR3JkZllZSUiLSqy7S23ivU6vzNGWLUEKJAQG4IDOpe +DEJCfn+j4m4PrYSBcv8qRGUDHH+EZOoFegMBKqVgIbIx0R8/9b7+fk5OTkJFY+IiMCbt2odRggH ARAAARAAgRohgJiCGsGOTkEABEAABEAABEAABEAABEAABEBASgnAUyClAwO1QAAEQAAEQAAEQAAE QAAEJEMgODh4iUh59+6dZPpFLyAgtQTgKZDaoYFiIAACIAACIAACIAACIAACkiCQmJi4SaRkZ9P5 glFAQJEJwFOgyKMP20EABEAABEAABEAABEAABEAABECgOAF4CjAnQAAEQAAEQAAEQAAEQAAEQAAE QAAECgnAU4DZAAIgAAIgAAIgAAIgAAIgAAIgAAIgAE8B5gAIgAAIgAAIgAAIgAAIgAAIMAQcHR3J Qe/CgvNfMS9AADEFmAMgAAIgAAIgAAIgAAIgAAIgAAIgAAKFBOApwGwAARAAARAAARAAARAAARAA ARAAARCApwBzAARAAARAAARAAARAAARAAARAAARAoDQCiCnAvAABEAABEAABEAABEAABEFBoAsHB wUtEyrt37xQaB4wHAYqCpwCzAARAAARAAARAAARAAARAQKEJJCYmbhIp2dnZCo0DxoMAPAWYAyAA AiAAAiAAAiAAAiAAAiAAAiAAAqIEEFOA+QACIAACIAACIAACIAACIAACIAACIFBIAJ4CzAYQAAEQ AAEQAAEQAAEQAAGFJqCrq+sqUlRVVRUaB4wHAYpSys/PFyMHJSUlIq3qMq2t9wq1On9zhhg1hCgQ kBsCg7oXfkxCQr7/YyKuj61EwTJ/agQlQ5x/xCRqBToDgWoloCHyMRHfv/V+fn5OTk5CxSMiIkxN TavVDggHARAAARAAARCQPAHEFEieOXoEARAAARAAARAAARAAARAAARAAAeklAE+B9I4NNAMBEAAB EAABEAABEAABEAABEAAByROAp0DyzNEjCIAACIAACIAACIAACIAACIAACEgvAXgKpHdsoBkIgAAI gAAIgAAIgAAIgIAECAQHBy8RKe/evZNAp+gCBKSZADwF0jw60A0EQAAEQAAEQAAEQAAEQKDaCSQm Jm4SKdnZ2dXeJToAAekmAE+BdI8PtAMBEAABEAABEAABEAABEAABEAAByRKAp0CyvNEbCIAACIAA CIAACIAACIAACIAACEg3AXgKpHt8oB0IgAAIgAAIgAAIgAAIgEA1E9DV1XUVKaqqqtXcIcSDgLQT UMrPzxejjkpKSkRa1WVaW+8VanX+5gwxaghRICA3BAZ1L/yYhIR8/8dEXB9biYJl/tQISoY4/4hJ 1Ap0BgLVSkBD5GMivn/r/fz8nJychIpHRESYmppWqx0QDgIgAAIgAAIgIHkCiCmQPHP0CAIgAAIg AAIgAAIgAAIgAAIgAALSSwCeAukdG2gGAiAAAiAAAiAAAiAAAiAAAiAAApInAE+B5JmjRxAAARAA ARAAARAAARAAARAAARCQXgLwFEjv2EAzEAABEAABEAABEAABEAABCRAIDg5eIlLevXsngU7RBQhI MwF4CqR5dKAbCIAACIAACIAACIAACIBAtRNITEzcJFKys7OrvUt0AALSTQCeAukeH2gHAiAAAiAA AiAAAiAAAiAAAiAAApIlIAOnJEoWCHoDAZkkoNCnJMrkiEFpEJAsAZySKFne6A0EQEDmCOAIWJkb Mihc3QQQU1DdhCEfBEAABEAABEAABEAABEBAqgno6uq6ihRVVVWpVhfKgUD1E0BMQfUzRg8gUP0E EFNQ/YzRAwjIMgHEFMjy6EF3EAABEAABEJA8AcQUSJ45egQBEAABEAABEAABEAABEAABEAAB6SUA T4H0jg00AwEQAAEQAAEQAAEQAAEQAAEQAAHJE5DS3QeSB4EeQUBhCSgpKRHb88UXnKywJGE4CCgC AST9UoRRho0gAAIgAAIgAE8B5gAIKDoBeAoUfQbAfhCoDAF4CipDC3VBAARkhkBwcPDp06eF6s6b N69x48Yyoz0UBYFqIABPQTVAhUgQkCkC8BTI1HBBWRCoYQLV6ilQcqNDnFBAQFwE8lfni0sU5Mg9 gWr94yb39GCgXBJAngK5HFYYBQIgAAIgAAIgAAIgAAIgAAIgAALfSQCegu8Eh2YgAAIgAAIgAAIg AAIgAAIgAAIgIJcE4CmQy2GFUSAAAiAAAiAAAiAAAiAAAhUloKur6ypSVFVVK9oS9UBATgkgT4Gc DizMAoEKE0CeggqjQkUQAAGqWrfyiuYpSF6dDNwg8B0E2G5sYSvkKfgOgGgCAiAAAnwCiCnATAAB EAABEAABEAABEAABEAABEAABECgkAE8BZgMIgAAIgAAIgAAIgAAIgAAIgAAIgAA8BZgDIAACIAAC IAACIAACIAACIAACIAACpRFATAHmBQiAAAiAAAiAAAiAAAiAgEITIElYSOYmYYmMjFRoHDAeBJCn AHMABEAABEAABEAABEAABEAABEAABEBAlABiCjAfQAAEQAAEQAAEQAAEQAAEQAAEQAAECgnAU4DZ AAIgAAIgAAIgAAIgAAIgAAIgAAIgAE8B5gAIgAAIgAAIgAAIgIBiEuD6jhfdkk7/PnzNxVfZNI08 rt+0wocGP+1/mpzH9V/SrZFO/5X/PEn+UpxYZqh7d2F9g5/+fFqySjVCjvUb31zQO6Mqrxr7krBo MhAzC8C2/2lv4AsabC43YFU3JeP+y048ScsVr0K6urquIkVVVVW88iENBGSOAGIKZG7IoDAIgAAI gAAIgAAIgEAVCHCcDmc83jvGjBHBbjfLO/TDyV8GtGCWhsocx/35X5/v79NI237FyYtrndtoJd0+ sel20gf/8wF336QXX4urWy25+SVkQ/MqqFOFpk0c/75IVK2CBKltSgZia8SBEUX1S7ztfeg2Fe3/ 99W77zIKhyLzrue2s5FV89HY2Ni4i5TGjRtLLRooBgKSIQBPgWQ4oxcQAAEQAAEQAAEQAAGpIaDe dqrb4j60OskPTwZcj0gq8oY6M+7F18mb1s8f3pZdm1LW7TrKtauOtv0gu87NNKXtuzNLrYGeitRg Fa8iyhr1GxaVqNt1yoSulJH9xN6dG2sIhoIXF/Db0i0vP5QI9xCvMpAGAgpHQNr+2incAMBgEAAB EAABEAABEAAByRNgmTqsXGzB+ApuXQ58FJ8tfEX9KfKkz5uBtrYttWszarE49u633idd+n0E7ThA qUECdTh2a2/lR13aMKqtVh1aD+ImWDa+76ZrvLz8GlQLXYOAXBKAp0AuhxVGgQAIgAAIgAAIgAAI lE2gYacxE23oKq/O/3f9XlyGYK2Z+ejfy02H9rMyUCXfk0XTFrT/aX9YQSaAXG7IUbcRZvRG+vYT 15+NLJEgID3yssdYPfq5dn+XLX6h3MJ99emRgXt/7qbLbMLvNG7pjjNP4tK+lLnQ5XFDTvwxu0sj pol2+3G/HrsTn11Gi8wIfy+XLvxd/u3Hr9x/JYrfe16oh1HB3v/+rp6717swamj3X7Dn4tMPhb6S 9MiL60cyvWn3n7fjYsS31eNlRgbs+9mWEWrY32XXheuX9i2asPzYzRfJH0M9eglzKOwNevrMV5h3 YPgav5C3gu4ISd9ts3sIarYf/8uxu0mFmoiOYPGkDHncm+5j+xA3AakUv3uKuXYdg5/WbBnRrFia CZ4wLUX/3y++zcKnAgRAoIIE4CmoIChUAwEQAAEQAAEQAAEQkCcCLPX2Q37kb/K/f+3qzcgUernO S793+nIrqy5G/PABslt+QwA/9KCw8DJDt43sOO7XoNZrLkZlhWzolhX9tkiF1FCPMWb2G1/8sDvg adiuxlcXzfx9f8BDZm2c/sJ7fi+7ldcsfwt4npgSMjbO3WXKYq8zd6K/uRrnvfGd0rvj+uiOO8Nz M+5ssGE9POo2dt62s1FppfsKMp/smz68/9zQ9t73PuSmhIxJWjd95pz1p+gUgMpWix8ELOEwuoY+ S1DvvuDE3+Qy2f+PlQf970clMu6E1NCNI8wGbo5y8g59/3IPx99lwrLtgTGlOSYIh62DzfrOuGa4 5cyDd9G7rR67Deo1YMaWp6npRJK61eJDvpNaFICppfvD7wFL2hclmRvnO7ddx83PO26hVd3Qj3p4 ZO3YZR5nX5RmWxPHXQcW6xYKYHG6L/lj3Tjmhv4s7/APuXG7fln419mCDBS2SzznjmzNZnGc/o7Y Zz9y08U98/s3VfvWDPbz8xNNcxkZGSlPcx22gMB3EICn4DugoQkIgAAIgAAIgAAIgIDsE2AZDpjt wLgK7py8fDviw2eKF3v5yMeBXSwMNGsVmFdPS0ejiKm82Evb9weTW1bW1q30VGsZ9Bo9sLlIDV6k 3wrXCxTVuEt7E31dY0ub1tSHG2fuRr7jZnyNu7Jp5V8J/Ec6DRpY9hxqSH3w37ft7J2nb9NLXfnz oi7v9ImgHvpfvv7iQx2OSTMm8eL95xHRKTmljEBu3CWvVcciKP12Vp0MG9RuYNnXrjkV479x16FL jGtBVUuHn9bAqkvnls04emz+ZXJyZk7ulzziKYn8Z8myyxTVpIuVZTN247Y9WlHJN/4LfBCX/rV4 b5khuxduvE6xO9r36dyqhb5hN6dh/CSR3yolScZc3HnqPXWbgV9b36Qp0zLyYcSbtJzSjnFQrS9Q voxOSAYKj9+d6UENOnDqytOET8SmzPi64xaM6NFMXUn25ywsAAGJEYCnQGKo0REIgAAIgAAIgAAI gIBUEahj0G/UJOY1dfLRf0/fiPn48vLeWu27WhiolrGmzIy6czGCNNE31G+ixuyWL1I+Rd284E/f aaqvramqUpdJy/fh/tUnrxITo+9c8k4gj+prqtWtrcyilBsZdidv3ZPvP3yV8DGttJU/xdLvPIY+ pqGhlpZKnfJXuol3/S69Jz0YaGnWUSbVlfWNu5W9/C6m/I1zgXzlGzdQYSlrNGDT6l0NefA+s5gj Iy/i+q7rieTwCGO9huqqdctXreTIs/Rtxtg3othNtDTq1RHbqoRlYDd3YT/hmKZ9jfS7Xdu8uV5Z YypVsxLKgIB0EBDbZ1I6zIEWIAACIAACIAACIAACIFBhAppWYxbQq0qKenI19MaZHde69e3eRq9e We2zUuPJAvmbJS8jhV6qU9R//3Pq2Eytrr7TXvoqLj0rNzM2+gXzSFNdpW7t2iKL6/D4D5nRJ8c3 Fw2AV+Lvq1dvO/3oi/yvflPVgn91cTsdWeZO+7z3Ubfe0D000NCsS3sKCkp8eHxCRk6JuIDiVuRl JCcJlB9oqKZUW9/pAKN8WnpusZyBeUlvXsbQz1S11OrVqSMMwagweYZDy+l/J+bHn5+qefPXBStP P69U429Xrt/+h1HMwRZP/K/efhx0K7116+badcUkHGJAQFEIwFOgKCMNO0EABEAABEAABEAABEoQ EK4qk++7z1oY3bJLx6aa3/N+vCTadrP2BDz/kJvPL3G7ZthatDZuWaRe3vuYm6/oO+b62upGIw+/ FlTm/+/SygH0vnqS8+/AvG6WJFVBh5kuQ0y/udOelqPcyLhbM9Eu8uKjbtHX+ub6ehoqFV/Pt5u1 L/TD1wJ14nZNa8OujmUDj3tn37y++iRVQYcfFw8pCqcKk7XgYAtyBKbP7wcTWrRp1lDUL1OaZF1d XVeRoqrK7PJAAQEFJlAdH3kFxgnTQQAEQAAEQAAEQAAEZIpA4XGJlKl9vx4W+mrlOAp02/Rh8iDG h0Y8TSmS6e9zWkZabl2TDjZM0sDY0Oj4lFR+hVzug8cxSRkabTrb05dv4z+kZxe+4Wd3bNdCr4EW P4FA0cLPnjht+90GY51HDmhdLz2h1D0KwkY65n2YrIHh795lfBLZ62/azqyZlkq53/xVTaxtmN0Y saEvY1Iy+DEIudywkGdJxXIaKuua8215H5P4MTOLSYZYvDRo1rZJWXMh8+6mkcNmbL/BHjtm4oCW yukp4ps4BQdbJD9L0TNtqVN+hgIbGxt3kdK4cWPxKQNJICCTBMr9eyGTVkFpEAABEAABEAABEAAB EKgYgYbWAwfQy+OOPzgMttQrngzgU1pSBiMnOy0zk47BF+ZB5J+Y8PHxnx5/vmZqJB+dP/K347Ft p3pNaU1v73fftvO/64+5mRmRoW/V2FpqWupmjus2DaSod3cevIxP+vjx0fUzJIK/3Zhp/a3MSg9l 4GW8eUFnT6SSo7jvXt1/p9zNsIhRvKyPfN/B54zMtE95VD3T0cs22OhQ8Q9D78V8/PLx0ZWA1xS7 3axJo+2MtYgLJDstie9q+EinMPySVfQyn6VpO3uHM0mLQJR3d/v7elx2Xmbk3chaBs21i2/zZxnb zaTNjA+9GfLizdsXJ5cMcb1eFLiyRkMmX2T8i+i4pPiwE39s5ldIz8z5/IWcNJHx7nEwvdkhOSo+ 9tXTBGWTIuEQVF5GKt93kJ2W9Sk39yuVnSpQXmAsRek0t2zOWE98NB9fBFwLLzhhkRxsMXIBMYQ9 YNzwrs20Kh5MUbEpg1ogoAAE4ClQgEGGiSAAAiAAAiAAAiAAAt8kQJbHP/7ex3zosJ6dDDSKBhTk cf2W2Xk8ZppGHl0w/7eDD5J5dQycNt24sG6Ezp3dUwb2nXNee/jIlsPnbti2a9/ZsIebndvoGDlu /dd/wxhd6v7hhcPsJi/bF16nqa4OW5V88a5vtfhYhP/Slv/Osmul29D6qMESr/8OLh/doyW79PB4 Zd2uo1y76lCUobFySpKqudPMxUxi/yc3/zt3PerJyYkDpgUyaRGSDy8Yufbg02SeeuelF29c+K2x 75RO2nUaWh/TWbHv2MkN4zuw61B5oR7t7dy5jDX33Se4rV82frLI5ZGA0LfZrGZOXqcv/DZChyg/ t4/lDwt9Yhq1a11aOkBWM76Zyv4bJvZ0/u2F9ZoVNkUZ1zMdtcZ3MckYEOQ+YdF638wOE7syFcil +5GAB281Ok9x7UVyIrYzVkpIUmnl9NNm2klBXA8Xzlx/EXlyvtnUfwTkF67cdcZv+5QpHvwMEUJj ldtOPLZllA7x0fzhtvVe3VaG2jRkprCa9Js0vNmwrp1aEMtRQAAEKk1AiWw/qnSjbzcgWVjIQ/HK FKN6EAUCIFCSAD62mBUgAAIVJ0COHHdychLWj4iIMDU1rXjzsmsquRWu0ZJXJ4tLLOQoFAG2G8nV Lyj5q8X5LVehMH6XsZmhHoOt6bACkqBh83wn2vnxXXLE1YiXHrj7zy92k+3N6GAKFBAAgUoSQExB JYGhOgiAAAiAAAiAAAiAAAiAgFQS4MX5TtbV6bT0n5dpSSFhtVoa6VUwPyVxg4oePBEZGSmV9kEp EJAcAXgKJMcaPYEACIAACIAACEglgbizzlZsJbbIj9VsvziBqtyzswsfDfMKzaJ43BsrRrD15x9+ wd++XqTkhXpZCesbez3Mk0qLS1dKhINQ8zKNlSHboGr1ExBJ6MBPK1ADhZcZce/i+w/3H0ZERdyK btbRUlwHWdSALegSBGqYADwFNTwA6B4EQAAEQAAEQKCmCRgM8Qm6c2CCHl8P27UBGfd3OhoItOIM 2Zkff+/AcIozYdvzwy5WalRi6NH1VynuoS1no0v6AZStXEK/BLgZ1bRN39O/wZA/j22zF2AQCCjd WF5W6LHDxGmCAgJ8AiQDgpGOnfsD5iLy6MLprvvPhbyV/AxhaXaasGaMGeV/4I+/U1r1aqlbPD8l BgwEQKCiBJCnoHxSOTlfoqISnz5NiIigs7OiKCYBMzOdFi0aWlg0UVGp2U134sePPAXiZwqJICC/ BOQ5TwHv5eGBDvP8EyjKcl7AydV9Cre7k/xpN5b+8mjUehcrLXpsyWv2VXMd/jLYFrh2fEuNUkY7 76FXS7vV0RRl5BbwwqWdsgxNCBJWMMz54OtCzUszlscNWjfOq76HD+03kbKCPAVSNiAyo061/nGT GQpQFARECMBTUNZ0+Pgxy9f34e7dTzFnQEBIYNasNk5O7Ro0kLrvRt89RvAUfDc6NAQBBSRQrV+m azqjIS8jcF0XO0/iKtCb4nNl3xBOQeQlL/LwuI1aniJ3yhl6efIUlDCVcRPM8Qwycgs5Ck+BAv4R kFeTSWKC8PBwoXW2trYNGzaUV2NhFwhUhAB2H3yT0pkzj/v1Oww3QUWmkULVIVOCTAwyPRTKahgL AiAAAgpAgKXRadBPtnTsfYK3X2DU5wKT0x7/97T77B4Cx4FI2gIrj8IsBDzuw7Mes9vQGQpGuPk+ LuXkhszoIEGF7rM9AqMzeQXyeVmRNw4vHcFkSWgzbOnhG5GlpD8Q4Z8RHXjYrXcbur7+7F33Yrmh J9yG7GJUyXroMUyQbYFONJCX6LeoIPnCorPcgq0SvMSHpw8KJNA9nnjIzS19fIsbm5t474CLlZNn EHGnBK+2bspWGubld1gkjwObYSLsVyTdgwJMIJgo0wTIMS6OIgVuApkeTSgvFgKIKSgFI9lu4O5+ 5cyZ2JLP7AY2EQt3CJEtAgEXSpkMPXs2+v33wXKwGQExBbI1G6EtCNQsAbmOKSBoP0d7/9Rp6mkS VWCz6e+jizvQ8WPpN9zGvZ3w33gjweuVwtCD5psC7i5m9hZkhnkNnbg6qMEE36OejrpJgX9Mt9sY TO4Ldx8IKrR1C9kzW+/Ggo7OVwbywxZ4WS9OuPaZc9xsbcCZme2oh7SciL7f3NdApT30mGnn+sRm +Y59a7tRQSU6ooptHyD7JkY6uD+iqMk+8RuHcJQpXuzZ6WOdY8YXdkeW/fbb7l3gG1iseUljiRdg aWunvyjKpiCmoMAELiUih5Bcsq3+Yk/HJhJ+K4XdBzX7JwK9gwAIyA0BeAqKDyVxE6xcee769ffC Bzq6dR3HtOnc3ViPU19uBh6GVJZAAjf17s0ov2NPkxKFb5ko+XAWwFNQ2cmA+iCgyATk3VNA8eLO kmX8IbLo5Sw49WJFD808rt96D2rKJpEVLzndoLP16tcUVeApyOX6rerrdCBBuBovvvugwAHBdxyw +AkROm+L2DXe+D29bvd+XiCKBAWMtXMNFllyi043kkdwz1jrVcHUcLqtaV2qlG0OxZb6BQILdCM7 KUaZzbsqyMXwSZCVQKh5cU8BSVRXzNiSngKioZCAMMVDcrDPI8OJfYQ7OCT2qYGnQGKo0REIgIB8 E5Cwn1cGYO7efUPUTTBhaps9h8cNH2kNN4EMDF51qkgmAJkGZDKQKSHsh0wVEn5Snd1CNgiAAAiA gEQJsAy6jphoSXfJPbr1n2ge702gdx2HvgZlfmHKiLobQsLxKaMWjXVKy17Ie3vn+F1apk1z8lKf Yqlq6dWjqLv/3nzLS3x03vs5CWFo0VCN6aIux9CE/r9/2PPEkucq5Ly8eoEOVeAYNtX7zvS6LP0O P0xqRVGN2FpiTLRYh2M/htm48WjbxovRPIoXef1xE0vJuwkkOlfQGQiAAAjINQF4CooM7927MYcP RwlvrfXsO+5Hm3r1vvMfY7meOQpqHJkMZEqQiSG0n+xSQc4CBZ0NMBsEQEA+CTToMGqkDW1awtXj tx8G/HPGcYSNZtnflz6lJaSVBYOX9TGa9iRQB51b08kILOjDBaiEVylZX+JfMy6Eeo3qqxbt4+Xr +MIQtgLhqe+exNG/q9bXKFa94mOh3mq8z83kr1u7vzu9dun+u28/VbxpWTXV2wyc2Jmu4H/mwoM3 j2/yunZqIB7JkAICEiFAAqZIlKWwkASHEukWnYCA9BKAp6BwbMi+gzVrbgivV/7es0NHQ+kdOmhW cwTIxBB1Fri53SHHZNScOugZBEAABEBAjARYau37jran8xpS/gv6OX+c0L+Z2L4tkd0HX5KT8wU/ oYvb1dVvziyvheUzN+Ylc2HSXL+uGK0SEZWbSDIg2vWx+yNryC9TOjcl0Q1iKXWNRvw4j0NEXd61 ZcvpWhYW6mLDJhb9FElIZqh7d9FFr5LS8DUXX2UzCHiR++0Ezwz6r7nwNluYWVORCMFWEACBChDA H/FCSLdvRyckCPz3JHOhTa+WFQCIKgpKgDgLHEYbC40np2kqKAiYDQIgAALyR4BlNGzpWMZVQOlN HNrLoE55Jmq36sVsWIgOexpTNBAgOzWDrMSUm1g6lajAS3zyIJGna2ZDeyVe341JKrLZwL5DK92S uwOEHb16V7S6iIb1G7c1+LbCJNPBgenWP20L0p33y6h2qiQaQkwxBaRLTYvhC/uR/yccyTDu2hRf McubNtX3XN1qyZWI/Q78DtiD1l58dXjVgBaqzCXLdMq/AUt0rcevOup3aFH/pt8dnFJ96kMyCICA dBDAn/HCcTh4kGQGFpSps3tKxwBBC+klMGl6D5Ltkq/fqVOFm1akV2NoBgIgAAIgUCECLA3rXqPo 1+PDV0ztpFG8CS8nLZW/vP6UlJFD/79ui/6OE+j6d4//9zSLynh58M9d0UwNrqdDh50P89g28xYy FU7Pm7n1Bn0kYUb09feNLHVZLCOHdS5ks8Prc7fDybmJmRE3z5GW/dzWDSk4akG0+8KOzl94mUVO MZjpsprfUWFR1mioTV/RboucrBdn9m6hMxtQ1Me0jK/kjXLmmyjmOvHJay73UYpWb6MirXnZAt8B 38dBlTRWmd2sRXO6TWZyWnZWZIjICYtaFmMnEDP1lvw4jGRbRKlJAsoaDdhM//pWXTq0aqSqJFQm 8+k/wU3+/HPr/8Z01oGboCbHCH2DgLQTgKdAMEIkejw8XLDJkLwrrt+APhcJBQTKIEByFvQfIggr INEoUVGF52WAGwiAAAiAgGwT0Ow0Ye1wvSmOfYxLrHi555faeTJZB6gEdwdbj4ckFoBlMGhd0Em3 SQ2CXfs17b01ossPP5mPdjvp43Mq4FmkCzlHsaBCKypoo4N+n9ke9ynrNrr0tzCWmtXMoxEn3Zoe ttPQYWtM9O+04lTEPhcrrVIBFsihjk/t3tTOh1q01q3oSp+4LYxGLfdZ0pv2SphN9LirN2gek3WB vlxxlkvpdB26gE492KotFZeo137QxBl8H8f5w9ejM2PP/jhmnj9jHN/HEVuKscrtR+7bMlqPJC+c sPpgahMLTmHMBZMPst+o/q1LuFdkezrIj/aZT/atOsrrPrRXGzaycBUbVnNzc1+Roq3NeNxQQECB CeCURMHgk1yGc+YIktiTLejIUKDAH4pKmP465v3sSaf4DVav7jJ0qEUlGktNVZySKDVDAUVAQAYI yP0piTIwBqIqlnJKYs1akHzD87r+/B9KC4iQkGI4JZEBTU73nKXvdICiTMd67ts8uwenjhLFe+M7 Y8PrkYt/sjcqjDHIjLy8Y/XEZVc4s9a4dXkxc/LWhI5LDv0xY0hXI63COAQJjR26AQEQkCoCiCkQ DMf795nCgdFv3FCqBgnKSC2B5oaNhLpFRX2QWj2hGAiAAAiAAAhUG4Fcrt+SNkpTDkd+ptJfhDc0 b4Fvl9XGusKC8zI+JjOVVbXU1esoEzdBXMDKrc+Gzp7Wx1DETRDu/dMP9h4Zzn/7nZipf+fcpQSK 3bF32xa62ppwE1SYNSqCgLwSwN/yUkZWj1NfXscbdomdgDBVQXp6yeOsxN4bBIIACIAACICACIHs jGR+RntBWoEagZMRdTckgXoUHB7PvfreoibjCWrEfOnsNC8jOYnRrL6mZj1lpdTQTb9daj129iBz rdoFPgDeG7/5o6ceSuw4ZczwrhbG5gYNQ19QVNveFsRRoA5HgXSOK7QCAUkSgKdAkrTRlxwSsOhQ GFYgh+bBJBAAARAAAWklkBfqZaXlsI3L6EfSCmi5nuUWOT9BUoo36PDjjNGc18ddNp3idOuqie+W kgJfoX401TXzXu1f9xd7/HwnK7bQTUDx0oP2uHg/o9g9hnY2bczRUEp68ySGojp2smihp61Zq0Ky 5atSSkpKpEjJyWHSlaKAgAITwF9zBR58mA4CIAACIAACICCzBJStXELzk5MLfzYP4ZQ8WFEC5rHU Wo7ZGZ+cHL/zp05MlkYUKSKQHX/Le1dC1xkjuxiInnTAi/xn45/Ey8S272lt3ERPNS/udoA/fVKC kUHD+hoKGVEQFBRkJlJiY2OlaBihCgjUBAH8Pa8J6ugTBEAABEAABEAABEAABKqLwMc3j94xsqNj cru5zBvcVqvwiAr6duKza/QhF/pWrZrq1Nesw4u5tMcvgTLtZdq4oZYalgfVNSyQCwIyRQB/CmRq uKAsCIAACIAACIAACIAACFSIALvd+J+XzR1iXsxNQI5GiI+5SUtoZKjbQF2Niju1fSVxHLA7dmxp oMMucTJohfpCJRAAAXkjAE+BvI0o7AEBEAABEAABEAABEAAB7UELNqydYmtQeNaBkImyvmF3+iI7 LSs79cXtW08TSIAB296qpUEjNjlPEQUEQAAEKAqeAswCEAABEAABEAABEAABEJAjAnnvX9f+ae+G WX2af+MUA06feZsGUlTK8/s3Q1JqKSWTZIZm9h3NDHQaFN2lIEdMyjPF3NzcV6Roa2uX1wLPQUDO CcBTIOcDDPNAAARAAARAAARAAAQUi4Cy1cK/lv7Qll37m2bXt1p8Pj8/6cGRDS698u+feEixrRV8 64GpqamjSGnYsKFizRlYCwIlCMBTIOuTIjcx7PwZT2cLFSVd+kf/h1X7zp0NTeTlJpw//6RGDkuS daLQHwRAAARAQKwEyLlj5PgxsYqEMBAAAXER4KWHXPmbi60H4uIJOSAgPwTgKZDlscx8cW7VUAub wdO2U7OOhTzOyk/Mif93WS/1l9v6qdW1dH3Gk2XjoDsIgAAIgIB8EAgPD2ez2V5eXvAXyMeAwgq5 IpD57J8/T5PjEqm0zJzcL3n5cmUcjAEBEKgKAXgKqkKvRttmhh/5eeSUzf4UZ473zX2zh1sJjjBW N+21YJ//sTm6NaodOgcBEAABEAABUQJz586Fv0AUCI8btLZ3mzbOx15mSs6xXyOd4oMgtQTyQj2M NNpOPfSMaJh8/n9Ow9cfuvYiWXLzUbrAEFcmCYASlpycHOnSD9qAgMQJwFMgceTi6TD10b5lCw6H UxTHdrXLQP1i2Wfq6A11Xd9PRTxdQQoIgAAIgAAIiImAQvsLMsP2egv3BeYl3T7jGZSQcPDIpYjq XJDUSKdimi0QU90ElK0WR+eLlLhdM2xbshV1cRAUFGQmUmJjY6ubP+SDgJQTUNQ/BlI+LOWqlx72 3/ZzTK3uw7s1L2UUWU16jmxTeD8z8qbP0h/4uQz6Lz0SFJnF7yIvdGcbfoIDJd1p/0S/vLRzWlv6 d0PnPSHcvITL6/rrM0/bzt0enMj3MX9HE9LqWwpQmY88ewsUUJl6JOjSkVX9hQooqlO73OFHBRAA ARCQbQKK6C/gcW+sW7cn5WvByCnrdB26wFZPb9K4/mbV5tmvkU5le25CexAAARAAAQEBeApkcSrw MsKunKS3lFGUYac2LeqVZgNLw8a2rTLzhN6n4OQ062m/4I+JOR/9BzxdMNBpmU847SxQtpp9O8CF w1Q7vO94nPmsvWe9nc0p7sH/9dA3aLGQO+5MML2RIfzEkvl7r3+gq31HkzIUoNQtF5z0XWTFaHDv 9huD4WsP05dEgVHbb6XDVyCL8xM6gwAIKBAB8gZOqbzi5ORUKhEF8heQFfuquQ7rr4pyYHFsV119 +tRnjIl69XwZq5FOpWruJ1PlzU08lzQBqZogUAYEQKBsAtXzjxOoVy8BXnZaSmJFu8hNCNi9gexT MLS1sahPUfXb9LJtTlb+s347/fITLUNVi81/mTF+5lRbAxaL3diMf36s+ajdRzc4WzVv0px5nvo+ rSA8snJNylOAqqfB1mB6tBnQv6Wa8JKbkpYNT0FFhxn1QAAEQEBGCcimvyAj+vKu2fpsthK7jfOu oMgMAfzM6CCP2W2U2Ozea89GvrrhHUj+seZx7+76cSTfTfDa1U5XiW3lERLnt4i0ZX6GeYXSrvu8 UC8rwR2r2X7PReQfCONmJQa6D+M/1Z+9654gyI+04nEfXvB2EzxSGuHmE8Z/VsFOGbUzogMPu/Vu wygzws37RrQgb0LWQ49hBUrOPxwYeHjpiJIKyOisg9ogAAIgAALlEoCnoFxEsl4hMfTMRdqtYNiQ /9JCWd+wE/3/sNvPksq0jb9ur1Qptcl3K1CprlEZBEAABEBAhgkQf8GUKVOoLzJhQtpDj+md7L3e Lbz89uvDjcqHnWzXnI3LpajP0Sd+d3L9tCIiPjlguv65TbOuZRN7WJzOP21YNpqxrPmmgMT85NDF 1gaOK04tsRS1lkTsBQUs0KNvvT6+/Uy8+VSv+z4TOFTCwSX99Ju2nhA3+uk1nymtKO7xVQ77gpmY O17c2QVW43elDD2an/Q2ZK0NdXXb5Ilz/npJnlWwU+ImeOm9apjdutDBf7+lhdiFTnUYNucEk2RR rd3iPwuUDA1+zXHYuIe+FFFAJkYLSoJABQmYm5v7ihRtbf6bMxQQUFwC8BTI4tgr67TuZMtXPObe 01dMaMC3St77N7ej6Ye69YuGN0bfi3mfJwHra1wBCdiILkAABEAABKpAYPjw4Tdv3jx16hRVuwpS JNWUF3l2retlijKy722mxmrUysaU4p7769IbHvXh+bVHFBUZfDMmi6XbYf7yje1K3R7IV7Seho56 UZVZKlr1mQbNR88d34NTh6Xb2ESVqcKZsC1w7fjWLRq3akhfcj+mZZGV/OdXl/wOcROCt5wPS6fU 9Js0pp8lXL0W8e33AMU75cVd81p1KIFvC8VSs+xqb0R8E2uWn4hmQhOE9TsPGmhCB/3xdRYoICni 6AcEJELA1NTUUaQ0bMh83FBAQIEJKJGEp2I0n+x2ItLEK1OM6pUh6syZx25ud/gVzt+cIZlOv78X 3osjw/ssuEJyFZiPO3Z28/Bm33b5xJ6b1nvK4WhqvO/j/Y7k6MS8MA8bG9fXlNGoY1e3D29CZyi0 tHaLIbsP+BVIisGh9iuCKGqm9yuvwXrKxesTpSvXhCpHgeI9fiqmwPdTkkjLzb9fCLhAZ8cdOrTJ 6tUDJdKnmDuR3Y+tmEFAHAiAQAUIBAcHnz59ugIVC6uQU8e+1YT4CFxdXW1sbPi1ldzobxH8krw6 uVK9SKry52jvnzpNJQQm+8RvHMKhEv2Wtnb6i7Lfdu/CCOov/iNKb9LaHSsn2Jry99aRpfXZ2frO x5mYgruL2zFJhEhs/1g712Cy884t5KiLFR3DRzYgdLZe/Zp4Cnz/2+loQOU99Gppt5p4+yf5PPMZ olvYhN81K+vFCdc+c46brQ04M7Ndxnl+FwWVK9JpXYHy1PBtEbvGm9alqLizzsOcD77+Ro+fC3Tm K8BPhiR1he3GFuiUTrnWcpU6/RRMIXKgwP3794VGy+IaQcFGDOaCgAgB0bNRqv47X3DV5Uhewn// PbKy2sP/SczJl/qfrzGP/hrFz0TYc4t/0tdiCnNf+bvuDoujDcm5tXskXc1wk38GbVdc8Kbm9PVI z8c5dKuMkNWGjBziKaDrZ/ivs2Wuiafgi0h94ll4K+ilck3KU6B4j8UVkPKxWLLqPH/a/PrrecnP W7H0KLsfW7GYDyEgAALVTYDE85b85sWPIyjWNfUrJfxJzk+Wyp+3AZsEfo0iRhm5BXxJTv767PKW 0cwOAlJ6z/O985ZvRbyP6O4Dxi6hHOIpeMu3NDHEjfkHmngKHtN3vgS4GTGSiKegSBOyUE8UwkmK Dzh0YOm8JVMFagkqV6RToQ5CgY99JjEq8M0pVJJfoWR9aRwj0VlU3XMb8sslQFyBop+UcuujAgiA gPQQwO6Dkt9eZOIOS8104oaLjLPg+sKJP285F8YVZP8j5xH6eSz6NdKufxvG21/PyGnx6p4cKiYo +HEqSUz49FrQa4rTbd3i4SZMkGN2WjI/U2FiKpPD6FNGMj8zU0paBtmdwMtJT2GeZ79PpYMdK9+k PAWK91hMAZkYDigJAiAAAiBQUQLCvQbCUIKKtpSueoUrfHrRHuVChwqQTQcL/ggM2TXPlrgLrm5z Wn8q8nM1as1LfOjj5qBPUhX0XfxzT2b3QaVKXY6hSZEGeUmvg1/Td2yaS2vEQKUMRGUQqASBlJQU EgAlLDk5BZm8KyEDVUFArgjAUyC7w0mcBc7bo+L9Tx6eRflMsdHnqCjpqihZ/Pzfx4bD1u6e016v jsA29U6zfYNOrGPvtmmgq9LA/mIbzwtBhxZ04kc67uxq58U/cPHK1G4zvM95jnTaHMpcn1xg8fOp 0+v7DlzPnLPADZo1ZP7p2O9pUoYC9NYD0R5n7vRcJKrAuQRJ5FKQ3UkAzUEABEBAhgjIhY9AxahD JyZqIDroUXzBCT25iQ+eJ/KSg30Cubw6ulajVp/52412FjwKDmcOGK6WkvZwy3y7yduCOWNdZ7RT yUh7X+lelHXMO/SmW718HS/q0dDr3ctMp9LS0AAEZJsA2ShBjn0VlthYem8pCggoMgF4CmR89Fkc yyHjZu9/IgzRf7x/8VBb0+JnFqib9lrgw2wuyE+8tHGcsAJJtfxUZKvF/imDF1wVifbf7TB85d3C vRhRdF6D72hCGH9LAUrdskiPPrMX+IgqQBIlyPgIQX0QAAEQAAFKLnwE/HFkadg6byRnEJDcgVOX ryOeAYqXFfmYq2eky6qnlrRz5vKz9CmDqjrNDRtQHLtBnZkVt07j1sw+gk9JGTmZ0cGh5CjDTxlJ mYzAzOQ0fmZiXk5aKvPbp/epzCnB2RnJ9OEJpKs0QdCfoMnHtIyvpOG7Jy/pp9yYt/GxT1O1uvO3 KghLBTplmQ5Ztakf8Xr4X43IIoY8uu1P0iLYuqwaZcR8QRQqye+x2CUmNgiAAAiAgDwTkLqMhtbW e2uctwxkNKxxRlCggIAwo2ENIgkJqVIOTmQ0rMGxQ9cgoAgESExvBbOIy0JGQ2bEMqODdm+e43o8 oUjywqwnp8MaWqvd2LZujvtVynaBz575QwRJDXMT7/3t5rDkOLfV6C2bV8+3ok4xeRAFhU5qOIv6 k0lnyC/NRx9b1XrlNDqdIb9M2ubT9rgznQGRX0jigLUdn2+fbrcx2Hbetp+H9x3amndqVV+nAwmc 0W4+i37sZ6RGld8pk0kxI/ryoc3Oq47TAYa95x2YP2GUjRF9WpIw5yK/x9Fum6jVrnTOxAIFpDSp YWFGQ5I5a7U483YX2I7/V4LAkiVLNm3aJGwgzRkN/fz8nJychKpGRESQ0xAqYSqqgoDcEYCnoJQh hadA7uZ5NRoET0E1woVoEAABBSMgM54CBRsX2TIXngKpGi94CqRqOKAMCFSKAHYfVAoXKoMACIAA CIAACIAACIAACMgbAXNzc3JQi7Boa2vLm4WwBwQqSQCegkoCQ3UQAAEQAAEQAAEQAAEQAAH5IkD2 GjiKlArumZIvBrAGBIoQkPbdBxLbCHD5/GPPdXf4bCTWKSajHBAQ7j6wG9hk0cqBkrFoUPci6TyQ p0Ay2NELCIBAdRPA7oPqJqwI8rH7QKpGWYZ2H0gVNygDAtJAADEF0jAK0AEEQAAEQAAEQAAEQAAE QAAEQAAEpIUAPAXSMhLQAwRAAARAAARAAARAAARAoEYIkFNaIkVKTk5OjaiBTkFAegjAUyA9YwFN QAAEQAAEQAAEQAAEQAAEaoBAUFCQmUiJjY2tASXQJQhIEwF4CqRpNKALCIAACIAACIAACIAACIAA CIAACNQ0AXgKanoE0D8IgAAIgAAIgAAIgAAIgAAIgAAISBMBeAqkaTTkVxdewuV1/fUtpvlEZfLk 10pYBgIgAAIgAAIgAAIgAAIgAALyQACeAnkYRemzgZcV5nMkLLNAsbyku75br3MTD//lH5ktfdpC IxAAARAAARAAARAAAYUmYG5u7itStLW1FRoHjAcBioKnALNA/AR4CQFblx9JLRSsrNPZaX5Pju74 yfamquLvDxJBAARAAARAQFoI5CaGnvVy7s5WYrOV2gxbEZQo8Vg6Hjdobe82bZyPvUQcn7TMCugh AwRMTU0dRUrDhg1lQGmoCALVSQCeguqkq5CyyUaDDc7OW6/nilrP0uu34lL84/3OxuqYcgo5LWA0 CIAACCgEAV5W6IHp1s6rLw84lXzZzSgheP1vu4OSq930zLC93k/yBN3kJd0+4xmUkHDwyKUIHPNW 7ezRAQiAAAjIKwEs26RuZHkJoRd9lv6goqRL//Rfc+he4euIzMhrns4W5H7/FedeRt/0uZRIq8/L enlp57S2uir6P6zyi44POnKWPtYlL8yjs0BI751kI0CC31zBpfHc08y5L3mhO9vwe1HSnfZPtECI kq6h854Qbh6TWYB52nbu9mBahyL19908vYKvpMW0HQ8S+H6B3MSQHfO72ZONBhQV5GajoatCuk5N PD1L0At9WbAlITPyptDM/kuPBEVmCYYi85Fn74L6U48EXTqyqj99yWhV8GImPTrQc64hw+f0s4Sg IxcTCr4gSd14QiEQAAEQAAHFIZDz8uqFYGKuan0NdZ3mvVpRHGsrM43qtZ/HvbFu3Z6UrwW9KOt0 HbrAVk9v0rj+ZirV2zWkgwAIgAAIyC8BeAqka2x58X6Lug3dk+J0KOdrTPCWbpS/13SHeX+/YFbI n6J9V41akbPscU7ihbn6F3+bfYNZXPMiTy/80S1xy4b9+gAA//RJREFUwa2seF8X3Sv/W3mb+bag 3OHn/47NLDRPz2HdheW6ouYqW82+HeDCYW4d3nc8znzW3rPezuYU9+D/eugbtFjIHXcm+NgcXSr8 xJL5e69/oIrU/+e61hzfnLTg3VOowy4DnHc8okMc6+haz1zlye/UdnVwRmLO1dkd6usOX+u7yKoI 6MzwIz87Oc162i/4Y2LOR/8BTxcMdFrmE87Yo2654GRB/Xu33xgMX3uYviRajdp+K50mwXv5z4rB m96vfs7NOTPD4OrvU64Lvx9J13BCGxAAARAAAYUlwGoy5MDN5Hj3IQZ1qpEBcROsmuuw/qpoFyyO 7aqrT5/6jDFBHF81oodoeSMQGRnpJ1JSUlLkzULYAwKVJABPQSWBVW/1T68uHzvC5d7afupBOqWm 36wx3R036MazJPqXpBc3wijq+e1bUVksTvs5v623rEfffv/s9hUu9TT4blQ6S89m+m/LzWuVqiVL RbNh8ZcLqlps/q3xM6faGrBY7MZm/PQt5qN2H93gbNW8SXPmeer7NCaCUVi/7+jRPQ1YlKZx/8G9 yP3rm9b5Rn57J2Y9DbboG5XchIDdGw6HU4a2Nhb1Kap+m162zYk/YtZvp19+YnoX1rcZ0L+lmvCS m5KWTcc2JD27F0Rxn9+4G5OprGs9faWnVekWV+9gQToIgAAIgAAIiBDIe+hl3NTOlQ4poKJX29Ue 5hWalRfqZUUnLCA/9CXFPTtbcGk12y+OVBSpYDXbO/CGt9swukL32Z53iyQ4yIwueMRm93Y7HBhN fOs87t1dP47kuwleu9rpKrGtPELi/BYx3RX0KFAwIzrwsFvvNsz9EW7eN6IFKQyyHnoMK6g//3Bg 4OGlI+hL/dm7RCIaMcwgoAgEwsPDnUTKhw8fFMFq2AgCZRCAp0Cqpkcdva6DR5GX/CY6GqWMjE7L Hh0oekXdtus0z2vRDQbPHUTHCDRq3bUvh+J6L7C0met56ZXmoJlDmlTZKv4S/dtFt77gRYV2s1aG pJrQnVGRnhNDz1yk900YNuQLUdY37ET/P+w23yVSTlHWad3JlqISD0+2sfhxZ+BrjeHTB+gpl9cK z0EABEAABECgOgkot3OJehuwyYbuw8gt4Mt/LlZqylazzvtOLuyVM2hjwAI9ES1IuF6Q4M7ra9fe 641aeZi+fH584fLCBAeZYV5DhzlMfW4fEpP0zmdCxLZ5dr+fivzM4nT+acOy0Yy05psCEvOTQxdb GziuOLXEsqidGS+9Vw2zWxc6+O+3+UlvQ+xCpzoMm3OCyXeo1m7xnwX1Q4Nfcxw27qEvucdXOewL ZuL4UEAABEAABBSTADwFUjXuLDVT5+0x+VyfnlzfNWu8At8V0a6e0cStF90nEe9A4uGFoyxGrjn9 gg7XZ7Ucs+/kb+PNaSfCigHdnFade5kuVVYVVybv/Zvb0fRNobtBUCP6Xsz7iuQbYJlM2HbDi3ap cA+6DbadQLIzIL2zVA85lAMBEAABEOATYKlo1WcCAoVFeKd5r+E9TNSVCyqkJaby4+w+R5/wWh2U QBl1726pxeJY9htI0h9oa6kpfYNpPQ0dddFHvLhrXqsOkfb2vc3UKJaaZVd7Iyrh4JrlJ6IZT4Cw fudBA03oOD5+c+7HtCx4CjBvQQAEQEBxCcBTIGVjz+M+OrTUqQVJVTBwoUsfZveB6NcJTvu5+y4H H3TpSVbJ/l5j/scP1yebDmbu9ffft6Qbubi+fsrCf/j/+EtpUW7UrKuRqG558TH36GujToaNKhYb QBIizNl66+62Rfbku8ytzS4rytr7IKUYoBYIgAAIgAAIlE+A9/bO8bvMP5IN6HjDSqc/yEu6e/UQ STRMaTfQYP6NVdZpbtOcohKuXouoSCBf+RqiBgiAAAiAgDwSgKdAqkY19dHW6fbT3W9xJi+aaq2S kfq+iHYfbh26lMCro9th4i++p1bTzgImXD89+PjZNzzylmHC+kN0EkSKunLvBf1uXpndxJh8F6je 8uHN8xjSA8e2R2udivbE30ZBUbdjih5Z0KFrhWTwMq4fvxify9LrNHrtcf91gyu596GiWqIeCIAA CIAACNQ8AV7Wx+gEWo3ojxnf8xrgMzfmJWNFAy2Noll9gl9zKxLIV/MIoIEsEcjJyRHNC0jSBIpq L/ro3buisbM1baWtrW2ESGnSpOqbeWvaJPQPAlUjAE9B1fiJuXXGu6cvaJHcqLcJb56m1behUwAI Sz21pC0//Y+JtFfVbdaCTXEGDOioS6nWS14643d6JwJLRa9Zcw6l62xvxbybZ2k0bE63jrj25B0v M/zETp/X9GX2+9SCgMLstGT+WcuJqUz8/qeM5AzmOiUtg3x94OWkpzDPRZrw1Tm8Zy/dY27C3cBr 5LKn6wonU37OgQL3REZyenbWyzuP6AMUhWLJTRIEUc/IaTHt6YgJCn6cStIlPr0W9JridFu3eLgJ PySzmBrFLlkq6rGrf/yV2WShqmvYXJcyHzfUqsJ+ClGk+B0EQAAEQAAEZIRA9Kt3Sd+xsq/LMTQp YmFe0utg5uuATXNOxQL5ZAQQ1JQKAioqKnfu3BFmBjx9+rSoWsL7GzZsYLPZNa4xcWQIS7EUhrGx scJHOAehxkcKCtQIAXgKagT7tzrVtZ4wkQ4K6NmCikxp1GHIqHXkkEKKCjztGxhJUhKwTJZ6zW4c smGgrlrzKa+Gel9aN1ifnL1U13DT9ulN7m/pr88xcnk9zvdfDwc9ZmBZJiPWHVvejaQbnNXHacMD s6EjmtO3yeWQ+adjScLlnV3tvOiIRBKGMLXbDO9zniOdNocy1ycXWPx86vT6vgPX06kHhU2Eio93 Hkb32NxyzA5q/JYTXlMtC45iUm43fjedTCHUa4rrobRmbfVYiadXFYgN9Ro4cmdYJqXeabZv0Il1 7N02DXRVGthfbON5IejQgk5MDsXMR0XUmLnTc5GoVudIHEKt1msPzGx89/cfVOpajnk58tjJtcOb YSpL1VSGMiAAAiAAAgwBZXazFsw/vt9blJtYOvEzFN69fJtb+agCZR3zDr3p5i9fx38WUUKvdy8z ONm/d1TQriwC06ZNKxfQsmXLiE+h3GrVXWHJkiVmFSjPnz+vbk0gHwSkkACWV1I1KHV0bVf/m5Of eGnjuCFWuqw6esO9HpPLGJ/ZfUzVKPW2Q/oY6JOQ+0uJdJ11g000mS8h5gMGmeqRLQmX4hNz4v9d 62hUeH6yptHwdbRA+v5Eq+Gr79K/k5+o7cObUCTh8lP+JfOzf8rgBVcLL3N2OwxfWVC/oEkhrXoF PeY/3r+gF18TfiEnOM714as905rDopR1h+8WEXt1dgcmVZK6aa8FTDW+vbbEQH5Rtyyihs/sBT6i Wg3WU1a2HNSvcTPLCRsZ0y79MrzMYxqkaoShDAiAAAiAgDwTSH33hD77kIoOexojWJazNBq0YG4F PYrnZT4/vu3Ya/ry0/tU+uBfOnwvjZ+6kH+n2CW5z7aZt3AC2XFIPT/k4nH0BQn942W9uHL2QRrd SKdxaybzz6ekjJzM6OBQcrTip4ykTIZxZnIak8zIdMiqTf2IAv5XI7JI20e3/UlaYVuXVaOMmG+B wvof0zK+lriU59GCbdVEwNTU1NXVtQzhHTt2HDhwYDX1XimxZevJFzV8+HAbG+ZMExQQUDAC8BQo 2IDDXBAAARAAARAAgeogkPfQy9jC+eBrRvbpeWbdZvvRXgOW6bCNvgtsSAbBqQ4Oa56YDR/WnK5A Lse4+MWR8D5bO08mDwFzx+OPpaKXfAkGQzxDL+9Y0pviHprXqsuwpUfDWJ2HtNeiGym3HnnEfTSH SnBfvnTfB+P27CS/dQ7ujxgdHm2z+9ErlIQkarVbvO+ev0vjLf2aKuk0tQ6wOnDq3pmZ7ej3ClkP PX4sqE90XuDl8YvI5YqzyGRQHVNFAWSWHVYgJQEFZByIC4A4AsoekIp4ExRgSGGiIhJQys/PF6Pd Skr0mT1VkWltvVdUn/M3Z4hRvTJEXT7/2HPdHX4FiXUqGdPE2Ut64Jp2zIaFnlv8fecLdxyIswtZ k7X59wsBF2KJ1nYDmyxaKSEH+aDuRT4mISFV+phU/WMra4MGfUEABKSUgJJb4cl/yauTpVRLqCXd BNhuhbvf81eL81uudNstddqRwP5NmzaVVIsEFFy7dk0ath7wdQsODu7evfu38BE/wqlTp6QOLhQC AYkQQEyBRDDLRyeieQ2uL7TXsaMzDqCAAAiAAAiAAAiAAAiAQFEC3workJ6AAr6+ZYcVIKAA81qR CcBToMijX0nbi+U1yCnIOFBJMagOAiAAAiAAAiAAAiAg3wRKzVYgPRkKROF/yx2ADAXyPUVhXbkE 4CkoFxEqgEBZBDLSRVNJgxUIgAAIgAAIgAAIgABNoGRYgbQFFPDH6VthBQgowDxWcALwFJQyAT59 +qLg0wLmV5zAveD3Fa+MmiAAAiAAAiAAAiCgIASKhRVIZ0ABfyxKOgUQUKAgsxRmlkEAngIBHFW1 OkJMr6ISMWlAoCIEEripwmrGpjiXuiLMUAcEQAAEQAAEQEBRCIiGFUhnQAF/JEqGFSCgQFHmKOz8 NgF4CgRsDJo0FFJ6cP8N5gwIVIRAdCRzshVTmjQvnEIVaYs6IAACIAACIAACICDfBIRhBdIcUMAf AlHXAAIK5HtawroKEoCnQACquWEjHd26/ItLZ6OwAaGCE0jBq508zD+zmi6GxroKTgPmgwAIgAAI gAAIgEAxAvywAmkOKOArLBpWgIACTGMQIASU8vPFedJs1Q9mt7YuclD8+ZtVOii+UmN85M/gQwee 8pssWNGl3yCLSjVHZUUjEHztxe8rr/OtdhhtPGNuH4kRGNS9yMckJKRKH5Oqf2wlZjg6AgEQkG8C Sm5KQgOTVyfLt7GwrpoIsN3YQsn5q8X5LfdbCovO22oySubFXqYoW4qqLfV2vKUob4pqSVFjakBV yUzXGjAMXcosAcQUFA7dIId2wgvPdXdEt6DL7PhC8eoiQKbH3m13hdLtB5tXV0+QCwIgAAIgAAIg AAIyTaCfLLgJCOGmjJugm0yzhvIgIDYC8BQUoqzfQG3m/A7C66Uup+EsENtEky9BZGKQ6ZGUKDgf kQQUkN0r8mUirAEBEAABEAABEAABxSNAnBrEX4ACAiBAUfAUFJkF/YdamrbW4t8i60CyGnwe/g7z BARECZApIeomIOktJk3vAUQgAAIgAAIgAAIgAAIyT6Bw84rMmwIDQKCKBOApKAKwXr3ay9z6C1Mb EmfBolnnN/9+4XXM+yqCRnM5IEB8BGQykCkhjCYgU2Wj13AybeTAOpgAAiAAAiAAAiAAAiAAAmIi EOs3vjlJhiVSDPqvufA2m0fL5/qOL3zQ/qf9Ycl5cQFLbJV0hiz753Hal2IZRjJDPXoJqxv8tDfo RTIjRRYK18+5QPVCzXllGCtFRiGjYSmDUSy2nF+DrAltbJu0MNKWotGDKpIi8PhB3OOw90IHgXBK EDeBHqe+pLQo7AcZDSXPHD2CAAhIgAAyGkoAstx3UbMZDZGJU+4nmHgNlPx0Fa/+5UlLf7FvTq8Z h+g3ru1cvA/8MqFDI5HXa58iD4w1W/Zl6a51y4e31frgN15/xBGK0p/l7b92Qht28fdweaEeZtau MXSFPYfnO/VsyZaVN968SO+BZlP9RTUnjpJSjOVlhv7p/brL+B/aSIlt8BSUPsNTP2Yd2Hk94EJs eR8APFdQAp1sGv28rB/JbVEj9sNTUCPY0SkIgEB1E4CnoLoJK4J8yS+9MG8VYV5Vk42Sn67VZMg3 xfJeHOjXa1og8RWYjvXct3l2D04d4Rk3HwJdfzrXZsmK8dbs2koUec2+bHzfP9UFjgNyp2iRXU8B RcIK9J0OinoKSjOWx/Vf5uSWM2X7r1M6SImnQFZ8MZKe1WQFuGjlwLWefYVpCyStAfqTVgJkSpCJ 8etGh5pyE0grGOgFAiAAAiAAAiAAAiAAAiIEWKYjV07WpW9E+l++8Tg+S7ivgBd5auObzva2rRry nQIsAzv3oPyksxtGWGiVcBPIG9MSxjJuggmbbmfnSZOp8BSUNRodOhr+sXf05t2DJkxtQ94hS9PA QRdJEyATgEwDMhnIlCATQ9Ldoz8QAAEQAAEQAAEQAAEQqH4Cfn5+OTk5YuqHpdnJcYGNDpGWfP5K 4L236QJXQeqDf+9bDu1lbaBG+wlE0hYY/PTn0+QvTO+53FDfdWNa0tv8209Y4/ekxCqalxl5cePY VnQF7f4/bfEL46bxW1IUeeTv5dKTSRGg3X7cyv1XIkukPxA1MT0ycO/P3XT5olz2nLkesH+R/S/H bkUkkz0B7t0FqQYMftr/NDHOb1pB5oHhay6+yhZ0yA058cfsLo0Kevz12J347GL5Fvg1ixubxb3r Oday/6bbSRT1cPd0K+1a7X/6fe0I0QQPNJMcrqBfkXQPYhqkb4nB7oNqBgzxIFANBLD7oBqgQiQI gEDNE0AUd82PgexrIPlwbsxb2Z81NWaB5KdrRUwlS9SOHTsuW7Zs4MCBKioqFWlSZh0mH8G0UxTF bjdr04G14zuw61DpAa6OtzrtmjfCRIuJKOClBy43tXNPLMxTUCszdOtg64XX2Q6/7V3ubK8ftW6y 3foAUrUgT0GDbH6FdnP2bJ7RLXl3v1GXrX5b/6vzAKumqlkv/pzea9oxfSY5gumrLQOGLnvRcenu 9XRChFICFlJDPcZZu95vN375Glen1h/+nNzX7YZIT2wWyc7Yw+nIG9L3Pv+1U9rkB7na2Xk8pqhh v134Y9GAFqq8N75T+o941K+wu+AkquPS44eXjyQGFt990CCzuLGkyix9pwMkncOsfQfW0rsPeJl8 E8i+DaEcipCcserDWPe5/ZurSuJ9vyT6qPL0ggAQAAEQAAEQAAEQAAEQAAEQAAFJELh//76Tk1Ov Xr3EEV9Qz3jABGc6ODv54cmA6xFJuVRu3OVz3IGdOhpoFGQjYKlqNSzik+DF+u/cf500surQpqme tnrjnk4DisT08iJPrHAnFfS7WJjqG7S0tLagos6duR/xLjH9a+ylDZvIGlu/S4dOzRrUVm/Td5gp CWnY6H7k0uuCmIZCinQewYWuFyjK3H5g51bNGxv2GjqsnOjhelo6GqLDwIu6vNMngnrof/n6iw91 OCbNVOmn959HRKeUFptRwthShpSl3nKix84ZNLb7/5469yghl8QnZMZTgxeM6dFMIm4C0jM8BZL4 sKEPEAABEAABEAABEAABEAABEJAhAuLyF7AMek2aZEEbnnzxyOnbbz5GXdz7pWtXc/0yVryZ0Xcv PCMt9A11G6qr1StBjRd167h/Anlurs9WV62nrFFfj15U33v8KuFD9D0/sm6n2AaaGnWUyWq3nr5x y28v3bMjrp6iXRJsA72G6qoq37M6Zul3HjPGjKIaammpFGZsrOpI1zHoP2UhvXEj8uiR8zfepH+N vHy7nmFzDrNfQyLle1hIRDF0AgIgAAIgAAIgAAIgAAIgAALyS4C8cU4W/ERKTSmGWxz+goadxky0 YVwF96/eu3HmwNFufezb6NUpY2CzUhO4ZY07LyMlin4e7/+/ER2bqSsxhwtQVGp61udPsVG36N/r NlBXq1tkr8Hb+Hcfc3jFxH5884Q57a6uhppK3TrK37UMV287/eiL/K9+U9WCf3VxOx2ZJZ4pq275 w48MtvvXrt4MCQpIb23RXFt8rohylUSegnIRoQIISB0B5CmQuiGBQiAAAuIgILrfWxzyIEPRCeSv LjWfmJixIE+BmIEqkjj2GDZ1XJYM/v78BYXHJZK39w5rDnsstDdSFVmV54VuNLFe9lqYpyD3TLGT BXmhHmbWrjEFeQq6Zf3ZSuSyZ0uRgwVJykD9EUcoffvf9u9b1L+panao+wDrpcGFaQWKII/1c+7l dPCVMP8Bu1hPtOhieQrqPiguMJcb8vd6l+XbP4/2PjBFffMPo0hSA2EWgxKnJPKKGcsumaegQEWS 0MG0r0cixe47eLDNjF8WDDbWqiWxGYOYAomhRkcgAAIgAAIgAAIgAAIgAAIgIKsEvj++oPC4RIpt 37uPhYGom6AUHLqte9nT+wniQ6PjUlIzRJx+n9OycnJzKZMOIzh0O9EKPG54WExiskbrPn3IBv/4 8PiEjJyvhcI7tjIzKpoNgX6mY97LihEUn5iSmfWpeMgB07xBM8vG3x4zkulg28iO07bfbTDWeeSA 1vXSE8R1cgRFaVqNWdCPdJ18JUuvtbGOpuTcBKRTeApk9YMKvUEABEBAkQnEx8dnZYkpuk+ROcJ2 EAABEAABEKgkAeIvOHjwYGwsE7Rf0cLStLafqEtq20516NtGr1jmAV52miD53+e0jLTcrxTLsP9M Ryb1wPWb4a+4sQ8O/vEnCSig18xHF07bdeZWfKu5XrOZCgc8dp6+8ZibnRF5/W0dPS2Nhupmo91d SdR+/J2we28+fsl8euW/SIrqMusnJ7vmmiV2F9Qz7j92Ci3oyc27z97EhZ+cOYkOXShSlDXY9EGP VPyLl0+5Cc+Pe2wmEQqkpGem53zJ52W8ecFcJ0dx3726/065W9HcixmpJKECKXwfRx5VwlhKWaeZ SXO6SnZaZsbH5zeuPeIWnLBYv/2EmSQfJHvsD8N7GPIPipBYgadAYqjREQiAAAiAgNgI5OXlhYeH k/+KTSIEgQAIgAAIgAAIlEdg+PDhN2/ePHXqlKmpaXl1iz7XtJn+u0Ojof36dGpafL3O/fcn5ohE erV9dP7I344+TVYycFx/zX/TGN07u2eOGbvsXL1eQ1uPWLzGfdu+s/dvrZ3QqyWnsaBC8sPDi4ba TV60L1yzqa4OmwQrsNStFl6MOPubsf8UK906GoOPNZ2+77LPhgnW7FKOSKRYBg5br13YMKauv7tz z1HuL+zmr2BW7SKlnunodSfm96SoIPfRUxYf+dpzRhfmKblcvu1SrEbXUa5diSvB0Fg5JUnV3Gnm Yuashyc3/zt3PdjH2Wyqv8A0xsdx42AJY78ot590bOMoHZK8cO26rSGsVia6wpgLJh+k3bDO7Vqw 61YOeJVrI09BlRFCAAhInADyFEgcOTqUOgJv37599eqVhoZGhw4dpE45KPS9BJCnoELknlMU+Q7K rlBdBa+EPAUKPgGk33z2PDb1XqCm7yhfKVGYnI9YqibER+Dq6mpjw+TYk+OSVzQjQpEUCDVi9odA D98vDmPsjSUcUkDBU1Aj441OQaBKBOApqBI+NJYLAnxPATGFw+FU+rWGXBCAEQpLwMHBgcx5d3d3 hSUgbYYjo6G0jYgM6cN2K/T5ScaxVRE4SkrFY9wVxUfApyMVnoLcON+5HUa8nHzca0X/9/v+ynWY bCfJXIZ8Eth9UJHPC+qAAAiAAAhIKQEul0tyFkipclALBMRNIDg4+PTp05s2bSLnqYlbNuSBAAiA QHECwr0G8h9KIDQ9Oy2Jn5Hwc0ZWzufcPEkcoVJi5qVH3Lnznop4GBEVcel1s94WTSV45IFQGXgK 8BcBBEAABEBAtgm8fPkyLS1Ntm2A9iBQMQLER8CvuGTJkoq1QC0QAAEQ+B4CiugjYOIJjLTs3LkM seTDCwf9b//Z0LfZpR6I8D1UK9ymYaep88Y0ivffuuPvHJNexo3qVLilGCvCUyBGmBAFAiAAAiBQ MwRIdkMchVAz6NGrBAmQOAISUMDvkPxC4gsk2Dm6AgEQUBQCiukj4I+ustXi6HzRcvoXR+umqpJf MrPUW049mpif/+HSdmebRjWgAE1D8mYrymcMdoIACIAACEiMADkEISIiAkchSAw4OqoRAvv37xft l8QX5OSI79TuGjEJnYIACEgfAXKugQLtNZA+/tKjETwF0jMW0AQEQAAEQOD7CWRkZGDn9vfjQ0up J0Cmt3DrAV9ZElZw4cIFqVccCoIACIAACMgkAXgKZHLYoDQIgAAIgEBJAklJSTExMSADAnJJwN+f fyB3kbJhwwaEFcjlcMMoEAABEKhxAvAU1PgQQAEQAAEQAAGxEYiNjX3/vuBwarFJhSAQqGECKSkp c+fOLanE/fv3EVZQw2OD7kEABEBATgnAUyCnAwuzQAAEQEBRCZCjEJDdUFEHX27tPnLkyLdsc3Jy In4EubUchoEACIAACNQQAXgKagg8ugUBEAABEKgeAiSv4cOHD5HdsHroQmoNECD7Cw4ePFhGx2X4 EWpAXXQJAiAAAiAgFwTgKZCLYYQRIAACIAACIgSIm+Dx48dwFmBSyAcBsr+A7DIowxayMeHdu3fy YSysAAEQAIHyCeSFehgrFSnOflxBs0+R3qMKHs3y4+aVL41Kj7zsOcsztLAqjxt6et/S3vqMnP5L vc+GcnOpzAd+gXFlSeOeXrrUOzAyvQI9ykYVeApkY5ygJQiAAAiAQKUIkKMQkN2wUsRQWToJkIAC krawXN22bdtWbh1UAAEQAAE5IaBstfjF7bUd2Yw5LQb9djxwCjsy5G02fVnPdMr2/2ZYtBu7Yo/v CHbkk7fZvLKsznzht3Sk7aFao/rUfRKRTFflxQWummztMMOHPePwmStBQfMt4v6ZpV9XSWNdqp7S Wy7TSamF03+mTcwfPYb9fPJ5plyAlndPAY/76OyRndPa6qooMT/91/j8czGMy+O9uXw2vCIuJtke ZV7czdX9dQ2nHpGAcyvBb64AslJnUZ9cNRDMC/PoLOir984w+fgkVgMmiAQBhSfA5XLj4+MVHgMA yDaBsLAwfX19V5HCt2f48OHk3vbt232Z0qVLFxyCINsjDe1BAAQqRUDZoFVLTaZF2y5tdLVaWPW0 bqpKX/IyXwRFdPM+uWxwW2Mj846WTVW/veDlvfGbP9Lpju2+n6yVv9Rp0qQBqcqLurRxPTlrpmUf mw6N9Zq06Thg/C9ef+8Zo82um/Y69otq3W+rWc9w6LLtOztcGzVt2ZnXZfonKmVqjVWWY08BL+ul 35qBVvYjx++mnL2D47k5+Yk5FxZ204jZac9Ra77q5ecao16NHac+2vH3I6EL5P3d4+7+FNd767mX 1e4W0XPc+viAbUVMy7y3z+dBVfRR7vDzf8dmVqQr1AEBEFBwAiS7IZK9KfgckHXzbWxsTp065S5S +BZNmjSJ3HNxcXEsKCoqKrJuLPQHARAAgUoQUBLWVdPWVmWuiJvg8JojDUcPY+ept7K2aMEuw01A 5cad2uTizRs91EQ1n93G0kSbrpyXGH6POZM2KSohU9OgKXNTveWYaZP6tm+urqqqUubyWUm92Q8/ /mgTuWPKij+jPlXCFqmsKq+eAl5W5N/L+jt5XefqOvv67108uAOHMZWlZtJ/9t6z3s7mUjkcVVQq NzFo6687EwulNOo8eok9xZkyf7CJchVlV6A5S6N+o3KrkTCHTb/sT5EDL1u5pqICCICAVBB4/vw5 jkKQipGAEmIiQKIJxCQJYkAABEBA9gmwNdTrCXwGvLhTi/9Qnb3A+GNc3aYtGtTmG5cZednDWV9J v7fHnbjAFXTugf7ekWQtkn5zm8sObqMuXVo30jIwaFibL0RZ19zajv4l+f6Bfy6+ysjnC1Fv0nNk lxbqWlp1mGrfkkkesVqNmNWP+uDv5eWfJGgsq5Dl1FOQGeIzd/kJOq/FyGWLBukVs5LVbOD/3HpL YOks0VlB3ATrZw389ZZopyyD7m6XEmMOjDPlB+fUdCFuArcpTiTMAQUEQAAEJEWA5DWMiIhAdkNJ 8UY/1U7A1NS02vtAByAAAiAg5QR4WakJObSOdTXUVOrUVlbicS//ui1r/hqrlNe1jNtw+DEGVGa4 9xwn+825c07++T/927/tPM2l2B3b1fnw6mNayJW/yWqxmbaOagOOnpowQIFlOmLDb7Z02+R/l89Z tu9xKv07y3S4ozlbR4uO3eLL3JK/+PzR1frXV3ie4lI6HdvVjrsXnUa7BpQbtTJvRiU/POR3/o1s hxXIpaeAlxFyavd1Jv9l3wGdjeqVnOcs/Z4ObYVBeunRQd5r+usXJDIIjM7kv/HOfOTZuyDBwdQj QZeOrOpPXxo67wnhMjViz00zLqigpNvGgw77F27Xn+bHvNwnmyAuCRIlGDrvDIzMom/mJZ6eVdBw 1rmElOhAz7mGSrr9PR/RXacLLklWhdPPEoKOXEwoCNWn0y7sK1BV/4dVfz9KyGU64T7YPr0f300Q 42qvwSjzrtTEAUSfQIEhKkSC982XBfk580J3tuFnc1DSnbbvZgETi2k7HvB74Zdv6VCSctE7vITg PTMG8N0Er1dYG6gI0xnkJoadLVCJQFh6nCSSKOisgB5R1S86PujI2dhS+xHJXGA893TpdcpTEM9B AATklgDJbvjs2TO5NQ+GKSSBO3fuKKTdMBoEQAAE+EuSrJSoBPoXA001Vu06n+5vWfZ82OI+X7n5 huZNBG4COg3B2KkH33dxHmTT3KTDD92ahJAvA217W7TQ1WZFhd2il4sN1FXqqaoKAgr4bOtbr/zz 3xmd6V8fes+0m7Ip5AP9u5JWkyZaSgKZH3pMG9BeTaNJ/57mT59TlHlvC46KjrYm429Q1jfpQv6X /OjunXciiyjZGzi59BSkPLrizw/Bb97HslnpJmp3sW3NRBWkR/ks+GHgqrABp2JyvsYEDwibZffD z39H0St2dcsFJ30XWTGS7t1+YzB87WH6knvwf6O230onFZoM/uOU53j+Rgbb1X/PsiQS6e36f41b 4v94r6MumcJhWydYDHCLneKflPPIU2P3YKdVp9/wSFjL8LUFkjPe3H1Kte7ayoRDXY94l8njvfxn xeBN71c/5+acmWFw9fcp17/y5xXvzbnZ9vbbM4f5vktMuru6J3Vr8yT76YeiiSIsTvu5a9ePN6Kr GW7yz8hPfLrYsrHDugvLdYvMSf6mDLsF9wf4J31NTDrV7/4qp/4LBPkOla1m3w5w4TANAu+81R/x y8lD5DLxsIuz182McnUob/Kz9GxmrnUbxR+XdSFxOfl3F1gpU7kJpxf2s9md6ng8Meej/7rB1HX3 eTbzj71kPHC8yNMLf3RLXHArK97XRffK/1beFrAo3plyh4VXiLE9l3s/Dts+vEl5uuA5CICAwhH4 +PEjjkJQuFGHwSAAAiAAAnJPgCz1ea+O/n69w9oR6m8yOa2aawmW/bz0oD0u3uEUu9ugjkZNTJpp pcZFvqGojp0sWuhpa6a/eVLwclG5dhFHASHGau6w89DJaYyz4MO/SwbM2fmCn0O9QKZ2136WuioG Roa58eGviMyOFvpaOtrqBYEJrFp05ffR0bEZsrzlWi49BTlpCakV/FDw4q/scPNOpMz69WqtRrIY WPTsZ0jWxstX+tK7V8gxGxpsDUaUzYD+LdWEl9yUNP55G+rmY5YusKV/i7h87RkTL/Dp1d1cp0V2 unTqzMjTqzeR9/zNB/Rso16vUesOrajwI/sDXtFNhZIjkrVaGel1cJzt2Hx8VzN1XtKze0EU9/mN uzGZyrrW01d6WjFTjeJFBxz0CaeuH/4vJIVS5zRrwmT3vHLvxftvJQdkqWg2LJLdiBcbtMWDbMpg 9GFR6q1tBpiRfIcLFv1DuxtIUdVi8xv0GWxHNiwUXCYmpPLPA6m8DuWNAy8mcD8Jvji32y8sg1LX M2zMNAi7/SyJ/v/7Z7evcKmnwXej0omjYfpvy835LEoUXkLA9s3Z6//8dbCJdOyzKM9uPAcBEJA8 gdjY2Pfv30u+X/QIAiAAAiAAAiBQXQR47+4euVRr4qhmH9O1WpuwhYt+XuQ/G/8iUQNs+57WLRpz NL/G3Q64TFH6VsbNdfkv/4tlEeClBx45zS1YWNUyHrG7wFmQHPDXX5dfk9WfUGa/HlY6DfU5deNv BxKZja2MOCoNtTVLLFR4n7I+ybCrQC49BRWfh3lJ9/2P0HEnOvU1mAgD5UbNupI389ygG/ylavmF ZTJsLh13wL21/dQDEmjAex3y0cRSkwbLi751mix0KaNOho2IdEHCvysX70aLblmx7tCyPkXV0Rvu dXe/s7F6HZ3WnWwp4q2YbGPx487A1xrDpw/Qo3VjcToNp+MX9Nia35ti4X3oReJroDjNG6oxA19P z9CM/n9Z7oYiBMSgQzGiLP1OIwbqUhwjNnFdlCiNWnfty6F9GZY2cz0vvdIcNHNIyXiB3MSQHfO7 7Wu+ZcNg/TrlDxhqgAAIKDABZDdU4MGH6SAAAiAAAnJEIOnNkxjGnAfvqP7j7al0qpkhR/Skg8Rn Qf5kIaZv1aq5AaeRan7MpT1+CZRZL9PG7PqC3QlFceQmvL5+M/hxLP99MCm1jB0XO/eif0uOS/sQ xyVh28+uCWQ2Vq+vp1fvNZHJpVr1NNWrXb++RuFZDHLCWS49BTote3Tgj8/rwEck1v/b5VNCTATz tKEW31MgLLdjhMkByhvqhpZ97ekgf67/1bAPWQ/Dvtq048ch8DJSXtP/jz4xpim9+b+F0wn6Mik1 o6wjAlkmE7bd8BpFdgFwD7oNtp1A9ufz8yaom4/b/yQxa59N/Ml1q7zuxfJf81ei5MXH3KOrqzaq z/cUCEvEm/iK5duosg4l1NU0dj7wOOf17l7vT69eufc2Cd8RKayWY/ad/I32j4SfWDGgm9Oqc8Ks CoJaH57snNqvh8sJ7s2TZx8zMR0oIAACIFAWgYcPH37+LJen5GLcQQAEQAAEQEDRCHQcv3LKgEa1 qCamRlpFXhnmxUfdpGE0MtRtVF+Lijvltco/gWJbd2ypz6z7GjRry49lFpak8GuXD524GvGxMLcA S1O3Kf2cbaBZL+9L3pf4mGCBTC3V+vWSaJlcStvK2qyBllax5RW/a9kOdpZLT0G9Ft0G2PIH/WnY C9FsfMU/OwUv1YX3896/uR1NX3U1ZF7kV6SwNHpOWkZefVOhJ48d+/darc7t6hdrxt+Wnyj4uTq7 g3qZcuvoWs/ZeuvutkX2dKjCZpcVgq0QJPPf32sGWpFUBYOXze7E331QmaKsb9ipSH2ho8SsmX4p eR9Lk11VHUrK5CXcO75qqAVJVTDcdUbXFsUq0NkN9vr771vSjTy4vn7KwoKNEgX16g39nw/JbkBA rfjNJyy1MjxQFwRAQBEJkEMQnj59iqMQFHHsYTMIgAAIgIBcETAb9tviqR0MtE1aGhZ1ExArlfVb MAuf7DRyQsLb+3ejk5idCFYtDRqx6ZMOVU06dKPf9X7MzOG/V05/efdyTMLJ9a4rdwfHM86CzBd+ f+z5m6K07afP7d2kbm3l2vqG9JKEyMzKYyU8vBuTQsvs18FMvb4Ou64QbV589G1ywW5j2rSBWj0Z Xm7LsOplzHOWyYjfd09h3vP77T/+sMSrZnKg4I4jYSQvhTI/1J9kGSj6Up1j26O1TsU/SKzmnUd0 J9UTffaFGnZpUQBV2bjDECZBoGhoAy8h/ElZzgtexvXjF+NzWXqdRq89Tmf4E2yFIMkRd8yymeR1 Xd9l+URL1Zy0xErHFFD8YH4q+l5M0dwGfTu1pLdHlFvEoUOxTjLv7XZ2mLfZX3fRwokd1DNTU4o8 Tw8+fvYNj8WxnLD+UPAW+pNZfKOEtkkTk/bz9/guIV6Vc24jVp3jf7BRQAAEQODbBMhRCMhuiAkC AiAAAiAAArJLIC+eq7Jz++Ke+lotzM0LjkQsYg7Hbu4KskBLfn4v4N5XndpvyAkFZvYdzQx0GjCx ByxN634TyXLx/ouYpE9f8qm8l3EGfi8eXTu42DL72uaxekpKShqtnO60cv/jzz3LBjTTbtqqqaYS p8/83/vQMu/fCPvSSP3dU4pqad/RSL2+NuN94Je8pDdRJEya9kroN9CQ5dW2LOte1tQmMe2efrSz gLxqnrrM009wmiB9ZmHQGc+FG560s2tHv9hnmTiuoFfj/HyEvKzH1y+THS89XVc4mTJoPmUk87P+ p6TRWwaKXQo1qNei35hxZA3OGerUu0khU83u0z3n0A6LK6sWu11OJP6qzMg78ew2emR+fksUS0U9 dvWPvzJh9qq6hs11KfNxQ610KF5mbAR9CCIVH/4mLuFJslYvJsVAYWnQuA2zhz8nJSM7Nfr6g0Qe Lyc9hTlmlMpJpr1p9EGgbq5kvf364vWnZEdD5rPgi2TzxeDVbo5GfKWz05L5DRJT6R0PxS7L04GX kcrPFSboruQAaTdrZVhQITPyFjkNMfPdc+Y8y8RHrxPjo9Lrt2ku2kq1XvLSGb+ffpFFsVT0mjXn ULrO9lZFnBoZyemfKJZBd9c1q3uSjAY7pgjQlewbd0AABECgkACXy3379i2IgIAsEujShT5+CwUE QAAEFJmAstWcnaOaqTdu07ap8MSBYjwa2vz67/Nbfn/MsDfNCw/wfcJsPTAofPmv2ePnbZN1qCd3 wqI+5vCUrSa6mNSp26bv+AWL5yz+4+7z+9eCgoJ++6FTpw6WxubWVobMkQr1rVZc+PD83B8zB7ZR evDf8ceUTseOZg3qC7wPjAK8uNvnblHaP8x3al/glZDVgZJXTwEZD3oD/MNXIT6HnantTvYt6tKZ AlQsl51NbDBknedcG/psArrUt1xw5NY518bbOxuq1DK0udhhd8At3/mWdH69zEeeI502hzLVTi6w mLnTc5HI5c/nRDIZsPR7Oo3r1m2uQ3sml2FBIXkK1/17bssoDveWu72FsfPOe5RlBw6Lyks8vUpE 1MCddIBDQanVeu2BmY3v/v6DSl3LMS9HHju5djg56lFZp7PTfLIYptqaU7GJuh0HjptL+yao4ItH A5hEBuptx66lt/Rz189eeDjdtI3u+1MrBq7nnxaZuNmur2doHjncocP8Q48vrm7iba9TS1fH4XLH tb6Pj8zuUJ+ulBe6s6udF71sJ66Nqd1m/L5z4QyRS3JCQZk6vDw532JqUJHuSnwqlNs6+dApGBI3 z1/xZ5JROw6rUefRdDgAp5vl16gEtuXQyf9zJlkJoq+d+eca7Supa7hp+/Qm97f01+cYubwe5/uv h4MewRf2x7AxexjpoV4DR+4MS00M8HZjPA70DgWLJaJDU0IJ3AABEAABmsCrV69SUorGMQEMCMgO gcjISNlRFpqCAAiAgPgJsNim7Y0a1C5DcG3tll179urVqXly2AmSo6Bw6wG/TR2DEWt85uid97sY /DaTHITAYrcwY5MXunW0OE2atbQmLenS1cKoCVskBWIddsvOvXp1b/PpGUmQqNOvfdGtB7zMB37b vNlTVo7v0cLQWK+C+7vFD0csEpXy84udD1ElsSRMg7Svikxr672iGpy/OaNKCqExCMgjgUHdi3xM QkKq9DGp+sdWHhnDJmknQCICyFK/iloqKyu3a9dOTU2tinLQHAQkScDPz8/JyamKX7ckqbDc96Xk VpjxPHl1stzbCwPFSIDtxhZKy18tzkWZGJWUeVGZ4d5zxkw9+JQ96Pdjexf2NSi6es94eGD+rKUJ /Y/uWdGvSWGugXKspmWOnXownD3ot72/TxvSjsPsaMjl3tu3zOG01vJpg6zbWFq2KnIWgwxylOOY AhkcDagMAiAAAiAgQQIkr2F4eDiyG0oQOboCARAAARAAAckRyAv1MNZoS9wEpMvk8yv72WwOKXYG nUa7qXv+8XVI8/I8lyA8H7FMBQtkkrPnicz/OQ31uv+FacC98Icvy+HPlSOsW7VubSLrbgJiEGIK JDdT0RMIiIsAYgrERRJyZJeAWGIK+OY3aNDAwsJCdlFAc0UjgJgCaRtxxBRI24jIkD6IKaj+wfqS /CIkPFHk4ENdc5uW7BJvy3PTuK9i0rUtzUo+KkVHXvKL4HA6B52gsPhCedlvn0d9adisqR6T1EDm CzwFMj+EMEABCcBToICDDpOLERCjp4BI5nA4pqamgAwCMkEAngJpGyZ4CqRtRGRIH3gKZGiwFFBV 7D5QwEGHySAAAiAAAkUIkKMQ3r/nH96CAgIgAAIgAAIgAAIgQMFTgEkAAiAAAiAAAtTz58/T0tIA AgRAAARAAARAAARAgBCApwDTAARAAARAAARoAiS74efPn8ECBKScgK6urpRrCPVAAARAAATkgAA8 BXIwiDABBEAABEBADATIIQhPnz7FUQhiQAkR1UlAR0enOsVDNgiAAAiAAAjQBOApwDwAARAAARAA AQGBjIyMyMhI4AABmSCAuSoTwwQlQQAEQEBGCcBTIKMDB7VBAARAAASqhUBSUhI5WKFaREMoCIAA CIAACIAACMgIAXgKZGSgoCYIgAAIgICkCLx69QpHIUgKNvoBARAAARAAARCQRgLy6ClI8JuroqRb 8sfQeaff+UcJuVUbh8xHnr0Fwtt4PMqrmrDSW+clnp5VoH/vnWGZ1dEHZIIACIAACJRB4OXLl1lZ WUAEAiAAAiAAAiAAAopJQB49BXqO23O+Rl1YLkgNPN73cc7XmMe+LiaX3cYPtu+28Fx8VZwF6pYL DnqPN6radEl9tOPvb3sZlHWH/3Fr98iqdSH51qUZlXlvn8+DanGnSN4+9AgCIKBIBEhew4cPHyK7 oSKNOWwFARAAARAAARAoJCCPngLaOpaKZkMVETPVTIbNmD2MvsHdsXznzYyanAO5iUFbf92ZWKYK yur1G9akjpXuuzSjeHE3N/2yP4VXaWFoAAIgAAJSQIC4CR4/fiwFikAFEAABEAABEAABEJA0AXn1 FJTkqMxuYtycuZ2YkJotac7C/siKev2sgb/eqjEFqqPj0owibgK3KU7u/tXRH2SCAAiAgGQI4CgE yXBGLyAAAiAAAiAAAtJGQHE8BZ/ePLn3msGvq1dflT8OPO6js/vW9NdnkgLo/7Dq76JZDNKjg7wL nvZf4xMYnVnq63GRzAUqs84l8GPt06MDPeca0ukSLKZ5XnuZzu/uwfbp/fhughhXew0l3cplOihD H2F3/decfpYQdOQiX43MyGuezhbEuv4rzr2MvulzqZRIhrzQnW0K0jpM23fz9IofmBQPFtN2PBDN 6fAtVqUZ9eBd8J4ZA/hugtcrrA1UlDp7hgr2IAhVUmk71/OSAGlhagnjuaffZL28tHNaW12VITvD Uqli6hWMSHH1pO2DBX1AAATkhQCXy42Pj5cXa2CHPBAwNTWVBzNgAwiAAAiAgHQTUMrPzxejhkpK SkRaVWRaW+8V1ef8zRnfp15emIeNjetr0pjkKdjvoP7y1BYXF6/rXIozx/vmlsH6dSjem3Ozh0x5 NcXfd74lFbLTycGNPO174NbpKUa0/yT1kecE+xVh3dadOjRfN4jU9Em23R141Lkli4o9N633lMPR lOEm/0eLLZVJ5Q83Vw3fTC3yWOZgpE4al2jr30vQaSltS7WPJDV0sRizh6JsVwefmd1BnbgeonwW OM66YET0WWBNhW2dYLMwevxffn9MNFZn8V56j7VYRdHqGSaF7Fs76smAW16D9fKifSZ1m0V5Pj44 zujjgx3LnB8Nv7zfUZC+QbTb9MA17ey8uOSW/fwL3stsNWLovrwTe26h4RCLymFVEghFkcV/C6cT FNV8XUjwAisaEu25uMdw7rA6+NAsvcBF3Z0C7H39dzrqsXgZQatsBq5PpIwc122YOKSj2tk59ite jDp2dfvwJpRQPc4Uz4ue4/RC+NrqLgoIXttH4/vmh4y3GtS9yMckJOQ7PyZ8DBX82F67dq0MbL16 9Sr1admtyh6Hb8nECXYyPn/Fo35qaurHjx/FI6sCUtq1a6elpVWBiqgCApIgwP+7ffPmTRsbG0n0 hz7KJKDkRg8HvySvTgYtEKg4AbYbW1g5f7U4F2UV1wE1QeBbBBTAUyA0nTNptcdom259LfXqkHvM 6npqEGXlcuHiL7Y5gsU/NdP7FVljKxc85S/U6yScXmg/5prdsbObhzcr4SmgY+/nHWu7lV7x0p0V bavMLNdvftvLUOrQFPcU8OL9yLr6CLfAcUDetFtau8VwGLHGSYxbQVfgOMhLOO3zsPOPA/S4jFEq o3Yf3eBsrkZW+zueWs8dVIqnQCCNKnSUCNb5fPktqTJZUaW6P0rxFHxiUJwUeFhYL44M77PgSnfa kWFST+jZ4a//1Yi9/c51/NdznKkmHVZAG8t3+jjqFrtUyA+3gnsKquJ9UMj5AqPFQEBZWZk4C9TU 1MQgCyJAoMoE+J4CX19fR0fHKguDgKoSgKegqgQVuD08BQo8+DJgugLsPqDPPshPJD8xPrMdB/Hd BKSwOJ2GjzenKD22puCFt8hwfXp162IQfa1TX4M8raM33OtxzhNP2k1QtPBj7zc3WVfgJqAoYVuz Zvr1KIqfm5Ab9M+tV9+f2i8v6b7/EfqdP18fIrVRs67k/AVu0I1nSZSyTutOtiT/wuHJNhY/7gx8 rTF8+gA9Uk2nZY8OFBV+YlbbrmQHRHSDwaW6CUQN0q1Ph0SQot2slSH5H19+2awqPMt5r+/+c5Ou 3dWQ1o6lpqVLdoHcPH3rtQgYo14dTUiYAEvf0fPpAdpNgAICIAACUkCAZDeMiIjAUQhSMBRQAQRA AARAAARAQBIEFMBT8C2M6ubj9j9JzNpnE39y3Sqve7GiWQ7zMlPIApmUpNSMb5/xlxO8f4b9gCUH E6/4XXqYWtCPsO2eKS1q66rUZjYRkMQEKaVnOSjcos9PFkA26seWUPlTQkwEc7OhFt9TICy3Y0hG ApbJhG03vEZxyNL+oNtg2wmr/Jj9//WMJm696D6JBBEkHl44ymLkmtMvvvNw8LJYVXia8rJSY2hv B3XYiU6doNKU3sFBcV+nZH2/C6XCnaMiCIAACFSRALIbVhEgmoMACEiAQF6ol5USm13408ctsMSG iPQbbvqiddhs57NlH8rFaB531tlKINnY6+G3vyDzuEFre7dp43zsZenffcWGoYixQpV43BsrRrD1 5x9+UaNnnYnNSggCgRojoMCeAio3MezvNQOt7LdnDl42u1MTQZbDokMR+zy2jH2wBgNW7FzdkyzQ z7kt//NR8b+GZKdABh3LwP95ys9oUKLoOW4X1qF/iaJ35hcv9fQMzYrcy3v/5jZZZhe8n6fq6FrP 2Xrr7rZF9mTtfWuzywrfSHr5zeK0n7vvcvBBF1pJf68x/zv98tN3zbWKsKqwYJLfIaMAS07+XWEW gwoLQEUQAAEQqBECSUlJMTFkKxQKCEiUQEpKil/Rwu/+zp075Pa6deuWFBSJqoXOpJKAspVLaH7S 64AFegL1Hm3beDG6yDuZXO6VC88nzh7Br8BZcCotKdlnSCm7U4sbaDDkz2Pb7AsEiz7NDNvr/aTA dZCXdPuMZ1BCwsEjlyJyqhUSbeyXADcSZStaEkOPrr9KcQ9tORv9bW9GteoF4SAgJwQU1lPAywrb Mctmktd1fZflEy1Vc9ISRWMKVA3bd2X+YkZfu3A/4VuvvFWaNzbsMcvHZz5Zh19fOHHxKaamsG3E tSfvCprmJj4KT/z+V+eC/QUUFfEmXnSpz7Ht0VqH4mVcP34xPpel12n02uP+6wYX7Br4cOvQpQRe Hd0OE3/xPcV4NMJu05sJKlA+vHlOfxkWyC+TVQWk8asoN7P4wYr+Jebe01cFVvC4Tx5xvx9MhTtH RRAAARAQC4HY2Nj379+LRRSEgEAFCXz48MGpaOE33LRpE7m9cuVK8gspTZs2raBAVJN3AiwVraYT Ni0QpLv0D7gT9bnQZN6bwD3JQ/u3rcW/pVpfQ7XCywGWqpYe2VpbtJB3+OvW7Un5WnBXWafr0AW2 enqTxvU3U6kB1LpWY5f3pjgTFg4xKvUlXQ2ohC5BQDYJVPhPg4yZx8tJTxG4MRNTSwt94mXGRtCn FVLx4W/iEp4ka/USfWnP0ug5c70zyWJAJfqs/v3vcDpoP/PZudOPikTv56RkZPNYenbz17t2o2u6 /PQ/EvZPFbTlBs2avyEojkfxsl6GJeia6tKwGzRuw4QM0G1To68/+Ib7IC8zNYVBnpGcTi+qWSaO K2gXQMTla8+yiMDH1y+TlXxP1xVOpiyKpaIeu/rHX8/RZzGq6ho216XMxw210qHqqSVtYVTikdvN WrApzoABHct0GR/es5feoZCbcDeQTnMvkF82q28YJch0QOUkp+VkRt4KI+4A7W6zV4wj/grq5AKX jTfpIxjTo28mNGrLIYcrFIxX9vvUEpsRstOS+WPJH8pilzI2M6EuCICAzBN4/vx5VtZ37uWSeeNh QE0QIMciurq6ltvzuHHjyq2DCgpDoBa756DRgvf/d/+9+Vb4VoYXde9M46F9zEqNpa08HuImWDXX gbzDFyksju2qq0+f+owxEaS/qrzYqrRgcXqs+yc5fuv4lop5QFZV2KEtCBQhII+eAnrnfy1j+sg9 plyZ2k1NqbNnaNEAJGWdzk50LADV1pyKTdTtOHDcXGYRG3zxaAC9tGY1G7zT33/fkm5MRkDD/kuP hCjbDrdUozIfeU5iNtiTN/frnbpueZQXF7RzB+N0IGH/Tt1+PpVI2nr4nlhHEgT4bx3YuN20rSFU 64JMiuptx679jWRS5K6fvfBwumkbxn1QrJCDD36mzwigS6jXwJE7wzIpqr7lgiO3zrk23t7ZUKWW oc3FDrsDbvGPMCSlVuu1B2Y2vvv7Dyp1Lce8HHns5Fom+SLLZKnX7MYhGwbqqjWf8mqo96V19PGQ ZZTxzsOa3N/Sv7nlmB3U+C0nvKYy8stjRZVmlHJbJx86dULi5vkr/kwyakfcASRPocPaSxdXE/Ov /+rUwmqu522qgyVNIOHUCsF4EfdKq66ig0VOOujKP8GRGcoZv+9cOEPk0q8C2+rwmQcBEAABMRN4 +PAhshuKmSnElUlg2bJlZRP6/fffGzYkGZRRQKCAQK0WfWYOZrYKJFxddTw4ne8r+PzqZlir8V3p r2XFS9ZDj2EiaQjyEv0WFeQ7WHSWW0ogP497d9ePI/lugteudrpKbCuPkLjCVsO8Qmmnqkg2AavZ fs+jL++azWRJaON8IIyblRjoPoyfVUF/9q57wjdovKzIQC/n7vz7XpejRb2zPO7Dsx6z29CtRrj5 Pi6ShoF7dnZBjgYrD2EuhdzE0EuHl44QmNPb7VhoFSJ9McVAQGEIyO0piQozguIwFOcOioOiJGXg lERJ0kZfIFAqAQ0NDQsLC3J6IviAgGQIeHl5zZ0791t9kbM5SOiBZDRBL8UISOEpiXmhh70phxkm YW4tHbbRr1ss5wWcXN2HTZFchh2u9Qlb0SPr/Gx95+PkiZFbwAuXdoK/ZCRn4TDng68LbibfWDrS wf0RRU32id84hEMqFatAXpOd5ctpving7mK+GGErG7eQoy5W5HBZXkbgui52ngnkoe3SU0d+tuH5 L+jofIj/HogzYduV6VqbZzl7P6eTJrxY0UOTygrdM9Z6VbDt2oAzU3T9f+nrdKuv71FPxya0fyMz zGvoxNVBDSbQd3STAv+YbrcxuNCQwr4KVMrl+q3q6/T2p5A9LlbUQ4+Zdq6XKWr4tohd403r1vhk ximJNT4EUKAMAvIYU4ABBwEQAAEQAIFqJkCOQkB2w2pmDPFFCJSxuYDsTYCbANOlFAKaFsMX9mPu Pzpx6VkGWbGH3Hy+YrSNZgW//9fT0FGvPNiSrUjehPpMeoPmo+eO78Gpw9JtbMLf/UDcBIFrx7du 0bgVExHD/ZhGNqHyok+t8CKL/+aDu5qr19U1t2xFPT+05zpz3Hgu1//YriDic+jcjw6MqKPb096+ SEZDYV8FitN5Gc4lUJd3nXicQalxDA34QILDP1TeNLQAAcUiUMG/FIoFReGsFe78j32TUM3n2Sgc WxgMAiAgvwS4XG58fLz82gfLpIsA2Vzg6+tbqk7IUCBdQyVF2mhZDBvam9Enwf3P/yK5Dy99GNq9 qRR9++/Xr2+JbAIkk8K//sQX0LyzoQ4JUmBpaDUiBgjyMmZE3Q2hYxOMWjSmH1ag0Bm/++pRekY6 alJkeAUURxUQqHEC+MjU+BDUtAKiiQCuL7TXsWPSIqCAAAiAAAiUT+Dly5fkBLvy66EGCIiDwMCB Azt27FhM0vDhw9u1aycO8ZAhhwRYxj0nT2nFGHb3+J7d/6T17mNc8yH3ZYPmZXx8Rdd4fdzJgslT wOySoD58zCC5Ej6lJaRVcpw0TKZsfZr/YF/vpFMrft8d/KaSzVEdBBSXADwFijv2AsuVrWY/zU/M Ef5cnd3hOyLNFB4jAIAACCgqARyFoKgjXwN2q6iolExtWJFjEWpAV3QpJQRYBr3GD+DnNQzeEtxw VKm5DKVE1+JqkEQDifnJyYKf/5iUB99TeNywY0vHtbb+86OTyyybZt8jAm1AQCEJwFOgkMMOo0EA BEAABMREgByCQJLJ4SgEMeGEmHIIODo6kiACYSUSYmBjYwNqIPBtAiyNToN+smV8BZw+fawblMmq fuO2/G38NVmUTSyH0eeRUa8vP31TcLojj/v8CZccsK3dqpcl/Sw67GnM5yJaZqeSw8tL0TszbOe4 iXPcr+otmeNspZ6Z+rEmbUPfICBTBOApkKnhgrIgAAIgAALSR4BkN3z27Jn06QWN5JOAaBBBuacn yicCWFUOgdykN1ERb1IE62b1NgMndqYovd5rhbkMeRnPH17jCymywFbWaKhdsA7PyXpxZu8W+lQB ivqYlvGV/j8vOy3hU5FWOo1bMwkFPyVl5GRGB9OnD37KSOLvY81MTmMqU7yctFTmt0/vU5nVfHZG cjbzJCGNSZAlbMJ0pNl5htdU2rfhv27BqiD6PMPM6NvxDdtwyFHfdVv0d5xA+xHuHv/vaRaV8fLg n7uYs8sprqdDh50P84R9MSqR+xnxz+gMiFTCw7cJcTFp9Vs2Z6qjgAAIlEsAnoJyEaECCIAACIAA CJRD4OPHj5GRkcAEAhIgQIII+GEFJKCAZC6QQI/oQoYI5IV6WSlxzJ22/eXUTkdp0Vku2dvPX113 /oGfy5Cca6ik05x/ZiF/ga2lw3Y+m0hf1DUatdxnCcmBeHqe2USPu3qD5vEjVsjlirPcN2d/HDOP zjUoXJZTlHLrkUfcR3NIxsTlS/d9MG7PTvJbxxysSMqjbXY/eoVmkYxYtoLuEq5OHeNy/F+vDvyz G4kvYF6nHw+f9fixoAm/IxbH8Zf//NeO5iQEr3dq3Xi2122qnZUuf9HCMhi0Luik26QGwa79mvbe GtHlh5/MR7ud9PE5FfAs0qVd0vmlBaYluDvYejzM07Uau5xYpGfT7mtUQsN2DmPd6MQNr6+d/i8o MkOGRhaqgoDkCSjl5+eLsVclJSUirSoyra33iupz/uYMMaoHUSAgHwQGdS/yMQkJqdLHpIIf22vX BK8fSmXYq1evUu+X3ars4agOmfIxAWCF1BJo1apVo0Z0lm4UEKhWAsQtZWZm5uPjM2nSpGrtCMIr QkDJjf72yy/Jq5Mr0gR1QIBPgO3GFqLIXy3ORRkIg0DVCSCmoOoMIQEEQAAEQAAEaALIboh5IF4C GRkp8fGRJX/U1amffppmbd2y1Kfk5ocP78SrCaSBAAiAAAgoGgHEFCjaiMNeeSCAmAJ5GEXYIKcE lJWVra2t69aV9nPI5BR/TZpFFue5ufy911RaWlJ6OhPNzZRPnzK53PBiyiUnk/X86RrU2MzMtVjv 9erV53Bait5s0sRceKmvb1qD2kpz14gpkObRkXLdEFMg5QOk4OrBU6DgEwDmyyQBeApkctigtMIQ 0NDQsLCwIC4DhbFYIQwlL+qJne/fv/78OVO48s/J+fj27X6FsF/ESG3t4Ww27TVo0KBp/fr65JdG jQzr1lXV0NDW0GioaDSIvfAUKOCgi8tkeArERRJyqoMAPAXVQRUyQaB6CcBTUL18IR0EqkyAw+GY muIFbJU51pCA3NycDx9iY2PDU1PjP358GxGxqYYUkclumzadpqLSoEWLLpqaujo6zbS1G8ukGZVR Gp6CytBC3SIE4CnAhJBmAvAUSPPoQDcQKJ0APAWYGSAg/QRatGjRtGlT6dcTGgoJEAfBs2c3Hjw4 WX1hAqqqlk2adCvJvF49TQ7HuLrH4tOnLC43omQvOTmpb98erabeVVU7WlhM6tZtnByHG8BTUE2T RxHEwlOgCKMsuzbCUyC7YwfNFZcAPAWKO/awXKYItG3btmFDRQzGlqlREihL3ASHD8+rlI9AW7s/ m21I2ouu8zU1tbW0CpOZ6+vTFWSuZGSkkmSKQrVjYwX+BVFfQ0TErkrZNWnSgxYt2lWqiaxUhqdA VkZKCvWEp0AKBwUqCQnAU4DJAAKyRwCeAtkbM2iskARIqoJ27dqpqakppPUyZvTDh/6nT/cvqTTf HcDhmNWrp8b3AtSpo6KtzZEx86pN3dzcTx8+xBPxfG/Cq1dh34pQILsSfvxxX7UpUpOC4SmoSfoy 3jc8BTI+gHKuPk5JlPMBhnkgAAIgAAI1RSAvLy88PJz8t6YUQL8VJxAVdbOMyiSFofApiT4gy+OK S5bvmunpH4mBnz/nlGtmpeI1ypWGCiAAAiAAAtVNADEF1U0Y8kFA/AQQUyB+ppAIAtVGgByF0KFD h2oTD8HiIRAcfOzKlbGVlWVm9hO/SbFEA02amImK0tbWr1OnXmWF10j9Dx+4xBUi7DotLTk9/QP/ UnTrQWzsrezsR5XVcPXq/Mo2kYn6iCmQiWGSTiURUyCd4wKt+ATgKcBMAAHZIwBPgeyNGTRWbAI4 CkH6x//uXb+LF50kqWfTpmNVVOqX3WODBgb16+tWVqtvZS4sJuf7VvuVVaZWLZ2vX5P4reApqCw9 1Jd7AvAUyP0Qy7SB8BTI9PBBeQUlAE+Bgg48zJZlAq1atWrUqJEsWyDnuot6CpYvf0percfGRqam vv/4MT429k529mM5t1985jVtOop4QEjyQpLWQUfH4MoVn4iIvfAUiA+wLEvinp2t73ycsaD5poC7 i9spUxSPG7Ru3JyjTX85tWOUibrCbYuGp0CWJ7T86w5PgfyPMSyUPwLwFMjfmMIiRSBAshtqaWkp gqWyaKOop2D16uiSJnz4kEDC8tPSUvjR+Fxu9KdPGeQXJoHfCVk0+ft0VlW1aNKkC7+t8CyDJk1M ySVJ9Fhyk8WxYxvhKfg+1HLZihd5eJTZvKuFnoK8RL+lrZ3+oigbt5CjLlbC/K9pD7ddomaPon0J cl3gKZDr4ZV54+ApkPkhhAEKSACeAgUcdJgsBwTIUQjW1tZ169aVA1vkz4RyPQUVMZk5XJDO8Mcv 79+/+/w5W7QhP0ihVFHJya8+fLhckV6+r462dj82u8W32pY8v7BRoyZ166oI6+vrf7Nt2frAU/B9 4yW3rQrCCsqMKchNDPxj+gzV3164wFMgtzMBhskCAXgKZGGUoCMIFCUATwFmBAjIKAGS3dDCwoK4 DGRUfzlWWyyeAjnm892mwVPw3ejks2EJT0EJMxk3gd3GYCO3AHgK5HMSwCqZIaBw24FkZmSgKAiA AAiAgNwRyMjIiIyMlDuzYBAIgIAcEciMvuHtNkyJzVZit3E+EBYX+9DHbYzHQ3Lca16olxVzn600 zCs0iyLLfsGl1Wy/OCECHvfhhQIJbKURbj5hibxS+ZCtB4sYaQUCeYlhnj/3IW4CUj16tV1tNtt4 y9njwjoF1ai4s85WTKtFZ7k4hlaO5h5MkTIC8BRI2YBAHRAAARAAAbkmkJSU9PbtW7k2EcaBAAjI LIHMMK+hwxymPukUEJ70NXy3wYV+jdvZTd72kjFI2WrWed/JhbZxBm0MWKBX1FZe3NkFVuN3pQw9 mp/0NmStDXV12+SJc/56WZqvQFnXccWpJZaFAli6HRas3DipOX2HxBR8SU6OWjhk9K93DkxgetHr fWDzbDqXgcGQP49tGzzT5936IRyEaMnsZIPiUk8AngKpHyIoCAIgAAIgIF8EXr169f79e/myCdaA AAjIAYG0h7vdVwclUPY/jLXlsFicrk62zKr9W4WlolW/XpGHn19d8jvETQjecj4snVLTb9KYfppw 9VqE4KjM4pLqaeiolwdOw2Ty4o1TWtFyVh0PTmd8Dpkptab/NMigTnlt8RwEQOD7CcBT8P3s0BIE QAAEQAAEvo/Ay5cvs7Kyvq8tWoEACIBAtRDIe3VzN51WU69d00bfuUSordfNfjSHoszYGt8poTTL WAa9Z4+3IU+4R7f+E82jPkf/G2HQ20CMPVQLTwhlCOSFehgriRbrpYH0CTJFSnrgUv0ilZSc/bjl A4z1cy6QbewRWsZOFF5c4Ir+SvpTvV+kly+1CjWKGCtUSVK9V0Hx0pviIyZ2pBAIAiAAAiAAAuUQ yMvLe/jwIfkvSIEACICAtBBIeveMOSG0no5G4bkXlVOOpdZyzM745KQjXbknPNz+uP6ucs2/VZul 1r7vaHuyBSHh6t9XHnOf3vlq0UETqxjxwK1uKcpWiyM/PL2wZTxb0FOo+7LNZ+I+ifSbG3flv6fj Zw7i32KP33IuMGgKOzLkbZHDY0pRtInjnyfduzCCc6JDb75IFu5yyby3zftB4T+xiXf/Wu9Pcb1/ 2XU1+Ut+9ZmsbDX/brj3LH2mh5zoBxGMRpLqXex24TMmdqQQCAIgAAIgAALlEyBugsePH5dfDzVA AARAQIYI8BJJBkQHfZKqoO/in3syuw/EUVhGw5aOpbMVBB3etex8re6tSLoCFFkhwGK37tuz18xf prXja3z/zs3gmDThgp0Xc2nP+8FWTQQ36mqoqWibdu3Zy7qparkWsrSNTDSL1yLv8Nf9sjX2bUzy F8Ej3U5j53Sl2IMmWKslpVerj74228yiRbFtMZLrvVxelasAT0HleKE2CIAACIAACIiLAI5CEBfJ mpPzhRsa9I/HYn0lIyX6p2vvpcf9Tj/h8r7EnQ0SeZ/1vQry3of67XPW5wufvCKQTiHP4z7xE/bY e1tgXGzgislK+su8X2SW2w2PG7yid1d9Z78XmaUnoy9XAirIMwFdMxv6vT31+sm75NLtVGY3a9G8 LARpD7fMJxkQgzljXWe0U8lIE19GFpZGp0E/2RL1nv+T0KKjcV15Hgj5tK1Wox4DHW10GOOeXA19 9j79K99QXtQtv8aDLNXqFOa8UKpdR1mpohiKVSRuglVT7Nb75374WOAnoChW4wHuvo/PLB3cypCj KfEsmDXbe0U5llIPnoIqwENTEAABEAABEKgaAS6XGx8fXzUZaF1DBDJj/JZO17eeOnILtdD3VPzX 6Pz821d/6aTx0seqVsvGP5ea7L1SqqaHbllh7bTh8sRDySHLjKgb6yf8HRT3YMu4GU6uLycGBIVs 6kwFbZ0wYemq9Tco7sl1Z1+V96bsa+LtC+uD3nMP/nM2QjT0t1JaFaucHrrtVFnbg6siG20lTIDV rM/MwbSr4PLlKy8yyCkGi4aufl1UB5ZGgxb0neigR/G8zOfHtx1jKnx6n5rNzzT47glzTgI35m18 7NNUre5GRdrzCnwHn5IycpiGGUl8D1dmchp/TtZv3NaA/n92akZ2WnTQk8ITFtXNneYS9SznLR1g hBVMNc8NcqCv+M/0ZTXrNX1oI1rz5PsHTvu/TmeCCD5F3bzXxqFlrbpaJfa8ZIZ69BZkL6D3/Odx /WYVJDOY5VfaAZnEGer54wDiJiBy33n9aK5dh27H9XNWUmKp6Vt06zUl6KNabSWRbALGzn7hkZc9 nZksCfrOO+5xM7mBboJe9Z0973ELvaqZkZc9+BXbOntcihTxt/K4oX6CR/2X+oYVScNQondm6HK5 oWe9l/YXmNN7qU+oSEfVPLgVF4/PWcVZoSYIgAAIgAAIiJ8AyW6YlpYmfrmQWK0EMiO957g4ud+g OBN8769f7NiWw/9Kpd6i3+L1930nkJxuVS15b67uvkqEqOqoq+s17cWhOAPbGsWF7A4ir2nVdLQ0 9AxNOJTZwNkL3Jb3oDgjVwxpUd6bslq6XQcut23EmTRiiFnRjPXfqesXbuBfi7d9I6v9d8pEsxok UIfj+Mt//mtHU4fmterisI2afeiX5kXVYZkO2+i7wIbkC5jq4LDmidnwYUwFcjnGxS+OonQ6Oo+m Uw/aNqMiPjayth/tPpVxPZw/eTk6i3vWxWwePadJA3cHW4+QOL91Du6PmBuPttn96BVK8ryqWYxf uXZSK4rrOWvuP2lmZrqFi5U6nL5DRxn16WPdoAYZKUjX4eHhZmZmS5YsEau/oI5ur1ETGFcBlXwv +OqThNx8Kv3WgXUNujdVUtHVKvEXTN1q4dZt/ZlN//Se/zRyrObRSWZMe+7TR7ElsxiQ8zpmLpk5 mKmhP2tPQFDQgY65UXUGbTw6hZ8lISf6cUTyF2WruVf+msTciT64ZkdYs/HrjrkPZlPcgy6d9TX0 J7wa+u/B3wa1INcLB7udTmB8sJn3PIba2rvGTTh4IfCfUR82DOg+48gbvhch896WcUOdXO92cz8Z GOTaOenhjdci04QzrFjvxE0Q57fQynpHbM/1j26d2zOrCxXkPtl6mtfj8tIySHzywVMgceToEARA AARAAASKEiDfyXAUgkxNivTQ3e5TD0ZQVCP7tc4OBrWLKl/bwGGG1wCxLMUFglkG/Q/ER8cf6G9Q S9hVHQNHt/j88wdGduy37q/8+A1TWpZ72hzF4tisu3o73sexpXrVvwESN8GucXZbg2Rq5KBseQQ0 jPr9RFISJuc//W/jEKOSazdKw8hx1X/5/AqjrB2XhNK/k5/QnY4kFqCObp8l9NOrq8cPb6fLIq4H 96fkMn6nSz8jNc6QnYLKdJPQxdYGjpuZtvyf/1ys6OQDLE7nn3xukjtPfaZ24BTb8M1q9MtoG+Qy LG8UxfV806ZNxF8wffr04OBg8cjU6DBugR0jKtL/3sNXyTnpIUFPlwxqkl5Hp0Fprk5WAwO26B/Y hiatGpapCUu1IUdLUKO2rnm3Xr26tmTX0zYxLvr3sW5jc/5fTH17R7tmn7826GrbjV+DPWgJmdiN 2/fooktffuA+fcbNpT5FntjkGsSl9E2MW7SwGOI0pI120lFv7xsf8smy399nC3lEmVkaNzfq2Mdx 5rh+RfJzKBfvnc7L4MulLm4/9kTTut/QPnzfx8Nz58MKczeIB3dVpVT934mqaoD2IAACIAACIKDg BEh2w4iICByFIDPTIP3piS38N6NWo7uXdlQbi9N3tGnBdyxeZuRt76WTCxIZnAyMFE0oQJ7e8HAe RD/VX+xx+RX/WV7oPuPaDq5MIvpoV4faxvtC87JDPcbVtt7A3Lvrat3W2ONJHveSsyBFghF9KSBY ao9fuX6rGB3IzziPUOHLKxJQ681PhaDv7H1ZoBvdV0HlZd6BNwT66y/2vPeefovGe3/Pc7kV300Q vcG6tpESraHMDCAUlSUCvNizU7uz+x+O5vEyQqIbdG2K1YuEh2///v3du3d3cHAQh7+gfnvHUb0Z A5KP/vtfyPM7l94Pa6P6Wb+pXu2KJSZgfd/4s0pIF95RMzDSVVViCRIjWHXp0qqlpWUzjboCv2x+ bl4e7/XN4zdppc31G2lrN6ir0UifJFt8fMH/QWp+esTdW/SBjvr6uuShKotSqqde3OlRtHeWfteh PbQpdhP13IQPn6laBbW/ZKTlSFcGme9jLeH5ie5AAARAAARAQM4JILuh7AwwLz0k+G/+Sd9GlpaG pWZWY2n26Nye/vrHy3xxao7thKn3eoZkvMzP2D343hY727UF2Qd5maF/DTWb7Pp2REjGs3dealvs 58z3iyNfFZWtpkd9ObWJ2eNttOnUl6jpVsqqVouPfKETFpDSeVPIk6jFbZU5/bYH/FR0pwNfJr/H Z+987SPcl9nNvRDJq8VxXBSwxLwo5/RQj5/N7Pe9XeiX8fW6l/I/9rab/OJIFjDS146Cyo+vvdYd tdGTvuT+u9DhYFA6j2I16rRgkdekpox+y0K+ROfTGsrOGELTChHg5aSl8pMHFKQVqFAzMVfKfB16 4TnlH/ac++ZmrNlgU3nMZZhO1s0UPzWAlJSSCXROnz4tFn8By7j/nImmzCR5cunQlv1JvTqqsbS1 1SvmJxDz5CpNHEulka5WMbcFLyv5ZQJd2f9/P7TSrqXUdMSxt2TM4lK4SemZqXEf+XJY6moViyXT bDVzz537J39pnx7g7vZH8GsJWPV9XcBT8H3c0AoEQAAEQAAExEwgKSkpJiZGzEIhTvwE8rNSU/mO gvILj+u/af9BLmU0uKMlCfhXN+k9mOx9PTl1Plm6EzfC6xMr9gWRtTb9tC7H3LwtFeG951ZUJd4q sVS1tIocJFZMZuduA0mCg8aazJFydbV0ihwtx4u8tMKVBEe0GNzbRJ2lbW5jSHH991yKZfoXVu4w fKCRuvCSm5qaVY2nkZePFDUkRCDroYdDcztPZnlEpxVo7nw2UUJdF+1Gs924tRP0qL+WrgjUtW+v USM6VHendylqO0Xi/KWnzJ07t1SjxeAvYDXp5zyMn9fw4YmQul318/PZHM3CjVXVDfv75Ofz/+zx kx8UlCNjTHh5PKryfxF5H17ePLnBaeaVvL6jBjQuez/F9+krnlbwFIiHI6SAAAiAAAiAQNUJxMbG vn8vvmPFqq4QJFSRQGL4aW86nYFRQzXmK1ddfUND+v/+j8ITv/KiQo/7k+FuamOoTd7HszQ06dxv /tduRn3+7m4LZAp6LExwUMo3vs9RN6/RKcIpQ0N98p5WWaN+fYp67388tDKuiu/WFA2lnIBau8X/ iSQRSE72GcLs25Z80TCZspUkOygtbYHklUGPNIGq+QtYmp1HLOjKHJfI7tjBgKWs10j1mxEFDZq1 EUN+2KoOm3Izq6FtaCHxL2JyTXr0YkoPU03N5qZsffPuTKxW/Iuo2KJ/uj9nZJW6m4DOgOgw2d1f Z0L/jg3rqqlL78YteAqqOnPQHgRAAARAAATESCAzU3QTuxgFQ5S4CNTSNbe05wuLfvQopqxVfV78 WyYPWD29+ipFv3LFxMR/5mWkMkkH3h506s7kKZh9kL5MSckQHDP+HRoXyHwfnZJVXmjC14yUFKaL o076pkpKpvpORxmjUjPKa/kdiqEJCICAnBEg/oIHDx7k5DDnXZZTchPfRDx791Hwp0Xd0vHHrrSf YGr/dur1ddjEU8lLfx5ymS+ELLA/Cf8GKWs0YuIP6HV4TuYL321/8HMrpmdmMwtsXlZqAqOAsJVO 0zbNmBtpWTlZL4Po0wd52Wkf+VrSN5lmBXc+f8xkOstO+8Cv8TGDOfDzU9oH/r/F/I60ey9c8AN9 WELQxpXu5+Jzyf3I6wm6JvU+ZtcxHjpmpDZ59OTSuTsJ+ekvDu784zXTNPnwzz9sv5dXoveM2Ed0 BkQqKSLxS70vVMOWzD4uaSzwFEjjqEAnEAABEAABxSSgo6NjyH/njCLFBFjGVqPt+Sd9xQSHfyhj Wa2s35Q+Ma6wfI4XbDDhv8YXFDoTQX50vuDnyGKrIvsJvo9E9JP4Cp9eSGc9KOgdGQe+jzdagYBi Edi+fXtycrKLi4uKikrZlueFehgr1W3s5L53cq/W2rN8uWThXc940MSJjdr1tuCoGDTVS/7XWamW lt16wa6u5MMLB7bRnkROByClnunoX3ZM6Egv0QeO+9+dRv3GWzHdBbk7rfaLf+X345Cp/swWGdJq 6oGQT/mUsqXzvl/6s0nGRM/f/kwwMlT+GPPvXLuN/L0zyUfn/rD1/qfQLR0Ed5Lvu89dePy4Rwe7 Tfwa9zc6LfzT12Ok3Sb++Z38jvJYzZ33+O74yb4FFbZ1mIHl+E23KcvmrM91G6qyWI3H7PiHPNJ8 6OVk0OP3Z9ZDJhkNmOX222+/7/E9aPfl9t8uRXvP0+08bg7tKGln/DXirWrrniNWOpH8M/GhNwNu RJGUFVJUlPIFuy7Eo5OSEh07UhWZ1tZ7RVU5f3OGeDSDFBCQIwKDuhf5mISEVOljUsGP7bVr18pA SIKwSn1adquyx6Q6ZMrRLIApckhAQ0PDwsJCWRl54WpgcO/e9bt40Ynf8erVzJv+sgqTp7CPK0lA QNmuDDkz2arooYM8bvCaCw1WTmmtzIvxHjh+qv974gt4QRIQUuRMgWnWrncp+w0RF0aaZt5e2nKC OxHCv2Re3/C4kY+oFu05tam8Jx4t6eMPCtrST8mZCC3p4w/I2n4/36FQcKegWnqBTM4E3/urHIuc 4FjQu6B5vfTALS3tdnHJWY8HDl+YYsj0/4X74BVlacphCSuP9Y13c+R8FmhO8S/JpmKun/M4p4Nv 6YyGL76ZzvDYsY0REYJ/s1avrvx23vJGQhqeK7kVRk4nr06WBpWgg6wQYLvR76kpkh0vi/Id5Ss9 at+5c4cckViqPsRHMG7cuIYNK7G7npf8Ijg8scCpqtmio2VTVVZ+9tsHIYlalh2MtGpR2W9D7r/K KtafWouO1k1VqfwvabHPn73+mJtP1WnQxLj+5xevmCNYKIquwP4QEvZKGEHF0jW3aclWyv4Q+exZ Qla+kppe69am2vVy3j66/0q4CK+ja95OJ+lheCIJDeAXzWYt6sa+SirQkKXWrIl67BuhxkxHRJPc NO6rly+JXIpSUtM1MTHiaBUc4Sh8pFSnQfNWOlnhcUpNGqmy1LQbs0v2bm2m8iH80cuU3DoNGjc2 4Og3pN49CHmVkV9LTdeQESot6R3hKZCejyQ0AYGKEoCnoKKkUA8EZIcAcRBYW1vXrSuPab1lYRQq 6SkgJhU6CziTlnnNG+5g1YheaWe+Crx45fCFejPXjulEVvukGjmJwPr3IL5DgXriMXSWa1CbTSFb FltpkmV5nN/ajk6HyFrddrnHkbU2nOxXQRFqPfmiyAmI/P0IhX4EctLhamaPgHBtzytY7VOcJYde bOyqWSiT4kzaFLjDoaV69gu/ezn2tu3V0wKXTrZzDycnfS0J+GtjnwYUL85v+nQnOpNCj+UBG9f2 0cmOfBShYW5Fa/6xoPLAAxGbp5hmF70kE7XAlcD5KeDFjMZh7zR6tuaUiFWFp0AWpj90rDECAk8B 03++NLnS/Pz8nJwEzlMhne/wEdQYWXQsDgLYfSAOipABAiAAAiAAAlUjYG5uDjdB1RBKuDVLvaWj z7vbIac8F1L/Oll3rUUSDSgZ6c+5ktKw79YDExk3ASksdavJZyL+2tT0H2sNEyWNWec6LQyI+INx E5BS28DRNch/5STO+6D1k/QbL/a4TVkzbgISKWAsSFtAchwuM6u1yo9LTjScKEglQPIOTu1Xy/kS l3t5Lh0UQBeu+4QOHk/yaJmrQkM2L7FtxD3o2kpjylLvJyzaTZDP9dvMuAlICXe3m+MRmk2xDBy3 7vDf9AOHurHerltj579uUyaMm4B4AeYUVL4w1Wylh8fvIpdr/Lgkk4Jq+/GLtkwyo7i7Jsw9nWpm UtJNIOEhQXcgAALVREC416BSoQTVpAzESowAYgokhhodgYDYCCCmQGwoIQgEpINAq1atGvGTNqHU EIHKxxTUkKKy1i1iCmRtxKCvRAlIf0wB4ggkOiGkrDPEFEjZgEAdEAABEAABBSPA4XDgJlCwMYe5 IAACICDtBBBHIO0j9H/2zgMsiqON48uJilTp3AlIBxUVBVuwgETsJWCJJWLXWKMRSYyJ0RgLEo2K GjWSiC0aIWosET/xLGhEzooREBALHEiRjgWPb3avcCAq5Tjujv88PPF2d+Ytv9nb3Lw7807924dI Qf0zhgYQAAEQAAEQeAcBksXQwcEBeEAABEBA5gQEfO4qz3bt/P54UKj0+16qki8y7+j6EOjj40P2 NcBag/pgq0QyESlQos6CqSAAAiAAAipFQENDg2x2oFIuwRkQAAFFIVCaefXvjdz09NADZ+Krs+O9 2O7CGztDSMKLhi4VzKitLw3tBPSDgFITQKRAqbsPxoMACIAACCgrAbLZAcliiD0RlbX/YDcIKDoB deMeQxd6mJlNHNff8QM73pe7IuBfWr16Rw7JWNmgpbIZtfKlQT2AchBQAQKIFKhAJ8IFEAABEAAB 5SNAshhqaWkpn92wGARAQEkIsNgey87fu7fnU3vt6v3gJ+PzZfNGrDnfwP5VZUaNfWlgH6AeBFSB QPUeHKrgKXwAARAAARAAAUUhYG1tjfWfitIZsAMEVJBAaUb4l4ZqhszfsGBeEUUV3QoaJj6zYH9k 5P6AkfQhZ/b26AySxkDAv7Z98ihhmCDF38tUzdA16JZoDUJhEjdodjtaVM/ZQZFJkqwHgoxb4cGz OUIt5X9Mw9QTfq6ik34nMkQS2g0LukFMoahXGbwzIgNIQ88Vf/BoG0ipyoyY1Mq+CDusICly/wrP doyWkStCLokN+4CnKtjbcAkE6ocAIgX1wxVSQQAEQAAEQOAdBMhmB5aWlsADAiAAAvVGQN3UZ+nR JR2l5Gu5LP5NfIYXlcIesW4Hfcg/tGzErqh8AYvd7fO1X41hGlitP5dRls1b7KJODgpvBA8d5uuf 83lMcubTr5puGDVswSk+PazPu7VhgZfv8kOOq84VZDyOWeVOzrEnbL6fwjRsNWTLz/PZjLjSlOsx gjae7W2p9Ki7aYXUK374sr5uvz0fvTu7LPnc+n4Ud/Mct68PJrwkdasyw61VZV/oMMGDkGXDvFbz Bu99XJb5OMaLN3XEsDmHmdyNH/C03phDMAioGgFEClStR+EPCIAACICAIhMgmx3Y2NgosoWwDQRA QCUIaOgYa1d0RHKm26CB9lqU+JD/PK/oXZsjvEw6HLycm07Z9uzZUY/Fduza3iw9JDwy8SXFv7TT /ywdVhjcw1lbXcve2ZXEBfj7Vv92q0CoVVPHUJP5cD5Xr7OtaachM6a4j+ljqy14FLnjZDp1dvvh OwWUFtumFVPpdlRs1ruxV/ZFkHoheNk+Ypa3p6MWxdLq2MObxCFCf/j6cBLjSS08VYk+hxM1JvAk 3M9OTVjsgnjCWTSC1Mil/dU4U0Pi8mssT7UaIFKgWv0Jb0AABEAABBSYAMlfSDY7QBZDBe4imAYC ICBFQPD430PX6GN3KzaZYMDS1DPToKhrf11+/K7QgoaxTuX0if1cHHRZFMtiyO7j26a00WKZdR3z sRllZmusVdtxSGnmtfP7+MQsI30det4DpW5s5W5FUennL8RnogNBoAYELHx++zOwuyHdoiSJdzku m9zZGdf2rImg+CHfbv47Ie+VWJigkLcnhFdYA9nKX7W231Dl9xwegAAIgAAIgICcCbi4uCBMIGfm UAcCIFB7AoKi50npdPNQv7Z0OoAOfqEpZED+MKdIwO41gywcoKgXd+MeFpYWPYjlkaE7e+qKsW2Z sft7io79lE33ym7u8sw8uvTHX6Ie1dy8l/zkB0wrfT2dJhWaR6XwG36Dx5o7hBYNSIBlZGuvW0G/ aTc//96U4aAJPfQz+AXiDBrnVi/e/yTlER1KaDQFkYJG09VwFARAAARAoEEJYLODBsXfEMpL7wbZ 2aqpCf/GBfGK68EIfrifp0iF3S7R1Nl6UAORjZ2A7Ypzr7Ozy0R/TCYCPZfFu6LPbR79+IfuOqaW bvvN1/96lPvtkFbNPshKwL/xR8C4tiRVge/cWe6tP1j/rQrN2Tb2FU6WZqZEpdBnhHMfUECgRgTU KtZmteq77nTa+W8HtTbjsHXIaFnAP7tsnN8abm7WczqbRuMpiBQ0nr6GpyAAAiAAAg1GwMLCwsTE pMHUQ3GDEFBvvzjxbsz6bjVXns/bfLR6w362z+7N621rrgEtQKA6BNQtOvoyaRGTbtxLFo+RBBl3 bwq3S+AdXPe477G7TATh8rbFn/Ry0Pmw1MIb28Z9NifwvNmSOX6u2oW5zz/cpHINdWPnzp70yQcp adIjNzPPPo7GNReHFgpPID8hMiTAk0NnE+D4bYx+lMrbGzBkI/OQLOQFkWipJNFAKT98luhQbVa4 ZIYJuVmP7RJJUON4Buzl8SXLCip6T6KvpD1Li9Ohx9TostZ6FD9662RX7zVcMmfmevB0V6MmnkG8 /8qzG0gSHAgbkuIXTq+MUYmCSIFKdCOcAAEQAAEQUGAC+vr6yGKowP2jaKa95kf+vngzVlsrWr8o nT0vCjKFa6oLs/NeMB8kZ57nFbx565CijM3bMlGnF5kFJYVJUfTOhYbu8xdNoLcwODZ/5qZL9OCq IOniM5OOpizBg4OT5myMCBqhZ1y+RSJndvDZJGYfRIoqLsgWTqNJz5Psq0gfFqT9R1IkktO3Hqen Jue1dLKqhLYKMyr7wnIYsoxe+5AUcT6+iBIU3b4akURRHnOXjbZlxjYf8lTpOrNRG5zLCxrn6LXs Svu15y7/L8y/SWg3K3O3iYH3Mh9Ex2cLtF0Xbdrcn0MTKkm6GZ9Hdv04ONGRAca/d/sJfQ8KHoVP 93bb+Nx7O/f2uV9mubziBk50G7kxusJ9KUbMHrHl1Bwj5ohJXFBg2uWzJUt9mBMus3ac5nK/71LM 6rN++7fe1sxJjyUrOhYnZgvYI7Zyv+s+aOWRScapj0VfAmXvN0QKlL0HYT8IgAAIgIBCEyCbHbRt 21ahTYRxCkSAhAm2j/PaxFUgk2CKMhIozQhfPSLwNmP67c1ek4N5GbeCJovPHJvvuDA46Fupw6Un yNtX9bajDgSOYVPpgV8H7Mqy62RKxgmsVoNWc/9cMbENxV03gtN3dtB1yq0dfaHKwj+03Ht6UGQ2 RaWemPfFZuGr1Yj5XR2Cb0nSB5i6jv2aTAgwc3d5k5hu4DJi7IopbSgq5cKx49wEZtuEymYYZlb2 hQzDmLUPEXPNN/SzVDO2dDvnuvto9N8zXbSJZUUf9lQZu7SR2kzyCP622P8kRbX5qBunhW7bAXM/ HyVcsPLqef6rMvoDS7+VYVMpPAb2bQykaQkSz+0IiaUuhOz+X06rXgP7OTMzX65cOXMhMY8RULGw NE1aV8hboKZpwNEX1WGZOPfq06eXk6HJx99s8h9GZ0Lk7v4nw9rRkF6kUOY0/xtva9tOrpZaqtFd iBSoRj/CCxAAARAAAUUkQPIXOjo6IouhIvZN/dkkeMYL3+XHIbkJengGnLyd+VaCNVLh2KEAzx5M fgFS5yiP/5o2R/AseuPXrsIwQdJat6a2apLUA+9qUsGLkkcxR4ViOX4hZxOkc3S/5vPI5N1JoowG noF7eM/EabkKE86GMNZOCgh/kBp5/BifvG0WFsmltwXWHz5IlgkBdVOfnyRpBbLLjs91NXVZfFzq zLa5i7dJHf40hF7f38y069RtacxSgoXdxOEAlpZD37l7LouXGPS1pUfjZHhmP/b3rQs9zN4y9/bx G09KqVZD9vDK5SfOJYkNxGMtdq/VR7LL7h1fN3GgqymL3hOBFn5vz+ceosULlcx42xfhMEzHtt/n jLXk78jyKb1EhlFa1fBUJpAhRA4EiuPPH6MfiYZsM30dE0sTTTVWs4pZLKswglVhhMvidB39qQOd /7JZXmrWK4olTkvwMj+vpMr8hJXyFlTpplpzpzFLZvYk17L3hey+kksJEv66aehmZuFkoV2d9nJg V3cViBTUnSEkgAAIgAAIgEDVBJydnbW0VOTdAvq4egTyeRuWuvmuPTtw29M3V899wUmMflKhoSA1 fPokt5+LRv8dVVYQvt6D4gZ+6TbpaAL5vcoy6brwy+CJlnR9269iXieVJU53JeOr9zSRFl38+BHr ozXnLz4Nm0CF/ujtsT48lQlAUK9Tw1e5uh3IGb25rOxmzHpPirtjktvK3xPoBd6ChNPzvHelrzr7 pmzXF+b/Lp0QLY4T5POCvnD03vV4UXjBm4vB6kekBFaPBGqpNgE63cDKtM+OPBanOSQj9syneyaw zawNar39oWojg3e1IPD80V3mEdpcR6uFpmaLWg1dtZ2nhv6beO2rTlkXtq7ZEfW4pBZ2VNXEoMf4 ke70hTtH/ziXcONGaWczQQsDTZWJE5D/KcmIFMSAAAiAAAiAAAhUIGBvb6+npwcojYqAIPXqtg3n Kcqy3+AOrcgW8uxOvoOFa1lFRZB4ZUdIPMU9djg6j9I2sbEku9OT6dm3YzMkb/IrA6tuE03H3p1N WFTTVh8P+IwsLOdH7DjzhH5fJnhyZkcEnzq/4fC9fEqLYyN8CXzvQmwORb3JiL0dQT27e+FWQmET dtdPVwe3F76uEyScWepPHLEe7GmvzTJydrcpF9ioehTOvouAoCg7PuNpfFJ6+WLvgof/PXndXpIs AOxAQCYExCsE1Jo2U6/dKPwV/86JnQGfz4xoOXr2EAsdmW2PwWr7acAMJzKr4NbBLUtCX7Tn6BoZ NZeJzwoiBJECBekImAECIAACIKBSBNhsNofD5FhCaUQEBIXxd0/Ta7PZ7UnK7KoKi9NxDJ1ty9hY 74MzaEXta9xEU9tYk7R9FnHhQQb5l2Xy0Zg+bMrE0VjzrZ99TUydO3qTqEKofxuHJUFnn+r5jBnO Joa9TLx8IYLWb2PDIT981XVatqQFHuIlNqa9xBvRnVsLV3XdF3N3TjM+NUxHnNGQE3A6z37G71OZ ZAEoICATAsbOfVxoQWlpGbnvCqfqt25HZ918RyGZDraOIxkQuYaTR7ZRzy5mvXhrRVjtLTX2nPyp G2meHZvWsmWzJvoGTWsXy6i9BfXaEt/kesUL4SAAAiAAAo2RAMli6OBAVkWiNDYCZUW5uR/YH0vb YcqeU2VvVns+Pb00YG/UY2FS+veWWjSpIE/bacratLKLBzyzDy/96eeop9IXWQ4jfr/2/UR6DsJf /t7jhgacSaBfEb8pyCEzDkg56MtxUFNz4PgepI+ScgsQKfhQdzWa6yR/gfuwxdvuSVYfpG2bO/Jj F3azRkMAjsqBgIZd/3Gf0VsM37189b/M4ofhMyf5J1fSq64j3IQ4LS7xSUlhXNjmn6OYGvmFxSQo ICh4FM+lD/mxT3JeF2uYe9pVaC8oyk1n1iO8LCh6Qe/+WZyXI1yf8DKviHlAqxu3tmGyKBbnFZUU JfwrtcMiS7vr+Lm+tpThgLHdW+kYqk6GAiEiRArkcItDBQiAAAiAQCMioKGh0aFDh0bkMFytGQGS XPBogNdwkqrA99vx7sLVBx8otWhCS2Sb6QqTZAj4t/cETOeQVAW+079wN6+orSm762e/8cJ/X9KL zBrgBn4/73CKVDSg2/qYu2VlSaI/Yd4EFBAAARCQFwFWK5+tZ/cu6E9FBI5u8/GWN4tWL630DKM0 HMZ8u3VCF7INwbqB477716TfeFfGOm6g7/LwNMq0h49/d7LvoY0dlf5Up4330CljjMnVu5fPRj8s ehQ+ecjUCHrbTip7/6Kpu2NS/prntUYY8M0+uGjqrhgSLFDv9NmurwYaUgkHf1j3WzbHRq2gWLJp AstmxNQB5v1dncwtLao9U0xe8OqqB5GCuhJEexAAARAAARCQECDbHJAshtjsoLHeEqLJ/BT18Ozt 9Ipv34sy80gGQTIPdu84ty8DuSZLvv3EVfNlbvoH5xTUvElm2l2ytzzluWh0O3qvr8LbG8bNmhR4 ib1k6gxXzYLcXKneEeRzTxxLfc1id/Rbt5lOdihas6Bh37kzM51X2pHX/JsJfMwpaKw3N/wGgQYi wNLpMH79ocv3r1/irhtu8ryYejteqeM6PfjI7SsXuNwfPnFs9dGS8P8ucpky0yIt7QXbe925O3eu BM0c1KezcelLG7+DKY94F35b+mkHrezmXr/eenRdVJt7YJhGarsNhY+uC1tz6RO8uGwBy+Ljb3+P IgaEfenZ/MUbXX3ptIVqzdsu/tStlbG+6k2nQaSgge55qAUBEAABEFBFAiSLITY7UMWOra5PLLuP Zk4haQieRezl3ix8Uxj3z+YN15jGsYFec4J4hQWPkrn04bNbKempt5+39KyQ75DsEt+6PTNCL87L K85P4P7HF5R9qInYtqRr52/nk8BA3OnzZykTj68nj+/EbApekH6X+4z8y7/1NC31UW5LB9tyb1ia OvwvJ2wKp7dUbGFm04pNOU4Z7mxKsXQ9xgYLHZm6cllkhoDEOBJi08ys2fjlWN17AfVAAARkRUCt qR7Hya1nH1K62+lX8RQiFSw79OhNV+jRwcbYsk0v+iNd3CxJ2hY1TXZ7+mr3DrbmhposNU3LznTd nm5OHL2m2pZuktp9+vRyMtSydBO3Jv+SE0ShmqaJI2NAbzcHI00W2ZFmKofTPyROQOXE3NZxs7No bVadCWKyAiInOXjeywk01IAACIAACKg8AWtraxPhakmURkuAzJTdtDVi/Sds7o9uOlN/+M/Wb5H3 xPUbw8J2x6TtWeyqa9pj4Nce5CZxdKH46WYdRnzmN4WODNw4tv8KkyBAs9P4LzeQlIf87RPmHct1 tGezmnyoCUU5zfw95uLTmCGZi/urqXVss6x0UdjOA6vcRaN6Uxe/r8nKAhMPF0F8eku3Eb6BdAjg 8dljp8+SAAHL/ufQsa2vbvNUczL3ffRZWPAmH7JpA1mfKnaEurTG6yNzv9+vUvau7KaNtmPhOAiA gEIQKM7LFGUREKYVaJBCktdeP8XnRxw6/m/c9SftXY2a6+uqVCpDEVW1sjLJMgsZgFZToyHVRaab 205pO05dniEDsyACBFSLwKCeFb4mMTF1+ppU82t74cKF91AkQdYqr76/1fu7pT5kqtaNAG8Ui4Cx sXHbtm0VyyZYU20C166F//OPr7C6o+MMa2sXCwsHDqfSC/9qi2v0FbOy0jMzUx8//u/OnfDi4jtC HsuXy/I3p+IwVltRPkTIXp6tOIbBEsUnYLjCUGJkmYp+QWTYC6W8ICc3f3pxlaiM+vXR/qmWDRHB LORtGDf6y7+pwd9u3zzVQZPd2qyZCoYKECmQ4d0LUSAgJwKIFMgJNNSAQLUJkM0OSBZDpCeoNjCF q3jrVsSxY/3fNsvIqJ+hoTWbba+hoaWra6SnZ9CsWQsjIzOFc6CBDHr16kVWFp3868mTBPLfhw9v lZTkPn58+G1zLC2nTZ68q4HMrF+1iBTUL1+Vlo5IQU27V5AdFxVL1kNJiq51l46Wb+8AW1O5Na5f 9jrv4e3bT4ooLbO2bcmCBBWME5A1F3V5//820mq+nHxPX2BOQY1vVDRofAQQKWh8fQ6PFZoACRC4 ubk1b062nUdRVgKvXpXs3z//8eNfq++AMIhA6mto6LDZorX/wmiCRIiSzkooKMgtKHgu8UIYBSDl xYsiPv+B8HN8fIXZbR/kNnHiTTJT44PVlLGCskcKBPxbp/bvDPA/ROd/9wg4euCLXnLe6VDAv7Rs 3ojfW22OXDXeSUcZ74Fa24xIQa3RoaEcCCBSIAfIUAECMiaASIGMgUIcCNSNAAkTIIth3RAqRGsS LPjvv0s3b/5Zo3hBjUzX1OxgYdH97SbSsYYaCaxRZelxvnTDd80CqJHwd1U2MhpuZ/fxRx+N09Ep D6DIRLLiCFHuSEHhjeChny3nms4/99vwG/O8/KPMlhz9d12veh6v593afIaaPdpFmMSef2I2x+8Q RVmtP3dtseic4vRvvVqCSEG94oXwOhJApKCOANEcBBqAACIFDQAdKkHgHQTatGmDLIYqdneQkEFW 1pMnT2Jzc9OeP38cH79exRysV3fIKoMWLfStrbvr6pqam7dR4QCBBKNSRwpKecHd3JanUO4rYvb4 Plrzse+Vj8MObvSxqM+c568yIn+ePkNzZdxcUVQAcwqYmwl5Cur10QThtSCASEEtoKEJCDQwAUQK GrgDoB4ExATYbLaDgwN4NAYCaWn0DPxnz1Jevix88aKQz48lhyUlz+tvAoLCUtXU7GJh4UHM09Bo yWY7kQ8mJjbNm2vq6Bg1hrjA2/2iKpGCg3Ndter/rmPCBF7romxXnJNECupfq8JqwJwChe0aGEYI IFKA2wAElI8AIgXK12ewWBUJ6OvrkyyGqugZfKoNgaysp69eFQtb5uVl5udnSKQI5yZUEpqdnZCV daw2mmTRRjLalxYmGflLTlpYOEs+GxlZkGyOslCuajKUNlJQdCtoLFluIOkPZvK/fazkJD2Yn8U+ HtDW93emzqQ9aeuGsF9KtZqw+dxw6sy2+YHnKfaYVUeXz+xqKp6MUJAUeXzfD6s3c0n2A8/5uxdM GO1uq5l5Y9OKzxYxCRGEhag4b7XTkl56QIrU6gNBUULU0d2baMmUmfuSpf5Th/VyYJZElN4KdvJa Lkx/P3HjUb8mkYwWs4mBe9d+1lnOGRbqfC8jUlBnhBBQjwQQKahHuBANAvVEAJGCegILsSBQfQLY 7KD6rFCzdgSEiyBq15bDwVSX2pGrTSuljRQw4+7y1QeSOQWpJ/yG+YWm0MN4+rV/9qWAUSMCb4sj BSS1gORMmzG7d62fYnJDWIG98Gjc0l66JFaQdytoppf/Xff1ew8uMj4/faxfyHPP3UcPT7FnUZWE ExMEBZGru3ttJOEDcaRAUBR32L/vnEOOq879PdOFukVnUoj/uDzfYf6lFU4jNpM9N9gT6JOcW8JD uWRYqM0d8p42iBTIGCjEyZRAfa5CkqmhEAYCIAACIAACCkKAbHbg6OiIPREVpDtU1QzyAp8M+Gv3 p6pM4FdDENDQMdauqFdyptuggfZalPiQ/zyviN69TpBwYpX/WTJhwNvTUYtl6jr4IzNKv1VLjXcY z2qhV/GaIPX8+uBDfMpqcA9nbRal7dhzsC3F3zd/wfEk4eZ4mjqGmsyHfv0+JnsliA/T0/NEs3oa AhN0yp1AKT98Ftl3T1I4AZH5lYwojA7y5JTXsAvildauldydUwyFiBQoRj/AChAAARAAAeUhQLIY YrMD5ekuWAoCICBPAi8fXj5H1gxQlJG+DpmA0IztE3iv7PKm6mdJzLh9KuQ+WXRgbaDFDFSas23s 6X8jbtzPKJWnJ9Cl2ATU2T4/PTy1apChyEz+rX9j+C+kbM7l/bLCn0smn5DSZfyGI5G7uxQn5pn6 bCu4FiTVKurcHf6r8mak1cpKrQqvxmULo1SNrCBS0Mg6HO6CAAiAAAjUjYC9vb2Bgcru91Y3NmgN AiAAAqUFOVkMhaznBbUZ2JempVyjm2uYtNSsOFB5kJL2EnxBQIqAlqWt6ctsyYmyV6/fiA8Ehbzf FvufEh128RzY1cmpS69eToYsiqXZUr+0vBVFmonvVGGrk+WtXFoZOXTpzTRrhKVROt0I+xkugwAI gAAIyIIA2eyAw+HIQhJkgAAIgIBqE0j971FuLTxU51h1q9DsJT/5AXPC3orTvBYC0UR1CbxIvHzd KWBqF6GHsamZ4kCBIPXogjHR7ScJbyXDLp7trc3MzUShJ9Iq2nnlXNFtFpuWVaHVdbfZ7uJWzpwW xpZmmmqqS/D9niFS0Fh7Hn6DAAiAAAjUkADJYmhjY1PDRqgOAiAAAspFoKV5+1Z1sLiFbeeuZnT7 lAsnb/JrMWfb1NHdmwhIuZacWWFOgnfnNqZkOQMKCIgJCFIuh7Xs3tlJHL8ve1n8gkmV8ejodwcN V3kbZ6YyVdt7drA2NdIWDfhJq3Bjj+6WxhKQJcUlpBnd6g/TLSONkx6JW7FbGBvpipsVJpwJ8muv pjYkiJcQGeCmpsbpHxJXixtcifoPkQIl6iyYCgIgAAIg0GAESP5Csicishg2WAdAMQiAgOwJlGY/ ephCi03i3k4Tj3nUdQyMmHM37iWXFMX9vXODcCfF53kFwnevLwoyC6XOVDpk6Xj4rZvShlRID1n7 w+/3i8inwvgT4bH0B0ochijOLSjOS+LezRAISvJyhYvLX2QWlJB/WLYjVs8lb3VTTl6NLRSQtpdP kk0R+61YPcRWOHApLsgWpi5MzyPXKx/KnhIkKiqBjP+i7Ntz7J1dWzMWpqVl5JDg0qvUoxt32H42 zLgsI+Ypfb5L1w7WZka6TURukFbtulpqWXayFrfKJTc208pp9kTtjLhb4lYcPWNRfEFQGLd3jseA n4rH//nPPE7U9m2hPIpydGme/TBPst5BUSnVwS5ECuoAD01BAARAAAQaBwESIHBxcUGYoHH0NrwE gUZCoOhWkE9b39+FY+7zU7sb+53IoD83tx399Z4lnhR1bL7jZ0HXzAbNF07GJodLT/DJDoiTmU0T hWcWBgd9K3VIKpRSLIshu8LO/U6a3T80tael54r90eqePs5adBOtDuO/WTWxDcXfOGvekTxHR9OM UwHMFom0EYEjPIJukfZarjMPxv+5wnK/l46xoc5nEV2XHo3fNddVj65Ueiu4M7NFIikR87tO3hA8 7wupQ6ELKI2BgCD//p03bU2MTDhsG9H8AEFRYXbcwe9Otvmqt6lGQXw4fTcwSw9My6cG5N+/S7kY v9HkmNubijAJijLvHPjupMu6oTqpyQn/iFqRpQcGwvgCvZah76RQ/sd+I7pa2XX95CPDGHLLVgpA qCJytbKyMhn6RXahINLqItPNbae0Pacuz5CheRAFAqpBYFDPCl+TmJg6fU2q+bW9cOHCe+j16dOn yqvvb/X+7niXzMePH6tGP8KLuhDIzc19/vx5XSTUqC3Z7MDExKRGTVAZBECgkRBQW1G+ijl7uVSe tEbiP9ysAwHDFeLE/WQAtVyWg7I6GKUsTbMiA76+1XPmSC/j61N7j/yD/Dj0WBL6ud6+S5bLJ7Y1 1c/aObp/4E2Kcpm1Y9PiMe62esI5BaTVssRhM9wstFKWDfTd+5ButWO8+uG7LsGzbYsLUw/OGBZ0 g2m11s+rezdbPTW6SX+vwBuGg1bu+Gbcxz1aF/81m+P7V5clG3+eMbQHXUFlCyIFKtu1cEyFCTRI pECFecI1ZSRAAkYPH5L/wcujWFhYID2BPEBDBwgoJwFECpSz3xTCakQKat8N+ZEBn97q+e3w3t1N E9f0c/vmX4riuHR29Vi2eFRrKxfbxBVtvALJ3BPD8Rv2LZ3o3Va0eQFpNe7BsFVdzaxtcncNdfO/ RLdyad9j+dq5ZpS2VfbWzh+LWoV+PrBbVyfDpoKEkIGOUyMoh7EbNiz27dPJPPuv6YN9Q5pWDEDU 3g9FbonVB4rcO7ANBEAABECggQkYGxsjTNDAfQD1IAACIAACIFCBgCA/hnv3Y3sjE2ZZgbpwvsBL qtuwoRZG1k7mzRJ5R5glKoberk4cfR3RkJdudW9YW62XusyyAuFsgJdUd5+RpqVvWjuwU2+GiVs5 auvq6ZAMmqUZsdci6GpObaxasc20yhLP7Qi5Rxm2c7A0aqkjzn2gor2DSIGKdizcAgEQAAEQqDMB stmBg4NDncVAAAiAAAiAAAiAgAwJFD+4kfOxvYkJnXFQg2NrR4t2+fTzYe041nZszVfJt68l06cc vLs4tjLWbybSTFrlD2un+dKIxBc0ODaOolbDHVroWzmxWQ9vR5PkmUwrW+2WRobNSCjhRVryffoc h2PKMdBTf3x03UYSOKgYgJChX4olCpECxeoPWAMCIAACIKAgBEj+wnbt2iGLoYJ0B8wAARAAARAA AYbAK370ns3bXzRl6TFTA1ha+sYmRp+sXPZJeyt7W0P1wrhDPy4LY2qSiQAcY8Pm5a1+edn8jcCI ji+wtFoampmMXrlshKMe28FW/yXd6k9Rq9YtW4riCxoca2Z35JcFJU1KH1+8nlRY8FYAQmW7BZEC le1aOAYCIAACIFAXAs7Ozs2bC39eoIAACIAACIAACCgAgVJekF1zTre5ocm75w8ZMDv8CYkUaOqZ D1o6vpe1fUfdi9PUmui0IfsUCE09/p3PiMUnnlKSVkm/TOrjE3CK7INItxqxemovY0Nr09hFzdQr tPIdv5Ir3EZDnd1/8lJXQyo7IfrkjTfW6o+4j8h2Cl2cWokDEArApN5MQEbDekMLwSBQbwSQ0bDe 0EKw0hCo74yG2OxAaW4FGAoCDU0AGQ0bugeUWD8yGtai8wTZcVGxGQJRS13rLh0tNd9kxycUWbSx 1KSKH9++/jC/olimjsZzqWYsLevObpZaguyE20UcF0ttteLHMdcfFlVoJqpDdqV4nZ0QE5v+qple y9S9A0ZteD12w96vPvXqwBYvaqiFE8rRBJEC5egnWAkC0gQQKcD9AAL1Gilgs9lIT4B7DARAoJoE ECmoJihUe5sAIgVKdVeQ7RK9vQKLySYIX33atwNbQ6mMr42xWH1QG2poAwIgAAIgoKoEkMVQVXsW foEACIAACIBAbQkICuNO7tl7k2RJyCtSNxTlPqitMCVph0iBknQUzAQBEAABEKh/AhoaGh06dKh/ PdAAAiAAAiAAAiCgLAQKeUFe4kQGD0992989KKZUWWyvg52IFNQBHpqCAAiAAAioEAGyzQHJYojN DlSoS+EKCIAACIAACNSdgLbroiP3L3IlZY97aWL267rLVXAJiBQoeAfBPBAAARAAATkRIFkMtbS0 5KQMakAABEAABEAABJSFAMvQqVcfqdLDybCpstheazsRKag1OjQEARAAARBQHQLW1tYGBgaq4w88 AQEQAAEQAAEQAIE6EECkoA7w0BQEQAAEQEAlCJDNDiwtLVXCFTgBAiAAAiAAAiAAAjIggEiBDCBC BAiAAAiAgPISIJsd2NjYKK/9sBwEQAAEQAAEQAAEZE4AkQKZI4VAEAABEAABpSFA8heSzQ6QxVBp OgyGggAIgAAIgAAIyIUAIgVywQwlIAACIAACCknAxcUFYQKF7BkYBQIgAAIgAAIg0JAEECloSPrQ DQIgAAIg0IAEsNlBA8KHahAAARAAARAAAUUmgEiBIvcObAMBEAABEKgvAhYWFiYmJvUlHXJBAARA AARAAARAQJkJIFKgzL0H20EABEAABGpFwNjYGFkMa0UOjUAABEAABEAABBoFAUQKGkU3w0kQAAEQ AAEJAbLZgYODA4CAAAiAAAiAAAiAAAi8iwAiBbg3QAAEQAAEGhEBkr/Q0dERWQwbUZfDVRAAARAA ARAAgZoTQKSg5szQAgRAAARAQGkJODs7a2lpKa35MBwEQAAEQAAEQAAE5EEAkQJ5UIYOEAABEAAB RSBgb2+vp6enCJbABhAAARAAARAAARBQZAKIFChy78A2EAABEAABmRFgs9kcDkdm4iAIBEAABEAA BEAABFSXACIFqtu38AwEQAAEQEBMAFkMcS+AAAiAAAiAAAiAQPUJIFJQfVaoCQIgAAIgoJQENDQ0 OnTooJSmw2gQAAEQAAEQAAEQaAgCiBQ0BHXoBAEQAAEQkBcBss0ByWKIzQ7kxRt6QAAEQAAEQAAE VIEAIgWq0IvwAQRAAARA4F0ESBZDbHaA2wMEQAAEQAAEQAAEakQAkYIa4UJlEAABEAABZSJgbW1t YmKiTBbDVhAAARAAARAAARBQAAKIFChAJ8AEEAABEACBeiBANjuwtLSsB8EQCQIgAAIgAAIgAAIq TgCRAhXvYLgHAiAAAo2TANnswMbGpnH6Dq9BAARAAARAAARAoI4EECmoI0A0BwEQAAEQUDgCJH9h u3btkMVQ4ToGBoEACIAACIAACCgJAUQKlKSjYCYIgAAIgEC1Cbi4uDRv3rza1VERBEAABEAABECg /gjkJ0SGBHhy1OjS3i8onMd/kR954Bi/tP5UvltyIS/Ik7GElFnhDWNDRev44X5ig+yCeEIoAv7Z pZ4cjt+euEJBQ1CidSJS0FDkoRcEQAAEQKBeCLRp0wabHdQLWQgFARAAARAAgRoTeJUavtTDa3/R qO3nuKRsHK6XdGheZz2v81mP0oprLK3uDbRdFx/4a5ojI4h/7+qtx8UNNhQXOcP2+e12YHfmoCSJ dzkuW0CVZlwNW8Pl80N/3vzXf3mvy8Ru5/I27xXFEupO4kMSECn4ECFcBwEQAAEQUB4CJIshNjtQ nu6CpSAAAiAAAqpOIP/y5rlb+YP79u1kY+fSo0+fj32mzQlYte7b/vlpuW8ayHkdC0fTBlJdtVqW sa19hSvqpj18/LsbGXoP6GGcx88XzjN4xY/ctHhzam7CQxJLkENBpEAOkKECBEAABEBAHgT09fUd HBzkoQk6QAAEQAAEQAAEqkGg9AHvCJ+iTp68kGNoqdeMbqGmaeg0aMGcHpoYiUoBVKsIk8X2Xnfu zvnvB7Y2YrN11ZkwwZpxXt9zS7MeP3tZDfAyqIL+kQFEiAABEAABEGhwAmSzg7Zt2za4GTAABEAA BEAABEBAQkDdvuNgY3J0dfMQz0/XhfH4r5hLTQyHzhpnSzIKSWcNmBoSeUiUzoDjtzGaL/XiXFCY cCbIrz29nJ/jF3Q2obAccX7C2Y1+TA4E0uhsQr4UfOn8CP0DQiIT3lrzX5Z198i3A5nGU0PipNtW 6kNiAMm10F+UamFjVGpq9J6AqUE82pBSXpCdKNGAJ32mPO+AnV/4E4kgAZ93jKRrENXsH7CngodS +kr54bNILZYWp8NHy6PVTPSapEdvnO5KwgSk0qOfpni2MbL/MfwQXUdcGL3Uk3A/oSGyyb+ASAG+ yCAAAiAAAkpPgGxz4OjoiM0OlL4j4QAIgAAIgIACEAgODo6KipKNIbrdpn/1iSEtK+HQVyPdOJa9 6SE7GZNrsG05mpS266Idv48Tzgc8tmXfi3G/nT4X+pX3q9BF3cb9EJXDnBcU8jYNdRzg/2BI6D8R Rz7PX+s9dMa+RCaOkMsLGufovT552uGL5w99nrXF2/3z3XHCMAJzyeubS92CzkUeWjnoQeDUT6ft vFHxdXzUXzfVBy7dtG+yE8UPmdpz4b6nL6ryWmiA19QrHfaeuxgZNulN6HBz826TAuOzH9yKy36t 7vrFhd/HCJUm8aLjmg3ecmqOEX1Ykn4vOuZxEe1Davh016Eb0z/ennjz3I45LlRE4KTBI9dfLZSk IChXrM72WXlqVptygc+Nun7xzZrh5vQZzqwdJN9DSE9Dzqd/LhvMgDXssmSKa/HTbIGFz9Zd33Yf ufLISMNUGeSAQKRANl8BSAEBEAABEGhAAshi2IDwoRoEQAAEQEDFCDx+/Lhnz54jRoyQRbxAr/28 n/8I/MxFxCjjUuBUL8duY3fdFc0LYHGc2zMDXqq9t5c1q7m952fTP+tNRsXc7xf9Ev2yjBIkHF66 nkvGyB1trFt3GjK6Xzsq4eDmvZcKBYKE8KX+JymK7Wyuq27m7uvjQmWd2fH72ZRiySWzjjYcTodB kyf0MqK0dYrSHudJJ0dw9+lnnpvZwq6tCa0/O+vh9Tj+q7fG7oUxvywmBhh3/ahDqxYGTgOmfzFK GNooys0XTpGgWDp6UvcAS9OktW6Fe+JF4pk/Qvj8CxtC//ec02uopzN9NetK5LkLyXlVxAooTRNb eiZGeVFroaPXVHTIMnXu1adPL4+Ry1avHGZD7L6+OzLV2taQjOwFzWzmz/vY2qGLq6VmnW9KRArq jBACQAAEQAAEGpSAvb29gYFBg5oA5SAAAiAAAiCgagSOHTsmjBdERESUlJTU3r2mlh9/seXY1UMb lox1EUmJ+2OGzxTRvAAyzhaOSXW1W5pZmmmqUSZt3Jj359HXIm/mvEm8ciiCpDrgOHOMjYz1muua cMil6xcj/n364PI/EXQ9c46Rlr6xgb4+GV1nX4+8Fp2a9eDyaeZSS11tfWN9bfNPt/J4oUv6WDRv WikhgE4rS32SBkBUykpfl1YeuZfGX/yFSwzQtzVr2cLE3EyTpdZMPGh/J5RKWppxPhr4KQlHWOg1 y+ZnvZZsQFj28nle7ck2d/5syXhXYkP235t3//uCepHw138cN3NzJ3MCse4FkYK6M4QEEAABEACB BiNANjvgcOjfDCggAAIgAAIgAAIyJ0DiBf379+/Tp094eHgt4gWlvBMn+KVUUz3L7qPmLv3pSHm8 IPFPZl5ABYObNBUN5NWbCM+/zs0vKsh6QH9Mi/jukzZGzdQ4o/6gD3NznqalZz9jah3/zrdXGyNN ju9O+ig1L/dZRnpWOnOJvPVnqZNhs5q2ZeeefXp3sqxxHsXSzEeJSbQoTT2tFpqazWs1BmdpO00K TUi69l2HrIu712yJfCybfmLp9Bg10Z0sdMi+fvSvMwm3Lpc6cASapjX2sWprECmQTS9BCgiAAAiA gPwJkCyG2OxA/tihEQRAAARAQJYE7lPUfYqMwxWnJCQkVHLw+vXrvr6+tYoXxG3fd5VZaKDWVI9t S8cL1mzd4EuvN0jNfJhWUNXc+0rKy4R1hCv0xeXnT22blYquuMzacbr8yoFPHQWvSylho2fJqVlv zRKoae9JbGQ1ayYKYdRUBCVIv3N0R4DP9xFaQ2f7OujUuP07GrDa+QWMonMi3Dq2bckfb9qbtTAy YHaYkEFBpEAGECECBEAABEBA/gRI/sIOHTrIXy80ggAIgAAIgIAsCRyiqEMUGYcrTiHzCKp0sFbx gjf/rd8QHPNcLJDEC1p37dVJmyTi62NvJqhyFP/8UWwqXb+Lk41BE3X7TkOZNftpvEev2n5EohWk 9HIw1LVr28Wthyl95QkvKdfYWXiph4OuoYO7SxfXLqJGV24kFVecuVCzzlM3de7iRTd5lpzxrs0R 1I1b21q/T2wub8N0t0mBXJNBI12aZudSVSZOrJldotosPc9xn7uRwEtyRJq2YbPmsgsUSJZI1Mou NAIBEAABEACBBiFAwgQuLi7Y7KBB4EMpCIAACIBAYyYgHS+oFofMo18PnPrDnzHCbQ8F/KgtW/54 1HXaNyM7cfS0pF5cR4UfPJtI8hSmXj95NoWius+a1sfWUK+Fbp9Faz6l5yBc3/HN8pNpREhhwsU0 fftmJep9Zm0ea0/PvQ/cEHg66Q3ZJSHhRpqZnd7zPHWPz9d8QrL9Udmntq3cHlNAPhX+Fx5+m5nd 8CIvU5hOMb+whCQ4lBw+TsuquDcCU4ll1+/zz0gKwzTe5euxmfmp4V8N9b9YyXGWjhGxg4lZPBMU 3QvdvCeZPnz5vPAF43TBo7tk6giJNiQ9ySkp1jH3rBhXEBTkChdLvMwrYoIIEpOK84qEJum3bs/k bnhZUPSiIOnCzfI9JLW7TJ3b34hyGDu2G0dHX7dWqyMquSM8xJyCKrHgJAiAAAiAgEITILkJtLS0 FNpEGAcCIAACIAACqkvAw8PD2ZlJ4f+BYjrjSAR3r6/O3R0+TdRIacJZ+bSzf9jK8d3tHe3MNKRa u3/iTh3379fE3DdEMOTzoLnD29vaW5KRb3OrKT+FbZvnbZh9Y/snrTjj118t62jX7GULPc0mVqN3 HN63YIAhdX3PZ23Yo4KuUm06aBaU0JccpoQe2vc1yaGYfHJJN91e/iHRTTy9jHKyc3hBo7wCbzB6 uYETVm77abH48NYvM4OOpbwVLGC19t16iGihIlaPavPJ+jcTdi+l0whKF5bDyM17P+9MxyzmfvZj dJshQyzpy+RwyfcnHpVRpj38xnYnJ1zY1ONMnbYeQ5dPpKc88C6fvfawiB8+2XEqk3+Ryj64aOqu qynhy8QmJRxc9M2umDyK0u40/odVw22p7P0//nw0y9Ze81l2sWhVRPPWI8aNNO/WxcmytYUMAwWU WplodceHurh610nfk4p1kenmxiSiEJdTl2dUTzNqgUAjIjCoZ4WvSUxMnb4mdf/aNiL0cBUEQAAE QAAEKhJQW1H+Ci97eTbwgED1CRiuYHYH/L76LRSipr+//7Rp06qdJ+h1dtITytJKtzjj8aNHT58z 4/Bm+uZWrS3Zwn3/CnmBA9wCoihq2MrTG2Z3YaUmPHn+qkxNy8ze3pqtJ150/zqPn/TgQUYRGR1X /1JZcdbDxMQntFK1ZvqtrFpbMAJfZ8fFxGaINjikKC1TUyojo0gMV9e6S8cqEh+WGyDgR3w7djUx mORH+GmBby8nQ+FWCK/yHsf/9zDnFa3Lwq5lcdzDLGZCAUvLurObZYti/v3bCVmviO/mrdjm+tST 2zEP84mfpvb2tnp5t68/lFjAMm3bWfMpuSo2qZmpsxvRUlb8LOG/hPSiN2pa7LZt7YwkmQvzuUG/ 5Xfx7NKjA1tWSQqIakQKFOL7BiNAoEYEECmoES5UBgEQAAEQAIH6I4BIQf2xVXnJokgBE1+Knxuv OP6uX7/+119/fdueGsYIqumQdKTg5y8HWGtWs12DVSvkBQ12oxcgVIoUyNcgwaPw6UN8ny6MPz3J jLv7z+YfeTo72ejVNuFiVbYjUiDfHoU2EJAFAUQKZEERMkAABEAABEBABgQQKZABxMYqQhQpYNwv W16NTQDkBWrJkiUkWCCtrX5iBEINWZFLPvZaf5tOTBC666fPnBU+UpAVGdCfWR3gMHbDz99N/Fg8 p0Be3SPUkx8Z4OgVmD5sTdQKpwfP23a3NnWw0pNdkgKiAXkK5Nuj0AYCIAACIAACIAACIAACIAAC SkKAxAji4+MDAwOrvdygRo6R9/OjmDABKf/+MrF9u3UxpTUSIOfKpbwgO2OpJALTlxx9JGcTROp0 XSf4f2JIHQ9afcKgl7W6nplswwREC+YUNEzPQisI1IUA5hTUhR7aggAIgAAIgIAMCWBOgQxhNjZR Cj6noD7nEUh1tSA7Lio2o3wfQ9GafMW9GSobLExD0BBZll8/T7p972kRpWXm0NbBRFOmEwoQKVDc OxCWgcB7CCBSgNsDBEAABEAABBSEACIFCtIRymiGwkYKwsPDyb4G9TOJQBk7qpHajNUHjbTj4TYI gAAIgAAIgAAIgAAIgAAIvE3Ax8cHYQLcGIgU4B4AARAAARAAARAAARAAARAAARAAARAoJ4A8Bbgb QED5CGD1gfL1GSwGARCoBgHpWdzVqI4qIPABAvJJJo/VB7gRa01AYVcf1NojNFQlAogUqFJvwpfG QgCRgsbS0/ATBBoZAUQKGlmH17u78o8U1LtLUKC6BORzu6ouP3gmewJYfSB7ppAIAiAAAiAAAiAA AiAAAiAAAiAAAspLAJEC5e07WA4CIAACIAACIAACIAACICA7Ao9lJwqSGoDAKz7vREhAfzW6cDwD dh3j8QVULi/8Er/6xhQmnA0K2MgrpFuU8oLsGGGS4hcuFvUiIWS0+PSscH5pVRpK+eErAkIiEwrL N4GsviENXhOrDxq8C2AACNSYAFYf1BgZGoAACCgDAaz3VoZeUnQb5b/wG6tmFP2eqL59rynqd4rq T1GW1W8js5pYfVBnlK/4kWvGeX3PNfX9ZsNkr1aaL59G7f95y74YlvfuiH0fm2hZmmp+QIegMOHo DzNXp3l/O8O7TdtODoYsSpB5eungz9Zdz6Yo60Er1y7uzdax7uxmqUVLKkveN3H4T2+GfT7S09HS oYubZRXyBUnHAxZOj7IP/n35KAfdOvsoVwGYUyBX3FAGAiAAAiAAAiAAAiAAAo2LwH2KIoNwxS+J FJVKUVcU31BYWBUBQfLpdTu4FGXU1/0jc1NOu279x3/z694fPjHSNcxLSXqt0eJD2ASpRxd4zL3y 0aLJ7q1MWpsbMANllrFzF1smLkC17+5sY9vFXRQmoASF8dfTPTZvnzW0u4tLF9eqwgR0e9uhK37a 1uXq6PHf//3k5YdMUKzrmFOgWP0Ba0CgOgQwp6A6lFAHBEBA6QhgToHSdZkCGiz/OQUKCEGhTMrJ yTE0NAwLC/Px8VEowyoZU1JS0qdPn+vXr5Pzly9fdnd3V2RrYVsVBPjhfhzfUHKhy5J9O78Y5cJu RlfKOvfltzd7jvXu3rUDW+N93ASPwqcP8T3R9odt80Z6dHUyZFrT5Un4+F6+Bx5R1LCVp3/+coA1 M3FAUBi3/4cDRlMn2qgbW9voSSpXrUHw36be7b6I/+TXq39OtWuiNL2HOQVK01UwFARAAARAAARA AARAAASUi8CBAweIwaGh9AhOkcvp06eFYQJS1q9fr8imwraqCZi27eNlQl+6/ueBfx4UlQlraVv0 HtTDumVL0WA+P+HsRj+OmprnRl7q2QDyQW10SMILMvLP5+6YGxJr9HF3V2tLS4OqRv6GOtot1IRC yeyDxT83n/yZxSstC2uRZLJy4UyQX3s1tSFBvITIADeSKKF/SJwwPwGr7fBZn7TK+mt38D+pIruU oRcRKVCGXoKNIAACIAACIAACIAACIKBsBMiEgnnz5hGrjx07FhUVpbDmkwkFa9eulZin4NYqLMYG NozlMHrtF91oIx6e+tp/4a4bBfRnDYfhg5wNjfRakGFvflzIQg/v3a8W/31xOZu7dMMekpywi3Xz 1LtJeZkxZ/7hU4bW5qZstommKCAgjAoU5WaU0B+a62jrNlcnJ/hnv99cOOsLh5LmVu3YwrpkisHe OR4DNryaduriFxzuxo17eBTl5NL8aXRSHhMaMGrjZkFRV/f98b9Hr5UmVoBIQQPf0lAPAiAAAiAA AiAAAiAAAipJQDihQFgU+UW99IQCibUkfKCSnaK6TrG03Rb9+de8TrSH0Xtmjh6//upz+nMTPQuO ntqr1PClfaeG8Hv5DunUsoVFz57OhRmUURdPZ04LAyPNpzeO3CTBAH1tXR3NphUQCYqyk5/RZ1rp ajVvpl4YveGr//pPd2M1be1sqS0MKdAJDvpOCuUPnDakrVYLm/49re5lkBhElw4cPWMjYR0NjqMj +Sc7MvrfdKXJVoBIgep+WeAZCIAACIAACIAACIAACDQQAcmEAqF+hX1RX2lCgcRaEj5oIHJQW2sC zS1GBB75cw4TLEj6e8mESdvuiMbl+Zc3z93Kp+wG92vHacG2tylLjU2jqA6eHdgtjI10sx7dSRYq bdK0qfSMAvpUmXASgL6O3puE33640CHAq3mpSRtbfXFEIYu7eXUInzIa3KMjp2Ur+1avUh8+pIxF MQhdcVoCNTIdgaLSnial5CnLlomIFNT6RkRDEAABEAABEAABEAABEACBqglITygQ1lDMaQWXLl2S ZCiQ9oSsR8C0AuW4ufMvhRx7IjZVw2ZkkDhYkHz894OnUwrLqBcJR34JJGsNDLt5uZrpcUy1066f PJtMmTvasoWv/cvefNBVwdNru48JRg0wExi2dTSSTDwQJBxfF0jWGjj28+porGfK1k6/epIstLF0 tTWhYxCVww5kNUMxyYugFAWRAqXoJhgJAiAAAiAAAiAAAiAAAkpDoNKEAqHdijmtYNu2bVViJeED TCtQihtOkP4g/HL07SdF5cECnznT+xjTh6m5z1Iz8ssyYy+QwTxFuTq21tZnmzVJOvMHPQugt715 U52WOu/djSDzUWwKI/jmU6r/6B7NtFs7mkklMijNiI2OoC87tmndsiXbRCPp3I6QWMqorYO5dsuW FTIeKAVMaSMRKVC6LoPBIAACIAACIAACIAACIKDQBN6eUCA0V9GmFZA8iyR+8S6UmFag0DeZyDh6 rH5y39F/4rNeScxlaZtaatNHJLlA6evS18+So+jVBRwb05aaejqZp9ct+5NkHOznZqutp6dDBsTG lu1av8tX4XSDLuO/mdzXuKVFGyu9CssTXqQlx9HXORzTliTowD+6biMJHBj16+Soraunw6w4kC6k mr7SbJOISIEy3P+wEQRAAARAAARAAARAAASUhECVEwqEtivatIL3Ry4wrUAZ7rjc+9euU+n7vvL/ PjgqlUkBkJ8Qvn3L3ockc8CSeX3MmzdVb2pi85EVufAyr+gVK/36teQSeiWCaxdH/ZbG+vSOiOrW 3Ya1I9efF76oKomA3aCVi6d2d3JsWylMQFpqcGwcaJ0vC4pelaZf5yWXkKkNjv262Gq3NDJsJll7 QAIKCaSWYR8HSz0tDWXASmxEpEBJOgpmggAIgAAIgAAIgAAIgIAyEHjXhAKh7YozreD9EwqE1mJa gaLfcaVPUlptenD74unFLkUXNow0U1NT03P0jekYuCVkxxeDWpuZt7HQU2vlvWBWd7L1wP3r3Bsv rbX5keS1v7ero3ZLY8PmjIMGXYb0Naayr996kF5xF8PSNH6LwNXz+tjbdmzD1nx77KzO9p7yY3cj KjvhOvfOa2utp5EkQtGlPAYhwvf80R2SScHBu4sjR09LWUbgymKnot+isA8EQAAEQAAEQAAEQAAE QOA9EwqEcMi0glu3bikCqOrELMi0ApLyUBGshQ1VE1DvNGVuG7XmbbzGz/9qjv/P1+5fv8Dlcr/5 pKuba0cnZzcXWz0yaYCl7brkYlbclZ9nDmxXdvl4ZAbV1rvCa3+W7sdz13xiQ/H+vRSfL61I3XXm umFO9h3bWVYRJmAqandfevH+/StBMwe2Ubt8+lAGZezdWSoGQVcRpJIEiilGgyb4drIQxyaUoD8R KVCCToKJIAACIAACIAACIAACIKAUBN4/oUDoQnXqyMHZo0ePllWjeHt7y8EYqKg9AZahLb0ZgVpT PTPL1k5uvfswpXsHW3PD8uG9WlNDxx7kdLvSu+G3KGNXN8nSA6FilsOkoJVTLK4dOHApU7gtoqiQ du2ZcMO7S1Mjpx5Eq8OLu1fSqbZ0+oMKSw9ybx7cHWI165upPa1trM3KlyTU3mP5tESkQD6coQUE QAAEQAAEQAAEQAAEVJzABycUCP0nL/MTEuhl24pTyEqEJVLl6dOnimMbLJEdgfy4I/v3kiQFgoLC F1qVXu83sRmzNniJ84WA6asv8KtKV/BeMwSFcSf37CU7LLzILXytK0x/QIqAH71xwdC/HTYvHN7N 3snJguzIqDQFkQKl6SoYCgIgAAIgAAIgAAIgAAKKTMDAwED6Jb2/v7+0tdKXHByYPHAKUzIyMkj8 QlKKi4sVxjQYIiMCpbwgO702U8n2iCRjwdHvfGcEXy+oKFrd2P3z4IPr+6aGbD5fcWLBB0wo5AV5 6bSZFEqLTj713cihwddf001K+Ud3hmkM/22Vj5tT27ZO0tsrysip+hSDSEF90oVsEAABEAABEAAB EAABEAABEACBBieg7ro4Iev+RZLFQFh+8i69F5fNjOjLSzM9p8Gfr1njZ/A8pwbTCrRdF0dk3b8i Eb3PW/BvXLaAUmf7LJ7j7mBn18Gtg2XF7RUbHMeHDUCk4MOMUAMEQAAEQAAEQAAEQAAEQAAEQEC5 CbAMnXoJsxgISw8nw6ZveUTyHXCcOjkY1myg3NTQiU6DICm9nIQCtCw7ONuy9d5Wo/gkawZA8f2B hSAAAiAAAiAAAiAAAiAAAiBQIwKmpqZkrYSkaGpq1qg5KoOA6hFApED1+hQegQAIgAAIgAAIgAAI gAAI1ICAu7t7oFQxNzevQWNUBQFVJIBIgSr2KnwCARAAARAAARD4MAFBUcKl/QEjDdUMyV87v+AT vAxB/tX9x1I/3LQeapTygl0ZSwzVXGeHN4wNFd0qzQj/UgjHUG1YMK+IvirgX1o60pCzYH9cpUxg 9UAEIkEABEAABBqOACIFDccemkEABEAABEAABBqOgCD11FKPWYeoSdEFmdll2XfX9qTOr2ivNzfq TcPYpO46m3tuoVnDKK9Sq7qpz+ro3cMrXMvgHVxznuLv23AiqbT8gqCI98d+YSgBBQRAAARAQCUI IFKgEt0IJ0AABEAABEAABGpGIDtq84Z9/AEzvhhgq03/HGKxXYYsXnd0t1fD5Z1itdBrqVEzL+q7 dhPtlvoVdJi6jv3ak2JPWDTEVl18QcC/uHHxkef1bQvkgwAIgAAIyJEAIgVyhA1VIAACIAACIAAC CkKg9MntsNsU9c/O/XekXoXr2I/8pGsTBTFRIc1gsXutPpKdtmm8k47QPgGfu3rcnI3cVwppLowC geoSiIqKWiJVnj59Wt2WqAcCKkoAkQIV7Vi4BQIgAAIgAAIg8B4C6hYdfTtSVHqUf7+uwgwFwsq6 PcYPb0X+rZA1IOR/J0TpDHrO3nhNXJWpX5jEDZrdjl7J33N2UGRSoWQDbpIEITLYrye9yJ8zO/hs klQ8Qjo/QrthAfsvJby15l/Av7VnxTCh2JD7753WX5AUuX+FZzuhou3RT/i8wyuGbL9Frw0ouhU0 TJRowC74Vql03oEvT/DFqwcEGbeOhYokqBF7Dt/iv2PYzz8xW5S2wNA1iGh4lRG9e66r70ZuOkVF LXezZNIZxJ/wcxVnNzA0pPVSlKSh34kM3JYgoJAEMjIy1kuV4uJihTQTRoGA/AggUiA/1tAEAiAA AiAAAiCgMAT0O0+eMYZNm5MeutzPra2x54r9keXjeamsASmH9t7Q++JAdsHlzROpQ4umTN9wQzR0 L7wRPHSYr3/O5zHJmU+/arph1LAFp/h0rICs298x1nHU8sfjzxWkxQZrb/f2Wxr+hIkiCC+NmB/t RV8KG5wUOH/EvONJkggDXSfl0NbrlO83++m0BfcPTZ0TFJn9Dm55t4Kmd/Vazeux9b83/P/2WZ3u 5uLs9vnmOGGuBS2Xxdv3TLQStyV5B5YeXULiI1JF8OTEdF+vn4uG/303u+DsCg8qKvBzr0l/VrRH XJ89aF2FTArNTLv6LQ+exFx2XxHzOLvs+FxXxyFb922e2IY5OXzzqekuZJUCe0hw/OZ+U/bE/jbE VGHuABgCAiAAAiDwHgKIFOD2AAEQAAEQAAEQaIQEWFpOo9dz98z3EOcQ5G6e79W1q98fD0TzAiRZ A8w8P/vEnd2M0rb/eHg3ZhpC8NGElxT1Mulw8HLyOt22Z8+Oeiy2Y9f2Zukh4ZGJLylB0tGlwVEU ZTW4h7N2c1Pnjm2o+/t2XHxIwgGVLnXr/TGbMjPXq7h1u9WYecNctNXFaQvyMnJfVNVDJOhw4Dv/ sxTVbcykHqasZqa9vb1t39+VGjrG2hUCBYkXfw+5T3H/PBb9nNI2tbJk8iRE3LifIZWvsLxB9TIp aLcZu/qrCXQU5tjq3dHMfAlBUZqm30pvNn54NsKvGlwGARBQTgJ4YCtnv8FqEAABEAABEACBuhJg aTkMWX7+3+hzmyXxgvTQOSNE8wIk0jVMWmoyP5jUDVtbW9EfbkfFZlGCx/8eukYfuVuxyWtzlqae GRlmX/vr8uPSxOi/IsiEfKtuNsb0FR09E3r4fe7fxJcC0SUzawMtIpPVasimtOx7u4fUaghd8uD8 aRKPoNg2lma1zMPI4nT+hH7/b2KoJ0lQWFespD2rVa/Ji/qRD+mBvx0nURVB0smbJj1bNZOBaIgA gfohYGpq6i9VNDUrhu/qRymkgoAiE0CkQJF7B7aBAAiAAAiAAAjUD4HS2BMnUhnROrZ9x0vHC0Tz Aj6oVlD0PImEAygq1K8tvXq/g19oChkaP8wpelPw/CF9IeWQbwcmfYDfIfow63lBqUB0ia5WYcHB B9VVUSH36V3GBc2WOsJQRi2Kdpvxey5nv9nU8+mxVQG/Xntc5eSFWsjV6zBsqCfd7tqh43czbt55 09tZlAKxFsLQBATqn4C7u3ugVDE3N69/ndAAAgpNoLb/X1Fop2AcCIAACIAACIAACLyfQGnKbq7U anw6XvDtvnXMnHl6SF8DfLYrzr3Ozi4T/fEWu0g2T7Bafy5DfJ5Zw68lEZty9+m7cg/UQLUMqr7K IBkQvfqSVAVDvp3STbj6QBaF5TBgAZ0TIT1qw+YVh6nunfRkIRUyQAAEQAAE5EQAkQI5gYYaEAAB EAABEAABxSJwd/tPv1fYVoBlam5PZhyzu3a0b1GVqaXZjx6m0Bc6ujsbUaLdEygq6ca9ZJK2gCmC jLs3M1j2HYcxuRJTzt57JJ45IODfv8t/pS6+RJ29eD21jjsLGrXpw6QnTHr4NPNdoY2W5u3prRze UUimg93TSQZErun8b0e7aL7IS5fVnAKiUL/z6FHu5F8+75ljB2v85FSsux/WgAAIgMAHCOCxjVsE BEAABEAABECgcRIg2wpM9w86IdoUUJBxY8vO7UmeC/dNddeV/oGUcmjLbyfIRoYC/vWTJDGBmfv6 uSMcmlOUofv8RcK8ffNnbrpE7yxYkHTxmUlHU5ZutxnBU+lMiRGrFy7j0rsqFiZdTTNoR9IiSi7x dwcsDWeyJxY8CD9zl/4gKMkTpi588Sy3WFB+mHItucpIQHPr/j6MAddOnX5QRHYxmDl3eVKlrlTX MTBiogkknFFSFPf3zg10ZgOKep5XQPZHEBQ+SmSOM+6m8Pm3c/Q8K2VEfFOY+5ypX5idR0yTWEi9 yCwooc9LcjeQCsVFCTFSOyyytDoNmTGlDcUeu2CkLX5xNs7vGLwGARBQXgJ4bitv38FyEAABEAAB EACBWhNooj//7OOy0192zjs2rhOdTaBJ3+/T3X+J/21Z30rpBa3GzBlgfnXDsCYufiHUmPVbNs5y Ea4iYLUatJr75wqSEZC7bgSn7+yg65RbO1P6t1Uzts+3xyNWjWGnR63xbWs+O/gq5eLKXKEvrYqM 2U5yKJLsid11Rq8IucXy7tdem1XK2+bhtZHJfJB+fuqnc4N+DhAdUin+U+eHC7MqVChiA6hDU3ta eu2hvly1ovLeB81tR3+9ZwnJGHBsvuNnQdfMBs2nX/Mzh0tP8CnjHkMX0rs/tGlPpWaYdRr02QxR 6GH/xaTCVxnhS7tOPcbUv73Za3Jw+EGJSemBIzyCbpGZDOqdRu3aMMaMVJiwPDTXogOJhkgKq1Wf 8QOsPuvjUiHyUusuQ0MQqEcC4eHhalIlISGhHpVBNAgoAwG1srIyGdpJvl9EWl1kurntlLbn1OUZ MjQPokBANQgM6lnhaxITU6evSd2/tqpBFV6AAAg0OAG1FfSvCGHJXt7wq/hLecHd3JanUFZjwo5v 83nPHP4GJyc2oPRWsJMXPa2ApE6Im+siy90MauejoCBy30nzMZ/SUzDkVAxXGEo0lS2X5a9cOTmg WmqWLFmyfv368h6R6bhDtqhIpMDX11ciMz4+3sHBQbYqIA0ElIuAokcKlIsmrAWBBiGASEGDYIdS EAABmRNApKCuSBUjUiBIPbGwS0DqqqOHpxhEbbzIWfCJPNceIFJQ17tIpu0RKZApTggDAbkSwOoD ueKGMhAAARAAARAAASUhUJ414GlyRpFSGF1ckF3MGFqcW0ASHTRMERTF3/ofP/38hfiM1NupnXrL M0zQMB5DKwiAAAioIgFEClSxV+ETCIAACIAACIBA3QhIZw2I8u9naRdML8pX4ELWSrjqjdjMZ0zk bxyh53+C3yAWs3S6+i4luRtC1/5wWMuzd/laAAWGB9NAgDI1NfWXKpqaZB8UFBBo1ASw+qBRdz+c Vw0CWH2gGv0IL0AABBRt9QF6RBkJYPWBQvWaEq0+UChuMAYEFIGAwkUKFAEKbACBRkUAGQ0bVXfD WRBQZAKIFChy7yiLbYgUKFRPIVKgUN0BY0CgRgSw+qBGuFAZBEAABEAABEAABEAABEAABEAABFSc ACIFKt7BcA8EQAAEQAAEQAAEQAAEQAAEQAAEakQAkYIa4UJlEAABEAABEAABEAABEAABVSMQHh5O 1mNKSkJCgqp5CH9AoIYEECmoITBUBwEQAAEQAAEQqAmBnJyckpKSmrRAXRAAARAAARAAgQYmgEhB A3cA1IMACIAACICAahPgcrl9+vQh7+sQL1DtjoZ3IAACIAACqkQAkQJV6k34AgIgAAIgAAKKSOD6 9eu+vr6IF0j3jYDPXeXZrp3fHw8KBXLrswZRKjfvoAgEQAAEQECGBBApkCFMiAIBEAABEAABEHgn gcYeLyi8sTPkbqkIT2nm1b83ctPTQw+cia/PpRkNohRfAhBQQgKmpqb+UkVTU1MJnYDJICBLAogU yJImZIEACIAACIAACLyfQCONFwj4l1av3pHzRgxH3bjH0IUeZmYTx/V3bFFf90yDKK0vZyAXBOqX gLu7e6BUMTc3r199kA4CCk9AraysTIZGknyhRJpsZcrQPIgCARB4mwC+trgrQAAEqk8gKirq2LFj 1a9PapIU4u9q0qVLl6+++mrgwIEtWtCjZbUV9K8IYclenl0jLQpdmYzYl80bsea81fpz1xa7qMvH 1gZRKh/X3qvFcIWh6Ho+5d/EXwEsatQmkDQlJDgoQYAxQqO+G+C8shFApEDZegz2goCsCSBSIGui kAcCqkyAJCYkGQdk66EwXuDj46M8kYKCpLP7fvJbdohPmU1ctfWbCR4OOjSTwiTuLz/N8T+U7rFw z47xepcfOkzpa8y/tuOrL5eF3pdAs1p/5rjNwQ6+vzNn3FfEHJzrqlXKC+7mtjyFPmM1Jmzflzpc sfzAvWs/bXV/63SvdVHkInvMqqPLZ3Y1FU4KFfBvnTl9bPvUzfQlynP+70tnfdaZXBNUTykjoyAp 8vi+H1Zv5qbTEnYvmDDa3VabiC+6FTTWy58RTE3YfG44dWbb/MDzlQyQ7Z0gE2nlkQISa9oiE5EQ IjMCiBTIDCUEgUD9E8Dqg/pnDA0gAAIgAAIgAALvJiBcjzB9+nTqtVJgyrsVNL2rd/DTRWcfv7m1 Tn2/r8cPJ1JfUdTLpMM/+vq/WBqfln1uOufk+lkXiok/LHa3z9d+NYbxjMwpyCjL5i12a+Wz9OiS jtLeqrvO5p5baEafSjm05e8056nB1/dMYFPpoUv6cSzbTkgdc+/CniltKP6hZSN2ReXTSRAFqScW uo7fnjP0YFnm45hV7tT5zZM+m/P7A3KtmkpJmOBByLJhXqt5g/c+poV48aaOGDbnMJNkUctl8W9i I3lRKewR63bQh1IGKEVvwUgQAAEQAIHaEUCkoHbc0AoEQAAEQAAEQEA2BMicgrCwsM2bN1NNZSOw XqUIEk6s8j9LUbbeno5aLJM27g4U/+TvZx4JqKz7F26TxRZRl5OLWKadF3y9zkXj3ZZo6BhrV7zK aqHXkmlgNWbe+F7sZixTc3thSjX2hM2Rq8a3tTZvY0Af8p/nFZGR/MuHZ8L38dOjNpy6kU9pcSyY RdXp5y/EZ75Ta2WlgtQLwcv2pQt9oVhaHXt425LYxA9fH05i9mOQ1O82aKC9luRQZEC9YoZwEJA3 ATJhisyylBSybEreFkAfCCgYAaw+ULAOgTkgIHcCWH0gd+RQCAJKTKDS6oNp06bp6+u/3x/VylPw Mink865TSaaGSXvS1g1hUxnhAW3JOgLvzdGnR1K/Cy9VXJJAj+1PzOb4HWLmFIjzFEjm9otWH5Ba 4gUIZPXB8W0+rajSW8FOXsuTKGrinv/2DDEtXw4gVM0qijvs33fOIcdV5/6e6VJwSqhCXLk6SpuL jKeGb47fPt6hOUWlnvAb5hea8g6NL8XrEYQGyCnfQk2/LchTUFNi8qxPMgbKU12NdFV6uMXHxzs4 ONRIAiqDgIoRQKRAxToU7oBAjQkgUlBjZGgAAo2YQC1+TFeZ2qBSLkMhUWXIUyC9el/qPrBdcS5u rgsr48amFZ8tOkRW/NNr/sPWLPYhr+KrM2ina9UwUiAaqDOpCiKi47N4gbvppAKisEJ1lFJvjfzF kQKhO+oSZ4WhAWWLFJAc28tlmbe7EX/vG4XrtXi4NQoucLIRE8Dqg0bc+XAdBEAABEAABOROQLjW 4MKFCySFoXDLA+UsZC7A4+yybNFfIhlXk/QApp0X/hwZs32+B0k4cH6z75qjCS/r0TtBxq09K0Zw SKqCjxd/0bvmW7o1Z9vYVzCvNDMlKoU+426lqDMG6hEnRIMACIAACEgTQKQA9wMIgAAIgAAIgICc COzZs0fJYwQtbDt3ZfIOJnFvpzGL+Ul5lXHzfoYgO2pPJF/QzNR19PK/966ggwW3o2Kz6o1s3q0N C7wmbY5ij/Wf4dKiIO9ZjTWpGzt39qRbPUhJk45omHn2cTSusTQ0AAHlJuDs7EyCmJJiZGSk3P7A ehCoMwFECuqMEAJAAARAAARAAAQ+RGDLli3Z2dkTJ05U5nkExEmWjoffOrIHAckdOPXr1SQyQAmK Eu7wzWxNWRpamdtmfn0iiWwcoGlsZaNPsb0GdWNG3MbmbW3pf19kFpQUJkXxMgTUi4LMQoZZYXbe C+aDoCQvl/n04lluMR2DKC7IpjdPIKry6L0Iyps8zyt4Qxo+vfuAvspPfpz25F6uXk9GRXmphlKW w5Bl6/uRqEfE+fgi4sjtqxEkLYLH3GWjbZkfiBIjhRorHX6oy3EdBJSKAMlKQCY6SYqBAZNAFAUE GjEBRAoacefDdRAAARAAARCofwLkTR2JEcydO1dFfnmzLIZs2hO2fowZdX6jl3N7vx3XKUcXdjMC son9gu3zOdd/GG3YxMUvecAe7rdDWtHnKfW2ow4EjiFbHgZ+HbAry66TYWb46hGBZKMEUm5v9poc zCsq5W3z8NrIJDggMYhP5x76K7jziM18pkrE/K6T958Imixucmy+49ITfIMufmPcyVWP1lT8cxM3 7zGBU+nJDmdP/Xk2qah6SilKz2XxruiIueYb+lmqGVu6nXPdfTSa5EfUJr8PSZICaY0Lg4O+rWhA af3fO9AAAiAAAiDQYASQ0bDB0EMxCCgIAWQ0VJCOgBkgoBQE6jXplzJkNFSKXmrURpbvfYCMho36 RoDzIAACdSWAOQV1JYj2IAACIAACIAACIAACIAACIAACIKBKBBApUKXehC8gAAIgAAIgAAIgAAIg AAI1JkAmTJFZlpKSkJBQYxFoAAKqRQCRAtXqT3gDAiAAAiAAAiAAAiAAAiAAAiAAAnUjgEhB3fih NQiAAAiAAAiAAAiAAAiAAAiAAAioFgFEClSrP+ENCIAACIAACIAACIAACIAACIAACNSNACIFdeOH 1iAAAiAAAiAAAiAAAiAAAkpOgOznGiZVjIyMlNwhmA8CdSWASEFdCaI9CIAACIAACIAACIAACICA UhNwcHDwkSoGBgZK7Q6MB4G6E0CkoO4MIQEEQAAEQAAEQAAEQEApCOQnRIYEeHKYFPft/YLCefwX +ZEHjvFLG8T6Ul6QnSjbvp1f+BNZ2PAk3E8iMojXMG7Jwo8qZJTyw2eJ9ybwDOIVCqsI+GeXenI4 fnviCgX1pBhiQaBxEkCkoHH2O7wGARAAARAAARAAgcZG4FVq+FIPrz9eTthz9coFLnfjcN37oZ+7 6HldePns0ZPiBhhnqrsuivlrhiHdDyXp96JjHhfVuUssfH77M7C7UGQS73JcdgO4VWcnqhagzvbZ cDvwY+ZibhIvOi77NUWVZlw9sobL54du2n4ujRyLSy5v817VCpTUE1WIBYF3EkCkADcHCIAACIAA CIAACIBAIyCQf3nz3K183/597HQ0W3X8qM/HPjO+XrN5y6rBefFJz8saBgBL28JOT7aqWUa29rqy Fakw0poZ29pUNEbdtMeQOa6Ght793Vrk54vCIq/4kZsWb0pOSVahOInC9AEMaTwEECloPH0NT0EA BEAABEAABOqbwKsM3olgv56GaoaGau2GLeVmyP2NroDPXeXZrp3fHw8wGbtib5c+uBHGp6iwIxGP te0s9ZrSV1mall7z5nuZaDbgT2I12d+U9SBS9kbKSCKLPTjw7wt/Lx3cxpatS3cjCROsGef1PfdV 1nOpOQYf1BYeHi5e2kD/m5CQ8MEmqAACqk2gAR+Lqg0W3oEACIAACIAACDQ2AoIi3u7pbn7Lzw44 mn12hW161JqVv3Cz651C4Y2dIXfFC9JLM6/+vZGbnh564Ex8Sb2rVioF6vadfUyJxf/+MnHEuBWH efxXjPksXe9pkzsakMF1hawBIafCA/qL0hlsjOJLR3wKE84G+TGpDkimgzMJ5REZQWHCmSC/9vQV jl/Q2QTRSnpaC7lEEiQIBXI8A0IiE/IrwyvLvH/kW0+h2JBYqbZVURbwecd2iRMuEIF7xe68q0uk EzT0p/WLzC7kBTE66TI1JPKMyEiO38Zoaaff49pbGhlXhUI5flujUx/x9gQMEeZMkEZMn5HKquAX TsI4wkI7J5agptY/YE8FU6T0CTMXNNHiOH/k8S03t2lTAT9643RXEiYglZ4GT3c2amI32m+0OHED MciOsYMf7if0WEqpUt3LMBYE5EEAkQJ5UIYOEAABEAABEACBRkCg5MH501HET82WOtrGVn3aUGw3 V0ed+nVcwL+0evWOnDdiLerGPYYu9DAzmziuv2OL+lWtdNJ13aasHGtMm5107PsxbhyLnl/+yozY 1ZqxrSw0Wequ8/73+0RmiX9S6JbjxeOCb5/7fYl3UeiiESN+uFgg9LcwOmioh7d/6oTQ05FHRmet HdBzxoFHdBxBUMjbNNRxgH/yyCMXzx35PH+t96BJu2NfMiNf5pLX1EtdDp47e2Rlt7uBU72m/cp7 Kb3iIS3ir3vUQP/f9k01omJDp46csy/xnW/EBY/Cp3u7bXzuvZ17+9wvs1xecQMnuo3cGP3OWST5 cSELPby+udQt6Bw38vQO+4ipXr0m74il62u7Lj7w1zRHxrfzh87kdfnip72TnSh+6KJB3+x9WMiY KHbtwZDQfyIY14bO2JdY9XQZho/X1Gttdh2PjDzi/ya0m7mV26TAu/y4eJJWgCRmOL65P6OsJOlO fLaZz7pVQuJU+v3bTJoGQWr4dNehPz3ptfl21Okdc1yoiMBJQ8YE35Lc4lL3HclcsPTgRKHxuUk3 E7IpdpeZXywdzKFPcGbtOMfl7p4z7aut6wYL1yx4LFnRsfBqXLapz2+3A3sOWnlkkmFCzONiKYn4 CAIgICGASAFuBhAAARAAARAAARCQNQGWxZDdl7PTAoe0aiZr0VLySJhg2bwRa85Lq2CxPZadv3dv z6f22viZV4m9rtOMzae3TnERnX4WtWG6l2OXUcE3xS/wm5s7O2nTVw27ePe0ZmnbeY6b+1l3isqK /n7F5mgSU3iRcHi9P5dPceztrK07DPEd0s4o82BIyKWsMkHC4aXruRTVypmtr96qt+9gFyopbMf+ 0ymFkkucjq3NOS5DJo/62Igy1ClOe5wvFSrgePv0tszNb2HnwOQYKEh/ePc+/0WVN48g8dyOkFjq Qsju/+W06jWwnzMTjbpy5cyFxLyq0i0IUv+3flkIn2J3tOFwnN0HTJkwwoJ6duTb6cExTCBDx8KR nmtBUe17djN9WdzSvq0JfZSd/fhWYvqrMkrsGqejjXXrTkNG92tHJRzcvPdSFYGJXN4vK2g+Xbq5 t7Fx6vbJ3M/7mzOiS4uFMzgolj5HGBkQFSO7NgxxcXmReOZgCJ9/acuRG7qdBwztzYQBMi+djLie V1WsgDKwb1NhO0M1TSOOcGUJ0WXq3KtPn16d+y/e+v0oI3KCu/tkYssO9oYsQWGO5Zc/jTLXs+7i aqkprR+fQQAExATwvxDcCyAAAiAAAiAAAiBQZwKlt4LtLL386SkFVNJyr6bDgnlFpbxgVzphAfmj Dyn+idmiQ9fZ4amkolQF19khkZdCVgyjK/ScvfFahQQHhUniS4aGniv2RyaRd68C/rXtk0cJwwQp /l6maoauQTGp4V8y6sQaRW4VJEXuX+HZjjk/ckXIpSTRGK/oVtAwcf0F+yMj9weMpA85s7dHyz/B Qp27oFoCjFxnb+fe/3vLkrEuovoJR+aNHL/tDjNmpkeXzBr/5vraLc0sTTTVmrLbOLemz8RdjLyT 9ybl8qHL9JEzx8TISL+5jgmHDDPvnI64mfPgyqEIMn2e046j31TfyFDfgH6vff3fq9GPc0WXDFvp 6hgY6zU3/zSEdyN8iUer5k0qmszSamVuqC75cS549brKsTHF4nQd/akDRenrNctLzXolMpnIepmf V/L2m/7SjGtnSJyAovR0tfWN9ZtR6q2cP7IgkYB/z3NvVh5+a5mwddUlZpWVvi4tEySKXHPmGBsR +3VNGNcuRvybVTkuUZp0fvspctHQjm1mZm6myVLT0CqXVnUHqQmJi0szTo++n5BRvYX2q/TsV1QT MaPXuXlVx00o1oeHM6zWQ5cs7EtUZB88fPDik1eChL9umvZsqcFxMtcUa3d2dg6TKkZGdGgBBQQa M4EPf7UaMx34DgIgAAIgAAIgAALVIqDuMjfx8bn17nRl2xXnXh+f66ql7jrrVNik8ubsQevOLTST EqfuOpsrOpNy4cIzs9Hf7KcP7x9a9HV5goPCG8FDh42Yet87Jjnz6Z4J8Zvne/14NOEli93t87Vf jWGkWa0/l1GWzVvs1spn6dElHSsaXPAgZNkwr9W8wXsfl2U+jvHiTR0xbM5hJt+hlsvi38T1eVEp 7BHrdtCH/EPLRuyKEueRr5b7ylCplHfiBJ8sUm+m5zRk7qrtp+4cF8cLko//fpB++V/BiyZNmwoH kU1EY8nXBXlFBdkP0ulzEd990saoiZrlyD8ek+Fnag4/Iz0rkb6QFvHd6N7kEsd3L32Ym5+blZn+ 7AH9OTs1/5U6HQggaRQ79e7Ts7Ol9ocyDwrX4UuXWeHEBW3nqaH/Jl77qlPWha1rdkQ9fn9Cihdp yfcZR3S19VqoMyrVmjBDgNiUBznvGH5LoRAUZDH2E9eI083UOKP+YFzLeZohPSmCPpf56E4y/W9z PR0DzeYf8q7Km4al3Wbqb//yzn/b8fm5zd/8HPlENrdWy84jx3jSou6eOXX+xqWYN71s8vM0TKUy WTo4OPhIFQODClMVZGMFpICAUhFApECpugvGggAIgAAIgAAIKDEBVgu9lhoV7JecseozvJe9trq4 Ql5GrnAI9zLpcPBybjpl27NnRz0Wu2O/gST9gZGe1rtGYRo6xhUmcwtSLwQv20fae3s6alEsrY49 vG2p9NAfvj6cxLx9ltTvNmigvZbkkP88r0ju2zbUe88+2P1n1ONixq+meuz2Q+eu2hSydQz97jg1 91nqW+PequwpE4YThGvgxeXAp/ZlpaI4Q8UrP3/q2JS8lhdeS0tOzSitEVWyDn9b0aMb5Zq4Yw0T 7j4ufsG/c2JnwOczI1qOnj3EQuf9r+01ONbinQWF0Y/SZ8lXH9EGObN1Xr6ohkFVOv3zp7ZNxU5L SJWJZ0GwmjWrNGOi2r0ryE68fGiF7/ITpb0mDrBrWe1276/IcvD5ZoEzidfc+vO3lfuK3FoLyowM 6nNpkIzshhgQaDgCiBQ0HHtoBgEQAAEQAAEQAIH3ExA8/vfQNbqKrb4O/Ta6pukPSjOvnd9Hzzw3 0heOJ9WNrdytSPq48xfiMxsd/Dd3Vm/ZGZVWPnegqXH7bp3JKn/DPg7mAjKif5tIaeajpBT6tIOL jQGl3tp1aDv6KC0u+ZU9WQNPl14OurpWbbp08WUW+6fxktKN6eXxzBVDXQfXdl1chzJ5FCne9X+T PrCnwVsW0BMQhHrEpaPB/e3j3CYGcg0nj2yjnl3MeiHe+KLqDlU3bd+tN33pcVqmaI2FgPbUsIuL DUdT44ODAXX7TkL703iPXrX9SOy0oa6djWGlxqZte/amAy9pyek577q7jC3bMcs53lFyeRumkwyI XGPvT7oYNtHSlF26QYNun03sQbRm/5dlaPCCTyFQ0OgeAHC4hgQ++HCooTxUBwEQAAEQAAEQAAEQ kBUBQdHzJGa6e9Lzgmq8/H1L7Ut+MjNznKxp16n4jjcqhZ6J39hKRtiPY2d9ve9f4QaAAn7Uls17 H7rP/XZke209PToWIyppEeFH/knMJ4n4r568QgbVLrPGeNoa6bUw8Vy08BM6Ix933TeBJ9NIlr78 hIvppvYaz9U/WrB5Ej2gvr57feDJRPJuvTDhYpphB72ibPVei9Z8SjfK/mvdyt9u0R2ZHxd+4ia9 AERQnJfDLB54+byQvN6XHKbFpj2vOksBJSh4FM+lm/Bjn+S8LtYw97Sr0I2Cotx0ociCImbGAMth 9MoAsi7myb9X72QJBIW3L55KoSiXT6d52hnqkTkuL/IyhfGL/MISorPSIVm10Edk//Ud3yw/mUZE 0q7p2zcrKq4UW2HZDp0+gA4V8KLOElWp4TOHfkVUVSgsHRMbmlMaL/GJID8udNvPwhrPC5jZHgWP bt+jDzMfZbxmFVKGPS2lW5cW5ApDEMV5RcKox4u8LKHxkjP6rduxRe6X5CVwb4o3e2Rpdxq1cLwD ZThgbG9jgZa+bq1WR1TyBocgoMIEEClQ4c6FayAAAiAAAiAAAqpCIOnh08xajOybs23sKyAozUyJ YkZm7lbsD+WaUxV2Ej9M5/8v+uK+saax20c0oRf/N+GsTO++4p81o1zYbHs7U6m56BzyRrvp8a97 NbHyDSkd/PlXi4d3tLK30FOjWFZ+O8K2fu5tTd3YNKxVx/Hrr1IdrVgvmxtoarQavfF/+77sb5h9 a4+fI3vU6quUWwfNzBJyqYXVlK3/7Fs21sUw++SXnXT7+YfEsDw/MszJLeFt6Oy1hp7zQWVfD1zy /bbAuaJDKu2X5cuPCVeIVCrqpj18/LuT8biNHZX+VKeN99ApY+ih993LZ6MfFj0KnzxkagQTXcre v2jq7pgXZDRv2OeHvac3jTU5PKZ1kyY6bqc7rAret2ZkeytbS70SXtAor8AbjA5u4ISV235aLHUY eOIp2auxudWUn8K2zfM2zL6x/ZNWHOJ0WUe7Zi9b6EnSAYpNbGY+ft0RUpM6+UNflz4/C2ZvmMfs WChVWA5j1n8/wcWQur564NAf/nXwGC/cHeH6Ot9Ff6VRpj3GfeJKDl3YZfHpLdv28v1mLB1k4V06 fSkuIfwLx6lHmNoJBxd9sysmhx++zGv9bdGZL+ZviiZ7WWp38vvuq/7WxP0fVh7KtjVXey4OaLAs +08e1npIV0dtw9YWlQMF4eHh0gkhEhISVO7+h0MgUDMCamWilUc1a/au2uQLRi7JVqZsLIMUEACB dxDA1xa3BgiAQPUJkB/Tvr6+kvrx8fEkDVj1m7+/ptqK8nd82cuzZSVWjnLIVgJj6e0PSEbDuLku zDic7G7QzW15CuW+IuYgyXEoPrQaE3Z8m08rqQqiM29VyL4UMGpEIBkLtZkQdnCjj0WFlzxkMwWO 3yEmo+G1xUKFYhvEGgUJ+0c7zj8vPqTIHg1OXsuTzDx3Hz08xZ5VXn/SnrR1Q9gvRS5QwkPliyUY rijfg69seeXlBILshw+oVo66JfzHj1KePmc27muub966taWZHpO/sJS3zt6NvAbneK/8deds1zep yU+ev6TUtEzt7W3Z4s33qFd5/IcPHqQXEfHVv1RWnP0wMeEJo7SZvrlVa0tGoCA7LipWstMES8vU kMrIJHtbMIWlZd3ZzVLr7Xu4rJgfe/tBzisiyLwVm2NAPb0Z87CgrImWqY29rW7e7ZsPJWkmyFaB 7k70MoHXefykBw8yaKulDSAXsuNiYjNEuxiSPJemplRGhtgESte6S0dLkvlPqrmalpm9vTVb7x3L /MtrNtctPPPJkO9JUgTOrJCIVRPaGQr3L3yV9zj+v4c5ryi1ZvoWdi2L4x5mMTERxl8LNX7snQTa N455K465geDJzVsPC94wSq308v67/pBsVykszUyd22g+vPuWs4LirMT//uMXESBmDm0dyB4WwvqC /Mjdfzbv7KDD6daBXcn6en24yfEpBFUgIDMCiBTIDCUEgYCSEkCkQEk7DmaDQIMQqNcf08ofKUg9 4TfMLzSFooZvjt8+3qE5PTQRDdSZkfno0oNzps8PJYnoJQN1QUHk6u5eG9NFZ2yLKhySkTyZ/35i YRc/OtcAe8LmyFXjnbSK4iLPl3QZ0klPPOynzJYc/fdbzp147R6u6lGiyELH+ef+XN6XDJvzbgXN 9PK/675+78HFLhRvx1i3ZVEeq879PdNFm4iXRCKENheKAxPlLjTInVZrpe+PFHxQrHSkYNeX/ekR MkodCIh5VooU1EFizZuSRRDTu8x9uiry9BQj7sb/mY52a6FtbaNXOeFivT7cam41WoBAwxPA46/h +wAWgAAIgAAIgAAIKD0B8q7ergMTJiDl2HzHj2aHp5JPLIdh68IWupMMglNHjPjhruPwYVZ0BXL4 6dzw1FLeNg86TCA+E/RzgPShUEKrIRt5Z7cu8aT4++a36T4s4OANVjc6TECKettRBwLHsKn0wK8D dmXZdTLMDF/NTEAg5fZmr8nBPPJmWM9l8a7oiLnmG/pZqhlbup1z3X00WhQmIBMQJovrE5sXBgd9 K3W4lNlTsFEVkibguXCJ/7O0LNnl0mtUDKWdlfCkXuYVvX8vx3pjJCiMjznN50ccPnLyysXHnXoZ FKob6tZ2X4Z6sxKCQUABCWBOgQJ2CkwCAbkSwJwCueKGMhBQcgL1+tpN+ecUKHnvqoT5dZlTUMoL cnLzT5JwsFoT8+ArV+VbgaEgHVnICxrq5s8tt2bkH2l/kriW3Evh3Z2TfWceKfVeGLR9ZXeq0MDG rOJepYxFJDFBbGysxDgPDw8DAwO52wqFIKBABBApUKDOgCkg0CAEECloEOxQCgJKSgCRAiXtuMZj dl0iBW+t2Kcka/wbD0BZeloxB8N70i7IUmmVsl4/T7p972kRVTFtQb2rhQIQUGoCiBQodffBeBCQ AQFECmQAESJAoNEQQKSg0XS1sjpat0iBsnoNu0EABEBA5gSQp0DmSCEQBEAABEAABEAABEAABEAA BEAABJSYACIFStx5MB0EQAAEQAAEQAAEQAAEQKDuBHJyckiqAkkpKWmgDIx19wQSQEBGBBApkBFI iAEBEAABEAABEAABEAABEFBOAlwu11GqPHnyRDn9gNUgIDMCiBTIDCUEgQAIgAAIgAAIgAAIgAAI gAAIgIAKEECkQAU6ES6AAAiAAAiAAAiAAAiAAAiAAAiAgMwIIFIgM5QQBAIgAAIgAAIgAAIgAAIK RUDA5x0LCfAkGx2R4hkQcozHFwgKeaci+aUKZSeMAQEQUDQCiBQoWo/AHhAAARAAARAAARAAARCQ AQEB/+yycUNHTD2uv3hHWCSXG9A+49gXnCZNdJY+1X6ZXSwDDaojwtnZOUyqGBkZqY5v8AQEakUA kYJaYUMjEAABEAABEAABEAABEFBoAi8ST+9cw+VThq69+rq0a9+l94Dxi38K3DDExtCwuOCNRguF Nl7exjk4OPhIFQMDA3lbAH0goGAEEClQsA6BOSAAAiAAAiAAAiAAAiAgAwKZsVweLSY7NaPUxNpI U41Sa6rfbcrs4T3btjbWbK4mAxUQAQIgoLIEEClQ2a6FYyAAAiAAAiAAAiAAAo2YgLFzj7aM+9xf d51KfiMkwdKx9RzR0aKlXjPmUFCYcCbIr72a2pAgXkJkgJuaGqd/SJygEVOD6yAAAqKHBUCAAAiA AAiAAAiAAAiAAAioHAENhwmLAjobEr+y//5uwtwD8YV0BIDlMOgTF45eC/K+UFAYt3eOx4Cfisf/ +c88TtT2baFkDoKjS/Psh3miuILKMXmnQzk5OQlSpaSkpPH4Dk9BoEoCmFOAGwMEQAAEQAAEQAAE QAAEVJGAjueKvesnupBgQTbvl/G9Ju2IpYMFTfQsOHpqlCD16IK+k0L5H/uN6Gpl1/WTjwxj0imq S9cO1mZGuk1UEcf7fOJyuY5S5cmTJ42NAPwFgUoEECnALQECIAACIAACIAACIAACKklArXlbv62/ r2WCBVRm2GzPKfsfiZYWZHE3/xhC0h0O6t3FurW9jU7u48ePKKMunu2tTY10kcNAJW8HOAUCNSGA SEFNaKEuCIAACIAACIAACIAACCg6gazIkDN8kZEs7Y5TJMGCrD9/C7mUVUYWHiQcXxd4g6IcvD/u bG1uqluWevXkFYoyd7W1NDXSRqBA0XsY9oFA/RNApKD+GUMDCIAACIAACIAACIAACMiNgCAr5UjE uf8KxApJsGDcl5+5MofPnz3k55eVZsRei6APndpYtWKbaZUlntsRco8ybOdgadRSp9EtPZBbz0AR CCgRAbWyMhJVlFlRU6NDkLKVKTPj6iCopOR1YmJGSkpOHWSgqXITsLIysLMzbdGiqXK7UZX1qvq1 lXdPJSRQKSlUYaG89UKfghAwNaVat6bMzRXEnPozIzw83NfXVyI/Pj6ebEIuK3VqK/AiU1YsIYcm ULZclr9ylYkpP3wi5+vcDfsOL+yiIbK7lB82gzPyN4ryWLL/5yWf2qZsGOTmf4nizNoR/vWELmX/ TB/iGxJrOHbD3q8+9erAFu6L0KgKyWYYGxsrcdnDw8PAwKBREYCzIFCJACIFH7glrl1L/uef+L// RlITfHdoAkOHWgwY4Nitm40q4UCkoE69mZNDHThAhYZS16/XSQ4aqwaB4cOpiROpgQOpFi1Uw6G3 vUCkQFV7ViX9aqyRAkF+5DeOXmvTqR6zdm74bmp3NotshXj0h5lzArma3ku+XfhJrx7dWheHT+OM 3EMZjv/5n5X9C2/8vX3xksPNx27Y+NWnfTuwxeEFlbwt4BQIgED1CGD1wTs5JSY+W7To6Jw5/0OY oHr3UqOoRW4GcktMmnSI3B6NwmE4+R4CZP+k4GDK0JCaNw9hAtwpIgLHjlHkfXufPlR4OJiAAAiA QAMRKH6QYvv71Yvc03Mcsi5sHuesptZEx3HuFaeZ63as/2pwB/s2Fnpq6uz+k5e6GlLZCdEnb7yx Vn/EfUQZduni1MrYsHkDmQ21IAACikUAcwqq7o/IyLglSy4qVl/BGgUjsHx596FDOyiYUbUxB3MK akPt6VNq7lyKDAtRQOBdBPz9qRUrVG9yAeYUNPwt/5qiVHAlXL1wbaxzCki6wuykB2WWjoZUXgY/ N/fZo4wieh1Gc31z81ZsjqGm8E1h2evshJjY9FfN9Fqm7h0wasPrRrz0oF7uPwgFASUngEhBFR34 669Rv/xyT/pCV3eTfoOcWlkYWNmYKHmPw/zaEEhJfpb6JOfsqbjoqApTCWbNajdtmnttJCpSG0QK atwbJEzg41NhHkHnLtS4iVSPnpSFJaWPZY01Jqr0DVKfUk8eUbdvUovnVfBl2jRq82YVCxbUa6RA 6e8EuTgQHBzcqVMnd3el/7+PXGhBSXUIZEUGeHsFFo/dsKExLz3IycnJysqS8LKwsGihuuvIqnNb oA4IIFJQ+R6oNJvAoa3eF195IkCAr4qQAAkZ/Lz2fMJ/eRIgixd3/vRTN6Xmg0hBzbrv7TDB/jCq nyqvS68Zn0Ze+3kOtfsXasU35RhULliASEHD3uMlJSWamprDhw8/evRow1oC7apCQFAYt3dO30mh fOtBP/yyc0m/Vs0aaWJRPNxU5ZaGHzIjgDwFFVCmpeVKLzqYMLXd2s0+CBPI7HZTfkHkZiC3BLkx JK4EBd24e/ep8nsGD6pHgOQmIPPJJckLyVSCuCfUMB8Ve2lcPRaoVRUBMqNk8VLq7OXya7/+Sv35 J2CBgKwInD59mog6duwYGdXISibkNGIChbwgL502JExAGDw89W1/96CY0kaMA66DAAhIE0CkoML9 EBTElRyT0eC4ye4aGlgLiK9MBQLkliA3hnSw4Kefosg+msDUKAiQ3+hk4CcsJExwIJxqpfq74jWK npWtk93dKwQL/PwosokmCgjIgsDatWuFYsgHMr9AFiIhozET0HZddOT+Ra6k7HEvTczGT5rGfEvA dxAoJ4BIQTkLsiHixYuiVehk0YHP2K64U0DgXQRIsIBkrxBejY3Nu3o1CaxUnwD5US7+jU47u3sf wgSq3+m19pAEC8iyFEmRBJhqLRANQYCioqKirovnNJEPwvkFKCBQJwIsQ6defaRKDydDvCSrE1E0 BgGVIYBIQXlXXrmSIjkguQkwm0Bl7vJ6cmTWFx4SyaGht+tJC8QqEIFLl8rXHQRtoewcFMg2mKKA BMiylMHDRXatX0/l5CigjTBJuQisJzeSVCHTCkgONuVyAdaCgMIS8PDwiJcqJKOhwpoKw0BAPgQQ KRBxJrPH9+9PFB6Qd8XITSCf+0+ptZixW44YYyd0gUwrIEkulNodGP9hAtKrzUeP+3B91ACBL/zL GXC54AECdSFw69Ytkp5AWgKZVnDgwIG6yERbEAABCQEDAwMHqYKND3BvgAAiBaJ7IDExQ3I3kA0R cWeAQHUI9PIsf6scF5denSaoo8QEJBPI/aZhK0Ql7kd5mt6xc7m2f/+Vp2boUj0CVQYF5s2bh2kF qtfX8AgEQAAEFIEAIgWiXkhJKZ+/18oC26Erws2pBDZY25lKrExOzlYCi2FirQlIZ6TzHlhrMWjY uAiQvbhJXElYMKegcfW9jL1NSEiotPRAogDTCmTMGuJAAARAAAQYAogUVHEjYOkBvh3VJCCdzCI1 Nb+arVBN6QlY2Si9C3BAbgTadxSpkmyuKTfVUKRCBCIiIt7lDZlWQOIIKuQrXAGBhiFApueQr5Kk YG+RhukGaFUkAogUKFJvwBYlJOA1EAlvlLDb6miypmYdBaB5IyLA5jQiZ+Fq/RAgAxgSDniP7F+x uUb9kIfURkWAbBXpKFWePHnSqNyHsyDwNgFECnBXgAAIgAAIgAAIgIDiEvjg+gKyMAHTChS3/2AZ CIAACCgnAUQKlLPfYDUIgAAIgAAIgEAjIECmQIeGhn7QUUwr+CAiVAABEAABEKgRAUQKaoQLlUEA BEAABEAABEBAfgTI/u5km3d/cfnxxx+FusmJMKnSvXt3bIIgv16BJhAAARBoBATUysrKZOimmpoa kSZbmTI07z2i/v77zooVoi2sTl2eIR+l0KICBH768fS50/RKtqFDLZYvV8qU+Mr7tZXr/UMShjk6 ijTejKfsyjfIlKsZUKZ0BI6HU+N9RVbL9H+4DUUiPDzc11fsEUWRcSzZgLyhjGmceoUPbRIl8PHx aZwE4DUI1AcBEmvLysqSSLawsGhB9q9BAYFGTABzChpx58N1EAABEAABEAABZSPQpUsXZTMZ9oKA EhAwMDAgcU9JQZhACfoMJtYzAUQK6hkwxIMACIAACIAACICA7AiQxQiyEwZJIAACIAACIFA1AUQK 5H9n5CdxQ37ozzFtoWbaov28jeG3018UcA/8k14qf1MoqvD2Rk/GEvI362S92pAePk+kSK3bRl49 eyvtlxzUNUTXQacqE8hPuBASMIijpqOmptPeb1M4L/1F/oUDx+r1G/pOnoW8TZ6MJeRvVnjD2FDR uPRwP5E9anabRA8TQfrZpYM4nBl74ooEqnxrwDcQEBOIi4sDDBAAARAAARCoPwKIFNQf2yolv0o/ tvSTgYeo2ZHJJWUZJbxlfaioZV3sBl58I2dDROq0Oy78M+xLV3koN/PZdGd3zd+DCIpu7Dlwo7CG FhK/zqdGrbeqYTNUBwEFIPAq9fhSjyGHqFmRBQVlZQW8tb2p89910RvSYE8J1wV/nlsol6dENemb +fx2c7d3hcqlGdFhay7x+Qd/P5FQLHUll7d9b30HJqtpNaqBgGwJ5ObmylYgpIFAIydA8hSQ3UYl hWw70siBwH0QQKRAvvdA/uVdC7dmjJ85Y7iTFq25mWlnn9k/H9w4njlqmKKhY6gjH80snZYmNdQk SD+36esD+DVUQ2yorswE8i9v/nIrf+zML4Y5aTNPCXYnn8UbD+4e25BPCT15PSWq2XMs7ZZmFaqq m3b1/boXmz120hAHTfGVV/wLmxZvz6imTFQDARAAARBozAS4XK6jVHnyhM5XjQICjZkAIgVy7f3S xBsn+BS1PyTshtTgV7vt0AmuTeRqiHIoI9OJ1/r5bbr4SjnMhZUgIAsCpUk3wtIp6mDI/ptSTwmt tiPH4ynxPr4ss36rT6Wl7fRz0hL+f42ECdaMG/I9VxadAhkgAAIgAAIgAAIg0NgIIFIg1x5Xt+s8 hE00nlzh3ovJUCAcA7N0PMYNMFOnSnnb2glTBqiZTtt1maxTYD53mLb1pqim0FpB0YMz26a1p6vZ +G2LTCgqdyI/KXLjPBthq40XHuSXXylMuLwnQCjQtH/AAa50K1EtQdq1Q8v6M2KnHkiQalsZ0pOT 0+zEdoZnFCZc2OjXoQXnk43RYkvebUZlUa8ybpw4IFTKGHboBp9ZZPwqI2brgo+8N10kkRXuCncd 0xae20RrEN4jnEgLF5Hpv/TEXdIWBQSUjIC6bWdf+nX5Sf/evZgMBaKnhG6fccOZp0RQB2HKADW1 Gbsijy/1ZD5zZmyNrvCUyE84v9HPXnhp49lE6a/zey+V50foHxBKHi6V1/wL+Nf2fNef1m4/NeR9 TwmKKkqIDA0oN4//iLc/YAiTVqD0ZpCdKNGAZ9DNQqo874Cd3/HydziCdN4xsQQ1nf4B+6OFT4e3 Sin/+CxxJgVGoIAfHTzdVRgmeOjvpq+m1uHH8DBJHYKFqUY9CZ9hp0D5F5TsVoW5IAACIAACIAAC qkwAkQL59q6u29jlU0xpnbGHl/p6W1t9sizksmQ8r+46++q5uXQogcw7OHJRb05YSV7UL1Oo/XMH +G29XSj8jUzW7W+a0GHAiidTIjJLbm/U+WWw77Jjj5hrubc3jvto8Pqn864lF6WsUQ8Z3X/pyTRm mFEYvc3Xw3fWvX5Rz/lJYeMeBC4cuOzYgxcVnT8ecqHpkFX76bQF/JCFA9Zezn9XYjCLwRt2iuws TY65IXDq42JL8a/ce8qkE3i3GZVhk6wNi/q5/5Lrcyij5HnE6sHUxcD57gv+oA1rZuo2c9nGmUwL j+VRBRkl52d3JnOx3yOckNk6y913RWSfkKSXGafnGCXHpsi3e6ENBGRAQNdt8rdTmMdAbOgyXzd7 K8/vQiIlQ31118WXzy0Rzrw/eOR/enPOFeTd3zqFOji325StPNHAPpe3aZzjsPWP514ryEsJbhri PXBpOF8YcfjQpSHLovsfJa3CJqYFzpkwLyyh4lPg+NaLTX1X7qfTFqSHTB28NvJdT4mi6KBRHl5z bvU48fRN3tN9rY53c7BymxUozMCm3umLS/uF326mmI3YcuJr4ZNPUgT88Om9hv783PfvgjcFFzd4 UBGBs0ZM2hdX1VNJnT1sVYVMCix217mrgsfa0tKs18c8Lyu7842PbyABxWhhe2/dvqgTeZ5Y+Pxy Ynf/BWEJm31IFAYFBEAABEAABEAABEBATACRAjnfC7p2fhv/+uPrj0Rq+Vd+murbwX3enljR23hN PcMWzLWPx4zp3YpF6dr1H9yHHF5cv1r4k12QcGz5+isUZTWgdzttDZO2ndtQsQd+PfdQQAkehK9e epKiHPv1aavFMnXq0Ybih4eeTRZQL5LCglaQl/M2Hu4dWrLMXD/2dqbYJnqiOboSAsOmje2sRYnT FvBz8orfnUJcYufFHD0XB1MXn2l+HqN7OZCf3u824y3UguTIX8MzqJO/hN8ooLTNbMyZGjeu/pf5 rl55n3DBE+7OEEKG6tvXjdOMYrXqPryflZy7F+pAQAYEdJ0mbuTu/9pD/JTgbpzq1cndLzRWlNhT U89Y+JTwGjOpF/2UcPIe3I8cXlq/lHlKCBLDly6jHwWDe7fVZpk6d2tDXtrvoB8F1b/E7ta/D5sy NG8p1FT+lJg3hkTsNERpC9Jzcqt+SuTyfl3pf4kvspDVqvewfsyo/V2FpalrIMkuwFR6kXj2j5B0 Pjf4aHQ+pc1ubUmf5Edc/u8dWQfEJr1Pi67ThO+CJzrTcn4I5QpjHIXZTSYvHMFuJoN+gwgQAAEQ AAFlJkD2H42XKhYWFsrsDWwHARkQQKRABhBrKELXdvjqvzLjw35ZIo4XxB6eNVY8L0AszLSltrBz jFq3saF/InMv0QNoQdKVY/8jk+ptu9qYkFdgohyB//vnWlLuwyv/cOkGjq05GuSlnXZLA7rVkSsP S1OuHblMX7ExoGWyWg/+5W5GcvBgMpyue+nrZq/LYmSe3+LnrEW9eKcZb4cdWJyuIweaUmxbQ5Gv HzLnvcILk25ExBIJVu1aG35IEK6DgGIT0HUYtvp8evy5rUs8RIbGhs4Zu+C4cPaQuJi01BE/Jdpb 008JZiD9IvHKPxHMo8CGTT8KdJhHQUT4lURBdS4ZG+iQR0uzVsOC0wru7h7Wujb/kyhNOr+bhCoo dgcrs9q0J02bcboPnkimTtgb68kwiQvLwnvaFBpp+u/rjpKoyouEv/+z6GVRSxsV+x6CdSAAAiAA AjUiYGBg4CBVWrSoFCuvkTBUBgFVIIAfSHLtxdIbJ84KdyPXdujpt04qXiCaF/BBawQFOSl0paTD n1rSC/utfQ/Th5m5BS8Kc4Sv4ndMsW5q2qJph0930EfJOYWlRbnJzIp98rnGG41LpSQQJVAgswDe U0rfaUYVqskMi913SlJ+6fPs2PJvdl59+CH33yu8OPcZ8hJ8iCCuKz6B0psnTgifEloOfSeuk4oX xIaEnEv88Fe4tOC56FHga99UTacpZ7zoUVAgqM6lzJwCRntdStaju8y3WdNQr+JMgeoLZWk7+O15 UPYmpPfTsB8CtkU+rn7T99VkaXcc+JkXWYLAj/jj9M30O5ffdO5KYp0oIKBUBJydydQYFBAAARAA ARCoXwL4hVS/fN+S/uDAGamf+nS84Iftf8yhMxfUcBhvtTomtaQsQ/QnXMMvLMJV/eJL9xZ3lCy/ TU58mlXTMYDF4F8Ty6URsb/6MHkWPljebYZUU0F69KFlQzuQVAXD/Wf0oN+KVq9US3j1RKEWCCga gQe7I6SeEnS84Id9++fQC+yTcwo+HCmQuOOx/mJBWUGZ6O/OYtfylfjvufTk7pPnCkFEwCcZEL3s SaqCgd/O7susPpBFYTmMXDiJhnkpZMN3R5t81E7y6JSFdMgAAXkQ0Namb9vnzxXjqyoPj6EDBEAA BECgAQggUiBn6G/+W7H+jwoJw5sZWViR6U2mn3S2qTKjVtaj+8nESLZHr7bGZCaxaPcEKiXytmQi siA99m66uk2nHswYPv7C3afi0cSrjNuxGazWHT5xZfyM+t+11BoMNGrDRvOdZrytuDD6F78R83+K MP1y0WedtQtzcz6k8L3CTdr2+Jj+/S9NRiiwJDuv5EOicR0EFIbAm7s/rP+94lPC1MKKvJxnj+hs /46nBPMOn+3ds60ppWnv0oPJ2xd/VupRwL8TyxdU51LS2TPX6/qYMGnbh35vTyXde/SOpCPqxhZ2 781ckMvbMp1kQOSaTfp2sptmYS7ZOFJGhaXrNmJRL2Je7IFndj1syAINFBBQSgK//vqrUtoNo0FA UQnk5OQkSJWSEvx4VNSugl3yIoBIgbxIS/TQ2wrM23aMl8GMnAXpUbu37Unp/f22uT11pG3Zv2Pn sbgi6lX6tcgL5Hxv/6W+DnRv6facvpGZg/C/ZYtXnKWFFCb8m2bYzkxDp/fMNX50si7urAVrueSn PtlM8Ua6qYMpy+ij2UvH0b/bYw8sXMnEKQRFCadO3s5lFL4oyC5gPuTk0bOOJYfxj9IqbY4gZV9x Xrbw+ZmRW3FFA+vdZlCCgtxnTCPR0L3w6X16E0Qq43ZKRlpifst2VhV6Q93Qwo45U5CdX1z04N/b 6aXvEU6xbPpOY+Y7/C/8zK1cqjD2MAErtPEnr4830nuzoYCAchCgtxWYF3ScJ9wUkDwltmzfk9Tr +32ze+pKO3Bwx8/H4wqpV6nRkWfJ+V7+q+mnBEu310xR3r45C5ZdoB8FhYk30kwc2KzqXKL4ocuX 7mOyJxb9F378NpNG8UWe+CmRW0g/JcSH8cn8qp4SLJv+U3zoR875k6cT8skuBjNH+ydVRM/SMWAi BUw4oyg2lDhIHxan5wr3byh4dI/ZJyE9MSX90e3clp4VpxwJxLGD4uy84goWFmTmC03Sb92OSUZV QpKz5iZculm+w6KWy9hZxDzXJQuHMU9VFBAAARAAARCguFyuo1R58qR8117QAYFGSqBMpkUIUaYi 5STs+PHbrq47hH8VJtuXz/CXTPWv/YfUqNBVUc8zKqQz9J77y7krmW9ESgtiltP5Cylq/P6IqNC5 vekf26bjNxy+kydlVd6VkxtGC7cUY09cfjI+WWJkZvzh1ROFqwMqteI/vLb5S29hm4++3B0mElgQ sdpD6tafuHz1RKnDmSEPX1dF43HIeKnXgTbrIwoqMqnSjIdho6VE06snip6GLSEmEXt27olK4xel hNCRDinLi9L+CWTcYU9ceSmNL3Tz3T5mlEjIEJlhF0+vaTN+/a9/hNHCZdqP0kyWLDslvG2+//6U nG5WWatR3q+trEm8V158fBl5uAn/bsaXz+qXTO+X0YfXF0M3XXxeViGdofeSrefi09+IlD6PWS8c No/dH3MxdAn9epxij90QcTOv3Kr0+IhVE0UPiepfykuL+UWcQ7HXkt0n4gtopwpiVlV4SqxfVeEp EfbgdVU08uKPb6BTEpL5UAvD7p1YI3xk2K6KeS0ClRcv2t+BVAi9uv978TPFduL+x2UFL9NOfE9r JWb8EZOW9/KpcP2F2cT1x+MLHoRJW2C76upj6T0XKdHaijcPLm8YSz9S2GODrz14WcHI1HNLrL8+ lypGKqO+q/qu2B9WfufI9ZatL2VhYWFSz3KKZAqvL02Q+w4Cki4AIRAAARkSwMNNhjAhSjUIqBE3 pP+XX8fPampqRIJsZdbRpGo2//vvOytW/CusfOryjGq2kn21Ut62jm4ryHKD8WF3qpsRQPZWQGL1 Cfz04+lzp+mo89ChFsuXD6x+Q8WpqbxfW7kyTEigHB1FGm/GU3YOctUurayUF9TZzZ8sNxgblraT eXWv8KX0ZpBTb3paAYkUxC2QSpjQUJbnczf+bbZgvJM8phQcD6fG+4oclen/cBsKXnh4uK+v2CMy LSQ+niQLbyhjGqdeSRco48+txtll8FopCODhphTdBCPlSUAeP5Pk6Q90gQAIgAAIgEAVBASPwue0 VxsRkiAQ5N98YNDNCv//w30CAiAAAiAAAiAAAu8igF9KCnZvSNb/P3mUXvMtDRXMGZgDAiBQDwSK 8zKFWUKePkoTLupX9CIozs9hsglQ4rQCDWFxYdK1iFjqXHRsxsPzT9t+Yodchg3RC9AJAiAAAopK wMPDg0ySkhQLCybZDQoINGICCrr6wM1tZwN2SoOtPpAsPRA5T/YC/Ftq+8MGRALV7yQgWX3QsIxi Ymq/ZEYpVx8wC50arDTg6gPJ0gPxU2L9xb8Xd1Lknf4KeZuGui3jlndWgy2ayI8LXdh3Tgg1Nvjo yuldzZrJ6f6RXn0gJ5VVqZHdwgdM0G3AbhSqxuqDBu8CGAACIAACjYEA5hQoUi+ru86+J50a8DzC BIrUPbAFBBSAgLrr4jsky6Dk77xihwkIMW3XBecrpPprsNwKuk4Td6cVlKXtnCO/MIEC3DIwQfUI mJoKMxejgAAIgAAIgEA9EkCkoB7hQjQIgAAIgAAIgAAIyJaAsbGxbAVCGgiAAAiAAAi8TQCRAtwV IAACIAACIAACIKB8BBLIniwoIAACMiJAvlBkaY+k5OTkyEgwxICAshJQgjwF8skacPbUnY2rFWCX RGW9kRqv3ZI8BV4DLb78Rq67JA7qWZ7Oo1HnKSiQ5Vav77yVExOoToqxS2Lj/bYpp+fSeQrkc69K OOlIpfNAngLlvH2qtJqMZxyZTVuxRaUK9SpcaXgCSMLS8H0ACxSMAOYUKFiHwBwQAAEQAAEQAAEQ AAEQAAEQAAEQaFACiBQ0KH4oBwEQAAEQAAEQAAEQAAEQAAEQAAEFI4BIgYJ1CMwBARAAARAAARAA ARAAARAAARAAgQYlgEhBg+JvNMoF6WdX9+d0mLYnsVDQaJyGoyAAAiAAAiAAAiAAAspBwMPDg+T+ kBQLCwvlsBtWgkC9EUCkoN7QNmrBgqIbew7cKBQzKM28FrbpIj9j/+8RCcWNGgycBwEQqIKAoPDm npCbkicGGIEACIAACICAvAkYGBg4SJUWLVrI2wLoAwEFI4BIgYJ1iEqYI0g/t+nrA7nlvqgbd/Nd 0JttOn6St4OmSrgIJ0AABGRGgDwxVn9zALtRyQwoBIEACIAACIAACIBAnQkgUlBnhBBQkQBZaLDW z2/TxVfSp1lm/ZaeSbvzq5+dNm453DEgAALlBMgTY9kUvzWXKjwxAAgEQOA9BMhbT/ABARAAARAA gfomgGFbfROusXxBOu+fPQGftFAzpf/6/7AvOkOytL8w4cJGvw7kfP+lJx8kXd5zJoMWLyh6cGbb tPamLTifLAtPSuMeOPGEnC29EdRNJMRzG1kIkB4+T3RoN+8YXYEq5W1rJ9SiZjrtSJJIiJqpjd+O GH4pk1mAudp+3pYo2oYK9XddPrZUaGSHaVtvpgt/5b/KiNm64CNvstCAorgr3HVMWxDVuRnHZom0 0IfiCcaFCZclbvYPOMBNKBKhKry90VNcf+oB7pkDy/rTh4xVYhL5SZEb59kwfI79l8498E96aY1B owEIKDOB0ptBdjpqavSfnV/oqfDv+jOf2/sFR0m+J/TTIZ13LDTAU1Szf8D+aPHVJ+Ez7Jgmamoz wvlFCWc3+XF0OJ6booVf0Xc3LORt8hQ11JkVnvgf01CsujQ1coXQEjXOjK3RoicDLa8w8UzQjPb0 JXu/oPMJjJZX/Jitk3t5r7lEPzH8e+uo6XgGidYg5Cec3+hnL5Sz8WxiPmNUKf/4rHLV6TmiOoM2 8orIs0GiguP5XXgCnxtyknnQoYCA6hLIzMxUXefgGQjIm0BCQkK4VMnJwVw3eXcB9CkaAUQKFKtH BGnhX340dEeO776SN8lRGz6iIoKnj5i/N44ZIb9ICls2emnJV3dKMk7P4/yzcvYlZnAtSDi2aPKK jIVXitLC5pr+77tvrr6hT6t3/uL4HzPL3TMbsfr016bS7qq7zr56bi6bObV/16FU51k7T4T4OVP8 0O96cVpZL+KP+zvqjzmmVOzhJQt2XsyiKtQ/clFvTlhJXtQvU6j9cwf4bb1NpypsZuo2c9lGoVKP 5VEFGSXnZ3duaTp8VdiXrhVAF8Ye+MLXd9a9flHPM0qeRwy4t3Cg71d7Yhl/tDsu/FNcP/rqo1bD V+2nD4lVo7dcyadJCB4cWTp4/bPl9/klf89odf7HKRcZj1FAoBERUO+06MaJr5mvb1LoH9dbzv67 LP3u7rFU6Nejxm0Rj/b54dN7Df35ue/fBW8KLm7woCICZ42YtE/4PLHwCdq5xIwh9ib56g2Bc28X R4rPvfe0gHzF3tdQ23XBn+cWCr/Rx7fsT3Wet+v6/jlsKjb0654cffMJqePu8f6cYkbxD84dse0y M8QnaQg2De00wP/plJj0kqc/6WwY5rvg+CMB1YztNnPtT6InxvqLBWUF5xd30qaoXN6mcY7D1j+e e60gLyW4aYj3wKXhfBKOVGcPWyVWXZAcfY9q06O9PZu6FP+oQECehIcXT/Z/tjA+L+3cbNOTy7+5 gOdCI/pCNFJXMzKY9wUoIAACsiAQGxvrK1WysrJkIRUyQECJCSBSoFCd9+Lh2T8O8PlXthy9mU9p cVqb09bxuZf+Y94aZMZdukFR969eSSxisTvNWbmmowZ9+tl/V//Hp+5FXUvMZ5m5T1/5tXOTKp1i tdA1qJybRVPPUHhq/MypHq1YLENzRyPm2Hn0LwfX+rlaWVgx13Of5ZXQ/0rqfzxmTO9WLErXrv/g PuT8xfWrwxLevauBho6hjpRNr9LP/bJ2fyxl4+HeoSVFtWzXx8OKxCNmrTz24AVTTVLffUB/Jy3J IT8nr5ie25D5XzSX4t+/dC25UN3Ubfo3G12r9lih+hbGgICMCbA0dQ2YtB9s70/HeJg1o7ScBg5x p58Yy4IOJ5Kv0ovEs3+EpPO5wUej8yltdmtL5nkScfk/0dhCU89Y+PW/mNOyowO7o8+8iR4Tezro fLAhpaEn+kYPmzfTk81qZmphJUxAwh77e+TGz9raWLdhJPPTc+nwHxnDr1jPpSjb/r07ammw23Zu T8WGhJxLfMcjQ5AYvnTZSSFIi44AALMWSURBVIpyHNy7rTbL1LlbGzIlasfZZKa6RHV8pl4bB7PO Y2f52I7t0VaHRZ6EF87RT8LLzJNwwYqvXfBckPEtB3EgAAIgAAIgAAKNiAAiBQrV2c3MegweTd4S 2huT371vFWOnXp0pekTdvse0jReS9AfPG0TPETBp2+NjNsUPWdjRfd7GMw91B80cUvdtXYRD9HcX 05aihANGrdvY0CMCcTijOjwzeH//Q49VbAyEQtQ5Nl3pf29cFYZEPlDUjdt29aCojP2T3DtM3haZ ojN8+gAz9Q+1wnUQUFUCmmYttYTfJGMLO1vmq3SB/io143QfPJHMGrA31nv/mNnTrY0ui2K19tl6 fs9EZ+3qN6xM1LbfkL5OQlukiiD5yiEyhqds3a1NyBeVpd2Snspw7p/LycLIYKXyIvHKPxH0OUcb NgmGquu0NKADHOFXKkYW3Lo5tiQ+thoWnLjTj1Zq0raPF5tKD5nq6u636Uyi7qCFg+v+JFTVewZ+ gQAIgAAIgAAIgMAHCCBSoFC3CEvLwW9Lchl/T29+2A8/BEc+rWCdhu1nm/4JnEiiAxn7F43uMOqH Y3H0+zqW06e7/lw53pkOIiwd8JHvspMPhGt6FbWUPnt0NYk2ThJuEFmaFJ38rDr5Blj2EzZfCqZD KvzQFYM9JpDsDPTaBxQQAAFpAixtB789D8rehPR+GvZDwLbIx9XFU+uGVSsQFOQwX/ik0PGWTJ4C 31D6MDOnoMqve2nBc2HEcIevfVM1naac8Tvoo+QcssLgfYXlNOmXPzeMpZ+EocsGOI5aFi7KblBd t1EPBEAABEAABEAABEBAQgCRAgW7GQT82/sCfK1JqoKBi+b2ZVYfSBWy6GDerrNRoXN7k1FyRPCn 3wmn65OptjN3RkTsWvIRObi4ZsqiI0mKPHBWN2ndg3nxKS6lacnR9Gfbrjb0K8dqFJIQYc6mK9c2 f+lNogVXfpq79H1rH6ohD1VAQCUJCPi8/QFe9iRVwcBvZ/dlVh9Ur9S64XvF266KeV1QVib6E6Yk eE/xYDIXiOvfWez6oacDeRIu/CUi5pclHkTqpTW+i4+8Z01U9UCgFggoBIGoqKglFYvQrNDQUDWp 0rUrMz8PBQRAoFYEPDw84qWKhQUmptWKIxqpEAFEChSqM3Nvb5ruPT3wCnvSl1PdWhTkPqtgXdaV fWfSBc1MO3/2bdjR5XSwgJmunx916MQjAYvdccKafXQSRIr6X3Qc/W5e3dDCzqq+/ct6dD+Z6GB7 9GprXF1dwmUUFHU1ueKWBZ17VEuGoODioX/SXrHMuo5ZdShi9eAarn2orpWoBwLKRqA080ki8/a+ cx/6q5TL2zLdbVYg12zSt5PdNAtz06vrT60bVq1A3bazL5M6Men8bWGyAVIE6bHiPVMqtdK0d+nB ZGqMP3v3qbj6K/6dWOk9HarQlB+15yT9JHQdv+ZvOn0jWeAQHVutWUrV5YJ6INBQBNq0abO+YhFa cuzYMWmTvvrqq4ayEHpBQAUIGBgYkC1IJaVFi8rZvVTAR7gAAjUigEhBjXDVd+WCp/fiaB38xMfp j+7ltXSnUwBIioZW5obPv2Nm2muatrY2pNgDBnQxpTQ1sgNm/EivRGC1MGttxaZM/bxdmXfzLB0D K+YH9wXyg7sw9vC2PSn0YfGzXHpLMeZjXjaTqZDKyGXm778oyCaJz0nJyaMnBgtK8nOY61JNhA33 79hJa3yVfi3yAjns7b/U10G4UlocnijIzi8uevDvbXqbNIlYcpJMgtCw9V1MRzqSuVF3csl45t4F bgrF/mj14uH2TI7GymZUsorVQvvJ8snfM4ssNE1trEwp53FDXasdp5DwxAcQUA0CSaG/bKdn2gtS r56JosN2qxaPtiNfpYJHwudJemJK+qPbuS09rSv6W5yXKfz6P8utOLH/Qw2pF3niB0VuIf2gKM7P KWYeFCSDIf0gqSRZt+f8n8jmCGTovmzmD2fpAX9RwkW+YUeShbE8t0JBZn5xYeK/vPRS3V4zgyeS RQT8iDkLll1IJbsaFCbeSDNxYNOPmEqqpTzS1Mj8esbXx+MKKZamWWsbM4o90btbNWcpqcaNAC9U lwAZwISFhb3fvy5dugwcOFB1GcAzEAABEAABeRNApEDexN+rz9Rtwmf0pIDe1lRCjknnIaNXk00K KSryWFhkAklJwLIPCJ5tHrN2oKmW1ZSHQ0POrB7MIT+1m9us3zLd4vqG/hy27dyUcWF/BY0wYzqW ZT9y9R9ff0TSDc7q67v2puPQkVb0aXI4ZMGxJ1Qpb1sPr2CSaIyU/039aEbIyY2jfH/iMcd/Luzw xdFjaz4euIZJky5uIrF+vN8wWqNVx0+3UuM3HA6e2lGU4ZBSdxn/C51MgRc8xX9fXuv2ZqyMY8vE YnnBA0dtu1FIaXedHcY9vNrwF3d90xb63v+023iau29hVyaHYuHtCmbM3LbxS2mrTpJ5CE3arto9 0/zaj5+0aN7x0wej/vhz1fDWuJUV6laGMXIkYDtxxrDW13701LMiCQAmrvptxzQ3Zlq/aY/xn3mQ f3tZUw9yzDoP+WwVM1w/f2z/+YRC6kn44hmBwmkG56Y6ugTxypMGvL9hIW/TKK+NogfF1E5fHDq+ pvOQNcyDhIzth0wOOxTU06tc8qxwPp13cDX3+IaJZnxukDfH0S8omnLrxAz8Saig4/gDayayKV7g NP9dua07kfABya0YGBaxipyMWDPE3HzGpqtUW1c6rFDKP75MSvXAoJuFUpyb2wdumW9x/YdBnCYO c5PHhnEDR5ANWlBAQDUIkCgAiQW8x5e5c+fiFahq9DW8AAEQAAEFIaBWVlYmQ1PIcjkire4y3dx2 Sqw6dXmGDC18l6izp+5sXP2v8Kp8NMrBKdmrIMGFjm4ryHKD8WF3fvWhoxiNvvz04+lzp58QDF4D Lb78Rq7vcwb1LP+axMTU/msiq6+tXO8F5lEjKgWyfIi904vEBKqTo+jqzXjKzkGu/lalrPRmkFNv /yTKduL+83uGYTllg3fIOww4Hk6N95XrvSoxREfqayK7/9eHh4eTHcclSsiqXjJZV1Hpq5RdlchX 8i07O5tMPVAph+EMCMiXQEJCQmxsrEQnSVuA75R8ewDaFI4AXrgoXJfAIBAAARAAARAAARCoRIBM Kxg+fHiVWLZs2YIhDW4YEKgjARImIGFQScnKyqqjQDQHAWUngEiBsvegfO2X5DV48igdGxPKlz20 gUBFAuXZAR4/5EvPwgcoEAABlSRAFhf4+/tX6dqIESNU0mU4BQIgAAIg0IAEECloQPjKplo6r8HF Rd7GXnTGARQQAIGGIFB6c4MkOwB3WTedDtKJBhrCIOgEARCofwLu7u5vTysg4QNz88q7Kte/LdAA AiAAAiCg4gQQKVDxDpale+qus++VZZRI/s7P7vz+7dBlqRyyQAAEpAmod1qcWFBWJvm7s9iV3vAE BQRAQMUJBAYGVvJw2rRpKu4z3AMBEAABEGgIAogUNAR16AQBEAABEAABEACBmhMg+SOl1yCQMAEy StacIlqAQBUEfHx8SFJ2ScE3C3cJCCBSgHsABOpEQLjxAUrjIlBc3Lj8hbd1IZAQV5fWaAsCbxOQ nkQwadIkIAIBEAABEACB+iCASEEVVFOSn9UHa8hUPQIvXryWOMXm6Kqeg/CoagIpZKdQFBCoHoGU h6J6nbtUrwFqgcAHCJBXnT/++COpRHIWkMwF4AUCIAACIAAC9UEAkQIRVXsnMwnfB3Hp9cEaMlWP wH93yycUtLYxVD0H4VE5ATupHeMjToMMCFSLwPMcas+vopq9ParVBJVAoBoEZs2aRWq9ayuEaghA FRAAARAAARD4AAE1shpHhpDU1NSItLrLdHPbKbHq1OUZMrTwXaLIy2Gfj38TXnVoq/fzzjFyUAoV yk7gpx9PS1YfhPw52ozdUp4eDepZ/jWJian910RWX1t5+k4xjxpRKZDlQ+x9XgStplZ8I6oQ94Rq hWTjcu1zpVR2PJwa7yuy/OgZystbrl7oSH1NZPf/+vDwcLLfuMSR+Ph4rOaVSbe+elWSlVWD5Wyn TkX07dtbQ0Oj+tqNjCyaNWtR/fqoCQKNikBCQkJsbKzEZQ8PDwMDg0ZFAM6CQCUCiBSUA9m5JfLo oUTh8aqNH3fuYoPbBQTeQ4CsUpk98aiwQld3k+/XjZAzLkQKRMDlFin4N4rq11Ok9At/6ofKGcjl fANAnaITKCmhBvShblwX2fk4m9KX749ORArkcosUFOQUFGQJVeXlZebnZ0jU8vlxL17kSluRnZ2Q lXVMLnbVTImjo3+lBmy2s4ZG+Q5HFhbOwgrNmmkaGSFOWjO8qK0UBBAGVYpugpHyJIBIQTlt6YGf sWnzHfvHaWg0lWdnQJcSESCTUL6aH57wX57Q5gYJLSFSILph5BYpqDTwO3uZ6o4Vwkr0rZW7qdKT UIK2UDPnytsCRApkRDwr6+mrV8XPnqW8fFmYm5v2/PljIvjJE25xsTgMJCNFSidGGF/Q0GjJZjuR DyYmNs2bkzgCZi4oXU/CYJoAIgW4D0CgEgFECioAkZ5MTt4Sf7ViMIIF+M68TYCECdYuPxkdJcp8 2SATCohViBTIO1JA9J2LoEb0L78lsAYBD4h3ETgQSs30K78o/wkFRDciBbW6P8kqgNTU+GfPkh8+ /FdhpwDUyjO5NiJBBH19S1NTB3PztpiDIFf0UFZbAogU1JYc2qksAUQKKnRt7vOiBdOOZGa8FJ4l I8AvvurXUl9LZfsfjtWcALlJfl57VhImIALkn6FAaDUiBQ0QKSAqv11C/by+/MbBzIKaf4lUv8WO YGrxvHI394dRw3wawGtECmoIPS0tITr6yO3b4nQkNWz+nuqWlmNbtGhZqYK1decPatDVNdLTq3G6 3JcvS549oyc+vL/w+YkvXuRXqvPkyZXi4tsfalqz60ZGZI+G2S4u8s3TUTMbURsEMKcA9wAIVCaA SEFlIjeuJy9b+D/JWbIMYcLUTr36tsHkAnx7yFSCS5H39+2+KYklESYNsu5A2BeIFIjuSbmtPhDq q7QGgZwhOQtmzUeCQzwiaAKJCdSyJdRJqYXoy3+kFi9tGDiIFNSEO0k3sGFDdcfkkpE/m+2ooUG/ TiCz7k1MLCQKORwVSXVUUJBLyAj9kg5ASKIM1Y8sDBgQ1q1bQ4TManIboC4IgAAIgICEACIFVdwM lYIFpAaJF/QfYke2wWtlId98VLhVFYNA6pOce3fSorhPpGMExLQGDBMgUlB+a8g5UkAUpz6lvpxb YTRITvpNo7wHUlY2lKamYty2sEKOBIqLqdg7FNnpQDpGIIwiLV1BtWigbPOIFNTkFjhzJvjff6Vm gjBtSURAW9vY0rK98N0+2TjAyIhdE6mNpe6rVy+ystKIt0+exBcV5T56dCM9/dqrV8nS/mtqdvX3 v9ZYiMBPEAABEFB+AogUVN2H92Ofrl1+rtKwUPm7Gx7IjAAJHi34qlfDbpCBOQWi7pR/pIAoJjML Vi+vsAxBZjcXBKkKgR17qHETG9IZRApqQv/YsdW3br1z3YFwEoG+fquWLU0l0wd0dAx0dFrWRIlK 1U1LowMBkokGDx/eIIfx8dvf4+Ty5fLa1FalSMMZEAABEGgYAogUvJM7WY5+6uitfbvvNUzPQKsC E5gwtd2gES4NnsACkYKGjBQIdZMEhyuXlW+Dp8A3LUyTK4HBw6lVgZSdg1yVvq0MkYKadMC1a+H/ /ONbkxbldTU1O1pYfCQ8FkYTJNcsLBylZSrmrATJjACJqXl52fn5oq0fyUlhFICUkpLcx48P1o4S aYVIQa3RoSEIgAAIyJ8AIgUfYE7iBRf+d//82STJfnjy7yRoVBACDm31PPvZ9vm4TYPHCIRAECkQ 3RgNMqdAclOSyQW3b9BzzqXTHCrILQsz5E+AZCXwHkR1cJG/5io0IlJQk26QjhRYWo5+/PhwTVrX ta50rKGust7dvo7j/FoYZmTUr7S0ODc3StgWkYJaMEQTuRGIioo6dqw8y8z8+fPNzc3lph2KQEAB CSBSUN1OISED8lfd2qincgRIdEBBAgQStIgUKESkQPpWJ9nsUBotgRaaCpfVEpGCmtyN0pGC5cuT SNO0tId5eTnk1frDh7fIYXz8zprIa4x1SVzA0NCazbYnWR4tLBx0dPTJ6ow//lgnQYdIQWO8LZTH Z+ySqDx9BUvlRACRAjmBhhoQkDkBRAoULlIg8z6GQBCoCwFECmpC7+1IQZWts7LSX70qYRbnPyEV Xrwo4vMfCGs+efJvcfGdmuhUsrpkqoVwr0cNDR0225Z8YFI20C9dSaLHZs00qvQHkQIl6+ZGbC4i BY248+F61QQQKcCdAQLKSgCRAkQKlPXehd3yIYBIQU04VzNSUE2RzOaCzyWVhXMTKrXNzX32/Dm9 X8C7CrNYoJaLIBwdZ7zfVMloX7qaZOQvOcnhWFfT5XdVQ6SgjgDRXG4EECmQG2ooUhYCiBQoS0/B ThCoTACRAkQK8K0AgfcRQKSgJveHbCMFNdGs4nURKVDxDlYh9xApUKHOhCuyIcCSjRhIAQEQAAEQ AAEQAAEQAAEQAAHlJODj41MmVRwcGnr/GuXECKtViQAiBarUm/AFBEAABEAABEAABEAABEAABEAA BOpKAJGCuhJEexAAARAAARAAARAAARAAARAAARBQJQKIFKhSb8IXEAABEAABEAABEAABEAABEAAB EKgrAUQK6koQ7UEABEAABEAABEAABEAABJSaQFRU1BKp8vTpU6V2B8aDQN0JIFJQd4aQAAIgAAIg AAIg0JgICJ7xjh0P8hukpmbL/E0KCDl9jPdMIEg9cSyhtM4kBPy74UGLOULhnpsj+a8p6jWfd0as sYfn0ih+aUbk0klqnK9C4go/pPA1P3Kzp9ogv5CED1b9kChcBwGVJZCRkbFeqhQXF6usq3AMBKpH AJGC6nFCLRAAARAAARAAARCgBIUJZwK8hruNWLiB+iQs5uqbsqSyspBve2o/2DypSZPeXzx4WVdI hbc3jJvh6//gs3PcmPXdKO6mCT9Hp/L2jnOb7X+277ns8PW2z7hrAn8OPb1nzSWK/+fqEw8/FJvI urrnLy4VH7r6QvyHqlbX+MLbm0P+k5Ww6ipFPRAAARAAATkSQKRAjrChCgRAAARAAARAQIkJCArj js7xmB3Ifcaesu36b9N9XE2YH1IsbYdei3/bFTbFse7OlcZH/8J9RlFaxno6Zjb2bMpxYDfj1PP/ 4xLRmnp62kY2fRwptku3fl5+X/ei2KOWDrFW/4BWox5+n3hQjhOX9nH8UNVq2S/IiFy9cXPOm2pV RiUQAAEQAAHlJIBIgXL2G6wGARAAARAAARCQM4HCu798vj6UT7QOXBXg0arSbyhWqxErFwxoIkOb mrXyWZFWdmq3j3m5VFYrn92nytJW+FhY9F39e1na2ilO2h9S2ZTdd/75slN7pjh8sOqHRFEUCRMs C/Ai0xlQQAAEQAAEVJoAIgUq3b1wDgRAAARAAARAQDYEBPnRZzfQb/spyrtPT7vmb0tlteo6pqOG +DxZp3A1JGASk8igh2fAn5EVsgQUJpwN8ePQmQg4fiFnRZeKeUHjmrqtTaJFXPN3a28XdLe09G6Q XXs3/2v0uaS1bk3HBfGKKf4ZP1GKBFu6jkhlYULknwGePcSpE64mFAooih/u5ynKp2C3iydZMFD4 8KwoFcIgv6BLTE2KonUJMy/YqvkdihRL4/jtjaZzJVACPm/j5MnCMEGS/4imatLaZUMZUkCgoQj4 +PiUSRUHB4eGsgR6QUBBCCBSoCAdATNAAARAAARAAAQUmUBezJlL9HwCirLt52RT9Q8o/V597ZkJ /sJ1ChOmRveOKXhQVvDL4OgNXh6rxNkH83lBXzh673q8KLzgzcVg9SPeHuvDU8lQXNN18YHXMV/Z 0hK6rY+5m7i4vbp6+8WJd+mEBbTir2JeH1jsqkmx+2059zm7Ai1GpteG6MG/EJlhU54FTl0073CK gGL7bFm9pGJVqvB20NBx3v65i2Juvnm6QH3DJI8FZ1NJrIDourFPVPnszRTOwHXHNpBDfuj3I36O ySerLNiuC9cumCiEsP7o67Ik2kJF7jTYBgIgAAIgUFsCiBTUlhzagQAIgAAIgAAINCICL3LTyWC5 ekXAj1j/K1mnYDu4S0dtFqVt7znYmmQfnLrgdIKAEiScWep/nqKsB3vaa7OMnN1tKH7EjjNPmNf6 1SwskrJAU6puRZkm3QZ3YVP65i2ZiQ+a2sbSVamXCYd3+ZPJEbbdPDvqstj27u1N+CF/n0lkcjFK KvfzHEjWNYgP+en5RdU0DdVAAARAAARUggAiBSrRjXACBEAABEAABEBAcQhkxB4LiacoE1sDLeaX VnOOjQ39b8Tt2IzixMsXIugDGxsOGcmr67RsSVHPIg7xEmsSKqjo60uxTAMDHZLToKk4wUHlXAp0 K0Hq5UM8+oO7JYfMB2C1aGlGVkzwDl2mZxWggAAIgAAIgICQACIFuBNAAARAAARAAARA4IMEDJz7 tBNWSjobl/zeUXVp2uMouqKGWcsWFX9pJSenFRXk5DBiDvpyHNTUHDi+BxmhuQW1H6m/EcvMySn4 0JYEgqKcJCbbQuhsDp2SoKdv6GMSqkjKKaq9/g/CQwUQUHgCUVFRS6TK06dPFd5kGAgC9UsAkYL6 5QvpIAACIAACIAACKkGguV3PPt5CT+7GxjIZ/t5V1DmW7hWuvUxLTmZOCOcRCAudiaCsLEn0lzjd VQYr/vl3H+VVlzad9UCsHRkHqksN9VSWQEZGxnqpUlxcrLKuwjEQqB4BRAqqxwm1QAAEQAAEQAAE GjcBlsPATbtH0ckB+RFbDv5XWJnGa37k3hCyMQEppvZ9vE0o6nFUcpZktwH6vHdHZ1Nt+86dmQyD D8/eThe/xn/Nv5nAr/07fQ2xzMdnT975wCoCdfPOvs60/qTbt5OZ3ASkCJ7dvPms9vob940B70EA BEBAJQkgUqCS3QqnQAAEQAAEQAAEZE5A22nKskg6WPCM6//1nKAzPNHMArIh4rUjQauW3W47sBOT PJBlNXr1dA8yGD95/TbZgLDwwfmTDynKc/3q/g4slq7H2OApjnRugqkrl0VmkBSHhQmxaWbWbPpH 2ZvMR4+YXRKl4wh5j+4yuy6Uj+0FxXl5wjeexZmF/2/vPuCauvY4gF8itSJDnCGoVVlOQEa1LgRR 3KJQbUUFi4vnqHWg1VpxULWCUir4HJUnOOooVFQqoiAiYkVGURxMcUCCA5EhSjG8c+9NQkBWZCjh dz/5vNck9557zvfmRs4/5/zPK0pSJsX38Vx7kFl0sSAlIOAu/R+vCp6KdiXHkGhAW/NvFzrSsYpz cxbsDaObUJAc8UzDsBN9fsnOgjx6NkSFp2SHjpr6zNoM9HkL7ofHIr5Q7x8zFAgBCEDgoxCQx0hB SezuvgpcpWoffd0TygX5G/hikOxBLqO5WnOOJtc6bXKVNSrJDnQSt85id9w7P2mIDxQKYoM8HAxY h9EbIwXFDdxIFA+BJiJQEutuoKCgWs3DKUDwHl8QBbGeFpJidTzLli3/2F3KgVi4x1f5rSJuiEwt LeGfdhKz1Kbwj10L9Wv2AiRYsO1x1qlTJ6dQOxeaavZSoKf6T1h0+mm7Sc4HlpkwvX2ycVRMZp9J Ouj22R+mqroKqk5BA5aHJv2y0kSNebOzjad3iNsUHnVlq+XgLg4Hr1G6JrxPSO871n2WKG0BHUcY 1cLhPL/klrsOm0qA6dv3HOMQwKf4F5ZY/pddspG/faax+60SUub+gzEHF5hTSX5zxqpabPeJVrSy 6a1C8QOWrN0u2vW/lsYHyHcTp/Moz/CDbvY9qXBPS01rB/d4yrQXXXNyLuOZop1Dvu/5jbe75Fj6 6Xm6GMVeM45usCdLJ27fvGR/Tk8jJr6ADQIQgAAE5E5AobS0tB4bpaCgQEqre5mmpvsktforcr5s NSSRgi9+pbb/4DBCT5ksGewx0WptOCmh+5aYq8vIHMC8tMBtK9e225Cw0rAeJgTWrmqCgCU9bE+U 1aF2R1W51+s0X/vBTicpytzl6pmFxiqV7FgQvdt28sYIzcXnTk6KdyQC3BWhV11HqL7/mXMTvM9Q C2Y1Htr7V7W5HDluaNltEhMj420ihVRft22jujNfNaIt/32+xIT8gHlmtj4CUsYC/xQvGw3m66Aw +cJvPzmsU/KSvCJjq0ri3XuZOZPfA7VdY+4trX7SsbAg/tAJytbRqLJbWMbz1nl30vmfaLqOfFWa u0WcWVmLKsnS0tfJfvY9F9FfWbUsvM7NQQGMgKrUbVJ//9YHBATY2tpKiJOSkvT09ORA/Pr1gOBg UbtcXJgf9bHVh8CxYz8nJYn+tXJxeZ+v6/qoBcqAAAQgAAGZBeQyEMxpv3yNEx0mqHRT07ZetWEh V2aquhzQaeBXq6wonuPS8br1EZ1QVFFvV311SpIjfCNI6F+1vVobrlZfLtXP8nPtKkBq07Di7HDP Dbuza7Mr9oFAkxDgdOzWW+mdmirrjVrqeca1ayM0QSgI3fLDUTYBurxvZBG4Gr6y5F0A7YMABCAA AQhAAAJNTEAeIwWKRl/Z9aq2YeqGixr3t3FO56Ebz2enH7DTY4YdNurWUsPa62bRLQ/rbu97sUmY YKvT2A1RjVptnAwCjS4gCAugB96rG5oZtmzgkwsFF9Y5Omy9gjlBDQyN4iEAAQhAAAIQgAAE3kvg fTuP73Wyj/KgvLQwjyVa9GR+g7kel1PYPALSuQCcggQ5on1GeyQU5CV4WIjTBDgFpdy5LMoFoL9k 19XsEiYfAZMawGCudzybGoBMPRAnTRjoQU9dLolzHyh6RWeJ7/lI39VT6KdMCWV5h4uz484eXScq jTt69fG4WiZFJhMuLDoPcc6gzx2+cYgqe1J6E/ITzu7fPFqTqaHmlHWHEsolL8hLC/cRvzt6s29Y GknCJOTH75o3ig0TpDtbqSpwJSkeCpLFNSd5EFYfDU8uFF3gR0FzdUREcwOyC5IZIs0pHtH0DqKn 5JC1QSlpkb7nMVDho7wvmmGlhHlJCQ+YdisaTZjATEYgA+x1ROkMdBz8zof5rbagn+o7eF2VuhuL +fEB7vP16an449b6s1nHqtuK+THe3wyz2nqF7BnubKaqoCqZvZ+XfNln9ThNZlb/6NV+Yck1rm5O pkt4OmiytfI8L95fOo/AHJ/L533Wj6bL1HXwiJGquJAfe9rdQZccq2mx/kTC8xrDFjW2VFiQel5E oevgfonJqVbVRs4etF/cWFKBQ7HiL0wHqRQSTLoHSaYDHYfTj2rixfsQgAAEIAABCEAAAvUg0Mwj BbkJHnaDx7s9XnI9vTBjq6LPNNJ9zSJ/LityrV39V5gwwPkPrt+m+gzqrcujIpIeF7Q2XHZS/Nbp 3w5n9lq6P+TYIi6VeGLVUAPVLgsz7SISTtrxqOwjix28IvNJARqTt5xbIz3bQdF4+UXRK2mXrwg0 bH/yo5+SEpbui3jGnLRYELh81JA9uTbHs4tehGwZT0Vs/3bI0mMpr2txzVUMl13KvOrWnd6VJDLI v05nZyBhggdBC62sdhVM8n+c/fS6ixkVtcPeat7hNFFsgqEYuy5uzClC4eOQ5eU0c61/spDDM1ri unUGk+ZYyy0kvzT7NpPfoSDx6He2tk63R119QddwzO1lY22/901kggVdx+/ct5hZAIoqSY+JE/Ya 3l+b4kfdflxAvU7zXzdtbdH3N4uyzy3RDN608Io4vFCLhmEXCDScAJkLsO3n4LflT6BotDzu7Brm s5x2IVKgaftTIP000W/N0l+usLcqyTXgbWdm63xpuH/ym9KzizreT6xpfnNLnumCbTsWMIeTefv5 pfmXmLwAeff8lplPWBc9+lR+/tv8iDHRiyzNlx26V02woDDafaq51brnyyNevE3eqOg1xnzVKWZ1 NBWTpSdDl7HfYNGXH3SetukI/VTgt3z6rvA89p7Pjd01z3SG8wUr/8cvs0IXdk2NIYnZq9lqbCnZ wXOi0Rjnx44xgqLHO1R3TrJdevpB5YuuCR8ELLEy9S6YdvJxqeC62zAq3MPe1OlwMtlbY7J37EF7 DaYmlgf+WkK+vhR5k35J8nZ0PBL6v0mNMTGk4T5mKBkCEIAABD5agatXr66S2h4/fvzRVhUVg0Dj CDTrSIEwJWDL2iCK6jlqeB9lDrfXoN4kQ7DfhXTmT9tWqu3Z9H9Jz9v01tYwtllo033GoJ4qREzy 1qS5/7Hgclp26Nqdne/MnXEw4JdZPbV66DLPswW5zLJEHCW1duUnREte0R4+cYSOiqJ4h9wnL4vo I4TpYb8FZFNBewLi8ikVDa0uTPFx1+48fe+PhTAt1M83kYo4cjomh1LhdevKLON0MfreE3rAQXkK nsnY4Vyqvab6u9O42fMXC0L3bDuSSGmZDzFQJ6O1+w43704iHU6bAtlYRus27dlDI3La9Nfj9reZ 62A+bZieCvX03pU4irp7LSq1kMQgFm3aatjqvVuEAyFQTwJ7bXU/aaFLfuR/90d1Tmu1dsytoj1q woheyorip7kC0a36KOQ3n3DyvsWIQbyWJJ+42aRRTFxN5k3Iv+i22YdP9Rxv1keFZE3XNxvfg+L/ vmYpCdhVXtjrZH93ZzIwoYe5hb46h9tnSF8e3+/Y+XQ2ntiqjegbbIi1VS8VyVNBTi69RhoZLRS2 24t8+2mPGv15Zw7F0RhkO5qs2Vb1JqyppcLkExvdCIX2aDND5Va8Psb6VKKPT2hqZbUXpofu9Uuk rhw5Qb6OlHlaXRjj0OhE+uuIo6I3fcuORXSAJnT3AVFEpiCrxbRNE957CpXMlwMHQAACEIBAcxPI zs52k9pevWIXF8UGgeYr0JwjBa/vRwWTv2tJpKCbJumvsmkC+eF/RN0v96etqXEvdYqiZ/tf/81B h44UVLWx3f76IOVoDvhyLJfiabevl+LoCnN4A6xn9CM/2LVXezepooSio7oqebfG1AbZsWeC6VkD Wu3Y+ilqag2g//+dWMYIU1010gvpNn7PpV0O/ZSpjr2GGVN0TEF/EJnrkdZ2/JJxjZtbsvne6mh5 1QJk7YM3+fH+q4bJnp2gIO16SCIpWbtvt451Ii7JvhHCLMTQsR19D5KbqpPWFyTmwA+JvFP5DB1h RmRAJL3nF1qa5AiOsnon0t+OPB6VUUVkQbp+ZPH2mHP06brqd21bq4rX1FJhetTxUDKfQntIj050 dVTU6WEBocGRoshFuZOQr6OvptNfRx0r+Toie7bsbGm/fBiJFcRu9zhNIiXC1ND4LiYkooENAhCA AAQgAAEIQKBxBJrzX14lBTnsT/R7HXt8wlX6xODrvfSz9BwyPf9Db2o6DgduFmXsGf4k0OWHfdeq HxVcu8qq9LP77VZ24f4hWSe3rPOKfiQdKJVQPM3Nr8Uy8iVPHlxjRlhz1ctHMtKi05khClVurbRn eQZvtyfRgewjy6cZTN0ceA/TD2p3/bBXgwpwVHQm/7B6TAtZT/IqV0D3t6vaJBPsSS4A9uEUIKj0 FnmddT+JKaWdukr5WN7f6VkljwLm64hLYMqZH8AXFuakMzkRfrdl8hR8Zvs7uSv5aS9qzG1AjhEW vsypMaFCuVbV0FJKmJ/DfCmk+c34jMmJYOtHP32aU+lXinI/x323Sl/ut8g6uXa919XH7/xuo2ww 6euhdAGhAacT7sdHvTUzxeoJsn46sT8EIAABCEAAAhB4f4HmHCmQqNGT+bOLSkUPdh7+h96Egujj 6yYakFQF1s7zB/Woj+qQFImHNo81IakKxn+/cAA7+6Di9ujuoxc1n0uxU7dB5UZYl2SlR9OHaQ/Q on9OrG6jEx/sv3DVb7EZ+cEwxOvr9aIJCzWfFXtAoEEFOGo9DbuJzlCcGfxXfC2CZjVViEyw31Oa Xyr12GPD5Ep8Z2ul2aP84P+SJ+l/M11veshAV5t9qeXK2WfDZgKhtx5uMS/KTpG6lMlL8uE2bdeY f8uazKZgeHcjyREPrZ5gQlIV2K5eOISdfVBua6U32WkVPSwhaKeX64kWxkbK+Nfqw11UnBkCEIAA BCAAgeYn0Jz/9mqtZTSIGfqedPnWY/EwguLshESpBQg+0CeiIHqPw+Rvd4RwVyyfZaxSkFv3NdeF hXHeTkPsvSI0F6+ZZdi66GW29I94Eoq0y+duCGoeUsFOIqCoa+nlfx81HtSn+iHYz6IOnxcIW3KN Z/3of8qFDhbUKfnCB7o8OK2cCmiMsGG6tULBZe+ofyvv0Vdoeqc+wy3pPnvapQQ2wYlke/X8pSwT HBW5fQZYMV9H6XzpxKU8q6F9Kp+ho9jNeDKTs/B+dEKG+BCyWMnN2iySUna6C2Xffmzd85/mVZY5 taaWKmob2zJpCKUphIJE0RIw5WzY5Ij2269orlo9y0SpKPdJZVRqxtMWjyfH8U/k9xzYvTn/WyWn 9xuaBQEIQODjErCxsSmV2vT09D6u+qE2EGh0Abn/6+t1/nN6/QGyFT1nU5BJNo6q2YKtDmSuLD/c aem2cJIyXFiYEifg6nFpFcmBOS8rjp6t8JawKC+HKfnVk1xm3O+rl8/ZM2XnMhMZJDtI6lDhkHdK KHh8N4IeGpydkJGdlZqn3rd7uZqXiGMH+c8r/ZueKnn+KDWDPkQSBBEWPEqiVzqkshIfZApuPW8z XPoHTAkFle3r8tMhZgmDgjtBgQnM1IC2Xfoy+caLcvJf5aZFxGcLW2nbrqT7+enhV2/mkiTqty+H Z1C8wVtWWusyGQorCkhq30r56c7/rA+g119sze3Woz3FGzPmc2QqKHd18aRxBIRPH9wVfSPk5BaI xw8wS/etcXQ40b4LE/QSvsrLYXqxrwT03V3hKZmOrzXakfl5nx4kn0sVJvr915dd+4DvYWlMr/BX 6abYsasOMyyHdMtfFaT+TRYI5OjYbHElHeOkoIg7BWRJhVsRQWTW0TDnLbZ6VXxNdzD/z1pHunN+ cs63P4fRSwzmJV8VaPTjMfu/fin66mNbV+EpqbjlAnv62y/k2Ln4wpKCZP9fvcKZusZunzDVPf6d 9Q1rbKna0G9FaQjXLdh8gQ5XFCZH8NsbapDsDyX5onAnG4YQ5j9KYk6W9c+DzMzE5+pmlSZTVDea 5kgayFvm9KUOUp82zm2Bs0AAAhCAAAQgAAGRgBxHCkqyA524Sh1td8Sybc3eYamjpLMkUGo5bpJp z93/xBYybT7Ec2yX/nM9Y6g+7N+12YHrxAeeXGYwdnec5O/mggSPqVJvfXcqcOvIsVuZlGMk4jBh 6cnjuwdZerEzgC/OGTw/IFtwaq1oB7oOIz1i38TtLHeIx1bxDkwJpIadBn61ivy+yBts+DZV0N5w 4uz1dEQj7fKZPy6n5GQHfjfY6ST7N73X2KlSdWMbSmo4SpRzga5Sb95csoyCYseBtkvp3/D1+1GP srmfj7VbQpZypKirwb+H0v12QrE7JGT/qsFMukGt0auPxiiaWxsq0wWq6E933USyIfK3Llx+JE+v Lx1JURmw0D/8xJb2e4a05Sq1tQru63Eu/PCyAcz+j4KWzy8T0HdPkOotcXRXey3sErNtLFe5u+P9 iT7nt4zXlD2NHO5fCNRFoCTW3UChhZ4tk0GQbCfnGCmJsgC00TT9ev72K5R2W3qwe0n8TuMJW5m7 mR+yaMI3nluXSD89Tb5MWnaetCX89E57jSBnsz4WPyd/MWNGv+luJ4/4n4rISq56IoCi4YyjW+15 pFs+13l/bjcj+mtH3WTp0aTTzp95DVRVbaFqFjzAOzTp5FKTqkfdc3iTPc8Fu5HUgFc2WOqaOHhe o4wNmThBQaznVEsP9quPtG6Bu+cKqaff0bkSON1stvuHuNrzriw31Ri/+V5vh8WT7V2P+B8Jikk5 X9mUgRpbKtmBH+5updnTwT2aMjUi1SHJGr7ruUj0lcWEIV5zB9iuoRMW6venHgk6fT55+hIm5HE1 8HhoslSaBQ7PbMb08bMsDdTqcrlxLAQaXaAkdr+OgrYC+9DZX1XIsE714p93EJ9Cx/1WPUyWqlNt cDAEIAABCMihgAIZZVOPzVJQUCCl1b1MU9N9klr9FTm/HmuIoiAgNwLjhpbdJjEx73+b1Ndt26iw zFeNaMuvzy+xRm0FTlaDwLMwr9AuC7+qalgF/GoQUJW6Terv3/qAgABbW1vJqZOSkuRjjO716wHB waJ2ubiwY4PqsJXccu812ZkUo/19zL15tU4fkhf7axi1cHJt9ifxiF6m2+gzuJ26t1L/wyYoqUbq 2LGfk5JE/1q5uODrug4fKhwKAQhAoHEF5HhMQeNC4mwQgAAEIFAPAsWZpxdrqk7zSX1N5SUmtDXU wT9T9aCKIpqEwL/8sIMrf2VXZcIGAQg0tgAJg5LfTiRbcnJyY9cA54PARyaAP8E+sguC6kAAAhBo 1gJ5STF/86m4y3cyM69kG03EeIJm/WloTo0nYYL/2ll6hjenNqOtEIAABCDwMQsgUvAxXx3UDQIQ gEBzE2g3YOYSe400vxWbTnDNzNTwj1Rz+wA04fYK+bcC3Fdq0ukDZq/2v/Pu2ACyQ6DPdgtRfoHZ q30TREuVCJ9Ee6wxYcMEadtMP5HKbiB8Eht4fLXFICbrwSCL1adi+f9WNCrJivFlix3n4H4lmcml LNqqO7wg+YKPgyZT24CUzLDTgfy3oqMK7l8QNeSdApvw9UHVIQABCEBANgH8ESabF/aGAAQgAIGG FOCo6Dn4ppSWpvguM2XXccAGgaYgUJCw026+rfOdsf4Rb0v3f9cxO7p8rgNh5vl5JvN/yRl9pjQl P+YHc+rK9tlOsw8y66tyOg1YtsLL/jO6nSSvwb9ppalMagNhZsC82aa/FE47c7U0P8DNnArfvsJ0 9qnkCmuy3n3CsVp26W2EvyPl5zzbfOkFspgTvVV7uDD53BKr/QLXC3Rtu/y9dma0KE5QkOA+0c7K OXd5TPzbx0sVd0oV2BSuA+oIAQhAAAL1JYA/w+pLEuVAAAIQgAAEINA8Bf7NDAnYGf6EoozHD9Lg UJ/wzCzGM0uhirc3qefP+PCfhO+8EJ1HqWh2ZqICT0IupzBrJ1W+CVOj9vokUeGBJ6JfUiqdtD5j lgsNSUjMFv/4zxzXWr+/Me8TisMbOWMEWVOE73PmfOob8nq1h7/NTkwIoZ7cuvxPckEL3oCvt3jp t6ALe5N8Yr8zaYj2QAtDNQ5Pd4h+J0mBzfPSotXNR4DL5TpLba1bt24+bUdLIVCpACIF+GBAAAIQ gAAEIACBuggUJF3/h15RVbtbt45Mj7vi9onm4BFkZVSqZ7s2tf7Li6Np+JV9T4rq2LFNpWVWOAen dZs2TM/m9uXEHPJ/1R7egtvPkKzGzPdz7q23yv3C4zY2X1nzWpBhCJHHmQVWh3ymSQY1cJTUNUh4 IvZ4pGiYQl2McCwEPnKBIUOGbJfaunTp8pFXGNWDQEML1Prfq4auCMqHAAQgAAEIQAACTVLgda4g r9qKc1R62fhmpb09+vnjE16rf4l6WJtmqug5+v5V+naLxeNza1cfuvrwdW0OKtun2sM5epMPXt9A By/4fzpb2U1cfZ5OcCAszEkjIyMoym8hk3BhqK0fqemTtJzC8jMeZKsI9oYABCAAgaYogEhBU7xq qDMEIAABCEAAAk1KgCQX9N1uqUlSFQz/8bvBzOyDGrd/+bGnVltak1QFtj/OGMLOPqh5U9NQZ/es /vBPeANm/S824OCqYSQWEL59w5ITGWXhADZdQqnokbpSn4wwwAYBCEAAAs1KAJGCZnW50VgIQAAC EIAABOpdoF2/4X3pQtMSEtLpHAFl26uXL1+RDnhe7M61prP3hvNsf5yv3zo/T1BzFYQFsYfsTFds D++06scpJq3f5AqqH1Pw9umDB3QWRXPraQPakDQF1R4uzAs/G5j5L4dn6PDzrzFuFqKkCYpdjG37 VWyI8El8/BOMKaj5imEPCEAAAvIlgEjBB7mer9N8p3GVFLhK046myDiY8IPUFyeFAAQgAAEIQKBK gU91Rk90JCP5qdhDp+8VUAX3/I7sZNc+4P/X0vhAbEnhg1vMc35GRlZmQq6aRbl8h+SNNt306eMp OrKQlxx+hy8szX+QHk6/9OSfDEFmwgt1ix6Vnj8t6EYCmThQkHYuMI6ihq35cbyRCvnrrvrDOa1V +StmegYkF1CUkoZWZx7V09G6H5dqa/7tQqYh5+Ys2BtGr8hYkBzxTMOwE/5exMdf7gUCAgIUpLbk 5GS5bzIaCIHqBfDNL/F5FrnOlOm9ix8TfdIaKoTeStvhWOq5NdyRYwZq13IwIT7JEIBA/QrkJV/2 WT1OU0FVQUFV38EzIFbwOu/y0UBBSf2epnalFcR6WjA1IQ+ngMarg/R5FXQ8Yz9I42tHhL0g8FEL cDqP8gw/6GbfNtzZRtViz50vJi7vN8Xt5G7/U6eyksmShx0GOUwxJy0w/4xKytUwHTFr+0y6P37h wpEL90lnnaxgYDRjxU6Sv5D/35lLAnN76vI4LbiDxq4x70SyIPan+AINg8mzHJg+fFzgkSg6pwDV oteqHTGPI2JmP1upqqugOnYdNcU/ZrvrCC7zt11Nh3N0f/Gb3u3abguFXl1sH8zy9/K06UwOFDek JxXuaalp7eAeT5n2woKlH/WHD5WDAAQg0DACCqWlpfVYMonEkdLqXqap6T5Jrf6KnF+PNay+KGGK z3SDOXdnHAz4ZZYOHZJvuI0EJsYspLZfdR2h2nAnacCSiwVn/0w3mzpYjVYSua0IbfzmCLPOn0w1 +cqsQwO29WMtetzQstskJub9b5P6um0b1Yn5qhFt+e/xJVaceXr55zNSZh3x/HFSLxWqmB9/9sh/ XZx/H+Kf4mWj8UEm5D4LWz/G0oNkHV/QyHUoiXfvZeZMfu7Udo25t5RexB2bnAioSt0m9fdvPfnZ zdbWVkKUlJSkp6cnB2LXrwcEB4vatWbN7ZYtEcevn6t67NjPSUmif61cXN7j67p+qoFSIFCjgLx+ udXYcOwAgaoEGrQz3ETZp36/+qsGDhOQvvWzhwmfTh1p0DTDBJQw6+yOGx31mTAB2Ti6jseLSm82 ftRD+ODc1pjO/ds10Y8aqv3BBPIif13hzZ++4Ds6TEC2ljwjm5Uevx+YrvzBqkS1atO+iX4ffDgz nBkCDSOwdWtf0r+9fv38/ft3nj2rRUqBhqlGEy01Pz83K+s+0Tt1are3t5MkTNBEm4NqQwACEGi2 AogUSF/61/ejgsMbZUaAMC0q8PZwC+Om2MUVFqac37N+r5rFhw5zFCRf9ly/qc0gQ3HAotnexmi4 rAIlaXH+5I//332OxOeWHavc58sZJrVZtVzW02F/CEDg4xdQU+NKV5L0b4ODF/r5TfT2HrJxozbp 8ZLYAen6kg4weZCeMPv4+NvVQDVkwwHk8c8/VwjI5csBxOd//1tDrHbuNNm/fyTRS0jY8ezZBUkF DA1/aqDKoFgI1IsAl8t1ltpat25dL8WiEAg0XQFECqSunTDj+h+RXMPuVeftIZ3ksKPrRjO5DEZv PhydLU5kIBREHyevay0OyioWlZgXtlnLdHP4M/EJJMfqL/GNvkVCEn11eGSCg/De0YmadIFaayPz yFLG/ITDq6coKRiQqcrlsiQUZ8cF7J6rz556S3gm82Zxdoz3Ei1yrMPemMz8uD+CRPkRq3q9rLFk vsBXbEYGJh2DUHB171x9g3Vh+aJdJLXVnOJyPjVsrQFbPSo3wWOSlsGYjUdCvMYafOV7r0RSDpOd UVIsW5SIRUl/WeCDstYUJEf60m2kzz56bVBK9WtQk1L4CYHudDPp/TdGCoiwsDDOY0rHntPW+mXs sNQpyyhBqh1+xsPBgC5cf8muq5IL1HRvUdS8IQQUtY1tNUjBQc5mw5gMBexty1EbbmdNTz2Qnr0/ x+fycVE6A10Hjxi+1H0pLEg97z5fn04uoOvgfonODCbe8pIveTjo0nkHNOd7XEiV/pBL50cYvdov LPmddcqF/Ou+60czxc7xSa7mBiEVCPNh91TVtFjvE1Z2omreor86+PEBopqPW+t/i18JcmHyBU8H TVESh/PlKlmYHOa32oLNqjButc9l6YY3xOVCmRBoDIHevYeYm/tWdSbS4yWxA9L1JR1g8iA9YfZB Osbsg3SSSVeZfbDRBPZBOtKSsAL7H6SP3RjtqfU5iotfV6ghGUkh3YTz5w9JmubmNoVtLxsOII/A wNkEJDzcmfg8fHiiqtN26GA9dKh9rSuFHSHwAQSGDBmyXWrr0qXLB6gETgmBj0kAeQrKdZ6nG+zu dy74R/NKJ73npfous9lYsujEtnmmPCorYMVQF8rj7A7rblTWX/vDPrMbGD1X6nBm6n6w9U0/O10y 17E4O3yrk2P6GPrY9k/DvV23+jyxO/m7Qy8mVCPMD1838uLIi99rXvVP4I621rm3aYgjtfsf16Hs r+XCzMiNjgszx+xaPWe4bss0X/vBf4yJCnTULgjb3H9pDl0H3lO6/Dcr2ENIkKLS1yt+8kiuBOtd ugeOjC68lKXYMmDVqc/3keZwytWWWxR34Ps1P1/+fJ84BwFZuEFcAVGgiYQPZlqF2dBVol8hxc6+ NNJneeeYwOvtLO10UtZLZWQoiN5tO3lP1x98XecZaSgWJh/6fszsE3Q/ZaqHyKpcLYWCC9sc1vDH /PTdvFHarZOPWk8I/PIs61ZemByVlxa4beWyzFEe39pONOHSOHu0z7P+8rkhT4Hour5PnoK8e37L RizyEXeReebLXH+0/3KEjpr4syLJGsAzX+l79EdL1dRDi8bP9hOMd4s4vNJInV6BLN5zotny8GE7 Y07+RyN05eczLo89cnb/JHIH5cZ6zjRdF2fueurMEm7Ikgm2IcP9I3ba8FqSWFvFt/yeW3mHnbMn H2kSnphoui6conj0gUu1otm0BRprQmNdR1QycObdCnjzLQ8kBTjqcYQFyUxtdUndlppQMe5TJzun jD0Q5OGoRxooPlBjEV0r7tOwzY6W7iGk4WV5Cgqj6UOuGJPGLtcIm2dme87K/8YuJt3ZO2/5CKYe iPdz1JHbG60Jf30gT4HsFy8/P+fOnfD79/9OSnKT/ei6HtGz5/tnnKnx3I8e/f3q1c0ad6vfHUiA QEdnpJ7e0B49+tdvySgNAhCAAAQaWgBjCiTCzNQDnlUVMwKKBYFrbTZS3wfvWmBK5wDmaJrZ2n16 9LfQ+0LhGzXzeTN1npQ7/FmU727xRAbyA7i3k+PDqaJjW3bs3OZJRG/rwd1F+sLkMzvuOozknNlG FlqeaqRBPXmQms2OOKC33ATPBbZ/jzn0y9LhuuSv/NcFOa/YgQ/C7IxE/qft2ihzqJZcs6+mju/J HlLV6xU/THk3Lx3tbK3/JjpL09LYcLjreQ86TFChthwl1RZPItTLUirQIy/izL8c3EPy2cmLO72r 7BVhyuldN8wtVM7tDO5gPfMLLkUyMuT21tVk5n+/TvN335his3UTCROQXhNHWaMrSevMXRGaWnSi ki59QfQeh//cGLN727LR2qRpr/Jyi7n9unVgzlxhqgh9gaYsyybIC61NuJzXaX/uOWn19QgsLdHQ XyFNtXy1XvYe4UfWmIvqzw/3mGNpNMTBL1E8LkCSNWDorBnDeByOit4Ia3pZsyDnjQHJZFyBMPnE RjfSrdcebWao3IrXx1ifSvTxCU0lI3RSA9auCyLpyseb9VHhcPsN7E0JAvZeSKcPKvcWb+Do4Tyq fRd1pfKKk5Z8ZawiSVsgyMmlF2N/Z6tQgc9HjNWgeJ3V6RtN+CjE091PwNaNQyn3sRjdkxL4zFn1 B1PzRyG/+ZCaUxYjBpHgBaez2aRR5ddre53s7+58hU/1MLfQV+dw+wzpy+P7HTufTpZ0rfCWyXir fpRGJ3VyFmwQkAsBVdV2AwfafP31dpJ7b9GiR9OmRY4Z40+Gzffs6dy69ecN3UTys3zDPRohTPDZ Z3MJFBGztj4/b17SmjWvFi06NXr0YoQJGvqTg/IhAAEINIQA/rwTq7JTD+xGVjrpnSTw+3lZgPaS Bdb0L3LMRqckzGL+i6Ok0ppT4XC6E54l6k4XxPiu8W7p4vy15FhykHQ2hIKstPQnF7aearPQ0ZCe jyDdFSf99v9tWEu5bP2Gfov+td9zwy7u9/amJPUZR9dm7RYNr63e9IB8Ti+7XQ7MT/pVvl7+AyTM j7t4UrcHladkaCy1/hFd2yPdPdaXqy2lpd2ZyftG2k0yLFw0LgtzkAERpBz+UPErwsLM1AwqaMdv qvMWDSCdlnL7M0pUX+NedJiAKY0OdvDEcYQKn/DchP2bNlKLNswzZTo/mZFuP+7pOn+6KZPcoULA Ii9y/7KA3mXIZB3KEzf32GjgA94QXxtyUqaa3qQtlwRJod6rzEUtSvRbNH3paamZMvTr7dRV2NUA 2nbr25X+/9DoxCclwvSo46FkRIL2kB6dyNscFXV6NkNocGR6bmpUMP0DPdVTi0d+ZldUVSefWH5I QFSq8LX4rY7tVMlBLTtP8srKv3WAHoYg8yauAE+7Ld1N5/BsDqSUZnkzP/s/iQ30SyRjE9i3KKqV Zo+ekpqTRdevh5B3Ke2+3TpWelphRmRAJP3OF1qadNuU1TuRuZqRx6MyhJK3tNqp0mftZuN9q5Ss FkEPl8AGAXkT6NChC5mSQAIHkyevJbEDZ+doEj5Yvvw56QOTB+kMky6xJI5AesiknyxvBFLtIYES 0kb2QaZpsG23t49nNYgMeXzzzX4CRcT697fS1NRr2bJCGFSOedA0CEAAAnIo8B5/oMqhAmkS06HV rGIxgtf3Lxw7yh86dYJBWWJ00r2/zZckNSh/eG7CgZ1eos5zsSDUb0/KtCVT9MTW9HCDu2XZEJie dnpe94XLxmrSf22X74rnxAcciRppM7o/Jy084Oi6iQZb38w972EnCjqoGy719unxh62DdwK9tLJk q+p16WuXk3AxhGqp0dtYRyrbuzA/5tSeFCtbi67i2hY/uRN3tyyu8W7SR7qc7LId6KcZKX3nrp/A 9NLL78/pPvDLodTtq9fp2dRMZsSfPcLNFiwZrVXJB5EdqmA3Vp9KjQzcv3ns8B1vF/wpXr2yvBI9 gsCLmi2FLJ+fUrSqvgRK4s+eFZTQpSnrjbD/WSpeIBoXUOOJhPk5ZFlBikrzm/EZk1DA1o9++jQn /3X+i6fM4XttdT9RUP1Ec8Ze+ll6Tr6wRPwW2Y05ex02cQX4aS8q5jko4adfpUturVHxt/6kdP5r 6lWuoPpU7sLCnHRmWsbvtkyegs9sfydtZU4keYtuTh1qj0Mh0GQFyKAD0gcmD9IZJl1iSRyB9JBJ P5ntMLMPtgstebDDEyo8vvhil6T7Xc1/1DIGQYb616Y0sg8ZJfFuZcgrFepMBgVImkMCJaSN7GP4 cHu27WSwAKvRZK8nKg6BMgGySiJZN1qyJScnQwcCzVwAkQL2A1DF1IOC2OOBZLhuAT8lvdwoAKpY cCngJH+8k40xs6yZ9OHCwoTLt96254o6z3mpN/6myoYqkDECh3bsoKRCEkxP28xxtiXbOS/ftabH JsRSF+cMVp569IGwzchd6ee3jKfnIIg3Trfx7gdcKO8N+2MKpT/LVb0u2YcZ9dD7y7Hlx1Aw4QPp gRUF/wTsDu5dNteAUGRKTz2g5xrsoBavmMQOZ6CYYgcvsTdnoh4Vf/mnWmnP8gxeVrLNsA1XqYWW wc7nw7aG+K4ZKh5iINUCdqgCP9ypN8/2wEOqk4VX3J+uNvQcBKZcMnJBKn7BXKCy+RrN/KZG82sj kHIghEwUEG90vGDz4SOLeOQFGfvAZG7/v/mlpaLHpZVGotE3FGXuFpEvfr209OZKE3ZoAr09uvXo RW1qWZt90m4/YCMTkk2RpzWk3Auvs+4nMS+wwxxqvfVwi3khaVpp6lKpFtxPffCsrsGOWtcDO0Kg SQqwXWjJgx2eUOFBRuZLut/V/EeFGIR0PEL6v8lQ/9qURvYhoyTerQx5pUKdMSigSX7yUGkIQAAC 9SSASAEDWfnUg7xU/wtUn8/eNRJNRli11La/uvjwOHMX+8Ek61jBrSsvunTNTRX1rssmKTA7CuKv 3BJQ0tkQ6K41tXjNLGZywbtda/o1Zhr/+R8dvhxvrqdMJiBEnE/IexK56zhZs4DeVAxG2xlHhSUy ayU8q+L1Cp8Xph9O2diPKv9jPl1bSU4BesB/1P/2/hUhNdeAru0bcaYAUmaFCAtb7OwVc5j5ApVM VaBHMqv1Hfn9ybvpRaXZdKMmGFYSJpDU1mTxuafZ53+2s7YeSuIjpD5n45nVGZiIhmRcRnlk9mCh IC2j3DiLerpjUIycCLy9tdntYLllBVpyu3Yng+x5k411y7r00q198eD2I/q55YB+nRTFqydQaZcS 6AwEok9dYrxAUbf/IDriQCVduPVY/E4x/2YiX9ha/FbahfM32PVL3nuTVIC6FHaNL15yhS2uU5/h lqQKaVfvPynXm2dqLn63XM3Z4149f/mK/J9iN+PJJvTz+9EJGSQ3Ads2fvxNvlDyFnU1KLqOLXjv puNACEAAAhCAAAQgAIEGF0CkgBDnpR5y23aRkp4tLxTEBnkssfmjkymdEq+dkc2MwWVj5gN++mZV hp2Xu7Mll+3d0zMX1On+M1n/L0ZpmPHLCLo7/cn9iPhsqsNnhprUs1x62G7KX8euUzzqflTfzlTq HXYBP3qiPlWWRlE0qH7Qp7cTmMXY1IwnLRmffeNiBLuUYEHyZY/l2xJUNFrnPAw5dDyC+Uu9IPX6 lfTBI/rRo/1Jn7nS1yt+kJiBDFL5AqTef3X32p0n9LqJscFnkpQ+6/CQp9O5JDaKTmbGBgKsLIwK I88m0EMYJBEWzq2gwDuFVDHJxSg1JEE0PmKAcuot0Sp0ZJWHbVPmXswtFghq6MZzVE0nO5nlxl2M SKP3pKcq7F649ZaGBh2DYMIrU0f2LAw/f4u8y9EaMdeGe/H40TP3mIEVeWlhh04mt/qMyewgCFxs IL16ZYPfUzhBExEgGf7GL3E/Hcsue0gWCt31X9+0YRsOLxwqNWiHvHN61+7TZI1AIf9G0CUyCH+8 m4sNPZVIbei3O5gxCKHrFmy+QBdSmBzBb2+o0Upt2AIv+350boJFS9ddJjcpWbAwLquTHkmLKH6L 4vu5rD3MZE8svBNwOoFJo/j65XN2ldKc3ALSwZc8ZaYMvLtJKiDwXuzy+z16CkLevdNn48l/cPSm uTibk1DB+YgE8rTwzqXzZEyBuOYcrdGONkzNyZlzqcJEP9Jwpny+h6WxZ2wJ1cH8P2sd6bwLJ+d8 +3MYffPmJV8VaPQjCU0kbyX6rNjEhFrIOgt/BdzMbSJXHdWEAAQgAAEIQAACEKiVQHOPFJCV9r5S ajPEySebYga6KylwmQevh6nj2gviUfccZeM57r/1uzaGHjM/6Of0/mv+Olw2Ep7icLv348V6Oa7Y HU0Zmesp0Vn6suIuJlDGhlxOOyP7JcNDbA11vvF9pGNt3Zcemlz8jNLUY6IMdF/6rvTcBDKonnqZ Fv20kz6bZVDdcJ67z+exjgbk1PpL9se3mLDFY8kQ7ithX8+jc1ROLdVS4Hacfq3vT+5s2r+CKl6v 8GGQTrgo/RZHz3rHVqa2DnuuU0YTzXXatKCozHuFWl9oSQYtZ94Mf6Y7zpA5HZ2sIfuo76FoJXPr Psp04CBTamIFMymgOClW0L6v9MABvt/GGRaDO7bgKo3e7BsQyQZB3t1UTB28thvfWEzvqfWN761P R7v/yi48wWxvnsRfz+5loU+HA1pqTPzh0P7+cV/31lLSnLLuD35X66/M6Mxu2CBQhQD324gXpf/8 YJx7wq4NmYqv0EJ3k2CoZ9LJH0dUHOQyadEU7rWfx7bQs/Wh7N1O73Sil0gkG8lHuCX89E57DX64 u5VmTwf3aMrUiPl8kjx/2/1DXO15VMjWCV26zPe8RvUxYYslb+0KidlDciiS7In6quNW+8QoWk0y VKGXSJxKr4lIbyfnGC1w91whfhrubLYygM2qUG4jFdgZG+G3ahiP//vs3hpjV/vFcCzHGTH5DVWM lp6JD3br4mOq0UJBY3KQqWto/FFmcUfpmgc5m/Wx+Dn5ixkz+k13O3nE/1REVjIzxYDDm+x5Ltht ej/qygZLXRMH0gJjQ/beI6kT91+5fnCZFb2Ygkkvi/UHozlDbQzYkrFBAAIQgAAEmqoAl8t1ltpa tyYDDbFBoFkLKJSWltYjAMkCQkqre5mmpvsktforsgGXF67HtqOoGgTI6OX/HkseNecrOh1jcXbc xZhHuQ/ObN0YNtH/H9ehlSwXD9EaBMYNLbtNYmLe/zapr9u2US8Y81Uj2vLr80tMXCjpuk80XRdO UQv8SW5/jconJDRqk3EyCMgqoCp1m9Tfv/Uk6Zetra2kLklJSXp6SGgn67XB/hCAAAQgAIGPXQA/ u37sV0he6vc67dDSMcFtTHXYkd0tucbjxlvbLdzpuZhKfZhdfpa1vLQZ7YAABCAAAQhAAAIQgAAE INAUBRApaIpXrSnWmV6TnXv78vkzsWyCBrLRqRD+vPhk1jxLOhkENgh8VAKSNAGP0/lMGgFsEIAA BCAAAQhAAAIQaDYCiBQ0m0v9gRvaUsN6y58+46gzsw2U2WQQmrZecW+7zd62cRSbGBIbBD4aAems AWQyf1sdOs8fNghAAAIQgAAEIAABCDQXAXTRmsuV/gjaqaZt/tXC325l0+sjkkfWn67zmHUfsUHg YxNQMVl6qTS/VPJIZfL8YYMABCAAAQhAQF4FSBIWkrlJsiUnJ8trS9EuCNRSAJGCWkJhNwhAAAIQ gAAEIAABCEAAAhCAQLMQQKSgWVxmNBICEIAABCAAAQhAAAIQgAAEIFBLAUQKagmF3SAAAQhAAAIQ gAAEIAABCEAAAs1CAJGCZnGZ0UgIQAACEIAABCAAAQhAoCoBLpfrLLW1bt0aVhBo5gIKpaWl9UhA soCQ0upepqnpPkmt/oqcX481RFEQkBuBcUPLbpOYmPe/Terrtm1UWOarRrTl1+eXWKO2AieDQIMK qErdJvX3bz1J+mVrayupeFJSkp6eXoO2A4VDAAIQgAAEIND4AhhT0PjmOCMEIAABCEAAAhCAAAQg AAEIQODjFZDDSEFJnPtAJQVuNY+5Adn1c0WKs8M3TlHSX+KbWFg/BTbRUkqyA53E4Ba74wqqaoZQ EBvk4WDAXprRGyMFxU20wah2sxco5sf/FeC32kJVQYF+jF7t90fA6T98vAKThbLaiIvSXRuWJ/PB sp4M+0MAAhCAAAQgAAEIQKA2AnIYKVA0Xnm96E3CsUVcFmCG/82i0mz68Tb9ZrDLjH61candPtkx hw9FUYkntgenltTuiKa1V0H0ft/4WrRMkWv9S9SeqTU0riB6j8NEx7W3p55LD9liTkVsWOgVmV8n kNwE70MJtahfnU6CgyFQTkBYkHrefb6JptncXak9f4x/WZpfWpp/co563K4ZU7e87aIl23dqCf/0 t5pm420XbU+aPtJUTbaDa7oyeff8tvmmvq5pN7wPAQhAAAIQgACVk5OTLLUVFRUBBQLNXKB+/zD9 eDBbdujaXalidTjKuqMX/nLARaNFPVWUazpz1mCq37RVY3QU66nIj6cYYWak24+/5dTyR05FFfV2 1de9JDnCN4JPUart1dpwtfpyqX6Wn2srv397yYAOzw2762l0yPtXA0c2K4Fi/uXNE43GOP9O2XuH nNnkOEJHjWm/mt6k9Xu9F9j27ybjN4Eib8Iaf3sSvjSZZWnAllVPGwkTLBsR0GmQVqt6KhDFQAAC EIAABORZIDw8vKfU9ujRI3luLdoGgVoIyGuk4N2ml2T/dZb+/Vmlz5DPVWshU5tdWnLNXf4surXL oV8dery1OVGj70PCBBsdbbeHNMyJW2pYe90suuVh3e19P38kTLDVaeyGqIapH0qFQGUCwoJ4b7sJ G8Ipime/ccvMfirldmqlpT9hWE91WemE6aF7/RIpDavRRjXE2mpfslAQ7bt+au9FPpRBd433vcdq fzrsCQEIQAACEIAABCAgfwLN56/I3JSYx8z1UzG0HsFMTChI8LAQz66fczT8/NF1o+mnWg57Y/j0 L+mCgCVSyQ4GesSWUJIJ+TpLAiOC5uqIDu/rzoyBf1T2CkmFUJB8mZ6TrznFI1qUxaAgOdJ39RTR LP3VR8OTRa+XxO7uK06sMHd/ZLjP5tGapGSDud7xopn80lV1Ckq5w5RMDtFfsutqdklmpAtT83KH MG0V1YHZ0+N8WgEzQKCm0wkFV/fOH8OGCTLWmnZWUmDaTm8k0UCwpAlKozcfjs6u1ZgDuv6dhzhn 0GWEbxyiKimQooqz486K5OnkBauPxzH4oi0vTazBJafzDaObIOTH75o3ig0TpDtbqSpwRf50eysX LrtwhMIpSJCTFuaxRIucziOBNskTPSWnCLwjCD8aLMCUBvn7rqtziwpj9vzgFk6K0VjktW5C53e+ OxWNJkzQkAwpyEu+fNx9vr4oi8GRaOmPNVWYHMbmOBi3YqffcXKn8SpMPSA7nN6/epwmffi4jWFl GT3I3IfwPzwdNOnkCJoW6wOSCyvcgcK8y2u76A6c7UHfv/zfL8bkvSYpFcghOosCMul9ybCIjRaq +nNOP6CfCfmxQQF0gbqrA2L+YiqsabHxQrnaUvRJxUkZ3n23zrAoAAIQgAAEIAABCEDg4xNoJpEC 8hO0947jFTLtqRguO+m/woS5KNHXHnS2dj1CP+X7rZ+2K4qkFtOYvC3h4DQe876W274lJooUOyF/ +dJzlz2tzcbv3LeYfVe0dS17pSQ9Jk7Ya3h/bYofdfsxfeKCxKPf2do63R519UV20YuQMbeXjbX9 nk2FqGiy8FqoqKiwvx9qfvnjycPkafaRxQ6imfzSVT392+HMXkv3h9CJGBJPrBpqoNplYaZdRMJJ u3KHkDNG77Y1n7b2udPVF/y0jYq7xkxZeUpAOgc1nY6jMWSB68ZpTKO6b4nJLCq9voy0nRJmBawY PHFvju1hkvHh6s7BVIjXvMnfHrpXi1gBqf+lzKtu3ekizV2u5rMFkk6LIHD5qCF7cm2O0yZbxlMR 278dsvRYCjuzOjfBw27w2HVxY06lF2b4OGR5Oc1c658s5PCMlrhunaHNXpeQ/NLs2ysNSXHVCNMX zlV8rfMfXL9N9RnUW5dHRSQ9LhAKU/5YO97tictdftGZ+Z0v/eQY8fbju1FRow8tIMyLObXzCpk+ Q3r1NiN5LaurT+G9gPVTe07wyZkR/PZlhr991nanOevOMj1zshUm+iyztVz0z6Czj/89MOTu76RL Lz31oJgf4+3Q39b3YZ/vzl73n65NXTlzPimPOTIv+fS6iUZ23s+twwVFWWcXUB67Tie/Kl8Tjtpw 13tn19DfTJYHkpI29Q7/juRBmLrOL80v5PoTOgQmfPk0iUr0ORubLbznY2Ni+rUteZev24JSHzx/ xkQexQ93338uXZzdQMiP9vpGb+z/HvT5LjTF3568e/jSXTbmiA0CEIAABCAAAQhAQH4FmkGk4Iit gdKnBpWPVG+l2p6diTBkzOheypT4KT/n5SvypzBHWe+r71yYRH3p4Vdv5tL/IcyIyZu4xJz5QbF1 m/YVciFIXonIadNfj9vfZq6D+bRheiqkSxy6Z9uRRErLfIiBOkWp9x1u3p308502BbK9YsmBI8Zb 6qlJnmYLcsX9AElVJ839jwWXU5aIgTvjYMAvs3pq9dBlKiM+5HWav/tGkheAOSNHo8/nfXnZvsfC 0mp5unc/8q/vXzh2lM+P2nUqPo9S1uzWhd6FH37lztP3vj2E6WG/kXUogvYExOVTKhpaTJFU3DWm SGFKwJa1QRTVc9TwPsocnsnY4Vyqvab6O9knRGevSVhycamk5216a2sY2yy06T5jUE8V4dM70eEU /+6V6+kFilzTeT94mNRXHov3hsGBH59ATkxoCB0nqDGhgPBBwKqpth63zF03rx3emcNRVm//KUUl Xr7/jPkNPzPMfcWc35+z7yp27Nab/kSbDhRNW2AmOFj89HDxAe/FQ7jZN4IupYknJpCxAB4LZmxN svc6vMFGTynjnMfe8GGTLPRav2PFVpVnZTNYh6PIm7Q7P2KnOb1TUjqf3P4tO09yPeM62WpoHy6n l+MpJhhBmaxavdxGR721WjumuKc5+eywmtzYXfMGrnm+/ITnMtP2/OiwC+8Of/j4LhVqBAEIQAAC EHgPAS6X6yy1tW797r+w71EqDoFAExZoBpECeu2Dl1HH1gx+n8vUSnuKE/NrP9ubpYRpCbn6/WrO czDCVJekMed0G7/nEpPFIDv2TDCdfE+rnQpDrqipNYD+f1Gv+H2qJjpGe/jEETpsodKbMOP6H5H0 C4O06AHRHOU2XPJ9FxkYlfG+vwa21Bg0nh5hodtRtb4+NRzNAV+O5VI87fbvNuD1/ajgcLoBHdVV SQNqTG1Qe2FT417qbIHXf3PQUWnZsc8AcxJhOTJ7iME3u8MyVK3njSkbQ16HK4ND5UlA+CzjZhbd oBoSCgjzruxdTOcdsFkyrT+dyEByIH2wsCDhxGb3EMm7woyEC/fJj/8D+nViBtmwExzoY7WyLvus mbPYh3I8EPT9CPJlkhf5y1ySImGq67JxdJyS7uRnlf61zET5nbsx7+b532PpEJt+F+Y9joq+2fge 5D/yn+aJRwq0nbrFVo9+Ny/lelkwQnLBOrajbzoStvjfynVBPHvH6dpPwvx+nDnDm5p+MGy1eb1m XpSnTwnaAgEIQAACTVhgyJAh26W2Ll3YX7CwQaD5CtRXn+8jF1TTtl6+4qvyCchqWWU1Aws7eoZC 9tGLCXk5t6LefmEqe+KxkicPrqXRJ+Sql+8Tp0WnMwOC630TFuamM7+A0kMqyOT8zxyPkArwM3Iq Tmuu9ZnJCAuHXemlfF8zvv/mzV5hbNaHum1qOg4HbhZl7Bn+JNDlh33XSJ9JspUU5LCDFZ7min7e rPZUdRDm6M789YoXHQTh+20cbz5zXYAooUPd2oaj5UpAWJjD3lBK7dq0rvprU5j8h8dBsp9khoIw Pep4KHnBhF4WQfgo5Def8LJ3X6dGBYeIfvwnRb9O9nd3JhMcBN62eh3Nj+R8vuhMVtIBRzLIiLx1 as92AYkpjBlaw1oGwrz4i4fInhqDjLXFP4ZwlNtpkc93ruAlWfCJ9P//StAfx4QYRDuLsyS8Tr8V TX9Paeh079SSEiaf2EjnZeD72XbpbOv7wvC7iKzH+xx6vRubkKsrjcZAQOrfoVh3HYVqN4cA5ntB xq1EulwL99gKUyNlLK0Rdy9XcR13UQajaiogW0sfBTiIvWtTeCM2HKeCAAQg0DwFmkmkgFxcdV1T cWhQ+ODC2cRa9887DHZYaE4K4J/x/+PE1RbG+u/+/l3jZ0exU7dBzLx68VaSlR5N/7f2AC32x8QG 29iZ/EWihzhBwHudTshPOLzatgdJVTB2+eIR9RJoJUnaj6+baEBSFVg7zx9E/+75zvbo7qMXNVe3 TsItuaaLPKOu/7rCilzmqB2L6WwINZ8SezQnAcVuxpOZnCZF7NSkKrYndy4zcQHxkodsLICihs2Y RiKMBWnXQxLL3i28efoYGfgz9KvB3Zkv4qeJkXH0/0/3z8ovzdq38svxJjzRN3TB49R0+vvCwlCr hu9s0SyJcikSyQyITuKoQWH8iVs9pxmpMw1gdxbXVpgRGUDqw7P60d6cjGIQtUXb/sjD0vxbvkvt rI3E1WlOVx5tbc4CiiZLr9+9/Memye1ZBatN/mQVNXoLO3fYzcmqByW4m/BQlJtYBihFk+XXY/c7 9WcOyU2LT35ewz85ubG/Hqq5Wy5DDd5zVxok0cdJkzm8KC0+qaaKy9bSrjbe+3/8nMGuTeHv2Qgc BgEIQAACtRVoPpECRe64CXTeO5JsP+L3v9+2qX3/nKM92Hok+UUu8ahrYrcRNf2hXrl8x17DjOl3 rqWXT6tvPKhPx9peK5n2U+xmMIXp2KRH375flpzsVkL5pOYylJmb4DnPat72KN7sFXNMlfJzn8hw bBW7FkTvcZj87Y4Q7orls4xVCnJzpPZrrWU0iFmiIu3yuRt0IsYatvcWFuZHHA/OKuZoDPjK9Tid VbGOyRdqqijeb5oC7QZMmWFOqi64GZfG5hcs28iaIGfjpe8s1Y5qrcjbQv5fP28+SVHj3X76hvyG L3yS8Q/5tZ9i382757+XTpFoOWawciq9yknJk/S/6V/0eRrqklVXhfwrgXSGlKJcAflf6tXzl5IE hkL+3+GSvIOSuoimHpDOf8/8K1fTyt04rwS5D6P9kwxtjUTDq9idRfMpyLwJv3UkzDHMmZ2YUMJP v0oXq67RRpIcpDjz8vn49x6W1DQvPGrdvAU+ad/LzHqsqXhEIqd1D9Ph9GYxZsa327x//rbDe/4R xWmvZ9SjlssrF/PDPFd6pmek19Qtb4xL9Un7ngY9qs3oWqEWsrSULE6lY6hdS5bGaC3O0dwEcnJy kqW2oiIyEA8bBJq1wHv+I/fRmxU/e5Qhur+zcyWJuplF/n50Gnu+fde2TBNe5z8nyQfIlvOSHuJe 4am4lRytEXNtSK+Va2djpin1L+Srl8/ZcxTl5LM/M0pekTops0crbduVLmY8cWbE3NuXwzMo3uAt K6116R5FxQMrKadC3YRFeTnMyV89yWX+cq94SIfBC9eS1RAo6uSyxT9H0qus5aVFCjrpM78KVtj5 3dN16NZbi2nZ85dFBclR9MqF+Y9v36Nf4qc+FDy4/VJ9CLODeCsR9/Pzn0vmQpf7lJQ8f5SaQb+S dPnWY1H/peDxXZJzkczsSMjIzkrNU+/bvewQjqrZgq0O/eh3fV1+OsQsElFwJygwgfn5pm2Xvl2Z +hH53LSI+GxhTcJVXVyKo6TyyOWbDUEppPvXmqvVnUv1s5to0jDxm3IieNKkBDgqRnP+6+3Io4Kc py11v5QsGi5MljM85XswVtmc/b29U5/hluSuYzICFCYedHHxoex3Xtq/nPkNn6OirkG3mWQBIEt9 xtx5kUN/+otvnr3xVlejJSUetsD//cgfyeTTSBZBOH4oVWsinQO1Y7+hdKix7K34v84Iupm9MxNB FIzQ6Nvxvv95lb7a5b7g+SGLtkaI5h2QwsTzFKi83MLiguRDS+Zu5Q/bEOqziM19oKhtbEtXN/bQ kaB75DuGLIJw5I+UnhZGmH3QpD64qGw9CCgoKpT/54x/9mxsScs2OmPsrDrL0mt+j7qQMMFWO8sN 4cXPXvz7Hoc3vUMUOOWxm14LUOMmLEDGC/WU2h49etSEG4OqQ6BeBErrdWOrVPciTUz2Sh6SkfO1 /A/xanzV8Ez1uFmUXZQfssVcaid7ly32Uk8X+Nz/t+yM2WQVw/Eu9AKHkpH8D33YhfrYjR7k/+4r ZcP+6QOfJp3YYs/8Tk5RZqs8ziWls6Xlx7hI97pnuLpIl0xnZKxQ1QV7j7l2Lzu39jS/Y+VLIIeQ kt+m3wx2mUF3timq37QtwVFP39budOTYNzfZqfvkwO2RNwuZV85toLNCkpqfjLlZ+CaBXqaR/ABq 7xJ0O+rYAik6eh3E8herQv0pim5UaXbhY/9VZMA/b/CKfb5Xs/j0Uoh0bbkzdp64+ZIuoTArZP8q USpKabGiUv79yE1M07gzvILvvxGdrirhon9vVl3DzL+DDqdkiE9ktfjYXdF1KbvW5a/jR/O69G1S l5uuvm7butRB5mPJ94zkkV9a2kiPN1kRZw4sIx9adutn73osNP6l1Nnf5scHu01nbzqrVd5nYlLe SL378i4dayBH+cek5D8+soj+762RWS9F9X+bErlTdCzPfNmBciW/zLq+1Z5ZlfWdt6Ta/jbZ35F0 7zVIeCLrbdnrD5kFDnjmrtfzy158GrqMDDvq5+jtv48+KVsr6dq+ybrkZc/ENpi2hCYJ3jaWc6Nd UHk/kfRtIvM9VuUB/v7+Ul/4VFJSUv2V/TGW9G/MNtE/uFau5x4UlJY+Df3xl6D0529JZd8+f/CI vJIf42YuNnE8EBp8gP6njf73cef1LHo3dnubFePvxtzGPPNVBw44M3mNqf5O+2Ofle0kJfA26/pO 9q4Xb9puMf8yO+QnhR5YJTolKSs0idzZNWz5SSHsycnN7haclM+c8t8YN8kfMvb7QulCmfrZe13P elNJxclXwbF9ziyHptP+xMorXouWvkwKEbWNIIUkkS9Bdnvob9etYuHE7dQ+tmIMnV8MXbd/s/zL /eHhFkMMHvrbs+1Z4J/FSmGDgAwCze3LTQYa7NpcBRRIw6X/Garjf5PMP6SEupdparpPUpO/IufX sVY4HAJyKTBuaNltEhPz/rdJfd22jYrMfNWItvz6/BJr1FY00skeBcy3WPzJ9hu7bJj1XZktL2y1 ieV23Z0xJ5dWsoBCI1UMp2lgAVWp26T+/q0PCAiwtbWVVJ1ECvT09Bq4JR+y+JLYn3VNv88gVSCR gv0LDVN/tZv31unM0ql92peN2hEm+86aOPtoMkX1sPrRy/s/ncJXzZh3OJnqsCDgltcUej2d3Fj3 mabOQZ1st/930YC2LxKOuP104G8yjY9ECg64OhpLlVXW2NJX8d7TJiwJyiLd8r2Hvu7ZguJw+w3p wj+4aPocP80fT60fq55765jrgj3PpuzwO7B8EDtaspKtINp94mTn8D7OfivHtr7hvmD9DatDNw7P 7MYhSz/7zjKdffQ5RbUf/+PvO/7T6coqy3mHn1Md5vrf2mejQT5BomPVbLe7Lhqg/uLWn25Ldv9N zkEiBSGujn3frXiNLWV3iBv6o+eWEaXhbt+vjx7025W9c3qRSR6PAmYMsz36oKxwsu7svAm2KV+f 9R7fLvNvvzXr9vzznBr2S8KlpQYt+JfWz5u6Oeg51f7zVR4/T/jcYEiv9q8urR+1+5OVC8y66X1u +hnWuPuQd04TPHdz+3JrgpcIVW5sAXmdfdDYjjgfBCAAgY9JoDjz9GLNcR6x9MSBwtxPvj2zfXJZ mECy6oGpoS5mE3xMlw11+agFQtaN7dZOk54L8KJifJKj2U+fzXuoP9SoowLJtNOXefqMf/sOn0z/ E2aG7d4ZRLrX/Q21u2ubmNvM/mYU8+N5tZtC6w6abT5hdyExgmHDhw/rpcoP2bOOLMXaRaeLqmLb fqMc51p3p1L/XPGDx0VBFRMUXiefcHMO51Oaujo9ehhMsJ3Qt8PT3318rjwjQVZOl776bBoGk4FG bT9prWfYl3n67OG9OwKS5Kg4M8R3JzmW6mmo01378xE2C+xGVZvQuMaWCpMD1joTCl6/LmqKGkNs bfpTz87vPXgho7JkscLU0L0+idQVv2NxSqZjxo3oyfT9r4T9deN5KcWz+GHLpklkTObzGwfCMnto 00ELYUutb5eM7KH3uQnCBDV9vPA+BCAAgZoEECmoSQjvQwACEGh6AtnXzwbzr/jsDk19dOWO1o9O 0gMHhILQbT/TqzligwAEZBCg1z4IOrxpRn9hZasnccR/UCl14LVpSYmflhaXlJDVSZNu/EXfcp20 uNwOHVorUJxWKu+X4iD7euA5uiSeinKLNh3btlTU1BtGtyHp+rWYhy/fVtIcsqzJcbKsCZl2oNmp Q4e2n6p20iT97ZvnQuJz6fFYkswAHKVO3DafcMSJAkqL/yWl5SVdj6JPp6nJJUeTNWIVWqlUlxG6 xpa+To1kVoShumh2UG7bsV3btiQv0PMbYdejMysZHsbRNJk4hUwoaKdS/PxZMdWihQj539w8OlXT p/1mrZpBZ29+fubXA3+/JgvK/nlH07RLl15dCDE2CMgqwOVynaW21q0xKkVWQuwvbwKIFMjbFUV7 IAABCJAMHoNmzDKnnqfHXH/Sc4I5yZgo2kr4p51a6FptJcsukBSJHpbGnh/D4mu4YhBoEgJk7YPh M77ftHqimozVJQN7ntErn5CNo6Tc6t0/vR4FOOiQiWBlm0NA5bE8skjKVXrNVKqtitKnn7C5Fpn/ yUrMEjxLOeFQrhQdh4BHZFTR8xTm5CHrp/Tu0ELhsy+PPSR968wc/tO8yiIL5dpWlJspWqiYo6LM ZGCufquxpSX5OezSSafX2w7r3aG1pi0zjS7zZe6TZ3nvziRTMVjwv5Abl5yNXoRt/8H76qMKueg5 qoOm2g/pQMcaTv15PvmfyBI9TWFrLoloYIOA7AJDhgzZLrV16VIvC4LLXg8cAYGPRgBfph/NpUBF IAABCNSbQEvecJdL+VmXNs0yKQsTkNIVeZP2SCcmTF1qUvslY+utdigIAk1X4JPPho/pJ0oBIXxw NjCxsgEG7zav+nQqXW3+G3aDJF6XbI4dMx8yS/1U2BQ7aQ3uLnpN8ZNPFKiSLHZBU81+mm2KVUbt fhAnVcoBx/bPHr4SipJUkGQHoWVvHv1aV1hS8xLEdPJY2bYaD2B36O+095x0bXoK/y2p5NB/n6Ve Pblx8YKzCiPtR3ZRf2cgBqevw+qpJFRA/RO4e9Wxt/oaSh3avd9oDdlaib0hAAEINAMBRAqawUVG EyEAAQhAAAIQqB8BRd5kmyEq5If8Yn7471deveE/r3EBQ0VuPxMz+uyPYlOzy/fOX70sfEO/0/oz 0+HS2zDTz5Qrq2/HfuZG9OuJWc/EAwKY/rVef612Sq2VlD8zKlfMcKPP1HqYTOxL75J1L71YlyQ7 oLdhempq3fXai5IgVA1DlmhlFnPJupf6iKmnZHuTX1j0bqChxpa21jUexCwC9Sg2Lbdjv8FMbQbp qbXXI/kIK/5NKiyI9bYztd8e3nbmFIN2LVRUXpGcDxU2ThsLu/+YkqwQ6SFZKu1bfopAQf18zFEK BCAAATIMDggQgAAEIAABCEAAApUJFGc/yBANeX+RL066p6z5yf0zPj/aWf71KUdYqtSCol6/fFbA HJ5X8IoMMqjwlOLojJ43lcy3f37j/IUowZuCe/6//sIMBaCSf//uW8/o/Crw23bry6wOSHfLXyaH x/OFrfSmrdo0sAOV9U/8rewiYW7CpfB0qmN/p68suqi1bVPp/IAOFsuXTaETLIb//MP2oCzS2c5L jhBwdVu9IO159fIZ2zy2dRWeUq10Jn7N/GR/63zQ34LSvHt+u3/JYPZ/fuS7Kbui3xlQUVNLC9XM nX6drktTbN+5/VzaW5LEITkuS0OnzYsXr94W5mYztRGFIYT5D+6G08+zk7LftCp4225oj0qgVD6f s3h0B0pv+vSBmqpt1ZChAHfy+wrk5OQkS21FRRVmu7xvuTgOAk1WAJGCJnvpUHEIQAACEIAABBpM oCTWXUfh0y62e7LZU9z42baHCpsHoIWm6eQ528OpT968banc6lWs+1RLtwRmp/Dttqt3uK2QeuoS kFVCcbRmeB/Y/R+r9v94TO48duMdnS/tLUY7/bhp0897/bda/pv5vPJ5ACpGDuu/H92DdMs3bzr+ XLuLwotXpSpf/Hju7IHFbU7OG9ChRVvToN6eB723ft2nDbfHZ21IzKKSjdPdYa+/93+selBxnpM6 G85wu0YZdue8+bRdy3h3Y0s3tnmkdcu3uS2ZJ/X0zyx6cYSvvf8gx6r942XbedhPd0wn2GuPcdq4 adNPe/39LP9NfafiNbaU6j5t74nDS8e0p274zurNm+p+jept0Dq/SCkv2HHCnFAmi4EoDKHIHTRx kQkJcmjplD56qN7bzNbRlg553Io8dzW1QEL2abfJdl92Gfh5r8+6dUWgoMHuh2ZQMJkO01Nqe/To UTNoNJoIgeoEFErrb41lcp76Wpjd1LRsofi/It9/oXhcfAjIscC4oWW3SUzM+98m9XXbNiq1gtTP RpUkzG7UuuBkEPhIBUST6Zna1d+/9c1pyfF/n9+LScx+d8S71AX/tEv/gdptSp/fu5pYNq9AuSOX ep5N1ihlN+Uen5uSRftK/33JT0tJzy58S7Vs16N3h1eJmVTXTq05yh26tK86V7/w1bPUO3f4haUt lDX0+uh1Eu3JlJWSXUhPPWjZtkv3bp/xxAsqVv5xLH7Jv5+SIqAPUFDm6upqM/sLy1Wco8xtT2U/ FSdI4Cj3MGYmQUiOVWjZtnvvjoWJmQrVVrwWLZWqv4Kyhq5uD3rBiFcPY27cl6RnIAtDDunV9jX/ bkLys2LSxi6deV3aUo8SYu7nlUo1gW5uXrj7//I+t/h8kAEPSQo+0q+jplCt5vTl1hSuB+r4EQgg UvARXARUAQLvJYBIgYgNkYL3+vzgIPkXQKRA/q9xM26h8EHAvAm2j5clnZutEX7g5KeDLfr10qpi VEUzZkLTZRBApEAGLOzaPAQw+6CBrrMwP3ytgZIClzy01kbm1SK9cANVBMVCAAIQgAAEIAABORMo SLv+VyIVEhjw982wRzrDOii3V6t88oWctRvNgQAEINBoAogUVEctFFzY7hFeVaKhai8SR9V8y83C ux4jtc1d7AerwbnRPtI4EQQaSqAk3l1HVUHh3ce41b4k0VjZaUmCLl8HXU2LjRekX22oaqFcCEAA As1QQM1kpjNJ1HjafcvZdsN6KLbRaINchs3wY1CvTe7Xr5+/1NahA53MExsEmrMAerBVX33hg3Mb 1sS206h0naLafGiEaVGBF42tB3evVFmY4vOVkoLBurD3ikRUPL8w6/zxiGe1qZWM+7xO853GVTLd HN4QhctYF+wOgQ8qoGi0MvXl3QOWTCpyywNJL0tLBbcOTO9HXdk+22zpwdTX4tq9Sjp30E/AD3f3 v/ZEKjN4YaLPxqPJ9TzAiEQlDq09cq+eS/2gzjg5BCAAgVoItNFfcuD6jSv+zuat/22twa103Yda FINdICAW0NPTs5Ha2rVrBxsINHMBRAqq+gDkJngucvQleX/fe3sW5bs7fOSYgdqV/+vF0XU8XlR6 03WE6nufQXIgCWpsjencvyG+0VppO5zILor50RyB1bpfJ5TQ9AUKspJu8ymKZ2UzWId8fSr3Gjth CNOq9KRMdo00srXuOXa2vQbPfKXtoE6KotdImGDZ7OM9jOmj6m2jwwSLxp/RGVh5OLLezoOCIAAB CHyEAp+01TYdOnz4UNOe4kSPH2ElUSUIQAACTVYAGQ0rvXTF2eG/7j589c8jj6aeC37PTnJe2Ob+ MxNdwn536FWfXYN361uQfHn/T6ueO1ysl6BDk/0oN8OKI6Oh6KI3XkZDYd7ldb0mbOVTJqvOBv88 vANFUmotmWDrl0hprAmNdR1R+TyjYn788V9+WL39iqboqHr5sAr5sb//stJpe3h1p66XM6GQJisg zmh4g6IWDxggaYaOjk7nzp3Zp/n5+XFxcZW+lZmZmZqa+u5bZLnxwMBAyetz5851cXHp0qUL+8rV q1el3/3222/r+Jafn19iYqLkdNIFNvRb27dvl5y3wrnq/taqVaskhZMxz/b29pKnDfqWtbX1kCFs fJPepM9V97ceP37866+/SgqXLrBe3pK++hUKrOVb9f75rKbA2n8+6/5xqubzKX2JyaWRPlfd32rM j670uerl41TV57PCjdBk/wFAxSFQHwJklcR63Nga1b1AE5O9kkd2UWkjP/hp/vZzT0YEreFSUz1u FrFn5988YM42b+SBqMI3N6/6LTbjUWY7Q56+zS56c/OK1zQexXXwTyhka/s29Rxz+NV/Tmyx59KH WS0+dje9QlFS5dNHFWaFHHMj5dCb2Qb/+2+y6UwHzHPeGv/st/QO+1cNpiQnept+dSd5KtroipWz ktSZuyI0lTTh/vVfV1hRVD+7Yxl8adWnSf576GLJaQav8Ash56XfLYraM7ViyYVZwdvp5nBneAXf zw7Zf0b6jPz7MT6ixvIGrwq5Wb4yjXwRm8PppG+Tutx09XXb1qUOMh9Lvmckj/zS0kZ6PA1dZkJz kc555tu3KdcPLiM3FMWbvjMk/qW4Dm/z4y/5H9m3atjgVWefluaLD2GUectCX75T1bcpMaeOHHGb bmLlfftl7EEyGMHK++5bdreXWTF7VtHfPMPcQo6sor8LpvtnkdczQ1dpSO58ErYgJ2IRmLN7M4dQ ZFBDSBaZItFIODjRxycgvkfOSz4sDfMfSUlJkluYTPKVPknd33J2dq6qwIZ+S/p7qcK56v6WdKNI 4dIFNuhb5AJVda66v0Uut3TlpQusl7ekP04VCqzlW/X++aymwNp/Puv+carm81nhpq/q6pPd3uOt xvzoSp+rXj5OlX4+SeiTFM5ur169kvmPExwAAfkSaNhfuxvmb5IGLrUges/6xzO3jlN8lJotdSp6 sgCbofDLLzrdColRmfjdmtnciGu3+QXZcZdS2kz9zmVqtu+xsDR2rnJOwsWQbB6V9k9ml3n/u1mY 4eOQ5fX1+sAU+l1m3sGLkC3jKam5CSR74paxE39L1//uJokI3PVoeWjX+XQhp5fdmcck6NDdbqQh Jzn4UIRw5Ea/c2uokJjUAjIxmaNsvMyfDmEwEY0zjtrlrydzoqf+K8ZPHdmPk/LXsYtC801H/Fd8 GnojTbxesbAw2XeJge3xvEl7SMe+8NrcZ9tnbTgroCc9M/MOnl53MSNNHtyDLlmYH7HLwUN1a9qb m/smF/531qx7rTuJzkhGYWy0HfzrA8MfrtFRFdeW2/eHiiga+HqheAg0mkDezfO/x9JnE2y17Nyi he7A2b/r+ie/ydq3bJSOGlMLetCBnpGF7Yz521OGjzYiE4I6jNgUzMQX6P7/400j2P0kmzDVZ6yu 6eQZM5x/1/qqT/pP42eTBAdpL5h12MmEBScrU6fb4yOe5//UYsuM7XxSyNA+dORRbcTPsWywYOqB +Eh6dAN9cn601zd6Y//3oM93oSn+9hQ//PClu/QXBTYIQAACEIAABGoW+O2333qKt0ePHtV8APaA gFwLIFJQ4c/2zMidF7ptcjJUYWR4Op9xW5b9Tc9kKJzY7cWt1mbj9Vo9eZCazevR5mVciuqgobrM 3/+S/fNuXjoay7X6et7scdqkKE5Xs68ncqn0NMlM5ry407vixD1wiiLhCYf/3Bize9uy0fT+r/Jy i7n9unVguufJZ3bcdRjJObMtnGc71UiDos/bV4fH1pB6fT8quJpsCMKU07tumFuonNsZ3MF65hdc 6tnDhNzeuppsmkZh1ql1Y9wpl9+3LRnCJeUx9aTK4h3C/JhTeyIkSRmL6VNTam1UFClO58Ez7UaL yhEWxnk7jb0x6g/PhSP0lClhUf7L4vJ0cn0ToXHNRECYF3/xkIA0lv0N/03WJS97yttWb+Lq0/fE KQo4asM3xR5ZQH8ZTB9pyk5GEMUXhn5VWXJTjo7jebpXT1IkGlIRe7bT5Y93MtNWJPMaVk2f8/tz c9f1TkbtVHjdPqPLkhQirozlmKFabCaU3Nhd8waueb78hOcy0/b86LAL0nVoJpcIzYQABCAAAQhA AAIQqCcBcbateiquiReTl3pob5TZgpWaJDrwjJ+STvUdI+6Qi/vkZj1s1LS+IHEB4b3rf0RyR/R6 WaA91pR9GmfusolZEFGYH3fxJH/q9yvGaVQeimF3GPq9qOeQm7B/00ZqUcg8U7oDL8yMdPtxT9f5 AaZMhsKCrLT0J3FbT83933Y6fiHMoE/05SbmR36yc/mnFS+AsDAzNYO6tuO3Jf/dPYAULlqOYSeT /4zkQdzkclTXMcS2j3h9BzYWINmYkREjF4qTMrbStl3pctRuxw4LXZdRXF0HN11mz4IY3zXe1Jaj DsbqdKmCUM81R7q4HDTC2pBN/H5A9csLFAsepJJkhpSGFTNYgMMznfPDj5f9Fp3cPmN9z3g/Rx1R j/1uTAyJJsyyNGDCh+IufY8Bht0rT24qLMgVkJkCffKSXi3NenmWx9ycBfH/20WnP1i0ZFp/FYoq 4adfJS+XxQVyYkJDSGW0LQy1xPuvXBfEs/efrv0kzO/A5kXe1PSDYavNKwxhwDVtngJtKWpA/eUp IIbSKQyMjY1bt24tgeVyudIDoev+FpmcXFWBDf2W9Kelwrnq/pZ0o0jh0gU26FvkAlV1rrq/RS63 dOWlC6yXt6Q/ThUKrOVb9f75rKbA2n8+6/5xqubzWWFiQlVXv8J3Yy2PasyPrvS56uXjVM3nk9WQ /lA1z3870GoIIKOh5DNAfhj3nDlkeZT0h4LM/A8UD+kX3jtqPWJbZ6+Q3Tak/0/WOJxusK54le8e 0mcWPQ22vulnp0s6A88i142xTVhYdixF1hq0H7xRx/8f16F0/7n8DpLch7acqNDLl3b/HPf5dvfv J9ODC0jQIXzdkLFnLI+d3WHdjX5On1dyoopP3/lAMyc6+oVP5M7xdPiDqcYfY9iKsU2g9kjnXGT3 txLVs7KkjMKsgBVDF2csOXV4GR16ENcw9fubB62p6PCoc785/WN8zHO5da/3Xl0St2UtBZDRUATV OBkNhfd8bEbMCeWTXAP3RJMIJAkOte2PXPKd1JWuT17YahPL7ZQkweGzsPVjLD1ipY6qcHlfJ/vZ 91wUZz59tbvHHBNlJgQoTpQoPordJ9LKO+ycPZMhlT2LQJJYUVQ3puh+9q5rbMwsJhoxMQdszVlA nNGQNiglmT2wQQACEIAABCAAgdoK4C9JkRQZh7/hf93+K0nClx26mGQoNOwunofP/LZ/W3Pq12bM MAFmzD81dOrMYfSg/QpTAOipB2/s5lqKfvan/6yPOrox0tzFnhlxQLrop3ftoBavmMSkFWDHF/DD nXrzbA88pDpZeMX96WrDhAnIxvyqb+Y427Ir87zCXAN6yMDdqhdiJMOeLx3NGrzE3pwOE0gGIIiS DtDHUkOtpUZEC7Mi/On9JzPDAV6n/bnHi5q9ZIqe9KeEoznZ9Q9nau0m37jcshpSJ5cZ6Mz0TaHU x7o/PfcjwgS1vQGxX5MREKZHHQ+l10fU19EkP/Izm3iUAaWu0UaJeeV18il6BsE7Uw8kQwzeaW9e 1IHNJylKSWuClREbJiBbQdr1EJLsXXuUKTOvSbTPO1MPRKMbKOrJnct03UjA4mFp/i3fpXbWCBM0 mU8WKgoBCEAAAhCAAAQ+RgFECti/yxOP7X4yc+vkKiYLkD2Y/jxlZWHMzAigCsjcBO4Kp4n0CIKK PXBmz4m2Fmzfnmy5CQd2nhyx9SdbtsvN9PZ5kqLYfUwWn3uaff5nO2trOuWBMDPqbHw+3UMgXX1q 8ZpZorwJorkGbFefbHQcgZIOZ5T7jLF1nr1iDjOpQTL1oLLJ0szb7GSEBSum92cmQZB5DZFckklR NNc6fO/he0xuNI5y/7FTR967fIvJe8hu9NIMj/90nTfe2pzEOISCq8FxOeXqgicQaNoCr1OjgkPo JpSlGxDy//qZ7uRL5iPQd01kQCTTw9fmpCYkF5aIUhuQLn3/wvAr99/JLiiemzDMcaEoGkiXV5IW 508nLOiq35UMG8+7d+oInR+BzF9onxR2M5fc+OzUAxKPMC6MDk9/LZqbUBawIMcWZ14+H88kRsQG AQhAAAIQgAAEIAABWQUQKRAWpgRstl2QNvJLUW+cJizOjrsSx+dptxf9si/qk5d1m+kO/NSRBqqM t2jyv/6bv+P4Qrb3Lt5TKIg+vu6rWbfH+rpO12ELk/TAObeCAu8UUhxV08lOZrlxFyPS6CzlpD7n dy/cektDg04rkJ2RWBaeEJ9o0Ke3E8iJRHGEqSN7Foafv1VJhnM66YCkJpLxCAOUU28JikmHnzlv +rWoVHodhIJ7QevnO96f6OO1bKgGPQCBaZQmvWhCwtmg5DxhdnrYoeNR9IHCwtTr1273Gq7PxlXa GdnMGMyPvXQpmVlPIS8tzGPFhn+4kp9dZf1IYn8IfHwCQsGVg8dICECSKaCYH39ozZzFPnR/3mrN b4vM2eFConEHSlRqSMirbnrKJWxqA56FWtrBCFVjegJR+Y3t8/Osvh5bNqCAohR5Wsxy5/lP814V pN658yKHzo/Q5fGlK9QAA3VK+CzjZhaZZTCq/d3fLnxiqtVKUdvYll4HIfbQkaB7JDpAFkE48kdK TwvpMj8+VNQIAhCAAAQgAAEIQODjFWjmkQIyb/9rLQNbr4gor7FjNoc/Y7q6YZu1PjUYuyGK4ket HailNO0oWdqQ+W1fHBqoML6AmQJAvUx79KmhMZkY3M7Iwf2n9r5DlBS4Sgo8B/+3I3dd+22REdP9 pjd6FgM/+6jvoWglc2smlaCKqYPXduMbiwd3bMHV+sb31qej3X9dYEqKokcf3JWEJ0gXnT1R9NNO +pIZyG+exF/P7mWhL4lpSD5sdEgiUxLOYMdBUMVJsYL2fdnK0Of9qfeV6VqkqgZbHxivifJ3Hc8u 4iA6V+zJw0di6IUeVApfGf0cNF/55DwDpRZaY8J6/7aTzV/IrNQ4x/2YSdzXvUk5BnP33eGMd90j 1d6P98OPmkGgNgIk0YBpC12rrVfo3joVOqdnGwUF1U81zey3X9Ff5e0fGn9yy/DOom9SZfUupMeu YdzfbMJk0qUnd4d6Jx59mKrhV1+JchBIn7OqZRE0RnzrOp6isqJDz0ZTvG7Uv3T2gdFjJ1nqMxMf lNQ7t6f/v0X/GTNN6FfUhn73u5e9BsX/fXZvjbGrD99Vn/y1ueQ7pzatxD4QgAAEIAABCEAAAhCQ EkBGQ3wcINBUBZDRUHTlGiejYVP9mKDezVgAGQ2b8cVH0yEAAQhAAAJ1FGjmYwrqqIfDIQABCEAA AhCAAAQgAAEIQAAC8iaASIG8XVG0BwIQgAAEIAABCEAAAhCAAAQgUBcBRArqoodjIQABCEAAAhCA AAQgAAEIQAAC8iaASIG8XVG0BwIQgAAEIAABCEAAAhCAAAQgUBcBRArqoodjIQABCEAAAhCAAAQg AAEIQAAC8iaASIG8XVG0BwIQgAAEIAABCEAAAhCAAAQgUBcBRArqoodjIQABCEAAAhCAAAQgAAEI QAAC8iaASIG8XVG0BwIQgAAEIAABCEAAAhCAAAQgUBcBRArqoodjIQABCEAAAhCAAAQgAAEIQAAC 8iaASIG8XVG0BwIQgAAEIAABCEAAAhCAAAQgUBcBRArqoodjIQABCEAAAhCAAAQgAAEIQAAC8iYg h5GCkjj3gUoKXNFDc4rLhWyh+LKVxO7uK3nLYndcwcd4PaUqOdAjtqSWVRTyEwLdl2ixrRu9JTxT 0uhaFoDdINBsBB4FzNdRUFV456Hv4Hk0MJ5fx3unJN5dR1S4hXt8w3zJCAIcxPXX8az1t0SzucBo KAQgAAEIQAACEIBAHQXkMFKgaLzyelF+yBZzhoYftX2566HEQtZJ0WTh7bfpVz2nbbmeXnRpobFK LfhyE7wPJdS2v16L8mrchVQy1H9ajbuV2yE3wXOe1dfOl+1C7111606FeDp6R+XVpb/T6K2Wrb3Y GwJ1Eehqsy+1NDN0lQZbyAL/lH9LBXf9l2n6rZsx2cxq3ukHdbl5FI2+u3JkQV3qR1HCgnhfn2qi DBo2/4s/YFW3czTi0TU1pxGrglNBAAIQgAAEIAABCNRGQA4jBUyzW2lo9RS3P/GE0wrPst/YOcqa Wr21eMq14aGKs8M9N+zOrtW+H3CnkrSrvwWR8yu1b6OsoTWYR3GtTHVU3vviNpFWf0BwnFoOBFq3 6agk1QzlXjYLF9jTLyT6rNgbXqdAW111hILQLT8czam2GI6KuijQUdezNfjxtWlOg1cCJ4AABCAA AQhAAAIQkEXgvTuTspzkw+zbarCZifjM5Df29ceS82SsCOkwb3UauyFKxsM+7O4cTRuP9NKbe2w0 3vPaNslWf1hznF1OBDp00+/BNEWQk/uqLqMK6uQhFFxY5+iw9UpxnUr5aA6Ws+Z8NK6oCAQgAAEI QAACEGhYgffsTTZspeqndLVRPx32XyUen8v3WbbEI1JQ6R/feWlhHuwMf4O5HpdTmICCkB+/a94o NkyQ7mylqsDt+1PQSSdx+gOys1PQ47+lsh4o0DkFBAFLmPwIZfkFCpIjfVdPYZMmjF59NDxZNA+C ehQ0V0dU2tyA7ILkyx4OBkqaUzyixTtIKUinVyA7l/eh8zKomm5Mp1/NWGvaua+7aK4EyVxwdv/m 0ZrMWTSnrDuUUK75wsKUsKPrRovf9YkkDa+k1eLSqLy0cB9xaaM3+4alFbBdqZLsQAmLU5AgR4Q5 2iOB3kFiO3pz4B1B+NFgQWNO5KifTxJKaSYCwoyEC/eZtmq0U2/NfjcW8+PP+qwfLcpoMG61b7ks BsKC1DDxu5oW633CUisPR0plLtBxOP2IKZkce959vj5dsq6D+6VkJp9BMT/G+5thVluv8Ckq3NlM VUFVpkwH1dRHcjpSz4BkfrhPUIVqVHhd+pqX8E87iXM6OAUIcpIveTjoKiiM84gtZL8EqlKqpjl5 okJUFTTne1yowq2ZfPDQTAhAAAIQgAAEIPARCshxpICiOF2GuviUBQsiNixc93uqqH8ruRa5CR52 g8e7PV5yPb0wY6uiz7TRa4OyiikOz2iJ69YZ2vR+Wm4h+aXZt38YP3X71T2OXPqlfnbH1ozt8sXC 69ddzHjkOdfBP3CpiaKGjcdVL/s9t8KWmSiSVwsSj35na+t0e9TVF9lFL0LG3F421vZ7XzZpQtfx O/ctpg8lfe30mDhhr+H9tUlWhduPK0mARjIXXAtdzLNafOxu+m82TAXKNjovQ36Mixb9SvctMZm3 VxqScwsfBC20stpVMMn/cfZTUkkqaoe91bzDaaIfSoWFcZ4zDSyX3RgT8rQo4ZhN2o45tsv/SKPe bTVTGpWX6rtsyth1cWNOpReRRA9j4pwsp3x3iMFU5Fq7+q9gh2/kP7h+m+ozqLcuj4pIelwgFKb8 sXa82xOXu/yiM/M7X/rJMeLtR3gToEoQIAKF9075HQ9h7m7HHQvM1ch3Y3Hm6eUmZntyphwvzX8R 4zqeurJ9ttnSg6mvGTAy995zopHlnJgxMYKix0dskjzmWK78I7mysQiKRsvjzq7hDVvjHx/nO6mr +Ngxzo8d6WN3qO6cZLuUTo7Qkme6YNsONseBuVtEfmn+pZVGtUmnwtQn+dCisWx93pYKTo2PWWc5 dpkPO5ZKmHxi5TfOT5YlvcwKXcgNcvnhMnsrVvV6+e8Y3iTX0GWiezw9+jbVe5A+ucevJD3IJ62t Rqmq5uTGetr1nOT2cPH1/JcZXp/4WI1dG8CXkzEUuJkgAAEIQAACEICAfAjIdaSAXCJO56HOOzxm 9GOvVvaRNc5uoWVLIZC/k1MCtqwlM/x7jhreR5nD7TWoN8UP8LuQXsXIYzWdWc7fjyT9+8Sjv4Xe JzsJC56nkF//qGzfY2FppP/w+sGdltZT+jBJEIoFoXu2HUmktMyHGKhTlHrf4ebdKZI0YVNgCtPT aN2mPTtNOiKnTX89bn+buQ7m04bpVdItEGZG7tiT47HvB+tetUuvQAnTQv18E6mII6djcigVXreu rekTXYy+94T5SV+YHOjiRoZLdB9j1lelVafPR1iS1Aad1asqXJh10XujTzarRHGUDcxGadGYP/iz 3aJWqu1VmZYkPW/TW1vD2GahTfcZg3qqCJ/eiQ6n+HevXE8vUOSazvvBw6SFfNw3aIVcCey11f1E QaO3rUeWveuRUxEh+yd1o78ZhennfQL4VNDOP+PyKBXNHl2YRsddvvOUvYlObHQLpyjt0WaGyq14 n48Yq0HxqriJyET9bR6vvA5ssNFRq+TYPsb6JDmCT2hqXWY8CB+FeLr7Cdj6cCjlPhaje1ICnzmr mODFkzuXQ/nU7auRqXkcjSFLN67pz96KVb1e8fq2aiO+x5+26a2nYTzdyUZ7+qA+qpwalCr7nAhT A9auo791x5v1UeFw+w3sTQkC9lb5rStXHzU0BgIQgAAEIAABCDQVAXmPFJDroNLP7pffxcECZimE 3Zeei67P6/tRweRvffI3azfNVuTncRX1dmS5hPA/ougoQKUbp/vAL4fS71wMvp72Kj8uTe/EGWZo QGRgVIZQmBHzQteQ/jWSbNmxZ4LpmQJa7djcgoqaWgPo/4+7xvY0JNsIU11yCKfb+D2Xdjn0q9Bd Fwqu7p1vvUtr0w5rpvdSu43DG2BNx0c02qvRQwIqbMK0qMCLJMDB696OdCmomlIblDy9EXKUjod0 VFdlSlPs1G0QGW3BD79SoSWmxr3UKaqlhrXX9d8cdFRaduwzwJwO0MweYvDN7rAMVet5YzQqqU/t 2oS9INBAAszaB/mlpfm3fJfaWRvxRDcaR3OwzVgexevZvpLsoML0qOOk703xtNsyNxHP5kBKaZa3 TeeKdyk7An+/jvs2G15LtgHiY7WH9OhE7gdRbsLQ4Mh0drTCe21PYgP9EiX1IfE7zR5MVtfQ6EQS H+zUZzgJB5LAgckQB8/zqWrjlo0nQxuoKl+vsgqmA3vS93jnSV6p+xx6kaZXq1RZMa9To4KZsRs9 tXj0t64q860bEhBVp0DJe5nhIAhAAAIQgAAEIACBqgRq3/dsyoZ0sOAAO02ADAc4sSPwiag1JQU5 bKd9r2OPT7hKnxh8vZd+lp5TcY5CWetb9Rg8xpx+SkID0XG31EyNv7CwI+Ny6fhCempCrn4/9ud1 quTJg2tp9H9w1cv3M9Ki09lf9mveihJ+XTp46PojseFHz92quk6VFESa/Nut7ML9Q7JOblnnFf3o lfQ+wvycDPo5PyNHNM+42qq8FqQnMTu0a8NGCiTbtfTq0w5wdGf+esVrGoHn+20cbz5zXYA4u0HN bcceEPjQAmq97A9k5WccNXtyYuMPv/zN5jAQbeQmYm5vftqLam6iZ/H/nWNisdhPEHko6KZkYpH4 2DS/GZ8xeQps/eiinubkV/rFIJ0mQIHJF0CSBVTcs4SffpUupLWGOh25kNqS0vmvKU6v2XtO7pxO ooeJfuvG9Jy6LoBNDVDV6zLQV6dUWTEl+S9E37r0UA7VTzRniL516akM2CAAAQhAAAIQgAAEPg6B 5hEpINYqA5x8fZeKggXv2pu7XM3PLioVPdip/lVsHO3B1vQEBH6479ZTbQ17cNoZjrSicwdcPOS5 M6uvMfl9jNlEP7yXlVKSlR5NP9MeoEX/kFirrdXEFSd2Dia7Rrht2B9TSbLDKkspzo47tHmsCUlV MP77hQPY2QfvbBm3H4iHV1RTHeklJ5ndJEGQQVo1DRFoyTVd5Bl1/dcVJLUkP2rH4rWiCQu1aj12 gsCHFRAKon3XT9QkqQomOX/3BbsuQsUt7faD8mOEyu3QasL6UyTBAfm6WLdpT3xuhYO1XWP+pccy sI+qUhIo8ibtEe/D7kmWNqn4FaLI0xpSrvTXWffZAB/70z1FJh0s2xMSs2eVOXlyZautOKVCVa/X Xr42SpWVxiZiEDf/5komuQs2CEAAAhCAAAQgAIGPQqDZRAroP5RHfV8xWNBay2gQkyAw6fKtx+If tIqzExKlcxlUvFCSCQjX2nw+sDsZfatqPHIqPV4h/HIHQ/HUA/K0Y69hxvSxFX94Nx7Up2MtL75S zx7dTRftObdhMOlmr52zLpDkPKvNRhIWejsNsfeK0Fy8ZpZh66KX2eXGFCjqGE9gB1iEhcWQ9I01 bIrsJAKi9CBLenQ0z3xY9S0R5kccD84q5mgM+Mr1eMgWpr9UccJCTSfH+xD4UAKF0TsdJ8/2COEt Wz7fSCU/N0e6IoraxrYazAuXwq5VmY2vQ++uugOW7A1dSSJlQc7T1rF5+yTHpl1KkOREEQoS4ytf nKV27WfnF1BpV++XH7JkOaAfiUzmXfUNeiDk8ExmbD0TsZO+ndlZCVW9Xrtz0ntVq1RZMa11+w9i vn6SLkh96/JvJvJr9+1W+6phTwhAAAIQgAAEIACB9xaQ10hBgSA9LeXR8wp/eXI0LJfu2koPhhdt HFWzBVsdyIhcfrjT0m3hmUKKrB0YJ+DqcWmYtl36MlN5i3LyX+WmRcSLwwfiCQgjxwzUpn+so9QM mAkI451sjEVTD+hXW2nbrqSnPKSHX72ZS1G5ty+HZ1C8wVtWWusyR716+byIqUd2blUTC4qevyyi WnLNl26gu9mJR7+e/1PgvUpGFjx7cJddJTEsgYklCAseJdHrO1JZiQ8yBbeetxnOzFiWbGpD53ks okMkfO8169n1IPJSA88yExwqaTVH12YtXYGkC5fvFBKimxEXyOnMnNfa6jEfoNf5z/OZsnNelhs+ zVFSeeTyzYYgeuHJ1lyt7lyyZsREk9qGScrVGE8gUN8Cr14+ZW9AKie3oLJh//mPb9GrFVL8mxlZ /NRc9b7MUijiTW3otzsW0d8lAu/FLr/fo6cg5N07fTa+3FyE/Kd5ZOR/5xErN7sNI2kCvG3nbKCH /UuODV23YPMFuodcmBzBb2+oQRIZKHbsqsOciBz7qiD179gqwgfCglwBU5dXz1/SgUCO3jQXZ3MS KjgfkUDqUHjn0nkypmC8m4sNfZe2bvV0zfw1p+8VUJzWGt20SPJFe6uBJIJQ1esVsV+/FN/jFa2q V6qkOSVqwxZ42dPfuiGLlq67TH/rFqTGZXXSE+eHqO8LjfIgAAEIQAACEIAABN5DoLReN7YCdS/S xGSv5FE2KUAyO6Da/8i86ta9DGKBz/1/y5fwNj3hiMvJh2UvPk06scWeXXqQO2PniZsvJW/x70du YtZN4M7wCr7/puyQwrseI7XN95DF/9gJC29Tz63hjjwQVSievyCpoVThlNkqj3NJ6aK3HvqwSzCy G7sQI/uWeMlD9h164cP7/tPKdtWedkyq8kWl5dtL9qObzL8fwky1sFq850zI/Tf8NH87uk/Tb9qW 4Kinb5kTvbl51W+xaDoG2S1U/HppFa1+GRW0Uxxkkd7/35vH2EXd2K3cPI7Mv4MOp2SE7F9FT6Ag lSGrPNbuIsp60Zvn/tK3SV1uOvbK1aWED3AsqbDkUTaAXzKSv/r/eOg/vVyvn25/D7eYF+WPevmY GQvAM1+271RE1tuXGf50/5biTd8ZEv+SGTb/JivCbxUJATAf71XeoUlkecL80n8j3KRKJ2PsX2Qd kb5H2ASKL5NO77RnRyVo2LudTsqXtOJl1vWt9nShGvY7L2W9rbR1Kf72UnedeBbD2/z4YDc6GQFd TfNlB0JF9SwtfXHr1Mmkx6Lakrf82apW+Xq5k/5bvv7lZw3UqFRpcwRJIa5MG8t51vLyYbdaC0jf Jh/gLsUpIQABCEAAAhBowgIKpO5Sf3DW9T8VFBTYLkcdCzI13Scp4a/I+XUsDYdDQC4Fxg0tu01i Yt7/Nqmv27ZRkZmvGtGWX9cvnEatOU4GgUYTUJW6Ter873Kj1RonggAEIAABCEDgYxCQ19kHH4Mt 6gABCEAAAhCAAAQgAAEIQAACEGh6AogUNL1rhhpDAAIQgAAEIAABCEAAAhCAAAQaTgCRgoazRckQ gAAEIAABCEAAAhCAAAQgAIGmJ4BIQdO7ZqgxBCAAAQhAAAIQgAAEIAABCECg4QQQKWg4W5QMAQhA AAIQgAAEIAABCEAAAhBoegKIFDS9a4YaQwACEIAABCAAAQhAAAIQgAAEGk4AkYKGs0XJEIAABCAA AQhAAAIQgAAEIACBpieASEHTu2aoMQQgAAEIQAACEIAABCAAAQhAoOEEECloOFupkoX3jk7U5Cop cJVMN4c/q+dT5oVt1iIlKxisC8uv56JRHAQgAAEIQAACEIAABCAAAQg0OwFECiSX/HWa7zSmMy/9 0JyyzicyJa+unwtOL7szj1PPreFSWtqdVepaWoXj1Ub8mP7Uf8X4qSMNVOu5aBQHAbkUKIj1tFBQ Vaj2YeEeL6hpNx2H048IUEm8u847pel4xpZUxKv2vONW+5wOTy4UyqU4GgUBCEAAAhCAAAQg0LQE ECmQXK9W2g4n+Gn+djyKGnkgqrA0u6g0+2nYXOq47ei1QVnFdb6uOQkXQ7JHjhmo3Uq2ooQPgg9f rWGwQN7NS0db138MQraK1nLvYsHZ41F5Ut2h2jSwlmVjNwjUVkDd3jU4SfC2NL/03wg3bfaoHm4x L0pL89/mx/uvGtaSolRMll4qfZnhb9+PeVvb/shDsj/zeJl0eqe9huhkikYrU/OLkrynip5bHkh6 WZq61ESxYmXYAu8esCTfMmQzd4vIp0t7mRWzZ5X5le1zZlj0nOoZi2BBbS8i9oMABCAAAQhAAAIQ aCgBRArKyXJU1NtRPPMvB/dgYVR6jXX4ypzvvWZ3ZF0H9tOd+ayykmt7QYsFZ/bGdelZ7WABYX7c xZN9ZY9B1LYO9bmfMOvsjhsd9dUkH7zaNLA+K4CyIEALdJj1w5LResqVfgFyVHQm//iTXTtWitO+ m16Hd9DU9CyWep9YJo4VkPcVVcmXB7t1Ulet5puVo6zeqXX57x2eyQznH5eZ0C9e8f41NBPXCAIQ gAAEIAABCEAAAh9WAJECaX+my80faj24u9hFWJiZerceLtG7Jdem0Ly0MO+f1lJmxuIeSOUHkdEK l3tLohu1KfjD7CMsTDm/Z/1eNQvJLIlaNvDDVBdnlV8BFZMZNnrVfflxVIwcHI2qmij06OzphBKK o6I/dCAZeVA/W6s27dl44CtBLgYV1I8pSoEABCAAAQhAAAIQeG8BRAqk6d6dIMC8Qo13sjEW/6pP urthR9eN5ippTnE5nxq21kBrbaT0WPqC5MgA9yVsisG53vECdtqCuOROUWz2Qeah+ZXvPXYUvlAQ G+ThYMC8OMXlQjZ5tSB69+heg8cvP5G+1Zb79dGU11VeY3q0Qmep6AbZMS8t3GfzaCaH4ujVx+P4 ZWP9hfz4XfSJmLo9STh8Nk1UgypeLzurMD98LVtDUu0SQfRxGoGU45taID2zuiqf3ASPSVoGYzYe CfEaa0A3vKoGCvkJgSJA7uiNkYJiYYrPV6zYRB9SW6Hg6t65+gbrzsaL8kqwSSKLs+MO0U3WWlwf U0Xe+4bCgc1AIC8l+iGTgkDR0GZM13pq8OuXz9lxS0O/KotU1lPZKAYCEIAABCAAAQhAAAIyCiBS IAVGd7nf2M21ZKceML33FQt3vJm2Z5uDsTqzX3F2+OaZo33zJx/kFz0+bP3Q8+cTlN1IQ9FYetJJ Dthsa3f8gdbcqDfZRU93axzddj6d7kZLph60GfFDVMjSGWs8ziWlF2Udd+jFocvcaDv41weGP1wr KuXfdG25fX9o2mtKZcDCc2EeI7XN99zlF52w060yu4EwOyOR0vmMK/5xsyDx6NwhUw4XTfDNyC56 k7Cw8KcvfzonyrMgzI/Y5eChujXtzc19kwv/O2vWvdad6MZW9br0p4mjar7lZnboYl77Tk/P7vP/ 13zTeZLWwTJszQ/+yeJQQTU+6obL/ojaM5XJAcE0vLIGCgUXtoyd+Fu6/nc332YX3vVoeWjX+XRK 1/F4EUnZOJjM3ej2JDY0S6WXhqbl5/qGDieySX20rCyMFdPO/h4uHPnDycOLqb/jkuucgVLGuwi7 NyMBYWaYx89H39Zri4X82CNumz1iqWGrDkZ4ztaRMZVJvdYFhUEAAhCAAAQgAAEIQIAIIFIg+Riw EwQ026X/asv8gs3rMTuYGrf75tVdDv2UmdBBYZy3k+PDqcG7FpjyOBRHSbXFkwh1yYoDwqxT60a7 PLE7uG2ZjaFGS2HK6V1HjexHaXFIP1w8qYEShO3//dVXv7jameuVlTn2xqg/PBeOIK8Ii/JfFvNE 3X5hWlTgRePygwXe/dC+vh8VfFcSrRA+CFo5fRu1MuCX/xhpkNhBSw0Lm6lUgN8FJmBBFT95kJpN qbVRUaQ4nQfPtButq8lUo6rXK5yObQhFdRzjsGgIlxBo9Pm8L3U3JauwFj6UMOP6H3HSmRoqNrAg eo/Df26M2b1t2WhtFQ71Ki+3mNuvWwf6M8qOm9B/E52laWlsONz1vId1Nw71Ou3PA3Fzhyv/6XlR 0/orUx5Fgib8BlhdAl8VEKAF0vxmfKbQpoule0g9eYQ7m6nS6y+00TR1+kvrSMbbv352MCLfLdgg AAEIQAACEIAABCDwgQXwR6nkArATBL6yW7rjT7LqAf24tWvZV0N11UR7FMT4rjnS3WP913riV+g3 JP3SZ1G7txztu+y7WWxYgeKQX8LTvcZrku46W/Lwz14cWPqfjJFLreluMLvRZXpTW9azYxaEglDP NUe6uNgZ0YMU6BBAeI1rJdDd70xxtIJkB3Rb49vNaaG1jvgU9IgD0rcXba20bVe66J7YsSOUTHDg 6Dq40YMayFbV6xU+nWxDytpIFWSl3aZ6s+GGGnyodwIfFRqYm7B/00Zq0YZ5pnRpwsxItx/3dJ0/ 3ZTkaGAiFLo9qDwlQ2PpflQBPyWLCt7ppz57Hg1YO7EPfMfh9E1XgFn7QHDXf5lVPbWBrH3w/PGR RcxCCIk+K9xO8eu+xko9VQ3FQAACEIAABCAAAQg0bwFECkTXX5gV4V/d2gTC/JhTe1KsbC26ismK n9yJuyvuydMjCHZQi1dM0n5XVPjsYUIWl3qYFhF9OSGVXzarnykzwnjqhH5USniQ72rbHu5vFx7e xg5hqPALvPDe0YlM3gH2IU6OQHe/bw+3YFMeCtPDfgvIHmkzuj8dd2A2NiOjpuiXefKCygCn/23v fsjByTOaGQgg3qp6XXof+of9cm1kwhDGw/U16HET1fpU0o2v0MC8uNO74sztxupTqZGB+zePHb7j 7YI/f5nFhDzoCAUZHtHbWIeNwog2uj7haT0WrJ5Ixhe8I9a8b2y0vqEElHvZrFxt10LW4l8nB52X zNKROrhl50muZ1zH068IvBe7ns2UTvoh60mwPwQgAAEIQAACEIAABOpJAJECFvL1/QvHjpZb9aAC MNNZLUtJQH5C/ydgd7B4xQG2Q175uHf2t3SnjWtmWQ/vzQ+5FJcjLppNl3hymYHOTN8USn2s+9Nz P1r3EnWG6Z/rpaYecHrZncliRjowj/QtQ+lxB8x5++rw2BEEzC/85RZiFD6KOHYm22zGJPqXedHG 0Zzs+ocztXaTb1yudCOrel2yD5MQgSQFkBT1LMp3910HRxs6MFG9D105fkpmubqVayA7r4Ef7tSb Z3vgIdXJwivuT1cb0eALJstD7y/HivNBsDViDxnvNH8ECVTQz2s1WaOe7hsU05wF1HQHfKYo+hTy wwJvFtSMIcyIjHldxdKJ6iZz17sNowcW8P1c1h5OrEVxNZ8Qe0AAAhCAAAQgAAEIQKAuAogUsL1O MoY/kqpmqD89LiBXNMye3j8z6n97/4qQ9OTZef7lN+GTtHSSWo8dEk//zs/RHmw9MjfxwbNyvxry 1vhnP/7Tdd54a3PSMSaJ/YPpUALTDZbOU1j5RWbXR/yiw634jEp+imQnI2guXTNNn4QS8sL3HmaX WuAo9x87deS9y7cETLbFKl6veEamIZKoBJ2I0XvH0S+2rp9Ad9Rr8GETDbwpG9pQeQNNFp97mn3+ Zztra3rSB0E+G58v2tOGyfggvTGxiRXLZ4mSTWLqQV2+B3CsTAJdJ0wypEMFwszwfVfedqoxAWFx 5lmvLW9Vy42IkT6hsqnTT87m9CuJfou+3xOfK1NtsDMEIAABCEAAAhCAAATqXQCRAkJKetRe2y7y uYbdmYUAqtpe3b125wm9Sl9s8Jkkpc86POTpdC6JjUon6xe26jHqazte5Enfv9LoyQVkEYQLR48m qXQn3V2pNH6c7gO/NA7/I+p+CT/h7F8JAhUjmxmD+bGXLiUzEwHy0sI8Vmz4h6tJVnEX/USvX3A1 OKHqbgPb/e7670NK4zNSczXjSUuM7165nk7XIS8tcMN/vk6ZesxzqXlnui+fnR526HgUvWqjsDD1 +rXbvZhZA1W+XlGBCaZwO6szvR26cCfH9DEnfhhLJ2Jgt2p82MCHlYVRYeTZBKaxFRrIUTWd7GSW G3cxQgx4fvfCrbc0NJTZLA99jXvRCRqlNjo28akknaTY2UT5ViK9xiQ2CNRWQPgqL+cVu3NRzstX 73x6hM8fJJNlOOlPuCC3ULLMBz/+rM8GR0t/5W4d2PEFJfm54uFCT3Lzy4rJSz69YeYMb6p9m9bs WYSFuU/YE+Y/zWNXP+WoGH3jzs5BoIKcf9gekIr1O2p7/bAfBCAAAQhAAAIQgEBDCDT3SIEwxecr pU8Nv/YmIwKyd1jqiOf/V7Tm6Fnv2Do8xNZQx2HPdcpoorlOGzJTOfNeodYXWvQvihzNCd//8bPx jcWDO7bgjl4TmNnNeuYwsjpA+SHxLTX0Bw2+uG7lhmsq5mMMNVopG89xP2YS93VvLSUFg7n77nDG u+5ZxKxZwGyCuEiBjpWherUX/s2T2wWd9Nk8f+qG83bu6htmQ+qgNOSXdMMVN0+KpzMIC18Z/Rw0 X/nkPAOlFlpjwnr/tpNJo1jV6xXPyTSEn+1ra6hMEiWQwk03RO1n1oBgezo1+DA7Zd4Mf6Y7zrDs l1XpBqqYOnhtFwFqfeN769PR7r/S5UsWmCz/US2XoIGUTc9leFWccjObq0fYsUGgVgKCAAfVFm0m bBUl/RRstezcQmF+gDgHaEGsp4VCm+62folMafyQRb1bkKUK6MenmmYT53iEUFrtyJyCknh3HVWl notOik4aOqdnG3Y38mjTc8bWcEp7SI9OihRbYO85oewZYrdP6Kig6hQgKCE3r8kSb3/7fvTLV7ba Ghk7nH5UqyZgJwhAAAIQgAAEIAABCDSAgEJpaWk9FqugoEBKq3uZpqb7JLX6K3J+PdYQRb2XwOs0 X/vBf4yJCnSsJGXje5WIg+ouMG5o2W0SE/P+t0l93bZ1b5EMJTBfNaItvz6/xGSoA3aFwEcuoCp1 m9Trv/UfebtRPQhAAAIQgAAE6i6An1/rbtgcSngnH2FzaDTaCAEIQAACEIAABCAAAQhAoFkKIFLQ LC+7bI0WFiYHHT/a2Xpwd3xcZJPD3hCAAAQgAAEIQAACEIAABJqgALp+TfCiNWaVhfeOTuyiZTj7 BJ+s5qhksC4svzHPjnNBAAIQgAAEIAABCEAAAhCAQKMLIFLQ6ORN64ScXnZnsrKLStnHTdcRqk2r /qgtBCAAAQhAAAIQgAAEIAABCMgogEiBjGDYHQIQgAAEIAABCEAAAhCAAAQgINcCiBTI9eVF4yAA AQhAAAIQgAAEIAABCEAAAjIKIFIgIxh2hwAEIAABCEAAAhCAAAQgAAEIyLUAIgVyfXnROAhAAAIQ gAAEIAABCEAAAhCAgIwCiBTICIbdIQABCEAAAhCAAAQgAAEIQAACci2ASIFcX140DgIQgAAEIAAB CEAAAhCAAAQgIKMAIgUygmF3CEAAAhCAAAQgAAEIQAACEICAXAsgUiDXlxeNgwAEIAABCEAAAhCA AAQgAAEIyCiASIGMYNgdAhCAAAQgAAEIQAACEIAABCAg1wKIFMj15UXjIAABCEAAAhCAAAQgAAEI QAACMgogUiAjGHaHAAQgAAEIQAACEIAABCAAAQjItQAiBXJ9edE4CEAAAhCAAAQgAAEIQAACEICA jAKIFMgIht0hAAEIQAACEIAABCAAAQhAAAJyLYBIgVxfXjQOAhCAAAQgAAEIQAACEIAABCAgowAi BTKCYXcIQAACEIAABCAAAQhAAAIQgIBcCyBSINeXF42DAAQgAAEIQAACEIAABCAAAQjIKIBIgYxg 2B0CEIAABCAAAQhAAAIQgAAEICDXAogUyPXlReMgAAEIQAACEIAABCAAAQhAAAIyCiBSICMYdocA BCAAAQhAAAIQgAAEIAABCMi1ACIFcn150TgIQAACEIAABCAAAQhAAAIQgICMAogUyAiG3SEAAQhA AAIQgAAEIAABCEAAAnItgEiBXF9eNA4CEIAABCAAAQhAAAIQgAAEICCjACIFMoJhdwhAAAIQgAAE IAABCEAAAhCAgFwLIFIg15cXjYMABCAAAQhAAAIQgAAEIAABCMgogEiBjGDYHQIQgAAEIAABCEAA AhCAAAQgINcCiBTI9eVF4yAAAQhAAAIQgAAEIAABCEAAAjIKIFIgIxh2hwAEIAABCEAAAhCAAAQg AAEIyLUAIgVyfXnROAhAAAIQgAAEIAABCEAAAhCAgIwCiBTICIbdIQABCEAAAhCAAAQgAAEIQAAC ci2ASIFcX140DgIQgAAEIAABCEAAAhCAAAQgIKMAIgUygmF3CEAAAhCAAAQgAAEIQAACEICAXAsg UiDXlxeNgwAEIAABCEAAAhCAAAQgAAEIyCiASIGMYNgdAhCAAAQgAAEIQAACEIAABCAg1wKIFMj1 5UXjIAABCEAAAhCAAAQgAAEIQAACMgogUiAjGHaHAAQgAAEIQAACEIAABCAAAQjItQAiBXJ9edE4 CEAAAhCAAAQgAAEIQAACEICAjAKIFMgIht0hAAEIQAACEIAABCAAAQhAAAJyLYBIgVxfXjQOAhCA AAQgAAEIQAACEIAABCAgowAiBTKCYXcIQAACEIAABCAAAQhAAAIQgIBcCyBSINeXF42DAAQgAAEI QAACEIAABCAAAQjIKIBIgYxg2B0CEIAABCAAAQhAAAIQgAAEICDXAogUyPXlReMgAAEIQAACEIAA BCAAAQhAAAIyCiBSICMYdocABCAAAQhAAAIQgAAEIAABCMi1ACIFcn150TgIQAACEIAABCAAAQhA AAIQgICMAogUyAiG3SEAAQhAAAIQgAAEIAABCEAAAnItgEiBXF9eNA4CEIAABCAAAQhAAAIQgAAE ICCjACIFMoJhdwhAAAIQgAAEIAABCEAAAhCAgFwLIFIg15cXjYMABCAAAQhAAAIQgAAEIAABCMgo gEiBjGDYHQIQgAAEIAABCEAAAhCAAAQgINcCiBTI9eVF4yAAAQhAAAIQgAAEIAABCEAAAjIKIFIg Ixh2hwAEIAABCEAAAhCAAAQgAAEIyLUAIgVyfXnROAhAAAIQgAAEIAABCEAAAhCAgIwCiBTICIbd IQABCEAAAhCAAAQgAAEIQAACci2ASIFcX140DgIQgAAEIAABCEAAAhCAAAQgIKMAIgUygmF3CEAA AhCAAAQgAAEIQAACEICAXAsgUiDXlxeNgwAEIAABCEAAAhCAAAQgAAEIyCiASIGMYNgdAhCAAAQg AAEIQAACEIAABCAg1wKIFMj15UXjIAABCEAAAhCAAAQgAAEIQAACMgogUiAjGHaHAAQgAAEIQAAC EIAABCAAAQjItQAiBXJ9edE4CEAAAhCAAAQgAAEIQAACEICAjAKIFMgIht0hAAEIQAACEIAABCAA AQhAAAJyLYBIgVxfXjQOAhCAAAQgAAEIQAACEIAABCAgowAiBTKCYXcIQAACEIAABCAAAQhAAAIQ gIBcCyBSINeXF42DAAQgAAEIQAACEIAABCAAAQjIKIBIgYxg2B0CEIAABCAAAQhAAAIQgAAEICDX AogUyPXlReMgAAEIQAACEIAABCAAAQhAAAIyCiBSICMYdocABCAAAQhAAAIQgAAEIAABCMi1ACIF cn150TgIQAACEIAABCAAAQhAAAIQgICMAogUyAiG3SEAAQhAAAIQgAAEIAABCEAAAnItgEiBXF9e NA4CEIAABCAAAQhAAAIQgAAEICCjACIFMoJhdwhAAAIQgAAEIAABCEAAAhCAgFwLIFIg15cXjYMA BCAAAQhAAAIQgAAEIAABCMgogEiBjGDYHQIQgAAEIAABCEAAAhCAAAQgINcCiBTI9eVF4yAAAQhA AAIQgAAEIAABCEAAAjIKKJSWlsp4SHW7KygokLfrXqap6b56rBWKgoDcC8TEzH/vNtbXbfveFXif A5mvGmwQgEBtBer13/ranhT7QQACEIAABCDQZAUwpqDJXjpUHAIQgAAEIAABCEAAAhCAAAQg0AAC iBQ0ACqKhAAEIAABCEAAAhCAAAQgAAEINFkBRAqa7KVDxSEAAQhAAAIQgAAEIAABCEAAAg0g8JHm KWiAlqJICECgcoEmmacAFxMCEIAABCAAAQhAAAIQaDABjCloMFoUDAEIQAACEIAABCAAAQhAAAIQ aIICiBQ0wYuGKkMAAhCAAAQgAAEIQAACEIAABBpMAJGCBqNFwRCAAAQgAAEIQAACEIAABCAAgSYo gEhBE7xoqDIEIAABCEAAAhCAAAQgAAEIQKDBBBApaDBaFAwBCEAAAhCAAAQgAAEIQAACEGiCAogU NMGLhipDAAIQgAAEIAABCEAAAhCAAAQaTACRggajRcEQgAAEIAABCEAAAhCAAAQgAIEmKIBIQRO8 aKgyBCAAAQhAAAIQgAAEIAABCECgwQQQKWgwWhQMAQhAAAIQgAAEIAABCEAAAhBoggKIFDTBi4Yq QwACEIAABCAAAQhAAAIQgAAEGkwAkYIGo0XBEIAABCAAAQhAAAIQgAAEIACBJiiASEETvGioMgQg AAEIQAACEIAABCAAAQhAoMEEECloMFoUDAEIQAACEIAABCAAAQhAAAIQaIICiBQ0wYuGKkMAAhCA AAQgAAEIQAACEIAABBpMAJGCBqNFwRCAAAQgAAEIQAACEIAABCAAgSYogEhBE7xoqDIEIAABCEAA AhCAAAQgAAEIQKDBBBRKS0vrsXAFBYV6LA1FQQACjSZQv18FjVZtnAgCEIAABCAAAQhAAAIQqHcB jCmod1IUCAEIQAACEIAABCAAAQhAAAIQaMIC9TymoAlLoOoQgAAEIAABCEAAAhCAAAQgAAEIUBTG FOBTAAEIQAACEIAABCAAAQhAAAIQgECZACIF+DRAAAIQgAAEIAABCEAAAhCAAAQggEgBPgMQgAAE IAABCEAAAhCAAAQgAAEIVCaAMQX4XEAAAhCAAAQgAAEIQAACEIAABCCAMQX4DEAAAhCAAAQgAAEI QAACEIAABCCAMQX4DEAAAhCAAAQgAAEIQAACEIAABCBQvQBmH+ATAgEIQAACEIAABCAAAQhAAAIQ gECZACIF+DRAAAIQgAAEIAABCEAAAhCAAAQggEgBPgMQgAAEIAABCEAAAhCAAAQgAAEIVCaAMQX4 XEAAAhCAAAQgAAEIQAACEIAABCCAMQX4DEAAAhCAAAQgAAEIQAACEIAABCCAMQX4DEAAAhCAAAQg AAEIQAACEIAABCBQvQBmH+ATAgEIQAACEIAABCAAAQhAAAIQgECZwP8BrL+dUOtkLsEAAAAASUVO RK5CYIJQSwMEFAAGAAgAAAAhAMLFAhHwDgAAeJoBABQAAABwcHQvbWVkaWEvaW1hZ2UyLmVtZuyd T2gb+RXHX9hloZdS2PYgWOiCLnvwzdBCTuphdRGF7cEXF4agiw4pmJZlWB0MOQRymGJqCCw1DISF QsH8IFBCdw3CUAJBRXsoIYgpOYSgVbukkAoR0sVo1ffGfvJP4xn9t2bG/g58iOMZSaOfZ77z3vu9 9343iOg3dL799AbRtvX/ow+IfvIDog8//mWZ6AZ9dnCD3iH5aXwrvUf08F0i3k0/i+z8wy/eo/8e vUu/4p//98fzndHP0u1D/v2v+d9PmI+Yz5g7Z6/7kFqj/bJPf77Be2TT4+xtOBwCAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAACBDDJjjoE/HxtCgH2BMpvAd86jVpaBh+P99jAkAC9Jj9k1AncYe /3+AMZmDx90B+d4+fddtYjwAWEB7dv02a88utGdBnvaH5Lk+a1AD4wHAHNqzw9rzwtRyqT0vmaJj RvXiXpFoeNJK5VyO2A7ad+rsu6bz+QDkLd6z1+qTqZZpOOjk+rv4wYDqhTINeunZHzKe+zyeTc9B PAiAKbQHbPtU2GfoSPw0/9/lZtmnVw031fN4Lfak16LXLQ/XGAATntW7jR61/Upm/a4Tpma6Y/24 kvyrt8yWH4yO22AM2yHfdU+P1fcqFYhaB+P2idc6oQoVV6YZpjMgv1JlmxJziAAk2QvbbC+8DfzM n6vEVQqlOhlH+gF2E48THakXiqEPFvSH5HhHdIcFR2PC4qO57KP9yRyyzffwXC+6Q1Yrh/ZKtJLY zSvROreRi7EFIK14ScMV26eX+XNV22aLVWLSPd3o8XEFd3TMC9bYjaohUy2E8S3R3DJrbqvhUbd1 PLL7WiesP0WPSqxVplpaOhaWB9sSgDTv522+n9t+OTf3h+jlJpUnxnfE7rjJdof9vezXxe1XW7DA uvTILZBrnq1kLlC00K+48MEAuAL+gWpEgzUiyWYTu6POdsce2zHqR+nrWh77ZcOTcL9fFnvoXBdk PDZ5PFiW6MVJQDs1Q732cmOTJ/8WgHUiz+Z60V3LXLXe/5M2p0BkDsUnkphMvN2hPtgnfOzGthO+ jn8k3y1RLzj/HhLL2SSH7rtl/v1R+F2JfTLRnyHrT9RHs99bY9sSP94rV5eKBYmmVVjT0p6PAyCL voxflpyf9fkGGkPeZM2w70nJf3zY4Hv/0LBOOLTHYiS6MYsP1uT3LLsPqMLvKXNXGkeWz/ibccP4 s6hUvUT8nqc5BnE+mM6N1fjok64J67p2TIeO6jJGyfHuq+bjArCOeXfxQRqu2APriz3r526yHiTZ BO3+kPXEhPZQt3mxDi3OB1P7RnynWfQ06fvL3FmJitQ/m4OXPJ5tr0UvTHUh/UhrnAHIMm+Yjz79 mn7/83f4/+vNeVatmBTD0TkrtWnsfX2mxJpg5wBp/cU8dRdxudJiOxXZdnppJDfo9HdN1sNa2Vs4 FnTrqxO69f77fF5f4doD4Oy5/sGtr1LRH7Vf6mzfTIo9SfyFCtUL+TjReXixMR60+6HfpnPsi+ZK iyYR+3a2NqpfZvtv0B8A8qk/s+bx6HyU5DCr/6O2T3STGPQXbmmU5zwNzfWRTbRmwFojftLpO43n WGs8KVyjmVHfDPoDQP70R+2MAtsZk+KyGjsRTZg1rpNFoD8AZEt/ZokB2f7QNF8N+gMA9GeevBjx rabpiurUNF8N+rOeeUv1T9k1HYvRg/SQPBHT6tKGcxpP0HjEgVuamEt3nfVHY0DRPCDoT/afWxWv NdVuBetDYpmFokeG9Ub7tcvzwgR9+qTkZrIHTNr6M2sMCP5XNvNW0+7xBsbtny/Y/vncrYe9x1vd ARnzkFxf8ldKmdQftT/SfI7pPHxSbFnG9bbpsEYVqOmVKK+9BNPIM78Mm0c2yUt/0j7KRI+3NJF8 /arfDj0dmZ/90nNGtofsqxvpQSWzpUQej1m3JXkbp8/YjvRfqDfCffd5X4c1Q2IRmmeiuWx8W1Cn UQ9fF72GZE62QA4d1aVPw2luvuaraB3TuteviauxSvJvsqA/02JAWqtxm/fnuTdj3vVHfOAC+8Dy DGj3+7RTN2Ht3XX2weRvusEK8dQ/7WEnNUCfew/CHlO6T3JGeqwdokUbrEV6rGjHBmuH7H/aH5Dr +rTpuGQ8qVVsju7N+yxAT5qNUEOi9YraQ+bY3OPXSA+ZIXX5d7f3mvw5kquSzrNaezmL9k7qo5Ul /YlqpIyjZ1phPmGVtWfZGnToz3Los1b9ZM1Nz7NPvCxSp7NFxbGY5Lc8Lr3nwYUaHrXjKzyKWp8o tdF6zcuaKRuOP9ZXT66ZLdYwfX+tV1Q/IK6u0Lar4uyudV7vEjOZlDOTtv7oOSZtMnbH5vBKrH+Y d/1RvbGvJ/lOJf77zZuLeZXsn6Q+VHFxTanhKZS88H5rD3oX6hfFd6oXqyM9F/umyMfoe+j8o2pY 9LqK7pdnuMO2kH089Ad9BvI69y42tfgQmoee1MPtOuWvSEwszsdQu97ep78TG2fANo6Mpx0bjvZp 0fszahNtF8p013XDeITdW0prhOx54ll6ZUF/oD9pIddnha9PjXHOEm8t8/O0bVy+//rh/beuvPTo ugK6zeIDqm0Q7Tc3KSYQV98T1Vr1m7RXjMQrd8v18HzU3pH4ssxJ2faP3G86nk6hEPZ4eDEYhDam fT9G5xm1XvGIx/+uy3+3wFCZ/wZ6Xhp/fuBI3732hc+cND5fBv1R/lA0nm77Kvr3jhsn3afH3+P/ 16WfDutstD4K+gP90Wto1vi+zvMWSm54NR4c1mmr8Lu15mVN6ouwCv1R7FjKpHvEjlXKpve+7JO1 D1xf3uN0Hky1KG485fvc9e+RVyyHNYcSQ5IsT42Nar2i6mC7Lz2vzFhtpN0La1adtuPF0U3tNznP Ow222UqSX3Ru6+n1I+dkz8Op/si5Pd5zwt+rP3ib9VauNegP9Md+htn3TZbRc54nH3UR/bFjNuuy 8dLo1WvPmds9/2TMDtt9enV8ei6iQXdNZ2xt9qi9Nc3/steBeDvoQH+uuf7oM0ieUzuuc2nrtU6b a5glXyQN/Vnks5ZBejvY/tQ6a2ri4lhyfTxrPR/pg9iEu7uNcD5atSTOdk7SH7vvxb95PNPOf74u ZDX/Wa8/ye98zBfLba85Uyxo7vuKfYdu9xXTncq3zODt60zojz0+69CE6HlqrxntB5xWDZSNxLzu uD453gE/twqxfUGT9MfuffqcPw/6g/oLe644zIWrZnsd7mn6kxSnTtpmrX9eh5+gMZ/L0ptF+3Al 2UxJvf6m6Y/o+d955we3/jqT/ixrP1/1Lc/6E72/xKc3tdrSay+mpT/fM//pn4zZVN8wvzXPaf/j AnW/eTK27/W/ujT8/u3U+MhVzLVU/Znnu2lMLBpzvkz9WaX9DPsnm/qj14qu+dH0xN9YTSwor/Gf RX2UvMUmJ/1tbLtPc54kXhiw3+Wal7G1HtPiPzKW8L+gP0nPd7nOdvg6W1XebF7jP/P0acgr8vff rfhj9o/qhJ3zZM/Tq98l18m9vdbM8We1neRae8Ofd53nv8TXrrCvvY7c9CznPyfdz3Kt7JS9zMWC 0tCfedd2yltuu6zdcL9aDXOTtG+Q5CTpnJi9vkNUk2Rc7zvF8LXR2NsOv/6R74V5jJpPpflKb/n1 edKfuHzLZWxi7Sk/qUb3OuhPUgxSrzlTq2YqFpSG/qx7Hj4NDWqw5pTd0zwgO1c5Lp6v46C6bMfx dR1RxzsMxzvoyc+n63+Klye581o/m0f7R3Oeln0WaT5pqSRxfImjXd49lgf9idPyy4gFpaE/EpP+ 56sTevkPfs58P//3mGXNTJC//mPLnPey+qNxj0f8/V3zjG3C2qX59lnWn2k5LpJntlU1l5IXtC79 WWWcFvm611t/9H7Z4vtlmetPr2PRsRcnAe3UzKX1Gcqy/th9/OJqMPQ6Edu5+cCNnW9dl+7s1Bvk HkiNZGmt+mPHXu2ehNCR66c/dj++ZeYj9PuP1hhkv26vXL2UWFBW9Sdu7tWOL8b5/mnGYDW2IH2N t1gL1qE/cT1FZ627B9nWHzt+lZRzMOmakNgN0c1RzwDjORfWP1U7R3riBkcy13Xq/2u/FM011VjH UV10YrXP+Lz3/wHgqto/9tzerM8U2ydQW1h6HUicXfupqF1k+1nPewH1gnN9ivbJlHyGba8VW9MC /QHgavpfi9ShxPW/1PxMu2e0nWMuOU+HJhj5WDIHXyRnbB09yXuplb2VxoKgPwBc7B+ZpfzneXNM k2JAmmNp5/Uk9eqNm9NQv6zOr+8Fq8m9w/rLAGRbf+bNMbX9Kvt4XWvA7g2Q1Mtb1xIO+91rHPos L1E26Qi3iv7q0B8Asq0/s64HP4vPFtWyJK1C/RcA0J9F64yTcsKiNZVq79QLhVTmTKE/AGRbf2w9 uecUqOwejHwjmef6i+9emFtXnaneJOocn8956e9t20hsok22idLIn4f+AJB9/YnrBWHXxtn1a7bN JOvU/vlJg9oPfT6+P5rPt/2taAwI+gMA9GfW2p7ouit27q4c/0bqca0a3uj81arqxqA/ACzPG+aj T7/OlP5MqzNetgYwup4b9AeAdO/1hlvMVP3XpH7fum/R2ps0YkBZHWcAshDvzVpersaA4vykZfvQ pbFmuejPLutP26+gZhqAyP1cL7qZquPV/OUL/UDP1pWdZ93dpDn+dcaA5DNr/JnoWwXARXugxvZA FnpKxuUkR/tC+G6JesFyWrluzU1jTV0A8lKDus3P5qvY1z9pbm3ZvtGL6J1fkb5dqD0FIC4G1HAl NoHYKGLPAKw/3gv/4Or7twBk9RmN+ZnrM78IQBZtoJ2Kn7m15vJu+zhs+6xjfUMA8m4DSX9/U5Vn NdZiXsV47vN4Nj3pq5iNdbMAyDLSt8s1nUtdB+u6cNQd0L5Tv/R1VQG4Ssj6Jrt+mzqNXWjQgjzt D8lz2ZftYm0WABbRoH0TsAad99MBs2uP7z1g7WliPABYIn5xHPTp2Bj2ITB3M4vv+qjVpaAh8XvE ewAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAXF1+SEQ/PlsL7UdnP/8fAAD/ /wMAUEsDBAoAAAAAAAAAIQDYAcSOJhAAACYQAAAUAAAAcHB0L21lZGlhL2ltYWdlNC5wbmeJUE5H DQoaCgAAAA1JSERSAAABKgAAAKQIAgAAAK+V9GQAAAABc1JHQgCuzhzpAAAP4ElEQVR4Xu1dO45W PRLlH9EZKRIRj4gFQIpGdO9ldtFIQE7O7OWTGEhhAewAiXSyDpiaPlKNx76P8tu+93xB6+v7+VE+ ruOyXWXfv/78+fOAHyJABHog8LcelbJOIkAE/osA6Uc9IALdECD9ukHPiokA6UcdIALdECD9ukHP iokA6UcdIALdECD9ukHPionAXxa/37/uPwSLCBABOwJ/v/9spzfR78OHD+/evbNXzJREgAhYWMPJ J/WECHRDgPTrBj0rJgKkH3WACHRDgPTrBj0rJgKkH3WACHRDgPSrBb24am5ubmT7a9Fng5+kbvnV /YLveH51dQWXDxLrTxaJJYtXLyqKKsRSEdPkIED65aC3lRduH3HYhPQTXuEnya+uIX2CQvFc/kr2 y+WCxEpU9cSCVGEVodNJ0njeI2W+clL56Q0KtTA6fbmkX0UVCC0bqHJ3dydMsBgiMWLKGfDHZQiM 5JpLVs3dotHDr5IXVSCNfAHtIRukXRxBKqJ2pqJJv4q97Rm03ZpE1704CbF7ylLXTrrfLTQOq1bz qEWBhDCkkFyJvRu9sds0JlhEgPRroRiqzWpeFmtdDO5btJPuEjG0TvjVrdQStATr5wrmTkpbwHS+ Oki/Wn0OMyIaHJoOnelh9ggJwp2S29tbd40n5ShD3KUdHupEF6WFlYLG+hzVuX9Rvq45dS4a0rIW ZCcsV0Kudz/v37/fTcMEzRC4vr7+8uWLV134pJk8rGgRAQtraP3mG3JlQRgaNy7P5uvIB7xqacZO o8xHQYDW7yg9yXZMiADpN2GnUeSjIED6HaUn2Y4JESD9Juw0inwUBEi/o/Qk2zEhAqTfhJ1GkY+C AOl3lJ5kOyZEgPSbsNMo8lEQIP2O0pNsx4QIkH4TdhpFPgoCpN9RepLtmBCBivTzjsBMCA5FJgJ1 EahLP+OVCnWbyNKJwKgIVHzHA46Khues8fzFixfPnz8fFRbKRQR8BJ4+ffrs2TM7LqZjypazkpaD g2E5kkvOgIbHQNNKs8jJNESgHgJfv36NKtyi5xUnnzJOhBfg2QcPpiQCh0egIv1wVRZPYR9eh9jA ZAQq0i9ZJmYkAidBgPQ7SUezmSMiQPqN2CuUaSgE/nn/qSES6VcDVZZ5BAR+/PgB4v3j/lOjSaRf DVRZ5sQIKOukDfWIB4BIv4kVhaIXR0BsndAPrHv16lXx8r0CSb/aCLP8ORDAPFMoV2meuYgC6TeH clDKSgh4C7zQ4kkCqVr+/v79u7gMpF9xSFnguAiAbJDP3c+ExXN/QhpJD0LK38ePHxdvGOlXHFIW 2BSBRc6oyfK+gEvufqY729Rpp36pvfwj/ZrqCivbQGCNM57JUtuFL4ucUZOlXzzWtVzgbXW6JYjb ErttKQdpypZmr5cpx0Hg8+fPEEa+fP/+Xb7gb42PVKG15JQ/34kHDvZEYHFN5Zos10AVhGt3T6Vg XclFcfKZDB0zLiAgSq9zSF2VhfPDqtjpnkptp3l+K0i/fAzPW4JLNrBOTJlas8brK7BOY8Rq75oU 6XXSrwiMpyjE3WMMydZR3V3WNeZ8ZsfXpZ9cd8ED75k91Ct7aNnCBVsv2VBvg5Do2g2sSD/hnkjP 0+61u7BI+WrZlHXuNLKjZVts3USru+3eyb3pDNeWoQ7vUrObm5vL5RLe9wRa8tMRASyQRIAnT578 +vWroyRRVcvQ8OjRIxnQu8j89u3bN2/e2AVucdOZ68TzHHr4N/Ty0e+X433KyStGLCd737wivHoL u0gSq7eW9FmTT4/fMix5Kz1JwMmnfbwsmDJcuUnh4touWEWbonQ/U4Tvta0iaiwfsX7lm2wZSNZ4 LHd4qokL7/NcK9kyKlikOlsaN1JEQ0bwRVRTY0fqhY+0BFxagWiVlpV6dXm31NaIenlgad4aYfD8 4cOHi5NM0s9FQFmhKhVyZpFFfVXQoh5l05SKEUuWak3ba9Ava/JZ3hbPUGJaZLC2zI2mDyODvSeS q9eMq3FXdI8RwxuBTPslBaGxDBJr48H19bX8hI98txQVZSeNBdZLtmiy6lV3zpIxz+w4Zzaum2pY vyzHg+tCkMFD3AyWcaH1AGORaSWNnrbMKOMsWZOv4lMHY2OkRGOxL6hftgX49u1bccdDFv3cdxjZ SWVP2bg/tDqNqOolQE69uueZU0hC3okmydBb4V7Utvxw9MPIseZ2X+vCkek3r7lT49PLmCQwtnGW WHPniVeDflk7n+Gk2TKNHtnx0HEFkrDw0935s+2OJmBl0cztYmus/XJ3Pl0/+7xOdjfQsfGQnFBd eA/sRBO/hPYmZ8FOJlQ0ap6ZXGN0RstAsmGv4HnHx1LUUDufc9k6ga67T8zYxSMkyzd3XitGtH4y qOA9fuFLpKNHAmZYQWDGg6S9OlM3IwY1d/+PS+7kE/Ydn16Ix9Y7y1TTYx1nmGsdDY85fo3dz4xV nrLpc+kn0kxn98bfGxTivX79GleVkHXbGo/V3XRKiEbl0k+aPdGOS7JruOyYt1aa7qnIADHj6YQ2 KKEW3fPD8qdl1QXryqUfjP74N0rAkz6sJfGOb492urygwuUX5SnbFGu8tVbn0k8bPzIDh3WmF99T mWgFHstDDYmGyk3Nuv+13bJHbHcqtC9tu8Yx/dHwHxSRbc3ro891/107Ub4U35S39Htmmu4y13A8 ZEW9CKC7fr8QtbJkXuzUMR16pVjnEilNp9Fr6Bo9VOo+TCu2eC7vwGvx8qMKHJF+26qw6I4/G/2K HNwGkg0swAYzo5Q1OXFsFEdyRbEZa9Av68QDprBYb4T+FqwGw03heuuTnz9/vnz5MnZRUSk9vIuy Kffv+09CLR8/fry9vU3I2DKL9K/o5fhy5mMy4k1nOoSEAzMO44bHcGtYv6FmmzmhYe6cMHZ47pje nea4EyKjKauhEsXRGM76yU2e7s7n4nHb8HhR8QNHI2xsuqfsotwbegoGk4J5XVgW86Jd786AZmny cAeO0gaYgkNdkc3DtFYgl67rosyvaysaLOdyGsi8ikAN65fr99Mxr6XfL3xzlWXoLZsm1lGuK2FY AB3yD+K/KgvuaUrLop8GW7tn3o8NXZSjXGHRGebUEVLH7tkurcuiH9YqskHXJua1u9GLCoO+urpy Wdeld1np4Ajk0g/NExI2mHy2jIRUK+d+2Q6Ddo+9yLzg7u5ulk2FwXX0wOJl+f0sO58hdp8+fZL3 1Ngx7bKxady9XPN52lvHlLMgMNzOZ9qunX3nM2o7sdkenRul1axSVtQdgRo7n2WsHxY5xrmW3e/X xe6Fg7F7fwE3KqOMVb0IpygxiiQeMeolIU531/rpm0b6DnheRHJfYWasHe/eOcZHlLaG9cvaetEr XsQ+yDowc4zBiVj59L0MQh10eq6MFi+zZ5l9DYEs+oleSqDZYW460zshBSxSbjrO6ETXtQQNNuRz gMqiH/QVNjC5nX3vHVOx4bqc656snI4/WF53kYmhU3VStRRr+BxdLQ5aFv3URBg3XTzp9U0mLR16 KsORrgwprhYtC3Rjp6ReN1RIf4I84U9uJ4oSSqiDptS3F+lcBi8/b+OjNgKYRT8ggvYkWz+joKWS qbk+1JUhpdDpUY4uXrzFdniDs85N9CeXY8JVOXaoS3dlHS7j69Gy/Tqz6KfWD4vA/druU4jPvf0b vLxhMs1cGxvIZI0RwFWX6FPMLeVfb02kujoWFS37wmuuAjyP3V/edTxYRLKnUf+BPQtTFkEgVjGK VLpYSL7Kjeh40ChHGVFCx4MbA9l4OMQoiA8nme3BH6pG1YGhpIIwuZNPsfiILQ536rEs7GLrpdLD uEMGVJq5RFpUzkGakEU/dwUVrqbar690B6h91YN0J8WYC4GsmM/dpsIKecnK2kOh3Elu2tpFe7QE chBUZkajSZUmjyjtiDGfG8tlnAyoes3uUNew1ts5mLTkZlsv7s5KpdtQR9x62R5I6jkDNXCBQSpp Y/nxcrmOpYkCBrPWftu9iM2PIlh4oW2lij2eFp65RS4Ddd9bNuTluft3cZe+F251136LrVpcEO4a 0iI07oXyOeuVEDDL2i/npYs4HAONcjfecA8Dosz0L3oBT2LVab6139qKxe4DtaecdHV0bLG7rP1w qzo0x33/jz7RX6PAn2/tlzDke9G39B8kYHjOLDrzhM4g6AyRaPoXyIzlhbeMAWWt0FppaTfHWORn mi4INLN+DVp3WOunU/bY6fg5h3m2+kgIVNz53IDJPceVsA4+UgewLWdGoA/9GJB5Zp1j2xWBPvRj BxCBggiImwHzKT3tHhaONU4Y8IiH+ReFpTWH9EvDjbkGQkAPv2+4GeHuC4Xuex6N9BtIjShKMgKw fupaQFiiGyyll1Agpf6UXGORjKRfERhZSDoCGvWy/QUXlOi9eO7rhOW5F4eoQTDqBlQrp08kV/cL iki/dL1hziII6Ntstr/gOjy9gjm8i3nXceUFhY4QOUz6FVEhFtIZAe/Eg1534Ma4wMWl0TCYrOqU tY8ltEQMtIl6sUjCNBMhwKiX3c6i9es8bLP6MyNQkX59bzo7c6ey7bMgUPG8H+bf4em+hPN+HdGk tMnghz5ur6g1X1xyjbUzRh3BsWhOH/rVhqlg+XOpCKUt2PVhUcXp92B3dZh2PFFzld22sUjLNERg BAQsml9x7Ydd3V1vTNXhioUTgZERqEi/glctjYwgZSMCyQhUpF+yTMxIBE6CAOl3ko5mM0dEgPQb sVco00kQIP1O0tFs5ogItKbfXKEwc0kL/eoTOhyv2zh0F5+vQw6VU68FKyVEB/r1eulfGmRzSTuL TiMiZAr6KeUgbVlHWmv6pXGgVy7vfvJeYhjrnSjkBYNaWVU2ohSbTIXUE/SxJWykJ/12wJxIp2vo R0FVc4sCqlNYPxUbN8qUlbkD/WYZ9gR3XIC1GzpcSUdji71cLiOc4DaKXVaPjZUmJHPXe8VVt2LI dUJTmYUIHAYBy4mHDtbvMPiyIUQgEwHSLxNAZicC6QiQfunYMScRyESA9MsEkNmJQDoCpF86dsxJ BDIRIP0yAWR2IpCOAOmXjl3fnIvxZW6QavEAxb7tPWTtpN/E3eoFbYGQiOdSB/Es3u2JuyFDdNIv A7yRssLJi0vURS69TZ30G6mXfFlIv5F7J1o2xCVKNjWD0UUwQ0MESL+GYBetSt9Q54WkumZwiiMF RVGZrTDLjYiWGwst5TBNVQTCbvpy/6laKQtfQ8DCGlq/2cbLdXkXj8PQAI7cwaTfyL0TLZtHNnIv GsG2GawHjtpKxdqIwBEQ2H0nhIl+R0CCbSAC4yHAyed4fUKJToMA6XearmZDx0OA9BuvTyjRaRAg /U7T1WzoeAiQfuP1CSU6DQKk32m6mg0dDwHSb7w+oUSnQYD0O01Xs6HjIUD6jdcnlOg0CJB+p+lq NnQ8BP4D84M0sW9XURUAAAAASUVORK5CYIJQSwMECgAAAAAAAAAhAFFzDPQAgAAAAIAAABcAAABk b2NQcm9wcy90aHVtYm5haWwuanBlZ//Y/+AAEEpGSUYAAQEBAHgAeAAA/9sAQwABAQEBAQEBAQEB AQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEB/9sA QwEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEB AQEBAQEBAQEB/8AAEQgAwAEAAwEiAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYH CAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHw JDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6 g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk 5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIB AgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEX GBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKT lJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX2 9/j5+v/aAAwDAQACEQMRAD8A/v4ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivzS+Kn7e HxF8P6amnfCX4D+CvHfxF1f9um8/Yl8LaB8Rfjnrvwr8FX1/ZfDzV/iHL8RfEHjfw18CfjFrug2j WWjXWnR+GNO+HniaY3UlvK2urE0nlQqkHiKeFXNLEVlh3SpRhOUp/Ws5ybh/DqPLFpyqZvxBlGDU b8yeL9vJLDUMTWo1KLjRniJWjRpuup1JSjGMfq2VZrnddyu1ZU8ryTM8U5NcrWF9lFuvWw9Kr+lt Ffl9+0p+1l+3X+zh8Mvhh4/1L9kX9kvxTfeMPiL8JfhD4x0Ky/by+MOmWnhT4hfHP48aD8E/h0vh rW5/+Cdl1N418Gx/8Jl4R8TeNfEWqaJ4E1vw/wCf4k0XQvB/jD+w9M1bxLPJ+1f+3Jf/AB+/4Zr8 M/sk/so3/wAR/CfwC+Gvx2+Ll/rv7dfxe0LwT4etvil8U/jT8PdA8N/DnWtP/wCCeniTXfiJPZaZ 8HZvEOr6p4m8KfCqOC/1+Pw/ZWN/b6a3iG+2pU51mvZpSTzDFZWpc0VD67gsDHMsVT55SUPZUcBO lip4py+qexrYeUa7+sUVUU4yp0q1aaapUMNQxdWdm1GhicwoZVh5WjdynWzDE0cLToxUq0qkpWp8 tOpKH6dUV8j/ALYH7RHxA/Z48HfCu7+F/wALvB3xZ+Inxg+Ovw8+BXhbwv49+K2t/BzwXZ6v4+h1 2dNd1/x54e+Efxs1yz07So9Dl3W+nfDfV7m8eeMD7KFZq+DPir/wVp8d/Bz9nnXPin4n/ZS0/wAS fFX4aftk+IP2SPjR8Kvht8eZvFvhLRv+EM+Het/Gjxl8RfhL8U774LeHNW+LEekfCPS4NVtfAmrf Cv4ZeJ7zx6dY+Gt6dI1LR49U1Pnp16NV1lGrBfV8fQy2tKpJUYU8XiMTw3hKcZTrckPYxxPF3DdL EYtSeEwrzbDvFV6MY1pUnOE6ao3hUlLEYWpjKNOnTnWq1KFPCcQY1uNGlGdX2ssNwrn9Sjh3BYiv LLalKhSqVauGp1v2ror84f2qf+CiPh/9n3xx+x94I8B/D+L43zftVeP/AIdWV7rGmeOrXwnpfwy+ CvxC+I3wv+E+n/Gi5Z/DfiW48U/bPHfxm+HumeFvBSQ+H5PFlnP4p1ODxLp0HhDU1fp/h7+1b8dP jL8R77UPg/8As0+F/F/7KOg/GTxd8D9e+NmpfH5PC3xd1HxB8PPE2rfD34heP/h98BL74TXPgzxd 8HfBnxI0nUvDd/4i1j9ozwZ4/wBe03wz4x8R+Bvhb4sgtfBNj8QOmnRq1FeNOcYrH4/LJVKsXQpQ x2V/2dHMKM6tZU6dP6nUzXBUK9Wco0aeKliMK6n1nA46lhsZ1qUIKbqRlGWBwmZQ9k/byq4PHRzW eCqUYUfaTr1MXTyXMquGw9GM8TXpUKdWnRlTxWElX+96K/NPQv28vHvxF/aM/aK/Zr+FHwK8Haj4 n+Hfh3VdU/Z58ZfEr436x4B+Hv7Rur/C3xR4Q8D/ALTGj3up+F/gj8Utf+GFt8C/Gvjzwr4Tl1Ow 8NfFC48Wa3eXsL6R4YttOmu36j9hj9qH9pz9qjR/E3jf4rfs6fAj4L/DnRPHHxt+Fmn6j8Pf2qvi D8cfG2pfEP4E/GnxV8FPFMN54Q8SfsgfAbQtL8GaprPgbxNrPh/xLD481XXJ9M/sKPUfBmmXWp6h HokUE8TRw2JopvD4zBf2jha806VLEYP2taiq1KpV5IyvWw9WiqaftXVjGn7PnqU1PWunhpYiNXSe FzKllGJhH97OjmVbCfXoYSpGlzyjUWFjUqVW1yUXQxNOtOnVwuJhS/QSivyx/aK/4KZxfs+fCn9u 3xjd/A3VfFnxH/Y4+Knwv+F/hP4T2PjdNJufj/c/HPw38JtY+DmseHfFGoeDnt/DVt4q1n4m6h4X 1eCLRvF6+Gr7wB4quWu9UgtykPR/Fb/gpd8O/hreaFe6V4D1/wCJfhDW/wBmX4ZfH+yuvA2taVP4 517xJ+0d8WfBvwb/AGTPg94N8D6xDpWn6pr37QPjDW/Fllp3izxR4y8EeE/BreDjc+Irv+x9Q1TW PDEU5xrRpyoc1f2rwsIQoQnWqyxGOyXE8Q4PA+xpRnVWZYnKsHiKtHLJQWYVMTCOWxwzzKrSwk6n CVNxVS0Od4jllOUYwcMLmmWZLWxHPJqH1OOZZxluHjjnL6nVjifrFKvPC0q9al+ldFfHXws+Mn7V dudd1T9rP9mn4S/BDwPpvw/1nx6vjf4L/tPa1+0dZeHG8Mx6fea34V+JWh+J/wBnP9nbxLo3iC50 rULjUfDE/wAMNK+M3hvUx4V8YWmv6/4Tuo/A8fj3e+J37UeieHv2OvEn7ZPwg8OXfxv8Fad8EY/2 hPCeh6dNrHg7VfHvw0Hhq38eTXei2+seGL3XrLXbrwI13rGh+HNU8M22qalq8dl4cvotGub2W6sq ruGGpYmvWq0Y0MHShWxlZV6M6WDpVZY6NGpi6kJyjhY11lmYTw7run7ejg69ejz0abqE0ozrVcPQ pU6k6+LqVKWEoqnP2uKqUvqirQw1NxUsRKjLH4KFZUVP2VTF4enU5Z1YRf1PRX59fHr9vrwp8LYv CN74D0jwt8QfD/iP9k747/tn6x478QeM/FXhLwH4L+BXwc8IeFNb0jxhr134I+FHxi8Y3unePtf8 deGdI06Dw34G1zxFBoMPi3xJoXhvxfqPhlfCmreW/ta/8FQ/hX8EbbTvAXw71+w8RfH2f4rfseeB NW0XUfhd8bfEfwj0BP2lPjJ8J/D994S1v4zaP4W8M/C3QPi7c/BrxvrnxH8B/DTxP8QNB+I15pEO geNbr4dan4VuRZ6jrGjVeJw+E9nOOIxOYYfK6VKcJQn9cxOa4jJFTcZJSXsc0weNwmKai/q1bB4q nWUZ0ZxUU5wq0qVeE4OhWpVK9OvzL2LoUsPlmLnWdT4Y0lhc5yrEKpJqM6OYYWpByjVi3+q9FfI3 hz9ur9mDxZ8b5/2e9A8eeILz4hR+LfF3w8s9Uk+EnxlsPg/r/wASvAGkahrvjv4X+EP2idR+H9p+ z541+KfgrS9F8R3Hiz4Z+Efifrfjvw6/hLxraav4fs7zwV4rt9Hj+Bn7d/7Ln7SPjy8+G/wf+IGu +IPE6eGda8b+Hpdc+E/xj+H3hP4neA/DniKx8J698QfgX8QfiN4A8J+AP2gfh7pGv6toVjqHj34I eJviB4QtV8SeFryXWlsPFHh+61LKl++9h7H979awzxuF9l+8+s4ONCripYuhyX9tho4WhWxLr0+a kqFGrWc/Z05yV1Iyo+29tF0vq1WlQxHtE4ewr167wtGjW5reyq1sSnh6VOfLOpXTowTqJxPr2ivj z9oj9pfxz8OviX8LP2ffgR8I9F+NP7Qnxc8N+P8A4gaNoPjv4k6h8GPhJ4O+GXwtu/CGm+NPGvxK +KWifDT4z+JtEWbW/HnhLwt4M0Lwp8JfHOr+JfE2sxx30Ph3w1p2u+KNJ5z4E/tu6B4+vb34c/Gz wc/wF/aG0D9o3VP2U/EHwut9fuvin4Y1T4t2nwRuP2ldFvfh18StC8L6Cdf+G3jX4C283xF8J+L/ AB/4N+FGsGK2vPCvifwf4X8bQw+HLl0YyxCk6MZTUadasnyuKqUcPjqOWYith3JJYmlh8yr08ur1 cP7WFHH+0wdSUcRRrU6ZNShzc0Zfu6brVuVOf1alHB1cwlVxfIpfVKccBRnjnPE+yisG6WJb9jXo Tqfc1FfGfi79v/8AZa8GeA7f4kXnjDx74m8N3Hi74xeCltfhd8AP2hvjN4zh1T9n3xtrHw4+NWr3 vw2+EXwr8b/EbTvAfw18caJc+G/FPxPv/Ctt8NrDUL/w2P8AhLHh8X+FJta8M8af8FB4fDXxy8Va Hod78IPEvwFj+AH7Bnxa8B+P7zWvH1gnia9/bN/ag+JnwEtLy18VeAfB/wAXV1nR7vStC8C6h8ON MtPhzpunan4l1ieLxr8R/CPg3U5/Fng6qEJ4itQo0YupPEYmlgqTim4PF4iliauGw8pq8YVcUsJX jh4za9pOnJLSM5RqdOdKGInUTg8Km6lKfu1pezzjAZDiI0qUrTqTwmaZlhMLi4RTnh6k3TqKNW0H +n9FfnV8GP8Agpf8DPiprvxR0LxBoHxf+E7fD/8AaZsP2Y9H1n4j/s/ftL+CvCfjLxd4m1TTPDvg M2vjT4gfA7wP4P07V/Fvie/Ohf8ACNDXtSm0Ca58L3Os6lb23jjwm+p/aHgT4s+APibq3xM0TwPr x13UPg/8QJvhb8QxHpOuWNpofj228K+FfGl54fttT1PTbLTPEMlj4f8AGvhybUL/AMNXmsaXp+o3 l14fvb6DxBo+s6Xp+dB/WcPQxeH/AH+HxOCo5jRq0vfi8HXw2U4uFafLd0uShnuSvEU6qhVwlTNM BQxVOjXxNKnLOo1Sr18NVfs6+GxVXB1qU/dnHEUcRmOFlBKVueM6+UZpGhVp81LFRwGLq4adWlRn Nei0UUUxhRRRQAUUUUAFFFFABX4v/tOf8E9fij8WfAOqaDF8Nv2WP2hbSb/go3eftgXfwT/aN8U+ ItD+D3xD+F9z8M9X8FReC/G+pL+z98dYrXxPbazqNrrseny/C3xf4ezpsDLq0d15ckH2p43/AG5f hH8Pv2z/AIVfsPeJtC+IVp8SPjD8PLnx94S8ero/h7/hUKXSt49l0r4e6v4km8VQeI7L4h+JtK+F 3xB13w1pEXg+60jU9M8K6mv9vw6kLfTp5dE/bh+EHiD9s/xz+w7pmk+PZfiT8OfhHP8AFvxb48l0 bRLf4QadFYz/AA8l1b4fDxTN4kTXpviNonhz4tfDPxrrOlR+EzoOn+FPG+gXc3iX+0bp9Liwp06d etg8ZSkpVKtPGfVKnuSpzhwxn+A4kzGXJVjKlKOXZpwPSnjPbRdP2OExFCcZQxMebSrKVOli8LWi 4ww7wtXFQvOFSmuIsoxXDeBlz0pRqxWOwfGTo4adKSksRi8PUjKNSg+XyDxr+y18S/iR+yH8Fvgj pHwh/Zm/Ze1v4aftF/ss/EyP4Q/BLxxr/ib4C+Dvh5+z/wDtT/Dj4y6toXgDXrb9nr4I3f8Aamt+ CvBeoHSNCX4NeFNDtvGGpRaPc6wukifxSbHxD/4J3/Bv46ftzeKf2nv2ivgb+zb8ePBkP7NXwP8A hP8ACuw+Lvwz8LfFDxn4A+IHw7+LHx58eeLfEehWvjnwTq+leFdO1jTPiN4IhsdX8OeIBrWoanoF 5Hqem2VvpWkXl91nj7/gp1+wZ4H/AGfvH37Tlh+1Z+z98Svg/wDDbxDo/g7xT4p+FXxz+DXjLTLT xrr01oul+Cf7bh8fWnheDxhd2l2NWg8N6jr9hq0+kW91f29rNFF83tC/te/spTL8NxZftMfs+6jc fGe38OXHwXs7L41fDGS5+MS+M/7fHgtfhaJPFUUXjs+MpPCviaHwrJ4cmv4Ndl8Pa4NPmnXR9Sa1 6/rLpTdWKtOjm2JzVNKrWrU80zjIcHlrnCUpVK7xGIyrBYfEYWnGUq0pOWJoKSqIzemExOCm4xw1 fAU8LiIzVOlTjl2W59SzHkk7U6UMNh8zo08JiZTSpqmnhMQ+aUr+Y/txfsmab+2L4R+CHw88T+F/ hd46+Hngz9pj4V/Fr4o+BPjBolv4n8HeNfh94Jg8SrrfhyTwvqHhvxPofiPUryfVrCWx0fxFZWui XBt5Hu9StHhh8z5X8F/8E6/iD8O9B+FPwW8Mar8MD+zf+z7/AMFAbz9of4H+Dry/8TG+8IfsweJ/ hb8QY9a+Bw0ibwrqOnLc+CPi58VPGOkfDnQhrdz4Yh+DkHh3TW1fQJdKtvCkH2vpP7av7MreGP2e 9d8efGv4PfB7Wf2o9O0S6+B3gP4lfHL4EQeLPiTqWutosFr4a+Hl14O+J/jHwV8V9bS/8RaFpe74 P+MPiDot9qGsaVFo2s6nHqenzXXpvgz9oD4DfEf4gfEH4TfDz42/CPx78U/hJPbW3xV+Gngz4k+D fFHxA+GVxeSyw2lv8QfBuh6zfeI/Bk91NBPFbReI9N02SeWGWOJWeNwMKeFhh6tWkqaqupnf9sVq E3HEUauZ5HieE69SpKknOjV/s6XCuU0askpSwuFx2aYf2lOhnOKjVqvOU43rOVNRyuWUQnedCdHC ZnS4lwkeSonCpCpiocR5xCmpS9nXr0cHVdKpicrwlSh+QHww/wCCWfx28GeDNV0PxT8Uvh74x1fw n+1x+yRdfASWTUPGNtpvw/8A+Cfv7HP7Qen/ABe+FPwkv/tfh7UZ5PjBY6NrXj7TNTm06FPCmu3M PgXT7nX7O10qfVovp34Q/Bb9ub9ma48b/Bv4PaZ+yx44+AGq/GH4z/GLwF8Uvib8S/it4a+LnhDT vjF418VfGbV/hDrnwW8KfBPXPCPi9Lb4k+Ktd8KaL8ZYvj54cutC8E63pnibU/g9401zwTceF/iB 7L8dP2zb74RfGnTPgH4G/ZT/AGlf2m/iFefCab406nbfAq+/Zh0jTvDXgiPxZP4Mil1a8/aJ/aW+ AMl/q13rdvJHa6V4VtvElw0GyWXyWcR1t/DT9t/4EfFnSfhj4o8Ja1Jb+Cvib8PvjP8AEBfFHjTW PA3w1uPAkX7PviXQfB/xf8L/ABC+HXxE8ZeF/jLovib4e+J9ZvND8etpvw21vwz8N9V8O6pp3xL8 S+Dr3UvCFv4nUKjr5dOMMTVeEqV81r18VOs4TrYpYLD5VmmYVK1XltWjieBJ4ivj6apqea4LNcPO rUoZnmGX4tSoxWOXNh6csWqeAoUqUKfN7CEFmOZ5Zg4RpdIZbxZVhhcvrSqJ5TLL66w8pZXg8Xhf zg+AX/BJn49fs36p+wr8S9C/bL8dfGD4j/s4eNPHuq/F3wD8UND+BPhb4MeJtC/antdc1X9sW4+H Gv8Aws/Zi8O/HybWvEPxX1nTvjB4C0/4u/E/x7p2q6x4N0XQvFd/p11eQeMPD36W/sY/Anxd+zr8 HvEPw+8baj4c1TWdW/aG/av+LVtc+FrvU73TE8OfHX9pv4s/GjwjZTzato+h3S63p3hbx5pFh4jt o7KSxtNfttStdM1LV9OhtdVvJb79u79iDTPhPoHx61L9sn9lTT/gZ4r8TXPgrwv8aL79ob4RWnwn 8SeMrNdQa88JaB8RZ/F8fg/WPE1ouk6o1zoOnaxc6rAum6gZbVRZ3Hl+z33xf+E2mJrUmpfFD4d6 fH4b8aeEPhv4ie+8beGrRNB+InxBj8Jy+AfAWtNPqca6X408cRePfA0nhDwtfGDXPEsfjTwm+i2N 6viLRzedrnVl7SnGhCCqYmo406VFQ9l9deW1IYOkormVH2tbB1sNSm51PaZonGco4yhEhrnp0oSn KUVOljb3ilWr5cs8wlTHTjBRpyqt51j6GNrQhGMpUMNTqcrwkUfA3xk/Yh+I/wAQf+Ch3wT/AGmN C8SeA7X9nuz8JaK/7R/w+1ufXpvGnjz4ifBDTfjPafsyXfhjTU0K+8KS+GtB1L9obx94h8dPq+sa Xqr6x4M+F0+iw3I0u/FfMXwe/wCCTPxX8Mfs9fHP4W+L/jP4J0T4mQ/Gn4D6t+x38TPC2g654u07 4WfA79iTx1oPj39jXwl8T/DeoH4eX3iu407WLPxNH8VfBOjeI7Tw4+i+LtT0Pwb4otGjtdXg/Uy9 /bF/ZG0y9+MWm6l+1P8As46fqP7O9vb3f7QFhe/G/wCGVpe/Ay1u7r7DaXPxitZ/E8c/wyt7q+/0 O3m8apokc11/o8bNN8lVdE/a7+AXiCXxRq+m/Ez4bz/Cvwr8EPCf7Q9z8d7f4x/A7UPhFefCjxZq XjrT4PGUOq6T8TtQ8UaX4P01fh9rl/d/EfxR4S8P/CvVLVZoPCfjvxLqnh/xnp3hjhpcsMPT9hWl TpwjPMqWLp1uSccS8yq57gs8+tJrmx2XSyuvhslzCpOTweW4KeXYR+xw6hDaop1sTXq1aaq1nHCZ diKE6aqRnhMPhsPkryieDkpRnhcTLNMNPMcIqTnXxONVXENUcVWjV+Cvgp+xr+0Bp/7ZGs/tq+MP 2aP+Cf8A+zb8W2/Zq+NPwv17Xf2cfHvjX4l6x+0h8Wvir4s+DHivR/Hfxz13VP2Tv2afE2n6D4Lu fhDc29rBeeIvjB4lu7PxxqNta6vpMmkyXev/AKn+ALTx5qHwv8G2HxvtvAd58TL7wNoNn8WrT4fR a3N8Mrrxrc6Faw+OLbwXD4sDeIZfBE+tPqcWgR+I1bV5NDa1XVgbwzivLLL9sn9kLUvhXp/x1079 qr9m6/8Agjq9/wCI9K0r4x2Xxx+GN18K9T1TwdofiLxP4u03T/iFB4ok8I3t/wCFfDXhDxZ4h8R2 dtq8txoeh+F/EWranHa2GialcW3s3gbx54H+J/g/w78Qvhp4y8KfEPwD4w0q113wl448DeItI8W+ D/FGiXqeZZax4d8S6Beahout6Vdx/Pa6hpl7c2lwnzRTOOa6Jx5sIsvq4enLCUMNSw8cNOivZUMN i8ZnWaezhRa9lRoZhWzfGz9hGEcPUw2GwtHDUqeHwkYGPM1iIYlVZxrVJV60Jqo+es6UMBgZVJVW 3WxM8FLLqdJYirUq16eIrYv29adWt7v4qfCT/gld8Yvh7+yj+3T8Cdd+IvgHxX4u+KX7M/jz9hr9 jnVrrVvFR0H4cfsleHfDvxds/wBnPw38TLyfwk+p6Z4z0rUfjDqGl/FHUPCOl+KNP1HQPBngq60s anfaebFL/wARv2Bv2xLmX4tfCv4bap+zTP8AA742ftRfsa/tZ+JfG/jjxl8UbD4reGtf+AOvfstr 8TPhPoXgLQfhdqfhDWNH8Rab+zfb+KvAvxT1D4haXe2V9rE3w78QfCqWC5T4o6T9d+OP+CgfgLQv 2+PAH7BPhbXf2etQ+IGofDm7+J/xTh+IH7S2jfDr4keF9Eni1O88PeHfhV8HLPwH441v4w+P7nQN B1vx74p8Natr/wAKbPwh8Mrew8bya9rFnqtvaJ7/AOAv2xP2R/ir4I8S/Ez4X/tTfs5fEj4ceC9e 0zwr4x+IHgL43/DPxh4I8J+KNbudOstG8N+JfFfh7xPqOg6Fr2r3msaRaaZo+qX9rqF/c6pp0Frb yy31skqwuKlH2Oc0q8ql/YSqYmtUbeLXD/Ev1mricZGo4z9niOMMtWOxuLUaNHM80w1WMalXC18X h8RtiIznVo4GtTft8LjKmPwtOMW6lDEZ/lOBpYaGHmuZudPhvC4fLssoqc8Rl2UyUKHsalPD1qH5 WfCL/gld4u8DfG7UrDx/4O0/4n/BG3+PP7Qnxs8A/F2X/gpV+3t4f8TfD+8+OGofFLxXpz6R/wAE 8JvCXiH9jaLxl4K1D4ra/wDD5PF2hfEjwzZ6zo0t18VE8MaP4w1G+8JXHuX7JX7IX7VXgnxt+ygP 2iZf2etL8CfsH/s/eM/gF8H9X+CHi7x94k8WfHmTxNpnw98C6f8AEb4k+F/FXwq+G+ifAu10v4e/ DKyub74TeFvFfx203V/HHi19RT4gaZY+ANIXxR96az+0v8G7f9nLxf8AtWeDfGmgfGH4J+FPhp43 +K8HjH4NeI/C3xB0fxl4V8AaRrWr66fAviDSteXwl4hvG/sDUtMsyniK2046vA9neajZeVcSQ8B8 Dv2sJfipomm+I/iZ+z98X/2SNE8WXXgbTvhbfftJ+N/2TUHxh1j4gWWp6lomgfD21+A/7Tfx2vpf Ea2OnR3Mvh7xXa+FNZvk1G2Xw/Za1JZa6mjrCr6u8RgKMFF4Snk9PH4aovZxqYudLNckyGviaEvZ 0Z5s6Ms1wWDhShGtip0HiK2HxOKy7C4nC44lwxCpZjU5H9aeYVMJioNVJxw8cVgM2zpYWperVp5f OtXy3E4+z+qYSjUpxoSwmGr14VeN/aS+CPx7m+PXwU/at/Zki+FPiv4k/DDwB8Vvgv4t+EXxu8a+ K/hd4D+Inwv+Leq/D/xVcX+nfFfwR8L/AI1+I/APjXwR40+F3hbVNMYfCnxjo3ivQb/xH4c1FNAu 59K8R6Z+dngz9lT/AIKIj4o/EP42+Mfg7+zZb/tHRft2aB+2voEun/tB+I7n9lr4ifDQ/sr+L/2J tA/Z2/4WlB8B7L9oX4f/ABT+Ffws1OLx1rfjXV/2UvHHw98deJ3tZ9D1fSIfF+seEfhV+vXxN/a3 /ZT+Cml61rfxl/ab/Z7+Emi+HPHFn8MfEOr/ABN+NHw48BaXoPxJ1DwrZeOrD4e61f8AirxJpVpp fji+8E6npvjGz8J30sGvXPhXULLxDDYPpN3Bdyckf2svB9l+0145+APiMeGvDHh3wl8Df2dvi9pv xY1vx3pdhovibWP2kPjD8WPg14L8AWWm31hZ2Mepah4l+HOiWfhe+g8UahdeNNc8d6V4a0nQoL+C 0k1kwaqQq0FhpTm3CvhMFTtCdKlTxmNw3GVfC0Y8vs39ex2ChnCjV9pWxSxNehSlPCZjPC1+iq5+ wx85wVKNfCwpZjUjeFfF4bFQXBEfayT9vWp+xzfD5I40U6dB08DiZxp4jKsNi8J+W2pf8E7v27bf 4TfDL4d23xI+HHiDTk+I/wC3f45+LPgX4e/tb/tl/sVeGn8X/tSftL678ZPhP8WdN+Iv7NOhN8W/ ifb/AAi8GeJ/FnhnWv2bPGGteDPAHi7WPFc+oz/E0Xvhvw9r0bfDX/BKn9oTRPhT8LPAdx41+Dlx qvgX9kP/AIJDfADVL2PX/HX2G88YfsA/tZ6h8d/jFq1i9x4DlvX8OeJ/CF1Hp/w1u7xU1fV/Eoks /Fem+EtNA1yT9l/G/wC0Z+z38M/DHxC8bfEf47/Br4f+DPhJ4g0vwn8VvF3jf4n+CPCnhj4ZeKtc sfDep6L4a+IWv69rlhpXgvxBrGm+MfCGoaXo3iS703UdQsfFXhu7tLaaDXNMkuvCfH//AAUY/Yr+ Gvjf9mrwN4o/aQ+Ctrcfta2N7q3wP8RD4wfCeDwp4w0NdPjuvD2v6Tq+o+OLB/EGhePdRntfDHgD VfCNp4ktPFHim8tNFspPPuI2OuEryw1alUwiipvNslx+HjFOrzY3BUM2xOTUeaTnVxTlhc4zGrh3 iKmIxlbL/q2GhiJZXleWYbBKvVqy+uVMQ3/tUsVisTOcVCMZS4gyzPMdXjyqEKFOWbZflixMIKGG pzjOSp08XmGPr4v4/wD2jPgV8SfBH7Mv/BVC2+LOpfCDwl8F/EF58Qf2v/gX8btH8b+L9S+IHgbx /wCEPCHg/wCI/hy/+Ivws1H4X6Z4e0D/AIVJ8V/hVo3izw74h8J/FrxrdeMbOGytrjwt4Uv7dhef Yv8AwTx+GPiz4Yfsh/CRPiTHAPjF8TrHWvj/APHKSCRriM/Gn9oLxDqnxi+JWnwXT5kudN8N+JvG V74S0Bn4g8N+H9Hs4Vjt7aGNOp1b9tj9mfwL8PLz4p/HH4y/CL9nHwRbfFr4gfBWDxF8cfjn8A/C 3h/UfHfw78U+KPC+p6NZeLNJ+KfiPwamt30nhDW9Uh8Eah4isfiT4esrO803x74K8HeKtH8QeHNI 7Hxz+1Z+y78MNU8MaJ8S/wBpL4B/DzWvG03gi38GaR45+MPw88Jap4un+Jk/iK2+G8Hhiw1/xFp9 3r03xBufCHiy38ER6VFdv4rn8L+IotBW/k0TUltscIvZYeOHp/vYU8BwzkuClFWq4bD4HBToYbC1 HTt7evm+Bjw5CVOtGNaX+r2X1eWpiaterLPFRbquU+ej7PMuJs5zClKTdCvjsdjcNRxGP5Z6U1lm PWfU6eIU6tGm+IsdhMNLC4RwoVPfKKKKBBRRRQAUUUUAFFFFAH45/trfskfHj4s/Hn4yfGj4TeEr W78WeCv2TvgVr37MviO88T+G9Ht7/wDat/Z8/aC+J/xj8N+BbtbzVo9S0bSPFml6npfgvxNr2sab B4UuPCvjzXNOm1KbZqdvb/LXin/gnB+1X8Sfh/4ltNU03QfCvxk/aV/4J0ft8eHfjv43k8TeFm0D w1+13+2D8SPgX460z4X6le6SNW1bxF4R8IeHfC+o/CDTvHWm+DvFHh5Ph58NdHF/ZXiXWk+HdT/o xornw2HjhcNDDUJ1IRpw4nhSmnFTg+L6XEVHPajUYxp1qmKhn2Fs8RTrRh/qvwwuVxwePWZ6V6n1 jEvF1YxdSVfhyvNe+4SlwpPIp5JFc0pTpQwrybFK1CdKUlxJxG+ZSxeDll/88fhz9kz9o/xb8Dv2 w/FWsfDX9vK//aL8X/s3fBz4SeEfBf7XHi7/AIJTaB4U8eaX8IPF3iz4g+Gfh/8ACK7/AGAf+EV8 LWd1oeq614k0LTfFP7QVp4PtLa08Z6PBpdxpelf8JONF/RD4ffDH4h+L/wBuPUP2rvFPwR1v4aaJ 4k/YS+Hnwm02Lx9qnwk1X4h+CfHP/C6fiR428Z/DTVX+GvxA+I2mQXEek3vgvU9b1Dwn4o1/wHrF zb6bFZeJtWvdHaOy/QyitcbThjqtOrUXs/Y0c2o0oUXJRis7yfiLJsyqN1HVqVKuJp8TY/GSqVal R/X6eGq/wVicPieeFKNOhi8PFycMasqjWlJ80+TJcbkGNy2nBtWhTw3+rmBwlOCjZYKpXoqzjhJ4 X+en9kL9mL9qL9lLSfg5YePv2L5v2ix8Tv2Bv2Nv2V/G+lW/xG/Z2TRP2efGXwJ8SfFKfx/onxnk +IPj6F9b+Dmtp8WtO8Wx6t+z7pHx41y91bwZ4mtpvhjcXh8Kah4k+1f2KPAn7Qvw6/aE/aW0Ob4b /Hf4V/seazqGt+P/AIeeEP2jfEv7L/i3VbP47fEX4ufETxn8U9U/Z+1f9nv4l/Ezx1b/AAJ8XjWh 41u/D37SmrQeN/DfibxDp+l+A7DQ/C0ereFfDv6g0V2VcVUr4ueMqqEqtSWdSqRtKNCcc6xk8dOD oRkqVsBiKjqYCcYqsqn7/G1cbiXKtLXEv6z9Zv8Au/rOIyrFSlTcueniMpoww1OpSqVHUnH63hIy weLhKU6f1SrVoYWGFhNo/ML44t+0B8Jv27tP/aC+Hv7Inxq/ad8Aaz+yEPg7PcfBfxx+yv4avvDn juz+Md942itfEVj+0Z+0b8Brs6Vd6LNE8eq+F4PFAimZoZ7ZJF2N8Vr/AME/fjVp0fg7UvjL8Gbv 4t6f8QvhB/wVy139pnwJ+zR8RPA2gajp3jn9vX42/A74i+EvhB8JvFPxZ8Y/AyPVtcsfCGieI/Dt r8TNVm8D6Bd+IfBd54p8S3fhefxJpVnff0I0Vw4ajHC0KVGm5c1DDcSYOjiW0sTTwfFWMzvMM2wq qU1CLoyx2dyxVHmhKdOrluVuU6kaGIjit1iakcZ9cjyxk8XleYSpLm9jPHZPkVDhzAYp3k60alHK 6dak4U60KM6mNxNWdJzWFeG/Av4e+Av2/fB3ib9nz9pj42fs2fFz9rLVfh54D/bO/Z5034TeI/F/ 7EPh79q3w58Ofiz8RvhL4j+DHxH+Ls+k/FfwF+xrrHiLVfB3wk1PwJ8W7z4LfES21EaBr/gO/i8G eK9TuvH+m+HU+LH7Of7WsnjX4p/C3wT+yXBJ8P8A4tftx/8ABNf9rXTviT4L+LPwZ034QfDH4c/s 8n9ifwl8W/hZF4e1/XvBXxUvvG3gNP2b/EOv+E7DQ/hDa/D/AMU/De6sp9O8Y6T49t7X4X3/AO+t cb8Q/iH4H+E3gfxT8SviV4p0XwT4C8E6Le+IfFfivxDex6fo+h6Pp8Zluby8uZTgADbHDDGslzd3 MkNpaQz3U8ML9dLFvBVcHi1KnD+zamExClUSjTnTwPEdLi9wxk04SrU63EFD+0sRXqVPrUPaV8Ph 8ThsHWqUHz0qTTrU6EZKWLp4vDOFNOdo42lhMFTp4WjLnp4ZYTL8HhstwlDC06VGpQhGtjKWMzBR xsfwyv8A4DftFzJ+1n8O7f8AZ0/b5sv2V9V8e+E/jT8EvAek/E7/AIJzr+0H4J/arm/aX8SfG7Xf iz+yZ4t1T9om58GD4IWfiq10b4r6v4F/bR8VaprMOtara+E/Cnw+1LwPceIPh1o/E+Mf2Tv+CgPx Ut7b4zfGX4U6Z8Rfirp37NX/AATL13x18Pf+Eq+Dnh1fjt8Rv2UP20PjZ8ffih8EdWg07xd/wrfQ fiNd/D7XvBOrPqUWpaR+zxqXxjurXR/D/jm2+HtrqWpaP+23wH/ac+Ef7SFv4pf4Z3PxCstT8E3m lWnirwh8Xfgj8bv2dviNosWv2Ut/4e1q6+GP7Qfw7+F/xDPhTxJDaanF4b8Zw+GJfCXiG/0LxLpe ja1fan4Z8QWmm7+t/Hf4a+GdSk0rxLfeKPDtx/wtPwf8GLG4174b/ErR9J8QfEPx5pmm6r4X03wh ruo+EbbRPGmi30OqW9ndeNvCeoa14H0bXbfVfDWt+I9O8RaHrWl6fnh4VcFPK6FN1VisFjspx2Xu rFTxE8wgqWXZXiPYzj7LE4jGVMdlOFTlQnLM3h8hy6t9ZwuHwWDCrWhi6WdVJShPB5rhcXTzNU6k 44enhKWMwWc5k4V6dRVcJGNXJcVicRWjiIzwLxmd47DVMHjK88ZR/IHTv2Tfj98V/j/4F/aQ8Vfs 3zfCbwv4r/4Kd/Df9p/XPgd448U/BPxD4x+G/gr4S/sHfE79n+D43/EZfh94+8efDS5+J/jD4tHw LNYaN8K/HvxM1bSfDOlfDfxNe6hY6/ZeLbHwn+g37Bvwe8c/A74UfFLwd478Mw+EZ9X/AGv/ANtP 4leEtFtdR0HULP8A4Vx8W/2o/ir8S/h9qtmvhzUNR0/TIPEHhbxTp2vf2LK9pqulTalLaa3pmm6v He2cX2xXL+L/ABfpXgjSYda1m08UXtnPrXh3QEh8IeCPGnxB1Zb7xRrun+HdNnm0DwFoHiXXbfRb a/1O2uPEXiW406Lw74Q0KPUPFHizVdF8NaTqurWdUZ+xprDUacY06kIYWFJOpNOpVw/DmX0/ZRnU k1WnDhzL6VGnD3Ie3xGHw9KnQlhKGGvETliY0lVs3h6lGvGajFT5cHiOMcZCNSSXvU4vjTNfazl+ 9qQwuAqVasq9PGVsb+W37Zf7Ifxu+P8A8Rf20U8Dafp2maH8cP8AgmdZ/s4+B/FOs6/pljoOsfFQ fEX40a9feAvEdpZXV54s0rw1rWheK9D0zxH4ij8PS2Meh+ItQXS7m91WynsovmH4v/su/tIftaaf +0D8Qbv9jHUf2dLHx18H/wBhT4Awfs9fEfxv+zd4h8SfFC2+B37VUPxh+IXibxEfhN8VPiV8IY/h h8Ofh5r+seEvhnZa34+i8aeKba7+IOn6j8PvD9q3hG18Sf0H0Vz4alHC0sLShzTWCqYythHNtOlW x+a8S5riqso0vZU8U5vivNMFShjIYmlhMKsNWwdPD5nTq5jWWJk8TUr1J+5LFPL/AG/s3K04ZZhu FaGEhFVHU9goz4PynFVp4f2NXE15YiGIqVcJ9VwuF+df2u/Afij4nfsm/tN/DHwFpI1rxp8QP2e/ jJ4F8G6Gt5pulrqvibxV8OvEWg+H9KXUNXu9O0fThfapfWloLzU76x060EvnXl3bW0ckyflj4I/Z l+Jniv8AYW+LHwQ8Kfsxftq/Af42+C/Cvwh+K3wd1f8AbN/a/wDCf7TGjeJf2mPgNd6N8QPhjZ/D DXof26v2vtU+F/g2H4kfD7w9Y+JdJkh+E/hKfw14i22+m6gF1S30392KKKVN0KuY16NWpSr5jRye m60HBVcHWyHE5pjMrxuCnyOVPFYbGZrUxcXVdah9ZwmArqgqmGUpVKcZxy6nUo06lLLa+Y1adGXt FTr082p5XQx+GxMYVI3oVsNlVLC3ouhXjh8VjacK69vF0/51vEH7Nv8AwUC0D4XfBu/0L4b/ABJ0 n4i/HnT/ANrf4o/taaj+zrqX7AGs/HPwH8cP2l/GngDxH4N+DPjDxv8Ato6B8RPhXL+yt4M8A6ZF 8Mfitq3wI03x98S9bvfgx8JL/wAL6J4n8P6U8Gpcp4R/YV/bM8I/Bb4bfaPg43iLx58Kf+Cfn/BG PwjdeDh8TfhlBqfjX4zfsH/ta+Jvj98cfg5oHiG58UR+G4fFUvhSz03SfCPizxHfeHvhVrviTxFp Nu/jrTdKtte1TQ/6VKK6cNVlg3F4RQw/scZlOMwUYx545dHIaeeUsmwWDjV9oo5dltPPsVDC4St7 anCGHwkHzQjiY4lKcvZzhUftnVV8RUqJKpicRUzzJeIcdja7pKlGWMzTM8iwtTH1oxgqkKuJjRhQ k6E8P/PrrHwr/wCCgcZ+Ovxe8Ifs7fFD4Xah8ZP+CiOhfHeTw/8ADLxP+wT4+/bH8B/ApP2Bvhp8 FLLxJ8IL39pDxF43/Y88MfFZvil4Wk8AfFmz8ReLtYk0/wCGGofE6w+F/iPxjHrPhzxTqu1+yp+z p+17+zr8Lv2Db/xz8B/iD8TPFvwu/ao/bs1X4t+GdD+In7Mcvj/wn8Pf2mviX8ctZ+H3xT1W7t/E vwN+Cev6Xp+meKPCmq+OfDfwptPD+r6Ra63cReCPhM02lSeEbP8AfCilCo4UaeH5YypUsPkmDpqf NKUMJkWW5dleHw8JuXPTpYrD5bRnjlTlGVTEVa9ajLDydJUYxV8WqXtXyypVqmJ56SjTdTE1KmeV FXrJLkqVKU8+xHs7w5eTCYCnOM6dKtCv/P8A+CP2c/2pf2cfi/pH7QE/7JHin9pzTV+JX/BVrw0P hD4G+IX7OeneN/CXh/8Aay/a9sPjh8KPjBos3xm+L/w1+G9z4V+I3w/8JxeFfiTpjeOU+JPhm01r wrbL4E1JR4y0/SvPdL/4Jn/tB+HPgB+0l8NdX+GHgzxr4y8R/wDBDnwN+xX8N7zSdf8ABV3oR+NV nrH7WfiDVfgV4I1bxdd+H9a0rwJ4Pn8dfBzRPD3ijxZpfhLwpq+m6L4V1K6u7fUfDmoQaF/SFRWN OPssJhsJFy5MHgqmAwtRNxrUcNWwmf4StHnp8ka05viTNMRGriIVqmHq1VRwsqGAdXB1e2OOqxzH GZmqdJ18wzeOdYunJTnh6mNp8RZFxJR5KU6kvq9GnieHcvwbpYWVFYjB+3q4x4nNJwzKnzfgyz1L T/B/hSw1lZF1ex8N6FZ6qs06XUq6lbaXaw3wluY5Zo7iQXSSh545pUmbMiyyKwY9JRRW9etLEV61 eUYxlXq1K0owTUIyqTc2oJuTUU5Wim20rJt7nl4XDwwmFw2EpucqeFoUcPTlUac5Qo0404ubjGMX NxinJxjFN3ailoFFFFZG4UUUUAFFFFABRRRQAUUUUAFFFFABRRWcdX0kahc6SdT04arZ6fBq13ph vbYaha6VdTXVtbanc2Xm/aYNPuLixvoIL2WNbaWazuoo5Ge3mVE2lq2krN3emkYuUnr0jGMpN9Ip t6Jhr27L/wACkoR/8ClKMV3lJJatI0a+EP8Ago34D+IPjr9m23ufht4K1X4naz8MPjt+zN8edZ+F ugy2Y8QfErwV8Bv2gPh18WvG3g/w1ZanNb6Xrfiq98NeEdRvPCnh3UZ4YPEHiWw0rR4poL28tJ4/ snUfGvg3SPC3/Ccat4t8M6X4KFjZ6ofGGo69pVl4WGmagYBp+o/8JBc3cWk/Yb43VsLO7+1/Z7k3 EHkyP5se7Z03U9O1jTrHV9Iv7LVdJ1SzttR03VNNuoL7TtR0+8hS4tL6xvbWSW2u7O6t5I57a5t5 ZIJ4XSWJ2RlYt+1p1KFek1TxOXY/L8xw86kHUp0cdluNo5jgZYilzQ9pTjisHCVXDynBV6UKlJyU XJppwcalOpFzoYvDYvCVYwm6U6mGxWHnhcUqNaKcqNX2GJap4in+8w9SdKtTaqRgz8c/2pNU8H/8 FCNL/ZV8M6X+zr+0V4r+Cnh/9uH4Yn4t6f8AHP8AZg+OXwS0PxJ4Nk+DHx1XxbYeJ/hl8bfBXw/+ I2rfCmxfU/C3h74ha54v+H9v8EvGsPjeD4er4g8bGXxz4a0j5H/aO/ZE+Gt14t/aj8QfFr9iX/hb fwN+Hn/BST9iHxd4Q8IWf7I+pfHa2T9n7RP2Rf2WPhN8QdR+Dnwj8OfDnxlr3jb4feHZNFuPCXjH RPhJ4S8RRW2neEdZ0W50iePwxfWNn/RV4V8beDfHdhc6r4H8W+GPGWmWeoXGlXeo+Fde0rxDYWuq WiRPdabc3mkXd5bwahbJPA9xZyyLcQpNE0kaiRCenqaFOOFnVnSjzKvm+X546eJ/fUalbB4rgHMa FGrCKpe2wbr8DU5U4OSnChnWOjCq58tapUpyqe1VWUuafDeO4WlUotUa9HC4zEcQzxGIoVHGqqON Uc+lTk/ZypOtl+FqzoNRdFfy2fFH9mnx94o/4KFePfEPi+3h+GGoav8AtD/sqeMv2PfjlZ/8ElP2 wP2nvih4A+AfhDwp8F/7O+GPws/bO+EPxR0b4SfsT/D268beH/it4G+MHwf+L3wp8Hadpel+NfH3 jT4mad4t8F/EP+029p179j+4tPgD+1d8R9K/Zw12b9ov4jf8FV/BWqN4vPwz1zUvi54g/Z88Pf8A BUD4LfEmw/sLVLnSbrxXH8CNK8M6Pq/xVj0/w/Jb/DWBB4n+J7WwvNR8QeIbv+iqitcHL6licsxF KVVvK4Qp0oTquXt5U804MzB47E1HH2tXNcdR4Mw+EzfGqcYY/wDtDG1qeGwil7Cc1H7a3tFB/wDC jl2ZOKi1TjPLsHxBhqOCoUrulh8tw1TiCpVy3CQg5ZfDB4ShGtXVONSP8w3w68KfGrXf+Cq/wQ+L un/suv8ACDWYP2tP2sPCX7QXirw/+wj+0f4c+JV18CpPhZ+0Jpnwo1f45f8ABS7xr8VdX+EH7Unw l+KWt+FfhF44+H3wx+HPgC88IfAq8m+F/wALYdb8ETfD/wAMaL4p/p5oqlqWpado2nX+r6xf2Wla Tpdnc6jqeqaldQWOnadp9lC9zeX1/e3UkVtZ2dpbxyT3N1cSxwQQxvLK6IrMFGTjg8DhbK2Cw6oR auoWT5n7NTc6sKbm5VOWtXxNRVKlR+2cHCEHX/2jH4nGRjyvFNr2MbvV43H4qEpNW9pVp0cZRy+F TkjOeDwGDjVdWtGpVqXaKRWV1VlIZWAZWUhlZSMhlIyCCDkEcEcilqSE00mmmmrprVNPZprdMKKK KBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUV8k/ET4h3nw9+J/xB8a3cltdaB4C8C/BOXX rK8gumOh+AfF/jv4i2Hj/wAW6XLBKgjuNPTStF1zVmeK7hudI8EvYi3W6a3urfB+Iv7R2v8AhT4Q +FfGzeJvhH4O8VX2tW8fjDQvE994c1O/8MWuqaJJ4osfCdv4a8QfGX4Q2+p+OdJ0DV/C174gsz48 tdQls2vdT8P+FNWGqaTpsaUk4OV0nCtTo1otpKl7et7LD1W5OP8As9ROPNWaVOFaGJw6c54dynXL L2rpJN3pupTajJupyYeFetTjBRc/bUudL2cVJypVMNXVqeJpn2pRXx3rPxV8eajD461Rp/DY8JeF vjJ8C/h7oum2+iePPC/iq9fxf4k+COpajr+sazb+OdKu7OBbLx7qNivgu48PxRytaQW3iq41jSbj WPDV7yGvfHD4rr4P+JOu62vgSbw7c+Cf2pZvCel6BYeOPDfiTR7v4H6zeeH9NudZ8X6d49jvL8eI oIJrm5Phaz8Eav4fuRBLpOvSzBZ4KSbhGo04qUXP3k48sfqmCxlNTTSlGpVo4/DuNNRk48/LUcJx qwpFFe3nThB39piKeGg4uMlKpUnKPuNS5XGHs6rc3KNOXsnySl7Sj7X7zr5t+P8A4B8beI/7G1P4 bWYl1/XNO1j4T+LbxdStNMk0f4c+PTa/2z4sha8ubZL3UvBd9plpqel2dv52oSR3uqRWEMktw0b8 jr3x+8XaPrnxH8KPp+i6ZcfDLwv4r8f674kn0XUtdsIfAX/CJ6PqXw8v49Hl8W+Gjd6jq3iLU9d0 rU3vPFek6ZPB8M/F08+o+HINRtNR0rxm2/bC8Zz/AAl1LxtdeLPgtpF5pPxTfwi2u6gPh3Lp+v8A h2PwTo/iKdvCvhux/a3ufB2va/YanrHk6hDpH7QWqaimlafc/ZvCdzr7/wBiQRywnyykouChQqtz 5VCVLF4l5bWoVG3end1K+Dx9Nyp1adJ4qlzRnGVpo1JShCdPmUqjmoKHPKcZ0cPDMKFaEYrmnp7D F4JuElVn9Xquk6clf6q+JXh+61n4Y6fpWj/Drxv53hrxFo58P6L4R8SeAPD/AI88PL4P1WS38NeM fBlxr2u3Pw81B7VrDTdZsvDvjHWbPTtR8M3Vxp/ifSZL37X4PvNT4fweNH+GcHhrxr4JVPEK+CzJ qMNxc+EtJ0LXtT1ptbWbw9fv4OvNRh03xEkEdlL451DR/DsfhEatrk9z4NudZs457ax+fPhL+1bc +MPjLrfgbX/E/gWXwnDZ+JF0+/fRPD3w+8QaXruneNvC/hDQNB1zRbv9oD4jeNLHU9fvPEMmm2Wk +PPhp8JddvtUt7ZNN0m5muZ9NtOUuv2s/FI8beIvCUnxN/Z/8O6bZ+NbzSZfiBqWkW3iP4d+ANAi tvFt3pVv4s8UeHP2hRDe+LPENxoth4ds9J8bW3wJ1JNYg1+50nw/4ltba3hoqJ1KU6dRuazBVcVJ 1FBy51Gjgq6lTqrkdecZUq1eNSlUqVKVOn7VvD0aNKJTag41aaUI4J0sPT9m3yKEqssVQVOrSbfs YPnw9BqrGMZVZ+wviq8q87+meA/jHBoOnWU/gf41R/DzRfFWl2g8C6f8Svhl4R+POpeEdM8Cajpm i6Qfib8OPiR4PsNY8C+D/EdxpiaMPEHxNtPiNq9pb39z4pvddjSGy1Pcb4O/GvxBpFhN4m8VfFKx 13TfD/wO03TB4b+MOtaBHaonxA1l/isNYh0PxPY6V4m8SWnw3udJ0zWte1621f8AtPULJ9S8I3M2 tsb1xvjr8dNe0iw1vwzD8LtEgg8P/A65v7LxJ4V8YatJ4g1L4wfEDWfAg1fR72w8caF/YHhu2tdP sfF+i295p3iS/wBV0/UU0m4u7BwusNwXiH9q74s+H/Guh/Dx9Y+FF54vl8YReC9RsH8GWWh2msy3 vxT1jwDY+JNDg8SftPaR47+xLp1nFqWpaP4R+HXxWj0q6sL2PUPE1rBeltFuUnOtJSjKdZ4yM3Fx qTqVKuDhQhOpNTvUre19tQVaWI58VVlipLRfWFTiEVGjTqRko0qcPYppwjGmsQ6mKjRi6aUKcFTp 1NMM44aNGn7Go+blg9L4sfDT9omHwnpXhr4eWvxL1S50nWvi8NA8RRfFrxZqGu6fpt94iiufhxF4 jvrj9qD4Mya3Yrp0zSWuteMm+Muq6VbWC6bqHgwwyz2+qd5rNr8XNR+IHxf8IaJqmteIdH8LeCtZ 8d6NpUPjHWtAv7/xl8TvDFvofhv4ftrmleIvDl/ZWHhy/wDDnxA8UWMEHinwxbadJ4x8E/2dqmgf 2Jaa1Y8t4k/aw8Z2/wAOx45ij8KeAk/4TDQfh5Gvi3QdF1G0uPGmneDbzW/ifpz6t43+PPwB8Haf a+HvFK3Hg3TJ7jxvLeXereEfElvY6frzX1vJpGH4S/bE8Sayp1u+8c/Al9H03RvgvqfjPw19hudB 8W+B/wDhPNe8AxeOdR1Gef4t6+t74Q8F+G/FM95qHjS58P6D4b0vWNT0bTv7R186P4iVFCPM/qvt Iz92WHqV5TjOOIpZhhqE1i51I80PYy/2PFSx0YU4qvCjPEVGsTOOJucmnTr8jhKNWdeFGEeV0a0M c+bCypvV1MPJYrB0cJKU50KFfEwpQVSlCdCHTfAX7QK/DhdD8SeFvjzqV3dfF+PUrSfwh8ZpvCvj zwr8P4vCPh/+0JYn8Qftg+OItdXWPElhqul2Gj+J/jL4u03Rv7WuvFMfhWNba18OXPZ+J/Cvx+8T +FPiB8PLjwj41nhfwP8AtR6XbeItX8ceDLnw742vfiJqV7cfCbSNMtx46vfEGNH0W8GjLN4w0Hw7 Y+GvKNnYzvprC5EetftAeItVvH8f+B9T0nxDpei/DH9rnU/Ddnol1qWp+DPF3/CtvGfwrsPCGt3t rpmrmHX2WwmvzDqthdxI1tquoyaPNZ2uoSb5/iR+0Rd+LPCnjNPhX4j0nxJow8bWfg3SPEnwy1Qa pqOtL/wp7VviNdeHPD/iLSJNfh/4SHxP4n0mH4eWeoeGrK48QaYdbkg8N2reN7bTLiInOUqNOq43 WJpQqeyb5PZxqzpZVRqVftwssvp1X8U3OpVpqnXm40TSh+7r0ZRaTw2JxKjP4ozqZbiMRiMTFaqM ouVeorRdP2lKMJ88HB1V6LBZ/Giy+JI0X/hHfGd34Quvi1o/jEeM4vGHhZfCOmeAIPhNY+H7zwe2 j3HjKLxm0/8AwnNpPqUuh2fg2Xw3LJdHWk1OW8Mq19VV+XngbxH8TPC0l1Ibn4l6h4cj8ZfDzQ9a u/Hmj/tT+DdH1dvEfxg+COieENZ8Mf8AC9/iF4i8eabqAsNe+KFj4g8O6J41uvC+q2mlaEfHnhZr TU49I1Pr/HPx++Oum6KniFfFvwh8FeGtU8c+M9KtdVvfAPiLVJ/CHhz4afHPw38O9V1XxVqGofEm x0nV7DWNAv8AUNT1t7XT/CzaDZ29wtlqvnzxaxpNbUKDu/Zzm489RKnOmqOHy3DRWJU5RdP908PU UZR9q4SlXxHLiKlenT5qMF7sY3Xso0sFCCvJVGp4nEwlBRjeU6qrVZSnaNHlhTp0X7P2Htf0Uor8 wfjV+1V8QbXUvit8PtF1DwhZ2um/DvxfFp+qadrHgfwt4u1bHwYvfGFn8UPAF1eftCxfEKXS/wC2 p4X0XTvD/wAGPE0B0+wur6y+Ilzc2t01h9QeOPjWNG8eeCvBGnfEj4TeB7O7tpV8Q6/8Ql/twan4 nguPCJtvhfosdt4/8DRaN481PRPE0HiGBNUu9a1B9Nu9MvLPwpqVpPPcwkIuboqOrrz5KaacZNKE qkpyhNRnCKpKM1GcVUlUqQwcabx7eEV1JKmk5dKdepOzUlFUZ4OnyqUHKE3Ori+RuMuSnClPFVJr BOOJf09RXwJN+1D4+ik8ePaX/wAKNd1fRPD3xj1Rvhjp1hrUXjX4TT/DvUZ7TwvcfFvUE8a6lJda d4vSG3QpB4O8EutzqtnJod3r1hG15PoeKfGfxkuX8a+CviNqnwl1vwQfE2ofCfxC3hzwT4w8K63q Nt4k+Clz8QotesL7VPiZ4osfD7aHPeWulS2k1prbavFFPq0Oo+HZhDYrhOtGnS9tJS5fq31lbPmT qRowpxknySqTq1KVNOMnSTqJyqxjCrKntTpupWlRTScKk4Ter5YUoe0rVXHSUY06d58k1CrUSfsa dRn3XRXG/DnVdV1z4feBNb12Mw63rHg3wxqmsRMFDRarqGiWN3qEbBQqgpdzTKQqquRwAOK7Kuqv Slh69ahNpzo1alGTjdxcqc3CTi2k7NxdrpO26RzUKqr0KNeKcVWpU6qi90qkFNJ20uk7OwUUUVka hRRRQAUUUUAFFFFABRRRQBj/ANgaSNfPigWm3XW0caA98k9ynnaSt6dRitJ7ZZhZ3H2e8eaa0nmt 5Lmz+1XsdrNDFfXaT7FFFC0SitIx5uWK0Ueec6s7LZc9SpOpK3xTnObvKTbHq5SespcvNJ7y5KcK UOZ7vkpU6dON/hpwhBWjFJFFFFACEhQWPAAJJ54AGT09q5HR/HnhTX/+ES/snVftf/CdeGLnxl4V /wBB1KD+1PDdmugvc6l/pNnD9h8tfE2hn7HqP2S/f7biO1Y212LfrXUsjqOrKyjPTJBAzXxLpP7K +r+GPCmg2eh6pFq3iG1/Z+8Z/DHXLL4geP8A4ifEXwPc+KvEFj4EjsjovhT4gv4w8LaL4Ra68N6t FfQWHhG2s4rGfSoJvCGtafbHRoJUre3cotxp04SpKN+apN0cfOULWd17WjhIcy1h7ZrkqOadK4xU nBcyi5VIxblpGMHKClNvvFOTUXZStZzgld/blFfmzbfskfEG1+EupeBpfCnwi1a/uvim/j7Q9H1L xF4Jj8GeF418E6P4YWVvD9h+x1bfDTxFdX11Z6pJeWS/BXw1e2lrqkl7p/i5PEbS6w3ceKf2bPH3 iDxV8XtYsNE+EehN8RvhVr3giTxRFeC78R6lrl74c0fQ9NurORPhJp/jr4d6EradELjRk+MfxM8P WGmabYW2jeFLbVLiXW7O2venFNPlhJxbdoyqLCUsRyuVmlBVpzwjqR55yqxjKlSqxdb6vmnpTb05 5JS3fs4/Wnh7tJczfsv9ps4xj7NNOabpup93UV8J/ET9lO+ufDWu+Efhv4T+EVp4Q1H4i6F4vsPB 2qaZ4PstJ0y3tPh5D4Z1e90mHxl8Dfjj4Q0LX9Q8Q20d/fz2vw7vb7W7Ce7ml8RaRqFzdrd8/wDD D9lf4m+D/iv8LvG2tH4ZG18CaP4f0vVvEXh8+FbXxTr1rpfwgbwFc6ffND+z5pHjvV2bXpftiX2p fHBfD9xpFjp4j8A6XLbWNhpxTSnUnBtwjFqMak0kptUI1JtK+kVUl7KEm7VLNq006SUm40vact5e wqVvZJ3lzQm4wotpNKrUjyvl6OX2qUZVl9TeFPHXwe8Nn4keB/DWswac3wa87xB8RtPuYfEBbw9/ wlyar45udYu9T1m3kbW11TzdW1e6vdNvtWWO4NxbTyQ3KfZl9E8JeK9B8deGNB8Y+Fr1tS8OeJtL tNZ0W/eyv9Oe706+iWe1new1S1stRs2kjYM1vfWltdRHKTQxuCo+XfEX7Ovi7WviDr/iO313w1Z+ GvHHxHN74+0qeK/vbrxH8Kl8JfDTHh1ojZwWqa3ceMvhuumTx3E9zpaeBvFnidlnk1O7SwTgbf8A ZW8b2yeCLe5034Xa/qmjeEfhZ4etfiHqus62ni/4NXvgTXL3U9c1H4R2T+A9STUH8SRXSSySt4h+ HlzLeQrDrMmr6altbW00byp4Z1Pcq1aeBVaC2o1qmGnVxTjJ+5KlGcqFKCq1MO8PVhiaNSdaKw9e tVS8ZV+TWEPbTpt6urH2sKdNSt70KidPE1ZezhiJVqVXCzVOi1WR+gVc5rmn+EfF1tq3gjxLY+HP E9pdadaXGu+EdcttM1q2udIvrm5jsJtW0C/S5im068vNKvEtJLy0a1uLnTblYWeWzlEfyXJ8Dfih MNZ8O6jonw21vwVbWX7QEGkb/iH4q0fWPF6/GzxeviWLT/ENpH8LNWsPBtlpNpNeaZfy2l58QY9V /dPJpgtJrmwbxmP9jTxvaaTfRwfDL9lm51fWvC2j6BcXsuheB7XUNDXRPHuta64sZ1/ZbvPh5rOq eJPDerW9pqniZ/gz4es7C90bSbc+CdbezTXzF+bSULwnTpyfNrdVY1uaEoWbTh7Je0jJX5akYuKq yp0p21ypuM/3kcR7KFn0jiMPH2yknbllRqVKsJcyhGdO0qnuz5fufwifhP8AD/xA/wAHvh/4Y0jw hfLpi+NLvw54H+H95o3hawtdUuLnT4NT1fVfDvh+DwbpOqazLot3DZWmq6laa1q8WlTyWdrdQWbv H6tX5m+Fv2JvEnhvwX8Qbc+GfgffeMfF+ieFdHtpL7R/BWowRWGn/FTxh4w13SZNVv8A9niXwbZR /wDCL6l4M0vSLyD4IXuhpqHhLTYk8D6VZ6NoVzafY/7PPw6134U/CnQvA/iJ9IOpaZqfiu8EGgz6 fcaPYWWu+LNa17TtMsH0jwR8NtGWHTbHU4LLy9G8BeE9JjeF00/RLK1EUQ6LXVTmnedGUad73jVj ryOk27uFOl7Om9/fhNpQg6cXi3ytckfcnWlBJK0opUeepWnorxqV1PkbUXy1aSfNUjW5fa6KKKzL CsjX9C0vxPoer+G9ctje6Lr2m3ukatZrcXVp9r07ULeS1vbU3NlPbXcK3FvLJC7288UoR2CupOa1 6KBptNNNpp3TWjTWzT6NCKqoqqqhVUBVVQFVVAwFUDAAAGAAMAcCloooJSSSSSSSsklZJLZJLZIK KKKBhRRRQAUUUUAFFFFABRRRQAV4T8Z7qZdc+DGnG9ltLDUviLrEt3EgbZqN1oPwo+JHiXQ7KVxw n2bXNIsNdtujrf6LaTRkNDz7tWPq+gaTr39lnVbT7S2iaxZa/pUiz3NtLZatYCVLa7iltJoJD+5u Li2uIHdra8s7m5sryGe0uJoXyrU3VpumpOm5OC9qledH34v29JXjzVqFvbUY89NSqwhF1aSftI3C XLzu3NelXioPSNSU6NSEadR2bjSqSkoVpRTqRpSnKmnNRR8A+Fvjr8WPAnwO07VfHXivwPfahH8G /gn428PeKp/C1/dyWo8b6vL4an03x/d+OPjf4H0PxBqcwt7K4ufHmp/EH4Y6Omo3t3Pe6ZJttrW9 v6Z+1h4pvPDnwP1vUfE3wo0Y+PfFXijw/r9pHY+GvEHiPxDb6V8Q28IaLqPgbwdpf7Rkc0+napZW 0v2i9+HniL9oS8XV9Q04aToWr6ZHLdTfoPRXVKopV5VpQT5sY8RyX932KlVdPDvTW0KkKdWdlGv7 GFWpRdeVSpPGUf3Xs4PkfIoKfxNOWGhSrNJtL38RF4ul9vDOcqFGcaKgo/BcX7S3jvUr34m6J4f8 TfCHX/EGhfEX4f8Ag/w1Homjr4l8M6Dpnjz4mxeCIb7xT4k8H/GrxJe6rrGm2N2jz+HfEXhr4K69 c6vpmq/2bpF/osU2pWGB8W/2q/iL8L/CmkzXeq/Df/hMrbWPi5o+pW0/hTStO0Lxm/w38Qw6TZy+ Gz47/aU+Gt5oN5qcNxaJceGtAn+NHilZ7s3Nlo9zBb29vqf6J0VjSvTVFTftHTlzVG1b2nJRwtOk vec7JTo4mrXU/arESxs7qDoYaVLWTjKpOXL7kql1BScbUpRr+2pNw5UnKVShKjVpxpToRwsIrndb EyrfDuq/tS3uh/tGQfDTU/EPgO18ERXdxD4kTV7Pwn4c13wlYW/wwvvHs2uX2tX/AMf7jxZdaNbz WtqsuuXn7P8AoPhGSzv2hi8WGa1hu9Sq6r8Y/wBod477VtE1P4K2+jzaH+0F4o0qy1HwJ441G+st M+BvjGDw1aWF7qdp8UdNtdYvPGdvdwXE2oW2maLb+FpElaOx8UoyRj7sooa9ykotxqUqVSHO/fjO pOE+WpOnO6mqVWUalONWVSThBUqs6ilKTVN8tSEqi9rT5aKr01+7daVN/vJRnH/d/bw5VUVGMVGc faUvZqdWFT4ti/aI8X3PxQTwtFqHw3Rp/Flj4as/g59g1S5+Md54evfAlt4nb4qRakvjC3gPg+11 C4lDQRfDq4sZdKsZ4/8AhLoNakbT7bl2/a7udW8A3WueHvGHwVtdf0b4X/C7xH4sm1XWtHGj+F/i B4o1u/s/F3gq9h174oeBNFt/ENjaabNbaN4N8T/ELwdqFrqksX9t+IEhKQzffVFFlZrWznTle75k oUalNwTv8NWrUniqt7y9r7OnRlRwlGjhqcxuk7vmnywSlZKKlHEU6znyapt0accIo35PY89SrGti atavU/LaX9rvx7o9hr2q3/xl+D91PqGu+Cz4V8Paj8KNE8L3kPhzxJ8NdL8T/wBtfaPHX7Xvw38M W/hy+1OPV7XStVuvH/iKHW9UtL5vCeseItIvtLttKyPDf7QXxR0Xwn4u+NviXx74VstU8ceOPAHh nQtD1jRrt/BPhrT734L+HfiFc6PZWfxC/a3+Ffwp0S5dtYTbq9t4hsvEGt6jZaw4Hia31fTbDwl+ sFFPrWa0c6cadJreglXw05ODespVaFCrh6spPmksTVacablQk1tTi/eUYpVL/wDL6SpzSnK1kuWu 4V4QilFOlTTUpL2h+avxI+KvxC+JGi/CtLa58UeHo/EPwv8Ah14/1fT/AIdaN8XtbuP7V8b6V43u 9Q1W+0r4D+KtC+K2peHfD+o+EdG8K6fZ6L4/0bwrpup/Ea01rx1rOvRaV4fsLs8NfFj4o+AfC9xq +oaf4gm/s34QfGzx34P0r4oN8V7HWLWfwb4f+Ac2oaJrVj4112DxVrmhwfEXxB4+stF174gWeu+N YPDtvYL4d8TJoN/PLrn3r40+G3w6+JFvY2nxE8A+CvHtrpc0txptt408K6F4pt9OuLhFinnsYdcs L6O0mmjRI5ZbdY3kRFR2ZVAHVWFhY6XY2emaZZWmnabp1pb2Gn6fYW0NnY2FjZwpb2lnZ2lukdva 2lrbxxwW9vBGkMEKJFEioqqKg1GeJly8sazxKhTg/dprEQxCVWM2vaKvQdWnTpylKrFYai6ND6tS rVKTTV54Vu840KdOE024ufJCnGcW4ttxrSg68mnDlqTlzwr1FCvD4hg+MHxk0b42aD4E8cePvhLa aZAviaymsNK+Hut6RJ8RdZXw38NvFmi+GfDMut/FHVLzSvGH2XxHrllpKwXOuwapb6df383hq6mu LW20Hzn4W/tPePfi547+FlprF54a0rTH+I1hPqdh4V8QeBrLUdNg1H4P/G7U73wR480Dwh8evi7r N5DpV9omjX0eoeMdD+GkkusaPMbnwVp2qaasFh+l9FKDcW2+WSSapxado3k5qU2n7SpLm9nq6iSj RhCChCrjI4oknKjOm21UqUK1KdaNk3KrSnR9pGml7KCjGpNqEIK7m5c3PSwssN8X6n+0hqr3nxbv /DXjD4RapH4M8Papq/gj4XpFc6j4+8Y6LD4Y0DXdM+KsXiDTfHhS/wDhrdnWZ7w/8I98O9Rjn0ix f7L4rOoma2tqnh744fE/xjcad4Z8FeN/gf4zn1L4hN4WtfjH4a8JeIdX+Gl9YR/C/XvHuoWWl+Gd L+LmoTaj4i0HUdFt9H1RoPiZ9hFvrFvcvBZ31pPpsv23RUOP7twUpJvD0qXtL3m6tOo5zxDvdKVZ u84U1Tim3TpuGEVPC0m23LmVl+/q1YrlVoQnBQp0ktpKhb926qqxmkpV4Va8qtep8AaN8Tvil4qu fhDqnjiLwJc6pqN78D/iJ4Wj8E6Rr2iS6HZ/E3UPFvgfxjoepza94m8RDxKtt4Tur/Uob+0tPD4i P2iOTTJnsbbUm+/6x9S0DSdXvdB1HUbT7Td+GNTm1nQ5DPcxpZancaRqegy3fkQzR291J/ZOs6na xreRXEcP2pp4UjuY4Zo9iteZezlDlXNLF4jEKf8ALRq0sLSoYZL+XDxw796/72dWpWlH2tSrOamr 13VjeNP6rhaCpNuXLUozxM61Xme/t51+ezV6atSi/ZU6aRRRRUDCiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigD//Z3w7u9VX9tL/gsle+FV8Q2mn2t9qd5F410/433fhC68GaFdX9h4W8S/Em11yb4deGfiFf 2Pwy8QeKtN+Id5beGJfiK00H9jzW/FtpeN+2L+2VH8T9P8N6hrc2j6//AME6v2A9D+MOg/D3XNY8 WfCeHWovCPiH/gk9p/xH1GLxnq/7Otn8P4vhx4S0jXPirNcaJ8O/DfiH4c6a1vp1npmVH4U/Zs8W /Fz4taT47+OH7dGj3Hw8+IHh/wCIlj8a/FP7CP8AwSd8e+HvjR49GnweBo/i38Ipfhr/AME7fiX8 WPFd/wCCfDkFxofir4p654C8OJ4D8J+GvFVvrviHS9D8C+Ll0HDGcL8RYhYbL34ufSFwOV5FlOOz DF5NmPE3i5WxlPO8yzrAYJS4fwWK8L8vjUweIwdTJoYrD4DK8bnWMrU45tPmyiphYYAWIoQqVsRP A5PVWIrU62DnRo5epw4fqZfjMdgFXnDHYiU7Yr6xjniW4YPCZPzV5+2q4lYyr93fCr4V/sRfGjUd P0/wF8Qf+ClBOoa7q/hcS+PP21/+Cv3wcnt/EekaLeeIH0ltC+Mv7QPgDxVffa9O0bxZFb6to2ga n4eg1vwL418LaprGneKfDt7oo+qNH/YH+AujatperjxV+13rp0rUbLUhonjf/goV+358Q/Bmsmxu YroaV4u8AePP2mPEfgfxx4Y1Dyvsmv8Ag/xn4e17wp4m0qa70XxFo2qaRe3ljP8AKPwX+Nv7JnwX 8feMovF/7Vv7RPx4+IWgeGdH8b6fpnxU/Zb0jwjq3hLwZ4r8f/EvwVffEbwHon7NP7FHwAj+IXhf xj4o0vxvqmtfETVLL4l6RoHg/TdU+JWg674S+HfjPxV4p8Y/WHh//goR+yJ4k+IeifCe0+K11pXx E8Tab8Vtc8O+FvGHw2+LPgG/17w98FdN0bWviF4m0eXxz4F8O2epeFrLRNf0/XfDniizuZtA+IGh LqetfD7UfFGl6Hrd3p/84cbYbx5oZxn2E4exvj1mvCVHBTxUMTjcT4lY+lPJ45FRzbNa2PxGY8Oc J1KmDwmClisTja+K4dy3C08upSxL9vgFHHV/YwcsDThR+tRyqhmEK+EozhSjhaUqWMx2I9jltCEV WrzjXxVX2dLBJVfa4usl7CnGo1Rh9mRxpFGkUSJHFGixxxxqEjjjQBUREUBURFAVVUBVAAAAFPr4 em/4KKfsu2+kXGsz3/x3iS21DTdMk0Z/2Ov2xB4xe61aOdbVrbwF/wAKG/4Te8so9Yt5PBmo6nZ+ H59N0f4kmL4Xavd2PxFng8LyfU/gL4h+HfiPp2qan4ca7a20nWX0a5F7DHBI7SaXpXiDSdQgWKec Np2v+Gde0HxJpLyNFdnStZsl1Gz0/UVu7C2/B864R4t4cVKfEfDHEWQrEfWHQnneS5nlca/1WWCj ivZSx+GoKq8PLMsvVdQcnSeOwntOX6xS5/SpYjCTjTjQr4ecZSdKlCjVpzv7NVVJQhTk3yUnQqU5 yS5KdSKpScakoRl3VFFFfOnQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeE+Lf2XP2ZfH2k3GgeOv 2dfgT400K81PSdau9F8W/CL4f+I9JutY0GHwnb6Hq1xpuseHryzn1PRbfwH4Gg0m/lha606HwZ4T is5YU8O6Otnak/Zq/Zzm8baL8S5vgB8E5fiP4b0v+w/DvxAk+FXgV/G2gaKRow/sfRfFTaCdd0rS 8eHPDw/s+xv4LTGhaN+5/wCJXY+R7ZRSSSUUkkouq4pJJRdehHDV3FLZ1sNGOHqtfxKEY0p3ppRB +9fm97mhTpyvreFGs8TRg77wpYiUq9OL0p1m6sUptyPnzxd+yT+yp4/eKXx5+zJ+z542kg8Ia58P YZPF3wY+HHiR4fAPiez0rTvEvgeJ9Z8N3rR+EPEOn6FolhrnhpCNF1az0fSrW/sriDT7RIcPxL+x H+xh4zv4dV8YfsifsweK9Ut/CngzwHb6l4l+AXwp12/g8DfDi/sNV+HngyG81TwndXEXhTwHqml6 ZqXgzw6ki6R4Xv8ATrC80Ozsbizt5I/p+iqi3B0pQbhKg6cqLj7royovEujKk1Z03SeMxbpuFnTe KxLjb29XmU4xqKoqkVUVWNSNVTSkqsaqw6qxqc1+eNRYTCqopXU1hsOpX9jT5flS4/YT/YgurbWr O6/Y2/ZUubPxLD43t/EVpcfs8/COa21+3+JuraRr3xIg1qCTwg0Wqw/EHXPD+g6143ivlnTxXq2i aRqOvLf3mm2U0N1P2I/2MIvEcfjGL9kT9mGPxdFrVj4ji8Up8AvhSniOPxDpnhc+CNN16PXF8JjU 01rT/BbN4QsdUW6F9aeF2OgW88elE2lfT1FTCMYKKhFQUKdClBQSioUsLiPreFpRSty08Niv9poQ Vo0cR++pqNT3ht3k5vWTq1qzk9ZOtiKP1fEVW3q6tfD/ALitUvzVaP7qblD3T5m+Mf7MHh74oeHt F0Twh458Wfs/3miWtnoUOvfCLwf8BdaluvBFnpfiLS0+H1z4R+PfwX+N/wAMIvDBTxLfyxT6d4Cs PFOmxtd6Po3iXTfDfiHxdoniL5Xf/gmSsvjDU/iHJ+2V+0HJ4/1rw9ceEtZ8cv8As7f8EpH8Yat4 Uu7rUr278M6n4mb/AIJtnWr/AMPXV7rOsXdxot1ey6bNdatqVxJbNLfXTy/qDRX33DniXxdwplzy nJMRktDBS+tRlHGcIcH5xiKlPHYj63i6NTG5xkOPx1XD18VfETw1TESw/tnKoqalKTfJWwGFxEpS qwnJzpUaM0q1eEZUcPGEMPSlCnUjBwowpwhSi42hCEYxSUUl+X/hb/gmUPA2n3mkeCv2y/2hPB+l ahq+r+IL/TPC37O//BKXw/p99r3iBdYTXtbvLLSf+CbdpbXOr62niLxAur6lNE95qS67rC3s0w1O 9E+DpP8AwSh8L6B4h0bxboX7VHxm0XxV4d1jxZ4h8P8AibSf2X/+CSuneIdC1/x7qEGreOtc0bWr P/gmnDqWmax401W1ttT8WanZXMF74j1C3gvNYnvLiKORf1eor6H/AIjr4lKeKq/2lw97THKccdU/ 4h/4e8+MjVo18PUji5/6rc2IVTD4nE0KirOanRxFelK8K1SMspZXgppqdOrNSrQxElLFYuSlXpuM qdeV67vWhKEZQqO84uKakmj8grn/AII7/C+9a8e8/aE+I92+oyeP5dQa5/ZH/wCCQk7X8vxXihg+ KUl40v8AwTKc3UnxKht7eHx+85dvGMUEMfiI6ikSBdK3/wCCR/ga0utOvrX9pn4s219o+tWPiTSb y3/ZV/4JHQ3Wl+ItM8J2fgLTdf064j/4JnrNZa1p/gbT7DwXY6rbPFfWnhOxs/DlvPHo9tDZp+td FdUvpB+LE48ks+yiUFTq0eSXA/AUo+yryc69LlfDDXs605SnVhblqSlKU1Jyk2v7JwHO6nsqnPKp UrOf1nFc7rVqUaFWq5e2u6lWjGNGpNvmqUkqcm4aH5NeC/8Agkv4O+G15aah8O/2n/i/4Bv9P03R 9GsL7wX+y1/wSR8LXllo/h3U7zW/D+lWlzof/BNGxmttN0LWdR1DV9HsYXS10zU768v7KKC7uZ5X 4TSv+CKnw103V/izrN3+15+1X4wl+N/inw940+I2l/E/4ff8E7vi54V1HxF4Tj0uLwzd+H/BfxR/ YJ8X+EPAVpoUeheHl0vR/h/ofhfRbMeGfC/k6ep8NaF/Z/7QUVrT+kT4vUquNr0uJMtpV8yp4Wlm NanwVwLCrmFLA4zDZjgqeNqR4aU8XTweYYLB4/CwrupHD4zC4fFUlCvRpzjDybLZU1Rlh3KioQpq lKviXTVOnSnh6dP2brcvJToVKlGEbcsaVSpTSUJyT/L9P+CZQj8Tan41j/bK/aEj8Za34bsfBms+ LU/Z2/4JSp4m1fwfpdne6fpvhTU9eX/gm2NVv/DenWGpajY2OhXd3LpdpZ397bW9rHDdTpJ4/wDC r/gin8Mfgxa+HLfwN+1r+1C914M174oeJPBfiHxz8M/+CcnxY8aeC9U+NdzqN38WR4N8efFX9gPx p408J6X8QZ9Y1c+KNA8Pa9pug6nHq2pwTaaYNQu45v2gorKj9ILxXw+ExWX4fPcooYDHU8LSxuCo 8DcBU8JjKWBwOOyzBU8VhocMRo4ing8tzTM8vwsK0Jxw+BzHH4SkoUMZiadTSWV4KclKcKs5KVOa lPFYuUlOjGUKU03Xb5qUZzjTlvCM5qLSnK/5P/Dn/gk94U+Dyainwk/al+MnwtTV5NEm1ZPhz+y9 /wAEk/BCapL4ZksZfDkuor4Z/wCCaWmC9k8Py6ZpsmiPciVtKk0+xewMDWkBj/R/4X/DjSfhX4M0 jwfpWoanrktja2f9teKddg0C18QeMNch0+y0++8Ua9aeEtC8LeD9P1PVhYwyPo/gzwr4T8FaDbpb 6F4N8KeGPC2m6RoOn+g0V8dxd4icWcdVIVuJ8Zl2OxMZOUsZhuHOG8nx1e9PD0uTF47Jcoy7GY2j GGFwqhQxdevRg8PQlCnGVKDjth8FhsJBU6FNwpxqSrRpurVqU4Vpqqp1oU6k5whWmq9ZTqwiqklW qqUmqk7lFFFfEHUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFfKnxM/bU/Z+ +E3jST4c+JtW+Jut+PYdT1HSZ/B/wq/Z4/aI+OfiK2utJ8E+HviHqE1zpXwT+FfxBu7bTLbwp4s8 NXja3PFHoz6jrmm+HI79/Et1HpB818If8FLv2RfHHgLx/wDE7QvEXxph8EfDHwf4Z8c+MNZ8S/sh /tf+CZLfRfGl1Z2vg2y0LSPGnwJ0DW/GXjDxaup6NqPhj4eeDNN1/wAf6/oPiDw34k0nwzd+H/Em hanqGbrUo069WVWmqWFcliajnFU8O4YWtjZKvNvlouODw+IxclUcWsLQrYh/uqU5xv2dS9KPs581 dwjQjyyvWlVrLD040la9R1K7VCChdzrNU43m1E+9aK+E9C/4KX/sTeJLNtS0j4zyy6WPib8IPhBD qt38Mvi/pWlX3jv48rf/APCq7PStT1bwBZWGr6B4judI1vSrvxzpdxeeBfC+vaB4h8PeLvEmg654 f1nT7HsPDP7fv7E/ivQZfEll+1L8EdG063FnLdQeOvH+hfDXXLGy1f4m6z8GvDer6h4X+Il14W8T afoPjT4o6BqvgnwB4hvdIg0P4g63BFD4J1DXor2xludpRlB8s4yjL2saHLKLi/bzwtDHQo2aT9rP BYrDYyNP45YXEUMQk6NanOWPtafLKftIckIOrKXNHljTjicXg5VJSvZQji8BjsK5t8qxODxdBv2u HrRh9eUV8MeEP+CnP/BO3x1b65e+G/22v2Yriw8O6fqus6pqOpfGXwP4d0waFoGnXOq+IvEOn6l4 j1jSbHWvDfhqzsNSPibxFotxqGieHLnStXsNbv7C/wBI1K2tftHQde0PxVoei+J/DGs6T4j8NeI9 J07XvD3iHQdRs9Y0PXtD1izh1DSda0XVtPmuLDVNJ1SwuLe+07UbG4ns72znhubaaWGVHZWl76s7 03FVFZ+45zr04Kf8rlPDYmEVK3NPD14q7pVFG5Pkm6UvdqxlUhKnLSanRVGVWDg7SUqUcTh5VItX gq9FySVWHNrUUUUgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK wNe8WeFvCp0oeJ/EugeHDruoXGlaGNe1nTtHOs6pZ6JrHia703Sv7Qubf+0dQtfDfh7X/EFxZ2nn XEGiaHrGqyxrYaZe3EG/Xg/7SX7PHgn9qP4T638H/H2peKND0LW7vS7w694Iv9N0nxdpUmnXiSXQ 0PVtV0fXbbTW1vSH1Twtq11FpzX/APwj2vaxBpl3puoTW2pWkVPaWiqfLeVWjGcp3ap0Z1oRxFZQ Ti6s6NB1KtOh7SisRUhGjKvQVR1qelP2fM/a8/LyVbezUeZ1FSm6KfNooSrezVWWso03OUYyklF+ Z+MP2MP2SPjv8TPhT+01r/gmLxh8QfAnxA0j4+fCr4k+HPid8RtOsIfGMvw80b4f6V4v0+Dwb440 3wp4m0TVfhzpmlaK2kalp+seDtd0hpLm70m+l1O9urvH8W/8E4f2JvHPw++L/wAMfFHwB8Naj4W+ Pfw0+H/wb+Ls0WteMtN8YeNPhZ8LNK0vRvh94EvPiPpXiSx+Ith4c8LWWkWkmnafo3irTYzqUupa 1c+frOtaxf3/AMga3/wQx/ZC8Q/E/wDaH+LWreMfjlL4l/aK+IafFHUba01X4RaTo/wp8Y2+n+EN FstY+Cdto/wZsLzwPq9l4f8ABWl6CPE1zqGu+ML/AE+81uXWPEepanq1zqBz9X/4Iafs66lrWleI rD46/tEeENb0P4PeHPgjo974A8MfsY+BrfSvBOh6Bp3hS/0/StE8M/sg6b4d0vR/EXhq21jT5fh9 pukWnwj8E6p43+IPjj4R/Dv4cfEbxpr3jC7lUcPXjLDVcLClg8diMNWxlGqqWIpKWZZAsLm1XFwh CMcZWwfJ/q5XqKi5ZnlnsK0FDCzq4HDRCriKdq7qN4vD0cVSpVKEpwnL+y84WJyOGHnNqVDD4uWI xGd0ozqKWUY1V6fs6+JqQxVb6J8Jf8Ei/wDgnn4G8LfFXwX4V/Z8/srw38bNU8M638S7AfFf433k uv6v4N+GPiT4O+FtSttS1D4lXeqeG73RPh54u1/RbG48LXuiSx3l3B4lLN4p03S9asvnL9oD/gkP /wAETYPGNj46/aN+C/wh8HeLPjl42+Hnw28Oaj49/aE+J3w2X4ifEWx8V3XjH4c/D7wJpc/xh8N6 VqXjHUPESXUmkeEPBtiNY8S6fbHw9Jp2p6BZppcWp4v/AOCGf7JPjr4sfC34t+J/iB+0Zq138KNF 8c+GNL8B33jjwNe/C3xP4U8beJvDmrL4U8b+Cr34Y3Fpr3h7wt4R8D/Dz4RaBp8Vzp/274XfDnwP pXjKXxZ4i0CDxQ32Z8Zf2P7z47eDfhhpPiv9oL4p+CfHngPwz4s8IeIviP8ACXwl8BLJviL4V+I+ g6fofxM8Fa94C+Mvwe+OfgDTPBXjBdI0eYWmiaDaeL9Eh0iysdN8cJb3Ou/21dSriKcMJj6EXVzS hUwWKVOVZ0JUMZl862WYT6vmajVq0nh8noYaGDzKOElXwGExSw1HL6/1SWDrTSoYebrYCtGFLK+T FYVONKNWlVw+KVPMazWXv2dOpSr5xicbVrYOrVw8cXiKX1qrXw88dOvhfnv42/8ABGz9iT40fDb4 r/D+PRvi58LdV+L0PhiLXPir8N/jf8SJvij4fbwbdeJ5fDD+DNV+JmufEnw1oMOh6f418V+HtI0v /hFLvRNJ8OavFpOlaZZQaB4WOh/op8NNM8PeFPCGifDXQPFT+LW+E+h+F/h3q19qF/oN54nivND8 JaDJYnxlb+HLDR9L0zxNq3h+80XxHd2droWg2kttrVnqOmaPY6TfWEQ/EJf+Dcj9h+H4KfD/AOCN p8Sv2mbPT/h3J4Ei0/4iReIPgZL8W9X0b4a+J/AXjHwV4a1bxzefs/3VxaaFoHiH4eaRe6bB4Xs/ Dd7p8WqeJ7GwvrSw8Q6hbSe5/s3/APBFD9mf9mL9p/wB+1j4I+KHx41n4hfDlPjMmkaHrcP7Onh/ wfrn/C89D8PaB4yufHSfCr9nf4beL/H2rpb+FdD1S38S+LvGGr+KNZ8WQ6l448ba14t8ZeJPE+va 1rRhSo18XQhN08HXxOZc9aFO6r0sqWIr8O4p4fnUqc81xGbZtHEYWLSyyrWxOKq4jHOpBVsKkq1S lhcRUw8Z4ulhcuUaTrLmw9XMqmHw+e4V1/Z8tSjlODynLJ4WtaX9oQhh8NRo4BU6rj+xNFFFQdAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//ZAAAAAA4IASUAAAAAAAAAAAAAAAAA 9iAlAAAAAAEAAAAAAAAA+McGBAAAAABwmisEAAAAAHCaKwQAAAAAAAAAAAAAAAAAAAAAAAAAAJBF YzcAAAAADggBJQAAAAABAAAAAAAAAAAAAAAAAAAACgAAAAAAAABAc3AEAAAAABXAWv3+BwAADggB JQAAAAAA9iAlAAAAAAD2ICUAAAAAQHNwBAAAAADoyAYEAAAAAAMAAABkdgAIAAAAACUAAAAMAAAA AwAAACUAAAAMAAAAAwAAACUAAAAMAAAAAQAAACUAAAAMAAAAAQAAACUAAAAMAAAAAQAAACUAAAAM AAAAAQAAACUAAAAMAAAAAQAAACUAAAAMAAAAAQAAACUAAAAMAAAAAQAAACUAAAAMAAAAAQAAACUA AAAMAAAAAQAAACUAAAAMAAAAAQAAAEsAAABAAAAAMAAAAAUAAAAgAAAAAQAAAAEAAAAQAAAAAAAA AAAAAABphAAAaYQAAAAAAAAAAAAAaYQAAGmEAAAlAAAADAAAAAMAAAAlAAAADAAAAAgAAIAlAAAA DAAAAAUAAIAbAAAAEAAAAFIAAAC4AQAAJQAAAAwAAAAHAACAJQAAAAwAAAAAAACAJQAAAAwAAAAD AAAAGAAAAAwAAAAAAAACGQAAAAwAAADMzMwCFgAAAAwAAAAAAAAAVAAAAFQAAABVAAAAuAEAAFkA AADKAQAAAQAAALrnEUId+hFCUgAAAKkBAAABAAAATAAAAAAAAAAAAAAAAAAAAP//////////UAAA ACAAAAAFAAAAJQAAAAwAAAADAAAAJQAAAAwAAAABAAAAJQAAAAwAAAABAAAAJQAAAAwAAAABAAAA JQAAAAwAAAABAAAAJQAAAAwAAAABAAAAJQAAAAwAAAABAAAAJQAAAAwAAAABAAAAJQAAAAwAAAAB AAAAJQAAAAwAAAABAAAAJQAAAAwAAAABAAAASwAAAEAAAAAwAAAABQAAACAAAAABAAAAAQAAABAA AAAAAAAAAAAAAGmEAABphAAAAAAAAAAAAABphAAAaYQAACUAAAAMAAAAAwAAACUAAAAMAAAACAAA gCUAAAAMAAAABQAAgBsAAAAQAAAASgIAACsAAAAlAAAADAAAAAcAAIAlAAAADAAAAAAAAIAlAAAA DAAAAAMAAAAYAAAADAAAAAAAAAIZAAAADAAAAMzMzAIWAAAADAAAAAAAAABUAAAAVAAAAF4CAAAd AAAAYgIAAC8AAAABAAAAuucRQh36EUJKAgAAHAAAAAEAAABMAAAAAAAAAAAAAAAAAAAA//////// //9QAAAAIAAAAAUAAAAlAAAADAAAAAEAAABLAAAAQAAAADAAAAAFAAAAIAAAAAEAAAABAAAAEAAA AAAAAAAAAAAAaYQAAGmEAAAAAAAAAAAAAGmEAABphAAAJQAAAAwAAAAIAACAJwAAABgAAAAEAAAA AAAAAP///wIAAAAAJQAAAAwAAAAEAAAAVgAAACwAAABfAAAALwAAABIBAACBAAAABAAAAFwAfQBc AC0ACQEtAAkBfQAlAAAADAAAAAcAAIAlAAAADAAAAAAAAIBfAAAAOAAAAAYAAAA4AAAAAAAAADgA AAAAAAAAAAAAAAEAAAAAAAAA////AAAAAAAAAAAAAAAAACUAAAAMAAAABgAAACUAAAAMAAAABQAA AFcAAAAwAAAAXwAAAC8AAAASAQAAgQAAAAUAAABcAH0AXAAtAAkBLQAJAX0AXAB9ACUAAAAMAAAA BwAAgCUAAAAMAAAAAAAAgCgAAAAMAAAABgAAACgAAAAMAAAABAAAACUAAAAMAAAACAAAgCUAAAAM AAAABQAAgBsAAAAQAAAAXAAAAC0AAAAlAAAADAAAAAcAAIAlAAAADAAAAAAAAIBfAAAAOAAAAAQA AAA4AAAAAAAAADgAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUAAAAMAAAABAAA ACUAAAAMAAAABQAAADYAAAAQAAAACQEAAC0AAAAPAAAAFAAAAAkBAAAtAAAAAAAAAiUAAAAMAAAA BwAAgCUAAAAMAAAAAAAAgCUAAAAMAAAACAAAgCUAAAAMAAAABQAAgBsAAAAQAAAAXAAAAH0AAAAl AAAADAAAAAcAAIAlAAAADAAAAAAAAIAlAAAADAAAAAQAAAAlAAAADAAAAAUAAAA2AAAAEAAAAAkB AAB9AAAADwAAABQAAAAJAQAAfQAAAAAAAAIlAAAADAAAAAcAAIAlAAAADAAAAAAAAIAlAAAADAAA AAgAAIAlAAAADAAAAAUAAIAbAAAAEAAAAAkBAAB9AAAAJQAAAAwAAAAHAACAJQAAAAwAAAAAAACA JQAAAAwAAAAEAAAAJQAAAAwAAAAFAAAANgAAABAAAAAJAQAALQAAAA8AAAAUAAAACQEAAC0AAAAA AAACJQAAAAwAAAAHAACAJQAAAAwAAAAAAACAJQAAAAwAAAAIAACAJQAAAAwAAAAFAACAGwAAABAA AABcAAAAfQAAACUAAAAMAAAABwAAgCUAAAAMAAAAAAAAgCUAAAAMAAAABAAAACUAAAAMAAAABQAA ADYAAAAQAAAAXAAAAC0AAAAPAAAAFAAAAFwAAAAtAAAAAAAAAiUAAAAMAAAABwAAgCUAAAAMAAAA AAAAgCUAAAAMAAAACAAAgCUAAAAMAAAABQAAgBsAAAAQAAAAXAAAAH0AAAAlAAAADAAAAAcAAIAl AAAADAAAAAAAAIAlAAAADAAAAAQAAAAlAAAADAAAAAUAAAA2AAAAEAAAAAkBAAB9AAAADwAAABQA AAAJAQAAfQAAAAAAAAIlAAAADAAAAAcAAIAlAAAADAAAAAAAAIAlAAAADAAAAAgAAIAlAAAADAAA AAUAAIAbAAAAEAAAAFwAAAB9AAAAJQAAAAwAAAAHAACAJQAAAAwAAAAAAACAJQAAAAwAAAAEAAAA JQAAAAwAAAAFAAAANgAAABAAAABcAAAALQAAAA8AAAAUAAAAXAAAAC0AAAAAAAACJQAAAAwAAAAH AACAJQAAAAwAAAAAAACAJQAAAAwAAAAIAACAJQAAAAwAAAAFAACAGwAAABAAAABcAAAALQAAACUA AAAMAAAABwAAgCUAAAAMAAAAAAAAgCUAAAAMAAAABAAAACUAAAAMAAAABQAAADYAAAAQAAAACQEA AC0AAAAPAAAAFAAAAAkBAAAtAAAAAAAAAiUAAAAMAAAABwAAgCUAAAAMAAAAAAAAgCUAAAAMAAAA CAAAgCUAAAAMAAAABQAAgBsAAAAQAAAAXAAAAH0AAAAlAAAADAAAAAcAAIAlAAAADAAAAAAAAIAl AAAADAAAAAQAAAAlAAAADAAAAAUAAAA2AAAAEAAAAAkBAAB9AAAADwAAABQAAAAJAQAAfQAAAAAA AAIlAAAADAAAAAcAAIAlAAAADAAAAAAAAIAlAAAADAAAAAgAAIAlAAAADAAAAAUAAIAbAAAAEAAA AAkBAAB9AAAAJQAAAAwAAAAHAACAJQAAAAwAAAAAAACAJQAAAAwAAAAEAAAAJQAAAAwAAAAFAAAA NgAAABAAAAAJAQAALQAAAA8AAAAUAAAACQEAAC0AAAAAAAACJQAAAAwAAAAHAACAJQAAAAwAAAAA AACAJQAAAAwAAAAIAACAJQAAAAwAAAAFAACAGwAAABAAAABcAAAAfQAAACUAAAAMAAAABwAAgCUA AAAMAAAAAAAAgCUAAAAMAAAABAAAACUAAAAMAAAABQAAADYAAAAQAAAAXAAAAC0AAAAPAAAAFAAA AFwAAAAtAAAAAAAAAiUAAAAMAAAABwAAgCUAAAAMAAAAAAAAgCUAAAAMAAAAAQAAAEsAAABAAAAA MAAAAAUAAAAgAAAAAQAAAAEAAAAQAAAAAAAAAAAAAABphAAAaYQAAAAAAAAAAAAAaYQAAGmEAAAl AAAADAAAAAEAAAAlAAAADAAAAAgAAIAlAAAADAAAAAUAAIAbAAAAEAAAAJsAAABDAAAAJQAAAAwA AAAHAACAJQAAAAwAAAAAAACAJQAAAAwAAAABAAAAGAAAAAwAAAAAAAACGQAAAAwAAADMzMwCFgAA AAwAAAAAAAAAVAAAAGQAAACgAAAAMwAAANQAAABJAAAAAQAAALrnEUId+hFCmwAAADEAAAAEAAAA TAAAAAAAAAAAAAAAAAAAAP//////////VAAAAEgARABUAFYADQAAAA0AAAAMAAAADAAAAEsAAABA AAAAMAAAAAUAAAAgAAAAAQAAAAEAAAAQAAAAXwAAAC8AAAATAQAAggAAAF8AAAAvAAAAEwEAAIIA AAAoAAAADAAAAAQAAAAlAAAADAAAAAgAAIAlAAAADAAAAAUAAIAbAAAAEAAAAJYAAAA8AAAAJQAA AAwAAAAHAACAJQAAAAwAAAAAAACASwAAAEAAAAAwAAAABQAAACAAAAABAAAAAQAAABAAAABfAAAA LwAAABMBAACCAAAAXwAAAC8AAAATAQAAggAAACUAAAAMAAAACAAAgCUAAAAMAAAABQAAgBsAAAAQ AAAAfQAAADwAAAAlAAAADAAAAAcAAIAlAAAADAAAAAAAAIBLAAAAQAAAADAAAAAFAAAAIAAAAAEA AAABAAAAEAAAAG0AAAAoAAAAmAAAAFQAAABtAAAAKAAAAJgAAABUAAAAJQAAAAwAAAAIAACAJwAA ABgAAAAEAAAAAAAAAAAA/wIAAAAAJQAAAAwAAAAEAAAAKgAAABgAAAB6AAAAOQAAAIEAAABAAAAA JQAAAAwAAAAHAACAJQAAAAwAAAAAAACASwAAAEAAAAAwAAAABQAAACAAAAABAAAAAQAAABAAAABf AAAALwAAABMBAACCAAAAUEsDBAoAAAAAAAAAIQDShd6Pq+AFAKvgBQAVAAAAcHB0L21lZGlhL2lt YWdlMTEucG5niVBORw0KGgoAAAANSUhEUgAABk4AAALeCAIAAABjhHpjAAAAAXNSR0IArs4c6QAA /8pJREFUeF7svQmYZWdd7b3nvc9U1VMmCCYRZIoYmTHIFRklgYQkzAQIyoXvigwyiIj3qqBG5YoM It7IEAUhwAUEIUwRUYYrTsgQQQgQBM3Qne6qOsOe9/5+6z3dTZRgQpJKUt3/Sieprjpn7/dd7+7K k9+z1vr7l1zydc8+TAFTwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUyBra9AsPW3YDswBUwBU8AU MAVMAVPAFDAFTAFTwBQwBUwBU8AUMAWkgKEuew5MAVPAFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAF DhEFDHUdIgdp2zAFTAFTwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQw1GXPgClgCpgCpoApYAqYAqaA KWAKmAKmgClgCpgCpsAhooChrkPkIG0bpoApYAqYAqaAKWAKmAKmgClgCpgCpoApYAqYAoa67Bkw BUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU8AUOEQUMNR1iBykbcMUMAVMAVPAFDAFTAFTwBQwBUwB U8AUMAVMAVPAUJc9A6aAKWAKmAKmgClgCpgCpoApYAqYAqaAKWAKmAKHiAKGug6Rg7RtmAKmgClg CpgCpoApYAqYAqaAKWAKmAKmgClgChjqsmfAFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAFTAFT4BBR wFDXIXKQtg1TwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBQ132DJgCpoApYAqYAqaAKWAKmAKm gClgCpgCpoApYAocIgoY6jpEDtK2YQqYAqaAKWAKmAKmgClgCpgCpoApYAqYAqaAKWCoy54BU8AU MAVMAVPAFDAFTAFTwBQwBUwBU8AUMAVMgUNEAUNdh8hB2jZMAVPAFDAFTAFTwBQwBUwBU8AUMAVM AVPAFDAFDHXZM2AKmAKmgClgCpgCpoApYAqYAqaAKWAKmAKmgClwiChgqOsQOUjbhilgCpgCpoAp YAqYAqaAKWAKmAKmgClgCpgCpoChLnsGTAFTwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBQ4RBQx1 HSIHadswBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU8AUMNRlz4ApYAqYAqaAKWAKmAKmgClgCpgC poApYAqYAqbAIaKAoa5D5CBtG6aAKWAKmAKmgClgCpgCpoApYAqYAqaAKWAKmAKGuuwZMAVMAVPA FDAFTAFTwBQwBUwBU8AUMAVMAVPAFDhEFDDUdYgcpG3DFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAF TAFTwFCXPQOmgClgCpgCpoApYAqYAqaAKWAKmAKmgClgCpgCh4gChroOkYO0bZgCpoApYAqYAqaA KWAKmAKmgClgCpgCpoApYAoY6rJnwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU+AQUcBQ1yFy kLYNU8AUMAVMAVPAFDAFTAFTwBQwBUwBU8AUMAVMAUNd9gyYAqaAKWAKmAKmgClgCpgCpoApYAqY AqaAKWAKHCIK+Jdc8vVDZCs3zzba3Rf9xtnnfvwabx4c/SP3/4kH/OQpD7zXsaMbsLr5t//2Lz74 l3/31a9+8XPfWA+Of8Zrznvs7cIbcL1D+a357n/+20995h+/eMm3/vXiz39z6o1POOmuJ/7wD9/t 3ve9zx2PSK8GdtuLzz/rWW+bLrW4/wsveMmDjrgFadpe/KbHPPvNs+Xq7vPit/zGg484lE/N9mYK mAKmgClgCpgCpoApYAqYAqaAKWAK3GgKmKvrRpPyuy/UXf75j739lf/z2ee+88sb1/c2xbc/+Hs/ /+JX/t+PfArOxUW6qxbl9b3WIf6+xVc/9DvPffKzfu11b/nzT/zNP31z2qHW7Buf/cT73/y6lz77 //uFN/3t7uoQV8C2ZwqYAqaAKWAKmAKmgClgCpgCpoApYAoc9goY6tr8R2D902981Qcvqa/Xjbpv ffqdf32QkwXH3Oe0M2+/6l+vSx3ab+qv/NRrX/Z7H/4qfOuaPrrZF9/28vM+tbs/tFWw3ZkCpoAp YAqYAqaAKWAKmAKmgClgCpgCh7sChrpu1Cdg+JBn/d5rXvHq17zid17yhPscdfDS3Vc+9smvzK/P naZXXvpvzf43Bic/87d+5ZlPvu+xdmjfJWX3zU++9UPfPvDl4QmPeP7vvOFP3/Knr3vxmXc8oFa3 8bH3f/KbByxxwQ886DfdSenX2SetmKbX5/G095gCpoApYAqYAqaAKWAKmAKmgClgCpgCtzQF7P/w b9QTCY864UT3cfcHPvk5P/1j0cGLX/blf127PneqFtMDpMsb3faEY9Lrc5FD/z3NlV/67CUHt3ns o571/51y0vHHHHH0He7/lKc8NDv4jW9/9d/29195/uTYOy+Pio8fPCI1p9yh/5TYDk0BU8AUMAVM AVPAFDAFTAFTwBQwBQ4HBQx1bdYp+0fc4R53Gh64+uyrl6+1m3Uru2511eX//h0Vjr31kdkBdDU+ 9o4njg98q5jmVtdlT4spYAqYAqaAKWAKmAKmgClgCpgCpoApcCgrYKhr007Xj7PRLWio36bt85Zw YT+Kk++s41++/I31g1xx592f8VsHgornnnPS9lvCcm0NpoApYAqYAqaAKWAKmAKmgClgCpgCpoAp sEkK+Jdc8vVNuvThcdl290W/cfa5H9+/2cmTXvGup564BFz9v77/53/2NV9YuN+M7/qC1/3Ww279 HU3KKy/+5F/+1d9/8dKvfvFz3/COu+udfuDWP/SjJ//kT9zj+Mny7e3F55/1rLdNv0vFq9/iWi+y /0pvesyz37xM7o3Pfs3bH7vyjxd96OOf/Fz1kJf88oOOcV/uyt1f+tTHWM4ll3zh89/0jjvpxONv dbu7nvyT97v78atXw3WLi9/0rOe95RvuLZN7/eIrn32rr3/0Ix/60IX/cEW3etx9HvSw00976L1u c9DJ5l6W7774//3lx//24m989YufvWx057scd+TRt/3hu93rx+915yMG/3lv12071/RcdbPPvOYJ v/TeA0Vc0dEPff6v/dyDjx9+b5L7H+S9/wsveMmDjji40W7x7b//iws/9qm//czn8uPu/xMPetgZ P7HjK+9/3ye/dNlGuX7p+t1+6RXnnKhdthd/R1jvPi96w3OP/cpHPvyhCy/63OXV+IT7PPCU0077 qXsce/U1tOvf/Pu/+sjH/+YLn/+nf7m88IKj73DPO/3gD93lbvc++d53PvLq2dT/eOUXv+U3HnzE 4fHHyXZpCpgCpoApYAqYAqaAKWAKmAKmgClgCtxABQx13UABvyfq6q/86K896dz/t79p685P/oPf eeId9lOgZvenz3vJb773a0sKdvWPYOXknzv3xafdDj5ybajrOl3EXfvq3GT8mJ976r+98zWfukKs 6sV/+DKhrmr3Z970P3/57d+4huGFR93n5371RWccWLd3ddQVjCbDfDr7j2+6zSm/+Yrn3Hvn/i01 l3/m/7z0V9/95Wu48ORHH/3iXzjn3kcfbDO77tu55gNb/5tX/cwvX7jvO7cKTjjlmc9+0oN/5Khr rjf73qirueyjv/3M3/7r9atd6rannnHCP77rosvcrU94/Ktfcw2oazwZL/6THMHqI379j557n9Xl ipt/++Rrf/033vcv1zQj8j/p/J8gmqGuG/iH1N5uCpgCpoApYAqYAqaAKWAKmAKmgClwGClgAcZN Oex2+rW/+NMLDnAuL7jLA+572wNup/6yT533+wc41/C4n3zys17wP866z23cSXQbn379K9795YJP /SPv+ewXv/CZDz/u4AKz+zz+RS9+4bPvdSQ9VNfxIt+1udk7ft9xru989Ls/9Ue//c79nCu47X97 6nOe+7OPudcxyy73K/7mD173ni8fqHL/D1fr5sFdn/qbr3zFK150+sGted/6yMe/vL7/Zc2VH3/9 Sw9wruCEBz75uS981lMfeNzyiZv+0zv/1+s/fuWByv3ru53vrGj1Hk968WNOuNrj3H3jwtf8/FOf /rILPnPpd9KM1+Gw1z7/7rce4FzDE8580e+8+uXPP+lr79nPub73BWbDH37ay17x6pe/8GozH9c/ +Mkv7kdm7d5PvfncA5wr+uFHveB3Xvnylz79fvtZHzqf/7FvW4/YdTgfe4kpYAqYAqaAKWAKmAKm gClgCpgCpoAp8F8qYKjrRn1Apm9+3kMe8NAHPuCUR/73l79/GfTzgtue9ZIXnXr8Af9Sf/k/fejj ly+/FZ38c7/2i+c8/GGPfvov/fxZxy5fsfja2z76ecxSwREnPuDBD7rfXY46uMD4h+75wAc/6AEn HhF41/Ui17y57KgffuDpZ59172OIENaX//1ffmI/jtl+n+f9rxedffrDznr6L77wrFst39t9/u0f vPiaWFd2u7PPedS9f+TEkx785J958EHnVLOx2A9sum996l2f3o+ygpOf+asveOIjHvbws5/3oqed tH9Rzaff88lvLS1ON2w7+7Xccfezf/klZ1yddnlecfnHz/tf//2557797y8vr8lN9d36VN/+/Ce+ tf/L0Y8//vEPOunEuz/o8Y/58e8M0/weD8ztH/3Tj74vozcfdPWZj80ir3r3htmlX/znA2Dv2FOf /MQH3/1HfuS+Z57zuLvtv1x38Se/sPtGfRbtYqaAKWAKmAKmgClgCpgCpoApYAqYAqbA4aiAoa7N PPXgNvd50sv+6NX/48ePOciCuo2vX/z5/dRleId73Xm/rWd0wt3udfT+pRRf/uq/HWiduubV3ZCL HPvAX3jtH77qJT/71Cc//MRtnrfx9c8fjNT90D3vcpRDOsHo9j96zwMutPLvvvpv1zA8Mt6xkrmn JxiMV+LvXufaNy++RO40fdzmLne61VKBwW3ueKfRgX1e+tV/d11kN2Q7V7/x8Nj7/4/fed0vP/ru /7HYqvvmJ877xWf9+vsuWVwH2rV+2dd2H3jZrU84dpsr8Fo9+vid1/YnZcdkfyvXYLztGuTo0x94 0OPPfpL7depJRy1Nc2E6sBmdm/kH0K5tCpgCpoApYAqYAqaAKWAKmAKmgClw+Clwbf8Df/gpcmPu uPvW37z5V57zwtd8+GsH6+Wb6d49B9w94WgQE0Z0H3E6PAhI9v7bnu+u8br6um7ARW7zkNMfcMJ3 muP7+b7L5gcuPczSA9XsSTY82NJ+1eV7/lMl13XQqNvYe9lBsnTMjpUDD1r8g6f8+qtfsxyJ+DuP ur1DPjdgO/95JdHK7R7wtN/8g1f/wll3nlz92SYZ+vu/9NpP7V1arL73h5Z98LsHlx2uHnXCAUB3 HfZ+TS/ZduIjnnzOU5+qX0845fbet/75Yn185Vv7ruf17G2mgClgCpgCpoApYAqYAqaAKWAKmAKm gClwTQoY6rpRn4vhQ571e0CcV577v57xsDtvd5fuZv/83le86PWf2rsEXM36noM5tenfnvtE0o7u 18MPTDbkNcU0/69rm27ARW591I6re4666Z5LD2K1j7/8cQ/ev56HXG3443dSeN+HVv18fX9K8z++ yZ8ce+cTD3zc9gjn9boB27nGFUU77/DQZ778ja953pknjb/zgm79Q2993z9fY+/Yd17UF4tNg0/d 4tK/fuvvPPusU04/+ylP//lnP+t5z37uS9/yue9DU3upKWAKmAKmgClgCpgCpoApYAqYAqaAKWAK XJsChrquTaHv6/vhUScI4/zI3X7isc/5tec+ePsBeff99Uf+0ZqYvi8pb+iLox13eugzX/rbP/ff Vr5zpa9e9NlvX0MW84be6rq8vy+/9mcvfe5L//jDX7wW2HZdLmavMQVMAVPAFDAFTAFTwBQwBUwB U8AUMAVMge+hgKGuzXo0/B0//N/ue+SBq69/8Vt7vwuyDO9w9q8so3z/8de555y0dIRdl48b5SLL G530+Je+8prWc/ZJB/OH12VFN+g112s77cXnP3JpjuPX6f/zO9MSJ7d75NOfcd+DYna7v/StvTdo edf7zbv/5m0XfHa6jHSO7/DYX/mDC/78wr/46FtefP/rfUV7oylgCpgCpoApYAqYAqaAKWAKmAKm gClgCny3Aoa6Nu+pSEfbGHH4nz6i1V0HS9PD1dvc/mCY72qf3Pm2Ryxry7/Xx41yEXfxYLLr+IPN XduPvcblnPiDR6QHKsWus1rB6s7jDr64bg5ivm73xX950UcvWv76zDI8eeNtR1fr6/pq9fP+tmNO 2HadV71pL8y/dfFn9uy/evbAs59wv9seMTpYhrZpd7ULmwKmgClgCpgCpoApYAqYAqaAKWAKmAKH nQKGum7iI48mO3a5KYd8zL9x+foBBnTZRS85/YAv6Tnv+NqB2YXXvLob5SLu0v5o+zEHC9cvu2Lv gY6w3R/9n/tNUg946E+//WvXYXThf1qpP9m5f7gk3/jK5Vcd2Gfz7596zbnnvnz5631fdZX4N3g7 /wHYzf75C5euH1xNv/dbXz7AmLzgiDvdZsd/eeDhzqN/6OAL5nm5rLFv16/4xsHy/uv1wMz2Xnaw Ei1eGQ3sz931ktHeZAqYAqaAKWAKmAKmgClgCpgCpoApYApcmwL2v9zXptCN/P1g5bjb32H/Nbvd f/0PlyxpyvrXP/tPB6Y0Dm9/u1u5uvbv+XGjXGR59clxdz7hwH2++Ym/u7TUb9r1r3z+YF/64O63 u9X1eExWjvuRH53sv/L08xdfmrvP17/8t/94ABpFt/mR43fqizd4O/72H7jzUQfVKj983mve/v++ dvnu3d/+0qf+9P+84e8Pjr+8zcl3OvpavFTjHUcfdNR96fNf3q1hAv2V3/ji7u+f9l39+MY7jjlo nqs35vnyavu+8o9fupGfL7ucKWAKmAKmgClgCpgCpoApYAqYAqaAKXB4K3A9GMbhLdj13f3skn9f tkT5t77HKQfbo772p7/562/48w++8w9e9uqL9hu5xnc++0EnDq4lMHijXMQtJ7n1vR/4Y/ttZsU3 /vi3fvNNf/bBd73uN373g455gaHu/oSH3vG7c5jXLoN/9N1Pue+BSvjPvencV7/9gx/+8/N+82UX fGX/e6P/9rifPGH5/N3g7Uzu/LAz7nbALOd53/rEeS/52Sc+9uynPPOlb/rUxoG1Rvc46xEnbbuW lY9PuOuP7dr/mubj/+d/v+HPP/pnr/+98//xhpEub3CbE+994LLFx//vO//q8xd//m/ffd55n14c 9NRdu6T2ClPAFDAFTAFTwBQwBUwBU8AUMAVMAVPAFLg2BQx1XZtC1//74Xj1msCKf6v7P+v5p992 afIpLv/0W3//f7/uvZ9dzmccHvdTz33+WXf4rz1dDg7dGBdxt/SP/IlnvvSx+5lT962/ecurX/kH 7/7cukM7wQ89+CXPPvN214/GREfc98kvOPOOyyes+8aH3/i/f/v33/53+8HT5Ecf/dKn3f/IA3Tq Bm8nus1DnndwF9dwZMHK3c956Qsecpvv4LDvda5H3PtRp992/x+LbuMf3v77v/Xqd2/c/r53OGjK un4PxNUvu/65t7zshc9+7v98S/CkX/uZE6/f9exdpoApYAqYAqaAKWAKmAKmgClgCpgCpoApcE0K GOravOciOeq2d/xOM9QXv3HZgSKs6IiTf/aVrzv3Bf/99Ifc96QTVr3g6Dvc+8cfeOYzXviK8175 wgcce+04Rmu+US7iNp8cce///oo3vfznn/6oB578o8dNguDoO9/z5Iec/vSXvPxPXvGC+18HOvS9 JIyOvvczf/dPXv3LP3PmQ+5z1xPGXnbUne95r/s/4vE/9yu/90e/8bR7f6fL68bYjnbxqrf+/kt+ 7kkPu/9d2YUWNTnhpPv85MPOfvYvveJNbzz3yXc/IrkOh+1nd3z0r7/ml5/yiPvoIpPb3uvM5/z2 y5528vYbWCLPZR/7W6/7lac84v4nnbB9fMI9HvjY5537B8950PE7JweO+6Dv7zos0l5iCpgCpoAp YAqYAqaAKWAKmAKmgClgCpgC16yAf8klXzdtTAFT4L9UgKEB/9/L/2bZ+XXC41/9mnNOvIEmL9Pb FDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAFNkUBc3Vtiqx20a2qwOLSv73ooxctf33s4htYRr9VRbB1 mwKmgClgCpgCpoApYAqYAqaAKWAKmAJbVgFDXVv26Gzhm6GAf9Xfv+rcl5/rfv3uRd9YuM6y/Ipv XHJgbmRw2xOOufYutc1Yml3TFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAFrlUBQ13XKpG94HBSYHDb +/zUcfs3XHz0ja+/8B8u/qdPXvDWP9uzHMEYrNz/x+58Q3u7Dic9ba+mgClgCpgCpoApYAqYAqaA KWAKmAKmwE2rgHV13bR6291u8Qo0uz993kt+871fW3z3SoPbPvZXf+Op975O9fa3+H3aAk0BU8AU MAVMAVPAFDAFTAFTwBQwBUyBQ1EBQ12H4qnanm6gAot///ynP/HJf/jclz//T/9yeaFJjnf/kZPu 8eM/8RN3PXZoRsgbKK693RQwBUwBU8AUMAVMAVPAFDAFTAFTwBTYRAUMdW2iuHZpU8AUMAVMAVPA FDAFTAFTwBQwBUwBU8AUMAVMgZtSAbOo3JRq271MAVPAFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAF NlEBQ12bKK5d2hQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU+CmVMBQ102ptt3LFDAFTAFTwBQw BUwBU8AUMAVMAVPAFDAFTAFTYBMVMNS1ieLapU0BU8AUMAVMAVPAFDAFTAFTwBQwBUwBU8AUMAVu SgUMdd2Uatu9TAFTwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBTZRAUNdmyiuXdoUMAVMAVPAFDAF TAFTwBQwBUwBU8AUMAVMAVPgplTAUNdNqbbdyxQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU2AT FTDUtYni2qVNAVPAFDAFTAFTwBQwBUwBU8AUMAVMAVPAFDAFbkoFDHXdlGrbvUwBU8AUMAVMAVPA FDAFTAFTwBQwBUwBU8AUMAU2UQH/kku+vomXv/EufeGf/QkXq9sui6PA7/OqmZd14HtJ4JV1Mxmk cRT6YVx5gdfUTVN3bVNWddd7vD5N06b3kijs/WCcJXw1CsPQD7qu67uu7fu2aaLA63tvUTdt14dB MMpiPmt7L47Dqm76rq/qOvS8oO8a97aK37ddGkXcN2/6qlp4nu8HQRhGwzRN4qjjtz7v4LVcv+77 PuALgT8vdeGMBQTevKga3Zo7885gMhy0nt9zl6bW0j1e7kdhkFf1IE3SwahtuXVf1rXHMtqGq3te vzabF0W5Y2W8MhzWvLXvQt9PQ79ru6tmi75rR4NMdw6Chq2FIZcaJjGryMsq8npuVbZtFidJEtfa W4MeASsPuEyQhhErzMuy7Zow8NEqCKNBHPF7vouIyMgX0Y4P1saSqqZmhX3LR+dzuyRBqLpt0Byt kJQbZFGUxKHH+9isH6Ai1+WzYRLUbc3BtBxJi8hVWVVFWaVpnMRx3bLvfpLEKDpd5Lw3DrlhH0f6 SFJOI0Iu3ssTsCjLjUW5miXjyTauMc0Xkd/HQVh3Hq8dD4asZFE1vu9x3GyN5XH0vtsJOy+bBvlT RIkCFGAxfMtHb7ffrvNGaVxVTVG37CMJ/Y0cRRvIMXvnAWVH6MMnXI8zXl8UPHvPeMbzb7w/EHYl U8AUMAVMAVPAFDAFTAFTwBQwBUwBU8AUuAYFtgzq+uCfnc/yYRDzqgEqpHG4bZACU+AIdVVXbQvl AGmBcjogTN/tnc3yosySeJymUCdAT+d5o0QEqu49kJGoBb8gR00NoQh9aJMPj4BNwD74jaMYwTAR lAJqwJvgazHwIgjSyIej1TVoA5gDKAF/eCtZAp6quw46Urf9vKrhTdwFxMY1wT2QIIEV/nYwhStV TVtU4lYgtkGaArbKrovDCH7H6yEvgyTin/OqguKwBUEZ1i/MJJTEXYBCLAa0lkRx3rNaCB134lZ9 4HWR7wFpHHbxgWjbhmnDQn0PyDUtcq4DCEvYNqJ5YCy/qtskAnH1MC/Q1zAFCzYbiyIJvVEcFqAy 1lq3i6JiMQ7jOGXZUZwMIpTvwT18sG94UBoFcZz0wn8husF9mqoDKfLBwoGA07JanxewsO3D4WSY QfeSyJtV1e69a7OyPmIyhEuKCMKnmgb6xRthW45PelwHuMUSSlbVQ7tgbmjjs98C1iXO1oHkOJSq 86Z53jZVgnxBWDZ92wcoMgCZca0g2MFnUVC0AcehHXp6ALIo4Ap5VbWdSBbbmRZVUddZOlzNBgBB v2++vW898jvIZtPzSEgP/oEa+2Z5GMXDNOa9kFatWQ+ad8qZT7YfQqaAKWAKmAKmgClgCpgCpoAp YAqYAqaAKbCpCmwZ1PXed75JqCvwgSZADUjJZJBUnr8oSow/iQNVctxgM2px5OBtakEPfEVES44q zDVdJYuV2BOWnChMwBgwFExbACMoB3anneMBX5zX3TjFOtTPZLqqY8Ecf5LxvnDfosQTNMkySFff gqWgaly5DSJeAs6QwUmkBd9R2wp1+T43FUHzfaxM4DFejPNIDMzZoOBNALUMUuP7iwoKJx4HPwLn Le1F3NzZiHqWwjpgXniP4D+8ERDDZTJ5yMIgSgZZCvcB8HFfSM+sLNCK/WdJwssgW9y0bp2Zizti cXMOstrzsVmNsxRjlwhdWYKBWANrxAoH3gJkgdmWBJDlQHlywFwUYLNyJxKwC8RmywKOXr+SRldM 5yxvkGXDDNSj97H+jaJq87ksYEjU4WULenARuND3AU5wLgx0mPEQNvNAmXHVe0J6eKNwbclPx0eL RPjm4iAapdlgmEHeIJJ8Q+oDz9w5sp1BHA5l9Iv4bl2VYErOkSOIwjhLIKS9TGFdhwcM2lXUDQp6 HSAOPCdfWNG0q8NMkDGvAFWcPJCM5wgxnTmOHfA+L/Y9aNq0rAscdnr+9KjJ/9a0AqB+yMW2DzK2 UMIRg/Bhpz12U/8w28VNAVPAFDAFTAFTwBQwBUwBU8AUMAVMAVNgy6Cud/zpazgtEBW5RdgEfCMb ZJM0zYtqNp+7BKI/yoSjunjM94EdaUiaz6taj0AbL9g+yuSK8r31ogYkTYbALHEcABUkBHoBwgAs 4f2BswBoSLdh0cqALXiFeg8YBFNpvCCOwGndvnmB74ps3drGDCC1ffsuaA7kC6ZGLBGIA7PBPAa7 wlkGjeLKq1kK5llbYIlaBvf8YRw5qxSOJ9Eusm+iZj6+M0gK6UjhE3xNnR8A4GQjw7EmoIMvyYOH +eTn8BzFSZalWJ9WB4m8Ui2oB4gGCCON2bKdKbynazF8jdLExQoJP/a8XXRGOU6oTIgFSeysbm69 MoD87ZkXQmaKiuLA0hqSKCJ6mcOAiFnGEVI4GOfxFfjOKCh2z4orrlq/1c4VUocb8wVAasdkDOvB TcaNWMyirP26XBrQojAqWmhgh50KKgSfIhc5yeJhErFTkcqepKoMXMBGjob7AwRZC26vJMSrpQQk i+O363kJhkuEumRmwy/GggSnorjzAgxb4xCwVeZNk0YcK0hSe4GQ7hgma3m1XlQ4BMdJ7DU+djBQ F1lTzgLaBbnjmsKCHBO6kWZFcnxwoY/uuMMGPCdhhHsQm9q2QQLzYgXOzKVMLDwPvkXoEvjlBRF3 f+Sjz7afOKaAKWAKmAKmgClgCpgCpoApYAqYAqaAKbCpCmwZ1PXud8jVBcfBN0S4DMdMSwaNjyTA 1jXLK+CICpuSuKK5SswIfuIoUhRjHSLjCOCAh8ApyCHCIsZj0Eq0vsh58RBGJaOO6AS+J5fLk+OJ f+MJgshwWYJonmqq4kkaYf9RcZUzheHhwt2TJAotwo4UQnQMCDKiICSmKhWDkYbzIHQk4Pjd2MEe fEy8ZVFVJA1dmFK2KSXqCDnSZOV3rExNXriKSM2lCTAIaxLIBVBG6pCrsQ2uA0YBJ8H1QHM0RgF8 AEaKSSopqYenbJS845UowtVWBslGgSSCdzi7MIKtQpV8HxiERCyb9WOUIx/ZqCdrAerDF8YbEQXb FP8ivOl6yWCCSCqZomzIFfCCIQfFXjAyVHQpSExRyg3yKk4EAsRFhgnLjwCLmLEaD2ZUcS7LoB/r 4HBIKkLrIHuyyXUtMcyAxeOp4kbUqEHlmm4jr3gYIFmsg9uRMOWIuQhXdSFQUby8BhKqkQ0Z0QQc tqi7pfuPS0EGoZkcFQt090EeoJ4+53wFIqV5CAEkZArr5CGBn4I/efpIpy7qepRFpF+BlUPWzqOF RkgUhDNYZ9ti+GPHvFjFbbBPP3jEWU/a1D/MdnFTwBQwBUwBU8AUMAVMAVPAFDAFTAFTwBTYMqjr /e8W6sLZtDadK1QYJ2PanaiXAtCQI5OBCzAVwlZUZF7X40T9UCWsI4yAWBAuWZAcocCjtBNKkY1U rUXRV0NflTrkC3iJ8xNBZwgwjtMEPw6+LVjPShrP6m6jbDA0YeQBUZFzg7Xg2wJUzYqSZnhej3sL 3xjAiMsJeKnyXqk6/uLiMJrxeJRGIfYfiBIkBf7iCSnhllIzl3qpPA/7FYvBGJUXhTKMsLqYDKWa 4FnnfDHfuzGFVAF94izbPhrxffbFvcoyp3m9gfVgJfIDyBH2tCW845rjQYoPzZXdq4IdSsWCIUIL urcWOS4nat1pYW9xQgGq6iovC4KGICooH9FI3gjx4ab0eWG8AipxTfXNO+qXpBkygt6wdLEVFkNd vLAcTqiExYdQMEgTYgm9CRG2ACBCgjApVsuJiG2phKsHKumohBmF0bgWMI5IqsKAwyHOLpaRhpR2 +Tj18LIRKcRuBj2khx52BqUSTwx8vFrqIkP/wKPCLQtAby0WMDQHdFYNt+ugV2o367oZa0jUlUb2 U1FSsqul+sjgceAtlaNh7wp8QqDT2Ywj49lDkxmOwSAYygMYwLmmBU32GgCAj0+ZWY9fHrlXyNeV 0wX7euITnmY/cUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU2BTFdgyqOsvPvA2hIChKNrHB7MUBxmY iQpwUIVL2jnOIE6hqYeObfU4bkgtTiarQIoSdtM2zq6Fr4eqqo7kHuwG75TMWRimmOWnenMRlqUX CVwFKhuri77fM835DjXnQjEuQshLVGnlaZAfTU8AEVgPr8GjxXqWJAswtERUGJFYTzIYqPscEgRt qTBhgbMKvEDUzy/7vLg4NA1T0cZizm3UkEWATgRKNio3RBLS4uFjYg2z2bRq++FgRIQRbMW6gDV4 zXCqAdSoHlv2lGF3Qhzn3MLR1sLz8ElBsliw2uUD3VFMDbKjrKImL8L7lh38cTZc0jF4FV9RaZnr 1ScRSmE/3wAmwpsSEJugnkqyAJHCfK4jDOrHvhUUlU+roxuffjEU01hJ2utl48IYhXusEhsC3kUJ 7+IGquQnTBgGcCLUx8cHzisaQqb0l2HjimBnjCHg5EKkb6lnU1qQJfH5elGuDNKVLHVFYyRJSzAl gKwoFUMkYDgeDlCTiKJ7ovQPSFmWMeCyofkNMMnfzMSkbx5OV5elzx2cQxA8p/inq/3nJbjbyIRS 9j8tW2f00wEBEBkyoEme+NE8Mqo+y6Dznq2desbjNvUPs13cFDAFTAFTwBQwBUwBU8AUMAVMAVPA FDAFtgzqev+7/g+ntWe62JjnJO92rY6mZQN3yXA9hQFjGQEisg8RIYzp8EqmRblnYwaU2DEajoag Eq/C9SSrDj4dNbgz1hDeQcO7mqdAE10bO4sQQEZGr84bD3B10ca1gHzBhggMsgBQj8rLQzqYhLVw ToFOliYk/GAM6SPRBg6DK5GVK6pGkbqm2TtbQHpWhth9NI0RJpRiMgJB1RWZyMF4FSRDzxiROmxE s6JOPbrw6XEv1xZwkm6SDVhY53EvFaQrmkk+jrYvaqn0dbWeU/0FWWNDcdhXJQQM31nEAmA3pA0x hoFgWL0mRcYh9VJVWYgKUkPmUBZGrZw++Dhi5eA5NqtmdwG4iIZ5PiepqIp3+ZtUPA/qwiGFBlRi rYxGsDzuuDqk2z7aN8+p5XLHoQmUACzsXXTkg/YAlCxekxld5z3boSSLhjQypFi0QnrWlFDkXUwG 0PhL1eELqzH1sRkl8RB+h4IUYHVI6LVet0ZvfFkihSroqcn3cXiVfNN1yaeoADQDAa6MBjwZG9PZ xnyO7w9GppdziE2n5KqGAzA7sp8xlbKpOWso6o5R1vqEZOPVxMsXC2xZ6LlzMgpijXSMA48uM44B 65aDgJrcyGo17SAMqZMjTIqGrI1wK19KkkHv09X1RPuJYwqYAqaAKWAKmAKmgClgCpgCpoApYAqY ApuqwJZBXX/21j9EiH2LAhKBXwlewYcar1oQlfqXQEJ0M4GCRiPCi1S8V/iAADQYrAijMbmvgSsx my9NeDel6dix4FxEILlUzHxGgoQV+CLP6Rt3Uxq5OJ3rTG1U8ZNDQtwHWENjGHgET1AWx6AirrV9 mBIwXBP60YuBMmAjyuChS9AiNT2R5MN6RpvVrp2gGhAXxAvuhsOLHq2wree4k0I61BNw0ozvJhqB yPVZpWYd0pXeaQHqhl/k86IkyMeaiyDEiMSiqcknBogyIndpjM9pigWLPntuXFd42TSHMgzhXPiR CGPSYx+NVri4Fux7ZBihRVS58wJkVP9Y3w3RENJXVCjtJhCqxIoFdXXNArCGuSmNsEJ1b4VxvK+o VN8l01m7wO9UQbt8quOhTnBGjFrDOMgmq1wFz5eOo+tGaYorSgXwtV6ME00XDKKqKuWhUu+ZoBL3 AivySs6BUjbOhDeDNGF6mmKpc1ejmXKeYcyL9hWcPO6tjAeGQQJF6w2HULKgpMStLFwglGcnUK29 KvNllEOetq5xnGFAW7Z08S6eC6Z8YlIjMSqLoHr8g+FwfNTqkDUgNtsEGvItopssltArF2Q7GlSJ 4RD4hwEPhqYRnACy8KzHWlfXpv40s4ubAqaAKWAKmAKmgClgCpgCpoApYAqYAt6WQV0ffJe6utRk zoBC7DQYeaIIhlUzWxCbjhrBRRlgN8NxBkiBp8AyVrIYcw2pvQGMpqdsqya7SJQME5MKvOJ42wgf EiG7DiMSwAVOw/VhZ3yfL/IJZV4uqAj6Is0WEQUELZFWBHtggMJG5EAGETel9mRJCpbupxa0tHMy Zg3TvCR3CVhTcRTApdEYQYXm0nTZha9ZimT3gGQeXqcedhbHShpyT2fFCtYwejVV5DW4nwBnMpTJ TIRZaZUk5Z7plLSm8IqHlUlN6hAdjYPEAibfGnXrqu4C8ai4jP1jUgP8UHLPykmCAnHcxEa1TPFy x2Z4DdcH/xEYRVg2JQhE+RRpxa5f4BbjGxocKUo0oTV/kLFrbggl5GKwPQ0EgLV1nZrfdW5UZrG3 LJMPjlyn8JW6w7qeCjO8WlwKWsf2lQCtypzhjBSKsU4gY8PoSYGmWnxMxNBlVN0USzWjqfJfDM7F PwF23J8qLo09xNoWajtELPmrLLDckTX18fSBGpedZcw34BY8A8Vihk8Q0xjDHR1nE5+DhrGX6ULT NsXsBEDlHUMTWr0IV9ILxtgBLiFc6ArvZWejwp+NUHMW+nvnCwyGGXVn8eCMxxjqsp+5poApYAqY AqaAKWAKmAKmgClgCpgCpsDmKrBlUNc73vF6lCA5qCxi71VQoc4ryxLQAJChnglaA2YYUVo1GIFv IBT4oZiUx+cZaUBXygTa2DakoBy3DcQEchNgCCKPBkCh7Il2L+c/ckMAMYsJsrTCThqsxzfU36Wi d3hJSa4Qt5Gre88gVt6etX2EKOkgZ4VcE3aTkUhMkjloqlU9FjgJ3OarKx9/k1gTWEQd+LiFKLoS a2vANGzhqnkxXVvDezQeDKhOx/rFhndkETfdM8uHg8H2Ca4j1eTnCwUVVwaRJiEGpP8oYvfgQhEj HeE+IVRKMUmMXXJlkUEMRAlRBuQDttk+SCAy+xZwMaFAxHHd8CqvEp3yPCARmGxZXwbXw6FG5Zl+ 4/vsWoMIi2q9rPCFYWqblpwJGkUZnfEkI6PUixLX00X+McJsBUzUpak2qxn1GG4bpAJ9qiNTKzzw UcHP0CMqiMI0bZH2ZFNLjxh8Dp7EqbCeeSG6dMR4EGYjoUaONoDuVfTQ8/WaFjUxRd6hDvzpnEKz +cp4hLCLPOfgh1kGOAORorZmEbS9UoeQMsrLegUhAVxwPA5lJh7HPYGS8bYsZiWcOIsH6kUgtiBY y3OeLoKuLIPnh9NDQ6rj+KJypiyE9jQ9ehwFCDI948wnbO6fZru6KWAKmAKmgClgCpgCpoApYAqY AqaAKXDYK7BlUNeHP/AODku+GUCMHEYe8/v8rr5qusBNwyg9vgMugWgk8QDHDZ9jPkojeayKplbH ecQQwwZ4A+rCKASMgTfBIUQkPJ/xezh/6L4S3XDF93yDWB/WINW3h2p5h+lg8KFGHfYkWKbBiBrP h22HlZA3BAOBUSA72JFwDwFclDpU7o2BjLJKQd8gRLxGziysSdoLMEsoCrsThi94jTqkcK0FwC7l NIkf5swW7FrykcA2aA40jxopnEp9tQ5Y4YuiM/tb+TEUhevzBfvatjJezeJ5XgCzcKWB1zBXgYqw hYG06LznppjjMFgJKlGYRbqvqkgBjgYZaUGcYCJlbpohmqCEJOGTIHAB0sRRM/r4CT9iQGPhwoRs gbugD2HMJMkq8pkkR7XZZZ28XsOCFQx17fjwRr7JeEKsVYM4QMk0UOm7Riu6SYyox60JeEIVNWrA ATglJwW5/BoYFoR0hPHS9dkC2EQFG+lLTeP0AX8MiGxkzGMvgEC/H4VMZozISAacKzvyyITqeOjI 50AhiPLX6ZHoZosFugLY5NSCq2pQJvMoRUihlW7spK+K+hZsB+GKOAgWhgpcD9AIhZR/EPcYQvOZ YrLZqQ9/1GH/A8cEMAVMAVPAFDAFTAFTwBQwBUwBU8AUMAU2V4Etg7o+8ud/ghJMKiQPCD0BMwEm MEuBUaA/gAdMQ1RrQX3ajib2RpjC89wMRF4V7RxTDBWvF9V0vvDausgLeqPo2MK/RQiSHnEsT67T HvuSjGPTRUU8jSQbSThl8QBhFEd1LVhK3Ae4Rt1VEBBsw8a1azSo6KwS5+pktGKhvFEfNRlJOq0g ZVwE+qZQYuBjHAIMCa4oiMc1MX51ADgYDz4vaqTgRHGSrgwH0Jw9s1nUA7lc2VZDMxdcTDgMGkRI knauSZoB3oBEgCy8V5Tf87J9eUkXOr4lmrfmeQnCgSqhFVvo2iZL02w4FtbRLEr5tPhWBbAjZhjF QDZxNO6CSSqfsSH4DgSJV4PwEtqz5BHDudUT3yMsWVZlnAxhaZSUqf6/abGygZvIG2rqoqrNGAVA EZgPcuKtrp6fQq56xlFW+PK4ILMKA8q8+O0c71UYbGcXkKxWoFAdYQqPxsiK4NBDvHUcQr3YIH44 yBKuz7ACatY4EQKQbQ9uA2lx4PjUOpjjZMjwgIhxl1Tma8pkgvnOx/Hn8BbxSU7H4xbwPDKMmLDY FLXyq2kYpQM0S6KYgQN8QCqJeK4X9SjJWDC/zeuKNKejmQBKzFzqFFO5WMXoz3Blki0IswYAOGhk csYjDHVt7o8zu7opYAqYAqaAKWAKmAKmgClgCpgCpoApsGVQ1wfeI9SFj0YOIXmuiIl5NCu5/iz4 lMJo+HEoJF8UOZXk4+EQSMEgPExF8AkHRzAQkUJjqGIJ4uENOHCgZIqz4TDC4tS22L7k8AIqwUtk ffKiAdXyNNnje2I2YompC4imkBsJQNqvMIgxDJHvMQzRfQWWBaBxXVceqbr1RYGhyHnOVP80WdnW NRWOIbmM2IW+BiZxkwrdLEBcUStDkn3eelmTc9R4ROhJEjnHUETVPaG/zFWJ0eweRAmcCCmI+EWU cqmpjHaqCJIEzeFqCi+2tKTTa8ZoQvrLSFa6/i7MXJSLybGloYGQOM00hCsBbOT/CnQFtZsBfeCA FYqH+MqUFeSvDv8b8CjLUq5PuT4cLQH2NWrRoihrUWo0obORxRoWie2LhGbTsGAYnJr2VXYVlJxa wtDJlCuAyzhccBadX20Ut/PZvrxGNMYgQqb4kJWv9/DN8YnSoBxiVRMUBT8ugR24zrnPPEU6Q3/b KOVdOTsUX9TX6GBDHq6UhBEGMQxaPEWsisWwUorqQWa6Jlcnihhq2gB3dqM4NVwSbUMGOzJosmGw Jo+MSuf5F4/GtvFwPBpM2XZV034PpWSpInbo0Der44yHqPUTsqunnfYY+4ljCpgCpoApYAqYAqaA KWAKmAKmgClgCpgCm6pA+OxnP2dTb3BjXfzrl1ys+XdYeGih8v3V4WCYETr08OaAOcb0uJf1t67a lxcF1eEwCDqV1KHu93ANvE2CJX2Lv4qZerAWvq4RhXxNvfL4wiJsPpR9qYMpJOjnwbywLI0z/FMh tGTvomDs4IBZhEEA0WB2IhiIMYuAJC4CfqPUHhwCGaFyHoaSqoO9p5xKji4KoajGV6U6N6A2TPMH gTBcGZBFTpBsJWvcO89BSjvHQzZIJRUgjHfh2hpCqWRkI9TIvdRBxtS/Gdtkd6EHvVrJoluvjiFc nhdieoJhUSePLJA04nWAJPa+3IXrWQ9IXW4fZ9tHw1GW0UfmBh0KeMWiWHAe8p4yx/FiEFHlxkTi RNs2HjCUECakmQDU68cRu4CCwYBSKReo1N/xOxAPgvhduzZbTOdzqukJUXIZdIPAgQHxXLHxcZYd sTLkFldu5ORR3XRLdf8D/Yo8B+Hh2sNxNsZFRsCQnnkBzm5jUWKwWptpNmYSafQjXJIjY27mvGTc IeX3MFCkq65cn80WOedAA9eO8fDo1TFHz01pJePwuBxGM+jVMoK6NItpuCesKvBGhBydQQ86xqHv GqvfbYaypFCjuA8omU9vtW0C5BpkQ0Zekg/lQFcGKWVtS4BJqxjicBfo2wYU0I22/OETT7qx/jjY dUwBU8AUMAVMAVPAFDAFTAFTwBQwBUwBU+AaFdgyrq4Pvf8CNoCFShky/DtABxlzWrKKUCdShHAv aAu/TbNRlgB9GkiFqqFIDmo8In4iLFyqOZdDx5c9B9uObFiYpOiG10A/Rv5FZAALnD19B7YAFRFl 47uuuBwIovoqKsDUdeXSfCwJUHLFxpxrD7MBpfgwI0iQzExu5iDchHQkAAieBKxJsiG+s3lZaMFU mNeUgwWMINTcQMrD3HxGmFRBy75YjK7PYkhocjm+Jb7jyuaJB05YaJa6EYd06tONBXCJievJecZl XUYS8xe8b5wN8G1hHsOohWlpbV7wbWrfR4J+ONcUnMS7NF8Ar5ox7ibYYBQXSOD5I3KWXc88RGxx sEVcUfAjcJvQGMFDHFOyzdWU5WdhsFFU3BtYho2LFWLIgpFpbkDPcMxmovKulO87C5tSk6AqBRsR mAmVoC4Cp2nKuxazDRCYaFbvcZYOdVU4wrgUhy8iR1cWW2DcpO+Cq6xBcK3T3MUeWxxFY5r6yDvx 5dVhKvOX4qnqenPGLs2U3D3N+Sdsa9mkxqGSycSGhvmMFyEmLWDO4+WtDBLwGeqgFNMGYHDrecE1 GSU5SFI8bLNCZV+qCGPIJLBMgx1V7qYJlQyU7Lw9cw3FfOwjrZbefg6bAqaAKWAKmAKmgClgCpgC poApYAqYApurwJZBXR90AUbQgeM1giAwCtJiABF6zAFXVHKtE+Rr6mE6nGTUpKuESiXpsnBRZVWD cAiaaSKiCslBIvARxuelICNcYHwdGw7XB6AQPKtanFAq1uoqHEWtyp88n/Qf9Ee5P/mkAldljsNM FV1euYB+UdculxBt6K7jvA8jkJdQmgYsVrx922jkYnG0O6m6SyCGtXvqeOL1rGzRtACoySCDyUxz /FUt4/+oJwOl7BwNBJBUFMa3NO7wyG2rvHHffAG8I8tJ2zwkiFAlUEyULY4AfJqZGIYT7HCiS5TQ t9NF7lFvPxjgVALlUP4F69k1GYGt0BX2BvvZPmKxMdpOp+uLWjYlDExjLEv0uysTqBgp7VUbOfVe 7ZhCtNafVRUoCtYEEqIibZUrwK0Ih9Lur6yoEpRYvUBdTFqE7k0rwUfmCeD3whzXqN8soO1rg5qs skAlbsFZk2FEYc4LKRydhBxR0CbCKJ8bxwvoJBmaMIDSY5yiC7TyOlxaHRCKW2O5Ivno0qYAqyWP ImuJKU8Qk81yBW4Eu6T0iyIzob2qZdN023PQ4EvhUK9fpkchnrA8FoM7kOuTY1VRXN/S+8bChD6b bt9s4dr6CZlqWyyVW5OxfcJTnrm5f5rt6qaAKWAKmAKmgClgCpgCpoApYAqYAqbAYa/AlkFdF77r TRzWtKzx+OwcD0AP/7o2S7LsyJWxV1c4p/B4AYkgUPijeBnMCAYBd8DARZwQ81TZycMFcQjBE3S0 lzmD/7AmgW+gKCT+CD/SDwUWAdKo7SqklZ4wYYFJCoOYMpBgEfxIkCnfA42tz3NXa6VZhH1TwYjG eKUcfYOiYAQiv8aNd62MgDWsUCVT0JyUECO5y2bvdDGhOz0MFkoddq4RP8blxPoIChKoZPTgRolP ijIs9etzNSXh1JSvx1YurZArefN8QVGXVifqEgKr6BQD38iylMS8lovwCS4sDFDsNOraI1dHq+MR fepgKWxZs6ajBp4rkDNUaRfuKgZBRpGGS85ngL+VNJUiYVBB8TA6iV4xG1Fl9gQ2lfjLBvAoZAGu kQaF+dReAAdSJ5qAIwFHsZ6wrQlU8l0K41k/UwvX5jmpSS/Q0eCfggmtYPXKxqtpDLDjUBh3uJFX i6pQfb8wlNrNkHqSZZMsxMQHrsJex7ewy7UkUGVe8wh1Yk3TBAFW0oQAzYFqvrq1fLF3uoFKkR+X DlOyWtxz8qL1vSuqb4hQQjkHCR1bPrlLXsQd2Thy7XeBEXv0GHzpcyIQQM096NpBHCPFel4y34AY KLJjc2N+Juun6h5/GynVRz32nMP+B44JYAqYAqaAKWAKmAKmgClgCpgCpoApYApsrgJbBnW9991y dQGGXJwwgm4ocBj463NwDZSqEtKKFB7ERsM0vSxOwUxk/gAcTMnDH0VnE131g1SF5coY1jUMg1we /iWaoQRKHEgCSAFoYCglzh3uEia4veS6ghZFIVQGfgI2AXjRDoZJCEMQ3AbQo2mMFKunJCnFgKbT 2WI+c53nCXwMWEbpFCSLV+IGwpDUUapV1uqJl8MLrxapOq2HiwLjGASpeYusSq/Vtljt9hEWtIAX Q3ZkatP2c5qwWAAwi0DgrpWh7GnkH9UYJQccWA9So5ouJSjpogrWp/irGhnQ1F/GTENBMSxvrp1K DV8RqUb5v2oAEDBxgt2skf+LZWB7wh8GAwIm4paiOH8ZPIxizVFUoJQCe5xlIQMaA/rFuCB99mxR MJHWMGmBtasl6sgbRQnVQdZBKDG1YemiyZ0i/OHKiJcRtJwXEC44UgdIYo8cIlfGZrVtPAJBaSPM 36RXSwFF4BdjIMF8EDFoGhMGWoYhQtQW+RzC2HhMY/RYrpxd7tTYMr9wvbEwVo9SlK0B+5zayCaX FtYxvkGfGgvAvAZpEwxrUEkRV42kTBJXt0/zGsgs5lzmRcXjMRpNOJ5pKamhcpXcZtmpDzttc/80 29VNAVPAFDAFTAFTwBQwBUwBU8AUMAVMgcNegS2Duj7653/KYeGwgTfhM9KMP5hRWQJRgCYABYgO cGEf8xcXOWCEqKDzJWlqIAQGRgTmAHyAHEAYe8kT1i14Rc3tuHu8njIpJgbCO5RU9PpRTD0WiT6+ mMLXaF3ig9tzF5AZEbiqqqcLMBElXLRuCSPluMbCCJvPICbb2OWLOZgmSxPATy47V7gtVcjuso0F Qb/VLIGJ7JnOIEfDRP36fJFkIOzGFejXXJoPYS+XtmORjVKAADHVTWngIOYv3oaxiOJ2KqvoQGeJ UDaVskPrfGAcO+Et1G+B49ZU4dWqVp/vej3cjd+TzFsdqgILODSgbItqfEgOsIcMoQYv9rt27VgZ ZDSyq7iqomq9ZI+wIsAfkA5xWQ8WrSQdIOS/75tyZQZEqnEMhqXZh+Q3Va/FQEJo3Xg84VS4PgBv u5xmYU6XPDvSTAAShezdH3F4wpEaesj2qCsjAtlV1SzPcV3RK+9zrBqbGA+8ntjgsuQetQBh3FRN bFTj1yULg2dx85XJuOo88NMQRZgJUBTiT31w1MoI/fYsSp4FGuUHo0mlLKWY3hiwlSboAyAVA4si DGJKd7poKglLjGwUqUHKOAy+AtfjGWLXQmtuqkAWD4OwL9s5IjCaEqxHhdcpp5112P/AMQFMAVPA FDAFTAFTwBQwBUwBU8AUMAVMgc1VYMugrve8840ogUEm16Q8bFP1NM+Z6jdREXy8HC44SoEZXZlX OJrUhEWXlyxXgVq7XKW9hhLyt7iTr9GIDMhrWsqf1D7ed6AxQBVmJYJ1vJFcGtk0LgzjgNcIm/Qy ZOmCQQDkEEIDr/AyUnQq+YoBWxi76DjH/lOpo73ierwYygMvczMYhWKgJ6uDAfylwTQECApolCf1 Vu8raqxRULCKEnPP3zFm+l8IosKlxduXLewEIQncAbX4JKAinVxhoAmPbNzVQjUuaOicVvKmyRPG 7WB4+KdgOkCXJGbuIvlNzQeEjbEvQBwrZB2qwnJl/1ifKD/TKMnhOAHoKM7oBkYCnXRNtMLdpsyg DFVIJEgkHZY99+iGsY7xhOwCUASK41LSWLvVMExEAwzxddxs3HpEMRgX7L3V4RDgxX55hzie/GWh uwbED3JIHZnqtwh+Fl2/tjEjv8hpwKA4jJwhmORDBwO8ZoBNKtW4oHKgk9VtjCmo6j3rU9bJTdkk y8Ncxhp4eiCeiqUmGSsfRpqnecX61MPTx0XIljLEQJwzwEjo/HoKb6KDmvLpaHNpR7YoaXgr/2p7 TeoMSUb2Vb8gsOn3aDeo/OiM0w11be6PM7u6KWAKmAKmgClgCpgCpoApYAqYAqaAKbBlUNf7XS09 OEnuKgbkgSbUJ858Q/xVFCdRFq46dobxkVfU8DzghXNhQZ2c90hhPdWKByEWHkjWDGsYhEq0qwEe QcoWebE2o7+Lrnp6llzYEJABGYKXOeMYOC2NAurJxay6nlurGkyd6SqY31dUMLUJswgVY0ywCAXC Z6GcZQJIGgJIkhJoQ/HTGjX5cTYZJDRAcZeVjG6wuFERFTY1rEPkB1mIYo74hGAsbFmox3m+cFGJ mZHHXFmBvzgXlv6xKAtYzDCjRz3A+AaHgtewNoxndHpBxEBRLBgZZpqbKIpHuZgSkZ0MYFwQSAbp QyIYDoTJsSlsTXROoVO7jzyhsn4iQ/sWZPMYKoilztnMWAGcKE23j9l/tqALX4snPcp6Ul6AIYse KyAgewFsySQHfGSHHqYwspMt1AqGBaWi2AqkhWgsXD1cSbJjmG1bGROlpExt74Y7cDV09djz+Bcb 1zDHoCeEOB5y7YQFca993K6qwYVgLa7M22BYR08Gs7xYNN1oOOAQ3dhNcJvPjEVsXFyKt6ANxHQP nj2dAWMWE1bIOQIxOR6WJqLlTGQyjoUh30UA3ovPi3MpMH0x1qDykKmTE64aRIxtXCGl+bBTTref OKaAKWAKmAKmgClgCpgCpoApYAqYAqaAKbCpCmwZ1HXhe9+MEDiK4AuYeuBXtDLRxwSdgUDJqCX+ FQK2Osw6mp3HTEP1o4MkwF4raQzdwQmlBnlHlGAo8LIFYwG9nhQhtfP4dHyyaSrBigj06ZqAJyKK DgNhRuLN2IW4+7SoHN3o1HIFPAK1gJvkLJNHSWMKPY1lVGGWIJUWxrL5AvaznJmHDeMgk2E25A3Q o4hhg7yTSB6OrIiFY5TyFosFoT9gmXqv0hRjEyE9WdU0UVIvgRbxDrgV0ArKI9MW7WOiPjGdZbAZ uMuMOjEkikh9RuAhUBd8xzXWw+zQ0MOstTrAMRbMCTMGtHR180q18bJwMVWS1F61oODfeb4E25Cd G4ySFNYDOwS0CSSiTa9kH/yKN9K4z7xIiBF4CLkwPskNBs4bpBjW6G5nF7BF3sUnClqGMkyJCbWQ ygZ5wxbPl3r6uRr4UvlEApVFzrlGBCeJN3IjkqVEBJXxFD7Ddsfu6DNDAD7RQ+Ep9cm3eBel/mpT c+VuACzgFM1irBPM53bks1TeNSMPC9x0rwRdcXEOjrmTKNLjEIwjOvuLssDQB4dj8VjqEIaHCmSH Noqygtz0CHah6KtPtX0Ywhy7yhsE6fj0U62ra1N/mtnFTQFTwBQwBUwBU8AUMAVMAVPAFDAFTAFv y6Cu97/nj4VUMHZp3KBX1GJApA2JowGkqFVSX5bn7cVrVBUO5YT0hZOqAx4lDBAEeZCwq+txGgMv VPLO5eBEOIgoiKd3PHFuIDw5DAFMNQkRBFI0XexpPKLwD41SSkQGLoXYrIiOEZprNsoaeIRtSiMa iU82Cr4duTp2obZgbb7ANYbPixGH63k1W1TAH5gaXrHtozGGKVJ4IC2w23g8Vl2X4pAdQUhwCQYj cBXVYHCuVaZCErj0gyvW5zJGgYNo1leGTqQJ3EMHPFJgYoozxif6+KpAgdtSjRe8claxa0xJeJ3Y 5DCWgDlGMEeRUBALGC/mjmiiiZQ0ZDFd0VWSKUfo+6uDbABr84Jp3hBHLNh/1yMpd+KmLGI+XyAd ynAK7pfHjjJmA3A0DhquDDUlYD1f8BruKKWYeFiUID52DVaDjgHvJgmQsZ2zTlibThnyiClLowD4 BcIbpTEGMbaMAtRryc/W9fj7UBh5sW5xZTdYQOM4RQlpFmN5nJFPMhQ4Fs7KKldtP54skq1Lcxnf gqDRvq9RmxocIDzHQMYGMxsr5Ijha4NUnWhwLgZc4jgbJIC2pfFNnWmOc+pfwEWU9RknyRPApuU1 7P1oHIO6HnGm/cgxBUwBU8AUMAVMAVPAFDAFTAFTwBQwBUyBTVVgy6CuD/2ZAoxAIkAGDAbCAC1i Zh+uJTXKhz4cBLcO5UqQBmgLowrJwGHpgonE9L4HIcMKoROAm33zHCtR2ANQGhqduM5kOBhlGcSC 64OaXJxRgcccZ4+ygar9IhrHCyhlh9JM8xIuwzdd8bmqsobjbQMMPa5JXSXxNbjKlaJ7ysSpEF4p SurBAlVK9d1qls3EQNpRSAQPn5W6sGBkJAThO1E8wDEEdgEMJfIjYVDqAExKSkJbQsKVdNj3TZlj RpOBS24mj4J2rVvFWiAcKA2d7qrNkv/KVzJxI6+U+iRkV1dJBHISQuIDVuWWSi0/IK/FHcZaEQTP V1VRVN+NY5rLcCdhB9Nf7EUlX65+nrsQ/cP0xBfhUKI/fY8zq6mLy/fuAx4hI/k9go19GC/YfF0D K+FQ7BT4qEwpZrcwHCaYp7qpuvAFoZhmyFGxK1RkgGOHiwtciV1OGcNqusjpEssGI1xUmoIpa5nY FvKyHeKUfF0OOJ/4KtoRapWvTJ8qHComCbFysUQ163NqomPLyn/54pyKGt/ZOZYnhsUrIXQ8TtBP tb1hHawJqArtwQs5WMVTQxrHQiThW+RLj1il3b69agrtLKJ4ZTTc/vDTDHVt6k8zu7gpYAqYAqaA KWAKmAKmgClgCpgCpoApsHVcXR9415s4rrqpsfzASnaMRyAdjWKkECogpQi/6EeqqI+YIuhGBFLl 1AF3wD6r4wneH16JT6pq6mEa043lBSmuIjAKJi/mOs5pnQKGtNX6dL4ocf1obB4kS0XjTPFzrh/o yaxW8zoGJ83do7+J5i2Zj9ptO7cTNqScva1rYoNQpzEgJompl6L0CUqybTjQAEhVmqvAnNCbQ3IV LrJF2aYpDijMTQQG67zIKbnHibZ/4qLXE96jHmyFmZKupwwrWaluflEiOAxlWGMKupKo6NVR1dcN /iNQDb3zSaAqK4gS+AaYpnurNt6xGi6gErPlRfh6R5JxQ6BNLezbx5OV0XjWeBsb62Ad8OE0LwBS LiLawJxosALxkEZEWNQmvwk8TLMUq1UqaBhuTKez6RRXGogNk1rjQBBYD0i3PluszeYkLhkHCVnT AMZQPWWozVlMsggAxm9hf2JqvvjmxsbUq8okzeI0KwTmArBaofJ+XH4RPi+OZhsusnTgwqn97tmM Pe0cj9nOxnxjadQCy40ZYaD2tF6sk954OQElAcJycHBSVgu8mwxS3lirnl9DMOV+cwgM8pXFMYdR 15TNt+sz4p7kNOUbXB0N2SCsURMAxN5kt6PFa3161e69G9DUXatHnn7mo+1HjilgCpgCpoApYAqY AqaAKWAKmAKmgClgCmyqAlvG1XXRhRcgBPDIjQ+sKFanBH44nhCj27tv7ap9+6qyGMiCQ5hQ1Uwk /nj9FdOFYBA0gn51puk10B960CmK6oeDIRMcCTmCJ7YPB7WnkYtgLDdtz1dtOzE0qrIS4E0CxRBL 0y8ZmiAdi/mCZisIUsP10zSLKD4vp4sSUxJ3x59FfK6oe+KAcBPK9HEzTecl/IbfwKEGxAAxkAm6 BHTDty2t7bk8XDRQYSFKeriNg1Nwphr7kxBM05LRE9UaDlldSwpyZftAtIyZk0ovcikcZYM0SPx+ OoeYwYKwlJGv9BMFHltNWwxd2xWEr60JLQJ0SGK6aZVB3NcQtD5OoXKDKNi3hg0ub0KynGC4uKzp 5ccX5d/qyCP591XzPOxbbrooCuU32zonuEeqURXwPm4vOB9OrLIsIGostlYzP7phb1MNP4YxuuHB bztWJoPhgKYvrHkcHjFNlYhV9ZWzheq9IJLYpgCLNNrDEcFqpCmHg53jIehsnwxnctLpbwZGliXQ yQshX/BNDR8g1QiWmufNKEsGkl29ZvLGyU7XcjWeBz0Zml7gr27bheUNy9je6Yxzb6uSwZjD4Rjf X6bnpaOmXrX9+NHqNiemim0vwowWk9JsqmLPtFiQls24KSQwIC86yuJisXbV+u6vffuypkqO3XXs U5/xPzb1D7Nd3BQwBUwBU8AUMAVMAVPAFDAFTAFTwBQwBbYM6nrvu9TVxZxCEnn5YkFicUXUh9Rg D9BZ5DnuJI1f7PrxaDjOQDNiGriQwDsQJ0iHOrlUgFVjAgKQwSnkSaIBKlZ6jimLABOSfctKe94L 7aKBy48SWJiKz7Ee0bMFVNGsPR+UBDaBdtAlFeznb8Jj2HkAQDiPcAMNowAOA9uCnwHCSlgJSTZq n5gz2PVgNRAV/iJsUMzu45qUyBOTxKZEv1fbqwedWq1pntPHzkbwEjmWFA2HA2UziyJo4Esd78J5 xvKgPzRkAZDc1uVrw8EkA5fzLpGLZINsjb3jPGpgN5A2N5+S9zoeFwPCyOER2sMNB5bC35QSyOsC 7iIfk7xRAY4twoB0lq2SUiT6x7IQVgFBDSVUM5q66gGC+oBAzQpNMiQtqBp92uDdyEsZ4uIYFAXq U3W+fFVqr2eDkCcuiF1rgoWLxjTXXk9u1WuJqepDwKv3UF4lX7yYsZKCj/V0sSA+SSuZRgpQDK+x leA1T81kzATQtMdAx9dJBKxw4LYNSsFiRVPX8tKLUz6BDvJdDHq8knOEb+6dF5SkcUt57jQSE8sd GoT42jggECFOOqSF0B25Ml4ZpYyzvHKjIOYJzZwVs/lijYr+psV2t+2cc86xnzimgClgCpgCpoAp YAqYAqaAKWAKmAKmgCmwqQpsHdT1fxVghFaAQqIWE1MfxBQtuZgeVeoqZxdUwlNU9sxVpMlKX8Jb pMoqzSqkYwtw48NNQDy8TDYogIivLB4wC1hD8BDnkCtl8rBcaXahTFJcNQLBgEvIr4FpMO/g61lN hV02ioYWKmJ3MBHoEPflosAhIE4W+2uzAviC+4ead3BM5gdwISgJLiKazlnGKArSQRal2RGDGB4E EcFIRVXWiE73NFtU7ULVZFxTmIbPeD1XBrkBsK5a36C5SjY2yAuzDlUHlYFscD+xcdaMVOydjGfl sfL46PGAwOCVG7O9sxkoCU6IP45ef5xjZa9mK3X+exraCBED8TGVcjQY9FU5K5XlzCKP0ndIFLYm ECFUi3QhV5CxigBjlrGM43dMAHmXXjVFrIxRAKCotqaqirwhxfYiT1iiKhr9Y3ri10uSp8CkHhrH rExuvhypOZDxLRoP0rzuKNcHe2G5IkFJFlJ/EgSt1CbvkJMGLNKnxpu5Uhb6mNGmeZU3jIbU3t00 TK+pSxQCwLEq8B9f5ikpeo0ycJMZCZMKX26jON/r98w00wDSSEqUhCnrB/DtmoxYHq9RLxjR1BRC RzhRbfpMxOQhBHXRIwY7w9MFvNSmPI+9gODiQLnInLBmOHr0WY/Z1D/MdnFTwBQwBUwBU8AUMAVM AVPAFDAFTAFTwBTYMqjroveqlh4oA3Soi5z+JmAWviHcPbhrADoABZxJq8PMeXlUSEWGEXi0qBjt p68AxSAVVGJh18KfhPvGFbfLjoQ3BxgG2qDLy9XDh+MkBmnBO/iW+uWDgPwa/h0cTfI6QZJcO3tG Y1TdzfF/hWGmdJtierRhqQEKYxA3xMTkeQOAWRgBz/y+LqiT5xVMOYTZef0I5BSni7LGSIVxaV7k gLs0G4DDVIIFWulbLGkk+7jkgjp8KtXTlH2RW1QHPi31wDna8uNYeIviLfXydxsFyUGsRUwClGGK 1zQtyTtNj+QXlIs9A9egciA71arjagsCzFPzRT4EdAVBjQcKe1OLlUweNxel7NDJTR5USxcXYgt0 u68gR8aSNBMTBWBPCEioE3Q1y3P5uJwJCyo3qypa1caON3GOAClIk3rI1MkFw+LfwEcUw0fmMXMA Wxs4CRwFdyJtymo5CVxveKs4bkiWxm7yevf+SUpws59WzShGrpiCMdic/GHo7flLxxoCQNmIHALs WABQj7cyURGMxcFxyqriD0Iu6IrpNZ4SGoZBjAGRbqaidiOSGImNQsGoSONRq0soqTBs7F7NhnDg 8WRFMSiwaUGcXcpggMef8Tj7iWMKmAKmgClgCpgCpoApYAqYAqaAKWAKmAKbqsCWQV3ve9f5CDEa DuEd69MZzVUACFm3fPBEsgIKiiPoCz4gmsFHCQMKuyWO4V3QCFxg4BJcV/ye6i5ewNDDvFJvFNdg TCBOIYEWF/1TqE+GIJ983Eqi7nm8PCAzcm2YhRgUCFvKEnw6oSKTIDYAmK+oIHfRWEP6uhzcwZIE IGMg4so4wwRVYDfyW5WYa96h0oDAIC4LmtHteAVvJKbn90mGGS2YA7TaZvsAupRdlTfrRUH9GEYz CAoVUeoS64IdmK/iiFmNXG3vvISzhQQUvR6ch78MkMQvStSBL0J7SAMUCwNqpJbGJWAT+T5CmKyK GOIiL9ams/k8H2skJR1ePRtM4/SIyZBAJ+MeUzZDL7uGYHoCgtjZum5tUZLoA7vBieqOEKJ67gn0 wem4M9vBPLWR12t5Ra/+tkFKEb0sUyrYwhHFbiK6+VGbjqtSwyqbsaxucS4gySGTOuxaGF7X7hxq SesFEdRqwBHQdwYSo3ZNrVkkK0mqwp00JhI1uCljB+iUh/RxSnwRBprD7ZizCSVkgCMzB+QUw/Im w5n4Y+9pdCVnIbQHSIv0NhYhwtVC03iclAdl2iPRVFKcyoz6G4t8Pp+BNbkyfjSII48OoU09RWHA YybOFuAcDJ/4yCdu6h9mu/gtTYFvXnrpwSUdffTRmthgH6aAKWAKmAKmgClgCpgCpoApYAqYApus wJZBXR95/9uQgv53fExdsaA7vO67cZaNBxmghM4qIAc+KOgJ+ArzDgwH0w1/QyWSKMFMBCuB7PB1 uYPc0D1+8VX1XTHxMKYFHgyE+0muKBjGiEGGYUA3+7Ss8CgpJumIEqRMYT8gB1SLTBt96tzTDzFJ YQQDcNB7hddpDu1QCXtDSxWUqW4YpNhG+M6I8oHVHFDDAwVwUezR6+h9p2Z953gE5NpXFqtpSg6O FijyjHipKOHy6RmriBCqsYxgJogO2xGLYLMCdPyjqmBnwD94C3YzmJ8ie9TDezAa9GkxfUHPZBTj fhqsuHwjrqdeBAsrlPN2yY5GHxnrQEHurEp+jw59IJAqyyJ1vZPHBBDhZcKPxqUGJC7VfM90yJai NExWlNizd2X+GM6Ih67VLfBA4b+TFy8MeONCFjZmIUKasM5xc/ZLMpKXRLvGE85nLWdQgE9PGcQN bxeEEEEwXMnbRY0Z9MiNk4SOyRUWBim0i5GLXEDbA6mRVcS65bRW877AGewKQpcNhsjO3Zlp6Qxi Pa44fgvUApLmBeY+7uuD4/BzVW0t/IWAcMZOEy2VqRRlla1MswXYMGSMc+KRCck/ojMmL6FSNg0B g4RyXE985BM2+Y+zXf5mUwCq9W///m979uz52iWXfOtf//XiL3zhGpdy8v3ud/s73OGud73rHe94 p5ttrXZjU8AUMAVMAVPAFDAFTAFTwBQwBQ5pBbYM6rrwfUJdVDhRtIRNicF/kBfsSJQrUYBFSZOC jW1Py9XqUDGyaVkzBhFKtQFy6v0VVUepZApIgfmI90zzEl6zOkyhPZh9VocKPzaev2deuKCfq/rK IFyqLcc7RJlVFvQK/QFndAeKt+jFYjZgocZ0Mn3QIGXc5G+SgyjE59UNQ3+UUpvlM0IQBxKhQgAJ GUasYWwBFMR1ZCuD2PQdjqckTsE2fBGAAplKA+6Lp4nAXTgrF/jUgErAJtZFBpKUnFxIAYyppzts tiiwl/EWeNhGBeli4iJ988v5ifW+2RwPFOsHZkGhYDT0dFFexkoXbUda8Mid248cZ3vzejeJPhX2 g4dEiXg7AA9kBRUiJIiZbW1B1ZW6zkh8gni0C+ZUxnLSIRowiX0kQQ+OXFvkWSjzVxonaKu+LV+o EQOVuuc7GuWBaA1V+IAwcBLdXLCpmMqrgPIyRUthS7jJBgJRIUMqZZMSdeKsO7ngMFiFMCZfbA7K 1Htr85J7EVcsOJumlgOLuqxGqJGFkToEXzLcgMeh6logG35A1bJ1HSHTlRRI14MLIWv4uOj7Yk0w u3UGCjCrkTyjDquDkdHQpf2zWdFUzhTmpu9y6KwJ8TgyuCn3Ycok9xQ79cPHnfbYQ/qHyeG1ubIo vnHpN77yla989h//8dOf+MTVN3/iXe5ymx/4Ab5y29vdbjgc8slisQCBbWxsHHzl0ccc86Rzzrnf j9/PrF6H13NjuzUFTAFTwBQwBUwBU8AUMAVMgc1XYMugrk9f9HbU2FiQPfOANSoaV9W8HEizHIcQ gIhc3oCJiWUNJgKpCGkx8g+3ETP1qHenJpwphK6lXqXmsAwwBRBH4bmAbim5ekArcjKFQlUk4zAE BTH5RR+TDslBgm+zvFgZDratjAjHbcxxcTUwJ3gRpV7gJwAKViLhkLbJixJ+paomGqYEYUjbNfV8 BhXig6Xhtiqrmvr2wYDuLE+F9K0HE2KQH0VR07zQkMcWW5MmJLoqK2X9iC4C5qBUbPmK3bv5+s6V SdH0vFLWJF7mK1sHEGTjCAWpobGK8iheCXGCk2GeQkVolkt3yuuEcQuWxyL1BdmhNFlyx0gGOpxp M1xztJK5wqxZKW/Xsh9NDV/0wbvGK1CVQpIYrDqPSCXXpGS/p/oMw1wQ4rzDAsX9ZvMZOHLMKlQj xnnKNqUTQXvNbXRzDtky8dLGbyva8Tt+0RAWRglXmJfFMAQXeixeS3K5RY5gRlqwYxSARkNyTbYJ c2K1PfvGzobbizq0EL6pGZEcJ+pzf0KMGOGW5WXY7+CNMuNRWNZgLWPLLTYz1kn1PCI46uY64HoP cx0PDtwOdyClXvBWnkaXlJWPkFsvP+eEkUXwS09EQsfaE09/9Ob/ibY7bK4CV1xx+de+9rXP/M3f fOgDHzh4J7xad73b3W5zmx/YsX37cccf/1+v4Mtf/tJnP/tZ3n75ZZcBvF78y79sDq/NPTO7uilg CpgCpoApYAqYAqaAKWAKHGYKbBnU9eH3vVNH41N5HsxLGBMYhcp0cErjhv0pOAavwfSFkYq4HEk1 CqggW6pU7ymBaoA+EA1cQiI15PCUQcOs1AM+xCwgXDJkwdHUoqWic6xYgkp4dpTL43Nqp8jkQS92 jAYamFjXRCbx++yZLgAlTH7cNhqsZMnuKd3uzF4kssclRXAILWI3W9Q9EUOcPiujEQuEAsHk4HUB KTv1UVE/z4JphoLg+fTTk36Uc0ojDuUecjk5zRyEwhCkY2cQH9gKrWFKa3oAIOxo/XgwwAO1nBVI io5PYGpQO7Y1iClHD2Yla1MXFZG6ZZcZfituvWsyZIgkZjfMX8JkNNl3sDN4H9azCNMWccX1grRf BxUEF7Jw1cBjmmtqle5j0fL9SZZsILTClWpwByaOOBxfSBFZmXC5bHlnoeyI4yygfU0NLYoTOcxw 5+GPyhu644FoBEPrvYucQZCjjKAn6UIUo/+rHScqGYMCqvDLXXkMwwv8ou0ATHPnSCPgyNBHHbuC j4JynCA8bsScRJKJSIDmXjsrcvX0Rz5IkGWrRU0PBswO+EXHW6RKNKxtVOnLNojvTEQulXlLjx9l Y0mccAqANQ2YdKhLjw5/R3EA6tLJyEJHwf3jH37mYfbj5dDZLoTrM5/5zMc/9rGDycQl3rr97W9/ wvEnXA9nFqawD3/kw6991avQ6Fde9rKTT77voSOW7cQUMAVMAVPAFDAFTAFTwBQwBUyBm1WBLYO6 3vPONyAULUwwLTAHfIMkWujVVGJlCSgE2BISVcO+dNX6AtRF0G/HOANMbDAbT91aMCx6xFXG5CYb QpM0cY9Lwc4YGrh3UWDacaE9zESMNVTNuQYquvl+dLTzT5m8/B5zFngHI5ksXYFH8TwvWs0SRdai EDLV1rV4GeskU6elqvmelrCq8/fNp7CYHeMRN8dtBtkBDMkc1bVFQa7Rw5pFaxiMBG8RAMy1XGns IOVWkBk8SNCUIaX7TU0ZPNYqgo28nO3BjGZFwX3BbeQoKVkfo0kYwWKCHirH10RfcC8R3MM1RZe/ HEpuy3AczGiE9NQa1lNGFm4fpdOSjqoeOOXyhh6oCTg2LUt2ORRc492aYYk5TAFAcFgQarolEqnx CikiSrWASVAxzSukgquuN0rSha4wixgju+W71OJ38DsynipXkzEN5T0ypD2niFUNDIhLi6QhiA0v F2uBHPEurjxraqKRR42ZYUm6swLCUdGvKjdPUBJzG4IAJJVWxKanb7BanR07ArgNY79oahxdbglQ r4oFL2de6hPoIP/SNEYO3VFWbZkl80G2kfWo1J5j5lK8ARqpLeuTkK9i6uL4I85SYwrAdkkWDc54 6CNu1j/sdvPvW4HvJlyPftzj7n6Pe/7gD/7g6urq932573oDDV//65dfgr3LaNcNF9OuYAqYAqaA KWAKmAKmgClgCpgCpsBSgS2Duj78Z3/CciFTWLdEWTTlEGMOvieYVEw3lOYeqniewifYAqE/5hLK 2YS7COQAzwJxMRCQqYt4l6AqZPCAN9i+4Ch5Ix/T6iAFjSwY26cgnjPsQC/8jt+DZsYUOXXdel4A jLIMjBSMEpxWmvMIConTFEKk3itIClaiGssRNVKqnwfBqB/fg6mRuBSEUkyPaB6GKTrOqxZwQ9px ludwK9xoBAC5GP1frquef6rvnGmJdV2CiYhPQnn2znLetTIewFmI3YGW9s4XhCtBNCJZUURhP7CH TzAhzcqSXcgaFmDOIqLXu551teyDZpCUhn0WyF1wmal0yldZGIlQYNQw6DTlsPdWsnjvvMAJNRoO wFvsQC6qWhFF19OvgYxESmV2W/rQwG9cNRT8AoSxHVrqSRYKaIVU2pMJxS/G1MVwWtTTRUHxPxth qardz6j3yoiUwsfIMyIWu+f48L8xD1PAjhPiUFwIklwq8UWa6dkYR5DXlUfHFpiwp44th56lMWX2 6NxlHKbX55yXyumhnDxHeojc9EtYHwoogij3Gb3ybadplZHWTypTlWZq6RceFclyLV3sBVFlAHTR RW1MjjB0A4ChfUIoVLhMTV5YybIzH3Kq/dzZEgqsr69ffPEX/+873nHQw7UkXHe+052uh4Hrv94y NO0Xnv98aNerXvtaSzJuicfDFmkKmAKmgClgCpgCpoApYAqYArdwBbYM6vrgu89HSuXgnLnGcQqF E2mIAr5Q5y4DDwMRG6hKSWiOMnW+CsngLSCYfYtyjmUJNAbbohYK+BKnbgZiMFT6UUVaCyXVMETh 0iJFmHA7MAeFX9wKQEamDrKigq260YxFTQSMwSoqh2+Iu1GJ5QqkyMelqcxZdGIByLKEryq052va o1JzLNt1nLMLcphgIRjTKAPrKDKHDwxaBMlTOhKe0ndqwCImqXGOil5y37whCLlQM5QoVaTpkG4o JKjFTVbk+qokW06gnGveZEs3PBwHBxxxPBrJRL3CYAI1AxtyFzDasrdLPWUBKBDp0sFI8xnl2xJm UpO9nuV+jXr7otyxMl4ZDkkZslZAGZFATE5XzRaY4kaDbImEkJPbcin6xVgF+ccIjxzErgUsMlIy du1eQpQu9CfepiBk09CJ1nYaIIBW+MMYL7DEhYiIjC7HqQ/WxpLwlGksI7CK13C7JHExwwbN0coZ 5XogF/4qObUcokJFrstnQ8HKWiMnNS0SkStcbPjjoG48M0J8XT9JYhSdLnIxOJW4uVwqFySYqYBq wHt5Aqg/o0gOc994so1rTPMFzjLa8cFkvHY8GLKSRcWkToFLtsbyFL10O2HnhD6RH2LL44YCLIZv uTYz7ZencpTG9M2BHdkH5sSNHEXV48beNTgyIjwqeMf1OON1NzfgGc94/i38p88tcHm0aP3D3//d Oy+4YLm2zSNcV9873q6n/8xP09t13uvfcKOjtFugyLYkU8AUMAVMAVPAFDAFTAFTwBQwBTZVgS2D ut72p+chhOYnqr+cHFlPAxRwh8Yu+IcarGjdCom2hfN8ujevAEAALzW4M3EvpjgcbtDKvtM0TCKE C+1YneAbgn8ByOAXwmQy8pBMaxZ5uXN1otv5/pw+e7rM45DxiMAUOAKt99wUygHSUpU7EKbv9s7w VJUE6MZpqimD5BYZ4JiIQFG/paibWwGgjUmMEAp1R4HQ6KtyNfn8xlGMgHAiUIo1wZvga7if+BZl V3A0ApMa9adGd/mtKAXTIMKug47gsppXFGaJjYDYuKZmCmLZAqwogymYwpXYI/E9+A2IbZDS3e5j WCNHCb+T96oj9SkX2LyqoDjO+QQQUp08O4QkOS8XvjkftJZEcd6zWgidLFYia4oNekAah118INq2 YeoarDwg17Sg7gwMJwubZlNSSN+rqoxsI5YnmBfoa5iCBZuNRYHdbhSHhZKjOPFaIqLyYWkfTll2 FCf42bitrGIN0yQZYQnB5KATzVkEcilE6TMRU2lIDt5ZtxjHuT4vYGHbh8PJMFOLWeTNqmr33jWs eUdMhnBJEUF16tM8xlMhtuX4JGjVA26xBHX0q4UN5qagIvsteKbE2RgTGXEoJEVxk7UEJxXUDCkA o5cNRQYgM64VBDv4LGIKJ/33qkrjL16YRUQyu7zSEEtIFtuhBA3TXJYOV7OB3It98+1967jzeLAZ JslmWQz/QI19s5ywJA+zhoQy78DJi1annPnkTf3ZcShdHBvXX/3Vx9/1jnfgrmJfjFB81GMec+KJ P3yjpBSvi1AXXfTRl5977jlPe9rjH/+E6/J6e40pYAqYAqaAKWAKmAKmgClgCpgCpsD3UmDLoK6/ /Mg72APEh2hbEKWqdhK3EryAO9C/znA90BIQATIhOgOnALh0Lawqr1URtULscDB04IOoGlim3bu2 sZEXdFph0losYE3tCsCA2XxNV1bL5KCidkANSMlkkFTkE4tS3eoOVMlxA2ajsJ4edib9QS1c1TpI BorF0EaqtWAZsCcsOTSJgTFgKJi2NGWw7bA77RwP+OK87khHQlBmMl3R8yU/Fv3uGH8wo+EJmmSZ hgBC4sBCGL6aluXzIUeQxkQKmWhsIsiHmYlCXErUYWUCj/FiOKAYmLNBCeeFgBXYl7KWOJUQDX4E zlvai7i5sxFRwK+hjTAvvEfwH94IiOEyDJpkMwGZ0CxFKACfWt4V5FRTGPunJp+XQba4KZLKzMUd leSTgwzKiM0KzTF2idCVJcfEGlgjVjjwFiALzLYkgCwHykN4k1IzbFbuRAgMSmy2LODo9StpdMV0 zvIGZB4zUI/ex/o3iqrN57KAIVGHl43xmhpi4GZZBnAuDHSY8RA2c7MXK1ffhnQ8Q+RMsb5hN0Mi Va4F0Sjl2ckgbzwlfMPFHN34Ty7dMyohxFnI+/huXZVgSs6RI4jCmDQqOVGZwoh8Cs9xhQYFMRhy UjKG4Shs2tVhJsiYV4AqTt7FJDXd0pnjNBYT3MaYTmjatKwLHHZuEIHQGwtoWgFQQpNRtJ1hl64H jYf2Yac91n7wXasC2Lg+/pcfW45TxFf1U6eeevKPnXytUxSv9bLf7wtoqX/xL76IvOQ73v2em4yv fb+LtNebAqaAKWAKmAKmgClgCpgCpoApsCUU2DKo6y8+qAmMBBQBS21AMK2NeqiNJuHxv/jjVM1c mhi439PUYonCJjPUUEWBG5U01W1e10CVXWMSdkHd+2VeAKNANhiBZkU1p6XL5f5gUalXcjsQFblF 2AR8IxtkE2xiRTWbz10C0WcsIFfr4jHfB3bQ3gWsqVRLRX6u2T7K5IryvfVCPeiTITBLHAdABQmB XoAwAEt4f+AsLJt0GxatDNjCAnoPGKT0JT1kETit2zdnuiAjF6u1jRlAavv2XWwK8gVTI5YIxIHZ YB6DXeEsg0Zx5dUsBfOsLbBELYN7PqVaziqlfi5oF9k3UTMf35mqrzTUkIQmniA/AMDJRoZjTUAH X5IHD/PJz+E5ihOayrA+rQ4SV3MF6lGtu/hOS+F9QPcWS8XwNSJA6uxOYDTeLjqjHCdUJoQoip3V za1XBqi9h3mTIDNFRQGRWgNNV0Qv6VDDewffQQoH4zy+wlGOgmL3rLjiqvVb7VwhdbgxXwCkdkzG sB7cZKo2c3MDfBCnM6BRukXnP0tRqxnHRHd+GE2yeJjw2ICK2AJJVRm4gI0cDfcHCLIW3F6MdgSS AhxZHL9dz0swHDMXGSXgCuPEOAWnorijjg1bXwjYKvOmIRcL/MJ4yF4gpDuGyVrOCMsKmDpOYq9h XiQWOQ0J5SygXfIn4owDC3JM6EaaFck1Q4BOtA53GD1rWPBwD/LnYNsggXmxAmfmUiYWngffInQJ /KIin7s/8tFnb4mfQTfLIkFLn/jkJy58//uXbVxLG9fd73b3mzE/CHT7xRc834xdN8vzYDc1BUwB U8AUMAVMAVPAFDAFTIFDSYEtg7re9c43oTuVVZRw7VhdgQStTedM9MMNBNqA4GBxGTlLzxwTEIVZ dFpVUCpQjtiT3FCeD0PhhfitMExBYMgiav7ggA95auAF0CnMOSCGy9bn3A6Og2+IcBmOGXrL9cIk wNY1yyvgiAqbkriiuUrMSGRNFCmKsQ6RcQRwwEPgFEA3rj0eg1aidbKRdU17+hLAcVN8Ty6XJ8cT /8YTBJHhsqwChgfdmEDx3OBFvo5ZCA8X7h5GIELfNMhQNU+6AmREQUhMVSoG04BCTYqkyCkErAj2 4GPiLYuqImnowpSyTSlRR8iRJiu/Y2Vq8sJVRGouJQlKbZZGK6pfX/lKQn2OJNIK71HORf6upjEK 4AMwUkxSSUn90WByI/eV1a6VziuDZINJh43gHcQRI9gqVMn3gUFqze/5Cp3xcB5UoydrAerDF8Yb EQXbFP/Cyud6yXQ0MlPB3bIhV8ALhhwUe8HIUNGlIIGfbsCjOrzUAsZFmBdJkz3njhmroemfLjZu 4YJ+rIPDUX4Vv1vNQyLDGH4/avmBjLoRNWpQuabbyKn7byBZrIPbkTDliLkIV3UhUFG8vAYSqpEN GdEEHLaoNY9SXr/egwxCMzkqFujugzwaT8nnnK9ApDRXCz4hU1gnDwn8FPzJ00c6dVHXoywi/cqj PmTtPFpohESBKC0YD8MfO+bFKm6DffrBI8560qH0o+rG2gs18B/72MfOf/3rlxcELd0sNq5r3M5T zn4iX7fGrhvrrO06poApYAqYAqaAKWAKmAKmgClweCqwZVDXh98vVxfl57ADeugJ060vFqAexiZO 89I1sKvD2w3U82V3URwPgwyemhArz7JUHgyBsQtMAPEBKICxwCXj0ShMM16PZ0okSdVUhMVU1YSz CaCmUGGcjGl3ol4KQEOOTAYuwFQIW1GReV2PE/VDlbCOELcZBWEqS5K3CB9ZGO6EUmQjVWtR9NXQ V6UO+QJe4vxE0BkCjOM0wY/DGmA9K2k8q7uNUswOIw+IipwbrAXfFqBqVpQ0w/N63Fv4xgBGXE7A S5X3StXxFxeH0YzHI5Af9h/NUlQ9fOQJKeGWUjOXeqk8D/sVi8EYlReFRIPVaUqjmuBZ53wx37sx hVQBfeIs2z4a8X32xb3KMqd5vYH1YCXyA8gR9rQlvOOaY8ZZOvjncnx8C+cdYAf60y4WOS4nat1p YW9xQgGq6iovC4KGnCGUD58db9QBuaGQGK+ASlzTDUMU9UvSDBlBb1i62AqL0TDNpRMqYfEhxwpp QiyhNyHCFgBESFDpVArCZAbsuZRymhyjCznqSFyrl+KrID/CgMMhzi6WkYaUdvk49fCyESnEbgY9 pIcedgalEk8MfLxa6iJD/8Cjwi0LQG8tFjA051lk3CTPJPRK7WZdN2MNTKN0df6KkpJdLdVHBo8D b7n4rQgXPsTpbMaR8eyhyQzHoKZMauImT/W0oMleDz4+PmVmPX555F4hX1dOF+zriU942uH5Q+17 7frLX/7SBy+88GBW8UnnnHPPe97rFpUWfN/73vvaV73qt/737971rne1szMFTAFTwBQwBUwBU8AU MAVMAVPAFLh+CmwZ1HXBO9/ADoFT42QwGE1oQZoviq4Dg4A5ZIohoUdxO+Bg5xDIQjxQVVnLVGDv q/EdgJDG2S7oD+/FbtPN1wEJRR0wrhHjj+9BhVRc5fl7F0UK5FBFkjrX9cEsxUEGZqICnCu7pJ3j DOIUmnro2FaP4wZf2GSyCqQoYTdt4+xaXJKqqo7kHuwG75TMWRimmOWnenMRlqUXCVwFKhuri77f M835DjXnQjEuQshLVGnlaZAfTU8AEc2jBOggC8MEHckCDC0RFUYk1oNULsfZgnYwxG3k4KwCLxD1 88s+Ly4OTcNUtLGYcxs1ZBGgE4GSjcoNkYS0oAwNaP1sNiUlOhyMiDCCrVgXsuM1w6mGdFSPLXvK sDshjnNu4Whr4XloCsliwWqXD3RHMTXIjrKKmrwI71t28MfZcEnH4FV8RaVlrlefRCiF/XwDmAhv SkBsgnoqyQJECvO5jjCon6ZoEhSVT6sDV4JEUUxjJWmvl40LYxTusUpsCHgXJbyLG6iSnzBhGMCJ UB8fnyyBjSKxAFO+w4PBGAJOLkT6lno2pQVZEp+vF+XKIF3JUlc0RpK0BFMCyIpSMUQChuPhADWJ KLonSv+AlGUZAy4bmt94gPmbmZj0zcPp6rL0uYNzCILnFP90tf+8BHcbmVDK/qdl64x+OiAAIkMG NMkTP5pHRtVnGXTes7VTz3jc9fuhcIi9i6ziP3/pS2/+4/OXWcWT73e/005/5C2TJeE4e/ITnsDY x6f996cfYqdg2zEFTAFTwBQwBUwBU8AUMAVMAVPgJlNgy6CuC9/7dkSZ5TkUAyMTLKuuCtwt2HeA IeAACIXoD0kuJx4GJ9CF4nj4c+KMr9CnNK9AC1ilsMZAahKih3hz+L6bhNhhSqLrCtZDQnAx28Nb 9kwXG/Oc5N2u1dG0bOAuGa6nkOtAYahSoncKOxMdXsm0KPdszIASO0bD0RBU4nErGtnJFA4YF5il jDWEd9DwruYp0ETXxs4iBJCR0avzxgNcXbRxLSBfsCECgywA1KPy8pAOJmEtnFOgk6UJCT8YQ/pI tIHD4Epk5YqqUaSuafbOFpCelSF2H01jhAlB7jQ4sq7IRA7GqyAZesaI1GEjAvalHl349LiXaws4 STfJBiys87iXCtIVzXSqMgdAAT1PreeARcgaG4rDviohYKgdsQDYDWlDjGEgGFavSZFxSL1UVRai gjopoSwIJfY6VsvKwXNsVs3uAnARDfN8TlJRFe/yN6l4HtSFQwoNqMRaGY1gedxxdUi3fbRvnlPL 5Y5DEygBWNi76MgH7QEoWbwmM7rOe7ZDSRZJVzKkWLR4ODRZUe9iMoBgqOrwhdUYa9CMkpiiNxKL oC4aymiFZ37nGr3xZGY9TxX01OT7OLxKvum65FNUAJrxYK2MBjwZG9PZxnyO7w9GppdziE2n5KqG AzA7sp8xlbKpOWso6o5R1vqEZOPVxMsXC2xZ6LlzMgpijXSMA48uM46Bx9RBQE1uZLWadhDKsQjR RUPWxqPLl5Jk0Pt0dSkNdzh/LAu53nz++cu5iiCkBz/4ITd95fz3dQTLDOMfv+VPv6932YtNAVPA FDAFTAFTwBQwBUwBU8AUMAUOKrBlUNd73v5HLBqSlVGlFScYbVwZk7w5uJzUuyQKg+MJdtDTvw5Q gbPwl2NGssYAvsjfQbI2SibuQVV83D0D7GFkA7FTYeDS0DswWkCs7Morr+R2+xYFJAK/ErxCxIzG qxZEpf4lLkg3Ey8ejQgvUvFe4QMC0GCwIozGMkBxeKl4L++mNB07FpyLCCSXipnPSJCwAl/kOX3j bkojF6dzHSuaip8cEtKQSY00FO7BE0RJGWvmWtuHKQHDNaEfvRgoAzaiDB66BC1S0xNJPvZLm9Wu naAaEBfEC+7GNunRCtuaOjN0GCQJOGnGdxONQOT6rFKzDulK77QAdcMvctr6CfKx5oJpAG3DoqnJ JwaIMiJ3aYy6UyxY1J9x47rCy6Y5lCGjA2S8IoxJj300WuHiWjAYsRY4o8qdFyCj+sf6boiGkL6i Qmk3gVAlViyoq2sWwFm7KY2wQnVvhXG8r6hU3yXTWbvA71RBu3y8eTwJcEaMWsM4yCarXAXPl46j 60ZpiitKBfC1XowTTRcMoqpiHIFsbAquwihVSubzSs6BUjbOhDeDNGF6mmKpc1ejmXKeYcyL9hWc PO4t4VQGCRStNxxCyQI4C84+Fwjl2QlUa6/KfBnlkKetaxxnGNCWLV28i+eCKZ+Y1EiMyiKoHv9g OBwftTpUqNbr2SbQkG8R3WSxhF65INvRoEoMh8A/DHgwNI3gBJCFZz328O3qWl9fv/DCD1y9kOuU U069RWUVv9d/hF7/R+e984IL/uStbz3qqKPtP1SmgClgCpgCpoApYAqYAqaAKWAKmALXQ4Etg7ou eMt5bA+b0bbxAATgPFaAinaely4Wh/lGNUYYgoq22Yd9q5HFCey1QtMSYwoBHhSN40zCf0TZE4ak IMKcRY0XFiz4xd7ZjFTazhGhNJm/BnIICV1AnTRQkFtHEQyrZrag67zHSgZl0JDHcQZI4fKwjJUs hqyR2oOgQTE2GALo+UTJMDGpwCuOt420KLALRiSAC5yG68PO+D5f5BPKvFxQEfRFmi0iCghaIq0I 9sAAhY3IgQwibkrtyZIULN1PLWhp52TMGmguI3cJWFNxFMCl0RhBhebSdNmFr1mKZPeAZB5epx52 FsdKGnJPZ8UK1jB6NVXkNbifAGcylMlMhFlplSTlnukUH53wioeVSU3qEB2Ng+Rs5Fuj7EzVXSAe FZexf0xqgB9K7lk5SVAgjpvY6OrViImKzfAarg/+gzciLJsSBFIuVQe2wC3mQCQHx5lMaM0fZOya G0IJuRhsTwMBNGqgU/O7zo3KLPaWZfLBkesUvlJ3WNdTYYZXi0tB69i+EqBVmTOckUIx1glkbBg9 KdBEHpY3aLiAMqpuiqUjoSxFDM7FPwF23J8qLo095NEKtR2AKX+VBZY7sqY+nj5Q47KzjPkG3IJn oFjMeIYxjeE6dJxNfA4axl6mC03bFLMTAJV3DE3Ar4Qr6QVj7ACXEC50hfeys1Hhz0bwLYb+3vkC g2FG3Vk8OOMxhyPq+uall376/316CbmOPuYYCrnu9+P3uxnnKn6/P5QvuuijLz/3XKvr+n51s9eb AqaAKWAKmAKmgClgCpgCpoApcFCBLYO63vfO81k0BGHRtMQMqUbChSRuIucWfKHGawOZUPF4kMoT 49q+K2xMTcO7EqYMwiG6XpYcGYZoCm8nGW30qi1XGxaFVoHKtvgtkUMa4EXWKK7CI9R7FVSo88qy BDQAZKhn4h0qyKe0ajDiRhAK/FBMyuPzjDSgK2UCbWwbUlCO2wZiArkJuDt5NAAKWI12L+c/ckMA MYsJsrTCThqsxzfU36Wid3hJqQmTLFh17xnEytuzBs1r6CBnhVwTdpORSEySOWiqVT0WGwS3+erK x98k1gQWUQe+lBFWW5QNmIYtXDUvpmtreI/GgwG9Zli/2PCOLOKme2b5cDDYPsF1pJr8fKGg4sog 0iTEgPQfReweXChipCNih8KIADyMXXJlkUGkPc1VwoN8wDbbBwkntW8BFxMKRBzXDa/yKtEpWfZk C1vWl3GOONSoPNNvfJ9daxBhUa2XFb4wMOW05EzQKMrojCcZGaVelLieLvKPhFoDYKIujdGvZtRj uG2QCvSpjkyt8MBHBT9Dj6ggCutx0nRFDlceMfgcPIlTYT3zQnTpiPEgzEZCjRxtAN2r6KHn6zUt amKKMgRyr+mcQrP5yniEsIs85+CHWQY4A5GitmYRtL1Sh5Ayyst6BSEBXHA8DmXmHmAcbti1tmUx K+HEWTxQDyDLAII10ruM8kRGH4IpRyEaUh3HF5UzZSG0p+nR4yhAkOkZZz7hsPpJB+R697vftWyd P/Eud3nSU865853utIUg1/Kw2MXTf+anX/jiFz/oQQ8+rI7PNmsKmAKmgClgCpgCpoApYAqYAqbA jaXAlkFdb7vgTexZfhwl7/bP41vUmveH1QhgwD9rzcKDUcWiVHIteSAVXuAGI4aUN4GbhgACv987 zwEfwCGm2nEBFZMTuyP5mESiV0mWi3RRSCVTkixZnsf8Pr+rr5oucNNQ9cV3wCUQjSQe4Ljhc8xH aSSPVdHU6jiPGGLYAG9AXRiFgDGsHA4hIuH5jN/D+UP3leiGK77nG2oZo1SLy4ZqeYfpYPDBngZ7 EizTYESN58O2w0ogfWAgMApkBzsS7iGAi1KHyr0xkFFWKegbhIjXyJmFNUl7AWYJRWF3wvCFSuqQ wrUWALuU0yR+mDNbsGvJRwLboDnoQY0UlLGv1gErfFF0Zn8rP4aicH2+YF/bVsarWTzPC2AWrjTw GuYqUBG2MJAWMJGbIioGK0ElCrNI91UVKcDRICMtiBNMpMxNM0QTlJAkfBIELkCaOGpGHz/hRwxo 7njd3Enugj6EMTm1inwmyVFtdlknr9ewYAVDXTs+DwDfZDwhJJTKNpRMsfQhDb1pbhIj6nFrAp5Q RY0acABOyUlBLr/mOQpCOsJ46fps4drfwH+6APdhtXXXyJjHXgCBfj8KmYQQkZEMOFd25JEJ1fHQ kc+BQhDlr9Mj0c0WC3QFsMmpBVfVoEzmUYqQQivd2ElfFfUt2A7CFXEQLAwVuB6gEQop/6AzMHLw RDmpQTv14Y+6sX5M3MKv89nPfvbjf/mxq0OuW2br/HWRkejlY84846dOPfXnn/f86/J6e40pYAqY AqaAKWAKmAKmgClgCpgCpsB/UmDLoK6PvP8dLJ3g2dKQBdEAN/A/9OCDpQkKOxEQwfmJwB8gpwZ7 C9QDDnEEMxEHyd55Macn3rVWYSZyfVABdiHYgxthp/p13rw0xgyFyyBrEXlALgVmAkxgluKC0B/A A6YhqrWgPm1HEzuTHz1giZuByKuincx5TOP1oprOF15bF3lBbxQdW/i3uDk94gpQqtMe+5KMY9NF RTyNJBtJOGXxAGHgt64FS4n7ANeouwoCgm0sd9doUNFZJc7VyWjFQnmjPmoyknRaQcq4iIZTCq/5 cEDAkOCKgnhcU3gQHZEGnxc1UnAi2s9WhgNozp7ZLOqBXK5sq6GZCy4mHAYNIiRJO9ckzQBvQCJA Ft4ryu952b68pAsd3xLNW0RKNc/STQlgC13bZMy4HI6FdTSLUj4tvlUB7IgZ0rwGFJPsaiurc9gj EUsPgsSrQXgJ7Vk6Uzn4iO8RliyrMk6GsDRKylT/z8NQ1eAm8oaauqhqM0YBUATmg5x4q6vnp5Cr nnGUFb48LsisQuKuAb+d470Kg+3sApLVChSqI0zhUZ4AZR6hh3jrOIR6sUH8cJAlXJ9hBdSscSIE INtez1sac+D41DqY42TI8ICIcZdU5mvKZIL5zsfx5/AW8UlOx+MW8DwyjDylbIpa+dU0jNIBmiVR zMABVySniOd6UY+SjAXz25xZDFXjaCaAUlFcKKHKxSpGf4Yrk2xBmDUAwEEjkzMeceijLiDXwdGK 4KEzzzzrFt46f13+C/TQBz7AUNd1EcpeYwqYAqaAKWAKmAKmgClgCpgCpsA1KrDFUBfdRjCp9blq l4h0yZkVBZiz6F3C8AVx2EtHvYYTCgkR+tsGxSH5VdBXpZ4jGaCwd7mWqyVrAKNMFBajIxyHEPME o12jZK2oFHVzHig5hOS5IibmcXfXnwWfUhhN1KyqF0VOJfl4OARSMAgPagafcHAEAxGWMYYqliAe 3oADB0qmOBsOIyxO2M3aRg4voBK8RNYnLxpQLU+TPb4nZiOWmLqAaAq5kQCk/UpRTfgaBiG4k74C ywLQuK4rj1Td+qLAUOQ8Z6p/mqxs65oKx5BcRuxCXwOTuEmFbhYge14Zkuzz1suanKPGI0JPksg5 hiKq7qWwqxKj2T2IEvWa9YQf4V/wLnFE9gRJguZwNYUXW1rS6TVjNCH9ZSQrXX8XZi7KxeTY0tBA SJxmGsKVxCZhVpxgjN2J3RUlHLBCcbKmaKJwKugqDIBHWZZyfTAlHC0B9jVq0aIoixmajulpyKFo JbYvEppNw4JhcDIBquwqKDm1hKGTKVcAlwmbMmNxkLVR3M5n+/Ia0RiDCJniQ1a+3lNCVisHP4qm ERQFPy6BHbjOuc88RTpDf9so5V05OxRf1NfoYEMerkRyFoMYBi2eIlbFYlgpRfUgM12TqxNFDDVt gDu7UZwaLom2IYMdsSg2DNbkkVHpPP/i0dg2Ho5Hgynbrmra76GULFXEDh36ZnWc8RC1fkJ29bTT HnMI/9Q7JCHX8rye99znXLVnjw1hPISfXtuaKWAKmAKmgClgCpgCpoApYApsqgJbBnX9+XsvQAiS g6AuoA1YC+AyzQuIA/xow8ELCBTUIEpSMl+wLTjOQCBFPeKYrWAeUCTZYVrsWslgMAIfQK/wNs2q UmTB7/BwKazmYm9cEJ8Ow/9AY9QnaSpiU3NZcNhqRldUc9naBjanQZqJfoShZvb5PUYkfEyYrtRo rrKtaFEA2bAUqfIJExOr10RGyJSsSErSATJw62AN204nfpTw5jmxR69fzVL4FEP3wCHQEbxDLJ7h i3KrURvPJ+QZBVNC4BRLpn4LTxPBtgWOINezPllZjb12fZGD4CBZGonoJhgCgHZP5wCgIydDtomn DeTCW7ALYUzDdAYsXHa0E4TEDwXAghLC+WBeO0bp9kFGhz/l9etFASXUtER5jiiJh8tQtR5Rb0+V GPISueSaqyP1+HMczCRUOb0LFarECxZFWA8bFL6vJAYzYfWShy70t48y2sg4C0AhDAi4w9XcoMxu giNuMGCN/JuQ5Kwgi6rWfE4ER904iV1VWcd26C/bX6NF29dgsHMywAT37/tmMS37em7iEaHUIMyn 62wVQxwkEpUgSjwGWLq4O5KybKgS16cYDc3lCfOZzFg6uRRxBTQSoVzPK+AcRjOSmLsmQ0KOiEaK lOOYQwB7fIKMvmTMQCAaRs18QtxQhjuo1hK/4mfjmYGLrQ4Trn/VouAwCXgC1AjG7hyS8fTqHu9b gML7ZgtOhHwucuH6yxcLvkIQkm+VvD5OYaOPe/STN/Vnx81yceZa/vOXvnTQyXXO05528o+dfAg4 ua4u5u+94ncJY374Lz52syhsNzUFTAFTwBQwBUwBU8AUMAVMAVNgqyuwZVDXh9//LrSu59O1jTXl +xLCgCqfGg8HFHUPhwPsPLmYEhk3oAGzGhMgVJYNBBeqEvYjwKIOeGYXVuMEIjaczvP1jSl2HhU0 UYEUJ1kiIALsyKtKDKjF/yWQhfUGRw/eGggLrAbLGGVbICB+m2ajLNHYR1q/VA1FclC3wE8E3lHN uRw6dIHJMcUsQvKCtD6pXsrzGPkXkQEUyuo7OuaheETZRIic/wv7EveiAkxdVy7NJwXa7oqNOdce ZgNqxUBpACyZmdzMwSTwAFUgEnAMuCfJhvjO5mWhBVNhXlMOFjCCUHMDKQ9z8xkBLBAo1dgz4dHz WAxIjsvxLS4LqFLxVdfjfaO1y404pFOfbix8WDFxPTnPuKzLSALCQD/jbIBvC06EUQvqtIYFj5p5 ZFIPFs41BSc5uPkC31Uzxt2ERS6KCyTw/BE5S8SXpyweQvo8f7bATUWnmJilEp2yzdWU5WdhsFFU 3JtMKISRFULKIFaaG9AzHLOZqLwrVdBVFjalJiMeCeRCYCZUBqobG6Yp71rMNjBiEUflhDhLdWnx uDgdOHz5yujKYgtwUvFE7F1yXwHdNHexxxYHhNLUR96JL68OU5m/FE8VCHPGLs2U3D3N+Sdwatmk xqGSycSGhvmMFyEmLWDO4+WtDBKinaiDUjzBJGfX84JrMkpykKR42KB7vFGgFFYLgdRgR5W7aUIl 5LTz9sw1FPOxjzykaumBXJ/45CfefP75l192GYoBuU455dTV1dWt/iP4u9dvqOvQO1PbkSlgCpgC poApYAqYAqaAKWAK3JQKbBnU9d53vRld9u3blxcF/2O/fTzGwwVyAAIRoiNwKAwE0apq4MlyROAA XhJpDmBRtsAQXFdqqa/rtcUCLrY6ntBjv3tjDkEhucZARJmm/GAySGEomHG4IOjA8RpBEBgFaTGA CH4twBUGonV8ZU09TIeTjJp0gbT9b+qpsqpBOHiRNBFRheQqzWexY/BbrAwjX1dxmO8pfdkyKdLD U6SK8YpMW6vyJ88n/Qf9Ue6PbnLymK5tS0VXLKBcsHFwnorqaUN3Hed9GIG8nItNGUPevm00crE4 2p1U3SUQw9o9dTzxelbGOEsA1GSQwWSwyHEH/Gt4mkApO0cDASQVhfEtjTs8ctsqb9w3XwDvcMPh VEIuQpVAMVE2/ER0ezEzETcZ9ViiS5jp2uki96i3HwwowQLlUP6FyrsmI7AVusLeYD/bRyw2Rtvp dH1RK2cpgpmRMKV/X2YnNk571UZOvVc7phCt9WdVBYqCNYGEcKKtcgW4FeFQ2v2VFVWC0u/U6cak RejetBJ8xFoF95TfSv1mASasDbxgZYFK3IKz5klAYeeNkymLyjOODhilUnxcWzwTgE6SoQkDKD3G KbpAK6+j0kuzO7k1lfwkH13a1A32FI8ia6nRnwo7OlMeN+KxpPQLA6DQXtWyabrtOWjwpXCo1y/T oxBPWB6LoeGL6+PVUlFc39L7xsL0zDcdfi7X1k/IVNty/XTK2D7hKc+8KX+UbN69Dh/ItdTQUNfm PUt2ZVPAFDAFTAFTwBQwBUwBU8AUOBwU2DKo68Pv/mP9j73mJPp4uOgp36CDKi8oYsqyGJaBtwgY NIz8JsD5QuAONEVlOACBrnGyiZpeR8ERjEqOG1fGhGuGhF0Gh8B+RR95B4gp6JOX5Qd3jVrDlc7b OR6AHv51bZZk2ZErY6+ucE7h8QIScRP8UbwMZgSDgDtAzFK1QwXkyGAWEAfSinyxLnMG/2FNAt9A USgp514YysAiQBq1XYW00pNnI+6I04rMpdCb/EiQKd8Dja3Pc1drpVmEfVPBiOjU57cQIigKRiBi e9x418oIWMMKleOD5qQUv3vECfdOFxOCbWGw0DhCgozu1s575kUK06HJRolPijIs9etzNV7GJZf1 VXJphVzJm+cLirq0OlEX0po+nWLgG1mWkpjXchENyiS5KabURl175OpodTwi2wiWwpY1azpq4LkC 5WIq7XK5VBai4ZLzGeBvJVWeEXkhmHA6EBECQut4waysWd4oG8CjkAW4lgkqebUXwIHUiSbgyGgB sR4SgwQN+S5BVNbP1MI1Jm9SHB/oaOQK7PoVrF7ZeDWNAXYcCkHFjbxaVIXq+4Wh1G6G1JMsm2Qh Jj6eKOx1fAu7XOumIBAeXcnYLm4+0SeSsgDNgWq+urV8sXe6gUqRH5cOU7Jankx50freFdU3RHGh nIOEji0fVMuLuCMbR679LjAPvx6DL31OBAKouQcdj3eMFOt5CcqlAwzZsbkxP3MZ5MTfFkXJox57 zlb/EXa4Qa7leb3vfe997atedd4b3niIBTO3+tNo6zcFTAFTwBQwBUwBU8AUMAVMga2iwJZBXe99 x5vQVHMVw3A8niipBUbxsL3g4QnWydFVzSRNt2Ua5wcHAW3ADtYWhZrWeWNV+uqkaphth1vriPFY veewFWBZGKyMiMQlu9c2GGS4bTRgnuDKaMztAEMuThhBNxQ4DPz1ObgGSlUJaUUKD2KjoeE+i1Mw EzQNwMGUPPxRdU+4cjhIVViujGFdwzDwmeFfotxdoMSBJIAUgAaGIjzHXcIEt5dcV9CiKITKwE/A JgAvWs8xCWEIgtuwQU1jpFgdMOcGGk6ns8V85jrPE/gYsAyKB8nilbiB2GvXqA9MPfFyeKlwColY DxcFxjEIUvMWWZVeq22xWgqzYHm8WEX+ABttP5/nELoQmEUgcNfKUPY08o+qvJcDDqwHqYHT8cHu GBq4PsVf1ciABvyheB5upd4rwo8yZKnRjFSj/F81hwtMnGA3a+T/YhnYnvCHcY7ARNxSFGktg4dR rDmKCpRSYI+zLIRlBqRXuSB99mxRMLGseJ1CoG2LTY83ihLWeLM6CCWmNixdNLlThD9cGfEygpbz AsIFR+oASeyRQ+TK2Ky2jUcgKG2E/q840nIFvxgDCeaDiEHTmDDQqqwNkpjPeeJ4TOXSUqRRcw1Q hi3zC9cbC2P1KEXNGbDPqY1scmlhHeMbtH2xAMxrkDbBsAaVFHHVSMokcXX7lOiDzGgla+dFxeMx Gk04nmkpqaFyldxm2akPO22r/Bj67nUenpBrqcNFF3305eeea6hr6z69tnJTwBQwBUwBU8AUMAVM AVPAFLh5Fdg6qOudCjDCYTBwDYbDhjFzNRE6ARFQFgwCsgNgkV+mKVUVxag7so1+AAQhKzevZKES KsEFJkag4YkwF72dninaqXBC5RXOLurVudrKZMTtcNjAm/AZacYfzKgseQPQBKAA0QEu7GP+4iIH jBAVdL4kecUgMDAibsUqQA4gjL3kCesWvCJrGe4er6dMiomB8A4lFb1+RDu+En18MYWv0brEh8rx KdHXFXuCmdMFmIgSLlq3hJFyXGNhhM1nEJNt7PLFHEyTpQngJ5edK9yWKmR32caCoN9qlsBE9kxn kKNhAh3z+CLJQHYqAAVzkf9NM/5ARbjcWGSjFCBATHVTGjiI+Usd7D2976qsQkHlPX0KpdBc4IxE IKaqIKB+CxwHZEQNApZQPDxxnA6/J5m3OlQFFnBoQNkWisuKB/MpgT4sYNeuHSt03svQRNKwoXKK PcKKAH9AOsRlPVi0knSAkP++b8qVGRCpxjEYlmYfkt9UvRYDCVVmP55wKlwfgLddTrMwL3Xc7Idr 5oQGaQfj8IQjNfSQ7VFXRgSSKv5ZnuO6GmeZz7FqbGI88Hpig2QYgaeoBQhzff2aQ5nXJQuDZ3Hz lcm46jzw0xBF+pahBOJPfXDUygj99ixKnoUVbF+jCWlb571rx4CtNJEtkY5/nokowiCmdKeLpmJE xMhGkRqkjMPgKzzSPEPsWmgN5Nu0WTwMwr5s54jAaEpZFcPklNPOunl/uFy/u18dch19zDFPOuec +/34/ajdu35X24rvMtS1FU/N1mwKmAKmgClgCpgCpoApYAqYArccBbYM6vrrD6mWfmM6BegEcexF iYiDzC7Aqp5UF2XezFWk6amrC7Jd4AU4C6MJaaTaN13HBITBCnIlCqRAIfSEuXa9bF9NDacAGYAb IDKYbgBAo7FcXRhk8o78ILapeprntNtjHMM3JddS4I9SYEZXMnqPtnuYEV1eslxpGiNIQs1auHH4 W9zJx8cDn4JrEKVU+3jfgcYAVZiVCNbxRhxn7IILwzjcWEl1nAvhiZ8A7BxCA6/wMjWUCXwAtlgy HefYfyp1tFdcjxdDeeBlQD98QnxAT1YHA/hLg2kIEBTQKE/qrd5X1FijoGAVJeaev2MM5QsRBJcW b1+2sBOEVBRUstQkRUFOfFXeOjXL4zli3iJBQ+e0kjdNnjBuB8PDPwXTAbow2RC4RI0a+TvYGPvi CFgh61AVliv7x/qE4w6fVzocMx/SV5yRGY2KmDqfGVrhblNmUIYqJBIkkg7Lnnt0w1g3HGTsAlAE iuNS0li7hb/JOgcY4utgUW7NU6EL9t7qcAjwYr+8QxxP/rLQXQPix4MGS1X9FsFPzIFrGzPyi8Kj HU8cpEzjFEeDAV4zwCaValxQOdDJ6jbGFFT1nvUp6+SmYqlxwnPKGnh6IJ6KpSYZKx/iS2TOwPrU czMuOW5cXpIbw52bgMnTq4eJGZ0aqqlQrTrdGAigA8DUBqLrObYkJBnZV/2CwKZPd104qPzojNO3 GOoyyLX8D4OhrlvOfyBtJaaAKWAKmAKmgClgCpgCpoApsBUV2DKo68/+75+g73w+J88FSxqORth/ 4BZwCkqvoALALGAAnU0wF+XpiA1i5xEF6oK+xeUSRipx54NYHEP0JkNia4rmQWfgOIAp+ArsgDop SNPq6jZuB06Su4oBeaAJ9Ykz3xB/FcVJlIWrjp3rkFdULRjwwrmwuJ/zHimsp1rxIMTCA8maYQ2D UIl2NcAjSBlFY2sz+rvoqqdnyYUNARmQIXiZM46B09IooJ5czKrrubWqwdSZroL5fUWFDhNmESrG mGARwpLGe+UsE0DSEECSlEAbip/WqMmPs8kgoQGKu6xkdIPFjYqosKlhHWLXLEQxR3xCMBa2LNTj PF+4qMTMyGOurMBfnAtL/1iUhXrTMnrUA4xvcCgUZ20Yz+j0goihLQtGhpnmJoriUS6mRGQnAxgX BJJB+pAIhgNhcmwKWxOdU+jU7iNPqKyfyNC+Bdk8hgpiqXM2M1YAJ0rT7WP2ny3owtfiSY+ynpQX YMiixwoIyF4AWzLJAR/ZoYcpjOxkC7WCYUGpKLYCaen4uTo9XEmyY5htWxkTpeS52rvhDlwNXT32 PP7FxjXMMegJIY55hKKEBXEvQq8498CFYC2uzNt49I6eDGZ5sWg6ArIcohu7CW7zwbJwVS7FW9AG YroHz57OgDGLDFXQMwDE5HhYmoiWM5HJOBaGfBcBeC8+L86lwPTFWIOKkaNBJydcNYgY27hCSvNh p5y+VX4kGeS6+kkZ6toqz62t0xQwBUwBU8AUMAVMAVPAFDAFbpkKbBnU9eY3/QEKVn13JNnC4SCM VeEE+JjP8xnAqGJ8YIldaZwNcOgQC0tVRO6lg7QN/BKTl9q81fMdyUBUAhaGY8iPPFCgIq5Evo25 gVCitfmcEYT8ltvhKIIvYOqBX9HKRB8TdAYCtez/gjsIpGHW0ew8ZhqqHx0kAfZaSWPojggaSUBH lGAo8LIFYwExlGUJtfP4dHyyaSrBigj06ZqAJ8Ccw0CYkXgzdiHu7vrFoBudWq6AR6CWZfDSGbg0 ptDTWEYBPkEqLYxl8wXsZzlV+w3jIJNhNuQN0KOIYYO8k0gejiwmVMoo5S0WC0J/wDL1XmF/I1mp uZBCXQoxumQi74BboSOUR6Yt2sdEfWI6y2AzcJcZdWJIFJH61HBMUBd8xzXWw+zQ0AMmrg5wjAVz wowBLV3dvFJtvCxcTJUktVctKPh3ni/BNmTnBqMk1XQBT33wAolo0yvZB7/ijTTuMy8SYgQeQi6M T3KDgfMGKYY1/H3sAraoh4e3QCNDGabEhFpIJf1ucdji+VJPP1cDX8otSKCyyDnXiOAk8UZuRLKU iKAynsJnwFB2x2ODAHyih8JT6pNv8S5K/dWm5srdAFgaBOomA4D53I4UmOVdM/KwwE33StAVF+fg mDuJIj0OQQaMdjT3Fxj64HAsHksdwvBQgewEcB0JJVvL70LRV59q+zCEOXaVNwjS8emnboGurvX1 9b/7u7998/nnX37ZZYdnXPG7/8OwnMD4vg9ceFjFNm+Z/4G0VZkCpoApYAqYAqaAKWAKmAKmwFZU YMugro+8W64uDDMgA5ATqTCSYQzqgxmRLOvaGvyBVSlNhiCHOanCiramFsdRQk1VXk0X5QTI4vtX bcygUwCX0WQCZBBgABkkRO6UXGN2H6iEaYakEIVUMHZp3CB1+GJApA25KUCKWiX1ZXneXrxGFZ4y 0RP6wknVAY8SBgiCPEjY1fU4jYEXKnnncnAiHEQUxNM7DmXDDYQnhyGAtI+5svOi6WJP4xGFf9Ss TyIycCnEZkV0jNBcs1HWwCNsUxrRSHyyUfDtyNWxC7UFa/MFRA94x4jD9byaLSrgD1rgFds+GmOY QiuQFthtPB6rrktxyI4gJLgEgxG4imowONcqUyEJXPrBFetzGaPAQTTrK0Mn0gTuoQMeKTAxxRnj E318VaDAbanGC145q9g1kuJ1YpPDWALmGMEcRUJBLGC8mDuiiSZS0pDFdEVXSSbhfX91kA1gbV4w zRviiAX773ok5U7clEXM5wukQxnNJNAvjx1lzAbgaBw0XBlqSsB6vuA13FFKMfGwKEU24WUigA3w bpIAGds564S16ZQhj5iyNAqAXyA8iuEwiLFlFKBeS362rsffh8LIi3WLK7vBAhrHKUpIsxjL44x8 kqHAsXBWQmGp7ceTxTO1NJfxLQga7fsatanBAcJzDGRsMLOxQo4YvjZI1YkG52LAJY6zQQJoWxrf 1JnmOKf+BVxEWZ9xkjwBbFpew96PxjGo6xFn3pJ/JAG5LrzwA+e//vUs0iDX1U9qibo+/BcfuyUf n63NFDAFTAFTwBQwBUwBU8AUMAVMgVusAlsGdV3w1j9CRPJcIJi2rtYXC+rYR4M0r3sqtOAF+2Y5 g/mOXN22yDf2bExp14Jt0V8+yobwFNxDwlSkGCntiiNQS+N1I6b7iUBALpjbSAeTqua5PvSkbkAt Ml4BMmAwEAZoETP7cC2pUT704SC4dShXgjTwekYVkoHD0gUTiel9D0KGFUInADf75jlWorAHoDQ0 OnGdyZBVZdyR64OaXJxRgccc8KZsoGq/iMbxAkrZoTS4zOAyfNMVn6sqazjeNsDQ45rUVRJfg6tc KbqnTJwK4ZWipB4sUKVU361m2UwMpB2FRPDwWakLCx1ICMJ3Ior+kxjsAhhK5EeCFnYAJiUloS0h 4Uo67PumzAGLMnDJzeRR0K51q1gLhAOlodNdtVnyX/lKJm7klVKfhOzqKolATkJIfMCq3FKp5Qfk tbjDWCuC4PkCUPLdcUxzGe4k7GD6i72o5MvVz3MXon+YnvgiHEr0p+9xZjV1cfnefcAjZCS/R7Cx D+MFm69rYCUcip0CH5UpxexGADbBPNVN1YUvCMU0Q/ckcKzgRb/DxQWuxC6njCGcNMcKmA1GuKg0 BVPWMrEt5GU7xCn5uhxwPvFVtCPUKl+ZPlU4VEwSYuViiUrIcmqiY8vKf/ninIoa39k5lieGxSt5 XHmcoJ9qe8M6WBNQFdqDF3KwiqeGNI6FSMK3yJcesUq7fXvVFNpZRDEzRbc//LRbKOoyyPVf//fA UNct9r+XtjBTwBQwBUwBU8AUMAVMAVPAFNgSCmwZ1PX2t56HoLHKrmqmHl62d59cSIoNpjRyQwOu 3LfBuMVjdh7R9bVmByrahTGH7FhMhnE0TFQeT/qMMFrgrS+KOOALCp0BiZgKqILvzoNS4I+BTWxL tnG7uqmx/MBKdoxHIB2NYqQQKiClCL/oR6qoj7CMuRGBrKED7oB9VscTLssrWWHV1MM0phvLC1Jc RWAUTF60489pnQKGtNX6dL4ocf1oYZAsFY0zxc+5fqAns1rN61A3zd2jv4nmLZmP2m07txM2pJyd MZTEBqFOY0BMElMvRTYTSrJtONAASFWaq8Cc0JtDchUuskXZ4oaDzak1v6rzIqfkHifa/omLXk94 j3qwFWZKup4yrGSluvldA5ofUIY1pqAriYpeHVV93eA/4iDonU8CVVlBlMA3wDTdW7XxjtVwAZWY LS/C1zuMdRsCbWph3z6erIzGs8bb2FgH64APpzkTM0MXESWLqgYrEA9pRIRFbfKbwMM0S7FaKeQV hMwrmE2nuNJAbJjUGgeCwHpAuvXZYm02J3FJVRtkTQMYQ/WUoTZnMck0qYDfwv7E1HzxzY2NqVeV SZrFaVYIzAVgtULl/bj8InxeHM02XGTpwIVT+92zGXvaOR6znY35xtKoBZYbM8JA7Wm9WCe98XIC SgKE5eDgpKwWeAeT5Y30yiERmEzuN4fAIF9MC+Uw6pqy+XZ9BrAlpynf4OpoyAZhjZoAIPYmux0t XuvTq3bv3YCm7lo98vQzH31L+xlkkOu6nIihruuikr3GFDAFTAFTwBQwBUwBU8AUMAVMge+lwJZB XW9+w++xh1Hsr83y9SkWpYruK1fPhGPGJ5YHGjhiddswG1WdCpuyJBhEwJH88r1rlL0fsWs78TaY Dh1WcBbZajqmNBKbwwbE0ECNAtSMQzKGNVVZwfFH3o7bAY/c+MCKYnVK4Kn3Ika3d9/aVfv2VWUx kAWHMKGqmUj88forpgvBIGgE/epM09MUSHrQyUL2w8GQCY6EHMET24eD2tPIRTCWm7bnq7adGBpV WfjQ0gSKIZamXzI0QToW8wXNVhCkhuuL7lF8TuNYyeK5O/4s4nNF3RMHlJctDnAzTecl/IbfsDcc cPjYmGYImqEbnr7+9TyXh4sGKixESQ+3cXAKzlRjfxKCaVoyeqJawyGra0lBrmwfiJaRDlV6kUvh KBukAXnP6RxiBgvCUka+0icPChXUtEVmAYjaRTjxcNIBdEhiummVQdzXELQ+TqFygyjYt4YNLm9C spxguNiN0MQX5d/qyCP591XzPOSYfG9RFMpvtnVOcI9UoyrgfdxecD6cWGVZQNRYbK1mfnTD3qYa fgxjdMOD33asTAbDAU1fWPM4PGKaKhGr6itnC9V7QSSxTQEWabSHI4LVSFMOBzvHQ9DZPhnO5KTT 3wyMLEugkxdCvuCbGj5AqhEsNc8b3ILqeuPBUX0/QFFv4Wo8D3oyNL3AX922C8sblrG90xnn3lZq mhsOx/j+Mj0vHTX1qu3Hj1a3OTFViGyEGS3mMW6qYs+0WJCWzbgpJDAgLzrK4mKxdtX67q99+zL+ cBy769inPuN/3HJ+8Bnkuu5n8Wu/+iuf/sQnLMB43RWzV5oCpoApYAqYAqaAKWAKmAKmgClwdQW2 DOp6/e//Ouu+fO8GWABLETMIV8bZCiQmSVTyRP8VaIG6di9mOF8Q0cKe4WqqSkqZgAiay6jwmk+g jIxdnec5Li6IDKFCnFIR0TtRni6kX94jFhetjk7gdpR/8ep8sSCxqHvRed/3AJ1FnuNO0vjFrh+P huMMNCOmgQsJvANxgnSok0sFWDUmIAAZnEKeJIxmsdJzTFkEmJDsW1ba815oFw1cfpTAwlR8jvWI ni2gimbt+aAksAm0gy6pYD9/Ex5j5wAgnEe4gQhqwmFgW/AzQFgJKyHJRu0TcwY7Ssg0JBF/ETYo ZvdxTUrkiUliU6Lfq+3Vgw4MnCJN2bARVHIsKRoOB8pmFkXQwJc63oWSLA/6Q0MWAMltXb42yKMM XM67RC6SDbI19o7zqIHdQNrcfEre63hcDAgjh0doDzccWIpjTAnkdQF3kY9J9DHAscXZ0Fm2SkqR 6B/LQlgFBDWUUM1o6qoHCOoDAjUrNMmQtKBq9PHvuZGXMsTFMSgK1KfqfPmq1F7PBiFPXBD8OcHC RWOaa68nt+q1PBj6EPDqPZRXyZeSrS0dWqx2ulgQn6SVTCMFeHI0tpIn0FMzGTxV0x4DHV8nEbDC gds2KAWLFU1dy0svTvkEOsh3MejxSs4Rvrl3XlCSxi3ludNITPAsGoT42jggECFOOqSF0B25Ml4Z pYyzvHKjIOYJzZwVs/lijYr+psV2t+2cc865JfywM8j1/Z7CQx/4gJ869dSff97zv9832utNAVPA FDAFTAFTwBQwBUwBU8AUMAVQYMugrle/8qUsF+vMynCANQYTD74ZbDIb+QIPTKYoIcVbACaKuWkf J9VF6i2lEx22tV7OmM8IPaC0K4B2EQijgUu0BMZBaxJ4SEk/8ITAkQ8GibYN5eqCVvD7qMXE1Acx RUsupsdVVM4uqISnqOyZq0iTlb6Et0iVVZpVSMcW4MaHm4B4eJlsUAARX1k8YBawhuAhziFXyuRh udLsQpmkuGoEglE2ExLXNJh38PWsMlEyDDeKhhYqYncwEegQ9+WiwCEgTia/WwF8wf1DzTs4JvMD dgolwUVE0znLGKHbIIvS7IhBDA+CiGCkoiprRKd7mi2qdqFqMq4pTMNnvJ4rg9wAWFetb9BcJRsb 5AUzneqgMpAN7ic2zpqRir2T8SRWOozjo8cDAoNXbsz2zmagJDgh/jh6/UGJZa9mK3X+exraCBED 8cEsR4MBvWuzUlnOLPIofYdEYWsCEUK1SBdyBRmrIJFZxjKO3zEB5F161RSxMkYBgKLamqoq8oYU 24s8YYmqaPSP6YlfL0meApN6aByzMrn5cqTmQMa3aKzSt45yfbAXlisSlGQh9QNC0Ept8g45acAi fWq8Wf1uoY8ZbZpXecNoSO3dTcOkTq5EIQAcqwL/8WWekqLXKAM3mZEwqfDlNorzvX7PTDMNII2k REmYarqC7++ajFger1EvGNHUFEJHOFFt+kzE5CEEdWFDhJ3h6QJealOex15AcDzv5CJzwprh6NFn Pebm/Rl3dch14l3u8qSnnHPnO93Jpgpe66EY6rpWiewFpoApYAqYAqaAKWAKmAKmgClgCvwXCmwZ 1PXWt7zKsST10FOoRVl5GiohyIA7cBW1StvH4+3D8aL1NtbnhN9gE7hy1NNO2K0h3oiVB0sXNVKO DcAQeqKMtJjDXJiuqGokUAIExmPyoR/edueP8BlQBuhANxj9TcAsfEO4e3DXAHQACjiTVoeZ8/Ko kIoMI/BoUTHaT18BikEqqMSCq+FPwn3jitsF2PDmAMNAG3R5uXr4cJzEIC14B99Sv3wQkF/Dv4Oj SV4nSJJrZ4foMQtyjv8rDDOl2xTTow1LDVAYg7ghJiaAIMAsjIBnfl8X1MnzCqYcwuy8fgRyitNF WWOkwrg0L3LAXZoNwGEqwZIsLZY0kn1cckEdPia3NGVf5BbVgQ8IBM7Rlh/HwlsUb6mXv9soSA5i LWISoAxTvKZpSd5peiS/oFzsGbjGqYDsVKuOqy0IME/NF/kQ0BUENR4o7E0tVjJ53FyUkjEBkZs8 qJYuLsQW6HZfQY6MJWkmJgrAnhAQrAm6muW5fFzOhAWVm1UVrWpjx5s4R4AUpEk9ZOrkgmHxb+Aj isFDPWYOYGvjGeBhgDuRNmW1nASuN7xVHDckS2M3Xb8b75+kBDf7adWMYuSKKRiDzckfht4euFSO NQSAshE5BNixAKAeb2WiIhhLI0S5uLKuWAtdshbCGqhBDIMYXkQ3U1G7EUmMxEZ5RPEz8qjVJZRU GDZ2r2ZDOPB4sqIYFNi0IM4uZTDA48943M31s++7Iddd73rXm2sxW+u+SPeYM88wV9fWOjVbrSlg CpgCpoApYAqYAqaAKWAK3KIU2DKo620X/CHCYWUibKgJgW1OvxH+GybaiVCRy8PYFKeAK6gUXydP BrOAhWEJKpvCvRIyo9508IG6xFUQrkonwSJIkCrYfbxDWHbI5t1qm1DXaDiEd1ANRnMVAELWLR88 kayAguII+oIPiGbwUcKAwm6JY3gXNILLg0twXfF7qrt4AUMP80q9UVyDFeAUEmhx0T+F+mQI8snH rSTqnge4gczItWEWUpU+6bkEn06oyCSIDQDmKyrIXTTWkL4uB3ewJAHIKDAj2slGCuxGfqsSc807 1EaBQVwWNKPb8QreSEzP7xMmUfbBHKDVNtsH0KXsqrxZLwrqxzCaQVCoiFKXWBfswHwVR8xq5Gp7 5yXSMbESHgPOAxoCkviFZw74IrSHNECxMKBGamlcAjaR7yOEyaqIIS7yYm06m8/zsUZS0uHVs8E0 To+YDMGSjHtM2Qy97BqCydxMtcgT61tblBwRRwYnwrsHXGNvBPrgdNyZ7WCe2sjrtbyiV3/bIKWI XpYpFWxxuuwmopsftem4KjWsshnL6hbnApIcMqnDruVJ6dqdQy1pvSCCWuEkhFjxatWuqTWLZCVJ VbiTxkSiBjdl7ADPF6SPU+KLPE453I45m1BCBjgyc0BOMSxvetLEH3uoqhCYJl0KsUZ6G4sQ4Wrd SFAFGdkpqIsj1szH0N9Y5PP5DKzJlfGjQRx5dAht6ikKAx4zcbYA52D4xEc+8ab/WfPNSy9997vf 9aEPfIBbL51cBrm+r1NAwKf/zE+/8MUvftCDHvx9vdFebAqYAqaAKWAKmAKmgClgCpgCpoApsFRg y6Cuj3/kHaIByn+VQV/Oiw2SccANUI9arFRnBEDB7SR4QDCwrCvglfJ2PbwCpASkwJEkQqTxiUJO wgtu0h3z8fRaWIGgirPP3P6Ye/Bd+t+BYV2xoDu87rtxlo0HGaCEziogBz4o6An4CvMODId38TdU IokSzESwEsgOX5c7yA3d4xdfVd8VEw9jWuDBQLif5Ipi9SMGGYYB3ezTssKjpJikI0qQMq1XDrWA vvWOPnV500JMUhjBABz0XuF1mkM7VMLe0FIFZaobBim2Eb4zonxgNQfU8EABXBR79DpkpGZ953gE 5NpXFqtpiteNFijyjHipKOHy6RmriBCqsYxgpvQK0F9OJQE6/lFVsDPUgrdgN4P5KbJHshRiqGGC LaYv6JmMYgqHyqXl3ojrqRfBwgrlvF2yo9FHxjpQkDurkp9oKhwTdCP/GD4s8pgAIrxM+NG41IDE pZrvmQ7ZUpSGyUo+PnXAgwJbHhKOnFvggcJ/Jy9eGPDGhSxsOmsChhrNGbJfkpG8JNo1nnA+azmD Anx6yiBucFIIIYLwmMjbRY0Z9MiNk4SOyRUWBim0i5GLXEDbA6mRVcS65bRW874eK9gVhC4bDJGd u/OcOYNYjyuO3wK1gKR5gbmP+0JaaaBvqrZ2VLblBVyX/KYylaKsspVptgAblvkwiHhkQvKP6IzJ S6iUTUPAIKEc1xMf+YSb8secQa4bRW1DXTeKjHYRU8AUMAVMAVPAFDAFTAFTwBQ4nBXYMqjrox9+ K+dE+AtGA6iBAFAzhV8nimjvbvAQYXKiFB4iA4kAF0CooEMUeWOigoCodh7+AGQSj5KVxrm6HJ4S NxBEAG7wAjWee/1xR96NL1LhRNESNiUG//E6UBrlShRgUdKkYGPb03K1OlSMbFrWjEGEUm2AnHp/ RdVRKpkCUmA+4j3TvITXrA5TaA9mn9Whwo+N5++ZFy7o56q+MgiXasvBcnjVsqBX6A84oztQvEUv FrMBCzWmk+mDBinjJn+THEQhi++GoT9Kqc3yGSGIA4lQIfslw4g1jC2AgriObGUo0Xc4npI4Bdvw RQAKZCol7wl5omoqDGflAp8aUAnYxLrQBYOcQGIAY+rpDpstCuxlvAUetlFBupi4SN/8cn5ivW82 xwPF+oFZUCjkpaeL8jJWumg70oJH7tx+5Djbm9e7OVQV9nM8okS8HYAHsoIKERLEzLa2oOpKXWdY 9Dgq7YI5lbGcdIgGTGIfSdCDI9cWOcMIQEtpnKCt+rZ8oUYoprrnOxrl1e9GFT4gDJxENxdsKqby KqC8TNFS2BJusoFAVMiQStmkRJ04604uOAxWoQKuYnNQpt5bm5fci7hiwdk0tRxY1GU1Qo0sjNQh +JLhBjwOVdcC2fADqpat6wiZrqRAuh5cCFnDx0XfF2uC2a0zUIBZjbIlclgdjIyGLu2fzbqn1zE3 fZdDZ02IJ9QbMSUgZcok9xQ79cPHnfbYm+ZH29Uh18n3u99ppz/SnFzXW/mLLvroy88991Wvfe0d 73in630Re6MpYAqYAqaAKWAKmAKmgClgCpgCh7MCWwZ1vfltv8U5MVjQ2XaUW3RkBN8TSTwohRCA 40Xqm3LNXC0gQAPwgCZqIZddS5P65OESIABguMAagS95iwSvnPUI2sH37/QD9+QtGwuyZx7fVdG4 qublQJrlOIQAROTyBkxMxD4GuuCWIC1G/uE2YqYe9e7UhDOF0LXUq9QclgGmAOIoPBfQLSVXj1Ab TpxQqIoFYggKYvKL5C47koME32Z5QQ3/tpUR4biNOS6uBuYEL6LUC/wEQMFKJBzSNnlRwq9U1UTD lCAMabumns+gQnywNNxWZVVT3z4Y0J3lqZC+9WBCDPKD/03zQkMeW2xNmpDoqqyU9SO6CJiDUrHl K3bv5us7VyZFQw5UUxDpice9pNJ0/GL018vCRnsaqje8EuIEJ8M8hYrQLJfulNcJ4xYsj0XqC7JD abLkjpEMdDjTZrjmaCVzhVmzUt6uZT+aGr4cleT1oCqFJDFYMXwATsUUgqbqqT7j+IIQ5x0ck/vN 5jNw5JhVqEaM85RtSieC9prb6OYcsmXipY3fknv1On7REBYy4sDz52UxdCM5WbyW5HKLHMGMtGDH KACNhuSabBPmxGp79q2nLsBlR1daUWtGJMeJ+tyfECNGuGV5GfY7HmCZ8Sgsa7CWseUWmxnrhNki gqNurgOu9zDX8eDwdOIO5JmFt/I0uqSsQC23Xn7OCSOL4JeeiISOtSee/ujN/tH22c9+9s1/fP7F X/gCN6Je6swzzzru+OM3+6aH9vWXqOu8N7zRlDy0D9p2ZwqYAqaAKWAKmAKmgClgCpgCm6fAlkFd r3vD85YqOCOWyrqcC0hNRy1DA10MUQABM5eHWUbVUg5qAX2WhUfCWti+RBCYYad3uuRiCK5Rm1Xk 6qtKDeVrosj7wVvdz92MyvNgXsKYwChUpoNTGjfsT8ExeA2mL4xUxOVIqpGfhGypUr0nZdkAfSAa uIREasjhKYOGWakHfIhZcFcZsuBoatFS0TlWLEElPDvK5fE5jjQyedCLHaOBBibWNZFJ/D57pgtA CZMft40GK1mye0q3O7MXiexxSREcQovYzRZ1T8QQp8/KaMQC2TFMDl4XkLITAKR+ngXTDAXB8+mn Z+NyTmnEodxDLienmYNQGARnZxAftKc1TGlNDwCEHa0fDwZ4oJazAknR8QlMDWrHtgYx5ejBrGRt 0p9I3bLLDL8Vt941GdK8htkN85cwGU32HewM3of1LMK0RVxxvSDt10EFwYUsXDXwmOaaWqX7WLR8 f5IlGwitcKUa3HkyGFzAGavonaxrVS1b3lkoO+I4C2hfgxMQ15YcZrjz8EcxtaCkFd8jGFrvXeQM ghxlBD1JF6IY/V/tOFHJGBRQhV/uymMYXuAXbQdgmjtHGgFHhj7q2BV8FJTjBOFxI+YkkkyUo1Cl crMiV09/5IMEWbZa1PRgwOz07PZBpEo0rG1U6cs2iO9MRC4Vm9XjR9lYEiecAmBNAyYd6tKjw99R HIC6dDKy0FFw//iHn7l5PzgMcm2Str/3it+l6ewd737P6urqJt3CLmsKmAKmgClgCpgCpoApYAqY AqbAoa3AlkFdf/BHL+Ak1C3eULUO9HBTFOXhWpZr6YPf6n/61f8NslEXFzPsXNZQhIUPmbkEHfRP El4ggyX8UgjQWcJk/VFMrN8xUXqIFiaYFpgDvkESLfRqKrGyBBQCbAmJqmFfump9Aeoi6LdjnAEm NpiN57rAyMrRvqSGcU02hCZp4h6Xgp0xNHDvooC1udAeZiKClqo510BFN9+Pjnb51jB5+T3mLPAO RjJZugKP4nletJoliqxFIWSqrWvxMtZJpk5L1TbZd9X5++ZTWMyO8Yib4zaD7ACGZI7q2qIg1+hh zaI1DEaCtwgA5lquNHaQcivIDB4kaMqQ0v2mpgweaxXBRl7O9mBGs6LgvuA2cpSUrI/RJIxQPeih cnxN9AX4SHAP1xRd/nIouS3DcTCjEdJTa1hPGVm4fZROSzqqeuCUyxt6oCaOa1qW7HIouKYTZ2OY wxQABIcFoaZbIpEar5Ai4ryBSVAxzSukgquuN0rSha4wixgju+W71OJrNCcZT/n7ZExDeY8Mac8p YlUDA+LSImkIYsPLxVogR7yLK8+ammjkUWNmWJLuhIkCpPCaMcdTUBJzG4IAJJVWxKanb7BanR07 ArgNY79oahxdbglQr0rTEJa7UjhRGIszhZs5A5qCtnps3GPLGjlnPVcEFOVJZOWBtqxPQr6KqYvj jzhLjSkA2yVZNDjjoY+40X94lUXxiU9+4s3nn3/5ZZdx8XOe9rQHPOABRx119I1+o8P2gkvU9eG/ +Nhhq4Bt3BQwBUwBU8AUMAVMAVPAFDAFTIEbqIChrmtGXcfu0ARGyJTa7B1Pc8YcfE8wqZhuKM09 VPE8hU+wBUJ/zCUUe8NdBHKAZ4G4GAjI1EW8S1AVMnjAG2xfcJS8kY9pdZCCRhaM7VMQzxl2oBc+ PWSyII0pcuq69bwAGGUZGCkYJTitNOcRFBKnKYRIvVeQFKxENZYjaqRUPw+CWRaQYbbCqgaEUkyP aB6GKRrOqhZwQ9pxludwK9xoBAC5GP1frquef6rvnGmJdV2CiYhPQnn2znLetTIewFmI3YGW9s4X hCtBNCJZUURhP7CHT4CMs7JkF7KGBZiziOgxF4CedbXsg2aQlA41FshdcJmpdMpXWRiJUGDUMOg0 5bD3VrJ477zACTUaDsBb7EAuqloRRdfTr4GMREpldlv60MBvjlyqVIshm8JWDclCF1il0p5MKH4x pi6G06KeLgqK/9kIS1Xtfka9V0akFD6GRRCx2D3Hh/+NeZgCdpwQh+JCkLj+iC/SB8fGOIK8rjw6 tsCEPXVsOfQsjSmzR+cu4zC9Pue8VE4v5Lp8iJx9ENaHAktGSz8XydNO0yojrZ9UpirN1NIvPCqS 5Vq63NhQoUz3PrcxOcLQDQCG9gnGReEyNXlhJcvOfMipN/Cnw9Xfvr6+/ld/9fF3veMdByHXKaec as6jG1Hh5aUe+sAHMLnyFa981Y1+ZbugKWAKmAKmgClgCpgCpoApYAqYAoeJAlsGdb3i95/l/ocf qCTHjauUd4BDhewgHVmZlq4uQAFoaok+nGnGBbwAGC7DiI/GURhZvlyGUZ4v/UNX05sgEVzrVttP 4rfKwTlzjeMUCifSEAV8oc5dBh4GIjL7sS0JzVGmzlchGbwFBLNvUc6xLIHGYFvUQgFf4tTNQAyG Sj+qSGuhpBqGKFxapAgTbgfmoPCLWwHIyNSxRhVs1Y1mLGoiYAxWUTl8Q9yNSixXIEU+Lk1lzqIT C0CWJXxVoT1f0x6VmmPZruOcXZDDBAvBmEYZWEeROXxg0CLkUjoSeXs6/vU2OtZZDNFL7ps3BCEX So6KUkWaDumGQoJa3GRFrq9KsuUEyrnmTbZ0w8NxcMARx6ORTHqHwQRq5lQOwGjL3i71lAWgQKRL ByPNZ5RvS5hJTfb6I9ivUW9flDtWxivDISlD1gooIxKIyemq2QJT3GiQLZEQcnJbLkW/GKsg/8iQ AhG7FrDISMnYtXsJUbrQn3ibgpBNQyda22mAAFrhD2O8wBIXIiIyuhynPuQE1GhI2CLmLEUZefJw 87mYYYPmaOWMcj2QC3+VnFoOUaEi1+WzoWBlrZGT8iYicoWLDX8c1I1nRoiv6ydJjKLTRS4GpxI3 l0vlggQzFVANeC9PAPVnFMlh7htPtnGNab7AWUY7PpiM144HQ1ayqMjwClyyNZan6KXbCTsn9In8 EFseN2V9leddTiTVfnkqR2lM3xzYkX1srK194ctf/+xn/5nvD4bZXU+6ww/d7vjlBEmuxxmvu7kB z3jG8w+TH5qbt01Mc6edegqtZz//PBNz82S2K5sCpoApYAqYAqaAKWAKmAKmwCGuwJZBXX9w3nM5 CgdDqHiHRMjsouiiomi0XwlgQQb4Df+jrhKjJcciRqbJe1SGKxcGKuB/ziEeS7rkarJUjU4PF4CG 97iwoz5WRncRGmN+ovrLRb/k/vKZ+kfvuAANC8EixHzAeT7dm1cAIICXGtyZuBdTHM6FWtl3moZJ hCxlx+oE3xD8C0CmO4LJZOQhmdYs8nLn6kS38/05ffZ0mcch4xFZExyB1ntuCuUAaanKXb353d4Z nqqSAN04TTVlkF0zwDERgaJ+S1G3JQWEHLmpheqOAqHRV+Vq8vmNoxgB4USgFGuCN8HXcD/xLcqu 4GgEJjXqT43u8ltRCqZBhF0HHcFlNa8ozBIbAbG5qZc6GIEVnYpgCldij8T34DcgtkFKd7uPYY0c JfxO3quO1KdcYPOqguI45xNAyNWoucYu5+XCN+eD1pIozntWC6GTxWr5DIAqXZqVzi4fiLZtmLoG Kw/INS2oO9OUAcxomk1JIX2vqjLwDJYnmBfoa5iCBZuNRYHdbhSHhZKjOPFaIqLyYWkfTll2FCf4 2bitrGIN0ySZiQDB5KATzVkEcilE6TMRU2lIDt5ZtxjHuT4vYGHbh8PJMFOLWeTNqmr33jWseUdM hnBJEUF16tM8xlMhtuX4JGjVA26xBHX0q4UN5qagIvsteKb0uDImMuJQSIriJqOuTn1mQUgBGL1s KDIAmXGtINjBZxFTOOm/V1Uaf/HCLCKS2eWVhlgSs2U7lKBhmsvS4Wo2kHuxb769b33fnj2Xfv3b X/vat1jbEUdsv9tJdzz+uFsxg5SwJA+zhoQy78DJi1annPnkQ/yH5eZvj1mWT/+Zn37mc55z2mmn b/7d7A6mgClgCpgCpoApYAqYAqaAKWAKHJoKbBnU9YY3vpATEC6BN8hEI2wj05VYhDrX4QtLFwwt 5cuGcv6X3jlyxERSQIFzaUFPxBQgFx6dR2r7gkookkZEjjdAT1p1sd/2NifzMogP0bYgSh1CgzEI XvBe+tcZrgdaAiJwRVdy78EXADiwqrxWRdQKscPB0IEPVsIS2r1rGxt5QacVJq3FAtbUrgAMmM3X dGW1TA4qagfUYFmTQULZPvFGdas7UCXHDZiNwnp2CJgT6FPVOjuEYjG0kWotWAbsCUsOTWJgDBgK pi1NGWw77E47xwO+OK870pEQlJlMV/R8yY9FvzvGH8xoeIImWaYhgJA4sBCGr0YjLPlwfjgglJCJ xiaCfJiZKMSlRB1WJvAYL0Y9MTBngxLOCwErsC9lLdEa0TgzDnFpL+LmzkbEqWloI8yLM4X/8EZA DJdh0CSbCciEZilCAfjU8q4gp5rC2D81+bwMssVNkVRmLu6oJJ8cZFBGbFZojrFLhK4sOSbWwBp5 TMBbgCww25IAshwoD+FNSs2wWbkTITAosdmygKPXr6TRFdM5yxuQecxAPXof698oqjafywKGRB1e NsZraoiBm2UZwLkw0GHGQ9jMzV6sXH0b0vEMYUp0bsMWiVS5FkSjlGcn4wHnKeEbS/Ph8hzZDuFN nIW8j+/WVQmm5Bw5giiMSaPCdWUK43kWnuMKDQpiMOSkZAzDUdi0q8NMkDGv+CPDybuYpKZbOnOc xzFeeum/f+5zF19x5V5EuO3tjrv9Dx1/m2OPEXpjAU0rAEpoMoq2M+zS9aDx0D7stMcemj8jb8Jd Ufb/iy94/gtf/OIHPejBN+Ft7VamgClgCpgCpoApYAqYAqaAKWAKHFIKbBnU9cY3vUjCC6C4hKJL Y2nWnfAWYxZFfpZ5RJdbdOExYouOdYGjMAHxbjcnUFdZdiFxEQCZLFrCIkId+GWgCSCUH7z1fUSp 3CDFNiCY1kYOpck7RjVVqmYuTQzc72lqsURhkxlqqOL+tQFf8roGquwak7AL6t4v84IFgGwwAs2K ak5Ll8v9waJSr+R2ICpyi7AJ+EY2yCbYxIpqNp+7BKLPWECu1sVjvg/soL0LWFOplor8XLN9lMkV 5XvrhXrQJ0NglvO+ETFTAxerlUcJ7w+chWWTboPuZcAWFtB7wCClL+khi8Bp3b450wUZuVitbcwA Utu370IryBdMjVgiEAdmg3kMdoWzDBrFlVezFMnXFliilsE9n1ItZ5VSPxe0i9yfqJmP70xOOw01 JKGJJ8gPAHCykTm3nTsUwqWwKAYn0pqf0FSG9Wl1kLiaK1CPngHxnZbC+4DuLZaK4WtEgNTZnQQr QS/Oz6V/UwmfEg/sYFi3Xhmg9h7mTYLMFBUFRGoNNF0RvaRDDe8dbimkcDDO4ysc6Cgods+KK65a v9XOFVKHG/MFQGrHZAzrwU2majM3N8AHcToDGqVbdP6zFLWacUw8UWE0yeJhwmMDKmILJFVl4AI2 cjTcHyDIWnB7MdoRSApwZHH8dj0veYCZuagIrrq+xDgFp6K4o44NW18I2CrzpiEXC/zCeMheIKQ7 hslazgjLCpg6TmKvYV4kFjkNCeUsoF3yJ+KMAwtyTOhGmjXwN2bziy/+l899/iuzGZFV774/dvdb H3urnTtXtg2SKS39Xe/MXMrEwvPgW4QugV9U5HP3Rz767EPqR+PNsZmLLvroy88997w3vPG444+/ Oe5v9zQFTAFTwBQwBUwBU8AUMAVMAVPgUFBgC6EuubrctEQAAak+ir3lUtHEvz6E3uBJ0f99u8Yo US0Fv1whFIYVdT+5D/VTgWzcTD7VPel/2JdtXwEjCrG0qC9c7p7J8Ie4CJVVlHDtWF2BBK1N50z0 E1wj/SduROpNlp45JiAKs+i0qqBUoByxp2VbGAyFF+K3wjAFgWHVmj844EOeGngBdApzDojhsvW5 1oxbzYNEcA/NKNQLkwBb1yyv5E2DfyRxRXOVmJHImihSFGMdIuMI4GDrhNGAblx7PAatROtkI+ua 9vQlgOOm+J5cLk+OJ/6NJwhRuCyrgOHRIj+B4rnBi3wdsxDoLwZIJQotOtHkquMK6KggJKYqFYNp QKEmRdY1vxs72IOPibcsqspBRuVC5ZDC4ETIkSYrv2NlavLCVURqLiUJSm2WRiuqX1/5SkJ9jiTS Cu9RzkX+rt6A+8ATAX4H8qssgMmN3FdWu1Y6rwySDSYdNoJ3EE2MYKtQJd8HBqk1v+crdMbDeVCN nqwFTwy+MN6IKNim+BdWPtdLpqNZJlyjbMgV8IIhB8VeMDJUdClI4Kd7mNThpRYwLsK8SCVq3WDF hqZ/uti4hQv6sQ4OR/lV/G41D4kMY/j9qOUHMupG1KhB5ZpuI6fuv4FksQ5uR8KUI+YiXNWFQEXx 8hpIqEY2ZEQTcNii1jxKef16DzIIzeSolv10zhCm8ZR8zvkKREpzteATMoV17r7s8ku++vUvXnwJ ko7Hwx+92w/f8fbHsWIe9SFr59FCIyQKRGnBePwxZMc8qipug336wSPOetKh8BPxZt3D6//ovHde cME73v0e6/u/Wc/Bbm4KmAKmgClgCpgCpoApYAqYAltbgS2Duv7w/zwXpWWTwe4DEAn8VNE7DDUY jEiHqe1J7Ee99QTCVCHPP2Uf0j+WSEvGreX4P80CpCXJdxk6OcBU3oXZy7nBZBA7ZtfdRNY63Yge esJ064sFt2Ns4jQv9/O0Ze09wMhxNUAAUAUiwBqXpfLcGGMXt4D4ABTAWOCS8WgUphmvxzO1NJGp DIqyKXBJXQPUFCqMkzHtTtRLAWh8byADF+sKYSsqMq/rcaJ+qBLWEeI2oyBMK5e3CB9ZGO4cZVE2 UrUWSLChr0qIsICXOD8RdIYA4zhN8OOwBljPShrP6m6jFLOjSwtERc4N1gI7BFTNipJmeF6Pewvf GMCIywl4qfJeqTr+4uIwmvF4BPLD/qNZiqqHjzwhJdxSoorqpfI87FcsBmNUXhQSDVanKY1qgmed 88V878YUUiX0mGXbRyO+z764V1nmNK83sB6sRD5gUrMgl/COa44ZZ+ngn8vx8S2cd4Ad6E+7WOS4 nKh1p4W9xQkFqKqrvCx4Rjh1KB8+O96oA3JDITFeAZW4phuGKOqXpBkygt6wdLEVFqNhmksnVMLi Q44V0oRYQm9ChC0AiJCg0qkUhMkM2HMp5TQ5Rhdy1JG4Vi/FV0F+hAGHQx5FlkHYFraEUw8vG48s djPoIT30PLNQKvFEPfyqmVeLXEA4N8wC0FuLBQzNeRYZN8kzCeJUu1nXzVgD0yhdnb+ipGRXS/WR icc19SVf/1ecXFe6rOIxtzriDrc7/oTjjpmMRzMcg5oyqYmbPNXTgiZ7Pfj4+OSUZAQEMdssgXxd OV2wryc+4Wlb+2fhLWD1v/arv/LpT3ziw3/xsVvAWmwJpoApYAqYAqaAKWAKmAKmgClgCmxVBbYM 6nr9H2kkmWtJVywRigQqkDfKtSw5T5N6xF38TVYXiALd3mom11dU9a36cFxgmsXYAYhU3SUQ0/mO NWGpaX2oR8YYu6KoJ5Nbczvg1DgZDEYTWpDmi6LrwCBgDpli4GQUtwMOdg6BLKq6B0MsU4G9r8Z3 EFYaZ7ugP7wXu003X5/OqPQOGNeI8Yf6MEq1KK7y/L2LIgVyiMypc10fzFIcZCx/3wxMRNm5Gza5 /GAD/ZJt9Thu8IVNJquEJUvYTds4uxaXpKqqQyrYDd4pmbMwTGFnU725CMvSiwSuApWN1UXf75nm fIeac6EYFyHkJaq08jTIj6YnuI/mUQJ0kIVhgo5kAYaWiAoZWQ9SuRxnC9rBELeRg7MK2CL188s+ Ly4OTcNUtLGYcxs1ZBGgE4GSjcoNkQSOoQyR0342m5ISHQ5GRBjBVqwL2fGa4VRDOqrHlj1l2J0Q xzm3cLS18Dw0hWSxYLXLB7rj8iHRnEqVTCEd+Ud18MfZcEnH4FV8RaVlrlefRCiF/XwDmAhvSkBs 7tEiNwqIFOZzHWE8dWp/IygqnxZ1cdBGXGzA1gATmZ63MAJjAcjEhoB3UcK7ls/tAEdiGMCJUB8f nyyBjSKxAFO+w4PBGAJOLkT6lno2pQVZEp+vF+XKIF3JUlc0RpK0BFMCyIpSMUQChuPhADWJKLon Sv+AlGUZAy4bmt94gPmbuC6DFD756b/7h3/4wvJH173ucZc73v6E7dtX3QhOoWHcbWRCKfuflq0z +umAAIgMGdAkT/xoHhlVn2XQec/WTj3jcVv1p+AtZt0PfeADTr7f/X7lV3/tFrMiW4gpYAqYAqaA KWAKmAKmgClgCpgCW0+BLYO6znddXaJAElnuK1m0VMUk6COHi7NWCYe5KJ8DEK6uC6ISqsCe5BxG rk7zCek/okrb9X17RAupmsryMty1c9dkZbUuW4q+d4x28O5ZnqsRjEGBuF+qAncL9h1gCDgAQiH6 Q5LLHTpXA10ojoc/J874Cn1K8wq0gFUKawykJiF6iDeH77tJiB2mJLquYD0kBBezPbxlz3SxMc9J 3u1aHU3LBu6SsZmQ60BhqFLSZjE7TdJkWpR7NmZsecdoOBqCSjxuRSM7mcIB4wKzlLGG8qv5oZqn QBNdC8LDpIMqMnp13niAq4s2rgUqwYYIDEotVzNPWlDjCwFAruVqaULCD8aQPuY5gsPgSmTliqpR pK5p9s4Wmlk5xO6jaYwoDrnT4Mi6IhM5GK+CZOgZI1KHjQjYl3p04dPjXq4t4CTdJBuwMFAk+Aw2 p2imU5U5AAroeWo9ByxC1thQHPZVCQFD7YgFwG5IG2IMA8Gwek2KjEOGU1ZlISqokxLKglBir2O1 rBw8x2bV7C4AF9Ewz+c8Map413gBFc+DunBIoQGVWCujESyPO64O6baP9s1zarnccWgCJQALexcd +f8/e/8BbUt612fCtStX7XTSzZ2D1EGxFQAhhEASIGQEAmRkZHsEDtjWWMIEj73Gs7xmvNb4W2Nj YzzMMAzIEmbAZsY2DtgwBBuTJCFaqSW11Llv33jijpWrvuf31m2GYbxY6itacLrf07dvn7B31Vv/ t87R2Y9+AbQHoGTxamY0mfdcDiFZMFc8pEi0uDnUrKhn0QwgGKo4fGE1ag3qYRgQ9IZjkVuahDJu Wvo7j8iNxzPrOIqgJyZ/gMKr4IsmSz5iCkAzbqzJMOHOmC+W89UK3R+MTA9nE+tWzlWVA9Ad2S1p pawr9hqKujWM9+fZT//Mz5FN9qqX3z3Z3qJEYXs8dANVOmIGJsuMbeA2NRBQzY2sVm0HnhSLEF1m yNq4dflUGCbdgKyudx6/n39/nFZ85crlP/vt3/72d7zjz/+Fv/jHaV12LXYCdgJ2AnYCdgJ2AnYC dgJ2AnYCdgLHbALHBnX9k/f99WuMy4AuIS1PbYkKUxLqEtgCHSia3fcUTt5jL+NqbF1itHnRjwGO hzSDDgVTSatgViGAil13hK9uK908ubF5mNWzdU1A+nYs9APJionSCkKENiaMSdocVE7KXRKFQfEE O+jIX+dkigFz8JfxRARQ5Eb5+O8gWfOCxj2oygB1T4I8DG8gcioEXCq9A6O52MquXr3K6Q7XOSQC vRK8QsSMxKsGRGV6IeWFbHnwcIh5kYj3Eh0QgAaBFWY0lgGKQ0vFc3k20WNQPTgXFkgOFZBuhn6t BF9kWSErJPIcDk7mOlI0BT8ZJKSSSVUaCvegCSKkjDVzrM00wmB4JPSjBwNlwEaEwUOXoEVKesLJ x/WSZrWzDaoBcUG84G5cJjlaXlMRZ8YckjAEJy35aqgKRI7PKtV1SFZ6qwUoG36dkdaPkY8157QB NDWLJiYfGyCTEbmLAqa7QIJF/JlUeSVaNvVQelQHSHiFGZMce3844eBaMBixEjgjyp0HMEblj3Vt ygwhfXnJpE0DoUKs1MtZVSyAvTYtjbBCZW8RBXeYl6bsE9FZs0bvVEK7BmjzuBPgjBDTNHDj8ZSj oPnSdrTtMIpQRSkAvtKDUaLpgK5fltQRSMYmMSGMUqFkAx7JPhDKxp7wZJAmTE8lDNp3JZrJ5+kF POgwZ+dRbwmnUiSQN06aQsncApJa5MYQyr3jKtZekfkSyjGepqpQnMF/+5QunsV9QcsnIjUco6LF yvF303R0aprKVOt0XCbQkC9h3WSxmF45IJejKDsEh8A/BHgwNFVwAsi8b/k2m9X1Bf30f/DBz7z3 3e+29Ytf0BDtk+0E7ATsBOwE7ATsBOwE7ATsBOwE7AR4Dfzww48eizn81D/9G6xT+hgj0OrlXVKb GMAkgZa5DKm9UPeYZCij2uJlOdiAF+ZCXbQL8iJdHYBwBNK+m6AZpN1gfG6ylZfhk1cPQFHb49GV eX5qQuCXg8xoY5SAAIzGClDRrLLC2OIQ3yjGCEFQ3tSHyLdqSZzAXhOSlqgpBHgQNI4yCf0RYU8I klwfcRYxXkiwWPzBcokrbXuIKU3ir0QKIaELFqxCQU7t+zCsim5Bk3mPlAzKoJLHUQxI4fCwjEkc QNZw7bFsKMacEkBngJUMEZMCvIJgY6hFgV0QIgFc4DQcH3bG1/kk7xDmZYyKoC9Sp3ysgKAl3Ipg DwRQyIgMyBjwGc4oSZLbq58a0BKDYg0kl+G7BKwpOArgUqtGUKa5KOqz8NWliHcPSOagdepgZ0Eg pyHnNFIs9wihV136To36CXAmQZnERIiVpjgp9xYLdHTCKw5SJiWpQ3RUB8neSLdG2Jmiu0A8Ci7j +hGpAX4IuTc2V0Ec09ho4tUUy2Y4jtoGwX/wRgbLRQkC6c7Rhq1RixkQycaxJ2NS85OYq+aEps9T UVwqBFDVQKvkd9MLqgnA2KSDw9cpfKXssLYjwgytFoeC1nH50huWRUY5I4FirBPIKLIq0IQflidI kSiPqmmxNCSUpYjBGfsnwI7zE8Wl2kNuLU+XAzDlnyJHcofXdICmD9TYZ5bRb8ApuAfy9ZJ7GNEY qkPD2cTnoGFcy2Kttk0xOwFQaceYCfgVcyW5YNQOcAjhQhN4LzkbjNl8u7EvB6s1AsMY42+QvO1P WtT1Bf0o7esX/9EP//Bdd939BR3IPtlOwE7ATsBOwE7ATsBOwE7ATsBOwE7g+T2BY4O6fvIn1MBo srmoflOae//au+/A6wO5ngZevLZXlhUv4HunIaIWJWg5CLREEoAG+M38MGrctHbSjXTbc8Inri7a qliullTjkRx/782nOB0EYV032AyJRkKFJG5iwBqfQ2vDwRU87kbSxJi07xIZU13zrJCWQThE20mS I+ZGUngzjkmjV2y50rAItHIVtsWHWA5JgBdZI7gKjVDnlFCh1imKAtAAkCGeiWcoIJ/QqmTIiSAU 6KFWpVr8SBdjTYrQ8ryNlIBy1DYQEzk8OTt+NAAKWI2JGP2RKQFELCbI0gg7qViPL2iOCnqHlxRq mGTBinuPIVbO3hE0ryaDnBVyTNgNfjeyqlagqUbxWFwguG2grHz0TWJNbI0y8DUZYbV1UYNpuIT9 Vb44OkJ7NEoScs2QfnHBW7HPSfeWWZokm2NUR4rJz9YyKk4SX02I0EqHIHYHLuRT6ciwPWFENXFW pVRZeBBJTzOR8CAfsM1mErJTh2u4mFAgw5GVT2UF0t/xxi0EJuvjy9hHFGpEnumDwYCrhu9wdbOi RBcGplwU7ImaOmMy43FG+pHjhyanC/8jplYXmKhDI/SrqHr0NpLIlCKIgpEKD3yU8dNzSHBnwrqd 1K7I5kojBp+DJ7ErrGeViy6dGCVePBRqFLuF7pXk0PP5yuTOyZMpkOosVgSarSajIYNdZxkbn8Yx 4AxEyrS5EFLk5DqElBFe1skICeCC47EpS3MDo3BDrrURB6yEHWfxQD2ALAUER7h3qfJkjAMIphSF zJDoOD4pn6maHbjF9O3A/e560du++duf3z9Lv9Cr7+sXf+KnfurUqdNf6LHs8+0E7ATsBOwE7ATs BOwE7ATsBOwE7ASexxM4Nqjrf/vf/yrbZLQ56rDjjRfeRmbioVIx+fQSdwlUSO8imoM4ps8Lp2tP L8gduBh8gRfycoNVrZ+18TDZwqt35WC5Wq3AZQReUc8IpHjJXXdyOulx5Ly71se3rtT3x8MABvwN FAMeYQUTpZJqyQGp8ABTjOgR3sTZUwDBoDtYZRwTOESrHQdQMDm2O5yPoS96FcaZSBeBVBIlSZLl OPT3Ddpqf7GG6BH1xVfAJVxtGCQobngf8VHkS2OV15Uyzn1KDGvgDRePUAgYw8oZiYiEM6B+D+UP 2VeiGyb4ni8oZYxQLQ7rKeUdpoPAB3ka7EmwTMWIXE6LbIeVQPrAQGAUyA5yJNRDABe5DuV7o5BR UinoG4SIx0iZxfR1LcAsoSjkTgi+mJIypFCtucAu+TSxH2Z0C7YN/khgGzSHeTQDH8rYlTPACp8U nbmWys+We7PVmuvamIymcbDKcmAWqjTwGuIqUBGyMN0GHVn+LkNFYCWoRGAW7r6yZN+HSYxbECWY SJlpM2Qmoqiq6UQh5RoDaWioGXn8mB8RoJntNb2TnIX5YMZk10r8mThHzc1n4uT1GBYsY6hJx+cG 4IvUE0JCiWxjkhGSPkZDbpppYmR6nBqDJ1RRVQMGwMk5Kcg1qBRF55ERxkNny7VJfwP/6QCch9VW bS1hHtcCCBx0Q48mBB+PJJl0HIr73rgwlZHPhkIQpa/TLdEu12vmCmATLYarqp+BPkoRUmilqZ0U HgYpG8LlsxEsjClwPEAjFFL6QSNgZOOxchKD9pY/8a3P4x+kfwiXbusX/xCGaA9hJ2AnYCdgJ2An YCdgJ2AnYCdgJ2AncIwMjO9///f0+yWmJSIElFCylPkQjYnoTf9Vxc/zcl38QvnvsB0yswEBmL1A TMiMiBZy/aRo0krxUKODWbF7uPQGxLcTtL0k9Ang8Cde+xqOhvGsF2RBNMANvKBXxr0RQSEnAiIY PRGPBznVnBvqAYc4QSdiEh6s8hU58Sa1CjGRyYNykQuxOFNhp/h1ntwLY1LhMsiajx+QQ4GZABOI pTgg9AfwgGiIaC2oT9OSxE7zowMsMR2IPMrfpucxCmZ5uVitnabKs5zcKDK20G9xcnLEZaBUpj3y JQnHFusSexpONpxw8uIp4B/Q0YClxH2Aa8RduS7GNpa7M0xKBinO1UpopSk7td4qPJJkWkHKOIjK KYXXBnBAwJDgiox4HFN4UPQR16Ti5BXaRfrZJE3YmL3l0u+AXCZsqyaZCy4mHAYNwiTJBo6jGPAG JAJkob0i/J6HHVIioAJEnJU1llL1WZqWAC4BJV9Mx2U6EtZRF6V0WnypBNhhM5Swz3A0zgKXzGCP WCwdCBKPBuGFpGdpT6Xgw76HWbIoiyBMYWmElCn+n5uhrMBN+A3VuiicShUAQWADkBNPNfH8BHJV S7ayRJfHAekqxO7q8uEK7ZXnbnIVkKxGoFAZYTKPcgfI8wg9RFvHJlTrOfbDJA45PmUFxKyxI6Dc Bm4LuwzYcHRqLcxxnFIe4FN3SWS+WiZDxHcDFH8Gb2GfZHccTsH3Ax5G7lIuilj5aeT5UcLMQj+g cMAEycniOcurYRizYD7M6GIoa0MzAZSy4kIJFS5WUv3pTcbxGjOrC4CDRoZv+waLur6g/0mx9Ytf 0Pjsk+0E7ATsBOwE7ATsBOwE7ATsBOwE7ASensCxUXX9xAf+Wo+3ICYmokvIRXFdRiTT1y2a3HDp aBR0hLgLfPN0XyPR5jxVUd1O1Hn4FuNs5RBetDerVmuStlAVFVm+8nz0WZImvf2rhbrINoJJzVaK XcLSJWWW7yLOIncJwRfnOCCjXuWEQkKY/jagODi/cvKqlHMkARTyLpNy1bMGljiWWYyMcM5Jn6C/ MwyP8tKAOWmgpBAStcMmprOb/Cz4lMxoomZltc4zIslHaQqkoAgPagafMHBEhk3eABlMgSegwIGS yc6GwgiJE3KzBthHDBO+OaP26hw/IVqeJHt0T3QjFoi6gGgyueEAJP1KVk34GlOGO+kzsCwAjcm6 cnDVzdY5+2E0Z4p/Gk822rpEMSSVkdLUDGDrmwpNFyDXPElx9jmzosLnqHpE6EnoG8WQT9S9Jmzs qCS7u36oXLMO8yP8C94ljsg1QZKgORxN5sWGlHRyzagmJL8MZ6XJ70LMRbiYFFsqDYTEqdMQriQ2 CbNiBwPkTlxdXsABSyaO11QqQP3Ton8DHsVxxPHBlHC0ENhXK0WLoCw6NA3TU8mhaCWyLxyadc2C YXASASrsyi3YtZDSyYgjgMuETelYTOLGD5rV8jCrGBo1iJCp/jbm5HLIauXgR9E0jKISKBpgB64z 6jNHlk5vsDGMeFbGFYov6nNksDEejoRzFoEYAi3uIlbFYlgpQfUgMx2To2NF9NQ2wJlNFafoL7P1 KHZEolhTrMkto9B5/sOtsTFKR8NkwWWXFen3UEqWKmLHHLp6Ooq5iZpBiHf1rW/9k/bn6nVPwNYv Xvfo7BPtBOwE7ATsBOwE7ATsBOwE7ATsBOwEft8EvPe8573HYigf/eh/kBfRZNIbDZfYUI+8BFP4 rEkvEg9pifFSQpfIF9wCA5ZkRioYxHuGrmfgRUUTzObl1aP8yt48E3ZCvbJW8rnSwVWBd/ftN/Jq H7oBBYAyjLDs1TWFdIioEFXtrVaHlAWWhetUMA+IDKSDXkI4glLb4UN1jbJmaEoGWcMwisfDMYAF KZPsfoAzhbJDwSp6ALVUxE7k3CPhIYVqMJimSRpjOnTQ5nBtI3Lci+r8/mGW50SHwyDIVFKGuiyQ YDB1OYL40FfRqQdr4fOqKDSqNuZBEjmL4eKVwQTMc8gLI5KpGsXopzxoycE6p3YwoYvQdSEadCeC gbgcQBIHAb8Rag8O4bqInGcPImWwd4RTSdFFIBTR+IpU5wTEhql/EAjDkQFZ+ATxVrJGLJwgpe1R ygUSSQUI41motjB4olhiMaAmPtQpsnzJZaqdEhZZT2L/3JTxs2waADAGgncaxsIuQyEBSVx7fxUm Z93Fdbk5ijeH6TCOySMzRYcCXoEoFvuD31PiOB4MIipNTSRbRPkApYQwIXUCEK8f+FwFFIzdjzQ5 V6H+ht+BeBgIBZpHy/VitSKaHhMlh2FuEDgwIJorLnwUxycmKae4Os/wo5p2S2X/s9V5lnHDoNpD cTZCRYbBkJx5Ac52vi4QWB0t1Y0Z+qp+hEuyZfRmrgr5alWq0DG68upsuVxn7AMJXFuj9PR0xNZz Uiy0bB6HA7NCr3oLai8WU7knrMp1hpgcjUCPbxY2fWekfLclk8WF6gedS8h8dHZjDORK4pTKS6SP bOgkiQhr6wEm9zfD4SzQtzkU0FRbvujelx6LnyR/PBf51FPnf/7f//u3fMM33Hbb7X88V2hXZSdg J2AnYCdgJ2AnYCdgJ2AnYCdgJ3BcJnBsVF3ve993G5IlAx2qHxXtCWLIo2byu/o8cdEvXtf3oV1A MBAHAiUSh0jg6garxslgBU2XFlX0+KX86iEyIw9OkWeL1sFG5zcAjbpLouS73v4mTletFkfzI/n7 QsyACp/i5T9B3WmaIOcBfPGWF5jg6GoMk4is9oSTinuU6GzknRPMqstROPDDdLHKZvMFch4FNBGB FIRxmGyPSWeHTJWcDkQmDxn6HaCDhDkNWA3qhGQM7gVt4cMoHsahah8hFYqGwjmoU6AnQsKlmHMp dDBqij/QRSiEJtOmdG5U/vl4AHOgXdeCLUBFWNn4qgkuB4IovooIME3PuPk0gaa9Ml9x7DROiBWD GbEBQo2mcxBugjsSAMRWAGvCOEV3tipyLZgI84pwMJcKQvUGEh5m+hlhUjkp+2IxOj6LwaHJ4fiS +I4Jm0e2h/aN1C5TcUimPtlYAJcAu56UZxzWeCS5Ddj4UZyg2wJlQg8RLR0hwSNmnjEJ+qFcEx5l 41Zr4FU9Qt0EG/SDnBE4g6Gcrx19iMjiYIuoouBH4DahMYyHKKYkm6sIy489dw4Nhc8ZGRcrRJAF I1NvQEc5Zj1WeFcko6skbLobQVUyNjJgGipBXRhOo4hnrZdzEJhoVuewlwZ10WegOciSC5EjK4tL QCA4MMZV1iC4xo2pnLhJStCYWh95Jrd45UUSf8meqqw3I+xSp+TuIuNv2FafpMam4slEhob4jAcx TFLAjMbLmSQh+IzpMCnuYBjcLMs5JlWSSQgUbpe5wr4UEYaHF1imYkeFu6mhktC61tlbqRTz277J xtJf/4/9vn7xR3/8fTffcsv1H8U+007ATsBOwE7ATsBOwE7ATsBOwE7ATsBOAPxxXFRdH7v/5/VC XulbEu+gfeK1utHsyCInr6IJU5f5T5wLZQ20yDzG9YpqUJLhBE5peUk+6Jrg8l5+eQ+qITVKlq3X qwVmsAqdipDCgHfuu/tGahYPZjNexReYy8II2tJnxq+bdm+5Plrnh4vVwWKFEgYvG7HgrIJzYw3M QV/olSJ/FEHasImJVSigaiCgA4DZhrgQtiQO15vvCiAEYMV3oFqInhSoBEcII7yWIgvwD554lJVS M6mJkYeRD6XrR1YVo9hqye0KNkdopFygG0iLxHq+tDGMN1KtXElbDMBpeA6x7VWLG07mts5EhoEz ICDy/VH7iMrMpJ2B1YBKXC/n96u8Jf2c7sCiXDMuQs3BTp2DHEn55V2HFkmqNOVneUAuSv3Yg57d wB1l/xSHaqmzZIVbk9E4FeBjTOjXqHsENiFNMgIiUvcD+AsRVBvDURpGUl0NQDMu5YOAlUjxaDoF 2EghXDySYkEywxT3BYPD1UgDJmQy7OsvjwBvdYPcCcCFxo6Ue7AQkf8bqWRPWZ6thH5AY2Z/4WUu 5xKoIr2KtDXUTCC/ddFcWayFzGgDKOWLnAzT6XjEBrWe3JTDYZp3iieD7nEcLp+RMhn2VyMjGUto kpy39nCd4xIFUzJ2oOcE1yJAijh57lMDcBVv1jg4ZOd5TvMmnAsiOEHfpcwvb0htpfLmHS58UXE3 62eYGCcaOtLNmEYPDbl3idVXbj3iO8WQsROQM3Rz3FfgYughn1fBYsT91TNZ1RfwB6si92sPMbHn clGAsJ1hjOEUaskjCQ6DDeer1e7B7Gg22zs8WqNuq8oXv+zV9ifqdU/gV375lz/9wAN/5l3vgjZe 90HsE+0E7ATsBOwE7ATsBOwE7ATsBOwE7ATsBJjAsVF1vf/Hv5flwlNEA4AOfQq98SiaeCvpXCBE aKFAHsYFKKmL5wpCQfRIrGoHa0Q+gTPK8/FHP7N3sLeEUywzyINeugNlSKOimxGEU5flf/dd38jh wUMcHw0XOeVzMqiyHM4GNuE1PxgIDJD6g9pF+dKgUYIiAX1YBFxCGpymhhrgChOJM2FMrBK7Wkwj I/Ir8shbUthz8uQl+YHuKDW84n1AGGzryaNlGMcnJyOnKoEOXBcqJU6CPoqHwYzktaSgkKQqwT8X 6gFyg+igTeOTVZFR/AcfQbuEzgtGxrkAFaAVpDpKu/JIpQdT5VwI5EUeSGbLxKAhA4fAptkqM7FW 6iJkIoyHTH0+RI4EuUMIBMPixDuTIWCIFSpkykHDJX63KuuDxXoMzvNcyCCQyyTiB+Al9Wb6fF6V AnPAWU4YlvL1OZqccErK1zemVFoeR3JW2ZqgLq1O8fweoWFkiqlPE0IUBjyWg6goE8sh8AZZXNuc nA6noyF56mApZFnLuiUGniOAmBTahbqKIkjfV7nkakni1CSKNBHPJTINDyq8THANJoigqajk+IsT bifGgnoONyj3WuW49FYqE82jvlD0UZMngq3APqg6TtZPa+ERzZsEx7vaGqkC226C1CseTWFhtTYF zjXPynWZK75fOi+lmzHqcRyPYxCtNHewNr6EXK4xLQih72DqhNypQYCV1N4IFaIwbHuUrQ8Wc6YE 1itk9YU2+tyZ0qJ1nQmqr7FQkhCXhGRsDRAf8iDOyIUzrmsqMGyPDkhwwI6AH9V70HJ7B4xilhX0 G2ADZezI3OjPZP1E3aNvw6X6rd/2Lvsj9bonYOsXr3t09ol2AnYCdgJ2AnYCdgJ2AnYCdgJ2AnYC v28CxwZ1/cT7v5+lwzLEQUzlH+9CtoyLEcwl3mVMjIpskoJJrjW9jEcGBSASxxjkOX1z7taFS/5H P34hw3XWVqvVknQmGcLE/fgjPRjH/P6/INSlXkU0SqOxjgpGcVopfjwXuQ2Cl3EUbcSq8+tlUCwJ qZeS1nliWaAxghIgdwLInRiNlHsOWwGWedIEJUm4ezSnyHBjmNAnOBmOOB1gyNgJ0WQpNRz0MCMz v4JSlUJavsyDpDORcB8HERcHTQNw0JKHcInMJrLqk0iB5fIYVhUXjy+PpHaSoQRKDEgCSAFoYCjC c5zFCwldQumjzC/yyOiyhMep3pHiSMWTI3GC23CBamMkWB0wZwoNF4vlerU0mechfAxYBsWDZPFI BE1ca0uoVlEpJ94jFUvbxIhYDwcFxlEEqb5FVqXH6rJY7eYQ16LLgxXkL5Ecl5+RhMUCJLnyw51J CoNDawU05ESsDaynLZaJFYkSWVTubEFCfY20TW4+gufhVnAooJjSqZTwhXSO5zJVNheYOI5CqhSN eonQtJa4NPYRmEhAFTqm3njoB+pRlKFU95iYGwIw3KscEHUblyiYSGqYZkGKF4IsadxECZVB1kIo cQVS4EiSOzqxdDLkYRgtVzmEC47UApK4RjaRI+MS3RgNQVC6EPo3ydUStQV+UQMJ5oOIQdNoGGgo Q4SorbMVdxy3KZfPclFgyUnqe1wyf1CZsTBWL3Mu1ZwmfUxkysMcitqu4guI5VjAKPQhbYJhNVOS xVWVlGFo4vZJXgOZBezLKi+5PYbDMduzKDRqqFypbtL4LW9+q/3xet0ToH7x697ylr/2PQL69s1O wE7ATsBOwE7ATsBOwE7ATsBOwE7ATuALmcCxMTB+/GP/t2CUMreQ3iitScY6hTTxCl+uL0EuQS/R IvNItTGqSU5+Rt6XwQvOAnZ46NGji0/socXJ83VR51JUKYkIRgE56tnZ4DWveJE5BBYz0r4VDm5y kJRdJUdeJ8MgX6dJcLFe4TJDnYTxC84VOOidgDw5iU6Qm0kcnRinPBeIQG4WRjzgBYohIZ4akddA R3EGCHMkC8ONGAY4yQAZAAuC70ntUm45nEuNf96iLBfAhgbRDX49ISeD9AaTYTIlQjykfQ9RFRor SYRAYzVgKKMSMUdehHyp6gYYAxHhpLIKMijInT+NKQoMTRiWRxkiJ+IdxtHHhBlyp4o+no5NEmLC 8VXfWFeAOZyA4CkZAMPwxDDkGg7WBfKorTRmN1aE04cYEmMOThchm8VqJYVq0KlRBQBuUzMiKjfO w8JYMwCR80qkJ3kXD3ZPjIeTVBIkpsO+kwnPsySjAiWaUgImxtWgOVJlgMyYfFH0MSMQa4ALkoix ELEUAfBAJblctfaCnko2fHMy4uC0dxLUpbZK6dTQlxFfFXNpfQ0lT4chgvt2l4AjDK4dvOlwuVpm GfoyroKCyL35wlRGio2ZkLTBVhoxTGRy0CKsq1Kf5TAkBxaJAIot49KgWhNSwIiSZ7UI3BB5eT7I cj8rcXRClE6MuDgcrKi58IqWR+SN0ROK4I4mBVK3YIVFnaSjxg046DSifACPaamazrKFrEG19lf5 nJZMbukwkWkRg2fjoAmTw1S3M42W5pZrWnR5yL4YIyo5cCeD4o6TH7jtMI1yBLbGdH3SDzDwqVyU iBLsxTcjBaiw58GdL7znC/lJ9Hx+LvWL/+pf/IvXfuVXvvjFL34+z8Feu52AnYCdgJ2AnYCdgJ2A nYCdgJ2AncAfygSOjarr/f9Eqi7DmwwuMRIsaZzgUyIUYge8ouddk9IlRY+4hkkfV1SRWw98VcwN us3/+OvnL52fdx69cjnx5sADSXdUsEeHoOgZEOT/933v4lPzxYJDuBTngSdIupLYRWIyXF2EeRO/ tVYeUg4gAHkQEw7dIPPqcDGDTQA1EADJ1CdDIVWJxGd1kn3VVd8LSEwSGEvQp2mHI6m6EMhkasqD YVULFUOWCMfQTfXlgkNIRteyWi4C2gX7MZIrLo7jK9JepYTG2AnpUTUiTAcmqGR3rRtygR8NsRLG Op4IF+IqODCYA1WXYrU6CbJ0QNeFs4CfCJTiuVAV1HNSCUWyPhJ4BmtSY2VecjwezKDBXqaDUaH4 CIWmSULsF6I55ay7wmpc9WFeQSIha0BBrmhrRPufx0BQafH0PoUd4CIrqMZS4RSFJ/JZaeuk0dNe G6OhUVpJmyZgyOmQRKGfAjlBmojtYvcVkEUYv7kutoAVsg4m04f9I31CcacqyXSkCDXZGU1hZKfM MkYIsuIqYUYSVDEiigYqzaHPuWduCOuoJxSxgkBKSoUOqkYNyAWyFwwNfyKfR83GqbkrdMCOYLI0 q1qul2cIxEpfBrKUcRamiHiqVyZi/EQceDRfgvbYDaM6JHeslD80SdCaQUuVp4VTEo3ZeLpBTUFZ 7c0WrJOTcpEsj/uUNXD3ICiTLTWMWXmKLpGegdkCeSQSPGW0UWKgclIX1Gf0ejJvMgcl5ZuSU+gb l6jR8FT+03Rq6vRwRnZlt4bQDYTLknLgv+0bv+UP5UfS8/AgDz74mfe++93f/zf/5hvfqDYM+2Yn YCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ/CFTODYqLo+9an/ZHr21Dqo5HkShQYCV8AIyIskUUrEEuPC RcerdlRUzMVU4UkB1GEs68hEjw9n3ec+ewl+pZfu0hKpKw/KY7K99KZIraZ+xcteCOlYEFiPjoZa PU9Ngoi7IEqQGjgLz8aNuDaAB4gA55JFjpJE3F4Kn4KeAJR8aY5c+S4hXwAdLGdwCiEDcQ43azps lKw2RTfkirNBHGAO5NmfHCUnJiOEYKALGgy5BjRF87zCwcbVX8ssw1dYN4RtAdrAIvzDdNBPYcqr yVQCecnc5yAZg8uwhr2jxf5iJaBDtjrDIXEp9FEO8XgunxUj70KUZARuLdbL3kSpePWBe7guSODC OMgxexsjyiB56PREURLMiRj5CMuHlOyt1mjpGILS7psm9QZy8EXRJGUGXGcIegLEIJNidyCGstdB 8WBJbAZyM5NZNR7RdRkwKEOFJGtiFJyFIH+oFCIjWR/x4Gm9aN+kUeJLXN2yIIpeKIuSQJamGLKB y91gosekMuMYxKxjxww9OA2BVp3boZlazpYLgCdWwL35CnJEOBtBbjgoMzYaiuR5G6NkYzIOVFMI DFRQGkOALgGMFkUJ8ST0illIKuXjbFVYnCl2dOYmiAuIJjkWKfWlvISEi3Eh49DbGqcbU2RnhHNp K4nEAiGxDN3bagmAxwoRnt6Ybg2HzJwdnxc18j7II8ckggzVHvf3memIuxHIGMcRtwicS97SboBk cMLN6OiRCNFIwUePNsP6WChiX35MVItcB2sGExuqKJejfKMh6xf9dFSDIFOokteaJdOnvpNvhQq8 6JE/htrvzjvv+kJ+Ej2fn/uxj33sN3/919/1Hd+5sbHxfJ6DvXY7ATsBOwE7ATsBOwE7ATsBOwE7 ATuBP5QJHBtV1wc+8N9wwcriEouqAQ2SIQl1oeQyPKsXcimqXqnqCugyyV1SYwEg0LMAZZzk4cfz 3/ith5AH1YMGMZakPJ2wBCY9ownTUXj/3f+VsrrKrj05GZOsRc+ePHR4A1fZcrnGJZbXBXKlUZyw DCLZoWCcN0oiaA1ORpgCMh9RDwmIisU6T0djwAGHgLZwJCDOOI2RSx2tVous4ENOJ3+cEfXA8nDJ YWNEwsSl9flfACHhEmAIqUyE5UfKRwd1gSQmUYDKB3KhBHlhIqUshZ6DP1OCsjjEuidUVBNBpdRy 0p50TOMGhRGZrCydHQzH2U2+GDIxURvSx5kuOU+qPzQCLsgRf1CzKTBLJQFaGMvmE8jPMqL26wqJ VhqnPCHPC5/x80wfpRHyLrMnrgNEWmUZdFCcBfkb2yNvHVskFZUy4M0z2A/mSNSURFukjwlLyqxH lyJMakmcGCPyQTQI9hCgYQlFmSfbI+H4MpMK3gmuUbMIH+M5q1Kx8ZJw0TVZt3W5JuDfaL5UfcjY OQFGTKFUgTOUXsYMi2eWyC1DheB9GWARsyF5WFQ66nkicZMkgkaJdil7C4ro8I4SyjwJppSwpX5G 8t0CD0hkcvo5GnRP9yeGyhwzYueLxEVsDFcUucAuujIZEQZGuTslT1RVoqRebCtQki/xLFyrSlMz XJI7H/gG8WOdICpzRQOWKj5VkB/nSPNmNIAcnI0DdDIR4uxgs/gr8yKH4fH9wuJhgQyGm8q0PnAt XIhLvhcfeXLQDoi2RwGJyrB0EjcafeNbbFbXdf5M/umf/qn3/9iP/cRP/dSpU6ev8xD2aXYCdgJ2 AnYCdgJ2AnYCdgJ2AnYCdgJ2Ak9P4Nigrp/4ib/eoy698MbShd4FJ5oJGzIgSGojwS+hB+l8pM1S ryBWPmmwkDCRVOU16Wc+N//I/Y/QRNdgelNUO3FY/SF4EU/OE1hDfrH/8XvfxZOoLAQZgJykCyIw KxIzwlnWNhWPo8YwClOQwwpXYZmBLEh8CuOQeKzFuhgDWQaD/fmSAwJchuOxRDpKDiO/CRmNnGuc j/XSZogLUVeHzEp1g8ThiwHhNuSkAClUPByKzx+gMStzg3I8op1w1QGPQqMXk8OuqhAZAS+UVMbh 4EQoiIx0TZRt4JCmzwXDxTCvgUBywsIc1SMK/yhZH0eka1yINQlljAM0My8qpoFmStooI7ziuSen yM0wtbmkR0H0gHdUHM4UClYCf5gFEqHN4YgNYVYgLbDbaDSKBVywAbYYIdkd2hHBVURTwblQkzEs JnNltgLfDMFBZHXJQ6cNBfeQAc8o8DwGMfWJg3XVgAI3ItULXl2WXDUjRQzFRaaBBpjhNTUUiQkS UMWDOSMzUSOlA6AZcF5thRoI0MHFqJLAOousxo6Yc/0EVHkuZ+KkLGK1WjM6JqNOAv1xuKKYbgC2 xkBD1GqMfJateYwEeKYlAaOiyCa8TASwBt6NQyBjQ2wajIl1cFFYGzkJhI4/IDyUgwRmcclMIEqG AEZWQgUkE2a8eF05sikWUB2nUWNROKmYLTZnGgPHvGUBhVUMmeCv6KsaLacxBI30fVVtqjjAfI9w Y/O9xArZYvgayi8j33MQfPENliB407eGjJbGLmyy7yhtVPFoPSDejjuAi+64I7qBPwpAXd/wzfbn 6vVN4B/+gx/4+Z/7uV/45V+5vqfbZ9kJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AnYCfzeCRwb1PVPf+L7 zLpNWR/J74qYNwRKSVUmSh4cY+r8YBM8DOJgtDnwFqQokvrgQvSb0f0fv/yxTzzuuUkD0wAJtMCd cFDVSoqHdimyS3qx7333t3MyegxBME1VztZrDFzDJMqqjggteMHhMqOY7+R0Y53NSSUnXQu2FcIn 4hSegnpImApjHqFdgQ9qqZ3W5KMLmWEbZMlGJ+RxfOhJVYNaJLwCZMBgIAzQIpLFUS3hcES0AwdB rUO4EqSBxxN4D8hD0gUTCcKI41BWCJ0A3ByuMqREXgdAIeK94jjjlFXFnJHjg5pARb0EDvskAVEQ IYZInhQPwD0JpUFlBpfhi5KAQcSqOh1tJExRyE9wiIQyxbgrmqqRJ45VygFKPJirSKmuncbxUgyk GXrsFMNXFhZzwLUH3/GDRN5GhYuBbHqBUgtgYvLGrEeTI5WOXV0Qzu5IwGUsqNBNrVvBWoplY3OJ LkM8J/0Vqeu+P89KqBCQKa/K0OcGEELiTSn+Wiqx/ux1gzqMtTIQNF8ASr46CkguQ53EPaJ/uBaF fLmyWHIWItUQPfFJOJToT9ehzMKDePngEHjEGOM43hwlnResufiqAlbCobhS5etLgYi71ktDxFMt JkeJB9H0IVgzxlqmSIFji4oLXIlcjoAtmgfWGVLAOBmiolILpqRlYluMl8sB8vJ5KeBwZSJi4x41 ujK9i51TeBfpFj0GTE5WWX1HQMeUZKcYr14AyV882LA8MSweye0qM6wZmaSDVYnGkc0xZlKPiaiR gI7Ohr6Ckm+oE9NREDT7C2hn7gd0im7+ibda1HWd/+PyX/3pd27v7PyDH/xH1/l8+zQ7ATsBOwE7 ATsBOwE7ATsBOwE7ATsBO4HfM4Fjk9X18Qd+UfIShbArPgqZFmlCkCL+Nt2L0iXxCh4gAggBoADC FOklg54Jm/f1ot1zoieeml+9ckRnXItuSQWGoAMFgPEvr/lNWpeYwGteda/RWGHMU1/f/mx2OJ/v zxYVNkBAQFnuH82QzxBUVNVVQkmfAtoHsjGipSKAaRiTYATLgg8grsElR1QS6IckJriBj5gKBAHM KQtsZcuyiFyZ5gAxqLbgNQR1EckkrRWBU4GIBlQOYgKeqRUOhtyMy1deFTQFgRFgiMsHzYDYAGrb w2iYDrkSEMypjRFZ5uWg1+x0RDUReE8EFYZHpdzrHO4kDngW48Pox1AQNDE6kp44nUAhDY/TIdZD ocC2npOX31aKFENZVgABC1aOMMrk1quvkEwu1GrsSuK3eOIIYod0oRfj2CRokaNVVXkNljFWSIaw ylarPAd+JXAfOfXqRZ/DZdaGE3MySqejlISsQv5JBuKzT4SkKXifkHhmIGWVJHhsIhtgAE6k9gJV FxJ/RgyVtz1MVCHQwoDCk9PNkxvbg2AIVySBCua4zOGJpK3jN20ST8I3pIBybSqEvuryzFUOGlfl c6GQu4WhkhAg5GgklxUdlBFBmLORRlgIkfLRzyjNmsK7VMmIsZGlopU7OdbOqtgRHRrCKN+DJB4e Hi3394oM7VdDAherHydxBVKlwhIASlpcUUZIyTiD6U2gCxJ53jBKqsaZZ2vdN5yraYa+Z7LDXJU+ hqGS6oCeqOTYGFWQOqSUsS8baWLaSiXvQtBHBhwqObaeL7ELisFrydR3kOPRASryRX8lSjwdWOld MDTKEigrhXpmxfpwsYYlJmF619332h+t1zeBH/nhH37Fq171ZV/2mut7un2WnYCdgJ2AnYCdgJ2A nYCdgJ2AnYCdgJ3A753AsVF1/R//R29g1OJ5ZW9yuXi57gESUMqg8oE0wQgkQjHXh8yKQCJe0kMT QGKIaFYFJXyj3/zw4w89dNltgy6QLqxSqx52Pr3qR15lTG8E1nfv+U4FDw2DwdEyI6C8qEuyr0w8 E4qZAbY8eNiJ6UYaD8tWgU1x6CZ+tyyyywdHThCe2NnE3sZSQGWsV7KaVon1kiCxWgQ8RlfFyUig AgzdcvIOTiecoPrAcom7b+AR7wV1Ojg82j88LIs8EcHBTKhoJhx/PP7KYo0oh0o+JDnIeTgLqIgw KA6dJikNjpgcGdJmmlSOKhcFawzLg3PJhkZUFjo0ouVNyaPAmpLO+Gq5Xq0VbjbwiLcH5YD8sE7i ymTxnB19Fva5vOqwA0rLFrhAmcWqQBXGB1wbCjh0bMqkN5SkaapZBjRBJkaefeCHHe4/+Bf4BR0S 8ifYD+wLj94oJfI9ZXUNLsjJJgHqlADQD6BkenCmS8K6i99zoax4qJ1gEF/ADwpGVNsiuEpePbhm hZIOuRl0ybRVukGnYLYuiAA9ie8eHiGDy2oPLydGxMBUaHJTDc6ePMl/91eZp5vKWee5/JtNBQ+S q1GkaABSBFQBncB2NBqwWLyxbDWBYtA8EdLAX5PG5flbk3GSJiR9Ic1j8+BiChErq6vLteK9Ys0K OFsV3F8ly2Pi0zTZHqVoqA4lOJOSTv8aRgggczxfgkHS2UjXDwMw2SqrwYvKeuPGYSmikXoKR+N+ 0J2hctLBdGMHhAqiPVgs5dEtlTSXpiOQZ6z7paVPE+GY9GhVk2FThY75iNECbuO6zPcWamAIY06K +szFLzqMg3x9tD/bfeSpS3xz3LBzw3d811+2P1uvYwLktL31LV//dW95y1/7nu+9jqfbp9gJ2AnY CdgJ2AnYCdgJ2AnYCdgJ2AnYCfy+CRwb1PV//jPF0mPlywtEVZj4gC04qwK54EAEJkweqqSuPT4y zYyGZOnragkMAmRMbjf81d946PHHrnYN8MvkkQ+8thGGUuoR5EXFiEqy/8t/6g0c7PLBHCyA35Be uskoRl5E4LpORf6Vev38xgka3IY+KewxRK0sEOYAERzywWReG6C1wWNXZWh8gEQBYA71EPliMpwp LJ98eQcdjT8d3srpUOLw6Gy95tJ0LjLvuw6gs84ylDsZTrW2Gw3TUQya0YWpudBzIU6QDvDSlJMA AP/0SURBVGVyKQCrIuwJQAanwECHJAc6g3vOZ1B4IcnPN5H2EgFVprWQMkRN0gQ/kbMFVGkaJZQR Ll9jlFMCvHuNvwmPSSVH7SOAqu0wasJhYFvwM0BYASvByUbsU+DDWcBqICri0SIm4yBJQwFUYZOk mpB8r6ZTDjowEIkZCiUuhCkZluSnaSJvZp67NXyp5VlM0uBKdHIhAKnfU4Af5FFmO6WY4bLk0wqi 4tqRvFG/COaTMMyE0Bseh7QLVyL5Vi5WR7AU2xhhyGuRm0l+ZRoPkPZpb9CgTREuYf1jWQxWBkG6 MsF8qtU0if56g0Atc1RctZFSsR4uGvJVszA+A4oC9Sk6H0Ue7lrSxxgLZlWW7zjjOOBiuQMBWijE nIYbQ28CXp3D5BXyJWcrAitBNMpAsU8i2lKlAHeO6RvlHyWTmU5SCje1fa2GQG0iuG1OKFgga+pR VjiUToYYSCkPBRnK6Ms+wjcPVrnEceaYktIZUqziSNR0Rj1HAhqjhdCdnIwmw4h6x6vzHJsnNHOZ L1frIyL66wZN38a73vUu++P1OibwxOOP/8U/953f/zf/5hvf+KbreLp9ip2AnYCdgJ2AnYCdgJ2A nYCdgJ2AnYCdwHFFXT/5ge9h6bw+N91xwhIS2KDqakhwV/ecgqf0Ol36Lgx9vQ+xB2GRPIo+HjW/ i//jb372iccuwXNQJOFPE68igAl5TuxCcWi5Q7MDknjXO4S6kM5M0gRpDCIedDPIZPCLoYGJoRRo kwYAJoK5SR/3cMahfiITHbY1K/DDCVkQ2uVCu0hTwiknWgLjIDUJPAR8UzS4wBHWQsffSKXqglbw sd8gYurcgKClAVBPUfGmGFJh9kiKOnoVuT59Cm2RIqvUVUjGFuBmADcB8fAwyaAAIgNWJZgFrMH0 hnLIhDLRUAnBaZCAAaYgGrLYKdDdA9Mg3kHXM8WK6XnzvMYCSc49TAQ6xHk5KAOXV1F6t5yNQP1D zDs4Jh5gKqT4UrntJJ2zjCFzS2I/ik8kATwIIoKQiqisIZbLKF6XzVrRZBxTmIb3eDxHBrmxafuz Oe5GydjYdMR0ioOKQTaon7hw1syouHYi50uHlQencZC67tX58mC5ZNPhhOjjyPUHJRadkq3kb3VU 2ggRA/HBLPF1kru2LNgmwuMdQt8hUciaQIRQLaAnR5CwChKJadFxbtkaA/Ie318wrJgqAFBUUxFV hU8S/2bfjMBg0WqREz8rat2sYNa2pSuTk/eVmpg0EZ2NFPrWYhsEe3HrEsCGaVDfmYJWSpM3yEkF i+Sp8WTlu3kYQgnOL7Oaakhdu2nDJE6uYEIAOFYF/uPT3CV5pyoD08zoKEi+aXBWcgvtLdVpAGmk cICUNrUrDAY74yHL4zHKBXMcaJquPVCaPtCYmxDUhQwRdoamC3ipi3IcrgUEx/1OflxWhaS9vf1b /qT98XodE+hR13d/3/fdc/c9/dMfevgh/n7Vq149nU6v44D2KXYCdgJ2AnYCdgJ2AnYCdgJ2AnYC dgLP8wkcG1XXB9733UIBJGvJEdgDEClrpNtBMaXQKd6MiZEmQfQyUuIMkKlg1xpUIAuUWfHIi3/t I5/75AMPi5Y4fpwkqIfQOMFEMPLxYet6V/cODpeL737vtxqWpBz6qkUP1kSeHIIU3IGrwogk8tFm OloTljRbYX6DTaDKUU47ZrcaeyNSHiRdNaxGbACG0CnrClREQBUEgjdQgq5FmM67ffslvAeU4QKr nDQswSyuDnUP6hqADkABZdI0jY2Wh2tq8TACj4jrErsCgZFv1bXE1cPVYHyob0xwuwAb2hxgGGiD oDETD++NwgCkBe/gS8qXd138a+h3UDRJ68T4TDo7RI8uyBX6L2LC5G6TTY/aQgWDIQwy3lEYXgIw 83zg2aCriPqveQQthzA7pxuCnIJoXVQIqRAurfIMcMegwWEqKdRYGiRpOPs45Jo4fERuUcR14VtU Bj4gEDhHjFYQCG+RW69c/nae4xxU4ya6NUghj6lpGZDoTZ4/KJdwp+x85I4h30POBYxzEU8RPpWa YLYKDRTyJlyurTRuxkpJtJpvmgcxosKgdAmIASeMI2ZJ6sRkArAnBgjWBF0RyCUdlxFhcd8ty5JY 95HhTewjQArShMdQKjAxLHljkVDBwMjYonMAWRv3ADcD3Am3KatlJ1C9cdOy3dzPqt3k8eb54wjj ZkeK2TBgXIgZG9ic9GHMG5euUawxACgblkOAnb5TTNwZjYq9k5ddVhS/8fyaYHrVU0LDEIihRTSd iroakURfbJRbFD0jt1qlrDBh2MA8mgtCgafmhwAUWDcgzjaiGOBPve0dz/Mfpp/n5f/SL/3i7u7u 5UuXeDyc6zOf+tR/8Yk/8y//lUVdn+dI7cPsBOwE7ATsBOwE7ATsBOwE7ATsBOwEfu8Ejg3q+qfv N6hLzXFK01KlnrBXhzrJtMoJbPHGC34lopMiZRgXeEFZ9jUFdunAS9BaPfjwU09e3HUGEb10hpzV qLqgHbAeOf5cF0UPJOU93yOJClImJGNqCGwy8o3Q39BoJ0KFLw9hUxABrqBSfB4/GcwCFoYkqKhz 80jIDAlUijZXYyAn69Vogl+KTOc9tEOsDm/e2Q2hrmGawjuIBiO5CgAh6dYAPBFOQEGBD31BBzRw aWakoLDtcQzPgkZweHAJqis+JrqLBxCdnpXKjeIYEq8paR6cIyRo4KDJhB8MJiGp7R7ADWSGrw2x EDnqsCV8muizZJkEsQHAsHlqkqbWUOo5wR0kSQyNADOsnVxIjtxo0PBccRZVJkrHxGFBMzqdEeNh sGO1IU2UnbsCaDX1ZgJdivezepbnxI8hNIOgEBGlLLHW3UJ8Ffh0NXK0A7LWiJrHoOh04DygISCJ P2jm2DehPUajrHxS2Hm6hEvAJvx9Cn9ntW29zvKjxXK1ykaqpCTDi0T2KgqiE+MULEndY8TFBAHW TW4mAUHkbG17tCbjjfN0cCK0e8A1rg1DH5yOM3M5iKfmWXWUlcPA3UgibkdJphSwxe5yNT4R8kyb jKtCZZX1SFK3IBOQZJNxHco5CzncTrWkWY4FtURJCLHi0YpdU2qWShNQVLF5amJkl0N/laMclAuV XeKT3E4Z3E5R8QXOSvyPxiiJ5E13mvijehyEwNR0KcTq62ksQoSrMZWgMjJypaAutlidj95gvqY1 YAnWVOsCplEQcYsqEAWYDsdtJs7mohz03vlN77Q/Wz+fCfzDf/ADP/9zP/cHP/Lue+/9wR/6x5/P 0exj7ATsBOwE7ATsBOwE7ATsBOwE7ATsBOwEft8Ejg3q+umfVFYXr/zzEnSDKIt4eKEu/kBnwAVq 30MsxGtvXnY7g5D3pdwhDslPnCD1kt2j9f2ffvzipatZhqYnPJwdDqARdX3LjTfdevbGVdHszo7m 6zWIBJrzt75PEhXj/yrcrljlc5xxwA1Qj1KsFGekAj2xCiRUaJCqUj2O0jPBK0BKQAol4ot2GfOc EfoYGqdF6rGwAkEVg+hecOaVfJX8dyWF5Wuyw6uuHcXxKIkBJWRWATnQQWm9pI9Do4w/k3+hEqEf IiZiBpAdPi91kMKhXP7wWeVdUd4XkAKv0kThJRiS5w0TUrxcstkXRYlGSTZJQ5QgZVqvFGpQRQRt NZFULBmRFEIwAAe5V2idKAo0Iew1KVVQpqruirzx0Z1h5QOrGaCGBgrgItuj0zJGYta3R0Mg12GR T6MIrRspUPgZ0VIRwjUgZ6zEQqjEMoyZmpc2VEolATr+KkvYGdOCtyA3g/nJsoezFGLYMp8G0Rf0 TEIxyf6k0jJPRPVE74CRQhltl+Ro+FdZh8goMWZE8mNNhWOCbqQfQ4eFHxNAhJYJPRqHonYQuRz0 h2ESlIbISjo+ZcCDAikuJG9Lp0ADhf5OWjw6N7FwSsKmvUZsCBrDYgjqqhse4u+MxuzPUUZRwICc MogbnBRCyEC4TaTtIsaM21jqPWkDpQrz3AjaVTVzDqDLA6nhVUS6ZWat5H3dVrArCF2cpIyds3Of GYFYhyqOD4FaQNIsR9zHeSGtJNDXZVMZKtvwAI4L8JWnUpRV31DqFuCCTbGpzy3j4X9kzoLNMD8u GgIGCWW73vlN325/vH4+E+gdi3/wI9/93ve+9a3f+PkczT7GTsBOwE7ATsBOwE7ATsBOwE7ATsBO wE7guKKun/ln/y1LV34QDjufiK4O+KKGP3KyeKUPDlDqN7IjcAamNTCYSe12orObO1cOyl/50AMX n7p4sH/E6/du4MNE+NowSaE1r/7SL7nl1ttKMTIOAUcYEND9ihcN+RDzF4wGUAMBIGYKvY7v88Ua DREiJ2G1DlkZAiRCzUkKh23BugAI1AIa16IC8uWoFOqSqsvgKXEDQ+UklRIb44s3n7yPTxLhRNAS MiWK/3gcKI1wJQKwCGmSsbHpSLmaprKRLYqKGkQo1Rzk1A0mio5SyBSQAvERz1lkBbxmmkbQHsQ+ 01TmR2Rse6vcGP1M1FcM4VJsOVgOrVrsaqQ1cEZnIHiLXCy6AXMjfOuW0CB53KRvkoLIY/Ft6g2G EbFZAyoEUSBhKuR68TACH7kEUBDHkayMSXQtiqcwiMA2fBKAApmK8HtCnuCOnrcs1ujUgErAJtbF XBDICSS6MKaO7LDlOkdexlPgYfMS0kXjInnzfX9idbhcoYFi/cAsKBTjJaeL8DJWum5a3IIntzdP juKDrNplUxXYr5vGWD4D7huQFVQIkyBitqM1UVfKOkOix1bpKuipDKSkY2jcb1xH6HbgyKN1RhkB aCkKQkkEJd4SaoRiKnu+JRBO+W7ckoAwbl6yuWBTAZFXLuFlpgGha1GTJQJRHiWVkkmJOrHX3Ijy 4IYmh05sDsrUOUergnNxh+fsTV1JgUVcVi3UqDZSMrZI5Q8peiQ/vgGyoQdULFvbYjKdREC6DlwI WUPHRd4Xa4LZzSgUoKtRskQ2q4WRkdCl63/aD2yYm77KprMmhifU69MSENEyyTnFTgfeO976bfbH 6+c5gb/93/2tD/7mb/4BD/5HP/zDd9119+d5NPswOwE7ATsBOwE7ATsBOwE7ATsBOwE7ATuB3zuB Y6Pq+sAH/jrr5iU9vi/8aD1JIv8K11rtSAQEmvAGlBJCkYBPfhombQFKoT0x/PkPf/pDv3P/1miL 1/TiCCAJ9C9k0LfNuVtuev1Xf+XODaf3OrgVHrPaL0vkN9P0MU5HsaCR7ci3aMgIgAwnHpRCAMvw IuVNmWSuBhCgAjygiVLIzYpMzDzxRryvxcqwhuFL2iLBKyM9gnbw9btvehVPma/xnjl8VUHjipqX AmmZoRACEOHLS8jQRz4GuuCUIC0q/1Ab0alHvDsx4bQQmpR6hZrDMsA1XK3Mcy7ZUlL1CLWhxPGE qhRwRvtgADbEd9niHMT4tsxyYvg3JkPMcfMVKq4a5gQvItQL/ARAQUokHNLUWV4wf0U1kTAlCIPb rq5WS6gQbywNtVVRVsS3JwnZWY4C6RsHJkSRH/xvkeUqeWyQNakh0URZyeuHdREwB6Xikq/s7vL5 7ck4r/GBqgWRnHj2W6Hp6MXIr5eEjfQ0pl7zSIgTnAzxFFOEZhl3p0gowi1YHovUJySHUrPk1lAC OpRpS1RzpJKZwKxlIW1Xn4+mhC9DJXk8qEomSQRWlA/AqaCrddkRfcb2uR7KOzgm51uuluDIEatQ jBj7KdmUdoTZq7fR9BxyydhL60GD79Vp+YNn1qPiwBmsijw1lZwsXksyvkW2YIlbsKUKQNWQHJPL hDkJ83LduutcVHZkpeWVOiLZTqbP+TExIoTrw8uQ33EDS4xHYFmNtIxLbpCZsU6YLUMw1M1kwHUO 4jpuHO5O1IHcs/BW7kbjlBWoVWOpeZ8dZiyCX7ojQjLW3vmNb7c/Wz/PCXz0ox/9G9/3vX/Ag3/h l3/l8zyUfZidgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AncDvm4D3nve891gM5RMf/0W1LvJSW8FX+MVU EYiKCnEJeVYKXkfFg6aF1+PKGwrxhIWDJJvXv/yhTzzxxOXxdNsNYuEYeJn4gjyQKHl2Tp+ZnjyB Qw8gMp6Ohtvjzc1RPJ3Mr36krFZOV1Azxx+ESpLaqEIRHZhkZEpkl1WwVi6Y4s2N1sX4CiXLMblI yoiSga1nT8IECMG0VP6jdDHEOMQ9cbRmGN9Sqg3PR1iEOAmAsyrWs3U+W60RzkQ0FoYhMjVYxZRK w8BHSTSKI3RlCi7rZFLjD049WA4HN0HvPcrwJrAZ40YUBRrIWyfQhUhH2UxkYyn/icUTZw7dkAiq dRLgBaV7bTdiRU13SKR8maNggn7QZXn18EjB/WsqFjFZAkSaYeBP1Q7pYfJDmLY9HkJVuHr524IQ yKTQqIEkZsJwhleC1ZihMcoJxugSsECq/BF0VRQlWrWMT4NseCKCI47eIDJraq4LdZLQmEoAu6Il kx6ZmLyiCfAFdRguSPAZTkRS03m+ibhiYicmQ04HIWIXGQx0Z6Xof9ViAqLoJQAnEQqmNgCUSto+ aQiJ6l8iTyPCvyxAg6PAXaxzhHEKzhdEdBCFcQ0cVjFabasr91X4CLriX10GGFISLXkRYVT8qSoC 5dVlWVXF1QV9jjoXu5EEqLfkzx2GcRpqbiJSlEL6wdYwGUVst5+mQ5LDuF39wE1jrhzs54uxEsOv gHquBYrKSMG46MyQaKF1qyJYawN4RHBGXSMYSzo27hf2Twl1TW3UiNInSpwnJ6PYrDm7nLpCueyT kewZWyP3EbvtM2V1Q8pmyl0dvPgFVoX0+f4oPXPmzO/8zkf2dnf/i094+zvecd8rXvH5Hss+zk7A TsBOwE7ATsBOwE7ATsBOwE7ATsBO4P89geOj6nr/97FySIQx/SnhiA+lRPHoTpzyX716pwBQHjFe 6kfxIF7Pqg9+9KEnH7/gxImLV1GpXt3B7BAlS1PmYTw8sXMiTeNonEajNCMvHHdiUZHQPUpHd9+x 6AdlPIfKpTeGN4V6w1xM5pYnrQy+RQdfmFLUjX4LfVOf7S0Fl4gWS+wk+ZFmRg8QAEL2g1sSfRFY R0ntvnN6+6WcC3YjCqZayQHUznMqIrHikAx1VDvUSrrIl/ZnaxANRr+tUcxh53TjmSwwvHKkLylh XBQOaqXGPQ6VBC6lgQfr3IxIoiZwBThGTjytTZomMtqlW0PkNeiATQAahGSSdLkOwfM8aBqHsqzJ N9o2VSXUxDrx1Gmp0q8BWMp2cLhaIPbZGg05OWozfJfzAvue6g1pumRaSLNgYgH2vZptUq48VIiD wMAYLrsDjUoJ3a8rwuCRVrHLPJzLywuCunLOuzFMQFlo+yB9LEEO1q5GiAR14nLZEYx7qKbI8pdC yVwyEVqI0aBTSg3rCCPzNofRoiCjqgM5Gb+hQ6YYlHBRFMJJUE/RVHVYwoFkAMTD6Hpqt2RESrxi FJQZyPXJVqqvEIZUVfMCd6EJzMLGaMoTEICxHWC3HoJKmMbkHTykHbuIVI2wLVRaoC5oIFouY6v1 eBZHXtYVQPPUiA5L3J3cKAi70JrB+FrAFOI2BoK4TG5Fvhf0BcMKDdCCcqbBAEQHgzVLgNCWakPo r0rmRDCWGhHgdUaAJqOtbhu9i7eR9fR8kvtOOVwgL12y3hH5QtTF9vvspWoK0L6FsZ+87Wu/wf50 /fwn8AcIu/723/k7r3nNl3/+h7KPtBOwE7ATsBOwE7ATsBOwE7ATsBOwE7AT+L0TODao68ff99dY t8nqgpyAhOjU4wU+7ADF01BmwLbs2hJHGJ16XeWXmffB+x965KEnUbes8ZohT0J8EkVoqKBBOL9u vOXmV7/s5befPn3yBKa6FHoAFkHYMj88enj30HUfEFlT8DqtggIfogSyK/Y58nrjQyVvqeoOh51i 531pakwwl3kTOxCb09/IYABAPfxS3pVxPxrIgDKmu2FLDYyQKaXZm5MYqIcAByYVIOlS76GC5wl8 gi1g+qOXUAvC7AhygGcZlRAaIvRHyNCUtw684WLhKFndgEumSQQaWVPbJyOeSAdqJn9ADhmwwx0R 5CQRUw4wimMwkjsMXZnyCo7mBFEEIVLuFSQF4VWF8ZAYKcXPg2D6ADICp+B3QChtBdY8guJJOCsb wA1ux2WWwa0IlEJkxsHI/zJZ9fwt+RtVAEicwETYJ9nWg2XGsyajBM5iZFLewWqNuZKxiWT5iNqw H8KYuAe6ZVFwFYpOd33WANkxOetK2QfNMFJESCyQs+D8VOgUtwHZ6kjtwF5uq5bDzpnEwcEq564Y pgl4iytgHzGEQuJMTr8KGbGUko8ljZaBaGoD9QS/uEm4HG4UnIXGsEqkPZ5QRGC0LnqLvEIIRvA/ F8JSFbsfE+8VYymFj8FNGZa0hh1nBO1xCC5Kb70JEhSKfVHCK0YXEGBfOmRsgQk74tgy6FkUEGbP nNuYzXSQy8G2uGbdh/1NZJiqWkqNQk63LZZNrkttlb7WjytTaj7pEYVHRbJMSpepDRXKNM8zFyYV HnMDgDF76c6Ey5TkRSxa/M1f8xb7s/UZTeBPf/uf2r1y5f/7lJ/5l/9qOp0+o0PZB9sJ2AnYCdgJ 2AnYCdgJ2AnYCdgJ2AnYCfzuBI4N6vqRH32PWXT/qlxsxSRWCfSQsUVuEaY711HsdztIokHy6Yf2 P/Tbn1mvUcpgVqxQU8FCFFEEsJASqnPjcDicBEk6HANVkgnymCSN09EkTrKuG40+a17wA5WkuDGR 8gZwKJBdAfZ9UJcSt2BT8qsJfRjRjJyKYnJG2AXSMhRGHMwIuwTC9JeO1gfnS2dzdlOqLhRWvbjG cApQEVyrBb4Q5y4BD+Y7uh+bYkhTI42CUQjJ4CkgmMM1hscSzxlPI60JtgGdMh2Ibkr5n6cgrTUP ZVUk6JPVr0zxAMYhNZSx+4mt4N1j3XAM1G3kyqt6r+XpNCfmWQ5pYsWcFPSjrkCcgwGIzUNKtq4R LilJar5Wgj5aH9gN+qZVVS1W+P6USDWAItHzGAWIi9AFYdE8Wq0ZBeIsFGq8w1NUXyjZmwiaCRJz wXZox6ZJAl+KPQRZGA9hLsAaCeqMWQ/RWIdJkMVyV7A5SJnIVmO04mitjK5mqoKDW8pob2aop9ga wtuJ9apKAv5hYgwKqgnw4tN4OYkqI/WfwH6ulE/tL1mq4vCVMc9UXQc7IRlkdAKw+8r5AtSV1Boi 52M7tMWgLpytAFkeCTeksRBoOMvJ3fdRQCElRIWVleuj5QJ2qDtE7Y+0hqJT0y5wCbqxBzoj4fqI CvEfKjZOXkdptCB6OBAV/VVponyK68AVC8dVurzi3szNKY4prRx3KH+bm0piNAZg/K2mNsFFiqgH 9IldEgNKe8gR9EnRXY3zGgjT3c5zaJsMYs7CakFsEjZ2/re86evtD9ZnNIFf+qVf/Ht/9+/+vqfc fe+9P/hD//gZHcc+2E7ATsBOwE7ATsBOwE7ATsBOwE7ATsBO4PdO4Nigrh//MRkYDSsCCxAkZXK/ FYdF8yB9dEuvzsk6cmrHm254ZfgbH37s0u463Tp34sQNp05vb02SoG2vXLm6t7dbZdl8uchbVwWL BIsP3KogPGrFq3wgC1Y0Xs1/x194EcdXMLdBDyazWy2KAmauD2lRnJOnDwABQASwQi/1kiAHdiIz oBASpAAgIVuaT/J7L/hCdgTIUYegUYDpbTJ8sfAD/YnKLxf9kvprQOsfueO4xbQwJEL0A66yxUFW ohKCHCnBnca9gOBwDqSUfbRFNBGylK0ptsxAoVDMROSFjCroCc60ep0VXCm0hSo/+B7QAk7EJOBF IBOyt2A/aKAASXndcuo01PVCPtBeIU0iDn9R1qh6FFNFYr1JXidCnYNweP5elYjROkyXwBQoIGIw UBFLU9+j7+cKQi8DPKFKhmpQcsEOFduuXC2YDlIvIqSkGtPstcFcHw/v1jJO8hjNR7NVuSFj5MA8 pYDBKUTKE+qC8DArwtOgXdfIqDKmFJyGWKxkQ11vOiTjf3Awm7N9ADvlxUs8ZbYKo2UfJ4+mTJFn fp6tSfvi8Uj8WJeo38Al2G2YxNyMmAvhdLmS8AF8Du2RXJeS31tlupPMhgMSRonijAOiNyP5Cs4E 2MyK1Xx+BORDgpcOifkKdf8Cr8jgbwkTk6wL5sVn2FfgFWdn2uA9SBT3CA/T/WNAlOR9ECujIjTd iUpAk8CQk5lr4UAQT1iWuV2ZNsozyb9YP3zQRLTpDdYlSZiLJ7fHYEZ4KCFjD2av3eWOH0vYxY3H ClF9cZsMgm/86q+1P1uf0QSKPP9z3/kdv0/Y9e73vvetb/3GZ3Qc+2A7ATsBOwE7ATsBOwE7ATsB OwE7ATsBO4Fjibp+7MdUWGY68SQ94ZW8crpM4HoBMykzv6tDCERL1tUomzu/8cATWze/8A1v/vq7 77l9OtocoRfqahKLsN8BDnj9f7huHtmdLRfL9fxovpgF5EmV1XIF86pW6+yOGy9xOuCUsAVxWpxH KVjG1qWzq3MRdAEGqNUNKDUWhEDmSJxotPgBEIxKC4TCceAlEIbeAgmckCUNi5zYDmhDMpzbb3wN D0O5g7UNm6VBaCyznK1yo0tyKNdDZASr4ogm+cuBM8HL4EpZpYioCbbDJBWeQ+SjDsjm4Gg+z3LJ pqJgvcao10ygX3TzqSZSZEPh7m1FOjsTBRlCdciG59BJpNOoaBC9kkF1MtkVal9Usjv2UVNiyGGZ o3AVaIQ2PhMnRm8jL+B5OnyqHvhkg/kYK8lRE0Qa4JtDFgTZGQG3RI5qLRRFEE46IqzMoYjlWqxW k9CfDocE/fNJk4GmMH4gF9o2ufcaAuyrwoi2FDvGNlUVhzQh9O7meAhj218S3FaPZDxk+M7+IuPg CYI10uJ5bl2zn+OUCH2PDC+2Hsxk+J3KHxGZQc5ypbu7CfH/ccxqNVl8l3WNzROIhbKuoemxLFGl RRgq/WAyDgOnOFrO9ufz9aoIBddohSROPoBrarfdlvMqskzmTpErPKpCSQ6nhj0JsVGPSPRYz0gB hewLRI/PG9Zm7ihoJXlyLYnyNdI8pGTKZEPrhdkQrSI3oe4pNlJCNtgVHlIpDZUphhpR0j0Ip+F4 up1q9Hby4CrOrM/sMm9SfLGqHv2ZN/lv2TuhZVRjXsxj5BZV2hd6RyG9t77+TfZn6zOdwP9X2PWP fviH77rLBvw/00Hax9sJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2An8PxM4Nqqu973v+wUFeF1u0tbpvzOh WZKxYPJCBQNd8pBStUG1Gnz6oUuf/tz54faJrZtvS3dOjJLh2a2N7cno5Nb47NZoaxiOIr8NR40S 0jGoxbmbNIP4MK+CttqEGNTNz/7r/0FDUmyWcSiKrMkIBiFAfSWVi+yMwgPGtyj3F5/TmmAJVAea gG891xyl1xZJMgYIQ6IlF5iYmeQ60n+5t537Uh6H+AcU0rj0KjZQIlCa5DhEU0VK5uKKARvqbWwa MrnAECkEzqifFNJUNZlwj7czAkOodrDIchYgUkMvYV5iymN5OqmC/buxPHTANaEWnsWqlKWFTU9u PpRKpiYQKVYY4GGEAK3WZLoPxtQEIqdCRgXhIJJcOiOFV8E/QDnQNlRmR+s1kfbbGC8hX1mRKbB/ QLMkp2DVCqvShWOQrHgK+VNKOsOG6fnDKGSwQCsi8aFrWQ0J0vpRh9EjiShsf0WhYMVVg4NWZYXc LKVG0+mWOaZFJ6bFEOTIDrkDTIgMho2AxMnHZ2yj8CBshQyQ82K05MKxYZJ+ZVLaAZQEzwvegELZ Otx+YMp+vEGcwMjYC0yEfAY8B5tj/cvVipB+rgygRbYWYitEaXnJwgt5BdVlMCCjC4cpz2XruU2Z BvcU1Ag+xCe0GhRVBiaa9k6lxTE9Y2uVSk36LHZEHFH3vLmjmLwUbybnvpddXSvP1B4gA+P2ZeVG /2jaHfWusdsKWHELgyRpoZQPEcMkPZU0TjoDeCcPEbBVLpewnKCskYyJikn2Ze5iE7HPp3iXFUgg hiYPL+nAffNXfJX9yfpMJwAXfutb/l/Gz1/45V95pgexj7cTsBOwE7ATsBOwE7ATsBOwE7ATsBOw E/i9Ezg2qOvHf1wGRqOEQs0EdEB6IyObMrxJRXKaGA2SOv3icjn45d/8+BNPXQrCiFfjZVXxej6M +bpH3+J0NIqTGBiAzy0ZDcnrGo43JqPJxnCcJCmoALCjOryjXxdCUBY3KK0iC0oR4OrdA2yhZyEH nlMr28i4EIECJpQLAABaMDZAvcE6KN3rO/lIqwJ1Kf1IaV8SQoGKlBcutjBO7+QgWAihLVvTCVKs o8WKRj+RCYROyLoGzlAhWe6qqomRAv+QY4VSSxPAsmfSwkBUPBAgAn6Ad7Bq9Q8mvMWKUm/q2HMR ByEf4qODNbSIKXaIxVgN5kSejseSmaLvQm/FkgWcHILPJd5RXlTdEM0FrFllpdsNkIjFJoYfH+gC KlY3YRiOhzFxVFAW/Hzo41AzwXqG8khKW6QMdjZRK+4lSAomW1L4mOc0JoLVJNNTgLo3jgOgEQ9h BxWqNVCZozRcHhvK2joYmcAc6/RpG/SWa52NTWGaIDCEb4fLNX4+uiPRhZHNNgRpesSTAQF7l58e WXV1DLoRESNrnwAsMvCBNgNmxD62NBjS08jmdyTTq2pwEoW4Q5V0xr2GsgzyVuY8HjYGndJeB+Al RGw1/sy8IHNe6VockxEDzhDEyZ4p7iRs2qe2QbJ6o6UytgS2+tsEPRcZX9zqfRCcIaam9wBMJvmg IKxSt9BY8QXkZ0+HbSldziTBCe1x+3ChWpm5XSXUM/5E5Fl82+jmVZEm9ZoKmzeP0pflJ2WD+g4G HUebot4GoS7thbZB7koei1IMaojF1P36173e/my9jgn89E//1Pt/7Mf6J779He/483/hL17HQexT 7ATsBOwE7ATsBOwE7ATsBOwE7ATsBOwEfncCxwZ1feD9f92wJ2VbiSGp+E8YSK/8PchXYXr/An+Q VOvqQ/c/vLc/k8tsM3XJbRJbGGxsTm++4dzWdLouuofPX13u72dFPVsCeZTtXeYwnBqGhYoK1PXO tymrS+V/BEgRkOQORHwGBKXjgYRoCA+JQSi3nlf7ipCX5ow3/dUjrV7+IpalLkD57OhhNMnhYhfo swR6eoHYmZ37dHWtToQcipz12XrN6ahNXBCt1fO0PvbewUIorgZsoFdRgiwCvExPopRQSKUG9CTK kYd7DYxFopQXxTwer10vImPNRInDgzgj4AZ4woWvi+popZLEKaIwhWzRyah0qt3FOvIHCMOyvEai BfLaSCMQDu65PqXf9DDKxtgb3Ah7h8dg/cMEipZpc5hQuAhsEpAUcZGiCvi4qlt8fyyPMKxVLl8j e4RECxjDFnB+4sCa1QI0Bv0ixYqMfEK+xjHB7QP8hhRBQpEY3zBwYzLLAGmKaG9YDI2QmBCVikXi mJieLpm3onbIb0/Jw+rwUTbbowSallX5wWrFw2B8bLzUVK4PrRsmQdlWJZKb9fpoNiegjGpIjKE1 tY+9kZDGSO6YXNWRzAfyKaVWi70S1RbcSvfn71Z1Sh4luAaiku8VEAaJU9q7oaOs1dzS3COGM0lt Jk0Zd58hTRqy7l+kc2AquVm5VGkauXO4DY2O0DSTmgx5cTqRRn036HiiwQrdMqjLkCr8sX7IdfSc 1nBXhfvrBu2FYEi9hDaNqks0uSe5qgfVAQW5jLVVx1bOV0fhJHUCA+8tX/E6+4P1OiYwm83+5De/ rX/i3/47f+c1r/ny6ziIfYqdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AncDxQ10/8YH/xrzk1mt8ZSgZ R5ZCtBGX+C2JVdIcqZQvbSsfSFLlhBL5463Ejd3C60w2U+dWbbYq6ry5cuVwnQ2yvJkvKRYksnwA oUB2Q1h7CucI/Ne8dJMTGA2MOIVIjRRkghO97MVAN/EFEtslfiKvSI800i1TWgdtEYkwFAKLmKK7 SHxXNpJ6I0EczSBI4pio9TyvxuNznA44NQqTZDimAm+1ztsWBtSAxIQbCMmqanDSdhrlMjO2SJ7W 6M1kXyTcXfFhqKx2RmRPBSvES+1qtlji7ENVFYdSa6FFAi3BNg7QUCmrXkbCEE5jyJcyzEpy34l2 qpdZCUWcpjGXCJYCn/lpkpjkJpEQ5YhpDrLTGYUUEASxEnQFwVIah1tDRHOCX8usAA/BhmBYmBKl T2o78BzPLTrn7OaEL11Z5HQKQLBgJVj7eCKoCwxXEuw1n2GixE4IjDHFgiBI3QH4HBGOoadS0phB SoyaC5JEyfNUrsl71E4OCKdHKJevM7gfZ+cylXGv4C++JgKEW7ACXZK3xmxJmxpGMQlUZJAhYEKS VRTZCmKX5XEUj1HG9aoqc5NJfVWXOWuvChCQMuPVdYhFEYGTXKXX6jI7OQQ5HU7P/taVqVFiKX1g PgFOFVkUkBNaZZaKYOsfzPvmanSXs8WICnlMVuZ9QLwi8k23p2iXZH8CVBKlmaZF3Xbc2bp3pHXj Rma8iPAYgfCagud7jyNfAeIKdRnyxd7q1lbwnPyPosvaNpEwzJBK9QIwS4LGhSEHG9AS4CLp4hBv +LLX2h+s1zeB3xV2/cy//FfT6fT6DmKfZSdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2An0E/g2Ki6fvIn hLr6xj3lHBngReqRXoV7FALiXcTGCIMIsNyRf4XpL1fxXUaauXxmqHSqbrWqZ/Mio7mwHVQNjKlG nWTERghtcjKV0EBNNsaTcXrf3Vs9WTPeMcEESbRMLrs5tdFuGdENr/lhBOrb0zhFtshBMrwAJEAs l9yQTleoBs98sm3R1MRZ4e1s74wnU+Lei6zcGup0yywTZQh9WBaiId9FI4VVTulaEA2kQJAgDGma g4lyh5FAqfwg5jPwl1VZAGiSQAFY6KvoBMTVx9eFKbqW8kS1+gE5cBdiNjTVinA0LqRsnauzBRRr GHgHywxFEQFTLFZiK6cdh14yHRPctb9YcXEnJin86TBjsNL+ULmIimrMBnRdRjCWSv4Iy68APCcm owhJF5n6pE8pT02B+pwXPoWxkfpC8I0QWFNDv5BHLbI89aGR7VFRDeOwKZkaYWHhBv/6HsbMoxzX KaeD5VWotY6Way40xVRIuWQQDZOUveCwm2OlyIdu88T+0dXZLOralWhXyfkgO4HS/T0GxQ0DWYq9 Lq+Qb5XgG8AUqq7pMIZNzdZI6xQPj4yJf5jGumjohmTyXLiyqbhOORRRggHB1LvJcEg6Y8JGqafO StMpyaUprYx97O8A9Fuq7GRSumoFyUu3xZ2GZAqrprCpYVVGI6gbzIR4cTOx3WS0GbOsyeoi9t4j 6UxVAuaBcC5FerHbYpBGpoUt0RgYOR0r9sT5sBxKtoWHUa5aIJcEhkJd4mcclVuXqzaqLpMoJy2j 2Bo0jc/IiskFgrdAePJ5op7TUGCob3mdzeq6zv9ZuXLl8p/99m+/+957f/CH/vF1HsI+zU7ATsBO wE7ATsBOwE7ATsBOwE7ATsBO4OkJHBvU9eM/9j2GOUlhAm4ytiqQE+BA3EkaFLEvORijYEyQ12qN Kodk9WXVloAOBFyrdTVfFEsMgVUbhSm6qzRRq+FsPjdFihzESVL6Cn0Qxlu+5qXXGJcBXSIOnprt RCWEugS2eLxywH2PJfReMz5EStO6dOSZ+HLJffDZZYNBCWrIADWD2HVHjpNupZsnNzYPM5AKkqJ2 O1Y8PCQL5ZFLxph691DhiIMg3YJQABWgGRj3OGgaoHISX+EfKBLsAxYG5djEq+iRaaW2PgYEqUmQ hwkSNQoKk+4HjIahU3ZIaBRHhvDJ8+g4IwLlBewcsuRZ9Fo2uQ4QtioKBEzxcDKlb5FHm5ypkqZG pzMNjNArERDeSJYC2CDRwikJ/UCKRaTZMMGnyMNInUeU5EoERSiYpgQnkvLLOD27JAhhkRyfDeNQ AK+NNCYQC1XXkgytzhmhC5NtTzZJgA7yLZ6KVkunRJzVDjZHdGwGnJVoMOZGjyayJ4bPciiPRJu1 xm5YsgtumugB4BuIFfuRAxqVTM9pYUCsGnDH8/KCPDGnVcaWjKYiqsxfTKvnnEZyZaLGuH4kcr2A im5FHmDUUuaeMdnzAq7mVIonQ/HVtMrp0lPN06Tp4vbTdcikKumYCXfrZV88RA/WcyXSMhI2gTQe bYLpeRf4xjuyFJoHKP++J2t6Fms3TQ44Frkr1AHZDcIgQiWmLSR3THZRBIaeWgZ0OI4uwZvEYlqj MT8a9Zcy8GUv5Q5Sr4JZFLZNVF1aAjfVm1/7lfbn6nVP4B/+gx+4/Y473vrWb7zuI9gn2gnYCdgJ 2AnYCdgJ2AnYCdgJ2AnYCdgJ9BM4NqjrfT/+vQYN9OTJpLubVG/VDyKekqxFPYwggtCPET9luYQy aF6yIgc88XoeSFYAMMiAonXP5LTHEUV83mqZr8jDkuMQnQxWMkKasje/UVldFX14RqDVy7v0qt4A JrESMz6pvaR+MloeaXZMd52n6j1Ql/F6wR0qWiLJOiqboBmk1Biem2zlZfjk1QNQ1PZ4dGWen5oo OYsuv41RAphYFOiAwHcsFX+cSYVXWWGQBF7e1IfIt+oaJAP2mmDSGwCkcLNJRWTshKYPz/VXZc3V kRzP4g+WSzoTaUUEOfFsIBGyJs4YhENWiCQHDmWsiciaPJEylFl5hlKJIeJzDNIReAbug71xnuWc hg+J4YLByfsmXx5h7O3s6BDZ0ckNMrUiEtRQZg26Zp0XnBt4h4iMyC02yW1d0NNivWR0iLCG6VAy KE4kf5xkTRwWUR6DY23qGlAlJRZUCBr6LSVOAQRHCY2LDqFg8tkNOFfEKXLS5WVGpHux8X2WVPuK jy9mq+UygxOKfwFDOQoNh6y8rECiCy0jHnKTENoWhRQYIOSC6vV5WijhalZBJ6YRSiG4Q0ZVG1Bl iBNTl3XQ9CGSwqWgeCPvM20DAkK6WXpCKoEfDzdmQ9O3aELieVeyKCNP7Imp4FffmWjC6fq/jYVW MIoFGL2XdFxsGMekF1JuWYn8mJ7uYwAWDwPp8hA+r4wu1SD0tZDciRhwI8VsqSIBjVvI34rfkvpP hEyBXGpi1BVxHr4HOBOPMHeIpGEcViMUBeNYRsTYOV/35TZk6vr/Z+WJxx/nyTffcsv1H8I+007A TsBOwE7ATsBOwE7ATsBOwE7ATsBOoH8p/fDDjx6LUfzT938/6wQ/GDef8IGyuiQ9wUIl7RKsqW1y pSTVCs4ygel6+Y8qKIgSHptBeBoIiF7XK6C9JcEdyCMxEhoezw1pAOTISIvqstzYllPQZFIh+FKS kpHicETRnT6Q62ngRTo4ZjiJynqnIb4xRSw5CLTkbYQYoCfyw6hx09pJN1IOHT5xddFWxXK1xMGH a/Lem0+JXHjuusYl50xIh6LUThdp3JUNgVnIzghc5+TRNT0P/Iy8KCoIWbrn98SBh/mmng8l1zgm jd4hpt0Qi4b5wI/4EAlVxNI8b0RSWOCu8uJgtQalIBYDiah9UM5EiiMhLy2qMWgHljuRPoMaIVuS D7luZWAPZM1klquXEKyEn7NnOCidSItfrhHXETom36k0Qb6fRAoOg0bxeB6q8ZqkfKAYNY66FTmy pq2lcNY+kF+mwDbLKwye6t/0uVyfVkeTm6aceK4Z0slGDCaI8rpuiTGV+dY4IKXtWiwX2EjjgPuH pkXoJbnxwk3xaBOvI4Iy8v3DYLAqllwghY3cICaFrMdTxH8ph56rVWpYL6TqaZVqBxRo1e8TyjnY qFm3kBB6Pz7H7ot46UBSaYmIGVbaMyyTQq8b19gS5X41me/iZyaES4I5I2PU32YOikXrLY/8CyEz ztZ+NeJVPB3qyQ2pkgNhL74xKFvk/JAsqQ5N86QMjHxvGOEXN7RQl46hNRlaa3Rz0nKpflEkt39c LxMzpkejIONvQTTWjwDQ+brXfNmx+EliF2knYCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ/DcnsCxQV0f +CdCXTATUSe9bDewQGoXXu0HUCpoSlmTigX8ge3o9bjaDo34xvVjOADPBXWZV/cyA4IAEL+AYJDp oOCRgRHHnWRYJGXVeTcTCDTVh70wB2YgwCMtjF78Pw0s5O2SS43jIHMxDks52jCaSVBDYDf8gQBv /Hd+1lLtt1U38ZWD5Wq1ApcRfE4KOUDkJXfdyemQSQEZyFxXjnvTritUTKJqMAn+BorJWucEPDLS Qh1EYTygUMqTV5bKscL6h9TrYJVxTBKulnQNGpAk4oPzMfRRP6FbkhjJ9bAhYkHDegZlKUFDsugB UKSZYiZXliCqjgrIrTTuQsV/FSUiqiYrgUXNME3J1F/n5bVwdWmLKogJ8yYBHsrIBORqVFwZOisB MyEwH12bC/+qOhOXT/hUUSwWc7CaKMxgsIXjEVWV77GhGBQRpqG0Q2W1LrPZ+mCxXpioMkDUAF4H OGIvZDgkSKuquTRcmUjPRiG9hDy7WONe1DKaslg3yvgnl010CJUWyfvjdPiSe15G6eOju+uu9csO zdmsq2bL9ZHjFD3MNLRTu8lzmQb7aILchar4o2wsroiH1oqlB3UxZLkEJcJTKpp0YcZOKCx1Lb+t bzYU+zNOQyihDIzKL5M2EIepCJRuOKNcNHou8TbOTjoW90ff3qjbbeCiv+MBoC6dWjlyhpHJH/q7 yjJuFI4vyyHP4O7QbewQb681ig8bVReAUn7Gnm2Bx0xSm+LGuHtFx/rvJh0ZyZcBgFI48g3DG7vV Syu/+lWvfPZ+Vt7xt3/n2Tu4PfLxncDD//0rju/i7crtBOwE7ATsBOwE7ATsBOwE7ATsBJ6lCRwb 1PVTJpZe2ecmmbvPgJeYRSCBl+tSWpF9ri+ZnCIJYnrVjAxZId5AFDeHywykkMQRBMv3wnEagRDm hHjJDxiOUnK6PA5CJSLdjP3EDe/Q2Uw8d/8hwhkTnWQ+NFoZCaDEiaAoMqOBbsimr0FMkkCRkO4n RZNWHRxjdDArdg+XSH+QZBXF0uif6j/x2tdwNK6iF2ShpeICAF/KuFeC+QBBmuyF0vzw+IEiugg1 NxlhJ8bjaRIerHKSrSBMEq8R3WV8j6OYBC2XxHd0aaxZZkOTamZEcLLDIbNipsijeJ8kLDx+rBin JCvRNbJ03ydpqxlwyBb1lLx6A28UxSiIkPWwSKyUy3W2WOcCKvBEh6JDmdqMfMhZVO26rnliy4Kx NKYKjPI70skUwg7q4mLJshK6NJgGzyMYC8IFy5PwjhD6CBmYu8yXs8Xu/tE+uju2kicn6N7ATIKd im/jhFyDeNPA4Zgq4JTNEMCndC1Ak1HmYQs1vZ0OfCrMC57o5zkThhnGZZeNh5Ufgvy4ljzyVQdp gsKEdIymTcFZxJ09nbQleCTBlNEXGrWfGBR/zJ2i6erGwdqn/CzdLWZPjILNxNiz17I0cvt6rtbc QCdxi5q4N1N6YO5htkCMVfI3k1j/tNqrP4UEWhqTCZA34fEdq+VG7LPrQVlMhLWBuJREJhzGMxg7 AWeI4wTrXJ8bLZDW0ZAubjcQqHohjZvVoDOdyNwv+mM0cejg1MaovHvBTam83viq+56lH1Ic1qKu Z2+2x/rIFnUd6+2zi7cTsBOwE3jOT8D+AvOc3+Lru0D7C8z1zc0+y07gGU3g2KCuXtXFG4Sg50p6 fa+47N5gZYyCnSkNVP6RrHaCKUaGI10Nep4aYyJmNMXPG00WCi8gTK+fGaSxcrt4Da+D8wR/3eMt IQPjOONDmfhkJMOx1cMvYQelRfXaMZPnbfgXbjEQhjQ+CLA6D99inK2cog73ZtVqTdJWg5opy1dY 3CL5BQdv/2qhLrKvIB6zFeKpFr4jZRYVgWEIcEHwxTkOSJLHqEedoifAsUHmu+se5c0SwVVVJYEf GXhk/GlhH+Y0jgNgElIsiBWetZ1heJSX67IkXZyrhuasqDUceNMkmaSAvjaOQ55MjeWyrGFmBGAt siokFh9nJ47ACvWX8qsKJX0hK1PIPWlcuOqmcTCM491lhqpLyeVyFLajNBJPbNwSSVvgI1OrlZBf DBqJk/KqKbA8BnESxjAUGBKG0pXBfIp4l/kO+IWqyz3EgVgtrhwcFGXBeaFd7GwAooH8odfiKR6E q4I7GhWV7JDsNsNkMxRY5alskRuEawCg+YOwaWA1dBKMF0tA28hx03Tkj0ZM8iDLDp1qPkmEL3tn IqZEqJ7JaScrPzT4SsI6dlueWG45ovQjCi5b3WDmjpBaS6iLXDAhLRZqdHgmwF/h8UZgyAab4HeT omVqE40l0Dg64bWS2RkrITWLfTGjyYbDs0n+mm71300EM0dV1pgJ/JIyS4WYfSuo4sNkNgSBGUuv qWIUx3QGTNBYMmFWsvoaP6Pgl0pNgWOgSNysT6MuNTka7qXvBG2xjqhzKeFLSO6N9730Gf3oeUYP tr8pPqNxPX8ebH9TfP7stb1SOwE7ATuB4zgB+wvMcdy1L8Ka7S8wX4Qh21PYCRwb1PX+932feYkt rRCKGOOvUjqSUBQmQIQ6CJTQW5FPxIt00zmnmG3RKjkM5TIzShnjMyO4Si/moTbwCmPNEyWBAUgu prSqqGr3+puD5yvcydjxrgWRo3Yx/jWtx/Q/GlNYryAzyhe187HCEuxALrzjDtdVdLBf7M+b3YM1 uUxwk6LKOZsK/mrxiD/1da/lCLgEoTOsEKy1LspFlsNLOPi8EN8BFdGSiEAN/ADbgl0lUrMpIBzW A0hJBb4IusIIGSbJEMQCO0PrtiwLzyFdvqV8Ugo0rtDEmYuG1EXJGl0coCHrqhwIEGBFwijhDGGX js5IaBPkqCoJB6s5JlwEUkVyO9zIwLKcv7E61hgUPW8zjTIUYtjlfPLRBQGrAlDm43CMQuRXzbrN 6zLLKEWs6jhKt7ZORmTTBx7gbHe+5GjQMD4cb2xzQUUDmKuOlsu2WlGoKD+h9FAtl482bZREcMdl Rthaic+PnUa5ZHAljlRUWxKymfgrLYYLYMdMKyGSu5AN75ogzxj7JE1OlIFT+kUSFrP51aBbRZ5Q lEKyuOdCniRhFuoxZcAZ6RMMStFu5O4X1A7g2VSQmylOFEbCIMvxTWAWazBaLPPG10jmN3otU6Bo 1FLXEJhpwOyz5xU9VquMkrta/MrANR3XQEBjiNRBtWvIBrkD5ZdUOJfpJ5BQy2A+5HzsTCMbq06J kRYzKKuWis5UL2JU9FoF0BljI+c1AfWmooH/ghNDhXYZQNdLupTYZW57VUaawgQd11zC19z3smfv 56n9TfHZm+2xPrL9TfFYb59dvJ2AnYCdwHN+AvYXmOf8Fl/fBdpfYK5vbvZZdgLPaALHBnW978eF unDi8ZKeukAjWlFgPFyG4HHogSAOMhMTSMS7vI7n8ZAvev2k7JK1S2hM1ABVVOAT3QQhIokefYqR w3SQKnQ+KJjgALP8qsCEIpFMyhL5Rnql33MG/jYSGLnhlHTegzAoHEYwBDVNjT4JG+OqcTKwVtOl RRU9fim/egiI8IqyzLMFXAaa0FQOz06i5Lve/iZd3WpxND/iRF4YsXKACZ7KURKSiwUhIZWdN/oM SW8ncAsHXxwnnLSkUbGErCnBDGUT0GcUQsTSxSqbzRcmBkur58riMNkeJ6iuiMlXhj6eOQOzQFdA E4ZEYNMwZmCyNDLqEFdj1e3NV5AloRTfQzgmEOIMqqKYg58GLkFWStdSXD/7EtRtDZkiL4zZAnpC 0t7DGG8hAjGuS4FZGmdeZCsi+YlVmwyHW9PNJkiJThvHHmva3d8/OJz5TjPZ2eQ4XDIkiwrF8TBM 40DitA44Bc9qwH2gHKPKQySlrVR6vYl9N/lWvWZK1BP4KZumVFRokzpkfk4FJYyakqS0dOJP1ofl rCjSUxunzmxc3n08dnYHQcPE4FFOUQzCFCIp2IURE1MkBkGu1lhLET7132+SZTkl59I7+CqvESvu Se5GvIclN2DghsbNKWSqgC7uK3NTGScjm6HlKZssCtHOGYWVK+Oocr0UpSXApqdSIqm7sOF6dHXQ RHkte5kerYsNSMvjk7qDQt9Z5tWa7krEc8SbEU8n26goFzeq7hlzMQAujgg141BG9gX4wjcphZfi 7vUwLVpY2QgX+VBHNAZIk03XQrze8KpnMTXJ/qb4jH6sP38ebH9TfP7stb1SOwE7ATuB4zgB+wvM cdy1L8Ka7S8wX4Qh21PYCUiOcSzelBBkXtnz0ps89axs8xJwgg5FmUEScnloiDwy3Z92EZLjjt9O YeRVQ2g71sUa5QqvzcEUQBYwlixomM5QbaFg8vwogtUon0hsywhpFN4OwFJWkzKqjElR3YB9BZ6a GdWLKEcXqOLpikKHU1KkmNUNOVa8D6a4urfcP6AVkOD8KsvXq9VS/y6WLAvAsVyhSsIFuN5bzBdF eQTnwFAJliDCqaz2V/kjV/Yf2z06v3f45O7homQVAVIsBE4ZlKsuKDpUplXoThJ/Gvuh61A/6Dnd OPQik3O/PUo3hwlOxkg+vFqcSZowE8YlJ5rYn0RkRZGX1TpvFhl8BLFSsbeYXTi6cpjtMyyueRs3 IuPtnGVZHeY4CQeknpEjz1RkmUQp5mA/REOEaosEeOm5YCJ5hf+yHIaDNMSq11GDmAQuX6UGUhvR 1BmhZSXVkatVsWbtUQwgw7nXzdfLfSLr83XVlkAnUEzdEeTFkutlQeR8sciyGdKwEu1bk0SgH9AX 2A/wx14D8XR25cQrRl43AfeK/IAmvi2A5NAZ4PnYNiFiT+1e5X5AFsb+BkilPL8cuIAr1HTo5bh8 BV+ZBC0xPd5w/+FJpCWAMWqSUmtdI6FGA9VLDpmBMbH2IkB9Dq7KlvBHiNakpBXUYuJmNLHw/sDf GcUnU8WTcXOyZYA5tkayMTRfpNfTvYD/s6Lq8drByeCClZmwOIndjDnXtCxI2yYxF/lfO5PhiY3R 1jgdpUSfyavI57lJsrKRHk/HFGjj6dfUYkal1Wfyq7KA9DqpDcW5dI3yMPa+Rk3SpOHju7wmWzsW P0zsIu0E7ATsBOwE7ATsBOwE7ATsBOwE7ATsBJ7DE/De8573HovL++hHfxGCYBKL9HobnQn2Kl7G IxuCPklwIxAF3VClnaRHKskTngD6ALz6ekREQ7xuh8HQXGeyttWEpyh2I9TiJTvvoRPiASSSq7mR I5kCOxMlrlQlzq38e8M2TICXSb+XZdFgOB1Ba3AHoJKChYXeGOnSw0+sZ/t507iLxWq9WhdZocx6 iWhcMEORZW945QtRMMWhvzkenjmxNRmP4Fda6mDAJ7fgXq4LGjo5jpOEYClvxJUHdEEqvAm7IlAM unG0Xi2zrBcUIa1CBIbnEVwC20NChippmVFEqDkYF6fWj8KHjC1DR6rY1CYeLVGWHTZ1EWPtHAym cTyNE5x5UBe43P46m2c5z0R5xB9McISeozzKG1RSmmeijHdNi3/5OoFT4EZy7VU32Rbongzkgak0 eBsjChqxVmKcxH5Yr1fFancxz4oVU+GRRYOrtMI/yvtmroyKlklWTmAYgKgkaoyuSEKrhMCATuA1 FzemIKUCtJTaJvUV+5vlBNUrbkt6P7AQaJIny18a1I4HA5zTvshmBGE69JtuVXWzyikcoqi0vApG KjWVyYaTfs0k6JtdVhB+n+SmG4yEN4OCFLRlXIvX7qq+x1O+P9SAqjzUE1FCmYh90wHAEfUcbi8w 1tV5ts7AuW1WVjwp8rkKBdNz+0PtSE8zDzeWSGOqFGMVkmKMtbLKVKMJb4WPtaYjUn2LXK/S05h7 g4ZRvaUmYk7pX0adqIQvU/mohWiCnA/Sa6yURobWWy0l95LdUX/4TtOy+Lzuw25w+9nTz95Pkh/6 T5eevYPbIx/fCbznq84e38XbldsJ2AnYCdgJPOcnYH+Bec5v8fVdoP0F5vrmZp9lJ/CMJnBsVF0C WARfqRlO4UGCGKIaNM0pLhspk68Yb3XvVVVJTnpGjWJdSbBEKLoRqUh4o8I7cAMoRGocWBJ/96V4 xvJmGgYljIEmSbwF0eKPHFxiY+jFhBhYAV45PhGHXhz5SQQv6jsfQWucQcWLocefvnUv3J81+7vr ddYuCaI6mper3CGbnYR2eh5XGaFVgAdkUPxZle2idlatv6xwh0Wb4/GJ6YgcqWXVzBFqBSRwJeMk ODGOp2k0iiKoBlqisqxn6wx+BjCaF/lRlrEe5ECMabZeX0Ey1pAm5h6tmAqsr92dI+qqTo7SjSRm AFdmi6uzIyRU0pRlKwaR4iUk1b5pUW/tLkmlz+jrQ0i1LNBPOee2xjdtj09M4uko3hwnZzbS0xsJ grKS3Pg8O0KzVoG0sDYSpwVl4iqLGRq2CnVWvuCrZS5zZFuRIAaigsI19TrP5/Pl0XI9H3R8FcXW ei3JEbxulSHhqqGDy6xc0zlZdej06A/ERShd1DonBR+WVmUVGizETSYz3cipQIHawU6hVMPEBw5S G4mwDH7HjiF+cwFtiVMGbQYvS/2SnC/uk7wiv8rtgsgH7gVUF6C1AmRyl/CJEEEaGJUbwrgOkeT1 NknxJYgqeIxtFUuSRMuEcCnqC5EZYWt1zYqRU4mZKv5Lq0JixYmUPI+5dDhKhqMoTfztSXxmezyK A7oASI+DQ4ILYYawTUOVYKnCbRwZXoYgDNUXtZt5QRKZ0vdlgPVIdqOkkqJJGjk1L6FgPKwMxVRF ElCPFxVlmwSK8t6a+gWNzagWzbcYD5WJEeLpYuwV9urvf0XgCePpRMxYQfeSWh6bnyTP6OejfbCd gJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AncCxm8Axe4FqYumBTShjpCzhb16N8zpcee2KCFf+FN19/qBJ gkHoESEuY91Qti1euctrJUgAP0DWY8K1gVOokUy8ExIvIs6FMZDZGP8WFXMuGhlsfWrZ83jBj6SF V/7CKX0yOMvhECrWkyLHMVY25cr31MAABufK7sHi4AglUramQJESQzgPZzbmSknMjPdLXZBcS7AZ R5PQT0hV8gPCwYumwzsGX5gmMYu5ui4vHx1dnS+uHM0PZgtORwYVqe8AJaLch6F3djq8bWeCDogL gpRMh9FULY3StWHXQxBEgWWqXj33YFkc5rXwWF2hvYp8Oh9bMrBGmOcGXl61BTiwreRKBKa1hL6j 2+I628M8u7pa8fd+tjo/W1xeYXmsQl/SMd8lzp5aRZyaIKiM5+KvrOpimriTeJAELAo4tZ7B/LIV oMbQsfU8m9ftmssgQIsHsH0orlChgQs7h2ZJQu9hZ7IuslQcimpElGrM6PiMEs0I+kRoVH1Jq4Ck fkqkulYayKdEJwOgpGnkRLgnJlpLNYZXVClXWAbBn3Hbrq7uVgeLceuPa29UuQnMMSfRH8xFwSKR Zx73DXIp9kTpcJxLLEi3lWkrFI3VSiS5MvhHQj9uOxYTdx35ayF/igpTJycmp99d5uBIOgq4VD8r u1Xp5C0ndJfAxRqhGgRqQHEAdkVJ1wr2kWWCRoVlicziegnkR4eoqyVXi89z9zE2Y6ot8DoCgs16 JObimNx4RmKnLDZMmVJrcYdLscUqgW4hk1LDo943Yi6HzxgbrKCY8sLMTzjl6vdvbBjfdsq5s292 AnYCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2An8sJnB8XqKKB4E2ekkNL7aJIxKmAW6RyYW3jtf8EAHj KZRBT6BKci15voxshRf1pqYO0tGHGBHI1QfM67W95Ct63Y6Rj4wtGAHuNoNTeJFPGjo0BKgCyyJ2 ikAo5Z0bQNVbCKV0Ee4yljIwmNGC8RhilOrKOTiYmVAvDIxVGEVSxxgM1j+Fs/PgzXHKHyBakWfz xXydLbOqQIaVoaXK8tVq3dZlNOhQJKmccZ3vzxeHiwWqMLDRk1evzOaz1TqbLYvdeXZhttpbrQw/ 60j+mi2X+3x5No8H7dB3M06A8ks+RDRT2AndrdTfHvr4OJFNYYG8PNsru+UoacfJAK0WyI2hAkmK FjkW5A1LHcyrI3hrKwVgOWoqxI2nEHQ4yrqu8UhmmB1HMZdHIPoaQdbBar67PDpEt7WaLfh7Oa9L XJBE1Rc1QKbNshLR1qqoMq51scRnuZIyD8JYUzkAZFRVJbNm12CJ6kE0BZoMczxMCQtL4mQ0TMMg Ia5N4fheQiA+4+cPHEr3jbGYVh19jqSDmdR65HBNlVRNWnTBujvhpXcMN8fLav7EBWd/uVUlaZaE 8yBaYkCNurzNVjVGUEyV3Fax66egNBfNlO4fkJPprITEQdBE3nrExjsyDHaALhLkRq43HHgjN5i4 3jgIJmEwqZ20bBPPHxdVOFu6q8zfm3V7C2dRYAtNKNIsWggb6q5J5I18vKFdBLxFNYg6T3JArmTA BkVDHKbo1cj+b3RGVFZgslyokrwwoJgD4QKH5lldFqwQ1AXw7GB2PY9DyCXvpURx9BJwBNCVEW8p q0vfBM3Aq6VQE9Jlj7FJ6gY3MjDRUBCaqiEFd+2bnYCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AncAf +QSOTQPj//qj72FYysoyLYi9poSPTPBRb04UcpK2ivSmaxlG+o8MWso1EoDg6+q568O2amLpXcQw 4ARwF6//xb6MgQvzXzvItTfGm2bsaa08XZAz2dbaXiFmosaRXumBKL/6SO/WCFyMXKjy3WT/yP/V //xQtiKIHRRC/JOHIVJpSUqLEn2Ts7Kp/8a7/xTPWq1WiL2gOOlwOEHG1TmiXXku+ZCrtPuK+DAT tcW5IBlofVib2zWxJG5Y8uROY41ZVY3TIaZORFYMBoYEkkABJ5UUeAvfme/hnkMex/GbJkdRxcOg fdAigtex4CFP8n0WRlXjyumKIBwb06gbE/wE2TI4B7xocpoYsoK0CmhYhfcQVNhinYMeSjeExKgl KIzUK3nxFKOvDCklXtUQFUVCYRE0iehSMGmHTMoWKfhMEwAJjdEmIkoy0ArXHuxSX9CFSrokiMkT +QRCJ7UPGjMpNlJRT3ntDPfUbcPlw8s4MquVoy9oB1EzjNrJRjdZ7eZXrh6AhG4+deLUqelNO2MO XXTtwqkuZavDdhesCNHicjmI7IlG08VtwSR5pCkGle2V+4NbQ2s1DIkbCtTV1b5Mhy73HsFhlIe6 a+BekdMC4Pjhq+69g3yyixevwEM3T22zwsVyRckAWqrRcKjtBC6tV3wV3Rju3A4doaaiJDmdExSF hdXc3V4NknJrN+JC2BFEj9QrNqp7DInt4vYOAym0MJf2cXcqcpBOTYbEp8PFNHdVL/KNIaWXssUw Ppp90H2uXDs0XPqK0un4nKF62oc3vPxlz96PM1tg9OzN9lgf2RYYHevts4u3E7ATsBN4zk/A/gLz nN/i67tA+wvM9c3NPstO4BlN4NjE0n/i479k2vP0UryvYuzb4AAafKiX8yYPXDKVgJfzuNjUyWjC tqVvMg+T0EbEBNMWci3St0jtkpILYgSRkVDLWM96W2LRx84b3gUuAC8o5N4wAeMJM18w9kMcjiWH Ulq4ZFriYxwcYhUM4stXisce2wVvIa9BWyNToMLFem+jwXVGEfPSu2+jN7Bo253pZGdjKsYBqKCJ EOceWqGqXBYZoGWo/jyl7YOclGHPA8dD4p7Q3QCrdKG0GRYZrET+TJaLAS0kPz4m6P709sbGhAwu CA8rC/A1grbIulrlq5YE/KaAzWCnLCtMi904DYcRo6sJAUNQxoBXCMxU+wdDqlY5xYe0RuZ5mREV hRYtyxGfLQEirE55+wOAYIlSjGsEyIjOqWJQGiCitUT4mI8JoqJiElGejIXGWiiE5UHzxG1MrpRm 3ePKUJ+GGCnV3RRgqhBR0jmTHWWUe4i/RGmEyozUSOTSbJvRdcGADAJrKnLdEjdKCm/UDc9EG7tP 7F15+MIN8dYtOyc3J5txEg9K8tKc2E2Gw/FmOJpOabMM1hQISO+kRfJHFBQ9YEi0l5HotRAu447F BeuLeqKGAioulu2Tj+098sSlx85feejRi088eunhRy489PCjFy9cnR8s0+HWi+950UOXDz9x/8cv Xdp9wYtedGVv/uCnPnv5YO7Fk+0TN0Z+6vrjwAtH0dANpi78irco8RzcmBEs0w9HTY5mDfdk1IFS B9RHUgwQhISZuWmbabc9P4GpwQ8RBfLd0Qx8Ntr4Fk0OPehK97/gq77BjNRQ32LcJgrhFz1mZ3mT mJEHk81vuhc1fPZJR+CLg9vO2Fj6Z/Sz1z74D2ECNtX1D2GI9hB2AnYCdgJ2As/aBGws/bM22uN9 YPsLzPHeP7v6YzKBY6Pq+vH3/S29njbVdVAMw7z02htWRVAXEitTMAeT6mOzzMt4dcyZnHJwkV6m 8/ocAkYvoEQpynmCGsGGkHeZzjxet8NMhKI6N2/3+9MZN6REWOAwkI0YhxGymOAvc3Cj/OrNXOIg 0l2BZEh0GnhN+pnPzT9y/yNNHTQeUhukRgRF9YdQKaGYEAfvuv/xe9/Fk8BYK6RfRDSZgr0oCjnk 0XwJneFxJFBFYUqL5KrIyjKDHBGKT5w5XrbFuhgnePcG+/MlB5wm0XA8VnaYiJr0QQALlsz5WG84 EBYEJDHJ1fIQ1FU1axK1WH0YJqS3E22m4kKT1QVmM9yInC2RvWuKOnk08XpqrFwsCWBmRK2ufSCP m2GP2hw+5NRG9yXqaJ6GFgwBGqBEdQEanv5In0dEFI9XsD9cDKQidiTXKF83kVwmG8pQK+MB7T8j 4CWzqvyn2nUlrMuiKB8jq+2LDglWp4kz58RkZSFtc7ywDoeZe/4zlz55fr9cVzfHk1fv3LaZJntt Hm0Nx2E8mBdk2DepX7OszeF4GpVDZxWWsxKv6BHrhQgq3g0joG4sOKG4a6Vs+MYJlbHl1GG57H77 dx6ez4o5gWOB0ugjU7uJYipN0uHm5ite8YqXvfDO//zAg+c//YnpcPTKL3vVf/zgR/YvX7z1rru/ 7mve6Mzmq+UajrmxueUQvo+OjzOuFxXs1XU++8j5hz/7ubvuvRu+uXv58ukz29Ot0zBa3yvIFRNV DdIWW69v3LguHZO0K0RsPGpGzZFeSXdAucHIQ7/HTru1H+GHFKuVZsvQYvK/wGhm2obPatpMODBQ sh54UcgFYXHVl9740ruevR969v8UffZme6yPbP9P0WO9fXbxdgJ2AnYCz/kJ2F9gnvNbfH0XaH+B ub652WfZCTyjCRwbVdeHfus/GLrSVOiASL5CNURoFnFPxIsT0kVuF948PlOUfIHsLlRHZVHwhy+K yZi8eMWXK0J7EBpawlcp/zNP4zGCWb0aiIMPvMIY5AzKEWcRyjFeSKPpMj2NfeMjseCiYNdyunRc FT1CSThXGz/51OzCxQPUNO2AWHPBH4gWOEfJ+IbUSRs06F5+3z1Z3VCkKPZW5Afzo7bKBm25JMk+ XyO6OZwvCbVK/GC1Oto9PFiskVCBF0ARQSxAhFBJ+G2SRienI85OlHlE+Dwh+l4XB/Amacd4fxg4 hNYrvpwGyIZRZmSU4yKE2yCH8hHJtTWdiYtsUVRrxgFFAm6gVELEY3yEJMcT0o/CS5lc5G3haTPJ 5rJ5QrRI2oewcfXo2iiphLHIdskFd7JbchDwnPEYymlo7lTNFKzSS/ZEJMXJmDmQzAxJWWg8GIbD +nmk6gWNTk+485q+yyTTm9pDrVechgpKVQRKc+QjAxRO5CrcmCB5KI1LZFq63is+9tHPPXFlf14W h4v5ZhidzEkOW60GdRrFBYxptsgRruEDJdhsltf5IA3Gk2joSKyG9y8YkZwPhpQqULH5/W0JWOWE unGaaHFUXb54WGfKfC87RFdw2ah2/brzajfYOHPu1S++92CecWfiTk2mW+1ks87zIEq/+k1vDsLo 4599dCPw7n/o0cnpm1586y0f+8zDn3vyfDcIntg9oldyMc8OVtnLXvmq8+cvPX7h0s033HzLTbe7 k500DQdREm+dcpOdZGs7mijQLN5IR6PpKOX90TRKR46/NUi2g9F2kE49n2UHURAPQ4X/MzFUa9fk XRLGPU0YjR/UUGQAWIqV1cAvgU1YZNfeeubkM/rR84webP9P0Wc0rufPg+3/Kfr82Wt7pXYCdgJ2 AsdxAvYXmOO4a1+ENdtfYL4IQ7ansBM4Nqqud//Vb2G3jL1K8iLj/JMcSNIdFFJSFUlDZFKfTI2i 8TYalGJClJT5RO4Qr+F9wRSZDVGo1KATVTHWcBPpnuArIBhe0N/94lP9zWE4EoxHcWDAExGTAWFH YiscB8yBjogGQ5gXOIczgVfw3nEkEtf9bvJbH7n4qQee8AdxHUOBcBm6XU3VIRdivJV9GljXfs9f /jbOFbD8Gki3vnRwCLhj6WkUKR6r7a4eztE9ndk+AZkiPQn2xHoimfkC+gjJZMetV7awKvgOpX55 gORogAKsAy2lMVlRCiPHPUiEGKBIcVLypCnSXx2MOPOQ9TTwKWMg1F/Kn5J+To8VdII0sXqj6kFy pfh+8J/pGiQuTaqxssZrWUj59XRIfx9tDg9RnLw0V4JNfA6caJjh06TL2BWhkBqgsYuaPZQ0LEaw Z4gjSzGeOZOQhXzPBK7xhw+16czSo/1RMV7G88jF+RBIRkvvpMx5MnfCG9GfObHv5kTL19Hsqdkv /8ffnreD1cAJ8/JVm6ffNL6hGVSH4+js2XPr+QL0OEribHPsFxUG1CCJK4DRySnVjqtmdTU/7Aim D9mnhq3iAhkkeWzeoPEiorsCpx4fXFz/9oc/e/XyeufcmaPscAklRFg1GRHHz+1y46233HbrrV7Z gJqu7u9RdnDv3S/YvXTR9cNvevObLh7Mq/X6bBr/509+4jVf8bqX3X7TT//sL+zvXjx5222PXd69 5+zOZx89f+LUiTd+1Wt+8n0/VfrBqdNnlmXzpV9y301xeWW2uFr4fh2cPDFyunU7O9w8uVMtS2L5 Y7oec5oC5uWSoLb1uqlnVb5X5SdvvWHrzMlliwJODl/AowzA7K7599r3ggR6kqVF5rMKtpfOi3uL LW5e9+pXPHs/T+3/KfrszfZYH9n+n6LHevvs4u0E7ATsBJ7zE7C/wDznt/j6LtD+AnN9c7PPshN4 RhM4Nqquf/fv/k+TFCRVll5dG2+ciaEHdYic9AHmJnHLI7nLiHhAMFjqCIGCfxiTotQnKJpUQUdk E++j9yrgPK2L8wzjIMSsVmy6e+7sWIjHiL9gJSaiSLqjnrIZ5+K1YC9RNTrs+sgwHiOaJlhQVBwt fPzJ/f2DlYKc0DpBccikvxZkjxhKiE5p+G33yhffBggLnXq2XB3MFqR6RZGfxEFKNpfnko0Fsbrh BNlcVP8RzhWP03R7lKCZ2p0fkZ4eEzIfukFAkBXmsxZHGnojFFi8jwKravOqIQ8+bzritsgwF7YC SxU1JYfkjAsUgpd6TsSVAbkAMaTvm7pD8vhFthQvzyCwzpkQchZtFG4mS0s4CWOpDmTMhZpCX0Go RC6S0U3lH5osPuRhpq2vj0GDQQpICqoJSorISZ+F5kxh9tpgPjQCr2vZaIYOShHGkCUy0gH4us9g pWDr/xg8J80bw5BtU1WDoaRjRsxndipq3afO73/uwqWZ7y/Y0ShuV+u7djZHrLYCsWEdbVH7hdCz gGQrNyStv8XAGOQEnFXdKEg300lVd0tUg6jeTM0jiwy1WLBXyYZXTrA7W+/P1ps7Z6YntteDAlnV xnSjWqzRrN10+szLXvhC2gO2JhOvqc8/8eRtZ27w17OHHnlkMpzUWX71/HmaQD/ywKcOdi9PNzef uHDxgU9/6ty5c/fcc/dtp8/Orx5c3D967X0vfXz3aHVl75te/+XoGa9evHTXrTc8ef7iY7uzW6aT 3/nEg2en8Wz34OMf/mjxyJNXPvqpg088uPuJz+5+6nNPffLBC5976OrF81fOPznfP9ibzW88c+N0 vNn4kYRz4EsVNhhirDSvaxWmhuRq7IzCtCqQeu8XnZfVfAv6t52zqq5n9LPXPvgPYQL2/xT9Qxii PYSdgJ2AnYCdwLM2AavqetZGe7wPbH+BOd77Z1d/TCbQS2uOwZvvkZkUEruEm413PN7n9biCsdRP aBhH6GNM8xIi159+MEgh9NzQD4ckVnm8Tq+BErHrJchr+Nv3owFOsCB2/SEBRG486aKUV+1Y+pAT 8SdRyBNCKygO6VuotxRGpTh7iAY58yh56MNrPGnCXIx5ERngWdVlKj5UCx/wCIik7HvIAFKmBgMd ZXjApMJDCVOXMrshYaLsLyOAa/Xpxwkr31OalTsgNGtrY7xFTPrG6Kaz2zee2Y6HUYV+bID1zfVB M+GQ6zq9s3N2ZxJJ+MUqi6xazbP54Xx/f3FY1vT8LYj2alv6+xZFPW8bIEsZungMUWYh4yp9jz+s TB5MPJxyclZkhZn4/AZ/Y5mROF+usKkBoAibClxi4DESOsR/oSijflAlfwBDEuip/BMaEckSqzJv xjnKYwYEiUHH8IdKc2W4lcnk0pmkHpOXky/q9KTzk9Cv0xt/qGAc1E44DA2duiuN45NDCGiqdRHE aaLiU5+NZy/DEIMdgi9sqoT3B96Q3kH5HuUqNAokpt5Uq2I2WxoESHBXkjfOfl49sprVYTdEwlYU lEhGEcn8br3Il1VxFcHaZHrj7ffedPu9Gzs3HjrDgzo6sXnu7MbpxCdAzQ06ZcaRUg8Aqp1B3iH1 qtcNQfFtELlVk21O0u04Co5m4/nqru3tb7jvvpsm09u3dv7061756le97M9/x7v+yrd948kzp7/0 K17/7rd/C/dWOhq+6q7b83x55twNt91263zv6sXdy9LClR1cdn716p3bk8NLlx/8tV9bzY4evzJ7 4pGHMEx+/Hc+9alPfmZUtcvD9eBolu0dzi/tT9fF7mcfnj/+VPbUXnFpf8E9ls1jtw35VsJaK4rL dsZkexFvT1tBIECsjkcsv4BS7oAsr/hbNQSZHMHcLygE16Uzr70uGJ4+cWJr58wx+CFil2gnYCdg J2AnYCdgJ2AnYCdgJ2AnYCdgJ/A8mMCxUXX9h5//v9CRmM49KXuUcC7tkcLgTU69pDrId6S66oOj +sh6GeIGMuehpUrGkqeUhTfeUNxQW8jySOyQ03hlcPrsWQoQO6VlBRundjZGhvYoHYzUJVNVZ/RE fIjXDoTGvXEtpd0VEZDGCzWZuBs8i+QsKIzvd/4T5/fmRzN5J6VkYildyN9VSY6V4pBcV5WMONfu uRVx0MZkcvbkie2tjTMntk5sjokFXwOeYEwxOC52o7jDu8hqSVivK0RPw1i54es6W5brjOitJu/o UqxzRFsKZ4cjIS/q0HZpYH2keJ/r3pBwpoAziJIgV5/3LreiBFkyJcqbBkwyuE5Dkz7LZNJL9YUv kkXDvLjk3jDK5w0DlHrOOAi1QfIMmg0wMf4m0J6DK8yfT3M0o9jqxXFyTHJodWQaAZ6JipKezSyj b6qEKkp2ZyyiGDMVj95XM6oBEJRGzhgrkBDPnF48zAvAbLAwOhl9f0jiOp8GylUJ8WXr7vwTF3Zn BTomsGeoNPk69btzk01IKoRrEo/yqvKKMvPDo8QbnT176vZbss2tOU0A0810OqXCcVEVqO24V2ar VbVasGzJ4Py2iYp20ARdsDrMFpeORqs6nM1H5XpU5tuOuxmEo67J93frw8OR066zPA6TremkGLQ3 7my84s47TozjzY3Jl77knsn2zomzN33Ll993emuylxVf8eVf8q1veN0LbjiHYHFv9+rUp7Jgsbd3 +KIX3sZ+PvnU+a3paG/var1c3XLmxkcuXH7qqce8urrw5JNdkdfLOU5XF6VhN1gTQ7+Z1pNxk1BB OXKmm9HG9ORNN4w2p6SQFcZYey2Bvq0H6OW0l4ScUU1JJeUoTSej8XRna+uWc2fuvPGGm86dvfn0 SYLG4i5/9n5g2v9T9Nmb7bE+sv0/RY/19tnF2wnYCdgJPOcnYH+Bec5v8fVdoP0F5vrmZp9lJ/CM JnBssrq+67veJrpk8rdMqV//JrBl3iRPE1sxeV79I40nznjo8jLdPvPOt3/L4fzwX/7sv/32t3/L alb97M//X7CPqs5Hp86+7c1v3zp54vGHPvvLv/HBN33Dn3jZrTf/+3/zA+aAUCGDiRwUTCJTBhbJ qWcMeKJCgJowUP4XK4FbERwFyYJztYN45MW/9pHPffKBhx1UUI4fJwkx59j5ktCPkogPCRW7undw uFx893u/lcMNowhRGIFanttEnqgVpAWTZRjFm6PRZjpaN858tjJBT+qClAWyqbJ6RW8h7AdZlsyc phMS+iOTmek9NMWG4D9zLYJxmh6Q0ESVCSvJvmjuGvU1GtMmHkZzef1EeTKBYGYcSqk3+e9ygypa zLQnsiFGYadYePkcgWrGU6pxmfQupTmpDsAcjYMg1TLZW0rpUvAYh1K2WIeKzjBFrVIPYwIVhlJs hBJwcR4lqbmDTEWHgyhEIYfASK0DMQSqvytgTdGgYy2KFFN/oMPahAhDZ92sSMYqy3NuHOzNfuFX P/zZw0HlR3MRQncyck872dfdfNuZZOI04eapswd1vpzPy8np6R2n0zOnc3K7XO9wmePt25psTsPg hJ/v5Uvfrfzq8MLFx5f1LG/y3MkGkZR5SRme/+T5+WN7d3dntoLRydPT01ubTR2VqRBgAQwMvCrx x5vDolysVvNCEjYmFSkDrRvEJ5I0TsJ0GCUp0DMityzwR5PJcLSB9O1zFy+zJRvTyWiabuykF2ZZ 0tSnp96/e/Dy6TA+tb39ySd3bwmqjz708MHe3qvP7Tz65GW/cCZuyI2b+268NfbHo2GazrPq6ipf 5+ubbr8jmm6sl7MUnRYtjdw3yusKgnR04sQ2AfzTNMQlSztnBKZFuabWRfR8QNaMIDlaDO658Vk0 MNqoi2f0Y/3582AbdfH82Wt7pXYCdgJ2AsdxAvYXmOO4a1+ENdtfYL4IQ7ansBM4NqjrL/2lbzbQ Rcogk+/Uoxl9TonyRs9lPiWWYjRfJjXb4C5Mhrfefd93fts7l87g3/ziz/+lt33dQ4/v/eMf+QcJ 8dpOc8u9L/72b3nnv/uN+3/tl37hjptv/XN//s/96E/805unj5vTmWwpPHIGpQGYcNNdExIZjGZC 6JW3DtsB1pg6RXxfeNnSgZfURfvgw089eXEXLNMQ2y5yVvugoaZd0w9JGpbr5nUzz/P3fM+f5PhZ WWEJNKqpDHehSWBXzhW+SPrxgiBStWIJ4gD9yBUICwPVFXVuHikhFdyHoQCDlE9legmZAe/Bh3gP pMXqFCVmwrZAdlKFiWeZxDOeTlGi0vcNLTS5+8bIiESplmgNXiSA1au5dMCeKJosLR3HBPNDy6Sr 0igU5CVFGeSG9LGKHC6KCVmb8uI7ZZ73b9omOJlaGPsuQgnL2GQBRaWDmdBzBG2+GJt22fQKSB9G RBpXXnIpIRPyA+m7HFojxfTUojkgj99vIG50LrppULr1OEhc59SSELWrv/2rH/9ktarbMKM1kbqA ph5G6y/ZOXPH9EznD5Nz567CAH3/3K0vCibuXpKsIZptdf7CFUDbLWfPRcMhQrKTo3jodu768GDv 0jLbe3L/guOtKzyhrTdq/Kc+/vj80b27m5OTxh2PnSQa1JU7GEUwrGEydMMAlyydAUGYeHHiRV4w HFVh5ARAPK/cnS/pAnVQ6OmWy4yAsYI6hjEjO1iSp+9MNiZM2UnidJjGUZxOR1LIOW6QJKEbcaes 2+LEENNr87P/5ue4K89sbD9+cFQNunm+Ho9HQMvFMgtC6Fb7ype/PJ6QuO+N4iCM48kw5Q+RcMKY SkZT9wK7mjXNomqLZYb5c6n7r6LmdID0ra1f+dK7n72fp/Y3xWdvtsf6yPY3xWO9fXbxdgJ2AnYC z/kJ2F9gnvNbfH0XaH+Bub652WfZCTyjCRwb1PXu/1odhSIyRrrT8xk+0xsV9V4vOjKx8bzx6lwN iyRpqcHQ/bLXfe03vv5r1k6QFyuoUbta/NCP/8igXLfVIt3a/sqvfOuX3nvH4/vLxWyenD73w3/7 v/2ar72xJ2d5CTxClIUCTKiLPxQdwn8gDOprNPnrioWSfZHWQ7GVxAlSL9k9Wt9P+Nalq1lWEWt+ ODsEGCFAuuXGm249e+OqaHZnR/M1xkMKCbu/9X3v4GRYI4uqcLtilc8PlsuurfDq5VwDfApJj4oO BaFIsyJUq28kdLpSJEgFlBQM4tQDV/UIqpdj6U3PMtSvVjLYNa4lgNc20CEGSWQ9D5B4S5HtHUTL uBjRhYlYaZiGMfVP5ciqsVTlouRbxt1pAvk539MFmdcQlpLhTWC70yBV429xQJfTcu7fbc8UGfNB LBgTAXmKPxO9iSlKbNuctDAT+8UeIN0yCjABNSaP8gi/JGyuB3ZhmNBWyaFzxt3UjAJpHOny4SBe 5l7RkC42albt2q2nkf/GzRNn99a/86EHH5tfycru8pwkrixuY7eszo42RpubtRtObr25O3cqS/3B eCtflKMT04Mw2ByUB1d2n9w7uuHsDaONLUDYNIqmVC1Wq939q1G7aOr5Xr531B4qXb8MPvuJi+c/ 89Qt4eZO64+8xu9Kv6o9IvxdN45IEjOQj32gfNPjkx50iQtrCEHjhksTpaH5FGgm8LQT6QTZGs2O lRugc+MmpCKx9Nx1iWG1UZEiNQN8HhTrcrsBz5IS62bQ5l6VD9qnnnpy1QWjzZNXFqR4ZZFaO8dH ywyuNd3YCUP/bW943cnTO8CyjSRkPdxV3ACYapnM+ujw0qqaFXWgYDmxyK6iC4H7QFn/RuqoW/eV d559Rj96ntGD7W+Kz2hcz58H298Unz97ba/UTsBOwE7gOE7A/gJzHHfti7Bm+wvMF2HI9hR2AscG db33vX+6ZzdKMRdhMdjraUtjH6f1tOJHXAZYY1KwUD/5Ze2+5Zvf8cIbb37g0w+/7ate+9O/8mtv eM0r/v4//HvLxdyL2jgOTm/eOr359m/7ivseeOLKXXe9+J/8kx+9+YaF4WhCPxHchNQrTH0ollxP hroGcCPvHoV0uOOkXDKNdI4Tnd3cuXJQ/sqHHrj41MWD/SOoEUiAnHq+NkxSzHav/tIvueXW20ox Mg6BwGnQ1u0rXjTkw2UOw8LZVrRNkWVrUAOYjiJEmIVa/ngKYfLQsppIpRb/Ik4y6a7UJWlci9ci w4z3T6IsYyJUUpahcmig0J2Z9Cwzub4GUUoprqtnW1wh44UoIbCSydGAQz291Wp1PAm4WIk4GkuX rxPdllkEDzcgzEBHY2WUQEzmSQ4F6gLGKf+8l4QxEN5gcCCzPhaMPz7iOQV8daidEI2hYioIojdm RhHGugVg8RCuX5WKgn7m0pT05ZPLxUYrnQwhFAvHmOl7ecmFjMY7506cvSEd76wuLz/15ONnw8HX ndjxdhflY5en99xOMeX9H/zNK49/LohH4Spo1m07SbJm0JzZrm46+5HFQRyHkyp51T0vuNo1ky5f LpaPHx650Wh7+wT+T3YjqIpzMdWXZTGfnY29YrB6rLhweT1vneSRRy5fePjChr/plkR4VeGgSmiF rEGHtCRAYJF4tbeeOJ2GEf0FXV7Vq2XUVB7xaoyrQ8VmyCSYMwgJjGeeoayY8mM6Qcg/CX+SBHVg kAROoq4GHIfRZBREw651Z+gGB6DTeuE0D119auWl3mi6j0mS2C1lz4O01K44GW+S4f91r//yszee heUersrVellgtS3y5RqMRkmCSVuTwlBh/0SySWYmLR/3tstmSN446O6749yz9/PU/qb47M32WB/Z /qZ4rLfPLt5OwE7ATuA5PwH7C8xzfouv7wLtLzDXNzf7LDuBZzSBY4O63vOed5oLUx65PIzXIJcR Hymk3Ii7YDeG5fRxXcAU+R1Jh6/9P/vn/vzmMPrJf/az3/lnvuNH3vf+73rve3/qR/7++YvnQQ53 3nvPO173en/7pmC9/p/e//7v+Ct/7c5p+oF/9t9zhIpyQjU+qtFPJ8bD5QY1qU9iQDWWrtAD0oA7 fIKM2oLorNBvw5//8Kc/9Dv3b422cvBM51QAM6NGAt6cu+Wm13/1V+7ccHqvg1t5TlH7ZdkV1TR9 jNOtCiAXjY3yLSrtXY4/GFKl/HsBI3PR8AklczWABux+0pfBHno5m1FVmSCzPhVeKAnNmWK0GIga EBWIxdfRpYkYgkNUoQhsMgBMy3yaGRrnZ6+XMwDL16NNnpaskMb/CEyipZBPCCyqhbGG1SiZS32I Kq4EyZksLqKdKE+UiVIfOa7JwpcbjnMAs2BVYBuYVQBRgW3R8Ag3oiaxD7Jn/YJ0MiVyRuoWDcBU OSMxXlK5kRsFQWQoBHm1HdUDdApSiVi18dbkhjtP3XG0ffb0C2661Aa//luf6w4v3Rg0X7I5Prk+ +vjHHrjthTecCbwHfvUzh09eDCfdoKDAwHMm00uH+XKa3nzffb+1f2FVlbgjt3e2y7YZdvWFvb3x ZBIkEbgR12CerSFDG8PR2Z1NZ1bs8EmqPkfd1XK+t1p/+OOfunJlLwrGrJ2ENdRwARlsrRfiUSQf rFme2A5e8pIbk6BNHGfDiQOuWRHwAMHQUT1ksV4s1stFvV4BPIUTK8RilGUyXW4NGii7bito5lXo D2qnymaYXt3heJhUY6oysW22adDRJTkaB2c2D2u+D/w1iNMfrFtnuVyVRwctokI/8pPo7d/8ze7O zlUKF4kTUwgbgfRAN8kjB1HU92aybX3FZu0Eylkz34/c4sS2ha5z3503PaMfPc/owfY3xWc0rufP g+1vis+fvbZXaidgJ2AncBwnYH+BOY679kVYs/0F5oswZHsKOwHzcvU4vAE4TAKXSI9ETKCX3reo zKkeRKFIAonIeGWMViaiXFFPteN3P/sv/tk/+l/+t6cuXPif/5cfvHjlyff947/7mccegyphwLry 1JMf+Nf/6kf/9//1b/3DH3j80Uc+8D//T3//B/9eH8IOQUDfBEvDShaGQUycPAIb/vLxn6VJlCC4 MelXQVZAA5J82fyHX/+dxx89f/rUjUE6bj0fC2IFwHHbOgwqfGLD8aysrx4tgraaTuKtG7dP33Z6 5/YbjsMO2DXaCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2AnYCdgJ2An8cZ/AsVF1ffd3y8DIm1RdSEuM fgsaZVRdAl/SdiFTMjHxvMluaCLSZa+T7EhfIRlJ8iJ0RD5KoQqpEFawEmGRF5KhhPsLakb6Onjr pS8ecy658mT6Iz1c0icFhHl0J075rxLqqxzzoKf89CgexOtZ9cGPPvTk4xecOHHxKirVqzuYHSoT q8xD4pZ2TqSkh4/TaJRmTT3CnViQ1e6M0tHdd8gvaU6hJcvYaDRYKjREG2WMg4h6OK3nBArdQnRj AsMZgsRWaqWUw1H5Zd2g0jNNSBckjkQlibuMH7AsAH9Io7gaE++FkkoOQzM6TdKo5STkknvRwD59 SdZFXHeK+xf2M5OW5xAVlQvp49isw4zK9FKaDDXORpxUZQoeyS8j8oyn4CmkqpJzoE0yqfdGNkZm FWeulOUlmRIh8o1T501JZJX2SSo0xGeIi/DxEV7P0ZF0cQSMpB0pY1les4mN22XdYDMKUmRhjVPm bhjvfPXt9wXZTj4Yb26NPrLe/4WPPRT5TpL6L7jp5Ms3m8/+zoePPvbglLyyZesl3vL8soybaBS6 w8n+fpmNxje//L5PLHavHB664+DUzjZ3FKnvWZYFaYpgCo3WuiyHjL11ppONQZyOV+tJ26XkbKVx kAwuHlz8lft/e+9ojmqtJFZs4EvfJ3kePsRxyGfqxeaZ+KWvuomyzahohqi9ND8EezgESS8bRDwG P2vphDhB9cxAHQAI4bAs1q3HTSJd26A+WGUV7QTFcnfBUL0hS6paXK8FVlgwa9AFcTccI0/bK9bl oPbi4Nzp05vp+PKFy6gRk+nGkdt+5dd+XXD2zGqAjrHFXAm/laNUCsKmXa3ws/J9gJPR9C7i1exQ FvKdguKRhyGc5B57xU07z95PO/t/ij57sz3WR7b/p+ix3j67eDsBOwE7gef8BOwvMM/5Lb6+C7S/ wFzf3Oyz7ASe0QSOGeqSSqvnKSYKyqRz6a13LPayL5PdPqgNv1E/IP1/tAuKMvDyXa1/UATxGkKp MCTqSxyT0KiQ2Kk+6R3a9NKXbJsD4ieDPXQBOVCcbdAZPddQZsCW4r+S1/6Y+LrKLzPvg/c/9MhD TxK/vq6rUiTA9aMIHiPPZdvceMvNr37Zy28/ffrkiZOTUQo3YHUEb80Pjx7ePXTdBzgdjksy6CE/ JiZLiMnEZfUh7wrgUjyV7zEBxYY5cAbjTHx6CvItCjjpb8CQ3H8GfuEuNO7OPrxfgVj86acjrCYk qCkxth4gmsf1OV19JhNmwA5romr4XL7aAX2Uw6VwMoU4aVF9KJixWCq3TJtTGQulTJSm2JEvP92S KTjYx1C5Stjy5E/ErMn0WeqAqCpiz4FZEawLEkm4VRMO3GYQBhE1lHIw0vxHM6FsmKAuTohzsaJC ETslZK0FZh1W/g2nbnrV9i2LTx8NL88xAF6p24dPbO9GCPnKnRtGxXJ5y7IdPPFI/tAj4/2uObvj 3rJRBZUXx+7KefTiwTJO7nrxS59arT766U+6QThO4pPbO/5oM6UksS5nJKm53mwxH8VYD53TkwlN mlvDMTbD7ZiKgm4Sg0TDSwf7H3zgk0889WThegtaCwIvpiCy4kbDNBg69eLEyeRlL7u1c+XX1MZR QGD8qSqpBK62Xc4gSoyMdUJOfZx0Xpj6ZMpHkR+nYTgNw1E6TdLw5HQ0Go5qn/it0ukKyg6WV/dX hwvCtvb3Di9duHCYkT/mulHETm9PNyRBDMKmaJIkjtLkqfnq5V/ysuTGswcellyHO0y3fp/31ltj dV/ILdtrK/vdI/We24ZvK3Apc73v2RQn2t8Un9GP9efPg+1vis+fvbZXaidgJ2AncBwnYH+BOY67 9kVYs/0F5oswZHsKO4Fjg7r+67/67dotQ1+eBlxiMhL5GJ2XiZS6pirivxW5UUY3ZFRKUiHpZTnv yQIpeiQdk/E69keFyMBcTG+hdCv33LVlbg6dQVHqSpjSuQIIBaniTe2SHe4g6UJAlUSD5NMP7X/o tz+zXiM4ggZVxtdIM11DUWBZoG/qqAMcDsl4SofjyShJJmEYJWmcjiZxknXdaPRZA6GANqJEJlLe rBzyYwLs+6AuJW4BjFi6ybESDjNXITBhhF29UMqAPxK8rgEyUTIzMxOcz1MUQCaUpfkIsfAu144+ ybhCjUPUSLTAX6AoErLogKylHRN965sfr3FEw0NAZwIjTI2lX9N8mdZEMTWFxvMmeKLMe2ESnq3J K79sgC1UrK4h/MmNAg+mxrBqjiTgqJwvtVo6oEb5RJF1CUYyVhZkJGSFqJ9TKl3djVzUTyzSW1LJ ONm489xNYZEkl/KbCmddFI9c2b0YRyfOnP21z36ySAdbW8NbkSI9+ETz1JPe/sw7teHddrobRYMo mO8vj2Z5HqWTMzdMR+mvffxj1Trf3du9+64X7Zy7dRCPxnG0e3hIUlpWFJPRGJ3g6Y3Nioww1Sg2 g7LY8b2dNB74buI5BPB/6JFH7n/04WVZ1BLNwUfhnIMQyteWt57ZeukLb63qI6ehDZTVu5GvZsmc rkyqFVU66Y3SBPesKhGjWD2To42dzc1TW9sbo9GZUTLeOBO6KtBswGQ199pyli+uLOdHBwsUjEXZ 0Dl68cLu+UceQx43HKZk2rtFvT6iAiEncZ/JgG/nZff6r3rd6JabZkZWpm8lcwuyg8zd3DsGdZnN FTUWJwV1aXt5MCpILv0Vt9tYevs/KF/sCdjfFL/YE7fnsxOwE7ATsBN4JhOwqOuZTOt59Fj7C8zz aLPtpf7RTeDYoK5rsfRGmNSrk8zr7x7XSIclQVMvUCIl+2kYhr3LoLHeXKd/DMwxBxEfkTev/9iw o/+Hl73ipWfNY4TI5FEzDEwiJN8tqrwsl16dwzXQ6HjTDa8Mf+PDj13aXadb506cuOHU6e2tCVHj 7ZUrV/f2dqssmy8XeeuqYFG53m5VAElWoBwJyoqKFXzHX3gRxxcpksFPYM5AuB7QQZoEsHAd8gGc wbQlXtN69QFmkqUpPh8zJeZH42f0Q3E8o8chzxzE1BO73jXIe9Jl9YozIxsT55OLUKHohmsIaXHh knUJSQm3yVsnzIbBTiTEaOr6nkeFzes4RimmjTB6MY6oIdPZZ3yPHNtwRgMOzRUajohmi3R/GUQ5 ErH8CNLUCul0IdWV/IOwC48eijeQCushBF0eRwemxOnXdbtYldFwFCTYU7OkysIy6NzxOky3t05c 3G82cr9aFjuT0aLMkxNb5+Lxow8/uazWF8r5hauXTszLzaO9Zn93YzM9edcLghObVRwSur6TbORB MvejsnM++dDDV/avkoJ/8w03BekojOKtNN2bzTGZbo3G4zjmGqYp1kZv5Abr+WJ5tBgPnCSQ8goJ YELBZxA++MRjH/nMA/vUNw78PHTXXls4Lojq7rNnXn7zzdR7IgWr2grnKdN1DTty2jwIYw9/4XQ6 2dxIx+MbN8ajJC2jNB4Ogzj13BAaGSz3DpfZ4XzJPTU/WlCe2OZrMBmVCqnnxZ6XVs1j5y8/+In7 2fC8KlSUoKICl5uRbwa8h/F02qXjN735jeGNN9H6yS3JTqpjk/8YNHntlhAk1T0j4KWEfPZLykHe WC53wCst6vqj+zn+vD2z/U3xebv19sLtBOwE7ASOxQQs6joW2/TFX6T9BeaLP3N7xufhBI4N6vru 7/4zhj3BPIwv0fAUESCjQ+rTu3oy9LRS6xoKE6FRYr3CpER5BFj0SF7JX4u2F0WS6VGHMu13/OcV Lz9j+Jcx6OGeE+tS9hVnIhSpLjNqHWExLsnz7iibO7/xwBNbN7/wDW/++rvvuX062hyhxgGZNGVV Kx4L1nS4bh7ZncE61vOj+WIWtE1eVssVzKtarbM7brzE6Vg64VP8gyMMN6AInpE/oRyDLwQGB9VG rMYqr/kNuxb5FqV/XJBYkkngQooFEOstkFw9Tj/+YO3jEqFmvGN0WxoL/4Wu9ODPgD7Dx4xzsjdN SqvVOQGdgWjYajAaRkZiwgz90GA1KyamhDQDEnsOKZAlTqIzCIMZGRin5cDQtN5a2o8c8IJeCaei 2hW9puhUqogD0msHQVkiKSPiqvDdyiMuLUD5xQJyehLLDt9oEIas6OrR8tSp7cbrgnrpzecnmzR2 J7uFs5ujjhqP/ekRE69WTlCd3Jmec9Kjx64+/NSFi9Xq0uJoujm+YeDMn3zsxZPJjbfcXm2MujRh afUgdMLUCZM4TC/M5rOjvcP1+vYbbkwnyXQ8GkaxrtpxYEm0WZKixSUtyqrdPSxW6zqnm7EL4yAc xSEtBnE0GU+j9erChSufevL8o8ssd7w9v5gPg8Wgeskdt9934+2DrJxo+3LHb6hC8INwYzi88Z57 T6QxrGwynCTxVAlbPp5OUuiyaj0vDvf2d+dP7M7Xe7vrvJyDularxeyoAXKts0Fel3g7W8bmYOVd LFbVfCl/Lh8H7oK6xtCN49hcYbJm+pPp13zt64OTp/MgquR4JRjOUGAZGdV1aRCpECffZ9zQfBOa olO2VBFxZru7V99hVV3Pw/8F+SO+ZPub4h/xBtjT2wnYCdgJ2An8gROwqMveIP/FCdhfYOyNYSfw RZjAsUFdf/nd38Y4DC5R/aKBVUamZTBNL1cy7wtIGfVTz3EUL4TsRBnvCmsHoiCfaQd17QWpZE0w IcKnOmLOMcAFsCKfVPMwuu9lJ3V8PFqoqly3BCMZlMZ/gAhdW0GXPKW0B9Vq8OmHLn36c+eH2ye2 br4t3TkxSoZntza2J6OTW+OzW6OtYTiK/DYcNahpMDSGce4mzSA+zCt6GDdFb5qf/df/wzXCxClN fBiLF0gQ3iJ73vgsDXsyvkVz3b0JEf0UfkAxJgWImTtGCjTJ3KTPEeHSEY1ZE+QG/4BMSWllYrT6 pko9vp9iHw7WS+UQWJnDKa2s7cGZND0gLB/BlxLsFdhvAJ3+mPXp8T3nUma6QYnSqUmQ1eeIKaXL nEenoddSBzH8EbSCKZSLg6v4tTdG2VQ2ELwlcC/xmsBLvcjr/Nx1WUHWOIAbPbztLlw9uPfmMw1n yKppVW+1YV20+8v8qGrWrU/CVd0UebOcrWfMYRKNg9qrqvaoLs8f7g1i52TkX/j0p14QRy+55fb4 zLluNKUFgJS1wA1x6BGJhYBqMV9//ML5F546mQYVAW3yfBY1rkaiwuqKYgHTXMAIQpcgLY2ePDWe HaO78oZRGJGQH8Zx2V3aW+7OVruL4tHsiVngXGqKe7/sK174kvu4ks02PzmMk9F4OpluTqYEb3l1 6XP0ddYROj9fHczX+wezxewgXyyW89nu7u7R4azIc8yerFOCwLKUmxY9H2vANovyihvbqVZNxckn m9sb08lkOvbCAKlaf+uITzbk1g+OsnLj9KnaBNhDFDeGo5PTsZ/G4WRYuz7AFlarbSIcjcNy4D42 Tklxxs5qyPGrb5cQ8ll6s78pPkuDPe6Htb8pHvcdtOu3E7ATsBN4bk/A/gLz3N7f6746+wvMdY/O PtFO4POfwPFBXX/lW7kqBUqZJKqeovRIyzAggI/4zNOZXEa1ZD4joRN9i8EAQkFhotHjuE1RB3Ht +MMmi5Vg7+ZRQDkgh6zIlXfcySu/RKjLKKFAZLQ0Ep6kLC3lchNB5TRYw6hDHDhxuRz88m9+/Imn LgUhmd+Dsqoky4n5ukffInHhcRJLYIOpbTQkr2s43piMJhvDcZKkiKFwuXHM4ujXDSRCUANKq2AL AAS+JPbTAR8gRbrkXncl/GQkbUbOJjIm2ZhxKXIBuMlYNKSLx5sIf6V9GZMZVkEOa1KyDKHgOOaY ivXiOAIgjTCOyR6/JoIT55Iz0SW031Q1gvmk6jIzN0yxtzcaKZzpxDSqLkoW65ahgUfSKEgiP8sK oxozJk0tTFQuDGhoZLxcMlNvSTkjqarNvWmb3JJuBURuOc2iXV92suWgTRrgjzc8cbKOgiurnJls eR6Ngo89efkr7ry1qaNJF531kk0Po+jslx78xGeuXlo63Y2nTpf5OvXbdV6N4wlETG2LjnfxqacO jg7Z2zAc7D35+C2+f8eZs7fde58TD4mIl6OzbMpV7oOPukGeFweLZUJdwKBI3UHYyELK4vr7sDfx aQpsOblmnluCU8nTx/TpewnyM8+bDifTYNgs8ukSUVb12cMndpezx+rs5W/42te+9c3hiWnatclq Ue4eVUfzdr1G7pcfHD15tLh8eJiveE4GySrUsoikTQSQ7wPuQqyhne+QW6+wtqY2af9+60VOGCLE OizXTewOT26fvvGG4eYUpSAWxALZV9uhECNdy+RuDdb6tqgO63qZEyfWrnKkaWVRlDfdfMuL7n7B HTecTDe2oJaLokLMhQdVFNOgLnlLTUpajzhffbuEkM/Sm/1N8Vka7HE/rP1N8bjvoF2/nYCdgJ3A c3sC9heY5/b+XvfV2V9grnt09ol2Ap//BI4N6vquv/Q2oS7gkMnfwp4lB5VBXX3PoLlmCYhMGrpg jKyOUCy9GG/joXfzrRtwCdK/n3xiURcEX+0Vte/U234LHdijWO/O28+e3Qnns+Izn8tf+CK5sYyD rxNDQi+jSCrhHnxgZVWYpkNATVKtqw/d//De/oy8q2gzdQEbCsgabGxOb77h3NZ0ui66h89fXe7v Z0U9W2bUF8KyyjxDEgSvAY7Afd75NmV1oXLi4gBSnAdZETwlA7hUXaCIMBOGpdx6CEqPigypMClk fSslT5CZjBwuORwVJ66PrlUqEuMlK2IvEDO2QdEynoQtkOujfVFaORRTDIsnkbuvYHg9CbjlVMrb Mg2NjB3CJ18i6VRGICboIVelgT5mF67lmPdsrmchyJ/ikLJFF8shSxNAvKYgk36NT1QcBzAUBJMm vNPfvN2djDfOHl258uEnHtwP85IsrWRyMjl17vZbL69nH39iP9pMkFGVu7PF1aNX33zzhje+JRhN kYw51V42++ef+M2PXHx0t6pRVK0PjzbjlC7Ck8l01rVF4G64ib83T51BOok/t/fUenb0knh4++aJ 0ZlzRZBwvbrGuokpfYQi1a0fhuuuK+jf9JyQAZQtNkA/JjLfXCpYkeuRtzXk0iom4aJQGyDiY1L4 Mik1jKgfiJOwLLr93e28LRZXqqzep+LgpjPhHXfceM+LYrc7/7EPPf6Zz3SztVK7agHVvldUM1X9 pW5xzpzjLpQIjnj7zsduiZVQhJIyz9LE/rNedtCDxWaxt3nuzM4NZ9007eJundNcqcS6aRopZ429 rJCNEX7fQLaWZYlc8akrVy9c2cvXCB/ZE8+Jo5fec/eXvOK+W2+/1fPjNcStLTmBkfRxE/RSPt0m 3GyvuvPGz//nzjN9pP1N8ZlO7HnyePub4vNko+1l2gnYCdgJHNMJ2F9gjunGPdvLtr/APNsTtse3 ExCVePjhR4/FIP7Ku6XqMmY3vbw2/XsGM5iYqb5OsNc6AQgIWTefMH1yYKG6vueuc1/9uu1gkvzm bz75wd+6MoxOtc7jnSDPVltHTjT/stec+9IXn9qfXzk1KT93KbhyYajnmiOKHxhXn9FD4RBryzrD Ckh+lO+mbeWvFnWVS/g03krc2C28rqhRgXVu1Waros6bK1cOCVDK8ma+zCUrIyyJlPamSuMoHYJ3 /Ne8dJMTGM1ab/STPK0PJYOvEd90LV1LnkTonhAL5j+T885nhN1QFAHEJGUD06AGww+IWAdioiMF VCwiaFJEuuvlOVgEngO+qyVOkvlMnY+OS8i57HAMjIcp9Utde/2IFbllxGSgQ5bU3zI9xdLfRU/d 5FVUaprSmzpiuVgsXkdoj+KsgHdSe1UtCjsoTlWi+YJMyUxXNxUYzGmjsBgMLh9UF/aio2rSrop1 dzD24xfecvbuF98Znnzo4atu0n3owmMffOShW++6/WU3vWA0IBY+f9Pd906n57yy8q/sf+7TH3t0 7/z5rnrg4OLjGUqlLjtandk63ZXd6Wi09pvcb5O62lpXW7Hvp5NPXXl8Nrv40un01dNbvTito4RJ wprgPm7T+Nw7eBU3knGaLKBo3YAvqEmg6rzGCJuikOZIwGQN6qrLcOCZSTSyhwZx54fwJwbbpgkx 9eOupN+xO9y7zcmmJGw1/mzzxODsCza3Tjx28OT5z31idFR6TaT5hvQp8q9TCjiqxpL3aR9AL7Ya BBWgi83lMyDQFHrI/GoIVC/JqxAhMv1RfPK2W8dnTgzSBDq2bvOuYbPxLw78cABt5V7h+yMvqqPZ ElngwWz52MWLV/cP2S6fbkoTXR8GgVoO0uHXf9UbXv6Se4DKyMqAnn31p+6Y/pvQgfV1r77zpmfv J4n9TfHZm+2xPrL9TfFYb59dvJ2AnYCdwHN+AvYXmOf8Fl/fBdpfYK5vbvZZdgLPaALee97z3mf0 hD+qB//bf/fP++CnPifeyLj0L9oTqXBgKSZZSiwKSYvEKyoHNDFQiL6CVdEeVeUdt28/8fjlxx+Z h8H2eKu4+96zTrdazlejyamX3/XC5Sz75//6/tHG6PY7ppeeMqHspnNOujAFY4lhoJupu0rqHg4K 3YB8kchEKlPiNEG5lg+MVKj9bHa0Ojzau3Jw6eLepcv7q3WxNvHzWMRQ31TlOs/nuCBBZUkSjEfR uVNDyEKfs2+wmgRrKL6wVYK6xE6MskzkywNYXCsw7EPrBSWMdw5Rly6a5LEmR68D9ZC7sQscN87K YHPjxMbGSXeQYrpcrNtl1uaETQ2irgmcOiRIHSmQLsmjyVGuR06V+CEWNdL3yTNj1KI3xh0pcyji JknAdF5i/dEA8XQIYy9Egr2gFwKgIEVLcG6yOwIzEihBtDhBpdJISImCz0E4OPJ8DKadswISwf8m 0+HZM9VeuRePxi980S2veN3C3dj91EPto5cX2fr8ld15W07GW/tP7t595qa3vOqNG/FGt3d5+fiD H/3Yr93/sU8w712vfmw2d0cbbjRG5YRpNExJoSrj2H3xjbe9/PTNpyabWxvMwxs3xc0D943hzsuT U3u4+gBuKL+YtdOlIcH3wXiY+NOUoKvNre10upmQMc+fYdzEfhW66AJbSJYUhHBFCfG4TZBJkQvP nAKNUWwWeZdwaVvKRlstwi5u8nZQyic67Lr8yuWnHnkkOZr5sEGkfSHQEZqlWRqaqF3mMOgJG88v WBkn1K3CiRQ7B+eS9K5z1k2DTTRznfWgw+l54qazdRzlJHYRVl9yasyO0uXVbeHKpqunUJPAuq/s HT342JP7Bwf4GtlGrLmjNKrrcpgmcRRWyyUI7MzZ02d3plntk1vP/a/ihL7JwPhSuSXPbpGt/2y9 /dB/Um+DfbMT+H0TeM9XPYsJcXbadgJ2AnYCdgJ2Al/gBOwvMF/gAJ+rT7e/wDxXd9Ze1x+rCRwb 1PVv/u3PXKtbNLnnJodeRjzYjBE/SedlUqZ6LdfvBqybWkV3UOWV72UvuGtr7+rlJx5duYONG26e fs2r71jkR48+dLVrNu//9BO/ff9nEGy9/MVniRc/f7Hs6ZKsgqbK0JAvdE1K6AaEQHKIToJ9gaRI 7c6ysliTcrRcLlez2fLoaLWHQuzq0e7uweFsXpYNVYtwk2EcFsUabsY/Uj/BfZpyfnR45+2nWPM1 E2YfK8+aOVEjuIY8ykSSiX+h+2JVvYRKXk0F6wsS4dQEe/CfAc42p8ZaWcFDBpHrjwbu6NT05NnN E3kxWGa4L71lxvPCKB4Np1uOO/LCcTuIynawN1tf3c861FXhcJREXCzhY123Rpklryi2RqRoeN6M 6MwYIq9hL+yIPiTF1C6qERJVGMyIwkeSpRCCwefw/PFFyZTwTjooj1Arwb5QoBF8XjtBU1O6iOmP lHS/DcabW6e3X/La137l6+/9kte1p28/XOZXH/xkuMqjOPUr78zG1mtuf9Hs4mV3tZoUiwc//vEP /+ovXvjEB5e7F4Bls8A/dKJH9o4WeUXNJRvDUZs2P1wd3bq989Y77rllY3LD6a1Tp850q0N/uf7S ZPsFJ0770/Diujys6gWCqV7XJjqnddbrBelVq6pYVDVUDjVYEsWbo9GJyZQE92GSICN0IUTo6pR5 ZnaMXQAfkhEnt58J6kfHVyssHidkUHlFTmZWGw8Styz2Z1fq9QqsORgHgxR0Sl6879Xsr4n8h3CJ CiKhCpo2oKNTiJHHQMRIpGfDqw6wyV7QQVCHfua5wXi8fe5MN0LPxb1ar9EWFiX7B4ejkJNWUCyw dVUfLTJ8nQeHi0efOD87OgqDyPGCYRqh/BqOI7Y6z/P1ajUaj5er+bxsdrbPbGxso1JD/Gcoq+ne 1C0pb++ZjdGz96PN/qb47M32WB/Z/qZ4rLfPLt5OwE7ATuA5PwH7C8xzfouv7wLtLzDXNzf7LDuB ZzSBY2Ng/K7vkoER3ZDhO6azTzIn05Bosrr6CHrZBoFBJlUd9sADcdEFQeNk9dlz1Td924s+/sBD P/9z8xOb9wXuhS977W3/9289sPv4emdyrnVhOpde/eXDL3vDl/zqf8gCfy1eZpiZKSFUxpXRdklJ phM56mEkpin046bxspyzw3nqDIbRASp8WFSB961uKNAzwVhNHCUQkdUyX2WF1GDoswhlJ7iryN78 RmV1VTgLTU68DIzmrCZdy5A8s6smDlw57qY80SRhiYRwtayOg8n+OMBc15U0FJZN0AzSbvD/Z+88 AOOozq49vWzfVZcly713gw02mGYgBkInlAAhCWkQSEJCGh/Jl5B8JCEVUggphD8hhBQgQEJvoZtm G9u4W7Ikq2v77pTdmf+8dyTZBhuwg4wFsyyytDt77513RtLso3POGx4VSRiWsq27X5fEinCoK2Nk DQv4qSYRhfnO5eWgrHT2pzdu3tDZ0WoUC5qqJuKh8WMSiRDmzwZ1ULQQWBEckVgCk5SR85D6HwKF gXiVXcV1ZNZ5kVYPSMdxBQv0CxySxd5zStjg+TwypojFmGgKwHpAUr49RYOJWL8uq7waqIjFwnqw WguNDUcqw+NQnQ05q8ijfYBptmzR0+lyrpxOpfuL+VBtnVM2wBT7OrYnt7cmsvlKiSvITlaVejg5 OnpCi2G81LLFtAzXsqHPgqAJ/S5HK8GxYliEqRDdCDj4PK1KR55aUzluyoR+R3lxU2u6UASbcl3S uVklIwyARKnvQEzglGgpEA3oaKcJvR0oFsCQGFbUoCzpyCBzOTubSZlmEpnyZtaEvxD0i1c48C5J EWSItGAXdXScGNmClEs7xZQuiJVKJF/MZizQT9QU4WMEx5CMxhXLrsZkXDi67GMJ3LCMJbnQbQEl ykGFIv2RkiY62bwJ0porGjkwOknMi0K0viZeVVlURFKTObZhkm1WodON9GFlHsn+Eo5aTyqPgK6t 29r74VtUVDrDJKm6Mm7ZBYTIFYuGUbR5QdbDYcjcVDVy0EGLjjhskSyqgpMn8snaJLDvEjo/Z4+m Tg7DdPP1/8NU2JE+rK//H+lH0F+/XwG/An4F3tsV8C9g3tvHd5/3zr+A2efS+S/0K/D2KzBiUNfn Pvdh7BXAFoCLl8kF8QzeYAN+ETQi/QsFGNEbb0GEhArQiPx0lJ2ODoZSyczVj86f8aFDn3u2/T8P d4aCDZzaN75J2rwlCeagh0OGm5k3p3rBgkBHV/LuO/sXLZyE6QCVqD0jc4ux9/MgBiI64MFWCB4C 1MIBPpQoOAvtBCGmgbLHtMuyqmPbook+ighTgiGM1gx9E6Q65PTiXMjByPWoILwKehwT5rJYBfRd lNJF+wX3Xxm8hZKgYKiDXsYL5BoEXjxmx4qA89jaAI2oIABKiEpnbjLXsCxJUctCoMQFYgEMrbR0 Zx3bzKGXn22HgkGId3QNKWFBxM2jk19bX3rFmtU9Hdvq4mFNBtTBpGVFdyeMS9TWaiZnyLYMcsf6 PJLGjXyKSI8CKCKTIi0FRkTsl6JoiDzHUaIYLuiHGIjEs7Ir1roalwXGUflgwAJ8QqAYSI0kaboe C4VC8WhFXVVAVjVJNi1OdOWoqGihKqOYzxQKGrFFXjHyQUSM9dtuNoldz4dCSkM8Zdjp5o6tq1fk 1qwodHUVnVJa5Hplvn7qbD1RCX2TBYGVUY6CDZWdapnjunrWbWiLhgJuRYiXVRsdHLNOVTRRMW40 H6vqSPdmi2Y4EAqIfMS1k/3dhf6eylIpEo6HVcR1hXQtxINsEsCDtgr5bBwAH0gjpG4YvyxrBRvy QQPNDmzeMSCzAk3jVNtF5wHIroCvkPivSE5JyKcjJadSUZN2bltvM6LsJU4DkUJTAw534qhlLgIu KruChNwvACek3Vt0wKFjVMuKHE3EATYN0+QE7IOTt0pJw0xa6JLougE9Xl/rKKiiWBLcXLEILR4R YZA5pi4DpdIUJZMtpLL5dDbb2tpO8VzQcqmqDq5Zhv2yYMHdiiNeKkuyKgdCUADyvCKGoh8+5eQJ Y8da0BFiLwh0UdtNnI6wps4cXfX2f+7s7Zb+leLeVux9sr1/pfg+OdD+bvoV8CvgV2CEVsC/gBmh B264l+1fwAx3hf3x/QqgAiMIdZ2P5ZLECVgHYioWneV1RRxUXFHKEtAPugNaSC9iz1IgFJxeSgDx UXX15gdPnPn8y21PP7ldUSKz5gSOP3rssyt6H79/o21x8xaNX3xQTX2Vkcr1ltMNr2zuIvZEuUsO tTVkciWAAowq8DIoFcx9FhnBgLrAeaCuYt0OmfoLzfkg6MFrgbpIRkPuPWAGMAOZ/GukjqJeeITg SIYFj2LJcNN0MMiuxkRrLhRR4BLUU5FaSTJXpifjwvAoAbkoySBIUjaY0ahDH2xllPdOTjo0doS8 p4i2k3oCjQS7+nP5fB64zDSLaAxIHfvKbiRSUSxxihLu6+pbtWEjDHQRBbY1JRpUE6GAYWRLvC2F lKbxNcFYwC3kJVoWi/qnvHqOFGSAPBSRxrLoAfEg7FJ0LAOdAO0iUqOgYCKjm+1y8XA4JKABYWz2 pLFN42thkitTohol1tsCdE56UIaUqcwbpVIyyff3dvZm7LwlBgLoXUjZ9hDPFYpuul918m7eKJhi MRztt7PxQyclY5GAKTS297U++XL3lvUFt7ef5/VgFXEuRbc0PhKSJyVqULYN6zdUCIWW7S2lvFOs rAnMnq9VRtPJgpjPSpDARSIFoVxEg8OSqcKFWOJrHDdcKMIjWZXKawE3DrugAMmVhjQr6LUs0CwN YjUlhFh8nIHUs9EBILIcACmAKdbAUlJxFF1YGF1NsItCuUAeVAldCLjenl7B4oOS1JHrsLt7DbMg i2XoCAuOyQMVIixNgHsU4FSSZAUjmFD8wZhKEj8prAQUXU9yVqqQ5i1DSJdKqpQW3crGpkRdVUs+ 2wU4qKkAqGWkdLnlDCRdLNxOYlFiWCwYI87BZCYPntXX22caiK2nZLhgLIJTPZ1G8Qslq4QziZfR 0ADNF1SkxokB3XKdow8/6thjloASOqZF4XBIEGPnJk6J2U0+6vJ/p+zvCvhXivu74v58fgX8CvgV 8CuwNxXwUdfeVOt9tK1/AfM+Otj+rr57FRgxqOuSS89l7Im8e8xB53kKKa+d9WAkVRQoGL2bZ8iJ 3tzDU0YeRxAVGfwhECiMmxzfuLGzr9uOhoOxSnvBvFlPPN+85bXNTQ019aMqAzpcYjmJN2w3UhGv o+lct2jBbEfyMRa4ROIxQANkMjHNC3uK3u+z2ComOqMcJUiqFAluxGQOyKeMZG8sRkIgVgD8wsnk EQRe0mQlFNBVmPjAuSAEKme8c2Bgv1i+Phge+xKiMArn8p4lXRiRPhYfBsZC5kWEybsw5aFnI7CT S0413SwHbBedG0P9abMnmYOgDHH4ppljrfxKiqzLUrDkysWs8dq6dWirpwXkhrqoypdrY+FCodje 0VMZj5q8G6tJTJrSJKm2WEaIPVgHop7KYG+QsGEW8nJSk0cHM2NhjgQjIx/QtIAelrVwRbwiDkNi KFgbDgbVcFAKU4fIMvLRgcNKRtF0YS8EHbIs+PaQHgZtEt/Zu3H92m0dbZlsEmnreiTaVN8Ql2WD 1F2ZPDpHljkRYirTycW0+OSx8fHj7MrqcWrIXN3ibtoiGJl+10wXi3kk3Nsmwtg52A+NAhRtlpGH DxFx6gE5sB2QavqMimnTMUcZRBDJa/kiyaEUKJg0nXdNELN0CoH2QdOazElVo6p14Cck6iOAjOMN Vyza5RTsq0G1LhHOU3CbG8FhiKDFImRQOu+YyZ72TD6fcbgsIuMRU1Ys8LYJ5ZupapC5aRwP4yhx Uzg4Ibpj2f46sFSxgDsJBRnshG0wB21gqQQLLLytYEvja0eLptReTOflUrFkA8CVMkaeN3KuOOeo Y6YtOWjj+k2PPfm0jV2AOMsp4VVFG10tCXExKjrQMhPmRLNoFvMFtErAo7quAN/iGwsVQzgXcJ6m aERORTmHlgqmiVM+FolAIFk1buLHzzw1GArBn0rfdNSAEzgS5547q5G6iA7Tzb9SHKbCjvRh/SvF kX4E/fX7FfAr4FfgvV0B/wLmvX1893nv/AuYfS6d/0K/Am+/AiMMdbGQeaiokBhFWefYT7xnp36B zO9Hu006r6EIIdjMENAu4l0/kppg9TLL2XS6oKtIXOIdJEaVQ8n+YnUl9C4ZrqwVcg4oEYV2q8rS o2Z5RaTQL8aV2NSEugC5vLZz4E5gUHj/TxAK1AeIS5axCcnJKPcbxkT02IOWhkVuISKJRz9DkkVh IBAVqGZYjj4li/MsGowxLHqaES7aH7JqQsbFUBv1PsSw9DTTjrFsLMa/kM0PORsZLTlOdUX4FrVi njNLSm8aAh0L+wlHZ9HIixIFxkOMA/KGWH1dC67fsMXMZ0JyORZRxoyqsgpl+OXMolsoGIBmWlix pPKUyQ3VDRVI0y9ZSKxHOhd2CN0jnbCsqIIucIqAesmI9VJE2xndUD95bEOgMqFGIsjH4kQ0mVSM lMmnzHLadCxDQntDVxUtSzAKZUxTyAG6gGOlc9liMbO9u7N1W2swX0pl8nbUiAUCET0glnnN4bMw e0pSmZdCnBx3JEeTtmt8Udeqm5rkQHTL2tVST7cIJGRbRSRM6YonxENJkJdPSf5lN8MZYVXRTLmI wzSpqW7ObAPdK4u2DN5ml6RyOYBZ0nmonCKa0hiONIVDlQrtrpXthS81UzRTORj+8ukC8tjK2bKV FVyJtE5uUNVwWIJaXEG6vuQGeCvV1w86lYOujxPKqlRyrVGRIByFBlyHWRuuSvS1RJY/5iVLKxyw YIeIAFMURM2h76OC0vI4QRFdT2cCSo9A+6JTrg0m8hk7xZnZkATzaFVJcPqLllbOKNpBxx5TOW38 ihVrV7z4gg0eh1dQEn7ZtE3qEUDcFv/RCQNAlc/mLdA/9CmgBxwgvkQiBkKYyqTQkgCcCywSJ6ce DIeiMTQOxdGJhiM6JGaR2PmnnTZp9CgD2XBw5qJjJGZhztaZo4Yxlt6/Unz7P9PfV1v6V4rvq8Pt 76xfAb8CfgVGXAX8C5gRd8j2z4L9C5j9U2d/lvd5BUYO6rrkbHaomCyFEqpIcgUSBJ0P9b5j2Mu7 4VGm9yJGwFAUNkZ2FLRXeAiR7SBHcIaRvdAVIbFBMJXpiplyWSFPGrCRhBBwad68RjYFiBIppzwd mdfhEUgJ/jsZMh/orRxsj7f8TExGAhdaGvQzlK7FEsXwAlAWMvuh1SD6+sETJnJQ6lC2FomyAGOQ saXaTq+3eGq8iGh65lWkvSADJhLVCavReiiJn+L22ZZAJKxDH0nZsEIL/fgEEX66YMFW+/vMvky5 p78AuyGQh2kbmA0LJREWE6hBMwUx1/aOLk0qV8eUiQ3RYsHu6y/1dmZFIWgD0DlWNKFKAWfq5Npx sycC3bmIgiqayEh3WJ58FN0Q1Wg8FI/HquOxKgRa6fGQAu0T7JyWI5nQBxlQTaHclCqGDpjFEsKl ymhlmIGpzsjkEThfQMdK24C2rGiUjALIDId1mRSNX+agKiIvIVcGlYQUjVcUdG9Eo0uUBvZJ5LQL roiw+2zBLQSUPGiS6CohVYsiNl4MhSLYT1QH9aMDYBlkRS3ZXKYo9JmcKQQa66Kj6gzetpMpvuQo 4UhttGIMqmcUUX0wpiJInA1BU66ILCzwOCKMDrLqDaRxQXblcFgHTKFZzu2HXA6nnyIrxODKoyLh qCRw2YIqyKYs9TmGFA1EQoBgVgHqJ/I8RqxcIZ/OiQSyBNgdIU2k/pUIywpqaHMgaSqCzHCkPEQK XIbTG4I6UFfOKAPVdUt2MqGCeFYXbLcta4e5bkWau/RIuaZq9Uur1m/YgNqUoOhC3WGXxV4TL6UY edablHAr1HQkFGPQFpQN6DIWDRfzWSTKYX4E6xfzBplweaGqumbUqAbUH7lgUT0A+rV44SGHzpxq S2gViY4QxHGxNLPsTKpEFtpw3fwrxeGq7Agf179SHOEH0F++XwG/An4F3uMV8C9g3uMHeF93z7+A 2dfK+a/zK7AXFRgxqOvTnzrL2y0vqIt9Rv8zCAW2xZROrB0cU00xxxbrXUgaJBZ7RcnpAD+ULAVd GG0E8xhYCMAU0sXJggeVlpfb7ZQXHNSE0REUBUoF4EJYjQXGwy6JGCYiZ17vQ4iGIJMifyLIFwc6 ISP1iCALbQ/IQrO4DhreoQkjJDxIogcGgSGRFkYpSLICmxgnp41uvJwlcFEWPsXtDyA9L7+LPJJe szvEfHkgDPQCOiDwiHIJ0iOsHga9IrBW2Q2YttrcYXQnIecSTcsCSgJCAmtDV0C8Wld1o1CAFqe7 uzsQkCNBLaZJoytjG7a0gUFhdx1BSxXsioAE6VJDQ7RhVGTa3DnBkE4NFwGq0giWUmtjtY3xuvpo ZSIYUIJhTouaUgiNBZFdVi6YgmGJmNwqKTYCwkynXOjJZ/pT6Z5kMpvPlnt7kbCOO9gTeiyirKRW w38CnxU5C1Cm7OquEEgiup7jYC+EEswpaQ6YnwDdnSXxOARgP7zNhyQtbwqpygBXl8iqSjkUAOoS +RJkSjicYKCI3yoUTWR+IRe+dVOH3F90cwWo6WAbRN/J2nCg2lHqFGVUQwVC9Ut5O5/PdeeSuULW NiBAc9H+ENuWVRlYCLBMAB+Fxw+QVBMKApez3X7HTYtiT8niEUfmOMFiYVqiukkNyLkskCdUcCWJ kyv19lxSFTldVWxBMB25AuH0+VImbXCcXIbZVdNlTQUGJVgq4gwl5SLviJT3T704cYJyFll1Xa0k ugW3XTRyNeGCYdTm7ZpuLq3b6bBeP2/6hr7+3ubWPORadOradJITZ0Sh6byis4r4KSgnRI4cVIfA rNgCJyT2Dfo22zKCwUAm2Q8lGF4Ti1cIMuhbcEzTmKbGxpxhKK5rCFLjmLFHz5/r6hFQ3gCOF9FE HE1nTHQvfu7s7ab+leLeVux9sr1/pfg+OdD+bvoV8CvgV2CEVsC/gBmhB264l+1fwAx3hf3x/Qqg AuLll39uRBTinn/9zfPreXbFAZMiA0D0Tt7LyqJwcEbB6BNHJNETbcAi7Mm/BYDAjIfU0RDwizQp hKPAxBgNIx7E0ufdcl1dnOAZ2SFFBKPDzoWPUERRKhgymwDSBAFwApIwBtZoVEh9ELzF7Iowf1Ev SIzGwrvgoyRBGNErjtxemBCiMLjU0COS4sLRzK9UoJ2j9C3KGiMIx3LuWew+CycD72BuR8a5aI+8 tQG3mTYPd1sJUiQQISi4ynJnr9HZWyjaQG9OsVgo5LOw9dloi8jICT4B5oCTE/sO1FdTWRtQQslk IZ0qIPi8AGxRsvC4oiBHzMAuYEJV5BORcF7U9VC0ob7x0OnTpo2bMKamDgKmsqAJJof+f8WeQqkj JxVcx3TLsM+le61UR1t3y/bu5ubWzStWr+lqbS2k+zP5JKaGdAskrMwhj982oORCS0NE1JftXrRc xMOu7NqiEpS1kO7qoqWhAyQMf4qApHZHUAQZcf84dWGgzKAvQCKgjK1NReWMU4S6T4U4ShBCio4a ZfIm2i8GEO1vllZv2LBl8xbsYD4oJeNyNsiZmtNUO2ZWdUVYdHozHa3tW7dv24Z/ktlUGeQSFFDT yVbo8lIYtlAepZXIsUrVc2wOCWCGxfUUrFTZTZtmHrMAV5k26F6FI/EAahCPaZIUDrRaWTkSGD2m UYwhtyyBHo04aSCOK5d4NPMMxWKBRBzhbYKqQC2HFULqJVGTT6YkZE1A8wagGZLNrJASgOgvx5ek RCSiaRIi+uEHVcQ872jxyObWtlI2C5snNGi0SssC5sLJRm0QIKwjTkrkiz3oQtXnGXHZ9w6PU5es uK6bSaXAY+lUpBqEItFYVUVFJIZYMGjOtGAozGuQxNWqGnovUFYXBoGH0Xa4GAL1h+12/eMdwza2 P/AIrsDlR9WP4NX7S/cr4FfAr4Bfgfd6BfwLmPf6Ed7H/fMvYPaxcP7L/ArsTQVGDOr697/+zgRO 0GqxZCwvmIv+93KsmI6LUBKhJcAs5nJkNkB4EpnBkF7HlFZ41w+Vi5e3RYCLhc0T6yI3FlOCcfyo +hieJSskzUq5YMjhQuNFRUFWtwBYRa0QKSIe8iuwL6ISJOTiHNAC2yJeQVSLujQiup5HzzsG50hj Q5ogJtTCkvAZgAM2QMNC6tyIkUCvaGCItqhJIeYm1sFImdcBkfaZLIuMttEItAbkifOCiYUpYrhU imxqKaT7jHJZQLAUgscRQE6Z9RALITfKKptFiL8cs1BAR0jsfmW0KiSGOtp7sHERmjAiXJbMlWWn FKN0fbdCCVXVVjSNmzh3+tTDZo4fPaq2ZlStFg2KAV4IgNeRt03IFtV0gculjN7t6ITYvm7la6tf Xr9u7YZNG7q3tXS3t6V7uo181jQKtl0AyoKoqgSZHWqOPQbww8pMAzHy2xCnVlGhNo21wlrJgK9O lBHRDvsjSZBQIHgWiTUi98wWxKwsuJGgXBFvl6W0REAR9lC4NFW0AZCA/wBz7LAsI+f9uVVr2tq6 JT1kKjI6LWZBk0QZ2fpO1tAV3SxxRTNnWGYRPSWDOqdrtD5o+3AHytTgbMVZgkeo2yAOPcVvlbiS 7RQBkVTJkHmQphIwGFfiCvkKWRkdRkC7K0SCQizSZhcq6ytDUR28tCyWDMEIh8KgXXA7ovVkXhBD VTW8rgFmyUy/x3YT7TolOsYgmiVosKDQgpqLh3IKeBb4kkdXBA6wMov8OYMz05zbCw0XzI+2bcJ6 Gg4X0ekA7ArnFrkeyUNLnHfgjPekXeT/pe8L0kQSCVahLxQldOpkZyO+Dej0g25LCwTC0UggGtND YXh1IfsKRmJ1VRVBTWWnqNerAYfI9VHX3vzg9bd9ZyrgXym+M3X0R/Er4FfAr4BfgeGpgI+6hqeu I35U/wJmxB9CfwdGQgV2RFwd4KslVRZTbLHOipQ+xEKHWDKXd2MgyIvxYv3maHtynZG+hTxcpGYh eQvJuABYPD0LtvI2xjboLYhnvSkIYCH4CuIr6JpIaQXYJWJ4zIaSIYldAvdg8U8QTCHGiMEFNL4j rgB+QCIsIgzI9YL5EIIsGhnYCx8ZkGNNCyHMYsCKls3EWyBauAN2gXSA1hCGwz/o3ShDXMSjRZ6m SjDBQbvkLZ4FglHjRUXEnZVEUPrS5b4eNFF0cpkcYsatvAFS49hQT9lGvojMdnAupEtR4Bf2wHK7 OrratrcjQT8QCcNUaCKJigpnc8V8iBOqI/FFMw867bhTPzD/oIPGjo6FY/EgujdCoWaKnCHa2ZKF jpJFrpDs6Wh5ZcXyp194+pVXnt+w+tVke1s+3V+CpKxgZqyioJZlHezIUqERUzXE44uaouIuCmYu 29PTnTaQDy81VNSVXKmjUJQaR9njm3ojCnyCkiCHKKldBBorKZKJ2vI84rFKuiZUVPTLIpRkFMol IeALPlRLlp1C2cyYJiggul2u3Lx1W9t2XtSMMp/J5jK9Sbc3m2/tK/abrXljZW+60+VKekhVwryg SyJ6LeoSsvCDukDJWTIprdC3EjhTwglAAj2cKa5QQgMACdldAmLXLHhD4bKEc5XXRAlORdDHQFCM V5ihQC4g80i5D2iwCJZ53GFltcmtKMswbJYlqaQotiQhhgyZXF6jTxyufMnM49Ri6XCYERAMcsGS JGPjMk4GnouV3RgYY8nezpX7ZAGELhION42qH9VQr2kqzjGKhyPMS98i6A1AYXZ0viAeDOFtcO2S J5b5ealtAjqKyjLEcDhxqYEoVGU2paPR6ScrUKZBtgVNmwOlWTQcroyESA7JvMIEZfHdg5Mck/k3 vwJ+BfwK+BXwK+BXwK+AXwG/An4F/Ar4FfArcABUYMSgLq9WXmaWh7E8Q99OX3o8i3gYNSlkIihC R44DxgSRCzYlpQqEMcAWTBNFqi7STxGK8mRTeI337+B0wF7YXmLIiz4CGlBPQXAPsCaSh4E0kaBH l3kEQikwvAkgJCoEU8TGiJCRhxFci2XKQ/tShvyIETqgA4oPw/MADR6owwsAIZAYTmn6IpK8YJDk ZTpERMVIPENB+MxYSfZGxC0xiRrr4sjAH2nNunr6s/0puBThXLTgE4ThDRAFFYCdjSRmzP+I/cXj 6J/HCWmK0OorlK3ebAr+PIGMgrByckjimnHwIR+96OOnn/fh2lFTi6Vgb2ehZ2uq0JK1t6acLen0 6t7tz25sf3p1y/MrHnniyUefeGTja6s6W7f0923PG/05M5VFW0UjZZQyTtmAtMwAE3QdA/xFBYWR XKOcTWY6u7uA5TQ9LMUSdgiZX0HkWGnZTK67NzS6Rp08bnNIbIMx05YR4gXros4pKl7LC46kcMFg v8C3Idy+bBUQbF+2gX5w+JJ5OwnMVygipWtzW8fmzVuQiYayGaZBuIdMgYBVTtYs5G2rM9Xfj3WR mAuKJSA1lc4PBKhhFg69H3l0AUC0vg3dFpCQghadIEFcWeBsATFVKCHp+cDZyAILIsZspWCmAnyU uprjuQljxwZCwUg4FItGQqEAzh6bB0kLFYE7ZZWT5ZLII33MViVAPbThzHO8DVanqXwwiGi3IucW OKco8AVRLAhinrgVnS0m7KoOH43FJo0dN7ZpdHVtDZybiM2C/C2XStkQx6HFJg43O/Il7CBptxjk Ir8u2XvpjME5ip0dbKuATbExFIwy1q7r2BUUCujNtO18LocmBiQ1RJIaA76oJ05BTy9JAsUR9oPk APjR6y/Br4BfAb8CfgX8CvgV8CvgV8CvgF8BvwJ+BYanAiPGwPjgA3fijTkAEhQl4FJMt4J35QPv 08n25TkYyW1Id+IVTHuCd/X43ENb9FGESAryFE8YxeyKjG2RPIUpwsiTxfONDXEak6m5BtovgmuQ 8ZG4GHUvJI0WLcMTWNE7fvZ6zImxGUhgpkUQDbAVep1nuyR3GhAWibYYnKJUfIpkh+eR2SkRC0+U jA2MLHW0MGSZ4t7yPYpHEV7MuIk4fmZoxI1MdWBlbll/bX13JlUEiwDhAG8BCmNmSCb5IvwmktqM vH486Be1pxQ4cBFOAjgrgnNpJaehomLeQYsvvfgzF5931qS5B8WrK2OOGEZylSPbacfYlilv7ulZ 3bZ++dq1T63c9PxrW1e81rO12ejcbtk5s5DhrbxtAiIVnRKyw2xSKVGwulAWFE5UNTVQFkqZVLJ3 e08pbQQkvaqqVolEs45QlhTbcXVeiMsqksNS+SyXiHDVVfmcqeXIxqiGZA02TXQ/hF0zFMhH9K1G MgNDIgXb8+gPSccLFkXo6wCb0JwxW1i5dp1lAs7IQEPs0ItQ17EUfNQWgi2hbGMkNRzScB4EFbVM XjyKgvfaH7Iwd8Tb47RhwW/Eiwiv4lNEuxOZEgTccw5nQ6CmKFh8RBSrgzGwIjGipyWhti7ByChO Czq0aNQo8brkwlYJ+imbksRHgo4iIvU/Gg4GQ8FgNCwFNC0cVMIhRVXBqHjANUXBlmnIBnk3gIh6 hKhhl6LxmlGjKyoqlGAImVoAVAXgRLvUl0oZRcj3gM5IrAjaxXRXpFWkyDd2urMYOE7CNwLwLflY 6XwFG4VEkc4TOo3JmqjqWiweCweC0Hxh4ciWA+wD8gW0U/EJO+MpT455d0Ngg8N28/X/w1bakT2w r/8f2cfPX71fAb8CfgXe6xXwL2De60d4H/fPv4DZx8L5L/MrsDcVGDGo6+67/8qS5QcyqzzA46Eq T4TlaZpYtBaPt+UEdQg/EYEizc1A4haBK3AL1srQYxYUTs9eROoUbzRgoESVCszE3GMkf4IyquRY 0GRB1ALgwQLkeVkhkRfLhvc0LRT8TWwNLjqgEoIL8CRiIgTnu5BqkQoGzQuBDWhG6s0InRZL3cLU yLBn0UmEU0hbhj6TLPKbGjJie4btCFWAlODVAGAYn/E6RuJ4QLiyJGjJtLthfUcZrQIpbx/5VrhB f0avZEn65GJjWWUwsjlAapSlXwZAQYR/WXEFzeEb6huOPXzphWd/qKZholV0jZ5uq7+9VBacYtlJ m2J/Pr9l6/qXX2ldvxZJ7rBK2rkUGFkZWfQckqIQ8kU7zBAcC+Sn4CdY83hN0hUgHAV9FLm+jp4S J0SqKiP1NWo0atI+Y/808DC4MNUyIq1KAWC7fBrmyJqKunCkKoOIM5I3uSqnYHML6CcSauNL24pJ xL/jQMkl0tih6EBaRaShkejPbW7bvr29EyUnBRI6Y0oSHKCWWUDZIXkCOaSDT1yyXBGPaZqGo4lC I8LK852SA5A6BIB1IbcLB5WKCnGcBZDGVHE2xxu8UHQ57DwPGAQPpKJUBcMRRRcU1Q2qbkgxUFly 3iL1jYLJFFlVBV1B7n5JxKug3hLCASmgY2U2Tg4ZSXBSHtsrskUwDnTVAVTCWUKtG3leDQSwKGDR YCRaVVMjhYPIwndUNRwJYRcEVVV0PRJLVFVXV9fWxeIJWdPgisQZZVsmo8PsHGMcldy1dOrTo9gC wAtnBjaADkxVVDyDcgVCgWgkEtR1+Bs1XQcjJl2bKFVEI5qKRgH0ajRbwB3qthAY5LDd/CvFYSvt yB7Yv1Ic2cfPX71fAb8CfgXe6xXwL2De60d4H/fPv4DZx8L5L/MrsDcVGDGo61/3/g2gh3VLZKFd oBBMqsWESh6pYvFVHihiKVb0KcuVp5R6T6JFsVxeRBchJLJxsax4YKYhCRg5swRSdQ1wAdi1KAKM ITLKJSKgxegHmxMLIL0L6WNIMEUaLiadoc2odyFytwB98DnkO2BiWD3LmCcWwzCVl0APW6Lpxc4z Zkd0ggAF+RaZWMzTm7FYeqwX7Q0xFPiCZ8HE3mFwyM9kXuvsMrdu7UHeVxn8hynCWLiY521k+00Y igXdU8c90olBsRRGlhRKU3LmTZn5xdPOPXXx4oSihCxTtW0eEixeczpSqfVb1y1/ec3LL25a+0p/ 6+ZSfzKfz2adrKtiINA8ICwgIwAZir8XZUqKoiaUbMG442tNksHjUkYGjS7j8SoxFIZSCXwOlMUA GyxBWIadRgo9hEXlgCpFBVXIl0uFUrCyupQI5qyCa5lQxZVEEUFdSU3eUkhnoUVzBdni0IiQrJ14 ChCK7XcmnW3b1kaIiZdw/KBHQrYaQsqg6ELrS6jLcDawYpRVWaqtqonH40zJBEilgElhKNyZj1SB rAuGR+98gu4JRlAcQMAwxHQhWc3g+aJTVvSAGgrUxuMNFZVBOQii6QYkTpdAjaCrQjFQa6BHJI9B 2cWV+ZyFJHslEIsF49FAKIw7+h0G4M5kJkHI8XC4oBzERCXYEnE6SIoWjUYTiSDcsbpeVtWyriL0 y4K4KqCH4lGowgLRKFol1tVU19dV19fXV1dXVcQTlYkKRxSsYg5F8XLuSMvmtWUA5CI6TIQX301I kmPJ9Uw0SCBVhKczHIlAXKYGQmBe4YCOz21erIxFEGPPWj8Q5gV9g3IOjSz35ifP3m3rXynuXb3e N1v7V4rvm0Pt76hfAb8CfgVGZAX8C5gRediGf9H+Bczw19ifwa8AN2JQ13333cH8h8S2EJ7F4BSc gAASxFNYDtdgVj15qrzGcKzlIrOcUQT7gCSMEaMdUMzzcxFf8B73pGGj6muhoOJ5BX360CmQ52TO lfB5GdiDh8pKcl0ZZAA6GIAy6GNwdwE0iJJJaHcI2ITse9dF7BW0QggKhzcOQpggwtg5HsHykDjh zgK/CYLJZa7g9cXz4sjwEZCDjHTMeeg5zrBAr3EeNY0kbAOtGORHrG2fwJVA1kpKR2exvb0fzjyH hxeQnI+UdY5xWDI+2e+ggILeyUFCFMmuwLxkjgO30Dhu0YxZnznqA1PHjIeAhy8UulNp9ONTkPqU yva89tq6la+0r1mdb212LYyfL0GlBpugAI8iGTk52CSdgZmArCjNikyCLC4NLjqa3TGxkybC+y0x GCm6paxF6V35UjlTstDKsISUfBKuIQOrXORt6NJwSDVXyRnGunRXKabXVsezBSOHEDKXByPrcIy2 QraIeW1XzNuC5RbKDnkmbUrWz+ZyqWS6iJaSdF6Qa5QaJ1KbRDeELHxEwgPuQOSlBVEWFFzRgtU1 lSgFZW+JEjLgy5B3yTJUVHogwKtaSZRwbIEFDVBCSmuX0fPS5jiE/COKTAgGtXhCD4exeTwUjgZj aFxoqnyas0Po51gqIYCeThCsA2n2jlQAQBQVPRqLV1WHYgkFofiBgBzQQY1M04LsCgQN3RMdqAAV DRQskQCzqkhUVoJkubJUkKQCuoHG4+HqKjkc0vFJPApll6gH9GAghkeCQUnTsYP0HWBbhWIx1d+H HC584+AcwKmEc8tjqCCcDMy6eAoUD37gytoaTBWKhlVNQzNLxIwlKioxPSgahG/oyYgel/gCAjRU A4SMdXYkoVnER13+L5T9XgH/SnG/l9yf0K+AXwG/An4F9qICPurai2K9nzb1L2DeT0fb39d3rQL8 pk1b3rXJ92biT336Q9icdFBMYYW356Ang/nz5Cj0YrNY6DY1hmMqLeqr6EVlkS0RlIyQB7ofYnsv uIjlZ9FbfpZPz17LUq24uTOb2OrQcY9GIEEZ2ScxCKVxgRJ5CyBiwmLCiB0wLYyX2MXS7uH0ghEM JkcZnRwBBUzkstsWJdlD5IUnyT5GrwWE4ZQsm44thwAUgSrib9TZzlOPUegYiJGFvQaYYBI2St8C bsDeQZID51s59PLKzhWrmkVBLwtQSoGWgRgpAEfkoiMnJQ1C6fYs3AqYDbsREIRAuXzEjOmXHnFM Zd14h1NCmiA11hbT6c7XNm5Z+9r2bS12rs/NFbiywelIZ3eknFCGMVEha6dj8WWLoIqoOW6BR/wV 05uRc5P0a5iCGgGCh/GqGAGYQhi8HSxnHLHPKiNh3RaQCB+oqakPVdfEeCnqQO6UTRk5IK2yWRZd hLgLKVlIB+Rx45oSsp7a1osUfZRli9Gz3Si6sAqWHdksB2TdVriCSLlgEMNZSMEvGOCCkJLJmmJa JjtSPDiXLov4Mo+mmaWSrEahpHIK5rjRY445eG7INIHMSAkHCygyxkj0x7iqzJtwLRbNUpaaWvIm hGV8ziwms7ls2QnW1FU2NXGJmE22Tbte16vEiImDLRmGZMIlGinbhVKujI6LssqXNSjBeEUNBcMg XJIWFpUAnToSh4NkJLN2X8oyjBKRUgjuxCC6PApSmRdtEpFBq1iy0MnTLauRcF1lXA8FcXbA+ShB qCbKkJzJTklz6CTJGGZ/Kp3p7epsa2vZ1rp580Y4LzVNx3kI3Fi0bJw5FLyFKP1y2TQNnGHUhTMU njhhfA3C2RwnBxRZdqvisUQkGogmtEAYpzFOVUFRJtZXK2oA0jOQL0pAEzmr7NYpQH/DdZvwzZeG a2h/3JFcgU3fmj+Sl++v3a+AXwG/An4F3uMV8C9g3uMHeF93z7+A2dfK+a/zK7AXFRgxqOvTl55D KMgL2CYFltdlkfkTiagQ6vI2AKjw0sSpryJMW5DnUAYU4SfvtZ6Ai5gIGwEPgAGRpYvjvPx6hkXo fbsXDu+l1w+iLmxFLIR5CZllEjyJMsvJ2+jNgSHxUnIvEs2CtosROiJsJdLZIPCJ8uxhbKQ4KAr7 4vipM2u8g0bpW7RyMmDCx8ZWAgEO6bkwDsKbwKmQLlWi5ou0fihrYKHESCYMgG7k2Re3r1ndIiFc XoPNDlljcM0p1DMP/wMDDqSSU5AV2BdWD3teROJG6cFPHHXc4rmTAyV03lO5gCTVNCW3bHr8P49m tm4T7IxgZNDf0EXcGNiTBBugzUHphkEBAuHOBI1BhBkZLmVSdhFL40n95HXpQyyUic0D8brRvKbl zSLgVqaQ602nYDQsgs4oCnKmRISyq3oc8ioZ5ApZXny0oVYSpLyBvoJcBunxAT0cibqmm8vnO7va 1mza2JEuiJrigGpCYgdfIBkSQXtMVJRwFToP2mZlTVUiHu3u3A72pKnok4nwMKRgQRnGF9GfUtCj oahlFGpj0WMXHVofreSgVrMxf1lB7JdlI+YdEj4B/RldpPjbom1l02n0kyybdjZXyBUKXCAQG1Uf rK82w0FeVXVHqOXVqCCnkXOmQb+FqexQqWAbqbRpuKKsagHOEsVwOBSNI3xeknQo+zgJXSUVgCVE gQl2sWgahlnkgsBXkgrGabl5TIjTE6uH3xNsTNWqKxOhsK7oiuE4eQt2VQeCNXT/1FjLA6to9mUz 2VRPqrurc3tnR1dvb39/oZjDcbesUsa0isUiy5MHJCN3J048LRAElo1VVo+uqZZgcHTsvGnh2EWC YT0QhJ4LOWtIAhPVoBYON9XE0WAAK2cpcC7oXbFUrteHMZbev1Lcix/q76dN/SvF99PR9vfVr4Bf Ab8CI68C/gXMyDtm+2XF/gXMfimzP8n7vQIjB3VdchaRLNZIjoUmkVCLTHjgU9QnkWUcefiKuudR 5jyFYlGwFmlmsC2JjMB38AXe3jMBF9kfmbYLT5E+ygstYh5JQAfGrejG9FOeDIyivSjrijXo8wyR zCtJT7FceU8dhs0IrlF3Q7IQUso5exCePFJmYQoshM1D8VCwgs2fXzeA4oiEse5/pDYT4TmElIey 0VkyF/CWl8cPHgPKRK9lufgoRB4xVk7omeXNGzd2Co7syqQLQx9CDhSIlm8BhzE8Rl5OrIncZ5Ba OeWKgF6jisdPmzYq3iB39gd111DEQFjp6O3N9fSE0yXVNN2gULQM8D0M6khw/IlEEAHgYF0kDgj2 hjtT0SFHn6Eui+rvYn2osmtzZVVXq6tFTaNicxIQEpXOM9CxxobwDiKDjBgTtHCiRF8DNkEEZ1sa LwQ0vcvhCuAx1VWgYWvbNnd0d+dtjAR9GQ4/EUSvzyDS6Rk85PFCdKFsHN3YVF/72ob1+WxOkDQj n3etIsXjBwJYe8YohYIR7Iom8icvXTqjabxhwO6XByUL0SlF3RjB44C6ZGjmsNuuk8/ljGIBzQqL +bwJ+6euiaGgiQ3CwVA4rHNCgpfD0GE55bxTNB0b64m5pllIp8GvcJ5C5CeElGgE6fFgd6qkUj9Q RYFVEcIophW08qZRAEzU4F5EKVA9RILBTCmjWDYnIDMeFsxoWJd0FTliQIsF8qSWVZ6DXZb6MZAv 1Uxns509PW3t2zu7e8x8HsZMxzLS2Uwf/i/C5VnCDkAUGAqF4pFoMBSKhcM4HKqqyVAAlkH5TOwd KopMLoWyxMgbqaP0Ycqpr45HHIToS2BrRDg1voRj4aOu9/svk3dj//0rxXej6v6cfgX8CvgV8Cvw divgo663W6n32Xb+Bcz77ID7u/vuVGDEoK7PXHomYSPy3xHtIX7ErIyMXnnZXB6MIj0XqyW1OMQ/ 1GOQ4rah7SL7Hm6MDhE6Qp48XoF0JNYwkNKhgFrAGyARQvY3DcFGwiyDehXiXF58lvcfhcrjWXJE 0vSAU7QIWMkQW47Wg8ghlxQErbtmTlBDLPgbjf/wMtm2bUkPQWklGujRJy1aTKhLgoXNBGaAlQ2p TuBAMrE6NiPBPZqDGBnmZKFdFDuP5ylITJZhUoQX8ImnNzZv7UZqGNERwoIigqQcCJ0kcCosiqRo lLJPdArwg+Lr45oaEOzTlh5x2ZIP3HvHXd2dW428CcRRkgXbNaOuXGFZ4CeWnYdsCjubhb0OYiCY 8aBsQ7QW5FO0NB5kCjbFMjALPmEBWUTWqBzQWPFCKByrH4WmlYYNXyFUWzzmwGaUac46DagwYpYs ZGhhYdTUEklYqmDYJvo66rygiIrJiSnOLcqSWjlqZce2jlQyVypRNhqOANglItM8yIiSI/++BCOi DQ8iQq6qq6o6O7abeQMB9gBejllEo0J0QgRgghYpFAjpkUjZMI4/7vgj5s61LKc3lwPfiil0btDK UHTIq8CQJMkkaR+lntmGaRtFMmZifwXOxAmJHZWhehKqNE0o0UlVKGRg8NSwQKOQzSYRUpZFgBky tMKVdbU1wShi5IMoo0ttGUXEiaH/JLgfSggOaIE2UXtGWQVNIhkfXohjil2UQJg0tFwMoCT4FAeQ 4t2w/5B8MYUicBrOyHIylenu7unq6yvmc+C2UM8Vctn+ZDKdzfWn8yR3RF6bKMVBrqIx6LaQNw/6 is4ALAXOLCF9Hycr5IeiFA6FQ8FgBHlkiqYgOCyGMDIdj1P3AWakVfgyUFedr+p6d36Gv69n9a8U 39OHv9zz8HfPv/bxt7WP4Qt+/I+PTkd4p3+jChTW3HzZFX/ayooRXvC1G69ZStcYu7+V1/zhjMtu 80IUuCOv/MtVS6veoTr2PHT1Odcu9wbe+QAN34z+0fcrcOBVwEddB94xOSBW5F/AHBCHwV/Ee70C JDvab7ef/PhH3/rfb8KetQ8zAtHQHaIkFloF0MPwAu6UXeXZEZn8CqwD4fEgPcytCO4hoWEdIpug /aLIK2hoQI9sm0KlvF6NEKeQZIYJtgC/YNbCpoTJ8K6ffIRMDUYZ7Bbymiyknttli8x8eI4SsIC5 WC4VArZYY0N8RBgSFFd6lFM1aIukQFjUY5wL7Q2ABonSJItvqGuEXIY3QIqUcFUV7HW4F+GpQyA7 7QzUPWAOCJFn+fasKx6FdoF2kCIMpkHAIPTeQxaYogGJQYuG/SDnJIgPEE8Z4AQuNOi4VKcklxBj 5cIKB4kQKeIos4u6UuIoUH6ZHnQi8XvWvHbFP29btW1df647Y6S6+vIpOB/jjXai1ghVoEshuIcl ukU4JQ0eijezZKSNYqbspmy+v1DOQaSFuQUALAiZyCnJFHhYMURCGtaJ3oIKNFLFTCHZm0z2g78I GI9ooOXaJgRHyM6CRVAsWWrZlmzDMfKOlReBqxzHgsaoUFBtI16ypFQmub0tk0xRiUFbCJY5kmOB tmDfqEAU2c+aJZKrUTQss7O7mwmqELsO6sghah6eR4NixkhkhaMK8gb9koXMsmgoXhWvq6uurq/B QQlVVenV1eHa6kA0LofjciQera6taWyoHTumcszYyjETasaNr2gcHa2qj0QrFeTDI8VdVhFX7yoy crVwCio4SYxSJm/ni2624OKJykCsJl4JughkB7IEcuXIMlAlh/NToah3SLaIs6oU4hWE4RGtHEU4 HGWYHwVF1iC60lWYPEUV5AvskmR7iuhqMk50TpGoZYHtkoTPkZSyHAiGIolEIpKoCAQDUIGBnAV1 LRTUIiG9KhapikGtpUUDWlBXoXSL6qqu4azEHT0XNWTsRwC2kLKPPDPWZhTlwjcXAJvXQQGnqdf8 wbP47sM3tf8SvwJ+BfwK+BXwK+BXwK+AXwG/An4F/Ar4FfAr8I5XYL+irkwm88yTT37ve9fuA+1i lkXQLtJmIecKbIqS2tGCkGgWswKSooUpvUhmBfEN3n5TEL2n5yK3I4iLygKHdLyTV/DeHHTJMKnV IeAIhEt4tx+AnwzxWhAxoY2faSDsCcFUEMXgTgFPCNnyKAopaQYgF8l+6P0+U0p55MswdT38kbPO OvmDJ5Zl5awTjz9p6YksCwwUzNRiNWd/5OIPffhjy448LpAYdcpHL77sks9DY0MyG/AX0AtS0xCP IvclVGlltwCwZJXh+aP8K8cNMF+bLKB9I4U8IQq+ZEDuJQd4OaIqHIARArp4Lh4IQFIkc04iHKiK RJuqasbU1MWRJo5cJwAipn4rCSUbDRIdoZAsvPTiK1aHU8qqfUapgzNX5XtX5Hr7Ra6kBvOulBXc NtFt5bkceh1apYLIJYNiq+B2OU4RXk/bBawySbKm8NAYleG1k1yT3IzgPoKm9ljFDb2dLT2dHb19 nckUPm7r7Ozo7trW1bG5a3trT1d7Z1dHZ3d/Z0/f9s7etnbwrHTz9vS2znxbv9GeLrT1p7e197W3 d/f25/MFVJ/Mpyg9A5xUdO+AeJ0FmLYL0f4wC06dNK66upKaTvIcDjpFtSkMDBKHgwhPIM0f+mji nBGlaDSkx8Kxilh1TUWsKhGpSkSrqiJVVfGKykg8rkfj4Vg8gj6IVTXI0a+ua4hU1YfiNdF4VUUC k9RUxxLxcCQYQPwWErMUiM3MMp8vcTlgVQn+v1ggGDN51eBlh44bwsUU2AB5WXWgkMNdBALlqZMm tT2gsxb7h9MYTA4AFCcHZIey4MY1gC0IABGWhj6ONnpdkpMU8kAKi8OL0VKTnLuAmNBeRaPRYCQS ioQjAYAuGSd/SFcTkXBFLIK4MHQG1QHPANggWUPfR0BYAcxN1NErVIdfEf0k4VsMMBbMOBcz95Jz lXEu/N0fd6Z9Y05d/+ZXwK+AXwG/An4FDugKsI5G/s2vgF8BvwJ+BfwK+BV4H1RAvPzyz+233Tz0 kENbWreBduEjPqfMrLd9u+fe24bi4T0FCRPuDMqdALRYhvwAiqJMKzziyaFEi3gGvT3H/8S1qCEj jUDbMEJA2IwsgaR0IkcYCb7I9IjHIQcjPZXX+ZHYCGvJxwyQjKrRpKTBom6MDEwQcnFHNU06YuGS YEV9b8E4+YjFliM/t/xZJtwRRo+bcOTiJY++uPq++x8YVV2/bOlxN/3pL3E9xWbxIALtC4NcDkib 1wASPA0fGcxDHBVwGPx5BN/oiZKgCkFFDJZMLpc1sL+xaCLAgsTRcBCSnIAso29fAR31DJjSSI+G GVAR7AicmpEgYtMLuUwGDrVaqHtkKVfmemWxxcy35tMdqTTS4hOKIwTl6sZJSjRspnqr5DCnaNmS mwPD0WTglKIuFcIhQ3F6ODsplIuSmxZKeREfywXX6S5ZHSWrxykVeA6NIYHtsEugNwhih7HOJHkc 9HEwPLpoVImAe6Ssg9kgIJ7soVAqUTIYZ4t8n+uksXuhaG8hZ2L/AYbgwWRGVdYNgEyZOJgoIO0d sslKhhYMGkUznUyFIlEvKJ90erDBgt6oyASDEs6FoglKpfpx4+dPm2BJ2FU66HAjygqL0lIVIFLg 0QA4KCRXEP2pKlATFgc/Ik4YuDnBjOSATi9TJWwCGoTEMTqpcHrJiOoXEbXlahoXDOmJSllWNKT/ ayqKT55HXVI0DRledNaxMH1ATYSeUfNNiXSMNthnmXgWxavxsC3KZXI7Ssg8Aw4jSMVuQHcwjiJr jjpyAmCiErC2kkCQWC7kiDlgXYzs8hoCuVRdULD7Shj5YqEQWBa5etkJRqo4cFvKUcPI6LKIKDGS ekmygiVBFKYpyARjTkn6LmBAls5EN0SW3+G6+b26h6uyI3xcv1f3CD+Ab7F85DSOnn7w4sMPZ/fF s2I9L27oG3iNdsi5X7xg2RLvqcMXHzJl/NjqoBdm6d84u2fFfQ+sSrFKqKMOP+noceE9VoXXK2cv PnLZCcfTff74ukSQ2fffgVthy2N/f6rdG0idffzZc6u9v4oM34xvtuhiz/rlD/7x53cUZi55k2q8 A7vtD+FXYJcK+Bcw/gmx2wr4FzD+ieFXYD9UYL+iLqCVfaZd9/7rLzvKwdLivSsmQk3eBy+pi0Es z0JHb8VZTjxLvKLYqIFXeV4rNgA94kUcDX3JnmEgi+x9DJARYsJbf9a6caCRobcYzwKJG/AXvgRo A+ZweGnKjLlTx05yeG1c4+giJGi8+/Jrr+HFThnSp5KiVyydN3Xy9Nm11dUFSX3gz3+cOCkBrIAp wS8wIzgMxvHWAOQGjgCOQWukbpKEclSBV0HyZBAVLaQEI2o4l7NeXtvc0taJ2HTTKnf19WTTqWQq FY1FmxpH6+EY2I8FfAaAoSHdPYi9JgmbJCGYPJ/JWaYZ5PhaRcVEWZdPC2pa5g1Vwl7HynwjDIi8 DakazITFTDpBrQwVExo11wlJen04YSq6oWqRSKJp1Khxo0ePqmuMxarzLt9rl4qCYKE6gQDUd6Bt YIJoGEmVxa6hgGBGCJxCneHChOfS4W2kX1HJ8ZQMRyLFkfGCo6oFRc2LogHopWr9xaKrSjZETIzN EHZhjjoS2DEsSSZGdDPEv3AQGnBVUq8CEB/sLKVUwTUK9yg4nUKASQ8GEPle1dA4Y8IY+PMobA0M DAyJrRIDwlCoKXhMLAG8MQEhfKjU4ICS4HkKgqeGnTDTEh1SsBQQKBJm8WVJjoZDgImgZqhAMBKF lgqSQkeScAcUI4pEdkXE0pP2kCX8k8YQWXQ4CXBKQaAFO6xFyXTwgmIJEoEzAnYAhHC2knARfIvu rPsndG7ERon00vmCxUAAiPh5LBIPm1AGgpoRRIPzVdE1Em4FNGxCoItqyMLmBkrKwC4kXWQCxunN Ex4MgcyBA5K6C3sLEyUNi2lB48JQEg7bzb9SHLbSjuyB/SvFkX383mL1vBwbNW7c+MH72Mr8ijue ah54kX7Ux756xiETBp/1Odcuxdwr1KVGqqoHb/F3jHNhPXtEXcM24+7PqFJy3cO3/+za79x41wsb srVvDv7e099R/s69KxXwL2DelbIf+JP6FzAH/jHyV/geqMC7EEsP9yI8jNB2LTr88K9+9Wsq1C4H 3u1znzvfA2F4Dw8y4oXee0ZIRsmY+sVjXay7IkgBMxeSysYqCSeefs7kxqbVazeddtRhtz365DGL 5v/wJ9flshlRdeAYq42PjTaNP/vweatbuqZMmXnzzTc1NVAkLPkfXTAcEfomhIJB2gSkAB0Oxbhz 1IwRTk1AGYIi0BQR1lHr45Vd/dajz6/e3ra9vy8FHZHLYwGICuNgQEPE2IJDFo4ZO86C+4yGgOoM rRIdwQWzMg1kgyGrPJ1q2bwlXygkVOmwRJVetnoNo4fTN5XS3aUi0NdiKb4wFIxy/X2ibBasAM8F SI4n9UEtxyGIPRjSdE5FtnsAsrGCheaBRs60ewrFNsPIwSkgwUcIjMUBqJiFompzuqKCAwGfIOQe MMaRgW8ECVjLtkOcEFAEjeRtkCaZ2aKBhHQQITUUKAfUZLmcNEuWom7q7S2rcpGpuVB+SLOI9yDT nZpwwuRHDTSRvhWN6vUNNelkNt2fgZuVlEfhSDabQSlodtcF80MAWzAUQH7b5IMXfOSDHwA2pMQs CQom5otlPQCYsg8aLkKNgFy4Y+GQplFwG9LbbZuH5RQIjbLhEdGFR+E4tXN5s4hDKIrFAoRlFjhj NKgJCopB8iddkcCYVD2owVqoQPnFIwPOMSm5jF5umXA1QrwHM6vplNHOUpFEpMjhBaBRROAIP/EB xNpjtWgqSqDKS88ifRyaRuJIg3qJOFFcx6Slmr29vR1dXSkEpRkGkssoaR//YxcZLPacoB5Bg0iL 2i+QuJB4KmRrBMOoV4MWD4cleGBF5IJBsAZ5lwgyJ0KvZ5VGhYcRdfmprgfez8gDYkV+qusBcRj2 0yJ2Tan3c+jfrOx7E0s/fIdvT7H0wzfj7kc+MKqxv/fan+9AqYB/AXOgHIkDbB3+BcwBdkD85bw3 K/AuZOyAbYFwfeDEE/c5t2s/HArmjiRpEAgAy+OGiIWl2jP3ImEp9jzbxuuHSGHo9F4frIUXahMR gSusWrW8tT+5+uUXDVFLhHR48jBa47gJF5xyzOnHLIHC6t8PPQB8c+VllxWRSW/auaJFHymZiwgb 0vNBHphCSOV5FS33NGhxkLkP9b8OIhRU3ECqt/TYc+uWv7iqkIOkKVDiVKOM3CYFaqmcWUg01DZM GhMbWy00VgsNdUpNbbAiHopFlFBwVH3d1CmTD1946OKFhyw+4sgps2fE4wne4iUDYipR4bgALwYc aXRtw+gxjcUSyb5CQFF22SyaKXCcdJnrFviuYr4nmTWK/X2dfWvW2Zvbq7YZFe2209JjdXeEyxyG 4uGlK5Z0MJxsUYdSC648y5QcO5fL9CX7e/v6+/r6+/v7+nO9fYV0Wz7Zmu5rSXa09rV2pHtzVj7t 2i3Fwppk34qO7auaWzozKdJoAeiALIGjQQZGKiiijFT/gTx8srrimGEb0BxN12Dpg0wMpC9fzOWK BTApQEXKpqL+mwaUU6ZlovEAGVExDgXAQYkH2ROshYh2U8CVQOZUGBUlAYgKLBKASBGBq5jgS1dD oUA0jB6FoZAeRGi8oAR4RRPhf4TXD2SL1FsKoBQTUiEyiyAXAuGRPY+R6XQiQse6TrJenyWKOGNC NQnPUy9ONE0At0LrgrxZMCzDogYJJdAsGD0po8xDsCxDjroZ0I0IF+AXYTFJhCRNUiHgCqHbYiSW iCJxLIz5sStMPUhh/pibMvLBszxvLsE8AdxWwNQSqSQJu6LFAMgfDJbU/JMy7JDyRqXFdwChV//m V8CvgF8BvwJ+BfwK+BV4j1YgnU6/8sor+xA3/B6th79bfgX8CvgVONAr8C6ouoZKctttf/7Db397 YGq7PvvZc7FOFgZG7kbAA+AVZnMktuUJuPABz4GBeY5JQi3MTCmIKlrXZXJQTZUSkVB3OtNUV7m1 rU0PyGgFiV54sWC4XFY6ELGeTI0bMzoWDFbXFgbLQoZKZof0hESK7WoEanhEkoNflHgHEh/VKSs6 rxdS1pMvr21p6wVYgagpnc2ioSPC64F1oMTBQidMnDhz7qxwIqqGAWNiIjr/gZ9ZbjwU0QPBqfWJ hBx+dXtHKZPp7e/f9Mxz9vMrIvnSllK6vVzOaGJeEA5fcvRJ1bUr7rkrYqY0RcsXrV7E9csqbzsa qZxKXE18zkFLt3Z15FvbNM6I8GGQqDyX25ztTVqiYXOIpELrRRvWOQkpUQoVUibFkYVWjWQr9Lpm ChLMgdi9Mto8lgMQPUGyFSpDpWUIiiWIOcfJGRa8jVosqoZi3ZkMIsConyI7PF7GvkNZ7iQ3I2kT iIxVRAx9rDJazBuUesULRdPEC6wSUBLC1+H+BMpS0/2pqtqqctGYt/jwc088XlRD1IUTNj0EWpG8 C7SHORpRUtLysfaYNDx1sqRcf+Zgha4K0Al9OcnKh1uxmC8Y6RxpuaDMyhZxyEpwOIZ0pHSpaKuo M+yFBaBBgoaWoPAk4hwr2ejXaZtGgUyuNo+elXTelYA9TTxOZxci9WXGp8Db0BYR6A0sijAs1kb2 Q0KuDH8BAELGRvZGomhYrlGSzHw2nU4W8nlIxop2KYs+Agb0Xq4MQSKYGDNm0vldtrFsrABfgNV5 Pl50gkCUGxo1CjLWC15Jhcd+QAGHkkocWhOUG/ccBfPf/xT0/yj639fwPTmC/0fR9+Rh3cNOvaWq yzF7Xnv60SdeXL1p06urWrim2dPH1E+Yu+iow+ePidJfqLxbec0fzrjsNhJS43bklX/53JStj9z9 wPJ1615LJeYectQJJ3/goIZAqX/dU/ff/9SK1a+8Wmw89OBDjzhp2WETYt4g5TU3f+jyP+bY56Hz r//zKeKTd9/7xEuvvNiqzz500ZEnnbp0esWuQaS7LixfPfngqeMmvmFh3M7io7HnXv+zD0XXPvLv R59ezi+99ktLq2jycrb5pcceevz5lauXr+3C32tqp82fMn7CjHkLDlswrUrfqW57o2N6fUGu8uZ6 faFuubhu/WMP3ffvB17pcMLjFxy77IOnHL+gIbjTpLnmJ+7424PLX32tWDt9yvRjTj1tSvN1H752 ubfFziq8Pc3obWl2r3nqMRzF5o2rV7bokw8eV101fsa8hYhjq4IifJdbCfV4/MHHl7+68sW1nQ6n 1UybNWnc5J033vlg7fzS0Pk3/PWj0wfOip1n3Gp6g8xZdNQRB40J7zhv3tbRWfHUU0+9uHrzulXr O63Q2HkLFxxyyBFHLJr8uvNh153wvxo5FWhpbv7kxz9WN2rU8cuWLTp0UdOYMW9n7f4FzNup0vtw G/8C5n140P1d3v8V2K9ZXa/bvZkzZyKr6J677tqHlPrhrtSDD97F2s3RdRVL8iIK4CXHs6m9ZC+v iR/hFq9bIrYCuYEiCxQB2AHaH2ALdCA0bCMArRdP2VP5fD6bL/Sns3ihrkqgGIVivqYaOipS5MC1 CHpCweokLwJVkQJqCLSIpDcQKUHEBHeeo2iCVshYz63Y2LatQ0RQekAnBZCuFUsmDxOdYyECqqam FlAkBfFUOrm5pblr65atq1ZvePW1rm2txWw2yJVrZK5idNWYxspRDaNCNfWpZNZsa1bKpbRdyMF2 p+Kun37yKTWRyJYVLysOEuORJV9OKQ4Xj2dKORkB8xzXp3Di6NqN2e6udFc4Gld1BRFaBY7vd7g+ E348kDmSVlm8W6SFC7wmQasG1IJ6SQgdc92g42iWHXW5iCxEkP6uq2E9CKNcQgpH9GhVokYLhduz OTAwV4GdE5FYPDK/sGsAjig/6sYgD6FB4jrEfFA9aqqIo1dfX4eWiP19SSRkmTYaYFpgOjQ1ia1U gB0wuGg8hhePnzRl+qQJhI8EUYNDE+QN0JI4DqdA0EWaKyJKxNVYAjxysmhKgUcMPfEwxorA2iiw C35AAqCc4NiQYAEqaQqMmWQXpOR6ls+FrgH4EIRCjBlhKWwLeAtSKQf7B0OioLCeiGTtZHIvUhSK CALDAPAO4iMJsmjPWS8DCgZjllo6dwjRUucFNg49YeOsQdg/QUWSJXKslyLwFk5UmCaBREnXxSLP qH44CW2LaBlsoQR2YXgliRxaS+I4whlLrA9glSRjqCS2onwx8L2oNowSUT/qYrh/4o3Q8f2oixF6 4PZp2W5hy5M7srp2jjmn4aye53/35Uuvu/fFtVtbO9MW/viQ6mpr3rTq2cfufnxzZObCqZUDnTPc nhV/vW81LOV0C0ScF3/78ztWNbf3QUXc07L+hUcf3xZuch+77us3PbG+uQsy4lx364aXn/z3a+KC w2ZV4jc1hwH+dt8qbwC5OpD++y9///hr7T0510p1bn71uQfW2LMPnVc7BJ7esDA319e+u4XtkiUf mzzJuf/a6+54aVNnqnHxmUvGBYVSx39++bWrfn3fK1vbevKehDnXs715w+rlTzzwUFt4/vwpCXWw sHuT1bVLQcZ4c7HRdy5U96pH/nHPI1hNjma2ku2vLX/sCWPy8QeNGvjJX2i+47uX/vDeTVibmepq 3fTqfx5fk3MLm5sH+gjsfLz2NCNEzT3P3PilL11/93PrN7ei+NjDvvbWbRtWvfjE/Q+uLI09aMao 4BB8Mjue+s1VX7zhwdWbqfi04lK+p93b+OGt0XmHTErgcming7XzWafMOuEslpL/+hkHB1n+yD33 b454g7AX7lzSNx6dUs9Lt37nimtufezljXQuIXQCp8P2rWuWP/mv/2zZ+fTbp1Pff9EBUoF0KnXP 3f/MZbMrXn4Zn7yy4hVc/1VUVOKK7k1W6F/AHCCH70Bbhn8Bc6AdEX8978kKvJuoCwU9YGnX/fff QVd6zKNIvRVZj0W4tVhy146Ee+paza6CmJ+ReejoRp30gBDwL9LAJRmvRvc/8qUxKRC4gMPClTAO Rgb0KVVXa8wCycRCiKZ3OTQotOHRQwAVkAR4jGWU0GHRKSE5SSgLRsEF59q8aRuUQNl8Pp1N5vLZ EkAJTQoc4TY01h86f+5hs2YePnfeYTNmLJ48bd6ESbMnTpxQUwMJGHBOa3fnU5u3bNrWvGLd5p6u fr2Q69i0NbtunWRbXVbBlOTEhLGRsWM+dPiCTGdP2+pXIuWSKrpkXQuro8ZOzBQLAZc3kC+laWVJ dQqFYm+v2ZMU8xbYS7AiEampzopuSnSligQfjUycNCFanegpZm2Rc3S+tq6qvramrrJqbKKyKRCt 0oON8WgloJQkhhUpKkk6IEqplLftftvMlEsp0BeBL9jlUDReUVUDE2LBKCCnHfs7EKJGxIsETQQg qQ0B8Rp0VozFooV8ATolxG+Z1H+SVGRARQqaKQJ+mSYOWTASwStnzZk7fcJYhGGhHSMoFIAVYBAL nkf7RHIQ4kaWR5dBLhYD70W5e6wKRx/0hwgX2f/oBtAGpVYeNXLdBJiWCsKlUD6XJIY0OYQemVCd MSpHfkP8X4I10YboDA0iybxISfCU/U4yKgqIBz6jFHuKsgfmQjtPUnMRecWFP0vG9xpQslOR1F10 YtI/1CDRLUMWBkEfJY+ROREnADVPRBYYcBcQG8LqcdpBEgj4BdUf5HQgbo6DxRALcznYHbF4NMKE 5VMGwyXBmETdBdi5zqRnpURgGFsw+leK78lfP//9TvlXiv99DUfOCG+Gutye//zsqzeuMBjxEMYv uejC0xY1OW2b2wk65NtebFUHQdWuBKevO77sS1d+ZNnswKZnXkuxn+Vm6/LHVlWd+Y0rLjhxtrbp lfVJm8Z0u9aUpp102Gi8o92ZnthbWqyjLvv6Z844pDa3YkWrSVv2rOuoP/K4yRF2beB2P3rd5Te+ Sg1nhhY2qrhhfRdExljY8k3CQUfOob914bYzTEm99vxLbbQNbh5+6n3yZ//7+5fT7JHwtNM/94XP nLm40V730tY8HjBbXt5WOzTprkO9VQfGt4W6ytpBH79ql0JxnNEnTD9m8WjosDku9/Ifvn7LBhSb blWzT7/gQ0fVtd33wNqBXdi1A+MeZ9z+6Pev/v1KT3EXaDrq3AvPOWqK3PNqcxK/x6zuV5/saDz2 yPEDfTb7n7r+8htXeoWVZp3y5S986vTDGszNrzQjjhMHcc22msXHTo4hELRm4rTG0sbVbZ52Xms6 6ROfPONYdOwcg46d7htmPHNRXbFlfSc2xiAvrRDnfWBuNdPovenRKb302yt/8mQKvyGRyrn4wq9/ 4SMnTBe3rNxIJw9Ov/bQosOnJ4bxV6RXd/823BXwUNePfvqTT33mM/FE/L57//XMU0/97fbbe/t6 NU1PxOO77SzvX8AM93EZoeP7FzAj9MD5yx5ZFRhGIcbbLMS555530cUXH2i5XYSmkOEEFRXbDdLD MJEXqAM55kjEQzlLiGqCy82LL2fxRgNpUXgAaeFkyeOAE6BtIgiDiHLKTweiYAIgCmei5nhM/IMA JBaAD0yBJxmg8MLvXbjNTMS7m3nOLvB2Hq5IDLmpZfvmLdsKppOzKLEKiVAqtES2hUtA2S7JttPd 2v7Yo/+55c57f3nb33/+p7/d8re7/vqvh+77zwuvbmzrT+ayeTMeCo+tqoCXbfPm5s4Nm+57+Mm1 r7yUSWXbMmngHyCP8aPqZk1oXNm6bfOmDYg/D/MiEtiLWCcnRRP10dqmZCBsBit4Ifraxq2tzdsr XK1SEHTTcQuIYHdmHjz/zE987MLLP/fpSy/90PkXzF1y2NyF8yvHj42PqqmqHzN5wvTDps2dNXoi PJ2gZFY6l+tJpTtT+e5MDqld/ZmeTD6VRb47p3ByTAtNGj1m4qQpB8+fP3PcxKoQWQpAeRAzT/5H FlE1mJxGCiOWUE+FxoI7u3s6e3pxDFBMdF2kJCpBQsUQoUUCOtuORKAkU+PxeGMipqoBEdZFYB38 p0uw6yk4iAIRSuoVCTTEiCawFKRdpJsC9PTcrGhryCHsvySRcI+aFIhMwOUi10vVqAljKABahCwv RQ8Q3WTGSxW0ihlibZwiJWTcoz0ioSePpXpiL9pEVMC4eBGnmgZvLHpEYvED8jHka7FTBQsDW8VH AC2QNVTGdmFlJTJrI16tBNRlgLuRqE6Rsb90NUZEjjqKYhs6R2FaRASYhbQ4K29YedNK5otd0AQW ikwfh46TgkVclwLsPKSIAoDKkbcXaIw6J/g3vwJ+BfwKvCsVsDtffOzJNAMNXPyQK77xlfNPWXbG J7965Rn13nKcVbfft8azHO56m33ex09bOH3G7GPP+9gJtYNPaRPOv+jMhbOmzz72wo8fOySUKmUK A1KwnYfQjv3YR46dPX3OYed97LxJA8oOZ9UzK7Z725rbnrr/2YGfjuG5l1/FFnbJN646MzEwyPo7 HtztwtjTMCguPf3c8w+q03iu2Lrm+d6BFwWOu+BjH5g/fc7iD134oZmMNNEuvrC6e/h+Du+2UAbs +Wxyt7j+xcch82Y3afHFX7rk7JPO+PSXLjlyVyPnbg7Azg85Lc/d+XTSe0SY/+lvfPWik449+cNf /fJHJw4UtvT0/U+3EE6kGbeueWFwd/WTL/josQumz15y1kfPmD4wYm71k2u7MU7V9KOWHnXwhCGD vVwz87ClS489anoVrhN2M+Oysy753ytPqfSujUvb//7IKgSh7va24+i4uXXPP5Ic2Cz4gTPPOmzO 9PnHnfHBGQOvc1Y/+FL7HkZ5i5r4Tx+YFZg9e/aVX/7yq6+tvfG3vzn+hGX3/+tfX/3SFz/1yU8g mAUmxwNzzf6q/Ar4FfAr8D6swLuPulD0Idr1ta9+paur80A4DAxbgUaRBMZ7q08KFvJ3DeRvA0hA fgOOQLKYMnxmBC+oVyACoCC6YdH1Xn9G9i81G2SfCDACEl+AZIZxDRAuIA4ZL0VzPiSZi6omQVeE CCdVU0KqonECYsDzpVIGjQ2NXLEsOKZtd3b3KYFQ/aSp85ccc+KHPnzeRz7+kQ9/5IRjT5o7Y968 abMnjRnfUN0kiwHYKFO9+a2bWpe/suqp55Y/+PAjt//jznv+cdddt/3t1t/86ZE77nvykWdfffr5 F198ZfNLLxvdqbIWssIhU5cRSo/eh4ptF1u3Z9Jpji8Uda6vXO7jxVaz1OEUY1OmRBcssiZNSkVi tqOg61+PwLXbdodrpgVre7anJdlVNIt9/R2PP/fEE08+et+TD/3n1ZcssxAMxZsaxx41bmayM71t 9Ybtq7bmUmZK5HMSl1ekcjgi1Y7SJkyKzplbs+TwsYsPizc29ZuFla+tevnVF1etfmnTmle5vmQU SjnKbgcXJGJD2AZXkZRoRoIqcEcyfwrw98nBQJTi9hHmL7gKOj8CJDpgXgA+GmKutFC0prqmvrJy 0pRJlVUJgLMAYubxJAKxFBHBajgM6DMI/RNkYqS1o0l4ZKEhRIx5AHlCaKT2IgaE8CqCntSkANuC p3HwGsZiESyA06OCij8guxby25DwD+kUXK4EjRhcMtEUkZKz8DB1cgRA47BvAkArKbXAXBFTZlsi MCNwFVfiSqaIULOyjVadIFDgZtBqefSJpZ85ArgXMtugDGQrEZF0rykcC/pSgc5od0DTmPm2ZIhu CZFbwGcQczngqpToleztSyEAH+K3SCweiCdKCjoeYCZs4VgupmWWXbvEW1YZeWSwo9Lu+je/An4F /Aq8KxXIbFm1fvBn0MSDZ9YwwiIEJ805eAAEceYLG9s9BdAuNzQU8WiMGowNWQ7lRMRz5Ql6KPIW UpyJY+s9u5+QaJpROTh2T2/awzDp5jXNgw+OmTet1ltYaMLMuYMQaA8LA7L7wBW//81PvvbZiz56 wvQw+qpoo0+/4Nzz2f1Dc6rhv6ef+bK2w9C3vbNvNyzuHToeuy3U0NhOLtlXHPhKGzNnQiWtTqqc MG3MXkxfTrWs2zKwvTRq/uR6r0RS3ZR5NYPDtG1s94ilW9LGnupV4/wLzpxT4xFJHl2Nh2bc0Nm3 myO+84L2MGNo7Ox5FQPbGes2tntwbdfbrkcHl3FDU5mbNm4r0O//6LglA8fr/HOPbNg5SW0viuJv eiBXQNf1pUuX/uKXv3zi6ae+9Z1r8JdvBBAjzOuKL3z+4YcfQob9gbx4f21+BfwK+BV4P1Tg3Yyl f119vZT62rq6H/zoR8iZener//nPX0CXTeApnpLH67bI9EMUycUyubygpAH9jYdB2A1bAlh4r2AR SrQl6bvICkZqHU/VxUAYITL8M39uHW0GxxzLf8KWgCfU508QzJKJhHXJLSngGY4oCaFihnt6dUui afIxy06YOm18NBQPUWxSiYNLrWQy8lZOFsqbe9K5bK6QSWWyadkpwxyXy4Oq2PkChDtmwURqu1zK p2VVaaqp7ensdftSSPbqTaXak0kpEV56wgeqayokq9S9eVvx5VUQqAG2ZdxyryRUjh69vb9PLHO6 qInkZANdMcOyEELeVK4YleV2zpBGVYuVCSWeCOqhOLiT5Fj5fMum1hAvTx4/NSaUN3d24mWRgByQ 5aIl5I0SL4m9EK8JXCab1Rwe/QITitbf3b11e2trsr8AnRTPV4uBueOnrOjteK2nzVbhpXORFQbS Q8ZPHBT4QimEnUrJDpVTW1uJCP5kqr+QM2KxqmK+VLRK0Yo4jgG9NqBOqK9FOlhdQ/3iubNr6hsS sTiCt8CukHLldTYkhsOO1S43FsXlHdmh1oOULs/AFEyAOBQmuwNpUWsDJviDtzSDg2mZAT0QCwVF RF/ZFryYJnkHAZrgHIQ4ihAUpeFLio7d4Xm4UiH7wjMyBGmwL9LZA/8iovaJvqK3IvkmCcHRcj2/ LQRrJLximV/YBgcIN4xMBkesDt2DisV0wUhlMn09PUYxD7AHnVrRQIZbPpUvwBKLFcRCuIUDAR3s D2shTRwOgSQjVx83UF3ARDYyy+J3nCmN1cP3Peunug5fbUf0yOOfunJEr99f/AOPPPq2i7DnWHp3 271fuOSGVz2HGpLmdxesLp3wjb9esTgKYv+6WHpv493nuO8San7I1/703WOrdo2l5wYf3P0IztY7 L730xg2e3mkPC9NOueb2yxaE8Dtl5zVIDR/92Y3nT90daHPMbF9Pd2/awC+n3hduvO62td6OI8n+ houme2xvGGLpB6q625H3MF3PQ1ef8/Zj6c2WO77+yV+8wtYfXvC1G69ZStdFlMXftmnbADrktepx 46tel4tkZnt6unvS5F5NLf/1N/643juldgTh76kae5px5+1rl/3fLz6/MP7mR8ftfuhbF1w7KN8T QtNOPP/cEw6bP/ENOfpvdrIff8zRb/t7wd/w3awADIynnHrqblewcuXKBx944Ne//JX3LHrNH3nU 0Wfd5f8t8N08Xgfs3H4s/QF7aPyFvZcqcECouryCetquzo6OL3/xi++6tgtp8dT2DoIrSoOn4C0m 6GEB4ORWZP4tIguUFeUxKZYPzuE9PwEGxEEJogIbIwJNYVt0bC9JHKwAki2Oh8kMkETlypJQhtAI EINuKALIhMy65hG2gC7HNqHNoQ6MogTVkCOoaKC3ektre1v7lnWv/uOOv/34xt9d94tf/frWP91+ 772PPf/slq0bsn1tQrEnrlpzmyJHzG06+diFZ555yklnn3fMWeedeeGFn/3UR6+87FP/88XP/uTq L1575aU/uPprP/nml7902Ue/8ZXLL/v0p2ccubRpwcELTvxAYvqMl1O5x1t7VvblW4ql1wThBbP4 Yi63wbCStlsbr4sowWIu1w+EZBYyZqGnWNzUl2xOZZNWKcNzWizeOHHS5FnTxo9rnNBYFxszOjx5 arxx/BHT5pw6cWbulXUbNm6I1MaFCWP6EvEeXobDrq+QLgrAOZyriYqmomTpro7ejvZcf59eckMc D+Of7kpo24hEMl1SoHpDLj0i8gX8MZWOD0mkmPqKMUkcBZZb1ba9PZXsC4V0yJCKRiEYCccrK5EJ r2pqZWVFQ22tDtAVCMTCIQJPdFRcUCQmksKRQaA7xmL+xUE138A3/87oi06GgTt4FmK90NMQj5gO Z+B4yoquKUEdVA1QUQ3pekgNBPG3P+yhhcNLFIuaEUC6h9MMpKtkw8+KPxFT10ZyB+IpJs6i/aIs N7AzOifpc8p9w8nJ0uXL0IPZJBHEHU/iD8yOJyzE2WOxfWD0j5gds2O6rGkjTLTwNqKnJt3QNjKX z9tGMSDwUQgT0TuSDjHq15dLJQ14GbNpo5BDbByoHOnSKKELXQdYohc6TPo3vwJ+BfwKvCsVcLK9 zUNdjB+/7pxjQQ3oftxQp0X8boZV/nV/shj+tbqFdMegr29PsxnpTPGNC9Pra2Nv4FxocXjrdZed dfKpZ3/8k5decfllV1z+rUHONfz7MuwzlHPp1O4mEcMNk6cP3KbtwrkKW/9z6w8uP/XEM8+58JLL UI3LrhjiXG9rtXuacecX5/uybzyCrz86fPWST199etPABbWTW3vPjVd/5sKPf+Omu59vyQ6fq/Rt 7aS/0f6sAIyNxx1//DkfPs+bFMbGxx97+zR/f67Un8uvwPutAh0PX3XKwLWBd4Ww4770jIu//IOb 7l7enN5FCoy/1rx+y9e98JSrH+7gii/98oNDj59w6R1b38C2rbZ7vrpjus/f3bbz7/xyuuWlR+64 6fvf/NLHz6DpTvvkl67+wU1/f/il5uxbCJNfdwSLPWufuOvmn3znqis+eerS44/BTn3xOz+5+a4n 1vbgHen7+HYAqbq8o3CAaLs+c8mZWAzpZ0h/5Qmy0IKQdVpkrf6YZgsGs4GejCzByQumL3PwiMlI RiJ7GraGRghpSbJW4qRguajxABeCgZ5+TCMGmxn4VuSghaSIgUwHGhzIiMDYmB2PAtap5yJX1kDT EFbOaVaOf+SZlS1tHQg5hysSyAFCGwTNA7cFAlo0FNJ0DfDE5jk9FFSCwWA4FglFYsGwrgcgF0JK E3knLQuMA7qmgm2pgC+BIHx1mlVOJTPtvT2gGmVFaxw7rr6yJsRJvZ19hb5upJTnCnkgk/5ksjIQ 6kOUfV8v2gsirhy7jM6G4DuQiQUJyglTp02bNG9aWhVg7ERaFeLZLXza3BZq7pRTZnN7qzy+Xp8y OQ3OUjTjNidY6FRYQiObPBYm8gHY9wyz0NtfMu2+/mTeLLTn0yk0YJTVqKuNq29cm+psyyctFL8M RyiPHwXwJyIlywtUY0ojtFakoTjRaWysicfDrW0duXw5Fq2NxCtgFQ2EgiEVrR11XZajkdCYhrox TaMjsURtRSKA/HUWpEaUq1SiI42zgLkUWZtNujFj6oCgizLayTfIzgs6VYCXSEOXNyjSHQ5I9FKk OC2PjOLgmhbOHyBSiLnA7QCaWDwX4JYFQRj1K4DJktywyArz1GlEvOhsYTwVH1jPR0p8Q80dgboe DOoO6WfZ0GIGAuFIoUZLBMajCXADqzLzOcNOpjN9XR04jnBBYoQiWoEWC9hJ6vdJKjC8UkKrAJyr sHzKGiLDJAVdIzVUiDLqUGtwPZqOWoM6syc1Dd8PUl/VNXy1HdEj+38UHdGHby8Xv2dV1y5CrT2N Oiiq2p+qrt3PRcKwP5wxhOGG1F5vLsXKb77j2i//4pndJY55uzzSVV17o0TD7pob77z6qhtfGswv e+Nhf2tV155m3Bvl2o55S5lNT95xyx9uf6Z153cVQt3RH73yk6fP9rLt/dvIrgByuOBP3K2qq729 /fHHHvvdb3+7rbkFOzlj1qxlJ5548MELotGofwEzso/6sK3ev4AZttLudmCgrk9f95zX9mQPN2HK Kd/5xicXelEDHLezMHn3r/AEyMrT37/k2w/2eJtIx13z568sju6yfXL5Ty69+l4voEmbcOkvbjh9 7MBfRgob7v3xtb94rGV3LEqrPeqSq684YULgbciSChvv//kPf/bAxt2NI4RmnPuV/7lgQZXXAOd9 d3sb5du/NTlAtF1eXzzKpacsctLOECNgN8/XxiAXdcADEgEIYbouoiHAEKrIQUU0c1blrDn1UyfV 6CQiigItlUyDWjJyusADJeVmzIged1T9ogUV8UiZ/GpkRwNnoH52+BcSMYypKnwQWeSiYzloFgh9 V6nMF3Sdr0wEYzGttik+Znpd06TKusbwpOl1iw+fseTI2TPmTApVJ2TBzSZTW9ZtevnZ5x/+97/+ /Mebf/3Ln/321zfcyO433/zr3/7ml7+68Rd//MNNt9/y2z/86vrf/eKHt/7tD3//158fePxfz7+0 /IVnn7z7H3//f3+45S+33fbKc89utuw+RYuOnTB+zryjj1/WNHvWlCVLln7o7Is+c/E5n7jwpA9/ aOmZpx587LHjpk4dP2VCvK6qLdv3xCuvvLz8pZUvrVi9Zm1Xfwp2PJClpF3qMdKcXIpForrDJYoG t61d6M/YqYzUneFa+qxN7S0vrnz5ySdXPv/s1g0rt21dZ2WSOpMiAQQCEdmS0F8CirPKIo9OkxAr EVEUODyOj4SFKOuKjhtQEERSUDbBswnnJkgfcBH6VNplOxTUKxMxZMUDGMGeF0TjHBlJVdAnGaaR dyxDBiokxkj0jAyp7GygXCzWVRN3cE3kVsHdis89dRWIGMNImBzPgRbxEU2MByRdYQI/3GURFkDc 4QiE/5DkY4gPEzgFEfFwIyJBCw8RFyUbKxhrQOLASwWnhDscgliGt7s4j5ABTx+RFOfgjDBdpH6V oLQykeGFMDCorlzbdCELK1lcycLGUAeCvArgaFxZRnY+orvALGFkNIuZHE6TFPBlXzoNzgW2ByrG xFoUAgbmhVWV2drQBRSCL9ssli0TrBTtEqhcxaJlGN59//6c8GfzK+BXwK/Abisw+9xv//TH19/w hvv5syMH3AXPXhzC/hf+8utBziXMOvNbv/7TPQ8+8MitVx4yFLi+F4ON/E3L/c/947eDnEuYdu7V N91+54OPPvCXry141/ZNikw46qJrfvfX3/3g8+cfVjt4rjkdj/7uS/93+7o9I8p3bcH+xO9ABYrF 4sMPP3zpJZccsfiwb/7P1RDXw55y0+9+/6Of/BTdD8C53oE5/CH8CvgV2D8VcNb98/v/7+mevZXi hpumjR1aYOmZNS2vi8202tc+jy4p3q3x4MnVA78fSi33f++bN+yec2FLo/Oxn37tew+0veVy3O6n f3HNT3bPuehtbG71bdfd9HTPfte175+D9pazHIhXfgcC7YJkBjcS4zDMwU4P8sZByUNiH2pah5yl smHA6Ajpj0thRSyKy7ZdI1+uT1QdPrt26ZIxsYhUyEHxFEF+ui7bqmLxUhkJV3MXjF965Jia0eJh i6UjPhAnQRAiyJn9Dv+xgC/2KU0MARDFjPMIQxfLkqbMmTnpiEMOOnLRQcctnn/cYfMOO3LuwYfP mDi9QQsgbqsrm+zg8r1kwEN2FByPghIOhqOJRLwiXl1d0dhYO3HC6LFj6hFihXuishoR7eGw2lCX iAXFREhqrI3VVupRjZNKxXK6L9PXvXnrxlceeug///r3kw88/NRDjz7z2JPbN22tSVQcfdTCQ448 aOKSgw85ddmJF3z47E99+uxPXHbexZ/69GcvO/vjnzjhlFMWHnrYvNlzx49uLOczm15YvunpJ/tX rnE2bpdTfGHbtv5VL/StfLmweUvzitWvvrhi7aqVmzetzyT76mS5SdWqbbeqzFWXxUhJVEtCRFBM 4BTLypbNrmK24JRNEZHwDDtB8ya6tgTxG0vs8qAkHSoolQiS9fb053JGKIQMMQVjZIs5HC40O8Td w1jUzhHFhx/PtIwiHVE6nBiHDjezrTIRF+gUnQSQO1HgFeKpBgyDlMvGmBfIF9N9Qf1HDQ0E6szJ ot9Zv07AL/aVCBgKnRf6dtIj+IioeFmG2k7GnWLCRFgpyeWKcXAOOOWCjcaXDjyeWJDIYrkwP03H RGIQ8LG1AIfBcGvB7wqwBTshvLe4lbAjZHDEzlIbBNbiEyn11C+S0uhBymwTfklFcFXeDUjgqhQF FtTVsK5Gg3o8qMV1NQG1oCqHRSEkuBIgmFFwbdyLPFLkbANBcmgSisyxt/xZ42/gV8CvgF+BYamA EK4cM5g/z8UbJg3a3Xb5d1yVF+X+zt5gG3+TAXdZGDYdvNLEz+ihi85ARYKCut78tkvDQfWEc889 bGIV/h7ynrpJ0UokoXk36LKH/jht96z5D0K+2f3R5c0eM8o1r147eP1fe/z5Zx42virATPt7c9vT jHR9MDhOtC4+dGq9nbGl8JiDln30f2/64/c/cshgmr6z6s//XOlHlL+d8o2gbRDLdd0PfjBz6rRP X/yJB/59H2K5vvfDH/36pt/gLUzTmDEjaEf8pfoVeB9WQD/u8z/0/iT242996riJO34Hp5++/8XO 3UGh2mM+f90b/oR27UWzkeSo1E2aMfTbi8tt2da9C+tytm94oWfwd4o2bVqT16WknH7p7t8/3TVY /JoFH/3W9bf85U+3/OIbH10cGXjUyTz917tXpt78ADktT/35/rbRFb8cAAD/9ElEQVTBbQJjP/jF H/zu1j/d+quvnT5l6G8umUfvfWqgf/H77mi/5QXWu1ORd512DVAnRkJYuLznYCOHHHX3A2xg3wfk buRFqK2YsoeUQAgBl0V92/b846/0gGQ46JdoIM9ICseCc+aNrauDYisZClTVxya0byvfevu6Vzfb TU0IR2Jd/AhjQCNGAjHCKWjSB8wBT5/A66qoKS6ETYpajiTEYLXAh82U0ZHNtWa7tuY6WpKtLds2 b920Yetr67f09qfSuUwmlzasvGnmCrlkPtNTtmA/NJHAFAqI8ahaVRlqrE+Mq6+ZNK5xyuSmaVMa R41GLHtdPBRQLRs9jBISFw+I0ZCUqNAbm6oroqqZ6+nu3LqtfePqjasefeS+G3910w9+9ut/3Pa3 B+6695mHHln37LPd3a0d2WRRdJrqo7Mmjzl03swlSxafcdYZF3/qE1+49NLTTjojNH6c3FTNh4V0 V3th3Zbw1p6adDEo2GPG1E86+pBRh82vmjddHlPLVYTLkUCoriFc1xCtGSWHE4asOYpmCmKqWOxA g8BiHrI3L4AeRIlgI6jNoI+QacCIG/KCBBKGIwgfY6FgxRKV4WgEdAmvQ7wUtRBEUJoswaGHXoUQ fyGBColXJnRfIF30KiTeo3mjqEs8rILEzUhERsgJL6ckNy8Yi2WEEXMjvgVoRv9DkUfB9qBkDsRn iMvHUOjYiOUixl+Aq5HGpPaRpAdEoBuS30GXIroS1mQdXR8hEkROvA3nK9EpSAIxghfixpLiXIrI YrJDys0v4/xDpBtFemE6vJ3C+r2P0GdhiV4KGMjdQNI+XmvZ6XwBbQtAxHRZQrMxrAfRcpCgRQMa lGj4BFgNiAzKr0I+l0qnkv39vf340J9N9efSSbQ7yKX7M6lkGl9mU7mMfxn/7vyo9Gf1K+BXgOOD 8bqhznsdXf2DV5k7B2187PbNw5FWsaPNX7pj05CZrnJ8HVNz7LKwnTokpru3Dal8qiqjb83gdm5x yMmx0AHWz0+tbhwysBc7+nJepcv9XVv34uyUwonKQXqX29iZGmSIZsfTN1937bXs/rN7Nnn2k0L/ 9qFfOnos5DVg3NvbnmbMdrf0D44Vr4y95eCw1n57KIfljJvXlDlBrT34Qx87dfzgKKVnN7TtXerK 3u6Lv/3+q8CDDz549JFHnnHKqYifh1Hxyq997a933PmFK744d+5cuDj23zr8mfwK+BXY1wqI1eOn eX8Mm3346V/88idnhgZHyq5a1ZLZzbB69bipb/gz2kB8pDhq4kE7vvW3bWrP7zSAk23f0jz4tTBv 2tiBP26lVj/+1NCvMWnRRy455/DJDdVVDVMXn/PJjy8CQfNuLff/Z/2bqoJL3a+9smlovoYzL/v0 CbPH1FXVTj7yIx85fseqhvoX72vJRuzrDlDUhXqCdn3zmmverZR68JGhuxdJD4BCzfgAVCiWCyCK AAKpgygFnLLrKS7cS0UXuGK2mMTltm2oqiHwFNtdUVG/ZNbk0Y1h6IoKKefWO1747a1P2TkrBIKT krxufaTC8UxwnnXOS4uCTMzk8kUnnbOQAY94J7T3g/jIzBaKqWxfd7Knu7+zs29ba9fWlu3btrW1 bd+OfKtUOolgMVz/8a6JLuXokwhCU4L9LJPCRls3bu7r7s6kUtlCLpPLgRkhMj8eUhUofAp5ySkF eV4FGbHQHzBrWvlCf6dkF4KyG9bFprqq8aPq3JLR0dHatnn9ay8tf+Cf//jrbbf85v/99sabb7zh 5t9c9+ubrr3hd9f9/A9//Ou/7r33kVv/8e/b/vXEM8+tfunVDWLelnNlmSdYxPGW5hR1x0yocgiB XshxzybVQr+Z6lF4p7aqIhyLirqatq0+9Ch0uGKpxCG9CvnxkZBekQhGY4iQEpCjhbgugl4oPQLO 2AFAnjw9KKKKpRK0R25/N6hOEe7BUCyGEHocImwvCQ4C49EpAGgKLSlLBnpdIp/dRu4YGlUiRAtd FA149aCXQiAYMrYQGU8HnforUv4701J5mfL4A7CFWHYkcLEzwfIUVSQIBGOCkZCAE6ovuCbvFHHu 0PSQbNFyMYyJto0c+hkqoqohvZ66GVDoW5kex+Em1ZqAsHw4OCkPDGci41s0uOsF2JeQaWYXKR3M KsBTiJ2xSriTsqtEofEQX+HMQsQ+6dEoGM2h/SzkSoahSDKP9otUP3LqeuvEvkMfhi6NiKnPZjKA WZl0fz6fMfI5MwvS1dvW3r61ZdumLc2btzZv3NqyuaW1bXvHiP0Z6C/cr4BfgZFegZ3tAy1PvtBs 0g6V0xtWrRzcM33+hPrhuN7JrlrTzDSt6S2vrBgKAQG+8qBNZNysyYPTdixf3c6ESDsvTEgcMnnU Wy9MCMUrhvCWncoNyGh3mfRdPIg7L6/UsbE95e1mZ1tyLxYlRMZNnzVYitzzq5s91VZ+0/Kn2weH GTNzrPcGIJCoH7KGFVM5dsB3PeJ7mHlnvdgeZty5qpUzptS/Jeri9XB0aCO7JzXwtkSUhx4UZjZU 77XobC9q52+6PysADZdvVNyfBffn8iswvBWQGuYtmTg0RWl9W+/e/mEsNHrGvKFfSb2rm3ceILt5 xapBDbI2bu6ECm+mcufGZwfivTguNHPRzLohobZUN2fRtKH1mE+sbXmzv5RYfZ3bd9SnYVS1Nihg DzVMmT6E8OBqep2vcniLeuCM/tZXWO/iWhctWgw98LtCu5jEigcIAQsglRUsbmQdQ2Y8pFUyHlcV dqNPZC+oS5EpbwvlEkRbdEy5lJPcHDxzaBAYCIZ6tvW91pxcvhp0SeFsR3P5sOYuXByrn9bw3MuQ AbEb0wXhXy/1HAlOCP9WFV0UkDoPixssdBQXDoERJtK1QCiU0NRYOFJTXzd6wthx0ydPnDdn+qwZ U8aObxg3rnrMmMrRoxPjxtTW1CQaRtU0NtTVVleEWMx4QFeg1enp6WkHG9vWsnHjxubWllQ+V1mZ mD9j2oKpM6Y2NdVUxGNRBNqHkD0Wieg18WBlRK8Ia7xjgNdEVYm3LM0Vg65UG0lMbGiaM2ny0R/8 wJlnnHjUEYvmTJ/cVF0J/PPaq6tfQFDnPXf89pZbnnz6aTdd5NKGULJE9FokcRzytdB00Sr292Ze 3cBtbu1bs6mwpS27ZVvfpi2t6zd2tLR09fcYPBeLV1VU1cZjFfFEIhyJaEHK16qIRGOhMFCXjrJR ND1xpoFejEQhgSCZIk9QCkXCQOB/KCAaYbLaSoooKBxl84PqIBse/RmBdorZNJpAZdPJXCaZy6aL +bSRz4ArAn6BIOWL4D/410B3Toi6mObOxgN4FvdCqruQ6TXyKbOYAzQDECMISoH00FhZwJNmIW/n s3gqh+oX8oZB42FW3A0LKM/Cqo2yaziC6Qr4gjVaJAIFbVfRKhVtB+H9edPOFs18vpjOFbLoFJAv 4H+jUGB3xG3hSyj60plstljEFEUD6A6LpJaJRPJAsKi7I+CbhWaWNlRgugTBoKwraBOpBdEmUhKh 8wopcliVI6oS1ZSYpoCCIotex2YwW1K0GNl5AeAQS4c7kr9YLr9/8yvgV8CvwLtSAWXUwmMOHbhM NLbe8r3/u/mu+/7xq+/+6D4PgXDC/POOnzI8SqiVN197/e333fGb717/8FBi4aQFM+u9/Fe59qCj Do96F1rG5t/+8Me3//vhu3977Y6FzThpyYS3oQPh9bHTDx68Djbv/8ffn1yxZs0zd97wu+fkvbLX DdPR4fUpC4+LD8aPPHH7H+9bvmblw3+59am3zBjZeUF87ZwPHFk78MjGP37/R7fe/9Bdv/nWtX8b jCqRFp905DivWqExM6YN1qPzob/d9dTK1Wuev/Pnv3pK3o0bVAxFY4MT5Vb+4w+33vPQM5vpr+m7 n3HHoQyM/+DiSfpb+l6F4Nhp84aOzkM3/fjmux665//95Ee3rh2YteGYD8yuHKba+8Pu3wr4RsX9 W29/Nr8C+6ECUiCyU6xef8HY61irxMS54wYXWmpf3dw3tGqnv2X1kOJ7p6Cu/u2bd0TkV00ZPSTj wiulqtHjhhgVl23t6H8T1gW1yk558+vXbd3RRrJi/qe+N2i69LyW78fbAY26cECgB35XaBeS5HEH y0LAEmxmeB9fpo57lMEE6gUbI97vU5Y8mtuJIqAEuIEFo5gD6Q2eCcJsxmllUQ1YVqXEB2w7V+BK za1bRMuMBkKSXnaU/NyFE+bOG7913cYVK1ZBWsTUReTB8xr50Q36MGrIiFlUoAb0H4SrDMgCch5V FkMIUYpGKuMVjXWjoFMEBqqorGxoqJ02tWnG1NFTpzRMGFczdkzNrBmj584aO3/OhEPmT14wZ+LB s8cvnDNp/vRxB82ceOjcKQtnjJ88ug6worm5c+1rbes2dLR0JEVdHz929CEHz11y+KGHHbH4uGVL l31w6QeAsI5bcvDhC2YtmD1x5uTx0ybPOuiggxcsWrRwyQlHf+DEpUfPWDA3VMi1t25va+to6eoq WoUSZ3JBDVZJ2PMgFsqXynlRSAX4nrTN25ptKxlX6efE1lxxc3cvnI9py8yikqqW1wLpQCAly0lN yiUCvSGpV3Y47DOKU0KquhuWxYqAht0Ph0Jk6kOfRKaqK1OyOzRTkC5BaQWiA60cMFEJLkX4DSGx woEMiKKG10DkZFoYUEatQZMsK48bWFc2k0qnU5lcKp9PAxpB1VYspPo688luM92Nj8U0PukxM71m vt/I9oFtFTO9BLnSXVmI5qCmAvgyCqBdhWw2B0WcyxsYHqors5jO57O4d7RkOlszXW1oIwmJXT/u bc3Jtq3J9uZkR3uyqyPX22n0dxtoPZnMWJmMnc5Y6bSRTBn9fUayp9DTmevuKPZ0FDpb+9patrc0 tzdv3bZlS9vWLdu3tbTTl5vbt2zsatmEYTF4qqs91d1Bw3a15TBjR1tnO5R/22FFRMw8SseiyKh1 JU5dYCxSL9KdKB0skWjaaFESGUdnuiCCuyEbDOcm2K6K7HwEjUliCKZL6S3fDLwff7z6++xXwK/A /qkAX33Epd8+e6CnkdP63J+u/+kv71iZZghemHjsVZefPmHI4fiOrmjKMccnXvr9D3/+95cG/zwr TDn14iWjB6+t+KrFn/jKWYML2/jITT+87me3v+ItjKs55JLPnDZlxwXtm60scdCZFwxqnkov3va/ V1xx+Tf+zJ31jU8d9I7uz74OFpxx8sePHIgXcdb/+4dfveKKG1bNXnb029u5gVn5usWf/Owp4z14 V2h58Hc/+d71g4VFD6kPf/OyI6oHftWIiYUnnzdxAPOVXvnjNVdcfsXX7+Au+urFM954oNXRR556 7CCJc7Y+8v9++vP7thZokjedMbLo4itOn/I2QCTHVy++6LNDu0+n3w9/+oeH13qatqq5F3z2IwsH F76vBfZfdyBUACFcB5xRUW168ZYz3T/t5u787qSO7x3+yPmjLxqg7TuVcJdXHXvH5JGkORx/3GJ7 aH9/OvMjB8KZ4a9hZFegjLCW/24PlOqmcUN/TnNWbGgtDtKyvk0rNg3+HWxHUNfrZhtSgg/+MgxG B//s85brUkdNnLJDe5y85xe/fri54F1jaFWDNs3p0we8ljsNV+xZ8+hff/G9b37p42ccc8KFl33l 6mt+8oc7n1jbs7vwZbN7zSO3//L7V3/54tOOp42/+p2f3Hzv883ZXRFcec3NZxxztGfnh5e/VGhd fvdvfvDlz37n4SHrj2Ni1tt/+YOrr/jkqUuPP/XjX77qmp/efO/y5h187i13eG83EC+//HN7+5r9 vH1dXR3M8Hfd8Y9nn3lm0eLFodBeXTrt42Lvv/9OXARRLDzy3UmrRfwJ4eJQXTFjIVkayQdHcIrc baTF4gVAAHzJCxpa8IVCyvim+m1t+ZZtwB3CtGmRDxw7pSyHtmzugw9s5rzxC+aOro1DtOXMnziz oz/P+jqS/Y5CzVl7R/AHzCCjSyAEY5SgziLJXREEjGWEUUoTPHMMUyAcCutkKwSTgE4Hmq9gUIGE SRZUFrzEOgCK8OtBhaarSigQACcKhSRFgWQswDlatr+QSxfS2UJLR1drT29fLudKjiJjfBjhOIMs chboTx9QUC6fTCc7ujqa29s7+vvak70bers3drW1d3T3ZXKQJ8GdmEFcFJYlKelMurcvKZdciVPK omzDhidIJVlF6lhGlvtkJaWoeVXLqFJSEpKSlFFAuNSMHkzper8i5TTeVJBAj+GQ7i8Eo6Exo+vG jx81eeKoqqpERVUCkiSwJSiWXLb/LKWrjGgrRGKBeeEjpaARRkQYPIghyIxMOBLCLoHXVGqQyKLe KQkLeWus/yH8hJTJhdwuFpdmU79Dx0GPQ6ME1VUZd8t1LWi1EJ2LRobIYyObYtlwJfgb0frQhUAM fr80gFl/obcr3dPZ3dHRub2jHRXavr27q2t7Zxdu+NDR2dna0dmKBzs78XVHV1dfb09fT093V09X T0dnd2dvX3d/srent7u3p7Ont7e3r7+7p7eru7uzm24Yp7e7B58k+/qQYJbs6+3v70N+FjyHOdhT caP2in14No/P+vvYnF14QTqVAuciYotzjC732clGrRBIhkaolZSMdGbhI9AuKsSeIO0WJcrRg4NW SiTAISbMdefMmr2P32xv42XXP+4bJN9Gmd5/m1x+VP37b6fft3vsFrY8ecdTzQP7r84+/uy5g32M 8JgYbJh39NEz66ujQfxaTXVnE1MPmjX34GXnXPz5j582s3LHn/XcnhV/vW/1gJB/zOIzl4wL4km7 Z8V9D6xKscHVUYefdPQ46myIbf9236qBbRsOP/2Y8eAouzw44Zz//cbZUyH8UUQzF5pw6DGnX3bp uXOrdnrjSAs7dtnBoxP4yw8tLF2qmbzg4AVHnf6Rz1167qGjdnLH7X4Ng8dbqZx+yCFjI/jVb/WZ sZlLjj3705+/cFFV38t3Pub1Fy8H5hzrLXtPu7P7U2f3BWH7+bYLhb9CRybOWzAmhN+cmVazct4R x579qUvOGrv974Ol3vl47WlGYMlg48HHLZ3XVKGhO7KT6crEJy+YNW3ukSee84lLPnrspMhOSfxS 1fQlBzcFUWmrPx+ffuSysz792QsWV/e9+MATm9mbFisx68TD2MHleL1x/pKZlfjTlpvv6CkEx86e f+jh8xuIlL1+xhRfM23evIVHn3LR5z533iF1O2LU3vzoyLHJi5cdMXNURUDWInqpv49vnD1v7rwj T/3wZy+54OgJ4QP9z8rv258q+2PHh/cCRop98oOjduvO5iUJrcbHThh10qGRUVu77t3ZU7XLq8x1 /9l6e99e61j2R+12N0di/OjPzh78lir03XV/95BR/d1a0r7N61/A7Fvd9vVVuS2P3vtM28Dvc2XW CWcNXUG4Xctv/dMzrYNAat6JFyyl3/VcYctjf39q0D8fn7nshDnVMAbt9sbLQXvbPx7b6iEmW5t4 zJFTY/iF5RbX3f+LB1/zBM7S0ed/6ugm788n5eb//HLo6oKbeMQ5R3i/rbwbb7c9t2NqvmnJcW8y tRLmuh98bOOAFM3JbX7638/1xsaMb6oJ7bF5Tanz+V9d9bkf3/Hya5vbOlMWV8r3bG9v3rB6+RMP PLDWHDt7VsMOgXSp55kbv/Sl6+9+bv3m1q4UtPLYuH3bhlXLH7nn/s2ReYdMSpBshG47Xx0pU8aH 7/vZ9/723NYOo3bgmsrqef53X770untfXLu1tTNtISgp1dXWvGnVs4/d/fjmyMyFUyv3VN99Peis mJs2bflvXr/fXvvKK6989UtfrK2r+/Z3vrsfmptccum52DW88Qc88tLi6Y0+671I2i5QFbJweViK Ov3hH7z1h+AF20EcU7b5QKAwbnJ848bOvm47Gg7GKu0F82Y98Xzzltc2NzXU1I+qDOiIqM9JvGG7 kYp4HU3nuvCp4V86ZSh93CNo0NoAoiFHiT1FkGGgJyBABBYDKx5gFgKkkrki0IyuqViMJCrhgApy kcmjg19Jk8G2dGAeDIKYJuAf5OgDlLk8YrykYsHp7Mn39qUDqg7Wk8xktnd1IdwJeijErUPXpsDT piggP9A9GQUDTfokWRUE2Cv1cCSmB0OQXAEdCXB7ygoWjHpIAj7hKU8slSzmshEUsVAMcXxIIClQ mKyhCorL6zqn6WUsl5K3MAR0VygenI2YGHoo9J0kFyD6/jmmCw9hzsqjfWEorAURy8uLAUlGD8BU ptja2bVhWyu5GFloPNFJOlz0I4exGkJYaiCowUoaiVTEYpXRCMx6AWTwkzVPDslQJwkIY0eRcCMd HRghI5vEg3QSLRECA+RCCD1+WtH4CAQj0yRFzvM8Dj2LxYdxkm7IzMoUYB8skFtwkGJCQkUBX+CO rMcBnSyskQGSsagHASgp63IA+qaSSxA+WeA4IlBI5sLUsA3ic0rTIgZHOfQoqtenc+D0pOYIA+SK UvVp/7FINDqgXbHJ3UlND+iUgjaR4T5wzwCki6ytoxdaz049urN5oeHCANSJkllr6YTEl4j9Yp5b irDD0+CAWO+Hz/3w8P00mPDNl4ZvcH/kkVuBTd+aP3IX7698hFYAf7f80OV/HMhjOuRrf/rusTta L43QXfKX7VfAr8CwVWB4L2Cgz7rp4Plvpcoqday/7OpXbxwyWe/yqvSd1zx6+vp97ZvAC+NlZ/Ow pgDtOgVUXesuHMw16l1/0edfvWXYjt2wDuxfwAxred8weMfDV336uucGTIOh82/460en0/dNObX+ nz/9n1/8ZzCKXmr85C9uOpv1ZERnm3OuXf4mqwxf8ON/eKPgXdO2e79wyQ2veuqwqmO+/csvL0Yq l7H59q9cctOr7MHA9M//8kcfHO1hoV0uJLgjr/zLVUt3/hvZLlOPPff6Gy6a/iZhBaX+l37/9a/+ ZQC0DSxYqz3ywksuWHbQmOgbfjyUuh/+3kevfdQDcMLYY84/ZV40/fLdtzzS4qE66egr//jVpdUM lLnbH/7mZdc97YmUA01HnXny/OC2R/7+z1c8JbtUf/6Pf/XRGQP8buerox11Cy/42o3XLK1zex67 9lPffWJAbj9+yUdOmqd3PHPn3cs72A8mYdaFN3znw29T5/4mB+UNT40Y1IWVe7QLn8DSCGPj3uzm Xm/roS4CACzxnGABy+FCbBM4APEvogGMozB2wMgA4tZLkAyBh0DVxQmmWc6m0wVdjQZ0HinjpXIo 2V+srpQcIcOVtULOAdxB4hOnKkuPmuUtkYxjEIl5MfiMLAA3eEFeAA5AHmAWtC5KBAOnAEeFrIuY A/iHZQEIUWdC0qK5MPOBZZBQDAOhq54kiDDq0eDQaXGiJinYlk5pEBC7bNh4VFa1YNGw27d3t3f0 AGoh2Qm7CQ0Ur2qw/3nStgHCIYiqDsYVU1X8DVMHjioBjCCWjHoRMh8mxyHeqq+nG9HmqB1i8RWZ S0SjddHKeECLIB0K4VBQvkWCaPuHHRIdEDLSxzmAOSgDyupQG0GWqg72x2O3mX8TGetoKpmHCKk/ k8/m8oWi1d+bgu4sA5cg2JHN+iGKYHnUmpDRQDpwtCgQOgXNBZUQBG/BIGKnQrqCvxyDdsk89R9E m0ukvyOySgJdxJrJDIn4eDoYWAluIET415M+UYIbmVmJJVEvAuqTiGkRlE/UCM+hdSOGgaCP/sNR Y8ALg2JMaglJW7LjSDSLjhPTBdKdwCpWC7YFf6woUtQ9DhXDrZ55kEy0jH/RpFgnQBhFetGJgM3o ADGJFgFRCuCnifE6bE3NJ3EDa2NCLVoB2BnbHWq1wBYw8K1Cgfk0LJWPsuPY42RsJKWj136UktAw Pg6U64I/nnuOj7r2+ueM/4L/sgL+leJ/WUD/5ftQAR917UPR/Jf4FXjfVmA/oi43tea1377Eur8F YhB5zk4Mvck1X/p/jxz04KBLSwh+7pjKhoErPmv1c523ZPZa1RWKxr+ybMKHjois+enj+07K3vS0 2O0U8VFVX50eGBDAFNJ/eSo1Qv8Wuj8vYFqamyGVral526649+C36y6oa4/7Jxz86d/872ljmBlx r1DXLlRLqProz285f4q4C/+a+fGbvv+h8QOe+HcUdWGthbbHf/ed7965K+3Cm8ymwy++9OJT59Wq O0mLna13XnrpjRs8wLTospuvPqkB+vLi5tu/fslNnkRSm3DpL244naIXnOY7Lvv4z70Oj8L8K37z fyc14F177sVffvyr//SEotop19x+2QImAtt1p+i5mhmLD5nTOHrBySdND3bcd83HfvgUe038kC/9 9OpljRLn5Ffe/NkrbvVy9dWTrv3zFxa+4969kSSq9nK7UAsAL2Cv4f02pEhzlkmEOCcSGxHIgY4G j8AhyEyCPN2Rak7h4UyuA8oCOxsMbaBHSJ53oZ3RE7EqTQ3wAkTwQaivKquqeCkgyAEXfCicUJGD l0jEIzFiBtRWD30DMSOgCal+MCWSyoEw4I8rQ78j8GWEJSGcHrxG1SD6KXMARCQqg2MP01HSOgAW ufUwVZBy6hQVYi6ohNC0EUwNUiAohyRJU2UNOirQHIFD1L2Ann/AO0FVQgYW2JtTNIKyUl9VO2HM pBlTZs+buWDW7IULFhy58JAj5i84bNGSI489ftmJxx532vHLPnzeBeddcOE5p5168rJlS5cuW3TY 0bOnzZzYNBr/VVVVR2Q5IAgqpFto/Yc0rJLZ0b19xaZVj695/p8vPHHP84/d88zDDz392PLnn1m9 cuWGdas3bV7dvO21trYtrduaN67fuHHLplZkUbVv6+1s6+3ZrohlRRXDiXhlTfXohrpEOAzHXm9n ZzGTxaojkWh1ZU1torquura+vqG2ZlRtXcOoxnH1DWMbGsbgXtfYVNPQmKgfVVFXFwwFcZgyuWw/ /ICdHdtatm5t3tDSunH9ulXbtrX09PTlMwh2R9PGjG3Cv2jDBqmAAoZCaAUAvhcIhCLBcDgYAkCU FYAyLaSj2XkoGgwhLD8cCMfD0VgwHAoEdUXVJUkBRASnIxzKgBahI85kpArUjPo4ukQfgc0oU5+p qOicogaYiJKnCHnqjMgYG1NReUwKjkwJ6nR8iQFB5sDrcEQhtMMNkI3stDg18ImsiPCoIulNU4H5 4PHEaT3QKxRnMzkTGenikXDGKCjOMUBVFktHaIzsn/QNQDI20ol5zUax5JKAZHp6hFoB0HL9m18B vwJ+BfwK+BXwK+BXwK/Au1MB1+jsuvLBFrrftfKw765cuSNeW525sHbR0Kqc/M8eYpvRvWOvOZcQ +e7Fizf8+Oj/Oalp0jC5c/c8RbK95ysDK2+5csRyrv18gmzctPHC88674guff+aZp9Npasrh33ZX AQRofux4j3Pt9U2pGTNuMJHA6XlxQwfeGOU7Nm0c5MuBSRPeup/vXs86+IJAw5Gf+cGv/ues+buq zJ2WJ2/66mXfuXvTQHoX2zzVsmYoPqxx5tSBVemNU6YORl0azRu3M/1bOdWybtD9J42aP7nes0SG xs6eN9BJkjPWbWwf6AC06+objvnyL2782VWXfPTCk6bHOC6zZdX6wRZmEw+eWcNGEoKT5hw8qFcz X9jYvq/q0jcp3EhCXdiN/Ua7SCRDd5IGmZASQTEE3ACRC3gEPGrUfA855+BHuNNT4AWs3R44BLEm uPcACCAVEqBZQpw64JUEXyE0OmACkCjFICGCZKcsi5YgI/mbrGzQ7BDQcAAqSAhEzjXKV0fuFeAZ lE0wwpUYA4G0B2Y3So+CyZARE/TCQ+QUkBKMdeAdaJZHWIKpw3DzOAUkOTDEhUC5ZMR2kVaIGv7Z YBakyALkyBct6pEti41jGqbPmDRr5sSZuM+YMH362OlNo8bUVtVWxOsr41VRIB8lUBmvmzph9twp hy2cediiWcuOmHvy0QefddKS80495sNnHH/BmR8474yTjlh61LyFCw6aN3fmjJlN48Y1MNgUq6kN VtagaWQgUcVroWKZ6y/aHZlsTx4x71YB+yihEUaorraisbamsaZqXH3N2PrqcfW1AupIUqgStFpQ b8GNOH3ylIPmHzxr1uwJk6dW1tWF4rFQPByKhWKxYCysxMNqJCTHQjI+4h7X5YSuJNBNUkSqPV8R UCqDWiQaBLAKQmUWjVVV186YPHXq+AnjG+prKxIVsSju8Wg4Fg6hF6Eui6rABxXWmlCRUcYAeR4l CMDQrxB+Q/TpxGHzoq88QyFjlw5YJe44sjjAIEckq2NY0/tbGst64+iwwgZI7kgHCMwolfERCj2c VeyUQJ4+PYhxcFpgCnyJgDCcb6T4Y7Iswy4VLBsfCYix8bElos0gNPNmJL8hi+LyBFn008txMSbO H5bHNXBjpzBJtLB3iHDDXMykyFRdTGM4kCdHWWbwVNIKPZEj2oPu849n/4V+BfwK+BXwK+BXwK+A XwG/Au9gBXJdrX9etSNhWhmVOOydulKT48cfXrdTkt07uOrBofbDFMOw6gN8yDWrVn3r6qs/dPpp v/3NTcMuGTnAa/H65Qmh8Uef943/+8ppk/dgFKw95vPXDXYzvGHgk/87dqgFDahNaNy0uUNYZePW 7XnHaln7wqBrWDpszvi37uf731RNikw4+uL/++X1Xz5j2i4A2sk88/Ov/+Lp/sE3gE6mv2OQOXF1 icjgmuVxJ3zn+oFd+8GZk5j8rJTt6xvcVq+rGArw0mPVQ+qrdE9qyBq90/objzvl6LE7iunmkx1M cEq3gKYOCk4VLTCkPe3r7M0Nrey/KcUurx1hqAtr3z+0i5LKWTQS3uZ7afSDPICMY0xgQ/8NgAUW pEUB9Qwo4EEgJDgKmSwMfAFoij5DqBMCmohDlMCxSCgmiyTgYoFLhLpYMJRolpDY5RgWOt/BC4iI eWAEMrKhAyEEWp5vEmwEgM22wEZAu0j9A/IAX5qEAUUepj8wOuSsA3cBxWF0DIIIKvjpSLPDDHHg FIZpG8TwMJ4MvgF6A0ckRErRaKimIhSP65WVerxS5YJcWQEnc1SN1zROlh1BdhGsFVXlqoAMrCZA I6aHoGSKxhOjRo+eMGnSlAljJ9bGm6oiE5tq5s0Ye+T86R88ZOEphx125tKlpx533AePXrpsyVEf OOLoZbgffgTEYB845JBF8+bMnj554vimMY211dWJeEU0Eo8FIhEpEBQ0XVAwC9AhHHTIA3AgV5OC oRrIt8aMGTd+7KzpkxbMnXb0IXOOXDjn0Pkz5s2aNHva+CmTx0yd2DRlUtOk8Y3jxtSNa6qb2FQ3 qbF2YkPNhNH1M8eNnjNx7JxJTXOmjFs8d8bhc2bMnzypEbKvigo5EHTR7FLVkEWG+lOvwcH4KgR1 Mf0cpbOTfY+dJBTaNmj3AxYi6yERT1KyEfJi0i0GvHAjaMXyw7zAd8oQU4iX0Xc5sJEXkoUBWRg8 8SUMUsSBRhAY81EykR9oKm6YxINqhLHQvhNBbGS6ZAyLobYBMoUHPRxGJkdSZdFqPZeldwMR8+y4 lOHFErnwLMBZAWcY67NABkmcRuBiCITzfKF0PtPivTtOXSTTv2M/k/yB/Ar4FfAr4FfAr4BfAb8C fgX+qwrYD23eqUNaIDR5p/4c/9XA/otHZgX+eNufb/ztbw5euOBvf/kLDFIXXXjBbbf9GR2rRube /Fer1o/7/A8Hsc6Pr//VTX++4883/c9HjtgJzbx+eL163NTpr7tNawgP5r7Q5hUT5kwY7NlrbGnu yndvWd87ME541qymgSbFe71wKDCUt/siqWLy8Zde9/sbrjh99k5OQCd9/5/vXjsQMerm07s95Hy4 YdrQDo6vYgq1ci6dequp833Z3aGuUTWJnUPmnWxv81Cby8evO+dYr1Hj8cdddttAfhrerCKU6J03 CY081IWK7wfaxZxbFL2E6Vg8EZWesS/W8NCLK2fiK5aiRTFJXgg6PlA7RApxIpsY8IZtQTvD+ALR LIpIZ+HjCMGSBmgFS5r30rhYwj1saBLS5cMBDYIjBe0XWSI4xgdVQHq6AuZDOjHocUA9SojEgh0P 0h/TtgomEsEQ8g7TJbXUA9wgRgEQg/irMofIewO0hkl+oNyJBfUKaKDCwXg4XJ1I1FbEahPh6rAS BOEhhVmR54yQ6lSHBDS91HROUR1FKauKq2tQRYlRpcw7iF3PceW86xiOa4p2RuItSXLwMZ1Pwx1Y srJonmgDw7kOYqvgugyqeljTE6FwdSRaFYlUhiLxYCAegrYqCG8gHJc8dh6B7CBvCKqHYEohmkZJ 9rIQZLAPqjRPY0Xp+J7MCZIqCUSyHNL56qiciGp11fHxDdVjG6rG1FeNb6yZNGbUlPEN08eNmj6+ YcrYUZOb6seMqhndUNNQEa3HzusgZ1R+8nOSLE5QUXLGeEg4hZAyJmACq8IhxWYwILKmjoBEUE55 8VZ0AuCoeD4+SnJjgWme2RBqNMahWGgXCw7DsilwHo5UhwOt9KR5Hrhi0jCQL4JS2BTh/9hZaKwC sgiZFUVxgUpCX4ZHYFpkZyC9kjyJFKTFRFssqo3RTEKhzBFJq2LnM+5YCfZPoW4D9OXAWU05X7QC EDFSKjJ9mBeH75FaIryDejHgOZZPTzQNw9LZ6TE6/+ZXwK+AXwG/An4F/Ar4FfArcABUoLO3OKSj 4AQ5Hh28VEMs/S1nun/y7sfeMXnn3GrpxIUT7rnyyG0/PalIz56evf6YFZdP+844722rdt3XTnZ/ t3MQfvS0q09z/3Tyi8cNvsnnhIOnNP39iiVbfnJClmY5NfnTY9d/be7vFobG71yTnddwy+LrNP1z Z8xb8b1l2R8edMebToFYentg5We6P535kV3q/Pam9vZiYJDTOz5Wd9FxM575zrKB/f3pEY+cUX3k e/GqtrKycunSpbfdfvsTTz/1pa98Be+z/vDb374/jY1i9fgdWGf65KaayE7dkPf1W1eoGj+3ZvDF bRvatrWs3Tr45YQFU6t2PqfEitqJO+ZBM7NdnHuuudN3rhQN63uHa6TE1OMv/fb3P7tkJ7i28eFX 2obBHbivtdqPr9u72u3Hhb3FVMNNuwbIFJPGeNqugZhvJsqhGwMMBApYBjkgAHsJi15ipjMKNGKg geLAWXY9AwXkFyOgQBoZ2oxeTriGEsA06rtHah7COAIUOhTTjiOkUKQWcTNY92zbMkyziDaKJTQq JJjl4TjPcEkIhgLNaWSSEZXxLHnPMA6EQRRXzhgOhEJFaq0HmgIlmITw9rLgotVo0THTZcMSLEkt CXKJk0oWD1OhicB9USjJAnLZoRrjQIZC4YAeCUFvRTlQkHhBACUrwGllhMvzEjQ+qVzeMAqIe8fv RoVzIFTUREd1S6pT1gHaKOEciiVkQ1H4OzLOsDZWZ3zBU9RZyTUgLGJ3+Oxwx5rRS9JCz0gkSWFX oTkyCo5liGVbcGzRtUWuLCAozbZ42PvI9InujRgfiVXoeVgWORuKMNc2eOwTYsvg9jOLFErlyZyI SkHgVkS4P9L4cUOdIZjjCNPBLIisfeq0SM5BFJb8gyULbLFMIflYPNXfha2SBc1731JeND/TbXlh 8/Cf4oVAnSBfTPxHqIgC5pkyijUhYCiKjId0yAAoUUZ4VKHkgsAK+05KPYajUIp00cwUDTwFK6sX tsVOJE9WSMTN0+4RUBuI2CIlmnfiYmRP3sUkYOSUxE4NPMIEYUx5xpbo/c/GIAYHtiiS+xXnM52p LFnfYniPWl76N78CfgX8CrwPKiBO/+g/Hnn0Ae/ut198Hxxxfxf9CozQCnSkzCEdBS7GK6veysGo JW782tK7Lptz0uzKxkoYOejSMpSIz14w7apvHPXw4YG3kRgtX3TW4kf/5+Az5lWPrQqEiKFJ6Ho+ afr4j1229MnLGo7aPUKSZ33ssB+eNm52QzD0Vmvc87HYt6mFqiMO/f2FUw4dExzY38qqo0879Ndn RN/Gzo7Q84IbNWrUpz/z6Ucffxw6r09d8hnf2PgOHchA0/Tpg+dv8uVXHnvxGa9NIVoyzpj0uqAu tC7bMWtPb3oXc4yT7msZenZUY+2bJOKV1/zhVCaPovspVz/cMfi68IRTP/mpxfHBL52e11r736H9 /K+HmX3ut3/6ejcoiezOnz1kp/yv5xgaYAS/QfVoV21d3fCl1BMG8QAV8yoOBS2xLz2eRTIaAhwe OGBCGDAmklKRH43gBaDAoF+NAQ5gEXqdNzB5F4fMZKT6gmOR+v8RVcBHBG9BZITmgBLACNxvZQiL ShJf1mV0DOSgyoE8J4gWgpREDtpAcjKwLHAtqHwo4sktk+uP9EnoIwmVF4nKkLIPUQ7CpfAi9jqs uWiUcgU7Y5ZzEH7ZgpWyjELJNEp23gAToogo7GLBLOWK0I5hF2RdjQSUmMRHZEcTTNEp8FwREEl2 bNkxeStLbEZyJR0CLAHYjC/ytimg2SQhvjIwEYVEweFpGw7BJ4NHMUvMEUh+Oui0aD9IfQYESCIv 3NEqMQgTJlZMoididtC7URI76BsYHIRsrE2mKqtRPRBVFB09IsHUGGuTAJVIk0eHE9gP2BDlQIUY KvJspp5bFfyNorHwBeyfTENFMIh1BSDNF/ErJtZjQey05hKcpLAGcmVShDHQBDBmYWTsIY4WNcLk 6I6RcBAJaxFmQs8CL58Lw5Eqin07kmKQrcKThjHlF3vGdVU60KQUw4LBOHH20PoxDkAYS/jydGHe KcX0YQTcSFzmnb20aiYzYx8ZD2VSNHbyeWewpwejfgheD0fqo7kL7cLiWEIcgB1VjJ4d4HRkYMSS 3rkfSv5IfgX8CvgV8CvgV8CvgF8BvwLvYAXEQODNpUrCKSfN/Nj0AdZU6m2/9baXfv2f3gHXkxQ5 +sMzrgiWV7+06c7HOjp3XPSZG5ZvuPORTY+10Hv1+NQp3z25ZhASWdtWbf3X8p7egXfxUt3Cudcd u7soJLHyuEXRQUZQ3vymU+ypIvs4NS593dxT/3zph7dtXFvYke0x8aD6Y97B2u9uqEE8McQphvGT 6669drd7c+ihh1755S8vf/kl39j4ThxtIdI0afLgQPl77vj3wDcPpy6c1vQ6D2Kifnx4aM6edduS Oy2g1LNty+BLpYajZ+2UCPbmy3TRSm3HFnysbmzsjS8QohVNQ49SmPTAzelZ89jDDz3s3Z/33IZS tHIo537nwdGqb2iiaF18D/lmO88thCvHDG0Wb5j0ejMo+3pcFdr4vdO3EYy6UArQrh/8aFhoF6xz 5J5jd1SdSVoAiehBdLsDVYJ7zrMxEmMCkFIpP4ocZjgvBp6jLnlwi4HI4FUDXkjWsA7bUBATww0s 9HsgC4q2Z6wKW0GSZYGY4Vxi0fcG5EMAJIxVMNENsr7IIgnQ4/EXlnlPzkiKZGISHa95Hh6H3Qwr xERgJDilTehw8McdygDDTBQzJQuOKpQVJtpCnr6EWUlQ5BQyZrq/gLh46J8kRwiISljVA6oOQ6Gm B8LRAK/JNhLuoTUiQMWJIV0IKJBvGRKfBxjSFV6V0QFQCwQCYYr1knVF1hRNo4oATVG0GLL5mSEQ K0R5YVfEzYN9SMui5DDQLbgaJZlHJ0PWWJD8oaLEQUQGXKcgVAsNGCV6GR0z+B+VEieXXIWDXVJE 9n8AHzlBc+UAr4c5NVhSApwWRPS9FAgzSEgBZRKWpQYlpM9jtSqRNXSq5EUZRBAhZ3RcvSp7fQjJ OOqxTbINIkI+b5UoEp5lurMemuRPJPUTsSrvKJN1kSR1xDq9ww+TKduCmBp5CfESNiC1HcBHYCrq xckkVQCqUAjisOEYK7S7FPVGI+JVTGbFqCjl9uOOG6aCho/abbLzlGArsS4HaySnKxkbyfLKzlXW ZpGFz3uwjGLpydvIuozi5egeioh61EhGjWQUB59gZmq7CMkhC7+njQfEbO/0zyd/PL8CfgX8CvgV 8CvgV8CvgF+BYa9A8OQ58UGlib3moZXn/2vrp3/72tPpAQDEh6qWzhVueXDt6X9qax+CQpyx5oE1 p9+89sr1AFryJ45trB94r+r0PLN86Q9eOun6Jz98ZzdydtlNnbuk4bg97AhMW82bO1/a0P/4m02x pyrs89Ru5uVXz/7b1iv/9eoXHhxK4MZ7CCk27AU/UCZIJBIwNn77O985/oRlWFNHezuMjTf+6lct zc0HyhJHyDr4+qmHNL1RlxidO3P060WCYu3EQ4coUu7VZ9d2D31Pud2rn107sMfC3JMOG7Nz5tXr K7ELQsqtfbU5PbSF29+6bjArjBOqpjYm2FN8uKJ2aI0bOvsGWVdp+9M3XHstqCjd797IvM9SOFE5 uG1uY2dqcNtsd8uQRCxeGXsb7k8+GK8bbPDIdXT1D/5I6Hno6gFJ2tHHf+z2zcMgnBjZqAsHoaam djhoF5AQ7qTPAhHwmBSBABZIz2xqHukg+QtPnQ3BGkAuQLsAZxi8YDyGaJgXwkVuMvLBAQ0QYyIL GHuOxsHoBauAOzoQFi3oqApGCfe8XUYAlgV4BYETxEx6ABQGMia6sfWAmcE9hygwE536IAbC4KS7 gfuvzBVNJEyhCSTFfhFuEuFkxDjIwy/DGQlGBnJCmiCGxShrjAx6YBlBDRhLCYR0atWIx4qG05/J pzPFfAGBYIzAIBoLSjPAIDAxylkvl0GgMBP0SZjbMBzD4JF1D+VasehCckTdI6HYIm7DUsJ4ltru kOwIv/R4AfHv6B5JlkTq6MexoCjIpUrYAGQLoBGyM4xm4cZi3dHLkLAPMzqSGox0TY6NiH04Hi3s EH7XYjcdQcJscBraNiWV4ZWIMjOwKWRVJImCtZMsoZTqD4ciE8xBJ0WSL4i6yJKIIHgvlYvBKzQq xCjAgIT0eOwI0CHi0HAwZVUEMFM5AQVhOIwpxPAPICQhLrrTCUT7y+gU2ykWjIUDyHocUG9PhivJ 70iYjMEneDuJX9J5glMNKjB23nlIE15XgoFgZ8wp64XFU1QXC6wn/yJqjIpjtzyNIcv+Yj9EKVMf gjIaChNiWBxVCr0fSIuDAA0HiHRvSAa0wLJwsiGo3kspQ1lxOrKFYUfwOfUGtd1isYRCj5BfQ/4y /Qr4FfAr4FfAr4BfAb8CfgVeVwHq/zT4kFg/qYLMhk7h1TXbX1rTwe49zYU3v9gLzRs99KbXXLcq uZGGcx58uWcIjQn1FUt2F7Ftt2344lfvG/vNpw76Zcu/9uXI7PPUbiFrMdOXszmzX69lB1zwQ3b4 4fzkyq99bU9F7e/v/+ddd5179tnLjj3ugX/fV1dff+nnPvf//vznb/7vt5rGjNmXQ/HefE2xe8tr a95wW9uW3UF9seNC9YT5tW8owLg5EzzKtPMtNvWQuUPEqfT072+49cn1bd09ba89feuvfv20J/Kq OeSSi46I9KzHvJt7zN0Wlo+PnjYUEMaZD9x0w+3Pbu7sYeP8+ncvDkW+Ny6aWjuQyRdpmjVnUFGW XbWm2evTml63/OXBaD+pcdaYCnpQiIybPmuQFeWeX93sKTTTW15ZMThy5Ywpr/Nm7v4ECDdNGzv4 TMuTLzSz3SmnN6xaOfioPn9C/TBwqWEYcr+f4sNBu+i9Pzm8SIRFdzAeJssBpQJ1Yv5COmG8boxg MCayncjQyMxfHlIgKETDeHZFLzsJ44BreOMMqLrYxqQjIlEOznmRATEyPgKXgbORshBYBV8QCBEk BcBL1XWokFTIjsrYnrnPmPAHWfX0QrAUQDcMgOUXLatoQaNlk4kRM7FWj7C8scBy8vKB8xglxwT8 YODDKLumK9o80uAj8XhFXU1VTVWljuaLvFBikrGMWUoVTcsolgtpp5jBXShm+GKWN7JiuYS0LKFk KY6t80z5hCgxmhQLE4AAEd1F4ehgWIxzkXwNVIZVh7kXSVo0EDcFuEO8y8bT5GpkGeqMH7kI80d4 mUyKNRoHv5ehTmMSJKZcwgEh8RrrGQh/IbNuYhsFhA4fYWkkgRaRMgwPwCcCaLFtmOOTmh1CnYXX WQTJKImdwt5Bihj8wiOUyMVCuRAZZoE1IjuMIsFECK6AfqhjI7sTFfJSrmiHGcIClvKIFbE6umOn WTdFdqLgdIIPE2cUuCYDpYjZR5Iau1PkPnm6QbkGR2enJWpD68cd68e4JNpisi0Myh6kCnpnH0YA tII4joXCkYUVJzFi2kgoR00UaAqaEQ/SMqjlAT4BLQNtNXC2kGkRwMvbQaJgiEADSbXBwgD7oPRS h+JI9/v3vz+hXwG/An4F/Ar4FfAr4FfAr8B/VYH8Y+tzg+/bhar5B91xzcG3HCX99VfPHnTt0+y+ /MKXqWv7Hm+SVhUael9p9fYMxg91F/qHEJmk1iXe+N7TeOmBDT/p+S/+aLrvU/9XJRu5Ly4Wi88+ ++z/XHXVgnnzv/j5L7zw/PKzzjnnZ7/4xR/++KeTTz4F76xH7q4Nz8o7H/nplVdcftnr7lc/tG3X szbYOHni68/vNwZ10RLFxMKTz5s45OnrWn7zNy//yDnnf+TSb9/8dMbbh2iNvOpnnzjrossx7+9X DDz4+t0LT1t22rwdSrLWJ2+66pIPn73LOHizfNAZH5wdG3gpXzv/hMWDifUrb772+tvve+Cem/7v mr9sGNhAWnLOUWMHoqdr53zgyMGTYeMfv/+jW+9/6K7ffPf6hweaLgbGf3DxJP3tuA6VUQuPOXRg ncbWW773fzffdd8/fvXdH903gPCE+ecdP0UfhoP3XkBdKMs7TruYdAY0gMRaEGrB5DbgqmOAiuyB nqgLUIPlqTORDMmjKCGLGteRXIhkSAOcy8NciKpi7IElG3kDUHoTHVccXMS1BxxXc5He7uCjVior dlnhXU3kdc7VSyXZtiTLFCxLxL1sy4KrSrzGlUCWZKckl8uqywVUKRwJJkKh2nCoJqRXynJMlWOa EtOVCIyHIvRAgqqJ6OUgg7llsqVk2s4XuaLBZ3OcZfCkD7NLQVGOwnAoqtDswJ2XzuXzBXR3NIpQ eRkFrliA2ExIVKuVNXy8lkvUOYk6O1ZTCsVKwVgpEHdDcTGSEGIJLhCSVF1RdV5WS6JsC4otyBYv OaIiIM9eoVR7fMIrmqTokgqLpC5r+ETHg4KsuxSPr5RExZWUsqiYnGgJ6O+o8rQxbePKahnbiLIj K6Kmq4GgEgjKpH/DyCpexUnstYIM6RkMj2UJsyOjXnYl2UEevyQhTR+qLDxeYveyyCOhH4ovh93B 0tDbEXTNEfGgW6LQfHAtoksKctCAmmyLAwCjLpsWKCay6ZlOjDUdsGxUrFAooGj4xDKRS4bof7uM oUT0pAQFdJCQjzttakF05n00SxY2Q+o9enda6D9gWibEZrQEFtmPaQzONuCYBpnEILTvKna/LGFH qJKWJNvweyo4erqi65Km4YAzbAgrLEyxHhDDJ7Rm3PEJJqVPLKuM2U2Do3R/E6cvaB/YpYgGCFiK aeDwA3E6pimUqaWmzLlBuHqRIAYuCOGYf/Mr4FfAr4BfAb8CfgX8CvgVGJEVKN182yt/bjYHaZcY G9N04ceP/s9PDn/gzNoT34ZFiROV6Fu+6d25EeSOKpkd298Uor1lPfd96rcc+r22wcaNG2/81Y0n Llt2wbnn/eXWPy9esuSb11xz97/+ffEnPjllytT32t7u7/2RKiZMH7frpPrhs8btTsnIadPOvubr p4zfc9BVetWT/9kwGNq1xz2RGo+74ttnD6Cp3WwlROZf9O0vHde4A4dJVYsv/NLpUzwG5Gx94Pc/ /P7Pb39hAKWF55z17YuPrB7cmq9b/MnPDi6y0PLg737yvev//pIXty9EFl18xelT3qbYga8+4tKh dTqtz/3p+p/+8o6VaYYKhYnHXnX56ROGHI7v5GF7j6AulGRn2nX33f/8L4tE4eQk0mKxWMzzxQKz 0BCQYrPIcUbtDol7CVDAcA5EOcSsmPjHsysO9mcEnyABjhe9BOERLGn4gjVnZEngZBYDY4JOCo0V WW9FRGMVivlcAXezUCzkC7l8kVBTrpjL5vOZTCGTzSTT2XQ6l84Usjm8xDbRpxBgAm4++ggEAz0Y MEkuXzAwDEAVXg7igvR5hiwwfLGAgQsuyBl21DAL2WI+k0/2Zzo7ejq393Zs7+7u7u3q6u3s7En2 9uXTacxdyGay6WS6vy/Z17O9rXXLpk3NG9Z1b12/ffP6ji0bO7Zu6WnZ0LN1Q1/zhuT21nR3Zz6J FyazqWQqmUz29RbSqXyqv5hJOfmMkU3nUv3ZZH8umSymk0Y6hc+z/X05rKCvD58UUkkzm7KytCVe kksl86mklcvgkXw6RWvo7cXIuGNYgzbLYAMaJNmfT2LjFB43M2kjk7ayWTufs/PZciGPEWhAbJxJ YRyMZueyeNAp5I0MRqBXFTJ0L2YwLxaZwuKzWDm9Kl3EXJk0qpHHU+l0IZs2c1kjhyNC9xxemPU+ T+Oew+wYPJ/HjGY2S6OxO16bTWGPUjmaC5NinGwGU2PGHBaGI0uz5rFTWHOxUCoWMAsd+2w2l8Us GDxTxIBZfKRtqA50MtCr8CzmYlWiWTBsFutng3uHDzvu7RoeQQWwGc2boQJidlo/pkinaGRUO5cx 89lCLkvLpgepdMU8djmLNRg4QfFEKoU1Y5z/8jvOf7lfAb8CfgX8CvgV8CvgV8CvwDtVgTH1ofiO saze3qEE6j3MYPSef83j//vg9o4h3oXQqsqa405d/PfvHnz95DeLDHqn1uyPM3wVuOvOOz2j4g+/ /338mdszKn7jm/+7aNFi1eu36d/+6woI9ZMOrtqZrlQtmjNmT2IlqWrRJTf8/iffuPzck446eFot vSw8ZvLY6F6uQqla+Imf/fnnV332gmVHzm3y2jWGx84+5Khl51/+9R/f/PtrL5xftStsk2oXXvqj /3f9/3z89OMOmTs2xGk10w5ecOQHz/3sN3/ym+9evHBHlhdG8hb5469/6pTjFs+mtWHjBYefdMFl //fb3//vqRMCbx8l0Tp/fPN1X/jkmccsmoN1CrXTDl503CmfvOq6//fjLx25E4vby/1/8835TZu2 vKMDvsuDdXV1fvmLX+zs6Ljo4ovPPfe8fV7Npy89B6/1grRgG2TpXBQgT8Fb0NYgwImFiLPmjOQw BMnyUpOgBUO7v8EgL3ot9RL0OvyxEfAA8Bn5BVkoOA1OEVb01wwvo4klg7Mmf7DX0SzMgMb64jHZ GGujCDXZoE2SddvDl3ChUcg4LZIiy8HiIFoiFyZ0Zsz6RlY6lpyPOKqBxVBkGCfaFhRDECq5RUiT 6AavG7kjyUqJASFhEzE17jQ17G5w1YWDoerKykA4RvY64oGYlJLvWX0Qu2Un+3t7OjuxE5Tq5TiI M2Od/ii2ihbPQtBZdv5AlpmXzu/dvBw0rzIsHsx7kOrHqk7/I4+KkUaKHaPyUgg/08d5jQi9oH92 GxzTS1sbaCw4uBXFpWGbHbOw5oJDa6DJ6NkBdarHJnEjvyXbWW+R3lzekwMO1oEWh9S8EsDUC9ny 9hHuyYEisMUw9yaZOWEp9NLcvAM9cLKxzzAL9SKg84E6D3gnA7PVEmnFKsiKSk8QbWUElg4/Kwtr XuDgKOHEYFleZBEdOOtYLemE88rCqkwOV3aAvP4HAyv0iuLt4+Ac9KPNazNKefyce8PPfjx0BN/x TyZ886V3fEx/wPdABTZ9a/57YC/8XfAr4FfAr4BfgfdqBYb3AkZtevGmg+cPZPA4nY88UXdz32Al +cNOOfzxs6oHnuRSt3/j0XO2sAvaXV6VvvOaR09fvysFU0NfXjbhQ0c0zatC+MfAzc23XfvV569K unt8+Z6G3e3jb76GPT87/rjF6y6sG9Cc9K6/6POv3vIme7T7cbTrvnbcl6Z7b/53FG33Iw/bebk/ L2DQU2+oCSOMiocdfrgv4Bq2A+sPfGBV4O2juANr3XtazZC2C80jbrvtz/u+aAr2ZvlMSDyCAIuc hxTdBLEXYAMIEbUCZAnhiO6SKR+ctQZEAhfCi1iYODAEGihCC0ZZSUwkRmCBxVPhcwppB49gMVUY i3xxlLGOO1RW+JxNwrNAKuTFo80dJ4NRMCUZPoFXEa9G4ztVQKo7PhFUhFnxjoRUKYqah/mMEuF5 uM3yOSi9+HwB2eEuLHSFfMm0XBspVY5gmuUi7pZrlUArRNzRZC8YjMqqjmAmMlmySPWyw5vYE6SM sa57RKnwnOuaCJyCGRDB5IidwvaIcIL/Dsa9MmWU/3/2rgMwimINb7meSy69hyQQeu9VpFcFpCgg IsWHigIWUFFRERUVRMReKaJYAVFUqnTpPZBAQhISSC+X5Prd7vtnZvcSJEASkpDArPt4l7vZmX++ 2bvb+/b7v98JZlxqrYPhQXAGfIjJaoOpAl9CWgAqyF0K9YlKAcoIISUdKQ4IWNvwv9gsC6glUrKS I38ScgvZkwnIRgr52WOdHWG5oB2qd4npnlIbopjQvGBdgIuDY3G3YLkFO/CAUE4QnnQwyInLie25 kCsX2LpDRiJqj3ZsdIZ2UjkTu3shW3vEMWHXMfgT3PPxhGDp0REoHExEIgt/BBsiIpEhPjLagkKX 0Br5d0GXcCYAi4SakWqL6DxxwhUGTjmEsw4xpAgQ+Bd5ySMWCgeugOPBfgtWCE5WmBG4sIGjFraQ R4HC8wgxVNHTgV3EECmL3MjQOQl28mg8dH7iQgxIr4hrNKJ6n+DTDyg5oHtM6KJzH70VUCvkTgcK QUh7RNmaELrE81X+/UaPpAhQBCgCFAGKAEWAIkARqCoEVL0beMo8F8NYCmNTy22GZSt+d/3xDs/8 /fjPqRmyQQXrETJuoOzwU2aMDmuO2+yLgTvdMkumgrvi8ibYMnPKHUb5kbiFQ5c/yFvasttdd9FE xVu6AnTwW4PA7UZ1AYpVwnYhGgEXToQdZDHwL/LpBt0SlO4TES2AOAH8L65BiORKQHCh2oiI1EAS HKz4QdorpF3CRvRKFfynhE6RTTnkLQJvgBgDm8PmhOKNsAOlBQwXPODhMadEBvPgDQ6O5VBBkQWf ebWa1yqVWrkxUGBAcqkUKg8WqC5wZneCREvD8VrgfuBfpMxilMBecQoPBsgyjZeo1sG0RAfYf+kZ MO3ilbzGwGjB30rPikobEpuxUPoRHKo4h5VVg9ySg6kAHwWuTzBzDkzNgUCBwn8sa3WxVuTuZFU4 XBpeIaqAfXNoDQaHhw+j8VB56PR6vVbrpVFqlWoPxNVpPcCYHTgXQAGxPxwvCdWAICRW7tgWHXbA EHuk4fKVSOeGTM2AdZPdzSTfdwiHIAx0I1KaoUKB+CWsccNiI3hJCao0tCMjdugP9Y4OgIkTX3bE UCLlEykigBYQj48bAoMJDu5oBPwsEsQRbRNiLNGoYPIOT8LXN+d0WLC7Pbi/I9YTVhG4LogDFhW8 /IFlVICbGtCfcD6ASk4Fq4ZYQuRH5nLCYjNqNUCEKDUXsJVAcyHWFLnIAxPncGihQCXyg4Plh1UG jyywO1MCHQmBQh1GATpVqVEBTTAag0HBIV6jRVU2YRkhTpRYi7WDkKwKvmZQjhItKLC3LgHNAPvf S0UdUStUboHMHxWKRDo+gB/oWwACI4HRIDBCeAhJpDPDoFxRgeTWfJjRUSkCFAGKAEWAIkARoAhQ BBhGHxQ2qllJVpr9Ys62G9hh+a1dOlJcPRrvww4P0DCi7fPfjn9y0izDyUdEeze4DraCNavITWOp Q2TpFROqD3JTbjbr5cJquGK8hUPXhZOtX7/+UFGRJirWhbWiMVYxArch1VUlbBfoY9COZDHAW6Hy iYgHQtIlTLngdEQpTw6UTSB9IpmOwE8olFAeEczsCRMDdvZALYAuBxMHKOELSunBhkRHIgNpfR4a DSodCH+gKnpY3oS1Y0geBP9zQG1HMC0H8gpeAx0TShaEf0EUhXdoDS7lwLmxjNbAqDVgfK7QefJa b9AkAfGCqhKC1svOhodEKFUqYKcgldAzIMDHy9tusrmAdysuZApyBTsUXVR6eXg49AadTgskkknl YQYll0JlBnGWRueCAEQFdOjgwKPcAlUPbeBIZSwyg4u5SuEVFe3hZRAYZRHHq/IuaYzZrFqj1PA6 ncbJKZxmkwPkSkCYOWzQBcyIpGdirgqzLbioH2ab0E7qGCLEcYYeNu+XshdRmhyWMiEiC1hAxLMQ WopQMaQ3JP7CJBl6jIkszIGBmTxJ1SOKLLQWKAJyCA6AZC6iYowkQjw8ytwkwQDaaGBMdeEaizxU IYRVAhpPqTcgrzQgB1VqUHWZQLmG7Of5IrMlwNugCQqyKZTFLtZT7xmuAcWeCgRWqAKlrRj4Uhtw ZAxnA6c3BS9qVDatRqlTgnG/FWpOqnhRqXAgD37EdoKfG6ODigKQaso4i/M50ar39fX1CYFyBlao uugUPYqKwc4eKdLA7B74PBcykgetHrjbWwJCYRJIdOihhzNQoVQDxDh7EefYEtRwsi1i/AiamO5T KoB1BejQ2YtAIyuCDOawKA+yY7GlXTVctlTxJx3tjiJAEaAIUAQoAhQBisDtiwCrCQ5aNCAS7SNa 73mpdWuNO/vQfGDH5X03mLk9v9hNVHE+vnBXHG02SIKQNiE7ozjzv52UUm8xpn0XzHJrZcMWfk1Q Y25E24BA+ahyMG5XR1l6iGvNoZqGvn1PFjozisCdgcDtSXXdPNuFf8ij7EWgukArBdwUqLqQzxFi sySJi6QqApICXkAZZ4gdAPoG+xwBEwIyHJVGDUXwQI6jAobAZnOApT3izaCgHs/pNCqdWgViH3gM XvGwAxkEuWEC5O0Bt4RS3JA8jDBqKGsOk1xAxCC3K5wTB8+gJ602rdbz4TFjht071KVUjRk68J5+ Q7EXGLBgNo130AMPP3L/g1MG9xqg8w0bPvmRGdOfGn/vCO/goLETHm3Yur1N6Tlm2PA27dt5ar0m jhpRZHM2b9r48emPqxWKCcPvGT5kKNAlg0aOGnv/SCjXB6RVo4bNnnhyduMmzcEQPSgiul/XTv2b Rbfw8wBCcEjHFogLCwzv0aF9kbGoQ6eOc5956pGHHvL19e/Xb8Ajc15+dMbMdq1bQZ4gJrNw2hxk O+LEOPwA7Tink5BcwKfAAiAmiCi24CiJaUJ5iKwdwEI8C8CCaSnyjsWUIjyLZWASQUNIRtwbWlHS J7BmwLohGgsBi3DFz6M20JKkKeJxyVGoPXkVNYD+wdFMpYXD+vfo8eDIkSC/u6v7XRPuGQwFO6H6 IajvBF4xcNDgUeMeHDGov4+3T5/+/R5/Yvr9M56rH+bXqGmLB+4ZWsxpew24597BfUEbPm7oEIO3 j5+v/6yHxmk9vRrG1H9h2mRPg6F9x45TH3pQqeAtNmtwcOC0MSN79R4MJJ/aP6DfiAlTJj740Nh7 FJ6e9w0b3qhrl2ydetCIe9q1aaf2DX5i+v8effrpJk1btmje8uk5zz714Mih/XtbOZCRQVIkhhKY QQQs0rOh1FukXUPJuIgiQ5hIkGKSi9CHIOPCPBgiKPFDmSFDZUTh7UE3igBFgCJAEaAIUAQoAhSB W4MA69282eyJHdE+umFrX7eSSiyOj1+8x3qjoCx7Ek0yUaWI7t95x/Q2f8/pMbe9XBbNmb91VwGq B+eyGy1u/kvfa2Knv6c0nhcG14HOH3ddSpUSHlmvdu3+fKbNuundvxjkK18jWo7uzbgR44bDvOYQ 15pE1Q19I5jo6xQBikAdQuB2/oF6M5mMRNQDO8h5sPMSElxh+3Ui6kJ2XSB9QYov4mmFZUZAjgEV BrolZEWFtFeCHXyQQEqFbcyRxzsod5DPFPJRgqftsGNGjWwos04FpBo8kJLmgOOAnEfIpMP5c/A0 Ei5hKgfluUHP4JUEpExAQEh4UHR0w/YN23Rp36JlSFg9J6iUWU6jVIeHhDZsEPXvyfifft/k4+nb Oqbp59/9+uX3a1MuXPTx8VcZAh0aby//cGBAzDanl5ePsdDkYQhSGMIg8zC4cbMGzZo1iYlp37BJ JPSJky7bt2wW5qNp3qxhsSi2atSY0xkWfrl65cZtFy6n8r7RZoUavMV0CnVWdhFo1uJz87/duT/2 /LkQrTru9IkTKenBwSFFJqSFxvl8iC5BbvmSEzyGl9hhYUixxoqwjghPYsUl71gXhmlFzLzAN7qk DMNJjahz3J2kw0KtpU3KxUMBEBWTTNpIIrGSXD2SoIf5HSx5cr+xEf0DwjmH3QuEWg0axDRs0KB5 i7atWoRFRUOaJ3bcd+lVqpYNIs4VmFf8tE6wWXq3rP/zn3++t+T9A/8ehBxG7wB/AF4VFKrReDis Fr/gcMBfLYoBPgEWi8s7oF7D1q0i6zepH9MovEUrNeSiCmKzlm3rNWhUP6YhyPYiQsM7Rgav+HH9 ss+Wp6al+nt76jx9GbVXRFAIZESKUF5Apf5p5+F9+w/46DSZRdZd59ODImKgpKfDBqZpoMUC0hJ4 LqRrg0kBpYp2nIwrOffjySLgsQUaZlqlGgKIHMN8IWwSgDjVsQ596tFQKQIUAYoARYAiQBGgCNwB CIjFiXHzPkvacGP5vXP52tjN2TJTpfZs3y1mYGsfb8KYOYsPrz+xIBG71zuzvtqRJ2dDsvqQ0IF9 6rXVo+vJ/Lj4VzdmIjoMberodjEjugUFSE5dzvQDJ178x50OeV3srz3EtQ6rsqHvgHOCTpEicOcg cDtTXbCKwHYt+/gTsOK7WZf6aj4jIKeM7NiVCVdLRP5IyH8K62uwtgtzPoR3Qyl7wPNglRmkGQaG hIHzk5LTDe01MMOi8NB76HwCOIUWtEfply8dPhk3okurGY8/3qN7t4v5hYmHD1rzcny8vTn/6AeH DX31ySdjGjV1goUWrwCPdBjebnE6zA61Vg9iNA+9btio0SanQ+Wh8QoI8jH4xEQ13HgwLiIswjs4 EliVE+kZhRnJrcNDWsY00is9/zd84MDunTTg6ASGX2qPzs0aTh4xrEnjZiaFtlWrVl0bhB09ecbp sMNpB3QLMHbItAqZsiObfSkrlCi60EwhaRO7oOHUQkzKIOczpGQTBeRfhWR2mGtESXTIIQ350xPW Bg6Xsh2x6RpOUURaOESrIfaLFDJELBsWKeG6ArDGaFyAVcppxMMBmwamWchujPCPmP1Cz4DWjxG1 np5ag97kcI4Z0EulAuNNpZeXNypayXFgw7br1LmOgR7PPDWj28ChTk579shBk92i13t4Bge3jIx+ aeyIHq2bOXklaNTskBsLDmkuh6WoANAAFjI5La9Xlw7+/kFFRqshMJhXqZs0bvTP6TjOWhwc7q/x 9bmUm3c27qTDCWmMfkoPz3vvavPKo2PrR4c6lVqI2dc/cNr993fs2AW41OigoH7t2sSePlNkLASh IcQGMkXBacPsLZosTB/ziIi0hQ3YXULo4oRO9AwiVpELP/Gmxz76eI3wc6hkASW6qvkTgnZPEaAI UAQoAhQBigBFoLwIgH9IUsLFdWt23vta7NLs8l2mGS8PemHr87+c359szMixggzMaTZnZOQe2Xf6 lfnbOq7POy8N7lrz474ZPyeeSCsusKHMk+J8c76dcGmOFT/v7fPGoV+PZiVlm4sRM+YsyDGei038 5sOtd32Y9s+NGTcyxnWGuBYCVTV0eRGm7SgCFIHajwCbkHCh9kd5kxHarNa33164b/fuSY88Mm7c +JvsrToOnzVrAmIVIKUOU1mY3CGKJeJVRWge9DcxrgKCBlNeQLgo7E5u6MixjSMiT59JuK93jzXb d/ft1n7x+4uKiwp5taDRKIN9og2RDR64q93plMwmTVouX/4FcDWZ2QXTn3p075bNW/cee2LKw6dP Hzt3PnHylGmf/vBT7w4dQw1e2/buve+eAQfikiaOffDjpR/d3aH5z39v8fUyPDp/QeHF2EBP70++ /aV9VIhfRNCfew5H+voXmUxDBgxZ+d3K1Lz8x+4bturXDf26d/NQK37btDk16cKTT84KiInycxS+ +8mKAmMBoZaAIUGlLPEsWQ6LoYiZFua2JPUQnjg8Bo5JqrCIFwCZkJHcTiwxQn5c+DgCIyi4CDmI PbikF4jHF/mTDEPMuTCukNGH/sMObFjHV+I/hfLz5H6RuAlrzERGybkslpCoxg+NHrJz/9EHRg5f 99fmJtH1Nu/YffzoIa/AEKAMgwOCQBA29cGRpy8VNAgK2rRlc15eVl6BpUmzxj3bNX9/xaoefYeE 67TfrF45Z9b0dQdj/VXcPe2aL/nxt5EDB5w4HT94UJ+z8YkQVUFh3qnTZ157/rmiwlxDw6iDW/ae On3ygf6dvvnrX8bkyM9OHXnf8HMpmb+v+/WRx564fPniwdgzM8aPXrfh7zNxsYOHD+/RtIkyIGT1 d98dPrjfLzAY04tQh9IG5XEIXOjswlgRUNAZSJaF4IupR/Sq+2yUFg0BiuhXJMJjPv7g/ep4a5A+ q7dWd/XFTXuuZgRqslZ3NU+Fdk8RoAhQBCgCtyEC9ALmNlzUqpgSvYCpChRpHxSBGyBwm6u6yOzV Gs0LL8ytzdouSbQFahpUSg8sv0DVhV3t4Q+UmoftwHH+Hs4jkzLLCFEDVQaDfb04xnzy5EGgmU4f PWzlNb56kPbYobeI+jEPDe87sm9Pxsn9uWVTvss1Z8aMhx4ap9cqrakpefl5rMPiB070GlV+cQHo kuY8MeXunh1PxZ0Bb/TioqLEY4fOxJ6NP3WgwGT08vEODQlJvZix48+t50+dCNeI24/HKbU+T06a 2rpFq6yCPJc5y15U2DA4xMdT7RStjM4zpnWXp599odug+zyDwo4cP3EqNatbly4gIEIeZSSDERMt 8EBWriHmhbjFY19/aYd5O0i5S7yD7Aj+xcyL+/YQomMwBSXZysNLcn1F0iE6EyCvVE5IxXl56ABZ MYf0YahnXD4TIQsJfjhC7N5OCDCyTug1iB50XQx4+Svs1jNHjySZ2FMnjhXk5nh7aKEMp0oDxQl0 w8aMmTBpsrdX8L5tWxPSkh959JEnZj5Xv1EkkEN2gTXlFXkxoh385i1WY5Fj1v0jR/XtfTrPVmAy +2pVSXHHElLSj8eezs1I1qq09cNCTSyzd+eBf/edDmvdITenICUpe8Yj/5v09JMg0WJVmnwbFAko 1Hl5qKAmAM94h4ZNfeTRbv0GgrH9+SLn9v177+rWwdfbSwCGC1gtJ5iq4SWAYpBYzIZVbQhMQADA woZpSMmFdsKyykUCEMLYcA4l6WJmlrCR9LOWIkARoAhQBCgCFAGKAEWAIkARoAhQBCgCtQGBO0LV RYCuzdquJ58ch9gEpC1CFA9mW0hpQOwXhgVc8A+y8ULyJUQ/EOkNYsR4NThGFRaDfbzT10ufZSyM DPFPSkvT6pRQClLnofP28HS5VOlZOXn5BfWj6nl7eBSaTMlp6eGBPpm5RqizFxbgm5aZY3W4GkXW A54NiiSevpAcGuAnOJwmq93b0+tSemZYcEhObq63lyG/qNBssTZsEF1ktRTZbOgQjs3Ky1epNCFB wcmXL7eo3wA4slMX0zo2bZ6ZkwvZhJkFxqbhobEJ53z0epvFVGy1GvR6yM+ECePik5jGw4bxMFPQ eWHeBFFKJWIq/HeJ9zyx4CJZiUD8EX0X5mrkJ6E30gNmuQiNI8uU8Okg9S+72WPCR9rwEVLFTJnH QX0g5gsTPfCkRIBp1Gq7xZxVCJiHXkhLjQkPv5iZCQSSr8Ebcvy8PT1VGk1G+uXM7Gxw1zJ4e5kE Njkx3mAwAB2WnpsP6OUVFmbl5jdt2BDyVyGpMD71EoQe6ueflpkeGhyclpEZERxUCAI9hdJhsxYU m8KCAj11uuSMjMiISE+V0mQqTrh8OTokOD03F06FiAD/1KxsKLAY6OUJTvY5xqIgP79iswnoK2+d Lj0nR6/T6ZSQeUkoO8CfQ8UWStmQoaRGbEpXIulyi+Mw54dAx5I8osCDpi5UNJP9eOmS6vtEozdF qw/bOt0zvSlap5ePBk8RoAhQBG57BOgFzG2/xJWbIL2AqRxu9CiKQIUQuIOorv+wXSPvGwlqrwqB VX2Nn3oKJTDChryTkAkXEtpgSylEe5FyeLj+I/Klh80Bvvc45w5Z1EM+I2LIwPAezJ5EJNVRsLzg gLqCYPZld7h4XuVw8iowAgPnKWQJxhWZLXCAmmeh+iH0A+QZHARd4jp6rNlqM3jpIT8QEWouEcgv hwNRG2a7A7UEDREDNQZVTgbcveBF5CYGnQf7+YYEBrhQPUlgThRQZhFMs7RqpcOJ8jFtNltBoTE7 J0dw2pG1vsRm4VRNzDFBYBAJShzEDApxf8JzldIHMXVVkuKPyD+kRUI9yVorNzWGDiOtITws6MJ/ IpkSkWhJSXlEruT+H9aYISZRTqIkRBnJsJRT/PBg8CRME2RNCD2ed4JRGNZ8oZGwEAyVZhZcKq3O ardzLnAiQ9b2kFZpRfo0hCbpAR+CSkNiURULZTixVT40RTUKQMeGTglojGlB+B+givDFBBMiB1F9 RFhllA8K9TqhHqUCPNdcLrVKbbHZcMonmp1GpXa6HMAtQpyoc+ywRfI1Yd0xh1hi4iCBKacrosle fd6T7EawTUPnCAurCMF8QhMYq+8DgvZ8DQTolSI9NSgCFAGKAEWgNiNAqa7avDq3MDZ6AXMLwadD 3zkI3BEJjO7lLJ3JCO5doPOqVStNhDJQtxE2yNaD6oo2uxOoIgiS5OKhtD0suiFcDfyJhVHAhQGj ZRMFm5JzKBXAQdmgNiPqyekAKsRhtylYJyfaweCLEaxOl0WjErUKoFBc4HqPnmScDocV2ihZJyvY 9BpWcFhZxqXiGbUS6BgX6LaATPFQKTUatQpRWZBAaIecQwa4E6joB5UIreb8gvxLeXlO4FuAA+J5 oGqAgUGMDzBB4LSlVnl5evr7+anUWpiDAw6EGUrpiIgvwcmDxJVLrpSIUxhlry3CT5UsF6KKgMkr lTcneXxhjgZxT9jyTK6kiclCQneR3Dy8E5MvIJVIeUvMiCF2CJt9EVUdIt0I5sQDX060ROCSp2Gp JIUdFoNhbRqipUClJdptQKaCbRgi3BAn5FKLglbBq0nosHYwGJq8U7DZoWCnTqFQAVbAXgGLh636 wTwMSnKix1hPpUQFONEj+BeOBdIRSDRU1BMoSwHs8znW6YR/Qf+lBhN9RlSzjBpGdtqBMQVMYDYu hw1yQDG9heZCnOYJvSURqm6UCeKYCENwkQ0XagTdH6wetCdMJGESa9VbiQZDEaAIUAQoAhQBigBF gCJAEaAIUAQoAncsAncW1QXL7Ga7wKW+9rBdwBvADkQCUdAAbYBkWqjkH9IIobxFXP5PrVYqlaDO QVUEERMDqi7cHJ5QAsMB3AawJk47ki8hFofHei9SMxAYESCHGGT8xQkg+2KVHAviL1RNEFybFCro WaUExZdKjfpHYiieB9EU0huBKgykRAziOMxmk8VigTGcTodU6pCB9DeQJQlmm7nYWFBYXAzEFxA0 WEqFa/kBHYMETbxGrfHx8jZ4+6pVUBoS8yPEmAurk0BfRugkoFbgNWyFhSRjSHqEKSTyFpXpF5Jg KNErmPZCzl/QAHNYmMQhrlvIuQs6RFo5mBiGAQnPSOFFWbWFw5CGQOwW4YCIMz1Wh+GwJFd88qfE f8HLMAwRqSkBSCTIwsUiEfsEJCE6GoR0oKYjiABUqL4mwIEUdGhuqD1ebqwfw3UeYTmR1gt4SSeU pwSWCi0aWPijTEPE3pEd+C+gGhHhCKvtgj9hB4JMBBEZnAjwKjBrcDiQWyIwnvAYP5BSFzGLRWbk 5rCIMRdEIXnCueVtaEaIf8QCO/QIu8kRqzUydVwKgG4UAYoARYAiQBGgCFAEKAIUAYoARYAiQBGo BQjccVRX7WS7iH4JUzGQ1ybtiAWSZUtITeME+ZQkhCKm9cBNqVUKlRLoEWRdj3U/5P9B6oUewf8D 9QHPwuF2bOsOmY9AUkFX8AcilXCGIHamApEQkEdoxywQj7gclHsI6iREcChVkLMIbBj8vxIegF8+ yjxERmIgEgLaC9EkxcVFWVlZuTk5psICMD4naX+Qx0dSFBHbpdX6+fj6+Pvr9F6QCQk8D/SsAIIN cWuYeiKRYHN0tKOAQdfmgH9Rb5jyIjvRJcl5joRswZssDEO+Wm5FEtYiITYNE1aSMkvy/yJG9aTm IkmfJHGDygqpz3BOJWLmkIkYeYHYgJGNjCnruVBLlFSIX5CkUrjaI+4dc1+IjINpw5yBxgRqDAg4 QACBgBYReC08UbzBFIgvPtrRY7QwmBKTN0Q0YVcyBA3iBHEwsi+Z7E6G0xgRh4UpKiDSJN0cZtTQ 8wQ3Mh00BTe3iJ/GNBzuW65WiQgyDCXgJp9FlOqqBR/nNASKAEWAIkARoAhQBCgCFAGKAEWAIkAR wIKSO3Grbdouwj0gXRJmRXCKHyQwYo6HlAwEHRTLYGsspPbCJAghsxDjgERh0B5n0hHWCTIMHXb4 DyRWDuT1Bc+ghEEnrt0I0i/gp4A8AsUTmEw5IRMSNEGkOCEMr4CBkCxIEB0OwW4T7VYYG8YEWZbB 01MLBmfY4AkxUsiRCrNDcCDSGyG2Kyc7MycnB+Rd8Cd0hdRgID4CDoVlnSILRR6B7Qry99dqIV1P CREDfQctkXJNkjZJ3AshfXAiIZE8YXMp7EdPGCbcHCuoSqXZkTRGQvJgcRV6RNIVpUhJmUbCTkmE oOwPhsEvodQkgJFKC0uaMPJ4x0o5zBnhfshCSH5eWNWF+EUcMKQfKhkGfMhAk4XEWXgqkG0IvBva AX0SGxaSwUIgfg9YQpJUKInUcFCEhwMNFzbzwgHh5EW0CJhmwpAA4SXTcYhwQ7UkcdwyFYinglM2 JfTkkPAUSG1FVIGx9IYpO7JhdDDNRwBByjuEBz4X6UYRoAhQBCgCFAGKAEWAIkARoAhQBCgCFIFa gMAdSnUB8rWK7bKBnxaQUiCPwiopoDUwgwFMD2S7YS4FW9Wj51GynqTLQcoj4D6AtQDKgeNVkMaI 3JuAsHFA0pwCkh15hUYJCXMgsAJGRs2A6RbyrQf6RYsoD94BqXTAksGgKNkQcWJASgFrhSg2xoXO DbC9gledomC1gm+YSWDsTgjUXCQ6bDY7es5htblEGyK7bE4WdGZAkVls5iKTsdhos5shXggOCcXA BYwDyypsr65SGgxeQUFBXj4+SrUWuZuD2AmMvxAlh7gZTFahYwijI+fKIQLLLd7C6iacdejWMWG6 h1BmCCJMNxEyDOVoIj9+2JUqJKBSoH9Rdiih1xBfhlMmsbxMItYQY4UpLcJkEdsvTEnhnWQ0kpVC q4T4JwBWBTtklGLKEjgjYKwAVvCLR5QgyPJEO3rWhkzYTKLLDsUL4WhYKQ5WDhIVeSXLq5HllsuK yEEBqEnUAlFzwBoqFSgXFGy77A7WBYdbQDqHogStGLQEYgulrKIzBXNehBNEG1GgoSIDmK+C4OFM wzI3RI7CA8IDokaYLyNcIGFdUYc41RJpvUjaJU5gxHQrkYfBCYZZNrpRBCgCFAGKAEWAIkARoAjc KgTUkYdXjhZXk73/2sY456NKtsYt06VuR4sruy9SS502GNDd4X5+acuHq2Qs2glFgCJAEagiBO7o H6i1iO0Csgl2RgCrJxU2fIL0O+TKhdgEEFFhpyTJ9BwZoWNLeqz1wi7hnBIKJbocrMMuoOp+SBLE GFklNFA6bQrRCXbm4Cdl5VmTy2l1WcG+iXOC5sueL5rNKigIKJidQgHKM1TyNpfFARZfKj9GqWUU UEOwmFVeEgQzLxbxriKbJVPJmzQam15nUips3ppAFeurZArVYqHGbuEdxSqlQ6F02W1F+Zk5makZ RmOu1WFTcEoHMpICYge84pEhGfBteg9dgH+AweDDKdVApyEBF6JhQNbkQF5UqC4kFmfhNEakUsMJ mOhfJH9CzvxAMuG8TuzJhUoWKmDDbBXx+cKJl1jEJKm3ABnUj7srSU2FABUEm8MBO7yKyCe8IWct UoYSU0TQCSbCCBlGsj5lXRjKaYQUQQUYqgE/B/ZaVhFIKJDU5XFMHseZYHXtvMKpVNqQmZpLxalY WGqwWuOMLJsnikWMtZi3ujiXkhcQIwkJkyyfITKFwMixooYXeNZezDmKWIeJg7VQFvO6TBeXzikL eRZqDgAhBvmkQHPaWMaCnMBIhMRgC1nbY+ZQYr5QzOSEIs77SB6G7dmwWg0QBCM3CTjJQAx/1qDk R2KehquCYtIM+ZEhpgyWlHp1VdEnMu2GIkARoAhQBCgCFAGKAEWAIkARoAhQBG4SgTua6gLsCNs1 aOjQW+tSL0mFMKeDMgkRISNtJAcPmASiVwINDxBhJHcO2AuVEsr5MXot17KVf6s2oU0bBWlhSmoD D2X3bFbImeMYLWiEVJriFi0MA3qHduvk5+MFqZFFQICo1ZrGLfwat/Ju0cErvKFKpUO8hVrh4RI0 VpD8sAUR0YpOnYN79Yzo0sGg8TCplFZPvdigsV+vXvV79vRv3UqvUdvVnCUokGnZTN24MdskUoyu z8bE6AyeAmNx2EyF2dkXcnPTQcIkCgpcsQ9ZuOPMPJiCQsXz3sB1efuCtgsb8wNFB2wYkpORtDiU oYfoPkTHIBYG5E/wD7E0Q77tKMtP8veSyEBipEWIGGmDsWRSqiQtj7xtpLxREFPhYDTgRwZKOJTu h7oAboiQO6Q+IyJzUOBY8USUViUbqQ/gAD5RZMwi6wATNTisXoR/k0beDWP0Bi+YvcPqcDA88Ie8 qRhuh/kCp+Ttz3bo4Ne1o0f9aBevzmeUeSyb4+vLNG8d0LSFZ7NG3hqtQ8nZICAkBhN0LmDJNNbG zfy6dGncrWuz4Ag9x5tBO8YzSlgwVrCzoODDACM1FnLtItBhxCQJHGK8JLGbAgR2IG1DhQHwxHA+ LDFgw+chPIlpRMRzoUZgWg+PMX9WqjFWINKNIkARoAhQBCgCFAGKAEWAIlBDCLAN1Hf6z9gaQpoO QxGoswjwM2fOqrPBV03gQJ507doN3N1/X78+JfVi1y5d4Zmq6brcvfz11y9AIOCkMWL5hMkUTOMQ bRF23yIKJkRiEFUNPIDcQofNVT8iuFfnwIaNArKzipIuFGqUfixjVPBOHpLpgM/RODp2iezZOZzT mJs1FD2DPZJS8jiRC44w9L07OipM3TjS3+k0HDyUpFd6Mi6VmnMJTHaLltr+dwVynM5R7NG0E+ep 90i6mBnTOLh31wYCEDoqpk3LaIuTz85OqxcZ1qtbs/qt1U0bFIdFa6E64/4DGTwTwGhNTmeGw2K1 uiBhUK0AbRKMCpUQEWsHZf5AuKQAcgkYN0i2RIZkkIHHiZBeiHRDqPSjVNpPEk8Ri3Sco4hyGzED RQguIrAi/AupB0hUVvAnJniIQTxxtZf6JMcBjyMpmIg+i3i0E1suKQsSDYfdsKRR3GwZocnkjRxn 40QL2KxxSuCQ1JBw2LJJaPv2Bv9g6/GTyU4b8GgKFRJe2TVKbwVv9/Iu6Nm7fkwUG+ArRkd5W0A4 l5epVDhiGvl0bmuoF+kb4Blw+kKBqdilVPs5OZ3AKj08mD69w1s28rYUFHlpdI0aRhSb0nLzihWs F8fqRNGO1H6iAp8jkooLM19XUFHY6gwnOOJNFmShVqiyIyG7pJdIA0lfhy3OyDNIgYdrAiC0YK3g +cEDB5T7fK9ww2U70it8DD3gDkBgZu/QO2CWdIoUAYoARYAiUFcRqNELGNGpLS44ffLyvhOwp++O KzphqyLc/INm3+2vJ52Jxft+T92CTHoZxmrl07P/RcPBnrX9orUmLtdY9cO9Gy55pOMz4XnvH7dU 0Qxruht6AVPTiNPx7kgEbgUd7opdMaLPwL5kf31rNvmwvMXbuHHjJz3yyK3Sdkm2T4hkIHwLrAti ehD/hY2TCPeABTi8AzRSkBGHJFLQEqgh7cXLph3HsoE5Qnl4VrCDUnh6e7RpFx0S4mSZfL0uINQ7 5tJF13c/xp1KdERG6jy9lHaHy9tPxyjMmw5kfv7Tud/+OMnZXUBFsUqHi8lXqfIaNfCAQot/bDm1 7redycn28JBgXsvWr+/hUpnW/rbn79+PFhVYYuoLCo358kXm+18TVv1wJq5YU1Sg27QlzWXV84wn tsPnHFazMS8zPT2luDCfczqRexWeJCr2B3okIIRUCj9fn+DgIA8vLzD0sgGjB+5fYKhPjNuBOpKK JyII4EgQHOGyjNhuClN/2E5dymTE6XQoRREaYyaMOI6hBv+RemHPe0KFYT4LgkGZjC7w8ocdm/ij 7tG/sh+7bGyPOiMG9sgaS5J4wWzApUsjikrsqmaHl88lX7pwOVurh+lYWV4LaZsRkeoW7QO0+nxB yAgPYaP82Z17Ur9YdcZodLZo5mcx2cCMLSRCl2bJXPX70ZV/HjufkAYaPhdItVwg6zIFBVubNoG8 0Avfrt+342CsQpMXGgD5nmZBsEBALKNVcDrGheRXxFQMl7HEZQ1Keclj7o/44JPSk6C0k0p3wlSQ EozY8MusHkoiJYZuyBQMJ4/Kxl4IW+mNS43pb/EnGB2eIkARoAhQBCgCFIE7GgHB9MGWlDmbyZ6+ srDaRff5l7Kfl4ZLmbOn4Ei1o8+P6dPq8JJBy6e26BOlhbpPdKsOBLZu3eLe4+LOpiQnk91oNFbH cLRPikD1IXArqK7qm83N9XwL2S4gf9w7saRHRkjoAbKjRy/h6nxgW49UO9i7HlgKor8BUytLkSU/ Mw8s4tVqsDMHEsjl5xfas1XjehGedmuRuUD4bu2hr77b4yi261UaW4EiP9cmMhot780ovPt0aDhu QKueHRv7QeIcJEQqdAqVB2RGgmc6sDwGT2Wov4ZzKlkH57IJvEtUco4ATz7CX88Jdqcjp7jIajcr zQX2YLUmxNP7zPHiC+etrK6+w2m0FFtMxQqHU+20mAvyMtLTL2fm5NlMJrCrAgIPa7Cg8CJkCfLg Eab39goGp/qgEI1O78LCKjw/nIqHCy9KuXWS5AiLtCCpE5MyWLaEKDCSbUc8qgjDJdFRRLSFFUxE sYUz+KTTBeuT0D+SJ73bzR0VlpQc6ElWoEyLYWEZZoPcvmDYcV/tYgws5w2SNMglBL6rMN/kNNlY JxhpKRyiVqXVtmmp7NE+wMsTamGKIX5BNps66Vyhy8jlG1kvPx8Pvb9aF+YUgkI9vcf0bDSwU7NG 0WFKBVTCtCk5BSdYlQqTKEJypFe9MJ96IZBtyVotjNXGqpVgh++AeoyQrUgqIsqm9ITlkykvRHuh xyRbUeazcK4jGPOjHVcDwKUuMa44pRQWC+ctKnC30Bt5hmRBXq0au7l3IT2aIkARoAhQBCgCFAGK AEWAInA1AspOnaPaB4A7Ld2qEYFFCxe691lPPDFt6hSy3z/yPtCpPDzhwfeXvLdmzff79u0F/qsa 46BdUwRuGoGaoboQZXDTodZEB7eK7SL5caCiUimUmEQBry0krwG6RwFFA5VKtQpv6AGYSSERErjX Q2lCAAUKKfKCTeksVojFSpUV6ifqPPTZF3PPJucfPF1QZFYxDkEjsp4asXN379Bm4fuP8umXwVVK dezgufff2LbhzwOMMqttJ6XWu1gAXizPYslXmPJ84s+zTi7g3vu6jxjfrkkrVWJW9uWkgoQ4q6AL HfHA3f2GN/ENNZ29kJeTY3JxGZwht1WfhuB5v/vQ2UCvMC1js3OXWMaqYD04hS/LIrrGZirKzMi4 nJGZX2wCxgVZmhN9FyTD8Yyg5HV6fYCvX4h/gN5Dj5RHWPqF6v9h23SECv4fzBq7p0sWUygJEZNT CD1UXxFc2oGFkTRJbqrLTX7J7uxX+EsRFRTiwsCSH3uEYUt8xBORjD1MPiKnKvIn8qDHYi7imU9k TpB1CbUEWEaD6jCKQH2BAZmO4QKcrF4BtJHDZTcVX0hNPX7q7LHjiayTt7tYF5SGVIgaqJ1pNyvs mUqX1XSpaNP62MWLDx9IPd+okaVhQyejylBqjAyX6xSsGdlCwgVdoG/khFHd27Wvz7C6Y7HFwK8J opIRzWqlVeSdYEsvmW7JhBSmvqRcT0Jv4cRDTP+hrE1E56ETCcgsONsg+5KYz0u0IPb456CqAFJ2 QXvJ+R97/4N1mkaJDc7cxGFNvE3pGBQBigBFgCJAEaAIUAQoAhQBikBNIwDihb83blzx1Vfz580D /gvIL2C+gPaimq+aXgk6XjkQYBMSLpSjWaWbWLLjj+7587fYlk+/3C9E6kUsSjt70SiJarWBMdEB 4GtUmzYgquEN3Lxly+fnzg0KCq6B0GbNepBwDThnDlEs4MqFpDTEER0JaEDYhcU4HG+x2UDhhcke RAIBN+G0FYfWM426v+v+fy/t2pqh9whn1LkNIhWJF/ItBVqtp94qFrZrE9ipky49M3/DOmCzWIOv ytePt1mL9Wp7166RgX6+K1ce8fAM9g8NOp942VEsqBTO4BClPlBxd/dgzqn4anlieqo1IjDAP8wR ECb27BKWeMHyzTfxClYb3sDRtHVQp5bBWzcm7z6eHRPRwlRs58RCpwgpllqw/ofigBwDrI5D5ASV WqvV6r28DN7e3kCS8CqVC+cAKkHMhmVTjOAsNoMVVbHJaLQ6wHwKqg8iOkYBlQ1RIidJrwN6DOUW Eot77EyFiwliMRe0AWiwGg70R2WsHnblkrzQCC2EDdEQZYaT+yTLeZLxiE24sMW7294eK8RwkimM JRFt0B8yfwe9E8cLgl1ApTA9ObagZ3fPBk3FZR+fZYVorV4fGpyo1HgnHgeneaFzV+/23QI3bozP yyoePKCpUpe7avlFf0NjRqu0mM9ENOLu7tbkxPHsA/uzGjfvkp+fefmylRc9Aj3svC4/vFlox0YB +w9f2vjbRb/gRh5qJePMBzt8IMZEXg3CLCQIRAQWGI1hllkNujyBAQ86l8DBeQTtoVYA0KroNTQT PGlkboa0dggQ0BMid36EjyQRQ+QrwIB4WISSbPKFTlpEWS5Z9E71vVNiXq1+UXz1RU97rjYEEua3 r7a+accUAYoARYAiQBG4WQRq9AJGHXn4i47t0S1P2IzrFmwfGY9cYhoM6B43MUTyIc459/Rnxh73 1r+7ia+/mnWai86dTvpiTcIH2aVECazu+Qmtp7X3C/XmivOLk4/FLzjm9/nzjaRfRK70xdP2zsEu YFf2HD/pqVMrSwGmN/i8el/DwU18gnw9/HVCQY6lqLj4XFzq6t/TVhiv1ECo9c/d22h8h8DIAA9v 9KvQWZBjysrMO3r4wpKt+YfwxfwVY5UaxRl7qMnClET0DNexScTzQyLbRcC9c52eJ53k7Nl+/q0D xbgB2TSL5g6Y3RzsjGGzH1m1dXZO2Nyh9dpGMHve20EQq4GtNl/AAHtVOQTGjB3bv/+AyKioyh1O j6IIVDkC1afqcubHbV4x77GJ0+d99kf8Fa6IrGd4s+byVr+28VwAMdF2xZ469dyzz2ZmZlQ56GV1 iMgTzBhAaUIkwEHaJSyvQUZdqCgj8uzCeXzAvMAroF0CioEBYy2noGJYL9HlaytU2W1KF8NbHKaG Mb6DB7Zu37WFkxXy8gqbNG3QtmVMgN63XpDh8XF3NW5Sz8tbP7BHs4EDWvTv16RxPf+sdNflLHPb lhF3t/Ez6JCejFHy6TmFod6enM3nt40ZySlWb++wYrsjO88Saoiy5Pr/sSmdc+o9PYO1nn5N6oWk pCh3H0kP8Qs3W8weGgjRU6XUchCdyyIwdpFxQiocUCTggmUym7Jyc1LT0/MKjTarhXPZwV0fTZ9B 5QMFltdptSH+/kEhoXpPLyCQ7Miii8UlAQXMxEAWpxMoMKI7wimESOUlmU8hnRVy8iJeW7ik5X93 MPlCXlz4eaLIQvIyaSO1ARD1Q2oXIjkZKLRwviU+BFXHJPUiyeEwQ7TDM3aT6CgQHHlOmKSgcTrB NA1MuqxMoUnDuVS8mVE628Y0HdStZ0R0OKdkLmYW5RfY+vduMuSe9t4+XgcPuvLzmebNIkcMbTx4 RMOOnVsyDt/483ZAol/XRuHBHhaTScWpcgsgQdUz0iM06bxj247zWoOPTq90cFanWmHjPVgVuKSB 5RqjQMb9EDZx1EdzhDMIBQkBsyzskCXqgnxYB/LgAkoQAADlJS78iZgueBIsytBZhx6g/FnId4QE R6jpCWcdLn2Jkj5hJ25pVxrf18g7hg5CEaAIUAQoAhQBigBFgCJQIQR8Yha93HFUaz/gueA4hc6z WadWi2Y1HF6iOtC8MKPnwoFh9f01GoXKP8C3w4DOKx4MgnvXFdoaNmmyf1Gf2f3qNQ/39NfB702F t79nRFRI30GdvlzY9esmpYy2tP6rX+799oj6rcP1mOdCcXn7Gxo1jx778N2/PRneu1yKCOWkMd23 w9TaBUYHAM/l7qTBlBn9ds+4ZifKmBbfzWw9oLFPAAqSbjeFwM8//AA6LxB51dTP55uKlh58JyBQ fe9q++V/f1yzL7VuJC5etdSE7QKJZs2wXZh8cdntQDMgYguLiRDJZbc77cDKgEYINEwuYFWQ4Tm4 MaFkRsgeAx4GVFBg5SQwOXmunQdTTp295BDsCqXlcmZGbLzp2MnMXGO2jx9nKy7afyBu3R+nd+9M 3nnsZPz5CxnphUfOZllNTH4mv/eg/bu18aKVS7lUcDguJ+5CrpPRWuyiVqNKSzctX3v24MEUHy+D ircrFHbQZMUnFX31y+GLyen+Pl528PRilAePZ/+59SwjGpRKg1alBPswhdpbozLotSpPD95DKfC8 gBREAnKDAssnkG5ZLabs7KxLGek5xgKH3eJAZBEwKqITOC8QRvEonzEsLCw8LMzL0wtoLaDKsEM/ qKjQv0TLRXbknIXTPyGfDtODiJ+C/1MiEgxZV6GnSnaSAok0WDg9Evy2MF+GmshO7MQFTM51RKwZ 5s1kfgdRQMjAHjgtRHxhFRRilMDSnxOcsExQAxHqISqAHkpJMW7+Jycvh7G6eLPVFH8x/8ip1JNn Eyw2NivduWV7UvyFooIc2x+bL+zceUmnMcSlnYu7GGfO5dMTFL/+funkiXRTkeXshaSTp9PsFhsQ m6xGz6n0+2OT12095bDrAww+rLOYFcwMp3Axnk6HCjgtgBdOGXwPDNzQgHaE15xK0aUEqzWkOmM4 OGNIfU9czROYRPQXUGNoRz5dUP0AIIQ+4RmesGaEcETnJ+K4yDsGAYJguRM+KukcKQIUAYoARYAi QBGgCNRxBHgh++jpxauOr0+0ui/flJFho8Lln4TB9SZ30Mvkkisn8eKmg7muYIOhQvPm/N+Z1rS5 jnQjFqdf3vTPhQPpcOcbbQqvkMnTmkyWBuTHjW49PlqiuMTCrPW/HFn2R2oquArjtiGdWzzfDvFi 5pS09dsSj+S4VVdicXLKum3x648YzQzj07TJm8OCpBqRjP3iyaSNB7NzUJEq0knbRf11Zc1A1apb vVBJ8FahGdLG10QA0hsnjh8PxvYUI4rALUeg+qiuWz61mw2ghtkuCBflv4FMxikA8YBcwIEfwnX0 kLQGpdfh1DpEXkAqGUigEP+jVkKeWrGCs9rtjhPHE9MvZ/OgvBFNORlFG34/kRyXERxgsDiyEhMv HNiffPRgwd69xoNHMs1GxlakPn40d/X3x75ZHbtu/XmXRefhGXTqZNpfm2KhVDGY1jtditwC1+Hj 6WnJeX7+/l7eIPg1cmprdl7xidMJhfk5IUE6uypP42nPvFx0aH9yUY4x1NdHsOe7XIUup8XlMlus JqCEgFlR8GBr70LMEUo6dDrsVrvVYrEUm4qN2TmZly+lZuflWq1mwWkBN3cF5CYijRFOWVQqfL0N ESFBocHBvEIFPupAeIEuCRgpnNgJjAwQgpDsiUouEj0SLjWImEKElGRMjxMRpQ1LljAzVVqJBLwO Lj7oprqQYRpWK6FvaCVy1EK+VKQNNMYe7cgBHv7FlBli3gRGI/I+AuvFIzYPnge/fe3FNFfsWZeS D1UqfBgnczY+a+u2k067FYzF7Hbv/DTt5r8vfPPdkaOH0v29g/VaXWFW9q7Nsd+sOPvtikOxx1Ij AsNdDs1vf59NSzN66fQMKkdZnJWemBx3UjAX+XtHmFw6k+BhEz2xI74O2YxxVk4JtmFKF+vF8gYn 5+li1azCJYCdF6tkeU8n78kxOob3ZFhPhvHiOC/0gDWANhA/48Gynix60iByBoY3CCqDqPJ2Kb1Z lQ+n8FCrtEADAvgwd8meHwN3s+83ejxFgCJAEaAIUAQoAhQBikC1IlCctnRZ3JzNCfd9eu68W4/A 8nqJlmJ7dA5uIP86FLMSnpt/cNCyvc/tlI1nyhdbSMeovoFSIqWYd2HevH2Dvj7a5ZUje0wSvcYG RkzoSIRd2p4xbmbNcXbT0fvWJ8364eh7R0zyULp27b2Bw0qPTxmz/Ow/maWorsQLI5efGrO5IJ1R /q9/RKh0KSpk7zvY790j9yzb/eC6LLghjzd1257hA64ZvBNKSR6JzU4spjdvy7fA5WgFxvYg77JB dgvdKAK3DoFq8epyxS6/f+a3xVfNSj/hw58mN+ddsStGzVhTRF7uNeeHl/oFoE/DK47qMnf1S02S /t6w+fDZc0fyvLt17z142MBOETpnbvzOv//edzT2SLw1on37Ln2G3ts9xqs0Gy/Yss/u3b7z8OmE hFMnU5jI1s2jQmPadut9V/sog/ShWxG4iW9XcEjIu++9V32+XdOnP4CDQo5TSKKE/ZJQcpzThZRJ 2H6ebCjVTqZpQAaF7cCB/wGxFNAyDuTxDhSRAjQ9LpHXgI6H520iX+hyqRinRgHZkQqQTCnsYCQl MmqtUnAU8qJGEFUuHlgkMyfYBJEXRC0YOinB9AmeAwUQo+LUqNgj0lqhfDUOkgxhOIG1gweXy6lx WUQ140LyMmBJOCuwUAolquEICYs81B6EVDkGqkIydhtUhiRZh4iHAvUQ0k0BXQVskRKKE+oC/f39 DMDOKKAFTAk9QLl3UHfSCbmdFos12wgOXsVAlamQJ7wAcjfoRqOE9pB8R0ReGCLEdQFKqI4jAhH9 hZM/S+gY1JiUXZSPIo9K8TXYz4uQj9jMCycCEicv8ozEi7m7hdfhPESKNQgZEWIwBZQsCFUMIUeT EQA9DSvY4C4VqKWcDNBRLiiq6IQJAosHci2g7JSQ5+mw4ORCHaArqFkXVMT0CIqJAC12Alh4oTNE YVExZpWocDgMea6YDCGaUXhYnHYDMFnOfA8+I0ib4xR0OdZAk9CId1mAstOyeVrfHJeLMxX62vlQ 1uVyWM2MVu8SoDoB2HqBhz4qrghwW1ygCgPoYNk4O/LkQuwkiNNg2eBEUDrsmVmJqWlnofCjAo5k OSATkdoOe6gtfe/diryxKta2Rq0uKhYabX0rEajNVhe3Ehc6NkWAIkARoAjUDgRq9AKmXF5dsqNW 2Y35hyf3WdGXSLjEwoP7mixLT2eYkM4d4mZEeRFIb+zVxT3wUJ81A73xVbVoOrq/6ZJLqeix7oN5 /Wc2JgyXK+mP7fV/MDKM56LpLXobyC8dx5lNxyYedTBM6TCYUm5cpW22hIxtO0OW5+IDfX5Y3OuB YPI7z7L7s60992DvnIimF95qHk2u7u2X3nzs35cR9VW6E/gVlf/7R/vGH7Zc/bu1Ws+g2nwBU2mv rqsR63bXXS+8MFet0VQrmLRzisC1EKitVFeTgcN9j/+2L7NU3EFdZ02/+8J37/5+rnRSJNdw6tKl 4xtrpI+x7APL5738Y1IZaZNBXZ587fn7GpepXr3++VEDbNdjj44hMQA9g8gZ9Aj9D4uPUHqZxMOg 1D0kSUK+7JiEQUwRODLBn4iBAhd2xB9hfgeUYWDkDo3AdQuoIKBdkBkWPM0JLp5Xgk6JEeDznnci esWFmCvkTYW87oHOQpUHUdcsZMIBjQFaIexSjpgujgXVGZBmkNUGXBjor8BgHZrbeQ64LayyYqBv qEKIPMUgbFBbgeIM8T3gIQVKJ+T4BFwb5GqiIYjVFqQUIpWQSuXpZfDy8tJpNChtDnFgkGyHajTC vIBPsTsFi9ViNBYUFhpZp4MnJRHB8R7S9pQqmDPKQMRgYfoFwUiYLoItkXHhf2WqS+K2iJ4Le6JJ jdH/4dxHidsinvegHkOLgleGdCVlUZJqhgwHXlggncK94/xBkOKByk6p4BwOCA1s1px2VCERgwPr gLRr4E0GLBKy13KBTzzIseCBnUcpmh6MaHcIvKd3dKdWraIjj4UFFEOFRzhGgxyzIJmUyRKjEzOj s2z1nVaFhrH6aVNC/FJD/C45XOqUrMZJOW2AiFKoWH/15fAo8MX3SEiJyLMEW22uS7l5QT4+VtEB 6kAkk4NzgGH14KLvtPuARBDRjCogLItFMUCrBG0dnBFqOLscrrTLxy/E71NzNnwiopXHJCLivD54 b1H1fc7W6JVi9U2D9lzVCNTOK8XIgz2reqK0v7qHQEqnXXUv6DoYMX271cFFq/qQa/PbrUYvYKqA 6roGl9S4Zfq8xuW2pVc+N7P/O53IT67ShBQ/tF1oL3/CaomFKZkLrjRz1nt5jIoxNPeH3y1cUPum DzWXfrTdmOpShGz7qFsfSRxW4sfPlAZEyPnmuV1TM+CHRek5iuYTBzsvSj1d9WflDXqsnRcwJOgq pLqgN2C7Xn1tfo0DTAekCCAEqiWBkQ3s9OTcZ8d18Zcx1kTe8/icuXOf7BRY3jSnuOOZLZ5csmzR nJFN5BAz//1g/ie5vV5btuzdF0ZFy88K5zf8diSPDCRm7/3ynZ8lnotr0HPyrKem398phBDJmfs/ +XRdXGUo+xrIZETF7ZDQCtEyxFtKto3CVujI+hv9h1P0kBoJ0gCBUEFt4LYI8Ctg9AVO7Zjgw87p 6BHS3QDFBZsT1E1AXIHEC9Rf8CSij5AzFtJ+SQZZIEQC43esf4J+UUFCJGBCZu3wP2xHhV3fcfYf qn4IJIfTIThsogvcqsBGDCVYujhQkyHFF1hZ2ZwuEIjZRNEB/ahVCk+91tNT66UHFkuNHMY4FvpC 3vBoRx7vNpvdarVlZmelpKZdysy2mC3ILwsxUNh3C7vDqxUKX4MhMjQ0IjTc18cXqDIgzMA0XaVU Q4ToId7wFOD8kEhDiYXCgjmAhTBV+IFMY6E/ET0FyGPOTSKukHIMA4KyGXGqnloFUwGhXMn6kLaA MXbyQsAS6RgGEeGInkEUIGKGYEfzQCQh4Aam9XAMSKzsvGgHFg3YQ1hmtGCoRxfMAAn4gENCENtB ggd0nks05JpjMoqaGgVfs6LIoD7fwM+oY4pAdafgnIFeQrjBxSqcKh/Bx7tArzQ6IXcUNFpKl05h VnMOkM8JnMLuYnKsDq3LZTVZ8ouKgXcsLrbZCy15ucUuCyymkGs0O832pNyiApPVZbFBnUaYpgqX SoAHQKDKy4Y05Ih1vTIblH6yUgQoAhQBigBFgCJAEaAI3KkI8AEeZdpfuTYeTZ2zOQXvF0vxXFzv Ls32vT00/5PBK57pNmdix9kT27t5rnJhyKsM2hv9xOSUPoar24iFOeaa57nKNanbpdG+3bs3bPjt dpkNnUcdQ6BaqC4uoHnvfr07xoDvD9mUQS179OvXv3fzgPKO12jMlDHdmzdv3+/hhweWaB5bj596 X+fmLVr3Hz9liHRrgWGsRRaSiO3IOPzPbql4rU+XZ155fsLwwaOmvTBnVCiJQjj541+xleG6cE3G VxcsqD6XemyCjty/EUuC7aTgAea+iPIJUy2YccDECk4gxMwW/AOu4bBjf3rihAXUCNJ1IUETtm5C jBEov0AFRPROLA+sEDiXExUUkF8oRVKpxA2QmZbDDhUEGTDDl/yvGHCtQqwXrjcIxBZYh8EjYNZQ NiPyNEfYAr+FjwCzdnDYh14El9XhsDpsLtA3Aa+FdnjSDt764HWu0ULGokan04KPPPSGtE6QRud0 oSKUNmtBTs6l1MuXM7KNRpNgd4LHO9ihI1CgaCBSW6l8Dd4hgcFRYRF+Pr6AkAWINcQZSVwXgAAz QCwe5gWxqguld+K8Q/ycRGGJqHQj8a0nzl9Y24UIKkJ2oYYIRwgK2D4HKqwIAjhEzWHuD3eENyLv gr8wKQYaNcASPPLhPyVaOswIIeBREihm7eAFtUapVkNxAQ08UEHFSmQXhgzhsVAPsWQCkFYKqIOJ uEnIKoVcQ5fCaPZKzg47dznsTE7z5MI2Vpu3h5fVi0c0pZfSFGAA1kyVmBaQneMfqLWGqiHnsdgB SyOCNRejQx7zCgfLaxRsA42y2CUYNBpfT73G08Pf19PLzwsq4/j4eqkMnlF+Xh56TT29OlCvBWYP qDfIGDXBCYC0hBAMSbNFTmkEU+SNhrNu6UYRoAhQBCgCFAGKAEWAIkARKDcCbMce7X58vFnXcC11 hy83aHWs4ccffJCSnFzHgqbh3hYIlJd6qunJ+npKJV+1eu+ScrQGT+kugdrD++qit4UXTsbLmYsN O7YMwp+YnEejNh3lrEXbofOX3G6GFZxSt27d3178XjWxXRIzhayeSP4dYroQh4B0VXjDWXfExgvp aCQSBiglootCG1ZHIX4HZSFKkiVgJ1BjaEOKBaLDMY+DRVrocBga2mBDMJT+R7gLoltCbAaSdRF7 dzkIt0kV5s2I3Ivk80EIQAohRRjJiwM3LZfN7rTC7nBZXeixBXYBvOd5RqnkVCiNEpmbQz8Om81m sVpNZkuxxW625OfmpackpyQmXEy5mJOXD9yVChlaId0TDIXqJSrVHp5ePr7+vv5BXt5+KrWW5eBE QUUPQQkFlBnsoIhDiZeIKAN5GyGkkAKLpB4i1oawd4jGAg6LCN0kFy6i5EIUGAYGW89DMiaivQjB h3c0eQINFtiRrEZ5xTBiqMgjVqah3FCcDukmx9x1DPG4aKEx4YlyG5Hyi/yBFx4CgUxIyB9lBC+z OdJqDjTlNszNbG5y6FjPXC3n1Ahmf22uVm+8XOibkdk293IDpV0Z5JOjVueAIT3De3FOFWQkovRY yH/kRKPDdi43N9NkARUZqO9YlYJVK0F5hxbSajNjRkut4nMKCmILjDlGk91sLbI6QIaHWC0pgxNX rsRnJDnZKvh+os0pAhQBigBFgCJAEaAIUATubARY72eHR2DTZvSboyD+zCsL/oiZsG7SNrDxotut QWDTtu0/rV33xdffwP7Bxx9DYtaYsWPBt/pmolm79tebOZweSxGoHAK1leqqxGxEU356SbUOyJOT +lBpdG47+tyMnOIyfLzKOVrbtm2rj+1CH/Bu0RHOVZT1QohTwUwWTs5DlBdWKOHURWhlB5oG9FKI A0K6MKBUMEtD8vaAOEPHIkILszvk/wk/Q9grQnjh+oUSb0MoDPQapn2wxEhCqOQwII1wlUEsi0Jl CHEOJuoVSCUs74LMObDAB58vsKJy2RxWiwN4LqvVabHYzECniC6wqbeCuTwYfkGuHjwAggx7grlA OqaESYguq7k4NyfrcurFtEtpBTnZCrsFjN2hwiT0j+g5lQqMvcJCgiPqRYSGhQcGBUMVSZwiiP+H wQBmD/FchJfCk8DQEJEcppTwBk7+hJMiWiVc9xKLwjD9RCYPcAIxh6crJS3iEgEoyRITXeg/wqJh eGUeDJ4jr8tcEOoQCbWk5SDaM4QlcgaDoEHGhVYN94j6gX8R1QVKMLBIc4FbPOfl5YrSp4XrMsFZ 3iToix0OX3VxoF9OntJ4xqY0uoKKTX7FFn2ABxukE3RQkNHBKhkQ09mArIMuiwXewih0Iq+2OUyF pqSsAntOkT2r8FRKjqPAdD6jwGpxpRqhSqbDWWTTFloyL2bFXspzFpqBsURKQQUyUJMSbIncEDFz t9EnSTk/DmgzigBFgCJAEaAIUAQoAhSB/yLgSjPKlQ/h/rSSg/qJeFM8PKDZ2skt8d7i685YieDr 1zJI/p0m5K39Mm5BvDWxopA6rDklxROhspb8u0WFfglIm2DLzKn8b8CKRlTX2xvAMSYqCvYmTZpC YtYj/5u2cvV3q77//olZsyrHef29cSMVdtX1s6Iuxn8b/UAVinKSzfIa7Fg0tj+Y6qF9gLvaI1TZ MFvsNyU/qSa2S6VUwK7GO1ItoSQ4DogVeFKJyRVJXYSTGYGYUamVkPtGKjMiQRZRHiE+hFeBpRM6 RuKqiG8XqR4ID2STdsyYkTRJZP+OXiJEF/FjQionkDlhnROhwwjxhtpLJJqUZYmZM5D7gPc8tquS fLAw7YU5OpRTCf+hlEaHE4oyolxElGJod0DaIdRYcSpQmUFGo1EoVTAPIIIg5xBUXECrOJCJPiM4 rKacjLSkC/GpF5NzcnMcliKFYOegZCEqcMggqkyt9PDShwYFhoUG+wcFaz29RF4JNBtQf8ghC5OA iMwiEJSo0hAVSNRtKKNTTsaDVtBYiXIPwfgecVJ4Tpg3xCouZMqFhV3kZCPcIRZ74cRTzH2RTUqE JKUdcY1M3CH6jzBfRBOH00OllcFB4uGA78KVKiEMRMHBlQKrg1KbnNIe5mtp1/BCk8hzSq0iMyvQ xjq9vYq0OsdFsy7FyWcprKLWg2d809JTFK40pVDAuowcOMsrHDyUFHBBsU3OT+PRENJHVUoPjSrY ywP+T6nXhPl46A36cH/vAG9Pg5cH1J0O8Df4+Xl7+egDvTR6HZxYSIKH3NokuR+KHp9BkqKtLn4C 0pgpAhQBigBFgCJAEaAIUAQwAo7TGVDvW7qM9fTRROCHUaF6nwoA5ErOc//c4nz8dEHSsap2nRve 17cx3hsNaYYTdPy1vu4fo6IjL79SbJRgzSpyH6gOCZEJrlC9m0ZjbNbLhTf1G7ACANymTYOCgocN G/7FV1+DzqsSU9yyZXMljqKHUARuBoHbiOq6GRgqcmxptuta/LTRWDHZLVBFiC3CVlDu6n7IFAsn y8kqLklChPkvRIEB2wV2T1i/hNRJOAlREhwRbgr+gGagRCKOVCQ/EWgJSYsj/5979iixERzTZT0Z eR5l/YHaCKc0En4MMXFyn9hGDNFvoDpC1mAsZNlxOOURpQ1iX3ViOQZmXSiLECmpWEguROZXwGJB kiMjQnVCJ6TWgd+5QsGqlJxayQPzBSomFA7Y27vA/d5uNRdmZV1KSUm8eCk1Ny8Hch3hSfCv4jkB JGUQAui5dB76sKDA8NDQ4NAwnacBChqCB5kKkYIQEsijECPjzhYkfA3JZ8R6LiC2SF4jwpAYo+Es zpId2iMoJCWT5FYvIYAgQAiX9qzCBTPdMjqSoijRjhgGBK80lkSLoVxO8qS8UjgIST+nBMZKFK3G IrODzdHoL2UXqFIveWnUjnrBRXZRcykrOLWALXalNwjVmApVJ88YPUVjQ1+TSpkNMjlBDesmqARB xbFAbSU6HEUKoLPUBr1Wo9fovLQGbw+bggfaC5bJV6vUwau+kBvqGRruHxrko/PSCWol8uTClvsy VtJFA3XqqshHCG1LEaAIUAQoAhQBigBFoBYiICTm2WSvF1YX4t0DXeHxXcN1qgoEK+w6k5ctt1dE BY3HpJY+KPjuSLfKynb+PPZPzrHkuUkqVunrg3+Zsp59G3vccMBSejHTvgtmmcdSNmzh1wQdzI1o GxAo92K/mLMN7rDT7aYRUGs0oPMChVfzli0r1NnPP/xQ0R/IFeqfNqYIXI3A7Up1tR73+tIlyz68 ap/Q2qsKpuxmu6ZNnXLs2LH/wJqZmTHziekVUmkSVQxSChEvKOzzjXL0gHpQIq4KyCoYhVRjtNud NofDjnIOsXES/miXxVngzo6rKkqe8cili/QjqbqkxkRvhDkdbCAFG5GGQVdYeUQ0TpJiCRNbiNIh mXekc8zCIR4MszWQ2gduWmqg34AGA+YJ1R9EdvkKBugr8DZHMinUDfQMyiLQMUGuIhpZAK938K+3 gVeXw25z2m1QThIs88GpXnBaGZcNHku1IzlQnDFQNTA783LSxeTEixcv5WQXm4pFp51x2EE9BlHB ZKB/+Aj29/OLiYyIDgvz1HvCjLFJGcIK+e+DPxhSeEnVGDFuxCELcVgkPdGtyyLZiZIADiVCkhxT BDjO/pRhkopnguZK4sEkNgyvKS4SQJzwsRiM+HxhAAnThXuT0h5xDiUKBERlBHZoAiwh8shSaBiV gbXpjXl+eUVRDqExZ4tUFGsC9WadV1GGUZuZHW60eYX560S9LjZX4ym2ivAM9ffK9vAqMnNau1IF XSmQmRrjqRJBCxasVBZYHdlG86WswtRLBa70fHtG7uWMzMykyylpORkXc5Kz8/NyCxVOAcoBKKDI Iw4XpoPPN3zquVFw52fST1mKAEWAIkARoAhQBCgCFIE6iYC4Z//leKcUOhtS/7X/NVo0tMXLd3lX 6KZm/unUf9LlXjwinnu1y+9T2u95qXVrjdSNmHVx+T4bGiYv91SmzK1xvsMmN/tgQPTXMzqMCcFV nf67ubJNcrcM59+hzdbprVb20OsZ54+7LqVKr7Be7dr9+UybddO7fzHIrRizHN2bsa9OrkgtDRoU XgvffqeibNeFCxdq6XxoWLcpAlXA+9QWZDhP/yjZf57xCW/UvKytfoC6Qp/V15wcYbvg5RdmP1ua 7QKe65mnZoF7fYVUmqQKINBFINZCdfkQLYSkWyjpDpVZhFw9Qi+AYTjyDMcGUChxDjnBowKBQBBB CiGx88I+U9ibChIFiWM9kVYh3ymgl9CckLYLZ6JJ9A38RRoizovjdBqVRo0yIYmVFRmPsGlIRIaS KxFjhJgxRHygNECUy4ZVTDAAMXHH/BsUiERsEcp9xEopTPjg3oA3g+J+IGOC8o3gZM8D1wZPQn1I 2EEfBhQZYs8gyR7VfhRBvQW8Eba6B3bMbiky5mVcuph5OTUH3NVtxVjChg4GcRmn4ARorFR4+viE hUdE1avnGxCo0GmdUAERq9KQGxgAh/IyccIh9hhDU8erLRFdBCaZ7sNyL0wsYiMteAkXtnQTPNLy kCKQpFQA7IiylEo6kuVD4JPDyGBE2EUIM1gknE0JiaCQOyo4FEpI3YRVQdo2Fcjj9IAF+Ns7NKpi nTbdpC8wBfoGaHxDFHoPldPubbMH+mgC6xuinWKj4+fsVi7IP6o9UIu+eovGQxD1/nZjuKAEERev El0Wp8JDpYfam1kWpwdS3rHeWoXaQ21RKzleBQb1jErl9FDroaqDRutQKS0qpR0YTFRuAGaukPlS xMSRrFa8040iQBGgCFAEKAIUAYoARaAuI5CZ+uU+o8wnqRv1bDV7XEy9y9nJFarrJeTO+zouVhJa sfrw8Hv6RLf2lTy5nIXpy7+IW07EXGLBB3+lF0qAcf7Nm8yc2H5KO/Gvn5LdurBSaDq+3JiU6Gbi dIbO3WIGNFSDPCw/Lv7VjZlYJwabOrpdzIhuQQGShsyZfuDEi/+4XW7q8urUpthBW/B8BTMZjxw+ VJtmQGO5/RG4jagu1sMnxC12Tc/Mkw0Rs7fMI6ZdsE/5MbFSOeBlnghXs12E58rJQh/OFVJpAiGF RVrYGQuUU4xoB7GTzWEGI3cb4quABCEeWZCDBvZVoG9CnJUIXzuInEL6IKk+I4t1YcgjHkyVgI0h hvG4OCMmJTDrBByMwyXAbkdsDOKx5GqMiIeCSGBoCAAYGODcgHiD3oF9QclrmN+BRkCwwTNkh74x X0TIMjQRRHhhugvFhukfaIcT33AXyFSMh1RGCBPt8ECOivjHS7USpYFgNvA6oaBEBRhzwdGCU3Ra RbuloCD30sWLqReSMtJTCwuzLNYCl2h2CDacOik6RAE4Oa2nZ0BgYEhQqI93oFLt6WQUAguJeEpW RKQdZGMCmwbMGvHgkuRWoAKDXEuU7El2UkUReXgBuUNkdig3EtRqUiIiDk7myjBMaMdVFJFoDDv4 S/+W6OmIsgynTxI5GMwSrNrQhQAy5YeFgpqSkNbpFIsLczKZQ6e59IuCpriIs9jScr2OJvieT/C2 ZIspF1XHzmsuZ7i4gvyQ4gKtMc+RXyAWF6UUOI8ke51IDL2YGnzmrCE+SWUpUAhWQFP00PAN/Dx0 XupGQQZfb00DP42vt5bVqyMDvGKCfMIDPCP9tIEGVZiPWqsQFMYCVXG+2pzHm3MZqwmdT1K6JaG3 EE/ntier2x+ZjVumrx4tlmsfdniApm5PtkqjbzCgu8ON29KWD1dp57QzigBFgCJAEaAIUARqCgHb 0i/3vvx36rlMi5URivPyT+w7+uT7KbkVHP58XFyXOdsXb70Ym1aUY4bfN86CnKLU5PRtfx/839x/ p8a5kwnFf7cffujzs/8mG3PMLqfZlBSf+Ol7eyaetFjkjERFkFcPefT8xPhx7x3bFJ+fUeREeRtm c2YRIeEcK37e2+eNQ78ezUrKNhej52BE47nYxG8+3HrXh2n/yL1VcB60+fUQAG1XhXy7du/cSQGl CNQkAmxCQjUpCc2xy2c8szoJT0bfdvanbw8Okybmil0xym0V32vODy/1wzVmXbHL75/5rcTHd5m7 +s3+AfjZshqX7tyz09zPFvSDAqj2tN9fmbr0IB5FEz35vQ8mNFUzLuPepQ+9shFrZBntyEVrnmiP E8arbANJFwi7oLvnXnzpm6++IDwX2SY98si4cePLM9JjT4yFZrI9E+i2iD8X9mwSQNcD8isiwwIq BufikbqKLvBeB28s5J5OFEKYVkGyLywcQj3AE/D1AhlwiEbB2XWoE6QHQ8wXegK4FGCjnED0QIYg olyQxEkqP0g0SDC8QCzwsT4MjSLTOvLkkNwM0W6oP9wS5+wh5g61B/0WcEuQzIhzCVEVP2z5JEul EPUFf2CKDMgsOBwxR0olGoVkSALHhNRjyGMe8WBYHoXoOWgJpB9wTk4ISanw8PT09vH18TJwDCiP OJVKTXgscEAT7C5QJQGBZzabjEVFJlMhtg8Dps4OWGJaEP5BUjkYAB6jzEHZyUuS0MH0ie08fgE5 fEnu7IRDJJmcZMmkr1PCakkYIeYPsYT/OR/c7l7wAkyOOKohLyzg4xBdCamfHnovg9pTDZUCeFbr EDwgM5RTYX0VowI1mlIt2sHH3wanBqKhQG4H6w2VAgBkJVgrAD8ILYHWQzwhOlsgnxRYTOSkBpmJ oghUKtL34RRXUPEhCR5UDUCqNJedoMFAM2SXBgwncG5AMCqhpiNZG4gZiEeQh/H84ncWludUr1yb mFePVO7Aih0FVNe8xsHlOsZ+ZNXmDput5Wp7BzQCqituomwDmxM/6alTK2tk1gnz29fIOBUbJPJg z4odQFvfjgikdNp1O06r1s2Jvt1q3ZLcioBq89uthi5gbgXsdMybQaB2XsBUYkY2q3XaI1Mhn6mc x27Y+CfIwcrZmDajCNwkAtWn6uL1Bm85uOITv6747vct+xKNNxnudQ9XhXXu21WSqlqTVr791vL1 f/366Zvv/UV4LoZrP35gk6rluaBXt7br3bfeLM1zwUsrvvqqvPZ7SGqDJFrAkyABFso8BMt2JIcC UghlCiJZEGKRkNM6qoOHEwmBngBDeuwwBbQDpPaBFAtlMGKRGASAGSRkY4UoE8iDw8mNqC/JaglJ j1CdQtQzrn1IOBxMmxGXe+QShocDFgZqKEo0k5zXh1si2ZKkYiK8Di56iJMhpQxAYjFmBTbG7oT+ SZFHkuCIfcTQpHBCJtJSgYYL0jFBcwb0DaTxARoCSi4ETRmQKiAWQ5PChv0uu8Nut1tRbUeXw2Y1 2U3F+dnZSQmJiQkXjMZ81mFhHFbs+QUMEYsqVmrUap3W2883LCwkOCwczk8PrQeo1iAEQBF4JcgQ RN5oKCKEJyEQCbdFpkJyG5EIC22ACy6NiSoEYLDwhlZK3lDCJjb0cp+3crFCqVus9CKWaZJZmKT5 gixBLMpDfJzLUpCXkX0pvSAbcjaTMi8fz7x85FLKsfTkY6kJ+9MvHklOOJ6efDb70pn8jFP5Gadz Lp3Kunwy4/KxtEtHLyYdS0k4dj7uQGLsgfNn/z0fv/d8/K74M9svxO1IOb8r9cLupMRdaRf2pCXv u5y0Fx4kJ+5KPL8jOWnfpeSDmalHCi+fKM44Zcw4WZAZW5B9Nj833mnPUqscQPFhThOferJ8rzrf 2rRvigBFgCJAEaAIUAQoAhQBigBFoHYhALzVqPvvL39MGRkZ5W9MW1IEbhKB6qO61PV6jehP6mgA hZO0bdXSj/5Kqt40aTbw7idefyCajCmk7l+9bOkna08YsY6Ga9j/pZkjY25czqMSgIaGhnh6eZV5 4M6dO8rTITBcOAEO1Dgu4vwObAISbIGOSkTFGRGdg/9F3lzACCEHduTtBUIkYKBAPUUYJ0SrYHd2 oGvAaQs8v5D3Fua2gOQC7sgGqYk2RCVhb3mQ6RBxGOKwgG9Sq0EghXVFiNBB2iuSgYjKKaKiiuhp 5K1F1FRoB3YMGV3JxBDJWMQ+7KhLbFuPs9tghyiJdRgmvlC2IByKjcgQMwYLBn9Cl+hJ5P0FQjRs jo+UZMiIDMgvyFoEPyuUBokyIBmtRqXVaRRqHsRMSI7GiipRUAsuU35ectKFs+fPp6RczMvKdBQX MRYT67TZHRYwV2eB2tIoA/wMDeqFhQUFBQQEa3XeCpWHwIH9ugASJ6Sa45AXJioPCTEjGy8cCoGY FGRENSdx0iHOTyRpioitIhmeV+xo/Ql/SKgzggbRfxHpG9a3oT9xAU3cEpJMXQ4H7E4LUF2saHPZ wYDfCqUqedaqYCxKwcK5ihVCISvkq5giJVOsZE0sU8SJRZyrUCEWq5U2rcKiEgrUYoGOL1aozKyi UGDzRTafZfM5MY8VclkhhxfzVLwRdo7J48RcwZklurJ5toDnClW8WcVbFKxJKRZzgpkRTDCugoOF sJGVJZ70KFzZv6w8p3qtbpOX/ee2+HXu/WCubPoAUbsyYhNLXtqW8E+K2xi1Vs+JBkcRoAhQBCgC FAGKAEWAIkARqCYEOnfuXP6e8/Lzy9+YtqQI3CQC1Ud1Maxv9xkfvTvjgYGdmgUDO6GPblrPE7yH qnVTBXT+35Lli56eNrpvtzaRnhwX3KxjtwHDp720aNWS2b0i3BVuqzAI4s9VVCg7Kl7Z9Q/ffQfC zhsOh3zZYQceB1uTY+oH8hZhx2l77mw/xJTwxOIKG6Mj83qtVgO6IsS3cCiVD0gIIEgQ+4DVVbj0 oRLJuERGBSl+Gg1qCsmMHKfCNf5Qj1ihgwg2REHh+o8sCxURLSDEAlIMxYR2JGZCllNkR5wOsFWw w0sOB6iwEInltm3CtmKEA8K6JcnPHoJGXmAwSVSeEZFWiC6xQ9VFCBF8qjjQVkGCHXijY8kUEk4B AYbGB7LH4QITLgfaWScD1vNKyMYDYg8ZfEEzmBcu6GhzOayQ6g8+Xlnpl5IvJCZdSMxMv2zKz+Fs JsZuFp02BfB+ImK1tF6egUHBkZH1Q8Ii/QOD1R4egKADAET0EwYY5F2YlIK/YRiIE+VCEoIKsz2k ZiZh9qTSiqTAosz2lVp6Kc3xSrIL8WJIMScxjpgFRDwfTBkleaJsVHiddTEcyPxMDOtASi/guxwK XtTyKk+W1QsusO4H4k2j4D0UnE7BgpuZWnSpnDbeZQfPL5XI6jmnWiFoFYyHgvVQMFpOULEOjnFC qiPenbxo5+FPhaBRMVrWqcCvsgIUw3TA4rIuh0J0qgW7khfgWA2cICT5VM7nvOEJXkcaZGdMXX5q pHvflFOa6rp05GzJS8vPzHEXKKojk6NhUgQoAhQBigBFgCJAEaAIUASqFgFw7Cp/h7m5OeVvTFtS BG4Sgerz6rrJwOrG4cBzzXnmmczrSjHBrq9fv/7Xn8/jT4xAlE+psnzY2QkILZQoBqQL9qvHPucs A5UWkQgL5dopINMPiBiUWoisuFBJRMJYIX4K1xRE2iPIRsSZZnA0Ul1hBy051xB5RyGuiPguYdZG 8pWXPaZAPIaJMhQKknxJrmFQ3hHZe7nDxpmMSKwEfWF3eSDvEM8lZSnimoy4aiRxEWNQ0iLqFBE9 2HQM5ojM7GFeOFkSrO5RzEj2BV2h6SDpFp4C0lgRFRXqgORQwhRxiUfsr48Ea8j5Hg5RIGd3lGqn VGg8PMDIy8/HG+zkBTDzAmBgMFbhBOEWGKK57Gar2WY2Z+cXFFvMkMGIx0bMEwAteYahySEeEIvI OGTPD3ESPzPsR0asuGR/LryGJZts4FW6WCGajsSaEbgIyYZzOxG0uGIlHgHSNlFpAgWo9AQnjIUK E8DkoQmmq1EBSgwwOY0kmzSIH6EE9CEUnCT1IvFB2BYMyDQ0C8yK4rqYWFkGjWDOyL0Lrx0ADAmw WIIm/YPC4qBbVEcTnW/ICQ6dB0sWvVN9b9pbY3VxhXUXNee63vJSr67S6FDzoOr7KKhDPddm86A6 BOMNQ6VvtxtCdCc0qM1vt1tzAXMnrHodn+Nt49VF1uH9Je/9vXFjedakPL+Ly9MPbUMRKA8C1ajq Ks/wdbpNeXgumOCKb765obDLLQTC9QoR94G1Qhge2VEe6BWk+AJuCzu1A38FRQKB+0B5fVgFBLQX uFshjy+cbAjcB4i2sPCGg57gaTvsQGoAtcFzwF6AGzlItggtRUy4kPGUgteoVR4eGr1e56HTaOAP YIZUvFoF8jBEL6HEQ9QY+gAOSSKZkJs55qQQ04RDx9ZViCCDrnHaIvIfA6UYKM4wBydarQ5IprSY bXYbVEpEZAlSlgmiyWy3O4C/w4QXTq4EBgx2XPYQ+B2o24j7RBUkcfVHbPGFcjEFF6iuoDnwNyoQ uql5lUYJXBF4zztgZIspLycz4dzZI0cPHzl+7PSZ00kJ5/IyMqyFeazDBBUmISFRp9P7BgQ1bNSo Q+s2zZo1i4yuHxQaBl73DiATISCouggiOuRfBiMg5gdYIsmjiziNSZUicVomZipxZiLWe0kZf0gL hQ3TpB1yUt0VAFBTmBs+BlF7CGe0dpKxGVprQBnqb9oVvFVkiwWxyOUqcomFTqbQxZhdTLHImETW 5GKh7IyZ4fGusIkKC8sViworw1sYDso+m+BVJ2NxMTaXANUqTU7B7BQtLtEqwJOiReBsvNLJq52s ws7yFpY3KdVWXm1lFXC4RWCgiI4Vzh3MbmGCDJ2joKyrusqmdfpDgQZPEaAIUAQoAhQBigBFgCJA EbiTEGgQE3MnTZfOtc4gQKmuyi9VYmIiEUNdf8vOzDxytEbqx90oEvo6RYAiUJUIsOpJA1rseq3v 5Q+HOVaPdnwxJH1x78PTGz0XVjpVWrNo7jBx9Wi8j0yf7N+rXaNt8/unfzpC+Hpo6vwOX7XT6hmm Yf2IX+b0uoj6GZH/Xq99D4UNU7sjLd3DsMMDPB/o0/LQ24Pyvx7l+GLwhXmtF9W/KjH8ysCEr+8p KzAGpFgQNonNMTeyYUDgV9N7XFh2z8XJfvLYiqGdG22a1xdFi5qNsnw66OIb3bdOqDcJypzSjSJA EaAIUAQoAhQBigBF4I5HQKfT3fEYUABqIwI0gfFmV+XYsWMrl39zNjb2Oh01btp02Ucf3+xI9HiK wJ2KwK3R/18/gVHj+/UL3SbHaORM31Jr4yzc9vWeEbvN2OoLiKoBs5ur8MuiMSXPHOYXUpoKcxo3 b8yK6h/TSFe6J7HwxKFBiy7++98enMnnCv0b+QI75t5Ec/pHC/6dmSoL68obGKK64iZKsTjPJW/z Dh8YCJEJGdt2hizPZRjVY+O7LhoSUHos96DO7KQFC4+9niUNShMYS781aUbVnfpBdcW8a3NG1e20 QPTtdjutZqXnUpvfblV3AVP6csL9TV1pzG6jA9WRh7/o2B6Z/8JmXLdg+8h4VIa+lm+3WQLjvn17 58+bVx7MaQJjeVCibaoKAXpn/maRbNu27dJlH37x9TeDhg69Vl/xZ88CI3azI9HjKQIUgdqCgGLS mLYTZZ5LNBsP7Ev851yRVIFC4dVnQqu5PleTYKwhTJmw8cjiNefPQC4p2RSGAcOjPeNiF686vj7R Kj/LerWKeTzi6s9nRVQj3cXtxxevObs/RyoByeqCHrw3IETubvK49m4CTgos0SQ1VXj1fbjNq2UE xigaRWGeq2TTN2g4d7DMc4mW2H9OLF5zZneGQ4o6IHLWaPegtWVVaBwUAYoARYAiQBGgCFAEKAI1 jEBYaFgNj0iHowiUBwFKdZUHpRu3iYyKevqZZ39au27SI4/4+ftffQAov27cC21BEaAI1AkEOJ9R HQ0SMyQaN360o8snx/os2PtFvI2Ez3oEj+57tRxKLDx66oGfk+ZsPPX05twSe7PitKXL4uZsTrjv 03Pn3c+yvP4KnZeEi5B87ulvEuZsjB25Jk0u+8r5Ng+7n3yWKwImdPaSObaizZ/vRIG9tuerBLt0 vCZwWBmBkRdFa6HxzJn0E5egMdumhV+Y3JHrQvxjX5+fs/HM8O9T86SOON8mIcPKkLTVifWjQVIE KAIUAYoAReDORoDlGhDFeR3c9Go+qg6GTUOmCFAEahgBSnVVJeAGg2HcuPHLV64CcSYkLZbuGjIc 4+LOVuVgtC+KAEXgViEQ5t/cR/7wLMzdehprnUTTzyeNsmheERntfdV1mGgusqejpkJiIRRfkDer LZPIrvJtRTdw/xMLLhsP4bbpCQVp7i702nBi2FXPt6Fe5p8s+XtPYoZLNP0dB1ULyFZmYPC8I2nH 3sFPbGn+1r+DNhcB1WWwFB6PTT+C98NnjSkkQJND5swYxuARU2evkm/ViUPHpQhQBCgCFAGKQKUQ cOzYdGzxqkN4P/zJPuyRUKlNb/BZMLZj/Ce9F0VLWX+V6uaWHMR1bBy+cmbv1Deb3e0e35Hz7WoC C+ynf0unhZJuydLQQSkCtREBSnVV/aqoNZp+/fqDOdfbi9/r0q2be4C//vyz6gejPVIEKAI1joBP gNbgFjQ5HAXSZZWYDkUs5WBUfvrWVR+YaLc480m3xQ6LezBe1yAKXbCGQGDuQU2Wi1K6oXA6z+Y2 rlAatI2uDsyS+f2ajB0lRJuwcfPxDgv3ov3tgwuOMZP6Ry4aELmorZem6idFe6QIUAQoAhQBigBF 4PoIuDYeTZ2zOQXvFxfIKvKKocZ5vflI93NL+rx8T2Qjzzr2G9Anut6GF/vvmtdlYic/79KOC4Lp gy0EFtjTVxbeuGJYxUCr+63BRQe8tGxWyWaj7k+IzoAiUF4E6tjHXHmnVTvagY3X/AVvgI3XmLFj IaK/N25MSU6uHaHRKCgCFIHKI+Drr/VyH+3feIVcxzBBdnmHF1mNwrvyI1TySJ1BfcMSOJxeLRt7 lRrFZDpv+u+g+sCQNXP65q8Y/se8u19/uOPsiR1n3xNS85OqJBb0MIoARYAiQBGgCFAESiOg9Bl4 V0iIuk66D/g2jBjczJPeb6vEGZ2bmwOe8cOGDvnqyy9ojlElAKSH1F0EKNVV7WsHNl6P/G8a2Hg9 MWvWvn/3Vft4dACKAEWAInDzCBjCfnu5y9jWPt51Lr/h5udOe6AIUAQoAhQBigBFgCJwGyFQLyry 5x9+mPXEE5MemrBhw2+ZmRm30eToVCgCZSNAqa4aOjPAxmvYsOHg5FVD49FhKAIUgZpBoCj9W8kh wm0VgR/8knq6ZgKollH4ccOa9PaVWC6xKOO7z7b1nvoLuyCeXhlVC95ld8puWK5oMrfsvfP7/Asb uZ2ZrFReswbDuo2Hcibznd2AL+fo2X4brzWdGkWgriGgWTR3mCipyEemT/aT47/y+Skhkwa02PfG YAtqObJo6d3bRgX2Qiou3Ozrju1Lbl8Z7pt3n7h62OEBbqUU17FJ5C/P9Lzw/pCilaPFlcOyl/aP n9v26876Bv8Fi+vVruGmeX2zvhgpfD00aV7bDxrrZo3tHPv2wPSlg/O/vrekT3XkYeiKhL2y+yKN dtaodsffHly0uOXDUp+KoZ0bQVfpn44QULNRlk8HXXyj+9YJ9SYZ5F+puJPSwnmGCOqhQzXDlB5i df+1ja+8QceqAZBdr/W9/OEwx+rRwtf3pC/ufXh6o+fCrqg63WBAd3hVinNpq6cAhzm9s78eBc84 vhgYO7PRrIA6/5P58y+//PW39bOffz798uWPP/hg4vjxr89/jSY21rXPARpvxRCo8+/bik2XtqYI UAQoAjeNQF6ORa5+yDC2wm2SQ4TbKgI/2FNw5KYHuroDXsldXdnR3eyKwDheI+coeCk5d7qCy2hJ vHFg2p4xbn975/ltJybsyd8hlZe88cG0RQ0gYMxi1+/hHv2Af/csZbtqAG86BEWAIkARqP0IcAF3 d/1mYpOuUR6YvuL0/gF97uv6+SjDda4c5FkpJ43pvv3ljqPaBUYH6PTAF/Eqf39Do+YNpszot3tG eO9SWY8NWzVfObP1gMY+ATqOVWujGjeY8WyXKW1DmoV7Bvt7eF8zQVLZakqPxffVbx3uoZeIJtVj 47v/MKMVdBXsqcAjsBpPfURUSN9Bnb58re0rgTf3Q1Xj+/Wr/QGQu2J8QnxUMCar1gQH+7Xv1uqd N/tsvUtXNiw+MYsAh9Z+/ngiCp1ns06tFs1qOLxO5n1ecdK2bt36sccfO3X2zLdrvh84ZPDeXbto YmPtf1fTCG8GgZv7BLmZkemxFAGKAEWgbiKQn20xum1PvfQNpUKE/MOT+8t3XEcVPxMWUfWz43z8 dEGk2yivMPedS5c5MRn5zl8RWEmFRL5VoEZuK1qNFlJO8bqbOsjg7l0oKpT87W90FH29xhEQmVU/ c1sLanzcGh/QSWtq1TjmdECKAEWgziHAi8V7fjuyeM35M2b3ZYqyYYfQvozz9JGEdf+kZ5R8ltrO HTy3blvCPylIHOzTpPH8oUEy9WO/eDJp48GsDBvpRBHSufVbvd1eoJ7PjKpfT+KqxOLE8x+uOv5b uq7llTqpMqDj/Qd0M5QWU+kbNJw7OEAaVLTE/nNi8ZozuzOkSw5FQOSs0QHIXdRl/Gd7/G+xxSWx m3O3b4tftz3ttLvmThnjKSaPaz85RrrrJ5qNB/Yl/pNokqTQCq++D7d51acs+ooXso+eXrzq+PpE awmIkWGjwm+TX81arbZr164ff/LJwaNH5r+xoGPnTjSxsc6902nA5UTgNnnTlnO2tBlFgCJAEagC BC7lnXOX+FH59u2uRX2ynn0be8idO1MSjalVMNJ/u1BEBY1Ho3Ej2gYEul8stqSRK8NLObH58qUg b+jcEZT9/wnMkRBfUI7AbJlG9/Uj5+mlxL1fOWg1zI52eR0EPNoIqx91rYH9EeHFNkxJhoaF/eUc extXnMpI4779lZ9zvO7fT6fnN0WAIkARqF4ExMKjpx74OWnOxlNPb851E0O4To5z5eYzI1enXSr5 trDGboodufzMnHggfxQP9AyLkFgoV/qu/f3ePXLPsj0T12aYpYC1nfqEDyCP9b4dI8hVAcOIBRtX nZ65OeGhVUmp5fseEm2W5MSMI+cKcxi2TQu/MPmj3XUh/rGvz8/ZeGb496l5Uu+cb5OQYdDAWTBn 5alnjxSVorpyVi0/NXJlysrr5PArAiZ09pK7L9r8+c4unxzr89qerxLsUveawGF9y9J1FactXRY3 Z3PCfZ+eO18CIq/XVeXX0MC+fWpsX7RwYZnnna+v74MTJqz58Uea2Fi970va+61DgFJdtw57OjJF gCJQRxEQcn887Oa6tJ0n9Dw8ve3217qOC5fvchZe/nHHVRUNq2SyHhHPvdrlj+ndvxjkK398i4Xx 6T+RqzEh/9dDRvnCT9djYo/d01pverlUYKasjfvkC9frxWPZlVAsX7UqGt7d8ruhkYtGt/+gv7dw vTuoVTJD2knZCCi8xTZRYlvYGwgTR7teqFfS7EASW3A7wpabyn2wQtH3Y+7Nw6zldpwgnRNFgCJA EahSBERzkT0dXw0kFjoqooX16FbfTeRYTh7OP4872bI/M1nuhQv160k07GFeYZKYnWGsRbEX0WVB cWrh5XLovx1p55594a/oV/d0+CRlI8MaLIXHY9OP4P3wWSORnOebHDIXxTAl+vSK41TPt6HbicGS v/ck7lU0/R1XcnkTGe0ddXXHVlsmuZDKtxWVj7+reHC164hGjRrFNIyBlEYS1oWEhJSUlAJjQe2K kkZDEag4ApTqqjhm9AiKAEXgTkfAuXzNkeUJkrKdVXu279agdwMPQnSJ5tyfvzr5ekmKYxWCJRrT Ciwh4UO7BQXIOQCiOWvNH1n40hY254qfj61yB6bz6dGz4YDGUmCMs3D76pML88tz4eZa88f5Ayap JesTOn5cx9kjwpg9pze5VWNVOC3aVUUR4MWuLUvW0ZHGZFbkN01FR7tV7S+e4T6NZyi5eqvwp+NS BCgCdwoCnCbQ0/2T0GU2yd8oxQ5LSQqftl4QahMRoPV042KyXCQMl92cXnjD7yHrkU3n3s92NxM2 bj7eYeFetL99cMExZlL/yEUDIhe19XL75N8M/iEBWsPVcTLC6Tyb+2tFadA2upkxbuLYTdu219g+ Z+7ca0V64sSJRe++27Jps8ce+d+mP/8aNHTo24vf+/zLr6CQWlBQ8E3Mjx5KEagVCFCqq1YsAw2C IkARqGMIWPOmzt/29M/n9yTkp+fbnYxoLTKlp2Xt3nZk+ks7HzhaTRbuoiX+xAOfnvk32ZhR5HSa zamJSR++t/+xC6XYABTYlimr4nZLgTGizZqRkXtk38nnX9reb7e5uJxA51+c8Mb+H0/kpMLsXPbs tIxNP+8ZvSIzz+1cwesaRF1Z56icPdNmVYGA3u2aAr0VU9FTVWBK+6AIUAQoAncmAkqNv1sAxZDK jLgcYemKjayUwafSKbE5QiU2W/pV0i99YMiaOX3zVwz/Y97drz/ccfbEjrPvCfGuRN9XHaIzqEt/ T5bZJadXIy+wO2+7dOnSd6tX9+nVa9TwEZ9/8mnzVq2ADvtp7bqnn3m2bdu2ak2VkI13Hqx0xrUP ATYh4ULti4pGRBGgCFAEShCIebU6ihnWFYShQPiA2c1JtoCQsW1nyPLcuhJ6dceZML99dQ9Rif4j D/asxFFlHcJuWM4/d056xdDLtXeg6Db0Tf6XH7RB9g3RiGvmudrKt66sBezW49yeZOZMCgtHd6kn RoaKfdqI3YNKDodOj21SjNshdT79UedUD27DQXbrOWb4FNcw+VZ4RjK38SR7NIM9nMR4hosxBqZZ lNizhdDam7nas6R0YyZQbBvANGsgDmktxJT6teFM5rt/zhrxsDCjnd2Yzfu4PxPY3TlMlybikM7C 8CgUZOlmpZEhIDClOmEaCTsmC+jWs8AuW8B/YiXNxXdfKJkFvJRwjl1/AmbBHoexWaZNjNg4TOza ROwVKZa+or9iXHfPlVrOlE67KnUcPahiCFTd261i49LWtQqB2vx2q7oLmGtdDJT9fIMB3eMmhkhf GTnxk546tRLWTB15+IuO7aW7VMZ1C7aPjMe3yq54/lprK7Uvu2fGb+3Su+/zJ99D9iOrNnfYjD+O rzUiGcQQtm1Bpz6+175t5kpfPG3vHHz38BrjXnOIa7Uv8/mKwVUVZ39NXsBs3boF7Lr+2rI5PDx8 796969auBQEXTCIkLGzg4MHdunaLjIq6yTmlJCdPmzqlPJ0Ap9avX//ytKRtKAI3jwBVdd08hrQH igBFgCJAEaAI1CACInvyXCmuqZ4YIn+ZZ5zlx77Lz97Ero9nz8EPDSuz/xz74w7u0aX8rD3stTR9 1nTu+U+5+XvYvVnyLFzszg2Kvp9zi/5ltyUxQBClpbE7YtlPNnJjP+CXxLFXeAFf1diYhRtv4O5Z zH97mSkzaVbI55Z8zT+3g92RxriszN7j7Etf8B8kVrXFvovdsp4fvpL76jjmuWATmePnESZPfcY/ e21ManA56VAUAYoARaD2IGA+8seRxasOXbWf/i39himKFZoFP25Yk94yzyUWZXz32bbeU39hF8Rn VKgb2rh8CCx9//3/Jip+8SUkKt48z1W+8WkrisAtQIBSXbcAdDokRYAiQBGgCFAEKomAwJ7ayy2M Kzm6QbQYQDicAm7Rz2wcIZZYZkhf4Y0hYi9vqeW2P/lvr1F985sN3NYrjd/TT3BP/iv5ZPGB4qxh wmt9xUaEXrOyX67m/iwoCSDjNPfClY1f7CFGkMYW9u2f+dMlJsMlRxWdYA9GC6seFT7sJ/qQp0Xm my1ssshwPsIrY4TppTxUGnUQ3h0jvNKYqehVS/Zp7plD0kSUEeI7k13fPigOlmVr2/7k/siu5DrQ wygCFAGKwG2CgMOa4/ZqZxwXj12csznlqj19pbseT9VMW9szxp026Ty/7cSEPfk7qs7+IS/HUuiO k+M18u0hLyXnvlPkMloSq2YudaAXUHLRRMU6sE40xCpFoKIXjVU6OO2MIkARoAhQBCgCFIFyIGDc wbeYq2gC+0v8mI1svvsQVhzbRCTpH5fOsX/LjFXfEa53+wmj73ItGi1GyUTSFwe5kkv//wzKMu1a i4/3ElEReZHduoeVqmmx4rwJrse7CmP7ud6VSs0zjItddZqV7u+L7N5Dcjy8+PpE1HjiUOGtHtIA rgz2lwtl1WhXik8PEDpFCf37CNNluxRXCnsgh+EM4tB2wl2hJSGGRovD2glDo8QKXrWw55PliTDM 2H7C8EZixxauWXfLOjOR3ZJU1TqycqwmbUIRoAhQBGoRAoI1q8it2NKEhMq58o1bphPTLti/7DQP vh0YJjPdVPIFpFR6kw9llS7Eq4Ifz4w6yOBOXRSKCstRwbEikOVnW0rqA5VUcuRbBWrkUUWr0ULK Pt7em5+f/6RHHvni62+WvL8UkgcNhhK//tt74nR2FIGKfipRxCgCFAGKAEWAIkARqC0I9B0i3CdV SWLjk1i5QoF4V7TkzAUeW3fJheEt59iUMhNQdOLCp53fjXXNGii0BWutYvDnkifoJ7bxkx5HR4ju wlvxlyTLLcbMHkqWG4eJ7X3JY7F5jOghP737UlmFFEPESGKUxTINIt14slmmqsRWGyhO70V2oYuP xHBpSpkqn86nRR6rEnDaF0WAIlBHEOCUKvdNCPORi25lr6pFh6COaA7ciLYBgfJkXJfz/sFkVHG2 uaQUs5ff3Y0RL+bTxK++/EVT7unbMo3uojqcpxcm0q4c9JpdlVJpXbPNpZxYd6C8oXNH/LnPevZt 7P5qciTEF1xD61zuSdSFhuA0TxMV68JC0RirHgFKdVU9prRHigBFgCJAEaAIVDcChiBx+ljXOz1E PRlJZLLzSsbUuX91KMWSIvImJuvKREVyQP1uwtCAEqd5wcykuu21fBnppj3ctg8RvnzUtQbvq+4S tWRYaOz+taJh3EkiKhXjNtHPzmHLUJOVNBa1pczhz+SVJQGrJJpi266umQPRPqOP0Jxhjyezx5LZ 2OwqHKKSkdHDKAIUAYpATSPgshst7g93fa+Jnf6e0nheGPwYdKz659Jl6RVW37rtby+2/2PmXV8O 8pV/KJr2bU3bQ8LNzD6WKX/os4Z7H++6aWr7fx6JdpNi5Z6UZVeCO21S0fDult8NjVw0uv0H/b2F UmWl3b2ZjTaz+w/fei/OabdubOjQ63yWC/m/HjLKtpK6HhN77J7WetPLXceFS19Noilr476SLssd Nm1IEaAI1BkEKNVVZ5aKBkoRoAjckQhY5yzcwE74Be9rafnFO/IcQJP2aCOslmkmYJr+eM658ynX zNYyz4U4JybLbSrPsM+9jbMdccKjXJcQEg8Z0xV+8hKc9XyY0vfjBQtb5o1uVie2iRLbkj1Uql3o MrEJ7lU5x/Uig85VtJArLcKLDjNTdQYsFT4FijO4z35UdHlF0WsJP/Zzftzn/BM7KtwJPYAiQBGg CNR5BJxZX+3IkxMFWX1I6MA+9dpiv6z8s3EvbciUS5coQ5pFD+0U4C+RQrbEzUem7ZYK3IIr5Ad/ pbvvXih8gwb0jm6lNqeVuH2VEyfXmj/OHzDJBJtP6PhxHWePCGP2nN5UIhsr6Sr9UOJvqe4kR1W9 VvVHdPHzL7PoiXSQc8XPx1YlWCX7Sp1Pj54NBzT2kObkLNy++uTC/OsdX85p0GYUAYpArUWAUl21 dmloYBQBikBdRwBqb4+UTC5WDzs8oJRwpe7MDCpwO9xWHUtbPlypyKukk0qNfPscpPAuRTNFiTE+ TJ08n2p8QayXuVmfc0uPoyKSdKMIUAQoAnc2Aq41P+6b8XPiibTiAhuwPEJxvjnfTugex4qf9967 5PgfR7OSss3FLka0WTOy82OPxi97d0ubVVmlSqGI/24/Mu2buH+Ti6EZ47JnJCZ9uOT4IYlTqgjA +RcnvLH/xxM5qfl2p8uenZax6ec9o1dk5rm74nUNomRnLSF/+tt7P92XkZRjRayby56TW0rnVeaw 1ryp87dMWRW3OyE/HYaAW0IwqYzcI/tOPv/S9n67zdeqSlyROdC2FAGKQO1FgE1IuFB7o6ORUQQo AhQBhol59UjdhAGorrvv8yd3FOxHVm3usFm+KVp35gMsVdzEEOkuaE78pKdOrax48FXSydXDJsxv X/FYqv2IyIM9q2gMdsNy/rlzUmeGXq69AyX7rbL7F9hlC0oEXNMfdN0lZTZe0TwkTAzGjijHNinG 7ZBe6jXG9Vm7kpvbzmS+u1uT1UjYMVmQ3MDKGviKxuHix0MFya2rdGMV0xSrwK7Vc5nBXCvCsju5 Yvriuy+4hiHjXfbv7/inTkuhtOkhvNZDiDEwOUf5Xj9LeS+lga3QxK+/yimddlXRaUC7uR4CVfd2 ozjXYQRq89utzl7AVPR8uB0ueCo655tpXzsvYCo9o5Tk5GlTp5Tn8Dlz54I1fnla0jYUgZtHgKq6 bh5D2gNFgCJAEbidEGAbqOlXQx1cUJYJLPFKEaMi5ExDd8ohfkB4rutvvIcY427hZNwpj4KR3XiU 20D2OJbYx1/RWCc2v3K4/2Q7XjFuqZ5vFFFlX7exR2SukFGKj/cRmhhKHMQq2yk9jiJAEaAI3MEI BAR/PbnlWrI/HPmw25TxDoaETp0iQBGonQjQ3zO1c11oVBQBigBFoMYRYNUP926y7Y1B/4z3cY+d F3tuyapDi8n+S6qsj6nx2OiAN0SAZQJK9FTsJbeTipF7TPbPavIeF1dmBcYrO2d1TIS7BPxlNls+ xJHDvfYz9xzZ/2WL8FFXNM4oaZxxlJfMwuYqhu5wl4YsNVJJz2zq5ZLnm/lWnXmKlUm1yz3zjFep wos3hJM2oAhQBCgCFIEyEBD0XXo3vq8v2aN7Eum6r6G+wf2j0pyQ4P7kpRBSBCgCFIFbhgClum4Z 9HRgigBFgCJQaxDgx/RpdXjJoOVTW/SJ0pbW/eRfyn5+c8ocsu8pqKOppLUG52oNRGxQr4Qk2nSO Jemy+ansAXlYjximXnm+9nVixyj5GCt7NIM8Zk/GS/QW/NEgWgwgT4NXfZjcuJDdc0lqHHu+pDJW t1DGTZ2VQCD3LJrYf5PcT4uB7kLwpdByOMF2v6ytiJGskG1s7tVFuzRMhNtv38UUSt74bOnYqnVJ aOcUAYoAReB2QyA3c1eSbA/P+d3/ZOvPBtb/7tHGreRLB2d6xt/J5bipcrvhQudTmxFI3/rS8IF9 +5B91PLYsop8Vi7+0j1PXRHrrulpjl0+VR5x+Lyt6ZXrnR51kwiU55r3Joegh1MEKAIUAYpALUdA 2alzVPsA5XXKdtfyCdDwAIHIpmI/mVKK28Y/+zf3y27+6bWsRWKfxOkdBV15kGLF7h1Ft7RvyY/8 Vwe5NRu5mbvlg3nx0TaiNBQr3t1ZdNOjH33Pf/Avt+o37uXjUmM+QhgZXTZPRXp+73tunbusVojY wV860NOj5Kj9e7nP/uW2XWbhKVYlyk0YJp17fyd3NJn7bTP3i7sT9xzVYvtG8h8Odvku7lAyu3MP 93Yc41keHGgbigBFgCJAEfgvAkVL1sTHmqXChvqoBo8+1G58cw9y/SCaM1d9Hb+CMl30tKEIUARq AQKU6qoFi0BDoAhQBCgCFAGKwE0jwHoLLz8oNpF+cDDbdnIv/8nuJ0QXy4y8X5gYWt4xglsIb3eV pFiuLHbxOm7+HjafHK0R/zdBGOJd0lVIa+Ejd+MC9tMN3Fv7pcZ8oLhojNC0TIOwMHG0HvX8lbs6 Dss8PEhsIBOuDVqJI2VmDmL4YAP3cwbMg+GDxLH1Skbftokb/zn3/DFGXwZTK97dU2wmP79/B/fQ 5/yj25kZE4TaWNGgvItD21EEKAIUgVuJwPm4uC4v7Vy27WKsVMkR10PMyNm/6/jTL+2dGnf1bYdb GS0dmyJwxyHgclWdbK1ug0crMNbt9aPRUwTuBASquICRWv/cvY3GdwiMDPDwVsOPYGdBjikrM+/o 4QtLtuYfKtGRaBbNHTC7OUl/EjK2/zs3zW9az4i2UR4aqM+dk3tw99kFa7N2lLTnendp+u6IiJgA rcJszrp8aeXqrDbP9ih/BcYGYcFzhkb3bmQI9EGBOc2m1NTsA3vOv/KP8by8zKVLGTpjD3X7UZj7 QMOu9byCdEJOeu6B7aef2VzSGB1UjsleUR6x1PkE/TdZmMJcrwKjYmjn+jMHRLQK9QzyVLCMaC0y ZecWnYtLXf172gpjyV1dWoGxUu/TClZglMcwZXN/x7KHkpkzKWyimukRLEZGif1bCR18EU/k3q5T gdHdJiOZ23gSEhjZw0mMZ7gItQubRYk9Wwitva/oirRPPsdtjWOPyY0b+zPtGgoDm4vhpRyy/lvi cJx4fC+3KYU9lcaE1xf7dhXGNbiiyqQ1j1v/L7sjmd2dxugDxTGDhdlN0FvOWcT+uY/77Qy7N4sx BIqDW4mj2jP/fMB9Itc4nTPDOVXm9Qoucz/sZ/ensGcZsXcjcXhXoYuam7uQW0+uBEuVmKQVGCt1 ot7Kg2gFxluJfq0Zm1ZgrDVLQQMpLwK0AmN5kaqadpBm+Nii/cRilNFP+PCnyc3L8FWozFile44e t+zDSc3JPTpXUVrCRSOp68NqAus3CIAy1NW9Cbbs80d2//lDbKt58/pKLhPVPWbt7p9SXbV7fWh0 FAGKAMNUJdWl9V/9Ytfx0Yjiumpzph84/OBHaf9I7FVpqotxuQSO5648ynFu/Y72vxiL8ddYx67t Nk6PDijVQiw0prBeUZ7kKfuRVZs7bJZ/iP93bOWkER3eHREWUEYloyuiKs0ZuS5lxfkENNeVDsp6 Ys3OHhuLcEgMU77JVpbqUj02vuuiIQH6sk5RZ3bSgoXHXs+S2C5KddH3MUGgCrmkWg5pbf7tXcuh q1B4lOqqEFy3a+Pa/HaryguY23X97sh5UaqrZpe95qmump0f5A7nn928fs333+/JgEvvLnNXv9mf Ul2wBjSBsaZPRDoeRYAicOsQ4MeNbu3mucTCrPW/HFn2R2qqjZBbipDOLZ5vV2aqFcOLxXt+O7J4 zfkzkj8FtFc27BDaV5qM/rGhEW6ey5mf/c++i2ddepnnusGM9RH157p5LmfR/j+PLv4l8Zw0EETV an6PMu4F8WEBkekJH646+t2JInLbCFLLWg1p+IjEl5V3suaUtPXbEo/kuMXOYnFyyrpt8euPGN3u mldPQN+g4dzBMs8lWmL/ObF4zZndGVLagiIgctbogJBbt9J0ZIoARYAiQBGgCFAEKAIUAYrAnYCA cHn/F6sxz0W3UghQqoueDhQBisCdg4C2Z4zbz8dxdtPR+9Ynzfrh6HtHTDIEunbtvcuSKYmFR089 8HPSnI2nnt6c6/4eYTUKb3KkX1CXerIiSzT+/snePp8c7PFJwuWyzbj/AzjXrXNIjHy0K/Hc9O8v zFl/+pPj7qi07Tr6RVy9SkLuT5+emrn5woT3T+6SqtAxrCFwkFQGqbyTTY9PGbP87D+ZpaiuxAsj l58as7ng2gVj2DYt/MJkPZnrQvxjX5+fs/HM8O9T86Q4Od8mIcOoy/2d896iM6UIUAQoAhQBigBF gCJAEaAI1BoEKNVVa5aCBkIRoAhUOwJscWbWkdh0aU+14QGFAoub5eF8/HRBZYQhmovsmPcREgsd Zdwy8df6uj9NTfn7zyOVVf75nDhTubguraIkNZIL8B8WCH25EhIy5FAzTl+2lxFUUeFJkiHoLDqX Keu6GJW/P/EfqPRky7MMrMFSeFxG8vBZYwo+KN/kKAnU4BFDjM7oRhGgCNQIApmZGdcZR0z/a25/ qdT6wAe+PGkp/elkSfzxabks+uMrYqUc6BqJmg5CEaAIUAQoAhSBW4KAOXb5VPm7b/i8LUnZsdu+ f3/ORPRded+0lz5edzD1yvwGwZy8c+W7L8588IFps+ctWvFPQqH78vs/8V/Z89b/3Du2ZMdu/+nj t1+dPXVU3yETZzw/b8H7K9btPJMtVcwu3ZerKPnghi/ffelJHFWfwQ8++fJ/GrtiV4zoM2TmtyXf 3PsXTujbZ+CI5bEl7vSlR5Q7+f1AijstBA/pil0+Cg7E+6jlsU5zKhr6uSffcMfvMqYc+fu7pQte njF+MBpi6gvvfLFuZ3zetWC4JataalBKdd3qFaDjUwQoAjWHQNGcT/7tsHAv7L0+jt3GBLw7IHLR gHp9g8tOWix/XA0ivfzdra02iXdy2o3W8lBdwq4T2elyQ9Y38uX5d28bG+p/4CQJFfYuP2SnXh2N w1EgsW7O4pKBFGFhHrhtdU0Wdy5s3HxcCu/tgwuOMZP6A5KRi9p61YDrZvnXhbakCNw5CBw7duz7 7767znzZkPaD7pY/qHL+2X6yoKSxI3n/X7HSnw173dW4TAu+OwdLOlOKAEWAIkARuNMQMMV+PGvi zDdX/nEkE11dG1P2//rZ3Je/PJArAyHakja8/uj87zftj8/ITjm2d+u3C2bMW3mkoILVDp0ZBz5+ duLMN75eu3n/saRixpp55tDBHb+v+Wj+0/976asDGaVZI2f6ro+ffXLeB2u27T+Do4I6WWcOkcaP vrM+QTLav8FS/XdEuZOlc6c9/MY1O7HH//32c2joI5I0wJl9cOXcadOee3fV7/8cOoNzJYuSjm3+ 4bPXn3h0/voEc21MnqRUVxW+jV3ZW1+XieH/MKlVOEqVdoVpYDnm17dmV/C9WslYrgAKOONyjAoV Jc7sWrd86YIXn3vkPhTwiKnPXYP/Lk1ID3xpS3Ylg6ymw67P8VfToLTb0ghAncRm+94emv/J4BXP dJszsePsie0fkqql3DKg8s/Gvf5XtvtujMLTr889XZYv6X9ieqPnwspwqi93oNU7WX1gyJo5ffNX DP9j3t2vPwxIdpx9T4h3uYOjDSkCFIEqRODblSv+3rgxJTn52n36dxjcyyC9nLF93zn5M0e0ntnz Ryq5uvbpOvqu+jfzqVOFU6JdUQQoAhQBigBFoIYQEExc28lvLV2y5PnhDUgVRdhSN++IM0qPC06s /+mYRERxXm1HPvrUo/cqT22Pu46x7dWhO7N2fPX62jhCC3HRfSc+NWfG5L6RhJIpOv7zK1/tyHKT XVl7v/5oYyLp37PZyDlvLVvyyrQ+UvaJcdfnH/+TBh2xgR1nzp09oUtJvevoex6bO3fOzE6ByEjk 6hGfeXRkey8SmnHXpwt+jS3rxnzxTx99uDez1ARyj3738fdHyA/roE6TX1207O2n7m2MAxcK9331 /u8Jkl9vDa1XuYahVFe5YKKNbikCRYl/vT9j/JNvfvTtXzv2n0jCHzhFSSeuwX/f0lDp4LUcAbZj j3Y/Pt6sa7i2lv2Uc3z+/d4HP487ku1wy8BYtWerbq3efr33rqE+DSuDazVP1hD228tdxrb28a6i cs2VmSI9pq4hoIhyHVjojCP7ZCG4rsVfa+MFhiv21CkIb+3aX68dJOfRvOeQcOnDz7Z1/5licrFd fO7Afsllz6fXkI5B1GSv1i40DYwiQBGgCFAEqgcBTcyESaM7t2reuv/Eqf3dtJGz0CxZc1guHN4u uwQoej363GMj733g8ece6VqhXxRC6t5f98l0WbcnXpv94L2D75nwzPOPtJYm5dy3bk+qpI+ypMYe yJGe1w14aMqg9s3bdL9/4v0tJSZOOHnoNPBiXEDzPv37dGpYkqIS1LJHv/79+jQPAKanjBHvGfnE S3NHRkk9p23ecqLgGpBqglr0HT5hVOcQLVOcsG+LnGGi7z3m/rtbNe80aPTgptKR5oRNxy7WPl0X pbqq581Ce60yBJx5e7+au3hjyrXePMB/v7Nqb3ZtTRGuMhxoR1WBAOv97PCIAImaEQriz7yy4I+Y CesmbZNv2FTFIJXtw7lh9+kOT2+898PjG88UWeVeWLXhrnHdVvVx318qd/fVO1l+3LAmvX0lKMWi jO8+29Z76i/sgvjreQWVO3bakCJAEagQAoThat6y5Q2EXZr6Xfs3knq2HtgXiz/6is9s/y0JP6kI Hdq7leHWsNeQgLlhw2/XtxurECa0MUWAIkARoAhQBMqNgNLXS4OZEU6r9yrD2aQ4L92t36rfrKE/ orhY/5jW9Sti3VGQEpsgX+NHtGwaSig1bUSTpsR8hGGsyecvS3mJTk29kQ+Nm4D3+9sEqvF9KFap 8XB/TV/OyC3LzLf0nMse0atBm2YyR5d1+HxJaapSh4b3fe7jzz54afrkifc092ZcTrv717Y1Kf4i ipE1NOhDwoO9d3iJrKzcoFd3Q0p1VTfCtbt/rl6/tz5csozsE1p71b7zQUjduXqLm4fgGgx/asnX K39Y/fG8UdHuYI07ftot89/w+VSv/wJpRh8umdJG0mfW7nWg0dUQAr5+LYPkrwchb+2XcQvirYk1 NHY5h3FuPJBwz1tber5z4t8c91eKtvOAyOFXd8DxmuuoL6p3sqXLOzrPbzsxYU/+DuLyT7eaQ4Dd sFzRZK60L0uWBxbYZfPdz/MbriRyTdncmo38E18oOsOBLyq+viwdJdrYXf/yL6zgh+Fj793BliO1 vOamSke6DgIg6QKGa9DQobOeehqaXVfYpWvYa0AL6duT5DC6jCf2bCcX3lzbYb0bVOSavSqXJTc3 5+MPPpg4fvyr817et2+vzeom/KtyFNoXRYAiQBGgCFAEKoGAKyvtjPswX08d+SblNJ6+FTD8FQrz 0t3qjRBf9y9vZf0hb8i/Xt8d3Uj6IvZsfs/kyZPQ/vC4EY2YpLOxaDuXVliBC7RrjMjpvf3kr3uh MNtYhh9+xIDhfaJL7rR7Rbfr7CMB4Dzw1eyXl67de85aX45w8qSHuofXPiKh9kVUiVOPHlJpBFjP 8GbN5a1+AGGLa9WWFXfgnPt6t/EDs6YObh0dHBAa02v8lCHu3JdS/DcQzKXn1CCgFhLMtQrgOyuY 0nUSRUdefpUJbRNTCmWFMcN4aOuRbz2VLqRc9DH/8OT+4urRZHfMjWzACIdOnZ/yy+VCeXnYEN8e V5/KJSUOPRoEufXTzkuXTOi4apssDkodVCL9EIoKa2F6/p11apdzto5s7vlPufl72G1JDGLARMZE bge62LU/8NM2sOvjWfKJm2WDF+lWNxAg3NbIkaMio6KA8Lq+sIsL6zC4q3S1atu650RB5vF/DhCa WtG1X/dI7S2f8/59++bPmzds6JCvvvwiLu7sLY+HBkARoAhQBCgCFIEqQUA0GcvMfrj2r9fi5J3f LZoxZtiIB6ZOe+KZmTOemTl/zZkKuINda8TS07HlFJZBdYUFXUHisaG9ps8a4ua+ik799fG8GeMf m/f5hoPJxgpwb1WCY7k7oVRXuaGiDW8FAq7cjPMl44aEB7qvwj3rNWngfqUkj/pWBEnHrDMI5Fjy 3OwWq/T1wR+ArGffxrJquNIzKd2zxrN5PaQd00d4hZbrTo+YXuRwx8UZdC0x5+xyCO5vDjGvMPZq hTJv6NwREWD6eoGtfd0f5vacHHzcTUyWV3I3KsBmyyz5XuM8JaE3N6JtQGClMaQHVj8CibHcVvfl DMv06iI2x+e+K5P9Jq5k+IhG4sQwpvbd+qh+gOrgCG5JF/BcEP7gIUPg3+sJu9igjgO7Scy59cC2 dev/2kmI+sihwztiC9vasv38ww+znnhiwvhxNLGxtizJnR3H1q1bIM2W6g3v7LOAzp4iUJMImBLX vv3s619vPZNfk6NeayxFcM8nl7w/d0KPYPdvDiH14E9L5/3vqYU/HqudZkKU6qoNZw6N4doIKJSl bjEnxl4okJvyPh0my6mXNFGRnkLlQyAv95Q7G53zHTa52QcDor+e0WFMCHeztyNyM3clycom1nvo 5FafDYz5dmJ0RLl+NwqHYnMzZAkNF97ok7nt103v+vekCFko7LhwIO23MjQ2uh4Te+ye1mHP7Cb1 3Z/lxTnbT+JIKjZZV7bJnS/J+Xdos3V6q5U99NcmvCy7EorliBQN72753dDIRaPbf9Dfu4SfK9+a 0FZVjwArDpvkWvMo2YVOJepz9lKpWjrd73V9NNzVNwCNX5jHXnR/ttYXvpjoeqKFeGscm6oejtu8 R7eki8yzSZOmN3Ls4g3t+t7rTz4ycvau/uUYIdrD7767uVyesTZhlp2ZSRMba9OC3LmxnDp58oXZ z06Z/PCaNd9ft9TpnQsRnTlFgCJQlQjkHfrh831SrWSu1ej5n6/+ffOmbd/N6eJZlaNUpC/eq2Gv ya9/s/abt2Y/2CXEnQGZsvuLea/+cKYW+g5Qqqsiy1vJtubY5VMH9u2D9+Hztl4ypx1c9/EbL88Y P/TBma9/vPZgGso2cuad3f7dB2/Mnjqq//iZ8z/4fuf5kjxcV+yKEeTwPgNHLI+15cZvXbXopScn 9r9v2nNvf/VXbN5/PdmdRckHfv/iHZAVDkZHDZk444U33l++fueZbNuVGVule+77+tZs6fe+K3b5 KCngPqOWxzrNqQc3fPnuc0++sTW9BANbVuy2Hz95Z95zj9w3sO9902a/CEP8cSC56GrOwJy0c8Wi eTMeHPPIM6++u2pHQkH5aQU+rGGHEuOQ1D8/XL4pmbzlWXVAfXfuZfNSiYqlgx/40hZSE1XeTGkw k3eemzbinomz3vry9xPZ9tS9qz56d96L8158cuLI5bFSZFcu2Zak7Nht378/Z2J/APO+aS99vO5g 6n+Uo66iZAQRWhS0TIMffPLlBe+vWAeIlyEIreR5RA+7eQTEgg/+SpezAjn/5k1mTmw/pZ3410/J V54nlRip6IMNKZeldyKrj2rw6ENthoeYT10qV8GE/LPnl/1rlJsqQ5pFj+gWVt+TUA2u7Ngzb6zP L+OGTpHxvNWrR8+o1rI9PJhZnv474UvSUcUm6/hyY1KiHAGrM3TuFjOgoVr6fi0DD9eaP84fMElk F+sTOn5cx9kjwpg9pzdVXVpoJZaBHoI/HZmoSLFtlLQHl5IWmkp9crUOEd1ZrzY7485B1dcTIyjL VUfOpP9IukjUDz08Cf69nrBL26jnvU2unKK+xejeTa9n/nfrEaGJjbd+DWgEQA9nZa/46qtpU6c8 NXMG6LyMxtpQ1oYuDEWAIlA3EOAMfpHuSB1O9y9iITv2n61b4CMF7QeS8cWaaEmKPSRfmauHjBvX o2GArkLlHlEv1xrR5Syp+a4N9b1RMscV8PKeUe0HT53/9VcLp3SXTbHNid/+ecRYZc4wVbWclOqq KiTL2Y+r4ORP78x88bO12w+dyXBmnN679qN5M5f8fuDvj5+b8843v+0+llQsZMTv+m3l60+/sKYs clRM3fvVq08tXAFVwjMFY8qRzT8vfv7F5Ufdnj4MY0vf8/mc/8396MdNB89k4DPOmnnm4O4/vv30 9ZnT39iQYK7gWWiP//vt5+Z9sGbbkVS367Qze98nM6Y889bnv23eeyIJvuaNKcf2wxAfvvjIlNfW XzGELWHda8+89e1fB8+kFyYd379pxcLH5686VO4qbfqYbv0jSsBN/3PJ/55dBBTVfzi7csFvS9/6 4dNzl/6y+XBKkTnz9NZflr4078sNe35du23f/oMHAM8yezHFfjxr4sw3V/5xBDcwpuz/9bO5L395 INfd2pm+6+Nnn0QQoUVBTwsZZw7t+H3NR/OffvSd9QlSGY1yxUgbVS8C4r/bDz/0+dl/k405ZpfT bEqKT/z0vT0TT1osboVSkFePSsUQd+TkhM/O/JtcWGATSc8fvndoc0E5327Wdz75Z8SHx/84kZOa Y4IeGBfkIRrPxV5Y9dm27gvPryjzRoktY+Eb+39Ah1itTntORta2NXtHri+QSbGKTTY/MX7ce8c2 xednFDkBDKfZnFkGb10KmvyLE97Y/yOMnm93uuzZaRmbft4zekVmnlWGktc1iKKUSaVOJnpQ3UdA vr8l36aS7x6V//nyYPAfSRc5pG3btjcSdukadL2rxAIAjlF0G1ZZP9nyz+j6LRctXFieKUMbktg4 8cHxkNhouVHtqXL2SZtRBCqKABhEw0l7/8j73l/yHk1srCh6tP0di0B29k3fXL5F2PGB4c3cQ6fn FZILfJcxMwk75JZvYz39gt1s1bmMXJnrcl7e++HChfCRgvYN53GPQnF+rlsxofTWV85K8xojuozZ GXLcCn9/rxt7W2dvmee+kgHlDUSuDm33wMQxDWVNivPs+bRap+uiVFf5Tswqa2U+t6eg+fNLlix5 fngDOavE+M9HL67M6z93ybJFc0Y2kZfkGuRo8Y51J5o+C03nT+lkIG3NST/9vCtNvtzL+/ebBb8m kbefotXwF95esuS1qb0IWyQU7vvm020lxQrLM63inz76cG+ppBc4Rkzf+8VHvyUSeYAusvfEGbMf H9UlAkcDQ3y1ZG2cfKYLxcc3Lj8i3/IytB8+fdajI9SHtpZyhblBEH4dJz4xwo0VGuH81qVPT5z6 1o9lKsiu05vp1LrPtkqcINdk+AuLliyZ0erEX9uvLVwhoJm4tpPfWnrFkjGpm3fEybPK2vv1Rxsl NDybjZzz1rIlr0zrE0QiMe76/ON/0spJd5RnPWibm0XAuWF3bLeXtwRMW6ec9lf9Bcemn7IVp56t /9Av7AS8P3VqpTSEdc7CDdKTE9aGLJfIzcTNe5Wk5RWN0anyz/4z3V7e7DP1V9LzrPiiUj1s6LD5 +l8AqPbivYt21HvqL+iBfXhDwFNbGi88+vCewlJ2df+dvJiVPg4d8od20oaA2bv6bcy/snH5J4vi P3QqcdCCbSGPr+cm/KKc9nebXwrg2WvPl0lMvTQWRp+xQfnwhsAX9gz6LeeQaJz4wjoZtC0j46Xv 8Ot0crPrSY+nCNypCBBJV7e77iIuXaW3Gwq7FPW7D+vgzoDgDMP6dSgRh9YNQHv26hURUU+rqhvR 0ihvYwTgbUgTG2/j9aVTq1oEMtJLZQhVbdfV3ZveN8TtCHEpKY0kKRkzknMr8jPPK7JVG/nLt+hk bDLhsoxxB4+6iaeIVlF+6ElO7+PnprccBcUS7WW8cOz4jVQUpfRiTNkj5p876q76EtG5SYn71jVR 1Hq5SzYyjryCYnKFzyvVMnXBRYUH1rqvZEp1Vffb4j/9a2ImTBrduVXz1v0nTu1fQqA2GjNlTPfm zdv3e/jhge58PafZYr/anid44NTx/Vq3aNVjzCMTWku9C6e2HyNWP1doHbXDHprcv1Pz1j3HTB7V XGpafHr3mawKT1oT1KLv8AmjOoegd5yYcfzvHZIsS9HtyfkvTLpn8JhpLz49KpzQ1ObENVtOFpO3 fdG5w0dkLZhP1+lPPz5q+MjpTz/aXfYgKkckrG/HKa88M6Q02wU/ytO3f/Pi9Cff+vFoRnkzBO3J p/bKqVWKu+8f279989b9xj7Y40ZK0LKXrMQI35Iae0Auvqcb8NCUQe2bt+l+/8T7W0ofh8LJQ6ez ypXFVg4waBOKAEXgzkWgOINb9iM/9m3FsC/4uZu4s9e6jyiwy+YrmswlO78Bs/LHNqHHz50rQe+T z1GDxZv4znMVvX4ucZUz7uBbzFV03sS6P7ZEB3vsGPfWj/yk99EhMPrTv/I/xLH/EarjIaR9WTJj yubWbOSnvi8FQAa2FrB/7OBfWMEPgwjnKyYt51/dxO3MLBkLNboy/t+yYXT+1S8UzaHz+YrHfuN2 Zf+3OuQVEb6oGPsNivDbk2xGWaVByzmdmzzPNm3bfpP7DQMgkq5hw0dc3dIt7LpmdhUb3KZXK/nA iP49mlS6NsdNTtN9+Jy5c284ZWjQtHnzVxcs+Gntukf+Nw2mWZ5DaBuKQA0gQBMbawBkOkStRcDb pwK/7GrtLG4QmLZBl0Fy9qFzz5pVfx2JPbJ1zU97KvQjjw1uP8Sd9Hdi+cJlP/616fcv3lrwg3x9 pug5tnc0KZuljW7eUf6Zavv71192H4+N3bfuw6/3K0tMWN0xsx6GYPcfx9Z+vXrD1n04sajsERct 3SQJWbiGfXo2Lkf+okdEi/byKlu3fbF4+e9bNny3ZOnq00T7whkG9Grnf6Nf1TW+9pTqqmHIlb5e Ggw6p9VLRcvQX76eOrIUWr33DUq21YsJI1ekqsDI+jLXa07JKcLckujURI+Y8NA4vI9uE0TYNFat LbmKLaWWLN/kw/s+9/FnH7w0ffLEe5p7I93WhdiTEn+ta9ypmaTD9Ihu10l+i1njzl8iBJc577Lb xSCqdYwf+jnF+jVsE1W+oUkrVh3e58nF788d21HOBybPWzN2fD53ejkzBF3G9FSZkVKENAyDmQAV bQgOv9Fn8zWWzD0Bp6beSAnwcfe3CVTjX4ysUuPhTt26nJFLUywqsuC0LUWAInAVAtYMbtZH3CfH 2eNG5lwSu24HN3olt6cCqvlKYuos4BZ+zI/7iVt1nN2P75PA6H8dZl9byQ9axZ29hlTRms49/yk3 fw+7t9StlYyz/Nh3+dmb2PXx7Dk40MrsP8f+uIN7dCk/aw97DX0t+9YnMDr7YxKDbiBamR37uWmf 8btKTfy/EYrM8fMowjfX8MO/5HcWXDHxyk2nkthV52FE0gWJiteie4iw688/N14jCpZXuq82OKWi tl8NBgQFTXrkkVXff7902YfdunU3GGqjg351Ljjt+3oIVFUW7fX7gXdceZbhP4mN5TmEtqEI1HUE yv+ZnHrRXQWnzk3au9Xw+7u6c6p+X/LizDnvnWhwX7+QisxEEdB94mw5i0tI2vTN4nc++vGQlHXk 2WbM64/0CpQJI98Oox9qJX09Ow+vee2ZZ2a+8j0z5pVHO1w9IhfZY/ygcOl5IXHX8qWLNibha7Tr jmjo+ejsYY3L49TJhtw1ZXIXafrWjH3ff/T20lWbTuOLN86r/cTnJ3X2LVcxroqgddNta/vFzU1P 8PbrQKdREw6FVWl0bjalOOFyHj7VPJsPmTR5Mt7HD2/EJJ2JRdu5tMrbZkYMGN4nuhR77CzKy5H5 a95Dq5TPaqVa575uzruUgyleV15arFtjafD0IG9dhc6rwhepqOLD/xZ8/vHzw1tcyU0Zd336wld7 r3Lmv2rhHYW5bnctbYifnpz6vG9Q9E2eI57N75EAf3jcCEAcLnIw4iVlBW5yAHo4RYAiQBFgD+zl 9sqeDj7R4ovDhPEKdsOl8iIT0lh4d4zwQGBJ+1690DO9GouvjBFeKnXVpG0kvj1GeKUxgz4kRXbr n9wqOYW9UQvxtfuEqY0Y8tWTH8+9tJctU1j7zQZu65UviAXcop/ZOKJUZpkhfYU3hoi9vKV4tv3J f5ta9ly4SPHzR12rRotN3JdQZvavJPkPkf3ztysjHC3MaiFFaExln/yNS3fLoys7nfKiXIPtiKSL 8FllbkTYBf7Zdd02e8zYsW8vfm/192vGjRsfFFRy07oGwaZDUQQqg4Cnp6dWW1JZqTJd0GMoArcd ArGnTtXdOSnCBz635JWH772rcbBOH92h7wPPvPPm1O7BFXybK4I7P/HeqmUvTx05oEvbaD2jCWrW sVOve8c9+er7X775SOcSLy+G0Tce99qHr/xvSK+2kZ4+ke0HjH7q3Q/n9I/y0stkWE5iuvwTnw3s PvPthU+N69ulWRBcwHlGt470kpr9d0SGC27WEUZ8auEXK18eEVPOeo6sOuqeV1Z+9dbsKUN6devY DHIeDZFtu9x1z0MzFnz2xVsT2wfUuuxFdL2ZkHCh7p5wtSxyV/bWNycs3CFF5fnQkl8nN0c/CKCc 34xnVifh5z07zf1sAWZ/oVDg/TO/le5jd5m7+s3+qOY7lEQcNWONxA71mvPDS/0C+LKfvFYPaMCk Xet+/uXnzfFE6fWfrSSwawx3nZ5RV6Wnc60VkKdZ5nSgolwpoPQTPvyJ4FTOzZkbv+2Hzz5dd6Zk dorQCUs+ndyCfNKUDew1VoEBj72xCw+SoSu3ZOjI4uSdv/38y9qtZ8qokscw0eOWfTipOWELyz4Z yjn1O7ZZzKtH7ti5/2fiDQZ0j5sYIn115cRPKnEWuxMRSpjfvhZOO/Jgz6qPysa9+Rb3LZGH8uK7 s13DvKHGJjdjMbdV5r+mP+qcGYUbQALgAv4TSW8lvvuCa5h0c4HdsLwkh7GkPcNkHOXdOYyGXq69 A6XijGIe97/F3B5MFSkbC38+JEBxRtHKLfmQ+xLfXWGU4udzXXdjgTEkMI7bUWrqLNOuldjZR+x5 l9BWx6Qd5AeuY0mwfe9zfdAJDVGUyI/5ik3GT2o7CDtHCUi6e2X8Lz3legh5H7I7f+UfPSz132uM 67N2KCwhk7t/KXeaAFNf+GOKEA1fJy7264/4RVKevSj3wFRoOje5iCmddt1kD9c5HCRdUAAOmKwl Sz+4TjOwygYLIRBDAUl0VbPSX8Slv6SqL+ob9Axlp0o700Oi4v1jxzZv3uL6YoFqebtdcQZeETZv YO6KFrs3Ffo3FUvXNiWNSr8F3KfoLQO0IgM7k/nun7PSD6ZGwo7JQt3iFKv17eYGEuznyyPs6tKt 28DBg9u3a6/WoMtSegFTkTPxDmpbOy9gbmYBnnlqVjlprC++/uZqi8mbGZoeSxG4FgJU1XXbnRu2 8+tee/7Nb/4um+e6Daar8Gs88PE3Fz8pSyhhSs7LW46nuOu11vQcTYlr33729a+vwXPVdDR0PIoA ReA2RMDKpLrToAPFZpi6Yg1i51IqreqYdcFl9oAsiWrVWAzHNyVYjdi1kTyagz0jZ4ZfEYBOXPi0 87uxrlkDEc8FB8UnSTwXSMWALCB0rWe4eJd8F9Byjk0p6+6MQbpdKnqVZSWRl8G6jVWjGor1yG0T XmwV4w6Hjc2WJGCVn051gHsTfe77dx8cfR1JF+m7Lgq7anmiosvI7DiOcmPvW8HvLbiJJaSH3nYI wKn7xKxZkGM7f8EbkGNLeC66UQTuHAQi6tUr52QvXS63Ir2cPdJmFIFrIECprtvs1HDl7f/1qyPS Lw+u2bh5X/y4bvP2TT/M7VQtE9U1nvDqkmUfXrUvnNT6RhZY5YoHNFBTS6wTXtoi16fldDH3PDG9 V4n3XeaF1LxbxHXlHfrh832SOo9rNXr+56t/37xp23dzupRTDlouIGgjigBBgJYyvDPPBGc+e8w9 c09GT6gbljFU88eMsYhxG7t7qmFAsomlk3JSjGV4M9TvJgwNcLeHI5hsogLDm84tcleKnu7LEBOT Vd4qIyVdFRRhDy+8RXhJeYvwuFEn15pHpX1ShETXVXo6teqsg4RESEuEkHb8sx1kJtffSeTXduyq RTMLDw+vQ4mK+RfY2Ru5tGq77nBWpKJX5VaxBoaoXGB17ijIsf3g448hx3bYsOE0x7bOLR8NuKoQ aBBTcovp+n2CuU5VDUr7oQhcHwFKddW5MwTKh15dltE9i+Lk02dkI63ggRNG92gQUGLoVTVzVRj8 Uaol3nhDRKPmZWzNGgRUw+2s0pVTGYVfSFg1jFFRjK4oeakeMm5cj4YBulpXfqKis6LtKQIUAYoA IJBfWEJj7fiZdxdYLJ2oWFBW1Y16PswVng0ik1XiT88+97Zcq/Eld6IlZB0ypgqVMcIrVGQq2wTV ECC2jZL2JrI5ZKWnU6tOBjdvBblUN9xJOkmdcOxq0qRp7ayo2KaXQGjT7ycL/6tfci7kn+bWXmkw V79dCcH6VIPrXKpd74TKSOO+/ZWfc7y63H2hAunpWO7Vj/k/S1Wr5wMFcMST2OGBonetOuNrazCQ qAjFQDds/BOKgcLZW1vDpHFRBGoIgYiI8qq6fv7hB5v1GkVtaihYOsydggCluurcSrvL+bny0lPd Jav0MaG+aCqlKx5qvfWkAGPVbgpPX3cpUVNShlG+qZm+9aXhsgJr1k+J+COM9w1v7lYd5OYVEn2A PS8jvXwxlabVGCb2TKJ7NMaRdTGJVDdFW1D9CN/r+32p/IJD5dYuk8VBLkJdeZnERK2ym1Ccn+sW Iii99XJNzMr2R4+r5QioIw+vHC2uJnv/tY3dZ51m0dxh8vMj0yf71fJ50PAoAhSBOooAGG9t2ra9 ovtVjld8QL9X5E6+lt0k6ygk1Ru2t0ybtmskPPOQ8D98sUW2X2O5kusQhimTYC1/cLmp3AcrFH0/ 5t48XHa1h/J3VXZLkT11iH9yCT96Nfdj2hVNWJ3YRqaG24aKteA+4s3OtfqOp4mK1Yct7blOIxAa WoFChGfOur0H6vSkafC1HQFKddX2FboqvvSDpy/hO9/55466PyZ0kf4kC0TnG+oubmgpKLbhw13G cydPVNlEOa/IRo2l3oTsXUcSbJg1Ml44dly+RahrFBNKWLbS8aSfTyOlVIsykt3FEK8fltK/XoOS q0rrps+X/XIw8XJ2dmr83h8/+XKfLPDnDN2bEBOZa2+c3tdfZv7M8Yfis1HUjqwL58oZyjV65vQ+ fm56y1FQLNFepdGoMuRpRxQBigBF4JYh4Na2uBMDyYNKqFemP1gifinVm9CpVK3f6p5nFU6nukOl /dceBFiNMLxriVwrM5G5XEnxVhlzuniG+zS+JCG36mctMv/8yW4rqPqO75AeW7ZqRRMV75C1ptOs BAKQvRscUl62CxLwKzEEPYQiUFEEKNVVUcRueXtr4leLl/z4x+9fLFq6Sa4AzzS+q2UQFrvro1o0 k9PnMrb8vH7PidOxB9Z99Okepb7K1poN6zCku2zFlfjdW298/ftfP3+yYNlWSYuqbzahX3Mt0d57 Nu7SRebecvZ8t+avIydPbPl5zc4yfYyvxpbVNO//YAc3eefM3PH5vGkTJox9eOYr3xw0ykyXouvE YS08brAynL5h684yNM69Xy35cv2WDd98UMKXVW5lWW10845yt7a/f/1l9/HY2H3rPvx6v7IGf7RV LnZ6FEWguhFguQa1sfRwdU/79unfx6vkd7xb2+JODCQP3OmB15s2ywSWOOiLUREl2YWle7u6pN0N oSwdocMJnmDSlpHMbTgq7Tvlr8oqm84Nw6INbl8EIkKYEou8HPYSzcK5fdf6PzPr168/TVS8Y1ab TrQyCAwaOrSch0H2PZQSLmdj2owiUGkEqoz+qHQE9MAKItCk7xCvA18s+ejHQ0QiBcqp6FEP3B1J FEu8b+dh4xtKJIvz2LcLnpn5zItrmUkvPHJDLqj8cbChvWY8O7wBGcWase/7jxZ/+tsxYhmvixz0 1LOjGsv6Kc6j9dAp3VGheNiExN+WPvfUc++ebTW6142YKTkaRdSg2XPkscoK0dBp/FtPDoooh9ze t8Poh1rJZ3z2sR+XLf7g98K2XRuWf+JltizdrfPwmteeeWbmK98zY155tMNNdkwPr1MIOHZsOrZ4 1SG8H/5ELlRQp6ZQlcHqDT4LxnaM/6T3ougb6C2rctTbty+Fj9jWPbs8poDw/AJzKaN65wy290p5 hHP5br0Ju2G5bLY1V/H15XLEwDIBJQJd9pL87cUYucfmyl29x8VV3Iq7dISnSyJkUmO5536W9jWX JNujKptOOWZMm9yuCCj1YqR7bg7GWMqr7timkvfFY0evMNsyZnK/buOf/kbR70XUZtiX/OI93ClZ Cu9M5jvPVZS2wCPWeJ03sW7/OmsB+8cO/oUV/LD5iiYvKsZ+w7+6iduZWdKABFU6hmXJjCmbW7OR n/o+/xi8Z0tb4zGSZR60gY0EIJnxLef+87kC9l7HjnFv/chPeh+16fcx//Sv/A9xrPuG49VDP3aE Bbr5s195ab7L+W/jWNPtek7QeVEEKAIYgQr5La5d+2vNwiaY0w7+/sU7r86eOqpvn4H9n/opoeKl cGo2YjrazSNAqa6bx7CGewhuN+Gl917535BenRoHB0S27T38yTcWTOvk676m0jQb++77L04Zflfb aL1ndNsB42YsWjy7X4yXl0wGFaWm33SxQkVAt+lLP104+3/DB3RvHW1guODGnXv0HfnonCVfLJ3T J7y0LbsictALb700ZXj3ZsEciWfxK1N7RJTfcBWN9eE3778yc9w9vdBYaDNEtu1y1z0PPf7K+1+v fOPh9kHl84HXNx47b+mLU4d0aaBnOH10N4Bu/iM9b7pUpL7xuNc+RCvSNtLTJ7L9gNFPvfvhnP5R Xno5qpzEdGMNnyV0uBpHwLXxaOqczSl4v7ggnuQO35Eb5/XmI93PLenz8j2RjUqq692RUFThpDVM hFsfl88mY5tGsZg9X82/Hb2DxFbyLC6f4s5gu0XRxP7r9jhUiU3LZUwnNqhXIhDbdI4lUpj8VPaA 3L9HDFOv4pck3qFiZ/nrxBjHJmDzSNHK7TpTgn7HYGnoqptOFS4t7aqOIcBpGP+SkNnsG74HXeze TfygpdxLW9m/zjNp+GQ8d4H9aiM35n3+28slUsTrAJFxlh/7Lj97E7s+nj0Hbx6ROX6e/XEH9+hS ftYe1m3b+p8erOnc859y8/ewe7PKf8313yicBdzCj/lxP3GrjrP7cXGJtDT2r8Psayv5Qau4s9cQ tR35g+/7Obf0MCvN9xz75kp+URxbdemedey0oeFSBO4EBED22Lxly3LOFIRdcXE159jlTNv07swX P/px0/5jSegzUygyEwceut3WCLAJCRdu6wneFpNzxa4YNWONdPev15wfXuoXQIUSVbSy2VvmjV14 kHTm+dCSXyc3p9BWEbRV2E3Mq0eqsLeb6gps6b/o2F46SYzrFmwfGV9t1eZvKtBbd3ANQpQwv/2t m+c1R4482LMaomL3/cZP2S913KSjMKcdk3OCe3F/ibPP9EedM6NwA4FdtsBd2VB89wXXMCkLHNRY /HPnpE5K2jNMxlG+18+y9KmXa+9AUWLqRXbDKv65OOmQiMbilGbihePctzLV1W6g65tekok1yEnc spReY1yftbviIlIs4GYs5raStwvL9O0p9PZg//iH3U/uqrLinCddU0ntkGvEX3b/Ivv3Gv4pVGYQ bY3aiA/HiElHua/kSxtlE2HzRCGETK4i07nJRUzptOsme6CHlweBanm7XXEGMv89mUGK+Da3Qw6u 9PuozFM0P46/ZyVLXEF9Gomv9xR0udyi9Wwcfn/wIcIv04XGJvavRDbxBPeJ/PZs1EF4JBqE+uLg KJG98r0zpI/QTV3qvcMw/2fvPACkKM83PmV7vUoTBAUVxQKi2JVE7L13Y4tGjFiiRuNfozFRg11j 12hsaGJPrFFjj71FLNhAQOqV7W3K//fO7h6HUu7gOO5gNhu825v55vvemb3b/e3zPO/i5tCugPbJ B9lrWerTT6ovl/sDKcohe1ijQ8qgodao4Qj3rgAA//RJREFUuKi6trlVrXwot6718rFWv/JGCz9l 1t3QPnwd+5t2vwGGjDUf28UuJ5a2X74StM8+yNrUpz79T+3eqoPYO9J69RBruT9fXMR10ZOfbj3o BUxHnlHuNt1VgZ75Amb5V//hhx+ee9ZvOjgO2V633XGnP9ABa04HR1zsZvlvHvrt+NuqrxW0QWP2 2XXXffffZuCKaOC2vHN19+/CCnT+I9QuPLg7lFuB7quAlZ36zgsv/Lt8/8/keZ03ynTfXN0juRVw K+BWYOEK2JtvbY2rtsD44l3t+Fu1331nH7PJCq6Tau++j3V0xYOuTP9SvfixBZxr3VHWpdt1tFmb WmP93xH28ApyUl58Rfu/p9s4l7L/wdbRbT1yO7Um1R63+4IZTvlIPf/hBZwrPsj+yz5VzsWwXbec Ts3R3Xg1roD6yeQK56IIB2xv7jTU3mZz69BBlZKYs7Q35ypa3N5jU2u7dk+BAWvZe29q7THE5mX6 N5+qFUasKNvsZU4cZx24nXn9oXb1eanc9b7W5gn+calVZdNN7JPH2tsPtxhwgwUf5tmjN5JH4FxL uFlz1XuqpFtf27ruUPPQMdbvjrKOrsaVTX1De2dRHqANf24du749aqh16q4VEMZRSlllNRY8r8ZP Anfpq1MF8DB2XNg1e9asyy+/rJDvhsjDzOypbc7s6KgJF//+14e5nGt1uDBd1LU6nGV3jfIWR216 9y+XXXaFc7/m+W+dBuF2/oepX1fLo41eq78r6VpdL5aha/S7c/y23167Z+6+A+37DrTu3HPWlT97 79QNJg73RzpUk8AV5+3Njs59/1nHLuTpijT2uYPBr9+7dN++zX/e9tk9akdtuv7bF+/4w7W7zrp5 39J5g4eWD4Ee6m/lEbjv9NjohiuO2fLLG/a1+PZve8+6eLM7Ng22TWboztuUKlseaF+70TEcYsL2 3zP/e/dtuWr7Fw/oMxamEIj/iRGu3D31twNyN+/85dkjLhz0I7OvtvnwwQ+fuf2317CNHGXetTt9 ed6oO7eIVKbUuYk5RbizTfXGzvH9LtjPvm/v93buho/sOnSeeu9G3kbr8hOt0ze3R8aVeB97322t u4+1xq0IgcTCNfLUWOedbP51P/vokfaWRMurysh17H23siaeYE462BrSmd+Z/dY37z/D+tMu9r7r 2esGFD2u7LCeffQu1r1nGX8aabeFgnX2HMkMT5HJtJ/hbpvZ5x9mPvFLc4eahcbrwuV0dp7u9qtn BYy2tC1F+XyGYzZUlfVG2uPHlu/WkKVc+urXUxfYD7ceXFFcxtawt6o++3JT1GmL/PguZF92hnH/ oeZpu1ijlqlTTvNstc1fNGQde03niGrQ3qLtL0RJ/WxRfYYaQkr5DUYooLi9SVbPK99d9WpbgdNO P6Pja3/ztdcefezRFU+7irlkG1BrGL52n45F33R8He6WPbQCroGxh56YhablGhi75Cxl3rvpF+c+ 0eK8HtTW2/3MY8cNyLx77/WTPixr9vvtcOFV5+1Qsbl0yQHdQbqqAita/z984w0e//X664UWlWZi JF+88/V9X8tWwlAW686D8ux81ojyS3pr9ouv9L+rbFiBNzU+esk2+/Vv/1c1P3lKYfi68fL7FGPy u8Mvm/YNXy00uG2YikdfeErGvL9e8Nrx0+UaBnV9cXR10FTLZ/noBo3tD2H88OaUNwcMPWCIv/0Q dmbGZee+fX5L2VnmPeagLW/Yp++iWJ4x6+33jvjLjP+UN+zoxHztitD+5Bffv+f5zZ7v+k/teqb+ f4U4qrrqueSO010V6MmOqu6qQXccZ4U83brUwDjrA33nf6hV16AycjPrl1vY2wxchBxycRbde2/Q /zSrXMx2fuT2k/Tat55n7uCoPtsPsvY48/Ed7QWkabHW5sUaGL9+Xd/zqcrfkPZGzvZHOeQX5sXD 5U/FIue/WGtkl14dPfnptqJfwHRpId3Buq8CPfMFTFet/8knn7jxuus6PtrW22137rnnrUgn46wX zv/VFW+Vw4DWOuz6G44ZsUz4v+NLcrfsGRVwVV094zy4s+iGCoQ3Pujsg9YqX/LWl09fee6ZZ15S 5Vyhtfb/9S+3cTlXN5yGnncItfaio9arci6r5euvb733g3s/SFTAjCf280OHn7Dsn/5oO+2y/p5V JGVn5z0+6b0bXkoNrnKuxZdDVZpn/PXed294ad6CyGFP3c+2WdDmfsG+0Wjt9C+uvOejx7/JV+OR PAO23mDf8Ny7Fh5BDfc/bJdYecfa4etdvEcb5yp+/8l3T70zd3YlpNPTf4tNLv3ZIl8HLGFixqfv f/3Yf2bNXqAvKEx5Z8pjL379n2nthA097xJwZ+RWwK2AW4EVVIH+m1hXb660yR8/ek875UZ9r7v0 ST/pYLjoCdhKS8vSprZwI8i2rdesXV5FVSqz9Dz7OY5I3r25FXAr4FagrQJ7773Prnvs0fGCoO06 8YTjyfnq+C7ulm4FOlIBF3V1pEruNqtGBXyNW/zy6nuuOfvEA3fccoO+cu3Th3HUdnsdd8bEm68+ ZeuFNDGrxordVXSkAnX1G/Wtvg2x5j90w8e/eu7bo6/95JVEhRqp8cYd119m1uXfeYN41Z5iTn/t k6Oemjrhrk+emrXUMPvsqw9+cPxz0yb89YO/T23jRGowuKiZpGdce/0XZz//9X43T/lqAWZa8gie Q7Zfo2pnNGe9+ta4ie/vef3rRz86u/q2JTjm5wN3XkQBlzCs8bfnP9v/vhkzF8SR5yc/N3n/uz47 +0sXdXXkWnS3cSvgVqCnV8BMqtXseKZq9wkvbcK6vdO+5sOH2DvWLNhy+hT14r/pBz6gveP2Z15a /dyfuxVwK9AbKzB+/ClotTo+c3K7yLM/8/TTuhp4ZSffdfwuOx5RlXQxo+8mTdhzlx2Pv3tyG6e3 CvMmv/TQTRMvOPPEfcftstPhE353ybV3/eudqYmFXqzjstr357vs6Nz3vWuykZnxzpO3//mcEy95 cd6S11mYO/nFh2768wXnnLCfM/j//fGaux5/5bN5hZ84z83EtLcZ83cTjthdjlKZyeOvfjZ3oaDD 9jPZ8Q8vzCtPs7zS8gz3ueCFihjY+ZGRmvres/dd98ff/fronfjpfieec/ntj776ZXPxJxPPUYm/ 33j57886/oAdxx1wwpkXXHLN3f98e1qqt76Md1FXx5+DK29LfcQxj7/03Ivl+4Vu+8XlOBNaqP8m 4w4Zf86f/nLPvynmC4/ccdX/nX7krqMHuTLW5ahqL981UJr++az3J5fv875IOpzGMrKlNmDj77+Q /bBT6w0N7dsWxFKc9p1jhLSzX89d6t+MUqK1/Cew1JxZWhOFfGFOebyWQmoBZlryCOGt125zbOY+ ea/lK2fZ/35rztTq0bQB9dsvImSlMxPrVKncjd0KuBVwK9DjK1DIKQvwlKrUdCSHULPXH2neeLb5 r2Otk9dboPCaPlk79m79fz99r9Hji+BO0K2AWwG3AkuuAG5EPInHnHBCpwo1+X//A3j94sgj7rj9 NtqITZs6lfsKTvIqznv79tMOP/XPtz384psfTUtZxJB8+fZ/nrnv6guO/9UfHvtyMbrV5LfPXHPG edc+/Px70/JLepVuzHvzplOPO/PSW594/o2Pv0s4g7/52r/uvfkPE351zl3vzGv3B8CY+fpfzv3V 7xjzrS9nO96Sykyu/9Opp1262JksrcDGnPfv/r/jjj/nmrueeO3tz+bIZBPT3n/+4RsvmnDCpY9/ XTZ1Ojdj9ts3/uboCX+889Hn3/rwu7Ripb/76J2X/znp2vNO/MUfF9pyacfsOT93UVfPORfuTNwK uBVYGRWYOW3Xy97YTO7/Pem+WcoWa16x8+Ardu675kIhV8s6MW+gIdxmACnOn1cmUsZX87s+uKpz U9QCfaJtv//NbBtNS5dybbDMG1zTUT+6N7cCbgXcCrgVKFfg+x/UTFst6u0hS1V1tW2s2cPWtU47 xnjuWHtstZuqOVu9f8oSTYKq0od2ENVbqU1jYCrFtt/VPqWhI8St86ewNtbuwxNDafumfdb+oI61 bun8wd093Aq4FejdFYB2HXbY4b+/5JLOLgOF1z8efJA2Yicefxz3vffYHeDV2UGq23v7jPnF2eed vNtabb8lG8Yc+Zuzz/vF5n3ko2h77is3XvjQd2VapQ3d/tjTTh+//ybx8qvfOW/95dq/T16QI7Jg Dqkn/nLtS4ttfdu2nf3Dyzdc9cQ3ZVwWGvyzo0899/SjfzbUGd1Kf3r/xTe8Mrfyi9VsfuPey578 sjwRz4YHnjXx2iv+cOJ2/cpmjjlv3XT3SzOW4YMRM/H+3/987zvOVLXYlsddcPW1l561TyXPJ/Hq rVc9/XUlS9KY98Y9Vz76RaUSa+149OlnnnTw1v3KlWDLK5/8si0lZVlPRvfv576N6f6au0d0K+BW oIdVQA399sgtv7l5n/f+PO7aX4056+jNzzp6g9HRpWeULH0Zmifk64pxln6kTm4Bg4u0TazcJ9Hp /Ni+f6KqRxYZ1d/JQ7mbuxVwK+BWYNWogJ3Xnnp3wVL6DlfWXMrraPXJuzzDz6vcr58q+w5c1zx9 +wWD/GemsiSVr6o01rVtrM5se2uVVb9uM7TE7Lpl7mC6xBMTjyptA3/aolQ5mzpr7oLd+kUW4LBV 4yy7q3ArsIpVAEvgpEkPLActWq56bL31Nvc88ECnoruW63g/3tnbOGL7ceO23XCBxyI6dMzPxo3b fkQjv94K37/+7H8rv4Kjoyac/9sj99ntgPEXnn9g9fful48+v0jWJYfR+m388/0PP2zLAYv7rMGa 9tZjb1TSFrXRv7rw3GP23GnvI84959h1KnsYbzz7xrTyr/L01E8/q/4tGLjH0UfsNHrjjbfZ/5hD N60syJr8+v+WYpRcVN2SX77536oSedDOB++77SYbj951vz2rqfzWlDc/+N759N2e/f7Tb1T+wnjG nnbJWUfstef+J539m4PXLQ9rffXUMx+3dump6Y7BXNTVHVV2j+FWwK1AD65A4IJTt/vjrgPXjrYF B/fgybpT694K8AKRRkKJhBuo0711d4/mVqDHVKB1nvrhVLl/MEW7+l7t9ubqzFT76FHWUtMP4pVG ILLXrLRaxkIezwI8tFnjAktj26JLCyRU9npr2W1/nF6eWmnm2DJdfbu6dQeI2yKquUAgtvhS1wyw t6h+JpL4Qv3aYV12Rv3vd9V9ovbGDT3mVLkTcSvgVmBRFWhqmn/3HXcgjzrzjNNxBXb/S5q+ffud ceZvLr/yqhEbbdTDTlFi6uSp1SkN2XSDsoZKiwzbaFQ1Grfw7lczF5Gvq8V3/u1tt1/921NOOGav EYvqGMU4Zuu0L76tjO5ZY/R6A8pjevoP37Rv9aAzvppZVo3Z/jXHHXbkUc59j036llmY7g+2/ZFJ fzW7dalBvz8pr1Uqtu3U+s2XM0RgpsbW2vHg6rG2XqPsYklO++Sjqplx/ZEb9vM7Q8XW2XSjqgp5 7ntfzen8BFbyCXdR10o+Ae7h3Qq4FVi5FYgMHXrC5tHqX7Tcx0//95gzHlOP/M9j85eWkLVy592V R8++/6/3r7zn3Z/cP31i1upThEUXlBeINMw+eP/9Lr7o92CvFRwY0ZUn1R3LrYBbgS6pwEcva4fd qnM//C7t9uq7FkYevq19wIClHsFedy27TRj15FPa9f/VHnxR/78XqwApZO8/zC5/Ew0v4F9vvaHd 8l/txR8Eja2xrr1r9a3GZ8/rF7ysPfFf/axH1Vz54Kpy6MZLJ27lLWtr2yas/u0Z/cH3tS+W2D9R rbUP3LC6y2ztnIf1R97XrnpAe6xieFE22MLacBGRjksti7uBWwG3At1dgV12323yJ5/gCuQlzTVX X9X9L2lGjRp19bXXXXfjjStP4fWTmluZ5h8qv0oVpb4uVv1tHanpU/2tqySaErmfvhgevs++2w0K LRmkGKmmpuqewf71kerWwbV2P/fq629w7r8/YN2yDb5mxF5HH3PssXI/fPd1lemfTZbblOlLbcG7 5Aspvs7ojarvcRIf3nr+udc88uaXpWFtxzr2kG0GCtWyU02z2wTG4WA1xEX1BUJtrbtaZjYtyszZ 3Vdyp47noq5Olcvd2K2AW4FVrALqyA3r16i+6bCmfnXOAzP/VmllsoqtdOHllPLz021vq0rff/j9 2c9P+8l91t/KIf3uTVHohE1UKoERuAC++OJztyRuBdwKrM4VGL6ldc2OVk0HStB/I+v89Srbma3q zU9qF72gflROawza4w+xdqjKvoZubO9f/fzenKte96T2j9ngKUWtsc4+yB5e/jtlK48/p/32SfWN 6ruzHXc3jxrUgXmwiWrv+nO7vrrtlE/Vix5Wp1Sh1aKHUO1xu1tHV/UHUz5Sz39Yu6PK+2rXs/60 jd32frBjk3C3civgVmDlVOD0M85454P3b7nj9s23GPPsU0/xkuakE3/Z/cbG4cPXR+H190cfI8Or Uy0aV0jV7Gxi1tLCc/OJ5IIU27ZZ9OlbV9Y9LeFmphOti/qxHh243ojKbYOhjW32Rys79dUHJk44 YPd9jvzFiWdMOPXMCaf/4b6Pl3aUJf/c02fscefttW6V+LR8+a8bLx7/ixPPv/Wfb09NtdNoWYmm aW0jvXXZkZVOjj/ffcK9bXjLSGaXIS1s+ea/vHu7qGt5K+ju71bArUBvroA2tM7f5g2xMoVvunYx xeysRNtnQXooXP6V61lnBWUId3zyVn4uXWYqt0D/iqhaUdbbaFY5tIv77WMuWDH5Lx2fZg/cEhfA aaecMuHXp7jGxh54dtwpuRVYoRWI97H33cqaeIJ5/z7WWkt9m1Oeitc+9GjpvXja5vbYdZSBqsIg Y0fY4/e2Hj/TnLCuXf28HU+JdeEp1kXb2mMHiqWRzYZVyVe/9c0HzzGv3MXedz17Xd4WqcrIdexD xlq3nm5et63d8Vz4xg3Nh46zThhpj4zL1NZdS4kvzbjvqbHOO8WcdLB19Eh7Sycgf+BAe7fN7It+ YT57tLX+ionDX6En0R3crcBqW4G6urpx48ZNeuihV954/azf/tbr9axEY2MwGBq16ab9+vdfbU/H wgu3C988/ofT//C35z7tYuWUZ8C2p15+wwVHbdm/7fd1fvZbD/3ld+N/fek/Pm7fAnJVPBMu6loV z6q7JrcCbgU6WgHrm+ZC26caWtg/1NkxsmafTeq64tejnZ+bbCNKwY02iYt9RIttMmilM6Ts+9+3 abZ9G27Wd3NZt7bvqMa2Zl/mD83/WfIH/ksvsubtman8S5/5Urb48vPPXWPjclfRHcCtQI+sgGZP +L3xxWWLuL99hnn53tbeQ+1F9l0ctcuCXW7ZtJ0k1um9ePL+5i3HGS9cajDILUeaE7ayhv+EUQXq rEP3MG85xZh8mWx21vAFgwRq7D3HmpcfYz7J3C41HjzOvHgXa4e+C0hZuZSLnUO10gPXsc46xHzw XJnqkyeaOzhz8Awx325b77FWv4VPi+q1R42yfneIefcZstcLp5jXHGAeOtyu9CirbrzIQy955B55 +t1JuRVY9Suwxhpr/OrkX7308sv3TnrgpPEnd5uxkWh8dGRnnn4aJkpkZbyOotniql/uDq1w3luT Hvyw8iF0ZL1Dfn/Tg/98+sV/33fe2A7tveSN9JphY4+9+G+Tbpt41mFbD6q+vcnPfvnmcy/4xyL6 Km5w1IUVf2XZZVm9HzeyXfhkF8yrG4boivdy3TBN9xBuBdwKuBVYIRWwP/q0aWb13YQ2eOilh699 xS7Dnzh1nTXtLvHuZV/4MlUdSB+ww+b/GT/yufM327VLONpyFaR0z39m/lCZmRrZZNQTvxv9rwnb 3b5gZpk3X5jx+jIcwiwmFii9I2OPHvPscetdsMYq+7fGNTYuwzXi7uJWwK2AWwG3Am4FVucK7FI1 iHXDF+RzLa7UW2211dnnnPNjY+MvTwBIzZkzuwtPENFgEC6i8dGRTf7f/7pw5OUaSos2DGnLfS8Z ZvUFu2mU2t4EhOrr2lK2OncwT7yhsbqHXSq1ffJdmjf5VfoDOPeX3pnqqLhy0ye/Pb+ycWDHIw/f bmhjeGmi287NRtHjg0fvfswlt9w18Zdjqp9UWF89+cT70mxFi9cPbhsvNnDdqsFyof8ObeygmrmT M1uBm6+ybz9WYM3cod0KuBVYhSqQ/ua7+z6pshk1PHr3Tc86asPtle9vfqPaiGS5FmtO+tdXb2eq fzA94U22Hrbzep5Zc9q6xC/X6Muzc8vnX5z/5JyqTNrbf4O19hjT2FDx0hS+ef79E19bWn7BIg9v zL3j5eaqGkyN9B+wy8/XHBWpxqEtz4x/su/KfYH4o+mUjY2nTzi1S5e46MG+/li//jn9pk/VXtcK pxuKs6hDqB/+Vyp27zeV/ncraRruYd0KuBVwK+BWwK1Aj6tAU1PT1199PWfOnPLM1h42bPDgwTXx mi6ZKPGmQC40XD2IcLUtTA3X9m8T6f4wu6kaRZWY+32bjbCxIV4Nae9kQTzRuurLaqV9/8TCrDfu gj869+v++bXzdiPdPKutSYg3Fg52FaKZ9cL5+1RfLR9/92SOEew3et9jDmlrhTnv7SmzeTGpRuvL 7Sfl9t2c5srrS3PeC3+o7r7zCQ991euaValff92unUwnT2B3bv74IzdxuFSuWDSs+kgo4PebilYy DNMwFVX367ppGoVSwTJLdLLxebwej8e0bdu0bMs0Lc6L6vVoJdPmK13TdU3z6JqiMYLp11XF5nHb UlVN1Xy6nilaQa/XMC3enIW8Sks2lynmNYVNeEDTVA8/0FXVo2mGZeWLJYYKeFSPR/UypqKaHMPm 9bTKYS2T7XWPZhaNfMliupbfizNaZ24qs2Nqhql7vLrXE/Jphq0Yhl4siBzd41eLZs4wi8zJsnWG CvpwPKmaptkK09QjAV/RtLy2asqiuHNUS1dspmRrrNy2bFvXVLZlebpqGyWzYHoDgVqf7tXYzCxl shnLlt2YosZDimmZxZKZ91M8pqjqls0cGEnlUUUp+bwqK/XoPsvi+ApTkmPwfypM9U2LQxm2GvR7 FM3OFos+rxYKeAslgxVH/T6PqmfyZjLHW2C7JhjwaDqimZJJTUqcBU4Ug4Z8Xo+q2paVLRQDXq88 nVT+ByZgWUxF4dQVDLkGpGSazpmVumuszyrJhGzuAa+ue73ZXME2bU325+da0OsplOyiZVE/D4u1 eCygeig45eO8yrnz6OIpYy66rpdMTquRp2q2qnMqTYsahDhTqloybM4Co1K5khTAKhSLhUKBL31e H4WTWcuNrXQ1FLDYnS/4TvNwYQQ88otk7112786nT28/1rDfv79il+CPXnjwBkdsUj+AjysSyW++ mHrz/VNn77bDY3vWOYTGmv3iK/3vapIv/YPfu23z0ZXPWRKPXfLS/l+W/xoErjhv57NGlDtRtdve +X6dtQdddsCwbdap7RdS0vOb3v735If7jLp5RycrhUtu8rvDL5smGWGLHnzRIw/deZsvju5f+as0 /8tjTv/f3zo5gnNwbeyma/9m7IARgyKNdaGwkZ+TzDVNn/viC1+d/0l+QVhAZyYmo6r+k/be4OSt +g5uDNf47XTLnL9f/d/jv+v6v49DXz97xV4YnR/9oEMPvWST+zq/X2f2KKrXX6ff1Kzsdqh5zSb8 0lMfuVM/vy1krt7+x+lmW7sdZ1z12fv10z+tHKJ+c+tf+1sL+rAt7ciJOdoLn6vvTlU+m6ZOyUtM z0b9lW03tMYN+7F9aWkjdeznFi9L1Je/UD+crb73nZIgk2iYPXywvdOG9hYLe7U+fM5z2MuVMcce ZC7kF/vJod59ynPU64p3Q+ulI6y2z1g7NqFl3GramFeXcU93t85UYPA723dmc3fbVbMCPfnptsJf wKyap3TVX9XXF4/utkWiHoKqPPPv59dZZ532B21ubn7t1VcfnDTp3bff4fH+Awbsf9BBW2yxRd++ PzIxL+NMaVp97733/OPBB5dx/3a73XbnXwcPGbIc40B8fnXFW+XPsNc67PobjhlRFnOVZj1zyXFX vu68Qg0MPfG66w9Zx6OYiTeuPerCp5wPpbW6Q66868SREnNlTr77gFMnVT4HH3v2g+ePa1yK7sqe 9czvjr7ig/Lr33VOuukvhwzlhXvmozvGn/OPGeV+hxsdf9ufDx4aUHLv33Tw2U+UaVdgn0seOnWM SMnmvzHx1D88V0GQkSNv+PuxI+SQC81k1K/uvHS/IcitWt655pQL/lXW4kXHnHfLJePIQWv/YN9x f7jh7G0aFCU/7dELT7zxPWfL+Jbn3XQxW9rf/+uM8Tf8rzyDjY6+6dIj1gMCtp/AwH0m3jB+dOXN y3Kci27d1UVdLupyUZeLurr1l84yHGyVe6Wo/+LYn9/9U9S1DKVZvXfphleK5ReISy3z+iNGHHzo oSNGbBiPx1f0e+/sN/pud6hzFPv8082jnM5osz7Qd/6H2pardtp44+R2HdnspPabidrTVQHY+JOM CUOWuiBnA1N952XttBfURXa6XnekdfX+1rAuDZ0zEtodj2rXTlnU9FRlvwPM34+221JVO4W65n+s 7/CganrtW88zd+iWjnE9+b13x05/79hqRT/dekcVVvtZ9uSn2yr3Ama1v9q6qADd8AKmbaY/Ql25 XO6jjz566l//evD+B8rb8CndttttR2/ELlqcDIP/8ZabbybkYZnH3HWPPQitb2yUz6c233wMr6+W eSheKC0GdSn2vP9cdtKfXim3kNLW2fGEfTYNTn/h7n98WHlk46Nv+OMR5WDFzqMuxZ71yqW/ufSl Mn4KDd75sP03DU//96SH359XXotnm/PuuninPvK5+txX/vjrS/9T9jDGNzlywpFj6vO0S7zn7VQq lXEeXYC6rK/+fsIpd04rwzIttvUJZx26YeCHV26e+Ej1Y+U21GXMfeHyYy97qbJp/20POXirhuT/ nn/4+S/LuWC1e11426nb1EHgfrrlmNj0VyY9+n6yfJgNT75h4oHDllHgthynbvl27Sp13PLNogN7 O0ojTzjg618Trg35dLRNtpnOI0FSawIoiVS+QFGDCAfRjciFDPRWluGInVAr5YuFTKFQxHhrW5GA SKiCQR+yI/Rd6UKpOVvMIpFCuWPQga1UG/CYpaLGd6U8h/B41YBPDwS80bC/Ph6MRf0BLxoxNezX akPeNWrCjSisUJGpHtXW0C4FUQdpmk/T0JoFEZjpGsogn9fLVpEQoi7USEiS5KazkwiIDKZnIbSy EKYVRbVkmmjA8kUDaRD6ssZwYM36mqDHp9pe2xL1mF9FgKSipMozR4RhNhewaLIMoxj02GGv5uMg mpovmVlGQbdkKjIgJmHLSuXyM1oSLVnW5bOQbjkaMZ/ui/mD4UDQgwRLtElIpaQ4yJ9QTPWNBwfW RmrQaCFQU7VIMFgbjvSpiQ+oqxlQG2+IRhtq4nXRaN94fGBdTcjvR8NVHwlG/d4AI4sAzC4US8lc rmgUKG2YZ5NZzORziTTVzSUzWc1GRseWCqK95kyuOZ1DnoY8BhEUxcgVCulcoTWdS2XziMVEh2Ur ecsuqVogGIgEA32izCdIZRFTofEzLJsjUfiA14OYCwUf4rWCiQrQ4sT50Jyplq6jklPDvnDIF/Jq yNy0XMlqTmfmpzOJTH5ua3peIvNDU2silbZKecXCr23Fg5wwRY4tFx9SM03ke5bt0fW6cHBAbYyC cJZzJcR4skPRspmkzxsMhyLxcDgWoHzo1FDcifjMva02FQidc+CGjx67kXPf4Ir12sTBq00BVumF Nvbte8wJJ9zzwAPXXn/D1ltvs3yvwzpaqclfwLkUxacMrans0m89e892yOmJz9S2jgNsMfML9bkq 59LXtHZrR8GWfMjv3tGOXwznYscpH2kXvqEuk8d1MYc11Yf/sRjOxR628tgj2v0/dLRKP9quttEe wkMl9aVpK8RLu4zTcndzK+BWwK2AWwG3At1Vga+++uqWm2/ZY7fdjjrscDjXNttv//tLLnnyqadP +OWJXcu50HOd85vfLBvn4mXVdTfe+NyLL51x5m8OO+zwceN24r7iXl+pjdv88rcHrVUmItZXL952 5RXXPVThXErfLcefvN9PG4h0/Hyp/bc58df7DC0ryLLTnr/zmsuvr3IuLbLhEb8/dQeHc3Fr3OLA fYZWwEzi4/suOXvC6Rfcpx118fEjfno4beCYPUdVIY6VfPO2CydMOOfyZ+bJe+wf3Tx9tjvs5K2d z0VZ36zXJ113xQ13PVvhXPExh593+BbCubh5+ow94cL9h1cqIVtefWsb51pr79+evVev41ysSp8w 4bSOn6+VuOXH/3uzhFXPNiFWLdlsvlSEWUCRIB/JQgFUVCjJR9p+DxjLD0ICyoT8Pp/jJgMtwEBC XjH9YaTD6YYDEWOgH7OZrWAo83l1dgQq8WIayxu2RxVyZBm5Yh6A5IeFgKSECIkfDtOeYeT4p2gK vmlKp4umAVXBG2c4Vj4cffmimcFlJ+5BTHZYGk0wnN+jgcRAM+IOFDsb9kKl/GXQpwNTwCd4FTFH Yh704tDTFRgMI5QsNVu08yUbdJcrlrgxvWwhl8jAY01hbDouOkNXLXx8Rgkbp7wrwGoH8ysWrVIJ isYjsEJ/2B8CYWG95KeOmouj8K3UBIMeFC4a8saCQaojMAcrpKrh2aPIoCInTg/bpscAIxoW6yuU rGzJSuVL3LNFI1cqYjxM53PZEqcDsCj/5vJ5/i0ZxWwekFXAaIjTMQfMcxgbvkQWzGjMCPbIXCBT UjVNa82B/xRxe+pl4yQ3OZvQSiBl31gkAJjz+6BGlDoPJxTDJsxO83PudY/4OsUgKeeekx72eUBM nF0uCc4m50Z8oEoA6iSI0bJAnfFgIOT1xgLBYIBC4abk6jA4+xydYZgWJxGMyVz4LWCYKmMIR1OE o7E7RkUmgLWWs+zz+QI+b8DnU7whCsudoEPWS9Xlf4o6fNhCEuKV+MzqFYe+/uXe25/Ff+Qho48a 3Wf9tRvWX7u2oWX6Xz4jCCB03F7rbtFQlj2bMz/88sJPupIY9Ipz2iWTnPCzAV0yzhIG+fbbb998 fRHp/HwEevQxx57y619vtNFGkchCfdRqZt61AmdV1CY9pn5QVPTh9lmb2uVXT6rPjme1x76vHLa1 Sd12C3uN8vVlq/9+VnuhqsvacWfryDXkd9BSb3ZGu+5+7ZOqVKx2bfvyw6xzx9pb+dRnplYSr2bN UsdsYQ/qIn7bMkU//T8LIN02Y0U1dtIYJfKD+m75I0VF/VJTDh5ul1NRZ3+jPTK1so4hI+w9l9iv nI+hprypTraUb73K4RtURlhqEZZng8Qaxy7P7u6+HazAin26dXAS7mYruwI9+enWm1/ArOzzukof vxtewLTVr/xK5rXXXr3phr+8+cYboVD4F8cdd+ppp+2xx56DBq2J9qJrKw3nuvzyyz6fPLmzw/LK 6qI/XLLZZps3LEhz7+wYi9s+/e1L/3pzRjmKq3aj3XYf2aftE0I9PHDTnXbbfM26cMDvs1rnJoy+ 643ZfMzP9v/FaaccttUaC4LY7Xkf/f2ZTytxXkO2OXD7tcNL1wxp4UGb7zxu08H16DMCVnJOsna9 MRtvMGrsHof+cvyxO60bayu+6mnYYLut1w7xXrGUyNRuuN0uh5545mHbNCbee+Q/Za1WMTRytx2H OtFi3pp1Nt18ULiUS8yd1VqMDh2zy/5HnjZ+t/CH//6k1VmjpW2w054b1svrPU/dujvstP1Gg2vB ILUhY1aTOmSTkSM3337fo8efesTP1m7XSleLDBwzduyGazag7glYrbPztRuM2mjU1rsc/MsJJ+69 UX2Xyvi76sQubZxeY2B8+OHbWEss4AE9cP5AGEiWYFXxgJ4kKsm0YEXQoXxBUAroBJICmDAs5E4Q CdM2SxKyhU7J40H1QzqTX/RVOiSoWEK+ZaNi4qfIn6AyUCRJ5uIqUSUjCsISRp0F8ODaQ6cDctFI jDKFyqgqJESynsAqRG9xWMvkqhK+gl5L96uqD8xUNIv5Ys5WCeqSGCmhORK7pRJSJelNKvKrEmsq mAiGgh7etRAbphpe3dABKYJH0ApB8fw+1c/cAW5Bj0V8GHePF3wmGi6AVCZfTOfyRFo5x+WFverT PYlcEcaCgMtGdOYgI6ASs0RK5vEQ7EWylQf4UtW7af4A8yaOSqgZ08sJsoIiopSTcC5JxVLhPlAy KRfKKWYvEjvb4msecxRL1L7k94CB7HwhR2mQhxE05hAfcrKIAPOkilbIB1fi0Dxocpry/EiXoDRo IRCNE8F7KWbC+M6R5XQUwXwG59EOBYBIQa8PJKcSksX82IUyCSYUwCdfUAeQHMI60BjfC/UzDVhe UFRd8DFP0fR6VYLa5ADcBG+qmhxOSBQ3UFk2W0yj5KKMgLNoINicJrWMsC0/j2imKkFdlImJmSYn FMLmEFSAl4K4TEChx5tTgwzPnccZG2TGqjnc3rvutrTnpvvzBRXozfp/bad9tn3qoMpfVDs7/+mn p30T6n/4bgMayrzBaHno8lcO/aKsLHZvnatAN+j/f2RgbG9UXNxcV6ijqjhV3/lWFSn8gHHmv3e0 21IiclP1/W5Vp1bndMIJxllDnRc7c7SDr9UqOV1e+8bfmju2BbAusdgtn+rb3181Rer2n84wD6h3 dshpv79Me6iKwH5kluzc+Vtoa/XFh/RTPqo85F3Pevooa5CzvOQX+g5/qyKwmP2P35obOa8tO2Vg 5C/tg7foF0EDo/a9Z5mbl1P1VuStJzuqVuS6u3vsFfp0W/7FGFP1bW5VE+WB1rVePtbqmgic5ZmZ pV5/iX5T5eMVe+K55t7tXEGZedqT76ivz6wk5Z39a+P4AUpPXMXCFejJT7fe/AJmea4zd9+lVKAb XsC0zaDtlcyKMCr+dJ1PPvnEjddd16krAKPief/3f12rLOvUBNyNV9UKLB1G9pCVF0vomXKzWltn JRJN6VRzJoWKqFDKz2zN4D1EKAStas4UU5AqsySp45I1D1hBMIUlDx7lNRSoB448Nez1BkE+yJws y6+oIY8n6vWhBUPjBiqJhwJr1NX0q69prKvt01Dfv6GuX00sivtM9/sRZmGqs0Tdg0TLMEqZXA69 koKUSmCNyTfIi7JgJGx0pLkjCkJqpKvgJIRFiH3wL4a9HoAOs+KLCNzOebvLfwA98YC3JuAL+/1h uK/H4+A8owAnM23Mc8SlJwrpplxqXibxQyqVLCBtKxSMEtAtUzDmpQuJXClnqGlDSReAfXbWUGel i60FO2eRnq4XHdIk2iPbjPg9ESyLqhIFvUmuvIFhkMcxb6YypXSG1YC6FDyYFBAZV9gfbIjG6qLx mlA06g+J1dHjCwDtUJ+pFA1E5QOBMROWwyEQWGHfSxWMdNHIGiaAKAmGc5hZlvh2Ve0TDQF95mWK yQKcC6okfk+WLFZMABEoUdWEKnq8SPNA7JxDR6iHt9Ib9AeYFBo3pHMwTsmqVwBYHjmtCNdgmcLy mBb4T+yWqOb8Htyj/v5YLCPhSCBEsj715ppJFkuJfLE5X2jOFZkkuA3IJrn+cEH0YR5f0B8E6BVl Wd5UnrqF6sMRqgE0zBp2AcDqeFEjPl+YOUp/ARGfAQRFJOjnKGqNX496tBBqMvgl1NM0YGDCx9zb 6lIB69/PfXrv1/myaVUNNexx4OgJu1c5l5Kf/NSHF7icq8dfDCvFqLjIqnw7VTgXtzF9lfZpqMFB 1kHt3kP/8wutnCw65fMq51KUIdtYW3eMc7Hj19MWhH8pa9ij66rTCdjrt9NPTWnuiESsAye4pHz4 +YLNNl7PHlhdXrTe3qDtJyllZjmyorM31R66prNPSn2vEvDa2SHc7d0KrOIVKM3TfnuzdvHr6ot0 hGCtNqr5VXzJ7vLcCqwOFaivb1hBRsWfVi+RSCwD55p41VUu51odLsXuX2OvQV39YjHu9XAGv1jE Qn6ytkIQDfAK1KNoaPmS2MSkHZ5RIh0qoMvbAMRHNGuMoQISHZCfGCniuZI5krmMTK7Uks7PT2Zb BZChJLKFjGiS2UQvQJMQK1COPxiPxhRPIFnSC7bPUlCChXQ9UDDwJGKU88UYFlgk72JFzQOICSE3 CgSgIWFES45HUVyKosPSoEuIiNB0Qb4gYkkyqEBjiNCgPQqex8K8RGp6c0tTJt2cJa8qy08hL0yH NaYLBkOFA3jiPIA2dGWIr6A/DqGi8aKC+5JAqMZYtH9tvE9trDYWicci/Rpqhwzo07e+lpaVYeCc RohVLlXIzksl5qVT85Kp6U3Ns1sT4Kc8DQdpTaha4KhMNt+azjcnAYtZ1EywIV8gonhCqiek6AFV WBM+P4Kx9KLNeyFWTStDXxGgZqFfgyx5Al6/3+PjHg1FayPxxlgM0yRTpl8mcLA5mZrTmlAtI6jZ um0apSIACHtkNpvLF4qJdCaZzvlsk7VJVBgir1KJNpcI5kKQRt2LIgzGJD0mBYwZYigVGZ2j3nPC s6TjJpwO5ITmy+R6EI0c4Ko1Z85NG0mWahK25Y0HQrWhSCwUihM9FgoE/fBH9rLkuOIHBVhKdhtg ClCK0dNWQKMqqi4Oi5+VkoLhJAnOds6RibeU8yW6Pk4cUjXuTLspmWxNpzLZbCGfY6mMrNkcvhqc 0/3PePeI3V+BfPPxF78wftKXr33dMjtFn1VuVrolMeXjr265+sUt/9H8VfdPyT1ihyswcODAy6+8 6r4HJhEY0VU9iTp88J9saKsfVXnQ4PjCmX+6vctWC0Recz5WJ/Me1VD/U26w49wO2sjucCC7Omvu gh3ja9sD24iWqoTakuHReHXVL7Os+rXT66h826K/6GvLN9WnRNt+IDGIy1i//o2Vir04Dfu5e3Mr 4FbgxxX4ZrL2QlvOn6qM3dIe0WE47lbTrYBbgR5bgVGjRhEn6g+0++O9wub69NNPdWps9FxwrpX/ +qpTk3Y37j0V6DUGxgfuFyUkNIMEK7RRrWi8wBCY9TTdwV4oaAAe8AXUVRbfAFYgROTBR0UghbsR /xqIgd11iW/CogYSAVKUVInHgs7gVNZI/iqgBjNsCcDCf+fzefKkUGEsRLhEUhfON5H8wDNMjoL0 CJxC8DnB7yiZOIhjJNSEwFhaoSTMJQPT4oU6cVpADsUE2CADIoKdvcjnItiL1HhGzRZLMT8BUhCZ YNgXAcQgYcOfSIg5rkEEWUaJVfsCAWl7YNj48kgtw80nkVMkvkPLQHiQoFAA+CL4Ekcn/6JmslSN OC3LKFomGV5aJBBGfgVfY86ouuBwmWKRvDAhSabBnj5F9+MrxMCJgRHzptgnAUPifCSoCgVYuoiU yuD48DZcndA83ruzOxOAN4m7VCGYzMYdKfFgwCwdHlWaK70jkNqJAVLVhIJhyaRQMCrsfhQV/Rr1 RYWFwI15tWbyXj+OSzEwwgGR5RG2xRlAJSeB8OJBJO5e45wySU4I+I9x2ZPvMtTLNhDNUTvmlzPl PHKumDdjcGcVECuuETyUnAIO5zhPyQtTyLwvCDElmQv7pJgTIYoUBDEcl0uB91iWGBXJ3SdjvmhZ yWJBpsuG7MJU8T1SedEMSgQbCIxzI25WgXHiVLVVPRqUvzS77LZH7/ktsfJn6ur/V/456JEz6E79 f8cLsAIdVQvMgz+2HTE9u1U79UrthSp7OuEE8xRda3M1eodbzx9t9e+oBkt95Hb9/G8rix69t3nf VgvY0yJL0d7iFB9rvjFWefMD7elP1cJo85pNl96Jw27WTrhCe6MytH3Rmeah0nNpSbcfGRivHKDe 96b64Sz1k6K95dr2wdtZW9YtlEpW+Ebf9g6VLuH6etaLx6xwH1lPdlR1/GLu+VuuwKdbVyy+J1r/ bGXq92pL9UnZfw27XyWAZSET8TZ7m7duZZdTZOys+vFciWKQm09Zf8CCRqhdUaQuGKMnP93cFzBd cIJXxSF65guY5aw0kq6D99+vU4PcdudfBw8Z0qld3I3dCnS8Ar1G1YWSiXvM50UQMz+Zr/H7B9XV DGps6F/fWBOJISFSMK8F/DWxSJ9YFLsbVjE0QFG/44kzhEXALiBjRLyTco7pTRrywWHYE5mYF/8c SMjAc4fVDjyRyubmJdLzWhImMVXgD6ySUDQD9ANv0ZOZPEgFdRPGRLFHenToF4ok2QZ8gyiMlof0 aYd/qVbAQ7qThlexMRIaXBtbsy4aC/qhbLgO03laLpqi/dIw0ymZkj07Vfh8VtO3c1vmZABTiKOC KNIifl806AOxYZYkCUtSq0yxSYorME+LReRQmeZky9zWpplN82Yl5s9umdOSampOzp86d+a3P0yb NW/6jHmzfmhuSuUyiXw+VTJLNtHsZIL5bTiOHy2aVhPy1kdRogUQyjExRFQgunL8FgXIcyBkSVIE yfTH7YiAKV8okDPPv6AjssEE/xWJpadVYmZ+IjWrOTWrJTmnNf1DE18nkUphMIQpYv1D8wXvQ2EH oMTlB0ji1Iq6ClDn9YEdvfgNQ0Hu0VAwFvTV0sJQ9FOwS2gU9kYvhC+VLyZJAmMefOMEvfMlofhw L1gWL8ichyU7i1dnDsAyoHvZAtvQZFNtThdS2WxLOpnMZfM0GIAcUl8ngwwoRXJZrlhgNOkhqfnp nykSOoniErcnssHWXG5OMkN7BEnvL0vKRNuFDgyUKVs7N+R7Gk01bbOIhM2nmn6N64HkMefu3twK uBVwK9DJCpgtynvlkCy/0r+cSN/uptbY+2204Pv7P1Jfm7wgvWvP0Xa/jnIu+VhpZrteh1Gs/p2a akl9+EH9xCfVx79V2veCXMIYZlKtBIo5G4U6maXV/JV22q3ate+qr8xQWuaqz7ylHXu7/ipYq93N G7EHO9+aU9Xp1ayxTi3L3ditwKpQAVUZMtgeNaRyr3IuWVmmbHt2bpv0r3AuvlZD9sjq9qN6Huda FU6Kuwa3AqtEBd59951OrePs885zOVenKuZu3NkK9BrUBQThrmjeAN5AfyhnedNF7GkS2w6d8KoK Gp58rjCzKTkvjT0RuAWVQFMjrQchHflcKZvJJxKZ1mQunc5n0vl0zswVpY+eyHNMswnznGnQng9E EfF5GyIRbHdooMBNNfFo/4Z4LBokgako8eNaXW2I+C7dq5FGLg0B4UZykwQoFEVZmh5aaJFUE9+c 14uiSF5b20pzrjQzlW/KlpoyRXASijNYDPMvYJHzBYqiVeK4wbUasGoGGjArarAyK4ezTwK5IC/Z bCaZTCWaW1ubkqmmFDH0BjlleUK4MBJaKhZCMqbwFZJSRRI7nrvaSLgxHsXYCMPC+BjyeMMeLaxa cY8V1q2oZoU0O8xOpH1lS/MTWayLcCKMlzAdiefPFyBYfh3fJtlV8BnUa0jJ6LtI+0t0T+CwQs4o gn6AN7g/SWuHmzXGwvXRcF0kFAdRhSKQvVAomilpyTznxId4K5GXDC8nyV5Mhkif4Ido5AjpYvdg 0B8KBTGPGjBG8rBwp5aAUxbaOiLQaCOQQyhnKvGAP0B0l5PehTeV5HsnwB4ZFydUxebK+pFnkVnm QFKJRasN+8MBieeHcuI79Xgsn56P+EpezQh60XAhGWMESJseCnjxewbhpFZRA4ra0vzStIpotmCk RIGhbyNMTESBZt5wwtpQgJH+BvIDiNJzslgqpvL5pjTKvKxdzBn5TArTaAspc626mefe2Sequ71b AbcCbgWSzWqlyyJ/8tondVVKY48ZZZez47nl3tMmtLWOrLMPXncpsqwuLG/iDe2iL7pwvKUP9clH amZD+6Ld7bE11Y1b1Rs+XsioqAWUhvIPC8r3C1OwpR/A3cKtgFsBtwJuBdwKuBVYYgXeWFTH6sXt MWKjjcaN28mtqFuBFVqBXoO6hDFgW9N9KIGQ+xDyrkk7Qrx4CHCMTLEEPcmXrKgPoCOLErebIl5C p8EfaiBS4QPxSKQ2RHgXwUzETQkrgZNhf4v49bqQDwNgEBmObaWKZrpE+pKeszwQKKgMlj2Cm0Bb Ya+CyREoxrf0TIz7ZX/6HuaIfKf/oKYXLZUIrXgwEJF0MCLoyUknBAslGcKlAFljGCprQvJTLJM+ HVgDnNHpHRkLhAhBVzS9JVeY0ZqYnckj98pK7BSmSzuAKEgxSXsC+IBp0IXBsPrW1/Wp7Vsb7Vsb 69+3fmBj7RrxSJ+ov97vq/f66lStpmBGimZE02oigZqaUIw3R62pbD6fz+ZyTYnkrJYEyiay/YGC qNrifgK2yMLHBCrB8uK4w5lIAZ0wLB1znoLXkiaSKtZLn1eDBEWC/njQjykR3kOZKSZYCMNgwZIA F9yK8XAgEg4B2mr4j5+Je8M+xFxiUsR+CoXkC+6wRb/YGE2MnGipEtkcsjjOH+/l5K7ZUa8Wg9Wh fiPAXvcEdQ+z5jvkYQi+ggjDuBq8nigmT0qJ7gtICJpTxe6KD1LOqmKhY8N7SF8CSB6tMFkZWV6G VWjJpukz6aA3Th1RZN4gXS3R4wk649SLwkv0a0WwlxXwKsSU0XmS2rBcss8IWeNH6UIhg8oMZR/X B/liTmgXu+OdFKMrnR/93ljQCw1UhIK6QTEr9NeaO7hbgVWzAmguKmqkNe3+i/rrHR1iHdKWH9+u Bhtsbm3YSZ3U8ldw4CD7qB3ssYuaz/IP/qMRsGdes7956HbmFQfaQ6o/+/R/6vftFbQBZVClCOrM ZJdPoUcMSIv3Ds2jMHfyiw/d9OdzTtx33C47/nyXfY8/98+3//Od6e00PR0axt3IrYBbAbcCbgXc CpQrgHvxzdde63g1jvrFMR3f2N3SrcCyVaDXoC7io7gjAsIdFgv4akPBIG0QfV7JX48Ga2MheM/g vg3hSBRPHETD7/VHMP/RWtHrhTGhFUIWFiSWXnRWPjRIyKxgOGAxwyJsChhFjD1wRMGoFgt4ASi1 IX8Ug6KKfS9HmLoi8ehF2BA2NeLVITuInpLpbHMynUjnnOwtwVqAGMANyESymirtCCUvnZnD3ZL5 0rx0rjVXImaeO5ZJMqoQoaVy0CGkVKIqQt8UDQfrIhFGiQX8RMoTOQ+tq4+F+9bG49EwGrAiHRUV PQ/pg0xhkLONtFFCRpTOkTpfQAiGk9LBcyYGwBCaN7RvqvQHDHs8GDSbyeQHxMCuJEwdwGRBCaFa xHJJcj8lwGHolUQt1GHwRKfJobgsRTYl8V0aNlBD1Wg0SRaYJOIHEUwF4qFgbTgMVaK2UDPMnqmS kSmVkjgiqQHVEAOgiNkoNNtAu9DTUYemVBbHaAn7oUGN0eSVCEXDZ8lDmUJxfjr7Q2u6KZ3DnSjQ iAuB88t5BIahPmMJaKsgjaoK+XKEXZIrBq6ThgA6BzKThcIPidT3LanZqVSaeHh+BEeTE4RQzYz5 PWEuGXb36mBNzgQADFFYBE7mjBnF4+rR2absO5RIfEkHU/mvV/K35FsuIcRjwM1gwE/5gHkkskmH TZ8PoyiYrahrFj5Zv19QGXf35lbArYBbgU5WYF7L0nyEPnvnzX8yqG4fM9Lu5l86++5vPjHePH9X 89Ah3eHXXm+o3depTXSAvUXbUucuCCSSn3ntaPUlz3eJpVWyk6emJ2w+berURx97dKkzsZvfvfOc k8+89NYnnn9vWsr53CX13YfPT/rLeSefedM7zd1xupY6x16wAR+WffihdulD+jHXeIb/znPoX/Uz HtHv/USd3RlvbGKONukp/Vc3ekac5xl+nmfcjYsdxMiqb7yjX3SffujlsuUW1+jnPqU9O101flQq S/16inbzo/pJ5TEv9hz/kMxqfvv2EZZ6/cUyiHPXn5RWiwrJd3x9zpQFw910q2xw/VR5hMSxLSrb e4bfpZWbwLo3twJuBdwKtK/ArFntgg+WVhokXYTlL20r9+duBZa3Ar0GdTkR6sSHW/CRgkEjP/Kj ChgRc6nWdDLR2traAkxOJn9oav1+fmp+qjAvjU/QwPhWMHAUglpE45O3oEKi8wJyBH1aiGAoBT9e AeteK+Y9w3DylfR0yWjOZJvTGaQ6SMbS+RL9EOe2ppvTOSgXIwGFiKqXRo2woWCwsTaO7Q12QwQ5 lKQlW2jK5AnOz5MXpSoBwps0Qs2JP0ed5KkL+UlYR3nEgUAh2CdJhwJGYQskPx5BGTY6yXnSPUjD sDe2ZAo8CFpCJkXHwwLkBkJkGLmS4bT8E4Me4VbgJ0LDYHlhLwq1YL9YhGiwiN8PySKADIdhwbJz tH3kMCqiMEdBpogADUSFwzASQMnkx9RXkC0VDgSSYoHYIaFdzIgvsBkKKWQXv78mEq0NR8P+ANot xmzJlXKGlaSpZbYQDSBd0mFzmaJBxVoAaY5/kwEFS5kGUjookq7a9KMEEdYE4Ug6u1ATGg5wF40W /0Oc5fVCLhuioYZYGOSHGq7GaaJJHj/SM2lPadqMBvpsgEv6iTNDc8cp4ESQlC/ZWzwiVM9WGKd/ LDi4NrRG3F9HO0yJ+yc2XiPDntYBNKFszeZThSJnhLJjeGSvbEkJ006T/pJ4Qp18N5GKWSq2So6S LWKulKsKv2jQ5wn76fDpI6kLeZjAQnHF2olcEcCosgy6ngTgrh5C+Gky1q7P2PI+gd393Qq4FXAr 0L4C646wNl8Y4wQ3srer6d4i1dtHjLK7s3VbQ8hpyMLNb9e3WTvzyvTFGBW7rHFk99Z1yUd79NFH 7r7jDl4KLWkze9arN1/z909bytto/TbYfMuRgysIMPvdI3c+8skSd+9J612JczFatctu1A/7u3bP R+pb9Cq1lY++Up95T/3TJH2f2/VXWjs0tRmf6r+4Trv4dfXlGRLCym3GjMog+92vfd5Onzd7in7S lfrxj6kPTlY/cs5PYq76+Ova6Tfpp72upqtHY1bX36nveZd2nRNaJ2PmlTc+klntde9CA3Zofu5G bgXcCrgV6EwFZsyY0fHNDzz44I5vvMpsaU6+6wCU1OX7+f+et6IWlp181/GVo+y4zwUvzFpRx+kN 4/Ya1GVbJnf0SplCAflSKp/N5gVh8S1x4HwMyR/1gq3WRiN949xjDeEwIhsIjpMmDmWCNNElkCaA Fg0GcaeRVk+mFvHr3BHoBKVrnsSSowVCnYWTEc2QbRrsiShsQG2sXy2hU0GyoYI+fzQcrgmHycAK EOpOrj3R6uSpS3gKIwuUwzQnbjUP/fhQLRXx5cHXsLYR0QUD8tMHUKOvomSlI7xCyUSmOwa9sI8J B+qjoUg4iISKSC+6/iHFQinmwDGHZ+l6EJ2RzxNHcUY4lK6hRSoDGmgROyBxSuSLzRnAjfAgoBlk CsNmhMSsYDAaRunmxWtJLpgPrRIWTsx4SLSEo0G4iKwiqN4PUMIpCn3iuOTiEyQv1j/BbRogSTKw TEkHY3PgV00wFEPLpOlUJuz1sUZpCWnZUb93zfoaSFZDGEoVjATF0sm4QCYYEZQK9Ea4PueOx6mk pXlM6RkATENPht2RIWVuHg+r9NFDgDcnWfL4GZxu9+SCOboqvivA1DJ5FHMgyHJEFwyrPhyJBYL0 juwbi6xRE5dge9uan0zNbG6Z2UJiGx084WBaxBcggD9fJJKMwmJolPgzOJsoubw+QGFLFpBHvwJ6 VvJyVpcuB6YAL+gjLkfKB+3KGXaO0UGBwQCyO/yNAtI0EBjVwNsoFy3XgLQEFVeje3Mr4FbArcCy VMBTboe2xJvWYB+03kJbHLGZVbu0vbr45/V2Ywem2sUHXb2HmzNn9rNPSYv3JTd6t6b99+8vVUQ5 ntHjb7j9+j/+6crrrzxpROXF4Ff/fOmLNnSyeld08au31aef0O6ZU9lgXULiDrRO2xBngNwS09Vf P6HNWpo4zk5qVz+oflHeTFeOPtB64ATr7BGVMVu+1C79sJJ0YGe0Wx5S3yj3dwjapx1hPXCsdWjf ypYvPq09VBFSqG+8pN1U7Zo6dpx170nWHzevzIoBL3hLLS5+Tf3XsyYeZB3SZ8EWY8fKI9t19+8O 96JzK+BWoLdW4H+ffNLxqY8YsWHHN3a3dCuwzBXoNajLjwUxKFFWgm9sFYaFczESiWiAKyiVDsgy 07jgaI6IMZDwJEmml0x66dmHRApJDmwljz0OwAXQALYYsDP2A2rBcdjKhPOAXTRoDpodbH0m2iKa 8ZFsxX6ohJArAYw04Ih0WlSE6RhWLldATWbksxLYLjIr6ZBIYnyKHHXLRKDEXtAiGBk4xxfwlfKF dDqbTtP8L53KZIv5PLZBrJnoyAiq0lGjoyDyCGciXMwjHQC9qK/wydFAEBkXuVDslUwmm1MpgBUZ WuAtAssc4Ro6J5RYOjoyLHWFYjHjWBqJmlKtksqSkTgZii8QqovX1EbjtVE6HBJVD6bzok0LBpBY +UA50kcQcINkHZJEcD4pWqpIySSfzLCYmk1/AAnbkhvmPZLLJINf9xDllTUNsT9C53Qd3dm85tZS Nju/uXV2U0tzIokVtETHRl23JI0e46FBCZkWSVhATKiiaL1ghQilJOIMv6QkhVFGCKSXJHgbT2OB dpiKUWxNpRNZ7JsGTlE0cfm8kUvnCpmcyVckwZOZlcvSfqAl2/rlzBkz580p5dJ5WJjiiUTj/erj AxprA8FQntaIgtJAelwBNt0sc9kc60fABsXLo9tCT1eU5gW0PwBScaHBzrCCqjb5W2AuiucJhYOx 2rpIXR9POE4dEMThZBWGyBVl6QEtEASBGopVsIo5q1S0YGPcl/kZ6+7oVsCtwGpbgcba6q8OQ/mx camtKKq96frtfsP47R3W7HzBNGWNAQv2ShX4IGjBLTNHe/KD6v0LNdP54X+6hx6z27/szS78pvzr LxYc8ZUqYlieww6Lr2q/hF966aVyQZYo7DKavp5chSED9zhkp2EhXgRqgbU3225YoLx7YcrMppX0 icyHH3745JNPLEWVtjxnvYv2teaq91S7LuhrW9cdah462jr5UPPMfpUDlL5QX0DqtcRbbrb6StVU GN7cOn20telQ67hdrNHVvd79VC0jrNR09YlqiFp0tHXchtam61rHbFG9gG310SlkudKHQn3po+rO fvv47a3Nh1gHbGeNrD726fvqt4s/s/2GWHtvao+uWTDpDdbjEWtUvIuq5g7jVsCtwKpeAd6cdnCJ W2+3XTzu/nLpYLXczZarAr0GdaGy4V4XDMSwx0FyTCNfFK5Frz6og5M672uMQ8BISvLgAcQ9h7oK CyFoDCsZDj6RD9GsUUW7ZEvGlCoER+6KlTfw35EEZSQAVyUj6tNryFOXuCnMZ2R+RaMBLHdBoplo ICjxS2K406KInTykm9sERZV9haAx5E+MHvBK/FPIUUbBbUAffC3h60wOB14wGIsRKl/btw4jYISE J9BIbYSdPNAYABCxVNki3KxIXhWOSXCSpG0Rpo7eDITiBZ/4wyi/HMEa47MyjJNpR47EgUBUWUif qmIGdBahMgrf8z80XaRmwZzShVxKTKD8hGNg0qTDIxVA56QyCjH+FIj4K6dLJGwKeuVktns8+BMj AYnwlxaR0iPSZo0s1gc0VCy6N2L/Ix8t4hffH/VPFEwUW2FJS8OPCVdE0CaBa8isagK+ujB2QZGq AfNYIDoxvo0RfQ9qEnEXgVzSPxG5Fq0twYvcQE2868JIiQMUzVqiUGjOFrkIKELRwHVoZkpWK9H7 Jm5CjS4EQxrrayNRG3+nT6+NBGuCyLsUQCSXQsxpDsCKWAnnsShJYjheCZgXPgguFOMirSFF7ScX GRiOYojDEsIlOj4dcyuRaIqRV4yclU8mkq1NiRZpZVkUhysNH6Mef4030DcYWSMcGxyr6xOMqpaP +3I9a92d3Qq4FVgtK1AbV6Llhf+gzlv8u9aFxF+L7tW49PJ528myvk5WDFbl3VIz1XP+oVXu/1W7 pJkhLvl2aWLq3IX4mfrZf6uH+4c2aeayxmxl1O+q7vFaHPyr0A08BOEi+uTyK69iWYsXdhXnTp9a vXCGrbtmpFIDLRCtq5Z/ViK9kjBgU9P8G6+77uD997v4ot+DvToasd/t57F5tvp59aBD1rHXLKu5 dHvjYW1TUSfPW8pVWvLZvxhrj3fuxw+1/c6uqq/6BG/3HOfVWxvXzv+gfuMYG2v7V/Zl9z3KjUWJ GW37nWAo/5sreFoN23tVjzJ+E6WCM7u9Yu4B3Qq4FVgdKtDxTPpttt12dSjIylujf82dzrn6+huc +2XHbLJaq3N7Deqam8xyh1vBQzC+4QjDX+iHxVgaLsNsyW4pWjNSeaLXCagivKkcNB6GzTi56eFA KB6O1ITJqPKjJyK4qqSoEAvBS2LxIzHKG/HR3Q/gYidzkCDUX9jUVLoFEv3UlMmmCiL6yeUyTYlE c5a2fQCRErInH2THJ+OQkg4NwbgGBYObYCTEUkdSFaFdfEH8PCDGkV0JNwKCAaogSkA6zIISo47t UQAZ3koBW07IlCEiMRAMufnY5cBDAQ+9F0lkh9twtHIzR/ALzj3MjyyzzItAPDgKozgaVbKoShgn iWGXTHcKAvoSiVaxaCI6c7KnCIAXByjHR1Sl0DMRjEXxokFfHA4lPImf4RlE/6SA7gTAlegxiOCp SDw9/CdTLDSl0VilM1hKC/l5yfTcVI4lg4nCAC6K4/Ph/QQa1kYinAWoESsCCFIHgBAYC2goRaNR Ju/KQJCShI+YjJdpGhVL5ouUAycqEVs8Bvojax+CCDek2QDF504FWTuiPxRa+Bb7xKN1jlWTfLGC 9NCESmngTl4v0gqA0ThrGDJhWUVDYteoULnnJBoyToBPw9CKtkxi+5ko3lHwKBIyTjAyPWigRIlR TYczIrVrSWfnJVIzWxJcNlyW+Co5MCeYZLcCPDbbOj/ZNKd1/vT5c5oTLUGlwH3l/fpzj+xWwK1A b62AL2avW547XW47k37d+QXbg9qpuhLfqjPa4Y/26fjxAXb5jfby3qL2Bu3eiL89Sz69qNwsZWa7 HOwN6paRxPBXP1nxi9lDVq2Pk8tsi25WpPwCvBYv7CpkWstGOEUJ1ddF2l4BNm591p33PfiQ3O84 cJiwG3PeC3+oJH3se9fk1PdvPnTNOUcc3D2RH7xfOves3+y9x+6TJj3wxRdtWGl5L7Gu2r81tYD8 DopVHIIMvu4Yc9JJlfsxg5ZylcaHWBN2Mcv3IwYpX01TP5yqfjhdbf3JLGsG2jtU4+dK32rH3qnf O1nNrbFg9/Eb2vLzkL31OtWdTXXiHZ6LXtc+LdmHVI8yYZw1pNe85O+qc+WO41bArUBPrMA6w9p+ W/XE6fX+OenRgeuNqNw2GNq4Wn/M0Wv+7lkW6KkIhgA1iDxJpcUek9eleyCuOk3vGwkOa4xBggQ4 ie4HeQ7mOCiOCukgvImuegYIQvHUhMLIjtCClcruRo14ciUvuVGY/krZItnknj4Rf00I7ZI3GvAE PDgbASFlA18p6LFraelIez5pJohOiQmpCLukj6Pz2kZskxLVRPK9ly37kKoeCUqLRueOaglkA5hB NITPEcsjX8OZIGIpYsdsG2omVj6Pt080UhcKxfxBssUKJTtTMBOpXCumxKKRKxBLbxIAli2UyERH qQSOcdpTonUS/ZX0SWRdiKQkHssLHCzZNA3UWrK5dDFPcShEOEgfQxRbCqImsBh3NoDTYOyUnpF4 PyUpgqWwLKdfJc0MVRo7FkFAmmr4ddOjmT4v1fDUh71Rv45aic2oDNH4DAx4IwALfmfTOVMj2F6E VZA+UFkZEvEgJwKY5qwdIyb1N1ozhVmJDKSsaJk4MfvFggPiIXpiksrFCaXElGh+BmNmsTlXgIKV TFXyy7y+xmgEKVkB7mmoqaLakreyhnRFDDMxiYNXB9ZG+jBLTZFEswKtIYnfgvpByEjE98i5DtKj kwPZmFET+RJTwjSKSC0S8DdGQ/URAWxUBFMjc4DqFUpFCGMtGf++ACFrDbFY/4b62lg85A+IAAyy SUODLGvhjOHxVLjmIHpcJ9zdm1sBtwJuBTpbAbXW3iLm7FRUZnWJb3DxMxiyRrumjTPV95urm9rq lO8W7Das3Vv9zi5noe01ZcTwBQ988qU6o2rvslvV9xcox+w+i4q7n9JSpQ/tpFuIWAZVVHDOn+ak +mn5CH3swaHlmmyP2rlN0lXuZlVu374YYVc+Ob963dAGBjF25eYJ1TY0lm8N0bLCaMHNnvbKnZdc fNs/P57d3R/SwOxOO+WUCb8+pUcZG1OZRSu24o32qCGV+/AOsFQ7rz77on7oxZ4tLtf3vUU/7Fb9 sPvVj35ybak11rmH2utWj5mYof7pPn3Hq/Ur/qt9XTU2yk6qvfte1qFtYVt55aGntAP/7Dn5Ke2V OT9p1NglV/BCnRzbWjpWmjZ2yRF62iA9VmnY0wrlzmc1rECnvOf9+lX93qthpdwld28Feg3qkuAt J0UKPRBmuhCx5Y6yif+TtIV0K1e00nnjh+bkLMkcB8fQmhDXmwXswH8m/QQVjawmArnQDInnkbAl iYRCk4WGCdykg4tARkTNw3WSeSOVL4lgh86CKm0BxY8YDUZioYjlJGehL8sUSiSOQ6lCAX9NOAjh YCBucJOAD2GUE7BlWgCsRK7Av0lJKC+mcoV5NHNMIbcq2aZJFHrE5yHMqyGCmkrHh1huEImOir0g Pqm8dHJE6WVhQbQVMunjQT/YJUCOWIFU/mKztA4s4bvkyiGEbE4qm8zRBlA0SiwS+RJ16hsP14UD yJGktSOPgtKkuaKEoRL4xYJKiKWQaOk0ggTnkUSGDM25G4jCJAUMCZRkZaFLI3Iql01lM4lsLkF/ gEIhXSwg7CLdHwMgvQOgbMzfQVLwHqFcmELTDu3hJsnsDiIkFAxZHKyKGpIxT/E4jUwOe2BdKBD1 edmsOV+cm8qC575vTvzQkqIUkjcm+E2cmDg+QyxPV1HY/ZBIz05l6AWJnG1Qbbw+FO4TCdOJEmrX ms1kCpTQaMoJRBNVF026BDjqYTySkFHpJIAQDyRKFZiyh6x98BbYNOz3gghpg8C5SBfRqYGrSLLH 8+irCQlQpCVnFtJK2JfXb+repKEkiain0aTTP4HmB401sbpYJBoOYGZkI+yXRcXi3r1Pc/dobgXc CqwSFfAom1RkXerMjmZi/Gjh6pN3Vd+UXqn/b7GJX0rNWvYBbTzIVK98VH92ujq7VX3nVe3Kb9rG tHddW36fL+mW0H51XuWIu7+4hGBse9NN7PrqQKUvtYte0P43T509T7vrSe31to8H+tubLUpFNme6 Wu5kVGxSP2zbuI9d225yrQmlHLgeHqwMWFYTZA+8jNokXeW5bbD++v369196K8aOryT96hP/XHDK O75fV2355eef9wpjY+fWW1Lvu1c//QX1o3adFhc3wsANzXt+bY1fb4GCzGxV73xS2+cG/c5vFmAs T531fyeaV4+1B7Vd3rby8uvaSdfpZ7+sza6y487N0926XQVuuunGO26/DXetWxW3Am4FflSB1paW DtaEv1D0pe/gxu5mbgWWswK9BnUhuOGO6RBOhQAHjuSomkoERmH6Q2ZDFP2c+S2tibRZKpLFnsyC IAS7AFAgUBq6JMKbSGISQ1+RXngKsfSlIuiDoHo4SKYkyeiGg8DYB0kUci/yzpFKEdZOv8U+8XgI b5/XVx8h3kLshBK8JcIl4pyAaBbd+srRYNQU1RI/DdEk0aPxRdiH2VDHIImqaFBNZHBjnI6EiJpA O4R8JfIGDAuVliAiDQEaaiPef/CgNCqEO5FchesQ+pU3jBSLdpRWBJ0nctKNEH0WbhaWgdUR3RAq JPANEVxsRJnqsA4GvOyC6xHdFjhNwueJGLPs1hSew1yBBpG5PK5EwFlrhoiqgoojlMaUJUm1V5yj ix1PMrOgZKT7K3A12JdfR8kVLIdwEW0fC4fISmuIkXOvIdRCTpfGB4p6qkSXAPRR5OnnZ8yb//3c eeA5TllNyI9vkYL2jQWoNpgQtMQhJKW+hISP6UvAGacDDVw8EGiMhklqo6SOfRCBGSHx2BMpmRny K41Rb9gHykNuZrZkEFJlktkMQfTQOqR9qLfkHCNXE+uiRsoYQ1DisquUNpcmGjabbgD0DqBFZIhA NUPRib2Ac2Hw5O5APIyltk8D0uFYzLTmieMi4A3UVaQrQdHO+QCVxUQ625yGZ9r5jJLPqoW0Xcra JaK8CmqpSGYaSjvUhPqKtR4t5y8Fd3e3Am4FemoF7PXWcsxKivLx3HYWv45Pt6RMmVbZeu1R1nqL 75Oohq1f7GK3BTy0fKuefpM+9s/60c+qba9nazeyd2rXsm2RsyjMV9+v/mCvde0l5BTWrmOd2U7Y 9cbL2kFX62Ov1iZ+uWDgPXawhy6KUplTtFve0T74RrvzP2qb2XHzkfaa7Tae3+KkdyvKTkPtVUbU 9SNJF6vjLcRRxxzDF0tuxdjxS0a2jG99+AUTV3rkRw83NnaqpPM+0yZWGwToA+0bTjXf/5PxxbnW 2MWMUjPAmnCM8ebp1h93WECyAF5X3KnfPn3BPp6wvfsu5jP/Z965n71jTfVxW3nmOW38y2rVvNqp mbobL1SBfzz4IO7aIw47BHctbU/d6rgVcCvQ2QqM3HTTzu7SVdubqanvPHn7xPN/ffROP8ehv9sR v/6/S665+7FXPpu38G9Hc/Ld+8oGzv0PL8z4YfKL91971hG78e2+v7zgxkffmfEjXb2Rmvr2P2/7 8wWnHi7b7Lj70aee+8dr7nqcgRdkKC5uEZnJd51UPdYhE99oatvO/PK+IytzGHfEXZ9Wu/VwrPee ve+6P/6uvIr9Tjzn8tsfffXL5vbdfLKT7zq+OuY+P0oeoAjvP3PfdZecO+GI3XfZcdwBJ5wz8Tb2 b1r8R59dVf6VM4769dfVP7YrZwIdPeq/nniATSXOSRRD8JMSDkBy4MnIgvzwJT8DPKH7AcPwAAa5 MEn24RB9FEFPgm5QcznZ4kiWIERIlNAZ8QCECCIGnYJq0fRQtEji2atIkBCRgUFgUgi2IFl437AK emxbjHC2QmYVYVJQIFyEaKUk4h3UxQzLafG2Sew7I1eNbzIVVGgcESMbhwOfSOKTDU0D63jSBURd dsgPH6N9IalPKhSG3HOccowekbQrzdFKCXNhPWS8Q6wYCcMhPj4QEAyLkXE1ShwpTSltq5xpBeoC iJE8hcqML/DllTVfGAxZO+APLCjlpS6mWUZ4eB55SELtWQtqK68XVRcypXy+QI49ufjka5mYEiV3 X0VTxr5YDvmPg4ZE/QSAo3p8gSGTJfEDmlJKSBY4TzyA0uERtgWDo1ySy+X0fGRp0DomT3dIzgEi ODATzSmlRyaPOxosxGUS0oan1JGJ2WjNTErNuxg17PPjV+U8MLGSrdZHQ5xLMCheVULOiPSS3pQI 1yzVMNVcpkTlUb0569AiqMBEOccJZDIWVwrMVA4EPzMFYjqdGXFycpfgM4heXcQPJswaebkmxGIJ T5OWB5xlGmvS91NXYkSCcalxHTFRcCZWR3mvtfPeHb303e0UZdjv294su+VwK7CgAl9f3NavrAeV ZfA726+42djN2i+vFJVTdFvz9T0qadY/OtzsD/Sx/6gynoA96QJzVPVTLXY/4QrtDWeH08YbJw9a ykw//68+4Z/q9EV5ruOD7GuOMreuOgSNqfo2t6qJ8njrWi8fa5X9CTPe0cc95kym3v7H6eZGi4dr bIKl66FH9Es+XSgFv22Km25nXb+b1VBd2YfPeQ57ufLDvbe1p7yhftFunnpf+65fmmMWuB3VZ+/T T58sPq+J55h716y4U1QZefh5S1zqYo7/3IuVRoodnB/vtxFwkUZfdi+Wb9isTjzh+NmzZv390ccW 7nI164Xzf3XFW44dNHrU1Y8cO6IaAvWTw5HV9acjLyvXd9Dul1592hZtkrvFTo0X1h2cdpdstv6I ET8fN+6EuquC3dvoZep/9V2frFyF2+xn3jGmImycPVV7p+rzja9h7dBXVrmY54X65hP6cW9VynDI L8yLhzvXLhLIy7XKRb3wM7d9xfLN2t1PaNdOqTzmHWm9eoj109hhu6T+91XtNy9U2bRu33ieuSPP CIyHl+g3VdRk9sRzzb0rdkskn/o51WHHn2RMGFI5xOKe3V1yHrtkkGljXu2ScZY8yDVXX/XsU5KL 13bjIjz40ENHjNhwCe3k3Bcw3XBqeuMheuYLmGWu5LSpU088/riO7L7rHnucceZvOrJl125jzHr1 pt9PfOqb9sbv6hHi25888Tf7Dqu+oAF1HXDqpEpyQigSLaZTC4Og2v0uuXP8mHj5L2hh1ut3XHzx I98twrGjxbb+9WXn7e00O5abOfmugyfcWxaYK1ued9+fdmpUrPTbNxz+uyecgACt8di//O3I4c64 xRn/vPD4a99xNh24z8Qbxo+OK8ac9++75vJ731mErH+hJYC6Tj3zvnLYRHTMebdcMq6/83Vx3vsP XvWnez5M/HSufbf89UW/3W+9VeaDwLaLp9eouoAF3EkrD+iKR7FBS7XRYEM83CBR87gZ/b5gwAJ7 +eQr7vRJLBh2azqbE7VWFr2NdDMkb8u20ITRdC8cDDXU1pDbFfT6EShhKQNLpYiQzxZoR0iHvnwB mmYHdD3gkYaAKJMkOirK9r5isQQeQpkDayOLCqTTlEgl0hkkPRmS64nMByRhdqO7XzjIBOk2SKe/ iNeH5Ekxix7L5CKWjpAQOUeaBEHDpof/DlojPIb0LNLcg/6g9DpkPX5bFGDyA16nA2qkGNJhUBog oksiicxiA/oCejxFWyFxH6on6EhaVZay+WyB1SD7ktx3fgBskr6Q/LekaAVepJF1FSSBnlwvcu4j ZHaRgAZH9Hm8lJYoKj96MF0B4zg2SbH4FSyVCuBMbElmZjcli0Rr5Uv4GD2WUhv01UYDNRF/Yyzc tyYWCYUYG4cf3SBhTYZCaBruTDPs1egeQCxawEePSzKyvJw2MTYKkVRpB9mcys5LZlozWURhGEJj NMKMhKKxGOPRs5J5pOWAOU6FxOuLyEvy0QBNhIMRiMbBmDjnqEUkfCIBgzvB7+Bxgq3EbWk4XQiE kzIC14eoz5yOl064v2NphN+VDIRvcC6eM5LRRXA9pUcIJtFlNjo7eneS25XJF7C7Ol0+FdgZKC5j Wq1F4/v5TTOaW+ckUvMSyZY0Qre8HN90jQRd+/fLHc2twOpSAeK6dnAYUup7dVbnU/9y86thQPX2 tuVXPku8rb+V+fAZ5rV72PuuZ6/rGA4GDrR3HGmff5j5xC8XcK7Fj6F+P6sCBZYsIiuPoAbsQw8z /nWCdf629o5rKfIGXFVGrmPvu6117a/Mv+6+gHP96IgDR1h3nGyNH2mPjCvxPva+21n3Hd+ec0mQ /3eO+EVf2960AzlKSytMj/j5TyVd5Wl1tbBr6Ii1a3rEgheexIYbbTRo0JrdzLmYQjy6oFvop20h cYoyfXIn+oTObwu/AwIHlvRMhlyDTcv3LZ4TQXqgzjp+F2uDajVKX6jSVrOdU3j4xfqHhGt47a3H Wie0ReKY9IXsgaexd0/p88mTL77gAtqGusbG3n0i3dmv+hWY+8adf6lyrugG+5996fVXX3jiz52P JPj9+eqtN/5nxqLTZfLaRsf94frrJ557wFpt1KTl9Zc/ba3UrPm/f72kyrk8G+9z7uVXX33R8WPL HyRayTf/evOL05cYW6NF1tlki8pHY9a8T76r/p7O/PD195VDeDbeRDCcmXj/73+ucC4ttuVxF1x9 7aVn7VOZFUu46umvl+waSnzwwGVVzhXf5uiLr7564pm7Dy3TrTlv3XTrvxYKgFxFroleg7oEYzh3 VE4SAaWqJD0lQFiWMj9TIMgpQViX82pBeuYJqZDwesEWCvngfCGwA+UOP0zki02Zwjy65qXT5EwB HsAXbI5NL+hFC0Z6FxY8EJQEkEsYUwHWwYHIgC+0ZjLoh+KovxzpE1IjpF7IgPrV0N4RqhWsi4b7 xkiR8oBs+Em+ZEkfRuFtkr0FRENcRuNG0An5+uR5ibpKVYnfIjoq5vfWBHyE1gOosPvNam7BKFcs 5nFfetFTGaU01Ir8fNEwgafASqwJ86bo23xk0jNjDmqUfB4FhBSDrlVPL4yGI3IsHgAOkbkOlNEk fB50KB0kpVul31MbBq75ETfVxiID62N94hGUVLAejk2RysVkRZQdglieiaOAEz4FDKsl7h8kJk0s 8QyahHDRfbAlCQTMzmpp/X5+C3ZRHJGIs6KhIHUDvbExaA0PJsonsRMSGF8ykvmCqN6QaHk8dQF/ fTgU9sIibYyWTfPnJVpbc8lEAqtqKmPmacQpOiyWU45dg4QhNyOHjRGpGYwvXyjQ1hItGOIyCCMq NnK4aiMhRH/o5ziLJL0FfTyI4VTza5iDUK7lM/lcMgcZQ6ZnZoF6pIRZlgTDaSLm4hxRNK5HmBnA knNHBj/8C0kXzC2PrZGWBzYeT71vjb9PnHugT02wIRYAlHIO5DS4N7cCbgXcCixDBVR7q02dP3Uz 1UV+NslP+m1qfnGZUbn/foGkix/NbVLKsvuOgKfy7Ajb3nVb8/JjzCd/L2O+cIp54yHmURvb/bwL zd4zxHy77aBVSRcfQnz/Q2WzJbsXF4ylKWsNtY7aw7zxREMGvNR48Djz8j2sXQfbP8r2GLVLdY2X IT+xGwZZEw4xHzzXePsM8/LdrVHtAukZ3G5SX3c+Bt1qY3uNbgnqQp+1DPdOXRFli+Lue+750722 23a7RSV2BWINVZ0br2j4ZKxyK82b/OoLL/zbub8zLbuMaZLLsN72u5x93nkdWT46mt9fcgmCtRN+ eWJ7LVtH9u2SbWoG2Fu0hcR/oX7tfHRl57VXP1sw/Ob9lsKhG+oWbNyUrwzXMl19+ydTDIWUYPXB Yqta1hmQtdr2dNAH2/15UeFT+rY9JU2luWrHISO0clPtITVdUgB3kEVUwDU2upeFW4EOVmCjjTfu 4JZduVlu+uS351cGDO181HG7jh4xcpuDjz54o4qMyfrk3U/nLtLDt8nhx++3xYgNN9np8ON2b/vo II84xhnNzn03+d3qfsG9jzp2pzEjNtn+oGMPGFE5WPrT1z6bu+SV1K69yfpVNdXn3/6QK+OM+VP/ V5mwNmaTdeP8lk9++eZ/K9p5ZdDOB++77SYbj951vz1HVJcw5c0Pvl9C+qOV/uLtF1sqf9/Dux54 0LYjR4ze+YC9NqzMzvr0+fdnLuOf/648VV08Vq95z40Ah7sktyNlEp7lxKQrGpqmgTXR9fo1NMYi /Iyrw+m6iF9NFRucYz0jqglyEpT4eSx+atRPrDg5U6ix6CHoWOJUsI+K648MepgUzIxcd9x5yHMk 2apsW5OoJuiZjfSnNZtrRQJkGOwrWEvXm7Pgs/z8dK45k5+TzqeL+BYlTwoXGwwI1x4bI9VCjMYK OB4QCmjiZMBLl0MZTZocKrC76c3JplRaM0oki5EjJnKhQpFUd2yJpKuLNsvEHkjelq+EC89Qylo1 KgLTAdCg+CJ3qmiUGJMU/LJXkWWWHZdwN1bcB3lUICAyuZJBOhc7goqhNqTLozsi1V7y+LNFMuOR caEgYwLkXsHFCOeP+pBiBXzlloqKFtQlSAtFGT0oYXmsgpB4YvKxHBK8jyqK4wa4a3pN0F8XJoVf VGmctcrJMsy5qdy8JJuToVYAOMb80trSyeFnfjJ7ikhVZ7emEqnMvOaWGXPmNyczwKYIDR0RqDlq N1yBfp8fP2qqAJ0UnRo7wqlsoxj04OJEP4fDEcJXglqi2GvOZBH7SQa9tJmUULR0UU5rupDPYmss 5jSrFPJIEJuALS/595wuLctZQOTlJLKRAkYOGXd2oVcjFZPOBhqIjTgzDXaKhA6PpMVy1WIRQ4ld JLQrx8p0hbt7cyvgVsCtwLJVYM3BytrsaapT2glDOjaUOu2HypvqjoKnjo272K3y6ueznB92TES2 nEdbwu6pJlVAhGrvNmxpOforbhJdOnJZ0gXPgmr9dOA2Yde775btD+WbP1xTxSbZpuZ022va0tx3 /nbFZZc592e/ynReLtilS1vkYA19Go854YR7Hnjg2utv2HrrbZbgF1vRc0FZeWD1rYEyWzvnYf2R 97Wr7tVurz4fvcOt3ZcimbTXGbKgw+mjr2r//kb94AvtkqdU34/7XyrRfvY21dcMuY+1/3tWe+K/ +gUPax9U17nPZrYIEwL26KHVh0rq5f/QJ72v3fyw/pdqklf9SHurVUXPuKJP8TKPP3/uPJ6VRx9+ +OkTTn3zzTc61ZNumQ/q7uhWwK3A0itgBNbc/6jDjnTuB4/s43deC6neQLjtLdkPs5vax121DRmP hsuaq3Z/QBcczzYCa+1bHvbIow4c2bf8K1wlzLptmymzm5Zs5lH7rL9V9dd39ttpc5xpNE2dPL2M 0AJrjxrmJAhYpWLbQK3ffDlDrJhqbK0dD66s68it1yivazE3RDNt+xe+/up7+VjLE197++ruh40d 2PbBytIr2lu26DWoq5wODl+AXtH/zumBKK34JJ+rRDNEAyoDWSHQ3cmWAgapoCVchEAucSBqWhZ9 DhBCMAjfsS8AyEPEFfHn3GFQAW8w6g/EQ6GGSKRvNFobwXdIeDzXtyfg5XE67xEIQca8ny6N9GpE eYQcCdMhmKw2EowF/A3RMMStMYJj0Rv06hj0SFrH4QjegqexoYAtps4x8cc5kAthFfiM6QC5MNox UR/PPFWJeHxokwi9CuiekJd+f+EQrkzwHQtCnEQ+mIq2yOuXqDIGwA5JtjrR+DCmIllmdKt0FFsa 6jMnQR+Fl2jQwoTW6ypp8fg0dQgP6rFiPpXPtmTSzZlUOpctkuReLNBNscQPcjmPWfIoJpAM1ZIz SZMEepyGUeyIHnnmi41RoA9QyxL0BVOjDwB+RUyR6Kf8Yk6U4DISsTw+9G8YGcupVWVFG/N0sBDN KA38oYS+OzFYqOEQZGWY1dx0uiWX82qmrhIiZg/u17D2gMZBfepjsXg4GveHo7gp4WEo/RCUYURF yhYLBvvEwqwXnyEyMwgpzRDxOQIATTOPexEIGfIpQS8JYh7Zy+MJ+31hr3RzlF94lgFapN9jSyqd yhe4bHjM6dLILoI+/R6OhRCMa6+UzLEJQjS6ZCLqok5cat6wNxjgkpHrSqdLQrJUSDt5XgUDCSFH Z4E98Y1Eb/md5c7TrcBqXgFff2sv51XPe7M6+auEj3NUe+y69tiNrJ91wL24/HW2c4q2lhxxz22W FIG//Ada6gjTZkomN+7FLX6aabTUnXvkBpXGi8ccs7hWVmVh17133424uboCf/+1h1Zf9n095ftK YAh5APNntFa2iQ7qX9ezPo056NBDCSO7f9JDhx12eN++PaBFvWqP2906uuJ7UaZ8pJ7/sHZHNfaW DLu/7GP1X5pysHF9a3x1KaXv1FPv0A//m6bsZJ09+MdXm1pjnbZHtUGErbz4ivbbJ9XHZlQ222as NWF9h96q9s67WDtW36dM/1K9+GHtuo+UsgSgdm174s5W49Jm1SOv9A5NqhrA3JYkvUK++FFQ1xJm 1mZsJE2vQwtwN3Ir4FZghVYgOmLPY489Ru6/OGzfdZXveI5ymzIjuZyRMlp0xO7OsNwP34eBP6sM XJVfdWRV/jWGb1hV+s74aiZ/mu3c91M+rnwatc4Om4hyF5H9OqPb8k4TH956/rnXPPLml6Vhex1d ncAh2wz8yaclC46vxdbZZPNqiKjx/i2//e11j735VX7YrtXdjz1ym4G9Bgx1pK7ONr1mRYJ2dLoT WjkxFWLHgxEBJSzaCGKmA5BgJwN7ocBCuwVdwpjGhRL2exEQYRrEjhgnZItwdMEs9OkDN4AtoEcg EbLeRa/lRF9JNtP8dL4Vgx7KKMiEpM5Lf0Bp5cgAXp/kjss3Is4SHyKx67qMAE0i7IoWjSRm4fuD xIJAHFmSIB3RdkkgO0lVkviULpaQj0k4O9ZCBF+6FvX5iYzPl8iMV7C/JQvkzEuDQcRlMKQi/2pe S/cR2mXYGmgvgY4slUvkC6wg4PfSVTAWBELRblIgGmIuaiWpWER3mXYqjygMu0KpKZObn6brJKgr DXdhGSxd5qaJ8AojHnIzXQyRgsqYN2IrJ5lKldR7XU8UC62SS29kLDtNdBd4TbODfvCfGADpqyga LikNc0B2JzKoeSmcgHR3NFMFAJyQRo4V8XpIvOILOUu0zCwQkSawiN83Ag4Rizn58fGgb3BDDWZD Jx2fg+H29NgeX97WUiWbmpgqLk5RzxWKRhoOypj0GTBLTUkqRHyZl9KxLx0wYV4S/6/pgDZmlYFO 0d6yBBotIe7jFPAykc4AVFInfM1p7cjy6WUZ9XmZDMCUa4lHOKfALxBdPldISc9Oq0AzT9VbtH05 aa/I3ZPM25kijzOC2qe+prEW3ZuX857K5sh0m9uS5N7hZ6i7oVsBtwJuBRaugMfeaSd7/Fh7dAgp cGduqr3r/uYtx5q3HG4NX5bM9M4cy9lWrbN+z+GONa/cakm9Fzs9bqd3UI2QVOzcn3WTe7HTE+zk DkuWdJUHKwu7CKd/7fXXqsPrNUM33KDyum/GUw/9+2vHq2jM+uCl1yt9NT1brTuwZ5CulW5UXMI5 8dRY551iTjrYOnqkvSVNSJ1Eud02q2TY7VDTgdPps395gnntOHs3MukCyjYj7YuOMy/d1K5tywTO K9MrocjKOluZz55u/YmN15U0OryLW65rHzLWuvFU89ZdrH7V8xXoZ113lvRePHSEvcNASWxYdy2Z 1UVHmv86ztymI7PqwMTdTTpYgTKihc92cHt3M7cCbgVWcAXSU1+5/4pTD9p730OOP/GUMyeceuaE iyd9tqiU+s7OI/vdq/dPnLDvHgceevT4Uxn21DMvvLdd7+ilDqcGhm68RcWU3jJlRjP54XOnfVux oTdsOHxAGWB5+ow97ry91q2ym5Yv/3XjxeN/ceL5t/7z7ampDiA7tc/2v7pg/8GV/a30Z/+85YKT jz7+wtuefHvaj3L3lzrl3rNBr+nA+Nw/76WqtWFciN6sYefzSG8kpT5bks54qKSQ0ICxwFMkcoWD dGYE1kijmWIJmoLciC6HPILl0Pah9yHNvSRtG4ERkgAmShv5BnTBTih9AFUi8oE0mdKwj2z4eDgA PQGc4dZjF7gMff0q9kCPB6ETVKlo2mRfwK9iQU+ZN5Vb/pXbGvI45MuLZKscGa+gNirinpMILdWD DijnNE6EMhG2RbwCGxN3BWTxef2G6osFkJjp+OdANLSPZFQRpIGmaPUouEe0QpgykUzx+gZCB2ST TpPQLmiaBG3ZHkL0dYlmF10VtbJszJdiCUVz5nS3hAn6VdUPEUSxpWKNlFSwSoHAUrKdRhQ95YLp iSGSqUDzDIOVIuxCpuYlVF9DJyX9FwU9lQwmAz5iJpSIOeL148g8wqeQSLg4X9l83qPq8XAQ/Rs9 E8GC8KOIx0PMFueS6C1OOe0FSoUCcipInhM6JuvBWgj/wtEpM5CodwvlGE5DTJQ+FSrokU6IHhUT Zy6bg4gRkC9mU0kSA9zR79Lm2JwKuUy4RGgyyaqZvDT1VJg7JyAPaHSaD8jKRSHI9AQBcmACufJW wdYsBGyxcMS06VDJoqXLpGSaEdmFlA3/Y1xHIZfNZMmFy2RygNPaSIwr+bjDftl7fkus/Jm6DYxW /jnokTPomQ2MVmgHxh55HtxJLaICK7Ql3JNPPnHjddcRbjVu3E5LqH65FSMb3HbHnRXxlz33jSvP +sOzFVGQ1m+D0UN8cyd/Mi1VpqZtzRbbd2Ace/aD549rXOEAjKQwHJQYFffce5+f//znHRRwuU83 9+lHBVbo062twj/twPjT4v+0J6P7Asa9RBdZgZ75AmaZT1bHOzAu9S/XMs9hiTtmvnn0snNufLNN zPyTjdc67PobjinnXi3UgbHtL+Bi2hoWvnrsgvNveb8aBPbTSbTrd7yoDozlHWa9cMH4K94UKZg2 9sL7L9jo8z+P/8PzklDv3/OyB87YItI2rNn69WuP3XvHP96a1T6WK9Bv7LFn/mqfTRrLDYkX14GR HxnJr1979G93P/TmQmH5Wv+fH3v2iftv0qdbPgZdMWd4MaP2GlWXyGx0bXYy8+3c+fNaWoh392iS WAWXAPfwQ+x5utdbG43UxeMEqwsSE6Miiid/lMZ9YTyFPlgJyi0xO2J3FAOhI9XyeKE4bAoZkdQu WI9XREBojqgOA4foDqjRgZGdCuRJtWTyrclMSypDFjv4JkbSuFdPE11vliBNUY8S92sEqNOSkCaD 5IEBdPAMEoyFjS6PTRa5k6WAZmBDYBQAiz8Q8vgCpk3APCAOoRPNFFFF0W1dq2M5USfwPuBl13yW mCmZCAyJ6HNNF1iHFZEQq0QynUxlyK0naJ8w+5Z0thWwYoG3sOmpEZ8PZVGIGCzNCPqsaEDrFw32 j4ZCSMKEOKkyTVsn0D0YBAgFIvR/FCUTdkcsktgKyUSjR2SeMC94DgWH0DlmSTCgFBIlHan2IcGN Jeggc0zl8tIYUVXRr7XmCgip0HbNz+QBc5wRWTi0yeMLB0JxjKKBkPAxcKWNF5UI/6LuUejKCGEU kkfglzQNUCCGTh5ZPpcvsinYCbzGiaP/Zjwa6lsbjYYChLQBmopgQJuYLU6tRr5YPByKh3GA+uFh yUwO/yaclJftQr0wkNKOwOevCfijPg8RYJA/HLCcU64HeKJfU4PIBQXrCbvEA4kaD22XUEnNW7A9 RdWfNzUwqLhoTRVjK4H3NJ6MhUN+fxASJ3lspMqputOH0ysx+p10HXXrrwT3YG4F3Aq4FXAr0IMr wMuAR/7+d3kxm81Ws+TLifI/vqPnWnvYsIWEXWqfrY8/9eANKzZOa/Zn7771UZVzhdba/6TDRzvm 2JVxGzhwYM8yKq6MIrjH7KUVaOzbt4dkyfXSArrTdiuwYivQ/O6Dt1Y5l7bxgRffet8/n3/uxfvP 3nLhFjadnoTZ/NYjd1Q5l7bBYRfc9tBjz7/03IPnjencUHXrjJIUVm7We1NmpKd/9ma5E2PjtmOG LeBcPKDXDBt77MV/m3TbxLMO23pQlePkZ79887kX/OPL/FITcjyxYT875pI7/37nxNOP3LZfW/+6 WS/dedalD32xeBLYueX0oK17DeoqAwLIBw3y0OkAdOZlCzOTOeyDUUhHMBgNBEnRKlpaplRI5OhU WEASlYI/YaPD50jiuIRYWcSrz0+kSZhCo+TkLiHhAmx5yf8ifyoSDAX9AULfMNvlSlbeAIWIPy6D Pw7Nla0QmwX4wjmJdIfTCGlD7ONRiRdFKmQS6N6aI/I8J6BFUSVIniB02ZRoJ18sSH67Lvn3Di6B 6eDog5cxAXoC1oUDdUF/jFQwIAtB6UUTtx19/1oyhbmJTEsyTXRUIpuB0iAzQpdFxD6Crbzkv6fo EqnaBZ+K2qsgai34TsDXEA0B8BBqcSeYv8SgQu5EAQaTac5lWpBNoQ4Dc0GToIV0EbTVZGvrrNlz p834Yeas2flkq53LGGxmYTZU6oOe2gBrhziVPFZBNVHWYU3MoZqSehNvX0JxJsFZjjZNfJF4Dwc1 1vepidfHov3qYsP619fEIrFwmD6VsaAPkkXqWSRIHJmZKiYzxSy8DcrWJxLx6gFBUE5EFiZNEu6J 48eSCZxE/wXSCgUQqEkxOUHEY/k0aUFJVBZfrBGvqY+E0HbRlDOD5ssiOSyke/xiPLQFmQETOWs4 NBVYKfo0lGgexG5WU5qFSPI/d92mZYHo2ugLmUznEuR2ZdBmAfeKHCasq5Ggrz4WGdBQXxeN6d6g JplssLUgZlDho84vK06vUdBMBjNItSfeDZspMYXIUJdgpe5Bvx3cqbgVcCvgVsCtQE+rAAALesWs EHZVs+TLifKLuL/5mrgX2yd2qXWbHz/x5qt/d9I+O282OOq8CIyuNWrnw3596V+u+NXWjSvvI93h w9dfKR0Ve9r5defTuypQNire98CknpIl17vK587WrUB3VGChPon+3Q87bNt1GhFodMEtPfXTz6oN GPvtcuSB2w5tDC2LBto/aJMxg8rzSX879eOvPi9DJ229jdcWM9CPb3p88Ojdj7nklrsm/nKMNGeU m/XVk0+838F2RZ7okM12O/ai2+798y+2rAZPWp888MTHnYkY64LydcMQvQZ1pfM0AZSWBJjjQj69 PuQZWh8cUhsiRioPljJJRyKdykamFCA1HC0QPkQkXU7vPIlqIuJJVEEYCb194tF4MFADDZIMJnGd SVx5EakO+Ak1lRbw+ZFrBf3eaNAH0aBZISwMpEJAGLIv2Bi6pDjGOrLDyG53RFYIdzgi3IsjxoP8 NIjhjylhaRTXoaVghVMQbElIOvyGRyTKqjYY7BcL1QakvV9zNjc/l23N59LwMQFG5E/RA7GApIi7 T4dtEVQlii/kTLgEcdKRcw79iYR8kXAAZmRrerpoSyIWNj3CsDRyo5wwf8K9nCQyxoTiwe/YC3Ik /SX9Xg4OhmMH2lQW0UR5fXQAwpVn6l7FG6A3BcI3eiGm84VkLj+jJckxsUSSkJZlqjn+zdhiG5XQ KwyOJPnjMcVzWBNCWRcgZN9QtHTRSpespqyAJ8oAukqXDERxTIMAryQCK90fDUTxcdLHEGQ2L5WY 2TKPtecNCzdlYzREbLzYC/3cOGleJFItRGUVCiJ9gxZadKjMz08l04UcDRWTACvToD6SMabQlJP2 l0ArheSyRM5ozTMZrKZkzMO5+L+XIZHteXSqJF7MRDLZSqvHZIZoLQnIB0sqFmAR5R9aPwR6GDMR 8aWxXvIFGK7At6wfhig2UuoNl6OJJfgvl8/wjUkvBVylGEilmWTl3g3PbfcQbgXcCrgVcCuw6lVg o402uu3Ov3bq/oc//mmhOvj7jNjxkPG/nXjb4y889+JLzz1+5+W//eVeWwyJLniBrjeOu1B+JPcL u8G9uOqdJndFq3YFenKW3KpdeXd1bgU6XwEr3dJUib6CD9REuq7RYLb5hzY6FKyJLLuUQRswbNNK VuO3bz39xtflRQ4buX5fYnXKt1kvnL9PtQXH8XdPJmUs2G/0vsccslF1g3lvT5m9+Mwucgn+0NbB 44C7JuMe8/fb/ODj9m1r3mv8d8qMDmR+db7+K3OPXoO6yunv2MRS+VIym5ubTE1tmj8nlbJJVJLG huSRo9MhXNyg3yJZUyj8sN15MdSVskYpg/sPUZNk1nvwuAWypjdVAPcImCg46fBlLhahx55KEhPK JWx0oKQ8trmiIfgIIEJmOyIh+ZEpmi/ABZKfPJDDIpwdFCV2OUeHpEFHMM/VBENgMux5GAMxxqHz 4kGfji8PTVO0JhQtmnoiZyYyYveDHOnia8QQSGq+EqJ7ogcDoz8c1OnFaKlY9ZBe2eGATmAZqjFs cVjzYqGammhDPFpfV1NPgHtDLF4XDsd01fFTFsI+JRBQfUFVJ59dMZgcFr0gO/lqwv44afIlw5sp qGAggFBAtaM+vZHGhtj5CL0K+Ej/QpEG58oXiqjkUDvxbJPoMarthRD5AgE/VNDj5T+BWCgSCoQN AtoVn6n4CpaHmPZM3kRL54WzofMylBQisHxJuBohVtRL99RFgn2QdcGagIAaHBB9lscnjSY5HZL4 xblO5ArRkK9/TZSNYXNewJ9XjQYpKOyOMmSThUTBSvt9pVCAngHZrJGCmNFgMRbAUyl8SlinT496 9MZosD4cjAT9YCmCSYIoy4Jevx+ah5sSFRYDGlG/GgsoAbRbXiUc9dTUBhobo336xOCjHgZiuSEP SfW0g/TpFCTvs9MhNRlQWjzWfKU0VynNM0vNuEgLudZiLpnOzk9nmzOo/Yxkwc5q3mJtVOHu3twK uBVwK+BWwK3AMlSAEKvBQ4Z09r64Ro3LMAF3F7cCq20FXKPianvq3YX35gpokdr6NrxVak1XsFfi 2w8/qrb/WMblheoG0CukfMu1prFecTMTUz75uLMDBtfe7Oflvrzz33/rE2cgz8Bt1h+wANUEYg14 g8q37LzWcqC+ynvo6oPxDQctoYOyGozG21BcaV5rxaxIklJ1f22jgX2WRZLW2aV26/a9BnUBPsqZ 4kRu1cAndJotouGSaCYCrvCxcSdCCj6FlQ6bI9lduNWgYFCPpkxqTjIDb6BfYMgXrAuGI8Ke+FoP El4OBjPBXiYgJpUjONzEiCexS2YJQ9sPLa0JbGtEn9uokLD+sa1BeBb9+ABkwDJ4U00IY6K44ggm J84phcYHKY+T5kSMfUsWklVsSmbnp7I8uZI5Y16yODdZaslazRkjlTfTeYPwqUgAuVmof12klvAv GU0i50maz5solMiGhw3R3FBD/5XKFZrI7DLM5myJr8nlR4zUlM3NSaWnNzfNam1pbk7Oa0nOaU2k 8llFN00dF2cpX8rj28S9VzI8BcOrohWzfVZRtUo2a83lC4lMel4y8d28+fOTsgKq4pXmiEUytpDC NUYiEZ8fxZZP03NFM2tqRUsPBkikCvsDsLGQokK4CKLyoaJCyAZtxG4ojCyTM0olr/gmqbL0zgSV IXlDckXDxJZMcWZrdkZzguB5iobsrTYU6ReLNURrYiHG9UAxnZizIsRoLgUk350ksFIpwAXg1XA7 1kfD9ZJoFkXxZSLgsjWTQDbVZjMC70nmSqTTaNDwHrZmMlwbEs/PVUSGGQ7MUjGVz4vKrFiEeKIE y5tWS7E4H12WpgS5vEgfw7JJRwHIFwQuoPlDWjDswRCpa1DOdCLV3JogO252U8sP85tntrb+kEzP Q5xXyKMJw/Za4ExKFpxHxyfphSCCLWlMsOC3Urc+292DuRVwK+BWwK2AWwG3Am4F3AosQwUOP+II 16i4DHVzd3ErsLIroAbXGrF51bBYePaRh1/7aPLkNx+74c63vG1db5dtjpEhG25QHXj2v//x+Osf fzr57cf+cvPr3khnGUt0rY3WX3iffqPXH9DGscgaGLJR27HmvHTz9Xc/+cy/7rv+qns+qEy9dvud N21c/DK08FobbNpWhH/fdvVdj//7n/dcc9X9n1X2Gbjjrps0LFsZevBenT0NK3EpkhEfCRB35cUU iLgHbkDelmi1nCx5ZoZ9DEKTLeSS2QyQRRRJ8lOlJuQb0hAdimDJ780VDLAXvrN8yUwWSvPTOYlQ ErGOXocqCesj6Mu0wl4Pd7oMQkQAOUGvD55SEyZryYeTMVHAc6eje4oFfEVbnZMpFhWtQNNDJ0IL jy5HlhZ+tOoj2p2ce00NQ6lsO6iSReXpEw40hgJxnzeCykuliR8OOSiQkSiUGBm7IbSI/zgtFJkO SioUTKxUpeUjefPkfIHWGKcuiGDLzGaJWc+hRAv7tHjQB6IiH51M/TjCJKLmVUWokK5E/J5av3d+ Kp/MFlPZPOPMaWmF3BHNTk9EQA/+vXAosNagfkMG9WtoqA3HYiE0YvV19XECxHiiEYzlSRbZGETo D7JMZFjSt1F8gkArBHcSQ6+oUjQbMR2ThcNhDCXLP0/KWCqPzdRCk8XGP7Sk5qXyzWl+ZPhVvcYf CuoBG3xmmPNTqe9bmpoyTeTZQ9mcKHpyvEgEK/rBTdBEIsoMGCKiNw7lM21/qqA3pUkd84W8IWBU xBugU4BBaluuxOnO5FlvjnMO7SLpDIoH3mrKcM7ynCXsnV7Nojg4VSGYfWsijXWxhlgEcSsmVkyL ZN9DSyUrv5RLZZItqeT81pam1tbmZCKdZRAqB2uEguLrpGEl3tOSF42fFENaT5LxT/obKW0BNHBM 0BtS9QD3lfhEcg/tVsCtgFsBtwJuBdwKuBVwK9CpCnSwK2inxnQ3divgVqA7KlC32YFHbVyhHsZ7 ky4688wJFz6gHHThSZst39H1ui32PnydCi8zPrz3kjMnnPm7R5Vjzj1hwzYFVgeP4KkfNqISTV/e IzB6s/Xa+4A8fbY77OStK9Fa1qzXJ113xQ13PftluYFyfMzh5x2+Rd2SAsjUPtsc8+uxlegva/pb 911/5bV3v/BZi3OwxlFH/foXW/RxwqZXqVuvQV3EIXEHfZAOX0AbZEjslJO4RF9FXwiOIK4yhFBk LpHURPs/gqroyuhDxEOseksuNzMBXskkcll8ghgbw36dpowwDkAFEVGERoGBEtlStkDsPUoiqI3H pwOzglF/gKzxonTZw+OGc82Hvow4+SzzUHDSSTJX/9pIY4xIey/WQtRcBpjKIrYdIZOQKpoVFov4 IREZEQKV/KG5tTlFs0TkSTQbzBdJc5d0eDRoNmHqOBWRLFolM58zihgAC1Y+bxZyfEHek0X+PVAH sRFNJ5uzeSKlsszewGnI2sMhb7xPtO+gPnWRcBCOhY6tOZFLpUqJNFCPAHnTq2pxr9oQ1EIeI+RH 32QEvDaitBj0rSZO4FcilZrbgnYqQ5q6ahYTTDiFQ5BqIk3y4faLBKCC3hJZVLkskyeoyywR5V5Q 8F2CoiS/TLRc+EY1pRCUwTGSEpVfxHqJqZBGl5glPbRHlBh8wuFFkOdRjGw+EdJLDSFPPOhtiIZx MRLqjoqNAaFRqmWQE0+VsEOWikRqacmcnS3pedNLjjwMkYfS6WxLMtWcTs9qav6htejVfBF/OKT7 uCogg0GStjwk5duwTLCg0CgYllRe3K8Ix/CiAhFzosWjk6eXE4IEDDZG4FYugzYsYWZTZi5rZFLF dKqYSsi+xJORhM+pgANaXtNE6McFIOn4oicVV6aXCLMSrExsqfIFDStxqcrdvbkVcCvgVsCtgFsB twJuBdwKuBVwK+BWwK3Aiq1AZL3DLrrhwl/uPnbU4Gjt4NE7H3j6xBvO3mlILFKFQ/O/mbVMmeyB DQ6deM3vjttnu1FrRZweL6deceVZ44bFSMMp31LTZzV36H2ftsb6Ww9awKq0TTdY60fSMP/QPS+6 5baJ5xy918/GbLFBX02LrDVyzNi9Djv9jzfd+YdfjO67tKT90JC9fvfXOyeecew+2225+Xr9AjSl 2WTLn+125Om/v+3mPx4zZiX2pVlxJ1/9+utvV9zoXTjyc0/ez2hwSzAEOizpUYhF0JZ/IUpka+Hs Q3hDbDzaGUUja0nDMYY+ylLxmhFWLrIpAqdQ22B/I3GKjo5E0BNHhSQMsVY05EeWVWBMywYaISZC ooRfNRzw0CkRfZBpFGUfzYN5Eb6WKaALI3YKUyFp45I1LkZIGyGPBzkV8VLIpjQsjgb0pMRRaoJB kI6wMVtHfcaUcOdhyORHGryR3ocqrATNks6AhLCbpWxzKsdP+IGjLWMmBGR5kXRlC0VpR6mwIqyL JmlWiN2kLySwzDbBanj0yM6XsHlxctKuEa2RpdNtUPMRAQ+oQnOEDAnVmcSlw87gcRweY51qtaab UcRRGnyU0UhU0UFOiLQU5FHgGg4K53OcmsA15FmmZlukitHKkVB5Whoyf1AgJkWZA5ooLJCWiT6L lSIC48ZDth6ktJKnRrfIQp44NbR0hhxEksowYxI1Hwz5Na+ffC+IGkCMRgE4CTmVcC/JCrPYqkRt OOk6kjvWToo/EjhuRO8bpi8YoMOjnFyFkyAgi72YolNn/sv1AlAk4x8yxVIQBXJ4zKQFTqWfbDQR c7Fbgdx9/qsh4dM05gUVY3zWxgxpcsBwwFUKKpNGH8eJZPmEiWkarRJYK2ZbMv+cwHpmpprS+9FX HxK3+N677N+Fz45Vfqhhv39/lV+ju8BlqMDXF49ehr1W9C6D39l+RR/CHb/nV2DamFd7/iRXgRm6 T7fySfzwOc9hL1fO59iDzFs2XWrL+VXg5C9YQk9+urkvYFapS63rFtMzX8As8/qmTZ164vHHdWT3 s887b9y4nTqy5eq3DcHzv7rirXJ8WGjE6Tddtdeaq57MqptPa69RdaWKyGkylp3P5ZNzE/ObUy2J fDqF2AkFFNFMGAV1DYEVQi2/avi1kkJPxXx6XqJ1TnPznObW2aifCvAY9DRImYhA94TCkbqaeCwS 9vsDpuYpkWHvxckoHK2WcQJ+7IGRgI98K/onQlNAGGGfPxbANKe1Ns/PpxJKPlNKJ7OpRCbRmmhp Sacy6WQ6kUh9N3s+AV92oQCWgYOgM4uS247IxwPe8tJB0o8+ij6CxLlL6z/JwKdjZBGAJd0SMWAS m4W+rEj/wawTw49JMCfJXPnZLawdOViR7CqQFrwFqtIQieCLg4YJ8LOsqF+vj9DQEJsnEFA4IAIz dEeCxkwDpJMrFImmggnRJ9Hn9xMBz6OMhYGRSSJNq49Fa6NRuFK2iN6NfpAo6SidySACAZ2jIE9i thgzyb0i3wqfJVyQLgAotYi4t0ihF+EZSfTijoz5dTLwQU4WAf8Eg5UYL49kCqupSOqMUistEy3H a+mlt6a/IUyWvydEKhgISvfQExN6B1/yqCwEuqT7IHmAOQmTF7DEjxja77URkfk9RsRvh3SrUMjk JTArkc2hoEtlchgZ8zg9oVesCUldKodgC5dlEWiZzXN1FAUJGrgdU/OTieZ0EgkeG5DGLyq1HF7L ErFokDauN/owSkmpqcT0C8riqsBOy8mVxyVRTsAerzQxsMK85E72vTwGbAMxdvPT3D2cWwG3Am4F 3Aq4FXAr4FbArYBbAbcCbgXcCvTICqS//3RBTP56223U132/uPznqdegLoc1+ZN5szWdh0jgpOsb i3IPioaG6C5YlYIsi/itpnR6dmtSHIIF0fJEPVpDyEdUFksFNYgcStGE8fj90UioPhaJ0ePQiVpP 54uJXCmZL05DkUWiOPRFHHnSGhDhD3AFEx8eRpPwc2/A9viKqqekefOKZnu84Ug0EImGorHampoB DfX9Gurr6+vQWoGCahCGeXWoGaijYBDsZMGY0HKl86UEpMUw4SaE2DdLoz4EW44nTrFIpo8H/H2j of7xcDzkh75Jkjw4z+sBBgVYCP0CTbLJFMLPW3IFdGRAJbgZ5AinZx6REV0jPUSViQgKugJjAcuI +Ij8ftXyaQp5XkHyrjw6Oi2S8BUkVKWi3x8kaT6MH9TjxU2JuMmjsa+ow/xehXIHvRoJZaSMieOS IC2DQhkgLSRvfp/m9SiEk0GpSNkH9yCvg4VxmQKSWBSiLWRezKgg1AnLYJH4fxyMnBuEUSA0ortI UvuhlVx8zKoFABiQiMr7JY+fdfBAUfR5loEXMuTXAY8e3fJ7LJohMpd8KSvxXKVssZjGc5jJEknf kkq3pNNJYrYghLk8oBDLJ5QrK8ldeQ5CIr98ISo9EW6R3gXSItMLylVgWaIAc2SD1FO+BZdKVUQx BtJCq1WWBjJLKJcjZGMtnE+2pPiI/rzku3Ht+vgCtugRTR/35X/quiO4FXAr4FbArYBbAbcCq2cF 5EXg6rlyd9VuBdwKuBVYBStg57/55O18dWGNG647oK014iq42m5bUq8xML798mMUBVSTSOdACZam KwAkZDWkOIm9T4UyYIgDfAGAyDDHiwdqKYij0QOXQJHDvxKYDugSp5kIhNDjsC/8CF+bEDAFBiEq HXhEOtfM93ROBKvUR0IB6e6nOSgEr5/u1+lraBQk6QlzogLFwMgmXEiS8EX0BAFhJiX0VMLXxP6G oocDM3PAllgGhQ2JdQ6UlilC7rysiSmEvEqLtO7LEynliH+gbB5+gL4JWIeBDm0RQwXQM3lUlENi i+MYtiyAwwK7nO6HJknqKJQYE8hCZZgbljxxZRqmTt28opnCWGcYerEAWSOBTCUGzSCfCxJmo8BS EKAxuCNNEtAHqiPn32vTR9GZvHhJEW+RLGbhXHTgD65M/IJInaiqjduxYHoDgVqfjj0RH2eJ/C/Y IbsJOBRuBD+i6WJe2gsIItLlNEkROVm8fiv5kG2JJM4HqeNsi3dQQJNTYYc+cSjDVoPC82zgFnbV UMCLBZUVR0mUV/VM3kzmxOdYEyT1S4cvSbNKEvOFqolXNISMDZ2bZYEYid6XWD+5liRszTE8osmC Tso1ICXTdIGG1B21mbTjZEKCvVCiITGDm3H1iHiO06qDDj2ICMFy1A8bqc31qgVQujkhbpxXOXc4 STkgcwGS0bETdR2AEg8rkjjwJDUIyRVOij9tRqX1ApUTnygOXFGiFfjSB0ATtsYxBbyJPTQUkF6k fCFXuYcLA2bJBnvvsnu3/U5ZEQdy9f8roqqrwJg9U//vOqpWgUtr+ZfQkx1Vy7+6njPCavh0yyfU N/6n3T7VvvZIq9yanltinvptpvJ1sNYeHu85p6g7ZtKTn27uC5juuAJ64TF65guYZS6ka2Bc5tJV d8x+ed85E+6qtEMM7n/FpFNGS+SNe1u+CvQaVdeM+fO4z0slMLRFwt4oXews4EIhBPQh/koltAvH nwTMQxrCNM/zEn2uCyTxIKXhLlQg5Bd/IqHydO/zEPGOYy1fwOSWzuRz5HjlMRESRwXNcXoZuDe3 Am4F3Aq4FXAr4FbArYBbAbcCPaACdlp97Fl9rz/rpzylfsQHee1u8UZ71JDKfXXjXD3gzLhTcCvg VsCtwPJVwJrz2Rtt+emNW48c4nKu5StoZe9eg7oQEHHPlKymPDHnFv0Ekxk6FBbTuUISF5oodUTe lS8VaXHYlEolc9lkLgP/wqqGlAflDdoXHHmIrEQi5QS9w7zI9uJHyK5IbQeLkbiOpgde5iiNPCTB 968J14awMKLfweGIBEmtCaAkIoxJhZ0hwnHC8fGsobcijEpUS6iVgGbk1hdxw9lWJCASqiAp8V7R d+EubM4Ws0ikJNfdyhRKtQGPWSpqfFfKcwii0gM+PUBSfthfHw/GaADpRSOmhv1abci7Rk240TFD OohPQ7skDRE1DTciWjOi7pELoQwiwZ6tIrA9USMhSZIbaVEY7VA2MT0JzCc6qyQWwiLev5KYB5EG oS9rDAfWrK8JenyqTYC9qMf8ZHBBAA02I7kMYZLhrJKg/2LQY4dpdshBCO0qmeSIOc0nOREWd7RY qVx+RkuiJcu6fAR6EbxFnaW1pT8YppkjEizRJlF8KQ7yJ6hl33hwYG2kBo0WAjVViwSDteFIn5r4 gLqaAbXxhmi0oSZeF432jccH1tWEMKL6ffURGmV66epIwJdCe4FiKZnjNBYobRhVk1kEZuJmzOdz hLZh/sSwiYUT0V5zJtecznFp+ZC2oT4rFrEyclG1pnMpErxQdLEeW8lbdgk3YjAQCQb6RJkPhlJK QtK8ilWRI4k/0etBzIWCD/FaAZurZnHifI6HlKsv6FPDvnDIF/LixVS0XMlqTmfmpzPkss1tJVQu 80NTayKVtkp5hXaXihWnc6SuyLHl4hMHqgBYeiboel04OKA2RkE4y6Sd5QzZoUiOnYXOKxgOReLh cCxA+dCpobhzeoK6N7cCbgXcCrgVcCvgVqA3V8Ccr13+ijrd/Zvem0+iO3e3Am4F3Ar8tAL2D/97 bUrVvqhtuOm6q5k0d4VdE73GwPjs4/dQBJCLEBMyswoFgp8AQNlSQcKniEkitQkogCmvSGZTEcOe 9NUDXyheHHkOUYGn8AIBh5xKH0Oxm/GlopELBfsK0nXPCX8jdxz3WslKyje2dDnEtsbQZN5jMYQP AYbENAaCcKLJgTxiiZQwLxCKARqBr+EExDgGicB7Jg34NMm8JxULqgQMw/sGaYOfEd3FhElyF3Oh ZULHYHChoA9DJYCCvVgBBy3kMw6tUEmIByXRiTFOP0cLx58ESMGYxCtn0vGQ1oFi94OhAVZ8uvjY OC4sBrAm3SAN+kuK9U9se2RMSYSWx6DBIMjMRzGopAFxospUDIQHaAHZQAWD/FQxU5A+n08Ec4SL SXaVkCz8dKbuZLCrKsvJEmbG/nzndCCMh+LwLkAkC6R9IgxIbHgqTkyDEgZ8ts9nQoicCDNMiVgT JRdMHKDSAlEqAJIS86UT7U61qRf5ZrKxnAWxfJJyr2qYQfEzcldIjuekcj2wTM6RzEylVhIcj6lP AvQlfN4O+v1hv49NOb3ALE4BqfUQNyoIZnJaNOJxhIyCkCwgZX0kYmk+qBM/IJKfanITgyddIIFz WAvFrcpuYvdkEELQSOBnfoK1VDtfYlcMtTGqhf0QLEth6dTJ/L2ah5IRQGZa2UwxK3ZIzQOT9Xu8 fgCjjf2Wg9IHQELBONdlliqWyDKvlasWosVPxDmpBGKOB1SunbLjVTyVirLvrrutsN8h3TGwq//v jir3wmP0TP3/auio6oXXzgqfck92VK3wxXfjAVarp5sxVd/mVjVRLu+61svHLjAwdmPJe+KhevLT zX0B0xOvmB4wp575AmaZC+MaGJe5dO6OK7QCvQZ1/f3vd1AIgp/IrYIxOX3tJKCITCbbygszUvSg F2kOCp5CtlTiDT6qK+hGIicB4ZCAAMIn0Ww5iUxCA4AEIuZK0XzPNBlFAtQ1KI9Fg0CCotgsFvD4 YRXIbExohQmrigf0JKzCtCAi5DflC/zIolUiXIXDQXCEO4BIzJKwBqEWHkgT0MYv+ipQF8wC+ZYN deOnoBGypWhEKNhIEVIDm2DSYdRZ0BrADTodR3PGAkUvpKqQEcl6ksR8oWOgE8FulWB0P/UgTKxo FvPFnO2klQH5HEgkbQGRvkl6k4r8qsSaCuTrE0yv+iQ2TEX4ZkBynLAttEKK3+P3qX7mDs8Leizi w7h7vBplRMMF34ENpXN54A6NB0Er0DP6WiZyRWAPONJGdOZkbXGemCVSMo+HYC+SrcizV6t6N82P EVUnjopQe9LgDSLlC45Ej1MmfErOMSI1YYmUC+UUsxeJnU2eGlI2j6NYovYlvwcMZBMwT2k4qdhU ybrirAEiEUKlilbIFwg5Ufqca05Tnh/pEpQm6ErOEBeAXFiM7xxZTgdIDREbYVuhAIHyXFxBuBKM lfmxC2WCeDI/ubo4KlI3w0JYJ20+GccGL6INpL6CulCMFU16RhLUJgeQq1RopyaHk2h7QZq2lc0W 0yi5KCPILxoINqeBeoRt+XlEM1UJ6qJM0kkTzsjlJ5XkdHGCEJdxkVH3nBoUmis8UY4E9mLVHG5v F3Wt0N+j7uArqQI985XiavXeeyWd+V5w2J783rsXlK/DU+zJT7fZU7WnPlE/mK2+950SHWgPiysb DLG339DapMb5DKrdzciqb3ysPfOF+t5XygxeRMSVbQfaI9pvbKnXX6Lf1JZY3LZvwJ50gTlKUz58 znPYy5VHxx5k3rLpQtKv9jNJlwcfau++iTUs1G4SCx3C/vOZ1poztMffVR/+TjEDytiR1uFbW9s1 /njmHT5RK3bDnvx0c1HXij33vXb0nvkCZpnL6aKuZS6du+MKrUCvQV3/fPxeChHw+HBySQS7kyLP P6hwMoU05KdESDqtFbHviSbL9OrIbUT2gqIIrCA2PKgLcelOqDk7FhxpDuY5LHsSl27L9gAmdiJ+ fm5iHgcAi4AZfDoECmgiKAeAIlsIXtDgMiAJuhkKqBFdDY8jrcLRhlBIWvDxCHSMu2SoSw9BzTRA GoSOiwAHHRaABCMks5LHiHUXXZip2YALkacBUzAMiqgq4EUsBmHBpRn2g0t0EBjLFJWWRLUrSN2E ujBT4s5lX5NGhBTJIWYekBFLA8egYCoWQVFostjRAAN5tJCt+Jk88Cdv5Fix6IF0aaWI/gyKB1UB 4xCsLnopZHQ+D+ZJ1okwje8hPmyjK0LxiMPHTFc08NDRahBGxrqQe4niS8ATxkcblIa5T/iLI50j VY2Zw8Nk0hwCxISdE+8hR8N8J1WlLgIK5SbzQn9W1SsxOgWkdySqLrRZYEORShVzjttP9dMlQNL6 RfXk90dCPn8ij/vSDojZk1PAopinh/k7QjNmYwITY0EaOmro7gRd2qS/cx2hHBQeCpxElsfCnarL 9SNXgRgKpR8j5xeeBCED7xWdkVmUXyyZAj7heoirMkXOOH5CudK4cjlNaMikTwJJc4jqvFQma5hw WS5PtIiyDL/OEbmqPYDcHDxVsSSDX/aQK1/0Y06KvkA3RwCIgM7rD4u/VC5t+ddQLMRrXMl77bbH Cv09sqIHd18prugK99Lxe+YrxZ783ruXnujeOO2e/N67N9ZzcXPuoU83U33lKX38fxfVJDFg//IQ 67Th0hGofCs1a5feo02as6glBu3zTzCPHCCvepcRdS1hJm2Dl4+88CHiASXxI7IWsm8909wh3BMv n578dHNfwPTEK6YHzKlnvoBZ5sK4qGuZS+fuuEIr0GuyumAL3MnqKooBEGoj3e6QtcAjBK9oHj8k RJokIlEiYysYCUiaEnlHiK38hNCLE9CmxSAEoZYfe/Ug4inncXgDdIZsLlOlw53KIbCx9YvFuNdH IEt0XwyE/GRthcRLKHlVwAsNSxrYS9rhGSWARkCX+SE+olljDBWQ6ID8xEgRz0WUWLZoZHKllnR+ fjLbmimmCoKEJIhKk8wmKA7uPgv9lT8Yj8YUTyBZ0gu2z1JYQUjXAwUAmYX/zRdjWKCOfFYnuI1p h5AbBQJhyb0iBEp8g0AQsBEGuoiPTpECuRBS5Q16ESJHoi8jUjCBJsR1zUukpje3NGXSzVnyqtC2 IUuymQ5rTBcEsYUDQCspoNA9eI8HPx1iMmGK8EECoRpj0f618T61sdpYJB6L9GuoHTKgT9/6WlpW him6RohVLlXI0kxgXjo1L5ma3tQ8uzWRLRTyNBykNaFqIeHKZPOt6XxzMjerNQs6jAQQYEUUT0j1 hBQ9oHqDlB8bJrZIXKkYAekroHp8RUvjMqCLANqugNfv9/i4R0PR2ki8MRYD4jFl4Fcml2tOpua0 JgCSQc3WbdMoFRVMnKVSNpvLF4qJdCaZzvlsk7VJVJj4E0u0uQR2heBKuhdFWIFLxwFaCLccUuWk Xzl9KPlWDK42yFUEW2wJQhWxoe5pzZlz00aSpUIvFW88EKoNRWKhEObT2hBXJ9Iz9rLkuFxG9F6E G4q+TIR7pMbZigcvKqouDkvEGiUN+bkkJL1LzpFpERAmfSK5AuGIpjhAmXZTMtmaTmWy2UI+x1IZ GXIq8NS9uRVwK7BMFfjwww9feOHfiUTFM7RMY7g7uRVwK7B6VWDWx9qvq5xL72Oftrd10Y72umUp V169/T7t6da2gqgvPrOAc226lXXnSdaNu9gDyz/PqZc/qU3lJYeqbLePdflYe0FQcR/7/IOsiftY /X+kEFu40j+dye+2tGurg//pfv2j4qJPjTbYvvUk854D7eFt42fVZ75b4sFWr5PsrtatgFsBtwJu BXp6BXoN6gIlcHci5AW8gBlEXeOIccg+yhWJlBID44BYZADQBemTmP4sGNP8VJZMdAhP2OfpEwnU hXy5YsmxHCLekRArkf+oit/nQdTDPYqEqmTMbk1yb83AanIQiJZ0dlZLcn4im8+biqn5SCjXtZgf iZMocjDXIb4xlBLYix6OYCnp+YhfDeWO1xclIzwciTmJ5tFQKAS5CYAt/NAiKFLY5wWkQNwAMV7V TGcAMAZcR6x63GEUALWCXcgr+YJWMLHskTCF81FCoEI+TyTox6HGqyAWBfVrTqbnJFNNmVymaLbm SrOS2dkC17L4FmtIRCdDipkheLOVhmiof11saJ/adRprh4GoGmvrIsSZR6OhiN8ngiTAViAQJP09 Hos11sQao1FngwgeSmCfUbSZZFwqFowFSWTXAh477tfCPnKyLLRWPj3v8xr96iL9aqPxSCgWDvWJ x9aor41izkR8RiS8IdyNmLCwJxDx+tHrob2CWwEQxU1IDXUJqwL0JPMlwCKKftnG4y0Y6PgMABFC PQ08hUQOUmhrfs0b8fgCukcxbKyA+UIJPyp1Dvu95NZDkShRBrhllJDjwdsyHL5UCusq0HNWIpsu leamMnO5WnBmomVDzEaems8TD9MhwIczEeEcgiu4konUrhz1zmXIq1aD1gQl7Ipck4Gydk+C6sUE GdT0IClt5G0BRosluXpVCZWT5DBJ2hIFF9Fj+TzXVQ5lGfRPVwO0CfVAXzWpc4Hr27B8mkoEfdSP yEt6hzIOzwVgIrBUvIoSvQbmhJWiEwOfAdokyx6SyI/BZNx7+u8hd35uBXpqBZqa5l9x2WUH77/f NVdfBfYq5H/qIOqpU+/GeX09R+X+4mTtyQ9+fC//qBvn4h7KrcDKroCtvvA6n8w5N9W+4Ejz5K2s Q8eZE3euTsxU7/nUCUzlVlDfn1J93Gf/ehdrmyHWjjtYJ6xZedCcpn7QIuOMGmntuZ4iOu3yrcbe aVNr75F2vyU8vRY1k6P3sa7aqjpIs/r4t4vc3x6/m7nDEHvMaOuM0Qvq2boYLrayK+4evydWIGAW azKz6xLT+s1+70f3huav+FEs39wT5+3Oya2AW4FVqAK9xsD4zJOT5CWB5ItL9hFGPCEixErJu3gJ jOJNPRABj55JVBPCGBWdS4lIJAyMbAEkw8PoZDCBAMSahwgH2xfiMIm+ktyushFMviZqqTnRxF4B Xc0WS2jBGsBUfox+chQAisi5yPbSbWRTHDtTgJoURXWmYbtDUCSCJvRjIA8GFPYhoiAJWxfFlQYz ETsh5kRaR4I7xPOIlsiki58a8vjo5IjTTeL3kZvBMmy7qTUJoIpGMBtKQjyyo1w+D6kDp9HyD7cj Tkrpe1giUMyAbLAX7AlFmbgZPRL6Lo8rCmyQWzjgyRQl9wlaQyI/hUT+5fWpiJiQGlEExEJ46MSU RxqZR2fyYB/ck8jCxNNJ7D64xzQFxnB04IxjyGQ42CJ7MUm4EuVE1hYKxnSPH7+eVI1gLAa20IuZ sBhJlGfCWdLvbSbM96SKSQw80EiiunR0cfwI6kTpgI+S4M4WCKZQeLEWR33GmQLxiSWRa0K1oDxy MdATgKaQ+BN5WGWcIASNJCtnVrIWiVqjTJqEecGssDTmSgZdA8qWRKgRQ8lR+RFUSaXgJmlwPFpu Zcg5dZpvipzL+QIXpwliY27IsXBxytmVoHrC19gScZaWzhf8dC/w8RqV3p3SVhKHoUNscSaCaqXl IoeNE36vcKGiT6RUdjZHeL18mCvWW1SDwFACzwj35yZKMolokyy0cmgXmyg2IjUOLacdiSOx9Ion 6Asw51322LNX/9Zy9f+9+vStuMl3g/4fSReoq20JDX0a99x7n6232nrwkCGLW1cPdVR16Wloyahf zlI/+V595Svf+1Mrb+qXfITN1gqNGpgZOdjedIhdG171e8j1ZEdVl14LK3mwnvh0S2lnXKY9U77G G6zHz7CGOx8rF77Rt71DTTkPe0darx5iiboqqz74mja3XEXd3vvn1hDZWH3yLv2cKgIbf5Ixwfl9 s4RY+kVndS1mJi2f6tvfX4FxA8aZ/96RFuc/MjDaE88193Y6gC05BWwln/7q4Xvy0221egEDvQq1 futr/Tac+66Ymd+Ry6PUsHkpMqAQHZSN9M/rC1huR/bt1dt0wwuY7qyPa2Dszmq7x+p4BXoN6nr8 sftZFaIkLHvADDHBgR+c3ojEXcGUeOVAVDnQAaoFQpCEd6OE/CcHpNC0IIogh3Y57fAkf8rp6Qee wIpoyt94yY9CSuTFwwaeyOTk1YjkR5EvDiLRRUoDw+IuOfXSMRFRUjFdKMpDEh0m+U28TkH0hOCI GTqGMrob0oyPZHRUSmI5hB3FQsFUvkhwPnzCI5DOjpEQpavSHNFJYncS6YmgsgWm2CikCBgrShQY 0Mok+FyVdoHkwTtrFzIFwHMSowjgEuAjGfQwES0viVy48PwIkVgdgVkcAdzjiKoEvmCujGL907yi YSIbnpgwnYaDzNyC0qERI/GfLakydknollQDxZT0XGRoUtb1ED0EBesJNWN9OCXlxRz6KSdZHtdn OIhqCqgktQEDkVkFoSMUH9jEv/AubKdYLKWvIuM7OjWEUhzQoF4S7CWbAXcQyImJ0Ok1iMYKwERB KBfrDwgfY8Z0JQQeSbBaplhIZQntIigNxZycaQgRVlZ0fyLLIi9N8uAltIsLhWmjkGrNSj05QxSc R0pCkkgok9YHItxSQJDSktLhTtLVkX+ZmHBDH5I6NpZQLgnXJ4NNmnGicdPLC0FuSKMAOnlSYAyg GBtR7CGtyxvyUy48RGLgTrkDp+QkI+GSrHsnWE0spdKLQPcIW5XAMqmkZPU7M2F+IiJkMTreWy5U w8pnOa0cGiRJ70ivx9+vpoZdxu19YMd/KfTALVerV4o9sP49dkrd8ErxR6irrRTrjxix5957b775 mHj8xw2he+J77y46hRCuD6aqT3wUfP5TPvmo3A494vCBAwf179+vT9++DQ0N7Q81f/78uXPmzJo1 e8aM6Q/e/0Dbj0YP8e6xUWG3TVZl5tWT33t30eXQI4bpgU83a4528LXap+XytOuTaGfVj+c6rym4 +ZT1B9jySVS7Wz6rzm5VWkQ5pb72lHbTjMrPlhl1LW4m7ZFZcDPrlQOsGIdaOJbeRV1ddX2vDi9g kGiF530aaX7PyIpQgNuaQwZvvc0266+/fiQSGbLWWnha2tdz2rRpmXR6ypQpU6dOfe7pZ9p+VOi3 fb5ueDo+eHVgXt3wAqarruGOjOOiro5Uyd2m+yvQa1DXE4/+jerALwRsSLA3UUk23zlciAxyPFq4 3sg2ovkhYV7FoFf0RsAKyTwSRAKXgAYYQASPEz4PE4IRwHhIy0I/hT4IKuLB0mZa6aKBpEpeikjk E5tpEAfAgtPmzsQtBnaAROCgxGkGdZBee5LzxBe8PpG+fFjGyq322BiZmMOCBEPBKnAs8l+JDIfU WEYSYZWwGMEXzI95BWQf2QDSBDeBpoGWmDAILODDXGnmCkXwnOSL6WAUhEVwHJo/YpqDQwkPYhZI lCSVX5ogonRDziXIDCoCnnF4Cj3+BKvBXbK5UjJbwKxYE/aXkFxJI0hJ42pO5xzlkQ7A4lhlSyN1 JsZK0I40whRSKBlWFKUsMxL8UpbdAYtAZsAXYBBHFn8llIqOkI5yqtz+UrR1IDEYIjuKZ9BRSznP AQcwOf/AnnA7Oh+ISjA9ZwEFlCxH+lKi1bIoPBMG6kE8SWHj4XROlFJgQngYp0eWJE0Rpd8i5wjU Ffb5YG1UmAmU082k2pAvkYfJI3zNoSU4i26SHEdaRjrkC+rkJPwTa0YZOFnwSrZiLNpZIqwTn6Nc WlyZYrblvGUKgqgIuy+UUgE/SNSTKig1wTAOVGyITptEBBHkbeGDlEOwWmnYiSTQNEWZ5dhYuV4J NWNbNIaOJBBRmCTTc3LJ7eJgXNGS92+ZWCXlOhOFotPEUvXWhiVCdtxeB3f/L5cuPOLq8EqxC8u1 +gzVDa8UF4e62oq86x57jP3ZzzdYf31/oPK+tQe+917+SwIT4hPva7e/VvE67bL7bjvvvPMGI0as s846HR985syZn3/++cv/+U8b9jp4tH30dtawvqugyMtFXR2/MJZnyx74dFuC9mpxK/3qE/2vr6mP VdnWjzZbZtS1uJks+nEXdS3Phbj4fVfhFzBYFCOJaXUznzcTU8t4a7fdd992u+3WW2+9urq6DpYz l8vBvD793//+9c9/vvv2O/LmK9yQrB3dOmDLjC/SwUF642bd8AKmO8vioq7urLZ7rI5XoNdkdfE2 njuOP2yJWOZqgp4YuU1eYAfJU76AL0TWfNgvxjVFyytaIccnC7lUulQU5ODzQZOEzIAN6K8H84J9 iQxIQqHqw4F68pxw6llKlsc0vV9NBPkXd7HL6Xos4KsNBcFJfp9X8tejwdpYqG9tbHDfhnAkGuRR THpefyQYQnBEgjqx9EEy8QmMJ5YeSoQMTfdIu0VBHiI2A9SBdBACgSuAFLGAl3ys2pA/iggH0Vk+ R5i6IvHoxVQ2K+0c0VCZRjZfSKazpHEl0jl8i0ReEdsEjItIwz/6OIqDDaGT8Blb5E7llKt56Ryh XcTMc89KLL1BflkqJ05QiJP0XkTbFQ6Sw8UosYCfSHlCxEA/9bFw39p4PBpGhFS09aKi521RitHQ smgbaaOUyueBSqlCIYejEQ0R87RoYkgFxL6nqeInDXs8ZKU3k8kvgjVBVKSqZYtotcBMQg8luV/I IzlTkisPkgIsSlNEgVSCNSVeHrKj6vRrNKQHkSaJ+EE/CVrxUBCOA8aitrRchBimSkamVEoWEDSJ rs/xmYo9lUKzjRPNJon7Takssi+yzdD9lUQ6BR5iPiUeyhSK89PZH1rTTWnitpBLQZ8k7YqTLAyL 2DSWgJ8R4yt9HmFnjiVVSBNYSWYr7R2ThcIPidT3LanZqVSaeHh+BBSTEyRaLSLewH+cIMAfbJIz gQ4OfhbBi+uMSSYXojC2ceChXDbANMyt/Bd1m/zHcS+CYcXBGiCySxRqdFqI4Fn1+TCOWh5PUdcs r0/z+7F9yt29uRVwK7BiKvDsU0+de9Zvjjv2F5MmPcBLvRVzkJU5KpDrwseie16rw7k232LMLXfc /s4H799400377LtvpzgXa1hjjTXGjRv3xz/96X+ff8Y4jPb391VGPuLWoJvntTLPsXvslVqBL17X j5y0WM61UqfmHtytwGIrQPzW4I+vjn92G5wLbe+9kx546eWXzz7nnK222qrjnIvRg8HgJptscsSR R0566KFX3nj9rN/+tl9jODDjuX7vXLzG1BfCxbR7DtwKuBVwK7DMFeg1qq5HH7mTRYZFyaTixwPZ cOdNfzwYgKqgbcFhhxtQNC9efFsiHcItSDO/VNGxN2JPs2i9B3coobORfHA4go1tEFoBBhOZFxwK gQxDiVqKHHgRKKlkZsFQyF0CUqDe8WNPFB2TWNWgYHPT0v6OLYVokUXlaKyQTaGAAoWU3WccCMLm RRou3QslKEq4j6NQw69XzpxibJRl4o+zLZrnYY3EhMac4BcQH6bEVETHJN44ma00knTkP8imJPEJ DMSYYsGzyReTdC1BXyL/ge+xDQflcTRRkEKYFNiFFCchSmAgSyLJgsFITVCHW3F0RFoMSRHYgH3h KlAz2/F7UiH0XUyHZTNhRG2sCcwHtWFLMZTKcmB54gdlWWSxoyAD3DAhkBIdJaU6TqwUm7A57Em6 OkriqsjEJHBLOlKKUU8aaoomTmbi8foRKTEgcjbnvCtgRAlNs60o4WUGejbcmib6N7ifE6Rm4aVE tMWBogGuEHojmngY2RFnKPSNeTtdJQUcMYhkdjmHE0eo7C9nBw0dZ5WD0tVA1HpiNrQkNB7JnAPR WBEhX4LiCMnKFRkAPyM8kZnA76JwKNRz0rNRzxu0KyioOrn4plf1BX3BTElBzs3VS6kz5NIXcgwa DRIrJtRMZHiWuHQ5U458TBPQ6ywHzRjVRuIGJYUAUnZRsukai8oVi3Q9EPmbKNXQ9llOVhePKHvs ut8y/47oCTuuwh+K9oTy9t45DH397J42eYyNjx35cU+b1bLNB7viNc9o0Ch2553MQQcfzBuSZRtq cXt99dVXf7v77rLIC4XXSTtaa9SuIgovV9XVtZfK4kbr7aouO6n9ZqL2dLlPsqqccLh55Dp2P3/X ZHW5qq7uuQiXfJRV7wUMSfMN3/+rrOSCTB18yMGdYltLPSnovN544407b7+9LPLKDDuktXHjVc/S 6Kq6lnoluBu4FVj+CvQaVZc45iA1pkGXQ9RYGAaJuKoJeIUeQWtsyIsZ8tnRgBYLxQKeED0VoRnZ vBmkVaJPDXhIUsqnCzlCshy2Iq/doQfFUjHqVQNQIwx04h/UiEqifaPQD0RkJdK+CsiXUvlsNo8c qsi3ZFqVTYIFW62NRvrGuccawmFENpEA2i/JCAcAISryIpkiZ8pDKDv7IDUSYsYdgQ6zklQqMawp qLPI0YfsgGvYE1HYgNoYjQtJ9SKbHlRBD8eacDgeDhON7/Pxf5AR3jfREInZEfbhUWmDyL8wMwyP 2NzKSWFElYGTIDMsCqgEn0J4hZKJxcZ8aIuYcKCenovhIEysia6NJnxQlGKknnloOSjtAvUgOiMa EaI4UzDoiQkR9RCMBrTHDkicEvlicyaf4sQQ965rYZpL+gORUAhwEw2jdPPSLBGc6EOrRNCZh96S OvqsItRJ8JbH7/ej7fIRIaZL2hrtKQNeDoK3Uie/il6L9GTMm0SJCQ1DJlcTDNHOkpAvKhP2+lgj xyWwKur3rllfUxMMNIRDNaEgjQQItmdc5GTQIkRktBfIShC9wuNU0sL3CtpipVSOydH/UZe5eTys 0gefypmo/Mwsg6uEiOmU12kuYNLEkfWimIOsApmYHgH09TTZDARxy/aNRdaoiSPTg7fNT6ZmNrfM bGlNYuAUQKdFfAGiu2jxiHqLzo+cn6w0D9VEyeWlN6jZki215IxUwSwaMDcdyMj2kFUwF50+KR8k MGfYObFu6v5gANkd/RjEjEognY9qkB8nFy3XgCBUB6q6N7cCbgW6rQJLiKvvtjl0yYFop3jwrRE4 F9qrZ/79PFKsLudczBNdGCMzPo5IjrXjRJ0ejl0yf3cQtwIrqwJ62B7WdmxyOatfWwn1qbYWpV+o NJHhlputvlLmXMhbRlsnbgjn6rKJL24mfMjY9uIgXKdIUJd7cyvQgQrgWBz49T9r//cXONdJ409G 4furk3/VtZxLngjBIPpfRF5XXXsNpsjw1w+t+dntxIF1YILuJm4F3Aq4FVioAr1G1fX0E3czcd68 5wwJWRfHFv49HZAB7SHPCN7Fe3v+dns8tFv0ABFIVRc3H/AFoQ1QiWgtsaopajgYJIIJksX30oaR FnsEigMWFDXoc3oOEpPkJKwnMfqZVhD6QaA9sh4NSRHqGxH4wIvgaF52B2hhoPOKPgtaA3MQ6Y0q WiHQElHrATRi8im1tGGU6ChLktwZAytcJIDOyMZwx1TYQnoC0owPaZKEhOnimSNpy1aRefGF0wlR wcuIWIdMK5bEjGEZEqMOCBFFkrx0cRKlJInMyadXLVRaFjMpIf8RHZP06vNgfKNWATL+A0GACbBH ovSFAKInkhgrQqsYU+opNcOziHjME4tF0MyVk6TEr+f0KOQQLNDZWMCKYxOFColAq2gTNGYA7DAb Oj5CRyBWVbTxDUIkOmkiE2PKkqdmS36V9L8sZ6EJwlSgXtIpU0UxZ5J/D9mTgC30apj2dJ3mBF7p i8geCK9E2sVkwD1cJIjOnJpomEzxV2bJuqI6eAA9PqotI6JS84qOTERZIrtT2MvJ25JTyIHEc8pZ dAyhphM5T4FYDkq7uckc9NLp2SgqNnpRUuyCZeWMYj6fBwWCugybc0eTSj/aPfyPiWwuk88A/rhy fBIFR4SYykpEOuZcclRDCuS0JuD8iaDNCRET3yuXDexWE9ordDAQsjB7sgk6NoqGKZdYNNhWUXqS SvNGA7DrZOh7vY0RiQLdZff9e/VvvlXvQ9FefTp6zuS74UPRpWZ1lavRPqW+B8pMOnXKckXlsid1 wBPvMU47/XSMip3afZk3fuGFFy794x+/nzpt5w39F++b7e1dGl1V1zJfCZ3asSc+3TLauZdpj5cB VsCedIE56icdGNvi6md/oI/9RyUCLz7WfGMXW159dlUHxsXMpP1Bh+5iPjnW7cDYqYuucxuvMi9g gE19v7ybeBg+//jDH//YWQN756pW3RqF119uuOHWm27mgeyQfWYN3HbZxumBe3XDC5juXLWb1dWd 1XaP1fEK9JqPT2FIEjxv07NPQ34DF2nJFGa2JGe1ts5MtM5Pt6aLGcPO07oQ2U0qm8GIlyqCPIqp Qk6Cm3QP0qGaUKg+Fo0iCwqFYuFQXSxaFw1hSfPTqw+xEkY7HRmRmTcsVDbc64KBmJ+GhYI68sVc MpMPSn9HgSQogxrjqLigOkKjMiLCUdkRXCHNE50AKbATuKIokfSAEsgGEesSfp83SkwM7JPI0avQ iPr0mnAgInFToAkyv6LRAMFNQbBMyYDoiN4JcBNF7AQToVmhSWIU8iIx+iF/YnSS44l2ojclW0r+ ukf6VJLhJZPDXBgMxmLRvvW1fetqItEICU+Qu9oIO3lQJmHBI5YqW0SrBGsRGyi2UIfWKX70ZjAc r8Yywyi/HMEa47MyKFjakSNxIORKWVCdqoaopCxC2jXyPf9D00Xxk6R6FXKpXB6aI65Gw6D/Y5ao LNE5qYwC8aFAgEABYpg0uQldhAF5ogF/hCQ28q3IYWNLhyeyWB+dEBUrxNw8OvloEX8ArkX9EwUT xRbBbSjLiH6nJSM4il1gTzUBX12YVpwiVYNcCs+yaQKgxaBpCMZE3OXARaGKiODgqvI8kvaRNgZV T8CxuSYKheZskYuAIkBIkYllSlYrDkSndUBtKDSksb42EhXG5dNrI8GaIPIuJZUtcCnEEKw5K2Il nMeimCBBhIVMPkcIHZCL0mHwLLezlA4MwCwhkaBIT0h0fHqaE0apjbxi5Kx8MpFsbUq0JLjai8Ti 2+GAL+rx13gDfYORNcKxwbG6PsGoavm4d/w3grulWwG3Ah2vQEOfxmNOOOG2O/967fU3jBu300+7 MXZ8qB6y5cwWde/rg3AuZFb33n9/t3Euls/H+A8/+ihOSdo7HnxLyE3v6iGXhDuNTlcgZG8+pLpT Xv2gIkZRP/lSld7ezm3oWnaj8wWfIUrvGOdWTKtZ5ws7o/73u6Udtp1ebLGbLmYmk7+qwDV2/Nkg h3O5N7cCS6wAyVz1H14F57r4j5f89e67u4dzMSMUXuR/PfLE43z0Epr6xOBP70ZZ5p6rzlaAD+0u vuj3b775BuKJzu7rbu9WoFdXoNegrv9n7z0AJUvrMu9TJ5+KN3b3JGYGZggOikgSFBEcQIKIsETF xV2M7IKK7oqifkbMou4aCIqLCotiFpdgBEFAkoiADJN7OtxU6cSqU+f7PW/NjCNh6O653VN1+9TU 3Lnh1Dnv+3/fulP1u8/z/PF2cUfag3QFex4f0VtBLa7Y3PyCI4cuXV/baLcPdTuY4GYWeqhoo7Oy 0V3vtTd63UMXrh+5YHWzGXZKy4uL2bCYjLlPqj1sYpWF6glgtNYOITXiK3joovDkMOFuwp7wi5U4 wpD0BLCYmY3LMOGxxezmUUaQVzsIyK2fB40TJYYAh9z0VtjstdorrSaRUrjncomOFPslvCSLn7sS eW2ftC91KxymkKCikFCqQbvCqVXuxMkozxIISBrvDAa7Sb6XAETo0Fih52r6Oo86AQqlcRJxE4yE WOr2kqKfFnxCoBUgBqEZkA96AwQDVEGUgHSYBRWjrsgnAJmkb1K0VRbMS/5GRVAhZzJ4KHS7TIAx mb6DhF6tNCFKanOJ+ZFpznkRiAdHYQdHY8OOiwnGSWLYlelOQUBfCNxmNERExGbixAiAl+CI6xNG ZvWoFPZBz+lEfg8OJZ5kWgYABx0LdCcANyFoiwUviKeH/8RFvjMeD5JxjKU0z7aG45OjlCmDidDr MVoopLRXHhH7bVYBaqTWlBqnCxACYwENVTRFm5kWikrCp5OjQq6o2DArKAdOVCWRyeVakbUPQYQb 0myA4nOnghJ5kcnmePgWD/U6a8aqidwsp18nU0fpRrgYf4NCUWhWDUMmLKuYkhomRiYNHaBLDT1n vo2hVYotwxktRF7gUToAsMBkmylCTOFlknlRYfps7o2TrcHo6N6AbSNFGkwUdRi2UOLS4LFJf3u4 c6K/fdP2id3BXmTl3Jf6l1Q9+LoCC1gBGi/+1M/9/G/+1m8/5znPPTCmxX+8xn7+bzZv2pkQv0Lw PCny57jyGGHwM/JW6qbtnLh6xnOOB1Bfrq7APlSgUX3ZQ6rV2070C//XefV77df/hf2id9z2Laf6 1i++FTBFR6pH3Yaa0g82XvsR+4PX27/zx/Zffzb+ZEfVJbeP7zr7F95u/8lHG4M7GfHnGMnLPnTr Y5wj1eP//Yz7MPX6FAeyAoqHx0V42aUgJ/LjwU/neJrY5/lDCH+AcfsfxczYzXbP8QAOwOXe9Y53 /MgP/uBTnvTEV7/qlR//+McOwIzqKdQVOJUKLM3ryHyCBS2JC3r5TZG+hGHQ63RWOs3tNN4a867+ putP3HSiv9vynS4qnchDKzSppgAN37aIcxoXUyAIYVcQBwK3ilFcJVlQzuJ02o1aUbNV0cQwCBD8 ZCWwBvwFeirAEKAGyZMw0AmGOOoeOOWbzuF2dMVmFxIk4CTdD/IcrGlKxod0EN5EV70pCMJyV5ot ZEdowSaE3pPsrqBxK1Nu1CwvJklRoOc61EZxhnbJ64SEuONRA4TAnbChTSK3WsVnSBK5MTDKKddo IOxSH0eT3ov2CI7DICIaRDbDQ53mRjtSi0ZzR7UkpyeaqdksztRukM/hTBCxEbFjVQU1Y/homg51 2muI3YKIbLF8UsV5ORilfSLTi2mak78uwsgaRD6cDpET8AcchtZJ+is1C2BeiKQUj+UBB/Fhognb S9JxkVEcCtGK8PGh2MLsSeK7w50D4DRTefbkEjQuRqbCtEy/SuL1G9gpCxAQrQgCp3RZTY9quOst rxM4qJU4jMq0PLRbihYjAAt+R4I+YihKgrAK0gcqm0MivslCANPM3MkZo/7TfpwfG8SQMtSAxPMf 6UYX9gh783BnKhVONKrajuk1WeymORRsUqo/QOT5m502UrIc7jlt0PpgL5sl9DigcyIDc5V5f/Fq +xCjtAnCLylgTEMAHJZqxohJFo2hB92DnQG8yI8bZBOGpIx5F8IYbHaa620BNioC1GUMUL18UkAY V6Nm5IeErG10uxdsrK92e82ArDk1QaBTw27CXFgxWXnZcxA99onxz9a3ugJ1BfahAhgVv/elL33j H/7Rd333Sx74wAcGYbgPJ12MU4hzvcaGMdFIi/iVu3FQvJViDAyA8dS0625ciPrSZ1yBI/ef/dTD abRtXqSdbPzcH9k/8s7G3vx0YfXN3zB74sqt5250Z9/06FuPtMrGr/6e/ZzfsH/Vmv3KEz7L/7nt Q9W3P/C2QVXWm//K/p/vbaR3Oso7G0lUfd8zyvvXsu8zXubz44FwLvohYlpE53s2EhtPsYr8IYQ/ wPBnGGLCLvpYTbtOsWz/4TDi1VjH33/DG178whc+/3nf8Kd/+icnTtQJaGdSyfoxS1QB50UvevFS DHd0yzW9Junp6iiHRIYbvIYkJd+p1tuAhRJRTbupXHDkU1YDEgOKUe4Tffp4q58VSpenpZ10WUq4 hwxAHdQyD7MdmVFKs8p1Q1SkHniVCBIUi6vMLDsgMR1IY4gSrAqNEywBzdfOKB6kGaAGogEQQ84E XjHNFdVnEEhhOJli6qUqapCODxXCoabvIYbCHAcYAtioLSB4qCTsCR6GYgu3GiZERZUzeECSbJWY /wjOdxwMcM3AB9kwwXmnRQ6QwkuWOqmnuK5MlKa/IXBmnGTMjiEQCNUEDGHJ9HyQCjIhPHoGk+n1 GN441EbkpzMSPIrSOjWUQw/b4iHGbKc0K2RKypQyyV7UEPpDS0eKA7LhdZlMfo6NVgsExiw5QNFh 6obZIOvetHyUaZH0L7UQtBv4NxWHhUOUDwjKZrI3ks8/r5ppWcn1CqiTvJbkhtG/siQ5TIerASep bZgRNXdgj3pW8iCjaOOs1M1RI0PYqHLCyEEDU9KAsqQJgFZBTkmZPVtYBFGL2RArIr10rRNDVGPg RNZCVeITjjFKOkE0XIR7NClgw5QVWApEiPMRpRjQjb00zlUhcCRRZaTRA0bVM9FYJgMtuG6UnOGa eDJ9adLNKA/mWLGtbDplEVk+sCg/ZQmMM5SYM60vzlDSvVhBRG7ZrIFgEFQ4YcrFhH3bxVMr0Rly MPaEoxJgerWqK+5z1VI8zT/XIH/5b48t9fjrwZ+lCrzo0ReepTPfftprr732Xe98J0bFZz7nud/x wv/27Oc89573vBde5Du57srR3zrbo9r38885F6eFMdEqft/Pf7onvOSSS57+jGf89V//1W//zfDB l1kXry0fsB9c9E2nO+v6+DOowII+3WzrsvvMnn6FtelbkdfY7VubF1dfcon1xIdWL3nK7Osuvo1t mQkfvqx6zCF16y5GjfV7VF/3yOpHHzs7MrT/z0dMAivNx+9ZPfkC81nDuvze1YM7Sqgd71rD0PrS K6sn3LsikvP4p+w3XX9r/S676rbj+cZnjGTas77iiupJD69++GmzR6+ZJk3zW9V4z9/b77stRf+x X17dx/ye+5xnPoPVOmsPWeSn21K/gJlzLuRU//vXfm3f4+fPYDs8+CEP5r3RO/7mbauDjxerX5C7 51pfdgZj/lwPOQcvYG6/9PyVzI//5E9+y7d+66Me/eh7XHrpW//f/3vfe97zR29603XXX+f53qHN Q7xDvSuzG/T7f/anf3IqZ/iyRz6S11GncmR9TF2Bu16BpYml/73X/zqzxc8Fa4IpAHpubSlYTVse 1CMFIPh+s9lcsUs0US5KIYiDkqGIVhdNmUJV+sMRrxtw4UUueUyEtdOCz0ZZA6vKJoiWZrYS2W2Q 0FpP2nNAkrxoUAl58EAloA0g1K3gKY5jLoBQDOoDbJGeCd1S0ASHGAUWFjmUQQUYDqZBCLnywi0L UIcREebCywuYXWjohnmRYYiPheIpxCLHw6XNKadgNSAdE+EO+YC5APoUHc/ZDTNDuWMYE7SmSvFB Wg1IBxCHYTM1h7x5q4LOwLCYFy9q0D/RnRDXJIcBgAyOw/KpvHnORxUYBt0hUc8VZI41LGP3c4bZ FCEUvw0hMnAuPkfSRS47DzeYCSWahFOrrVCVnFX8iB/QB1A4Sv0tZ+Ab5eIj0ALwqLawQgY3kyKM zxmlKY6C8/l6nlRVlVmWz4ocgRYJXGAbxZ+ZUHy6DfJA6oP6DBkaEBPRk+sCJRvIo0QerQaWT9a0 2woPd1oMT9iMFQxsCJRIZIPeBcKLXJcV9MMQJMcqI0BjBnMeCpMC5LHTuBgSObkuFYIv3wHbIZ9W 4Eg6MDJ2QVUbKCZsSgFZozAgbQ2A5oSuN52SXi+l4Fx/xyxYIEn9eIwwnDLSDOObQcTYJHMixhU5 9lZyRy3UFFSAFrFWQPy+Y43SnJ+yOhSLmXjse3aDfKICtUrcd2VBpRZf/cTn3vVfFnfjGQ5Mquvd WMMDeelzkOqKzj9Nsy+43/1OXcC1iDnZd7r8/3xj45m/pj94LAjnun2wR48efd7Xfz1B9a/9r7Mv vWLJ+snWsfTn5nfO0j3dzk1ZzrerLPLTbXlfwNzOuX7u53/+3JsW72QP//qv/frP/fRPN8K1mx/4 ksxZVl3iOXgBc3sN5w12aHZ8e8gaETkf+tCHfud1r3vLm/9yftgznv3sL3/kI+973/ud2W+POpb+ zOpWP+psV2BpVF03X/9v6LlQV5GDTigSxCCFDDSqUOocCw0OrEd/vZo1QDoQFtRM+BArSBdv+yEO pC95dicKVrF7eR4kAJAkVQ3tDu0KzEOkFJBlNp3QdZEzRdEKSeWygVlyhkGscNWpsSKdHOUn4yEV ufVKlpLzThIt+BRqJuEiCajQOpGbiJpJX8jXaHAGP4NacVKIjZpI+oE4kxKvBFCMBEk55VAb/ILj PB8RRqXxzyBHxs5mxF8zyyiARH2U7cSwBTYY5xzcaDwwHWKzxllBaPoQO6KwmvL0QTDz3KdeBKNR dH6byPnAg9EIq2CbrGY4/jgJ/jkkQsjHCMaHqsCjiKwijgs5mKLqHWXGI0mD/ZkYddk4GQxUCBpp BFmVwukN1dKoSHanRK48lXJgGpoDolP6GNIq8KLpzMhJ1GVRsVqeHIco//XYBhoKQvnnuWOgPvga mBKkNcomEEwonoFjOPWEdxihhFqGivaM95MDsF4S0MbJ2BBGDyj3IzuAg9Qh0XO6kQyDpn4Wqf9K 22IMt4bjz+Vi6KqoMIlYaABzguThjRAu9X2cZuo2UJbEjVEkBkyh0V5h5+SByMSoA2NOMzp+qlTz /pgkkyHIk8/S9ZFioQ0TZLThfWZlzTYDaArFIRRs2DR2vGit3QpRgU0ib+pZhdfgEyLzqyhwmiFR ZTw7VkJ5NPFxQgb9hhuQ6U+Q173udZ+z/avkrJ5/qf8oelYrc56f/Bz8UXRjY/OCCy44rb92LqjM 5HPsFXLov+N3m8OkXDTOxXi73e4jvuzLfvd1r/vjDza+7kHEJi7Tfl9kmcky1fHzjXW5nm6fbzb1 z8+wAov8dFvSFzDk0BMDj98NPddCcS62yFzb9a6/e/tavhWv3m9qvClLdzsHL2Bur8lc1fUN3/i8 9fX1+Td5k4J6+klPetI3fOM3XnzJJag33vaWt/y/N7/5r97+NpwvKysr7Xb7tEpaq7pOq1z1wees Akuj6nrrnyu5AzCh8HQJspA8iU+BHpoueCijjR4sJ+d7khFJKTSPiBLRqFwJiGgp6EFXwEBW4AB2 sMMJBsGPuBl8hvtRJjjjW9MznIcQKSU/GvALamH4RSInoSATI8Dxh7Lo1oRxHo5MB0IiY2DFd+Ey DBgDGkBEwiukZER0FVIMgT/Q8pApD9FgrCAuQJsCveBZ6Hok2ipDEtCDIJnO4vGYcCyEQIAYM258 dhM0WUoxtyoskWiLRLLET6RBg+sp4MlYAqFmUCCGLUFXxcGqgHELYpYsISLrnRbziotC+rIp8VVl BtgrZ0RAIR0qJmoDQBWhNZj1MiCQxRlcQuPBfASBsQoEpUNvBH2YEfPGOmiC1GXTUwmZjkYDoZFK yjAjSsEIUH8FmmxJQ0aQGHOEnZnDRP6AlYjB5pyQ2iFjYhHl4JvOaAIgmdiE/DWnTZiZItAaADjF 4auTgIL2KXlSTEF+rGk5nQC/yPAS/ZINkTUFyTW4LuMn201tBxwf9ocdE0FdCB8K2SNuw/OhW2pP mYudIq1jd0ntJXQliRauRvRhSOMafsDVWQukZC62St4ZB2TBSc2HAXM25RHOrGD+0pPJbMljRUpZ VqRy2iBg2VE8ZqxaJ1G+uflVWfUqKNX2Gp3ITacZiXXmHKBecU2p/CC8U2pvxUMUZ9oAfME5PN9f 73Q4yeOf8HXn7HfK2bjQ8v5R9GxUoz7n7RU4l38UPfWyL5HMJC2sF/xW9P7rJ7/+6lfRAPHU53gu j3z3u9/9vOc895J1709flEbL8/f7RZaZnMvlO9vXWqKn29kuxfl8/kV+ui3jC5i1wQ2rH/lf5NCT z3Xu+5Oc4k5+2Q/8wBt+9/eyix9/9LIF/Z/XnU/kXu/83lOc6X4ddkdV12ee88Mf/vArf+M3bhd5 PeKRj3z+87/p1Lvu1Kqu/Vqm+jz7W4GliaUvCgLck73xYCceZUUaurNuSCtDKAGWtaAxs5HZjMZp PByOx3maFjGqm2IySJWchCAL0IH8qECxNKOXInorMo6Q4ci8h0EPtZcX+Bb8hdSmIKyIODehXNyJ iAIqAM5AGIMEMmJtx/kte6NBKjcat7muyrQiVLc+/sMxnFh5XVCzyhpkxU6cb9E1bzweSwrEUVgR FTBPggMp5k3fDvlC+eXgJzBWrsCoGRnweT+Oy7Lo4WnE64gdjfFwdts+skJ7x4j7Wqd1GPGV56Ib 4icZGi6SpFLS+0vmDsqB9eCXhKshF4IlSY9mJF10GATxrIQ+ofUozlDAHdvd2xuDvDLIjQd8myrk K0FFxlBKYTszJ5yhxJ1V2Dh9RizeB3SzUCR1ySG7bTdBgYySq+IbKKrIXBchU/j8DKCG/E3dKgN3 tRVEYUDC2mq3ffF691CvDSuST1SXVJkoJjOi7ElazEfCdygpk0WBtUrcP6o6NbGEOZbjfEL3wb3h aDBOju31b9zeG8SJVU7BT51mRN0MMrLbkqnJSypjIIHxk+kwy4UxrUbLddfCYL3VbME9Z1WSZjvb W/yZIh0OBv3BeBRDAQF4TIXpwKgQGHabZMTR/JDjZ9Qsp4tvntPWEg4F8eTPTLgsQYSr7WYr4KZV BKBFPt8kI98OUBRamDSzOEuHKewR1ViZTGk2KvEZwj2kf2i0WCOKJs5LVJel7pBk8IMBaYAAUIXW Tml5UAFwncMrwaEe9/DQSrTRDdF5sQZahvpWV6CuQF2Bz6jAr77dgXMR9LuwnIshkx2mnow7k5f/ 6VL+6b7ed3UF6grUFViiCoRlsfmp1zPgX/ylX1pYzsXwfuBlL0N0RpTYoa1/WaLyLuBQd3d33/72 t//UT/7knHNdcNFFz3/BC06Lcy3gpOoh1RWYV2DJVF0IeRDjtEIfcBGDjaYVuhxypYpJ6siUZoEV 3IbitCACavAn05npV1hOEAvJylgpsAno0wRCQLzKWZuILam19BGINspNbv1MfzuGySjuieglYuxB C7bTwnWGfqpqgE7gSCAJNDt8A3gDA5HOx6AuQIya60mfMxeNIQKiP94EOAIGAoQAWUzskvyNCIMA Q0ALZbcTZq8QKGAO5kcixSVTYvTIfMB62PIAO8h86LE3D3hStj2J5joSe6NRUkkgNKPJJGZDNVuE c80sSBkCroRYKaRtrgtIk+nShGzls1JasGqaaUxSq9lQqapBrjnjZO6ea0V46hwXsELoPUI2ZGIY 7kycPIljgEGVEpkdSe2COyi8lIQFD2qMC/oGuGT/cypoDnPH0oeKjaAtI9AjPUuyO04EN+MH4EM0 a5JhKeXK7gY+MWUARtZRkfCY/Uz+P6RJwWNliSiqtErXvP3hPPwXsMMOYV0YE+WSAA/dlJGSiZwx KkyRUmaxMSSWUjLXdAYuJSV/Ti2J4d+FMk6nnGoYjzgD26CHUVDKvMpWWJmwo4ib7JLzZgUEaTFk 40CVJzTkR/MKSGLGlsO5aCEXmwvWFEYGymKxVDgDTJkXwy4K5XyZR+lqbAJjjzWjl2qPyLAKVRdL z8OMlZNFUU8DOXQVzk9UmRoNqKCMhpk2BOM4/xOvrlVd9e/8A1iBWtV1VxZ1HtHFW4XffO1rF82f 8pnzeuF3fAevwv/3N8y+6qrlCO1aZJkJf4Ta+ui7/+Zv//EDH3zfB68bWFbv0gc97CFf/pgnXP3g i5u3/W1k620/+OyXv1cr0XnoS3/9x66e56Iv3K1WdS3cktwdA1rkp9vSqbru8fE3etvv4w8MNMO9 OxbzNK5JnuOjvuzL/dbGjVe9MPZPz3B3Gpc5O4eeyxcwn5nVNZ8Toul3vuMdv/Grvzb/8quf9KSv fPRjTiuc9Pba1Kqus7NN6rPe1Qosjd5jWjnc8c/R8m8wJn5dUhZoD3d0NCJKhvWMcAiW00GeIqE6 PhzvxfkwybOU/KspSqnKpKR3AhKjlBk/SNJBnILPEPhsj5ITe8Nhqhh45X8jQDJZ9XCGjqLoEdSI UsA40BnFOhtd7+A+6u4Hr4BS4Y6EZCAPIoYc7ABXEpJQXvu8QyAIBh8lh9MjknZ7RIZFnSDsNZsb 7fbhTme1DXOLbH5qucQt9Zpt31Uzx5YftEg4Jw9eGh9lVDHN1XbUDYONTmuz295sN0lxIpGqi0Qr IHaKtHj5NzlwTnOUrn5rGBbICFAllQ/8CArEQH06+oEIXR9tElUMHbfp+SvNVtNHcwZEkjfRgBa0 RfQBhPgoWAy6AtdRBpbojOCUUWzZMDVYFjQNcAPNaoHYnMY41So4MDcZBLNRluzF490YEV5SZBn9 FUnAn/CDNHXLiWuJ46BaMoMsYZBR4HfUglCdQQiSTycTjV2gUIZW9S40/TEj9FNB0I4i9bqEXbo+ +jfPDww8ms0VbYwTKmXP6AA5DR2EaYJC1AfZWZ7HjOrkeLyXpuwOyCnZWZce2bjnhZuXHFrvdnut Ti9odTApGncqcJOpeUjZCPk61G0x33mDSwgjdkiMnya4DCxFjljZVA8mQUY9Sj0f/ZZBZmJ0JMEV 2d5otDcajzJEiCbKTbCLhygLH3bL7sK24zYIkuMQhGg0dhR5Bet5Da/lRSFbRvvKGZeT4SQfT4tk CgRWI0c1agKj1re6AnUF6grcoQJYF1/yRvVX+9Ef//HF51yM8/t/4Af4+FP/L9qL/71lXL2kd6zA YDDg/cznr0m1/YFX/8C3vejHX/OHbzeci9vghve/9Q9+6fu++Ttf/f7d2zrwff4T1UfUFagrcNAq gHURzkXLxcXnXJQe0dnPv+IXi3h7/do3H7SVOJvzARES7f+Yr/xKwgHgXFd90Rd970tf+sY//KPv +u6XPPCBDzz1Jjxnc4z1uesK7E8FlgZ1EQjPHYYQx6MRNsa9/u5efzQaTidYxlLIk0UeEzayIhsM dvuD/hi/WRxDwdDUcE+yvE8SEkY4JVFNZhOpiYAOqHVGeQbjSLJ4nNNRkVAlEr5IRtId6U0qUyF2 PBgRUGLWHyWcg8diJwN7YeKThRJZE3Y1qyIQCtETpkFUTZzZtDgEs9DVUTHnfMP01EMzpRwmzo9J DQ3X9jjrY9AjWAoygeAIORJuNRQ7nMDz0VLN9IU0VvIhIv+hyR7QjR5/NH0k6AmshLqHv9IqxkwK H5MBD3PRtaBd0BwS2bE0Kl4Ka+F0SnxYxydJCnsm8fxUrhrmJXAJNRa6LyVe8dH2Zo5fQXYqG7Q3 SIqdUTrIcmZAyFQr9LoREMplpJwWkRm1Qs7GwFDBjSTFwoA52YnT7XFM1tUwGasDIxZRtYx0qFto w84YokK+JA8zDSBJxIfhAJIYDQswKPI+y0ubxVk1nk5z8JqtCPaWMQAiFmMiwCbpnbAPVjY13Brh BMS7Smg9AE6kkWu1PaWk8YlWqYStFUP5IRGxzRtQ2nTkBNX1Iv/SjRXMhjolVIwROW7l+llljyYV NSkbasKIpiwvpjS1lOBLYHSyM6RCM1LA5nFdLR9zInwQyRjdC2aMio1VoGCbgEbZeKbZYoPujXRp JN9fbSVN/8wqsJ2O7zEYgKnCxWipOUErpzA4cO0IyMfuouFjwysqP53a2BYnlTvMaEHA95XQdWh9 ZXO1R4Y/6z5K0p3B6OTekPv+/Laoz1JXoK7AQanAH/2TjSWQv9vf3o9pwWc2f0tz03b+++9Zmtct 57ikb37zX7zuta/FRn+n151u/f1vvvz1HxrPD7KP3OdhD3vA5b35V7NP/cEv/8G/JOd43PXl6grU FViMCmBdPHJUzOg7v+u7FmNEn38UX/vUpwLmwHNAus9/9Pl9xPb2NkbF5zzrWUjhaGHJmwqMiq98 zW/+wi++4uqrH9vr3fo/gvO7SPXsD1oFluYl42iMCihOkxF468T21tb21snt3eNb20nKtwdb/d2b Txy/+eQW+qDIJ8ipbAbEG4E2qnECK5gipyG3qEPUukeaO7BLkqwG3q+ymJUFMh8C7kEQxGTtDMfK eJICqLqgG95zrU1AO4IrlDh4HjGqFcWULo0Iy5DucINtcagMbbIlEoklFAVxIn4exROxXfwI4MU9 nRT4CmmtCEVBcQX9wNOH5ow8cvAb1j3ZDWnkB/KqZqStk7kO9uo2A/r6hYEP8ABXZboqrjUNcJxP t+L8GOlcJtnK6LTmuqcp9A1DJhCHjCeaJK5EnlyEhiiBaMY5aWVzLKWMKQ6jLIre4ntKf2pM8Uui pWq1281mFAT4LgM6FTridzhE4XpkimWzmUoa+R2Qk4uWTTSOE8LOGIOoYTlbDX0CpEBuUL9RwdTQ lylzDJlYMaFvJoZIXKFOGAbNKLwV+mDMc22d0HOO9OgDCQ9ye+3mxkpro9sklkwlKsn/SpFfwb9i I3OD67EOLI1fWV3i/EnyIlcelV+SISjrJzn9KE32WbI1QBhV9IQnZR6cy9+oAKPKSuaFJ9NJpiT3 y4PJGqf0TUR3NilsdhH7xPhJAZTIAVFvDZN012wFo9GCi3lZae0M0y3Eh+TC2y7iOLR7LT8syIxP 4yRL+yjaRkM+9pN4L80of4fpm93FuooqZnDJKdFjuHTmxkZF+E8xrkpWSMzaWmdlo7vWbXURtAH0 pGKz1AEByRjwiz1nVkEGW7RyJQ5cwG59qytQV6CuwG0VQBj1o39mEzn8tKc/fYmqwlsa7Ja/8LbG NSdqYdenrxuSrte++tXHjx17xzvfcWdrOrvpnW/421v/+uE+7AW/9hu//JMv/5n/9VMv+MK592d6 /E/+7l/Gy2ERXaKtWw+1rsBSVGBl65/z3WtJb9zfP4F88pOfvOKyyz/zzvf3pSxzMLdx45/vy9kO 8EnQcH3bC775fe95L0bFn/q5n/+NV77qOc957qkHzx/gytRTO8AVWBrUVZUxdzxcYCzS3gl7Wuk2 CWTHHeZ71mon6OJeQ45VoUZyA/rnIf4JvIzErqbX6YTrq621lVZoAr1njdJ2YQcxBsesiIFcaJLg S8Ibs4nfmNpE1ytV3j4+jK89ub21twc1cCEJaJjgMORWgUHUOs9b7bTXenCYyPc9XGbgojAIOs1O t4WnkLaIQAi6Q6qBHzwFP52kWq4HDONQGAc3EAvKHhnfxJrkyGsSMWVb6NEKQs6LfC/O+sN4bxSD crDydcFLnjOGyZWTwLU6rtULbALUJ0XW4sSWWv7hGQTnAWIysuWRO83UxQ8QxpsDx/GCsOn6YVkR MK9mky5CJMRcMDnLXmM6HRN4H3o8NEsAcRqIAsnQpjmCepCjwSgeDMdDqeQygvYJs98bJ/1YUVzQ NWbd9n2URU1Ykz2N/FkntI90ogs6zSYQizQt9UhU2BSB7lFErnvY9nECqsGlQ3K7h63QRia2h9Cu yJWh5SrR3pglMZuqkCjpSLVv4tijOyQuwQzFE+RNbk30a316EQhHFttxRo4WK6KJg7JcvxU2exhF w6aNj1DETaFmIEiHPLJIvElhZyV9IWkaYKH6YvJxlqVZwaEAJyLUWLggDHqd5uHVTqcZkjGGoROq xc9pDgAwpDtkr9XstXCABhDOYZzi30QFphwx6dd0hsgPVsIAREjePIQMByxryn4g6gukGAEDleAv rRwsFjUe5EtCQNvLK7doBFlpIyKTi5YsLg8Iy+Zvdltw3EhoC0GXEJ4T0MwAw6maPdYGxgP8O7ye Wl2B067AXBj1/S972VJYF+84vRd953fy5f95x9K8dDnttTnTB7zvfSZWy7I+j7Br55oPXXOr7Ct6 yn968hVq0WuFlz30K6689crZjUe3756/jnzwgx/EgAmzO9Ma1I+rK1BX4MwrgKSrd8vbefwzn/XM Mz/L3fFIwNyzv/655eD6jd39YWd3xyTOxTVro+K5qHJ9jQWrwNK8XtyL97jvjof5pMDaBs9CtqT8 KqdBc7tWOwQ89DotqMHM9jvtFm/+ia0CGq11/F4TvdIkRqCje5IVyTgZoRDjVHxE1UVuOFnw5CuN 4hGBXbgf54AA8kGDvLwoADpbSX50mCKyQYPTi6JOGJGiVczseJIPUjoV5shwRvAnbHT4HJN0bCLG x1m2PUB9RXT6PHcJ5Q1gyyP/i/ypdoRmSnoftDnpBNETKET+uBh/XErjRIvYLCaBcxItETsH0oYl D3UWiVnCKGneT7PdOBVosRrMB6Km1ocW0U5+lwR9V/llsBLuMB0cffAyBkB4/1orXIuCLqlgQBYF +5e47XB2km52chDvQQGxiSZIl3IEZLgikReh+crATZMRAfSNKvcbSOEItacGNo0CNjpNAB5SJO4F lk9OKnLHWEqYzG4a7+UZEVnCXNAkaCHQsWoM+/1jx0/ecPMtR48dz4b9Ko2nHDbDbGitR+5qyNwh ThN3ljfKbEqXTe4SWrGIHJdjmiQ4Cwo092ziPbxkc/3QSm+92zmy1r3igvWVbrvbatGnshv5kCxS z8iMd9xyVAzjIoG3QdkOtdueEwpBmYgsTJpIwBDoYckETiKpA2nBTqkbxVRgfwMapxaURGXxyUW9 lfV2E3kaTTnjgi6RJIc1HTeQ8ZBWA6A69fIk3Yw4ORLR/ABM6zrpdLYzZiJIxgruyAWhgCjhiI0b jtMBuV1xEifAvYLLtJwG8rb1bvvCjfW1TtfxIluZbLC1CDOo+Kh+rVBxTJ52ycmmQF/i3bCZtiwr MPf6VlegrkBdAVUASRfCKORRi9x18XMtFd0YGfkb3984ulcLu/69SPxRCsJ15IILXvjiF9+5sKs8 efO/3KrZ6nzhfS6Mbj2H2+zebl3pD2JFRp77287O9s++/OXPfNrX/eIv/DzY6/M5Mc/9AOsr1hU4 yBVA0jVNdpB0ra2tLd08//Pzn8+Y146+delGfm4GfOUVV9ZGxXNT6voqi1aBpUFdtj3l3oqcjbXO kY2V9dVut9PqrbR7dJiLAhKSwBDgBj8I54Y7uAaqmVbowFn2kgSGUM1yTJBZPMrT8SRPMH2ByyA4 mBBzMZSEHDDTABDOkY+zgjurhZOu6TvrTfde69Flq01ipHDhpSXpSKRTVciUQlLD0QLhNUPS5XkY BhXVhJVNqiCizb1DvU4vCpGUiXBgirNkm8TehhsxJr/KsjH5IdeiYyNmQIhGBz2U54FU1C+RFoH4 +4Kg1wzJO1d2uxFZIdzhinAvrtiL+GmEV44h0YwPOQ+YCRudhWBLIenqtShtV2WvRtGRbhNTISBk N0m306SfqRYTASPyp4phliMp4u47sC2CqqT4Qs6kXpaVml1Cf9pNv90KYUY4D8cFrkky5NHKufRx BM4pDYxwL5NExjmhePA7HgU5QrjEHLk4GI4H0GSgQBPl+Q3UT55fOp7lhQ0PDuXOSpKw8mGa3bw3 5Jp4LElIw/pHjD1Kr4pmACb0CuApY6nvk1m2wi7wQ0L2p5ZN28fxZLaTCDxRBtDVGAnfVOiKAK8h Aisn6IQduhamBX0n863R4OgejsOcrpp049zswE5JunfdgJv6ciKR2iMqK88lfVPnxiIrsu3RcJyn dmM2BFip/SKuVICjg/+TmkxIQJtZg3TazxgMAfVkzMO5+NfjlBBYvLD00mSzDUCro3iLXo9JqoB8 sKQ1k28RhksMnEMbBAynJM1PaHBJWeKcL5m/uj/C0qg3+zXNhf/SLOYL7IqNWUlIGLlobJL5vb7V FagrUFdgXoG//LB+JTz7Oc9Z0oLMhV1/8cGlefVyDuqMaRHC9bznP//xj3s8wOtzC7tm6Xh4m2Sr d8Gq+vOam7P2iBf9zhv+r7n/zNOvUL+C/3CrRtf/3f/5iW/5usd/1RO/8Xt+5Y8/fPKsZtf/v7/4 i+/7npf8l2/6z69//e/R1escFLC+RF2BU6nAiRPHT+WwJT1mbfd9jHzpJF3zat8u7FqJD/IanfHW wqV4to2KR285esbDqx9YV+DsVWBpXiziCeQetZpktCMligtABmxiAo5pKh/daTb9METhYhVkmSfZ IKG3HyqiYjDY293dHuztpPGwAXOYkQ8OrSAYSzdwCWnilg3RAHs1SNBSf7+ymKe/YxMbZRPymE4O R9fvbJ8YjSoLzMTDySNHp0P01JR+i8TC80oR252HoW6STCcx7j/lQJFZ7+JxC5OS8Htwj8BEbtLh 51ysTY+9RsVlSQBL6SQ5zbDNFXSLtCqACPFLiIT0o1KaL8AFkp8MyDEjnB0UJbuc0SHZ0BHMcytR E0yGPQ9jIMY4dF5803fw5aFp6qw0O0XpDNJyEMvuBzly5GvEEEhqvtWke6KLAi4AJtKLcdbAqof0 qoIVrrZ8VGPY4rDmdZsrK52NXmd9bWWdAPeNbm+t1eo6DeOnzFu+FYYNP2o45LNboEkiuqKIB/kr raBHmvxk6sV5AwwEEAobVcd3NmlsiJ3Pc0F5hJ2hSINzkaWFSg61k4eijFpTbQ9CxPoGUEGXuDA/ JLarGbamBLRbfmn5+cwlpj3OSFJD8kZnwwaLMwJgZhNxNQcmiD3SXWtHh5B1wZqAgDYcEH2W66vR JMuh8C7WepDmnaZ/wUqHg2FzHuDPa3QiCgq7owzJMB/ks3HgT5ohPQPAqCOIGQ0WuyGeSvEpsU7f 6bjOZidab0XtKABL8Xf0CGVZ5AUBNA83JSosTjjtBI1uaLFzGXer466shpubnUOHuvBRlxMx3aZL Uj3tIH2HgmR+NW42hqG15862rclJa7JVTnZxkeZpv0iH42R7nOzGqP2mw7xKbK9Y7WDvPXu/QOoz 1xWoK7BkFfitd0nm+bjHP37Jxn3bcBF2kTKGMI0OMfWNCtwu6Xrklz+S//8DvD63sKtKRwOk2ubm NdFN33Zzmiubt97WO8GnvTKcHPvTX/gfP/rav/8U1sLsxAf/6Ne+5+f/7Pr0bBd/++QW6WPf8l// y4v+2wtrY+PZrnZ9/lOpwO/97u9+w3Of86d/+icHj3mR6U5K17d+x7cvo6RrvnbPeKZ8l62tfzmV payP2fcKoEnY93PWJ6wrcNcrsDSoS0lGwjqkW/FBHjjyU7G2AZ7Q+JClpUhyTII4xMBXZMQrGgng UfJjxDNqbihGZcFMSFuHmFQ036uwkdHaLoFt5ZwjI2zdBMhPS8CHYR8EaaEYEpHybTRcimbi7AyA OxFS8CmsdNgcye7CrcYVoB478ejEMIY3kP1FKvla1GqLPfE5w5NkicGgNwLEjNJ0VpUY8RS7VE4w tN2y1x9gW2PUmCpnWP84dkp4Fv34AGTAMnjTShNjolxxFbnpU1oNTiTlMWlOhJHvJZCsYmeYbI+S /ph2hNOtYXFyONlLZrvxdJSV42xK+BT57od6zQvW2quEf+lswBe4WZWVKJQIVIcN0dzQRv81SvMd Mrum5W4y4XNSzqnnTpKeGI1v2t051ockDrf2hif65L0nllOWDi7OSTbJ8G3i3ptM3XzqNdCKVf6s aMwmFXNNs3wQj7eGg+u2treHmgFV8dQcsSBjCyncZrvNIqHY8m0nLcqkJG3diUISqVro9vygSRI9 fI+PqKgQskEbsRuKkcUpuJLs+gYLKSOlEvvBmUiuiGTbi4uj/eTmXTp04sQskb2tNttHut2NDslv nJdsexRuxJwVEKOTFDBOsKAy55AN4Nm4Hdc7rXUlmnVQfJUIuCqbGHvqxmHAVZK5BvT+TOQ97MeE 0NNtoKKuBIMxIALVRlkmlRk4VnWm2rO9othGl2VbEduL9DEsmw5pZITMNdzQDpp21HIxRNLqAKHh YER3UbLjju/s3bK9e7Tfv2U43kKcl2dowrC95qyksuBcB5+kp94EfsPDcPnv72fu+q+M+gx1BeoK LG8F/vnGBo0XsagsXUrXHWv+X1/wAr788I1L8wLmrG6Y2yVd8w7xAC8+nkIrxlMfVHbT0cP/6Sdf 8Qs/+S1f2jM1n33wL95z8znLrv/Exz5WGxtPfbXqI89qBbZOnPjfv/RL3/jc5/7wD77sXe/6hwNj s432lHK1vH8CYfAPeMADsLeHN7+F0LGzugfqk3/WCtSoq94Yi1mBpXmlqPBu7h6cCQsZXIf0q4p2 fFOa+PEl6iQgU1bsDZLhGH8ZceDZnmE9DRuZEiREff/o/lfaFUYwvGwz4AgZRna01lu9aHUd4+CM 85Gx5HE4aEAZ8e2QuCsPUyDiHrgBeVvznn1GS4UmB/Y0TfJ0mMRAFimS9FNrpelfttG5F4KlwEvz KdgL3xldEokZ2x6nilCSWMdZQ5WE9VFgbdbyXO4B0EYxW0Hk+fCUlRZZSz5OxkGO585B99QN/aJq nEDSZtk5ZksTodV06JuosDGaMiranZx7u9GCUlVV1CCLyj3UCjebYY9WjKi8GpU9U9tFHICDfMKZ sRtCi/gPJkZ550CIWBMbzLSRFQV58+R8gdY4z1qEYKtMEmLWU5RoLZ+GjD6Iinx0MvV7CJOImm9Y okKO1Q7c1cDbHtEAsaANIuc5sdeH3BHNTs9LQA/+vVYzvPySI5ddcmRjY7XVpctAb219bb1HgBj1 JxjLHRYcDCIMIqaJDIuagwYNtEJwpxh6q6Gi0cNywmDhcKw8Wf5o+rCqYjOdocni4Fv2RlujbHfM j6ZBw1mh36MTVuCzabk9Gt24t7MT75BnD2UzUfTkeJEIVgTgJmgiEWVTGCKiNy5FH8xglDs7Y1LH aHjQBEa1vZBOAVNS29IJyx1nzDdlzaFdJJ1B8cBbOzFrhoV2wkb17BnFwakKwTy80t5c62502ytS kKE/U/Y9G1lZ+ZN0FA/3RsPt/t5Ov787HIwTTkLlYI3qFsqw+MjG5zmhEHvMtvQ3CDHwhqS0hdI4 YoRtNpyQ+2L+9qlHVVegrsA5rsA/XqP/6T/8EQ8/x9fd38t95aMfzQn/4d9qc/Z/kHTNiwzwon/8 52/FeDpL0vrqpz31YV901cOe/PSvvsQ8bnrTP1+/czpn2Jdja2PjvpSxPsm+VOAf3/WuH/nBH3zK k5746le98uMf/9i+nPNuPEl37/2oZaFFd+MY7vqln/w1X8NJ2oMb7vqp6jOcbgU+dc01p/uQ+vi6 AuegAo1rrrn2HFzmrl/izX/+y5yEDnyAFgQxmAbpt0jaURzTQhHrG2qiRjHBMwiTQhVENFWJDowm hGi6YCQETqkFIizGccjHkncRyRcUQw0BK5LSEStBixxa8ZkXzxff4wv4GNqcU90ZQVDgH8RB7aAp GRHcQ1nxiqufFWPEYjyQgHBoGFeRemwe3MTJ4U2oeqoqiKBbDoohE7QFMePMKMh0ZoQ/bTUgtA0r ayBbmnA+x1Me1XSKm9ACvYGHCrpANkj8gvNh+uNRpIlNcsLvsdUBAKFDJPU7UA+OJCgMDRFXr9yI 2eFn1CgsjoOI0XkRZ6NHFhfqOLx7FLLMUUMBhuBGlI7cKc4KjCKaikeT9c/cHFLwAUCwP3Uw9OiZ 6KLvYkK+WxImtZdmXIJ3UYBCxetbVtNtZoUTUizHSqf8jYUzEUyGvxAch0gKHRxeTfgUrSGVekYz wRExWyntDsUW8ZRC2cA3DDjLaH7OsBXwbqgcn8idCZcEccJ+5EjVQlI4m+mz6qTyKxzNdeCaSVHA BJVupQCzGT03AWPK+YoCUVKrGk0mvD0g3Iq1VhK/SsQGUeAVQ2XfcFfvTE4y0U/BZ1Ax9hUrideS ODXi4XBNwhAtm3/BkPx8irSKHYmZU10j5XjlZC5yKwbGFgGKEcSmlHqHro+K3CKgXjRvSg9QXVO9 FLVhEKaVWC2p0nyC8Di2HDfmg7WTSRZaSJeIsQnGUeCo8rm4Lnlg/lqk3OGnPP4Zd/05eDee4Yof fv/dePX60gtbgWt+5EELOLZL3/sVCziq+ZAe+3NRo3PhX//t3y7sCE9xYM951rPomP7xl5/VzKhT HMtnP+y+LyXZ4LRvb/mrvz6tx+DsQ/H0vS996dVXP/b2B9LEkHB3Qrte+erXzKVet93Krbf/xDe8 fL76lz/nl3/l+VfdHtf1GZfdetsPPvvlpq1j56Ev/fUfu/oC/vf07w/vPO8X3vRNV9Fa+LPdHv9V jzmtWZzZwfe76qonP+Upzw1/LOIPbfXt/K7ADQ/9+3NQABomwFs/64U2Dx9+5rOf/bCHPezw4SOf dsDiv4DBvbj6kf+F2vfbvv3bzkYZP/nJTz7hsY/7zDP/5dveSsbWPl5xfqHs4scfvezqfTztWTrV Yr6AOePJnvqv/U/7H9YZX7F+YF2BU6nA0qi6xqSS4zWk/aI18xFnldO9YXxidwSz4JvYD+EPQADL AnNgYqN/HXdYECBFCiL0TyRHOTbwKgCzQHUgIOp3WFmgit1hHylNGKLg4XUz90nTdrkDFlrN1gUb m5vrG91WrxU2wU/Y9Pbo0xgPd0ajAvzkr4bN1U6nt7rS7baavW7v0NrmBaukLa22fQLmAxof5tNq hKNwZ7Q3yOK0hNiQUt/r4mzENRd1u+0KsIUF0vZbzQiSNxrHOzsnB8MdUtjBd/jsOENWVoUJegfV DeJiQOBWUbp+QHRWsxnkKJsKAmcH2wiA0jEEi3R7N0RAppAqSZJCr91qtdsrytfqdoKoQ9vB0KWV ZKvXJt2/A4GRmAvlHAwp9NdWOpsbmytra2GTVpMkvhMqT5JXM54ot5+WjSkh8UVKsP1xAttLZ729 ut5eIb0LfkS/QpyGtAtow+3aTScIg6jlRG1sdWBGBooGD2kdyGYcj/vDwd5eP4kJOJsCvEyhI6BO rI6S0B/QVulF9NMMCA8j3SpSZDzCOno/Zmkam7AzYBwIiSwsGiM2iGFD2mQ5fCIBoIXdsbMStdea za5QZgX48+mfmdv2dpxsD8c7wzFiNPyhkxmci2z4WaRQsJaLgRLSRvUxKc6yMh+V2VAPbkatTrvZ agfEcNH5s726sra6sbm6cmh19chmb2O9u9LprK3wSUQf0E6E57JFl4QgpPqtliy2CBRhpThQoXbc UGbh3bQrSFcyIXefmDFy1OCNIFI1G/UaIdk6ol+kfDl8SdYXRlo+mIYAopgQSjV5hMKx1RWND3Ir +aTijcj8Xt/qCtQVON8rQNdC3ItPeOITD0AhHvWVEnZdc+K8FnbdMaXrjmva6/X2Xdi1gHuGlOX1 9Y0FHFg9pPOwAkttbAxGN7Fky672ZQqAM7Rp7d1/Og934N075dNKr6t/b9+9i3W+XX1pVF2vee33 a20k3OFNv2K7eF9PRhI0gxuiGIRLNBkEaxEw76BtAhTwFp/YcroNKU0JHkB2u0PrQLoZ0pvv5M4Q 0yFuRcdFngN+QohlYVeTN7GadXv35kKkWEFAgA7kv0sKJgEUvsAZAVCYBM2RsAS13dNIGo1RBrcQ dkEDRs47AitAGyNF3IUUCgkPdAejI8PBmqhIMY28ws5GEhZpTqCKUY4US6ACoISCCbRhOX4y6M8l aZwKuRdD4lEopIB1zBUBFvo0vmXzM9smFR/fGkAFwCddUqk0dcaPnAoDHtgIDRSPygsqhoXTpJ+h 6ppmtPbjKwbUCb0CZEJFOCtzN88JQ5QkUuOkyvRqWMwFSVTgWJyNIciTKMGbkTGhiALGIImyfOqv w7iY78toB+iiXYBVAc5oXpnlZLqjY2JOFUIuOBtjJmWL6UhxxwV1QgnfUGNJHYcIy0xfRWYkUqu5 dN6kMkiuoJp4JHFQ8nlcTCTm06L5yNoYD6FdrHE5xUBZIvmikFg/AyAXo0Vz5XqAS1K62F3yqnJm uBjSP2001GfypyJ/oyejBFdUnoHpE80aoVupETM8bU7clHxGVfUVjyK2X/bWihUw7QKkBeOxcDy+ iW+UGxVFJpgXuYZjWgpIW2dyyJSWzwjYKhIvssSsOBORp9XcuI7Z+RXqOe0Wqbok6+LqpLnRrEB/ 0v/axz1tqX+1Lf4fRZe6vMs7+MX8o+jCqrr+6qP2C3/H/vVXv+rqq5fgL953vi3f/e53P+85z/2Z Z8ye8iXnLDPq9J4o50BmMpd0vfDFL37KU7720wb3OYRds/F7fuW53/8nJpn+4q/9mV/5jgf15g+c bX307z58C38t5P/23Sse+tDL2tZdUHWdXqVuO3o+nc/72LmY6yEPeShEj4MX9un2eSdSH7CPFTgz EeU+DuAzT/WMZz/7yx/5yPve936L/wLmHh9/o7f9vo987F/PUobjOVN1sQo/+zM/8xu/+mvHH/rD sd8+q+t710++mC9gzmxe9Gogw+4UH/vK1/zm2W4HeYojqQ87HyqwNKquWZlwz/PhpBjPynRSxOS5 QwAAJQh/kArRDBFTIEHtViOlVyGR37OKGC96KQqL2bPMmpGYNT6xs/X+f7v2kzfeNByOoBO06MPO 5tiZZdOuLmu4ZDKVtjtVNpcXDLOyP84IRKJv3uFuh3uEcxLe4blgIdKyiN/aGY+P94e7ozHR7YCP jmtvNH2isqgsFALTHDZG3yfEPEAJtN5t00YSWkEqFJKzQToZZsUNBFmRKA4MIRwKGyQPg3Y43qSi BSRORMvxwsr1kX1NGCL8wvVa7U7Y7jQ73dWVlQs31o9srK+vr9GgEKCHXgyjXdu49wA0ULlQWid7 nE0GSOCmZUArwcl0V436Erx++ANpBoh4qRcGhzvNC3qtXjNYpfUgSfKO3fHc9WaACRGzJywLTRnh 53tpTptCiBHSKkLgx0Rk4fGkaySID7EVIFLmR0mMsGDyHSiVb1vkeeHlDFyHwHsYImvHKgZBhP6r hbzM9SBWkCzX5rGUrgo8fJpe5NkklJEyRh0hWbDEHPUXp27QpBJVk9yQTZ9UNRYML6YloimjKy5I TI1SSzEiRH+prKwF8f9AJNYGZyhAjeguktRu6ZOLP1G3RgXYyzoKkfLkKeQbkC+y/qc0WGwGTkjz RAdshxtRY0E/qHiuCf7IMXn0cUIk/d5ovDceD4nZyoo4zcbo8hL0byjgSO7KuAiJ/Ppkbq3FnqiY NfoS8Nd5Y5ykA6XC28qSeupLGUmpipgdiFEdGQy/m0Mu0C3fAqdideRAtg1rT76bsX96rDveSIoq Glff6grUFTjvK/Apo4G69NJLz7gSV1x2+T7ez3gYPPA+97kPH//p2vNX1TWXdFGERz3qKz+zkrcL u/71Y3dMEbKjdve2NiWDY3v/3jCrOvne//Xyl0Oafvblv/Rn14zuytKcpcduHNpEqsZ7pFf88q/g 1pxzrvpWV2BhK/D7b3jDi1/4QpK8FnaEtw8MzkWg+1niXOd4+ve+t1EqjI+d4+uez5fjf0ZveuMb T70CR458usn31B9bH1lX4HQrsDSqrtf+1vcyN5xviJ+Q2uB+AxgBhdJsqjAoBzgibRByKqUsWcrt lvAL95rXQukFLECXBFqAxOD7481/4AZJJnVPp01UOzImE9Bk9DTcrrpcYSugmsE4VVgShkAAkmxu Ux1CozwCqGYl4ItzkWGO6w3Ukk8UxgSXQLjERwWmA7q4cVLGpzFY8CNJhfT6HAbBRfFjOuN0l6/p nAhWWcfVqO5+tkEhTNYJyBcrp9j5mBT6JSgGyjVxISmNIDb8TZvx2BPUPuJrKoukQDZnENiClWhy QiU2KC0uIHcec2IITc/aU+u+zKROicqhIuIHRgVnU0/SwDgV8is8c0iluBDiJSWfqUwUwCjPbGhj RkA65wSyUBkjNJOCijwzh7p5LjRKOWFTp8gha5YbNGjDCKWUEbFCp4YtkZff0pcZYiNUhxDKq3Cq msErLAsRFoZEBEzSO4F2tNBqEGBWdkLUvReGq7gbgUTAnziJFcJmpiR1GL7PkuC1TO0FhIhQQjEG Car4LhvGxxeIVMzxIXWstvLUBJpMhQ194lLTqhGJ51XALRoy0qkdfywz7pAo33DirBymINAKIRXq LWWoKcyMCDjJ1Dhpk3wy5FuzGYiR6H0jqeMf9ohCucSqHOik9oBKhnqLociryPxoxynRGHfJAT0P bsbuYSdJPueADt18QoQ/qE5yMzR5LBp9GCQFU3SX1g6vIxdkLEAyAuhcZwqgRCmGLJEnBTVoaoeT 4i89mml0ak9UAKLfClAcnypkTGzNRN1prZxGM1QvUpNjx6ZjY8AsOeApj99nh9Ti/1H0dH/z1sfv SwUW84+iCysz+aE/cN74/sY11193xsWHc53xYz/zgXdlJJyNwTz48ubvfMtwH4e0j6c626quuQYK +vOc5zz3sw57Luy66gu/8BdecYc/tu/+7Y8/60ffYZRw0dN+9vUvfJDSHK1y9+9++ut/9O3m27dl eH0WVdf0+F/+yH/+uX/QUYf/yy+/7hvus6/m+M+l6vrqJz3pKx/9mC+43/3+Y+7YrZNe2KfbPu6l +lSftwJn++k2H8CdZHV92gjvqD1c8Bcw3Wx3859e/q3f8e3f+z/+x+et85kdcC5VXfNrxVc86/iR B5/ZaM/ZoxbzBVi9+nAAAP/0SURBVMwZTP8UBbnzMxMi+du/87tncJX6IXUFzqwCS6PqwnnHHXaB U0zOOQgE4Aca4oe8W6f9YKG36KJLgCT4B6iAJHV4FBSD1CbPIaW+lAvP5u2526Hdne8cRmQV+ZzT QCPlmuNyk47Gqm7e3uK+NRoQ3d5ueR262M2AC3kT6CMrH6ytQGGUFlNIA+lUClnnEIWDI6VRD0eo QDPASIjKBo+dDWzzMA9i2EvScZylJIQRJFZgb4TmLKj/4sy2VP2ougJ1BeoK1BVYwArAufjT/QIO 7MyG9Oyvf+4/XffvuqQzO8mSPup2SdcTn/ikzzWFubDrox/5yAc/+MF/P2bl8i++/62+nvRP/+DP 5wKu6S3vffv7b30h4t7vyos/rWnv6KPXnqRnimVlJ2+6+dZTXX54bV8512fOAlhAevEb//CPvuu7 X/LABz7ws3KuJV2+etgHtQLLqD3kr9Usx1wMdWa3NE0BTHdyu+GGz94Ske/f+QPPYDyYXHiUW6u6 zqB2Z/QQUrrm+uJTvD3yUY86xSPrw+oK7EsFlkbV9Vu//d2aMMCrnPBRehi1UPRmFaFU0gYRHuUR 9MQxwncK9UachaFNqd0Wzf5MKJKkTWiD0AWBmBQEBefyfAxxqYmo9ziR6WVor3fuy1lSc3zL87Dr TScTbHd8iV4LgxtDQUuWT3FQItgCrpm2jpzBckM3ALphHkRz5Ekog6BIGie+y+iV2oQQSUovE5PV ILSehpIZl0MiRCfGlk9UluLn9+J0JfJp3IjaB2kQAiswmTob0tcPoyap49IEIRySbggZjzLJHGel FSmkjKwuabLKTMnuCMds7siGuArmxDiT5I2wLcmdHNn9+MSjzaLEYjYP4VRNhEDS9ig0irnoc/kK NWBmpuJLBYYgq4G/j5gsSCJFv1XEppWi4PP2gUjtGistbzKzsrxRFNQI651dTGPwICiwieHODxHP ZUpVQ1qlmPVm4BKlJlGWmlUqykoyPdpZKjrfJuMKypnxLylgpOg3LEXYl95Ka1UdALKMBWL85oBK zTppdOii9KNj5hhnJ7qoeZdCzIdIvrq4NW2ciuR8sd5u6IcI9HRTaJsaX9KHkiJyEpYLw6b0g9qB aK8cJH8MDs+pyQGT6mre0TIvLYSH7Cb0XywoKjxWnZ0jp2E5a3l2i21nVRlLJ2hL0j8jaWDClIis Qifl0lyyafR7LXpuljPMp7gsuQoyPYiqduoMmVuDUjCUudhKEjcZHNUuEuun4zYRBtI0kycC/6r1 pzFAMgSWrKpS9i00FiDMFVskqbGUpRLSqLdtqQMA5UUiGEiGiKzMwmYb47JknELIakraW10z+jOl onHGBMGg9qH1tV9dq7r25fdzfZLPU4HF/KPowspMiLMBD/34T/zEGW+shVJ1zTNZ/up/lBetLqJH +6zKTD6vpGu+xJ9N2DXd/Ydf+bYf+rPB/Aj7yH0ecml48uMfvm7+Dbv3NT/+qu/8UpkD/13VRa7X c17+y89/QPn+33jRD/3JMVr3uJd8y/9+5bOu3N8/ls4nBSx48lO+9hEPf8Qphrks7NPtjJ9l9QPP oAJn9el2+3juRNV1J9rDBVd1HTn+T61r/u/rXv97D3/4w8+g8jzkc4m2zuxsd3zUmSl/+f/UZOMh N973mXd9AGf1DIv5Aua0pswfXV76ff+TP6ic+qN++Md+7BGP+LJTP74+sq7AXazA/r5QuYuDubOH wxe4E4AOsZEFUD4w3vI32s1IWVLYsAA9sqqh7RIg4U27wJYshxgbsX+JQ8hmxVE2BAFMQSg7bAgA BPGR80uh93AD2cTwYWFCi9aizmZ7BY9j02+3A0x1Tc7CALg6VEgh9yRwQR1o84eXjzvSsKokkAmL JJ620MfZxSA4o65eYJ9sNFLMYlxYQxUbQ5sGUUmKnDs/2UviG3Z3t4YDOFU7sLNJcWI83h4NBmmM 95JM/ZUmNjifPKbVFs0T0Y5pJr5rtT17rel3I4+r4MbkYNgWPjrMdKutsB0EbYCSLUSS5+Rz4Wuj vSE9HDO8ek0fEoQejbHCpSCChI6l4zxG1HbDzvbWeAjIQ3kG8DL9Lif47/pJlmDPEzcRyMMeR0pU NwzWCRAjcN7H1YeT0QUa8TlBWgAvPJIs0DzxnTE3yS9TP0Q7mVQ7IKicZK0yTnMaZ5JmtTPs7w52 43xMQD8cSe014T8W+VgZoWNc2MWUB12bVEkyHacKf4cwBg7ZXn6P/ossboNlVl6V5/jwoHHB6rvr 3c56B7thwCiAQ/yHiZMAB0hKMuN/tVSfQVIM4rwfF7vJdCeZbI3y3aQ4OY5PDke03dwajfrxeJgQ yjUYEcs1HgzGg/6oP4oHWT5Oi0mf2Db6bloN/IFgL9LN4JIwpYhekFwbq6TtbMVpnE9pOwAHY5Ck lckECR+sLBb1wpX2aqcdNJuW4+wRspZDyWB2HmtPewH8p2ZLNTI4mdXggW0RKxK8MLfSfwAChpkR rMr5kCliRMVKWfUi9nN0qNM51Ose6bUPdVvsT3aOwVaCd3glOTnOSiCnugLIIsrDrfGkHE1Kasja rdDCsxW1oqjTjLrNiNh7mhkkecVIWEqQn8CkAc71ra5AXYGFqgDtFxnPxRdfslCjuiuDmcsQUiM3 Ot9ub/7zP2fKJF7fcP31d3Lv7+3xDvw/CrvctUc87/ue88W3Krtmxz/xnvfcxrks+17PeMnXf8ln hGBtPuSKT778aY9/4jO+33AuSNfDnvqIS/b95ePFF1/8Uz/387/5W7+NJfMUOdf5tu71fBetAgdG e7ixcXCamaoJ42xn0bbKwRsPeq7T5VwU4aqr7n/wSlHPaJErsDyqrte+kDqqKyA3JQhBWEBFQZrz 3p5uh6i68obNizAFNPFTE2FkNFqmaSCCJIAMGIEsKombTMwRvID8JQXSOySC8zDpmMoZ7RvdTleR twQ/8UB0RAiNoFbchCOIt0chZjlQFY4poVSTCWfDsYjsZwB2MY3z4AgmIsokMqnFnnoBcqIRAqQS CEH+ur4BpEknCorisC60hqQpi+Ak9D4lCe690EHchBBMQ3e8LFfzR5gMJILLoc0x3fzwdE4UsgW3 c100O0iWAtiH6yDoKSbIeWhfqDwzaZQm0yRPlcxlET6ljCgG3YqUYc+XABXCvEBRTJBvSGdEl0YE UBKi4bMEWKmvpZkNxwXUA6RYlEVWpEiRJIYSJ1EkPywPxZLSmxoNtEvMCaEToVJuQ+ohyu45aMEk IcJsSsIV0Wl+I5CMyLYid0Z8GHfXY3GlR4PjxFkxTjOIpbmuTegVvTMHKQI4kJZfqceisrZYJ0Zp ukyiXSLZijz7BpI8tkw7tAOMqA5xVITakwY/RdVF00Mpq7T6jJk1hqspXo1yoamaK7Z4LJ/zPe0m 9p01CVwwUAWSozQsKto0pF+smmkD6o4K6GHYNFH66lM5m2b8yFFQGpotRHYsBDuTkcw7L86TyOj/ OCHFH6VVCPtic0XsTkKyGB8P0Q42GWraXVy1pCcj7S8dJqbumJVAKvqqSJ0BgLYuzUW9BgowXUC7 VG0mDaOVmo0b9DBJijGcSy1Iy6oTRrtjBGxA3oDv2GVDQV2UiYEpI5/tp0qyXCyQ0vJAaK6XNiLZ fY2gTNy2qpg1l3vKVz9hf3/rLfgfRfd3svXZTr0Ci/lH0cWUmVxzovHkVzg//4pf/NqnPvXUK/xp Ry6UqutP/viPX/Kd3/Xn31lecfj8UnVhSPy+73nJaS3ipyd2WenWR9/9N3/7jx/44Ps+KD1X79IH PewhX/7oxz36QZd29Dtct39XdV3+nF/40Yff8me/86a3vPe6/PADn/C05z3ryQ84dNtxpzWQ/T94 MZ9u+z/P+ox3WoFzrOo6Le3hgr+Amau6/vJtb73yyivPbJctmqrrZT/wA2/43d/71Jf/7JlN55w9 ajFfwJzK9BFzveOd7ziVhrmfdjbakr7gm7/lVC5RH1NXYL8qsDSo61Wv+VYxI8W4z7GRo9h3S8QD fQ9uPOKzHQcbHe+34Qm8+RcZMNhlJg+hBQMShpDZi8hw43/Ue3SaNlrjhkvHRk7t4sHjGNsJjmxc xeVC1zcmR9kjFcYu+FVJaiQzGmIYyVgEVsindyoZ/hDyTBBqmRB3qAtx6SbUnAdijuOGIAtJjmgc Ih2BKYXo4yw7OdjiAmARMAOiHGAIIAziAEDREZqpDZcBSSC9EqgBkolEIbTBPygPIw/hO9Ax7spQ Vw9B/JIgDRRZ4n4IiwAkNFZkVPoeBYLWSIkGuIDmCAfSFJA5RKFHU0sIC+5DZGuQPhAY05xOFZHG kcYyyLcxE8rSiRSORoQUyRAzaeWYGtXHwIh7jhObbLWpdGN2s7ICBg/8yaYpM5aF01ErRQybUDyo ChgHeyjYDe6Dvgm/IfNkdfka4sMxjiWKhzovRWo0JSsdoROMjHlJLMalBZ6mDBmUpug1+Val7CNV jZHDwzRoOVBns1ioi96RDaRMqip1ESjUTeNinxiLqVbSVoY9vSMLdQaYgg1pGYAszsiYGqjDTFq/ PKno5xCsDbIym1TYUWGrkEi2Guotxs/SsyFJ6wcmYpwUU6L5AQyxIv1dvRRkB3Rc4CQNQJm4qbr2 j3aB4bjGiouoTYSM3V+YMzMp7LFYLgGfcD0kdHHBihOgr53GzmWZEJCpT4Li6pCSUZlkWsJl2Z6W kvYZucMV2dX4JSu6PQC5lMGvR2jnq12ASdHXc88oEhEnekGLg83W1kfEjS3ctZb1NU/4nPExZ/ab a8FfKZ7ZpOpH3fUKLOYrxcV8712jrru+307rDGfvvTeoa2dn+7QGw8EPechDD2TXwsV8up3u6tTH 38UKnL2n2x0HhoGRL++kScJnncWCv4CpUddd3Htn/PDFfAFz59NByfWe97yHfovHj51Jj0ta6NaK 3TPeMPUDz6wCS4O6fuu1325maDRdvDOf0foQjuVDKCJMagrjKgAI6FfAV9MZB0jGpaaB6pMoyGX8 XB7v9IVaREDUqTFwISDxzEmEx/DvWQFdAht2cPHhL+RicCJkTVJlARRgO6TRT4tkkjVFRuyJ4IjE ORAQEJhaBFbQormmjIh7AsNKOE0vdKEYhH0ZgZkHC5g1EGSpsSATACGRSzXLFQ2bTAoytEBd/IxB oPIhCsp0U4S4WXwpH2E18YXIEFuRp9TAWsl1k0LCnwACUVVjJW2Z3DAqItcePj9XrjRwTAkRK5sB Ef1SVmGmi1yb/K9hViI48uc5ZdKEMQUaXCrSjFOi2yKPiooxJw2N6jg+54Q6icCowyFeOTijhEt4 E8FqMfIg1dFi4sjUoDl5nqY5xMuPgjYobAYkcmgFKCWVnIgVIyTPykRficxRVkFCPsFgqLZ/KKCU rSY4CYtRfdU5UOorRiG9meAREraJdEcGGvItPjIiXJ/gpZDDtXymK6M5F7CJGCw8oFNxJLRg4lwS RDFgUTVplyCK2B2ZOXtLIFIznLHZ+Ajq4lSB2JlFzRktkyXSy7KZCl7FUMsrViUHq3atAuQa2BuB SWuBg86LbgacGeMmgI99AoYb53SynEAMFfIGqxSMFcRlYaBaoq1wLuPFbbso7xjDrKiqbjOgA+MA Qym71JXmyzw8pE+D6SCJ8VMdSdWm1Mgh5fQFcFbYbFPlr7Gv2ZCl5UvwqEfTTRLGZ9p/Gipr4B9E TAuh4DkqILkYW73QJYS/5GSRObjRCtTn8UlPfMqZ/T76XI9a8FeK+zvZ+mynXoHFfKW4mO+9a9R1 6vtqX448N++992WoS32SxXy6LXVJl3Hw5+bphpjlDNojLPgLmBp13V0bfjFfwNxeDahWlipIendv jz+uXH/dde/4u787M8I1PyduenqM3F3Vrq973lZg38MWzl4lGSp3R8Hf5g7mQGwTuLy1BmEgdeG/ Juxd/kS5+YgwF82SJ9E4r0zwkLQnUieBLXgs/CglKR6MgzbIdZsuJKrZEakA/UgiZG5CDkZdY8Q4 4Iy0IHFcBsYLu+0Le12SsIzpD+Q03R4le4ny0MEoh9oh+VnQDWM5RLyDy0zeQ8ZIvhKiHu7kbRWT 6fH+kHs/Tos0xXC4N06O7Q23B0mWlSSJ+Q0bi2Y3QOIkRQ7sC84ztSb5FIIG1AEGQftAWrLydVqt bqvdjcJ2FHaaTYLMMMQ1A2iImkISkQ4tg5tENtivHMcJ+h3S/GXV404N4SV5lWdEyJOtDvsg9R/n I85Pi5T0dhTgUIPaMCnIzu5wfIL4KmKnirKfTo4Nk+PDpB8n+BZXmqSqswQIiFgya6PTvGCte69D q1durl6xsXrZ5upau91rdTrNNulVEDIyx8IwWut0et3u5kp3s9MxB7TxUCKPIn6MQfZUsYg8Mthi 6Fa9wG75iPRmaK18J/O96ZG19pHVTq/d7LaaJFJdtL7awZwJpLStdApFKp3Kbrlh2wvQ66G9YgvB h+QmpIaOR9QXGqVhpoB8oI+OcT1y7gnVYmsh1IP+WerkCQoirN1ru1hnXQgWVsAsh1La1BnWAx2D qVGimIT/6QQ53iDJaQVQTiYtpxE5jWODZDyZnBzFJ9ktODMF9UTg6NzZI7WLnH72HlAJfIayDtwp 4+StMkRSsdCsYVdkT5LFJu3erEoLmSAj24lscrzc2cSCmmr3QjoZIShLTQWk4CJtLcvYVymczAZO NohTA3gi81Kdc/Y3TQzgsz6zQOSl3qEKg3OIpVODB3kVYXKCjHhV0YlBZpEfAgulN+THYDLuZ++3 QH3mugJ1BeoK1BWoK1BXoK7Aua/AGXCucz/I+op1Be56BR7/VY/5xuc+91v+63/hjl8er+Lvv+EN d4VzMaSnPe3pd31g9RnqCpxuBZYGdZFGpTt98Bodq2pDXZBSqY8cehnojO4EITmTgp6AUCwitfAF JuU0Q5ZCojaEC7WO79I1D9iUZeRrlaPZbIiQiEAkYNCh1YsPrV3e62z6XndmhRLDqOtcmZfCCgAC 8ACt/YAiRIPzfh9JCzgDZnFzfzwiPZ6gJkWIVW3Pb2FfJH5+Mt1NiDPPZTpDKwRLCwNiv7MSCDIl f30Qp8M4IbkLkKG2g47T9T0msj3MVoLgkrWVSzY3LljfXGl3XT/g8WS8r3RJE++AGABgcLoOmjRl NkkFZlrm4TQEaiiMDO8aojaHRwawDMWGJZPpKJ8O6eNYzlBybQ3GW3sDMAqMkDgoRb2rlaRce8OY WPoqClAnkfgltyBCIqCNjkFQRJqVovQRwc2gGyFZ5q5NLPpmu3npavceax34HZ5B8NA4wwUp6R1g ZFhY8aQ6Pso/dmzn2pN7J+J8ZnvwNyoP6+pEPrVDMUcSllKrypLlYahjoEyRJGm8O9w72d85urN1 bLB9fO/E3mhnd7h9/cmj195yw7Gtm27eOnbL7s4oJRY+I0AdhyESsYYfVA5J9TBAe6XprXciIBLS LQaGU1AiJhO/RQEyLkSQvIogSR4YFf9mhg4NKpTn6APJBkO0pT2Q5/1xvD0YHdsdwSJP9Me37PD5 EAMtbTqFkySgE6cSWyRwC4xH+Vyn22z2miw+0FItEeilwF3J7hHtBUT74FygUpMRhmDQGmXFkCQw xmG6Jspny25GO6aIN2Ev8+15F1ELBgsRQ3eW5ByDK7OxO85HSbI3Hg5T0vZxXhoXp8kgA5qJvhEw lxeBF+J5rUBVkEZFcUkDBj/tp+mJYbyXJAYc63JsbZ4LAFvgmdLcDAAGOM6meVUWeGr9Bg1G2Q8S gele3+oK1BWoK1BXoK5AXYG6AnUF6gqc2wrs7e2d2wvWV/v8FXjhi19cWxc/f5nqI85CBZbHwPjq FzP9aYmWJAIHkGokfdWEPCbxGd6Ko1rBSoYGBv0NyeO8o4eBQVkQeJVkahPkxGGyPSp2StldhCJh 45pNopAedmFeRqUV4WDkhzuj/NIjalOFKKnp+2AOGAd4TLyMUHQPNx+6FURShJEzIDxfGNGwk03A KGmO5dCmfSNqJngLpkAIkcLLNU4GBiMrJXox8fqIafDrkXAVpzIwKj9KJAL7oukQaQCEcuplH0OU RJvCwjAJxq/8JjRuiJ4QHDFCRkLXP0Wky62JSkkJXyTZ0yYPbkJwPiTDVZp51SUhymnIKGiS2E0i PRFUFWcA1+GaQ0OlKDDXpSUkZBF5UUwevJk7dEUZTiYxCgOfmu0pg57IMDtTIhdmzADswuwIzOIK 0EAjqrLHWU79O9At25OGiRUiJsyxMP7JCFkqqQpQwpFUGQMeq6tqoJiiUAxsSsq6AyxSbpbsdHLL geIoAccjm4JYYeOjNyALrbx/+RABfwrpAmAhpuMjFsZAzR8JnqdjIAxOOjWhIvWRZNGAgzoMs6Z6 OxozI0NBY4UajoJQLuYfyqmq/oSowdh7CJriAq5EaJcEhvoBbS5dN2J7YYpElkVemjahQrtkDlQE m0P/Sq2+CYfnOxPDVZmZEs6MfKvpOswIrghrkglXMWpaH6SCirFn+5GNr6x61kn/Ego2nwhyQ9N7 tKDA6B2Je0Oxh7Qum+qnbDyeIHMwrGaTWmQkXDKMmmA1wBrPI7LMXEEvBZapkvKOmpEwPokImYxj 6wnG4VnCsnJpKGghY2NwZGWFh1z9lP+0v7+sFlz/v7+Trc926hVYTP3/Yjqq6FT4wB92n/31z/3x n/iJU6/wpx25ULH0v/s7v/PDL/vB8zCW/oyX70A+cDGfbgey1Is8qXNjYDyzCiz4C5i7bmBM0/Tm m2++k+LccMMN3/aCb/7MA3791a+69NJL7+SBZ5aUz/+nJhsPufG+zzyz9Tpnj1rMFzDz6aPq2sc6 POKRj/zh/+9H9vGE9anqCpx6BZYGdb321f+dWYG2iIbn/ThewMAjhEjaJTXdM8nZrgkLVxIT9IB4 InCETQgRhCkgqEv97yoUSRwm/Y1Il6sw8tXQxcW3M0QTg4WvLTOXG11wWO/YlWmkxC4lWEF3+Mpw IfKhlBRFbBVaLtABmUWRx2ll68LCpXZ6Mp9BA6ZABGVQiVYwVDUuTCczbIjEfkFFXCxt5WxcTD2Q iTLvTRZUZUMcAAumzV2JQgzsAIlA4YPTDOqgXnuoc2SHVO4SaUtYxuat9pToZFK6YFC6N7D44fwz keGQmtl0mE+4hMlaEq1gXMiy4CuieAosJy5KsVMMGASGDAkNWGr0XIijGBzhUHwfmsYI0RyZC0mI hY5OqfxqgojQDJmRkBlUBDxjeAoxYcJqcJcknVBpFG4rrQBxFVFqdkMJ/7vjVIoqm+AzKCF6K1ka qTMKLKEd02KAUZuwqnnS/NzSZ1a7AhaBzJTWxnzhM5gHoVR0hDTJXfM+Boq9B4nBEHmgPINQJwiR bvMGBfoAe0IpZbSOCqZnFdhJmo76UpKPxpYDBqEmpN1kY7UV8u1xmiL4AhPCw1geTUmRV+q3yBqB ulq+D2ujwgzARLvLRSsSpsR6fYfPjVBL4jw1TZCGy5AvqJNJ+EegSBlMFwZFZ3Eu2lmisJN+TFuL nSmzLesW50JUhN3nk1EYgETdUW6tRC0cqNgQTZvECdsTBRtT4BLMVuFsqOmUF6ZGihSL/UpfSo4l A05xZWxdbUh2uEvaPxdjRyvtjvyv2UT7TAIz08Sy4a22WtTu6q/Z51cYC/5K8dR/4dZH7m8FFvOV 4sK+977vS+8q6trf5buLZ5u32frgj0wjNcNYuNsiv/deuGLdhQEt7NPtLsypfuhpV2CRn24L/gJm Y/eTvX995V1sznvnC/a5WjTelbaPd3LFGnWd9vPnMx6wj6gLzvV93/fS2vx71xelPsOZVWBpDIxz dAOmAGW0ArsdSl0lKuCgSAKkoB5SGJeClGyyyU2kuN64i/4ACAA60B3Qhsx9kBMbzgJPiUK/NYob N92S7G4XybhCXDUrvDxBt6Ngc3xe+OGcxmwlcrvkNnnADpKn/NBvtsKohcWPwHg7s+w8nSY76WiM f1JIAcOg+isq3Jv+ejAv2JdkQAqFWm+F6+Q5uaiErITv2c6RlTbyL+4aITbG0F9tRuAkvHer3Xav E612m4dXu5ce3mi1O3jiEN0EXtDG/Of5BOR3m2EUYVLELsdHErn8meOq3aKQh8RmgDqQDkIgcAWQ oht6OOZWm0EHEQ6isyyN09QqiXQvcL2pnSMaqnKaZPlwnJDGNRinpJ5jx8MWCoxrq+Efpae6Sig3 8fqSO81TrrbGKaFdxKtzxzJJS0fyy0ZpDuiCOKkhANquVkQOF2fphsEqRj5l/M/Wu63Dq71ep4UI qaicwnKySkoxGloW1XQ8neAeBSqN8BXSGhINkQyXNDGkAkpnA11SFqyjGDR3Y3CiLgiiIlUtKdBq gZlEDxGdqV0hhIg8LNcHSQEW1RTRuCzFCgXH2Eb08rSBppAghkdGGObHXjOC44CxqC0tFyGGo8k0 nkyGOCKpAdUQfuS/oEkYltAVO5M67IwSZF9km6H7U3D9FDzEeOg5kMd5sT1ObumPd8bEbSGXgj4p 7Uq2SxgWsWlMYVKSYc+wAFsmNd+Qprky0VF7x2Ge3zIY3bg3Oj4ajbNUokWgmBZIWi0i3sB/LBDg DzbJSqCDg5+1PQX1c04yuRCFcczcd6gMep5NgE81cTDcVFgRMyj72otCIrukUIs8B9eqzbOO9peu WzjE9ft2ENAeU/f6VlegrsDiVeDBlzff9Q//sHjjOsMRzV0qi8m5znBK9cPqCtQVqCtwbiswDTpc cDwen9vLnq2rgdU4dRmuna0L1Oc9nQoQRV9zrtMpWH3s/ldgaVDXPAIIJQ76mCHpSuAtKVA0fiAM mexGTFRYDTgXd4lQpL8R4KKVIW//USGVdKlTej1Z3i4RRR4qrmFSfOyGEye34moKQNvgt+PWbr61 m83JGlQIUkAMdz/LSRDvx7FtwSlIkBpukR413E2KEdFYrabXagWHVrqHVzrwEZRi6stonHSMDyKD wS4nzmtSkFO+NRidHCiv3G9UYeAjyUoAdEagNI+uz6ckeTHDPIvH6ag/Hg76/f7eYDAYDm/Z6d+4 Pdoe5VvjyU48JfCKwHRYEt4xOFI2U8C+hFSQCN9uwv4s0rLyQRITFZ/RElH5Ss6YELE42R3HY8VR kag12R0nJ/tjdFWSY0kz1UA0RG0RxcHcNld7yN9gN1jXoCR7Sb4TZ/20IGufQYeEN5HVzmpYFexs rRlgsgPkcSFQCJ0fkUEBo/LJLJnMRgAq2d+kkOpFISKmvTjnm6Al2CTtInPZS+WMI+lMSVUk6ANW IGvEq+NcJfTdc9aa0ZFum2gwugGwpgSQgRJJRKO/AJo1TtWNmlEQUVGQHIiKnKx2CNPEt+nmOtLi QiApbR7y5Wl0IImetIEihWpMEKy0O6utDiiTDcc591KaF8yG6YS5d1gzB1HbLC6mVGwPkCYtGKIm Bj9LJbizoUjoz/B+gghX8LLOWxy4TjMgwwteyrYAbYLbPMglgf0b3RbIb6UZrgjF2dJwSQXGUlac DfS5AZekESRdOKcsgfyTqKuMjdQS1assznNBN7p0tXlRL1hD+wd/5Gkws2kXEGdTQvGxTI7yghWh 7IizeFQysVp+FHqheY4o3w08CCWjZwNXSYqKWgq+Aft8l7ahfuAj9surSrDQ83C8DtICwIjwjJR/ bvgkkQXSA5V7fasrUFdgAStwz7X4xutv2N3dXcCxncGQ3vLmv3zmg+pkwDOoXP2QugJ1BeoK3FqB zFChj33sYwejItvb2wZ1rRyM6Sz1LL73pS+l5WKt51rqRTwAg18e1DXzyAKS2QsDHW++icTWm31A EkY5UsN53w/7gjHBVqArBmEYfoRLz/WaDWRJIQgETx5SrCBNqqx0stzZ25k5ZccPNy2v57ltJ+xw hiJHpoQHzcJ9BkhBjcVJibhaCYmNV/4Thj/fKZt+1QntbrMbuk16KnLRJCsjWiX6JLWTpJSN85SQ LHxx8niJZCGdKjpeIwRFMGj5B22ikmjfyNfc0SvFeY58aZQlSSaExZdkWs1NgnnVWO20D/e4dzda LUQ2EByTJq7eeoiKKAneOsAcYV6k1RfY9dARzaYIdBiVUqkkeyOOHaUStkIeNOWRiMIuXO3SuJBU Lxo4Rn5AD8eVVqvXaoWEupNrT7Q6eeqKQuPMgnKB26ANIh8hXBgesbnNk8KIKoMBYWZkUrBIQrvA fCiZmGwXXOIz4HCdnoutCAnVDhgGlENyPnfBMcOzyBpDZ0QjQhRnFrRIJsQ5oIEW8QAkToOs2I0B N+JBQDPIFAn67WazE0WdFko3j7A2aKaPVold4tJb0oE/knjPnQC0IAgASj4RYo7S1sjFJ0jetCkE t5EUhs/RykqixMQrgV8rUZN2loR8UZmW5zNHrguP6wTePdZXIFkbLShVRL/LUDhIAfMwIigV6I1w fdaO71PJGb5X9QwApqEno0GBcsOk43KZJZ5cJy1R+ZUJJ28QIuZQXgWiwUlhanGGYg4ECSNkeDCs dZpshhGaxcPd9kUrPQXbV7Pt4ejo7t7Rvf4QAycKLstu+yFsFzCMeovOj6xPouahtpRcHr1By70E kEe/grKY8lTB7atOpgAv6COdPikftCudVqmsm04Qhcju6JAgkIYk0qca5Mdp07IHaN6o9Lf6Vleg rsCiVuDB9xQYuummmxZ1gKcxrvmf7i+u/3J/GjWrD60rUFegrsCnVyBz/GDtnpjBD0Zprv3Up5hI 1rn4YExnSWeBmOv//N7vXX31Y5d0/PWwD1IFlgZ14T7jLmsYqha0LaQZYQlUGn2jGfjcUSRNsIZZ SHwU3C2ZDbId3qA7/MjKoFaQBkcGPxhHabnjpNzaS3b6ManxaVGN0gmyI9QwnJ+Yc5iLQp0qiyCt QZoTmm4aDmIdU3tHEImi3GdVMp0lcQrNmU4AU7TsG6XZ3snBiX46SiZZoej2CgmMoAah+PgTfRec 0Sf9ajrtZxMa/GEPQ8IVYEGMImKRhG+qBgwL52K73SZVC7wBY0I3NcYFR3NEjIFEQ6ntIviL3CSb 0DHQDnArJbi+AHABNIAtyp3ncbq2scWVcB6wi3Lf0exg61NYFc34EqKmUJzNJDEDGNnAEXVatMR0 SHynU+RwOM1AdlNqDrqiunGG1RENU4lAiUdBi2Bk4ByMnUxpPE4QQg+H41GcFPSsZOFsBx1ZMUlI w8c0RxQ6axD6PIrhECWmhpXEbCHjyjlznAyHw93RCGAFbgRvKVdewjV0TiixHHRkWOryAomcLI2Y TBskRjFlJE5Tyw+ba72V1U5vtUOHQ5lOkWqhTYtCJFY+KEd9BLXOSrxnixC+BdTiO8yN+TK0yvMZ JNtL7kw6EvIAq1GSQWY36Ogp+yN0ziFzbbq1258kyfZu//jO3u4ArSGWxJwNx0KzABxACRkW2wKI CVWU1gtWiFCKPSS/JG0NBbMgkJ6LzAxPY047TGta9EfjAdwUjRj4j0z6bJqO0zxOSz7LEnZ/libj jCaJ/U8cvfno1olJOs5gYZbb7vSOrPcu3FyF62a0RhRKA+mxA6RFTJOU+ZsEOofdmaOnKwp8nrb0 XCTToe1CcqbQNsXoS7zlNltRd3WtvXbIbfWoA4I49rAYohLrnNAOIxDolPSvWZHOJsUMNsb9IP2K rOdSV+DAVOCyDT03/+UjHzkAM/rXj36UWXzRPerfNgdgMesp1BWoK3B3VmDoCwwdPXr07hzEPl37 Xe96l1BXbWDcp3qe7mme8exnv/I1v4mY6/DhI6f72Pr4ugJnowJLg7pMf0LUT8iRMHmh8fEUpV7K aNcfTwYxohRiuZUbJQteCRoiPBxW5eeYES0eENkNCFdQWmE6xX7lloUXjypo1/XHdvZG8d5o/Mmb jn7s2mtPbJ8cDgeytOFgrOjZZyO/wTyJ1e7o3vBYv3900N8e98dFPK0y8a6SBnxxkmcjiMa0GOWp gpscF+nQSrO53u10kAU1m91Wc63bWes0Q3K2MEUiVpKzDfBU4gREZcN9LQq72OMgOfRbLMS1IvV3 VBA+yqDNHgSMpCQXDyDuOdRVPBA0puaJJkAK0RZTxcOojClCwpSbr/D7bIr/Di4HswPiTTu+s9IK 24qbwnxG5lenE2K5i4hmIvVf8Usy3NkdxE4EitGssCQxSjAINIb8ibOTHE+0E70phXxMD0c+J8NL g2Nxoqjb7RxeXz28hhGwTcITaGS1zYNcaAwAiFiqpICb4SvFTyqcpLQtdHHozUAoHvgkaKH8MoI1 zs/MME6OjRyJC4GoEkifECdXVMA+Z+Fr/kHTRfFhTujpgI7kdqE0QltG/8eEqCzpnBqcha6HFAhK qmAt4Bk3hiiw4+JPbJPERr4V4j+OnFXMkcn67CFr1mRs+BA9ws7k+6P+g7xEsUVwG8oyot9pyQh7 5SHIrFZCf62FXVBSNWCeAukrMuPsLiFzoCaJu9gF6rOJXIsOA7LWKopL8fgYKXGAsj0GeY5Llk1A EaC7yMTiyayfZQBTfrrabF62ub7a7lT4O31ntR2tRMi7LEAkW6GLYM3MiJmwjoWSxHC85nEmPsgT RcZFm+w5JZWJf0Ec5eJEd+Y2peNzMLdiC7ammTVNZ9lwMOzvDPbww7J8PPFaod9xgxUvPBy1L2p1 L+2uHYo6jZnP/Wz8qqrPWVegrsBdrMCVRwSG5u8Elv32vve9jync54IadS37StbjrytQV+BurkDR u5wRHAAPI/Z8jO3TlauQqt3NNT3PLo+M64d/7Mfe+Id/9IJv/pZLL7vsPJt9Pd2FrsDSoC7T5U3o AykKseNIbcAQaJiQn+AFA3qIzzQ8ZF4CPBJ1KYmJOG+0PEAbwMbUiJwiN0JKNR5P4jGiGaBEE40S HRjJrRrHg/5ob5wMEE7h7eIOSuNy2POkfynpWOddsbn5BUcOXbq+ttFuH+p20H6hhiGte6OzstFd 77U3et1DF64fuWB1sxl2SsuLi9mwmIy5T6o9bGKVlWHxI3CqHUJqxFfw0EXhyWHCHW7FDDG+4QjD XxjAYmY2LsOExxazm0cZYjICqghvmgeNtziByU1vhc1eq73SIqOKuH0UNrTZa3Bm4SVZ/EiM8to+ PQHVrXCYQoKoHDa1Bhn/iOR24mSUS/STpvHOYLCb5IRSxRkdGiuC/JvyfErGBg3BuAYFg5uwAljq SKoitItPiJ8HxBjZlbgREAxQBVEC0mEWVIw6tkcBMryVAlsmZIqWkmjRlAwFlhMeCt0uE2BMpu9g hy6NTYiS2lxifmSac14E4sFR2IF2NsiiQjuHh1UKO45kpkiSJFUqEZ2Z7CkC4OUA5frqadCjUtgH PacT+T04lHiSaRkg/ROhb44A3CQ3y14QTw//iYt8Z4zGahxjKc2zreH45ChlymCiFoCL4vg+3k+g 4Wq7zSpAjdSaUuN0AUJgLKChijYzzRZBkErCR0ymHo9UbJgVlAMnKhFbfA/0R9Y+BBFuSLMBis+d CkqiiHzP8fAtHup11oxVk3yxnI3N1B3TKdKyCKPjbKwahkxYVjFV7JoUkHpGzBt6znwbQ6v8uYYz WnhHwaNIyFhgZHrqwqgwMT3dqDBSO54SBMwd3RuwbdiWPLu4MAuMjjKHxyb97eHOif72Tdsndgd7 kZVzX+jfefXg6gqcrxUgwf1x96t4J7DscV38vwq7zYMuo+VrjbrO191cz7uuQF2BfapA3BOb+KM/ /MN9Ot/ddppPfOITXLtYueJuG8H5ceEjF1wA23r+C14A3kLD9Za/+mtkXI94xJf1er3zowD1LJep Ao1rrrl2Kcb7mld+j4EDwCx0KGh3cgRM8BPHa/OW3hVJyLlLiYXcC2Mf79NptojHKmiZ9/wGSRGq VflHTw639gA+nC3g3f7RW24sJzk9CiEwXIGcKxx/z/66h/K1GvlZNvle8pxJwEQeeena+SglMKpo Nzub3fWZBdFQwjk8RTlhtkuUEiY9aEJOsrwoGj0ccXnpH0bcazcZLuozLgXtGcYpQEs6NIs4MB+S QNo3E/DVj1GN/AAxwCJwiTyTitifcWni3ekNCUIh48kIkhBEcfCsMSugM/AazgDJQqwFhIJzMACh KHLH+CZGxVDaKPAQ0elpjhLNKH4ECC1sfqAqHgOQgpiIJ5LIBblpNCRzwkmpACml/nNFUQ8Od10R E4esKyXrS5eEDku9Fws+QyVEEYdpDi9rg8AaHkY72wsI5OLh+BZHaVpByRqKdzdCPa4Au/Ho0sgk mDWQEbkeZTTyMTebieYIlgFyjNgvhmI6NiqqQQZkAWnRxbCB2IphsL7aNooYU16a8WyS6aYGmdxV JViY0vjReCmMDBUYci6WWl0CpJRT7wOgj2iRLJQChCyxgvUFwhSxBk6kEhwAb2VUOUY+RFtSwDFB yjUbGisix4AdOSOsk/H02hGCQZaNMzJlTJro+1h9jif4TN0yVW0OCNoqlKaJxAoKyXCoucidXQEH fceSv7NqbHZbDGqLdozy4cqAqYg46ba4mc6SWE8JmcNqK4RLZdSScpVgeuXsw8VmTAHIB28FzOFX hcnh/2SmLE/JxWhBoP2pBpMV10PTmE+GSZ/nDXcVhbt4X4tSP/prnr6/v1UWvFf3/k62PtupV+Ca H3nQqR98zo689L1fcc6udboX+quP2i/8HfvXX/2qq6+++nQfuzjHv/vd737ec577Q18ze+4jFjcf 8IaH/v3iVOwAj2SRn24HuOyLNrVFfrotxQuYe3/kf5eD69/7gfevre1/AiLRik947OM+c8/85dve euWVV+7jXvrZn/mZ3/jVX9t68EuHy2BgXMwXMPPlePvb33b7ulx88cWEwMy/XFldrXnWPu7Y+lTn oAJLo+oySdmwB7Q23Pm8UTYCbGC+xENFPkFmhMSLN+RkP9F6UIHfCusylrLSJsUJDtNouV4a5/3t 3bQ/mGTjcpok410gA5FVyoNPErxdY6yIWXyP9TXua+3uWhsBEO/x6diovnu+U220MaYFh1daq50Q MAGFUJqWyXCSFsbIZ9IUldaIgCqoAgAFzqD8q9ms7THWLI5jrjUajeLx2K2mrkLHdFeIu1L0ATqk hkvZZIiMpFtpMRtn01t2cVCSOQ5CojUhrjcgH45CW/0ELRuERCAX0MZckJh3xXjh+OMMoCM4X+Sj /woo4jCbjrKJBDt0FmzQFlB+xE7U7jbbM5OcBe+I8wmJ4wA5LJcrrahHHJosdXQSxE2JMMoEbJWz UT4hy4yPQyWUk1WWb9HMcYTcagK/IwodUkOY10YbNZWDD3HeIBIdFY9CGzXK1MkR+AO/Yg5k0vei YJ2Md5hUTip/savWgfSvRLGEjXF6YpRAzYxqD9bTAMZRp8O91lorRI4kAiWmw49kQIXBEPjFhCYg OMgU8BGEQ5FLZGjmPkUUphQw8RvJwRhKwdrhSB0k4EwunY+LHGEX6f7gQJYbZsT4JZAyYXDSPVEo pGVzFoaHlu8LsVXI4lBsUUMy5ikey8jgAH9r6P18j8N2s4K2nntJeuPu4Ja9EaVQ3pgBrki2cHzC 9YBQKOxuGYyPj2J6QSJnu2S1t95sHWq36ESJCbGfxHFOCac7aX5sEEvVBV6k84DrsNvx3gpPwnzF pqgCQ3bJ2m+HAQK6VuBNqhltEFgLhIccyH5jvVDMrTQBmA4tOUmjU9iXF5SON5xaQyLqaTRp+icA hTdXumvddqcVYmbkIPBnYc24n4PfXPUl6grUFTiDCnzJZfols+x/vf+LP/9zZvGo+9WSrjPYAvVD 6grUFagr8OkV6B95FN/6wAc+sLylQa0M53J6ly0F51rwOpMof/v9vve9H57E+b3mXAu+cPXwPrMC S4O6pop4172cYaybiGtAW+zGzmgcp+NJmYlTiBjpXb1EMEhZAB62ZDUAiU4zgDhASXb3BvFoVCTx bJqBdAgc58CCnot8RiY9ZycRKS+u2x1yjyfEb4EAZqGHxAmh05TQq36SAwjgGWqup9Pa7YCsdLU4 BHDAGuQOxPVYFLv94XFcX0holIJkEaO+M4Yg0YYxh3b1x3wYJ8qajyOfrpIRpkM4FcZGZkaOFXcU Rpj+kFYRRX9ie68/GJeTgiz2YQKCEHYBoECgNEvCm0hikqGvkD6IWPpJAfpAAQYH0USgPAaB8Zi5 Boe8c9RLGDzpt3io12vKUumvt9swQXQ+Ct5qoJqTLAjyhZ1zHg3GjkHlxE+bNEl0bT5p+ZgNHQyS hzrBJSvtSzd7dCREgAbaIeRrkE1ZD0ikEBHIjzaUxD9ZfFONCuFOJFchW4J+EdU/YtISDlVoigap QCFCOdRITAOtF+I6orLAN0RwcRBlWsM6GBLBVuF6xNEITlP4PBFjs6o/wnOohgLo6nAlAs76MRFV OX0N4GosD8uBjIyry46nzCwoGTItC64G+0Iu2AmieQgX0fZErZGVttFF52TjekSJNcYHmiq6P8lY cPL0s5u3tm88uQWeY8lWmgFCPAp6uBtSbTAhaIlLKKV+MkNPB4GDQrIcJNT3wnCz0yKpjZIa+6CY rpCadHxlM7A2O17LZ8sjDSv34niYxcMkJogeWkfuPZouI9kTfkMmRsoYp6DEc1cp2jRF2FUIDekd QItIRIXB1HLY8WxjBGBG2cazhO2NlhBIh2Mx7mfEcRHwBuoq6EpQVKkPqCwG42R3DM+sstjKkkY+ riZJNSHKK29MCkSK2Cgd3etftXUF6gosZgVw/D3zQfIwzjsYLuON9zNz9+JFqzXqWsYFrMdcV6Cu wMJVYO5hfM2rXrVwIzvlAb3j7yWkHW4+7JQfUR9YV6CuwMGvwNKgLt6HCy7JlgcXMQYyaJYNg2jR NxDVFTnfvGd3rPK2pnuEmwcYvRrT3Ksy8FG7tUJU02pXHeX83ko2q7bJox8NwVIiU1k6hYug3wF1 FMVqu8u9E7R6UbNFBrntFrRGtBHLoBdqjFC2zCyTVZQiBCPIiazuURoTTE+gvDWbwEK6vnPxeu+y 9e5KhOBItAhkRpO9Rlk4s2lkV000R7OSrCMD6XD5EZYv6RrapYS4KJKXnIrw8AyxV0XWN7yjTUKT yW2Xgw7Zzoqyr0oAXFViJoshRwEiJoxnssThcJSODHMiUiNGTbQVqU9AENAVfkbZ2eSbnI7S0c5o MEiGe8l4K4m3UiBaCcNC6AVoQVGF/gv4At8Be7EEQBGoE/lZuPLUQJAvZxXeuj1URf3xsVF6C95Q cs24qd0lZjhS0maouiR5a1jdJh43rHv4/qA5M0qApA402Ir8Q6sdcBJkCQjXCp3DK1FPBI07GV6s JuI6HkuAlYLTFaNeVQNsqCaOX/5Vk3HPl3Aw/gsUa3teB7Qz7xeIGdD4GMXvUGMp1E03g4G0kQSW DArkQF3KDYzjEGsoj3PjbLo1Sk+MUPzlu3EseZdLSBZZ71Yr8HtRiAirEwVHVtqkzlOAnVG8PU7A Xnto56wGXAnqxydcBC0Vsimcg91A/QlwEU4rCuFWhGh6fhA1m0r+cmU2nNHDsUB+N8rGKA2LKflm yPAAXGT0Z8jNENwREBZ6zFUdAlDPEQMH2ANRsUaJpGbSnokdgi8V4o+4LMDSS6bYoW6b/cBCU+GA Ta3nAJwU/qiupjT+RF53ryMrl6yHa1HZ9fJWI1n1883mrBNOuy1rbcVbXUXut4airh2ttsKVKOiQ Gue4be4H/xdnPcO6AktbgSd+sX5P/tXb/2pJZ/DG//tGRv7fv6rOBFzSBayHXVegrsDCVSD22/mR r3jfe96LPXzhBncKAyLA8Zde8QoO7G9+0SkcXh9SV6CuwPlSgaVBXT7Sm7n6xqQgoZfBMxVBRZSj TQQ9cVQ0y2t4juUKZyDnUn42aEbyKnBBnkCl4jRF7IMsaXN1fWVljYgtlEpeGME6wEPGwqa0e/KM gEEK2HJBVCXWvJ1xmuRIeLDdlXleGkhlIU/aw083GuJDJNQe4RScq8QXOMnHeQ7vQJYFD0KppMT0 0AnhUuW0n6TEwKNyAipANA6ttDrtJn3uuJNWHmr8VeS7q51oo9faUNQ8bsbAj8KZi7RMn3GnT2I+ rfpj4vNRayUAEXUzZOAViVGQlKAVNTdWV1aarYh+jw7NAy26EY6IkE9y2hHSoQ82Rn1Ch1GpISDK pNBpHOlwvF8wATx+OOkIM4NIYY4bjAaE9sNu+J8JkfmYBzG70d2vFTFAwr/o9Nf2gEoeRcGPiXdQ OVcgSSNNIu0Lmx7+O0yk6sdIS0DS3KMgUq9D5hNUUoDpBywNqjoVQx0G1QARXRLZZzMOIMHfdUFL KPEEY6QuA7NNkoyVkbDK5L7zA3aIYtX4L9o/oq0ssGBEAj3x8eTcA3b8BM9ihfnRo7QRUf7owRzA peCgwrlsNxdwLHEm7g3j4ztDto92UMzUrNXIx7i60g7Ixjq8Ai5qcm4cfuqrSXdIuJVS7suWZ9M9 gDaX9ExoowzzPJZNxkYDkpAN7o6SrWHcjxNEYRhCuzTCJPutS6/OiJ6VjGOsC2ovKV7fhH/JONkA EVa0ceRiDJw12pOETxIwfLS4dIGGCBPRYxF/ZroQAIfVlJT9IfWZoYEm3N9YGgnwR4TIt02iGUVR ahylRwim1LAKnR0yRtL44ywH2ZkunxaOUSR5cTnrF9Mbt3du3u2fGIy2BsO9MUK3TNc3wXP1ra5A XYHFrMCXXjF78OXNn/vpn17GcHrGzMgvWfcecI/aKL2Y+6seVV2BugJLWYHxBQ9h3HN7+NLd/uEf /uHG62/ILn583Xtx6dauHnBdgbNagaVBXRKaSJCk2ClljPOuXOIlKZ6AX8qjAnCAOKTx0Xt5k1yO 227WCuzQQU3FG/ZkPB7v9vfiNBup5eKU3oxrh48cuuDCZqsjxGWSy3kU6qWCN/hFsjce7MRkKKWh O+uG6HG4DtqmoDGzees/GqfxcCjMlRbyxhVKrcLASCZS4FWhT+47Ihl6KRbETDFOtE5cAF8b8h+y wAFpFU30grBSrJYhGbYNHlMEVKNB0tMAhDWztuOcICey6o1iibxyBDqKV+ejiYcngJxPBDuQuvFD AvN34nyLrnnMFheaRFclhyNbilBLBaR3YcFTVDlwSGFMOayDC5V80kevVBa9EHQl9yLlBgVSUMRK xHVxX+u0DneREbnUnZ9kk5n6MIq3KXsLiIbOS/5Sdau0yPNSohYR7FAt10HuRHI8sjgAFXa/Y7t7 GOWgkEiyPDRF08kYapVBuYRKQJpmTpg3ldqPsArQyeShkL5rgZC60LXbNi9LxxW5Ft8ADg1URGAk lAd0qA6S6lYZuKst4FrQbkar3fbF691DvbYNOzPhXSb5XsVkRpQdgjgfCd+hpOoS4DirSOpAYmpi iRKtJIQLRd7eEAiYHNvr37iNMzbBEUnEVacZUTcJ/xrSTEFj2aqyE5YluqxhliN140Qt110Lg/VW s+XBIiuMljvbW4N+Px0OBlhVRzFaPdPuACxFCpZi1yBh9CLAu8sZqRmMj32MtA+5GngXwogOjRyu 1TZSRGnGWEWS3iI8tiGGUzuwJXvE+IgUcZhCxgihLxOgnsRc6gDg2ZLvsUYUjf0IM+MJwdrR/BL+ RRg9zC1DMzZBE4fHE+VdcKjHPTy0Em10Q0CpqPPS/FI5q79a65PXFVjcCvy3x4wZ3Gte/erFHeLn GNmtkq7H5HSTrG91BeoK1BWoK7BfFei3jkw2HoI9fN+FXWTPX3P9dZ95369Mev4K/5M//uPUoX/h l+5XNerz1BWoK3AwKrA070qlyTEJ6+AG3tibCHC9VZf9ULHuEhGBNhRKBGRA3oLOp4C64O9D7DP1 rIldTXd2d2+48cZbbjl60803jYYDUMrhzUOXXHgRJkXEKPJI8t7enATfG3fSltabncPdVYLby8pW iNOkJIMc7kT2FLlgqHrWOs21drjW8jfb0eFOcyVEHWUjESLFyVgEp0iC8hKZWNkL7XbkknLPQw73 2pdtrm92mxAqyBECHBkMleakToRQF8WkWzaapotXOvc5srHZbfMzJqnZynGHuU5IjGET1QQ5iVQc GvY1OoHaNfIRcREQULHkDbBPY0Jg1pRAcYQ/+CIJNSPrylGyFU5AKXrUcpEKIP1Bd9ZHAjSd8lhh LcfZTcBn2fY43Y2zE2Osc1jxlCeFeA4GRDYWByPVQozGDEzXP8haw2TAM5tKZzO9JGF3N+0OiVez 0c0BjAoCs3JaQJLqTgAa6erSZpWEo5O35U9oIzi15lo1KgLTAdCg+GJ18DxyTlLwpTSqKgn98LMq bh+vpXMIeVQYSiaH348UrRyfC0xnSro8uiPy2pTHnxRkxiPjQkHGAMi9YvPg2cTwiIWSvgZiWpYd OQrSQlGWETqGnq2sCInH1JkUE4L3UUVxXWLaQtvBv7fWIoVfqjRW7dbFmpYnR+nWkMPxBuYAxy6e WkNpBfe4tgGXVPV4fzQYxVu7ezef2CYjDtiE+xK3IdsDtVsbZ6IfzBrOKIdOSqemPpjlpJoWkTuD v7FxSWlD2Qe1RLFHPwTEfsqgt9lOCkUbF1pWWi4kqXLb7Nmk6SqITWDLI/+e5bIxPCppziSymY6Q uqtLA00laXZJZwMbxIae0YadIqGjleWM6YKFrTyrCkK76NU4cyzu9a2uQF2BRa4Awi6yrkjwXa7E rqNHj84lXY+9fy3pWuT9VY+trkBdgaWswPAStQ/+5Ve8Ani0RBN461veMpd0YcNcomHXQ60rUFfg HFRgaVAXVIA7ZAc+oH+R3Ewn0BwIhwnVlnpIlEWNDJUfZbK9pC3C2MiXwDCkTLuDwWA02uv391AU DYd7OzvIfDa7XWgCbi60VbxVx9fG237wEXfQBkBkMKbnoaQs81Qv0AlnBDfAj0bTMimngzxFQnV8 ON6LcxyCGWnlWApxGMLaZhXUCcMhCEdN/QhKnwC/ptuj5MTecJhO4VPoZebp4PAF6BVBTqYHolrx oddBZxTrbHAjBbqjM4LqoFMCLcllCSnDgWjbCfocqiEMwlfifgCaCWVoqAchDCr0ok4Q9prNjXb7 cKdDxhPeQYgZSiDCnno4+1z+Sk7GfECXRno1ojxCjoTpkGmSkNUNg41OC+K22cax6EWeg0GPpHUc juAteBoHCmyZQC4YizRN82w1o/IBcmG0Y6A+iK1htV0fbRJVDB236dHvr9XElQm+UysByA2TQFtE kpQ3j2gT2ZkRjQ9jKioyrxQRhmLLnodUkUEF7QILtQitdxqkxbMKDoQH9VhBulayF493Y0R4SUGS Oy0IFFNfZGnqlhPX0hZCtWQGWZIDhtOwg+rOpTL0fESHBfFR6pnQF0xNeVcUx4nQTwUyJ7IgDfLh XB/9G0bG+XacK9oYp8FCNKOcQkAJfTfZWajhEGTFjOrkeLyXpuwOSGjgVpce2bjnhZuXHFrvdnut Ti9odXBTatvTWJRpOoRyEVsWHeq2mC8+Q2RmEFKaIeJzBACWas6AW7Zs+lbk0YdRXJiPqBFbZI4h emRK5H8VGRl1eyPyvxAhymxoujTyEKHPwOVaCMHYe5MhKWE5QjS6ZLJJqRNbzWt5UciW0b5y6JIw xK6LyXEKG0VCyNVNOFh9qytQV2CxK/A/n6Cgwx962csWe5j/YXQ/+RM/wdc/9tRa0rVEi1YPta5A XYGlqQDCrnliF/BoWQbNn0Be8p3f5bc2dgynq291BeoK1BW4YwWWBnUZzZG5yWSIq4236NxRbIkd 3PoTqAgCIGUOSeTFj+BcAC8SrOBDiFOwyMnmpm9NszgGQ6z3oAl2nmQKhC8rt3IJnz+0sqE4KGRc eRHTrxEb415/d69Phv10gmWM/KLcmk5aaFyKbDDY7Q/6Y/xmMRKlHHcbd2RM/RHtFdMCAoA7bAKV q4AOvcgb5RmMI8nicR4jKmK4oDWhHYfuhDPy7lN6J0LmiHyazkgW4xw81gSry8QnCyVCK+xqVkXA OQIitGbYETkz3fegXYQ0mZqALaBHIBF77sw00VfKZtoeZ30MeiijIBMgQkf9AdXKkRN4PiY1Olbi wjO4TU0euXMGaBKqOlo0kpiF7w9jGwjEyJJUbGm7lKJGUpWqPy4myMfAQzA8jkL61PEDmjdmEwtB GPa3YV4Cl1DVIS6DIRV8tL2Z4xPaRUY7aG+AjmyUDrKcGZC1TlfBbgSEIuleEA0xF7VSKhbRXWU1 yhCFYcCc7MTp9piuk6AuGl/OmAZT19hsCa8w4s3RJyMUXQQOiR8Cccj2p2DOoMj7LC/6r1k1JroL vGZXUQD+kwGQHgDScKk0jAHZnWRQZNUP5V0tRzkATqSRa7U9F97KJ1olWmbmRKQJFmnLysNKcwMX VNeL/Es3VjAb6pRQMUbksAH9rLJHk4qalA1cnFLP0VpgDAflnAKjk50hFSK+zKN0PJYOmDAvmCCr CGhjVGws4shIqUPYBg7WNmtUTd9Vaj3ha6a1I9Onl2XH9xgMwJS9xHdYU+AXiA5cO1LPzhn9EWYN r6j8VO0VubvDrIoLvs8ZGofWVzZX0b15rPuIELrB6OTekHv9S7auQF2BBa/AF92j+uZHVryl+ZM/ /uMFH+p8eIyTxpG0j0SSthQDrgdZV6CuQF2BpavA3j0eDTYCHoGQlmLw8z+B7Fz8pDqlaynWqx5k XYFzXIGlQV3m7bnUQ4zYpFqh/xGOMlQLGZEgl9xgxj2nDoPzn8xFUMi1rAYuMmKzeSTv9uEkaTxY 6bUvOnKIb453d9R6b0qmu99pdy4+fNFojAooTpMReOvE9tbW9tbJ7d3jW9tJyrcHW/3dm08cv/nk FvqgyCfIqWwGxBuBNqpxAiuYIqcht6jTDNBe4aQzSKVokI6l3PACmU9DmqMJMVk7w7EynszoL+iG 91xr95oBgiuUOE2wGw8opnjuEJYh3eEG2+JQhDP8i9tP6WUOl6DdnhRPxHbxIwWDz5AjFXQAxNQG RUFxBf3AAIjmjNwy8BuxV+oIac0ykFc1w1kZqImfTYfEXgutmw/wAFcpylzpYxrgOJ9uxfkxXIIm 2crotOa6J9yLCMfUDAArXDv0VyJPzSENUVK/wJy0sjmWUsYUh1EWRW/JqUn6U2NK/j1aqlZbKe9B gHc0sBtNR/yOVohwPTLFMgyGlDTyO8YmaZCTtG+wM8YgaljOVgmBN40LoH6jgqmxH5Q5hkysmFiD pESmR6QZdsCmsuQN9MESS8o7J/ScI702Hkyu2Gs3aQiw0W3i31SJSvK/UuRX8K/YyNzgeqwDS+NX Vtf3miR5hWr3OUoyBGX9hER5mgWQfZZsDRBGFT3hSVks5/I3KsCospJ54bB1kimgVZuaNU5TbJz5 bFLY7CL2CYVm60ymyAFRbw2TdNdsBaPRgot5WWntDPFIZrTvZC8gjkO71/LDorTiNE6ytI+ibTTk Yz+J99KM8tO1gG4A7C7WVVSRFo+TKdFjvH1EY0j1eObgS2R1IZzErK11Vja6a91WF0EbQE8qNkvW SiRjwC/2nFmFGUFtaOXo48j9HP8Wqy9XV6CuwBlU4DuuLi/ZCHhLs/g2xvnf7Rntdz2h5lxnsNT1 Q+oK1BWoK3BKFcADCDbi0O/57u9efBvj/E8gRIyd3Lz/KU2vPqiuQF2B86wCS4O6eNutOx3gTFK4 xEOwE2ETRU3pZuRdss+ZfCJ4kJrWSWckKiRiwJtwLF4Z5jU6yIHJ7PWV3ubaeotsJlQ/ZCFNCaS3 yWnaOnasKmPueLjAWDA1+gaudJsEsnMm37NWO0EX9xpyLJLv8Xw1sfwFqK0yq/KbXqcTrq+21lZa oQn0Ri1mu7CDGINjVsRALjRJ8CXhjdnEb0xtousNxzs+jK89ub21twc1cCEJaJjUdlA/BOA5nrfa aa/14DAK5sJlBi4Kg6BD474WnkIf6xnKLZkdsTvKQGikWvgxpUOyYRxK7WrgfJQIiCnPoSG9LDEq okcrCDkv8r046w/jvVFM3bDydcFLnkNDSeLGiP/quFYvsAlQpyUhTQbJAwNY4RkE5wFiMrLlkTvN LKgTIAxJk+N4Qdgk/h/BHDFOwEkXIRJiLpicZa8xnY4JvA89HpolgDgNhOrgF7UdQT3IESFWg+F4 KJUcMWE5YfZ746Qfp2wB6Bqzbvs+yqImrMmeRv6sE9pHOtEFnWYTiMWGgSwxzAqY5UcRue5hm/6P UjJhd8Qiia2QTDR6RGaEeUFSKTj7x5glMZuqkCjpSLVv4tgrJ6iqGCNtDdQYsdFAv9anF4FwZLEd Z0BVVkQTB2W5fits9jCKhk0bH6GIG15UIvwLx7XoyghvEoUl8EtNAyxtTuWRZWlWcChbdEouvk3f gqBHuNtqp9MMCWlDpgjV4uekqAEMyRfrtZq9Fg7QAMI5jFP8m6jA8Cpqd6IxpB2BH6yEAYiQCDC1 bsCa6jnsB2LfQIoRMFAJ/tLKwWJ5kkC+RIltL6/cohFkpY2ITC7akv4PQFg2f7PbguNGQlsIuoTw HNOH01OMfm1gPM/+L1JPd0krgE/5559BS2LrW7/5mxe5GyNvt3jTxTi/76vT1Vbtj17S7VYPu65A XYHlqADYaG5j/F+/8iuLPOIPf/jDt1oX7/nERR5nPba6AnUF7sYKLA3qUjI5MiDut76Rnmu1iMdW gzzenyu+y8iZ5p370AKZA0ibMkjMdksl1GPna2Des6aNbmc1sKPerEH0OHnvBv1UaTzeO35i+8TW XrzHfXc8zCcF1jZ4FrIl5VcRHt8KWu0Q8NDrtKAGMxshWIs3/8RWAY3WOn6vySiJLacxI/ckK5Jx MkIhxqn4iKqrwltGYtIkH8UjArtwP84BAeSDBnmErQN0tpL86DBFZIMGpxdFnTAiRauY2fEkH6R0 KsyR4YzgT9jo8DmSOK4Qqxnx6tsD1Fcloh+Tu4TyBrDlkf9F/lQ7QjMlvQ/anHSC6AkUIn9cjD8u JcLfIjaLSeCchAyyKSFtWPJQZ5GYJYyS5v2UyPPU5KA1FCSPgkeHEu3kdyPy25Vfxlpwh+ng6GNp GAA9Adda4VoUdEkFA7IQlF6UuO1wdpJudnIQ70EBsYkmSJeIkEcLViEvQvOVKf99RJfIRpX7DaRw OdVD/9QK/Y1OE4DH4nInmB9JEiMW9myUMJndNN7LMyKyhLmgSdBCoGPVGPb7x46fvOHmW44eO54N +6z3lMNmmA2t9chdDZk7xGnizvJGmU3Jr+cuoRWLyHG5eiHIOSvYqp6M7eYlm+uHVnrr3c6Rte4V F6yvdNvdVos+ld3Ih2SRetZGPuaWo2IYFwkbDMp2qN32nFAIykRkYdJEAoZAD0smcBJJHUgLdkrd KCYLRDyWD4Bt+ERl8clFvZX1dhN5Gk0544LIOpLDmo4byHhYCZkBE1k19HRIr3z0cWBa16Ejwc6Y iSj5nztyQSggSjhi44bjdEBuV5zECXCv4DItp4G8bb3bvnBjfa3TdbzIViYbbC3CDCo+qt9YVByT p11ysinQl3g3bKYtywrMvb7VFagrsAQVwMb4Q18zI9D3B1/2soX9A/73vOQlvOn67sdWX3VVLela gk1VD7GuQF2BZa/A9uWPdze/kNYlC+twR+r7XS9+MXU+cc9n12n0y77f6vHXFTh7FVga1AUA4Y47 D60WbIroKBRTMqtJsmQES+ZmoBZx6UhhlJWEbQ35lE2zQY+cJSmsem1caoCjDhBmkkyG/YwmcqAc FDVgMz6SdvWAq+5v21PutNTbWOsc2VhZX+12O63eSrvXlcUOkgaGADf4QTg33ME1UM20QnoRFntJ AkOoZjkmyCwe5el4kieYvsBlEBxMiLkYSkIOGFKzqSLSc7o6cmeZcdI1fWe96d5rPbpstUmMFC68 tCQdiXSqCplSSGo4lA+vGZIu0ztPUU1Y2aQKItrcO9Tr9KIQSZkIB9I1S7ZJ7G24EWPyqyzp15Br RYHMgBANmhXCwkAq2NAoIDVFl9RrhuSdK7vdiKwQ7nBFuBdX7EX8NKK2DKnAD9hwwEzY6CwEWwpJ ZzH4jqKsVqPoSLeJqRAQspuk22nSz1QL+lia/Cl6IOZIirj7DmyLoCopvpAzYT/EQ0fKGfSn3fTb rRBmhPNwXOCaJEOelXdLm9woE+ZPuJdJIuOcUDz4HY+CHKm/ZOBxcTAcD6DJQIEmyvMbqJ88v3Q8 ywsbHhzKpRfiOMuHaXbz3pBrohgkIQ3rHzH2KL0qmgGY0CuAp4ylphPoCrvAD5EATi17XMzGk9lO IvBEGUBXYyR89DHkPPl0iMDKCTphBx0hfQxBZlujwdE9HIc0nJwFvrvZgZ2SdO+6ATcWzUMitUdU Vp5L+gYtJBCuyLZHw3GeIlgcAqzKKfVRxpgFpGXLAa0AvtYgnfYzBkNAPRnzcC7+9TglBFYRdDS0 nBYD0CqtHocx0VoKyAdLWjP5FmG4xMA5tEHAcErSPE+OCWWJc75k/sLFsDTqzX6liSX4L81ivsCu SDdJQsLIRZsH6t0WnXf2fmvVZ64rUFdg3yrw3EfMCMDCAwJR2reT7t+Jfv3Xfp2xPe7+wfO+XD00 6ltdgboCdQXqCpztCpB7dezSJ89Du9797nef7cud7vnhXM/7+q/njzSj+/zn3d6lp/vw+vi6AnUF zp8KOC96kaD44t8+8qG3MUi9w5dzUeZFpWcbWyM+LWNulHeR1osoUHh3D8nCXAifwas3mRDaHWA9 PHp0Z7iXIE4BkeBOy8aJ1yIc3L7l6FEMgFHk8w+6py97xFd2D0/gDiCtCh2YSaQC0IAeAq8Bl4AL iFrZpDNBmARWMCWqSV1ZDEeDOB7T3m86ySV7EaHCK1YZb5f0Ng1HY+MTspvgDFjkOGB97TDf4WiJ iHIZ4gYZfropsEpaHWWrkyRGu0YwkFr7yZRJY0UkS8qhh0UpHQnwx50QcRxnWN/k22xAPUSpkKPx M5AP8izJeYjrIsOL6Th492RX46dAKkmBgH6lst4ZjwDPlOaAqrtpTCkHJAOHaTB3GgKi5CH8HnmS rKLmm4A4PG40cwS6QbugOcAQzIzotUwePGCqhGQB0hhWpwkiVA1lgwMF2Q18gsTPU0myn0j7wmvY bnbIlWpJj4ZjlMu5AXbLAoXXDOoHyUOtJQEfG0AxbTo3mimsiVDBaUkQvhoncg0sgaFr95rIo1Q9 hsh45x07kchhVxTLMw0OmIlco4j8mJWSvETKmiFGzqC0KG+AdoryUGHlpsEOtT7qyUmp2QxklBly h5DMBSY2PcW38zkwka1qzIK6DHFozFfxViVBaYTu0xtA3QCULNeAPSGjY1mglxAxfJFTOjqGElQR Gw9FJVSOeaLaI2VMO1HxZJbVDpViRjNIs0DEn2klpMzTtNhaKOZmLbVZZF7W1LZweDabaBKjTjuk 3SaYU+ZS1RMVo+iYZfHMKbxG7lmZbSVYehszUtEguSmdSNnh8D05MuGoyq+HH1brHboHWPe99xfv 76+UX/7bY/t7wvpsB6MCL3r0hQs4kZWjv7WAo7qTIX3pFdV129Fb/+Hj/PHmqvtfxZ+GFmT8cK6f ++mfhnP99DNi7JbLdRtc9E3LNeAlHe3SPd2WtM4LPuxFfrot6QuY3I2K1S9YHXz8D17/uoc87GGX XHLJguyB2zlXdvHjj1/0pQsyqjMYxmK+gDmDidQPqSuwyBVYMtRlErnm9Zz/R8iLj5JzCSUQRcT7 dWKslESO1sSauYQ+gXpgC9ishoOkmjmO5a21O6utlh1nG2srF6z3rr/5Jqxb+B25gYiuvPd9/JVU 2e/wCaEHvgvvAjoptwt/H/gGrqWcJwCQEBsMAnDBz2VRRDwDa5DgCHriQkno/ueq+R7+u6ok/wnU IsTBHSZl0N1FRy5So0NGbNtItzgA8hAi3IJAaW54MkXVpKMCt9Bojzkrrgz/2hQ/IznoCLbIomor iCow2KeBOgxIAzgzZsQqyaeAFc4DfZNEbMZIJkiZgFmaoTr9obZS98ZuEwcbQiEQ1QypGhQG5Q8j QchGnBP0irFQGG7MBB6HjXGkvKq5n9EeZeU447G08xPnY4IUhESwNVyfkC3j2zP/CvZBuLguI9Sy 2ajCbNRueymp+ZWMhcYnyEQHObZNwtcHcZ5iaiSoC6+dJH4eQWjUT8QwnSIQCxFR4elz6FTZcEn8 kvRJIesTIvlRRe3GSrKi4ngJUTAxR6giEVPdEPhGnhbwEFlTOW1I5oYwEEUYsEuuPcstcZc2eAcI 5+T8DlQLYRpbgV3iQafUCQC+KNcsaipJ7GZWUpQ0hdwZIQ0TnGWCyME6Em+FUcSMG6RiybCJ/isr Bhmng3bKhts0eWEQOzL+EZG1o4iNSHNM1mEiXqYuBBrAhMR6Sc6Kggmqu6h4nyFfPB/wlmZYDOGw GkyJXEvfKcsEBgnwInHfARSK6CEURC3nyiopt6QC1SCek3SUjOjZkPLflED8IWKvCZbHgh6VE66L w1RPNfWIgPxKaYmXEoRHee57ry/c3996S/pKcX+LUJ/tMyuwmK8Ul+69Nx1cv/K+E2jX6/7o7669 7trHPOYxdzvtwk35mle/Znk5F3t1kd97H6RfJkv3dDtIxV+cuSzy0215X8BAu6zgcLj1/j9605sW hHbdkXMdvezqxdmBZzCSxXwBcwYTqR9SV2CRK7A0qOufPyhVl1F0zes5j6kXgUJSI6OeGJiUXKCb YgLdkIDJmqEzAmD4SdG44ejuzm6frCelZuWAn8QepZZnPfx+97rh5A5WPcRgnCOwZve89OJoA4ec GBOcBUwAOEARxc+RcM0zvCeyz1njZJKTkWVJZwRDIQuJh8BChA8AQqTEw4xM8BVSGcRcWN5W2t21 ZqeY2pgrgSiiWba9sX4BhyBZgpWgqIJWgA7UYk9Sp1vR0JScKtxidFIkKx2OYubPGJu+u9GO8MHx gLwoSUnHcggkgmbR+pDYcrQ/ICSsjsjIGDqOOKxqoDROgFCNVH7UPystRFSe9GKzKpvZQBZ0PRxD fz0seMiSFJOGSQ31EDWxLDyNEj6hDoLKKc9L7C0ksSkIuxAiks0QCUlOVCK0M5ona4JpUesni5tq asCMaTVgQCU/nVDDieF68D6ng8wJuqeek6wFzIus9yrN0L3R8tJGMMWRHGJgi6hQ6Phbo0mGyVE8 ZkLylDlVg0QyQbNGhefwwsNrxGnJ1IeLs9niBt4iwV2hb1gRFetmI9+CQiHO0pxUEOCgCmVC/NGy uaqh4FLONgLlIb9SSr8cjg7AjLXZVeoV39RehYIFNj7JgJMw+7TIdhIy1lIgE/BRwWCYXQktU14Y sVxaFW03MKBaa1J2b5QDtrg2ijYaA7BwLpBunM/YwuwCLkfdcEPiNJVizKrAWzgQobx8Kf2aC/RE aqfGlFBLYvnx27bQ4yEPNE8f4BpXh5hRW/YwFBFrIjdmB88SopVfUkF3FEPOWQfMxyNt/KH4d00L S1EuHJg27Svl63Tvc8/77e9vveV9pbi/dajP9mkVWMxXisv43vt22oW2C9r18Ic/PIqiu2u/KYf+ JS953Wtfu6R6rnndFvm99921smfjusv4dDsbdTjPz7nIT7elfgGTROvV+hetjD6JtgvZ74Mf8uC7 caeRQ08TFXyL6LmWnXNRxsV8AXM3rm996boCZ6MCS4O6/uXDb5H26HbMZT6RkEp+MEmfpH6S1Gtu +UN503TddstvY6j6wEc/9ffvfM8113yqf5IcrfFll136gHvdN7Cd49s3b660Hv2Ah9j3uNcXf8GV V1x46eFLLr5844Iv/sIHlME2XjDAAySC9/kwKNo0krpOOjnv/IWZQEI5TQYbcpC56vqIbwxdFBSM h4m8GRgHsJLCah7kjTaM7K28MD3ypOCRcMyMOmyvY2QDJpiQdU1AkeQW/fVCqBdgRgKvhlLeG2VC /r4JJUNuJPERQi+a+qEJAq9APXwbsc4sQg0kGx4oBBMi7rJqlE6w6YnaGJQmTVUD+5tHg0BEPlgY hZRmFrIrsp2YRQaNazSI/KLZ43onQpYlsohKynAu6NVUDkBZ9yA1pXiNJhVPCLqCAQnimIZ+3FUl hawbXZKUd4Az6kigFDPGLIkyTfKrGcMWdWIoNP9zHZjdKMGsB+XBuUdLAAyL+AGjtU5Iufs83iwB q5EWiLNKaCSSvbZrtzEpWhOWAwkeq0b5SOyiYyDwLs7iYUwIVeFzdMMaZgjEUHExE9OqsmGrK2LD JkkNooiHEL7D1JBazafAWpNfRpnhSyjSgGQcjmoNNyjoDRUeIjwEXMyFVHnZI2GEyAutKaK3ltdY ATJhsAw8eB4aKK7E+VGc8Rgeh3QQlR+paIjDCGhDvUclUI0Rg4XcDoBJeZOiIL8/LhohXS3ZFRRK DRURMc7chjpFmo2hKkNZWQP2oBmHzJVsDzaVMTi6LDQaMu5cHWRHU8lqQspcgV1yQnR9lorsafqS EHISFqdiHgTd07qzIYEbux4VIRhRvl3TDlWix4omDyxjdZ8rrtrf31ZL/Upxf0tRn+2OFVjMV4pL +t57Trt43r/xr655y1ve8sUPfOCRI0fO/X775Cc/+ZxnPesD7/unx92v+uln5UvnW7y9Yov83vvc L+vZu+KSPt3OXkHOzzMv8tNt2V/AZH4bJ2Ov/7F3/u3bPvFv/3Z3/SGEgPz/+vxvGvQH5HMttW/x 9mfoYr6AOT9/gdSzPsAVaFxzzbVLMb3Xvlq9xudI6zZhlzHe4aICnhhEwhtzzGRu5eUln5Dg3mxa zseuu+kd//i+NMngI0iCiHB64FX3X3OCT910zc7NN3/hZVd8xVc96ZIHP+gBF69s7yTv/Kf3/Nsn P3HPK6+YHDquN/vKjS8BaryfN1qpwm3gh1ROFh5JfGCQBHRA2NfQxTAYDkMEhCpIrEsMTpFJ8Clp guhUJw7hxEYyo9ipwAdsGQhW3fuyB+lySGb4vu+pR6HSq/QxM30lwTAp+d8NiFvTsi0IDtlOMlPK TAdnA5BNyZxiHNAuOByFAWeYEYCrPJyDCJAgUCAJAJhx10FhrFbo0ikxxmoIbeIxttuKWsRvxTnM YyIuI+2PCCLIB1kWCVi90B+k+d44gcowYTLJuMpKFM3gMmoj6YA8GFJkUvH5EYIsmUsbnryENryK KDWnnCS7o3ReIwHFBiMBqXlolDAwygZoCflRfIxzuDLVFxLEVhE4Ja0UwiKFzRPUhdEO9yJ4kW6D to97EZQDeeHtGqo+xaVLUSaVlFmUWX+8G2c5pVml0WG7Yzm+OnqWVkqcGP0LrJl4FaMmtkzxazJH whpp5Yi8DujG+APXR6anMUi8x6XLcTFhpgjNJHITZIoEsmzoUwnQa5QT1HRTXQQmBjhFdFgBDm0v YL8gXmBT0CgAayRLiRdSIkI5PpFTycTqBBKZAZvwKhpaalEWXIJ0eNTiqgMpZcoU2SaRnNipJqyA NuE7fTSRZFhdR1nOUgYOnkwO4mE5ufv8Fz7H2OWcZO+yo4BWglswTfSQ2BIdDXo6MeFlRKABDIlg gz/yuY+h0gTW66kJm0O0uN6UEuQpj3/a/v5WueKH37+/J6zPdjAqcM2P6Dfnot0ufe9XLNqQTms8 f/oB+3/8vlrW/MiP/9jTnv70cybv4vfhH77pTT/8sh/k0vSFJC//tIa9aAff8NC/X7QhHcjxLPvT 7UAuyrmf1CI/3Q7GC5iwLC695g3TrY/c47JLf/GXfukBD3jAOVvl3d1degTTn4SYfPotHpgc+sV8 AXPOlrW+UF2Bc1OBpUFdv/mq76IiSnW3bRM+pRuQhDfttvK5JDVyABpOFGcQA2c1bPf30g997FPX XnsdYqeV9Y3mWm93t799y9ah1Z6bT+Nx/1AUXLl64SVXPeBLHv7QI4dXrjvZ/8Q/v//mW45efNk9 k81bdAFzJfDD3BomvYx6JgKfsO85oC6AgHAAvkk5vNCSedgSxcFISFe6uUPrQLoZ0pvv5M4QdyE5 4Y5LIJfwGZobIu+FCapZt3dvLkR8k/CRXIv46QQyPPRW+O9QCRltDrNVirtjRtJojPCqAUigKiAk qA+IR/qjChiHtQweBDlSLL0j2gXPELebVe3ABZ+haQJmjPJCwh98cNNCQi8Xh5yfDPoidiJ6ldRY zE3RXDaQibnCVJRhVlJ2IZJkOqGBI+CJpHYmThk4LeOHXCEQA05B0XhUXlAx2laKVvJT6Ayt/fiK AXVCDwGVos6MMmjeExQagzFR1lSTHs9QmAugjhR7zsYQEFZJvAd+MTXkrNgrcTRSfx3GxXxf7kOC yfCaQpdoyukiqRuBiqS9g1URTEUfxoZSt5iOLHlcUCc0Vs0GFFLYCFLJz1Xkee6+7SKRozLsOQHQ 6SRk1R07LiZioFo0f6b0eYybPJJBS7GGyI5ConcKwGmMFjem6ylgi7rMoLPyKkKVBAW10abyJkJt FS0PjBN+gutRGYO0eGgA4JOyTmQKL61Suqiq4VRE2jfYabIuyroqCsyRPBZRGN+UZFANCxQ5h1BM w9Ex6A11NgnEqKn6KXAgTzaTSqaFoY/k/Cagp51fIXLUbpHSUMpErg4j9FaikOX72sfVqOvc/AI/ 36+ymK8UD8B772tONL79deFNO5OHPOyh3/f9338O3tUg5vqhl73sfe957yUbwc8/I/mie9z2v/ml 3eOL/N57aYv6WQZ+AJ5uB2k57q65LPLT7WCgrvnKHjn+T61r/i+fPPvrn/vdL3nJ2traWV1x/v7x 1re8hS6QXMW/6EtvvOjxsd8+q1c8lydfzBcw57IC9bXqCpyDCiwN6rpd1QVCMnlPuhnxlPFYKUAL RoCHEA7Qjmb21k7y/g//2y1HT3RaTVCR3+18/bOfet3x/pt+5/UhcKG0NprNDT8ALDnNqLOxOcTe VyR429zp5PDq2uaX65cpmGQuipEt0cRqqdOiUplADyJuoAU8a3i2YBp6u2+RZBUAlxAbmZwvRYPj HJTwqiQpHecdbe+QbhWY1UADgiemCeDqygO5HLIsTGSourrtVjuCitiojcBZGonIToWybMaYqjJw ScVCA9VouT6x96kUYCpIiuvQQiWEvS1EJwWrAcEQYS6AAqVTbBiKp1mHlCWqph6LBphMLQWBiejR c9KfEvyEhgtNkzFXAlwiP2BOjLiFkgdkVklyhTqNRwFWqMNqFMWTKs5LABHj4TrckT3hiAldhEiz k6MMMoJCCSMk15XhjWwv0B7wTs0lsdfR7E+qPb6JpxDfI/yFKwoWGdRI4poLrJN6TuZA0URdWh0o FdcFPiuBVjLZ8U1SzygB12G5oHhmr4ghodniv+TnkwdPDBkiNQ5UB8oZ7A/FnIdKi2/CeVgJqbDk 2FTUPFFXnNPQLmmsWFBOGeP4m05NH0w0ZMJDcgs6YaLH6AgGDzJjvzI8Hq1UL8yd1WxjtcV+Qqel 6LkKxCkno9FlycHJ+CW8MlR33pZgHukFf+TOmjLqjOFLvmU0YEqIr+i3yM85CTZKlHqwSugegFM2 WoXZNVgRpcwppJ+jVDmlrkEXjcdRO3Q+Tq2v+TElo8xGdih4rPw4df9ky5jUOVcNHv2I4pmkNbWr hH/h8eT4pz3+6fv7y+sgvVLc38qc52dbzFeKB+O9d1pYr3un8wtv028A3tX85+c//8orrzwb+w3I 9duvfe0bfvf3OPk3P7L6jqvL5TUt3rE+i/ze+2ys4911zoPxdLu7qndgrrvIT7cD9gJmJT5++Oa3 Iu9i83zP//yfz3zWM88S8Hr3u9/9y694BX//QMy1c/GTTm7e/8Bs1/lEFvMFzAErcj2dugJLg7p+ yxgYb9U23dp+kTfxepNu5DC8/5YWB8taK2rm8fRd7//EjdfdLGQCmppV6/e4+L+/6Ns+8m83/eXv /e50NLpg/dAj7/OQh1951Vov+PDxrWJGn0R/L9/92IfeQxITzekueNQGlyMAHs6gZnYTpFiCXWk2 VV44Eem2tEFqTCgWABLhSN70+4HXQukFKlC0FpSGnneMoaJdY5BkUvd02o3KRsYEduCU0tNwu+py GV4wrg3GQBgpppDu8EM4hg7B3kYuFFxJ3sMKJgd8A2PkcjTio2yoHR6xUMiQ5r0gOan6OTIGK1Lv RhkC1ekP2DOb0ZaRE6Mbgn2oV6WaCdoaI3lPttOJGKNnQraIfxIe4jAZ4pAnoSFTkU14PdoyTIMm El5yHqEdPIYOTQ/pvYjSDcjCFRgnGilchxxomjbaRD2BWFg2hoEyC1IDLmoHeA/l/oS2gNIkRpuW vSjkCoa2gWBQMU2zsoBecSFKfisjNOxGUiqjeJuUnoR3CmzjEiRSMWJpqxBR8RD4Ii0CGavOaT6y bOps2RDp4wyy5hngBFuazia0i4xQkTUaPJCz02KAgH7+a5ZNXkK0UoAhuliiXcMpSYI+P45CZuPj 8IPrGRpF7BaxZRMl1rMVCWgjQA25HDTLsjF+MpmtEYo/UKASsEBbrJ+BXEZBRblohaBdrHh4JZqp 46GdFqSJaXYwVWOebZDcZaAWgjjbwDvORi3NEs6fOMZnqIGbBRRrFB2GYeoqHKlHqakCuE1GRqE2 SJmkbjqWk2h0xhgr86uNsTIo7eB2i5F0iDZqSllNn/LYGnXV/3M5FxVYzFeKB+m9N/Ku//Ou9hvf m86B15Oe/GSCWvZrackY/v03vnEOuR50mfcjT82uOLz0Yq7bi7PI7733awUX4TwH6em2CPVc0jEs 8tPtgKGu+Q45tPUvqzf9+TTZ4XOA19c85Wsuuuiifdk8cyXXG17/eiAXJySBvn/hlx4kMdftVVrM FzD7soj1SeoKLE4Flht1AQuwvvEGG32PYAQAwQ2LePaxTx695pqbBkSO55Oo1drYWCfI/ZmP++pb tvbe+tdvjfv9C9c2LwjD0A7avn/N9lblhJtrq7YDPLA21zbvffnlt2x+gkWSZw7Jk8KP5OAz+fe+ 0cTQE7BEv8KlFZBkoaWSBspov3wSt4zXUKIbE3/ETea+wENPBoHIsd+BDmz0Nx6gSwf2Ol/AQXAT yZHUeJGmewqA50vjJURxI9UPLAYpDfwIdMK1swlNIUOuD2ThNMAvgAREg0EbK9sMdY/MfXP0I8ZE 1JQaS8qSJtimUH9OX1aKnBd6oV8hl0EtBskyifkcD/PaSwvbtSNjhxNPm3eYxEioiVWMlIj5yPVp v6gIqNKcRARq7tOEcxGjToRWBZBjbAqjnynraoXIKrtBP0xwjyLTGw7Xihz0R0AdzJi4M2Xk5BEy 1Vmo26YIu9BrEYCOB5OfkNclslOi01KoGulsQBtDqyoGYfidmKPEf6iUMDWGbUbOYgLZuAIzokEh bDFHhWb6eEJ+EnR0kwn4FPbkeXYbSRurUFpZpl0AqqLILAcHhyF5+RJdgdSQa4nUMduG2wzwHaKb U/A8yfeTImcMhFyB62SWbFRBU8QPZkTrAAa+3e+X0xxbq/RqJkZe24Rei+wus/SycGKlNPFtwnyU aBpXADqzN5jr3PFqBFzifrIzCiyqiyQbQXldRoYmTqfzinnxYHyObAdD1ISpjH7t1iPVWNMjVY3g ebaIaTJgLsZ/eAgzc8j6DyKy6bmu+UenZ33nJ//aGnUtzu/4Az2SxXylePDee98ReBHU8sxnPfur rv6qMxZ50S3+b//mb/78z/5s/k4GyPXfvyr/0iuWO5nrM59ni/ze+yD9Vjh4T7eDtDrnbC6L/HQ7 kKiLlSW9a2Xrn3u3vH0OvB7/xCd83dOe9iVf8iVnJvKCcH3oQx965zve8Ru/+mvzbXOAIdd8gov5 AuacPWfrC9UVODcVWDLUJQ6gXKHbilORlySxiqxsymInHyrc3Ynf/d6PT4rGyspad2X93pfcY7XZ uubmm244dqznRXmWXHfjjXSvAx4M9ka4uZrddoxVgzQmVxoXqIkfBk/7b0YoS0YRUAnljN7L4+JC p0MoFegAVdOEuHC9rTcUBvgCCrAlfCFVyfi8RMSkc0H8IwmRkqeUpkTbOnoGwksYrTRRRnOz3rkv Z0nN8S3Po90etIXoLr4EH8Cb5s0cCU/HmmbS3kWGdAaL9H0BI1RaYBeyygx6YTgl3zVNECXdkeHO BFoBIjgJ5EgCnaoqMPeBRZzAgDxDK5AzZYUcfYzelDlynXar5URNGheO0xSPJyfa6ERwQVpJNn3U W9Vqs8ns0wKl25S+kPTmg5WAfVL6+U0gWcp016hkADSRWGA7KJVpT6nV5D0OEw8jxEswPXUbREc3 nUbgSJpfNkjm0rgAZaRIhUGI1kqOS/yKTFkqJH5conFzGkwE7qnlYu5S/ZlpctKJQv2dIGzh79Mo ZEyFUBUGuwGWIDs2oWPH+tls2ggR1aFbIgVM1kihJMaHeVQxVeC3uYqN/81jEVVlZRwEdTEkSdNA mGTjyznY8G2LvodxkpLcRudDXyeT7IwCZXlq/LczSjoYjexZwaJAoVhlUSftBORR0mRRGx6unpjy PpLAxeDIOEtsmxnB+xC+6SEKzpcai10q/ktEvNlv0m6Bz/iHWYuzoTzkdBKyEXWPxI8LGhkXM0JE p6/kzDWyxAYGRaysxp5p0CkbfcL8GCeNOaMwaDaEunTjp1K+SWapL5/62P+0v7+/Duorxf2t0nl4 tsV8pXhQ33sDvP76o/bc0sgN5vWEJz7x3ve+9xdcddXFF198J+n1vIe5+eabb7jhhg9+4AN/+eY3 0yp+fobvfmz1mKtmB0nJdcfn4CK/9z5IvysO6tPtIK3ROZjLIj/dDvwLmI3dT7Zu+Qe3/9H5QpPw +KivfPQDvvgBGxsbd/4XEfLmb7rppuuvu+5973vfXN7LLVi75173C4eHvvhAKrnu+FxYzBcw5+DZ Wl+irsC5rMDyoC4TS6/QqzkcufWG+Q7lDwzCJI1XXtNrXX/z7nWf2r7y0vt90VX3v/LKy4+0oxuP 7x3f3vYrK5xWWBr9apbPGsM0/+TxW7a2twZxtjcYDoeDKGpKdQNDydKv/ZbLuAKaGBQ+snspNAr2 EUZBdzK1iklOcFYYcAgeRtEE5cGDb4RZ1HEQniEdmBgF5kp5zkBTiHIQQjWctLIzaapKVGHmIpZ7 0doX6zOHFCYfvVee52h4gDzJJIdykBROVpRy5VHyFMAjHHWybnJ+pGBACbxkPkcb1sFlFexFbJIA mz3KJaqKsMuZtycMlUpxJo7MpJsimspvBk0egVGR+VJhWJKi0cvpXMAFyRK2A/kpjF9OQDyW0Byi oAjrouhAFBNbBklRlwD1HxTokQ6J03AA+ialSelkVEC5Y3AsevnJMKeuhdBA9QuEa0GKlCFlGkpi e4TQmMAvZdar/wDyLkRk0nmVFDpTiTR3MTuUXablJHIj5gYVY6bk4oOi5Lybp9pj2DRNCVFwmZx9 RFVqQylRlgWhwzuJrk+CNqY5LgrqLIcmSjrXxdXpi981xjmTIsVfoI2fcQlEYJoXEzA7k7YDCL9E iDiAUksbOEMhxWRDpFDk+lvWVpywx4QlUQOSJgaXpW6cRO0N5TmU+GuOR+e9F7TJtAslvMNpKFVZ jLFSgAuuavAWIG/uy1Sc16wRYCGlD4ARFkL15nHystze2szBODJJlXe1d1gzRs4n7CJhNSEtqbXY eAZ1Ubopm5qf8jAUhGA4gucAYS5pd9pW+iAVI31PTVuBp1xdo65z+Wv8/L3WYr5SPNjvvfnD0Idv tP/5xsbvf8Ant/6Om4+/6q+urt7xO3t7e7TNuuN3SJ3/6vtlX3bv6gH3mB2MTK7P9fRb5PfeB+lX xsF+uh2klTqrc1nkp9uBR13zlW0V49bg+nDnX73t933aWmN+/7TvfOqaa+aq3ttvTu+y0eoD8s4l B6bB4ufd8Iv5AubzDrs+oK7AclVgiVCXyeoyKfG3oS55tSBRoC5Me9Igzdw8b3ziE0cH/eLKS+97 j8suv8dlF6WTyWBvDFnAU8atHfUO9dZXW72W19gdjbb746BspNnsaH/UBjSE7goEazb9aP4mAwVE TCQYkygJthSkOb0F6XbI1fKGjaXOQJ55TJGAjIkAV/NEvITgHuxckAjT2M+IXhinZSfkhkuto9gl 7Gm0b3Q73ftwOd8BxJiYe/nKFK6Eva5CzQNqspwI1AI3KXKjq0Kbw5H2IJVWihOFvmxoRgNkgIfp BciJRvgDYS5iEPoGwISCBK5DQLu4ku3gtAMShSSOz0gBk78QJx5inwJ2VKH/otWg22s2GQ5XIocd mZgi5wErjSpRA8dGgNlP7lEFnwNjqANkB45zK8Shal6ggHPpyhC/TZCS5dA6tGzNJq0bk4mMb3Ai GBDwbd7skhQtgqZAP3IVNgSSsFnmWTnJJlG7RW7ZIM04H+H9jk9ZyKtnuPAuJV4pz141pyOneKCo nkkEI8ufzoGgLynGygaSMaCcLJ9VGecxhBMHaDatmj5hZR5iNNCN9HRmZVlhFHMcDKFT+hjhaAYa iiQh5JLEDuyo3DR6ZjL1fAIjypW1pipb9MOk/yFTa2JZrWa7w/EMWnprO0rROsmyRKy0bBLbTTN0 aWrjaOChSd2ai7xEH9XZcTomSs50XRTpM4H+aAnnEWQEumkVsB8azSAYTCWd91gkRW2ui9QWVQQc rCsweXesM/I7qbKMExOlIv5TGXA5BaZUfizNF002zQaXOM7CQDqnzhJzGTwqnR1fP/mxz9zf34Pn ySvF/S3a+XC2xXyleP689z6617hpp3FyaP3TtY1+Yr31Y7cKvu649x53/2AlyO57YXXPQ9Yl69VF qwcnjevOn2KL/N77IP1yOH+ebgdp1fZ9Lov8dDsPX8AQXe8n21583Ml227OdfPfaT1txkubj6PKZ G03bFxTRZh6tH3gN12fu+cV8AbPvz836hHUF7t4KLB3qkqzrDgZG/IYKpDdeM2l9Tuxk7/vQJ44f 2w3tKIyaq2u9k8ePI0SBADRpa9jteUGz2equr6wf6m12wwgd1SVrm1UQTT1/teXnRXVsFCNm2R7+ vpiRMW4ZbCQ5FrY/6BPQQyhmlqMBAqRIhiRFFyhnnnoEYZFQC6MhAIdP8I6ZWHgTW48Sxxo33Myk ZBn9DwFcTnBk4youh63SmM5kjwQfyGvm0NZwzHwnkgfZAA/YChgICZIaGSJQoh3jrEF3QumBCMJn LOpdiEtRjA4hkglHB8jpeKEKpY+b0HIzIAaXTqpRPg1QAIm+KecKytYOAsKkgEfK1RJtIqzKONWU eo5ySjqseVY55ZG3ULZKktbVXBHtDzXJCpkQqQ1mxsAP8lJ9GEUGq6lnm0gzhxIBbiBuyvUy0e3q FBm6dPojfmtKB0l9BkczofA5TQfzWQiUiYhLA2uhVmOosERAGzooxoIQDFcgbRZBhQ4Y0thINUgx QIXuV8VMfR5NTJmFIikKULvZiKompHVBrtDuqZUgj/bnrj1jXlTnQU6TTydUlcaX82R+7KUswFii OciTRGIu/3C0yypA5BhgwaiAg4wO/RPTZCiMgZEUBYjQNF8kP0yeQSiV8R5qr0mURjNQ6m92juqj dCyHhDK1AJAlFqg0GYG6jAOUxovzzC7Mldo1JlFu3i+U7Yd3VUlt6qioSkhFxzGyz9LQAIaGqxKA 7Cp5i9aZhrjJesqo2eJyW+pcaq3I9uE4k4tn/JKmCij85qhrntWlHqgmFOxrHlejrrv3F/v5cvXF fKVYv/c+X/bfnc5zkd97H6QFqp9uB2k1z3gui/x0Ow9R1xmv43n1wMV8AXNeLUE92fOhAkuDul77 qpeIzphMqXlDwflb7DzFPoZWBcUS8UPu0WODD374U6NhCqBAsEQvRGQ+uM+azXa70+O/SZr2+wOk RXlBjlSWJ0UUtjCWYd5C30LCU5LhGWw89emHzAWMpsuE36PdsW0fOoMQCbGRpDdljgKGQU2NtU5G QkEF+iQKchlHIw0BRSuUnWVSjAJ32rDjmZMYdxtOsYDgrIYdXHz4C7kYwISwJqmygCMiCiRn0Wkv I0OKS0wAQ1JwSZAFAmNsfMikLpKKDDqGDAfW0gtdpkbYlxGYea3AhwJCccA7TADe0QwYvdHtmEZ8 SLFQk6VGroXai2vDtwAaLV+581AbDJiQEUAUJ0F9JNWU1UjBYFNJeEIkSa4HPVMPSJM+BRIyafxo whQKZnoJkm7OT0w0mcV6UQzIl0XguxFeuf0kW2sB5US1mAqcC4YGrHMtxia+g9gKTyctChkU8DAl GEsh7UKCQCDYlsLMyN6aTUzCOsumNgFwLsgLfBCVFFldkddIIE/SRHEkD3Q7odpR8k24Uqhmg04s 7RUGTF3WeFerTuiNck0Wo6EcfASHUUzWW7I7QsLwMkoFJVWVgraI+IJzKd9NeWQUHHkUiycHppCp eaio3rynISti+KPwkOmQKOGbml5OMwWTqdTaP0jFAFYGNhrHJ0+BWQzqMlvl1saLlF/WRSPX4sRm E+pcCnyTz1GzM9ecmxwpl0vXRkm1GIMH6kLK5s95ppyQakYJVNXyQQ4N7xIa47GAQH7OckvV1cjm 7kXRSIFC9fQU6nrsM/b3t2f9SnF/63lgzraYrxTr994HZoPdlYks8nvvuzKvRXts/XRbtBW5W8az yE+3+gXM3bIlFv+ii/kCZvHrVo+wrsBpVWB5UNcrv+f2iUkNM0+ER2SiN/L0FlQyFkihP5piV0Sk AuIIm0G3TSAU4eiF02wVNLiz/eFgdMstx7LxOM8ydEfwBKxhRCf5AalDoCGUSQ5Y5KlP2pyzNNnP DOoyNkHe889lK5LegCRM50BF4ytNy6AEpYALR3g0BAT1KCsfFGVbtLODwBSTkeNmDa/EMGaVOMhC 6NasdO6xKQMjPAjtDNBAOVmKf7LSaZHmKXFadG+ESKyEnuV6shbOJkmR4zTbHWfgJZBNt8kx1KQa Z2AuGeeEHhpuF6mOCIwwHDfOTAYWwzTZZhouoIbKdFBeKVFLmp68BGSBpuBR0C2Cz5G+hU1Paeac lYejMwK3iGwJtchUirmSOmGVNG3/0BCVCr+XkEr6IpgLtMvY7OA8itViNCtRiJ4o1sXU4BLmJMEY 2WMoh5Br2Y0RLQ+hZiVFNpyIMHTp2rzAs8lZQzImzZ1ptgiaYqBTa9YGl+EeZHZQTx88ZBAMP+Is BG8xUAOTTCcAeQOxaiKwot7sIYmzDNZECge0MiZBaKAEaCyxNpy0TsRUsUcQeSGdA16qhnA02KJW 2Zwermi2h2Alq0wPTNLftT3lDVS6vIrP+mK7lRpxnkM/D+Qy49IHrozuj8rzbYiVOUoMVPFrkoax UpPRrILQMTbYks6uWHq1+6RWao+pU0oBKUmXPIym64B6LGozG22X45FXL1DpOE2sm5JuaTdzCtpD ojbLcIyqJyQmXKAxNAyxnuR1eFfVKGBastAR55gHdRluaiSHomVPfXyd1XVav4frg8+wAov5SrF+ 732Gy3mwHrbI770PUqXrp9tBWs0znssiP91q1HXGy3qwH7iYL2AOds3r2Z2HFTAh0stwQ0liYrdN tz5RATm+oBhCEMb8hRYnDIIjaysXX3LxBRceOXTBIdyLnXYAzSK43J3l43hvN97NZmleTbcGgxtP nLxla+vE7naSp0mezKxpt9cK22FrpXXFlZcBWHSfIjvqWFXbd9tIqWZKshL8MXcHRAVDI/0cMiIB 1jRBjIMcqRBbUT637yJy4f2/xGOTcjSbDREkQQlwih1avfjQ2uW9zqbvdWdWaMyAcibSapBUKdx/ SZbBekBDTR/Zjo16C1XVsUFyc388kjtOyerc257fwr7YQGY13U1y7qJVaJ08LyRe3vfQ5pDAFaf5 IE6HcUJyl0x5pnYt3z3cDlCBdQlXtwkgx6M3GxG3XiKe8iMn4CNuQt8JAscDbQyyIi4mpuOhlF/U HEkb9ITJSi01nYJIlChPbvm0VLIUX2EPnOG+tDhrobD5UuIvo8kbZsU4I0ZsxqMix4rTZDzcy+JB NhoMB/3d3b0iTtB3SRUl4gQOm4yLdDcZHBsMaOwoz59dprMinuYjVnCCLo0Ye7EiBt0KsTnavkRL yKYkguIEVHWrP+5TCzReU2pIGPwU8VoT/Z8omJRo2C6JQ3NJYZtNUkYzHo0THpAAHEdpSsQYgrkk TdIsSbJkGMd7wyHhaUSISYdV4lic0E/AgEQJwTJiyQCLMg4qQoupg5NQTpH0xbfZtyA5SBngTKIx 0xJBzMjo+0CrMieabo/GuiiYJborjZ7aWRrplWmtaIRa4mJKFVPMm5LoJaYTn+KnkncZ0yIYSlcQ JtYDm4qvUxS+WKJJnVPelnCVvIksfegFvksyHZhSKkWEgMyFXgQ55ePOeOmKQFiY7r6gLd7e+b2+ 1RWoK1BXoK5AXYG6AnUF6grUFagrUFegrkBdgbupAkuDumSOu9W3KFJj7nKNIX0BS/AWXV6vho2w qMjKlICr/rC/tXXs6I3Hb/i3W2645sbrr+2fuOW6f/u3j37kX2+4/sZjJ7bgC51OdHhzbTDqd3DP 2Y3xeITQZ9DfvvbGT3Eq7hUqJYs3/BEyIyOBgnPJgoYKCWmMa/vEc6sDodG0IMNRZJjwDosJ0OFz 4SDfb7TgRl4D8kK+eLvZidxoGBd76STPwQ92lisJnjuDWGv6vdBpBVxduedczbRfRM3koLoiLR7q JrIFzJKKahbnaNbESGAVQCgciGqdJ5SBKEukw8h4uAo6MPDY3JQn2Y7fqIZJdt328JZBQuAUSi0I CFgNkyezUmrXVInqYKFMsGZu0KMXIQoqO6L9ngdL8hiStEJgGvkbS/BcnMZplvbj0SCJE2yG0BH5 FdWbUiFfxmxouvxJGsRPM0PQduKM77dbISMm7b/h+d12a3NtJQqAYETzg1uITGd4wm9R6DSbLrI3 THeba+3D6x18kL3IPdSLogAjK55SilkyKbUDQBlXTFLcjkbFRlQ8RBH/Ykp/RURaU64/YYKDcUJ+ /zimKyLlEgVDqqVYMQe114wo+ZbfiAKAENXlTHmRx1kKiownExowUlLRKlMyhF+qNAUFjAEWWRyp uebha4rFkgrOtB4QbJL6aq7mMgdITSXRoDLgEZd5LLg+IQJesjUjOrv1XPwspI5Gqcd/zafUWHfg VUCZTEIcXGtue5SojCXgijKn8l8aQZJyphx9zmzciPSjdNsBIi97rt5ijIEL6goUJSZhosLI6IdJ iU2TSuSH4DMHJyjKMnMHe/G57nfTb7P6snUF6grUFagrUFegrkBdgboCdQXqCtQVqCtQV8A4s5bk dmuyu+kx9+9ZXaAJEAw0B69bQsQ6FjN1j0M2k5eoqcZJHqfTXKIcmIbbmIYBqGHW7TY317uHNnud btBuBxy03d/e7e/gaiSqfDAcmvArZD6Eek3iJANs4FgMPQyGussOZsLqhRDMkcbPp6FBp5DZoNYx CU6KeQcogCv41/Hbtg/L2sxTb2d7eOL48RNb4729PLBAaVPumOz68XgPkVk5wScWhHYQYcT0RD48 wp9gVVC8YjCK8wwQU7VDb6PXWem00ObImZilSZLC5FQQ6asUtA6bgb0JrkDCwDeTYmcwvGV3sDNO 0Dp1I5+7wumlI4PfFAmyJTnXSJOSmCp0GxGZ97bclEwJd+RenJr+g2q3xyUUhkVWF9I1csXk5BTF aYcRTRu7zQhXpdAccilMoQVtMNP+OJHFssi3hmNUXQCyyLEhLICzVhi2mmGbLDSX6eA5hFkRdwUX 86g4iDHOEjLWcvRW0wJbIYqlIHCbLdoPoOASmUTJRU3Is281g3aIpkv7wrEmfqNscjCCLxBPWfZH owQamjFjjZrdY5Ld1VmAErLWECpYlwE+yKrU3xOnI0yPUYQe6VfqHMCjsEiK2pn+i1CmedaWuNIc NvLRWG1NVwCjRVRTSLN5xJr0Y5Ev0SORI6yCpouiZF1KlBcx5dJScgmtzlVyBn+a1DMAq2RiJsfe ofAm3R9XJF0sTUq9ifjiSGXUzztRAssYuKFjpmEoYwenoehT60ZUe+pLiXDR+IJZyJIUMFvdLQ0m YwnUkIEuATxBWmHQpIFlGGHLdf9/9v48WJb0Ps8Dc19rr7PefesdQGNfCQgkQAIkLRCmLJKiF9pj 2eERaXFsjSLMPyY8O8PhmHHEjOcfhSRLlkiKlERthBYSFE0SBEgCxNr7XfpuZ6+9KvfMqnl+Wec2 QBAiGgJE4ja+6tPnnlsnKzO/N/Oc7nrifd+fCZfF+cU4AQ+Cyh0hHWvqoRRQCigFlAJKAaWAUkAp oBRQCigFlAJKgT8lBR6arq6f/+s/g0RrfrS2wIhiMr6QN+qYr6BMVDwx1M8oaRaHM0AuDGqzMpJ5 WLIWpWm53oQ5exRl0dOu6xhfADDJIg+8cDBcJNLcRQd81ut2yPm940kJYfGmnffwAlE0+s4BAdhk bFAC3wo8WANmoQIXEKXmde07RAO2IqXmAgjEZGbFuYzPoxGJAzMSEmaAoWj/cEhRGKYaQ8NPFuLQ On+xKatbSjMUHq6mizUG3gS4A0lIzMzBoVRpUVqlxYwso0Ttlkvf8VtBk9VgJ5L0HCRL+r6wj1GH L4E1Du2TYjR1gocwLKqv4HQtD/eNaAd/A0wdT2MUpYoLmxM4hdmL0kiFocmSevh5ljGMD5HpKaPf HWsVVCMuVhRsUVmFBwn6h7uHE635mrjByA9KcK+qoCAECDkQVVZRkuMOog+LXUN5amuQFHmBadiY 8Cdd+4iX04FVF4ClxBGlzV3KuXgd+pNfdHQq7cFFMsWQ17Nz4pnSq05Dv8aoSlrw5ZCh1K7VnfGV GM0Aniw8YyF5Adej6msSRTAdVJJWM4n+0UdlN4Cg5grc59oyhlHmNAobWuHWkuZ8/HHSnoVo1MNj 68q55SCQEjas5x7W+USufp1VXIdLBU7Vt4HAUSnor2v7uYWAhBXms9MOeGGFYpuqGW4dRpRRAGIi lKYtmaIomUcBVvU0Rj7ETKdnVMUhPJcY8MWQg/UGQDHuhfWrHvyYCIdlCTIBEisi1JS7iVeRUfV9 7hG+A/yVsZ41cav4maLgy/RWoCvD4Z7gdAWNUU4vkzvXUylriGcYvunXP4qChTllriQuNp754Q+9 /1v7C01VXXxr9XzN7O3bs+pClQe9Zm6wb2Yh387lQd/Mur7dXqt+3L7drsifyvl8O/+4qf+B+VO5 Jb79D/rt+T8w3/66qTNUCnxDCjw0qOvv/S//JxYmkECm3a3XWM9GrEvQBRAInwBDSFaP9+wwMEqI qC3KCgCQ2FlANrRWQaLqlm8LQwueF3wzlgb84v17TSBoUpcphJCIY8EO5BOXJj1Zrq3ZDu/lcc2s B9rRSg6MykFL4BqxUEmkkRcwok64w7qPvKghieP6rusWxQq2kmblnbtH2Sxr+h0/aFKtHjOJUDPf /OYNDhdKeow85gqvER/sse3jlJG2eNxPHELSZ/aS4nbWThAwcLx5LiEyCJOB7ahKiCmS0KuZkvAX VosqwoqI+0mcj+YsSUSyRvk2hANUJ3P7dOrhsRlx2kkJ5LJpnMI/hN6IQdV74LoAH5KAACwW6cn+ SBV6EmuU1J2OI0hijHjBoEpVRQRSvFh1ok4uWFXN4pyzZqdyJnwlZi3p7GdFVI9BDF2L2QIlf99o +KFLLNSYxoA5A+3QJ8mj+WI4XYxBfJbrojTwR+YhahoesHoUwCrJ5Lr7jhc6rtixDMkUku6UYjNM WgVF+fApOQE2FkPaSgNvNYOATnzPCtA5X+WTeLxcRuYKJiobSOX+umwe3Ak7q6ddcr8J4uRuoKMN NiRsh+u+/hDKKrCLe7LuqJdIoqjFPzKCU25UEpYwNP7hgtaOKQkbrl1bso0Y5uSmrqv469eCuuTL 9ShMucnkByAFdcntL0awGmueOsJOBwXUFV3rnYh3TH5Y1pV2grG4qqRCZdoBCAuTGoMiubL80HiW wzxQCBdXqNIc7h0q9Vnv2tlVOxkFmNVITgKtpgGoXVeJyb+SV62NbH/2A+/8hn4Nfd2N1f8pfl2J vjM3+Pb8P0X13vs78278qlV/O7/3fi1dIPXj9lq6mv/Wa/l2/nFT/wPzb31ZX9sv/Pb8H5jXtuZq dd+BCjw0Aca1n2Vdvy0WmHr2H2/1K/w+1A1BdbC8GAXv0E0XnFMURhZracR0QQt8Je/mGc7XCr22 bzc9GqPoE8oNLTG01DBS1875CP2y5ese4/WWkA58WEw0lFZzn4ifLQE63sRzJEhW3R4uRhoKsjgJ ZgDiTloXLomryw50QS4+IS9ptTLcJF6llZlm5ni4NKum421qdptmJNNrsoc8g5/JID3cRzSmExRk p1TFM2+xRjIwtdIxq8BZNT2jFbTgMrrmctA4lfRfy+GcsUClC2lnB8HULhtxGzHsMG/aukdAj5Pm zGAYhk5iDdMXlAjTTug4wJsIn5hE16i4ktQeKwVUMdWx4WLhskCEGKZk4p5pNl237YNCcGBBgVYI ZGuVjZ0sxzsFsmIqokaSDZJWAyIZNQixg/oA1wohjvV1q9vXZZKgqYcuMyKtbuAErgV4Cl3MZeUk zifU7JOqowS9kh50bE14zdi/TEGspE5+SRO8jNZMk2hBLpKm+FWR4ejL4vloPBxPhvPZOEsXvl72 PSxMnCHmNYYt0j7G9MZSnHpLbX8UP3t7//bB0a2D4Uv7o5v7Q1xshVEkmpfpXmnasDEOlmM34zOX QZr4hRyxQK4MPffgs3pIQU1/uB2lS15yhXJz1snGujteaJaArtOpCkiOFW9pEBC0QaFAo5pi0ZG1 /oDHYXxzsK0J2wVy1hbGeuRoXT4vuJEXEZ+U6ZYAPa4atJegKv47yUUKpltzRDx09VkIjat9dDJT tBQdxFZGSrVOuJ421ddBTHKUZILrHOV66uhpZleOTogRHiYAk4IwULJMZqTdSz74mvvedhqOfHwH /iZVS1YKKAWUAkoBpYBSQCmgFFAKKAWUAkqBbxMFHhrU9XX0WrOvugxe7C8Gn6WwvqYrQm/W0wIl 8wXvwAAl4KLusq+jbuvSrbp4CwRAGfnaX1OPzCs1+I8k3WrHChsxt46PuogdsgPAEWojzhap7SLn xbc0ycxJb5VED2EHlWYt4upkHA8nEWQuyVfzpIhxWDFKr9SohsJ4JZMNV9oiL6fAmLyglgnjDUVI gCMgAqyEwY5xuWQe4Hp+YJnTrzVP0vHx9GiSzGPa1aEeVINJi7kN8wB14HiKqaYCRJXlJC1SavBX yyRNp4uEgYwnuKQizlRievR1QSsarrXV9Fq+zRcQMc+RMCBoo+4pl6530AjIjAPPGJgY8c9syLTE 6bjIEqxOTKzE+iMDGYsS6BbjpEKGFU9SqmW0Q7sLWqM/C4KoVRjPSIzO01jIIYYvOQepWkcUUod0 cmErA2hRHUadFtcOvILEYJ+6REvQD84zYB5bSIZwKeiKyYiLKJpHs9liMplNj0eje0fHd4+PR7Mp FLFmUnVNlmllgovsfqu929/1vF5aeJ7TPbt5wQm39eDM0u+XbnuheUeJPsj12dKca1a0clIpXBej HzJzUUCggQvi0WjDpzKfmrAWaVh6/S3DZapjXeEvRfC1pWqNk9a3GtgO/yDRTwAna5fmLzrn5HYj k4hcoMCaENYbr3vreUqK70+TkbI/8CUrQk5ALt35tYTwN7x49WwErHrruYqnJE5GfNZRX9mmZNRk ntBBJx8wSixd9exGOGOcMRkzmwEPs7R2/EkWU168lHgp98Dav0hbGxQSE99pK52AYAZxZpNMPr5N frup01AKKAWUAkoBpYBSQCmgFFAKKAWUAkqB70AFHiLUtZ5Ut/5YP2rkIaXwwpjWJULSGbT+Qr4F qcKFhSkGbEWlEZxCsnNYmjC7aJCSkjfzGb6gKJ5HyVw+x1kC9oBe1MCMjBpVWa5N3RXWJWCXsJLJ ophGkjqUsqX1VMYKhwzkBVblwAdkaKPjE+CrNLfSvKS0sZZVuR3NV9Cu2wfD8TwazxfX7+09f+vW 0eB4NpvixeEDjoRjisooznwcZXvj2cFkssdIyMVkkUflCgOa0Ia5DDdM57CKMp9niSzPhLm4nSDo t5gqSR980AqDXqvZawaeyzw+8BcgBmZlYM7CJAU5E4IlGuKrclq0gUkB1irK8/3p4mg6G86jwSI5 mSVTyAeOL8ltyhBKOqpmaUqtFmwl4RzieAruShhGmOCHSmvw4eMjo8Qca5JGmxjuMCv0LUruM1gN OmnUQhUx9e9ZLNMyyzzKEy4S3Wd1qhIfWJ4ki2k0tVepWdFDz0JnyyoGQcoYR1NmC8pe4Cy1IYnL SPFZDC/MAXG1G6muhyd3Bw2iGktcYHrFK9EYxx1Qsc6GSpEZdwfkUW6YaokvbDxP5omWln6ULRMa 3yhBA5ka9oqpghj3pKkd6mdIpxm519ruJAerq+dluqNMYAQBlXip6g9x6nEvvgJVTweHriRKKmlH +b4kXuv7VO4v8KnUXa0b8aVjXjxc61J5uVRyU4pLiwOvMRbJTEllUr0P4RWkJWyXPeELk54xzuR0 YMK6SE4a5U6nOEqy1gjwB4LnQhf3IQfh0MwCMLXSA5PW4ExaxziJmgbXIzQp/FoXc63ZHRdBuvPY LTCUF7ABti8+vgN/k6olKwWUAkoBpYBSQCmgFFAKKAWUAkoBpcC3iQIPDeqSwXXk5mpnzDqhV5ux 1pmu2mskb/7lGzI+Tiwtp91HAhbEPSUWGPjH6YcgAN69437Cd+WaNvVT1Ka7UmsuPfd1g7jsy6SV PaLwSabarXLwB5s6wC82pu1dKqWkaL1Oe7ExHMS2HM92AR0lviXN9i0fK9ViUUQLgmM4vAKZcpjg r0oW0XQyHy/iaVbGSV7xAUrjcKT0+CyNY4Z9bXPzyZ2ti/3eRqOx1WpywksNq5i/0exstPrtxka7 tXWmv7Pb3Qy8JiPzonw5y4sFH8VqnNFUr6VLisbMXsMLiJlZFj1cxCohPsJw+BuN79Jej9NJcm5o HNhm0zbbzDGkDMzQCUiGjg3DGcUJwThbesk5VcxUGYVXBC2bnt0O3UbgtkOvFbJrjUovoI8UnoG3 CskZTqFjWNRAXxL9pAmeRrE0ThIgFCgqdAy8UVzHeRaP4sU8XsTJfDIZTGbDk/FwMD45Hh7dPzma JxF1Y9jkWAe8yrPtmi8Jf6QfDBAGyxKbnKyGUyedacnf6p+zeuCgOKxgesCd2kNHFs9gHUkSaWVk LqfJ4nAyOVhmM8rvtXSeRfPAWHYC16f2DY8cwwfFtYcLD9+fwCeZQwCiktEBwrqEIq3LyqS7iyfl zOR8aipUu7DYQmxSfJaJmBpjERyytTrzCqkao/hLnFjS4sV9SkRSUrGQOorppWptPfNzXXkvKLfu /RLaBSCV9rCabcm5rI8r/9Y7qqHb2uRVd2rxqvXpI4bcDpSBcWQpIGN6gFi6cBSSy0xrcMu4AIk6 CheUH6wVdzwmw9o1xjxPYrN82HTkY8eraSphXafju3x8m/x2U6ehFFAKKAWUAkoBpYBSQCmgFFAK KAWUAt+BCjw0tfQ/9zellh4j1alxZ428MLTwVlzsW+uhgnWJuLyr/0OPNSVYP7UOv9UPadbinb9r eQCF+mnQQL1fTU/SuWyBo0e6xoUD6Fqu0XNlN3A4Wca6zyur6/BhN5CIev7d0qRvig3IOwqSolRr 5ewdz07GswS3l+HSu7S3f7cqMnEwySGl0J5Bfj/277+dv+MI4nzo95LZjZhoGNe4JKGWzZMp+KER NDdb/aVG+G69yKX0hBmU1lPCZPmmhq0qEopWcCoSnrNMzrjdCDjdOsKpYwGaRUKsxLQmCEQYRx3b rLWoOQpUhdotSWNKhz0uH7xvoEMrbDTo1SI5SfotxgGV5zU8Ygwjn3UKxkAnvo0pTcsZZUnkEEuR jMAUpxaUp+HTJY+FChMSmIzW/QwLltS2G7Sd6SQWHYYKst6q5OhsADEKPIf0I9QGnxOdWGHgMcKS hipMReKnE8aFiek0E7i+fBxITsmxGa9pGX6ULOCJZY77S7dhkJwecKf2JfmkUD2PKQWLtCTwSevX fL4Aw2WmeWZ3MytzTqnhmNi0kJGr4lLYZjtY/spkYa4Yx1mbwSRzKIirbv4ycpx9DPgkhGlSuLW+ unVBF76o09tPnqtdXnKj1qMm6556QbQ1MBMehjtqxckivCyHO4H5jHU/Xd12D9CiyYuvMlSXAQn1 LVi35ptAMJnVuDaL1U318hLJNtYwTu5u6b8HtSEFSJabDL1qeigAi3MmzsrmGNYk0KijUKtcUV3P VaUpX1xxYkeTpn2uNiNKDarUAF810JO9Iy+mQo713ne87lv7y1S1un5r9XzN7E21ur5mLqVaiFLg 304BVUv/b6fba+xVqpb+NXZBvxOWo/4H5jvhKqs1/qkr8PChLinGWscYa4BAdvCrUFdtpXlAs9be rgecS2DCHyJdMraRWiopj6oDWbyJX2+QFTM+C4+oQ2rSrURkUQb8OfAR2AsN9aftXsLa1i1MvMDy vTDN9UVc+G7Q8VrDSX7rzsFsHhW82vGJSh4Ph8Tc6m72UhhC3QH/f/upP8erxwm7rfGHTuOVYA/H XPZCbRrPoDiOSxu9z/zDSvrlARwQK4CLmS11yuylMqksMDIJH5H6/JVt275LiFLiZlJsXvufavSx wpsj1WXQEYn1EZSrB0guCcRVQDqxTdlWiwXY5hQTWo5Fi4gk9Mg8ms4JNDoCRqQkXgKBsq0GqBIU JKMbi9EiokqMheHzAWXVINDcaPrwGPKPvJwusiItMEuRjBSIUpaLKBbeVq9KOtKkUh1atOJbPGq3 VIVjDN7EAbkoKf1cNSECKmHU4hlOm158QUX47jgT6t51N6W0Pk2IanIFoWwgqDhDc82zSSYK1KN0 H8OWbzvUUI0mU4AaknW6bdQCC6JVkuUyA1HTfdiYDNDMaLhiVqS0axma62AARAfglIAkPH2sAcMY bEja2+oFCXuVbwsNWt8j6/ChXGUWyq21YlSlBQMVV5omQwmkRczyGHnJ5bClQ45Sf+ZVigGTki1u xXqP1K5h2qtvVtmXmApJRMLaJJxb35Aip5jACNuuuNBcY3G61U5IGUDqiD1RViLnJblIbr2UQjhg K/yUbcwg8Dsr/ibXBstYvW8uq3TISTc9p1aS5IU3y+Kkyo5auV4oMxnf8/anvrW/2hTq+tbq+ZrZ m/o/xdfMpVQLUQooBZQCr0kF1P/AvCYv6ze/KPU/MN+8hmoPSoGvq8BDg7p+/oGra426TscMkpIT iiA93DXGWqcOT2lWbRta+5Yk31hHyeoIWP39NQHDjEMPF13jdbqLfCL2GbGnzNPhGnWtp+jxDekq YlhhkkkTGKRD6r/o/yLSVwqOWPcpMaLO9okeUnPkmE6aWUeHs729I5kP6DhO2FhE6WA0WSwWpuOQ VGQn9bC86qf+sx/kcDbF4Lrh26TzTAYNYo4qV2VIS9gqYeeOEwRBx6ggGhZrxmAleTlq2sXMVLLx ZDZHhQ7uJ8viuYwaKtOAdrFIGY4IV5IaKQNsBJwDwxBppMqLYicQDwwFngV0QxISjhAQgBctXQvg R1WR9YNYIZ7MopROLfrIZewgxi5IDXhr3ZFGBpPv4CsDTjFdUfiM5CJlQACuLqlJpxyedFyWQdfo QmfJGM4YE8mDLYk42tilcHYxIdEE9KxfAWgTldAP5EPclCoopgTwEnAV+EmmXBomy0/zjFZ4KM5c erbATgaLBurB29gVhA/OxtcWkUFsdTmcSGO8AA/GCVI0NV9E+M08z2uEYd1fL5lAmbBYVdwEoph4 qZbEDkF70o9GGRvNb4ww5CoRbBS4JQ45qJJwQ7CTBA+5AkIS1yMR60p3Xo42pBXFHSWlXZoBxWNE gWtzIThD0KkReuRHuTTadDbn9sB4JX1YXGb6yYiRynWS6Qoy6XFNfCWWWI8BrVvC1la3+hbnqsnp cKXWKd91dFHsYpbTaoRShr9moLW1jxPEmgXUZKYiDFA3g1JuBI5OGBa6x6JX5DllbTLr0VjGsura 0SXjIHTTaXg+l/W9b3/y6/7qURugQJamfHY9T6mhFFAKKAWUAkoBpcBrTwGFul571/RbsiKFur4l MqqdKAX+eAUePtTFG35xqwisEqsSrhI4wxp11U99GXXVPOu0rGkNudbPgB1On67pmGNSLSSuLuE1 RLQ0nVDeJDqqdweqwAhTG4CEZwnb4IDUh0vzEkMS4V/kAyUFJnvAAIXXBQuUbtJb1S2y8uRkfvdw NiZHF9H5DqkQEgTqEkxQSifTusPp//pXfkIOx2A/SqHqjvF5Blwq6I2CRYyjeYN+LYuOsAA3UiF9 T7RE0SAuucSabsgLoSlSgq4ZWbXEi1W3LK1IHYoHS9rbV3mWzaR03ww9TEMACtxYxPpYVF1nVleO wXTgGBycRcnZgXIKqXuXFndiki5OJgtKxcuBMpLwxNRlYAeT6i48U2UaQ53kTEBNdaVUXRZlNkFd NJdXFScG25KhlevBfvVn2R7SJv9KzRXcrb5STARwawMZdWkp0UUYHMyxytI6dIlQxjhOCWrapkYn PQvi6Xaz3Qh9RkBG8xkskBPuNhqc/xhDXZJwNVkuzfdgLxxhrJ15iZjAqMefz2NgEqQGJEjA07Rt 3FB5WbE9eyZUSSSS4Za1+wy6J6wT25ok92CNNUUVS5kMRSDwKMBU7g2BoCvPo9mNKn0mYILneB7Y RQwTUqcBINkMWie3ra4lSSKNZktq4Z3QDRB8NBlHceSJpcwCSHEt0ItbwTYKnH/SHiYuPeFNXB8u mjivhMkJ6qrTnTXArZu9OBxuOdbPCZC1pFau0wwlw1gLzq64kWU8pYR2sdRZ+B0dt1Ewa9Jx6sWJ kY0PHF6Shq0vmFuC/QTO1a1hrN0OXKE27337E+o376tR4JOf/J1er/f440quV6OW2kYpoBRQCigF lAJKAaWAUkApoBRQCrwqBR6aWvr1ampDlmCFmpGsk4qvJBK/xoLrOXHyeDDC7rSx63TT0+8+2INE GKX0iIJt5vzJBzEtQ2wtGKBCpirahtRjiW1GQI/00ptAMhnrKFavehof2UZ8S0UGV4miJJnMYw6+ 2e13Oj1wgkw/9Hw4kEQGQQS13Yc8o7QrSU5NMmuzLB8uGFBIKrGaJlWWVXS2Sz4uL8fzeDafRYsF pfZFnlBsVRW0wGcLGBaTI4EpVTUvgCB66Jm0hpNpnMTJEMjD95ZLqNRWJ2w2AgAe3iwPRqdVKcMQ U6q3wFQSj+NksFW5sJqKHTMgMZnOZ2LGKphWSYu8lObD4HwbpFNhtOo1bc/FBJdEGX3yDLKMGN4H 98POxRrZRZlnS/KKGYwrK7KM5q31GEGE5YOLCXshhFgiCvFPx62ji2I9Im+Ih8sGneC6Io+oG7ud Zq/VAOJInVdVsYTNdhP+lZagKLrDZI7hPE5JmLINkAxzHGtBGTTiToAcsWeijpCiZhg0wxAuAwuC cMm8AIdJlJ6Rr/JZNh/NcyKnDDDkOmvicmrYlq9rJF1rjFQ3cElwT+xUkE0BrOLpkg9OAwYnQKnG QtylmLMyzIfSqybYCH7nuCQxvZVmA6fKpZ4SiV3qo0VGVRZccZHkCRHNquAC+o7Ap9ppeHojr+vw 8YLVlFNw4ymlfMBy6zo6iSWCHPmQzepCMPlpqe2NdS++nFuCJ5DQoSQd1zev9P7DP7EZ6TouPMxc gpDFvyc3Pc1rclAIoFTKoYt41shryiQHW7xg3FFmwA8Ll189Xp0C/+CXfumll156dduqrZQCSgGl gFJAKaAUUAooBZQCSgGlgFLgVSlg/uW//NOvasM/7Y2+8Llf5906SIZ35/iI8JGQLMOA1AyhBrzp lqL4OlQn8S3e2xPFwm3DS6RnCDSmWy2hIg5+JMJrNf2qq7Wx8PAmHziypHOeiGHg8Xy1SiqsQ9K+ XY8lFBYg2EIyZPCAOtZYj/oT1CHt7OuMpEwn9G0hH4AK+uwXi/TgeJTkgAwNAkNBV9jutDudOE6y JF4jE14FUHnv2x/HsgMwmmcZPiF24tkGYUEcRhAEwU8Fub8ypTsql8SiIIxVlUClYB6SpMOfpeXi 3KLIibSahDzBWzhxGkzFq6HReuSk5bjQPJ4hisixoxquiBdJQy5p2uesQj8QF5IYdcAc0sPUZQoj XeV4okitiSMLJ1ORpjGvhWDNMExFixK6t5hDsyhxktJ9AXn4tySwmMC5igJnFvP9WIlMBsQ3JEMK a4ZDnk63G2GAYlHKd/XQdULPncb1iwXDpZAWxzKOphHIjYGPCVyuwuBldZsBFfTARtxbXAbuAXgW h+CIRQ62k1mcnP8ixVGFjUu3mEbJQupAoowRtB2QI2Vb4B7p+S+W2SI3l+QoHd9v9NrdbguWJj3z YECYlQw1rCca1mCoDsOKpUqWISFAGX1YkmuVHiu8XdLQxbWQSi8hRavcWhVwKW4WVIdMzhg3AIdL mWiphX6DEOF4OqccDQcch+SILJ/zgo8xMbKGS9KZJoSQlYiXi4EIcuj67uNc1m1n4kBcRyllziKv qW/T9ewBOWW5pcVXhpcNrxj3ZIx1TVrb8Hs5DPEsK9xhBEkdSTLSYsb2csdzxTgssy3lwIK66p+N VSk997i8CMzK4EkSj47DsXd3en/avzAeguN/7nOf+4W/+3f4Wfvu7/meh+B01SkqBZQCSgGlgFJA KaAUUAooBZQCSoGHRIGHBnX93qd/lTf2QC0GAbpk8CyTd+r4Y8TAIpBL2pF4S97ybd9vuS4t4l7g +91mSAMVjVQYeKTsaaUnCc6nEvpAyXkjCOE8ITutdKiI5wbMnHMpXI+zlSvhRN7iizdHiJRkAAEZ vKOv7TWS7JNYn5QkCe+AqmCJAgPJLLtVKRXlhrt/NLp+864QjcUCnoJXbGdnd6vfHwxHs8mEvROl k52sVt/3Z94BzoBLNBy3GzbgDoQQqTunkIrUIx4mASq6DqwLXQ+Lme+YLY+Mm03RGNYbDl5HAqUP Sggg/iNT8wVO6U02871W4PeaDdcieSeeI8EiyyrOxcCFGlme8XndioWNiLAcC8HyJQXkdT1TlGUj mvWltYrm8hzHWd1wzz6WwLc0xb62oJ8dfcAsyARl4uu6rvzL9jupmpLgY135JNVRkolj7/Ahhi22 PZNue76mXR4uBlUjXofMMpCxqiBUEq7EycUgzGLJjEpJj8K22CCTQi4yp1wV3GG9htf0HYKCRY7v TCYkcm9wDaXeSkxk4rKCE1F271HNjh+PhVQVsG44JGOahU4QohYPP0BngGPgmL4riUHio7atF8Cu enYBzIz9C019gJUwctElRsqRA61nIwjqkjirFi2SyWi2fzC4defo3t5wvMgH42Q8m8M8adh3vOaZ ne1ZIX1hcLqN/g7Q8e6dOwejycrhJsVSxlWWiZzQvRUlWzb5U2G60Cn+KnVf3EqY/0puqFJglxes CqmiZ3uqxOTeAFaJ1cyg/R6fGDvkB4h4rpwkN7WwKzZm2KJD3xZKEZKsDJOpl3KpgFxsLsVjQpXF cgcllkivVubrin0ZlMAGdWGexvW6cG7zIfkF+Kd5mj/7//i/DweD+/fufeSjH6Uk7k/zVNSxlQJK AaWAUkApoBRQCigFlAJKAaXAa0iBhwZ1femLH2dEHYgmKQT94DaSEJyux6S/BBbVqS6Jp8lcOnnr /sCONU+yun7LTrIlniS8UbRRQcskOQjloc48yyYAsNXSDd2VEYym88PhyA3FlXNaJPWAItWWnNNO /NMaepk8J76htesLIMN0QgkBLil9D1+8fXB//4T0IEADKhSn2YWLF3Y6nXt7e7PBAHJCATvGIk79 fe95K6mylJOhjImmKmljLwB4ADI4FOYqHDWcD2401pIU+RxLFUCoXMGB+IOXsH+SeJwH5Vy4iIAs iySLBANZOLXmaSGjFCu8URiUCBHKwosSjCb/oABdXhC6OmRIpZjUvlPIXsLNQEYlYKvANCXlXKuq Hk0oDEtsTNL/BPzBrRVTfy89URJJlPNniiKQad2WVht/VtDJ2v8m4IlniOVJ67zAvqWxqiaLGIuZ dPzL0MBVnFftMGBp4JVOw5Nhf9LRhm9LvlvvUmAS5Vf43eqq+5wj4WOTHjbDiDJhnxKtk6PKSETB N5iPqnIeR1jSGBoA72P/0WJ+fHQyASoVGYjUcXycTXX+UJun8WA2HS0QLwPsQE5JsErnvWQRdRGK 0xNsCFCDPwl4FZ8gN8C66b2UUZX4u7i5jo9He/dO9u4fxxFBS893W6XmQd0ypgG44fbuRavVMnw/ KTTbDdsbF/eOk3s37y+91uOve/NGa6Pb6JWG2ww7QWPDNgPda3daYWU7UWHNourkmJwrmM07Gcxo g2s2W16ro+UriB2oC/dhsTJcn4J5p9QJGjKckvJ+r9TIoEJU4V1AP9ewufndqKAvX+ZmLi3XFJRs SwUXaBAoJmiWi2fiW+NF0t4m1f1iM6sp4nozudzcE5fObb+Gfkn+O1kKlq5f/PmfW+/6LW97++7u 7r+Tw6idKgWUAkoBpYBSQCmgFFAKKAWUAkqB7zwFHhrU9cXPf0LKrEAKJtSFSJdLPTdmJqFc4njB 5oODiYJ5adeSN96UbQNpABOQoGrlW/QiQSFM/FZ4oXzHN3VXW7kNL6S7STOcsNW9tL21sdFPmWao U94Ec6kfwrboZKp7vMS9sh5sJ2/pa4gjqGudH4MDYOmydPgFeUg7K82bd46nU7qrKBRn6F9JkvCp J5+gCOyFF16KJ2OJ0smMRLPf7r79zU+xA2gP9qilRAFTvoalsHeyeJiQaFEKHAOmE0u9uiQBsWHx EpJmnAQQjQ/gCl+vpyjWEwINTEkxRfiUmQOEKgY+IpxQn3oyIP3ogudgXeyR3rEQkCOmH54swF4y 0rCgBUvyhgTmau8OSA3nF2suOQFoW5olRDHxdTH5kONd3m5J+XpOpZcQKVaNZwz4xD5rtrWeCyig imfZpq5mr4OnECxGTILzlgLvOEeQJSX3UiWlrYazxTouCnYRZ1E9lQBKV69j/U89PQBzkyZdYkkG LhRiw0rAT7wEJkj2jigiEy3ZmoCkZzmcIxnO6XA0HI77zaDhCegiHiid+CTzlsU8XsyjOZciiheL JBlNKUpLG3TWSw0WkArUJesHpdFPL7cd8IebUexVSyx18DtOHg43GIyOjkd5ugz9Vhj2u52dZrgR +k3sVElWea3+5UsXOTi4koozz/AZdLmYFXGkv+7xNz99+ep0DqY09o4mgdc5f/7xyZiUq9FrtVkR NkDPcF64ca/V2+yGrWdfeClstS5tn2s12qGDE87vcHv5zUa7z/SFFgVhbgfDn+s0TbtBQnPJHFAc kjgF/UbbD5u2z2RRmUdJylUmWJrEVrkwUu91+pMgfjbJ6WIqg+YumRkqQ0Olhp/lSms9jjPmYZoX z2x85/0u/cZW/D/87P9zcHKyfs25Cxde//rXf2OvV1srBZQCSgGlgFJAKaAUUAooBZQCSgGlwL9B gYdmAuPP/Hc/LtyJfiUITV0jxaNuKcKqI1wHJ5Lv2jiRcNXUreEy/g+GAo8BtLTbrUYj4HnYBNs7 5BQp81ricrJoTeJVph9cOrsDFTg6OR6Mh09d6rJ/4Vh1Cq+ufAK8yDHhLfX+5fvygZ0KAlB/G2cT IUFqxculG2XOJ3//hcFgoRt+TFNXklx67LGPfOSH7t8/+pV//E/SKHal08n2G+HFnbPf8z3yRncW jaAqIDr2Dkph5+2mT0M9vAmvjOc5rTDAOJbmZOhgDFKdxNfE6iR/xgY2p4HrSzxH8iFtWSuCkJia oHHgN1OXvKeUN1F0XzdxMZORDB25OTrsMe7Q34VdTiKG9YLYhUAhywF2EKPExCR5QFxhdYqzrota 31aSTeSQuIAgcfVlEsMd8BFuxcE8qIvMyJSo6foVMCAmNfJ6QoKE6eQlhsVVwHzHQfi+57i5jGEU /QFaZBIxzy3S0nOstRsNJCNY02RigLi2MKxxfmy5iMVshRGMzB3wiEuBvYvBAlIV71jzLLIhkly0 koGF9L1lCRfCts5tbZ8kZEVXAY6nlV233jNmEkaYarTT60uqzXgypVhrpTVaoelbsxxjXYrVzfUF koG7qAmTxGvN7yT1mebceMC++4cjzsp1O5MpAU5rZ+tMq92fL47H4zm9+BevXNns9yaDsSNYUWYs Bo3NJC1PZrMf/cAHbqeLyXR4rhH89peeOX/l2g+87/2/+Vu//fzz13fO9BzfuD8cbAbWvdHoh9/x +uduHv7m737q8acedwlENvzvevzK7Zf3lo5XWDb3PrwPdBnYdg4dW5fLrZYZjsE8n85iatFQkckF jEQwGo3e7hY0sCSeKfMT8EKKd6s2x8lMSVKvfDBmEvMkrf5cSn4CKDBjnigb9QMJ4r3lzY+pX7l/ jAJYuv67/+NfeWWDx5544v/zP///lGJKAaWAUkApoBRQCigFlAJKAaWAUkAp8C1R4KFxdf3iz/91 Em5gDlgV/U2UldO3BM+K4pRJh7xpn05n83kkcT4J6IlbqB6uVw3GM0J8tH9DQKaTGR8pJeVpFi1i Jt4BlabTOZm9hBL0YjkaTg/3D8bD8ZULa1tKnVesS7AkISm+FVOq1OUfCS3WzfKnMyClMFyG5VFC D+Aw0mx1d28wmcQMtpO+qGp55eqVt7zpLfPJ/Przz/NS7GbUftOnns4XTz21I6+riFHmHAtsEtA2 Bp6R+qOq4duOtJMx/9GgWJ02KB7ArhRU51rN0KO/jPJ0wmoy1RETFpVSBd33C2mpF+uUdIYD62CE 0DAcS9AJnFkgI3CODOSrW6WgaeK0qqvPoBy0ftGjxSvXcTxcV5ivxB9G5FBQjiRE62Z+UYWycqrG 8GEByjhPNhd4Vbc8SX+VaZKElGYudgIoq/OlyAUgkiuY5qfRRt1gzbI/qsoKaZgijMkIAoYqNgMX SJfiJMLOZwsU47SgZJiPZEAhBV5Ubhk6jfVY6tAGh1o78NGPTCi9Xqw34gKTKLS9Vb60K8vXPK80 s2laZprrdUq347U3ghY99B0q2xh4CRzr0tZFb5nFiElPACl2s7qPnjsrLXTfabR8HwXFblZ7nxAI T5ck+IiaSr8/zVYOG0+jkoEHGzvn/XBDs9xmpx12QuKnK8PZPXfpyWtXV34TtjUeTJ957kVyr3ae 7e3di/KMw82nY1KkN2/dOjk83NnYMpf65774Ra/pPXrtYpPWfCe4fXdwcev8+Xb72duHr7987a1X L1+/s9/xfUxsz73w4vmLl8q8Orp/eL67MYpWezdumXF5/87hwfW7J7cO7r907+DW3tGtuyf39k8O j/fv7x0dHTKYcmdnC2sW6C6VoQcS/KzxpEwlwE7JF3W7fV36lRUgTASWvjCkgSpKKnV59vzWt+TX 02t1J19p6WKNNHZ934c/1Gg0XqvrVetSCigFlAJKAaWAUkApoBRQCigFlAJ/kgo8NKjr47/6T23b pWKb9+AEGEEljBVkoCDWHiYQ0j3P23DayYl0MY8PqsMz9WdCWA4tS3Ss8wwvSFP65kFJHhVaGdYp ytNLSI/Z9BvNoFUSvpOyJ/PsGd521qYtie7VgwKFbJHfAv9IQT3gpi4I44n6PT8+qBr6QGFkZqHm JIm2tz+KZSYhHASvTGt3++xbH3mUKXvPfekZ0ACsiWqsfBEli+Ta67fhb7M4WmI3gpCAsliNJP6A WWbgS2sSUAyGotseQyfhYDJccLXqN8lg6nCYJAfyMOAQcxuYCLcWkEcKt/BTsRkmpXXju6QNKTuT jem3SqS0K8fuQ1oTl0/luVAkSB0mMhkmCN1hmViZxMu11HoNu+EBrTgyvi/JIdZdXQLS6hZ2YVQb 7Qbz/EAfcCgEB3XV0/twutGYRt6tpkIaEwDcNUPhrx4ZQ8OQtjEN7MVIQD0gzVdbh1gUHi3PsYFo 87Q8u9FG2fE8ZlGMaPQ9T4AndWKV2NM47IjmKiiVaWDmGswiGJlcWsNgA2FnurnZapFM9UrL1byg tBbD+Wya2k6YkfUMQ912ScJCibSqwHEHySLDx3EM2283KPIKZoXW621ub51hYCK3omM5DT+E9sXQ 0zwnuwrq4oS5QfDCcSvCD2nHZy5lO2hf3L7YdvsNG8zVAiEtbe/y5avveP0Tja0NDHvvevSsZtsb Z8/+2R/44P7eAZfno3/2+/cGQ6rrr50787uf/5zhB08/cuX+8eGzN663uh1nc8cNw+PhSMuTfr/1 wsHxrZfvLv1mXCyjyRyweO9kpFfFtUuXD48G0+OT3Ubn5f1hfHB0sHd0cOf+3p294eEgj1PWG4TN gO6vTpc0Ixqe2d3tbW7R8ray7bhi2qnJ5YR8QTAxAspUT37kKL8zJW0qGVDdyldGUq3ipcY1lgmY K+3SBYW6/o2/xrF0/b2f+7tf9e03vPGN589f+JP81a+OpRRQCigFlAJKAaWAUkApoBRQCigFXqsK PDSo61/8yi9T3CRGLmlhsviMgYYOdzxGNU8RsiB97lLRJF4hOA3bk76SGiipiTLjCeTDCE3XoqUI g5HpY2Si46rtNohF8p6/iAvIRnQ0DMPG1japOvFtrW1LMmCO9JYE/8S3Vcf3Tvu68FkFFBRZJsYn MZRRTcX5mD4VW3v3B+TbTN0zMRNpRrfRuXz20mw2ffG5ZzkuWIRCeJJlb3rdG7YvMvZuycRDxg52 mg3m/5GJ83wbCiQlUGKyIm7JhEGXSW0N/pAYp5A36rBoqa+xVB5FC8qjAFlQNCCTnHLt0aKoS9xY y4KbGORVU6/a2CW+LbEesUpAhtTOA+5wq0m8TcKVKImPDHePzJdclqEgqdUsxnoG7uODtcqASMJ6 MA/oHAyEQKI4nBAKlw8FUS7rkIQhh4RACfeSlBsV+wRRyTjKBEMcYJxcSESRbeoYIEfnlIUX2lCq ZbPhtwMXU1boOOyZS84ipfzLBFAK/pPV5blYiurqdLGhsSspmKLEjUvLsE634QfIai6NIir1nGZ1 xy2N6XA2nsae29Y9P6Ww3XbJebapgQOocp+YBAGbm61ev93favd02z+7fe7q+aue354SHK0KKA+n xzHlslcFzsG6Rg1L4YoVMSkyp+ssX0bjRTRNG17XrfxVqlcZ3WeOsyQUGfR9n5BnOZ3pi4hs6vnN rhO2sbA9dfncpZ1dGsbefvXCmUuXB9PZd7/7nW988trtw6PCsL7nne/87tdf22g17x0Ol5NRWi2/ 9MKL89ls68KFaDa+dWeP63YyGkSEOk3/2ZduTMezWVodHp7MhqO7BweIjjaLPHM7re3zZ89evHz+ 2qPbZ85w08yqantrs/QbwFPHsRq+mOmkjY41VitxTEr/miDetKan7WZHrjAPuTGlyV7nGln2lTO9 1+pvzG9+XV9l6VrvcGt7+81vecs3v3O1B6WAUkApoBRQCigFlAJKAaWAUkApoBR4aLq6/puf+s+4 WtiU+LwujK+tVlhhhJhIwlA8JsI4IF9wHNw9cBXASZQmzWbj0uVrx4eHs9ngwqVrUZIP7u9T4k6H Nh6mrY3tc1cu7A9GLsRipW1fuASjaOo35BDCcNbHq/+ti9Vr0FNDIY2gG4m0IHRo79bAAHXbOmMB AWleGhuf+NSXTg5nttOEi9Ak1fKD97zve/pt99c//mt5msNDCgbgrfQf/P6Pti8es398TOs6eamr 0mlzXwU2QIfTkDGPLL3uxhLXlzi+pPg9ny/mzGqUCqV69CGxOMxmcCvX8YEUdYG+hZ+pHlfIMMUl prM6eYhuBngPapaVxmSRB/i5qsKjyEqOKyE0FsvIRl7pOyal7OgJbsOcxWKlaktq5kUOsWXZFt/C ikZYkfmWwEFJXpqMelzBAHGNQdagYrU/aI2fYGIkL3UMULVi5AGL3W4wijCkVaAxpmTWBinJJHIa Usjl030GZqHbi+p4jTGI7BShzvQ7+I+kRAzwsgIyig+OaYwcBesbmVWuEwRLpFjpkMPju+ObL92x V6sLdqfX6C1mizgp+5cu2zv9OdfMd7Ya7tnAo+IKpbClAdfaWL0aQW+zXenWZtMbz7NnXrr53MvX R9Nj4XH6EtvWNJpijxtNx9jQgjB0TYuT6W/2Go1mw2ocH4yslfPUhddbJpnIluG6rVYjK0axzMEs ZrDP6aig+WtZ+p2253uGVlDlphFw1Krt8xecdj+Zz7nLLFCdrtE5d/7KZWjgZDJ9/oUbs+PDwlgx fKDf6W7vbMLW/OUqKct7B3vf8553HxyPP/fZz7p5fuPll5kD6unmaDLd6PXP75zZ2Ng6d+nSE9cu bFw4o9sOt81zL73021/84vbmZtjpedZqb5Fuhh53FC4tUrPCeXVJjzrAVt/nyrY9p9HsCmYm8YoJ b7nM2MKRuQahIT+n6vFHFfiqlq6v3OBf/fq/VoopBZQCSgGlgFJAKaAUUAooBZQCSgGlwDevwEOD uv7KT/9FQV1SjSTF6mLaqlOF4l6SKJ+wqLomiZIkQV1E8/gGMToMS6Vu/rc/8R9+9qWbn/7cZ/+j /+CHX7x9/zc+/huUVNFK3t2++J/+6J8zQ/+zn/70Zz777He9653vfeeb5vPRx3/r52vUddpFf4rX 6mr1eiCjFFsJWmHvpkujlBS302FFIg94I81FXpHoX3zm+uBkju0p8DxIVDqave4tb7525ey//N9+ Yz5dSNU7LKlaff8P/FDzwpDD0QZVzyiUMvZ1Hb7vUBZPQE/W59mE/kwhOpAicJS4whgLOC1LVlqP G4RS0Q0PeJJoJVxIwoDIQKKTLvn6zGy8OGxI3rLhO5jTJvOEOKdjuwALTaeii2+JEQyExQ7FnyRk akXakewhz0DK8G0NZzH1YRyUq8AlEBOTzJd0SBrifav7yiFF7ATeZcH4aKXn1fX8RIFo4MNcsOGK OjGoHfQqzrJFkmJnK5Z0YNmzReaRfxRAJdX48LDNToPdHo6JLppe7Vij1Z5voz5s0CF8CfnCdcdR MPLxhyzSonGfw3HmUqcOm6vsF7547zd/5w98Q7vkbb39yafr5jFj99ojSdONMey1Gpsdv09k0bSX adHzHc5nRZH8du+RR88EjRad7nv3Dz/5id995tkvTKYDUFRZJrNsnlZMiyyGs0mURJrtctMwQ2Br s7vR71/cvUhDWtttNszeRmP7iY0+5zPjsBYWsyU4cJEsxvE8zmP09+jp4hY2lrCqo9l8ECV01dtV NRmPX97fZ0CBJG9dtAm8sLnV2XAM62gxO3tmJ2w0QI3bG12j2+/2O1yHOIn7Z3ZWSdxN539wb/jS 3uETm/2D4fhzz3zpwoULb3vsStDf6m9vN7ptnH50RY2K1c3bt5598blrV664YRPbJO63jUbDdrxW q9Vvk7wMPZK0FMZxUS17UWkhtJSe/mU1ni6Gk2gwiaCQVzpSS9/dYQSEenwNBf4Pf/m/fv7ZZ7+m NH/tb/zNi5cuKdWUAkoBpYBSQCmgFFAKKAWUAkoBpYBS4JtU4OFBXX/5fyeoq6TKnffaJl4TaX0S K5M4mgShCAIS1xPvxKVIvaR/asWbctqpNnfP/Pc//V+8dH/4mc8/+yMf+fDnX3zp7/ydvwMUMcri 2tNv/Y9++COffun27/7ObzmO/hf/gx/9//7cLy7z5LFLsqt1Idepi0xYErgG4xX0B4MSqAumBtmR yCQfhgadMfCY4biZZfqde6P9vf1sUVDVJJ1hWmVN061rF37kQ+//R7/ze8dHJ7q5jLPczPPv/u4P tB7JBHWBDbAuSYe8ZuFmwjS2LNgheKsosUoxGVAwl+2QyuSBCU1atKRXS+4CoX0iAaRtHRKU0ZBE /ALfcE6mcZSkWHPWhjQQFUiLDvj6C7Pfah9P5tjjqPqStnEZlSht67y8Nq9xtEI3xN+GC8x3reE8 YyfC9FaVDFesuZosWxCTyYXB4MZ+4FA18CJcKn1m9TmjnqwRU5lrCXzjSyn6Qs069iltX63GgEGA NNATI9RpGZO+MM52s9NKCqFvhBUxiCFpM/C4Ori9SA5ibQs8sSZxe3CGhOzwRCEo5WScDxk71Itn xWc+8dzdl27TM/X41sX3v+W9B5NZlOdXLl6MWZ5lb57fWQUme6HfrUxLQE/g+ZbmnNvshTu9oB1y +Yd393/r137rM7//qfHgmIgjYdlcyzF+pctiHMN/KpAfNBHmRbByY6P/pje9tdPd6vtN2+z6ZsNa 6jHfKrJlPJNZnnLJVrMsIQq606UQrF0ilyspQIiWbnq2F7CO4/F8vqDULYE0IfpikXtu03UaWqWP RsM7o2G/3zVdY5pmDFeQ6rkiHY6GMsrTdq9cuOgFDa6SYyxfvHfvpbt3di9fed873tra3qG8bmVq M+YXjAeRHYRafjwaPPXoI91205beMivAFWk6gFrNgv2B46JZTiRYgozHMsah8vWAG1V++AjjFjk/ GJ2Q+jzt6q4MMFWPr1Lgj7F0seVf/Zmf+eAHv1eJphRQCigFlAJKAaWAUkApoBRQCigFlALfpAIP TVfXP/+VX5J5ghKdk0mAvJ2vnUfiu1pDrjprCBKqC4UkMCc1UHR5Wa7zuqceO3flsrFavuHy1UmR a6v8S88/Gy1mQAFMOSvLeO9b3nb16bc5zcDfOfuxf/TL0LHLZ2WU3Km4D/6UUiop8KrzgnU4UErx xUMl7d6W1QhJdNn+Z5+9+Vuf+L0bN25OjsdxvLh06eLTVx93DfNwcH+zE373028zLlx945OPXDtz cfv8ucsbu298/dOVO4CbAD44eU4b6uTh2DGXeZZChoRjLcnlSXk80yRtEoRS7MWKZZyh9FMhS425 QEC11a2OeNLItNJJ85E6pN0d81ddxCVnbdF2hh+KTi9plBcjnFjDmKIo+2JJ0gMGpcLBQ14tcJw0 zyVIaMggxcEskXNgRqTr8AGao9W+znnK5EHxndUpRXYkxfqntjsD0xYXa71AtqSZC+zFiZFHDF0X s5jvcFKckr7I6JWHn4h1C6pZO7S4RNi4Sixgoedx+dkApxtkkLVizBKfkZjr6BKTYCYnMKUKDSK5 gj6aTS9oNdrNZl/PjE994vOrVAu84B1PvuHKhcc63Y1HH3n00uNPWF7H8RuPXdzRG+EcZgNcLCnn txqNELX44rjgZqriOFsOJ0c37hzd2y9mSdMOAsttu16v0eqFbUjVdmdjt93pNtoohmfPctxzFy/5 jbYTNFsbW612m0ozx7M15gy0PKsVep1G2G1eOEtTU7/RadB2BcpL8oiRkkvirYtYA6SlYKTS0ayG E7SC5m67+/Tjjz117tJGd2u30YRKxlH8/W953Zvf+fYnz5993YULbyKsy621da5TVd3uJhlDxw+5 sPcHx/cODyzP2b1w/uqV86kXJPgTV3lqGItl5beaW712f2Pj2pVrF3Z2tgOopkf0dbzIpuPJjfvH tw9Pbh2Pbw+ng9mCyn8ckdayhM1ZjuX6rMniA1oJsuWHc7vpf5O/mF6TL/+aLV1fXqmuv//93/2a XLhalFJAKaAUUAooBZQCSgGlgFJAKaAU+JNU4KFBXf/yn/+DmmfVrVwCU+ru8drTJUBFuJQYkORb 8mW9LYMCbdMPG09duawZzhev33jjY9d++eMfv7zRfuHm3fnxYKO7yag7mMZIM955vnl8vHep4x8O J/CdZgDVOZ28+JXXA+5CgFJShrWNCQhj6U65tJelYYExSu1LL9351O9/LprFvIqKcljYua0tK0vv 7t1cDI53/Wajt3v1scf+zJNXtrtb4+Pjg4P7lmOm7kRYFIRlyYBIKY9fxOk8TrBTgYR4rOOZrAor l0xNZI4iTewleUQCfBYRRfFUGVIiD3cDUmGsqqGWEafZPFogj+9ZgC34IGanelykhDFhagQej0bz MKDSnV3RyiSz9mq+JKVe7TqzNk+kip6/M/kRUcgYAsISKc/SJOOoab5LCpJwpVwQNmAhlK1zEbgc cEPOXyCUZN90UBQv4FWQLQ4lMzFzLGC0fS3nSQFBk8ypuMlOC+Y5DWBW6ANSWJrMfeTKwrYke1iH OTkEMxzZEzlHlgc1qyOtGrasw8NxFRW+6XedxtlwwylcLVrZhtNsts5fuJoHLcey3vrolYtXLje8 sOcHXtNJLX0YxUkeuxzJMl3PeXkWO9pyzhjKrOCiVJPJ7Zsv37l/d7aYSQO+XkZFLN46AyWRjXEJ SxSzdCOHmOnG9u6O5Xmm7wfddhmEOair5S/b7aLh0bdfNoIyDMKO74ZO5ZkLndL8xaKKx+VyXtDA VS2yZH9CQHBKs/1oOjieDCbz0fHxyXwyPzo8OTg+2R+cRFHsOP40q5LJ9GQanbCLvNhw7SfObF+9 ePna+XPXNvtnmSkgei19D7gYdHvdSCY5rHiWAjC7KLh/mFXAnMydsJ3H5e390cF4MZ7Fs0W0TOJo seCCcvn6Uj0vpWzM0KThy7QDDHsVO1jqUVZBCYHMlHud66kA41f/Gn/hhef/zt/6W3/ML/d7d+/+ yI/+KDz3T/I/AOpYSgGlgFJAKaAUUAooBZQCSgGlgFLgtafAQ4O6/sU//4eifo18hGTUvipp6arJ Fqmu9bWpO7vWmUapnZKtTefamd3ZaP7sSy9hQvnEb//W+W73mRv3krLAUkMi8Ie//72PPXrt+vW9 //lv/CJ04899/wefeuLxW7c+v04u1oc4fchxOUA9rhDCgzXKtbyEVvCV0fXDaJb87hee/+Izz9FT tbG1tXX+LFQgXkRVmQ1PjmfT0XYrPBO2nUbj7GaXHbx8fHJw5/p0MqTIPLIHOYGwLK0AWHlGCxdA CvZkYLyidowxk+LgonWekit4TrHE7aMxMY+6J7xU0CVsXTp/DV1vu9V0LCeOy5SScBmBKGYzOBdq QScclzIvrE+aZTHMEJ4k7JDQHyQL+xjRQ5KhEDosU6yb9BrxQCgPuIsYIX34BANl9qA0oqGDLUMn 103zMqGPUv+lGLk4xdUSkxp7RjEycDWTlCIwThbjT+DbTOvjKdgWNWGMrMTvBYYBodBJz3I4LsY5 yB9V5z6zComFStsWVecS2gR3waI4PldBsCczENl4WYFZxOq1pEBLDH97Nw8+8+lnrKg4vHU0P4ry 8eoTn3nu5u3Dtt+7uHNh+9LlTrd382T/5snRLFqcLGbzyWiRxtMszapiUeZHs/gwSbqtxuF4fG84 4iIAsO5P58PB6IWXnj8eHc/SKFvm42gS5UIk0zyRqQBZksTJfDYlxbfR7m72+iQr7x8cTaJ4sVpN 03R/Oj/Iy5FhH63Mia7PdG20zG2GT5rF/Wh6MjlYloOcmvc20xGuLVutuWPObH1haDdmk2ePDp45 2n/25OjObKZX5dFkMMoWQTvodDsxWG0+Gw6GzGG0yqRRJuPR8WRlkpCNF4tkNrtxd+9LL740G02A gkArsCApVuglvq20NAbDyYRm+7yazOKe7YxPRuNomuQ44wpuEkZs8nPBREsdgCrBVSggOV6ByUbM dAeZ0Ei9GxeDeKVvrxxL32or1PXV/7H4W//L/3Lrhgy7+GMe73zPuzc2Nl97/5lRK1IKKAWUAkoB pYBSQCmgFFAKKAWUAn+SCjw0XV0//VM//kd14S23cKg1lKo/r41F2InWvV0yJxDgZDn3j46x8IRh MJvNn3z0ys27e03fS9PseDxp9LpeM4QbLMbTjbO7FDRhTXnfG1oC1uosoLy1rx8SFMRAJaPoJFGI pUvm+7kNf2mcDOM/+MJL+3tHzTCAIzit5n/4Yx99+XDyD//uL3i0wVfaRhBsOC6xODPwmxubM2Jq eQxesMpiu9vb/K4Ge2aUIP1YddG7ZAkp3JLadAYsmpLg44AgIyAQIUUMOvVASEfXXKnkWkm9l9TD V+KNEldWBdLyGJUYBICtfAV94i+1AY5mLSmHhy1JxJE+MEY0QqGQS4q8cOycgkQhejAYxiPC1Kjx wgJmsgUms7phSmuGDXxM0ygCkFHQzjmTWOSKrKdk1keCkADBxE4ErZP5mIbO7L5e2/dtexZlowm1 8dJxtoip39IDqYYSmsbCRWhNkBaoEsFpYsfRNYoXNNrj0uIUOTmwHIvg9Fwb0FZEaRm41P/js7M8 XX/5mReXcXp25X/+xb2O0+g6Pdqz9sF2QWtjd/PSW14fNsKD+zdHR0eQ0Tc/9bo2SLDVnLX8GcMo s/linEzz4uq5nYPDwfD45NzWdm9zW4YEDIf3n3lmdHxwb0ARW6QXxQaNXnLiBhY1bg2gGIlCzjrc 6Hc2+sfzwcv7R8lSc3vdja0zjhva7b7e3khLpkmajBCI4vFGaGz75vHJ0fTkrqUt9NbGh7/vo295 wztKc0WZ2HCagKUS7Hl5lsCjysJvN19+4W41HDPYAK7Ux57mBlvNJjMJOs2m5TVhvPcns2K2h8Eq gd2V7GcOsxsncWabQafZ2+h2tzYaGzs+CcRGczUfIBsLOImLnd5GFkUlpjJT82yuBZVqpUkaUzMY 71A7C1dwUyxhYGAil3ScrUyPGG8SxWkiNwObvPf1l/4kf4t9+x/rzu3b/+V/Lm2Df/zjJ3/6pz/y kR/6elup7ysFlAJKAaWAUkApoBRQCigFlAJKAaXAH6fAw+Pq+hgBxq9+SEm7oKEarIjTq66sWk9j hOPIE1L5BCfCKyW15VJe7vIunfIq/sSkAv+CJQFhKEoKPZucXBpF5OHObUvZUF3IJX+eHlgikrI/ eUpqu0BMukfzfZx/+osv3b97H1a1mM/nUWQ3w/e8/72H48XLzz1TJvFGr/eeJ9/xg+/64He/4+1e f7ffv/DYhUdxfqWDE8kcWnr7MniCain2TooQ44xMNgT3UDJukPOjr12I20qwDzYbmNSSfCKsitCc TXl87TSD+i3F3CWnvSJpmBaF4wDWOGXK4IFZgCh2KWMixZQFqNA4OJxrxVxLNlqDPYk0Oj7gC2bI aeQUxFOJL4iKLiyxXOGvcqSFy+Qz3fPlUsPuhP7s13NM4XErzgqHlk2CkUuAFwwDloujjN2zUZEN BuP7e4dUydMG1QxDbGK1FWzZaPqm6RRUu5f0qUl81DAZ8mfgRIMvyn7kFE2XNnpNDFT4txCGHCaY plq5juaa6FdqDcdfLZJoOG8v3XKct5DI0lJq3Eptc3v3/LXLiblcxNNhMdebjcWq2h+e0PTV7rbb m+3UMpl7iBNqmiSLJFkPxGTMJXHPuFpyfsAeW9MRHXjYCputdisMfUrjMuCjYdRBU4tOd0lnoo9j b/Z2L198hLKwwb37+XgWLGFZWXwyzQ4Hy8Vcn0fVNOoHfaKvg6ODKl8uM4GdZ7bOp3k6Ph7Ek0ka MeAx5m7d7vQubG5d2G7s9Dq/+/ufPbpzs0rmQbvl00PW6zTxKLaCRrexudPubfh+y/a32hYV/dwK gRV0AlAiozoJiLbbnY2tne3Nbeq40jSPiUBmAlFxZiVZ1TW0YsU0TXsNiwUf80PGRWCyJOpS+CY5 V2427iXIXwD5rG8naGvteCSDajtne6H6vfuVCnQ6ne/78Ie+/wd+8IMf+r7XP/30e977XkLIB3t7 fEFu8ZUt4zj+0Ie/X0mnFFAKKAWUAkoBpYBSQCmgFFAKKAWUAt+MAg+Nq+u//ks/9jXXSdhtjW3E ZVVX1j+weNVt9dKTLoMRISe1E6u2fgFchCpBXlbHg5HJu3iPvi38Uzld9rxt93z3A++5WO9WWFeN ztbv+tmPzCU0IR/SSgXN8PJo+fz1vRs37k0nUZoVfhgyd48I3Y9834f3T8a/+q9/NZpMzvQ2dz3P M9yG49wYnOCC2ex1DZN2Lm2zt/no5cv7my+yf8JgTEeUGYVEwoR44F6i912cVFAUOnxqqsVCxEUl SxbvF1E5uy6wkgyiYCw5U1iEyark72a20oVkGLRUEUKTEn/ZSLiT5M74l+2lfYzjkF6UoYK2mxUF JEPcYUQLNTMtKsfzWp5XQzHJOnJutbxiLRPgZ6zSNCXCyAFlap9h4C9LczxHAkQ8x0ZHSrvgQzZE hXimEDCPTrHdfncwzbhsWJao8moFdIO5TEWcLFKoSbcZYn4T5rbUmCyI6ws7GluvuFI0qTMyU3Nm E2rbK+ZJhr4dO1TR692gNT8YXP/0l9qavbkKthv9AkOf25ykudNp9/obn7x9Y1bG7Yu7qantHx0M 9u53vfDa2Uvbm1tmsxWjjaXbhhkVBReYXrDpeEpHmuP65zqdM822k6TFfLE/GFRpvphPIsKx0wnX DBHiMkNdb2k4hrm5vdHd3jp/5vxWr0vY8ubhARQUxxncaVRQ8VUmepmV0crVz125qK3SZ5//PEMp 4a9WaLjbm5XPBE4orRP4AcMftza6/c0zm5vb7abhZtpnP39zejLcbjfoswcEOlU1nKdiBiQMqWmL xexoOhvS9jaLErrtqee3bBr090ezeV547fa5M2eCdjtZrSZRxP3k0OhvkR615obz9keuVu1da7RP zpFbkbMoNa4MhXIIUi3SzNZKKetiLIJp6aVVyo8F57kS/2GOTU/up3c8tvXN/Fb6Tnjtxz/+a//j z/7sX/sbf/PipUusdzqdTsZjvlj/VT2UAkoBpYBSQCmgFFAKKAWUAkoBpYBS4N9agYfG1fUvP/YP a5fWH/qoJ/zVaz/9htTR1xzrwUfNquQJ8RcJJZJWezxOYkSS2YlRlGC4aDUbNv4uG+TlUiPVajV3 Nz0hRqeF9w/klWqqtS2KoCT8BbhhjifRM8/dripze+fM5WuPfddb3v7GRx63TfsPnn0+m0ZNzyfX RW/YNCtuHZzcOjym/ehkOt8fj48mk8PR9MWX73zupZeeeucWxe2Uc3Gy9SREUoqcubXigy/lbCm9 x1CF0YkUohSUcfIsXrasl8jZ1DYo/F9Sa8VGPAEkMil+0nLBW3WpGaYrloVNTCre+VgZJBMFYHFU QUorlswaWSQ18xyVsY8SfTRt/hDnF21bRNRKbEyoKbSQg3FoKaivU42stE6NCkdDNTrm67GMRl0i DwyR6n3Zlenwr2ZYDG8UL5D48dAVYqX7FkazCqTXCdx+s0EtGSeW55VtWGdbTU4wTRPZjUY2s0F0 Mplnq0LzdA+8V5lLoExgefE8O7h193g47fpt2w5Lw8F1tbK03kbPcM1pnmT6MnW0Z+7diovUc6zZ aEwI0LehnkwYALyVDtJzsy2rjutyBrMo6oV+LwjpUQPxwOByZk3ScYbfzXaarg/7sYGLlKIxPxN3 XaUxmdBy/Har1wpau63Wk2fOtP2wzNmzvt1obvfbDaxupr3V756/cLbveccnQ1xdtu4w7DHSpJ8+ E3C2zIF6gdXpBLYP9iIwS6N/2DWNs3T1m2ZWaE0in4vFi3f3hgOcYYf39/duvnw7Oj5IT46i45Pl dK4RYlwkdw9O9g8P02SB3c8NG5NyiWQut51HMX6jFfgtx+RqbLQarh8ss5SrAsYKhH06muPzWa4v rHdVuZjsyImSv+RfKuvqKQYyFqDi/AUQn+krV9fX+Z1869atT37iE5i81uVckHB+EfH4t/5Vrl6o FFAKKAWUAkoBpYBSQCmgFFAKKAWUAmsFHjJXV50n+4qH4KEaYwnE4VEbouopfQ82EhoDkxL7EZ4i 6Yuq01bURsnzyzl94TGZMRJpRAeJ+ckkQ9/3vuedu2wmtiZwxys70xgyqMOQCHLV9ig7sMPb90cv 3xw8cvGJNzz1ukceubzT8O8ejg8HA8CAx3DHvHJWy2ypz5Ls+uH+yYAReel4OpvNpgzlEzZASDBN fui/vCQXg9p10pQ1rKvzcNjLWnhrCOutVpnnsslSjGWkAMuKBKW0t9c5Mhie+MCI+RHVk0ynxPpI JlINr5sJdEiMXBXopj5IvRkLWdMpgo8AJl6DgsxVZAtcWVjc8Lexa1xhRBXZJywLiiSNW2C+Uhrx ObpoIJMuwRw0NGH1EizFhL6W70ZpEeeFb5t4uzBGAUbIkAqkq0vOTNuDf+VSDqZTYM9pSPc+ZwzK EqNYPW5A0yAwUVHioWOKI/41pJCrJrHOnNgnwT3SlvP9E8+wmnZzf//uYHoIXjq7c54w44tfeimL k7Ner+92KL/ndARldvqlbx3l6WhVTp3l4eCYcjKAUXI0ONPqbvU2abHSvbBMctKR3DWYzDoMDciy jWaTuZx0ul/ssjdKy5b41pIsu394ePPGzWg2w5VG0zuTBYzEQPosigl8tvu9s+fOndna2Nnaavfa ZpUfHB1N5hGhVQq+jMLmhrT6zf7jV5Dl888+Mx4eGZLezLyObrdtaNjG5qYMO3QdxwmWhldpFqFT ydYeDiPsaPf2905GDEXseO714z0ycbSVTaL50Whoc0Worp/By5YQPM12V164eeb8ucuXts+cO3/x YtjvSxK2Igi6wtXFv4mmUR7nW1wRhl1a8NGkWHaoaYOXNhpc3ALIWKB9LjlYzVyaVosZB9xp9czN hCECRe7W7XnvenxD/Yb94xVYt3f91Z/5mQ9+8HuVVkoBpYBSQCmgFFAKKAWUAkoBpYBSQCnwLVTg oUFdP/W/lwBj3Z30FbQLOFV3xNdwS95jS9v6g2GM4heiPIhZg2JGkWmAwmkIeUl1V4FHinF+jGm8 uz/IebNem4/E8GWZm/3e97zrHHuDdNWvWgsuRV2QKFCXTPwTK5WVZfqLL+5NJ/kjFx+/cOnyhUtn KQ6fjhfQK4gPj4bf3mr3u2E7tPXRfD6YLNxKT9Ll3mTesC3Ds4AUAK5nM5kvWRdeCUMDxwFadN1N MsNzPAqpKIOSCYRiUBNYJ34uSRfWU/DkxKBOGLVsGJaUKMlzAv84T82IDTNjdbJucWCZjEkEsIAq YDfIhSXLhojVcU+IEkgrcJ28rGODK07C8jwf4JWmjCC0CYuyPee3dpjVvWjCEHGczbOME+KsEAoY BPcAf7AUsaTp+iKO+RqCxV+FrNlOr+UTaaScHmQmMzPZWya+IPrnjSXt7pwxPElPyszSQXuE9Gh/ 96WpK+hMoxnfarY7Lce5/qVn266p58vf+Z1PGfEoL81HHn3ykXNX4vmKaQBOutRS4S8YvsJWu719 PthoWZ2m1mvta9VnXro+nU2pkd+/dcM3jUcuXAaTNZpdTogGqkKGXsrdQh3XTqsxm6ZmWaVlSYEY 9w9YazqajA4O5oMhXq20zBkYCSFkHAAwUG4QbGSO1dvs7+5sNrvtsNNqhS51cVEcszqon2u1Xcst m+GVtzxtBuH46Dgdj91C27A0N0gLBhy0mul4tDc8cfBMJfl0Nt472T86OaEPziv0dDg/2j9Ok8Rt N9qdPq4rLjzmoFmRRWnqanrP9OarwvF95m7mjpc3mtfe8PRT73znRr/rNFskWl++f/f3n3txPB48 9eijZy5egsotsvx4yIQGWVHoGH1TYzQjGpzb6RqOGXPajB2QMZcQx5XLXAapgpPoKwiUe4m0qV27 LN9+pf0t/CX1mtzV0dHhf/LjP/6f/sW/+Bf+wtcYuPGaXLJalFJAKaAUUAooBZQCSgGlgFJAKaAU +JNR4KFBXeuuLvBPnXV7RRxQ1+nf1wBMTEZ1c1ddscVzkDBYjHR1rZYFccCVWIvMYlnAbYo8Yexc lJPbAnnhWsI5ZGkF0wu1d72pX6MuwTRfdnWtdClDF1fXUrqqKvNomH768y8eHow8w/f8oNtrHx8e YuWCvQUNPElt2w2CsNXv9Lfamy3PBy+d722uXL+0nW7oZPnqYB5RbTWY/X1ZXZ2+rJcodiw6mOjq ogMd0EFdORXwMjlRjGuAKZZd5xq1ZR1gtOi9AmDJzEMJJ657+fmU60z0s1Lp0F/iyUIG/FjUvLvY wiQECK2QLnUGOEIx2B1+Lp3MInFG/DpFKaVatuPFmJxAGziKBJmVogChRjHKMRdS9G42PHxNKCL1 8nUhmhTIGzK90aXUiSaqPAsdZIcdrUhGSiqTCGFO5RaRQ8iSkSaVkQlXCuiq99odP2RbRhkuksWC uYFFRfe647f8oH3+0iM39w9OprOdbq+5Wr7whc8sFycmJe539y4xTzJoXn3qTb2da89ef3k8i5YT +qqG2w1v98yZ3sZO6phT7G1eGPZ3jix9f3C8LIrxbHJw99YyT8/0t85vneFKUc5vWDpzI5dZQcyS JbQDfzyaUBiWL0va3xnFmCeMIFgsRpPpeEZhP6sqyIKmVe5IDhMF6LoqllXYCntbm2EjCDyT3n1u DOYoNsvMTsoQz1bQXNj2hSeuEmjE3ValRnV/Mn3hmefvfjJOZqHTyBbxy9MBxjo4Jl35R6N9NLHs cKu/Q5AyimK0A6L5YZvmeIYzJhpFaHUWNC9cw42NqtHpWkHI0M/2xQtbF89evLDrNJpzK8iyZHFw +3/7wjOD6fipJx67evmyieZZOmNFk0kWgT2LxWzxmWdeyKPoyiPX3vj4o2d2drirnWaHtOYszY0y Jbsp4zjFHrYCk4I3HanO0950VgUYv/4v8A994Hs+/IM/+N/8t3/l62+qtlAKKAWUAkoBpYBSQCmg FFAKKAWUAkqBV63AQ4O6fnJdS7/uiX/AutbmpjXiWi+5HsRYB+/qv+HlooiKEXz6Kh9Ph0yps/QN CogMmxr1k6pMLLPle1tHx4fbfUsz85wGKCMs9O7Tj8ge6pmE6yxd/aDgPWH0IRE/IT3l0to7mH7u Czfns4RIGuP2MEuRxvM9hxqpRrPNn3GSTCZTgl1MMqS4PYtz3wtpZLeZX2jbRMziFDOU/tE/t67x rj1ddfk9g+2AF3iDKF8HFkESyirDtcZJldQlCdgC1kG6qIYSyFUnGinglxIuMa9hYqMqy2J4YrQ0 Y8FjSxKLLp4k3XAN3aXpvqhWFMabTNUrUtrpEco0bdv0EDXJiOEB0SosV5SXTeMCQsYrsS6B2lCb sibIlwQP8W7BxTwXDxQmKNSXuYzS1SVwMS2XZO9sx0iytB1Q+LWkZV7SeHLqq9FsIU1QNZFMIpKe Nt3n5srYdMKe7cC3ptPoYHTMCp949KleZ8v1wnNBIw36H/vU56fT2Wa3u9vxP/ep3xgd3r7caQTz 2dksThk4eP6RYPuxl/buHQzH2Wx69/7t7bb39LVHdzbOryxvmsVT8oleq7G1BdJKltXB+PDwzk0a 7xnCudvZOt/eXNHSbsqgzSrNcjx4uuZRwJWmXC+q2n0uri0N/Rnf5SOvAvq8DItLucyrRM+nGN/Q wHbYP6ir2evSAVaWieFqER66PKZLrJ1p/Y2zncq5cXC8pxVef+Pq7rlNuzG4dW/0zB+8fOf3ynzR a/SYe0hvV8MPuS7UjY3iOdDQanabvc3S9rDNkW7VsYaZ+iyFTqGu1PZHBYhT5nGmht7f3mYi4/bu 7sb5HW9zs9/vt5p+YTcKyx4P7n3is1+aRbMzuztXdzaeORwPTk7yyWR4cHh4PBhPxq7tlPDIKKLD vnfm7KVLV97/9BsuPvroynWxji2SynfDOswo8zpTzmZZ2PWP3lsvtl71r6Dv3A3/L//n//6Tv/3b /+rX//V3rgRq5UoBpYBSQCmgFFAKKAWUAkoBpYBS4N+BAg9NLf0//9g/qOFVTZweCFEbt9ajANc9 6DhMpLO9bqUX6xNJK9APGa4nnrx85cqW5WijEbE8MAv+ntSxCsNxqL/qd4OnX9d99InmIxfbUbEA Tp3ZaK6NUVLkLtAGV5RkJ8FOFDbVhiqgEt4rh077K5cvXb508bFHL7/pqavXrp7dPbN59sr5Vr/b 2ehSdJ/jJoMzGdKFDzZyPeYb5piGSgrHLZOucc7x8Ucb9drohq+daFKfRaMWfVgyu7AumJeZj3Wh lgQomYDn1KMnT6cgAog0g8WSbcQ3hUULkMZQvLyIcBehDWcN3tLNwHR8cYqBqrCl6byETwwZtOUA oDKLQYt+vxlKURfWL5eScTG1QdzYsO6pl9JxTkLMY9jMZOajUB2WhhuNGJuUiQlqFGsYoTaooKyo DiMCYiTVWMicSSmbZzVkSPEmyTBIsyp1rFKcSTRP7t/bf+Hmy7fu3rt7fz9Jk52tnXc8+cYL/c7+ 8eD5G/efeeH2733q91++eTugBD5wNjyrF4Ztqb4i/ze4NRrcHI0G0WKmLwdEE1fZ0XTkOO4jFy5u bG2tLFxqdrPRCHmJHzQ9jxuG3J1bFGearX7Y9CjkogWf0Yh5liZxEi0ovfJoQau0jukInzM0RGxS f8UwRaxwGK4wWXGhqPbPcg8VA4+EH8ZAt+E1++2g2/Q64dKzwWelraWrEvcW27d88oxetRg/d/2F weDwnN+wo3z/xguTvfveaFpVebfVaXY3LSfUDJ9xmhT5U9S14l6mNL7VsYMW8w0yICXXuMhjjIRi naPRXpK4BZzLtkpSpmFoNZqNXt8MAyrTaOPSXU+jc0ya1nAllqMI7Ge4foM7Z8QQwNFoPBhOxhM+ KAWDZkoIt8lwBZ8pk4fHRwDXdrvTbLgrLiTzHqGY69kNQpvJmdJhRwZ2db4nfWLq8ccrcOP69eee eeYjH/0osVOllVJAKaAUUAooBZQCSgGlgFJAKaAUUAp8qxR4eFDXP/+HdSBx7d96ALsANaeWK8jW +mnJ6q0f0C5xGFnGhfObH/wzT547G9w/Or7/8nRVFCeDO4a1WJLAMuEIzjvefOHatdx0xxfOdHqd 7snJvBcG4hY73edabUE2EvGTHUsHGG4lfEB+o9WEWjSbQeg1fCqrYura6XofR/OYBu8yn8wXJ4Ph 0XA0my8weXFCcZpi82m3m4br+A3/3Nmdfgu4BEICpvgrit0tMoYUlEtfVz02siZ31CHR1qW70rm1 EoMUVEHquNhAwBjIQTrqwTQ5KI20I54egzl6eRAE/faZ0NuEq1FBTptVVmlkNlmM6/tZZc8SitT1 oqjb+2XPtE6lBNwIKPoefiabQ2DygmfRLs/qwSky25GBfZwiZezkJKX2nvImLGJykjxD0LOesegW uVUWFogoSkoWFSc5IwCjVHrvUz7nS7xQFa4k7FI1/2IEZhc9Wx3N9c6fv/DuN731nU++sdtoffrm 7S9+8ZnRrfvpnftukm+CuVzrmfs333j5WjKZHr348mD/5P794/tVNTe83HfnWjHOyfoZ6WLRsNzH zlzOjdXe4mCj33/LI09ubJ3bCRtNmtcHE3M47i31q83OlXbHqZbz2SiP4nyxiMdjPYrsPNNpijfL wi1KaJVLo3zouK7OyE7XCgMndGUOI3VVdK5ZRBerjHL6SitczFMNd7nRdjqu4VQNH5scjWM4pXzb awYbj5hx9tzzz8XHg10/3GEu593rzz/3GZxsgNS5s9Q7jbJhT9L0OI5mVRGvcIRJdJby/yRZTbKM QYqQR4AmNw5hVFK4IXyz1KtEaOPSMhzXsIEoYYiFEDbJN7mFGXBpuT44eD4dHWXZW89vXD1/rtlq V4ZFLNOx9N2NHv1e3LHcfK5JARiXXsOV6OgMubSODw4m0+nu5jYcDtoFGCVkC6xcoy7qutbI+ZxC Xa/iN/RoPGII43e9733rIYzqoRRQCigFlAJKAaWAUkApoBRQCigFlALfEgUeKtT1VZzrAX964PSq +7nWH8LDhAABkJZ6OV2MNS8/eya49fLe7VszwE3YMp58/QYxuuloRQnUO546EyeDX/3k7cLKX3fx yudeGPZD6dteT3Ws3WJ1+b04oUyBUnjJDMMy7DSH18iYxRR8Fc0W85PjwztAhMl0Tnf4/sHx/XsH x8fDwWAMlWg1/F6rNRqP280Wniy8XZajLxbzyWz8xOW2lIJVWKWEc4F8cMdINLAu4pcCrzoYuMZ3 pBYBK4QU6wp4OIewL/4VTlW7sRybbnRGFMqrQ9+lzT3OjLiEnUmqTQhcodHARQgT15jnhNSg65XJ CY+n0/F0PJpNjFUVeF61tJJcNKSLCXwjljmOvZTG+LIsaUnjMylIGvTrAjBL+FlByRejGingwhVk gYno5nJsvxOADuV0KQhjSCJ2Oxc7lLBImzCnFOSvmGqJSc61/LDEWtVsnTl34b3vfd+1a4/ZTlil 6WjMaMGkRS29oCUNZxFY5TMvPDMYDveP9l8eHBzPx51mgBNq68xOC15TJmPw4slgPh42Q3vb92Yn o1vXb8aj2fBk8Oz151946YW9O3uHey+PTu7R+k4bl+c4xxCu+VzGQZIFzHOXqZAZcwgp3cq5vmWc rMTqhUp4n47maRIx9JDBlMIaoaa+7nvmdGlXFjjQc1y091yHiGa34eGJi+sLik+KJ9vmcnLzxuGN G2GeNX2sY4soOuGaNhudoNWjSN9xg5bTpwGLGZQQUdcJmBXp2r5lOly/0jAyrcrhi4wikKsiJBTE VaTLMqFNTcsMroRBcz//2LTh1xx0lqbTJB1F8XQ2Hy0iHIXDVJsWq3kCD5wfHx3dOzyK02ISpdPR iHvVZRpCmkCvuMYR25NLJcKZJRu7565dvOSYK4tVyzAH+fEAZ0rpmswBNXY7Mi5UPf54BXBG/rN/ +k9e//TTV65cVVopBZQCSgGlgFJAKaAUUAooBZQCSgGlwLdKgYcMdf2RZZ+W0Z/6vE7jfK+MaJR+ Kt6Jx/E4CGZvfGLnxssn+3tZf3vr8tXuB99/GcvS7Tsz6M/nXrrzW39wazHO3/CGR0N796Ubg17o 1OSIxzqeJY+6CZ7oX42UltigMCLpdJdjhKrJSFqki9lssoJ9SbyrHkGoaTR02bbFCL4+wMO1i6LA drWgbz1LHJeSK3c4mbzxsX7dSQ/t0imBwplFRzkU4bSMTJiV1NTXZwPQAK2IswpbFuSIBnU+424T IkW00MLAIwwLD9fKCFpuM01Ww/FiOltEMZ3ommP4DWJxmtPwm003WBVGEq3Gg2meRExnXC3L8WRE zi/wGmHQdqnSlwJ+6s6FYsiHNKFJ3BH0Br0ixsdkRsCaudSdlenoHJ7KLcAMCT87y7SGQDWqxij/ X9Jo1bJ9W7P1JcyGFrLAAu35jaDVb7R6Z7Z3Hrl0+bHLl3Z2z1+9cPGN166eaXXLgnhj1fODq9vn Hzl/5Y2Xzj7yyOtpnArbTbvb2Tx3brYYvny8P5jN/NC/3N/dbG3iN+u3uucuXmiGrS6uJiiRYfcN N7RdQoGOVt6/f+v+nRtHg+Pj6WAaT6MibTf8fr9nB/4wzyZxlEASSalaQJxKYqK6jCwAQVo60T8o JTCPYZAeMAtDEyjLrCrQzqZlB4a+0W33mv4SUVyzHTiBz0oJgnrIE7pem/MhAWvTvm8t7t0+Ojho OBbRyGyWck94S4ARdyw5wJhEITcVLfiYpgBdbd83EBaLXd2EhkGOaZd4CtutVrPR8m0XToeFsWIE p+VWrlWZlYXRsNOmlIwWuXlRQOVmWTqP48nweHB4OC+rRiO4fWfvxnPP33np+o3nX/zdT3zi1rMv 7N/bP9k75O5yLYsUJ+CPajUhfnlhAiIdpyqLRrt39cK5wvW4++ReFMIpoxNopq8tiJpCXa/mFzQ/ 1H//F3+RuaDvete7X832ahulgFJAKaAUUAooBZQCSgGlgFJAKaAUeDUKPDy19D/5F1hPXUj/5fmL eJykKGi90FeeXs+EE0RF2mtJJRbtWI+8vvuRH3j/P/7Hn/z87x61+u1OL33rW8/euDX617/x8tXd K62mO4v2z+x6H3j/997aW/yDv/8bH/nAG9gltUMQqzoiuTZ44V6iIp4MGTYrQmvMPaSpndyfGK1M A+tMhtuGurBFaVquN8mAVzamJ4CI60BsimSRB144GC4S2sWlYinrdTtRHL/jSZvdL+nuMjza30EH QrHgaCubfiu+xfw+jcAYIxHp/NJpQ5LyMMq76CPD5kMWEQxHqDLOMV5JwRIHZiQkccsyyfcPh2ma w6AMrRn6QC7xYBFdtDzOabV3cPLs8y95+qrXCcQqZBRFGVergoqnMztnt/p93ShOJkOT8BzpvFps GB7JTMfxLHJxOu3tUmFlr3R/SX073WdmtlxN0wwE1Gt2qGjHTgYMJNVIXVmDB6Ym3/csuxV4KUvz 3Y1mYwcWSAWVbeaVNpul1lILTXs2GBVx1aVzSgiPh7VMT0ZFZuXxbJAneyuj1Qv3otHN2zc+/7nP j+8fXKEFXnOmzDd0rSuve8NuBx7mLJMiXGrbLi1pNpzmZHj8zL09szB6m7taqzkq0oPpCZX7G5v9 jV5vlBQn1Lnpq65PhFWPZrPFeNxY6V3PBcoRslxJVRp1aKhtM59xRiyTMra84lLR4cX9aUIztRUE c3urt2yEVqfth+FGp5m3GjNpNEP9yreX7sq99Tufck6mPU07ODk4PjqgWKwoYnbuus25pjdaO1wk +sC4zOmS+QAMATCIEGLf4rZZoKdlgLEAkpxNGmdJHGP2yg1nFbQXy3weT/Sm2+x23HaDW3NBuXye yaRRgrcyXtKyG63u1sZiOr93/948ipi6OBycLBaLZqvTafUoZqNTbDYZFUniOnYUkYo0O+0uWcis KPqb23/2+z509cnHTWrKarsj7WQEKmPmOuBn07S3X1K19K/m16/2E//Rf8h2f/vv/tyr2lptpBRQ CigFlAJKAaWAUkApoBRQCigFlAKvQoGHDHUJ4vkKqiXTFtc9QTUGO10v/V31ZjSvi/fJABPEV57o fOCdj/7TX7/1zGeOAj+wvcWFq43pJJ4N7abfy6vZY69vvfstjwzH1d/7x5+cD4uPfuBd7AFbFrTr FZYmsxEpiefNvPi9ADzk8PBhAajASCY98BR5EfSrzVU6swqTnEl8UqIlRUlECmFmZP004JcYtGTn 2hITFJzA0I45nK5TD2+mxdLF+ER3vNTMg1TgUZhlYFtUj+PxAXVxUGnK1/gLHAknj2nllG2B2vBY EZtjQF6xYsxhmpV37h5lMyJyHUrYi0KPc8Jtpgd9aTAlMgDaDE+mOL6SxSQt5o0GLi67LJLh9CAt k+2dnUevXgOPTWdjPF/kPfEoyXjHul1eqpwoShf2w8noAqd0Mnw+JwgNAnXRFtYMm+Lacvxus3mm 2271ev0evq5wlUe4kkwp+aILH9dRQEBTLhvEZLHYv39wcjzEBxUyITK3BgMozF1NQN8qThfT4WCi F4Mii3X97OWry83O9nZ/ma2sadyLMj1LkbwZNLr97ipb2pxFRZgv0WIa004GR3vjJNILq9M5t/Xo o1sXNu8m86PJkHwidfFBoxM6XplypJxMZQOsGSXpyRC/VZmxYBlRgOuuMkrMfSwKTx63AtM4DZ2i K68yLe6VyWJG8b4UlVlkM13b85eBu3Nu8+K1i2ZnA1TJbVVq+eRgsvfs57S4bLt+dHKXTCUMM88p c0uX6WqYJxal+SGySHxQ/ITcbNXKkXt7hdULrrRkZkDoYb4SD2FexTFAbLVx5sKFx54cV9XnbzwT F4nfaa48mzK0BblL7gluMqxfNNNXDAHQGq3GcDQt0mxvj4qzJcaweDHjtLc2d8rVcjoaAlcBWaQU cSYCjvnBsSjn8gItiZ963es/+sMfDTtb/PxxJ9Zl9BWTB7irOcibLzBmQT2+vgL/0//7//UvP/ax X/rlf9Rut7/+1moLpYBSQCmgFFAKKAWUAkoBpYBSQCmgFHgVCjxsqAuq8oeMXTSfCyKqW7G/bOvi rXeNv2SGIb3q03j8ujds/3vf9fS/+OTN3/zXz7davc3t8KMffnLv5OhXP/5C4LQ2N+0PfXh38/zm /t6Ncmh/4flF29uo6ZlYp75sGCM4Vk8ZxMVSawu2EFPLaZdXTd1Ov1UTOXlenmILqZeXs1pKwxdk RMCcPEPJ/SrJMkOL2RpihXemPm/8N+ASq8C6I3SjoO+9BlxsA6uSyYlgIWF5SyxPHqMMZXcrvaoo H/eygoAbheL2eJhEs7Ss8JQFLT/Ab4ZLqchKgBaNWkwwzLOMUYntZtczV1m5wAZX8735y3fvaFWx 2Ql3zuxun73Q2dzSVtlsdpxHU1vTk3k0Ho+8RlszQurpGfRo43CDoGkk8wQYbXZ7mW5u9dpnz+9u 9jap74f3YUtblnmZ0oGfVauQrF2aFlUSG1m2TIukosYrIzB3f3//1vMvnOzv4zSCrLiEIpeglclo MQTpSG7UXSaOkxpWpGvd7c3O2bPG5kaz09NGi91UawEJQXKBTRBxPp+M7902x5MyWdC9llUFWT6P ln6vsUi1ZacV7uxEnjVfZuN8qpt54G/sNLa5oqbXzJNJHkVupp3xWkwihB8CuPKKiYeJVnDCCfMk 43i6sIydzV3q+12Sgya5TGOQ5Qfjw3Ot1qWNXQjg8clRVMR64K5o6LItLhwV/rFmSLU9TG2RYMDD uUXAkGmG3UbI5QEX9T1/jnfO0PM8mccRvVtFCn+DcZVcZurRAKBkGDlcpiM/ExeNeJ5Wmnn5rW99 9/u/66XDwe9/4Q8GkxPAFpVeBS/RVsPphFurTqQCRik600PLwVHIAQ8O94DILj32psVwypxeNt3i M11sYSO0MYLV0cU0Tfdv3Oxsbhf6qt/d/Kn/6i/unn+EO5vDg17hnzA+WvK5k5/cEpeienxdBT7+ 8V/7H3/2Z//a3/ibFy9d+robqw2UAkoBpYBSQCmgFFAKKAWUAkoBpYBS4NUo8PCgrr/0Y6ynLon6 igBjjZoEI9WVWqcLlh55MZpI2ZZOJbeXa+XuGf/8meaNl49u30hXutfp+x9495U/ePbFF5872Ohs v/6JnbMX0jnQKZoZWfjZLy3e8YZH2Nu/EXXVTdzSwS0TIE9dZXXOsYZdp9X4D/Cb9MXzIOjHSenY hZibJ1/TJCYobZkzxpAyLJ6AWJFJlGhiZllYvZgH6ASuC+qKEjJtJT4oUoT1rEYRQUb6mT4V5SCw 2jdGgI4IpDtPl3MAV4ExJ6Y6nO58GqbwVWEvm0xo7Ep81++0WhOayE+OYRidTrstcb3SC5x2h5b6 7MWXb8WLiWNqG9ub165cfuOjV0Jn9akvfmF0dODjXbI9ySfmZQiKchtZKZ6ehutf7vbP7l67fOlM u9/WHUfH+KX7BpwpWZC8BM3Ro07DO6YpPYpdgqFZoSXRcB6Rv5suouFkCMGJ0uT+vT1a6PUi92m0 YkAmipaQqxmX3jNdzfUNDkuvVpHdSWfLVlN3rcPxyWqW7K68TQ/XmBs2fa/hsYd0MgEXUXN1sdUq yspNl6Q0k0V2MpkbG0FwdiMPw4W2jMs5ycEmVVpGwJzCles7etk3rdbS8ZaWb8MXy5M5mDDNSJ3S Sx9RtZbl0yjX9H5/c6XVHfyGVtn6eLG4c7IXOvbVjc3Q0I8Gh7Ybtje2jpKRZXq9sBeVywkDFXWn QSQ0I/yYz/IECfH3GeLa0lzDoAUMpuW7ttsIsQwGbAhaw5VHjBToCPOq6VVhaileqqJk0kIUYSe0 rr3t7W9+x9tAXV984UvH42PyhMRrCVTCoWZRRIyUBCLYkZ3xlFyLHP8aQybT+WzCHRQEYbvVpEgO BxkFc2zJ4AGZ+olXsBGCK4/v3ett7pDlJLH6I3/+R55+/RsE8BKppaCuYnCnHJA789ENhbpeza9f 7YUXnv/pn/zJv/ozP/PBD37vq3qB2kgpoBRQCigFlAJKAaWAUkApoBRQCigFvp4CDw3q+qkaddGc xePLVEsMUDX0qR91i7wMK1zPYeSZakXsy2q22o5XnAxeJmaoL8+UNEqJr2WCgWi7d96yWvPFxLWn UTacxzhb7Ia38wPf9+hXoK61ipIjA0mQJNTXqGtt2cJYJhGu+oiySd3cTmO70LZXMpXrc8eIQ96Q SJpkGYV7rfBIaWHgMtWv3qGky+BZVcWwPvJ59koLSCZyWMvIK8xMAARq9mUPdfGV7mTgL+rSsQux XMtbrUhHWnFWxVEVLdKD42GaFHAK5iFCoyBSnDjtZY1ABjOejIbj4bDTbLZ6bdKV8I52v/fIo1c7 gZtmqaPl0/k0ztJWu/XkY49du3T+5GT/ZHwUYkoTS1kVJ+WsMC5u7V7a6Ho2zjJAWdC0wEqchrHK OYkZFfzOsjqYpvkiQfIM6qbpsJZiNprH2fFkmsdRNJ+W8K15OplFG1u9MxtbVD4dDo7TxZzQJ4pg dSNtB0khLopEJBKpYAOvTJiJuCom+qqEBxGcXGp919vawEa2YYVhMZuQ8KMIv4slS7OaEEAKpxa4 ptL5eBSNpjIVcrO3yFPimRSbBc2NbjPElYS3brWIKweMFzRQOCqsZaEXyZ3RYJouyixe5kmBE006 zaBnGkHQqKomZTHldmOwpqtTW38maIaF5jAL0UhShyhmsLV5vum1OMlZGh/OZxTK26U+GE/zAhIp GVVoESVmHneOpoGfYFokMbnFiEca9MBx80DU6nq2PGV0AdayojQx1WH5woynJwyK1KwzTz516dHH KSO7cetFgqmgRcm+cktaJlMliwJGhidMCKtNv76mj8cT3GTNViOJFxwKgkYzF3MWSGhKeBfQRWq0 Ws4Xi+7m9s72jhSzueR/7WWx+jPf9b53v+999eRPwrPU0ldaVdIlxvlf6rwyGuLr/Qb6zv7+0dHh f/LjP/7nf+zH/uJ/8V9+ZyuhVq8UUAooBZQCSgGlgFJAKaAUUAooBb5lCjw0Exh/5WO/WFfNr6nW qXtK6uF5Tw0cECeVcKb1BxykflLedWNFIZHGu3z60S0x6FCxBEKRgYa4mVaGRf84JemMyfOsMAja 7WbPMJwrl7tSQrRGVZI/rFOSQg3WScH6WGvERhLs9HxO44qYwaTWSV724GNdko9pi3J4ywHdyPE5 MWwzNcyBQtTUzKJPCQgHvQEdEFaznJBhf+KuYTkYdGrjWL17zDRYwJiC6FIHxTM5TVGENS1/Hlfz BQRpCXZI8+VkNodr8FpoWpREjIqsO5XSwXgQLea8mMop3dbn0+F8OvZ87+q5c71OtxUy1Y/UIVP4 XM9rXNravby1oZclffqeY9Jb3my1tvq9C1tb5zY2Gk6jZfqtygLuaONFEaV6nK3iTJsny+Fkejwt BpP88Dg6PLl97/7+4VG5oPtqNZ0nWZwss2w+nU5nc+KLWKKs0DMbQeToi2KR5UlMwJJiMIKPrBA3 GZ35SzDhkvwnky9NEo9Fea633Wv2es0N1Mkpr+93epfO2Ttbsb5ceG4aNjCqVaY5SdK4SF9ORhTs N+if39nu97d3OlseEFBbbtiNx+zuhdUqTuZ5nBKuDPyw6wdd2y6S+GQ+mUQCyYoaF4GKTGrROBGD dnuTZv1hlRyVSWLqqa5HMl3Abvm+S9eaWc2t5KCYh92m2fWny3SWRuPZNF7M6efHCUZ6cMmNQOG9 oRNwpBYtcJ1Wo7lkliLzBuhso6nNAOzVwEvmJHACCCLES0YucAvYjFo0se1lsDJe0gjny+WNu7ej eE5bHEX1URSlec4PRAr1BM9RJpdkUvRvmrP5gnkB0n+35muOQy293CR5lqRJHC2Ao6RA+VYQ8kMS 0LR2dmfHkbmOzZbnbWxs7p6/yM8aMyHXlkoYmhghNa3nK9T1qn5HM6Th7/6vf5tfDh/68Pe/qheo jZQCSgGlgFJAKaAUUAooBZQCSgGlgFLg6ynw0KCuf/Yrv0ApOB+1V+qVDxm1uP6Q9//1qESxXPHO XnKEwnigRBjBGKVYD0/EVcO7e97zMyRQpihCLJZFTBSM7QBLtgTRTMfVzu92ZF8PwpLyTr7+wNJ1 yrAekK4/NBGylnvN4tbKC4A7/QLuJa1eDkcBbslxNNrsSa3xfQhEfeb1QwgdNU3gC9xYLlTKeJyu AADgj0lEQVSDLSWPKeAMtsE5QUcsUBfYh7GCfKb+HisR0cVlZQ5G0WBEJRclXLzGmEzHSRJJ7K5k HiQ2nQwDEZBpQXt6kTsMQvQdRkHOJmPSZxtb25fPnW13+5TYc5Sm454FBzY7juPfG01PxhPfWG71 Gud3tzY2trodKuZbruVTt+Umq+VwsTgcr+KKLOKULOLJcHp4cu/Ovf379wZH+3fu3b23vzcajlZZ tqLY3XcIEkJtRsliFC+iPNdtOzeNlAGFvof3bEJVvsAaMXARHuRzzRpBgETkRAKAClHQbqdnMj6z 0YQTxUZltoJV0zFa3kivRsl8WErpFI1To8nk+u3bL+zdifRVo9Pd3NzqbmxtNpuBafpOuBu0QkxT 5BOjOc6kttGkQB/Yh91pVeZjPGDzyTyLc0sMUYAhrgPCMleTyQTFCry1mhvVTK8SIqCGBc0kgdh1 XPxZlb2MrNzfaPo7/Zlj7e5udxrNeFmCIitzSbH+aDKMkoXrWu2GTwe/aywDbg6YHdxK14kYcocy UFP8VtLOhvWLE5CKLuq3QGw6qFK4GLTOmMdpqRtOo8HkgXuH+6lMvVzSBoaZDxAFxppMxkRccfPR vcXSMI4hKV+5rsN9iLmOmwzs2mw24GlZkrADjieMeLlijAGINmw0Lpw902p24GsBWDVoXL50aU25 1j+QguBq1Nz1v94vHvX9BwoMhoNPfeIT//FP/ISSRCmgFFAKKAWUAkoBpYBSQCmgFFAKKAW+JQo8 NAHG/+ov/fvyjvqrmrpqmFTnFR/EGAUVYYKh1IqCbAFTJLhwdYkj7NSEVXdprbET9EiQQk2vTEtm HYI6JIGov/tNV9m3hP7E2bXGW/IdMlrypl4SizWbkszXaQU9T5yavx5wLtnklWKx0+Z8qTiH59Tf EJCx3iArZnxmb7VXi7mIMsYOIOY7DhiL3Blt8fWwSZqaZLxjfSheABcK01xfxIXvBh2vNZzkt+4c zOZRwasdP4ri4+GQFBrnDOYSb9hqSdE9r6UiSi9WfuA7EMAMIlK+7i1vesNTr3viwkXP8Tu2aZVU sOfUPOnk/FoUhqWNRtDpNx3fwiy0ijLDDuiGd/KqnBbmJC0Px4vBEOvT3bu0nJ8k05n4g6rUluUa OT3uDB40CBOGthc6TQtkhLyLJCnpiwLLQe50E7hDSbvTdGkhG08nXACHwnudyYOWR+0aIIU8qtjj 7Hy5jFdLu9v0dncL0xlF8bQEoWkucVDXgepxXITaCttNzZwejw9v3w0Ma5sO+XYHrOmYdsi1jGKj 0FrlajY8mSTDXq998cJVb/MC1WFkXUFxB6PB7f17OLqgiwCmJEmYFIDfbsF8STKEuUQYR2ZxoqVj ySpa/bAdEOZLF+faDTP0Gv220/ZWECw4Kt6pLMKktzI9M9yoVvHRnaPJ4dAzHA8LXbONLCjh6JgQ YU9BoYvdjLsZ8pURWCyLCNy2qqJSGKaNK5Dbz7KwCXKZ+IjjhFqzRq/baXVmSTxflfM0ORmPRgwQ 8NxGIwR6cuWhV/Asbh/EB8eht9ylmP3gnlgbwWpY2+I5qKukHz/LgkYLk2TQBP01t3Z3r125ZJqN di/s4IHsbL3tDa+XYjydzjEminJxTmvpL/b+UKHet+RX1Wt1J7/wCz//t/76X1fN9K/V66vWpRRQ CigFlAJKAaWAUkApoBRQCvzJK/DQuLo+9rFfesUqVTtHHnysU4MggbrBqwZKp76qenu4Fg/IkeAq jDESLRTYVecQBVatdyT5wDp6JW/88YBdOrtxyqZOo4rrzU4dZBKclC3rPOGDNOUrqOvBiazP5PSa 1ifJcSW1CMWq0Zmgsrray0jLCCwneEDOAZ8WFjR8W/o0EhJEmPGUizHmT3YKExMsJmzMoHCJ1iXL s+0oXQ0Gs+FglMW0nq8shyF6RUJuLQGCUL0kR+CzlJhLG/6yhNekGQgmmSUb21sfeO93PfXIlf7G 1llMXMi5ZC5iYbnmTje8dq6zvc2URiYFTifHg5N7R/nRbHTrztFLLx7cuDG4cXt0/dbhize/9Ozz 17904+TmvelwlFeZ5VsBy6gM4EySRgXobJWllG5hgBonixkJuSSJ01W2dJayLuY/mqslpq/CrsyG 3+52wWjAQNshIUdpO0FOE4PTOjYYr7TUMtq7u+bW5sxYRlW2KBLI1HA4HI/GWhRvBxs7bvOs33Qq bTSeVEnax6jlOFWaLeaL2RTjWzyYzBdVStbTSFYJXWU4l7o9u+NTyQ71oYJ+OJlMpjOuNl4tpKYT yyNwWK4opee7ua7lljE1VyN9lYCeLKtB5tGw7SyztYp/rVbY22z0+g3wYnRykk3S0A1Xrt08F5qa k87iJTHPSmuFLYr0DdsFDuJeI7hIFpExkCl/2Lj2zGEUQzqXjp27zhRFg4DxBivbhzbqpleV3FWM X6C83vb8sOWFTTdgzEEEOcPFl6XAMp+XsB3/Ug1W34WUcElvlxj95KcDsodXkPuVZREYZdgnRK9I 01avL1tbdrvdYXhlo90m6eiFhF6tdqdjhb2s0nLys7glJVu6ApVxY7aUq+tV/y6fz2e/+Ru/8Z73 vW93d/dVv0htqBRQCigFlAJKAaWAUkApoBRQCigFlAL/RgUeItT1iwKC6oV8ZVnXH3JS1c30UpV1 SphqvCW+rNpCtf6Qei82EmdWzbbqOq0aMK2/rl+wvHRuSw4nTGidhlyTtPUL1kaw+jTEGfYVMOv0 uKc8bW3y+krgxaZQm7pdrK4XA+Rohk2XebFYc7q6zwuyQ/sRBMz2iJxJVZd0NXEq1JQzaZG6L/rL CbpJaRS95HRXkSWjV0oa65dpZUCCiATOFzGQC0cYsIMW+YqiKfmDKXwlZ0CxekbDuiwQkGE4gfe2 t7778csX2l4DWsE//Vbn8vnLF85f7DGNME4XR9PD23uHt+5O904mB4fzwcG927fu3L432TvJDo5P 7u9dv3vzZHAPtFauMk1nPGCJ0SjLkqWzYqYgRyVzJxFNaJODaUva5YlMUu6ulbREcQaYjajAYlwg TA4HlN7HntRq0dUlXqqypJEdxgfA43NOBTpiBU7zzHbmOfCmXFvePTyaT6b47VglJiWq90E5+rIa D8fxZE7Z/NJYpfP5MInaW5sMO1wwn7HIaLJa2Uur08yK5PrBrfPt9iLUh/f3b9y7+eK9m4v5hCth ubbumLVTkNQkFf9WbuoUsGMi00w7tpYEGDPcWrrp63aPTCjzKFu+12vj1fI65Cv9amVMo7TVCO3A b7TCpNL8TrtcIGtEPtBttd1mm9wskMl0vMBvrmxPszz6tAIa921rkab4C8VZZurkHwtDp2ONE4Fh CcdlYmMjaHW75BmDkEmYzJnMJ9H8eD7Ja9UlXVjhm0MM/sTVRYZW5jDWGV4xC9ZBXXAoBFZufzaT O0We5ea0uXPcIOhubbXa7X6r6Te8MGyGjW5g2VeuXGk2fKruAoKRrg2QpKeMy9my8CGqx6tSALX/ 2T/9J296y1uuXBEnqXooBZQCSgGlgFJAKaAUUAooBZQCSgGlwDepwEODun7lV/7emj191YJfqb+W N+undq3T8vp6S3nn/pWN9XVp/Sv7+HKTvTwF4Dk1ea0unRHUVecla69VbbkCDKx7wdao69RY9ocL uNf8a90f/xVntD6XGigYNG9J+VZdXU9Tl05NflkswBa4lnDWABcgXK4N4qqzllA5CWHCufD7cIKC wfioIZ64dARKiFnNTNLiZLjIilUzbNKIPoN0ZYXtuBi72MNpnRlkBGMPz1QlxiHEoA2LQ+9ub33o bW+7ut3vWdbKDrdDpx3aGrFFTjFNtNnMmE6Gewcv37g9uHt/eP36y889f/PF566/+Mz+vesHdGCN Jqtp7ibGKnCZFyhzMemHYiU23jRZeWFRwuWQt6R6KqNOnsL8tT2NgjSwnc/cS73KpEON8nsynFRE UbDFUtthAENKsR2Jzc0upS5LqzgGRKndzsOgJK6oLQez6Z1795ZFRYeUC9WioErTwXnD4ejg+Jg5 jPBCqZ6CGXrezvYWiCo1VvMy1xAaFIVVSytJCP7+revXX3qhIntIlE9GXYLO5ETZ3ZLWsKqQS0LF +7JKievR3WYY02U5yOIC7sTaiJTigKpKABD9+mQuIWJhkzKwAGAEu8KvZwW+5ZMbNWfD6WQ8I/3X 6PTdRssLwhUcEzncYKPd02FHPnMtjXmWc2CJdjKGEh8Z4khWEBi1ZCpnaZleu9Xf2t7s94NWO2yE jmPO82QczwezCT8vqMudgo1Nrxu48GtxUaQNroZfqCnMaz02gQyv3PNCuqSSzDCDZjOJE7yB9P63 2p2mD1ElU+swviGAtRpmf2MTBxjXMpNeMHrEKq8OnIYmPkT1eFUK+J73Cz//c2DEd73r3a/qBWoj pYBSQCmgFFAKKAWUAkoBpYBSQCmgFPhjFXhoUNfH/vkvrY1Vpw6rtc/qQXrw9At4jzhd6kghcKTe nPffdU3Xlz/q0qt1upH39w++XrMp6YQXBHXhzAZfJDnBrBXMCYcLTeH0XzEjj0welqva+CLF9xJ9 FGBEV5FQMBmTh4lGt1rNwPccEAWJsBqLQRJs0Ai95Rot8kvdNen5DoAZZrVKKmb3yTTG9ai906pv mANDFuXvNaDj6dr2tQ5riiVtWfmUNmlE/ji2uVikB8ejJF8VSy1O6dPSwnaHlBktTlkSr/kbryJl Vnt5ZKgjO4abMQbx2qXLH33vd9mtvmd7vdCnD0uMVUQNp/N8/+AAtPXc84d3bkzu39rbe/mZl69T O2/nRt8KNtxQuJDjN4jgBTh7eKCQSdc5ZypJzeUSD1bmmF6zwbZYy+JEmq7wBdmOBwyTDKYFF2Nz 8IpcOtYI/uPs0gqW5bc6naXtkLsTflYK5yqxtwWB0enkoZfoq/3p5N7RAY1a+JSotcpzgo8VcwyZ EghTWyzmMBqPGnUuh67DFLobG0tK1lshlBHOh+h0w5ueS2RyIaX603Crx31EHRidX4W2LHXK8Gn1 F6wIL0uqKuHiU3fFHVKW1PtPq9wUBuQHjtczna5pdVoNrx122w2mRYYNjwtZJtTvRwxmZBhBo9UA ns2mUZFXuuO53Q3ND0w/NFyvFTY9L7SdMNHMXNOZqwh1olnMp7seJGroHDoIfLq74KJUyDdarW6/ 2+t1vGaTW85wAHeugQ0Nh1er1e92u51OGIZ1Tlbu7TUilTio64LM+HlZd9HVqAv0KahLSBfWP8sO wwaXg56vbrfXBsZhZ6u4tW3fp/eMG1nf6myYS6xmXMBVYK5wCxK65P5v2aX6xfsqFcCk+fFf+1WG Y6ohjK9SMbWZUkApoBRQCigFlAJKAaWAUkApoBT44xV4eFDXxyTA+G96vOKsOmVY6+1OC+el0+rU 6CXPyJd1arH2iNXFXrVlS17x4At9a6uLjwao1QgZckh1O/Eyh75zMfgI5JJQGBCq5fO2n85235Xc md9thtiUAD50o2N+ASwkSQoegftg0SFSVxQ6o+yMSmemoEcqDAsSMCXOVu6SPBr4qe5LkpORMq2l lCvVS5O+sfWESSFrAiKAOWIqsuXv4B/cQu7+0ej6zbuzeTJdLIqiNG1nZ2d3q98fDEeziQTx8PXI TlYr1rOEZaw0B7eTaQaWubm1/aH3f4DBhAwltPJotYhX40V2MLz/3EsHUK7P/P4XPvXJ55599mRv r4hiV9e7VNe32s12m2IoMFcQhLIvV1AQDjPwWwlRExuW4BOGJi591wxC6qiKeppi2GpgLqpIzFGR jkzL0pEJfpKvlPGGZEtpTsfMlaWDeKE7Nsyu0vQFjCzJ4FMFGKbZXDWCyFiOs+RgPKR6K4kSrg1T HVEPD1ej0YAtNoMwbDXbG0yM3LQBXo7XbHU2NzcYlUgITwJ3hpGTXZwO3cDvnTk7c5Y2ZVetxso0 4FzsCJAzZ0KmqUVVHi8LZhriBYuXFYuSlGbJ8+WszA0cWDiyTKdn6S1OD/DV9HzXXurQOge8lKUL oqOux2Z08zN/MkizVUkwkuThmXNOp+chZqtDVTxhTqrB8KUBBAlmUsQmyU1Dx9IVc+2CoL3dN123 1Qy9+s7kXmuBEQFggRuw7Har2e2Gnd7Z3a2drW1AIbQLDAbsg9sC3EzHZq4mjzTLuBzcXtAWiY9y 6fnAPcdFqSeWYjTDDghPg3kFHtVg8EhrE+7Vakq5l2G0Wj0quvjgpyit4FxiB4PPdihIU49XrcDt 27fVEMZXrZbaUCmgFFAKKAWUAkoBpYBSQCmgFFAKfB0FXsOo6zRJKBnFBxYwoVo156o5WG3rEna0 rtwSQ1FtbxHD08VzXYEwhiaZOlquNNrIsa5QWgUtWrfT81lnPCJuI5li98CONU8y9u9YdpIxRRBY A7uBSzDqDttXCVqAL0wAYKulGzJRMBhN54fDkRuaErOs+VbNCmAjlY4DqzZiSanXaTFYnUSUuBkr WNp0mzOTkAmLSyNwwxdvH9zfP4mTIiHyhnkqzS5cvLDT6dzb25sNBvitiC7aNgBuRXUWpeNsA+1q txrgtstnz3/oHe9p60Y2OM6no3u379+5fuPFF5+/feOZwe3rL9994Sh6GXtTN2hRNtUMGqz+tLmc 6vO0wH+1jEhhVitbRCNqxwcYC46GsGiX4x6Cr+H6ASwRnaNqynWkfEpq05a2lEkxrZHOfM1ekWWk 7UrwHryEIqpoWea67nk+Eb6UuBzuJAvbU3icxfuL2cF4NBhPE+ZQMtASaIbPri4745LCcXrd3vkz Z3sbfextgRc0mHbYbHd7XSmhcmyuTa5b5cqMpmNOuN/vzqzSDBzNcVNktq0KR5/vLl2nohLepATN IEwY4eQqSzioZdiUb0XL5ZyAZjN0vJDeqg3P70CVmgEd9SlhQ2PVbknaj+tq2rb864WZ7qzstm26 XqPT2Nh+7JFr53e2AFKtZpPQJ/iMIYuUxgvahGT5BB8tqByutEa7ubXR6+5sIAsdXjjOOITTwC5H gZYvhDXwVyQfQ9hgi/Ga0D3dZnwl4VEb89rxaDg8OQKSwqlwqHFblUBTmcmIoDgQxZjICazpqh82 N7Z30wJU18Cvhhr45CBp271uA7eXJRnbjd5GPTWU8Kb8+ABeXZPP8Fzl6voG/tsD6vr8Zz/7fR/+ EPJ+Ay9TmyoFlAJKAaWAUkApoBRQCigFlAJKAaXA11LgIURdUm7+oP59PQRR3piv3VprVnUaTlz3 ydezEiWeVcMsKXiXFFfNv+rurXXasQZeDzblBRfP7JLKsjDqmC4V5qbpQiqk3ahutCf0BnZpelRZ mRIwZA+0JuV0IdGzLtPrmN9XQl7I71UM3HN8xzd1V1u5AAkYiWaQnute2t7a2OinqzLTSaiVpwsR tgV1kYouPksn14Pyr7qITADTg+DmCrJAcCxjoqJOAZZ5887xdBpx2oTeqHyn0P6pJ5+wTfuFF16K J2NihQQSpV+p3cXu0wlb2SLhVDAGGfP4e5943fsuPzK7dxS9+FwyTw9efObzn/+9Wy99bu/ec7cP bqTpDNjSgaY4DccNUYXFwa0waAnzEnpo4qYiOFnhRhK9YVw24wM5IHY13XFSwoBglbpzCg6HPwtD G8VPMBdYC0E+7E0e0bvAxZSVwgSragFEs52V4wzT+M5wkOtGu7tJX3uOAraTVCW5xYPJeDSdpnmJ JjakC6cbecga1lQpj8R0nDO7O71+b2W6bS9og50wo/W65D0dUn64pdAqTykPKzR9Z6sfmVpKpM+y cinV8kzft5shYVS72bDaDQOQ5LgVMxB5cRCSAoTb5VyFdmPj3Jmd3Z1tspGdHh3xQbdvdzskVNlD abkopgGg2r1+fweCNCldO+x2Gu1Gp9c7c76zu2s3WqbjEygkrwg3opXN9JzSNRiuSRE+9wHGQTek 5r6hhwyIlNkEfuh1+t3tCxcuXL26sdml8N7wfPrpSUc6bOy7vuPyQmxrGOtQfTqfRfGC2xTlOQKk C63qXi6q5AV11XZFbpyCnvl2v7977vylM2dJezab+NOCfq/bbrXENdbqVLK5jUduq79BPb4r5yMm L6HE9c+a6ur6hv5zc3R0+MlPfOL7f+AHO53ON/RCtbFSQCmgFFAKKAWUAkoBpYBSQCmgFFAK/FEF 9Bs3bj0UuvxXf+mHT8+zhlqn8cNXJiCuQdGDAY3ydd1MtX5OTFcP6uzXL1zjMHlRzcZOs4518fz6 0W9iT5J37byHlzhh/eS616tmBHqWl8TT8pIpfHiPZIYdVArGhJeKA4BUGo2A52mGZ3vqmEBU5LwI o1Eez6tMP7h0dqfZaB+dHA/Gw6cudeVcxCglsyBrZIevSY5Zt+jXEct60TA1mcZYf9ulON4i1peX SzfKnE/+/guDwUI3/JimriS59NhjH/nID92/f/Qr//ifpKQOJZxmU6l1cefsVqu5mE5wNp3f3Hnh +c8l9/d++M3v/uF3vJORgM/fvuk1Guno9u9c/8Lo5HjD9cSnpRMotJswDSfQvKbhWKymENxHg1lh wH6WzrKSznybE+L0kEkq2EFdMmKS406LvMCMxRxEStvxOlUVpiopVqtddnShSX29RBhLg2+xhWHG mM7azTxwUksbVSnFVdvdLdfwFtN5UZbH89nBbDymYJ+uKMcTZTKYYUXJt297RP6Y/5hHEX6ud7/9 XY9fu0Yxfc914TRcPBKpfFOuA+Mp48XR0fH4+OhkOnrbU49NG+YoHtIulkRxC+OSaWHSA9jBjTJN evMreVGRMLcxzW1K3hfZOFuU3bB7YccnLLnUQ7NrraxmIzR8vaDLS8fdR9SSpVeubbYdn4GZLw5T 12u6JvY1z2223FYDiSGpDsCyZHRmArYDUE3x66WpLW3vK7gqgyANz7qTl4ssdcFL5BZDv9WifKyF eim1X4Y422Cv3Ctg0DIrpzHDJ2fxfJrOxrfv752cHE9GA9RjUgEFbvwc8AewCzDp+b5M/5S2O6Pb 7vS2t3vtbmjbaUGfPlVwVa8psw6ajVav3coMspreRjO4evESN6fY6TQtYx6otuzQscZsTCt5KH6r fJuc5AsvPP/TP/mTf/VnfuaDH/zeb5NTUqehFFAKKAWUAkoBpYBSQCmgFFAKKAUeXgUeGlfXr3zs 75820ksre01/Hji61pHDtaGkNmcJpKrR1mlBPT4u6daqY4fEAyV5SOiq3gtYYG33ql+5/quApWg6 iyN6mUpYVUplFHYtzFM8H6dRlGRZPp3O5vMIDECUjQAh7hhMYrhiBuOZKzyLWvhyOpnxkabwiixa xFmasecpRe85HeUE+Zaj4fRwn6ap8ZULG/U9VOcV6/iiJCQJhUEf1jhPKtnXoctToIfhCycNSyLp BnCj+Onu3mAyianIwtyEWenK1StvedNb5pP59eef56XYzRz8Za6bzhfXr7/ASL53vvld73/j26bH 9xd3b3bMcv/+zcHt5wf7t+6Pb7908vIoOmGaXttwu7brmYQNqQPTaWsy7Qb8D7BXd8ST7kzyNFvm THTE3YWjypEqsapi+SycMigUYoYf/I/Thc5QFKVZRPfwk8mqKNXCc8RCkXs8nmDRmizm42gxiuNp lg2KdJAlgyQ6jhbHs+lgNhsvFqjPBsezyQIRySuuE3T4ysSjBTYzKFWjuYrnZvMpDOjahStndnZw pDV9h+yiZptZuYzBPXjMEJTmNHJ8RREl0Ua/v3npjNT1a0aWMX6RnnyGPxqdugkrxNBEPrDR9Lqd 0rRYkG+Jngw9dLsdt9PEN+fput/YavW37UZnReYRQ5YDnyJU6KeVHuWrUVQeE4DEjWcydNK33YB6 eEfSliDOlW+abSKTVGbZNi8kosiq6Pk3JK8ZBvi5GC8ZeG3MgZ1Wr9vqU7jFaTEYwCO1KMYvj444 G7+V3fBsvIgwU4Z01natknvPdv12q91ttdmSfTLqkwtG2jJsUHDf6uPk2t05e/YMn+k2owIMc56Q V9CVy1m6zGjAN0YnHTiMzxvtNoMj66mlIplg2hX1cqtsuWrYqqvrG/iPQrSI/tk//Sfvee97r1y5 +g28TG2qFFAKKAWUAkoBpYBSQCmgFFAKKAWUAl9LgYcIdf3S+vxrQrK2dT0YHVenE+V7dSJxHWpc B//q6YjSdfXK3+Vb9eMrQpBrelRvfmoF0xhEaNuMqIM12QQY6UgHP5jE0OiX0gm+YdJhxKCDeYk6 pRqzrD9DchwogEHfFG1GpoXpSLiF5VGhlWXk93RK5Cn+akrjVavMc3FAGebZM3T01Os5nQC5ngsJ utGFINQNXmtSR+W7NGDVzimgDAYxYSSakyTa3v4onmdFRqaQUvbW7vbZtz7y6Hg6f+5Lz4DEAHJ5 nuULhg0m7LLTbD517fEnL194+eVbjUnxI29413wcD+7fX5FmTCajCIxk25rZMEVwjhjoLc+kdJ9g ps0swgVhzSqHFhEzzHKca8x7FOhUBXZFJ72+KjRBHtiBxBGEacshFRcwFREHFm1amMLAYQy4nETz KEvH8dxxMLr5lF21m01wTLvdocG91SFvGGaaHA4dstoRhxQJ1qc4njNWErrkh7bpSDwyL5ZAxLKo w6w65wWVxES2u3tuZ3PLx+zF6AAMa0URJ7iyGBXIVbapu2KfgWlA7Kij3zh7TrMCmSioQ/gC3SXA 2Nja2jTcoON3bI0K/naj12G4YYdSfuq1up3eVp/yrGa/44pJzdAp6m80M8NMVhWsVDDkykSpdBKn 06yIy4y6e7ruGXKAJGQ2MQc6eAflcnL3MOgAUJUQLaTi3uPeoRCMiKDLCEZWAGSkgCtsMr6yQVt8 EMCg4JAuN4Vn0utlMM/St4yAe1ZYpBTAcYOwUlq44GnsImjStkY/PRQV8EjfFudBkpRdNna3idRu btNr1ul4sDg8YvWdBy/zbXrNsBPqdN9zY+IwC8lK+jZ191LOJcCSVQpvJujK16FRqN+3r14BrIi/ 8PM/x13/rne9+9W/Sm2pFFAKKAWUAkoBpYBSQCmgFFAKKAWUAl9TgYcGdX3sY39/nThcO5tqLHXK pV4pna8L3AE9DzDXl1f8IL54Wui+bvFaO7mkAesVL9ipZQpqtcK2Q2k3UAj/l8VnauKLAosM5iYi YhaQi+F10AS+AEABbtgehEHaDEgAHYpBGxqlRa7lN3CS6aZP+VYWpzhhZDChbhZxAcyIjoaMt9va JiYovq3asibD7aBLIDD+Lis69afJSQMoKDmHfhCjFEMZ1U4yRdHPUn3v/oBGMFP3TNvD3dRtdC6f vTSbTV987lmOS/F7kWbk5N70uje8621vA/yMZ5OySu/feH50cBgCN/IkjmZmU8/ny9IoaI1vgDQo kK/bzVYa509D1jImlbes5lUGUsuXZlQIc4OnGLpT6Q4uOI5SAaZgXzKhUGq7OGWAFBMheQK3G/sK 7ABQWIBPVjqczKMAjGmUTVxnUEHHwb7kGLFeYgQbRpPD+WiaxHiTuLLz+eJgOGCqJdALCxiZvnar Rxk7QTyMdaT6wiBohiE5SYxMKNhrd85v7watDlMP242QeZnwtTiJgIzoDCji2ud5zsU+GQ04Q39z 23ACXFNbzVa31Wp020arndtMbgx9I7B0h/TjFsXsOKpaTbfdDNqtJfSn7oMPYIM0htlW0/e4rbI8 0cqiyvLa4ZZnDCyYJYtFNEtzuBsTG0FVrYbXCAFuUvhGJxzEinsGYaV7rLbtCde0Td/DhGWX4CSL vne9srx+AyMX8Uq62k7RrggqrXK157E2OtbpUR1/Hyo7km1klKVjOWK7K8SEuMR+yMJ9B9E4meZm F4VCw3UlCUk1ndCypcdyPIrtgWh6g9kHtW0sdG3+pLGr6Yb0ojk46SySm7oL7yI7qeuOnqtfuK9e Aa7D3/1f//a1Rx9VqOvVi6a2VAooBZQCSgGlgFJAKaAUUAooBZQC/yYFHiLUVbu6asNOvZi6ZKse UvfApyWFWWtHz1estvZJnW7/5adrW9d6ywdcbP2iBy91zBCkJdTKsOp8Fi3s8pk+bjFwkVVcSfBR opCGlFmBbEBdUKckKxw3uHT58SUsLE/PXX6EPqbFeL4sCpfqJ/KA/e1rTz2RkQP0Q7jV1Te+pbm1 5eojWRIzB2v7lpRW1dhO+Je0d60bxSgRF9RF8AzAxXhGDGUYmoqCNjFXr5y9veFsHNsWkwpXxCPT RWyHbd8x9vf2pBqfbnw+ud573v3+P/cD3zudLj7+G/9qb/9uOVus8tlkeFtLk6ZhEA/UIGiOFraa vcZmz+kzjzAqM3xOnDNf6Cu7MhxIjWELf4FMhb6PXYupfH7QsDWPwnS4kukCTULq922W6VBRT4eX SU5wMp3G82ipLaMkWc8CgAJhPMJGxMhEiYPKUg0ZDwmbW66o7U+WBd6xAmSUL2fjGaX1yJGQ2NQ0 YnUcFDsV8cM8jbE10WuFSUrGCNbGLrTErHSm38N/1W81KhBhWWTsCCl1OscozxeaWej6bDaKyzzc 2W5g1Gp0Hc+HMNmtcEU+kbGDzfaZoL3hYYpqdDc7ZpPRiyGEiyCfFYQwIhTwxCMFAmIYY4BVy8PU xwzJvIAVwpmWWcESA5e0YGjX0xJxsfFBwX19U2GJQiK5L+XuwvknIVaylfTKW/i1gE2S86ztiOgW sGbIFzMAgFZLIWMwqbo2SxAZN72kOkGlQFgZSSnP0ziGMtxaUZpRcQajhV6hP6IRf3Ro/moyc8Dm lYJv5VviK+Selk9sxKABIqhsB+yCmWEGc4k0uot8lVQag0mZNZrnqCtJ38BWExi/sf/ofPzXfpWs 74c+/P3f2MvU1koBpYBSQCmgFFAKKAWUAkoBpYBSQCnwRxR42FDXOmr4R0roT0usvopznbq2Tsuu pO/qAc5aG8FeyTi+kl48dX/p1L0HMp9O4oK8w7eFHchDIoo1ahDPVe25EQLAZ6rkgQGuZeMqWhnO f/7RP0uT+Wgy+5F/7wcd17935x4pLzw+/Z2L/+mP/+gjjz+2TJPbdw/e+ba3/eB3v/vKbu/WnS+t T+1BL/56hYK86pb6+hugNDkWjjC8OCY18BSc47/SNGKV/qq0Z9MYLw798b1Gs4s9J0o3ep3dfvv2 /XuEDAWhAFFWq0cefeyNb3r82eevH9y+QV16t9sojGU0H51r9Zt+B0w2K+L9VXkjju/H81E6jMoI iLLV3m00+qlRYeBKmVtIy5Nn63TdM2zScwnA0VO+tE0ShTSts1hGBKIRGAm/G334KMliiFCm1HcR 48viwWw6l96zmJF/eZm7gJySVnotKUqMUDCcZUnTuTYrs2E8xwm11Cy6tZI0J2tHwxQmL1QXe5sc yCDOCL9BpgRwRm8YIcc8WUTUYhWtbhfSFXZ7EChZP2Sr9geyUsKM5Eo5vU67FUUzuvOfevxKb2cH 5NOkiCtwnIbrEBMESZE2xNvFOALiqHwPCsYQQsvC5QTeogpf6uCZxgkHYjMf8tWQv3HRl5BDkJXE O5GIrKDTDJ0mWU2fCYaedPOLj82lBI1QLMFXiQFquK7kPGWapdRgCX6queBpyFUHdXE4U3qypLJO JhXQeV/TUtmKD6Rbz7us8a/Greo59KNxz0iPG0bEgLPn2FTvc/ZBQFsX7kJOhJMQ1yJnTx5Tbm85 L1AXDxBiTbg4bZAezMsDnnIiPg1sbCCochnlS0Zkdn3V1fWN/dfm9u3bn/rEJ/7jn/iJb+xlamul gFJAKaAUUAooBZQCSgGlgFJAKaAU+CMKyBvph+KxLuYS/IPd5fT9+6mnS97rix9KxiS+EmZ8gLIe +LZqsCW0ag251tBg/Tj94rS2/nQ+o3hq2CE2IKkfkkiiBAml35sHGAIjUk0SSCXK2cg+cCFV5WQ2 Bxxsndm4cu3a1UeeOn/x8sbOdkpirKQVqupvMdeu+fzt+1946XrYc9/y+kf/h7/2N//nv/0LdQ+X GHnE01XX0j/4kGO9kthcVsTiyjmjBRMMUsayJIDmurpHGu/2/ojCdhZJi/4My9Z0rC2i47075wKn 3+6Q1GuIo8sJcO2UURFPbaZFFqs8zk8GiyRlwF9rVZKGs1Y2Zergn1XC+MiVES2N4yReVHnKrl3P cByCbZ7HrMGWG7Qc+szbnVZnq93Z7fbOdrqb2xevXrh4+cyZc80OAw8DvGlUZmWLiLxjmkjVlsTi gClL6ustnSUUulYKs6O1PdOt0nKWtpPb5tL1M9spNHKa+OnAikAWIo5grnavtwlbW19OINJ8Ph3N RmQmgWNxwWd6+itmCSQZx0tLbTWNF9MsYctI+vKpDlvFmpXqVo73Dd8XHi9ba3ekPI1mLkq4itZG ZBhzQBJpPyJ7hPs4sk8fvEVOj359rodn6vzNpgxLE7K1NNyVCQqjayz0mhtuq282u26rbQGPWs1O t+2FAVoB3TY3NzY2e0GTjCNwq46nikOP1CONW6yRfKGUwJGSlTgrDxTkg3ubu1H4Hx9VPUfBxCin mdLxv+KzzK+s+CBnSzWazEdYMrQSqMhyxW0I5+oEYuoDbFl00nMkrl+LvrF22G432p12p+P6IaYt 4JeMxlxjXLFz1SFKgV3gS06TsCVeQkgYPxkmqoQBxfZgP3mKMyfkycdD8StFnaRSQCmgFFAKKAWU AkoBpYBSQCmgFFAKvCYVeGhcXb/ysdNa+voyvNK9tSZap5fmQQHXH7lSX/HW+ytY2Cub/eHs4pqX CXiq8NfA0Ohcr61VII6aR53CMZnpiCOJzF1F7G4FUyBwV5EvfN1Tj527cpl02RsuX50UubbKv/T8 s9FiRqarTGYry3jvW9529em3ibVn5+zH/tEvJ1l++awrh1w/HvxJEE2KumRW5INiMYJlHIf+e7iL 1QidRtP2P/vszd/6xO/duHFzcjyO48WlSxefvvq4a5iHg/ubnfC7n36bceHqG5985NqZi9vnz13e 2H3j65/e3OjdvX3TXVa9/hZwDFzUM4y2a7fssNJo0DIWRr5YGlXlmMuka2pdpwE8imaLLMlowHJa jZXr5GUqvWh1oFL63atyFCfDvYP79/eOjo7Hs+FwMU7EaLbSHYsaeDKG84jO+5j8HGzLIujXotyc hiucUdQ/eSAZbEqwLVaId6xgjOOqSlZlvKwijF+wTImOmiQr87KI4wgSA3EU5od9jG+aJheJa4RL jMsBoYQPNVqt7d3d8+fObu3uBo0Quxi+M8GSXLeqlGJ2L5iNp7opIyAn8/m5S+exbQGAuZo5p4Ar DNKmmeQSKccyXYcZiM0Adc1sCZAiWmizEFxsYnnC+BWEJnVejkVrFfBVBnRS2I9LSzx5Oqt1Am/l uCsX/5+MSsT7xicIlCnJQRlxaViEGakPo9a/gmQREYUMCknldpNbT9AVN6hFJLG+5aR1jjXxOqY9 rklvXUTHacO7xAcmRkSJIOLfwpWFj4xHKbSuJqr1wEoIGG4uicjWhXGyD5YhA0BlAoJgrnoPKAPv Ysu6zV6mNNgS75X+e3HXoYRDRtJyuRtW6Wvy1+W/u0X97qc+deP69Y989KNYBP/dHUXtWSmgFFAK KAWUAkoBpYBSQCmgFFAKfCco8JCiri9Tqm8YdX0V4JK/fg3Uxbi6B64vmUInZjIZRgcIgEasUZhA AfnWesKjmMGWxM8w9jx15bJmOF+8fuONj1375Y9//PJG+4Wbd+fHg43uJnMCy1Ux0ox3nm8eH+9d 6viHwwmIoBkAYep6/D9UNCYnUVcniaUMZEGxEs3ozEAktyj+q1L70kt3PvX7n4tmMcsgG0hS7dzW lpWld/duLgbHu36z0du9+thjf+bJK9vdrfHx8cHBfcsxW747Ojw5v33uyatXt3utC2EDcuMv7a4d ZkY55+XpDG4Bp2lZ5gZhPs0H+aywewn+WSUl8cBsMJ8mZeZ49iiKTibj+0dHg8FoNJ3Fi2QSz48W s0WRCq0rVkvKuOpqMXFdSXqOeZaWPM2EQp7Il2maH44Hx4vRcE7b2GKWJOuPYRS9fLh/OBwcTYaT 6Xg2my1m8zhl8GM+n83AT8gOgamddhXDFwE3/BXwxBfImWcZibzaR9XFfWbYTm2OqgcLFnlagDJB RIwprFIw2bIcRNHOxQtm0JDhmjJCkzyh3aV3i6AeZIr5khVOL2CU2PzgYeKtYlgjgErXXVvmHoqz iT42HGHcDXJzrFKcVqZJBBLGlNcOPUKERAlt/FWORBd5kAWUZvr6xmIXYDtSn1AnMVMRezQkAsnA SCFb4vFa5YIYdT6D94CDkqitpxmsHWDMA5DBi7VfjB1yenXdlt7kzA1tnFS+zrzKih4xplkCrFga 1GoN2qTZbv0Txd+EzsmO18ZIma5YT0BY0y+IIUcvViZFXdmSOaEMSaBMLZ8mOAXLtqO6ur6x/3xE UfTJT3zie7/vQ4y//MZeqbZWCigFlAJKAaWAUkApoBRQCigFlAJKgT+sgH7jxi2lyR9V4C//5F/4 Sib2oOQLrCWRz1c6v4RPSc5R/F5iAQNM+M3ve8dblyv78zevf/h7vvsf/pN/9JHvetc//s3fH44H Z3q9RV78xI9+ePPSI/u3Dv6nv/53vv9D7/3w+969SIpf//W/xW7rKnEYzemRBTZAiKSfi96lJQgH khalMhyv6zUm4+Tzz9+8devlMis6/Y2g1x6NJoP9k61u28rKaDHZ8t1HumfOP/X0m9/19p3tzsvH kxe/+Af39/fOXbpy6dwlJ852L5zvbvbHgyNvMr31xS/dfenGdmEM88F+dPAlSuPBNprxnp1L9GON Z+PAbOiaP6uyWKumwBQbsLL028GbnnoKQ8/9g8NnX7xlJKVLjE83Qss8SimfT33N6Vq+VuoFZ+/Z cUbEknYvF38RpAZ4YrAszYiqZEb1vEnL04rRfTiUxN4E9XE9opRxle0Pj3FyCSha4opr9Pq9OEpk AxxVAl94BWk+cxrHsBrMR0hJZJLRh4CDSxcuPvboo4899rozm5sMr4RPUfhVxUzIZNShAaOyl3mn EWRx9Pl7++9+x1v8/gZWKx+LGOzHNBtEDS2axEz8Y3opdIz5mcKDlqsM61Q9HKGe6ClnlyBKucSW k2elRChTHhENZFjSkiSnDx7kh/UpdJn+2PTt2mcF83IdFiCt81LLZVZFPltEeNPq+Z4axWjogmFQ 3G5y88koBBAVNA5ahgTANTKFUsX14CG2OD6kvl8eYnVbaaGlzbLqZJYwiRODnZYBSBOtgmfWEFfS wKA0WVFNXJl7IN+oOa/cm3jTQGIcsz5j2ye1aPtdLxA4x4lYFlvH3Hi5QK5LvfWr1OPVKvDxj//a //izP/vX/sbfvHjp0qt9jdpOKaAUUAooBZQCSgGlgFJAKaAUUAooBb6WAg+Nq+tP+PL9y3/xy69E Hb+yAEz8LTU7WH9ecy7pBZf4YV37vtTu7B399mf+YDwcf+nZ546OhvM0u3dwGLrueDa/d3j06edv /danP/trv/nbk8FgbzD67c989l9/8ncv7XgPDlf3jK8fUslEOI+68fqAK5NedqmTsuzBaPGZLzx/ 7+5ei3F+eIKawY/9+X+vf+bcS88/p+eMUCy3m62toEFb03A6vH3v7m/83qe+8Pnfu7l3bzEdFYtp NZlWaXb5yrnLZ3boXd/qbbeWOraq0WhweHIwquK5bs0oItOMftC+PRrT7dTw+prjnzBH0LEGy2K2 zBkjGOu60+1qjns4mQzGkzb17ZTc0xOlr0Z5RHxOSv1xMbECS2d6Y7FcNl1mQuKYMqKsYHgltIv0 XdAI3MDGv7QyzUJfRVlMQ32706AqS7eNQqtmSUThPnYnwJ9Ods51MT6BvcSOpBme5TWZNek15osZ VwHJZPJgkcO/QF0dGqlaLcqo2MJkIGFZzBfxfE4jfoLjCa0TMXgVx9M5sOfi1mbtrxLow8Xk/Dzp W69HFZJEJULJ9aAAnusigEvCguJ1IsLIkzItka0lCEifFnSJpRPrm+f5At8ahBL/Xp2CpP+LYnff Jc4o4wzxuNXJSwFoMoERo1uOVIQTxVtVj9+s4ZPYx2ojWv3H6aCEukW+7tU67d17ZaKB3EEPJjPU oUba7hlnAFwk3GmKB03GHHDasjRAlcQhH/yYsX+xvpWcdR2ZrLvo2IdMAaCyq84rCpbVMcrJqgtN n+cVRXJWjeRadKmpxzeiwK1bt3B1/dkf+iHl6vpGZFPbKgWUAkoBpYBSQCmgFFAKKAWUAkqBr6GA Ql1f+7b4Fx/7B3/0GzLJrg501VGzGnXV/9Q8AU5Sf0dIhEGSC5oBVYFlAGOyLOfPZVky9Y72J5BN p+GFYo2xGULoOua5bb8GE384SikRSdmffEtquwAwOvaZJM4//cWX7t+9X+TFYi7Qxm6G73n/ew/H i5efe6ZM4o1e7z1PvuMH3/XB737H273+br9/4bELj25sbaWDE5tMnqWPxuMXX7j+4gsvHlx/OT4Z 0GKejIaH9+/cPridFosJtMWloz3c7W9f6u+8cHQkpiGtE+nmcZpajaCkOd4wolWZWyu/3YyK9Hg0 mCxm+8QM6fNalrMs5twDDw9attnre43mdndjs9VjQl/Xa3WcVqnbm/3tze7WZn+z2+42whYBwUbg 97v9brfjN31sS/iY5nF0eHIyns+ijJZ8vfJDr9sNGg2tWjIBEG8VCUKicyvWZDn///b+A0yy+6zz xU9Olas6T0/oySNplGUly5ajHMDGBuOwAXYBP2DA3F0vu/Bn9+7ey7Pgu6y9sJfd5eGavGBjkjFI TsLYkq3gJCtb0uTUuSvXyef8v+/vV9XTCpZGWTPzltuj7uqqU+d8qjR+/Hm+7/cFKlSVifcEqTNS VThmozE2Mz4x1qg3qrWC6wZBOAjjTn8AaN0orHguisaCJMRo6Hy3jxDZRG0MwS1MGg7fUCgtymvB 3eG9z6nqC6EuxKjoJ/Tq025EA09WsSlTRQU+PhgoXRPaCukzDPlpUFtwnQMfFf96vQQkDk4LDWW2 pqIhCy1dmGzEJ4j2G1B+imwpUl1hGGKNJF6Fxi1F3A/SjUJmQmnR7kUabhR5NvEjlWuJqVq5ohFf stpL/kDrJvMcmylx/vCFcGTUAqYbyLjBxkFy+YjMJQmlxugpctcjetASZNKoh47+S7kvqqY3LFH7 JVaB0tpHHEPHhxwcbV3BGGk/xrYDpVFg1fXs/veGVdez48WPZgJMgAkwASbABJgAE2ACTIAJfH8C rLqems0tT6W6RGKIdjHSaCFZBCRdkOeiEbZ1SUVrATNUIMEGYLSLwjfY8gdVAfUCTdZqt0U9lIpe rTDCHNkAUgzBme1bqlJ1CUMw1ATkLsixDJUaXgSb7sJe/PCjx08cmx/0sGsxRshremoKZ7SjMt5b ah45fDjyg5LtRd3VRw499r0Dj95x77cePXxgafnU0uJxdJ2P1aYv3LV//9ye8eJ4f7V/6NjxY6uL B5ZOLh4/9tjDj/YH7Z6tNCMDWw7Ngrfq+904Xel2S5ViYbyeF72OkUJ1aZ7XCvsDmC7bXlhaPnTo yMpqk3QPPBq1njulcuWaiy+/9KJLds3tfdu1r73skqsmx2d2Ts1gBSAiZz6Ft/QpaK5aqRm2OoPW WruFZJUfxr0gghhaarejMFqYR3eWD54osypWypWx8er4mFfw6uVqqVDEPsc4RnE7IKOgKolT7FIE jxBwRb5OBMowYoddkYUiXFul3ih5hQhtViQqRVrKtAqlMlwU9irioW6xvH3TZLU24TsOdJZj2SXH QoKL2r9EHdYgoZKyokWRKHr/ob1EnE8mpyDcMM2KX6D6i9rbhBtCWgv2K8Cmywy20Cxi+E9MpTp5 ildEGz9CXQhIUZNZkqAJTA4SIkxFqiuBOqOKLFmSNUx0CbVF0TKoLRgrTDhK2yUau6Sqkl+J2BK6 vmJUfidegKJxik4dYVgiiRFOzDkGqGBLUP0Fq4eDIiWGlBkOgUFHTDhiNwBFzMi4aUYBJ0x9+pR8 A5pqsYIRTKg7AxOVeCfIqdE2yYrNA4zP7n92WHU9O178aCbABJgAE2ACTIAJMAEmwASYwPcnwF1d T83mwx863dW1/giq/xaWY9Qdj+2MWHO3nqWB8oDOguugcTHoLVHkRXZBNI5joWG6vLxWbzS8ghsh upNgMo/iQ/jxsgsqeBWKjCHFs75eMtcwsQiRotORIL1MW3fWVvt3fuN7caRWq3A+jd2bt9S8woET x4/Oz1dMNwwGh48dc7DFL8/bzS42RHrlYt+PcGiUQeEYMQYQHbtRr1csr7/SRtgJvknLw9IgOnbk GF48QUmVZpXHK+00aaKwC1N4mbp/+56ZzVt0xzm4cKLVbaKaqeX3Wv2uPxhgASKGAgcDn/JI0CS6 jlefm958+fY9PvZTRgk6pJa67ePHjmRRpKZJGMUYq3zT3itODFYOryytrLXGi4WaW9HtWqpbnaif o7LdNYuuvdJtkgcytPn28oFTR6mpSywZNK3SxNikilWRWOUYhbTfEC39SDZRXRRkE20WBEwx5aeP TWAB4/TkzOy+3fumx8dtA+IJWxsTM01gm+xiEUOOlq700HdWLOyYLKZmKTX03iDBmGXd03G4kcUk fSX62qk6Xu5DpOFC8ZbJaB86+PG6mALEK2BWE/mmFOsg1byspigH6wUJfQKQ2IoiLY/tIiJetkcb KHX0uONScFbQTdg0GaPjC49R0I2F/JQFrzVc/EkvIl4C5Vi2rSLuR91lNCeJ76FWpSUV845KRN6K YmKkn4RGtfHxorgX2VmahsTyTCyyRBFbG23//UF/ENOJQciiZl4xlazTXVtZXTZwBSbeEke1HFjD WrmM7BvUGxq5Sqhcs+vIehlUrG8gd2Zi3WSa4tXGKusfYv7b94wIcFfXGWHiBzEBJsAEmAATYAJM gAkwASbABM6AAKuup4b08x96nxAHj5/DwuCXmO4SNd/kHEh0CeMxOgoNmcElUM8WmtGR1hGt3pSk ofuzbn/QG4ToqMKvafAOBkLXsJ3v9ddMC9VFg5GnVZeC3YIqQmKaJgfITM8sHDmxdvjgyq6t+y6+ 8KJdu+amiu6xhebCygqm4Bwsd4xSC5sNM7Xjh48tnFpeWW73hcvotNGjhbNFPAdhMsipAkYSA+xy VLySYyRBA2G0IBrzPOSpjoT9i3bvgeo6trwAb1QvVvfvuGDb7h2lovvA8WOnFk4trq2stOHBBkXH qXhFDANC31DhFMxOmCB6Bu9RtJy1VgtjcZSuMgxHzU/OL2JgEz+Ol4t7N+369vEDtWIRTy+ie8tA b1UNy/tawaAb9FqDNtQJ8kODoI/++6X26rHlEyj8ovawXG1MbdmyaRtG+Hq4qm5zEMEhUb88qR8M 5w0n9hQq89e0ycmpzbNboLsuumD/5ukpFLVDdFHJf5qbjlspF4uOifewH2alcmHzRCE3rAghLGwW NLSCjZ2aNDsqhiLx0sM3+ektDiJr8E8YFcSWwwj1XnmGFjLqwvfTYBDpKCyDUkpCA5aL4lAWImVQ XQl67OmksDUyyCIk1lDsRaKLUnIajTfisyTOQXhV2KZCUcXMJn5MEwTYsNLSFBpsOAGLmdmUPOrw KdBxum7Rw6DNMnoKPdJIQ0y7Dlbb7Vani0hcJGr0gW0QJ0qc+L1mv7Nma7nhOLpXMhzPcRyvWFIN Ez4Pghfvvi1Ul4k1nIYBt2YrqS3qvrZMrLd+ncFfQvwQRWHVxZ8CJsAEmAATYAJMgAkwASbABJjA C0WAVddTk/y5nxGqi6zWBtsFOYXqJlF+REEehcqJpPQSLUt4PK0CxD+R+pGpLrglCCy0jGPaC43o MA7HTq3ApODZ1PyNVJChjzfqr792FocQU2rrqouKutDrBdWlqgnZs8zAAsNHHjmJab9dW/du2Ta3 ZdsmTPG1mz3YK4gh3IpuZaLSqBUqBVNd63ZRoGWnWDiYnWx1ixg6c4yqYyuoEIsTPc4W292JWn3n eLE96IZrq7fde9+pUyeyIM6KZmNiCmOGURalOINUwejg9q1bx8brx9vtpeWlI8ePLiwvtNea2CGI q4YkwWVijyBSXekg8FwX/Vy7N82NYzFkuVSvVqm0q9U+sbT4yJFHB34fBi037OsuvKRcqpxESi0I VrsdzAZiho8KoVSl2W5i62Cp4MG1ICfmp+FyZwV7DynBlKuzW3Zu2zQHZdjr9Tvd9gDBLspe0V5C ROrWxz8xYocCdcd2Ns1u2bNn70WXXr5leob6rdQcOwfxBhXQoV9CaZaB2T2EsFzXKpYc1GtBVKG+ HXYKpWY0ngp/JgTT6bDd0/7LB9WFwwfQjlkaZHmCGBUkJt5c/BwmBuWeIrxryJIJkYXTVNHmTm9J SpsaoziEsEwQT8PZY1QQX3CmVD4mIlrSdqHd/gmqC2OWIlRGJVsU9ULSbdTVhadQ2b1OSxsxY0h5 NIxG4vMJ7Ral/mC51V7r9tIwCPv95U4XGgtjnmGvGw66sd+Dv7S9glmswHDpplUsl4soGEOSLsd6 AKtWmKBhVNOByaV/AUjtUqprzybzhfob6jw5Dquu8+SN5stkAkyACTABJsAEmAATYAJM4CUgwKrr qSEPU13DivH1xwy3+5EFG/ktWsRHI2JyrBEmjOqVYFJQuaQYSOngB2ilGD3ecYSVf0YfyR5qkkLs SNcsQ4lV7L279rKGUF0U69owwIh5QxgLpLoyGstL9cXV4JvffWRhfs3RXAempl5ZWliAGaEpyGKh WK6YtucVyphKnKiMlx0XearN9fHcdlEkXytYYSTK15HbgejRjTIW50GykJVLynnaazYfOHgg8gdt vKprt7MA43ZYp2gb7qax6ct3762MN475fru52l46tbS8gHwSeqvQKoWwEIQgBujgxYK1FkYU64Xy 7qnNOGmooqnxsVq1EutYH5mutVYHndbnv3Fn1TB3zu3sZEGoRFu3zFVr45GKJiwqNseUX3NleW15 8dCRg9BZaHfXPAfBLlg3SEOM7k2Pz6DPfmlxJQxjP4pEX7qwPCR0qNpMrBTE3CXlsBBEqtUbl17+ qh9442vLkzM1qC8TAgtvkEJbEF0b0KlrnoykmogjiJ4qUl3D6T+ZqBI7EOXnYPTPp/7kIG6GTwNU V0jZLtrEGJPxzDFd6eBQGOqEF8ywnRILKIV4yxXqawM7jC7SFwZCE3ymdIMqsSC1CCzFA9dTXRlK 5W23kOo2nWEaK4izoTkLI4my100GDqV8lf+kkKGO5BVNseI4lOoSU4xx0Ot11zq9tXbXh+daWe73 ByXHxIei12qiqyuJQzwKUS7VLaJtDD4N2gtxNOrj180ietO8Kl0Fkmf4LOEs0xgvgFcPOscv2LfP dpyX4K+wc+MlPvH//e5ffOpTn/7rv6lUaJaZb0yACTABJsAEmAATYAJMgAkwASbwnAmw6npqdD8r Bhil3pDdT+In6gAXImH9Hmrlkm1I+D2sURSnpglZEjXbq6Vq0VDHFNXTzDwIltPEN/Sy60wsLi1M NgxFjyJF8bRCrNYu2SX7lCjVJTY6iluuhn6CHyAokCZLMuPkfPueew8iAkVmwzJzI8f6PwzBeRgD LFXwT2z6a7XaGRVCYSINm/8i14HUkjOFJhzHIKCOfISJsiQN+z4qnxzLDPs9NU4M0SbWh/0yNLtS sIse5YpMd6oxdfHuvZddetHWmYnELipR2Gv324MgVnI0OikB5JhP824Df7XT6yxhtrFt6/rWSgX6 AyNu417JVg0MM55anL/zvruOrSxigHBmcvNlF16queax5vLU5JSCDX664vs0T5dEUXN1xe+0W81m q9deGfTqY/Ueir6ypFLCokbHtZDEck+dXEAyCeXr1L9ONWG0mxBOCdKH3hsq0MpJJmZKoVzevueC a199w749ezaPNcoeCuHhj2i8kYqpRP8VLRug3jVasIi6ddoHACOUYn5R2qKh54KjpCnN0UdGfvOE XYM4F3xI8CvKdqUZslx4jSRTMFiKlxiQmqOcl5pSmxYUFpJQUF2oDkNVF9WI4SQyTG1i1lAsPERb GfXNy4p5inSRG9N0i1SXRcYKuUJ8JmntJ30Q5enKb0Rxl1glSQFBVJnhHYYxpSwi6NDaxghrCLrt drfb6SaDXnNtdaXVocJ+VOn3uj3fx3pFnAFaujC9iPHFOFN7meJQKb2C8clGpbxpy04IMFFTjxNJ UDEWYfhRUT7y4+/En295+9uvvuaaCy+8iPXNM/4F/d8+/rHP33zzF/7hy8/4SH4AE2ACTIAJMAEm wASYABNgAkyACTw9AVZdT81npLqEcTpdzoQUjkEKikQK9ZXTNyQSRCEXqrVgHrBfr2Dv3T1t6MHy 2srRw9Aljm6EutZB2CXTy4pShte5aI9Xqmd6rj50cPnEifiKvdTVJW/i5cifUO+X6PRKRTd6kmmt boJxRTRfYS0hRu/KRRNzcn4c6dgtmMKwWCivOnVqHlkdbGfESkKMzyFygzWP6A2npJKJviY9xqxc nAQklWKsMiw6bntlzcg1NGZhj95Kp6s7+lJ7TXUs5J2SIHaxm7BYnpqe3jy3Y9uW2XqpUqo2ts9M 7dq8aaJUiHFaCRWuR36IFvqoP1hs9lZX1pYXltZW13r9HmzZACmh9trCiSNHDx9F/qdar2iujnzQ 8bXmifnFWqMK49bs9GP0fKGpHRXnilo27RRN7abSR2xJV5H36kcBhB3I4CEo9F9bacF6QXFFmAsM Y2gh8lqwUwrVolFxGPJxSYILxgLHqdktN7zmxmsvvnByalpznHql4FoG4mwYTYSUwnN0PByqJk5j VXU9ynXhDU5o4cAoxCXiUk+wWvL3FGnb8CGi9BV5KuqAp82FNC1JUSoMjmLQE/ILj/UM6CYUpmHv YYI8F5ZOIs81rL2Hz6LMl8yOadBYcJ9UsSaWJ4r1irTyAGslkXCjTwsiWpQGhGvC3WS4aHECnoIX FaqLPq7kYxG9MnBi4g46B4TFzNRvdTsLa6211ebC8nLa63QHPl4Ic5Wo/Ieww+gpmvJDPIv4mhhN LXku3CJ1z2tasVgcn9mBK4WSw+XC1YVhgD9x8GMP3fmVL3/5wfvvl2Cuu+GGyy6//Oqrr0Z1Gv+N /JQE/q//9B/vuP12Vl388WACTIAJMAEmwASYABNgAkyACTx/Aqy6nprhz/6s3MAoE10j10Xzi6hd opvYticiQUOhgB8oiINpwC1bJ9/6pv250vmHr99zz11rGFVsdk9W65mh5o47pZjV66+c27atM0hW J6tTy6vaV24/OjcxhueTJZEdYOLQEBSQCOQ3UIcP1aAhRGX2I/wCzgI/K56VLnVakDSabi71/FQz kyiZn19ZOrUosl1U114tVfuDsIxarEoZewrRrzRWrQ76CG/FgR/0e/1auV5wSnam+1jCF/QGsd+J 4tVu23AsyBjM4FVLJTRI9Tsd1dCgW6YmJrbO7dq7c8+O2dmJGiYCLdcyocngsBxdxVilA+EVxJ0e etZTWKdyyau7zuLRY3d+85u3ff1ryBcZhcKp1srRlQXoEs9z4ZcKpdJYYwpSCGOZVa8YINzV759a WMKv6mO1zDRCI0ehWBCGruch5QadggYzpJuQhcIkHi0oTLHlMAQ8JIyE81GRNSKvQ8X+GkrDdl10 6XVXvuryfXudklcqFz0bj0MNPMrbUUimppRKyjSYJ0Mvlq0QCTpRvY54FIbz4IpQTQUaDrJ1wiKt 30ZOasOnSEw/kqWEUoKdgsNSVAgjE5X9sVBdqlKzkOfSsWwRdjBP4n5ArGgYEZIOw44I19HyxtEH ELqMWspIr4q6Lirfcl0XjV2ktoR1Qs5L9LydPg0oV1Jc8ilCdbnUC6bTy4tVCS6qt9JBp9ddWG0u r6wen1/sNtfAETGyjE4eDzARQcSwZ6yoSKDRACPWQSLlhUnPBAOpuu16JbdK2TSKsEHkxVBdGKfF wX/kLdfhz3a7/eCDD9x9113IK8kzu3D//quuvvqyyy7bu3ff8//L61w6wk1veD1CcP/qX3/kXLoo vhYmwASYABNgAkyACTABJsAEmMDLQoBV11Njl6pr1HY0egxCRLKqnm7rSmr4LbbckXZBV5ORXXnt tqsunr71H++587YVVbNtz79wf6nb7C+e1Kpjm3/o9bv99NAXv/noBRc1rt9z+R/99YO7xqs4IumJ DREyKC14IQR6YE5okEy3B4GCYUGYCpgRLFpUld7y2gIkTKbZQZqvdMNON+j2glazA89T9pyi7S0t r0FmQQAh4+OUXD/A/kfV0k2InFqlMj4xWS438lBtrTRPnTje63d7g/7svt0HDj6GmTkthVTTUESO K8aMHUbY0NPk2nBZkBoo16K6MQP+S8mLpaKDWbs890qNerFUdV1kfTBHV/bczZPTRa9it5a/9fDD 37jvOxXLTjEumMYHF0+dWFzBwaHh6vXx2fHNmmFh2C/G8GeeQkMhkwU3A+XTHvSPry76cYhmdfRe IbwGZYdRQ1gj5NOom0vHLGjU7rRhpmwDGxXFukoH5emYUYRhVMZnZvZded0Nl19ZrdfHxstj9XIB 0gcIRS8X0mMIc4GiDjGV50XRsQ51FEKB5YoFO4RRSR0NXwY8F00QUpsajSFSt/sw40dziGSd8Gtq 15LNbZhEpMlDaEo4LMitMMa2yBhPm3SNLEh8DGCKnYso5hczhgaCeZQ0yzDnGFERPeXIMEOZozwL 6TDRO48AYY6KN3KgGGoV6UJaP0mKFNcjlkXSx1PFjCKORqdE44q0TwHvRSZGM8XILSKIcTTodLrd VruzvLbWXGv2Om0LwswwEnpCjtWQdBW6jk8BdcWheQ1vB70CVY7BbRUct2CaWEmAH3Ad9I6lKbYz 4uA/9U9ogHH9hozhQw8//O1vfRN1VPJOhARhdiC8uNILNBYXF/75Bz7wnve97yd/6oMvy/8M8Isy ASbABJgAE2ACTIAJMAEmwATOJQKsup763fx+qotSPOvPkPNtFN+RtgtqCEohHwTNfRcV3vMD19x8 60N3fb1ZG5+amdXf/Lqpo0dWb731mGVWojxaaS+iOutd77lyx/juv/3CvTsmEFaiaAwVoNMLyCFG qCQTuR2KBymIKCHhpQcBRtbQ7hRjtjBNOsvNRRSBxYqeqEY3TAdhhqlBVM2XPLtSdHVFW1xci2Oo MdIXjfExjAourq66loPJPoy9obprZnrWs2quYqJgC9mdpt8v1sa+/Z27MwyjhUl/0KmWak7BxWAa ph0nGmPIK2GsUk/zTjCAL3NM24+CcrVSr1Rrto0sWBigVisLB/6phfluuwPzg54nPH9habHX7ozX yvOrzVw3G9VyybH7YVQulRq1MYwlYu/hqfZqL4u9UqHd7/U6nWKhQt5KUaDSMIqJ8q1UyaIMMSJ0 WSFdhK2GiR8OwkEfL0drLulOXUP/GNJiGiYQFZpCTCLMLV50yeWvuujSPVu3VsYKxVpJzp3mNiBQ UitLsHWQgnRo+Mf7gnwclA3pJZolxH4Bck5izaMWwjjR+4PAF3koIT/pZ/oQ0OvlCLYh/CUOhhyY eCRmISEaUw2ey09j6KaaqUemhrIt7J1MA1+NfWqJx1OiAX2KaAUk8nkI5ZFjo6ovxM0QT6OjkgsV bWT0Bw1aih2heBg+A7BN+KhAz5H8IncmarkwVYtMl2FWS4VUs0TVF126jqBeu+X3OmsYW+z3k8BH 6Ro+JygTw4NRjY/DOJB0Odr6kWWk4UdsdUQ8TQTgYhH7sroRhj7xFpH2o7WeuDSs+FSUf/H+d3y/ vyW/972H77nnHuS8Fubn5WOgeC64ELfzt9ILQH7p33zkF3/5l9/4xjedS//rwtfCBJgAE2ACTIAJ MAEmwASYABN4WQiw6npq7N831bW+h299io0aksguQCggjAQN5djhrv21d7ztxs985o7v3rVYblSq 9eDKKzcdOLT25X88vGN6e7lkd/qnZqadN9z4pkMne3/5F//4jjdcTKqLitFpwmyYE8P2QSgGRJzI aCC5g3opDSqCHAbm3zRkdEJNC6FSegl24zmtEFN9JswGjINtaViH5/cizymsrPZ8au6KBnFYr2Ge cVB0bN8Pse0P7Uydtp/l7taZucnGOOraQyVHJdOxxfkInfZ+kEYJKplgo9CNDuFR9Uq4TNSbIwd0 AhOISVIplKv1xuapidw0bE0vTNXGiy5yPlHfb6+1V5E6mz/Vby7PnzqJo9GcI3JAECOaDrMGchA6 8GaoqmpUayg0P9Va89OkUK9goq/f703Uxx3b9oMA/WCIF6FOHt1iWAnYDSFmaDIQbm5AxmaZiuBz 1R9EU5u2XHLJ5ft3zY1NTRZsF0N3PfRPpcFanG1uTF44PVOtunrBQa0XVmSmcFKGTV1oQR9dZwq1 X6n4OXZKRhJ2BwP0rGOeTze0BMVbUeCQeIJzpMlIpLOEVIKCo5uIO9FnwrAMj4qzcFREq2inJhRY EiGvRuOWSRphNDJGgAwDpFTulqJsTUt8vOvowwJk0mc0I0mCj/SZ+HRhEaZY2igjZHgtqdLEgKJ4 UfSMDaKYervoZ9oWSWeFq6OeMKKNS6sX3Vy3oQKFMUNizUcdPTYwdnp9BLswe2ihqQyvJRQaxbby zKb+eQUjoOh8Q2xQfMyplgvYLUx62s4gwmylgsulmv9c6YexnI78F+/9gWf86wxRprvvvvsJlV5v evObd+zYcb5Ven32s3/7P37rt373935/67Ztz8iNH8AEmAATYAJMgAkwASbABJgAE2ACT0+AVdfT qS5akDfyTiQXqBBc7GSkO9cHGPF/8ulhkAgUqtFSQx9s31d9wzW7P/sPhx741qLneqbT27Kj2G4N Oqtmya1HaWfP/vJ1V+xabaaf+swd3dX4h95wLY6A5A5s16hxScR3kClCnxSNqcFvUe09ZBgt5kM7 l2heCmMUVEF9qFGaw/5Al1BFuW6gWZwsBYbuFMgvGoKjgysZJt9UGJEYlfRIRMF29BdQRD9IXMtF bxQOBJe0eetmHATyomRbiEgZuusWqqhqwu8hlExEpzA5OQj9bifu+5htU23dKlmYHuxiyrHfRxbN V9Ju4MdJmGLx5NKav9ZsnjxVNO2C7VVL5UapEkFQ9XowJ7ZloYoeKFFIZeOc0aDl2gGKn7CIMUlg xvAAlLuLiToa0bNN2hmJ6jKEjtA3jzb31ebq0sKC3++bpmuolu2VZrbNXXzBvgsu3o9RSs8uwQsi d4ZaMlRNbd++bXPNzSwH+Sfso8SvVJPkYNbrJJoBxwelZIW9BFYo6J1q9YI4KFjo/8LsXo6msCxD n74F1dVH7Rnep9gPEZkDbzEhSIE8dOGrGqihykrFyKlpNgom3iM/DA14uWiw2OmtdXGyUdkrThRt jDou9QYDv192THy4/EEwEKVdDY/OZGXgg4dFKxhRvAbVRFVx8Il4n3sxomj0H5GpoulBFH4hZCfW J9I6Soqbyc+psGIwjBOVAsJZ1AIG2lR1hs9EPAhpWSdmRbFBAPsKCrhSuCta5ogjIdUW0cQjOTs9 BCYqj7NwwfgPLB72epqaRT1i9P6oAaJnSVzATlBF+fH3/uCZ/80L53Xw4MEvffGL6GWXz0Kl142v f/35U2P/r/+PX0CFP3fSn/lnhh/JBJgAE2ACTIAJMAEmwASYABN4GgKsup5WdVHp03A6UTxORGKk 7TpdqgWnQINqQnYZCEa1B82LLp78gVdf8rk7Dn71yw+Xy/XxycIPveWCk8uLX7z1e55VHh83b3rL 9Pjm8VMnDySr5r0P9yoO1dLjmKJ/fCTRMIJHM3DUACZeHS8kpyXFGKWwbsNfCSMnXJwYZ6NTJH0k u8Q1+C7K+uAeHceHdqEEDvyR2EC42BocX1hJY/QpBbi+iUYD5gNF857nIbnjmK7nNmyrkadaFmLL YgAJtdRuttrtLAyLhmepWjPuho4eomoddWCVkuY6A02NMP5m61GcLCyvZc1mfPKUrWqeas3UJ4uW kwSBaLcifwRngwWRupI6DlrzbQzIxVQ6JZJs1IIO5yaoY3iwHzqWu3XnnFcoVgoFx3QGfriGpZPz p1bX1vpdX80MGLLcNHft3n3BhReYpl32Slgj2IXXC3xL02bGG14RY5HVQrmsO3bRplKuNI41v5+a rorNg35/deFYEgWwYE0/UNLQRa2ZmmO0D7E6uKsBAnSKiVCdo2vLrXar1y/bFrZGOqTNVB9N9hit NAxMaOJaxkvOeMk+1R2cWmvBw8XQZ8srC4urqFQrlSqT9SpSYkvtNkxcDXJN1cJogDPC/GmjUMRA 5snVFdtATRvkIY6u2LqB4FsvyFxVbw4C9IxhiyLMlGjKAi4N+wDwlmFhgTBu9BFAAgs/Q9BCF4Il PhIIqeHhIUXidEwgkhSjhykBVgDALY6q7XFoV8/Qw4bwIERWmMGlUmdZqhho6MdpIzuYIcGme7QT QAxMSp2K3ZJ4s37ifY/r6jrDv4Jljf1DDz74hEqvc7vGHlf9o+9+F3fSn+GHhB/GBJgAE2ACTIAJ MAEmwASYABN4RgKsur6P6vrQ+/ALqhXf0BOPe6CayCHQdrtRZxetYIRqwhd0A1JITqQk0zPu5pnS gcOLRw4EaG+qNtw3XLf92w8+8shD82PVyf37MGYXdCGd+h0tLHzn/t7VF+96OtWF5nf6NXyCqMWX Lky0jwu5JewY/SHOVSwFpEp7ugCIEktTxPewH6TSMjFzBm0hsmKqpVkFP0gwvxYFGPTLXMv+zj3f olZ3B+Nr1ImfpI4fmlmY4kE4wCBA833XcZxKpVIr1urVWt+IH1mbjy0VqgXTj+VCwTLhwCyMN64u r6J0C6OL2M+IFvN6oeyiVD7NiwV3emKy0WhkWCSpq2UMGiJDlCcwW9A2sDO4GEwoLrWaKLNCSAq2 SaGcmTFWLE1vnQ1RHGVYCDblYRhjIJOSSXEQpN2Wj8lGt1CYmUDXftLuD6CiIH1wtog/IQ+FMU/D 9fbObdu5bdarYMEgXtAoKMlKZ1CsVAq2fnz+5G3f+mbQD+D5LAwUqljDGKpZjFJ8RJ1KttOLVWy2 xNjiRMFt4fjItWkqyv5B3oZngpbDBRoWqtPsQnF2rFosFxcRamu1qkVHwSbLMAwHAbxRgsCaB9dk Qh85pua6NskjVKrl4bGVTsUtoo9tub1iqIhWJVixSP1oVBdm+FHuUuN+jvY1fECpbAyngxwcbjBh eOeACzsf8eZj3hWjhfBh2JmIJB91aYlWfXSB0TVoBc9Dbg6fFsfEpeJhGs2T4oRS1HPBikZlGwfA /KIapCgpowWh0Fuo6xIqDSk25PtcsTOU/rVAvgy6DIk/3J5VquvJ/wau19jf/tWvbqz0uuLKq869 Gvtbb/3Sb/z6r//HX/3V6667/hn/vuYHMAEmwASYABNgAkyACTABJsAEmMAzEmDV9dSIfk6oLurb xqjXutWi4JYcCKOb2JcoesGFZ6J6plxHrqVUrlhOvLxyGGOGajaTKDAJ8DatE6dOTdY3G0a522vZ ZrsfrnYHlq2ZRWfqbW/evUF1yVOiziWIHkgpVaouGdlCsAxdSfIVRS0TJtYolkO2bX2mUp47BvQ0 VKxThToJCKgueA2l4NmjB8KSoXJK1cwqaqtQKBUG/bV259Shw5iTQydTnolm+gB7D7Fqz86iuOgV aQeiAW/lmRZyUQWrWFxIOg8P2mgJQ7FXIda1OPE7vYJhIQV26uQpCBDM6UGUAdjE5ORYveGZZr08 tmV2tlitGp6n2Ra0Cdqz4MpgigYYxUzQJJWudftwJ2RoEhJwLlJmhcLseC2L0YmmuhYCXFGv1T6F lFS3T7saE6Xfi9D+jhZ5bHXMC8Vk0O13oI0c9IiheMuFvsFpaMbE2PjY+ITluEDqug7I9Qd+pTFW ttQDjz2EoVOMJ+qWCaoISIWYZ0xRtI96sLDoFVzb6wdpqzcYr9Us24HeCsLYH/QHgV900dFv9fwM Dq9ULOn4bbFQrlXwRlRsvVD0kM5CV9ZqL+7003B1DcVjtm2h6aruYXKTaOIE47C72MSuTrwfkZp0 /aBPM4pKRr4KobwEyx0NzDJWXRtvnvRT0FF446lbn1QX5CFSXfSJQaU87KiYVMSqyXzg+/T5xQwi fqTD5QE+FNB/qmpBeGFk1TDQMU8BQFhHFJ9FA8/CcCyUGjJceBRFw/qAK2wrdZMpOCosKk4Bggtv b4pxTsww4qg/+2M/8ox/9ZzhA2SN/TfvvhsjfvIp191ww7lU6SWnFz978y2245whE34YE2ACTIAJ MAEmwASYABNgAkyACTwNAVZdTw3nQx96j/zF40Nd9P/vN6xgHD531N8FpYAZQ8s0PWiBIGgjMaQr FU13qJk862MZXhgjv1NGSZaWDOKoH2aIKllxrL/htdtwLNo2SCEZWDPRSY4VflR9DhklU13QE9Si 9MSbmFd8wp3yDiokV1HPBD8i5hmFLEMlFo0Eykfouh9l/UFsoFwcMZ44arY7SH1ZVGxuQ75gnNDI Xdua0LFdEaVUWCWIcBBG8yCLqpUMaqfk9Izke/0+rncw6FYye9AeYBOkq6iLmM888D04psxCX1WA Xqqi52Jjot/3bdN0igXTcdEhhWlEp+iWPbNgKrZX8LDNsVTCpBzqxHz0t+MlbKeoay58neVsnp5U CqWgtdZqdg4cPdFcW20tLxYrNc0tQTSmWEOoYQmkjThTr9kCR3hKqsyyIIZQB5Yh9IWXqJUrjq6f Wjh1+OgRBJwwMYjme5qVtAzN0RxHc70C6SFDKxfdIPKRc6K1g0pectxBiLAauUOcJBBC4WG+z8Vi SxPPNlDTjl9unt1SKlehnvBEt+iqmAV1vaoOX6UYedrt+vcfmE8HPhkp2amFNwrDpLSsUbeLRc/U D52cN9UYWALEsfLYJRmYk+FT8J2D9FyG9ZeI2xkOklUZ3jwNw5ckSvDWQm0iuSU2McJkGfgiG5bE 4aCXIOhlWOKTRZ8MCmcJVwuPNTwJEVekKn36lENchfC3oojOBEeUxmGlAJXW56j1F2YVs7DohkMA Dkk8mDKAoo+Z8r4ffOE3Ccoa+3u+850nVHrt3r177959Z+nf8nL34o//5E++//0fOEsvgU+bCTAB JsAEmAATYAJMgAkwASbwSiPAquup35Gf/pl3rf9ilJV63COp/EoUG9HuP6rwwhAZUjTkHJC7ERsZ xTyhDH9BEpATgIWAuYKxwA9iER4VG1G/+bWXb6MfzkB1icqtx93EJr6h6hJzjPIms2ZolYdog+oS ryUVGh1i2FmOB8S53g0yE0N3ihJA+QR+pVYvmdBcrlVwqYI+8ULfg0Ry0TcPpdX3MTy4ZXrT3Pad W6bH9ZK9qCZHwtDV4eWSilEaA5Aoa7YHJ5dXTswfX1xdQcNXP+gG4YDOHQOBfogwmeW6GL0Tqwwx JIiiLhouLJaru+e2zoyPh5lewUpHdEuhmF03MKEYRFgDqKNCDNapu7Tw4OHjuT/oDHyomn2zE5pl HVvqtps+ljBijhSRtl6MYi+76LogbpkWomQr7fb80oJTLM5OzyCM9MjBg4cOHcC74pYqwgbpY7Vq o15sIJNnOsjTNQcDVGUhF5XCTcJTkWg0EAtDzg8MkYGCT4IWxNAipA8CVmNFrxfGRai5QnmQQNVh qjKGA3PQ3i7DVqjARwgvTptrPaSjMMUIMWVhhpE696lXC5LJcbwdY6UjrVacBgXHQKM9MlWGjU8U etOQIXPaqV73vGOHjmD3JHhicSTWfoIJfB6uBVcqP3KId+HjhFPG6+LAWP2IpZbU0Ua3YQhx+OGk bOCoAU48F2eFujRNwzxlgFenDCFlCdHyhTcLjksWwmEoEh8jmz7ikF1YFYpdjXlexDoERXnvO9/8 4v1N95SVXje89rUYb9y+fTvmal+8l35hj4w5zV/+pX+HSNen//pvzqLTfmEh8NGYABNgAkyACTAB JsAEmAATYAIvOAFWXU+N9Kc/RKqLdts94fdDLzAaY6TpMSSHdDJeVPQuRJaCGiPKf5GEEn3dskoL ggAhLVEYD3EBiYKnQHVRwfx1l+0gF0UDk+K3YnoRXwiCPSHVNVwKKW2WKA7bGDM7fbpkL8i82QYC TaKWHnEb0UEur0sqDxgSPASrEbGsMArTbr+PGb9iDbYqoSgU6qU0rA20jLiMevtBHs2vLHXanZJd vGTrjrktmxu1cuCYHSNvKVnRou2EBcPFQ900WWp1Hj21sNZcDtKkD+eSRzRnia4ndLxTLTq64w2E oeIEY46RApuDJYcQJ663aaxuuYVMszxdh6Cha9B1eK408guIKGFQMVcWlpYWl5bTBBXxOhYCVkse rAuaxGIfKx9j2lEIsFEE+BEGSoVhxKGw2BGV6o2xsR1Tk1jgOL+42F5bBXKE1ZB0wq9owtNEvCpC WT3SVJTQgiYDIRUzmzQoSnsLUzWM0Yel441E6gnrEXGOSIThoZ5tYk8i3jqckqWbeDsxfSnWdtKM KXq8cHSRukKTFk0lIltnoyQLCox0FIJiiFpRuX2tWsqhAVHjTxs5I8g9ZN0S1MBRy76DBFXcjW69 +/64tUYfLZxxSFsQaRYSaSyxIxT/RMUXBc5EzpAIQCsaJg25xpgkhafDpC310EN+YYRRflzxTOFa KVSoksgDARJt+JygmQuHIIxKDjWLt4Baxegz7YnMIZ5Bv4W1dFDbpSgf/MA7X/C/qp58QKiiw0cO Ixj1+ZtvXq/0wnjjZZdffsnFl2zdtu0lOIfn8xKf/ezf/o/f+i2OdD0fhvxcJsAEmAATYAJMgAkw ASbABJjAkwmw6nrqT8XPfOjdpISGbVxPeoyc/hPTXtAE5LvEHBqFaSBnhAOQzxHiAT6B1JW0XeLu x80bQjy95lV7cO+66iIzJtJXCBOJDYw0FCYVxHqqiw4pVJdoDRuaLbEHT7yAHIGEMYJzUUx6Mo5D heUUwhHV9kKo5ZqN+nSvXPGKPT/p+VFRzxFzyrIIZVVGwepjO2GA8cXxIFVW8hBF6dEgKFnli2a2 XrB1CzrXY9uI0ABmamXHSjXHztD+rqG+an55+dETJxeaaxBJ/Twvm6qJeBTg0BfcG212zHUbNgaX GGOQToUVii1koBy34HqII9m4LpGfQmc9CtdRJBaEgZqgHj071epCuCDjhKvqdjqYv8tIo8G2GLis IIroQpGrEkYPT1/p9iHO8G7qttuo1zaP1XHYh46fDHs9pNYizCoKLwlNFSRBlgegJcY1tUEYQEfC HLkOaupNLdP6IRY25i6avBSljU56eE1dL9gWOs9QaoVzg80rWFbRshIEoGjbYYaJUSw0RE88vS8Q okmy1hug2Qy+DM+CaYJUBGzMMNKnJ9PcavWCndN20aKmd7y0Z8DlIZ4Vp+ogV5B9C5c7X4DqajdR 8GUUihqCZjB6lgEDRZ3zaWKhKT/E+kgIL6BFzxdkrKFZEJfUfE+fSfSzwQNCe+HqMHhIsTQRL1xf LQq9h+2feQRBR21xCI3pRpjErSCSH2N6K3NlzKvDc+EiYPNwrVjsiFWauP3TH/2Bl/hvW4w33n// /V//2tfWxxunpqcR9brgQtwuegVmptBB9gs/+7M4yd/9xO9xS9dL/Gnhl2MCTIAJMAEmwASYABNg Akzg3CbAquup39/Hqa4nN2GJJ5ErEtVatANv9BgaaRv+Uion6g2nhwkJJd3UaMpQzIHBcGT5jVdf iAdDdSFWI5vvhRrLqGYcOSjZ1SWmxjYOMI5elObf5OtT+xKs0cik0YZFHSNmlOqC6oKVwQPRqzR6 ACqddMuwk1SPEs1QqXKp5FhYsRdlPtb1RYbWDSMzsotZbTmK17Dv0IghXapO/cJNczfu21Os1OFC YNJsS0NKC4Io9zsYb+z7wfGTJx87fLg/6JdsA3sRMfw4QHwLASiSLsBl1huNveON1HATxajYeaAl vTSEVYFXwsglCqICCBjU/MMcQTHRLsQ46Le/feh41mtnlr1pcmxuorbY7hw7dtzPlU1TEzNeAXsN 5zsh7jQIqsgf4f1I0oVWGzEpzBOiUcu0LHR+QZhFfh9V9si14R2D1RIVZmoQ4TQH6OJCRTwSVnh3 8SxIHGSmbMdF7qvjI5GlQDFBxiH5FYQh3mDUs4k0FXwQSvlR1U6LEWn3oQELhwlHlFlBlRmYacQO xjSLTzabiP9hJSVlynTDxRAjStNiKuHHMUr1xuU7t5hFd0A7JiMT+xmxRTPLWijuSjGMqXd6aXN1 UUPkyrTcUrUKaaioGJPE6sR+GC20u4Nez6OZWgP7HtG4D2OG42MXpygGo5WNeAtwIng5lMhB6Qmx itzZSNdSlA5TmREa1rCtkTJfiIShdw4JRvoI0ueTPpf4VCYOcor0aLGBES4MTfX47Qf/KZnil+Um tzdCJG2Mel24f/9VV1+NSq9XyAJHiLl/+5GPIIn2u7/3+6/89NnL8j7yizIBJsAEmAATYAJMgAkw ASbABJ4zAVZdT41OdnU9eX5xPY4l4lGU0ZEpKZoZoxsiNaIOfPS4Ycm3tF/CSA1HDqXOouwM7s9e e+VF+BGrBhEKEsVJQo3h8BtSXU9WXXSE0wOM9JL46bTqEgOMlo4OLpHqwsGgs7C7EGXzIt4lTgGF TEoP/iZMMU6IDYPoge/4vbV+G8VaqkNF7ZpvKgM3sq1VS+mXTM+2Z8uTl8xue/2ubSWvisFGGupT Mf+HEifyfqlhp/7gxJEjjz1ywPehwjRMAhZtB5VdtO8Pk3E4j1SbGZ+YrY/nOibedBPaRk96aoCo EGJWBrJG6MPXUYxFhVceJIsF/ZWvLC89euQQlhSi+KpSLFRLLnZYdpqrmLQzbcz7OThjpI00DDbC w6Cei6YFsXQxbg+CHFoHjVOWJ9Js9IeDM4EZhD3C1sA8MSyLQm7Ye5iHmCiE5YK7wbtKM6UYXKT9 h9TFHmCVAKlDDPHRGCN5TJHkwzQiHld2na7f77ZbNBiIsjMs2MTrp7nnwWtZMcYw4xiHKhUhGB2Y NRnBo7I3OhO89Rquv+QU29j6iBFBPTYwN2iotJASey0jjHnaruNFCOsVEEXDC2qW4TXGiplpjbvu mGtEGeRViiQWjBpmNqGzWnB3yKaFwVqbplPhZUm96Tp+t9TuGsiNYT4UZWlZZqOHf6RhIfLyDIOR kaZiNFMMkaIZH51ftBIU+S9cM83WmplFqS4KL8Ln4kUTW6iuH3vvSzHA+Ix/68EoHTx48O677oL2 Wn+wnHBEmf3ctrmXJU519MiR//Pf/wo810f/68cuu+yyZ7wKfgATYAJMgAkwASbABJgAE2ACTIAJ PCsCrLqeGpdMdclZxCffZI03eR0yHfKGOBINM6KtiaJXp1UX1UuJucWR6ZK5LPk0kh10e80VF+Cn QRBBRTkmRtVgDVLkcEoFjPjBxmAbIVqnyIoheQPjgO+h4agBijYDwpCY8D4QEIN+MAgCekUplRIF BU5koqiRy0RJO3WJIzhmmpGWt0JkdnJX97QIVgmmQ0ePez/LO4Pe4qCJSBfSRHEaF+JCTZkINHVV SSNHr5WLtfrE3s2br98957hlrDLsIxWmoBKLdFqqu7iofqf56GNHjh44CHw4rJprmxoTnUSzdRXx LhgnAwEzuCXDEnVVpmOkYZ4Ecbfb99uddojBOqdQq5RxqNbqSub7oeVNTTSCgd9ZXkLDPbSQYhgY u8ToX7+1qmOZZaa4pr3UDwEaQ4URDFmSeY5dsO1B6Le6/SAKUTbvWtQlhUgWNFaQYCMiJgs1jN1F SVwqFiChwjhEdiuMcU8KJwTPZSGoRXE7eEiQ1l0D4am82ekhomabVrGAV7BxMmgNozlH21zpNPs9 nGEeZejmsjyEyCjuZQJnEMXQTo5jN4p4qk1zpJQ7oxgeolY0VKiijQyyy2y1A+r5UiAYSQSGIrcn hBuOY8E21SaKpodCMRXisIhvkEoznZD6s/IwpXMp2rGWhHgardvEOxLGx1oDWuOIFnssTkRFWj9S gwAGTWpaeptQDgYfKqZu8c8kExsYSbaSM4WXxcdKfN7guWg2EyfeD0Yfa6qCg/VSKug2U5R/8sM/ +Kz+GnoJHgzB9NiBx+6/776N2ms97TUzMz05OfUSnMatt37pN3791/FC7LleAtr8EkyACTABJsAE mAATYAJMgAmcnwRYdT31+/4sVNfIe8neLir2FoOJQwEmxIFIeYnCbyEMZE3Wuu/CN1ddugt/Qkl4 NOiGOnZaeyc9GjwX4jn4DxxXwcbsmQczIqNbjqXBjiFvgxk0bCrEpF671SGZAq2FBYeO2+lGRbiJ EPkcpVjAfxw9Cgc9v5ukvpYtRd0oTxuFse2V2UKe9AaDrh/4adLN1WbexlAi1FCQBvWsfJG7y7JL A5yTmluu6Y43pmanLtwxlcBWobee7ielQleUYnbQX1tdufeRo4snTkKNuI6jWsVrt+301QIeUDAo AAVn0h348EfwXJjCg2ZBT5aphsfml44cO6ZEvlYoXzG3KdTMex89HK4sObXa7t07Kq47v9g8fPwg irsU07po587xavmBg0dWoMNUY8K17ptfVrHWkOq/qGbNQtTLgBFSQwwlxujFp0ou8kCQb3mOdZM5 fBZN6mHyLwMvTDf6UYjydoS8BnEs9BUyVRTugztDPgytWhXXwohcqzNI0djlOPBhjWodQ5FwWRXX hqBabC6TlRRf6LXCW1Hx3H6MHrSggPZ6dMUjsgZMqkrPQU8WCsZgIQ0MP0Jx4o3OMAyKCBVlyvIE ANGlheN7jgPfBNvmoywsisemGk7RQfuamsRYrQj95NluiDWZ5OSwsxJ2sauZAY5c9Dzk+BBUmyxh 2JDGEaHQkjBsL7YPHltG/gvl+7B+cFgYjaSJTWF3TQ1ajHq/oLQw4Enzj6aBb+C4UPKFTzPOEw+D KKNRRqqTo2gbXFjNQ4pQ+aG3vumV/PfpU2ov1GZdevnl+y++eHZ2dnp65gWv90J9/p/80R9i3yJe 6Jf//b/HNOUrGRGfGxNgAkyACTABJsAEmAATYAJM4OwlwKrrhVJdYmYRJVNim9266pLFXeS6NnZ1 De8jhyJ75a+9Yrs0X8hzwSRgMA76x9SMXoAZOngZCtHIInk0QGG0Dw+AHoJnanYHKFEqOJAsOTJT 7Q4iVpnnWhAW8A7QIrARfoCVgOZ4o+ZY1V5ztbm22o7CzNYTRwmMvO5Ut5e2ZANEnzrIhEVoV6+U 23kHM4FeqQi7UvTdS9ydNa/WilLYrFBX9+ya3bp1otGoRIoRIlgk2vLxKx1X4DlZ6rfXVh44cHz+ +An4GggUeLcLZrdjoSNOxzHUWDNCb8JN/SwKscEQOimkdYSZrcSPnVo6NX9KT0LFplWMOPEjR446 yEK57qZtW3dPVA4uNo8ePWZTi5k6MzlVKJYOHjuqhgFcHnqt5BwdhBG+cIMiQooJMgeV7yinR6c7 LI8cGcXDqIwLJpE2FdL8HvHCzkfIG6hFHcXzFvwR7qYGK7T0QwjhibpeLzhox09C5OwyzXZM2xkv leGAsM0Q0TDUkakxFedjxg8nM4hi6E/YLmjKAIVfhlkytXavjy4tWC6oLigkhL/Ktg3DiURVhBPP ciS+TI22JeL1sSETRVkF160XMHqJGJfi40G2VaiV67USHmkhvwclp6Cb3k5wARgsTJA7S8Ksldmx 4zj1MrkuGFbkDeHvsGcAp6qGfmex+cChU2gro3lELAGgtB0ZQCpSQ9YsRq4Qz6CgFm0AwGwjdmSO FjXifiTUIO0wSkkBMKG6khwNZepYycV5v/umN5wtfydCe508dfKhBx/EFzzU+mlL87Vj586xsbFN M5uqtdpzk19oDXv00Uf/6tOfljsisW/x3e9698syOHm2vCN8nkyACTABJsAEmAATYAJMgAkwgedJ gFXXM6ku2CjRYCQWz9FNji6uzyiOWrqGyxXJSAl/tX5cjBpKtUXHkGpLHmn0M7599ZU0wEiDjqpG GSnqmEIrPW606A7xLiiQomNi7gwuhw5A/VH0X2gZpL9qxYKfIVelhz5NLzq2jd4nfMGXdQch5gFt r7BjquEVK8cX5pdWVwIl7irpGiYDPW9ffdPllbm4Mzi6vOSnQWpk877f0voQZgWvgFL7cse6yNg2 UW4EEYba1FDLq5sae7fj//9XMwOLCGlyDydFp4OXhPhKwpVW656DJxdPnsDCv2KxVCxW9k7PqpoH z0T1WYat+W3onuMtVFJliDDFSQxPB0vY6nTCfgeZI/ibAOLGMDO/Z4GCYZRrdfigTt+PB77vD6hd 3XZV09YGHYA0bAud9/0ko1AcGS8VYSQkkRzbaQ/6g0HgIYyEw+ErVzp9hOFi2BscECOgUFqYG8T3 wO4HEYYTszhByzsOhfcCygwI0VrmIOZmQu1FaH13yKCh+ctF1zuuHsksCDG8K8hGufV61cP0IM0J YowR6a1mL4COLBedfpo5ah77yJj5WAGAZ6IzC6qL3md6LD4ANOxq6NZ4sYRjxjmCXZikTOHESgjH KfgYYAIVTjMNNKWMjB92O1L8SnPwcqqlQr3R0kULYaxetJw2T6LCDKIL2S58Kjt+FMDYCSsHddfp BUcW1mz6eNMiSPqYYVmAoeMDR2eiYP9iZiJSJkQk3lxZS09bFESeC3eJZjZUq4lCOQy3iv0MSL3h t+/5oVfcAOOZ/EXZbrfn50+dOHECc47Hjx3baL7k09HzVS6XYcHGx8fxY6MxVq/VnnBkjEniniOH D588eXLjOsi3vP3tb3vb25+bLzuTk+fHMAEmwASYABNgAkyACTABJsAEmIAkwKrrqT8JPy26uugm pNaoypziSwLb8Ffi++Huw6EEo9Ki02sS5ROl3aLHiTZ7+q8cYBwJsUYJzog65kkowHGJw4t4DVQL ubYwStADBe2hKTAzIn1Dv8oGQYgXqFTKxaKH+9FqhcfDykCRQWpA1iDSRbVTrrdt01SpWFlcXlpp riaG0oyD41kUlQuXFhrXje3orLSPLS11kxCbEE8mUctNfCSNsFjQzKc6+u6ohi2LWBVoWy4m+vpq PLdj06aZMd2yMQOJsBJKr0j/wVtZJuxb4vuLK8snjx+L+j4KsyyncNGmLY+tdpMY45Z6tVhCYRbO pAfjQ8XumMsj2YLrxwNoYjOOoXyQKcJUY7noYkQOxViwbDA+aJCHhYngwqC6MG8YJxVsf1QR6aKN jbrj4nhQUtQklaQQPVic2Bv0u92eaxlIYOH0DEVda3eDwMfEIkq7kIhCkArEiwUvxnxgmq012zgD HN5Bq7xlYaQxCAKILySoMK/Z6jZ7g64NxQbTZRbwVMqCZSmqvooFGDANawh76K4X439QjfJtjjCW aJuu68Y49YGPiUoNsS68Bl49SSG8cDlhHGOxI84b93qWA/kVgEOKjFgiOuZxPx6OkUfk+XC++VgJ mTKEv1TkvHTU+zsllPfDm8FIImcXBEupvybODbEreM8cB6dpRMCl7QcwcWo/yhA+w4mg342EmgFh aMGGIZ6Gzn4k45Aaw4cwiEi34Vf4HKIDDekzi2KFuDTadCA3HmAGNMwS0B2vVnDF/+bnf+bc+BsW ma+1ZnN1dQXyC1e0sefrTC7wZa/AP5OT5McwASbABJgAE2ACTIAJMAEmwATOMQKsup76Df3pD/3I 0GKJgBaFWcTgm7gT4ZvhnXKdH7mn0waM7oJLkMcVzV2ioEs8RdxEdZf8vUx3wRMEfXyDxqfhhkaF ipNgV8KI5s3w5D78CBrr4RJoTR4dDVIMaaeF1Val4OI1XA8aJQ6DCAbIMA2oLjyq6LntLub2NFRZ 1Rp1QzVazdX+oK8W7MBUFpXYLzr77cpWtd5fbs6vraBeHsXzbc8KKupqhr4nPDHeElg7g0pJL+Ua PAiyPJnp6pOTtWqjgBdKNAs7GeOYyqfIx2B0D/YnT1udNkYRUz/E2RZLpX2btn3tewd0LFRME9dC GsuEbDJJwmCKUOTmsgwXBckCHQOVg//IvnbDxALH2NQtzPZBCWGqDrEjqD56U4ii6hhmGAai4d5E yEg37XKxAF/jBwFiaVBdUFX9Xg9vCXrw3WIBmqaPVFh/AGWDubyC5wboog+jRq2K8wZteDGkzCAU sZIStgvvAMYSxQ4CvVLw/GjQGXTxwhhALLpl5JngJaG6YKHG61V07C8vLq91enIrIV4CEou6wfBm GHqtVEQirdftBv0u1efTDCOK/xMIN1w7PgZ4AyGm8J9qoYwCLlwvkliozcK4JHQb/CjUHZQdhKDf 6zRQR29Cn3m9XoCNArrp2o4LaBHK6dXMNALNjgNE+mL6E2c/Xh+DPQxCahLDdCyCcj7OL0VJP1YD 4NNkQIqS3MQ2TaoJy3DhEGRofcPZy48lXJvQkdhlmeN88NmGfKNIGtV50YQtPpq1chmf5F/7v/7D OfYX5cbLwWJHFL3hHkw+DgaDJ1wpqr5Qk4f/vDQ99+cwZ740JsAEmAATYAJMgAkwASbABJjAcyPA quupuUnVhZtck0eiS9zIwZDrEgmtUYf3yFqJtnnqppfzjsMHSE9Ak2l0G44uDr8Rlfb4DwIz+B3S MwhIwYzQPbARlJDC/By9Nv4hBiFp3Iw2+okwFPQIfATSXhgDpLuUvN0eIPqENFIXKidNMb6HFiqU MWGKsFytBSEiSvTfzLE6SrKmR6nn7vUatY7VX2umauZUi201OQ5z4SXH2n1IMyXx9xTGtiaNPFbd YhVDjSikQmEXElEq9gVaZqNSRQqp48cN10b0LIwCtDrhhPv+oNla6zY7cFvVSqVaqh85eTKPAgwf lgolzbIUtF+hKIpmH8WOSqTQ0hRkiSAgC4eIPyFQaEQSdKiLC3suFVg8/FMsowQomYAjLBBHpWKV auexOVFUqtOWQ9gZxMTCAPzIqJF40qAIIZgQ+8Jh8TzRYa9Aa4myKqwehC+iui3KTyGlRRswKSWG Nx6aMcsTH8sLYYsMq+AW4XqQTaOiffGOIMHWj8LllaYSZ1BacZ5VqmWYMlAaRBFeDp4RFqlsW35/ gPcRJg4vIZwRva30ScuzTqdb9kpIWsFAkURDJbyhw4LhMpMkhICLIr+MUBnJN912ip3WgCreDAxX UsIOU5v9qI8WNbtsprSFAOOaQdEt5KqFarMA3iuIHAT/ikV0e5GkEgX5YiiWRmjBkzKDIoxIa0Bp h4D4mOLccIokGenfA/wXATHMNFJJWYyVlcjVgYlO+ygV5bd+g5YM8o0JMAEmwASYABNgAkyACTAB JsAEmMBLT0D/8Id/4aV/1Vf+K95881/IicPTrVvy//CLe+SN/g8/JAuJmdPNXOLShpEuUjDiN6KE Sy5iFIN+w1TX8IB4uJ4bsDhUfU5L9bDEUMWavhgDdQmSVcjKYCKNKslz/BKPhWWg2T4oGxq+Q687 hugGrUGgaOjWMtwiapRU3UX+KhwEFbuIsUiUv8eDWLec/uJqoVCMB2GkZFqpkCMV1AzdPtyGTbv4 kNoyTSxZDNdWFKegh7EVGxW9vMlw5xcWkFSK/CRHk1a322+tVscnPM0wg0EJASWnoBQqhcFKYXbO sgu2rcdhL/chQVLDNSoobo+SoNMvYMlhqVLzPESJMCQo5SHxEihpb6A0hQIhiSmIFqggKRbFgkvQ FissybwIg0jaER39pKxMs1YtwXRBvoAX/Bgm/2L0xCsq4lmGYyNXhS4zUTIFuUPjiHgWvRl4PqRh iAk8uCkYLt10MMBHrVUY9cNxkKWC5UL2Cm+PZqleqWA6nq5bWp5Gblkf9FH7j64qWER/MMAbFKB+ HyOQqYZ9iyixKhRrmW6tnjiuUFIqytG4FodeYxItY6Hllis19H7lyJFBEgV9vOVaoa501gYkxTTP KaJ1DbsFSE55TqplYbcJEBXk72LMtNYU5PwQxfMqeCvTXkdzbaNQsCzUh/WNyVoaNBXLKE9twiLK npK78SDSLEjQzB/4yNGhSR6xLoWybPhKEePC1sgwRGqMolr4QOAjiP+KX8O24n6cPxQh5B+F3cJI DDGShZUKGG8B7CC+ecubzppa+lf+30V8hkyACTABJsAEmAATYAJMgAkwASbwrAiw6npqXDff/Omh cBlqLFGyJebm1mXXMOb1OM8lXM3jbNfoCfBRQ9m1oe1rpMgsvQClRdYK1gbeBCOH4k/sEIRAwMti QSAFoOB7NLR6IQcEZQYRZPiwUba3bW4vBgcx5TY7t0s1nF6zm8Ux+qRgaWqNyZ0X7gt1E7kezbB3 XHpFaWJibX5eqzcgQZrN1tZNc7ZWzsNgbGwi1aG2zMKmzUvNtucVJ6uNPFLsQbh1ersfJGqMmUa9 Mt6wCqWg1839oOxY9W17y43pQbtrRUGlXBmsLZeoR99cWVmFD5reNWe6VrfdNUzHLdeRnEIOqOmH FJIS86AwV3i0iMPRZCiuCJN0kFdCDyLsJRrmFTyAWtspg4V6LXoYlczLX9E3xAqHoUgRZigx7AkB gyQcWroGAx+KBg6NdiAGfrfdDIJBGocYsYRNRG9W5A9y6Kc0wcQl5Z3CgErWaYowCeGDoLFiyDII nwhRsH6/g4QYZA5OEbOY/W6v3e0ZtOwxqYyNx34vHvQa05swF9kctNFsZrgowVcrpcrOuWmjUB4M ourkzI65ufGJyX6763pmrViMW62xzTN4DRCu1CfbrTXXK07v2I1GsmKtUZ2cytGCXyjUZ2bw/qah j/xftVErTs+2O11aLukVxnfvn5ib88OuZuWlrZvby8ewTiCtOCutlcktW6tjk5iLDcOeNzNXro/b Yw0thfEK9EIdYTccAe8B3BUiWoQM848kvShJCAuGUCGoI5VGMTex2pJGVGmaF/sZE+gukpPozodY pHeF1C2pLkW56U1vfFZ/DfGDmQATYAJMgAkwASbABJgAE2ACTIAJvFAEWHU9reoaxrg2yCshtmTU S0aLTj9/mNqS2S2aRlvv4xp2HckHi2SYEGCj9Jeq2IaHtBIltGhajfyIGFCEzaH1hqR4VGSSaGwR v8efCsJfMDvQUp6HCq2f+KEfxDrCtVbnR3/g7ZbtHj96HFVeCBY1prb++Afeu2vvnizwjxybv+aq q97+uuu2T9fvf+ix7bt3X33dZXfc89BNl1587QVXLofZT7zjLSuJenJx4Wf++ftPHDtaqTQ+9O53 HD2xNOe5/+wdP3xsfnnh+AkU21916f4fveHyR4+dMrPkNa961dtedeneejHGvOIguP6aqx85uoCZ ure96oLFfn/7jrm33XiNF/YPHT/5jrf/yKsv3Te3ZWujWD505GjJsTHKCKNE1fuir4uqqtD2hbFD mXqjO4iRbEgjgyjiXjRriHosGajb8AD8IOwMnBQmLFFQFSGjhFsURbQyEAYngsXCSVIPF+7Bl/gl yS1q5iJvQ4Xs5HQgeKi9PUG+KQhRDY/fYtYyxYNxD8WacBLIa8EJkRnDHoBkYnz8PW963bGl5TSO fuQdP4jJyJMnT6EUH8ObU5Mz77/ppn37dveaa4jXffBdb3/NVRfPbZt95N4Hr3nVZVObtjx85Ogb bnoTNvmtLqx88J1vO7q8iPf0x9757oPfe7g4NvZjP3jT8VMLF+zZ+57XXbnc6S8tnNo6t/Wdb3jd Vddc+dhCux8EV7z+xne98TV7tk4dOHWqVLLf/UNv+cajB+rTkz960zV3fe/B666++p2vf3W5UT5w 4sS7fuCmK/bsufbSnZONxrGltWqpNOj7+EwZWKOZk6IStWLiYycGMqmcS9Uw24h3BgVhdD+0ItJz w3FcGja1RORO7G0gO0jNdHgT8+ytN735hfobio/DBJgAE2ACTIAJMAEmwASYABNgAkzgWRGgEiS+ PZmAzBnhRnNcoqhLdEeJu6jViPIv9O3Gm9BbQnQNv2QZ/dCLiTG90YFk29HwHnoA/A2+FDSRUyEU jSRS67qoiaKWKQgY2BiM01Er/PBkROFUq4NwkDcxM7Z9584duy7cvHVubGoyQP4miVFq1ZioV2ql h4+cuPfRxwp1+4r9u/+f3/393/6jTz5w6BAuasvk7IXbdpbGNvU6rX5nWXHx4lE2aDm1ccP3N5cq m6bndk5v2j87M7Nly+apSVTaNxqVC+c26bXqji2bV7vdubm5P/78P/773/vUbQ8+urC6kpXHnaJT rhQjo3j4xFp3rXVwsf3333jo2OKyUqwfbPYOr3brBQ+GCd3yaA/D7F6lWq81xqvVuucVhG0RLMh3 YbCOxJeozcK0JpFBfxnSQ9QZJVZiigo1BJCQMyKZBVbQZ/iCb6LhuijBxCVK6MESGSR4KjzQslA0 T18UjsNAIikdGn6EYsQJOLYL84PXhvWCAwNfGg1VNLJa6MMXmwsxORoGyWAQ9vsB3hD6WTcbE9NT 23ZcvP/iCy6/avPc5vJ4A5dRRJ98nqhaVq44dz7wve/ec/embcjNpf/59//kd/7kk0cXT6hO2WuM 79qx/aKdO+uVBvJtTtn14xjXZYzN+LlSb4w1Nm/Zsf+y6T17venZ2C1hMrI+O2dOb0nt4txkA7Ot l195zZ/+3Rf/3W/+z2888ODA9tqaU2o0arOb1epmvZ+USo3bH1255YvfPHFooVJu3LcaLkeOWx0P I8yh+jBZIKCQwSICFBuEVSXCw2I5UpCYThWt+dRzJn5BThKtXojRUcxQbgeArMRR8OaANn3xXylM gAkwASbABJgAE2ACTIAJMAEmwAReLgKsup6Z/FBRyVjRs7ydVmFP+8QkDfGFdqk0haMZQLZgQk5V oHugV0ZfaIaK0b41QNgIvUr4wno+wzZ3bJ9di5OiZ77jNdevdLvlouUUrCAa+Hl68vBDt3/jjmsv 3PtP/uVPX3r9Dc1C+fixI4ura42Z8YabXbZn10c//FPvvOGq6lglhibKw37QintNvbVcNKzrtkwn 8yd/+g1XXXXVlQ8fPzHZKHcG7cntmzum9TePnazPTnd0o+0U7r7/vu89+iCt6UO+zLaveu0brrzq 8qi2qeUnmV3cOrvj0osuL3lVxzEv3L5t1+zMPz58yC5Vt+/eu3ffRfv2X7r/sqsuv+KqV197rVOp hpEfJwhJxTQeJ77I5snvKaMFMgGCV8N7MmwSxP3IWCGlhdIpegQq17Fd0Uf6CgcSGwRJVaF8PVdI SylqqVKvYvwP2yhr9UqlXquNlasNauyvNRrwSmPjmBhVqNWeOtrxRfoRz8e+QtF/ZkCQ5Yg7Waj/ SjINte7QQ7BZY9ObTvjp/l3bX3vtdY8eOzbRyBUPb6ef5+GJI4/+48GHb7z2gp/7+X+x5coLl5P8 ofsfzDpBsVCoNCZef/WVP/kj72hs3mF7NUxWYvYy87GIs2AWFW/T+I5dcyda3auvuMypVFZCbevm zct+sGfv/vm29u1Hj14yt606vQ2d/weOPJr40WRjeqwytb08/cF3ve/NV79e0zaFQaNe3P7uN/7A T/74h3dc+HpL2/a6XReMZcbf33L70uJSL7ZUjMdiqSXgiflQKqQnUUuuS+bmDCQNqRWO8nKI1dEM rfy1+DCL7BeOgN2XWKtgGpploBUOJWkKhnD5xgSYABNgAkyACTABJsAEmAATYAJM4OUhwKrr5eH+ pFeFZ6BgEsQWBuLkn6LUHBVUovWe1t/JDY9kYEg+oFfeUB3PHS+VTp5Yuuv+h0pl94tf/YoZY91e UQniRrGGJNNjBx/70r3f2WZ3i1l3LF27eP++mamp6fGSY6nfvPve/+Mj/+mzt3wRVfMLJ4+Y/bXN Y1A+E66iFA217tr33vcAetxNr/Dtb397plqcndt67WV7d49VrqqZWyZrbrWW+t09l14xdsFlvleO 3FJfc/74f/3Op275UnPhaJoERr/1ja/f9be3fL692syCdobS9laz2VyroVsdbfGaCmuV4Ko0TWz6 E5X94uv0bZSQG/5KqJj1m1SPo+FQ4WOoZkt0eokQEm74HskrGjeEI0tT5MLwhYf1EftKEnxfsC3s NMQ+ANgxaniXA5ViIaaM6J2O72FslGZWMWeKO8V3WHZpO7ahTRbsb99376PH57GG8qtfv9tTBjGa wLClMddM21o6efjW279qxi07DLaqweW7thVtA6sI0P3/pS/e+m9+7b98+5vfCIO+jmFBTa/MbClM z4Zri6udvmfpX/jKV1d8/8gjD37tO/c3LL1YqWyd2bpjW/3aC/e4e/Yp5ZJ/+MTOPZeVJ6aMrB+n /om+/9H//t8+8ce/v7ZyKmi3/G54y19/7jf+06+euPcepNX8tflWL+z4IXWEJSlq4KjsjEJbw+FF mmfUDYwrYjYWv8BlivWU4vMmmurxeFoCQJ6L6rnwAPGRFdE7+mDS1O5wcPcV8i8WnwYTYAJMgAkw ASbABJgAE2ACTIAJnGcE1AMHDp1nl/xKvNwP/+z7109rtJeRAjY0KDYapcT9ZFgoaERTZNA4mBuz 3NKbr74yy83vHnzsLa9/3V/97d+849XXfuar31htrszU670o/rH3vmV8265Th+b/2yf+5K033fCW 11zX8+Pf+dM/btTKV+6//OP/4xM/9f53NmrTf/Knf/ahf/IjO3bvjhQn7Ld++w//9Cd+4PV/d/f9 O7dv1azyHXd+57033vB3d979w+9824GTzXsfvv8tr7rsH75+92q386PveMcW17zzwccOrLbe8bpX f/rz/7B58+xNu7b83ue+um339ptec+3isVP/8M173/qG13znngcwRjhVKn3jsUP1otPD/j4Tqw4d 2zL1NH7syKF2a3W0y/JJ8bmNd2y0XeAgBuuopQsZLpqrEzscaRIPfWV0k7VeGHSEx6mUy/gesa9B 4JvYBlkoIJgEnAHcWxS5ltXqdhAPw3OF4SG7I/rR6CBwQHBmcnISCxyh5xCJSrIU+w7f+trX3P7A Q47rXXHxJV/48uffccNFn7r1Lqwa6LU6E7MT77vpGrM6sbR4/C8/d9t73/6O+qZZrRv8zv/83Wuv viJKlS/e+sWf+ol/sdZs337HNz7wrrdPN0qBqq2dWPitv/q7f/Wjb//fn7v1VTe87uAD3y1Vxq7d teUz3/jmP3v3+79yx+2Bml19+cVf+eodSdh/79vfXCw2/uhv/hq9Ym++6Z2/+zu/uWXrtrdec+1v /f4f/tQ//2d7N00cn1/8+7u+8763vPlL3/r23k2Tbhp/9ra7kdHCDKYcuqUdB7JADvsjxZSiqKEX qTaiIOwheUOR9qKti3QXHg/0Iv4lQ2Cgr9qIxSnKb3zsV1+J/5rxOTEBJsAEmAATYAJMgAkwASbA BJjAeUCAVdcr4k3+hZ/7wJPPY1hsL6WC+FNsxsMPYtSM/kvd9JZhnVhcsgyjUPA6ne4Fu7cfPHay 5DpBEC41W8V6zSkVWitrvWZ7bNN0sejFSbK4vDI1PZmEIarTG6XSwtGTnXZnfGLCcpyx8elDBx7G 4sKZibFWf7BtdtPRo/Nrbd913G6v53luu9VCHsotFOA7MD0I1VGu1bHqEEOAGBgcr9fmNm9ZXl05 dmp+/4X7j548gdL3KqYEvcIjhw6aqooWLTim/RdfKi8B/sjUdYw/nlo6hf2GIwjrZmtjxOuJhMhw 5Sgvg+uiLBd2A2LWUKN1jbIlPUdICgsdKfYFJUMeR8S18lwUykeGrkG0gSBkFs1GRqFjWKidp2E9 GBzqViPdQ0sAcNoJ1jiSFIM7w8FwCWR7VBVRqQD5qCxbaTY3TU23+71We7lW8Y6emJ+d3mLb6N4a KCZVvC/Nn0BJ2PT01iBWINtOHDq0ZfOWEydPTE1OViqV5dXVQrG0aXZLu9dDBG1xbQ0xKds0Br5f LhXXWu1Ns5vDfncQxY5X6KyuXHDB3vm15WOHj06OTTSmpvBGLJ86hbotCCkMck5MTnfbndWllbHJ qeXlJcTZKqWi43iLi/PjkxNZGBbKVYP4Y9sBtW+R6hI9c1JviTYuKpgXyxfknevd8/RBHD5SwZVR LZd8t8TSgIyScYryXz/2n18R/17xSTABJsAEmAATYAJMgAkwASbABJjA+UfgRVFdR48c+eBP/Mv/ +Ku/et11159/SJ/LFW9MdW14Pu1fFIqG9hPiG9GLPrQKQoBB1JhQXctrLYSjMDIHEVMrF1danYJn B4MAQ3kBckmOXXDMCKOIwn85BfTPwwfl8BRoCHNNS/PDIEgME7sfTT+K/UGv4HiwSK5rI9CUJfqh Iyfb3R5erN1t4eVg2jCXV6tWOt2eH4Q4P7zu9Mxs0SsUy5VKsQSRVHAdzCdaFlqcFNexV5dXUBi/ uLBgGjpqqtB+BbWEVBciU9gASJXmOs6Irm2kTeQ3z6y68BiRLcql6oK8gdHCMyFsxGENcoMQN+TD lBCbGWkKT0odtHINi6do+UCeQY3JaT2aXBzt1oSfgirCrkN5D+b3MA4JoyQmGOXCQsgdNUySguut ddBglgj/o3ZaA8NycF2uJw4cBeJ03DhEr1hm6jYEWRgMCoUijlNwXJxBt99HzxhevuAVQ8xcpqnn eYMocB306Os4B9qHqCiuquemallGq9VGIVmqqQBetG3HMm1dw7oAzE7WKjVYRaECKcWGz4bnOLgq MO8HIcSoUIS6uFKRhaM/VUwiYnJWqi4Ky4ktobKHHt8SWYTghAwbIaQ3SX445V1SdX3s47/2XP41 4OcwASbABJgAE2ACTIAJMAEmwASYABN43gT0D3/4F573QZ54AEiB22+77ebPftawzP3797/gxz/3 DnjLzX/5VBdFGwKFYhAbB8UsmdjuSGKCbkJXIEqD+A+SQxRPQgFWFEJJpDkm79RWu427EGxCX3sY BX5/gDE90eAeIaaEO9FuHyLB1QtwMKoKw97GNHJsGy7MRiAKx1Q1348WF1chR/BbCjgh4hRH6FDv dnuDQQ/PwQELhRIcFvJOhoWUGfyYAWmCg5AtUXIUynuQNBpVl8OkdLudbruN7JKaJfAieBReT3Z1 yeItMZMo9/3JP7/P13BZIISgSHJRkRcyWuSZcNVSWomyKXmT84goWSdlI7Y9aiLARC1TOEucIC4B AS0wl6pL/ilGI2GvqMQK55EAkRjrk+EvyjEJ7kLxUI4MY6bYGID8nYlaMDoKGS3sOwR0EmrUa6XY KLrC2wJvZELzaaZuIPuGXwI5+vtNyxxAhpF/ku86pfkCTFziWAk2QeK/+DVOJMJ7hk54vDqMGhwZ 3hS4PDJT4rzpDMUl4GJxktRVRq8Ob4WeN5F6MyDOBLkhKFpkOdw1OjJ9IuZFx6Lf0ekM67vw4/CZ QnQNvyd7SPxvuukN596/pHxFTIAJMAEmwASYABNgAkyACTABJnBWEHhRVBfmwq67/vo777jja7fd xrbrTD4Hn7/5r0YN7MJliS+heUZKSxiuUUvS0C3I6JGsTaKZO6rwomJ2YT80zNb1+361WsUEHKJf SGw5KFF37HK55Lji5nmYebR001B05IGk68ENeR7SRghx0bCeEUXpwsIyDAtiU8guwftQcZWGmi0X lVWoJ7csp16v42CuW/AQJ8PYnufQy+H1TIsegwMZeiqW9+FsyfCg9VynVncNKw8tPIBmA0eTcBu/ ebpUl3icmKej1BuNdsqRPCm9qENdTC7STwKonLwbaiyqVKf1ijSWOOq2xwOhCMmUDUf3JHx5hOGZ IAclKtwJMeYSaf6RVNfw1+JNyMI4pKwZKUpcLIwYBCWFpMQPOBVDzXWpy+RFU6JKBqxw7oCv6zgN PBIvIvNSuDCIMVGqRQxFvEpUkonQlbwkeCxxpuKEhewTE5fiFWgGk0JtdBXiIXSNQieOGrqEHRve ZH+XhDb8Q3xL10uoh5r1cW/N+gwjnBp+warrTP6t58cwASbABJgAE2ACTIAJMAEmwASYwItB4EVR XTjRYrHItuvM3zCZ6hKGYcNN5Llka/gofYOtecNU11BCiAlHoUDQ4EVxHWF5oJVIe6VK1ux02t1B b0DjjBhoxEgjqtXRXx4EEX70MduGH7CeMEnRwBVGFPeyHAgs0hqwRxj3i7EHUtFL5SpmE+GziqWS 52EGseAWivBZGJuDMoPuUjXsG1SFKILkIZuFsUZUW+GM4cY814HTQuYLsg1PxFMQQoNawxEgwmhj 4un+/ccj+P4QR7CGASzKGYmFgDKCNDRcQu/IhBaJQoS25GZGUBr2fMlKtGFQDs+Wfmf9ZWXNl3RD VN0lNRAlroR1ol0BQBeDK0qyEL/KlAjt7TQYSXYIxjCFdVQ1TFBmtGOA3lOoLpMMpbBv8uAiJYbM F/Xn00Fo9+H6KkOgIr0mDig2SxrSgEpJJj4QVEBGg4e4DHoexg9pNlOn8cXRYehOOoIcuxTfSf8n I4PiJlHK4BseSLVkI9slCcg03PDiRORNPHx0DTmV9+OHm97Cqa7v/8Hl3zABJsAEmAATYAJMgAkw ASbABJjAi0ngxVJdOGe2XWf+xt3y92KAUcR2NjwLfkRalaFPEPNlwwCN/IYG/4S3kXkiZLwwroiA D6qY8jy2HbPZ6dJ6QTmrGIVxikoph4SJkCT4crGIkOJX0FAGsl9IWMGI5GLToJA7FEiyLadcKFVK ZRiuYrFEpV6GiTlHCnA5Du6EAIPxQk4MY48kv1yvVilTzsshxYW4mGvjD3SF2QiTYe8hgmBIh8Fy 0RQh3RIp9Z50e7pU12nVNZw0FGkuwVEG3YRqwj+GTocmATMxn0hKjpSWqK+HGhKtVeJGqmtDhksK Iar6khKI9CGWLtIcoDBSOX6goBPdT3aKXJOW0gzf0CohA5aqSqzmGHtE6iqhmFaqZjHGTil3JgYr xZsqxyvJ1OVJjqeIKNcwt0WOCWdINk56O1qRKCYNZfiK3iO08uNZ4moFBCgnclJD0ycXecoDrLur 4SUJByjTYNJznTZf9NP6IOjoQzg8X6HnKK1H0TZxfqK/H6+Ko3Cq68z/3edHMgEmwASYABNgAkyA CTABJsAEmMALS+BFVF04UbZdZ/hufe6WvxyltKSrGXofkhrD9XfSalDN1LDEijSRKarRkTSCQklV NLAjQpRRwTv6n1DJhXIpy4ZZKlYrxXq13Bir1bwywlS46Qhc6YpjiVxSrito3IJ8grBJqDEK6R34 oiyKlAT/QBNWjEFD3KdQcivS0oFl2V6hamD+EP/FpJyBL8zyZQgSQZThhPDWYzmkcC10TmS6UERl oiKfhBd+8ONYiDR0WSFnlmHGUTq8YWSI/MtQvkgYT/klRwvxFNEVRS9Gc4zC2JB7ERN9wwNKfGJs jwrwUxogFNaH7hmKnlFMSZSvi5MRtkj6IZoKVBQ0ZQ1XM9LMJAWabMsWU4x4P8iiIdWE94KeC1gZ Bd1wh5aj7QsSTR7NyiNLMdFDj7Yy+Cka3sTBaLEmTh8RMN1SVRuRPIVmSUm90Zwl3iMckiYRMxJ1 NJ+aAFmuwqARLLJ4OcXGNGoIy/HW410ceq2hQRUGkFQYOSmhAOn8qRNMNvELqSrU1mkvJp2h+EwK CKKlXkxVUnpMQ3BPJMPku0DTqQImp7rO8F98fhgTYAJMgAkwASbABJgAE2ACTIAJvOAEXlzVhdNl 23Um79nNN/+lHKLDnzK4JWI2NIM2dBGQWSRtUEUFuUBzcah8yhQjjEn2IMC11l41sH5Pr+ZGRXcL YdJPkCTSkcGa7nYHY1XkqGC0sqLj6PZYGJUtBVXmqWdn7ZUl18xs1Sp5ZZyApidaAoER5Ek/Snoo PHed2NTmHTvS86Znr7hYCZgs5soAcgVOrFHVbLeZKEuu14vCpUyLdLW3unq87y92+stJvKYpEcyR azu4ENgRCJwY35lmvVjwHJtOXlGwbXD79OT8WhOBKVw3wmXwZmhYF0ZFRJ423EScaORbRrNzeCRc D04fQ5JIkVFjlzBA8E/CxNAfUh+KABU9TQxNjkJfCFylaQT7pg23Xo6eRcX2Yi5QzECKA1FDPeYz YQSxxxCTmmJkTwTlZGCKIl7kuVKD5h1VuKpQ06NG1csSTIaWVL3smjXssYRnNGgTZRpHgyztYL+l prVVJRiv7sitLZ2wX3SyOF71PFtJnST3lMzEWaL1HtsdB8HJPF8zjJ7pDtJwDXOnrlM0cfVk2/CK tCsAexgpeyZuCIGJCJ2s+JfRLvgzkQccSi7ht0Y3Yd/wQdsQJaPrp8TWSHwRDZJz4hO7/gURK1TX G8/kY8+PYQJMgAkwASbABJgAE2ACTIAJMAEm8IITeNFV17rtWlxa+rvPfIZb6p/yLRxuYBTpmaG9 GVYx6SJCQ/knYSQgvGS3EoSKjuIp+C/LdfddMLd9+4RhKWtrMDOI6QSWHlhGrFkWCqIaNe+Si2q7 95V2ba30416r1XYy1dXTiVlv5+7S5jlnYkxfW/ODAA4KE3ZBoay3uqfqY/all+287OKLds5NpHrv 5Pyymvu1hr51z7YrL9+yeXaq5Ba7g65hKzNbxrfsmK5Njo9PTO/YsaXiGpiUxD5ARKNoRBHyRVdc y0NVFEWtREUXSSJpVrCfMctwZ7ngrnW7KIeSmxxpji+FFhPhrsffZPmUCBbJ34lFhbQaEKEpSmMN RZgQhjLxJD3VxsNQCknmlOTMn2j3xzGgpoQbE4ON4p/Dub/RO4Pfw++IyJiIg4mjUpRMrhEQz8FW REN3Db1kGI5t5LW6N7d9tgRNuNhWVA+7MJW0FfurEHJ+2Hddddvc5EUXj+3b69Wq2cLKqhJa3UE2 PV7ct7uweUdhy0y120erWl/NA1UZKOogVwPDjnbtbGzfOT21ZawGdC3cj+QX5BpdBakucnSy5I0o 42JEx5bsoRdfKAxDzA0fKSnwRjcZ0qKfRP5L0qc8l4AlHyyFFw26yndhGHCjn+Ao8XtOdb3gf1Pz AZkAE2ACTIAJMAEmwASYABNgAkzgDAm8FKoLp4Js17XXXHv0+DG2XU/5xtxyy19JvyBnw4aPkVVd dBsWN4nfjzJNOTJBCBhpWzaPv/G1F8xu8k4sLp043M7jeHnlqGb0siQwdRfTcFdfvmXnzki3m5Am 9WptebkbdLrYAXjjjXPXXuZNjSsVL3ngkTUbjV2uCfFUKGn1Ceem1+/bu33sxPwARe5XXDyZ4Bdp 8prXXrVtbnZxKdw6PX7hjtnldgut9pfu2rt10+TsdHXvtp3lcuGhA/OHjs47FoSPnecWBt3QbKVr DkJXsC3Ia9H4oqFDDyXUnEWXoNMeRiPwA7TU46JoxlCkqcTgnfQqG7/W5zslK/qVaK4ikyadl5RY VD8vWD6V6pLHpl9K2rL4X9a+izeCUA8PMsp0yVcR/6XXHdpI+Xwhzegc9NykvYyOYRQhxCwrnJn2 XnXJnKknBw8et62CialTZdG2IsTbBkG0eWvtta/evmUmypVg3/ZJzH+uLq5BFE5um7zq8sKObd5s rXjoRLPZXNVReI/djHpmGcqVV2y+8qKpXo/WYl5+4W7Hq5yaX81TE/OtQnCJ4i/xqcGgpjxBTJoO c23ycmWqS8xErn8mh9wEuuGo45C8GG8cqi650lJuflyvA5M2kbu6nvLfb76TCTABJsAEmAATYAJM gAkwASbABF46AnKV20txw+q9X/qlX77uhhv+8BOf+OQn/+yleMmz7TUe57mEj6DWJ7gisj4oikc9 E34c3oMOrQRV81l45PiRbz58v2pGSo6lisEgjsr14r79m6Y21UI/di1ny1ip3w6/8JVT3zpwdHai lqrmIA7KY+XM0r/w7RN//uWDN99xYmGlG4cI/thxnK8sd4vF8pZN1UdPnvzM5z536+23224yu71s V/S5nbPNXuuzt9x269e/2VOb9UaysrJ01z0P3HL7XV/57j2dqL+wtHLPQwe6vS4cDKbqfD/o9vxu Z7C62my1271eHwX5WRKjOR8zdch2obMepfbVUhGjfPgGPfe4bFH6nomw1oYw1iiNtB5LkqJqYyJJ Jq2G90jz9Pjb6FdCnMlE18gwilgWhZ6k5BJzi6Pc2NBuIRpFv5TnRWsSZZu7mOuT5yHiZrRGEZG0 RIixJAvW2vOpvtrPVlbbK+jZQrvZ5FRjdveE4Zr4l2J6Etcff/eRR26/69GmH09NlfvxiqJ3ZjdX 3HJ2+333/t0ddx068j1Hj5QsRBs9nJWimLOTlSjrf/Ub3/6Hr97RDPtTU0USWbRVEtk50bKP9YlD QTgUVMJFiWuS07FCm4pzFqmskR8caT3Kc8lo2/AJQ5Ijxyd+HC1yRKJM5OQ2zJaebf/y8fkyASbA BJgAE2ACTIAJMAEmwASYwDlC4KVTXQDGtuvZfmpIQ9BoHU3XDefyRqN4pGOwzE9J+721pflHPDPQ tdwwtbGpxp4Ltr7+NRdcesl23dJCv/cnf3/X73zq/uMHB5412ekVtNzG1KPtVqy0vGti8/UXzM5t sqbrUCNxHmaeWqp6jSS0fd9sDwZlx98yhruDXhhrKsrACljlOFlTqoV+J1rQtCCOMfmIzNFSEWXr cf7Awwdbq6vTjXqvnwyCvD+Ie92w14mWl9dWV9bazeag34f/QmoJnVP48KGWCy31tWJhkKQo8KJW dwsVVqZcOii1i+yXkm3q9CV/FL1lNG+4rqxkwz2SYnIlolxn+LhIl4iJDUfwZIBp3ZTJyBIdbH1g Et7otOsa3Us6jCyRnI4U9Wmi6p5iTlBc8F+QXIha4VRilH+h/qvfWj212j7Z9ZdUNcKj4iTftW3v lZdd5pVK5UphsqGstk/d+Y3Hjh5qHp9fwwpMw0q9Qlpyo8jvzNTSvbMGqtaSNMIJ5oqpZE6eO3GG YwXVUtao4HPQV7KBqqJoLMNngJiI+coROHFhkqAoxl9XYGKEU5aMiRHO0Y2uAv8R9kt+YkejonL7 4rA+jfq5pCgb/VrA3gD12X7c+fFMgAkwASbABJgAE2ACTIAJMAEmwASeN4GXaIBx/TxR5s2TjE9+ 12iA8Slu6xXioxTSeiu4kBQYUEOwq+Dmk5vLey+44OGHTy6f7DouhgTbpbJx+NjavQ+crBeqtaJr af6uudLlF1x58ET3tq/co+d6pezd/9CjDz1ydHqmcen2rQ8c7pxYWujFvV4cxvF4PzZ37NozNm3O NJK9uzeX6qU7vvzY4qn8ol17ChOFPXv06a0Trj329buXmiutar1QGmtcesFFq83ols/dPlYsIXlk WQXDKqgKOrmwgVBPojSNsbsQSagsTLHaMcLkYozkloZJRspQYaaxYJsl1y64Lmb/kPfq9nsj1UXa a2ipZBRLfslqLCqSp4ou3GMaFixPlCbQT3gh2kQ5bJcSvkY2e4mbOMw6cZl3kvaKQluUikJhOxkf Ogj9KTUW7UlUxIglRibp93LGUh4c9+AxWRalURqEShipaRLk+aqWR9vntiFzd+CRZU1z0UOGGFvQ HXz97vsnJ6d3zZVVM37wkaMFo75pol6rWw8+fDSJ4k4YfOn2+6rjW666aG650zt04pTjqq5l9YIA B5yaMco1fW62umf7uKXbi8vdk6eacajhMyFTbzQVKnrohQokqYUfh9dP9pDOWLaSjWwVAZXF9YKZ XFY5bNsX6TkxFkl2a3ijCUm8oNzXKBDhYPQqqnLTTW943n818QGYABNgAkyACTABJsAEmAATYAJM gAk8FwIvteoiU7DBdq2srlx+2eW457mc+zn0HKm6TodjxKWNhuyG5eujyxWTZ6I4SfQtQULElboz U7Pue3Tt5PFulqoDvxck4cpKx1KLcEf9sLV7f/3GGy5a6/b+9gu3RUE0WZ8KwkGcLhWcXqGmbJ2r Hz3S7nT93TsmMEMYxjomI0+dOBj6C6VNU5Wyed93D97x7WU1Mw4fPbq0eDK1nD2bNz38YO8fvnx/ wbKdUnHb7p2bJia+duc3Aj+enBxPclRyORp6z1WUb7l4zyFJyKPIHi4oIpHVwoXhR/RzmY4jnApK 6W0FigYrFDUNA4CWZeM5uEqEjIScomJ7kUOiLzFYR7Et8mAwU+RuaJAOuSrxLMI02oso6IobPWVE VmggIjic6RNt9FTRRf+km3zK8NdCleHETBwcqkvMJ+L5ZLwwb4l9h3QCquE45MtgmgwHDzX0AHsy N22e6XeDA4cXFKWA4JltRgurJ1IfmTzVLar1cXVhYQmyb++FW20n+u53j6DSyy2nSbRm6736+KbY T+ZPnKxXyqV6PQiWvbKzsvBYv7MYKUa5Med3W3d/50Ac4vNQyBVb2DpxjbR9Ep4xFkEsXCfeA4va yIS1UnOIO0pvkROU5lB08wtnJRYhEDyRnBPPoN8jLAZDKXJbxBzvhm7Ib6UekyE7ULrpJt7AeA79 9cSXwgSYABNgAkyACTABJsAEmAATOKsIvAyqC3zWbdc/3noruuqR8zrPbddQdYl5xQ1SZpg1EhGc 0QTdsCqK7oCG8BynH4STk7WLts2dWAwOH1px3FJjfPxtN15ZrpQOHl5D/dbERPG1N2wam62F8YmL t0zZTnG5m1912eVXXT5Z22zs2j6XtIt3f+fhV12x9car9wWJcfRo0zbMKO0GSWt2wsqD/G++cAQR q6nGWJqnmDzcjqKonvWnn/mOlnia6W3duf2ai+YOHj35j1/59sT4RJ6beWoEWDOYoYAddfMusk8o FiNFIhQMXEoao80qkzEiC5X1JpY2YtYPMS+yS2I+UbEt3HAEqpyPk0RAEEGq0TAnORXRHCU70uW0 oogYkZCSqa91XSg1l5CE0urIOJY4J3EnjT1CC6F+X4a4ZHcVPW59yHEYJRMlVvIsSJyREZOlXWJS EJIIhkkXASnTwLsVR6myc/OO1kpw9Miy6xTR13/9lXt3btvV89UoycrV8p6ds9ObipMz45vHCg99 b+G++5f27th76ZU7MYu6dfummVr5nvuPrvSj6665dNP02Imjj2Zx2ut3F5Z6E436/tnaV75x6PjR FdMsqKktrhdtbjGAi0lDmdkiS6jrVPqG9JoKb4i8G0HCXWjuX58CRSZsON9ILMQ10Z/0hpEuo7wb dd2LLwES5lF+LsXD6WWwPRNn8GZOdZ1V/zPAJ8sEmAATYAJMgAkwASbABJgAEziXCLw8qmuj7brj 9tvZdt1y819KE3NaaYlPmRzVOz1wt16dhMV6JBfgFUxFx/pCs90fHD+x1OsgDGVCsYzXSt954MiJ o01LL2ydGVfy/mNHT86fWGuupPc/3On2dNst1StmtWh22/nNX3jke48ehy9TDPOeBxbXlnqIYOVq snNbodcJ7r6n1VnNJqoTnX5o2PpYvRAP1L/94qOrS92pxpZCqVGuN5rd3he/ercSJuOTM0EAVYRK KVPTHMwUwn7Ak2gKRAnqnzK0WMUJ8koRauvxzxhtVmkW4A+ki+gxUEQi/gVXJeq6xIpGw0buD74l haPB3aMJRhJbchsghAtq+oWWEUXq0k4JeBtU1SjSRTklUV5PgoYWRIrWKRFWWi+eGvZXPdF20VFF cRq9mMw/SclF2SYx5Ih74W1F6CnTaJOAGkSqZdYXF7trqz1V1+H4SrXpQTv79n2PuZanZtrxpV69 6JVd7bsPHrntzgMFvVprbDNK3qZGKfL1+x/q3Pa1x4pucXZm6uRi6+H7Hi1X6v4gdh28g+VvP7R0 5zcOTNaqcIN5auM6hLPD5YCYXCYJpUiYDJHAEiqLHJU484T8FJ5Dp40vBLxS/ImRTbofb4SBd4Nk mIaSLzmkSZqMPpASGRkvocLEwkfCLV6dU13n0v9G8LUwASbABJgAE2ACTIAJMAEmwATOMgLqgQOH XsZTDoPgox/9ddgubGbEfkb01r+MJ/MyvvTPfeh9eHXR9ERZp/UzgWIYmhRpYUQ+R2SZKHOU5nqa Gug2t5x4eeUw6pnUbCZRkJGKkrR14tSpyfpmwyh3ey0bKixc7Q4wEGgWnalKZZPjKSud+XZ3DV3p eZzNTMxOjFvN7tqBw8vTY9vKlTFdD6Nkpdvva6lbsqti22Bmufkg7K+t9fTUmtu8oxektlNJlV6U rh08fnzHzHShUo9QyZUbYZAbOvJccFUmSa6oD+8j/RwOhIQXBBYpIWgsCzX5eqNaRUkXSS7cSyVT es8PhjIKiixN2532WnNNpwMIwyVmCWWoCw+HaUFCisqjhMSSBgoicBTXEkZG9kmJGioa1pN6Rq4t zFEyRX5NDDCKUJcQOpRQEkkxmfAaBsogsUT6jG55Ts8ahqdkYIyqsfI80XVSX5go7PaiQrHQ6bW0 PDbRWhZGhdq23soSslkzE2OI0Tlo4td7iysHFASyNKPsbXXsqaXeiX6wmvhhnOjjtSlbVyt17+Ty qSTob9+5NYmxabM3iLpYCqBnpmOVDHM8Tcs0OmkqqdLNES5TbSGlMB1sJyisJ8MXghPyXDm2b6q4 c0A1ZxTSomsmWKMbdCPtejQtSuHRVkcKdsE+0pSoDHplCcDgzaK6MjJfVNaPQJwpGsE+/vFfexn/ beKXZgJMgAkwASbABJgAE2ACTIAJMIHzmcDLrLqAnm0XIHzoQ+8ZmZONn0YRmjktvoa/ktZFzPPR DkPT9FDXFQRtwzR0paLpjqogOdRPlRRbE123jLWEWgIz0g8zzdStOEZQyi6X7DDt+4N+qWgbrqdo qK5PYE+iMCk4E7lmwon4Yds01DTUsxiDiFAZSZRHQegj5ePobqncIDeXGJ6btfvzQZKY8Gc50kMW LFOGvizVQPBHdMZjYhEjdWjoEqJKCCZZf04SiRJQRr1SdQtlqBPU0ruou9L0IE7weJG+0jBzB+m2 2mpmCVrz0whjhllmGZBoMklEpfWIhaUwRcJkSY0le6PW1aGMYsl7RC+V1G5i/pF+R/YK3kpOSlIc S/yIO4etXUIzyhZ6MT6KuJPspsLmR1ElJg4t+vNh9XDyCEDhbiv0E8Sj4iw2Dc0yssSPFa2qqiEU I7QeUnhpFECBmUqXElc2tisWs6Skpr08azq2Eal4A+040mamJiqNvF5L+wFa931DHeTaAMsqI9+I wppi7tPVqTDBMbuuccgthHkWx4GdxNOJ0ggVq9s7bucnPSdQcrsTTcR5Vct8CnvZtg45iluCE6ad ivTpSuBcocyUomNnCN9hrWSW2waUnAmfpWZhv7vS6TUdnTKHEiaeBFCUv1OU3/jYfz6f/1bla2cC TIAJMAEmwASYABNgAkyACTCBl5HAy6+6cPFsu376Z961/iE4Ha3Z8LkgtSLMDSWZ0DBOYRsKWmmq peQGjZtJgyTDX6kscBfb8cjCUH+4OBjcBe0WFGkpJHUy5KGgc0SQCYEmTK7hHoOeKSueRhVMSUzp LCSe8AAxFqihSh6vAtWF2nkMJ0bZIFfiVHFVJRKBMyxDxFmJF6K4DxJFWMUIkTMcLKSZORJPZEmE DzJct2Q6JdOyS6WS7bjYwGhB3A3nE7GlMUe7lx/0ffTeB9EgCKBfEhR4ifTVUDHhDOKUom3IwlGg Chcrkl9isE6Wy4s+L7oqkdUSFVZibhEng8PhchAok0/BoKWoDCOGOJSoqB+KLYrUCV8mFhxiIJGe SNebQ7cl1OMuklDkgEQrPok/TAlGyHlh3lTVUiKF+7CYEg4QvfxZhvwa8lMJ2r0QBcMZJlqFWrHS Xpb7mTbAzsZUHbvqootmd2izMwFcnxoFtpEUSiZWXA56Qa9davYv9jvVIMqccrx18sHa+EqitMLV emd1TzuY7cf6WrI4Y91bL67GUWlpsGspnB30F9YGoUeiU7Wg6ETBPp02vT1qREOymWvqss4r0ax6 aSxVnV6S23FrafmxY8cedBKfAl8yAifeTQdBMKiu/8aprpfxb3V+aSbABJgAE2ACTIAJMAEmwASY wHlN4GXr6tpIfb2l/rzt7fr7W/5c9rXTiN/GL/GDdAlEjLJDIlwkJxuRJ6K+cEgj/IOeCtOFBBT9 Uq7ho+p3PBBhJ5JfJKpooo8G0OSYH2wNDBeas+jBZMhIaVEqikbvRMAHz4cGoz/xFPoTd4rJQXot hJWSNIpIutEL0XQgrBqVldOORNkSJU4gR007/U5qJpmLEt/INZJiRBBpIEgfslGIF0l5JOcZTd2w 8F9xs0zDdmwMurquB8c0bJiiCjDaJohHkY3DSZLEkhZL9FSNZho3aERpv+RDxEmIh9GYn+inl7xx iPVCLvloYe7EoKLwXPgnpJg8lLgiuka8skiHiVenOdNMyENye1g3KcYs6dVw8nin5HJDCDLRrQ/1 SBw1w6ZjIhpGx8NluqpRmZ2amZgwKlXsqEx1LbAtaMkK5h1du+PYmHQsBkEDL1cf6xTLJ+ziQHEq Vmwkg4KfFpux6RrazHg8WY+sghfnm/2svuL7KMmfazSaCSJcOrYCDJAKi7MIX4qGHi/UwOED4SdJ pujlypRbaJgi2FW2rTDqt9YWMStJ7+uQNF0sVCH+5Fr68/p/VfjimQATYAJMgAkwASbABJgAE2AC LyuBV4TqAgFYjBtvfB1Mxt995jPnYUv9zTd/eph4gigRAaThl2hWH7oEGUwSmkXeLbULhbjkPKPY r0e/kWOC1CwuDyQqxYf1UlSoRKIFjovcC/SW0C4QLzJhJRYIil5zuY5PdlSJI4sHkmcbVl2J1iyR oBIngHvhtsjNyDosKYaGWgjpI7G1TxgmeR3C1cncl6oj6BQnOYJaISYkUUCFCioNtoVSWMNTJw+l 6tjoqOsY2nQcG4fAPUP9RDN04uyGx4VzQQu7OLxsrhcvt666xHniSiVLKRPF+YiBRGEXxXmJVip5 nvJPGdQaXp7QZJTfghSiAnfyYsN3Z+jQJFw6ELWwyQWNIi0n3laZKqMvqsIS75NQZCamBWEgkVwD L2hHVS1YTn2yMVatZ4XCIE7tdifrdYqd7tggsFRjRTOjKKz6/QlEvcbGH/MKp3w/j+MxO4uNzEjV 8lrguTl+FdZKTcPResFElNQNRR1kuWN7meFU7ELRLbheCdVvhUq1WKiUi5VauVqtVA23UC2UK27R NMxEeLuCqQz8Xnt1UcsgSQUcYSvpQyAmTm/iDYwv61/r/OJMgAkwASbABJgAE2ACTIAJMIHzmcAr RXXJ92D//v3np+26+eY/XxcxI5ElddZ6lEgErMiSkEwZeRtafSdMC82a0Rf5KZpazMm6iAwXeRv5 W6mlKDhFuSXhUMhhiXyXzDaJQNZoIyG5HvFEkRsbjQpSekpHQ5NIbMkH0CvKcBb9V/whW7LkTRwe koruEUZJ5tGG/xUmC79FNixOMqxlDAI/9GlQEaOXURhSTxRSRYinIa5GDo1iaUiTiYyXaWHM0bTQ bY9FjQhGIQwmx+hILJHRI1zihYaua0Oq63TwS2ou6aZGpzwaByVjh5lPHJ6SanQZIsm14VrlooBh HEySlEcTlyuUmWgTk+6MQk/it6MgFD1Qbi0UoTChv0S4TGyixBdyYNioWbYLY416vVLLTDdMU21t udZrjg96xST1Hafr2nocTvT7ZddZq44/UrAHx05Wmkvu1IRWrNiRUm71C1quN8b6ZWcBr99uj7X8 ShCGS/2g2Q3KioERVctxLdezXddysNnANgmsjaCca1qLvXDBTwoIemHOE037qtqP/FZzKQ5amHmk rQHALarQ8HhcAae6zuf/UeFrZwJMgAkwASbABJgAE2ACTIAJvLwEXlmqCyzOT9v193//KWlGnvBp WBdGImI0Gp2TWSS60bCeyAjJxJGQOhSrkrcN95NfWQ95UXWWNGIyrSRfV6gKMXJI/8HgGh2N5Jd4 jEggDSNmQugMs13yfqHbZAAL5yS/H4qeddsDJyZWG0rdRY+iMxAbJ/FqKSkj2q8ozoIOAdFDLVmU bYqDOMZzqHYfj8CIJVkn/IOEF0khNIHpUEIWjmaI88YEJDV5UciIbkIskbQ6TXg0g0iRLlHgte65 RORquHVRziFSf5d4RemnRhcr0dFLyHSWyKzJVNlIbwkOwlZSHRipLtFzP0zkybyXGAAcUqKRSLqX 7BG+0P1FnfdxpjiqXW3UqmNjaqmUGqrV75WUhFZtFsrNSjkxjXq7PxUGpuO2y/V+EDaWl6aVtlqa VEqFPE0K7W4hTLR6rV1xF3G4peZYM6z1w6ALrxgFrqbYWtqKYi1J1prtoNWJ06zVo60FaTBoNtda zeYqVm+mqifsJk64O2i3V0/lcVfOYtJNfCYxdoorevNNr3/CJ5l/ZAJMgAkwASbABJgAE2ACTIAJ MAEm8NIQeMWpLlz2eWi7br7l00N9JcNFo9sw1SXjXXkO50JGRlgVGZyiGNMGzyVUlziStCjr3wu/ Ihq+hjpN2h1xqKHuEqN0G60FWSD6LeYEh0klOqQMNa3LLClyqLZ9GGAa6iQptaTBoT9SbEUkMYc/ KUuG/i+SPiIDRWEgOB4L5fQ4eymtENfCxSIxhME8jDP2BoMYJVKY6kupHMpCvbsoysI5W4bhOXYR 5eoedjd6nuNSY9YonyXSUXJwcLgrcDTgSIJJjGfSf9aDXVJIyRyWPIi83I0OUvSJ0YuIYUeBWQis obeiN0dscpQrGimJtl5Stv6+npZuMlImFR9eFKeLo8NGJZTfo86yVInSzFDs2vTE5PSEWcSaSrXY 7Gu2HTamlsamTzp2IQ+nVrt2rnYalbbjFI4vTvZX64bmuBXdzpGQy9ZiD4Sr3up4zUfh/tHlup83 +lFkJrGNnZ2Glmp6L1eKhhHGUItpsVxEVf6YY2NfY9lGA5pSt7BuUW+nCt4gfCA6g2Zr+bieBeTm ZBJNtJdJw3nTW9740vz9xa/CBJgAE2ACTIAJMAEmwASYABNgAkzgCQReiarrCbZr3759xWLx3H7n 5ADj97utZ7uGDks+bjj1NuzTkhJFdkCJqUWZsBIBpWHKSgid0aDh8LXI45wuB5P5HDn5NxyMFGX3 NL84GgekdY0Ug5JzknQecrJypJDkqQ192OiKhAkZarShPZJrHuk+Oiv03Ov4ksVd9F/KUaHgfKSE 8rzT6662mlEUUXm7kuFwMbwXNdCTNaN4l4aEF+ZfsROSXJkL54X6fKFhqHKfHjvMmkmTJX4zbPdf P2fxkNGJSg32pJu82CHwUaRp/VFwVvCPclZzuMZSvhPyTRMHlHTkEWRf2DDVJd68jMSfCdMH14f1 mJoWaZrrFcbHG5PVumG7Whi7Sy0UjQW1+srkptU0rq+s2j0/bdT649Vm4LuLq+VDq2l5atOeunH4 +PzB5WZ1oo46sYrVrleQktMW2+ORWqvrSc/3cQpl1Pw7znSpYNtWreBUSxCHVtk1bc/yCrblWa5r V10PdF2LavA9R8vitt/FAGOXQnHDrB5dsvSDrLrO7b+v+OqYABNgAkyACTABJsAEmAATYAKvZAKn p91eaWf5/vd/4Md/8iexk/HffuQji4soGOLbRgLDMnlZar7+BdWAmq5hPxU5LFREjcYaURclQ1+k kMRGRlGDTlN34oseKf6kNNIwqSOTXo97CRochGsiJ4MDamIGcv1rQ/xpONEoznk9EyVc0/d5I8mo Ua0Y7JVIf9GQo8h9idIwEmxpGPY6reWlhWOnFh49eny52Wr3+1TrFcUhmvFVBJ1sw0a/erFSrdbr tUajXqlUPCS9LNsU+S4pmCjkRcOfIp01GqmUZ7Uxi/Z9z/TxFyCt4uNSbOv6R2baxGAjvagYY8RQ pvjCN8Mv2XwvC93Fwkca50QlP00xKpjcjDQ1RfAKRfuG4dpGwVYcK/Ni3w59J88cPCPEDk01KdZ0 zUkGQeT3+o4ymN0yGaWl4yd8LYm3TWdWJcbrx3kB45AG6r9MfaCpAzXrKmGoRXEedOL+fL+16rfb UffEykLUXeu1llZWTqphp99f6+aRU3KqFbdQdtBQb1oo9xI7Q4cU6boonzf8MPC/rUyACTABJsAE mAATYAJMgAkwASbABF4eAq/QVJeEIScZv3bbbXfeccd1119/Dme7Tqe6oJpG03ej2UKREBqmk0Qg SMSeRHuW2Goo7I1UV6K2Xbot+pVwW6Pc1uih4glidE9+g8eQVhrmjSghRSYL/8SAIH2DX2A2TeR1 xO5BalgXuajhkKQ8Bumo4fZFmela/xLGTdil0UmKE6ahSJJquFPHwTKkujLUPImQ0/qCQyHdhrOC SIFpmKTT4jDw/f5gMEiiGPONcYicFykyeCFxTBWVXrmh2S62Fjo2vmxHdHqRScKjR8cmNFgnSCJK XIogCnM3jHUN01yPD3ZROGsU0JLji3Ll4kZ/J2v8pVATM33ibZBDjpSDG+bt1v91H0bvhoX09FRc I2XVlETLE9XOsCrTMZ1Sbcopj5UKGNCMNaM+iEvYfWjovaLlJ0qln6JZXqkXFb9fXVqrDjK3MT5u Z06zGSWaMjZVLHitQbTqWZqn23E0vhR66P13gmCh16k5qJLXenGkJtFaGxkxeDMfhKMEmzBbnX7T UKJB0IdCRWu943mWY5l50G6fWl2bz/yuuNJRRE2k8cDjppt4gPHl+QudX5UJMAEmwASYABNgAkyA CTABJsAEXtGq6/yxXX8vBxilIpKSSNyGxkjWoQt7NEzNjB4g0lVyXlHOy5FokTpMFEXJWTm5cVHO Bp6+SWMmx/WGBxcJLhF1EukiucNRJHdQkZ7JL4oaoUweX3JCUkw34j+kdE6PSK6HumT7PM070lmm Ob7UBEeA0sHR8KP4k6JcihoraoSvXIux6k/VsOsvypQgz4MsD/Mcv0pxdqqSwhrBzyVphML6NAnj 2A/CfhgFOEZC04NimaSamaj0otWJMDm6biLJZOLFTMsW/okWTVKxvSjSQiAJ9ff4otZ7QEEtmuyg f6oBxtEbJaANLdlptyeByuHE4T/k96ffUdJeOP76bfhOkzijd4TGLQEVCo4axrCyEcrM1DWT0CW9 NGz5vqF0o3wQ9QOsp3SDXr3fcdJAVxKr3bNWOw6+sXK1ncRKMKjoeQtbLTMrC+y4Z6G63vetdoCj Ieimeq5RKhYKFHszCpZRKbgVzxkv4s9C0aOQXKNccNxCrVAqFVzbBJhETfykv9RqLqytLeVRKD9F EoPQXmKAkVUX/28LE2ACTIAJMAEmwASYABNgAkyACbxMBNQDBw69TC/9LF72k5/8sz/8xCempqf/ y8c+Njk59SyeeZY89Kc/9CNDEyUzQkI1ybJ0EQ8a3ikjQSLoJPUIbiLSJCumxPgbqQexdfC0XMED h01a4gnDYi3Ex0REi+4SASXYJ/FScD4ifCReQoSSUCQ/XC0o7xyamw3ibPStzJ89bihQVOHTlkR5 0nJhH9Yoom1LVHOJmTfRL7b+oqK7ar1MS0g3ZLUQJ3KTVImTRPo4GZiSF4crMA2nUhrTbY+iR6ie sk06TTGtKbUdHB01tGdZvz/w/QHCYcLU0bPjJA3jCMLOMkzQRAoNSSc8myYcN3yEZKZr3Q/iN/JM xFWNiIkLwTSfdI504cOljfL9WpeYp48rrkVDmxg0nWVgBBVPxGkLKtgkkCU4FdMqWg7K903NjB23 gWlCSzEVU83s3FRNNaV3MjMg7NBhZkBDerbhFOw8SJUsNuw8xG7FWElClPonpm4HsIWa5dlWkqdY tugYBq4aOytxApBntqFHaW5Seg9eklKBMG7wkbjCIIoG/V6OQdLuWre7CvM1/ESJzxWuFE4O93zs 479+lvybx6fJBJgAE2ACTIAJMAEmwASYABNgAucagbNDdYH6uW27pOqS6oQySesdUiIrI7WVTGyN PoDSEwkDNRqqW490iXE5qVRGNmxds4gHC/Uj6qNQ3E4/C60ivBPUTJom8hH04rS0URRIyQJ3Oo9R ZGl0MkONJt2adE/i+MOHiyfS/KAMeolLwYUibiUGM4dCT1zgRq0kuquENhOHRee8hYL0NFWTNBZn K83ZaSDQLPBBhmFbNoYWLcOyawX0dHkZnmpgEpO8FdY4oqA+CMMgGMRR1O32eoM+pZtkNAmajxwV Xa5BKu503f76v/fiJOXQouBLHk0sbdxwo1gWhcvoDKXq2vjbIcTTT6HXw8OSlIyiicp9HBAzoxTL o+uDoqNEGnyV+HQgyKZbThIhF+bi0hKcI56I4U1UeVm6cJKwiplhaoptKRGdnWZqCOXlSY7ZRF1J EeFCog07LqMsAVdR1Ub1aGToxFvvmKZYB4kBz1QABgmDzjDLBv4g9ntZGkEPgrGKXQLiEaMPLHHD jx/7+K896ZL5DibABJgAE2ACTIAJMAEmwASYABNgAi8FgVf6AOM6g3O7t+vmm/9CzsptmHSTMuv0 DJzQRzBOp8cbR3DWdY+I4Mh6LTm4ODRJMtU1PKCIVYnXEiZLHGQ4giaeO0pXrSutYXZqOKMnz3E4 2ydWPg5/lGezbnXEy+NLdsCLtq/1M6d7RjJu+LSNdmx4XeLAw2lM8nKwLRa2NKLMXeo2EaYaQqMh yUwZBFQ0RSIrRNmUL36HkNRwvBLXCulFSTYaC4QSgxrDl+PSbkEDD5Vl+LJ+izzTcD/jE/89FLhP z+xJJ/i4B5GfoqZ53D+cPX3iMTY+nmyl1GoCy+jY8v0S95IvUyC5Ihq0jFM/iAeDEIILOawcOa40 zhNSeNhJGYdhElHZVhpH9BX6WRQkYYA/MWyIe9B2H8VRf4BuLh/l+HhCIr7SJKKWfPwjDOIoHAz6 6DUDSXzhePRdMEjjQEnjLENZmGq5Npq78EBYM9zkekugk6rrppve8FL87cWvwQSYABNgAkyACTAB JsAEmAATYAJM4EkEzhrVhTOH7ZreNPPFz33u3Gupv/nmT9NbIwyOeI8o/iR2IpIwkTc5Ffj4iND6 XOHj8lCiM0k+cmSXNmS81j8D8mHSTq2rF1Jd8vVHQSc5IbjhRiZodBojobX+66EHOh23khqIzJGI jY2+REJJ3uTR6bdC84y+Rpc9pEF6SkfDlIZBu9MnTA8Xr0AH06MYESgSQBEJL3R4JWivR8U6foS+ weQjlBa8jHxlwzTRWF8ulZD80i1TQ3W9aeJxMiQn43WPu+4NPwxFl3hrnkJ1CekjTvL7qa7HAxUx MZEOkwaQBi8FE7qH4neaGMZUcqrX17E70chVHeEsC1VkBhJhtGkTNsygWn/Kc1EJP013Jsh3Ufm+ ktLz0BivYUZVGrwMLWVUaUYqDnYvMXTwxQvRxksMbuZZLH7EudCPeDw2B6AJLYwDJLlMtHZpmPqM FPi20289yVhYOdxx01u4lv77fXb4fibABJgAE2ACTIAJMAEmwASYABN4cQmcTaoLJLZv33HRxRd/ 5q//6hyzXUPVNYxxibdcKgTZCzUaYHyc5xqmtoYhIFHsJJ4xcmNDUzPMc9EvhlZCOJyhSBp+uoS6 EiZKjAwOX1S4sOHc4WndNVrruNF/PT7W9HgzNjorimBJ2SXSSuuzifIUhqc5tHPwK2Koch0E3BPt M6QBPQigkYMaHk7oGzyUYltouRIo0KGVwXf5yCWR94oCpJPiBDYLIoxyW1RnBnVGN8NERMkueS4G Htt+ABdGlWWkeUguyp60jRcog1qnEYrLHSazhtKK7hG9Z3L69Psqs/UnDvlTCo300ugpcnqTFl8a ukHhNCVHxxjeItHij1nLKM1CWe2fqZg9ROQLRf5o34oxe0jTiDSRmcRZQtsDaKwySsI+Mlxh2Ec3 F+5QsojujAO4LcwkZilGPH2hCgdIb2Up0l4hxhXTNMQz0d4PrWaSeMO+R+Tr6Ani4ke9chh0JN+m cqrrxf1rm4/OBJgAE2ACTIAJMAEmwASYABNgAt+fwFnT1bXxEu65555f+jcfOZda6oddXcPc1vBa pXoa+h7yT+tZqnUYJH5IIK3LpfW6KzmZKAXSyHbJx8n9gziWaGcaJa0oVTRUYHKSbhTdQl95Cjn0 9P8SbTiDUTZNjN/RTVoo4c+GZ7fR/MgRPagTMUI4ehVKOaEj/fSLUmO+YRkuSruk53p8sZe4itxI cwO5InkYkZPaEJODLDMMZLjgjDwPJV4uMl2ui/J6g3rr6Qm5leeLrQ7NAMb0Hxgy5MIQB6OQE3qs SBbRJCYGKYdBNGEHoZRwETBoQlflhmmg3l26MCnL5OTp0wPEQ/G+yOuS74q8ibcJag+l+0bf91FC hjWRjuEAKBrv4bEkNDwDJwCGOBn8iPPFhkdcqTzIsHZNxXpKGjeEg8OFYXaThg2Hb9PwrcIT8Rg8 Fxdu4NdUNDZ8B3EcHJBGHjE1OTKqwnGJPZ2IfIn8mayl/zjX0j/9+82/ZQJMgAkwASbABJgAE2AC TIAJMIEXjcBZqbpA4xyzXRtV1+nZv2EPlrAVZ6a6TkskKbieSXUNVzeKOUaZHhsaomejuh5ncoQ6 GXme0x9baXFkcGyD0hoaK7pbjluObjBblkn5oOE15LBLhpqjQx13DkXQ+uLJoY7BAsLcRAxr/TBD fyNlEARQmqBwCvoGSS7LsjEI6KGmq1AwLIv6612HBgMVhUSVqdPBMdeXxP3BADVg3W530O9j/6CF GUhSSMNFjHi8tEswROJkcxxcfC9E29BzPf2/vvJNGm4SGKmu0yjoraeWMX0QhZZOPfCYYIS2gqca WjGxrxMGCmcm5Bfd8P36iKikjgfTjscsh8HCxkm6jJELW7eSUnXROkhSXRiDhAtb/wzRmkg8HXjE h5HmIZVMhf9b30+Ab3AvXp03ML5of2PzgZkAE2ACTIAJMAEmwASYABNgAkzgGQhIx3H23S677LKP /tePLczP/9uPfGRxceHsuwA+YybABJgAE2ACTIAJMAEmwASYABNgAkyACTCBF5rA2Zrqkhxktgvf QHtBfr3QcPh4TIAJMAEmwASYABNgAkyACTABJsAEmAATYAJnE4GzNdUlGctsF76B8IL2OpvA87ky ASbABJgAE2ACTIAJMAEmwASYABNgAkyACbzQBM5u1cW264X+PPDxmAATYAJMgAkwASbABJgAE2AC TIAJMAEmcBYTOOtVF9uus/jTx6fOBJgAE2ACTIAJMAEmwASYABNgAkyACTCBF5TAuaC62Ha9oB8J PhgTYAJMgAkwASbABJgAE2ACTIAJMAEmwATOVgLniOpi23W2fgD5vJkAE2ACTIAJMAEmwASYABNg AkyACTABJvDCETh3VBfbrhfuU8FHYgJMgAkwASbABJgAE2ACTIAJMAEmwASYwFlJ4JxSXdJ2/fGf /dnU9DTvZDwrP4980kyACTABJsAEmAATYAJMgAkwASbABJgAE3geBM411QUUk5NT/+VjH2Pb9Tw+ FfxUJsAEmAATYAJMgAkwASbABJgAE2ACTIAJnJUEzkHVxbbrrPwk8kkzASbABJgAE2ACTIAJMAEm wASYABNgAkzgeRM4N1UX267n/cHgAzABJsAEmAATYAJMgAkwASbABJgAE2ACTODsI3DOqq4n2K5b b/3S2ffm8BkzASbABJgAE2ACTIAJMAEmwASYABNgAkyACTwbAvqHP/wLz+bxZ9lji8Xidddff+cd d3zxc58zLHP//v1n2QXw6TIBJsAEmAATYAJMgAkwASbABJgAE2ACTIAJnDGBc1x1gcO67frabbex 7TrjDwY/kAkwASbABJgAE2ACTIAJMAEmwASYABNgAmcfgXNfdbHtOvs+lXzGTIAJMAEmwASYABNg AkyACTABJsAEmAATeE4EzgvVxbbrOX02+ElMgAkwASbABJgAE2ACTIAJMAEmwASYABM4ywicL6qL bddZ9sHk02UCTIAJMAEmwASYABNgAkyACTABJsAEmMCzJ3AeqS62Xc/+48HPYAJMgAkwASbABJgA E2ACTIAJMAEmwASYwNlE4PxSXeu2a3Fp6e8+8xluqT+bPqp8rkyACTABJsAEmAATYAJMgAkwASbA BJgAE3gmAued6pK269prrj16/Bjbrmf6ePDvmQATYAJMgAkwASbABJgAE2ACTIAJMAEmcDYROB9V F94fwzDYdp1Nn1M+VybABJgAE2ACTIAJMAEmwASYABNgAkyACZwBgfNUdbHtOoPPBj+ECTABJsAE mAATYAJMgAkwASbABJgAE2ACZxmB81d1se06yz6qfLpMgAkwASbABJgAE2ACTIAJMAEmwASYABN4 JgLqgQOHnukxL/Tv0wf/8Id//pNdedgbf/FTv/LGcf2Ffo1ncbwwCD760V+/4/bbr7vhhl/6pV+2 HedZPJkfygSYABNgAkyACTABJsAEmAATYAJMgAkwASbwiiGgvWLO5GU7EbgtGC54LtguOC+Yr5ft VPiFmQATYAJMgAkwASbABJgAE2ACTIAJMAEmwASeB4GXRnXlaZo9j5N80Z/KtutFR8wvwASYABNg AkyACTABJsAEmAATYAJMgAkwgRefwIs9wOgvP/Kdr93ytw/u/1f//o3Tw8vJuycePtbO5U/uxM65 cVt98a/0mV+BJxmfmRE/ggkwASbABJgAE2ACTIAJMAEmwASYABNgAq9gAi+e6kqa3/vy3/7pn/75 HcczpfSqX/6dX11XXa9gHGy7XsFvDp8aE2ACTIAJMAEmwASYABNgAkyACTABJsAEnoHAizfAGJ26 888/SZ7rbLrxJOPZ9G7xuTIBJsAEmAATYAJMgAkwASbABJgAE2ACTODxBF481XW2kmbbdba+c3ze TIAJMAEmwASYABNgAkyACTABJsAEmMB5T+BFGWBMH/yDH/3wn/SeBLf4T//fT/+LC/X0wT/84Z// ZFf++sZf/NSvvHFcx3ePe9Y1v/y/f2Xv4c9/9ovfevjRb69Vr7v+dW99x02v2uwlq4989fOfv+M7 D377kWDzFVdc8/q3/+D1O8vGhpfKwuWHv/7lr37rgQMH7r/vqLL1kgu3zey87LrX3XDFtgq9zBnf PvnJP/vDT3wCmxmxnxH+64yfxw9kAkyACTABJsAEmAATYAJMgAkwASbABJgAE3h5COgf/vAvvOCv nC9/9y8+d1/0pONaF7/tPZdNaPj1pz/3wPDX267/kddsL1C47HHPKhbSb//J//rrbx47udzPeqtH H/7WP9x5rDaZf+E3/8Pv3frokYV2FPeXjz12z1c//x3rqjfuHzNksX20fPfv/duf/Y2//9ZDh4/j MbkStRZPHDlw353/+NmvHCzvv3rfmHnGV7t//37DMv/uM585evzYtddcaxgbhdoZH4UfyASYABNg AkyACTABJsAEmAATYAJMgAkwASbwUhF4UVSXolqTuy7YnDz2wImBuBBn6w/81Ad/+E3X7N2xbaKg nonqWunV3vpz//rH3naxe/yuh1fEtsb+ibtvu2/s3f+/X/yxt17iHbjj4Za4N187Ee19/fVbPCHL bvutX/qd7wbiF9qO1/z4P3/XdVuzEwdP9hJ6+reO26969cVj1pmzPRPbtbi4UCwWz/yY/EgmwASY ABNgAkyACTABJsAEmAATYAJMgAkwgReJwIvS1aWNX/i6N77uqp2l0Umbk/tf/cY3vul1F46f6evt fs+/fM/1F154xRt/7MduOj07eMkHfuJdV1940SVv+sC/fNvU6OBB15cJsXjhW/94e1v24Neu+df/ 57/7p+986w9/8Jd+8Ydn5EOz+/78cw8+eazy6cm+//0f+PGf/Mk7br/9ox/9dexnfMKDMeT4//z6 r79I7w0flgkwASbABJgAE2ACTIAJMAEmwASYABNgAkzgWRE4U/X0rA76Ajy4XvLkqbnF6umZw0qp IKcI7ULVfdKrdA7d98ho4eOuq/ZPiodqhd2XXkWRL7qF33zsZPqsz26j7UKGa/35sszrwfvvv+ee e571QfkJTIAJMAEmwASYABNgAkyACTABJsAEmAATYAIvNIFXqup6DteZ95vz/dHzPMceddBbjrde R7+6sNIb2bBn8xLrtuvffuQj0nZJzyWP8Zm//qtnczB+LBNgAkyACTABJsAEmAATYAJMgAkwASbA BJjAi0LgHFJdWXfliKwGw+0rv/G+N930htfT15vXtz0qSjLw0VX/nG7Sdi3Mz8N2/e7v/s6658LB 7rrjjqNHjjyno/KTmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwAReMALnkOp6wZh83wOt266/+vNP P+FBf83BrhefP78CE2ACTIAJMAEmwASYABNgAkyACTABJsAEnp7Auaq6Lnn///2bH//v/++Tvv7p JeUX5ZI/f/PNHOzif9mYABNgAkyACTABJsAEmAATYAJMgAkwASbw8hJ4UbzPy3NJWmls26h/XqnN 7r7wqW7bx231OZ/exn6uJx/kS1/64nM+Mj+RCTABJsAEmAATYAJMgAkwASbABJgAE2ACTOD5EziH VJdaqE0XRkTmF9ei4ffLX/oPsrQLX//yzw8+l1Z6OtLTey484C8+9al2u/383xI+AhNgAkyACTAB JsAEmAATYAJMgAkwASbABJjAcyPw0qiuPI6fq2F6FpdV2nrB3OjhR2//5pGQfkjbj9537+he94qd M8/pip/Rc8lXuOWWm5/F+fJDmQATYAJMgAkwASbABJgAE2ACTIAJMAEmwAReUALPSfyc0RnoxUp1 9MDevX/1h3/6d1+64+CLGnqyNl39hmsN+ZrB4T/66K/9wWc+91f/6z9/7HPCeSmKdsUHbtrrntHJ P/FBhUJhbGL8GZ+KtYwc7HpGSvwAJsAEmAATYAJMgAkwASbABJgAE2ACTIAJvEgEXjzVZW+58Yfe VBsePzv8D3/8m7/9ucODF+ky5GHVidf+7P/93jn5mtnxu/73f//N//nX97ZFoEzb9aZf+fC7d65P OD67E3nHO975+3/wR//xV391z759T//Mr371K8/u0PxoJsAEmAATYAJMgAkwASbABJgAE2ACTIAJ MIEXiMCLp7oUtX79z//2f/n59970qgumNEUrzu3bUjJfoNP+foexxq/+qY//wW/8qw/+yBuuu3Rr SdOmLrjquje/84O/8ht//PF/c+PmYeTrOZ2E7TjXXXf9f//t//HR//qxt7z97d/vGH/+yT8Lg+A5 vQI/iQkwASbABJgAE2ACTIAJMAEmwASYABNgAkzgeRFQDxw49LwOcL4++eiRI3fceQcmFp8M4Bd/ +Zff+MY3na9g+LqZABNgAkyACTABJsAEmAATYAJMgAkwASbwshFg1fW80KOZCxOLn/7Up5YXF9cP ND45+Xu//wdIgT2vQ/OTmQATYAJMgAkwASbABJgAE2ACTIAJMAEmwASeJQFWXc8S2FM9HBOLDz38 8B/9we8//OCD8vcYcrzssstegEPzIZgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEzpgAq64zRnUG D/ze9x7+3C23fP7mmy/cv//jv/lbZ/AMfggTYAJMgAkwASbABJgAE2ACTIAJMAEmwASYwAtGgFXX C4Zy/UCLiwtf/vKX9+7dx8GuFx4uH5EJMAEmwASYABNgAkyACTABJsAEmAATYALfnwCrLv50MAEm wASYABNgAkyACTABJsAEmAATYAJMgAmcIwS0c+Q6+DKYABNgAkyACTABJsAEmAATYAJMgAkwASbA BM57Aqy6zvuPAANgAkyACTABJsAEmAATYAJMgAkwASbABJjAuUKAVde58k7ydTABJsAEmAATYAJM gAkwASbABJgAE2ACTOC8J8Cq67z/CDAAJsAEmAATYAJMgAkwASbABJgAE2ACTIAJnCsEWHWdK+8k XwcTYAJMgAkwASbABJgAE2ACTIAJMAEmwATOewKsus77jwADYAJMgAkwASbABJgAE2ACTIAJMAEm wASYwLlCgFXXufJO8nUwASbABJgAE2ACTIAJMAEmwASYABNgAkzgvCfAquu8/wgwACbABJgAE2AC TIAJMAEmwASYABNgAkyACZwrBFh1nSvvJF8HE2ACTIAJMAEmwASYABNgAkyACTABJsAEznsCrLrO +48AA2ACTIAJMAEmwASYABNgAkyACTABJsAEmMC5QoBV17nyTvJ1MAEmwASYABNgAkyACTABJsAE mAATYAJM4LwnwKrrvP8IMAAmwASYABNgAkyACTABJsAEmAATYAJMgAmcKwRYdZ0r7yRfBxNgAkyA CTABJsAEmAATYAJMgAkwASbABM57Aqy6zvuPAANgAkyACTABJsAEmAATYAJMgAkwASbABJjAuUKA Vde58k7ydTABJsAEmAATYAJMgAkwASbABJgAE2ACTOC8J8Cq67z/CDAAJsAEmAATYAJMgAkwASbA BJgAE2ACTIAJnCsEWHWdK+8kXwcTYAJMgAkwASbABJgAE2ACTIAJMAEmwATOewKsus77jwADYAJM gAkwASbABJgAE2ACTIAJMAEmwASYwLlCgFXXufJO8nUwASbABJgAE2ACTIAJMAEmwASYABNgAkzg vCfAquu8/wgwACbABJgAE2ACTIAJMAEmwASYABNgAkyACZwrBFh1nSvvJF8HE2ACTIAJMAEmwASY ABNgAkyACTABJsAEznsCrLrO+48AA2ACTIAJMAEmwASYABNgAkyACTABJsAEmMC5QoBV17nyTvJ1 MAEmwASYABNgAkyACTABJsAEmAATYAJM4LwnwKrrvP8IMAAmwASYABNgAkyACTABJsAEmAATYAJM gAmcKwRYdZ0r7yRfBxNgAkyACTABJsAEmAATYAJMgAkwASbABM57Aqy6zvuPAANgAkyACTABJsAE mAATYAJMgAkwASbABJjAuUKAVde58k7ydTABJsAEmAATYAJMgAkwASbABJgAE2ACTOC8J8Cq67z/ CDAAJsAEmAATYAJMgAkwASbABJgAE2ACTIAJnCsEWHWdK+8kXwcTYAJMgAkwASbABJgAE2ACTIAJ MAEmwATOewKsus77jwADYAJMgAkwASbABJgAE2ACTIAJMAEmwASYwLlCgFXXufJO8nUwASbABJgA E2ACTIAJMAEmwASYABNgAkzgvCfAquu8/wgwACbABJgAE2ACTIAJMAEmwASYABNgAkyACZwrBFh1 nSvvJF8HE2ACTIAJMAEmwASYABNgAkyACTABJsAEznsC/3/m5QjOpmEBGwAAAABJRU5ErkJgglBL AwQKAAAAAAAAACEAsTUtWXCuAABwrgAAFAAAAHBwdC9tZWRpYS9pbWFnZTkucG5niVBORw0KGgoA AAANSUhEUgAABuwAAAHaCAIAAABkUPgEAAAAAXNSR0IArs4c6QAAripJREFUeF7t3X90VNW98P8k 5etVGpCL+tQJ+sACmmAXtWgiPlVuHVBDvf7KSkTv1SZo0OpVbJcskjxYar3WyiXJwtUKrdomQmi9 1+JkQdVWognp80DvAyZqlXUxKbrQQkavlgJJ/YEw+e6ZMz/OzJyZOWfm/Nhz5j1/9AfZZ/947b3n nPmcffYpHh8fL+KDAAIIIIAAAggggAACCCCAAAIIIIAAAgjIKlAia8WoFwIIIIAAAggggAACCCCA AAIIIIAAAgggEBQgiMk4QAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4BBBBAAAEEEEAA AQQQQAABBBBAAAEECGIyBhBAAAEEEEAAAQQQQAABBBBAAAEEEEBAagGCmFJ3D5VDAAEEEEAAAQQQ QAABBBBAAAEEEEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQkFqAIKbU3UPlEEAAAQQQQAAB BBBAAAEEEEAAAQQQQIAgJmMAAQQQQAABBBBAAAEEEEAAAQQQQAABBKQWIIgpdfdQOQQQQAABBBBA AAEEEEAAAQQQQAABBBAgiMkYQAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4BBBBAAAEE EEAAAQQQQAABBBBAAAEECGIyBhBAAAEEEEAAAQQQQAABBBBAAAEEEEBAagGCmFJ3D5VDAAEEEEAA AQQQQAABBBBAAAEEEEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQkFqAIKbU3UPlEEAAAQQQ QAABBBBAAAEEEEAAAQQQQIAgJmMAAQQQQAABBBBAAAEEEEAAAQQQQAABBKQWIIgpdfdQOQQQQAAB BBBAAAEEEEAAAQQQQAABBBAgiMkYQAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4BBBBA AAEEEEAAAQQQQAABBBBAAAEECGIyBhBAAAEEEEAAAQQQQAABBBBAAAEEEEBAagGCmFJ3D5VDAAEE EEAAAQQQQAABBBBAAAEEEEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQkFqAIKbU3UPlEEAA AQQQQAABBBBAAAEEEEAAAQQQQIAgJmMAAQQQQAABBBBAAAEEEEAAAQQQQAABBKQWIIgpdfdQOQQQ QAABBBBAAAEEEEAAAQQQQAABBBAgiMkYQAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4B BBBAAAEEEEAAAQQQQAABBBBAAAEECGIyBhBAAAEEEEAAAQQQQAABBBBAAAEEEEBAagGCmFJ3D5VD AAEEEEAAAQQQQAABBBBAAAEEEEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQkFqgeHx8XOoK UjkEXCRQXFzsotZk0xTHv3DogjTd5njvZDOksj2GkeB4d9MFTMZsp28+Hcc456tG5vHqeO/IjEPd EEAAAQTkFGAlppz9Qq0QQAABBBBAAAEEEEAAAQQQQAABBBBAICxAEJOhgAACCCCAAAIIIIAAAggg gAACCCCAAAJSC/A4udTdQ+VcJhB9rKzQnt+Rp+Hy1ESSsa1+1LGghiUNd7y7mYzJXwKYSPLFaGI1 CrZP5Wm4PDUxcVzlklXBnv5yQeNYBBBAAAF5BFiJKU9fUBMEEEAAAQQQQAABBBBAAAEEEEAAAQQQ 0BAgiMmwQAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4BBBBAAAEEEEAAAQQQQAABBBBA AAEECGIyBhBAAAEEEEAAAQQQQAABBBBAAAEEEEBAagGCmFJ3D5VDAAEEEEAAAQQQQAABBBBAAAEE EEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQkFqAIKbU3UPlEEAAAQQQQAABBBBAAAEEEEAA AQQQQIAgJmMAAQQQQAABBBBAAAEEEEAAAQQQQAABBKQWIIgpdfdQOQQQQAABBBBAAAEEEEAAAQQQ QAABBBAgiMkYQAABBBBAAAEEEEAAAQQQQAABBBBAAAGpBQhiSt09VA4BBBBAAAEEEEAAAQQQQAAB BBBAAAEECGIyBhBAAAF5BYpDH3nrR80QKBgBZTIyHwumw2koAs4I8D3jjDulIoAAAgjkiQBBzDzp KKqJAAKFJxANlxA3KbzOp8UIIIAAAgUnwHm/4LqcBiOAAAIIGBQgiGkQjOQIIIAAAggggAACCCCA AAIIIIAAAgggYK8AQUx7vSkNAQQQQAABBBBAAAEEEEAAAQQQQAABBAwKEMQ0CEZyBBBAAAEEEEAA AQQQQAABBBBAAAEEELBXgCCmvd6UhgACCCCAAAIIIIAAAggggAACCCCAAAIGBQhiGgQjOQIIIIAA AggggAACCCCAAAIIIIAAAgjYKyBXEPPEYPts8Vo+8ZndPnjCXglrSzvh775LaZnJTRvb076wTGS6 tPvP1raA3PNIQDWRGBg29ltgbLi/u/vnLcEpGf18dWn70919w2M21oOiJBJgMjrTGUxGZ9wp1TEB vmqcoeerxhl3SkUAAQQQKGQBuYKYhdwTWbX9yODjDzX1+7M6loMQQMA0gYB/z6aWqyZVLKyr+3Zr 3JTc29V0S93lFZMW3t89fMy08sgIAQRSCDAZGRoIIGCDAF81NiBTBAIIIIAAAskCBDHzd1Qc9/f9 eGXTC/nbAGqOgDsEAv6XVt9cc2trT7rm9K+pq1hyf9+hgDvaTCsQkFKAyShlt1ApBNwmwFeN23qU 9iCAAAII5I+AXEHMCZUr94+HPvtXVk7IH0UHaioimGtuvvzBfgeKpkgEEFAJBN7dunrFmvDqS4+3 +cmtAyMnle+x8ZOjQzu2tDV4wsl71nxrzdZDx+FDAAFLBJiMlrCSKQIIxAvwVcOIQAABBBBAwDkB uYKYzjnkVcniCZZH76gkgplXnUZlXSoQONb/xPLOvaHWzW3o6Hlu7R3XV3oiX6wlpeXeG1b+fLD3 Qa/Sfv+G5T/ZyVPlLh0MNMtZASajs/6UjkCBCPBVUyAdTTMRQAABBCQVIIgpacekqJbYQXx7+23V F6/oYiNMp3pu165dw8PDTpVOuUKgp6fn8OHDUlAEhp9du1GZjJ7mHz/WOLdUo1qneBZ9t73t6nAY s/XxZ4c/laLy+V8JMQy6u7s/+eST/G9Kvrbg9ddfF1+JUtSeyShFN7izEmKcc953tms57zvrT+kI IIAAAgjII0AQU56+yFSTseGX2m8rr/hmU1dk2deaB6PPqWY6mL+bJvDFL36xoqKiubmZnzSmmRrM 6P333z/jjDPWr1/vfChzbGToTSWGWVm/+PzJKRsy5YLraqvDf31n6CDvKjfY5SmST506taur67LL LiOUaQ6o8Vz+5//8nwsWLKipqXE+lMlkNN59HKFTYOLEiZz3dVpZl4zzvnW25IwAAggggEAeCVgX xDw23Letu7NlYbHqs7Cls3tbX+pX9J4YbJ+tJJ/dPnhCk/G4f/C3cdkG8+wfHgu9LcPfvTRc2l3d fvXxJ/zdd8VnnJRP8eKWzme3Dfp1vHYjdGz3z1sWlqkbV1y2tP3Zbn05ZDFCxgYfv7O6KbIA09PQ 1uPbsPKar07MIisOyUlg3rx5F110UVtbGz9pcnLM4eBrrrlGHH3vvfc6/pPmxJ9e9YUXRVddfN6U NG0qmfm1K2cpfz/y/hFWDubQ/fGH3n333a+88kpdXR2hTNNMjWQk4sjXX3/9tm3bHA9lMhmN9Btp jQmUl5eLcc5535iaqamrqqo475sqSmYIIIAAAgjkq4AVQUzxyHN3y8I5FZfX1C1r7VfL9Lcuq6u5 vGLOwpbucNjRkNvYW90t15ZVXR2XbTDPhRXltz26R0/8MVie2FJyU3I+RT2ty5bUVFVenq5uIny5 uWXhjGAd6r7dGn6VR6QN/q6mJXU1VWXnLN2wx2/duzvEm0O6BgZ/vvLKcq0HVw2BkjhLgeXLlytH 8pMmS8HcDhNxk9tvv13Jw9lQZuxdZOOP13rSvozs46MffpxbszlaS0D5ZSs+hDKdGiC1tbVK0c6G MpmMTg2AAik3Os457zvS45z3HWGnUAQQQAABBCQUMD+IGTi09bveusQAX1zT/f2tdd57Nr+lLJ/U +Rnb23nPkrrWHu3k/q4VF9+x7j//O3NmH77UdnPNranyKQrV7btbD2lULfRC8KqGtE0Llu/vWn7x zRsGDbUuc71FilO8zR29Q2/tWFtf6TlF1xEkskZg0aJF6oz5SWMNc7pclyxZov6zs6FMPc0PvH/g 9fCazXO/Ov3v9RxCGj0C6l+2Ij2hTD1o5qY5//zz1Rk6G8rU0zQmox4l0iQIcN53fEjceuutnPcd 7wUqgAACCCCAgOMCpgcxP+r/ySOd4d/q1cGY2+jJ8fDns5GB5zqaw/vC+bva23r+rDeKGXi3+7v/ vCy8F2SReJbaNzASznd0qLej2RuEfKGp7l+6Mom+3bpqdXAFpVjP+ORWjUyCx/s7H/lJ/0cJOQUO Pb/6Ww/2K/8qarBlh6pp4+OiGlvaYjtU9v/q13vMffHIqRV3bduxtnFReept9zK1nb+bJXDOOeeI J8sSciOUaRavnnyi6+/y5CfNp/t3vhi+A+P5+oVfZhsIPZ2sN03CL1txGKFMvXZmpBM7bCRnI3Eo k8loRq8XXh7ivB99AiDaes77dg6ECy+8MLk4iW9h8lVj5+igLAQQQACBAhIwO4jp/z+bWgdDfpXN vb8KxtxKo0Wc4qm8pnHNk77GuaEEezuf6N2vK4oZGHut+7FO5W02Hu+qnsGnVtZWesL5lpYvalzz 3L6NRl5xM7eho+e5tXdcn5DJwDpvuOsHN29/41jcMDjy2r93hoOznnt8r/x85Q1eVdOKikQ1blj5 1Cu+Ro9yWHIOOY6qCaWlp+aYBYebKBB9siwhT37SmIicJitlJz7NBDL+pDn2h47VW5TaeuqvqJps 9hevPeiylnLWWWdpVo1Qpm091tTUpFmWjKFMJqNtw8J1Bf3DP/yDZps479vT1aeddlqqrxrO+/Z0 AaUggAACCCAgg4DJv6VPjLyzK9ysFK+5KJl+3d1Lw++36Nmz9wPt1/fE0xze8+tf9Sv/5G1qv//y SPwymqqkdE79hueiIcgMsJ7Gf33k1rlJG0qWlF5wVX11OAbpf/3A++oA64m3dzz+ghKCqH54ec00 7ae5S6ZV373CqxTvf//I32ToYepgjUBpabotSflJY416XK7iTQtpSpHpJ82RwSfXtSrr0z33rP/O AlZTmzs8zjzzzDQZEso0V1szN/GO8jSlyBTKZDLaMBxcW8TZZ5+dpm2c9x3veM77jncBFUAAAQQQ QMAGAZODmKoa73rhP7U2liwqMrD7vpLdsTe2b46s7vx+fWVsaafaJy4EmRausv6Wb0zTbHfJOV+7 siJ87NuHR9VBzFilR7Y3zkmtdmrZzEgONvQeRTgnMHeusqA43YefNJmELP+7BD9pjh/qXn1tk3IL ZG7j+qZUt0Ast3BvAWJZbsbGEcrMSJRLgrKysoyHSxDKZDJm7CUSpBOYMWNGRiDO+xmJrE7Aed9q YfJHAAEEEEDAYYHIhpUm/ffR3ubwWsbgL/aGtl/5xK6YuvP+fKAtvEhzVtvA58phJ4/2ropkeadv JPyvWll+MtQRfddHQsrPR3x3RqCXdAx9kqJGqmSxCuiufTChKocG30js0Nxz1qqGyqvB956eijo8 2gq4ePEM1NDQUBRAT2dln8b4wMi+LH1HytDzjz322F/+8hebuiC6EXDvg95IkZ5G38HoFsHG3fQd YTiVDF1jcx0uuugin8+nLtSwmv4DmIxavSv2gti5c2d+TUbR5zZU2Oa5oBSnfzhLmNIRMZ2Fct7X CWVdMs77mnPWOnByRsBSAQnPQVQJAQQcETB7JebkS5Y9HI0k7u1quqXu8opJxcXFC1s6u7f1Dcfv M6nrey7wtyOHwy8KmjV7+lkTUh906syvzQ/HQNPlPHXKpDSZ6KpTUqLj/sHfdnd3P9u+rLLuieyy sOEo0Q82lEIRmgLK6gxwHBRQVmfYWIHj/r41N18efhuYp2Fj349rtNeA21gnihICyqpMKBwUUFZl 2lgBJmM6bB3XBmOD7QtFsthndvtgut2ArE4fbo6Omts4ypKK4rzvpH6obM77jncBFUDARAHJv/NN bClZIYBAegGzg5hFp5bf+mjvqvAryGNl97cuq6u5vOL04uLFLZ3Pbhv063qjT/D4T0feiaxfu3Rm We7hxwyRUB0DJuAf3CYiliJkubQsfEX/d2VVV4tfxUuausLxVh3ZkAQBBGwWEEvw7CoxKWiyoX6O 9lYYdtWIchAoUAEmY4F2PM1GQAhw3mcYIIAAAggg4DIB04OYRUUl0xY98tzIgK+tQXPTwJ7WZUtq qsrOWbphj/+4uZoTymZeam6OCbmJ8OWmloVfKKuqERHL/AtZqp+Gs9SJzJMFEp5pgshmAeUJ4t// /ve2lHvsrc5/qVSvwUwbwXw99MmuYuLAnp6e7I4t2KMSnmUuWAcHG57wmK2VNTE2GQ8fPiwmlPjP LKokjhJ3N7M40PFD8vfaQPKac953dmy7+Ly/a1fkLarOElM6AggggAACTghYEMQMNuMUT2Xtyk1v jo8O9fq2dDQnLcwUL+/uWn7xzRsGx3SvyNSho3o3uo7URpOM7e28rbrq1tZ+jQM93uYnRYhk68B7 B2ObbxotwI70juxZ4HihUVkTa6Le4DJ9zyk/Y5YvX67n9SN2DAJHyzCxC0QcRE9Toj9jamtrTzvt ND2H5JQm4N/z6L2LlnWG30YuniLPtAZTvNn5ggsuqKmp6erq0hk9EclEYnGIOFDPuyZ0tsjE3kmV lbomJhans4FK+HLr1q2XXmrtDS+d9XE2mYn+CXuMpmmXEr5sbW0tLy+3vPnGJ6P4ln755ZfFvhOP PPKI/lsLIqDQ3NwsjhoZGTGrUSb2TsaszKqzI/lkbJ2JCTjvZ9fFJnaBy8778+fPN3TeF7dJlPP+ WWedlV1fJB9lYu/YfN63oeYUIYmAWaOdfBBAwDUCFgUxIz6l5Ytqb2hcu128n2d0aIfP92SzN/be n6L+tvt/PZwpijlh0tTIqXrXOyPp9mCytFM+6vvhrcu69obL8DZ3hGKWI+HXdIzsWHuHCJFcX+mx GNTSNpK5AYG9eyODIfVBhC8NgFqQ1O7wZVFRwL/r0duqL16hbCvh8a7qGXxqacanyEXcRAwVsUvg 0qVLRRzkjjvuEL9qxG+V4eFhtYr4v2KNmPh3kUAkE4nFIeIXnR3BIAt6x8QsP/nkk4y5Eb7MSJRL grGxsYyH2xq+zHYyilbcfvvt4j+/973viUiBCDGsX79eTDoR0FTPR/G/ReBS/LuIdYo0Yn9Psfuh OOrmm2/O6ECC/BU4cOBAxspz3s9IZGmC/Drviw2aDZ33xUNgnPctHT9kjgACCCCQHwK232MR0Uxf LJRZ3TGkel2vHW8nT/fa8ZTvED851BFZTerxtu1O/b511RvSpXw7ue3dLUWB0aloYm02bdqUZoYr P2OSi7OiJhqNkviFyCZ2gRJr0PwoP2M+/vhjG7vg5Oi+jQ2xezRzG9btjNzkyNxo9WvTDZ05xMqg zLmnTaEuLses9BxuRXHpl0cp4cs0I0FUSU/Ns0xTGJMx/fIoJXyZL5NR1FPnaq+EqSq+9rMcJKrD bDpH5F7Rgswh/YpjzvsJg8KKwZxmbnLe1z8prTgRpynd5uL0O5AyXwQYQvnSU9QTAdsEzF04GHsf ZVlLX4o3kZeUlv/jsvrIa0nfPjyaYSlmyeSqK+rDoYGB3fuOpPyFH/hg7659hn7/60584oO9e8Lb znlu/f63q0pTHamug5PrRnW3jITZCvzf//t/NQ9lFUa2osaOE4vvfvGLXyQfY/8qjFAdxJtDfnjt ebdGXuxVvar3xafuu1T/wmxlMaYxgqIilmEqYh9++KEmHasvjY6orNP39/drHmvz6ktTJqPIJM0N kjRELMPMevzky4G/+93vOO8721maXzWc953tFEpHAAEEEEDAZgFzg5gTv3zh15V4o3/zywPHMj0p LtLNmjopYxUmn7+4vjKU62DrDzen2EYzMPZa92OdmZ/wzdV34tTTJ6aq8XF//0Y76pBrGzg+VwGx I2FyBI3wZa6sRo5/9dVXE5I79DNG1CLu3cdFnoZ1uzc+vGhaxi+2hPpnEQHJLtRihDk/0ia/r4nw pZ09d/DgQfFQZEKJToQvTZuMYosGo4sxxfc/ux7bOersL4vzvv3mCSWKPRwSvmo47zveKVQAAQQQ QAAB+wWM/tBOX0PVqkn/xh/+W69fM4w59sZvNu8MZeSpvumS2ZmrMHX+jbd4lZL721Y+opFtwN/7 yMq2fqv8Sr44ZWp4Mejb/Tv+eESrnPhAhlU1IV8pBJ5//nl1PQhf2t8rGzdujBbq3M+YYBUCh55f /a0Hw18+nsaOvsfum69/CWZMzuhiTJZhKnYisiC2L4w6Er60fzL29fWpC3UofGnmZBR5Gb1DkMVN CPt7ihJzEUhYA8h5PxfM7I4V20Fy3mcX7OwGD0chgAACCLhJIHME0VhrJy/4zvp7QvE+f/+a6srb 2p/tG45t+B/wD277ecu1NU39ynsvau9cPFNHDUpKRUZtV4dqEs62ezASIB0b7nu2/bbK6jVKnpZ8 1I+0v9B07Xfbn+0fjr1X/dhwX3dny7Vll0cCGZbUgUwlEhCvelBqw88YR3oluiLG2fBlsO2Bd7c+ 8IPwm8iLqlf98qFb50zO2sRQHMRokCXrWkl+YDSyQPjSqZ4S77dRinYwfGn6ZDS0GJNlmE6NPTvL Fe9b47xvJ3hCWeK8r7w+i/O+g71A0QgggAACCEghYP7umycP+Brn6mib2DbuoOqlPsGKaL3YJ1LB 0Tc7GtJm62n82ZMrZoULvtM38rmqaSnf2BPf/DTJPjvoU4KzaT/eVVt8ayKvAFLXQWcFDPaGfK+M MNgAm5JH+8yU8l577TWRYaot/NMXYW5NUpYVGxiZRmzs7962gdQvrMoZztyGi7cqpdnC384uUL3y Sz91UVHcW7/i6qtzZ0wRLcq5T8IZqOttVp52bvAvYpepXt2jcyQIAQsb7vbJqLyTKtWre3R2gSn+ pk/G9C+MUk8czde4Zdcoc78qs6sDRyULcN7PYlSYO5jFW5U472fRC5qH5Pt53ywH8skXAZtHbL6w UE8ECllAxzpII7/Ng2lLptf+eIuvORLK0zw8i23jSuc2bkidrXeVr//R2y/MHGY02ppI+lOm1Xxv 6zrVy4cTM5rb0Pbi0HMP33DF/Hl6XkOUbT04TgYB8ZN1+fLl7IDmVF+cf/75YhvE2tra0047zak6 hMode+03m8Ov/DKpHjoXY7IMU/EW73cS4bOtW7deeumlJvUA2RgT+Pjjj0Wwr7W11emHHM2fjDoX Y7IM09iIyc/UEydO5LzvbNfNnDmT876zXUDpCCCAAAIISCJgQRBTtKx0Tu3a340O7fD5nmz2qgOL ItL3K9/WgZGDm7LZNi6Y7XMjAy/4Opq9UT9vc4dvh4ge1paXfnz08MfKv3umTvmi2U0r8cy/76lh 0agtbepYpqehbYvvhYGRwU0rF5eXlhSpXkO0efsbKV7RLknvU41sBObNm0f4Mhs4844RXeB0+FJp zF/fffPP5jUrmJOenTHZDTNqLoYB4UtzR6DR3M455xynw5dWTUaRr567BTpvPBiFJb1UAmKQc953 tkc478vxTevsKKB0BBBAAAEEggLFYhmqWyRO+LuXl9U9EWzOrLaBt1ZWTnBLy2hHbgLFxcVKBo6P dnlqkpuo4aPlabg8NUmFKHb+OuOMM9IQi4VvJv6YiYLYM0FsLi4Nozw1MTydcjtAnikgT03SiDY3 Nys78Wl+xDJMsSo/tw6JOzovTExsbyFkVbB9Kk/D5akJ533Hr8ML4TvHfW0s2As293UlLULALAGz lyuaVS9VPoHhzsXi2yv4Wdg+GHtLUFJRn468MxT+x0tnlhHBtKAvyBIBBKwWSL8Yk2WYVvuTPwJq gfSLMVmGyWhBAIHcBTjv525IDggggAAChSOQB0HMkklTzg53yNBLfzwYSNU5x/b8el1/6I+e6su+ 8qXC6UNaigAC7hJIExnR83yruzBoDQJOCqTZGZPdMJ3sGMpGwF0CnPfd1Z+0BgEEEEDAQoE8CGIW fekrl1UrG2v6e1av33rouIZH4FDfv61t9St/WXDTghn50DAL+5WsEUAgfwVSLcpgGWb+9ik1z1+B VHcOWIaZv31KzRGQTYDzvmw9Qn0QQAABBKQVyIdYX8nMxXfWhl8P5N9Qd9Ed7c/2D49FV2QeG+57 pv22b16+RnlLsMfbtvLG8lOlFadiCCCAQEYBzfgIyzAzupEAAdMFNBdjsgzTdGcyRKDABTjvF/gA oPkIIIAAAjoF8iGIWXTKtJqm9Y1zw03ydzUtWVgx6QvhfTKLT6+4/J+auvYqf/U0rPnZXVWlOltP MgQQQEBKgeRFGSzDlLKjqFRBCCTfP2AZZkF0PI1EwEYBzvs2YlMUAggggEAeC+RFELOoqGR67c97 dq9rCK/H1Aaf27Bu5+BTS+eU5kmj8njYUHUEELBcICFKwjJMy8UpAIEUAgmLMVmGyUhBAAErBDjv W6FKnggggAACLhPIn3hfiWf+fZsOjgxs9f2qrSGyKjPYGx5v85M+346h0T9uuu9ST/40yGUjieYg gIC5AupFGSzDNNeW3BAwKqC+i8AyTKN6pEcAAT0CnPf1KJEGAQQQQKDABYrHx8cLnIDmu15A7Dyg tNHx0S5PTWzudHkaLk9N9HTB4cOHzzjjDJFyaGhIrAXTc4jRNFEQeyaIzcWl0ZCnJka7LMf08kwB eWqik7S5ubmtrU0sw1y+fLnOQ4wmyzsTow0swPQF26fyNFyemugZ/5z39SiRpqAECvaCraB6mcYi YEiAIKYhLhLnpYA816/y1MTmjpSn4fLURGcXrF+//r333mttbdWZ3mgymy8NbS6OIGaygDxTQJ6a 6Jw1w8PDFRUVf/nLX8RqKZ2HGE2WdyZGG1iA6Qu2T+VpuDw10Tn+Oe/rhCJZgQjIc+lYIOA0EwH5 BQhiyt9H1DBXAXmuX+WpSa6mBo+Xp+Hy1EQnoViU8fHHH59zzjk60xtNZvOloc3FEcQkiGl0RqRP //rrr8+bN8/cPNW55d0XlHUUrsm5YPtUnobLUxOdo5rzvk4okhWIgDyXjgUCTjMRkF+AIKb8fUQN cxWQ5/pVnprkamrweHkaLk9NDBJaldzmS0ObiyOISRDTqpljTb58QVnj6mSuBdun8jRcnpo4ORBV Zdt8Ira5OEmQqYaJAgwhEzHJCgF3CPAeHHf0I61AAAEEEEAAAQQQQAABBBBAAAEEEEDAtQIEMV3b tTQMAQQQQAABBBBAAAEEEEAAAQQQQAABdwgQxHRHP9IKBBBAAAEEEEAAAQQQQAABBBBAAAEEXCtA ENO1XUvDEEAAAQQQQAABBBBAAAEEEEAAAQQQcIcAQUx39COtQAABBBBAAAEEEEAAAQQQQAABBBBA wLUCBDFd27U0DAEEEEAAAQQQQAABBBBAAAEEEEAAAXcIEMR0Rz/SCgQQQAABBBBAAAEEEEAAAQQQ QAABBFwrQBDTtV1LwxBAAAEEEEAAAQQQQAABBBBAAAEEEHCHAEFMd/QjrUAAAQQQQAABBBBAAAEE EEAAAQQQQMC1AgQxXdu1NAwBBBBAAAEEEEAAAQQQQAABBBBAAAF3CBDEdEc/0goEEEAAAQQQQAAB BBBAAAEEEEAAAQRcK1A8Pj7u2sbRMARCAsXFxYqE46M9WpOC7Rm6QOaut6F31FPAhuLSaDMZnfVX fzPLPCmcqpvjveNUw91XLl81jg9muiDNtLKhd+Q577vv66VAWsQQKpCOppkI6BdgJaZ+K1IigAAC CCCAAAIIIIAAAggggAACCCCAgAMCBDEdQKdIBBBAAAEEEEAAAQQQQAABBBBAAAEEENAvQBBTvxUp EchVwIbHdnKtopXHy9B8GepgpXH2eReaTKG1N2FkyNB8GeqQ/YSx8khkrNS1O+8C700Zmi9DHewe dvrKQ0afE6kQQAABBOQSYE9MufqD2lghIM+emFa0jjwRyCMBNjbKo86iqggggAACCOQowHk/R0AO ZwgxBhBAIEGAlZgMCQQQQAABBBBAAAEEEEAAAQQQQAABBBCQWoAgptTdQ+UQQAABBBBAAAEEEEAA AQQQQAABBBBAgCAmYwABBBBAAAEEEEAAAQQQQAABBBBAAAEEpBYgiCl191A5BBBAAAEEEEAAAQQQ QAABBBBAAAEEECCIyRhAAAEEEEAAAQQQQAABBBBAAAEEEEAAAakFCGJK3T1UDgEEEEAAAQQQQAAB BBBAAAEEEEAAAQQIYjIGEEAAAQQQQAABBBBAAAEEEEAAAQQQQEBqAYKYUncPlUMAAQQQQAABBBBA AAEEEEAAAQQQQAABgpiMAQQQQAABBBBAAAEEEEAAAQQQQAABBBCQWoAgptTdQ+UQQAABBBBAAAEE EEAAAQQQQAABBBBAgCAmYwABBBBAAAEEEEAAAQQQQAABBBBAAAEEpBYgiCl191A5BBBAAAEEEEAA AQQQQAABBBBAAAEEECCIyRhAAAEEEEAAAQQQQAABBBBAAAEEEEAAAakFCGJK3T1UDgEEEEAAAQQQ QAABBBBAAAEEEEAAAQQIYjIGEEAAAQQQQAABBBBAAAEEEEAAAQQQQEBqAYKYUncPlUMAAQQQQAAB BBBAAAEEEEAAAQQQQAABgpiMAQQQQAABBBBAAAEEEEAAAQQQQAABBBCQWoAgptTdQ+UQQAABBBBA AAEEEEAAAQQQQAABBBBAgCAmYwABBBBAAAEEEEAAAQQQQAABBBBAAAEEpBYgiCl191A5BBBAAAEE EEAAAQQQQAABBBBAAAEEECCIyRhAAAEEEEAAAQQQQAABBBBAAAEEEEAAAakFCGJK3T1UDgEEEEAA AQQQQAABBBBAAAEEEEAAAQQIYjIGEEAAAQQQQAABBBBAAAEEEEAAAQQQQEBqAYKYUncPlUMAAQQQ QAABBBBAAAEEEEAAAQQQQAABgpiMAQQQQAABBBBAAAEEEEAAAQQQQAABBBCQWoAgptTdQ+UQQAAB BBBAAAEEEEAAAQQQQAABBBBAgCAmYwABBBBAAAEEEEAAAQQQQAABBBBAAAEEpBYgiCl191A5BBBA AAEEEEAAAQQQQAABBBBAAAEEECCIyRhAAAEEEEAAAQQQQAABBBBAAAEEEEAAAakFCGJK3T1UDgEE EEAAAQQQQAABBBBAAAEEEEAAAQQIYjIGEEAAAQQQQAABBBBAAAEEEEAAAQQQQEBqAYKYUncPlUMA AQQQQAABBBBAAAEEEEAAAQQQQACB4vHxcRTkFCguLk6oWJrOSk5stFHWZZ6POQu9fKy2dXV2H4ho UY6zxjptR3IGJPk7kxGSYFKAIO776suxEwFJ/qJw5Bs7x360rs6MEEZIxh8g1g0/R3Lm8snqy6cc v+7U1WOEKBrEfzJ+TZFAcgFWYkreQXHVM/FLXH+zHSlUZ/UcqZsjhQKiU0B/zMW6fsw959xzSMWV JmdHCtXZrY7UzZFCCxNEZ6vTJLOus2QrVKcVIPrPBTpJs0jmSC/orKcjdXOkUEB0CjBl0kNx+aR/ hBTmTM+vESJzH2X3lcVRhSZAEDOfetyR2yaOFKqzV9LUzbpqW5ezzlanSQZIAk6eglg3xgBhhKT/ nnFkhFj61Zdj5oDIMGVy7ERxuMu+VC0FccQq9xblmIMjM9066hw10k8Z66ptXc4FC2IdqSNTxtJ+ zDFzR0Cs698cNTgcAasFeJzcauHs84/eJOEbKnvE0JFI5gjI4QiYJaC+98s3m1mq5IMAAggggICc Apz35eyXPKoVQ8iszkLSLEnycVyAlZiOdwEVQAABBBBAAAEEEEAAAQQQQAABBBBAAIF0AgQxGR8I IIAAAggggAACCCCAAAIIIICAOwWij0DxLJQ7O7iQWkUQs5B6m7YigAACCCCAAAIIIIAAAggggECB CYjwJRHMAutzdzaXIKY7+5VWIYAAAggggAACCCCAAAIIIIAAAggg4BoBgpiu6UoaggACCCCAAAII IIAAAggggAACCCCAgDsFCGK6s19pFQIIIIAAAggggAACCCCAAAIIIIAAAq4RIIjpmq6kIQgggAAC CCCAAAIIIIAAAggggAACCLhTgCCmO/uVViGAAAIIIIAAAggggAACCCCAAAIIIOAaAYKYrulKGoIA AggggAACCCCAAAIIIIAAAggggIA7BQhiurNfaRUCCCCAAAIIIIAAAggggAACCCCAAAKuESCI6Zqu pCEIIIAAAggggAACCCCAAAIIIIBAokBx6IMLAvkuQBAz33uQ+iOAAAIIIIAAAggggAACCCCAAALa AtHwJXFMhki+CxDEzPcepP4IIIAAAggggAACCCCAAAIIIIAAAgi4XIAgpss7uHCa19XVdfDgwSza e/jwYXGs+M8sjuUQBBBIFtgV+nzyySdZ4IgDe3p6sjiQQxBAAAEEEEDAEYHXX3+d874j8q4plEtH 13QlDUHABgGCmDYgZ1nEeOST5fEFdlhpaem5555bU1MjIiB6oicijThfNjc3n3HGGXv37p06dWqB gdFcBKwSOO+88xYsWHDZZZetX79+eHhYTzHiDoRIPH/+fHHgjBkz9BxCGgQQQAABBBCQQaCiouK+ ++6bOHGiofO+WEPAeV+G7pOhDlw6ytAL1AGBfBEoFoGyfKkr9UQgjYAISoqgySuvvKKkaWpqEv85 d+5cEdysq6tT/tHn84n//H//7/+J/+zv748mHhoaKi8vhxcBBMwSED9j7r33XiW366+/XsyvKVOm zJkzJzoZlfn41ltvHTly5K9//esvfvGL6MxtbW01qxrkgwACCCCAAAI2CHR3d0dP8WnO+yMjI++9 9x7nfRt6JO+K4NLR6i5Tb4VJCMhqbfK3VIAgpqW8ZG6rgPr6SX/BItxJ0EQ/FykR0CMg9mcQa5z1 pExIwx2FLNA4BAEEEEAAAWcFEhYT6K8M5339Vu5OyaWj1f1LENNqYfK3TYDHyW2jpiDLBa666qqL LrrIaDG333670UNIjwAC6QXE/gyPPfaYUSVxR4E10UbRSI8AAggggIDjAqeddtr//t//22g1OO8b FXNxei4dXdy5NA0BcwVYiWmuJ7k5LGB0MSbLMB3uMIp3r0AWd9RZjuHe4UDLEEAAAQRcLpDFYkzO +y4fEwabx6WjQTBjyVmJacyL1BILsBJT4s6hasYFjC7GZBmmcWOOQECXgNE76izH0MVKIgQQQAAB BKQUMLoYk/O+lN3oZKW4dHRSn7IRyB8BVmLmT19RU30C+hdjsgxTnyipEMhSwNAddZZjZKnMYQgg gAACCMghYGgxJud9OTpNrlpw6Whdf7AS0zpbcrZZgJWYNoNTnOUC+hdjsgzT8s6ggMIW0H9HneUY hT1SaD0CCCCAgBsE9C/G5Lzvhv62oA1cOlqASpYIuE2AlZhu61HaIwT0LMZkGSZDBQEbBHTeUWc5 hg19QREIIIAAAghYLaBzMSbnfas7In/z59LRor5jJaZFsGRrvwArMe0311ui+KJRPnoPIF1EQM9i TJZhMl4QsEFAzx11lmPY0BEUgQACCCCAgA0CehZjct63oSPytwguHfO376g5AvYIsBLTHudsSomG L8fHx7M5vrCPSb8Yk2WYhT06aL2tAhnvqLMcw9b+oDAEEEAAAQSsFMi4GJPzvpX8bsibS0crepGV mFaokqcjAqzEdISdQi0XSL8Yk2WYlncABSAQEUh/R53lGIwUBBBAAAEE3CSQfjEm53039bVFbeHS 0SJYskXAHQKsxJS3H1mJmWPfpFqMyTLMHGE5HAGjAmnuqLMcwygm6RFAAAEEEJBcIM1iTM77kved JNXj0tH0jmAlpumkZOiUACsxnZKnXMsFUi3GZBmm5fQUgEC8QKo76izHYKQggAACCCDgPoFUizE5 77uvry1qEZeOFsGSLQIuEGAlprydyErM3PsmeTEmyzBzVyUHBLIQ0LyjznKMLCQ5BAEEEEAAAfkF NBdjct6Xv+PkqSGXjub2BSsxzfUkNwcFWInpID5FWy6QvBiTZZiWo1MAAloCyXfUWY7BSEEAAQQQ QMCtAsmLMTnvu7WvLWoXl44WwZItAvkuwEpMeXuQlZim9I16MSbLME0hJRMEshNIuKPOcozsGDkK AQQQQACBvBBIWIzJeT8vek2qSnLpaGJ3sBLTREyyclaAlZjO+lO65QLqxZgsw7ScmwIQSC2gvqPO cgxGCgIIIIAAAu4WUC/G5Lzv7r62qHVcOpoIOz4+ruQW/R8mZk5WCNgpwEpMO7WNlcVKTGNeqVMr izFZhmmWJ/kgkLVA9I46yzGyNuRABBBAAAEE8kUguhiT836+dJls9eTSUbYeoT4IOC5AENPxLkhZ ASuCmOpl5PK2nJqZLcANN7NFTciPyWgCYh5mwWTMw06jyggggIAJApz3TUDMwyxMPO8zhPKw/02o solDyITakAUCEgjwOLkEnWBXFTjz2SVNOQhkEGAyMkQQQAABBBBAAAEEdApw6agTimQIIOB6AYKY ru9iGogAAggggAACCCCAAAIIIIAAAggggEB+C/A4ubz9Z/rj5LySTN7OtqZmpg8ha6pZiLkyGQut 15mMhdbjtBcBBBBIEOBEUFBDwvTu5tKxoMaPaKzpQ6jQAGmviwVYienizqVpCCCAAAIIIIAAAggg gAACCCCAAAIIuEGAIKYbepE2IIAAAggggAACCCCAAAIIIIAAAggg4GIBgpgu7lyahgACCCCAAAII IIAAAggggAACCCCAgBsECGK6oRdpAwIIIIAAAggggAACCCCAAAIIIJBKgNfcMzZcIEAQ0wWdSBMQ QAABBBBAAAEEEEAAAQQQQAABbQElgkkck/GR7wIEMfO9B6k/AggggAACCCCAAAIIIIAAAgggkC6C iQ4CLhAgiOmCTqQJCCCAAAIIIIAAAggggAACCCCAAAIIuFmAIKa8vTs+Pi4qp/wnHwQQQAABBBBA AAEEEEAAAQQQQAABBApWgCCm1F1PBFPq7qFyCCCAAAIIIIAAAggggAACCCCAAAK2CBDEtIWZQhBA AAEEEEAAAQQQQAABBBBAAAEEEEAgWwGCmNnKcRwCCCCAAAIIIIAAAggggAACCCCAAAII2CJAENMW ZgpBAAEEEEAAAQQQQAABBBBAAAEEEEAAgWwFCGJmK8dxCCCAAAIIIIAAAggggAACCCCAAAIIIGCL AEFMW5gpxCSBwKHuZWXFxcXXtA8eMSlLx7MJjA0+ulC0qWx596HjjteGCiCgU4DJqBOKZAgggAAC CLhMgGsAl3UozUGgEARODLbPFj+6xWd2++AJW1ucuugT/u67lErZXyuFIBPLcf/g5gc6X7MXLEPv EMS0dfhSWE4CY3vWfWt5p9/jbXvgrsopOWUl0cElpZX132+uLPJvWP7A84cCEtWMqiCQUoDJyOBA AAEEEECgMAW4BijMfqfVCCBQWAKBseG+zpZry6oanj4sV5CCIGZhjcR8bu2Rwccfaur3F3lq7/3n eaX53JKkup/p/c79jZ4if+cPHtj6rlzfEK5ypjFmCTAZzZIkHwQQQAABBPJLgGuA/OovaosAAghk J3Co50ffXtbak93Blh5FENNS3pwyD68rLi7OKRe3HBwY7r6/6YWiIk/1w8trpp3ilmaF21EybdHd K64uKtrbubxtKw+Vu6x3XdccJqPrupQGIYAAAgggoEuAawBdTCRCAAH5BCZUrtw/HvrsX1k5wdb6 OVh0xnbKXLdUlSeImbFbSSCBQODdrWsfDd4F8DY9cmO5G0ftlMpvr2j2FAUfKv/JzmMSkFMFBLQF mIyMDAQQQAABBApTgGuAwux3Wo0AAgjIJODGcJBMvtTFDIHAsf4nlnfuLSqa23hv7QWlLh20ky9Z 9vASweVvXfeke15bZEb/k4dEAkxGiTqDqiCAAAIIIGCjANcANmJTFAIIIIBACgGXxoPobzcJBIaf XbvRL1rkufaWK85175A9dfbifxI7YxYVvbDup3284cdNQ9g9bWEyuqcvaQkCCCCAAAJGBLgGMKJF WgQQQAABiwTcGxGyCIxsUwkE/IPbnu1sWRzdyrO4eHFLZ3ffcI7PRou7vl2re0IxzPorqibHRmzg UPeyMlVp4n+WLe9O3lDyWF9LQrLia9rNWer4UV9LVVwNFncOp3wvT+BY3/1xFUlKXDLtG7fUV4qW +jv/Y/v+TxlrCGQpYPtkFPV0ej4yGbMcLByGAAIIIJCvAlad7jU9uCDP12FCvRFwo8Cx4b5t3Z0t C9U/xReK2MO2NMGHE4Pts5X0s9sHT0RVTvi774r/Z/FW7v64zIM59w+PqX/n60kTg09RtM6eOe4f /G13989bFsYHNcqWtj/bvW3QnyL8EG3X7KXdfxa/1UKvGo/GamKBmuS6Rf7lf9Z1va1U8e2mqv9P bRR4q3NxuDJlLX1pwz2qGEi6UIlOikgyZW9TPhIKRHvSrLqph4ZZeYbyOTrUs64huIRQ8zO3oePN 0ezL+7C3ORjXKyqqbO79MD6bzw767kko1tPoO3hSneqvA23ihTnqj8fbtjuH+qgzP3m0d1V8BZZ0 DH2Soq3RhiiV8VR37IurafCwWIae5t6j2aOFjzR9COVcIzJI7BrRR6aiODUZRSOcnY9MRlPHEZkh gAACCJgtYOpVmaWne82Wc0FubECY2t3Boi37HWesXaS2TYAhlIL65OiQr9mbMvQgfmV7m31Do0m/ s8fHPx9om6Wwzmob+Dya/ecjvjtj/3xyZHeqwIb3wd6Rz0I/2dOkWeUb0vgRn6JokVes9PhaKdX7 bGSgK21jQ2GFhvW7lYrFfaI5z2rwHRjdtzExXONZ1Xs0qJRct9i/JEd3wnSfDHUE98ELFR/OJ0V/ Rc8dmtGPLOeTub+fs6wEh2kK5Mc318mDvauqtaOXsX/NPo55cqgjnLvm9Dh5wNc4N770uY2+A5Ev rZOjA+u8CZXzrhvQ+lLLchCO7m6L+w5NPTmP9gbf2xP7pAh3RpNl+DrQVV/Th5CuUkmkQ8CSK1Fn J2PwjO7ofGQy6hh4JEEAAQQQcErAtKsyi0/3mj5ckBsdNqZ1d6RgSy4djbaK9DYKMIS0v4sO+kLb r2X4eBo27kv6yZ85iLlmy4vpIxvBSMJfktZIxVdGK9qQVRDzs5HeBxNDGanarVFoLIh5c9v6VUlh 3+iSqayCmOOxk4LGUjNV18ViIGkWexmeVwQxDZPZdkA+fHPFrXP0NLRt6R2KLHI8OtTbobpvkN2o jcX4U61MPDnSkzgnPff4DobuRSQGNcSNgsaOfbkvcFQPAdVdCKXDqjuGNG78JC0T004mcn7P16Dc IkpeeWp46Jk+hAzXgANSCFhwJer8ZAyGMZ2cj0xG5hsCCCCAgLwCJl2VWX261wTkgtzwuDKpu2Pl WnDpaLhRHGCnAENIS1v9aGN1c4cIPUR/eItFi891NEcXV6kXNoVzyhjEjJjPbWj7j97wgkqx8PPF toboqqm5NzfUhIKoIo1vILz+MWG9pMav+CyCmCfV4dpgnGVH3PLS0aHeLW2qxZXJharWeCoN8zZv HBgJeYlAzX/4Bv6iuKSuWzQuobVI9OS+jupwODn1I6SqX2cpox/ZzCqCmNmo2XOM9N9c6nWO2mst VXMvu/XDepYfayy3DD1Unvxwq8Z3We5dqboLofSYZrg24VnyNDXJfJmov86mDyH9RZMyvYDZV6KS TMZgGDN5+bNt85HJyLxDAAEEEJBWwIyrMhtO95p+XJAbHlZmdHdcoWZfOhpuEQfYLGD6EBL1tyJP W1lGfA3hNqRY7qN+LCwpaqYviKn1Oz3xkcrqVb3xO9gFA4Oxxy6Tg3rGg5iq+1XRFVpJ1upAZ1Kh 8UHM1JlkGcQc1/FEeSzQmV0sKOXg4sU+6jMC/9uIwNjA4yvb+kNHeBr/9ZFb55YmHV0y7R9bHla2 S/D3/P6/PjCSfTDtsTe2bx4MHVRx5dfOSTFYS0orb2uP3/jS3/mD+1vX3L98Q/B9QJGPqORDNdNN H/Elsy+5KXIXIlTUzmd2HkjcXjfWEMUrzWvWTzl7xmzlpobf9+qfYlsOG7UjfSEJyDIZhbmT85HJ WEiDnrYigAAChSdgw+leE5ULci7IC2+2ubLFShxTHc3Mr2aeGHlnV7jGVRefN0Wj8iXTr7t7aXjj y549ez8wPHW1IwaTz18cevuu8kve2/bD+xdNS4wqTJ5/4wqvksL/+oH3U77sVx/5ibd3PP6CUlz1 w8trpp2ieVjJtOq7o4W+f+RvKfNOl4m+CiWnOnX2gm+GF776e7YPHE5OEdj/h2dC72cuKlpw04IZ JsZhTMwq2+ZzXF4KBI7t2bquPzQoPfesf+iapHmstCoWkiva9c6Iwa+RwPsHXg+HIStmlp2a2mlK 5V0PxO9Nubdr1YNdcSHMNJXMrQNKZi6+s1a1NYf/zaGRsbgsA8cGXt6sqkzCa9bjiy8pPWf2V5V/ env/ux8aJMutKRydnwJSTUZB6Nx8ZDLm5wim1ggggAACOgTsON1rVoMLci7IdYxPkuSHQP5GMON9 d73wn4c044QTKlfuDy/ge7zWM8Fgr1TW3/INrbDGlPMuropkVXH1wq8kL94qKjq1bGZFOM3bh0dz DGLGmjGyvXFO6pidqtB0TS2bN+NM0wN/quUjg5u3v5H0jvJP9+98sSdUK0/zXTeUpwnmGOwlsWrG 8BEcgEBQ4PDA9nBc3VNfe0WKmwNigE1e9MiI8j2yf2Wlsa+RwNjB/W8q2rNmTz8r7cGlVXe1N3lT ds3cxvVNqe5g5Nyfp0y7orZeHcXc/PLAMfX31vH3D+xXxTAr6xefPzl1qSVnz5gXzm3onZFPc64e GbheQLLJKLwdm49MRtePdhqIAAIIFKyADad7TVsuyLkgL9hJR8PlEpjw5Qvrwj+T93bWXXNb+9Pd fcPxi4dyrPDMinO04pNxuaZfXJVjBSw6PMXC1RxLUy0f8ScGQIqKAgfEA6qhEjJEP7KoBUHMLNA4 RP2gt/mDMuIb+NuRw+HY36ypkzIMVY2HWKP9ZNGD5LFxELfCPPjofNyC6tgEDh3hqV5cNTXdGJp4 +lkTlb//+c13/8poQyCDQOwhL0kmo6ivc/ORyciEQQABBBBwpYAdp3tNOC7IuSB35YyiUXkoMPmS ZeGt6kTl93Y13VJ3ecWk4uLihS2d3dv6hpPWAhptomf2jLO1H9yO5ZRxcZXRQrNJf9w/+Nvu7u5n 25dV1j2ROQPP1ClftCLup1o+kvREeexZ8ozRj8wNSExhRWOM14Ij8kxA9Xy0BYMygnFi9Ehkb4Wz p2QKYoqDkh9iDeWU7ml3s9ynVi1W74sZt6BatRlEqDr1V1RNZt6ZJU8+ck5GB+cjk5FJgQACCCDg PgF7TveablyQu2840SIE8lTg1PJbH+1dFX0FeaQV/a3L6mourzi9uHhxS+ez2wb9WT7MPXHq6RPl +50e8A9uExFLEbJcWiYitsHP35VVXV1XV7ekKW7/vJSdal27YstHEp4oVz1LbkH0Q75OytMJVVjV Vj0ffWXVeVaF5E6MHv7QmGtp+cKrLzR2iDmpSybPr1nhjT1SrlpQHZvAoaKuXnHjhWmeJQ8mmfA/ Zl4a3o/YnNqRi5sFZJ2MwtyZ+chkdPNwp20IIIBAoQrYc7pPEcTkgrxQhx3tRkA6gZJpix55bmTA 19YwV6tuPa3LltRUlZ2zdMMe/3HpKm+0QiJ8uall4RfKqmpExFJ3yNJoKTmljy0fiXui3MpnyUV9 CWLm1GmFenAsvDjrq9PPkkUhMDb41Mom5TVeqo9/w/IHntfe+NfEmpeef139glh+0QXVCc+SV9de d8EUE4slq4IXkHMyim5xbj4yGQt+VgCAAAIIuE5A2tN9srRzFwCiLlwDuG7o0yAEkgRO8VTWrtz0 5vjoUK9vS0dz0sJMsbtb1/KLb94wOJblikwpzMf2dt5WXXVra79GbTze5id9Pt/WgfcO+u50tLYl k6uuCL8dJPZEeWDstd9tVt6fYs1juwQxHe30fC38r++++Wfr6z5h0lQjAdKxgcdXtmlN8iJ/5w8e 2Pquxd9hp85e8E3VN2h4QXX8s+Se6psumZ1xzp3473d2vW09LyW4Q0DKyShonZyPTEZ3jG1agQAC CCAQFbDndK8JzgU54xABBKQUKC1fVHtD49rt4+MnR4d2+HxPNquejCzqb7v/18MWRwCsY/mo74e3 LuvaGy7A29wRilmOnFTemDyyY+0dtbW111d6MoYWrKtiOGeNJ8oP7/n1r0JhGY93Rc18Cx7bdb7V lrNSgPkCp005e0rmXMf2tC9U9m2oaun7KHP6xBQTJk2JvADn/SOjGb6Bjgw+/lBTv+od4HG57e1c /tDGt3Le6zdtG0pmX3KTamNMv+/VP51IeJZ8wU0LZhiZcoL5NONuHFFQAhJORuHv8HxkMhbUHKCx CCCAQAEI2HO6TxHE5IKcC/ICmGM0MZ8FSkrLvbW1d6zdcXB0yBcJZfp7nvnD/vyMYgaGf7O2dTDU Ix5v2+7RHWsbtWOWqj2LHeu/pCfKY6+hW1B/3fkZX/eeRcWNRFSyyJ5D3Clw6ulnTVJa9vGHRz/W bmPg2J6t65SooveWG+enfR+3dg4Tzpo+O7w35NuH0wYxk59b8Xgbbvaqs/V3LvuXDmuXlJfMXHxn bWxfzLf3/PGd9/b+/tVYLaq/uWD2qZlHxIfvvhleiDnprNN1pM+cIylcLCDbZBTUEsxHJqOLhzxN QwABBApRwJ7TvaYsF+RckBfilHNlm5WXwuRt08YG2xcqTShr6UuxOklEM/9xWXSTtwwxBGklTnyw d0+PUjvPrd//dlXKOGDgg7279oWbseudkROOtCjxifITf3rVpywt0xn9MF5rgpjGzTii6NSymRUK g3/PH/+kudnE2MCTP9wY3gih/qoLSlONtCOD7dcUz24f1JpyJZOmRoKY+9/9MPWkTH5w1dvUvuGx 9rar4/qqv23l4wNjFnbfKdOuqA1vCREsZecz3d27Xoo+GK7vWXIRARo98n64khUzywhiWthhrsg6 x8l43D/4W9Wr7oqLF7Z0ar3ST+9kFKZSzEcmoytGN41AAAEEEAgLZH26j/3sLy6+sXP405Sigbc6 F0fefFt8V7c/duGt9xpAigsA0T6uAZg2CGgIRMOXeRvHnPjlC7+uLBiKe4dMmt6eNXVSvoe70r1Y /Li/f+NjnZFHzh0c9ZMvWfbwklD5g75X9w3/cU8oAqI3+pFFxfO9V7NoMofkLjDhS3Pnh/d/7O/8 ac+fE5dpi21o71kWfrjb2/TIjeUpxtnxQ92rr01+FU+kgiUzv3ZlOIo59M5IiquuwLvd342UFT6w svn79ZWlUyvveqBNvS9Gkb+/6aHHB4/k3v6UOcS2hAh+u/asWrUu+oC759aWG1I5qPMLjB3c/6by D9Xz535pgoW1JWs3COQwGQP+PY/eUVl19ZKmrthGDP2ty8Qr/S7/1774N/rpmozCU575yGR0w/Cm DQgggAACikAOp/sY4c5ndh5I9Wxl/Dbucey6rgHkuQAQdecawOC8CQx3LlZWuMXHrw1mkzJ54FD3 smCE/Jp2S3+ImVVdXfmIB48eDS4LLFvefSj/34Ktq8mOJ1Kv+Nv4w3/r9Wt+nY298ZvNO0N1tTCI ZrFFyRenTA0/3/l2/44/HtEq7ri/b83Nlz/Yb3FV9GUfeyHB2y88vjbsb3QnPX1FhVIRxDSARdKo QEn5dS3NlaH/u7ez7prb2rcPK+sxA/7BbT9vubY6sg3t1W3tt1VqL8M8Ntz94LfqNqTaxjKY24Tp F9YppQy99MeDWl9Txw9tbVsef//B03j/d7xnBg8qrbqrvckb120vNK18ysqHymNbQiSMFk/9FVW6 NrX9+E+v/qdi4pk342wmKLMuk0C2k1HMnR/VrBDhy+rmjt6hUWWb6KNDvR3BfWT6H/zW6ucPqadc 5skoKirVfGQyZho6/B0BBBBAIH8Esj3dh1vo8XovEffXU+4Qp2zj7qluXnFzsknmawCpLgBEA7gG MDSyVUsoZs2efpbZSyjG9qz71vJOv9ja74G7KqcYqpnEiUtKK+u/L34O+zcsfyD+mlniSud91SYv +M76e0LRPX//murK29qf7RuOPWcZDkTUhJdSeWrvXDwzP39Mq8K1RS80Xfvd9mf7w8GWYNuPDfd1 d7ZcW2Z5BPPvp3/1XGXMvL3uqd8owfqPxzSfwo29kKD/6S5lR0HLniUPZq6834hPIQiov7Zyb+/J kZ5Vcesck78Vq1f1Hgy/QSuhvJMju9c1xLaPnNU28LlmjU4e7V0VTtbgG0lKcvKgrzGWS6gCnnt8 Bz9TJfzrQMJD5crmuHFZjQ60eSO197YNxP/RqNTo7vjln0rGlc29H+rK6eS+jvDbgXQfkjrfaJfo KppENgo4PxnDI21uo+9AwiSNTKuEEZhhMgo86eYjk9HGIU1RCCCAAALpBXK/Ksvq2jt8lTtrzZM/ C15hLukY+kSjnuGrgiUdvV0NwYre6RtRX5pzQW54dOfe3QlFmnvpGJ957KeQp7n3qOG2pj8g8lss 8TeaycU4kl3k0lfjcjr3+uTVEMq9ubpzOHnA1zg3Oe6Q9C8agYjPB9rCD3nGBR8+H/HdGT48ZVAi 1zQpihatTpXzZwd9Srg27ce7aotvTfgB2cTvbT11DrLrqlusFglnh2jHfTLUoTxRrnw81R37tANB urs6TcL8jE1n6k3+boNAiefKh5/esq4hxZeIp2Hd7o0PL5qWNMLErYPOlssrLw4tAVvRfHPayam6 C/HSwL5j8WsxA+9ufeAHnXErOec2rm+qmXaKqvlT7H6ovPT866J7Catm8eIqXa82ij3L46nWeYgN fU0RkgtkMxk/+K/f94jJc+nVX0+cpCXTvnFLvVgBPfL6gY9UUy7tZBRAEs5HJqPkA5fqIYAAAggY EcjmdB/Nf8Kcb9y0ILhju9YT5eHrT7FwpuKLWjXigtxIP+Vd2sDBP740pMQdvlpRZu6rhAPD3fcH tw7zVD+8PP43Wt4xaVS4ZNqiu1eIdzDs7VzetpWHyu3p0pLptT/e4muOBO40C00ZiLCniqaUcsq0 mu9tVa/6Ssx1bkPbi0PPPXzDFfPnheMpA7v3HTGl7EgmEzzVjUnLs1Lt8hd7ojz0XaJzJ70s60sQ M0s4DhMCJZ5L79u0a6jX16H6HvE0tG3ZOjBycNN98z1aw+vo3k2PtPb7PQ3rd488t/bGCyamp4xu auPv2T5wWJVW47mVIm/j3dXnJhZq90Pl8RNYuSLQ+yy58ixP8Ajvipr5uh4/ZyQiEBQwPBk9tZuC t7cer/UkPzSkvALV//bhv8XdN0g5GUX5cs5HJiOzAwEEEEDAVQKGT/ex1k+cveCb1dpPlCtvwk27 fxwX5K4aR/GNGRsZelNZFVJx5dfOMTM6IO5wr300+NMm3TsS8lp2SuW3VzSLEJJ4qPwnO1O8Lzuv Gyhl5Uvn1K793ejQDp/vyeAuWLGPiOv9ypcuECFlc1JVqsQz/76nhkUzt7SpnmAtCgZbfC8MjAxu Wrm4XOzaF9sCeHDz9jdMHoSl81c+1x/cbEyHXOyJcgPRDx35aiYxYzkneeSHgHoAOFfj93zN3w9u wReqQXj1csqV2yLJydGBdcq0seABBzWD8iRFzo+TixoPdcTfG9L9YHjsWfIUD/sY7LZojxs8juSW C8gxGVM18z1fg3jYYlaD7734FLZNRlGsOfORyWj5UKYABBBAAAF9Ag5dlUUeJxdbN0WfGU98ovzD 3uBO96GLzxGf1uPkXJDr62NVKtO727pLR9XFUqoHRQ03P3RAdBcCS562zq5OFhwVfYr26raBv5qY fx4NIRNbTVZ5LGDqtnjpHcy815JtHJXjCkrg3Nq1DzUuKtf9qEJJ6QVX1Ye2ifRv7n7ZwoX6nx79 cNSMnoiupoxk5r3lxvnGniX3NN91Q/mpZlSGPBAwLBA49MoLL71dVHThZXPPij/YtskoijVlPjIZ Dfc+ByCAAAIIuFagZIZ4WazGE+XH3ti+eTDTSxhsuwYw5QIgeCERebyJC/L0I1pZhxv6VM+f+yXz 3uoTGH527cbgCk/PtbdckfSonHum2amzF/9T6CUNL6z7aV/cWzHd00bDLenq6uru7v7kk08MH8kB eStg57Z4BDHzdpgUTsVLym9ouTUUxex+Yvs7Wu8oN8Mi8NGB10eKPF+/8MsZHnBPX1jg0G/Xrt6i SuOprr/qAu33syfkdOS133SHriGuXnHjhZPNaBN5IGBYILK1pafxnxbPToqk2zMZRaXNmI9MRsO9 zwEIIIAAAm4WUHZZSXhHeeDYwMub/UWeeTPOTv+70J5rADMuAILXEVyQ6x3JR/btHlDSZh4DevMM 9sCx/q7Vwe3XNbbVChzqXlZWHPcpW96dvFTlWF9LQrLia9oHjxioRcqkH/W1VMVVYHHncMofmYFj fffHVSQ+cWQ3+SJ/539s3/+pGdXL+zzOP//8urq6yy67jFBm3vel3gbEtsWrfrjBa/G2eAQx9XYL 6ZwTKJnsvXN98DVk/p7VXf0Jr/cxqVrKrQPdm1dqlnrcP9i97v6Edw2Je94z9EyzwKG+n64T+16L p+ZXfLtyiknNIhsEDAkce2vjQ8s79xZ57ln/0DXJr+USe2/aMBmDV765zkcmo6F+JzECCCCAQEEI hPcs63lxZyzUcnhguwg1VdYvPj/THXQ7rgFyvgAQ/cg1gJHBrESNg59ZV1785UxjQH/OyrgSH42h VTLtmofWx795Wewp+cDz8csYjww+ua417g2uHm/bA3eZ8ytpatXi0IN+0U/cpEhoZrQtyr8n7x4b zW3L6o4/mLwpoX5ymVLOmzfv+uuvf+WVVwhlytQtVtbl2B86wgu59EY/cqpNHj91T9UNCqgHisFD rUquY09MpejoZnye6o59J82uzsmRnlViV2DPPb6DnxnOO7x5kPY01L2P518H2sS77cRpMas6pKh0 tE6GG8UBFgtIOBnHxz8b6X3QGxyEjR37jqYGsHYyBmd71vORyWjxuCV7BBBAAIGsBRy6KlPtiRms urJ/n2q79qO9wdeSeFb1Hg1dX6fcE5MLcmM9b3p3W3XpqAyA4MeEFwNEjWL7bEaHVoLfyQO+4AoV 9Ue9dWZsH/ZYCu+6gVHzfgWO7o5/7XLq35gxIqUuWq8uiKZJ1V5jwyeYOm+GUIqm+Xw+de9edNFF 4l8+/vhj4xIcIaXA6Acj4fl4cnToxbYGZTqLOw27lXefWPrRs0QspyApB+ciIFa553K4i44tKa28 rT0Y5rNiMWbg46MjH8z83u7BdbXTTjETTSxn+84CPbc0I8sw5zaub6oxtw5mtoe8XCxw3N+35ubL H+wvql71y4dunZNm2Fo6GYWwNfORyejiwUvTEEAAAQT0CihPlEdfYht5lrz+iipdT/9Zeg1gzQVA 8Gc1F+Qpx8eJP73qU1Y75rynlqqM2IakKZ9yK5le8/C64CKS2Gdv5/K2rcpD5WMDj69s61fXWtxi /9mySl07dOmbDKXnX1cvtoiNfhK2WYj+e3iOxBJWf3NB8oZLk7988ZXirZjBn6rbBw7rq4HLU3m9 XnULWZXptv4+9c+/mveF0J4MX5hU8c2mrr2hr5Hae/95nu53n+RAYmmIlMxzEYj2ai6ZqI9VDxOz 8swxH90rMUPlhO+Y2RTg19u0cBuSJqF3lW8ozXI2dfbKSyHFAjjfQfPuL1pxB0+vCekyCUg2GY/u 6wjtSC4imL36xiCTMVMXJ/zd9O9zg+WTHAEEEEDAYQGHTgQJKzHF8w77OsRztOH1YsolqGphZoaV mFyQ6x1Fpne3NZeO0Tdri2vAjiHTfoaEf9oEn7xO9widxnLL0K+hzw764h82L7Lk/eaq17IrulpL LMejbVHSpKpJTFL3c3gZxlKeDKF0rbj99ts1w1SsytT7PSJ1uvd8DaHAffST4WE+MxvDSswcAsAc arNAadVdP1vT4PH3N7X/eliaXZNLzpx5mfppCI+3+Unf1oGR3kdqy3Wtwhwb3PzD1sHUuxDarExx BSYw9lZ3y5LzlnX6PQ3rdm98eJHWTpjJJEzGAhsmNBcBBBBAwCUCJWfOmFcWXi+mvJfcU724aqqB 1nENYABL8qRjB4feUao468qvzTQrMKCMq+Cn4sqvnZM61+jC3piSv/MH97euuX/5BvVmmJ7Gf32o ZrpZtYsWFt4iNlb4zmd2Hkh8u0+sLaF0Kd+0fsrZM2Yry0r9vlf/dELyfrepen//93+vWRKrMm3q AGuL+dLXl9Z7w0XMbWjzDQz+rDHdw3ym1sbMiCh5mSoQ7WezclUPHLPyzDEfYysxcyys8A43fQgV HqFVLZZkMoZ3nxS1MbBw2CoTd+fLZHR3/9I6BBBAIKOAQyeCpJWY4yeP9q4KboPZ3PNe+H/0xh4d 0rMSM2NTSZAvGxoqy3JDMcwG33tm9ZtqheOdvpHPM2SbuDdlUqTD1BcGxFcmccln0iLK8GSJ1inN KktjrdZhbfo3hv2/Pn70ox9lDFyxKlPHWCBJooDptzQyDlQSIIAAAgg4LxDwv7T65qVr+v1F3gd7 n35Q38Jh56tNDRBAAAEEEEAgB4GSyVVX1HuK/Jt/urrjOX3vJc+hNA6VWeCD//q98grxogsvm3uW STUNjB3c/6aS16zZ08+akCFbsbC3vcmbMpGlLww4ZdoVtWIuRD/+zS8PHFOvxTz+/oH9qjWhGm9a jx5bcvaMeeGsht4ZkeaRQZM6Nbts5syZk/FAVmVmJCJBsgBBTEYFAgggUHgCgXe3rl4RjGCK+9u/ XLXIY+orrQqPkxYj4GqBscH2haG92yOf2e2D6Z6Vszq9q7FpHAI2CEw+f3F9ZZF/a9fTew0/S25D 9SjCJoHAsX0DLylleWbPONusS8HA344cDgf+Zk2dlDnYoPFQeRTAogfJY8DKXIhFMeNfyxM4IJ4w j/01/cYLE08/a6KS9s9vvvtXm/ow22Lizum6/0+a0jTzqKur01lBQpk6oUimCGT+XkEKAQQQQMBl AoH9vU90ht4i599Qd87faV+9LO1Wb0jkMgGagwACCCCAQKEKTK1aHH6KOOXLowuVppDarVpmeGXV ebpeT6+H58Tokcjruc+eoiOIKfKcUnnXA21xbyoPFSReK//QNfo2a9dTMc00sbkQ+vPg5u1vHIsk DOz/wzPhlaqh6tRfUWWaUtYVdvJA8XvB0uKjoczh4WFLCyLzfBcgiJnvPUj9EUAAAaMCJz7Yu6fH 6EGkRwABBBBAAAE3CISfKBfvJa9ffL6e11C6odG0IVFA9Vafr04362HyoqITo4c/NIxdWr7w6gsN H2XCASWT59esUMVPVU+Uf7p/54uqq+WrV9x4YbrJMuF/zLw0/mXNJlRPrizExoRWV6ipqemXv/xl eXm51QWRf14LEMTM6+7L+8pPqFy5X3wd7l9ZmWm/lLxvKg1AQCKBCZ7axzPvEb2pVrVNkES1pyoI IIAAAgggoE+gtHLlDo1r7cmL1o6Ifx5Yu+jMuHw8tZuC1weP13q4NNcHnL+pTrz7qk95h3hl3YXT He3vwNjgUyubXki09G9Y/sDzhxLfF262eOn519UviGXqjzxRnvAseXXtdRdMMbtsx/LL/CtAK0Wa 6mpm6PP59LdQhC+HhoZaW1uJYOpHK9iUBDELtutpOAIIIIAAAggggAACCCCAQMEJBN7540tvK62e WXFOqXntnzBpqsFlnWMDj69s69eqgb/zBw9sfdfiMOapsxd8szpWeviJ8vhnyT3VN10yO33g5MR/ v7MrDGoeppk5RddR2rCgUqn3yMiIngYQvtSjRBq1AEFMxgMCCCCAAAIIIIBAKoHwSq7YOosMz09Y nZ6eQgABBBDIUUD9DvH5X5t5ao7ZqQ6fMGnK1PD/ff/IaOYA5JHBxx9qEq+a1P7s7Vz+0Ma3ottU mldNVU4lsy+5KbxJbPBf/b5X/3Qi4VnyBTctmKE7bjLl7CmnWVLRnDNVzuM5Z6M3g/feey99UsKX eilJFy+gezIChwACCCCAAAIIIIAAAggggAAC+S2geqvPpTPLzHyYfMJZ02eH94Z8+3CmIGbyg+Qe b8PNXrWtv3PZv3QMjmWOhmbfISUzF9+p2kPp7T1/fOe9vb9/NZZh9TcXzM4U5/3w3TfDCzEnnXV6 psTZ19UlRxK+dElHOtQMgpgOwVMsAggggAACCCCAAAIIIIAAAnYLfBgJ0nmqL/vKl1Slnxhsny3e Qh38lC3ufCt17PDT4c4bwwmLZy/t/nM0j5JJUyNBzP3vfngiXcuSHyT3NrVveKy97eq4o/rbVj4+ MGYh0SnTrqitj+0Ev/OZ7u5dkYftxWvJMz9LXlQUGD3yfriGFTPLCGIWffLJJ/39/cmdRvjSwoFc MFkTxCyYrqahCCCAAAIIIIAAAggggAACBS5w7E+7w0G6snkzzkwREfD3PPOH/amimAnvvVF5lsz8 2pXhKObQOyOfppQOvNv93WXxD5JXNn+/vrJ0auVdD7Sp3hgunvDub3ro8cEjFnba5PMX11dG8vf3 rFq1LvqAu+fWlhvKMwVNVI/nV8+f+yUzl7Za2Gors3711VdfeeUVdQmEL630Lqy8M83HwtKgtbIL BA51LysT9/yuabf0NGYrg3iM4tGFwZudy7sPHbe1ZApDIAcBJmMOeByKAAIIIIBAHgtwDZDHnReq euD9A6+Hg3RVF583RaM5nku8l3iKel7cuV87Chl+7031d5tvDgcsY5lMmH5hnRIQHHrpjwdTREGP H9ratrxzr7poT+P93/GeGfyX0qq72pu8cdV6oWnlU1Y+VD61arFqX0xV0Z76K6omZ4yZfPynV/9T EfXMm3F2xuT5PoB01H/btm3RVIQvdYCRxIAAM8wAFkkdFhjbs+5byzv9Hm/bA3dVap1uHa5fdsWX lFbWf7+5ssi/YfkDzx+ycr+X7OrHUQhoCDAZGRYIIIAAAggUpgDXAHnf7+q3+syefpbWssGJl9fX Lygq2vnMzgNav06U996I56yrL9I4OhoQ9Pf8/r8+0OIKHHr+geUb4t7m47ln/UPXTAsHJ8Tvo9t0 PFQ+NtgeXAoS+ixsH8zlifOSyfNrVsQt/1TqXVm/+PzJGXs8cPCPLw0ZSJ8xwzxPcPjw4ba2NtEI wpd53pOSVp8gpqQdQ7WSBCKvrvPU3vvP80pdBXSm9zv3N3qK/J0/eGDru4QxXdW37mwMk9Gd/Uqr EEAAAQQQyCTANUAmoTz4u2rZYN2FX9Z+9Hny3G98s7ooxRPl4WfJxTu7Z2tFE0omV10R3mLypYF9 x5J+3ATe3frADzrjQphzG9c31Uw7RYU3xe6HykvPvy4Yt43/eKoXV0Vetp66Z8PrUkUCfenzYIzk VsXnn3+e8GVuhBydToAgJuMjPwQCw933N70QPDM8vDz+DJcf9U9fy5Jpi+5eIXaw3tu5vG0rD5W7 oUvd3AYmo5t7l7YhgAACCCCQJljDBbkLhkds2aDnqxVlqZaGfGH2JTeJB6w1nyj/4L9+3+MvCr6z +zRtj+gWk/6e7QOH49NoPEhe5G28u/rcxMCE3Q+Vnzp7gYjbxn30PUuurEsNhjC9K2rmZ3723AVj KEMTrrnmmtbW1vLycvc3lRY6IUAQ0wl1yjQqIO7XrX00eG7wNj1yY8adlY3mLkP6KZXfXtEsXoon Hir/yc5jMtSIOiCgKcBkZGAggAACCCBQmAJcA7ij30vmNG4fGQ9+RrY3zkkZDiiZIVZaaj1RHji2 b+ClDO/snjr/xlu8Qa7BzdvfiP9pc8q02vVK8bHPjvsqS5MrIh4qv29HupSllSvF30cH2kJFmf/R +Sz5AfHUfajwBfXXnS/z84LKg/fmOyXlOHVq5uWrNlSDItwqQBDTrT3rpnYFjvU/Edr4eW7jvbUX aJzhXNHYyZcse3iJaIm/dd2T7nltkSu6hkbEBJiMjAYEEEAAAQQKU4BrgELrd2VlYvIT5YcHtot1 mCLCOSN1KKGk9IKr6kNvyvFv7n7Z2ufMPj364agZfRNdUBnJzHvLjfMzB+Oiz5J7mu+6ofxUM2pi SR7R8KU9cUxL2kCmCIQECGIyEKQXCAw/u3ZjcNcUz7W3XJH0oIH01dddwVNnL/4nsTNmUdEL637a xxt+dLuR0EYBJqON2BSFAAIIIICARAJcA0jUGTZVpUTzifJjb2zfPFjkmT3jbPUWlklVKim/oeXW UBSz+4nt71i46X/gowOvjxR5vn7hlyfm4hI49Nu1q7eocvBU11+lY/XMkdd+0x16lvzqFTdemPkV QLlUkWMRQIAgJmPALIGAf3Bb989bFpZFXg8n/vurS9uf7t426M/1lCXu+natFnf7RAyz/ooq1SYj gUPdy9QFijLLlncn3+g71teSkKz4mnZzljp+1NdSpWpycfHizuGU7Q0c67s/riJJiUumfeOW+srg ub7zP7bv/9Ss3iGfghJwZDIKYafnI5OxoIY5jUUAAQQKXcDK072mLRfkBTnkNJ4oDxwbeHmzP/F3 mZZOyWTvnesb54pfNj2ru/qTX+9jkqiyEFLf5pWpijzuH+xed3/Cu4bSLzUNZxU41PfTdeK1DUWe 5hXfrpxiUpvIJpXACX/3Xcqv79ntgyeCqQJjw/3dnS3Rt9QXL2zp7O4fHlP/JteTRqvE4Pfss50t i1W/9xe3dD6rO8IhxtVvuxODJCJksbT9WT1hkmPDfdvimibqEWzdtr5hzc3nYjhRHa1WjQ22R7Qi iJFk0RxmL+3+c8i2T9V80fZujaJzVcpqtCfsMsH/dbGAeoCY18yjQ75V3jRjz7vKN3Q0h+I+7G0O xvWKiiqbez+Mz+ezg757gvf3VB9Po+/gSXWqvw60iRfmxCXxtu0ezaFCqkNPHu1dFV+BJR1Dn6TI O9oQpTKe6o59cTUNHhbL0NPcm4uaUodos81pLrmYJ+C6yShonJ2PTEbzRic5IYAAAghYIGDeVZnV 196ajeeC3NiYMK+7w+Vac+mo0ajPB9pmicJmtQ18HvpxMtQh3nWj+mGijITo77L3fA0i+awG33ta QCdHB9aFfidq/vAxRqqZ+uRIzyqvp8hzj+/gZ4azG/E1pP4Nq++3WOSXZnYVSFvj/B1ChjvCwAGf j/juVGSCI/TkyO51DQnRgLCb98HekdCQSJcmTZji6FBPqqxDA7phXU+6EMdnIwNdzWJkpv14Gtbv ViqZ+Dk5OuRLe7jH2+wbGk2IJcRwovNXK3PVHrKRaR5JFs1BzOgDo/s2JuJ6VvUeVReao5KBjk9I WpT9oRyZbwIWnPw0whYaU9XT2LEvy4iccuIMfVUkzJmQ/skDvuD9PfVnbqPvQGRuRU+cqgTedQOJ Ez6Hjhzd3Rb39ZT6DH20N/jentgnRbgzmkyzvQZravrJz2D5JE8p4MLJ6Ph8ZDIy4RBAAAEEJBYw 6arM8mtvTUIuyI2OLJO6O1asBZeO2m1KCGKOn9zXIba2jP4wUX6qxH6npA9iiiKiYT6tn3JGWZNj Pfs2NjamigRlyj1NEFNfUPLkQV9oKzD1z89Mher+e/4OId1NzCKhKoi5ZsuLqxJeJh8fFQj+6v9L 0nqm5DRJy4rGj+7rCHVshhhkqhDHZyO9D3ozHR3+u1ZoIjKuMpXfsHFfXFjDzCDmzW3rg7cH4j/x kf0clbLofdX3YU5Hc3BeCZh+8lNPME9Dm29gJPodcHJkYGtHc2z2VncMJX8/ZNb7ZKgj+K6bYAwz xcrE8P03dduiZ53EoIbIJftwaorKxmoYroJ2S5OWiaUEUS4FxCd55Wlmr4QUpp/8DNeAA1IIuHIy BsOYyv1wZ+Yjk5H5hgACCCAgr4ApV2XWX3trAnJBbnhcmdLd6lJNv3RM1aTEIOa40vvKD5PwLxrV 77KMQUzx8nBlzYfHvIfhDHeH9gHhpiaFivQ+Rxhenpz0IKA51cvfIWRO+1P0WXQlZsRnbkPbf/SG F0WKBYwvtjVEVzjNvbmhJvSzRKQRgQplzWPCGslMz3p6mzu2qmIco0O9W9pi6xO1gt1xIUgRItmy I27JZEIOGj/51Y9vVjd39KoOF5V/rqM5GrpNiJ6bGMSM6HqbN4Zbf3So9z98A3+JdEv87TTjSjkO ElZi5giYT4ebffJTzRPtkJz6LkSa56zTGEbncJpnEDSWW4bOJcl3qi25Sxa7NR321WxpwrPkaWqS +TJR/5gz/eSnv2hSphdw6WQMhjEjzw3FmmjbfGQyMu8QQAABBKQVMOOqzIZrb00/LsgNDyszujuu ULMvHVO2KCmIqQ5cJjxLLjLREcQUF4fh51Kz+z1oGF/vAYmP9IlHdJ/0qSNW6TKKXPHqW7Opt0qq dPk7hLJorO5DVN+Boeik6hHMSB6Jjz9Wr+qN321OJFSlSVgppQpBeryremJLtFRVPDmyc10kVJpu L7vUYyPudlTCUq3YAuEUS5rU4zYuCGN2EFNX/bNT0t3hKRLydvKkey/8g16BT0feGUqb9hSP96Z6 8QBC8PPq7/d+qDfjaDrl5XfBT8WVXzsnxWAtKa28rT1+40t/5w/ub11z//INwfcBRT6exn99qGa6 6SM+/Nq+WDk7n9l5IPHtPrGGhNKle836KWfPmK2Q+X2v/im0XzEfBDIJSDIZRTWdnI9MxkzjhL8j gAACCOS1gPWne00eLsgL+oK8ZHLVFfWeIv/mlwf+/FroveTVi6umGplIJaVzlm4aEdGIXzeWn2rk QIvTlkyv7XhTFSQZ2bH2jtrrKz26fi6KK977doiDR9bXTkv7lnaLG1HI2Wv/up98/uLQm3KVX93e th/ev2haYpdOnn/jCq+Swv/6gfdjP92PvPbvnZ2hCIKncf0vH75SczCUeC797iP/qjxwnvg+3hNv 73g8+KKn4D6wDy+vSTE2SqZV3x2twPtH/qbqxRMj7+wK/9+qi8+botG/JdOvu3up8uRmUc+evR9Y 9PWUpv45K+U8anXN0ZxLIQNXCpxaNrMi3LCeR3+0bnv8W8BCfymZ07g9eMoaH9+/qfZcowqB9w+8 Hg5DVswsS3POm1J51wPxe1Pu7Vr1YFdcCPOe9Q9dk/QFZrRGWulLZi6+s1b1AK3/zaGRsbiE4bf4 Rf8t7evzSkrPmf1VJenb+9/90KJvJTMaTh4SCcgzGQWKc/ORySjRmKQqCCCAAAKmC1h+utesMRfk hX5BrkSF/M91rP6pvveSmz7yyRCBZIHK+lu+ofXrfsp5F1dFUldcvfArpRp4qu/Stw+PRoOYx179 dehd82L/hBSZh/MqmfaNW8Kh0vgFTBMqV+5XAuMj2xvnpI61qSqQsm93vfCfhxKXRoUSxwoZf7zW M8GawVE2b8aZ2vXPXSnnGhPEzJmwcDOY4LnixsjLavZ2NX2zYtJVLZ3PD/qPm0QSGDu4/00lr1mz p5+Vdn6WVt3V3uRNWfDcxvVNqe6E5FzbU6ZdUSvuT0Y/wRuVx9RfOMffP7BfFVCtrF98/uTUpZac PWNeOLehd0Y+zbl6ZFAIAjJNRuHt2HxkMhbCaKeNCCCAQMEKWH2614TlgpwL8qlVi8WzdXuf7trq F8GdtD9kCnZy0nDbBWZWnKMVn4yrR/qFUIlVVt2wSbEKMnZE6TkVM0P/z9/z+//6wLzGT/jyhXXh UMDezrprbmt/urtvOH6BlHmFpcsppYAMSgQx7RkELi1lsvf7fRtjW9sW9bQuu7aqbMbCls6+4WM5 tznwtyOHw7G/WVMnZRiqGg+xRitg0YPksQbGrVoPfpVtHzgc+2vggLhBE/u/GR/BmHj6WROV5H9+ 892/5sxIBoUhINFkFODOzUcmY2GMd1qJAAIIFKiAtad7TVQuyLkgDz9RHhwfGX/IFOjMpNm2C3hm zzg704P8GRdCxdVadcOm6Im6sv+vON3ntIplW8JHv38ktpYzA8Nx/+Bvu7u7n21fVln3hHbayZcs ezj8ZuOiIrFQ7Ja6yysmiZqICEv3NjNiLPp6yjN1yhc14y82KGWuIUHMzEakSC0Q3OLkqcHdG2Mv yRJp/f2tyy6vmLOwZXNuqzJPjB6JhALPnpIpiCnKTX6INVRxj2UPksdclPuT0c/g5u1vRIO4gf1/ eKYnthAz7bPkjDUEshaQajI6OB+ZjFkPIQ5EAAEEEJBfwNLTvWbzuSCXf1RYX8PITWJ+yFhvTQn6 BCZOPX2iubEs1Q0bfVUIp1I/kK4+MOAf3CYiliJkubQsHA/9u7Kqq+vq6pY0xe17F1/aqeW3Ptq7 KvoK8sgfRYSlrubyitOLixe3dD67bdCv+aS5oYqnS5yS12ylrGpsbsdnVQUOSiEQDf1LLlTimb90 7e9Gh3ZsaVMtygyGMhuqKu94dE/WE+zE6GGD7wIqLV949YVOcJVMnl+zwhsLY6qeKP90/84Xe2J1 unrFjRemeZY8mHDC/5h5aXivXifaQpl5LCDRZBSKzsxHJmMeD2CqjgACCCCgR8Cy071m4VyQ6+kT 96QJb7e3f2Vl3FZeZy5aOxDc52/tovgfMufWbhJbAGbz8gP3kNESBBIERPhyU8vCL5RV1YiIZfqQ pZZdybRFjzw3MuBri7wDPT6RePh1SU1V2TlLN+wxbR+/POtCgph51mGyVrektNx7w8pNI6NDvR3N 3mgt/V0ran609ZBZu2Smb31gbPCplU3KXryqj3/D8gee194U10TN0vOvq18Qyy/6RHnCs+TVtddd MMXEYskKgSQBGSajqJRz85HJyKxAAAEEEHC/gCSn+2Ro5y4ARF24BnD/yKeFCFgvMKtt4HPVi+vT /8+EiP/Y3s7bqqtube3XqKbH2/ykz+fbOvDeQd+daZtxiqeyduWmN8dFdMW3pSPusdfwcf6u5Rff vGFwzNoVmekqmYtSbn1IEDM3P45OECgtX9S4dsfoPl90pokY4k92ZrVB5oRJU88yADw28PjKNq0v iyJ/5w8e2PquxfP71NkLvqla9h1+ojz+WXJP9U2XzM4450789zu73jbQcJIioCng4GQU9XFyPjIZ mRIIIIAAAgUjYObpXhONC/KCGUs0FAEEFIGPDx/9OLvgwUd9P7x1WdfeMKS3uSMUsxw5GX5l+Y61 d9TW1l5f6ckYEgjnIL7ha29oXLt9fPykePLV53uyWfX0Z1F/2/2/Hs6uoiZ0dfZKuRauVy/Xcjje RQInBttnK8+6z24fPKHVsNI5tWufGWi7Wvmb3/fqnzSTZTCZMGnK1HCSzNvlHhl8/KGmftU7wOMy 39u5/KGNb2UVStXdcSWzL7lJtTFmqNUJz5IvuGnBDCNTbsrZU07TXT4JC1FAyskoOsLh+chkLMTJ QJsRQAAB9wrYdbrXFOSCnAty904tWoZATGDCWdNnh/d0S3hPr26lwPBv1rYOhpJ7vG27R3esbdSO War2GtabeXD1fW3tHWt3HBwd8kVCmf6eZ/6w31gU89OjH47qLVMjnQlKOZQePtRIRCX30sjBZQJv 73/3w1ThySlfW+jNbWdH1QxJtV1u2DP5uRWPt+Fmr1rb37nsXzqsXW5dMnPxnbWxfTHf3vPHd97b +/tXY7Wo/uaC2admHgIfvvtmeCHmpLNO15E+c46kKAABWSajoJZgPjIZC2DI00QEEECgEAWsPd1r BzFjP+y5IC/EMUebESgUgQlfvrAu/GM+7j29utt/4oO9e8Ivw/Dc+v1vV5WmOjLwwd5d+8J/3PXO SCygMjbYvlBZLVbW0pdiBZaIZv7jsuhGdppfy2nOFIGPDrw+ortFWlHMXJVyKZwgpgl6BZpFyaSp kejkwO59R1IoBD4+evjjzEJHBtuvSbWiM1ZQuis2rQdXvU3tGx5rjywFDdeiv23l4wNjmauUdYpT pl1RWx+LYu58prt710vRB8P1PUsuIkCjR94PV6FiZhlBzKy7oyAOzHkyHvcP/lb1yrzi4oUtnVqv u9M7GYV68oPkDsxHJmNBjH8aiQACCBSIQA6n+9hP4uLiGzuHP00pFnirc3HkDbrFd3X7Y7+q9V4D SHEBINrHNUCBTAuaiYDZApPPX1xfqWTqb1335GCqQIf4xf5u97Kvpos2pnt5+nF//8bHOiOPnMc1 YuKXL/y6Ek5QvSg4bTNnTZ0UXpdY8sUpUyOhiIPvjGiGPQLH+rtW96R6elWfp4lK+grUSKV7w1IS 2i0Q7S2zClZ3f055ntzXEX1u2vtg78hnGrmNvtkReZ2Wp7n3qHZ5nx303ROcaak2hf18oC0cLvW2 DYxq53HygK9xbvzIrmzu/TCYeHR3m3rPiGCiq9sG/ppT2zMc/GFvc/iLL3GyeVb1Hg1vhpE2j5NH e1eFv32qO4b0HJE6O9OHkJV0hZW3FJPx5MjudQ2xqLu6TsnzWs9kFH0o0XxkMhbWnKK1CCCAgOQC OV2VZX/tPTrQ5o0U7anu2Jfq0vLkUIdqb/c7fSOqt1rouQaQ6AJADATnrwFy6m6toWzapaPk84Tq RQQYQlpj4fOR6PtwUr5VJqc0cd+E3lW+Ic0wxtF9HY2R31Dq8ILqh3zKsMNnI70PRr+Ug72c0JCj vc3hrD3eVT2RzTTjMWJRjrhvdXXlPY2+g0lf9ydHelapwyOJhnrogjXJTcmESV5kQh5kYY2AxN9c nwx1LImdSj0NbVt2DI1GZ8nRod7/aItEMIuKlnQMfaIldHTItyo8gVN+B0UvQVJddUXCoKoTu2rG nhwdWBf3HSGSedcNxKpqes+pv7niwpipI7kJdYhda+o+JGUrTB9CpnsVbIbmXYlmPRmjc6e6uaM3 Mn/F5O1Q9lhJOvNlnIyiM6Waj0zGgp1eNBwBBBCQUSC3q7KsT/fhC0uP13uJqEHKG+RK/p7q5hU3 BysaH8SMxQS5INc7tHLrbo1SzLt01NsE0jkrwBByJIg5Pv7X6Is9Ql0gfig9NxBbsyV+K/lU7woP bXyprmgsBCm+UJODJOpjIz2cGAmJ+z0VzEP8UIsWcXJkYKvq3T6ee3wHVevJ1Le7guWv64kGYcVb zsWPvFCZ4dNBcvx0XG8QM1elnKcWQcycCS3LQOpvLo3brXExu8j/mdvoO6BxyzdhCVjKIKYqDNHg G0miPnnQF70JEi4xYSYnfg2Fpm3Cd824+h516iWfOjtaY/mnKDSyODRjJrGvHt2HpM7T9CGUsfok 0Clg5pVodpMxPNI0ZmhkWiWMwAyTUTRcuvnIZNQ5HEmGAAIIIGC9QK5XZdmd7iNXubPWPPmz4HNU KdYWhK8KlnT0djVoBDG5IDc8PnLt7qQCzbx0NNwaDnBAwPQhJNqg5OlAY0wrUk+ULec0Jw/2rlIt TNcMcijRwIaN+xJXR2ks6dDIwLtqi29NpIyEm0aaT7ZpVqJ6VW/CakutJVwJh4oVXUNbQt/zyY/D 6qGL9GVOSrkOCF7sk3pU8pc0AiXTa3/eM7AxHM7XTiiC/7t7fl47PX6QHRvu62y5vPLiFV3+ouoV zTdrP80azrFkctUV4V0mXxrYdyz+zVuBd7c+8IPOuC0d5jaub6qZdoqqPlMq73og/qFyf3/TQ4+n 2eEix34vPf+66D670aw81YurIm9aT5t/YP8fnlF2qdB9SI715fC8F8huMn7wX78PjrRLr/76tITT QMm0b9wS3A5m5PUDH6mmXNrJKBAlnI9Mxrwf3DQAAQQQQCAikN3pPuo3Yc43blpQVLTzmZ0Hkt9k G77+FK+grPiiljgX5IxDBNwgoISO3NASS9tQMm3RI1tiz4xqlzW3Yd3OwaeWzilN+CF1yrSa721N tWFXMKu5DW0vDj338A1XzJ8XjoMkvWVEfNv/eIuvOW0gNRhp2fjwooTfcSWllfc8vXt9iv3CRNR1 /e6n76mMbKKZk2JOSjmVLA4miJmrYOEeX+KpXLp2h1iZ7PuV6uFxZWb+yrd1YOTgpvvme5JG2NG9 mx5p7fcHp9DIc2tvvGBiesHoxrH+nu0Dh1Vpjx/a2rY8YUNcb+Pd1ecmllhadVd7kzeulBeaVj5l 2ZvKT5294JsJXzme+iuqJuuZa5/u3/li6I1mHu+Kmvm6DincAUjLYwJZTEZP7abgZczjtZ4JSZKn nn7WJLGd9NuH/xb3OyflZBQZyDkfmYzMEgQQQAABFwlkcbqPtX5i6ALV3/PMH/YnRjGVN+qmfQUl F+QuGkc0BQEEMglMLq99JBTo2KJ6eDz0K1083+17YWBkcNN9lyZHOoIpSjzz73tqeGiHb0ubOpgY O3Dl4nIR+oy9HkfrTeilc2rX/m5UZOJTPTwezD19pEUkOMUz/55NwwkhmtBRvUPDm+6Z71Gv98rE kOHvOSjlWHKuSzk53jKBaM+aVYJ6qJiVp/F83vM1fz+4BV/oyPBG4SkfJxdJYouic98jMm1tlYfK c36cPGGn2yC67gfDY8+Sp9pI1Ji36UPIWPGkTi0gx2RMVb/3fA3ijVqzGnzvxaewbTKKYs2Zj/Gv KWAyMicRQAABBBwTcOiqLLxpUvBaO/rMeOJu9cq216GLzxGf1uPkXJAbHjamd7fcl46GfTggo4Dp QyhjiSRAIF8E9KwOyzFMyuEIqAXOrV37UOOi8lK9KiWlF1xVH3oZun9z98uHjus9znC6T49+OGr4 II0DoqspI3/z3nLjfGPPknua77qh/FQzKkMeCBgWCBx65YWX3i4quvCyuWfFH2zbZBTFmjIfmYyG e58DEEAAAQRcK1AyY4HmE+XH3ti+ebBIPEs+O83Fp23XAKZcAAQvJCKPN3FB7toRTcMQQKAwBQhi Fma/51WrS8pvaLk1FMXsfmL7O8n7+JjTmMBHB14fKfJ8/cIvZ3jAPX1xgUO/Xbt6iyqNp7r+qgsS N8vQzOPIa7/pDj1LfvWKGy+cbE6ryAUBgwKRrS09jf+0OPnHjD2TUVTZjPnIZDTY9yRHAAEEEHC3 gLLLSsIT5YFjAy9v9hd55s04O/3vQnuuAcy4AAheR3BB7u6xTOsQQKCABQhiFnDn503TSyZ771zf OFdEMXtWd/UnvN7HpFYoO5rr3rxSs9Tj/sHudfcnvGtI3POeoWeaBQ71/XTdCyJfT/OKb1dOMalZ ZIOAIYFjb218KLjVrOee9Q9dk/jGn2BOdkzG4G+PXOcjk9FQv5MYAQQQQKAgBEpmX3KTeLyp58Wd +z+NNPjwwHbxpr/K+sXnZ7qDbsc1QM4XAKJZXAMUxGCmkQggULACeqIrBYtDw6URKDm3+u5Gr6iO f+PaZ4dNX4wZ8L+0+s7VPSJw850FmS7gkkz83UuLlc/flVXVNXXtVafQ/WD4kdf+vTP4pvXs6iBN R1GRfBY47u979F+Wdfo9jR19j9ROS7Hrs8WTMRjBzHo+MhnzefxRdwQQQAABywXCT5S/M3RwLFyW 8iy5p3pxlY69jyy+Bsj+AiD4G4ELcsuHDwUggAACMggQxJShF6hDRoGS0srb2tuutmYxZuDjoyMf zPze7sF1KQM3GSuomUB3RDKyDHNu4/qmmlTBo+zqwFEI6BIQEcw1N1/+YH9R9apfPnTrnDTBfEsn YzCGacl8ZDLqGgYkQgABBBBwt4DyRHn0ZbiRZ8nrr6iarOdXoaXXANZcAIj+5BrA3YOa1iGAQIEJ 6DldFRgJzZVUYErlXQ+0eT1iMeYPnxyI3D42pa4lpXOWdnTcM9+TYulZ+kLOmv5V8Sbn5I93la8/ 9XK2uPQf9f/kEbEM09P4rw/VTGdOmtKpZGJE4Nhbnf9SqUQwezsfXqT1HHlcdtZNRlFMDvORyWik 10mLAAIIIFCAAsoT5f7NLw8EN2jS/yx5lMq6a4AcLgBE7bgGKMDRTJMRQKAgBQiYFGS352mjS6vu +tmaBo+/v6n918PRrXycbkzJmTMvE/t1Rj8eb/OTvq0DI72P1JbreTY9MDa4+Yet4kGeVLsQOt1A yne3wNhb3S1Lzgs+Rd6wbvdGHRHMEAeT0d2jgtYhgAACCLhVoOTMGfPKxD7z2wcOFxl6ljwKwjWA W8cG7XK7QHgLtOJitzeU9rlcoHh8fNzlTczb5olvGaXuZvVRNEMT88xR98Rg+5yqprdntQ28tbJy Qo6ZcXiigOlDCGKzBCSZjMHNp25euqbfXyQWDj/xv/WF3c0yKKx8mIyF1d+0FgEEEEgScOhEMDbY fm1VU7/qWjtwrG/1nMvXFDX37F684+Lg/+h9a+2i8I13sbNkWV1X0Z2+kfW1Hi7Nsx/Hpne3JJeO 2YtwpEEBhpBBMJIXkAArMQuos2kqAgggEBVQRTAf7H36QSKYjA0EEEAAAQQKQKBkctUV9WJ/ps0/ Xd3xnL73kheACk1EAAEEEMgTAYKY8naUsgDTrGWY8raTmiGAgP0CgXe3rl4RXIPpucf3y1WLstsQ 1v5qUyICCCCAAAII5Cgw+fzF9ZVF/q1dT+/V+17yHEvkcAQQQAABBEwSIIhpEqQ12RDBtMaVXBEo dIHA/t4nOvcGFfwb6s75u+gWOXH/Y2m3v9CdaD8CCCCAAALuE5hatbjaE2qWR+97yd2HQIsQQAAB BPJSgCBmXnYblUYAAQRyEDjxwd49PTkcz6EIIIAAAgggkLcC4SfKi4oq6xefr+c1lHnbUiqOAAII IOA2AV7s47YeTdMeNoQuoM4ONdX0DaELDdC69jIZrbOVM2cmo5z9Qq0QQAAB2wQ4EdhGLUNBpnc3 l44ydKuddWAI2alNWfklwErM/OovaosAAggggAACCCCAAAIIIIAAAggggEDBCRDELLgup8EIIIAA AggggAACCCCAAAIIIIAAAgjklwBBzPzqL2qLAAIIIIAAAggggAACCCCAAAIIIIBAwQkQxCy4LqfB CCCAAAIIIIAAAggggAACCCCAAAII5JcAL/bJr/7KqbbqDaFzyoiD801gfHw836rs8voyGV3ewamb x2Qs2K6n4QggUOACnPoLcwCYdd5n/BTm+BGttmIImZVnwXYKDXdWgJWYzvqnK12cq5SPvFWkZggg gAACCCCAAAIIIIAAAggggAACCFgvQBDTemNpSuCWizRdQUUKXYDJWOgjgPYjgAACCCCAAAK6Bbh0 1E1FQgQQcLkAj5PL28HRNZictHLspPXr1997771NTU2tra05ZsXhCCCQi8Dhw4fPOOMMkcPQ0FB5 eXkuWXEsAggggAACCEguwHlf8g6Sv3oMIbP6SP18J+EFs1TJxxEBVmI6wk6h9gmIM5+IYIry2tra hoeH7SuYkhBAIEng6aefVv7tF7/4BTwIIIAAAggg4G4Bzvvu7l8bWscQsgGZIhDILwFWYsrbX6zE NKVvlGWYSlYsxjSFlEwQyE4gei9dOZzFmNkxchQCCCCAAAJ5IcB5Py+6SeZKMoRM7B1WYpqISVbO CrAS01l/SrdWILoMUymGxZjWcpM7AmkFovfSlVQsxmS8IIAAAggg4GIBzvsu7lx7msYQsseZUhDI LwFWYsrbX6zEzL1v1MswldxYjJm7KjkgkIVAwr10JQcWY2YhySEIIIAAAgjIL8B5X/4+kryGDCFz O4iVmOZ6kpuDAqzEdBCfoq0VSFiGqRTGYkxr0ckdgRQCCffSlVQsxmS8IIAAAggg4EoBzvuu7FY7 G8UQslObshDIIwFWYsrbWazEzLFvkpdhKhmyGDNHWA5HwKiA5r10JRMWYxrFJD0CCCCAAAKSC3De l7yD5K8eQ8iKPiK8YIUqedovwEpM+80p0Q4BzWWYSsEsxrSjAygDAZWA5r105e8sxmSkIIAAAggg 4DIBzvsu61D7m8MQssJ8fHxcZKv8Jx8E8leAlZjy9h23SnLpm1TLMJU8WYyZiy3HImBIIM29dCUf FmMa8iQxAggggAACMgtw3pe5d/KibgyhvOgmKomAUwKsxHRKnnItFEizDFMplcWYFuqTNQLxAmnu pSsJWYzJkEEAAQQQQMA1Apz3XdOVTjWEIeSUPOUikBcCrMSUt5tYiZl136Rfhqlky2LMrHk5EAH9 AhnvpStZsRhTPykpEUAAAQQQkFaA8760XZMvFWMI5UtPUU8EnBJgJaZT8pRrlUDGZZhKwSzGtKoD yBcBlUDGe+lKWhZjMmoQQAABBBBwgQDnfRd0orNNYAg560/pCMgvwEpMefuIlZjZ9Y2eZZhKzizG zE6YoxDQKaDzXrqSG4sxdaqSDAEEEEAAATkFOO/L2S95VCuGUB51FlVFwCkBVmI6JU+5lgjoXIap lM1iTEv6gEwRiAjovJeuJGcxJgMHAQQQQACBvBbgvJ/X3SdD5RlCMvQCdUBAcgFWYsrbQazEzKJv 9C/DVDJnMWYWyByCgB4BQ/fSlQxZjKkHljQIIIAAAghIKMB5X8JOya8qMYTyq7+oLQJOCbAS0yl5 yjVfwNAyTKV4FmOa3w3kiEBIwNC9dMWMxZiMHQQQQAABBPJUgPN+nnacPNVmCMnTF9QEAZkFWIkp b++wEtNo3xhdhqnkz2JMo86kRyCjQBb30pU8WYyZ0ZYECCCAAAIIyCbAeV+2Hsm7+jCEbOgyJbww Pj5uQ1kUgYB1AqzEtM6WnG0VSF6GedFFF4kA5WOPPRatx6ZNm8S/XH/99eqasRjT1n6isMIQSL6X fvvtt4vZp269+L/iHxM8WIxZGAOEViKAAAIIuEpAz3n/Rz/6UcKVgCDgvO+qcZBDY/QMIS4dcwAu ii6Qiv6PXHLjWAQcFGAlpoP4GYpmJaahvlGWYYrAZUNDw4IFCyoqKk477TQlB03J119//Y033uju 7t62bRuLMQ1RkxiB9ALRe+kiRrlkyZKvfOUr55xzTsJkFP83eh9YpB8YGHj55ZfFHQXx7yzGZIAh gAACCCCQRwLZnffFqf973/se5/086mjrqpowhKqqqqZOncqlo7ng6tglizHNtSU3mwUIYtoMbqA4 gpgGsIqKurq6/tf/+l/l5eXJR6WXPHjw4J49e7xeb/RkaahcEiOAQIKAuEPwt7/97bzzzkueU+mv nz755JNXX31VHFtdXY0qAggggAACCOSFQI7nfdHGSy+9NC9aSiUtElCG0IUXXhhdgxItiEtHs8wJ YpolST6OCxDEdLwLqIDlAoSDLSemAAT0CXD9pM+JVAgggAACCLhBgPO+G3rR0TYwhMziR9IsSfJx XIA9MR3vAiqAAAIIIIAAAggggAACCCCAAAIIIIAAAukECGIyPhBAAAEEEEAAAQQQQAABBBBAAAEE EEBAagGCmFJ3D5VDAAEEEEAAAQQQQAABBBBAAAEEEEAAAYKYjAEEEEAAAQQQQAABBBBAAAEEEEAA AQQQkFqAIKbU3UPlEEAAAQQQQAABBBBAAAEEEEAAAQQQQIAgJmMAAQQQQAABBBBAAAEEEEAAAQQQ QAABBKQWIIgpdfdQOQQQQAABBBBAAAEEEEAAAQQQQAABBBAgiCn1GCguLpa6flQOAQQQQAABBBBA AAEEEEAAAQQQQAAB6wUIYlpvnG0JSgSTOGa2fhyHAAIIIIAAAggggAACCCCAAAIIIOASAYKYLulI moEAAggggAACCCCAAAIIIIAAAggggIBbBQhiurVnaRcCCCCAAAIIIIAAAggggAACCCCAAAIuESCI 6ZKOpBkIIIAAAggggAACCCCAAAIIIIAAAgi4VYAgplt7lnYhgAACCCCAAAIIIIAAAggggAACCCDg EoHi8fFxlzTFdc3I8ZU+6XvWusylzVkMkDQm0lbbkToLqwIEYYQkf4laN/zSaEs79hghdo4Q68Ye J8eEfgQEkPRX0IwQRggjxOivTEdOYVw+JXQTIGlACAEZndSkl0qAlZhSdYeZlUnzxZ3jd3qaWuae c+45ZIHoSKE662ld3RwZITpbbekYy6IO1vVCFpUx9xItuwoAIkMv6Ow7RzrLkUJzB7Gu2tblrLPV 2X2pWlft3HPOPYdUJo6cHHNvTu45AKJzNjkyQnTWLbuZnmPmgOgHtG6e6q9DFjM998ylKlRncxzp LEcK1QlCMgTkESCIKU9fmFyTNDdYrLv3knvOueeQhaMjheqsp3V1c2SE6Gx1mmSAJOBYB+JIL8g8 QtwHYt3gsS5nS0eIddW2LueCBbGO1JGTY+7NyT2HVGMJEP2nXet6wdKZnmPmjoyQHOssDreuswDR 3zvW9YL+OmTx1Zd75s4WKjO7dbbk7CYBHid3U2/SFm2B6E0tvrIZIgg4K6C+w8x8dLYvKB0BBBBA AAGrBTjvWy3s+vwZQq7vYhqIgFEBVmIaFSM9AggggAACCCCAAAIIIIAAAggggAACCNgqQBDTVm4K QwABBBBAAAEEEEAAAQQQQAABBBBAAAGjAgQxjYqRHgEEEEAAAQQQQAABBBBAAAEEEEAAAQRsFSCI aSs3hSGAAAIIIIAAAggggAACCCCAAAIIIICAUQGCmEbFSI8AAggggAACCCCAAAIIIIAAAggggAAC tgoQxLSVm8IQQAABBBBAAAEEEEAAAQQQQAABBBBAwKgAQUyjYqRHAAEEEEAAAQQQQAABBBBAAAEE EEAAAVsFCGLayk1hCCCAAAIIIIAAAggggAACCCCAAAIIIGBUgCCmUTHSI4AAAggggAACCCCAAAII IIAAAggggICtAgQxbeWmMAQQQAABBBBAAAEEEEAAAQQQQAABBBAwKkAQ06gY6RFAAAEEEEAAAQQQ QAABBBBAAAEEEEDAVgGCmLZyUxgCCCCAAAIIIIAAAggggAACCCCAAAIIGBUgiGlUjPQIIIAAAggg gAACCCCAAAIIIIAAAgggYKsAQUxbuSkMAQQQQAABBBBAAAEEEEAAAQQQQAABBIwKEMQ0KkZ6BBBA AAEEEEAAAQQQQAABBBBAAAEEELBVgCCmrdwUhgACCCCAAAIIIIAAAggggAACCCCAAAJGBQhiGhUj PQIIIIAAAggggAACCCCAAAIIIIAAAgjYKkAQ01ZuCkMAAQQQQAABBBBAAAEEEEAAAQQQQAABowIE MY2KkR4BBBBAAAEEEEAAAQQQQAABBBBAAAEEbBUgiGkrN4UhgAACCCCAAAIIIIAAAggggAACCCCA gFEBgphGxUiPAAIIIIAAAggggAACCCCAAAIIIIAAArYKEMS0lZvCEEAAAQQQQAABBBBAAAEEEEAA AQQQQMCoAEFMo2KkRwABBBBAAAEEEEAAAQQQQAABBBBAAAFbBQhi2spNYQgggAACCCCAAAIIIIAA AggggAACCCBgVIAgplEx0iOAAAIIIIAAAggggAACCCCAAAIIIICArQIEMW3lpjAEEEAAAQQQQAAB BBBAAAEEEEAAAQQQMCpAENOoGOkRQAABBBBAAAEEEEAAAQQQQAABBBBAwFYBgpi2clMYAggggAAC CCCAAAIIIIAAAggggAACCBgVIIhpVIz0CCCAAAIIIIAAAggggAACCCCAAAIIIGCrAEFMW7kpDAEE EEAAAQQQQAABBBBAAAEEEEAAAQSMChDENCpGegQQQAABBBBAAAEEEEAAAQQQQAABBBCwVYAgpq3c FIYAAggggAACCCCAAAIIIIAAAggggAACRgWKx8fHjR5DegTyS6C4uFipsAyjPVqZ/DI0pbb4m8Jo USb29I56/NtTYhouJqNFY0l/toXcBWmUHJ8a+nuQlHoECnmcyzCYC9k//fi0p3ekOu/rmbCkkU2A ISRbj1AfBBwXYCWm411ABQpIoMCvpAu8+ZIP9ELrnUJrb8Lwk6H5MtRBzlmJjJz9kl2tCrw3C7z5 2Y0Z246id2yjpiAEEEAAARMFCGKaiElWCCCAAAIIIIAAAggggAACCCCAAAIIIGC+AEFM803JEQEE EEAAAQQQQAABBBBAAAEEEEAAAQRMFGBPTBMxyUpSAXn2xJSnJjZ3lTwNl6cmNndBquJs3mnI5uLS IMtTE5tHgjxTQJ6a2NwFeoalPXvVydNwF9ekYMe5PA2XpyaSjHObT382FycJMtUwUYAhZCImWSHg DgFWYrqjH2kFAggggAACCCCAAAIIIIAAAggggAACrhUgiOnarqVhCCCAAAIIIIAAAggggAACCCCA AAIIuEOAIKY7+pFWIIAAAggggAACCCCAAAIIIIAAAggg4FoBgpiu7VoahgACCCCAAAIIIIAAAggg gAACCCCAgDsECGK6ox9pBQIIIIAAAggggAACCCCAAAIIIIAAAq4VIIjp2q6lYQgggAACCCCAAAII IIAAAggggAACCLhDgCCmO/qRViCAAAIIIIAAAggggAACCCCAAAIIIOBaAYKYru1aGoYAAggggAAC CCCAAAIIIIAAAggggIA7BAhiuqMfaQUCCCCAAAIIIIAAAggggAACCCCAAAKuFSCI6dqupWEIIIAA AggggAACCCCAAAIIIIAAAgi4Q4Agpjv6kVYggAACCCCAAAIIIIAAAggggAACCCDgWgGCmK7tWhqG AAIuECiOfFzQFpqAQF4LMBnzuvuoPAL5IsBXTb70FPVEAAEEEHBEgCCmI+wUigACCGQWEL9kMici BQIIIIAAAgi4QoDzviu6kUYggAACCFgoQBDTQlyyRgABBBBAAAEEEEAAAQQQQAABBBBAAIHcBQhi 5m5IDggggAACCCCAAAIIIIAAAggggAACCCBgoQBBTAtxyRoBBBBAAAEEEEAAAQQQQAABBBBAAAEE chcgiJm7ITkggAACCCCAAAIIIIAAAggggAACCCCAgIUCBDEtxCVrBBBAAAEEEEAAAQQQQAABBBBA AAEEEMhdQK4g5onB9tnitXziM7t98ETurZMnhxP+7ruUluXWtMDYcH93989bFpaFcwv+11eXtj/d 3Tc8Jk9zqYmzAqqJtLT7z87WpZBKZ3oWUm/rbCuTUSeUycmYjCaDkp3sAnzVONNDfNU4406pCCCA AAKFLCBXELOQeyJj2wP+PZtarppUsbCu7tut/X5V+r1dTbfUXV4xaeH93cPHMuZDAgQQMF2A6Wk6 KRkikJ0AkzE7N45CAAFDAnzVGOIiMQIIIIAAAmYJEMQ0S9LafAL+l1bfXHNra0+6YvrX1FUsub/v UMDaupA7AgjECTA9GRAISCLAZJSkI6gGAu4W4KvG3f1L6xBAAAEEZBaQK4g5oXLl/vHQZ//Kygky u9lbt8C7W1evWBNefenxNj+5dWDkpAI1fnJ0aMeWtgZPuEY9a761Zuuh4/bWj9IQKGABpmcBdz5N l0uAyShXf1AbBFwqwFeNSzuWZiGAAAII5IWAXEHMvCCzvZKBY/1PLO/cGyp3bkNHz3Nr77i+0hPp uZLScu8NK38+2PugV6mZf8Pyn+zkqXLbu4kCC1OA6VmY/U6rJRRgMkrYKVQJAfcJ8FXjvj6lRQgg gAAC+SRAEFP63goMP7t2o7IFpqf5x481zi3VqPIpnkXfbW+7OhzGbH382eFPpW9YvlZw165dhw8f ztfau6LePT09snQB09PRESWGgZiPjlah0AsfDn2kUGAyStEN7qzE66+/Lss4dydw5lZx3s9sRAoE EEAAAQQKQ4AgpvT9PDYy9KYSw6ysX3z+5JT1nXLBdbXV4b++M3SQd5Vb1bNf/OIXzzjjjPXr18sS R7OqofLmOzY2JksXMD0dHSZTp05ta2urqakhlOlUP5x55pkVFRXNzc3Oh3iYjE4NggIod+LEibKM 8wLQ1mwi5/1C7XnajQACCCCAQKKAdUHMY8N927o7WxYWqz4LWzq7t/WlfoP2icH22Ury2e2DJzR7 67h/8Ldx2Qbz7B8eC73Mxt+9NFzaXd1+9fEn/N13xWeclE/x4pbOZ7cN+nW8FSd0bPfPWxaWqRtX XLa0/dlufTkYGIgn/vSqL/wq8qqLz5uS5siSmV+7cpby9yPvH/nEQBkkNSIwb96866+//t5775Ul jmak8u5I6/V6RUNk6AKmp+Mj6u677962bduCBQsIZTrSFyKOfPvtt4tQsuMhHiajIwOgQAotLy8X 530ZxnmBgCc3k/N+wXY9DUcAAQQQQCBBwIogZmBsuLtl4ZyKy2vqlrX2qwvsb11WV3N5xZyFLd3h sKOhDhl7q7vl2rKqq+OyDea5sKL8tkf36Ik/BssL+PdsSs6nqKd12ZKaqsrL09VNhC83tyycEaxD 3bdbw2/aibTB39W0pK6mquycpRv2+E17tU7sZUfjj9d60r7t6OOjH35sCJTEWQrU1tYqR8oQR8uy Dfl8mIibNDU1ydAFTE/Hx9E//MM/KHUglOlUX0S7wNkQD5PRqQFQIOU2NDQoLXV2nBeIdnIzxXn/ Rz/6Eef9gh0ANBwBBBBAAIGogPlBzMChrd/11iUG+OLI/f2tdd57Nr+lLJ/U+Rnb23nPkrrWHu3k /q4VF9+x7j//O3NmH77UdnPNranyKQrV7btbD2lU7bi/b83NVQ1pmxYs39+1/OKbNwwaal3memdO EXj/wOvhNZvnfnX632c+gBTZClxzzTXqQwllZguZ/XFiUUx+dQHTM/vOTnvkaaedFv1lKxISyrTI OU22ixYtUv9V/hAPk9H+QeKCEpWVgNGP/OPcBeYJTbjssss477uvW2kRAggggAACRgVMD2J+1P+T RzrDobTq5o7eodGT4+HPZyMDz3U0h7dt9He1t/X8WW8UM/Bu93f/eVmX8obuIk9Dm29gJJzv6FBv R3Po0vKFprp/6coE8HbrqtXBFZQeb/OTWzUyCR7v73zkJ/0fJeQUOPT86m892B+twZYdqqaNj4tq bGlr8EQO6v/Vr/fY/O6XT/fvfDEc4vV8/cIvT8wkwd+zF1CeoEw4nlBm9qDGj7z00kuTD5K4C5ie xvtY9xH/+I//mJCWUKZuPBMSnnPOOQk3FUSmEod4mIwmdHoBZqFeCRhtvsTj3IVdJM77F110Uf5c evFV48JBSJMQQAABBGQQMDuI6f8/m1oHQw2rbO791drGReWl0SJO8VRe07jmSV/j3FCCvZ1P9O7X FcUMjL3W/VinEsH0eFf1DD61srbSE863tHxR45rn9m2MBRAzu85t6Oh5bu0d1ydkMrDOGz52cPP2 N47F5XPktX/vDAdnPff4Xvn5yhu8qqYVFYlq3LDyqVd8jeE4ZnIOmauVU4pjf+hYvUXJwVN/RdVk s3s2p8q58OCrrrpKs1USx9Hc1gvJcWSlhTJ2AdPTytEn3rmhmT2hTCvV4/IWOwZqliVjiIfJaNuw cF1Bc+bMyZtx7jp8pUEJ62GjreS879IOp1kIIIAAAghoCJgc6jox8s6ucCkp3kJTMv26u5eGXz/T s2fvB9qv74mv6eE9v/5Vf/j6pan9/ssj8ctoqpLSOfUbnouGIDP0tKfxXx+5dW5pYqqS0guuqq8O xyD9rx94Xx1gPfH2jsdfCB3hqX54ec20UzTLKJlWffcKr/In//tH/mbfkDsy+OS6VmUBrOee9d9Z kPol5vbVyd0llZYmjSBVg2W8nnZdf/z936fbM0GmLmB6Wjv4zj333DQFEMq0Vj+U+9y5yr1J7Y9M oUwmow3DwbVFfOlLX8qTce7aLkjfMM77BdrxNBsBBBBAoMAETA5iqvR2vfCfWhtLFhUZ2H1fye7Y G9s3R1Z3fr++Mra0U91XcSHItJ1YWX/LN6ZptrvknK9dWRE+9u3Do+ogZqzSI9sb56RWO7VsZiQH +0bS8UPdq69tUmKscxvXN6WKsdpXowIoacaMGRlbKdP1dMbKujOBBF3A9LR8aIltMTOWQSgzI1Eu CdLf1FFyliCUyWTMpZM5tuiss87KqCDBOM9YR5cn4Lzv8g6meQgggAACCEQ2rDTpv4/2Nkf3hSya 29D2K5/YFVN33p8PtIUXac5qG/hcOezk0d5VkSzv9I2E/1Ury0+GOpZEOjQh5ecjvjsjf1rSMfRJ ihqpksUqoLv2wYSqHBp8I7FDc885VTU+G+l90Btpm6fRdzC6B6nWEQx4pwQee+yxv/zlL9HSDY0q w4lVE6nB957hwy04wCl2dbm2dkF0I2Aj0zMZXl1/C7olmKUMXWNzHcQGjjt37rTBNnROiJ3VmIxR 86ampqGhIZu+D02ajOrJYtFkdGo+WtccG3K2+dvDUHG2jnO+arT6hvO+5hw0NIxJjIBUAjacVigC AQTkFzB7JebkS5Y9HI0k7u1quqXu8opJxcXFC1s6u7f1DcfvM6nrSzHwtyOHwy8KmjV7+lkTUh90 6syvzQ/HQNPlPHXKpDSZ6KpTUqLj/sHfdnd3P9u+rLLuieyyyOqo0AvTLw+/bsjTsLHvxzXai0xD uYt+yKoUDjJBQFkaYEJGZJGtgO1dYGx6ZtssjjMsoKzKNHwYB5gnoKxWMy+/jDkxGdMR6bg2GBts XyiSxT6z2wfT7QZkdfpwc3TUPOPYsDCB7ePcwrbkadac9/O046g2AggggAACaQTMDmIWnVp+66O9 q8KvII8V3N+6rK7m8orTi4sXt3Q+u23Qr+uNPsHjPx15J7Je49KZZbmHHzNEQnWMloB/cJuIWIqQ 5dKy8BX935VVXV1XV7ekqSscb9WRTc5Jkn6Vbaifo/2sfc5FkQECCBgTYHoa8yI1ApYJMBktoyVj BBCICfBVw2hAAAEEEEDADgHTg5hFRSXTFj3y3MiAr61Bc6f/ntZlS2qqys5ZumGP/7i5TZxQNvNS c3NMyE2ELze1LPxCWVWNiFjaHLJMqMqxtzr/pVK9BlNHBNOpR9Us7ZN8yTzhsbJ8qbab6pnwWJmV TTM2PYdDn+zqc/Dgwddffz27Ywv2qOTHyQuWwqmG2/h9aGwyfvLJJ7t27RL/mYWMOKqnpyeLAx0/ JH+vDSSvuY3j3PFBJGkFOO9L2jFUC4GsBCT/zs+qTRyEAALZCFgQxAxW4xRPZe3KTW+Ojw71+rZ0 NCctzBQv7+5afvHNGwbHdK/I1NE61bvRdaQ2mmRsb+dt1VW3tvZrHOjxNj/p8/m2Drx3MLb5ptEC dKcP+Pc8eu+iZZ3ht5GLp8h1RDCV3OXf4CBfaqje0C19zyk/Y1pbW8vLy3X3sWsTmti/AlYnk/Iz Zvny5VOnTtV5SPbJspqe4tHampoaEQE5fPiwnqKVcIk4RLyYe+LEiXoO0ZPGxN6xOSs9rRNplPDl 1q1bL73U2hteOuvjbDIT+0ic/nS2xdbvQ+OTUbwkSuw2IObU+vXr9d9aEDcSRHpx1Msvv6zTIWMy E3snY1YZKyNzgoytMzEB5/3sRoKJXeCa8/6ZZ56Z3Xn/jjvu4Lxv4ogiq/wSyO4riKMQQMB9AhYF MSNQpeWLam9oXLtdvJ9ndGiHz/dkszf23p+i/rb7fz2cKYo5YdLUyOsgd70zkm4PJkt756O+H966 rGtvuAxvc0coZjkSfovOyI61d9TW1l5f6bEYtCjg3/XobdUXr1CeW/d4V/UMPrWUp8gt7XvNzA8c OJCxUFt/rmesTUEmsDV8WZTl9BTRbTFUROhk8eLFYuPURx55JLhdReij7rToP4oEIlwiEotDxIEE x/WsmyN8aekXwNjYWMb8bf4+zPpcefvtt4u2iK30lBBDV1eXMvXECs1oG8UtBOUfxV9FmgsuuECk F39VjuXjVoEPP/wwY9NsHucZ61OACfLivC9uqYp66j/vK7dJxHn/F7/4Bef9AhzVNBkBBBBAIE7A 9jswIprpi4UyqzuGVG/TtuPt5OleO57yHeInhzoiq0k93rbdqd+3rnpDuvlvJz85um9jg/rl7+t2 RqKotndjwReYfuWR8jMmGSk696z1k/gtpSY2PH28QPkZY2MX5DQ99S/wSTiBaQ4zQ8jqDA0dKE/i 9HpK+DLNSBACFralMCZj+uVRtn8f5jQZxWDQv9pLPX3EUbkPJJvOEblXtCBz4LxvqNutGMzp52Z+ nffFJUp2v0g57xsahyRGAAEEEHCfgLkLB2Pvoyxr6UvxJvKS0vJ/XFYfeTPs24dHMyzFLJlcdUV9 OHI3sHvfkZSn/MAHe3fty+6CINNRJz7Yuye805Xn1u9/u6o01QHqOpi8blTsF/7Da8+7NfLmoOpV vS8+dd+llq/8zERTsH//3e9+p9l2VmHYNiTEegTNsmxehRGqQ67TU1mMaZSO5RiK2Mcff6xJx+pL oyMq6/Spnrx24vsw18koELJbUJndUVmbc6D9Am+99RbnffvZ1SX29/e75ryvLMY06sl536gY6RFA AAEE3CdgbhBz4pcv/LoSb/RvfnngWKYnxUW6WVMnZazC5PMX11eGch1s/eHmFNtoBsZe636sM/K4 t3UdNXHq6RNT1fi4v3+jNXWIe+Nhkadh3e6NDy+allHOOoYCz1lsXJgcQXPi53rh9oP60c6oghPh S1G4OdMziwhIFoe4csT89re/TWgX4Us7O1q8XUo8FJlQokPfh+ZMxixuKhBZsHPIOVKWOO9/73vf k2OcOwLgfKFi89lXXnkloR55fd6/+eabjbJy3jcqRnoEEEAAAfcJmBsHU62a9G/84b/1+jXDmGNv /GbzzhClp/qmS2ZnrsLU+Tfe4lXs+9tWPqKRbcDf+8jKtn6r+qfki1OmhheDvt2/449HtMqJ/+1k ak0Ch55f/a0Hw63zNHb0PXbffJZgmkpsMLOEtQAO/Vw3WGl3JU8Imjj0MyZoatb0NBo3IWiijGix IaY6skD40v6J3tfXpy7Uwe9DsyajaI7RSIHR9PZ3EyXmKMB5P0fA3A9PuF/lgvO+0cWYnPdzH0Xk gAACCCDgBgGzn5D/7KDvnui2jZ6Gti29Q7EdJE+ODGxVvdvHc4/v4GfqCmjtian8/a8DbVdHuUW2 vug7dcQL0Le0qXaKVFLd6Rv5XJVzys0u45ufOtnR3ua4Vu0YGo3u5XlUvIJd4w3scZtv6qyAVm+c POBrnBtpu3iK/KBqE1Gze4/89AmIQInSI6n2ekuVTXQM6ysn21Ru34ZPvZNUqj2wbOoCU6enoZ0x c98VSyFSn8ayHXBOHrd9+3alCan2vsw4EsSxFjbA7ZNR0EXjdw5/H5o6GUW79O/wYMpumAnz0cIx SdZZCXDeN8pm7gWPW8/7hnbG5LxvdBCSHgEEEEDAlQIW/HiL+yGRJs6rEY9LHcQcHx99s6MhGsvT ytbT+LMnV8wK/8XsIOZ4XHA2Zau8q7b41kReAaSuQ/ZBTNU7hYwEzeNeK+TKoetYo1577bUswpe2 /kCNTST9Y8bbNpD6hVU5Y5v7Y0Z5u4LR8KUVXWD69NQZN7EiaGJtLC/nIZQqAxFZMBq+tDV66/bJ qPwCNxq+zIvJqP+mglmRBfVNBctmDBlnI6AMBhnGecrau/2rRrlf5crzvs6dMTnvZzN1OQYBBBBA wI0CmZ/l1h8FCacsmV774y2+5kgoT/P4LHZ1LJ3buCF1tt5Vvv5Hb78w9upuw9XOcMAp02q+t3Vd 0orP2FFzG9peHHru4RuumD9Pz2uI9NZv7LXfbA6/U0jvIaSzVuBvf/ub+D3T2toqnv+1tiRyTyFQ WloqQifLly8Xj2I5imT+9NT5UKrOZI7i2FG42KVO/K7bunXrpZdeakd5lJEk8NFHH8nxfWj+ZNS5 wwMPeBbCtBBvD5NjnBcCdso2uvW8r3NnTM77BT36aTwCCCCAgErAgiCmyL10Tu3a340O7fD5VA+P B0sVkb5f+bYOjBzclM2ujsFsnxsZeEE8vO2NtsHb3OHbIaKHteWlHx89HH5JrWfqlC+a3bQSz/z7 nhoWjYp/ej34yLzvhYGRwU0rF5eXlhSpXkO0efsbKV7Rrn8M/vXdN/+sPzUpbRAQ4RLClzY4pymi urra6fClUjvzp6eeuAlBk+jYEMOA8KWzk1GMWDm+D82fjAJWT9RATxpn+4jScxeYN2+eHOM896bk aw4uPu/r2RmT836+DlzqjQACCCBggUCx+vElC/K3M8sT/u7lZXVPBIsU+1G+tbJygp2lUxYCmQWK i4uVRC6ad5lbLVLI03B5apIKbnh4uKKiIg2rWBBk4s/pKEihDUsa7vi3kPyTUUyK5ubmtra2VPNR RBbEqnxdX4L6EuWFib6mkCosULB9Kk/D5alJqlkhnio444wzOO/zrYEAAggggIAeAbOXK+op02Ca wHDnYnEBEvwsbB8cS330pyPvBDctCn4unVlGBNOgM8kRQEAGgfSLMVmOIUMfUYfCEUi/0JJlmIUz EmgpAtYJpF+MyXnfOnlyRgABBBDIR4E8CGKWTJpydph26KU/HgykYj6259fr+kN/9FRf9pUv5WNv UGcEEEAg7UOsBE0YIAjYKZDmpgKRBTs7grIQcLdAmp0xOe+7u+tpHQIIIICAUYE8CGIWfekrl1Ur 78rx96xev/XQcY1GBg71/dvaVr/ylwU3LZiRDw0z2lmkRwCBghBIFTchaFIQ3U8jJRNIFUEgsiBZ R1EdBPJYINViTM77edypVB0BBBBAwBqBfIj1lcxcfGdt+I3f/g11F93R/mz/8Fh0Reax4b5n2m/7 5uVrlJd4e7xtK28sP9UaLnJFAAEE7BDQjI8QNLGDnjIQiBfQvKlAZIFhggAC5gpoLsbkvG8uMrkh gAACCLhAIB+CmEWnTKtpWt84N8zt72pasrBi0hfC+2QWn15x+T81de1V/uppWPOzu6pKXdAzNAEB BApYIDluQtCkgIcDTXdYIDmOQGTB4S6heARcJ5C8GJPzvus6mQYhgAACCJggkBdBzKKikum1P+/Z va4hvB5Tu+FzG9btHHxq6ZzSPGmUCd1HFggg4FqBhCgJQRPX9jQNk14g4aYCkQXpe4wKIpCXAgmL MTnv52UvUmkEEEAAAYsF8ifeV+KZf9+mgyMDW32/amuIrMoM6ni8zU/6fDuGRv+46b5LPfnTIIt7 luwRQCC/BdRxE4Im+d2X1D7/BdTRBCIL+d+ftAABGQXUizE578vYQ9QJAQQQQEACgeLx8XEJqkEV ECgIAbEHgtLOQpt38jRcnproGfHDw8MVFRUi5dDQkIhp6jnEaJooSKENSxru+LdQfk1GMUGam5vb 2tpEZKG1tdXoRNOZPu9MdLarkJMVbJ/K03B5aqJnIhw+fPiMM87gvK/HijQIIIAAAoUpQBCzMPud VjsjkF9X0iYaydNweWqik1fETURKG4ImBDF19ki+J5NnCshTE519qtxUsO6OgqhG3pnopCvkZAXb p/I0XJ6a6JwI69evf++99zjv6+QiGQIIIIBAoQkQxCy0Hqe9Tgrk3ZW0WVjyNFyemui0FXETkdKi ZZjqoAlBTJ09ku/J5JkC8tREf592d3fX1tbqT280ZT6aGG1joaUv2D6Vp+Hy1ETn4BeLMT/66CPO +zq5SIYAAgggUGgCBDELrcdpr5MCeXclbRaWPA2XpyZm2eaYD09VF2z0lsfJc5w7VhzOF5QVqs7m WbB9Kk/D5amJs0MxWnrBnvcl8acaCCCAAAI5CvAenBwBORwBBBBAAAEEEEAAAQQQQAABBBBAAAEE rBUgiGmtL7kjgAACCCCAAAIIIIAAAggggAACCCCAQI4CBDFzBORwBBBAAAEEEEAAAQQQQAABBBBA AAEEELBWgCCmtb7kjgACCCCAAAIIIIAAAggggAACCCCAAAI5ChDEzBGQwxFAAAEEEEAAAQQQQAAB BBBAAAEEEEDAWgGCmNb6kjsCCCCAAAIIIIAAAggggAACCCCAAAII5ChAEDNHQA5HAAEEEEAAAQQQ QAABBBBAAAEEEEAAAWsFCGJa60vuCCCAAAIIIIAAAggggAACCCCAAAIIIJCjAEHMHAE5HAEEEEAA AQQQQAABBBBAAAEEEEAAAQSsFSCIaa0vuSOAAAIIIIAAAggggAACCCCAAAIIIIBAjgIEMXME5HAE EEAAAQQQQAABBBBAAAEEEEAAAQQQsFageHx83NoSyB0BBCICxcXFBY7h+BcOXZBmBDreO3bODkaC 491NFzAZ7ZzyTpXFOOerxqmxp6dcx3tHTyVJgwACCCCAgFqAlZiMBwQQQAABBBBAAAEEEEAAAQQQ QAABBBCQWoAgptTdQ+UQQAABBBBAAAEEEEAAAQQQQAABBBBAgMfJGQMIIIAAAggggAACCCCAAAII IIAAAgggILUAKzGl7h4qhwACCCCAAAIIIIAAAggggAACCCCAAAIEMRkDCCCAAAIIIIAAAggggAAC CCCAAAIIICC1AEFMqbuHyiGAAAIIIIAAAggggAACCCCAAAIIIIAAQUzGAAIIIIAAAggggAACCCCA AAIIIIAAAghILUAQU+ruoXIIIIAAAggggAACCCCAAAIIIIAAAgggQBCTMYAAAggggAACCCCAAAII IIAAAggggAACUgsQxJS6e6gcAggggAACCCCAAAIIIIAAAggggAACCBDEZAwggAACCCCAAAIIIIAA AggggAACCCCAgNQCBDGl7h4qhwACCCCAAAIIIIAAAggggAACCCCAAAIEMRkDCCCAAAIIIIAAAggg gAACCCCAAAIIICC1AEFMqbuHyiGAAAIIIIAAAggggAACCCCAAAIIIIAAQUzGAAIIIIAAAggggAAC CCCAAAIIIIAAAghILUAQU+ruoXIIIIAAAggggAACCCCAAAIIIIAAAgggQBCTMYAAAggggAACCCCA AAIIIIAAAggggAACUgsQxJS6e6gcAggggAACCCCAAAIIIIAAAggggAACCBDEZAwggAACCCCAAAII IIAAAggggAACCCCAgNQCBDGl7h4qhwACCCCAAAIIIIAAAggggAACCCCAAAIEMRkDCCCAAAIIIIAA AggggAACCCCAAAIIICC1AEFMqbuHyiGAAAIIIIAAAggggAACCCCAAAIIIIAAQUzGAAIIIIAAAggg gAACCCCAAAIIIIAAAghILUAQU+ruoXIIIIAAAggggAACCCCAAAIIIIAAAgggQBCTMYAAAggggAAC CCCAAAIIIIAAAggggAACUgsQxJS6e6gcAggggAACCCCAAAIIIIAAAggggAACCBDEZAwggAACCCCA AAIIIIAAAggggAACCCCAgNQCBDGl7h4qhwACCCCAAAIIIIAAAggggAACCCCAAAIEMRkDCCCAAAII IIAAAggggAACCCCAAAIIICC1AEFMqbuHyiGAAAIIIIAAAggggAACCCCAAAIIIIAAQUzGAAIIIIAA AggggAACCCCAAAIIIIAAAghILUAQU+ruoXIIIIAAAggggAACCCCAAAIIIIAAAgggQBCTMYAAAggg gAACCCCAAAIIIIAAAggggAACUgsQxJS6e6gcAggggAACCCCAAAIIIIAAAggggAACCBDEZAwggAAC CCCAAAIIIIAAAggggAACCCCAgNQCBDGl7h4qhwACCCCAAAIIIIAAAggggAACCCCAAAIEMRkDCCCA AAIIIIAAAggggAACCCCAAAIIICC1AEFMqbuHyiGAAAIIIIAAAggggAACCCCAAAIIIIDA/w/LCSl4 NId0dwAAAABJRU5ErkJgglBLAwQKAAAAAAAAACEAngTYKxERAAAREQAAFAAAAHBwdC9tZWRpYS9p bWFnZTMucG5niVBORw0KGgoAAAANSUhEUgAAAS8AAACpCAIAAAD1Iiz+AAAAAXNSR0IArs4c6QAA EMtJREFUeF7tXTuSFTsSfY8X7eHjtscCwIUJIGATGO3BJgiigVWA2csg4gYzgMksALM9FjBeG0xO n4kMPak+kkqfVNUp40Z1tZRKndTRNyX9+fv37z/4EAEiYACBOwZ0oApEgAj8DwGykeWACFhBgGy0 YgnqQQTIRpYBImAFAbLRiiWoBxEgG1kGiIAVBMhGK5agHkTgz+z1xn/dPkSQCBCBJATevn07Fz6f je/fv1+Qm6QfAxOBgyCwzBr2VA9SDJjNARAgGwcwElU8CAJk40EMzWwOgADZOICRqOJBECAbD2Jo ZnMABMhGK0Z69uzZ3IqRTMTJo//Fn6L33CJTKMqTYCXP1OPvCJCNVkrEP26fOW1kMSnkqnyZjDIp ypUAGusDYivDlfluFWAFpl3rQTZaNC+asrkGDWTDyhUoFD5g15wEJR54iF8QG/9CRK4nNy4cZGNj wNeTW/VwAm1WCbmQkjae2rTKC9KVFyHh6XRC9IXmej0nDJGIwF+Xl5eJUf4ffK6blCeNsaQtur6+ FlTlV/gAwshzfn6OLiXeAdTV1dXFxQVe5LtEkQf/lUGjvMh/FyRIWhJG4uovvoCTmpwEoJXLlsxl POkZVxbtPtK0WeuTPFONRmC5/8+eajSQhgMuzwAZVpyq/Q0BspEFgghYQYBstGIJ6kEEyEaWASJg BQGy0YolqAcRIBtZBoiAFQTIRiuWoB5EgGxkGSACVhAgG61YgnoQAbKRZYAIWEGAbLRiCepBBMhG lgEiYAUBstGKJagHESAbWQaIgBUEWrORB7RYsTz1sIdAazYKAtxObq8YUCMTCLTebYx95eGBK7Jj XVj65MkTE6hQiQoIfPjwoYLU1iLlhIRPnz5lp7py2pDcUZX3vHv3LiMiYoVx86RlKMAovRDYh4mf P3++BcBlEFr3VPVspezahRGJwF4R6MBGngu418LEfG1EoDUbN6rL6EQgBgE9BTN8iYneKwzZ2At5 plsLAcwUinR9GWUm3wobf/78Wcs4lDsOAu4R6dBaD1P3jk53KectYuPMUvf0dAmAu0ncX8SyhU32 BFHZKTKV9uDBg2yVGNEyAmULzEJO3Xl7eff+xJT+3Nz+KoC7mlNdrYp+/PiBMFtWdVZTYYC9IoA+ qjZ6Om7U/Fq+X8RKTzUsHK9evZKP/7599lp0mK/iCAjZcJUIOr0YMaKDCrcTPZfdXDdVdF1tmucC lO14rEqTNvPjx49QRt6z1WbEXgismriBYv+8fbYkdKye6lxlKeNJtJbuw2azeNuyY4F624/ZPNrt qS5AJsz0/stBptkSZkcx9GDt6BNqMiQbNRtKSw4yLRcy6haJwNhs9DIp5AQ/OfcTaX4GM4XArtjo tpkeLTnCNFXsqMwkAvtkY0hLIScISVqSCWYRsMJGdYkKl2tLYYfWUmnJiZ9SwJqSI0uL0Ad+cKZ0 W1XGChsx34X77gGlOv4WX6UFLXW9hLRcLSUDBcAGWlFY73uWP1HXe7/qHmAnd61P4pjL+bK/EvDF 9LT6UuhLKTSFluGSZinhlBPjkrZl+g1zeEhFf7WdRHXv/TfjhvYXL158/vw525q2TuKAz27oD5Hn qCFy1ANYJG90s1AXDTr9bPFWaeO8tZoKCoZbrvQLPqLwpOa0qi9O67bRbeXcCubly5elzpJTj8T4 CkyXRrwoUlXr3CzGnPEyGdJDIKZt3A6ajBV1vAOfVfSh5KP863Q64Ve+4GC0VH+Aqm1jazYCi9Aw gGa7MVYlyMlljx8/9tK6e/toXFFvjp8STKyLqSCScxVtL0Bq0U+V3yB8VTZW8RrXDmTYKZ3bV5bR Z0jtY2h4dR0OXzQMnNTlmfNQ1+/arc3W5yARW5q4HqRVe6rl2ei5yYd/Tlqlu6lQg4RjCaXlKutW A9QrIkNI7m7iIigNxkadp5mcrVkdfBeBbLuQyQkhtJarrOMk0CT+ZCOosVA4y683Ykzo7uxs0Jtv k4Ssf+CRtRA8k+mGyyRc0mxjoNFTKc/G0RGB/uqEMMe3GFp6W0xEFGm5j+JRKRdV2IhlDHg/VNK7 mdhl76rI1lK11WZTnWbpN9vMlPYTKs/GNgsVbZDFetTZ2dlqtZJKS3Wa5Rawsqb0DnqcEz5pUHXG LKtSvLQqbAQhdRE2XhuDISUvNzc38VVMKi2RZe7MLGh6ODzPCdSOWxgAvbnVmregqp6o8mzUQVdx b+96KMRITrXTdloecJCpfqp5L7Ajmkd44eBdP8KI8sC5XF3J8ZLhthpTchLCZC8GzM3VhmsDMe6j +5j+XgYzcoHEE7KbA/LamFh9UHU5wXU4Uc/V8KP3rzlTVl1vLN826pYWrajiu3kJtUi/oNmzU9mt JWd6Uq29XOTcvigW5Iy47JVnI0aMOpg2ks9Ucy6ER+62jC5SaYmZngN2XPOspqaBpcBMvThUJzXw Ly2r2njkJVomVvGeap7ANt2YPN0aeJbkdWILZqeBqOFMPInJYD1VVBKyTKcD6DLVhjEp2uZvaSQ1 T/Gt5Zb9uMYgpDo+AlV6qugYyG+Rknooo4W09AaN7lrIoZA5QmZr7W90++sxOLbZihqjSV4YzI/n xV2OpY2ht+VyuA2W+1jx+v79e72TOCqyEYUschZndDbmbSRPYq/bR3U97LjpOQnGjYG/ffv26NGj bCHL5bxiT7VSW5ENRNWIcqABbiarlwruBfK81fVAynrpUnIzBGqxEYPGQxFSMosDVxo8Li2lzVzY 3tVAGSZRCoFabFzY3wh/pVIZMCinZe6UloIDOWmwMCSp1GHciMLqjSebnVKVhM6IgYWT8Lf89evX iPob1/nOnTtv3rzJVnJlfiR72TdvMdfCKVXZWU6N+PTp0xgf3VSxMeHd43x24+kak/HaYb5+/bol idYncajjvFQDk7MaG93Ksqul9hFxVmWvRdewE0t/1/ZlICnF8uNG9QOc3N/oXraRpOiggYWQXdjo urbqZKyw8eHDh4MieQi1s5vdvJ7qKGfGZcMyF7Fjl9VT6Qg+scXNpwIH66m6dViXZsFmJdpyotVF AI3k69ev9WO8T6xNJHesVfmeqo4bUzfL7xhlyRqWIntVT5N3b636xO7bIgZzV4WNunoR6RZnEJd6 KlX115lTe/ksrHC+h3sp6xWABcnl2SiJYSJRni4lrwuOkYlie2svWFb3f0w630VmjcEKIJA93o05 Fyd+6qLsnFB2ptpEjIelkj5JK5A665MUq5Lm3cUONovjrjf2GiYVqKVqisAiUK95HUnavZRyNaNc t1yFqFSA8j1VdMZ01bGUovuT031Qnbr/Q/uxYgv4xNKdoGyxrMJGNIm4G6esuruU1msYqY1k6pxN SMtUCbu04/ZMlWejTuHs+1yc7dCrBMzrdKy5sP6R0dBx1qdgMRBR5dno9lG7d8bKglVJWsuNkctZ 2NLEcfVye/Eoz0bRSc+u7Fjfb4emsQQLUztoJLdwUqJz1ie75JRn49G2/GdDPxnRQm9C6JTRaw2z w+Flatkoz0Yc3swpnFRLuOG79ynKnm7O4WVkYSjPRpQkPVY9Ug8G8xDQG5Q6IqOzO97dr6kXSLlZ oM/6kkGznRvmvGfc3f3xTieH8sWJx1wBHBofOPHI7+QLfH2EpfIrwfA7+WLBGaiqL075c3FcFxNp J71j1OC/Go6ORj9PtU0Lhn7Hjk/i0wmkyU0nFg50rnqe6h/x9bQXMq+2RmUfxs2Tlq38uBEFQL1p cNxcrGq+4ByrbeyqkBoBBmsbV1uJyQqeZ8at4jYZYPcz2BijSmfqP7ePC8Ldu3e9L3kYJsXa1Zlx kw3j3McaddteZaLBjB+oD4cDTsFzh45dhpFV28byc6rLNQ18MjtuX0iqCAcK7F3Q232NpDh0WCZx nRP0ApKN7grFVc0W2JqNMqlj52LnbNTMRgwneHZW8YGT3qnq2X621uzYmo3W8r9XfZSWepP2nlpL vRrI9RlK3SBm0PRko0GjFFbJPcN2T02ldlyVk2VdiAqbIUIc2RgB0o6C6ErvPva7hYNJsdW4HVey cUdUS8kKGsyOFxOkKLsSdnIwOWLHlWwsWCrGE6Vnpuzg8NtwMDlcx5VsHI9CNTTGJXNYfLLgsJ6d x3Aw6d7Kni22TUSysQ3Ow6SCHqzrSKwTP6MQdXIwOYQBynuN52WbXuN5uHWMpR6Obi8XNFZrKpPR 9sZEUWlFNl7DMcD1QccErHoOpAK4K6/xOYcseo0P56pWW2EtEu5Lnse851IHzfMc63blGZdaFTH8 YREIb3PRXjTa2Hh/hnAwaRNVjhtt2oVaLSGACSft+sZMOy24udq5YZZsZLkfGAH3Im1tLRfazMmV SemyAoIiZ3NtQZNs3IIe4xpCQBdptM2cW0TVlUlrmz/IRkPliaqUQkC7spjgnfTODTm5fMtlKd0W 5JCNDUBmEt0QwB1NOiGk0z/am53kZNIdXgXzRjYWBJOirCMw12ZObtFqn5nWbJQ+w9nZWft8MkUi 4CLgtZnoyuKQdR1Mtj9boDUbJc83Nzdmd9nZUcyIJkbUwPCvXoWiR+Pfv39fTr7yZndcx50vX77U U6O1Zxx8pkI/OCNnxtk5gs2IJkbUwOpFm4NkJSFxuLl37553pitOcw2PCE4i54oHaLZ/U54vm3sS eXbSjEgEBkVgmTUdeqpSPbSp5JIqLQYmAt0RaM1G94yW7pmnAkTAFAKt2Wgq81SGCJhCgGw0ZQ4q c2gEyMZDm5+ZN4UA2WjKHFTm0Aj8dXl5mQfA9vUfWWO8uLgQOVdXV/AbxEvjGVc9xfD8/FxUkl95 sNYsL3ngZMSawwHfG2MCHK6vr5G0C1FG1vKiIFH51ULSq4Tk6T8Za7l492wb1WlQnXoblznghWle 7L7BrhxQsbEy8NWCSm7S8TvcSxUaLEHpBiUXolJJxMjR6Xc1TXujxOhZMExPNnrZwLa0xhyADvCQ AB9QJeOlINAxotxDnIBDl8O7QAMv+100QXkIf2PALBtGjxdAc12pbJhgIypj2Lt9U6DGFvtp81jW lpHSXAYqDtpLjBRSJJhXLboQFZEfKUTVmEQmUkjBYGilCwr0RWV7GOV5xrnJ4fZP9+rsLveB6j2k mjpUykYmLyIS1ZvY3XtRW96R6qqhOPS6qlUzPodMHtQdYy2Xq9Ze4xXrFYomAuYRWO7wm+ipmseQ ChKBFgiQjS1QZhpEIAYBsjEGJYYhAi0QIBtboMw0iEAMAmRjDEoMQwRaIEA2tkCZaRCBGATIxhiU LIaBR0joMYMv8BlYWKquu4ptEbABdCIbBzDSnIqeC5uuZakLyzLlSEhrticbrVkkQR+0jZ5Hq+v+ DqdK+ZU9GdqQygtO6CMbE7BuEpRsbAJznUR094krHnsv1AEdrr/uBhEJ3MU1vw4Gu5JKNo5qTt1M 4G2/AvfcllAbT2SVTaJdk2d70Lb3q85W9SARFywS/gv++gdBxk42zZ2nardmGlyz5f1oYZPI/qo1 g7Onas0im/SZIxj2UnvDy00pMXIFBMjGCqBSJBHIQoBszIKNkYhABQTIxgqgUiQRyEKAbMyCjZGI QAUEyMYKoFIkEchCYNO5OFkpMhIROC4Css50Op3m8p/PxuMiypwTgToIsKdaB1dKJQLpCJCN6Zgx BhGogwDZWAdXSiUC6QiQjemYMQYRqIMA2VgHV0olAukIkI3pmDEGEaiDANlYB1dKJQLpCJCN6Zgx BhGogwDZWAdXSiUC6QiQjemYMQYRqIPAfwFXVYY1QgggoQAAAABJRU5ErkJgglBLAwQKAAAAAAAA ACEAspGxYPxNAQD8TQEAFAAAAHBwdC9tZWRpYS9pbWFnZTYucG5niVBORw0KGgoAAAANSUhEUgAA AY8AAAFHCAIAAADWQ1dpAAAAAXNSR0IArs4c6QAA/8pJREFUeF7s/dmTZdua1Yl530Tfn3ObvNlQ gArJ9E6VRE9CqQCRoAJRUEmXkGR3b0IBVYX0R+hJJlk9CZnJJCs9qKl6kAwrmcmEIZlJZjQJCZlc MpO83emi8QjvO43xG9+ce3lEnBN77nOP34jIWOHhvn372nuvNZsxxze+Zi7/tf/z/+3+vfv37t37 +OOPf/Nb39re3vrv/a7f+eD+vX/0j/4///f/5/9rc2Pr3t07X/nSl37kaz9088bNX/6X//If/9I/ f+/BvR/+2tduXL++tLR0dHT0G//2N//Nb/zG7/x3ftt/99/9d1eWV37jN39z5+MP//vX13/XzfWV 87PT09OVlZX19Y0N/9/Uj+Pj48PDw739vWdPnx0cHuj5tfX1Gzdu3Lp5a2tra1lnr6ws61haWvbP lVX9ylP8pQ6fwF9z5ur56fLp8emxLufo8OBwb29vn+PgYF/PnHHkbdf5PH3i2uqaLmZr24duZOjo FzT0Kp2sD11dXeWSLxz1zPNP+xz96fzs/OTk/PTkjBte4dmlpfOlM5r3+Oj4UDetW93Xfatd9c93 rcZQk62treXNz/m3dO7jjO/5oTfJbzl0gtr96o1bV67eUiPR3Ktra6v6tq73UsNt1INVPa2ndD86 6CO9/PDw6Pj4aLRZlpbPzpdPl3QlXF/6y8fpmS7PN3nqh3rE5eqBz9Inffc73/nWt7+l33V9ughd g242Z+ca1MVXtq9cvXb97t07d+/cuXnr1s2bNzfWV8+Oj89OfJ1qkgtHNVOe8x/dJu3wtfDZQzd4 dHj4ne9+O9eprlhTnzAfNPC2travXNEFXtna1NTY0OjIm+tj9C8DRl0+9HE6+aMPP/rwww8ODg8z 5mlRf9Obqws3Nz3qr2sCX7/uKbm+ruFUzUr7e6SkF3iW5kwHuCfyaztf77n06NGj733vu5p3773/ /oMH792+devW7Vu68vThCUcG4cmJp6jG6u7erobqdV/ENb2rxu3u7u6THR86QxAhKLh27dp1wcKt 2zdv3tBg04xd/cN/8afuXr9+59q14929Z48fb62ufOX+vdvXrn7vW9/+zV/7je2V1Rubm3euXX1P 3by9/fCDDz/4zrdvbV+9f/PWdd2mZs3J6bNHjx5//PGXb9/56nvvba6s6Nejpztf2lq9t7WmdtZt M+B9eLSvreWOj3Xdx0enJ6ernhGrujj1nM5o04kxQc+l8dJhbnpGUP+Wx8sCI7XryanuM83BIVQ8 UEupffVuGgqaXNvqKQ2QK1euXb165cpVPfMidrxycBgiOV555nMnaEbpHj2GjL71ZejJV5sjdbeM G9+ph5ofMH9PdUcChQPBvbps99nTpzs7T3b0TeifQaDpoXbQaTMc4lFmnj5HDe5/GlBra6wiG5os HsTphivbN29pYt/RLFdjaWTrT5pOnmFr60bbmj+6KnelPkgfp4bXDz1VS0hvo3kerKhnz7NC+Uv/ cqHcfdogf6rn8tDgeHhyfLLhO9jQ3ahhdaYGVbukY11eblx3qgHIba/QjsdqyDaH3b5u4szU2Vd1 SL+DAvTBXtc76jIE6FopdQ30o69Q01+Np9UnK7DHhrDJY1z4YrTgoZtz8AOXDvb170A3fFVdePXa 1qZ6b0vvQjepwY7VRJmU+jg1iJdDXdHpSbAyt8kyloWBq2GpS0/0Y3nJaCisefTwodZITS7929SH bW7p3d2h6dTlZVY0utm3f864OV3XsbaqvwcemFFebNStmrM69IlmFutqN6+7q3/sr/70rWvXb129 dvjs2ZNHj4RWX35w/9a1ax985zsffOs72+tr17e37ly//uDW7Wtb2w8/+uiTDz7QX+/fvnVtc0uY rPfeffLk6aPHX75776vvPdhYXnn25Mnp7u572+v3rqzDfJZ1mZ6ibRHOlDNi62qWzjVZBBmaDhpw vh/ahsGpG6uB2n9MUEpzuLiBX6CG9iAVyxBa6WYF9Pv0jMarJ7reWdPS7XhlW10onNI/takuj8sY OxZGK6N15ozHYMc8z8/Z1KwWCFrXSIFGGRs0yoXColG7giohlVDKX093nwmq9jRIzaq4a8ZbvcoL In2fptDn6zI0DoQ/RqmtTYN4YFxM5MrVmzdv37h5S78JvNRo6hoGlomUodZgQqtzeKCz+OgzdF8h j4OHJg0wVcMbWOqAlXHAkAh+9ZVCM0xPmylD/3RRaqSsWMzM4+rc5SXmBVQxXOP0xAOoZmSGUjU3 6+O5P4mPm65Ji6GVL1jDT1C1YnTQZ2pYapyyvJ7aenCz1mqeTwy1AakYKYMHM+BQM0vs6erVq3Tg hhc5TA0fxydqLwMk33T76kR1ZdazAqtAuAmvCRR/mMFU71+9g9Hq0SNx+0AVn7bpMW7zCAsIY8Qd JKgq8NMqcqYRGKD2OiimQmPr6gR8ELJjfThWmf9qzPrjf+PrGqF6e63ST3ae6A5l6N24fm3nyVMN fvHm27fu3Lp5Q6RMo1t0RZ93546euaXr8qxbWdHHyzi4f/+eOLY+Tj2xsbry/vXt+8IzTQVf/KY+ TGe6e2TSiG2trmotv3rtml4ivifsMOdipdNc1gxq9p7vNeMFsGd6sC75VljRjU5Hxwd7u/uarrvP Ml3V8gZsvdXGhqahwAl0Ek0wQun91VWeH0BAYeLIgFgYrTQk1UOMvzbzMk0CvDFyuCh4d/Fv3ebe /sEuzEn/ACkRKf/0k+qnA9+1p645mFYqL0TqXb2HLcSDfREtWIYXSI0coQ58alP4pNFsyi3OfeOG ukOsW+bSlavX1XDYy1Bif3Nfwwr55utvNmlxHw9NjOwMwaFDV0U/M0FnUAVCMVkB9AKsOlE/dGF0 qNdFtZamKOa/7eLMdgwf32muSo0L6zyWda2XxFSJvRNuVTNS1nBWkPR0UJQrcAu+YD5+9tgxf1lZ NokwH8xIducalSJQ5O6ASQMDJMavSluPU3h399mZJp4GvO49oIQVdmxUEuk+OzVq5zNpB1O5xmND qGiSWi/DuQziYUoZvk21yXzUGwpZAugmaloPGIr6p8a3/cPIzpt7IqxXp5jjm1i5d/QnrzOHVm8A hCX9LZ1gTP+Jr/+n6k11quxGrdGayA/u3dPYVa9r8HsE37ghowniI8hf0s3L1tT017g0WHn6rWnQ 37h+Q2t0+MLW5saDqxt3r6ybgG5vZzCpJzRhBC66R129ponN2zu39VYijjKEpbfoZryWbwHMNXDd jTWJmby0g4GKJYLvAmPbQ09k/B4datLKSF7ShRmqZNeox2z02aLRb5atuP+y4wdHXsblwmhFvwi4 s1oWCqeDS5AxTkGiohGgFug2d5+Kwu48s+Vn249jR48FW1i8GLxmT6ZOvsV1qXIbaiUD27NdzGGt nPqre13trxksPmVLwVClTm7HTZSdLWkAQBVCWzOhAKyav15HaoYV9bHJHxWyt8+8D2wUe/moiRCL MNyqEexM6WYoMjpWVjTkNDh1gbp3DwPLH8+g1lpWPSUCypq3unS9uxY3UQC1xvqq4YO54xYPnW/M 3VCRScn1B038M/00vLytBDQ3me9+Cw9hTFQbX60R+y3TxkuwrRhTIwsp52K2LamPZcfrc7H5VjSK DrWkHZ+EeGKRGaEwqblX7OjJUTLi7OMREmpN4SrzPwgkbNWtqeU1OSWk6rF1BrN32zQ+x1jlca13 0ADUbNRV6c1tejHW/NFL55nSISSwdcNOVIvVn/47//nt7a3b2zI0V66tr79/68aXpEZe3d5cOr+x uf7ezRvv3brx4KaErSs65+bW5t2r2w9u3rh//ZrOuX1lK1/3JGzdvK7v0slvbW/cu7L9pRviViY1 V0VntrauyAAzTTRRvH7txk3UtWtXrglCbItsCP6NekITnWh0S1e5Ld2AWYuAKQMV1p5vCsKrljmS YXS4t6vFQ6e6pzNMIVZqlAgy5qeWjbPEFZFpS8XYgFgYreD8tnny8V265O4MKV4VTRv9JcPW4tPx sVYOtMFP9nfFsazGeZQzxbxodkRB7ObmTM1sM1rAOtJpJtPMW31Lm1i+4x/2n81ANz5rhc7UODX6 MZNmS3tUiqyoABdSUPTAsitjlA3RqkzO+EuyakPcXviX0cA5+aHp4V7e2jLgZHQzEUMhI5LagIUh en1CPzILWNKiawHCq3cJ92JWxnpuGHwIq6+rqilalGNwhfPaCcwHHcybmstI16THYXfQGRm2uTUt KmIDi0BV1hD3DpZ7SI1tPRlWReSLNWl4hU7pEw2OK6tNrCqVBeQq4RBIqwOyWCsXro2VeK50ssVo hCe9kpGDDSgkYjAF32L0qFvS4TEWQTMvH+qgonT1Q712khG3/P/77mMhxubmmhSf/b1DtdO1q4KV DcPv0VFbXPx2ehwql3fvS2vagsu3XWpyu3R+dfnk6opw0W9Q3OHs3LB+cmRn3JrYHAqI1itm3cHR vvi7JmFWmhi38EnGYQ1Gz+GayLql3JzdE1pYD04PDyRRWoXZ3GT+XWFaWhuOYAE8qM8Md7ynXxji MIZVGFNZVUZfmMlTayw0SrcfMmVfQNGqLPm+v6z5ogzf++4H3/vuh25iREBMM3uLogXoKMsR9QG/ gt7ZD2Qv600iVaZxCp9sHPt/dHTk9U0JWebkqys7z/afPt2P5Pfc0bWLF5d8JiFDZfBQh0td9fdu E5cl0Mzh5hzMMm7ugN+BO9qyiPFYouvjTx5+8ujRQ3tH9w88A80ivbzb3Qa+puWvaXG9d+f2rRsZ x5kItUCKBxmBLXjHzRindOaV18tjzaUxodNo5bdfRruQh8TXV8feHobpsa4jy4kWcZFd/EA2XwYb sp1eM6iZdNhgsUJCzaUm2Go+OMiKJVtKjFqf2GzBzudaJ3P7WSpyTJf5gJo4/kcf2Rvp1t8/0Nve 9nHrzp27t2/fsV4rDQTdSi9n6ovu+V8kM41VcxCtzTi2ZRLIP6jH/PVMdpiO1b/ysz8v/Qk+s6pV VV+a8/Kgqp02RF/XVvUleVAwqC/1+ZY+U4NAjSuLg68NaQd+cnaOTlsXDz/3+k+DtUURDJcHV+PG bgjWQmOOFjitBniggSpGQ4Sqcpo2rQqgwf5GtbJ4daJfRK71jhpltoGYmBx+kKCBjNSQeayLMsAz EEfHxGyRGXyl2TJ2bW6rUNj3Es6Y/47wCE3A2hVz3H/8WJbfU+u1WXszt5qxUPMwynfNc7erhrsG 4/Vr12XvGZquiuleRcYz1cWzUQp6jAW3Dwq6Pl9ffVrmfosx8bkh5rCX4i9WAw2gE+9Bb6ZXPVDn YKi5g8rUeIFblWhl6tjoRy56ZQUhxgSlOAVCSDS3XHNpQZlVMpHW17Qea6h0nT2yDJOw+EDuOFEy YZS6xBrPg9yKNy7aFk6Sjsuz8Q9mfdJReM+tdW43OMrC3dxQUam8CMpXa/ef4TfqmD5UtknsDF2Q /ZUrq8Xy4r9iDoJRtEygnXduxAqo9+pp+0jNHc+G3lmIzCiPRMX9Wkr3CImjqchOqWHWO3Sl4Z5Q 0eI90XyEg3ZQCDx+/E/+af1dnZsO0xiXqYEX3OMVO1cRPQII8yL5mtSweoDrDabkP/lJTuZ8M6jj w72nB7s7SN/PhJS21vSs3MZWYeR933+6u/P48eOHjx9JaHgmI65YFZ5UKBVwa1crHH9Gr/ir+WLO yTAVYl7Z2sDSxARcV+gKgQLlveIkZB0P6Gpjw5hQfliG+By6FVAV960bNkQxKOUG8iFvZmxcf68H /nmkEYfoRnd3RUPgHiLWVoYMM5TdFYHUXR137t4guMbrtjza8vTZKN+KjQRLtL2ttnR7C/zVferN YzG7NkyLS4aAyMQIWhmnNhwCwTvJZWNBkKXATTzy5cuNbsXFNH3KbzWBrVKyYgmCavHbaCjojnUr SCRy7VRAg60B2ickMVRUl2p72KJAaZf4WewFbFBV4Iqpk4lZEg3GGrbA8OELiUYROt8ZfXxHeFY8 RovGFZWR/ApsjR+6FzVLYpdifxiScDVkfTaXOT7SbyYKLD8OZcDcizEUuKKZq7mzYgHiscziVDSH RSZ3U0eI0NAWAgW8MrqEZ1aoGSw63zNYF2b70E1KHyLhSaLChlVnRL/NZDDMKqzy9/3RPyYhTguu TYzlFb3GBHHP7n9ZEWrIczsQTICNSZEHragIw9QY/iJCESYXMPOTAqRn+/tPCFSUl04asAi0fVJq RH387oGY3tPHou9PHssA1GTUHYkP6WqZmMa/BlFIN8CWP5dWqK7tjbq8pFu4Zie8vAz4O5uvmtuv IZbHWTFN9KH6zFKH4Q0dC3OrIlWBKutS4YaG+Yq8AKTi49OP4JVGnPqBUC0fDV/wLmUZRF2IAKG/ ei4gJIg8y1crvLKWLpjC7nMz4UfD6W9pJO/DoIGtmpyrs4CNzFdYBhPHoQ/FrNbt3xTxznd7Xayz +q1exaVe+LuhCnEhINTXcxab/q+5zqJP+FSmnE1dQ5VhyOEYcGs7Xpun1SM0JCvyCkPE085XG64d AuGx0ZzuXhDCrAAtvuGnWgCtqqOY3gErRGVjQeNWuhKs3dI07a4Wz5I8X9LB0Phk0C9Z7Wbw+JqX iAuVrwwKrTv1enl0rAe6Kdo99rs7uzULq95kbS7MTpOodYLmzZJ228miWjESxW6AoxzHcpHYIGpv Ag5iskhLUTVAMSnMLkPfsN/XDd3COy2eTIdc1epf/I///Pt37rx/++ZVSV+CQAlakpZOT+Xe21w+ 35I9uHS2eb7kX2X9CaFPjzfOT/W8n1k610t08urp8eb5uXT6TSlHikI4O1k6PRSL4mocbILtUAty jDsMOgfvZXYhm2zpPuWa15HITuGYbxfrD4OxBgpwQ8c7asjL+vWr29c1FRmpXuRNeBmBzOISQxj1 DPISidU4kbfHhsLn4FZBXvPPcNyYfp1EccdG77r5MCyd4VDghM/lfkqcimsJ/mz8xfEkMBIwiUjJ mytadee2gn8VS4KrFW3ZTUT4m0ccIGEcb4AVomamsubQ6swrmjrBDFlVV5c9lUM6MsU5zpdOUa0X Qqtaxpvrj8Vl6g/zrZtrJW4ioOaF0o0DN+D6dH8FCJlTBlj/S1yrf4v2BxR1ClGjIqgcY40wqGBc CbWWyTGqhrlVBqMnAs1dEIDw7GvooKjb0y2qU9XljhY6P0+85ej41AirkXPiqK70Ti11XIsuBGTS CoTtj2GoQ3cs/SQrH82cfzlolzJkQRU3kmW9mJwE0NgzGHuc0eXFDm1p2dLhqgI0MyFt92Ve2goW 84XTxWkTMYFAJXEUL5xq9hDM1Z/5yb/4/u1bipbfFpDr2ZNjfa2dnQiqDD3CryU/2Ja2pQtUoMrR kZ7cXl0OWgkDVwVwR0f69YpAR4kFCuWwx+ro+BwfBJ3RRrwvJ8QvaKWbzHiyWLq1pZH35LH00sfM WvMK25a66ClU0RJTj49moGLgblxz+kIIg244M3BiHrHcAFWMQg9tNSWs8RLRymZyBZr3KIyy+sq/ qZUkoOUlRSHrRweHGgfqaVk5HkANqmIde1HCyWRmgYIuj36PnZK6KfVUtMpefMcc4RaNcFAmYKAq /LMCJzR6/GkbkSinB03HFBZcZWn16CTygEmoYehWH0Yrz+RGb6JyIuM+R6xq0nY7cdn0QaOoxkOp lQ7HqbDDsAD4kq48PAm6FDu1mFWAL5YrEziANj3CsbS2MRKH0cq30ohj54+FAjPQN2p4Gu87aGxf ariuWnElst1H0UpDzDlnx4oI9xo/W90Z+fln4mZ+59hxAvoUOOqAUrmGWQyaOMdl0m7VeA3w0/9+ 1vIOSjLYpAhxI6MXL0w7vZU+ogJ3wmJ5d52gIazFmNURc5JULpYoQ7beMYNSLa72E36tfv2nf0YB gdeuXBU2KGpJzZTbMx+TDGGi1rz9WNca1X4zvK2xh6XrRo1zW5eSdHos0FziIuh7/xUcxY62sq6b 1wRLrodmhu5Qf5WQ/PCTh0+ePImIYyOwTPpoMTjsMYgg+w59SECNYuBEq5zpU8ZLWphuygqZxYAl Igt25qdZziVagiZSJIvAitwfcRbYGAzTKg3LDVDkC4NR5rmC1xXCLgiLK1o34lwzxUw5YAqA4tAv EqcqKAERJ6FGacAMxPzoQ7DPS8AoHG1byKc2zhgqShKVoh3VjLHT8IljJvlf6Uo1MDM8P/Nrhdjx bo9FwKCX2qQJRNRpRbo0hnm2epjXGHlj73tJS1aoh4rOiUSilhY1FXbh6kOZKUwLYy80piUKtur9 iaJUry2AVr4eHI+1iGYZJ/yN0EA3cDiOnSo69vZlZOjC79y+I448ilbShRUqrXHjxvFNoHsEtmtO +CMdqpJEroMDxRprjSQj7Vojf5jGhVNhVWX1tclV/Npk8EhrqtIDAjUVHqWVEW+svRz6aJkMXlwd pOHUAjkN1RcAjq1UjETLodEiE/ud1tbwFcR4Ef3b3/ib0jP0pgqse/jwkdpJN4f0Y6+KPgYAYhEn DkLtnpjU/hUpVE9G94bPnJ+IGy4rXtYkSD2ScA+ZsfoUAs3tl3EUtfMq7SnQxam9FL+vQyH1zFWj ld/QVrfnWFn9BLv7ixjl+LDFqeTSmM2kMq3b2KPD0tw1BQK3CcO8xMwbtYCjpcwWbZPniB+3pYvE 9Jslj6jfRLGUBfjw4UN1rRinrljDRH3ksQVOOaCTMHSJ6WpVJxXJ5dcCuFnKJnygkYhiGc1cCjBx KHRPHkN70MsQjOEdTsIxmz+KU6LHjf3uKkuTw4fQymtRVN1CrUas8lGT35rGHfsqU8gidpytVmFm dnFWM2EWFpYXy/0DBSGf6s5YxuvjQrq63dhMwGY6Zp4uilYMXQ9AL/0F6G4qNVT0jBK/ne0qH5eS FpSo8FQ+KNkKD957T0EAo2ilGFm5kDXAmMWVkGCyYxzHrMCTK6Jg2+L4SAuhRGQthnIfazixbjDl 6MhaojKH8CsWvBcttAgVLzYc3U7Amp6kbenNPMgPDxWToLMcNbq6JsKo8arxLNlH/ZWJTLGB9RAu yx9ME12A3hAAOVn9u9/4hu9mZdma7sG+4F3OXfy7jigwBJH+r77WkHfIAiidlSdf+tWAhDUqHZ9A WnWFw65IEfQABxyMeZp77rNSdf2Mc0l2dx89fvzo4SfyIGoqaraENAWP6lEFJHidRJzyvwgr+gjx zlWRxcYcMg/9vX3lcfqA9drTl+XNlnYtHDERylDPHAln5Eg0RgXWNYdJSErGeYYeb8a4NL7bPUr4 j27ToS5OmNlVNIrsPMvnBU22doXNCNw+cKASw8AZahO1hEaSv/yfFIMksNvRZ3xyzsAGiccSbuD5 ubA8KADJCGxaRPH4IhMlAMfwE7vSiPYbhBN1eJoxntYykJ+05GxSwXEHvxJUknU9MFI3ECOkcR0u j4IQNi7Sw1Epa5IRwqoXwJnQBOzvS9/xvh6SejbGgRb7xONCcGlthBKdlloTMX7d+X7e7s40EQBU j8I561sbRTPIdgKKh0jGE96QCPsz6Mc3RvAJnwIqrIjf6hI0bTWAvH57Gs70DaA5Lv+69fxVb5PJ rovL7LFFL4Ekn8bkLdvQ6Ti2XqxtiRARU40kqpRvqV1+JjMNAcH3QEtjl9SsQA2HC1nIpyxHLPLQ VL2tRzECtKKwLGmBPo6CXFsTm4uRGN9Rrj8XhAmZWesjq+3yB//yl7GSpDV5nuix1yIZnxxxqOt6 olgg0CowS1LFqjO7Mk7F7ZB/4kty/y+dH60vnWzUJEkAZMxjCFPCEUpntteQ//oml1UqTtSEqtk2 M1lgSG0KsiBmGdhYWdpYEWNsx6cpCzNSUPM2NmLNtlLjwxWCTXzTsk3v+Bc+QS+OhYs/rJT+LDhZ SdR7qVnjGi7KYdzbTzBCRmMIc4kz5EkwTCvkiuiFrq/bwyAK9SNf+9GvfvVrNcziVCKIgEnL/G3z J5M8F+n6CjKRIRG9afKgoOXlDSXBU8lPm7aaG6uZvMPMPHvlX6ev+szHMoqiHnJdAb9CwHpci0b+ Gog8Oycn8JBQWM/cFlbc34SYfpvaWttxUHEcSBR68kgRiKgK5mW4Bv2+akVWhGuKaZSPQuxfE1Vn JaxDc4zqAmmWyXXG/9MnMfO5L3XGXeA24gMDqeZ6EyQI3KnyITL69+N42X2qqiiPtawpM9dxljdv aZkikwY05kgkSbMaloy6vJf+awh4XBCp1M/P9Cm6tLycbBAvngf7Wkk/Vnznxx/hQb6WvFE9gCFt 9RuM/ZQBFqqFjeL4g7hbgXfjoJpco1+3oIMaTrsapcqgl02gN1czx0mtwRpff1o0PEPRVzbGDo+U Bys2Y+BQHMN/8Qs/ZzS2QrYkGdYaUqwq+yfMD85Pj/VYzzhYdN1fiQ+G38hzbR+4w/vdPeFWjqJe Whd+EFkPl8maktVADQrdVeKb4o9VauKh/ituVfxXjfL+e++99/571AJIOojtvBbDWIZJIGay3p8r +77C+NOHnyaSXJwxBh2HEZKF21HQimjnXgyrGBgsr0GW2P8ABC790q0dzqfTE+lpNv7s2c6TJ9yf b/OTTz42bCVXyK5lT54YgyxoPiryhEi5MqvPz8WgfuzHfttv/+2/QxEJ2HtKMne0Z9KJooWnORoF 5ILlby1DoDoi3XHx6Oxz+sANIxbRlk8m4uTrszHo1SrVS7pHC2ly1dqRKZUlofG9ZqcS34VnU7ep tqM74qQMzbJN6PCxJnpmDqid4ojQ35+5zM7jqByJ6tZscs74/l4QJdltFbW3vBTSG+spxCHLw0yP Z+oWmWcpzDd/FUCE+vM0FXEysnwF5Wr0OqObkhUvc16zWWPk1//tb/zav/mmBobpD+noejkXmAXU lh1rdvU+YoxUOYW/etZ0bji1MqbjJJKC5pmwQyTlk4cPf/03fj2mq2uWkIoV0SUXHo7pgFPCBnML 4UH60CR4RSPTnzKqs0YE7Bil5As7LmFf813DWXMoSJ0pYEJX1qupXGaCbTiJj//FL34960SUs4yO WpzrWdO7DI6oGFx3NbYDshIKhWxci4b+rKQByV42xR3IqWshM94sSoaRHjjBkjmpPhfuqV2ET2JV WkaEuHOvyXWifKS2Ridyyjzv4PHFOhCbpciOxxG9n4j69q3FUPt3rbBCEP3TkFCjuGNOTnRjGu7O rpZVq0POTdXSIfdYy4qdmzhA1Z8m1G2wFtEkVA2FzumsHkBbDkRQrvjtm7fu3b//3oP3VQ7jZXDz Wc8hMRvL5mmNC+csq028Bg2/cMEXEEI/+mlaGMioRDmtV0N58q9448xGLXFe0HOo4mCy0EGc0jix LEu5y/BOvgRIYQMwyRGJ6gyilU8h+kCO3P6MsRY2Bcpa29TVzRaD5x7Veeem4/sHujERECzPioTS I4DbMrlJU1tPdUG2s2JVjU4GVb86OZVWoWHs+GH7vpy9qwvPbYb7cEQrLDQGN7GSIy/HyOWq+uQp IPfzzLVWSDL2IKBmLdtvi/VUyw0orpvz08QGrv69bwitjNPQi+a5NWv1delJWzo54vbGOvAnRlQF y4k69TWnv3zJG2uiaonTtRDjuiWQQX0TVCklUOGixIPZ9S526EIJV5GJVcsBV/3IsTBaZdmmC3JX BVulbdXIa3/gPP1iYFWa2Y0bbhMcscJiBeWLIRqpTKMeOvRVZXeePtMti0aZN52eee3ZnI2wGMOo ebg/WKMEMK5yo9X1xnWyo24LwW/eum3OPHh4QOPXG2nLDMg3A62MVUKTLLeoQRmBHa3yS9O08lhZ /nJF7QmyGlQlPRuFBqvZlrwS1lrhWbW6eJaLJvHX8imUXhDMapZrWYc1q5ueVDnbrRfmRSvItlxV BlWDsu2SEw1Ax+JDncRNTFVS1w+m4IgzZJyIdEOHDCkRCbEcmTMhZV6ymyailgWholZ7JfOnlL0U tuVoAUCjCfOl5RlGoBJZo1Mu0SHkWUrTZSWtaDGQZ69sA2L3qClSFPvv/ae/cC5+p7QAwaiGqN5W U992oE70k5JmlA3olYFA5VN1pWrEOuXGtszB8YEL7Z6qUMuRw9jP5DeUGHh2qNoRCWaNZUxiSUK4 IRDWfSLHVYUESgKQjewIz6FW1smfB60yBGuI50ETRmrkF8OajHsWHN2WbD05NYRKxOUDUEIopxLt ikrqltWb8GojfvKBzOQh0H0pjlnhpqDwrWZFqQau1oOr7/oNNY7s70GwcthLoiJH2/PNQSurNBrf xWEwvYpaZaXB9GirjBHNM9BSpMKSDQNt/Z/N7VgDGqvqX/2IDsiauoUZdOFAHIgvtLGrCRufWLZT 4j8vWuld9ek4E6zKaNZkgY9bzg+U2dcKtDE2KBbkIIAYaGNHbDezSGfXhlVZQYc6kRYDdUqcRwNi 33UaAW6VlvaVNABCLLGyVt6QWfMeKe8HVkQ3dcOufB+OsnJvkfnrMAjf7d/7Oz8v/53RSu/vBRUV ymjFUq/55RhUfH4Kjlui+LkkS8fKyh1Jbohh6+AYqJJZeLak4XOqft61LgZegVhwarwqXFkSIFJe N2UASAppFUTH2nlxtIIyOo97dtS4rZLK0RfqrxUJYE+z7ih2n8Dq8SP98yHAEly5rKdz602XvNTg MKa0iYx/R4RgRvgI+sTSTikukXBpvCkrdpNSUzrk7lVTNdVjALKKE7/FaAV0MEOqNGCcihO73p1X /ZvlHcSSUE6NXXdH6ZGlqjiBztaAo232NGb1Gg1XVz3a3nb+PYoHbkM6Mqq5mczMEGwf32UrMHI2 yOZFK89VHy5xJz6oj0yaUVRylkCjba6wsR5yelgM+yI772QC9F2HI9aufIu4px3AJFd+PLPt8CyO kciRtN0s9Wh29ld0N1DsCQeaUNsHr7dd5bpENadOzrKa0iIEsTvdT+9l+cwSv7dr8A3+vb/zCxXU l7oEmDp6FzsmWX/Ekv2mhAeRtEY1SotP5AAe2qwTeMHJ8Qni0CRLkGJMjkcnU4kC7SERFdOZdBsi GLtuGu/7vO3bzvs83IrotdmRqiXdKzhz9LBgYVmcC4BVuVDwJJEqpS30Xb4L528r9Nw81x4uL4wW 4n2g+LmgkhonrRw10aGt2+JTjvG0m0Q8yhGeSuqzju6SN6rmo/p2DkbqwnMpjK/8EQNeVzDanm8K t4oKESu+on67q7W5PSdmPNwAD7pGnJpWXRkfR5nqQBBPlLeDtYSesyvc5TepX9Ihy5+ewYJTMTSh LyuetVCggtDWC/OjVVZ2OxP9WnLlQtISYxkjN9wcKxb3WMytcW7FlTozPAab7i0+IL2VbjquOjcI CVtI5kVemTye4Lk2fY+mUXQJRhBeBq7lsPSuJ9Xk5i4AlV9Beok+OiuQpQxE83zA6t/7278QI5G2 Bhz9H8AkNEuv0V/tQFTERMUaqNquHYsJPojDnfxc5ilC1aOnzz558sR1h1ttbCeeaY8ZGIR5VNXJ oypaRUvjuCgTd2x+LYxW8QTifJ6JpuX5s8AXPYC2aIRIDadA4Y8/+ejjDz/aeWackodV6G2W2Yof ZfFDxC0tkPXGgoiNC/gU/imrdQIo0ajiUTfsM3b8lDyhTq3KliceBBDjsSPzzJrr6PFG6FaetFUL hVmSGcFUaS4TWA3N1yaSNdWNNdWBFI3VyxOa62U3Y7gV8jc4NDHb75k6c67T7YU8GIcrj7JIXbmi l3MZmd7wq+eW33nRik4zKlk+RuKJ6WQrVcU5xSAoU5fFP06AhFEsYAYGZSzebUhuboU9QA7rP04+ afskVFu6cgPDm8IbzfwIw9RL6A1ELpcGNQLqd5DGhwMUWNE1r4hF8zgHbFwPXe8Qwd3vzgAOsKz+ Z3/7563yEiSVyEd8ozjyGAp6TjAkFUZEyXrMnqIcBVa7Vo/1ZfYEfXKOst9SOx+p2x8/e/bwyU5s P11oYpLUphWTQPG3UCpWf24bZE1Xjx6LoZXHQcFxeR8YgRWqUFw/XvCCrHqkCE9taCYxveI8nSTk YhSxGYMoCbLIEayBsXrwaQ10WiRZ6VRycQayXYyKS3Butuu5VFxCRrxfTE8OHiF2bzNaxaQmRLuT qql01XEqfDaHhp0jare3ZAsQguusOq25WfTDEbLgs46atAnGZFPogxzk6QSPQquEdIbOZEGCIDBT a+2lbBFDYJRbmUNhyOoiZBAhHku1sa9QFo4+VxOzB1UkA9SmFoEF5ZAbmUh6c9/wuuNCIGeeATEq Q3Yagay2xH1na9Q6EfcXRubXZuuWZjzqTRHpU6Ijs6naTS2p24oOl7mvS6/YEIwS/RWANh1b/frf +uuHpyrpouTCkyNxv3N/12Mlsx2cysA73lfxl4O9nf3dnb1n+nqiWi96sL+rkMfdw4M9hanOXq7X nhzqS6KVGZcVO1b3mKXky1AezwsFcWuE2AagK+x9Yt4PtPSCaBXGizkB4EeSyMIZk6D+tYdeFFwK g/KUbr4SPCeNPbnqmJdhqfCzU3lyDFCaKy405f0bKI3n6DI2yapI9PRrpJA4dzApZvDXcfCzH0Rk KE1noDn1eW+CT5DuU8s2UhVJ6rl/xakalBhGNMGcrOGwxp5ZwRTDAAz0IFnYBomPIkGChjC22Gra FYScTioCl1W3VpZasXpvjqJVBo8hUqXsqHaf6HCWVJMA/SjOhaaUZakWp2GXIA3H4eGaaiUU8imT szS+KPgEx7k1ksbsq8qKnBHbVHmP3IQaZBBHT9Nro48QKSZQS6Biontd0gsFqxQMT7cKlz5e/dm/ 9VOCKocDGa3al844Pdbze8cHzw73n+4bpwxVuw4f2tnbE2A9VSF0ha0I5E+Vrfjca08OcNnTgFTt 7gH5YuE9bwYg63TB14dmZpfN4LEgWrEIUMmg4VWcqwGqdlSSZNAsMocJ5YFeFFMv9vb0krv4aqhC AcyXSrko9MFRvK7kScUp1fOkdL190pTf5A1xxpd96g4O7A9Sqxa/sgBZfWPQKokBcNmq5/4iYEXX Cqa5LUxVCJzUQFMPhgtQp8XuIAwMoQA75UiuQiggo+0ZaXeuwtbHTFE7+r6vHC0QCHhJRdYYLXXM awlGgNALo29mUfenIxLrj2KFZM0ZxYgVi/ZeEWGDc2h2cZqCM8gFH1PiPOpehLvATWJoa8TaZVkT Sudwy37PDFo0XEI1q1Ca05D1VzNB3l48Nyt03K82AAlucOyu0mOJMV39Gz//lyiHF6O9KWCKmifs 3Vq6BSqntmXnI0d1EiIU16k+PQlBFd1ruHSLSaGSLz7z0PHWbAyBZzHrBSZ/wmToUg0dKjGsBDZG G3oxtNK1OF/IRRESZtjy9KilzG4VJOulCDEhuUkOJy9CHNQNXewpTtymNap72LABW0/yOe69W7dv P9Au2Pe0AdqNRKJbsyuu6xyR3HuIWLf9Qk4xlof36Yubmxk6eLwhaAWm+w5LK0KdQnquf13D6nCV MxORrUYhccOzTo0ci92uK6oCkBnns+1lomeck8ZmU2E3KOvIvQBVpmVIQb5Z06EPwgFH0Srn5+7C /QBHeB+Bv84mWV2tgBiLzdSTQpOegOPcXc/UpPJULNlOFD1NjSk9hqvtZppJrVcZwgL5VNQxHySk 06M3aWFwrAB6sBXcr8sMK9Pfq+VFYAWZ7JFMgOZe6MPqX/sb/0krVolQT7idZyopty7n/kwbC7pQ CUhFskigqjngG1BVZaCAqB3xN25OoKpcFX1ChsqCVJT6JxNSd0JdG1e6GDoWRKszefecdYFRDFSl SkurihCEqqIIrahLsItOLXeQCZrVyFI38aN6P1eHI7BnIhkzAqvb96jk6YJTjhVOYLR7qBueeR/6 kDj7QJUXTSrnjR5N6R1qTHjCG2AJhih5KkcoYuUre2ZiDgawImYx8cWVnIMmqdo5A1euJNJN7azZ iOziOkLgTohz0M2dAoAVcGS1Bq0CVV6kMW8Qhmv9LurdxwYdMS+3yloeA8pxT7Z5JXpaw9L4ITLZ 6dbeFCs17VyA1xvWR0Ia7XSPQ6I39JnREJK9wlh0uCn2AWQjkaKp6ufGCRqnZFDAqoo7pUEq6LxK OHgdIHpJhfIwDU1eHNXFYuHUaP3zxUiSwrMnb55vX6bMf/LX/6fyuu8pEEEA5S9rVXqgQCq5SSSl yzMvrEr5depUspjJKNFq5FQi7V+zKW+fHuuLEg12/iUSW0pMl6jCLd0BfZmLZMW4CJTbn0i60GhD L4ZWalwxRjkOgKgqqQM8hU5RgHiCXZBPn0jpBJNTp3DDqHTBTiEi3hXzjrh813JRLLqwSmhFjqi2 +d4Wy0VigAcEm5IGkBEWuQBNLw4V/2C+jaNVtOcFjjcBrbitgAn3OMOqQELBQudawTWwx14kTQf7 fZwI5VJuLBsELuPCoxA0jqpyyEJqsjd6660wO51c83EarhL5GY6lie11DKF6DK2YL7i9LKgxLLy8 Z2kHTkjHUzBUkJgtFHWDePMD4CNH23w7c7LoIqJEtaWJXjUiPMh3Ff3b3KfsJQKVKC2WHRJwpFEE Iu+Lrer8XBWHIWwtI59FxR9lC6xtMVMlKbXbBYi5+mf+6v9E+5FLgVJNigNlyJzou7/kkFdugvY+ frq/S1C2I9RF7xzdoeqMQijtkyMv+5UtRSXogZ/ZNFopG1L1n6+rfM3VGzOoSixqLJOyT9zFxRU0 IDBz7VKkVt9IG/vcBdHq9FRQrGB0jMDZ4eoh8RUZt0K0pn8ntm3PeB7DLYDr/USdMeMSQUKn2+ZS /ma4uskOitevO0lQg0lXXOJseDPTpM04ejRLW8Z6XKWo7JHa5/4abcbZ+W8IWtWcLMSK6y1HItqr VfsiAPTEx6oxZonaBmBaFsWXehZWTDTBwt3w5JYlEahqEQv1rqV9hFlh+0Qkrl+8n08CAsbRitUL 6l0xBOp5J9VSsTpqtIcSwVc5TUfqYuvWLMkPHYEfV8sJT+1wFwcnSn7xTV8VRph0a8cwJdQyy7bs U0rVZte3FqIETBUKtCGOz4r9rviANK/exGnFIkvYc5qWuqaaEj/xl/7H++ZWaFfkS7PBjRJqDF5U q9zXr6fqfb0f5qbzu+VkVzCCv2tHZsrhSZEMvSKa69r29Wvb14oHFoPKFJwdGSK2+yPZwEKxsIZJ 7GJopc906szOTqtwVIBVHCoyVeSq2IjBND+pwwWINfJRPTYUPoaLj9JTmH764fpTKuOp2niO0deX 6tq7jrh5O8MiyofnVo2E4t/xlwJTmDC1oI1zq6HBOj35TUErXE7PhSmETNXGdQVYkJTCKs07PTRR UnObLeQcfasKQBEKCRcAZ/ryae0oykwLKonHqwKDWkJtjPe+2ujzbGAaU3LMawkaKNs40SSBqTEB QVkmsOtlFkknoEETWAuw/i43sy5jrP9ZN3V7wamoVzGHKqQgbJ+nNIa96/Chd7XSXPCyTZKZPpS9 ARxO6Y2HFYvTIsXMDcG8LrTHWZElx2RNa4jR1gVFU/wAddhsQHdq5eRP/8U/mQI3xedw3sNxZgKO OTBqWQCT2E72F81FVVGX2plObekaxCJgy6rBUHKdrUZHr7V9vqlzoovg+ihfSIy8PjKUb6yVF+ZW irl/tqtI9ApZSCWJZDNOynA130PFvkZwtenukGhX65HNZ4NPVdC1a8OdO3oowJLTzyVH8Gt4GLHp pT5OhqceakFwNR5wyoV3bDNX6cEs+IYqckpZslTZ0B1WVHTuH0yNYeiHmrwBupUuE/y2dOJFzyjU 0IBJPDMHy5gJG7Lh5goD2mPJsUtl7NvPteyoBf3TqLbH0DuAufKUF9EcRC50gz2+edJFABH+zw6I lic6RUFhQGNo5cwHVkcNBr87vnXQYlbiWR8h7d+wpT3Zj49UgFcp9fLhSHjQhBubR1GRGqEL09Kv auFgbzLkddNqllRkxjTZMaaIOZ4nh2xD0JD9K52KIaurQrKqO7ikqELLujfHpTse02uHWYtKDx7s K+Q6W6jq9vO57DK+sfoTf+FPmCdk48BCqsQaRaNxaUV9jtitsE1Uir3frctIQs7e5OylInLlo6X7 iEavreh1Fod9q7G0UQdtyhomDVVytRhKs2OCFH19rCfl+Ba10hu9scZnoFxkjIuHbm+HAi8Jr3IZ yRcDGNq2fwQwJM6miiUIl3X/aghxKaOUCrh5W1pBlSUrZ8woKM5jyAEjEv9UO9RYtbfrQFpBlRO+ cIZWw9RGeAx+15m0+EiAhcY5raKAYNahub8Aq0XQSgPvDagYE42pMmyxpBFZQldDYQJZ0UT4u/mV hqkqOW97Q3KVIXOFDAIXXG9XawzlpVSb3DuTw1ZslbCKIbMwgTPfUlPLTArLsaAKVx0exQQNmaPh Q/xMtMK5zBCtHtOVklLnQkN6vwwnVP5gnxmH5eoNY6s+SJenCa76H9/93ndv3rglZ45e0ibEXOql myrbwcdvX3q6oSSleoFFR67r4zVlFSogcFT+mYIHiMBycQjB05WrSnTVFlQOJdR85yWe7CVPTToi EY7GYmr4qX0NuKpB+OSx5ombXTWpuUHmyNbyP/iH/ycJxhiHCVWty4wzjFXcqwckV/PKVijAQ80+ GBdssXbeyRWpzdeXVYuPKnfuLEet9ZXPzZCFanY4bF7/9KaKexD6MbLKvdNGWb9LhuBUPBb/VBhu 65F4Q60FpVfd51iZxR4LgvVxQg0hiI6+nMSZ0rSkrilRk7LGk6eB1g6RKYV5OjZEwO/E7GA2pdog zLSDlbgEmLpjsCmgvlDrZvZVBEjaimUzF9++a1xKCZQxqTJyY4evYLxyCAzE5TguNPLYJ4+eTa7f C8vJK9+lPDfgUgYvKJ+QqeysWc1ZA7OwW6eJsSrg03G+kXvUZ1rDNcrTcVpXPDJqgTlwz52dqhzQ J598onFEemtVbUH58K6rqJYuoaHDNtCG6q96GHixX/UXcBS/SqW2+ErLz0YIcVSx5h3TwI3RY06l tSrCQKPL5X7BjLL7niQTyRoiJri61hUjA61wgU5ZABhb1aWek9CZLPB8aHfoZQqX7E0JQAvj0SUc lYYMgpWgbGCHSavuiOq56bLf1/He+/cVp/PggbAK7UxbYhnp273lrQ3dMhswaRXm7vg1LRhc+45K V6rokhyBgmpdGwRJCzkFjf/JP/5/4It1MK4BxRPTIlIiGAxpok2rVhyzEVNcey10YgIos+XMT2L6 +zqKjdNJmYCYuLU/LWhCmVo6SlAoSPaaEKjKclnYFFREJm3mdEDNT+CbppqzP5Hh5708hUxuWqt1 btaYww4cy2bELh4USjaDRrrvOZI2W5ryh7sP7n/laz98597d2GSIhygYllRjhjczpJbMWlcyrQp3 s9L309u9BjoZlEUM1OVyZMg3/crZ++IJL+OUr3qbuvthe/xV7/vZf1+EA9JXXOeEQk5uuROt4lt8 huRh0SXtH67tJIh6dM3bhI0UzFmrjg2WKGEARaPrW9/6zW/+m2/K/sG0cOiDzUkuAAvIMb/i1lIt XXBD9TZl7HgnmLNrV8Q4VPnbHZuFL9MhLKYmRguIAXN9Q5nDqX9QQ2XShDUx+PS8Byuz18Rf+ze/ 9iu/8ivCGGHHg/ce3L1zT7Q/Yij008XRAVkuCbIWybgBYvG7at3ZZ2OBznhABZ395m/+5i/90i+p NO4P/9APf+WrXxVkafMLJ40TW0/wgdJrHHLFOmILxvsSHB1RwfmOZpv8Vlo2JCJ73wJ/29E81SkJ ZlKvoYCvrH7j5/6afkiys88xbJawtsxg7XziDFt2T7G4h5HSg95wwl/4yvbgBBy5dl+hihlLeerj t3DFUQBKzRymHtCPWSSG1TyHAShfSYakZ3ChM4tnCKFtZpN9oI8EbN+8a0LL8GKF9KFfnddIgXRb uCwnMdMYkMQiMOiRI2gQ+GczfrV5kYV0HRao7t6Voh7jlyhzNjEM8GWd54tfA1+zeA2cI3GD9Jdw Wr2kvYq5mNfK5ejNI6ud8ilzfw3Yjg27n8frzwdEc7167tt5/sbz7rOXp8kuHrR28/aol9XtMgBZ +AhK4B2yZHp4lqHBn/HHc6wzop5pCOMxJNQIY5MOchmn6O0mC3AVwqBOtZJq5oi2NSsV66MDaa69 9XRbvXBAhRzWfgovXzxqGcydYBtqTKoyiBiKrtOuwwp2on5DhimJEQnU4Cpw9mBV59On7deH9KwP +aSYWwnZUWkDWaCacfIqSR9iJ3BAvEiuJmXMHfI5KrjR8VOpb2DTMpJgMsUdJUU5BzdwhT0ZkaWe //zP/pVa7Rtjyf3HZskFmTKMHGm4yavKisqP54+uM9h/7M2v9YmFTnxoM9MqFDN0zEOh7CqkJqBP 7VA1tl15yqRYHFXsUlgtG1ts0QsdOI0G57J/4lw6ISp7GHh6wtzebk9XCBRCUdPUcQnS06VSKQlZ pF9NM9Iqi5+LMW5MXPwt3r1y0gKIudKDcOnU8zEl862NuPbHjhN4zoVxHp6R2/Xdw0mDlp13NXRi l8RJZQPTLmPZcepBApSaHfHiLCjVqqwFY6DX0zYshzpQtoMQl0wJQaRvSyMcqT6sowzehFyiwzoD ycEcTb8f+jip7TKZdbOaK1rfI+Q1TEulIyflRPzBpM6+W9rzOI5yst/gsXDdMBozB6hfgbnQ4/VC K5nbSZfr44PBE5YLF4oa7tsm6r6CC5IP442AYurBpTieKWJM1UBMO9NSun1TGiI+9N5aFsS83KOU PQqQpUagnZ6WIZyF7F2xqD2VsClBmOTNKaQO9e7oye/QarTFPvt8TRyhlcZSqHsGmykMrkUkm26v 1zvlHJtLrPywsNpDIVK3bRKqPrn+jHy+aKV6a5sL5PLJWvE6W4YfkZRlcNSn8bEheWX56VNAq+Hw Q70oIfV6s8x9zZVUg8HFRr4Oiz0k0tXcU8ArXsHR1iasXAGcqgetcEJPIhOjFLEIbKlqFcZmgkh1 p9guVDfwlRAXEoyKWVwMpmyTIr+vHbcCrdSmaa/Q1BR3RJZPtBPhTi2JD4fmoSJYtcOM9+qTxeeM RsMTprhdbyWb0xSEtLFhXyowa6k8P6fEtdMvEp6R4CnqJGD8Yf7lsanWlauKg/U4GO/X0XGQ89+h 1WLt9mmvYnbYVmsyQ4esjDnYVVMeyxQC1ohcMS/IacRzG49sRpApoknnUXXgrdiwaMqo0p/lA0/a WQ6PSiZmuQEmHoYmUpmLRYcavX1hhcgeHoPKGdK0Qa9oijNFk9v4rWA/g8JCaGUAirTkbUpdhdhp QNSfMXK54IL2LqCmFMRK95q4BGT/VNONq6NJMTHjE1+YDSlYIl4vbuUoWNyOGTBNR4hNC49Kkh5p xVQlTRllh5nI5suWDYGqMKnYnhkQIWZ+MR5rK+9E9CeAIHEYCSKjQILTZ1zWheoueFb8D0P7iprf yuF4zP3osHuHVou12Ge/qiJDzqReh8XE3Ggcq70YSaqWi+im1lN4Beuo/24xCE8dySJZCB2FHQoF WvmvgjXRq0ZngLK4LvFbdh9BUb2apJ75CZoZbQRZJxrDQgrWeB8a6sjIvk3EK9QpFB49SiKf0SoG 2OBhxdlRDs6mFkkwldPOpFQzN+VEWkk6G3HRup1lQgtc9MrN1Cw/N1dSe1q6SzU7WGXC+FrpVhaM yCsiiD/8MF6ZhKpW9YNwIsvlCigNt9pzwS1XGW4KVHSBLpRF7AKtHO+bXZDVKXJJfPnLX3Z+jFJl JEs5RUaFhrUvo8PbbJbGDs/OQlgJeheH4SgOa7yY7OAwqNPfcavF2u3TuZVxwEpnpzGfVXE0fjMf 5gB0uoaERoZno2v42kJsG9a64EykT005zXuy5bVdgoK8FE/UwpCbIAWiIdDYSdA+iLlqCR9TYAG0 SjZe191xjBk0kLGjpIghkqSdHKNeJ28cqtQaulzMS5VUt4gu6NbH6NM1bQzoIHvS9QOOqPQEP5tD VJBJeGhCDpKhkzZhDleMxWuGVuxFqqbN0kRpgxRyqZ3WifWobXRMjfJQ7r9da1WueN9odq2EjIBi 3VjODpvPlu5ebVYFVT/6Iz/q6HNvuCWc8saiYlMiUUAVgayMPEZPhaVkjXhnCX5/QeTS3g1OI97t Mn6NXKFqv1Ai2ZcEcMQoictQUy1JHcR/eudO+5u9CYFJvWZcxrDFmpNTKi3ty1Ol9H9jVASaEoyS 0Fu6eqZxDKBc18JoRRxkAuzts0sOnnDE+wa4HreDkwVSDk7C8W0ghoOBDy93Pn5G71DHwNjtCcsq 7qiL7McZjz/7TVRj2zIsRttUPLaDbJFRyfJpMVIY3YhrusLXC60cmeyQkKo3VmZfMaGJ9We4oT5X 1CnK29QWFoG5WTB+SV22Cgm3jQdH8WYy6BTs8bWvfe2HfuiHZPSRJ+AjNiHx9onWIM4AsxkAbeuj hYbhfl1sQr7jVou126e/yuuXJxKzM4E4L/wrGkBkRGkoARcNC6XtCAhChkSenErF/pj6cwLZM+dT cVTjUwNKryjJiq33CJPoF1gGUKL2euyeTrL2Om4JUtST5DoqSaRGfMKh42UUq3HdCedyWQ4veTg1 oAYDAHQP+iyyU2R41t4/DlHCPebPsrjnRmTVr/eHiRw7dDkfiuMwOZdcQiKomHZYyzTda2YJJsJC F9gUqgZIMfwQq/Koc62gkZasKHSBqoRcwaGo68K+HTGkhUmUx1Mtl9v3H9z/6g999Uvvv6/8IQfM UsCT1cjhArRtwj0MVYkEwZqE01ao1Pd7Hr3s/d6h1fe3lSNj9gqZsTheClidfMUSjKmG49i5HFEb UlIIV5cv0yECbN8d+YZovmOy/tfj0QaqIq+XnN8mK3xscvgd0HpGb5/38Pj1xi0KubJRptQc53MU YnrfQK/BwFPVhCliN56laypHnJO1dslzse8qwlZbivk2dQHe6Z6YeHtO7cA/MFZW9n7ddjgpCTDe E9DNmJAAPKivF7eqPiY6IX4/C+kTiEotzxh0iV5o+TtJOYo2VXpXBkQ8qUmnEocicIqyU94E+fa9 e/cVRVVBr6kllbWN9bSTp1Lr0U09QlmSFliFRoddzn+HVou122dxKxt93i++casS2nvcZsnuhWEF VZ7auL4ckpCIBHIzNCKs3XBosOlzY8RlpGocCq0Eb/EJ2gzEtoF0GAIzPwnWJnizHeDdIuXeiJ1K /AADGnM36oqvDcefniQIw2M8Aai5jAVGNZk5DuAILfIEYV23s0vla5BiSE5STWQHN+gCyHlSnXuC Ktyadc/NALQPAzdbjO/UTX7N0CpBGeFE/A+NgkolV94Bnh4GJVemB87P5B5k+xlcpOxn6wXQQQmO RfCmMkIoAMplPFXOhVJ5Qi7xqYr4KBdqxaAiJ8ycI2lNBmv+sTXDuIW/2Kx7h1aLtdunvspkymAR jYj1AH4x4VelYWW1KGlYg4pgaW8/wf6a5JQIZ+TIdqYou6rpnQJSMeL0zsIMpSRryFToYzne/Hkc LdocbZ44qUg3sxil0dtP+irA4XgrTauQRFIvUgzHW1EoEjGaiU6r6FA+d/TjDHFe3v1Nb4/tBl7D 5UJJ2evYafkUbnXmjQ4HYG+5sk2sQFL7UJkTZNkMGhrMF/V6cSuRIt+EUoSCU9SRKquP7geo0LgD S0CVDoV/6kU6ISMvBWpcIEG+PaLQfRim7PIDplx5StKVje0Kk0OU8hvW0fssbJlhFYrtlvXS/E63 Gh3Xr835ySztuhUPerBVjxStEsmRhxNCmfAa06ZDBVVZEtBIYyuQ7FO7pqmoEZxxq0GKlZRNrMwR EhsZKzDvmXnaeVkemOcYE6En45ZgwgfNp7A0QuGqkIMKnHAtrgz89CllHjRrDGROyOdKRnuJlRt6 6PJblNzKVhd18eZyhNEroNplvnRfbPFwSB64ckJSH4lFISFHlfyU+Q3NQth6vdDKFTyciqX6XlGs Gp9yvIDlqWRtchOzqOOsUGK/Wtzkz/PO7DdvunDnbSdN3rpjPiWEUrsIv2THp26ETbpG11lh4OhR 8wiHqZYyrMf4u6CpN5F2tGcXOf8dt1qk1T79NbYB1ZmV5so0kUTinxePzrZK12I6wVbwCauMjyyZ tXhsBBDmRAlxYv93/dTbwckdb9UsMHgCTsZa+2I+pupo0ilqcyOL1glTGr19NsNzJYIM5bzcMhYZ uBnJDpGlymii87P467M1iUY/rkxI1BM7HymwkGU/a7x5F4HyjvBvZS11WdlD0+Ulck3AeDHQXFBU PsfWosC/VvFWMv6U36cgT0tWTaJKpa3u8nO7NvqTezRUOavAEXFiTOJStvZSZlhQ5ZroN13Ew6XB bPdZrWjKQrSFpnZVtmDDQz4muM5I7h8n1zeG4juf4OjAfl3OZwu7MsUuXlPig6rQqP9Ui77nnScU ScAgkeOV0EPtUnblxbarcEYUFhZI4XfQ9CWtIgMqZC05xlQpp9oogNXEJv1Bk9XFQhZCK/kg9cnO BLRXziM3WfpWtYMplO4SGvTNBwQTsmcFH6OdFOUtWAXmJFihbpO5Fp9Dq2tBARndvsCK8t9S+syq AlUOamsitKHKLNhc0w3zWqGVrEBnIj91Mc8oUxXt2arKEPxLczSjPw8c5Pmlr7z34P279+6IUake nlL7xKTYEs48s8WYVVgeTUIbZgCVRoWvtUFhfsY0nDkEQ74SJPNOtxod16/H+WgsfIFK7ahHFSBp YlDDomGXgpK8hGpMuiY6O+W4GqXr5DlG1NIyIc3BpKx5aO3K7hIXN4XxcMWNk6KjOnAh2lhM6EPq PUS40gh1wgb6xtCB5k0OPJZmYkMtn2NQ1IDP5smpWXx0pJRaebMkkWj6DH2WTo484jykZpKkKUMe 46kX20hxYFebO7OLUO1mtLqmeui6VBqoeSFIb44FVckDCWN9vdBKgXRPnjxR8YSeUIWlT1JVAxF3 eYOJ2IA6vvyVr/72f+d3fOWrX1ElF0PV1euK3rMgKtMdlhv486JHanS8oln2PHiy+tRRNmagqozD gFTt8AFavfMJjg7q1+b8jlbYgMxpO/P1L7SKf13Har4WnSMxVQGWGgbeMYQdtqU8WFiQf01jDNKl 0zV1TSjYZwEZdlclGzSXm6IfqSolklO+uL4lQMqqM3ClwSf9dgG0IofahMSGJWU5HXHtiUBkPDIW bqhNDXC9v6Dq448/1iYM9+7ee++990Y7ysSHMtwmkK7nVQYgxZT0uZ5EEqpUp8F7dwrNle6mMq2O JXIwthow6B4pxgQFWQZq4g0mfCMU7jNaZcLjhqSR/Fu2k5Y2plIXaxeKSHYnCq9qbssEn1YpHb0L pb68z206PfpiRaXj7MsGhUnWy3ctI1KsVOhFAyL2l+E5hBlkdU9GDExaHzXqk4r8FcHVV74qi8+7 s2cf3rqVcnDYYczBNRMSF92g7sBglUATjL6+zM7UMf2pMXn7khxYMu49gYNH3B34cmd5vX1XMWZ0 Hn3q+RXKExbUjybouq8DZJa3oEJ8i/WkTvTAY/c5TSHH4kXshHRbZFR0aO1IDO2A28gVFqmKMUAE H7t7ZoIS0JBKjj45V9STBOOGLmGrmMw0Nqs7gOINMhIl+YYZ7c3HdEdZdzEnGOdiX6tr+QiFSeS+ 5CnXpEIvNrvxZPEbNSuV920x5zXlwxPBgRSZsP/RL0SEC+HqTFNvpOmpj3CVgKsuL2NnQqrrVh1p DB6+UksVA4e4MB3/8p/9Q029IEJit+J9zP5UNb/zI8ZX2qPiIvJMcRMco5DP5RVdka6JxBlXa6Us grbPERCBUpRwKVpIyArWnXdOTgmh9Gp6qCJHQpMDWnaypDWyEeHy/bvv3b/3QDcf6y2JDZU72lhZ RyuGS1GpjNXnBvXMNOQPL57goohaP8fRKgJq+/B5557aUxsNKeN73he8O+8zW8BC+fmx0pDp3erg DlwVUpdZkj9mgJyfu5zj3p5XcapERROvyOHAVerJ2ZfnBFavwNrp7tmzDz/49gff+5b63UCWUgdZ wE9Pst7Kba0dSOQOkuoq64g3jngN9ecSarF8TqoI3LYnc0fUPRJXSKxVlcIMsaq5kdlxdqbSVGwG oR/a9+qAmMUTqSiiPFXPzVE+zvFIi7YEx9SQYA/UbMDOFRD+Q42KJijr1+zSQUsc6c9uBNvRLgzl req92arlf3ZvtL899IpoS2ny6ia/tTcuVQDEv/qlf5gwj1TqItvQjDgad0Kbyt7uIAWUAicOkWhN aoSJGO1IfHLsEiGlxkgV51QxpYqnQEs7Frp+YxTu9ITNePbMhvZUIUR7jdmJJLyK1D3XNwt8hc6p dL0ql+vlY/M0se/jFTk0dLSCzBIn5v7UbDAwSsrU0OvaQ2h1cAuTua/qt9qJNn9OD2S6jd54wmu0 BA+9UC/517/6L/7VL/9SRrjGe4pvGyH2dmMqKFL5vQcP7t2/T1TgrdAorffeOUklg2tRBRQi5wQZ +BeZZCKVeJsMAZYdmImwLA2NFTyKRov/6c5v3ZHm57/55r/+V7/yK9pRXCbhgwcP3nv/fT1U0I+0 8CyxZMyYhASjA6Z5cngenZ9//PEnH3/8kaaDd8dJDIiNUx/CBqVY6nvCv6nRub76jZ//awnSjwvV tluBqF8Dc4J1la8iFLMxK0MTUae9SaxeO0hEheC/973vffTRRw9VcvXxI/n51DHZAhvL2QmlMcK8 OWSzvVyNL6Zcds/BnuRCC6ryjFc2n0KoWzlSLFPGazN0tPVq6EUsYJWuMPbC0jsH5XlzexWjNqV/ d3wfWoC5TQ2GwSPqZxTP+Q/NiH1tdHR4ED0r3EGTy8qRHdnEarG1PfPJSqv5Ueyv+IhqfiWspohU gMq/NB+RX455E0swcga8MMN8avTWM/UcZ+mqdmX7HB6KWKGvONhVbxU+YnOsybvxqlvhKZJY9RXm b5Oc6UJPOztqkB7NIYbi6y8qlPsgMkTioB7+4i/8dMypMjObnZLrCMHpthiMx2SGy26yNMZlD1ay onZw8N3vfueb3/ym0Orxo4e6IBdIcKFh175RO/E+KAHWHnPjbj51Toix9q1AogKdnocqR+vXLkjk UmIfW9YZtbB6N462sl/4Dq0WaLXX4yWXjVbaXcehpGJk0t0TMWO0IoBZUOUgeK25uqoK1Do71Ygm ZCplR1ci5HT6FPbR/AHBKx/N/IozbhitRDSyxY6YjjmCt4NnC8vYk6Ql+7dwGm88Y81Ftpc3Rljo ePJk58kT7Y9nRS/CT/Rmaephl9Z8sLdl1RrB/9Y3fjrYGSGvT/hock2oK3RpaB1nGd8oEZF91xO/ K9lJhvCHH338wQffS1R6i0FPAXV4JJV+Wh4VVfawOQWLqdnpxmo7pbrElLchNGUOz/JR1IpEct+R rfQFQgrecauFhtmb/aJLRitP6pNjlANLPMxLMwby6O25NqvSnwmTSZBBGS+U3dTkjMTF38m2qMCb KFbhTg2vYBGets3OmJ9bUWDEiTKab6kxz8WUz4mrJvkMpsJlMP2PbRVe9KrPNTx07Sr3qxiAVLUE FY1UjmJtUIW7zBwzYt/q3/rFnwnHiZFi3sQRsdAOUPxroFgV1mpufRAGsbzK47V6U5KlYpG6kEs7 SD/HARE5jDIvVbM4CYDHxyJN0vYUdx77j8otKTOVung8AMm8F+tkZ1ygd5aEPFdrVTfDkMePd9xq vM1el1dcMloRJKFC6RF18Whh5Xn3SZclILOCcAdNjxKmgkDZy29lJfpJZXGEZhE11YYub4y0jWsb h+Q4WsVqpCpWSasJKgAOZWZ1V6PRAH9dxabrQqBgs9C1uXr6/FxW1+PHT3R3YYXBHxfkwXZConb7 qLGivRmt2n3yecxc4ibMgPCo+smEwcfH15lUyuGhPNrnF8ef/kk9FMGTUYrdl8ZlXQhyJR90VuQz TWzTVG5NhaB7o+PgUyFVICrbrnbzsHbGTbACno+5mui5k95xq0Va7Q1/zWWjVWb7sj1oCbGK2yj5 cfwxRQYSDIlz7dSuNM0LoozWEijd1PHS2ePOdFdAduLohvwknqBF4QA28+hWerMoP/ECcUV2QxUl JCQikQH6I6aXQsy8lzuxF/guh47zc/nenu0+LX2cK7ePzrJOlRJ06BkRIOjhp7IE/0ZFUBHsEf3I lJASrf6l4nHNhnrZzr6bKL5POfns2rWHIz/27A3VvYav6QrKaKwcoapH5VgW7xqkXbOl6CkD2Vu0 C62U6Gf/pp2cqoUv16Vr40VsL4m9s6pYt3YFeuvTBfDqHVoNDbC34+RLRivgQgxFuxCuORjA8rWX Xw1aTYhUHhZQhVaEZ8no0J5ygoss1K0KUmdXlHIvI62ssOAUoTkTJ9iIyp5X1u7rgIIdEYSSJhra 6IiyH+wAVh3kgY4EXo4cOlvYIYKjFyUSoqzg+N9aTBWGrXMP9aGrf/PrP11+hRZ6AotyIbGkO2Uf rA5ULpEfR67DNAJN3mS0H3peZ6B55RLc+vqkkFmykn3DQXH1hPrPW/Zdd4KfwjuVkJxdcCFTfR97 r0TsvxpKZRHOnCphcxXdv4hN9w6tRgbYW3Lu5aOVYtklTGndVUahnG0a22IPhKo7xEl2BiaYh7Qe aIZoSmlXFAGBioho0Y6InPhTjsaqEt2YYOd+NNxKb82vW5Gm45DGRIYXNvEGxbBILUo4RDyqjjUv JjcGVbm2WV6RvaDgoEAQ1ItchQZl3Eqo6OrXf/6nEvEFTlbou3dJoOK5DjDJQWRBJr4VmarAMvbw A8IU2ZlynY6iiqnpCHoWAd2UAw/WXRVPfSBzL7nHyuu7d1eRcfpSfp+gatvGPKgUaEo2glux/cPy D/esTsLOfIdWbwmafNG3cdlo5V2HbQay9jqpkEXcEQNCKhJ+KjxbExWt5cSCyv6eRr32hRPGJcvX kxNu85yoDlQFNKL8+PdZPsaAJVjGCQZpfy9KuQEl5RvjhyNnsT1bHb5FOk0AYQko+5yz22ti2fQg MNsiyZDRU40PP0O5G+ySRJcKVAmPHNH57JmpFGRKcLU7wa0oVv5eCTQJ+VTQaosKaRsgq+nFjcRr lbGe8nhUcxFGaZN2Q5XwSjG0lEwlYp1sT+AKpR/vQDwBxIDMoEqPyFJ+h1aLjJjfgq+5ZLRC8VhW nrHQyoVliGCyUE10eUhMZr4wgT2Z7FVX0I/oheQRETGjWoY4EnuYT7hUQ6f87I6yGdMZ4FZFxrJ9 QU01x4glpcbbfJVbTJcjbNEU1OzUHfkcyNHYWOL6Nc9RclCccMElhBUbFGhGjovbcfVn/vpPtggC 5+oQicB2Q3Alx54/3dl9JtSSBWhg8g4z2foYU7El+CRQjTBP0pRIT8dFS8EtSVEUclExdFebEqmS xcfuMleccaU6h+zMqGvu2ZhR1xJaQe8292mFqJmtZtnQl+6uKiOMNVjJj4Mv4mMXkvXfRYcu0NTf 95dcNlppTgqq1rw9isNSq7BI1ZzJiqwhLvogzzhps/shL4rbUeK0lukQKzxWBgWmOXZb2FTye/ti Dk+ZGYHzc6umxncUdHqjo1XLxYY0lHw2b/yTzxSNIER8EKroVEceUYcrRiUmpjV7DM+mTPHeCGRn qz/1V/58tmygxrn3X2DTvtqiXVilgAhhVs9CTrBCIqRiTAP2PgreMThdauqat+Rz+qI2bZB+7jrD PlwbTzU8r2sXrCuqEEtXOfUR49HxqC1evrFb4xRYVkEdFsLaMmCQ9H2oK6uK/9jYfqdbjbXXW3H2 JaOVfGWCKimvmoSaWppijjBKLPT2duKcNVk1rZyR5qm3ryvUTM6+5WFhJffa/vLcjhZigOrpzjyM Os0iXlxnfm6VuWC9fwZ8NmIS7WWnG9sgIBu5MnJw0p4BrmlUZddF6t7lcNDbBCepYXHqXESqL8Aj bRQmXsPc6i//5J8l2qmKCCfGM3UR1GrK7HNRhL09zmB3GcJwgxchotHDcNd5h/ZUQzcy3bmj/CJB VQ5Zfz5cduqKSJcDPNHQ9FZ6T8ozuOKiw9JbRfqKICmfZUPgApgKOgnwG6rG0iFq2r1Dq7cCf8Zu 4pLRSgPX5pTqtWNImTdp7q0s28W0tRUJ1qWsYATxaLU5pdrq2T6rpHVCytsqnijvXnW0lfGL0wn7 Eulnbm4V6zHEyqyK3x3MhB/AlPDY4WB6S03b2LOOcg8OgF5j3QBa6ShszfVSbaboCJEcqFjZzvp8 9S/8+T+d3GNCFnwgWCmlUBUTDpSJLMCS/RevBHM7lZ0cE+HAEYVwOsnJOYlsySfCdEWYdF8lER48 uOE9RW8as+zzc5IBGy4670cXlmTIhIlSptn73CaFeXrb8X803A/c9mheIxXoiW027pd4h1ajI+wt OP+S0QorR/Qk+zQ5+cxqyYq3+VKAQkatsCAbZCbtOJVHDFUKdcTmSPIgwaWlVs0KufSio023agv6 GFqZsoVVUegJc8colbI2Nl+oNtUsQKeORGyCPBp5RseGDUmIFZBn8wnQC1Fjkx6TLaNE/JCrf+7P /snYfgFPkmf8K2Ge+8/2dlU+QaiPiF3QGxXcfEqtDVSJLAWncgic7iqL++69GIOuNyGvrTx9HInq soVKbDvxohUwqsYS+BGKwooQv0BkyLgHWrRpmBW/+vDdps724PEOrQYb7G04/bLRSohzIkXKvEmT Lxp2YgfldAqPyNxjcwDjVUQV/Sl6S1xWLgDQiE8SzpLUz+NyQ0UY1flxcw1xK02f1FuOVuWgy1hc YSqEhTbz0xeCBudqqrohzXAh7+jgyG2GziVB0JmJlPly6nErHczOsgaB1T/3Z/54jy/H81BG2YEY T6LTD5SQ6SbWO6ZpHLhJDkwSnYRE3vPDYCUPRkDr6vUb2lTmCg4FS3D5YMMitRDTbQIaoYsaKOX1 ALvtcjEErQKQELpGrxxei3GdI6Ef+qOb8R1ajQ6X35rnXzJaaZDKUtl9tqPPFRa0PDLv5+7AxoRM e2cap9pkwdVLMk+q2mdzALYZHXd5T+1PiE8VJNF7JC5+FK10PS59srERpLB30mwwdfIiKHcvvVMI TRePXBxVsKtIiwW2nyhrCP8mUOWwAXhWBKKKimWfekdXOQUJ2YpdGwATl82qUmJsQvtyoYoSntoO zDX0Xe9VRUt11DZYtxLh6dCSKOj6EJfkU81XDsVuuTSXmmB1RcG6chpKhtcGNXqHEKvConpQElwl 8LQwOe7Fa5HdwE1K+605/d7d9evcAhq3WvblrfLuxCr9Rpa+TA1dcwwZzQ7NW63XTpF1nuxN17Si YEMlqTWnFsX+4tyKBh2pJz/riMdwgcMytqiDqgwqYiBqMj5KXUYCoyK06RC50I3oKg4O91MMKgGu o4duwYhsMqRNLpw46ZoGQhPveLZtd6HKPZ9RpBO/3/L/4X/3vyYoAWUKDyJk1LVDdQTnylWpKhbU GnZCjFOM+SewMdWi6isbsrt0y5ryy828DO9YfC3LyUyomal9J9FEpFsN08elMkZuO33QrMFQrDw1 7R3/RhNuS0zLOaVrxblAv1b4a1GxomZeTMjn6R9Xn1D9P/vRiHU+XOa9VoGNpZW1cOXKzq41UvWP /GlJV1/1ALBxnqxxHA037PQdOfRW6zIdXGz/so4WH3JZn3epn8OOdlKMhyeYysPJ3nC00cihRXV3 98nusyeRdzVpCDJShd82L5rPTxIRkdZZ1EXHvAGUq5VyAEOKxjQBcc4/hg3mjDfdwXnlKHn7r+BZ SMPUEsA5lizdKDDOAMx71rvmvZNt423fQ14EVymQoCOKfp3jDWxacYLjYwGCApQU8q1/1qbBY8/l fBLTEUUsvn0yePAs4j6wY42tXlT1wdcA8Byb2eztKu4sVdCxo86X//7f/1/SMLv4+4xXxN1XFWfB nDiTGjiQRSi8YiSoODytgdCizKmtauzZ3lpTGBXv1KCl86XW08SMtOaaKOQvhpkVSPQh0u3x9oxS d3ToqlCxknpFB1X106xNFgzzdEQ7XahdlTduVGxbeUzdoSXeJ+Y00bNN6NfPdXHKK9dX1jZjK1MS 9Zk0Pv3TM/lMQlFWNXBEQZNFpOP991Qh8j0h/MhotwCpkNn1y6vLzorCoBq6zjflZLpUkDXsRTZY KatMebwjh1YvqSoK+cQ3VaGhyUjry2/JHfH+Wc91WXRVf/rwgw8fPfzEgQOYQGSJ2FDTxElNEhMR KhOnRHKqEnsyXL2mxbKqneDlTyglIUemA3gWFZzpupbxLgoOyFjZrzoECMUz0lbxQ75zjwwsnTjs mAK7msH379+/e++udhjWxRh32OEmBQwyARwZBimpElFcn8Z3KtQ55tJlalqkaGlHpMc4veZ49T/8 D39cE07XmAAq3UcMSEPTusxRxUrdUNRUtmwwg6UcqShhVQtzLnkPNE+WtniEwuEElVSY6UfQ22Ge 7Quoyt8xsuvr1SMBp8H0K/UZdLOBRCNL+Qqjxbu2IUdC7FJF6FzXSalScSsYmZkd/pfGMT2ibQk3 f4BhjhCz8+WzJRnRWjOdheRdxVRYzN+8GFLq2gF+xjrXfa7Uay14qXitlq21bM4fehOtAaxrl/Ul Tdd7tLyVXxFBO1uZsxN0WqwwjZChZrGDLZviyf9V/OU586Ct2U0biralZwUwmpWJWYiJEAm4syTf BbItVRdqNtnOURyTXpIrZomuMIi68aQiU+0gij3XluilInIVeko4QwXJQ44qrrJthLi+LiKm9VqY leRem2Ay4pxdyHglRCxzoCpweqdsN0qEfIN2OBuyXYw5B0lYeU9uHTcnfvdH/4M/JH4gY5CX2FXB TjLegDb7PjuGU1VmIuOx7wyJe4AZz+R+KweRG9M/FcNIHZfLOWhrti2r4hqxayukzisJbdHi2bhq khaN92xeBKqxcxcPYJl892+V7RgY0yEfhRptR9Uunu7w5YrzSkpykioGvBqbGtOO6lDoBlH70D+c pKQaDBwWFLwIXFZrZvm4zE8baIzvx6kh/OPEMUABpxg70CPGGJk+QHSKYp4UCMCxn5Ec4EtcaMul 9TTUs+45R5ZagXeoVCWbMBdSUCEeOPySgEK9MyELDqbXMXZvvk5lvzhY0h7/4Aqki0Az/vW9CGIV VYUoCSmV0R18ygXlCJxnEvZj9Q//+O9PHrLO0ByW0cfWs7Y/bYZqy+xN0SijpfGpwInaLUGsAJUt QRov6TJWafQ1eteLn++7I4CrFMiE/vKftiNPIV5SrNgUUvbdEO6Rvuy6XUeqWssif6FtBs7UYE93 93d2bAJ6e4xnrkERsq2z4uVVkKxECrcgOOVKExzelWgwju4dWi0+Ml76yjcErXDR23nvtF+5vCFm XTwxVWETnai+9r+3XZHDKjRaEyGkYZycnUyFcKMCqiYkJQQ14Qrzk82c6Q+xWG4yrpfbTLGVbTwJ QmBMIEk3P3+CmZLqZytnTrT6I3/kD0R/0+yVWaQ5ZQIgzcwFhz3HUmLY3LKqpFNjCmYVzKrNG8Ox YvUYFkQ1xxevhUcl3J4VBFdBsCpRZ/7f09NzcUEsglTZr7GYWOPMpXmFppVCGQdkIEuF54+kfgqj UAGdLJEoZJ2sps+ihwfC0Wi1l0eakUZ8h1YL9/P354VvCFpFNqoRS7DoDGiy/RYUCSriElRmImi1 NqCk4tvOqgy5MMnJOwBWTVaP3J3hbewYPCLk+8NPzmRJIu0b8qg66KmmoY8Y1UJcKb2piZDg/gG0 +vEf//1RneOedCwCiTJGK20lFoUYVApYZSsayFYRrjJ8U0u50MC0daEtrBYcjWQnFE7RKO7oGMyN OWMG5ij71X3qzHLq29Bbpc0nGSo4RdAsD1oyqv6kdC6bfzu21clSMjntNkKM4wTOktnt/xRuLsB/ h1YLdvP362VvCFo5ycYe9hqwRqd4moGnjCIP8Lb3DKGSSdzx6wJecRSV8SU61hQojC5HKqDmzuSO CiQfAawGdC4zJVPDgrgUce+bp9wVAQj7bsWAIepC0xL2s+FAqSP7++blVj/xp/5Y2JEzkMu/YGLl 3UtNqWqDZKhU/IDGKddwwSq0e76DFLY0zUG1wQWEgc8zHJ0ukOOCYOaOqtGZoLkIVGZiwhdjzb7E ppYJhTsvvyXWhZTv9lvzMtrDeuDs1Ij6kfXdzSxeaZrmMiXRyH4QU02jmApODBrJ7yzBzzMuXvLa NwWtPJZsRiG2VF5ehO7US8qAt1nB7n4ZuMKrRM0gX1QofPSq0oYYpmkWlnXbiSkgtQBUxRJEns8u MofYOS6UGiMs6ovZA3pMy0R0kRkt9ckOnhet/uyf/QlAaB1RhdAslOCURM+WfuFWF8y+QFXnUojr acMoZbKgcQlc0gFEsWV0o1P9g3vge4C9K+UJI04hecOSCOYsBQicivu3srmBrpx3Ioe0ZEU7C1uM PYIAwyFoVYne/oljI5R76Twhte9U9ksaFp/2MW8IWmlWJW2kJlrtOlNCdNwgXnbLUehERIK7jyIf W+Ug3iHONqAqEldT1ikKigM84gmVrUK6Rr48IzAxsuUVb6gNaMonF+gQEBIwAC0k5MpBnaj6njtz 6lZ/6S//BcckbDmKVLJ6CiToCWt4LspTBYYB99kRRb2OEBq3SDhqGtGVHy5tUM7243CvRK0qJlXS u+w3x5ucpC4Xi4BbNnuIVeRc51DhVB2uyIEophXadSTvifqooky7OzzGZgxmts7GpauIltZ6CQV+ h1aXNjBe/kFvEFo5QMiONleDiQRbcyyhLBB70KYtpXCr2l8rKR5mTJmqBGh2PtbsgllUvLcdjf9u 6EvTierBijC3pgQYVII0/CWBTZRtiDQD6QtUWZWfH61+/uf+OuKKg2IpcQ+lItg6LsmENaJHxeFX Nw76FmtoGDUjNIaqxWq4LDSQQQPHB5tqwo0T4dZ60U2jxlHqg9Mfk/3osGTvhkH9iUqvbsZfchkJ pxPCUSmnZV77XD0lsUv+E2xKQDk7L6VyDmF7pHuLoBr6K7xYIf6K9GUlGLrLd5bgUHO9+uQ3Ba3w phk+AKlgVfmMihUQYAg1KpEVLxvTgXqePspejBlYqYioI57jpl4Rv22pJXJyiFjpZE0tBR6yH0TK tAcSKVwj8PJTJlZavkMldLVk0jgrQKc0o2SOCIa/+Td/wXIw285odiVRUigEwzxxng0lwcpHyj5l Bc40IfO0onIDcL5Ct4A3+Xj1uPk+nRHbuAyu6r74f2FUHAcKXjGRAqpSRh7uWgYe+2XkSAxuMarO qnqlCIzEw2OfHzdHhbRlHzRVczZECf8TtkbjZhdExbIhOnokjRzv0GqkteY4901BK9Y/ICbmQhNm I3iUERNXeEWNJiLMAgR6TJLYjBwJsoxI00NUmbmJbMA2clqPQyIGD00qhfFIseJ689H+mKhmeayJ oVnANTmuQlxMHnV9OhJSxYOWihR8AUqej7f6u3/3b8bNF5wqtgb+yeatLUgL3dM+CVLzkbTKBI0n VaWiBwjJX6gU8BxD7WWnGDFJAihlKbwoqQoof7juCqpSrdlFvA4NVciT9bqGVg2wmsheQAZxtRtR df6d+V0ZPGrbyFXiVc5XdB0KkrccBIKFTXUKiJX25mUTgZHjHVqNtNYc574haJVQQeYs/iGMuvJx N9SBFUWQjVDuo0FGXIYVp9VmLgAFFuSADbWMC2omx1ic/5CiotQ9ffc2MU4psZkhBCA5xmilD0v4 uJOGXGTm2DvTHBx41qSi1Zy61f/sf/53cfP5X8y83Etc+CICogeOoy+LM/Qx3ziNSsdUjQaugvMu BaOk7TnGzffplLqYFnlARG5gqh09LMq0ipgDfBik0WSr17AqvpdslWqpRbPaKX5CKntDK38yUFXF 1cSnBEyJXDdQ1QFWOa28Nrwcuu93aDXUXK8++Q1Bq5pkEZ8IY0cAr9nNjAwHgSgYGhJnqPjk7EvI TL04DRtOGcbiFqugI0ecl8wzP07lzApccHqzrQeLbFRosOGi6oOACVxoXVdktDo5dqnP/X1vEEP4 4bxoJW6Vu6VBGuQm5vR8iewR6yyBg9YwRvogFEFKlUTc4ydlSDueO4rapRyCJvYRoz5zbDzCoNjw woAV+CoKRcookbWkKidOvY5mDcrKm0VahVHBw4ihw+Vqmy8p75b7VJLQiFTFB4NN3QCMR7mvWqNN 8g6tRlvsFee/IWilaZmlMpUMyLvJYTiAM0WEwawIOEErbDxyFIsva6/RDP5QuIeEkhhvWH+PixiA LHElTTVNDQvckbyjHIMq+iynlCjQwZUbXISKvR9cQ9VuqCG0+s/+879V5lsYU9nHvlU9TlUYNU1h WVA+UJU6MH2W8yD1d3SdMkbXq7r893mkvfTtlEiscE1Fpk00qYRtun6s/pUM5cQCaovRjt16DRqB +kQoVLJhixGdPVd3q7w9y1MOnQXPzacoBzKBqxRBTdhHc6DWCBhtkXdoNdpibwdaaRzGO+ThajU6 epKda3HZC7SSwI9Y7DEd4zGUK43QDaGcyASPz85WmAPN8aMlF9ixiU6gGTvsJD9USPpZVLZQFACW T3RerTWTVobG00nP68zEmQ9wq7/1t78e2yfeztxr7D4dbJZsnQVVqFOrCFW2k1Pagn8GKviWQy8F VWxJdEnk6tnus8ePtTfP0xh+JD6yNw+bX4QWJda9sccaz/q1/gBPnLGsnk3ZAnUj+AXZFZ5w7bq2 7XHRQXK/i1d1PoUTkCFFC6S5ylmb1h053qHVSGvNce4bwq00uRjO8rV5wFgNInJIOFM1A1wmtK25 yY0N0yIIawpYhjsCHmBmxjtimEEpB4H7h36P/DWGVY4OtXGnj8vAJjrU7xSJ24gAxEiAsf+K5B59 vN13VHAZQKtf/MWfI/7RMV2FVsRlJM07cY66hhjEQUun9gaanv83q3uQLYlGp+UcA+3lp2gb7k8+ +SRolUAqgg+IS4fuxYol4jz9Fb9qlW0Iy6KFfbT7muHTDNE4Qar5nTsPbt2+E1XKmAW5cqIS5eeD U3nDFmFsSq+PjQI6dKfv0GqouV598puCVirOt+9s+XCHFosNVbcH3E9mXsL5/R3JvVyEer7Rq1qj Y0pSvcFCRtJfkl0nUcMqOAZTBLH5v1jDye8RSp6w+Tyh61Rh8WYQFGlwUo7mpH4VKZSfXNJuMmIG 0Oobv/izkWr4MIS3OAlaHkvYB1ZeGEoT0gNeIZwxIMFkA8GqrmZDdQx7eBbPc+3F25pFPWGr4bA9 qKtKDeKhyGub8yNFp5oSjiD16NHDhw8/0V7S5d2L4EjMV9muDa6667I7faO/1d9zPVyolzL4UWx6 upb6zpubN2/duXf/vdt37qaiDttkUPwLIWvqWs3bJolRTWenyWKZN1paBxlZ3cpCr6Ie08B4/UGd jKUznDxvq6r18hCPSPjezP6Y78WO22Y+vxpGL55BaQPTCCZ1HWig7PbOfWd9ZQqy457nj5UjPq5N N05D3XK36nWqOVAFZyonhSgrFZxK7HvN0rCkSLv9SERAjK9KXkn9Bs70TPKfAMQIIDEsRBwEVfqT ntJ0UplT705GcEYggTq7xSG4fnbcQVwK7thE/bVf++exlXgJWZSrFMrh8oIJppHNt1B0qdnH+TWf 2KeHWnJ7Y317w2bwhRy92JIpltesWkgNiCfP67prM4se9l4jcivBb5GebCLjzfN3h24iLlFg6qlu I5Z9AmjT5tj0bjAKMCb9j0AEOqYK3ARn20vdk+nNVoyD7aLz3/d65apqZ9/TXhnceJVhrEf11AWQ 6PMp6vt4dOjSmsoqj5mPgd3ZeB2aKi6XNhbBOvT237eTAYFhFIhd1Ufs/FeTYL0A1vyHBju1Qw9G YTV5Exqo8dIErnrJ4NkMJTC8wgMhFY8eP3706FEWbwwnGxrBG72D/UKbW9SF1CbqN1TFQEksCWUm GFUMi8U1sm0JPNXIniIU+UyRu0wT+wSV6N8z2EJpMMdwoCeCyDNWbU6yhxxThisKfKYwcspi+l/U rlx14t1pcKbXN7/5z8KbMrZrflKoUPfGvg+65+NEcE3ncMsMD5q3iR24leC1JpVd9k7Bsi88t4AG n6Tv/N5hS+fqolX4QVJ1JmacHWqvePTs8Ds4UByaSwq7qJSrSiWDRpdabAgyFE4UipjPVLM7jJ1m S1Co3lKvIjtSBd19bjlGGo2i/iKlcqrgRCuR6sVB5PmqhPb5h2zO5C0dkDL6QgH/AuiRXltgWlbM 8zg+GqTHXzXaGjlf61DWnlF2hVDtaT/6uUGr6I/zHxp9Tp0/2hu+Trx7if8eOj7+6OMPP/pQ1beF kqpmJCCRxzwxiHofbxWhXWrItEvBlcivCgq8cU3gdR1SwA5V4EXW+FyAZxdBVchdKjBvQ0JnaTKq ZWydnbnEVWZ3YgVaWFF55GL0hLU4fFpbZ/HhiUYUggUWNFGVwavvrZ6VBfTVb3zjZ4vvJOa1ZfvF 6YkFfGLOF0inJlT4F4k4kfxmUFWBqX4f3BQVt9ZQqfCpoxb3FW9GbiHwdHysysvaSGPnqfSoJ5LP vVQ89oLxRFK6eNTuM8GWSbIUQfigXlgKP6pTSJR37IjYLpRSal+2BcOZmtR2gg8oNdHr4MQ/km9V KyeIb3PQcBYodFq8fBnDwz0xcqPwoem/Kh9OI0rz/1yYW2HXs1qMfFVAdGU7zH+Zi57Zgv7KJTTy Nt3GmZk4czxiVOGhGzq87573jhpCHNhDt8nGXur961Pljj0IEyQZsaZRjuTeE3resu7Np5nLkVNK 91HCL5wwGlFN+Uz85v6Tcs7mWD4zwkfpH1hRcVnGN9mP3le2O/UsVkusGz7LpRpizFTJPM0a3cln cyvoiJMVicsnPztwlaxB7rc/GcULyFM7n7pIhEWFuv4eR2pPomv4A1QTblX8iFcnzMTpMi4dFdMv XFdmPMYxiU3N6F32kue0I6S+0jL47BZ2f6Gl2upvVosolesGiSiHg0JVxSfaI3K2vOGYT15VWrz2 BB/enHYxbqVBu76yLPt8bMz2cJvRl/mFqs8/arh4UctoGP/ARV4RpULHS3v5s9+xe8CHPriiYYYt wXPtfa6YpEvjViq+rV0CtOTjUTzUwq/FP/kbIYbqJGwvV9/UQW7Yph5kj71oYeYNTtyvMITCl8BH L7mFBSPmJRak6Rp+gsRSfriJKWn7MMFPM5mYyeqdSRP8Q7ViMHQdZ4G5GUTPK4Re9gq0AiC8m1g8 aRVRNgGsUtJidUkkc3F6qIPyFU+PG1JV7EXC3BPsEM9bNG9/A2MSaWoaavrpTcRKoPJmQSqGblpZ Wn7WiML6ZXWHhCuve/FcYjMH4BtkV8lQW9ut6k4y0rta2KUBV1Awm4pNyLYg0CqHk0S7WllfWt5Y wDhbGK3ksFBZrKGplfU0I2PshXFDj5d+RZWo5XHsExc72zsz2c54bt1+5ZtlmdToe+WZz50AXzdn GXqhukBQJaI/9CpjyqKWoHfupLhIYg41OYxWro0k0S0WhhP3dCT8mxVZlbm3bgmrbt2s62x3yRxB 52sh58i4VW8LbnFq60W718SWamgF6JVEPPOvTyT7JLjpSthDmUig1KpS5j9zU+dGac468Qq0SkRV YzGFVskq6pwyMDWzIc08hVSH+gqtiqYOrpZqVQoWu5YWkOPbEY2SGJUNY2byYGogaFAy84JRidmP RKXHLuS5o90ltXTMFLHwuhAoARTRnN5gWv+jRwnF05ZxhAb6MPlm/3it67VS0J1uMvFQxtOq6Mfo +FsMrbQGyGbX5Y5+XNpidHZl4VB9stGPs8cn7qpLOTy2WPPippr/iGtvAXm++cTHWkbXeXyq9fby 0KpCnRU3IFKnIIh967xsvqVdhw0rGhWxMqPcx0iSg+vW7VvahxjuEROsTTYmR1bqLPBpbcOYdpGw 8zJl/yYhoZAGLKqAVnCsqARj7DwAaqKnQIrtLbnXA1jiWS64pGruK8thMMJfXfwr0KpbU6inoU3M 7JKwY/cxhTWlgr/E0R4fql2UY+2afPqVULUQqy6vl+cgNxODUS0rlUrQ0+7QClQnS7SOnbQEjKAA kWuu3/US9sJ1TWgInI80KHDjuggJNaDm4NVURdCbYF8ecQcUs0ae95YZqIntiPvDCl3Q6vR8+eRU gSTDevKCaLW8ouwnEbz5J2TOROME6IcO9Zd7TK8aQ4EM4MVCJoYuMCc3l/kweYwJOdwsIaqRN0YO jW2HT54N7yWzMLeKBWDKo0mo/ffYMdS7idoe9D/xlBg6ZVswcUVpbt7ykcmFsNsFESURe3J0nMp8 ho7Ya58QxZlsVcEM7ZwAlvGqzglaZZNXJqnTbO0oNMOys9IBjJtbuojIRb723b1XoFWANCMwN5Zv XYyvJyqUqrDWLtv9XX0VSsGw+tF8g77DRJoGrXT52pdBcVPiSfG9lw7WEZ1LiDDV/pdM5bWD2LNY f8G10CW1uJml8viSxAd2Jy1Gf8dLeICRm9MDhELBqFhR5uTWLcUuaHVytqSPGtwE0wN8QbRalFu5 QnaTKgbmlyazkiVkyw9OS/VLxu3AZ32+U2cRfyPvw3hE1bykA7nG6D92LIxWOIg21Dhxg8shKOkq B7UPTGmiC4dAZNZoUTdW3bzVt2R3GSkkLa3tATCzEqKSEkKNc6w9aNGXXVZPM5flFwsr2hA+Dn1n s9U9vY/eUxMse+apGAD7RPm7Zq4DP5d0pt2ar0CrKFSFWW3pnCBYwVctqUCaHotn7+89Pdh9WnAb j98ErsIMW6ZOZCtfvrSnjz/+WH7AqHhJeky+JSaGP6AFJMzKvGiGpGCV3jNXklzy5Ohh/AmkjFZJ 49MjK4zr2oB6WS8Uoev427SwMjbhWiQ61DZIcMdl7XuqCJpzLZdjo+/zoZX43ujH2U2xb9f16Asd ynfiuJChF6pTkRfsJr+co/GcsetM9GKnCfNfqv0rDu4Y5dSIgOM64MJoFSNC9+Ug8kMl/GuL9v3a S86Cu+Er2hZhClWNUkuzoEpCOyXtnKufPeWsKW1tRxKJG1AmUwvFgnM0KT4t2RlDMQs6KQDV/47/ S5agsdOlUFrRUaGhKy155y3toq5gpm0ow0rqFbwarTRhS4loY/ezuD79qM/ef6YdrHa8nhDJgLEX kcExESjtgazyCsb3KQBVnIJ4klWiFkHuiDUUpbSF61ElbA5nAYnUpSdENxHSEX8QSlVbuVYhT9t/ jkKLaK53i6MkIFRhnuXjLcdnnKA2EnEkxNXg3U+PlFB+SWilC1MSk+jd/PMqZ+rWvAdJdt+d+9CY EsodWnYcu0GNeq2AWrfn/qjPeWKj2INvo67XMJClP/g6qwoVOz3ySgdqSyUZj+5dHK3sXLuqwepp IoUflzkopW3lHKxYPEuoxd+IsnZ8uINFr1/DW7hZJdqcVWbYypyyuG6wLm4VA6+mdR8tn752tL/U z1RkSF2nCuw6PXNIkR2Uilz1BhHsa7ee+f4KtIr+5vCHWWQA8BkALSx1kNS0+4Qmgqrdp090TzZr O1iVVNXEpbICgTLYofwXCkkX4CZ0N7SKQL7yNOm0KDEJcDBgocJFSgOnbGxh/1ISwXzK1RES50mo bsGPvfTax5SALKAqxmfhUemMCTAFqcKqEssubrUvL8JloZXadm0BSX9pySvq06dC5JHJ5UVw/1AC h0s7Dr1QZsfOzlPF7Q696nOdnJyKwbfQBEhd78HXLaXSvobD0As1blQ7TkNv6FU6eWG0Snk1zYmk y2UzmphdbNbbDpUtAcIorGRGTAlJb3isVR6B1+8U6bvsQeYCcwe/ewIdrI+aZOUG0Y5idXUNadJP +TOHX316qugHx6CnQN/hQQKsNHWDVjaLtregZ+ev5lbOn0z6dqfdtkW7CTqxSxuiCWz3d5/sPtuR jBG7lTtD08ArGHUD3S32Yd5c4fYkagc+6ijl3sGKzertGnxpnmfnNhgdv+9apyom3Iy+gir/wUmL boY+YpLVROG9kzg5il+RIVlxVdCtYNhEtTO3ukxL0HcuI3dULF9a0tDcefJEbGdsnpyfq1iashiw 0AcOo9UTVe6R7HhJR5aQUctMs1EUQhNx9CrtVt7e1qI49EJdoEaf88kGYXVhtCLNflvTPtwnUWmG gsMDoYIlXk1OdsQMdiXcQecCSSVVafIQ7m4NiZgsp+2XA7Gbw2ACEQZiZ8exkRMTVB2TWdwCoPoc yxoTAcsJM+J8xfokuvs9HbldJZjMPOKwfwVaRXvWWyfmEgoE9jQpqj+IbJZvap693af7u3LtTbhV arMUvcqjwuYeGwWQtT9gKZbdCKLlNnrQa6U94/7IRhiOHNEOWNQabtH8SFRkGqfR6iMbWqaiBCvC DK96pmW1c4tC5Sz3x/FpVPYx6qEXLqayu1GIOBuaJDpZxEopABqgQy9Ud9hi2H+mDhh6oei8PLOK Sxx61cInJ1QOH8zYe2j437p1W7LI2MuWliLlaCwNvVADVcxefp1RwWthtGKTcIUssTYnzhIzQmaX QwHUu892BVWK+hFi6SB21PmPicysGpMUbAvDynwSfFjvxUiJQwpIkm5wjASGXtnWdhxXWfnLQoEB +H/NotYcMkUdLmlSrmShp1F5cMrblW+Zedu1ZTS1X4FWmZ9Fq9gcA15VMakzSy4RCg3NXCV+75lU doKtqqx0kamI5KBQDzXIuza7w0Ov1car4lQ5MxeTqIKk70VH10HJlqvqodyVAQvwco409QzVtjFr 8rHJ1Zy6aRo7DRw1tto5a8FZjVJFq8gh6LSfwWMxtBLNVtiMvgY/bUnj8ZOPH2o1HXqh2ufZnsbO 087t53y5poOgSmNuzvM//2mJCEqfzX9o3t2+fefGjWG08v7l165qjM3/WTpTV7h1RSvo5aGVI24C KNEuGNHJpZFIRPjVntHKEe/+Jq6F2l2KQeaWWJY8guyGJbSKg8pGmbOf0VV0ZGc8Czi7T+Xoyupe dgn9ErdVIjSnYU8BrHAgLkm2IDQP4cLb0xPXbYWRYCNs061Xx1s1Mw2kApCaOH4hKL2ZsRV7f7i/ d3QgU6Kq3sTeK02941WLXcRQLDszI68MRqzupmqxQ5lCugv6EzJVEecJKtNforIHziqxL9tHg7kd OqOk6QmCGS/s7ZyBeJE15bc+JfTqldMzZaaM6Rd6i8XQyomTT5/t7w6jgOyyjz/+SImVQ7NL7fL0 mUBnRzbE0AulIsb9NPSqz3NyAoNG38FodeuWvE6jL5SSojRgrYdDL9To276qQthbl8atcnmVu90y 7QgsEEdXKKR9caDVUzNh5egQ2SBjEETTBl/2a4VhOZaabVFkEuqH607are4NnRIPpZkmQezxE5V8 kG/dKIRDPghVMaXJ00vQKWQYsFLcIukpUp8JtLD6QMy9PYUR/jUUM6M3HNj9KrSCBwkvAiYVm9GD 6aOt9dgw+zIBNOUBnhzuHx/tBa0IEW1Hl6+wA0OaZkZkUIEKXgWOjVIRjuGmsVmtnWXYsjXauXAp 6qD+HFutCGe9WaFMY4j5aajSmYG1PvgSg1Msjx+N8dUpPKVKQoIqvWpYN10MrSTkPVU695MnQ5NE JyvU9qOPPtD6OfRCo5WCbZ8+GU3E1RqTLPKhj1v45Ea0h7M1NXoUsa39iEY/WnRMeXQaf0Mv1FRF qt6+NLSKUKX2YcWuLH183vYSJq5FNtfTHalXPlzXZJas4+By4UysFvQjH46Asnhf26P4fmShIbOI FqkWpoo2xW3ekuLwjVV9luSPJjZ+JgRTcECB9RQgN2bZPYjL0rsTyo+pv2cC8jmvsgRd91JRAgU6 3rlMaFN7KRjH/I9whJLNjTL+d6qKDydHcip5tneNK5ELPXShHpcF2dHJOJU4TSfAKLDcsQhUqOCK E/kWVkUcgu/foH/1imVFjPR+hQn1b8QtRh6wHtmP2FEdM7SKudjhM4BVydGzPzmiwKmCw6v6Ymil Pnzy6NHOw4dDkwS0evzhB8NopRZTGtOTp49H0Uqtle4dvc7Fzu9oNWoJaiCJWIkmjH4uOb8KR9oa eqFiF5jkVy4NrVIZyWhFDeNk3gUwND3IvT16uiu8MlrJEpTAbVZT/3djXEfY1jsYq1za22Gb5pZm mC6OpXuK2KI3+uijj/RWlUVb++j0wgd28JcvPoAFudJXvJSaoJHck2Bg7yBpv84WUhIexZGJnFx/ dVazJDQygCoTNIgDHJBuxH58/ud6y0Slu2Cnnj9Q1fhOY+L7a07BJAzyRLhV8wnqadFCPZnNZMI/ kytjHyq7icZgbnddthgB+xoNitukUAwFj6uYl6sJ+iPAJ/dBil8Rw6XkQaPgi3wKwQ1aWPG4jVsR 5ubgp1VF2Q6v6ouhlYbeo48/evTxJ0OTJGj1wfe+Nyp7q7me7CgB6pH6dPQTe6zJ6AsXO99J5/Gf jBwaP0QwjIGOPkFgpUOTf+TTnFYibJQT8tLQitgq2+Ne3hNxKKEas82iLTEN1okEWDti0datELMS 3FBMPAxLRxKfCYq4pmJYoqX6RmGs6xbit7ZkUX7vg++pmhM5LgVziSjy9+xQEPEYGtL97AhnO5pm ntEtnBCw2tPzekPhoFOHFNZAmPbyr/zqP65sifANZrTuk0pa2gDaYfry8UM3KiVxJpIHtfjXzMRC NdHNExVLbKyGnxG9mkOx3rD+MnNbYhVDQtVMUfSa0UfWkv4A8bogNokiyTYUcaIIeipHZP+aglp9 at2dET82td8mOn0GXzhnmYJgVX/uuWgz8z46ktfVVCk18+JvF53WyymfFYWRBjUaTvM9e2xahX3A ThVY/uzpzt6OvBa5urpYKGAPLenxHTPsVY+rMJu6fGh26V3lMdKrRrlVyblj0OFLk5eIgvnDOmDm wdDdMYfXZdhoAo++UKXqNFc1KmfhLn3OZOZ0J1iep6c1Ode3NrUwhtV7xOTcfkpe1n+t10Hp0YFH rzPsCe4pzxrGiHP4bZYx6ry3E/tqukog8VbOHBTGaXlTNTmbSi2hF0PHa1Ai2gVZ4lYcoJWkrK0t fZxhRTvLc+WZYA6oJvW2stkqbBEJOdkxS8uBR30E01013BwpD9NwdQMFNQi4jFX7QZKz5X/+y//f hG8GqJi1rmyX6EsBrgiZ2EqLjShBqSKeuuo0seaIDXKxRF19ptPzmnU3rXonRENaWpJf+Stf+eq9 e3eTh5gEmkST41Ho6YlxdMymhZX05VVvD03vonoFW2s2e4o3Q7CMQY+c4ryFVrNB0XPI6sFEdPfD 9ZXzzZWzhPvFuswQrLi42chrF0nn6FQvMGx87ZZhUNhgt7IZqaFtY6/AU0TBsr3VoGK4R6wZuqGY 1tZM66jU9pkjhIY/V7yVtgJ6nGV24Dg/l5NGo3g04697YAY+i1MZe+O75bbAntH4UEVHX7l2dUOy 9+ChuSoTSItnLjg1U9AsKEVKmr1BwdnvEHgXlbNcdHS+fFQ5/+XFz3guMac0aM9u/P1+oT5BXKDi mAav01POI8UTmlnDEh1tmz8lsySolKwXDSHBl+Qnpb4R6mAJKWkxGZ1xxYtKOQBLvlGbgzdsJCoA TaYOTr8+A6YhV/Fi0WAzTM4NOZtG+8svnTePoc8K+7H0XluqJ9KdijH/5J/9vxM/Rat638CQJcLh jxKln0Qz4K0dDaeYNPHpNXOOd9hTwNeeS7tmwQjQNPSpLk5v0Xu11ty5c/drX/va/fv3x3vnUl+x sXJ2ZfVkY9m7ZvfpxmrRkDDLY1skZycxmo04BMkGoog0zo6QCdCn7qAr5pCERUE07+pAIUbQKq0f FbEnMvnMcnQ0tJbzxy5hLXojhz7kRD6ZfRf8GXmdo0NS1nboVaDVcBTC6EdMz1/fXN++flXFyUff JOUBSJczSMXV5XwPq9mOVKJio//zJ4OWDk2CZ2fneyoamPsEO8J0Yi5Fm8h7NUpinFFJTjGLBdI8 s43AqOEpB8nHn3wkMg65caqOy2IR6S4syKKSoM0EYaktcAyKp0qBt4bFylOh6pn4zWBDqAp0t0mh X1OuXcO6HImghKHGMZve+dWTIdXJ930Nqz/11/4iizoKFxED2inUTBKiqBtwDMEEiWBksQU9XaKw QxHqP9qTEikdgRbZLLKrriOZMbq/+ERZqUwoxa5dBOymquvcEmBLSR8dRpd8/ury+frK2drMcGld 0Javup6ZZt+tyqhhVv0Q/epo7ed6uGzyUe6IMpzVnVH7Q4gtS1ofDHsMz+pnFpQBbMmzH4UPjzeF zk8KGRbHneMHol5Mh6Gv2nm4hvgX/MNooQ0vHYI3dgiUZFMZCKBznlpZjMEgfQOeikH07BON/mP1 hblOHQ1M/MJAWJvEmIrl1lElDEcGZvoMHT33YuhVGkgULM6WDSRhw0XCVGKGJEAI2dzcA3CyTwzh yTu6uypac0v5033XdRUs57PlXc8GOWgBfIX5lxf4ndC2U8/dP09W/9Jf/gsRedgSRgaHB7iIV9XB 8dO1GV+bEbwFc2KCUx2vPM/0ZuT97OrOQ690Y/GGUlwVbM5+GzdvSLITUAFZN4XWguqMhtf5WAta zbLqWUWmBolrYccjmqMZpAyA5k2NGejuoPag25///GvBaZi1ehmvbO8UiCo7sILHmlWIDBZti54V Wg2L5YBjlIaRL+i5SfTgUQN0Np2/WLjyPblmtRMbhr7s33FM32zf9mAVM9nfoInxqJXnWU9IoHCJ IdAK9pHRUitbP7MGEIpFerq2RB9kuPqExdBKH2mrUMXboiEGdIrNB62a6cp6BHPyWHNpTGWJa7fj cJk+6BtUNYwq2aRNFjYEY2sFjZeCqpK0wCsG8pQSLf+3/+1/jW6ixgyYwpdOTtl/4TCJfrNPx0nW ozzbdOnyVV2nLkLxXfI+xhOQzWMci+rgfYXJOVIjaceETREam/RpbbMKSXidoUrXJtFqe+10U7bZ 5KhVY/LMZJjN7ojlp8pTz/YDqYoSyq/JIxDMFMs9ptOXzxTixXaq/j8TrNzbrB5wWH+DZVWakiPu 9hS0MpjV7FXvfHm8GF/WM287Mngk3GYBEjH4OW2RF1opbXTCjed8H1sCjmDYrInroKQqXEeiCNXQ iAo0xjtkwIThfGX54GxF8syUiHWLbybKRpud6TsrZcSMo9VilqCGj82t2FQt5F3CdSrMRFU1FOIp dGlkl9T19BbluHPntkwlYLjYZvhm4kElKrOQ8bjBs94q3jC/Z4scNbUCI0N/9Fdl11NA0P6+5f/m v/mvUEzYqxlNJIxJWJWQjchKAaygkUdVQKWv7hNRK8/GqEznpV69/aiJ3icsoQXyuzqCICtVz/Wa xNTOOXp+UKdtrpxfWTvR91xAGE97wDO1LE1P8PPFf1h+KhakuFRjV5Cs1KKN3BhJUFAl+AhFq/Yv gbGC2Aq0Gu3NxWjQpYbRaEOtainVJBt8WUaIbnLwdR61miAuun8ph70xslrGGbxcgnIEdbSKuyuV /ikva5gKXhnGSp4Vt1o5OF85VCxxM/imM7mp7XEo1ozuiuc4UrkFF0Mru6dReAxZjBxU62QbP7OE ZFWhCmfHiYDatiIZ5979+1J14uwrZ2d5x/wE5zaVurR2X2dWZT0IksWZmFuOlYFsRcFMC1MHy//V //F/w0bs2q+ixRaw8GejGcPeRc4wsUP9Kf5/cVBnCcHAtylrE5csGJepcRw/GT98c6QnZRMSP6WT nWBIAOulDNrFP0Q4dVXcavXCzt0h7w23Y7dV1FawA6YdhSpNHVECFhWbcCZkSbKI4lfevxXxZX3x EfVs+XVqeYngTgRbOWh1svpVXa23H73Vdfm+FQE7WjQgsD0+wzQmxcUXuM7R+8r5Kmfh9l0aNpAN VsRblesucURslJTI70TeBLDaHDVaHa2sHSr5IRZgaEYpVkU+Zr82I9gea+a5Th69zQXRypPdUJMy Si6JRUEZxxIQXC5IIDBoUtIeNiW0evDgvoSdOMvyJglpDGSVPyKhVtCooAZj3BVQKiaJ12cEJcwC bhWVn2p8f/9/+79CUIdbVTCkF2jhl+Cqj7zY2jNuFyisT0mLB1e9IKu3iB27nrY2E0y/0pWzrm2d qicDyJqImmDZivV1PraEVuunGx2teuhA/BFmqaGdBV7dklfklP7ZczfTnEpvjy85QyEie9YP5HRZ giI7sQTzYZhckQ8LpnqobZDQ3Ql7H9at1IMbK4rP2RhGq5mKOtZ7yAYtiX/spYuc7QgbxYOMF0pH Yr0lR1iGemEVPxK4l3RieJV/xhJy0PLq+vHq+iyIprvKGjaVM3kmank2+23J2Ri9ycXQStfKNk9r 8U8bHkyvbAaKYmUfB0iM4q7D/cvCUnXkB/ff04xvEp5/tkbA1cmuhcDXjGRZ0QNzCjcqUCul9FTg xJOBOAZsU5yCq3/0j/6hVBGVJ7A5qCh31/Z0rpp+bDENP2iqOcUlHHamBOlEcCb6Qh7N61fv3b37 4P4DyegOgHWCkaPRkwxJ1kx0qlTOqgAQhBeYwfjiPNqdn/N8KR4bq0v6XvJhQkmbmVy0CPQJk2ru CMUQm0Ro5VIdYbb7VjNn2yc/jGuW3UPiCKkIhph+vYZ9i9yrE3x2fK+zT2qXQCjNaHuqU1Ta2dWd swLNfYTsz0Zksf9X/7B/WQ4ZJYtjR3zRX6LEFiooZzT0xbYKDu4NU7b7DrMFXbz7u+JNzbgmukd2 zdLKWd9grTFQGERXU6q/iypjB1l+hqmNDtfFVHb1k4So3GAdKe/WvR/cqvc1Zqx3VxuOfu1zstrZ fdkZyCM5Jm/JY8hVPVkx7qk+kM7HM9pk9L4gr/7hH/99cCvvl+0RHz2q1ZkKvHnB59m8uy6L0gdG KQpzEoDhLJnUf3dkgiI879275zpeEzk9xXGS4Q3l89FoSMsafO2hStcsh9ImaFXdl8EbbwhNG4tP D0AdT400r0nE7l5WJ7btVuMGtUpmryhRvIO8C8TJRiP2OV8Z0HwOolfeGz9igzgU92R3jrcnaEWi w4uOg8+cNxCOxrLnxjid6Btywn3JNq+Gt893hoacW2uwepduPaNXk6nv3FKz6iJgGbxKyAzLlkym QG1ZghnwcXd5rDBmZjy5dWCNGk0R72U5yWOdE7YWRCsTIh+6xOzvPOvDmeXUYywqzFK34VKY65vq k5Ja2+YxHae6YOcRQoxC1PgQn9K/sAahXobI5kVvuyTTSKt/8A/8Dx1VhX4GJFWDpj2DVvpTLj3W HDl81s1dAa8BVdUYhEIpzvW2nAS3beFLqiIP2S4ErxNZerORRVuj4qrMJ3IPc3bKD+w0c6s1BTHU BXSssnjUVO+LEJKoyZOUSWQD6goGjVsEVtWQKtgjR0WgyY4PigbOdl9so7o4WIU8RJhsOlZswUW8 q7ZBzK2G5ZKglSnz4EHbuDzI4OsWPN1oZc3Qwb1DR/aZ7ON2Nv5Qc4thMVXyJ7AKw0ZQ1ShSGy1t aasVZrYOpTX0XY2ZLP7Rgb4YWiWSQLPTV4wiHZde1iCAxrMzQNyVDo2ymL+664gSmcjcPS8AOppO xDOe/TR8wVNj5CYxGTxWr4zqjXrG4736B/7g/yDzRX+LupTQXN6gDIEgFGGr5lLZUUO45Go39u85 Fp+yN1VgMPVc9E4loET3tQBDM1TXwvm68QAO+iUT9jjaSZd2vnJuNte02/sMrbJChgkFbZoNmBiq MgZJHN11ISERKvIZW7INVjgMqdmN07d0NlNrzABi41nQrGJV/dPbtrJlnwxSJN0VKmgFTPSl5JUP 1LHxdtWAzrCe4yuN5c+9lMNWNUbY6KdJR/KcSOJUPApFr/kRhlVhd3G5YDLpL8I4KFJRhqY91mxs wyMDwGyZhUdRYRJWknU/dCyGVpmaLRomYURFgFiBYufPxIH0l56O/1O3GpOtNU41RpAuCn7/Z7hq rKoYjPDGOniCEKAyjpYimaNJHKu/9/f+7iSnderkgF0ba3Eo+qgoBMiU2dQsEAGUonazwSpxVMhX NrbJYwtMlWeLLjW2ArW5eQCSwgikHcCPh13gQ335+U8WTm2uLSkcOvAPqS8E6RqVQSSbTPcYhZMT Ksw6/7PjVAztgqpm/XWHYNPRacjoBC30oazNAqpuBTZj0HxMzVjUYfSWPVqmVcn45FcezRM9TMoy HONNvoRDnebqt0vyXAweCYkEiJrvk6vmK42cOZqjIZZLt8hvkRfBqZDL6NNuvFeEXRM7FcSSwm3i CKPdtyBa4aTzaPXIqRhRJmfhUVlFM7U2i5N7rC/VuqPMiUaq8G2WGy5mXsiTvxG2pH1Vi8NhB3rn NLsmmncdCpDV+Wz19/6+fz82IEFt1AkmNqoebyqYU3t7+VA8hXM6SzZXYXn2U3VJLjOpGHtoggYd bKKZltJmWmeJ1f7p5n50DWu0ey75/I2glZeTDlZlsk0k9diC5eTzw7NTcyuhlfOZKlqheBjn1tIE JgUa2ruXpZw/1N+qDxMUWt5BXhRvhQ+vQHFLjR6SvGZv80qYqhN0tVl8e7PM+YCLviiUfJGgVaLS SjMmyqZ49Q9dVN1RZJ0GTwGr4iFcOaJ7uJTMQM3LDdXfmK1tLdczbNj/iyO3DWR4QhNKKvACe/Ms hlYaV67NwIbn0RACM478x78pTgGSgDbl/bfWpGkQyQglKWSSAAKfmLAzD8NZAEOZmFXxL0GxQbLC M94gLorgYNpn9ff83t+dDhCQEwJF7XgVOAd+1Fik8lEiogEVRQStT4VGpSAFbj4S0EFHx6HKzcmS XzOINSVpROHIrEQTAW5F4mVNtNHJdcnn2xJclRR9Ea26Iy4IFMZUqxVy0skJBYWeKpylRSm0fgi+ JPCgWf7TqW4RgMqsHY6aR8YOEJ6cNXRDFwsfDIVBtHIoscJdStKfF6sICg1XmROkZljPI9bzyziw 2VIJeOxoco3vVI0Ux1bEmf6t3UBZiaFbygZeWd+0ddPIeJsUE2plgnmBI7tq4PVrMmZGh/diaKUh pMgmBVhFswGSmjQkbkjgPmYd/KgRabsC9ULiDBigpVYFrICqUqecxe3XtyAGQDBx/4DfBebVuVXH cn3KFK1axDn6VMqfy6mnHD7FfSkKIduLZXv27NEgcCPw0wH4LegLYUpYi/HdqBRjMbS4xPQY7heJ SVlTnqyj3XPJ58/Qajr4aoWcsKUWi9CdeK6HKLSi4mCM8YYFPZAqE/cCUrnppOo03Wjm9W4cqsNT fxB7uq+Ko+1jJYx4+vmhSmeq7xPgPIpWcfNfliHIuu0oqCabzm0O4it31xWvilJV113hHiVzMJ3T 7P6Yjc2VDRt04Sxek3rownSR4zHRKD68k9iN69l1eehYEK3OzhK+7hAMLruFRxK/Hf8Jsh1gZcBO G4pXJXy07hfsbpyqlalooe9p7LIEgauZKBZZK4Z1wheKdMZ1dSLd6t9Tw4H+KigcGd0H4ecbAqpb t2/re+0x3f5snGLHYww/XI5xFgSP+Bwk3rJxu1Xf8CksAmsDc8kdhL78+kOV2lFotSWVXaUY2uCr dm2A1ZbIBFv5O+mZZyowpvKy4lZ9rJaOEaOPFbuAPSJu/71nNZffG4KcVwVR6oefbysBlg4cfmis m4GLFit8spui88GPhoDXLXLxx47M6ss64kIwrAweatpE82Ag5HJLaW6us1CP5DfP0Gp1c0tfmeG1 Zs+Ug06YG0FuThIl1irdXyxhqPt08mJopXGa3QajLjN2zEUgJCmSQ3oDURvp38jh2jtVw7qXUeuK dGNmVRKnypInQBRjkICQiW7lPyAGt08wRNThKbX6P/oP/hCpL+sSyWXiJYHPO3iRbCz7T2H1ep5f rWnl5PjvAoq9VyKr5zA8Z9lpAmRMPxjVlFNN0LNZy5cdwdBF0bkHhWzAxFvldsry849ZOT3SqqQD JPITq1Bb17im7J4jISexBdPHfUHO/G1X5G2lyxfe8b7hQedhtbK1Bc62TkJwB4UkdZTvwxUBm4Ok 6WXlL/mUX205EMidVWvgayGkynCaUfT5AVJ3KG41s0jmBa30sIa3Y4bwcnFAI5rHqLmN8u5ufLeJ jBUZdCVlMWhY3y6EyrFmtyLg/rumm2ajJltTVCqgJUuUvxoLr7ES9IyoNPPdPb8IfNowV3cTvb6f yG3THuSgBGEhVuEOrelryAZ3lKnjrR8UlRMgikpV9uL0Z7SscBs9qu3YbZaFoGa8BwlxKyVuIZqV j+X/y//1fx97QldHXnWl7uUW+z48L73Di9OsVo52ZmHAc+c8/5IXkaLP0Lmx4/Oc2GJDxt5ja+38 +sa5Mm8iAJKokPxL5yWIElNCjMxxyjD2WHP2+NamuFGUZ/DSHShtSa7uC2Z5cMt2ZCP7NOBzUzTj ZwZbdRKlHjKmhw7ikc6OlEU99kKveVeuip8PfRoDtO5r6IVqhLT56A06uHlLMu1wRos6VPG9UsI9 1yLppPpeprX9TFnLqxoD81YR4hubN25t3rhtXcqrAPGNqYipOGGObnT3CB7NPnEagoEkDes9HUKk 6Ql02NefIRK7qlWCKGjBmLrYlgXlTNJqrwykNL8PmThktRwk1dFpKgmTJFeOT1uOgy7FxJsr+1SV 3bURnDa+TVAhDu4WT65IY1oENZx4Asc5cVNsXSVbzjp9u9zuCQwoBakS+Ozg/l/5lX8cD3vgKek8 +pe7Gxo9b97JkMEFYpq3VpUneLa5UnHkCa8lI4DcKg4/UnKTA0GJVUcGSqV5gt9ePGYk9bm/2Vmj 4viHn1VK4aWd5X0l9/YWyBJXErWrPgz2qEa2Y1nGVeH0wuh4s2XtTQ92MM0GDkUsX7t5Y+vaMKpm EdLHhUEEqjKpsydTwZYDoYEs/ONr+ov29bp+C4+bx4kZDBsvqncqo6Fis4uthByp4xTyInSjuLBc 864urOmtjyIa0/ABl/UWockqScWAWtVo0N6q0craktZl5HKQFm61KV9jcbKKTtu3wV3hgxIJtTmh d6hnXy2XbXimLb/8OKFRup3sUe/qyLjrkvySXD0xq+QGEKUw0zTawlzOYl3A6i/+4s/FFo1gFqjS w1zcp06ggbHxWp+6GChrXVtbOXX9IZaAqvFiqy92n4/2qEV9tjCRkOqhRqEj/VGf8arn6T6/m/eB kkMfp5OtQY9bZ5rAYRejH7dYL+hVFAsQtxr7QCGNS4CuDetrmU6ZfvZwWbWNyRTvlqGj9txtJdp9 pp7VXnjrm1ZlW9JGmWtJftb1VLXjON2KajBz7WLTh4QCmYJhF+neNWETr4RH3jTO/EgL25FM+J6p GC25vuVNGj5lgremCzCVq6Aii17RrJHDnTjF5uetLJ2uWb80ylbx8dbpaSuLXyVNuA6fPtDeNW6s CJIlQTySWJ5Bp1DO1W9842c9GXDD1RlYlZ2Rjg2EN+3sxeaJKx0vn66wTX3IryM2JpvtFE8mQTnW dyyycOwXaPqrW+2VaPXSt8BKNSd/KZZ91pP4fSpiZu4XgwJOWX/1/Vw8Y7Fe0KuSbJmhO/9hlFm3 XPLZMtyLf43FbUEwpfbKGIywU3Wt+BEgi/feeLW0uqFUQdQYAjUkzcMLAm3gnN/LodxpGXs6mJ/U zlJHxBHscRVPlNZL7y/HDnMYa3pGJ7DnsfdM9jBzrShYS9f2y3HD+zcDcNYVg4tof2GidGC4RpoJ 1TUYReH2nVZ55EJV5D83RLwM8TEZlxqQJ7RrJmadnb9Dq0UM3tWls9Xzk9muCWWsNz09BfUIAO1o lQnZXBNj3ArF0QvWKAoQwbMIWtl7sjSc7ge3sgdp9DoXRysVrnDS2FigFvGOth+YJgOHoSpRSCFX hVFsHhG5KpoVf4BTJfxbtcLWnCrYoiYNZTYhsSKVtpxYRUqdO5QXqmg7yKDjQw9zlZbhDZdeR3hR KJyYl9V5DTk2VLDaresIhTEO5Ks0oSlKxdgKlxsbk9MuTjiGrxeNP4EAwWPBdjyC+p7lxNcC55fx EdFNrzYxYw/UHo/KX1qkBHxKL3qHVoug1crS6crZ8bkDKGvjGivt9vrVXobe4LqhFcTK60fQCk2x C+KZp686jFYeEKMoEOanV7rfR74cOFnenPkpi51s2Y509DoXRqvQWS5x7PZk66o1Q3rn/9LHJEms fPOo6XGSB61mD5tuxYRdPde2o8IszT//bi6F2mWtqznZU5jEMz0Qgl2Gd8/PZPj4R+KdnP5LYlwM rrh6rD9wkJUitCIZsrDKmUZF22bmYPPFlUm3KGZxxQnmILvY38rqg3PiMiWEivlCtT9LgLSnuJhr 7/EOvBDbOUGkpofZc5Pcj3dotQhayVJfOjsyWmEFwqTyPTtr8bP5p7MqZkIy+Jzj/ip8eu7vTmyz FDl4xKfCojcymb0LpXLhP9MN/jIQ65bN4GVWayzwqjigsprPfxgRbGlVumzs+Xm+Qh7DmbpgxfZc NucwgoNZsQ3Fr4g/k1KptFmRnUBXds5JqhrgDkVqwaUITY2glCE0RfMEfQefWfY8upL4ku/q8byz 7cc2+FrQEHZh51EBq9LJgKpFCRaUE7ACyGMXE/zOAi1zL0mILOh2NdhiPdAfdAooZjswq0DEOFKc fYfBaL/w6PgdWi2CVktnJ9py7+zksArBEEzVaBVV1WMH9qjktguruidDZRCtmv4wOKE7Ts4/kzMN VBiNsOXE0837lQmsb4OXuTha4d8Otxo4UEewPQaPHqsQKzBHD11wBJCBqD3rvyVhf32F6NCcG3DS t0zvBhjedjCHKSrvK1BCD/UN6jXJjYtcHXkoezLb9tO2g1WAOPvgybxU+p73IfZtIpvGO3hBYq/P bmp54j7HD+McTLy5HcpbGhdE0UMWB9uqFCXOLoEJ9GA6+B1iMAavyDdFwsf+Jf7v6B1aLYpWxwcn xwdBpMaowqmqUBU6wkxft/M49L72XxqbKNR/Gx5HkWZHZR2PG9AqcXzzf4XDM5fGjgUtwWxIpzJV gyq7nWuub9hSLOdWrpKbFkAKf0IFh1iVcRejMNUqo0VBMYRWW9v+W1Ep2BSEJBwpDAduktgI12hS szgCRhV+KeKUsnL6kyaxoepYmxMr34UCxMfKPGWrK10GMVl6H/U7WpeZfYusisVWEenF4BqRm8U8 jfUersRcdYrU99ZhMMTKTCCWA33YEYJdmvfiEMUxiZVs6uprVwOGqJmRBa2syL1DKybKaO+cnxyd Hu0eu+AqHMpcqu1U06LjZhndzQzMpwAfw9zKK+84TfetcXdJdJj7KzR8WGXPmI0wN3QshlZuzGTt ZdrP/eUX4vAbukidnOqT+OAqHtN0KjYgRZCJu3IkZ3P2QR701PaVjStXIVWIU9g+CdTutcmyGkUg NywqbJIYFHEjh3LlQ1kJLP6wRGryx4A1Tun28TPSBW4MJ2slGwHJK+XlvI9uWZrhciVmh1+93Ff4 qmYCau21MBtv9mCp5JSj0L+koGGrtpiekxPdpVC9fIAqExDEyxDyqm5eGCslDqx33GoRtDo7Pjw+ 2Ds58mbUBVVl+tET+h/FqC3aDRBLKB2jVeWATrnVsSP31jWFmB5zHLYFRa/mn/85E6zycB27yobd o68K9Gsujt6gnGqeOyrJN3gIrBTiKOwo0cqBB6ZV4FQgK5GijV7EO6gnFOJ/9ZrOjRQQxhP9gMFS +QbWccA972fuvSpUO9gE0Em52nWVnrNdx8oYQb1SUoQW0aexxSHEmew9y40l0mBF6c6idTHgEtfk gwEzvHjzLtDE+EoZCD39JytzKiTVdpmt6psLMCTCzK/OtRdkEc5AKBbB64Hmd2i1IFod7T89OlDG H8pJk6/yOKnMgata1mYJBRPJfW7QIg7Fg2pwcs1OL0Vk7h9kdiDljhwM2eFq7p1pLnB3aUJNtvnN 1VAbV43WlmeDR8qQGK1i4zVR3WYaZQDwDqI5lZ2YubcubrW+pTxBz0FMG1dqjJ+LNc2eEN/CqhUr CJpNTs1jP0/MrWa1Hlt1aCHIWQwDDU0N8y6jFUkgFjZL6StLMBYgHAiMKmZVSYXQmUVSoLBiK6Sz G+UVWp7YiwoApSQb/2n4Jd1rrZ24A+NsKJ2Lqw+m607tPXjnE1zMBjk5Ojja3znaN7eC4baEpiJV 2aOgumnyERX83sj5vHAVU07wscB89mtHD9w7LI4ZPPMeUVpr7I5c62K9EJjT/1EOKILhQmvxuI8c Lux99ZrMtHi7SqBKoQK0KyMWYrsF9vjFjCXoVhub2VICFSbagSlSYiNzMY4Di+5VFqWRTkZgDECd XtuAkl0oONC7g2vYiQQ06a2kRtN/DjRNJFREKyOKVzz3T1+JwIYARIVKLCCMuCMqcwdzMt/CqVrd wXDAOLZNwlJIhgw/XRD2cSXSFMvTpo8gG1OpQhffcatFuJUUq/1nT4RWUTGh683+67QKI6V6rElj cYsQPTVwlAa5PKwHMXAWOQBHB84Mv7iW1BGs+hyWYD5miFjBrWwPmWOYmQx8qaq3Du/VTMaMDcGK smrxVt6hhgjR/Em+LcLaXTh0dT1E3PybEtghGkEHJrDD3yt1p8nz8gFqR4TYvNmR1MnQhCmEVWWL FgIliBEHAaF+Dkb1utj9fM1ngs83PNhQ1eR+0IpBO9Z54FI0jzC7Ur/aAE/MF9YuDIzAUVrPUcRq BxbUuBRrqQN2eVWLhkOgfBfLHjgZPI6Vir/75HD/WUOrMvxKq+p6VdzGqLn5lKL+ZFXM/2UNldVo 8DIXhCqvdVJ7ibpa4Bi9yLYKD/dCkbgFdsrRvAbhTHxGDrjVVeFDycHoLBWUkMDQ4lmJTtd/R4MK 9k/Olw8wZ4iHsm3jcSHMxAKKCgaPJYoSi0yzVb/jBrRz0DZRCh/4tXb2hvnm8mM9dVqTgHrxqCBG ng8iJKiUj43EVOwqr14MrbrHIADDkOcSExXNj8hhFeUgwmg6uCUMqg3hcxmNmXPV7bXKcm2R8u+4 1WJotbf/FLRCSseNzkETd3V9Qp9qCttyPPa22Cie835pHLFl1gJoFW1ilHx4QC8Qyx4qd2loVWvA AoBqPMbwGDy8y9NVBSIYrbBkcNhHqKpQqh5RReqfT3Ljy3nsUhjehtzcKksXOb0x2uqouM+UiT07 F+SwNctWJi61Zg5TPEXPBKB8JZnkkYI4ogDBclDNY+YxFBIh3j/2ebSqkJexPsSwmHmVOtQUdjXZ HjCu4Ay2S95Uk7iwsvYi49oTC1qrV2CPvCGuhtXlXVbzItxK0SJPH8sazOoBzzUCZRXJejKFqv4R cYsgJQwc6ijnWznRbOxYbCJ7VItYjTOzsYubnJ22Wvjlo7cpG1CrfHLShr7YiWDb+9c1WoMuFUW9 /UxwQygPtEc3tnd0/HR33+6/ZhElNq0fFS+CEVWRDc4oMrfSe7EOXjDSslGyBPc4dojGYFti+RO3 Hdjl2Y9YleCCLFqNSzUa158Is7Eb0cdoR+glKHEzfYNwiTq6jzG2XgKyCLZfV8iVtoByBEbw1sSy B9oTw8sN5K+++NEre3d+sD961Qyq6GeH8bw48ZLSwNEWjgmUzflw0XYfncmf8/xFL3Px1y10weXq n8DFXA8tXXeHfyFAMdcZ9oXjdIcb4QFy5GnXWwVEKoBb5hyZmyvy3mvK2iRStuCGTUfNSkcYyVt/ eERpNCVLHAu/hH8K9NLeCLdu3dSh+lCb29t6Y7E16kkpPvRAtE2DTBFMOk3vqAZtqk+zxrqZFsJ1 8WCQLniETEWSO04GWgJ54myCHKmZHCS7saENHbyNlgsVX1OghmAdmPSBnK4S2ylXqG8YzimpGgX2 m9/8Z1BL735qAEvYBltsdVY2fBN2W9pPFnBsUm9brEm1btwvCduwwGh0Cy2yiy3OunERUR2jN3h6 sHe8++jk4GnMvxa8Ns0QNIHqFMvvz/BwDba93ZItcqclayZir9kGIejNU6MH2o5OX6PXmTGwAGtZ PVcccbj5wBEQbx2Yfq1teWsQIHGnx0tk4TOSXekB02Jg64NzVi5h+jinJXffe3uN8TLHWwkW6iLq gsqKbaOxTJMW9801rNy8dePWrduqgRmHVkpL1X/r6y1qNDQGg0vXqQt8tLf/aHffswsG1uMbaq7q tBIQqkomysKZqNztW7c1t5n9wjsNn13tmfTk8ZOdnSd6sLOzk5AtfRByu2NKBQHU0TlSZ/gWs+Gz Qr7Ym1gnxIfInqGEPsSXqfG1vJLKkYzWWZPOZmQbSZMR5dM0noEVVfSsfi5KNHM9tm5PT/Jd3fZd Hd/5jqoSurhV5kuY3blL5bih8Hi60iiX/YWglYDu+GhPDoxce2N52K09grAibo2s0f8yHBOkNDBF ODVq0WiWk+D7o48++vDDDwdH+9KVjbUb6ve1VXTPI61sLhDLv9Sv1dO96fvir6mqooouFynpinyI WJC5X9RVgk6aVEvsnNd8vDWKKRxuFvvIvRvYYBikYnwUFWAPz9jRaWKN2cglFfyHT72k1DzFf071 /k77NpRqKM+gooPahYngWoH0OlUPj0bHi+JCFThwfKab9KCbqjq4pppTv4alrzZryd17d+/du68y mMluzr5QRIeqlEKy2wLEDhivQ5748zMVDDx0UaqyPaMj0hrcSMhFXlIDwg8hStc0S0Grk4QvKNHG xTnr2HPdWolZGIN6SVouOavZvURPuELn5qZw9uatmyI1MhX1zvn8xLKyqfG28IHy3LV1TVrV19G/ tZCRF1FryjFoqoZK7Zf+5ASvzp8+1Q5QT4VWlGSg7u7e3iGJhHpDNZHmAjtwZVf4q18QWp0d7D07 3N/NyhgvacVX2EXrAJT2e1G5jBFdIrl1C1jOGGWtfS+2cv3WWj2/+rsa6Nvf+e53vvWt0cX5zo2b X3lwXz1P7cqjvV2teeZoLsFP0+tf0CqToRy0KysqB6uhRmn27B3n280gjXdIrYNtkCCalBNxKSYF yak+4xh4LC3Z570vcPysEskvfc+Vs6VF0arNmfjLO0lMf+P46kFc4Vg6NE41+1z7pen0HeI78DU2 lr94kqvRKMt+MErH1eZOSU8VUBzqmbro7g4FiJet6G386hz3H9x/78F7KjgcWZ2OosKxvYSGKvVU QArnC6XFvIqen62vSTMf7T59hHicvqcWeRgTeJJqyRTU9ix3arOneMlY3t3PYe7EweuyZUUKj27f El7dUrFkxWEIvXKHbHa8YayS2rW6lvaczRAmRg8yna5GHbAs4lPeRS04eoNxkgaCNX12VC95Z8cM cm/PgT6usLQC1G5l3+UvRmXX/cnxoWL7rFSVvdRGQX6dCXzW3rwkYX66Ulc2Ihz6AhCTYYSRmTUh QcNO+3ZOZPZ3qF4GWDRD1DxqnPmDCXKmaPS25E+Eg0phkrsHNuv1jUvojulcWK7Nm01Adrg8LrG6 fZY+ktMRakur1YCjPYa5FXBoljc6jJxjNvxpHta1qHawmTyoyd+ZDH8KWhm3peZcrHEanJqhVTgI R602bBGcYT10g2pz70VdoQ8zbpXhGuSaXnh/rPAFqS2a3vHjVb9WaltTN9oVpnPhJiqGouKGw5PZ waLUxYZvIUMhlU7iji2ux97ORFt3lNeaZlBAR8SPyQ6erkit13RgfskNF6giZciuAAVYkJ3fEjCI LEWamenlL2llTOXWp0O9oJPL9ms+9XjTQx3iokBNhLXKs6Ep9UX4BKke5NKaDFBb8OVCKcDCfveI cARiGyHw5wTa22uWmJJ5v7DA5alx/K7eBxzQUK66P9lfhH0dUr8f+GajRz3KpilDh1ZS1fHX6Gs7 clEdqVHx+JnMI4JSiaThRkntkjv7dUcr9R+G5+hX4cjLwKp4Vh/ZOSdolcBu9f5FmOiCFWe+cMDE F0OrJSGcVNV2CQ2bXj+0wnxm06Om6xBtlSntIqJgeAZXK4Cwvu4ts1BjnYS9ve2N9dYcmyoilh2Y 1HTCL/1NNqwUfr0P6yixNY1MlYjcpORaEl6yLuBHwLQdRyvoCWIIj7oKVMGiWRIainxxEQxeZpPE VFu+ViHrAqxkv9ZCVoO7lezpok/T3eb5aT8u24dkYwFtyGhOG9psVUS/7fMbm2jBtPhxqFK5R9Nw g3lgS0uZynZoOYpEZTqIy9a2T1sREqFXX4mYXl3BPHflhteeW82YwTwN0s+ZsqHnMOszuFWi1TqN eu6Fz8+BpvBkbdOC0VN357xU+W4FVZb0CzBfU7QyQ7dTXwuHRxej1FiSghw+4HghIBilirq0Go3s rXrHEn3sbdSJeiVl8ByuBX9Rjp7pe+w4qhyYJqQBwz3zoB/TXniOy/b+GkWrAG/TYXDr5J5aXccs 94ls0IlfDLcy5J/pExJwUmEnrfJ+wmkTUjsNbNOlqr0o1VWizzwolXNkh0VwTaUvQ5L3fuMH22Sx fVbt9AdkZU8324jTOJE5h7tLZ3ujj5RqZOs0u/MikjN2IrpWSnnjVsjJYndZ3F5nS9B+rLHsoMwk Hy8lVjGvanmanJGzbePEiJ4ezRKcmn7TDtILtNLYxz8KV0oPFnt87dEqbWb/S7EPjDJL8jRbRS+V XwbJ3yGXMv1k0WmkaY57Ly/VWSaOKrPAVMDOem1oggPHTkuXGLVOHwk1Bk2jco4NbHpwPZg4acun wNY13U6fH7Ni9+mrWZOlGKYmGFaJccI2Izz6C0Ir3ZDRqqrdlIJJ4mbfBIgGL1jF4DD8I7mpVRMZ Pv8hK8/uNknYaN2y9rKln111kCmSq/iiAnHZ/jPDc0glc2yJG8/ljC0BUP/eNxYhI3wKx0LmZzDZ N5yd5F53tGJ0OzJgELA+A6o+G62iUvb4jZgURdMuClWZRkUBwq0qKDGGy1xfrsiuCVD85PXVrVjN I8LGJieHppAq/nUIkjYAM1TV1sUCLFJXFU/g0ce0crmV0KdpHxmXCBbXfOCNPQc7RQhyNQCbRRaV UtXMvmrF4Nbg4X73WsRKD/oqBDqhIZFRIFVsM4EE+4WgFX5AvmjtWNXJ+y7O3RSRksNpFn0JTBxB d2AdeuiQH/Txw8ePnzyRx01uNytTds95B8ZYfmRYhQWjHqLk6UFMiflJXDtTVPvQJqSvsoLfbPK2 0aNYFmd2gFUJOaIvzwVW+DtEB15rbqVyREKCLhPPSTkzcJ/Tnjpb+gxulffPi+t8FvDwtXzP1GoL P1XmtOZQdmXOy+unmS1oOe2fNAkLauLEa6GyJ24iOJX1HGSd1jDwzlcEIcQ9CbkyWoWoOhKKysLR H2onRHMGSvpVHk+oFjFgOKZ6mitcC4mMz55SZ3q6vhWbXgStWF70ThFKmgFITjiJkyE07FdoneeL QSu8N9p0L+NhFvJH02f01dBpsz+QLlyx/K0tXhMYO/fXzpMnDx890r69ZlQ2/aBoGJWRlhJEB7bM co6N16Jb8poNuiAtkglStc2k549YZDwybIcyiyNzL2bp4Gb9nfg+1Zl5zdHKsb2g1RgO6F7LfXLB oqtfPgOtMhdmaMUvGSvdIRXoJPoUzOJwLcTK8h25VClB6prXHq2iTj2HArhwMty68FDqQ+1uv76e igV4AK1VoeYeTeltCJczimk2O6lI1Yaqkh7UVoZQWzV4UaqZ+Xyxj9NlowdQMIseaUIKJiqyfavL nsJeXxhaaekKKHVgKpxmScxYgz71SnZ6oCVAvEicKL6e+Y9nDjPb0cvnfwmkKjsqD9fnVlSiuLVe n74MYe+0gH3VrPzUXdYWUL5/Y9UbgFbs5uNmGYEAFvMF0aqrXW2MNvuvxk8Y8WTgVPY4ERrohnO7 j5F1l1TfwD6RYgivqcqeq2vh/a2N2sZdBVmlC0eBqKAKY45nF9hjS9nw1VcLWrLmH/SfVdb7qmkv J1SSCsWZdX9dRvPhZqj7pbmoxaAq6EBcdL8J45TL5qSmo4lVwhuy+8EXhla+kBSCDo0sE3hmSeH0 hz2Z5eV6xIeIL7AOPXTI/Hv2dMdJWJWk9PyrAaWXfbGd8qgl6MKPhwfCLCj6LEwn+lRUyHrPcOv2 CQ4jfe3RykPEaOUAqCG40p2XE2eQW2UVbSJfYorraDgVPkVFKGZim29edIYuEpq7LLTSFHn90aoI SyFCaSkt7rYpo9F4EvbFg8QdB6+Y7U7BC1tJW3WqAFjFDeehO50LMQiK3mIf6avJsFNLeRHFqnVv LMGKxyVWLMp6xUMIQVLfxN38BelWZWt2SmnrKzyKTEfPdS/dfoCZZqLSolqFVgvoVoiI1NKnx2zc w+kwFrhVwkliwFt+95cirWzMz5BlftBSWqXSRLTbSlttLErpA5lPQcrE6eq+wE/fsX7gBXDFUYYM F1iCwGsVHXp+quZU9sMYWLmGZ7wMLwOrz/IJTo2ULNfVfU1Tn1DxmYcE5DIXGEYrRZzrGlnAg5PN WC3R7aL0NpuZlxwd6mapK+zyejUP420K65EaMOzOlKShCB6Pbd1I2zLM+VtIIT5i/SVjggxB/8kC rnMqKryr3hG8Yg1uHt2EwTaFrwjqqA2Y81ntY9l26odJmWsIqpLVzGr1BXErErKJomYdLJOvgGMy mTOpjVXAFTH4ylN3RNLQl97SC3LL6jBxhd1MoApvoz/AOOUv7250qBM1veaHqZyprWO1DOkDmFel AatBU/a4by0YPlcBWbR4tP/XH60c/r8AWjmKx46GUbRCgknAsI8IujM8j/TLajdTgTPtPL5a4Yu5 QevMVUPX1PGvP1oBCwVVsbfyy5R+FgmaaC6J0NGUZ+scBTK7UzRutVrH3aSXVHQodUfVjsnn0afp ibgzouKY+0zAPHZEF2rt5Q81GNes+osa+tWaxW3CASuAH8clw+GLQSvFs5w46rKQ3MaenTepJNF5 R/gOtCrU6jjJMRL/GJwDX/FkZDUopktobI58ph80uZ1r8JbrKac29zivExUgtCJxvbGAFLYq7ji7 m9xTqftkAankgz0Ar3106PmJRvWRLMGxEAYN3dSmWwCtErLcVtxmSkdxiX1Si14JLnEJxqKZL25h Ft5gr5oIYHHA15lbFTB0AbtYZ2IaPH/ZxBVruWRgjL+kW2kFoAxytsDS9jk2aPQio4KSMchyTJtr UZW1oaGKTr8Z2p++aMSzmYzPJVEWmNaavQDBQmUvfhYBLODLTfkf0RWxQL6YWHZ9WpCnSgoarWKO NaSygZSM4MatYjc5sHNWwGB+ypO1t8TDDOHqvcKsLunXH1m41U6p3UH3DHyph1XH08mNzd4MJDb8 1SYT9sMglCVuAlekVzAfoeKvsyXo/AwnZlesRyI+XvmlSZF9P0fRqidYdLSaieqRqNqqE8vAfZ3O hlyNLjZmBAoxKVR9fdGqRf+ksMOUvTB63EYzf1Uk4Nh6ahgBsrKUs9u9I4qEVrSiWjjuRAKY1xIf 4HQwBS2fnjjDedO7XcQh2/ux/JB+nYGvwp3DsTILeMECBygUNlWQl1trMklCzQzX/uAFPmCelzhE IpuH5QiBIkqTGHLHkzsjBh0pXwnldETB+MHbB/4wK3mHshVaTm8TR6rAkOdV2yZkflgsS9BJNjZe Qln1QdRFcwqi5X4Xwniy+/SpfsHFSdRX5ej08k/ztOIP7pymgIxgeFkkC1x06bcT8aLPk45fGbVN owqboL7VwCpTSxKvWOAyfxAvibBWhtZ0dXQBuSZ0kORHKodIgiaZLpRaV5utiJVDslRl4fqN6zdu 3Lx+/YbqxugED12VdsikaSE+TJvgXq30BEOWuR03Xm+8IOgCoQvPN2VoIlEXCTKKhydzVnxCEpsK RXwhaIXJieGFwo0JGJPIuXwBr6T0tay+JMyUfDUOVs7Xeba3CzxIxy6A0NuHxahpHFEijqtSOcry vKLq2teuXb+uhHoRAXj04GEPeOmMenMCW3z9bJfNUUH1e0LjziEBUOtrr/9xIe5koG3wAi9ylNYd OXc6+icyFoF6swCs3mv2bgx9kXXzJhzhVKU4gAylc9QMK68VVkufUZpemuEa55LQ9fIyO87PpGGp 7ujtO7fv3Lp1/fp1Ldqab1pINWFSsEECrteD2NcEuJeJGaIT45MKxtXLFbqwkGQ1af6Mr9pNLDYI RIOPcZwJNaYc/zpDqzgfpn1Y7/Lc8OlnTAwD38NkwPhuT5S7V9hEZBJmXogPyIRk5USYaEmG9qZr ZckY+hJ3ET6kpkK0oQiKQSsQ2gJVKhFqkVFtL1X30k/MwJkFOSfJwr6GEZcxKBRSkWwvbM3vWLmJ yUesm2zR4R51KAMRD2ct2vyDNS75A39eEAN6DO7QZC5XYPL+R75yI7Mx9MKay0C/cFTg7OQGMwWm b9UX8IoOMaBN2yzzaORr1qAwhQl45fG0UwgbrCNQWipLv9d8dkJ1uIqKMvx+4OFkQoZYUWs2mmwM ZK+BzdeXHQpPjiV8b7nwwoYayn/FpNcTolYqRnrr9u0bN24Yy/BESc3S1buioIpzyjkIp8jKWgFG fFT/TNIJSyPubdXzb54jxZ/dBr1lY9nWUa6Touq4zmwPSWdb/rVf/xfheRVHz9s7CQYAIa6f8jcF XcUJS9rLgAaK0cVqLOr3/d2n+7s7DvpoZT0qDNSuNN9rj7BJyD1MZVUo8/jx42e7T0c7GnSSHjTG XPq8Gf04iZPaSHedqvslkJk4Oy9UR0DfGz8QbVyBMNj7LpF5sG9kzpKn/y7Z5rGO9inS6xpvyqQQ qiKR4mBeWT47VszMcDU+2aayTNWqozd4rAqO+weWP0YOCbfXr98UbQVwiiaFzPdZjlrb02vqNKeM uD7cakZtYhubQ8bO5Vj2nkVQ9lr0VQLFQoIK/oxdp9S11e2t1S1NTl9olp2KDqdUSHyUzTBN/pjn x1d0fPWrN25cr1ANfDREODn2OrcV4M0V+qARz9ZXVZBvpC3BwtRgUOtBPvocrAf5hFr56u/Rm2RB qBqMXtD1B0FWokzVgCEMWAEKbDRZ8HBVJYbj08ePHz189DBBc07O9+xXhSwXhnRtB3+b1on0Dq91 sislSMsN+rdFuF924f/sHtIUAfjp0X8vi3PyfvrT8m/821/O2emS4LWLS7iEwYFKFyrGpOnQlbAT pkZUJ9gek49Xglj+q3ZdPz7YS95vyJPhj9mJn8Hhqi2xyV7WbIMhxefb3/7Whx99NNqvKNxVnWf+ 12Yd0Mib/yU5c23pfIPvNHlr0dZXOSf2TFk1jWV4dqnScW+voBbDLoBNTUoXSVObzNBK/aKI5HG0 Ett88uTxAoXnl7TKHJ+MUjqVfbtJdcrenheMOppj2tQh9sLpVA7Q/OjzMOMwrQOgt99rbfSJ+uMz FSd/qp2HxnBcvhVBlYrR+YoKk5KA33PRK6Ci/bFGyg//6I/8yI/86M2bN5KGkr/Wusspsx6fDAbd 4Nnayuk4Wk3fLe02JX197PUm7X9FpKC86gTnpqd1JC0SwoBVU/7yL/+Lf/pP/2kwiP3tXRg1hZLR ULznD1XSVYzSR3KqgbEtDd2OVi3ogM9xl+lKSoqC5xVETWjrHFPQFWO+/rMTU4S3NKQYsOzDxjlv o5iSUF7IhGKtvIFoQgoeEEaUwqv+JrFG76JKD5E/MAxTP8c0tWNdG4HBPv9Rlpzq4+v9LpoLr/4t BG+OO75wSpb9WhM7hZ3jgVIghXBlRUdc6Ee4Q6MPKKSzKRq/4WThhUxUB/C6LGupS1n7PKHlV/zL 2C3K1E6RkLGX2Y3s0uwv0vvPbhuNV9f83tqamQOlEUcpfl7giJagIyu57r9PnrCGmp24Mpo9GFQI iVlODIwfDR2iUgpfSOGkSpnSo2TR2mWvBayPjXaG6aFqBatasOZrWiZjpxOx+n12gTOD1ok+47VD X91rF5t31uzBqb7aPNc4XHfRJ8ob8bWmefjw4cMPP/ow7qM0Qg6D2ew/pVNMdRJF4V1aU3QkEz6U ry08PA5oFpjVbY1BFci9+gtf/xvNG8pHNyzxNkHeI8yFcoj/boVYwCZXInfxTaSiiEUKtqzSnC6E p4oPUvlsAhBAofkZH0On9+FbcWSkCpWgDieat4l8dT9dPAN6MmYOZOHK8Bz9OHXLDK1GXhx7pkmW Whpmq2XGh7oc8yIlsgq1vHzT/0NTUieTLGDVbPSFGprRdkmAnPdLF+xdVZA/hg47fbxlxvMktw/x DPbnFDE9Fz3URGfuiywwUrX7FheW9aosP+sSMet6movXEDOppaUbkqlv3BDpCJ+qygEt4yU2Ub12 wmR0eQoq/0LQamTsvfJczUWVBnj06DFDjySYith18Sck2ueU7crjia6kGw99akZw3Igv4Xd5lxcW r1de4NLqz/zsT00ELhJHKprS40B6tVPwJpCUaixFo0y1UIwqdqASaERztD2aVD7TdUJDJ8EM5KK0 yKsKOCCyAVS0qz9RIUMHQP4GoBWZIn3DC/XZbP1rqzQQ5YMVLyNGU39BtLK3VOvEvHjTJr1n50RR nhN6NI23ttlJafAAnS2PPu/oiROsWdsZElnquUBtreTBNFl45vpgcwdBFdGhiFJl8LUfF3WrlmSi s+RKky9ZciIWpCtKpoNQr5JSzAZFjUh2eiioegPQ6uRYmzhoo5OGVj25wKhkZtSEsjgV4FGGJjcF EG8DJy7FMh8CVy7yX+SqujBZh9P1Z67pvvxP/uk/6oPA6TlSBJlSKROsTRYke+tBzsnc6oZZexyC UNRdT+q6r21v6gtFy1s59pLDetuEMiSeOyezjHmshkCOiruzZhsklwxWk+K5mmpy0ppk0/OmW428 OLCMWFxSsrA8UOvV3eu9WIaUTFR2fQFYap6FdSttOSepbOQaGYgnZ8vxII8cEjnEPhQaMvIinyud TjDnYMW0RdbnmPeRrhKE03adyzlqOjlkFNdmjjp0CK3kL6MEcCIw6yiY4gfGEn8tg1Gf8KUvvf+l L31Z8UpCp8iL1nViSSl2D6AKtQiJrpglXa3SWTaGVfahe/r8J8tk+ta3vvUbv/Fvy/1TrsZEW3n7 Ge9SYQ+Q7zp6O8W11iVty8koWu1rCAhlQeGa6uHUu9l8EenH+a989a/+lZ8s+4wB0pRz5xmLBalu 1CeffPxk57GVqaobZekKjyFh2k0kLn2fhU//qXx/GksvtKnzr4SKJnpzElplUk/AQfaxC3rP+5VR Pv9t58zLtwSz+BCbaGpAYmrKvLJWe4EiEb1WN5dL1sJtg5rlYC7mMDmJXCM7H54jD6/4FelGscWz mOX5Xq85a/11nFtlV7vIJTPq1Ds0D6bCSVkRFHDFzp3vAmewtCxPKzWUZkbfxAacaOwFVfkIBept bynqRZIsxmPMJa6+KvO24gFZdMMwLDHLyKWo8Ot8CKQS96O7gSRmG7TmpdW2LMkryBqSn4xnAzuy YxaVBHi69mwU9cxLHBNwqjhiY1qOzdnVn/zJP1f5IcGe6MCOcKXAuSFK+zZqP4hUwqwdaKFDCTaq 4RV6VbxLwny9OAHqVRgveBSztuNFCQSMmt6Xnb7N/2CBcfADQSvfO91Ew/EbXYhE4J0WYwAar/oc MO20/jcizrh7Un5Cw2dwMltfLWfByCu9/x261eh1ZsXWFMkCkpVkJmzUUzPAsmjEacnpsqoydChM QbNrVaEhjVTFiON4DqryTP5kj5giLdhQzrpVc4nkQUxG5GegqplD7mPdmkTo1xutBEveecg0yiDs +ciSSoi5LYDMXJ6AHDiXwDcJcLtNQj4qfbMHXXe5owWuNGMZp8oIYK3+x3/+P2pCUg9L8EcSwOlk P0GM3hNqXHYszoKCqglGxBng/7oH7ZagLBR2Z8xhuSqkvht6Wa/692DHpFHmRypjXx/S88PW5aNV 44BmSbGqufCYI1RyB6xCrabyQbjF0JTUyRXPpQTs9N7cX9pZPrH6Q5/o2ByCEYZepZN1q9ro1QXh ws17Z1KPYWJQFGB1UY0wGm8cO3BvRLVJt5I7A3AqjJohF+Dk+TdRzfNX3RxJwhwpw49w1eKtEBgh JMVBEl1rbqVyWsLi+cfmD+JMpq9uPWJc1tKJOd4ZVb+5hKeexj2ke8e376fInSnvYPwj0ekZ67Oa C6Mm0eqf+TM/QYD5UUXEVnUmZ8joaV2OWjjFn9mFsEI5s+7lXkqYJ0oLScrflSWoBabBQXz6TQu4 aGB0lBkApxdO7WbdUCdfPlpF4eOWDVj45UGulKb26I9H0KqzpWfiWfSHRFQPeb50siNWHeDmYkYD RyhG4w7zv9BSDpE4vd/nfGBbSip7Kkb6NQ2hIO/VYgGyhhQJd4jVgRFNWe/5vnxzrsFQADVjVZ1Y 9T0gp1Qr28lXWcsLPogW1UT8Twsy96MQEd3Em8CtMjhLp4NG1GxKuEK3pNrzgSMRLEGQA1DYswpj sCXtwKrqdd67p/5BQf2NdxjQHFf/1J/640lrbOH7EZS84ZXQR5dLOKuhqrs0c910RNmOMR/zPmTX HOty9KIsTgkbYt610Ne2IQwzto7wzODfYOIXtYan7TsfaP0A0KpdWACrB+Tot5gb+AIrgMFTOK3e bOSg/vxfURg0Bseohy6FDUDmm/sziDDkWH11HZKhA63WbjV6BDRP35ReWwyLZ9xmOUFfnhjnbLI7 J1C105a0lV5eFgs8I7V+qc3lC6YnlmAswu5sKluVSV4jl5jV500h9y57Nb/e3CpNjlfHvV94HFaP ZjobsAzjGIVCJuI/HMhe6dBtJucs28zNgnLzdakw681IvsTqn/gT/wEI47SVcr84uTvRoW1fv0rH NusuB03bRZ2eis7gI6uyYEpy5PVrSsRjW2vCX/3FNkJrEigMYPHL+w1gj+aPTi9cIMe45WrPVoPX Fa1qps0ur2zYajqEXmgsxEoTmJgrPRt/bIPXeX9qLMi9q2E1hB3uQtUSGrMC/Qka6LKVPq1izGdc NNubOzC6oKpjOiOruZmaStoWef0tOvaoxSoe61J8TY0qjCruX1A1a7FJCo6ahIthnuKbd/pci7qv 1I6KvEdhblNDUPX6RzAAK+BJ7LVwWX7vphGpsdaXYyfq0MS1JciCWhCSBNMLMkczumnruG/0Knee kGS+2eoP/S//y/8FqUPHTIqY8r7KGBExEtUf1e6Bpeni1xfBLHd0pt7l+tWt61e3CYmA7SVE3p9T Wwa1fZQTY+UNrymP55E3Cjr+RDNAi6Zz33ghbOB16FX+uEUjGPJBmDCmyz2SQ8/orlnMnb1JhmlV odWtqUmygcnodcpTotg4deHoCxXBsDIewSDFQOlpSZcdOvQSyfPOvGkybpJouwOq+aIqrKFsEBXD VImLQ+UJzj/gfV1SN04ksjQClwnqb80A7PhVsxcLpq0V5mQlV2HABmprr+R1BaBsoJmkvCBeTgmI yrwRm3v9D2b3rFhbtg9W+kqS9JmsrkFX5TPDb85ShlSshMmEj5R2UaSDYzyId0imDnmvLZp2eSWF nuYPYlj98T/y+9lm/bCzJrsAyLbRtewpNXlnR/GhKUzlvBnHSZ0JlNR/qCueWOZQFDZQ3RwdiqG7 c/uWvvyrvL457C1y7lsSQSGWVd/TGIbc1bNnRjWsDKBRmGur/RjGacgtHMvO0LXa2i81ONVWcmZB WYBlfUdVyYo+L6dq52WRsN41v8CeDF9NT2bl0JeuPcFHo9eZlDRR7vJyxyXY7eSLk3ymXqXc1Wh8 Bjcn9yq7F0yPZhUy5WbiO4JiTi2lgsSMpMdW5Tbkj8i2Nr2TJjV5fxEr7wb82h9ltLUrR8Ax8rbg dkwiOEFYVb5P1uCQ3YpZ8D7TGUAvHH4XtQk21fxLzeof+oO/J7VbrOKzi3QWLqeSusicj8woHXFU pe6K87y3nfN87RrFom5cUz0K/2wlv4RSNgJ1lD1Y+duE0tXSU75M5pMvH5K/gDFYusM4t4r9OjqK FkarWmz1ielRWEGQqzkh0NkrgiFS4edAK2SF3n3zgojnpRtlRCIzwlGUx3A17we18/QarbvWPljb 0x19cpfvlPYqqYgXZqhY+Bj8PI22i2hVhkmHqcKtDMkmurhV8OVzOIbaxe+SyZG1nFAgxxnRq5Xy iV7rxOY3Aa1i5+LOCUtKloEZUpTr7mRompWVQ7orcQ0GkXRcrJY0aWW+TpVr/hwregCtfv8f+PdT Fw+5i2AuQonD8VI6D2nABwunhVRqRKmqnerZXQOqlJOg7/4BWl2/1hK2hVXWrQxYNm1025mMQFON OQ9NxoSVANKbR7lVklV0hUO4c/ncKv1Hd5b+4UeBzHRvpd2wmiE8W+OjZsiMU8w9OVHZayvpizzi M39j9fTm2oOHLh5euABaUWtNdlMPImwdibCZKdSO5pPygCksHrtQc2N2P60ZNSG3Nbsau3J8YU7j u2ZXK7xWUNV2Jql6auJWuV76EQ0Sp4m843auvxFH1cVNxU40CdOTbdBqnbIw5VKgkgg5GQyzHCyN 3k26+HxlJxHsESdb+d6CVn75AFr9vt/37yXeajIa/BDf5JkQ6e69ew/uP7h318edO3dv37p149ZN 0yiZebZWLaO7T5IAKaEKsYodDVtkhQTNcr7E9UJkkSdupDIfGP8WnijXN6yzIE6/EWjlG09Tt3zP 6qyOVg2vmhMWf0TWkblhqk6cDKChlxqqeoDM/K9kin7qLhKf8T7hkhEQZyZgIpfy3YEMM3Uq+qie TI7OaGCHI21FdojwdNtOTLYZqzJXbCDYlhO7gtrh1FeTKtfmlpOKqEhHTMa1qMEsvYZKZY4jk5P/ DUCrxhE8NWH4pBi4wppSwTJhYTMm7BWkiRTdvfkZ2LQaImzMhIwJ07TaMh7PkYl72nMArX7P7/nd KRXYBwMLvQtd6bh7997Xvva1L3/ly8KsO0Kr27dv3rylRHQpU6q9KSNPPUJEUMXF+TWE3huteJ8W ZxxoLQaUAhMNZ/1ktjbTNbhKsCrGMBbn/0ow5evPraKHmBRgcMfuTh/XGj7r4ufRymc2z/2cDxKF vADMUS4m8trAoVvL6Bx4DadmSNsSTFPwrcqSVPvUb4XzbWnNPEnrzX/oA6y1ZHiWCvESdhZ2wLTi yyWtG1phc1iC3t/Ho37ora0JHUrAXNWvo46dftfG5cOZ+j8IImYdSokrTM2U4sOBYPEnkQfqhqS8 6P5l/SZ0qXv0m5XgU1F7rMJG18jACHSFsaj505wDaPVn/qM/pdQnVd1TETV9yazzzyvXJElJmLp/ /8GDB+8Joayi25iTWWf5Ka4QfXQsUn821gOiQ+IyEoPSGGK8ggmcIxwlYfwZK3oLKxdGq6UUdEix FCY2hmllJUUw8BdxWay8jZro7cRJi6hToovAeSxpMAHsw/4q36UJhPhKdLqhLwcnn/HKwSN7eKtT uezaOC+4EMZZBkTr4fyKLKyPM8Ee+iINxK9i/M19OGJCEQw2g4Zkdi/JzSFQZLCIUVEmjw/4DE7s 0ir1EVl244/G5qrPzfBqZLzgCGrK6CKGwPEZg83iz3YZhrDYCMnFAsyMsANiJVb8tTvMV54KbWSX YMS0h6klXGYsFgb6mme+XmAJWI2pYnUpNlvyPGWJE+4Q4twUzJlkN+jrvIBvzV4eAr1Af5DbBpGd LbFgNblaTUTPKD+ltnKN202lIq1kD82+7l64leTjwX4SCuVUGV6gxxkMNDAsOsiQHABmeC3kjOHl f/AP/mtqOZ/EMjffI8KHGX4mIJO9JyjJdXQi0Jtx8kzBRj5z6exIG/621Tl0uwz3iHHBnQrUT5+d qdDqk+98+9sffPghac8wbO8uaATzDVr3l+s57QlPb64bTclT6rW1EQ1BzEBPkGM7N6EkaGZeyTWc DHuDh5pjW3n1g6/S6Rb8trZ0la0BDKuBS18Sy1E5eykpmxx3N5/3Wx2OYCimPXqDuiAVVlZt3MHZ EoHJVlv+ASL5BjvpIzNdwBj1Cag8xOyj7nI6YTTuoBge2kVqsqmsUSKla5M9O9gR8tCdastbwrsy Zuo7oydPdKY2O2FpKdVEan9xxqcDFdlCILNXoItnqSr/y7+k+Xz1+tUHP/TV+1/5sk7zUsw/z22W uhSzxufvJNF8cKg3cNxn2MBN0oZBzMGDD84y0iZNtUXF3CQOs5WmA7FPP/zwg1/79V//6KMPAyAh MRiQVaYigrW/w9N6lLjLhd28qclJRC2dil1Zfsasl01f0Pst/8tf/ieM6VDjiqjWRzbq03OoB25b H3d0uHt0+Gy0pRUt8d3vfle3HVeLy5Kq6B/VSSVnpl5yYa1ZiP+nTMHB0cmBbrOiAYrCs3DWoglJ QZSYuaa9SKfs/sC9cer60rmqXm+MJGTmI7TXjsQ+jaR0SSCbgSmXEUUYtC1lQlMIuIqxb+Hq5GR5 HK0S6DYMx1JfNGhc6XgMBxyMig+/T/UM37R5sZZ6KuhVnpaAWq0oaCbljiBJOM2E4zo7TrKNGxwn esVo9yk873xzU7sVj76QciK1O4l9glxQCFeGulecEtejWNm8lffpx37Xf+dHf8fvVIFLhy25KJzs x3IlVWRd1Z6xmqFltqNVm4ZjVxreughajX1Onf2tb337l//5L/3bb/1mfqcvyxvYrUBwClBOLTBP 3VWV3/nyl74k4zA19bIVVtJ34v7V+zh8LV5JOeh+/ud/Bq6Ttoa0NHRf6Mr9Ir2h1DfFP4y+g7pQ 2JRqkDow4spmNPtknZmse7w9+XYSDTxNSsTojUbb1UWU0cg4wISIxsZnjF6n0vYEWAtE+xFHaJdZ XUauBcW9TVhyZTvIMsfdJdKSRzKqckfPCfnz32bx8PlfUJ9Hq77oDSiKxRzqSDZ5EDd35Qe3m69b h2pFVe9HSQD02yJopZmsDOoXSpW+8nZrgUm6RSm0iA0v7FmZPqxJK/Fma/NsdVn1TDQbvTJJ+kg8 qapjyT9uFcVNThSRB2cmvO0w2NUrL+y5E9LIl4ZW4RNaUnpAJXdUUNDgJaJNLP1amRSFKWHJWlgC 1XpZXZcrArYBvax2Xs1fK7QSvGrd0aoJNU8wRXQrOIiLFzpT2t2ZZNeKlV8SNKreO/0KlYUDN3cS 3iR+malYwcBETo+PBrlGtOHNO7R6fhZFnVgIrYjB61x4JrFpkJatll7EC9ghng8cnswuNXWJaOUw yJVlsUEZULpeTT3RZynFZgtSr4GqntvGgix/upVc3Xva8zVHK4qVm5pgznqLpsy+GBCd/84iG8Ag 9basZmngJjeAFfeamzVYoQx4HYuIoCdfL7TS3bl+A9HJzWPTzJE4CKMw8D/IRLtEX/e9ZpXrlCWG vxeujGsAaqYZKaOotJYp13n149WlcxEkZZ2PHu+41Uu5le0FwkrjB2WzS35yFNlnAuSfiRar1kzj GOqJy0UrTTaNMy3CTsGGIJszOIjOfKEvq7mDkIiM/yi8C6yml8ytPOlUSleby7miDrU0EC6bL40o kyYRevkJ9RP64FmJ14FtD+OscLu4BRz5VGw8U/r1Qit8fRZ3w/6mVpzHZ9H+KogcNh5LUcQqhQ0b VM0QB45NfHwMv4lglGUrLTd0aDXUvrEl0o688h1avRStBFR2NCMzxx2KOeh/NVqJjy3bAp0qFNtO inHZ8ZK5lS724ORY2zqGO4Qv2DWMhNzj88Mp4pioMQWdHBlfde4lo5UuWMuLoQplSp8egpB8b1Mn 9uUrGQeXG/cqClm50KkJzQl2j5f9B1g1O8nT/PVCq27HxVQtFlV9xz3OqJBxCc3J/xBi+yJUqlDJ Q5ECCq8qcyK+1JA4NUhcL/Mf8tyoT0SvRkfSO7R6KVppkCfRIQiFBKtdk9g/lgNZQI0d33mbxJRm T7XIsY64XG4lqqDVVNX4uYt+eAhG6gj4RsxJiCK6qsdnOU3Hbo9Q8kvUrYhBKZ+m8bd4VAyh/DZT LItPcHuxjxLVEIKmS08N1kxM/U/n4hY+fe3QSkJ/hSU0E6BhAjprLa8EdMUw4HCVowuuIXMmn9C8 wGih3DL2Ylz7eqjmkMnxIiP7bORaWz5XnogAa3AULb1Dq5ejVfm3XeyYiLSqk5OhUPEFrUuQocsm TBDBa45WtgRlFnEbpKp4XxXXHXGNAFOPJFxh/q57ZzRWX/0149PRb4PHJaOVNKjEYHC1Ez9vAIci M+rJ3ESMGyLVKoYD4mAHAwpPxboUp65iwiZWes3rhVa4LNkwl1zvuP9aQExutirJeuiWz9DSq8Go iFcP7CqVy4tXRKuSgHtiqnesSMmD+VlVzpQNuKFtUwbTEnX179Dq5ZagSyRrv25lj4JWZSsx9lt1 8OrH5iPOSuRS3EfDFWMu2RLUBBRUSbnRIDRUEUDoHYKRljVep+7QWn0JFteAid9hEKwunVs5c25D aBukgdjRdZPALVhSogUqrDYxHJrErlmBjYy70Law9XW/hwE+nrD4DF8ztKooHKr0Ywkaq0KUG4+B 4rr7QCOnpkawM92KODeL926BuJPezmiIn0JvFVV30BA0Wm2urGoDvNFh9A6tPg2tpFupK8oGSHho abF0zkRwxOpHf3RYn1MfXnNupXuRT1BzUROvz1VHcsMXfPHRX5nNUXpiJskSTqTl6DC7ZG5lUgEF bjpwwn1hXHGeOGbQJ1hqrJ1qDNqZ2FmH/Md0eZ/sFaEy6/zXC63CJHEDBXcA2NIdQaWyBMs3RLEQ zAKtXTRZ2bs8DBfrUSeBqYx8MN6mI9Fq6+ZdI19q+yurK5KCR4fRO7T6LYhW5laCKjawigWEx9+J 0FSgTPhWFFkbCiViuJq+9SArQYPHpaMVWjjJJHblWcYilJ1g9oTRBZWkMyqMw3uVkmIZJoJ4ZUO5 Jnu274oh1awiKj28btwKdJoxouYkSBpHMwmLbMV/EpFLYlfKiCdk50JsXvQ6p91fcAAGClPRI4Rr /mNjZXlLmdjjw+gdWr0crVKDwZ7vkOY6ynrHYeTgdSf+W9Cp7UqY9q+/biXDTOk53vW3ZSO0VdWh ZBrjBjGFYzmjRZHc2W5SbvHKYnz9davADR758KM4Pc0MnXGCjw8vYSp+VgaCHmm2kt4cC6o4VRC7 68sJa4jv//XiVsZmh2MUKQo8lX41YVhJmw5jxNZ1g8gYRJF0H3eWFZZmlK6XZxo0L2OhVRIdBo71 5dVtOd3Ha9e+Q6uXohXmeDzfFUDHUpxJ4EGe7BYyZ5N/5cqd+p2NmV57n+DykqBq92g/4kMCIx3B AN0Qw3Ktb+Xk+Ft5DDSMs3cLk/5151YVwajYIyYeLt2ecoOYtbpa4kv1pdO9tQRhPxGMMvGfRd5q as2FmLPXC61AWQLL2lFcK0mwjVyhWBXDovKEORZVDHoyINgOKeWvBdvPPdCvSuXSvwGg4lQpVlc2 hFbDgsI7tHopWpGiUhvY4eudufpZjG0HODfQFMQAleoFRi2OQTvJ5splxrLrdvaPD5Tv2rUKBmf3 kdlArJxRhyx4ZY1RoXNSG2r0Bi/ZEuzqCp8LcWTiwSUc7VjB+uX6MxpFTtd5walM/ArrBqD4Z/UO vhbqtfJ6oZUvrfICCVim+jsNgIOggc6MIPk5D3WPP85F3CtDkPhSfEqVnFRiVses0FW8FWOHWJUK U7xDq+dnUXO7poVnR0Zxi9F+/q/1J/d3i03wGEbNaTvKYPtRVCobXcKvsK3sVhs9LhetdHX7Rwe7 B/s2HTKXOXLVUImKFTT5UhgsE1VwrIZK8YLh+8tMGY8HHP2guoUob7HlGkoBM74xXCJmx+bJ3HHq icr2d0gHMRx5n8S+V4RRk/NCvqrRXqs8wUYAKebEzVcMfl8sYixk11DuMIagKsYIhWsTTDCogbyN xWYR1zSJO7FOcKhE3mzgSzh1hTJro737jlu9lFuxFsUITKZUbZWULCm2bNB2ut7EBMCiAjqFo5Ku MdoLl86tlvaODvcOD2qwZk7HjGjfW7COwcv5Z9T6E6qplrjKzI3e4GVzqwTsegJGtqFyXEvK7amC CeVG1bL3QJauOlbmb7xeOeKs71JV8/NH4Vl/vbgVDk5LcckzClpRIZwHSVIox54h2fdPfwPh7lOH ojU+GpDiW3eV1qOmYmEqji9BW+vrV1S46B1aPTeNFudW7peK/p0FpjffPsHOJbLDsHp9q4zs0cl8 yWily1NpUQntHmlM6wRgZFCKZfSiMRSSO9cN7u8fPH32TEbCzVu3tNXB6A1eMlrl8oLBjW21sAOc oF5UoFjmDhRGcimS9Q1BFcUbHLPRoCpyPFSEyAgneMPEnC892hBf7PnwxpjxQqDQJ3T2ivbNnqCp FE3l65S4r73K9Cx2fnbq6N+qOiQ1SsPDK04iWQIDnKoTsJnb6ottj98i747vjzz8bBSOqp4ysPmV 2nspsZvaQCX1LAJVl9+mFGLSiNQn68Idxi402pfynroFa21ruyuq46dnVMrt8aNH3/vudz7+5BP5 +i//ekc/UTjlqAWST4LF073bzYkpOSng8R40W9va0kE1v1SRWLXeYhEHqrwmkZ1AeTt7HcyjvVkF UWkCgl/9lV+KhwV5z1QuUzq8LKDpb6wJPJlfKkyEndPqL0UH64+H5ycHo7ft2oHcG0EHRihiDwqw WZCa2poCjigXGryPd549fLKjrTA7k8wwj+eoRj7DH9PBW07rMpOOk0qE3NU0KdpWdBMUqily81rL b13Zek8Vobe3APuQs6avVaRFjNiwuhntz4Zlad4Q4N5EkSazDUOSMHRULIbyP13falygaZu+jXWE rk1xQK3C3Pyv1S0FXtIkM30qThJaCAO8KG9bjJe13qhdSN3gRfR+Xl4GggSsaFRJmaJsqFjWs10f o5i1rBJjW9tL43qQcEQwoxGVFK6MsBCl3pUTYapSyGQpPDs+0pacFQIaGkJuXSMN9mLDKc6yJ54C MzRvb9+588Nf+5EHD+63+l/1iuykE/e3LQ/PkEZqWOFL4p2ZFa0zJp1iksMLpwJXqgTM3+MXzgw0 tLnU4ALIKBOPN28BZnr8RMfjx4JupyRoj9VDVcoSiJfnRBeo2aBiu/qnEsbamXT5X//qv8hSlsuu 9Gd7JTJF3bAzsIpBOStn0MFhFnWZGb26fKpysqO37eAxl6ZX/meZb3mHjNoCzZrnBZkB0I8ePvzg w4d7B3YSV6mgNrITj5fVOplZxqcKED3NTnA1JZr9nDdhNDbJD+BqBbHObl+58t6N6ze3t9ucKg9H S3FsheRAIIYmu7koX4cSiBVU0WYzcxOoavpr3yMIF68/ROH2qv832p5NAhh7oVtDE3K8diiriVst IN2vtjqzPRm/7+z/0pJ2A7h564YKszWr/IUkhL5G1uB3pyuj+aMPffT1Zs720RK/evXayraJzNCx v+dqto6ZaEpNRs5zb5KO7euY/rx3dnpw6ldNj5zmKLPeVo0DZCWTtKOd8LS8xXa4ov0TvPGdtxnW 9xSYZajVO+Ricj3dad7Bv7JiEGoz5FqezGxhCMEdapNPPXnSLLmk9r0wLSxMo0VrgEoEa4vlx08e 7zx5LBP46VNhu2oFu3otq7b2gvZNr37jGz+XVT6NG5+FdetYYbXUVeO3JOIwN771H7wHZMR/cKYB e1kOHXphFqtqAt6xGF0eN/BKS4d6aJkROGvza2rszmTcaTtWIE9/fWSSpUrdytOtNWc3VZeDbhLw ynfVB7iqbS9WtR2mnwDYsz9v016yVUULs+gugbDFLACtbavFE3Ohb7OiPrS63lvf5SlawGifDt+B Iajb0E1drMQ6z8vdfSxl/XPrQTl7ZmtbUK3IiaIEvfupNiMQg8xa+NLjAjJYAjo7F7F6uvPUA6YN lXkeKFfY9ErEZFAFgPQQwDmZii+O8N5WuWLdkPZEdT2UF46aQjRFVsjuH8yUY0Y4nDIr57RtM/Ua R5+QdDCwQKoGWXmrQl3Lc1UMlipbLfw6HzBPX7/6nHC2TqO5oI4vGfQpiUVUMGHrvsmSAcJeczEA gmX7BabAq6/zB3PGq3BRV9VVwB6ERRtWXRIjBb0Z5Z3vrlvYCRQQw5JEukBPmMhy5q82UTPO2lyt n0HEH0zjXMqnpgUgs+NHhSQkMGHOL3PkKYmb8y7du+OulTnf/Pt4mqaxeIXKBxPz7QpfsoGFmLJG d7WNnTez0w+VeXepeE1pj9dk52VPqn4kU8/bdJRxGkPK41H/ymjACvxBDE8vxpRCSe1n7apl6489 tvR7slN0qerr1W98/XluFWAOT+gI0NkHM28yFWc6Vm60btfcalZNb94edJzJlFvN+7ql3b2DXRV0 d4hKMGEGELW+UT0n117vCmcxhFM9OfJTFrQmRSHB16JdzxrFllZULuCqs85V58p/n60f7azGKptF W8T8PFHMz1mCM5J1QbGptg/Ien/GuVujn3iBjQy9HIYz9Ao3uzfFtPP9U8jRpz5t+YYdnpvaONdP dbfmqo502MChObu+YW41eERSmXH/+V6uASeT5qXc6rPfQLjjrc69BRaVcxrbqihL6GnWv5CWmBqp utoKhbUdxxpONT01rVXTulkFNbnnu63vw1mZdwgIcZt4VyvfEL5+gMi31a/1LUGrvX2hlQzdmNyt ExqShlFGeQrUdkCKl6Ab8n3YR3fReWBYatIyInis2AUBlrIFq5t9ss/Ny/3mjlblVxA/MM7S56IP bytaqZ3j0OmWzZwPSjCWFDVyiIBJmZXbiAVj5NCCsfFmoJUYhm2lBlTB+5jQeZhAb7xj8XmjjUDK KmfWYmpZD0nsSGO1NbsLLVnJL5VcQfBEjMLqgpWVS9dc977kwlRVOn47uNXewaEAy96oGVhdIIAX 2Fb8JiZOxvXmTADMWyBWGqhLpYyHwiKdplj2LSe2VTsWrnVi1V7X3qOBo2tXU2euie7TGQYUTr1h bx630uoocfTIJX1T+HDer/h21FzEKcz9xY5+1mIHwUoqu4jV68+tZAdJUpdBlNVOY5UYjxwFVyZW pQHN0jhsB7I9cQhX/DcNqbqHI3ag9aILFsT3gTPN+xagZiWHJsKyTYJSz8HWmshykr0laCUfqL7U gbn/mT93qh9l6ajaz81tZTSP9ufvk3Hfwg+KkVrequ2FV1SKb1XEyoZE6Jv/1cdGW6+1ayKOxKAk LsF554HLtwyttFq4FggFyIeOJO1r3oxQq0xZG/Kg/MAXGzWvL7BD1yVbgqGcGiSJlAibwhTwKAOJ uhKVsuAJVzwzt9rMbg4XnLMRaiwv9sCj5MwEslLz5RIVvW529OnA/bR036QAO68lrvm3Rbfat0/w yL5zKFCjjt3NN9G54wtsHAx0s48oG50VTfYviT4rCKs4oPKZuiK74gkIrk9x/PDnQslYgHUZpQCG Y9sdyxZGbydaCT4c93i4X569+Pfm+EqYhhpyLr1qcpKl5VFmZYN8bbGs5ktGKxZRI04CzeLDaazf tZJ8K8HpZAJQQEqwBinbTjr0DICaoVcuIUsj5WZsAknp1PNSo+/DeWWT1nqTnUPi5sfSiMLiyu1U iH5LuJUiGPSlhSXxC03sZxWZ+Dk6E+qkJqBeQQRFoxrPYhTUnkkNuXKmQH9NURo4zgX8fo/IVsXc urOhC1uFm6bnGyrp/nailWNnKOI7OowtrFR45LzGY3OPUBu76zLVka/4oYmuFC0Vuxu9zktGq/j1 e6BHgCrGnZV0DhsEOIM0oR1RaOP4UHagPGoavyVvldWIac7igVRUgSZtP8Yu0l52nEBRBCZWfFDI bDWdejyL7uVtQasjFSR06dgLhiC9O3FaThhQyBVnd7SKsVfiUaUmGt/dgO2kwJy2VRJULTvVq0VZ xTvo71Hmi2z1CwhcJUcoFYveTkvQ0bbDySIE0cpDnwS6eQ8H4JAfOwuxe65NP+1XBXYJrcbL6l8y WglTqOklo6FYVYOqFs2euvXyobHrhIFK0fb7B2RMbKtZQkPxttndlnjByqJgQMYMrNUVdbar2qNQ vuD5qY2DYIURqJFACb/QjhipLRz9LUErT5Gj4+fCRVq8woxdTXmWjb0pscJbEloVrS+p5GB8sS0a ENjXPDk5lspibJp8ar0joR+TzrOtGOBM/upbi1byCWrRODqcEzT6aaUBXxAZu1/kUx+oRUUuxCMG JKvWvyoE9/qjldAlFSeiM7OaElBVzpoqdoT67mRvcuuEVvvCKoV+a8w5bq1EeQv0CceN+OUIlYKq GqolfoyD+II4xYzIEl9TDtUqpKGv9lm7uOC3xSeo8LjDI++4lQkQEtXVxOl476TGD9qi3CxBkChp fQ2xstjEX8Fj/qBPkuNcgNVk9mrcnu+Q9C0IHF9ljhqrtrYS7fb2cSuv8CSd9jyiuR8QGjJ4qAET B5lgpPm/NEqEVrV98MiHXjK3EhFyVm8S0RLGjKzuI+FX3g/ZjkKiaSn2ziFqpYFGQIkTy1wVzCc4 /Qx2lelPYCjRH20slt4x0iSf69zEFXEjdYeYJf7i8hJuXeUN9Oxbwq3kyFa3KvqkOwRxflwMY80C NWM9bQS0YY6t3DGkPSqc6nEqPltq8NnhvoYShl+09tIzA2ylsnfAMgVzoytMVwMpu5i8hWhF8r2M jvnko+lZk1y5uce/0cp5l9IBx2xBcWVVvXv90Uo+T8GNoAa3dQwlRGhaLu2UVH2IVd/6y5agGka4 5Odc7d1FdqoGK0gVC7CGaoCip52P75I5d4+9cCJ5Rn0qZEBkapSDMrYqh/r4bUGr45NDZ8ZnBEaf 40aTvjc5pg3WAD1SFduCzXDeymW6kQjPYmIZNHbSKIP6+Cgm3gQAGwr1SCy/YTW3/kY+ubjV24lW loStsCg2ILE/837F/brAoTyNbLo59FpzK1WtHHfVXzK3qvFLDaxAFMuiFXK1c9L1IV9Vr75beUm6 9N4UrvhO0RUMwmZVWV7NmlrBOzC3OOYmy/niKDTnK5menqh9ia9pm5tsmUBcnZOK3hq0ksoo/bAF L9Ba6ptCqgZYvRFDsiZolc7KQTe2sCxwqp7uirxw6mR/T6GQ5tKJXOhWn2UuY1bxtO4mhHI7g357 +y1GK6shShIaPNAQJ7x2TuxZWcl+9KNopTVNRW0WKDVw+WgV4p6aHiUpYMtFqKodNYCiacqaTteQ j5KF8OUzorE3quImT7tFuW9q7eWiVXK4p6t9LEDMoszWMAbUuvW3BK3UGUdyCbY7nwXyP5e33khW h62Qz6wnbVWJIk5LTYhoEImmWxJaHavm4WF3flWdq6klyeQDt3hV+K5Ck99itAqdxWyp+Ok5HyTk xLkFI4c+RBL7AtxKISeKf3/90QooKeNPDx0sg+oEp0pGpu1uhKi+C5Q5iE42ohnDEvVOrJYojM4q gAqpKiGs9NpEDYxTzjmZ1Iunldg/IxFoVfwaVA0PqDBYuQsX/qTX64XFZC8M9kaKZhJs/3Nh9uT0 59bnCvlw9F0lVaZzsxpBwExW42Gtqh3UMWvSfpmgafa4DUnxadEtr1fzfd+upvmtBsmVHddzItvk tN4ZIxhXRPn7dsdf4BupSVwkt9Vg0EjSqiy6pMoLqgalMlD6UsUtbfDlXGsSORL+LkyjToOKFapy lE5x7reCGw7li6K6ffkGkzfbFtSYFV/g/bzw1kX0MoU8i6pyjuG1+EJY1ZrkSYlxr+BWxcmKkjRX IjcZ8SsPynoqePBkJs4Wc3Tkq6IrIiqOfGlxUQ/oehDK85Ez66wzJfJums1BuffmFqQhfcHclL8Z eYJGHe6z+ttTId+KJHaVCpttm7a+uuFmdaU382pH7yX9bdkRfBXKIoeg2t1Ix2IHujUVMaMmF1CD ZuZh9j4ZgcLxL26NgTjfl9vIF5dajENHWw5H+s4dHSaLbTJyULshG6+PtIuqBar45GmrnTlns+g0 7zOs6CfPpYEP1Gzxfi8ZYkNfjrpn8GTCZTKr8q0PomdcijNpwGXZRYIlUuFkWqrUayzKV0n0LUs/ Pcy4dx9YyZ7l6/SFmImfshwd3ZDl8VF6cDNTarGev22CKzM6VaNfl8OTiCz+X1e7uvzNf/0vwhYZ Lr5WFi/76KEMVKVomnUgKp+BQFYwNnm+PlChHgsoA+oPgY4+smbt3D9Uim9vzzVS1Zy1IWwRYSz8 cpxAjVOMt9q/Suw1NCbCt3y8nMSJLYFULULhPU1kVa3d3z+3JRjyHMKaxSnGYid2rfIep21vX5Ul KFDjrArB9vgg4WC2zTRjCi3B7yh2t7mysrHYuueLGjzUsccny+O1Q9U0CUcc/DyXLnJTD9bYVdOk MPTox52sLB+trhytDTeMalo+ffpEXrahT4wT/nTibBl6OV7+NjKDGsw/66MtUmmGQ4kHXNOIqmAH lY1Saip1suqghBT7GWhvA2//XunQ+uWKKgpfuWK7kRourQC+u4dx5HePWzIbHZIhIznf2eyaZUP3 1ZCkFyZKd8RumVKNvoovvwKt4newfD12qHGPT8/HOhV4p53GKyupWKqi4qQmdoTNvgPJVs8Plzlu 2xLEOWJGxtFBtz3twt91Tn/EK+L8XTk9Vxetjlt1pgKsB4BVUjgLpPyAvXk9dsioyCHAUokZoZWC Ssc6oUW1DuOVluvjE31Nad88H70wWqUviq7P80ntnHhGRl7hc0/XVo42Vo/Ht1DZ2XmqQuIyqUY/ cbHziy84ZWLsUFks1TPPxu59RLlYsqPTqJpM6Eeq9aWwjMaZMOzO7du3bt1OWqLit4jVYq3XVgmY ITozb2Ko2/SmLXoDnagirjJRx65y/Oy3RbdqVCbcpjObEJ3Bgxd89quq4NXgG7M6Fbt92Ry7CJpB y2nF1/HuXfQVoX6jR1p+xrcHH1VodczfOb/o9wWucxjhFm3JH8jrhPwk2yS2wUV1slrXEzgTg0SO KKV+s7NBRJCy1QzpU9m9gKm0ZPWWpTOp1HrSA9M8wEq/g04HefFizfIK3SrzZaKszPspuPaH14Q+ 1uf9mHZeOFOLeoU6l/7N5KnA/UrjzBruRg8TewmVK4OX62nf8pKY9wqr1fdZFMS8sFWd3jzHHRQm AtEs4qWzBn2quFYvpzV/4/TLmv8lPlOjU5XPxLBa3OycceKmxuTgjn3cBOCGXljWdKurOW8fOJCd PU7GwyCZyAfecuWyjqbsjH0e6RfWECYvj2s6QlDXRxnNNhk8C/THUK22032snNqys9X0q+SY+BYz tzBZnEkydpXjZ78laFVySYT/9g3lz1Wn/b2FiQYQOxAEwDLQC8KYr0a02qpy2r0NVTyZLUjNP0Ny ZhxaoQP1WgZWt2j6dUwpiq5AMUWyEkf7d0G0EhYrwrOHjM19k7EEiXsbEKF1cl9Ghm5QHxcX2Ch7 1tbmp8pHeHvRyuGvjPwUDu6Yhega1SvPhbzX1mc6P0n9tv5EmBL3kE1MSmNNwk+5DuMKRwX2vwUM +aHu1slvCVrFYmKOYIywhuBtqK7xo/CtRpXU6L3D0modwvJbACvohptuBmcCGJdhGAarslmm0996 YsNKPhdHJJ+Oz8fjRbAoJVQiwWjvLo5WkMfReE1dewIRMxUGjqLwA6/I3LCMgsY3N6L6RO3IdCx6 Na6CvCncShAlpKlNchDmk24z/VEyA1BFVs6xatMEiQxVOLtw9yc40zFc8ScyERrUJRTiUqDqrUKr LBfUTWi5LjhOAkkVGDXxywR97GupLISiNRNEiF89bG3CvlwxBuqxCLeq/u7YxwNgIR8ckKrBkCAU F5xTJXjlmFwaWq3JDFRbDmGA1nN5LShxMq/q1MSpJjgsglbSeWu1ny8+Q02tSXlsbjXanEtvDFoR 5uC82ZSysm3BJEhMU/P34/SeuaOEO0GiChJgKAJWPe6BuCSed3k8oMoJPZ53L06f4eZ95QveEm6V YR5OFNO8BW4y9Q1W4FUzrTtYPGcJTtsrPVdAVaFQmRcKKXCECcA4Ju20pekijwuDi4kavGqCWpi6 PkMbGStR9ZXd+dwJi3Mro1VPfpj3Y4X8ibEeQx1uttUxGXipF1u86KOSueOt3na0Ui/gZk2gUcw0 fouFWKE7cZLb6WfPH85HnQBdMkF2BFzzYaSWrk4gtCk1AsvlrieyUcW8A2XR894itGrJB+CLV4UQ KgAKLOuCVgTHiQPrRU/i8+3ZJgRw426xyj7OrZ6Dj/wa+zVoy+pXUzdDSr/rs7Y3NrVV02gvL4xW K2eWrmoL3Lk/VVcbT9MA5HAq0SIhAiOHKkcDV4X1c4tlgv+3m1vZ50e4YJl/+MkKqlLePd+KG/UM HVeXLPYFyFUITQWVemUGqYgzqvAg/9AwS/DN3CNlwRPfErSCU3m0FiNFcQrZaiCQ2NhyW3TPSCZH g68We980rG6f+f29stT0j25Fkfux4znTqndazdFYRE1RcMAkA0ofpNINV7eGd0JfGK2WJQLiY40h MeeXBq4zO7xl1thRYbiOD537wxBiUj0lOv38XwrUFL1Ss45OmjfFEnTFK8hRrI2Yb7gCWbYnUoNp VOnsKWFi10PyLKKlO9e1lvaSfMG66FoFfCJWcia+Q6u5h1Nl/4BaPRIWUT09FUMwP3NSsKe8HkVy 6JcGSfns6YQnstz/lF7pHIVBsPJbT6LcO7lro6fCKbr82R6cKMbl6vaVa9tX5m6OOnFBtFITnZye 62uKnnM8dokSYnfm2Tni4jmq6VseqPlxTjdZaHXBcH71hZ4unyuCQQ6w0fZ8o9Cql1tod9nQC91q sirGoY5YHhUlth6hgWSyJTGW2VBLQkFVYZZ6QQGnqSzyhR5vDbcCZSoQoAnWnVfRT9iCs/2HZ5Yg 1dwbXl1o7Sm3qnAVQlbgVtGtBo4y7EsNm33QdH5OoKqNIelWK6vXr1y9fvXq6FBYEK3UVEruP1R8 YITUeY8kBixgCcYUnB+ncqZ7FPSf9/r6eWIPa6syI0fb801BK/sESxipnPxmFFRREG7cbdiKKpWw haXgQm+zRb+pt4lXgFcxSNl8OpTN9aZVwfWdJTjneLIc2ENBYFOw3yK1yFcRsRpdqtiEkrSDShCr ktNDwKZH/pgD3QrAGpTZG+cpitfn58XlzqJAn1kaHHLR37h2/ca1a3O2Rj9tMbQytdK89N7XY2iV gGmxpEvzCeqD2CTHiu/Al8bGurZrHuYCbwpaxQHYo/qmER4ZG2UT2s6YRV7p6dTCsJNqJqPUIK1M tEqRmlqZywoieWcJDsxNI0hDKzAli29mTVuI6zlE7cBS+2tHq96XL372DNGaT9ARSYNHWaNxXHbb NIscC1ZLyWa/pUzD01MNhVvXbty4fklopQvTfsuHe3tSoCZa6hwPcYcvwJK493GVvYIbAfeRL+uU Wxsrm8MRIW8MWmn4CnSapkpKanaVZ61laW4jHL2XQ3/ydn6mnPh94rVogVqYh8nqTyldyxr5hLYD zzBXHZjhAdnPrsHwpmQ1p9XcvrhnSSBPGhOjmGKwLaGcDSLT9ux6UJZL0KLBBqEo7Vuz8TsVkgit rOaVwVSDvHm0SQsDVfBhavxx3WRg8WcGx9LS9SvXftvXvva1L39ltHeN4K3W4Pyv1VXuPnz07JOH ip6a/1UZ3Bn6Q6/K+h6X+dALM5fUfEOv0slrV7c3793euHV99IVvSlazfHtsHtf7pOhUv9+XtrSE JxUIUSXu2IPJE1AqjnepJ49ZMTSqMuVqrS7h4OxmkvI5Z0Pxy8NcdbT93xLdqtnbJbAjUTHTE7pU FCtuQdSstrzE7QVut/jMWZdOYkkvtqtw0cGTKdw+chTZ4/MmZmAjVwFI6Im5FRkNekoj4/aNW7du 3Bjt3cUsQV3k/rPdvSdPndo6QlrgR9N9Oua93oq7Dh2e/0t1o1hs5mB9F05Z0sS6emV9e9jH+qZw KwI6U8ajF3El8oaQKUZFxnvEjzr0V4rLEBFCNEkLgw/t9z+dnVDRQFngzDWIne087LWYd3z0Kxx9 wet5Pg1vyZUqU+6SFLhIt7THE8MtPkGeID2TkztzfgGAgmaf/8AELWAMI+dAO2gCQhhYhbHDHTSZ e+zF5bS/YNKRCINHj0gcCSeIl2nBwzvuETMx9CWivQAju5yW/758ChvOV/nWNqj7TwfeTEbyBa93 fklnWAusXQotDLhMg+NTfOhPOkHjN2UnI8J8X678s9/kC4fDS7iHMKPgTS0YL8GWlo4566gKZLgA WFNA6oD3+VGqvcMMql7WuxklsRIr+UqwZeUqFuFlHYpmVllEWMvoF8lll3UQKORrHDx6LZTLatDL /hwNt1aM70LJ6Y5YzTmUqZM1tHmZ+CWqhRgpC0GrJePtwo7U4unlLPB8wCWh1fK//tV/nt0zcrmB SX1vIs5iU0Vz6+RsabwaHxN1gXWvaFTx1wRRoVDjbo1UBACgGRXJVT7UqepVq45fnovW6/9TX1iF /PJmzcsuxWpda8u4YqJ+1yfSpnFnZVSw41ILMM4lhG/oTwoJu371+o/90Nd+6MtfLgv2xWzj0MSW qwWlLM7mxhxEOr3k4fc++OTb39HOHOPzbBaRGM6aeri9UGol8+PE7euL8nUODg+cYMgxQbtKRygY LwOltDG9B1UwN00H+otaH01+dm97TM3zjRvXr7x/78qdW11r7iF6swdtOemsQU36ZGfn0aNHC1Tj 4/qH27JR1OFXLq+tSzAPCPUPzoO0LQ+s900vSxCXkqLV/k2Wdfdlo9lWgHRLFbu9weqWKo/KGLx2 7eqNGzeleNUdTolWHicd7kW5ZZCSLf/qr/ySVybFH7ePyiiis2WAXPDpz93eisI4WVoZ02j15rN0 8bk/KSem6kV3s007pnfPrKuQs/SriO7TnWc7T3dADJMYy0W96Ggv9ToBLxDkVJa9iquvsRXX/Ide 6sr/T59qWjYHcfmQp6On5Qr5OZ5funZl+4fe/9KX77+X5S9rmi1egiOfi6jPX6NJ6INcR3ywHpMa 4/GHHz367gdaWOe/O52pBhT0C5FBztUVFaanHqqL7aJwpHxSCqimrGWklN39vWc7O1rBO07VSKjC reoVOsgGsiHeX0vnepv3v/SV9957X28T1K8lKQsULtWmAk4Xm6XVK1ubd2+u3bieqKKaUKx49XuP aJmGtiwvP378+OHDT1SndqhZWJk8woZexcmLYJxfp8BXfV0Ukip+59PfF0bWMpkugmvYQPbIQbTK tyrR9+DBez/8tR++e+8uuBRQiuLBt253tsVhtgDQ4PMfq1//hZ8hrbEIPK/0G4RS5JMn/s6531mZ ZrV/cV4+15c+KkIr5HTgy1mWk8TYfpWtlWfG+5QZqyEB6sOuDDcZPgmGsJuLAd15TgNBldVdFnvk 0MUcaCuSvT0Z/5lamXdTPK2uZrrnS7NZNWVvXL2qcHYPXo/5Er9ydyW3eezoX8on12Ey71k7Vh5P n3KkstH7+/IiDG5E482UdAFyInnrrF5MlxU4FZ17T5T5wVQmAt6omoW/NUj5PzIgAt2F1vLOqwvW 1u/euadJwtbEdlFlU48qGF47hqaKXPvodL8Cr7c3lzfWy23WZmZhQ5tw6duaWsw8yTf7+3uyj0a6 3UM64NuxeO4HQ58zOZlE2RnfbHzqs98uoykY+eJRS0CqkUrCcmlxNcbe3u6uHIIKBtza2i5rBl9R xneIQbm8MnanzTuodolb/bPsPp2uyvDX/6BVxtY4WinI2LuKjDe2N/tdYBU60eanbEU79Il6yc4T H4bqicVXWZ6JfGhHwjUbt1rZUErtILfSte08eaxPlBQwdJ1Xt658+d799+7ey2AyHYGTBJiAKR7y ROArI08Da2phzfmhaopnimD4+KFEoTlfktNwKmnnn/VGXosb1NDqRKE96E94yWDP4fZx+UszXfyw /k2u50yo+OD9Bw/uP9Bte8xgTyfqRMOo6nEdk9gAAE3gSURBVJnwM89m+jh8e3N9+frV5atbjQLU qlO/NoO6Q1WnChoqDz/5ROVDh5oFtPIx9KrPdbLIZnb0GTmKPY2XUnj/S1/6sR/9sXv3PT5r/eyV 3IqHxRJg1Jpt4ZqM83eEcf6WR6udnZ0nOxrdZWm0PSNtSTTXeDCro5UGvOoTaEYuEF4iqHq681gT c2QULSmf+f279x/cuRv7zusHeDUjUkXN0YeCZYwa3YLRapQLnJ3vP3my9+ixo9JHjvXNjevXzQIh oLHLkv9a7ZfYt4S8tgyOSIzFOxp8xERLkZLJkdU0Ll+SBG/evn3z1i1PAQqhsG9OyZT5kLIAXYe3 2KwRdH317Nr20jY1LbpS1e60KMbFmZ4L02B59PDhO7R6blDIElQ44J07d5L5X/SelRSDv/sny3hE B/OBWDwAV7/F0epUopWEKxxwhA0QVZpNPuQVc1Bpbe91Aa2isguzRg/t7/R0x7rV0Au3N7feu3P3 3q3bmaoBrIKrjlltU6/o2hkNFEU4HNWtbAk+2z16+mzUhNze2rp9+44E15KQDBXUTvI+A95tIAsA FftoT0USBNeaqdKRCTWubVPWSoL3WkvR5fR3bSel7a9NIniHMq4rSQREDKXq7gxO0543J1vrp5te brpwEhvbx+znrINzGlvePB7d3OWt51Z37tz90vvvs2y4ixzRkN27rIJViGnFZ5WrskhWZJb56ZV0 q5/NApSe6sMlhmutM4MCDW8U3Wr4mM/Efv5t222PfaKn5aFEpIldhnDma6i4UubRC2In0aHjdqC2 6cOj0Rv2AnH49F+kxFyRKrBxob5VXC3VZV3YnDyptoDduLbBUDf4XRWsL/Lobc0HjitXrgit7ty5 LU+dNKtsBNVF9Qjq7eiRZ2Yter6iGF242NGG+J825HrSl0BQh1xOOvQR2wYof9fjzc1tZ6jVxozI FrE4ykQuoT9h2f6OqmXFan1NWc2c/ryyHpXFqJVRMJGbvCHMwaFmy1B75q3ynpd09I1dRz4v62BY 5NDhztrYUDtS5jobQdci0d4HDZYNMqPP4yTWXiVjzfJbmlupTbU/9+6zZ1l9HbqAltg3M7IF2PZT 65agThG3WpWoN0Ji023a7Fs+wdHhvrm+ce/GzdvXb2SqV4wJ3MOKVclXNQYiNcbVZRuMWL6hwWfA PjoRsnorgpFDzu9bN29ev349K2ZnWG7SGNPZerZUJD/IeKWZ5Xk4je2XWyhLoqIcMXybSh96qW/x UBWyNHW4U6m8ef2aiQFmnCyf760uHSatjWdyZh0vqOHtrCWPlZ2dUdnxredWV65eEaHW8lFB7llt yNjRpoVJ09lY1yhmq1V7hPX1/2/vW5vkOK7s+v2cBwBSJCWtrJDk9Yf9Zbsh+cc5HHY47HCs7U92 hMP2rsN6WfYqvJJISSRBYN7d090+j3uzqgckUNkghxJnCo1Bo6eqqyor8+S59557k4BVy60eOlph 1UZs2akph8NQEkjFaqm0YjTWStIgu/ftBmjFknWVGyMo5xf4wqrj8ISfHR2fLo/CBmxpFsJYymHM qh1FNNcfKF+yOviAaxuBW6GQTh1Y9cBfwIAggPJxDUMp5KLwiyZOxD21di0Wr6WB7Hs0W2pUWs6g DRwraBxELZlQIUQZ0L0DQ7om/Fj1txe9zSUXkdOVtkBNs1bYpmmhBgHAP3p853tkvMOD/MajFUAJ VBfyK80xWh5VbIsrpIIfiyN7wVRxZvwSxiK7rOaSik720NHq6pJBWPZLhZToS9Ff4xWTDNLF7thS pNHebsB6+7WcBcPyCv0dp6tDq/Fw/AT1reaL4FaiFTGijVPy7BR1QGFgHHnSJ3UYU80usATGu/6E xaPqjAJeVUjAWhqLtP8CbRQ6FN4omq1rhbADMACb3OcL/4NskgCkooNKZSxBxhqPVgwrrK39AGLL BIuBcb3bnu3WF9tb7xgikoy6R/A93fINZvV6jNhfXKBrVLXnNx6tiFA0+UNGJ6QimZJ0FxuFpHoP nAJgkXmhy2J3R1K697KH7rcyQmlYIE+5ZRrsx8yTJuTggUcPZkvNtOD+bTDUgEw9VYc3cLBgzRuo LP0lreHnwZ1qpL0xZMFN6GCrRhfRCrVriTuNPMoQ8/oNl4K50vG/MshLHJN9WnKocGhpsvVG4YVS amk6sKdzbg4PrZQZBK7wpBcVKG32wLW8vcYt1jInmxwUjybQT9RlR/lQrtSl7w63bBC/tsTO3pcM X0YtcnxS1Z7J3ipIRO33393/fv1WvMFoJlog0UO1kIv7p+alWKBTdkwkb+AXkjd3nRQfOlp5XLGX l3Fvy6A4WYtF0x4SYCswA+t7rcNUYey8afCX3+OJjhFlsXaW12YRra64JbYLtNXgCFyLW6nr/Ear CfJVO1+hdzQcYwsNK66isEFRfweKwuUtKaf/gxsKIyJ8+umzCoEwgdeOMDLFqOTDgQGgwUXegYHA rFBzGLeYN+dT4F88b7iubj2vGwhT3KUYZQqCfcZG/SAXASWsj2i1PzEqDwQNJbGz1p9wDywoFKpv DSzmQWkOgLcB+Q7s111zQh40Wml0cQSk7RFIkNGgCA7aHVLQIdS+0CLVo5WVRUaZ7hvYBQ39ECh7 KZpmOgqo3Z+h7DBu4VkFYAmtFIfeS9Z/8/UaPwUNEeILoy9l/3YIMZoRfncJo2Vui7A4PMdv8cUb obwalCWeTrGSwIQf2Z6MQeJbzuZt0Lwl++A9cT1BuK6EVglVAe7FgRWUyvDYyCB84rvctkPTNo+j w85fwi73zq0kESk6dWZ52I4v9yIXFZsTcruiuMZUBfOwbcu//t4fNFpF59SaN8FTBARiVhwqHDaR PZAQE/16izgWRlFtx7IzhINELuOOL0blpZko4ySYYLnsJFn43LxGdxA31Z1pxwi3JYg8ns4UvbSD 4KmV6aJECF5VNqkHPwFHSU/Uj6xW+NC+LGxNhE4KjCIlNWB5+bswNmFTC4mMBYnPgvKi7UiMDdw0 Wg3ArXq3cnx5VBnaC6ly/ntxY/qpOfPQFm7tc/9mo5WRSJSztEwSVnVH8tVIKNADdAhrs1F1vxlt 827bI1qxqWNOj2Fe4tkZynZ+U6tBOZkTrSqVAcq+5MB9M03Z28OLVmBQtuGjAJZ3LVeIe2kGU35N t84Qe+G7RsBHuRpaQPDmt+2zEHdiszjEvMglaARR3pAfeLNSzT+ikJcjxN8osyc1KX+Zrm+xp7zO gKrIaym1NQw+Au5IOOXDcxPJtMQzWCMR3rO/F/ZOVlW8YsnmAi3FCAO1qhqzzdoOOPDAQ+6XWwm+ 2byecPQzeLm6LTf3gegEFgat15DMIfOh+2I5DxqtrP0DJpm0ahRwJmCAMIqK2lGUbCXxgq4T1FGo RyuH1u14rtqUJN4M00KCbPO30UqwFWxGaRAVXszytdBBwKVvm6zqVaAzEaA9/EPKTj7FXHJilX9w pmXugMgWKigxaVD5TpHtZ2WWyG4Lqny1xWiTjS0wSxJaKLPhPBoc5v+gh6gelmsOq5PXSnIg4hSo ZMGK3ZryyISs4QBiVZriQOg54LD7RStd4N06uvSqc8Yi4XKzmho38fbV7fLo6OT4xOvXdtkeNlrF nNp2ShOcPCw8V6fvKjArpkqi1W1tGp17rcCxcvOyycEGdAkZ+y/i4/Y3trDMCqU2L3xzr8DeXtGn llulItQj3E4f0qIAnZbDyUAQJqH+EZtSCVAVAUxrj4RMVkZ6Dn2fIb1C7n1xKnmPeEwlaGIyRdzN VsAn4NKKCQbMt3hVXLmMzmYrmgbv+eYWfGWPb7Yl2MyX+/26NEMqhRW1kMfRmVenyNZ5+gTBlo5N 2tUb3/Hr/ux2U9ePv6WpzUby4yZdxE9F2+E3WiEvyZPodMVe2bPI2okhRYP99pN5kEubPt03rCy/ xspekE+htgr+4gfyFFlwktbdHRzKogi4bODU5cXlyzNsSC44pybtkmUSeTjTCwkiACLBFEKVLJel cuAIJ/X3VubKUvbBiTSfh6fJ/2RWIjEsH6ztxZYxLarQesKtFj8Eqg7vK38mR+YMkq5H91dN955O nKsg6syKyXyu6CNasLKKq3516lC4BardOuEdqPdi0nMHgXilAApjUt06oN8Bp721ckpOs60SxoM4 drHc3vryChnDtd2JE0rlE8IpoPkdrjejqIjgogH2DChu501rKzk5RWXS2IW0JAlD98TZ8DyLmcWf 5uP8LG5o3OuPU4bU/R7RfqyDmxVHi1EWllkhGGFaF0q7U74zfe3Z70Oib5d9SS4KT7klOvKLMW/v +sazSAgU1ATaIq1W0gmEVVO3CrHVoH/Z710PXJ4osteFqKUYvaGVC5T5kclGjPHXeM46Nk3KLzru /va7QUhG70blnKo2ZNX22gsIM1s9q6Woyak9fBExH9gqYK8eDP7yL//ZD3/4I+R75vQfVGCPounZ hgPyK6pvNWDgpXoiCjSuVwYYxPdnxDe3ucwG/i1xavlKVPiZfCBmBHVcTg/utcFmV7c7FHOu2TBd m2BUzSc4w3i7O1rvFgxCUtJSSgtw+mIHYXfg0NSKJRRWItFXte/Chdy4mKMbhDe08MoIx4lgWF88 2lGLWtlv6XtSlK+J62WantxLOd7TBWSlSBhwWkZbCc7qzHmlGkL8T/lhl7l95mJBqBlosPICL771 zGP2f5Cf7V9qR5QofbHdvCQM+ZI546PMqrxocvOjNCCKZKGpEYHhAt/BtADEkc9Y89wzQFl1jO8s ttoj28G5qmMDLaqOiWfVuEcL9OQjCuJa/us3eBDf+c53P/jg21gVzI+Psy3qzHLe1XI6KuCAN5p3 +CC/Em5Fx0d/O2wt497x9vFwHHjpuH+z26ElH/wNggBPoixs4pAVe60qNHjWFV5FzU8NMrjlKyev Xo9J/DckEVU3ON323r3tP2OisWZ8XI4uzKslerKCaYRcB0jBmak3c0LWmDRQCCsYymxhkRCPbQ1x SyYbGs+dkeo3q64ygQYEzUd72T0XZpcDQTbJwmlNF3bjtd6hGgLLI5gDdt9wncznmEwNts5AErc0 z3QnT5qlWzX2XW52f1ytnt9E9JE2q+1WPR3nXOEycAsJg8FfXVYfTdr9Ij0yffKqo4xWnhxrD9RR e57W2m+o2p+NX7+QBI46OX1yenLKVemjsAzn2tmUHVipOlzDUMkOXM+Q+TpVl/UN3VkDK+dPhQdb U7tS0XJgR68NEnL/zZFzX5a5ahhHyirDkW9/gazcnJ/b/q7W5+XjdumBA+9MIMVTe5Clpz08VOal +k3KRJuPikCg/dHr3/uhuUmIdHaoJ+WKeSj9fYopWqnOSwzeUuIIunlXwTLzDbSNMGO77arFVgc2 5gM4zLNp24ZRhMX8QBMyf0roQv/n6sGjVUSboguWcF1xtLu0XaBUQ0hqa7J/aV0vzKAcoiwaFJmG YRYVvDBkeOztIZX/0wIs50u0yU6tTV1ur7BjnbpBm9Zb/aJEiXwtycX2UOFN/zFFNVQZsaxE2cer O/duczQAa69hEq3lH97b2uGLL+1BPvgv0kNAaN3LwBm0+CcdiNbkpdRFePXg0cqWYFHx6L/pM5Fx lGZTVEIUZ6UT5G3igof2VEOVtHdB+CLFJUVVFGRHcop8w6Y3e8DUCIfi8xakWHEUmtD6i7S7il9x B6rauGVilVclK1FnFeeqehmI/YyKJLG44CigS3VrG7F0ccbSvJAAzL2rJAEUapGflqutj//Ut+ID OiIzGqS6M3/yRrvcG5fQY3xYK1Y8ohW76l0qYR+gvTquLR1uEAXOUZ1HNQPurVsFY7BzmXDlDN0W 0zNaKUzGQd8UFIxyCAWRTBwKWgRqtAHLvzykdgNhP748ztAmV+GCaT6yslkIFZjaXEbHd8i8UTp0 PipBlSlWYwgGayxfqSBFAciCnAHqtkjpYtdiYFo3BP8aho2Pj9uX1QKWBCudwRhFmCIyobJSrKjD gk4qs8Q/X4k6VGKl3WBPs9L1Bk3Bu+5d9kPkptqJydFlYmXA+jxnTgBZxmVtFKJCa49VMCud5TiX ffm1Nzja9Rbb/gILofLIkggYVo+GaEQGTQuDLRb7KtXobq3SThFYM5ks49vpKuP+YEwKV/UgpPBn dMJyrXiULY/7nrnZOJGcJFivuR8MWAF5Ps8gQSo6wioMihxPufWk19vN5e2Wi9BKoaDQrwRBUqUa O9v9sDSUZa61j89TTG1jBtweNBwOOqjqUe/tXNq79itwoFlucu0yZZTxmCp4LfR5fwSh9k7uY/+w FJRp00rpSGsrlDuKpLr2tCovaXW8++RWTVPYXWUhlTfUjbUmKUCKCjJFLzlf4WdE4yLHLcytxhZz N1ElCq1mHwknkkYcuFkF0QLAxEd91P480HNfbBMY0+UfTooWmHmpQqFW4rXlRh63qVKMuG9x/79K 4Uys7pC9yGIU4TqwUR4P+7wWCEuQ5OquFehF1LVcoTaqhi8eNlqJWHkaNRG0cq1VUTdQwWXkCmRV FeX5EjtqKOyFVYr5aoVP1GCk+FGPEjcDzAFFoH6IKgwAVySvCLAEXooVtkbqHpSloXTAuEwGGohk wHGozugVP03xHL/L96+i2JvbLURWVFONWI2LuG0flr8t2J1sOMcphUMGcMmpOLMHYN9BLpW12sf4 Iot/85U97tGpBdDmVrcp6ifAko7E5qDr+hKwCFmsif/ALcEQLWaYO9PMhF0aSOIy6ceNCXyImuwQ FtF06PRMWjsdbgnu+nOkt+WATxFlMJA4gxzHIkiRkkf72EO37Z1L8y7+LVZgy4E3QjnPKQXxVTeI 0xsFShD1LkOS8tSzQaPEV01RK89D4Gkl55teOAoZ/Ejjx7EFpMoZPfm0MSvAa7u72W4vbzewBJ04 LQkbg1FODhGoMSMkJUvZFxL+qtpEj+zREvz8NjOx37dbo7pAPrgo4+O55qGjFVsqih06osVWiqaV zRUdzZ7tGIVEK8wBoC61Hfct0GoAv1XiZw5JDXkPShv6giqt4acNv7HyU+63zAz2/YU0qSkvg28R x6TuFVA1mtWjVbamRZWB88mwfNEpSd/TXUqyzLVQ3gxRLQwDsxRaLVjhO06YVqgh2BY+/jSSBOoX YB5fbui3cmU4R80NV3ZkFc9VdgkjX2hBah/6I1p9UYsRgW4Vu8Ar6bBwKoRwLUcFe/ZDRys3hzUM gVSKwEWvl/OaqVMBVXSRwAxDBjnManiGajvuW6AVvexls4hBhEL161Q/k1Zg1KpTCZZbyNz7IC3Y Z98xXEhVpmz5NrIqIW4eUDWa8cDaG3TujJ1IUVzYodVYWFzOJf9g8l743yhhhjcQeOXVm7q9cADK I2G1lXDSR1y0uebi5m+kFSJMq83WaCVgZ56gPOxRpobPCEPIzAozWXBPMqxa/7pb7xGtXoNW6rYq 3JYOA5OE9hZxGiyL91WsfvrnExNEdXWu0pxAVUhV8bcU3WGkC3sQoncjropM8trB/GWhVegrwh1E oKENKKxydg5Dw6s1HryzT8yry9VGwmu4jprhmMRyB6gazxAXrEQrwXpk6UX2SwQDuAxp5PuUbBhL Q2gDMnoR66TaJuz0gicR5dwQFWQGWWmKTOYNX6QQK8RVLAhOVIexd7nbXVvqgZZqgoKRBujAn6ax wJt2EKb2oT+i1RejFRu55S7wFJx+zruG/PZBe9lDCxBMtAVVbi6rrTR0LLMiA5C7XR6Wr6npfGEh twolGF3MSaTx+L2OOzyXysQWILf8xXYW+Mfdj+Wz8XRXOyaxP64BDImpXVxiOX/o/9pa/2oXZ4Fx AcJcyElrM3d9OR0ygoEZElS2vnKn0kA2VEmHFtmgdK5FxmLxrTeSL7ZYqFSz2SLL0fZkXY7nAc34 cA5hRCuMf9sw3JLom1HFA+K8gvWGH07TfO6dNoCVvr7iE2J03OKA1ktsgAqGr0XL3kLRyOAty9L4 MfPxyr7RpmW+QyBeylvk+Ew3ckJXdo3Gu1zdNYChQHUtBY/EVMJT8yIozfkBPkLSPV658TMeUf0C 2DFQ20rJTt1WkW5Fna47AdAEn73IaGkPvJHjCvQqYEvwTQA7DMSr2/HBHMBJtzH7Q4FyB7aKWYDG f9hoJQKxZyLZ95y5HMFgWm4UIVXIer6WTmVHtbmVV2vgQxYVDLTCIHM1WfmMA5w0Xtu5JjH4klwp yF+SUijdPiBVEJcA/un0eTOp8opPyKPEuhoCJpAqn9S8AcqB7bIZMvGgGMgZDdxz1ropwKpCzhD/ 5117vQpBkloq9GcxvWf2Umhdvpbn/o08KXqt+2/4OK1BcXRm33dlF+TDRiv7aT5/YIqURsCd8GV0 8JsDhNdv2dtk2mQCowlzy9wPN5tvpRBoDrx2gq6HpZ+7bRqxhfYn+ZvDrpadb6DSNfSZq9AW1+z1 P/xwwg9RgkvCtRSwWcbmPate1D1YEIpXszbgnq8tcrqL9CpoUtMGgWdOGnLikD0D2UZuqfL/A0D8 sMZ8CEcJmOi9zJ8mxxE3luR5D7YePFo5zL2fJBRSbAsGnJiXFlcpDHVYps+hXdCemJBTNlCVSsjG 3I80hvDQON9NNo55xV5ykW2/SDtqgViE/euvFVcJE9nQI5QyZtGBntJaJQTwd/pE6CXUGnEX5wp0 fuEIdXQGA0qb8JGFSlXE2YlKCT8Fdeybu4NJiVLOkpR6TOiF73mkVfXd4c1HaGQVZ0ZAVVQVkbng R1l41vCf/+RvbL7j01QUkZ7RHhLGtRLL3nx67/E2MUFfXO2WHtWuV1iu0wVECpyrhRwJ143EIChf G/O2ClmsUY3P7mEGtBzSisWHGe0qt+CDNWGH1SlzvWKb9Pvz291kndULNMj8vJxtzUJltAf5h+WG FRaEIlgIharnsXCf3McqcE5lhrpDSYcWm0jv8pZJgmMGTF0x1StoSQFuH7wHeyJdNg9YPOEDS/Vl Bp6041A10altzDRxaRpUTa0akfKX123aPxx2hSRluo2BqkxE2e9x1yjCvcICqMi7DOc7ZT9xhxAv iJ96ouLk7uxxt5UGTrItue87vaQ9idFU0UX1RJh34Daret0nB1RHambM7nfYpk6eOThSmAgXEjl2 GK1H5F7X/8XP/14+DpZD9Fk90tx39TO+peIiDq0dGqO6+5lyTy70jirAtQfGitiqzfhKsL4genCs 1pdDGvryxTlEDDISMsLmrA4V8vbG9T0IAEp00R/e4D6P63LJ4/V2eX6zOA/BBMYOXf3j8BA5AwcR QOQqnJ+dffLHj/HnObZPPsE1WHhpsoMEYHqJ9A+WnVR8E0gY/Szc7FpSaf50vng2h+wg1fwp74qA pFDCP1zARr2HxZbJjGIJk+h8xqbSF0sSeWIWGkRXiDV7u66C2QI7qTY1Ddgtlw2eBUvtURcOR4fe 7SC2+myzfXG7tXzdy134B9I+LL4yoZIHM6DKl3dATBBf4BLUXZ51ex/1HXr8aw88eBwdcCLjxgG1 Q31g+4z2vbYe7t0xOfzJj//aqOZOW+Y3fRcOroYqD/yDazDUsqocbHetue7tjq7+uSd9zTeg+0OT ibayzyUWcEgKi0Ecx1qV6KlRF+h0k1oSgZoP45v18AqqRiWFaEbHNdP7L0sqqv1qcT1kXGHgGbwo 0lbJZuc3aIaXXlxXYLjxpWr2cg4dB/dgAsITK7rrE7OuvBXdllIk9qXwslYJHJlsGNZny+xq98XS 7C4Z5mK53V/FlR5QKHR0e2erN5agzquVHHt9zMw3O3CrmLSb6UXvBGquDa1xKNrAphoO9i3p9qle +763c4vfGZxv7KJCWTb7G/f8enc4mFvduexXGvEudX30Wx3yoE0lwlAUJW0ydNTLwz0oeHjVsj3k lD4monocVCVz2UmhzF6GlcdZbqTA2/zo6AirS4JIoVg7HUVwKYkdSEfqhBPHDbHGNzN4DWf2chq/ 2oH/4Nly2wcvLy56W5GmUq5BXrmRywdFqrB30l4Qc2285MGygs2GeWAj2mHPCD0c/hRqj7wDot1R ju6y2pN9k/d/RKsDn26bjslmD1uJ4/SLPG+10vBXLg1d12BBA8FJuFr4QASKVieOwCQ+mUyRjxJo BbX3dMpwHNFKmqwt9USSvGuZvyzpFGgjwOJuWTazjVnpQyhAlvxEOECk8Txb+xIS125tP7kxKBA2 jMII8b2iNBNuHfjYDzysxGpr7/HA831DDxv+5Cd/w4lRj4/S0igQFClRwagrb/5tLMHKU8XuckEe aN57lFZtDBelVlAAABsjo08tqyc9fmzF4h6qOpF3Hmy2o5v16HpdQEJWWYSuHPEqunRh1o4ifMAT fd6ADpW7iSIHIWvl+DHAuFCo4gp++tS8jPs71hyMWJpuMfxtGZnMNF9fooxdGqcyTQ8x5+MUFa5k 3oEt1EAp+awUyouyOKVMTEpkvSMKLFwjW5DFEXmoKzE0sQR9n29Lk5AnI7bLAV0lvkRCi7Yyoga2 Dugy93rIl2UJvvGiH9FKvpjKzXJC+qOdypSucxJ3RyccA0z/ibtsrduiXJTRagi0UmpbDFF6X2nU hRNbwT6H21ycGW5d/GOgVLAysoVing/fs5EUKweGh8aLjfZGADx8s28oGih9VLwPftROktCdYjw7 PHoAWtWM3mbfJl/GwGT1eUheI4hpb5wgKDa40BGwWPf694ZWRryD7rGya34duz+iVV2r3z+3Sj9N AJY8L4KqEoUtqgV5o+3mqrur3Ltwq9Bz0tnOKgF0QRUZgawwgyLd/3S9EzV0PX3WO2iShVIEKQeO MUgdzkVe+GaLU/aBVg3eBmTlP+ZTQb3jzlmw2GhVe5tm97Ub5wvrKtIdHcDk4GyJGQir/D/bikCr Vb+PnO/7Qqt4MrU36Daubcz73/8Rrera/J7RSv5dESqhUKgfWoQKVy8cSetJ4SVgQQzvupsLS3B8 Q194wxtijJJbkdcYqGD1saQBBQHwtOMDL2RvIZOuSlDlPDjZgTwO65wqVmn9FdGqj3IzBESPFh4U MssYPWXgtUlk4VaV9weiKriqBCwyy5SMJKUidkHf5fRlLwtfZGJJrUhKV4PBbX9wP2ilyYDh0wO2 g2e42kfwNvs/olVd630NaEX7SX0pAEtdUVdt+Aio8kcF0mQn1t2b/FZQMBCtPKA97PJfueworac8 QfWPrR9HZBB7wouOoWtvZHAljuxQHMYlww5MuBIN6G2AVj0ankne9s1aHfbq2AMmjhWArB6WcUN1 x+EenBFpDBeByjILAVQErPKrYg9CwXA7GKJR7g2t3C9qt+g5h1Ly2m528P6PaFXXdPeNVlGSxNog qY5sUZlMeTAH5ZEd6D+0y4wzdfTeaDW6IUvyScqGERqnF3djSnvhVpMJzoWAIerH6PQCOmnK43A5 uhQCdFk/kUTB7O0OcmGXpHOkLTlWC6Zy/Ak39Z3iVvSyWynQ/WWZU+1gxnXJN446E3boxZbEyqvX FILVOK+wsNftcLhlxWoe+VV72Q1V1bcXghBy3brBcO97P6JVXZPfO1oJfPzDM2CwKjMsD+7gWCbz xgKPqoPRynG5fcSSvemerYIMsgK9TTFeIXGAqkrmHn1YgTwiaL5mJx+aKEYUEBcJM7APcLw7xMq5 ZBzyGwLJtKO4FeGq7dLu+L4Wqry/C+OEvWeGVTBLb5NjeZfYkHmzGY23w9F9oRXKADJUWXuPCGYo 9vGIVoEGjzHBoA1V6KjRnSmXEDGJPpFeNUhlEhJ/g1vFeoJBkbqfkdxqdQtLUJMYNNFhuIXDDOd3 jog8V15TmvuNhla0Q1QlP5ttkSYsYIWY9av6pdZ5doofMpkGjmzaZgxM9A2KKxQS6f9xw5fBEsXo ivpanf9pMLRmQJsWUakf1l5AVdxFwbDAL5Ul9Ks/2I4mu9E9oZUbllSTP+w47PRinFYex+5d5WvZ 85Fb1TX7fXMrDnKWa/dwNwKEfMHjTf7rHNON38oD5wBuNbm5Ha82VppHbXOCVhhutAcjDdvlzaKm qBc+gv7TYGVWVqBTpI+BAgcyGWi07gim1XDTo/ghFQwRQMi4gTAvQgjBv2QJ0hRlm3SGqdjR3LNW z44zu7CzaVNDnYxTcrGXHyU+QX4F5dRkshuP74tbCa1uY6n67oDMpF097brBcO97P6JVXZPfM1qJ 3GRhVqfGFc+6xrgqvfPTYiLac+XRWYtWUD6BWOGFkgvSeKoiIMSf+lIqGewLkzno4nSiWX2gFfJ0 sf5UEMiITBYNsJNsBFWKrikXB4sprKC3GowHuTh0fnexHItFGkfrO3i3ym4FhXTVhs4vXTdZXtjP tqLf+IIWgeUlxK3Y6JZcpapBGFYCgqZVsQvYynTWG0/uDa1YAHDDwgFVGwmy5p6qo+5/50e0qmvz e0arTC0BYMmKCuqR/m/poBK+9GtvKKUiQKhFqwHQCpbgaqPFFVReRT/tOWM5FszbsjTSax5lEcSt sB5VVKcggEmhQKmCKrnLVxWpPKQpLIVzg7/9SX84JRgWAZlNP5uM+WzEsBKp/QbH4KdXuer+ogEr 61XPseuLlqASiPZcYxEatCI0PFnywjf0qzccAa36k3tEK16NtCY1LzwhLAnwiFYFC/7UTeI60LrH vWPklzJREVWLCiokLbLajA7iDTwivfLVF6riAeRErmZFF7pECtCAogSEXRsMgGHsYqlbYNQV658A rUBwMExtamE3l5qhcjSWmSFJM2YBSQlu1zdYIRcEq+2VNhzIvrJ8IuThsaBxywZD3qFXCa96OVWx ky+n5fIhlOZ1hRd9zyRs6dcNVS1Fe3jfqp/DgQeYcNe+IgJ84Dm/gYc9otUhD1WkxCCU+vHMrbEJ pk1eLQsDW9th5yNYBfBJpeACnVGvhrXJyXm0/AHpG7CCdWNoAwpQQjOBq9AiPlwbGaIs/MUXmWTB nKSrBzX8tMS3zNV0CTcCL7+z4yWUDQlegWBabaf6Rc3UAWJ2XWFznL7C30NHnkljiE4ts7AGNfUY hzyJQ46JBeC6edb3HPB2fj5u2QIRE0RP1YAKQbRcMe7/4fGoarG3zGquNZRknLAnVl2kd7YfufZA eT9pTtkx5Xw6D4IABtszcl1piWVxI2R7aCEa7m2eZYkTrSeTJ3vN2y85tFE4dLWZrLdaKizWtICn x8RCi3FRA0olpLMG7da3RYYPLR0lfMZyyDYIfXHex3XpblRABtUcpovJaD6h5EJSh+gEoSbTdxuE 5aXX9ccbPINgYUVK0OENkxpZjQ93VMFAQJhcfoKcSSAkhb4RNeEpenDEE9zmONuUdzhlfhHin+tN bwUx7C1+Au8h3sDniDGMtqjx1R/2BnDiDWE9wiWIRkfkE4Uu9JPf5TtXvbN4bPrQj1ZhYzryHL1o GdGdepwLqB1iCfLhM7m9/uWHW/dyyGnIbNOKV5wku36nc2btUC+Srj7tTLGcRTu16/5OOPGwvx3W F0UoTpDak3rSrO0NMiWYsV97OiwqOhhO0Xvt3eVUbncuCz3yJ/74/8VhYjZycX7+8uyMjiT7tQQY oa30NwVtiXnCU8Zks3t2vX16s2NZV1E6AsQWJYzpZ7q6vL6+umLxF9h9nHX4FB0WDBtU5fdY+Eo+ ej3gHtQ/Km+1hsTh6hLG3wVe11jSlX6r9fB4NDiijegY5IjufVRymDgDUd4z8jst48C/9uuX+a22 PZdHi+PjZW2CIdqW5u4V6ik0E5Xjs7E17/xBWBJHg8m3xscngznuFcbvJWquog4sbh4LcJOR3oJb xnAAQI2HPdWvwJ1uB30o4IHe9t9jzxvl+ai+5w7daIt6ll5mhAUw9AiQ39PbXazOXl6/vDsN6/L2 rjHke3H5aF8kJKDBa9tTCFo9B3PsKwZZezpc36zfO+QqIyLV9VL7//uX/1PV3ML1W0wYuyVt8Nxp 0jfezNuglZ0NbzzFl7ODGos9rXYbjHbDyW4wNuIUKU0pVFKcPnCBt1WBL89e4oXRkGgUFxDBK4ew 9KdAF/acb/sfbIbvbUZpgEWKCUtT8a8WRUd5PvqaJJWMmYZcDP4trblXvPN0c2E8ASYdoOR4v8KL iIWRD8y7Xl1f9m4uB9c98IphHzClivOWqQNSQn7qosZeD94rAuFmWC8LYtTKbb6YLo7msE2rjkNr wMkWK2aTw9DslreQKxPFGkXxX/8j82HQf3cz/tFq9sF6Qi/d9c3Z5cXZ+fn55QVagHC1WV8DrQTA gKrteNKTzY3b3I7Ht5MRHh6oEnjo+frmfEU+yqKGuw1L0GAew3oYXBVjMpziHcuKbfu75zfPP7n6 9G5Ro9DLJbQSt/Y8M1x2F42MmEDt1kf6J8CzKwoElrO0EGeh2rPNd7uj3XZWebqIRNNm73rC160s b7TSnHl3knr917+NJXiY7dn1dl/ZL3XndV9AOwD2ASPuYQHg+EIMwwbMzmI1pedRTwwgUG5V/8ZT bvCsYj/qd/5O2CMnvdERwIMfOWYWHqS0ecTKXIBfkGbKFpRHKY08k02TsF5Dhh5JdsZHXxAs1t7t NajDbkNVFbmcrjYdcbh53LsMQUfxnNTDml9O1CtY3PGN15HDqVO+WXScr32jmoI8XTYwrpzcKmpJ KEc7X4li/P9yN3x3Ozm9HcAGZGaO1rXGd+ENftq+ZjYSG2wEsoQSFsBjePiw3AWqHXJI6JRcH1Ud R7FZHcF1PVjsAq4Cdg/vtt1c315db2+YTxBFGy3zNZURCSN3I1SEQEXkVStXxircdR2UtmhnDGh9 tSpgV3Mr1NyYorh+nfnILmXTAv92MgPvYHldizzufUALpLtdbnrPBOmDD41UiNJNBZIQxCwcR7tb tLcUWAUUOQrAhYuU9+tSc9RKOklF7ItiRXZoG/+0Er2onzYMINajob1o4ii+Z98Y+ZuVGBJnKQrI H3SeObRXvREfLPaqfN0fDW8/a7U8hjXbTB43uMEUoyX79CNDE6jdJSFZXaNZxJpI12zNSxUnaHJx f6dKKRfda8NypQ9Qsz95sdUBg+DgQ17HrdDij9zq81s2uZV/G3zIM8WdLT3U3tM1iTH+TVYThOJb 8qv2zolHMO4NjndDcyv57MMjqFNR3BXJx/6vZix9lX7KNyf2Ew7U8g2+XoGWv0nQBSI16F9vri/W l+KAQkZ5A+xUadNsh+BsTRsF6ZOvI+L6TuX/KJQX9KzTm4wjtnHbxqC8rw3W59QQO853g6e346MN MoSIvLDlbugBFFZK8YEXL4hNATI1YPVosQ44DpEMGeERkU28YonJ0ahHJxWBiC3FbJsQsvEUiAew oFYyP12I9gZvI+o5VstlyuwQlG+QThiR5OrhfRC3IgQfxK3Aqia9Xb29mp7bzrf3iFYxTju3mGEm LEEf1R2ttNAfnVY5DRCwmqDkK+TdCIHg3HFvtEy0MnWm2a/8vXZknNhUcEql+EitxEAUrIQp4mCl DVgiVCQIia1p9MAfP7pcX59dnyOzJawcG5Hl8gRujL8JXvhD5I26AQ3lWnLFoBoMJpiSlRvuPjhq i7S+Ea0WQKvteLkZUN9htAIHcqV6rcaB+cSmLlqjjVaALaAVkdAIbiPRNhtihrGKN0sao3nUKPQp koL21mvIeGX18Ol5E8USw7IqRW75qErN78QeLttc1znZPw+xBB/Rqrqd3dGrDzv0gMP8Voejlcyd NlrdufB9x2vwmUlvcCS0Mi0yVPFlDZHJUfmvA0IyMQEkYZ1hBx7Fud2HZ/A93mv0WZiBUTe8XF2c XZ2BFNhnRVW7ziuQE/ZZ1qQ0Fwq1IqOI2IEh6Avq/hKWqtZg5eYhfwi32oyXECqIW4FVkVspQkp4 2ZFbhWNuH62w0hbBK3z5NOqwUqAdTg6MiiWqiZxxDdOb37la99crxAblTPQTb8K18qjJceV6P+Jx +gct7Pao7t2PaNW9yb7xXvaD0YohPObBRKnPu5bV50AXR+K4PzjpD5d9cG0NTDsoiR+vmJ7CI3mv iDByT9GXFKIvWShhgBpLcqDbCU0XK42Swfn1xYurl5jYQ6svVAh9lc5un3wSK0m2wBm3O68dXcut yKrq0QqIIG0/tfhl62oJbmgJ2qnnxX8czVQaZ1iC/M59tNoi7gl7MBIVRkgXwst5m3Sr45VLRcDF R6uS66gRsVZ91BiDS99FGALyg2p5DtH84eeP04oHI3kzag12H3qx5yNadW+yR7Rqhuu+30p2GUO2 NvHcNe84epqBl650c6tjiNjcp31EYaHJroRf+Z3akx5xxLmgeCTCtM8TwlR76wOqPOw0/M6vzz87 fw7xRQQRJeIKcIxVuuCBp9BI4lJiMMY89qHUAdX4KjfZgDTCuidC078EASeuFTGB+0IrmIEsw2pg Z84SuBWdTkSr0dBAJl7F+0DQIfFvfdNf30AiGtqUxn4gy9bsET7BVnEY2dqS7D1aggk9j5k33UH4 S9iT0GAJngu/kP3HH35iZ67dH/lrh7gxdWOo2MsLe4Qvvtd/4w2D637Piigo0GBegKD7iJ9sh/0N 0qp7O1g6q+3tDSwgjChQAAwtsCTM5PBz0ReD5bk4CFWyeDoeYS3CKSShFDDZ2BEkAl8YIVQoT9nQ 1KmSn0gmW/vSmGyl9Ulz9+bNtXfqndBACBhXrHeMBun3UFkVCjEwXv5Xb/BbSz1p8/p+840hG9pc 8B8ADTzqcIbjhQaaDUaz4XA+HM1Hk/l4DLH8cjzBC+8hs011Pa1nm7zy9TkICseZ+J3yK7FRpIp0 T6Z51puBX0I//RP9ike0utcHE36WXF/elarKluZYgFb5ldAqsYm4w1cBKQFWvjDDF0Tjh5ALjVB2 DiQEJeg4GrniyxZL1IPJ3ECNDUpDpzLd6eBRDHIB40CvoIdkRB35zwxTUQ8kYTsjZSJZ1laFI1ny VPh+EK9Xpk/1S2rWyJWpSafjIYc9PwK3cCowCwt2CbPwAkgZrQpmAZYCraRnU56NoArlWnt9GKIw 1SH6gNwAUDUbjfFzMRovxhO8jvhzit9mhiJvla4+sVJbi9Z/QAdCiLI8HxuUujc3X49E47A2/eqP ekSrr76NW2dw2MWbcqsMUEmv8r+FW8Wvi8pRsGXw0kvxKVOefCM9pH6FPWGiSHpOcoQJHkAGsGP4 ja4ZLypPFbxlVA4dyiOGy4Pkh7ofUKwZ31JKxCxo6TjtxrIxateYvszSrjdzos/ZAyzJgYOK16FI FScBKknwoZcDFy0ydedCnDpHaHRcw/nTRiAtyQH8glIWTQOvOxgpmmwxmx8tlkfL5TESi+bLGfRT aEZRVjwVLkxrrYNCqDQeZVNLxHbLrHSUUQSY3Vtex72OgwNP9qhgOCgEeaiCwfk09lXJFSQ3UzqU 0iOVH8qfYS/7oj+aM29UTvbcWt6xDAracR66A7IgST8JXZl4EodLdSFplhWq6SPz91+trtabKzpn VEkr9UZNBM6UJoOS9h1jWYYhxiTX2qncCBe66KoXoFySTKZDV3nZZ7vB6Wa8UEwQf0La6oxuQK7K +oUwouC+9VZqUD87ApetRaEmmyAeQjj6GeGT3hbG9Hq8uxlvKR91/pISXBz688Xrzgl6riMB16Zw ChY/HQPVg/vRy969yR697F/kZVcYyA7v0GqWVvUACAd6vgm06g0WveGclofc6yYCIRGImT7O6Fhh 4I9kCUqvpXSaqmgqsOR1kmZUUlL+CR+v5nkJHVbbG4itOJxUDU6SIIsZBXaCOGsmFJSnygvfh/0g wQaCVIIVL9n0sWrD/l4Q7AC0OgZaQW8ltILPCAoGRQZlmYl4ZgGFYKnWgEoaIitQzcBMZvu0oiXY JixIIC2CBeoA78kM2YX920lvTLoqA5v8SpCFij2x4TtVjdDAp42JP3Jkdh96secjWnVvske0+kK0 yhBhQ5Fi+Edel+GscC6H7mDpTXuDSQ++J+aqYhKnm4l+YuSpweXE4L8+Ydpa88Kh6OkYzPBb0TdP LztG16a3hVudcXprnHQgOQL9NfgGqhg38OpgCSsXWyC3UhWHWKdCV2cZhZCLWMUgPLxksIBmGIq1 aAXcoQbASyZ034a4OUDCtDYmiHz0JZKVtwN67ra31+v1NXK58VPp4eseXXvIsSSIk1vFC9clkVpo RwzRoEBsM61szW7f6wF+puCY8LJPp8vp9Gg+X8xn2/lotxjNlwts8xleyKuZT2e0C41cElpZQxIq fJzLqPfIrQryPFqC92oJprQmTLWiKSjm2d0pQSaC0IqqTaJVYAr85XKNA7koOWcCjXHKQEZQw6Gs OTLBmKa7ikjHvD9otVeoNsNoIH7K6EijxlDFXHaWHgDKMRHOPv9iS8YVpl9HVpBWU9zRO88KJ0Sr CmkodpbKkjSuO1J5T1lV4zYj66K3QnPMt2PUf0GQAVVfrlC+kJUn1ljLDP9lm+yjlWMh5FYymAnR 0vGrpBahCsSSfivOK/3pcDQdjWEQw1N1tFgcA60W8918dLsY8918AWNZPqzIybRCjU1sa506OaeP q1SsmqQ7UXjkVjaqK7ZHbvUaS9DLpMqgC4vCmTeyqmLYxw76RzN2f9Yb4hWfe8/0GtnZa7ssTIiU jgJrYHlg2vYXZR6eY1JRlCZOHQ6x+AdVQ5TaHLkkYlXMWQs6ZU+NTphnJLcCuEU9pkq4omhpKjm4 TtTxhf3tAapFK+jXlpsxvFfp2XahMObI4I/b00QycKqgVcQANMOp6gT1niKXfJ7cX4ENxCYQoeAG AjUbz6fr+WizGCFoIb9e1KkOW9BQ5cAKiVWQK6cR3mcNBoeADqjBcG95gvWwXYFaj7u+0gJ2rst0 UT2mIibXiONHYdaUX9ixnkZY25vcAEzjuNn3l7NOQNZxx8DGJM4RYXYgbLO6M9Q/KUjAJ9iFgSs6 iaMyAys1ERpos5gLKDzIeCQtTjudVJ6p1gzkyGdIs1FydHynoV03j759j1S7qS4Fmg0qda9jofxC FlmESShOxNr3gKY5jL/l0dHx6cnJ0ydPsT15+gTb6enpCT46OT4+RszwaL5YgnbBOnQYFuSUf5XZ fIgZ+PY3+af6Df1f/PzvKOpbrcIBnGEfRbRluUfMouIOxK1QO7RaC6OeYEdw5aactcpj7IEQqanc tr3BeoeaKszkEqmxVaCLcGRbUW3Ft8Nn6pO5xGgZzyZI4jkRD3eOssVHKp7AfWG5HPfGR9uR96JW xwmzSkGjzEkjhkPHqT0uc77douvDUQLAcg4cJYfSHppJqNIpVEfCSREp4Q+9VMfHo+MlV/TR+ZKC gHuwLIyrw7heDCUQVKHrhnH4FGxiMpGXX6DoBOCyKceuQWxTRxisU/AOFoYqx/no8tOJxBkRjSgm fgsSg3FNvEy0j/pRJibxQ/enycDbYjN8djM9Xo2obrq5vri4RE1XVU9lSUKpOVbmd8w1Jm7gxcIu 8P0FveWT0dPDLSoAwR1x5wSoGT1VsPjmcy7zMRlvR4MPB1cfDq9ymdYQqjktMR4eH4dKK8LzH0JZ 7cYg5Rcu7ZWcvOSmB/cdjvuQrLSix536d9JJT2b6UYaUMkX9ONLS8rjhj8Wud9rvzeMkGe3W3vvP sfV8daXWmamblMkKwg4+Nn+1ksOVgCRhWv9nP/0fLhHnG2UYSSfx/FEm7U632+y0g9ECwKqd+LIB 6uFDQ6oed0jdHYqqusGb9e5yBZdHPMw4r6FFRoJBsH07fk80WcUiNI0B2OYjaePFsTSwsLhff9mf LHsjAB2enXJlLYBG7QCuMUNtDtWFWsCBS0AoyQPl1WezxXKhpd7d9V3pKkTU4lSaG+JKI5aI9jhZ Dk6WXBow8DGqZMXgcbZu5MCx4aXP5gBWKM3A4MRd9jy56O2DYdFk5vaJFZmWGRZY69K1Pxtk0/eo Gp2dVHFIYJOK7smymjmVuBVH9SjJv82bCMVObgfHV6P59cCicWDV5TkLPQOtrq8uXbxLFw7fv1fq oERKOUlWFMQwVFpCZCSwXCiZqHmRlQq0wXHrq+HuH7Yv/+/ujO6wsNY9m2jqyc0TtaYp78j6Qrg2 6ESZl9rGCb0P+9Mr1ucXmzODqy2PZph3qno1j9QMlpIyXQQeq2izHqpWi7R6I/o6dz7q95+Ohsss K1QeYmPWxlPNvqHxhvFwdXmBqQK7sb1MLIXvtu41UW5Q1haPCfMIytv2f/q//ruzJjw8dF18+N7V 3eQAvxXqsg/q67JXNW57Zw1BuEfrYA636U5YFE8dL+BydXt2ubrC0gM1G1pYEwPU43U0EKsYAK0W /XHYbJqC8dAkftbiNpGxwdSNqCQgeoVnP5/P8fhF1NSjc3R4AjcLi03IRddZb3syHx4TBHyc6Jw3 XgHNxzU1ob4cl+rTKNFe+BbDv50gCiqKsjUSI5s4QiBBEaX1iM5tmUSdH8YO3tNfFV60VDrg8wVo zGJeO9kAreZXw/FVD2WOsQERrs7PMWxYmR41PsVQBY3gUwhRsDK6SgBDHYtIADFLHqqBrGKbxTKS Yy8v+miSyj2vere/WD3/2fpTRGNr+ksPA/TlixdgfjEwC6vxZMhKGGxz/lSzMzygx3D69OTJ6TEu vOp0ONzTn77EPlDnHQUvLQQ3JuNArO3RaPjOaLyk2zFCBHqO6Xfzc5YUNh65ZiR8yRm2ly/wYElJ 5wtYxYvl0r49yznwILhuwNXlOWtRnw1//OO/ls810pECRZVcgc0T3QFohY5u6e/9bJ6pak062rlq 1wxwSUPU4QWJ8Q3ZRV3nQ1O4hqcnhu4bOMe4P8SCpHal4xI9lXqpHVeHCVrC6Z4MwPnJmOQxzMyr CycvZ/fzzZ6vHdgz+WZMQx4ih0Ak4ZNXXhbzCKiS10uVjTVgeFHqL+yYToW0dSkyZW7VYkqFRDH7 GgbPDatTEBo9WISccmXzFfQzyYcgFFfuYqfFAmh95eveIh1puh1PWT9OxoacUMJcRvfwtVRy0Xkm XhXiqKhIT3G/vXn6BT3m+KFPQ0zl6qviV15EDRbyp7vrP24uPe67b7IT6fonzeNNegWQGP3BHqNS dlAdPs3tFgiOUvechms2XJ08F8oV1xJuDge4AQJ7MwbCUh1cqojNAqHGyWSyYDwnZWTufwRt6fWK NqV1PTidk0yxg6bVGeBKf47gz+NP/gvf3tHJCRx9p09Onz50tNLgqjMD0SGMVujh3RHHex6GVuik EFsRrdKd4H/JYjTgNKWYrIf9z97F6V7WS5KRcrVtuDQQFCzjVzGDBBXZkfLsbJDkVFqlQuxKtahs cXpij1oovIQim7L5l5HFlpjKNmAJN/SxShYNWZWWSrhqIEvxNi4TRuYc1pLRateD+YCZuBatgB9z SDV7oJyOa4oSuvzNFtnfQQw52Dhio/ozqJMKGnP8yja0zRdSBOfUuN4x/uFA534cvhCkf7K5+v36 nHei2+74cg4hWrnhoYFX4ajj92im0hwWrlu8h8t+sZzdiZa+EbjwRfaWkiZOfAc0z4xBeJJpYHOO F1RhH+5wNJkc40FY7qogTEJVxEsCX5NKe0rDU5W79RY740tAkhFwIEABoRiAQBxC7xGQYETiBHD1 0C1B9YM68x4PFdqcMySn3FTnx3sJ49oaIIiYQW813o0iGiWDDKPLxePsiLLPnZZdWGdp3mkQBlsR A5UtqCLrLpUQ7ChKsJvRnIy3xxhl6Z2QtaHjAFOsvhAD3CM8rUyiJzxbuAaXRrb72fNqeK7CADRy AchiBHGY9HFUcHlVBQw7Io9Oj1FxNCrJEk59zMnq+hUbWNXJdjG/HdMQhFsIFuD5GV1XWPzn8hKA DAvZCA/lFMCKpqAykChfw9jSxeHCQOogStOaf2KyaQnaEOT4libjcrv+u4uP/uvFbyFwq7hKG2CS pKraRZT3c3aQSuyzrQUvNtmM59yOjuG3muOyqk4njkkvf2Iys4ZwC/IAbLWONxYD4gSFr6VzSV46 vJb93nK7mfIa6EQq2MQYtPO+DLd+6HrmJpkvPvvsxYvP8CliQUAq8qeTU1ApoJSlHlqAh11SsLZ6 6GglalUdPbm8uT3j4lB1aIXpD/V0iVaVfiuvt4ThbB4UWEGoCUvMGf3hRZInyQiiGiT0ZJU+LawS F9Jfgh73ROg9HFvoGfBbvTvrP0MBFDoEPN1q4jbM4f8CLxlP4eG1GwXONLqqr64Y9isTqZ0W6W53 h6Vpk0MRb8BFZnOM9eJiz65tJ3bx0ufChQ4RuvdTRFE1KLFAS39y3D+a76ZaoOuaqwk6JnhxiUUF 4bfCixcN63uERWAnKKtgsgCdOtfF9nnh40emgE0djFsU2hqxwk4wC6yRI4TG15zv1v/t4qP/fPbb dSyR0/VyESF58uQZrDrV5Fnb18ziDFr2kGpWslFilAMbujRyW1woxL3my1WbPV8U3M+pFEMEA7fj ADQc4mgfidK4AhvBhFMFxa+zzRZNicAzHwvJtb12XtXMxVAjShG+AdF9oPGnn3zy8Scf4zqJVkug FUjUKY2/Y4hq56Jrkv6i74vmP2i0KsS86oli56vV5hAvO9GKobtatCI2QHa9bhZ8sMFkYuS3RpJ4 E1GdnbzxJDv2agTd8j9RSo/xPJtxxjhahb3d09Hu6Zjr14bpSfd8eEbcofcOkB2CmR16Boz5q4sr LWTsjf1SIGQPof1W+m/+Ht0WmXTzBWZyc61wvRMSSjZKUXfpK/NQlTGH9rVyVI524/ntdLwZu5TU FRAWaEXAEloB3FHpU5cKbgWKggwZxwgAW+RZyRWn/SGSbGT6MP5H41BYxaHZcKv++Wb1X57/4398 /utVpcgG8qzvfOe7kGg5i5HLPnKJVlWU4cqPXLuVj0PinfBNRgAV9YG0nHTNxj4gUIA9BqYDAAFk 4Fac7X0Oh/jLl2guXAu+lYAGRxMdTMt35otvTafHuRykQDIJVPSD7A7xG+/Sc3AIp6COFl72I7qt gFs4NYkVeK2X/EHahjQMDx2tUrdR81RlCb64XGN95LrDFJMGWqF/VR0Ip/bqEmsKr1LeYMlBhLyt zyrsKUAlHfmJMaFTCDswShTbUxMFeIsQDF+83K6Wu+uBuEA4yzJkHR7wVGvoVvg7/APj5OaKCibL YOzY5w8tQBgopTfNr7TbdDoEYH0xt2r89QK/glaKCc7ntZ5HrD4zuOz3rhETJEm5AmsgWnH1U5iF KH4T3Iolq6K0nrnV0Xx2NF/CHUUKAzsUC3bLPDRCEbDktgm0sr3bH7xcX/+n3//q3370cyylXfXc 33v/vR/88EfvvfcejXzYfRzaDANTMSfZipx9lpXISmeONR8F14cGIa8Mc6EvgLHd3FydnjyB00il bpYAZ6nrVrTaPvsM8TnsgrtY0iW+OOGepz/69rf/6i++98HTJ8Q7uwFF+u2lcB0c2a0Mz8j7oGjG rnd0Qk86aJojFYAswBaiFbAuPVGh/3Autjd+vXrQXnb7Jm33V22Mt1PxpGzempcFgfL1VGx4+uiW MOlcYZOOpnBHBx2Kr5NUj0UCkjTa6a4RRDpuD7zLCag2VkTsrIRSvr83eAtux1vUpwtRS6COtPc8 lj/CG0XjTOewZ8qwFSfVd8Wu4bKwfz1n3xy7NLq0/JhJGt/RgSwvDAefuZMCluVDfc5cljndyeXS u7wB7diueqhDSF8ItSCaQyTkwQPiSouAFUmCuLQRVFdKMcB9Y0jBPgGBsqWDN+JWJFeKh/GTdDXL EtT9QxT6D2d//PnzD3GutNc7/QuJKYwjWEl2S6GVSNm86iPvnH+0lCHDlOHYlq3FkATzQZn02f2V SxiBN9EyE7WiOeYORb3MNTo+7oar6jKtaArHOFQHy++9/8E//f73v/ftb5OUkRlRHSvhLhtDHQbF VsOFIbPSEZrNs2fvfPDB+0+fPYVbncSKhcBggTJG4aPsSqNIUduD5layjjgJVM142PkwSxAHkvrS y153RoATfBQ3WDtZAzloUnm7D32FDZkUxd5WDYbRKM9WY0b6vwpesygeV8I72lzhBUuw4Em+cRS+ fNyywXYM8ZzJXX2nPT3Y9uy1fXczbEA4h9CxGysxHPP4wFHNEluURSmDERjB6NHJMX5d9QR3t/3d 1WBzvaPXCi/qreCUAbei3goe3R1EA5KEwKeOyp+kfUKhk9n8eDYHMBiCXdpYzAoOLFQNlSUoZw0K X6Tfqg9u9bcf/vJf/u6nCLJWXSdu7v333j958sTomFchOQgX7+JDk0tLsZvrK6ulAMEX5y/OLj7D r6pOB18iAAggdPrkKVBSMgIQyQm+G+d4/tnz55+CW13gLORWQBf4xZFFdHL6w29/56++9933T5/I w4CC9L4oCZadPYF3oG36HusBLUv+wQ9/8P3v/xOUKtS8yeI8mM3Q8Jhx/VUWFtLm1Tc9aG6FRrcH R+O/4oXmxjSDiaaCIGlX5qtQxVrHrTgtMbmCtIjLl7vguhyq+i+rwWDwemXzKCKqX7EAudY3bb04 4eJFDQLrxsQbfgiwUi0H0PRZbzvvb4AeZlB2IeFfTeAaoFYchezI2YRMuMFgAQCUQWKEpCpMyUQO IVpRmtpSGgpSX1igEAGAsHLt3ZfgKr1u8pppAxQ7yG6Artkw1UNsNWKlHcUMnC1JgZkWpiWFpPqT VZ7hsyJzMaVBTBA3n143eIBRKFHgMfB7mIggYnjxv+BlqsoAtvbry+e/uvyEVTFsyXZ84VgUh6Cr iBYfDgNgMdgvMwwsBj9UkAYMiNIJPxTAJfZHlBPo4Pbs+ALKwsn9zrfeffbs6TsgPM+Y08jwnE0z oQkL9KASztFSaY/P8MLO7z45faYYZHhEI/U0lhhR26aQOCZGuUf7vXe/BTP3PdwBBcRiU3rO1Mlw BXBYvFfMhaJ/H9v5xcPmVqz2q4h75XaFmOAVXEl1cxdOwmnw+qZEmjueVtn97P9mVVbWeBSnWz1g IV1YMXI9PxVwdGcqXcpv5EnQZi27vOzvT3rvjXeTSLRI063lLS+uo6JRxBuErP74+z988vEfOQ34 StNz728up7BcLPS8u91sMVksYDikl73IFwQETsZ2FqM+SAf9cGi7o5Zbsfj87ay3GdEHBI81rvvs HMQKXvabS3IrvHwaFIZYckkIAQH8VpPpMQcuA1XA7jlWjjC3EoqBiIFeJdkyvHMaeXF787d/+NW/ /ujnN5UxQXcPgBRlptMxiA8823TukAAp8RkELlvENrh/fvSHDz/68DdX13dJ7uv7Gx1z0GIeQUJA fxLall728Ui5XKsX8LG/fEnohEVGLztbnmnZR8fvLObwsh+Nx/nM1T3lYtEzVu/KzsrP1Y3Rhszy fvoM5zXvjr6tvW0uYnjKa2ay9rC97GhGuyo6okbZDTk3Z1f1CgZn3tTHBDEy0Bkwp5ZeIGxp/Q09 gsJ79mKqQ5TMmACLNlzJGdpGq4Im8G1+/2j63ePJLFdRNz54XBgyHOtrB/ewA7JXfvfb33z04Ycq zxfiB/VO9tlGWSHdA7cok9mbH80gEcIADBwiU5QvLUCK/7MPLgQ7qWCQPJNHVT3BIdRCvcUQCgaZ glAwwBjkDH7JmBuW/iNaSbQNAIKfGUOWcsnxCEtCnNCtIkuwP0AiNgDLTjr548d0Ditqz3oXulqQ xhfrm//wh//zb37/i1UlWqFvAkvRY1ypT5JU+qBZ3AGopWpZS4YZGGyQI4khf2y//fA3//ibX4ON 1DXLcCCx7ZRhueWS+dm41+HIsIHMF/AbCmLQPlQwEDU9WywgQbi9nWy50CJjKqmm4zzjmUZPs6Rb aSbI5SmlEctZ2MIb/ZXKy+4tZXrz3UPnVkIPxjiqNpiBLy+r0QqnkP2NWnh1MUGYF5PZEio8Uyq5 2UWazLHNs/VcUzcacCDNMcfdXbSy8EH4ESqIMNkILUCrv5gNP5iDLxSZgXCp6YX22wRoFdTCxPvx xx9/+umnmiQFRcZOpydGIJLv3CEzvgFd+XDKWHlUquL3tlWhaY3mWeOyOICV3lKLVkgPHWymCCQo q/n6GrJQxATht4Le6voa1pe4FfkTnOhYYgt54S6X83S5fLo4wopbdKcNBnMkuJUkJ6AV/e5Skabb n4Z5v3+2vvn3H/3yX/3uZ1gYraqbkVlQV4XEPbFV0CwsseOEoOmEMDVfKDVFFOfomNCFMMBs+ulz 6Jh+v1o1JnmX86I9JfhEbE5wSC0+4VfrGG2knLiyNBXfZt2+NJyzKaShq9UYsg+rTzjHSO2uBFH5 3JgMprdKjQ5Vy0BrJaW8pukxpeNIuByzMjvSQ0crV1Dp8izb+4BbvTxAwUAvuzUy1WjF0TyZh7Yq 5hxJUCK4q5oMsmodNBYP36IzwcGBjmU7zIjUQqoGs8y6whLc7r413LzbX0OtGVYXMU98x7Nk8XnL 1sk/kPAhY57WVUOcbGIqDGqNPUX0AVwCMe9K/SuLapnBBY1r6RbKvBwEL7ieF+ZBBmwdt9reDm5v BuvrPsWhePzQLtDRDhfJBRP9kdK85sDDhcyG4yXW2pJwG6/3T5++/+Tp0Yw5t0arRT9igjQDZQwa rBRmlXuxBwXD1b/7zc/+xf/7O6zhWNXT/MA0CykwpkWK6MNTCWs6xGczJ6Xgz5PTJ1Q/SdiJpEvU B6IfsmajX0zwz1vFLcutj4dhtuMFD6U8Zu+1AE2O//F0s5lQHapFdqVnbyR2bEcnOVrCEiXdXA3D iRTRB9LHGV5JdVh5LIuTt/f/ARfe8qGF0KkWAAAAAElFTkSuQmCCUEsDBAoAAAAAAAAAIQBXS6dd sJYBALCWAQAUAAAAcHB0L21lZGlhL2ltYWdlNy5wbmeJUE5HDQoaCgAAAA1JSERSAAABlAAAAUkI AgAAADMHTAYAAAABc1JHQgCuzhzpAAD/yklEQVR4Xuz9W7OmWXadh2XVrsys87kaBKiQTcuiLEYo 7FD40iZANIDuRoMAKdISZNnWlX2hH2D5B/jeEQ5f2Rf+I47wv5AUlhiUTJpEoxtd1XXKqjx6jvGM Odd6v+/bmTuzshpQRL+5c+/v8B7WYc4xx5xrrrVe+X/8v/7fX3z5xWeff/7pL3/5yq1b/96/9+/9 u3/v7/23//yf/5f/1X/1/rvv/vv/s//pb//23/pv/tk//6//2T/7V3/xF//6L/7iB5988j//9//9 f+Pf+Df+v//iX/zz//a/+7f/R3/n3/13/u7du3f/6tNPf/HLz375V7/4+c9/8ckPPvnbv/Pbr7zy yl/94q9+8ctffvnV5/WIH/zgk9/5nd+5c/v2F198fv/+/b/9O7/zt377t2/duvXwwYOf/exn/8V/ +V/81/+f//rO41uv37r17/zb//bf+3t/76MPP3x8/8GTBw/qnDruPLl198mt2094l+PJk1v178mT x09u6Ucv9ebx41sPb9VfXj9++Ojxg8f1rl4+ecQLn/jklVdefeXVV1995VYVtY5XX726uqr3r9b7 +sL//LE/OTz4r/fNk1dvPanyuEiquRtAr/W7XrpN9OuBj8ePHlW16+fBg/v3v73/9ddf1fHtN/fr M33uo+pZ19IOVe16Wffi3a1XX3lSLXD1arVNNdDV7du3X3vNv29fvZZ/JQCvv/66v7n9Wp1T/197 rVqPdnK5XvzgNvSE63Urdbn/4MsSps8///KrL7/66utvvrn38OGjEic61891g7i7VfIq+507b7x+ 980333zv3Xfeffddl/fO7ddevXv1av1Wd+vXrVfVuPqXRr5J2Z88qYdXS1ruJGu0erX/w4cPH9Q/ FU6f1Z9vv/32m2++eVSfPHpcbfnGG2++XsWq486dajdJYRVC/RCZjKBaVruTXplqdiNLruuePh7U k+qR6tqHD7+tbv/m2/sPvuWJur3+Vy1vuT+rz6or9eD6VW/4X++q23Vay0YUAgmRfEmASpfvP7iv bvjii7p/3bOKqfvUHXyXt956+51333nj9TcisE+eqBXyT22ku1g5JaNPHldTVfNUE7lvP//663v1 ur6uJnr99Td+oOO3Xn/9rgr/J//b/7TasQS6TqqbvP/+B++++85f/Oxn/+Jf/ssq4g8++cGbb731 87/6q7/8+S/qVl98+eWbb7zxW7/1t958861/9a/+f//tf/ff1V3efvvtaq3PfvWr+vn000//6q8+ vX37qjqi9KTe1vH557+qo4S6+qca9PMvfnXvq68l7K+/Do6U8P3FX/zsL3/+l68+fvzqo8cFW598 8kk9qKCmRI++qZZ+7UmplOSxfoAftYDq/vDh44eP1W8lK+q5B/XsB99KV+v/g2/rA0mPvqrOU//V PYJNwJf/q0ctvwu4SoQMYN8TeIFAJz9nmoIOLvRUx1d1fUghqnqW0mpMiWy1g8RJda9eLy3R7/v3 q5e/uaf/kuNv71dLuCFVaUnr1WsFPXfu3CkFKiC6a0HR8eYbr5euv/XmG2/pePONev2Wft6s/vG3 HNWddw1eul3BnJpyKvLdkV890C1grC4FqJ4vOK7+vS8pMGoAGZsyq6n0Y3Wplqr3Zcpk3Uolb73i ezx+pX7UvkE8tYogYvALAH4GgFmXsRngta2HbIuMqi8Gfl+5sjhVA1VLqZmurnRWyaRFOiaXez16 3LgxlmQMgtBjK1XKRx0oL6anoBjzrJ4RUtevAkmbmfqvQ29UsmC3RN4CJwOWmyGBMW6qhiyUrtZt 6g563JMn9Wnds86qkgmrrYrV2lUGf/JQVMKQ6v6S8sdAzq3d6iIYMr3GWiGAeIbE8/btEr6Su2q2 OuHqH/7v/lPsQB113nvvvluiWRzrX/7Lf1nN+1s/+K16+1d1/PLTr77+qoS/JPi3PvlBieu/+Jf/ 4r/5Z/9N4dHbb71dOF8Y+avPv/j0089++emnVZ3X794phSnk+uVnnwpBv/hVVbEQrdSt3nx97+u6 sLSEJv763r2f/7wQ8q9eM0J99NFHH3/8SamFkOuJwKt65KqapqpKBwugVXPDt81JW5xqmkKrb+9L Y2UT7heEFWyVIhu/rOR1qeBKtsdKZgvXH/AaZKP/9fI5LPAzhPzwtR/xdPRqjYpq6XLjVf1Lpe+7 frSJGsL6rH+FUd/Uv3vVa/SwfhXjsq0sfYlAG7ZeuzPgVbJRQFTAVPgkAKu/DVhvFVrVm/qvk/S/ zOEbpgx3rQKvNVktDQXyrT5gwfHnedoJ3V+aBE4ZvAqFC44eQUPUIg9k/C0zEJFIxi4iYpq6YZ0v aarX1cNVYkmjupyiDlp2Sa/DLxiwaf4CL5ClPjRJFlqJwfsQo0H99b++s/0pckG14M2iIfUqeAK0 CIRoS9Dp9BDcxLhTC3sN6go93MBipBF01Y8oFwBWPzu9CmzXY7idqmIs9wE/LVolDKz/vp8Bizrq vAcPHpUClgU1qkA6bG9kPHADpL+ujK7KjellGgBhfvjQjaHTCrnqsNDdrQLUPa7+/D/7z+rhdVTd qiBFo6o6RZS+/PKrksl33nm3Cluc6qt7X9eV777zTrmNBS5V2/L1ii6VyFZVZPoflutxq/Tk3r2v 33337ffefb+aq96VopRVfufdt99//716sFywW0+q1+oOwtRXbtXjqnRVybKFH7797vtv1yN+8OGH H5Q+vKKuD+uoTr6q023P8ss1VFu4ljKtZuZFv+p9vjEZHY+SxnF72Qtyx9pTgHqtv27NoeiLRDy3 yl26wDe+lsydOah4QOtGrZAQjkelv/iG1RT1QvBUsCXoFuWCdomIAVoF44bwaryqr+xnCbKluX4V 5RKLEnS9ATWuD8TCDFV3+NJWB5kp0UUhqjetiw39dnuCy0aEC5p24bOntS53dPeDTDLPkOlqhtL7 EgAZqI4zOEaAYZNgtB9nf6UAyxbJCPCk+A8qaF/K0pDuN4QFCp9FvWBb9FN+bSQIzDGYQPRFuqrJ 5LvJjTIfeVCmpqRSJbFSVKVMYussijURjgsNdWhOIUmApl4g376THUM5dNYAymAzjl4BTwD3MK+8 4T4pjB1DcS9o9pXtB8GLohzSQrmu+iNYUN2J4eiXkMuorkeUCsIjArfGLsxA/CTdVTcGaQVdd+/W +XXO1f/hP/8/ffzhh++/V6Dx9ttvvVW4XAJejyxUqpPqJo6PfFMPKML1d/7O36lYVdnhKl/RqS+/ +KqavHzdMoDvvPPO+++9JxF5/LjQrSJcJfD1rprpb//t3/kf/1v/1ocfflgSUghYTE2e5qNHn332 WRWijLgjJncLsH7nk09++wc/+PjDj8rYV/MWVAEgKnrhupkXmgyCgeAEF+QU+LWcglfsbL7izggq RXAKNhu5OsTRyIVYAWah60jxd3d7Wtjox9blEZX14qKmbwEcC0c5yYppPLR1Kh7uyFb9UURL4YKy BEU/A2H1woAlWSLqYtZud8KxKRtjG1H6wRDlP8BTfS7NrnfVfTLYRdIU6nJ0q2QXcxtWodtSmwBA gOs6/DpnZNc19vY5JEddD/9sl9le5P37NDbCgGzgYwIuFhy7UuEwrxQUv1OBmbdL5BRvGpDwCYa4 mwXtFniBXw20InZxHNX7kK6iPjwL37JKWL1W7osYw7vvVRfYqXhE/NCNLC0HVaYpKNfeYviqgRvH 7voao5fqZ4PdakG/jP+Zz904TbaQVn3jktvo94tpouoEGwWZEXRSaGY1NeIqWEkH2EVIuYHUavZB rnrYeFZEenTbQrpXXqEdbD3NvB4/vvrf/x//8w8//KiQqyCt5LQ+unfvXoFq+ZZ3b98uwa9omK55 /fXf+e2/9T/4N//NDz74oCpTOlIkC6X5/IvPS5J/q0Dn44/lxN56Ujj14QcfloGumpSw/87f/u3/ wf/w3yw1UGe8+mohV/me977++pef/rK8y0K9QsN63Afvv//he+99+C4ErRxf8Sw6pup2VZ5jOc90 DtxLQOmYof46/EWwvo5Xg3p1GwUd54gYqA8sRcBVXuRvDM/QrpcMXhtypWo7hqF4I47WOPnO7SjL eBGGV7eq9op4yVN8UJEusa1yEat3OMefx8Mk+mMlVhOVTANABUiOFCvUJcno/wIsBRlsce4UYNUP oVxi3xxp2a3pDog8wh1daBB/+t+n4Be9j0+o4K7hGGTSW717CHCiJ+CW2rAxiMttQURP6tSqdInk m6+/jiwYIOLRcJ+blHohIz0IdoFi+gVWggBFW8QiCEzIBquAjxx6/rqEvzSiIKu6rWpD0LvDGyBu /q9SGYFOihmM696IV4GkuyApJUzHWoN35gh9WKRPc3Sw/bumgdxnlURY1RYFTWxr4Tubd6XjjGB1 v6oVYztVQbwemsrulu6FG2XtFgtB4KqzShxVwgKvn/wn/5sS17pXXIlXrwplZIeKIGmcoAYKKgr2 ZolxBTvqzLqpnJTHNXzz2ttvvlkwVbD1O7/9O598/HFhUBVEUbC336lICJhfMl/AVzWVUt2/X0Us XagS17dl39977/0iWQq/+ahC3CKSZ6WtEr7C4NnjeqHAao0zdZCeGE+axAZN7W59EuEFgiQsUrD6 AnMBUZY0lCWLTSPsZWJuZh73f5z8ZeluIsVPPSfcflF8q0msNBY+4ubbKOqpwIF9QIXa5ZUrhKVQ HqNKkI0an9BLxrbsw6edEl5AmNCfkhYHJRunHH00xRJS1f+MEwmlgPdqKbXPBuf+nFhhHzdpG/vK BodnHcPIOLFaBnXbFKCVRcMvOqpECsQIbVWF6mFHrWWjKOf+TAtJhtKqclfqB7WZTkvYaGnmc+HX PAU446mBLosUPVy/4x3Zk6pzyjEvB6hKXr7Ol198WT2pSJyi+h4HZQDE2pB/POlYsdyawWJ+9OxQ MPrK14cWivUlVAemTCMHdnW5iZlgLOAuXeHRsRCE4R15pDiEolPeeOfhg7mbh8fixhYY4ZDkWW64 8qYJCOjZCthDQj3K/Zp76/HV/+If/lnhU9kBd3/527ffEBHSURj07rvvvfv223VBlYmgAEpRjVkD Th/UsODHH9fgY3EuOXqvvVYaUdeVCUdi6jGlGiU5Vi0FlSVhdpdLdQogK/JbVqiKZ4L/8NZD4ZfZ kxicDBY8q4BMA5GPFGIF4BN5N+nuuKZgSd5m1VVXhlIRMeijJCFKmSAAsa8mYZYuGDOCwbHL/ct6 fW5BbfrCExF9PEGGU4qrVjpA/brvILUGFUOu7BYCZj6GY9kiWDkc5KKLq/MLrhK+Mgk/QS6RKw9A pl0cCYozbRfg+DZ+YhDpZk11E/xq3bQKQGD00jxFEqFPsHIIQOmCArqVsaFxA2kXp1H2vG2dxT1p qKrXHsZ59ZVyhc2TzIrc/4NcT4GwOW0/xwpI4VuWJgrhu9tS6auqQ31TuvbJJx/XiPC//lf/6rNP P5Uy3k3eCdIZMaTC8Rj71vnb0ts+H2QP3hRxbgPATdAm2tTBJcIyxjbcz2AKrIyISkOxz+xcB13k GJZOQQ5pgebJCnDrGTYP0rkEzcTru5jpYlrOzqcKpdsW2LkjJZzFderbcuN+9Od//vFHH5cEt06m lmNb62EGlse2Z7fretXK91tuQyrVHb5JcHcTrnDTP8h5BvXapNbYZ4laU04xBaEVkehHtx48fPJA AR70NlpqsDffRVBUT4XnDXswA1MHAg3CqHETjWYUgR65wLkGv14WYAFJp0cDjCPw+tGggzz+B/dK lpXfYLLF+Kl+VRxLuHUe0FEb2z+0vZLcUUu7ehWTF7Uq01Lhd2dCKDRfNknWxKPeZPr4bKioVcZ+ xnI1BsoHfXfAupGP9eLNGeVqBYOwoyGqpiJyd6wX1gwPpJIeIKa98UTizlUOidaTx2ImkiJrqlUL DV1ghBqCESfMcav+CcBxkW65a1e/5mTHPCQR1ej1VrksclCKK9zG+oZz+ar2vQZeFiwFZXY0m4KB IFaUajk5qUZ9aBcQAUmYYgJjbbmFV9aQNKG1d+yBzsRIuoR6SWYbAG17Y+cd6TRaivY6VUQB19sG r1BCFTWh7CQAuXWqMO4UYoDquGJeP/6P/9fFmwa8pvxDNwTJ5oQkheRzdeDNKUlQHltne552bWCP JSlhqXGLKoOqSjIIjq9fPHlQker98AAH4Yy6QaFVghUFkX5QnEXXOsxrjSeBbAwAuU9OORfIRQ++ uLZtV9rQMc4CX7S541eiy9XOwWbGB8tHBL0cygp0VUTr23uKaiGPoSLWE93d6Uw8lmAJI0z2CvUf 3NKIc/n2b7xuyLpy8F3Q5UvsaY9tqWBEg5e7HS9ok/MbuIEvrQFx6sI9rCg+6KzSg4pF4IwYxzyO 6lid2Zc1pBlofVByVcNTRUw1rg0E2qZWa1QzQOhuUvJdWeZ8JwjwbiNghscIm6sR0kPMzoGAupsK XEJcoZKWS25D4Qn46RPLfENMeBxF9oUpS6DOj0PtQ8n56xzRacaq/zjOGDD6HFVxYEUqbN7oQvmQ CclL3CINNDZy2VuzTS33ABaDG1j95HEixbAAFDNiDb+aoOivKuvH2PPXZbI6ZnxXP/3z//gHH3/y Zjl3Hsurj6tc1W8KaNtXrkh5Bc/r8+KwstH6vE67dVUhtz6HM/2hr8XJ9mmudr2oWLvuX5dUtyhl vUZ8nAKjrxSJr2S+R1cPH9k3rBoWWj0QaGroUxykPKgaTHISquI7zpCo1imzSTDUHdP2gKzgbmow rK1YqDucAuSajl7eYiMXf28kwTeRcocCRKeRmXRyxgs7wn5foXey1JxUeo83Dr27V9UMRnYJ3YpX j+kUahuMnNMjvlURLSU4OC5PBgRCU98BVQYunBTnKzbIW8+UZ62fOCMm3kH2G7XPRSfx5p7jgOV2 CZ0ewLGyypITtLOBdIjESRxQsPpsPLg6HbdSCVaV8qYsSvi7NEfXbgE+UydGCw2RPPiSRGzMpc+O fwfQLDyB4R3KHyGWRqDV4ELQtokR5opxFzphxPlCwRYLBmHAG2NNI75Gb/wW6Q2zQp0I2Jk3+oyM byyLnrtiPnOWby7XgTvX5Tyv5L2UtkwyvgGtWoXXcPXVayYhvo+H3gAvnAiq3CUjkVydV3e/+kd/ /ud/6+OP3rp75+rW48pFqBzRAqBqudv+rZ9X9KLmT9wtYaiIVTWuPnly+5U6xydsP1xrtALg9KJO fq1uXg+rwUeBl96+Wly9sNO/X6nKPH5YjvKrj+rnwa0K9dWEnkKu+98+9I/qfP/bxw/uVyqMR9ru V1JM1S2ApXxLRV5CC4ooyExIWARQchAm1tjeEMQsBMPXcTlItjTzhsq5zGlbo+ZxG6RVNxBfx/Qp aEVGKUdyG+wftpNI0hapmElMNuNX3rZzMqstYJ8IUhVeIFRpLpP40PlaGkv0h2RoOeNPPlX0nEik gSzMy0lbNryeIUTrQsFu3D7B/dW0TROCAMNCrn2B+owJ6fO2kiDXoJXjmHhHRuQB5JJK8CcKbF8m +Uhi7Zh8n5ChxrqDyWhD1ZAoaLPa4oBovj4YMR7mgrzUAyYFjgV83K6xNx5Q4h8QXH2tYDES093M 674RToMLg30B9uKxxaKBXAYic8/GNdcCkLJTaJtep+Hj0ee4BXqL9liZ5h48YHBMSOWLlV7u7udR 9UnZX02KIimzo0OUl0FjYKu+8vQmu7PEvAYvHcjAv67j6p/8+Z//1scfvXn3TmVigDVDvkyjxKQK sypWH+RqSCoOpcTRnMOZuRbCZQqmywsWC7+MYvlQnOvJo8Kv+l1zNAq5BF7F3h8+eKX4lPweGUXA y+Tr/qPSeU3O8lBanVMXKiov5qzmVlRrCYW4ghuagcRDnCvvlski9jawdUa1AmcbCp2+PDCztsnI 5n5q9YQmLn1r8DIFI22UXyur1NmlATTnynv6TzK0HHrXXRUcc/qWGUMeVP1alKoy5CFaPaJ4x4lb HlQ0FyOpmlE52Xn7WbxIHJN0J3NXI1eDRejXXq9ntM/glzsrP/nwKW3aX1G10fVNjIMA+todyHgy 0dQQMdMuV6/8B+iDR4DMNtCT+u1C1feiI7yVijOQ76woomPnvY7a6vO0x1CdxXn6hIX4QD8tECwz /nbje3ajwUu9/Kg0oJLvZbrEEO0rwJt0C4IhS3ZXv4DEJ2XOifI5+UrIsMXy1HCEdGxdH+Eo1nPJ R6GMvolaGTkcIpc37UdSFJ3pc0qnNenD03i2g7TYepxBz0EVP5rX6jE1F9ON232i+QRexbyIeTVz TH2fzuoRnOtkr7s038uLxDlXlTnaMlRQvsKVBuL6VRpZgS1BsGf8aB6aHHKLWhH8b74pplECamcH 0ZJ1rLQZZV54mEPtyvAiA4h47fo6deecHBibxbzzcUvXCMW1NaUyY02XREZNDy1U/eK5lkUbRaSM WA6/4xN2WpbGDvNWTg3ZpTOxSTbNk+Wg2fXKmTLl60ngC50q6bdiWkOxIFyewgFi+VfH5pPnvcOW KczWLFGwRDwyTTdYFrq6S4JVs8Em7XZo85Y8RGg/rhOokTSkZ7+B+EYGFZozGEqkhLZIvkCn4AT5 VvAPv5LUVLyPuAJKW3Re3GRyDao9NHx5MFC+aZR/ujizP1oiFnLk0iGQWzXSAjAaxDZNn8LjcpVE 1Fhz6UglLVUWWN269IPMnySNFhlxEZ8mqS3dEDgiaOZNFiTrpPUh4FUfEmASx3eso3OyVM5meLpu ULL5V2OIbmfkApDEOXSTpC9ZAsjBDYymVJm5rD9pTd+I+nX361YvAF7I5nXIdagNzy7apUcPdgVT AdvMrvaIRE3bc96hPSQnzGsgxs2saS9ff1VfFXeoGSvh14aw5PzCaOU/2DH2YS8IRGs8MyXLzzCu M/VaJOGpNW3w0v0G6gJnZw0k8DKrkrPYVMuTx0kmNYz1a4EWCaYeVYQmQLZlBzTR0wS9uK1jO+Su Fd1Svu+bbykbwnMknCVfs19CtQCuzPfnrxqmtaDbrdtnEMZUFrkBcxZEHSThBNWuE5KBlRvSsR2/ DqYiaESsZtlG2fthNlhN9JIHmwyUFDI7RtokqVXzVrPLLGAxve5INV3FCAe8zLaGCRoFIFA7lvnd Ke3hspEYCpRi629npsRf4LYEgu59fa+mo1TxKkW8lk4oGCiiXler1zXGokuNym7+88PlG/lMwGuD LxCMJ4JeUKCAl2YyKM/JoYXbECBw8uAfJ8AXRtYMWJbALqFnKRLRc3vDreAge5Fp+YDp8Ig4x3Qg 3z65+if/0X/4g08+rnlrDp+LPNeXjrjHhXyuF1xrV1E/Fd2vCFfFyJRuGrNgyaiaiEF45FR+ohK4 KmCvJNUaVXR8r2RIuV2+qkpemYSVWsvSIVFVuwMmX4FyW6+2XyPPC68juk+B3Zt8tXOLlpamGB1Q oGMEL561pkk7WtNB44aiWR2hl9eXwQfYGE5/e4hGK+xjawjCVUwS0qkZiW++XrOltejDu++8987b 79Q4ImNszuMW5WLiYfzBINX2Pjjf6Bs9X/DkCGKC9DdpnzmnEedpdu7khnh8Jz9nlmVYdFIlB1bJ Bh+g8c2jt3uUJ5qMYhjJFKBpp8V3kz/FtThmnSdHlPXyYQgIag1ybXwLbDE+NNZSgAUtemfcNTLU 7Zg/4mVzNEBcRapEvxKm+lwL25DUYlQ2FhHUGwLWgJYb0hxJUnUNFwkZi944L6+5xLWWiqkMQxM9 D1t7VjnJCqnj7nbS4uk/zIcb105uXVg3KEEt8lhiWTXDoShzXqfJjAZfs9oPrLD7MM3OZCNyC6/+ 6Z//h7/18cdKJXVGuya5e0zQESuF9Z/rp2GLIH1+PNSo8UqlPHsswrmmEhchV6EVWFY+tu1eOY9e laqC+h6ONAy9fvd2TUArn6iGPFWwhGg8Rd+euAeULBnjlY9obOL/XOp3wYYtyeDVkuQRhVAks3MN Lmiy9Le1iFb9eJ4hjCp/NKriRBuHFRhnaZ5ljTFyhUXgWZg3CrmZe6h1H954s1IcyypX3m8BltqD BIg6ajqF0s2JCA0lHV4a6JoWckh0JgCnBZ01d23L7TTsFIzC0W+MX63eO2AFkEJS0gQOZfosK2Ra hcfDPtJsKGyUKthhnwyWItdEoujIhemGwMyCZwpgJ9ODaW55HM4GxOYaqfUgVwJKm9OzyB9RWB6/ kcjAWtcbZ8zqUiUrsS/CVX39q88++4u/+IuSFHeviFDiIyoZETjAdQfKbvygcdw8kGWEFmdksNWT WG9/9tmv/vW//le16EtVPaEGLXuWyKIfNgjG/QL5wa+AsL7iDmVMaxm19z/4oEquTKCvZdC/+OIL pfLcVdYOLYPYhxq2SC0RTaDA4FVZ8gav2KCkONBDz/+jgjq1Qj8GREnHE40MqjxiW046jRvsMVIP rtZPnaI5QG7eqkQZFU3dcGPVxEtFb2og3F9D+DPpxw3f0OXXfGIxNp6l4jvWvBiKRRCOymipMfNO DsRaxoD1/4p11ZQPJ8c3cjnXFnfQRJN2iAJxHwjXHPMmaQBKN6020XyImspVq7DVhN6KyyO2YBK5 p3qFhqPl+Ib9jjN3sOD19D3IdcEJWlL1tLZcxv3FWjyKGP+Bco+aGQISMRrca9lvu73Zbk4fyLOl JsNLOdt0IZAGhzGK0dm5kFcGh9Zcvj8il3FkHSBs2hXUbCcvwLq+1lfgZv33nMHHzix+vVCsVpeq 9RdlnzJNRAMUkMvxtnbZpKYHrqgPmsoPwvdpKD3lrPLXtOVa6apuTZQ0ixrYUrhxUdU5NiY3T+3H uXdkQyuoUSa2SlC4VZzLg48PlTB9567qZMk9l5RRYEqHvarRRoGXklRdjBgLt+2LCBv6RsM7ky5B LQYRPJrjJVkcle/Yl86xKu/LSJRrRIYOLk/98VxWzQyoQ50GjUAp4VxYD5DLAb50ykjGtZT/5nVd vWWsomJ2Eh2QcpohSx+KXmkRy5rEXusE1WKP95Q8zUKAHhfO0pERu4aqcTkXbAUY0aaKRHgdCIWz tFZNiUJZM81tf1OhQOfw1EwXR/+M8UuAB6awuBsKdAtFsa2tef53BK+bt+x1Z0ZYm0AZuWKTkPUg WtTYbaTRH2feH0iQKoWQ6IWVBD0NQfSllt/x1huTWjEBf7G6INCh1Ju5iUGbs07O39XL1en7+IFw PUmU/9cnVaMyhCU3Skx/rRKZehm/Br+UOch0IMqDa9OjYCsII3Y5iOViVCvUIBHjSPUN9q9etJkL vBr0M1gJpfABdeo3plHSjCST32JeWvVPZQFp5rlyiRVVxCtg1LtvdKSjywBEWq/+6X/0v6rRRs/N bsrpWuF5P5N5qRHdzuG4IJYzTlFmJ2My0NsOkafLF44pqkUinKRNJ5Se4fiqGpVzCGA5IMn4semU mh2ZxYOyJciLxrCFXOHn0coXQuSRqxjj0CLXTr86oK6/SqKdBIha0NZLACpR/l5NTPyGnC4NRIRw Wkkc2EKReJTt/+HYc72JwdeoBSsF1hTUOjzZ5261BI0eVE/wt2NDNFkr/uIvZ7CB4e3jOzGvayHp Zoak/Su4SpiWGirmysJiCRibTJrjCW9tey3AJoFGC4v4cOvYW0woO2nv9G+6IyzeDSp74OZJ4VLF A1FO89Fv4TNU4XigYcfDuBXHKbFT5dNWYargysJ9tVIhH8ZD67nfIWsOLi8KcqRGFwoKhsUZjrWv ihCVrS89FlQJoGIhauTRhTiN61HbrJljjVyTnPfkFquQVgehCJrC5YlQ9br6hkW7cjqcxAUceN21 4uo//qf/wW999EGtGqElmOteysm6dduJqc5TfcaP8ksfPqhRwgpXKUVLqfCVX/qgEkorJ74mX1SW 1qMH3zy8f+9xUcT73zz+9v6Th/dfefyw1rfpDFjnu756q1YYe72Wir792t2a4F2/ldCvlcXtOdaC AfWDAchUW81YVOTa2eCELZPepVhdgMBWe/DrgqCci5Ixm+bvNsd0wa485hsg1sgoKQ4ZKHR4q9cs VVaLloDw3ESvkqYBVMZxgnc9hqjA/GZYEq5i0UuzLNbZ4qcWZq45iYatOrQ6OPMTLWS6uRQS37Kz tyAnzVMa+FsQQLo5GiTyQbWFC4c9iCjx+kAZ3GKXP0d/D9+tTjmB6ZO3iK3LZ6AZl5ESCbvaLvGu 4bu+bXRmwkf0wLSr0YSH9YQQldBBLg5wC92uT/ESm4Ep6JhGcbZqg1X/zfX9qLZM6+bbg07BK6XI 1BE1QA+zAJ3NNWsYIZmcCFVQYpACS5/1OPohquMBZlOj1ZRAmHQqwzrOaq+jrsv8kHgchKVUVUY4 0nLmI6sJw/JYzlWspL500Ffrd9O85Z18+eWXVQUPjItXVsVUDOl05GZT3nxy9Z/8kz/9wUcfvVmZ U7UyIbn1Ffl3Yv1JAurFt7cKqgqwaiqp1oSo1w9vGbYKvJwQ7+TSb+89/PZeDSoUU3zy4Ntbjx68 +uRRZecLszQc6STYVyqD/+puLX5fC040eHmKi/ur9n9gWrWq48kqTuZy2CuJXZ5qq28briLdyPgI z6mgbO9t3KeP6YAY3qZcnnXlbGDPufRSDk7dqoQtr5xvJ1GrPzCb2qvYaEUbTQwg3aGkjHEW5A64 GaoluVStNSYd5ArPYhFTEk8VAKx8iArS1wqSWs7UQIUfRCWIa02A3jreqfMdEUNzw2B37AS8N6x5 eszruiYdRT3ilu97xkCe1i/2E4Of8OwQ64GuIB51gQ4DpTkWIo2OslROaLtHw+MhYqT47dBGvEhY GOjl9i6xfVpFwjWQvSN47V7VGXK1CLoTiLnRlYmeRA+EXMhVRIjZhcsf5D7+3c+bZk9NOKPxP+y1 F0zQ4zyiTUOZjj0sYW5f1q0kLjGTLzILMkI4qgSuEby2SygVqnnKRLf9r5CrVoyvO5ZUl6CjXwY7 T6ffKR/C4467+vP/4M9qVQm7jTqmP+iWZ/7IUXIOCGzCG4Mwbc9xHau6XULWoiZflgHEYfqADnWL yuEh2hvc+MKYUsdzdApcAr8xkhR7jGLOh5b+ac4lMKlgc4pRM6R0XKe5Ek/RATti7coM8iREZvfU qlueRf3Nt0Y2re0JxGWCx8QEMeMUq0GEUUJNnmZ1cCWXKsP0dbLlC7NMuStfxEvJa8W1ChZo9LpH ES2ongh2HFccJQ3pSqQwT0YcRip4R7FoqURFsAuXmvE66HkaRqVHTkTs/E5bR7pL93896tDFdZlt HQQ5JlILt9LgLSBEziw/2Y6pddkV7sCNxNTq5xX0Ys+anCjHVQbz0oGK5fnrFXixfs+lDdB9RTyG 6KmVwxrbyfdycx0s1mSyb75xit/bZcbAW3sjFZtbfTvy5mfza2m5WUHjghsmzkyrFl61Y0APK3pb WlxCWOJX7Vzq0OHTzlXZta/FG9nT5kDffFsltFEuxqKYbO3+9Nlnn1YNvc+QhiaKGWDGY5JbgVNi S+LVf1Tg1atKBABao66TyP1z5sEze4B4PNMuetIxtqtWkY/bZXGJ8VQvwaUAIEM9cARlQLJODkDN btAGWoNrmz72heM4n1ZoqQ+yNCqMkuZoFU4dmLvQIO16erWte9rzw9BVvwvOwDZncmVWYmJbJu1Y o/3Asjq3QYOIrG2qX5pWLadxpiUS6fKWPtoqw9bJ/LqVb26bZuz3PcJxaFOauflp8LTNlt4SDzmd IbQTs2ted3kuf73xopTPbX5mYCwjOIA24MhEMn3qw9axdi09oC2nw6Abl2/1LpAwBiN7RtRTCdmM MesxEs/vM0Mg7KzPvaJL+Jcns18GLz9ks59Loga/Ti7c8ctFUp/WHeIoYrF7nMHMR9axBrK/+vLL CiDUyqD1PXvu2CmplCcoQOq7laBt1S6EzMJx4bBu0kSDGG1jZ/FRRW9/9flnFdR57/33a5hI0SvN e7HObgLtcpqraGitdfbVV0pLKmuoPvQg+VuYoto04xe/+EUV+b333ivh9ojeQ+IeNELrp+Wz9fTq P/zHf1pLznt6kLo6fLtJwa4P6kKfw+gmw2rMwuMti7baGxI1IQXC19iztWioeeIADqcKgyKMOkSL dnBBjGMtBoBWfvbGmhOaMl7HtnaJ2fSlu2jTH1d3cyOIeTGehzCXIapG8AKnXmaLFPqeam03kkWZ nRSh/NvWp/hx9JCzSaFWTEOU6y/w8vYYvd1FJlTfrcmLNctA6+FqmpTQZWM4bQ0Wx1oGIrbC/Ppo FqajkRKj1cC5caBJqVH3oraefnjT8/o6YOXsLu5j1rQ4HMHbRXwafWQv7eNF6EIWu3/dVrvnSR5l hijOuSIxIi991SrAWJ7lzIsAM9V4D44ju+u4jMv5fj9z8CvyH47XWOJ+4zIUjIHJ+lQbgl1dsU9W VSdp93Vui8f+FBonKG7FBBO6c3l4aEZibxJ4N61bo6S05rrUa42eVyDII9uSr802dJchSBE4b9bz oES3iFsJPcOpGHt7F3ercRVZq7WarRfxlyueawtCKen7q3/6Z39SG42VWtjF12JadZdDs4caOafW ETvPydMa9iT7CrySdpk4DsR11MBPy3AZ7cFUxFCrmFL+LAltdaJZQbKj9F7/js64YMYvKd3xxHRx D/hgag3ANujUCz/KxEpmhwR6LyUv2VEs7EEtxXXvy69qkzcNNJJVzzohhLfw/5Wb55HDNytNXlRK W4pl9rSXY87CzFo/3tMSnSShuT4VcMkUWQzKpibHZgHDDFVjF3kb/TrVMlgnHWFBrOIeYl5HGLkh kF1q+AufBb9iXUdxgYoWgWgYSGftdB8R2VFPWR/8RatoZCGA0rfv2YhO8nKnulkMS9M8uifRroZD a5D6v8IhHq1RvFPrAENZ8DkPxbIAWY7Q/Ya5c6impgMlaSIjCd2R1HbBreTxcclQbUDx7nvvlez9 4hd/VUpZj+kImYcUEjtfIfSB9emAERl/kg4WSkYGLPWeolivSkjff//9gqraeedXn35WFU9kX4sY E/biDkS0gjbICV2o2EhBXy3784oWjK4mKdmu7XsKukqRvvzyi6qZ55yyhKBmudWgZ/WrxlhTRN3u 6p/86U+LeZUCxRMydRp1gIhRhXoNcnl/Mx/OunT0LclN5NQR70wbqEOdKkTBnRtuH7FdxWPMqv0C dGjrPNrkhj8572Yq00ibfvNFCYIm38aCggfcroP60v6/uJZ3iWTGIgy0xLlgq/i8ovk1jjGDiYYS 5oh57mFytbI34pu1rpq3bvV/7+vDZGonJ/syJibaH9R0NnIqB4B2SELgga3xAnZkS88iDRuEV0NH vhs9EulZ3bE64nnp1danl3unNTcqHGVuPBlIDcbEjhxuZQkURHSd1E5UJaJvKbI8Ysod8/InWFBs H7/pc4kEushhJNJyc86GYucqSXcYoh89kBq4CoAlKnkErt2E7OWcfHnjAr0UPLKIPinBqPTk8r8q n7Sy4QtLM3PVg4WAZpc5reLirH4LXo+p9zOGl7t5jF12nOtXuXvlMNaDfvHzn3/62af1lBJlj6eJ kyQgEqyKRYmEulHrPARaLeRYU928jHaNPlWZauvqmpCk6bqZhetpJFqm7TUThfvLUteSBP/4H/6k AvZ1ppUuR+JWeIZs9ZDtl7/VMuq1Q0BxLntItiPynvCUxE0YqMmcCjV+NQTJpglrMRux/cOF+rRi V3Q36TEAY5N2FTyoYyCvBWNYSdPAo764oI1P/ZrBB2MV+QzM1vH4B4vQMMY4jdNLcnXjsQOGBiiq juRkVZRKG769o22TvLfJbDqdaYn1ARsmzpo1znKYVAfiCTCmaoXscxfzsLUArccR5DIbWCYh8IaG HjjGXLt9HrMJ+dou3R+Jrl86sMLXfbVRjOEal1/0k/lrduRVDXbEXahCltYwf3DWxyzdl6bMagEa vLYjSZB7WiVIN2GzWj7Tu4tP4CmZM8opy7R5TxSJP2cPa6BuF+fBCdAEJtZaOQC1w3w0A5I9rkCT Te9VUQa0ylPSJhDxjMy6qN1K3N6szCUo1wexet2kbiEfaNGgK+qMpgD2TjH9pgCj5JJJL8Sx0SGK WHdrr1a7n7Vah4rNs8wTKuGqBr68ZoHi9K+WTRe/sfiL+9hO+FutHOnuVZde/eOf/qQC9oBXWZEJ wANkDV0ObzGY9vW9Gtcs+MIVsldRC6A+Ylv5YIHXtJi+ryKU3+NxV0f1ku+R9rU6st7FCKktw8FU gmo2GIdw80Ezcg5n2vJFMlhtbiMXkSmMVwSIlmecnaid8BypNH4ptJEpiQNUXth0Jv0Y1IT2dXK1 fpmXoscVxCpsIifLqFXbbNVEak1I9D82PBHnquC8+JgtDYMXci1FVMH7EHknaG6BYENTi93WIJx+ bC5kmhgoxndvaEQqoou++70yOPvGzYHyfnrtAkY9HbxCcJ4BXIDsunmzgijZaHj3aP11EGi+bw4V yGsT0K4zTZkfB6iDc7v8hUUp9Zc8FrMQSYWjmWwYmgW8cQstf27caELVYbOgQ66alLXourRH/Npx ZOQZFDNHiD9UL1hchLEk/MzATgMeUgyvZomsOicVdpH70WlvatKImapUVYu4lGaURJaEVxlK6hGr nBE6K/9dioDdSJVP1DyYTfS8fktrbisHpYrq+TVaRdKah155E8ckLRR4/cmPzbyKlQWteh8t7xfv rWsU0hFsKX3J01y+rrVNFeNU9bFVbg5vPmSWR9zGy4eL/2XllYly+cKu1MQZqGNL3SJhmxhtgH0i tqvp3as0I5at5ccqsF+2SJYHGnY+5fnUHkiFZGUqYtK7mErtf2Cbnlc3r4dp9607d2q/aS8DUPkM SoB/+613vadvDREqyUHHG29plQA2d/WOmdkVkfjk7COc8aWkuFkE6n+ZIyb9uD52DIMu6oiMbugT kGoWlo1JxQgkSdy3dP8NmuXG/cmcGZN8aPl02kUIuoZ1pRtSgQugt9/TX08FB8di7c6EwMvGh8ZM 9Gk8wXmWaJbaRiWU+EK+3FysLdNEoRvZmlnXKBBTvVNfY+kMB3E2kDizwgCOHzxpUHn6XmaDy26z /ezVtReEvIExXiynw6ydk0WHq7Sxub28T5tmNqaJJQyV3/TioCOtLm7K9jZD6qEi5nl2M1GFPMXt ZzqQ9STavq7uPqmbxp3Wioz4Gb6nfR80UBEtK7S+rmf/o5/++KOPPyqkMbZVsEb0SjjoqDzp4Yo5 F+PSjoHeNfBbLQpYpbOfm4SGKlz1awbLyFLCVy3k0jTSRIxxeBYb2EaR2rhjAtCvGAMadJr1srz3 OcOk0s1GsjHeB9FhiUhS0hS/IPqqxUu9yzTw5RW1lBzf+wIY/kPToLDuPYRIyKX0dzOrymiomTtv 1+5x8hrBLP3yOhBsTF3o5RD8LK/onNzkNMe9trb3/BdVs9pPi8+5maJzCLwBy6QCNUeULeAETfon E4bcqh7SDn4FD9eZ/iKL1ge8PLyZ/hk4vIReoNTFHxS2u/ykQ3fQy4n038bBFuCuB5gkwBbQug2E hrHTXlqYxP0FlVfMy+iV7U8pXf4vg1cd44UcvP8zY80r05hRjSmjOa6ZTX8WqUesY1a3wiKZ3YsH DDnFsDUcweCP2CCckPGZOr8iFyXKyUbUcERPMkwKWK/PaXzNwxrCpjsmwESPq3ombF5wG+RKkLDa gx35ZMl7+VPFyOxR5rwoSm6/CbClFi/Dd6ZnlETpg/ANIWdYgv37Aq8/+dHHH38i5qXxXmuw85KM dhn79+i/0MxTNbUwfhVDMWeHZbSuUHZw0DQlz2fxAIF9n2TGpyf3XP8NnJadoxmjEgNn8R1aCk77 st9zMdYsvztAMDq+X0tcy2OAahyYFhOqaYRJCplceE4eQsejwHEDhHasgkyR8WDA8hiiORZrmnIQ kXQLRurcq/GmWw2Q+Eb7gLrBS63UVtta4oYbThu4toVNALbFJaf2BTvfBRM2aHI5hAk7OqFlfLIh 4kDjvNjOO6IbPTw3Of1yex+BaJQMrDTIHkTB9WoACX4FEfy4ARY/t1LFDE/wMi85l+q4yqO46xGK kJTODPpYeOQzml/oZs1pMkrik/XPSr6cDW6JX5COi/2gSYImDSZn8t7xEPe/O7f/0QIkbyiuo33U yxfTpqslatjcpGRFJRtK0u9nOElfxyYolpVHEeVy5n19X4rEYuXGmscl5VqoRhJaF3g1iq59v4hJ oI60G7KbFqmUrKCRuETdt+6Gnuh87LuN1dZRrUD6SoFAuJi2a9aYmiaCa7SSxR60rsOc7xECB2jY 1lTUywv07Eyr7ehg+7X9c2hEIAJvIDGEsw7dPzAt0n/FJnqE8JI4OjhHRAvyZdwy66r2cgpIsVAz MlzImdODHBCYFHZ30oOSsAqpONhfzLskMpne+1HHM5ysNjsuUeULKndW02EV5604nwApoPlIw9yp TcQoyZyak0eEJJ2YjuEEvuFNjmvBafDu+rtsD4QYAKpTEPB1b61UjpPXb9yP8ZzbFjQllQpugJGL 7YKZyTjNLr2pbtR27zgcpT1xPS2TCUFUaFlW0FteRaqYj6E9YtqjTFgDCMrYQl6N5UXU0U5QbjtC CjOGGgJreXK5yEyXs1OE4/PPPyvNLZkr41n1shlmODb3XLfuGMslFTs0Kt6Whi+IS2VC21XVuALi tT5XPaTkvpovGD0R8Ev6bmnUf3kvLj9j6qw67bRYxdnr5jX25ShYQEAIlZFQN07dCE9QuHRLPqfA mwE1e1D1iTxEd6ITO6iHmSs0wizWg/uM6NNl6Mm1IkfPbDR1660VMLwWuQIl/ad973D7jkx06H04 lafusDKgB0vZt0dbJPbi8h4Jh3G6+s6+TcejIWXAvUuNp/NAuoh32Tds2iXogm15q00FrWi35UEP GUnVm5uMbd4RZGiLP3Q0IjyIbhxV5i0Ri3aq+vy+EMc3PMhEwflEaxQF2OCHJ+WT027duzgIsnX6 8VvG944/vcmaxDA/rG8Cgq0CBEG3qlLjLtqGX1l/Qk3dM/cttXqGXWr8x171MuSpp+IpZqvZ8O7T skwFZl7bUfqFgwO+yEY6BCoKX5vXYe2QGTlT3cbRPOJjzRxablGChVmtpum4EwSjE9DZoLX1Mf6X BKye64E1qW0Q1pEjz9hLRsCmdku/TvBr6KEl3k5eAl5WfCf0lL7XV/ZgakxQazdW2QYp6Zs4mWcQ FoMDicQy9GJY9V4o5GVC1NpbFPvqz376hx9//FGpVZ2hVL1XWJNA0qtdfYoVP2FIsdZKrwUrtPlF uUFFLrwRcVC3xge01rDeZkhRLqsOrUFo+WjzFvE6Md4Rwu6MyOpmcENNqCQdNv08yBXr1zkOkiR2 8vAgCzOWwClRyXs1+PDVV/cq60N/awBVL76ptFtDl9mWJa9awIClFaISXnULaxFLb2gqrG5boahf rfqgpR+0YnWlIb9ea+7XCYZ6b1mpHtRKGUSm0F5Xb/Cildz7IvXYYuOdN0rSBnWtb3VhZSVm4VsL V10ndm/0qSjz8vj8FPUlcObWnNeo13yYIol5eSmPwccBLCPajmKYqBsdJ/2/Q7jjUAu80BXQM7lO aRZjWafKN6quIs1NrDWNgOt8Al6qpllOz4Mk5M9ug2ico5Cm1/UXOQYJS8A0f1WKo/KRYB4AkrAI MWTXM0VP34bzZEBJzw3m+lFxB9v05Fv7ZfXZCQtLxgQPDBRaFYyjRqtbZVFrJLuksyLVNYeoJLpE 2FzDfhxYorszzrEOzIUlInYyFjFGrD90sVDF+qaAXtHdt96ulvj6q6+VlqWNAspDE5XSLF35kloN rWF5VJlHzSOVzMFCUQWKChi/rW1HSk2VhMRuynXHf/QnP/zok5oeVBn2bKttjTFXcrK9Fzi1MYZb 3X3jTtGJUkwzO8GZQc3vpKGmbMIqOt9suWlBurb/HG3yKn2ai5akI1OtiHC+afxSLzLu54FUBw2Z CE1Iix09MpINdmkAQmDl/zq+/OLLLzUi4fUCWawZsmUpWBZEbUPMAMLsJHiTTA+pvlazEgVeRbtq vS1PTlRysH318aAhXMYvkAsBiiIl/MKi/1YweQNWZmbYF2y9VoCSKYf0zdqdjEi0EUq/o2x5K2wC iepy3cHztnym4ax6y2cOzfLN/Qg/LjffOBHFe+6frWqwTyron54TtxeCp1iwGtCbCW7cDUFLkRa0 NcHy5S2M0ZPW+8CI64gr58SoOuhrBwZqIS0pngM/sj3yEL0Lp22AnchATDh+iV9pTTFuaIpvm2Px 4CBGI5aL6OIcwST3XviyoiijCO2jILD1cUVca7Zg0b+aOfirX32q6pBvidfntt/UrIsSCwBCBb9A 0DA8PvMHnAH0ldhX8n2NSdWS53/5s59VfVVvcrI9abdcEQigaKpreUBJd0r9x1JCsorw1rLRNWBf uvqzn/2MHiF5u8CrmNfHtVjBeEMGnZRZgazsrSxG4diNAjnuj24AxwBY8dTyZU7RKLrqvoHRKOvq ig2jBuux4diA9W9sAuEAkAv0UrKg97xg+NDuIpF4PEVDknmXD4+iMoiqcVTtTy3HMUMTpKeCVgqC +mEltL0AL/XHMc7KIY5mmUB7/1NHIYi3TDerab0H7rYUwbK0J+0RaXEjtO/tBkmgPrKUr5oEYU37 yDn5wIIWwtG93OJ7XTHQjf2WQMA84bTUz36/9WxkYbudywE285NfyCTW8OJxKBKSM7XnZctglKTv AiIRsNezk46Z8EArqrIlFQuvr+vM6ml7WYSZ5HL48iCOcpNMf/Sht6onqUhRmbOJ3NQz3TFYsqvQ FHQDtMXe8q1KY1zI2i7y3UxDHPW4X/cnKu0h06zxmY7IEhoLqfbO3fArfmV9G/bUmwCpybyKXNWy XmrizeOaq5hRKSaHQPeIBEZ3Gx3oYtXDzWUGCn9kA/Or0r5S27p27rmBV03MhmgiphbV6pIK4jj+ 3NsuezE8YjfBleiv4j7AGZwaURkZdcd0/xw1axfD3JMT1+XpT2Rr/42Uk0Ys5PIaNQYtkkJwFL2B T88VILjV65sGvZxh6/+e6cPU6rq27lt1YhIWRFyBUEdEw7xtvGDFiOZMziAO0S2QWlJ4QC1Ncj1y RVe7LQcu1D/Wvu0JMx6/ntivou/zdul+6/PS6lXks8Kn8SNgI2lTyEuXPuOzAx5ablth256rRQ83 Cfpce+NRgUM7LNRDQY7i6PkvIitUkXGeYUjohAryao3WKRAhqbAQ1PmgkntfOxQyEO8Xzhu0j1Nv 6nLv4WQlNniRRpDWXPzmTOwPgoN25jgBL96S24XoEpdnhojDPgpw1KMQ1j5UixaApMSM8ZtnBdR3 XXU7DlwywE/VHeHz5ETtx07+A3Feta0rvgYLxgym59w1arQeaqt76hoPNNj5q+R7J28v5qU17O3o 9aCeOkaP96Q6xvOJ6zCwzzoZArjSWXmLtihhGrvKjmIcfepY1YARIppbpktPwCso4C5KbMsdxZwm 73tvgNIMQ/4lVat3o3aiR2LyrPzQYXmHVGUQEilQQ9TjSLFlLRoslTPmFXkXBdb2Khp+dopWNUPt Z2eJVjxeEOcKteYFMNJTpkaA+TOO1BqoGyiclNTYKwgKbleLYkjE4i6k6GBTNv0dyaU0x694GyaE iPsJUbp6kao8qx6Xv+87ocK6WxegsxbOILTPvFTOLvzJbfdi04xL/V07W+sjKGAP/b9PlsusEDd5 QtKGDKVh7g1HkkccPuuwJAogI0hmn1jCwudAUbfuAgprrz2n82bLRQ0UIFZ7ou2GCLTazkfSBCKW U8bXssS9m6JBkK2MN4Oyt2NK4mlF68Czs74skutX7DZkzHIgiWENAZDTtXriFMLIkWkRuFM6zBZg 4Dqtim0o8kQ5/y638Q9Ywx4XjNuAJHF8HGwfgKQHWpkEcF6IGRd66dqScS7mvqSlDEleomnsCl9b 9Yn9jc3X2VVGJgo4KW1GCQVUrD5jROpJAZAor8oMqTKtEoKR/6AhDFkJ4jksjzn+uZaIJ6G0qKfW APSAE5+U4+ydqJ2L+9odOQ3eurR30lMsP/Layg9kNJbQHk/TQL6dwyDSRsFQlSB0QlQEuYMvC6Tc b30nwOtUIXxZfFjmu538xIHLXVjuie5NJ69SfodXvQ5m7rw/xQ9aBGw/8+ID59ou5xTY3/A10ABE yFZFYQI8hoMVXKcRkzJeO2NVZFrxzNIuD0AnG8d2VOu+wywCK5WvBNcwYNnqZ/tuns8P2rv+eF3l 3AeV7H4Lv/F3reZcjda3vuMTAJa2rsh2yXidUsKfwVGH9ycqEuAZm3sqLEHV7WNQkh5iCDJMa1YS qK+qBRQ+9jwWfpsfuB8GdOBbOxrbdojB9do45oxmTm7VgJd3DwpyVVni+yVo4447snewEByzryht 3Q7UZCnK0RguabSHiTEwcfCv7sgG3VFip24Qt5KN88QdGJaJFpF2Ra689vI3rKUFdDFhgNnjWbiG vSMpi/s5m0p72dKCJ085rEk82uhWuKVfSjf1tB5F4rWsB6vTFHLZpvo+eJYZtjBGRVMGFfrDM9E4 +6CbdMtFCgA6qQAQIaaubsskoYaVgMvCzAadAzz5w6jQ2Jb9hYvresz902yDXzcB4qecE9bduDnI OBI1156cefGevpwynxmA+cL0Zg7F4OOtwCRCD4jdy7oN7nsWi7OMtRCLUv8cBfMokeRxICR8yL4k WRT1uBb17F00RTB5AVQHiVQFlHJnO+ZejVJ+nUt4HhWEYiDWFkuQq4aV6nXpTmlDj8J7kl+Norqe BiCUsYVn2sgNdGRePD6cXyjgsQDHfZWqDvOqQqGdjuFowQJuHhx35VLNbOYzeK6vDLuzpEqVXxUD B5p51RpglHLZaT8goMJrjaeE/kLNEt9Kj5g5bSC3ZGO9WhRu9DL4lfeoRWjlSQt6ljxBLBu7bJgW L58wV+iYpnkywwdXcmZSO8uU6Z2eZ+tm1pSAO3cLrWoCYv2wmdh7tfDH++++864n95CzZQQT2fIM TrJN7SzaVWwTh88YMRzYPsGFS41z/tlBe/uNwMh53tYovdl1lAZMZ26kdbuV+3k75pILItt3m3vO tUuq93u90OunVPzkfjdrtkPtzksUDT/Q0Lg/oUtE7BnDwieyahjFtP8LvkvJG0Aib4g9DZwcAIYM LUJdJ6Bcb5E7WCCndVdi6uggfbkpZap+ALLtQRt2gb85vwpfIMLqloil+Y8W0ayxqnJgNCL57rtK jfL+QOR1jCTsYgM12onIoZbBD4V0S5tKV2ztvHapUb50ssb2qxg1d65OYFLKVlnqu7DMPmx3pSlk zd+pk2QJrPPLbeTS5ZwEmHDnOIJXUx+QzUddOBEec6dd2DeJA7x0weYLcAvwvn53N/SQWvpSdyz7 xZrwJLMIxYCuRi6tb1Hx+JoP4L0JklqbadWct2Jbag4WW5YXcLdgqxBrwKte16FVbGpaIrDl+JcT 3JjZA3p5qBV6bruVjgetvtuxic6529hfukF3adth5QSn6JanINHFrnshRPqbeFFjAXI+jhr0hn0e cb8O4LVgwPkQI+hFjCzJyqrX7BgLXABvC6KBUIyykTBYtq/GwU5c+GssRyCBLuOuI1YLosCrfO3a NGGsEirP9vVaJ86aT+Tp8eNCrs9/9XmV/IMPPvjoow+Fvw+0HI0SFz2kcK7BPNwFClFtScP7M0+t oYnaA6wm8b71Vn0b3XROSWVSfvbZZ6VN3v07m2Y8RT/qST20JVgoNS0fiJZ0/tMGXjFHDWAMsj5F /YJnzstBsfyLmPF12MUt9/jFho3dJyJEBgO+Y9aOjBrh9koiTQ6W4lykw0+kiyR5nQ+uddwiJtTg WNHWgiwHsLQMfK/zoKVM60M2u3CCiHwDBik8ZhK/sIG2nWo5ul3xVfuXp7odCpQdcytnGQkAy21P N10EphcAr++Gt3/Tr+5WqgUIaTx/UGjlYDRKibSYKy0Cs7VkNzU8JANstS6gqAFbI0frjo3RmqEY cXUXsgnSgArBi3PYWN7hwO8x4NVkz/dpkwlCV/asRYUhUJPExw8fPyxx0iICb7xe4o1q1XdSvYzI KdJU1+9Ez+FBCmB+tNfOqA8MdJvWzmKaU02iZT27NLp0qjZ2LyStAm3s8FqZCVJC8FwBJda1q7UF 7LtQrQ/PkEIwaCHXEoVngpdBLvsIDFNxpS05ItV2yuq0BLkMSbWTWCVkMfHCWaQV4/KyF94fsTbA MHIRideacMIuT4KYhq4beoz1ttZ70CJaXuNBqCV+VTCmlPjiYT6H8GYjVhim+SFDFn0IXDr+5Koj Mi/xCDwFJRXSSGZoC8tvwOu5IDOQ351khQe54A9kIVlqbMsHtjbHxA8ULswKfK94Hr72QwHRrJxH 3Yfs6dYS7Pq+zY870PmAJ2Z/o+4Hf1GFw9MdX8sfLQ+3OQKlzwi9iwQHLDmvrXpq4KmWi/nlX/1S 4IKhttxr6dJybLdNLQJa14CXU6ubwjjdTGux1Eausx/KK6+UdtUiK+XCKBcs4bMbuSauqKceax0x Rc0Ew7vbuNqme+vpAmEAit0Cb1fXPpV50evo9gYCco8pFlarXntgsVad/vZrbzedQLwD7x5w1Lx5 QvAiZ17xB4yfaEXdcsUunZxXfVbjhICXgvF9FGx5wfjJSklGiP3CcWtVsCAFleiocGYCXrKZz6VX JyfTSovSNjgSJEWpEPy8PnMJR/EGTOdMnpVHfJdS/vfn2u6frdnUhcg+TkdjVmye4ECwlgM7YiNr Y1v9UOax5K7e1gBOaSYcnUgKae67c4d71x8mLTbeCIqxJ513B6Ex8Y3aQQn12f3TgVqUMXYUwFxE h5SgCn2Upa66lOWveSaZ52bk8hoxOoC8AXKPly42uqHpAaMFeR6lcIuK8RWnq4oxxd1TrEb0biQ6 lB86YuQX9F8DXmdBuYtPCGDt0r/px1O0mK4fnRmxkDkiIOV4XMGToldFt2opsZp0qLk7/qefb7Te bsJ2LAeX4CrIAg5aGDz/0DRKWVre7aIGC0W1antLLV6qNWy8jk+lwbARfCds6QVDKAwdItnntsLY 0d13am1v1DvXnrSB13g4YKZh9Dfg9ULNG3ocDbKYSObhPY7YZsEszwTutRtapAfpgBbmHmuelVdD KhP4WkVGS+bgC6Yehx1tIiwLTDLwZa1svDnq99KmhVwNZhb265ohInI0UPHwrCOs2q5hSCd/SuI9 Qwa4Kz303UmrCAbOs/zUHc3yzRqTdHVCJZQcJ01q3Aq6Pr0DBymbZjrBoot06jaue23NdB14QQuW z3Iz8GqZoadiE7AMJECQeJoAl+yC/nlHRK+E6Mwtzwcqi5eFmgGvKDNC1UMJGmVhSNG5OUxwesvr mWrlZSbEmy7jwRKDZ9BXb2NFF8G50BryN25EgJ9X134DXs/bYs88v5lskKLPtyY6vEjgGSixICWm 2x9sYJGQjACgTq4BPTYEYy9Vkw+BQ9lZy3nAzn/EI6z6fizCtqhSs8EuQQR69L5RIhb6xGk6NgG0 HJ9u+XyGACw+o3gl9KEUyVUisK8COo8t/CBS7oIt8NrQc0OuYIODVdZH+zBdiWDHobBY/os/8eFD HnzPJy8BvFQsyhkvkmJBsE9/6nOQmMZINC9L0xCPJ3blDK177IioqJbXSGK+D7tDKi3CeRD2F80j kbkqRfWHqJYPZ5fOWjUkbAm/tPIy0wUEV0GuRLN66HOh8dOh3P7+dAOC8rKOIHKcRz9Icv4b5vVM mHraCekwA8YWmVLDNnLhH7JUhMfZO8YRMMt4icI3YFxJHON0ZnImXdYLTRVmrUodaHAy5jZnjsVX NNQuyOu1f0/q8BSHBo3T74DkRQHE5/VpbaqlKV6qUPb7lbUNJTqFs+bFgz2VeuTwRCNiEwKzZGy6 uqFKTQu0D+64tLobTaAf73KuP52iAqM5HhQe5/XlgFdbM7WXhik3I3UuQZlEbfwRzep0LAYTHXj3 CyWXsuq0Pui51T3SUNTMUXves8oWPVcC1DlymsVj8PKUcv/mpfJLNZIIyVopDgS3kqjl3mdk/JK3 uNVsBy+o6LJ3Y/he5EX3c8/siZnQohFtIRYlXJ900eaTXZCDhts5T1eJ7wQSf1MvXj1qXe7spcxP kk6pFwewnOLIBKswpF4OhBkOtlYkyyyiIzl6jYREByiRJwcr5fnE1RAt8aFx8NuvEc0ReNmJA4rC mMIEn87xwxvOUS8y0J4SWaweeVeqEInxVRgSuQe2HJzJ2Bd36ED3ia8BTwnFm9XqzGXxheXEHKIv bmU3Q5YW06uOsl1GsK4Vyv5dwWsNs41darU6Kmsem/J1i2R3CwiUlqzN4fHEpGqxMx6jvBNBhNOC Vn5gA31St5yS5fUBvYIpW1ELyew7Ksu0QvPJzQKgOsuMUOvRbG0rQFzUxiwiA2RZli/zzotk9Gkf Bmh0ClTLv7xePnaWqoOtvwGvm0PlmTnqKGyWiPHKOxlSNq1qNq2AegPKwEGd7SkWkqHz+AGiZR4W 12lOisW154ICZRTSz9Sn9l+nfxHMqWZed8c3adhAZISkRTLKYnWpJ2gIy7EU5S54zo0SSpWCX+Hm e/Vt+SiFOQzax9CHUHn1hlOrHvTas+HMbVuhunnDEodFNb3qwYXErLclNDdW6foLDdfcxjOsfroo xIrQxkCHzQo33vUfgKGaxC+VaWK0IrGvd0GcuTtaeJt5PJ5drfakU7FjOWyQMv4HY/JRgKVkeM9I NO/y8qZGLidvTYqpM4n7QiQstoWO5QgcPaMxZrSRhr25Fj3jzDQktaSl9ULLcqVsq21/A143b/Zd 6zYr0Kod7IBz58P0qwBlUz2pgZYrsvz351E0eLsAwhRD1jbrpvuRZC5twTVUuUO39C+BqOh+o2mb rrFYY7g2/AIhIxS7NW5tdc5jDS/YIVEYzuvgFc0q4uBl7r6s4fjK0y4j78iMdmnUQV1dKvBLWr0g Vd8MePH80ayNFqIjCcSj0U3EMuh1SDQJUwnsc8MXB6891WkpUhf2IEbxarXYloYRPY+C+dUCJq/2 zfQdT+Zx3kPHsphaYM9QB3/rLqoYSVtagNkr5nvnokIpZiCSaOod2Fh/OVH7DAl7BQycxIGtHb8A X8vriOR1qoEiNGBthvHmunTtrZfmsGhXwMvmAWD9DXi9SDMvGxsVXwrWOoUN3uwQ7Y2+NuuIcbOy Uo6+QiexdCIzB21oETYZoWWCFxYK/ohWd8KZA92RgbC63TDqru1/8OA+24LLhfUyFj6wkg/Rf602 IY+R5e8yWMZElEdPSneqwKzUUkALdqEykXem8vuAqzYfiHaNyA5+nfQWgyNBLkF52AmDmw1wwFnr o59Yj3tx8NpL05am6W0KqP7zaEVH/TLz0NuBGKycPe/kLCDKY42OEHpB1KROGLY8GShY1muMsD1P ZT3ULy0/q+Odol1yDwVr8uc9nEiuBCuxrQ6YKpy8WAbtBcDrJdKuFgvLZTwGS8O2uM1vwOtFsMso sy5kLDCRpcP9fJK/G1tWQ8velGQWa/E3TZMIZYQzNZqJipCto83ODV0JS7M0gDtYiGBcJApEApBY knOS9TslTlmnuDucUa9dnjsZMzCSsi1bq6o7D97Ty62CdYPyVEqryvGp/NXKAivSGMeGVcwoahDc sXl5zVnDwj5QmEBCxjgKjniFO9HKWePTnEv5Jhni5G9cyGGjNCy18+8XB68D3UhJcveMHphwOWsr +aPemIdVIL6Nr2g8yo7DBn6QywTLJIvq8MYfJ1bnqa2a4EOivHHLf2r95ZrGZbxykvwglwcVs7Hd 9C5tzO9luDZ7+9zM6zfg9aJo8uu9bviyg/OHwf4THItplt61m2RuhCIut8jaOGvXeK2rnORciNvZ tqtj9VlNVLF5dNE3NPfAWkvkLZyh2HqaB+RyB4Y5F1gGkU8sMQEQzsLtBI7zwkipIQI/C/CqMz0g /+bnX3xeKy/XgBmhFka3WA7C9cbDdZAlw6jJkvRjKHoXPq7CwdOUVXBtm36RiNDx7LEo7UoHtlpR vwt4MUqQBiRDI82eAQqcQU9KLLjKUlqsuqUdLlglgxQtMv2IaI0nTC8hE243NrwQp0q6lteBKMrF LtRsldi7IrJuGdMkWTrSec82cQfauMHWuf78Brx+vZjy1/O0c/DaJVsphWg8URmP6MdQBz5ABFO0 xpR2hzquQ1DJ2/aUBJYMT1VntKjuYgcDJ4RR9Cw6gJvpUSo0Yn41Jw+OBZVawceZDWzlc/749xQD JylQojQmDaDVQ3uuHi6v8lc9gLGWGGsI9S0bH8eNTUF3Fzxa7XaMm0jLZvTRGu/9FUIFjJS5+SKW Lw5eQCR1YN1cAIgesoOXfFNvHuZdazOP2kkRXoNLayEZoSa5AxGgRWFeqpLHoZNqWuErAlxv3C1H sdaw8WbUml9N6Gs264a7TybEOWYBYS1+J+w7ffob8PrrgZNf71OvAy/Ez14NBVraxSee0weJk8CO rbX6rXhOvzGpUK65ZLjOUf7ULU2HHh5EZGVygLx37H0G1ksLSlHssPI2qoJSHxCkoYmrdoRamDK+ Rjc1Gg35YcCxXrJ0Mso946+QwezN4Ms7wraBV4PMuNEuiwod7miSEvA6pHY1kFF47r95RnlbOXiX Rxs3PL4sRYCVa5ppOtluW4vQbMsDarzQqOUVA3uE0RZFtBgHX5Vww5CzoAENJ7bjS2t/Hq+7bLbl iHwdbzlLvuYnKlFeWVvZJrKj8OnjtT/iGj888OqJD6STjz55Gv4yrnXLjAMy7Tw3jU3liTt6Dm7u hTlv6pxml4IO9P/fxLwuS+V3/jQwhJuCU2QYinc4URijmXuDFD8LyA50iDaZnc0nQME6V6vu1g+l LSlHh7H6pLYiKyiFLLdjGyqHxRMKqP8DJ3EMF2gNnC3kGhTrk3NO6nhwR6tC9lU8WdvBLJWEVWEd DHK2JojeZMO3O6oKHwT4p0jVEH3asg60IDeE0CwdSb9CJPzbfy+B1+ZG7dzkRDLgWIpGMVbovWmZ J83qDnkpzpUF5c2FE9VK4kPTt2ooZ4wSTvekaC8vXX2slQI1I5EMU1Msr2aTT+wnkiZvhOBXopvV BNwSeTgAmIWPxg69ondPm/9ouC7oxxl4ORAwqMTozATX9rebOYHCG5+24zfg9Z3x6EY36Nld6krC FvptZQzBSnRLiip4CVxZdlpodFWizNlrykKeu/mPLU9WfJKElNyWqMhVZPUYb2qfhAT3PRFZ3LQg Q90jJRi6A9Q1PXGNT8FrozB8fd4ugKwYVm2J1Eui64XnmtclVcJSZgYTmirlWS3ux1tbddqNzpkh p1RGyBZqO4TM7zkO6oBqNhBKxXbwQn/2a4KkJxrFjZ2xQBg986idsOXNL7KCaXJPawkb1sfwkGGD dsi4u1SN5ciUm4q4oOdDZx0ILwWhLVy1ALPTtkg6ZR3B3nVtQKJzoFz7AYuBD6QCw9lcZu/LQ8e6 7s/BvFqgMg4Q0Npo1w5SA14HafsNeN0IcF7aSaMqINHhvgu/rEz65cBWv4vt8zfCLg/2bwfKyZet jkS4ydXx+jkmMWJpWmDPSzZjbikJQeFSNe9vZAcl21U5kNuE/qQ5TsGrnYGdmyF1XX0eloqZdnmv ytpQGq+2V3lRhsCD+yXnLEFM8gQIi/HdH31mjvsZqVsvjuj2oWXdXnGNN1UMOi+E2kcbQcKNde1R 7UMFeS7RREGYYooeNWQlLXK3vFstc3fqjTcW3jt168/Of/G0HJbAdud6yyIFuLzm8gSzeKNVmDWg mHWyvexWNi+iEpgtwyDDtqnO8JvmWA3Qu+R2H9wAuRDpJfQg45bj2suWb2XwOTrgxrTn6vJNDDnN HfYbt/FEPb/3t5d6REo88LZwDmEK0TII9DHEY6REE4uY7p/F4lrh7Txivnk0oZmO32f197oMW+7F a7jRnnMZ0Vqt01rfkj8cJVTlCF4bxFZ6rSYP1SaHbFiJpj+q+XpffPFlPdwrpL/lYTErWwNI2i24 1Y2EGKv9Dh1HvB7EOrTaphpRkLiMS4N74kuejBKh+3lWPtrpiLkl+QtktTmY5R2mO9zlcLwT5f2p c09FzAjlc7n1PvyHCtfvLKvstR60EYanUXtxU/GsTkdl6o/WrRzatbRc1PyAXHTv/Ax2BBmO1nYa +4Idvpm+cIfUqI0SAbwh+vT2dPiuJxdR7GZP/s1ZL60FRiD3FxLSZQeRs9bRS922uUg+rSd07LxG Ql6REC+LqYV8a3H3q9dIMqjKZPkBh2K07KZmytUu7uzv1SbS0vaUmh/ErpnRlPfodEQs+baUUUP7 JEp4fejiAlWkKku5UvWuuEVIYtJupdZmkXtxTkx71A6FxQNPKw7+HSvjZtzu2MHF4xr2Q3HPWgJw 1/fMfprFTGvVrW+0T4837nHUS0jGeON2TGiMCdZacVBB+YIe739oAlqbiHk7294pUqQ1jiRr08Kn nW+SuH5HshZvmfZ4aWL8zBudM68Fj5C+bZZJcA3huEBoj0/bOvU3zOuZHfGCJ5zADnI+5mduqtNM EhKm4YtRU6LYM3stnmDuU5d2MNaW1CucA2V4nAKjGk+XF/ZaPCfrmgMtmqddp9XJBM+V7NkjoEve rzN6LYxHcn/uLE5F9+Wd1BTZ9MHbOVeRWIi4VmGtyE09fVbmt2tshNj8cNropGPmezuJGaIlfLSa 9KzYC22H6Bz2bTwDL3oRXuqeUkJbIlqGKofmtQ6zovKzQE0W5VrgpdAYcxkNatUEQiH5hB5LdB48 o4p6TXZpFqwxOVbk3tDVmXI4gywTGDdY4jbM8gXl+IUuuxa8CLctJNs8RGsCPfU0BPsNeL1Qjzz3 Red9dPkWF8CrhI7RMfRw0qrlnQTaYF3mSSxgIiwDzFo66oWWb7rzet3OcWEvk2KfsY7E2DL1W8H7 UsP6MCGRDCZcjjx0sCvuVNfrKeDVoXQjy5b5paHG+qS21qgJj0US61Ysyhwxpv6e1hPX1DMRVkte oIfxrtOIjX9WZX4tSIawcdRfVf/PfvrDjz76WJvQZZdzOsDFWIea05TK+0yzLaJWr6lD6215Z0Sv XcOeruK4TkbVFj5komaJD635X0OGWjJe03k0Ylg/Cr97zrQXYs4CW4I3IlbONWURiAS2dmHbIoR7 sPC5BfiFL7AZTNaHZzjorRIRz5FrJ8C7l0+g95yJdT2PCOhlWEr+T8KU6ddNROjtQ2OdMfPg4/PX /umw+/z324X8aU7Qd7nz8167sxm/TuKDzY7CylnVJeEaEgiczOXDw4KqixelSIikYzLaZwxWYBSr 6LjseUWK5Rp6L1spjqkeYQ8G45kpWVI2uKDLvQ7VjBUsSYjGR+830XgaeJ20Eqcq7GVorjBz0Qvx U8trHL9BlMVZ41usINTEUrp8akFjzbCuuqXa2W0dr3IJcHuXhq864eof/nGB10dFd2hsh7KMn6JH STe1m+gFtJy05eUBBVpff2340pJbBrNeR9Df+kMB2APCW0Wc7N6/Wdsg1g4i2gxRq8g7puXdp03D tv3EWF+SHu6JU7ZU20hCqNZQmmuMz/PK7KXzh1/1i0hjneuMERizg7a2UEhkeoGYOx7CEhvZKC6s ph7kOsEazPZ8qKLJC22+SS/3evYnaDVCvN8TOJsqvjB4vYxGvfYeewm/1wddd/NpMTC6W8l4tIzM Is8eREd5oo47eEUVRyu9CFjIl8XE6RFyNio2XNvKlBeD26JHN8hhDksUqhBanXXSBpxdsZFCwaqk M7wu4bNofCr8HODFFSCjkLRzj1azjDStJIdc4/IjwoqfjOg1oi3m1dGvDcgMuejQaNN0RF1Z4PX7 H374YQGH/FkH4OPzBa8m6+FheYadwsVapw7PewtYJjJIA2c0meikO6RyHNiAWru51i+TropOVspD lnyo2JbDghmDAauEXQAAINBrBB7xa3DacPz9gtdR0Jf+V71rMynydTlscVVuTIGtKLZgAghNa4ls eDJEbGk4N9UJbFl7nAJS94ybEKFsfEx5TtR+sGmHv+8OXt8f7RrA/WvBrKc/tI2PldgKnf/RQavQ Gj9bzMskglWmEVj9NLZ09MvQ5LCMtIklgpGiKEC/MDyarzDq5e/NBJMsJf4SqEN+Uq1+0Y7eQphV b87Z3b2YXYDJobchentnjUjYurcQD26hw70MtG81tNW36Ue6Hv6X5qLRgoBTUKnY//3/+n/+u3/3 7xaqsG0reQ5wL/ZI8ux2s7DO6fKsH1JTrY0sU+M+Q5FcEhVOKFQ7UHq8EJLF3EPtduHYe3qlh93E RJoP51bNWKYHh4XMtQuxnhe8cNXOgk4nH1zwYVIqfVPVfvCgNnoSwzeVogra6bOO2CvxY0v7di+a qIrMOQ1O3W3Td1pSgAUIWfmxJmMVfi3m1VjW9zuB2Bbep4MX2rdJxtEyPzUw932gzAkEf6+IdgLE F5siHwo2vC4mO9R6iXo0w4rgHKFkEMV5pJONXGHfGKDadYgBw2BLm67aiKzW0vr8iy8++/SXn3/+ hYd7ZhvmaDHhMCx9qVOFzyv24jm9d6Hg9YWXUFGmt0eLdhZ4msYRFzfi6/DdsbvRkWi/1bOLxBqw kayEqRhGH4fJBCY55JZeYy7SltE/AYgz5NAdvwVOMA6+IPhG9i8G//HVT3/8Dz748MPiR1kYUCs+ aOAwm7xmdo9JlyP0zLrCq68bUlAns3nGodZcVlBLa9TAst6udJByE98tX1F+YjW0V9pikRqSiSe7 K9wpDlejE5i7/afDlzaOpD8veN1M7S6CVyyLB6CqPQq/3KQrdRew8sR0/2SJsiwJDgfrPopNs9zF bo1EIN9F1t0KRAJALqqdT4OEZzU6way5Zk7khJu1xK/vrF8neN2k+n1O9VRWbZbNawYTbSSDaOHc Cv/0WjD0GuAVhypOFSJtMl36XnJERNnKkfVl0AfspamF9BwUIFTkcFgoWCc4xusaWzTwQV+eGm5o 11EexiUAQIY9ITQY27xYYNbcybUlG23JbIfjg5RCEj+zamHeNTgYRV8kLIKKrl398Y9+T25jOdv2 E53pkHW1PJlHufFmsA/iGHYIAJdQa/ffqTUeKphlr/CdWuXh7XcLq957V6/efgfMqsVq2PCit3El SyWLSE9rocSux2jZAaPGZRy8d593Y38/4HWusmHWFt9qD/xmRjksTonY1h+ydx2+0IdefoVxxqlX Sr+9F2elUjbNrBC85b1qJdW0UlAsjXZa0gGmvTVPpPMmqnsdaJ2Y6JeFbVPskxt+l6I+V9mWzJ1q suhWEiZQMTiB0wNgC2EurYTciriNWx7fsa1PjDByr99YNEW+HnxbNC7Z1+seQYoQF2gMzMrl0Quv vR9WYIelQYoycZy5G09tIAq/XzM3GvCKXO1fuHbtvwa8aFFutcoeSVoxs24w3MfFVVJ6a9jVH//R 737wwYfF60gxLV0zuerYF4tzmDlkLa0ORRd2sUApWXYGLkFXIVf9K67loPw7Wmyr9nZ943WFtHyw Og0BvAP0UK4WnLxqIA6gHaAsb3794LV3dElsTR+oFlLky8fEtYxZBAS1ZJligq42BT6Jwk/NOjKS DwgVWHqzkDrMqytvobW3MMe57n0f4PU9IdeIwLk2/drAa2/JYzECXhHVOD/xhMgsaJ1EmE2ITKcP wl0B+5gnFHMDFd9MXN4LRvUMINkvR7a4U+jdhFlLN8ODDBdxZ0zZjBnxtsKQfIfz5n3GJwPVYUYJ sJ2C1y6HJlHS9m1xwv4+0RJq4qCKa+6iBekPTYbPkWcTxbr6yR/+3nvvvV8PYJWtCsTXOKEWOhVg Ze3SLAtok0Mgq/LpPBZYLrdXLStvsFZ50OLLtRAg8w2ZOc/kBaM/Gktq3srjPTDUDWGB210hudaj wokLfd/g1eEwE1q3qp1t4nzaFheX8EGtrFjbkGu0oz50wJVE3WKx5X5raqfCIRpQVInZDl7bXxl0 ar943x7H2LEtvalzSKNmsMkLC+hFOpaZ522QNosaA966MkqT5jc0qCqLrJ65CbsQX4dQ3x9yuYQn nW8cOIjDc6ve0y+4yc11Tvs53E29FuaF7+YRwOY3wBKEmSoh93T+cAlVbYMShX28GFR9xiikwcgx FnK0LT91mOZnAeKSNwi/wcLZZD7X8HlS6g6ApQ7xfk/a52KDJGS/dQRk0sDTLnQ/Mv5srUk1szUb f9NCYNUmSY3izU3jngVB3IAV7SUIJmW7+vEPf/e9d99/9daVsk21T+I3X3/1taNbBO/34RMt8+gF PRSvsj5VMP6Nt96sscO3tdTD62/WXiSVA9JcF25lsxMcogDuyGBZ8HTj0qefBHCNXRHrXwt4EUxY NrGXamOhMlaFdShQE9LvP2TbFe0uWZgvzCrmz0IbDGx45TKFJV57jQwfkEvgxXy5RjR4P9+qzhXI UJM5wcfnSHBj00OpUe3zn5HIbsPY2100T5BiR6XrXrfYP7/1vgQhJ3rC2/mdLv8+kauBvqXrrJBT BgsEwQILRkeU+6/DYdlje5FjpN1dbeQCysyVXc2+IYDYYqeEezsr9QETpMnaroMTNbzm1Esb0Foa T1gG7dKCBg6T5+6HAHzzm/6wXc/Tap/h1+IS6Z1dN9IowTGGXI0y2tSWCUaJ2bn6A5mRMeQ/VNUv u5nSeHifULJat9prNl796B/8/Xffe68+LUWsvKyvvvr6iy++YDWbUjvWE5TTQ8wmi0AyYKj5hV7B 9G3lmd6pZePvkPIeN6cVavxlhGBB0M6suunghWGHh/bk2kQPYst8w2WUX2bMCwK79BPLBu0SeCWn RMhUI41MU3dTiXzVCxYvEx/TrIpECj1HzNP0HcEMz3I1CQ+kOrAtVZhlzfxbbItdA525HKwaShvw OseHRoNd+MaeXKux+32+b561P+sEvC7B3a/1s4MO4+Nsitqj/a2MjQgjn+MlzCaB4cDuDW4e7KNa rc2kCZETYf03eOF/uQCJ8PTYPyCYSHQBl7eBj+qZ4Ax/WA858J5p1VOQ4ot2PzYK3EwJwM0gnpvI WGG8uPWkCk3hA16pZXzfwLXxau1B1GodxJhs3p4XJcb54IFHYZMGUeP9D2uc9le/+tVnPurFF59/ 8WUdtQ3SV19XCmohfOloPafapQCrwvKVvFXL1Cj1gQ0yL6FOKu+OanT9tcrfCz5sxnQZRsSW4G1n UNeDi/Eke+H9yediDV//MNYxo7+W2oGPlG5DkXwHMo8F56J8tBqSD69Frha8F2yDi5dZPuMm8PqF j12RDsp76cFpopf95xLcX7Kr23lbe8MSdksyBnVM66Efz3p+3ffcSHioTpt3eHHzXuK8p9BBRjjq LhqCFBNjUSpni8nYWljZlYfmtiDnOK08UbUDfs3993MH03fkSpqjpZ75zQqseDALFtZ3MGQbqq0L 0z5L2u1xz48t+LGydWGnkICUNQdIW7blqBx6D9cyfVFbZtRZ4geeQVoRrkqE8FY9inCJcl3H7bvM N7LyL1PLbnqvdgZoYUJbAE4OjEky21hKwx61fveeRgwr+izRM4Y8JhcukJewgeVjE/JdL5pK7jpK F/MfnIJAzxG7cdoFTUWv7ZqbttCF88417QVuNgp1cu1LufkNy3PSOLyd1r98k27vdGOf3cMxPZBO R/l2/s3PZWTsprBKd3hJHJ1NZLznaJZWcVKSZwJn52QHgxQpUwTD+81nPRfJopenck7FZE8REUEv z5raEnZ0CWmEa/U795aySPJdDK9mneSqaIE9kIBno2daJbqwxQfHQ8MLGZQex62mB/34h7/3/gcf VjOIRJBr9/iJW0mL/7HCMvmWWif+LQ0pvvduZW29x/rLlSoh39q0y+o06x01DQNkj0ue3VCqjqct ZR4qHpsw0rd7kLvuX9fqdB4Lj7DE60BOLyVeTeLxRNkQR7C0MKz/1z9lwzEPrVb5Mb9iV0pZP2Kp sK16BLMNnAx3h2hqfEEExYnLxOxXkD7d5jxD251d+vfmCJxtmjJMADU8aXAsXQTyqY0zF56IeIvg C/XkdtEgBXjRSnKw/HP69wHBN6nABmQJMqckwySqh+33OBVGfzlBkbGYm+HaVC0DJlMjm0wjSeLp MaL0kRY5Zy8/TX901udrr7Ul8/RA0Yo4at6Ny05iLR8I9XdgvAoSI30Igd2kAdIt4y6vXvOEx4AW 60+wrl9tW+GVZuqXhxFUK5JRSRviZX2olqrcgx7Z8EiV5J6A73FIXufSUHVPAva/V6kS3g7AyOwn reWzKpLloxqr5vdUJsR7yuF6r9K37shbLOTSig8BESHXpbZQfPm7B3efA7yuUddr+slLRBKgYm6U 3zD56T47hBiPmBQ16MWSsb6g0K1XDhHK8XlSuwRfhK1I5C27wEKLhqYMO0lCe1Q8UUHy+vxhlvVY q78a4/pokrCs+17P1p1D3V8AvPbrXy4tujl+/XWB16o7gdB2duZ1IxNO0IoPLTQ2YG1teAJew4EO /pwBTKpMEjgrrRjExMQc+9LeN46r1iJfWf0Tw4lTZvdAEyQtacAm38Rfey7oOsgVdaO8dlNIC63H sdiMZzdrhYYCKp6IHe/1MgijOAXEm3APt+phDYPzdrQZhkAKJa9+IvB6v9DJQ7SvFGyJW/kokHJu vLK2inHV+0yofvutSqI359rWgrzEM11bI/6vEbywenu1ny70IlZ2CBNlpCdYb98QpOX5syw/eyCx 4uJat8yT1mcT8CBa4mS2CSyaWZLnvY1qtTlPK/AKGbYtaiYGhBmMTTpJ3tvft4XqStG/za426hQg WFEwXICTY86yrt2Med1cyp/3zKeDV9tFS9Jfd1GDVr3IZ6Z8RcobsyZIcqIRdkYhvO1KQo+Ga/VY 5saMEOVRtBCU+qymFyWukUi51EyhfVtG45sWNbTtMikTxBixiIyM9zKaQgt376W0W2eGqeNgpTvq znhcSGf9AaLM+LIiNqMNzvnQYeqUnBILunQB0cbxQLajwZHQJQUuuxBQo412G98vvaqqlT4VTH30 4UdAlxLl/Vt/nYKq+T6eSFUEQmMfncbl/MnjsXvNv3bwMh4cfp4i9m5r8yfiUu5nJhfwT2zLq/5s ay56sk9Wv1Y+V01TZ8sqY5jtXWbAeeKGRaroKzGLnmkQMEJowCxwChBjVkXS4uJUNnwhcmjC1sFL w0G2TX82NI/stQSeo9vz4s93Ov9vOHgd0DPjzxttCRdzCwjUeiW+zNJpPUccc+jUo0B20BVcqW8b AXeThsFy9ECHXTCvAbC811omz+xsG91zVIRw0KNcnv2+AxRI1iZKzSPP8SuQt/DLGKaEfkmr4MlB GDOxckUAYY02On1qkBTIhlgJurQLCV4ISuu8EhrMBmuJrq40iauwmpjXH/yDmh6kKZ01hvjaazUT sYDLC9Lq0AuWp9XfNysJVUtBmMESoQ/lG37cmHwQ518PeMUEpKpRahQUng9I+GAKGoMxiS0qk9SB eJxHx7wynVN/2GcyAUgHuhKMd0rqIyWpklLCsKTX/LZLr6eSW1gyRSiRhRV78ISilWuJDGB/hoZt 4HXow1CqCYN1w+9QtqMSELEfsaTnX3wnKHr+i/+Gg9exQgSP2gWUR7GvO7r8acBgvMGIYd/LfXHw InHk+qOFR3vikQVl9TtxI0YRzYB07HMh7V5lCqRCt3mC7uHbWp56kV9fnec381uINgXvMjR+Id/A EunWvQMJAaj6slNr7Rv2Y/ziFe2+LWWo5KHkfxqvVpMHP2OJ9SCMBeNpVz/9o9+vxQhr+o4W9q+o vCdPK0mEMD3bT9eC/x7qyLLxWYoWBRvcWkbmVIB/DeAFPONOuTkpQxore8CtwiY/noUT7QquPY8Y Zy6/nYWtszq/SViCYR7CyViiAmUVb6gYgyelzXi0ZaGeayHBHtKMdZD9bI61Chu2Fb9xAzFDWQzu 5iqOJaeSFupL+NTSddIpg4SnnfVrf/83Abw2WnpA/026u5UbZNza2QQNHavuJwiEuXSEBynYcYoe 35AqqLGftMBriEfLsovhJ4ybwP0t/pJwcR/rdo/43YefOeqkL0qckjLmK8ATe6/celedOHCw+LF/ 8IQ5UkGnQ0CYyAt1q96ypQ7X2S/UiQ7nJfORszlow0sRdBDZvx9f/cmP/uDjjz+qGJZd5dvsA8A6 zCzHPAg2kfuJvcR3mWqJNTbH2XXg1wJeop19uHbpinkdY+gcLccUvdKrF4CtgwUzWPqB1+zg5k/8 s2JcIFemZpA7PzteQXe7CzQ3ooRKvaeRxtsewoV5KWAP6Phky1HGg/3HpqFN5AxD0sNLwSRVSbf3 XY5SFUt9imkjhquoe3f9ml//TQCvm1S5VasXdw4G7ZdKp9L1QQRjxncAr+lRgwpumSGG3EMgBwoV si4u6EG/LK9QklwFKDfJ/qO8Chap0RU1ypdhSYr5eIWmw3ksU/ycSdHIeZfIum+LTNICYuqQiTd6 Q3IB9Mj8Kyz+jvEYrWkcPQV9XxmDUC8DXjXLpy4ufYJerdDwZpJ2aNgxceHllOpEFr5P8KJ16xcg dXp4c8ksP2a6ZFjyoq81iZODVV+/uadYFb2eFDumR2Vk17KSQd84D2mc8tWvsmQNaXXBoIiUhmNl A2QGCrnqJ8sb0ZmUPswxMXtdySKyHQdLHPPYsEsMuEeL13TaQby2a/vU34DXapRziLkG0QRebUIO 2iXkMoNBD9pqfifwck8pBOYnwW341/6i5Q1zCL1gJfX6uB4sUJPt1Ff1WoPmNd9bgQ4mrTk/ikXJ /GN5hI5dwKprGuTwcV3msQEdtBKgGmfIqNv5XvW4Yl4qM05ThNhOjAZJGcrYsIamTMMWIv/Jj//g 448+rnUCBcTtLD4TvK7t6TMjA7B8T6ONvjld6a5VvfDeyD1xpBKKREBLCRBOdxBm1Wju116C3/j1 zT3FELyhecJeTGIkcz5ztZYgjr1TTxd4TZM1awJBWHecPEO7jVqZkU8CMS0l7YQvKwoIJgxm87pD 1BigHZTOqNdlefsNeJ20y42Rq67L+g0mESfUgKCBAewlgRflTEhrU10LYrxFz78Rsdda6kavIjvG CO3RVZcDbHW29p24d6/wq0S+hBwnD4wz8VIkH1/hJjh1fg6AhxICXow+TRiHLZbQTCe835I7UmzJ x1RW5yjBIurGV+FcNIeHTq/+9Md/+PHHH5dGabygJiJo5kGFY8Z4rxfX1Uc1h/nk4Wdk76WA11ai 9oZSpUDVkCZCVu37sSOI/7M7SA6F3xXp8voZ7GnkDDou9haTamgnf2kiI6RsmnNGWJSjK2tnwfFK /B7ryQZIjPvUbxHau9p8pPxG7WDFJNAtTrViqO6rxO94rZ0e0rfL8F60E7HK6avF/ffOa7Z3TYee c/WS6dJY5o/rRwu75kW9/o4/npxuF0w312sekdf6SvPy9FAv4bCKsRfpO74ulkJFUoypKY+eb6cd VFrZzfrKl9izUvHMy6ALvlbQwLzCmYCv172iYfqg+cUWVZIIqOctIwjKfOur0HUP8znG04OMyUvw GZhJJRUWEVMUTKYZe0ruwsS5CNfal3S8wvTGdRl5uEAhT0VoCamn4hzAI9gk3kc2kXdx5axMnxNm iSt4VlGlTq7bH9ENuX5y9ac/+aNPCrwqYK/IvwdZO+3iBMBuCMYXjNh3AS93Hd025RnwgkAaVlZm lgcJB7GyrRH7GvX+Riy+7ymtTJ5241XPKqUr+Q7htkmAIOd0i4ySqGVO5fStO9r+yItce9bBDmDi s57QcSezFiI6PbywYljp7fYiu8ZIqrQEy5b/3SEJtbbd6lO2nl+Bi/ZS88xn9SkPFHJdBbMELtFz vXj8ncGrhlmNhuu372+8EDrUcskqgH78+eOX8pOFPAYK67bziA2O9VBjUKP2FMlLGUUeBL7mKqXq FTaKhwdDaMC1h+B9N4xDJxYigX98f9BKpGkySzdi0nAX2kX0fXxGdg8sqURf2C+wuI8DYVKS0hZO rgud+FVCPZQHKJRZJW++MXL5btA0wDOXzd+jp6kqOAO1/o+Y1eUkTZpFiDnwlYcUkqrvdFehlxpQ NiHwTY264fTUqz8r8Prkk8qDYMUwjwy0R3NErynBInjbltyqVXtupyrxguB1MDUbiBMp0gdwXTOv LLhvjw/4Usjd/zrBoffvruZLYKsnrxL7dHQLPzEJDyV8DCr6+wRH06Nl1pxhJ1ZVzuCbb2VTb21D SQ50//Y4o2TKGar12imrK3GL5upo/d7oZv1pbvchMrObpDUwtSTkAFuX8Gnk79KXZ3plnmUosSyt 1/MJn7/gT1bYM5wS+TgyrHVbA/gLPmUvnlqxQw0bZXMBTsnX4XHwPvfCsK2lx4Rdl20hXtNRGy6k GHscB5eIu0RV6Z50eEuAJT79ZYKgL8yTYFckTPEa9KmTe/3l4nByPXkAQ411D6f3yDurMwtolMRv h4HLYSGNUKb/KWeTQKvhibjYyhp8GSM3/gJMNrq14UPG8FErwvgE3BLoyRxibz5ibSCGfJRqvb36 sz/+8Sc/+EGNMaoVZs2NI2zxLjrWDXuOZeqn9Qz6o2v/HBn2viY0+awcxi3feAMvBl6SGS86xcBh ckgz4ccBSv7PfPtMu3dIS7QKjKKRe0Cnsx8WNrgrxMNUiHK0b7/hlfoZ62ATXe8Cnn3AgbGYR/uX rNAYx3DAacLzq9IH773F27I/+LUJ2CUkahJ32Q5tAnHx4v4wg1Lr7VPPfs4vUSl36yaj1s6T8h0k +DmfcjydDRcjShuQrQe6OL5oK8Z8GPs5suiTPEdl/EE78CNL7rStDCcq33pzdDKaahybwTeNi5cs 5qyYhETJQ9QJDCwEQ9ij2b+VtVAlUfDeW4CRl12+Vy18XAKsecrErZLr0E8XLLk9jt7raU1oMwGO pdy7TBLYqXt6FpF8HbS1HuPk1WK3dn5YgcuYqsFK36AzwFD7Zn1+efWPf/qTj3/wSaVHNHoni/8c vqbpz2WIT+LUCMJOReXYc09XlKZVR1cRtJo780pNTE2ZVeVlHQh42Ssk35RFrR3NsqnpTFIYVmZX dIB1OfYxFzantIZHTcxdXbYSBWjXW5o+9S70SsnEi8Rvm387zHX3tuaK0ikySmNejYM9nMyb/Hat W2tGUk4V4Skw9GJfLYJQ10O7OPrF+uS7wMgOB7sQITALQfIM/Jfv8sM9V/PZ7kd4lwO0MY5L+BUO PK2RC+0IAFOWGtOwhrBNBS7qu0UhNAF6aYHPuUed872NRvq1hLONXT0LpjMy5Jf6oG6MoSdPSIkU NtiVhF7gVdyNwQb5jG0/TvQ9ZWxUp4DdIjFBsc0GnjqBQTAgzIP3CVNLs5ztaORioF9/HY63DxsX xSdtoEkZrv7RH//440802qjnHYf5T/DrXEaX/TeOHO3MdvppSt4zpb25+YaCoawEB2wEvNiD13rQ xtxMNxRmedahV7AOxWp885xF3MwdqjYW4yDFaCmAxQwGgqLaaqQ8bCXDefNJb0Cpfb/LZ3zdCS3a 4ztbiHKZ45+5QyXSeRybpgaVBo95H5ENWsnftiyhEYy+HEJcXVj0mYMLtk/6m8HAUyA665BEsswn +nUNHnU8W8V0CF/7d/nDiz/xNK8NsU+ZzewqWjQ/uUQuCAHvl/XT0JInOohGXdagwfiG2+AADqN+ MmiAU+FGBmq8M4fzF9IFrRHuNz13bRN7tOXq+OHYiIBT/jp7gCUoERaQgp8ONMQE+gSFbjOqnqgu eYvJCmKDe+cHoTQJbAlFbtV5mgNXG1loQwZKe1Rdl2AhrIvSKNEG2cvDN2VSEdG4EU/CPIE8q4qs eNO6HkjUjb0gLHu5HsBr2uFvJnhdRjdQhsYCxFmhhsBWtXpSTJ0HL/YpQhauBdvCKzyRLyzl/sjp ITqhwwryCAVb2g6ptvvWjt+s3F+B+FoyQs3MvFiuAruYoeiP6m15lV5Pgu44Ba8G5sZs1MBDQrHg Ln24QmCPSETzlNRlq872Etnbrcp1ZkSAZWCSSndgvnWmMavg5inI1aOHmMyLP452OcjtYPwE5hdS fCeSdYmgeT5N/ZSxv9JD9eh6nHc7VNOsZYcuDWvGb9rCf6Be0tSb78SUO3O5bYm60k1/gXadsARz iDFuzcJCvzf8SuChTZ4rUGKekfYK7MYJe+g1Hji+rtVGATKBlBfPqQIVoNRaKGRR1Id2NbIQhWmp A2ZBzDPsIpMrfmKkH6sPshmPpHQIrB0aL4aDLmf2SBvdDHfoqaz8M3c8YV464W8U83KgyeyCH945 IuWQPKOK3tyTReKL+Na6+16pRiBGjjxp8WGnufqcGILfqyssA85x8RAJYdDDQI7y4z3P88272j+3 pvpo0lRF44tQedldRna2rX063ui7ZmkJRnNyGlCyChIq1riGjUVeW2r75A2FEr5tJNrV44BWNwQv S1Ui9G3qD07iSQjsOgSkXke4PD13r9vo9mFA4mxk7trH3fiLLhOIrwfkiSFNB0O2OSt0wwX04QKS m2DJc0Sgr4Gtbt6lmFuQwFx8O8RO2r/ZRLdZi89OgqgRgrBtndl22584nl7iJ8xaBwN8tRD+oXZM xMG042rjHlxqIJCKcyKuC7yMfdhva5y1q/0RhiM9TzNTf/AAGfXCc1lu49Ymf9PAK7F3+l+icGxf 5Vt5+M8TD0WGnWL6DQvVeC8M+Y8E56Gq7sUMnZyId+xD/6GVjFue0ONsLcYMZ4Inc9RZp5GULo3R VFpgb6DrnDw9uO3iynZhngTgtaUUjxyGg/OnxbkFoYUCBTL5ihBF3E6FaunF1Pp5wWtduMW8nq7A Jy38TPCaUkbe/V5DJ1E+vWtoGRw/e8iNP4j190NMhcwFnJ1FKMGqFzXtIp1gyLUPuzl4mY91v4U5 jdq3Rc0Q217kUGw80+26ZdkMWpH2FXyGC7huAEdJaX3C0nUZoC+Lf/8B0NasTzn6aKJEejG8A6p2 6+zgFQOLV0l7ORNrfJFkbmvOo9Yd9D7zjnSRfa9Capsfg5dzksh0PemJ7wG8LBlHk5FG37sdAQ3S QrAUzFOYitdOsp15OtpBbFaWdfqDHEZN6/n2Hiuc1k+vCuG4X/M3vEVUvmMJLJ4leCdbC+pqmqom WxM6lbPcK4ebaGl6j2FLyVze5MBxLv+YWxm5sC5NifES/UTZm+x9fWpLJuI14AVBWM5l4Ckn7ix6 NP+EJkywzLmTFsVFCdCWoxl1DzmYJYkaZOkkVXjUBKR4HXJ16YV3+H7GD4Mf8vDLd0lqds9nkCA5 zYq9A7xsx3f70ThXtIQsm3D7jM25Ouo/rwri0ER5YryObuVFTGs8gx46cnDHAszNvZcnj2D6a8DZ PRF1GK6zVJ4u5tylNO497uHgLOiGiNBvWeQiTgWLxZXQlXCTxhq+Y1Sok8aoE0/RVGrCDpHgRlih oJ+Wkb/IjZCeBxOOiCsz3kxiBTG2ih17VqWjKLg0vGPVVIbBAM7EWnBlFPFLGoNrixz7uf/P/9v/ 5d/5d/8nH7z/fgI0dnxmzNU36gDNhj2oQGJIvhcMKcTPfTnPcD6xV1Ps7qorGPVLMCfdkTVq6p3T 6rIOJNEqpvqQ1AAjA9kKt0A4+9GtoH4Q1aAYbi8bnOXGrKpRE5Z887AzLp5fN/JXuzoHTpEvDfEI rRggvKol4LS8h9/Z/OqZyxJ1tU2X9Y8RFpcEyx9s3ajXSESAcGTUsqqSn4DX3G1rZwRLQZ6ysR7I 3xMEyPNnH63pGRWFIH2FhFQ2ZVYqL/SqexDw6pyATbdOHqyLAa5rD9SwDuf+fFvd6KrVRWGmmw91 y2vbalz/KTd85lcoINDgZ7Wy5BU2TY+tbmT2H+e6JTiG4q5gNr1YC+o6tBqLXDLZKtJJgr5ebWdg m/u5m4JE/DEauOmkSONOOVwUX441GzCBnJ6S+6KAqkVRmznWUaPt98pJqcCWg8K1p4621vnyK+8a 9q0Z2CPdpyY437mtoai3nLro9WTsfGjVfEXQfVAVwxpPF1K6Iu5yf8gBltdBeZlMGbhUhE7+UlPA YALK51WHvVwz0wf0q9LTzAQB05cIXt1kIEj7y6rJBfAiaOU1FQPwdRURRsitpCerN6xJij3Iqh40 pPUALFYOiruziX4tNlU5LLjc3bYMNRvgRNS8WZTX2Yp3ZxPh1Tx0mvs1g9MZn06Y8oknZqvQ2mVP JtZC3lK4qdQg12jA+jIy2/1OOec2rcdoD7p2jl/bo6JndVY1GnkjG5fSmvpMXTq5RA2o3IhCK1wG v37yqH76TCXWr0HQk+tPb/c08Jpziz9XJLkgjGig7IXDHOkqd6IjBLUg+m4Fn/7si98muly14lli JtipxCKlF3WUOrFHwX6XuF6tsGI67o9oae2AFwMau278iuu1Ma2wslwGTtHTHA1l9RfnBBWBKobI KQlrj+r7GhIGDCJDsutl9XIFOOrCitZ/fe8eg/Jfff3Vl1984Y3Bvvr66686d1Qx42oJg1cFeBXj xePQ3BFPIFGLOVMMqpCBwEhjlC8lcw1DKbKImCcmNP8vNdagJ+BFYpOj2tzZqLWSvdk/CViOH/PC AfsDRnQXymduNrlxSRrzYIfNldjQECuhn/iJ9h8JX+XAZWSlrV5Xq+f3OLyVqZ6RHZ63yRWTvCSh yGYk1NjEekPZXcobjhDq6rliBnwLeKgZ63vgalpYvKpEgvOWPtEi8auw1h6L6d0FxvKPuEZox2J1 48LPjFILBTg5Nre/vYCGhjkxr2pbD1+0J20+ZRpfQHGu4u4nD8ClDZMtMWfeNGB/6IGnAQ3rHFTv r0Bh6wSKXBd7YTWmOsR7eoEXvlMAol9Bn+d5cBgtsqy8wId2DkYDjUTRxnYIoWRlhdljLEZbjS4r Nhfjj3G3Mezt/yym0sjlSsf0UuiWaT1vGbUIebcSJ9JkCEkdBQCV4VO/EQgjtSwyNKqancmGs/am 7Bw8wHffmSAlohmXgKSAsab2CprUIqueJISNgCHS5pZO35COdyQOplnyqtBFFIMIjzSVVnDVXn0J Ma9WtOWep87Uk/q/Sryv1UEr/ghloTyWCAe5JMQJI4pOZmIPqzEv9HLIfi1manfS8sDvPOSg7a55 khhozriGBVNeyIxp1WVZgvVsdBDPPBtMxZEMbMXlUPULAhYX4vZqbexDfKDNE4o4hFktVE9LToMG OyyKzctG5ehWhJYlTM5dOC5ioJbIH3ZcHp2XSKwanYPKXwt4KdVbqyCsVm25dQ0rI9z6pdDEdzgW eA1IhLIMeoUv14M08lPu9oFMGbEcQCQ4QJtb6iTLmf4KPYA/NIrFv+jzD8N6QYQYqenWDdJamOm8 KNZBUJq6NbJJ6XIn5VHXFoW3a/EGmTzE2BOZtYtHqVmRWg1/1YZp1jgm7lQFFwdUWKpBy2DUSoYA xoS2x9hIhKJt2oAPM+zBMQJJJfqCDJNsLw8MjLIxqa+li4W/hOS8Nc2Lu41V+ol5gRg4surecdXp 1hqrNaEmVoW7p7T3AR1LgUuMf8P0Qhax0Vq3dZUdyol80bYRjhHmBRvkVxH3M8slyVSDGwFzdaxH NBgC8T/vkE70flqYlm8T1GZbc9uGGr9a+xTXDw3d/B+sOtWzcK6F4+uEa5BreROWWN3Rhg1E25/A w0LKiRNCBNJqMhUt+LSUf8W+jHZY3xTzalJcUYpXHz98VW6jGpy0rPgxUd+muSf13X0qRIJxg3hB lhYf9lmUl2RVmFyT6Cjq6oS99sJ81Vnr3gDVuDtPQIdY7zG9TKtIUzVGwNMWy+h110dhbZfo8fpv wKJu4VfhYdKI1JQaez3DsTW6R/atDaWw62dZwS9xr9L8HfHqcg0JMhOnSuugRViLqSQcbGJ/P6vZ w4p5/exnf/mXf/mzzz+vPaY/L1+yUsHKSBhMXqvdwt5+R9tWVHZj7VyhtehqWRT7j167+exxbgfC hhatZk6W1OVLttiWXtu1crSwdJw9uzxnu/4h4gSa65GVDl5eLEKuoEdNwf7uMa+06ha/x9y0xSpz VGs5LkDC7wOtJCIxYfqL38tiP0wWKLxiZzjmDfDhAfVaYjfkSoIWu9zBntLM7TCi94Er4Mujs02X JNFHk5zHABk4ZPSN4pQCr9fCjvrb6MGJQg16nQNbbrcuyAOiKnkYBrYfLcWxWFNY0r/VVrNoddC/ nZbqGfC1ekfxBROZFIoFUqwhShlNZKa66OErjx68ok6E01fsTBtuLeNhL9RwcsSTKHFQ9RCfZMCl wuGGWwuLmYyrM0ZxV1fIsR7RkHf6uBtgVyAPtWivejSM7uS2XiCrAtQKZc6NdR1rkFo6vAJldr6h EyhafdXCj2ORA03xGQevS4/YQCcBAfc7V66arfua7ByEYfD4FLzwuer0o1urDv76q69/9pc/+9lf /Oyzzz771a8++7yiYF987rXthNzvaKdDb7tjAKsFnfTvjTcrydHLz+if5S4HqvEM8BrptbullSUe IlqKzOJeVfZmxTcV2X+kcQnFq+/eUTnefhuTJdpRRPIAXlUOhtj8fCJ/GCV113bQhtRwFzXFrXq0 2PJo0lQ7fXgPXY0l9KZhUbrpV7+Y1WlMrPKL+xjOBGKkpsDgNkSxzXIvOffKK2t5o5CsUBbQiaAi vbYuGf5jsNW2xH1hCZKRhuPsEImZbWJVZ1TA3uBFg0H+sepHZX46cp2B1w4rKUMozqFIcK3oozVf g7BJe4tZo/h46MCMyK+bsmFHf21L9F9sa9q/CPzj+4Vf9IdSBwhRhvtGu/ZWSmtVaYU1eSdnJGYn y7OESfXUUXRbckXnJ4AU+XKOiiaxhYecNu7NoCuOTZp2lx+Qa2pRSFla6v1Mu/FqFrHlqQyiT5Ww QdLBfw+3ocZubbxLQDkI1EgUsuY7S1iQmeAA2+hQHIjXCCDXA2hTXJ4XB60l+yhNKdPBuLhfKqv+ 57+ofz8v2lXgVezrV59/9uUXX7J8VIGFNrUQdPmX4vf1q/CrtuJ53eB+O7yVals16hcpCyRMBdgi oHMaTcTKFvHN9UqRbq8VWuhlClb3qCavEU/mtKBehZpa8HnAS5psF8u5Am7IfoFi79IBcs2RrmGT JfUXA8aBmNpb50Ftr+Op0SwRTyjBJrRl0EYXb3CCniice6q9IeZd369UYIOX+23KioVhhLWIMila s1SGnb9kTkBdQOjkhoBbkx8f+ZFcndrec/B69bWitwGrSDDex6HRIqkHCVqRkFPl3xETicimKYpq KwDqE9I6jePWfJFXlkwiNLhG15fuFK/SpkeFJjRfezuGjTJ3SlCSsy9y/OjBq48e3HrUbW7mZeSa 4Zb4MMea2Tx1wh9+H71Gl5pME6kk1ctVTIwopy3SUoksXpuDdmjQuSFgHU4zLgQo6Nr1fsiz143x GiEZjTVcP64lJd944607ngiMpVTSjFZxkCS7U3oXr5bcINcOYObJq0z1UjtDTyBgmAxuI1LTCEqX 2w5ZwIJK0YGg30DVXHcEr6iTlKogSoD12WdfVN7EF1/45WflQtYQZM0lIjFIXMuHAaz4V/CrXpSC zew3nBV3pPfxG8RIIQEVVDY1MsuW1sOmiRRps0GDlwZBv/q6Ti9V7iTL14FDD6/dbfB67z2yAOgS enTAC7frBLwcNWfRUSZDqUzCTucBJjcr2Vg15PwtuRxcQ9pRiQMAMnq72SdVhroCYQB43VhaqXkM qjTtkYiFwafAq0yCJu+8rs0lJX+QKyNTAHLmVVUbNs3iRkO4eHqAa8eR7gmYlwqnaP3Vk1ewxlEL XhErO1GdgyjukY9dSOcayxlmvhqHcQuF6LTmGh6XbYX8L+tHhTEEXsz5VBSpyWxy5MZrqeav7+pS j+kAXoIMp9+RmUliXd3+gdxGMy9jVoGX8zy3yEA66xxMtmzlwa0FSXiLXg94l4F15jJeLJCiDnkR xOprItiWmVBnOj6ZGQeB2gc0aJq7b7xRe5iWLndAVXEJQYybVecbXm0MfIBZK7S/rMfUw8lwiVIE jLoUB4hrW4U2DEaMjhC4a2O+2tPlyFtgA+NBmUucnCxRwa5Ko/i6YKvAC/z64vPPjZKPwa+qdUiY +VfhWPGyioHhPBJ4CXhRobFINEVXipIvAe+61CeWKfloWu/im3vlwv7q88/r3LuVreHdTrXIoq1X uaxFUBZ4iXPBRejIEFH7Vj52oamak8SQEKByiHQImrxphfdwVazYulAROaWSyof1JCoTRUzIxPW6 hnS6hRQuxHOhGyVCnoKd7JtEWitjdELzWR1QCXWsAEjeFh1bB0BPL7a9T7vy54JjcQ5eSOf0R5iX GtYR3D1qujfb4XXI5CISi4bOeV4hygTT4Q+cQTeFys7wiJ3pTnwzcYJ+E5TlS78I0YEN+XN9g7LF oVNXycooouV+dAc+fOVhMS+tnWLeFPBaQU2rQTfmVsdQpANvuNAcHbAz0dC/RAxyeTQNkby2NW/2 RYxLk276MDZ7SDdRE5txwCCetvyXgq83yzZmfMc5NoLUIGCNCJmLETOQHBPG86/tv14uViQlSMCe +yCFWX6JiuV+fTM+aublZ90UvJKo4SUwygo9fMQODvyqpNUvPi/U+vwzu5Csn17PlkNz967XU3nD 6xG8qT2o33233jMD0Y214oPdzhSzi49+bXUZfsI0AOGSx5fAli9UkC9KlpMo4D7BN6tG0oLPcRvf e4+IEd022qxG6XVmd/GoZ3hQwJtIf/stIfT6Ibkhm+5YrSzvGtdIVDbp8F6gesKbVDBue3qV0pAC Xl95JkElTjqrrRRYhTSsOuoukSHPMDMhDgtpzQCiwMtl2iTi0KK7TYiAHLWiAS6DQRazarIaxdRA b8OZZgnJEJ0zrx2qiGes5esu6CaIQx/XuZoN9c238EHrNIMYXjxDbAi/G88vLwr68O4hyKYCqpL6 yyPiIG6lkxPj91yrBxLqyktwv5ZFqp9avA5CVIzMmyEPPdIdA/mnNUCB0b64Eosa4PpuVnL03YSs cwmCXd8pQ2Lvw/bKzOP8eLyNJfZx/kYJDCjwem9zoXn4jq+U61L/IR2KPkgOb/s3gTDb4Et4K9mf MunuPg8u3+ULZOV6GupwvAB4cT19B5pqbMfTG1g3vYYai4VV0P5Xn3366aefOQn/S0xgVbMoVy0Z rPD9W2+99977dbB+oZYwtPpNbSksZR64srIcKuKGNV/SoL+mrNRRHe4VYR4XdBUBrOJZ7gL+GGbh V2HCJKlG8U6A0UWAfJ20HXmx2UwMd3CT/9piWgFaa5I9l7xGpxQWbrOk+qVknkKVtATLOkkMDVNy APHyhFDE5QnOZ/sLJhHwT4SeiYf435YnWq/DWe2yRkrP5OyCuCyuZnvsnsjlibKqCnlQmhLCMD+I D1KEW9ENSxDw8ONeyzpQYtQa7vgWl81xeUcJxGTxyXthfnvuE1Oqk90X4sI9RSH8TKZE6ZjgVri0 6dcDhcQgb868k5Mptsam4NmOBKkCMXm9H/ooYS5/S82JdaamqXpMwjRE3yWCMQORJw94/rf9hDNE 8eLotqc9opFiLt5nZhD2130WeSpEt2RMErTzJ7Zjh54FW5zQNg954oNo/sZa/Ml8qZYccM3ZLcYD EPtD92sNBMFPTCDCiEyONiRGzKcTqRTrUYlLtRjIN/Az5nhiq7fV/Lvgo+zTNoA1waNhTTQBa6rq UoriB8ewWeo28MrXdUJ0a9o2un9U5snKRealQ1qHxjF57y9Wf0IPFPpNaNZNlVnC9XratzssEgJC CXsYi/UbtRQpumXgwKkcJMNPSilJ82lVGOkW4hjsatRJYM1yaTDKzxCpFpM0SE5wh0TtbDYXlnXo ITK4pHicCAMX6GX4Gh56hC8c75idJ0/Y3FvEx2ABpUoKCVCDqxjKy3WMP+pLnwnMGIf8mX18O/Md jhRyaTMSo5Wf4kQXEhfPcaoj9x3hmZjKCq7A+I7KFEuCk9RNHQXifNSmI2u8WU35XV8lUHXWRTyV X+maqmEs4OJTHUqXMIR4kvuJpTygFW61q5mu3FUJIm1hanO3JLIt6jQSMADGxn6qrINkDXDHxkbx A6ixH27OZSa8TIm9H7RFKucSqws68iChoR9KwQgoW0m1OIRPzyP8x6LsqrtN1/K1O7C3pZ9wXSpY fwh8CCVcwdWCdVdjWMCrhqHVJBQ1UuOK2Z7SMqMtdU/vNK0RTZbSIjsDODN+6QMJPfdgqKZ1tVF8 F8WAtFvMLceKP0orjTNoZEpKabDKCRFamrQhzJN8On5oIuumaU+YAemtn9vgg1bT8ksYzhXEUtCo h6zZQ9RjfAOWptveanccPtesGuTYUuNUT7Eqf1gv+ClTk9dqOXGoCjY5fK5h5G9r6ENRMPtuSrny VzrhySOlj9Y6XP4hlbQyHoR0wiWNmdQAY93EwawiVmVq6m0F9Quq7lduRb1+XEOK9aHGFlnjgTUY Hukt04xVcmyP+hQC5YUf3Gjb74GADva4EVqHeG0abYVpZhp9cmTNrWRG00yNZYC+0w/trx+WJIzI B0xC8SK2rdlSFPloLj3sLBolhcyggyhL7CU0Im0BDG7QsbeRT8qZAy+t2qOuB/7lMtgGBr0ihIGy BX8Nl/088CulH6JF6xoP9T1BGBZWKdVSz7iqiXYaDwwUtRaBPD1GWO2BE71GMOJPBExiCbrYm1KB tEulgu38YUud7GCE3gbKnbpb3y3wml4BsJYzYNNfNzIiaQ6BFmTUKKbGJwJenhu+Zh2a7zGezng4 jUYOkevXrWgtjnCai3rlxGS7m08516FGRjVoq1Vo2lUUbvmfjnYWBXGZgtjWw5Ezw80K5S07dd0r Wvj68zxEp1t6DyGPkFfXVUxOaYv14pValUEopk/0YrYOqxUalEKluc1GH+0eluWPa+DvIUshC4lA pUKTij09/Pbxg28f3f+mXlTKgk67VbpdWe91vl5cPXnw2qtP7t6uNfJfvXN1645GXkrIHlRm/K3H BU/fPPz23qNvv3784JtHD3STxw/r9zePH9zLi4ffPHl4v55VJ9cLPaJGGCuWr0c8uFUBeyfWGUS0 tRcuVhZEthK5NQDoLHh/hDMhlxM6nUzuH89I4C1r7Hiv+cAxT/FK036i9h/jk7z4DgtDe+lnV0Tr 1ZC25vVUvWOFZNOz1uIu8LrmLQmAVH8c+fBBvCmx00IxmcsabaxQp7kLGDksI0DZaBXvNFDaZjVc fiOpTblaHrmgqVz0efkCOW2BImK8h0RgQ9zBEMjggR7kbaedalRZdRpUzQncTmcIvMRjavDX125c uOqc9lnhysAQYNR8lqcH4IY9bnfKycTaWg1JC8aF9EoPFcgn5lXjjsHMuBWQpXgm/CVvX+MSRi4t KXvvm8paSAKEsyaIggnujFw22+RwtxlzdUKez5yAcK1Q0QIs/VP4KqCFd8j+PP3KH+jC3qGH1EGb g2bwz4lcg1mbkQiaEfcgL0c5y1I/6aRea+nkQiur5XqtD3lrJTQ/8nrtYBaMqTQHLDMqWaOEHTIZ IkQVQCz0uX+v8KUIUWHKrQKXIJcgpn5qxcm7r71y+6p+tA5EIU5RtkcPvy20enj/mwfffv3g/teP inzVD1BYKKbf9bZg69sgVw0pGsIEWMIRg6O9TIg6ImeUgRMFiZpVhWGEHPkMvCWDGgAX/Mq+EfAg L9rVaNIIlc9DVLFyL+VnsV0jV9OQ8F/jkxmr6K2d4ozJCqjskaN40BG8cDkZCnkpVaKk7iA4+qrR ZPcXT6SrgSjw1uStgezAv2JZm/rjUWw/UfQg5wEAA1xL95qzSaRLjbw7svKBKzjfs7msvaFVoiLK FuhVsOKJ2G/Ex4yvuKYZ7M8ymiNE/pjLm5wFVGlfSFwohOP6DibX+TXmqEDHAi86RVbEoirAGvBy WATfUCsvl7foNbDLY9TphjkD1orZDrSrcJk/N/bExvrsqD7Puj3mX4q32wu0Ayk+xuRED0E6TGi/ cKIMza98Zyh1mPK8fjbhetYZuXk6LPYqF+XdlmcdN30JLBQzA2dIQ//nVWLedSte2RhoMMh7JmjR mES1bGOaHzunwV0mj975K06UqRVm3VtaapY5t7Xhp2OSSbcj2EW8K0F/L9GR/GLur0J5Pb5Wth5A WC7Y6JI1qI9ukHzSfqJJsMkwWk43oXpzYN4k533w6sycPPcHDQpRxvhLLndsKm5qP64NrfQBKhDs snvvs6IsWtvfs0Qy2u4ReCE+NUstXWD8pTkiT8grgpsr5kUU/Vjd43mb8C7qFu7Hd1v7bjdaJNnr n29nVoWH2yHMTsqTMPhuQhYbMo39YYUckU7Y7Lq+2Qpi4w8ipcyLJTZ4Gb/iJBvm4ggOeN25Y7TC RdQrc+EebEocK9xLjqMiXgpz2QMU17ZJWrqHTFhAD4LZnXEJvLx2BwueeaEaz56q/x7HNq8q0Kp/ GYM0cDEcaV2Ib8gMnUYuap3+aOE4GKkTm/WMtwNbkQVL5EgGash4aT/ZLX9QSiuhf7DhqIm9FUHb fFftSRBdQPTVl0V4Gf1zMLGtSw/oEVL1YImSeA1ZSt2pXwVgDvTPCkOgVWKUJBpz2xkBaEuGblJQ m0yRry302sZ+cOqokt0Qrn7IMFOokIw5TrU57XWCXC8FvHJraUEPebsPwUU/4gJ4uWU0qltqi8dn 30a9ha+i9pcv4zFghkegaD1WtkXC4qc9BbzcRKDcJmjzqa9EqEfXNwAcIMh50zsH/KLN00V5WDOA RdlKw9xTOrdTcYQMMa6OClWV3Z/qYUbN6FwqSCn3w+8SB9SFgBfVSYG6M5JPPvqUUL0cwcrW/oc/ +sMPP/ywHokN9ig54Ss2FYNg9VRvZ3UZtbRaiFY78lCLrY+tkh+Pw5YMLJw+VlZOiMpxKtHT43G3 3GztbMHurcS2zMEIDk4syx4bsfjOS3AvL+im0aZnuqdPrPuhQW/4ZsBrDZLhDbH4ENvPENyJ3BEa 6xguNMbmww6LB1MWQtR89Uy+8azU2lxP+PPtva8LjWoIl01IniimrkB7pU5UoL1cvwrGl1evcL46 q3zMQqt79bq+KuhzML6SZSroXsamXM4O9tszYpZ179wjv1U+rNzYKhjjAAQmZXdZycuyltHr1C4L pTHXyrPclfwm6sw2AKw0hGuPfGs4GCud9mmTYvm1yfO6wDbuMc0x0JjpF/yJG6CYoFXIkoJpTygG 33jGJewLJQTsT/FQJihs0a/giD9NAp2Mv24XdDbFjF76qRujzGu01b8wZWF8fDJhqh4baXGlFcZW HyBiJzj7F80cL9DYZU0aPdQ6Gq9IUJ46eU5EymriVYEwV7c1zUlPWRLDlUntVzEAKZ/PdziRzlP2 u3ZDVHvaAHfTn9tAPLr66Y9++MEHHxQ2KO3ds0/YQ6wT17STm50Pz5I0kGkkMcnbJs7kxdpKAsB1 xN0Tc1Kc3SilGUpEA+1U1+KMbMCTH6+3kS15GEeUhPvwUlxeD9Dw3qkPAFiTyumrRo2A+YiNT70h SF132oDXbhAd5FIPkGzY2/YS/KKwAS8R1IS9bLaDZUi+RhXJ/3SWljz1b7+pFUa/KqpbyFUGJSnv NT6oLhBmFUIVzaqIWPWb5mBVeEtvK85VqypXOLLiZXVmRbUqmFWhd8AovxXPqhCbfjSAwD5gitwF uRzAThgbZGFLIVW0zWRmhjLkZNwSHsmrd7awFvH1yArzHZTfyeoQrFgb/6Ij983RMQbTWxPq7mXO XxC2DnjXWDG6A7NEbfxfrjJT0N07ikUCYuZUmXxgdyOhMUUurVeeuitPyi0lx8G8M9LHwr1JHoJb 4nw1Bw+ABWNQ2kGKTf3DyzbyNV+m7Yb78EUL/7rlUcwDXetD+1VCLmuiZ6R5IqcCS6bhFJ8JM9Us YJeA61UNXA6FleiQyXyufzCuABl/Ib9bc1B9N4NCbma41c5XP/2jH9YC9lW47GrhYInxajLoPR9H q7UKuEgCMn92s/th7tR5a1fRicvKOPYUSq0ia+QS4YKDMUq4/5RwM3KYXUOyEWKvX5p01cRL4iWC 1me4vnpqmmb49YvDF/DIHXfwsmzGiiz/FQyFTbdYkOEZxVHziXnRfl4DSBMTnXYq7ltRRoWratHx 2tm4Jio6QdTnePxRKQ7kQOin4ExX+bXeysfRCUqD8EQfLzSDKjoQs/00pcJh1UwvoAp6aKWuX1QR VcnQuJmvf+wmkOMEKpH+o4U9ksAiuMIUAXBACX+bpQ6NaIW1cEXzuhwo5nf5aTRY+NjaMQCCdOP3 iVv0KHmAig6zs2hEQ4mtXo63eK3qXhzC7oFqOlITRxzDH0VNEUZL9cIpGpvgtuq3BAYLLpGvWPAW 9iMLO3CufDVSDRvgcOHMkNWr9ThU23gbuAV6FHpu4oIdqwsIJAWLqMkZfplr1aXJlZuSoRbhY35E sMZlMgA9LPD6/Q8+/KDkSgxLS4nX/IAvvRUiHqK3GLMHSZQ3ad3eUFfljsmA5ULq6M4aXc6et7XL YU1Gt+n1jjtZZDmsKgINw3JSPAQrc3potsysZgWSYHW38GXw2ghws4Tv7HS0Fp2DV/gf0mll5vlN LZBfRTqZKQWVVitNiznCaPhRhKrGBwuEtMSlbYkyfhWt96F80SzmSBKypvJ0wJ0AvO5loEsiKj4O A8Ctaa1wyCKymijcMnwG1vpG+R4Adzzjrum0s0E6sOWEJ1vrTlQBwWmZjv60lrQ9zl9PcBx8ae1r ZTq+f8F36EGqDIIsdtMAlvYZCoB1tjcIsmlxxxPkqoiY590sJ4dKudLW/8GEbnN9sETZ77pw07Zq 9ghde6LNcfqc9kphksBZd9i00gZhh5ZbVhkOtPGjYJpP164zrAjAA3wy8WniPPDpPN55FfBWFd71 jSqOqe8SeggvvdKL3CMDvopnUTYwzWNTVz/5g9//4P0PStYYnvIqFF8XdplmiW0lgd7Z80QmsUJd krC7aReQWHa4Qh5elYYlNRLvIO7e/uCOXLiHzAzj6ARVJ4GOk+Fo0+qGftzeG8uK7EC3dckLCn23 Y1hWW9JtNJWetZ9ISG4CczZZjudWYEvtR7p7pZDiithlN9vV8Y3fKO5ebxRRl/eXsLrzSMXR3BtM 31nhdua9+8e4BmYRtAk/XrAlAbEijqBYZAweh6nDAS+LT3hS08ldxbRorUeCe31aWgJgc5yMjmw5 Xrqazkm8Q58f6AGaeOGz5+9J7jskhpqTtGYFW+DOVzRQ2BXgtTAL94krHcsfpqhrorc2aDBOpDWQ N2aDEiwIHeyZiBAKjORH4qlEkGqT+G6R9kc4fz69Rgs2nVrgFZjyc8RFKqve60HyUI05ViTBg2aZ 0MksKzmVwLtn5gLTRE7E3FINupjqhH9N/56E9hq7dQmrpFn0r378w9+tOZZ1h8yx9ug6E09I08u/ s7XDKQyH50SLN1W4vWac14KtmoDu3UccgJcrGPnF9vZxAl54FEO+wrhmPNHS78qfKk6U4aBHwWp6 Laj9/KJ+csUw3+5X9wougg/1cYo3tMu95mCBqSsrkjEYogwGZZ5oBLdYr7ZiN1p5uLACjea+mkFN MoQVSR04G725J/fkFlwS/wSvGPZyP+6KGbV0qU/8iBKW5JO6cpXEVlm3Xm0xyGVIckfETYaIeSTF wZEt0hf4joyifpjisTv0W6wSccyx0jpTF/hbhOi7/OgmLoCfQlE25kVbGIMG0IeWqVBk5XPkRRe8 SqefEUL7iULcXvKnegGzlxiLb5wuzd94MvSo+7p/xYGNLPumYVtmbhviLByCAC7va9UOpbipOac/ /EArb/GsTB5ybWWFK6uQpBty2YkxeeSvGpJg6G7Jo5XBMQAMerVqNapHMWNyQ7wk21c/+ge/+24t KVHg1UepUdyT+BoG0R5JnKe2Y6TKgFx3bisrtxY8NHBp9QzH5rWcqTcPITTC6RP9GBzD4xh+xVyL RuvliI2YH4Fq64dukJ38fi/g1QrGZKAFXhlQiP6i1WqtEqJalLEi6x4MWc1NVoPB66tvvYdu/fMH X5cEeNSdPJSRZrYydeySvnGEOJjV/mHjFdB18BZHFPzisGBDmrXnOIUteYKAIcfft456gLVxx+ij /Zjatcc9RE4MGPSCASOsIdcHTkbv011oYXe0V1D7jj/rblAB4++BMC3kChFbKiRakfnb8+GMsDHL wAnL3YRxcmAiwQo3hZZm247JjiHmqJ5svtdr4vt6+axBWuJEgV83aPDFLzAKAdiOG02R+8UB8da3 uzu7A1xrr1YqrpyAUmAXySvYONsGQ9u+g6wvcFYFIWHAvtSMZjWVnOaiPoMvzXARgZbYjQk/enz1 R7//99999/1SLhIjmPorqPIxgnmAHBMtdtxxRkMtj+8ZBa/Xij9ascwJDxo8JCaf87J1pM73IvMO cjH3Jz/EvXA5YNmZ2LhcRlK79oMoyvFIz42SrL4868Ln/GBnXgu8YsfdYmhnu/mUi6Yv9lTY9M03 8spZTki5pOz7rcQ55ZQqe64ySS0D1SHEg32Q3a4jU4im5PAqv5VZiq1dYWRwbY6Wg1MCcWyJ5Sc6 QcLDjCNZ7a/wgR1iw0DzsQYoSZ1cg1YCGsIF5VUKpeZqecuX5/2ygOekt5/jbbrMZR3m0jNOUwt6 scq2KVO+MnitCcbQwRhlh2a3DM0FYW4iJ5wYe6qmmjo3VKszgrFPy/9fDMJjAP6aW+ih4TFL8mlj Wn4QjFZsITwQLWjhCfk6Qa7uNzhmnDwen/ComwpJYgRyG8Cghq6UFwZg8T+wTDqb1kBDVneshjuW JhTUmRnE1a/+8Pf+l+/VMqoFXk326hHogAvpw94ckTnTJsHN3RobZHTQqy0rx+H111koVsjlnSqT x5VVa4xzPhL4GpgKWoFbzkK9/khpKNMRxeC03SFji8YKnSvEc39yEbwwDTx3TOC04YBXDf8pqCh6 xZR2Zfp6bMRzFpzlAKI5Qq9QFjexeKw4MpNptqIfwIuxAL7HeTza+Ua3df3BZ+yPE8fTtZZbL4XS CHMEr1TZWkM8fi8b2qNfB1mML0PZRqumvn5x5sw+B0xdPjUuY1R89ZlRocErjVvvD/gFJJzMDnEm TMZXtTdHx0TslYZouusSCaJl3KQJTNvpZ70PgjVOc51RF3/NdzSHsDLjIO1no/4vAF4n0HWNNtAX dn71kD7LyfRauU41Xbjl6lhmgS4NQpWM16g5vkbpeC11XPo9he5mWh+MqOFyp9H2kRQ/5eqP/sHf r2XFCi8S4TVyNhdSQoM22wWiJrvBb4pd1U4i7KbrVZfFt2q5snrHqRo6BIcmegvgHDFnw59g5RbT EH72z7WSC86GJcYjsSAeHMfnxqmLF5yCl5/MfL0BLifnIaQhGO5CzazS6m6VK18jI5qkoFlWQFgB lnODHREj2o6l7XuEqqDRZmFJDerFcwEDy4vci+TprzH5AKCRDe1sVUzKuC1o7q9Jtwli62mKOj32 LrSki0JAIsuNo7MQ1OWGJ5TbelCvGbxLrHs4o7tMlQCtN5pDhOz8WKWmbYjxbD89ZSEfBju2Ymdx jJ4Tgp/VzGsBKOBVtW6aWO+I59lR1s/mKMSMZrQC2V0DcRKa5K1WUdVLZI4xVpx18byuZ/6LWRSA 4QBtAo+C7OAVw7LkEVWAk50cSPNNDppkv4lhhRt4BGbx6Hpl6O6uJMHHqy/VjwfwXi/+Y1s3St10 MeRRjzo1X2Ao/esUjKsf/f7vffjBB4VGxsta6eLV4kckjgaQDE16422P8Afrb40gamHrwq/6R2p8 fWUyhk+Y0MfEPWC1T//J112lEN5DDU8wjI5s8G4AQQDTXZa5l/STB3XuN723Avay1ksWbG4zp73I VY3kaondL37l9cKNX99UDpcGE71NRvEs+RN0DOqXGgRT8AqBp0Yoh7/Y0EcdyDI5jFB70YRCOWDC at0zoIVf/PhD571sqzYIsCZgLe2qHTmMcb4CqfJrS1gVsqdf039zDi+Wq9X1CE4GNXSLZj5uPK8K ZORywb3ggx7k3yc/qkSH1tUwbgkWG8pPb3bkpl2f9whjIC/bxaKjY3327gS8XOVW+MakieUS1YgH Car5g3EmrBSEd1lWY9EKI5fhqnJkCHx6uNnxUB2l/5rve/s2QlVX5kkNXkjerg2RxSDJAaWONOpp AHYKIjnXltnIVfoe2ucE+Wq0zJH0sHIiTiYpVbZCjeI73Uyd/IYOb8gF8h2KFWMX21JfXf3kh79X eV7VIihNOXTOftdxx/hlsGLfKXuD8Qi9JP8bbzpNXuf4i0qodrLHFpZapuEEdW70doecGJhrwGso EZqw9eINbctN7A+kwtYKoaPNm3n1LUyYeaNF/ZRrqkSUIl21wG4tEl6heZYM18CilujSBj8eE/Qs dyhJ7iBnazF1PjR4cZp+Z/kDL94Flo2OegvgRiL8NpfdWahGtF6PKBgQzIqW+3zlLpl5+dlGrnYU 20nuD23gZTP7Z1+xIRQJIPUc5oVlac+GRdws4yLgxSX+ffKj1VEyD8CQx22NU1kXLc3Z1KC/ytPr EYkmttkBvKYvRy6eDl4Jr2wzQeJMSrcJgsipdF8q50CL32eYktqWqGgc2tNbmM2i3/dqjsXXXzH0 XBJSSlluTb0onFMkp5YqyGLakRFDwPzqsrdTeZTxoNwNBP8SfEUuK89Lg3VRTi+1IVjpUWhTTABc fmJ9VXnqFQQXoDtrf6dfFqfmgqHqaBl+Q9SKvhHzqlSJWovacK6ku0oj5e7iXyTEkxGf/5qjlvVp lD09qy4nAj+cC2dQyAVXvtEB0drpFrowsHXGxIiUj3IbWDbJi2G7KTe+QTfeALwi/G5qT3KvLPiY UG3mVKQrcxVIyCLpQbzXk3w33BoEbG9Ovab/PskdyKUGvF7ew6HMxLk63NUzkH0FteQFhcTE9SdZ K2FF6AWRhujWC/wFjpCH5r7zBU9JB/juYb+XvL8hxinbZnPnDlG1oyA9o8e2eh0xKM9ZxTPzaw1s x9rtNA+kQtcxL5MvJ4p4zDwp1h00ngA7LcaS7WRrBMHdA/VtmTEnGchnJNlykpPrvqwFRfaSpi7U 5F95kQuG0v57y6P9Z8fNmdd1jRyc7EaMpjsKOONxRAQ5BTGvr9RStfSZl+FrjxbNd4scjvCQlk8M qHS8zi7w+r335Ta+Rpit6FYRqszcEXhlnQemSTtLPrOsPc2WRQBnqi1jhMOKXSA97JnuYoPUrhYn LqRh4+QHGhEuFHWxOIwdGoL/kvjXDcALecxBFEMxeWU+aIep+qsECC9+5llWw7RMqBZinfbiMn/A TeOObuDVcnOQ1tWH75I3Z3c8IBeBpoYw1odRU/pPjS6NblvLj3YEnEKaEdQNF1wCVJ/fjXRTnovg tcPW6s8z8Hpqgy2MPtQdNwrZjLoM1YrbmPMDL6ukWoVvNCz4nbAtMS9yHhUGAtyx4pPl5FQJZ/Ga rbgQWCzr+ZXD217LyGPOSRF3CLTu4X2UtWxW3Zw573I/0QC6pKu02v8SckVXLmDaiYxc+3b1g59Y hS9AUE3rnVMi3AgAl6MTeBZa1lGiVQ1g3+6OhnfMzuoeXYE8tLmxe6pt7ipQJR/+9A9//6MPP6zb lHDWLUS1aiPJPlKIfezPbNUsOEOHzD1MB8biLGfeVRs0eVbTHFozou6L5/WpEHZF0Y0Jw9qXsBLF tD3ryTf8/hrwWkmqsS9M0H1Yy0JohIWYhbl/yeU9IZqDl0auSbPCqpwWZDBKYfiQLLG0zkNl4c8A lv+AbDDtAVJuvf8IqfDHDLaDt64BUbVcTphJAWYV0VoClPl1bCeNHXVE52O6chXY1p3Jh1GiqXNo iHuuT8jIBG7jyZHgIGGQtFSzTn8w2Z4L2xMf76Z0m3bMXkXqlmvZ2R7b/nLXwzUKLgl7ssycsplm UDwuozOd4ioJdUp9mPMW8towV2EvZcuwsCdb2LQxAp7IE8CRIvhlEeIDUPkEl6MZFyX8xMbcUAsO p5mnBqg9ACluRbQr7K5l2n1gnGKxWY0LVW1AN0FYzN92+6j+aMUafKyTrv74Rz/8+KOPK2yFlSD7 AfIb6KQbqj+CUF5IAF5IeGsLcXETUHmKkCqM5jwF70+/OipbuuVEAxvXwkx4u1v6FMRR5af93ISc XQterVjkJngtNA0a1ViiI1wsDaixxfIgNZZoocuomgvojtviW1Rj07lGK+UFEd0ZCJuEiKzVHNAJ eEWs228LhOF5AnP1V3iI2jtqJkgMeBnClPZllOvoe8MQ3IGjXg++WM+TRI9GNUFoBYOh+itg228d 4fJn3IoPAygn8CuFbbAOSgNFbIrLz0BYxkIW7AMMcdiXl9yBMzePHw0DdUVhneB1aOZMGGHxptYc fEjDFUtCOUeIF0IuIR152ABYAkOZfuExnBPwqprUJ+VSiqZdvVrEXesYE7kXn3HyWtp6NXJEK+Wf vnppL6J+nv3g7E+5ZFUM0UnkC8uCoG2OvHtK0/4zCguMn7CvRVwocOQLeLn6kz/6g48//qji7XVa oRIzEJOeEDblGfGQX89YbI82TiJkS81vBjjI9UKgHgS6vmnPrC9C3iY49esPt/s8BTKX7j0Tv07A y5IhHjxaax9O0EXkQkva1BKoHlisfNSsbOMdyEKNUHvQ/gy8UjJ3e30J8yLfcQFbK+mptxhRscp1 qOvQsBEvJItASrCgadfE+GtLvs4GidfY6uviW8+bYY9au17duO3S8H6zbbyG0zXbAs0GuYwglzow 2E73Q0a9eGmawo0EWp0dYbFcCZVCthjt2Boqbad+Dp3cK4W9JlbSuUEAkUcbsxRd/BUnSxrSgC1x gow/QgIqYym7cLGiOpyawqguplrcwuGIyg30Qkk+La1tkF2/pr33Nj/IwUt4U80HRniS4xXNbmNm g9eGSGLsQWRXR6U28orUh/MYyanApfK2gLjIV//wx3/wUTGv11+3Wb2ldbg26uTxkTSE09FUQuf5 exaIORe4Zu3bzfD1LbI7NudOzkFu9ptcwjWAyxqXf7yP4aaVnud4FsSdg5caur21eprEyzO8yD39 qoCrJrpr0qI+0Jr0HlJsC6LiNtyTRHWptM1MqOiQDNd109VW0LTJofKXGqJ5i0WNwAuNifq2x63+ JylJZMppbZuJosQRkg2t8pmZUyQIeDtKZX811a7nX4Spi71IjTeCuoN6mhk4y5mWl/7pW1rzqIe/ gzIcJqvnVPD5gMgH8Eqw3kGUFf01Klld+mOnT/iNc80bwOoTYMhpX2JeVexCKxp4DsXpvYaVMm56 PmydKWV0Z9Sf+PUu9/CJQxu+bCwbQwUyRUrUrJ5t7+hIte3kMNulz1CSeqBhx8n3B0FyHczMoyAp eoHXH3788ceVoBXsnPwsd0r/UsGyrl6o8MS5mpPBqrcWGXG5YPbgkBeEFMm5LKj+9PA1ckujuMV4 mZtsFPW6e559/izwoooHkKnHaFheElbP97o2nnJd2fTO0bGEyWGsz+3hu8yNQzQYuDBe11Rymk4m rOulCmZ6SRjToJaBBx6yVS3m6qxlW6MBexpPh5AMKunK6mCeNuhD8v1096IheIYHhBrcggyAXAfa ZcH0o9y9C9WPxb1shuLoArjhWWuhh0Yyu8G0zILG3N5t02WOhHXtebs/+QbgFQbVaEXIyyF60wKC +lrNXA6mP7Gf0+yNZncyhDxIKghADnhxyzKH7ODFHI06oUCt4kakKQiNazlAWnezLweJ/+74tXXS pvuiV1VNtZ5Fgn0bAgj+hGwPTxuw85hVN6Cirig4fOmYLwRen3z8ceWaqpoME7AENUQ47MpvOs5F hN6GJci1m+JoAQpKgQ+W0dVFncYE8oLjQli2v1q8Y9nOvm6oKfFdtCD3khWKAtOT1/1gVa/9OSWy AKbkqwylwu/VGYVQSs6pOdbGrfpfkS5GuzuYvjtoS/u2CNbm5ozP4xPtIGrTXhKZ1B75zbrNEpaV iaoJif6hvvPWL2p3R59ZN0nal7YE02mcDNIAWPIZK9DqHqsXFV3JjGw3lc7wrG03eCMUAUanWFSw TMFbkhH4mUQwm1Q9hH5MMhcZCSS7JuVV6uAP63f/VGGMSFp9PNlvyfCy7EkM1FRusMluRfrythd+ cKkisaj6ZSv6FPDyfF/iwA7I541XMmdKnJ09wMypEiZey+ec0Ex9VwUg1R6+5ml1SumS2TJbAd20 vBLYq5VdKtIUCzFY2HoFPLeGfXfMOmLKCbNTj7TKySX015Szyp8lDGyswYqixhICx/IJEALm20Mw yNjlBRtXf/qTP6qtzyoXdQ30bhN4wKik29Hsxwg9iXb83hvouu4PO4/MHtsA03+CaPP2eO682+yi GgjY4kPfD5XSrxv8XPOM6fVlxnS72OR6nJcqZUaaF+NSdP5epUVUeqFWEnQaKvGkAXRjVWN7ykqK ZVCq/yai1SgW8NJypyCXg/dRYsLLwqnAUHI6gbDjT2wLJkRANgCnHdq08gELxOp3xYcrq8adBvQE vDIE4k0NgZvBJi+xMB86MJ8fMC7heePkujD4xYVgmeV+/T5aFmN4wIis1CLAKAVvaeLdAyRRyze3 kOSGMJ5hkRuj2IXievCCXJWCTOaQlMV7MBh60B2jm+NftdidkQtPR1ejsb5KWRQsZIyfQxS8PtRS lh6CZMOnko0dkJR2XykUQ/U6iyLS/33i16aPA/vCBTJC/K3hCrOxERrV5YEguLqyCu6VlmvAUMyo 78kNEXEUPMfVn/3kR5988kkxLyJXk/TAWK/Zl62DYMw8N3jGOK//+RjwCpo6ahKieCYJJzC3xOMa mbGUAWNnR6NXTPf+9il3ewZMPftr62lsNKTLaxhppx/F6GuEsXCrOBfk31Lm4vRyAoNcCwTN4KCq fQTyAnqzNCX7PU82qVMb5j5ce4O6N8qPVC0XCY6b+2gVr0wiUJW3n3ZklgYtI8srt9AY3vigtFtb Y8t1zj2UmgdxfZ/cT8xfVwHw3ul9WnBo/0l3Lm3yF6xntFQ7JPKSDBzBy61hHy2aYnTKjn2mYD3e CCh5RRicSL2WhnmYkfhMnBnfqmCoZEmbQlsD9QiP3zHZMdpA4TdIImei2sLVkVLW68nG2Jvu2fL9 omekPGbkMl+1fn+wy+T2mAAYJmW/uDqrSmzOCuqISDYDHow+FOvqH/30xz/45Ac12ad7oKP+AFbd abZ4YRB4srqmzzZv/CBDDjSgVyfHiYpu3w7fR9mPPxcVEj7Tt0Dz+/+L9sANrls9osQI0uhrGpDX FKxARHmLXvnfm2B5oXr3xASrsD1oHBbFP/CrvGl8a8aMfwquMNsYwOpb5O0NCs8pWy+kwQY3D4aC hQN6+dMBr1isVnsApgMznei3wCc5iAv0jWSjfg10Kf1o2jrhBLdaaQF7umNs8t4UjD1Oq/RD+xpU nblPPlwmvzh3rzbwAseNXoy2j9vohIimWctXVMwLR0bLKYt8Oekog43mCgIyo2GNMRRMeSa2Q1gu mIIS3pj+vGxUXJEJgbkqTEinblVFEZCZ2N1YNL7riZibzjt188jZtxkabUVu/d6FV86H6alwpggY XboXBbMyHwm8Pv5BMa/keenuC8Y2QNuDXxta1Rk0JT09+hCGhzT0t4dyXJAMvr+eMzwlHHbCO75r 4z/7+nYw9GB5jOTnZA1nr4rmHTG8xgmz9AjGgVBNStcrCd21w/rNL9K8OEUDXm6zXT+fXXoaGmbS ZeOGObRjBHrkno1SIZBS2BU9Hgn1ffAzF86ENVmsbI0RlJzAsgywFx4FHswBgvQl2xc5Y+HvkC9E iLocMasferhnynNIm38qeHXxLdluBu89YVSyV4iPZ/xidZb2KJ0UKR/RQ404NECf2iYhL8rjDlfa ODMX4RFVJXaErDP2voraGLVca/1mEMDXUretGW8oHy962tJ3Bg7cbT0q0Svh7LoOeIlmOsGVfLnX XnPyx6EQMeP0bCUf/qM//rFiXnfvRkoAKSJZ+7G9Rdw5wU19wl2tD4Asghm7uKlGX7XElMf3hYfP W+h2qd5P4L6bwC/j+aLt/+zrdvCq3EEGh7wsAMuY5DBbavIpqWTJgziIHhQXuNlYOi2U+P9sNC/t 3IL6VtaaSQyMY7oOgnwu1NdXZfAuXWVKv3c8QYL6bfAy+3Kvtyzq5FAFUy75lugz4tF/B5gCUkBf MKvJhzwj0wUkK6s1BxDRgMG4eZEWGGekZ1BiAv2USOcqFStJGTC6LohdpGhkcFpuCRugMCo1FYyX IsXzelHCLysgDMxjiw4XW6mKedmfiQJ1SNt362LrewXp3R5sIFhXZsTGAfuTjp1Con4GMayRhzxc P+dPhB2sWjxb2F/kjNRFCK9G83BrNc4dF6ALFu8jdbFfR6pWjLH7qStxsPg6IeBVzMtPiCQZtfs4 othAVbyG68AL5Or+2Gu/JApX4fBzDnRLhM4wMF+dPGW12hG2b9ID43Q+/eTDjb1vneKojA8ZvPSq 83SG3ViKkr8NJgFe0Acy89l5nE+MYus19xF4kWQCfq3M8JvU78I5I8SoTkAnc4wzHqaYfWMNmr+w QJ+bHxA7MnptaLUWj96hJ5d3xMgIaCcHVOmSbBztnHXpLIOX8JsegfwDQny44KBL3GUPtPXbgB13 2Q3+QcnxfWNlKVKHVxxar5/s79cLbvZEFGEY8WINMVYkyI4cpt2w361KF3hsv2BQdbxV2Zeaxlwf Wia8SbWdx4tH2KgXZQ2AZ6NNPawXvuXS7xfBlpAY3DUV8/Yd9ZZCI+kqhFxqrz3W5FdX5evDqmRd Xo3mIW9EvX2FRrEFXoGrZu3BMQHnEtWp7hLea8ArzYr32Cg2hG5kd7/PphCXPz5Rm3USAtXkC9Fr qUWOI9jXdDZxgoEJu3YH0BgwWS+6JX2lN8fwFmRZlKm8SOVOePXLU/Bi9nWj03ZHz8m5mA3uk9zb 1jElLKDg4bcdTDjV8Kgx6nbyswnviDDoga3k4CxP4sjj1rhhNytmXqXz7nvb3faOQOn320Z70nEH xMibKcNFgaBB3CaTtJ2GOvjUXdOtRuEmrcFu04PMHIo6aG2bnnEsa1qNGPYaK72nMlsph30xUuhB x4rjJ14s/XRcJ24jQ7e4d4vWub3IjPEOFi6rQK2SuZp5dfWXXE8De7iZXyY6TSZ0p4SaUuu5+BBr tWP2Un9ibZMyNK29KMkTtdHtO9UsXryz1nMOaEGp6NrSKs2uY+Ed3MZiXj6B4KNfWgafglzRnKeC VyJfR39vLhzutolmwgiXhPXarxq1Fu9H9EYcbUwvAhcfwnGQhEGWZ8LXhj+a60NiDsvJmXh50YhW puBrNsSOt5e5d42bWjWPKTon5MvdvvAr+1+0fiGYF/V8mvqkPQMbGzFpNV4njvp2plVf5FSDXdXt jyCaJT5WlGP7py+2DjzRNgpy0kk70p3LAwVYBmcL4U1hptEu9P0uDwbnDgqlNUdgFnKhIeJNpJsy fiWKlUWGTbq0v/Kd2ncmS0dlMraTvRjvqkN3gGta5yBgUb9dGQCgxuY6iSeUKLAPIRHVvXY0NTFV Ma8OhEFAgF6y8dKH3ZG05I2FftncZ75yeTqJzrxLlW0ZCUD52VW6crirkBUwrnEvyihir2bWWhqq lGew4BFf/Rng9cYbEP40qJrTo6xEIJbR3kzyJqFpPje1UyQSiolrmzNhR1uQ9pwQ4H5c/jGYXvqB 5w8PcXeoxcIbup+iX5dAzMxpkS+G9dydlwnY8dMsuuxYqqhXoVfvsfiQM1106XicxTCGBO/T/azj wCk9arH4VhvBKr6pEfsXABI7p/EHgx9nrwekgKINg9ZV/vwA9mSBrhbmle8VJOM1o41Oq4ZP7fc/ 9G1kAZ7m8NNO2UCS/PCs/WetbkHTAe7rNxOMmqW6aKSxLXf7KAU9MYs6SbfUzF2otLHKoHC7J1Tj LTqSZWeIhbZq/6xaC08LeQJdxLyUWx/kUo5XXecOzBbTUS90jOD6dOk4wc5SSsJXLVvK3B+19DJp J3JNy7g+RLqsIm5lq8bm+bun7G2cxtEu6cpzf9Yd1BeaKde/gRZLjoyePW/lhbF/mklU4u9VaNaf sHsjh6a65+pPa7TxE89tBKoUvnCQjLeql13lpSPIbZRk2WAHApmagelrA+i2szsSCY3abRrWaniq hevzp4BaJB3laSWLbq1lVFy8g0auxhTqu0Xo8YVdJyGnMTIxbYiOp1hX5QAvZw9qjpCsopcrgeIG UBPSskzFzi2uB271+8XaacopvAf/1D30euvWobFOgOME0Xg7pTrDuwMekua+mtfNvBVmL9jMgURc LKT9It7RFDj9RfmPSHcCVblJ39OO6zTo0g3Lnq38EcsmdNHSsYBM1aJ3kHBatH719LoE4AywZi7l 8SXhkQhNFrTTIp5evVPMqECs2Bh7OGj4LIuvvOZxRl1G3hyeh5WM6NeyR8GmdFGyxVhSD/UWE5EP FbPL/Mc5rIIjzWI66W1QwwOe6KSD42p8c67vBbz86PYAWwc1YOHWYKyFr8kjqRe1jFlNsPM5xOl8 BzcKK/2rl+vbAq9PyLDv1oTPygzssY907RIytzXyP7dvpXY22oxr6pmUYelZ24CRo7wYi9xQdDjh 8rdjLzdsWo01PXrcvebQ086O9xLMmRXbjluY0AX+lXC7a8kEnI54Bb+ccq8pQTJoXbJmXkO9Am1H RgZpoMvsNejFqpxHqtoiOVSyRckjlG7rWfv0AA2R40v41V818Wu5W1RwteYqD6+CQbMT2gKD0IQB 15kwgtqM/CwMXZ2T2m9SkOeCjHRM2st2B+cihIRk7tWa5+Tbc+6wDC3MO4akXqvopDvw44H9RLVq NFD8qzYBZNFOLxMortTZ3b2ilCNdmq6wc5/FDDYRHoLQKym30ymYk/fHsjOuILn1lHaQi1YkUSct 2nYiCeaGaPhGLOmh5V/mm9GAkTFMQUNCRKg+qSqxlH9cbPDVdasroAjVY6K0AS/leY0lsNxPhx1f jOifyNzoWsuK9a/9xhSg2w7Ju8CzWpovPRxzfKk0lxq5u3Dw9DLtcu+KdlWbyKGWuxjHn7/XkS90 ht9IvgGQtQCIeengflPAgJQLNz6p2ilsNSi5oZd7ja7ru0hrmFAS1Y91mKYBqxALrhvJHm2cfpx7 T4dOv6VP3Y0ZIFqEejX6rnAhRUtXNmxqe7RVJSB9jl830RtUbusgTE2zf7+hbQ/4RWt77XvATgf2 JzpyjehnImLwi0AM+NXxLS0wnxWGlRcR5CLRC1qBK2TXf/Xe9NGZdLfFj5s3kaIkgDE3kmYQfxmb tzVffavmVZWjkuM/mvlpamGk/CaN/gLndO/yl84YcmEaWPGsQQONRbLL8iwXaDiyDDsdrP7Va7U/ bmPtrUG0fvhXQ0Vcx8WvEHYrRzRqk/olOFv54MWtf61SkeqDqbkWYK5rsrk6LxZjcTPpMmzx8TGA RYx0ibw3KWfVStZGskp0/hXSf35AvurudfPqA9CqI1+6zdrpmtn1emwuqqtOgs3TrecSeAL0jvf2 mJcJMk7IEv2hw2OzLkHYoVG3E87Aq+MAabQDL1O38oG/7aVlFpVBXDaYDbAiIPn8CK8tYZd6vVuH HjwN2O8BZ8p0RK6gGGP185X6bxNibEE35/43MfpeR1ALQ3gqYy+J7vwubxMIsjku5uTVV+UzeiUW wU1V28PFW4/55Ym7ETU7gTTi7nZePWdSs7WrKh6QNAb0MWBBQ7iDDB19XwfvoV2JU6frt5u8AFhd vGQKNpbEqKrx02qzdqlUOsC0NEl1tIVGOlT6Ci9bQTNa8tOf/vgjx7yIAgvknCBolrYm92McgMyZ JetoSK/iUJLk1do8M3bkn6CZ7k1AobUpRD2TZJkqC4Gd12cvzjoX8XcqBi/QJDePIk10i62z6mOP yh2pFijyqT3KlRav5ZkrKb7okreW8yRrUyeWmW8WFXSKP+iQln8elwtXPyy84qUllCfhjUIfqrW1 Wg4Tpc3DIOib4s00Kpe8GxT1JzBBeFhT46QKtytjxmpgM+4jQ8QdRiGYgmFNNEV/l7G5Rjp3gF60 c5ZYTbdOaHyPDVgpLLb5DRpCsefD7YRRMaRiZNRXJZ7XvdpBEURrPX+GWTxOS69jFC4QW5feAcro Ma8kzwBwq70zsOKLq2QEGHHWlXBV4/mafl0uoXaQENuyt1i7MN+9/frd+qTCX3WO4lJa6lnZ9g57 3a4ONGNWNq7vT9e5h/PrFKgmczdFWAWRMLBZ/ZtvvPnO22/X7971S+vc1x2NaGZ6PAhWnOGjNL9V JOo5sDeO+MtCrgUdTQ7DvGL11HMIwaxXYFzLhkMBX3Ud46tewrDI1x//yY9rPa9aBpoIfX5XLdfK JxYhvVU3+2eWKzEW0PcJ2KOALaTgoPHdct0gpRduyNMfMOjCj2/JQy79xD1S9X0IsxBO+FM1jSwf X8tPFNkq3CrkYg+yep2R533HdaPX+ICDXsYskiE0aivwes0JhAIvRrDBQeDtoaMqGEiZFSxgWINJ AAyu2aCWtUHxLavsSVMCWargtVVwPwh5Nni1goWA8THmIkeDBJ89RS5Ttt7WKPzSzd6lXvsfOmso 88KRhTx0odgBv+a59CJWlfKsv4E8tM1P2OhoCHVEx6gFXgG3XRi8euQ+mNVL33FDTIjtH2tLGL+s NK0IUhARpQyQeFlUgxdheOHUXW23pSBX4dfrGm0s5KrPi5GZbznJ3msHVmjf84dk6LQsR2cLdK6E Gd5On9NthJ8TyiTKTjqsSN7dO5Un8M4777z33ru1DYVLJCpY51Sdy+gu6wb5mPX/gBPGhRVBqy3I bNpaIzHyL+lnpD+tjQykT915EmYNQWjcQB3mhMH0XRTac1CsUUQD1A7FvGo9rw7Yj8GGMWE4l1jx 1BE15HMpQvnPftKJYiCDFgwoYN/yXIGu16ndqm/XtSXmudTT9jgSrRS/iZur8toZrwgX65x6j5be qOW+HECnBSaUsvmO7UQudQgaW8LkfjdS+Nqmb3YcFfnqNoEX7L1CLCl6RG22A9q0bHOgyirVMJVz Wu6DATuTmRt2V/HleU91SSy03ZoxkhG4FO+kjzEtIyjd0W1SLDUjPINWyNIOXvkEKTsXjzRUlwzQ byNQp48XCfOadl4XoLJbc6spbMcjnyDIgMiQWjq6N51hdWe/a79RzqMDXgCWD3VSr1CTFCs9TkYl qdvbxMPNzkyrXH7hWzW2hVsVSakYtsc+i475Tx3a51GDBn5i6wb8C1Wmocd2YCympy71wHf7DCDI gZD144xc6JyoqcPkKAfZjy1A6sL6pHjDU8ELqAF0Nlm6FryQrV8reLWOsXSTOyhDfElWx0fOL23q acLFNj613KlnUNfS8gp6uV1F/BLwimIsjZjaDZ7D/S2kTr+q2bOJNzKkmwD+wESzmAGGYP314NUy qr8RWK2tNZ6GO9lOyGHARfK5icmSuCMmnONXY2ucMy40FvhF2OERYvvuNAvhowjJMnUp/ADWaMgJ eNELw+B2XRncH+nvgP3gVDOv1tUorQzSKn6geSJEjbVRZpqy/S2aNe8ToPecH6d4ZRp2XipTYiEX A5Pt31eXLVfUwLEgsnFrA/On4FeAa28Zwa9XYhCCCr3I2bjLCjPVnjP27c0TVj8Gu6zjVVp3clOW 7wZTJ1dfskUxi9WtSIvBS7vHOoqvKIepQP7TZpSvkKuYx9UfV8C+mZfDlCtU2cCVv6cKEBYVcmbR CHjtZhmqdUmVss7csZJSky7h4ZsOewBQa0BwgIkteQwX2TcKt46J0nIS+a398MS7tAAEm6r780Ia 8MERMY8VyjP0i4wcQloFhXb+zF+1CEl54DIL9b+kPOmunoHmZGAdCsDRe7Z+Q68whlhEI+NOHVBz 7wdlCzIdAzFwTxGkPAg6rdYEaalsnXRCl+Amo9O7cvcKh6vIGz/c7k+vj9FSOY2ZKfPegyuWfMEQ Nsalzi2nnEkDTVHneTHUsOw2OI56JfvJgpIY4wZe2Pvc0E1rGc0MHKc62nPcgvZ4bfJtCrCcdiXq pWg9i0ewAKgHHb20hEPzjXhZ+Y7bhueEEfCIxqz5mpOu+THU7m1Ia4nkmfExBFqhuKJdrN/qNowf UX9aCNN3pzA57lQ/A7N11NOL73aufCprXHBa7Hwa/45ZefWZ4yJewxpEsbpTTmxgKVdxEDEvwKs7 6wBeQSZfsMrL63T7su+2zwzkUFvk2K/rw1lmdzRLCo9RXz9dydPWMeSqHnsQisk4mgcdhPI+w6CT d+20V2icUnjLmJWd1MXA6qr6X9OoAKniM/WvXrCsIOtEAHxOPc0n9SJ7gXqDoPq42B7NzHJKDqEo m05TtB5pnS+RXpQkXajWq/PbHrrPPGDd/m7ar1kvgw0hLxP/iNhhK499tOn6ennge+NutTQHRK3u HfcObuiPmWwNPzicuH7Ut5kaoDaYKmLclwhLZrBMOWWHpHNVGO42X4FfhrH4EYYpmRi3Ww5FG+P2 61vkXpGUpo0tjio4jaZieVpTRHlGrrzw9I4hZIsaH7R8iwZO5JQJNEj6UnjLdGvIm3h5Er+DgHpi 8IcO3SZLnALJpfewgUtMBqXUSJyDX/IZq4h3bpfkuNnUgiVydZrkrbn0kebafeZf1DykZDO0R429 4Gs16GxfqXlhJavkq5PTDMCGhyBjs/UnkbL4yvZzbCSfiHkR82pbEx1BGwa89sbqWvnbrRkRECeU b2bNzTZgtiFgr41wwGsL/GDjJtSUpW5WQFPYYVcweyP2MjTGGJOr2h3RS2oJqRSM13v98S7E+lx4 lKu1nK5zGpzm98orRZTqlEBW8uU7d2smL/buetr7umL2ZovZL0tjG/IhBYImfgVwshvpBgY89CD5 l+Z1EQvH9hMT616PcEeG6dMVvG3RvhF4RfPzsI3ibVLpl1scab5iCt1AWrhLWPBwo3D6tl1bX0dg EYtNfPuU40cTf9qRK+DVIzBgb6b4LewSyTXlXbCF2nL5dqz1AiRX+1QMJ56og6xFi9c6LGBuZfwy w8lKzyR9iXjdYVByuZp2PxNH872sSh75Bbtie57CtU6/Ws7OGerX/QywchwrhK8iaSHmRANZPYAh penbxBcarSJUni1Id53B1gUWdtK6e2v7qyg5/XwAlsZimkbIFZLbGQ+wH7dX/cINtzDXprPXgBd1 CL/K6zTVgBdc4mADzqTEpbbJpSXc8G3PTpCYE1KJExEPeGn1boEWg4iOhpPawMCevhT5KodQW6Ub g5QNEarkxWpMpLJijRgX8XWb6EKcx3xNvGofeNwTJTpJIqkShULK+nW2F8SgrqYAPLpaJYK6qa+D jno6imX81Jl+R+OzDXqC88O8yqxnwfMO2g9BU/tuonWitJsGN4vZhK5l9Bjjs5RAX0J9Tqjy4jQ7 145URFiXwIYi7kq3zmn+uKrT540yuBQd3VSh7J0v9ep3TbiWim64ecIVmP+kFZQtz2pUrHuHEdEF gZC9wzXF2tHxSVV1utdsOttRyfYeGQhoAV+QiNfaP2dodPmDxeKfdYGKndaLbZF07SHAKlSdYC6D IwlS9IUXcP/yM1cfndoJ63XjNJ3bjR0IaXk3MOxxkL7UjS8DQb+4mNczr5cFXoatowPRNdlgeDUH wz4t7evzBi8nlNYKuXbRGNYTAHlujsnYo+JVjCLKqQO6giGibOXlHZIf2gr5RuVIO/41Ua6znK6G q0mD8CPlUWoeaQmyHccqTgOnGZ6qA9mdw9Ex8vHDWM0kqxiN2vRAx443t7HAy5ODdUf9xzBjt/oR IFh+tzBdBK9dve0ZxuEL09HXwXbLdkTUd86b9ueoG7QZoYvopdIHc5THtpjNRblw/5wnuTr6uwUG KVuASonZlFZ/TxY1mlbtDqjmb7dRzCtTHN2ERBO9RtkyHlYaz8LewKteG68gO+Ta12td77yc9BJz sLNuYzoqOeO8a929Mfu6DB+nnxovq2gTb3Dz6JekDqOd6R8dA/Rfo7coW+I9E+945mMPij5NvYS+ +qW5V8MWUru8VDeeGy4NY91h6EN1aSZY9fhemBe4OPYR5tUSTEtEnyaOsTgC1/qIb1jYAAR19Mr4 IlXHe6vwlPmVAlWciNPXM3W8AJAvGCaFwCejYcIlp+eY0W28DnBkHCDUzVBI8Eqkp/DIGzcqmuZw GEMBVWGDTAbzaYq6GxwwlTVQ0jCDX0OtgmTueTOv7YNuXfWr226YyB7k2unJAqx51UpPY7jx20Ob kHcQZIIgAbDVnSq5A0mbJZ3qLIu0gfhuwAbc6vIsn+kzU0aHJGwbSGfB+2FcBMefDzwZfrGxbFMS 0I0oBoT9oe6plZW7VEN0sR2b26hlD8oRI+Yl17DXR3Vgy6EvbRuYG7g6wS9uFUSOee7JCM2iDtj+ TJi44Ql+ZIRqWP/4bm33xDC8YJarC7lReySKa13eFHrj9jcoBtUdeYXR9nvnqByGnMiY1TnYK9qT Pa69FhkpPvr1ksErYpXKmpyGiO4VjrbtzTF4NdokYmL8Ieiu5Abtr/lNIZOBi/iWwvNkm0KvtG68 BxDNhgxb+HhHNsVTQCYwMj4oVIsxRj+dF3P4k4TAGCyQD6swn+6RUhFO83qEXCzhsDwALPR4fc5Y QILKTnqt10d9bonfKE19RKpEIKLBC39qAtQbLjnC3iO0YxiGtTTeLXyLUTZs+dqJefuchoA2S4GY lAgrOsu2uLJLM1GndSRqnnNQ+E3Wj8iVNEv72kkHNoDxq6qo7cASUXB9wT6etl607QwyMlrcpSL3 pLUr7Y/uwbASrydmzyZAjsrzbYYqW1nDkC+AF17cgXmNhr/cF1X/Rk4BwRCp0K2E8t2ZTpgA3GRf W1Y6aqj3e+fdALvS+0IjuKxq3OH4GdJtKx1nHRkRN5bVN7etzNvQngQOyoq/3JgXlXUzZF0v1VYL 8TR4ifZDD+xf7T8CkQOQmJjcL8Dy/B3BV8GYvcYsmJUIll29JEj0V6JZTJJCmjHRTSuiEsg5Pz6z Lfmc25/4g0AYr6Qmsfx2jU27ugryQcEvj4VZMyU0DEGi/nUGY5rAiolhL8/S2h7PA1mOfjnj3r0e hR+7AKj4dqinmnmPCB09rs1OuOzxwYBylzGXt9fG5bs9clfrM8PXilkZrLrUB/CKWm5Sf3Imdnow I/hCnGvRronLD+ei6amvOiuAxdRr/xxAc19jpKeOLMho12WnDA1eWpw+/qAnPvQ0LQYiWdPfx55q wXyghHo6o/+Gqv/dT1PdHYAnBwRt7MiRQIrRWA26ZlAozllinZGbUO7N8GxBgmtLmcYAugbD/Hq1 kBENJEfUkac6TLuSOdEE0Pn3lZT7vYFXe9EqAHBVvxi5wA/xeHzjRVtPqbxJE0kQYlSAl5Icml9V REvAJP2HBm24Yl8QMR5l3HUWE4yZbbsyBobojouIEsCUWv/zCeeMN1Ndrj4ZYqxWL1Rj1qQGEK12 khgjlFFPd6oKakKlR06BRoI21vVFs60GXgClD/krjneFLPgKeno47dRuEGrzJtMZh6+amAhbO7GA wizYcNcFFA7ok6hmHI54YMt5tKbEYJ+AV9dpW71H6e6zYoa+7+JakrovbPoAKsxSD58Zch0RTaNQ ix1JaeF12NflDGsLI17RLFOVHIS2SD6VljuEzF6nbJit9T4WesEzMnq2pmvuccEuRLPDrVQv6aXY A/Wt5M8qjTZSNIiMtHgQX6SiFnLIVPJXi1tIdKVN0dgM2LQTuntSbs+h03ic28/0cVp1xULmrMa0 1Sv0Ozcm/oi4YiKRysvg1RHE9PiGhls5rbB7C0fpx2ds9bbatGxVYzQ/AWWgMw67240yG8H/cnLW ffMuQZegLAlc36pLTGSc6WAQy+288UAjWHDBcCNrPNoIR2l4C4o58xSb3gDWpgfQ9TFYltaUvaIX 1B5WdxXBE5BSTj4XeDn3wkilYU1XtsBLBaaFpj0tDFYE97U1ICbd4OVV2tI/bSemgxuXN3iySm/H BgSHz13Nw3WWk17Vh0fFMqq4yCzxrA1zzTQ3wPVrYikHBuRGW4DGW3teILaOAa+2K4oMckw/0zv0 Er9OdP868OKJuI3Y84zVA1896BhT7ylBBi87ih5a9Jx5QxkbBbXnk3rZzNCR3L5VaOnOaVlfEmwd b6MGcJFZwZXMVaS1/z2qhZhdG61NKK1C3q286Qkgb/nY6f7VrQjBKXbRo028aNk2dd39/nsos4N1 ROvraneuRI3IV525wOvE2Lvpt5be7psv3NmK6ui/AcRR7CBJEESG0bOg7xuQtL1hwululiSBlgNF WgGpWEaoSs1SRqnf4CqyxTnPQFiT2MXnhrGsA0HEaj4TOLDSAyGuHDh/WQECb6m9TDma3XVj7Xlh 1rX02w6JFLzO75gXGRpwQsfCTBAdodM/B+s8KcnDn0UaBF6O4bTqMRhMWr1Vhcivg12lHwYvMwKz tOlyde8Rsfrdmuh3dk7qEpgYarPkCCTItOXBLoRNdN//NUHKWkFBvRxCyyzwiw/s30tg44DlWz6f TcFGBBHcVRdQqo1Jv65TwuhdF4XqdyFeuhEq0jDq1VNcD+slvtMCHa9toP9SbCdOaUey8C6v8wGa aea8vnKX2diwVJEOt03QHYw/Gv7vBa/mpqOxeqhEi+cDs2N/qhnpoOpuCAHuiynYUhscF4yxnCmg bEGPkeIIF43nxi837xYY2MFrhAusDM2i+FaICkBlwsIRvPrOaeSYUxfjCIo7eIkyEWYiyN1e3IR/ BDHKu/oWZNEwnOkGnqNSDDqXwTg1Y4qdFO+xQzuRvTNiqBqzrPnCT/XnwU6hZApjC1IokuAIoMqP 8cy0T29xQFZoDF62mfTDWzRplKTOVXrEZJRRrB4oqMK4ps7hEKx5PBIMq+EHOCOS4j217T1ayKwk bdsTasminNMxbbgvIle0eZV2hcNAAKRwcMHomeDGnAByWbPPzSq7OS6W2JDVmZlGtBO02t/6NVoT XMPSSsQtel22BV7LkTd3d6eZPlCjpssWVLxHc8QsKtfAES0zX96oQxQM9OnfMhXKkPRMbPuHvX+8 F9ZSJzEh+5DEkgnGWB1XcyjC0XH9HrFLj7WBgyKnEBgWFv/Z4yfmVba4Uiq5ScSMx4H0QvJVYV1F qCwm9IARO37R13gPyZ1vY7bjVsyw2FYApuUttq7KIFrsEJjO2N1GnjCuPqQu1b0evDIah+6PLexg EaJESkNrMvzJI4blW0GtnAtPNoSHDSe7s/iaWZWZS4b9EFVDDWN/fLmOPjufDDTsEr5MN87jxsfi G8Z9inUf5U64/UC+UPaEuRPOO7heYGIEIumvqlXvqK2S+R62N4GXcbQsZUYxxEDMK/LQ3LhdAJQA VJUUjt63gM4JC636ZF/Fx7jXOXd7c9m/OZGTCKvjFC7m4lmBqE2c/H1SQsGvCHoj4qjWMK9qSAJe kq4OeDV4qSPbokSxqMteSN7PhwGvkfYDTQyeYr974WexLdXFuRFNvJLrFd42LjQIgdPUfnTT5dV9 09zfywuoHqZHhWgfDm3PIw0bTMHj0IKluqTaLwAWLqQPbeN8Q1fOf/sxVG8ct2aygy9jmxZ46frk jqSk458OlKX5Gpee1Hpek2GfGgW+KMq09gaGTbb1kWerm2Ig9eCDxN1pQsCz+cV9AtVWYNMOrU3j 5R0cG2LCtCbuxKlqJ1EDdnqEp7BlsVNjzcazcMwgUfhuiYOt6NpI+x75OYOsFe1h2GqImJ1FfqLc ay+nECV6M9izqMzGtwVvKbXpnhiDatT3JC7WohSOIzEo6e/MomDYYjKNRgEtireRqUSA5r7bCTx4 TqazuhLpT32YqJOf4ED4LgwnuLDEcfcN16ebuLrkDVYHX7JEkAqiAIPAm4xhvky65NiAZQlt4tnv PqMdnYWhfvBWi0wPisXHX0TjTgquAH3HvMy9vJLpxMCcRQlhszsGz/FcocU1Um2qPlB1qYVe8mfd 2noqVSOfdvwqW0+WJdDAdx3w/uoABDegLwKkXIIuH8C80JnWXeA19sstql807e460mITA4lfD/60 WxphMff358eYl/sshdp5bZ3ZEYeSCyNEfESFnO0iTeidaHtC7iZONWAo8GqKhNOk+Ls9RntSOTz3 uZ1E9LzdPdCq4+mO+gNXiYItPoO/uFIjAkMHKtRq0HDboWDhbxyP/fzle9gDCci0Q9JuVvwTt9S4 KxtYKrSVPKQD3UNoEgnvawfDqp8mgdsSYjdyE2wD3mVONBjaEJfTTgBuVKg/Hwy129URopH+pQZ7 MdhwakQTiYZzuNi8QHARQJvbiDJOIya8119AvZMcNzDbcB+P3v6iY6CbxaBGR3SAUA0F2+Q7i2vm qwavzETZUM9J3mTYJ9876RH2G5t5TQ0T6SFGEzpHjYYWdMsvFJu+eOkvpq9Uz3BBesUDpV6XtdNv tUF3TXYSui1BDB7JY+uZ5UF5WLZ/BW3OwKue1JaqI6EYJy7x9X6Us476obsFapAdutrMq3bKBLb6 oRXCjBqVVgm5tFC/Due0y9u79829Ohx/FqsihONvKtAuPlUrlOqMb76p89vtK0wz2pEVz0yfnmo4 XqGIFDY1/DV/GFuaDwGzJEv4AX04COLzGEfhzc6GxoYTaBp6QiRuCwPZ8J/JEUjT68oaO9Z6M04I 6dShJqICqDymY1sBziyv5CaGtxGyiZQLEkqmvDq69uO0LbQ+YLf1+9q8wevAa7AspK8rOOA1yt8i CdT0cUpJQB5zDqwr42+8d9qj058aFgbRWLV0LiLCNIbaT9OD3XBqnJCwXspZpXe4YhhxbACnL+yq 27jhQEMaLXevB7izEreOEnowd5wrl1uFY+GbBi8Tr6HEaH8tmEMPJVfdqHYAr1Fxo5hL89Jx6uk3 pBHoS1C4oOr11++++eZbtS7r66+/8eZbb777zrtvv/1Olb6oRJ3GOmX1olqbTnFD0Xvq7nT1U8Cr WRGklqfv7Kx7i46e46JVdv9qGWjcxto9yOVpPYBeJCrN2uDiOl4OxuAkYPr666+LOhF7Cp8ihFXI 5ZMKugq8Clgcz07AXnTLFzAAt6L9JlkZ/TZxUuy69S8MbLiXRwMTqp+JQO0xBrl8rZHvMnht1tqb NtppygM7coQenB/eAziMrJeuHkzQsEiCWB6IVNTAN7KirXA4WrliTF7lPuVYAqJQsA3hndbzhH/p e/LDLxdyPC5/fVKXE2gDOvcjWNUKNth14PtHIAt48afBjMTNhOFHzH27BI+MFME7HrM5eemmlH8C 85gYG6gD66oPDeitoQ3ECzL9DbsqhAd5j4MmZcYtEKfjcYYuFpVwnGtjXpkplO+cs8ouG3Gl+lYh oktpe9vNvwb4Cgdy/YS5hU3CrDdrFfxCgrtvvvHGW2+/VRBW+l5qDnhVPR320eIrBpp00Qwv5/14 eu0Uu5eXS/dSwAvEX6k0It/yd7elqxw3F4u6p5VlgKKvC7Luff3V11/VftzCr6+++uKLz7/48ouC My2OpRRT+8wEngw5oFQG1xS6J7UC5rRG98R4mh5F1U7IEjFay2tfvw0OHmka74iCQbsufa9TlKDQ DGTUuznIZeR6AVOJYTGRGL88bzcrs4erPALq4+TC3WpBJF6gPM91ScuhL9oCz6nUoBrW9ABne5Ta 6HA4GTVoGsTtjwefUMl5MR+eQO3FSuURh1MnSp1PowbP1Sj/vT/5gmwXj6yFdARjb75V6FVYVjaz JLAwoNS+mMamR8yf0wJUBRHyrhhdR92uiWO8rDZDKVaviVuZHc0YfjKucAO9cHLBljBLPzXPUOD1 1Vdfff7FF199+WV9TzKX8SsRqD1Ef1+pApPX4CdlDM5LL514iJCmw6eBrUYl0rLOj6SA9Rd79sPk SOTFmcVeYFlNcx3nevEO2My71W+tLBAF85SpDbEHvLL41AK+5hXfP3BRXbiQf/xuPj3Donww1+wn bNjUd+uvB7R4WtAKLBtEA6fb3dhRaYfXeb29aJSkMlsXcvsX79P/fl55nWwXEavFo98wehXzKhpW xKyAwqniX5f+QgTQn9LhUmjTG+GakwKSLfnydeesnesRVz/68R9++NGHha/DkoCYxLAEYFmrodMa nItV/Mkz98SiCoWMNKoP1yZkzvcmQB3AT2LWycI0zY4gQmaAOHsLvZKDP4wraajmcAH8LOXnMBOD UUPkDAnxz9aLDRsIYiWycjHOdWw9W63I/P6aeP5h2/TNwGV+/7py99NUPqbCqDAEtDV6pYDXndfL Bt4pt1EemDagSYypg6RduHMt9K0ONnbO8acnLueceWCIDRpGmsShidX63fz2BqIOycnlOmzOlrAR jm9yI+xQxO3YAmZ8nDAT3bIYcWqaclIFwdlaawUHcHzQRskE15JxesRKAWImiDsMRDW6/KRx6ci6 N+UbunZeDFqvLQvOAtNoo+sPB+2axHNy+0Eiu8eQuxnfuf7Fc6LkCd88QZOngcuYk1uFWdpSSyDl SI92WrP/mERkG4cqvJgW5kbrUoTV2p3Xf9sejJD/ESdr+dqtoF/fOOZVGfY/+tEPP/jgg+oD/DtB 6axyzJSc7LBD1miysHZqRcg7Eaikwnu8jzFJk7miXUTn23nsOL1PGwrVQNSJBLh+oJnRCo+zg/Tw uySg6iaEQCztJ/H5BJpO8MvB+SU5HvabFoxUXRQa99r2TQJcfOa1rQ/HvOdZPiy+G712kH/CYTqh xoIVqy/w0mYKta+Wg8EGL6dC9ggeayLgVR0cvC6DvrDvufjahnL7S52w8Asx5sJx+IIKwZYVhmcS AIMJTqtFwgeGEmDZ0WsAaymD0TEX9dOX76x6HNrPb11CqUxUYq63koQvApqtmAfv1bqlFZ+JszmI RemTgEpbZ6VnJ6kShgfLxKANP1nNnroT0Z4Me5aPpJf8e4lIoPlZAIZwbgL0FDQ7Qa7I9MFS+m7P YJyy/1X3UvaKE5X6lhiKi/VojBA/ulaZdxoUdsxAbQV3iBLOhCvLUDolYNYsu/v6+cDrj/7oh+9/ 8H6NoZB7xYSVDlE1kIU34fV1tMq+nn1gg4WjWxPDyqJKiicpzr87iSZjzckaZeomC8JWqGq8ws6R CpGbsBmpBwlp7V7f5nxd//IIHo0oga9TBDoRlkPHH889gtfhu9CqvtiQEqECufYwV9kWR4jZCab4 F1oNtWHAkUHHOFONu0+RyUtiffZZX7/Fowa8CMNHBE2xgjdSe8e0G1c35AogMauuP7e8gl/cor/j rGAJCH9gXmkvgYCaM7zFuC36JKXsw2VJAdcQwGn71DVrBEyZqCZamcM4BU52hAGLKY3k1OuRXijE A8Jaw55V66mAmyObcXR3HQUNgzcjZKfcaxX2aJgIn157HDpVN28xXMxml5rL96kzqpKFCRXjLkyo WH4JYnXO2MjwRnK+1O6ua4l0xsh02x64ofb0yDYc07axa/cczEubU9ZPOYHef7X2A+upz0kZ7b0m 7EwyvxCMyiB1mh7yiOY9d7CurslEHYCxCVv7nET291NI7A8TEzEDNq//uR7A1hgBmpDjuSvxNEna v0OqRr2gWGMbAfMT2girGoVsrd+M2DaSNjCzXcJLgC632u8IaTk8Ar8nhL9pf6SvV+TJ8i9hNJhc P6bhy8wDgb0sudsjUzB8vvNj3Tw1CSY1LAZiFjw2fzuY+tz3vKsOfdJtcWyrbeRhYS62xxEKxscX MUSZfRwDd3xkxDLy3sjMMmC9C+UWBXmW6AW7Mqq9uwFPe3jdtQYiaxRSETClUCh4L5/aqJlas9xT +reWD1TcnNh2aShZLDtdHHM4Ag9ynwxzP6tC+f7qD/7o9z94/8MqE9uBgQiTob6NCgY7QrQcTR8+ L8bg+NRSwuQG8MHJ6EOI8hRxGmIi90Krjk3Nt0PZLCohef527SawinR8SJrnnJtv9isUZhXr6aZt b+GnMa/TnpgM9XYBgvhV314ugSBCPZ6lhYt1vS7qVcwrrpl8ldFw4lYtU/O4VfolGyIn6OSF0yxG /XkM+wHS1lpV6+GbF6Bzl6+4zUoe5AJB90t4jZZvN9U5U85Tcdlc7/pqaOIUvlGjYdpVWq3V/vWq KkmzTWRNlfB9F3nkFUGuXsMeEiHRL1msdvVmidm3ceikbyafcdVv41mtL8GxYyxidcU4eJ3Ocabd l8IFey9fvnOE5lqsEP987bYWEfjm28Jmr9FfdCdcWBrqZP0ODqp0wDFMbzrWAcKkytCSgfYRxR3R D2W96P9EoK9++Ae/++7779fdtHiDdhjED2xHLGGkuG8ulxePT+ZBEt0nbJWsCEe2lM/mWfjOhEua eMcyI0wAU4hcHp2cLOIInCBqpikLjaVr9g+RrsPhrqDOy62PCbzQTQc1PsLVywevuDoTfkow3Q6P Y16i3M5fpo+dgnO7kKt+6gVyUsi1YIRo18i5IyonKIwQc84SkmNwKyccKU/LmUpDFHy+P8Es5FE/ s0dCuJJidFQn6nu8UiDVLqM9x/0hO/x2d3Zduo7WliY4VBIPVD8HB3KvW77oMtaE4Wzjq0J680NA x7gTMJtPVANXt1KHPv/8VzXmXhKsNSUqKql4tv1K1vNxSK4qpTJCVhhHCtkI7YJsn8Rj+22finc8 PmbczGtElHP74Crw7Tk4TpeumELVt+SviFhat2mlwMsQ1QZ4gGLEa2ZmiZdFFGOqtPhQJPfFwOsf /P7vvvfue3UXr3dwf4gVsNH8pjBiawrXisDVeHPE5zJpEfB68kSdebu60zNYvZhIOrWlirGM+r/a umNYQHjAbfK6GrbwL89hC0w+MIvdV/rrBi9jRLQorw/UbwXWsfwKolQCoX4EXlpqyWFnwCtgtNll xnMweUjrYNa85rMqxCJkC0wT+B+42V8ckc0CCCR10GqNHK4wlhfnG+RqUB4EC+cy+CU81Y9Z/RhL tIzw9O9GFN3ph75eNzq9s79ZH9biVj3FDxbhscYgVxZ4Bsj93RT+8y8+/8XPf155Q4VXFQ1SbLL2 q9YuHNkqETuECcUUbyxgQYu4wB7w3L4BrICdhPcs4RPFii9xAn0+YfeN6HPEP6QgL67BTIMjQ3FV 3wrUlwGt60v/3FVq6I49GpZ18oy8uTQipemftLVlc8wT7Q+sxvqkdEfv+ILORnKvfv+Hv/v+++9V l4h2eSoA+IIo2JCntloLonNNWTGeJWxImGcmThWo+lyjEpWkW1m6laf75pu1KaTyduswf2DsrD6s j+u52BxVPjmo6bo4OPq8A2HLpWVssyXBiGvZoGvOzVE+3EGt2+RlM69IyUHTunMaVHiPzWoo0XIh jsXDUlnkrpqNGRuKld6+PeHfCjzsMc9DhWFeUWS1x0hG9+mgUMzxhkotWib2KCzFnGM+3MgUq1L3 N/0CZecO22fxEyniXJQm2WAmGNG9ORKugcVd3N23hwKqabco4ELSZmTINZ3gste4LrzPCOWFIoxf waopfTIonDzxSk2d0Z4J9Vkhl10qbd2IjClziCiY5DJxTBt4rHzv0pQVyMIZTvS0UapF+0Syn+oY XOMnbih4ARROP4JAgAnVXqYwt9iAxA0AUQaE0qIGIwunZ2+LZS7sVjsYW5ApkW4iTniai1qugiw9 2gq3wOvvv//eB9UBAqBy9CJsS1bVtWZAcn09adFLM3vyj+P7AS9PeqpRy8Ktgqx3363ZUe+8XWm6 Fex78/X6ZQUUfikE+Hp9oi+qTez4EfFUhG/IblUe/bQrSoIE0Xy/2AgamMVyy3tdB6o2yT7v8JcB XiE6fg5+3MUDfZ1DOTGHOE+7KMyX03h8IZeaD6dRbqOVS+tMjHU50KuTx4Llu6rPCSYpzd3601Ua 5DXQE0TbYGvL5RrF3i+R0rNuDKNtK2/CgAGg8XLh2pK5OWFDzpM2XZA89XOCEUB5aNk8KA+NYfb6 KxTDC5L1aAOljfU4JHyk8amKa1StW5cZttQ13lgoK5QSDbE9XoGNjIvjIwbDklMA9won2hyKgE0z JhR4E+KLus1ZF4hqf7Fz7qdj2LoJXK/wRksuVn2z5JGHF5HGXexZM8davWrjlqDTyamoK0gfcJ5Y b+16yEt8KniV2/j++++X0QC8YvqP/W0nkdkAHkQwltHaCByT78r+1KSCt+t49+133nnnrbdqnueb Qqs3lKVUtikz9G4rAq1B17t3LQcTzHulR6d31+qWtzOryQfzfA0owL8BOyjy6oQMlrubT9oUmno4 XgJ4He86EDbO24FweVHNRHY2cz+qAXBh/F8VZ32jms9TG739k/Us4BVtN/PYncSIODssbLJ+iqix gR0bOiBpyniAs0BN/KZBoAXBA2SWaJA2ZQaPNqwawJzHboi2eaPX2oHWmPYS0tuI7roK5LW+9IFY HE9RbA7qNUjrRenXoa+oEAuneiXlCpBpDM6CS86XvCdrSqVqk+hoFZasOgjDWJSEdxTJgWT090w0 2z8MZdlEehP4pwNQf4vz+RxBr2OEzI5iNaUXZFQLpOFxdjb/r961fwyjtL9sLKu2oOlaLlhvmhFJ OkR/tmZ4BvMKeNHKdB23aVV5Bc5ThaivqpsUiHmtZnKWWjGP4M3Cq7cKr95++z0dNR+9KNdbdYIC NXJ3FPrizu52D9o4rllfFbOo+2gwjX/KlMnGuNXWVTVN8675ByUMmsSt3gfTQfQxMm6ktkzOlx4d 3uX5ewKvTcU2frX5Lt0vCVBLn3zilfusWqU2/PO2QFprUPDlH/FUaJfWGGbv0gp4OUTTqOQXoNRi YQPcBq9GuSMQ5JwFUzlvB6OOVixVR5tzJ5iLKxCBAbCOfiLnx01rEGsZW1AzpnsA41jeFHSDIIdW 3KNNePFX4jbzZrsJxQ7h5HExB3mk0KuJ12meV1ZOCJ6FgFUktyixQl3l1Gtvak3qTUTFMRXyi5LZ wxoo7CwZVyl0K313tK14XKkg5qmN8cj2zXCr0WXOfi4I66sEMAind3ijZGYy4g7d1HrhYHjtphwq KZrhT+o/kSXDgKyF0bw2Nm0BcRc9H3hpepBm+Tym50Y0ETubjodSMM8PqM7yBKiKamnqueZwvv1W Ea1aQ6NcxXIYnRfyRp1UqCVK7WhA/GNmkLDJ8NVrd9+4W0hX53tUjf3SvTOOV3SsarFnY/mnkgEV 0Bv2rSyNQ9QZMW7w3taW28T+ecALpbjm2L5Z61MNUpDgBI7NDxzAy8BEdfkkP1mofrq2ejjYhXFv 1Ah4LUzapvhs+IUa745qFWQMXL7d4QFWl3jEGW4MCkCg+tjpkgso4elQV0MCEY4u/3a1O+bk/eHM nfkZeFgWausTh4YbsCIB0FMd9WowcQcybpBHd40S8sIXWArWju04pDLvZmAeYSz0Im21pFPI5TWh Ohxci0FZayFdbL9QP8FbO5D2l6aUJ9JGmQHgQHQ+Wice2+OmaPYC51lstdlbVRgYhUBUBRlB0Nc1 V6F25Nbi8A+MbY75JW6t6nss19m9kndtAFZwPvXHMN4UvH74B7/3YU0Pun1H82oqschxyqjWRqHr YRW1Ek75eEd+YYFVRbbqf968+XZB2VtFFpwLk7UwXSzx4o0rgcj6VwAHRzMLuwPdILmpzv/qqy8/ /+yzWlSn5KG6vhohrdFr0HgJzFN8WeoaLKM2Up/zk9VMJq3zVW43JO4G4BXRiubkqVx3hgEtiS2S wAuFW6AGf3m1hj5IHXIMuXfTmcVcjE3oQUJbtA/PnYJLqrawd75FG/ZjGU+kVDakucoGAb7kBHFO IawZzaxvv51woUko7WBQgmINatOScxM0B8GaKO/Ut6mKJa9vm6cuc8QTF4VEbfR+zWrsZpgij91p UFR+C26Rl/D1opoTn9WWfFAcMrple70lTz1HXmccX3eEbpiJ+rvEjexu3eVe2076tYEXDb6K5BY0 NIs6SYlMuessUqZovioqHpL/P2GbXsrsRhMlGU3Bwj0HeNXcxjIg5TTKp22bw10YcXFI605Bl+JZ HkPUC8PX24rLF4K9U7BWJ5SHI+RSdoxVMgKpbvOgS0JUIFcddUkxtfot5qVUgHYrawDhwYNPP/v0 53/5l97csIJcyyuk6yagc9J5LZFo9TRL9G2XDCOXGlZ38HqCB5lYntl+Ub/eThWbavkGEEbf1uP7 hOVK+swFWMmSTAqXGl+rxNlHNx0dd54FvNomG3n3YMnx6bsv3ba7nYidlYx+NqJZDnX4UWvR3x17 dgjbw1s5B1E+It0ZzUpzrU4c5nWkeAeYxTj3EDOx19VJPhVw2zvigG6bEXE37PG4HmcIcbQO8nj/ Wb5bbAKekzLLtWX7YXW5Wv1KIoCvoOwir/uN64Xnhddtqhi+uNdlIcWyNX9d4LWkmvbFRdOyOJUX 5cYVAHsAEd8ZC0w3wNGqmRQXiVkB0z21ykedPylyjV9PjXn94R/9/ocffVRuoFTXCbWMdcUrdWhK yQ3KdqjwS61t4H8+oEigVZyCNGyidCnwk0fC2G07CMZbDC7y+6qeXsu+pm+r7yWLNbdT+xLV5rKP qwiouFd0zTSq3WcHykCqbohImD+V7lk+z5hXz848AaeWHsPvReBC5mY9tpA65Dq+oUCoQlpWXzIg 66eyIORx+DVBruLf5G0pQUI/vE7Y625RYkfqmYkN+pQ41FRgCuknqlki5SeoeZH6catNG627WE1b rH6RPqVOGlnjnIR7mqn4M/UQF+8xbuLfOnF+piPyfH+Rg6hoH3Pbraw5MX1pqx5r0ZhysBbmVtGq ucsWngOl9FiX2u6gvMGOzLvkO8ts3GhoJAjNELh3h2LDu21ZXzCUuW0sgFUvhhjjoAYNA5IH1gzP ye8hPVRyk8zju4sy+90/RGHzZJqz9FgpVpXFGln0jnweS60PNILRzKuhQJuc1qEtUiuI7SiYQU0t WbemL0xmm69dCM9Fy7WqxEcffVRBYV1ZP5j5TtJT5ormNxm4FH6vwKT9VT/EYhehSNvM0J9pMofj eXph8mUO3eBWNa9g/FdfaYlDLWnmBYOQb+6s+QhXr5p9Z+r2GdDggxqiVM/p03mhj9HM0VhMMv66 o425hxB8mIufdC4WSJPvlXW5jSYLuQQ9UhpnkzrCBVoJqvRaYMykg9dYBtk/fNuvNfBaYxweZKQ/ 4vQGvNgmNT/ZMnUV1a/ytpXW5e1/B/5j5NqpBylO8xN3dYEMnTOY04hixTcW9HenFGbQcOuLjOEA WSQQhcTNAwZVAUKw0lEnwAQF7+7qCrvz/Esnpy+7mgNbtC4Nn+IDwlMO64Ubb5oURIovSAIPXCNL 1vWieAVW6GEjl1c4qLFya6aYtZNaQ7+aeCF1KCiVOQGvUYFz4fzuEPWUOyyh6pOs3Broo+wyrzGn a0wdmhJ26oEKBwcrnK0l/uwzVsKExjTqDnSH604DPJV5/ejHf/DRxx8XsVLJ2M20bSfiBPMinr5j VuQv4tPYZYBtjshAqePr0C56PEirSpUhIrTJehUANuBFp7lrHQsU/mnun0n4qtIYoCDQysns4Rwa LgLRIkceTk8uGpDSM32jqH8HEk/EhX6iL5liX8PmoUWd/g7zstDbeut3dpbYiQhzl4E26QxC0Gsa aLgDUxGPxcxL0ZHdp91b46DEaGzrgCsVRYiGD5qBajT7CSpxhzoGpHJOqtYuVV+Ipp2gWyMK3wXq 51kjcRtenYFjGzSaa+wbKj4dJNqOwvfB6w0Ewh+NUCFd29IHxwX4G2Xd3XR5kMuaFfNMznQJcYHX JCSSlsjDM52uV56rQhYXE8djIpFqU/ISqNrLn9fTfDgSPs6h5HtFros3dzDbfh8ljHVSy5bDBKEg qaonFOpdwYJiYlL6BxghIob1iAKc6h6aVQ4TWHh6DPP6yR9+9HExr9chLVicsZzD4cW0WkrH3LkF LSh9/w1WFu9zcGLf1cIJxnC0ZDxs/bHEUBKFX42N9dlJaA3POa+XimQD0HU2u9JO1L1SrKwg7N7L gakckY1dIEoFjAmXNWEnz7TbpioGApdsqWyTvuj1pst90s5hRwzsmI9XnquMXIdFBE/6d4k6Uj7N gfa1btNxu8rw5gTgpKQbC9iKfngZNQf+XAGkWVC1t8SFqh8aRcrc5++tFr5FXfBHy+Ea3HYTYDpR 7EvHenaaJY1j+Gvto+QDo3UmRdqEPO9sli3C/uNAPbvzrTweNUtTV8on1a0dSx/cjyXW/cUSjg7q 0QitFloytUT81w9a/cQQEYcUaDLTZ4eT7typCjJAQbUbktQMpqLa/kI7mtx+De6qNtY8UC1NUV9h xUeRt1o2eP3kJ3/0sZjX3QwWIBgmClD417JnYOdPpJvTdDcBr+xG0YQL5ALAgKMlgZvYgVnF9/Si +FeNv4otedmwBwXYOs45pT1EWbKpajcahjAHcQl7o4ucj1klRrZr3NyNphyjp0Ls4OX3aalWwYZS w0J0egeQaFRvOTc0wV6YkCtZb4JElCz7Fq3evM4Ip7RNPUCyUVT0YxT6BLnmK4OC/l8HW+vzBmBC RYDXgU4Muxii2iCB7D/lETh2fUJYk8N9yzbbqmur1FHsEwnZ779V8ISOLvA6bZxm8TR9h0WimNeB l8/FdYr3VAStRtDtUSi867RPTYfsVkOs0l3TgHFGWuSuAehfN5IZv7yAqqmGbK1jTRXxKFWt9Ezz hoCX00V1iECUz1grVZRjd/uOFxEsb9pUyynx1aa0TCzHVKvtUzXeFeBl5qUrLUGEFIZ+xZPcOhI9 RI3d0E9nXgLKTs7y9CibH4/zMUzaxwYf7rm4cJUcm/xMasJ4zZK4U8949T0t5RyLhMMC2nto399g uJfttqtKIUaSEHd/1i/8voZ2AwReFiIuRp2T0YXGQRM017GbzsgZMdWGNrk5D1KknwO3cwR6By84 0d6GRxo2+JXLN/WdipxAxqqmv1i3P6r+4apOvwrALEU84BHqGFp6DR3jtgunWqMJdEXc2hnPPA8B GPCg/1NmD2U0cgQLTuqat74trn0/GwNev1JO477zahBdSVWpmYFL2si6wT12NmJGY44cRot9rayQ 7+WhqNU2VVF6NM2/tGwp2tbhx5dLSK495SV+kTZnGiw0e7x7pew+IQildmv88l+mHcjMGOVus9dq 3S1un1ueBNGAFw3OnjS4oJWU+pOfCrwqh2u5EFtY11F5r+q9CeEIUKv2M8BrQy71aZCLUtioz7GD l9FEgFI4pUVEmUXk7SPNvLTChk7xIfnouxzQ1E63YVLywN+RpC63pN1eLL9yp8jPhhpLq4/gpX0a AyJSryNCER0DAyaBpPEruzPa8YxiGkONXLR8wEuSAfBZrDPCkNcnRngUlCbZ367XqPjxmPNXTYN5 ahNEYL/hBSQAdTi10YDTVlH6ubrnJbaVD4eKORMlFrUfCbak+1koHYZo8NoBfMeRXW9zJwoDeqF+ edUlc4WxYXV7K45uyYDUtvxvFitHunhQJDOKF/XrsK/uk0X73DT1aOzUFuFMeWlNaiWduPSzW7B0 0l7b7/G17TfotWJL+G6KfDmG5dCNvcUk2mfWegW1y298jb0vch/7fPY8N/CyzUhz9/IVtW/jjz75 5JNyG9OXgv2gV6PoKZ9/fvAiiz3ySwAAlpRJREFULT68JtgZ87XAwjp5JBERxAhjwJ1+LjgPIC1I at3jm3RxRgoQJpCB8U6fBa+yW5RUztFPP2S5gAfFPgev7ZOZRjd6bjDijCYPMadLpkaXFjpoNoLR KyxgMOoY8Do22gX9bIU/x52LSBSU6Ro1XgaCVjn7tuvvQFGDVyeTHHzVk+sQsx1Iw3mCHhJHSyT6 0YTbbyJXrrMtUAAsb+lvQIRztgrXy2WR2yk12zLutpMW5LK5idXve04I1oh2sljAgrDpkcE1CAth EyoueYROlqvkbCWEnh888AMkr2hfqkWV54jOfI+YtR4V7wTaugIGhi+v5l+F9RvVrqrMNtViUVpm Wj3KBMS63DvOaCaPoUtjhGFe9reMXsM9nhi8Pv6kmJcbKYKy0OtSJOK5wUuDiM6TgCgt9FqiFmjY 9dBEI7M83MMa7HTmCHNiyardOPxyQJHj/EhG2I067mXsomGsu9v0jcENPyu60pMNdplAWAbjIl59 hq89YzRz9gl47Sq7vIS+nKlUwi6UKc8wD9jEtBvtHExOPrkIVZevWsjV+o5uBcG6hEfuFs41pzXz 4myU7/pCHj225OTakAImgFfzI3oIC8htg1JU0s2TiGZToTTaWaM02cpDQvJiZbrGvljcYRtjEp9I 3CuxnH2FuYj0QpRFxyyJqw/JJCJMUSqrbEpvT70dqXn3OtyOG0a2/lrAK82+erWZKyGwmAKQq4BM dKoyWms5GnZRg5lWRSbfInNsJpP0dtxGcV7mt3N45O7qp39SO2Z/UuvVRCOsi7QaCqy7H7XxecHL iV4Grnh5rHtz4Fzn4JVYUwtNFUSc2mOO8G8H+hm19F8JcfjN7ovKN92PQTWDEJdE0vlgwy97b0tA RhxjxlsuiwD0LhrBtVafhhucmY1Zqn2xlF26U9bpLgC6YF4LyCX5i3ydXHio7PHN9cCxvjlVuQWZ aYfx/y5D4SIzEZoR7P38KRe4FHnbXsR/cxDWsBnuxZnqNoNaIqebTpNto3btY+KYsJsD7g8gn3iv PVs1nbbdn9gt9yfgRSCmdEv7wh+iIN37eeaJxZlCxhWVDntF1hqoa7ohxQ4+JRqB1hxF+hJ+uZl+ LUeLcrsVgx7L2smF8b/qTq+sdV/q6wagmACZ0oYs79yq+sQxL/e3k0uU32tNJ+egmNePy20ssI8u 8+d4fEfwIsK2gVdw66QH/ORlj9brDeY0VduT9euOWGqgFTQbCZw7nyLXiTKHaq2EQIvFwDhQGNsf GdruEC/AMDzg5VosGzsiBAhTx00XDgUKX2ABe+vNvnjILo3mGq0bh3u+BHnlQfM4t0jCN0+/+5nY RK6DOBvtmvtcxFPcw7l4F0ddiFlz9QGPwSnyCmnoHuLKl/rwDFnm6YdHLC08wbrlNo751+MbJwmt XjzcmIv5UYdpZLzbUk4mM0tTnVkkDu8DFuiXi1/2g1ZH7f32awOvsB3KmJI28fj/t/dmS3YkSZZY 7FgSuXRVZmV11zLz2P0hZE9VdTW/iyIUoYxQ+ET+HPk2I8Kp3AAEAhEA9Syqpubu98aNQAAFZKfn zYBfv76Ym6kePaqmZpb1q/ELUixVklqQhMPxZryZxg0xNBYARx+a5I3CgEUOX3NqITXtzQ3B62vG vLpaSc6yST9C8Ip2RTa2U0TVD4HhBeZEomRu650aZ5bFkxOxZOuKgVlZ6haDr4u9shoDuTAalRtF cm6kvFgoILFNeJvLJiqp+7DBCF4ay4ADPhsT/qQEzIi/H1ze/VdU8C4FXbmEEp9bmdcav/aDV0Hh FniNCCfN2UC29wVe4UemE7FmRK3CDSaW0IZlIlLohroOjX1lyY3F2JgogOozjjd7VSC5YlcFcx8M vIREi60fxIswbCv4JkaBTat51IjVOnGf+IrFgC4v2QNAb4tEmFMLMWsso18CL+R5JY/oFe5ifWzg FQZKIzHhPhhmxUCRyqwYLUO72NvfilooueqayKOaxGBarafG1DIvdavxaRC0nL8f84EwG1jwJdwb ODOLV2JrguwMBLatLaPK8XpHkdMOyxQnOep09d3h6fY7/L3Bq5dwxbzgxNGtkGl5v+AFZ1EcjyIj IdjcKBAliZY4namfDPQc7YzWTcsr9j2ZjEbvBotvMjzk+fa2fJgzhrjzfsPA5+1VTvrXGPzHESMW 23jV0KQKbJJsYkYdToWGufwCxTCvzOWlhn/TS3eoMcHr0SNlCsjwpwYaTz828IoXI5dUDJcJFPId kbuMbBF17lHHJU6Umy3JEmuNX0W+ShqAXMAoTMQ0BtpqDNtYohsIBp+Rw4PUhal140soOk0YR5N/ 9Szb8at4NcuMLgpNFi4H2TIq5Mqv5ZA8jCj6Lps+EIXDcfD105bml98fnHndBl52I0WKy6cspeqX V4E7eVDNyj6s1VIxr9TPoTJpuzbaoCOXqkQy2ZGrG1mO+7CwBvOOED5DfDDPutIUr4i47thqPyX+ QQXiLjdzBbX3BLtkzYVIc/1H6ixHfUaCapU95DxO4kwNMSva5U8//fTixfOXL17GkTg9+jGca8FE 9wm8RvHccKzpFrJX9FSokOZDArqW+IrIyxC+U8yrXLmAXCzpHSkhwi1SEkT22HaM3rMeGOTl3GBY nH1MHqBBJZrop7bsRavuMIuIvULbcPWG25orYOg0NczpFAXgN6Qbirst2ppanzLrRmVNZt1ZXVS5 fB2myXjiRg3ZstSXcunM9wNed5HVPLfDs9Us9c3qvnSXE44n3FPXmuHDLJPyVn6+nKvQB/SwO+bF FvG88TTBPRombrRslAXajq9GL/v4qmE1q7rKMytDIXoFPUej6511iWauioJr+pts8ASwpF0FXqlW ZvIqki/jADkG2VgbEqjiXJzkTo+1Ri6g9z4t+gDXGC8S3eELQmFd2fGAqEJQjwodBl3QOG2eRE21 NoT6aoiMUt1vB69FA4vESLtUp+8dvJooIDSAot8IgNwjilcjtyb/wv8hXlwNBMPJOed0zZMh0FPf Jatl9NCXoAh85HnkX0c2SiFMtASXNCI6M8VHogwWONSGuNbQSudONVm1zVAlelu8xKkKK1FOnCtB X4HGA8jc/W7R7JhvIMAo4BCKqHZc/h0Q0qDKtdSVURyQXr00um1qidzUMr0M9Wr9ydVDUMwra9ow 5IakRKB/prRtdNeIMvvDuUZyAH+iYb1zeQTSoQ5eek3Nv6piI8Ibs3uOfCgwsZFsYYX0PQrrhpbe ry0f6KoScbnXlmKPggSKMfMSPhNRiWEa9oJwSghke2WXe9wpmpVTPLL2bwOv1vOt7uoUuQ8HXiiy 6xGmB1MvxkzYDGGrQ919EmdI/koDiUXYNOmYYFtb7ikV2DkpA02S0Cd2hQBJDZoy8LuiaamGNYuh iylhbNyr4RepciqtipuaKVuUX9MlzoWmWsA+fZpqgiRvDyRt73KbTWM/UZ6kP6YnK+RagLQrJU+b XpVTGiTZGmhVUKWGOwS8Sswbcg27UhUiA+X4imKceYSWamJ2RjHWCJsVNxS7V4dL1tUEXnpWgS9O Zse6nB6MDYxedUeNVDfmWpQdb0NJx1PepVXvfm1TpQEZTbajoAh8IYUVdRLvSKRuGhSWW/PCIz5W DENTfZli3gZeWR1CLt5lAvQPw7xspsgwYZDexBK+zmEQAtEZjLw+Dh5UK4ax4tRjat/6yxfI9ZfM ixLyso0skq0PXpzXkQg5gKhBSqCmBmSgUF9tsZPPDxgjbFWnYcp0Tiuoqi4sU2+jN1NEFfDTAK8d bHCAFY1JGpVhXXCCrPMwnBne1HFBAWViEbAX37oHeFWpDgGvRKwar2t9clfRrOkDoRrLBlkf6r1k XtMNOEpZLysXNWqKo/Uzi1XCbdn5WMBrUGUp48SyqbrUA3jTAVJ0MuIU9krSI2Je9limmj9zehWk fVVG1EHgZc7i+anKYpg4pMbZYgwEyG6Hd415ZcRLVogTvKEJi0lJwePVomQODCEOxgHuAe3Fpodl Sp3JN0hMsEdD04f/lcNj4qWAPAFLv4peCW+UKlGCBHwUYmb4nuNw6SGk1KpV5QQaXzfBixP09VSJ TwO8OvVs6jjUrCqLIFYa2Li+z82kL3aZl6EhYij2KJ7ib0WFkjJjXEm6jQtEHUja9F/45XZqDEKE aGwa51hcjMzLzKExMLWwAwapw5vgJZu6IDnhQUWM18LFZSI90TEnJrDLOQpPRBhYYVJ2d+L0AFeY EtoVMuNJV0k2iM4P49JSQKWDeT5BziWjYVLQtZxk1frCf7bBS5Uoz8q9mLmzqN9qZb2uGZ12Hw68 ZKaiuBbImFXCbmOFrSxtishjMQ8uuJMYn06m8L7y3vhuRDfPAtlkB1JJqY+PFwmVoHIlYOqLBTd6 f99i0n6+NepJCDUQqUTBamE8VZExT2eyRTnmOix/WF6jRjfmyGyUv7kDkpKPcFNkS24bxYHl5L4K 3OW7xxj9RrIUOM/ze/AqD/PXzG7MYtFUwvqKeElhDMZ1NHARtkyP7qjp5mgmrhh7FhX2rNDLg7TJ iCT66kSYfEeHBSQM7WVao6VRk1RMslGtLAeZE5aGIMSMh5FDgEV3tMFoZoVyP2/z9wSvQwQyPSpU PRb347JknPiUs3wxzKf7xLvHCgEYV3Qd44rUR7OjtzGB0avHiLWnSg5Nqbqugr4n8DIxKdNH8tw8 DVtrzYTGZSIxz0YUj+F0bYQtOSojBCYVknIQMsohgUx6+gBSMFlZfLgbgzUVwkcv6ulbrKXo2gD9 FRtLapECrGcV3LtQaRyynLyNsNm9jZpSHf0uSQpM91TtHxV4NQGwrwPVpsJj7g1pGlV9sswZSEp7 4Zciy3X1iWYI+yoIqWw7TDWptDtlMGacS83VzWhVl55uU9arPiFMZs3+TFaygMv4ldKIdxGzG2sf Q+50kc0uObolIlQRkmnWjQsbZhWEdeVHPgEGA96Im7yOxdUuL1GlDPfGnRA2yvtLKPwezcgdgiYf 9hzXT7w9Y/NcuxXLU3MRH/ZI0kSxzm9uXrx4+f0PP8BQkRVBIZykyjwvFx2Roww6kHoV+1JTpM4M DX2/4JUP1bMVeCq3kVZG+fFmlCpMvB8nCUIUsGuUG5aeCt9YY3EADnbpmJ7KXnfUHAWyaJY1TyqY qYTHXNqnWiJDNhMa8VyRvpxgQpCaaGaEHerEN5Q7LOpVlV9qnw3xcTEv0RDWrJiXm0ONQuVP9dfX JDNy/9RY3Fc7aJemQvMus4sRGXi5hro4V49/4Q50KQvI9PQhvk3FabXSqLRWK+zp4l2uIiMHXlRG PgaKt9L+pjJpIY1WWRaJdxVgi3+p8AqxBkhjUdurWJsmXjrm8AOwIfeQ+Z8VxxCUqRVUVN1gbF2Z V8X+EAdcQgWvM97FtGwohqWedpuzMDBTPNQZKfgSsA3wUksqoqQde0LMVKmtNKZXwntiXm4HNnNI j0yi9FwNb+osG08pZnIpHAo7d1Yh69R4C1ItvieZ2Zh5A7kkXJJds8/pNs6kt0gwRoEpA+1dV+1U 3MZCWaIwwjcuQY/wVCBy1H/OAl0TS/Ay4mbpobDsY9hST6gjKQqOA0m9RY1yZgbZVFZtIk1SXLFd b/y1sEw5BFyE2OMclOslqNJWQLkAr0UtNWG+H3iNmNf7Bi9VKSgYV/nA8pAc4Yz/cQRzKNM9SlNO 0ie8qmrx103V/TsJEH0h+RnpEMlscxZhZgvE+jtQaEoUiEW8bhwIhV2ClwFrK9RFtSkndBvBN8GL 3SROUk38t7fb7dxin0hp1Ep3SSgF2SxZc0MYv0TdsdEuYaMsW6BZaNxD0EyBGHhsx5jjFfIqA0O+ F64lFeOUmgLOmPcZUuNpzgGD8BwHhermDmDXgtMGXkVEbCcS1diE6lfKPD1j1EcLXrKHBVwFWz4i +O9bpkvXMq2Eo1hT2lRKdMrcTGLE5AgvJOZFCHxPnQaznGUwYRtAulTQlch16+xG6dcQZfEc0cpC S43sl2TyZe32JqdKUZH48T+5it469ZuZ1/T0ICAc46HqEYJz/mTUB10kdk+le60SDWbb7yVl7rxj WTcf4jvF3grImK7mf+P/zAeJ0Jc8FfPuXHZIrTzAyxXL9pzi9Lp90q71O/Ua2ASvINfNkRg8IYuu F8CnJmlvrzRwio4984iZVE+n2O4YXi9lVHbG6W6YQwNoM3NzVVCyHsXAFA+j1A1khSz4NAqk1G/i 31G10c2p2fQgtQJcT1Rk/4nsjcn36U6lgg1dZ2Olh+7EYo9orQwBK0EGVPLrx8K8pLhFfPjVDKyM f4gdAIoDr7iADDUwtrCnohRY2VDrKNvp82otrirRaqGcJgXO4Q56hlpHXpLAJCG1YUXavuGuNZMy +JjVW9xWzS9H0TMh5FsVaunRkiFfZaZsW2y8KHGUNszzAPPABrBUxWLHOC4wxx+9gJkWGYqZroJ0 wz9J2Pr7gpdrgLXUcRyt5zRKxkyUIIE/0dwxYggrX4ZHRaM2wEu3EC/QrIRCMTWgft1E6gPAa+lu o3hu4AIu0rqBY54vImFt2EjZlozElUxJtMvYWMTxipK52bMitHuYu2+dEOb7UFT57gBJz5kGDPIM kEY4ZkAwmI5kCaOJgm9JHQc+lQ44EDFpFpqKjr6RtXokPBJgSHPp0kcIXkk9hBp2WIRpcuuIO1qY lSs4IV4DD0gABuzKRfDSDYSeGhOlsYIt0eqRJ0HQLMDSIxOu1Pg6sJBhtb4MVHf7Haof0JO2y9Ej uO2ESm/1ALUOpRSfEaqkXQMhT7QaepvKxaeNhIn42l7CT0iIdlWkg4FpSHPj4Ob0QTT3qOPBdp3y XXdodKH8e91xLbX2sPBQkeJXZg1g8T9mqp6Gd/zixQsJj956zCrhdkUFbyDXLpheiEKr67J8crES v+SDS89FUhp8tQbA8T4lqexKtgFJUqCK70Y9MXSRackYe6DbwJRqjASvfOnW8ChfZkEkHvG1oEIO A+ewb4qjOJvnQYQ4WBMkwQNz8ivfOUsyYl4Yg4k5cUsCaTw0c3+6mh2t6halLe9V1A68efeRbUzM xsiK1CrUPwOb2RJ/0r4wTu4h+ZVaUqExTRRPtpGRs4QPYVcFpevw/pKrutdyMA6268ejiIMjeF9R ttXDmoIM8+8pjfhUiUWKynKn36+Uq78aqxJ1ybg2bsOABaQYIstak97I/yJ4i4zk/wc27YOeNr1n 3nlUEIvG2I5qB7PFaHVXYQa9nGJesfQZlQAKs+JcApLNbfFGu8DL41YZa+/yvX3TZkHGYwUENZ83 34xCkK2XvEtuhKMl9aNfzuUt8OLvFmCyOS7PIVYqXZC5powocB8/daNBGpjTXNrYjoBX6YZxJ+Zp 8nu4ESROCG+RHI9+EoXAJsfReCW0+tjByxo2MMqNIiemtF3kxUjEejdAyS9kNCyzUtyynqgITbRI jZBV1GfaKDlNOAcLKwks+FgcWcBHB473AV4UpcltnIvdn2kroEpjOTV0RAmBxsSoI9+uSaUxbGFa HxSebr3ZptElLhlsHC0BHqNOtNSqmTMp/MS8qtYqN6K36K2lYe3xP8qKNZx025XpgFXywmykTQgz 1eq/GXOHF0sAIKYRX+VggR85sGtlkMChdLRSeqn8qlZWM6LeGDXBqcJZ1aRqdAiVQI0rlccW/SQa Fu4uy7nMo94ceeCoXhen0tBikAT9+1zyQ6itfhf1LhRaxQ1TPFn4BmQHtdH7PGmJGg1GFrTFnqAK s2DZ+c62MWkB4l8Bn+Fv+IXjldSyvXF3vW7eNVV7vcRkykndYfEKh4BXCdtoJUa4RsA+yVeWZ8qz 5xNrtrjRnZpoRZXrFpZdVZnnE6u3RgTxWhqpLjxkbluoDWbvUxzuf+9qR2lf3Chei/wRr6L7boNX ad9Q7JSJW4ojbzX5n6hpiFusWNaVzHiToFGPA4XJLxmGoAHJF5CgU2OpzYk5nPSG/OUshhoAlWmw 7XRUh4tlOmGgQC01CDdNQ4eTXBg6cJh6h6ULCQhwvL6JpEEgHFMlYuYT1aqq2qsYqgFUGWZwrj5J ZMZ5edo5+oWx3MCoAfchW6oH1cKrDwj+uMFryEvnLHXUrc0OkySaTGvTqAJNYF8mOD3QgU0tdWsS 98SdSZKb7Lpp6ucUpH6g4c5otrsyL185rDkOIEm1FKqBVz5x4FfKSYdN965W8WSSieqYtoCdHtd0 EoKtIJ4ISWadapISOhh/Z7fxTqgmRQdaaQ1Gf9sDXssgUzf8Ox6dZjc5l4FMPT/EnXysZ/6bZWcW IEXDmreaVC5b3U47pZuXiqUEsjA0gpw2Zl/bdk1eQ6cxRctrakAcoZsh+aBexXcpF8QgDodoBNvi WCQE2pEFz9WqoigCMgNrWW/ckxUj14Zb0fs4HxOPnV1wvQG/TKKhkXtIuxmokXzU9p0k4v2cvGJe Q2wqPln6XFQWRkvYxb9qUGGWaWcDo7Q0tpDGLNmJ9ldfC9mHIeH1JXllLOriErlbwcv9BckE7bEm ze8V3MOuol2SLYpDfl22SImJ/YaMChrI6vQUYPwb5yCenes5KGLo90UuFZJvJF/i8q699yMMD3VX FlLLczCwl2qD1YO+1upByy1dZLGcLeZVar+3lOL5XIQ9H5vyN0hVsauWLQFHbYBXnttxZ5QqpTaO IIDifBxMa5aZD+PZZILiohaLKn/il6CW5KshY/QE0nQhoz5YUiyFi9FY0R2CWLvUrmufK7RXzniq /cbUo5NY88pTF0ljXUVFMfk9IW9isRD+hxKTfMq97zfASxI3g9nAG2N7Gh5zzIFcE4JL2RLUSk4L gwqNCm6EWQWOsiVZpWvCn+McsrGnO7e6mPgPZYz4xXgTOVIcUTMtKrB/F8ZxhJCd6topea47LJ+o Xo2WrKNLzOuzamhlmX2YDSpES9GC0R3K+EkAWIaaGMnRdhTg9advvsEc9gkolhQRml2tuNlCOMjm UFNlg9kw4VaUsmxXSdTObRnzGoDbBMP1rht7EyCBfXEbZ7tAFZTDHRVCac3nq60Gam31YNirUeoJ F/RVLy6BKwIJzJYIsUg3Z4686hFI/0ktKgmHXsZsv7gZ19hUy3DjnlBfCkKDDSuUVLbV9IMIoR5x mFlqDZG7quO5JAM19CJqJ74o0SppV5Eg19UQDZ7TQgoW3rzVYFy2kBtyJfCSQKuIJNUzz82arwIs 3lBQIi4TO8W8qtPIZIk/92s1Bk1HTNDy5xq0sVGbuSbYBGHs4AAbMLMcyMXXcoxDtlTvG1oQriQi 3DnhjPiXVOqT4V/iUCktsX/6l7/G0mdfx5jIUhX+bsHxQaqQyVrTos3qng5aEWBpLDkpQXnnlZwV G5Pi1taxLwHQQkZhtGJYq52NTXPoRFYSrrHFteKE41B6tyTyDbijGNY0xtbiQ7XJuR6wBK5KCnlJ sqjbdjVjCY2XRE1TWikthpKfw23UV9WcL0+xZwE55NdS5xevyry9Re5yxv0gbJNs9dowfhWKJa1K gBr5d6WN2BnBwKVwSmXpq48Kn56YrekmGZZu3EoIaElf4q9rzYxeIXSAF9cOQn8e0yY834hNeK/p pds4TDueNCjYmrKtyB4Gyti6mIXUmyZcabZSVKPANkPAuUiPHSnZ43zruwjGhz83TbitTxQa7PLf 4DZ+/fgx3EbpQE9MlY4I76rE+2Xapnt0M+FAAkehjh61hVyFnGUnZ87fQcx3sSz6buJ+YtfqOhS9 yiYfVEsUrQRGp+Q95zkCo1QawagtwcxIE9fELD3JmQqw1uC12YevYLXmfeV0a/gON0RhMimV4oaq SVQROgoSHgedEbNpn/uL2f2Qq/iFytYbqIpCqyRJS54gCWtNX/BSdVhWrHbGT42ztSCpnlN4pYpc iZztsqc2NXjlhYvqYzg189E0NSYQjFulfZF0aRql+gBHGL+wUxLLXCU+LojYotoXnCuQq46wHtKb au+Vb8yZyZV6whEN7nqPiTy1pB6nltbSRFVR9xeX93+l1cqMmT2nwby+/ubrWnTWZzjRfmDXTLyy pLORdVR+fg1P7p7MK3/sHGhcYJvrA4Nd5IFk/s0BHfWeGFfAhZam/8jJIVJuGDTQDZHsMHqf3a+Q vw7F6xo49CHj77wRR0ClvnYlWWivcKVvNBYEL8xgRLeRjE4y2vRPqRv4D/2bqf/jqU1L7y1IHeKz irLeVqRg4ykLnU+u3nWjkL2D0QJT5voZfGqBX3FaVNQCjXDtyijaPUpDvFk/Bal7as9okMZQ846U BGmuA5GdNfT3QR44LQmX4WzHUwd42VssqOSLusL9YpZAGwIKmkpTeYsapsasn3QWoDaoxk9po0hE gPnfwm2MgL2YV5Ks4kSWquaxj3eUJs3bFn7JLq0nC9kS/oVud0+1aYC5IxugGqz2aPWcCRkPVrZq Kr7mWun5MkM81NitWGNwRqmcJYZnWUBhcyW36bomsiyvSkbUH0F/CBsD9hwgRB6R5lwyKm6leElM xJiTauigbmtDvK7VZRvtl9KOXyKrUoC7ynbpVUF5U60lA7KYJTipbjvMZfRmAV8EnIqcFbEa7kLd 0WZ4KS39rRb2YOuFC7zUOiFmGnyDfdlCE/wN8MJLKd7FcpY+LJhXifmQS4PhENpePzzf/mPVDuuT wQe2nG4wvA8OYIjLSL8Q/WCC0Se0aXzzUYDXnxbMi4QeY4SEZVZXRY5q20SuwWjEj+2UmdJYy3S8 uNPk5mRkzaqqaGJtUlHLn3Q6b1TSr3Zl6cY/IWNyUv0r14gFoEoCrZ64aEUIJxWa3t/voZvydkJ/ JJAaTYYPNVR58QjKGxdm5IzdTnEKkVJ4ZXr5fKKc1rz5VCUrYrfxSpPCzhK7Rq6pzQ+GsDVgpRQN kO04VM2ng5MBmR0inSA3EVc53k/6Wpt2a85otofPb+ZuevWWu7BHidkoGqnkmFcm+XuoEAUrB5Rb AYRYHCKXKiEM04N0kHsyFlSXOoabWRYSiEb1VNt3+c/y2/6ZdskCCREDZJnwaSZPLQrPki4KjbAU a+bRNs57audD/3R8+td//9OCeZGR5UZh6yphSBA4raTZB3TSoCZZ3bQSgy0potM/fGweMN1wURLJ XKf5j7jeXNGyNmgagAlLyWRH3Npm0uYyrdmo98y94pH+7vWIuJTRC09dKeiyKXXxYdV8eaFtsllp Ur0jSRTGaUZyPadMdX+jQhWu4ZVbwLgknsD/+ZZG0dSQUpUsX1n4hanvIjcaNWFs0crZsNtyOlma 4dEYOdSOpWZ9f9GCVjE1gahpVmOLbjUY09EOX4VuTZJ1uzKInkie+aJMhWIdRjo1J0vb/MQUJcFZ Yg0FhuffRiZofJBS6LSJOI42i4kBeQRxr/zoiD+Y6kRWNjcxXI7Z8NSvHEurK2yMiwJv6R1VrMGN L9Tl4zHZcJoaLBJYMV7QQ+IxW4MjFW4pKUBJhdV4gWofGrMs9VGKYF5//uabbyLmZdaT6kdhGBri ErL4wosm3q34aT36OWVG6rwVwfEvs+dNHEsBltAtJZ6lWfIlWRceZpSUZRFXCaH08ESFwtIYzRq8 ah1UxtxGqgjwMoqWANSKWcUUzZBU4Q58Pl8jPyOP7QgeoxZrRO+l18XRQ8Wy0ne0kmZ5Er9cUWtR GoZ69dvyp3W7Zivvucnyrmw214Xe3HqV8K12HKiid+x2XlW6A+kKv2CP+IVdHKziuUfSFV+tMLBL dNkbcCSHLOyCLR03YDFXImQnUEzz5wPROLuXsEyoIyMhmBuAZF4mrDRirQyNSjfwjbImQTNwrfFr vIzDzDg1g/TqZlDPu49HCP9VTDFz9apm98CkYLm0DUtAqYPMCj5TFlekbC1z7/GIpQKFO/0r3EaA lyWMSsgYssRuF87sLF45ia1+t0Q/7951eTbdZSRZh1TiLuWTuA86Z7ZcTxdCqe7ZbczJCkhslmbJ 821taWM2mGzYPC0XEJH4VWVMRiSwSuWRQjet6VPTHtFnxHpW6tAaidFG3SEy6RVN6l9PWZV+Z0tt tq7wS9d05NqpLTturwYZ0N0QZGLbTROqQVXJVpbGqOKIOVbG6Qu5hiM5w+Ko/A6OertWcrkDhN19 eldwoMrh7EieBiO+34BmDT61vtGowwHuG+5Lv3A2HpOns0D/vMqVBxbX5gvKfU+0YjBjAJh00fMb eqo1ZIVhSHxUhlZzzriIa2hSvX0V9vC/DasYMa+//vXPAK/Hj9PQyVY6YaIe7qafUWjlNeolh/2u Cl2/xML8WsgWkmfSYZlKDBt2O4VQojceIhlS7EQGS8Ia7eRJ7NIkqamrwUuiF82TX4e1HoEZYFdO OCUf1izJyjCETFjWfXKhHDeNMZJ6UG6wQpIVOE/SzT18JrVRjPKhwEtPHNC/0Zq3S6Qtti23K6De tDWy26xqWztqPr87z1Z3mC40vWqeorLvu9vYWZtRaVXqdwEvCcwUsKeYTZ3XW/WkyL4arH7fbxhK oRYUYFeL83wWRZ2Myb+AUPQW0zjqJ2gEZ1XjHGucYU0ZrfFzOAMxiCSK2q0JHYE7c5rbhebgMwo6 Tv/93/8cMa9HAV7UDOKEe++XyGW5noR7euIazJoUrkjcgnKZB7RaWRK/NObLiqO8z6Ig2GIdk3Rl 2EUpedQE9kGqUZpFzPfZAV6pbCNywmpLL7XkKdXWbhPvJs1TUXJrDpRWHuDNWCicwtIU6Zz0uX4x aGdlLSqHYLotaVt0OIG+N2uB+9DMqVlHlZWRsKTwPTZU1N7S8IJKP9Fqitaoh7j5j50SUlTtWfn+ LZjV226UwH7YgMYB0LQgafwUQNzehFaekIxJXrEfBN4DaM11jGyTMyhAzhtn7MBPORC8JNOqoh0F HGS5Ws3itKKEWc0eiaIYa/yPWaU1xSa36EwSUNORX/CGXaX4QMcjYP8Xjm18JHpDlTFTryJM0p/1 JnxfFnNJzUx5jFxzirs5SosALRTNeFba0ZClYGJqSGqpgEvV3CVblpkYcRZXRUMl5XdDlzVcG5aG NilBawcjzamgKiNVA7jMkBpgFWFCwRjAkYxlsaWhhZgWW2Dg4u2kfjOk6ia7NHFU1QGSJisuSPHW VJOPSWre2mOYhYrW5E6pVqli4Rc1xUP4SuFVIwuMIxq4smSlhGBdJAZCDX+OD+/skmvfCeaNic1I DfGMmBeYC6GKBYz+OQS8pNyMoMqLVKbOhJYWy17Xk0mpcq5lb7yCe8FUFVwraGtbvL6qelF7+qr5 CziFDsN2mkENM1K8id6jyDpEjy3qE2mSqhzjw9+VedVLB3ghYL8xttGd0ZLMVkk7QT8DSzstw3Zd t1tbBevILvBaNxntEf+MwGLhB2GEAXsABMd3cdWsWi3LGilPUIAySZm4zzjIfqq5ELqmINX6nERL l4vX1mkpB7430+rLrra5gAVAqVdx9hj96IIls+StZ/2gSm7K+O7ji9ND8qu7fftO/bWXssJ445by SH/6T6Vgpf9dNox3rC3jQhJevaOQq5uZrsaUDN+jI5cegSMqOTofzeM3cYTMywIDItZS7hO08q12 VDsPL0R736msFs9FzDfS/PQ4vAu/6nYN+IxfcQRXAbrUuaBNMS9/icIh5zCWoJcp4EPhmee6ZLc+ 95b3eaCfT//9f/nzNKuEJMDR+tS2/rAFtxpwISGwON67eF3TegtrP/XfQDD0skqVhld5bAYZAgf7 peIesCZw8bF2Frc0pVYLCVcqYd8Z5nELwT34LtPi80LYcmuUkKu5jV2IbSyk6pW+n2xukaUp/DJU qb2yzFVglZb1uVHcrNuNnyadN8RYXdfN2vITEvQTP2bRYDkaihk1kmZ2NStQmx8nyJrQMR41ehih YYVdG4itit0oBgGr3bhJzaIEWthlAV4Jifo3f9wLXS7dmpztOqKqK9s20KkY8Fpi1zXc67/eojx0 PSI2JkojZadWbFCfrpbC6JLWTcW9Vf7eF0bM6y9r5mWiskkOV9JeZKfE4t6lSWXzDbbAq7N4n6YS yW72wpAv2VeTlBO+cCiwCwkuDFEynQrC0ZnXJnjVey2IVzWnUA9LQy9VrBSa5RcGq3h5U8Bq7hd+ JerpfF7RxwDqKVpIW4/mVg3BCtxNlbfm0unIVbIuhZ2iBA3cFxKcZkYxnuXTF8C0+TjRggWGNqEa wFSP1kCqGcrGFWgTwW/peot48OBYMsot1AzYgIoZvBTzSswqd/Fd7fc+9dk3+1G93CZw13Ssg4IR ajlaWzGBWIJIoS4um0AkBlhDxDjHTkz9FMNAJIT+uwkR76L/d7l2F3hJFDZqYZ8u3OXBu87tj9wE r+WFrZCkVXmRSI5xTTrN/6hRWGj4+rXWeCgTpCuMRFvMq9pM7MkDQAt9EjhwjxzdUmiSuOL781aq Yz+QwxUNPa75BUnL+9MKDsQoOjcFwhLQt5rQJe7Vq0PFIbKrykskUi/db1XIqJBTbw65Y+MmlP6m 2+WkjMxJ0Rj/oCgMu1uR39Lu1vfHHRWLEaEbrCq/19NVKHW+ZeEmKZKfOBSy7c/SFld35oVQfXuV /u4PoQqb99jTngNjW0CyEG2C8gIgHU3BNkjFkXBLIgWMFYagAXNZr6OGwnGMXysxZSPq/d7efH3j Tx280gKoARIqhhOZgNAhKfBLES8t8JCzrVKAi77Io+vImMItHGzoweZfVi1dtvlymQMmQMTpDpwx /ZUTRSTYjqtyUolCOD6F0YfJWRpAtnzoRsGaEO/4VQYZEKIoSK4wVspZUDJDwMiw6yA4wEvx7AFo tPUDuYxZQphCRu2vH60LpXpVDFRrw7IFkvrORrthhWu+B4tPB7JPELxQY6ttJiItojobabGuuMPl 5cvnz58LpwK2Li8vIx0fXV0XsYQ1ExIx74nrcJvlvH8U+9TBa6E+lGPK7KLqBgnjGUpjoWbKuMAw T2qwGGGXrKfduZDJHGHZhNKk3AxAGVkR4con4jyBVyFmAZK6FntoiV3Yc1bTHHnqynwn5pV67bWg qzIbGGU/GiGmQteFUEmizKUqBpwMC2jYoUj79Vy33SoQVm9U5wvdiuuZKqqjMhlehaANx54TfCkf sjNltuyZ0UQtjNInwryWsLGQzD5cpIkZdpUeEa/JiMoNxtuencXu5atXMZIojsTKwK9evYpVyMJ3 0RSyS1WTjX//yBVP+FmBl2tsC7yG9LO2GcEhYsmx4XJbstrJ3uw7bLaC1kMuXMren4WjDdGfYRRS FMtrr+65DV6ye1SrPuxFUQmPSDZqLcLmZU7vzrwKSlzyBi6CGQOButgLnLJHcnRZoevdPSJErMwi 6HSoWqrh18LwrO1QmicWhxcmS1RPYKaL69/EskzO9JNmozfAi8dHZ89C7T8J8Frg0ZoW9ROI22Og e5AqnS/mHSGuIFnRtxXM6yW3n3766cXzFy9j8dfrmJcVHEyOuy2vXAPd9P3j1ycEXhXN2sYT16Fs AQntZAMc8TIbotzTzNONYewLLr3q3ia3WDEbQy3Sdcl2Oc0/TfU0BhIQl4ZpMK/kdAqy53+0e9nw fkM/cyJeljVNWCgxVDeEuKGGo7Kq9gTqJWFF/HqFVsUUVBWTbTRHw6sMXeqzdZIQM4W03iLBTcDl D9soXcKEnol5ZY9kFamD6Qr3WPVebaXWp1UwAB+gp4qpXuVm1XpgAW4j285NLPYr4jXr4AK8lP2m rozuDW8C7gPp8gNgwjDPkoHJMkKUaN1RW1y//SQS7mMU5NVVEK5XgWKx9muQr7gQizdECEzdUyNm 4/DHB4CvvxN4Kb60/CRIWKujSkbnSp69dZUk0X/lOSYK2RBIcrLho18R8hXTzigIjeXtQs4lq0SD nKYUZ20YkgU2KCiaoj+Ll6R/eCX2DfP0gV8UgFZG3VOHiIp1X+SyIt/jBDN7IYqWM+QknCnlMtnh ZlUnuZibwPDkIFOlRqko5aM5cu+xJGGXMbSX623F/0hA4VATOh5ErxYpVzxrjuuvIWkG03yu41nd 7VTrGUiNniyPhrs4pMm9jNyx0y2bYxiJBC/LycHgFW8nODaFb9G67XovoZ9H6N9y8rA0lpEDz98+ LdGr/ypZo/lDNItoDK8wKk6zsBYix05UqLqrJJyxpEPcMoJl6XrrVg+As/sR/8OD156++9TU5ATW 5wQeauqiOVLFreiuL51kKtto7EAHahD+fxNGxrQhfi33SwhIiMuLWmN4YDZTR7NRc9po46QKiw84 oGaqbCaPxFBNbHRbAmKog9VsopCiWgFYvr/Mpr5W8teYFt0VInjtH5PJWToEXnFMZEKIld6ZeUtF sxKkMK0K0IJj4rjIaWJXBRYzUk+tqDtvo1jWH0pe7K8C/S1gJv1i6K2PzhN0Eb0GGSSEloVBd0u2 BlrQzMtNNKDtNuY1crrwVi6aqeWWcR6yey/kskjvv/MhvzbwKqlM6k6xjNchcpVZUKtJVsmtX6dm oP8xMlqRBVaLeiQJ2A897/7rhwevW8rc4XoFXhvhol23E5Ut3CvkkXlB1XO6EiUVQgfY/Ucrb09O 5iclWndqc6uuvDI2P5f1WdkcMzq5cymDHbwoMXAyVWIF0aJMJWbkWM5jZf6g1lhjLD//Wadolras a2nXT50ZqSiywj4OgU4vzF2SCHwVjjhhyLna9qXcKwLApk24Ld2m+zXEgsSFLE5F/R3tGpM7wIlU YdIwuDGYX2KgZzNohiI3K6o+2+Nw8FIvRG4TJXx3zdxxhwegM716VQfLCl847ziHrUbx1PtGXXLf lpXMS/EyBW26UX5flfEzBK+USTdL4ldBSiEIhlkIoMByqFYh+oV5imSoaY0p1QqkKCn5bv48c0I1 w19pEpSmfMOydiJgNQ2GkAuXkrolhUv84pSFnrPQmbfqfezZX/naSUKXAnQn8EoChhpJouoFHRRQ YtzJiRCK9ebXY+s0Z68dsN6weANYWTGtsgdCJJYaLkkNBKIG2dprOomWXEC9w4Q8CS3fV/lzpR8Q 8/rEwauh95BhwdOGzAu2xgQqoGCxSRPCc4w1TC2rHg4ypmKupuzNOiHmvfDtA4KXSMyKsCyKvWBe +19KYR1RJd4bO4711E5iTD15eE/RGLTTjlLn8GO1n6Ip8vvoWfBGdZeElKmEBEaelHinIz4uw8Q6 UPFy9mENrMNDabk4ww5lxZAmd5lxZSZ5aXlIj3gSbMmXVBSsSnubTCzseAbLDLn1GrS5YqXIKpkY R+ZOLB4VlaownfowBhwJGvLseoSBvMGWvUrUi2xL29KoyPbo/vwZFax9opU6ZbF5xUIvV6dJdPyb PO4cw2Vnnyajl5SNvxgedG/weif+9E4Xs65EjPRf1Z2qrQ6WlzMeNxlCWFtpSbjtngcsYvnxX/js nKJRP0q93DZlMuum/chd9z8UeBm5bq/36QzVZobANjUx+4lyeJqFGKyl0HLed1VCFT0TgAjxsTIy hWScvRdfiFc5Enj4PAKgDGzJ4RsDhkWylVCRwEr0yVeIn06U1a37lMvKC1FC0Rh1bMSvTmklQnGR DjEvzB+tcWdiXlUk3GeCzVaRvqcRdZo7VPDUK7rqkyhBDAN8pTuJb4UaCVJ8TWo+p7Ib23QC35Qv l/keRBM9PAWej5sbXkwUfw1hw3PJ+8ul9mImudCqDmjyDgwyrmIJvHRP3dz3f3/g1eX6rir77ufj 9SivHbpUJAtptQOBrjSwBDgjlyC+r6+jHzK6I1++fBH/MwXsNZ13aJesfQzOskQ0v2NTne908IOA 19Ju7ithA69y4FIXdl8nnFrB3AAdVGBe7uwgMR3pBptSqUsyFWIX/NU3cdvqJmrFAUb6hoPpJ3YQ zn2fIWgjZqVnqLsSGuzFgn/h4fVagC1YTKlgLu9IPcREP2vwSmncrrVNMwKUVEmI4H5X4anBizsj +G12InKTZMcptArMBdDqp0GPWFs6ic8SgQHNtdD78VlPraqJLsYZvrtGG+TCJYlZqCB4MlzdXFv0 69f6JsR7F7mQi1GcDwde+5Tgvf/GWswuphTjDmaFXpPYq34yYIKWY2ekekgijSL6bmInjtCZVxKL UvxwDcleolfdZ1aiO735BwGvu5RoxbyGCt1O28aDTIyoJqO2EyNSL0mvrKloCVY5/3X2jpCtWmsH Dqw7UKXpMHAtNEZNVYnypQZ9YCn1KZ+RnlduGZuPbp2Yc5XglZvJxIPEvFyyBl6JXY2Q8S0IAMYr Tjeg/1k2LuGGg+c4KqYYZ6s2QCgZ3z07PQ/5ZooFOyzD62BHJSG6Za8l0AEgEyMTsvQcPin+5/px 5+ePYu3xCMFgEXKuhhkfFCJWlktOJsJadZvTDLFlkoW9R7fxLhrxPs7dxKlDwGtRmMI4GZ9QnMAx kS82qPp8sa6HTDNtg1fbtJfwDq/3cwWv4kbkTTP1K1IhFlG/O8Ys55ELig760eGG+xPnLjyi8Dfy 1R4sip5R+fkOMIO60uAlChZKXn36pc2cmSTG93PeEqOHNX0VsBdQ7gL9PT+xJCVWrZZkQY8xRbWf TmQygAi9ACaYf4B4Et+8DHi7HQJ35zFM7vwspDucDiY2VHIDUrexhGW+jnZUAWBZZlqx3BJRC7jF //jEwCytPa6NP18QQOOGJGjqqB3IJXe7j7cytbsbeE3xr7v4Gu+gvfe+dMIpCeYs0imt3dTufJqQ C4newb/gQ14GagHCIhaGaJiYFyMfrPeY6bAxu3u/xNHx//V//5//8i//8sWXX+gezc5TSJveS52k 0qX11SWnyxckpSytCyi7yxN3FFlh8ebhlcvlhNWtC2eMSThSly1dP0VQXO7s3ebqdUrH9goEzFOi 8/7y8tWlpvPOkXJ+e6NK9ZAPmsSHyQ9V4F+1ZDeUYBB/gkxlBap4+g/uqeiePSgPa0l0QqKz9C7U 9dHFxaOLR/SDrI7lNuWAcdXyW6Xsb2bWCbkiH7E3hP3WKHasNW6RUxU6BKf3GzGvbPWN1nTD+Z8o j8K6ekG4jcSQYFsR4q1EfETxjo9iOZigTYR6MVVslH/5zdw05DN3hUd4J6pdypuqvbW8fuMNS2Ph LGfAHrVmUN8I2Ec5Qy2xRBg3KKu63PgFE6v6B+boPfSmOz7IfTNan8nYrguH60fQ3q9wu89TaKiw RjRfiGhMccr/Hj95/IQb/vniiy8///zzMDPvXj0fHrz2eWEjubKhnXaZbb+z4VagKazCXyOJfTEe SOy11NUQFoj6mxgA8fz5TzGMC3wAoUflMeUYkOpAyTwL38wUbsRvqGxeu1swZvDy40Vn0meNf6/f HN3EYloVAsJTqZ8jKwI84+QE/tDFI0dzMLtlQRgVWB/ePP7ZtY67kGsllYIIrDOuNxYCY5L+rIE4 ojn7VX4hiIioLJ8OanKiwhGCF7ZeQbLWHL5DRDTxPA7BD+GPiwMR4qCi/qpDvCvfPB1ATY3rr340 /1HYsvhQDRAy3vZQS4JX/qSIpMl117Fd4CUki/cwqrV89HdX0bqDXK8H2bKZmnRs9T3yiTuRq9jT YkchA7rpDDxGY16cB4Z99tlnT59+9hts3wSyvfuLfFxu457g3T4fZ7saHO/v7c0wpbHQuyAB1DoF cCNs76G90ldn27mxZZkqwJlOmelqEm+pEG17VwCp9xzwUlnGDU00dJQxguQZoF74DwcYXqpIuOCD f6ubrGhP5yFTNak+V4JZRMnUgV6iX0jFzxIxFC8BZVxLHpw3RcrbhwEpcsbYQpCfxHpVYYcfPw1x ju3Zs2effx5/4mscjFMwY2d7mvw9PI6uYT0q7on8bka7hOYjQp8di1WD5WOLrjFip0qLnl+3l2rE uLsSR1DSBog9w75xbJuld1fOxR1u5z8HP3LYmHbJAoPy6x3Ay8JsS8MomBYnScIQR9C6j2OV2LDU gPrQRxi/e20/Y/CS9RQDM0cwVjhwg2/CLvggDoN4SAS6vdLdnPFFcDD3hBq9knQ0PCogsyqSFPUb jn30axppuxjZM1I3I79YQa2CRi6BV4JmgdfOmNcO8EohIm3UlkkEBFl2bCpJVn13jtM70KVwl0Jg yw1sCpBF1HryBHY4QCs+n38eUYsvv/ryiy++ePrkafwcV1ecMsqpGwG5ZM4RfBdQDQuvEzApQvY/ Epa0HnmiFFiavgqqRnVxlGiZHJPItUKtwaviBx8AvO6l4NsXbYKXm7vFvzLHKCVhRvOS0nrGONLw ixIObk4UuwnoCkmIXY3fitYIe3O/V/t5gxdVD+xpWTk6kFAhDRdVyiRM+Rux9ns6YhO9sn0eYV7e wtDR4irTg40XVJy+WZIEFFUonjSQK10j83H+U5xLD34o8DIjc434pUrfSYBMvwQZzYUbzhxR1mCr o+oMVGQdju/FBYywiBjDIgFuCMLH2k4MR5oic8Svb5V4GKfl+Cg9hwVC74ZbBN/i+kRzM9NEq/Ro G3gx3ywZ1wYjVXv9RwMvG68mrF121+BVlrhCIsG+1LUTmxz56E8J06PYSbQWZ5bG1NL32H4m4FWw kdZjVEXvdHB10xcigAyGT1VhwJr5KblXedcW9KYOqahhzR0FFnwtOzfLoAm1hHtVvpIAqc4w//Jl oZrkHOJcdp7sRQ3wGunifMjKsV1Lxi7mhXvyyegjyGFITOhUeAu0S5wv/x9+ZEEtb5BQMSADh+St Fu1JUGBSLkNHMM+cBcJRS65WpzWfBh5m/6NRKWG7qtq3lfI1KlHfVShdXuue6Vc1wqYu/WzAK6tF YTR8hhwuqmtl+evMXkWLg/2r5AP1zC32Fd0M8hxBBHUK3wO54hKAF1YPeuz4WUpaUujFXdMFG4cf KoRoOdv5Fqn2+15zqrJJ/BaltHTqKCPRlvZAHnWGKXCNkLkDPXZHSuilqKI/hD2HTRZWuzRYiNXU Ij27BjS6l3GCeAEfcQFYjPwUTIhw9H7+Dl6j62xVbXvAS8jT4VKcdH73lryQMD5edgO7+D5ZO7FH r86VQIfCwfU4pons2QQ4o3Ib8jnwQYSN3fXL+89sN8tUBksHCj/jnvGTVqeon+TYr0UtEbaFcMZw 7OpXfl8xr/tp+J6rZsTZzlndZKGH4Fc9tyDFnnXMUnn9Oqo9+HbQbtBwgdcOg7Gn/Fgx++uvvw7e rpOSRFjUpisTuQigqd+tA0RcsbbF10Oqfk/5DwGvxSPqbk1pmkSCI4l6VbHFtzLf28yWMWpmRypF coTHu0nJ9c1chgx09yKt8ILlylJykbxSRitY98hwyIBpf0wHSjhG86t96DPvrnZq6PyzCyMMNZ3E KepNLIySJe2sRy8yZNoPXj59qmwivpIwGNnNiVk1aYV6iRkeF9JUWfUYI0RFCWFAJJj5S3v3KljW Vf7L2lM1lfHR0xabhLlyxvWoAlzrA4qg64x+CZKCykM04EOfs8AvS02KpaRokuH5NRYotglqEhvX CysIbaqkbOwgmAsTRXu07/1XgHL6179y0dnquZQxsx5NHtAG62pd5r1w926BhwUvatQoS2mmDumn Qq48MTtGBM7QYdS8YYs1o7xyI4laBXJsBzNFtz0jiyDwcsDFB6GGKtgEtZQe06sJMxqa8QRe6BbD fdKspCKzE2Dro0hgF5YshtlfN2NCLhTJjAy8TCXscLBsd3skI+WCB7Ifk4ZYfMsTsyZaoRMKHNhr Oy2gR/UtmOpS1w9azjOZowrZjUO+EpLhrKUDf4cdzluh3BqrXzP3K6PPQCY08xcJythK5D4SFNPr 7VY3Gj401jSRwn6o6r9O+wWFCfjMj2Hj3iD3JQKhcGLOYmmPPeCFdvBIo6zx7RWzs+5naVwQq31W /Z7w9eDgJQjJdqJ2G1UgWNxP08JB0FQMdN/yPydKWEEdslYnu7FF4kmHYRrOvPn+Bou5Glk8oaQm A5NUGc+EF5p6UGgRfxCtzoHFxlWt38G5KTDmm3/1idUUaj/jG5bKCbxsryzTDtaT72hQokkn8Uop 6gtv1S7xPvFT3UrJc2YdTl2oSVnx94Yj48jDNJkgmZd4YBLobCk1rfVM72ZoND6qLKg/1qf4Y8GK EkukLwIvvWkHRbUHgdLgVSuIEKdGHpkUCsGcvJ9aLFmYgUyC8fdlYSWAO4oh4aso2NSiC/wqOZ+R q79jEyxZUcWUXXdv2M2CGqE5nPn7rEU2Tu3g/cFrxSg3FNZWa1OVtw6+H/CysPjmAxbGUl0ui50t WvV0u0Jbcf0cVWKnllAMOcriDxJP3WrTWyNYACIX1dIa3uugqfENuoPe4KFo5uMYXhGThzOmTsiL z2l03cQO/+5Crs7CNFcFZNTQaYAg64ZAVXRdsMWxifgvkYtAKhTnyYZ4VrYAQUgqiKQ60B5gsg4M 5wVUaepoJAN7NBzW0+SYEjqRsMycktG4NdQouaSfZbMzVTwfikpUwYjy9irqIHdQ11AmlRNxZRqu BK0kdKgmdyg4tLBErnrFuKTa1KBl2Wj0/GCleJATlxZz900lwoLd/cxrzz3U5P6PbS+dkARo1Iob OpbyenMDRu9OqaBgWodh/miGO80Klx+A1zJgr4eugYQGiNuQkrvC0/6WeB/glZilJy+IQYt3+ed8 t3RvUIfmXHIZ+RdT0YiO4IZiEePVmvdmFZA0lBu1UQvoGZDUTH8l+4SpUoOzI4IX7ogP1JK5tPAh Zw9RKFYHuZtepGGrmIJUN/9azy3HIHqZLKXXzomyJrdRyj9eYvGa4iaSWwzLUqKPhr95rQxFuVK8 sVOGQC1n6C/wMrD1ujdQuuZUfw1CpJnYMpUCqNTkGO6hZZziIUrHIyy8PN3VBge9mJfrLZ/j4gkR HgSM3t9NSgeztJPKbMBCFqX/JA6VIOgzXKk1O2WmsEbrqycZ0a8Qs+gES9+jvyZTL5fg9acI2Eey jWVOujMuHuo9KXq2qO7eIWwbzg5rsvcBXv39i3wNqRpeo/0OvE6+Ed6lq6P1J4VfKoCxNBN4uQYk +VIzNeQO5pVtnA6QSqz239rsMyYQK7V1YMZSrkfVjzdMILA29ScLnVnikj+5ikTLOcylUtanAMbX CiJyK/1XiMsgICQgZImXif8aQmwqs0poxDuLSHdyYZV21JzVqd5LpQtXltO3sRoZBNDHmJX2WtjU wGsOeKlgtltTG8wK7+bYI+rLBnwP33dxjoWwkzezIvg5GLmGnqVsS6KHMJQCRo3C8EcXM+u55Cty wfp7M+DI6V6SOkWBTv/tr3/++ptIlajexgaZtBMzeg3F1PFFLbwLcklhd21ikQ+xUS3LkcE79FtL nflqTJNMoLJPQQU2MdVPcZJohIpHtJKhNseZcFs8qOptEgdxs6kW+LjlFpwrfMYaUkGFNmlqnDp5 equyCQmMqErrZxWIdvGTVIWlUUq9o1wMfpHlRbEmRtOQaqaP2aqUzpoBHwMaK/adXEYSPDlto/xl DWZoUI2ZuFZt4YDIsTz+IsoGWFaqbhTW3DqhrxV3N47aCM3g1aZjbOjW27r0c9bbccrfC792IZdq o6pFxZszvI1im1g2y/LEvGY2NzVf6A6mP4hOm5vreJhmCsEEBLGoRwq1NF9xBvl+UqAArz99/c2v I8k5b5nUS1c08MpdtZW3oYdTkbrGTD/cVnG77pJws/P3W39wVfCf0tMBzZRlhIEMZXz7KdJFzsE2 QUxX/8XJmnSN1sO8oAG8a7oKl9VuN4QV7A/22YVgHmWeJgIEKhSr1SqEdPb2+JxfGeRCtCtW/oxP 7J+8mT7xNkEkYrgm/8J6RXH59U3kO4e1O34bc/bjuMa9RwAIyHh6fBaDNk4R54ohhrE4X+yfB4Qx 2T3GHCoExixWTj3N2acxLwPGgfPDrwrG6c6YS5OfyECNleYingUYi0xUrPWL4zEVJIqEEiI9tV/i E+JX1A+KzRfBhRhbji4FTQeryZzxFUFAtjTLib84bqfYc8dSMVH9RCtMA6ItvlZ/IuG2pXI5pWPg m+Tf+uQvhoAOXiUA1PAeEbtVbu9/wmaca78C9oepPibTyzdrwjxh2fhJCuYTczbPRhncvlQXB16Z fi9rHxGEWKA7ZkaI+cCCEOORPPNVzEDy6lW0lBlCjNL/y7/9F7qNj0Q3GnRZFXtZK+jVwWv9frvq +9aKe3fmtaIpeIvZsXG9Js9pxMu1iSuIU2oDIZZymzJ9XtyKm7ALvfvmOjOyL8hpRk9MLhbgFUfV UJz3ufajAKexAigbOvAokOvs7UkMojmOoRcBW2+OzmMHn0Cut4sPEEHINQAL00IQuaLM/Bt5z1T+ wCPBVgRPhVnRiR2wBeQClsUkg0Q0nhbL9AHO9MnZ81Vw8jKiCQGZjJIBLC7vy0g8ICy+Eq2wY2jD DvBU81gIwgxq2CH+CuO0U1gp8CVcBoQhaY7FU3Qyiyr8wvw37tSgeqILgfxL6sYJjO3A8t8BX8qT gP60gw2yaPmkQrmtdUH41eDs/vC0eeUCszaV7lZNFG55PhG7EBNy6dHjWbSBsgdq+mnu9DaNcf6k E6Q0mJLq9fUrkvE3gVkvXz5/8fx5HOcYVkbCjo9ipduYrMr0G09+I+b1dTGv9FPYANuO2gqLH67y 3x28dsjKOOxHsApThkrkjD86W6dYCL1bmJPEizGOiuCqPdMUTyjmEjS3cVmPRK2pBCoGaVcEAGru pQjYk3ZZDMS/IGiWhIGInIs/RS/+Jfty/D7lRgJkOqqBhJQWTcQDzVduFz0whlWzk7EleWWN5VxY rRk8EZeRniRVnYkAAa49Y79VZhzf0+CkPsz0VK8n5xt8KgDJXjPbCnIusupMWpudmQjpYc6UjKgl a0obmJc9xykyr54E0bQkW9mqDsRYl+NRsRePn6Jzk3C/RyVaq8DiyEHINa4ZVEvSuL5/06n8US5K fbNFJhPIm9QJQnNFaRgCw8y6r15dwcpxUkN+u4p59sLBhD3LfLCd4DUczmVhb6n3ddVsvvBmFb8P 8NpR13w/yu+oYQZcpcyJXXkgq52/KYTp/wleMB28Dj+yr2og11QhDby2amCjbtNnNHjF7ZEkkeBF iuNCrmuewOAEtDJZ+Vy+RQswO6WAAwkR4EpskvIrtQtpsT6vkMJVU68D9FFDpmUV2Ej5mbuF/0NK jUBb7o3xqzHTYaaLt+BB6kZIN2Xu/FR6L/uwqsPY5TJS8laM1gO/tAm8urvYMWyRJ9lRjPVpAepa 2iSspO1Dg9dCNg7Er0NSJdzWaXopFjb8evHCq6qTzcqR3kVVR2Qr8Cq2QKwXL56/iKlBX768vHwF L5JDI9mayIkM8Aq3cQTs01HagFgBo63gENVJB3dVyoH49WHAKytU1MvW3hU9dK6LnBpEkud4e4cA zho9wAuCr0zXBDE+0ZfXPUbFlaZPpjp/x5XhB1EkuApLgNeZXWG8AkJdzEQVI+kfrYKkI/MPakgT scJwAKWnmx/B+CmNK0WzZLSaTDx2ZI0VjRTZU24Pck9FvbS6zJzZa/4i0VfC1aCN3aJU1SWrEtKK M2YKnqBLve/qY7BW+erCDyFXMS+ULLM1CsPcJwpcm9IkdC9CGOMGKyDuBxKsb89nnpTqob8ciFwl f/35O5gXG7/xgBbzwtWKvJApZ8+7IW2JHjRuyr+PQP7VZfzHLfaxnBq5NjMMcdPTvwC8EPOSwDie LdnMhqnWHtLE5rJpbW1GQV1W9oHIpZfctaG0D9qKrEy/YmGSPJFmH9MHkfso6EoZLOSrCepSjqun faqMUQ+sZj5p4ZqjMwCF0tvmO8epCN2kHjBgz/HEtIwR84o4l8JhuBqL8GgqVLEbBOnRrYDtOn7A V7QdHSCZI7Ul5Ur6j2wbdStqZJTy7FUwZZ/yr7bsZjB8Zp9pX69JeM7goKUz4lzKprKnWe3LbpPR BHqongWkRjzF6aZwaTXjhJzcCNJhgn8CVptdVvP3ANvMCZgnnHYr9gu8AL6M1iv4stzwk2cMF6iN 5haipWh4ULm1ReRMvwPF86oHlea9N8tHD808EL8ESHXZbYpMyS1UGvIhSfZt2KDtxk2ri7JZ0iRY zKXA5NvMaI5bOWkn+mEUsB+pElbSMn4dilLOW00ta2EbvUr8bmmwDwleUgdFZKyELl11BkGYB9dl u4z3rYrBopvQS/3G/CBDu+S53hlkYgFMNCDjTtSoFfgbxEbMK6L1SkzlxacI0nfkAniB1EChCF7u 5gO9gNdjyHIHpAtJOBN70coeIudCLqCG64uFcWfGJAdRcMNWM0IZk2VUULmpHPYDVBXIDk/L8FQ4 ZYQRuumJ/E2YL2hFMJfolXMRGrniH6JYm2vM0TDALmvYf0WbopowgRiPAbwyj6PhF8pcXwVehUdq cFYuqtjixOIO6HI3mRTywyGXnnQgWq2LNadK7C52eSaU8LRzi/N5s9ERhXKRLhjAVGFq4mx9ZIBj XalYpSXCX6+vkLiDpqVNXVRjmghV8fxjAlPqZup6kfsGBB+6ce79PBFae0GinsphUoqQGS+/ZuSH kes05zgDgSD+w8uaKbE+SuVsD7RfW/10yxu0prANcUdBS1hO3aKSSQG1s0kkZmKhYYSKQ91Ltxbv TSbVJRpanSVRtD71OrNCew00nTdn2aOBndKqYp3Uooa9jbKbMaadAXkVLawCEmpHfoSRq7JZfd5S U6d3GE7lvWr33vJ9/wstcQcWt0tNCaiN+HYZ9uBpiSYsnYa+3lxH4Ov7H77//vvvvvvb3777LnZ+ 4PIxyRUoJvYgyjz50Bal8vnVSGuP8f5V95BXdrCY9w3yedCwIqQRYIl+6JN77nfL36Uuc6y4oVNi o3GtUKzj11ruN2xg1vPUXmX0ZfyhV8xF8AAyfPNoGyIH8xVS+QrsiFwMpTtL9K4tqQorWBZaF98u Mlp4yxU3jEpjp9Cg7ageClLlmuW7isjaKqh+F21tu7GD0ttEFzy6KHoa65NbVqzjdvVLlVyFzLrr bMuOTyHvgVDwkNK/+14V29vcUQZjBRg87GDVRno1ui9Vj4aUfLIbbFOk62BZoKp5EXVZ1XAYX7x4 8cN32ABdf/vbDz98f8+p7z9M5X6opyRrzedZCcRem9xLH7WVG6Mv0t2trWOXu/AUgtGWYeU6sknf 9ghgqZloTY7XJxiJfiEAihD5oFoQNi2HpKBYIgIUVeKiVX24Yii9PGPNwETnhxQdcQCr67OdvGEZ UJtIFsNSilwxI+ZCcRbPjsrr753UVVH4Pkv+WBXbC9xqcmqlpimCPzpPJ3QzyNq/89MSHQslO4T1 H41rqpwpjr/CZQe8PpRE/12eY2+cKJb9YAt851dJ7Chil8DNgutshDI4f3T4kJHFGqt8Ieb1TQTs lzOpmnDP97qnp566fnuNfvCYF4s0Xuv2F9SYN/1FyJbG2X3/2S5DjEVrqSHmXVvUoGyOKmhqWB5h 7iXHXXNDhqqjQIxGMdAymXtTBfXnAZDMybBsZEYZEBjHtFm5xXHGETRrBMGZPA7dpoox6T0qtmV9 HAEGFZzc3Q3pGRoSX3G3eIYW+ZHfXSR3mAU8YohKQbxdeSOXF+TIpbK9aJFX/lAcbJ5bvyBUDJGh F9e2AvZRR6p84Lyd6JHVNVMwu73V1cG81U48JlFP5dxu3Dp1IQa3a8u7n3EQD7zFroxiK6IrOUHZ chD1hla3s8ZbbIydLL2JnWhcrRFFwaEJe/ca+A97h2q2nQHK5kfJo1GY2Tv2SYt7ZacY2UiDuxSG HRXdfB/TginEpd4EsimPJmRsSxytA6XIg7SUrCvDDeBe8p6mMFmSjUE8TOCIASps+o5+Z8jf2RnW P8tPLMSBWeg4Of/gRKomdRZIH/SNU2B7lWxMH4z/tC52rY/txc+KjLUlhTJSSbFXV4RaY67Y3kmo cK5ROWtXPKv7T0vLMXE3EdtbkOvnrESk9outYLPXjFB+cwN4gbBfRNvRwEA+fwGv+4tNaX71Vy7u Zb1IV7OTCytqAtfWEjxeAUOcTc7nrWVd+S8DcsqDrDiCcpprcyYUEagiZo57dNQqHzJ9gKGaSV6m WBJLjp5BAU8I4KMLrNsY6wU9fgIU45pCWsxMdInkz1gmpkXA4tJDxCms/8jFHzkJulZt5D/4aMtF 2LQ8GiaJwoe7vKUsh3plsmKN5hl7niCoeGYhvoLBRKbctoJ27S7TuQexnlsb/BM+gepzu69j67WG v90LKn/ClfIhi76n7s2MpRjqe8uY2SBfLea1xq9kIjXh4I43q0Jox8FT6pTZklK+GMdiGEs8LHU1 lJB+IZ09XqWcLHdSFt9Skhb73UZnYfORB7mYGF12fYBPgnadB+543VmjjwNgWiQJa5q09WKZH6+1 l7VmIxkXwSs2rMUsUOOKasHraqVbLczLtXkNVLhRusXZ/aJMEDuQqt8ZsxahLIe36uiI36yDXuNG 4569CTvt1XM/pOi+12dtvsn6BR1n2F2UuqRbbtYrpPCXmNfdY14Chcz0oS4zd2pEs0cESu0yHCLn D5hNVRo7oj+rcFi7KrLqI6vFQYHztycXb084dSrWej57c3x6ozHb+IrVnyOWhQ+wSI7Pccx5kYMf NXmhZxb1XIbI/SRGxqQR1Ha5Vby/5vbCcAyIkm8Y3zQWbaS4FiUZgQ+xxgJu0ByQKK1+RFdQby0o qSUpxZZqBWyuL5PEisgFCKxlsgVYlarq3DQaCC9KNykH0iCQtDGc5ADkmJMlOmiFNgHtrzG/Oufr qeAXnWknTJBgOV+OddLsgOq7f0ZQcKGkk3lLOXmvmLK8+UFweXjMy6LOf7DERgp/NLFFd+E91NdW FaJjiswOUjxHURz//AW83h28Eswo0hXZ7XZmwFDtsV2k3D0/bHGmmx9DgjS2EUJw8SbA6/TsTXj/ nFXi5gj78Yk7IVsViBOJ9IFflIE3EYuGLGEKBiZ8Yi4KQhLgCTNHowhK4scErZhgGiMYNZaD6GZB VAAoZUuUv1JonGST+pHwXG9pJ40dgQAvYgueI+BCfnz2IArGhF+xBXR5vdr4N2ArncRAQUIY/c2c pZpQuJG2UqxKiQ/MSnW0LvZf3wC8tEXAPsCLkcI6JiLK88lY3ZWRLyvcjvpAR45qJR0iASKJlTpk M4q3QquFfn4IFHuP4OUaSPOFCliA9ebXMvaqnqoEnDw6goSSTpz+EBX1s34GFKaSKnqrbMBWHrIC M75D9ZPCemhLHnT0Z5GJ4Q6ztG3oEET/WfzlStH8SMM4yLicQaVG2CU0/fBsObqKM4raL3RQukL4 ciQ9fCYTL8w65Gk28tXFDgEvvtZZDOAZnYB45fOYdS7gKHzJtsV4D6yh/SRiYvogQBbL/CHKD/Ai EQvc4qx1wDhD3eBrrsoWO5tSiGcyUWaGFSAUshdXsT5hkJBLe7ljxzG/4l8HCschUzWRvUSxesjP WjMaN6iUiDIkWc2qilEP1SJ1yAagpeyEffklYP+uorMnlD7lT27BWO9oNGx5oIuD1kUiNktpRavI cW9qezZSNkKOWKHGMgm/nKs1lEzCJPUbetayXEc/Zut6tDMl/yn752Q4nWTvrF6nX2lshyPziGUx ioVAPsL3CIcRuZ48efo0/n/yhJF5hrgePY6TgF12KXMUuZCe/ZA1UEid6fXXvCb7PRatJnjP0JSo 06RPriPWTw0OSFWqSkv3kQ2R1WobYI3NiV9KP9eK+q4S+TFez+ol/eyla5WwgV/rk4doYkzeL+C1 bGnFbru9mM6gDqi7SlFkzpZGwpTOlYM8uizHpmSHoboNlTg19nHEejbwTIFm9eVjxSB6cDEtKB3O moBr3IRDlNrXomDl53kHtj95mSLvmmRVcGXQAsRhdLe8JS9hnZ2QhDe5Tj69ucuTdFZSrxMdysPz 5GCALm3BqbyhLzG3x0qqELgVbCm1VdXj+McYB1EDItLfnToTE2gGYidoGW6SfQ0swytCKEZkq1/c 9u0bItMkGRYjYwPIXL0GsoO8tgeAIvH097Wpz5rvgqdkJNRfuzYNl3run6/jC9XTbZMKp1lMs3lz 80vMq+FUddxKqgrCxsQHTjrSUGCAFvErzoS0sjvPkRGLvvFLPY5b2ziqRjfi8XCJeMBQRLVOMbsy Qp8R8HqEr57kJQL2Mc+EQ2eexjIT6O1AQgiAZVWOeqxGZfKjyVDVtcnHxyUM+WtiV2WpZvDdollR 2dQMx8mF8c7NqqSHMcTKoKzcLYa8XKX2nwXhSpmoaW4YyspjmaKVfYUr7UTeaMJrWmwslVI5bzGd a00TcYXZIgjEhGtF6zE0If1GE9JBuwCCvY0IcDIDxkeLkB3QAR2sWtfjOsJwK8RUxGDXjppmDiwY vPpBAs00pcf6hmrw/U/EGRGvqGkDaK5zGmCPQ7GE6VYliGnaC1tHGw7bjElEBvh6mLiSVDBx7i9b 1QClCoK1aC+fIH7EIAu65pVsFL7M+cUjzVabiZVorLR2vhXFdHwyOtuOqKFJtRwOq8RMTsKMT8zG fhR/x9zxmJgQD8ZsMPUXp8VXnwlpcS+kJ6KgLGEWac55THrFnZgthzDsg8DjmBru7bX9RqTrCwZE uCo01jFCMf0EYr312JJXmmeyfwBxenRC4uOcraS0DuHb/2tpDsSxBZ9Y9PIN5MrUDiOS4MtTIipp xFMGLdIiHAKbaJbDeukEqTo0NxmRayZZ+toP1pGyTHdSv1uR60BEu/d9Ni/EK3CwBj5p8jviICbL 2iRigsRyLu+0qeRW+RNOyH1agnIOPKDNg3NDaH8BrzsJz/JkiWZgSahZoNizZ589ffo0vJ8KvlfQ qhSYtsw2pnZ037LDPjmX7aFmIwfzNDIELiIfnakGrU9NfQX0LxPwmNnZcsfgd8oDLVJHraSzWONh IguMmJShZVFBHkjEMgfJeM/Cg5rirn4nsjCnU7kM6us0gqeYL2pjd8N0r/6W5svikQ8ZWTJ8XmFB ucB0gEme5NzViM6M/9VPvqkuylz7LVcy+zCm3wxbfN79IOydRPbDXPxAHrENIctclcj5BfDtF/C6 f2O2moUXGbGZp08/ixBzgJd7DStoJXCZu8kLsBa4pq8V+xLR08axzFqTwI4XvbmMdMn0GfI0HR+W ++G8grglsSsfK/OWwXjhF3JQG3oNcXFugBlX6rfQz4EJ70rK5ACPolXoi7kXVRbCVn/9juw7GiYz y8rHv7UBKxAvUOL/4FqZaWsEMpvMjo0NMMIKILlpio4l20q0Mhgm0DsE1jWwSp1V6JDBrW/zqZxQ lVPIU69/4CsUAKrReu2pIX+JefWY17x/Wx2LKahW439hhZoKM+hx5kLqqYPuJlZ7g6cmI52dMZ6g RTe0nd8cP2JuquJTp4x5MV3LoSsP5FbgAOkwXIxYgIjY/ykX3PawaKWnerWizO1yoIFuJmGIsRyX yu8h2FElGcIcLMM1CpfBNjqsJa6Y+V1bqViDiBlg0882qTTQNRMgk2DtcEC39aare1XY5C5WoDNW LlJ3q7ZruM5H8ZcrHCHJ6wqzdnI9b6V65enGK44SncFNka74uxigLXgXMLFGhvXYiHwdzD1vE80D fz+IH7mCb73lFP+seVPtE/jqekHubGd+ufHb7XRyjxIIy34Br/uDVyqzbUIyCPpzkS7K9Gx1gg1H sXzDDJNX/+CIppcPmCcDcQgxateLm/gcn75hfD2mhH6DtFVlw+O0+sCdVXec4mIxkwMXLgu3E2n0 9iMRnucSalwhTZcj850p+w5V0OdCwj7RsOibJdLvDwwz4SrupQvMQxXKYzej/FndqnEvVmFT7yJl Op4/CjRlOToXKu5ErILbx8w0opQHpjPGFVjlkDzBC0E/pfQSvK6urwO8uAQEQIw45dPkXwq5NLId 9YSXJpYZPrNaFPqzbesQgKv6JkEqu3UrTDzYCR8cvFJliOUzfnXgnkE8QyLLXvOYw/6XKXE6YX8H 8p5xKEwDH+0irSqJ7DfeZWC7XernGJVSXZFhfxPMCxIQoIPhQVxxFoH5xESSMk2WLLZlygMo4dDB Ydg0ZTzj9KBJ6q1Ayd3RAw0EsEWIIQqFrHi9FR3WwbykyXhxE1JRQa5NyyQszamlWlIxJzQq7V2C 2VDzwrmuvRMDGu4aWZSS29zFkPRLWNaYVxQcM2Rj/WDwtICtWKlGk6aDpRGWnCa3RC5hUzwhFWuG gxU4lNPuHtje7fgfBLwKv1LCh47sxq8NuI5W+SXm9QBmDCraNjKLs8hOiuB95Ct5BqIeX29eYT2+ ImKTUU6YKLsshV9vWpFMkiGAyIR9jxl0AJ+jAJUhWrKiRXPpQXHMtl2s7lpl/nyyKyBaEZ8kQAyg YXKxjHkzWLG/glcvo6kDNypBcT5h60rRxfVAiEh/Kt9fHAmHHXZXyGvh82URdQfxLM7k6DkcfXq/ Tu8s8pfvr3Ah84BZi061145P5aPqkgcQvk/2Fqo+1sYU0sqDrvReXYtm+wW83rXxpUc9vh77EWKP sD2ywwO8NCJmVsfOnzt+DS8yVVSX0t+qrXkZhCx3JLYzqjzIkgrAyqSDDOM7CBYXK5ve9CT9K8PX CA1RHSU7FSkfwueliBz+F36tuiOt4sv6rjeedtY5p5OfrZsQfatQQi6VnMU0CLuv1F3uBLNWNmOJ tKhQ2LitsY1NZQYqF2Y5GYJdsticObFCSEFYQt7fefWzdxX6W64/9O0Kv/r9qo4KxRK/BPr1+SVg T5kd293dxkUDMD6LGhakIVOqRXcXjd558vKnFtcPB06Ooe59EUGum5hAVWFyLH0Wv/o95vJjKTRj QnKV9NjiONPsvdyk8EtXU73d74jIEfzJN2/weJONzMPRgtMCtPypYUAHBORSKXSfxOld9SfxKXMV FOMy58mFirwKSca9nPFh5zJL4CAh3cZoq1ic+VUsVxMr1bx+HTMIqR0yJaJ1I6oPA2tpmvRl3Vcb LKcGne3XBrvcIw/vWl2b199Gi2UiDn30WAiqLTmUB6dwfvM8yl3Ak+aT+tfNmvkl5vVO4FXIVXLJ IxBf0TGmICjES5eqWZNqtpUP5LMUzY4bYgYb9i0qxHJ+cxRB+gAs0aA1eMVx3VwJ1OJsgI3oC82C xo9MZNLAl3Szci1CrPPFtT+19tDbeEZQNCQ/RWgbXZW4Px6vTMNCbEeni6vEzeMqZmDcwB/kemQu DEtyqGKszhNeDgc33iCzT+W2KVbvZNQW4qqyqTnAPTmAADMJ8YLArVjpNKL1Ea+PSuDULEfumzR5 8lAhVoKgjRBKLBd6U+nVR+ZPvSyYcmaLzHD2ThVyn5p8WPDK5Y3jjcec5awOr/m3hU1ZLdtz5uil dslJgNe/fv3rWDEbi87qPJzKSMSGwKwOOVIzcXoFcsdHPUqHVO4eYXb30iF3udM5Vre8Zot5uZdN fW3tU9U615VvoZrsyBWS71qZLU+B104UC27FDkHBRIBXBOyDeaEAbwNYjpGgARaQ+pJNSealvGZy BMiRihf/xEqTznRS1IEj8lIXjxmzgUIiVM+EaKeCAecY1qpOPFyqXj35jQQVBskUf8KFAQNiZw6p MfLtsJA8AWLAIR/cnilpWhNL01Uztp4TV/tXfVfCQxXQz0UPY3l2QHlUJ+95HcgVi8sjZh9R++tr ViqmHh4OpNAK3a+Waq5ASa+TlVsmbUji6NiovjPhP2/hYxs6cld8X8DRRkm6duwFL/WSb8VX15BL zGib1lzoB/Qi0zkppYU8i51b9fj0z3/hAhxcMbu2xK5FbQ7NdlGq1jdfpw4ehlx8t50Fvh947W88 EQbJmx88g9e6xvfUvu6gJ5ZYKycSMMBFeHw54cN9/2UwEtHWEBbd+B28wm08j1WyAUt41hlH/4ya a68A6NAiGCpb2wchIq7guIAqC4/FY3FrwQ2uIloBAoAF9M2u8T6O7o8Qd5zlvFdyEW5JiMDjDCfw pe3QZUAJ+Kna29x65ElxOS6ffIWkBuVkXWMCwYZlRC2gF+eOJQOjU4j/lLtVPRNAdCxAfkKUuwng evnq1Ssscwq3UfFMRbJYWk4aJCc7rTIbgmZwkOulJCcv45kgwkPqDjbulKzbtv0yXyJNEdy36efq 4d1/ss/VST2zkYMrpl/nGw3MuTsNT/BarR6kUs8VtUFL7moZ9tf8beB1e8v1+99idhJsCrsWJEyv v+8Fp9+Kk6U5dQVCxkPqtYiFIj76P3NIeRffKolsgpvANcAr3EbHkwOtbpCnGgdFVzQlYWqPVqH2 54akycvk5HAyvTfcLDmOUMeKxVcTDzZEHAqQcwo+/TMTr4Ioum+Ma0m/8w+Ogm1p2BEXYSOYpbsn fkd4lQuWFxPCxMP8u15J3YZ07hiXiklPDVlGK8CVJrvmTl90hETPnQo5woQ3h7ZhPSahcwDX5cuX gV2xQnNcj9GXQW3xYFYrF2AKK6B5HEdmiXDFeSKkuTIY9Y+MGr/LlbesSsxs73ywS9YsgXdTgYW6 dYner7nELW3TE/dRsaRa6vdehPQ8/cfKfRlqczh4yc4evd0NXnL7UP2OuUHfiqG09zoAkg88xTXV zh7GQVBwyI0SlAbU7mTH/MGqns4L3/kgESkeDJ8+veR00gRQyAANuw0aQycJZzL5nuKqTxRAqfK5 aQZTGmiVzf/AUYEjF8gVk0zED9QmxhM4i0Tg1DX4Ej6BdfEBeAG/HIxJNMBX9elzBB8rQT5isjQB CbU0Cub100w+hCE8U7ADdE1/kYecOy5oKwdTCQM6KCaEWJjcsWwFd0L44W4aBcQBjFqNjTM1Iyh1 Ffmk5lYBjl6d0tmoQlhjqqGq5gHS2HN7xaBBGBt+fAKIfX1zdRngFSH72Ls+ur6J6RIfRSYM7Mfb mLQWOXdvgu3yb8yaHYMfmBscc35ocu2IRWLObtknhhnczKwWTU6rCZGGmCV+wVOb7WUnSmsoWWDT 5leZ8NLmPGccTMnNglbISDtN6XZzqEk38xLhFxOeD9k2cS3kF5HbEhCKXr5DgNe/fvP1N4t1G/V4 GkNZilmZ+7eD1PyQSk7zQx6Sn6bRByIKr+icaydy6cx10YZ52vhxcboKKhltZbZcusli9pqAFXQ6 hiMW0jsCmOMqPlRdgxIr1fuw2wQqdQhiDhy2abIt4JYwKx4A5ErwEu0iu6j1nMm0ACIJXoIizxgh c4X/E1wBHAQ5MCaBFnOpCHToQnTRB/gXVRrIlRPOcGIw0yEyOQKIjKLwmpBonCrwEv3K8Dy81ht2 CAK8hFkjOJ8JqLIWjG0hPCcCp38EqXwFbFhV6/QiwEtRNDiNL19dv7q6ef06YPLRyVmMsw/kChMU /HcgV4zQwrAtgVd0+DLmH/uYUxuTcfNd0KfbJ1ZDbVFcOLxApkmgZN+qDhX1mfHrbvrWFeFQJSRv ImqJD+bXiTp0pCtsqoNALAXMxsi4HcyDar/9oXh5k3osVPv0T3/+16+//jrmrtRZrikVeVy6UWU7 eOWBVbTjNJGSra3ZqTs8IpFrFwyVsmze83bw0mUcw+cmIOALf/gq/KsIcgVu2q+qdP+fTTAKY2ID zxM6oDtHV+M55/MS8YlQM9adTSCe5dWQkhQpR9456M7AV9sESi5GlgJhrHgDRscgQZ7uS4KtoVBq NFHKdIBINAog9JolUxJD/XHIjNOhVdBKCx15Q9YChuswvsVgl2jX61g3I/Pmyep6aKyQHY+BQ0tK 6PYaTRMHuKrRebyHHhfLMcdqzHje1etAueggPY+JiID2k1FUI5Jb+N3EENy4cOthRYRWqjnVBk9I w9QUd5zZUK2rYe7vw6/ZbB8qwCVwBZSG0hm5VPJtFtXC8x73VdEu/7RxoetqyPtib0KrerXaEfP6 Oual6i8wg/3MvNzucw3uAs+7Qs6DgtdArh3sS5SkRGqjDvNltxts4HvamJmilnFFrhQdNNl9KVFH /8lUNDq4Bq+4AQP2MO9UGiVSLMFLDWz0EMMgaaJzibOp9JrQPoPmPCvNl9wbqRGZF4idnUI6PcE0 oLUa8COdNWvQ2xkmzG5AcIYKx4nsyNCHmQ2KqptJadFqsSoiFWLzAC70/xnFon+R+e8jlb1eROUk l9Qn/qd/LjJkqlNQugavl5eXryPZK9jym5tzzjJmqjmLCIArprZNcgLPPXUbQAYXkdyZRaCYKWxm LiPMc2XRgA3xMSPzP4aSUa871T1BcucJC3haCHYvBEvZEaeQSwPglpfOwyJqWRmNihg51stHlA3g /VTurChj/QK2GN9ARQbz+p+DeVVvo2pT5a660qFUhlEv47R9lZmCvP8c/TorcZaA8rHFLRe3nM2O 68GuCOtkeX46DptFk/u4pIKm1NWLrD5hOAoqIHDBjoLn+4sjmkW55gbmmXQyMgaiHWIK1EtKxlvR 04HMI1imXyO8Ep2PXM8MIQEp6c3x0TWCXEfXR0evw0s9Po4dnJDxHexTiXQwyhORKl7MikEcOgcz mosp/RKQgGkYDL2sE7QHVmIjFGCsI0MTcXcLjWBM8CTN5evYHzRskXIFRgGTYiD01etL9PAhTSE+ L19evri8fPEytuj4Q2Q+mNbVa3zi7MAVoBi6GDGCR5OQEYmV/ZGdpyqGXk6xRdM/RNsZjqFrc4LZ hiILjnTv5ioCXnAbr2OIdlTRk+OzJ8fnMZI0PhgDf3RycQTjcRFT2p7E/tnF0WkcjPgXll+CL6l9 jDRVjFPez7AmHhtqwVS1UMBV2LbNEHaIAu08h+I5nAGFVVcfqRmxRmP6c18BePq6XKepxeNZkR6R lgM7OOwf52AkB6DuCJMCEJ7kL09/F9rNoCnhhuIuwaI8pWJQ4qP767/+H//bv/zLP3/55ZeFHvQG DKtWX75F2g/Xj237DlIzNcEB5/jpE8mbGpIBaCtH3VwGzShlN4SmzkdleEVAVtCVHL5eRPDMy5d3 ba8jfAXXSC4BtKooosJHDrFoHr9Qfvo78ESoieprG5uIkT0g7ZkvqI8u7n4Smd5yW94ePbo+evwa wRdFuk+u3x5jslOhCxNik0udvI3EeGU84FDkWh4jXR7fIyMzeteO34TnRW0fI4FEySK13EH4oF1X x/GVQ5TjpTExBeY/lShqFR9MUoEVggIHOKkrs1Ep/dRfuZKBcp5fA11trMcAh9gwG01vHoM12y1o EWe0H56Bm1ttmvVuhOBvkgDb8ewJ4dNkeRiJ0ZzaBOIYyvX40ZMocyBlOIw/fvf99//j+xfPf3r5 4kXE7z+/uPj8/JEoalyAkJb5AW8kXkEz8+ro7WW+RtiPl2/fXnK5IQY7Gd6LX9lmjPO7i4Yj10PF UZ85Z1AjKjKhhRx4v3r8fjSTDEtcuZ8RVB7fMYJHys/mUgO15FEWuH7JYqhia+IUNC3EBJJGjFqW uLvftpIydFY8NSmrqn7mrwzLjv/iWDCv/+nrbybm1fmUXp1ysMlDtqjSqko36NSOai/Cp3rvZ5WR mC4dPNPv3nBSkJXI1QCp7lDHamclF4uyuz4g9iVP7EDss+Cy42+wJ1p+x8S1A30Wt6I/gw53SpO8 nJ7rwK9mXo7Qg/NESiVpXSC6+hnfmnYFMoXIRFa4+xwzc8EMLskQmUmEn9/ERxxJls1kgOBLJwyF jVxWpFQIOaURId7ZBUQhJ/nCb2XfcVinp+XwkGk+p9LB4C/KE2RKexArwPslqA/4V3CxOCJkE9XC irAO0iv/N+eHKCdUgl5mO8PlSTyIptTPmPlHBea6QwzYR95FGBk+PnoEbq4C398+PTmLD0Ly0cl7 FAsIHJ8H+ATDih0AdXC2WMUAi11yKAQ7HInZbAtWiyYuxnsnjrjCaA4gCER5VpdBo8m/eATfSUpw y7YpshNyKYC6zbwGOxJNUm1pXBeknpRJ9Cm/EnNjxiVSLA2rF+2Sc0lmNj7EaOB/dVnIrOjFbF1y cn3LHBNkKoZKrYZw3WFgtvF4IG7aNtXujk01XT/eVvMP9Lv0jMjNN7XSJZT7Xz1sDWtz8/cX86vM pRS39lbtsJYh0Q1PTuOogXpjbB4SircrwZKe8Oyr6vrVRWx41YEqwhokXpZYtbhMJspypF3ZrbyL 6B3+V4jJQaske4V6ubO8Li9zQIo/7xvHi581YlGDfjLl1FlnmTLWW9aPMISNnvUSxVQT8nzV/93l Tu1rlVPj255JVVM7xp1lxShyRSqqZbyjlDsJbztp+RL7iuvWzlMW4k1+kGsurXRW8l0Km4rr11mH uoZqS0wEdMNz8yxIi+fgqkwKqYpav5Iryz9YgrsoKuy7Fvz5SJ5kNW1tvVDR9Z3WOnx3ObnrFTK9 w1tU8dcgtTjYJXiu02GkmshWG0+in00/ySy+WL41rK3HO8d+gXxd3Fpn0EjsldYZEXlE6upP1UBD LxG/gWZ6jh9CkZNr5dvLuvY3KdiijnHYo5ZF45wT9HxB2QRu9aDRKWDP1uA3dRZMDZSsOZ+nABfT 6AeQKdm1MuU7frWX7MKzwKj6uhO7kg4kSLGqBr2cxyiqIVN1R1am767hXFnfrbT2kGhpOqr5612l f1ubhySxODveGCQ68SulzAypg9oS9JS1ZjMHcOzmfIWQeUBzZS5KssAj1km+URIRuqVx8PS//AkB +0qV0FvV87w/aN1GTd6KX4fX/uw2TtcVfhxwN1elbV+66cOgrAxBHWAaQP9sNVPcyITYHDouyDmZ ScedpcXmNPuxwyXF5kGKcTwM3gAKnM6OHYnCFVocxh6ybbBiEF0e5EViThu4jZnehcgYk1QjnK+7 K+YJ7Gb0yXgVO5E2jqg74cqbJm8u+GP5SBiy6w6d/3ivorbye3HTOARHAla1al4vRYhBppiS6zks SX+RRMKU/YI8wRVDMqwnBoihDHa9U7/Lyy55sN+lF04pJvqKFEgJB0FQWeMTEwadn57HYkvhkaL7 IAL2L16FBxtDkGKI52cnZ8/kNkaSSriNR8ePYibuiNnjcyKfEV5ksufwnsKdjGchQhahLPZpiJTQ MrAY4AxlMzRX5GBDfea3BWTovQ5QAb1/qxu4ipgLN/uBdt1GPqyxKK+XiPR7jvFo1hfJumqX4qYZ Zt0S6S5IuNQFm5i+pE/ZQYv7cJ8yaYuNKqzOsgCvjZhXFn/BIXdW2i5wPbCW67QHAi++cIIQ5SZl eacV4A9E7SJCm9bD5KSZWEIVexv5xOo3LOkopYpTMllSjZL4xfdX5qdAgJqL4lCRO3jhEVxgkV2c TId06rlS6qOHkX2OcTD+xoZMfDY+sCUKquQw9hSeqd9Bb+36klA5uKV683BFYYmcmvLJ1cmIk4Rf Ih6ea1UHApci4wA5uhyUzQxVjjDi4EpyJyMXgJ2+LUsoGSa0o2IjG8T+LoJIcocTZ/kOKURZe7ZV hn03fAkG7pgvHXGri8jlKvBCzOvyBkmq19EZ8uz0NMArBpCevnl7/hbIxb7FQC4s4hQ4FRliAWQx Py1bDMfjINCWpgULCACDnQwmXGfmsKKNHO6KmP1O8BrkgK90oE4VcvFydC2zlhI0vWOLOfi6b08I Y6+oql8ncG9IbuKva5wITVtMwQV4ZdE9prZgKkNalsteCgmkbWM1LMWA90vnjzE1W/zR9JaJNAzb dTWxk8KJ3FlcM2o/VeXABlg01brdBjDtuaOlmFW7d5OAD9TbQrpCNBmoslHsEeYFbTiEXlfqx1uz 2xiL1fLsDGlqr57eGsjFVZmseAQb9sXzsDvNyMy0nKMw1Jn6FneIDYEqkbHfuRCMpdfWAxd4ScgJ 1I0QAv6okUXy2ZjhziRcDVrkoB+mtTlrQen5yuRS+qkyurirqypd1jEfXU39QJa8T9b53sbQRTqp FYOr6AgBbmzJyHRkIQydLauuK6jNSskxXBrJxaUzwcUCdkCECWdY2QTL0mGe2rPj6I7lggGoyph3 W4FsVi3aTJsME3imsNjesn912eeX2BcfvE2tqj6WZGe+UFajOhfwrZ/QCJ1pAWozg+f2IHsVz0/r 9V6EjkItHdnQUop1SiyvtzrJhtmW9vAuT54rrq63KmUjq6nHxw5OFmiULN97CxZcdIlVv1nBCX9q 6MLXNJ8dd15CaZ5CTFjhbD9yK7qNJmQ1u5BpnGjM9UMVSi1l/OLjhQxKfiF6+V920XSgG5jXntRa sNp6gF1izyCPa3FWa8+buobA6RyOY57D8F5SnsiKRpy+Be4Z81LEaw49JZTRU2ygk1CmQ6tYz1AX dQm0LYBsCnUtL6bQatulzLZ5XZRu03v9ngLnwJCVja2otq8WxepMVYWk67qBZZBfUXLx2TQpLDTq w847vhNEeAJrgol3ez8bbY6LnVaiepkbafq2S+v7bUt/rdpE4+ZJSgMSppI9pMSWMc86sboc1gas RFVmIloxt7rDXnS+BQV2U5zxSjuKauydAaxwQgGW+kgmSoY6Jtbt/arFU9iLu+uTuJLJdjv+HQgl LOIfZasavpJtsw1HuYxaA77abytEp7bU0YElhU3j2Vac1S2sLKqhVJ8FcuVFQRDwNFJDMkROfs+9 udUMNRjqmOOryb/CM1TqQtIwT0ejtHmyruVnQqXUoeRe9d3aKlwklmGUZYfJ6YvfTwo/Q5hfpJOH Q1XG5yG9lfkQIUTBvOAVaj/cSfiMSGEN1/LR0cmjE3wen5w+Oj55dHr6+OTscSzueRr7J5HdGolw F7HuetQwCJrohHl858hDHXdj8eINEJ8kxR7Al1AuOqtvOz4D9BvIEfxIxxRz6OYh24Trx5ECL5aF I9ufLpkLLKDZCRkle0qCa6lwx4x5zWMbFxDQUfaODX3n020St67b5JPZ3i6jXq3edrYP+2BCZw7n bQME6hE6NauR/8JNKwhVeGBAP2MEfKPe6hBUB27wK5TSgrEg6ZZqMGe+GrGEOdyOiyPNTBEyOjoM WUlvw6l5+zYGtpRcMFTGyAWdoLMIe9FmrlpcFMFvGnfFeO8KSMHGSo4NDzaFuARJpXgzak5gG+Er eglzFonMynIKfK7VMQs3X4LVFk9gIbpwl1njC+eWuj+3XM9H6Wc6xmfLHSh9fnIeNayxjcj0fxnD gzBVWBTwyenZ0wjK810DrYBQbHL5jIChOBJTfTByFfUJqEL+VzQPCGyssIkAPxzIeEbA2VmAF5J6 zyLMdnYROb5Ylo6prxTBkuFsgcbytp2qLW1BA5DVGakUUNXgJH52oZdhXeKLSFPByizSlqku0sWQ o+Uj+bjcL2LXNhQYXjdcYV/gtu8uSeofoi+fNnhJqu2wZdgoUXwGr92A1H7ppGbzAjlTljESKOhY Ay9GmmXassGaUcmYs0Vi+AxJKox7rbGJIgxBKWQW2sIgS3ZHMzRD+UjBG5SDsx0kEiA681aL4sYd Y0zeGblDZ3f6ybFssAtgW1yPPNXCXoIl/UN4L0MwgTS4HLVB9xJDf2JmBka1OCVFjAPwTF7oaJQf FDdjuSeK5CKXxG9argl2WfIWbkzklaiv0Fmx+ruD1/HRo+hw5DzYcTEgiQF7AZn24wSAl2UkiNhR gFcgV2BUDPAG7WLQMw5dnAV4RW9BdBWAuTVWzkYQZrdNTbcNA+1o1qRlKGWxt9Tee0CYEpt8onoO u0kWtIlSsRU9PsRRUGpFY1t0hDtfy2v7zBG9VLRdWQPab3EMNip172e4LZ3Og4DroJMUv5KKk3CV By5QMPmX9En0VL82RPq2JYG6o3+rf2fWMWmzb96fJPplO6WOrS78ue+eLyt4f+8M3JAAwGdk9vnZ +XmshXTxKD6PH52fQzEFCjrdghx+Q8xUc3X58sXzl89fxPby5YsYnRh58jFkkVkIitJzPTH6fg6f qQOyyXZ3T8YrV02OoKLfzkr+EQgypUMxaytYVN5Z1N55rIQXtYdNfx8/ic+T+PsklpjCKlOPVcFR zzHQMj5cqg5Z66rrd3w5BdZkUicg6dxJ+3T+5uZIAmcEEg6JvWmHf5sFsofv/hrdVp8cYatr9ncL NvwqIEvJI4d4x0r5WC+fkEj+4CEfIc6+D4UygWlCrgSpUSWOzhKWJM35d6q2lM68ccWFu+VpgKe7 +HGGy8TN6b1947p/4anPmg27D0Y1IdiMxR2DIUCBqEwXwC+qVvhY4+kpPxpMdIP5ZIBbLwK9Ymgg Bla/ehmTwseM8JFgynlU0Supvse4IifaIpPTEtTWH3c26i2HE9G1uFWCD7+7kr+LPKuRbYCY78aO RkxKEdWHAZocpAn0AnI1/Aqb8Dh+CXh7FNUctoImAx+udf7OSppA47pcA9biCBuzGoMoMwU9WuRL v6iVxmkKfgm0qndBV+G8rcbdVfNTKw/xhjZ/cm7jklTJbxQu5McRn1EdO1zudX0p1rJzo3jyV6Ym ZJNhabC8LlpH61NQ57Kd7B86lwCKamFgoMvRUBMOtXCZ2vgGR47czRsCQTgQOUJxJsc2wn/DUEf3 YTE5y8E37GiSQnljmnmCLp6+HjM1LP7io+xWzsKKTNdIvo3pWF8fH7/GqG78xLkrTiKJKaZEjNHN McNFpJvxKy6PMdaMpWNu1agWDOaG/8C1eahCqT9wc9Nl5Lsq7TYdxszS1juzT9ZWo8Fz707JgGD9 PK60p98F3yFLHYpZng+NeR0jT1XMM4ocd4l9Be/hhzIHJpCfi2JqZV+E6uNDIDoPTvWIca7ji/Ae yWbPEbU/PsfIwIA4LQy88JDM2puA7ZHQBJKB+Dii6UiGbPegxvJm9isgHxLD3c5CXuqHVlSMt0+3 EeJcaqV6s+qwRIloHRwdA2PDc9h6ehQpCfawPyHw8is0kBqAtTC5iV4afnr4Rk/71tPtykmbjBBj CIPiPeLeiktXVMCJBJlmz76b4fybegvLmKJpdGRML8ejse2FXAAgjs1GLF3T43BUNsfscB8wevyG k+RgtDZG+gdyvQ0wAlohOnciMDJyxZkxewUwSLPrENcCtq6Ojq9OArkAW7GE2ZvTs7cxMTISy0+w g1mpsTZbzIrI0H68MxK6nfTPWa6EYbTSzkDky2GCbL6tOvEyp3qeQLhmXGi4ZZ9V/aJ5wohtoY76 TwWBPAitQO6Vz0dyVjhohwTs1+AV8UehKhhSQBAyVCNaz4XJT4JzPT49exLEC673WSye/uTsPP4e Pzo/fiwq++j04vwowl4AL1A0dvCGWI1+JxtL85oRHNzkT014uwQx4HU7CtEsJ740QtCPF+XHzgZs 4ajm+TXvosnixCLt5otytnch4+MWVRH1IWm3BcvWc6t/UgH7bdrVvCi9NSClMSiHG28FJV55C0lL lKyBNngS0tnLMGo+zx40mKSMaU1ELSkzSqU/2HQ0j4MaEU+90IPQElimnH5loqIAujQQh2CpqbvM vOpIsn9SKtxF3gj5FGf1IZUTAnIKfLwjM/WPX58C/pDQD5KFbFjseFxB7HPdQ0zpmiWBrQekMgMf L8g3HmxSFkjApRfkewHLJKmEaBTQWFPcSzAEepICjbRQ7+tg41zKBp5+9YXZMR1f4R2jtzHm/bmt t3EGL00OJJiBh4gECGBPHAQCgW0hJB8ZEqfwtiOORRSLdP2L05uYW5pQFZfFggSB/s6ykwSj5SUG iRFxRDN7pAdX8kzyEhXczW7rWCS50W1SeGJnOkEWFBKU/rdvnqwnn4XyuJLrkFRtTDZJ4WK/lcQy ldYXdsefJZ84l3RFxUCmToBXcrgK/6GqRG4PUelP/Zxskvf9HoQf/RmEyg8tekzZ1J8WXdOYN/Us Kpclu84zMbvafuxY0dOfTL3fOJPqOx+PAjAKmDdZX1VuKi62rYNyouss/kLzEJRBdEw1TCXm+6I/ kjXhTiiNAsqlg0xONTCIoS/FvNqc1L2pUluyhPOEBMS5Vic73r6/5nTBQhffQUZUEvE6VhigE67g ebCu+A+f84v4n9ldPcXSEcupXKzwqOoOFjJu2hwAb4MTarLLjWwsNYaAbzmpxWxcOyaOMWsDqpZm 2fec600GdVGXdkino4s3yndjwMxNtuCg7fKfCXhNFbWqNULF7e4gle6WzaZvcZZtD9vRVIMYZkXW jW0z61JilTAscWsFMTVhpU5Sc44/LRig0B8dr35Pnp8fgsuiOwL+qAun3F2eXIsh6SvKJbwCYiF7 FaqJbkjsM8uSczh5qDOHHfP9FY+LvkTF6hkI0xK1zpq3b136aGu90QouSGnzCqQy+Fk/uM4WJ2aF N6RAtY4nqtn63+Vv4/fWwomOoIwJXQpitU4P4hhdyiaPrn5ljBdCKz845x91+ZKnLBGEvR/TtoAI v44hb5+Q5130kg6H6ODmZYvjKqj+OhYgVq3jyAKci2bmlcMG8kG8ra1mC3paC3SLjyzmBbo83q0D Tq+UOoN4kP+zFtyqqTzmx1n3JkTFkpc7vtee0/CwnP9Py5DGE2PBsZrk0cNkspN40WcMcymf0m6j AgJF2PQDG9uFiPd2SB9Uhn5czR8tpwwRrnQVI7CFD2Je2KE7gfibp35GQEpxMayKFjsR/LrC5+3V ycnrk+NXR8evjt++OjnC3+OYGjS+4oT4CQEvzrnIRcO0TBE9TbmciMfHxNM06/g44IWX4GwD8RG8 GVmNNBqTgA+TymoIoFN/hbzQ4fELNVqckfkc+J7OoR5ijsmDPptsdhgJ0cgcmO04O+Y7xDKzMR1i 9JBybkTED5+cnsaHQBxZckijZ8CeysOeQN0nNjiMSDkFNBHTgezM8EJ4EcvQQUDR23p5dHOZQXRM VPvmzRXAPBGd40MpaeRLSUNKVsq/kxPJnDn5X8NGT1DDfp0JNnaY8i3aKpfAVnDw3PIH893dLM5o Ow0HWR0VdP40oNfanY7kgDnezJpfzDV6M9D/ehGZJE+efvYZM0ueRHdH3CgkLLKIPy7wStR2FUHq 51pewIrZjEIt1TmbuCCMk8lYGqat78KL9MP37Tigw05FMAvqszqCNQe0MUv9gBq+pyF8OfxP8yLw iWJkdv4VeUh0VotCzDk7K1l8YgFjXpxJAuhJhEIwPuZUDsFnzF6ppPQhMLZZEMap7gVesQPwinj8 ccTjiWL6K+QCePEnR8GAUwjtI/wfj6BCsDtSmgEEZ4wOJdEkorYNHNRMUiaGSWsDZWdgS7IKfSee 8Kt9ZjIRjWLXrNL6m+AlhqP/Nd6LuCZ/m8GywjVHlDSI00/CzUQq+Yx4FucMe81Z9LGyWqR3IMMe UfYYp4B2ink4mJgq/hvgxclsSCcAsNroVCMtlSxVTnXUPDpM4BmDggZyXSY9DcR69fbmCu1GcPNg T0nFAK9OiJqqs+FnTpRdIjisclKmIAb62jFjMAUd3tgGeBWKeYflYIuRIrJi9epctSQGDyDqp14I 1TDioJlMYksmVpbzTEjz1eRKLQnIevbs2WfPnim3JH6IW0RsMhYG/njdxh0sdVHbguvEgIKBgi0K nahOzZi8Z+c2amY+5OyEZDbErKRUQq6My+s4xVZrQ3Ab8GprsyFDxm1ybpoqErI+lYlZmjqilwHY pG64EGIr7PO0XxmJd9qEZtHpH+BaELSTY3ZT+uZ6BFdA0+BQZgVE4TC6Bf1twtP0OjlSd9vCQ+qc XEKqpibM8cisjBGMSwOm6bBGwgghSnjYWRV1if6Jt/VOusLjhMlvXIrY+L6jtdoFggcUswhiekwl nBIBD+v0kIMFcqWYyHGqPtXcMZxPSRUqxEJrlLLjGWlo43XW7ldc/iKWpG2zMZM28H1y4hAYgZjP O/7DqiqYHC0SlOMOAWRR9rqf4+6AewZQsSFBF2klyIxDBu9nzz6PFTZ+9dU//PpXv/71r7/+5je/ +cff/va33/72m9/89uMFr8Prd32mzZRpV7pi6YmZEKy+7o9kbnG1Hnowexoc0F4s55HJ/QyaUswE Y40nltlLYUlVyMgTBOiw4N271N76WvPEKqqMZhIX+oOThJePTygz+CIVapJ/VtXqYXXMrCiRxvqT gT/1NajGVC2137+OgytOoYDj+DxUlbk8WYoqJHGIxeX4qdw0G2PnXDJ/VZxikEoadsDRo+fFe1wD JR7q0tAdPIYLD8A9YUn190B2cFu1sOS5bJ0WqsPY0BfPnz//CduPL356HrnKse5MiE9E/OIF3JS0 L8rCZfwUqBW5cPFhNnRA1+PwFj+P7YsvvvqHf/jVr3/1zTffBHD9/ne//8Mf//jH//SHnwd4TSah EEHtRY/M5qFPYrDer/FZ+0+T/ye+rlsrrctdaxJLJo83b9G31EkZx0g85MUaEpkBLrav/Er/P6Q5 40qKLjHDq+9galXyLKd9gUPFp4hVuIo4wmQufmLHH53JLDCmUMATVOYqHEalgDHOhTVAlOSFufaw z+XuuWg0dJPcB/kcomMTQkRegL1fvmO+KFuqDdEdaAiFt3fJXXMaR7oWro5yvtZbYl/aAXE4fpax iaaudt6o6AKU4uaK2nleFnV0EEUKUtO7RYH84RBIRf4oL5KGhbhZcDixttg2yBc3JIKhx5KJ+Pyo BwA9mPTOgGyxi9yPGIJv+8hYhgRKdPeBYKvDWhdlLVJac3Sjm4bTVGLGWvW2ouQYC4VRBR4tpREH HDOl7emTzz4LZ/HZ588+/wL/fcH/vwgQC/b17W++/fY3v/0IY16jTiQet211htQbXyESil9xS2GB 0SHgbH8GzmVT7Po3zizSJWal5VsFS+TPa+SStCa2jRtYjDMe1tmcLhDiDarG2LnXBwrkev021i9D 2CtCXVw3SB+EfBVqichN/AVmGa2UpFrI1fDrRAcj15TLPuKrsmGRQB+5SIx5KQU/jgRsxZI6gDN4 lzdIf6VrLMNOmqVEfsfjOcmVHL9oE/gPsTfN06d4Fb0iaKy/KIxE2sKoypy9peC7N4XiFfuqXVOe FA954YxV6VDoFftcW54XYl6cVeL68XHMaeOMyyhwzAyBrkK+FKPQSB3hSCB1xio27SA1E0mQ9oVM LvJOpepGzOslxiCgUSN+jxZMedCk/CV4KiHeOqCLuRbcNARSTwFhcS9wTF+BtP0Y+n3KdXyjWYi3 DDZYlRRHFndeadcW7ONtdfJiJ2sPt2MLyasFO4xoPVEKMIVxnESmwKnALg6UFWI9CXJl/HoUZ3CE FEZKGcACrL4EXuGfALJA5yiEomCxH3X5MwCvDm6oZfEVqbs0PvU+v+yALyPfbWBJcRgxJulqTJgg 8YMPwHwmmdWMv5qEFe1yl6NLBDDsia1GPuVK+c58CRKVIETKmBdOxZr0sfiiwQsQhqh8HGS3I44D ubAMLYCpQZi42DERbZAvrVabafcnscOoPHSVQ4XIv7DebWAZaFd8jXUjo0hcjVLkhL2NsS9pL/BS MrqoCfrrKPSOVzHgm/RK4DXwh56FEEpjlYVNBXQJXgPF8mSysIS2Ule8jALtt4LXzQ0n5xJ4YeoH BOJ1T+MVdBYvBK0FfDFO7/+V0BVlZ9JvmtZY0hHghRqK9gzwujqKDkdPQptdO7ZaAo24uUBLxEug oEC4AuJQZrJT0rKYp+eE6/hGj4u2MbOICiHUehDwMramqQD1Q2ouxnJywU3gFHjU4ycquHoPwbE0 TF3n5MjPuITno2MRVIs+Y3Cvp0+fgtG/eRO/fvb0abxvVNDPw21c4I3YlsLp5jWEkmRiu4lVZps4 /r77qyDRHy9D2MBqE7mSlqVc+h70FImILLb/7oFQujDtXeTGqo9ASnboNkWLhtUtHkPFEa2gbhhd ZIWBP3JqSEWIOCZIgCLoLf61xRZZ8f2WZoJFtm8m25DupNvx0Bc64Lw1uTC12LpWVd06WqTzrgf2 sSWEIjNCW85uY4BLtXbHaBKY9rhs99F8wP/cBDFi9iPWzgp3iJuooJFFJGjANbXOB9uoZm9CEIL3 PX362Ve/+vVvv/3H3/3u93/843/+wx//0+9+97t//Md//Pa3EWX/JsLuX3311ZfBpsIZjG7Ep589 DWh78jT+Br0KJ5Fs68s44bNnnwWOYTHzMJ+U7XidCP//8OOP38XCwN9//7MELyNBIVeC1a2xrENP 6Oin6IXkWw6juxod+5oCXxkXy8jJ5NzOyHULBPnkNQ7vQ70GbdIhaXKp14Rf1d8nlsIV3HNcjh9S 55OFKF9KLKlTHgV8pPEVofIdSh0FuZlBInBWRrj6ku8Eyvt0VoGuhFuTtX1hL9satYf4k1DY9I23 YMpE6xi078uarXOxP2hgC3ek3UrrMxo3G8h+q2pJcKZImPBL/CsOcEIKbOCDH2orIYyCBet6/PTp P3z1D99+++0//dPv/vCHP/z+97//p3/6p2+//e1vfvPtN19/E12GEX0PdApKFT2JEdUKVhVbwBdi XIzPx38BYc+ePgsaFk5xvC6Qi8IRfZg//vjD99//7bvvvvu03cYWzRpUZIlZjFMOlrO7RRVIvb3F 86nSKvUVVUwqdhX/oq0obzGZUTp/3aO1ao4UMO3Ff9mfziwtbXgc1zrLUB54QdmlKPyY7o/00Y4z 9V9Cb+lvkeo6Xu9OZONsCdRzzA+dOoNoVg5ki7eChxzzP8ckXdfXTCdDYic853jejYdpxz4Hf8bk EsV+3fFoHLE6FiaaNtPaZnuOSKJWIeK0YK5XvKnX+KAB4cQ7DdgzYs13Z8CLCGbuHFfHK7y+jlW6 kaD604voILu8fPnq8mX08ce0zo+pPXEtp06NKSi0VWIXmSfnR0VWZjlQiVzxoyKMyDVhsPLF8c1P 0VBsm3jyK8a8xgRn7HxU2Qi1Lqf2XVX6AjNBpzH66M7P4YtxXrCgYPGi8QtiY5jMQrF8EWN0K8hu iczY7kZFRxt5kRgCbkx14fmQ1E/AWBuHk+Mh7BGsTVORPQ4Eevo02NNXX3z51ZdfBZMKPAqXUGSQ AMvMNycPk5kTf/VHGRK8Jx4UhCzwLPbitGhNLXuAVdWvYrLbaKrLTxu8aJknGUVjyOdLYy1KlOcN X6/7fdqXld+1KBT1SU6dwlDkBoSNEEOLm7wMi+HgXBWmn4ubvCuDZUa7DJ7pMRX/8o5Akbk73Mz6 VBW6Q60mrSIWfpkyyG57pNCGc1HqYWLFKdvHeayl8SxM1BVZPJ4t9ejm+m0EuuNbYJnwRD1Pb9+c IMk8Q3gyE1wqjdqkEmV7UqEgrrVeGjBSs+BjASGOn9b6QziCZYhqmnwvTMTRSHbkUS0J2fbahF8q RCDX66vrq8tXL18GZL14/uPzFz/+9PL585iNLCZXjJhXZEaqfBF6j2A4J9kieLFXj94xuSkBYniJ JFm68PokwpQaKo9elOdH1wFetHlYF05JqhkhdW+j5NcuYwZvCbrM9oyID5le6H3AVgBHuF/hhgWu CCICzCIeHlgWv4mI5WbKTXgMwOK4UotsACyDd0mfFXCP4BXnv0CAPQLr2R2YUSsHr/BPINczdhF+ Gczp82c8NaiT0J4NzaZOjQWAxgtEaoQIe4b0UOAgXMiTePYsihD1lG0e0qb/0PzH//t//V//+Z// OdLAyiab5fIfNbkSn2+nJHmGqrrO7/sq+oG3QjfX3scSdAxVNtCajCapSHIuAthtz9W4wH1bFh3I RZzi60QvG4bOUKdjJ0iIO4xyGWnRMDApNluNj20eUeKOQxssruibgv9c4tD+E4Llb08VFIkTPUE8 8bpHfVHYmBEnOZ7KPv6q59WLNSwby86R+FowjjdYnhaATdDRQBcCIzFF5QtKcXNzfH11dBP9B3Kf g1WgB1Lnnh+9OcMoAMf4FDUWHYDu0xnK2Brmupanic8w12Q4+kGum4Js6ZsRRKycdF7HlTL9+h0X REdpTgPNcVJR6ptI2g7w+vFvP/z0P7578dOP8e3N69e/Pr/41fkFb3vy5PT884uLiDxLu4N/PAbN QZECwuIRwTHsMPLRjNdji3z6GHQV+BXb1dub/3509d+OrghYb17dXP/45vonVFrO9E9iKTnMblbW Bl4IsS11blDBY7IKhO5BihD3vsClryEFgghkXcXnKtbRjee8Qh+BqLK2nAyydDKg5BwTYbDeMOcY SJP6H5K38RtWHUnAsFlAWjF7Ei6ePn7yZYSunkVVYQvQpmkRHY8aDfOClc/jSBRSEwHJ3MbrYVL/ SPXg9jl8ys+jyGiXyxiwFS+CLmD2RWD7GYKXfa6Ed/+L9nRkfBc83Q5eCVgCL6EX8yTQQ8fbgy1E yxiuLCNCLuHn2DxlVx5IYiVewyucccHbtYvZ2Ye1rg1elHpBiUQ8/pc4wnHLhXZUlhAimz6ILtb7 wYUQI29Ct7i8cDygKh6G0X0MqnvWvIzd2IOSjsZpN69jAjHdJNQIn3gkQfUkcO36NZZgDOWh+sST 5Qep61HOFz2h9M0YRaJHoQwJUx6NyLHfxCv44ipEAhvvqlunv2Wgw2AZLITrt744vXgSQ4COTyOX MvTkx+++/+lv37/48XlMBXvz6tVnb948c6LaSczGFRr5NLCMRXnK1KRQbgEiksLPztXMcogEqlEV AV6XSK+DAMQwRoDX8SWXcHsTAJnghQbXwm6MzWPTK8mEA6sZ2NJrKnPCvliEmujH8YBcO0w8LXJ6 9fpV4FfovM06cU0SCVNU1cbZgUQfRxzUwc5qJ1VxddSm4WCFyP2LcgTpirGImEgjMgHlHaQRjQKZ kUsAWEUx1W70jcaT49oAQJVILmSUUMO2yLXjFXgjLIjwc2ReqfhLsBBK7EIuCEe6LnvO0R3Y6AYB Mi8kRkkaaDVDAhnuEnmqAoH7sRAEMcjNuEmFyQrMBq+BUDfUA3gFE+LVeiLwgTtwIh7FbHfozQd8 ACgiFGVoE/f282GiMQwZN48cLepJvp2ySF24ANlYuQND8bgxvxCKo2iFgh8wy+GbgKO9hntIjQAH uL6SQ4eIxdXlm6uXYTUhhcjExtarOnRCdOnsPNIZy94rJCLTzx41QQJf0GiUh/IX/FoVpkfoq6AW +nAZIS4//bPHn3317KvH54/pNb54/t0PP/4tmBfywq9fXl5cvrq4vKR4ELwePXoa5eOTItT8+Wef RVWgJMencLHOzxmXwRB1FVVPD/B6iYQVyERkRfz3t6/+29GrwHWC182PN6+fI/ZVvHmnk8Dic9WU CI0zd0K5I3C+2OcYUW+kdUZywZfBWz7XPRHNuwrmFemAkEa5sqULZojDCvHdWMVLV2StPg3vQMiy DyF8xUcxd5nMjToYMuQKjVAikd6X7RIVH1uczQBZLFqy3CjeiBiwVgO+ccLPknk5RXTgBAFDHtK6 XuqIoGSP51hhJiFPciTOPgr/DChF8MIaEwvkEogJtOjBMgojCGQLjhNUbn5n87qRdRHIncFLOhmB X1hrQVLIiTi33zPeGVFzO4xSYCszRpAzwbYdlJLrvpQ3FRLI5TEdnkphhHbKn4A6ncTChW/PMTsh tTsgjymu+u/t61dvAr+IZdHh/eNP+E/PYpwKhFXyH9jLqLjjNIjYMGrDqcQEUKBX6FFIKJvJl9mB g1HyPduGA0zhlfWI1XtiMtPoSKQGgXk9//++ex4xe8S8knmxVgK8vghOIWpwdBRZSHKOcJOTcLLC y7qwx13eLonfq5ix4/ToVcD51dXzq8v/9+X3/8/l9y+jd+AqauT180j7wuh+02ULqRR0iRfu6Ihf CFnyuKPZAewB8xgMyF67SEUIp0uVSJ7EXD1R3DS+bt8WmkHNsnqL8ikRT701SWKFRtwctGQ7ZB1H I2K2a5aOk4U46qRbULiHIgiwwweUJQsEhE8v/VCBa2NxAV2agun4+P8HcVAWNlXe+hIAAAAASUVO RK5CYIJQSwMECgAAAAAAAAAhAPsDrYS8fgEAvH4BABQAAABwcHQvbWVkaWEvaW1hZ2U1LnBuZ4lQ TkcNChoKAAAADUlIRFIAAAMzAAAB2QgCAAAAPYnyLAAAAAFzUkdCAK7OHOkAAP/KSURBVHhe7J0H eFVF2sdDQEqA0CF0QpUOQZogCCIrFsCGa3fXrmthd133W0HXRXfXsopdd3FtqCsKAhYUlSYoxYRe k9BJQg8h9BC+3zlvMgzn3J6b5N4w8+TJc3MzZ87Mf86Z+c9by506dSrGFIOAQcAgYBAwCBgEDAIG gQhAIDYC+mC6YBAwCBgEDAIGAYOAQcAgYCFgmJl5DgwCBgGDgEHAIGAQMAhECgKGmUXKTJh+GAQM AgYBg4BBwCBgEDDMzDwDBgGDgEHAIGAQMAgYBCIFAcPMImUmTD8MAgYBg4BBwCBgEDAIGGZmngGD gEHAIGAQMAgYBAwCkYKAYWaRMhOmHwYBg4BBwCBgEDAIGAQMMzPPgEHAIGAQMAgYBAwCBoFIQcAw s0iZCdMPg4BBwCBgEDAIGAQMAoaZmWfAIGAQMAgYBAwCBgGDQKQgYJhZpMyE6YdBwCBgEDAIGAQM AgYBw8zMM2AQMAgYBAwCBgGDgEEgUhAwzCxSZsL0wyBgEDAIGAQMAgYBg4BhZuYZMAgYBAwCBgGD gEHAIBApCBhmFikzYfphEDAIGAQMAgYBg4BBwDAz8wwYBAwCBgGDgEHAIGAQiBQEDDOLlJkw/TAI GAQMAgYBg4BBwCBgmJl5BgwCBgGDgEHAIGAQMAhECgKGmUXKTJh+GAQMAgYBg4BBwCBgEDDMzDwD BgGDgEHAIGAQMAgYBCIFAcPMImUmTD8MAgYBg4BBwCBgEDAIGGZmngGDgEHAIGAQMAgYBAwCkYKA YWaRMhOmHwYBg4BBwCBgEDAIGAQMMzPPgEHAIGAQMAgYBAwCBoFIQcAws0iZCdMPg4BBwCBgEDAI GAQMAtHPzHKXjB82YnxKbuFc5qROfeKyFomtrZ8RYz9YkpXPf/JzU6eOHdbR/rLjZWM+Ssk6btf3 WNk8FQYBg4BBwCBgEDAIGARKB4EoZ2a5q997+MFX1544DV5Oyvt/WZY0/ptlm9fPf7dXytiXvk0/ GhOz75f3Xkvp8fSMVekbFr7cM/n5cd9ssgib58qlMxPmrgYBg4BBwCBgEDAIGASimJnlZ0y974IJ Ne9+ePDpaczPSZk9LabbRYPbVIupmNB/2MW1tqXtyI3JWfXDlONJQ/q3qRYbm9B7+NCE9LTM3Bgv lc1DYRAwCBgEDAIGAYOAQaCUEIhiZhYT2+LXk54c0bSSBl3+4ez9hyvVjI+zx1WhfmKPgzOWbs07 nL3zcNU68ZXtmpUbJbY+/NXStDwvlUtpJsxtDQIGAYOAQcAgYBAwCEQxM4tN6DagTfyZU3g0Y1Oa e1LzMjaleJhqz5XNM2EQMAgYBAwCBgGDgEGgtBAod+rUqdK6d3jumzX1rj7/7jBl0sNJ1WJiclNe HDXq/YsnLRmdVIHmt0+7Y/i4jhPmD5zb/6rvbiqok5c19ff9n0yctOTOmFc8VF4ol9oFj4HwdNK0 YhAwCBgEDAIGAYNAqSKQtnlTqd4/4JvDzKK7ZH5+Z/NhLyYftEdxIvPzB1p1eyH5hIxp29Tbu/fg L891vFTW4GjVvEV0g1PYezOQiJpHMx0RNR10pmzMSNkYRZmZjjIzEPNclfx6FcXaTE/kMzauZq24 Y9k5hy3Py5i8XZuSY5IS61eIq9kg7tDeHJw0KbYSs0diowpeKgdMak1Fg4BBwCBgEDAIGAQMAuFF oKwxs/ikQSNiZn40ZW1uzPGs+TO+29+hX8e6MfGdLroqZtrEb9bn5udnLZo+M6tz//b1Y2I9Vw4v wKY1g4BBwCBgEDAIGAQMAgEjUMaYWUxMfNItfx+646+Xd2vRrv9ti5PGj726DS6Ztc+7dfSIHf+4 rFOrtn0eXNLj8WevaWON3HPlgMEzFQ0CBgGDgEHAIGAQMAiEFYHo9wAIKxyOxvAAiBqDQZ84mIEU 52MSdNtmOoKGrJgvCHlGjJNQMc+Mad4g4B+BALfpkF9z/z0Idw3DzHwhGkUT6fvBMAMJ94tTpPbM dBQJvmK4OOQZCfnCYhiEadIgcDYiEPg7GHjNUsexzGkzSx3RiOxAgEeKiOz7GZ0qGwMpG6NgYsxA Iv+VMT00CBgEog4Bw8yibspC6bDRuYSCmrnGIGAQMAgYBAwCJY6AYWYlDrm5oUHAIGAQMAgYBAwC BgEvCBhmZh4Ng4BBwCBgEDAIGAQMApGCgGFmkTITph8GAYOAQaC4EcjPWjJxzAjMG6yfDneO/yE1 94xb5uemvHJZ9xdT8oq7I8XQfn5WygdjLpOhteh714vfpebaIceJbZny0dhhHV3fF0Mfiq1JrxOX u35awYR2vGzMRylZx4utC8XWsNeJU3fMTnnx6tZ3TM0qti5EWsOGmUXajJj+GAQMAgaB4kEgf/0H d9/9ccxvZqxKT9u8fv57PdY8cMufpm4V/hITk5+7/qPf3/TC+uK5eTG3ejT1/T+O+qj8Xd8txzFl w8IXOq9+/OpHpmfkx+SnfnTvVa/svG7SMuv7pxrNfPTh/ySfyUeLuWtFb97rxO2Z88+7/pA8ZBIT uurdi5Ofvu3Vn3KKfrsSbcHrxBX2gtPCB2NfSinRTpX2zQwzK+0ZMPc3CBgEDAIlg8CutQuWJ1x8 1ZA21Vj5Kyb0vOaGqyrO/DJll3X34xlT/3Dh+Ep3PXVFyfQl3HfZs2b+mppDR1zWJp6WYxN633rT 0JgZs5bsyottc9vnm3/+920dq9nfDx+asP79+RuiSyjodeLqXvjU3LQZDyQxodXaDxja/PBXS9Oi a2gxXieu8LyQOvnJN6PztBD6M26YWejYmSsNAgYBg0A0IWBlEN7y3ZTphTove1+fMDLBHkNss19/ /NyIJtG6J1SukVA9e+aU/6Vk2SLA2PgLn1yx+eURCRU8TFCzWtWja5g+J04GKIkH293Sv62nEUfw U+p74o5nTH/zlUaPPHd/+wgeQvi7Fl2PZ/jHb1o0CBgEDAJnCwLx59//3gONpzw2qk+71i1GjH13 ypcFPAYAKiYk9bZlaVFa6g743T8favzDX6/q27YFFlf/nfbF6bGdHlLu2nkzDw39zeBW0TVQXxPH 4I6mvntT2z53TIy54q7L2yEajKria+LyM75+6smYsWOHN48yulnUGTA5AHwhGEUhg30/CGVmIEV9 3s31BoGwIuDjzYqQIIKeogFjET9r0daMX97618dr4wf/dcILtqbPLnlZU3/f/8nESUtGJ3nfC/82 7umwohhiY4+Pfcx9JdbkXy/cvPeX98ZNXBE3ZNyn429op+hmfsacJ+99cNdvvnltZCPvzGzW7Hkh diislw0eNMDVno+Jo27O+ncfvfbZyuO+f2ZEo4oe+5KVtTOsfQyxsYSEBoFN3L45Y65/qfWrk29r vOzFUaNW3zW/UL6rXx747hZ4zRAHFsbLTpniHYFWzVuUDXjKzEDKxnSYUZQZBKL8zdqf/MJVrdo/ PvvAycIZOZH5+QOtur2QfCLap+jkweSXL20+YMzs3YUjObDunXs6X/Jy8kE12Ogdo3vi7LEcmD2m ffc7P98WvQM7dUpN3E5rBgvm62DyC8Na3f55pqeBBf4OBl6z1AGMLpFuGBnp2dVUmcmic3ZNmxmt QSCcCFg6r9YdnpiTU+iLGRPfununuHDeotTayk9998oWA8fO2VPYg9hqrTsnqbFZoSVuvuyTJi+/ c7dlLB9lJaiJq96gZuUoGp/3iTu8Ye6M9WtfGNWpVesWnUe9tDbm+9H9ozSeS/DzEXXPaPBDNFcY BAwCBgGDQEzlVkOvGRrz2XPPTpdAX9iMvzdxTtM7Lz8vPuo3gthWg28bdvzjZ16ZlmpHjcjP+uXd D6c1v+bK82rH5C4Zf+1Vf0juNeGdRy5M8Kzmi+ynw/vEVds+7d6+Xe6dSnAQy7t21rSCIUf2ePTe eZ+4Zkmjv0amYP+snPRQ+5ghL85f6kvJHj2D9t/TqH8h/Q/R1DAIGAQMAgYB/BUbDX922mujYt4Z ZskhEtv2+eeu/k+Ov7NHtNmMe5rL2GYjnnt/wnUn/31xVyuibMuLntjV7+WXb02qFpPzy5f/XXs4 Zu2EOyy/B4lD++C0rGiKLeF14mKbXfHEy3+q886Algyq3SVfthxvDTmqtnWvExdVowj38mI8AHwh Gk0Gg+F+Mkx7BgGDgF8EzBLhFyJTwSBQrAgE/g4GXrNYOxxI42c1LQ0EIFPHIGAQMAgYBAwCBgGD QIkhYJhZiUFtbmQQMAgYBAwCBgGDgEHADwKGmZlHxCBgEDAIGAQMAgYBg0CkIGCYWaTMhOmHQcAg YBAwCBgEDAIGAcPMzopnIELCkZ8VWJtBGgQMAgYBg4BBoAgIGN9MX+BFkSuH72egzAykCI+6udQg EH4EzJkn/JiaFg0CQSIQYCj1KNoHDTMzzCzIl8BUNwgYBAwCBoEyisCOHTtuvvHGz6ZMqV27dhkb YhQxM6PNLGPPnhmOQcAgYBAwCBgEQkRgzuzZWzdv2bt3b4jXm8vCgYBhZuFA0bRhEDAIGAQMAgaB 6Edg7dq10T+IqB+BYWZRP4VmAAYBg4BBwCBgEDAIlBkEDDMrM1NpBmIQMAgYBAwCBgGDQNQjYJhZ 1E+hGYBBwCBgEDAIGAQMAmUGAcPMysxUmoEYBAwCBgGDgEEgDAisWb06DK2YJkJFwDCzUJEz1xkE DAIGAYOAQcAgYBAINwKGmYUb0YhsL8BAfBHZd9Mpg4BBwCBgECghBP734UcldCdzG+8IGGZmng6D gEHAIGAQMAgYBAwCkYKAYWaRMhOmHwYBg4BBwCBgEIgEBF4aPz4SunHW9qHsMbP83NSpY4d1JA9D 6w53vrIkK9+aW+3LFh0vG/NRStZxe8pzUqc+cRk1rZ8RYz8oqH3WPg1m4AYBg4BBwCBgECANwL59 +wwOpYVAmWNmOfOeGfFizKNzsazaMK3frGv/+EHq0ZiYfb+891pKj6dnrErfsPDlnsnPj/tmk8XY clLe/8uypPHfLNu8fv67vVLGvvRtOpVNMQgYBAwCBgGDwFmKwN333cvIU1JSztLxR8Cwyxozy0tb OuNw1TrxlcE2tnrNejFrFqzeE5Oz6ocpx5OG9G9TLTY2offwoQnpaZm5Mfk5KbOnxXS7aHCbajEV E/oPu7jWtrQduREwKaYLBgGDgEHAIGAQKB0E2rZt26xF86WGmZUO/NZdyxozq9C2/03tT+zNsURf +Qezd8cNvKxX/ZjD2TsL6VpMTOVGia0Pf7U0LS//cPb+w5VqxsfZIFSon9jj4IylW/NKbzLMnQ0C BgGDgEHAIFBaCOzYsYNbV61W7fx+/Q4cOFBa3TD3LWvMLKZaj98+ev6023piOtb24udi/nTPFY0q 5mVs8iSWPZqxKc3vE2CboDl//F5lKhgEDAIGAYOAQSC6EDh8+DAdbt68eXR1293baN+4yxozy8+Y /uf7Vv12ylLLzuynpxs++5eXU7KL8pDRjvunKA2WyrU8pqVyX3NTg4BBwCBgEIg6BGrUqJGe5l9y EbHjivaNu4wxs7xdi2fNrNR/QJeaPDGxjc771fmbJs7dGNMoMcnDE2SpNSP2wTIdMwgYBAwCBgGD QKkggKnZkkWLS+XW5qYWezkrUIir2SDukBifxcTYSsweiY0qxMbVrBV3LDvnsB1YI2/XpuSYpMT6 Fc4KRMwgDQIGAYOAQcAgYBCIRATKGDOrUL/X4KH4Yy61wpjlZ/zy7U+JNw1sWSG+00VXxUyb+M36 3Pz8rEXTZ2Z17t++fkxsfNKgETEzP5qyNjfmeNb8Gd/t79CvY91InCXTJ4OAQcAgYBAwCBQzAlu2 bOEOcXFxxXwf07wfBMoYM0ODeenjbw/a9fhFbfEAGPJV21f+eUdSzZiY2ufdOnrEjn9c1qlV2z4P Lunx+LPXtLFGHp90y9+H7vjr5d1atOt/2+Kk8WOvbmOF2zDFIGAQMAgYBAwCZxsCh3KtuFGNGzc+ 2wYeaeMtd+rUqUjrU+T0B8P5spELvMwMJHKeDdMTg4BBwCBQuggcOJCzZ8/eVq3C5uA1berUPzw8 ml1PfSjdAYb37lG0D5Y1mVl4J9K0ZhAwCBgEDAIGgchEIDv7wJat2yKzb6ZXRUHAMLOioGeuNQgY BAwCBgGDQOkgsD87mxvn5Zn46KWDf/Hd1TCz4sPWtGwQMAgYBMKPwM6du1Bjhb9d02J0InDokBUe 1pSyhIBhZmVpNs1YDAIGgbKPwOo167Ztt7LomHKWI5CTczC8CGRmZoW3QdNaaAgYZhYablF2Vdnw Y4gy0E13ixmB5XYp5ptEaPNGgRWhE1Oy3ZLH4OhRCdUZhrJ9+7Zf33hDGBoyTRQNgYhgZqeOZq6c M/k/L47709133nXHvX/62yvvTZ27Muuw8Rot2uSaq0sZgbnz5i0ycbSLbRI+nTSJn2Jr3jRsEIgO BI4ePVZMHT1y5EiwLW/Zsm3v3n3BXmXqOxAobWaWt2vF1BfvvWTIlbe/PDPtWN1zO3To2Kbu8bVf vnDflf1G3Pvi9BW7i+uZM4+CQaC4Efjpp4U/zl9Q3Hc5m9v/acFZB++xY9aSGHY11tn8FEXp2JWt 4UE7CFl4S4tEKxLH9u3bg2oWGV76xk1G1R4UaB4rlyozO7r2o/uvf3hGzCXPTF24evanrz31p9Gj Hx79+z899fqnc5MXfjPukpgZD1/6yEcbwv/YFR0404JBwDcCBw8ePHHiRFxcFQNUMSGwf//+rZut kOVnVREBidFmnlWT7nuwxfEwhJYGQKRlFcqXN7NTRARKlZnlxTa+9Z2v3xw9snebupXP7Em5ynXb 9Bk5+rWvp/+mxcmTRRykudwgUPII7Nmzh5tWqWKYWbFgj57l269nFEvTEdzooUOHlq9YJR0U4Zkp Zy0CQsiqV6++b9/+wEH4+eefQ9BRBtj+/uwD1rHBbNkB4uW9Wqkys2rtBp7frHI56V3e0aM8Z/lH M5d9P/2bBan77QgtsZUbdj+/fY0iD9M0YBAoaQR27drNLbdtC04dUNK9jNr7BatnidqBntHxvLyT SkCyaHFy2RiUGUVoCKDRjo2NbdSwAZcHGEVlx44dN19/w8MPPVRM5OzgwdxKlSoGxRRDG3uZv6pU mZlC9+j6Tx+8rM9T8w/s/v6vw6+558F7b73kjhcX7TEeAGX++TMDNAgYBEJAoEqVysWhwwqhJ+aS 0kLgQE5OxYoVq1WrRgcCdM88fNiKfPbDzO+8HWzS09KKMhxMOKrGVS1KC+ZaQSASmFne7lkTHp/R +O4rW+2d8/nnuwf8bfZPH92dN+GN2dsNNTPPqUHAIGAQKEQgt9DWu1nTJoFLSgx+ZRIBRFO1a9eq USPeZmbBqba3bPFsoLlk0eKePXuGBpc5KoSGm8erIoGZHd22fvWJgcOvTIpZ+f2Sk237dm9Wr1nb 5ieXb99tDMzCNNVkcg1TS6YZg4BBwDMCmD9jB1as6KDNpP0KFSqIpMSUsxyBKpUrCwJHggxpdsif O6d4AHgjcB5hJ14G36PNPMsnJSzDjwRmVqF6rdoxu/bs27X2px+zG1zUpXnsgfSVG2Ka1aoeCb0L C8ymEYNA6SEAXSgz1uKMRYqCs5gsZkKYLkLzp6ZtDOHCYC/pkdRVLglQhxVs+6Z+dCGA5CzsT0Lj xo0BwS+BU0AhMCO3Okp2FKzRhV5k9jYSuE+lFgNHXLzjn8N73z25yuWjRzVMGXfLb98+cc3vftU6 EnoXmfNmehXxCGQfOHDOOeeUejchMckpy0uGMZTAYBkIlu/8qNN82F0BQGzejz+FwGVLTJtTtWrV 0HRYJTBB5hYlj0DlypXxBtixI6Pkb63uKC4Ibdu0rlbNsjMrbuFxKY60ZG4dCdyn3DmJV7/45Wdv vvzSK6/f3y+ucoPBd4+f/vFTFzcs8NosGSTMXQwCYUWAaFuyfZZu2bNnH4wBk5QQqEbp9tzj3ZXb 14L584upe+L/GKzhjsB74oTtU14iBfkEUgqzBZYI2BF3E/11bphguWfu3rPXby/lPHPBhQOXLFni tzIVlF2j38q5uZYYmxUPmsgHUbubEjICkcDMrAUte+uque899cB1Qy/o3fvyv0xduXn/UWP+H/Ks mgsNAoUI7M/OFj++PQEs3FEEG/tQsxbNi6PDshsFKwATJodvWnF0yWObXTp3tKfVZMIpMcgj6Eby vNWsaYWUgg/Fx1cPpHOinRRNpd/yq0uHrV271m81qSDvCxaQAdY31XwjEAnM7NSJDf8bfcMzKS3v eOXDSZMmfzD+5rpzR981doZxzTRPb3QjEBtrxcLeu9f/Wbb4xokUR6LdloFTrMiHIJrCgc7v148P EgggjEWAEhlA4CVw6ULgbfquiU5T7YjharPMtHP8+PGMjNLU7kU4kkj0/fawVq1afuuoCmSIIuZt 4PVNzchnZkfWfTd1SceHnv/7ncP69Uzq0X/YnX99/s+Nv3jr+7R8M30GgShGQNyUSleNCIOpXib8 +KBlElu1Zo2C0NMXDhrEn5s3bYqER6RUiC+m38WRMzES8CxiH9asXTvh7XeK2Eh0XR64KwxxMYoj f8Y55wQtMFu1eu2SX5ZGF84l09tIkJmVq4A3R7kKFcoru7LyVapWjjlyIs8oNEvmKTB3iQYEYHjB qtiEFIqKIVi/+kiDRFGfLVs2S9+aNy8WbaY0jhY4KAQUQwowGntQjZvKwSJwrMSVy8H2MLz1a9Ws eeTI0fC2GVprbORcGIgIOSMzi0QpJWkAENqISuWqSGBmVdpdcv3Q7e8898a3KzdlZGVtS13w8fg3 V/QY0aHa7qysrF0HjhrRWak8G+amRUWgvK3N3LXbStNU9LJ23Yaffl4clMW3MkYpDr/6oo+oiC00 aWJFW83MzCpiO47Lhf4igQiKB7Mvipo1204dWDIFzo1TXsncKxrvYnZ9j7NW1Rai79vntFBMTU3l +xaJBcEvicSRmZEZ4LzjmiO2/4VKdl8eACxi69anZuzINMHPvMEbCcwsb1fyrJl7t8567p4rB/Xr 32fAsBuf/HLr3uTnbhrUp2//Ppc88o0xFwjw7TDVIguBiqLNDDI8t7cxsPbBFdas3RAUY5DWxK8+ stAJtTdq+NjP/frGG55/5pnA9Th+74mUcd/+bJuZHSVyrN/6qgIkoH69elClklQvoqcO4WEIfFDR W3PT5s10PifHCuVQYoVnhpf0ZInk83bMe+XKlRim32MbLpk9e/cSYbM3+1eJMUvp3r373NmzA0dP hb31ewluKxkZmTkHyftpnV1NcSMQCcysfN3+D346ZfIkzz/vPNK/npm5IiKQtjkibHGKOIqz+XKl l4QB7Ny5K1goWDRZykvX4i3YPnurvzE9nX+1bduO39eOGsXvMIY0I14aCCNipNnAA2eALdy3SlwV XORKkiqJ5qhsTGu4Hg+9nRLWLB84cADPg5J5AMRDpWrVAhYVeKyKVq1bBwh1UfJMBAhC9eomlYXn 2YgEZlauQt3W3ZMoXdo2a9yoccsO9h+FpWubutZpwBSDwNmMgLCErl068TsEY3Pxrg+cakQg1GK5 0qpl4q5dO/lAnAB+q/N9uDoc4I6i3w5BBYbMqH6qxsWxQaqIa+Hqko92CrNZB5czsQQ6dhbeAk4m oy7JmHYqSoXIzAKx7iqBqcFJ07fkWBlxnmOibHiZj0hgZtapL2vRxLFXn9/tvD79e3fvOvC2cZPX 5Brz/xJ4h8wtogSB4yeOV6pkLb4lrC+LHHiEjzZv3jQ9LY0PderUKY6+ic4Xe2qMxgJ0AlixcrVi Y4ErdIqj86ZNEDg742XI4hDCma04npnAnTQllVMJizaLY8hhbzMSmFlBPLNl7e97vSCeWf2Ff7r7 kc/SToR9uKZBg0BJISAGtnXr1hGTlyKWnAMHUVoRUhJFW+BSGTlDc54O3GGqiP0svsuVb+mSRYtP nTrVpWtB7sgw3nHZspW6EVsgLbOpRIhPXCC9PRvqLF26jHgZGzZYxuwlWY4fL9ivTp4sFrOBwRde +OHEiTIixLTpGyPIQEWoleRlshccS3JMgnO/dm9iNmCKG4FIYGZH0+Z+TTyzZ5+4dejpeGZNv/vP 7DST4ME8s1GLADH3E1u0qF279vbtO4o+CBXIsVbNGoFbjMkZmvN0IA5TRe9ksbZw8GCuWsdP5p0Q 4ZZoM1UOzaJ0gF1knx1+E4UpkrkAm9IzSSujnwCvDaGaZHQI4cKz8xJ8+0ts4Hl5J8qVKxcbG4vN wKFDoUc/5u3Gb9GhVcfHZevmLU+MGSvnvZATPyBvjq9R89ix4/0GDszPD3PQA6VaFckx3JHQCm78 YWxlxhup+J6uSGBm9uiwnq1wZjyz4hu0adkgUFIIdOvahSD1RQ9HzhlUrGX9mhZlZ2crkxffVlMI 4aLFeJzVXI+AkJ9/UiiR5JmRnDNFLEoTJDZ5ARbddK9kUtNIRgdTvCEgbKxGjRrw+JKM4Zeff+qc c87hFIRAlxeQ36HN0f792fgtOrid8nFJSUmh2RCsIaUzJ/JOntez967de2+97bfbA8uAHojjs7yM IpjXi0drM04X9B9btI4dzg0NorPhqlJlZqeOHtjJe3SgZu/hF6e99ffX9XhmG4feN7Sd8ag9G57B Mj3GevUsz+KweO8HYsMkqbiVYsV3yhR0nSUZfKso86xMvvQgTCHvTz56gnNlUKIvtfcYUZagunz5 8kD28qI8DL6vFTaGm2Sjhg3RaRb9UBRUV8uXL9i0QhabZWZZDi66LFbvwN+feoo/Q4vMQrgyiWQm R4gThS4L3gYogc0CcXyW84kI5inijuDN7kKMMerXq9ugQX2pGS2rUFBPQhErlyoz2/nNI72JWNb3 guFjvnPGM9sy88ulQccGKCIY5nKDQLgREEP1Ipq4Osw4fPRRNBR6RCVv1rjUwYmMk31x8Jvwokj+ lsOHj0ibM77+mt+NGjfit2x+5DXfsGFD0e8oe2Gb1q2CEn0pIzNdlFV8kJak319okF49YuSUyZND uzaMVzVt2uTyyy+jwW3btoexWb9NQdBr1arJS6fk1n4vcVTACoJvHG7Ue/bs4csnnxqHThOCFdpj gNK/apxFnnDxpntH/MVZDNnxGco1eNAA3Gi4l+93QRwXTHEjUKrMrG7/R7yGMZv86aP965oZCxMC rVsUhHUOU3ummVJAIHDGEEisS1Un7OYmYYcGPaYSIfz0009El23Xti13kaM2ec0RkBT9prIX4mMR VFP0rXmz00ZpYgSt5CVhlx5xO9nwSqBAOkNTya1du7YEuufjFrjdDL/icvH7K+GCzAy2gb1ZUe6L sZpDKrZrpyVI69W7N/Zh5DQSzX6wYlqU/o3ttBlIhY8eOVIuRpkP+eqskELfxaP5o+Nd0FtAnNa4 cUN/rZ7V/y/SA1RU5CrUbWNFLeveoUHdxl30IGbduyRW2rZiSxmJWV5UmMz1BoHgEDhxIgi3ZqX6 DO4eJVVbt4Q7cfzEtm3b2rdvz+bHtiS7F4fyrVu3ulPNBNtBh02S38hkiC5EHqAzZvUZTvbzzz93 bt+h6B3zOxBvmi+/F/qoACeD70appTZuN8UUVKUokAZ+LY+3N1ETAZYxiMRJBblXz/OSAm9TrylP adVqVdPTfTl4tmnThmpCCv0Wt/mjxxBrMi5OF6fdBQIOT+O3D2WpQqkyswIgT2YvGX/HP2ZlSgLz U4e3L3j7D1eOemTOTmsOTTEInPUIqOAXfpGQhS8QUcfJk2H2zPLbt9Aq6GodgrqtWbmqa9du1uJe qxbpkPlAyNmffpw/6ZNJobWvroLf6D78Pqz6IFuwrmEXD123bj2Xe/QYePWVV26+/gb+6y0HThF7 K5eLhE/CwYe3CLn3K/MDiu+//z68t5YHOORYJLhC82yEvUsl2WClihX1yDg7duyQ5LBClSj4Dtep U9shRMemPsAIfLSQnb2/fPkKOwJOi+l7+B6fEzE7c6hlRZys4mvw2Xi0eMQ2EphZ+bq9L+u98P/u +sesbbtXTXvqjpG3fBpzw2vf/uNXoWVlys9aMnHMCPR3rVv0vevln7OsDSg/N3Xq2GEd7S87Xjbm o5Qsideckzr1icusL/kZMfaDJXZlUwwC4UQAc43soqnbVPALv93CQUzq+HW6FG0myhe8/f02W4oV dIGQ0IVKttWwYk6jf//7Ib8aun37thLr5NsTJsC6hg67FB83bopLGgrNenXPiHwbFgWrY0SSlcvt ARe4mjtwiBS5920v9eO8effccee777yjtyyhgItS2L8BMDRzPVyha9YIwru2KP3UrwUoZf4fcpvy 2qLNVC1Aegb2609yWJT4fNmxYydvQcICie9audCx9+BBKwKZX4Qx4iTVpu/h8FhCoxsmNHBX85YM oDie2JAxj8wLI4GZlavQcPAfXxzdeOrdg3qOHLey49gvPv7XXRe2qBaSZ2b+1i+e/OPHdcYs25y+ 7Nv7Y97882vzUJPv++W911J6PD1jVfqGhS/3TH5+3DebLA6Wk/L+X5Yljf9m2eb189/tlTL2pW/T LStgUwwCRURAJCXCIZo0abzfDpQVxuJXgRVgIib2EkXmwtg9moL5IcsJxOLN9331gaDNpLKSHMiF nLnr1g2DSWrgVtXCusRkh4JsoFWrxMa2U4IqPy1YEF48aQ2vPXS4jhuF/S7SYOBoUDntTCpGKGDR 4eKnSQCIrKydwQrAhA6GYAGpv3cykKPHSmJJV+ecIk6HPO26AdkC14N05HDowdIa1G8gguGdWZmr V63kg29ydvsdd/zvw48Q2vkYl4hssfp314Eswiz98r8iglYmLy9VZlYQNYPAGTuz4wf84R+3ty9f rdXAC5NqH99jfXU0hIAw+emz3p3RdNTlnavFxFZrd9O/18wdd2HdmJxVP0w5njSkf5tqsbEJvYcP TUhPy8yNyc9JmT0tpttFg9tUi6mY0H/YxbW2pe0IQ2CkMvmgRO+gcJvftKmk42WnpVlZt1u1bGmt s5UrFzHYrG4CJQos38SLBbHolKiIM85mjBJWD0IWSIOYNjnW8QCXdR9yGuQQe/daRMF3oavV7ZgC fovcq2ZNDyozFQUAHzq/7YRQocRUP6dO+VIfsBn7npf7772X0Y1+6KFtW7fyIdioMfL0YgHJmztt 2vTAgRKZU307VA0FD03RAxZfmTZ1Kj/S4YoVzwn7jRyx+uJr1Ni921LiF7H07Nnzl18sYZhvt/EL Bw2ijm+XDsw9vYXyR5DGOuD7FsjPotScsYhT4PvyUmVmhVEzCJzRv0//YXf9e+3JA8nP3TTI+rNv vz9+E5Dl4Rnjy9u1OmVlXKvmDc70yjmcvfNw1TrxEju7cqPE1oe/WpqWl384e//hSjXj42wQKtRP 7HFwxtKtxritWB+4Em6c7ZZULR9M/KiE75ucksKugDCf+yYkJKBhKUoHHCZQfpvCNcz3zuq3hXBV 8CGQk0Tg+gbPZ07YOulkTd+y1VJTIkVo17b10qXJPXv3cvftsssvR07j7WRPIPLlK1b5Jmeyebgt xpYtX+nQC9ND7kVlYplaPTkzoKiPKAB6dp2Q4Y2NLcez5FdVHXL76kJkZqKbczuI2NLQXNlNUXUl NGqIWMVxR4EIekrcYxukIE7ZVBaiwzOMKeHyFSsDt9XbZbMWNQthIbI8lh4fHp6E55599qXx4936 PqYpKC8cj/Mlr4ZKUl7wvNkmYqHNL/3UAVm/xnKh1Y0U3W6YgURy5knwpp3EGI5b6OHK3ISe41CA p6/QRh2lV5UqM6vTd/Snn03yEjijMGrGyTzxDAioHM3YlBbT/OSmt+/uopmO5WVssgInO4td2V+x TdCcP/4uMv+PFASUzCbwxT0sXScoUYf27aUpkXKFsQN+TX2JF8DOiv2Hj8OoJJNh9y36FuJlX7E0 jz4IIhlm2Hf1gJzuYGxqTe/bpxdavJwD2a1at3bfTrSZvunv6jXrfMys3MgdMgND7NS0jfqFeuDN AweyF/58htZSTKH1HU71Cpry5RdfFPHpqlK5CnOqdusitubtcnZKuJE3qymHOLZlq1a0g6eqtNa4 adMbbr0trlD6WM0+nFAC333VA8kzLDlnAxe5iSleeB0z09I3wezdWPEkvPX6Gx6Fo7yAil8GNUeC kuT5kFcDeRXclw9t27YVCzPOaSE7/Opzume3hZU+L+KG6bAW8Nt/K6C/F2EzjA1xmh4BRIhgUMGc /XbAY4Vo37hLlZllL/347XnbYhI66BEzrDgadiyNNrXzMlOmvfinv30XpDh67cqNiX/8afOmDQsf LP/RH/82fWtRzPrTNm9y/4T2rJirShGBwBf3sHcS50HaDKOcA2VlIGZACJ/0ZdfB5ySZDCt14PIM WsPKIMAtVqRl3vpJZsBt250hQKUnbj2sHjAshNlRwTU8Xiv2cPIv/eiv5GfiAaqKYlo1a9U6mXfy 8OEz/CJnfvut4y6bNU26SJJCLtC+CufY0dvtEMEht+P3Qp4cZTjl9gAQr141TdWrn44ABz9r3iIR GFvbAeco5QuN2X0bjfF2KMN2aZ8nk1sj/W3RInHxkmS/fZYKGHS2bVvgwCjf+HUv9duyX5tOtxWp PEghxKORR1F3XUR1npBQEPpLPIG2bt3i8ZhHnBe/asFKlSvrD7k3C30HJj5OAjJrbq8UvQX3IqD3 QQwAihiL2z2J0b5xlyozqzfovt/Ez/r95ZfcPOb1T39YvCJ1OyZnWZnbU5cvmPm/18f85pIB/zer 2jX3XZzg9+UprFCheq3aMV2vvGV4Ow4dsQldL+wRM/+XjUcbJXoK/GKpNQNu2VSMbgQc+2t0Dcax uxANKHD7rcQWzWWwXly3TuEIFsg2zzZJRj97vxG/5kCLRybHbif7lv5f9wpOFAA2D+zr5WZozZo0 OSPXeCB8V93CY09gWmw8J2xZhbtwuMd4SL+LYlq43PIvhyJP9rC8k1Zr+LWpBtUM/vjjj365Av1E CeieFDS9lSoWxEwvJkmnjgCx7D2KzYST8VuPd+pIKt+ocROHctl3lJZFi5P5kbtL+6J346Ft0qQJ x6oAH3gMOvVwJw0bJpADYN06X+LSQJ9j7/W+/XqG458S5LaIXs88BjBd2Py57QPKL8nAfZ+aJK2n yLfqN7BcKbMyM/wO/1eXDvNhZ7Zps2VHKEI+j4Xb+Z47CKXfPpyFFUqVmcVUSuh9+/hvvnzhmnob 3n30huFDL7QszM6/8OKRtz768Ya6V74w7/Pxd/ZNOJ3p3O8EVWzQolWcu1ZczQZxh/bmiJOOrcTs kdioQmxczVpxx7JzDtsytbxdm5JjkhLrW4edMlc4QJS5MZ1FA2JLDiHyO8pKMPIW1J6tHWsYFmuE GYFs8xAp2TID9OVUekzfwhLdzEUucTA/B6Fkr5WJF5mWbpTmTZupmJCoh7Ao1z1CpIWTJ/M8WjET f4GB63cRo6KRV1/Db4nYpKuWZA/bb3smvvzKq1jF/e/jj/msdKC/ufmWp+3Uhz4K9I47epwUpkym tVjD0YmmW27krSDqAE+i0iPToo7DUJ1ZU9MhMqdj3n0kUUF6oxQ77KzblSpVlt0de69Zs+d5CxtB BW6KWafqc+VK1q7/5VdO5uQbf4//9S3Ukf7r0S4CFGx7vJco+3g1Fi9axAdCK/O7Q8eOIXRbv6R2 HUvpLwK5du3a8XCSYlc/dXj0lvAdHI4Tb/369XxkzhDxWICC9iIOsCxdXrrMzEKyXOVGSSMfGv/l /OSFs76wbM4+/2L24mVLpo5/eGRSw7iA8kecnpDY+PMu/+3xd160NZj5WcvnJFccMaRTfHyni66K mTbxm/W5+flZi6bPzOrcv339mNj4pEEjYmZ+NGVtbszxrPkzvtvfoV/HMLjfl6XnI9rHEohMJdrH qPdfyA2yHNuGzNfbjUimQgWrmr1rWuwkkCI6pkBqIgATUhX4oqxkZoFfIj0Ru6JVK60oAO4Cr9VZ FxblW7cVBD9TxkDuTD7sjkRad9M1hGR/fPTR9h06cm3btpbQnZhe6qYiQitnq/CImXL5FVeoEBJ6 Hd9is2PHfCEsdFApE9n5HNkL3AiAZ4AyJ7mWxqHs6rNHVAWxXr16kwpJVcDArk49awlVKk6yYqen px/IztZDpzoaVHZI+nMIB7XaOVMYI+aAotRzF3fIDKkDXQvZO5ucrd5UhCImvPs+ywtVjiu6kg58 AnxT3ANR7ZCLDJGVkkiJRNZBgj1C4fGNroVCqfCoRpqEX19//Yb163RDT4ICStQ0veB54DsEjG+P ZjlBEStX2nTncTJJzT3OYOkzs4JulatcIyGxvWVg1q19Yr1qQcjJzhxXtZ4PvvPP9l9e17ZFYts+ L5+8+9VHiZoRU/u8W0eP2PGPyzq1atvnwSU9Hn/2mjbWyOOTbvn70B1/vbxbi3b9b1ucNH7s1W2M ZNXjcxKtX4oSE+NxDKRKbAzedojQOhCIJYfagVhn2RLEABnWFR9fYH/t7dbUDORwz0JPgZZRPMbj kAqOuyDqoHGPG5vHfVpZnQcYjE3djm3Gt85FbXVCUJRsQORS/Pcc+2SvF74k0rrH3IvI7bCSjq9e vWnTptzX4ZqXsWOHsgHr1LkzbSqroH8+99wf/vQnvlEp2AHN4f4JCKLH9G2lpESSTLFfQyjw5MGA 8DF8budD5iQIKFbN8FV/FDiF7L/A/z2h4ekEiCqTD+gpNwjkhVhi+QjjsnXrdhEJ6/MuUxZve89A RlFKkkpIYtZ7M3tyhMzQZ3PFCs+s3fcriZwMxLydE4QhYZjvsRFJnRmInYCPPqAnPf/886sW0lOh pMuS/Vjd0eEFPy0ScaNe6tStx9QHG+WVAXoLASMPklArbwVxmsPC1eEwa5Kae4QuYpiZ71ckmP/G JvR9YMLPtgHgtHEjLYMzxMzV2owcN2O1/eXqr54aSWAzu8n4NiOf/KrAxl9VDuZmpm40IMDKfuSo 5TRXMsXHDhFCB2Q59mHJodp06BnRrTSo7wz/qJtay4XQEr/aTCzMhDbJVu3eq1KWLl+7boN7dLIK e9vb4G0qnwzNUhxOWz4cHcToWG3S6Fx8hPNVxEuYmSO8HHtVsNsVvosocWhK6XoQgxEXQxDYs7sg CTTUjT9FJ0Xpf8EF/fr354MyVoOLQFLx5FXQeSNkYCjhshCF+iDTxNb6cOJEx0QIlyK6nM3Pjolt kO8ixEKkng7ZjwTUADGxQhMHF3eR5FR6IaXV/ffdp39DZ4icwjdxcdZz4i6VK1nfcysCxhI/RR4k XWYGOVB6RreXj4R6RqqnixUZDpZnwQq0klOW+WC0bpMygc7vm+UYMuJDR55yXvx+/fr9a/yLgbtM ihhs9569JM5S1n5ADQfauyfEWGgeQ9LIRPg1FGNEvl0TqBB4Xil/T24Z+X8ZZGZlZGbMMAwCQSLg Fku4Q86yKyenLNcbDtA9k9M/hTUUUZx7nXUEeS80uyknjMebqRmkkKYkWJR0XvZ7tWtCpLwZ2IlS j72BynQMWxy3ITYVPEaiQr0lciyhQcIwageQbFH2p6rVPEgikQwp0QJbYN16Fid2C/MI8EFcCUUo RdylUw1vsqXNm7eKhlEklx63fK6FdLrttYXgqn00cG8YpoPp1nWvoE0RUWI9W3GpF2SBojJzl7wT J7768kvmSMXXoA7EVO+MoOFGwPY69BADnMdszdoNG1KtqM4UoWiNGp1OxtCta9c/P/oIs6D3h0mc 9OnkGTO+CfL18qxFRV1LO25SKHYCwcrMGJE7BhvfjBg5MvDeytMF4ydx1i+FuZXEoHP//tNnAN1w DTb/5htverzFBQMG8P0X00MP+FKrZg3G5cNkAqEa60CwNgyBAxKNNQ0zi8ZZM30OHQFSAlBCvz6A K1OLnDdQv4lfXZWqrG9pynDewZkQLLECCvUJJNGe2inZY5DWwAngBx7Zg75/CxVzC6K2bNk278ef pMONGjXEGJnOcLi372JdgpzPZorWZ8J48ltREHb0OvXqiU+ZKgyBHYhdp4Fm961XkEhUIm1C7KGE cPo+UamSxTM8GryLZE5NweTPPuPPLl268NtjInO+p+U6devefNtvxPqtc+cuuv8mA7zplpsVeRL9 nR7wSazvGZdDqLN12+kgIzBaj1s+Hp0XXHCBQ3YIOHpTEsbCxzapT65Mt27/J2poh5IXL1Q4GRNE fC8dfDio/AltrVW79tChv9L/y2clLNT9OWwrtwKBnNj5nXNOxarQ4cLQaFwl/Yd2w91F4chVSCib N2vmuAVddSThEC6IuaFv2zuEeQ6rf49aVFx0HXeUPwWiQKLb6JcDrxJBhRaeGmSUUhg/FdVnYWZ7 9xRIc/WbQlWfGDOW1JweB2JZpN14Q1Hy0oq834eJAqkCmEFli+axG2fbl5HAzPJzV0x56c0520+H HeOb/z3x9IxtRQlEdrbNpBlvAAiwRX308SfzfpwfQN0Qq0D75s79McSLPV0mK5oP7yfHRWI6Ldoo iq5nOZV/Sidqvp3vVLOasbnFb0Sh6SZnHnMjireaOg2js+CzKLDYL9u2aYUQTpeUWCZfhdHaJDl7 7qECYUlycsqvLr1MRGVasVwIUTO1b9/BB+ZKaiLEAplWoRjJokE+LpTbMQXwTpRxL784Xi53X6LC Rmzftpl00dhNy00xSnvptdcgFuqS1q3bCHliE5UMB7q0QOLMMX1Q0sA9MyBqbL0MCuYqcezUBOm0 DMYsoeR9bJMiEFXxMrAiUvprIdxonB0at/P79SOXKIZljPSyS4epkfY47zw+b0xP37vPIt9t27Xj tx5fQ1FSoSPqT4YvFo0k0oBVcyTYsmVz926d0SBbyXwOHpTwv/tthwAK3p2IP3nYunS1SLOjNG/R XElJ9X+5Q97r/4XAZdsJUilCEL15HrjvKN/waAUVOEOmu7rtO0kRZqZLtnxLlRS73bc/G+tRru3d p686A/hwE1EUEEt/R1Qa6QlfutM88L04ZPi2M1MV9EwADsRIFeA3erZcAhfndOcN8LL0fekys0Ob F3yFYcTkSe+98vKHn9kJyOyfzz/+4N0PP1u32zCzsvSsRcBYWItZhoqYxdLHOHAB+3bmd8U9UBHk eLR6gdaoLC7SDf3UrnZrj1s+6z5+EiKC8jEEkT95NIdyN1uozbRIFhbcQoxEaMFGAsXBTsvhDaA4 ZbVq8bCKKrapEB82pKbR2rJlK/S+QauE/AmT8JF6uXevHvambvE8+kngRD4gFNm1a5c4BPhwZWVX e/WVV8Rw6ubbbsWInr1E8TP0dwyhRnyNgRdddG7HTjfceGPrVq0aNW6s+smfdHLg4MEykBYtmovi lbRRtvbqDK8jmSPoNWTLIXFpVGhrL8JOXaZCs0o68vTf/45UCdPA3ba5mwgdcRSA1hw+csSvoFQM 2lSvZAZ1Iq4TU5CMiytgEpj/16ldW/L5UFQaAERT69etV2jstJF3FNgeMlQeDAniL2X9hg0itGtQ v+62bduYqU4d28MKQUY4ioRsoKuIXY8csXiM27CSLyUbh6Sy1Yty0XX3R55kBakKCuiuKd8cPuzB jBW6GZQvi1T2YVEKc5WjwprVq731hO8B/Njxoz//tADZbc1atTF/xOCM809OIdH0di3qeBWVRq/j 8UtVwa8Jv17BWyonv4+l3I5FIH3jWREBqnSZWaWqhxf+4+E/jJu4Iubw96/+fvQfHpafPz4z5dTl /3dphzIZW8zHK1Vs/yJVRbG1HdENswGjblDqDDy87GX08LLlZ9hahWsMLBxyi2Itsna7T/Bsn9Aa XQiEAIYv2WZE+aXiqQqvcuikRLgihkQ++i9X6Vu1ImR4hMlnXdcmkgMlH3K0LBICfT8WmV96+kYo 5p7du4V4iXiD3WXzli26MprGObILIK3atBX9FF5pQltl/2bnpg5bGpIP3eKNYSLnQ5qVafvtejNC 5yrui0xIev67Bx7kd4P69fhGKCwUk6aaNmtG+PtLhl2CErNwgqy7d+rcpYbteDhg4EB+E761SpW4 G2+5hc8ixnBwU4AFYaHX3mzOqABlOWizTCnuKVNiM+aC/vAWgBVCUyoDhzf3RrlKJFjEaWPdEBGX N61cZkaGHo1WjK4QdPFb99nctmULgTP40uFCqzTg8L9z27Xht6i2pey0kwXVrFlTdzJQzzBdpRCy QQyzJNSIHlFMtQNcDF8JwOR7UgX4SHYuJEl+k4LCo8QaBa4KJuyI2MLjh2inOAynRPPusYjsaq8d Sw+N5hdTp/IhPr4mfGvO7NnB6lX1W4hz6Jy58zlcebt7gN/LlLkrW8Ff7Mxmvh2HRTDvBha0Axcw B9jV0q1WusysQr2Lxy3cnLbkw98PuerZGelaHqT0b8ePamciWJTuw1EG7s7m16RJY5VTfM3atRJ+ 6Zi95oa9+HAPDDk9FAflAD0HZVOXQckZVIgI4pP16y3HSY87vTAhFjtZu91xmNxBTR1qGl0wIJ+l KbFYt4PZnpLv3eHBCGgCc9q6zbKslyKDfe3VV+vWs9iPPkcrli3jT8V9paaSsY3+4yO7du9lIOs3 pJGUk3+Jk1rLxBZyamdLEMrIMwBl52GobMurEHiwr3uLoAtN0aV6sDHZAmlK+JCuAKLPKDElmKfc XRHlZrbRVcWKlnLwXttFURLVS8x6x77CVaI11oevPvNf6IhOfyXOhTvGh8OgUGgN0kFvijndQFAF +1DTXejYUbBriC2/kogoUXS7dlYgif4DBqp/tWrTWvJKtWjRQg+O5dCAI9hDDKb23eM22UJIJu3I G9S0iWXgz4yIwSJQywQpzu3xXMEioN5NRdECzD6pUlA45oKHEzWuWBOSqEC3WkPZimhnq62qDtwJ NEDloMdHQk3EGjtPef7JPNJicrBRcXfpHs65+rX0XAwJtmz146uLew0mayCM8p0Ab2qC/AbS89ZV 9/e8m/IwkArCG/9DVi0X6jHY+JNzHWgH4nEceH9KvWbpMjMZfrlzqjdu33T7VxhxvHj656VPV3jw xil1wEwHohYB5GRs6h07+jJIKo7BibNYyInhWAd9hCVDf4f9k7vb7NxwCMXVCPVdyQ59KWxG91FX 7pNSWXZ3fXFU/ED0mBQfahpZ68WEX9z62OBRqMmu0/7ctnI7SXkkSzxKOraNEycKBHUi9mjYqCE9 yc7ef6bO7pj1ZSFdc8DCvyAcsmrre4bDq58OQJ6osG7d+sqVKkn7kBVvO6ie7WfElVfmHjokRjwM ShSF5SsUxHhrUOidIGMUKFRKQUzgbUxiCHjGB4WqO5kB/0Xpo8ebFaDcFnGqEXqC7Co/3yuT05+Q uCpxgQcpQHEm3JoWHPFU+Rfzq+hgDTumKKVqoX4TMaJ8AyANbVUsGZKVBytvBFOjHir+K0pDGIC0 eSDHemY4OQj7kTcIjqXze6vlBMsphOHv3m1pyWFpHk0eFQKSWzOxRQs9WIn8VxyQ+eCWKeqRHWgf vX/9+tZ9eZb0NFzSjlD54/bjEXiRh9aHcpCnwm+ICgw1j9gGG/QqPr4afhPX33zL4CG/4rVasWK5 foqg56Tg5I7Zdm99FDTU7Tt2kgq8qkrCLUeLAAfom8bJayIx2LCTc7fJcqEOSA6nKDE3zLBj3ZWZ EgnM7NSJgzvWrl6zpuBnxYLPJ7z60offb8wJ7rkuM3NiBlI8CEyf/iUNO8ywwnsr3YKN+Ajhbdxj a3jFuwNHUZMdC28+Nuwfvv9Oomdhx8PvAjbjKR225GuSu+zavUfs9D0WXfmowuhLTSUzE6EdGb6J Y04ddln2NnYdWcrJesQGLJWFmijJnNAa2fYoekZw/kRT5hZMSrdl0xIK6NGhVW7EvKBAZKbwJnSM zq833K2/+Q1xM2vXqU3NzKws2cXZ/USjx/CkQVF+nSFKPHJEpBewHNHXwDOI06EAlwsLhVLlNm7c TDXIonwjdFM0WcJaHNZ+ktFBjPfRG8764Qdp0KPJIFRSHzhGSCoSm64n8mHMxHkARSeJp3RTrb59 z5dmlYhXhkyo0p49e8qXFcpXwJ5YerVt+46EBvURJar6utKQsTsUlIQ0k/Yd7AQRpk7vqBC4mErH gc9I7ERMK3OnmwDqwSykfdS1iMr4MGvOHBwW4EOO1oL9Uyc6WMQftQ0DDh0+QkIqEalaVoleskwi /0OEKSer5ctScKVEnidyxEGDL+I3n9EOe7QY27bdv029/t4px4vABygmAT7q6/6blp2cK2y1zEi7 tq15WsT8QBXVcllSaEYCMytfq98Db074z78Lft75dNaMV0Y1iKtdw3PwwcAfB1PTIOBCoH49K0Co WuXV/995972QU7ioRvSt3WP4+LBPiIom72hZFimMhP7vT4/q/5JdjQO0Q3nHaii7u8hd4FUOc2wE YMqIR3YmEdhAGvgTC24sjqmQkZkpsg1RMhIfH9c8PtSsUVOiyGLEzTKtb65yXGbXUVbA7CIJDRpI fbZ/XeukWzUJwhL11P5g3VEMhDle/7xwsQMTuRF0AV7ITOlhL+CFDsstx7XpdiQUyWoAEYA2CYk5 t317ZKLy5EjAekeRLUccDiQxw4ED2Xy2o4Kd44i5oJSJYCv7jcSGlTaJKKIal7HLRCgc5L/Y4b31 5psiz3NwFCGFjoQHmOSLHIsRORwz3cORb8Tsj8RTYpyXlNSjW/fzsORbscLyz0DEdZOd5KdFYgt+ 33jTjSocV916dcFfFH+FMtpzunY5nRFSkSFuIQ+JjA4pF/Z5HAmsoHoaO2FOofvVbNdXFa3Gh4EX YHpM044YjKtoXN4Lkg3wlEKPPBoSyNQgL2xWGKSDIfNEiaWUPP9cLocN+b18+XKJAuOj6HFn1Ltw 0o7mamdxsObOEWBZtfbCv/417OKhHGOIi/Hf//xHXCnbtGm9dGkK83vIdnBeMHeuj7tjpvnXp/7u 0VAPZFgZ1q1dU79eHVwulOyKp0Xkx0UvMi5xVeb59HZG4m1q3KghHdDN0ZRXQVD+FkXvc7G2EAnM zDXACo26n98q+b15qZ5l88UKiGm8TCHgTlDIasv5z2H8y2aGotOHr1ZQoLCLEN8yqEtCruywxFLt yCIlDmsU9mmxjmJX49wJP3BEf1UGwpij8K+mTRojXMRgSzXYoEF92aVYEw/Z4gF2IFthahEUNtEN G9bXrl2DdpCFsOkqaRDG0fYefE5dWydl9aFKZdnRJaaAuJuxIyqLMe7QtVs3LK9RXbH9r1+/fpet qMKumd8O8yBvPpXstettqdjevQUxnORGyvCra5eu1nDsscDSIILK6c89HXSDL5FaAc6q1asxazlk C36GDLkY6iD8GPWQWw7HxgzJkIzmCnMlCKxWtarHmLGItaSfPMCYCdrTV9kxZcoiEKcPaoKDzAWP 8Y5t2/RmFRcRpwG8RPQBSmAFJC6qfQd34XtH+DTV/4M5B6nc7txzxRcV3iOsC8ZJaDG56rPJU9Tt 6ta1zkX2uI7iRSH7MZ+V/lEFLVOXQAvYp3luwYoGHbuvzCmJuflNZwqv8iQTtv9HpgrlTKrfwvbn KDCfET07IY5xSsClV1WTdwSyq9BoU5iaiW4wxctsO0gpXK6Hg0F4TLA33/lSAcFtHY9HhjSocoEQ XUzFwkB4KSkfRJD804L5n/zP4mRS4Iv5ecfzTx7fumWrD1tVvG2oPODCC5nEOC28i2pHRLb79+3j AaOHivjywRXFRl3k4YMPiw7a6djhXLkGa0h3YGFlhCeGwrqhpLL3cKunffUmsv8XCczsVN6etKUp Wlnw5Qef/VS+df2akdC7yJ4/0zsfCCDJgG+hupJEOlJY+t1RtuVfPny1AsFZfAZ79+7Vu1dPbwIz O3FhcG6hevBJ6UbgOYBVFAn2aUQLsjpjdK+nv3Rswyx5LKDsPU0aN2TJ47PDDl2tiRKpQXZKjs6k EWT7RFvKVVzCIZvvMfdevnTpuvXrWTQ5jiPzIBoqCkH+ZbNDzzuoCskx0PZnROomHhsQDo/mQQKL 6idEhZFSRGv22eTPHdMHNYc3iKG6KmLf4947Be1uPXqc26Ej9AhRIjciuLzjWhD2GLLBivx28qSY QKkdXYgawxSsmAL3pgKlU7EYqIbij8v150q5CIhxvcpBLh3TuZRMsTf1tNIOy46rMpRLPtAtW7bC 5+DKfxv3tDs0a7PmzWgc6IQfw0FnzZ7DzflcnSfBZhW8g7waevfsNBKWYEmVw4dhyNb2T7x4fovR oTyuwsaQtAlp801uhM5CoTxG4i38V0yDBgmSuEm8HERSqJ5GbsdBArJOB07HRqlZk+HjA+gtfRYD UGlD1bjoj4gtN2/ezG+ZdymE5sKkXbEcYS3iO0LhSRNlrlTwFgwMYwaCxMKqJQ7LzBlf61k1OSo8 8qc/EYT5g3ffadbsdBgXDXjrI+MiL3vz5i2sDlT3IPcVzrpyxQoYufQwNKtZzmxA2rixZVbhLkSi kS99JGtS0wHrhUDTDcFHGC0iN3e2UI/3ivwvI4H7nNwz/+Vrr7p6lPq5cfSE7QOf+NPQJr5iQEY+ tqaHpYyAxM0adOFpHzHpEFzNLdvge49fBj4Gkeuc1yMp0Y4d4LEgP8DcTXm9Bdi4w7hEtxH2GAFS NcuGJ58r25EFBlxwPlm6+cD6CCEQjYDOzNgR0QYSyZPvxSKNLQHJloNo6t6asgXSJdkSlPe73Bc1 2dBhl5YrV37vvv1iXs2KynYF55BNSy3xunm7khPAoX2nKpe7yH7MQKZOmcwuwmLNSNURnEXcAbhb MIOFE5czTHdgKtGpETjgV8Mu4QPqWpShzZufEWueazFck+AO7sllMxM3RmWhj+0dfx4/AbU9BCZK p6lni8o/BTM7bbrkNbNk5cqxhXFZafP+e+/974QJ6l50TM4hzLVE/3I/crKpoxMWki18CAIkCtz4 GjXFcYQiQqBp06arB6B58+aAho4JTPgX98KYSWRvdWrXwd1Xbjfh7Xemu9L7yCyj9eNyuBT0CKGR KH+FJBHQRIlymQUH9dQHIqSch5A60n+HJFIsC/keFkIdnlmxahBNsa6d50+G2bpVS8eBhBdHoqGq XPJV4+LEzsxjYdLpCRNKfVSZArIe25ap59ClAt8L+1HMTL3yEuiE+2bZMUT8hnWlDkk29S4xR/hp ckCyID0zYkWWbTWPowa2gLgV89kjo5XDWHqqJRhWsrcQjLpEuOVNesf3cDLLW7lKFR/8W4wRMTUj Ro+dzNR6R9RxoswoNCOBmVVocMnfFiz8ef7pn1+Sv3/+hg41DDHz9tqb7wNHgJ0DSyBEWVwiW6Ds InpRgRiKkrhpy+YttC9+ZB6L6Ez5VwiLmscGCcss37PxK+Nxd01O6sp5Xv4rViwk0HT0RJRKIKar OyUig2qWGzmMedmJqUAkVTYeOzKFVVe0mdCya0ZdxwdbilaQcoc/aUEYoSboslIS8Q1cjdWWNtHi sU8MHjxYj7Mg3ShQmdmcgwKdoj5h6xDIoVTNyLD2YI7gSqOqS2jYutliL7tsmBsoBu5N1mKNZehQ 6142naVULRRv8Jk4qPv2WUJB3DN1SGVSGHjyL78gb5DBEqZ/+uefY+AO2qgJ+QbGRkgDdaHiyg56 4TH9aD1bucM2/9PPi6nPBvz7P/yeb8R+n6ZkJxPygZLaPWr5hr7Zpm8FUVc+//xzFLjj/vEMUdps 15BYQOM3nSSvkQTNskGuI7MmdyH2GL953ZAR1qlbh0uYEampu+ZhjgbTyrOVgxToiBAy+iBjVBpn QYDf9evXtz8TfbdAL8/3wrDhPRIsjatQSXuksGJZKAgLwxOJlP5gqEdR+uBwzuAbeAOMgdcBq0EK tMyb9J3KTDosBDUr9T+dNEn8N91CNeUkK5TCoRwk+6o4eKq093IqE9JMEbnmD98XOHy0Pfdc5Siq XJ0kFbrUhKXJhVKEbDEXeLEw9bkHD/IquVPNegzco7vv6G16/GxnPj0jZofHam3btOYRRRHMq+GQ fgGUHv1Y2bqpWZOHwaNfp9/uRWCFSGBmMeUq10TimpBQt1qFcvYfdawPpoQPgbTNloXyWVJQYr7+ xpsOzcuvhl7M8LFb4rfsIh5JmMi9QijsauxYHTq093YtPgfhzafJjQjLrG7nLVUlFTbZsz/HUjMV FHYd9mx4A2Ho9Q6LLMT2GYxljWYplKO8qCalKMWKIgroKPn8wIMPysYjii12JgzCGhcKFXokdSMB NjuZ0A5UP1RQ+jhpGTszRDucg+Ft69an4h/Kl63btFVxFvhTopjK5MrhW2yW2SeQi1ww4IK1a1aj gJMGcw/mSFwAPW22kADd9N7BxryRM7VriiJMp6eW6ZIttxsx/ArFWvhTIAJV9LA3XX99obrQctpl CwFb2VeqV6/GTkMkJzEUO100QaJHWkZNCChcRLw4s3buIUSCpKCmALUieWK4I08+iLm3SdnYZOy8 QWnpm+68736WZWZEor5RlD+BqIkhAejLdBGI2LFZloiHDksqep1cKr6FjM/KYWpbGQoXhJxJ1DTp gCNllnhOEKiCf/JZuY8IKdSjvEJchLuI9FGUxQpPXaAiubPcynpYstzdrWpXMbcsszgrm8IZxu9W 0qq46oT7SkvbyHMuJnTSDQAfdumlPXv38uavQx3Yjy7KFeEZ2VdV52F44teMo6tYPaqycqXleEEh cAkumZLQCTGYXsfjZ7pU8L3trzN58qduz0eBUSToyt7jscce8zEW970QAfpwy1D1eZj5EfEkRq6O c6PiwdIZIWrKQrFL5w64n3v06/SLQwRWiAhmxgEma9HEsVef3+28Pv17d+868LfPTl+f69WIMwJh NF2KIATYhrHa4RWVBADunukhxeW/KkRWyBFoRU/RzVPaPr4XnwN+ROoeOP/ztpw5TDEQkOjhr9SQ d+3c1aZd2y9tRZIeT9Je5WuqasohS1gXKyOLIFsav3HI5BtlZU9/cFtj3bTNzy0zKUu4lX1gzpw5 HK1qEO2+MKgVVym9HoRDHC3Zb9TyyjcOZmZNRGEmRBZl240gllW4a/fuui9t3z598FK0d2trBLY9 We6+/fv5cNFFF52w90IpmEeh/eEZENdIVWyxnyWiYGPO3r9/48Z0PSIrRNNtRnPhwAtFpwPxEkWY UtXxuXKVSjxvIqVI1YJxuIP0qj7s25fNpiK8QdgJvWISdfEkFGGnFcchB5D14Pj6WPisU8mEhIbC OYAOKuwxeD2+pY7nSqUeF92QEFlp57NPP8kulJCB5DFb9oYXIr+JFsFvyN/ateulS8TRxf0Filmp cmW3CySbLrQY1obmlMo4Kuh6ebwrGL7OyaDCipry/ew5c0TEBWj169VV7aMa1l195XvROUrKcwdc +p88Vw6tpS22y1dTo1cWsrVnT8HhjTHq/z333A7kfOV2B3JyEK3ReZic3J0jBzypVevWKbbGX4o7 vpc+9aK1bN/BCr4oFIReuYOZiTGDqEqffGrcmLFjrx01ysd4Hf+6/c475Rvcm3kFcN4UO1FHNdEp 63nNGZe83fJeS/EbuddbaiZ3h1smWrI9PeYLRxflHsEHXnYiSOsX8iyJ/NhHgs7AkSn1mpHAzE6d 2PC/0Tc8k9Lyjlc+nDRp8gfjb647d/RdY2dsN9ys1J+PaOyA7OKwH0kAoIYw6tqrB1zQnz9lP1D0 iCVm5cpV7CjwJ87loQ1ZWvMWJVLoCJYuje2os4HzP9Es7Ny10yHhU/xGlBcIz1avXuXuOUZR9erX R2Cj/0tWXi3leV72gRw5nh4u9OX0CALmz0uXWTbLzZriuRnftGljxitqhU0brdAYkL/UtFR9Sxb7 tlUrlyvJiuRKwghMl7WoeJXCflavWiFumHaw+7ixTzzBfkkseOnVuee247cujLElI9aFbg0UX/IM qF0QHSJ1oWXsQ2x1sEy4SGZm5of2DqdoATur2p+EDeQePky3IUmSMshR2HJwTRD+qidNkl4pSZiY JfEl4QmkBUDgpjXi47t26cSfDEQEh2of4gGgk0gQve03tKarntH6Sctx1WpI7udAjLUfHm0JX3kG 9Ai0hAjhS3ZrSSqPpomZFY57Mi/vtLhFwwJqIs+VBOYQWs9pBErHts1goebv/PdtdYUeowG5rKKY DIrOwITYAhgddIFGEFKKnJK5488O7duKchZeQoQ5PqiTABQHia84ssD21Pcyp2K9B5687xtdSRht /9yTmADSBwdHEWmrDJAAZshoXQ/CGV/YlnnWa9WyVSt+E0kfWZeyoJKxeNPxQewO5x5AvcgpSMnS xPgMbTi/xXJRL7169+7YsWPXrl1FfRlI6devH2B27twFAaHSkLptvOBhIgFVziIEExZxoHqL77zj jl5JPdy9og5GF2ic+QCeHlMzubsq3gD6Oc3C3H6cKGRlwJBUeKoeF0auKhumZpHAzI6s+27qko4P Pf/3O4f165nUo/+wO//6/J8bf/HW92kmo3kgr5epcyYCInJ3s59z8e23iZGYgqkKpDoWt3zozr79 BQY0DlCxel50pgbBUUFa82ZkxmIHLeMuzW0i5SgE8fd23EQcxdr3zTffTp023RFuTYRkt99xh7SW q2VR1Ntv3bq1/if7jcMNkA2MvnmU7rATyxYrayLbNms0LIoIGqIpQ9gjOkfWXJZIooZmZWZJwFX+ haq0Y8dOBFjiR/WBqxCseUzbzFYowpidWZmFZnPWfZHGkZT6vPN6IDwDDYcWSWqKsEff6ekqLh2y fSp47TQ+bL3WplLAA2zB24qVK/lNy4qcKQskiUvSsmVLfut0UNgeeFLBG23iRroQSPg0G7/Kd85G xU3BRDYVmCWsApkTnx2G0twISHURhUAq5E+cNigksRY2z32x1MEfE72hJToqzPFF+4qUUw0eOfHj j4U64DpHTZ6HTbYjocjMKLJni4U4JwRLDl2uHBIg+a9euKku+CFOCmKPHklJF/TvJ0/dunXrOnbq 7L6QyUIkxvcSTReRlcjtAFaIHRDxQQ8yzMasvPkkbohSfjFAJlcYBh+UG8HOXZbufu3aNdKBli0T sfuU90ifJih7YSiy05pQuQSfTXjS0qVLm9oBcvW0Xe5BQeXFck6cdmFO/F6wYIH+sivy585rybW/ f/B3Pc/rTvAOIt1wrcy1txDWOiFDfiY5TH0XHkgyOJEngBdn+lTLhVlsvNRV4kDKn7Bk8gdAgjnb 8CdWCogG+YD3tziAz7Zt3TxGJ545c6ZkX6VIXDffDrZSk3WGh1Y+e8ztJu+CHDzc74W/oUf6/yOB mZXD3iSmHFOvbMvKV6laOeYIrs+RDp/pX6QhAH3RA/F7655bwzV48CDEHt7ykWND9u3M70KLLU6M dUye5aSOIoyTOjsfqzOEjEDq3377LUH8lyxevHv3HmUurbrN/hRf3Tqps7F9MPEj1QF2dPGsbNWm zRVXXXXDrbfhysefirXIiRwXQs7QevYYpbsR8dL2HTuWrzgtbGMfEdWJKFPgWBL7SlgC+xDOfd26 ethZWbVRC0oCIlXY8yATPlzqpKbqEv6bsGohbZLjjwV30yYr2BIFYv27Bx6AVyl6JC2zNEMZ2TDY AnXOkb7RutByldXy8EjITTnuy0ZOlCY2byRzzAjbMyZfkiKdOoK2iGBlOxRdnvB72V127sx64He/ 4wPTKrwQAqGENPSWTjpkGMrzVAHFiLiXKEP1IYigot/51o5OkfgU6ir5IPxS9n4QoJ0NqelY7ts9 Pyb8him2HiTbOke1zwPJtcuXL2MLF8cFgQUmx6wx9Zbu8uhRZGOb7Ti0VSpbtBKyWyA6KhRg8KVy GFRCL2WahqJZdwid9Olkj6mTYGb7bEIpe7BqB0iVCBamKFaPciQQKlMQF+bMxFUQDiVUlsuZHRgV DzBxSmmc55kThWQylcnSNcLAKNzdzZbkkUMyBDhcAtoIJpV/pWNqVN4t5NaALJYV99xxJ1FhHTX5 U8ml3P+it5yF7BPRGa61wvBwXuY3qkb9whtvuokX392U+xuZzbp1ahV6A1gPmJKzokMX9km1i4Zc xAe1RmESTp6xgf3633zjjW++8Sb/qlUbn3fLpUAv/As+p845K1euJK6bHj3EWyf9Og3oRxdv/p6B IBCZdSKBmVVpd8n1Q7e/89wb367clJGVtS11wcfj31zRY0SHaruRne86cNSIziLz4YnEXq1Zs85v jh36zaLpSIurVAZu+qVMVVR4MPfI3UkFqMPpkDMigb5UfXZWFJqEDp89axaEDCqzySYQ59iJrh22 YqJT05WDG+3KFLUSsVDWqmVZYcu2qo7+hclM2mIP/sGHH1qLb9267CgqGhOXMGR1RzEwh8/Jlibt EygLmZm0KUTQmzKCrC8n808i3MKGz5bKlGNFZlPlEkceZR06YYHKmkT6UD42VilWWJ257xuvvy5X NbQpkWLeIIPSCkkPX0Is4Dd6dA+Hq6xMYmFaT4vNSATaefPmpqemciPR07F9ItKTjglWSAFVnx1m +MkpKZkZGRgtU0GFi8PWSjgfQjU94ZVqRKK6K0czRsEeDwjyjcPYiDlSbMxhNCY15TGWYChHDh/a snkTjw2W+3I7kbW4C5QXPRrXki5J/qvb9OCbIRKyGjXjf3399TL10C/hxCLWYmNWzcq1dl6EEyJp q2xrQqWo/otGXo7bQn1U4UQkdpDy9lnRVQpzMak6PGMpS5fxp+4RKS+7iGnVmwKSYAWZAKJCPsrR 35K28j0stlXLFjwP+G0gUpWXWrf3twzNLEptmV7pndQ/q3sRZUaeQL3I68PAcXwBNPJO8vbxe8Z3 M4kc5vY1VtfSGXR//OZVfWn8ePU9DXIicnhuEgKUCqKJRlXqravyvVikuYX6ooJXT52oCFl2mE10 6HoySmGWP9sBX8jloG6Hg87zz1gngfYdO0rYNlV4FOVfOwoDuUlEIT16iLdu604DhX5I1nqiFxA+ VBgYyPfwo+6/kcDM8nYlz5q5d+us5+65clC//n0GDLvxyS+37k1+7qZBffr273PJI99Y77MpBoFA EMjaeYatt7dLOnfqlJaeLkFf1R4gfn9u+qWYGYIHb4s1wh4s1fTbsbZyOpRo6erwzabSpUuXlomJ LVu15rAbVzVu0iefWCudvbvbNi4F5rdjHnvssmFnRHZgZVy2fIXcQi1tyuDd4c4m7bA5sVOyJUD7 6thbpm7vrJMAK9DnYUsIpAosp3wskehjJdWjqNgcRaXr5vt27SzzL3YgifOJpFAq+/DJYnuT2EUF +8EJksBUaNGiGeY4tWvVEFaBn7/CX4Iy4J8nzTJkLuRMzw9kyFalnXaXU1aGMq2I0J4tTI9D95Ys Wfzqq69JD0X2sGnTZjU6t/W6/ItdVvcpoWN0o+I5FlNRe15CQgOMDq3kPHa2H0aHKryOTXT0Imlt 7IARVaXbwnr1WUBSgjLLCXrh33rIU5l9RJ66UG3DurVEFVZHDl2uQHAJ6rPhqdgKlqle4bPHY4Mn gVTQMdFlumSNPP0vm4jYpwiL//DZo8Gf2GKS/8jC3JaQqeJw0eD7ZcuWi6OAKsL2cGsQv0V5kkVN JgDSbV1NRjUmRZBBUMdxSE0rsNijO4QvbStbk6tzbui7bf9HToUztJm7du0Uzq2M+fhsP7qnlTvM u/xItyGCQmdFzor0FFcAqIw364U//v732IwSPxa1oO6SrOOgPhOeHXE4bS5OSUZI5rGOfClyNYrS UKvKLA4IJpUZgKgFQWbT5q06LeNNpyYCVIw6WANbtGihWiB82gcff8S/CHAjvgiqKBGa4k+9elne oO7oIT46z7/E/NSdb0C8RjxG6fPdYOT/NxKYWfm6/R/8dMrkSZ5/3nmkv3NRi3xYTQ9LCwFxiZK7 s/x5SwDcvXs3KohxGKRKNm9ZPfUIC45RoNCc8PZ/QxhaTmFUJ3U0b9iwIef1y6+4wrH+KubHGfec c06rru6847dXjhwht+ZU/fmUKXwgRILqjLJHhkWRMhIrEN26S6qxlHsLpYZBidgqpaeli7muXlTc f52KqbWSPsMvREGsDMgkpiV+7Bk7tvtArCA3ouglc3JYZMV4CLfKH+fPFykLwcD0FiBi/Clkl6W5 VctEfLLS0tLYntkF3VFkhawQ4PfIEWu6pegWQmI6s2jhQv0u7Ojs+micc3IsOzPZ1GGvuk+J1I+v WVPdlI2tXr16MFq+F/t3HidU4T379PUIAgyAlh2sWmrqULsHRYUF83/kN+BL32ADcKP9+y3dnBTY J79FFMd/E1s0U/9S/FIpoLHooqhkRNIljwRCiDKGVvqIQJ67W83a2lWPgb7kdRP7vHlz5+D0IFJA GgQinTii/EKhRmtuLZXyZpUPJIXkt+zZSJRffeUV1Ss6T+Yi6S367urV40W01rKVZTIIpJgxqcdV Z2YIihsmNBDHYV3L+d6778qzJBHjpNjqeotBit0VF7Y/ty3JYVUF+V6p4CWkBSpURSLV0YUvJRwG K1IgMSkQUBEjw57cM6wI9HmRz7I46FYNqk7Hju119g/gPHh4AskLoiS7gvDACwd9+/XXmKYRdUe1 gKK2b9++H3/yicOmk6PpP//+D0dnhEPDPp977jnE6rxf7t4G/o2o+yVvRBkrkcDMylWo27RJhV2r fkaxMFf9/JheoW0SpWubuk7TijI2ByUwHOyZSuAuEXKLho0aii0XtlmOCKuqh6L2UsEyPG4kWCuL DYqc9WUzc2fR8e2Wr0sd4Bzso2obJi5R96Qk6RL+VlaICitrzUHkYbIjJjSyxBIHbYsrGAyL4/l9 +3KgJ2jk9zNn4rfVxc78ePGQIeQasnYaexPiaiQER49Y2UscIb+hXxIVVm7q2PbYVPgSdqWyIrLl y7ahNg89KIYCk5VaIlhC6QAWoGAwbdtaIjTYANuGj0QFbGx6T2DSHMovuWJ4RgabU6rb7A+IMH3D tl02Nv5CkVenjkVKbJnjqR49ejieQwQnN990AxJNoUqqPPLII/KZcCb8FmsqVWgQ0jTxgw+2bSUh zzHZ1MlXqBgwyjVJGoHTqDoMSDh73VlSfDl18eQ59pB1+RMSJod4BiKlwpo7LudPxn79ddfNnPmd 9FaYGYRg9arlumnU3Fmz2B3Zt5houIISOaDA0kPJi7iRDqB3po6I01ThySTPkvwpAPJSEDFE8o1y LRbiwL5n9x55SJCa8Vum1fpgqz71IvIqhCjKfn/NmrV8s2L5MlUNi0P5rGvPRTSiK8Jgb4TMQCBE 50XWqKeR/XHePDIXYcfJ95gQiFqTwnZuWxaejn0KEUQXr+5eD+jr1JYhOIL9iqRTdzSRB4MCl+S3 5XhsOSyflgjut8ei3iBZRiD9vKQiWlNmFTBLwpcwHDSAiNv5l0cuJdLZZUuXIqDyq8TUkRca5yj0 ljnXkxmwkmRm7qTDJK7t0P5cJf/jwlHXFYTkQNivYg8pjwRFOuUWiP2WLFrEBxS4Y8eOVfcVk7i3 XnudYT72f//nPge6O+ntGyaKefS4KLkvYUSvvvWfwBsv3ZqRwMxOHVnx3zuuvPfpT35cvXrNmsKf tdsPnmGJULo4mbtHFQI+YnOrcbBVi6uXN0chrJV/SU6xMwhZa26b1pbWg6IHsECK9uL4l31kW2Ln UOEZj9teYOyjS5ZYQiBokxAaXA4Lv1/Ch6++miEWJHqRwPqWL+Su3XSbdCus43BQkkknJrbo1Mk6 iOvyMHjZli2bHSG/4+yDr0RDoDiMxkR8lZ29X7FJRSLFkIgN3ncCY9Q9VCPwKXK7mjWtXYd1Hwbj 49lxiIvYI4EUbZE4aUpprp0r2A/QBXuU5bDdgm3XbgWGz5ijKYkpV836wfIdUwVfS4Qo/EmCmsOF Ojv1JEBo0MYSnY3Nb9HixQpYkFcJJES5ZkWvr169cWFwFoZDJC2xnZetXURE8DmlUaoRbx3xjxzO BU9hSCKe0aFIbNHcYeyvi80QqOixRiF29BzKAo3IOZDNVqpEYjAAmBAhBhQLlzwHilvwMOOxiNr0 lyWLVixfCrCiLJYC9VT7LhcWGCMeORJbPlas/TioIFyh5wt++gn9I9+Qd5zfSq1MhiXVGqSfrRSl mF3t9PdSYZ99CpLiMayuXChkHdyYa0bNBJ1//vkFDqo5ObrzpjT1xbRp8kFRKEbE+Ucyc8uFEjxP 3V1HXpeZ0biMCzCFWNMAUItEWSEM1SB+hDKKkJpKWM4o4FtIr9Xt+CAW93hjQJp1RaFHLiUSMvHA FX/Pohd9wSRWnDTYpEkjhJoAheRbvtGFczz2+/fvQ48p8+IuSuxH7k6Oyupwcuttt0376kupj+sJ CRI8Xi4yY7FxRLqppHd6ZbrXt0+vALOqw4C9pTMuOoBhbyESmNmRtB9nru742NQfPvvPhP/8u/Dn zdH9CnT1YR+0abCsIIB8xVs+Jb9hD1X2TNZ6xR5E2AY8olWk/ZdfeU3UnQS8kA1PT+UkoTQc6Zl1 dNk5OBoiwpIvaZbtRP4kVIE02Nw22mCfUHlRdJsnuVCRgyOHD4sxR7WqcezWkh1c5BCyn+khqVRP 9JXXbUTFqqcWXy6RrI6spHaqAGuJsFOjVGGD9/3gqM2Pahgw6adt3xcqoiAkWIXXVyaAcA52uFdf eZmuqp1AZTBU+OtDQ24nElO2PSRJ7N/SByFMys0elzSVZ5CIBo5+QpodHEISAKhCRCg+kzVBfcPA mVYlgylke+UkuZMqUAHwFBIgG0ahRZT1bDjSI7ZpTYSH0/FW9Kj3VMa0a8rkyXjJiecEpuJcLuCL ss9jkY4dPXaC2FR8gNJN/3zqNVddVdPWMEJBhKyAPDmCpHvyTWZGJjIl4GVGMjItLk5Rwipd+MT3 PEJiJEDhuWdrJ1I+n1PtvEA+SuvWYrV5mibq2yo0SD3SegQWh50T12/auNl9F8XD1AedgUl9uZ2S QbKe6I3DSNPSN9qq5II9VJxeQV5EzkwHVgce43vBtxyW8nLHrVu38q+RV16JkMmjtEwfiFjWh7fI 4QGaDrw8P/JwEjyMsB3qRh06WYH3pLD+oMf01gd5JDp0turTlCCJ8R+GccRdwy6NMSb16OHNH0Km VVgva2Mgh21q0nN3CF/poa6+CC9uxdFaJDCz8tVq1SlfJa6KychUHDNcpttEvjLvx/keh+hWOzqq sQDxjYi7lCuZSqarlCbsQCik+L5b165//MNoPdzGylWrpU2Ppml6aB8iPsiqxG9UY6Ii4TgoJKNr t278JiqWtIYCSLeCQtIAL1QCFQQ5ZKhEI3DtqGtxY6S+8BLMm0hWqI9R7LGksEHi2EiYALYiNg80 O2w8devWllidjRo2ZPFVZEV2INlp7CzRFdjFPZ5Z7Qqn1VW6dTwhu9whyx1ToP5U25t8szHdYhi0 psSNkIDTEUOqVy9vn6fVrikiBxgAwxQly/z5pzmWUBAtpY/FzESkR5k8beqIkSPls+IxP/308y47 9lWnzp0RTHLa5nKZNUkAoPXc6S8mpAq2hGcA0InGk6LuIn/ieafi2Im4RazmJZKWQ2CGVEDp/hjO 23bOclUOHsz58osv+BNBGipyPqDZ5IeHBPWrXlP/DGlYu2YNJFhnNlg98mwQ1ACuLOIKni7FWuQb ijy36emb3Kp8chvojFzi4XksHlkLNUW8VGiHcNoD1Fs7ju8dkXXbaQnTRM++fTvq6TOcLi0Zpy3P dhRbm38CwBGgOk6ASN3oJ+JhSxton69InOqYOKwOPPqJM0fyijHvnBMcCYUgLgiZyK/w5oT/XDho kO9RBx5UNhD0RFNPr3okdeX58XgJ6wzfy6vnIxmdulZ5pssCoiSLz7/wAj7jQ3/1Kx45b0+CNCIn q0D6Tx2eXkdWXznTkjUrfePm0MIeBXjr8FYrVWaWtycVtU3K6v0New1Ke+vvL01fgBt6YVmauifQ 2QgvJKa16EfAb3huhihbrIPNIDzTbZtaNG9OgCuWBmJiCSoYgJMISD6j3RDXAYquO5Nv9COaLGdS OErie2V9ssUBbJ/t2raFfq1bt/6AbWzOn4SWOrfj6bOpOi+KtYpuFCJtCotyhEMjdMKWrdukAkSB LYR22CogeUKhXn/ttamfT8YQu3FjS1thx9GoujTF8sHUHVRZ6RDzSeYTd0FOtn79Osf3ovQRy1yx dfNh6mtZjhVagKEmo/KaVatat0oUW3v2HkzsN27ahLs+UdPkRu3tNAAUh/yPYXKOh0VJukyKEooo yY1QNOXPpcI+wcBEWGgFqdq6TXLv/Pb221u2bIVwDu7ojjZHBWHw6jHgs3BoYpXanz0ssHKmZ0Mi bAoyKgWdXKhixnoC2/oO2sS1egj+FctXOBIpqvSR7kaUBRhJI1577TUqOLw6kNw4rlJxdDemp/Mv AnOI9jyXoBu2cIXP+jEAVaxqobyWcZWA+7yYQjrx/VRHF4/Aehu+7+/xiSEeG/m1VDUkgrqWH6Ip EZt13bFlKmCbCzi0XTxd0K+Z335LELJnbRmVCqHHm0geC/Fslbdpx45t4uyitHiYV149YqT1MFg+ 16dtc1QMWBSFONhmZu3y6FE+xC4+xjt3wXwfjs/uC9Xr4xdbTgLe0plgO2GNyBbPizu2t4IwDOHf D7NnSwWH0AtyTxE/ksW2OZq3smbtOt+2vD6uxQhy7boNnCjEjdpjohS/aJRKhVJlZnvmP3fV1aP4 +c0/vidqxviHbr3a/tP+ufaZ+daaZ4pBwAsCcopFJvH8v15UVUSJqYyBfDhUy1btOGGL8AxRnPAz 4s5Lsh2lw3JEo1XLjdKdqZ7o4oo6nmgNdE14Ays1TGjy5MmyBSJa4/uevXqRNYU/OS9e0L+/NCt7 mNu4RDaM2DN5AOGbkP3oixpZDnTjZcsZ78ABhDGnjZDOqbDMTu2nn/UP5FiOBbpBuj4h8fHVPv7g fY9TJBxIbN1gIUR381iNziupjNBZDL90O3QkTwSofO6f/1Q2ZCpERUGmHVtvQhFbFgJWSTARZk0x Myxa4BN2kAsLK3cSAjYJkWR88eVXNtQHPvjgA/YnCGLP83p4Exl6U2QTp5ZI8tIr2bnVRPBEEaKT b8b+9Qk4liNKi8Pk3I2YxIkgzJhu7e4xV5K6FsGwQ+QDWYmLqyYOKAKFCp9BpjL9ppJfi4JeaeWK lekb1t91912cDXAuPnz4ECIKKDvTN3/ePMnp7ihq1NTv06dvfPV42fJh2yIQZfgqZIbMprjKeitK wU2MX1VHZrlDh46DBlvRUDdu2sx7DUNCX0a0eontJ7Ngm+JZT4vio7gOEHW2IIDfmRI+mBn1EX21 Pffcn2zZPM+qnUPCOiDZAdKs5w3JGb9xhV631hKi6/PCn6CEO44eL42edLP9VBCv4lkMRESZlrGI yDOQwtCguWgGPEbu9diCb4vPQG5KnT888kjrNgVB8jxmZVXt8GzrWdVlNXAcA9Bu+5bssiAEPkD3 ECSkjsrXpHuZBDje0qpWqsyswSXPLfp5/kLPPwuev8RyFTPFIOAPARZ0ibJDYamCPCFmxx0PMuFO Xu5oTLlnyvey1tAa5IwPtMMySjuoMv314oz/YwWsOyRKIC6R5KWnp6vliQ3ytt/8hi8RvbB884FT O4IWggawgQl3JCCqSIYWLVo0a/ZstljWO078fGCxk7BesjMdPnqEZANKE7p2jbVPyNYoLaAhQvyl OkoPOdeiqVHaBOWVqbKpUFniZnk7Q3uERdc+KBXnjK+/9o0hCyjEWuRPSNqw3xczc0LpSgAk9xl9 1arViEAQXkowORkm8yV5XZTmUQJ3keIJNBiIEEGHPYoMEBKDHxLCDySFB22/hw4dO+iR6pRFvPTT W1bKw4esIG0irUEsx9RrzOwgiVCvv/kmoscPueQSoSNKhIOazBsLFPREPXRez564Xso3q1avGnbp pT6w/e77HxxKf3Smbdq263u+xfhxcaBvsonC8FR8dlExqyRmKFtr1qp18623EDqLA0mrVq1xJbE1 8o1ycyyXFHVKcRjJSceE+SkKjv8jWkW+IYzwnDlzCoZma0sl0YIUMQBQBcyVgjst1VJ5U3hUpNm2 tghn184s9nIkZ7Vq17nk0su6dO1mZ0e1WoZC8agI4PqZirmW3KYO3iDeEtxxyMWW7AoOwcGGd42c CuK94Q7qQTWHF4Lb6pxzhQiSeULwLLYbsaDu1q0r06qP19tnDMKwSBO25/DqDeTyotQBN4+p1VSb Aq9kBdBL9WpV3S5E+L9XqHiG4abjKtLTyTcBJtxUl/PqKeX76jWWUJ+ovySBLcrYS/LaUmVm5SrX QOPipTSoWTloE4OSRM7cq7QRgMGoXR8itXTpMlEtISdDpUWw6fvuvcdbIkvVd1lGlTYKg7BfDb1Y H9kNxCcYdW2wY1UUhwtxX8L6G91Hf1v0he4JaZkorVhbCYDJB9ETUdj/UCVgUX78xAnhc3JAT05O /sNDDyHIwUuA0TVIaPjj/AXsE8hsWChldc47kbe+0KpJcOC3CP+UY9SxQoGEWF9xrkVTM/7F00JH 6Yaet4Bt22M8LR+YFMbCsHiSMth3R24j0q9NpwtkJEKLJUQ+kjYWBlZ5ITTZ+7P57TFu04bUNBW4 TnZKLkRJrVu5obeFEAueEA5xdGU/dvsobLODla9dvRpgaY12sFi3N/5y4qDq2Gixv+BLb7QVlgyT 4xKZZcpu+xGFcJB2iA9V4uJEqaecGX0bOwOWCAAkoLw8Gzu27yCAqZhve8uW6EhZJtsnvrQMbeHC RYo1ErBqU2GSbwftkPs6vH3ZWdVjoDTLtm2iJZjUi0BE0DNIMzeVrF9S0LLVqmVNjRTF3nhllHWd /EvXiCGVlC/pErdjJyaOzMb01DF/flSobaXKVZo2bSaOe6pNBuV2r6FCpu0qQTQWvc/CTREQEo12 4OBBCLR44wRzsgi4J10WE+UoIPFX3TzG8RgjdxRe/tvf3u7Nz1HvFZ+vvOqqyy6/XL4MxHYqqOAa jnu5/xSS5D4/iJWFLH1z5xQoMdXl4iPsaI3Q2bwI2GR6u6lS13okwd6uYpFc8BPna0v8T5FGfEv4 /I66hCuUKjMrGOvJ/QteYXu4y/nz0NgXP5676eBp55wSxsbcLrIRwGReNpiuXTrrhvlBSXfcQ9Rt hvgvgitF79C8QJj8ovLA/b/721+fvPGWW+68527O2d26dUOycv75BcGEOMkhn8i3z+goSkToorZG BDas/iNGjIBYYJ5iLSh2kIUfvv/+j48+qm7NUi7yGMJHnUmbYq3MMkI9TmGhHCdCQbWuKZuhp596 SrWm9hL6Iz5TjkCXejguv8OngvTc7c2uByj5eeFCor/OnjPXWzpwbykZHB1A+6YHrkNqyPYGvbho 8CBbwmEZxFBET4pC05aZFXzpaIpI/Vj7sY7v2LZNBQdm1xdS4jH4J7fu3buXfgAQgiK+rkRlqF37 DPu8DRssSU+d2tyqwPTQ7TLmYxOaMeOb1PSN0m0s8NSuDP9DLMfcecyWCC1zm6JDIw4eyF6wwFLS paaltW13Ls4QKLwIVSXZRXVLLAgrjAoJtJicFzx75ctbb0ShiOuULcKRol/LE66/U1agOMuBo4CZ 8chBT0nKSZwREfeKDRNHFPHR8VakMpZKdmWieGwn/3fHjh34kyyQcG7yR6knECIoenydT4jLC/pK slqlp29cv36DhLM5csRaVSjCwjt37ty7T99/PvMs5upMjbxcbr4F+1HvERPB2yrxV3n9vQ3Bh0+3 j4HzL+zPaLwwuaf/Fcm3lMv3vbz9131a03mzGD5KhDO/pZx9SnEUkbwyy0FxMi7hOURIpq5CVEZg tqjLExAJzKzcOZVjdsz+fu6G4/XbtO/QoWmVzKWzvl9zNKHusfkv3j7q2e93G08Av8/22VhBOM3o hx8cMWI4xkA7bLOzohdZcdhLRl17taM1POEX2yuOFBWYQ9iPskXDIv5AdvZ11113229++9TTT7Ov s/qrvWrL5s0INmAPfMlWJyL3gQMHSJu33ngjh04Cet1y801o35o1bfKrX1nRFthRLh56sTL2Eupj BUfQ4jUgBIIWEBdKUlWyVQCL0q2wOiM3evKJJ3Dm57+O6K+KM7Vq3RpFCbYyOhvzuDiyJZN36Msv v46rVk2oidhWW4vjqVO6OIrsMcgP+H7ZsmUKPezB+czBVllWSUTv/dnWjksRZe7WbQVODOpCPiiR DDIV4Rxq75eNs0XLlmDIcRwrH/6c/vnnAmP1agXhRvXW1OeBgwZjtijT2rxFMxoXbXhBIiCXabxc yLqvtybMTNRwTASw0yMSDaFPBDERlSF96dyls1WtajVxq5QipMSH4iY1LZ06KjSavhf+/g9/eO0N Kzypo6CfFYh0i0PL3CrnwLkdLB5DmTv3x08/myzECp6kC2ulAqE0kENcWqgzFapKvgZ+E1ePaLR8 cFije4wdxalB4p+pTnbo1Nky/qtUETYsJA/nCbxNE3zSMuu+tpi5x3nnqaY4BSlds9iukageuDAs I5SguL4qxIBFLA45a1122bAd27bOnTdPHsWvv56hx9xpbZNR+sy1NOIx1hoVhP2I1eCDDz98z733 SMfc4kO+FIKLKJffwTIPNV7RZvLAh0WhqR4q1b7vD76e0lTLH5lXPkDXUUdQQ7mvOmAjU8QaD0fy ADtmpxnLI7dbv/M5NPVAVEbgDz0rQ4DtlG61SGBm+Uf27dxZ+4bxn/z7qUd///DvHx//2Tt/7n1s Z8PhT01843fxX/33uy1GbFbEpyRtc8GWWcR2IvByIVLYlvkNkxFg52lw4MAL0GlKqAtHkVw9UsSm DTGM+Gkrq6aNadaeQaJrKyb9qVMiYGCxgCTxe/nSpYiyuEveyTz42TYX87jJMu62SCeiBGVns27t OqRoSg8ie5gkvFPedi1aNGdPVQbIslWoPYamZnz9FfsfUgr3uMS6SNE4Pvz8089SjW1YwnM4Crpj kuogSiHEkXTMm/MX/+WIjyBh/o8/SiOwBHd8f2xucrGO1xg2fEsUTFlZmfrd1VIuYTmpplwvWYz5 xrbOxlg7H4u35cuWOaiMiBQdBUgJnnnJJUO/+WL6k0+N+8tf/nL/ffeCpzdtkZIZeLRlFAkNx3c2 GLrBjMP5QExFkUA4aj0ktWvrbpXCeJS1E9aKOkVQHKtOnYJYoLrcgqY8anuVZ6IwM2AhmNa69Rsy MzMdgjRlUpYvkTtsiZE822yxks5IB0350AgfEgWlW0FMCyiCZaMV3hxrMzOVt57POo2DAjJwFbLf MU0CCJaRMhzdMItTkKrMgyRJ2Rnjpk1pihOjVl621FJA41MinElC8KduWK9HW1Cxo9nmJX6HCksr 1VSIGXVHMR37Yvp0frsNImkH9x2lVG3Y0Dp1zP7BshRUFCdkdVuAHppCoL0VNKR+g3T4uNz9L8WS g7pKKjPL6skHH1aGAMPJcq0YinCskvQGIdw9Ei6JBGZ2dNuqlH1de3ZvWGAJWK5KYlKf+ulLN+2r 0oDM9Kk7D5wOzxwJmJk+RB4Cuk1V0Xs3cMAAtdOr1uQ0rKxw5Ht2FDIm6YIrvkTD8slnn0kFpbDA TfLDiR8oRQn/qlfX2jnEWD7nYG4tO1+klD17965Zux6X778//fT7H0y85eabLx46FBrXu1dP2baJ BaAWdDYYYSoi6lB3ZCEOhLBKTEvJEYk6Riy1+TDd3mModuqbAv2OG1tERGRnl++V8xdbu/tIjTQu xXb8tAZom1uhhlYNCoEgYZ9+C10m5DH2JnQTV49rrj4de0Iyauvqs/nz56u46iLQcu9kMCdha4t+ XshvwpjJrR1ROXRXAMWKPBrhCdVAtKlaSEqy3PHkTwbr4yChdE+jH3rohX/9SwFyWvlVKHMSOZDt YHuGzlTHUHKLUYQK86hM/PCjlStXAAIyVPeEImeSLyvb1lo8imLN6Vej7U2YZDl27M+u6DL0hpJC sNTtVE+ECk+bOs2dt8f9PJNFW2ROutvBH0f/fv36tUwBun4kcHhNApG8OJDXF196CfzxL5G5QBpN N+iMgvfwoUN6DijVMf2xcQu6yCRIzblz53IM0CkyuYNAhpcIHPbb5pLqMcBtwm9AbPcE6d/IYxaI qRnVfFMlv0E6fPcktP+qaH+Oy3nsx44ZU/Acnhnb2feNxLqUOQ1ZEhnaQMJ+VSQws8pN23U8Z+63 MzfkiHVM3v5l33+5rVX3xFoHt2/YlN+0VtVQepm7ZPywvndNtRYjVsjc1Kljh3UkfWTrFh0vG/NR SpZYj+akTn3iMutLfkaM/WBJlmf7k7DDbhosEgKowIiUIe6TUuRELuH7gzqrydIQyCUe2YmIRvQI q/zJ0RNffemYuHwTH8u9erLVoT2RqF2bNm8h9ikmzAiW0P1J3EsWdFR+ckYX0oNbg+iBlIcUHWCv lXCv6kSO2Onbb75mUKJg9RYNSBLYobuUdDHi/0iUS3Hd18PnVqni1X8KlytWQ6XHVDMiyewYtRI/ 0KySD82ZOw/QevY8rYqi8+zEeiQqaQq3BvngMVONYKJzI5Egtm7VSvXk44kTde99vpfUT3hBquWb WZCYI1988QVT4KivmvL4wSMzk5b1Z4bxMqEIlpg7ZegmaSUdkTaVzAyfCV3prGZEiQxpE5nrpo3p PnJak5WSO6IHl7eDA4YdOthiwMIOFd0UpTyzWaNGdeJ6VLDt35HtdbbDuHssqln+K5I2VYTJiRRQ jh8KDRW6Be8H5ahBYFW5ljYJ8/uvZ59VqQ7GPPbY4Asv5EfYsyMZ2jm2/bjEn5OyetWqd/7zH3me USgjAJa8YVIIj5KZkcExhkXDomiFYUHmzZ6VkpKMtdmGDeu/+vJLqawHMVEex/qur5i3TEHmjh3u YIpKGsfrIJrHpB4WjaP4dVhW3XZ/sAWB5SBnKiqEj8r8y7FM+a7s978Ot1m/9QOsgK0FR0Qee2W0 h6EFLI3vvaXR01uWhGyOcNAB3jqiqoXCecI9gAr1Bv927OBVfxs6+PKb7rjr9muGnHfbpDq/ffza uC8euO+tnCvuu6xV8L3MTvnPP19dq+Li7PvlvddSejw9Y1X6hoUv90x+ftw3m6xVJCfl/b8sSxr/ zbLN6+e/2ytl7Evfplu7oCkRjgB7jKzyqB3dXQ0hOVogl0ggCREkyEmXTUUWO9bivn36qJ5gMmz/ 10rtxwf2YzQmpFd0uNOLCxu6MIl2awVPyjtBnCpZ4jmg4+JAFC4+k3JbaSJq2l5sFStVUnmmqblt e4akErdvd5IG59ghFWSFcjAzVj1H3FcxLFP9517EuSWGk3zDJu3RVkb+K6ZvjiwIJ/Pzxavuw48+ fu31Av95cRsUPRTym549ewinEZRyDx1KT0sHE6GeahcBE0FAdU//4K1jgRi4gJsu2EP9KS3ffued jqiYQqSQw6lEnB474/5ScsuoIt6dHOglLh0NVrIlSY5I/aKCUXHOVBw4YU5W1ksbMSmwW+LFe+wP ymXmGvrVrFlTvAs5hDzwu9+pQLJ6PFguh59B6MVhAkXnX8f8RfE/FR3QvTWSKdzh9al6IlF2aQQd qPAbiUxh0TJ7jnGC0LutoqryVKBQ1mFhFJBU+NAOO9DGhP9MmPr559gOLlliJWcTnuSemtxcyySA WWaPF5Wl/FYsFtdLxqueAaLokRQBa7P6DRJUyiCP4eV0TatDqrp923aHE4bi2XJmEGYmbj16KC8J Tcyy5s4T5XFy5UskxKwztBmIqVmAjp8+bqf/y5tQCmMJ35FjPbbPssDTBf36w8OjSXOu1xk3btx3 M2fyPU5LYiProxA0G5N/DMsCHEXEVgue8xTHUCq3v+G16TM+fHxU304dOl9yz5ufffH+Q/3q1Wz3 6+c/nf5/Q+pZL15QJT916riXLGOCgpKz6ocpx5OG9G9TLTY2offwoQnpaZm5Mfk5KbOnxXS7aHCb ajEVE/oPu7jWtrQdZxhSBHVTU7nEEBBixMaG2rEoNw3qEClJiK+44gp+z5k9h9/du3UTbkE7yBXk M+HBMBnmOMuhvF69utt3ZBA3SwJ5pKWmC73zcV+8mSR7Jv5xhK6Ir1mDFZA9Q0n1RJzTMrEFoTv5 wM7HfkOkSuKXiu0LoUHjqlTZq2Vq0mOgC1zEfeW3N7MwbHHatLPuIvqdV18a7xFk0Y59O+MbqKcK CyeW10Tw5ze7ptQR0zGJ9w0CSHH4kNS9O5hA7GRvO5WfT3JsPpCJEn2QYxcBAY+e/x5th929dfiI EZjDrdAUTzdcNxyBIfgSYQnEF53p4MGDPELh+FJGxK5fx46PT2l/rmXxvXu3JaOiABfPALkwMzIz e/Xpi9paQofoPEDOHgjw3nr9DWHSErZt757dkJ5AFFgiZ4UFksqirR0dFIEQUy8KxNatW4m0TPJb SPYLieM6e9YsSINE+rDiRNiMCv8MBMDisCLutHACksnST4/CDGRmkgfMcTDgaSGYGZeTZ0wFJ9OD oCrbIJEXqlME5pvyJP/www9gAjKwmeP2NxTRpepaXfUvqaAXCD2vCccAOq8zjI2pqYigT+SdFA7n UDUK+9FjNas2ZcbFnLFb9+76vdRw8AJx94RvxOWlwjkVGB1yXLl14EVOle53XG+BQxHh+ANvM5Ca jthv6hJm7YkxYwNpQWSf6lSMqSv0i28kvI6SofJ5yMUXM920LBV8FGYzZHO9QPpcYnUigZnl566Y 8tIr7361OH3/8VMxp3KzVs3+5I2XXvosM/GSwd0bxgUd1Sx/6xcvTGr43D/vKVgVOYlk7zxctU68 iLsrN0psffirpWl5+Yez9x+uVDM+zgahQv3EHgdnLN1qHEFL7OEL+UaihXGbgnF89xaq3uO9RJMY SOFIx2aAYbjkpcHHkGXFYeyMeRByDuK/FKb8szJjstSSv6hhQgO89hPIwGjbUPs4vNarXz8zayfr i2y9bKJiLq2KsDpSAkvKy9OZzo8ePXDAMn2lqHO88Dkl0HJQMdkOVfB3dQu6JztcVtZO2ADWMD4g WvDjj3gqHLQ5HEUi2YooS9n4yweRVYAA7E18IPiTmHOCBn2GpvCBqEivvf66+47Bev4rmRPxCyTS EjurTBlSnHXrLS22XmRr1xktf4ohlxAmOuxQXOpmZ3pTyoAJP0f53o6Yfzj/VL71PFSuDKo8e6Qx 5V/NbVGipJrQmZnEOSO9IHsSdEoZXa2zU2F6NIRyg9bS9gFkr9JlonSgVcuW+GQMuKD/8OGXQ9GQ PZM4gZriG0HjqJ7FQgBYKtiUQpzjhJPZGe55riuJUBZ9IkyuUmWLIqjCf8XwUcVwVxxIbcZpaQWz oBLXyuXKY4NggsuXLUfbLj5M4k2Jbw2/MVuEcCglphxLdK0uAbSIXaLrMVXfeMvkJeXU9M03M5Sa ldz2v/3tbXzf7tz20LJeST2wUZOr6LMws3r16sirpxeZcUnaEUjhCRQ0eBIq2Nfyyoi21FtIF2/N 0g7nE9+ZJRHjYaUQSMcCqSNyKY/G9boxqDuJnKNxWbsk09r9993/eCGfYxbgkUqGun7NWp7Gy+0j MYVZd6TNCKTPUVcnEpjZqRMHd6xdvWZNwc+KBZ9PePWlD7/fmOM/TosHvI9nTH9xXMxdY0a0USeU vIxNmgBNXXM0Y5MVW8h3sU3QnD/+LjL/L0YEhLKI+6TjNqywPgxuvPUpcMkZaZFku8rMzGBZcehA sfagAxJgSZJi7tljCYEIyyTmyWLy77HUtTebI4cPb968FSKFjI0Fulr1amyiutOAfq2Y1ygTk4yM zGzbo5Oi6Jr0UKJoUhzpWUSS5NGmqnJly/594aJFVwyzxAC+eQAWcgTu8gGvSr8jddgOdU5cGGai mui/injkFZEGgiIV34T4BaKdhCgLZcTuR88ZL70CSez8+KDLXWT7RMHkUFqpwXo8CQifc5jMiy7P CiRbaBElz5Iw4522URRF0X2JLQdt/e+77/I9Rlfu3KzeMHd8Dxp/esTamNue237UjTfxQeSCMBeS WzBeZM/iYaqnEVM4yIzITgxKyHRBTyAVqkr4YvwiPQqT9J6oA4lCxpF8+qAd7YVCZGbJN4Vh4vPP PDPqul/zmV1foq6QKp7f7NYQjpYtW8oliijA6jgC8Q2P97nt2uQcyFZ9UGcctUrAO0mWiqeFqgMm vDV16tVdv3693nn6LCsPZgmO+LeqmmQPc3tjuFWiPAZIEyW1uUw9+XmF+QVoNKb3DTNTB5L6f8P+ 2YdcSjcGdZgEeOuGOscuKcyeSdw4nUdyMsTv9aqrr1YBHX04JKm7RPvGHQnMrHytfg8guvx3wc87 n86a8cqoBnG1a1ivfrAl56c3/rL7gd9f0ihMI+Os5v4JtlOmfiAIIIX63yeT9KjiHq8SliDyhrCU QCRn67VY57ClRlY0sjzZ5lWUB1GHSYAlyAq/JcoAC5lsG4S/9NhhdiwsgfgXwgD2POJjiTZQlCne 8j0ryxI9VCkaAZRHumoDiYgjazv2PZdcMVysfDibeowaz9qXd+IEy70IXSZ+8IEPJ7L46tUgDexb FwwYUMeW0okxE+aAEBe/oebE/gaRWN7xYyeOHdGDlUBivPkwIifw6C0oXIq9MGunJYHztogXpj48 bbBFZZkmj+TeIzOD6SKXcc+p+IfKb48FyZPyZIy3eY87OSDTKpG02OHEIEnPzRpUkNLHxz6Ono6m iPEmFvcdbdGvt0IdGa8cWlRYEAmcIdZyUgqYmW3mL8VhfoQ1G9I1ZFddu3RSoia5iqTmjg4ctDO0 UkaMHEmMVjJ2y5/CeG6/4w5HED6+dM+LSpruHl1CgoewEVkZmajPeBHUu0C4kHMqnCMOBKoo6iOJ ATwWxJBsFu7nR54E0fm6lZWSDJ6zmVQIxGLMcXfaD8o6zVv/I+F7BGaK0skphW+wcODLK4YXiM0e L3Tb9NHhaN+4w8RfwjulFRp1P79V8nvzUr2ew73dLzvl7bdS7nzo6jan/XSoWqFRYoEnzBnXWWrN 8HY8YlvjABGxfVMd+yU5BSNl8nUE0lVvgf6DtdEO5F5quRS7crjOvffeV6isOYVb4rffzmSn1E1/ EJIRkwzpl8SC91ZkX+E3q3n//hd07Gg5wSUk1Gd7k3TF/IlVMr/dJu3cDgsf/iVm5ux/aBU3b94E 50Brpu6ITgdrHr1vaGrYoflNm5xNxWBZ906QyuJiBllEesHJ/kdSVnspOCVAGqZ8PpVRiPXbggU/ Y0ltXV67tt/IHQwfVRQrL74Ljg1GnadpSuJFqWLHCvG8fOHZhx5Wanrc59SkKA0pgw3kIO4AAFET UY69T66ze2Jfj4EOPUSfKLpmnXoeKkjpfRzv49p161eqUlWMtFBDf2d7InOhcCyP+Tp5FN1Osh67 hybS4/fC8jG2E9wchxaeMe4r+UbdBuAyEEe0FORtffv0QnaFnEku0ZXCKqSZLihVUkNOLPgpc2iX MPpqss6xqbzDdlAp5UlbJJna3cWj0Xp66gZq9r/gAvUuMEwi28nlYvvIs7FylaVcDq00aWLRdxTZ /BZGrvsFS24JXeLo22jM3QdvsWBC623Rr0K45THGjY+WFeCQcr0aTwvfCFfjeUAgyo8eArDovY3M FiKBmZ3K25OGefXpsuDLDz77qXzr+jWD7V3exnnvL1z/0qhu6B9bX/3q/v2zHr7gvBdT8uJqNog7 tDdH/C5tJWaPxEYVYuNq1oo7lp1z2N7G8nZtSo5JSqwftLtBZE5sFPZKyEERA/zoe3kxYUBoftUy djmkAtyydasENWUFFwrFOisWOSL9osA5RJCmF8XMRHtYtapFPkRyoxziPI4CXacKM7Zr9x72kp7n JTVrWmj+ooVSFeGiWGYgHhMlEUW2NymSWIYdQv4rUsmRI4cjJ+jQsdPIq6/he4/xaeVybkF8V0do IsLKi32bbLoiWbRW1SVL3AbjrL8MweG7qrqH5Gbl2jXK7kS+h/t689tfs3YtqZ/wkqOarnGGsxaE O9IiLEhrgjaikmB3FNVJ9wd1a6EaKjzE0aMWnwYWt0cwbhAYvL/8ymtEhAF8HqqUpUupfNGQi7CX 5wM+JTIo5XKh3/eDiR/xo4S4Di6r13TbaMp/JUYJ4Yg9jotjD08denMojgqqrgJn6KZsHi8Xnb6S dJIGKnW9RUpUVGfREupeHTB75l02ZhHuonGuX78B8i2HGZMCE8HVBRdc4GNe9H+JWSRFjw0rQlzR Ksq7gB5821bPmAR4I6rJgcdKE3vsmO4XTJoKcfZkmPKoBKvQFDGet3hygfew6DVZSVgrsB/44MMP A2xNQg/KKUXi+OgFJa/+JxaoN918M9+4Y90FeLtoqRYs9ymOcZ3cM//la6+6epT6uXH0hO0Dn/jT 0CbBGv9XSHp4aaHyMW3y72rVGjz+x19GJ1WI73TRVTHTJn6zPjc/P2vR9JlZnfu3rx8TG580aETM zI+mrM2NOZ41f8Z3+zv062g9H6aUPALk+RH3e07evv3OxJzcYxCpQLrNmugOmhXIhaqO2DUXloJw 8rKdYAwUZ/MqdTpXH2BSHOtFJkEHxMdNa9NiBqiN3OMin6C7e4Vhoiz9KWIVDLG9ObFz0ITZLFq8 hJqIx/5WmC6zeYtEYsKJe5RIIwgDi1W1uhdCQfFuA3D9e1UBZaUwSyKKKR4pu1qz5pZbH3pYBGaS IFJmTeRwfHZYvEmbsEOPDphIbtw2KwzKd6yTuXbcEN3oB67j2xZqwMCBn02Z4kY72G8QAbrtz5ha vR09c4AdiNSiRGlpaaie4fetW7Wc9OFEqKfyXtSvRfPlZrFKWoZ0RyRtGJNJfFeeWMQYyp7So+ZL tkaRhzmKonHIfqQR3fdFKe/8hpvS5dw4T9SuU5e8Xvq9iJySnJzsLRKK3JokHPhVPPTww45HQn/+ MVRypAZKODP8vUqbMXvW99IBXQUp4yVTgsd5DyTCjuNCPWHAsePHRR0sAf8oTGUj2xeH6aZxmKvY RURjufGmm1BD0/OgInTwsrRILLAX1EcN+0fa6sABASrHp5nffhuN+ATe50hgZhUaXPK3BQt/nr9w 3ozpUyZNnj5j/pLk75+/oUONYImZ92HXPu/W0SN2/OOyTq3a9nlwSY/Hn72mjTXy+KRb/j50x18v 79aiXf/bFieNH+tQgwaOo6lZRATI88OGJOenb2fO9Nuag8EEnsV8x45M8t16VAb5valdwXoqSZtN VIt//2eCY5PDGAivSWFp4r92us1yFgGiwsN/fIQOJKcs129HDgD502075RZgKDex/bZYjuKgZbpp GpWJHIZASwz4EOnFFzJLAJ827QsVtxMdAXHqkdaoXf+hhx4gTD/VcCz1CA46SL5Xe+Tfxj2NPICk BfI9hVC3cCOmlWb1tEsOXzzVuMMB084EGuIa5Q4TUGgkfjo30/ETEnG6oCQmtnAYCSkzJjHqD7Dw AOgzUqDFs03sKcePO/ddNJtHbMW0eKdS2tlRSxo1bMjE6QrKDbZZOvFpES1zgNH/JQlG4UxoQt/6 9wQhZzIcaBBiDFFN9u/X9//+7MFNTwiHb6tt3jK8jKnmI2eiX4hEdnjuue34QF4vPuvZppNTlvqO hCLpMknW6eNGjALJmVSQsMmrVq3i1OMmVaJhdKSMLHAp1YzneAwkJnDRCyuGSNMVXatUqTIHBoRD 8rDRSaTvQdmNyXkjwBxNRR9C2FsYPOhCOi8mrXrxeNpkcoddeqkjQi8vAjbKYe9YKTYY4qoX3h6X q1y96v4Fr99/9bDhV426eviwK+5/dtra3CImy7TkZyn/Himh2GOrtRk5bsZq2ypw9VdPjSSwmf19 fJuRT35VYOM/bdzIdmec4MI7SNOadwQUOejevRu7+NKly/VdXL+O/QZRjdub0kd2Go+3lcVRjNJU zG6V49LHXMm2MWfOHIfrlroEQyt1Itf3uZN5J+Jr1Fi3br2ErnAso8rpXbJ6O4pDxSYbjNv2Wck2 9u+z9MJK3YM5FIj9vHAR6C1bvrxDh/YwnsULFw4adOG+/fu/+/4HdTsqsK/P+MZixghRiEAmEbz8 ut2pbEizZs2mcVR1mLJxYVM7XIVkT0pN8+UKLWGrHKn9yAQqtllBFaaV8WI2xFlcF1eIugdNr1KD ipefjwLIcGU7y4LXLAg+LpfZFB6j2Mw2WzYskyWiNdgnDzwaZ6CD1F57zdWQg46dO4lqfkOqhZuE ijiYe4jv27Zpw9Fizty56C7Vu0OwfmaBuHrwMMh0usZdVLQUYOnfv38IUh81RiXp8TZqb2GupD5n FZJGWBNtS1IbFoAQhKbCNrM7RWC2oB6J/fv36YuGIICg0kp1f+mwcYWCZGmTxYRgGbHlYwv9rC3C WseL+45c4htSeX3wSiG3Bw8nXhQcLKEdYirX1baiU8puaQrKGDg5EwYjCc6jqCjthwjY3B4h3saC HSFOG3rsDHQC3hI9RREgelcjgZmdOrFpyiM3jUtueccrH06aNPmDV+5rnvyne8fO2B5lD1qUPgIR 0G2xeGVfQXggu7g73bV0k42fNzAQb0pvwxJ/SQRa9lpg7XxKE8fSSWI7ty7VSqtXmAUcYoZEqnKV uBUrV7lvQaJiey+31laHcifvBA0fH3nl1eoqog8oSQyUqGaNeHY1h+2USIwciYnEXsfHZiD5v3Ve iNgMSBGlTJ/+JX4DK5YtW7d61QX9+4k4TXUJdRKf2de5hbTPFsKWVi62POY/c+fOcyTGUReqQLg7 bHsyVHXdunXhQmHMNAJdWLIkmc8VXPkTpRHcP7FQEVtvVXzkHvA2v3w/bNgld991h3UvO9STqqm8 AdxnccQ29Naj9RU4KHmGj5t6/JeezPE0yIWxIfhGZFqgtGjRoq++nsHnmd9+c/+994z5y19Wr1yF 9wYyy5X2kyYkktTyfE98V57VFSus7+fPX4C04PU33sQMAAtIWPivrxvFWLZs3qKoueiOiV52w/XX BUXL3P40NucmUqzTy1gejAb169BnHyjhDaBXkEhpwVomkCdeaV29RTPxO1N2DDBLeElOVbc35do1 2PuXI1iaBBvjLpKNw1vx3Q2et9179iLhljvCqnXzKcw09bAsTBCPDauHb6OOwrC3QYhy/WJSKhXE I4QnNqFhQ3dsRUeXWCeRvstZ2u2y4+08XyrjKuJNI4GZHds8d9p3te/659N3DuvXM6lH/2F3Pv7P 39f94q3v04I+LRcRDXN5aSKAIbB6Sx0RsFS3UtMsXYaP4jcalg+Zv1vNxI1YIu++886+5/d7cPRo +77lJC2xVEaUIsuE2PCqvT89fRNxEHQBGP/CE41wG2orwtxED6zA9jzggn4qVJJIoRq6IgvwJQs9 t9OlQX6nrZN9OmctI//3H/8wGloml8hSqCyO6TP3ZZXEol+1CQ+2Q1RUmjvvR1ItKXKm+2qo/V55 Gvbp3ZsbqUbQXtlaUaiFtdW5pwkjMzJTBRgDyfd4bS51hlGX1JfZ0UURijlhKqf3Vm9ftg2/CHus oAQh+n937MhQIkZ5eFRQLj6Thwp5gPI+Q60JbmTmlha2b9+GlXRD+0QhOxNmAHBrgV01y5TBXRSY IpuBd3oz/FfdU8E7RHjJ0cU9dqx/3FFG5XDlThnpG7fq8fF67nnflT1GLpWjS1AxROQuxAATgu4j RypWeoI8UnA34xR9sRyulJjc2xCwJCMajiw+ZIYQwaEUpkmXSfOu8VZC9SR5rrciD7M6Y1AfG9Zg nTp9A16S/+WIiHsmHrLum7LgIMiX70kfwufvvp/lsFCU/0bv8N2jjgRmlndw/76YRvVqnaPof6Va 9evFbN1/0DCzknw5Su9eu+xUQkphJPuKx+74DW2gG1b7GJAeMluZucgh1aFEWLBgQWLLVj169Lh4 6FDZt6rEVSa5jzQOndKdKMWBDv2XKCwIlq36wO6YbadhIfOmMiY7Yds5+XCqEnsjd2G/lEUZx0z9 v95SINBPiC8/UBCpL26YCCn5nWPvMRTy7JAyCIGTngYUK37iU2Bq1qePdQnBTaQybAA3Q6VphfOp u7sZgGivMJziN3Hn9aBlfFNEh1yPELm/RFeIaaAEAZYSmo4ywNv5qKY4E7s1JBjOMXxEQaymR//8 qPJQwyhQ/CfczMOdfgDwFewwJBGF4v9o/Q6MWZIGnvzoEm5XhKBYF4j0sfhK4FzcR80QJGe8nt5i ODNYCU3SslVLEJCx6wsLVk3wgxfHv4xtAJaRvMUqULA3oDhxkczx6DGCUR/BFlCPVYtKF5qoXwg5 g/AFZTcmBpq+MwEU3yQWvWUxPFXWgSIbExnYqlWrHVaqLNQDBg2WTHd6cWTvLXqvSrGFSGBmca27 94z76dsZK/ba292pvP0rZ3z5yzlDOiWaCBal+GiU4K2P2ca2fm3FlLQm2KO5PhQJ/q7n8pPtGdYl ugbH6ka2pfPs4LHclLgA7AGETd+kGfGIl+VyOwxba9u3kTrcBYWFQ2smISU5iCNK4V9btm7DHcG+ o3VfjzEgunTprOJceJwQGYgy7hG9jEd3ThiAkoRhWyPuliJbEonX+RcM4DdZevjSrfMikhbmTRAC Ua5JgesoTSucr2PHDnzpI0EWDgFUEJamF5lZR4Qqu6kgLJA84uPjy2CVaMG2r+qLZE7Nr+5WCUOF LsM5zj//fKmPdxsRudS18lLwgkiUE0KtitGY4xVgXoZd8it1lZwW2MCQ0WJiFaCjHAJNrtJPJt6k jyFD4b7Qo5du4O3Lg+rNMdlHO7ykvhccHvi69eqJzAzbREX+eP4x74Mr8D1mo9z6iy+/mvD2O7i/ ODKEerw7lp1IyLSwNtbxwB2UWI/Vx3GU9YQfpOz8eJQMYcCnshQEjl54a0JY33n3vbC0KbIxAVnO GHgcq/WfdQwz1jSX/sRjKJmw9KfkG4kEZhYb3/+ul2/Z9/yIAUOu/s1dt18z5Lyrn99zzRu/H+CM /lTy8Jg7Fj8C7B++3yhOTvJOypJEXqbzeniOJBlUZx1HUrSWsidJtCFpCiPTgYMHs1OKrI61T+IF NNTixyJm4Jt/PP00v1u1aqX64LbR2b3HEg1S+BfqSEcHHLsL8o/Hxz4GeyDOxa233eZ7aMqlUbJT e4tHpRpx7OsF1k716taqVdMhzeISxAbs+t98MX35smXdunYBCm/2HHLw9ZEgS7R7kiFAL96yPwVl FOV39oUXUhyBtfxeWMQKMmqZ34MHc6znxzb8chdR8nrko7iPiMerXKtLalEIYlumpKGq2aVLl6F8 R+0dVP+DzU8aVOPuyt68dINqNrRQXjyoSHzdOTCEsW22Tl/l5CDHa6iY2azZs5FByvED0gB7UBp8 3evCW//FGxRdJBwrPr4GAe59j5RVgkuoTwhDK/9Cbq54L7kL9M5hmqZWzqDADK0y4sMZ33yLMavS PIbWjlwl1iy0RrMn7BgithLzB0kr0tnOYIG6wJEwJj093bdxXlG6VMLXRgIzY9FqfOH/vf3lh//4 zeCuHTpd9Jt/Tfzywz9c2DAUZ6gShi9abif5gCOwcMokKChvnQ9zE86j77z7PmbOIltq07q1X+ma 75GKsESlSZGNkLWPxRezFe4iqycFFnLnnXfy7Uvjx+M8x1IuC7RuPyQHZUWGIBPSsl8vtgCnA5WW OwFAgNcGUo1tRsR+1asXOFQ6rrLjblhSQ4oo1+b9OD+QlvU6AvLhw5Y62K1xlqzV3lIgF0VEqvcB 1S0G9fREpKQejcCCHZfv+tgCQuXd7ore6IgPXTy57XVjJhJRcGvhB74Vgn8b97egBnVK0/YGciHH DIlEWPQi5wq/pcI5FdQd5ZDgO9CGjwZnzZkjOTD0IkcLIsJ6vBD7fVxnbr7pxjtu/w2rlhCR+++7 h9+ZGQVBT/wOgYhlcCyOgrp8VL+KdUaWO6EatgLUcjRmrr35bMoxRp33OM3KylkyZAXXImGoUNUA E1Ho49XjUfM91izybEuz4ugND+aH2eHUujNzBzjgby6NcK5mHaMmUab9gh8VFSKBmeXnpnz43Hur q/S64tb7H3549H23juzXpubpAOVRgaPpZGgI4GtJsHheqtq2KbS7yEsOQ+KdFAunoiuhxMxLhSxX LvSseuVtZzFVOJ5ypIZ7fTV9+ratWx0bgLDJ5cuW5p/Me/KpcXKVD5MXMvSplvWIUH5DdAaOLREx 8k543lH0RgjoqiIpYNUnoS8R6rSwE137KGwPkBvm4uuvv3ZX8+F+ISAzUsQbbo/LlStXoBX1xjB8 p0MIHBwr08uI4UoOp1thB95IUDWBtFvXzmIvL+CEHEgPHqnjw0TADESDGS7mKkMjD0FQY8RLwK/1 p48G9WeGz7rrifsqscBDG+64Y0naCyKzp5M8S/ADHMnRI2M3KUH7DuRYAtFAiopwIXoAtwMBy4gw MLxz7BNjeQgWlm2r7VxtHosjE4Ao+wDKm0A6kH4GWEcJ0XkmYVQIzwK5UFeVuM8kMDCWGtrBgPXG G34t3FcVtP9bt2xGqDZmzBjyAfABHwJxSQ7k1pFfJxKY2eENcz98a15mTIWodwCO/PmOtB6KryXk wJF/UJmEi1HnqGutYBPp6RtZB70xs0CcvEQ4RLRMjH42b9nWoUMHVlVWQLXQ8z28TU6r9opp8Yk2 bVpPnjaVwOIO9NgpWRRmf/9D//79GiQkyCLrjZkhEHrk0UekBcRpotsSc5OcHCuQh1+vUvfcuc1r AId48tR05znRL8eYidCj6hvRFwRYJMLZ1zNk8T0jso1f9wuClWBkM+zioW++8aa6HXRtZ9ZObw7z rMsiHwpv6d2rh+/4DuG9XYCtOULVq6vy7AwBjgIzEEbijblGstmNclbQnxmxugsQK1XNNnwsoQhL LA7EJVG35nVDj4zhKd+wGvh1ZFHO2pILhIJDAG+E+6wiLp8IwFiLZBVCt8BvlU4qcJR8xxEMvB13 TYnvitRQbjH64QeZ1vPP74vsKpAAFhhcuh1Z9LsQ/gZChpoenJWeRKxE0LmzaOCJhanf6Ice4hvk 0D5cx4oyzFK5NhKYWdX2F13R8aeP3nrvmwXJp5NnLk3d42E1KhWQzE2LDQGhRA6TLMmBzRmRKE08 EVQQJRr25j70HYE4eUnyb8QPrVomWpm/8/PbtW1HriRRX7IasgiKvkCUAtTE4gGyhcpDb18pFEgS 0rNPn4qVqsSWryhnU9ZuR4xvAY/LyQDYscO5Xbt0UiTMkRUuWJglVIejCMWR0OeBl1o+A2laDRam zmSVtHMXWvoFIkvptxDSHEhS+blzZqsLt2/fviwlZeSVV3rsLetyEZXXHpt1x30IHKuQa7Zr23rf Pkvj4y7CVFR4M0eFrEzL7MYdJ4Wr2Nv0GPpyoXzDlul75/PYE4yf+L5YFeghA+jxQihL4EFZA791 czvDGCb3+iW6Yb6jKVm1fLRPMjRFkpTOESMKjPfd2lg5dGEXwdCEmREOkNlEcOrtFjA82nGY3HGJ xwRfgePgo+YKO2IL6kv6xsFYXn9JK+wtIKXv+zqcGxi4/u5ziuZylRmZ0D/4Z/Tq1YsoM2EZTkQ1 EgnM7OSBjWtXn1j28V/vvfXq09kzr31m/hnxACIKNtOZsCLgMQ05RIeVjh/E4xSRRQUiGAuka0ra JGGBZGWUL2UdFOMMWI4jwWVB47awC7MSXAR69e4lhE+itCtlmUd/sQbE4rTTNkuRQWHbG0if3XW4 hfsuBMimptuu2cctUIctSbHCwPooeurMvXs9v5oS+os4qD7aOWJzceJ1qRDe4v0eYGSH0ICKkKuQ 0iXY2Y28FaHsws9UyiA2Vwlr8u4777pn1qPtf2jaPdw7aN8j3S8+AIMyTpBThxgIilTGbySzoF4E fZhIwflTQlJ7jBjswETYsI/kJZxCEQ/LguNOSOBoTZlY8P2PP/4Iq+NyggJKKgKyDNOY4xKWAvtI abWvSmhWgAjDPvr4f3ibcl9vZmpIy4ilh8KRNZm+9e3TW24amteO2KH6Dnvh9ulG8y5hvSkc8Irv KS35liOBmam8maTOPP2z4PlLfK1hJQ+VuWO4ERCPS15sj+oqdX6SjUpeeL+hMgPsoyy1CLdQF0DL xFpf4j2qVCdyrvXIQpo1a47mJWtn1i9LlpywQ85ioisCJFojmNkZGTO99wmZGYZHhw4fCmp/Uu0R 89O9j/br1490zj7iZwYIkY9qMmQKvlHKXk2+IV6rKHfcRXYjZXevNMgilggwskPROx+xLeCKqzvG 6tZjuiCk+Cgsd0d51KlTR0ceyWJFLKgnX55qPZWq2O35WBYELr/B5T2M0T59EUwLsb2sCe6ctvpV BdzCDs3oLoSB4EucB5ctW+r4rw/3BTGrQOiFng6CjguLHGKhRKlpp1N36A06jnkhWAHiDklYEMzU hHLpSZDkRtjdsnQjLcPeDoXjjTdc745QGOwz4+BzHvXC7kelVu1aKrj35s1b1GcYKlGQ1J/BdiYS 6kcCM4spV7lK7K5lMyf/738ff3z657sNlk2QKWUXAbHox7LEoa6S6AZz5s7Th05kr6ILzCBbjRs1 hPOxELDaQqEwxcXIAxkDwgyH/EkOix43QhLq1a1br0WLRIz6O3W2zFTr1a2TtXOXco/yq1qV6FYs ynghEezb96If1CMg6Zz9dkBvk3UQxbHfu+iBuKQyiaoCj0clObXYKhy5xmfOnOlIDOq3J2dbBUk7 IeFPAylB0R29Qd5EHp7XXn89kLuUbp2PPv4kcK/DohxUVJQyH6pMoBDMMY3wAQsSPsRmfkPVqzvC MCBnAwb0RzLKjwrewUq4basVrMdRbBvZAiOgo8c8B+v2O2sEW+G8x7IsifK22lGBVJEQu59M+pRv zu/bhxWP4u0w5vFeEqfXd+w3VCVuwxWGz8BFriw5KjAURg5Zx7Z1IabPL7/8QhQ6PiNQxMFr95lR uP0OPKIqRAIzO3VkxX/vuPLepz/5kaS8awp/1m4/GIRZckSBajoTAAKculauWNmoUUN39CaRWitL Bdn7WSl+c9utfhv2LVSDmfFiI4GADqEkPWDJzDxYM0KYOClifIaRmcPQRDogkSGF/YikDYtUzODE 3sUKM2uH5PZRhAX6Tv/sd7DhqiBaY7+tkUDp/vvuW7XqdKRZv5e4K9SoWUM3Kftw4kSavf2O4g00 H0I/i+mSQIzwHLdmi1LyRaz9ArEAU8wscN5cTOMtjmYlmVVxex26LTVVlltvg5IgGsicfI9a7P2V m5GjMnJoqI8KPLFx4ya3OBlbroMHzzCAk0Z07oikLbSjLAsvKUDE/5QW2JRVD6HCEv2V5YIjq48D gA9eKHF6xYBYLyzIhCvzDR1bgMiVOT/MXTC/Q3vrs0o8DysVWHCwkGVZGSBCBB3Bz4rjyQxjm5HA zI6k/ThzdcfHpv7w2X8m/OffhT9vju5nIs2Ga6ZbtyjIyROuBoveDgdHjjsXD7nIr11CWGJR0mE7 WmOBxTqH19q1ajZq3NAO4uDMNcHSKeqPz6dMcY+Unot9WL16lhV8VfK9Va8O4ZO8xVLfb1ByrLl7 JHXDDj0Px8gTeX6ZXNEBD6QFFYvVXfne++4jKiYsKoRgRdKakBKdmox57LG3J0xAYNZSi9AbSD+j t45oo/xumbjWOhRwEsziz3/5S1BjD9e74/emDgm33/qhVcBoDG5ao0ZNuZw3UUKPhtaaj6venPCf 81w+NLzmhGL2cRW8wVvgBt1PVnIxeTwTwh6++NKKR4Nxvdxo584s9wrp9vlw94re9rBNToMq8nYr FyUooDqzsXgq+xPqiAjcY+Hx9uZ2IC0wZaLe1YuPCNUe7wJh5RzOUty6dRsqtO/QPic7m0Ue+khX 5djM6ZrPfPPWvydM+nSy4nBBYVIqlSOBmZWvVqtO+SpxVUzUjFJ5BErpppIrM9i3sSidFSUCiiE7 O4pF0YQlOJQUSMtUkHpOWlVd0i9Zqvbs2a1kZsLtWC6xYJWg4X5NhqFusvwRNjOQ+kUZuO9rdbGK 28ZWXcs6KNmTVq9aRZIY+Z40UG58vN1OSAm/RXn93rvvkmsIvyoimal8ecU3zOhqGR6s5IjIYlXn mza1UjMFXvxSwMCb8lZT2DxmSTzPIWtRA+wGLtIff/JJq1YtMbqCAwl9CWQNEeVX4AU3ah54h2cr JMBvSFtvgRuUJQDdjourSqSPL7780t0fQqcS24z7YknGf5l6DLnc1dz5HlQdMa6C9hGRGwfq8hWs M2SAal94IQIt6nsMf8O6x1I56MKBorsMzU1ePDa6du1Ca5J2SZUK5ctjHRH4HEnNZk2bcDTmw623 3kJKYj6IgFkFUEStifeufNmwsVc2Gex9i7t+qTKzvD2pVpSM1fsb9hqU9tbfX5puomYU93xHTvuS K9PjUq6+JKhjyB3GNNgR1VMsMDKzdoo2QVlV62YfxNka2K8/UcrkvtM//7y5HXyVOqoamlC+EZ/N 6V9/Bc8TCRk8jwURqzE+Bx5fXqRlJZwSR0dVxCpsF8rv3RvmEqP/yy++UPs9mQ8En0CKOujLVgot C+Sqs7MOtEARDv1ZCoSFlDBiis3zuhU3M5OhselidAUHIgdlgIMNLQGrnNzCFchXLFbJH4B7EFxB JVnS1x80id26db3s0mFUTk5Ohpl5hNSbbTvI8K+srJ179+4TdpXQIIHnZ6OW59cbYrC3WbPnyH8V XHJ6RNBFs3JTUgMjqSLApMr1HuAUSDXxvuxpZyJ2FFxP+G8ggdD0Cwk2qf5sYLvHSgrj/PyToAGZ ZrlWit2KhY6cQfW5VCqXKjPbM/+5q+wwGb/5x/d7t84a/5CJmlEqD0EJ3xRBlO8znCxGrE1J3bsH tQSIXoMVhKUES1vWNSRYSoPJn6KFhEshFVu2dOm4v/2NZXGvLeWS8vwzz/C7ok3dDmgHuEWLk9eu 2yB18BuwYnoVeBQ24T7CrsRoTEKjBR42VlidXxlbcc8REbTRxfjeWUVGiNNDCFoSLpStwo1MsKHX ihuKYm0/BCGWMNq69er+8dFHg+1buHyZg71vIPVV9slAKrvrIOuFx5CmybfpXlFcWSX+S7hSUEAl CZEvSQ70xwBFm4xOSEmD+vV5Da++aiSiQcJkOAYu2QKOFzpHO/6rDDNY9JBO4dnD8tKxQ/udtl28 tyKRI19+5TXkWHSPQBhKfyrvLIukaCczMjMlqRSmfr6tULzlNRHtM9e6I+1J0EpM0IgJwgcflhXe BoIJ2qFDuRy86TBPBv7yEgIQPIPyiArtgQzvVaXKzBpc8tyiMyJlmKgZ4Z3dSGsNTvbOu++xEk14 +78/zl/ge5dq07oVbxQvm19DNBlm7149JVkNr6WIx8TCQKWf408VoP+GUaNwO9+3d8/3389icZdj q/iHY03VThZEO8gZBfEYwjBRU6anb7IM/KtXO//8Pvwp7j/iaClyL133FAj+XMsZOrQAVKp9dwzS QG6t1wnKIKmCzVyDLZJQSAx4sRY63Xl/3hLB3iiS6zPRcIWgplsqN2ncWE/b4HeMxWF95femQVWA qRRFBIi2jtuhpfIYDVH1BOnj4pTkQNwm3J0XcWAIFMHdlDAVRZQVUaD/FHEakFVIyBD6Sj2EimpQ j3Pmvou+VK4k5dlKSyWKBhw3Ah9TgzMmnIxucAzG0cqRjkUuZBlF1fjll1/Nnfej6qq3NoU0s046 kq+LP6a3SWd9gK5xoWhyfVhW6PdVzBtF8+CLhtxx++08D3hoStxvIbusySF43gT1MIe9cqkyM2s0 51SrS0g/D6VBzcomW1PY57t0G+Rt4cWznaqsheDCgafznDg6RoAcUh4F1VvWsmbNmiHBEoGcsgjR lQXy5TbbD5zg0bffeeeMry1rD/oDLUOPaa0IDRp0794driPh2lkvtm23Xm/Kkl+WbrGd1XnPRW4k GSeFmYVWCDzbt89pmhJaI34dDkJrNuxXqZ3p19dfrxoPXB8a9v6UfIM8ToR8CyGrgW/+4R4I+18I CQBKHpCi39GvGDJk/sfjykIkQe2LWFj33H6yHBrF/gxuxG/M5uAZAYo5PXrdsr4R/YcjIhRt9Zq1 6GH5wPmWu3vzTMTkXw7JjnB6Z463HKnUCcbGl4QHQjInFh3eCtIvBPCsw1AxFR2DD/icOmzLHC2g xvU7m45L4LjNmzUjZRY2cBQJdg0pRKc5d968KZ9Po37dOnXl9Wlhe/VGRSlVZrbzm0d63zxhhQlb FhWPShg6KUYGkhqP98dHcFGMTIPdvRCJQbCw92TV4HUl2j6LFFpCWUREpymkTbzfsaXAzpdqULeM zJ3K6h96xwLHG96uXTtctLguIyMT22YuUauMomK4XMGKRKqhazPDAFbJNhGItxc9ElkX+kd16A9N GiHOBBQklKG1ULLwlObdghKwqY6iCJOY/sVdYBKh2YMi5OaniN1DNBgglQntRixEAcrs/bavi6Xh TPDmj//3yVdfz7CCk22z4tfvyMjw4fDoo31EbsTrJ2AHdTDkQOOJIo/P6GFhUbyqRNJ2GN1Kayxo mPzHxVUZfoWV+MhbOZhrybpE14lHQqVKlb0ZurHMclitV7ee2PLyW2UIdTtjum/Hqqt4cIA2i6we F1zQv127tuhDWMTELARV5q7de+bO/RFpGaI+QYMSLSdYulqqzMzvs2wqlC0ECA8GIZOI/1jCFnHJ Ey9uFepJDCBEKqaYk5jE2omBLf2msDQRcYsYnCVy757drFDTplqnqxnfzVR8kaM21E2kYi1bttCn Qr3huBH0SCoIeS+pGD06w0fyNAonC3D7J2oDbJXVsK1meBvy6NiZR4wcGfLlZ8mFHFEsBai95QRe oBQe1WGBtxB4TVEsBlsQcvvwMfTbmhAyAif4rVmSFVhPfIuFpDOcTnFiaNbM8rSVQw5LGdTHbVgW SOc5jlINCy3MxV57/c3Zc+aqq8TxfNCFF3psJ932DLhu1LXejsF83yOpe7kYa1HdsnUrb71f8zLW YZZcrNzohm1JUqD6UrHKRNrnV73ol5nBNVn8WZ95O9av3wBHZ/En1gwf/jHub/v375Mhq7hxeEJc OXJEIHhGQh3DzCJhFs6WPnAoxHqMt50kMGIJG3KBACGBg28puTqfOSCiHOSEpOzJhHBQB26m7qWn A+JNXrjwZ/6VmZXlUX6DvT88zJEESY8Qq+fGlsOZOqKFPLrALwzcCdRbmwJRgMyMsAWwVeqjay46 OdNzcQY+5LOwpo+cV2chGvqQEZZEFAIOdwH4FvklxaTdXYSjiO8nzgz8Jv9SIMPBak1nSJjnEyCD CyWgjx5h37ZhPSMvuLSPogBOQ+4paI0P7QQcq0ePHmL6hpGozqQ9is3kS9SpLVsmDhzQH3kVCzWa CsnmJLcW2aFv7bzbSpKW4WHKnYtmAQF3K5HJbd68Wc7k/Ga51s1YFZ7Y24mTQVSUUmdma1+9qjNx UN0/7R742nMGsqjA1XTShQDrBQuHrKTBairdcPJaikU/r33hS5vPAZHXUl+zhKKxDEHO1PdLlixR ry5pW0RHyRHNo/yGJQBaRrO9e/WAcvHTtUsnnY3pfSs6Twr2wfF79PTbILIHTEyCnRHq//rX12Fb 7bd9jxWMBjM03MxVOgJ/fvSREpMLBoU8Fk5Sf/78BTASMWl3Sz0JtQ/dETK3bt166IhfQZE0y4qH 0YXqEj5MDRMa3HzTDRx3xbjwp58Xyn9ZADl0YYCVtXOnqs/KSVhHOE2D+g1wyvYxNN3FUvVN/EN1 ZgZnIvg+VE+WWVQWolUgfgcKTYK3uRNS+ZYBu00D2TvgYeL5TpGOqSXdkad44IWDpBqSM0u7ahOy unYYxWgppc7ManW/9p7fPfSg++e+AU0sIa8p4UAgbbMv35xw3MFDG9iWcl6UoM+UNWvXYlERrpVU JGQiysLekzdWvlH92LEjgx/RLToSofy0YEFSD+d6hI+6RxxoWVYZ2Fi3rp35QSwnNVn1HPkDMI8t JjAjs9mQbasjczimV9GFQBHNIYpvsFg4CTmDkynxj9vZkP5zLhKbdwhcq8AyYSDmZ+WRQyB2VGLR gQgKgZYoSWlQrPWlyPJ1Xo8eikspPUPz5s3a2UniPRbqqyycVFDMTBIfKbMNzsYskjiwix5T5bCC EYrdCGrQCW+/o25RkPgyGPEVtE8sVZTnO0Ng7VUPQH5hche5C17MHTsUWLIqQ7eSVGUU/dEqdWaW 0O/6+x8ePdr989C1XfykHiz66E0LxYYAnAzbUpYbyX2LXAorV0Tcod2Qcw+pgvWDF2SL5UliCaoV R0XzZ9VYvyGNn6XLCpKcyH3FlZq48+4Mx9WrWxJ7LpQQslIkXkatmtah1l04pxJ8SP8e0sY6GEV2 pqFNR1iu8qhxCEvLppESRiBAVXgJ96oUb4eKEHIm7pCBB/8LMHAa8iFM04Qe/fs/b2NYxocOHU47 kPbt01tPgyu2tqyT6BZlCRVHKP60A/2U82bOL1HTdtvJWvQi61t+vhWTiIVdwgnRlJBFySlMQawO J+ND9WrW+ZkiniIeEwzwPSutMhrWb6fWZNqXQEh8Q599hyjr3LmTo9swS/dYSvEh8X3rUmdmEYuM 6ViREBCrAlYo1ghOThIqrFOhR16wTaON5J1U0XF4OW17gnP4TSY7XlE5PInuEjoFjbNWhOrVWTjE /gChOnWkGyQlVL6BqictW7VBxrbklxSCyu7YkSnfswLw21uA/rZtW/PjGAtKzzatAzIWCRaEMlbf GJmVmQkNVhVeZgbubSCoCFn6vvxqBhVEJMb645e/BugfTWuiT6QIlXGoQSXjO0VFdf5x/nzxgjp0 6DCcBlUgPoyYl0jMDreqkS/hbZJJCY2kfUdrgXUUQhTRILIrOoAGU/yufOR9dwdN1I0xEG4he8Mj Huh0TwiR8GEtJyrLkyfzC78JzicGRijuXFFRSpWZVarX4Vc9mlR35pOOCuBMJ/0iwAtGJGuqIS1b tnyFb1NTj61JelrWCNYUCJYcmKiJVw6fJbyqJKCUhYMFC04GtUJaxp+iapSTHKclLP03bLDi+D/1 9NPKzkmnaFzFWgO9W78hlfQmLFhr1lqn3qAC9LP++l2C/UIXeAXdFyHwqyKh5oWDBjVpElwWyEjo tumDNwQiP7ZtCcydkCEoCPEvhDZJLqO77rzdL3/1Jkzy3W1C9g8edKFexxHBn3+R43Lt2nWsh6yf Ehyb3wi0RNOH9Mt9i2PHrLzgXOIxirUY+EL2bKerOiJF4zfPgI+kC+KVry+Puh+AaE5Z5x3+LtyI u0hsNiooCZ9y8wpEEiYJZ5VmswSehCLeolSZWa1+D7817po21sZpSllCQMwLhgwZLL48eSfzEKH5 NjW1T3KnMzfLG8jygaicExLcSKy7pA7/wupLzy7M8oH5BVYIGzdtdliDitj/rTffuOeOO92nQ4cp eru2rfud35slZtv2HRywuB3flCTTCvYxaNy4EVK6YK+KhPr4eAYV1D4S+mz64AMBY3QIOI4UZGKP j3GtX1pGtUDqOPAf/fCDhOz3ZryrIr3BF8nUOf2LL1knWUv5k1BqiOjQgQpdk2ZtvQQhGnM43/IZ XQRqh152zDlFg6SmMDO4lEOW5tGQQ9VhgD6cjWwbtVh3DhW+V2YqHh8/Qsx6jLurV5ZdYM2aNdHy CpcqM4sWkEw/g0RgyZJfuEJ5gPO286fvoEcwMKLyIHKHPMGHuESukpKatnHr1u1oBESK5lgpSA/H +8wSk5yynGtbJrYQcibSsrvuvLN/nz5fTJ1GUIy+fc/HidwxmmuvvVa+YVmB6PCbNYv+iOgbU7Ig R1/S1b05ipZ0P8z9DAIGARuBoKRfYlmP6CsE8AL05SRGI9IsdBcsnjVr1tyyZQv3wl2Ay7EJkRXV TkDHQsrye4SVloOu6CKEbDmYmeqqb85ENQRagZvecy8VF0PdgjXfwf+U2Ey0unXr1CbDsjf0VHhw XCVS0yxdSlQUw8yiYpqiqZMIzPBIwthTrRokMbOzF9f1NgzE/vKyIW22zmx2HH8OZGLjuXPXbgK6 bt6ylSXDjpGRz5eZmZk///zzcjvf3Av/+lfn9h3ee28ii0uPpG5wKUJtdexwLhb6/LdipUoffPwR IWQJilG7Tu04Wweql4IT56lTKmasWD+I85Ex54+mh8/0tZQQMKrMkIGXhEJdgmFmIgEKvHCLm2+6 kfppaZbPpiQ4F4WGolxYjCn3dnzedc2Dne7pjFS5rMychNFaOBQUHrvE0ZEkBI5/OTilm5B5Gx1b AEXnarTvjaESFiRrp5WLnUs6duzwzReeA8sFjmSJ1QxugkusW+ZG0YiAnL1WrlrNb/0QgyUpVhc+ XNwx/LT9wK1MSvorymuftXMXvK13r/OQY4kHgPCzoYMH33z9DaMfeohv/vfhRw//8ZHEli3Xrlmz a5dlr4rqE3727Tff8PnGG29uiXl/G0ut4LFItBtImxI+ifHWjozMAI+k0ThZps8GgTAiYFSZAibn T6Rfwa4bWFYFlQ4hhOMiXYI9SyB+Epy701OKaAqFI9Uc7WPa71i9WZmpyRrrTZamHi1MQeCdPc9z puFyQCSBjdyMU0m8pEHRvSqZmeSA8iG3E1UsZfHixeL+FS3FMLNomaki9ZNAvkW6PoCLkRX/85nn Fi5cRCgdrCvUi8dnRzgxR2PymolNA++/JUOPr85v8biEtElTTZs0PmRb1PJm4uopjRD/4p3//nfk 1dd06GhFf33xuWfWrLZ4oZTPp0xemvIL7WTa7kWBFxEAIDOrXt1EbgkcNlPTIHC2I8CSheFXsMys ZFBT7FkPYCTUCtsyZW0WAu3z1n8WUiFPnMx9R+EWTYiYDmNJIvoKLFuQ5PFBhY1UJEw8QG2hHdvE GfI8vTPWYdt2b6DBN15/vWRwDstdDDMLC4ymkYLsItiZWolvz0xUjNBLMpB4LKwI4nTJGoFFAqcx zli8eMtXrFq3PtWiR1bQHcsD/GQekQ+toxWGnH989FE+wMlOxcReevkVFc85R+ki7Xd735jHHkMH WjWuSvX46tA7H+kslW+56p4y+Rd9qCkGAYOAQSC6EPDoI8nCKFIoFcBCSA+KCJZfRw66oo8XLigr NvxMHDO9lcLzuWVGYmdz2seKjXEL3Auy69a3qKhpyjHfW8uYDn/77cz0tNT06DEyYyxljpnlZ6V8 MOYySffU4c7xP6RabsEx+bmpU8cO62jngOp42ZiPUrLEGyUndeoTBZVbjBj7wZIsi7ibEgoCWzZv QRZNsGYyhOiSeeFkPoIoItDi9du338qwRkQxTm/p6ZtWrFzDnxkZVlwxsVGYNnXqlMmfbdueMXHi RGLbDBo86Kl/Pjtk6K9aJLaE+fXp05Oj3q8uHUbmpTffeLNXUg9UnMlLfrl46FCEbTTFq+57VML/ VBH7CTdpCwUac41BoKwjQAAqQjOU9VFGzfigZYipHN1lgjg2i1JPaTNZNgkJCSdzKzHDN9pTLNEd 2p+OhSsto/b1tsByVqer2L0haeO4rtzwoWi6S34gPSQwB/FBLr/iiiWLFgdSP0LqlDFmdjT1/T+O +sfeUd8uT9u8fv7rLb67/c8TUrKRofzy3mspPZ6esSp9w8KXeyY/P+6bTRYHy0l5/y/LksZ/s4zK 7/ZKGfvSt+lnBG6IkEmKim5g9c/phK46El37DmyNvPqwLa/eunXbvB9/IqJYcsqyLVu3cbBTMXJ4 eznSvfjCCxzv8MHMysxq2+5cXDnrN2hguw7FdurUXirDAomd+PwzzyjESHgicflp2YfYjPoOGb5o V6MCedNJg0CpI9Cta1eOZKXejUjrgA9dQRG76tvAy03L1O1YXfmsAmrwWUzBgmU8gfRfBZJFf4pP qCPZgMT7YAsgSAeh0VSDrPPi+2Ubt1jx4VTB4s3tTOCxJypsL7I6KGDXrl2jK+lIGWNmldvcNjFt zRu3tmM6Kyb0H3ZxrZSJczfm5az6YcrxpCH921SLjU3oPXxoQnpaZm5Mfk7K7Gkx3S4a3KZaQeVt aTtsEZsp/hAgJ+a3M78j/cjfxj09bdp0CWB23nk9iKno0ZRVhe93NMzBSOwPEhrUR7hFsnCcKwkh hsiKz3zPZ35PmTy5fGz5a669hs/33ndf3sl8jlOtWiZynOqR1L1F8+bSbM+ePb/92oq7bX3u3Qt/ TPmMnya0TBIDBFjan9u2Q3uvGeUCbMRUMwgYBM5mBAJMuBQCRO6kvQE2Quh/ibJWAmXw4EEQKXJa brY9DyTdk6OI15fkwRSjNAiZhLeVpAKOAo+Et/mN/q0sUvBUgALSSHQlHSljzMw9j3FNa1WNPZy9 83DVOvES0rZyo8TWh79ampaXfzh7/+FKNePjbBAq1E/scXDG0q2W5aEp/hDYkJrGGy7pR5CWScLa OrVru2MeyqHHncVI7nDs+HFeIV7FDh3ObdPGkm9RH9lb3z69+DB40ADcfxC/PzFm7Lkd2nfo0IFX kjOfBIBt3rwp+cX1aF4qoH+zFs0HXjhI+WNKlNqD3lNz2HF9LEtYVeiViRPm7ykw/zcIGAQ8I1Dc ul2Wx2D9YWUpRo854IL+JTNtrKv169c/dSoffSICM5LsOe5rc7KCL7HuR24HJ5MgagkJlkrEYz8Z SIBuCoEnvCoZQAK/SxlmZvm5KxZ8d2zobUMT8zM2pXiA5GjGJv9x52zTNOdP4PiW1ZorV65iaFgP cPwidBmKjOHDL9cl5GrgEnTREUYfCbZItuPsuDiONxAqpns4/7JkCTX/8thjHJVEys1r6TEAbJMm Te6+714iyn7w4Ye33narDn6jRg2xWsMVwOMr3fO87qKKNcUgYBAwCEQ+AsjMfOsf9ZSUMhxZirt3 7+ZxoS7uIROlUjI+6TfCP4Bv9PUfMheWnCu33XobN7r7rrvV9o3lcXGPMYztl1lmlp81+/nHPmv+ 99FXNPLMuwMEMW3zJvdPgNeW1WroLuFkhJBmgFj+DhwwgM8YmngcLwF+dNImOZf278/GNVrVF5uJ 1NTUX375BZ/Kgf368yPkDAuzl8aPRzVJGGvhZPwWFae7wNse+dOfiChLZeFwqoiXpfL0LKtTY8Zl EDAIRA4CpeVCdNmlw4YNu8SBA6SHg7RKzVJiKEmM8V27rIxPEgXDUXyEugy5k5OnTMZ47uP/fay2 b2XcEnKbJXlhGWVmuas/GPO3JUOff3ZkM0ZYoVFikgdQLbVmSWJdZu6FOSdjwbKSrG3eCJkaLG9d 506dYHLY5mPpBS2DnJ3My+OX5F/auXPn9u3bONkMu3jor6+5Vp1sJPzMe+++R9Cyvz31lDQoHpSi nQyqsESKu6VH24WgmjKVDQIGAYNAIAgElaYpkAYDrCOxIR2V+fLX140qeccm6YnEpzx06BD8DHMU RwjZAMcVeDWGSfxePRWpj2DjgTdbYjXLHjOzA2Rce/Okxo+//VDPgkAIcTUbxB3amyP6bFuJ2SOx UYXYuJq14o5l5xy2Q2Xk7dqUHJOUWN+SyZjiE4E1a9dirMBDH+BLTiAM1IiIsqXV5OTkgf0tQwc5 P82dM/fxMWPUDYl8QTIlNJJQNMRmOFqioFQvVePGDUngHaCRgWMQShNqptcgYBAwCBgEShgBTsXQ MqKUoQlh8bfD21o+AT5CxZZwDyPndmWMmeXnprx23cWPpfR67u0nLkpQg4vvdNFVMdMmfrM+Nz8/ a9H0mVmd+7evHxMbnzRoRMzMj6aszY05njV/xnf7O/Tr6DW3Y+TMWQg9QaIbwlXeLkFm5g5O460y WksiB+JKQ7wcjMP27Nm9aOGifgMHbt60SZSYH02cqCLNvDnhP9iT9e3bV2z5RWw29Fe/Uo3DyUI2 zBcnUPH6McUgYBAwCBgEShIBiRkLJ8MngPuqSBnFoc0syXEVx73KGDPb98uUz9bHHF7/3h39Wxaa 7d8xNSum9nm3jh6x4x+XdWrVts+DS3o8/uw1bayRxyfd8vehO/56ebcW7frftjhp/Nir24j/pile EZAAGb6dlqFiubnkUjrMb6FlBLOBVCHKzsjYeeHgi2655TYSjdMOgrT/e+z/fn3jDXAy5GRDhgwR ezIpiM0QoRGKJizzgUksfQhN3haWDphGDAIGgbMEgUqVrfUtQK1Cmcfk/vvukXB3YkxCzCN+k5HJ EeGszOMQ+ADLBZ7jPfBGy0xNjJ/CK22KXmRIiyk6e2KYTfp0Mm+arsLXx4UBweHDR/TniqhjmPzv zz4gMf03b97UokVis2ZNqlWtSpyzc8/1EDYMPSZOAFSGsUHXwoUbPqFhcfwJV39MOwYBg0BZRUCt mWV1gKGNC2tjdgc0GMTLQFrG0Z0AGVlZO/nMThFamwFeFUUbehmTmQU4QaZaEAhIUNnXXn/z9Tfe 5C3COICLvdEyzkAIq4kNBgEi4wcnJI5HG1LTV69ZBy0jfdOypSnZ+/YSJHbXrj00JaEF3UVJzpKS PDlvBNH9M6oaWhYqcuY6g4BBIDgEvC2SwbVS5mrLml8cKQfKElSGmZWl2Qz/WNBdfjDxI4LK4oFM vP6NGzdmZWV5zJUr987IyOL33n3ZS5et+CV5WcrSFalp6bt27RYdIqHLXn/5paFDhxIYmgPTjoxM jM+8dfrJp8bhChBsNMXwQ2BaNAgYBAwCBoFiQICjvu80U8Vwz+ho0mgzfc0Tws/Ap1HpPX1fJdUC qRNgtQCbCnwgqiaKy1mz56CavOH665A2vfPu+5iOQcs6d+4kwcwc5fCRI/v27pMklQS2IHgY5v8I qPkmsUWz1LSN69auHvPn/xMElvyyFL0nUf5D6Ji5xCBgEDAIGASiEQHxCWNfIHYGTpqixESbiW9W cZvlRZE20zAzP8zs7LQzW716zeIlSyRuGXFiJWgZ8jPJwkRyTHcWJr7fsmWrrS4sR+Qwd4jFffv2 XXPVVef36/fU009TGZMvqFvIjpbRuCSZPhsEDAIGgbMcAcXM4GTKvIztgAN8ccvPooiZGW3mWf6a eBg+VmWTp3wu1vo333SDiiUrBxpkZrr7pLp+79590DLi0yQk1BdaxnkIQ34i+0udSZ9MImYs+cjl T5OY0jx5BgGDgEHAICDbQXHTsujC2TCz6Jqv4uotbkT/+2QSnGzuvHlYlXXt0vmeu++84/bfJCae 1ufCzPj+gv79lMwZe380khLK/+DBXH7XrXs6Ov8f//AH/CuJ7P/cs8/yLwL9Ex3DI6srrlGZdg0C BgGDgEHAIBBtCBhmFm0zVjz9nT9/wYYNqXCyuXN/JL4/OddwLHInvh0xYriEpbGp2EFSLeGJefTo MSwG8vPz8YUW5wCkZbCxb7+eQfh+ft56/Y0PJ05MT0urUaNG8XTftGoQMAgYBAwCUYAAfmCql/rn KOh6CXbR2Jn5AruE1dIEesH0CndFH8Ed1q1PRc9Yq1bNzp06hCtoqhiQDRx4QVL37vAtNyHTMSJa 7L592Xkn8wjcinFA9oGcmjVqVKpkZUbjX1Mmfzbyyiv37Nlz8/U3/PHRR++59x6+h6VBzvhALFnS jZfg421uZRAwCBgEDAIRhADhx0nQhNU/dmYc6ckH4MPZP7z9LuENvSidNzKzoqAXzmuhNQSYwKtx 0eJkKJrHprHl4l9NGjdqmNCAkBPhuv3UadN5N/r26YOaUqdlyMBgivhjkvaSxJdYa3J3OwhZBWgZ OcmJ4D/x/fczs6xIGZQpkyfDwFBf/vzTT/wptIzSvTAmWf0GDcLVZ9OOQcAgYBAwCEQ1AviKlRgt iy6gDDOLlPkiqAQKwbZtWsF7iMu6bPlKxGMIpfT+ZWbtrFe3DhFc+bJSxYqZmVnoDYs4AALJEqhs yJDBjuRlcDLikPEvOBmBM+Bn3AiuRuRYQgUSQhaCuGTxws8/+3TF8uU7d+3etGkzxG7ugvlU++C9 98mqpDom0WKbtWhOQswi9tZcbhAwCBgEDAIGgbKNgGFmETG/W7Zsw5e4ORmLmjTueV5So0YN6RZa ywU/LZo1ex4sbceODMRWUCX0mFCoevXqHj12DJ6EBGvr1u3iRxla2ZCaxoXuDOUInEVmxi2gjMdP nIAIbtm6jcrEKsOP5s033nxizFiM+m+88XqqtW7dEiEZBv4oMVu1bk1ictUfosUSM/at//wntB6a qwwCBgGDgEHAIHD2IGDszHzNtQRxLe6QZunpm2A8yHXhZLrpGPrNPXv2wcBQy2POxb8aNKhXIz4e O3oqQ4Ysu7STJ8+pUIGsR7hJVq9eDcKEjZpv+zPcMBHFId+iYFX24viXMep3RI6lcaRliMo2b9na scO5DRrUBwcxcevdqwdByMR0DFr22JgxVapUOXteGDNSg4BBwCBgEAgZAd3OLORGQrswiuzMDDMr OWb2888/f/Xllz8tWPDiSy+1bdtWCA0c6KefF6OgbH9uW2X4L8GRpWeES5ZqCMyQpVWuXKlmzZp6 p+FkSLMUG8NTkmDKcXFVdIpGHQpXbdmy5dPPpjRp0pSQY/Xr10cwRqD/60Zdc+qUdQsqwPlq1apF VFi0lmnpGxNbNG/cuNH3338/Z/bsnj17tkhMpOf0rVdSD/SVr73+emhviLnKIGAQMAgYBM5CBJAL 7N+fLR4AJTx8w8xKGPDiul0YZWbLly+/esRIOoq5FQFXoTXo+9D9IffCqqxrl06oCNUwEGtBjOTB Fb9izhn8JgssekyPEflwDsAoDOOzChUwAytwS+YDNA6lJP/lt7TPi0HsMYRtiMTIOV65cmWMxtSt UVxC0bg1MrO0tPRfliz834cf6fjS/19dMuw/b765OCXZJLUsrifPtGsQMAgYBMoiAuwvwsxKPrSs YWZl5IEKIzO7/7779u3d+9933wUafBix0OIDUSQaNW4KSerbpyBIGF+KJtGRRIxvYFRwrvr16/kG F4s05Gfbtu/g0Y+zxXLwOVtklo8V//ETx+vWqcM3eiPHiNx/4sS6tWvefefd5//1AmI5aq9Zu37J op8PHz6EgK1rt67t2rXbts0yMoNftj333KQeSZJkyRSDgEHAIGAQMAhEPgKGmUX+HAXUw3AxMzIU EUtCj+ZF2qJ//P3v33z19b/feW/L5k17du9ES9ihY8fWrVujtaRzRDVzHClgS4EfMrAJox3onXU0 iY3dviMTq7UqlSv1738+jSM/Q+mZnb2fD3Xr1iUT+fvvvvfyiy/Sw9Zt2iG9gwgiSGvRorkbJijm wp9+vue+e++8666AQDSVDAIGAYOAQcAgUNoIGGZW2jMQpvuHi5nhxvj8M8+sXLvGYSy/bNmKffuz F/684L///nfX7t1/e8cdPXr0oO+7du3s0qVLyIOACC5etGj33n1dunSjERSUJ44jbyt//NixipUq 7czKWrt2Ld+3b9/+xptuwmjs1VdewZxfEUcJeIt5mccOYC1HFFmiY5g8SyFPkLnQIGAQMAgYBEoY AcPMShjw4rpdWJgZ1OeyYcOGXXrpI3/6k95R3B43btzMN3379ESdKCb/aDz3Z2ffeuONb074z5Ah Q/T6WOX/OG+e/g0yNiz6+aZ58+ZNmjSB9iGK++jDDyXg/vU339K7T9/8kyfff++dZcnJfNO0efNt dn2KmLv17N2LCBdYkj351DhYWoA40hNjYRYgVqaaQcAgYBAwCEQCAoaZRcIshKEPRWFmIidDELVh wwao0oR330FfSZ8wFzuQk7Nu3foqVeIITpbQoEF8fDzfiyW+uFiiMSTpJOSpV+/e8+bOHXzRRXh0 QqR8DKlD504PPvTQ3596impEFIutcE5WVtbQiy8+77wkIvdv3boVd81q1aohPyMhpvCqaVOn/uHh 0XwIipaFAVbThEHAIGAQMAgYBEoWAcPMShbvYrtbaMwsOTn5q6+/oVN5J06sWrWyUeMmjRo2xMyf bxo2bFirVu19+/ZWrlylZcvEloktoErp6RuXr1ienX2ACm3bthlwQX9cV6ZP/+JEXt6p/HyY3C9L Fvfq3avneeddMGAApArBGL8RXH0x/Qts8zEgIyzZ+Bde2LFtOy2gZ9y+fcf8BT/16tXzgv79fGNz /XXXLVm0eMZ3M9u0aVNsKJqGDQIGAYOAQcAgUMoIGGZWyhMQrtsHy8zgTEiqPvnk02rVq2dlZiS2 bIWOkggUzZo17dat665de/LzTxEVFjZGDxMTm5N9Ekt/5GSkSNq6ddvJ/JMpKcskDxIirs6dO+3f vx+axTf8SXqAhg0TyG5JvvCVq1bzLxrftm07LXTs2IH8AcuXLSPeGBK4yVOmUvnX143yiwP0jggd hpb5BcpUMAgYBAwCBoGoRsAws6ievtOdD2oiUQ6+8K8XBg6+CKbVtm1rNIlbt21Da3nxEL45Z/mK VZjVt2vbGg0muZj4zW0coTH4JiMjY96P82FgSd27E6NfvoF+/Th/AcFjcZlUnUO6ZgvhEjZu3CT8 DEHahg2pfMnn60Zdi9ayjEyDGYZBwCBgEDAIGASKhkBQG3rRblXUq00OAF8IBjKRyMlEpnXzjTee 339AbPnye3btfP2N08HxUUcuWpxM0NcO7c89dSqfcBVySwz2Cf3vCC3mez5haampaeRKwi9SeJuU RYsWfzvzOz507dI5ISGhe/duJR9euahPorneIGAQMAgYBAwCxYZAIBt6sd08uIYNMwudmaEKnPTJ JMz8VRO33H5H/Xp1u3bt0rdvX/Xlkl+WYgfWq2cSMjNoGYRMYvRD5oKbK1PbIGAQMAgYBAwCBoGQ EDDMLCTYIu8iHxMpsTDEEfKiIRetWb26arVqP/28aPjwy7t17aqGQkT+9RvSUGKShpwkldAyElZG 3kBNjwwCBgGDgEHAIFCWEYgiZhZblueh2MaG9f3TdnyKydOm3nPvPVjQjxg5skOHDtywfr3T2ZMI xA8tQ+2YkNAAWoaG0dCyYpsT07BBwCBgEDAIGATKAgKGmYUyi//35z/PnzePWGVdNfGY3hCRLJYt X5mRkUmayw7t2+7bt5//YlUWys3MNQYBg4BBwCBgEDAInDUIGGYW9FQvX778+29njnniCeRk+sVY +suffEhOWZ6Tc7B5s6atW7XMzT2EmX/NmjUkiqwpBgGDgEHAIGAQMAgYBLwhYJhZcM/GhxMnjn7o IZIa9evnDOIqmcjLl6+w4KdFGPt37dKpZcsW2P7zJbSMEP/B3cnUNggYBAwCBgGDgEHg7EPAMDM/ cy7BZqWQKfyJMWPbd+jw/AsvOHKTSwWkYmJY1u/83ugucd48efKkkZadfa+VGbFBwCBgEDAIGARC RMAwsyCAk+j8D48eTTgx92WnTsV07tSZ+P5t27TC2P/AgQMnTuTBzypVqhTEPYqnqs4vi+cOJdRq 2RhI2RgFU24GUkLPfWC3MdMRGE4lV6tszEjZGEXJzXo47mSYWRAobt60idpNmjRxX3PgQM6u3XsI nNG8WRMi+yMtI3QZH0zQsiDwNVUNAgYBg4BBwCBw1iNgmFnQj4Bbj/nzwsXJKcuqVatWvZrFxcih hLSMAGZ6mP6gb2MuMAgYBAwCBgGDgEHg7EPgLGdmOalTn7isRSLS2tYtRoz9YEmWlWrca8nMzHL/ Lzs7+8iRoyQyX716VZs2rU6dOoWorF69ulWqGJP/s+99MiM2CBgEDAIGAYNA0RA4u5lZTsr7f1mW NP6bZZvXz3+3V8rYl75NP+oDz7lzZv/6xhtUBfwuSVj5xZcz+KZypYp333VH+fLl+YyoTD6YYhAw CBgEDAIGAYOAQSAoBM5mZpafkzJ7Wky3iwa3qRZTMaH/sItrbUvbkesNPhwzlyxa3LNnTyqQWZwM 4i+Of5nfderULh8be9VVI7H6R4kpOTFNMQgYBAwCBgGDgEHAIBACAmc1Mzucvf9wpZrxcTYIFeon 9jg4Y+nWPBeKP//88+NjH7//vvtbtmnTqHGT1994c8Lb76xcuap37163//a2xo0a16tf7/jx4+g0 T5w4UaHCOSFMg7nEIGAQMAgYBAwCBgGDAAiUwy7qbAUiN+XFUaPev3jSktFJlpxr+7Q7ho/rOGGh /GUX8Ra+5fY7dIiOHj26bu2atA0bZn7/vQM6ov//9+23P3z//bMVUjNug4BBwCBgEDAIRCgCaZut AAuRXwwz88XMIn/+TA8NAgYBg4BBwCBgEChLCJzN2szKjRJbl6W5NGMxCBgEDAIGAYOAQSDaETib mVlsXM1acceycw7boTLydm1KjklKrG8M+KP9mTb9NwgYBAwCBgGDQPQicFYzs/ikQSNiZn40ZW1u zPGs+TO+29+hX8e60TuXpucGAYOAQcAgYBAwCEQ7AmeznRlzR6TZfz388PvrrWnscv34Zx8d2a5a tE+p6b9BwCBgEDAIGAQMAlGLwFnOzKJ23kzHDQIGAYOAQcAgYBAoiwiczdpMH/MZXNamCHswjqa+ e5Odb0p+Lh2fQvjc/NzUqWOHdbS/6XjZmI9Sso5HWLf17uTnprxyWfcXUwqCy3npfO76aWNGFAxz 2JiJKb5za5XGcHOXjB82wsZfStRNzfGslI8KH5u+d734XWqubZTpGfkIfsbys1I+GFOQh63DneN/ SJUpyU9998rTb0rieQVPXAS//qeR199iLx2O3BdEf1S0tHj5698bIWuU/VOwAkTwc6UWFetN73vX 1O3yWHlZbCP4uSoYiL3wtnhwWlbhyuvxBYnc5yqoNzqynyvimZniRODA7DHth4/5fN3BU8cyZz91 afMb391wJHpQ2j37sQE9Xkg+cUaP+XLIpY99vuHgyZOZ3z9xSfeR76w7GaFDOnlw3Qd3tm/Rqpsa g8fOnzww+/HOlzw+dcOBUyd3zH58eKvh72yIqCEdXPXu7X1aNR/2YvLBQqSjbGpObnhnZPM+d76z igHIY3PpC4sPnvKGfMQ+Y0c2vHNjq/b3vLvuwKmCN/qqF5P3MybrETr9mBXOUuS+/tbz0+qSl5MP njx1cPGLl3To/NhshnTKc4cj+AWxOtxBnqWDyS9f2nzAmNm7LfSt7/X3RWYkYp8rtYDuT37hqlbN u9/5+TZfHY7c56pwIPZD1ar5A1MzZffw+IJE8HMV3Bsd0c+VkZm5RSnBZW0qDVGMz3vm792y8rjT yTRn1Q9TjicN6d+mWmxsQu/hQxPS0zK9JqIqzSEdz5j6hwvHV7rrqStO98Jz5/elfD8npsegQW3i Y2Ib9R9xYc3UTTtEohMBJT9j6n0XTKh598ODzxAFRtnUxLa57fPNP//7to4YX8pjs/79+RvyvCAf uc9Y5Ta3TUxb88at7eJjCvKwpUycuzEv5vjOzemHeyQ2OsMfO5Jf/7oXPjU3bcYDSdViY6q1HzC0 +eGvlqbleetwBL8g8ReOW7P6q9E9q8XEVuvS7+JaWyX5Sv7OzasOt05sVPmMNzhyn6tCQVPq1HEv pfhbryL5uSqU6H/20qtrD2vge3xBIvi5CuqNjuznyjAzD8wswKxNEUAAPHUhNzMttVaHZjXP+N/h 7J2Hq9aJlyXPCuRmr+mROILYZr/++LkRTfQH03Pnjx7IOlixTnycPYgKjRKTDi9ZlqYvK6U6utgW v5705Iimlc7oRJRPjTWWZrWqx3pBPnqesZiYuKa1qsbG5O5I21azY7Mz/bHzo+L1z89aNH1mVrtb +ret4K3Dkf2CFLwY4hSfdNPAlhVQAu7YlF4rsWndMyMXRfhzlb/1ixcmNXzun/fUKnzXPXc40p+r /IxvXnyp3j9fuEPbOTy+IJH8XAXzRkf2c2WYmXv/PpqxKa1Ud/Ui3dw+d8aseX2UbatRYMORl7FJ O9MVqf1ivrhiQlJvBHv6XTx33oo/t7+YOxN687EJ3QYgzDuzRPfU5K6dN/PQ0N8MbpXvGfkoecby c1cs+O7Y0NuGJsZa0uWDDVa/MuIM48vIf/0ta8W2fe6YGHPFXZfjS+6lw5H9glhvhmVV1r3/bR/F 3HTzZW15WWwJTcKKN6+wTc0KLUcj+7k6njH9xXExd40Z0UbRSS8djuznCn457t8xT4we3kKOunbx +IJE8nMVzBsd2c9VjGFmoe++EXmlfe6MqdLhvo9IEJa2akydj/74t+lbI0XJF5GQlVSnonlq8jPm PPfUf1uMHjO8WVQvGflZs59/7LPmfx99RaOKMZYI81hMx/s+4U3ZvGBcncm3Pfl1RhS8KrZydvPy r67bNXbEE9MyItmVx+e7Fdvu1mmr01ZNGpX1zNWPTM/g/UjbFhOTdM+nK9M2py97usHHNz3zRYSP LuenN/6y+4HfX9Ioqt+KmPyceW+PzRg12vF2R90LEnUd9v5+RPcDVTx7alRnbYqNv/DJFZsnP5xk y6SrJXbrETPzy5R9KPuKB6wSaNXSVLpvU6F+Yg+lPyiBXhT9FtE7NTnr3x/34OIh7z433NqBvCDv eZqKDlsYW8hd/cGYvy0Z+vyzI21+edrayfqjdfdOMTNmLdnFOKIiaVt8u6uuGxEz96vFBzx3OFpe kGrtr7xpqI18zdNWdNifte6cZI3Omo9IXbuyU95+K+XOh65uc4ZhnJcOR/C2kpv832c2/PaJkW0c dMDjC7KvVuQuvMG80RH8XFlLnmFm7oW/LGVtsmwC4hJqxsXVbBB3aG/OUXu0tlzdafgcxg0w3E15 7nzlGgnVj+/NEcsyWzTtsh0Od0fC2l6UTI3lIX/zZZ80efmduy3Dc6t4QT6inzHbQ/7amyc1fvzt hzA8dxfbDCiuVo24ClGVtK16g5pVvXQ4ql4QC/kzNyPLDIjRVY6J2Ocqb+O89xeuf2lUN3Svra9+ df/+WQ9fYAVe8dzhyN1W8jbMn7h24atXdUeJfO5VL2fHfPGHPsO1WD/ysqgXpGqULLz+3uiIfa5s uA0z88DMojlrE76No7t0GF2g48jN3LSj3W+v6hYf3+miq2KmTfxmfW6+2A537t++fliJRjE25rnz tZOGXBgz5ZPP1+fE5GfMnzYnu2tSh4jOehqFU0OUpmuv+kNyrwnvPHJhQsXCKfaCfOQ+Y0Rpeu26 ix9L6fXc209clFC45ln+sx363jdVdP25GZt2t7vz8vPiK0Tu65+/ddq9fbvcO9VWuR7PmDVtWvNr rjyvrpcOR+4Lcgby+dtnfzKnKchX266NLj83Y9uO9oyudkzEPlcVkh5eih7c/kmb/LtatQaP//GX 0UkVPHc4NmKfqwpJo3+RUWzetG7KgzVjrvjXwukPJ1Xz8oLUjdiFN7g3OmKfK1lnIzSmVSl368CG zx+/tHmLVtaPBDaLnnIyM/n9xwo63/6OF7/fYHf+5MENn4+xYtUwog4S2CyCh3Qi8/MHtHhmXjp/ cN3Ux4bbI2rRSgKbRVrJ/PxOPZ5ZlE2NHbio4C2Qd4EfO9aRZ+Qj9hmzw4A5BnL755nENkv+sPCl 6HPnCzMLX4rIff1PZi7+oPCZ73z7yz8UPPNeOhy5L4hn5L2MLmKfK225OZH8YjcVz8xbhyP3uVIj OZH8Qo/T8cy8vCDR9lwRKM/Thh7Rz5XJzlSMsh7TtEHAIGAQMAgYBAwCBoGgEDDazKDgMpUNAgYB g4BBwCBgEDAIFCMChpkVI7imaYOAQcAgYBAwCBgEDAJBIWCYWVBwmcoGAYOAQcAgYBAwCBgEihEB w8yKEVzTtEHAIGAQMAgYBAwCBoGgEDDMLCi4TGWDgEHAIGAQMAgYBAwCxYiAYWbFCK5p2iBgEDAI GAQMAgYBg0BQCBhmFhRcprJBwCAQkQjkbkxJzSFgbMqLl7a+Y2pW2PpoxQc+b8wcmvZU9swZc/XY OXvCdjfTkEHAIGAQMDkAzDNgEDAIRD0COXPG9rrmzdXQp2pJo79OmzAyIVxDyl0+6a3sB27tE++5 wboDbr1i1TMfp+RGQSL0cEFi2jEIGASKGwEjMytuhE37BgGDQDEjYOVYLI5bHE397KX/9rj1yjNT Vut3im3Vd2TFzyZ8v91Qs+KYANOmQeDsRMAws7Nz3s2oDQJlBYG8lPHDRs+KIZ/08Lumri3UZtpq zWGjx97RlzzNrYc9PSdj05wnRtifn5hm6T3J0ZyV8sGYy/imRWKXO16ZJV/qJX/LT5+n9zmvtS0w s3OiD+totdCi710v/5wlXCy2+flXNp35zqx0Q83KygNlxmEQKHUETHYmX1PAKlzqM2Q6YBAwCBgE DAIGAYNA0REga3vRGymBFgwz88PMomUifT8rUEwzkBJ4nQK8hZmOAIEqsWplY0bKxiiYdDOQEnvy A7mRmY5AUApvHaPNDC+epjWDgEHAIGAQMAgYBAwCoSNgmFno2JkrDQIGAYOAQcAgYBAwCIQXAcPM wounac0gYBAwCBgEDAIGAYNA6AgYZhY6duZKg4BBwCBgEDAIGAQMAuFFwDCz8OJpWjMIGAQMAgYB g4BBwCAQOgKGmYWOnbnSIGAQMAgYBAwCBgGDQHgRMFEzfOFZZryFw/vQmNYMAgYBg4BBwCAQCAIZ GRkrV60+euTIiBHDA6lffHWiaEM3MrPiewxMywYBg4BBwCBgECghBOBAx48fL6GbFd7myJGj+/bt l5/c3EPq7gcPHqQ/69atm/D2O4sWLV6+YmUxdeykXfLy8qQP9KeYblSSzRpmVpJom3sZBAwCBgGD gEEgzAhAg9559z040Jq1a8PctM/m4EMHDhyAGFGL34cOFTCzTZs2vfXvCfRn0qeTmzZtMnDgBX57 BXv73yeT+GEsfiurCnRgz569u3fv2b8/+4Rdjhw5EvjlEVvTMLOInRrTMYOAQcAgYBAwCPhBYO/e vdCgbdu2U+/Y0WNFxwu6QyMIwBBBZWdnC/HSC3eUP6Fl5cqVq127Fj9Vq8adOnWKa+FYH0z8KC6u yvDhl/9q6MXXXH1VjRo1qOyNcvE9hOzbmd9BqjZsSP3p54UBDoHb7d27j8rnnFOBTsbHx59zzjk+ ru3avXuALZd6NcPMSn0KTAcMAgYBg4BBwCAQCgKQpHfefZ8r77/vHn4fPVZUXd6BAzlIofidm5t7 6lT+0aPH+FMpSWFRr7/x5muvv8l9IWEnTuRVrVq1fPny3LpixYr8zvn/9s4DzIrqfv/rijTp0uvS pLdFRKqAWLAAomKMDSPWRAMx7R8x/oyaxAp2Y4koipEogg07RVQEWekdlt6lrgRQlv9n5l2Ow9w2 927h3uXMc5997s6dOXPOe86c855v3bMXjnXqqU2HXv+r9u3ade58evny5atXqxaFmb3/wSQIGRzu uiHXtmvbZv78BUGAgJbt2LGDv5UrV6pSpUrVqqeUKVOamiA20+0HDhxFUmnRU888E6TkZLjGMrNk 6AVbB4uARcAiYBGwCMSNwDvvvsc91w255pRTToEPbdq0OUoRcCYj7vJdtnJlNvIn2AyCK8Rg/IXl iPHAfg4ezKM7CLQgauI9P/ywjy/wIRVVokQJbkSKxvez+54lohbzgOpBy1B3wuG4uG3bNvv27cM6 LeaNPB1eiDSOB/Fcns4t1Jna8mXNmnVffvWNyBniNIzPaNHiRYtiFpskF1hmliQdYathEbAIWAQs AhaBOBDAnAslZt++faBlMW/jYvSMK1asDL0S+7A1a9fNnbcAay3IDVKo0qVL8dcwHt0Cq6MEuFrp 0qX5af/+/VwmgZmO9PR0GNIZrpwMMVuoGjT00VJxNm3SRD81bNiwbNmyX8/4JrorA/SLOsPJDC/0 qUo3btpEadmr11IHrNCkdf2/u++OiVKSXGCZWZJ0hK2GRcAiYBEoIgSQJWA/hCCBvz6lTxHVwD6m IBCYN28+PAaloQorU7o0Cj6+IADbsmWr7wlczJnNm/1CNQYADIafqlWtyl/IFrznhBPSvbwKtSY/ IZ/jcZcPvqxH9+5cwFGuXDnvU5avWIGlV9u2beUmiXQtJjkLHX4XXtAPujl5ytQoCKFjpZJlypTR NcgCR4563JAzhH+MbTji1q3boI9cUKFCeQjlhnXrCgL1oijDMrOiQNk+wyJgEbAIJAkCLGm7du1m bUOQwF++J0nFbDXiQgAJFqEo2rRpbe6qWbMmZEgCsIWLlhjSg3LQxK34n8tUzOEY7M+cDYOpXr1a nTq1OJ+9eg1nZmfN4a8uQzSF3nDqtGmSz6HirFu3DoVXq1ZVOkQd1OfTTz/bsmWLHAIgQ4w0MbOq LueTj4Lv4NGcqV27tjnfvHlzNJtUOJLilSuxgUNWZwRmK1c6gsBFixafeKJDaTZv3sLfxo0yaB1K T6RlEEqvbC8unI/JxZaZHRPY7UMtAkmBANPrtC+++nzytAULi9TZPikaf7xWgrXqiN10Zcx0+F48 QkAdb/0pCVbXLmf4Gr5u/UadWb5iFWKkf7/08sxZWTNnfcvFyI18F2/YsAn6UqN6tVo1a7j6x9zt 23dwBmolD00dBw8emDr1C8zzkc9hsIVgDGmrr6hvZ2dxplGjhjA5Di9pC2hzZgps07oV3yNJcxmu ED4jroPAwUdp3RfTv8T/wOFtP/ywfv36E05wysvNzS1VKpDFW1KNH8vMkqo7bGUsAkWBwIYNGxV5 CC0G8y9zqHcWLooa2GccOwRk3F2K9co10+E7Eoiij1BaSACYsV1I5SdJsYiUJMHCostUqWLFCij4 Nm7cVLt2LT6IozAdy8hoWLdu3R49evz+juGNGzdev36DuZ63HukaJKxWrZq4NCJbqlGjGv82btSw cqVK3paimoT69Ot3Hidh9qgIF3uM9GV/xkeemN4bZY8f5di1ezclB0SVChPLY8+ePRKYwTsffmTk G+P+y+09unfDdWDBQsfGf8nixatXOxZ4lSpW4N94eWHAyhTqZZaZFSq8tnCLQNIhACdbumwFnIwt qSZxFBnaa9qj2CNAp3vlDbQX2QNnWHqLgcEZxlWMbWRF6sevZ8zE4qpY9ikRZYngaizM1EYCetWp U5cv9erWbphRny/4VP7440HDlipVrAh9MYBIYFa/Xl14OcOgRo3qbNLat2vToEG9cuVO5jIiTfB3 586dlID5FxRHCsqt2xwVpDmQ3mHp5RPgiQ+hBo2EPx4JUDoKRzcasI9QvrOLcFvq8D8cRWkOAjNk gZDCSy8dVKaMQ/LOOaevBnbNmjX4wiYkYPnJc5llZsnTF7YmFoGiQEDKDlbi9e4XZnAMh63MrCig T4JnsLYhGsHK29QF2QNLcvRFNAkqHrsKMEtomcY2fxnSqL02bHTIR+ybU+oKiBQCoZYtWvhqjTQU U7Py5csRYww6wqaLC374IYdArGHbJ4EZ1v2IoIgT673Gq4vcsnUr6kvMv7hAstVdO0mClBdqH8EV lSHsBTI5n8DMWyC8zed8QDTa6dO/jAt4AqhRVYYrreO5SOlQsPLp07sX5VSsUFH2/uXLO6KyypUr M9TXb9iYWhZmAsQys7gGhr3YIpDyCKDjkPqSjS8zaSpuKFO+D45RA1yTsv8p5IG3CvpX/nepeyA7 YVSLjiDsUbQtzihMfHE6NmxwNJKIun2N2rJ1u1JI6nyTxg3nzp1Tp3bt8uXKia3qFtJZ8hdYAAfz MsRaMHXfkDAlQ8V+8GTDlAys7MlljTn/ylWOhLJ1K8cyLMqBYMznfMDFnEG7CoXy3SiG55PMcSY3 97ChWVnffceZPn16kyhdrHHtuvXGJ6BSpYpuCI8T0QZI8pdah2VmqdVftrYWgXwhgLqH6biOu3oh Uahdy5H26yh+ooV8IRXhZsfxbc26JUuX4zmBSVNhPKLwykRgRuFm9fI+SP53hffoIih546YtNE1a PBxO5XPKmU2up15xOta60R/q1DlKCciwpH+xrzKse926dQiWYGNGAObdhm1zA8ZKaxm6PZNUFdWh WCCHvCwhai43Ssd8Tbaq3303B2Vi2IBqyOoi2S+KHRJjFnUkOlZf74iZRc80tXDhIvS5RkrHzMaE 1qhhhooiLppplxSgqXVYZpZa/WVraxHIFwI7d+1mmm7cuKEma/lqsb/kr2QMQQ4mwcKWQ0ATk5P3 LFq8bOWqbOzzAAowg8B1DK9hNfVaj8n2PxUNomNiSDMdFlKtGiQDNka6R+QxDHLOSFxUnA6REtOP 2i2gmoSmbNq0ySRo+mbmLHSIPgKnN53rFSnjp58cvhU6JMTV+BUWKBbFWHKTARwET8zX8CpAdyxV ZqtWLcPCS8yzgoWdp0szy3MRkXr1ufBvuhtFJyOcy2rUcPacGN7xVxuS1DoKGLjUarytrUXgeENA 0zGtRmzGl3hVmRCmWd9+R6ikVdlrCgk6aB/Lxldfz2TeTzY1BEsgSwKea31698RAJ1KG5kJCJoFi WUSJ6q4bWVlZsbwWZt4Co4g3EnhuEd/CPkFW/zVrOgZzaMfQYe3dm4OduNdeqohrVUiPEynp0KG9 KR9vHnYL/Htq08YmQZOyHhHtDNZlzPkVNmze/AVcz7u8b98PDInoTB0WeMopVbiL67fJ8P8EJ1QY RmNIJQOqMlVVY5rGd++GIVQtGwU6sT09t0mTxrqSujHUpQqQuhM5nER6FA5nJSoQs0q3M88spE4p 8GItMytwSG2BFoEkRUCWJZVdCRlis9at/BbE0evNvd/Nmc+Mz9a8MEgJ5c+ZOx/ap2WGI9mCoG7e 7MRV1/KP7Q7ak2TzZyQRjeka9ETeCOyqqgmb7uvrAhdvFOU7QGwIthwMSyzfeS62U4wlZ6CWKycm qkW6eBwiJfXqOj6YOmg71nU9e3Qlagb/Yrn1t3vvJyY+3xXtzEtPkaJVrEjapbQlSxbBtBghkcg6 N+7Z62Q3b9S4Ed/hVZh5kZczo0F9eDyOBZxcvHhpJFUmv3rzizfMyPBGmlWAWR0B94fewbxm9Rrv cyXC90ZrYyTQ6fh+rlq5YufOHTyOWeXaIb9KlTFwnDCzg5uzxt7Vr1WTjIZNMrrcOPKT5Tmydd2z fMLdFzgn+Qy4a8yszaltApsqo87W89ggIMsS7YC9h7EpiVItxFezvs1itWvVsnn1ak5E75iHNJJs VZUohokSeRufSDeuWJnNxlcSKZYZ6aRiPqXILnCEea4vmxYSqYCJrlRkFYj5IEXgRFdFrVirkBvJ rFu2Puh0WClZpKFo9KbPrFCShlSMaqZVuV3b1p1O6yCIICgyP2Jg4+7AF+nsisexZMlRZEhyZW23 dCguxrnnnH31Vb/0OkvS46gjr//VdZjGt2zRnFD+zZs1Y4REIkYMdW2N0AjzFzPEffv+BwXU9Yo0 y8lIqkx+NfnFvcgzxoiXEaQvjFpWF4uZlSx5En9JfkBsNlMIMxsDW8TUrZXjwkkNFyxcOGfuPB63 devm5cuXLl0aO1F6kIoVwTXHBTPLXT72lkFPbLl83JzV2ctm3Ff74z8Ne362Q/j3ZL3ylzmZoz6c s3rp9NGnZ9312Ecrj8pcUQQdYB9hESgyBKTKDNXvGJsSX02YyjF1J0MAH7K1MAuz/inCQsxDTA6N JFtVxGCUQLIXiF0kYRu8DeMtqkcsJRXOopI8UdZgM7SCKiFoZGn5zxvj5sydQyWTipkpZY1bqxxC nyM2kJ2NDumtIMpffvUNvUnPejtRaW1S8RB7MKuymtCoYQP+ij0Xp4NOREfpJSXItGig2W4hmuJf dJrE95IVPIciYrgxWvdKdMr19evXZ4QQYiNKUAlIHoIo5VZyhtCP2G85rz9aUURozA/AbpKRB8R5 0qQPiZeBMhTpHfVUaaH3GrVs3qMPHjRBa7OzV+N/QL3MXezojEpUQdf4acY3M5HwUQ5BaAGNndXU KZMDVvKYX5aqb2NcwKU3HfL26q+fG9KK5KvpNTv3P6fm0lemL/spd0/W5Ilp7c/q07RcWsma3fud XXndig2p58QRFxT24uMWAYlJqoWkZ4kECFQJ3aVC0SLH4tO1y+laAKJriLiRCJ+GySEAQ8ymEiIZ lFAxLOuhjE2bOHoTHaiiOJ8k6kLpMdu2aQnjeW3s60z0JKshZQ0BP8kneGxFTUDEokv/IlRgDeaD IKRy5UosujKXJuWOakjHbd22HWGkAsR7o7BqeU5F/9wdO3fBmH37DQYqAw/mEUQenEJzwipXlXlq 0yY/k5JwzRc/M4fAwSVC+yLGBmfwpyT6V5T4+wx1LvCKxHyJAXbtIgJthbC8So9OT3dEtt5BRQUQ d3GSv8TRIJJiEPBlRiYKrijZ+B/s3rNXTkKa2bxSQ6exCJCPZAht2dIx26ClixYsCPK4ZLjmuGBm YYCuX7l8eu6+XTv3lapUoawLQonqDTvunfTd2tR2HE+GMWXrkJQI+PbWoXX0TqB8n501V7rL5s2a IsfiYxa/SBoiVnrJxhwdR8vmhsmxz1YJkK3Q57KXRX7Ds5qd2sSrWEkqdSGERrHfCCKAuUz//hei KiLg+EknlYSiETjgWPU5CxA2/gjJWIqwIeOgngrm7sxq7l/joYkaC5xr16pFn6Kr8kZhFTND45la OTRlT1alcqVI+EeSBx+r/srnc5cuXQbDMMIweaR6m1/KtasLuwXas3uPxgP2+/wlRD4ipSj1+enH HyFeBCqD+sDSEET55OW7dztUKYqbjh7HvfyVUPP9DyZ5n9i9ezfeo5iYyHVAkjAsKWn1ypVOSGEp qRUUQ14O5tBJdlAknuKN4CmXDBqImj/ms5LkguOPmeUsnvbxD+dc16dx+v6N2U7v2sMiAAJIPp5+ 5tljK/wo1I4gbCOLcSRXNcfT0DNtKW1Lx8z2AXWX7iyZl4MP2VjHzHaGHHgbFSpsQ7mGllAmzL5n aSpPBnWhd/1TKCmy4rA6YgoN1yF+Qfbq1YXad5EKZ7iyOiIYAzoS0YBY2HihhJBVuAFph7WG1atb xxeFVe6ZFJhC5Ey0IDRR9zHpjsJ+KL2DqAl3S/MgiZHkkqKDQBJsGwx1M+edfcWRxA9sJ+h6hu6O HY4mNNKxwQ2WQaAyNnVYmPnC4KEllKtmQDcdKdahSrwv6i84E4WHVpWfIGE0pNNpp0lk7puWV6xY Xr++Y/OgzeRe19ZTzh/ul7L45JokVJgacpKnhH1QYXdZwuUfZ8wsd+OUh+77d8bwEf3rB2y56xzg /yQMt72x6BHAPSemIyG0DMkHE9D27duLvoZF8EQ0jCy3AVWZzpZ0VTZczWe7Y+pJZG2+G2WBzssQ m/Ue2ZiZJX1N8wnbqJUehOIJKU4oDvDI0LjhRQCX7xESN2r9W7UqW8Yx5qhSubI3S3ThVY91yOue xoNEoTDljhTAnV8xu2FhQ4rJF69JFvo+1tpV2atNhVkmYXjwM8iZ70GF16h8lhzWyMxXppP6OoKw RFFgTOQ8eB7ewch9CSMcXY3Olajsi9jlE1Umb0T9enmGmFQeDR9nvK8bvexLpgka9Ka78akm8RX/ KqowmYsi4e9ahOZJmNjU5fzwQ6Wjc5xzIxXgcYiTo3ciynTvBV3O6ExaAs6IM4U9kHhVr16ddlFP 1VY+xao/kWAa1K9nNpPUk++mHN4FxIoPPPCAzowcOdKs4PkcbEV5e0B+UpRVKrxn7Vn6yr23z+w7 +qH+tZ12l67d8GdtfaSnrlidHfopvCrakgsQgTVr1owf//ZTTz/rE6H7HsEMBC0j+RrnvX7d+alJ FFP3/BSb8L2SPKG/CFKCQkN5Tb58d2kl8MVvVKTHUMfPSE80tmUtmp8a6Rr2vj7+F6T+BX4NEWVZ 3Wm18hUaIx4xVGJaskEvPGmrwntCBSCIPgki4ETJq2Nw0DLMIuozySJgOtwOsaV3VZM3X1hmtmPH Dm9IqgLHOYECaZF3VQ5bAot6JPs5ZMO8qpguwbE4iL6BWBHxLddHEQXxK1cCHcaR5okwucK20kPg 1L59+02bnTjPDAnGQ6mSpaIEA+MaDrqSYYO2mutFkiRD5cuWLVsi7VqJzUGaJ/caJ7Z+6KZOJmK4 aVNCpIYrUpp3LMGZGjVq1CDD8c+IclA9xrZomSgykjCG+gEnA4GzeCsdkw6FR/GV9uSTT+jMo48+ YlbwBAbYsbrluGFmOUsnjrj6gjfqPv7STZnl1Or0spUqlz2wa88+l9H/tDV7dlpmw+rhU78eq/6x z40TAYWr5iDZ7cuvvIpnIJJzVpQoxYi3kX+tQNRSzCnMg0Qu4MNqyr/GpSjOphTk5VrRI8nA+Alp lvy2mIilW4wk9wqtFnchP2AlYyMbvNIQglDbsuC3F9SViusRpTRWHQBRaj9fvkIx3WpuDJHCk7Yi wcL8WZTIF5+MoVUqsuBBjTLZrFkmfTZJSFGgNYQCiSQfWr58uaFifLl00KAnn8hb8AoK//yUIyMz n1l6zAK5i7ijyMb4ILKV98C8+QsxrOReFPES30YKHC+nYwqBEvF0inJzdP6Axys8L+bT83NB2bI4 NJSBoxw4cFABhBkAPst3Uz6vPMOGD4SMvoOTIdxiq8N4gPGQGpy7uTgSMyM2h+z3FWon1MsVEzEC c2gnFiQjCA6euIteeEE/xqGCsZk4sT5MaBQ1JPIFHgZOGlA3WAZ9xOuWnb0GcRoTNWeUDFRiS5+R WX5ATpJ7jw9mljNr1GWD7ph9+gsv/aFXTYfFu0d6hczeA9I+Hjt+cU7awc3TJ32ys2W3VoECNSVJ 59lq+BCYM3fuCy++REQD+NlHH3+C1mnYb2/rmJkpbVTYA10nO1GmDCboRo0a8p0zCQOr2ZC5g+mP nToTojcIe8LF5v9GtDneyEahBbIDZrbGeB+rLyZBciHHfKjZKK9fv5FdNaKvOnWcGNxRDnnvw+Gg AhAC1rbgdmwx65PABVRDcT2iSDu06khmMGXqNP760t3Iuw1ZGiycEThx4jvsCryVyc+IYpWCIiAt YIPhhgP4WTHEMENxEyVMqOqgELKITLxGZqZ6SEZpOz1ozpx0khMvCjo44s47+519zgX9+n366aec +fijj9auXiOj7yQ5ZGQWMzSGoxN3ea3brj24m5gwp4xA/G3xVuFthaRCy7QhiaRGZzAYp2Pip7AV oSjOiNUVavg9uql161aOSrJqVThTqVIloSxly5YJK6V23XWdWch1w3Qku/AwRfxiPDCKoD6uPX3E g91sXlrxrdskMPZdiu6baZOnU6zYW/SD0QuTk6yLe//8pz+ETbXJr0fC7zmaeqkytbtQWER2R/IG lcns9u3O6xllDERxHY1V5WP5+/HAzHL3fPvevxfvS1v8wtAzmh1ROd8+cfNPaRUyr/n7ORv+78L2 Gc26D5mZOequS5oGcuI9lj1mnx0ZAVzkWCZhV/AzrrrgfGd/Fh2wb2dncYEiZWd26MDtb4z7b2Ka KRZRNnA8kSmeSYdJkC9wEUnRKB++aER6RdyNEERiKMR8KLO/fCpj5rRx0gAcsd3BDEUcK+Zd5gL8 3qlMFIWpqqptccxqJ3yBqsERZdOvTKOsQBgjyivTN6hKlizFsNm1e/fkKVPfeec9bLS/mP6lqdKS JUvQpwcMrRnakCM5yMtQB2/ScUYUKxZ9GsXCTKVJ8sG9YV3YWHEZpfSgebQKnDFjxn9eG3vPffe2 aNny5qE3/PrWW+8YNpzzK1ckkdeUFI6i+1EOZWvgAjgZLAouBaNq364NH9gVzpt0rr4b/hFWjc7b zb4FmmKcjkmkQUhkaT8THoQBb9yzx6GhxNCFjWGPj9WXEt2ad4o2EltVEnpYOFwcA0S3dacg1q1T p3bn0zvykxuXX7WNRs3YzdauVZv3muFh0oSHrSrXAGkUBLzbCVNClJnZMLPNm7e4yTpztbtYu249 fJRAaNrOiXGywaNHQoPlBswrEBD8or/seGBm6RV63TPPby72+ICaMO5UAFiyAAB+oElEQVQKTQfe 837eTxPvHdgs9tpV9F1knxgLAd5kCSpYODt16vjrW29mMzf4sku055MKLywlQpLPXRKYcQ1/EbYz JUlpFe/Byse0iFzdTDp8oUwtisSfhi/yQawSb8n5vF4C//JHe5WHLZMVKwjB4l6jI4vLt0APxahf YdJizp4xqV5+kGEtUTWY2SNt+pE9KP4tY0zGiF7zauM9SmSmle7BBQgGjF8Y1ft6xjf8nefGcErg 4LmhUdoZZowoxlWUYFTmWcKQ/hLJCxV+IA5kUXcj/W40exII8SOjRl551VVPPf00X1Dm3nTrLb// 059mHS0OTKBFBXiLJMEBBwnSMnUlBAVGFb0aXjGbuRL7S8ZM2zatvE/kO9pPyoxp7hb2iXPnzn3o wQejm1voRizq+KtH/7BvHzXRey1KRA8izqSLKYpBq8h2hrUrJIpeNxNXViPBS8pNDSXlLV2mFGwV zhpdsC1xcqS9jXc7EbDrJSSTsT8H/5L9FdUzbSzn1lk5CWRLQPNlaeA7JJCT3jMVj+OBmaViv9g6 x4EAkT8RVKC+5B6i7/BOGrE5Z7xh0H2FysJMAjMdErZ7c7oFrIc7Ue5DtRS6F9SePttN9MaXotcH BTH/x/EQjmLi7wdsNZcpBGtww38DL8kNgz+lMK6ULox1hYmeHl+/fn3oU9S6enVry4ysresmEnq0 b9fWdez9HtNmxSbQ2sZfdgvwJ8ZnAoJY1lfXxj9PkG+CwR7hWEfJiiLZiqGdVJwzeExY9qBhuXDh YrYNjz/xFDJjTHywCurRs6daOmDgwPvuv/8Pf/wjYQ4KoyMSLjOgJFjClWXLV0JrENMGMaA0YjbV DUtEmAGPY+sS9nadjNc9guv/+fe//+vpZzDgi0nOfjyIZZhz4KGLkZljw/qjc0YMBokabAZRHzyG X+FkkRKkahTxVxPjAZev+w6NJRGgmEdwhWbMonQBb4pm0c1b8sKtHc49jBTQpcuOmNCnoIzich4J hIA1OYaXWWZ2DMG3jy4ABNAWsfideWYP1r8ouXVD1y2UU+g94XA+AyxppkzNkMaRHhjaF31lhW8x G4a15dIsU6Z0GSzefClHCqD9AYrQQh7F/J9f2U+z9Q8ofuB6eQzwJXqYtNDaQQ5gsYipotdHN2pN LaQ0AJKTUY2Vq1byZcGCRb7aUk8lymTdldOuz8JM13MZhJ5QlnRu40aNtJ6pzqTt4y+CWP6SLSBA Xx11CZQa8ZiJIyWvNFbiffscVzuvxJHHYSNIAIhQpRLDUnHOHB4TzjmXctwABD+wVNeuVXPhwkUQ ygYNGqAL81X4ZPf2mBwi3mYmdr0QDiIJlnAlLncT78CDA2GJCOPxpg4LrTOOCPHGgcN0DxkkkkgM +L6Y5pgwRjrccHQnwcmwG4Ovy3VUjpb8BQrGCf4BzF10JftDRmwkNbdGURRlIqLT+QuccRvdMtVb 1TpuSokCeU81gBneiLsaNMjQU9gxokHmc+DgzzzSTCBRZpKAOQYSG4GFepdlZoUKry280BFQUOwz e/a86cahV191ZejztMHyicGYfaScQpXpuwXNFPFydBJdJ9IO5gi+/POBh+BnGAzJXMzr0+R4Ov34 E7Nh2Ax0TJFsjilESUKK/sAwOfgkG7B68hhgLg4eJs1bcsDtuNbUSF5yAasa6TJ6mbX2y6++mjHj m71795Rww296L0ZBwxniyXKSWLKywfeVZoIqEcfyF5cPNjjLKgiWQ7/D27gXZ7e4KswCzLDxSmEJ DUoJTmyyI+GdTIEYzMlREVfBsE+R5iuSu0DdOrUZpa1btz6ce2j1qlUyUQotB7rGyfw4NMSFQPSL g0iCVQIrNzGTMQgL7m7iHXgStnVo7xiiBd+6xGzp119//dioUeee3+/mW27m739efz3KLaNfHuPk b8jZC40Wa2RHhMEZXzROXCGZI1tliGKCFsVXUaPIWCP4HionKqY7JtXgk4ZC/UVytAprZxapscYT E3G1yUPPgAR5DuXB9N4bRZ3Ndj2SkDtm7xzzCywzO+ZdYCuQLwRMUGxe0eBTyYSJ7zD19Ot3Xuiz sVowgUMx5e7V68yLBw648YbrBw7oz4RFLl6Zi40c9ThOoEjsYGmKhU2IgbAtYb1cvXo1j0O0jj4I QV2+Ghz/zQi3fPG74y8j/B2ai2M6x4XefMyDtss8jogDkCe6xvV3S5fzlzkQqsmdjTMMCW8a6SgA aieAoAUGAz4dOrTnX+6Nt98lgfCGoBMtkCG/t0NlDOcoo+vXQzAW1hZb7DZSQLvt27fBTdPTT3zv 3XcrVix/Qnq6npLMR8xAMN7K04lxkSoTSxl2zvtLOqO4bsdQNbpkkbni6it+iXfFX+68k3pePGgQ wrNQ81YkYQwk+heBGQcxjU0nMnolAWVX6HrvBvVdoyG8fbJQ5AsG9V6gpNwcev11bHSD9z6bUgbk xk1bQm+JZGcWKZSxvBMcbwYngF+eY0qknOsM+Pr16kSqJ9v11Ir7722IZWbBh5+9MukQkMubN79v 2CoyDXknIOgUS6Yi64ReX6liRVlwQ6fQdZ7atCnRg5gWiUl9229uZc7Shw0Zq/W4/74FS2Nd5JZ3 33s/7NO/+nrGfDeTLrN8pYqVChZEGAYeZz5K4X0E6zRHWDVWfmqipYu5OKxHfZSStfcNosrMT/Vi 3itn+3Xr1jES+vbtU6tWDZYKH4zIWRXGE45F/waXuHDL/gP7pcpEv2mGaFwemiy3EhWEtgWBh3et kodpw4z6RwwZHXGd74jOYxA8y5CucpWqv7zySowl0WzymvgK0XJeeJHbYvaa9wIkwYW03+ApJpay 2HmQrjeZx2BdZ3brjulYlOa8PHp0/YwGDz/yiPTjmZmZ/J0yebLvFkxX3cg7O6gPne4zcZOzUVij w+hImkGFZNSXYwPZMFJedhfBd7l6FlJwKGzApAgwTjfjkzPN+g6lEcMql0pitbv9+++9SlIf38WT I0jXxDWukuRiy8ySpCNsNRJBYNlyx4c/rPWPtzgUlN4JaM7ceawx3ijS3ouNLyfLYrVq1RB08fLj VeBEoD5wEH7GtMXBhuz3dwwXS6MCElSEKnrk/tmsWTNWTeajihUrcllBxc7goQRSghfizB8pXKpc 6xMQa0XvD2O7E9YxKsq9cmQL2Nkm/lnA64Nfpgzlq9esYR3C3RKDd4y3vDl8vGHMJBNVeEzfobge pHL3rkmUidqFPE580QqnIaqcm0EOxf0PVT5KCeW1a2YMiEGi59LQNcHrxdo1MmlaJB6jbIy8I1zW uGlToswzyPn++eQpvqqqFVu3hBGNBGlUwV4DZYmXPcRVAZgBoMmdM4jAzGQeg3U5fb16TaTHocck Islvhw0z/QhDQqH51Vdf+W6RSwGemF43JrOrMSrLSAw+rvbqYp6YmNV83bq1QYnpCFeJmPwMfTwj nCTpoTXkJ87L2aJ//4ugYmvW5oW24WL4nMnAkUDrUugWy8xSqLNsVf0IzJ+/AFuxmEHLzG1YUcjw v0f3bpHQ1CSInTVyMmQqZh7UMqAdKqudNnxQNBZ1vihamKyLvAdJTvj3tI6ZTFtMoDHjggbvY9jA V1/PpCbY0CiSO4oP+Bkfrz5LCscCfK5qCCwQLB4dJCatt1HgEDNYhrk+yIoYHDFzpclQzkggvLDb feVZKliW0FDrMuMfwHd5ccLOITHEWOKjMxyqIeCvOxKslaQC5ctXYEXhlFSZHE7c83p14WoBa6th FjqwFdjJC6CM4YyjKyOB8Arbtm9neIi1i2KiHoq04pKNkQtq1qiB8rVly1ZcJoEcI4dNRajjC4rO iRMmEO4hYFsK6TKjziuk8vFzlHFqXE7EzBiwrl9c+UtujGTeMP2LLxCYnXPuud6ad+3a9aMPJoEt 0k19GGNS+TFctf/xGYdJkMb5/LzdPimUN/lYXMAyJonTC2jYTii9m/fwTUrYSvJruKHluLxo+pLt 2jGMARlX8wv8YsvMChxSW2ARISAdU7NmEbMumnrIugsvSwKBYviP+MosmaF1hWmx/S1R4iQkJd7o rCxXzIMHDuxn7mC1YwLSzCKHrJo1nYACoeE2CH5rPEYpATmcc5knsGdiYCmGPvNgu7atIUlYqTOd kcsPJzI+JksMJzds3MRqHZwMBa8PC0O85jvBCy/UKyVV0qpWw5UPcdA3xIxlnLAYSBDFgjfqsSdg J1LxcA3rpcLuw+F8aW3U9cioSCoAhxMhlipTBxQweFZW2XSH9ppyS3hVmWvXbUAYZqKW4CFIxebP X+RG4HQsxEXyfFmMKB92JZGMfGj+O+6NZcuW8kSGloLTNm92Ki4vcnzRUEcbm9mp090j7iLq7CUD BpIkoFC76dgWLs7NuxOX5h2REXddNnhwpMqDOWEyBl/+Cx9RPr1zZ00gLjlzDq7UED14wAGfw7hb spFQABQx9cSkXNyoaC+McJWfQGAXbzOZEAh+psgd5ryasHnLlu/m/BzSz80N5T+YzBWlb8/eHLN/ 8xrm6o1TVJpif1hmVuy7uNg2cJ0bgCqmKpNrSru5BVHZYBz217vuvPWWm6OI2fhJCd2ysr5Dm+mF T9as7DIVaBt/dTlGMTMyaYaa0zKVsBi3atVShTBJ5W2Cw8UQiqufxC0IzqQlmcWD5Rk+0bhRQ76Y LDE+gUpcj5B8KN5AAHE9IsjFUioFuTL4NTIeQn3JLUZPVNdV1SGcgIgsXrzEYbpz57JaQMvocbrY TQ99kOUH3zfIGWpio3CkNK5HTIWMikLk/mZUmaqYKGAQRbbyo4eNm8WS7CUKqI0YY7Vr/ZwUC7U1 azaJHPr07tnptA5Cz+eYyYj9/R13wK4I4cbtvBdNmzRG0iP2CZMTKRk4cAChQJBJs3LLtmzShx+1 au1EdCPqLOkBuCWSWCh4XyR2pWyPjuQdSqyMGHfJNFOeuUEOad6JiYrA7NRTne3iItfQ0HfMmTOH M126dvGdb9rUcTk8sH8/k0zNmjX0UbhUJhviYfDFMPKWLU4lGRpneKibfynBbM9YdODqiJ+TOJl6 OZ/RWcHNu2lRuDVqzkljcaFp0EcEGZauU3BlNr3QO+GjqVuHig3o1h2ky5L5GsvMkrl3bN2iIbDG jd0axNZE2yzM+TEOC4Jp82bNMHdAJeTL7Ka9KeRM5mLML9JpyuADYxHmZW/5UmUS/FYntcFl7ff5 QwWpku8apQzyLt64QQAFmSuppNmzrspeDVFLzEhWNuPEaQvr65dAnRO7hTm6wCuAFQsLAKnbqZKJ Wim1JgpayPSatSTBPMAZgqogRUOu0OzUU7UwyFqLAQAXByJZFymVuxLFuE4DjszJJ5fNM9KKJS6F 9O/d62Q8DJKkWYFwpffRAW/zBm0XehKbGfc9wmihOOMM1EHBlve7wrPu3bs73/c7MmAduLaRuJov kvKCA26baOJ+c9ttgy65hC/YVEFkH35kJHYCELgiC6gR3dU0sZHmu4tXCWV9cIGZ6BH2DL169/YJ sf78pz8hrlf5UmW2a9cutJJQOiLGhfohUpq4vjd8v+SpYQWrcTW/8+mdmMTEydTL+ZSvCwfzzm7Y 6Ph+smPkjUOcL0rtFarxLxXgPCellk0gS31cTU6Jiy0zS4luspUMg8DyFSsDBjJo2aIFu//Q0GWR YIVj4Xcpm2jvwaTDjhbjdxZgZkn5qxtHOUxTfeoqhbAy3LFkSSdXdLNTm/r8oRLoXWJx+eK5IzyT jET6LMrE4AmJV/Scd5EezcTKjlayQEKKS0bI0kvQ3ZdGvxzqtZdAE7iFXTIJGYtYKYYAidYRLwMj /VDxAPpH8kaj3YPpup65TagnnAZtNdGVIGRaHYEFWgxENMHJbN2gnhgbaCMzgLJTsleVqWFAsVG8 aIUhBYJ2kP2G08WxVNUKCOxzzCR0Fvbm3A7TUrDlCW+P54y4qS96nDYnBFMwrOuJp55WUoHrhw79 5ONPGBWsrNgJQNE++fSzuEYCRp+wurhuKbKL4/Ugxq6fuiGClaMleCKVfO3VVzm/YsXKb7+drZpL lRm2FWf17cv0IlGuORhLUKWYuUEThkUbBk1c2utGSjQe8BFyNpLjkWLH8IX6S8g3b/4irwRXnuMM USYZfmVQyYLTbEvEjFVIgRDHgK045pdZZnbMu8BWIBEEZGQmWUXMg/kueGAb7erw2gvrBAQf0qzB Cs2k6c6befL20ASdikBtZPva+LLo5jOKuuK7StvCd1/obe073cDlq3lWYgIzTay0lHmWNiqj1Oys LCZuqAOxQnxWVjG7wHcBisI+vXq1adES4Q1Ksei3+3bY8T7Ld70UwbQL8u1N86Lug5dgBc+X+u7Q 0rrVoX17qBhqI697I+AgkQJt9DVQNFSKjA2DNkGPQ9mV0mtGrz/IuxZvYeK5+G4MoqpWQGBvgAmC ZhE6iwBaSGhkD4Axpc6Y8sU+ZabNITW9GWY/HjESwjSqa48eXDB82O14KEu+GHxgcCVGn7zFCUja FG4tP5bv+RxFvtt5o4lPBtR9+vRhuHIQqKxT59OJD3fn//t/V/zyql69z+IWvfhNXLoferRt29Z5 y2Zn8Re+C2dlC6ReCC66i7ddGmnKeoJeO+BeN+ZTJDNbhFWAa3NGK+CXzU5tQqevWOn4wei5vDtm JyAPU/WsYWZ5KaTcOVmx1vJJHGPWPEkusMwsSTrCViM+BBTIwGcHFl8REa6W9QM+VtHtLRCViTHI xPXZZ5599pln+II6gxKefuZZzKVLlymDmMRMJUdsiqtECpYdpP4IXcjDw5VVq1bhQew1+bC2SazF IaUVAYGYHEkXFKRM3zXyUpSlOfMpX5g90XdQ7T69eynRwuef+2MvBX8QhQ//7W9r1KhBqmwslqLf aESAwcuPfiVpiOgIN8fRPmLX+S5WoASitqqzWD+QJDVv3owvoWyJzpVak/UGsRlqRG7xygx8hcP1 wTCKnTU/KRd1kMbKezR6xlKtcG5+yTzDHQXNklznhPQTMSObNnUa37t1c7SWXAaN8ynUFPXKuK1I 7EdVN27aVLlyFbL9cBccFwdkzi9aFDQJFXH+1EwRxLAHY5iUUxjw+X5VdPh86t2CgBzwGiMZ2rJ1 GyFsIM1w+tuHDYPynnRSqcpuQgU8c5lV+KJUCqEH6ZU4+dmnn+CrhACS8YlzBsElnPgSuYcjRVsN WMMol2FqhgxeFpAB97pRShOJlG8pG8iMjPrm4jp1anfr2ln7VW2QMNVlINE0tj18OCM7jeTp2fzD m1gJlpklhpu96xgjICOhwtg/aabof9EFvry5vgabiZJ5BFr28AMPvDfRMSVxEjrNned4PM2chYAE 22rvjW7gjKMEaXHh+PWMmcz73IIFDNo0uQrKGcoEbc+zRN7hWLwlttU+YpaU5wNlVncKbNSoEcsw 0hG21wmIOtTYJ594gmhPf7vvPlJld3ApQmj0cwOLCeAZF1BRLsb9zUmp5Cp8feSb83KxJGqrKQGB GfFTIrElqTVDpXphbeMUtxZ33UjVYzGOkovad5fCmEW3/ja/mlDDSHFQtEG2mjVvwbMY5AxdUjfK NMobl8FEaZd/nOSmrOIs4bwjr419HYkXJXz95XRVjJcRPDEn1wVYnkXhoIrzx0CShhfhEP/y4S5v M5GvOJbja9f5BMOFGmY2gZEmY39jySf1cZcuXVaszv736JdU4B5ygbiRseuGC4wHG1vsZlZ9d8JE Xi6C/QIO+yvZLHIUHjNT1pMomWETAAQqv2mzE/fOJ7OHctWo4bwFeDHzF82m3GjcrFPOXkg2oOaJ omiKRoTg9pjnDkkAisRuscwsMdzsXccYgdAEagVVIRmiRopDa56ieYSV6fbbbtPahnooZ+/epcuW T3WFELIcb3C0b5ekUIiLgit9zBOlxEScQwZAKBcLJ5MaOZ9Y2+QqKDZAxYzDf2KY+BJmKwwbT7nw gvO1PHTtcgZ/v3XVLvEesFhMbfDskzOa5Adho4GrZImgZOqe/0MawGpVT5EEKKw5F2ID75addTQu O2sjMwitLSp1ryuc7wI6VGl2QnNRy/Hz88nTDEHBiFANiY6JoeaC8dNPP0WKc9XVV/NdITO+nTWL vxf1v0jlMHJ0HorGFzgTY6xxo8a4zigHKCyNJfzrGTP4O/iyS/qe1XvJwkUmtln/iy7kXrJiEGsD y7MoHNTE+UPDS1RC9ONIifhwl3FfVdYp9dH69RtpsoyQaLiThOrHnwyZy2e4h/yPK+2LDHmSNzG1 nfbFV0RRgXg4Y3jTpmXLlqHi9PkH0O9r166FjbEBwODh0ccf5+JvvplR7uSTNQ4Lj5PlvWJu1pNF ixf7vIkThgVqhR9JpJ0DcjLEz7gPIwmU6/f33+c5ThkbUPNo7b3Zh2OSiGFx2HSuCdczmW+0zCyZ e8fWLSIC8B4WiQIHSFN8ECsfTZe7du3CUurZF54nLfGdI0ZkZnZgZSWjikm76/PxlvxpQP+LUMfE 63IoUZaJe6lJTQlztHppTeWQzzl6wLjwQVyBGIx1wjhJmdtllo58RcIAic1YF+Pll9jZiMVeedVV rs50fygL8dWZxcmxzHMNhPN/GA2gJEA+ZgbRwZIMvaT3QawxpUpFs/rKCzrljhxzkGMAImU+rNAi VT17dGcVhLjgSOHjE0dSdDsjxBxa3fkgN3LHm1NtipIRYXRVprccZH504t/vuw9mgCyHn0j8s3Pn jkcefBARmgk9g2hN44oVEUUtfQQHgq4RNfDksifjbarYH0jLUIOyeyFhAJ6G/x2XF56Xu26/7de4 FPCJ3llfffW1jM3R8AII3//8pz9grKbW6V4p/YkQQUtpPhRndtYc+NnkKVM5v2DhAkPmEOAdW3KG uIuNmWRmriGpQzVciZfjlti1a2egPpR7OGv27MZNjjIy4w2iRQTS40VG+Mq/mjq+37Zt/bp14AMa wXNiJvaCSHLsDYycWDnmLgaM8rdq5yBnKV+ZyNIYk7w7jgfA3ry4zcYG1HcxuwKGHKOd1yefdUuV 2y0zS5WesvX8GQHtqk2M0AKERva2AZkZ0wpObczIffv25S62wr/4xeWHfmKFPnx2X8fgl8OnEqXk atWqZmev5ovxeQxYfx+TI+atcQtVFFxjSyvVVZCwC651/55t27ZPfOddxBXTp3+pp8CHYGlGXzlv /gKYBASF1V1KrjatHTP54EZFXKzwrZ9NnTrw4otZb6gt9Eh74rCRnwws1atVZcXy6bMCgua7DHsa aQDDOmZiB9PljNO9+kHWe9iJcfII+9BQcsnaTIUheazK+tB2pD4aD4iaiKvHQugVKXlt+7xPQSXE vZSA/prz6mISZXISyhIkkBUCDGrClUTSQon857/8hRKoHsf325xYCRKhhR7cJSs67JwIItOjR3fy UxnLzj59emvMk2XIG9iMiiENhcl169aVUHC+YsET4QcevrCurm5ML2IHXnThhYMuvpi1HBPws/v2 Fa3hwORIWVnbtmmJ8Tg5JwCWtm/b9j0D/roh1xKbkA94AibkLLEhkf+7lG0JrHD4dUzuatWgTPiW UgyJ6GMtdtJJJRFYdurUyTwRZjl+/ATNNpddegnkDOHZe+9P4kuPnj2YWyjzf/ud7RYvncL/FsZh JMQ+b+KEn6X5hyGnnQOd5U3EZNpC64jZRso7JbFVj/uCAem8ODo9Ht3CJOEKJ+GNKcbMDu/fvHTF djfA8OGfdq2a+cm773w6Z9N+J5adPYoZAlHyckgVVRjm/4q7HVOQI6jRTTz/7L8uuNDR4JgDUdiW TRshHOx9kSuEdgrz1CeffrpmzRpmKBO0NkjfeWMfsJAj2fIySNZIChSvgpNF8flndcSohdXxw48+ Rl0lstWoYUO2zqhU1rqyGYQ6Tz397EujX1HFlAVLflLSPM6aOZOlYuq0L2LKKqgqNYf8Mb0So5Xo piCgMK2Uw7zcrkOH6M1XvK78KzRlkqx9PMhXcTPeRD9EB6P7AEp66uXNkqK1bNGcVVkf6BERLvQs RE0oBxkeLMxG6Chn2NDUllIJUYKCCUtNppNhQ9GGNqde3TqK/eGNpCXDvs2bnSoh9zJ3ic2b3FMK hlerVq1Z334LXWCMMbD5ArOUuJFWn9mr173/+PuqlavoaOAiAgLDiW5t1bJl927dAJw2IlqWh8qE CRMPHvgRtfjlgy8jRBxn0E8h/FYwGqh/w4YZPI6x4cRWOHy4bp3aVAZwuIz2VqlciZ8Ak/OUyeNg P5UqVb72mqsZYLE6M5HfGSfeaLrgRpAXr1mkvDKRO5IJHqyIXKOUsgg1eQcJinbkqYf5t2q16i1b taIJuAeRBwypc8+ejnOri4MzGhUj+roh10DgslethswdCWp9iKwkiTQgwD3aHRWUKpOiFAE4khEk w8PbFu0u9KL5jMxUdwkRw86lARqXqpekEDM7lLPo9eHnnXXBP6fzCh7eNu2BKwf88obbfzf04l6X PDx928/pIAqjK/AP8uom+I7BijLo8cEY4nfDf9cko6E+xLBRHTBrZf0zHybiwqhbsSwTac3Y19/A YCUsaFJFFbj5P8sMs0bAVCfMzvjDo8qRbsgcTKkK4/mLywcPGNA/bO8w1yBOYMsIu0JyBnEx1tYB ezNU66pqsy7ylxUCW7SwAhVufPyJp2BgF154Ppm8Ma1lx//9ju+huayX3IvyDqMZJ5TXqU0hYQ8+ 8MBjjz2uLFiK5rBv3//oHWI1TZsyhYuHXn9DlBSKQAoho0BoHMv85Zddtjp7FUSHanCvGMxdd9+t 3I6RDik08QQMCE6ky2SSrH08tVJCreiH4tVFZ+piZl6pAIHjMaPxul+ggwYKbzOllzFrPESEB/mE tcYqTpWki1VIEAsz0y7ay0dJgS4edIm4oBxomjZtgsGfd8BHEsIpagYhcBmrw3572+mdTocJMfUB IxD17t2nXfv2a9as1bBEKswIZJBwNa8qvIprYDAQSl6WNm1aI/lg8CvYFdI4boERuiFaKs2bNw9b K1ZonoWKE9Mras41DFQejbVcRoP6UB9YInfx7vBE6s8IwcTNuCfH6tXwv/Mg7PDgYXQKqav4Agk7 PbNjv7PPeejBB/nO3H5mt+6Ix2Scp+PFF17g77333Wc0wup3V9KZgSkVbaFi4ty9z+rDMGbbM+bV sRjdXzxwAJWX7YGcSPAChpYxs2U0bJh52mmcyYjgyJlYGyPdRbhH2QgWyCGjRm++UbopksOQLmaE hBqZeSvjM9gtkHomcyGpw8x+XPbWPQ991eTGJ27vWjVtz3evjnpp6/n/fH/qlA9GDv5hzD2vzs0z sSkcsJnO2NMY3YQbZv1H7fM4WFYRnBACgA+bJ72rHHPmzsPtRVYXrHNh0wMXTn1TuFTeYYCStEaZ Ydhfqj2822w0+TesKir/bZbcIojDNnP31Vc6wSPGvPZaYs91w1IQVcPJ+cMCpunbW5SbnfN7Fj8j VmE/beyiZFLmrSrsgaFIaejIEIlFsgCj5tCsywdfWqd2HW6Bb1188UDEG3xnFWSUQtHIHIoZWSV3 gZk2bbpcxvDKZLm67957qdgcNzngQw8/hNqiUZMmxszIV383tvj3LE74w7PYLFq0CAew1m3yMtC7 y1UZV6NR8zR3EYpy1K9XB+kL/lxGnBMv7NKlKhyGtOExHVcVry4IUwc9L7emZJ9LGtyI565b70iq OGDDCB35smDhIv7SNNBQ0ATv4YuLAdqME/il0RN5L/Zqn/ku63gTl/+jjz6+5vqhO3fvGTnKMTAn ggN9/dTTT2Pw5y1E0g5vGgD9SkIzJFuu+DOHPoVvQbUZb7AKxgs6qdmzZ2MOxXA1LJYr33n3PSZJ uljuw4wEVl+GPddzl5JVSARrjNzJooG9IyUTYWvZ8pVHkhQ5ZJoCmXXhZMQ6cYerQ+spmeJRUsPq tC1J7FDGqpuH3gAPg36x8eALJAzmilkkpParL7/kOzM8mzGE5eYp/MQFPst0Fgs+KMexjnA3IQca N27ELWf27s3cBeOECd32m1urV69OK5BB0nyR2hbNm2vDyck6rgsnb2ViLYrrLnxTCnCji3ST5GDm /aKP1Ne0Uc30+jRIIE3nysgskvclmvS4WpTqF6cMM8td/c2EbxoOuf36fm2rlzi0fvakZTUuHXR+ q/p1W553zZB22ZOyVjvWL4VyKGYxOwCjm9AGyGVmzlYPuRmJ0ggBwOecc87BmMPYYjMBscjx0RZ5 lZuuxx5REIBYICdjJWPv2K/febyob42fQNBFRI/o1zD8nzdvfkBVVLw4syAxTcT0hGIzzdxN4f96 /vnQrJ1sdvkpej5BZQtApMryxtoDcZFm07u6s/gxl7ECeWOyM4+zkqHH4aSJ4wDP0Er87L+e4yeU Qeh3WICNsBb0MO7hRtZ1yuxyxhlcwCrLc806ygLAT4MuHkjlIXzU/zc3O5HGLr5kUPUaNZs0bkTd Hhs1CvMw9LCtWrUa+fjjZJjp1+9cJBxr3QDi3oNnQYNYKakwPSgGSbJn5aXhXz3XDZZWeu6cOdVr 1IieoFPMBuE0NuAxyRkmPnPmzvfapSFnIv6CETXpJ5MxM9I40WVBjA5BL7rU01Fs1a4F/pQJLUP/ xdqPVTi5WXmE8pdHUmUaOZa+hHV5wwpQ2mftXvgu63g+fGckYClIp6CFpAQGHsMvbCBlaUh9aQCE j/I5MqGx0PKBizD1iVXwq1JRsXNYeCRZJNoxXqj9+50dCJdx7/i338apU5nNuEuaU55F281LR61m zpzFT1u3bkcdJru0N98aD2WkhKXLlgKgFH8QWc7IAwaJGrabwCiNfwKHMlbhyqPPpE8+hoRN/XI6 zBXnHoJffD5lCt+Z4bt267b6iP2cXvN27f2plohsp+B2Onhr8EEmyW7lyg7rAjqYEK+bSfZgmm/e RzyCGzTI4JaiYWYJIBb8Ftd0zOkmvDJ1FzHwzO2aHFBXsA9hqIRujBkSjKUCJI7Ba34Mr0wdZrZ3 5zoicld3KNHhtXM+X1a1T+fGbkDGEuUrVUnblvO/oyQOBQmpohp6HWR8Xm/Zq7N5f7QbwIyAvxjT +Gog00U5WNkjEgJIelhL2M1j7MmryGLQ77xztas2okd8pwOqouLCmRlfUpyYd33mpqB5c/x4xX3w HVoqoh+Ka0WkAPgTCw+TkTSbtItqsMajKmLFckPI/kzXXJOaitAdfqKekp8hQUTnq5WYAAQASCuQ hOH+KcT4cB7PJvgKQxQ6gjqJG1nevJyD724YDke1BEe86YYbYFFs+rmLiZWLZT9+4003nXZaR9aP rt26ciUmUywqFSpWMlsRTsKcqCRLqURlZtUZ98Z/+p3vKEw5kHwY9wXEeLTaxGMLCx11QD8rFe3c eQu0WQo9FFoCJz6wMos0hIxbFH9BqkwFLYtpTQzOVD6IlT2lYSYfvdPr1nVmAMR+r702FgdJFvvT T+/ErLJy5Sr6JdRoLFRrSWxhSsCovEljh/2bgzEA02L7x5ui3QudTlx+rOP527//hXVq116xbNkF 55+X6Zr0fT55Cn8xvY9U4SjZwwCEEc7h0/ACps5gyyG+4o1Exb/sHxje3lyiclvhp1DygfkjvYZB vaKm0SLaRQmI+qCeyqXhDb3GC4XtJp3AwEhMp6l4Frjy6ODVhoSF7rt4rjFXQIn/1xEjONOsWbPo Xc9AAg10x1TPvA58N+GL1Raf2WjtOnWgdIbEeNsb/XFJ+KteImaYsL2j+Ma8s6iYQivPwGY5SMJG FWqVUoaZpZevXA+rjK14dP+0ffF3c05sf1qzSg40h3cumT0v7dQaVZydW5EeCOf1vGVLnfSI2mFH 8TWDcMTMzVKkDUi+hyksuHFs5DubS5YW5GcSPWKHIaImcpPYIS2V797g8TKmTplM5PpIkXXEzBAs RakbjWLVJEYAQ4KFx7WVKY1siSUHNgYto3rEpWQ2J9GLZjSVVvKkk5jZERVIU8CNCBS5EYj4YNnW qdNpIh8AJcT4cJ5NpxRAzz3/AoIHdG0+UZDmfceM6IcfznTDwaOoVUhMRDs4Ur46ZgxLFxIvKkA+ H3ikZFeo+FHKsBizlGIbBDVU/gBeBO/2lwsgdt3dZD4cLknKi8VFhceNe4M2RolqpruY3ztmtuNB y1eEkT1znp0PJBI/PrcteexNSmp8G7HOVjlYTcUk0CwhwBtEYOb0S8mSMVNIgQa6VPjWoEsuu/W2 31JbVFfcq2wWoQKz0BD/UhI1btzQRxaXLV9BCb17nalYFfyl00Xc+Yuh/WtjXi5f/uQePRyzfUYC DInmRxJCUElYbCTuG2VUKxJb9qqV0HrGgMo3Ak52DjyUDLbeEgSvF2RFcNi02UmDDdODcTKw4Zf8 O+2L6fxraJwXBI00ukwurlEqGeknxGC+eBaRrkS+y09o9i8ZMJAvCNhiBtnileGl5p0lc26b1q3h x0raYfaBJ5zgLMRe70vhv27tWlONgG5JCbS9aG5hhuGF0msSOvnIIRcuXjSVSf6npA4zy+g8sHP2 6EeeHDvu+Yee+KT8heecXjN9//bl3/z3yUfG5vS8tEv9wvIpDtOJMlqU7SoHcwGGDlraeUsRNmBM EHobiyVzYrwhoJJ/DBVgDeUD6FszMFQ3Z4xfdxBrsLAVo5uYJfmg+OM7f11z4718j2nrTYFKO6jI 9WEP7bN/cH3ooh+IGTqf7rjQo+PGMgyVzcyZMzWKVqxcdUqVKo0aZcjiB9aimRrq5p3UCOwEmxk4 oD8Q8dHjdIGPKJBiCNsdkZX69Y6K16W7zDr3XVYW9jRIBGlInrHU4Vz0mOh6fnHFFbpYsW0Z9uB2 1ll9OPP551OQrlFJaBAwyqjZe2DxxL9eN0Dvr/966mlWLyRtMWO8wU7ggnQZj/M9Qvb1uCIi6uOL KSo0xyIyodB09b7S1BEFm5mxebOmX03/YvnypfTOhg2bhAZyAtpuRCnIUJUwPkiIfy6TXSZUjH7n 769vvZm/3raMf+stOPGw4cN1UmnLJTcNe5D/gMGwaPGyeMkZzINNwsjHHuNx2GwxPBgnkGCeIv1p 3759fEuyqEmo/QDm/jDsxS4OvPIQSqYFJGf8q41HWMYM16EotgcMDymOwzZQ/pXmIwkfw9sbzyIS OJzHCpO/vBFsVP49erQi5kQ/QAYRMvVBNI49HBdDxdyk8Hkpd2FsnPR6LMroauOmPMPEWE9Igd/p mkgaf+mgvGlnU6A9hVzFlGFmaSc1v/yf9/X/3/i//vGRz8tf/eCf+9Y6YeNHf778yj+OTxvywP0X Zxzblqxds0Z2A/QXhgjr14fRWopVZH3nWJbYIxQB1hh4RnSPOeZomYjGVEX5ymedlv0pm3glwHH9 xZi+CViFYfI+XxCKSB0ktyylHYxyRNfNmRtF49Au4SzJSrZ4ydLRL7/y5Vdfs7fUZE0ccP5SeWZq 6dPNmoRyhyWZFSssFD7lWkaDDO79dvZsx7LYfajvYN7U6N1/YD/2NBIDwPYQV5Crm7WWf3v07Gnu YoFR4gEWqs0bNxBiF64MU2Sdg0Z79/cshJy8e8RdBH4LqyyWAcCWLZu5K4gRN+aeDANUlkrgaA4F Q9LK7UQUO0KOZTXlpfJBwhQHl6FSuCSOCEIYUXD9SDZzqIlHv/hCubKOJlrxETDUYrU2/M8xq5r6 BWpuaiirOM4ofAx9jQEZvM23tZPpquJicJg9DFpFkQ8hbzTvTd1IpwpHF/YAKBSmTvakmbN9CEe6 xZxnFCFVHfP6WIgOllvQXylGv57xDa+t2TyY6+kv1xUgj6BwnuEEVVXb0V2aUA5t3QA0jF5GuyzK w1YGDiShGttmyUrNwftF/gkAwXcHc36d5wvGCQjA+C5hWMxDSPJGnNmrdxDjBy7mncUqjveLRjHP wDhhIV4HFOlnvQyV7K6cGfvKmJj1SZUL4J1SVoQK/1DTI6lt3eooeWqqtKuQ6nls+UxcjTqhdMPz R4z9fMaMb6aM/V2vWoivK7e77pFXJn3y3xFn1SpRhBIz5lPXntHrwSQqludf06IFTj2hbWM6Rns1 a9ZsXWYPHwIEvOJM9DziXNAHtY1ryBzkcG2nHDaGtFI7ac4g8mRaxAoKMRILOYfmxCByuI8//lhp B6M8nYVQ/owxD6ZsaZdYt7Cl+P0dwwmbiXk+IlhM0HBSE71gpEE7KlVyNJhibBwTJr7DQoWTROhT QpVrWrAxaerRvVtYeQPxAta4ds3nHzEFU7HuoHXiToUqcLUsAS/LTEZGBsXyBXImdwSkgEhKsHbH WwI3NwTJv7vjjrCAUDd+vf03v8nNdToryNvRoX0bx9h80RKvXEQJMfUIqJKRGoKhNxNfnjCs1M9s IGytAspQdS9th11JJwXXx3sjrJvCUtfsAQWzqZ7srj77fApw8UQTRGP+goWcZwCMf3sCdoQKHwOx g7f967kXoE2ia9yCApdhgH7c2wpRYUaROs6LPMzGYduu2WukA+5LWFcI06rs1TGlmKGFEBqDVwAX B4gXsSGoLQKzVq1ahn2cT2DmvhH1MBQBIu4y2k8aSDOVhZYvocpf0xHyi2QbY3QaEC8I2QX9+pF/ AioGqcJ357777+fDLnr+/HlUlZc6kkA3tNpIyzgZavgfeqWREhnyjQ1m+fIVMYhkhBhsAQr5tLdR SP4ITqj3S8XG9EyK0qHJ8JN2L27ASL+3KYJwxMkBDTqToS1FUIcUYmag8ePudcsXZH35+eRvs3eh STw5o1vvri1qli5kVhY6N4WOIUV20O68hhsnKWySZuVmIUNZEXRtyj1CKXWlLIhyKFBnkNYxLYqQ yfJJU7ZxFYSHKSIrezhYGlNnTKMilkaEAV27Osbv0Q8tikEOrT0djwjhqlWrTn06duwAYyMoP7IT GMaaNeugHSxHXKm5XvbUoeoh80SfzIz5HRBor9cE21u9F59/fvmK5YAQSjol0yJwv685IrKM+cGX Xw50vALKp7lg4ULSWiMFRPbz8SefXXbFL2/+zW2jHn/8iy+me2OrmtJ4ooKPTPpgEm0P4l5HrxEB n1uMwZnPXp4AE0a2xADwuu8E2QAAMrBHD/3vRYNqI0TUAHOX2DKgHUrOFOsVoQvqG1WP73TNiuXL gQvp19p1zgaPkAoVKlRkCEFwGQBoJzFJRE1J6Aq+MIHA2kXXJk36ENNV8RXv8fLo0Tzo4UcegXz8 4Y9/9PWpLydV2FHKYokcDuEfWtcgw9h3DVyQChAZhNoqRWZrdxTFPKDatH3Hju9fGfOaY5fW8mc5 Cnmfwu5DwpYJD0BGxdSN7NB1g90JCcPjEhdLfC+MBLFu3XqKPohrS0ABGBffPmwYfDcIk2MgaVZh hIicrV6zmjbCp/EyVlZvHT5JEsOjUmXHZmbTkTB+qc7MTP0LL5lBzNGVQhekEDM7sGnyI1f1HTT0 1t/+7obBZ591x9hlsU15CqQnvLHXVaC4mtFZICFr0aIFb6D2N1J1eaMRmmqwVYUZkDOuQCpWzAqR XXZMehS81Yo8DusCc9f81jnC7sw4KfsP38Ec6t3aSubRq7eTlybKIe8tVCdBqsraw9JrCJOiVRHi HBEay3CH9u3Y+pd21T3QC0IDEKYS+cryFY7Rt8+e2jwuVGbGyERJNPT6X4WFFy8zjOeaNGkSFgSW sZlZs8M6orLYIM+rX78+j/7H/fc/8M9/4pmBXIf1GOFf925dMG9HJEYi0Xff+wDyAdVA5IMoyGSt Vp1RsLJwrlq1EkLgkkhHBRb94EqUbhBWljfeR6zgvVG+TC9LqFbGVQTrUAKJ6PLRI0m6EgwlxWCT HZ6PnE364IPBl/+CpytsmFIibtu69YP33oEWLF26TFnDoSYsY2d07sS45Y1ARsy8IaknX+BtDBj+ osgGUgi6LwgnAjNmJJImBacaYaF2xcmV4RAJiM248y8jRix0JX9sMJQiM1aXOr/Lvgp8GEIXXtDP SyL1/gYpxC3BkdBAr5EdwqKI3AZJDR3Dyr2rYC4BS+YyhILw3XjhRUKPsmX27Cw0mfijgG0km2MN G0V1kSd4MTjoEYaBq8a1Zv6x+zN1mNmhle/947Ufrnh4/OTpUyY9c2PGtHuf/zqoXCI2DvFdIa9M /EzMbbx1zBrSozMrIRtH7RW2UJRxTKYmemp8Dy7WVxNkMqZddnAAWE6Yl92UL6USk5Ojcfjyq2/Y 2i5YmCfjlMwjrJ2Wt2L42xN8EtUJKsKYFWbtwZlOSw5Ltdfum2W4fv16MDNELLKpJ9mL1mOWcP6N vlAZB3VtGMAh0vWYBNGuKDKASNpbpEo8BbEZf5u3aI6qCIWxmkxk2mVLl3347ju//vUtsLS2bVtD JhD8sN6zVCPv8aX0Zm8Dn2Dxcl0B9gSJfYDSTY6EJPyGzOGSaTraZBmSGFsuOzqiJ5CQLwgHMozg PCC0l+G4kpwZbRT0Fz2aVGBK4EOdmS6qVa/G+VIlTxLN6nJGZ3llInu7+qorb7pxqK8a6PUYMPzt 3t1xoeXwBeHUWn7OuefGHHsxLyCZgePxumZdvN4AlIxpfM+e3X/I2YtvLzk0Yz5LF2gbTDoKhgrS 8YB3hV4m0HbtcpYI4xEceplsy0wwl4QfF+RGBqchmo41RSVizewPy3plpUq2A00jQQpPiWtAgH5J dSfTooE6dZjZtmWzltXrP+jctg3r1G3Re/AlmT9+uiC7cHMyxeiCg0f7/sgASLttFF5ITXbv2p3j OAodlXmGFYt98Dczf87vUTQ9neRPYQGLFP0ysZprSY5kjxKzTCQxSAuQALH2mxTLxPuWzCPmQXRK 2DkhxTFwkX1xkINszVyGc5z3YpnLKMkjJjhkfGL8sIQrnVzYQyYdxs4mugSI8al2xSsDELxS4RHI Q1Y7y5c5YkVIIJyD8CKy+meJJWu14oxf+csrkAUSCoHuxijNEBfZAFAZFi1oWZCMokz0mKcgflA3 ecPuG4omGYyJysv3SAkkZI+I2Th/EQTShCBdFuUayLRXOfu1KykX/ZViS1FCcHlhqJDKmu/0LIGC Tdp1FvIomkfARMDmE0fBBVnLWdET6M3QttAXEpvhDRA9g1ZYHP74pz8NHXr9u+PHSxEf5MCDgiaz fgeUsalMmUDIHMIcjAHVOUq8MYl7o1C3IHVO7BrtFnxuCipKvixspZhG6ErOKEBxYg+yd6UiAqnD zI5C98TSZZ1N57E9DhxtyK9dmlYaKbyWLF2ycNHCSwcN8pIzLsMKG/ORSHnEjm2jjtXTZZaXnyhl vpoTpNHEMo23UQgJkMTIXUghdlitlR2yS9ejsmRGKRkrH9QorLKYuQTUbHIxC6FX0Qal2O9uADB6 dm3MHZvKTp068terofNVQ5cp6L/rKvg/b1wG38Xj3hjHmcGXD44XJV0vHdORinWaNnnKKVUqfzF1 GnI4NKShkQi4GFkgbnqo5FBuopxFeMa7QJB0Slu3bh0XKJdLED9NboE90E1QNG/9RcWgZbQf+ugV mmKTHmr/Tv/KHpExo3iYwY3MIuEGJlLOEqKFviDQLuNBNg8k2TRZBTGIZiP38YcfUg7BLMjAiygl YGAnaC5yNW8FvnS9Ds/qe1ZivRl6F9jCfQFQUdPiPRQ3TrZ9QQ4nD17lSkGu9F4TVrqJ5BXG9ty/ /y0JZdgD+TeB/n2pb+N9emLXmyEaejtD0cyEkDMHwG3byNKLDD6xaG2J1dDedQwRSFFmdgwRO+rR PiG/MTXTC9+qZSvkBKgqWG+8t8msdV1InoBkadWxqIdsn2MqCgNWTRbciWmj6NOVq7JhSFrsj+SQ 2S+ZhxwVgxzQC4xRMHAJqNlEr+EuyUctS6yIBFbgceIreq4iuUcJLyLvJ1fJmIuroDFDDq02hFgi lpjRMoM0WZmpRj700JKFCwj4xHd5D4Q9UMkhPMPNFlHHt7OzFNV2dXY225iLB/T//vvtLEKRAiDF rAy4ceCVCb3zip1Y+CFJ+OZ6S+ApPMv1fkD+V0Us0/jAxnxWlAsYPFJDU427/nr3Rf0HyCUFjSrm j8RKUAg9vDX35eR07dIZ+t2ytRMeIlL2QN+zXIPCHzFyN3rzt8ePx3MwrFFgYg0BRrgvmmLXG8Af Qy5mmXqjt27ZEvNKLpB9ld64/B/fzprJcGrZsqU3IURosQU158RbYYBlz6DgKd4jNLG3s5lxggvW QAZ//333xfugfF4f1pUtn2Xa22MikFrMbPOXrz81auTIUSMfe/69hWk50//zGN+dz2P/nVdE7gBH I7p581ERlYypGVfxwpPNtoErLV8wf773Pi0VB1x1mz2EAIZTqOeicym4CwGW+GjBdlP6/IDiKdQm Sfq7xBQ6xNhk0mzRPI+BKW0OD1Lo/wTKNJrN6Fte6TVCl6UWLZqhK/TK0hg/oQFFvQOJxZ6lnUhR qITQ8XFvaOhXXf/uO+/yN2GBmW/0omw1ZxQCLTpFQHaFmy39jkMAwCpX9KOPPMK9H05yZEhB/DQj vUFIHTDaY8zgp2mukc21LzKLHA5kKgp0WF4jzygoVziERnQEzoYtWrbE+ExJq+gRUljxUD1FKK1Y vuLpJ59s2qTp99u3B7TrQsN1emZHjNz/7i7Y8h2+8KKLCnxWYRQx6laucvwK4y0cpjjLjQIY/eC9 VsZ3r1FgrJui/T516tQxY14BakWfz09RhXQvkIYKhrdvdzSwPhDgcK+/8QapPLHFjJ6Tt2CrqhzB V1x+uTQG9igyBFKHmZ1YssyJO7/777NPPvb4k489+fKnq9J+zBr/BN+dz9PT1ieSkiMYzOy8w5or lS1TFmWRtwyZj4gWcCi9I0YkodGtWDgVHdseIMB6CVZhUywbfFhiZTUCD0NDx0EiI0xl0VjJptt7 oMpkiU3A8J/lgco0bpThVSkyuW/bvj166P/o/YhmU2ZnBFWKNLHKIsobfzJKmTGtcGi74RZRrG6l YisQgRm1NeVQJv9iZBZkeOMfJwshzNSweGPtgaI98+STsqBPgAroofBSffEucqGRWRhOymBoRgsv e5C4EkGaZq4hs8Lll13GQFLSKh6Hn96q7DVz5y1UNFcQQ3hZsnSZcuXLz5s3V17A0Q/sF8mMzrgi ZwNcFtlGVlYWt8T0HY5VcPjfT23amHcQgzMyk/JB4x+wHLIexQwiQy8TnQ4mDfJBwgrGfDQjRwFu 3J49ydhcxryxKC9gzwCkPicA6d+9IEDl5c8or44i8NYEvV/feis2suZZpKKyhm5FOTZSh5lVO/+x ldmEogn7WfrE+YmnUYyFN29OWEkJMz6ruHfl0OTu25+1adM2NPAsa1iUtMGxalTcfpfAPEqKZX49 4lJXhUmKHPOo6hBssJV3LLH2O7EtvKCgwkvAgpuOJrQm06LS+5iDCXTfPof5xwz9H6ljGD+oNd+a OIELCLvKrMc05zMZCbWIKuxulrdgwZo/i41dP3QojOGywYFs12pUr84tiM0IgoVUgESEBFzgTE7O XsglhBXJWQL8DCdQvY9esst2iE2RVzQLv4ecFZQGLVKXsTfr0bOH97mMMXKAIlGDkaAlHHLddbT9 mmuHUMLcOVlz50QUUTBskGE0yWiI/SJoQ/pRhnIXPgRSZRaSeg4YyYvFSCZYSVg1XKS2mxTgUcaz tiWEtzW5TfM5+BG+UkLrNo5qWPktgnj75vOh8d4e6gTAFOQYmVU7ajVjkykNO+DLNKKwSRIZveC1 LFsydSAJFa8z2wBGnRWexdvLiV2fOswsZ96bo16asj5uWXpiuAS5q2JFRylJYIX/u+/v5nomX8I1 eG9v4iZO8WnrCS6FJXKQpxwP1yh5SxQ5ELZB2I2RSFvGGRAyFHxsr5mw+CvhClI0lnA+KKcSs+BG esG9JtGNQV6i0PMuvDCfsiViJr0/aRISjh3ff880h/zMO80xIzMqirK7vd6CBfXcCy68ELoAUDDR gDGi8kyRthHBvwoBR4i2IG8AzMaRFrgULQf+FCTIma8VcAjcOLwnN2zc6O1ExhX7KOkx848AvRkp TopCHvoegVyEZAaMZzJNzV+wuEHDxmQRoMKkHmdFZBXEgIw1GB7Pd3369Or18uiXEd8yikiChD8s g1/KUEK6FJIq01SbMCUwJ1KqQx2Mw3JM3E52tck+F3XfXQQKcUwqXcuBAjlkQCKrUJ+rcoGUH7AQ 7C6iBFbVxCJWqkNqgZo1nb1K2OPaIdeKJAWJyBOwkr7LlEACIa52WXiTMMbuve8+dg78i+leYsXa u+JCIIWY2aqPR/13zlaX9Bxa/ubvho360okYeQwPokS2atkcDQXmmUp8xuHa5OYl0NQZzcjyijcH 4mn5zNtDyVtMEPywgMi7UH5eoYcCeyI5YwnnkHQtAQtunNVZJpV40XtABNGGnHtumDxI8XYfcxxZ KTEZQX6GSS86AsnP0ONQVECPvHgfGul6qTITsJyLUgHc3KALcdUQbk3PelM0imqwBrBaAz4fFldc 7eIqlothEl6fTTxAvUpzVk1y4FByfgRmsDG0inxwv6U3UVjToQxXnZdlofh3o8b+SP2cpIFdzji9 XdvWEDJMjvhLzBSUVoi+WBdZIFmD4fHwMNZFLY2QNn5iFHk9ClmtUQTzayGpMn3Im3BxQXpEPBsX 4Eh2lgiKFMYvSGkBr3nxhRdMnlap9Q8dcoJQFvHBGPPmKfc9XaEWvcoTAk0zBUVhqLytxjTC6/mR cLsYrg89+CCCA0g/1J9/SSBBab+57bY7R4xgmtLLqF0Tw1LCSHsUNgKpw8y8SBzeu37ytEXbjr38 jDWjaRMnT3mJk/L23KH7M4kEVq1c6W2BNCy+SOiF3dnJWb5SvHvTsPjqKUdLk3gubCuwrUaKxkdO bVGCREQBgeUhrNQKToCf18nlyhPkDAKBWRLqpwQCo3sfjTwJHQFrreRnb7ihKwrK9jlIRxeGKjPI c8NeQ4Th+fMXKF032TY5zuja5T+vvy7S887EiatWrkIOytJupIzQWX4KVQpHqgO07I1x//Um/EED Tpn5iUjOejb8t79Fq8hHeh9UsQiukIbC0jgJUZszZ07MQL5sBmCQPXt05S+rNWMD7g7BJZsQSyPf 4WGsi3xGPvYYq+Mtt97qa6YyhgUJg5xwH3lv1EBVQNSYB7JwiCP4yM4yNO6DfF+MXWDMAsNegFdH tpv4lQNtL2p6xLf6V0rthP18E6tPwLtwzVH+ZWaVWd9+hySydq2jpLyh5YicibgDaX7Ui7xBDFc4 PRlrQIwNgJSYiofH4RN7YzIYatQbsKX2srgQSE1mFlcTC/li9jc5ezGIyUvkciSq2VEKzVAnAGXe KKQD0gCHIDA6hrpz5h7lFlpIT8xPsatWZTOrRrG5zkuwEyxYpXKASAsW1yFLtbBSK8yxX3r+uZIn nUSQM6yCMIJG/ZRYPkFvlZj4WGtZd5kQa9asRTTOArF9DtjqwlBlBnx06GVn9z0LckZaJzJCkm2T T/NWbb7f4YSC0+fZZ51UV8uXLYfxsO6ys4fOcp6/bVoQFuGoYM6h5cP5nnr6WQRmlwwaqJGmQP/o MRNwEzHl//53v+M78XFk/IoTLqpY6BQneeXnL14Ei3p81KiEA/kirvAtjSJtoZZkEsgFDIOccDeZ GyXsQcYcpCiptsEH9SvLP1TVd1dcvi+RnmhEv5B1aXu9MkW2aknqnlmuHGyMnR6zCpyV6CQIemOi SmMh7gwwuDi8P+b1vgt4ECJemSoqO7u+sAEAN8qM5KxNmtFQm+l4n+69npogq8PVoCgdTvNT4SK7 1zKz2FAjsIl+EamgS5x0koQoWD6FzgIZGRkkrjGFcOVBt0xf0OrYVQl2hQKlmvj13ry5wQoo0qsA IVL+R9UDNSWQBl9BFcsqYBvQCMiC54UX/80t3mDxKoEt6fvvvceX7t279OndE8UTdtA+HUTAZ0W6 7LROnU6pWvXgj4UoA4a70FI4DesWkyCzITKMAldlJowDYpVfXD54+LDbCXJGKifyOGEqcMWVVxp3 nxf//SJvFgsGqzsbd3b2iBv5VTToi2nT+Eu7aKDkaiho+C7jTiTTcD6yWlE+8QVVSVmtJZwignsp HAaAs4KPJ0GnSJsNF2H5/MUVV3ANF1/Uv+AjWXjRJsw9C2oBBpiN2ZVYcUApyGAWXHgMVWL519vk PQrW94XygQKS4X2E6wSQm4ROAFIFsNNjw4ADhM/3KHovMMDg4vD+mDsTbzmQod/fcYdef14iJPci Zwq2Am6M3kgGtUozWoAHNB2yDtvDL4oJqgBLTvWiUouZHYln9tjrX+bkLHvvBQUziyOeWc6sUf0G jMoyQvg9yyfcfUGede2Au8bM2hzOFAENiy/+pyYjE5Vg5fLl/Guylyj8o3dkYIjK+DNi/BUrs1et Wt2qVevDhwtl/CjDCZoRPkosGCQ8EkIjKB2fKGlYWPNY1AvQ+JRVE3MrMtJEAYLNbnRVZkAQkb2h i/R5+X311VdMTMxQzZu3wArphuuvZ4JAyM+6zk6OLkNII/G+noLiiU2t0UEEfHT0y6pWdSxsyGxd IKX5CpHWj0QU6D62bNmCCGfC229zDU3Gg7Iwnphwmc7i1Pl0UjnBn/r27QNlR9ZlSsPZFp9chEb3 /O1vcDLEjfwEDZLtCz311xEjoEHod+gycofzfexrr3HNtC+mo8Ts06e3kcsyBriehS0/QcumTJ5M 4dHddXv07Mk1yM8KyV/SgMNSyoJagAFmY3Yi9nC8BSRumvVtVvDETSz/vE3GMk+bIqwI0OnHlccs bPVIrPL4E0998vEnoQObCYQNNq8/fkIJ+PnGRCPgBcr74nUWll4YVxVcKxIQmYvxK1pKzAMCB/LQ MjY27HCIg81LxFsgTiYP3+iH0owWoHxLmYgR/kETmaAC5kqJVc3i8HvqMDNvPLMn3vzuxx/Xf/qy gpkFjWeWs/DlYbc/udjDmfZkvfKXOZmjPpyzeun00adn3fXYRyudnXTMg+QqXKNpna3zx5M+wKdZ eYh1ns2Z16xBkdDXu0H/YXVQJYQumDQR+P5v994/ceI70Z8YllJEuoWLoZLVqzmJ5Phe9ZQq5IHx RcRVtb0vGLUidTex7/ksXrKUEkL3l6zxrHlQTBa/uAJDc3HY6/GBAMkB/S+Cy/rCXpjWSQeRH9c5 uObXM2ai2MWLFl3k4iVHWbAySSHYOK9fP2YolKpVTjmFCQLZPus6S4i6DFsf5UgxB3E0gvumxRxO 0uYsmD/PXMkcamyq+A7ycW2LVQ7kg9VOWj/oL0q3+//xD9ol5RqTcj5dTWO2Kz8XkL4JKReyLsjZ HNeCnjHAmMRMnmRHXj/NzI4d4WHyWKSnsPRi1YGmQKZp6eefT2bBJh+aoWVuPLzdvKSRou8GrPZ7 774L5YqOIb9Smb/ceWfAMlPoMqw42rdrgwgZxjN33gJspIJUntzhrMRey7y/P/AAk+GMGTNWrljB eTZ+iS38KMTJJwHxqlOvXijJgJpj5MA7Tjwd/HxhSAyD7Oxs8oNpdBXNIVLoNWUBRiTxuLsmVgEY PzsToqXEvJ3ZgO0ZCDPj8Y54Vb2DLrmEM0FovfKTFtRBlXhDodH0CzSxaAKCFFTlC7uc1GFm+Ytn lrtxwq09Xqh007A+PyOauydr8sS09mf1aVourWTN7v3OrrxuxYZANq3eXpGLZUlnQ5+n9JRXoFfI r3gQa9askVyK78xolSpWqFatGsvP3HnzTQ5N1mOkNT4j2XnzF0EpIBZB7MYwpqH8qlWrUDHmIAgi Rlek6/ExLUxkECAb61HteqlVt66dyVuATS77y82bt5j9JVUicRuzAAu8Ewj06acDDk12aUSR5uOz VAUfVtnc3Ny5c+cp7AVcJ1QzciQbd4JBDdC2wDVxj0IFyUfpyY0EUWsAOYXEjdq0aSWDGD6KPSY3 pdCMTJLhFdTme+u2bQcPHhgz+mUYGFwKKQKB3SX70XfY1V1uiC9zQNRiLmBsjiGX0vrJOEnptDmC Z/8M2MuFcVm/fufBlSFn77zzHisogwSzMIV0xwzZkLMOmZnsFmSAD53D0kurDqrP0844Y/qXXzVu 3AhRnGroxova4Rr+V1CMqMQOwIcIXjxoUMzbqUxhC8xi1qHwLkCE3LXL6Yh82MsFEcxLtmeU1Gx4 zuzVi+r9+c9/ZIjCYvk+4i9/iWvjp9ZhR9+mdWsId+06JPpyMn35DqWZwsYDlgabxLRrzKtjP/r4 E0ZXSntiIfGCbEXfufErswGvCdMa4POOeMHhzfKdiTRgBOyihQsLZEQpx6vxJmYwyC6zQApP9UJS h5nlE+n0jF+Mu2dAPWdBPXLk7tu1c1+pShXKuiCUqN6w495J362NYVMWoRqwMSNEUdZCr3WaNta4 6COXQv7P6sIcgUkB2szTT+/ETwvcsQ47IcsKMz5GnVAljPf5sBl1o9L/TCxgG1HAUOgHDkUEYCZi HfJWj3tlJMsXPMuefOJJVySzs27d2qxVGHVxF1HBWAWZ5kzyEGIP8mL/7b77WGZ+O2wYS36Q2RM2 pniYkDme5b1FDllTpk7duWsnaPA4VMC02scgkZlRjcQWUYcHr10HAmgKUEHyQQXDQ9etz0v/B1fm X3IKhYZTEhujmUwWoXEllCMZrPI5Krnd1aztJ4pKpzM6w8CwR1SIBCyoWKgUK4EPk6+c5KFufKBr XmLtq4YYnjbH0vrpoCGy8A0YaSz/rctPCQjJrhty7V/vupP0TaygrC4Ea+Akb5OCaIgZKygDh89s ef6CBS1btcY7Z8ZXX5qtDqOOFxN/zPxIYXkWGmFg7NatW34aWDzuhfEwddCWUMF8kAbKjUBxImCx L7z44rZt266+8kp2dEFup2dhycwz7P1+fcstPx48UL169UhKaiaT1atXP/f8C0xEVHvQoItvvulG pkq2RkGelZzXiNlEUWjy4iAtkwYzny9+kAg7dEdAAX9oYOTrb7iBhSnmnjM5O6Jga3W8MLP0mu17 NvW5Q+7fmL2ioNAkFY5WWf6GdQJAj162bLn0E05o3aoFHy6ThcHBgz+x8MyaNZtZ48knnoD9MOMT L4AUcmJF0DKYnIgFdmPwufUbNobV/TFJ4UWo0A9SR7ICURknSd/uPfAebWr5ady4cZJ+oVzAXGb4 7cNgkxXKl+caCuHGtevWu3GeyrnJnh0Nr+IDSeIt6xnZ2eig/FBxF0XBxngQcXHGuBY/TLgfffTx f94YN/Gdd/l169atq1evUVImFl1kezwacQg0Ecnf2rWOcBFY4lpEJUzSuw2GzL/eiFZYcsjwTgCS qRC0WelDwykxB6Gr4pozezkTn++Qo4AJEUlpuFlAoyHNfI9LlqZCWrRojic8bAyJgkIkADULlWIl 8DFO8ih9uB6CxV/5V5qD9QZDDfRBYnjwudCtMIUT7Ta0Rcl85oLz+yHneOfdPMtxhjQDmwiCiIQ5 YGZ0IoB4FYvG6v/iiwdMeu995YFmdMm8LKzhv4ZNkP0GYyxhd8tkxjnhuvFaoSyOi98wXegdJHET E5R5NE6mzBXkGGVHF6Q+9CxbFKTLjAEW9W9nfctdoZ3INMK0888HHkJOptmGybBa1arI7C+9ZFD5 cuWZ5VBcMGxSTn4mhSZb+tBwJAJQiWiZ7b0azCDYhr2GWZHlKeyzeC/YENId7BuV3CkKRaOPZEni fQpbHfpRmorj/DhemFnC3Yw6iXtfPeLBJ5PV0NIkJzNCFGiNL3zOpYMvJ2RX8+anMiUxrJkpWGCY GhDqtG3bBs0js8aSpctYYDAG2rFjF+xk9+6dWHJghcBfCqdA5rKKFcpnNKi32c21512S2TUSK+j5 517gJIZl1IT1Ry6KzJuwQOYgcuUuWLh47Nj/VK5StV3maV9M//LSyy4b89qrl//yCqYq6oN4ibtk oIoX96FDP3ESqvTJJ58QsM0k22EJ5P3EzmbRokVwGhgJssDv5sz3kTNMfyRmk9Drnw8+dGrzFt/M nMXrKja2bv16lLkdOrRXQ2BgChgLQUTUwb+zZzuWrcGZGSIlCZP4/PlPfxbf8vlpNmnseFZmr16D YInrL71scKR4GYoRpRQOvkPunxs3baLtGLHRfNgewhh6k+/onb1xU6OPPYnrXBlqnSimHopuxfSq SFcQLLg+oWJVONMcSnDEBij1YNsytPJKy7x1CLLxTfh9KYwbWfXlEGBMghQbRWZDvClvjh/P4DRv HEvs2NffgMyhD2U14p1C9mkin4WlZYwEDRtH9HLrrX+7554oBunsUmhmQaWBLwzEir7M2rVqQG4C 7kmw6CCmD28K+xnu8vlX8SJITWzWdRgzAmN6hG4SgeaQ/JiepX95Ndhv8EYsWbiAG5ctP2rLzV70 tbGvY26IIRpuv7ff9mtcWdFxE42S3SCzFhMUsxz2i8jsv/tuzsOPjER7nn8MNSCj5ADI/yNUwp// 8hdmWjb2odImtmoyaSgolfqNN90EA9azWMg41DU8CMmcnkWPIB3gu96UsIc29trkm4M3WgqZ/ARp U2kmbYb3S0EBXgTlHM/MrHTthmEWXR/oc91XfejQ6735OkM7hvzR7oywUuwEwyZey4sHDsDrkG09 m/X9/9sPXSOsOcm2UamwnPCpVu2UunVqb9q4Zfq0qQiQGmQ0PPjTIWQAt/z61xTy8ksvGS9IJhfX 8Pl/0BSY1slly6hYzV8sJ+wanYF+5pk//JCj9FBeQsMc5CYsKok1RvkKFfbu2YMFD+mhJn34ES/D WWc5ZgerVq10pREVWNI6ZrbnTNZ387Zvd+T85cpV6N7zzImuuY/aztTZ5NTmm7dsh+rREOR5p1Sp vPpIkmOIAjWX6Q844DD1wosvffjRx5iA/JCz99prrkbjwCTeoEHGeeed660nazDiKMB86aWXeIr4 bhDHTMWwxjYLysg0jRavfAVHJLnvB0fu6D1Y1DGLoc7btu249Te3t27bTlKr0CivSroXmlRHpbHR PxJ5oQxRiAgTis4UKz1s9WgFdoFBHNa4BvpY5+gMQpFee3ibd3ol3yXTMTgzJyKMxO+SVuPlhC1t gWyOI1XjmJzHIYARi0mQscjUWNVeAmNEyVkZVESpfWn0K1Tyl1dcrqEFi5X9Cu8da3BYoi/FikaO og3LID00QSFnEJgxsJPZf6Lo+0ivj4xcox8YeGH6yTvCwX4m7KsnDTUaOoY37zWMGb8cxMC84CLQ fPhOH9ERGLDzasiKnB5ks0fUYhO9DDb/r+degNYTjeXMnj1x+/UOAOZShHbj/vsmtzDbYPjbsSPp SDqgPX/6mWcZS7FaE+13MbPgEXwSfpbjsHzfvQxLX+wJ9hiKjhFpk5bAE73PwnaND10DhjyI0tg6 8izeOHaPqlLYFJ+sWVLChL5ESoCBskVsL4gMO2wrwubXTqC9x+qWE5IwxEshYrF5wo1nPNdy/Lhh mdgJ5e6Zck/3W9Men3F3rwrpaT9ljeo0dNHd7zw38GfrUej27Llz2dvBVLyuzm5+xhwizlNVtg68 D8wIFSpUZD1mR84cwS6wRIl0hDHEpkftwiry/Y4dVSpXfvqpJ5Fd4fbPS0tawE8++fTcc89lORnz ypjrfjVk2/btrD2U2bt3b97nV18ZvXjRIuQBLBXIYJhQ8CZzRDUbNyFYKlsW4VZFHsfQR3uFmAQV QFbW3O++yzq902lMVdWqVfXaW7hPdDyS+IIn1CWXDGLeIaTnOWefTTnsHS+/7DKSLiN7kC3C0qXL NmzcDM+rV7dOzZo1582du2PXLu07u3XtKkKGfujll19q17Ytr6IGEi2lgT26dGmbmUkdrrvuuvfe /wAz4V5n9mQSnD9//q9vuvnTKVOw7WCDCtfkFqiMNyES7JMNGa/09Bkz+HXTpk3NmzeLKebhHWZq wL7VGFLM+GYW9/5h2G/pGp8sCvY8ZsxrpUuXQVq5xU2LBIwkyQkdeFJ+xTsgKZ9QAojQYG+Y/EVy vKLfEUzC1zudlpnA9E3dCLKqujH5/vLKKwtqWxxve4vmejYnLLEMFYzPfE8EcEmUGXuTp0zdv/9/ /S+60JuGlXdk+vTpV199jV4Z3+2sE4haWUi8QbCAV/EFwBaBit4L1gl2QU5E+0ceSWBgFA1Qx+op CI95dNj3SFViEiNwF50FJyNhKF3G+Odf1AK+OnvHNmhfdfXVSLlYxcHfJLVjAxl2wKOOZB9IYDyE 8YjK4GSIVy8ffFmUtLzcwuzEBUxiVBIrjvXr130+eQoTJk+58IJ+RHJJAFXYIZMkk4Dhgqrb0Ouv q13bscwr2ANw/n7//cQfQaxOyVoXfKO6oJ7IsxYvXgw5pkCWnrBbQfMGcYFx6gRk+pFwg8zw0Liw PUjhxLvhpZOZr5qjQ5SufPkK+DDRNUH8Sb1NZkGHsRUUCIVazvHMzNLS9ky564z/t+WPLzw6pGnO lIeuH7J48CcvXNv054k7LmbGKGE7COFgSWYVMd12RufT5IQIRcNwGA0UmiZ+RQjMbuOue/6PhIyE OycFp2t7sfvNt8Y3bdoMuU5u7k9wPoTD55x7HuchT0wfrhTthPQTT2rR/FTs+hnojHKiBnw2eTJz CpY3WbNnI6iv36CBL5czT2QqnDZtOiZNfHdsbUqX+vKrr6tVrQZ9PPHE9NdeHUM5hFd4+NFHcb0s WbpsRkZDnHF27vi+dauWlSpXRj7xzjvv4kl68cCBNBOnBERlGOqi6GSjCWtct24DlAvJ3OzZs1dl Z1MZHsT7c92QazQt8sr9asivXnp5NPpKrIOgJlnfOS7roib8ynPZByuEd7/zL2BqQ5iRk7OX3bDB kwVYZl7cbhLMocsjAKk3YyPqEsR4t/36Fq7s2q3btUOG6DWGw82fP0/2sJ06deIyToKV1xwt/68c ViysOiqHAJJhE+HJtyPSr0HqIJEqxhnHCUtA/sHWhUXX+FoKJQRpn3z6GZulPr17lypdii2QAGHA a3PCIrFu3br09BObNAmTvJIxjHgsdJ3gLjTyEgbAD/DbZR+P5IbNkhWYhY5PZj+IF/MYkwC/aivL nMarzeuAfwC6fjhZ/Xp1EOEf6TjHJTw0Uy0nNbYRnsW7+nIXJmUoBAiVgugrdLREf7OYRZnBmBK5 jL3r1zO+gduxHc3s0CFKnpKwZRYxMxNosCVGMt+RozOZe2lNkCklrmvQOeKuEcWvkzcIGxtE+6ZY mNbtw4YFpIymOYbA6VU1pWGUhvFPcBWBZWZx9W8RXnyUzIznEmn2kWHDXlnqVKHtFaMe/NPAZo7T 3ZEjXmZmboQ0EApr2hdforBjqpIkCWY2b95cvTkMNaT0XM+oHTtuXP369bdt+x6ZmTOjVSi/e89e 3Dfbt2+HOJo90DP/+hf2NExn2rK3ad/+rrv/D5kZDn1Mgv969l/I88uXz6u461+5n6d36+aYSfmO v//9n/3792cTidhv1+7d6FJ1AfwJaqVtFv9mduqEmm9N9qprr/vV5k2bDxzYd/bZ57CVJI42+XM6 d+5MSB42x6KSTkAjlsyJE/YfOHjlVdcQSo3y2dqiX6PJrIWiZVyMfQkyKhgdeoozzujM8oY6j2BI 0NDSpUsylXAZLIqEd7xslACj/fqrL//wuzvQWeBTzfw+b/5CbywrHsS8f/DAgZtvuOH+f/7jtNNO c5WhjnUdEUbcYP3psD1WU4qFt+nFZpXFhkwCEtEj+sisFgU4HBkG2JyJ9smj1sjPsLBB9okaNK6o 3wVYtxQtikUXwS3R/M0yCS2T+pL8Th0zOyC6dp2RnWjPcoJhAwObZ5kcM+aVU5s2VXBBthyGzkLr ORNlDdMmij0Sg4e/BagbStFeCFttXnDtc3Qw7HEP5+WSzw1nkB7h/JSAeDhelCSa0rT2+zsCeRKY RyDlYpYzAjaGzfi3JzDkuCBeklf0zEzSX9gPm1tYy7+eDxSlLF5447oecqaokBz4wkvMRg3JPRBz P6nmYGAA+eM7b/pNN9zATI5LGWWidHp1zBj22KwO1w65NmZpPNcys7j6LnkvTpiZ0STNDt2792Bi Qo6ELAnSgKu/tCFcILENQcIOHcr9z7hxULe9OT9oCuNYsmTRrbfcTPIKDNQIuMWw27p1C04rRLi+ oP8AJxBau/ZQHORSUDQsJNBv7nC9nKBr7GMefOCBhx5+SDt79K3ydqlVuw7CuZYtm/c6s5ex1l+7 DgVBzu5duzp1Oo1r7hg+HEZ17/33f/b5FKzfENdldjwNftO8WTNqSDV4H9Zv2ES4/I4dO2hzjPLo +eee40u9evVr1q5TqVJlsk81zGhIxYw/AY/DRYBpukaNaoiz8W+vWaumVEIoRrG1Wr5syRuvj8Ut yyvf1oZbmkommtuH3eHkNmh+KkDxOPFCOKt3AEkvKZGV4VtaWSHEkD90vgg/zC3a6GMflkAA7iAD V2pfVJYilJDFvTk5fNeKVbCCuiD1SfVrwM3dG5zOMklbCBb61vgJfDFyWeRkXEMEY4wITOQaTrKv +NMf/8BLJwTE9flijAGi7Ly1I9KKUqhCiFTvHbYi8oLatHmLpjJeWHmXI+MvpFcsLGis4lRG1mxx oeoGZtsnSxVzcPL9DybBz6B6OKNg9RikzKJnZtSKuQ49IxsSIrgmoWRXesxQC5NIeLJrIvo3Zs04 n0r25r3XSLUDGhhYZhZk3KbANflnZuecc47rq7+/du2azZs5IbL05vCFlwclHQvD1i1bpK/kDAJ/ ZrRSJUt+9vlnuBEhmkK4BVViBbrTTRD0uz/+cfv3O8iu3b5Dh8aNGklUtnbt2p8O5TInymbL2M0Q CxvJE9aUXINk+/AJ6UR4uv5XQ2A/CvpPpAzm0PQTyGDzhfoDqUOXLmcsX74CRUDXLp2/nD6dPdjJ 5cqjFWrapBEyMK5B7jVv3jw8TWFXNapXN5tgbGZR29Wr51jvcmWH9m1lp4VeAx0BRniIiPiXZXLF iuUSzqG1RAIxO2suJ3/68cfqrpqjWtVTfJIkJwDbzFmt27bHio42Gvk5zmAP/OMfzKXtMzMJAg5W ACj3Ba91oFZWbSVDJwVoXFhtY4EMUApfvmKVGkUsEkYCcCGeJItAwoG/C6RiqVsIIxNPFN4O/Few BIpiRQTavFa0VJYAZvuOUQHLA94SnGGTwAUx+RYrhHIM5DMiVOrCHm/NERLj8Y0o2vEodzWbKXFI VC9hvK/CRrlpNgbRW3RMmFnyg8xsHNwi1qgv4V64haKOCb03uEWdZWbJPzwC1ZCO/Gb2bNRtQTwA fPYQkplhaL9li2NjHlMqI3N45EykD0KaglCKOQIzC5QyvXv04Cd2+WQsxp5mx/c7Fs6f+8c//793 33tf1tAvjX65cePGbdq0admimSYU+ZOrkUibJIt64MGHMNP/85/+oPMSVvEF2dKSpUvghUuWLMU+ A69yLGdZtBDa6Up2n5AerifkBJSLGQf3Auav7OzV3AjZghUhqMOfAAcoArcSngP3K1mcUA6sDgeI Pbv3NmnSiHuRHbKRRZKnBRLC1Pfsc3ft2YO9lK5nQfUBbkRuH3/4wcxvZhi7Mb2TIEMIAzaI0qeA XvlyJ/skYVpZjaQkUPcX9EUsUYRYI9pt4RHBgq5yMpYnsZn7sjgCjJYtWkSJq4ILM+PTJ62R1w6D AaNPBUaPybcYaatWrvTlyU5GdGyd8oeA6JSofNiSSKaHrS2zaMxoPpaZ5a8r8u5WKBCv+UFosQFF cSnEzI7nqBmBhg2MhOt8ez7CUgTMhYxCj9vRYcWU5MNXkBuhm8P266QSJeAZch1n04mi/a7/u7v/ gP6wLoQEDRtmQDLuGD7slRdf4C7Ma+BDK1euxGPA7PNuufVWfMrQliIiwmAZWsZ6RuFduvycthbF pyDA4x1/ckT0nU/vBB2UQ1OrVnl+f1zgOi5VwV4Hqc/Wbdv5V3oicGDZg8xNn/7lnLnzWCkbNWoE 7cAzAK0i3IiwYbv37KFdJU8qWatWTVgm05kMAiCyiAxZFJEI/uO+e1etWHZG505EbpM3wKLFS6BZ CkfpBBlyNaFQvdtuvw1aht2Y9LOEQoDYoZaS3J4bcVOAbkql4sUcs1Pkc770l4FGQMFdxCjCzsbS snwiqsTnFEJcDAZt9AUSU+7QV0950I2ff0xaxrPQdVpals+OS4nbTWRKX0BKU3kcuvmeWDQHrFl8 81JKYHJsK8nbqqgoUarxm9tuQwCBFRoWz2HjRdNfsLdj25C4nm6ZWVxw5V2MFUtAZoabj5OJskG9 mI+RsySX8de1oP8BHoOwatWqVdg9rFm3AQmcAlhfdtmlbPfZ6yuoEib/OHVzy1eeiPBQMXgPLpOM abGWrO+clMOtXfNnHQpBBJsxSxfBfmBX0DLSEsDVfHWuXq2aDKQ4L0lYjx7df3H54DZtWrOJxAij U6eOZpk8tWkTZYzesyfHdT7HGK4EIjEsTrxqAhZF5HkEtkFSrcfxKwyMB61Ymf3V104QV2SWxkif dmHIr2iEyAVhqFAubz3Rr8LnNmzc5AuOYFfWmCMwhS7AyAwngPyEHmDYsHXRpiWFGm6rWtgIMJ1y ML8xa4UlZ0yS1AFNOlp1EzUtYK0U1DpK/I6A5djLfAiweirNDHE9FS/al92LeCJEHkgh3Cwzy29n YRFPERCaSAXFlJbpRqYD9veOCX+lSnxHYEaEfV5+VITwHoQE/ftfyGV8gf0g+0EYhkOKeWif3r2Y SryhEZk1CPE6ddo0rjHJaryTAsIbaJA3NQpXEryHUI2DLh4Y2hzIHydr1azJF+4yN0LCdDGO5eYu ZGwIwFwZGO5yeVYm2J2EFiuy5ZVbcC+iL+zt4FiwNDSbxHH1+k4qcjTkDG7qM9zmXhrFjUFcdfLb 9/b+Y4dAvJbdvpoybNi6JKGJ9LFD1D45DwHmtyOZ4vagM/E6g+sKNgaMHIwdmWCxADbRj30IystK trn2KGwEWEcgZyyLfJRCyjwRUwRcf/4yYkRh16EAyz/O4pnFiRxq6c+mTsNkyhcLUcnLRU1kohga v66gggpSjvEwYgrgeyT1DUlFqA/KTbUSPwBFSsRujBmEM3zx3YtMDtFXcD/2SMby2P7DhEJDgBq8 meDwRQ++mjKpEVcCq/lIwSyAfdmyZd44Z+ZZsp+zno9xDnZ7uUXAIvAzAgonrv/RYygHsRcgZmaS gCnyLRvm3r3O9M2u3oDkuhEyx2b7r3fdaYEuVAQUCI0AN7NmzeJBJmKttTMrVNiPr8JR2RhCg8Qr ilUNgQMMLXOJYxWUkkwc0DL+mmQ1XviQ5wWnZdwYyUbq6quuJDFwlI5BWhacllEOtcIkK0qMMTw6 w9Iy7oXP8Rfd8PE1UGxrLQIWgYJDgCmLvTdTELa82K2yy2VDDtkyKk5m5ptuJG/fddAyyc8iCc8K rlK2pEAIZGZmch0SE+KW8wXVylPPPBPozqS5yApak6Yr8l0ReBtWX74PMjNoGeL3/BjlxKxaAnGD YpaZ8AVShlat6ljX2cMiYBGwCCSGANtg5GRMbig30S0g9UeKphx3KpCfmFeZXYnhwr9EPCZNRWLP sncVIAJIJRCYQciIZ4upjHERK8BHFHZRlpnFQJi4oKGJ9gq7VwqwfFKUkFrEl82mAMtPwqIkb7Mu kEnYNbZKFoFURADdAvIztr6yP5Prt/fALcCNdVyF7GHEMML4LBWbWZzqjFIFQpa61saWmcUYjQnb kmPClQwDHU4W6mWZDBWzdbAIWAQsAqmFABQNKRqZDkKrDW/D0Bb5GX6dONF7nbFSq422tsmAgGVm hdULCuJaqDrEwqq6LdciYBGwCFgEwiFA6B9kZpiI4LNp1JrmQnbCQ6//Vb16dfGpTyzmmUXdIgAC lpnZYWARsAhYBCwCFoFACCjqJBlQSPxF0rnNm7f4PjC2K395BeRs7dp1XPm3e+/Ha/7b2bPjDX4W qDb2omKKgGVmxbRjbbMsAhYBi4BFoKARQKFJDG3FPCsXcuA0gDaTRAJoNlu3dsJ6o98kRvcnn3xG zPCCrostr9giYJlZse1a2zCLgEXAImARKHAEcHhX6hQia/g+hNjgcXIRUJwg9JvXXHMVmdk+nzwl e/XqAq+MLbBYImCZWYxuVYJIe1gELAIWAYuARSA6AnIRILcehmioL00Sv65duxBugxR2aDkthhaB mAhYZhYDIjI2ki/JdxEZLQPmzYzZAfYCi4BFwCJgESg2CBCvh/08hmheZta+XTtC//OJkiil2CBg G5J/BCwzSwRDXHIsM0sEOHuPRcAiYBEo1gig36xZs4bUmvawCCSGgGVmieFm77IIWAQsAhYBi0B4 BJTgOD39qFSbFiyLQEAELDMLCFTEyzZt2lw/o0F+S7H3WwQsAhYBi0AxQoC0AZVCLGGKUftsUwoR AcvM8gvu+vXrunbrlt9S7P0WAYuARcAiYBGwCFgEbKRZOwYsAhYBi4BFwCJgEbAIJA8CVmYWrS/a d+zIzwr6bA+LgEXAImARsAhYBCwChY2AZWbRECaMoMvMShd2N9jyLQIWAYuARcAiYBGwCICAZWaF NQz2H9hfWEXbci0CFgGLgEXAImARKKYIWGZWWB2Lz6YN91xY4NpyLQIWAYuARcAiUEwRsMysEDu2 TJkyhVi6LdoiYBGwCFgELAIWgWKHgGVmxa5LbYMsAhYBi4BFwCJgEUhZBCwzi9Z1J5c9OWV71lbc ImARsAhYBCwCFoHUQ8Ays2h9VqduXX6uWLGC9yLy1PKvzZuZeoPd1tgiYBGwCFgELAJJj4BlZgl2 0YknWugShM7eZhGwCFgELAIWAYtAJAQsvbBjwyJgEbAIWAQsAhYBi0CyIGCZWbL0hK2HRcAiYBGw CFgELAIWAcvM8jsGdu7cmd8i7P0WAYuARcAiYBGwCFgEXAQsM8vvQPjog0mdOnXKbyn2fouARcAi YBGwCFgELAKWmUUfAzVr1ipTxibNtC+KRcAiYBGwCFgELAJFhICVmUUDulSpUjaOfxGNRPsYi4BF wCJgEbAIWASszMyOAYuARcAiYBGwCFgELALJg4CVmSVPX9iaWAQsAhYBi4BFwCJwvCNgmVlhjYBl y5bXqlWzsEqPs9wmGQ3jvCNJLy8eDSkerWCI2IYk1XtiuyOpuqPYvCDFZlwl2/CIUh/LzAqxs0qX st4DhQivLdoiYBGwCFgELALFD4HjnJntWT7h7gsyGrInaJIx4K4xszbnHtXFeAAUvy63LbIIWAQs AhYBi4BFIGkROL6Z2Z6sV/4yJ3PUh3NWL50++vSsux77aOV+b1edUrVq5UqVfJ138OCPnDnppJOS tlNtxSwCFgGLgEXAImARSFEEjmdmlrsna/LEtPZn9WlaLq1kze79zq68bsWGnIAdecIJJwS80l5m EbAIWAQsAhYBi4BFICACxzUz27dr575SlSqUdUEoUb1hx72Tvlv7U0Dk7GUWAYuARcAiYBGwCFgE ChqB45mZ7d+YvSI6niVLnpSenn7w6OPQoUPmrg0bNvB9//4DG0OOgu4pW55FwCJgEbAIWAQsAsUf gRMOHz5c/FsZvoU5WSMHD37l7HGzhmeW4Ir1E4f2v7fVCzP0n3t8+ulnrVu3Dr37wIEDZ515pjl/ 0aBBlStXCb1s0cIF386YcbzCa9ttEbAIWAQsAhaBJEJgxersJKpN5Kocz8zsp80Tftf9noZRmFlK dKGtpEXAImARsAhYBCwCxQaB41mbmV62UuWyB3bt2eeGyvhpa/bstMyG1Y3ArNj0sW2IRcAiYBGw CFgELAKpgsBxzcwqZPYekPbx2PGLc9IObp4+6ZOdLbu1qpoqPWfraRGwCFgELAIWAYtA8UPgeNZm 0ptEmn1k2LBXljod2/aKUQ/+aWCzcsWvk22LLAIWAYuARcAiYBFIEQSOc2aWIr1kq2kRsAhYBCwC FgGLwPGBwPGszTw+eti20iJgEbAIWAQsAhaB1EHAMrOwfRUjn2Zy9+/+5aOvcjOB6nP+qCwSG+Tm LJ9wV79W7plWF4wYm7X5YBK3Ijcn64kLOozMygv7G6HyOUsnjhiQ18x+I17N8mU9TYL25cwa1W+A i7+OlOuag5uzxh4ZNl1uHPnJ8hzXXSY88kk8xnI3Z40ZkZcht+UNoz5bri7JXT764p/flIan5Y24 JH79f0be+xZHqHDyviDeoeJJWJy79OUBmqPcT94MkMTjyswxzpve5cYJ6zWsIky2STyu8hriTrwZ t0/cfGTmDfuCJO+4iuuNTu5xRTwze/gR2D15RIv+I95esvfwgU2T7zu/wZWjl/0vdVDaNvnOnh0f nf3jUTXmZN/z73x72d5DhzZ9evd5HQa+tORQkjbp0N4lY25okdG4vWlD2Mof2j35r23O++uEZbsP H9ow+a/9G/d/aVlSNWnvgtHXn9G4Qb+Rs/ceQTrFuubQspcGNjjjhpcW0AANm/Mfnbn3cCTkk3aM /W/ZS1c2bnHz6CW7D+e90YNGzt5Jm5wh9PMwO9JLyfv6O+On8XmPz9576PDemSPPa9nmzsk06XD4 CifxC+JUuKXG0t7Zj5/foOeIydsc9J3z3vdFPZK048pMoDtnPzqocYMON7y9LlqFk3dcHWmIO6ga N7htwiatHmFfkCQeV/G90Uk9rqzMLFSykq98msdeUJP7/Zr5B/3hP/Ys+Gz8wcy+3ZuWS0+v2bn/ OTVXrtgUNEVokTbp4MYJd/QaVerG+y76+bHhK78j69MpaR17925aIS29dvcBvSotz94giU4SHLkb J9za44VKNw3r461MqnVNetMhb6/++rkhrXCL0bBZ+sr0ZT9FQD55x1jppkNeXbHomWubVUjLy5Cb 9erUVT+lHdyyeuW+jg1rHxUpJ5lf/6q97pu6YtJtmeXS08q16HlOg33vf7fip0gVTuIXpEKvexct fH94p3Jp6eXadju78lqlxcvdsnrBviYNa5c+6g1O3nF1RNC0fMK9j2XFmq+SeVwdkei/+diTi/d5 wA/7giTxuIrrjU7ucWWZWRhmltr5NHM2rVheuWX9Skc1bN+uLftOPqWCprzStRs2cef0JKAwIVVI r/+L1x8aUNc7MMNXfv/uzXtLnlKhrFtCidoNM/fNmrPCO60c09alZ/xi3D0D6pU6qhIp3jVOW+pX Lp8eAfnUGWNpaWXrVT45PS1nw4p1lVrVPzpSTm5KvP65m7955+PNza7pfmqJSBVO7hck78VQuKLM q85sVAIl4IbslZUb1qt6dEzJJB9XuWvffXRcrYf+eXPlI+96+Aon+7jK3fjhyMeq/fPRoZ6VI+wL kszjKp43OrnHlWVmoet37Hyax3TNj/Fwd9+Ztujpwa6tRp4Nx08bsz17umSufsmamZ0R7HmrGL7y TmTgnUnbkvSa7XsizDv6SO2uyVk87eMfzrmuT+Pc8MinyBjLzZn35ScHzhlyTsN0R4S5t8bCJwYc ZXyZ/K+/Y6146hlDX0276MYLifITocLJ/YI4b4ZjVdah+5CxaVddfcGpvCyuhKbmvGcvck3NjliO Jve4OrjxnZH3pt04YkBTQycjVDi5xxX88t7n0u4e3j9DW133CPuCJPO4iueNTu5xlWaZWdIu7olV zN13ppVpeetYEoStWDDilLG//9s7a5NFyZdYm4rJXancNbkbpzx0378zho/oXz+lp4zczZMfvvPN Bn8fflHtkmmOCPNAWqtb3+BNWf3lvae8NeSeDzamwKviKmdXz33/8q13Dbh74sZkduWJ+uqmN7t2 4sIVC8YN3vzAJX94ZyPvx4p1aWmZN/93/orVK+fcX+P1qx54N8lbt+erZ/6y7bbfnVc7pd+KtNw9 0168a+Pg4b63O+VekJSrcOT3I7UHVOEs2Y6yr3BKLoJS0yv0umfe6reGZboy6XIN23dM+/i9rB0o +4rg4YXzCEdTGVpyieoNOxr9QeE8uIBLTd2u2bP0lXtvn9l39EP9nRUoAvLhu6mAMcxfcTkLx4z4 26xzHn5woMsvf7Z2cv5p0qF12qTPZ22lHSnx+ldoNujyAWlT35+5O3yFU+UFKdfi4qvOcZGv9LMV HfZnTdpkOq1z+iNZ565dWS/+K+uG317S9CjDuAgVTuJlJWf2vx9Y9qu7Bzb10YGwL8iOysk78cbz RifxuHJmOcvMQuf64pRP07EJKFuzUtmylWqU/eH7Pfvd1rpydb/hc/7WvEK9O3zlS1esWf7g93tk WeaKpkNshwu1VvktPEW6xvGQv/qCN+o+/tJNjuG5c0RAPqnHmOshf9nV4+r89cXfYngeerhmQGUr VyxbIqXS6ZavUenkCBVOqRfEQf7oxcgxA6J1pdOSdlz9tGraKzOWPja4PbrXJpc8uXPn58N6OIFX wlc4eZeVn5ZNf3XxjCcHdUCJ3HzQ47vS3r3jjP6eWD96WcwLcnKKTLyx3uikHVcu3JaZhWFmqZxP E9/G4W1bDs/TceRsyt7Q7FeD2leo0PqsQWkTX/1waU6ubIfbdG9RPb/coqjuD1/5Kpl9e6WNf+Pt pXvScjdOnzhlV7vMlkmdjz4Fu4YoTZcNumP26S+89IdeNUse6e8IyCfvGCNK01OXn31n1ukPvXj3 WTWPzHmO/2zLLrdOkK4/Z2P2tmY3XHhahRLJ+/rnrp14S5e2t0xwVa4HN34+cWKDSy8+rWqECifv C3IU8rnrJ78xpR7Il1vvaV1uzsZ1G1rQuippSTuuSmQO+w49uPtZ8dZvKlfuM+qLb4dnlghf4fSk HVclMod/q1aszl4y/vZKaRc9MuOdYZnlIrwgVZN24o3vjU7acaV5NkljWh3jau1e9vZfz2+Q0dj5 KLBZ6hyHNs1+5c68yrcYOvLTZW7lD+1d9vYIJ1YNLWqpwGZJ3KQfN719myeeWYTK710y4c7+bosy GiuwWbIdm96+wRvPLMW6xg1clPcW6F3g48Y6Co980o4xNwyYryHXv72J2GazXzvyUpxxw6MfH3kp kvf1P7Rp5pgjY77N9Y9/ljfmI1Q4eV+Q8MhHaF3SjivPdPPj7JHtTTyzSBVO3nFlWvLj7Ec7/hzP LMILkmrjikB54Rb0pB5XNm9mUQl+7HMsAhYBi4BFwCJgEbAIxELAajNjIWR/twhYBCwCFgGLgEXA IlBUCFhmVlRI2+dYBCwCFgGLgEXAImARiIWAZWaxELK/WwQsAhYBi4BFwCJgESgqBCwzKyqk7XMs AhYBi4BFwCJgEbAIxELAMrNYCNnfLQIWAYuARcAiYBGwCBQVApaZFRXS9jkWAYuARcAiYBGwCFgE YiFgmVkshOzvFgGLQPIjkLMqa/keAsZmjTy/ydAJmwuswk584NNGTKHocMf2KSMuuWvK9gJ7mi3I ImARsAjYHAB2DFgELAIpj8CeKXedfumzC6FP5TKHf7DihYE1C6pJOXPH/WvXbdeeUSF8gVV7XnvR ggdez8pJgUToBQWJLcciYBEobASszKywEbblWwQsAoWMgJNjsTAesX/5m4/9u+O1Fx+dstr7pPTG XQaWfPOFT9dbalYYHWDLtAgcnwhYZnZ89rtttUWguCDwU9aofsM/TyOfdP8bJyw+os101Zr9ht81 tAt5mpv0u3/Kxuwpdw9wv9890dF7kqN5c9aYERdwJqNh26FPfK6T3iN3zVdvrzzjtCauwMzNid6v lVNCRpcbH/96s7hYeoOuF9f7+KXPV1pqVlwGlG2HReCYI2CZ2THvAlsBi4BFIB8IkFh60sg+aeST fue5gfWOKmjxqhOvemvZgnG/SXvrH9c/OH/Ayyuc72/e9XLWnrT9y1/5/eB/fD/4o7krVn35eJ1P b7z9Zb9ScuviL+c27taqqkvMlr/1+zsndnwqa/XKOeN/ueHRPz81TeZlpRu27VhpbtairT/low32 VouARcAi8DMClpnZ0WARsAgUUwTaXXxlz9rp5Vr0PKfmyjrnXpZZKc353mDf5l37HHnYd5VuuOHK ZhXS0mv3umP4FWvefPvbHV4gftqYnZVWpVL5Eu7JMpVqV9g3+9O3352T0/6291dPvbeXy9jS0krU bpiZtiJ74/5iCqJtlkXAIlDUCFhmVtSI2+dZBCwCRYRAtUrlI81wuT/sXLtv12OXNHe1mU3aXvf6 vr1bdkVmV+n1L7r7qVGXp4277ZLujSJoP4uoVfYxFgGLQDFHwDKzYt7BtnkWAYtAGATST65cv3Kb //tw2ersFXmfrOcG1o2CVXrNzAuH3Pc+2sxPnvtV2tgbH/i84GJz2C6yCFgELAI/I2CZmR0NFgGL QIojULZSjbJxNsGx3G85/42x7zqG/wc3T7n/goyrXl5+lMzsKDVlzqxR/TIvuPuzzbnp5WrVrpyW VrZmJT3TVXo2aVi7dJw1sJdbBCwCFoHwCFhmZkeGRcAikOIIlDv1rMvqfT7s3ItHLwnsIlm66TUP j/vloefObtcko1n3W1ef99+Hr/ZFx6jeolu7zVPnbXDKLNdx6ON/zZx5O6rMJq0Hj6t52+jfdHV9 Nvdnz5u9q11my+oyR7OHRcAiYBHILwInHD58OL9l2PstAhYBi0AxRGD/8tFDL/nm0g+fGlg7or3a 0pcvvvGb68Y8ObC+3eYWwyFgm2QROBYI2MnkWKBun2kRsAikAAKlmw4aOmDqm5+sjOgZkLvy6wkH Lx3at66dSVOgP20VLQIpgoCdT1Kko2w1LQIWgaJHoELXW/5e7YmXZ0TKmznt5bdr3TSgfTk7kRZ9 39gnWgSKLQJWm1lsu9Y2zCJgEbAIWAQsAhaBlEPAbvVSrstshS0CFgGLgEXAImARKLYIWGZWbLvW NswiYBGwCFgELAIWgZRDwDKzlOsyW2GLgEXAImARsAhYBIotApaZFduutQ2zCFgELAIWAYuARSDl ELDMLOW6zFbYImARsAhYBCwCFoFii4BlZsW2a23DLAIWAYuARcAiYBFIOQQsM0u5LrMVtghYBCwC FgGLgEWg2CJgmVmx7VrbMIuARcAiYBGwCFgEUg4By8xSrstshS0CFgGLgEXAImARKLYIWGZWbLvW NswiYBGwCFgELAIWgZRDwDKzlOsyW2GLgEXAImARsAhYBIotApaZFduutQ2zCFgELAIWAYuARSDl ELDMLOW6zFbYImARsAhYBCwCFoFii4BlZsW2a23DLAIWAYuARcAiYBFIOQQsM0u5LrMVtghYBCwC FgGLgEWg2CJgmVmx7VrbMIuARcAiYBGwCFgEUg4By8xSrstshS0CFgGLgEXAImARKLYIWGZWbLvW NswiYBGwCFgELAIWgZRDwDKzlOsyW2GLgEXAImARsAhYBIotApaZFduutQ2zCFgELAIWAYuARSDl ELDMLOW6zFbYImARsAhYBCwCFoFii4BlZsW2a23DLAIWAYuARcAiYBFIOQQsM0u5LrMVtghYBCwC FgGLgEWg2CJgmVmx7VrbMIuARcAiYBGwCFgEUg6B/w/nAWhutA9u/AAAAABJRU5ErkJgglBLAwQU AAYACAAAACEAaCTRyB0CAAADBQAAEQAAAHBwdC9wcmVzUHJvcHMueG1srJTdatswGIbPB7uH4HPF kiz/xMQpki3DYCtjdBeg2XJiZltGUpqOsXuf4jhZQwmUEZ98Evp+nve10Prhpe8Wz1KbVg2Zh5bQ W8ihUnU7bDPv+1MJEm9hrBhq0alBZt4vabyHzccP6zEdtTRysMK60q964RoNJhWZt7N2TH3fVDvZ C7NUoxzcWaN0L6zb6q1fa3FwA/rOxxBGfi/awZvr9XvqVdO0lSxUte8dwKmJlt1EYnbtaM7dxvd0 e63jCmnjRJqdOjhxx/AotJ6SnE/T2VzoT3ldTbtuWroJeac3a+ESjHXLxbPoMk/L2nPn/r+EMZUv 9rOx82qx123m/eY5imJWFCBBCQaEshhQnueAMwIDHlDGgvjPcT4iaSeM1McJs1xE3gju20oroxq7 rFQ/O+eP6iD1qNrJPATPckVq9PbHhbgsoftO0K+GTRoc7zU2LguGIxgDFCcEEM4ZYPEqATFnYRJE nBcJPWMf3fwi61bkVnfmLvAnYjQ7PNGd4uSvf/6RN02Po5yvCAURDHJAEMGArZyEqEBBDN0Uii+m 162phK4/9WIred3aQlhxRw2z4W8dLgJEYYQpcLZSQAK8AvR4TxijSRhFGIYIXhhlI/adnRiLsb0j HsY3Acsi5CWlBYA854CEAQerJECARAwHjLsQkBNgmFY7oe2TFtVP9xB8kw1zl6u+YIb/g4lvunh9 E67frc1fAAAA//8DAFBLAwQUAAYACAAAACEA2P2Nj6wAAAC2AAAAEwAAAHBwdC90YWJsZVN0eWxl cy54bWwMzEkOgjAYQOG9iXdo/n0tQ1EkFMIgK3fqASqUIelAaKMS491l+fKSL80/SqKXWOxkNAP/ 4AESujXdpAcGj3uDY0DWcd1xabRgsAoLebbfpTxxT3lzqxRX69CmaJtwBqNzc0KIbUehuD2YWejt 9WZR3G25DKRb+HvTlSSB5x2J4pMG1ImewTeqgiCitMCny+WIaUgDXHo0xnFU1tW5qf0qLH5Asj8A AAD//wMAUEsDBBQABgAIAAAAIQD1IsKspwEAAPoDAAARAAAAcHB0L3ZpZXdQcm9wcy54bWyUU0tr 4zAQvhf6H4TurewkmxgTpxTKwkJhF5L2LiTF0SJLQiPn0V+/40cSh/qQvXlG33yPkbV8OVaG7FUA 7WxB0+eEEmWFk9qWBf3Y/HzKKIHIreTGWVXQkwL6snp8WPp8r9XhTyBIYCHnBd3F6HPGQOxUxeHZ eWXxbOtCxSOWoWQy8AMSV4ZNkmTOKq4t7efDPfNuu9VCvTlRV8rGjiQowyOah532cGbz97D5oABp 2ukbSysMZxug+ewiws4dftfRaKt+CZQqaNKCkCC6oOS72kYCXwWdzn5kuEFeR/cq/9YQGyQbQjfO t8hsPkvHkOxWuhkFo6W6lmJt5KDqLe55WAtu8IbS1ho0xWrJczgSvNhsSonEs6QVwe7pe5ddpnzu gi61JceCLhaUnAo6myx6TK/YoMoanb1DvHwTnMOV4vZd+KLEO1zVJJ33O+ggXTPLzppXkoZ8EK7x cxvddXfwLf1I4Olo4NvueOCkzXsGXBTYiLx1UcFGHeM9jtJOemhn0PpfLyPSZdBy7bnAF0ZEc3Nz /KUpEafzZ8fSPdvVPwAAAP//AwBQSwMEFAAGAAgAAAAhAD4Mf9yTAQAA1wIAABEACAFkb2NQcm9w cy9jb3JlLnhtbCCiBAEooAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAISSXU/bMBSG7yfx HyxfkzpOKZqsNEiMdSCt0kYzQLs7ck6LhT8i22npfv2c0AbQkHbpnOd9fHKOy4tno8kWfVDOzimf 5JSgla5RdjOnv+pF9pmSEME2oJ3FOd1joBfVyadStkI6jz+8a9FHhYEkkw1CtnP6GGMrGAvyEQ2E SSJsKq6dNxDT0W9YC/IJNsiKPD9nBiM0EIH1wqwdjfSgbOSobDuvB0EjGWo0aGNgfMLZKxvRm/Bh YKi8IY2K+xY/RI/FkX4OagR3u91kNx3Q1D9nD8vvq+FXM2X7WUmkVdlIEVXUWN18rRfk1kiI7OH2 Ujv5RO6/CXKnGnTkZwc6XUSWGL2SJRtTfV56hOh8tXRd0q7hlFxDCBZg4I7VfhMaQlympa0VNpf7 auUM7OFPp7tTckdWXlm1hZBi/6J92uNW9fuvOC8GZvxQHsb5chk2JI1BvAztWLmffrmqF7Qqcj7N eJ7lZzU/F7OZKIrffZ/v8q9Cc2jhv0ae5bO6yAXn4ix/YzwKqqHj90+x+gsAAP//AwBQSwMEFAAG AAgAAAAhAJqaZbe8AwAArAkAABAACAFkb2NQcm9wcy9hcHAueG1sIKIEASigAAEAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAnFbNbts4EL4vsO8w0Ck52LLdJu0atIrE+TNQOU6kJGdGGtvcyKRA Una8p32HfcN9kh1JsSy3bItsYCBDzsf5+WY4IvvyuspgjdoIJUdev9vzAGWiUiEXI+8hvup89sBY LlOeKYkjb4vG+xL8/hubaZWjtgINkAlpRt7S2nzo+yZZ4oqbLqklaeZKr7ilpV74aj4XCV6opFih tP6g1zv18dWiTDHt5I1Br7Y4XNv/azRVSRmfeYy3OdkLWKwsz2KxwqB/+uEP5u/X7Enp1AT9k88D 5tcyO8vzTCTcEidBKBKtjJpbuK0cwUxtUM+UkJb5bSAxgoYiqFZXVYDBreyYRCNKiJZqA0cfhx+O me8AshnXfKF5vjTBYHBCmP2aRZlIkfb7zH8T2VRZ+tdjfi2wG5GmKN+0tH2wZmE4zkReKXYiixKe 4ZhICuY8M0immw12g7xsgBkXmpBrO1xjYpUGI/6iFjj14JkbLKkdeWuuBZfWq2H1opKz3FgdXClp DTwYTJnfbFZiG9uWxcfgpAKQ8FNgbSumvsB32O6/w3ZFH8TCZmje4aKsk8tHtaiIJPmQ4trH7Zyq bh2MDz61Ka+iqAmvAwpR6K2Ch0k7yEY6I/eZUzPmmXjW4ge6Fak4hNwunYAnir7M4ICZRnq7K99V p5Eml/EV3IfjsxierofwSEwruCsoIruFEK0WiYELYZLClKMJ6MbD5ZpnRXVrYKyFRYrPaZuu/nOG K4joimE5FJyooxuxWMJXXGN2DFGxWnG9BTVve4kSlFQ91QrEgJBN3O7Un/+k2ok1Nmn8+/c/cC4W MBOJLTRSsrhxHq1ZGCs5R01TmNiFO3VZnZ/ED50Yrrv93qeem9AWwEmnu8yUEWF1la8ByrkwWLLw S397Stw1aKYktMedE3vOk5eFVoU8GBLfsLJL5rG8TlV1Nj5M0W6UfikpS0WdQqN3s7TKeWLLBOMl eVws88K2TNLPUYPWlHd3+2GE+wYawkRardIi+WFAh0cnxhT0QXUzGk3v4WiG/AUisZA861jVmSpB 9bovvR07T4VRJ4omodvi5ePdT3oJouv+AKaqc481Fwj73o6qNwF9Ld2Wu/1B77X7+sZmybrQ5U2E UKXonkbVmQEc4WsXZt3p+VkYuVP6FfBgzn4zWceKQpHbgOqCGVVZ56rufebvVOyrkC/mIY/VBY2P 3dfxcJNFS64xpUdH8/VsNtgNTWmdlfjxkssFpjvM94rypfFYP77oBdLt0V/1qNjtlW+F3aso+A8A AP//AwBQSwMECgAAAAAAAAAhACVdbGV1HQAAdR0AABQAAABwcHQvbWVkaWEvaW1hZ2U4LnBuZ4lQ TkcNChoKAAAADUlIRFIAAAJpAAAAmAgDAAAB//m+4wAAAAFzUkdCAK7OHOkAAAAEZ0FNQQAAsY8L /GEFAAAAY1BMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABmaHTe AAAAIHRSTlMACBAYhyCPKJcwnzinQK9It1C/WMdgz2jXcN9454Dv90qvHq8AAAAJcEhZcwAAFxEA ABcRAcom8z8AABxvSURBVHhe7Z2HYuo6DIYZLZSepoNCC5Th93/Ko1+SZwaB0NISf/eeZpnEcWRZ kh1ncPs873XlfMzAGPori6HuvAAbWdBJO4Bf678R7+gG3R7nB39esDIo+G83dpS5mazy2YX7yWBN 2/gv2k+rw2DTs9RlQJhuqFv2lEp0qs/B4NXuiA4IftcH/mBzTot/2FDM4B6LiaxTCv4N3R1WI/QQ w2sjWtAKn9qCA1tZlaOyEv7WUbHrCCIS+B0XP63oNeiJnHG6wYZPgx+DVxTOq72hM07nWeMPn/zQ 7UQC3yj9L/+9697L0TGLF7jDnhLquMNCVzqwInGDpNC/RcfHgl8bq9QPsugM7ley1SVz7rek0i92 Nmh0qwkcqG7U4j7KuofWbZEkl27KCc5GSh9PJ63EOKRLM5gv7Fm0+Cthlb0avL0iUZyQzyb/yx/R +HQbqssF2sXKXzGsuQ0l4985zGCsS/2DNqQ5b22Z04nkbDgvVQAskHssT2bqfsWneaMFzCUuqC7w SQwaccme/OuEnkBMgE5n65yVRujhZPrK6E5XypDUfa/gHQEX31FrgqUqIquP2EN6e+P1a2DLhY3+ TcEOhyxU5w6epvh7BQpttGwWB0YVUOCKuGM/jFx3uJ3PaW1OZrhfOP5VZK6QEiZxQJPKG/hDu2tK WRqzUdTWy3OppfmoUpEIu+ifHjEPejtop3BrKf8kJf3Z6U+YcL3ER+NRS0Ui2sV7DdcXtLgjFBh2 q22yE6yJgNQz/OGfwfUkkNwt5IDHtGo9jDyQgOA8tIqCkucTXILtF2P00ctu+hM52pqc/m23C3Lq ppyfF03cLm+6dOzUeTarDR2bGiqcR1iHyOC+lJqYUy7pUiPzbN1i8IkaZ/gp4zbgKVIi2uRTPFad qESrRJ14hRuLy1Dhvc30gvayfLdbQzr4QEWCDfxTRWIT/TD2smYGw5K3zIr/FoU9NrmE3XoOkgFS OyQrH2qTYp/NGBGs/iwLroNyec4TqSK0rjOp8br3SjwfCVKyArsajQVzxVLLZDKNmAnVz87RgU7U K4gd2TYVNuKPgWjkwsAYF2PLLSTT18wavBjKwxp5kj260KCc3fp57JXhqMRZU6tjzT0D18BmDUsj kU67MOZLNq/DVh21ufodYrLZBXOtrOl1Xx5k6XYE+bm/Ut70sj4yX85atP5zfMplzZxdefp/RTpW F47rZM3E1a+QIIAuLKZkb7oYwxTe8KygBBposA5oioSfnO/MmPgyCa0KZF/uI5Iidgv84TD+vTPd I76MdmaMw6Pz7llDWxqDMId5J9dXNtGpwc4ztkuJ0SOyQS725AyHRy+QtXJBGHo+PhujoRnLqt+X wFlLjyJrkw1766YwJnEsK89TopSKdhxQqVd8ZI/nxGtmsNS0Sw3MyFaQNZY2UaLIGs40GZhpefhB ul1NKZXbwTqP/knWPtIOI0dSaoZ71ilrT+SyUROJuIf7pVY6TVkLHy5fz+/AGv0zI4QiaOVVLRUE YvwJah8onia33hqcwR+2heKU9aSpHmU4gdosA2pJ5HHImavYoIZOqIjCw/aBfrouGT0BJwpT1pOm 0sIw5o6dfen90xhXGBJ2rDiWNTBuzAmD07yaB7rDhRYw/tEmp4qS1vHeKlUXbLb0H1+P/wDONCsw KvYDSl71AxHf63eAC/h/kEta2qDoFnsP9rD2eOAfFpL2O3kO4qTadq6s6Y+QmrGqAOgCBKs/iG1q XnH9dzxEKT/KT7GIYsU/j14WKlM1OhfgYPKhoXTi6lkTQw1sn6Ls7Nhq+HneRdg+Bk9Wn5CWofLC qsv2leALP7JrACl7VpuK/knbfcWsSZvTwBVjpscK5WqFRi1zc6/xFXPGHQf1POsyk8lkMj1hSh5B 3xm5YSCt4CjEloN85pq9GNfFuEhisXuxAc9CQMdKaQuoL3x/WoHfDAV7lAiQcDkYjpjQ8jAXKrYI GWAFDIJAfePLxOPwRjbqFIVLk+ApYjAO/UGfmKikOT4kdtpUahIenvuuqr4V2zTsMQQHiYijKBg9 HG8lDLVS94ZETO6SLrLBOEoQb3mepAu5L6yi0OtYK2eIiVLEW55e1dFhdLd24DLXUR5mgg1exltl HivK+2ZZeEsNZqsFW8+0fJjr6I1kq4I+CZtJrI7zMalCrOCTH4kxezWkl8bM0d+Icd33tBQ3g8xG 6Zy1ydHr1pKVdNsze/NEJ7c2e8rh/F4hyqqudYYyqGsNaBotB926kwW6EqV47OsIPnmrpmZs9oVx pTaTfqzahzk/v9QeLldqJDe6Vs+D6gPDXYSz+BdmrUr22e5+sIajcb1Mx9i4stCTT+uy5Uut8O+J DAYvuzUcR3lToobScIkOtCg1TXLQ0iDx9PUJPf1z6di2I5Q0+dY88VJAjQ5IfGBXaq4WHSm1HasF Tb3kjE1CXV+F9L9fhrrsed71Tm0FROV0v5ICIyH8sns0edXAgVpcqVFdlZVAK1J18OUvpUbX4C1x wXXD5amG8GFLHk9EfwuM9SlqqUphK/YLL1BHnQkjLZW0FW0p1VC3BGHbIKW2UbkSYXtgF2diC+XN ekJLM98G2bisrB0ptbjpebOvEclOjZs8GafVNPlb9Cu6Ax0ZpSTXDEuNL/Bep4Ok1OxD+5Trb5cF xmQzGCIipcOv6o+c4qBT/6BeCwXbJdfH/89WRC+/NrlJowtN2CgXwVVzVOsgb+T0CxZtbTNSO0xL zR7kDWLjV7tS21jVQm2X13DfwWy3q7PWQoqdTFGEMn5+nrMFKds1pUZ2pK4pH6Gy8uvjFdY3LA9Y U/iYQopd1/4sCytqOuQnKTX/LnvpVh99YdCaVGUyH3mpOsMmcGqb2cabfxJWCiPVdTs08HO8WE+y NHkK2/KSAWke9tJUz4zz7OPqvqwOeIvl2g++EmEjA2DNojgx1A5rA0uJgrKVtnKbDOlIDP0bJ7lZ yBF2bXWpvPp6SwYBo5uW0Ha9fZaR7TFCNaMSMSRJcU2kwhID4MAV/D7xieM43e0TxWH0tXb27hJF tUNJErZ4ppHJXh+RuVFCKeFWUt77sY7cP5Ux3VTjUOxlx9dp3uIN4PuX9qSurPnudNdSggy6n9eZ 0M4Yqxz2iq6W1tCVda+otsDa8i+aKrRHrMMm4USs+9FHdufKS4/LLJPJZDKZTCaT6Tf9fLsgJpwr 5ygzGW+GAOSwZ1HIkI+Tgh1LlJe8jfDet9itp938wQkSq3zua6mx5DDTtRsSMuKXpYrCcCd+vMVo 4Len4aGhdsAvDemqvR3DWUh0W4cHxVtgYUu6jyFvW8+wRF/KRPujCsysBrCRbBFLN4Zrd7FB0H+I te2009C3EcFz8z8z8Ra65cNxb7rSI9J71rF/jYU2C4csvp0ypOw2mKRDNnXoUFOhjYL2gOifqJXu WHc0FRoXq5/Dbt2rMR3Euxu3qnwYecmTiul+ubY6PtqitoKRLaJvopbe7z/7GrY1MqSg4q2UQ78s 3HTgT7r9FI1xibc873ZYWz94SmpnqaIdolFZ8ZanX/UzEZ3yzcdDm0sDnZVo5NbNExfCplz77NAr Id7yPPZpJIy+u6FUOUTrqJjiLU/8fsqNE79CqupcpoHR0fEf4o/HW2X6VGjRYNGtOOUrKQD5a6dO iLfKmB4NugoLgawNhVtIWs53W1t9460y2/4oteY3AQofkSTirRRyEnTt5rncrb4cP9OUQ7/R24fF zj4IHOKVGa1wBEGSNzynKkIN/VL//homhdDV07ng603Hc2Hf4NA2BeOj9xOMkcb6ly7xdp9MsuCS t3c2QoPo3Wx3h1qdUa+cjxO/k9mJFrlwU5HJOHp5jfhFJ5yg0pHdrqdGk+/bhtOHBzqHrlPZ4y3i Q+1EAp0K7fgkGy0pvZhfZi63r7beRCVOQ3Om0FbGfc5Rk9uJy47xbHbBW1+yVm8+tjxpFS3utC0t 5tRQs0QrUfxqzMzY+3CT3dnMtbw/KBqX1MYdat8ab3nSKi7oMprjtUgzqm/Xxm+jPRpbl1zha/JT XsBySe+0f9FocLCESzne7XwVRsN0tPU5IT/HOP5+vK1nOvnGPLo41U15VfXeqjSbfH5CY+VOudJH uKmZDc6lJK1O6kKeEz0e1PBAM1Zy5PApHC+0L0nhXn6nzPnwABQcH3Df6PkSYUnaOb4nj+61uB12 MrP4yQTofsnCXlS77Kv7heWSbvbxOQUka/QotSOHSs/XUOzk+ulOw8nHT4kTonNNWHSvxf3YFhoZ ZLwEw12gRGX/k9gzItRq6R+OKOd/wSm7crzQRDbC+kJbmkGegpLrp1ONknrrhbEFLg+20B6DXNHZ dI2QddtOsG7XWOHTEeUcRcckkyeivyVqK4KjIsGHraEyAgcpXHvOyWtbv2rcJawBerTQeIOWJPIq Qisn/r6RDFuGHy20SrG2oibqjuqn6yqU5C28sxCX+qB+xCKYElomaFIkpU0vgilP0J0DXwRnDkam 2BHq4rDncLR6PleJvf2Ij2jidxIQexZNnp5VH5ZF91rcjldVhbXWu6S06cUbIaX2RN64qFzvnuKT KUPfhP9oQxAb0qrCH8SOmmktJb8zMW3T+qkNgEX3WlwerDjUZkoO2C5JsVf/mdnuUy+3+KS6I6vs ufgTtfVQ2kD3q2s1RNcaaintZWnli9x3Ww6afH+Sd+yvoWu1AUA5/iFqQe3n5PlooX3y7kfNMOEv 0pljdlo8N9FaxN32l9pjpMJU+9rkgTt5HKre9tYm3Eqv4lkMAvS0cr09S7UNHVi00GSx9PUkNC87 ciSEg0BPoF6oMvNYZ/HZybFSV8YWkUtOQvBRd+MxEzodmEsAgLTa7hBphIDxVt/FJ9N5TsmkvZTf O6MoKTQ3xcEFI/u+YrVj5gOQ30RjpNmzdK8I35ndnOe4kkephSaqY+lLqjzvEGFrNqYcVHcXMxdR ywZR5s8QClFraOLRV38Qa2K8qSLUQvviAuEvVgrlIACVhZVDWtXBemz7WZPpVZZUbyIpvWBNvxJO e2qTpoV2z3rCGW3QgOnsm+bVNleQU1k7iOmi85g8qsYPyh64S/5Z7B18qaVm523h/UG1eirdqrlX PTg0zoTXpX5KREMCZArxQrmkG3slPs2ealYhs8OPdhAaNgRHZrV04WSi5BKQ46y7zNaOz0j6+arr 4Q3M20diQQW1qYhx/NtFwzXSWyXHWXZx6WkMJy00XYmwUb8+kMa7qW5KoVCz60rHKTcgwln+ZnSP RninDi1tcqGQEvQ+FlmAfjjRs1kURXmYWmnHDXMXe4xwnKHzRlRIO+8XUfkFPiGTmP+X9GJ/P/HN wvp9Jt8D5WVbSYbcDrXK5AdpZZw0O1E3Rlxo2FqbLw4+x63kXhNqQCB1mdSU6wkf3hEl0ATOzDOH U+PitJvr6ghQkvjGmUYRJ66DpLHob2rC6aafz14WwjQIB/eBsGzks2QyXkBHUgwW2hxoOmujuO5J ZtWr2om7d32P6pVKpMlGLbSwHrVZ0M0gZg7Cku8FQf0M7p1sCO0nYp/+Ve0P/VTLe2zLzupGTNws 8vVTAPtLV0mjqWkmL4/txeBAwFXhbeXQN0GjsqoZI9KaYY/8TstZ80uE9HK+hCP9SMcYa0y9X7wl HX4nUtu3eNug4/1sgm9D94sObcF9/6ZKsJyvliLftWecW8fee1xmmUwmk8lkMplMJpPJZDKZTCaT ydwWo+XzlkdrzHvbzZRRRh+1kzV1Bh9yeMWgvhFe4paPO2AQ+Otff10sczIkZ8HgxUtDkmZV2SOJ mh0Vekfyl2WtXzy1mHOrkom+3F1F5TGMgXTjj4ck31bDZXrA9GAO0ejz6ReJgPIYTSvAw2Yj/ND/ pmOWGe0N3zZ7M2bbbt6CzN+H1MwmaMR2kJHDHV6HGPF6KDBlafLjHZuOKbhULFj3h9qZezK3xXCV tJyQLv8+HM+A4i04kqbaAchNx5h3Y/alN3dw/XhmmcxNMtz6GZyF5XwevNvPispLYgdJezlUyBmg ljp6Jypzi4yp8Wr0BfCmuUwhy5wtadN9YgsGQNSyD3rbQNCam641JM1PmnSupD37KZpmpWnyoFdP m0U188fAI25+rQS+YuIRnCFp4z2m0BPofOmsCIMRCXyfZpPoH3js1V88VOAdRA0bSdN+QjY887GO FFH9MXw9IqD8Pgrm7+vjq4p9AVHUkn4JGG+MOSQxsbft6uGtKF52r/j6izmEgtp07AhPlP77+igy 1wWKpKntxNNvfB0OnZh18+w3HauAP5pzgmRm/hJQPNEEqRFwBZoUHniFONWcoulYBeiEz6babYIJ jWuf7eyoUwreIE014th0rArMA5Pbz1sEU6ZXdEwKmJq1RXfkC85RM9ao6VgVUGpHPj6Q+ZOgdatR aeREhn2RZENZiVzuws8ksUtxsDuajrUBSu0EHZj5I3CgrHrED2mXfSAiBdlztqucdJ18XJThBtKd o+lYGz7xiyhskrkFHuixVjdW3CuQoIekv/1Z5O4NHkVgyzUdawP3e+VhHTcHogqVE1Wi6Uzxof/p l/8cymYSjwpqOtYGtOe5q/3WgP392+YoS7pYMxdk/cBdgVeYLQza57fNuojPOpz0kbpKgk9QgcUu /goHMb6zXWakex+X9rgdZhw8DXmdKxztkpx9FX8n5XJ8VX+ub/qkHXuLk0xggt8VuobHhcv+uvGH EIDa/vv2sMEIfT0q3lmmomcGn3gvgbsXPLbAWBUxOoSiid/HUT4+O+8avcHYNZvT7YRmxuicqapz 8LKekbspsnXqJN34zc9LGhtjv+5dES7h7s8Nr185gcVLMcF3DTBMKhiPSbIVFD79EKGWsAbSr5Mn jrP783GLXzfu7hxeuG6AkqRBK9lLob6cONAKVeTnJY0/QX+FRrsZfmuhez8B/F7vw3K4xVl/0GKB KUiCEmiwPSk4iJovGPjDiejHZ+f0Ve3cedDZHihD3AucSgVE3O9DBTrNfbqOpHGn5K/r0OZuhc5f boCzc/DVHeMInHBxFLHuvumHD5wH336SJCYjkpOzs6RdfrwTzppIBTdDSbzypFfBryNpqJi/b0px 7iDrHOdAbQ+ePdoc7/s0xfnohySRSO/6TsglSDrskrPHGvNiVEhalC9Se7jwSZZ2SdIKsQeJj+QO XnCXIedbz91+/W1cIltc930XB38Y2RtSbDZU6wLSZpBy9oA1ASmwRPD57L495c0aQZtwXKGOZtuu LGkc1w71PQTnpOhBLGlDNGsyg8AUV1t4dYlmWmx4dpnWRVGcXZdYefxSSeuqalH33Rs2U1TObWhp 4WPpxKrchFLRs4HGvWIiqvTrxJjF2V17+g/We23TiSdbT93YBqEsafzIwl18+qAZP0okaf8gQ65c 4NdsrcmAa1tjuaHPsh2c7V84Ggw32VHS+NY+qRoW7xI2W6T1ccRGavokUaga8MBxLhzSWEmHHZ99 RSoJSzp6191TrqQsaaw9w13QPBWOOuuqysBLKGnscgSVCBVIb5XtWifBqKn+zbjT4Wz/unCahHw6 hjng/gur+eu65lwFK7ZDJIP05DTsg/dnuP2k8k/EEWdfkRR/4TlXv716EcqSNqU9kZPLkqbrAby7 cih1KGlcSoFCZF9MboclzVkfkNsu0TCWtCrfHPt/Br1gDKuKTpLGlbVFL9s9+0RBQthBtv3grrpx tEsIVQEe6bf10pYljc3H0A+GGqowgFhmKpv0UNKwHmaedbWMxR+iZOzveX9Sodbb5LLTB2i+w2PV g2NJ66IUvwkO0neSNLY4dL0ZqLWgCHbhFpyyOc6VhFxwdtue8hNJVF7EZT0C1vehqkKSyojQuKgO 44SShoIO5SWQtMEIJbPgKob73YSVreAnFNldpAN5Dh9SkxVKnlXxTdppOEO7kUcIEwc6guTGN6ai GammJs8MZ3dv4cCrTcy4CFbQtZzqEaRONNvqzeKaEEoax8iDMYisCZ3lz+dmtoEtN3wgUf98oYIK JefNKUcEfEoZYhHGB3B/Gdyk6fpZcLtX45At4sYGtdNHO5J4BhsrlVFbf3YuxO49GpVA0hLziHWo t3jQeNe6vZWEkpb6F6LEhdk+FXLlnrUbnSaQNOgsa4NQzSw3J7jQL41ydMoWnlCNp/OPDgV2GZ5U 3KkUFa8Y1ol/n5y9xlK6BNCpqR2NLlt3B1AgpwkaS5qT3jjijIplJzaDP9TUexRLGsYY2OYVirJk +uA6TZr/OnB08vyPgsp911kFkAsb8hhzKCAwQCBYkdHDGiQx+NOzs2L4hqlrRkvW7aX4zBBq+APP EvmPPefjoKYFo/ZJc9HJuAjuo7aaVbVjO39M/KtY0iC+usoqv6TjkaBjOOEbqHWJ24ECE6rsl+WE b1rYP4bPSR5sfGlYLlHUtuLsovguOiI9yKMSZWKtudjHM3Uehw0zxj/1pb2lTRQWlJFsIfvwcLOk JWKp5mVpb8gMwU9QqrO6P32a02JHvBdhrqaaNqbWjoWrkwblMz8MNFxQ5d7giYbNRCxpCMzZtgE9 faUKwL5Gtd2n3EsPYclJwvRExP49lEBW9/v5ZK7awcrSPffspNTqANYRTTZC5tuBDoofEB5KYGjE kgY9Zq1Zkrqy6cMWUeJZpWjbEMsj3m0nojNylMkKF2Q4sJpZawdqjvyTcMxNDAKAv8967BcI20WN DgaNhJISSxo6EXQTBl5Fm44THnmoZH/CQ4lcOXJSntC6hw0vFGjgBlGlCFw2tONhPumMtS5TG/HP fDN45EEEdggdFyobdiwCI3dI4jCH7iAjO52XimGTqNF7meEMlCgcmUJCNYG+DFUay0edfcoh4vAy o6IINHEMB7EajcfMDyCdwo6FE5/YK3Ut1VCHuaWD3BSWj8aADDWe60SXkpZ7Zx8sbMg5oFwXwERg oHWEGD5Pu56kzB8Cgtj0WCGKI5YUG0gfbTEnMlRaPAAPDWSNx8jybKXwZd7cMvIo0kYvJfMXYe+z ofkkU558XbTa2vFMugsv48BqT/pJWNTMY4WhD//VijNprOaJ+7jxPDFMlPkDQGQalMyem00oJelw IV0G34CDq6mlxa8fkiFZakNtSFSodzoZZOj8sG3m18IyE/mzISRY7Lzi8cP0I1+AGzbYUlXiwKHg VGuxlhKxHJEwNvfTcU9crbeQ+cPAUqvVISRh7AUihEGqChEPbLIvWS2e9xBKswr1VqilSLCbxQht cLbSbhIexRn2HQWQFSeNJjTNA3wBMaDgIYRxuwg4C6FW49BLrdZMgIr9haNLMpcA8fuaITavNpIB h3DrVBX7kvURLzStgRxis+b8ZWDq5VjarYIGqzI8QX6mNd8xUsWNe4ZshornbRf9HBrPj73hQF84 wmtcPX8OgzOfON4q83fAIKxKh/DRS0zY48qDnoPmEdvBz0d0umBWEvSbhiO8ppS6zlSDVP7CMcCZ S4H4fsWLKzCwrLIaF34cEDuYQd8WurS8LMHT5B4wgZ2HQOXBAanVWqQ5j8RAMn8bjOpJHb4pC9T2 M3UWConQbt6cYtKRdKv53ZxXJ15l3fMgjpQ6lQb119Z1yPxNIGq148XaMCt0BKRunwNCKVnQbh2E Ma7s86ERzoJ2+yTvDP08JGgnfSEj81fBxAFXDM6TjRa9Jp25YehhX22o60fug+oTs0M8ydiPQRcO PxyUuX12J3QcXQzMq109hDJzw5CsVU7K+X3cZTnrKY0vsHwHP37BTCaTyWQymcz3MBj8B/cRGJMf A1oOAAAAAElFTkSuQmCCUEsDBAoAAAAAAAAAIQBzWEd6tCgAALQoAAAVAAAAcHB0L21lZGlhL2lt YWdlMTAucG5niVBORw0KGgoAAAANSUhEUgAABLwAAADACAMAAAFOqbo3AAAAAXNSR0IArs4c6QAA AARnQU1BAACxjwv8YQUAAABjUExURQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAGZodN4AAAAgdFJOUwAIEBiHII8olzCfOKdAr0i3UL9Yx2DPaNdw33jngO/3Sq8erwAA AAlwSFlzAAAXEQAAFxEByibzPwAAJ65JREFUeF7tnYliqjoQhnHp3trWVmutS97/Ke9sCQkEBcVK z/2/c6okhCyTYbIQQwEGgaN/AwUZA5dEq3GAlfl3M9Y6zxSQ/68LRwaIbdAtOVfJ9Ulc5oj86HBL HxPv0M8bOUgYccBiR2f91Y/2TZCXZcCYFj/iMo/yqBjd0ceW44oJF86LDX0+S6Yo1L5YF7f+NH1a xl6L4oe/LR764NQsEvraFe90MB5RptXfTnnUR/47wvvxp34FvuWTPeWEKx7oTxJdclC7iDOmh8FL vraaX8valI44X+Jiv0iC6slBGD7hfD3Gn2V2CyqixLsX9zOJzTImhIvKqgxehAUkhyvuWcJ7dYuH BjEopIi3mN8Us3mx/TKfkHc6NbND4UWjiVWlIWNzPvoonjSPesICumJZ0AmOQcQQrmpPNjAphPnz F/1lMybhPOyvyu9dTOSkP+97ETRyS6LMb4aLZqNOkrEB4RxrVnmjDA0xXcPiVcQ1QIFRpoaZsTf7 BuBSfFMjOkSoazJEBmvwQQesEgdXl5ahpM8+BFrkq50sbezoiikd8JiSr2u8lE5YJ7kcYVg/+UGO 7cpHGsEpUVR0uOVRR0k0hIjiU/yFT/7Qjy43MoTQwaInHNFBMkYcU8TuIwQI+aKU00FnGZcwkbyt /Qka3hg6FKP/L5RjF06Qx0JC6ljR4yh+j/qquQodegsa8sVu8fNjTho6BilJB5cPPvSLJe9P2sCR FIK+pnJsf05GszpUpD/JgHautEvKR/4zly86FCnUhpzWHmzLfBWf8sn4xoJCbnkI+UIBJCbx4K6n /BNUdRgZ+LGvyw4QvVPlNSfXsmHEebOUyQAWBUcTlKgtldDs3MjnWNxyWI4PQ3irRx7zi1u+6C+E I8hpYprToc7BXAzLhtoJc+SJ7sVfwLLircRg0NtTPwfF8HKkvC4GKCxmqPkCAPzrvLnibnCDhuFC HdbQiwNHGbGotD8OQK/wADfB/EEDiYRcOQIGeVJ5ndZI7h547uZauNpkFfHQ343yeh9PGPQgL54l OYlPV7zrIoEEyZJ+0N+ieCetpyN1aDueBsmTO6MXy6HRdL34b0lQdEB5HNOXPQLmE4bIa53WV5sI fZg07PeTHVSRYPzhctMoC4lxxlnRhyh3Gq09UWFHHIT9Vs6Nd0s+4nmnTfHNvnoVFTCyy/Jlx4Lj CVp3S90p9U0vl+Kti7ctTwtu9eLk6iCvJa80ES/5OBjhXLTy1bzoT0NOWOtGY+fWWhJSJzs3ZqWy sARfGcFdQRGIOL70Sz/J4XjONQriw+kXQ6nEZxLYj/+k7GytLVBUcfHlXDzhkcryo952hRDkJWKX Ca2jEdqccfAqHC8AoiMpmU7Je9zLXGYc2St4R+eZ8TcLkKef33XVFP0nAZtD/sog5kc5cm96zE61 puZKkNDFOwn7x2RGBZ1vXiiGu8zlQV4lDfKa2Fy1/LWOkLz0P/9NWIuoG8zKKV1i8tUvNgYSqg+W 4a6/10rWiF9NqVN8olzlJHU5CujyAfOQr9byUuRUlwhDWD1Z2i89cWPGXVwh7N+iLq/AGSX6o8Jo wQF5gQypvP5dvQBX52YD9TpOcj/aN2gG8uoERAQAAADk4dV8aCjbMyNZYejYHndbPNuvE0ELXPH0 +p0+5QAHmJDEcDu2Zi/P1T7UAQAAfwN7WF4yuJ8cDI24XdQVAuAQkFc3IK9u9CAv9/Q/kvP58nrV X39ei/dspl1lkdQZzIq7aKx4vrzoopOukymRzJU6UyKfU2qx5ZjbbXVs3H0SZNYw7n3ILW3QpTRy scdWDlW4UV/+3FlqRnys8op9CFnFVyOK0Mkvj3lNZuXKmlvRtWW8suej+OIfjFZWc/LsUjHZfJPq xAsxzSF/ZZCGNIjciYy8mq4Xf/rQ/6Kv3iuQk1eLRZXhkvTKqjMg/iyvm+wkkpMFaBzIadoajzm2 XDllkE+5725pBOyXdq/HxWhLVbLI7AYj8lqXPxe3uHkl5Ex/kBwulxP0of+L6d4rNX8YOXmJ61CE Hw/FN6+TVFfETIoy/+E1nML2WcUk4UReIq70KnIv7nVDrnThqnewdpZBxHNduIm/acKiT4uXO+GM OEReFlBhx8E1o267lfWB9CF9+fjqSF6uWIjuHl+E6p72vD0de9mVE92lQF2HVr7G6lPCv5lfqu+L rhXXCNQhh2UQu9iSYD7oUFylVyAvL77clKFyeQh6T/X0LnYkeBGlvILvXHejOxChBTVXZD/9ieDB h45/3c8+LC89U54XHkYcbmsDMwtSrvrlvyiIXRwlkiySrRDkxeElpxro+Bpb8tLFcVHImrzo8/ii XbMGrFMSaCefVBa75NjS43TPDDlJ95wtOLZIptrmkMMX0gfR83eaqOI+1WGfhjhYXt+cH3bLL8zU P/6xWXx55O2JvSry2lLLPdfSHI1QDtQ1oS9vpfjKEKZ4zq0ULk8r5Cbrzb70Vy6MVkexl9KGIMUd W4W5GztqOqzPZQnuM3s1sLzchFdSu2L3weaAw3Zakxx7JfLS/50WObNLdzvx5zot1Ra2Wy71lk+/ zMVizeR3FeZg/yiIxLUlAXySBvJNwz55s8jI/cgW+cWXhj/ob0pNiu8iRZerT0LsVcpr4siGv/sK oL9WEbLr3nEx+GhJottRWC6nhYu+muTVGYuT0CNbhF16R4i8iAfen0dDNFyurkwksVcpL0EOuiwK L13Vk62Wtp9I3J2iW1MTKXMd4+UVZU9uk4C/nG0KkZxTYq+cvIrPpGAHIwyukbWq8umxK82U9yev 3yUuk8orkLq6cPqVg6dRXjJGOw0xYf8ojfICWbgh9MS9UpBlYl0iwVtk0EQ5VCASBwC9cKujCNAZ 3IxtICF5w8XjWnCESKkgrxbw9BL0qzXxz+g3NqkJmoGN78RHuQMzAAAAAAAA4Azm87k+b32dz9HN Bj3zru90LJZLtxAPAPrDfax5LsU9YNoc9M7YFa+6xs1V1m8CcDZ7fsqw2RSF2DAAekUaRvm1kC3L BwAAAAAA/bI9joUE4AT2uiItw5bORGvgATiBg+plhwCcyCH1wipbcCaH1Cu7nc+FeLl9DvtugH+G YaiX7Lkx/b82xtHPYo/QPuRAGIR66Q4zv9rX+9F9dq4/NF522p3vlV+92Y41Va2IdEJf8jrJPD/x r74P8WQ7KxEStYnO7e6+XVPlaQ4yROoVMtrAaNVWMfI5nLhF4faVTT/kvB3nOC/RSXHPlx9MIfBc 5mTpnO2gshbxvDn5iVOzg5L4ke98LA+84WEnnttqw1q3J2Oaiznp9gxw7XemYv2QyrvVjajkNbdk JjShj1L3mpJO1CtkNMPy4Nka9Rxq01hYdRRb3XGmWSTM+Yny9kRhcYil2YAjdagdWS1LZM2OYhzK kYvlxlV2k2zB3jZ6OkZZa9/ZF2Mzs5ZxBSYhKpOj46WnE9uv7nsjOrP5sU2fWBi++BXaqldnAdVy 6HeSkxt6WtaffmfpIdEbZ/uFRWk24O6edYml++btwoRVPCnY7KAb2rdLuViihKki2PVaqRDx5nuD mhAzdGmARspaa7zgJ1TxgpPhvdGc7dinsG+a+ptfbBqp1zO/8Igvcze8V50bldvX0eV2VCGvXmT2 Ni5eEVZefnIOl243oqutUrwehIh570uKwloToYdESb309fVM490tLB0FJtM6cROKmPeDZG4tBaHZ EbKajSURPtm020xbRd4cahYkkFxEJ2PMlwm19tA4PV6GJyNGVy/qdqQxdZPjaPk5IQXbcIEeHFXW KwUsu3ppliJy6nUrUcYbnn2XXc2Tc8hvyQ+8+sh9oDut+7jH0UeiwU2ENPOwsCg0v52/bOqYRHZ5 xyxobjaW+JpKFCXqXd5f+VA1Qq1Fyl4hiYk6Trm7rCl1L8cIKSL5zstoG4qUVS/dKNHQ69beKAgn 5TDdoqqmDvzcqmILekiU6r3UyDJQjkeJx8lIomzqhGU8jss6QinysYTTDO9unlME8p6GnimRXEQn Y8yX8bU2Cr7+fAhXi8kOY8g3m3pGvTLk4yQy6pUJmtb0+Tm0/UCJKCZqAWMD13eiZZpZRHnv9RGG j8S+VmHr5LyDEgt3SyYWDkYNuzJys5/8u2+4OxbNXixaPk3xtbYM3QbqbWoW/f0YCY7a7rwcK6kf kGOOfJxEs3rJHr52/B2V9fwcrpel3lggjd4cl0g0TjPHOkrMp/Tp3NvLy73qTrODW0FTr1wsjB0+ qB9nWTzCbIYQXxAuOYqvNcqS3mWrOfdD3l3ZeG1l3CcJUA9VJCZ1Xkkvdr35WZ7+1eudpOUFZhkL Oek9h3aJdNz91OPFE/19dMPpKknWF3Gr+5DtCeTwtRagOJduRSIsbeSSxhl1tmFegUhSz4zAD5NW QkSua19S9t7ycwTn5zCTr8sneg0WbledfArlpEEH3TzmoFLsy8b0KNVam5HMntjvPurh5aZVX/3E QCV1Yl3Ok11UvaL6zZztI4eZfF0+0aFBjcXJueXZnEgKW92OXw6SOKcZUSqV1J9Kl0TdJmdxDhIS 9WLMIWyT2d5x84/XTs+hnLdj5RcSBT1TrcNApF41+CHhr3OVRMF5nKReALSD1CtuckpIvcqxNAAn QeoVlqsk2FMVAE5mTDrkkhG8Z8MnbE4QAAAGyRhbLoFfpjIvCcBJkB7Vn2J9j+JFOwCcjKxEKh74 WYEivlAv0AsN40OoF+iBhS58m7wExBvqBfog24W/kfkw9O7BmXw3PBMC4Gzmj7nVigAAAAAAAAAA AAAAAAAAuCaz+dx+GBQOAOgN/wD7tnm3MQBOxW1FvUZYJwEugHsQq7X/6rYVNQAteHAF7x79tb9J 93cEoAced7L/txvpPvgA9InbFm5TbO6Lck9yAPqCbNbePZFqoWcPeoffUDFzvNNm6707AWjLN6mX 7Eb6rj8cAqBH1kvea4IONmgbQd9Mnb3T6tb2QAcAAAAAAAAAAAC4LN+2X9wBMMcKTuVRNlk6SPyC agC68cMqZMcV+Ez0tk0AurM7pF5TOwTgNNh82WEFOoF1heA8Xg6plx0BcCK8iaodVoB6gbOBeoEL Mhj1+prjHcT/HgNRr6n7KWa8FBv8UwxDve5kBTYWMv5zDEO99L0z/1v1av275T/3A+dBqNdI3vH/ /Zs/IhlrV+9p+yPOa7Jxz3Z0lJlb2dGQ2M5f6SPbdR6Eesk72b7cL/btv9xaXiHu3J16XI2x29hR KzbtX6JCFahV6L/zjFZt69mt/Dz7IsTNr/T/Grunh3zttVKvKLIsP60rKZ9D+l7xDgQx6+h8lrMT XfOv7tq8h5cfnHnD+kzH+qMX3kqIkTONjil979izIRbJRhd2h2SSUErv9sBL0WdVwR/gObazFv2X uymKj1XDTdJSvewgC0mTUmhJLocFV8DCPz9/Uc/14Uh7SJS+vVAszSZIFNbrYc3R8S0JTt2HHfzT K/nOx3LClgut358fEqY8NN5Fyw5i5KKV3WOLXkT4og66l/RWCrpzvnpNut1/mRyOxLDqW0vpTlAr e1i9zk+Uv6e+XJZmE+7L6+d+E13OaK6bHaTaPulcLA8dbIfxFmzgEXwarMNNsvx2T3bUjlfpJQsa /YdE7VsS0xkyzuLsQ70OnsxQy6G9lG2Rri07rF7nJ8rfLe/csZuYmXtxL3Z/+mgeRDGbHeSyVrAh ls7LBsZtzZcvaHxUxeejNaW11Ui14+Z96Q6iz4kLoc5Wr1nnF/ZVc0iaxJ/3KumNGfKD6tVDotTp WXrL4dNsYEuKqBe557mtUrLmQGl2RLLLxVKepUqZ72QUO9uYPirj+VL0lJcOi0d8zUHKcI1XzMoV fbebOcv/7jPZWLeW+iJsQaKRyuedT4DuIP58cJ/qPl+9onMn5tC+zDEytTqoXj0kSjduiMSn2QAF 1ItmfGCKHQ6YZoddSeRieS0bbRl1fHD4xIpv2du6NxbTumXrGFJ+j7oGKSEIJ8uO15CKkEk9nNcD ESKf5AK9U8nei+fVyFvszuq1IcMXX/FTdh7In890ziF9LYqbnVUx76XClOp1T9encfaRKH2Hdsmn 2YCb60UjqvgQDYlB+xtMs2MRmrJcLPehICM3pkGAo2Iv48xM3IiKLz5aIGIW29o5l7Qk1syQ1Tmr bY5JaBvXKwruJmO65yJVzKW+92JTf1Lde871LbvmM7LMYxfJs5t6UfL0GV9RVtLJOaSuyO38U42M b8wj9ZqKAU/G4z0kSt/lWtwjMz90L8pF+13ZDHA87t4ODziWQRdysazKjUbp7qfrFlU7qt6sHmGw uSVFDbRRrzLPFb55SyTP0pGOVAZMmdQ/va5arFsu4GQvtwnfOHuyrdrbYbqplx7EO4AlV5+WQ2os IryaBPXSQA9xoB4SpSr2KkpeTY2H8EoXrKk14+/7qGp3Lkq2yRHmT7Kx7Cr6EMYBMeRLWlAq9ke1 tA2ECxJxxYQuICOtsx1HVFN/9CqZCUw+M5ZlOfjopF7z+q6+ydWn5TC9RCbTiUi9TC9KekiUrJl8 CT7NPG8U8pFMKJukpP6n8a8RGhwh/Wws+7JrTZBW5rbu4wlmvot8j2vRUb1oIKcHNdbleJrIirGW +pe/F+uhb8iHul90KrTendQrDvquIkqvPimHibQ4i0JQrydXPm/oL9E4jpBmHq6dhZuKkU2Teoud WQeJV74bYlm5+JEndb5y6qU/h/CNPOtuMoppxKfiJ1VHK6/WJBuTY8V42lFMNfUXX1210OO99Qmo f+6b+C7q9RLfBnP1S68+KYcvPi+MdBGZsmv/TpdbQ9ZfojEhzTzSuXCfoqiVixNnznEb2t1sLM/J rCrlOI1eGbO3lZx5K41Dq75XmFSlupb6pxtWk02sFz++KvsLgWrq3/6pejavVbqo13NcLiO5uocc Bn0o1UtaDTtSek601MEcY8mIDhHKcaCSxJVz7H0F5mOJJiYobDomLyHvSdTyruOZ9hbqFSLdbrSk 7ttkO48q9I5GR9k3GlCsceqH+l51uqjXqlT2yXarDzjiq3vI4SJoSqxe5OD24EKJlmlm0Xfh6FU0 AOevYh1P3Tc7osRysRDhPLfj5CqdEeQdn8gHquPDhfA7ff45//Qju++ymR25Gdn7XKeukvqn76i2 ykY361VW4611iKKre8hh1Ksw9RpJb+RG7/eLJHqsJ6NjgLl0krx5sDg2cVOXcVDufGK5WAhveOf7 DXlShm8LnjQrr2N41s/GvwSZZTs6gg9H37zvNx3c8fhi4qd3+dDMqCMjqPb+jX2Sn+enqdM5Gpbr gX4fpIt6vUpuxtKA63NCPmW3ft85NPX6kSAver9fPNEMkyg89QK9+XQfIxqyyu3W7KBCWKrZWIj3 MkKSKk8Oa7coyWTa5e/8SJvuPs2FzndS0xqe2ViQB0pWXPpNB9GIozLgCDkLB4fool7s9WlJe/u6 Nfn0nkNTr51bj6kS9C67eKIZwmMlaof5WPo94y3p9cpsebPjJ1wcDuJYGMvNTvtTU7eSUlOt8JdB xbQjJnTcjuGv+jBpvTuKd3y/ofhsXoQaZJn02VrH4dO6/OFWZNLU6w/XDtJJvYr73d66DcHLDnrP oe97Lai6ZiaOiyd6BbIN+zZZJ5PkOHl8cZBqQec0jHAL8nwuT+RXTCRXptHUlwYcpJt6lYQhzzJu gUqSS9Lr2+Uw6dobF0/0CvhVOgl+GCIkj7A7LYa2A4NtFj/ejU+kemzY2hUlfYD+Xna/W8nxRPW6 KweR2evPz2FGvS6f6FWoLwd6LUeWKzodsk7D34NzKCnVgrJTWtb4RPYutu9q6qwt5URKKzmeqF73 pU924Wh56ak5zJT78oleiajHWIEqIe5PlvMsLagU9N3ffO9Ji/iZ/w2GUEmduoZRb7aVHE9Ur9iH Rm7NBvvkHGbU6/KJDg2qnV1kkbvBUijLOvGDizEdxE86x+X6hgrV1HmUpchz3jZybKle1VDp8GWa sbDK6TnkZ4a/nijomVbqVWNc9oJ+j6skCs7iNPVqPzrukaskCs7iFPVybn9yh+BkrpIoOJPTrBcA rYB6gQsC9QIXpPylVhWoFzgbXutphxXoRPPEJQBt4AW42RHZB50Iy5IAOIU5KVHWSj3Jifi5HQDd 0AcvjHl4uEemYKIcAADA/xLMQIALklevEcaN4GIsw6+VAOiZZ/fZcqMMAA7hcm3jlvyhXqAHVL10 mouxqS6oF+gD3TqGfzus2A+4oF6gByYNj36gXqAHtvlH2lAv0Af2CwnreBHoe4H+sIHjS8C2b4B6 gR5oUKNR59fNAFDjJWz2FGPtZNPvoAFoR7S7LQB903qzOwA6cvOwgfECl+JR34cPAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAIy2K/kZX/QLv5Fswv2Z+80fAAAMBn4PmHP35vLvoHg0FwAADJSt 27w7tzFX8eP232S8sKECAGDg7NztiKyV7SE6d5/87qaZugAAYKgsnLsr3pz7Ycdk57byZswnOQcA AINlxZs90kCR35hDw0eyWnN1AADAgCF79aKT9uPi3m1GRfFAx3jSCAAYOHO35K+lc4uVk9dhPju3 5m8AABgu1M2SXtaUulv6apMxHeRf2AQAAINhZq8xeXJur6sj7sl45V8HBgAAQ4EXeKWWinzcDsYL ADBkJvqW6FdzEmP1ib0AAAAAAAAAAAAAAAAAAAAAAAAAAP4gsj3q2cgifAAA+EXGi0+zQKfybHt+ AQDAL/NjZsicrdjqJfbrIQAAuAbPaonM1QozXtieEABwRXiXVMJcrVDjFfa4BwCAK6CvBjrBeM3N BQAA10Dfc3aC8ZJ9CgEA4EqcbLzwHkcAwDWB8Up48qtHllgFAsCwgfGK+HZuo0Zria4lAAMHxisw ol7XrR1PndvbIQBgkMB4BR6j94Kv0fP6HzH6WfGblvvlybnVF/ZPvyQwXh5e8WY/GrjbOzfVw3+M lw3X3XJsThod/+/XvIxmrM2X+LnIDa8Af+7ffj3PA+ZDvDCnGuC7vl8P9p3JyavlmUh/Ee0nmnnY w5XRui56M15ZiXajdxF2kiF1tlh6Tz8kytWBPft7KOjVSvq4eShGZL+sh8m2q5fXCd9aMvOJeXgm 5r81tzL6eJ6zDq3mj4tUU7uemVr883mwEd3yMiEZrC9huZQb0qpl3+aLt1T4EGP1Yj7jl1tqb+V1 0Z155/bsuV8RPLANqt5EE80yDXBcXAVkTj6lqh5JLem6DndVb8arLtFOXEKEnWTIb0m6K0bzndvf +o6JMSaNL73OLOj1S/rt7PV2dN8mmpKWsxOjHSVCVEzhi/omawIfaIjmVt8v45dvsUYuJHnKmUK6 kptYnB3ywrK48O/cXimFnrdeYQ2s6Q/flfK66E6MSQc2l3m7zivVzFtGnTijpTpOKNSHHlIFUb/B 31R3ortx3TyxR7IIoFfjdeodeUERtpRhUZDcrD/Cr6xM7BIFDZNh5xV0ECXlH1XQF/fA0vs7LWc3 2EIS/oGHwqaBSCLlIULQHbYzwXHKGepocQppfbTOCzf0u4zEemVCfaKTxZolq4Fs1M0KtIfKf8Fu 50NaU0aijiMSzo8d87zNZ9lLvSOnc6H7zFbOuXQB0xCM12VF2EaGekd7o8J63pyfc4zXEEoqVS73 VKQpZ7Ny+weK99OcwjP1OXg0E2scGe/4HqO8+bOnnCG4T5aaqdZ54eoIlX45FpRKr9Yrp4Fc4K4/ N34ggdzb8WUgqe+r8xmJOlKAtR3y2629HWPojiQfP7/y4TZkvSqzLdc3XpcX4XEZih6v7FD2CQpK /r7dbu/ju+N04zWMknKV77mPng5mauXsBA2OZgWlEynSiMzKC6cdD01F3aqTUcIpZwgZDKbV0TYv Us91W98/b5ROnyPHnAZyb7PjRCrbu0sXn4uetBcVdYxaW+5Cx6PEZ/dGNWkj4We3fCe1rbS2Vzde vyHCozIUMfi65/FUNHswJgHH/fHTrfQgSiplXf64TXW4VC1nJyjZDxndhfE29ez2Ex4VJGPTMbnz D3JPOUNw56nSrWmblwm5M++Clw4a8RY74y5BwoKkxqxN6FzJTGxIxhRF9b47h4wGsow6JsEqXyt+ p8K3KLsUPdW0WB0521qzoxVHFmWHTr2zbeOmmIaMUx4XV9vWixuvl/s5FWA5v91m57ezIvxtGarZ N8V+INsVN9AUnc5vG9mC3v1IMd1qPn1t0KGBlNSqvN4DrJazC3rtlOL1WedGcyRJh2GBQF1DOpOb ZzrljE6MpIrVOi88O2BiL6Ge3FrSUyV4surJJi7TNGtuLhhfCZxyJU9sSfPtwoNMSzfRkGpGA1l/ O3abOVOVMXOnwrcrO4+ZUyEn6kiZYG0dPVMU5D8pxksbOWy5FnUkSfX3JPM61dvuosZrzLOI93Sn sUHI95wzIryCDKVVDkyTlKhDHt/6mYLyLPzum+3Sgk8mcy0lwygpwVWeecJWLWcX6FpqQHkIZ0nT iIwf67GxjSwrw/kjjc0owyln+KatPGNrnRdWzmqHbnK/J3W1m+Kd41rfFbeVlR6BySOH9k2IDdfl CUJ6AUVTqROPPjttIrXKgboGiv5W777DkEWoSq5T4duWnTNLAUtSdVQB7L/oUC7e+yrasdkii7Wk fPBiE7pNahOHFzReI55ote4MtZDZZj0jwmvIkBrhpk4392wTY1IvaDzMpDslP70xkJISNNLK2K5a OTvA1/KkFH1pLVMSrIRsdOq/svpSE72s32ydz4zYM+3TtM8LCypfVypWt9u7XT0rdTS07/lwtJVO 0Bd5Vev+DOoayJ3Ijs8EqFPTMInRqfBHy87j+sSrqo55qLpYG1dUaRKaq7WmtZczXnybhTnwomH1 b7MIf1WGZH4q4QMULs1graDcoS1Vs2n110BKKhGU1VJSK2cH6FpZPsXNKNlNnh4X88mtV26yaqLt bWb1ZsczfNNWnrG1zwtH11BZ+pi+efyeYveQSpWlXbUj3A1MR89nUdNA8WkoShMkl2qn2OhU+KNl Z794FkY8jhuvlU5sUGBusuWgPqK5mPFi27XJ9WbYhoabq1mEvylDbpWbHmfRaEOkF6irDt8X+8wj 9wGWVMua62/UytkBfy2n9MXT4xuRJt+zTXJlsxP6jwldzvBsREVq7fPCFdnQ87LRTDvbpe0FwVrB a50rrYlmPNdgnEhNAznRpGc3k58SzONA+nOLcokuj6Ib7F2nwh8rO6/eSgx3qo4NTC39yfZHbAhb k/o07aWMl4zCs8LhrkMYnxwQ4S/KkKxPU6+DrkssQc54SeSkntVu7QBLSqfn81xZ6+VsD9lDbRXZ QjzS+NjaSM5pebdUILOSyTzT/gyXP35CRXTIC0s9mwyhNdLW4PCUAEGWkG+yehPVPOfVz4R9/YGA 2Oo4J9Jhj58XsSgb1rh1KvyxsnNj2dV43WwFcxE/6lGVx6WMFwdqcTccEOH1ZciQXauYnIzxottK Z9Lzj8SEgZe0Xs720LU2tcmFDBZBOpTNvTm+nfIa0voM37QV89MhL9wfyicjRpHJzA1mYZ1g40iy 11FOCueTp6TraM030VAjVQ3keYvK4JlKQNUeSjdaOhJM0s/kkUC2uexa+CNl50dKiZRb3nhtuJTx 4lppkcVGEQ5Ehvf1xrmqOh7+iW+4c+oMu6SZcraGrKm/dcSE+z4A9zfieYpZZWhDp20S6pQzhHQv 0vFl27wwvM4r95hmTAJecEq1vlojOqDd8BOXXIR8srlZ60xVA3k6taJaM/fGTZtl5m7vvjhv6lL4 IXemC9698IfLLtWQqNYfMF7c2MSGXtFeQzRL0yDCQchwOt9QZLXJl6rqlPCAzd86f6ikDeVsDbVT 3qD8kDnwEYlmRSrA7khuLCATzylnGG4gE9vWOi8KCyaR+ff8vuDlPZuxb1GoPZlspjpXJpFkWgtC zBOTq2GpkXzH6zSqGsgzEuwehZ49Ga4PDqZN6Rc1cpz5pJfOskyEd2rhD5ddMpu0yn/AeHGgcobt K8xns8yi0VNNhMOXYVpQav1Dm8pZKAvz90vaDl4HUB+8Ws8n6ojykC+ogWzh5LtFp5whZKYxsmVE 27wY3FeOIvSr/aQXo+3J/pF6zN7wsEeDEZIEcs01weP4NJtnUrnVZID8UTzR0NCr4iMvEyHhiYY8 u9WIHZVeJg8Fein8obJL4dOx+R8wXqIYK275Jl/UsIfQ1F9I5nYqIvwDMkwLSln0/R/eRnpfPtD6 +yVtwz03+2SAK8MWW9hA9joUmcsf8VZK6pQzxVQTph6oeXTJi4eUdFfGqV1a33MzKUcDMnZFzVEM S92ts0NDfsrQq+2q3WrW9N0H40TNG1U4NZc0DhhtpBNBmajMC/A0YGTPTi/8gbLLDz4qD3n/gvEK v2Vx7pNtmMLzDEnTWBHhH5BhUtAxP6wPrKMZ1n+gpP8DWDZJN/UAPIlUGaUGuIuYlzFbllxn8Axq t1qVqc52kSo8PakSsbJVL+GctZ3UO1T45rKrWleGD3/CeGWhayu9104iHIIM2xX0Xyjp/4EJVWfL XYoopG9qKvB2IfnGRHZSzTczp3NMA6lPL70s6s5vbJkbXVLv/fFMRvU2bqKx8M1lJygHtWdKParj Lxsv6o1Veq+dRDgEGbYr6L9Q0v8HY55Kqy0hqkFSfEsbo2I8n3NHmQ1UMiHgmfCD31nloh44poHU zRKLxT10XV7G7WZVLRgaULey3PXCHys7w9vL1U1mj+r4u8aLxVkXYlsRDkOGrQr6T5T0f8N4SlI9 Vi/18yzYjUzL5cRLA7b1cZt4Csc0cG1d9nEYsPLjZD2qwOt7Ni3GzbXkDped4Y19c53OHtXxd40X iSqdTFbainAQMmxnvP6FkoLDUFPBbJrakktxUANHU+qZu3n8aFVfzlF/XKEsSG121dc2HOVw2SeP lGTDuR7V8VeNF68qbFjFeZoIryHDVsbrnygpOMKo8Rc8F6WP91elvJTPidpyuOzTTAPd+Oqzk+nN eLWRKI2a8j/xUk4Q4RVk2Ep1/omSAjBoejNeLfh21U0+/1X+PyUF4Gr8kvG6KR4+aLjUfqXAn+X/ U1IArssvGa/H+fxxm66d/Ef5/5QUgOvym8NGAADojZONV18POgAA4BTMeB1fuFQC4wUAuD7yI99u u2jaDgjmAgCAazAXS9RlBstvJ9WwBhMAAH4Be+Np4+/l64zDHksn73kOAABnMHp/3JgZEjb3R/tS ky8dMnqWP+VOXQAA8Dvwb/QqHJmE1/cppeB3JwAAAAAAAADwf4C3WsTjQwDAn4O3+LTDoyw27cMC AMAgeNB1YVheDwC4PpMtcd9iD5fRzq0/Gnc4BgCA3yZ+l9bkJUOyxQt1vmC8AABDgF9J6Tf+tB87 piSruWC8AAADYd1p508YLwDAMOB1Em1ex2XAeAEAhgFZo/LF8g88fV8leX0qjBcAYBDcJZNamPMC APwR7p3b2WErYLwAAENgRD2r1rt5FaMX2RVnhs28AABXZtv+BaljmwIz8FpVAMD14F2dsSkXAOCv MdnBdgEA/hgye7XfjswJAAAAAAAAAAAAAAAAAAAABkxR/AceCaZ/629bCwAAAABJRU5ErkJgglBL AQItABQABgAIAAAAIQBRqQWgFAIAAOAWAAATAAAAAAAAAAAAAAAAAAAAAABbQ29udGVudF9UeXBl c10ueG1sUEsBAi0AFAAGAAgAAAAhAGj4dKEDAQAA4gIAAAsAAAAAAAAAAAAAAAAATQQAAF9yZWxz Ly5yZWxzUEsBAi0AFAAGAAgAAAAhAEv1Pey9AAAANwEAACAAAAAAAAAAAAAAAAAAgQcAAHBwdC9z bGlkZXMvX3JlbHMvc2xpZGU3LnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhAGyQGG3AAAAANwEAACAA AAAAAAAAAAAAAAAAfAgAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGU5LnhtbC5yZWxzUEsBAi0AFAAG AAgAAAAhAGhPt1XWAAAAvgEAACAAAAAAAAAAAAAAAAAAegkAAHBwdC9zbGlkZXMvX3JlbHMvc2xp ZGU2LnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhAEv1Pey9AAAANwEAACAAAAAAAAAAAAAAAAAAjgoA AHBwdC9zbGlkZXMvX3JlbHMvc2xpZGU4LnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhAEv1Pey9AAAA NwEAACAAAAAAAAAAAAAAAAAAiQsAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGU0LnhtbC5yZWxzUEsB Ai0AFAAGAAgAAAAhAEv1Pey9AAAANwEAACEAAAAAAAAAAAAAAAAAhAwAAHBwdC9zbGlkZXMvX3Jl bHMvc2xpZGUxMC54bWwucmVsc1BLAQItABQABgAIAAAAIQBL9T3svQAAADcBAAAgAAAAAAAAAAAA AAAAAIANAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMy54bWwucmVsc1BLAQItABQABgAIAAAAIQBL 9T3svQAAADcBAAAgAAAAAAAAAAAAAAAAAHsOAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMi54bWwu cmVsc1BLAQItABQABgAIAAAAIQBjXCO0wAAAADcBAAAgAAAAAAAAAAAAAAAAAHYPAABwcHQvc2xp ZGVzL19yZWxzL3NsaWRlMS54bWwucmVsc1BLAQItABQABgAIAAAAIQBUKrbG1gAAAL4BAAAgAAAA AAAAAAAAAAAAAHQQAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlNS54bWwucmVsc1BLAQItABQABgAI AAAAIQDuDIdh1wAAAL4BAAAhAAAAAAAAAAAAAAAAAIgRAABwcHQvc2xpZGVzL19yZWxzL3NsaWRl MTIueG1sLnJlbHNQSwECLQAUAAYACAAAACEACyiBiYgBAABRDgAAHwAAAAAAAAAAAAAAAACeEgAA cHB0L19yZWxzL3ByZXNlbnRhdGlvbi54bWwucmVsc1BLAQItABQABgAIAAAAIQBL9T3svQAAADcB AAAhAAAAAAAAAAAAAAAAAGsVAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMjEueG1sLnJlbHNQSwEC LQAUAAYACAAAACEAS/U97L0AAAA3AQAAIQAAAAAAAAAAAAAAAABnFgAAcHB0L3NsaWRlcy9fcmVs cy9zbGlkZTIwLnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhAEv1Pey9AAAANwEAACEAAAAAAAAAAAAA AAAAYxcAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGUxOS54bWwucmVsc1BLAQItABQABgAIAAAAIQAb pwBcwAAAADcBAAAhAAAAAAAAAAAAAAAAAF8YAABwcHQvc2xpZGVzL19yZWxzL3NsaWRlMTgueG1s LnJlbHNQSwECLQAUAAYACAAAACEAXZvVctcAAAC/AQAAIQAAAAAAAAAAAAAAAABeGQAAcHB0L3Ns aWRlcy9fcmVscy9zbGlkZTE3LnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhALevtRzfAAAARgIAACEA AAAAAAAAAAAAAAAAdBoAAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGUxNi54bWwucmVsc1BLAQItABQA BgAIAAAAIQDvq9GX1gAAAL4BAAAhAAAAAAAAAAAAAAAAAJIbAABwcHQvc2xpZGVzL19yZWxzL3Ns aWRlMTUueG1sLnJlbHNQSwECLQAUAAYACAAAACEA+aoQlt4AAABFAgAAIQAAAAAAAAAAAAAAAACn HAAAcHB0L3NsaWRlcy9fcmVscy9zbGlkZTE0LnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhAEv1Pey9 AAAANwEAACEAAAAAAAAAAAAAAAAAxB0AAHBwdC9zbGlkZXMvX3JlbHMvc2xpZGUxMy54bWwucmVs c1BLAQItABQABgAIAAAAIQBZlQOr3gAAAEUCAAAhAAAAAAAAAAAAAAAAAMAeAABwcHQvc2xpZGVz L19yZWxzL3NsaWRlMTEueG1sLnJlbHNQSwECLQAUAAYACAAAACEA1OFkF0EDAACGEAAAFAAAAAAA AAAAAAAAAADdHwAAcHB0L3ByZXNlbnRhdGlvbi54bWxQSwECLQAUAAYACAAAACEAfEtjYccHAACu MAAAFgAAAAAAAAAAAAAAAABQIwAAcHB0L3NsaWRlcy9zbGlkZTIxLnhtbFBLAQItABQABgAIAAAA IQBNKlN0GQYAAAIYAAAWAAAAAAAAAAAAAAAAAEsrAABwcHQvc2xpZGVzL3NsaWRlMTEueG1sUEsB Ai0AFAAGAAgAAAAhAJrjZp8yBQAA9hQAABYAAAAAAAAAAAAAAAAAmDEAAHBwdC9zbGlkZXMvc2xp ZGUxMC54bWxQSwECLQAUAAYACAAAACEAOksYJ4QCAAA8BQAAFQAAAAAAAAAAAAAAAAD+NgAAcHB0 L3NsaWRlcy9zbGlkZTkueG1sUEsBAi0AFAAGAAgAAAAhAI0lMYBgBgAAhBYAABUAAAAAAAAAAAAA AAAAtTkAAHBwdC9zbGlkZXMvc2xpZGU4LnhtbFBLAQItABQABgAIAAAAIQDDDdP8wwQAAOMQAAAV AAAAAAAAAAAAAAAAAEhAAABwcHQvc2xpZGVzL3NsaWRlNy54bWxQSwECLQAUAAYACAAAACEAWfeh iWYFAACcDQAAFgAAAAAAAAAAAAAAAAA+RQAAcHB0L3NsaWRlcy9zbGlkZTEyLnhtbFBLAQItABQA BgAIAAAAIQAQOTghBgQAABcMAAAWAAAAAAAAAAAAAAAAANhKAABwcHQvc2xpZGVzL3NsaWRlMTMu eG1sUEsBAi0AFAAGAAgAAAAhADTHLITBBQAArhYAABYAAAAAAAAAAAAAAAAAEk8AAHBwdC9zbGlk ZXMvc2xpZGUxNC54bWxQSwECLQAUAAYACAAAACEAYwC+o6UEAAAuDwAAFgAAAAAAAAAAAAAAAAAH VQAAcHB0L3NsaWRlcy9zbGlkZTE5LnhtbFBLAQItABQABgAIAAAAIQBuEXCWYAkAANFFAAAWAAAA AAAAAAAAAAAAAOBZAABwcHQvc2xpZGVzL3NsaWRlMTgueG1sUEsBAi0AFAAGAAgAAAAhAK7BBK1k BQAAFw4AABYAAAAAAAAAAAAAAAAAdGMAAHBwdC9zbGlkZXMvc2xpZGUxNy54bWxQSwECLQAUAAYA CAAAACEA2JFhuHYIAACjMwAAFgAAAAAAAAAAAAAAAAAMaQAAcHB0L3NsaWRlcy9zbGlkZTE2Lnht bFBLAQItABQABgAIAAAAIQBB5JgAIwYAADoYAAAWAAAAAAAAAAAAAAAAALZxAABwcHQvc2xpZGVz L3NsaWRlMTUueG1sUEsBAi0AFAAGAAgAAAAhADDXCYXtBQAADhMAABUAAAAAAAAAAAAAAAAADXgA AHBwdC9zbGlkZXMvc2xpZGU2LnhtbFBLAQItABQABgAIAAAAIQAotj01DAYAAHASAAAVAAAAAAAA AAAAAAAAAC1+AABwcHQvc2xpZGVzL3NsaWRlNS54bWxQSwECLQAUAAYACAAAACEAvFO9PuQLAABc SgAAFQAAAAAAAAAAAAAAAABshAAAcHB0L3NsaWRlcy9zbGlkZTQueG1sUEsBAi0AFAAGAAgAAAAh AJaAqjMVAwAAnAcAABUAAAAAAAAAAAAAAAAAg5AAAHBwdC9zbGlkZXMvc2xpZGUxLnhtbFBLAQIt ABQABgAIAAAAIQBQ15iy2wMAAAEMAAAWAAAAAAAAAAAAAAAAAMuTAABwcHQvc2xpZGVzL3NsaWRl MjAueG1sUEsBAi0AFAAGAAgAAAAhALOE9LNlBAAAzwwAABUAAAAAAAAAAAAAAAAA2pcAAHBwdC9z bGlkZXMvc2xpZGUzLnhtbFBLAQItABQABgAIAAAAIQBA4ZyUDQUAAMAQAAAVAAAAAAAAAAAAAAAA AHKcAABwcHQvc2xpZGVzL3NsaWRlMi54bWxQSwECLQAUAAYACAAAACEA1dGS8bwAAAA3AQAALAAA AAAAAAAAAAAAAACyoQAAcHB0L3NsaWRlTGF5b3V0cy9fcmVscy9zbGlkZUxheW91dDIueG1sLnJl bHNQSwECLQAUAAYACAAAACEA1dGS8bwAAAA3AQAALQAAAAAAAAAAAAAAAAC4ogAAcHB0L3NsaWRl TGF5b3V0cy9fcmVscy9zbGlkZUxheW91dDExLnhtbC5yZWxzUEsBAi0AFAAGAAgAAAAhANXRkvG8 AAAANwEAAC0AAAAAAAAAAAAAAAAAv6MAAHBwdC9zbGlkZUxheW91dHMvX3JlbHMvc2xpZGVMYXlv dXQxMC54bWwucmVsc1BLAQItABQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAAAMak AABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0OS54bWwucmVsc1BLAQItABQABgAI AAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAAAMylAABwcHQvc2xpZGVMYXlvdXRzL19yZWxz L3NsaWRlTGF5b3V0OC54bWwucmVsc1BLAQItABQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAA AAAAAAAAANKmAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0Ny54bWwucmVsc1BL AQItABQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAAANinAABwcHQvc2xpZGVMYXlv dXRzL19yZWxzL3NsaWRlTGF5b3V0Ni54bWwucmVsc1BLAQItABQABgAIAAAAIQDV0ZLxvAAAADcB AAAsAAAAAAAAAAAAAAAAAN6oAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0NS54 bWwucmVsc1BLAQItABQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAAAOSpAABwcHQv c2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0NC54bWwucmVsc1BLAQItABQABgAIAAAAIQDV 0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAAAOqqAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRl TGF5b3V0My54bWwucmVsc1BLAQItABQABgAIAAAAIQDV0ZLxvAAAADcBAAAsAAAAAAAAAAAAAAAA APCrAABwcHQvc2xpZGVMYXlvdXRzL19yZWxzL3NsaWRlTGF5b3V0MS54bWwucmVsc1BLAQItABQA BgAIAAAAIQAvG0xVKgcAANovAAAhAAAAAAAAAAAAAAAAAPasAABwcHQvc2xpZGVNYXN0ZXJzL3Ns aWRlTWFzdGVyMS54bWxQSwECLQAUAAYACAAAACEACYkvD9ADAACvDAAAIgAAAAAAAAAAAAAAAABf tAAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDExLnhtbFBLAQItABQABgAIAAAAIQCkqUtM 7QIAAGoHAAAhAAAAAAAAAAAAAAAAAG+4AABwcHQvc2xpZGVMYXlvdXRzL3NsaWRlTGF5b3V0Ny54 bWxQSwECLQAUAAYACAAAACEAeUNcZtcEAADFEgAAIQAAAAAAAAAAAAAAAACbuwAAcHB0L3NsaWRl TGF5b3V0cy9zbGlkZUxheW91dDgueG1sUEsBAi0AFAAGAAgAAAAhANWuQRieBAAANBIAACEAAAAA AAAAAAAAAAAAscAAAHBwdC9zbGlkZUxheW91dHMvc2xpZGVMYXlvdXQ5LnhtbFBLAQItABQABgAI AAAAIQDfRwwQmwMAANALAAAiAAAAAAAAAAAAAAAAAI7FAABwcHQvc2xpZGVMYXlvdXRzL3NsaWRl TGF5b3V0MTAueG1sUEsBAi0AFAAGAAgAAAAhAGmiXyEVAQAAxwcAACwAAAAAAAAAAAAAAAAAackA AHBwdC9zbGlkZU1hc3RlcnMvX3JlbHMvc2xpZGVNYXN0ZXIxLnhtbC5yZWxzUEsBAi0AFAAGAAgA AAAhAD/B9X4cAwAAuwgAACEAAAAAAAAAAAAAAAAAyMoAAHBwdC9zbGlkZUxheW91dHMvc2xpZGVM YXlvdXQ2LnhtbFBLAQItABQABgAIAAAAIQD1yNJpGgQAAGsSAAAhAAAAAAAAAAAAAAAAACPOAABw cHQvc2xpZGVMYXlvdXRzL3NsaWRlTGF5b3V0NC54bWxQSwECLQAUAAYACAAAACEAfTih0gwFAAA6 HAAAIQAAAAAAAAAAAAAAAAB80gAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91dDUueG1sUEsB Ai0AFAAGAAgAAAAhAFs5Y8pBBAAABxEAACEAAAAAAAAAAAAAAAAAx9cAAHBwdC9zbGlkZUxheW91 dHMvc2xpZGVMYXlvdXQxLnhtbFBLAQItABQABgAIAAAAIQCNWR2NgwQAAFwRAAAhAAAAAAAAAAAA AAAAAEfcAABwcHQvc2xpZGVMYXlvdXRzL3NsaWRlTGF5b3V0My54bWxQSwECLQAUAAYACAAAACEA lKkS43oDAACZCwAAIQAAAAAAAAAAAAAAAAAJ4QAAcHB0L3NsaWRlTGF5b3V0cy9zbGlkZUxheW91 dDIueG1sUEsBAi0AFAAGAAgAAAAhALl/7nPtBQAAsBsAABQAAAAAAAAAAAAAAAAAwuQAAHBwdC90 aGVtZS90aGVtZTEueG1sUEsBAi0ACgAAAAAAAAAhACSpqG4yswIAMrMCABQAAAAAAAAAAAAAAAAA 4eoAAHBwdC9tZWRpYS9pbWFnZTEucG5nUEsBAi0AFAAGAAgAAAAhAMLFAhHwDgAAeJoBABQAAAAA AAAAAAAAAAAARZ4DAHBwdC9tZWRpYS9pbWFnZTIuZW1mUEsBAi0ACgAAAAAAAAAhANgBxI4mEAAA JhAAABQAAAAAAAAAAAAAAAAAZ60DAHBwdC9tZWRpYS9pbWFnZTQucG5nUEsBAi0ACgAAAAAAAAAh AFFzDPQAgAAAAIAAABcAAAAAAAAAAAAAAAAAv70DAGRvY1Byb3BzL3RodW1ibmFpbC5qcGVnUEsB Ai0ACgAAAAAAAAAhANKF3o+r4AUAq+AFABUAAAAAAAAAAAAAAAAA9D0EAHBwdC9tZWRpYS9pbWFn ZTExLnBuZ1BLAQItAAoAAAAAAAAAIQCxNS1ZcK4AAHCuAAAUAAAAAAAAAAAAAAAAANIeCgBwcHQv bWVkaWEvaW1hZ2U5LnBuZ1BLAQItAAoAAAAAAAAAIQCeBNgrEREAABERAAAUAAAAAAAAAAAAAAAA AHTNCgBwcHQvbWVkaWEvaW1hZ2UzLnBuZ1BLAQItAAoAAAAAAAAAIQCykbFg/E0BAPxNAQAUAAAA AAAAAAAAAAAAALfeCgBwcHQvbWVkaWEvaW1hZ2U2LnBuZ1BLAQItAAoAAAAAAAAAIQBXS6ddsJYB ALCWAQAUAAAAAAAAAAAAAAAAAOUsDABwcHQvbWVkaWEvaW1hZ2U3LnBuZ1BLAQItAAoAAAAAAAAA IQD7A62EvH4BALx+AQAUAAAAAAAAAAAAAAAAAMfDDQBwcHQvbWVkaWEvaW1hZ2U1LnBuZ1BLAQIt ABQABgAIAAAAIQBoJNHIHQIAAAMFAAARAAAAAAAAAAAAAAAAALVCDwBwcHQvcHJlc1Byb3BzLnht bFBLAQItABQABgAIAAAAIQDY/Y2PrAAAALYAAAATAAAAAAAAAAAAAAAAAAFFDwBwcHQvdGFibGVT dHlsZXMueG1sUEsBAi0AFAAGAAgAAAAhAPUiwqynAQAA+gMAABEAAAAAAAAAAAAAAAAA3kUPAHBw dC92aWV3UHJvcHMueG1sUEsBAi0AFAAGAAgAAAAhAD4Mf9yTAQAA1wIAABEAAAAAAAAAAAAAAAAA tEcPAGRvY1Byb3BzL2NvcmUueG1sUEsBAi0AFAAGAAgAAAAhAJqaZbe8AwAArAkAABAAAAAAAAAA AAAAAAAAfkoPAGRvY1Byb3BzL2FwcC54bWxQSwECLQAKAAAAAAAAACEAJV1sZXUdAAB1HQAAFAAA AAAAAAAAAAAAAABwTw8AcHB0L21lZGlhL2ltYWdlOC5wbmdQSwECLQAKAAAAAAAAACEAc1hHerQo AAC0KAAAFQAAAAAAAAAAAAAAAAAXbQ8AcHB0L21lZGlhL2ltYWdlMTAucG5nUEsFBgAAAABYAFgA kRkAAP6VDwAAAA== --_004_42CD495267BEF24CACD045A8B3747BA72DC8AC45ORSMSX103amrcor_-- ------------=_1384234099-3409-0-- From versteb@cisco.com Tue Nov 12 11:27:19 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 526FC21E8102 for ; Tue, 12 Nov 2013 11:27:18 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.024 X-Spam-Level: X-Spam-Status: No, score=-10.024 tagged_above=-999 required=5 tests=[AWL=0.575, BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id mTGvvUHmqfB3 for ; Tue, 12 Nov 2013 11:27:11 -0800 (PST) Received: from rcdn-iport-6.cisco.com (rcdn-iport-6.cisco.com [173.37.86.77]) by ietfa.amsl.com (Postfix) with ESMTP id A09F721E80C3 for ; Tue, 12 Nov 2013 11:27:11 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=5827; q=dns/txt; s=iport; t=1384284431; x=1385494031; h=from:to:subject:date:message-id: content-transfer-encoding:mime-version; bh=Pf+cKax/CCnhh0ulLb/fo7BQhEgNuk2zYrWbZX5UnQM=; b=hnngzqbW1T0ztQZi8mMKyw0cubIl2emDe0VabUn1J4oPHT/nHyr68rDn kfvKtFNz3DQby1TPwVPo5zvhXElABZVVfzJpysL5+fF13wG7uFgcz/97q 3AhlZju43GoJy9zcgiOf6wp/rhUgz6k9jpDKimgNWaBuUqA6L+9H7/X7y M=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: Ag0FAF6AglKtJXG8/2dsb2JhbABagweBC78XgSoWdIInAQQ6OBkBKhRCJgEEARoTh2a/PY8ug1iBEQOULpVrgWiBPoIq X-IronPort-AV: E=Sophos;i="4.93,687,1378857600"; d="scan'208";a="283871779" Received: from rcdn-core2-1.cisco.com ([173.37.113.188]) by rcdn-iport-6.cisco.com with ESMTP; 12 Nov 2013 19:27:10 +0000 Received: from xhc-rcd-x14.cisco.com (xhc-rcd-x14.cisco.com [173.37.183.88]) by rcdn-core2-1.cisco.com (8.14.5/8.14.5) with ESMTP id rACJR95S022146 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Tue, 12 Nov 2013 19:27:09 GMT Received: from xmb-aln-x05.cisco.com ([169.254.11.196]) by xhc-rcd-x14.cisco.com ([173.37.183.88]) with mapi id 14.03.0123.003; Tue, 12 Nov 2013 13:27:09 -0600 From: "Bill Ver Steeg (versteb)" To: rmcat WG , "randell-ietf@jesup.org" Thread-Topic: Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 Thread-Index: Ac7f3Sb8XQmfyjEQShWI11S2Wqrbog== Date: Tue, 12 Nov 2013 19:27:08 +0000 Message-ID: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.117.75.39] Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Subject: [rmcat] Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 12 Nov 2013 19:27:19 -0000 Randell- As I promised in Vancouver, here are my note on draft-ietf-rmcat-cc-require= ments-00 In summary, this is quite a nice start. It is a difficult document to write= , as it is intentionally quite broad. On the whole, I like it.=20 The main concern is my item #1 1- In the abstract, do we want to mention that the RMCAT CC algorithm needs= to be robust in the presence of a wide range of cross traffic? If not, we = certainly need to state it strongly in the introduction and the requirement= s. Perhaps a paragraph similar to "While the requirements for RMCAT differ from the requirements for the othe= r flow types, these other flow types will be present in the network. The RM= CAT congestion control algorithm must work properly when these other flow t= ypes are present as cross traffic on the network." should be in either the = abstract or in the introduction. I would also like to see a statement that = the RMCAT CC algorithm should drive scavenger flows (like LEDBAT) to nearly= 0, taking that BW for the RMCAT/TCP/"normal" flows. These points are refer= enced in item #11, but it is a rather thin section late in the document. IM= HO, this should be one of the primary points of the requirements document. There are some minor issues, which I discuss below. 2- Requirements section, bullet 1 - Do we need to elaborate the other cases= in which the algorithm needs to adjust the BW? We state (in 1a) that topol= ogy changes need to be handled. Do we need to state <1b - Changes in cross = traffic > and <1c - changes in offered load from the application sending da= ta over RMCAT> 3- Requirements section, bullet 2a - If we are enumerating types of traffic= that we need to be concerned with, I would more concerned with MPEG DASH s= tyle Adaptive BitRate video than generic web browsing. Web browsing is burs= ty, but at least it is well-bounded in time. ABR flows are bursty, cyclical= , and persistent. I would either remove the reference to web browsing or ch= ange it to include other bursty flow types (I note that OTT ABR traffic is = now more than 50% of the peak load on many networks). 4- Requirements section 3 - do we need to mention that there is a temporal = component to information sharing across streams. In other words, we may con= sider a previous 5-tuples experience as a baseline to seed a CC algorithm, = particularly if it is the exact same source addr/dest addr/dest port/protoc= ol? This is hinted in item #12 in the document, but the guidance is quite t= hin in that section. Temporal hinting is particularly valuable if the old s= ession data was from 1 second ago. It is less true if it was 1 day ago, but= may still have some value as an initial seed for the CC algorithm. 3b is a= lso a bit awkward, as multiple "flows" on a given 5-tuple introduces some = difficult SSRC concepts that are currently being discussed in the AVT group= . We either need to define "flow" or describe the concept in broader terms.= I am reluctant to open that can of worms in this document, but if we are n= ot clear it will be the source of endless debate/confusion. I hate glossari= es in the front of RFCs, but we may have to do that. 5- In section 4, I do not see any details on how ECN, delay and loss indica= te congestion. Even though we all understand that this is a complex relatio= nship and will be difficult to characterize in a requirements document, I t= hink that a few paragraphs of detail are in order. This level of detail wou= ld be important to a less experienced reader. This is my major concern with= the draft, and I could write a paragraph or two to include this discussion= (if there agreement that this should be discussed in this draft)=20 =20 6- Is 5a a requirement, or are we starting to discuss the solution space he= re? I suspect that we will end up mandating AVFP, but we may be getting ahe= ad of ourselves here. Perhaps we should soften this to a suggestion to exam= ine RFC4585 rather than a MUST use statement. 7- If we are elaborating AQM schemes, we should include PIE (I know, a sham= eless plug for the one I am working on - but I think it is a valid comment = nonetheless). And to be rigorous, RED, CoDel and PIE are buffer management = algorithms that operate on a given queue. One can also apply multiple queue= s (FQ being one variant of multiple queues) to the problem as well. To be = more clear, we should mention queuing algorithms like RED, CoDel and PIE an= d then mention that each of these algorithms can be optionally mapped to mu= ltiple queues. If this is going to cause a debate of some sort, we could a= lso just reference the broad class of AQMs and the broad class of queue all= ocation schemes without elaborating the specific algorithms.=20 8- I am reluctant to bring this into the conversation........ Once we menti= on multiple queues, we may want to mention that the multiple queues may rep= resent different QOS buckets, and the activity in one queue may impact the = drain rate of lower priority queues, whereas the activity of a lower priori= ty queue will have a (very?) small impact on the drain rate of a higher que= ue. I am reluctant to start this train of thought in this document, so perh= aps we do not mention multiple queues of multiple priorities. If we are to = mention different priority flows, perhaps we use DSCP markings or VLAN tagg= ing examples - but once again this quickly goes down a very deep rat hole. = We may just want to state that there are often multiple priorities of flows= , using the traditional SP voice, SP video, general purpose data, and scave= nger class as examples......... I am torn on this one, and could be convinc= ed to not include this in the document. Bill VerSteeg From mirja.kuehlewind@ikr.uni-stuttgart.de Wed Nov 13 04:19:43 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 07B7E11E814B for ; Wed, 13 Nov 2013 04:19:43 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.062 X-Spam-Level: X-Spam-Status: No, score=-2.062 tagged_above=-999 required=5 tests=[AWL=0.187, BAYES_00=-2.599, HELO_EQ_DE=0.35] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 3Ji93ChgAfiV for ; Wed, 13 Nov 2013 04:19:38 -0800 (PST) Received: from mailsrv.ikr.uni-stuttgart.de (mailsrv.ikr.uni-stuttgart.de [129.69.170.2]) by ietfa.amsl.com (Postfix) with ESMTP id 9898421F9CC5 for ; Wed, 13 Nov 2013 04:19:38 -0800 (PST) Received: from netsrv1.ikr.uni-stuttgart.de (netsrv1-c [10.11.12.12]) by mailsrv.ikr.uni-stuttgart.de (Postfix) with ESMTP id 6D17560296; Wed, 13 Nov 2013 13:19:37 +0100 (CET) Received: from vpn-2-cl195 (vpn-2-cl195 [10.41.21.195]) by netsrv1.ikr.uni-stuttgart.de (Postfix) with ESMTP id 67FF86028D; Wed, 13 Nov 2013 13:19:37 +0100 (CET) From: Mirja Kuehlewind Organization: University of Stuttgart (Germany), IKR To: rmcat@ietf.org Date: Wed, 13 Nov 2013 13:19:37 +0100 User-Agent: KMail/1.9.10 (enterprise35 0.20101217.1207316) References: In-Reply-To: X-KMail-QuotePrefix: > MIME-Version: 1.0 Content-Type: Text/Plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Message-Id: <201311131319.37208.mirja.kuehlewind@ikr.uni-stuttgart.de> Cc: "randell-ietf@jesup.org" , "Bill Ver Steeg \(versteb\)" Subject: Re: [rmcat] Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 13 Nov 2013 12:19:43 -0000 Hi Bill, two comments on your comments (as an individual contributor): 1) I agree that the requirement to compete with TCP cross traffic is=20 important. I would at least like to see this requirement earlier in the lis= t.=20 The case with e.g. LEDBAT cross traffic is also interesting. This might als= o=20 or more be a case for the evaluation draft... 2) Regarding multiple queues, this is only a problem if packets of the same= =20 flow end-up in different queues. But that's actually what I got from the=20 discussion that this might be possible. Now with respect to congestion=20 control, especially delay-based congestion control, this actually might be = a=20 problem. In fact you would need an own congestion control for each queue. B= ut=20 as you don't know for sure if there are different queues, that might also n= ot=20 be a solution. I'm still not sure if it is a good idea at all to uses=20 different dscp within one flow. Why not opening multiple connections? (Mayb= e=20 we need a SYN option to open multiple connections at once?) So but if this = is=20 done in reality, we might want to have a requirement or a note that=20 congestion control should be able to handle a situation where some (few?)=20 packets have a different dscp/priority and might end up in a different queu= e.=20 Or is this not a congestion control problem and there should be another=20 mechanism below that applies different congestion control instances to only= =20 packets with the same dcsp of the same flow? And/or is this (again) a case= =20 for detecting shared (or not-shared) bottlenecks (on a per packet base)? Mirja On Tuesday 12 November 2013 20:27:08 Bill Ver Steeg (versteb) wrote: > Randell- > > As I promised in Vancouver, here are my note on > draft-ietf-rmcat-cc-requirements-00 > > In summary, this is quite a nice start. It is a difficult document to > write, as it is intentionally quite broad. On the whole, I like it. > > The main concern is my item #1 > > 1- In the abstract, do we want to mention that the RMCAT CC algorithm nee= ds > to be robust in the presence of a wide range of cross traffic? If not, we > certainly need to state it strongly in the introduction and the > requirements. Perhaps a paragraph similar to "While the requirements for > RMCAT differ from the requirements for the other flow types, these other > flow types will be present in the network. The RMCAT congestion control > algorithm must work properly when these other flow types are present as > cross traffic on the network." should be in either the abstract or in the > introduction. I would also like to see a statement that the RMCAT CC > algorithm should drive scavenger flows (like LEDBAT) to nearly 0, taking > that BW for the RMCAT/TCP/"normal" flows. These points are referenced in > item #11, but it is a rather thin section late in the document. IMHO, this > should be one of the primary points of the requirements document. > > There are some minor issues, which I discuss below. > > 2- Requirements section, bullet 1 - Do we need to elaborate the other cas= es > in which the algorithm needs to adjust the BW? We state (in 1a) that > topology changes need to be handled. Do we need to state <1b - Changes in > cross traffic > and <1c - changes in offered load from the application > sending data over RMCAT> > > 3- Requirements section, bullet 2a - If we are enumerating types of traff= ic > that we need to be concerned with, I would more concerned with MPEG DASH > style Adaptive BitRate video than generic web browsing. Web browsing is > bursty, but at least it is well-bounded in time. ABR flows are bursty, > cyclical, and persistent. I would either remove the reference to web > browsing or change it to include other bursty flow types (I note that OTT > ABR traffic is now more than 50% of the peak load on many networks). > > 4- Requirements section 3 - do we need to mention that there is a temporal > component to information sharing across streams. In other words, we may > consider a previous 5-tuples experience as a baseline to seed a CC > algorithm, particularly if it is the exact same source addr/dest addr/dest > port/protocol? This is hinted in item #12 in the document, but the guidan= ce > is quite thin in that section. Temporal hinting is particularly valuable = if > the old session data was from 1 second ago. It is less true if it was 1 d= ay > ago, but may still have some value as an initial seed for the CC algorith= m. > 3b is also a bit awkward, as multiple "flows" on a given 5-tuple > introduces some difficult SSRC concepts that are currently being discussed > in the AVT group. We either need to define "flow" or describe the concept > in broader terms. I am reluctant to open that can of worms in this > document, but if we are not clear it will be the source of endless > debate/confusion. I hate glossaries in the front of RFCs, but we may have > to do that. > > 5- In section 4, I do not see any details on how ECN, delay and loss > indicate congestion. Even though we all understand that this is a complex > relationship and will be difficult to characterize in a requirements > document, I think that a few paragraphs of detail are in order. This level > of detail would be important to a less experienced reader. This is my maj= or > concern with the draft, and I could write a paragraph or two to include > this discussion (if there agreement that this should be discussed in this > draft) > > > 6- Is 5a a requirement, or are we starting to discuss the solution space > here? I suspect that we will end up mandating AVFP, but we may be getting > ahead of ourselves here. Perhaps we should soften this to a suggestion to > examine RFC4585 rather than a MUST use statement. > > 7- If we are elaborating AQM schemes, we should include PIE (I know, a > shameless plug for the one I am working on - but I think it is a valid > comment nonetheless). And to be rigorous, RED, CoDel and PIE are buffer > management algorithms that operate on a given queue. One can also apply > multiple queues (FQ being one variant of multiple queues) to the problem = as > well. To be more clear, we should mention queuing algorithms like RED, > CoDel and PIE and then mention that each of these algorithms can be > optionally mapped to multiple queues. If this is going to cause a debate > of some sort, we could also just reference the broad class of AQMs and the > broad class of queue allocation schemes without elaborating the specific > algorithms. > > 8- I am reluctant to bring this into the conversation........ Once we > mention multiple queues, we may want to mention that the multiple queues > may represent different QOS buckets, and the activity in one queue may > impact the drain rate of lower priority queues, whereas the activity of a > lower priority queue will have a (very?) small impact on the drain rate of > a higher queue. I am reluctant to start this train of thought in this > document, so perhaps we do not mention multiple queues of multiple > priorities. If we are to mention different priority flows, perhaps we use > DSCP markings or VLAN tagging examples - but once again this quickly goes > down a very deep rat hole. We may just want to state that there are often > multiple priorities of flows, using the traditional SP voice, SP video, > general purpose data, and scavenger class as examples......... I am torn = on > this one, and could be convinced to not include this in the document. > > > Bill VerSteeg =2D-=20 =2D------------------------------------------------------------------ Dipl.-Ing. Mirja K=FChlewind Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart, Germany Pfaffenwaldring 47, D-70569 Stuttgart tel: +49(0)711/685-67973 email: mirja.kuehlewind@ikr.uni-stuttgart.de web: www.ikr.uni-stuttgart.de =2D------------------------------------------------------------------ From lars@netapp.com Thu Nov 14 07:08:49 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 1217A21E80E7 for ; Thu, 14 Nov 2013 07:08:49 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.756 X-Spam-Level: X-Spam-Status: No, score=-4.756 tagged_above=-999 required=5 tests=[AWL=-2.157, BAYES_00=-2.599] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 9KcXHyWm8KkM for ; Thu, 14 Nov 2013 07:08:45 -0800 (PST) Received: from mx11.netapp.com (mx11.netapp.com [216.240.18.76]) by ietfa.amsl.com (Postfix) with ESMTP id 6A13111E8132 for ; Thu, 14 Nov 2013 07:08:43 -0800 (PST) X-IronPort-AV: E=Sophos;i="4.93,700,1378882800"; d="asc'?scan'208";a="75020522" Received: from vmwexceht01-prd.hq.netapp.com ([10.106.76.239]) by mx11-out.netapp.com with ESMTP; 14 Nov 2013 07:08:25 -0800 Received: from SACEXCMBX01-PRD.hq.netapp.com ([169.254.2.51]) by vmwexceht01-prd.hq.netapp.com ([10.106.76.239]) with mapi id 14.03.0123.003; Thu, 14 Nov 2013 07:08:24 -0800 From: "Eggert, Lars" To: rmcat WG Thread-Topic: [rmcat] check the preliminary minutes Thread-Index: AQHO219sCrp3x9p0WkGE77S/npK+EJolZhGA Date: Thu, 14 Nov 2013 15:08:22 +0000 Message-ID: References: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [10.106.53.51] Content-Type: multipart/signed; boundary="Apple-Mail=_51F1441F-2A92-40D7-B81C-8AC85456F7D9"; protocol="application/pgp-signature"; micalg=pgp-sha1 MIME-Version: 1.0 Subject: Re: [rmcat] check the preliminary minutes X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 14 Nov 2013 15:08:49 -0000 --Apple-Mail=_51F1441F-2A92-40D7-B81C-8AC85456F7D9 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Some of you have made edits - thanks! There are still quite a few unattributed comments in the minutes. So if = you spoke at the microphone, please check that your name appears where = it should, and check that your question/answer was correctly captured. On 2013-11-7, at 3:16, Eggert, Lars wrote: > Hi, >=20 > thank you Gorry! for taking minutes today. >=20 > Everyone: please go to the Etherpad at = http://tools.ietf.org/wg/rmcat/minutes and check and correct the = minutes. Especially if you presented or spoke at a microphone. >=20 > Thanks, > Lars --Apple-Mail=_51F1441F-2A92-40D7-B81C-8AC85456F7D9 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="signature.asc" Content-Type: application/pgp-signature; name="signature.asc" Content-Description: Message signed with OpenPGP using GPGMail -----BEGIN PGP SIGNATURE----- iQCVAwUBUoTnY9ZcnpRveo1xAQJCfgP+IZBgry5OJNdK7xjzvPqIcahhrmCMQBbc tT5aZEM+rPHYpIgYo0o4w0P5oOK16VXRJCMvljPwn0CoPaORu/bVKRdt1pjT2VxD TpWX8QGYxuWCxUZcqbhnyGgbM5Zmd8+AsSvpSvrxJN40H9XXg5P26HYvDPO32VD6 xzrg6yp6yk8= =qOoa -----END PGP SIGNATURE----- --Apple-Mail=_51F1441F-2A92-40D7-B81C-8AC85456F7D9-- From dave.taht@gmail.com Thu Nov 14 09:02:59 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 1BAC611E8163 for ; Thu, 14 Nov 2013 09:02:59 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.449 X-Spam-Level: X-Spam-Status: No, score=-2.449 tagged_above=-999 required=5 tests=[AWL=0.151, BAYES_00=-2.599, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id E-NDQQCnAm1w for ; Thu, 14 Nov 2013 09:02:57 -0800 (PST) Received: from mail-wg0-x22c.google.com (mail-wg0-x22c.google.com [IPv6:2a00:1450:400c:c00::22c]) by ietfa.amsl.com (Postfix) with ESMTP id 32C9511E8100 for ; Thu, 14 Nov 2013 09:02:57 -0800 (PST) Received: by mail-wg0-f44.google.com with SMTP id k14so2257376wgh.11 for ; Thu, 14 Nov 2013 09:02:56 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :cc:content-type:content-transfer-encoding; bh=4rlYxHjveOV6JZqcK/QuUHJAsj2P3HGU1td/Fi/ejAs=; b=nG3J9nnpdlQElVyJnlRIDQ6TyPMQC7ZQk4eqINx0r2u1aO7Eq5tyBakeecxNMFrCop /9eX5K3KHij1PZ3g7d0Y4383lbEGReoGpUPcZUriY4nQMPlfW1XV8Gr42qV1Jmxt8+/L Ol/FxiE4ck5YHM+zyQnvXqZUQjYQUiZOY0bTag6vGWbdi088ahhc6jNekFXrrOiXrg6h ce/WNwPWu56La2wFvY0oM6Y1PKw45dOlmsaxP3hOdbgphfxX7vb0SJs1/0TrJa4h7qQG mRDn3GVWjr7ziexnRAFzW7uJEtYMJBZ+PgEIHWM+aqPp53SNL8Y8QL3dS6qjKPMRPNsy lUfg== MIME-Version: 1.0 X-Received: by 10.180.189.132 with SMTP id gi4mr3758611wic.19.1384448575512; Thu, 14 Nov 2013 09:02:55 -0800 (PST) Received: by 10.217.51.5 with HTTP; Thu, 14 Nov 2013 09:02:55 -0800 (PST) In-Reply-To: References: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> Date: Thu, 14 Nov 2013 09:02:55 -0800 Message-ID: From: Dave Taht To: "Eggert, Lars" Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Cc: rmcat WG Subject: Re: [rmcat] check the preliminary minutes X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 14 Nov 2013 17:02:59 -0000 On Thu, Nov 14, 2013 at 7:08 AM, Eggert, Lars wrote: > Some of you have made edits - thanks! > > There are still quite a few unattributed comments in the minutes. So if y= ou spoke at the microphone, please check that your name appears where it sh= ould, and check that your question/answer was correctly captured. I am not really sure if the various points regarding the differences in congestion response between audio, video, and data flows made at the mic are completely represented, starting here: "D Taht: I think audio and video have different properties." I know that what I was going through in my head at the ensuing coupled congestion discussion is different than what is up here (but I'm pretty sure I was merely mentally talking to myself) but since this thread is about the minutes and not the concepts, I'll stop, and hope the other participants elucidate what they said. I would like however to talk about those flow types at length at some future point, but not in comments on the minutes! > On 2013-11-7, at 3:16, Eggert, Lars wrote: > >> Hi, >> >> thank you Gorry! for taking minutes today. >> >> Everyone: please go to the Etherpad at http://tools.ietf.org/wg/rmcat/mi= nutes and check and correct the minutes. Especially if you presented or spo= ke at a microphone. >> >> Thanks, >> Lars > --=20 Dave T=E4ht Fixing bufferbloat with cerowrt: http://www.teklibre.com/cerowrt/subscribe.= html From michawe@ifi.uio.no Fri Nov 15 05:34:19 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C92B211E812A for ; Fri, 15 Nov 2013 05:34:19 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -102.485 X-Spam-Level: X-Spam-Status: No, score=-102.485 tagged_above=-999 required=5 tests=[AWL=0.114, BAYES_00=-2.599, USER_IN_WHITELIST=-100] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id Mdqizg6Oe4vN for ; Fri, 15 Nov 2013 05:34:14 -0800 (PST) Received: from mail-out1.uio.no (mail-out1.uio.no [IPv6:2001:700:100:10::57]) by ietfa.amsl.com (Postfix) with ESMTP id 9D45011E8185 for ; Fri, 15 Nov 2013 05:34:10 -0800 (PST) Received: from mail-mx6.uio.no ([129.240.10.40]) by mail-out1.uio.no with esmtp (Exim 4.75) (envelope-from ) id 1VhJXE-0000g1-Sg; Fri, 15 Nov 2013 14:34:08 +0100 Received: from boomerang.ifi.uio.no ([129.240.68.135]) by mail-mx6.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80.1) (envelope-from ) id 1VhJXE-0006L4-AL; Fri, 15 Nov 2013 14:34:08 +0100 Mime-Version: 1.0 (Apple Message framework v1283) Content-Type: text/plain; charset=iso-8859-1 From: Michael Welzl In-Reply-To: Date: Fri, 15 Nov 2013 14:34:07 +0100 Content-Transfer-Encoding: quoted-printable Message-Id: <279D6A01-FD1F-4F5C-8F6C-2FF9393C3428@ifi.uio.no> References: <681728B6-D37C-4804-943B-CB6A5B7720A8@netapp.com> To: Dave Taht X-Mailer: Apple Mail (2.1283) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 5 msgs/h 3 sum rcpts/h 7 sum msgs/h 4 total rcpts 9841 max rcpts/h 44 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-6.0, required=5.0, autolearn=disabled, RP_MATCHES_RCVD=-1.05, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: D5359E523CB62FD0D1BF1836286F0B5A8F1903D0 X-UiO-SPAM-Test: remote_host: 129.240.68.135 spam_score: -59 maxlevel 80 minaction 2 bait 0 mail/h: 2 total 3708 max/h 16 blacklist 0 greylist 0 ratelimit 0 Cc: rmcat WG , "Eggert, Lars" Subject: Re: [rmcat] check the preliminary minutes X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 15 Nov 2013 13:34:19 -0000 Hi, Below: On 14. nov. 2013, at 18:02, Dave Taht wrote: > On Thu, Nov 14, 2013 at 7:08 AM, Eggert, Lars wrote: >> Some of you have made edits - thanks! >>=20 >> There are still quite a few unattributed comments in the minutes. So = if you spoke at the microphone, please check that your name appears = where it should, and check that your question/answer was correctly = captured. >=20 > I am not really sure if the various points regarding the differences > in congestion response between audio, video, and data flows made at > the mic are completely represented, starting here: >=20 > "D Taht: I think audio and video have different properties." >=20 > I know that what I was going through in my head at the ensuing coupled > congestion discussion is different than what is up here (but I'm > pretty sure I was merely mentally talking to myself) but since this > thread is about the minutes and not the concepts, I'll stop, and hope > the other participants elucidate what they said. I think the minutes are correct in capturing what was said (else feel = free to fix them), but I'm happy to have the conversation here, based on = what your thoughts really were. So, what I got from you (and from James who went to the mic after you) = is a concern that different types of traffic have different = characteristics, and hence controlling them together may not be the = right thing to do. Please correct me if I'm wrong! This was my impression of the concern = and my interpretation of what was said. Assuming that this is indeed the concern, here is my answer is: this is = meant to work over a single combined bottleneck, i.e. a single queue. = The required behavior for rmcat traffic is low delay, while of course = trying to saturate the bottleneck as good as possible at the same time = (and working reasonably well with TCP if possible). Low delay is an = overarching requirement for everything, really - e.g., if you 1 = interactive audio flow and 1 data stream that doesn't care much about = delay, you're better off trying to avoid delay or the audio flow will = suffer. The next issue is then the fairness between the flows: how much of the = bandwidth do they get? Again, different flows and different traffic = types may have different priorities. Getting ideal fairness is the very = point of this exercise - coupled congestion control gives us 100% = control over the fairness, much better than what you get when you let = them "fight it out" on the bottleneck link. Plus, we're trying to get = even lower latency and loss as a result of combining the flows (but this = is work in progress). So my argument is that if done right, coupled congestion control will = only be a good thing, wherever it can be applied, whatever the mix of = traffic is (well, *in particular* when you have delay-sensitive traffic = in the game). Was that helpful? Cheers, Michael From randell-ietf@jesup.org Fri Nov 15 13:17:39 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 82FAA11E8190 for ; Fri, 15 Nov 2013 13:17:33 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: 0.001 X-Spam-Level: X-Spam-Status: No, score=0.001 tagged_above=-999 required=5 tests=[BAYES_50=0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id u3FjHjsHfC3k for ; Fri, 15 Nov 2013 13:17:25 -0800 (PST) Received: from r2-chicago.webserversystems.com (r2-chicago.webserversystems.com [173.236.101.58]) by ietfa.amsl.com (Postfix) with ESMTP id 6ACC511E815C for ; Fri, 15 Nov 2013 13:17:22 -0800 (PST) Received: from pool-173-49-144-199.phlapa.fios.verizon.net ([173.49.144.199]:63104 helo=[192.168.1.11]) by r2-chicago.webserversystems.com with esmtpsa (TLSv1:DHE-RSA-AES256-SHA:256) (Exim 4.80) (envelope-from ) id 1VhQlO-0005IY-0P for rmcat@ietf.org; Fri, 15 Nov 2013 15:17:14 -0600 Message-ID: <52868F5C.2030706@jesup.org> Date: Fri, 15 Nov 2013 16:17:16 -0500 From: Randell Jesup User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:25.0) Gecko/20100101 Thunderbird/25.0 MIME-Version: 1.0 To: rmcat@ietf.org References: In-Reply-To: Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: quoted-printable X-AntiAbuse: This header was added to track abuse, please include it with any abuse report X-AntiAbuse: Primary Hostname - r2-chicago.webserversystems.com X-AntiAbuse: Original Domain - ietf.org X-AntiAbuse: Originator/Caller UID/GID - [47 12] / [47 12] X-AntiAbuse: Sender Address Domain - jesup.org X-Get-Message-Sender-Via: r2-chicago.webserversystems.com: authenticated_id: randell@jesup.org Subject: Re: [rmcat] Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 15 Nov 2013 21:17:40 -0000 On 11/12/2013 11:27 AM, Bill Ver Steeg (versteb) wrote: > Randell- > > As I promised in Vancouver, here are my note on draft-ietf-rmcat-cc-req= uirements-00 > > In summary, this is quite a nice start. It is a difficult document to w= rite, as it is intentionally quite broad. On the whole, I like it. That's a great start for a review! ;-) Thanks! Now on to the meat... > The main concern is my item #1 > > 1- In the abstract, do we want to mention that the RMCAT CC algorithm n= eeds to be robust in the presence of a wide range of cross traffic? If no= t, we certainly need to state it strongly in the introduction and the req= uirements. Perhaps a paragraph similar to > "While the requirements for RMCAT differ from the requirements for the = other flow types, these other flow types will be present in the network. = The RMCAT congestion control algorithm must work properly when these othe= r flow types are present as cross traffic on the network." should be in e= ither the abstract or in the introduction. I would also like to see a sta= tement that the RMCAT CC algorithm should drive scavenger flows (like LED= BAT) to nearly 0, taking that BW for the RMCAT/TCP/"normal" flows. These = points are referenced in item #11, but it is a rather thin section late i= n the document. IMHO, this should be one of the primary points of the req= uirements document. Certainly upping the emphasis on robustness in the face of a variety of=20 cross traffic is good - though it's not something that we can guarantee=20 is achievable (just as we can't guarantee success in competing with=20 greedy TCP flows). As for driving LEDBAT to 0.... I'm afraid that might be desirable, but=20 unachievable. We may well find that LEDBAT will put a floor on how low=20 we can drive delay, driven by the delay constant hard-coded into LEDBAT=20 implementations (100ms(!) in the spec, 25ms in Apple's kernel=20 implementation used for things like Apple's TimeMachine(?) auto-backup=20 stuff. I'm not sure what BitTorrent is using, but I suspect it's 100ms. > There are some minor issues, which I discuss below. > > 2- Requirements section, bullet 1 - Do we need to elaborate the other c= ases in which the algorithm needs to adjust the BW? We state (in 1a) that= topology changes need to be handled. Do we need to state <1b - Changes i= n cross traffic > and <1c - changes in offered load from the application = sending data over RMCAT> I'll check the text; I don't have it in front of me at the moment > 3- Requirements section, bullet 2a - If we are enumerating types of tra= ffic that we need to be concerned with, I would more concerned with MPEG = DASH style Adaptive BitRate video than generic web browsing. Web browsing= is bursty, but at least it is well-bounded in time. ABR flows are bursty= , cyclical, and persistent. I would either remove the reference to web br= owsing or change it to include other bursty flow types (I note that OTT A= BR traffic is now more than 50% of the peak load on many networks). Good point, though MPEG DASH is not the dominant transfer protocol for=20 such data (or not yet). The major players are implementing DASH in JS=20 (i.e. under provider/application control), so that adds another wrinkle=20 in verifying this - but the idea that we have to coexist with such=20 traffic is important, and HTTP streaming and DASH and proprietary=20 protocols from Apple/MS/etc do pose real problems - HTTP streaming for=20 example may look like a very hungry realtime protocol, or it may look=20 like a periodic maximum-transfer TCP flow (Gettys showed a NetFlix BW vs = Time graph that showed a ~10 second square-wave of bandwidth use, for=20 example). > 4- Requirements section 3 - do we need to mention that there is a tempo= ral component to information sharing across streams. In other words, we m= ay consider a previous 5-tuples experience as a baseline to seed a CC alg= orithm, particularly if it is the exact same source addr/dest addr/dest p= ort/protocol? This is hinted in item #12 in the document, but the guidanc= e is quite thin in that section. Temporal hinting is particularly valuabl= e if the old session data was from 1 second ago. It is less true if it wa= s 1 day ago, but may still have some value as an initial seed for the CC = algorithm. 3b is also a bit awkward, as multiple "flows" on a given 5-tu= ple introduces some difficult SSRC concepts that are currently being disc= ussed in the AVT group. We either need to define "flow" or describe the c= oncept in broader terms. I am reluctant to open that can of worms in this= document, but if we are not clear it will be the source of endless debat= e/confusion. I hate glossaries in the front of RFCs, but we may have to d= o that. > > 5- In section 4, I do not see any details on how ECN, delay and loss in= dicate congestion. Even though we all understand that this is a complex r= elationship and will be difficult to characterize in a requirements docum= ent, I think that a few paragraphs of detail are in order. This level of = detail would be important to a less experienced reader. This is my major = concern with the draft, and I could write a paragraph or two to include t= his discussion (if there agreement that this should be discussed in this = draft) Let me take a try at it. Understandability also can help avoid=20 confusion among experts and implementations. > =20 > > 6- Is 5a a requirement, or are we starting to discuss the solution spac= e here? I suspect that we will end up mandating AVFP, but we may be getti= ng ahead of ourselves here. Perhaps we should soften this to a suggestion= to examine RFC4585 rather than a MUST use statement. Hmmm. Let me think. I think we'd said that these algorithms *if* they=20 use RTCP would need AVPF in order to respond within orders of magnitude=20 of the RTT. But there are other ways to provide the feedback needed=20 than RTCP. But that isn't to say that a new profile (AVPG ;-) )=20 couldn't also meet the needs here. > 7- If we are elaborating AQM schemes, we should include PIE (I know, a = shameless plug for the one I am working on - but I think it is a valid co= mment nonetheless). And to be rigorous, RED, CoDel and PIE are buffer man= agement algorithms that operate on a given queue. One can also apply mult= iple queues (FQ being one variant of multiple queues) to the problem as w= ell. To be more clear, we should mention queuing algorithms like RED, Co= Del and PIE and then mention that each of these algorithms can be optiona= lly mapped to multiple queues. If this is going to cause a debate of som= e sort, we could also just reference the broad class of AQMs and the broa= d class of queue allocation schemes without elaborating the specific algo= rithms. The specific were more to indicate which ones we thought were most=20 important to work/test with, so perhaps we should go to a more generic=20 statement and move mentions of specifics to Varun's document. > 8- I am reluctant to bring this into the conversation........ Once we m= ention multiple queues, we may want to mention that the multiple queues m= ay represent different QOS buckets, and the activity in one queue may imp= act the drain rate of lower priority queues, whereas the activity of a lo= wer priority queue will have a (very?) small impact on the drain rate of = a higher queue. I am reluctant to start this train of thought in this doc= ument, so perhaps we do not mention multiple queues of multiple prioritie= s. If we are to mention different priority flows, perhaps we use DSCP mar= kings or VLAN tagging examples - but once again this quickly goes down a = very deep rat hole. We may just want to state that there are often multip= le priorities of flows, using the traditional SP voice, SP video, general= purpose data, and scavenger class as examples......... I am torn on this= one, and could be convinced to not include this in the document. Fundamentally, we have 4 classes of packets we want to get across the=20 network in webrtc (which also came up in the W3 TPAC WebRTC discussions=20 this week with regard to DSCP markings/etc): Faster-than-audio=20 (typically low bandwidth and/or intermittent), audio, video, and=20 best-effort (maybe split up into interactive use, and bulk-transfer). =20 Outside of webrtc we would also have scavenger (below immediate=20 bulk-transfer). Typical browsing traffic/etc would fall into the same=20 general classes as best effort and bulk-transfer. Note that "audio" and = "video" don't mean they have to be exclusively packets of that type,=20 just that they they would have network priorities of that type. Interestingly (or problematically), splitting the congestion regimes by=20 DSCP markings really needs to be done unless you know (or strongly=20 believe) they're being ignored at the bottleneck (note: not necessarily=20 ignored everywhere). But splitting the congestion controllers has the=20 side effect (if they are in one queue at the bottleneck) of reducing the = feedback (per controller) and slowing the ability to notice changes in=20 the bottleneck (and to respond). > Bill VerStee Thanks again for a thoughtful review! --=20 Randell Jesup randell-ietf@jesup.org From versteb@cisco.com Fri Nov 15 20:30:19 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 83E7C11E8187 for ; Fri, 15 Nov 2013 20:30:19 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -10.599 X-Spam-Level: X-Spam-Status: No, score=-10.599 tagged_above=-999 required=5 tests=[BAYES_00=-2.599, RCVD_IN_DNSWL_HI=-8] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id XmGXSsuOZvcx for ; Fri, 15 Nov 2013 20:30:14 -0800 (PST) Received: from rcdn-iport-8.cisco.com (rcdn-iport-8.cisco.com [173.37.86.79]) by ietfa.amsl.com (Postfix) with ESMTP id A0E5C11E817F for ; Fri, 15 Nov 2013 20:30:14 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=11433; q=dns/txt; s=iport; t=1384576214; x=1385785814; h=from:to:subject:date:message-id: content-transfer-encoding:mime-version; bh=UXYRgEZHzuGrxQWcMsLPr4hr76phz9U4Ons8r+llBFU=; b=W4BZntLnBZni583keA97pUGCyebmZte1UROEIPcG5rMn4DWJ3EUzePof UKrxYzruuX2db7L+6KL0hhzlspiNb+1J45N7cpiqY7dnOGyaM6/CC2Y7v lwh5b6bSM+qSW6EOPE9VAN1U4T81y2N+ryziJukLOAEPViksm9ZnC8wvT Y=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: AiAFAB30hlKtJXG+/2dsb2JhbABZFoJxOFO/LIErFnSCJQEBAQIBAToxBwwHBgEZBAEBAQoUBQQ5FAkJAQQBCQkIARKHYAYNwRCOFIEkPoMagREDlC6Vb4FqgT6CKg X-IronPort-AV: E=Sophos;i="4.93,711,1378857600"; d="scan'208";a="285538226" Received: from rcdn-core2-3.cisco.com ([173.37.113.190]) by rcdn-iport-8.cisco.com with ESMTP; 16 Nov 2013 04:30:14 +0000 Received: from xhc-aln-x11.cisco.com (xhc-aln-x11.cisco.com [173.36.12.85]) by rcdn-core2-3.cisco.com (8.14.5/8.14.5) with ESMTP id rAG4UDxv028265 (version=TLSv1/SSLv3 cipher=AES128-SHA bits=128 verify=FAIL); Sat, 16 Nov 2013 04:30:13 GMT Received: from xmb-aln-x05.cisco.com ([169.254.11.193]) by xhc-aln-x11.cisco.com ([173.36.12.85]) with mapi id 14.03.0123.003; Fri, 15 Nov 2013 22:30:13 -0600 From: "Bill Ver Steeg (versteb)" To: Randell Jesup , "rmcat@ietf.org" Thread-Topic: Bursty sending ---- was RE: [rmcat] Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 Thread-Index: Ac7ihISL5tCCpUb6TpWSvoLTGbEHcA== Date: Sat, 16 Nov 2013 04:30:13 +0000 Message-ID: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.117.227.37] Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Subject: [rmcat] Bursty sending ---- was RE: Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sat, 16 Nov 2013 04:30:19 -0000 Randell- A side note on bursty ABR video flows follows. Not 100% germane to the disc= ussion in RMCAT, but my pet peeve nonetheless. In the enclosed note, I was using "MPEG-DASH-like" as a place holder for th= e broad class of HTTP based rate adaptive video flows. All of the legacy AB= R video flows send in a very nasty square wave pattern. The square wave cor= responds to the length of the fetched chuck. The 10 second chunks are prett= y bad, but the 2 second chunk stuff can really cause huge swings in bloat. = This is particularly true when the video rate is just below the line rate, = so the "on" portion of the square wave is much longer than the "off cycle".= The worst case is when the chunk size is large relative to the TCP window = size (with large tail-drop buffers). Cross traffic can get really hammered. The notion that "bursty is bad" is not new. We found it several times over = the years, in old video servers over-running QAM buffers, VSAT networks, te= rminal servers, etc. We seem to forget and re-discover this every few years= . The PIE/CoDel buffer management algorithms clearly help, and so does FQ. = Bursty senders can make buffer management quite difficult, though. There have been several recent papers on ways to reduce the burstiness of A= BR flows. Some are client based application algorithms that try to move awa= y from CWND based flow control and towards RWND based flow control ( SABRE-= http://www.cc.gatech.edu/~amansy/GT-CS-12-07.pdf - presentation from last = Spring's ACM ) and some are server based algorithms that use either TCP or = application pacing (http://www.ietf.org/proceedings/88/slides/slides-88-icc= rg-6.pdf - presented in ICCRG in Vancouver ).=20 Bvs -----Original Message----- From: rmcat-bounces@ietf.org [mailto:rmcat-bounces@ietf.org] On Behalf Of R= andell Jesup Sent: Friday, November 15, 2013 4:17 PM To: rmcat@ietf.org Subject: Re: [rmcat] Review of Congestion Control Requirements For RMCAT - = draft-ietf-rmcat-cc-requirements-00 On 11/12/2013 11:27 AM, Bill Ver Steeg (versteb) wrote: > Randell- > > As I promised in Vancouver, here are my note on=20 > draft-ietf-rmcat-cc-requirements-00 > > In summary, this is quite a nice start. It is a difficult document to wri= te, as it is intentionally quite broad. On the whole, I like it. That's a great start for a review! ;-) Thanks! Now on to the meat... > The main concern is my item #1 > > 1- In the abstract, do we want to mention that the RMCAT CC algorithm=20 > needs to be robust in the presence of a wide range of cross traffic? If n= ot, we certainly need to state it strongly in the introduction and the requ= irements. Perhaps a paragraph similar to "While the requirements for RMCAT = differ from the requirements for the other flow types, these other flow typ= es will be present in the network. The RMCAT congestion control algorithm m= ust work properly when these other flow types are present as cross traffic = on the network." should be in either the abstract or in the introduction. I= would also like to see a statement that the RMCAT CC algorithm should driv= e scavenger flows (like LEDBAT) to nearly 0, taking that BW for the RMCAT/T= CP/"normal" flows. These points are referenced in item #11, but it is a rat= her thin section late in the document. IMHO, this should be one of the prim= ary points of the requirements document. Certainly upping the emphasis on robustness in the face of a variety of cro= ss traffic is good - though it's not something that we can guarantee is ach= ievable (just as we can't guarantee success in competing with greedy TCP fl= ows). As for driving LEDBAT to 0.... I'm afraid that might be desirable, but unac= hievable. We may well find that LEDBAT will put a floor on how low we can = drive delay, driven by the delay constant hard-coded into LEDBAT implementa= tions (100ms(!) in the spec, 25ms in Apple's kernel implementation used for= things like Apple's TimeMachine(?) auto-backup stuff. I'm not sure what B= itTorrent is using, but I suspect it's 100ms. > There are some minor issues, which I discuss below. > > 2- Requirements section, bullet 1 - Do we need to elaborate the other=20 > cases in which the algorithm needs to adjust the BW? We state (in 1a)=20 > that topology changes need to be handled. Do we need to state <1b -=20 > Changes in cross traffic > and <1c - changes in offered load from the=20 > application sending data over RMCAT> I'll check the text; I don't have it in front of me at the moment > 3- Requirements section, bullet 2a - If we are enumerating types of traff= ic that we need to be concerned with, I would more concerned with MPEG DASH= style Adaptive BitRate video than generic web browsing. Web browsing is bu= rsty, but at least it is well-bounded in time. ABR flows are bursty, cyclic= al, and persistent. I would either remove the reference to web browsing or = change it to include other bursty flow types (I note that OTT ABR traffic i= s now more than 50% of the peak load on many networks). Good point, though MPEG DASH is not the dominant transfer protocol for such= data (or not yet). The major players are implementing DASH in JS (i.e. un= der provider/application control), so that adds another wrinkle in verifyin= g this - but the idea that we have to coexist with such traffic is importan= t, and HTTP streaming and DASH and proprietary protocols from Apple/MS/etc = do pose real problems - HTTP streaming for example may look like a very hun= gry realtime protocol, or it may look like a periodic maximum-transfer TCP = flow (Gettys showed a NetFlix BW vs Time graph that showed a ~10 second squ= are-wave of bandwidth use, for example). > 4- Requirements section 3 - do we need to mention that there is a tempora= l component to information sharing across streams. In other words, we may c= onsider a previous 5-tuples experience as a baseline to seed a CC algorithm= , particularly if it is the exact same source addr/dest addr/dest port/prot= ocol? This is hinted in item #12 in the document, but the guidance is quite= thin in that section. Temporal hinting is particularly valuable if the old= session data was from 1 second ago. It is less true if it was 1 day ago, b= ut may still have some value as an initial seed for the CC algorithm. 3b is= also a bit awkward, as multiple "flows" on a given 5-tuple introduces som= e difficult SSRC concepts that are currently being discussed in the AVT gro= up. We either need to define "flow" or describe the concept in broader term= s. I am reluctant to open that can of worms in this document, but if we are= not clear it will be the source of endless debate/confusion. I hate glossa= ries in the front of RFCs, but we may have to do that. > > 5- In section 4, I do not see any details on how ECN, delay and loss=20 > indicate congestion. Even though we all understand that this is a=20 > complex relationship and will be difficult to characterize in a=20 > requirements document, I think that a few paragraphs of detail are in=20 > order. This level of detail would be important to a less experienced=20 > reader. This is my major concern with the draft, and I could write a=20 > paragraph or two to include this discussion (if there agreement that=20 > this should be discussed in this draft) Let me take a try at it. Understandability also can help avoid confusion a= mong experts and implementations. > =20 > > 6- Is 5a a requirement, or are we starting to discuss the solution space = here? I suspect that we will end up mandating AVFP, but we may be getting a= head of ourselves here. Perhaps we should soften this to a suggestion to ex= amine RFC4585 rather than a MUST use statement. Hmmm. Let me think. I think we'd said that these algorithms *if* they use= RTCP would need AVPF in order to respond within orders of magnitude of the= RTT. But there are other ways to provide the feedback needed than RTCP. = But that isn't to say that a new profile (AVPG ;-) ) couldn't also meet the= needs here. > 7- If we are elaborating AQM schemes, we should include PIE (I know, a sh= ameless plug for the one I am working on - but I think it is a valid commen= t nonetheless). And to be rigorous, RED, CoDel and PIE are buffer managemen= t algorithms that operate on a given queue. One can also apply multiple que= ues (FQ being one variant of multiple queues) to the problem as well. To b= e more clear, we should mention queuing algorithms like RED, CoDel and PIE = and then mention that each of these algorithms can be optionally mapped to = multiple queues. If this is going to cause a debate of some sort, we could= also just reference the broad class of AQMs and the broad class of queue a= llocation schemes without elaborating the specific algorithms. The specific were more to indicate which ones we thought were most importan= t to work/test with, so perhaps we should go to a more generic statement an= d move mentions of specifics to Varun's document. > 8- I am reluctant to bring this into the conversation........ Once we men= tion multiple queues, we may want to mention that the multiple queues may r= epresent different QOS buckets, and the activity in one queue may impact th= e drain rate of lower priority queues, whereas the activity of a lower prio= rity queue will have a (very?) small impact on the drain rate of a higher q= ueue. I am reluctant to start this train of thought in this document, so pe= rhaps we do not mention multiple queues of multiple priorities. If we are t= o mention different priority flows, perhaps we use DSCP markings or VLAN ta= gging examples - but once again this quickly goes down a very deep rat hole= . We may just want to state that there are often multiple priorities of flo= ws, using the traditional SP voice, SP video, general purpose data, and sca= venger class as examples......... I am torn on this one, and could be convi= nced to not include this in the document. Fundamentally, we have 4 classes of packets we want to get across the netwo= rk in webrtc (which also came up in the W3 TPAC WebRTC discussions this wee= k with regard to DSCP markings/etc): Faster-than-audio (typically low bandw= idth and/or intermittent), audio, video, and best-effort (maybe split up in= to interactive use, and bulk-transfer). =20 Outside of webrtc we would also have scavenger (below immediate bulk-transf= er). Typical browsing traffic/etc would fall into the same general classes= as best effort and bulk-transfer. Note that "audio" and "video" don't mea= n they have to be exclusively packets of that type, just that they they wou= ld have network priorities of that type. Interestingly (or problematically), splitting the congestion regimes by DSC= P markings really needs to be done unless you know (or strongly believe) they're being ignored at the bottleneck (note: not necessarily ign= ored everywhere). But splitting the congestion controllers has the side ef= fect (if they are in one queue at the bottleneck) of reducing the feedback = (per controller) and slowing the ability to notice changes in the bottlenec= k (and to respond). > Bill VerStee Thanks again for a thoughtful review! -- Randell Jesup randell-ietf@jesup.org From dave.taht@gmail.com Sat Nov 16 09:35:24 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id ECDDB11E8106 for ; Sat, 16 Nov 2013 09:35:24 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -0.193 X-Spam-Level: X-Spam-Status: No, score=-0.193 tagged_above=-999 required=5 tests=[AWL=-2.181, BAYES_00=-2.599, FRT_STOCK2=3.988, J_CHICKENPOX_63=0.6, NO_RELAYS=-0.001] Received: from mail.ietf.org ([12.22.58.30]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id dksWokhcPyJ2 for ; Sat, 16 Nov 2013 09:35:22 -0800 (PST) Received: from mail-wi0-x22d.google.com (mail-wi0-x22d.google.com [IPv6:2a00:1450:400c:c05::22d]) by ietfa.amsl.com (Postfix) with ESMTP id 7550A11E80F8 for ; Sat, 16 Nov 2013 09:35:22 -0800 (PST) Received: by mail-wi0-f173.google.com with SMTP id ey16so2260067wid.12 for ; Sat, 16 Nov 2013 09:35:21 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :cc:content-type:content-transfer-encoding; bh=nikDtH7ZL8M/I6noTTzTdhhKivdQjQp+mxziH48Bfng=; b=xpHEmtiUX+1Bm8yC9pkxlR2Eb0yhSgagS+qKG2DK9ghbOi068nUCkePG3sQmmRGHEM Omt49SFbWhREMXbTxuUPa5SiW8MgIfraZlMtHLllNBdZ7JY4rq6jVP7DYnrABtWj/tM/ PicEMNg343QNnsZ/CCl5wtaThyZeMuhsXR2m0wMqrDthDq5amoZFbqVHLoMigw2ThY0d hbd9sdgpWGLekjKAazw/ytmcjidnskPFR5KXFbldh6H+kAFVnBBzEJpR8zThsgWU3t/x rRVrHpd2/Fq+5+mKtF4AJjX9xexNVDqhcUzNHMtKoG2/6NPyX80LUXHLgNTIvLzxEsvv IO+g== MIME-Version: 1.0 X-Received: by 10.180.79.230 with SMTP id m6mr11330864wix.19.1384623321595; Sat, 16 Nov 2013 09:35:21 -0800 (PST) Received: by 10.217.51.5 with HTTP; Sat, 16 Nov 2013 09:35:21 -0800 (PST) In-Reply-To: References: Date: Sat, 16 Nov 2013 09:35:21 -0800 Message-ID: From: Dave Taht To: "Bill Ver Steeg (versteb)" Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Cc: Randell Jesup , "rmcat@ietf.org" Subject: Re: [rmcat] Bursty sending ---- was RE: Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.12 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sat, 16 Nov 2013 17:35:25 -0000 On Fri, Nov 15, 2013 at 8:30 PM, Bill Ver Steeg (versteb) wrote: > Randell- > > A side note on bursty ABR video flows follows. Not 100% germane to the di= scussion in RMCAT, but my pet peeve nonetheless. > > In the enclosed note, I was using "MPEG-DASH-like" as a place holder for = the broad class of HTTP based rate adaptive video flows. All of the legacy = ABR video flows send in a very nasty square wave pattern. The square wave c= orresponds to the length of the fetched chuck. The 10 second chunks are pre= tty bad, but the 2 second chunk stuff can really cause huge swings in bloat= . This is particularly true when the video rate is just below the line rate= , so the "on" portion of the square wave is much longer than the "off cycle= ". The worst case is when the chunk size is large relative to the TCP windo= w size (with large tail-drop buffers). Cross traffic can get really hammere= d. > > The notion that "bursty is bad" is not new. We found it several times ove= r the years, in old video servers over-running QAM buffers, VSAT networks, = terminal servers, etc. We seem to forget and re-discover this every few yea= rs. The PIE/CoDel buffer management algorithms clearly help, and so does FQ= . Bursty senders can make buffer management quite difficult, though. +1 > > There have been several recent papers on ways to reduce the burstiness of= ABR flows. Some are client based application algorithms that try to move a= way from CWND based flow control and towards RWND based flow control ( SABR= E- http://www.cc.gatech.edu/~amansy/GT-CS-12-07.pdf - presentation from las= t Spring's ACM ) and some are server based algorithms that use either TCP o= r application pacing (http://www.ietf.org/proceedings/88/slides/slides-88-i= ccrg-6.pdf - presented in ICCRG in Vancouver ). I am tickled pink with the paced fq scheduler (discussed in the paper above). It is in linux 3.12, which was released last week. There have been a few teething pains with the TSO fixes, but the results have generally been spectacular there too. I think we are not long from the day where the default linux packet scheduler can be set to paced fq for servers and desktops and fq_codel, for routers. The machinery to do so is now a single line in sysctl http://comments.gmane.org/gmane.linux.network/281378 I do hope more people experiment with this stuff now that it is so easy! In an rmcat or DASH context there is now a new setsockopt to control the pacing at a finer level. it was originally tcp specific, I am pretty sure it was extended to udp as well, but am not sure what name it retained http://lwn.net/Articles/560082/ > > Bvs > > > -----Original Message----- > From: rmcat-bounces@ietf.org [mailto:rmcat-bounces@ietf.org] On Behalf Of= Randell Jesup > Sent: Friday, November 15, 2013 4:17 PM > To: rmcat@ietf.org > Subject: Re: [rmcat] Review of Congestion Control Requirements For RMCAT = - draft-ietf-rmcat-cc-requirements-00 > > On 11/12/2013 11:27 AM, Bill Ver Steeg (versteb) wrote: >> Randell- >> >> As I promised in Vancouver, here are my note on >> draft-ietf-rmcat-cc-requirements-00 >> >> In summary, this is quite a nice start. It is a difficult document to wr= ite, as it is intentionally quite broad. On the whole, I like it. > > That's a great start for a review! ;-) Thanks! Now on to the meat... > >> The main concern is my item #1 >> >> 1- In the abstract, do we want to mention that the RMCAT CC algorithm >> needs to be robust in the presence of a wide range of cross traffic? If = not, we certainly need to state it strongly in the introduction and the req= uirements. Perhaps a paragraph similar to "While the requirements for RMCAT= differ from the requirements for the other flow types, these other flow ty= pes will be present in the network. The RMCAT congestion control algorithm = must work properly when these other flow types are present as cross traffic= on the network." should be in either the abstract or in the introduction. = I would also like to see a statement that the RMCAT CC algorithm should dri= ve scavenger flows (like LEDBAT) to nearly 0, taking that BW for the RMCAT/= TCP/"normal" flows. These points are referenced in item #11, but it is a ra= ther thin section late in the document. IMHO, this should be one of the pri= mary points of the requirements document. > > Certainly upping the emphasis on robustness in the face of a variety of c= ross traffic is good - though it's not something that we can guarantee is a= chievable (just as we can't guarantee success in competing with greedy TCP = flows). > > As for driving LEDBAT to 0.... I'm afraid that might be desirable, but un= achievable. We may well find that LEDBAT will put a floor on how low we ca= n drive delay, driven by the delay constant hard-coded into LEDBAT implemen= tations (100ms(!) in the spec, 25ms in Apple's kernel implementation used f= or things like Apple's TimeMachine(?) auto-backup stuff. I'm not sure what= BitTorrent is using, but I suspect it's 100ms. 100ms appears to be about correct for what is in the field for uTP. I still see a lot of tcp based torrent traffic tho... I note that there are two sets of dynamics in play here, slow start and congestion avoidance... and that any form of aqm or packet scheduling turn delay based e2e algorithms into loss/marking based ones: http://perso.telecom-paristech.fr/~drossi/paper/rossi13tma-b.pdf Some LPCC folk think 100ms of induced delay is acceptable, but I don't. 25ms is ok... > >> There are some minor issues, which I discuss below. >> >> 2- Requirements section, bullet 1 - Do we need to elaborate the other >> cases in which the algorithm needs to adjust the BW? We state (in 1a) >> that topology changes need to be handled. Do we need to state <1b - >> Changes in cross traffic > and <1c - changes in offered load from the >> application sending data over RMCAT> > > I'll check the text; I don't have it in front of me at the moment > >> 3- Requirements section, bullet 2a - If we are enumerating types of traf= fic that we need to be concerned with, I would more concerned with MPEG DAS= H style Adaptive BitRate video than generic web browsing. Web browsing is b= ursty, but at least it is well-bounded in time. ABR flows are bursty, cycli= cal, and persistent. I would either remove the reference to web browsing or= change it to include other bursty flow types (I note that OTT ABR traffic = is now more than 50% of the peak load on many networks). > > Good point, though MPEG DASH is not the dominant transfer protocol for su= ch data (or not yet). The major players are implementing DASH in JS (i.e. = under provider/application control), so that adds another wrinkle in verify= ing this - but the idea that we have to coexist with such traffic is import= ant, and HTTP streaming and DASH and proprietary protocols from Apple/MS/et= c do pose real problems - HTTP streaming for example may look like a very h= ungry realtime protocol, or it may look like a periodic maximum-transfer TC= P flow (Gettys showed a NetFlix BW vs Time graph that showed a ~10 second s= quare-wave of bandwidth use, for example). That was me, actually. It is generally my hope that with the short RTTs we find with the movie traffic that the pacing stuff is going to massively help, and fq + a drop strategy on the gateway makes it nearly invisible. >> 4- Requirements section 3 - do we need to mention that there is a tempor= al component to information sharing across streams. In other words, we may = consider a previous 5-tuples experience as a baseline to seed a CC algorith= m, particularly if it is the exact same source addr/dest addr/dest port/pro= tocol? This is hinted in item #12 in the document, but the guidance is quit= e thin in that section. Temporal hinting is particularly valuable if the ol= d session data was from 1 second ago. It is less true if it was 1 day ago, = but may still have some value as an initial seed for the CC algorithm. 3b i= s also a bit awkward, as multiple "flows" on a given 5-tuple introduces so= me difficult SSRC concepts that are currently being discussed in the AVT gr= oup. We either need to define "flow" or describe the concept in broader ter= ms. I am reluctant to open that can of worms in this document, but if we ar= e not clear it will be the source of endless debate/confusion. I hate gloss= aries in the front of RFCs, but we may have to do that. My brain crashed here. I simply haven't collated all the documents in play in my head enough yet. My own take on things is that distinct flows should have a distinct 5 tuple, on each of voice, video and data. I don't care about the impact on "available nat ports", when compared to the ephemeral usage of a given web transaction on NATed ports (hundreds opened, closed, and left in a wait state for while), it's trivial. (I hope I'm not alone in this) >> >> 5- In section 4, I do not see any details on how ECN, delay and loss >> indicate congestion. Even though we all understand that this is a >> complex relationship and will be difficult to characterize in a >> requirements document, I think that a few paragraphs of detail are in >> order. This level of detail would be important to a less experienced >> reader. This is my major concern with the draft, and I could write a >> paragraph or two to include this discussion (if there agreement that >> this should be discussed in this draft) > > Let me take a try at it. Understandability also can help avoid confusion= among experts and implementations. +1 >> >> >> 6- Is 5a a requirement, or are we starting to discuss the solution space= here? I suspect that we will end up mandating AVFP, but we may be getting = ahead of ourselves here. Perhaps we should soften this to a suggestion to e= xamine RFC4585 rather than a MUST use statement. > > Hmmm. Let me think. I think we'd said that these algorithms *if* they u= se RTCP would need AVPF in order to respond within orders of magnitude of t= he RTT. But there are other ways to provide the feedback needed than RTCP.= But that isn't to say that a new profile (AVPG ;-) ) couldn't also meet t= he needs here. > >> 7- If we are elaborating AQM schemes, we should include PIE (I know, a s= hameless plug for the one I am working on - but I think it is a valid comme= nt nonetheless). And to be rigorous, RED, CoDel and PIE are buffer manageme= nt algorithms that operate on a given queue. One can also apply multiple qu= eues (FQ being one variant of multiple queues) to the problem as well. To = be more clear, we should mention queuing algorithms like RED, CoDel and PIE= and then mention that each of these algorithms can be optionally mapped to= multiple queues. Nobody from the codel world recommends codel by itself, in its present form= . "If we're sticking code into boxes to deploy codel, don't do that. Deploy fq_codel. It's just an across the board win." - Van Jacobson So please feel free to mention fq_codel. :) >If this is going to cause a debate of some sort, we could also just refere= nce the broad class of AQMs and the broad class of queue allocation schemes= without elaborating the specific algorithms. fq_codel has been *extensively tested* against google hangouts in particular. I'm looking forward to finding ways to express those tests and results against multiple algorithms. I'm kind of hoping that the ipv6 versions of the webrtc code land soon (anybody?) so as to get the stun and turn things out of the loop for saner testing... In the interim I have a pretty heavily instrumented setup and will gladly "chat" with anyone over webrtc who wants some data. > The specific were more to indicate which ones we thought were most import= ant to work/test with, so perhaps we should go to a more generic statement = and move mentions of specifics to Varun's document. > >> 8- I am reluctant to bring this into the conversation........ Once we me= ntion multiple queues, we may want to mention that the multiple queues may = represent different QOS buckets, and the activity in one queue may impact t= he drain rate of lower priority queues, whereas the activity of a lower pri= ority queue will have a (very?) small impact on the drain rate of a higher = queue. I am reluctant to start this train of thought in this document, so p= erhaps we do not mention multiple queues of multiple priorities. If we are = to mention different priority flows, perhaps we use DSCP markings or VLAN t= agging examples - but once again this quickly goes down a very deep rat hol= e. We may just want to state that there are often multiple priorities of fl= ows, using the traditional SP voice, SP video, general purpose data, and sc= avenger class as examples......... I am torn on this one, and could be conv= inced to not include this in the document. The dscp discussion is a rathole, indeed. > Fundamentally, we have 4 classes of packets we want to get across the net= work in webrtc (which also came up in the W3 TPAC WebRTC discussions this w= eek with regard to DSCP markings/etc): Faster-than-audio (typically low ban= dwidth and/or intermittent), audio, video, and best-effort (maybe split up = into interactive use, and bulk-transfer). I have long been showing off a shaper that I thought met these requirements and it has been deployed for a few years now. The problem I have is in generating traffic that can exercise it well, as however it is being defined to be used. See simple.qos in the ceropackages repository. The package works on current versions of linux, can use all the new aqms and packet schedulers, and has dscp filters to sort stuff into 3 htb buckets. https://github.com/dtaht/ceropackages-3.3/tree/master/net/aqm-scripts/files= /usr/lib/aqm > Outside of webrtc we would also have scavenger (below immediate bulk-tran= sfer). Typical browsing traffic/etc would fall into the same general class= es as best effort and bulk-transfer. Note that "audio" and "video" don't m= ean they have to be exclusively packets of that type, just that they they w= ould have network priorities of that type. > > Interestingly (or problematically), splitting the congestion regimes by D= SCP markings really needs to be done unless you know (or strongly > believe) they're being ignored at the bottleneck (note: not necessarily i= gnored everywhere). But splitting the congestion controllers has the side = effect (if they are in one queue at the bottleneck) of reducing the feedbac= k (per controller) and slowing the ability to notice changes in the bottlen= eck (and to respond). > >> Bill VerStee > > Thanks again for a thoughtful review! > > -- > Randell Jesup > randell-ietf@jesup.org > > --=20 Dave T=E4ht Fixing bufferbloat with cerowrt: http://www.teklibre.com/cerowrt/subscribe.= html From michawe@ifi.uio.no Wed Nov 20 07:00:30 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (ietfa.amsl.com [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id BDC5D1ADFF5 for ; Wed, 20 Nov 2013 07:00:30 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.424 X-Spam-Level: X-Spam-Status: No, score=-2.424 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-0.525] autolearn=ham Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id CTPU4HivzlPu for ; Wed, 20 Nov 2013 07:00:27 -0800 (PST) Received: from mail-out2.uio.no (mail-out2.uio.no [IPv6:2001:700:100:10::58]) by ietfa.amsl.com (Postfix) with ESMTP id D2BB71AE3E7 for ; Wed, 20 Nov 2013 07:00:26 -0800 (PST) Received: from mail-mx2.uio.no ([129.240.10.30]) by mail-out2.uio.no with esmtp (Exim 4.75) (envelope-from ) id 1Vj9GN-00045E-LQ for rmcat@ietf.org; Wed, 20 Nov 2013 16:00:19 +0100 Received: from boomerang.ifi.uio.no ([129.240.68.135]) by mail-mx2.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80) (envelope-from ) id 1Vj9GN-0007Ad-0w for rmcat@ietf.org; Wed, 20 Nov 2013 16:00:19 +0100 From: Michael Welzl Content-Type: multipart/alternative; boundary="Apple-Mail=_F1893D26-0E50-414D-8D10-DB9558A45C57" Date: Wed, 20 Nov 2013 16:00:17 +0100 Message-Id: <83DF0463-6043-44AA-854E-645994E5092D@ifi.uio.no> To: rmcat WG Mime-Version: 1.0 (Apple Message framework v1283) X-Mailer: Apple Mail (2.1283) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 1 msgs/h 1 sum rcpts/h 4 sum msgs/h 3 total rcpts 10042 max rcpts/h 44 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-6.0, required=5.0, autolearn=disabled, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-1.05, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: E811A813EE5A67A6B32271EFF8C39192C13641F4 X-UiO-SPAM-Test: remote_host: 129.240.68.135 spam_score: -59 maxlevel 80 minaction 2 bait 0 mail/h: 1 total 3771 max/h 16 blacklist 0 greylist 0 ratelimit 0 Subject: [rmcat] Fairness X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 20 Nov 2013 15:00:31 -0000 --Apple-Mail=_F1893D26-0E50-414D-8D10-DB9558A45C57 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=us-ascii Hi, I write this because the requirements draft mentions that fairness is = hard to define, and we have gone into the direction of rather defining = "unfairness" and all that... as usual, I think it's overcomplicating the = situation and I have a much simpler solution, and as usual, it's = probably broken: as you already know, I keep trying to simplify things = here, and often fail in doing so. I therefore suspect that the simple = view I'm going to present here is going to be met with the same = reaction, so apologies in advance - let's not have a long and boring = discussion: if someone can make it clear that this idea is rubbish, = fine, let's just stop there. With that disclaimer out of the way, let me try to define fairness, for = the case of fairness between flows of a single congestion control type = (more below about TCP): *** Send at roughly the same rate under similar conditions. *** Define "similar conditions": - conditions that influence the behavior of the congestion controller = (rtt, packet loss... but also how much the sending application wants to = send) Why this definition? While it's known that fairness is in principle a much more complicated = story (I guess most of us know the "dismantling a religion" paper: = http://www.sigcomm.org/sites/default/files/ccr/papers/2007/April/1232919-1= 232926.pdf ), we have to start somewhere. e.g.: if we have a mechanism that, irrespective of the RTT, always = converges to the exact same rate, then producing a different form of = fairness becomes a matter of locally applying a weight (multiplier). I'd = argue that any form of fairness could be achieved by applying local = weights. Demanding "irrespective of the RTT" is maybe a bit too much as it limits = the design of the controller (it's possible, but not really a simple = thing). Most congestion controls are RTT-dependent, so I guess this is = something we'll just have to live with. But, then, if we don't require = them to roughly get the same rate in case of the same sender behavior, = same RTT and same packet loss, we just get something uncontrollable and = useless. For instance, applying weights as described in the previous = paragraph won't ever get us what we want if we don't have a known = starting point. Again, this is not an argument in favor of "flow rate fairness", against = the arguments in Bob Briscoe's paper - it's an argument that, when we = don't know if we have e.g. conex or coupled congestion control = operating, "flow rate fairness" gives us a starting point to work with = and a "known" behavior for all congestion controllers, and that's = perhaps just what we need to request. My definition is a bit vague - it doesn't talk about the time to = converge, it doesn't define "roughly", but I guess we'll have to live = with some slack here anyway. Thoughts? :-) About TCP, I think we just need to state that: *** An RMCAT congestion control should not starve competing TCP flows and = not be starved by them. *** Cheers, Michael PS: please don't get this discussion mixed up with our proposal for = coupled congestion control: this can enforce any kind of fairness and = actually eliminates the need for a fairness requirement - but we = probably can't simply assume that all rmcat flows will always have = coupled congestion control installed. PPS: sorry for sending this so late! It only occurred to me now that = this simpler solution might be possible... --Apple-Mail=_F1893D26-0E50-414D-8D10-DB9558A45C57 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=us-ascii

With that disclaimer out of the way, let = me try to define fairness, for the case of fairness between flows of a = single congestion control type (more below about = TCP):

***
Send at roughly the same = rate under similar = conditions.
***

Define "similar = conditions":
- conditions that influence the behavior of the = congestion controller (rtt, packet loss... but also how much the sending = application wants to send)

Why this = definition?

While it's known that fairness is = in principle a much more complicated story (I guess most of us know the = "dismantling a religion" paper: http://www.sigcomm.org/sites/default/files/ccr/papers/= 2007/April/1232919-1232926.pdf ), we have to start = somewhere.

e.g.: if we have a mechanism that, = irrespective of the RTT, always converges to the exact same rate, then = producing a different form of fairness becomes a matter of locally = applying a weight (multiplier). I'd argue that any form of fairness = could be achieved by applying local = weights.

Demanding "irrespective of the RTT" is = maybe a bit too much as it limits the design of the controller (it's = possible, but not really a simple thing). Most congestion controls are = RTT-dependent, so I guess this is something we'll just have to live = with. But, then, if we don't require them to roughly get the same rate = in case of the same sender behavior, same RTT and same packet loss, we = just get something uncontrollable and useless. For instance, applying = weights as described in the previous paragraph won't ever get us what we = want if we don't have a known starting = point.

Again, this is not an argument in favor = of "flow rate fairness", against the arguments in Bob Briscoe's paper - = it's an argument that, when we don't know if we have e.g. conex or = coupled congestion control operating, "flow rate fairness" gives us a = starting point to work with and a "known" behavior for all congestion = controllers, and that's perhaps just what we need to = request.

My definition is a bit vague - it = doesn't talk about the time to converge, it doesn't define "roughly", = but I guess we'll have to live with some slack here = anyway.

Thoughts? =  :-)


About TCP, I think we = just need to state that:

***
An RMCAT = congestion control should not starve competing TCP flows and not be = starved by = them.
***

Cheers,
Michael

PS: please don't get this discussion mixed up with = our proposal for coupled congestion control: this can enforce any kind = of fairness and actually eliminates the need for a fairness requirement = - but we probably can't simply assume that all rmcat flows will always = have coupled congestion control installed.

PPS: = sorry for sending this so late! It only occurred to me now that this = simpler solution might be possible...

= --Apple-Mail=_F1893D26-0E50-414D-8D10-DB9558A45C57-- From dave.taht@gmail.com Thu Nov 21 14:49:34 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (ietfa.amsl.com [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 153BB1AE3CD for ; Thu, 21 Nov 2013 14:49:34 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, SPF_PASS=-0.001] autolearn=ham Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id rJsCmmUwnZNo for ; Thu, 21 Nov 2013 14:49:29 -0800 (PST) Received: from mail-wg0-x22a.google.com (mail-wg0-x22a.google.com [IPv6:2a00:1450:400c:c00::22a]) by ietfa.amsl.com (Postfix) with ESMTP id 784EE1AE3C4 for ; Thu, 21 Nov 2013 14:49:29 -0800 (PST) Received: by mail-wg0-f42.google.com with SMTP id k14so682225wgh.1 for ; Thu, 21 Nov 2013 14:49:22 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :cc:content-type; bh=33itvoxzjorg84cfuVy7G/6Xxq/2QHlU7MqK65wSZEg=; b=NPnQL4RmPVhBYMoIs9d5xmDKlRce/PMt89IPbKKQo4vra/I3NuJ6OwrvTrAfZA4sUm b8vdY+exHslI8XEvdtzakIkvCkh8+3p6IE+huno/t3BDAe6Yq1yDykMBbywS0HuPQmJ5 BdgTD86PVuaN6zn4/dhwzI4iq8Wok2lSB6kza3m+wtYo06P4ldPuNDZcchJvsznXiWW9 iHlwB5R/2DEVmeI5L915NFwYm7sMtvpa9dk+XLOM5HdK0ydDrHolr6xRJQoqhsbxHiQk +VzY4x8OzCT6cXKDPZTXhmsMTM+WjFhjYr1mLNJCBNG+rC4eDXDjwGRjH3LjiEG2I3X+ +UFA== MIME-Version: 1.0 X-Received: by 10.180.74.52 with SMTP id q20mr32534254wiv.60.1385074162218; Thu, 21 Nov 2013 14:49:22 -0800 (PST) Received: by 10.217.51.5 with HTTP; Thu, 21 Nov 2013 14:49:22 -0800 (PST) In-Reply-To: <52868F5C.2030706@jesup.org> References: <52868F5C.2030706@jesup.org> Date: Thu, 21 Nov 2013 14:49:22 -0800 Message-ID: From: Dave Taht To: Randell Jesup Content-Type: multipart/alternative; boundary=f46d043890796d3d6004ebb7b51d Cc: "rmcat@ietf.org" Subject: Re: [rmcat] Review of Congestion Control Requirements For RMCAT - draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 21 Nov 2013 22:49:34 -0000 --f46d043890796d3d6004ebb7b51d Content-Type: text/plain; charset=ISO-8859-1 I could have sworn I'd commented on this already, but don't see it On Nov 15, 2013 1:17 PM, "Randell Jesup" wrote: > On 11/12/2013 11:27 AM, Bill Ver Steeg (versteb) wrote: > >> Randell- >> >> As I promised in Vancouver, here are my note on draft-ietf-rmcat-cc- >> requirements-00 >> >> In summary, this is quite a nice start. It is a difficult document to >> write, as it is intentionally quite broad. On the whole, I like it. >> > > That's a great start for a review! ;-) Thanks! Now on to the meat... > > The main concern is my item #1 >> >> 1- In the abstract, do we want to mention that the RMCAT CC algorithm >> needs to be robust in the presence of a wide range of cross traffic? If >> not, we certainly need to state it strongly in the introduction and the >> requirements. Perhaps a paragraph similar to >> "While the requirements for RMCAT differ from the requirements for the >> other flow types, these other flow types will be present in the network. >> The RMCAT congestion control algorithm must work properly when these other >> flow types are present as cross traffic on the network." should be in >> either the abstract or in the introduction. I would also like to see a >> statement that the RMCAT CC algorithm should drive scavenger flows (like >> LEDBAT) to nearly 0, taking that BW for the RMCAT/TCP/"normal" flows. These >> points are referenced in item #11, but it is a rather thin section late in >> the document. IMHO, this should be one of the primary points of the >> requirements document. >> > > Certainly upping the emphasis on robustness in the face of a variety of > cross traffic is good - though it's not something that we can guarantee is > achievable (just as we can't guarantee success in competing with greedy TCP > flows). > > As for driving LEDBAT to 0.... I'm afraid that might be desirable, but > unachievable. We may well find that LEDBAT will put a floor on how low we > can drive delay, driven by the delay constant hard-coded into LEDBAT > implementations (100ms(!) in the spec, 25ms in Apple's kernel > implementation used for things like Apple's TimeMachine(?) auto-backup > stuff. I'm not sure what BitTorrent is using, but I suspect it's 100ms. > It is impossible to drive ledbat to 0. 100ms is torrent, yes. Yet, It is highly desirable to get queuing latency below 100ms, and 5ms is achievable with modern AQM/packet scheduling technology, which turn all delay based TCPs into loss based ones again. I can't remember if I already posted this (forgive me if I have) but http://perso.telecom-paristech.fr/~drossi/paper/rossi13tma-b.pdf and the followup modelling it, which I have not thoroughly digested yet: http://arxiv.org/pdf/1303.6817.pdf > > There are some minor issues, which I discuss below. >> >> 2- Requirements section, bullet 1 - Do we need to elaborate the other >> cases in which the algorithm needs to adjust the BW? We state (in 1a) that >> topology changes need to be handled. Do we need to state <1b - Changes in >> cross traffic > and <1c - changes in offered load from the application >> sending data over RMCAT> >> > > I'll check the text; I don't have it in front of me at the moment > > 3- Requirements section, bullet 2a - If we are enumerating types of >> traffic that we need to be concerned with, I would more concerned with MPEG >> DASH style Adaptive BitRate video than generic web browsing. Web browsing >> is bursty, but at least it is well-bounded in time. ABR flows are bursty, >> cyclical, and persistent. I would either remove the reference to web >> browsing or change it to include other bursty flow types (I note that OTT >> ABR traffic is now more than 50% of the peak load on many networks). >> > > Good point, though MPEG DASH is not the dominant transfer protocol for > such data (or not yet). The major players are implementing DASH in JS > (i.e. under provider/application control), so that adds another wrinkle in > verifying this - but the idea that we have to coexist with such traffic is > important, and HTTP streaming and DASH and proprietary protocols from > Apple/MS/etc do pose real problems - HTTP streaming for example may look > like a very hungry realtime protocol, or it may look like a periodic > maximum-transfer TCP flow (Gettys showed a NetFlix BW vs Time graph that > showed a ~10 second square-wave of bandwidth use, for example). > It is my hope netflix has changed their setup by now. There was some awesome followup work after the "confused, timid and unstable" paper: http://www.stanford.edu/~huangty/imc012-huang.pdf here: http://reproducingnetworkresearch.wordpress.com/2013/03/13/cs244-13-rising-from-the-depths-observing-and-implementing-improvements-in-online-video-bitrate-selection/ So I think that data on netflix's behavior needs to be revisited. (I have retired that slide) http://www.ietf.org/mail-archive/web/aqm/current/msg00195.html > > 4- Requirements section 3 - do we need to mention that there is a >> temporal component to information sharing across streams. In other words, >> we may consider a previous 5-tuples experience as a baseline to seed a CC >> algorithm, particularly if it is the exact same source addr/dest addr/dest >> port/protocol? This is hinted in item #12 in the document, but the guidance >> is quite thin in that section. Temporal hinting is particularly valuable if >> the old session data was from 1 second ago. It is less true if it was 1 day >> ago, but may still have some value as an initial seed for the CC algorithm. >> 3b is also a bit awkward, as multiple "flows" on a given 5-tuple >> introduces some difficult SSRC concepts that are currently being discussed >> in the AVT group. We either need to define "flow" or describe the concept >> in broader terms. I am reluctant to open that can of worms in this >> document, but if we are not clear it will be the source of endless >> debate/confusion. I hate glossaries in the front of RFCs, but we may have >> to do that. >> >> 5- In section 4, I do not see any details on how ECN, delay and loss >> indicate congestion. Even though we all understand that this is a complex >> relationship and will be difficult to characterize in a requirements >> document, I think that a few paragraphs of detail are in order. This level >> of detail would be important to a less experienced reader. This is my major >> concern with the draft, and I could write a paragraph or two to include >> this discussion (if there agreement that this should be discussed in this >> draft) >> > > Let me take a try at it. Understandability also can help avoid confusion > among experts and implementations. > I'm all ears. We stumbled on defining this too, in the aqm wg. > > >> 6- Is 5a a requirement, or are we starting to discuss the solution space >> here? I suspect that we will end up mandating AVFP, but we may be getting >> ahead of ourselves here. Perhaps we should soften this to a suggestion to >> examine RFC4585 rather than a MUST use statement. >> > > Hmmm. Let me think. I think we'd said that these algorithms *if* they > use RTCP would need AVPF in order to respond within orders of magnitude of > the RTT. But there are other ways to provide the feedback needed than > RTCP. But that isn't to say that a new profile (AVPG ;-) ) couldn't also > meet the needs here. > > 7- If we are elaborating AQM schemes, we should include PIE (I know, a >> shameless plug for the one I am working on - but I think it is a valid >> comment nonetheless). And to be rigorous, RED, CoDel and PIE are buffer >> management algorithms that operate on a given queue. One can also apply >> multiple queues (FQ being one variant of multiple queues) to the problem as >> well. To be more clear, we should mention queuing algorithms like RED, >> CoDel and PIE and then mention that each of these algorithms can be >> optionally mapped to multiple queues. If this is going to cause a debate >> of some sort, we could also just reference the broad class of AQMs and the >> broad class of queue allocation schemes without elaborating the specific >> algorithms. >> > > The specific were more to indicate which ones we thought were most > important to work/test with, so perhaps we should go to a more generic > statement and move mentions of specifics to Varun's document. > > 8- I am reluctant to bring this into the conversation........ Once we >> mention multiple queues, we may want to mention that the multiple queues >> may represent different QOS buckets, and the activity in one queue may >> impact the drain rate of lower priority queues, whereas the activity of a >> lower priority queue will have a (very?) small impact on the drain rate of >> a higher queue. I am reluctant to start this train of thought in this >> document, so perhaps we do not mention multiple queues of multiple >> priorities. If we are to mention different priority flows, perhaps we use >> DSCP markings or VLAN tagging examples - but once again this quickly goes >> down a very deep rat hole. We may just want to state that there are often >> multiple priorities of flows, using the traditional SP voice, SP video, >> general purpose data, and scavenger class as examples......... I am torn on >> this one, and could be convinced to not include this in the document. >> > > Fundamentally, we have 4 classes of packets we want to get across the > network in webrtc (which also came up in the W3 TPAC WebRTC discussions > this week with regard to DSCP markings/etc): Faster-than-audio (typically > low bandwidth and/or intermittent), audio, video, and best-effort (maybe > split up into interactive use, and bulk-transfer). Outside of webrtc we > would also have scavenger (below immediate bulk-transfer). Typical > browsing traffic/etc would fall into the same general classes as best > effort and bulk-transfer. Note that "audio" and "video" don't mean they > have to be exclusively packets of that type, just that they they would have > network priorities of that type. > > Interestingly (or problematically), splitting the congestion regimes by > DSCP markings really needs to be done unless you know (or strongly believe) > they're being ignored at the bottleneck (note: not necessarily ignored > everywhere). But splitting the congestion controllers has the side effect > (if they are in one queue at the bottleneck) of reducing the feedback (per > controller) and slowing the ability to notice changes in the bottleneck > (and to respond). > > Bill VerStee >> > > Thanks again for a thoughtful review! > > -- > Randell Jesup > randell-ietf@jesup.org > > > --f46d043890796d3d6004ebb7b51d Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable

I could have sworn I'd commented on this already, b= ut don't see it=A0

On Nov 15, 2013 1:17 PM, "Randell Jesup&quo= t; <randell-= ietf@jesup.org> wrote:
On 11/12/2013 11:27 AM, Bill Ver Steeg (versteb) wrote:
Randell-

As I promised in Vancouver, here are my note on draft-ietf-rmcat-cc-= requirements-00

In summary, this is quite a nice start. It is a difficult document to write= , as it is intentionally quite broad. On the whole, I like it.

That's a great start for a review! =A0;-) =A0 Thanks! =A0Now on to the = meat...

The main concern is my item #1

1- In the abstract, do we want to mention that the RMCAT CC algorithm needs= to be robust in the presence of a wide range of cross traffic? If not, we = certainly need to state it strongly in the introduction and the requirement= s. Perhaps a paragraph similar to
"While the requirements for RMCAT differ from the requirements for the= other flow types, these other flow types will be present in the network. T= he RMCAT congestion control algorithm must work properly when these other f= low types are present as cross traffic on the network." should be in e= ither the abstract or in the introduction. I would also like to see a state= ment that the RMCAT CC algorithm should drive scavenger flows (like LEDBAT)= to nearly 0, taking that BW for the RMCAT/TCP/"normal" flows. Th= ese points are referenced in item #11, but it is a rather thin section late= in the document. IMHO, this should be one of the primary points of the req= uirements document.

Certainly upping the emphasis on robustness in the face of a variety of cro= ss traffic is good - though it's not something that we can guarantee is= achievable (just as we can't guarantee success in competing with greed= y TCP flows).

As for driving LEDBAT to 0.... I'm afraid that might be desirable, but = unachievable. =A0We may well find that LEDBAT will put a floor on how low w= e can drive delay, driven by the delay constant hard-coded into LEDBAT impl= ementations (100ms(!) in the spec, 25ms in Apple's kernel implementatio= n used for things like Apple's TimeMachine(?) auto-backup stuff. =A0I&#= 39;m not sure what BitTorrent is using, but I suspect it's 100ms.

It is impossible to drive ledbat to 0. 100= ms is torrent, yes. Yet, It is highly desirable to get queuing latency belo= w 100ms, and 5ms is achievable with modern AQM/packet scheduling technology= , which turn all delay based TCPs into loss based ones again. I can't r= emember if I already posted this (forgive me if I have) but=A0


and the followup modelling it,= which I have not thoroughly digested yet:


=A0

There are some minor issues, which I discuss below.

2- Requirements section, bullet 1 - Do we need to elaborate the other cases= in which the algorithm needs to adjust the BW? We state (in 1a) that topol= ogy changes need to be handled. Do we need to state <1b - Changes in cro= ss traffic > and <1c - changes in offered load from the application s= ending data over RMCAT>

I'll check the text; I don't have it in front of me at the moment
3- Requirements section, bullet 2a - If we are enumerating types of traffic= that we need to be concerned with, I would more concerned with MPEG DASH s= tyle Adaptive BitRate video than generic web browsing. Web browsing is burs= ty, but at least it is well-bounded in time. ABR flows are bursty, cyclical= , and persistent. I would either remove the reference to web browsing or ch= ange it to include other bursty flow types (I note that OTT ABR traffic is = now more than 50% of the peak load on many networks).

Good point, though MPEG DASH is not the dominant transfer protocol for such= data (or not yet). =A0The major players are implementing DASH in JS (i.e. = under provider/application control), so that adds another wrinkle in verify= ing this - but the idea that we have to coexist with such traffic is import= ant, and HTTP streaming and DASH and proprietary protocols from Apple/MS/et= c do pose real problems - HTTP streaming for example may look like a very h= ungry realtime protocol, or it may look like a periodic maximum-transfer TC= P flow (Gettys showed a NetFlix BW vs Time graph that showed a ~10 second s= quare-wave of bandwidth use, for example).

It is my hope netflix has changed their se= tup by now. There was some awesome followup work after the "confused, = timid and unstable" paper:


here:


So I think that data on netflix's behavior ne= eds to be revisited. (I have retired that slide)

<= a href=3D"http://www.ietf.org/mail-archive/web/aqm/current/msg00195.html">h= ttp://www.ietf.org/mail-archive/web/aqm/current/msg00195.html

=A0

4- Requirements section 3 - do we need to mention that there is a temporal = component to information sharing across streams. In other words, we may con= sider a previous 5-tuples experience as a baseline to seed a CC algorithm, = particularly if it is the exact same source addr/dest addr/dest port/protoc= ol? This is hinted in item #12 in the document, but the guidance is quite t= hin in that section. Temporal hinting is particularly valuable if the old s= ession data was from 1 second ago. It is less true if it was 1 day ago, but= may still have some value as an initial seed for the CC algorithm. 3b is a= lso a bit awkward, as multiple =A0"flows" on a given 5-tuple intr= oduces some difficult SSRC concepts that are currently being discussed in t= he AVT group. We either need to define "flow" or describe the con= cept in broader terms. I am reluctant to open that can of worms in this doc= ument, but if we are not clear it will be the source of endless debate/conf= usion. I hate glossaries in the front of RFCs, but we may have to do that.<= br>
5- In section 4, I do not see any details on how ECN, delay and loss indica= te congestion. Even though we all understand that this is a complex relatio= nship and will be difficult to characterize in a requirements document, I t= hink that a few paragraphs of detail are in order. This level of detail wou= ld be important to a less experienced reader. This is my major concern with= the draft, and I could write a paragraph or two to include this discussion= (if there agreement that this should be discussed in this draft)

Let me take a try at it. =A0Understandability also can help avoid confusion= among experts and implementations.

I&#= 39;m all ears. We stumbled on defining this too, in the aqm wg.
=A0

=A0
6- Is 5a a requirement, or are we starting to discuss the solution space he= re? I suspect that we will end up mandating AVFP, but we may be getting ahe= ad of ourselves here. Perhaps we should soften this to a suggestion to exam= ine RFC4585 rather than a MUST use statement.

Hmmm. =A0Let me think. =A0I think we'd said that these algorithms *if* = they use RTCP would need AVPF in order to respond within orders of magnitud= e of the RTT. =A0But there are other ways to provide the feedback needed th= an RTCP. =A0But that isn't to say that a new profile (AVPG ;-) ) couldn= 't also meet the needs here.

7- If we are elaborating AQM schemes, we should include PIE (I know, a sham= eless plug for the one I am working on - but I think it is a valid comment = nonetheless). And to be rigorous, RED, CoDel and PIE are buffer management = algorithms that operate on a given queue. One can also apply multiple queue= s (FQ being one variant of multiple queues) to the problem as well. =A0To b= e more clear, we should mention queuing algorithms like RED, CoDel and PIE = and then mention that each of these algorithms can be optionally mapped to = multiple queues. =A0If this is going to cause a debate of some sort, we cou= ld also just reference the broad class of AQMs and the broad class of queue= allocation schemes without elaborating the specific algorithms.

The specific were more to indicate which ones we thought were most importan= t to work/test with, so perhaps we should go to a more generic statement an= d move mentions of specifics to Varun's document.

8- I am reluctant to bring this into the conversation........ Once we menti= on multiple queues, we may want to mention that the multiple queues may rep= resent different QOS buckets, and the activity in one queue may impact the = drain rate of lower priority queues, whereas the activity of a lower priori= ty queue will have a (very?) small impact on the drain rate of a higher que= ue. I am reluctant to start this train of thought in this document, so perh= aps we do not mention multiple queues of multiple priorities. If we are to = mention different priority flows, perhaps we use DSCP markings or VLAN tagg= ing examples - but once again this quickly goes down a very deep rat hole. = We may just want to state that there are often multiple priorities of flows= , using the traditional SP voice, SP video, general purpose data, and scave= nger class as examples......... I am torn on this one, and could be convinc= ed to not include this in the document.

Fundamentally, we have 4 classes of packets we want to get across the netwo= rk in webrtc (which also came up in the W3 TPAC WebRTC discussions this wee= k with regard to DSCP markings/etc): Faster-than-audio (typically low bandw= idth and/or intermittent), audio, video, and best-effort (maybe split up in= to interactive use, and bulk-transfer). =A0Outside of webrtc we would also = have scavenger (below immediate bulk-transfer). =A0Typical browsing traffic= /etc would fall into the same general classes as best effort and bulk-trans= fer. =A0Note that "audio" and "video" don't mean th= ey have to be exclusively packets of that type, just that they they would h= ave network priorities of that type.

Interestingly (or problematically), splitting the congestion regimes by DSC= P markings really needs to be done unless you know (or strongly believe) th= ey're being ignored at the bottleneck (note: not necessarily ignored ev= erywhere). =A0But splitting the congestion controllers has the side effect = (if they are in one queue at the bottleneck) of reducing the feedback (per = controller) and slowing the ability to notice changes in the bottleneck (an= d to respond).

Bill VerStee

Thanks again for a thoughtful review!

--
Randell Jesup
randell-ietf@je= sup.org


--f46d043890796d3d6004ebb7b51d-- From michawe@ifi.uio.no Mon Nov 25 02:28:22 2013 Return-Path: X-Original-To: rmcat@ietfa.amsl.com Delivered-To: rmcat@ietfa.amsl.com Received: from localhost (ietfa.amsl.com [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 284321ACC89 for ; Mon, 25 Nov 2013 02:28:22 -0800 (PST) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.901 X-Spam-Level: X-Spam-Status: No, score=-1.901 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001] autolearn=ham Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 15i5YLLSJSwv for ; Mon, 25 Nov 2013 02:28:19 -0800 (PST) Received: from mail-out4.uio.no (mail-out4.uio.no [IPv6:2001:700:100:10::15]) by ietfa.amsl.com (Postfix) with ESMTP id 09E331ACC7D for ; Mon, 25 Nov 2013 02:28:18 -0800 (PST) Received: from mail-mx6.uio.no ([129.240.10.40]) by mail-out4.uio.no with esmtp (Exim 4.80.1) (envelope-from ) id 1VktOr-0002BO-Tk for rmcat@ietf.org; Mon, 25 Nov 2013 11:28:17 +0100 Received: from boomerang.ifi.uio.no ([129.240.68.135]) by mail-mx6.uio.no with esmtpsa (TLSv1:AES128-SHA:128) user michawe (Exim 4.80.1) (envelope-from ) id 1VktOr-0000te-Br for rmcat@ietf.org; Mon, 25 Nov 2013 11:28:17 +0100 Content-Type: text/plain; charset=iso-8859-1 Mime-Version: 1.0 (Apple Message framework v1283) From: Michael Welzl In-Reply-To: Date: Mon, 25 Nov 2013 11:28:17 +0100 Content-Transfer-Encoding: quoted-printable Message-Id: References: <96FB83A5-23BD-4BAC-9D1A-204913BC342E@netapp.com> To: rmcat WG X-Mailer: Apple Mail (2.1283) X-UiO-SPF-Received: X-UiO-Ratelimit-Test: rcpts/h 1 msgs/h 1 sum rcpts/h 9 sum msgs/h 4 total rcpts 10187 max rcpts/h 44 ratelimit 0 X-UiO-Spam-info: not spam, SpamAssassin (score=-6.0, required=5.0, autolearn=disabled, RP_MATCHES_RCVD=-1.05, UIO_MAIL_IS_INTERNAL=-5, uiobl=NO, uiouri=NO) X-UiO-Scanned: 1614CD53395E3D5717CD8E49FBE378C20745BBCA X-UiO-SPAM-Test: remote_host: 129.240.68.135 spam_score: -59 maxlevel 80 minaction 2 bait 0 mail/h: 1 total 3846 max/h 16 blacklist 0 greylist 0 ratelimit 0 Subject: [rmcat] MW's review of draft-ietf-rmcat-cc-requirements-00 X-BeenThere: rmcat@ietf.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: "RTP Media Congestion Avoidance Techniques \(RMCAT\) Working Group discussion list." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 25 Nov 2013 10:28:22 -0000 Hi, I'm sending my comments on this draft now, as I think they're largely = orthogonal to Bill's: Abstract: "needs by low-delay" seems broken. I'd end this (the second) = sentence after "Web pages". (strange to say "the TCP algorithms" and = unnecessary anyway I think. 1. Intro: ' "as fast as possible" mode' is weird, somehow fix this = wording please. 2. Requirements: 2. (fairness) =3D> I think this goes into too much detail and leaves too many = questions open. Don't get me wrong, the things you say here are good = "food for thought" for the group, but as we make progress towards a real = requirements document most of the text in section 2 is probably useless. = We need to specify something that we require, and agree on something = here. I tried to address this with my previous email to the list with subject = "Fairness". Let's see what happens. 3. is about coupled congestion control. I think that the right way to = specify this requirement is to point to the other document (whatever = document that will be). Say that "the algorithm should be able to be = interoperate with the mechanism that couples the congestion control of = flows traversing the same bottleneck, specified in draft-YZ." and not = more than that. 4. "leverage existing information about the incoming flows" is = strange... I think this item is based on a "coupled congestion control" = way of thinking, but I think we should really regard the congestion = control algorithms as per-flow entities; this gives us a better = separation of the problem space, and is more in line with the current = proposals too. "packet sizes" seems out of place in the list of = "leveraged information", btw, as it's a pre-determined factor that the = algorithm simply knows about. The last sentence ("Extra information...") = seems unnecessary to me. 8. (interaction with TCP) =3D> here I suggested "not starve TCP and not be starved by TCP" in my = separate email on "Fairness" to the list. I think that should do it, = rather than this somewhat lengthy discussion. 9. "stable and low-delay" seems grammatically broken here, and I'd = rather avoid mentioning particular AQMs but just say that it should = interoperate well with Active Queue Manamagement mechanisms. 10. too much detail here, I think - I would assume that the first = paragraph, without sub-items a-d, is enough? 11. There will be a test suite RFC, right? In this case, just like with = coupled congestion control, I'd just say that it should work well in = accordance with the tests specified in document xy. I hope this is helpful! Cheers, Michael