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	<title>Talent Acquisition &#8211; The HR World</title>
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	<title>Talent Acquisition &#8211; The HR World</title>
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	<item>
		<title>NEET: Creating paths to the workplace</title>
		<link>https://thehrworld.co.uk/neet-creating-paths-to-the-workplace/</link>
					<comments>https://thehrworld.co.uk/neet-creating-paths-to-the-workplace/#respond</comments>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Fri, 31 Jul 2026 10:12:15 +0000</pubDate>
				<category><![CDATA[HR Strategy]]></category>
		<category><![CDATA[Featured]]></category>
		<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[Uncategorised]]></category>
		<guid isPermaLink="false">https://thehrworld.co.uk/?p=83438</guid>

					<description><![CDATA[Emma Hughes, Partner and Head of HR Services at Browne Jacobson, will be a panellist at the Westminster Employment Forum keynote seminar, titled ‘Next steps for youth employment and improving pathways to work’ on 8 September. Here she gives her view on the NEET issue. Q1: As the number of young people NEET continues to [&#8230;]]]></description>
										<content:encoded><![CDATA[
<h2 class="wp-block-heading"><a href="https://www.brownejacobson.com/people/emma-hughes" target="_blank" rel="noopener">Emma Hughes</a>, Partner and Head of HR Services at <a href="https://www.linkedin.com/company/brownej/posts/?feedView=all" target="_blank" rel="noopener">Browne Jacobson</a>, will be a panellist at the <a href="https://www.westminsterforumprojects.co.uk/conferences/westminster-employment-forum" target="_blank" rel="noopener">Westminster Employment Forum</a> keynote seminar, titled ‘Next steps for youth employment and improving pathways to work’ on 8 September. Here she gives her view on the NEET issue.</h2>



<h3 class="wp-block-heading"><strong>Q1: As the number of young people NEET continues to rise, how should HR respond?</strong></h3>



<p class="wp-block-paragraph">We have allowed entry-level recruitment to become extraordinarily complex at precisely the moment we need it to be accessible. Multi-stage processes, automated screening, recorded video interviews – these tools were designed to manage risk and improve efficiency, and there are legitimate reasons for using them. But there is a real danger that the system has overcorrected.</p>



<p class="wp-block-paragraph">84% of NEET young people surveyed by the <a href="https://www.gov.uk/government/publications/young-people-and-work-interim-report/young-people-and-work-interim-report" target="_blank" rel="noopener">Milburn Review</a> said they wanted to work or do an apprenticeship. The barrier is not aspiration – it is access. The most consistent complaint from young applicants is not rejection, but silence. That alone should prompt every HR team to examine its own candidate communication practice.</p>



<ul class="wp-block-list">
<li><strong><a href="https://www.thehrworld.co.uk/hr-event/webinar/the-lost-generation-the-challenge-and-opportunity-for-hr/">Register now for The HR World’s webinar on this subject: The Lost Generation – the challenge and opportunity for HR: 19 August, Free and online.</a></strong></li>
</ul>



<p class="wp-block-paragraph">The practical steps available immediately are not complicated. Audit your entry-level recruitment processes for unnecessary barriers. Remove the requirement for prior experience where it is not genuinely needed. Reduce reliance on automated screening for junior roles. Introduce a meaningful commitment to communicating with every applicant. None of these require government action or significant investment, they just require employer decision.</p>



<h3 class="wp-block-heading">Q2: Does this crisis lie outside HR's consideration, or are there genuine challenges and opportunities in providing meaningful early careers?</h3>



<p class="wp-block-paragraph">It would be wrong to lay all of this at the door of employers. Increases in employer National Insurance contributions, the extension of the National Living Wage to younger age groups, and the costs of compliance and onboarding all weigh disproportionately on the economics of casual and entry-level hiring. As employment protections expand via the Employment Rights Act, compliance expectations increase and greater scrutiny is placed on employment practices like probationary periods, casual working arrangements and zero-hours contracts, employers inevitably become more risk-conscious about hiring decisions.&nbsp;</p>



<p class="wp-block-paragraph">The CIPD has been direct about this: the government must recognise the link between the Employment Rights Act and employers' ability to invest in jobs for young people. That is not an argument against employment protection – it is an argument for policy coherence. You cannot simultaneously expand the cost and complexity of taking on a young, inexperienced worker and then express surprise when fewer employers are willing to do it.&nbsp;</p>



<p class="wp-block-paragraph">Milburn himself, however, pushes back on those who use this as the primary explanation. This crisis is structural and long-term – 1.6 million first-rung jobs have disappeared from the economy over the past two decades. That is not something that can be attributed to the legislative programme of the last two years. Both things can be true: the structural problem is deep and pre-existing, and the current policy environment is not helping.</p>



<h3 class="wp-block-heading">Q3: How does the NEET issue connect to early careers as organisations introduce AI to take over entry-level roles?</h3>



<p class="wp-block-paragraph">Administrative roles are down 56%, sales roles down 44%, customer service roles down 59% since 2022. If AI is removing the jobs that used to bring young people in, what replaces them?&nbsp;</p>



<p class="wp-block-paragraph">Those figures should give every HR leader pause. The entry-level roles that historically absorbed young people into the workforce, e.g. basic administration, first-line customer contact, data processing, are precisely those most susceptible to automation. We are simultaneously lamenting the rise in young people who cannot find work and accelerating the removal of the roles that would have taken them on.</p>



<p class="wp-block-paragraph">At a recent board meeting, an icebreaker discussion about people's first jobs revealed a strikingly consistent picture - shop work, hospitality, warehouse shifts, paper rounds. Those roles provided not only income, but an early introduction to responsibility, workplace culture and confidence. The sobering question that followed was what the next generation of leaders will say when asked the same question in twenty years' time. For many young people today, those accessible, flexible entry points simply may not exist in the same way.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">The concern is that we are using young people to fill gaps in the current labour market, rather than building pathways into the economy of the future."</p>
</blockquote>



<p class="wp-block-paragraph">That does not make AI the villain, businesses have a legitimate interest in technology that improves efficiency. But GoodWork, which submitted formal evidence to the Milburn Review, made a point worth taking seriously: AI career support tools must never replace human intervention for young people who are NEET. They need more contact with the world, not less. The organisations automating entry-level functions have a particular responsibility to ask what they are creating in its place, and early careers strategies need to evolve alongside technology strategies, not lag behind them.</p>



<h3 class="wp-block-heading">Q4: Are we looking at a workplace revolution, and will the future eventually work itself out?</h3>



<p class="wp-block-paragraph">Looking back to look forward to the future, the Milburn report identifies over 20 years of government youth employment schemes, e.g. New Deal, Future Jobs Fund, Kickstart, Traineeships – and describes them as a record of failure.&nbsp;</p>



<p class="wp-block-paragraph">I think, an important passage in the Report in paragraphs 666 and 667 is that Britain has not ignored youth unemployment in the past. Miburn sets out that the twenty years of youth employment schemes is not a record of passivity and it is a record of sustained policy effort. However, despite it all, the structural NEET rate has barely fallen below 10% in twenty-five years. The test for the government's response must be whether it is fundamentally different to what has been tried before because constantly repeating the same non-impact initiatives is not going to help here.&nbsp;</p>



<p class="wp-block-paragraph">The immediate response from the government to the Milburn Report is 300,000 work placements focused on hospitality, health and social care and construction. This is a welcome commitment, but it conspicuously omits the sectors identified in the government's own Modern Industrial Strategy: AI, life sciences, the green economy, creative industries. The concern is that we are using young people to fill gaps in the current labour market, arguably following past initiatives that have not made a strong enough impact, rather than building pathways into the economy of the future.</p>



<p class="wp-block-paragraph">Learning from the past and designing initiatives that will tackle this structural NEET rate now and in the future is what is needed. The autumn recommendations will be the real test.</p>



<h3 class="wp-block-heading">Q5: Is the NEET crisis a chance to find untested talent, or is it simply the natural result of AI and an out-of-touch education system?</h3>



<p class="wp-block-paragraph">There is genuine opportunity here. Many young people who lack formal experience, polished CVs or confidence in structured interview settings still possess the adaptability, resilience and aptitude that businesses say they are struggling to find. The employers building relationships with young people before they are job-ready, engaging at 14, 15 and 16, not waiting for an application, will be better placed in a labour market that is becoming increasingly difficult to navigate for young people and businesses alike. But that opportunity will not be realised by accident. It requires deliberate choices about how employers assess potential, how they design access, and who they regard as worth investing in.</p>
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		<item>
		<title>Q&#038;A: Understanding Your Talent Needs Before The Search – How to Unlock the Power of Talent Analytics in Your Organisation</title>
		<link>https://thehrworld.co.uk/qa-understanding-your-talent-needs-before-the-search-how-to-unlock-the-power-of-talent-analytics-in-your-organisation/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Tue, 14 Apr 2026 06:07:32 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[employability]]></category>
		<category><![CDATA[skills]]></category>
		<category><![CDATA[talent]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?p=71005</guid>

					<description><![CDATA[A webinar hosted by The HR World and sponsored by Cognisses Talent Analytics, part of the DeepLearn Group, recently delved into the use of talent analytics to drive more value for HR from early careers and general talent management.]]></description>
										<content:encoded><![CDATA[<h2>A webinar hosted by The HR World and sponsored by <a href="https://thehrworld.co.uk/hr-directory/cognisses/">Cognisses Talent Analytics</a>, part of the DeepLearn Group, recently delved into the use of talent analytics to drive more value for HR from early careers and general talent management.</h2>
<p>The expert panel explored how HR which could make the most of existing data to generate smoother talent management practices. The webinar is packed with great ideas and examples to inspire HR leaders to pursue their own paths on data led talent management, and is <a href="https://thehrworld.co.uk/hr-event/webinar/understanding-your-talent-needs-before-the-search-how-to-unlock-the-power-of-talent-analytics-in-your-organisation/">well worth a view</a>. Meanwhile, two of the panel members, <a href="https://www.linkedin.com/in/amaliajohannareategui/?locale=en" target="_blank" rel="noopener">Johanna Reategui,</a> Client Services Director, Cognisess and <a href="https://www.linkedin.com/in/cian-short-66827a62/" target="_blank" rel="noopener">Cian Short</a>, Talent and Development Manager, Kepak Group answer further questions on the topic.</p>
<p>&nbsp;</p>
<h3>Q: What example can you give of the most value driven from talent analytics for an organisation?</h3>
<p><b>Johanna: </b>One of the most valuable applications I have seen is in internal mobility and talent visibility.</p>
<p>Many organisations assume capability gaps and move quickly to external hiring. However, when they start connecting different data points such as performance, skills, learning activity and behavioural indicators, they often uncover internal talent that was not previously visible.</p>
<p>This shifts the conversation from “we don’t have the talent” to “we didn’t know where to look.”</p>
<p>The real value comes from moving beyond descriptive metrics such as hiring volumes or turnover and instead understanding capability and potential more deeply. This leads to better and more targeted decisions, and often reduces unnecessary external hiring while improving the use of existing talent.</p>
<p><b>Cian:</b> Over the past couple of years I have gained the most value from Talent Analytics in supporting the recruitment of Graduates &amp; Apprentices to our Early Careers programmes.</p>
<p>EC recruitment has historically relied on academic achievement and then the assessors intuition during assessment centres and/or interviews, but that isn’t always the best indicator of success.</p>
<p>We worked with each of our functional leads to create a functional profile for our programmes, using our Values, our behavioural framework and the JDs for the roles these people would perform. We then mapped cognitive and personality assessments against these profiles to indicate potential to succeed in the roles we are recruiting for, it gives us a clearer idea of future success.</p>
<p>By analysing the performance of candidates in assessment centres, during their programme, then post programme progression and retention data, we can ensure that we recruit the best suited people to our programmes and then proactively guide graduates and apprentices into roles where they are most likely to thrive in the future.</p>
<h3>Q: Are talent analytics reasonably easy to understand and start using, or does it require significant training?</h3>
<p><b>Cian: </b>Getting started with Talent Analytics is often easier than people think it will be, the key is not trying to analyse everything or add more data points in, but by being selective about using what data you already have available to you.</p>
<p>Start with a specific challenge you have, whether role, function etc. then pick a couple of data points to help you build your picture, you can always add more data points in later, but it is important to build your confidence in using the analytics and build credibility in the data before going too wide.</p>
<p>The important thing is to start building your, and the wider teams, capability around using the data, recognising patterns and what they potentially mean, and then what to do as solutions, once you have done the interpretation. We have huge amounts of data available to us, but the key is picking what to use when, and then what to do with what the data tells you.</p>
<p><b>Johanna: </b>In my experience, getting started with talent analytics is simpler than many organisations expect.</p>
<p>You do not need a fully mature data infrastructure to begin. The most effective approach is to start with a clear business question, for example understanding attrition in a specific role or improving internal mobility, and then connect the data you already have around that question.</p>
<p>Where organisations tend to face more challenges is in interpretation.</p>
<p>Much of HR data is either perception-based, such as performance reviews and engagement surveys, or descriptive, such as turnover or absenteeism metrics. On their own, these do not explain root causes.</p>
<p>This is where capability becomes critical.</p>
<p>When organisations complement qualitative insight with more structured signals such as skills, cognitive ability, behavioural tendencies and emotional intelligence, they can move from simply observing outcomes to understanding what is driving them.</p>
<p>So while starting is relatively straightforward, building the capability to generate meaningful, decision-focused insights does require a shift in mindset and, in some cases, upskilling HR teams to think more analytically.</p>
<p>Overall, it is less about technical complexity and more about asking the right questions and connecting the right signals.</p>
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			</item>
		<item>
		<title>Talent Acquisition: Managing volume by addressing intent</title>
		<link>https://thehrworld.co.uk/talent-acquisition-managing-volume-by-addressing-intent/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Fri, 13 Feb 2026 10:33:34 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[applications]]></category>
		<category><![CDATA[candidates]]></category>
		<category><![CDATA[diversity]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?p=65213</guid>

					<description><![CDATA[Simon Reichwald, Chief Progression Officer, Connectr explains why TA Leaders must shift from application volume to focus on candidate intent.]]></description>
										<content:encoded><![CDATA[<h2><a href="https://www.linkedin.com/in/simonreichwald/" target="_blank" rel="noopener">Simon Reichwald,</a> Chief Progression Officer, <a href="https://thehrworld.co.uk/hr-directory/connectr-talent-technology/">Connectr</a> explains why TA Leaders must shift from application volume to focus on candidate intent.</h2>
<p>There’s no getting away from it. Application volumes for organisations are going up and show no signs of slowing down. According to research from <a href="https://www.linkedin.com/company/high-fliers-research-limited/" target="_blank" rel="noopener">High Fliers Research Limited</a> applications fare up by 23% this year – a number that has doubled since 2023.</p>
<p>In addition to this, the ISE reports volumes have risen from 86 to 140 applications per vacancy in the last two years. Make no mistake – this is a having very real impact for employers in terms of costs, resources, candidate quality at later stages, and candidate experience.</p>
<p>At the heart of these increasingly unmanageable volumes is a weaker job market and candidates using AI within their job search and application.</p>
<p>At Connectr we have delved deeper, identifying four job hunting personas operating in the current market:</p>
<h3>The ‘Casual’</h3>
<p>This candidate has low intent and low curiosity about the role. They are speculative appliers, often simply browsing without the driving force behind them to commit. These candidates have a high likelihood to leave applications unfinished or ghost employers later on.</p>
<h3>The ‘Optimiser’</h3>
<p>This candidate has high intent to get a job – but not necessarily your job. They will use every tool at their disposal to maximise the volume of application they make and the speed at which they can make them. Thanks to AI tools, they can look really good in the early stages, but when you start interacting with them in person, their weakness and lack of connection to the role starts to show.<span class="Apple-converted-space"> </span></p>
<h3>The ‘Explorer’<span class="Apple-converted-space"> </span></h3>
<p>This person is super curious and will do lots of research but is not completely convinced enough to step out of their existing role. These candidates are likely to have just the behaviours and experience you want, but your employer brand, EVP and candidate experience needs to do the work to convert them.<span class="Apple-converted-space"> </span></p>
<h3>The ‘Purpose Driven’</h3>
<p>Finally this candidate is very focused on securing the right role for themselves, and will be values-led in their decision to choose you and apply. They are a responsive, proactive candidate. At the end of the day, you want lots of these in your recruitment pipeline.</p>
<p><img fetchpriority="high" decoding="async" class="aligncenter" 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Z5cqE7tgNk3lJAvDusuCmP/jAX88JdBDORUPxuH5YKt4kQAWU852H/dVTPmiuVCmWFiRx7dY/WHGBj7lMDdo2GPILnqPETntBlZr3OYSR1AYkV8x4p4jmGHGLk2EMcuOuWBTBjYxx09uGAg4/xKhIsVUsgVIwCp4x+1cuOsTmScF3y1j7xp8MG9YjwxHUx0nX9A85NueWxe9ab7so/xmo8et4DgoJeKb/XbRtLfSXkjMusk8uTBmTtyhXtveHozBYtoR48YO1f0yI8Y8XvedFjwjU8kNsZqOrJ9tZW4eHdZLec31XqDvasQY7+Ul1Fi2L2r4+5DqXqByhmcXU/NlcZ/420cDdI1B+rmAklsYEDccGhjqHPkqOOMdRyXnf+cmSjq5hnBSAzSJYY5xcUvuPBDzpvvnLtrYG7yK3qJ+ZAOmcH27IEuyAGfumQZuKlv355lFx3RJIcwSg1CAzV9LBQlsdAXkhT16Ysj6tCj4/QlkcaG/AlGXht4BDA5ZsVDvZj038iuMcYRABybqTIJYGALHryo8z7lrGpp7E9d3HinLI0tQQlf/GaITQ4ZP7/nowImLaK7KNBaRBMqsnAjwZlYP0KFb/rhyB1RTn4YwZniE1DGd5NAlsVFwSciY0IZx144GdvNEHuA9m6LOHXjgw8c1hcWm4iRjQkL+K1Xjk5pZbw2K2c/hIf08nOxghds/X66NyrOf3iAunPjGfC88ZHY0ndbwFvODF11lGQxvDrXZ1wiXPwuo/UZDPrBrT02heaj8qnDpTEuE/q7MeUpNvGD716IJRH56kQeu3k5Z8QgY+if42JXBnSatKh4o+SVS1T72bBn6s25ihIrtDlE2xSonWZJx2I2pflvldZpn3XSvPDpcsaQM1bDXRPpq63bc3q8GsZuuokYRX/SYWzhxksi8KQePRRSjLfjrW9fWjc6nb/0uI45bzwL09je8akmPRNP5mc5FbdzXAGCCgFGZr2lYe6C7wDx7Lpl/Zy5Yo6uXubXNVRL5maqwLorJBiOGHPqmR2f5CtjrnIxRSgyjc3wts16iP91qzl4Ei95czeKtK5jMcoqvYzH7iM5CM/09wKEHWTSZ/5rcvrorC3l3fOu2Hrhln4Amd+8XVvBqWM5TeETJX1Om2i4dwa7eyA2iEH0AsW4MiZ3aomHc4AN8CKTT/Ix0gbkpJ0BCsotOZe/TS9AdEVeGYdXYLRYcekqZpMgjYAW00CyeAZDzLWWDWL76aIO+mIBjCMETrRgArvjy0TdId7ERI7lXQ7XCWX0ocdY3gUEQjsM8hFcucSucpWnjyQAxGSesTHthnert3bKF1+KB6ab5NiknydKL76htGNwa9LCDp/gW8pUe1GCTTdmfO7v5TJoaXytQmYKM4ePlCbZbOSA2lfZaU4M0GrSGkDiqHS3Ubjhh2xyllvO5+YPbxBM0olrxFn8vfxFNRjcFAJjDCQApy4GzYlRfeLlxsoA9bU3es1fDZcywNE3zjc6T89CMS7ZpMhC/EGfOri091w/yqkLM17MYgYWM83D+mxM1kbocAPUeS22uLGzhfYWarxxiUjszYMiGYCLhfHhiP4+0UIGP5yPGcd2Y1NgfHTuglHbxWTuuIZ3De5NALLdaGGG/EPIpXTTaS6Cp1z6kfFpAnkEFYfX2Ms4N4l9Wi0WF4iWnCNIa8yktvNDHyOTb9TYL8Lb8uhJ6/Wvdq+1VXFispxWrkZrA+zlsLHGIW5UwwQajYfxIUbELzkEn/ygK9/058e28UudopXMDfITKeXjkYNEYefbG9Zga1NwzbWNOQykEAvjAb2xQ38vxuF+g2k/7XGS862/td18Wrtdz/jCXpWxGFE/bXKuMe6TreYFHC8iyzh5UltEgDzSAa9YQUtP+x1EP2/8K3YvyMgYE/ImP0DIcWJPY9do/iEZMkjZHdIBzIu+/i4ntQyf42lMU5lt6KhiWLPiwUlNrVpMwk3i0A9e3vRvgZx4GA77TQBsge6GSL93BmCQWIMbGTbOSk7gI0vyLXOw4WVCio94Fw6TiDoYJEb54jHA/JBoTJRG0k/JQJrFJG79t1R0os9kwNl/hxU5NyC09A9s6nMYXJvB7RPiMm9cKho7we4GBJe0AUrpPzkoLo5wod7fXSqA/8R7RCQvAWpF8WOhVK+5IZZdRMjQb3zgO7pg6xOVs59cokl890LbWGt+uSCTjvaBT7xiKfDMCR/pQa0baSop2EKmddq9WUHQJ64MbL6di5w5DR7A+sZZn8GiG7zBzIg86CFe6ecCibt9MmVcxo51jjtenIzxIxy+ZEw/0pFYqpnq+rFY+7Qov9jjAoBuMWMqPOQKp4zBZTf6SwZcZFNyZu1TeoNxDeyGo89RCLLYYLpmIgom+X43jnS8YEVJPNmeMi7/dJKt1tNXPrSxPueonzxnXfIkBk+TIHoocDrPsewwfSmrz/mOX64Cm/XajxnBazzLgXo5SJWwieFeM/FNz2B0E8bO+pEhC/7zs1iVaa52zTCYOOdXDnAjd7nBJM7uUewDeYXNCSL0+Jy0OAtzyprDmHsGghg2MCvWueFmt92MI5efhyg3bzs2A8I0PvaboI23PggdgE0aqnCOemMKBSOhrXv38IaE9qg2h1M2eUq9a5dzBtW/5kTyscOZvOsapc1P49W1nAwe3YxqE2zzIbKNA/YMnADE3Txh0vlBfjjBHHzG0bDBGRIK9yCRYcNwx1GOYpQk0jNq049gxvCL0gXVFg6yyE79ipRo+nEAorwpyg0QdWzYJBi8Z7KUW3l4Uea8Y2XUoFRgXQgi2MNLDhHgjC5JCGcKX91lQtJt4cS7d6vl9DSD8XP9QB8dF2mSs7htKxpZUPaOvRPe5Mfs6nqRxvb4DKON0wZbnjHPmbHGvtufixGyKU2E+CdW5wM8/K2nyJCUGUPBzWISClJ05oQsb/VcteXUWSIeNzpIpo0/Xdgls3EKDEBiyTtGeLWfoaC64NMbboywWYyEYn6sFDvwcSRnbFWvXOAEZ/vxZRDKlbmIPiel6k+CaTybn3Cp3Zys7zzQvwXZYqcPMzlg07kxnuhWevX0p8ZlRT8+EbNHinNQ3xBkfCPhEtbegEeW2MHBnJh5qs3mvXMQO6iR44xpWxwspzJwnDWfXrqwiybbYDGnfzG0x03hAixctdOKXtdL7ZIjI3vaR4oyHRs4eiAzBRwKfScXfLfQ15yhWQhygxZz0zlG71FMPFw+9VSeYrTfeKTa/sbOukYuLntDhsXOaWS9EFU3R8gP98493FivneP6tjfirk9U5EJeR3Z5xfatL/x+keJco4DjyKCRGEmVVmIPznQU3B6VHoktOwt4PL2jB8f6NXuQOFyg+qsb8tC1H/Pw3Hly7U2fvILpWkvuOLb5EBkyvaTgmtbYxVf90aeJw8gaB7Xwa2MPCn6jl8GU7pSpSCy9jDVgZomCtPVrDKJYmQZDUPoY4KhNgUC0lwObHG3kAyARyEhIKUanTUBWE6irdcq7oY2eiQZPymBT3Udr6hK7GdPZ9IKzHHr3IoQaDTiLDD/2nVp4bwwIC089Hc2F7cYnkyO0SFymEgxeTPZ1gUqSTHKCuTHZpN+YdDNu0iHj438Q6Tdp4OSink0Xm5DjjeVsFusndZ9AZD1xU+o6IOvHWEm68643w724PAJtYjJFcAabctkanIxBSR8zXv8bRwYal3Ih//ULfujEr7qRTIhs8yG6vOJ/4xbQyCqHrdn4K58YBbQ+9YsB/MUN2ujzxwg4U+B4W2JaOT+yiwEXtCHduBZDO+rCqbwgBN7Gohs9Qs7q9JcXcnBABgne1W/NfI8Q/q0fxa29paT/0QZHOeajndPXiw8GdgNrbCrGl2G4OJw3aI/MZzFLAn743rPre+y0pzzLuz3yDdEwTgfHcn/qqW7W7R4bkdiclac8MgbUHK3OgZg7NysRwW7OxKxrD78uis1TsBrHIot+CpUnVjfWdTj9nbBaQ+zUaW7DpzIMLXb9tT99nZqMIavIZQcd16l+9d/gAuNFyEBVrdX4lRfHAtUO+UTP5jZ4xgQg6FW5NLNe4Lwx7/6AY5Ty8sKnbuX0UauxnX7avBDRjnjNkV1H8Ow88YW4fLEQjlHwHUvoUccyOMiTiyJPu4ywkw7l4lfqrtEqVXls7V4AqDja3H1q/tE9ROoQukNAdDbAbA4TCwD4wfStvA5IyBFoVYim/TWubvrAMRCMicc5TqmJ/TGYdkv5GeTZ3DYAu5kqB3cmApmpe87gbuTqjV1k1+YGHxzsg7E26EOOwhm9TmR4ZoLaqQsyv5ePRG4vbFVchJuJTmbs7/ECWfTl39YZCS96mad2XBEysZPcPuk5V32KgDs+PXjwVJACjq8k08y1cbKPmCW+ZCmS2Kc/+us/vmBRnxwnvhEeGeTpUH76xQmfxZMC/KhwFIcbBPpm0VgDFu7FQ5q7SuTgPzvHcLw29MIOdvT3Lr8ce/fInO28cWaBwWc/VnnaWHWREpPKlk95d8zNVK6N3cbqysXGz5uI4bJ+o6bt+OdFMeONX/s3XsTbOGScsjLE2ZI5b9/kvJ2PzhtdykSFMIsZu2CY39Ru7FOnoKNPkaVufqCd8JJvVio5x8jjD5tsXuUlUrRayEX0A6B+Yw+Tzj3j/SIY8mNTBcSbR34UZ9/M84yjsUXuoJ5j+LCjNQ8FUtH4Gg/GYXLdPDQU5aHPM45kvxuwOYse75at3caVYdrMu/FEVMGJ76y9ruWu250fyF97EVHLi3UQn84bZX2ok73hHt6R5WnQ2BLDvOCh+SHKWLnF94yRs/SRMDxQuw5LVr3mA0h9RVsdByshN3lqazDHLv6IP5igyCE+yA25vBurG9Twcd1kjPnUuhM7shE9+9KPew5TFzI84Gqjc0fVEjywCWOnIhWSGUB+RKAyypeeEmIgXzkMS0XdJUUfjHgRHBlSCyZvx4cNJilnnx3RAaKK1k97Yd3gjR5SegdTsNnsqM0Le4jCiZKZPvkjmIJtqgTMhEu9Q6ODzMi5kTmO3cZhFwlt36O/i/nWb2MyC7ETH75yjAWMGretciGKRoZ6TsU2fSDV99pNcpD4iuTAKDIsxLxNqBio78BEhrjh87z79enOj+NB2bIXr+KjVj364bZtCGCdA/C02oN8+gQk3mObGoswUsYheuhXDuHOqbjB3o3AEQHLoxsFGBlBToRYlhs3B92U5ZS+vbAxRzVWq/KiWhg51QwY2AJ+NjdxZv5Sry02gbKv/FwMCqlzyGk3duqPpB+xCSk3Cx2q3UKUXDlc+sTukod8xoKxGyi6+LZzQXv5yiVtYkLp7+MvPOQi7BjA2Co/EDvUT/zg2TF1RsN4JAfRQZ8ylqyjs2874HmaZOyKrbrDpbboGbuabv5ioBzwGP2VG74nRuVYH12PleO4LK1Fn/Et+ES8b31STs8yxl6QGWgh3ysZpvGHMQnZS8iW665l9KgPcX2grk3jhURKdMHu3DvjyuJzRwqxOdubxQzp/8QtTUD0P9Vd6zsnxjlcmrOd/+41nVO4e1Pt3NeXZlo4DFdkilsfiIMMwfXFRRs/rk9s3LvgpodCBY6412kw8T0WV8T1xJMkhfjLLUJgawe/8+N3GLjq+9vC7J2dzIwJWvoIMpG7SHdyarSyyIt+kiSoGL95MZEUFLHJCTtpe9HJebojE73ZQBgf2E48ilzA4iBDHHQwWPJIV8/FRmBxCT40rkXDKOMkfv+BdjmlV8MYuJHHTTl3nI3FBA2GGwdxSGmQuyni2xn0yDlx9Izvu+Bqj3HiVBywkdvNKZX6HUxwoNikXz3N45QIQ+f0RbyIYEveBJBYRqF9dNRH7ALUc/uwT3ttIlt/etG1Danxzzt8Y5S+dvcU6OUAFpbqMWcupr24dKHW/xy1iw15zZkLLzE0frG9sVqO/mm6WSyorB0qUMVN7JnfcERm7CMtT+ek/56wH48iM0j6Wz1iWn8aJ2Mcj/tNQlg3zieu0Wgs6GssiwUxOLH2sHXaK0HbbrDDjW6pwAu/0u5mU66MJbLR68W+eLWBLgVf63t1TrvkiGNgdj/ARuUnDtHfkGhD/oIC/Y8UL8rBNL4ZPW3PBaYXmk4IY7tu8Kzt2U+iy9jJNcRoMw8G4kYePfp4XaVz1rH2dt7gxPpe7Mu/vUko/sZqdWuD1mLuuRKOhNXklpx6Ue1+At7ks6Iz94FFJ6An+403PcwCFwsrqZ8eWpnWKNhyHne+rzXXjyAxTD52f9t43HKG9saHc+OPXvpnb+ocNl8qmzmZdLmNiTfP2IouPpDX6wE22ROWC/iANH+nFUxc868jMaoNvn0/n8xV7FznzY1YyB2bOHFG/DC4nqBByY8JkIqEw7CbU61AjR/I8ipJwysLdYNTQogCyBB680ZdjOJUthi3Qeq05xiyBES0CBdGZKAtyFQeMcaw2546TH98YTXX4MjRvygsrH6poxepbrToQxexy79sLF5AM5ZxeWfcjSCyxmdMaW4CDpCbGD6lvpaxx4Zj4uW8xU0DKcdm8gKiZ+L3fzFAHrxe1B2Qk3GRV/pS2Mxbs3lzB3f50cV462sXBhow463P3OmesWPDTEKzCaXPzRNLIUU9FX3gTAt/4eqCMUDdEJwr1LATQe/qCtPOyNK/CX1tfMM/6OQr3+Bz3tj8vSPH6mZI5jU42JZaRrQns5Xj3NzvnLcO1V6ky1eZ4LTMJgaYhQq25uKun5MrjMdE56c24XR9wYCcR+YE0x+7Qno/fRghusVaTH2zl3zTEFOhOLHb+ElCvzMWnu0vx+XGmY+QKbNlyWXHAa0HYFx5RHDpZ3Rtdn4h0nnsBJD35Ew3QeJAQTa91rUcGfUd6ziDtTJj0cH3gYhQ1cGkbzlzbhmBYXrJYLc6yFWvZ3y5bW/91vfWl9vlz/KvX+VQm72gqHF7MWAQ+0Ri7EK1dYdyIE6PlmtdoYtIL3jsL4uznrddfxE2NBkkz3d9dV8KTPjs2sEuMuAjVz0Uuxfc5q7sXIvgjy/LGh/zpp9CkwuqMXQu0zGyxsH40DOvqPVJtHr4Q2EvQhMcbQ5+ema89tbHZE2o51vukeOPYWIhXSOcBgu5Sdp+gREaOQlHXkeqpjx627dkoI6hxdhxurcwtoVxcChwMEijTx9qSBcHDg0CGMorg81UqBtgXKZPNvVUEfC7aSF7ssgYdyvd5FUbTkxoHuEjy0ZRe2jNRMAzLTZ8J4cJQTbnjQdWTu5OXMawdfrapDcmOJ/i3ORioy9p3c1Xme0/442VFP2jDqY9rVc8XPIajgY27f5ZpCwWvm2maoThHTnwKHDpnLTN31Dtf/eiKIzkxoJBAl3kMUubXi/66bgnsfqy/w5tx5H3xiKxAGM/6nBug9ELWXMJZOeeGGWOn37If6EVpwHhwIKCf8YVceFy06Pm5AXDKlRXTKMEiIVxYtTFNvEbHQSs5oApYshZnizOlasJ8Yx/+m996dz3osRc+88Maj11sOsz/pGTxpS6/jHRzTtUyrdffsGTKDQk+MY4QlNDnadoN7eJOYmDHDJ1AY32uCekeif5Ry5ugQeskHQO0mKzwX34OPfBx7p9OZ/fcE1vebAW08+NFtgIWhpzdPVXLre2J87SxGDwIE4JxqKATSkGPIJHW97Vs0MZVOHf2EK9Fq++joFYTODB5ry2sKhv7A87X+kbKtTyvoo3tfiJNRRTyrP1vVAtAPbqz4VxygMxs4L2LV/2MGzLOVJ8ikGkyHHmCoxbeZDA4HgbP6yaEznveu9TJpj8F3XYKXfPw7d/2pGAdZ76nYWuXdwmAu4pisi++0/apAfE4eFfx8EJ1npOxIy8CyuE/KGPBxiayCuX0f6OOvJObv1ltgW/JrCG6MbBXbSgKYPRKbaxncJEWhjP+5wkyEDe7gzAJne+9LnBMhaZvjKkZKEeuVsMgHe7Ox6Y3vnVBZ3GMCV4lOVHUlD3IwVs5Y1fEN3JREOOkaNvL7LKpk/e4PsznId3edWeF8L0owIPLxC26aAw0Tlm0sDuIzr/+LR84On/LYZOXj41RJ5EZQxe1NEVi7u0TSqNds7Q7cbZWGg0GuhQyoZ5sBmMntsux00YByM3JsXdP4umVsaMb3wAznkJL5K8nMuV+W5sO8theNJioSPPGVL1L2Cp78c42wdH7WUOzSFwJN6Y4SOyvOED715wBjft8s2oY3qRvo1dxjc/2CCyeZw2AYzSbY6EuT4XZeeV81wUJt/VNIj4iS0cITaCesTO+rnythFTt9wZo6CJndPfdOyFu6gZ9+Jy5Rfrz9jE2O2m51whi60tguQw9uG3+Yf8rlHjGB3GKZ3P2KTtPBAl6gtVIyvPh0CsU8Z7c5gqsnn3wsocA9BiNR3OY7oMjbaog429KqDXN/LkJzZy0EBOea3swI/dWWfaAaP6xsmYX/sEKO4pznVRYEHxHFuNNXmB6RzklTyYTzSMa2LAmfyCa0sriycAnBVYnM5pfY8W2HkZuzbp0u9efEBDYrDlN+svI5Tar9WM5IX/oxE/dewk1abYrHNzrtjyBGze/vOd+FgD8I4ubQuzwY1w/leJYGgtAsQDwa5Lqh1jv+0eEJxIe70KTucUwF7klYd73lDGHuwarzoxT4ZpjBDqQHmEZYoAEKKeF4YoPWOARloQnA3ROzxgTTrFaxjZKHSyu3kWoHg6rwhBWjuquPCmKyrFkBEbV0R3w4PD6SRzwOLnFeUGa5hnDDyssFXQ8C42PcvD7hwMYeT3kZp+0gPdlQV/n7Dwey/ejPtRJbzQiBz84enGhS9ztwabhohjOWfYryBXBvXYyWR6Jxk4JIkhCfsw/zUOY+DTh6x15JBJ8sABaca40LCheQELDLrVCTfupMBO4UnwNq7e9IGCnRR05E47P15AqMx4TM6mjFS5oGeS58y3cLXNRASrc5kIG7Nm3s4vehhhToDnKaP2E8f0lX/6tX3FQP6jE6HocgMHm5ZzDtKFX834qafPixcxxld/ckhxE8y5UZ0Y0srw3szAXc7gupGiG/K+G68zVngmv2DWRNDyYnPQJ/pnYKy2TV9zDGSLZtpKKAeXoJUneXrKip0BbdOLMqeeG7/64Q0buaJAfTJm+Fn42cwGAxjebpLkM3FEGZ/gsr6xFvprC58eHW8f0thAXt0cePHDYU4ZbwwcuuU+3JBO4onVuDHXYI8e49iIDHPe+arti2fscUFANBO98YfFRlR9/MyfEORVYXeaxjyOKL9DaRc/kv6D+cFXN0LYQ50/MlC0HFOTfPnR/UhuECxkOCUWD8KlFyzUwKQ/7xRiy56lStqEjjdtMMfdweo+Q6Mfp2dcBZAK2LkifrSvPimnb/97PoycD1PaLUeXDLpDCDxk2Uu92QqQe0Z02L8Q5bpTLBo7f3oaKaAIRErAm4ptc5HtzGTs2rhxj4QbJfTmRQ1H1kkwraczIzRiHyMhkcRibJN/dRGznqPjkT1fqS82eGIqzkUmrfwwnkPqfWtZwVMaDWjeAEwVjBSTfjZ9AoasuHPueHSQ16cwZIGym6SYGNZqR9TIlSMLGVaN4RlLcLzzRVrWnsGXq96Wn5t4q41nbHvRWC7h5UXdWHPxCqckCP/7tAt3OBYbW4ObfpMnWdAETUcKdR9Q2pxYXHMx3TpLbDZW0RR556QXNsgUH1nqLeA1aTc34ELpP2+oT2Byp8xFhPH+noyk52JMTHcTZWquOjhelBpMYLRHPCjVjT79MzeNQf3Uk4xhEz84r6/UnfP0L17PGFv/WqUFj/vxAUs3o6ds7ZYoMRE7owhT3/hu6NbP7UeHXIOXaiMom3BkvNzJVzyDRXDzWvnlVb9oZSw4OzfVKR8AN341he1+BLZtbHaDiab5JWLksBx8BXIKpV5gmg+MbgGrfDhPrs4wMd35RYayewKt9a4xolWT8kY4E+9TxeLap5TGxKSZAl9uLDfePuVM7OpGozMujdwooz+c8dncAVThDDiYc9rmUNr6oXpwb/K1uoBlEPm82PfgRS9F/8Sm1TW0vNHTHOrZk8C4Cljx0+4+Ue2Yc3BaKGXtpA9+xqOKq4LpiRBmyDts5Q3RvE2XkTbWEAvGyStN/OPlPMdPL3jzoAMa8eC9+zBwasApb2OX8eYH2MujbKSDnclP9bshyPHuOl7qN4GO0vqDUkb6Mrp1xIWj0BhDcNpOZGT9Vh1YqDiGpRbI78SBxVs+0fMjvnEStZVDp4/UBK2sJySnTEBQ8H1ODJgx65HxKRdu+3aiULdEdoNfn2I/AztMIvtkFS5MYjcuFlOksDnv9X3bHeuTl7hoRBbei41MMdOH0ULe2NCEfLgb8qPLaMMHHHDJS2DPAn54aiudK0tckEcP7/ALNeKDrDHIOWn3CB7Syo1sTpZ60Tt8OvxTaTmD1cTtN7/2rtIFMOPI0/abYSY0izaIcrl+D1EfrhiBC887uRs3iCG2G42ydQ0rwbpiYJyIGx+5OIfYyp/Uyw2GvqUfvFBXBh57J7vxcSEGGb+JHLKMUWq7Ovalm6GO1y8FtXZtUCuzOQZv3osNV7DBkTexSt0Xypbi07dtZdJE3PmfEezs3LBhGDv0YhbLjNlMgw2RJyPMdB4Aqa2aKhb5h+3ziSdSyBNb4l4caEcQBPiLmX8InXlkjSOHDq/+uLqUB11yjDLMgb6AlH+HT46x0zkedZQRzwuZVC07n8A1D+vb4rgeamZsYrv7GTKupSDVH7C5ccHWlTs0F2/lN6+2DRniDj/ko2AbPd7NoeJsn2fWMgU1ZPMpT9uNpw8TBHo8xoY2sZEYgUv8eHHBYgwXmCfiB5dItZ4z/eiYT9FDgLoXJM94MJ+EIZ+bxeZe466l2EBqH6C0iaHQLBc9EBs5BvaMycYChzvCdYKbadkgIK9LrtrkXf3zY1I6IWMRqNXt8hwbt3J+OQGx7NJjY9RrVGJUXTQX6PZzxqYTbT1QXNxmzpZPN2Ecx04nP1raDGo4bYlDkWGCKNXoGFjYcwROvroR+BQ2iSMeXKCbyUWybzaOuVDHH8ZMsIzvZuJHivCPFcauUj67uKIs5mKQLGBoOwnSBAhKsEyeZCAxZGPABgX43gjgAz2RJRlTyxYjBv72QsPvIKNjMhM3kvv6Nir2OhfgpOj8xCZNkpDNoD6FEURjVBws4g86Wr89R9dYda4i1FghDxfU1jac7rA5RgaHAoNdYsACrH8ec3OVL+8wlt5a6yYKhc5FKuJ1Mfebmkii0DMn5oM5xf8O1R6gbEj6FyPMTO3Fd4MNBu/GINKpT9LKCrSWW3xm8/ZjWBFu1g553hwprjkbHfNW3vQ0Bphxwxg7nRsGsYJvs65opQ/cc77SV9+QBCi4AWZNqW2b+YhfDmBVgXJBh54b7nTRy1oql/KoYI7p9++agoeV6BLL8ug6YyhijvnxY+rFog90CgI5JU/gdK6hGZcTOec8jc6eIlM8YtNZo21UeQphnFeh4ws3VtfH6F0nFxfEbkspYKO54Lxps3mWa3/G6rtrXEPF03YBcMtCsyLoUMffVPAHnJzOC1jqjRG9U2dfyVsf43C0xcFAfU2bOGVs9wDkwVGHGKHPWo3MjrEWGM8hff2Vi/LGbjmWH/3GQVIcopMXbPgxUolXbZb3MMWL8HPjQjH1a62yVvpJEVjxQa7wvjCYB/TvpVN7RfFo8EZW32dsrx1D+gomNUnmvGAGQaTKaQZjJEDAOTBf1oaEE58OwrPyO26gnOloIp+g07dl67uYF9yNf+VyxjRaBNoFkrpcg0mELkQYttCHjN+KpJ53/aW2UjDepGufMuiJywY5CZc2/E882nlBE51ik2AsvNnAsRQ5hDjtxOjLygST4pdIYsunv/EZr439OogtXhoFduzART70odMFYN+Moek7J/qR4yLImUXXL7HUEAsEWeeBGvaI1IllJwNipSW08UAsTZKZtgUb59zXJ2wGTj1vDOIL8sTInGJw3sR88+zWN2KBNX1mbqIr17oR9WkjpY88daLRxd+LohTUOzkFd2OE6xtn7SwvUFJ3fkTdQ31ePXWwGVm5KeYs2odNGUWt/lS2F/5iore24b43WGEpBlK4vPq04SU/5NHPq3xBwKcJEsKp0tdx1kN9oJe+HPjxrPiouoGyPq6h4CJxik4dic4FeQBuARkOs2DAgX5GW6eSagCXt4ODL0ZFbnxBey98kchFGf3bWGpBDA7165zDyMqFEeZFmjJaS/rnupAXc0euY4e9Cb3KExv2AvnXq/qBzej4jg1YrAwj7pE5w2N9r/SjsdiYqBM//Zh4sWhCxvZVT+f0kQ2Sndjabb0SVdz1UMawyCv+qZsjfvsJgDlMNsYWcznYEca4p2qjM3lJfrJHoDSI1NQNifKPJQWw21EhI09zbz71JnaqU37sKTw14jKyjN15x1s+NTDpDJKdkE1x4qgqPWcGUq6kyCCTdrvxqgBtHC/mBgxiZRA9mdT8jgu+9rCjPDAlcnKMrpoTheVjVBjLRPCqJ5i65K1Hr+NdaG7wkdmFATYJbxJbX1L1J12PFHghsb//uuWJIJNMMb6ZCPhNhxyNdQUy1o2BBUNZbLixQfNNKxINW+Dy9/3WV8JxLt4gn/XopiGe/hufnYKbfpDSxBYx5UJTz2Yu4JMX8wLOVVLPT59winfaRseuyw7YvahmcLjhR+OE3OLDZziRwNxip9y/3+9+aTTjbtBVUx6WfaJt7GGqTbDgz+YkUs1T9avaE3MvQsHrhZhMzkJFk7glL/bpEazeqV7zCxYLsTdK4DMP6PVmaH00VpEFg0HGzY+Ja5rVm3xAMggejS0K9BBLXmnXJ2IXDmn3pi248E9bGfHXXuUYB1+uqcHRPrk3H9eHU27wVO2hdvkEI3DEGJ2rXHWw4YxgZVqvj9Vo3OgZPeyhEU5lW7n1ydEIIEP+Yrpz1QtH414d54z1LY/2ncdbzsHpxaxxRsbhGy5JCvt27moXFushWl3L1ODLhRBcCr74FESGqdZ+gik/mzM3/BMosxGtYkUsZdqxWZ3YDhicztHUz75gWncvIneZjxwjvvroETNyHV8gt2sIVHNL/PFTngJU3Ge/y2aZBJ98JWbxf/eBK3Zg5ZUxf8WGZYKSn0umcWJ9M3D7TyZgr3COFHOQG622xND24GHLXMm4vzNUHUfz0nAG8EtSEEkheRjnpx9H4AxBFFUZdHkp154ap44Yyik9UgP7atnc9o28/WDDsbOFctTzlDD8MujEyYk+7voissmgrkq1qrq6cIh/maAuoOixIWYMvuijuxuOemrMmHXsdHMFsojoTX8w9uvTxKfqspMjctOp3WjZJBGxfbUfHk899YUuzvS6uIO9IWkb8dg+x1O5KfVnOjSD7ElAf5lrMHpnhfW+0CIOfPS4cRVJDjc3EpkXZSUGPzYjILmYF5utYfuMdRpN0uoiu1w77yA2jiwCfxeQ2PQJk/ysvFKx4SLTr/gW2y6sORebUDenCHHneG1euZMeefBk3LnIGPGBDTYBT7mNYeXor65taFS0emlcUe+0s0FuURuZk2Piciq0sjHaO/RyQbN+YZ+4eNGtk8MB/QXbj9O3DZfU8yPfzCV2bsvapa9jO47d9rWnmKhXp3FDjxFkp5YzGsvb0fp+8iwG/qDYGKPf0twZe9sZ3W68szfUoKPMJyVIHvsEbpc9mW3PznBs8nJ9VQQyIwyXxdnBcsUnXsk+BxqHqqKzcezHy+RXxHK4jZVz60UFiGAl98q4No1/gJsnmyPFcK0JOV6iOEV/G8raTT9Mm9sr2PhpqYS1o+s5YBvk5svYsM2+nKcuXpGBR3mChIs55oe+2gwOlbRWtv+sC2F6M8gPF+7oroy2RwdtinHIWXsoBaD6rTMeK64J4wOV4XMfwbTP4kZoi96MyqMkuAiuExDZRaY+cgQnDUgzf5LFOE47xmDG0sdrZdJTnQ4xUodz7t1HKvCcTba5IUJ6W+pQ+4qffshwynvlbMPJvvZyVH/O+9RGsPaCswuBxYU8beCVSYX/b+/hw/lnCzoQqZxNOgTxO8X4ptkbicYFQeS8GAfdWAWfL4LAS25Rz0jGsrDAiw796OzC9sYgT07IUTAJljG0XVmfXhlkQ8gJTspxp5YXcdZWB8eekMYS+4u5d42dm4DJbeYqNorHvAx2YLDHn1Fi7LaAyWvzamPOjQS+0uaCgYnNJ/H1ox878XuZSHZ+xG/+GcO0PRP/yaVeqBsj5yaEwCYwXmhZpXsHH977RAjvzn1sJeZu0jf+uNAQmsKaaM6UD3Zp68fgOxmwx5+c+ZIB4+bchFbe8GsQIjXxwg59KWLiq/iRoDs6p2x8r2jHiOHOpwBzEA29CDcmwpyj9scvomU959puyBqTcjLuYdASHZTgSB6kUfza2lySdtHLf/xDj59iDmQ6XD8jc47RliAQxce4T/wZ2DxYXf0APvOTQWMGV/p9kiNvhm+f7ohl4pM40N/SuJY/PZfftuCUUp9ztkWMYzMY4tl3K3PtBee+JC6xQ4+fnB8pIMOtvjCELvgU7URkWtE/mTDafmTHX4b7yYhdYrhWlY2cuMMHbbsenV8wzHvyxvHawTbhc41EkXa5eiHJWOLiusDM8Ix+Mjc80kY59jkha0wgkIIF/hkHfd7gV11fVp5YdGcXFBR1C0Ff2rtBTGgKrtOIY6BJiKqAaXsxGd2d8NXPqHLqJiD8Dgy7nSBQsGtHedBRnxix6jh2uTBhTxECAZf2UVdOrRxSamPwMRUZ3tuPjHhuFt2UilkbXdxMGLaaeG4iwegGhpHaYeJYaJ7pTsx4o0f4yh2LLeLBWR8jt0lLn2/kiGK9TWfIVre2orMd6XZxohHxflw49lm0KfoSDC/W9uRAdqZwxC9tcIIL/elbW/DnjfRexNJIi3Y3jPrcOEhEAXj0QrixoJtS/hzz6tSIl8OMl5PxMLnLVd0oYG97zov0aIIu13x82UKbeV1+05tufNi56u9om1P4cD4tEzsWZ/qaC5d9uNf3Ia4ei7Ztx2dh1+rOUW3TJybY1ptz8CIw7ePY9Qmq82UPh3Ipj3Khd/OXfmKIHGdwi4ZU5OfifOY0fZkz8j6UnF/GFgOd1Wfud6z6sNt3pfRDzMFzrRBb5iKyrOHBrGxadMt5jRH325hPPaL0972ynNuXWkrAYqf5UkswxOhpg3H6ptDvkzviGUH3/JhTbC44wFaPvFJ09E/f0j5tWB+BSJfPtG+4BDJjxFs3lAObHPHfDSsgK5XFV7geMMORzvuKMcNb8IuX0jV2cgHrkl0lJPPOj34lL9x3sTFc0OHpjjdaaLhPIMsnS2OPfHGfjN5T/kqm8dECayT9RpSOWx0RM0JO8kIub/cU7KkHFnr0c8aXxpl254F2uLKfKdIRGQPsKMIYHyOcV7YgcQ7ZlG1TP+VG1/HUQfPUg3L0CgGMbwLV4DHE+C5geFm/4UEfavS7MUchS9E+9Je79ZHt5h+9KBaP0asYrLNZ/5lwuBQPfPS74Ss6nBi/3SzB8h1tfYlv6Jl8c1FqvDpe4t2Y8KffBm18vSkJiJLMFyU+YHrjv936QCJhFU78no1AEaf04Q9vCni3bfjIFVkBx4jqiUfOctbXwUFnjHNGT5ycSU4TLQvAuWUsGMzD/gI7Tds6Az606QwT/JZrOnhCpA0Oc5BbqdaHS07FUTW62Bw+LjoxCQO8+gWhfmMwBg1VN4sxrgx+rC/yiW3KbcwGNqeyFsz6782HRD92O55j6sUub3NKCLwLmhefzpOxC0nkezN26cMF1I51TqhTzJcZo6v9vSGrfeQ7f24kc5MBoHEeHJHSd/6pOP0QePSJCyw4wbObN329ky93hq/8oNUCL7nVkaziRnNzMF5DXpmusdqCxuZwNRZR8emKEL5nyPgPv5XELv3GCrzk2Jbl1Rlp7xkSEdMnHuiwjCTzAd7Nm1hW7hoDDRli5RymvnllLIBM2YtwuaxL9Z/WLX94npwlShuUneOdh+pjjzEvWoQJ4fDg3T0n8juvM9Y/VsH66lrkKiIHLnLZ1/YPj3iDxmLWx+CqXy4cYWDUsSdJiGKN2CFBqwdjlDhRHAeOvA2HEXUM/9see2mzD5Dj+1/aNebQQrLyxKEfk0pErDoFHFgcUqpckpUqCA4g0cnoCEfJ5ujHqoznRR8j1kOCzekBC08Eh8QJ454RpywPzzcTFNsEG388u4iDlw44qc5gK5mvXojqOjiia2D9o0dfqKAbpB3jbMmJWtvTIA55K0/Sy6vyyLHpb2KB0QREN1yZYHUZSRGSL4rwtzbjz+AhYrQmIdB1gaW/eNEdWZMRKPHrA9AuUpLn7GeBMkIB3VPrBgh+9U0u2ESXOYqU9tWCXGXBKD7zOPVIr//dKNIPPiC8Uw2ytopPlowOteHgx9Dxv0+9sZcXeyTJbh24wSOPljNdllT2iZsx4gb2eWesIGRS4cdYT05FzosFF6ixpp+L7VnvCUUkiCftSKMLXg72YIdKimPGFPzptB/+uRHAIeQHhxqFbsTRMZTpWCwHEJgCXwx2vDb1wYsgQrFNDNO2Lp8EVvyur3Kobj0LXsY3f72xYVfUh7XXzQsLFOOVmELNDT59k7IzjsnBzdyQX3Bev5p1jRNzt/mnNe0GJg39xQdJ7ywknmAjot+Z/7T4W7ngb9G3+OVN82zkjrm/FLsX6NQZF7FuizNyNV0GFzZtdFaetUv+XX7uWIXKlRjvBboxjH4mHdZ+y3t8NU7g9yf8RPPMGP8nJHwbNy14SCiD3/VGTJr35VasAp74oARPWVjkJ7t5cry6cmU+IIDJFNY/9xj952EQAzM+RCbd5hJ7ALF1v3RNd87cAyPOnA8cgJGeVnTAINvEc900Wx7w13ry78GWDnujXNLROUavPMUzXU4rcExj7aTCFDphGEMxDjSxCrITw6h60SU4FLHRI1C8CND035hUVcbazkjNnLLirv6JkeHCKQe2F8YI39phcneCtQmXeeurMNOXOn2bMJjaRKw3j7DWLjZJavgz2oTFW/xGZPzJZHrhwsdxEGxs1Ub0SZqNQWRqDX/A4gA+09YRTzdVvtwyFiOqcdtu+MHduATE+rYbhwIZm/jD2UQsW31Ego9dK5/G2I6wUthUD4b6NtwZBS926yPpizvNpf1SDmNiRvZ6ym5MaLNQcKsxCkDsLy51+BKb2znHRjq0tnGrDpsxPBqLcsvcwyE6zlXOG7t0gjRxwI9BIwbYPmOKZHmhgVSGqjz90kldfz3nIDrn8MnRJzbqE7eOoEOt+D7xRrg2INH+jgdDQ9ivElgVGt/TL4d0d4z5QwS79KWaN5sUEBuj3mnzkXPixa86qjIYNulRnjPYaxucnZ/GuPKYRQamvJWhHoX66Uw6urHes+u7BIshmCggqbPhXR9g7YYIn+T777Uhici0DP9ALU/YgEVPY0U7Td6Ws6IcMow1DggwTmcLPqif/o1X8W9yICrInHFKAxSwOHdjv/QdycDefFzWapn8Z0zf4eJeOVJiDzoXfobluyh0TFwwlNK1jU7iqWw5db6rj7bzlYrr79zPLiw+zcIjxtXlig1m+FEaw8invzdx5OjsExtoJSOLTF7sDSuDSOpl9pwAAEAASURBVD0TzLV+1yQI7duQomjRf+qjto7TzJtth0v6klHSjSTjN8bEnsC60Szc2gHOGa6TrYJWJh4LflHJKIVggW9wtEEvGii0xkdhTapFHHvLI36BUQ4Qv5IJLpdugyrwYMdwQLsoumFE1zG6wyKT54ROHw50s91xZLrh9vGB9tpMkmYiQTR5MEzIc3ezC9fk876onL1DyxzUo8jCBj+JDbFiDAN54xsHZOxDdgoi6OFTY4xobVwySc4krTcgJKIZkXPsgJufJhmVsdn+jGuqMQeXC90meno1UXtUS2YX9HmRXCJIDL4LR9+CIW58y0a3nLCx/lSFDQh73RCALEZzy/7o8/VtbCC6F0fmBgz48iKOxWrf8s5QPRg7yCyfjmCV2GLbwASHrXrr8Cs2ELyXy7X5gLHyK8OGQr2+Vbc8zUn8UevaZEC5bIUnSpFKCIafAunCFl63yrkX3rRphMvauHwilxx8BIubaeOBmoVITon4xtWNDJsx4KcDOTu2+vGHjQ8CfuOdfU0y2Lww7YJ5K+MFzeI1tvjWNqLbh0+Sb2erSxV9OBga8PnJAduziac6XIh9c6rxQY43PDhGMECu/3QsN/qEqOhoIIrf3QvRlufI0L4KXEQQXzsMuo83dsZXEpErmTDt3ld/ioZNZLHFEzbmdm9LbepoZMR8nnF8kEJikDN7DEXJwZN/+vD/3OugA5ZH9hj2mur25mzy2VhHWDs9h/6pu3lKTuOf+YfdCW0DnDELQhRwPDeAK2s/hnQiEgDOy2SMjpOHPnJFUY52HUozrx13ghIZIAkof7jZscgrCVkScYohyWGTxTFszduFY+KioBfycLLgiyFG4BO5JgABDicWVAofv4WiNkAQMx0EU7vp80IQfeyDZTv9Joc2Ij//7goZ5s47Fd2qLcTkEb/rY3DOzZcl82jpBbA++aSUuPm0Jn7vpOpzYkYSkujDeecC78GRExexkHDO6A8OfnujNBu+fy90nggDxbAFneUMdr9SXf8RwC7SjdnOD0/IeaoIb8Yrg1Q/ImHsXv4d4eJiA3/IHG84kJTAZRs/9WXP8WE5UgHbiyLnLJaGv9yIoRzS5MzC2k0bO1oWrFyJG3LYEws8FmD0L3k4Nq7YrY/YI8eHmbFJF72D55Agl1wx0UkcxQK7MRW3Q44Dib2WylEnbsgyTiwo8ki/Z8dWz2EPi69/0008Nt7UmVtK/Rrf0maNbOlc0tdY4BNFP5ALhvEcvMUapkiKhz05aTIHbV9+1rXy6fxoRn3HAtiNszYZRVrcHDcWni9X3DDZlM+NOWPgu1RuY5t+fIMj/pdr+HHBXqdDBNr1BQYt5JzyGd+9B532FVNwGbe9tkCg3i98FW994HzixK79wWhOXLKNWve5mtCRCuTo2sMIdRwPz2JlHaTlO451LXSeqFeDeGSdpXHlTuaCi6xzCGraqbNuErD0t8+/IkW1JouPNWwp0mM5rX9w655w7mes0dHADXidcUnd3xmCxwBEWwKUCkRbw25I2htZd/RIDFudQWECpWyCRZdj4Yp/lzyimeS8mrB1JhKK9HdebHzddMChuIFGVJ4bKWhkrOYr6QSBn2Q8nc3QyT/y+88RmOC9sIEkwujij6/C1g6+KLUTETAnZXxIff8pgP3LEz1sKUu97Ye5wFCME2caGXNisYvg+OFAJCrbC0E3cmLZpBoWWRT82bL2AVPd4D+C5YDxBAEZYr7xVwv76XdDjw1KL2TIJhLIk+SORDJ9/MP4XgimM6PGeaQ2BmTIbc6xkJkLeBM79/Tg4S9v4u7vBx3rXPlxSmTg0LtE5PG7trtAIhuZbjaGM21MILty9ROc5gTzG94Z1s/M094koQFez0hsqV3k4WtepY5fGx95VtU+OFUOPkhVdwimje8owIa8WPnyVYdAaQBZuC1WfF0HK5DByhAjY4NqZFSPHvNmW6BicfFYf0PPceZo9TUePcqt7dbJEdEzCg7xp50KR0/R5Zzuri2HtMPGgf7+bkzMCBqT6JAFqg5fMBb9iif7QGyMDONb8Jzfdekf9hELInPSGlk6c6I+/ki6cYgvu1bbK5vIjJ/tTO7UVzihr5REyraYra8M0eCag1lK/ZkYpY+2Q4JtLldYHQ8ZzA/zpdU4WN/gg+KW6m3fxaF22Bcors0g7brzYpex7gPkP3Ejh8ZhbMg1Y1CBs321x1hGosO7ccY2HF13kZULY0jhx/RVHjt5eJq9IyKJGQ9SgxoszKFD3rF+dn3ifWNY2/eZyKuD0bxNXpQ6lp4G0uEIzOKkH0ulSYM00rL1nYBePBkvKTburet0OLBoIRkySMkJB4pO8i+2qoi0IA5pdbcLjq138hDJpoQdN1uwMMXE3EDBgewL1oO8dzK6GajQ8CSg5VVduUUPefFjA/NQQg5va682/WsTTBb2894NS9uRLXfiUb4RyWT3ry14IQkgk0rpxavOLg9tZlPvePXAMNEhhV2M5FxPygv53ztX/YVzn9JRxZOTJ83gdANtkuI0f9IRUS6Ke2Mgt4xtItIvHBwKrNvUE0njKPyMmadYO7k7OiLNl85TY8EANfzrX6iIncxRQsqK1mbtdnYCy8DlW/6rm5YghMM+zTfu9HVzqJXyEjxq68/GiXbfxISNo3zHwNg0GvpH/2KsFyvLmRhsoVbN9uwIm8FTX3jKi0h5EAlyDX/rvxd3YqLqjX2qAC2Y4xzSkQWjL2il2enpPuEanry8fBxbN5z7T31y08M8jH3qlGJnJrgYYcK1xFj5KQSHhL+QZrKNxqx+tj444m7Oi5DDYAYEDmCR+8aHegyggU9eLGM/NXMHp2GzOa3f6VFWmo5KedfTOWcZGsba6wZEPjAvXdf6bgv+sYqOnAQPbnhtvLJWOgco3PbDVwry3MZyVhrfokx+9Cat+CfXiQnewsmYJvaxok3a+uJe1hhiET5+4xTe4dnd0BEGhg/spuBcCr/+QYo532/SLxfXvzHaGBAbbrrRaHzAaAy6t9LaPGgduTAev5mv9vPN/XvHfdoaDKIXP6MGJAMFUwUQOlK4Vjp1EiFFoDOGvJAiQ2/7F2dlaPcOt5NRhxf94mMP3or1qGP6MfA4bCLGgBOkUzzXalEeJOXpZ3rALsPUwYlOJzd1Jp5T+npBBCfYLBoWJ/2po+NsdFj5+lJZYTPGl7jkaGJ385lIqgmWbmLDRVj7dmagFxC8CZBkqZN8F8rWN9aOZBy+LkgG1E/f6MWJ9FEgOOeMudEjM3J7N7YLgjgwBk6xuqBsx0cXVxJ35ZBtHLHHgs3XmIPBgkanmwhxqW2eBGDLGLS6WbW+T62M8VE6Y1w+iR9v/1zb1AEBqbmWmKXtP6UgRyPTDSgVC1yolJ/zEH7I1e/efTrvkeJOlNIL7Z38ebi7+etA8Vl5gRznwNx/7nNPUjue+cxn6dcO8g0/bD31VG9etv//j/Nj9x87nvzsZ7VHvFpu/E0HG31L/SdX4Nt8Jddn2BPz3LVGN/HbG2bivL73huERRbWXA+ezPjEm7mur+cbFKCV0WacdRIYssVtMDq6PnBcT7mDxhsvWVcA3ZVnfzJdZF0zyAyWlbCPJOKXY8XHWDO2uq3Ijh3mhs+fGoXjLsa3xSWAuFdgoDl3lf+0z54VvuMCzrASQ9uqpOzGViz7hVDT80avosIfUr/0Thzi/MXQw8saI/TdxVJ6bbHgQN7BT8PfIHzanlGIjcBJzgLHoRdRPX3LxKaJd1rNSI1lMVG739JUCAxji+YB9gr90I1C5Yxmq594jHlz5xGk51IZzmLXn3KQrf46tCYEOH7nh/K2TvVMCBQJsPFjiB+ob2ILDynFHq1OniilWOthQTBu8yM+WkgvCBN5tbmQahJHHznKJ8i1fp0GdPgn6LUUWxFzE8HPYqqfNIaCNJQMGCc47fSZ7orlB5mMbNkn1Z4EgiG9+3r+6xHM52odeP1bERDfX2mkohh2NyJdDE81/J5MEYAjbzQJiUU3jn6qJPlxu4yuecxOZYLidR1ceiSd6+pNBMPV86sQbv5y78OriD4iFCwiI5QIOCYYV8FpISNr1mQXIBkROSQBG0cGvc/GDCA8hgpc6T42bF/sXkbhAOncZz48ug8vfL7WdujEKznLgjtUcQuCmYEuZ8PGCp9nqd64Qfni8612/ebz73e82Hp/73GePX/7lXzk+9alPHp/4xCe8QC4kGPcfu3v8+R/+r4+3vuWtx2Opb4HTBz/4O8ev/urb/Nhp+/fME9S9XGT/v5b795mfyx/wn/HYY/UdkMyLNw/6xIWN2DPAobEj2M4BsjvEaLB4bc53SAmqKZ2nDWfn0Ilz/hr3tsUCT/urV/uLaO4FdeeC83JUxjZdzavU6g88aOyxjeB0HTrEISB+nEY9WPg2JqLZtkMcUuDc+cd6WcLpbIGn8+1b++gix3qhDzstaednY1H5rhdEFY/groUr9zKmUeLVvJQHcUhOU5e/XIovX3TEnfGJ/ZDp6UZneVWtuOC4B2D39AMbbaPTQjsl9qjYTX3a+EKecjYfkU0hfuzX66sXqfS710z8mEeuU0+7frHSm2rAweKlUec7fcEkJsj5tEmdH/vgtnHLXhSpc4A6jhEu67cOpy5A7PfcRbqyKuSA5n6sUJhO2o5zRqbJeZscuFAb4FunnZeBtV5ldPuEUGfA7ERcHOmjLJbj4M7MsFn2o8exkX4DExm9z7mTFj/TsRPkk9ZMfkSCrxEnUug0d0MyTsOhMYNnEmA4ZCjJnv+Q15dm5HvedGS8suNHdPfisbbAWB4rGyVx2Lhu++jXJ855c4nVVuoGmVPqd/K0chs3OLfsbOt0VFQwXvRQ1AvGpU/i4/PlC8bqY3Gd63ECPV78TsJ6gNGlKJczm4EbqcFPmxd6tvGRbw93kXhzl/GNO1jI46c6s4HsuHYytnGp6c0buPB0d+/4yb/5fx4/89M/k/qd48knP3P8yF/4S8d73v3bx1/8b370+MhHPpKLHhzuHE888enjl37xzcdHP/bR4wtf+Hy48UfGGeN89/iN33jn8Zf+4l+OfC5cz+jvj7mAov+BD3zg+MV/+GZxwCq3PpWiCw6FM0+Yv/a2Xz/e+973nR8xVZ6bnM4X05UGP74hsbjEhAhTzPWRUTV93Fg89ozHPFvPRfb+Y9xsJI4Tz10jRLj14tV+YzduaIfD2Y495lc+m29yzUXMPG4OySew5LaUOU9ZO70oFbv8qDfXzljgbkpzahppM46OPByvlLaoWoEsviXH5iK7dkA60SCbn+a/3p2jybCxEZzIMOoxuOwfwLM/bb1rGbPNzQhHjbnEf87hTXKlINMHD/rTFi+Yg3vKI+va7FqQSPrAhY4xvpilNutmZCLVAvUp2ku78dtYlhd7tvGPIl8Q5CJ3PZWmby5gyOwbKjoBIWgRGJlw484YfZ4q5zB2E190KqKeMUK43Y6d1eDcBxxMkDXEmZc7DnoNrIYjeefhJGXq9BHMXpx24+3FlD42FAkMBlg8DeT6P2N1gns2xq7Seie6HKoX20xeDDthKjQalSUAwZy7Bv0RKuhGjKeO8uwfBGAQPjtZRiLcmhw+zjO6v5w1CGA1VmxaTCiYfGGFgr+0Oe8iOO/uIo+f3NnUX+6E+j9O1LIQwScR24OciZ0+4klxzjK8k9szcWo8OUfYhn6nCSf41P/wcN7h2s/pWXQ861MezO/LGmP8PfxTSREIBtD5HWLihl36Gls0iUs50KKA3/9hol+sUcacSc0Yxb9wgz1wy6ubKfbarz3mNfaR8Ru65mhzRw6ZF+ckcvWTjaO/MyVvWIyYIqabW7aIBcYp4V8d+MG/HDq4nO8ez33ec3MhfCyL+Qvh8/B41rOemfYzjxe/+MV5Grt/vPc97z0+/cRnjp/92b9zvPzlL0vfM/Lk+Onjp37qTcdHPvyR45/6I//kcT/r4HnPe97x3Oc+93j/+z94vO1tbz9e89rXHH/v7/6947u++zty/rnj7b/+zuP1r//ymH94fOLjnzze+c53HS996Zcen/3c544nPvXE8fwXvPB4y1vecnzvH/5Dxxvf+FePb/qmbzy+5Eu+OBfWZ45PrNPmazw5/aRv1wm+da0yD80VY4xe4v35z37++L/+9k8fn/rEJ48v+pIvykXxGcdnP/Pk8ZKXvSTcXp8L92P9U1puzAlguBI7gnnZpt1yxjoyjqe7uWaFTuehEOQf80h3sUVhzvJi01s8zpvj6F4XRfRSCiseTTdffGQ4B+x035ucdEAvzl+lSAzlzLn24yM29WPkqWMfPOtJIuskl1SCn1efYuqDkLf6wdgcRpdSGz3XQHWJBMCb08wluk8Tj+ii7n6C2JbwZ+8CWdwE2JfrLfxn/2GMgg2exsBGB13XaIbRwx/1MwiPIhe7vNHinT5qIWV/6v1yVHOQQKPfeQWnWvUNC21biw86l076kV1M5rZx6yeE3edQ7tOnMeGCDO/hkljVQMREcyCw/Xc6dGGiRfAlgHkQZ5zaI1iMEEgGplQ/qys6BNOgEVQCLNwIo5dSvAbNDnWqSxtuBl0z+NEewNDV/lAgOQYWZMf2jlPsHFw8oSA3pPKE4cRQH2qee6BXO6mIJ9/IaXf6sNsSbiRoLg5+VXjJsMpvyrmYHU+S5QwuMaI42Wk3NlU0KTfQkUcGX7iQkBRymH4WMaJGKsldrl14TbhietTP+SYfvsqVuUGPmMxFFZy8KP1yROu962Sj6MYDnyihXv7iFwcfHCKJeTlW/3lKBKQ+B4+QZpz/gNVzmiT7fky0PPAzP/pIDBIRxC1waoMKObXzNLkRAXTgjN2d02pzfHh87GMfO377PR863v++D+bLKvnfRILBx6Uf/djHj//hf/xfjpfmYvHRj37Ei93zHn/cJ7iv+7o3HG96088eH3j/B3KTVW5cwH/ndz58/O1ccF79qpdH/jnHT/yv//vxkpe+5HjRi15wPP74c4//7Sf+1vGF/I8lz33us4+f+7l/kKfNtx6/+EtvPr7+G74y9t+Xvp8/XhZ7L3rxC4/nPf7sR/jic2eINbCbRPyOj/plMAhHfV1/47UXh2c9+9nH23/t7cd//oM/dHzsox87vvKrviK5cPf4r/7MDx9/+j/5QT/q7bw3LsBdWBPmK3DWOg/Evtxi6pGyeQkOpevoEmo/DNtHznQdIO2uoG87b6B4E43PiKRjOdJAjrexmlxQp6KIW9DWNk7mh/rqVoD+2ue8ZavaSCd8wULCdTcGGBc4I6z9c/0iS9uLFOQXuXWx1ghdxi1YqW+3zqFmXzkiRtzYm7BLnOWYgY0PbTD0FR55LTxjXMxWZ2U5awjp1K/1hb3+vnBzEazTFtjhA5ftSwOU8qYx3JoT4R18/+cPxii5oLqfZO2yby6Xcisf+8DJq+PhtZWigLtX/yuKAPJ5a3pgdQaMXQn5Pn4TUCj3CWQ3NzagLRuetnVPTIICzr5XnmAgwPm8GDBpkSUgcLGe0/lRQFmeiYQVJrp3SP3IDkUgGKOsXX2J/pjVJhu+j/IV7UTJ9brIsBGok8NiyR3svKlji4TAVzZu4uPFahIJPQoTvIlDX+szueFi4opZ3hGgEiPRzbmJ1KewJouwaFhhPogHb3jJLXpaTxudnMRBw3lEmz4OwekFehYRNhkTvXd5VKEkVhKSnNBm+s55TC9c6zfz0TvNM1YTB8cHvCyR5cKGzcYbTG5cerFOf2zqOxwiq2RIrg34GX8Qomt7zusHffW5fij0yOHO8UV5CvyyV7/0eMUrXubT0jPyxPR4nvR4EuYj0Q984P3Hd33Xdx6v/LJXmEPPec5z5uL2eL5Q8/nBr0WeDl/4whccz3r2M49XvvLlx+c//4XoPBTrySc/f7w5F77n56L4bb//W48/9se/73jhC56f9uPHc57z2PGSl7wkH9XyRZk7x2P++8xbohup+J9c6RwyfuV5ZudU2BpyG5vH8qT75a/78uOZefp9/PHn56L7suMP/xPfe3z/v/onjjf93286/tyf/XP62493+zvKrmnynTjmbjxrBJubX/4fnDPnPFE/I+/mxsM8afJvTFlTd/OE+wyfuPl3p82VwAxJ5od55OPbZ+QpvZ9A4MrtLKbFDU3k7gWXp9oAi7U5Ir981MzY/f0d60Lk3IvSrJEYt4bx/MCpvOZMN3kNjSngV4tgbM6fo8qyx5CryG5xz9rGnBlee5W9LHmzGbl9qjPHB2914IzevoFlbNci55PDclGnenWM+I1eZJi3+/yfqoODWvHnHKNwociRmKXuQ0EBxdN/pZqnyIRpsSCu0drt0+ReW8htnrRHNvM9MHKSF5zWt9T3j2gEfPzNWjNICKazSgM0wE4i5HUUapBhgyk4vohR5mkkefIaH3We8XMTBAcC4lcXG4wDoYGc6NOanbMhDU/0u6iGa06bAEB0s+wkYXupRS1j9VV7Jh+T38+u+RSGCUpWkrOygAmLEl17vOBDc4jNxR5cvtnEIvYpBT8T/E7X+g8IeE0mFhlc+ViuI+V6GytuQrDE5FN4sLPICa3OCX3OC3bT3ctFki8vv2DChjBW8GfnUH34YGU4e7ETpzlB218+R78Mo+UFZ78BGWR96rxjH7nLD/QgyGInIuEIl+BS1E2fmxMd6eca75/5Gh50uynlLEbUwfePExRduF78Rkao8LhZBPvxMxeAeyEamOiVf6pnMW/CoZyQugq0n/zMZ9KRb8WyyQfnyXxsyAXuk5/8VPofHi980QuPn/xbP3W84Q1vyAXr2ccTn34iF7jPuwifeOKJfHuUvz17HJ/NRewz+Tj1cxn79Kc/rT0ubE985gk/Wn3f+z7gl3KelwvhX/7RHzu+87t+f544X3l85skn8/vIJ4wTsnzRhgvOO97xG8fX/r6vyRPk8+MX8eXdQhzoop+YM4csZfaO9q3k1Ua+eVoc8oaN+wvh+/u+/muPN3zNVx1vfesv5wn4Y94UvPM33pUL/gvzMfLztMWFjd+bfvh3P3y8/BUv9yNV+H7kwx84vuxVr4zex4/fyke/z8zT5xve8JVekN733vfmwvtsL75v+9VfOz79qU8dr37tq6P/stjld+wJeV78rvQTH/9Enozff3w855e9/KXHq17zKp9mn8rf9iXAXCB/93c+lBh+MnPzSS+ur33ta2LvWTrLeueJ+/3vff/xu7/7u5mr5xyv+8rXO69smDoRSS5MxAHL3kiynpjAdDV/U+VGMutt1wjyykSuWV8cI0lKqVtM7DAH7J3q6CX5R/7ya4nBkrUIQ603HMzt7onaB5uJxUxwvfF2/ZUzfQVoO8RT6lvlwxhO2OV1+i/icMSFvPDbPQVM4tF104t5GoPNSbMIpdP1DLfobnyNWcb8lqixLHZDXc7I0+bQj84LnMwMTh9MjBucznhim/kph+4JsEjcokft3g/8m//ef5iaTuCY0mnWFsba1wlSLBwIT4uBomVHz9doZcBARyywM3H1BjwCQ7OIBNUa3fSt3o5nVNlpVzliYKoSO3mVY4O22FqCC7K4FYyOodMkxP61iXcjLe/yWxt7Pu2ysRArrQs+HMImTRPa8UiM3GJgzzigrsvwYuNaP5BkqDZ6l40wPsRHfafdPgewcfpHtbHcvtMelpMkTbDGXhRtwwG7GAK+mGuGxDXhgd74E4cIX3e5DLIJMBdcmBlNV/hop8D6ihl8JlZc9XduTr8yxual/1HuTQdYaNZOY9bY0Ut/fU9VnfiajyU7x8QT/cpUFx1igv1rDJm9qHIx+Fx+Z/elL/mSbMCvOD7/uafC6U4uBq/I7wCfk483X5rN/qPHZz/7ZH4X+P7j1/IR49d+7RuO1772tcfjz39hLiZPHa961auO5z//+bkAPpEnwhe60T8rG/SrX/UaL4zPe/x5xzd8w9f75ZyXvuSlecL89uN9+b3i5/LEyEWDTZ6Lzstf/qrY+Wx+j/iy42ti4+O5uLzq1a85nh0sfMMPLwzG2UDhoIUc2BizWSKPGPmAngFIB09873j724+ffdPfOf7AH/ye43Vf8Tr/DSO6P/1TP50L92dykf6O46/82BuPv/rf/ffHN3/rtxxf/MVfFPW7x1vf/Nbjh/6z/yJf7Hnv8d1/4Lvy8e4vHf/lD/0ZPyb+ki/9Uj9qfeN/+8bjPe/57ePbvv3bjr/+P//48Rd++EfyhPwsb3R+4x3vOH7yb/wfx1/Px87PfNazjq/gI9rQhNMv/P1/eHwiH0k//oLH8zva3z7+0o/86PHOd7zzeG1+x/r85z+ujz//d//+8aFc5F6Rp21uQH78f/rx4zff9a7jW771m4Nx/3hfPmL+1V/51fzu9XFvVP7aG//a8Qs//wvH13xdbihyYfTmikTYbE2d2DR/XEWN4U28NJxDY3uGcbvJLtcNjiCzcUeANi+LU0De0Z6+jpTD9JOXFGhu3o7YuQ7BrN1dG7Sro83MVYSzULGEreYGdS+0Ibk3ml3LJ8tKZ7x4kQ8W+wKEmktlc8aDZoTZc3vB7B528S83JMJiYoISewh76fRpEDPwvTh3Kmp7uYoUOffLnPEJ23Aw16PfJ0PspDjBweQOw8AZGQAyCHkdnkQImJtRBmqQC0y0dHICGcXzbj8QGwzlaK9N4MWGg4II22ewZrIXuxOeSY0ohaclr/qjE8bqxpOO058aevqIrbyZezhRXzDucpClMMbGtxdPN/hgQxG/nHCwTKL0ghOdHByrjcaiibTIgmuzWPwzg9oTPQ0TJRs/cN7xZZzCZFKKBHd8rH93+TKO7Y6XqeJqNC6Id56M9SQYGnLIsIsLX1K48DDP5cBCij35maaR4MKXO6t8pNfERiv1gBvvc+74klHucPNX5NUfjBMr3cTbu8XY26Q1zuRa7JrAg4vs3jFrkXnIe+PDnBEV9Hlre3k58ZUFhxjr13DoR1Y8OfI748YhYJa27xzf/T3fafupLzzIBfC5xx/9Z/+I/87wDW94TX4n+A98Cvljf/xfysXsyTy9fDofM7468nzk/uD4Z/7oP63s5z//4Piqr/rK46u/+g32f/mXvzYfNz6di91XHV/zNV9t3z//L/xzyhLnP/H9/3L45MtM+feM3/wtL6YrF6UHx/d8z3fbD6HXvz4XqoxzZ1y/yt9PB6KwT8bEEpfAwyfOygNCHHjliZn5sMxp48s3ST/8oQ8fv/Vb7zm+4zu//fiyL3vl8e7ffPfxoQ99SNx9oiHXfv1tv5Ybhy+dvDrSfrtrBhv/yr/2/T7ZvSxfMnrRi190fPB3PigOT2jf+M3feHxT3v/49/6h4z/9j/708cN/9s/nI+dXHt/8j33T8Td+4m8ev/yWXzn+jR/4133afN1XvD5Pdc89fjC/w3x/nqb/g//43/dp/I0/9leOP/nv/qlcDF+ZG5eXeZH+mZ/6Gb8k9olPfOT4sTxtf9+/+H3Ha1/3WvPpBS94wfGn/q0/6ZPhD/w7/7bBYGUQK+JD8vqpSOJi7qS9a9DNdeJF3+ZnYzFrRww+6eka8eaQhZhYb65pL8bO82D2SZ6JIPcLdDvHXRPtZ/+AK/Ps/KbOjaTrWOzOMTLseOwqfIucRHCfi55Op43+eVHJWmJOyYO9sU1AghBew1M/0JNBZZcHHM3BmMpnGWKzpT3kd/5wyDhvsNwDxMgeCAfjxH7IGH15gMFsZMznrFf0sG/+Yss2/4i/vqOA/IP55AAnsce+350VEnoOqQKlSyBMNZj0VE4wNsVpW0EuLx0BfEmx6OJEMQYrY+lQjeO+Cfh0Oy5JcHhxrsoo1FZiknLZM0pEavxxmAPKeRNAsCiIYcM3PNUr/G6iDXKtVGcsqswNAclx2QNbPyIM79+rUz/wJROZuPC7LmywkMCyf3gtQqmDRtoW098N5e5YV6L7rPxj7nv52IjJh0+Tt/7Vh061VCFVmOLFHrzMgQh4Q+A4Hx/nY6nkxP17j/m7GT3loJlmTawB8P/Q9R7gfh3lue9I2trqzeo2tprlXnDDBblhAzElxg4BDuRJcpJzOeTJyT0nwIVLySHtXAJ5yCWAIclNAoQYMB2DbTA27sjdWJYtF2FZVrW6tNW22r7v731n1n+L3Dt7/9eaNfP1+aasWTNrVfsJX9nk0CFZ3ggpWplWtb2VP1ojc6a6on+eEUE4okUe5A3P0I2NGoRgZUds59EifJEf3xRP2DKN0srQtORjnJMXfIsn3IaD3NBUgnhLNyqQbOCyBk6BDocfAfwDBxgwDKnzGyrz588jtfzktp+pU9ztqTs+JeNpN+U0WHB5LkgHCC7nX09rsPAmDgxlZf7qCAnEoc2v5ZNumpoCbGWLbWxr6UeQVtbNF/XQYGP1dgQ2gQVgTPfS6X3rpm+XxeqEfuf3fqdM0d0tU5QuB/GhIaMcuAM+ZoY6bgWmtOctmK+7sMmeAmWalMHAlVddURbpbq5fz/5OXKQpSt31Qo+peaaeZ8yYWX7rHb9VDir+qO7amIr+7re+q9Wss3wHyR3foO6Ol1zx2nLpFUvK47pjfFSLjHCcddpq8uV//Nfy+GO6i9RU6Rx1iG9+21v0bHBU+fnP7lKHvlmrZQfL08ueKU/9cpmmu/eU1+oO9qAWRDGQaX7lQpYO8V20iUVwE7mYg/1O8WZnbEng2g03wDUYr8vv4QDroDzHRcLlRGKFN2+BcUkZu8yEBzzx3jX1UrT9RxR5ONJGAW+iJmTfhmClQw6YYJiuY4p3HRK+GnggCdCk08HHdGH68OY/7S6DtIoHAlnQc7zJ42ThUk/xIeogtggYx3RwxCr/YXnIartBmynTCoaduEBF0Ciz8ADCF3oRSGVoHAxpxjQBEYR0K6Rz6EgwRdxYVAkRmEw37mFfBY7RycNAoS5QOgILIBzlEXL3lc4K3glgCLfCGg+e5qs8N3qVr40ns9E5iZflw5CCtXw6o3YoElMcA+lV5baQaTqZcixDOqBWawChQ0EjDzxaIWLQhg+HwKeR4hqjgxOOiE6Mn3RV5zAS/r6SzZVnOykJuDhObJeylAzqPA6oUVip6aAd2uDNqsQ1a9b4WccFrzlfz4tONx0aRmTJK45Cj5Fqd5cHfewDbxTVtW2jaJtG9ENq5XM9sHugbFbDcfwJx+c5GbrJ/u58hdmTu9IDr9I0H8XR2zah3BRWa18ez5amT59uWdG5DSRiJ8TKXSHXqRyyvZzm0CGNdCVXOlyoiT78pItHgVW21jgjK5a2loKJLaAVPchpPKHGNfajeClEpOean6dpfQ1FaFR/1SV2nzptmu7ifq/TiYp4aCjfpxRCF2y7Wgb2f+W4LDgjLeQVujzxToKO5LnwksSxJbUUOgk3Aq5PFRc4ylsBfVN+8bHUp1ChHGjAWhCo4Vm5ukxTn1s09XjBay4o//kP/3MZp2eiPHdrdC06ekFfMkMGUXOtDdPye6aG2ZLBdLMbSuX39et9utZRZcKdgmhQlgcOHijcNTMtvemVV8oa+Q13f316w04oRye2r1yiO/a7NXW76sVV5Yqrryiv013lD777w/LYo4+VBQsWlEuvvLS88U1v9EKlZer8ZuqO9YQFJ5TDGlBYf1H8r3/yPvtz8xH7sf2ENid6cCZQg/Ap7OM0paNP8zvk8125AYCPXbhMUD6zKjQ60KCiK/gogmnHWrvAgEydCaD4ZKURv0U2sIKvEjUdYCMYJ8oTJpHReUpx5xVoH12nhtHChxoPOhhJ0bFBe9UK45m7eICKLMaRkNjHfHV0rHWApNoupB4dx07Wx/I7WwdSI691H5Is+Eu1A1CpU4H0Gg75sZoJBUkdNtVuIQw8MeToaw6PyAQSu7iEjiGcVUUJXMRqkChee/JaYc0ApbGMTo0O6ZapRaoxmlKhQ2VASWhWo1TZTEw0bRTlEzOpqiiSxviw7uE6XbxwLnh4+tOwkceFAlX9t8KPaI0wXNBX/Dx1SCMI1fBpy7ZtP2XA205GXLrYDIJvDSdwls88kUl81Ik7HzjSayBGo0Djv2HDxvKLB5Z6kQTTaYys6Qy/fdN3yjduvMlTSv/be/+w62CoZIzmadBzZxa6OEFWgNHoiIF+yMBzFLYM0JBOnzHD8hzWiwE++3efK3fffU/5yEc+XK5RY8LdAZ0CNLGXAC1zGjNs0uxIVjWUTvgbrwlbvvyp8pEPfVyd6wnlk5/6K90tTPGdA5rjNl15KA6tDNronFTmsqcbD8FC2k5uJUJf0EpXJcCmAuh8vMmBpIKHB3YwLYGGhHymq/yqRSFhWC6AwTYJeB8ScyamoNNhTwVDnwRqIo1YT+bW4BtYuayGPKzXsaWhQzZQQjONntKwA3VC3IIXjsCSAi/2lVEK5qv0+JcyFYgHMrAuM+fkkPwa14mGs3Ei1aUrGscee6y2c5ylcj+kVZ5jbQvuokarI8KWFt1kFEeGIDsFvX+drzNEHSuha5PD/MC2EENaxTrRHSirSwd1J4ffvaK6cFhlQT2DNuXGdpSxeraIJCxu+sP3/Zdy5qvPKkvvX1qWPbGs/OMX/qms+tWq8icf+BMNKgdz9ye4sVqwg7Q2E7LgG9DE8AqROz7TbBz7VBxdwNMdJ3ojutKoU62j60bGQOIj+fcZew/n3WRJYy1AZKBMQlSwNa500lzWIkA818iA9Ulzb0A0eU4TjiuOCQBs2MgQfkpIuUsHaMHachql8VG6/iJb6iLXxjWC9OKi0ncGCcjpZGzR9KKjVXuka5cpPPXnzrfRDGp0Ic36Qh5Y2gnBK9LsAW7kq/IDVAMi0a6YmI6KCRShK5CZCwrlMFYKl2I2pMAgCo4anEqUHBse6gqOk5nL0KGFq0kprBhYwE43TdFp/FDSeco2hHnWNJ2gZgMAJ4imvOVXm9EaNDeeUKgOgVN3TkXDKv7hDY7A0Ms0iUsewURAeOCwCFSFQjBIV9mARRoHRWw12i8FSALus+ggB9d0cuGhu74qIza2XO4AmQdXQakj3L5jR7nhC1/0Uv3TTz9NDcQkP68688wzyif+8s/K6994VfmWFgD81V/+L+0H2+YpSCo0NmnTkQhAJ0aj4jurpq/ko0Fbu2Zt+Ys//2vvlxurjdvcAWPL3boz9IrJA4OoY5mQEblsp0qXKS6mtyhHGgFG16RxF4Bt+MGfVYFsQt+raSnKI6tm47Dg0CmznJ50fi2komQRje0tPtC0LMigkSt68SJwuCEHdwx+LRmNLakgKNjGpEgPyraFlL9kkfz80IM0pQhEZ8MLD9s5LbTAN23sAo5hQ5f0NK6p+KZdBXnkoUe8ER/aiONkHfCNJCGjxRQHRRwiS5NNRcQQwLhiL6gGF+jYKvHhU8c0BuShEz5pXw7Y/+eRcuFlA8CxfaS9KQjXdRkiSOWdjqBXdshK2RA66aQG+iIvaY6rLA7oThM/SJ0LPPIt1HQqm/1ZIMPr7VhNiiz4EAF9eIRw8qkna+D4StmhBTaXv+6y8sGPfrB85oa/K7/1zt8qT6pT3K6FRsfp+eOKp1doWvUx1wfaCmyBvzIg5K4Vmd0hWU4ExVckKb9cmj/XaVglg/0FaWLT3pk08lVSgumCSMEDev/R/pRJfRwjBPKhl5C650EI+A74J+INv47Mzc4AOF+kkMUI2L+lV/rUNdLgaTgEdB6wmSVp/h/dq88DI1DLJQywrJfoGB5B9B9+qQ9I6xWkBk5eo90eJUV35ckPmqyxdfxXTKrkIi/7IrMgu/ITi//AX4T4Nz3RQqoqAdA1WGzyCD7FuB5lGAc2vY4FQS1gMEQTYzAqCB6jXuLhFKM08h1KFcy0YCuAGMDSxmEADknzQziP3qqM4deOKJnOgIKtitRGKYVsA4ovFY2AcUlrwZ2JnBGJifcC+KEfGcQTqxoXfDIpnMjSOiVgopdABM4I12kqOFvLsuj5j+/mggtJtgF8+1vf0crBwXK+Vuux/D53ZUe8+pDFM+/7o/cq71y9Juz+ctNN36p8RngKlTeWsOoQ3iyD92IHTXW5AVDHQUP2/PMvlI9/7BNl6QMPqpPaV7ZowzhvS6EB/MhHP1w+83d/q1WNF2tK7FAZPabf/GmMWEzBvrg1a9aWl3WXCh+mc2lUDukNLWvXrtVCi9WWnTejHFCHetbZZ5bPf/Gz5f0f/B9q2Kb4DoNOC9wtW7Zold9aP+/hmpJptmdqjEZqk6ZsV4sm7wJNR5/Oj/1m2GjXrp3ugLmDXSP+G9UwusFMMdsvbHdKlvLVjwKh/O0DuhIQpVjz8QCQa/nhJ7UBBpSbUCop5Y6N8Sc3cEphujkNFrSSD66p6XzzD39U1mr6EXuBTwZ8E6pf+QIMsnMWKdOLHqlv5Dfc+Fr1aTLAtP/5wnFopdEaxrTSbfK7UcEvNQXs+mR7pT5EFuHqf4amu1lByxYGpk4lnAdXe7XaNB1tGi1k8BuEIoaPyJwbeWZthnQn96AfBzCIwr826tV0U/VCgYuXXOJ9lVe87oqy8vlfldt+fKv3BuI7DLLWvLymnH/h+eXCiy4smzZuLN/6+k1lcN+g7cQzyje/7c1aTHOc6sS4cqlWxnL3+MXPfqnc+qNbykat1t2oxw5333mXnzt6QGV70cZUYS2o1PXdUtIoBxcbenWAggFHeT67Y6Hh5zrloFj3V6n7urUD9jibtnX0MIIkPoq/R6ikOkt2zjO5DCLAo8wCl3P134BXv6yCOg2fz5+FFXFf2dkAsAI+0+HEf8RHyW0wALwh4Zt/XUYGaDpXeeiZ/qT5KDn5NRuB1eLQcLzREgXXM9mWeuUQBMEhT9UjOTpSjsrQLzphHWyU61F/8r6P/HlHqAlakUlHWYJVgJ8unS6C9KGtETGT4fmVgZFNgBEI/6EIaNfoVKBGN5eBhLsFV2MNTgvkAq9/B065Jq0lOjX5Kjj+6r87ARzGCYYAnwZRAYMJkEYAC7SQkWquR9QRrg0usq3AwLFephHMKk1YKb3nyJW/KhYw0GiiQ4dAGhV2q+70PvO3/3e5UJuuL9QSdKbqoicj0kyFTpqot5xoYcB9997n5eSXX3G5p4xuveXW8vm//6JoHfGetgd+sbTcfPPN5etaCr9p0yvlDN1ZsvF5lzqWpzVSXqftAOeff65eLTbd++nobO6774Hyve98v8zQq75OWnyiXzP2uc9+oaxa9ZIWSRxflurVYQ89+GD56pf/XRuxH/A0Lnd9S3/xoNIfKl/7txvLz++8SwskFvlNKS+oIbv55h8VntugzzjtM3vkkUf1rs4XPcVLg/rNb9xU7rzjLi3bv8gjdxrGp7UMnrtX4J9++unypRv+sQxoQcWZ2vMmU5W7fn5X+fvPfs5L/8eO6y9//VefLP/6z1+2DBdp1eMULd5Iw0x5q4xrOcbu7S4gPmSfrmWgkqj+ISYCTpnSibbSpVyr/+DjEqb5NzA9uF6ccsOP77/3fq0ePaXM0eb5w6zM7bmcfSCNTuR1lkVofHUWL9N3umSSr2cQF/rRA74G7Wg2/uAPr13A82ty42u3//RnfgvNtOlTtZL1XOd1dGUNGjaeA/I88flnn/NdLe9J5S7t6aee9r7JS/SKOfYhsh2DgQmdFitxGx3Kd4VeWs7zPfYmbtL+wOnyNwY04F16+RL7GoPA07SXEh3vufte57MKFHh+b9XK3km6c4TuHT+9U88PX5IvT3Pnyp3gaWecWk7UXsLZc+eUadOnlRXLV5S777i73HfXfYW7dLa1XH3N6z11Cg3/aDRlX8qYazzAZxmVTsDlgoFrwHaUbToIYtg+PpQz5UmbSFroNVwlKAoHTvgYrNOp9V4jSVp8LoMwAxu2+VraM6g0XzQhGGss1uskgUhZNxpKYWQi2YCNP+W61ZcIB4g1E1h8v+ONfSAs3raF4aILyeDxw+/Qg2xsaxTlg5P6FL6uA7DCGAq2OfjgIGNNh5bjysOCzmPgOowH8Yan1lpXSpE9Rv3vf/TRPw8hmEYIUQAVnkrgn2uFaEesC00IjAEcIQL9mnEQ2MoiIAoEDqUajY7or0UQPEa3iDpUAwsOXBwl/EFE4Mjho2CbLiTTAACTNCTmuupaz1wZt+bAoydj8JVlmOYEXEDbzq+45a28oi9GR+50luRH9iYTYkuuZnbBEaDJHcNyNSjfvum7WrRwfjn33HPUGbL9IDjQxAYk8BaT++99oLyycZP3fvFezH/+56/4ed/ZZ51Vrnjd5eXVrz7beXRk3/jGt93ZXvCa88os7ftauXKlVt49Ua5/+3XuEFn5d0iNzxe/8A/uAF/3uiu80Zq7mZs0JTtF/M4//zwv3Ln4kgt9N/YNjcZ37NjuZ0unn3aalt6/xgtlbvza130Xe/XrryxPqtH83N/f4E3Wv3ntW7045/Ofu6G8/e3Xe/k8KwjZJvDcc895mwGN2dKlD7qjfdOb3+i7g1O14XtgYKB89rOfdwd6xlmnlX/XUvpbb/mJN2AvUsc7TQ30eL3e7OJLLrY8lNPwsm/2buWhzIzudR7ud5QFsCmj6m+1nIOb8mqdkP2lK0sXjShQYPz3vI5O4T51hmeccYYXc/AMzLgw7AKEzIyjQnzHUZygKmE8XSNP/ByIYPR0sRK+i40uzQ8rDzBqA2P6SmZxDHsn3/SWa7QN5FS/ZcfT46YNPfmf7oTZ93iRfID9k+P1Rp1TNFU5b/587ek7Tx3oOfYV9u6x3/BidYz4Knd0WIMyGaWB3DPqmB5+8JHy3/77Hwv/FL3fdU8ZP04b4Rcv1F7KOZqa1epmsWSa/9wLzinnaxEPr8JjZoIXHZyugR2vrzugtAkTJpaLNJCapgVN7OdEQ1a3nnzqSZlVESFeLffayy5RB3m6ni2eWd70m2/S9ZIyRjMYlIVtrQaztVNdW4PdFVJWDKL0nIs07A8nol1+4FpbYVS1pcEVtOKtvLizrJSDby7g1/ZUqf6rfOxvpOjadH2I74LKgCzlrAsMZ+KmUOOVgU5NHnAAxV6AWx/kgrbOnknQmTLHrwiBq3XHmHnuRwa0DAHQMKpcSXGBIKOvDAy/lmYZyORfPz8yUSKdYxWnklYm6R0h0VMS5QItLozT2OhMPSGQqyLWu4KF3BJJi+LEahA8faArvJI6w0AcKiJoAXRhok4iriuMOvwW1nkIECEAMV13khLct/jQbc6VDgTjN2eBXxhziuweJYgqecjKoMZxcwIXOpUr+ASdqfSmp8tWqM5tMBKQZxx++wvw6NhkRvjK3zr4UvLo3Gg6as7ESMZ6GpHpA68xj+gr4tRmJxgYmLvGjlTZqWk/pk55kwf1goIlpJFDZ2w9VGaqEzlZC2tWr37Zz1OmTBnnzuqeu+71MxIaBr5xR8Pxx//tj/Ry6GfVedxW3vrWN5fFJy1MGYh0XtbN1oqsjmRa85GHH7UNNGtVLr744vJvX7nRL4U+WZvA92sqiulUOkY61d0De8r8BfOtMzY895xztLH8OK/y2z2wV3dyZ5S5atwoV6a3NmqVIJufb7v1J+Wd73qHR/YnaFrrve97rxrMPr+p5J//6V+06fsyT5EO7s+04knSlVWGt9/+M3Xg13qa+HvaoM3dzBlnnu6N69ded63l5g4jfiQDyupZLasydcWKLyFrpl5Y9RhfpEja3SQ2b0VKeXaB4tBl63TAoVwDEn74j1PxAxpPTXXVFNHnzqP5WGSxk5iZYtCr7Nww6lrSOTcVHhiesTUg4BXnUsgt2fJFigiMjJYiuirRKOC6fumKwRgvC4cvclJP3SAqr/kfPLA5swlvuOYN5s3CGui8Snv8CIfUqfLauVl60Ti0WTTk6Tx4Sk70cZ0UXV6Ezp0jnZz5yQ9NT3nAoTP+zltpmP40PQ3auGvEZwVmWVlMc7LuumNn+KgGuq6pTRDsQW1XOWb6MWXJ5a+1nZAJPVwewrIN1aC0cqWCk2Z5pYNtrzP8jaNza29cR9FLzNPJARddm9Wxi9PQi3ZQsod2zQBAATuhN8Dogk3g2XWSppuSFFCVkcIHhWvhD/PzpMsG1gVpYldw8fWGl7OOphH68IcmglCfAAfDab6DJZ2tUggle2j/IvLCw0gQcEz6QgQwbmiUDU7wSGv6KC45/QwdPMFQ9pA3rOmaoA9uY0UPGSgLshu+5YBPxWl6S0p1hsNCCBvXqVynEFGCH6JXYUmRgB2O4jgGzBFSYAAkngtdS2Glu3NzRxRlfMve6Augoyt8ExIOacRTqDQiqKM/JbuQFW8OJ0CHTmKYCtD5iltm80mlaY4OvABtRM78HXLFip7gwY/0REhPQ5fGUnzQn3wdCeRb5soXPeBH6OLYjYpg+pGCOJ1fq1iswKRRWqHP9AzsHNAzmfG9im9W2EJ0RvVrGnKOR1BMOxHaQIKNpqnkapg0wp4xc6Y6r/O9EvXFF18sp562UEJhK2Tj2UNsBXnLrAg2pN0mICM/EtCFcuQ5C4tVkD0r/fIcjOc+YzSah79lwJDVRizC4A72GD1z+sIXvlTu12pZpkaXLFmi1bKLdffYV+7UnrCXtKn7ne+aY550JGxcnjhhUvn0p//GHbGkFE3o0gAfpym6UXq2yjtAkTPymisKKdLsois05WQ9yHS5mVbSujIjzSr3GiN0RydXPGjBrAa7ufLBb7YnqzVimKHxJoZellEw0E0AQl4lsraxaJmnKrxx7INApmDMv+IShwq8EcvlpDynd7zxaGSu6YLlEn9qgZmIIW0P6YLyg6GzCDuuxofAoCN8Ih/bYKBNQI4j2saA/jZThAqNGg+knrXKR1mkQ4A+wZbQBY0hoT03jz7Ij9yRiXzi+GHPlkoUkOlxVj4y8Ry6lQmypj6nngINTOhCM3aCV6svpqfOxh2JMqI/8v5au1PtYFTQ9WuHtBP1ssL1eEGz0kN3mPMjscoHqea3sZOlCgiSCNY0QBNsWipilD75Ka/kNtqmWqWBHvAKigYnNndCcnzEoxATnkxFuqNX3DcWQPTIKAogloocXBJMnzTYwlCBrGYTX4tml624aQihDQzxC9osMBkk5a6XshQMgwPTC3VeWeJYksMI7jGUsgQd5nIME4WsxTBeFEkaBZFRKooFJhUl1/CwcSREAKxGokJoDY4lVLlQ+Rt/MzNkj0YbHbgQYcgvJK0o8loTEbEONZN4Fd6FBBMamTBTnK8hABIop1s3YKAo+FQ+IASIkDUI26jsk0KfbsrBstDY5YOziNAqsGnSWKoDQjbusNJ40WCkEeX5Bq/6YtEIz80uvuQSddRsClbDKLmAIzCNwHMYvjQwfYb274lPVcUR644skpnOdeHC+cZjWolg6a1ObEaanQmBFcAjQIcUl6liTlceqaoGXb6wFQ8t4CHT8YCUfjRovMrszz7x0fK1r/67v+H3xONPlBu/9s3yVk1b/en7/7sX57AJms8TnXjiAnVymU6M34qiCHs1Y8S0PCJb5SVfvBQ8JaU2G89AlZbOBdJjR848WLeOHb3IDRR42IQsVCB40FIbk9io2oRMpVOeAAMfnmkkoWEWIgoNnhmGZejja4kpVYjQRq7IUSu60pocsIMLNBpu8sDDf5VrGo76EP49uZwPV2UQ1398TL5FvHfnMNLvSGWaksaORTFMqfKckDs7PmtFA8QvtMBNHYrWSNZkz6KxfBz5cNm2dZv1BiLyJQZ0Oo34OwqknBCWCw5JQ/YWonPs15Wd8m1L2Z0zIWfqafJ8Xck0cq5zgm841kEw2KXhNduT53fgCjm6K6XyFQEYNhE7W1thZ2UQRfl7pqYKAH5r4IG1npUmnXuTOW1C5CQN9Mjc7AJvfi1E73yqKTCt7lfWqGMzN4xmA8qEgZzrgYRofCwbwJYruLENiZWY8jqboGu1SbsTbNd5XhrYwCteOzTfKeOf8FHd1UZxk3c7CiflYWvX79pWsh0Oo6Abvz4MHQcjowrYgHSZXIDjHEBAtBdAAoqTCm+YYxmmCWcopwBes1RJUj9NHwHSmQpEBEMT3ji+JKXIdzI+AABAAElEQVRlVyqyNAM1udCD0CqKC98Uhk2HiSB4FAb0MCRofp2YUpxOkmCMDxw/X0tQRRPvGRDZWsEDS6FwplI02YRl6dJYhzZyoA+LUxR1RYJBW3oeDMnOHZr+58+bp2/VnaOXP9+hhSc/1rOS8/2sj4bGOslEOA/iMkXJ87b5C+arIYJ7gvWtjmCZxXi8Gi0aLqYs3V5bX1RIeaOPK3gjgtyKm5bTMArXdCIKKrj+/omOQ8qzBYwNG11gbTNg8+PEi6zPOvOs8sm/+V9eefrwgw+XH/7gR+WbetflkiWv1fTaeE/vPqbnmayYpZxZdQgRfM5TjuKH3ARs2kLKMuVkmaRo8wPwXGaih2apSKmQ0KAhazlQTllDXFc6gdF45owQALazwapthGD7C4uoaMvKpumBoBuRNCquFth6uCKCd6NTy7Dp53Pl2fSzLIjJj0TLWeuRaKZOGbPWK+KRi5jxJU/oKN28TcxpSvEqz5XLVpZ/uOEf/DzvnXqt2R69dPwZfcZq44ZNelfqqX4+OFWfoWpv6wltjjVUHSmH7Vu3l0e1iIpnfVu1opg7TCxkJbCXUSIj5YRPOa0ebKsAyR/QlXqFJdOJWxddcfbgFwBsY3vkTBI2Do7oQ4bLqr8Hnpr96AImMUD83xJBimSbK7ZGDl82OsLh27CE1mYiCrJ1DTgJCohDFJurZUG04q+XkVlhiKZdU5Ktgp0QXHhNP2BqJ+90HXIHjBdWHorETnBr5CMHOE6M1TGbgrjV7Ng/tMxTwIDkCGTaqHZNGVofciSr+YpYOrTUQbChT55nyqy8iSZd1+0mQsq4HWDAWwWttpUdmg2UBz3KMVW0J7/sRwWL0jmTSRoiW1trYcOSXv/EDes7L0rAPn++NgAy1FSdaRiHhzRiNGRxipYPhtUBVz/iSnJIPBccnS+PxajwdUOrdMUaiuMgk+8KVPUDwA2h8rrRHoBgCCax4BGn88pfnAg4O7l1i6xKMi4FkhFxZAWOZxHgE0cO1CbOpnYUhKev1YM1m6AZtmep+rve/S6vrrvzjrv8bsa2V5BtDOiL7ryqiq0Ev6tXZE3WqjieiWAjbMtzudF9gVOSZdmmF0ovXnyifos1JZU0ZABn9Gjt8dNdqi5kXe5AM28/VrOvo9UgDKkzYnTFM70+PWtD06ef3FROWXSVFkfMNny+yCG9lDlyvOTsp2NUpRC8BLbcfNlg+fLlWiS03HqefPJJ5ff/4HfLp/72k36fJNssXn3OWV4k851vf1cvHXhQHe5or4DlNXRs1XjiiSf8wVs0ILTOET1aoEi5JskP4pWBrs1ngEMiYCh9Dy4kLH7KdfMJyqYNdsIPHpRffBARzEdKG880SRM/Tz8DqxwLlJMzgSYNXvqDT+NJ5w2MUp0P/cgeOK6TAYng4WsY2xRNWvFKv4OHl9KSnrNJVXqu9yLTw8tdLr4A/xPmHe/9ojzLXqQBGJ+Yesd/eke54qrLy3e0FeijH/6YVoKu8izE0Txjo+ZrLMhhG82mjZs9Lb7kCm170B2mhY9JEMKli+29J5frKidprQ5XjW2O6FZtEwuZpOsXete0Rgd4l7dp04hioMhaCXYngSjbVq7n1LMQxWgC1cm0FTe85A0vJVSZmxDYlBYauMjd9FP9wxfMWSD4bHcFC2gywEv7GlswMD6aVkWv8qetMWyFSz4+Bt/Iiy8FQUfBIbuyHAe3yRp7oqtkMy5KoCJSg5Effmwb0/iZj9KR23ABIz95aodkE/MUmMtFPE1YR2h52rXVO+EFtvLjpNBsSVwl5CNpwFo6dII+MsKbiwjMCAYlw9SEmtDJqUJlpI3yIYoJIQSdJm7ipKUwoalQT60iJDHJNauRaVndmd6cYJ6Kc5ZZrINlRjE5ixMalYrT5qvBaMaAljlL3w7fTiUoDKZceASHhpypAGBJT8MQPWJk3uqiDOvb7vJomO0UVV7bQgSgRYibm5NlaHid+CpsphLP1wuKP/CB/+El7J/+9Ge07/C7TudZHPvvmEa8R2+JuVwNyWX68Z5KTw+IB88Pf6A39j/26BO1IxntT+PwVYU/+MPf9zNICV5maQEOfHm348tahMNSeT5Wu+/A3jJSHdnmgS3l+fUvlRde+VUZf9z4snXEjvLg80+qDh/RK7K2lWeeWlcmT5yhrQ/TxBU7yFYqtGc2rCh9p2sT/emjyg8f/XHZptWmTK/t3adPIcleTNN+8YYvadvEGtuAsjpmxjS/s/LkU07Rne5irTS9zotnPvbRT5TPffaGcsuPbylf/8Y3y5e++E/edzl+3BjvKcNWbM1wkM1djrWsWKzDj9BV9ICYLz7Ac890pnTitfGXTSQmpIA2LDHipOfMc4n6JhnpDi0qbLwn19g28NASruhxbThFRNmdDL5Fhs9QF1wryzaAM77shP9FAvw1DCySEl0XwNcfgXzwCMaHp66dVmVxeiB8REbn68rTzLWThiYDBr5ziM3yjO+QffG1Sy4pf6o9pGtfXls++5m/9+pi7I7N+7VHFXrUA96+w/Nk6hjbI67/7ev0u77M1iIsgr9tSDMle6SuK9HGiLB0ovxY+MKzQfwcWVojSofKNwpRH562oc7IksbcGba16UqOXkjZktT05+z2oIJZD+lEWga4iYtqJy952MqDf2Sg8caGotFoWzbTRk8kiNdwgZ0aT4GYViCAqgNraOpH4G4Pn+DX5HaGDtAhtDzy04kiV7hi68SbFAhqtJ4sok2S6ZsmMAGCHjITjvZNC680pquwSQ1ihraWjUTA/Et661Ctn1ngs4CJh4zIn22KZZwOPpHI5DzbJ7aHj2VUGrgE0rS14mN/bmImlMI/at9LVVBkjORT8M3LI/+aRV7NijDtAmYtB1il09h0aaHsa/NRvs+CzZwwSrQfBvB/zjjAMD7uaCgIyQ0N8DziEo/2hpewQ1c6TckCTIXnTghyuQ5pRAZG/wo+KC4egrR8NnSc0XgCbCMg4Cn8yC8YKFTc0ELZKicyKI7kcchUAuCoSNA+SXdNF150gZ3szp/d6W/JbdTm4tUva/Wo9gmycu8i7dtrz2mo9Hx9nWlHlqfTufEdPL6fx3tN2WPIlwG4a8W5Ro4dUZ565Zny4q7V5b7lS8tzW1aWp19ZUZ7f9kIZv2BCWbNvXbl72QPlqTVPlwnzJpXtQ9vLHQ/qu32zzyi/XLqhbN28u4zU3ef6Dc9qlZ8+caRngUxvDBzQ9+QGd5W9B/eW5S8v1/7E1WVg3S7fFS9atFBTaqd5c/699zygTvKAO7enJferz3617gpf7RHgWdoaMnPWDK2UXV0e1PL7Rx99VHe+B70SliX9e3bvLT/UFDJfY2dAcZ72w/Ear1bxm72xo/5dfsN9hxSXKWUQANmEEgN4+JmUWvZA1PJrDUAr60ZL2cbvaDohlJGNfYa8U3a23pMZGDxASEEE2fKGTHwEXsDaX+rZ/CwnsQT8htBsAF3Iol54Vd83cViSzwW0uQ6+iehAB5I8eYvivODgp7fd7gHW1W+4OndzIo7/sXqYQckdt99ZXqUVn3yJY9u2LeX22+5Q56hPL2kv4Le0tYeXbm/WtChf70CvrXpeeMK8E8qtP76t3HLzbfbXxdoXiM3p3JY/uVz7Hm/X6mctrlJHt/L5lXrTzM7yy8efLHfdeZe+fjHVz5bZEvTir14st/zwVn81Y5v26t6pfYe3yEeeWf6MXix+vPcUdgOGqrc7/CgfG6O884hUy/pUbSWTuCwkO/bln+zeM2yusFuCSVc4IF0OAmlliV0pHw4tLWU9rCwgAhBkzU9x04AHeL3OMeWZciceVXL2NCnD8cozeFXWyt9plLuuaZd8hxYl0rbiI8oGy528eYQG9rAOTSZLl0NkwY+q3VBAwe2xzpFFEZJFzp1iyJLpxNgqAxElKEh36oZi8K7KSu7Y0hCqc6EVYi2P2xgjRWBwa+HEYuD+/4RYHge1kQRl5aAnXBcwmMijv+4aKRXaPG8ETlo7NiNldKdUevgacptL55AOR6Vhfs4Wr8ijK+Sn8BSlQ2DEnisgMUKlyakVtJIzkiEfdGVWO7TGJI0BcOngTKuSc+cKonCazuFCh8gID6LKFj8qKlOMjGKBp0LCLgDACNjs0+h59CYa4NEIfPj//KA+RrpVHdp6d7zH6KvrfCaHVae89YV+HgJVfL815vrrr/M+xTVr1nov2By9x5FvxLFkfZQW9gzsGyg3P/WT0rdAX2wf0l2WOsyN+zeWDSs36H3m+srERKUdPlAGD2rlnf5Ganq2b4S+MLBjZrnr5mc1barXt8kWY8eNLu//P/5IDZKeFYk2acfNnFve/673llXaiP2Vn3y9zJs1r3zw3X8q3hrN6+6At9B84IN/qv2GW/zCABq5iy5ij9hUddQqO+nOXcK7tO3i6quv0j7Kjf5A6/Hq/NmrxhJ8KtV73/tfyn/V+yhZzMEyfhpll4YNgYGxCQ2FYjISPkHxkeZr7Kal4J2/tjwwJYPJkGYsRWowrmyE75ILfqOR+pDRtpxN+S7ohlqvW1rkMy4OUZNDN3xJavRbXYGYcWCvQPOSV6RKoY4GfmvsKif1sMkVvI4GYLYR8lSiAREf0UeNagPnmkds2Z4vUu/OPOtMvY3oW5ppeKy86U3XqIP7SfmXf/rX8tvvfLvKvd8f1X1FG+XP1b4+voTxqb/+tMW95s2/4Rc83KCXRXDH98Y3v158mZ4e6TvRufp+Y//Yfr2E+wf6xuFJ5Wy9f3T+gvn6zNOXyl987C/Kx//yzzxw+txnPu9FZ2zBmKd89jeyqf5Ln/sHv+j+Ax96v2hSP9EnpsIn3Elw1+m2odpW+fhJ4Kyw4xyor2kj4lsoEYhWnlynjrszCZKO9Q4On4GObG3fklHdFunc7N8VgyOiK57xO3xNcJVGx9f0kB0aMGSGqS4864gBLVo62RfwT+VhA0LKGvvUxQfUE+Dh1RHOtZKCo6PtJzqwMXyNQ5Y0B866hlN0bLYlwRBO912cEyITvI0qodUKBRf6omT/7ng0RuiBjZBZBW0B0q42WeCvBTQ1oJhCU5TW1A1Ey1aO7050DQHgrEaPgBlaJNGKaNVIxkFUhKJxoqqSSAKRGhRvK4ZaehMWMAT2qKF2HKDz0mwRbRTcYLULwyqPu7QwBLTGdcaZTKMh6Eyh5641olE8piPecPHP/IVPmhsX0jNiwi6t0XSja36yhhVG3hQeNJMIjVpJ7Nzgp8Cgn8bLggk88nI3BFk2LfMRVeJUHBydvVzuSDVtSeFDw0Ew3P2xB5CvRGAHrplewjhU+l+u/GV57Dne05hOjcYKuCP6ssbQoJ53qmNx4yA5C2/512+UGqjjpp5cDh/QFNioPP+cN/+4Mn/BcbaD7S3bTBw3sYzRqP7sE88sH/+dD5UjB0aWSWP1NXQsJxsyWOE1b+x/ZN8XgU6Qjtp+KDno2A4fHnRHzrfvsBNpdKgEpopnzpoZmV15eW7LoCX2aXoiE3/2Z/kj/qH+L3zaYNAUdVC64QWD7k0WZ8umKaOUX7wjaSlevCf2b+WQegF29R/LQueSZzzwggf5+asUIKg8AhSBo2PgLED9p746z5gAAQe8yrfr4PFjdO/lQdNwIFd56RssvQDT4Fa+gChQZnxxBb3EwpmuT8DX6Xl8cpbKg0HJ1q1bvdKUO2C+VcgdIx3Y+Recr1mAve7g8MVjjz9OA7yNGswcLpdedqn3rT6ptxStX7fRL1KgzPFbPrP0M91h8vHgK666wtPnDJaufsNVep3aPeVH3/9h+dDHPqw9tyf5jUXQPUX7DelYWbnMiyUe1+eguAvljvzIER6Y25C2AHrRQZDUguuf7Ra9XfaSEwR8yS0b9ugaXGW5jcFGsqhoARdjQTvEW3mniEmDRrUtdsY5a7mYSBWowWDntM0qWfPowVNOcB+qPuXyctvOwr3wgBzlHTkUaXLhX8JHH9d7eQS8kJPQvUTe8JHb/KSi/UswaQuFoH/rG7BOGxMSTX8lCFmFCI+sJJXVqoxu0wSM9fxCeuwsOAoJnYbLTDx6Khmjc02b5c4T2ypqGdGHPBEVCZ1ArEgwU04KDBohCuFU4lyjD9j8EWDYYJyAoP5Lno2gDKCNFbTEEVLBNElXxPwVbwZo+BgpcRSikPRTGiOHBhPZoIMhkw6euaNoYsPkjfHBJxeHaPq0xobnVza48tkykc5VmmAXIzElksYMPp4esROGXrQLb/gwbRhEctAjl6EH/wQaSJyQZyKE6JhcYN2BqLOgwyBuGB1i44bjBOeBQ+FzxweOIQWGDuJSdg/uSZoScGL2gx3RApwj6mzgav54OXH9RvRJ1n7J4SFV7DhGz4MmHzNCb/zYZXhoA9w/sl93l32Wc9q4qWXqhMkw9eCHadU+LdYZrTeQZLpGvF1m6qQ02LENTAhZKXs1iJKJjhDiyNoCHSOdKnraX8hXZmwLLnGzjnwVEd3ciOmM/Sh7d37O79FvfNq58YA+wfLZJ4MDTWiHfzpl4yjbsgQreCQ4BB7BkaUdyQqf8Io/qL6oTJrfGl2IxtIBPbApzMxPUfLckAAnuZtsZk+mgizls+GQA7srxXYi33gq/05mwIPMwqsjKgNBe+aBZ4L8kIVFX+jEx3Z5BSAdG2fKkLJmTyl4dIbjNGtxmV6yzafD+OoEsxe855T6hU4//9nPPaOwa+cuv1h70yubxadfneCH9NL6N5j22HFjLWt7owz8EHOmvsjCy+I9eHSdR/6mF3qkrjT9SMEW1l9l6heUu11gcCt9ZQfbSGdvg+AagjFj4vVI1xM7KhPb1r/GS5mWEXppgwSmtC4f6UQ85Z6yhxXBPMnsQvzfckBXITQjA9ed3PitfhAhDTngQQhvdOQi9QhqaQsNKhak6Kd/6h/40NLR0RAOiDteMpRbJezkQD7SY9Mqi1K4UcndawbG2M0klGd5ES7i+ppBBO2y6WG/2k60a4ABb19L6WuGUJqDjV7FA9DMxQSBgxRjQchKwJ87EP5kuHRAjJH4U6jSQhde5qdkOjyvrMP4gvx1g4CKbrhUzo519CAOjWpPA+OsR/EQkO4dwpM8jCw8Cjwj8Vpo8BEhaFH4yNqc29OYGFK/8KPbQBckTOAuzyMMCxrcOBx4wxvDMGq2ANsVTGesZdiOdmxpDgingI3c6Pmacois5KE3ZQ2d4Q20rrRna6sbkc1a8cea7Dh4VUA4yD6ku7rZ0+eoQ9ILuPepo1RH5/U9sjEjZBMXyyFtoEZNd8S6W6QMdx8ZKBNgLvPqpS3lK1/9chnqe2t5/euu0UrDvfqA6k7JsEfPl+iAowNlgd1coWVzfVjTHaI7P6WP7h+lRT362sSY0WpA9dUL/fp1Z0ljhsu0copp8DdGebI1mQhcfcNlJiD4RW7lO8R2pPGlC1tXfgyNUaNQmDh4WFRXHBSj7Fpo5ZiyQ6/g2wcNZCSXa8OVWJJRGMoCjjKFNxU9tI/mEb7hGHiKAn17acTsgyKF/OAEBiaijsz6b7TScWInyQFO1Qn6pAEvUwhVB8tXacBHjYsAlIqfZ1YB/gRsP15ltnePvnCiKffjTpheXnhu0J9cWrBgft5DykBQYZRvxYmhe7UdciAWqYoyqFly6RJPhfJ8/E1veVPZtWNAMyLTtFBqv1+qzfaNk/WskU3zBHCZvpdwuoOg7jnZ6cRSvspHP/TXH/WuacqZ0PwLdyLY9uiNn4MlXOxm/8VuTo3cHnwoyXZthMmvxJGJuDxS6oqB+JPgP53dhtr3KtWmBJeCam0LtuOfQ3RJ+2YJRaf5JxAJyE3MEDWuq6pHdDVB03MZk1hla+0rvkHcs1uSB5mga5t4zweaJMRuyWt6JkfUbQjyqBRK1Qmb2P8MVOlUXeADCjYfKfvQnDffbbq6vsupueFg8NSjFRmsOX4B/Wp3yyHCXrtsAykXwQ0jUDVVJmRxjFnVqzBIbwEQMKTDWESbKWwI5bVr00YbBVYf1qiNCoydi0aMDLHzWbCRK3wqtmkGbphzyDo4u2wkQ/HV91ZZRV0GtJyQRgfomzZGZa+ZCl0/Y7jhjNQ4P8Y3AhSoOMbPnRmNt8UFxBQ5R3Z3vmLiiqU028P8iYPX20Jh/iBW++LwbWARsmag8kNqZELLdEqc3bnrDA4OFZsN6QXfWkwi/d52/bUaCR/Qy7j19hptyIcKB3eEwuFZ5KLZ88ucyTPK2u0bPG14SBvbR6pTVAG5LI6oM3NjqKQJY8ZpdD2nTNUKwGP7F5Tj+haoM8sobNEZbyyLFiwujz20sjzx6GrtGRvQnWgt756xLKNNK1GavK4YyCZ1uVsco05wzNg+vVBZ77o8ba5esHysyxI9bXiBpnGgvGKX3MlLOZSUPaFNZwNtx5XRNkInL2VK2XIX7s5Ccoau+BBEI41B7G4/s/DN+xEH/6Bhij7Qbj5qEiilQEfQYC2j5AQWftyp6MrXxjcGKb9OX/VE/mr6Fca+KZn4U0Z8oa5YRibMwVFZCcirDMObAdmhCW/I0LhQL50NvPKJEySxjo2YrsV7jPbdbFw/UGYfO8P+wOIubMXqUi9OoyUFCzT9ejYSLyhX4tDi7osp89dddWX52lduLDf/5OYybdYx5bwzz9PzHQZI+i6m3tm7S19OGaM7wEz5S1bJyDNjnjtHbxr01GNsjE6RHTma/LZC4JWWDjryRd7gAw7N1oFHfSUqA0rJFy0BxUeVJt6dngKwbSEKhumht34mg60Vt5vpDBQniHMSfqb+Kw/araBycqhYIVnpg0c5wBv/pvlA1ugPcfyPa7j3/NODSOWaPTrBruoDg/hHry1v9KCRYEzTdE0S0wzc8CXitPfOERPhYAeC0XRQPsFtKPkWmuTI4kwBN10ykIVupoHhYdsH0HXMtKuf4xcpGw3GzVtM2rM6ZLAiyKW/KEe8BrhaplpoEGgCKm6caGKF0riBAxw0hCdFKhldK06a/8jNH3ebhKbIcCMbWvTaJsw2Ogi80eyI5oFM5h2DJTfHXzeUOy9lOV1n6yJjITt/bSqyNZbtDGQLTd6MmpRqXcMb9SNKKoqpkuCAHbhLkXMwWtSxWiAFX21MOvya7PCjg4p9YNfo0TEO+asVv/v7v+spyLzcu+aLTBwM5rlLmzphWjlj4Rnl5cfWa0pTAPodHNAnlHiuKLQ+fQVCL1MtI9TpjVbjc5Zg3/KaN5RpwjuohgvW7Dcc2LWg3PvzZ/U8Z7Ua97ZfMU6NzlZM5PEDAmmqTj6jH7rA/sDgYd1Nampkx76yfv1OfQ1ho176vF1fKz9VDWsGTbaSdYagsOuI0YSrrVz+ynRnpxqvLlvZ4ZFBiHCxAa2BQtIctRy2sWgfFh9hWU7S+CEnqfDmmsaidR6UpMsJPQUCbspJNFTOxvfghrKQYWvALuSBQ2hla1rwkZxMEVPWLQ94aZC6JZoeFNWGxHzc4AhZwaSp8xLeskcj45ivoXSwzDmbhtPRApnyIyk4akwYOKm+8KyZRSqbN+7wi9Uvv/KycpaeDw4OZmYAnOankS02NS0OCj05hgpfu7jt4Z+U7z75/TJGd553rbynvO2it3pxzk9u/Wn5vr5kf/07risTxmvBlAY9fO5p3dr15exzzhaloTKuf3LZtkmPEhZkoMjdGA16C+GFDOjEEfuTi3WSTiblapuTEwCAbAu3BVgGGJVf63jgj52HcQPDaSosw8KHNO5koW9aKkPaAftJk8N5tcxhpb+0pVV2UQkl5Qg/MloRx+PfFd+Q0YOyTAg2vuMYNEamZgYc2dCPuif+gvOMhpCTVnkK2fjoLXsihwcGSEzTxqyT8DGhdSBC0LnJbde1XDCK7UM1R1E1rv2o4pu2yTDYEQ6IkiF1DRrSy0DhF3sgI8wlVu40coFgUGmZpJLm3l9nBCAl5+BwHH5tGjWLeFeBkcvAVWEbNPyML4Uwmis3gGLVXVd6JIZelOGOx9c130ZXAYIbOTmZa4VQlhtgAVQjAccIFFwjGl460jCCqmuPwBX1KEbE2SQPfBqw6NhjENqtg4ZsCgxSEExD7DRdtb2PwFl+FVbyUtiuGBRglbeDk56524nd4J/KiqPiceq3sA8ICpiFu9joqQQl06BGD43+UVY8LjnlwvLAsw9pD6Ke+akzOqjpzZFjtJdszhShCEkwTB3v2rOr3Pzgj8uLr6wq733DH5S5x8yxXZY/uUafwnlWXyHfotH6BN/ZiYnxePCN+aFjXWX3Q9pqcUSrX3NHhE6aBuXuSDg077wP0x4nnof7xpVf3M8LwUeWy67iEz7Rw5US++iPeGyGrcVMoflURqFOIVW/5PfO5MG6pTcYRuNJb1nkBI/yJ69StNzBbw2i5RI/Kh9p/KSO0ZFaFEwtvk1FVhoFBgiE9e87EeFDGX4ETk3W+BZ5yURX5+my0Q9G5IVocCWB6bdp2uCRlwYsDR2tl7m3TlQ4+CkffOYTWsiLTtSlqcdM0TPjfbqb+6Ze/DC5vOf33uMtETSkO7T9h+d+W/WyB+zQfBV+THXu2rVbsxe7tE1oQN9HnCHZ9Cx5Un8Zd+I4TY1qhkG+8tzLK8pX9+wov/PG93gv7Jf/ny+XB/U+25P1FZN9+p5if19/ue6332b54DNl8nHazD9gfeioWIC2ceMrloNtRrGDFPJ/fMgX2NcWj7FTL+koZBtZVeC17B3zBbSav/mOUDCt80i5C7Z2uMoSg8Cn7BEAWgQVnOKNXutsGlwGXPaAKgfwkQc89IQG8PFP0vA/yrtH13wqXDoNtItMlBe4XsBjOvCDRz1bRrOJv8JSv+CnDQp9UlBH9VnNX9rb6mdGR6bUDV0ivkNOJghTt8O0y40/PplyqDYUKKveYWYYoiYWnT1Y1HWHLy6o4j5B4vK0GlNEAcUttpgQc8MCZYKxkg8x/tK4QI38SqcamoJPgLYU0F9MYlDTc0E7JxUNrubTMOEJLxTS2YYApMJ1SikfR23XFDrBI4Ga3hwAZ/AdkfItFfTho+DCl3ldmIJLh9bL9ztDaRUNWwvPuqNdgq2FrMjMT/FWYFzTgSXUxircAk8GhCyz7AcdF37STA4Q0isosIyGkwZPZeRAJOlGRCbsEhl8N6lybnahMTuo1agLZy8oJ85eWB7Z/Egpew+UMdMnlr4pY+3sXsgjWn4mg2oHhsrjzz9RvjL6xvLeK/+gPPXEuvLQ0hfLzm2sBtxXxuol2qgDV3sDthAfpD98cKDs2rqy9PVPKhOnHKs32oy2rDqo02ORAwyErRM4nP3CA3Xwy5etLWedO19fSJjoZ4jmQUXDLjpn0ILd6RjFXIG1NtZejYT9QgQ73dWBGpcGBJ7KCy0ukVzplQ7pDjq3ziLaMTBLmQHcfBDY4OBHQYWv6btRDD/Ih2dlpGtkP0rWYbTsXyFnPYiaD0xMXydF7Zm6bvXD2cCCYKbBS0OJvfAPl5ah7Cey/55d6mQ0BTpWP2wJL/7YDvFq3YHx2SQefezXi9HZ8rD2pdyZnXLaSZqW11uCWOilafo9A7vL666+wqtH+Zo8nwgTMcvCh6QXLlqgz0XNKtsUn6e32+CTW3ds8ysGWWilCqHtQ/1l8/bNZYz88hN/+T/Ld7/zvfLC8y/oRe4v6bNPp5a3XPtmrzzlY70Txh1TTn/1dMmUD1tjct57yqrly668tOzQF+970474GvXfqlu/tIdVRGVwpxM7Ky4w2pLYzBb1te1XbR5bkhe42M1XooMh0amWj/niV6EFb/NCnJpmfMVT/pbUbYRRaz0htcGT7k5Ij2TaXRqk4M05fpryRlauKVd3EOq5TMfwyBS/brRtG3hhCTOtcV2bDijkNnmlJ9cAmwaXShPV4Ou6oykcQ3LQz/oq37SUFH4QGIZjjFyDDWzXRxnB4J1/N32DJs4vPDkAO1DNsBPGTMI8JMTEDbPAm6BNOClk5cCxcrmGcMRNeqPTznYiCkUJjDotiOWQcaoBoctfzTRBy2g+Vb4ah64VpGHsEBq3yCByLuh2N4hO8IKwyKQyKNJGUGZMRpUHaoCnQ5XN9Jc7MjTlZ0sq1mhCS46oHD9fpLKJnlmCQZzcKgP4DrrO85x6qXScxnwrLGK54oDu/Dg3kOq9Ko/QC5/kt5FZozx8VMaevbuX3VO+8J0b/DmZ0dO0aV3TpH1ayAJZf3GAKUrpQdkc1oq8UWNGltPGvraM3jJXOy4Gy7YNz2hF6bwyfpJG9lUuGnXeTzl7zuRyxtnH6tt2rPQ6rOXxL+ubdRogWCdpUXVp5WHplRgtpJk7kCPl7f/pIu0vO7bbVoHTp1GTVhgGi4AXRNsZfZsd2OOJ53WVRSjO68pHvEyHsmrxnn84Cy6ij8/l2TH8aFjiS+EtAAHbT6AdIdzJcefOtoJP/l+fKu9+z7u96bxNxcMTOuiBHClnaJm68QGBbvOJyGmM+DEUBA9cs6B1VEIoJw8AmU+nYb4oRMYGO7btLff9bFnZtH5r6R/XV8696KRy2jkLU/6iyspJdB+t2YPx7EPVp7yYcXh++a/KSWecqG0VGkiJ9mFNl+/T3eL+/XpDDXf5qhOpY8gYGyO/3xijF188tWp5+cEDPyg7BnZoVq2vrHxppQZJsu1oDaS0sGbmtFnlE+/5n9qverzf38ur3KjLY/kOoWizzWjNS5u153VMmaFZjXWrt2gfrr6fqNXO+CIyE+wzMar0j605+85ccmE/24riUHBZ2EewF3aTcobItYFqOrj4BvngYWfIOA5gvQ6DwPrus8IC0mhbBmObmv0BWPibh2g5R4XmNrC2a712zMSsD3DWUShNlp7fwkkBepZDUf3hZwTg+SPN2ggMWtQjIFtdCI/oDh4+GjkbT3w6bSF69Aa80Qd5LBsyqJNPHEpQhr+4uRzUc8BaaU7khF2Fz0ADvi3D9cR4YIeO0XRJXndn6FwdDGbDmlajjwZKQGk5o/MxkKUwajMqCH7+KHBGpqRbOGU0g3LdhACZjpAE5G4jL/hUMZRoFjEIUcCV6Hziphc+5qe8GJq7BAMDDZLVAD4joOA2nVzMciIw4ksp5HTXIPPvQ+eMSu3xEF2m8BigUVDEu6A80gjWX3RS4D2Y2DDpCOpr0WIhUJwNB4IAPHUGBl764zI4jObSUXFtWDUmhhJ/eEO3OSL5gIU304GHy1nzziizpkwv24Y0haT80Rrpw5B9hUfU+bk/EsVR6iD5ICud5QsDy8tJI48pBwa2q4E8qJWgU8US4tyVUVZD7gSXXLa4zJg1qfSNH6M7hSP6wvgG7V/UJ38sCEqAZqVcCJbT2imthsiaxsOyY0v9pcJRZmirgwJ3+xbCV9Bm9KuGEJ1dHLEH+K18qJiuRNV+oLpyQUlprfxCEtiUYeUoSsjWk1cI4Qc95dIoOGoCSiObBKJUYoWM5ns0XPHxTeCafYJoeZC/y4NUxyCW8ICn+cVRfMGsJJVP4wp5EtHzQU1579ixtRx30piyZYM6xjueKjNmay/ojMkCkb+zclPnsVrkxDPD0ZPGagHN1jJFd+1TpukZnugM7tlfDu4/oAHSxDJyx35NreqzTJKviRh7Oil20/7FpSseLPc+dV8ZOzpbI0Zo0dGY0l+mT5juD0Vf+9pry3HHzNUd56Dl9Tt0JQnbhqh3G9btcIc3e678UIEOeL9WSY+RL7sOK41BoW0OQGdTLhRsQ5IpKx4vYBQS9a8fLTBeR3S4/EAZyGdg5RuiwYKe1nECb7p1cMtABzoemIlm1yKEkepqyjZ1nfYAmWr7ULkhfu7mwt1yQdO+WWWvMkkg4+eySpwe5Sh9BKb/5AMLT+sq4k1nIHSho/wWmfTXy0u8vYkn+kFJOKKRXQhGdxp40bX6IDwBVIWGtztT0J2ka51poNA9bbjsUnFsd12kHkQO4JpsIueQ6+S7M2yFYL1DzSRbxawiBds5VseEMYNH65V4mJMfg4u/hYc2aXSUuYW2ZM5DIIRDWEI6Qi5CtKMJHH8CNi2Iugw65iGiS3SyIQXrXNiZemiQCASNVuvs4B85RL0KAx/kSSPBVQJUIzeyKDThnR0oN3qCG97xNmxGptRFj/g7fJxcUsmBXSaMcHF8JCUdvauQyOxN8DUdW5HHz/Zzo4Zz6Xmc7tYYZIwaQUMgRxO/UXKw1mCncZZGot8nvGc3PF/2jxVFbTscoRWdvCiAO8LD2koxQo0fDRN7D49oc/VI9gaqxRsctaPsPLyujNp7UHeEer8pr7WT7DA7pFH63LlTyiUXL9T3B7WnTDjcPezVFw4OqHMlMAWLPGl8VDi1fBwRETu34OhYx+sOZaoa2mYP2wQEhVZ+wNNoeDSY4lAuEfTOixWoSPAUaC0/aMCLs+ypvKND4xFbd34MlsslZKJDyms4viszcArwgA1+xNF8ubIwQMgmKgt0gHa786RhNRb44Om/kjRONIRaQjvDIfICj/fUxstCQESk3LAQIa5Xre05oEUwO8vkmePd2Z2waFxZ8ei2svyJ1WWmOhl8l46HTnDi5HF+8xCrSQ/LVybqjUIbN2hgJBvu253nvLv36E1H8h3uiLl7S/khJ9IRsKv8Xb555XmXlmUvLStrN6z1Zn1eqMDLH66/7Ppy8ekXlbFazZw9gtEfG1FvsNNGvSP3sHzzVfNnqhMSVbVy/eqss4AHeMqduobe0Rc75TL2xVYYpd0tW9bqDyMkv2d74Om7DxpvxdEDetDyUQddk+TBVcswM/yD7MA7SfY3jXp0mnCozwRAHdWBP+x/dMcZjGZX4Ckf7pKRqPHrnRGAdNkdBxCM5SVWecAbW0V+5ACOX2BMV+UCccpOwjoPUi3gw4Tm2wymDaYDdWyE8GmXTFdCU/5qATvbGNn4jXNPiiZROlGsAj64eTYvK5OSNrEZUOe0u7SVlEPg/QkndMMYboARxsapwus6qiOSAatsEqMarCdi6ADWaJDigvM5JCCAsn6mg7ByAp0UohoOwq8VQFdQouE/nBL2+sOwhAajnCheYRhRk0bo8uqFxLfhxUwVFIMIpvKmQCw316LVOTN0KEBwsRNIgrTTVFwY2cCOBJ7KRSG5QRRGV5ksvzjZO0BE/uoI2DeMkFjUwheeyEYKjhbZkSOjRq9I1eWREbvLgZED/qoDzjGq6HNNY/SVcWyPfRRC31EtPBhdHv3V4+Wrd91Y9mlhAY3WSG2WFhN3fnSMqARriow3jehhn68PaaXg5qGVZfqRmWXapONlo7ZB1tll547dZa+msqZP0N3j7gNlnJ73HNQCnb26azikzrTPWwBCnLpjZ9XeR3TmU01sQ6Esqdxnnn2C3j/Jq9Z4DoJNkCv6RJPISF46OyqahBZIygwoaIJLOhn8Y+9qezDIAw5bUT6VRUNJAjAKBgWAX/wi+EqxbNAmC1nTOADXKqVBwBZ64HQBLf3bHxRp8ipGZoKjgWvplHVgqeiSRZCkcYcDQXeysiUBfpEPvlVGJSIqezsn6E7v4OBe3Vn1l907WQ06qpxy+vFl1nF6TZ62y9CpUTe4M6S0GOQwdXpAd/2Du/brs0y7tc+QxnioPPv0hrL4lDnlbD3vlRTVDLUMBEGwabTP8/xTzy2f+eNPlWfXPFceevqRcsfDP9fim21l9abV5fXnv77sP7jf8G4EJS2y09Fu2rhdPjVY5i3Cz+VH+htxhFe49Zdd23mpRNoHzuiN/hRtV1/JUAA3VsB6gaVc6pVtGPyUSzAECK2KjS492/b8ijSXi9uRcIIw9ktpQeRoHwpn8Gje8WchID/CUW45cbROzQd884vPQds0jea4EgEPDV8gQexjuSGqEDzwiUOLAH98iET/O45EDQYatIvgtUEFF7q0jKbL9IwacfCAtT4UCtQqLm2mk8TbzaQoxJ+xqfBhADxUsAl34UrKgEb8Kj0nKh5w6UEdALUOQvJWaiUgog2gvIgiJCCNqThMFG9GMg0kUDAv50kIcPxzDgfTi6JUAUa7OkuWwGL8VFqvrDQxcKJY8JEl8HHaagBg+Zk3kBhNeHJ+QtPHRtc1NBIwfJwTGOTwSJEOywBKoYAqQs8ZlI7hVCKGt23M3ljQcmeHPqQgi34EOi1PC4BDZq0IdIopdBoqskLbhaw4+MgafAGog4IE6VC2XZsZ4imekhkapbvBkfvKhlU7y6Z1O7VIpV8jZW1kPrJLb4OZahoWBHFEkE8yPb9xZfnXu28sO3ft1F2k+Og5EA7PW2hGqmHEyeyY+3THqj2Ho7TM3VOL6KJ+ce/YnWX6tOO1SZ7pMW16RW6XZNFCiQll5rHTy6C2anA3eUgd4RMPP17mvmqiePSVtas2Wjc+fsw06szZk8uK5ev0zKm/zJ6rV69J3z59NWPBwlnl9DNPsMw0vFnRyx0jcRpYyVLtk8qACqThc3WRi+xGsWDXFlJKuWq258plg12xt04Y3R0sTBzATH4bYABkP4VJF4an4UfhTUdiHoKLT8FKsPARWdK8cKQjFf9IUUeGxgZfIBhfcegmyPewDTo0YGWQncuqgz0KPODUGWoa/KIrTi533vJ4WbZ0s5+3nXPRojL3BF6Xp+9WavC0T5vf9+rFCjNmzPIzxVG6SwRXJi8HtJhm5qwpmT4do3LVMzu23rQOzEpWCZHV8mE70vQ8ee7MGWXu9FnlsrNeW+bNnVe+esu/lWmTuSOVPyJ8xTG4OsItmwb8jHPh4jnOp840dcdoT+I+TZPGZ2Ib8nouoAsFyBJc1vhN/YtQaF3t63RdKAE6KY/YV0f7InWnhWb7jr6QeuUTqOH+SIrzoU+jDT/0NaiOwm9lydntADDWOVCtMzBKPTSe4LS4iFkbsJymCHKavgxkvrQDkgN7+Ksg1k1AVqgnC/lH07aokVp0GbCgTmCsVRdvcsZG1R8sJzIER5eWx3yq3MpSDH3ke/wJiB99jX25psGNf2hQ744Ajx7qL7CVPrse4iB3gbiRahowbmjStKVRrwIIsN2WgtQMHGVN3NxNSQecTP8OCM5/jE3cF8qTYrUnUgpUe1kgkxjEOD2FZAuGuHF0PVynxsONiypkU9ek4GceulIGsMDRiMahSEOQWtAysiUQLE5I5c62AfgLhtKu8D26ShKadaal8EXkpmBIpzEdqX09QSUNMtCnMvjCtEl3I6pzs7PlA0YB040afURfcThY1r+0y5vXt76yX1OLE8vE46SXRu7QDTDbfjS1tGNT+Zc7v1a2aJXeaE1x+iXPguN5IKtHmSo9pDtB7gYPbtcrsSaPERO9cJyN+OrcRmqDs4Qre6fsUVq9Y1PDSCOBbtNn6jkhsiltnBZaPP7go+W2b/+ofPRvPq6R/OFy45d3SH/xUAe34KTZWuwwuSx7fG2ZMX9Kue4dF7gccFzKA6cf3iFJSj+78gCGKRfKpPMJ8XSZUuaSRL8UTlM/tqDM2iDHVhcN+wzSq1xAw2LI4DjlTJorFBpSJj2/oDwaHJzCF3iuaicn+HZ3Cy5lKvUDVPmR3jrZ4FZfpARFyz5DxPyEhCC66DpGXUV/A1QdApN0I5Bgeemg2kCR51zHnjCjXPeeJbqz3+uVpNNUjgQGSxt2byrfe/KWMrTuSLlkzfnlggvOLBOman4dfxZZOsZJmgGgfSA+eco4rUrNMz7KbBjnTkdo+0PAItA/Bh/WdLxea3T9kt8s5yw4u4wdqelRT/s1m0pXmX3Htj36YsqucsLCmbYjdFwelQmviONl7oPy1/Gaqie0NqHJYXvqQpaKzRRLLVRibY88KAXAUIY8ytYpIxMIjECgkXToOcF5NNRuKpSJB7msMZx+nBQJDaKEikp7kzgJrawVVzAGzEyANH49n6Ve0CElHbDKg9EXZEH1YIj05Fs2Mtq1cLBdgIPfcPCdxt4wxoI0xOOv8LR/mh9+HH8BtIfrK8vXyWhdQtAzaa4rkTF4aSeJm5l4+uUm0t88VI/J4mgZaI9Z3aWAXfjUqxtbCx79Q0fpYaBE/TPdgEJNwYCaawyqBK7iLIpxIQJulByVAAYPoBUMEcMIxI0R6cjkqdOQMP8oqKiEtmHBFaxXXJIJbUi3xklwTVbSnQlt1Zx290C+73jRC57udEQIONOhUW+NXBoJ7kDyZht4CUeUXemU7gAC9JocciziTUCLaWUEB38LrqNViNMCm2vQ5FwmjFjRPY2VskSi+oN5tHw7gDL61UllwYDg1Bi4EYh4tjVkWam3a+/O8m/3fL2seuUlbTxVx6e7wBHoSUdHnNG8OkG0PaznSCP1VQrSWUmKcOTz0ubDmjYbHL+1nH2entUM9JcVKzaIMZU1jf4+3RVOmDJedwojyoN3P+RvKI7XS5wPaBpu9Ogxfl0bZcMzqL169phSwRb8MoBJJ6iyEM8DmprdoxWKvPlm0qTxsg/KxU6chSUa2J+y5Uw7LTr6o4wAJ71VZHzVZZlc+4mv0VG/0Oe5VGTKIKXypPwpq45XaxSO5kW+2SOfol2ocSRGV0oaAYFvcrkuqaORRZXFoAm/Cv9Gx/RJrZ22gDqZiANeT7roDRp6eGqgMQy5VZdJ6sSmsKpYNOkgCdz537byrvLUdn2nckJfWbd+c3lp6fpy+cnnlQXzX6Up1lHloBjRUTFtynPD3Zo2HeS5s+liw5QB9HJnQ0yzF/KrgZ2D6rx4YTz6Kk14syfNVkeYgZbLTcpAf0DbPjZt2FHmadbA07V18IKulB/lzXnSpHHe+jFBnaEHGCIs8rVc4UxdA7rKZjo8Qsl+ZkOobCiX1j5hw852goegr23C+JyAQTFlHcLT/hI+0OC/tUuhYTY+WAd46kcnIFCIDeNlZlWP+Ay+SGgzT74TEk/0TR1ytmWNX4uuaNvHjFt5VX3t62ITWeIDUEAWywXhGo+u5Ckt/4mTX2UQ9ZR506vio1j8m3JIQHYHJdAPYU9T1wno2D9tJ/WbVCtaCbTyaXZ0wyk8AmnoBp6fGQKcBjsVgUbWCJ2AxjNCYmKHEv6Drwjzo7CqcB5N1/eCggNDG0dx8Fq80ePsQjHvYak4ZLs0GzmsnWtYZwcujqgQFRWXPBQKCXbwSoT8jKwUQ14ZI+aLTOxla8/zvK8upEyr5yg9Z0DmnnNBXXRsN2yaazRIeSITnalGzU4QTOpWXiUlcFdHwUT2KqPoUGhoAk07nzmlMBW1E0MbvaF9+BCr+0aVk86cUXZs0Zcepo1Vg6Zl54OMiiMHlfyQ3tb/jfu/U55Y9WTpV0c4pEYMgwypYyN4sYzuDmkEBzXy5q6yX3dtR9QRkQctgveCCmX3oF6NNXNfeet1l5U7bnuq3Hf3C6b38qrt5Yfff6wcd+zoMu/k48qq5zeVRaeeoI5MnS06oSqEFGcBxtixeb7lZ1C14bB/CI7K8avn1pZf3PVM2bpph+4g+sqiU+aWJa87Wx2+XjUnGTEgNClLaPKHTe17XEds88Y3eRbpRMFYHvzOZRs/M2+JRzEQ9y8S+8gULHRagxZ4NArflFv1GzEPDdFz5RVYRBZvOjr8RQ1T5Y+JSXMdhWTlS37TgyR4EDyrYroGRoSKX5UGSLhB7qXZf8BvMgGnbMyfRRhBGa0B1EPrfll+ueGpMm5MVnvuH7m/3Pbi7eXh55eW6y+/tlxy2jn6Usl434nt0zNiyhGWnC0n7O3T8V34kB5ptDFeswsabbmsD+glDHsH9xfO/oCv3nAztk9rSzWDsUfPnNev2V6Onz/Dd67UxagmG0t820gxavdEPf/kztCVESD+DRzeqBtcIj0fie0jGfFQoz6GFx0jknd6SQ9gCK7PQjUfJQETnpwhgeL8J902qOVoAoAYDt+DWvhECmUgj+mKBr7pdjCygW+ZTDtxy19xus7BPGDSg1FMf1yHv68cDxx0nAcU6UZuB/kv+ALIjYOuBAMgU6zpfGKj0HFmyspRdIK+LpLlODrj25YFhhYAf2LAQxsZYPI7eUjiQj/fTSoagxJRUHobIOt1bK7dypfHC9EjNBvL5MwYwYyn/LBDDsVquikC4PwUquERQHANx2mANZ6ShNyuUCpJyLqi6IyvdHy41shAGJBSHIMl3mCaEQVqCIHA0Fc+KNpkd0WpnVKno18/hONFD2RhkQDBfCupjqL1E5DOwVGOkCJHRIe96YMEfJefQkOP4FKwZhVuOI5aInBxKlsSgfi5YtCpZtSqd7kERwTcaRdNWx6ZoBcn63ndovGiK1oaaY/WNBP0+IH740duK/c8fR+PaPi0hCsUn2dCjJF6JsgKUi4O6yOuhwYG9fFfPTtEJuWzrJ47RuTBpofVKMyZPLOcedLp3m92zoUL9CHWtWXLFk2dCm7d+r36ttzu8sgj68r+waHyqnmvkhq1csAPp9YD9c0bd5X5C2foqwWjyyRNu8WHpL3KhLdhsGfsxzc9rM74cJk4dbQX4Tx03wrdURwu11x3UexEuWInBetSG16mJZWhH3ez6XjSeYg2/J2HeZmepPMSLGWmGJSa7cgncN1y3bF5JEkeNk5+42MEDqIHJ4ian65rsngiGz+Fytc8lYYdzI90/FPywbtCm5+nnyAH+QpvRh1UZIoejIhpsEjjrhj65twdrJvlhCad9Miyff9AuX3lva4XfMJrlAY0e3fog9HbdpeB4/vLN5//cVm66ZflnWe8pczWgq1D4FF2+vnOUtfWW+alfpOORfzxbZluQF9P2bR3S9kwsKms27WxbN2jTfcH+SD1YNmxbpM/MXasPv67aMrCMn5gcjll4Ty9ZWaiX+yNHuhNaAMKFm+QxIKggZ17Vf+wDWWf8oW3cZTu9q+zFXjBRXfLXHFJ78oKm1U9QHWdrfxJ52YmdHQhOgmVbuXldgwhEc7/OZNkEEfAlBRk6WBw6ZEbjQonhqT3YCKnbayMpHNu9GGQIEzTooxpGw3j9ge5BFPl8gI9l5no1LbAmQJpvNGXPBJiq5SLSDhgS3g0uRDMthdAZytBWkwONVCXkJPgjhaGshlpllfp4BManM9KSnkrvdZd9JGq1hn7+5khiQ3ZBQ4hlMBTocsPxJwsYRO4wSvLMADBPM1lcEhDH5QlNDoWWukYxcjVaVtBdbBBC30SmzCNcAfoTB8aDzMT3zRPwnRjEzjrYADJJGMgHpKA6w4FG3CRgzMz8idRl8jtPF+Its5c66ei4cLGtlPoqlVO7jjtRFRcKqL++Jc7mGYqqq4lKzhMzbq4gYEJPMQb+b3K0naL/NAhjzDiiKYki57hHGSVHyxEn3zFRusLEA+vfLR8/5FbKADdTSlVHQ1bKNhPOFJ3W/DiUzx8y/CA3g86Up3TEIREf6SmXa0vjgU7Kof0GjtSHe5BTZFpBM6inHFjx4jrXoOMVufZ1zdVG+UPlEnTppcTTz7ROqAHxcJXCqC9a+f+MnnquHLtO8/zGYmRBbUYWa54Uhv1NeV65mtmqbMcrUZuZFn1wvayYtmact4lp+rtI8f47hKbtUUzGY1iI8mJ/RR6C7awD8RlA2SRvV3pFEe1HIgkPzEdnZmr6EDngqfhPykf0vETWNIoY3sUQRfaPTc6FZa8wJmED5a00iChyQ4s+vkfOQQDbOpjYk0GmLmxhS9CKztyGlGXNFSRO1OXkS/8ELc2VILkWeH9ax4p63avtw8hzyFNa+7euK1M0kKnCXqjDB9/3r5/p74swPo88UBZ6c5MBT7NJXjIYO/Xebdey7d657qyavvq8uzmlWW9OsHBQ/sMixyWQT52aPyhsnrV6rJx35ayun+b3hh/oCwbml0uP3xxOWPWKbw6V+XKVGzqEk7adMPn92il6UENzGiEHareR7UL8NOvtVeu54Hujq1MbUwsiI10NtWuLCCEsvzHl4IHVGyCHVKm5JMGZ+WDlpPzwbPtyB2Go0ugHYyq2NHyUgcqWYgiSUPgyvqHNg6JzshhmQTeOnyvWK+0rWu1m4lBFjvXtqCR950Ycus3nKegnYa9HAwTvcRRSY1C0wUaKg93YulEkQ//dxmZPjhIglE//wAAQABJREFUnmA9SFepcAOVnMaPdpd4T1dsq5USgIFEgfYch8aHtGZYw6BRhbeCQMipmiOFYVVUgotEaAIn1AgaKNNzIx4BnYdR0AVYxzEiUiA0xuqp644ahwZOP2fqMoWY66YDJE2XSA2mp/jwTk+qSeZ0Nq0iQBf6ZiX4+IClh3UkU2I6PAhEHuTIHQGFG/5NTGBaBe1JVp+7qbNh5IWu5OGMzdZOJE9/xhMd9t3ZMVBIgQaGKGUJDNO+NLojvcM8eMAM6vVY33v4Vn1iaZennEaM03M9dXrwYjM9jcV+PedZePzCcsKY2eW2+2/Tfi09J9SXLI4IbpSeu/DMkMU13u9IR6cyf3njmrJs5VPlN15zVVmx7ldlxfZHyrhRc/XiEL3blNc7eSn1kD7DM1krRvWRYTp6yccCh8Unzy6L9DvmGN3OSkf2IN5z+zPlN99+vhtSJWpxxeGy+ZUBPQvVXsPp2v+mPYe7JefQIe1dU1+6hxeL276pKP/Bp2PYCgMbfFM2FQ54BPuQYzlQ2vwPf8bVdZh4gGztjgcnUWj8YQUcukC60cUvhvudi1PAPbwah5hkoyJVsX02LWVRRuDQx8LZfsAzPVDIZ+qXSAvDeQgD0ZDdQGaQhia6VtrD6igLrVbtXFvuX/uwN7HDD122v7xJL27vL+OP0UZ8yzqinDfnrDJrvAYljLLgIDlhAU9mJPpHji77dae3ZvcrZdkrK8qKzc+XTXu2lH3qFHER6HrWQ/7d2Utp/ZPHlulz55S+zdpnulCveBuztzy3Wa9i2/FyuXrRFeX1C5cIni4xPMG1XXTNc0zi+/YOlknaF0mjTl1Kw2kMy4kBsScHtz3Y2PLravhAttY18hxAQkmuifsEEWyJsRWVPPhTAwUwcqTOpt0JfqNreHxUdJrfkNd+8CYPBk0U6MY/yCUfsUS3BvTi3+VFGug5yB6kcyko0yXafNKApkWa6UQx3NS47MMkZDBW7W8/Cw3yGj3aIrMQP+tm5kBYAMNRP5uuLivnwSuCRq20g8nq8Qkv6lvWPAy/K4QHz7PxEeJ9dGZ0LDCLYgaRECm0ytdCtTgMbLhqDCojWNDpaESXeg2PhpLbejCaoc0bxWoppPCFY2UNKDriIIMaFpnJIy2nCismwwocQxMASecATxqIqitCGSWOZfhG15iCh55+Hg3VQiar2atVJNtRuAHXQ37D0EgyKpFqVAL4mn6EJpqRlwDUAth+zo88SjVOCqviSJs0OOq87GDRj0UdDErgBzx3ZXlOw9YNGgE6W+Rg6lUdnjqmg5Jn665t5diZWooum4JP50YjelCv1Ro/cVJ59+XvLJPGTCz3rlha9u7WG2m0uIXvHY6mMUFsdNafF9Mgrz72+/Nl95XFxy8q37792+XpnQ+XKeNnlumjTijTh/SKtqFputs8UOZoef4kdYjIyLOca952tu/OeP7z+IOrrNSVv3FaGcv2DRmw2YBp0mNmTCgvr8KRSddeuAm6s9i51YuFpkxVR1pt6Ge/w2RU1L7gs8xpX0B6GCi0s3VSWrt2pg+UQdKb/6V8M+UMSMNBhDwfc6oOlFsqHvYi37BhHZEBVWg0EqcjhG/wq6i+Rhbqm31bIExBNlifBcwZeh0vlbOKGSznuZ67XsFNgbyK4zwS4K2/Qd3R//SFu8ruA7s9VQnwgd3aJ6otFDMWv8oN/UF1cNP7p5WLjz3XPKk3kKD8+rXimG5mjxZMPbX66bJs27Nl1b51ZWDfLsFIVsH16d7OH43mGnmiumcEiONnk47MKtsPbyz79J1OaOO3A5pV+Oaqb5Sh1wyWa855Y707NAWoGI5XvU2cqH2TemEEuuULBsmuIPZpmiGeIVNPhpeFIZHL9gntjoPkaHHOyOQyIF5t0K6BNAzpNQDj/Gp7krEZENBCAcvc5XMNFKFFgKO8SaN2ppFv+cB3fABxsDd2MQbP4DY4cCjDNnBChqOCCi3aiIvFSBvUdAE27Tb6AFD1dHrkoRGn/XT729nExAyfpOhvf/o1GvAy73aW/sCRhucScftR07rRo+2G/jaYFtB0zMWXAEIFSkIUIN4YBFcEYKoD8JDpOkUxj9CtGMSuR6bSNzoMzcYCKWoFBBy5hGT51Igog4Kg4oOROzGj2pCWWzDg8UeliylCH4OFVJg0cVoja/7QFVDrRKEeeoxmGcFIL1cQGhKuM9XW0RAu6aNU6YxbOyZoWBoVODRaIxl5JKXSMyqK1J6+kw6MjLtpPvjqDyUsh+wQHQQnfi+9sLE8vvR5LygYN7G/nHPh4rJw8bGWF0GlfVn14qayXUvQ52tD8uyZU8u1572x/OrhF8qYoXHlgBa9jNQdIZ2/9xCK5jsuva6co8807d63u8yfc3xZ9twy3TVq6kvTTN7YyihZDTCvtUKGkTxz1DL4Z7Vf8a++83dly/pX9MxyQjk0Sqv9+l4oW0e9XCZsmF1mHJyrFadXqsFRxyxcVoUuvWdlWbdmm+8GabTQdcPaHeXyq0912WMfKjcd9dkXLNQGbi2gueMlveNUU8FaTbp714FywZLFutuc6k5VhnJ5Y6MYinJnQNLrMCgXQhtA+C0vLt/YGBpHVX7bHgxTJWL/iG81HCf7EL8ABtlb4yY4+bcHJNIFXPwImo0OVwSuh58jJ2nUL6arFZNfpgHjwuDt5ObQQEq2D4qePUxwkoJErGR6xC2Iki0TOM4D7IgWq/SXe19+rNz/6D3umEbrK/R9ujvfs22X7pb0qrOdezQ1Pqi31uwtb7h4SZk1cYYWvGgBjPhQngf19ZFtWlz1xCZ9ceKZh8r6nevLOO0n5QOsTL3ykob/l7I3D7L0Os/7zqw93T3T3bNvmA3LYCNAECBFkSDEBQIlUiQtM7QpifKaSpVTiZx4ScWpciV/xhVbS9mVciWVKqckU4xlS4wsBaLFVSRBEgCFfQcGg8EMZt+37unumcnze57z3nsHUhT7dN/7neVdnvc97znn+777LRlrwiEs2TGNSYz1K3o7xpXzl9v4pZk2d+1ku3RNvx3OTeqZuSv93NyxyQntHC62bx38oV9DtnNmu688ze0L2KiFVjtyq3Tz/TmNAY4Mx3QK37cL9X7FfZyV4UEDXDB04sgpnX1Y6/GqDrOMihn6duAy+TXzXfcpDTck2SMf1ZyFHieTZS5Jd2ghKdvxvnG5Q5RngQqGxAlSMiY8CyumhrGK4IGWLge33lgPBuwgFult+PnZxf3fYw+axEPkIyJNKofL7eUX6LPoudERBB08pR9ONPrIzjnZKlPKPu+IYLvp+phFhj5JfZ6XkIoT14NNa7XPBqmCtvgqvvQagpQBFnBFFvMss06Xk0xMVJUFAzr15QTaEUCn8aeh2JVnoGWx6rLiNcsadRayorT/xjIAF23GIF7YrVc62RaWDgkkWmRVWwKRC486V1O0261cORwDfyZVnGavuY7Os1PEh8yyzxgHICRYiQ6jPR/JqSDtNiWYhntP1mPwwz2mDCSC2yIRGv3I76g5miN5r7/aiRjwKGXnQHn9v/jMgfbHv/9j32bAwOCIaP/rh9tnvvBgu/OeXaLW1XnaG37sO6+1g2+d1o3uM3qKyDYtepPtE9u/qEvTT7b9l59uV9bqFNUii/Viu2Xilrb48mL76iuParEZb+su39x266hxiXx08sp+nVK9oklQvyHqtCWnVbEBeCSOLk/raSEruJ9LeP38SnwmgoXLp9ve+9+rt5Pf5kdmgZ/Tocf12C92AlZwz6J3Dlp79okDeovBxja1fcJPO1mio2eOBrftWC/bPtie+N4rerHrZV1NOtYe/MTNXgwTI3EsfYR8J/Qrg389+L1Hy2BJM34mlomcEaZwMMKUiPmSYj2oQQUy+id78VAnRthCS4zCG375SPEHPuodv+K3ONf1CdI1tCMPPVESWaogbrWhGRr0EBeWB73rpVd1ibnkLQ4GaOXzTEqSRVlcntjVTtl/wnruyoX22LEft9Xb9AAEXfzEwnf51Pm2qEVqpd5YMXvuou4hXdJu2b23/eStP+FxxlOF5vX73Ztn3mpPHnmuvXZifzu/Xw+COP9623DzVu9A+Wi226CNdWJMbl1Q3Oqo8+KJM21eeta0TW1SN/FfWHGyLTsy1mZu2pQHyGOLfIEHZvVs0ueOvdJ2Tm9HnERRr2bteBw+eLL94FvP69mpJ/Vb9ET78MfvbbfdtdOn3lkUk7Twa4E9c/KiJ9Yx7fxd0mn4+B4KfGQPxl92Y/yOfuaXomVb8407qDQwzwhW6MAXjNTZduyh1hXkUv6zdb3PbCPzyVBWaC2O7Ih6dCueTSu5Ft37uvBaTPoeAnCwU+P4N1R/SSbMokPcjUo8b3kxwjaThYdCuMwaPsvucQgG5PofSrqW8RccxV2uycKH7NgV14FJdZbFKostUHS5IsImxjq+KPmDC2ggh9hEIjaj5wW1IBdhqvciYwHQowCd754sqNRHyRMMz64zcSDVYoCLM8GEFt2e/FMUS4R48KvNJcAo0ZfuDOQqFW4Ud0rT+HcqcGuyc734LKE7BN7scccGyl2k6awbGh3BgBtnRFctZvIDsqBGsJnxY9/jUltOmajZA5Z6OkG+Udk2wK0K8pBwmwJbDyTkIVfJR1Kdl2omMX7/+NF3Xm7zvA1Ae74Tevfbcj3Z//Sx2fb9b76gm5A369TQuLFP6hQRNz+fOn6xff/oq34ayHKduppas63t0hHdmwtPtNmrF9u9e+9p771+b/v27/9pG1s9o896vZdwU9s9sVXmXW2Xrp9rpy8ebCt18QqGYBf3GOowcbDwLeN1O1yII328DJgHKn/63k+2z/71n2uTUxOanGSA7MU4zEOGAxMfK2nu0lWiC+0HOmL83BceyK0PooSF3w1vvVOYdYTLpfIcGa7Sb1Zc/MNvAPEl4hlg7FmKyUqijX704ifsJOiHadgXqa/YGaVLf9NOfEtTrECN6ryHO5CpSv17YnCbCk6KC+GqPnZsqT7joceFYY1iwybsEyG/v8Iv4cQKSkCBqZWMv1ekuusrAm2DF5nZWRQA16EkbTqiUhsXzbyt3wvHxhVLk2pbOtXOHznt+03X7dqMJA6r2s/e+cm2VVcUH7t8qr16al976sgLbd/Zt9rc4lxbrgtqrs4uaTOb1lu2H5RtG0pXUKKXz6we4XdWC9cqHcVt23xHG9P7LOemz7XF4/Nu52EP7luxsVPGjqd219rzOvr86O6fbBPL9cYM/bEzdlFXkX5D8TyrRX3zTWPt7KkL7Wtf/UHbctP6tn3nJh0tXtR9rlrgtejOrNdFQMqv37jeF9vgW8eMfEPe7hdujjrLR/RJ+oZWPiTosYU2V/gLHuQRJ9QP54HQQF9y0Tbkp/+ChfakYCid9kfnKd2DHSTx5CgQTsnqcyJY6H8kcpSPDCPvuKn3AmUKeAtfZFznXmLJIJWdlid9LhtqfAGNoTOBy3+epFWHdnicTA+hbLVO7JYsGpHpfkiM0Bxy6jWvajDYHpF6HIZFvFBJAsqxD2DOa97CduVvODKU+BAhAGYRIMKLRWcwDWIRqL/sVaZsZXCKL50iXv0NEsKUaLMcbTNZhQZ5w04GA/ypo8018OJ4HGI9WeSs0zQjMkRiLtFBn73N1AlED8jQD08VxNmwwEsyJip6PosZTuf3t+zR2N6uL4tfFryiRUYm4OD371kDDOjCB0PfcQtALRC0oR0dCVyCOh1/Vk+DOXdm1peNb9iqJ8ywQCnQpqZXt6Nv60WpuuSdm+1ZSD+gB2XzNP9Z3Ti/UpeZ50kU0qoAWrdkmyaC29vlrYfbl37qi+36Cd1psXxSbxrYwGjV4sXpLv0WKb5Lf/JsO/7avrZKK5ZgaBHSIqiJiS2L0TItxljDjflMWAs6Oti1eXf7uQ9+Sq/e0ekzPQnEBonGSb7BPzyqy4NKfrS3NVi9xy57ZLo+iRNoufmaqxOXTigWRMy9ie4D5RMrwO6DCyXVmWSZhAT8uhdgVYhnGMcQJxU/viZJbXTcAN4tg/qKbfQFR8WjdiP6b3rYgiwm09LhgSxRNXGVHG9F7BhElZhTB2wACZsnMpW0M+L7PYkTTyxDo0VZQ2YgCzkkjycyFJlMJJfJj2buKeQqz+8e+JFP2xPb9obOHszpRbwTWjiwhduPdq/e1dZcWdW+8uy/b69c2NeOXzyh+FBsCAv3BHL7xQo9H3eMBy3oObXLfJsOSvXpk1jF9bzuHzz7zom2estMWz++oy1dWNkurz6lRe96u3z6QhufWZ1eEGsWJRZzdqKWWe/+swfbvbq6dFGxx47ZscNndJXy5Xa73m24YmxJ27Rtuj31vcPt7X3H2449W3zK9ML5S4phnnijMa3xwhHiBZ3+JY0uIvQFurwFusKsu1KValAh/UW/kcdC/XnRSB0yM/kjCV9L5yBeEWJB3glHFjKkybTkKsFXupBDCi0wMibwCf6lGRXEhloUL8hNvWNB2bKTemwqu0wW8daRhYedD9EwlrDZLf1LZe/YGUxshibyur22BwH6mJnIQi++YmzEz8ZGPeNwoCQ+xgZRyyZtNUehAJXYQSQrZx4hsGzamO+M19hQH8P84xYFiOOAKAkAtQT9YGuZfPHvSSr00pSEztFkhXg1PNHLgIoHMMXo0ePsECi6CZBAgB5SyVHFAGvHUjiR4aNX1YcXB2egZO8DjbF3wGPJ1Ka+q8BAYxpAl15QGEPnMYF0pU46BY+OYBKgjgXTWMNpP1JmAsJVbmNCkxL0eg+sAtzKpJOO9UBRHgymZQSK3zi0+Ol+vI3bphIUqj/4xnGfGvJvFeK5pslrx8717eFP3d2+oys0L5/Tq274vYYFTfq4KnX3mjvaxx7+TNu1/qZ2SBPZhm1bjHpezxHluY48BYQ8bx+gwU+o4S0FOjrjbRYsfFyRWoOABXRRV62uHptsX/zQz7cNq9fqCSS8ZJUgVQI/DlPKTpF4HfCqU5v+bZ+vQPWwM6kw61U9OhLWu4U1iWnxH0n4R560nwbVEkedJ1qcHJW9L4Rbp1/NZ1zES2jia/zd69Qi8U7ptxQYiPUWhuINVadnQrTS0NfkDQ06kuh/j4YeE6lNDBdfb5es4IUmxhirSo6qGuwWgWb4tJNSe/1dp/Wp3yO964BaOJhI+F7QQxn++M3vtrOzZ30rhfFy8ZVOX/JmiBV6FRfxiZB9T7zcXpl9pi25baIt05WbPNZvmU6l43fGBEd646v0gO/Fyzq9qt8TecCDsQifdqLYWfVOiWRd0inYMR0RrhvXi5/nxtvFqROOlVkdwTEpjs9MeqHwDqkWL/zgh0DIHfO6iGf/6bfb+7bcJZnCqpi8pAt9tC4K81K9cWO1X0e1VK+zqH7kStMrOvXPDhZHrFPTa9yGC6EhAZVsfE8fpM0+UTt9VTFWcuFzvIuYtzPYUWCNyBF5GRPR1RvNG9oaM7QgpeRTj36GkaNsyApV6MJhTtWo1HEAwvR8Uac+dxl/qok5R9ksuLSpETu0zTyFxqTgJo+cwteLYaNWvJ1HMmJDdHUgtgV+fIms8jViPQ/L3thKBST66vKxKzLZIsUaTWfovYxddTBCPaTElG6671wQVl5bE6DIBkCtnIRkb4MjlfDZsSG7oY76THA0KrGwVBJrrei2RfrcytY6sVE1onMxRF0jsqCTE9VuHeIuPlTYDrHHkXGqB5naBpMt8FFhHd2JGrA5euuBySIKbumn85EBG4rRgSx0RE8PMskgQJl4kroS6qvG2OPLVKnFjcilJvrdpgoCg/osgt0GMTDBcHvBhk2rdXvBpbb/5TO62Z1TlbqzT890vGnPeg1qnSpyFGki0IRz+53b21q9/uiZHx9oB/ad9KKCHRz1PfTxO9ttN92EN3Uv16n22LffkG5N9DrikCp/gHdGN0Bz8/2ibmNYod9UuPDGt1sov0xHgxCyd817DSdXTrQd5za3Fx/d116b0HNPV01qkV6uve5l7SMfu1P4V/uIiSM9FmQ/mUf+wb/4laEyCB0p5710b7x8tD352L42pwn1ljs2tw99bK9PlYqcnrHb6FwVfaRAhU8JdZmhCV386i4WVfqObf7Bge/76ThVgslPq7H/eXINV+3m96ZMWGhNwq/4L+Mq28pbgWQgD8jZC06MZWeipKi5466a7NnXAi4ZdA5JsohXL2SSadmuJx9b7BXJw3p/cADJOkCF7bFzuRay54++3J45/LyvPqZfgcvFLvOX9Bg8PfyaU5DwXD5yvh1/5e22/r3b2xqdskaDfSf7wTunh3nzW/QK7bRdmb+gi2F0NkOn9LlVYZCMX/6WjkVdfDM9riO2+al2afqkdrSWtCta0Dg1O71tg+KOq4xj91U9bGG5Yg+LOF2q3m77zuxvJ8+e007fXLt0ea6t0zNVt+yYaa89d7xdODvfzp2a16l1XQQkG3wkqPifn5tt01v0KEHZuVL1V67MO5/xhz2xP30YXxUGbCBf/YuMTkFTT+V1R7XrMCExJtSKl5IdBuSR633kLP7M3BMaWmV3FaDufcqmuwgo1uPMKLG5w0w/Ol59x53iy3jCFztjH3TeiZUcphYQWORAWTAEh6jlC+dH8IxYNMCLjMIeROVz8avC/YCuPj90o9xmkwPGrPYrOGnIEmC/VX95J4p5mCRi+sBXk9q4bhB5z3ohw04lvrCcDyVXKgtIAKusf/vCX2F2u1HSJsZBgh5eobRYKNURCBE9ef6HQpUV/6gECl7hkeu2brj4JcWTQsmEb5SbdoOFVh9PlKDxYg9lX/xU5wmuZLpTYWUiAqolmQ+sWaAjk7bQiVB8GSjplfiC06tZ5AJYOnvw4QPbgA6w6jtXmCrX9ZY/JyZWtYceubs9+ntP6neQi15o4F63car91CP36GpOroyL9cjiVN0WvVvwgw/taSeOa7LQUR6t9/3E7nb7Xduth9OVqtICw+lR5XV6Z5kMBgP6b960RzdZH9Q9fZdb06mlFXre6BLdUnFVR4FcFm9/ypf33Hx3+9Q9j7Qnv/p6239wTrdq6CXBS+Z9inOVjuge0BsQ1onON7xLLgshLh0c+er3SQa+mpxYhE4eO9++/n8/39ZtXdHWbVmmi2he09Hh8vZBvTSYFL/3WJAfaoKpvq1y0ZbP05fhGxkjIuva3SfCxxOKXMXEkf70aZc++SG3kgcv+M0QevLVd6Pt1i+57in5YFCOshvsqtP99Cf44SJOecEt/NTnm20GvONY9dl5hABK+JztPKqnSSX8zu98337zMT0MQUdMOovAaT5aMZ8jQ84EgHNRR15nXjnWJvWYPi6woc74oQa/2LjadKXeOk88LZkXD8+eFTS/kQMQ0Co2Ta/TmmtW6nfqs2va2dWH9eSk+TZ/er5dOnm2Ta5f46NC6LNTIDz8Xr1CWz2RCHBjC6vbmcNX2mvjB7Vzt6Ot19syiDdu4XnqB68qbuf1MPgN7c77duoNLbPt5ecPtjves6Nt3bFFdHpIheBwEdjCBV1hzSlG+RAz0t8dIwBEiJ0e9z3vajzoepU0QVd/D+rUVwBN15aMkk+fht9A0GL34HXykTeQ1fvRVxT3PHJpN717y4wuU6SNPzCw4xXJpUf6PQCIAbtzRJZ08xcFqo/IEpwdul6JDAlGFv3ExBE/IAOb7QHHmRotYiCONnSQCq/GWmGGHhuYy9BZ/Dg0fkGDakVj60qUt+o/+PvCGRxI5jdDiyojNdmlgm8DAUwQsVUyd6TbSFVFh1SMApOkOC0AA1iU+keHecpgVQCuFiUIMKIm/qp3nRrNP8AxIh9nCAN6Q0Vg9b0tKwQ+3JJvmo4jEh0YHmDFE0oR4R8cTxAHmzOSNZSXjrEdoO8y0jl0AHarXnprEkzQ55AdushPgDIIgQp+d3jHb70dgzg0UV1te27b2r74tz7aXnnhbV1Qo9OSqyfa7Xff1GZ0Px5XkXbzdIQHXiaRsfbCS8+2r/6Hf9Fu3/mx9tOPfLx9SIsJesAExrw5gN+ChF0fhOA5+uKzH/50+8yaB9t3//QH7aWn3mjXjmrh1KIrydKndxZummqf+dlH2gN779fVqcvac7roYVJHk54DJGUpA4Tz+0pMJGDiE6DJp5U6yvHNCi20B/ef1qTa2u47Ztq47i+c1Zs53nz1eHv/gzzNpvzVTx2pv3y1ohqwIb5PfNgYyaYufUM92oSHCTAFKhJD8g04+DOHaNO3iTV+q8N39CEySdWeCSHyaQMnJEVn4k5vL6uRvxpfWfCgQq7keOGlLEEaJMRBZEbvYAFUCz3nHQr5uc4s4FsowVwWqSg6tKpG9ct1CvHF46/pdOMB/eaXoy5Adwr/PswpUsYbR4UsZGtu2aDfi5n49K/O5gjSSXHH6fNxnQXQ7pVodfpVZxV4NRg36/tCmlAKhM5SXBlvM2M3tSNXX23nD52UOJ1y1ZHa9FY9om/dGtvvvpCv1cOaFLXTdGV5m1jQW+21va73iV1aebYt3TSvt5/oVgxdvMVFVzPiffiz7/dv6Ksm0atbNnT6//ChU+25H7/Zbr9Hrx/TWQ76xeNGTvIEz/hX3XDHA7DyrXzKhUy1o1EmiLT3R2ooDydtFfxPLyRFbnolMZE+oDULbY+p3nHYXjz2A53vHqUPRWsFxBmxCpboqnkUuZYhYBWv1mtidJKRbchStn7yoZZYclLBcnvRCOSnwgFfwTIeM8Uu4teyBmPFUTfECi7rDh16LFljuNT7TJ34IxZpolAjHNk5Jd5TnR1qFVRBXw380ec66tDn06ThkTBbIAFUvDshy4iGTo7ThKGDsnJ/iVm0QxCIDh/Ci88WxkzTW4FhDAGYT+JsZDfY0MCjvzIsutJx5JkvuDewEnQlFZnkXdfxeuPK1FuVvoK19KScgOt0yHLnZsvvcH6aieQjM78PabpCvytY/JiiaAu+YRu4pcttdrZykis52FIDAN7YC2CO9q7qVOl0+8jD91gHdvHHBJDOl18kBP0EP6ckt6zb2m7Zsb2dOvts27LlZ9o7bx3xQ67Xbsi7A5koxBwjkKYsssDCfWP33/3+tmfD3vavD35bF/DoHYUsFlpEFnVq9DbdxP+Rux5Ud15rZy5etD4fVXbcYIbeR6DKC5nHHjrB6D7VYsnFONhMOwP5qibRGT2d5sqc5J7Q5e5trB07NNvec/9mHcXqtK0W/kw6aAC65GqC9wtohd1yacC1BAhbkulSoGu4hWM01U6KOIwF3xqzvvGJ+0WN1kdL2dCFEC9wDuPNAGxTeIhbpLPtOwfoANdIiv9ppw2cQqJBic2UTS97ow9G5Vl8yMLTdVAkgcJWix8BzO1JCG/tlZP72qyODrnHEF5PpowpZKlvOMXIInfpnfNtao8elK2+udF2CUG0jtyo5x7VNidevfj3mh7EDa9ERTcghX354lib1Gn1y1OnJE+nMWe3eHLLUWX/6QEefn8T7hULq9rquTFdqcpVrloLpy60xaXzbeHqlfbSyTfaB7bdJw8oluSF2jHirRgrdaSI/7gnd6/OiOzfd6I9/9QB5bfpVWeTertFHpyBD0n2NXb3CR/cgmv85LGPPnA9+KQTV7lPlM+WdvL0YY3njAV44YmsxIFrVAEPyTul7uuOKQTmSdxItvHmmzgNbsI9PKpC0iDeSqdrodE/2KLRlJ5roBtU0mhx0JfOVPrbeIl3pCIv9tsOy3dtFxci0zDWhcF0nQ/KjL9gsVpzZuzRLqbAs6hus+aiLOKJf+QOxoXykdPriH+l5YO9hXI4YPQHcbfFhP5CaU+0uaSMO8nEAYLikJZKtkO5iACO9fQRiAPyR6tSV56FAmdmEkgjDh4SRR/OKQksPr1dW+9dAYE8G9Mpz8STiBVNsMJFItAHISU+eLKX1icuAIjH9WEZ4PecY3oL6RO06JXAheXlH/xvUK7TEVPn896usPk3KQR2fGDyYqwqILBXjQ11pSJ4WOzwBAnMsDrAoVfhuq4keO6JfW1m6X1tg041/tHvPime6+3Oe3e0z/2Vh2yTVco/nNZCJzLAhm4m33ldvclvfNe1+OvmQP/2Qz3DiIsWuOGa35NA4QVNAuC17z3YmCyyM8DVhmvX6ypDne6c1oS6WTdjj+nJMzBzegu9AFjUlX47b92om+53t+eefLsdlOrtet/eTzx0i/2AvbZb9HGZ+ld/0eJGl+lrcFWfyzSXwVOLUWrU0BP2yzLbYH4wKUFfqRYlmhIX+E08louW5NnKmz0uqBZKMYU2sVFlNYiORcC9oG3aAVyy2dKO/iTKXQ4VxiodvoBDZdETBxVHwHEcmgsZnJa/2s7ryTC+sIWlRPRgSoKfHS09TebIBd1nONZW697Vas6YkhThYYxxTyJ5brOhfEX37c3rliCegctFOPahmJdd04PZz2xplydOt7lluplfp2WX+/fH2JrFWHL1dpXlump17Moa23xxTPSrT+vpR7r6WEd6LNyMn/3HD+hpOZd1AZeeSqRxED1cYKMHvKvbeNuJvSr6PbqfdbXes8nzbXfu3qgb73VhEG4UBWOHLWVsZEGMz+Or+IQ+NIOK3U+mL58NolB04S9/MgUUS9qyWBYditGLX61XheKlHmz5gI8KjbPOQyt9Tb1JtfW8J5syNyR+3YqOLo9RY56+2LkaCf6PjPJH4iiYjU/+jBz1ab9oC3ym60hLXumDr+aDzInxu9tlHlg9DsRI34aPOUljVn9VRk4+apO/HIvolJnY4zacokQZXo8/GUY0kLfzTKBKAsQBgLWpVG0EoZX2StDVIuEtqPTxY8w6EUphAUgljJBI10U2bLR3fuXQQ03wqJ48TkOn/nAa+UGQqJ1Bl9NV4OyDGOz6Q3akdL3gRKiSTxmBk6RtR2J9wLZeLopR/pqedBHKBGc6EY4+qZQYlanNhEklJXQqr39u0OdxYtSng3sAqg0bckQj6u4/ePzcUmV8oQT47QtEqFsl3n61xAQkQW2/i5eO9mKr3+JWjs+0lavn25hvmFebJqUly3heowJLcrgRfkGT1VIJ5S+BJTrbYBcZMw89ZpFapmdN4if45XVT2VLhu+qLGtSmBZcFlOeQbuBlv75y77peLTXRPv7pO3WKd14XxSz41o+Teiwbl8Kv06m1HVrw7DC+ZceHPr637X3PVt+isUG/jXJFKf5ysNtO5YGq5L4RLnzgI6M+Yakqfi2kYqiFzW293ZZgmMvxJRg8kHA4/PRD12gMqmIspG8kQfzuQ0vJV8rEKkcg+LcQq28VFxIafnCjBRnUSlfZQp6PJw+10beszdrYFrfDpQp4kkRjzJFJXegyWZtO2Jl4ll/XfYE6eruunRSSd7z4DZlJRn/z+r2tXdHve7vX+wIWq8C/nFVQOxqhm9VTaa5qISYWuffvvt33tj3vfbgdXHpCj2R7yQd5S67pnZRnNre5ifNtbvycYtDWSqnsNzTx67VkK+cn24qr3KeqC2rGL+jVTud1Mcwxvd1XOxeKpyTi7Fp75+xcO3bv8Ta15Wb7aHTO2X/8nbZsXK910ltcll7VTqcW2Akd6d68Z1N75aVDbVo7Zzt3b5Lf8GV+i8wi3/3oTfkVrO/yJ30AjT74t9rJ2t+0V8yqR0ITeeDMAtZ5BypvjCPzWLYWHXY8AIs+8vxRVjt/NgQYqiv9alSeHcAcBcNHO2x+PGRfEJFDlRCrX5ijlFUMYCB52uChOnAid1CZli4jRMgMSnjgtJSwqEwd4zHzleIJfaYaLpqxP6y2VVlsRdJANtgNLvUApOjxRqZ4lL3halJJSKMDEQd0Yq+WaauFLwYgmMBXQoEn5vCkcvgNfQFGT+UBX3qp4486D1ixu71vLRlelOEw4XKdZwD6Bl4GauTTFhd2HhtPKyl4wGVbLAi68Jqqt4VNLZ1/SC8kIswpzB5Qlo3eboN4uugEKTIUdMhfot9imKD582Ij/1WqyRFeaNOh2WJnr+xbZJlKdBlYTGaWLFqaYPHukRj5sf06H+X5bZA2cNQVsHTpLXs3+RmgqyZW+EpNPxlGExBtG/SbIKeaxnX09sBP7GknT1zwjfznzs36NUpgzQ4COtmTQ3nThTtT+n1zY9uxa515ORKE4JzulXz0957TI9VmdYSqxVT0TPDsgYMDWxmo/NWCtWGzLn3XH/bWUbEXBalKHGYLvX0T93QeDNbHPjMEYA4TBei9kU6V6Q/Hp/gygcRvyIjvESmPd9vRi/9po26UJpIZN4oS9VNXNaDxImqeQAw/kIqS+uTLH5lA+1gEdyc1BhvnaHA9ckZlBQ/yw4js5boYZ9fa7R5jPMyA2xYEyb8NLsMm7eBc0dWXa3dt0e0V+v0NJ5G0WdRizo4cEsf07sHt09vabbfe0t576z3t6tFlbdPadW3vLTvaH7z8jfbs6RfEs7xNndvWroxdarM6KuSCLYZCruQWlsWVOQrUkeP1lYuiOaMb5M+3y+9c8GJ9TffYTO2Y1q0YupDLk6ZsFZ4FnZ04cOmdtvf6zYbGF49+O6EHAvzvT/12u7Rwsa1eMdkmdX51UjuCq5boaPPqirZtlZ5sc2FZe/P1I23PrXoouHYAiMsknEA8sIgktrAcf9Nv9qIchX7/XOKOqP7Hw6b2NjzKxvNkeh8kdqF136uerSebPg5MzJfre5yJ2wuh6rIghs+9LWyJFcZmzjQELzoN3rpBF13aciaBJurQ5Zy6B9tUyhgnr3jo44N6EuMSHvaNgGgdlDsBdRbSt5Kgir7QdoWeV7GlkxYKFS0UDoS7Xf1hmfryTqHkZexBdGPKhXoZ126RAMbPDYshkyyCkG6HFFrKXZ4NJx8krmWYpb5vASg5lmFe6mGJlOz1RcdgAYXHVBbpr9EysjIBIAnZI8EpXdQ6ic54NJEagyrDoW//B5fjlsW0yx12UiZyTiUEf+RYJwOeeNcmluR7US8dTYoOahMK4lInod+U+mLLa5C48g+ZHGU6OIWDVL5UbHnv1pX+io0egMgUYPbo7D8UkCIiebR2mTZcjQMs8t2crgaU9brYZbXeK7hWC9/qtm37jFk4Qh/T2+z5zfHwOxcH9xgyv+G3Dz90m946oVskVFirIzcWpnFdkLCo34YOvnXSbwcwTiEB53vuvUmPUFur3/vG9di18+1ZneI8p1dCfebz79MVpnlwMq9kwr1+zc8yvKDFWvqyB9dtYyKQPPqFBTMLlOwUXv4GsSTy7KCo1kdZ9EEGmv0vWnwy3HEgD1ZVOyET90EHhuRDn6ND06steIJTIWOe4mNCRA6TDrJrMhpMmpYeLM7qC97CkTw7L2KmjQ9K2Jqux2ZgShU9is3RyRYuaIveQlxLKBN/nHIiN0yw8RD4e7ffpfdTbm5Hzh7RDpFOV3NUKPULemjDot5eMqkb7leo3+Hl91p4yE/ovtLtM1vbnnU72561O9vN0zfpYe86E6CrT/fPHpY+nSrVRS3rVunKUx2RTZ3f2q4u19NfdFO9d3DpT9X72aM6HcpR28KkTq2OX9R9rrPt4jvnvBhPrJ/2FaoX3j6nC3F0paquQrWtig0Wb3b63jx7QI+E+5AWQbUJHNsX9YaMYxeO2O5Lc7pvEf/qn4uAFnX7xwdveqD9rff+pH5HPNK++40X2s17t/rlwf7NkUlXfcCOV3bQ6BP6jKO52I8niYuhU5V3QZ7FuSqWLBfSUc4OFjORSapsYCsm+d3x7vqhHBWtV8e2HptyQGly3DhaLF+yWLBU4ViAsVYqcQShvvUPnfHDp/+aN8jDm/HQfY0+yakYp91MyFSeUsnoJcv2BXQ0WiVxiDzOSOgIX3UMnTpyNhbEoCvixNftsf5giTDlEav+IHF2MNjEaTwdt8cmdlOvf9ngWysyoBHBHnkmFepQ7KMQNdG3BBOM2QAsgr1VQ5UhADgSGWbkyEeiM92wAI+RSEV85LgABhQjQ46Ko0OFjoGTOpd5oBaPJ0Yw8IloNztg5SjqockEZbMAjapsu7Cwyi9yXu4vs4LoRnaK3lqeJ99SGIvdMaYVtf7p8DzDlKMCz7QBQHAqYeui9IGRB2B7Aux+cMeKiz9fTi1VDGIPzAoA8fvUTqQJKzqk1/7kis/59p77tuth3rt1P5WeI6oHFx85dEa3WVzRUd+MjxBfevad9tQTb2lSAWvfy7O81u7VJeksNnNz8+1Pvv6ynm062yb1e8sm3bKxRU/22L1no/0D/jG9YmmnLq7gis/vfv2kF0FBb2v0mwzOpq/wYha2KKidMo4uODOdfqYP+ukq+yL+QUfaw4vnLdOYaWNQJM7sNRFouFGl2GaqILbgwe9wK+8ZKGMhsqn3tNJlR2enVhtYkJEaMBUuY3F7+hZ9ptc38VJYikfVjsnYgGcYD8jOgmUpXZcnTsXJqN7iE5tS6XwXNsuTVFxzLWMBaujxEbMqvztv1GP4fv6+T7f/49u/qdPXuqdUR3ne61a8cTHMVV35OavFiWeQ8maT3et3tdvW72k3r9vVtq3Z1MZ1ShRxi+o3dvrmr11oW3ZlJ+/K9ePiWdHWX9zRrlyfb5enj1v30sUVPgpcqStKr624pgXwQpsb046SFtC5E3pkmk6dj8+s8ZNveOrRgp5Us6C4jf9kgSc5topZHdEev6iHeut3w2ktxtg3q0X4aT0ijkkh8SC/2sFUaVLWTwjT49OyaanOYGxqb791Svcf6jYStfFbuE8pqn8TV0jEXfKhfYdDSWpn4VEjQxr5jgeaUnC8V1+Fl/EMkJGEHmTLphyFiT0aCR6EusSOtecxhajngVgmQeA0mWqUVOBHjOhxT3tuLK017oK9gCceaz4N5tgDprIBXaCj7KQi/kQXfzUeaAsFluR0PHqz4PbTohalOtHCh2zHv/Rhc8lDlluhod6COwaVLQb9qq+dSIS6Hv/B3+cASsvD4vobvsygGhuHLlr9FTLq689NAOopHU9rErkYlTL5OJdy+Dp0y6TGwSY6T8OqoL2wILjaB3Jdh4OHzqKNP+vSlv/gVlZ5ywYkvOwx2WspizCYLSN13jHozR5sahsGaTCiC4HW7L14W+8y4q1fGU7t6RUPos2kOGoTuLxgsiD2yc4BIz7Xg6nbYl8SJErxRU53+rRVLWLsuXlwwsfvlBrUoufG+8N6MwS3Y4Dn7nv1cGN4PKi0F63fYNiDY36pZHcJh4NXW+5FJM8Tal7XzfAHD+hVSlpgt25fJ5OW6CW8s+1bX3upnT09K3l6nJvokYdIFqPCL1EeOFmgujb5klO3dqknLxvd7cSXocOnyPHRqH3c640genzamP6SsNDLSP3TT6MLMW0SdoPNSAMvfhvwspLgUyZdOkPJvNpSJh9afJ7+jOiKY3ogtAj3UZdr+MIe9GFgtw2Z1U69yp74xVwTc+zoeonuwDKtpQpA4qd0BzO/r3myFz2+rlcbLego6Sd23tcWPrLY/uCpP2pHzxxt+tlON7yP6crj1W293ld4+013t9s33tJu0QK4eY1+O+S0r4TQFzwKDQjYv3xMC4qO/k4cvqCb3mfbpu1r2gbdmjGjU5uvLxzQbRGTOhKcasvmdYXq2EK7PHNaR4s8tk+//+kJR9xsz1HjzI5NeQA8fScd/KbJb5To4GgLhcR6FOsBEZf1HN3ZM21m1ZQX7XcuHGvHLmbhxZ+JmfjT7pWOzZMbVG/kejDEHe5HHvdH8vi2eNqRELob+xts6hf3H2T0iej4p1NUHvSNmzN+EQcfbel7swxoHV8DlSISA0U+EGVB6XNBlNHS+zx0wZQYoQ0JjAHzS5AXRBqoBappUrZ+ZWu8uIxe7IHOQCzKheJ1vds0l6jPwIl8pLMY2Qra5XuE4CvmICjiB3LSA70+pEEcWz9HkwSuqCibGqrwWJDlQhKskc544DoQxns9gUYFUoFKKc5wvQTZYdDYspT4BgSC2HqV1Tb9HikG0vmgdzConC3oZRgTi7MqUEafMPFnTPBDwAfhSuyhVZsrsMHYyljVUqaeJojcbup8qc2LBIGrNv8JSmHMHlRI3eGWJVQSls6QEzvGYEGOTu9BRz369IEGHNRWgp/koy7a9PGAASll8yIF3uB34FSnc5SEr8Gtj3VipMARcINBLnnkSTVpEmjP6I0Qb76phUtPgvFDtcW3tF9tSACintNeHH1y0Q6DpAY4be5rhAobEwe/Kb7vAzvbvffv0MSuWyx0ipVnIk5Nj+sexlva1x992b8joTs8YBx6BP95r1uyLV9kXHjDEWhiDJ1RSHtsoQLbgyd88UXaY3fa4bVqAEeJyo7bQUvJU1/oDyDuQ7VTxscDu90vvY87f/o9PNUn6R/wGbwp/WXZ4FE9qvgir0/phAWfECt9/ctEAg6Rq/fNkYuv4CdFT41LbCAhs2LCvunqGLvWN7A1tjvWpBSeh/QWiru23tbePP62Hsume0Z1o+f6ybX+zOhB7ivUz8jhCJAFFLyIj2/Bqlsgxq5rx2ixvfb8ST0diSOt5e3Wuze1rXqE4DvPbWyrlo61+VWX2iU9du2abp5XNOriLb2EWjtT548qTtdM6EIYLbZ+wlGPHcllobyqZ90Sm1d1qta28HAA3ConzS3O6gHj77Rb1++2Z17SvZMX9EoyFm17W3yeSxhXqtHzlPQs1HXKIaDGTvep7CJmVT3wPX2EvR5jfW6q/nPMIAciHNKTfSOeSB1pgqw6WrTIIW78oIIuAB7PWfK32zpN/d4fFaJCp+zP4kcx/UyfgCs716JGZzbSx44M/Z9UULA3NtUYwGfs4EXmMLbDaXPNg5zU2RfYgjClzCWebCmlTJNwwgIdGKVYdcS5cFFW42BHQUXsMb/I0AE9tKY3r/hY8MDb20RlOfav5wL9XqgLGZeju1Z605R+F4ZfNgZHSaDkSj+E6JAAZTnnSrXPE5MxMJpEJNq0whco2dDajTZHb0MeItTW1aQsxfwlSGgXFQaTIxsqlzMZID9BZ0eZzhJMY3LBHzhQMjzBaEsHd9GmNZcCmIQu2LIXR7DKBjtVdTrN4sCBWYQOWPhU9L127jyKvYN7GbmWAWIGCvJgkkRbQWQqDRYQyS+MVUdfsHjYFwS9aOwrGKkkIUZweBoHD+p2cGKvqn3JuHLI4QNptCborhH8/PVKBhg3/a/TlXcPPbxX92dNtO9981XvoX/+lz6oINQzJnUZ/dabpnXhzIb26otHvGhGV5RiA34k8B1Lkk8de/l779rcPvDhWzp0uISnlCuPSRnk9EtspRI53sHqdVwAwYQHDTwku8G+ooJ+crXzydH/yKUhBoOLRP+Q0F2JJkMTKTsBLFDpn/CmH0QkwtJVW2TEr8hlgtG3Gqnz2BQG4thYqGesSTVjiBbvIKLGGMgoazpnOzA28W2vjXx9+8EyVQkvUrufsfiqjvDWTc60DbfobRMqJ07lZ/qNBRBDRB+8iT/4PJkyZnXaBXErtOPF1YjXFnng+wq/7Hf86qo2t/J8m59YkOUs/LpPt19Aw0Loh3VvmGmrN057USBW7MM+3uZ1itS3aaisayrzk4H6GywSBtj2xsm32sd2f8inSF889qr7z31YPrLLsOVaW6VTu9OrVtv/eMJu4Au1VdbWI0H1gwlW+dH+HuQ7nwRqPGRMyjWizfiuPols2tWGjepjfakMuGF/uA4Bqjds8kolJyhTtjC36myRdWcOsi5x020e/53fpP2r4q3GV/2EgF1GZlzYIQZ9qCVPdfkkJvZK4lVe40gMrIzGoY8QoqTxRp2zCNLHNpIf6A1fUMADWfg6MhRFBjaSEy8iMqfFp6btgMum5VkIhdQKIySTEnriMBgNSs0c5bizrAS9HTwUrOBs9Gcz1FTtkLljGciwABCezCsqAjiLiumQP+IE8oNFB1nwjiQ627i7HstDqRJ5fqBFnGUbQInvOEzXj/NEiIMZHIagNnewJ1Qk4uT8AQNxoQumsiM2DTsKzMGV+7jY42NCwWe0sRcXGgaKdGKTMGCF5wDlRSAsqtdiYT3WHzuHesETLDwPkiO1FFNHr/DEGW5zeE2nNusdgrRaP8roGG0yKQcLvw15GhAhuMB+591b2116HuX+N062r//hi74tYo/uBcRfTIa8YullLYJ779zs06i1cOMzZBB/xq0KtGIXz0l98KO3aRFdqxf+ntKrdEYemiyE+LAWIveTRh3YkekdCSSpUG3eGxYe2m0UeqQbvchxu3DY1yFyW/yJHMSxJ0ywikd/6Sf8jMz4mzz26VrKzh++UFi56utKvs7oxrQhv/ocTCTgOPaMCzowy7c4FxGqYuIi6wtYqK7JAV6JKTsG9LabMYH/xdtttnS1uc8lB5udpK98U1s61+qhcz/2iV4s+Ae/SrpjmnsjFvUINn6f3qKrPid0Sw/3lVI/u/xSu7L0Qhu7pqfZqOzJV6fneZHv+aOn25ot69rEWj11RlgMx3GXcYDtPJcU++IHbYUr8R7/aX+vHb54VK8mu6LtsXZILxX27Un0N1fJEufukvhjrY54+f0zE6es777ED7ZY8sqfdAGD17Ggec04TEc1jVVATPpIRK7GlshJHA7622NP/Gqnj/E3PI4/28kIVBWfXn/DVvwmo11CjKlX2H+qJ54qRlTEdQN5LpswtcGVcUIbyRYg05zSUTHU53aPD7drPEAjeRk7nEHQwmz9wiD7sMunTRnDnUc1XQ993nFIh2Og6wcXeszf+eDnDwd4fogYy4KW5DnW9F2HqimyU9efTSoqlFovgjBYW6r5pjwYYKmNUhMEANVW0jtARQug2tnIKVA4h0XVxsLbqdhAY+eFhUYbd12DJSk6cGwmClMYh49Qy7HlFMkBmp1t50ZbVHYMah8EpGhsig3PMYYhGgfY0ondUcYLAj/OSoSDm0IxnFbJK6zuRJVZABFPwqoKBIK/sNKh9g/YAasG6DIhqShG+9F0+Ci6/PuTZFOOzersLovA48q/Z554rd1550aBXWivvHzYflzQ0/7R5L03/ba4qKePBF30eDgLRvTrHi1dNHO3LsT5/rdeaftf05WAWsQ45coC7HskNWrx1QU9w3SjLq5JnwqjpNonslUQUenEFaKrdBXrJ372Lr9i6g/+7dO+0vX+D9ySo2pIuw/Kx7bR3OUbJkcm5gwemqBJwj/JOW6UhQ4ATOjxO+X4ER0kdBq7S/qSjFiRCou0r9E71IcueEu/+0KcVZfJIH1MHX3kSRghqCmwUSO+2BQ5oOYvkyPxBY7oDEOwdGZtoOa/xNZkZB+KOFrpceXlBubhtCV+MhJiE8rQaCy4TPna0YmcfEeXYmJeVxvrDRi33q1XKHFRmMbyNV0punFS70LUa5mu8fu5HamQ1NEep0ZXb5hu41OTOtrjiFE7Yo4X9ABMOLVoLl7REaXudeW6NU/KViv82IMR+j97+Ww7fPZYe+Hkq23+ql4dpVO0V3VvrA1GVqdF7uY1G9qEXmYdMOm7ob+xUy0yirimnrM9TugVJuowgyJ9Qtlna1TDhFs7NuIOm3Ur323vtRZmPb3Pa74wFuTqj3ZS8uAQNwKMQ1nJJjHP0teFTw1DXmgUdzB6RwSfGUwHhACVB3NjX4qz2GhcmF8UqFI+GuEhx/UL+pG5zz3Uubb7BarQwerZxTUl23Udq/u8ywx+OIXVF3uYzTYZDn4h46St/qlCQ2I0O4E203TxpxZDII8YgRy4e6p89pRUosmWh4D2wcSAkfCaRoKUyDpAUOTEVjwOQnLKi4guKB0xVpMT9FKITveX5aeuaAkIPp0aYxBq3mSRr1znpc6ekdAEU6dCD5aYVF9WHlrzAMI0sLvG5UFwqw1HY2smTgsydncDBqgt8jkaif4hLbjFr6Dl5ubs3QJCfMCh3/vAyGKssv7ih1FMYNanJ08Iyiew0lNgPPDm4fbq82+1hz/5Pr3V+4xuoTgr/JoglLBvUVecXtViyKknfjeku4ZHHkwGXE260P6f33ncz3bktxx+4+H9g/N6PBYDkFZdxOwAAEAASURBVAmMCWFcRwQX9OqdK3OXtFgyOIRbl25hL2DRx+Q0rnsaf+Zz9+pFrHPtG4++5OdGctQquD3hY/U0A9t1xAZ1mZwiC1uLXBm18QcP1ek797RK4QcFE2kGHH2IDPY8iar0U8Uw/JGfiSCxKwyiJkEHjnfnXUGL+ElsB4sLyDpbUJYEk0pa/rrYgYy0dp3iX6rTi1Bih2220KFOxxz46Mxul+3pE6W1Cp5tNQloYj90xNqf2SmQf+0QZHABC5LxtRxj2XYmGNSPi6sVH1q8dLjGQ79XLR/T747rHOL83sdO3KLuXz1/5ISOBvUwhclxLaKJSZ6iTB8xlgcXyoh+5dKVesKbdHuMo7z3i/DypCXu6Oc9is+8+mw7sPSIMfmUOePJkyn+oKezY7ZRF8+s0Ns6uPjHYwvZ70r2ie2WeDDJL8gCG9T0MB/8SPswruITxDGec+GSdz2YIpSEWX6LyuByrEPfBbMogYtkZAhyTLlkxcwPnovUxPgj1YLoRc3gYEusgBP5hEWuW4BjpP/wZRdfZzb64bRVw4tPbLRZ6d/ESuZ5tQYGCvQfYej3YgpPPOZ26vijT6h2yUqwq58JKJ5uS9EQ95FGX8i3HXf0MOaGuqMFBaqTr3SCnq2U0KFURpY2DPowll5v9eUOwkH6WBAd2IkIK2S8W9aAt1NWGXWhxTEEFjX65D954YAmbdLkf5X1x6Bzgk3JdG5RYYDRDQkMyLGLf/Eybdj51CEjTc4kqOIXVVs/GJgosyCBIIqxuxYe6xV9BoraGSTYoLoK5MGkgloHYZff+Ry04uHWAo5QGFAsLhpbPWhFKF7vbVJJERt6Kj9QBHPw6kIGnniDTi1077x1uD3+J0va7Tdva4f2vd3mL573RED77IVzuoz9hI7QuKFaugHSY4QHba/QhLaoU1Qnjrwj2vPa+5V9mkQWdDHDJT0vmR+kV+jetHFdebhWR4vPPK5TqGdP+BSoDiH1iBu9/keRN6bFjut2li1cbh/68HvaybdPtMe++5q82R8xJ93cKC0P2Ne2i9PV9Hs3t8xOv4TO/dQJ3C4dDi3V2U/pNsuknt6pia0EU3ejT+k/9SUjBh8iQzgq5vG1Tw6qESyZBJHL0TIPZYhS5A6SqhLXvcZmiUdbmR6ejq94RjGN8mZcigdGkjEmXo1X0tBt2DTbMRBBH1vLR2j2BCYaXM2OjuV1GYgv3WrGMQg0jdv46sn+dRNxqh0l7gNErv6mV023mYkZ/e7Ivbp6Ws2J47qoa7xNrJnWSQsWqPz2elVvpZDbBTVH64jmt8yPfvCj7akfPqFbPBb8bkXsw/f0ic886VToFT2J5rHnf9iW7VolEdghWVqMr1y+7FdRsQDTV9w4vubuSZsxsA2cInffYSIdU0llJ9qHsz1uEr1olbEcCbCvqUCSyix6/LbsGpexfpgci5IZHMwtnMslJjIfGY84fBSletOjtWOyf5VPHxcW8PTJBmFSiL9I6Ane8FDrqMB+03TB+FXYoQUTsYYMrx9dlgisF5nUx+eRC25jR5xkkA+GURzMy9gJsiRoTBvFqiy+TtDlWZZowop/gp5+M4m+WBId8+jXp2g5XxEiqpQfNLkYQ0FVTo3zafS/IXqiZTCYW222QuUOKoBUb7WhM7GzKUNTiSwLFDVwVVsowCRjHNPBAVUtLqULvA5wZHRBdibWS3JMxWkq6997m2pxEd0Q6JOgimbLxibRs1dHMHuvEEd3WutUu/3U7+Eq32VvjY7o8qSOl+4ahQZwV2+dPkUHNJRZJ5NrTgEaG/XyuRdNMWaxUwbYBCyGOHVb5bBTF0+1N47s0+KzpB08/3I72061H7+hG+Avb2tX9SzIQ/qd5vm3X8I77eDZV9vpq/vbEs6cCq8XQ2Un9H7Cg6featcPXmunjp1sJ+fe1PMftRgu1/NJtSPOy3uXnjndXnz7ZT0MWW8nv3Cx/fj5H7QX3tjvBW3JovpKR4fLri1v3391pr14elo34uu9itOH2jMHLrSXXnk7sIV/Qe+Tmzhxqv3oteXtzh13+N2ItTOB+ST3afddTmvm1Aw+qj4JZXyZuIgf6Wx2BJGRo3pySSWf/iS22DpB4C6hv6KD7qz4yxGRuqbvwaKPj1+irP4i7/4Lt3VbLmK7UmNHiZL36FXvya7HjeuVL9mRR8f3eBDesgNaxIK/dmKtR5OUd97UxuRmo/RN3MZv5uwohLkPImRZH/pZGKSobMqYlU/etTCaSDrABE1F54J2EPZs3NH+0af+G50pXczVqLN6xJtOt3MWYl5jY14PtFjQqXzeqbmgswcLHFnq1JjLTTfI776/rW2r2++//sf2JZOz1rnYL4xX9TLqed3KcWrqWpta0FGo0qxeJzV75qLzK/W2lTG95SXYWtukeyvdv7KNv/g2Nt7gb1xmjfiVnaMhLS1ptnNglv8Sa2mh7zpGS6EWDtX3cvqgY1AdyTyyKW2ZLwZxIUY0aK8FyswHMInee1b0F8LRYCWU0yfUpjJxjPzS7xbzBa9jJt4VDTGkRqn1RU/KZ2dRZWRLTs1PyLGPiU0wic0LqcZuxRE0oykyMIEFPDug1Se0xWfh8BTQdboGZdYoO6WTfMyO7jo46KZhQlvy+rMXOps25CwwA99HaqoSmoFxNoIyf502bFjHf9rYVvKeGg4YaStDkFEpHF1XX1yrDiyjex+lp3izlaz8G4c71BVgxYxIQ7fxqWin2g5q9Cca77GpzqcYsNW8sQ0FKtpPo36xHcjx5AlvaNwNXU861cx8KalBkwS8dA43OjNZDBSKwpOY6pZrscF+Fm1j1JHawB6110LpPSArBzfarUX3/i1vj7/6ZPvV3/11ydIJAXQ6irGZ8ZKBQV7ZERsloMqWpYmdvgA7ZRYJeJgYlUlfRwYU1GZhr3ZsZbLm1Cg3Y2vAiLdiDd3IA4estx/HdDrtH//iP2q36BmTTIhgD06k0B/4UNRU6r8WpvIPkm5MmTBAx1/RWTVfSkjuIFTCNxnYRQtNcKhFetOHWTSrDE2l0Z0V7y3Lh/js5MmT7Z/+L/+s/crf/ZW2Y8dNf+YIEll8atIb1QnK6mP0eHzIEfQ6IRDfKNNTFnZ8lbEU+2KwqfBfT6XHW0nyf7dT7nCZrRc+in1iRERhtSjRZEcjPI5P5FhI+AZvnBdJ+j/zT/iDzyo7PGjwiftFm3OXz7df++a/bEeuHHdMgxk90F05P9fOvnqszdyxqa2cWtXOHz6l2zX08O6Na/Vs3nHTZexdazO62f7v/9Tf0YKo+wz5EVKpq3EGzLWQu9Ht6XvKPo0rP5inE4CFCtAGM2Mt8e9K09HH4cdOEn1Je43L9EfFbWji84zxXqMNdhPTQ9qUQYDI8j24+8IEV2Cat+hLRpXh9m+3XXbZY8GWEd0Yw7SAzEFyIbrDB55OrxzziGs6k3VSo3+L6fz20w2CoyEYo9RrheRRR/Icqm36B5nCwQLpbZ9LpGXwcl9z6UtDVPoBmRo2dWVUAcSJrjcyhOL4G5MkSA7mKYnYAWAPqWwbu2OgApQUMozhsbOsII6A0nWIE13RIzoJYovNGRDh8STR6a1POlwMGM8WKcNHDl3YPQwQKUq96qCyTdTpH/+41apTQE46BfLw4hkC2vT0lf7494QtkoRAjkxyRaxkdF4kcP8QkwkdikYGrnqyKwdHMNcbCdSajhYt/MasoxRuGt6xYUf7qw/9VfcFv8/oUmLzexFBDhrEtEI/erNg8joq+g3sJG/E4xgRIRw8ocZbNbJQZ7LruMRTg9pYhL0mT3CTPNjVyMCLbapXmXYvGpLO8y03T2/20Tg8hSeI8Dn8bOmnykd++gNfoIT/4aI2lBOZdrtopLKnirXSJG7rgSB9ZsydIX2ErIEA08GTi7+YTDNWuMCIN8lnLLEzEB5oazEpXeEHWPSDhjoS+ovOPxmouk8rIsfa+Aee4Oq8xgRWfYgp70CJpXBYOiLgh6hXkHU5O2BV7yW4kxU2OGCrCd2FmGFc2OPY0zkDZZMkm1iweV1Xjo4hEZH+0V+4hK6tXT3dPnbXQ+0rT/2emuVf/fPORGJzUY+O42k53Kd46fR5HxWu3bmlrdTzVJHLh7il79bqlO30qly5CnJ0WI+BE4/xJdjS70DGwtBJSPqul8FrX3Tb7Ddk9nrK/mhEISO+QGZsND0+ML8NTyMATDMcm0MS8CTRTexsx47MiSOtsc1EIpPM0bizCqusuJFb8ZcbohdcQSI9yKHfqPEOfmyIbskWSVrpMVAYnLZKCBmceSh9XbLlIis+Qgq+z1gbWuO5wnNV7C25sNuPpUdbznLZDtsOGmGSTP9m6FM7VFBdXpBBDNyQgjftMNkp4LdGtsoNvBLlgHM9YNzWCdiMZC1DZTuNK4NGZNqx6JWRhmU1IoZJhhirGjDWP17T8aZHKYJUYgsLhovWp3vQrzJNDvAINKEnc3V6ORAduT8QfpEo5RFlFio6dRIGiofkb/GwB0XJdqlMgtSYlR92ZgYBS4ppRDoiCWValCKHYPLiZFrVFaV8TRpMOuiTOPed8vaPaDfPbG5/5SOfD5fqferO/cSRAlrRk4GOLOzuojNhigI35pSypAdy5IsRXuqQ5CYTpJRTIpoytZiii0nIOwRdp1hU7rYjhwnarMKvtoU+YWFL4o96a/Egwy+k+EQ+1bkyY0GGktvVT35ouLaD/hVHxYCjB93IFUZj6DpSq3r+JZh+LCwlq+rRV/kM4ky25C07sKMjg0MY5Gv6wqrxZSavyOKbVIyRT5E44oMv4Q1N+o4+RGdibWhzHIu/0+ZF1KxGR85i6E/c57iyvUDOGIMEn8a9NZH3uBetFzD6sFMMkVMjLIAHnymCrbSj1/R967Jk8Rc/Rav55Sc/T3TX+9rjB/60vX78Db1/sb9STFebegdN9zdePnNBV6ie1KvB9MYLyeJ3SydtkE9/blyjm/9XrJIOnUan0fiIN/pC2vWB2HHhvEm6Ncgj7rAO+7BBf/Ix8mmjzFwBTY1VjwsCP60QGkviBxzxsfV3OZZkf9D/KhksWJADEVjJMy/0MacsuC3PgIRSpGDN2BCpedkqeYxkTAV34sVtYrQWybM/BMDzmnGI2vWR3Sf/0CNWuMA7WB9Kl+xMvHZjhJTJe0gHXmzt84fz2NnHoug9DsoZASq9oJesjhUgxgq/6mw/W/lRVzJUogGw+sBMQSlmJ++yQSAwbaYdNisXBWxN73laxF2k6d1EBdM7LKF11vyqV9UAR+kUYGihxiCcV5P4Ut92gTQz4u3wUzZYypVwbDo3AQ6b6ozVXz3Iwls01gUWggwoBLpYkVXJjqaOjxqqnAklVDjer1wRFdaAy362bSnbMwSDLhJwYEsWMlA2VJdONLeaPKl3GfaD/Qsfe0K54ICJo7As8W+afafFeDPoOUMUm0HREWKyhFSgZ4KFLrRp0+CRHnQRBZZh6yr4Vd9/R7UnuszuFW2kq8tr2rMnIcMWI7PnmZC9Z646I0TnQF/0u0b1UHgAEQ8DrJEFDYn6ws+YKV7scL0IoPGkKIJRu8QNgxO0g4SvxERd6kMX3uhH5ig/eUS4Xl/Qcvo7YtWPDNqOaaBHmdxKAy223ijTulXJlqZRLK6zjn5UDoXo4u/oz1EadsU25AePI1T1kq0/x2bpQRF5/UnKgFcZp/zWHj3YWD4ReRIsStRnJycxGrwsLhmjAYKe1qb0cPCP7/1Ie+v0AWtd0PsS585fbBdPnGtLdZHW1VO63UcXdy0un2+n9SLrlZN6os1WPWkmxkjCNb1dY4t/AuDCnMDucWZbQBRMRi6l4Ct+LXFiyWI0GF9qj00mjuNYDPhdz7bSp8wh5aeuQ3VxQS6aSUyiXCjNhygy4Os7NOhywh/xly9wUd50bk+MwEOd/akcW2PwApo8ogb9QqHrRQzzDOeH4DN+Y4kN9gn0tskZfVUsRSYx7WTIfAW7xVBS0djw1YjeolOjaFhUsTUEOVqM/AjnO21e9JgLuyxarLGXPW+qzkt/BGKMkgmihEBGYbYihaALoMPt1ApMN0UGJJalLZaxsHV/aauOEgEdbGerkbLptXU7GmFyz9Npqu959EdW9rAsX/Qc8WEUexLGaEdBK+vQr78avmiLbtXSTou2NdGjgOczcosDyTTUYU6fDOMXcep3L3CCTySSm0CrPVjbZSn4i9sE4jeROqEdwYQserCHjx/CDWrqlFh44S1bIhfMqh+5Kg1xcPA7nSdHCpJBANJn8OX3RcniiEI1mXDVL7QLPxMQdL5qDHnw936OhOxI0P/oxwD2UEufFyr4/cHC/A4LHbX2s3IkP3sUe3XqInzBkNb+3TGAA/0c0drPEed8/B6dxYs9pdMwKXl04df0ueMN/RXe0qF/8w183/1i/8tOfDugsXXCJVXei0Wlkm0PkceJfSWioV4o4jfILLvTe6O6LG74wyJNTy52KSMaY0SUCviPNuhrAk2ZKxfTV+AwnfsCvvQlW/ex+enTJE/sko1/weW4VtkqRetc1+9KiPRvHXYqNFSZyEIHfnVvulFfnU786GLBJOGvSrFbdla7RBIPSOdCm/u339Nund7TThw41M4dPuH4Xr1Jj43bs9XPNF2+akzPSB1rk3qizew5PQD83MXsSMhZ4ysm2t4NezQOpU/yC26O9PBn1wQ+/ZV/U0+5L2rdf54bsRr88OAXkGocglhkjv1Uho6dmowBG+axBd0wRQ911c8Igy900SEqyaGOWKDfMvNRdr0E4m2x2S7ku5/ZmZIIcdr/9BOfLF74PWB83yQ2IUU82McV38hW1nN3xQt1GePoFL2wYGPOnIEvtqjJiTIy+CT+Ywfza+lHdmIB3JVHlsaTGMUqfoAQxyp7JxE6MMBDU/GhLGV6MCl1PU9l56x6qpxPm5WRxUD/kevioOttsQw6V5geVHF06ukq6GMG8pI88XswqKUscWsoIxNqJjYGDR3EgGURU70K6krnkUUnwDkc0EVDba/HbJyqcviRn+QOwNkEgFIcrgxKlaqzPBFQAa02BMFgwTGxcHjxVCs0+phXW57vmHfBqR4sHTM0YEIWyROXc+Dko3o6mIBUvQeWfvvLYgWGJLYJVKgik1bbpkbbpHqp6wwsPgp0B1ba4wfkgCY+ZYEyjfBGV9kYMR6QEmqtdl8UEPBOtHWluSoU+YUnecokyOBjCwby8S+NUIhQ9V3cyFb2yu/GHRLnzWT6nHJEAgl7GMy0W59ytp1GpcQc2gJsuE07eEMPlsQh1MgrW61bZejQxWfIhzGS2nHAE3k20nTBF33QltxMNr1efNYfhw1c1GEPbMPe4i+J5c7SjY3QgWNgL4C7sOCPPxSkFmN7bBe2pA15/GEbiQlKyoONrD7ErkV4B0YVvdXy9FW2B3drq5atbD9960f0MIjpNrV9Q1uzdX1bpZv2l+rqVMbDlMo8EOLyGd1CpCudl+uDHmJngy6a2bR6o30hL95gG5jtlyg2ZlxZO+iMXbAEzzAe7SO1ZbzJHue7b2xO4hHfWUefE6DoXnK9vvqEnnrvVMNPMpAeF5bPNABe9PHJzoPl21piQXrtYCxVgi5fnnPch6mm1XZ5XhGf6fjutmSrPgSjdVqY+WhjcRokk8Gbe4eDz6yW67nOMqiLDseIBCAlssozkQsd6B2fHVcWv460xxfgahzFdtjCWzvnfmsFbP7NTVvUosYqK6NtgSrFEA0CpOi1LeYylCoSoiyFjL3mCstNiyXbCYCsBSdUautOBQfBjwzL7PXQYYedqG3wqqw9TDtH7XaYHWDwYrCVAzhpUhuBrb9hR8qRDGZr1Ld1W3tkqsUXumgR9iInQUg2ztIHNxM49ZJlbn05yLwgUCO+LpvJ1x2tMiLKHi8qokRAAoYJTHLVw4MfhmnGL2JEqgrecj8fcqlnwKTRRagkA1JuO8CPmfBcjywTS47zRDV5VSNGuu2fnrfflAc3n9q7Nq94mDzgRU8WCQLdolCnFD2Wr3xNJrYilZaLvMHra7ox+JbF1PsHnhRiK3bTlkUTmdgocEps4cEntVNCnfsJ33qvRP6lD7v+4utqbQ++AKuVi26UtqyLStEUY/cR7dD7Q1vXQzk4Sp7aROsjXOmqcQLe4gcbibrEEeLgG8qNfskhDog1tTo2YFSCd5DEVrKQYx75hDrGCYm+UU3XE0761hSWpTYvauHzgyXwlVLJdkyqbB004Hc2NjlxkKc84YtgDkwAakdSt2Dcd/O97eEHPtG+8eJ32tIrOrrUQuffjyVn5fiqtnyLfk9UPv0k2bKdnw5468aaVZO+pQM3mUiKjQUl+pDHXeIajFvPLVpoYfEYZGzjo24z1CSXRYSXEv8lm/lAcxQS0Nfboawyoq5LrkF53NEGJosGmueq7BRYkhskDhFK0eWfSyxMBwva8mcZECnr+UoZxq/xisZYGd/SyxhHFm3YCo/l64udc/sG+WqwD1Shau3Ec1aK0TNMVokcxLhBmZH+jv+sDk8kiQ7SxCm5bpdgoYdGW6Wdd2OUcI8T4aDsHWHZUP6Hn7FVafCbYTFXA4IBCpJqcxEFNDoNYHbAnUEb26VtHcEMeRAJQYAjhjbqVKX6WIUzS03xdgqonApX+MUqHv6KnqUHyaSBPBWpsSuxRU4qa0yoQpwNIdzCw6DVoMkTWKDH+wRxLlSgsynXYudTvCVU2wSO+NLrwdntpM5ymMBrcihE1pHBZRsclAiWlfCLl+eLggfRTPQJOsDwT4BqAVBbAh1Thhf22HZk6OIUjuyYUEInBnj0xUU07kPJCz12B5Prpb/KYCwb3A/Sbb/ToGT7Jcey+R0TkKBn0NDfSu52jFFik5uNQ+cJsOtOO4sbOw3uHlVFhvWYP3LqaDCy+f10GGPo9ZGvB0X3K5g6dnhjX3Zk0IH8qiOPDD6VBxu6KlHPh/7BXmWHiRirOFPtqKwiog696GQbe5Ck+i7M8mVXisETXdE9lItUAGBjsHuPPOFEY0/IQDxa0AkB//rrtlkmGKBzv0BDOQYmFiBO2TsTSEcOQKMgeUVb7LS4YFA7sSGk5qlxRB3JF3Ihh6LUXJNuJrTP3v3J9uaRt9qbJ/b7yuhrc/rdXTwcHZLUpSqDS3Zp5483VXxg5/t8apDbfWyjSL0TJKryLYrQ7Ziw3vgG3EbZbSp8yKcKbP4IH3SZ45CGvNGdbtVJFuTmVbtdCV79uTaNEEo2E7so3Q6L+l95FrMa73BW8lhxOT1BucRZn+S5X8TQtblP0lfoSXzVFutpAzF9g3XE1CABTEm1xmjLVMULAkoDeiGDsjgrDszbfeodMOcz/0BdOKwGHKVvJM8slvGeWCoacNa8Zj1yJDecdRjAGYUkdQgdcRiUNiiZUAvAgKtECSi8nFv2nqMJeneqHjLvfbxLIzy1eIpx4B04SkeMQX7H1x0gajsDGZXgIZm/T4A0u15fPppR+3XfqNopaR+xSS5UA0GiTkSXxUuiotRXnRGN4FMbwWn5jDZjlm+43UCVBGk9ziwBAx+/P+VxU9DUZEceeQw6RLFHhwfwDfJzNae41ejJQsIDLXzxkUNz4M/qJfjVM1rkcmGOkfZJQoC6n4O/+sIDC7i0S1F9KMfmNPplvJ5vVJaI6IQmtsWmPFFErhKOoZ3IwhZo2FMeDFSVweyEf/lTRdo1+UoPvy+ZVwUWnMKELGvxjOGs6b0n7rZgC67eTwBTkiTrYv2xHGYlmvpET11EoCMYsRd8JDBn0UoZenCBm2SZpuv83S5oxAnJIBVP/ID9wV22Got0O35ismMDwmgXPXbgLrXXZF1xZioWB2wTD2OePJMIspE7sE0kqKhkXuJPPGC335AhPv5IttX2px699btTtUNp9SBWwX1MvIPD8vGt6nv7ABd6Oj2tV1XYsHpd+88/8qX2v33n/2z7Txzwewx95oYrSImx6gMJnNVN/p+466fa3k03+wgxC64k6YI89GGDz3SgQx9YvYAxMLt/rV/+LX9mHEObfhSZF2OzYEN1hvJJ8rH7PT6gDpkAIHSzUy/LAZRqtasgorTpNimNYfSTzCtmh73q3BdSTnP6vtd5PgG42eQXaNCTdlGrQY20C4/7MaT2Qc9GfgfMeCmBNcdTU7FAPrZ2pbQ5zsRl3fEbWEt3/BVsoxiu6WK8oY+H8tI/uYeZ+LFcxMkQjhQtQwrAAX/ZPHgCjWkJvGQ6Y4ywGpyR2u6UTPwqmIMvqbLRBPrKlXoKvY0DAAYO6QYM/4kZ5FUadUrVvXsL/X8M3bv5KP/H8epFpDqim9dzPLNQycEaRNXxoM3ES8jiQIJeR2bKsneE67KAJOiCNZ1VuNkyDcR2BX/3o6/IQ5BSJt30T/igZ4+oB7ephr5gCBmOAoHBwV8tfJB2jcqphckDOfrzJNJ9mgEfrBVQy3V6Ap4gSV9V3ydog1cESsglEIVf8muAEKzYY3+KfEmXCebqE7b+SAZ0xsvkQM4qoif2W5koc5RrT/s3C46IOTXKkSL9pIHBjgszCM7psoMrul2tFjTyPzqx1KAET3zDDsDwKDy2atcws4VBgc/9KZXpX6rRrW98MkJb7Wzh4S8+DL2ZzCesmsTAhl+RU3S2TcXUhQN5N5Yhj3/9CDY0SQb+IzkeRsZy+M0Cm4TryzrJQ8+iIhluCz/1yMRn2FgY7Gtiwn2ATM+s0Sx+MERM4tp4jB+bTWCdPLFm94ad7Vce+S/a7z7xB+3J1/60Xb50SbrQz/Ny1YOKs8mJNe3hOz/afuFDP+97bnkXoxV0+5HvxVFY3RdSAWaxx1brlGZAYZSZsY0ySQ0U3Bfw4IlqN5OxQGn/FH2X48VEDeUHy+0yxAGb25br2gAefTia6H/o3Xe2p+aJG/s7MmjjrMVwQR2VFZrgJT8aL++mu6GMY4bOuKHpLypUPPxFNLSBw08kYjx0PYwzz4fyoa/YVz0wMo65TS5+q76KbzOmdFYBwJKMrfqwqR5zZ+B8BKaF1uTZTfL/UDhBRutydQwGkebm5tqc9r7WTE0loFz7n/bFG9JnL8/Z8Pn5eT27UFeETepp9t0Bf540nLKgh/+uWLHyz2v+C+uKFz0MAl5U+/+VfIpN72ezL3A0flGiQ3IaKiOHvE8PqBk/8fiieDJB63Pbqimb6Dx1q8qQ+UsZknqiT3Qo7W6WPrWoHj6FhuhqwlDeRNSV/Axu+ojBXTJg80RUE5oguk9Vz2KEhJzGocHi3M/G7u5mAs7EFDvMYZmJB2HHIOvVESLy6k/88KRZ+DwwDVgyuy1iQD5XrokNUMZOf0mJ8iwCmWRBh11eFBgs+jNE1cdmaRZ96PoE0TFQH7w5igUTygYTtycO5NAAiGCvdlPjhy6fdtvZASAbbNZh2WmITiRGV+TE9ooL60Or+y07PIOJT3iG2A0LdFCnDyhIFfbYZwYWLI5XVosw9L6QX1msIkJN+sOX1R9IjnNiSy/b7/rKb60oxFY24AND6rzAFA/V+jisbAexPFzQiUv8Y3WSR0zkDMtwogN64eHMxzY9qOHvfPxvto/ufbC99M4r7cDxg21Ob2aZ0iJ40/ptesTf7e2Obbd6zHBECZ7it62qM2bGm3JESzCkT1gV0QlucHm8U6N/6IgzChxlutrE3Q78SKW46T/6LfRDu5HhGOo4RBwHUR+t1sMDMkYTfXtaDxgYX7VKb4EZ99Ev8/Dq1auFU88VHpkTl+teTJ6C9JXf/jftS1/6Bb2TdF3HEYlgQClH0PO6NYUdyKmpNZrLbtQ5qp/8Ik+K0tYP5n934/9PeUEHF+jlgArH8TPOn5egwXbWBDtY39Q5ruyzlOEtb+Nv8sM5AJRqV1fQ+y6wxVEMsjBSr4/+UcCHRFvP9DrqUWDSgOmTAO8ve/mll9qv/epvyKCFgBQdnc8n79HjKLJuOcA49qY5ciL0sn3pxZfbG2/s0+I23945dKh97dGvqUUP5tX9QyR4oGfLu/AmJpa148ePtn/1r35Tz8q84jbwowe9RTvKm4DUJdbjy9rbBw603/rN326XL1/S641ekeOumhfZuAv+8ocB6CuTE4NVBPqvgUq7fWr/0oSntGWjL9xqWV6AqKM1/mRhw7ssAB4olOkL6ugn0fLx3rcHFP0Ev5IV3Jg1bycAZ2RCmonFTcgjo4/bFYfWWPgGeqII3gxY5GQhLD1sgQElPhjYpomAFEtQ1f0GnRhoHV1YoCXRBpdt94Tp6vJob48822DaoY9zf1t44r/4wHZSLRy2RdmgUFn2uh9kBPpNKxjES3ZSVFC9WAcpPolNQMZO5JIvGc6DvPMFb0SQzwdIo3n4YRBfMXatput9gw4+JOpNi27lerXxgKmwQUuc38DXjcIXsrzLQibUpOgY6sMP6fOSM1AoanR5BwI2yfCOVxdWC2PwxGY0jKoy9m5ALVwDPRAr1Tgmz1kXLip73+672y8++Pn2Dz77X7X/4S//t+3vfea/bF988C+3e/S8W+RXLGBO6bdpwhb/pb5bG9ziS4xgh/pWfzwzdTRWmANoI5UczwvYgP3s8KGI8UCdPpRxCfRgc7u3KsGmjeUqY1e4Dsok/LKgxeHX/tmvt5c1d01MLG2vvvJy+41f++dtn+bQL//r/8tviGEuY0f+3Lmz7cjhI+2tt97WAsaBxnAOBsPC4pX2xBM/bmfPnPH8+9hjj7Vnn31Ob5jJXArNij6vMh8zB09OLmvf/Ma32je/+W3Jqzl32WC+zpto4tvK029gh/73v6qj+SeebGfOnmqvv/6G+agvH9bagMX2E05RIhYoyyV40bFG32bssTOT+ROv009JUCf15T1eHhyAIFtVJlMeJel4Oqh3D3UQ0I4sgFDkqyfaV+iZmCSOFgO2tdNy7JnTZ9qxo0fb7Xfc3p5/7vl2/wP36yhytp0/d77t2Lmj7d9/uN166616tdCh9tj3H2tf+uVfkJPXtA0bNrj90MGjctbZdu+972kvvviSjxZZLNF56OChtnbtTNskWuas06dP63O2HT1yuL3v/vuUP6POPdvec8/d7TnpntJRK3s7V+bm26lTJ6Rnddu4cYM6/fn2737nd9t/99//w/bW/v1t46aNbe/e29q+ffstf2pqOjbJPu+5apu5SiDkiJx6wPHK21n4iCCm4xmEKeMzFjz8yCPSKs8jpQgQynauZEBDop4Odc9YtnQzqCQLHaZXa03u0ZE+Cj9dJjolZNSE4GBCjOS7t2FUMg4WNP0ju5CAjbjJhTkdAyTiA1bO0aOLfOrNrTKLp2p6fYKTQeF4U5tlM2Gjr9OCwwurOO0/ghq/2AcSannBDMb4W9XKQ+dc34KxknVhWNclz6spfVVyQpsdBx8hSWZNfriGlBi359Sa3y9jD32BrbEvqnrejPkypsC0feUPA8MGjlG0GchRjW0sGUOTXE9cxjdZHCAb4CBelKp9qIN4xSC1owuiAe6yjbahbAgrniCHa+AL8wZ3JnJRwy4dXizUh+iznySHSYtkXO4SZIm2T2aWiwYGN3qsG47uX9eoJL0kHviNfHRzJMEiuaCJvyZJscE59IvADeLBWOQLixKufuYnfZCLsYIH/uzwRC+OYwyR0vfw1BGtaVxmDPcEXmOGC54+vlWyqdoOMHeWd2/AwtEYvlk1nrNi43rqDmI5MpzRvHhdi/aJEyfb8WMn2vPPv9Am9aDyVbrN5PTZc+21195s69ava3doXmbR+eY3vmedO3ftUDy29ulP/6zeT3qxPf7Dp9pNN21vE3p4wQtPvtR279rdDh06KN1aYCWH+RSdi3oM5FHN80ePHrMv7n7P3Vronm679+zRIre8vfTiK+3Ou273ESfzMvG+YdMGLbbj7cu/9RX9dHG9zcxMt2effqZ98EM/aZ433zzUbr75ZrwxMB9/8sHH9DNNzBWuMFXFUNg4TY5PvXPb5fhxbO4NpFRyFkXKKODJOaGk6KxYHcbErkbl/O0ADvXgOx1PJy1p+15/s/34qWfaF77wc+2x7z3WfvCDH7WLFy+po/RMQP09+oePtr/3D/5uW6lDefWpFrqXbdT09BotljlduWXL+vZHcs4Tjz/ZPvCB97b/8LU/brt27Wyvv/Z627V7txbI2/2uvaeffrbde9+9WmxfaH/p5z/Vvv/977dnVLdnz5722uuvt/e//17p+6N26223tmPHj7VHHvlEm5qeaadPnRLdM+0Xf+mLXhjXrp1uTz91Rvw/bH//H/7XdgGvNhpNOJUjD3CzrNlnelWR90DwlSYe+0YNtKXz4jnk4FYGIL+3QoEvqLTvKA/8rlY1eW8UmeiiX5wYxCmDhV4xm9qRiFzqqp6BRd4Ltaq90JghMtBJEfkEaQUOAy0LsSz1hNqNQp8keoGQrkxww0lMTUM5+ETCkc1E58u+tdXuwMCeTEjRH9zgHabyJ1MOmoPVCExEXXkGzAys6FQzNukDgdWKNjZGPk2Rd2NZZPxjnb6FR//xAdosTqKZFNHFadtMduBAnjGLm9O5nDYnjsAxphvCOXOxeJWLpZKgd4FtT5ErnzLRAlKJb1+cpRx1hT26ysfaMdVf/I1AfcSYRb0XYdQ/eHwEp+rRhDzz21LoYieyjFXE2MupNEQhrHhMIzpZbhmUS4cXRAmwB20zzNFFzNkmgFHbFVFnme/yDWRuwzzl0Rg6VVBHf1hGZ7RekKrZ7WCmnHaTIlPtXaD5jcl+Cj5rQqGVxh9luxdAxV98TZwRx+UbJuSgNC96lBADDRvk2DcjPJw6zAuKTQjxIJlX8M+eOadF77y32H323Dnt4GtO1AEER16f+tQn25e//Hz723/7l9uPfvh4m9CtJyyKv/3lr7T/8X/6x9KrV689/Vz76UceFkYWtqtajMa0OC1t//OX/037hV/8QmOR/Iryf+1v/HL7LS1ef/Nv/FLbunVDe/zxp2VnFpv169drsdze/sk/+fX22c/9XPvDf/9o+2t//Zfa9MxU+7e/8+/a5/7SZ9uBtw8IzyP+Kew5HXnefsctWmy36gzf6bZ+/VT7k+8+pgVyQgcz7/UzfWusun9kecZcfoOuvqCP3JfdRYPYYf6ytxgrHHFyKlZXIqfDGNHpfNM4y1c6IYGnEhOfBJuHjrKyIR+5orUcf4WO+rFVS9oLL7zQjh4+LIeOt0/+zMPtN379f/XR2hPqnM//Z5/373wvvvBKe+D9D5ibSeLtAwf9dvZM1oE6Mz3VVo6t1ISiRzHpCG3lyhXe89m6bVP78IP36bTqMZ3yXKVTBa+2I0cPtzVrVrVPfvLh9qv/9Dfarj27fSTJerFmzRrxrxa2le1f/PN/2d7/wPvaQx/9iCcnHM5hP+fYP/GJj7b/l7T3jr/rqq59l7pkSbZsy1ZxleQCrpIb2GB6sCFAChBaCAQIhISEEAgvj5d2LwFCQuACaVySC7khhHADhN4NNhgwtnHFBXdZsmRZkq1qdb/xHWPNvY8J/7zPW+f3O3uVWcaca6619t5nl29dcmm7+spr22NPPUWy5wyDWUY7wLfqvX6f++wX2g6dXvWC4E7A7QyO0U8it7uzoKSNCagoGAL2vHuyd3SfFOxjSQxN7W2jAx4m6KJQVgld/nK1aIhq6lQe+4qJJzi9trkNmmSC3QLCw2qsgeKkDUV2AKjDZyTw90y22OdFL3o6eaB0UgrY1Z1jPpe7oA4nk4cNi2i+y7/lxyE2LZuv+LTq2YIUj4WHCQmMBUYNJGMKpKiEqyfTsjMhso6HDTJKjOuLpYuOHk2imi126p16d+u0/Ef+6aPay54rwfSpBSpvYR0DfUd95FtdlA26QJWjJWVEihxbWfoh6In+CQ5IyScZL/iLUNuuptfQUtRUSQqYOjZqSjZ5kmWBxaBTVw2mnajHdAIq01XH0VWi1TZZYfREWmKLPDpsl7V2bBYaymCX3AmZtMTntqZzFn0wRG+O9Eyr5owZIsjIvI2qsjW84EmasIc620lcKokR7PBPuKPzudn1LKwsgqtWrWwXPPGC2DFQJWMZiq25ejj5IXpTxzxtkc18OFNHGOxg3H33PTpbN62drrNjvH6Nh8bPn695UNdJYA8XpsycOVtH0Qf8myIHJlJrDBwxztURIfMxZ9HmzZvr+fAQzcknnXxiW7T4YM27t7cHtRh/+1uXtZe87IU6SDnSc/ptP7lDeHhnpN4dqbmX3zWPP/7Y9qMf/ai9653vaS996a9IZto5jcvR46GHzm4//5yLdNDzTR24nNiOOnqp7I7Py1fZacJ+/Bg/sDMdv2qiVx3zberwPWamZ+hP5qacw8R7JDbswVTZHAz26vBO4w2nM3pHqoxgrs7uOKgxqH16TNJOXfzC3uKDD+pdd/rhlfPTa/VmdYjPOntlW7ny9Pb3f/ePurprVluxYpn2Kq5sT3ji+XL+I2o7o31eC8y3v/3dduGTnui9kxtuuEWnOfa3HTqi3L79YW23td3ay3hYPxLv0Tvw9DOhf1TdunVrW6RTm5d88xIdpj+Eae1xjzuvHXbYoT61umPHLvFvF66HtICe3x7/+Me1j+m3wjVr1uuCHb1XTTbt3LHTwXDUUYvb6dqjuuKKH7ZzzzsHHw+JfAYGv1ceZL7qEE+0nZYN5SGFccLfvaUPEkoZhAzq9Asy6ELEBEMGUU4Zpg7adAR06mh1n+v6MGdIIiOnnoKnQ/RkaljoMdZorJgoXrA5dSCpDzaDU6ODTFv8zlw5uVBOxg4IsqCEv2w2v8AwwUNTGGM4tsUGNQG2jM82lfY3C3UkBBOS6K+SCfYh9SxtyIzdnFLRQIJOf/FLsFIBi2X5izL6OALJUcPQToYkOvCUHI7iOd3EZFSngIsUcmPoPJQNgu9OFNOjpWzBPvsRLD1vVqNVrttmu7yjFanxaewxqXzvhW7gC3a0Rb5FpVWVYFGH2Q7bJ5CGqfqKq+IzBrWWL1wvOmLZ48TCogefIciyUNH1a9OVkqlW2qEIL3Ir9lzpr+rbiZqub6x5dM7SsceGoEE15hn1eqwR8CEe2/vkjUSazC05clUvgbXjFQX9khRqxxRZyeYIbdas2baQxeqnE/gefnin2vUbHE/ZER8XwPBbImfh+AlowYIF7Srt2J933rmKu4Pb1q3bLJeLDqHlyJMDgSc9+QKdPfuqD1g4wuN0J/PqbC2sD+lnqh26Spd5eM+u3ZrnpUMrptR4/mTO55Trty65rJ1yygr/tHScfgJbo+s++Olqgebs7eLfop/GfvXlL2n3rl7Trrv2hsjztR7T/XPW5s0Pt5Va/D/zqc+LZnVbvHiRsZbdxBlnWXLmilr6Vpu+w564UkzieeoSGt74rJhyjMHpvo/HUuEWFc41cYKFbAWWJIVG3whG0NDm2e7RAYaoedpzOOPMU+WAtXbksuXL2gsOO6xdeeWP2rHHHtue/Zxny+lTdRrzeZoQ5rWn61Tlhvs3WQ/OPPjgBe0tf/CmxinP6/UbHsFHp5522mPbmtX3ynk3y1FnKthrL+IwOQqyqeqAx2hP5YT2ohe9oF11Vem7WG1T2tp71+r3Qo5Az/JeDWXOp7/gV56vBXW393AOO2xhO3PVGe2ee1br0P/I9vSnP0W/U26x7NHxmeQF1Xs6L37Ji4zdwYt3cKD85L7RFzHue18U1O4I2uW7moQYtB5s9CZtfNUeJHSce6DKYlPOwJKzxZK+dqv33Bgs6SMw6NScXsuUo0PRQK8pnoHn4FHJE1jYowRuqcy9jRpehQ9kNo6t5EhW6fLEQx0xAb/b4wPvHKjsoa5txrL06tP/LAszScHV89qIKnUhd0UWe2VVB33x2NdhHb4d3vCWfMIdoUrwlYzUjN/UY04nZU4KveSYT6RuL1mwdnme3GFIlfxCzHAhVtOYeLDdo7104obTS8YHtnTnYItEWXfhm8RsuRAoGWe3iXzRIXfSPvR3FuOBrnTEydijGtV7x6WIy6ioMxM96/iVA8a+77rF53lCCjIpjduIiNbST5xBx1HBTydHEJC6rOyk48u+8FMvDIWHmGKHBCf4gxKlceyCVzs58Egu2Ev24KsOAk70IcfytIEG+baLlj42vIOhxvikd0aXU3gYB7UDi0zbBg0y9YHXqdtr3d0O6rlak6sohzMBoQaa5O5vK888Qz/ZzNJpd50e15HfyjNPt085zfjwwzk4ufLKq9pll17mazDOOus0SeB+xRntTNEyR+zZc6Cdf/75Onqb266/9vq25Kil+glps47yjmm/9EvP8++NW7dub09+6pP8QINVOqiRc7QgtrZk6WLrOf+Cx/vo8Iorbmgn6hoQeFmg77j9zrZ4yRIttk/U0d8M/cR1h4/4nv3zF/sokR3DY487rt2vtWDjxq1t6dIj2q9obj5WR5H2YbcX3/OPXs+NyqVf0reQ0d88uQif+gEkxIvqHAf4FFezvflHD+mxn3G8OwS5dAYbd1L/hsZtbtAXICqPwHSocj63W0dABNiMGVPkWO7pgj6/o7AHEr1o0vla7YXs3bvfKzwL2eTtDByWo37PHr1xQfJYPBnc7A1ABy9YkE+A7d3LYFIQzNCVVXt5ugqyo884RUt79n641TIOpczgQFdkP2IZ7Ll8/rNf1unRx2oRPsU2TDqDoCQ4PShgxiLpiD/HTnG9vkKRTO0BQksH+d40fKnVFax08HAKyjaqrI9loUTCas8998zR6bSmTxwI8hkTEn2Cb6mDxv7X6jnKI2jcYFs8CUpStee+uQQXGvAVyXYzEcj3vmhIMvKbZW9TGRm018Q18Ko+GEdZyEs/YJxVjHrAp2RM5Q8mvO7V9C8TFPZBGXrbCteEPFpJ6PLASLHTjFgj0xZ0acgOT3waXxOL9BduKXnwxt8jjiyOuZpv3br1OnX/1+3Nf/D7bdEi3tmYpwgBBT+MKdhrwsauaq9FAV9VHXyDj+2fYKadf3CVfyZ5qg3Vw6Qj6NDLMi8IyMYbJEmxfYyx4i1/mWDiq/TQPplSjz08Ou3R2Io2WADiv9Ap5viUXlp8RaywucXxiSHShr3akIUGuf5X/Md/9HcWYOTVf+lnOxmn4E+/4idKUiE83k5gKho1Wr9v4TDR2H92h2GWoNiYPgB6ybV4f+H/snu2jhInE+QzZzL39d/CdK0G8xnzG1eBfuLjn/VR2oVPeoJ29Ne07du26cjvNJ1RYxxx0SNXpGYMuazTqRyYcDEMcy0pmFhQmGfTzjzNfEsd+kkcJDDXMncnRjK34hfsC2/y5d/pwosMYgE5V191nS+0edozntKOPPJI22Hh+sIHudcQ/+O39DL1LqlouTil9wG+K7qMY4EU73SMgj17PaigxH9Scuo4ARsSghVEtLmWopRXB+3z+eZcYIJBu3cHGI6BA8eiw7yAFj8LIVu/b85X4UGbRCeScDaJ06ckZMNEB0ymTEZ6y3XvXE/Q6BO5A0jEHD2G16A6ewKUjggOOpHFdIZPjR656Mg+cUY/TDjaR00SDjrk155w3VMHHZOYDVQ7A4LEpRLld2xh8mLQ00H4AioWSDxNPyGbydJ9JlokWhRf8LDVP+fT3UV9EgRjgjcy7AMI4EeLBamgOrR1yepi6KMfLMgh+bSldJBSp1btchUu7xkXrWVgm2RpQPJBZ+yOYn93ejDUjpTtEX1NVvgFXmvGTiPQl3jGAsUcweLT2BrXQPbTCVpPtAaRVuLHv0l0emRYARuE6M84lKevpwkXuhyf8gtudx92UFlsE/OFzfLkbY8FyYHG/pOK6i+X0eH+c3TZJYWHdlK8KokqZgHEbjfZNvLggb76PlviLYtPqIsndcE9CPKtA3Z+52FO4FMLob1i8vAEZ+KiS9ZmLIOLBTDb9GvJsn9FHRlwx1ZcKrVJ8POpMQPVhE/cr6IpuztX/NXHC1cV2n/0oeXER1GHTnzR487js5QjDd2jPSoar6Jb/ZBFw1SqZ/jb1yUvRku2xeir4j52hhbsI340U+8dMGYb3v+qyswd4St9NffBz1xW85l+SdJR2xJdvPg9neLc2R772JMbV3ju2oX0pD193qyy52YVeJwdupJqyxyZObTmaXROzskMbXxYvDV/I6d4I5NY1xtI9oyysYMjSK74P0xnFEtG6MexgruZK+k3+04g0q/69rwjDonVsmufQkPy3IJPNRvrAhqae+oC0hE9KNREwKGFwabYkTKMi9KstqLovFCzUiODwK7gpD5JjF3euKUlOkLz//V7UvbP4v3/I5sJZlpbtvx4BVRu5C8NOHKfFvG86ql8QODLVxDhE8xVSidSq3/9OThooAq/mjADL3V0JUmy1MnDoBM9kyNTY/F5EhSxF1k6ViMvC3KIKigoRVYGsVvFZ9quBxwHJJCJ0AGjcvobW4IogRQ7vFgzkQMGu5DjyWByomUwalH1DgH+YWD0m+4ls+QiAHdESnRRoraOoFNWnQgz8dsNpqEOWmMBj3BQI0rrqJ2i7JikT9AN32Sir1w/DPzeWpDiEOu3HSrjk/KXsU74zf2lGKKdfCVw+KxHVWibRQtZqYyvsTcVtg+f4efSiY39qrji69xIlExKxJDGY2Zl64E2baN8KGtBDZZMMO4z0RsH8sRcsqiLH7NFRrAhP7bUggWdfSAZLKbesVKeeQV9xVs/JVA2j8Ya3Qm9wk0y4m/a+FcLpENy/BIN7kMJV4q9oTe1ZGVBDzc22j7ROi+i2EvGEgBDJjqps1D4U++Nxg/9xF8nhxC2NMPTEzl4y4b4a2ynvjB1DeJIe+JMDyjXdRmciXF/lOCfsQXTAS0u5557jk6Fnmm5HJlxRmPEO2r5GSIeVQWPcY9wH9X+XwuxJbbG8rIF2tQroybj6QL4rfKII47QHMKZg+w80YRfiKUaXxPOHrLZkVa8SCbyS8fQL6qfqquDiINcQOMuGkEAEzBszSylLqmCOrovHSThBKn500IrbdzzpzWxpwkHd1kJ/OgQg/QwoUa+V2+JI6AxFAyuQ1pXQ11hotrGebWOfgKMdvMKI4FT8qsNPuQNjhFBTYTYZB1l37AHGVuwEShe+HobggwPmcpJe/R3G7ATEMg1pYkBgUwmK7DryE+Tt+1VNbdaHECuZNgG84oOmcwK0j0uCgQHOsQo3FZHFhX4xpho7n5VTc6zq41mp+RtnwT4KA39ERLRKrCAUumFRe3pp0xm6FKIhrfLDCTPZoMe/JBk4R23/BZl9hPtnJ7DCAZ7tYEXHZ40DZ5JEnnwA60GTbDFR/QHE15itvSnnzsGSaj6QZc9CG8wcIuG+48Z3LmKs+4v145YQxVbB5NFAy+JSCldtaV+xFV21VgLzygr2OChZZKPPHSZNDiNqzKY5STvCKuvIif2xz/liwlZJop97h7JtIyuMy6KXPsPvaARMQu+eXoZFtpCQIaUfgtvauobGcZsJVVLD6j/+6cmPS8Qgl/zRnyBKlV2GwoL4wy9gCuLjbrbhiZwBqsk9IV1MobgpJ7YM610uA7MnnPM3YWkLn5XleNV6j1mVRYp2MAyIFLBPgHMREr/oVe+Vf1eLYjQhZaa6IUuKRK5hYlFpep36d5q+0hSMkMGP3Xlwy4gdPjKtkW+x6T9Et1uE4MR4AvRjnMIvJnPgxCekl6+CV7zyT/A5yeo2ANNeKgnjVjSBuak6J6ksW/ECA/uoWxsnUNnZ3GmBKmiBNGJSUyotNAOt7M2IGBANCqlwYpcmw5F4VCnvGkQM0xIKkgGwVw6oiarvjVIGR1VAY9MdxRy9HECp/4ILNSUk0q3j4jQxOJi3WGzhO4/5Ia3TzoSDT8aMpkmX1jBW7JKJ2UHqPDAC07L8KSD83UfFgtbn5TKDr+VuvsAHNNFhyz2hNATmQR/jjC8GKJU6OqIh8DkVAYvEDXmoR/Vt7ZZ+LsvzSq5lI1dJqff8QEakUC+L5wMOgcadd0vbEWjBpGKnn/YlAgy5CAfOdidWFIRUn0qBqBHpipc7wEmojEeuyxp5QEkAABAAElEQVTpj+8UDZYdu8gP/qHeIKhLnKTv0JE67v8LLjQn2S/YaLz0N/iCPxaMdocD2d1YKoQfm1CNfiacyEjsIjey6cOML/DwoPboRn5oqCdN2g8s+Ko/aO9QlQu9EXQZtCfRv/md3vI6PlRgHxjhy04gHNBHXvkiOLEn/qFcujPRmS0wzGolLg/jVlWWCz6P0SxEpQPZsdcRBTF/itvEBMg43YVi4zZGyZBcBZdDDxoSMmkujKkVDfgVQ7TFBsZmH+vh9DjJOAgX3yUPxuDtGNUGrRANPQAeabJ8eK2HjO3AG1Ryxix1yOMpOSKoP/sBGXK4ZblBuRrDaEhs93ixzPTNaE98UH2FL+ITeGBAamLRfgcP9hUOcKHfTKGFx+WQ8q0OSp390nWYbqDpvFYaxciI+SOWiq34Fwm0xebMHdVPzAu0j8k2qui+RbKFj+2ukGp8A23FGrbyQT5zjt9niHcME8NpFhMECKEe6LWl/dEpZZ97d5bACH0vItEgMBQVjD+o0MuevUKyi0W39NlZkuFAxd+iZaLtyUa5DmwZVBYBMxnqxOKjFzTZrlKRcjpP+kRH8hGY+VUQffbWZIn9AC6AqY2NmbCKDzS0IwXLwW0Epi85aRW7GhGTiZSOgJ9/2rQXpIWMIcCTHJIyUXliVRDoZ2z3jfEAxtLCT5F+8wIqecgkQSVnpKxKgoGEPuTEzyAPA9vU83tD9iL5/RMMYLVWbUNv6aKHF1nQ5ZSNbY92w8yRnMgAZjZt+aMYMc7jGxZ0CgSpdRon5NFKOFQcJFatfRBr/LVYSb6TeboiVYwLQSZIJgXHivTCb20D1oiIgrTRXjEaPjDFB5SzwKAcn2iyEuhM+KFDdKzRVoX6tyUdJnXBAg//HaOkBm9kBR19zAQGhtQUf8mgXIkwCF0nFprYRBlZvRyjhzpkla8ijpggroM1PpAkBroIiHHaqbfMAYI5PAarDbq6+MpjRDyZW7pst+Mr2an+gh4MXpRiTNcXK9HAXzBnxxIMox8AM5YHmzvWGhtddIT6e+SZqByy6AWbfaFa7zS4lU6M34Pb4AaM6Lf92K2P8cjWTPSxs5RkGINfyXjT/tNYkVG6jKcTTNc4JbGTQX9aX/eX62GC1ltqwEN8jf0c2SmbFdqe0EXyVtX4JGMOv3SivqlxgbyMG3MOuFkAEc2WRL6gGYPqHsFPYASIEvMCuguS2xSTriM2fyr1m+47QRdC8PFJijOYoGsA2LjeHAWMKv+JRbKkx6qgIU9Ft94Brkq3YziDmw/5LsS6PbmCAAHa2KgYh3WefO2NKB7xwoNOvvQvOeOkFDvLNNg9SanCPlY5e6KpRw4TOp/QuSbOFAYwJ0kutHzrz7X6os400NJOhWpd6hX40jhUhtZPWhdVBUQWLg1idLGGSQZPGzEe5WuwIrn6h7yVS1OCAeFUyY5eZyidEFnIB4cv0NGWOu+J9n7wUOH0oBzrhTnGBJfkUOeA9mCZWEQpo5nfDAGhVIFP3k+eYaGWPtsiuZiKWtusMosoEwS/QWMFp038G6QIGZwkJOMzvdVlsBl7bZcXRfKPHhxwgSULDEdPwef4luzCiYz4JyNxODof+gEZXZfqyI8+jactE/9LVmjZaQly/EJdxkbo42N8ULHQ+WKu6OMf2oNt1EMZP7GNfcHjmFE9KTsq2EX8BRdxxUPQR770a2fx2IiLIs9yLC66opfaYA1I+qNjlKD4IX1kfkHNQjFVVxZyQ/h+3/Pm+3UhIKHDdD1Gul2lL/7E5sjFfNvaZ86i820bshcbs5MKB96vFPnEQ8VCxdd/1dFt7KyloyR5G1cP8jNW8UX0VDxCW/KNWw6DFb94PsKBSuiouIgfJ21OvvgHp4kvtN2JXQ510YLk9B+5yRQ+u9660Z9xFxto4ecIIbW/4SXeoJv0B5jwMuPYY42S4A5xARabGDuxEd/UHOgtwtUMX+T3WKDazL0RERCJ2GuL8yoq1eJIHhnwgRd8JP1mCBA5Qx9r03fyNIeJmakcA6ByYwa2iADjvz7hQU8F/NSrPSXX6Ctl6+lg6fSi8lPe4e2TF8zmZ6JRfb0mCf4M6hFz6EJf8mtP2XuqmoBsS9drbGLKlZ/aZr6zgMIDaivGzp6PM6uYYKqJDmIflUpHvTIGhOxk2FeeUSZ8IDHgsEzpN87uM47MqB86jW7qGGlTozo5E6Y7FT70dpkgHHi96NA3RmPe0uuJWEwsOtTBA0Jsoh9U4bKKygprD3Dkd6/0oBKXbSHAGBgxGRsS4MSI+pDFzx/OQlCDb/hLLNqX1KqOVt4dee/d97Yf6X7RJz/1ybph9xBjNEcHkD4JPx5AVgQgQf/684CQLeGLz1UrbNBAHmFsa5AwgFMfmhqksQme6LJOycmrY+CJPGxBZSYy5OOL/Buj9RrRf8ERbCCrlP7DFu+oSVygj/gH2d15vWWwwbayCPIxPxSKI32yEOieXS4qQIlpiDHl9Rd6YgD7aB/tDEL8xk5LfGwmpMDYt/FrdNdRPfMRzyj+3Oe+YL///pt/zzs/iX+zWndAVLlkqpZw6zpGGvlKdYYuqBVz2I/tXYrIq//ITvSN4pxP/NXJtSkaapBftlm+MVDX6XGR8qELLxUjraXItRCGjq0xIkSxh3FVZl6pSb30imBoLzzGPCEPGvRmQo4u/GIVhVU03glQpeuhn0jUVcqYkLg65euG4CDLGKFPiXvrsazo9QLf7S15Ez2gqt5vbgzI2N8Blx2001/axI4azwBNv3jusm7mtMTCIL/3dfnMN90XLrErG8BmQCACXEer3emywcdjULnBdFKQMoq1utNmtGJ2inxoSpOdo4FFJ2cRQRMO0YZ/JXeu83Aq48GcADePCUes8NhIbRnkDipd/t/FRXd2c6F0ffBHV87Pj3qjU2U+ZSOyhblwppNTRyfRwOkN4xBeD3yMlk/ZS6JsH0BLteppeUSHN+gwKUdUymcCMRkgRJt6YzEArBgT+SwyqYNOP0603Xv1eB75msfXMeEhLD9QKysmEIC3km0Sa+EpnwqmcUBHPzM4JhMDgcmygtF8RSO/W08xSL51Wk9kZTBlEuaU8Ze++JX2D3/3D+Kbogf2Pk70B6PZEmybc+l/ZNl7mCG7Xe5b/G5D+RbdpF+NAQ3dnupP9IA32izS2vgqV5WvoUHOUI8jlKreeWMRDi8m2tDuWIie6ltoaQ1+7aj4yHjse4RW/9O94OWTI/pwe8cKKVICTWT1fnGlNRhfZOVB+tu3bfc9Zd7pQa74d+uiuAO68pBHJHK/FzdL1yX7hQNp+Uc/etBJ3iW+VE6s2Gb7Ij7n6U6f+NdPtIuedZF3tsyj9sge42uyn0Kj747RClyM32gPvVqQZUuTjy/G/gFrJszoxHf4UxL0p0Zty86UNKTUv8iZ/EdntaMPGaWrdswtEzqUmjoc+IoqfGS8HMVOyI/+8JkXaChUiqxs4fnpZNv6nActZVH3fzAyV8UeeDuiKBA9Mmu8EKOVj22hho/kRVVbm+eayEtFaMt3niM0P+aAZMRtP8CrtuwQ4JcOR+iy2NKGvGHjInNPjjAzLtzzIis8jBUcx5iq8eYHdasuGryh1LswOnrj0K1u15d53NUTg5pFx/1gey25kyZPJ/BxD1azVSIpRw0VeAQR9Ha2peQrR1jkvXQM7aLWR8I6jwMCbPzwzl4vHH0RgTvBQCU8sZr68RRKmhzM7BTI+0hy0GjrgOoDGz7KKLFcHWIOR629Hjkm0Denapi0qAGzF1XlwZmJlXsKsb/LFQ0TTxdlYNkDx2oXfYuHMVChWjqZxzbxdpC1a+/z45N4LdUuPTppk54kwWmpM844Qw/FPUkP6J0dP0oBdoIzCzmBndewOIAkF3z4ADsT0Jmo3O9oln5Op5Dog58eOBhtn6otNmAkngg9W09Eao89U3Q/1GOo9Bu93S4lFcT5jRRMMCbAfdQMoSpwB7Y44x2j6Gde8G0xABYiL4LkuiBqkwwY2MJbdegLZmrIIibbDMTBRi+uve8Q1cdIZAVfJo9MUJWfpKOXKXcjlJd0l8fJGF/ZX+BhcsFg4+r15IV/EjcEQYAV2kmaNUOv9Fnb/uSP395maYfp9970Rj/+igdLPLTlQT9a6wc/uKK9851vb6foIRT0QeSBBx9gQz+tj379g9NxQh/Yz72vVEofps8uvPAJ7X//88dCo29STZjl39RGl41TBTrREdvRKSBDfdFSg61u4HvwlQv6qniCqHxU2+gYeaJhpKPdH21J0orl0oEH1Ef0Bzg9pjsNRikZt7Loih4KorVMk9DotuhwkSrVaQxJA9hV6MSV7X4xktTBD7ZKsDCO0et1UttghCI2mda6JuTTBi0gEOfxm3bbQ1b1xHL5sOwb4xtGUraZd7CNPlOd5EdHZBQ/HGoarDA9JbPE88aAZOQoMdcOBy7i9YEJLNjOR4NxuvLKxj1eoeEETCR0pYCDTslfUQJNhJqaViURdBoAJcA8Am2YUEW2nAdQlgPIh4FuRZEHZYGlOqKFFtCkoTNKDpaQ9D2SWHZ4OjBRVPC4Bs/CZd3KD2RdCHGmTuVjup7LI8pUI36/bULkON10nZXBnEVEdg6YMmFQn8Eevt5sHV5oJDeLQsemFvuBLZ8+AXQP+hQnMtGPXG3al7/0ZT3e7n4fTfFAXPbqWag2bnygfe4/v9De+tY/bK9//evaC/XIOp7rmoEBBNBgr7ba2F+Szb4FfnK/mybluBB6FklcQj58XmTA1evYgJJFlTrHQfcbZT7pC2s33sVLFunp9YfpObIP4m79c/TM5KpEhTejTvMjybS0sorFw8inM7L42ZPUWKcxRgxMwaFtYVeOWpqcoif5ivX6Hc5q9GUahoDI0q8DZBS43XGO7fpXlZJiWlsmKIpZzCMLm0IT+8i7v82jgirin9CVPHDYdtGNfYJfkoinfXpS00knn6RHC871o7d4K8Gxep4k96LxFJOzzz67/e3f/H3b8MCGdvq0U3371Ogb+iM2xE/oLyyq72Mh9LbK9laZqyo9HoQPvoyN5EGI3dWP8QEyqMcv8U3dxlHzyYgNSugtpOMqf1IHzuhAt69a7hWUK1U+OoMNMuSq1Ml6vhe9IDDvIUfEk5gsR3y2lXHT/ecdfklDJm2JrcIeP3tRYUBSbRDxdWxRpdrwOWqJH+REN1hgCZYsgihDUPSxJU3a6wq+RMZV64Msz+XMOZEHD3YgjvmIvmC+QH+lkjuU0dcxUVd+oQ65tWAXffyRkv0rusyzwBv71b7jpybvMiAYm7AzyXrsIN1nGEFmh0oUuN88PQ93lanq7XIkBk0aVXyDKvh6J1mI2FVhDVxUwxIW4DTgQNpRgaQxodFt8NSK4mbRddKoAU+wptrC7CQ6I84KDYw5wkFQFyItUyUgU2wCyzaaNh05YJFCDrFp90KBFOXzm1uXia0KBOrRby36QgZpDM74ZCAQDk9mnglhEJaeV9b8NZnRZufYr3avfQ79R/UmhDvvuksL3lvafD1RnqMgFmset7Rs2bL2xjf9jl5ZdbCe/3e/8QRTAo+4QLAndg9kApreck/YJuwCDUd39DN+yKnRWGj/gpZ7J7Ff9NDkRnN8g2nxdi2uw4AR1vCHhsEQfZTRatXhB5cHPX2WdscdeXa3pAK55jcvstTGIIaCiQIZMCOWxp7Ily+ogha6TmqqkT76qcwEoPjQBxQMRpTKDUqyXZjgM6+EeQsmaCy/PD3WgcMXMCBLUKEz0j4masLwouO+CI3tm5iIQFB6yKskWQAzOMfqLL2pBfno4x/63bv3+WzCS1/2Ej+sWVXeuSJUuF8Nfug4Bc/Y4GgSXs5AUN6tnS3arAtXi5a3pyd1H6iA5excIot/+o7J18nyZ0puLqTCtqIhrngD++7du6yP07gid5/xIBCSJ3Ft4ef1WZxBGB+lmDjhWZ67RU9MgL1kcBsM9pDsexqcEjdkqc+24+9jx5X9C7z0Hwl6Pt6JVt42O17soBB1OseU+3H0DQSMnehNPIyc0oMqyR3nmoxX/EYyn1xbMVLx6kZ9gScmCaUy+Q1XGoxfY0fteKHGD2cFaLSNnd9zX/lqBGe5xCwgmUNse6cj1g/oZy2uFeBnLvDjI5Q538chPJERfBJmnCUL31Yd2/EMUeixiX9QTKeRDFrMqAJNqYsg5NE2OlDUAsEYLEoziA6+CFeLaKo9/DI6nrUTrVYUtMEHLXmMDS+VQULXmSpF0ysELAJaEoMskqhXnfUHj9uhRz/1as7kkRZXqIk9QmhMhm9EyORi/OaLLoam3yEHof7sG+UrqF0WKXz2BworUa/Otu/F44WAZsnha1i0+yPOqEde5LAd7bSHFVBMSMxnLEb8xvb1r36tfeYz/9ne/Vd/ofeGHaJnDWoygMbCmIR4VuAUvzbrjttv1x6+n09nhGBj8tih1wshl3dNMrFkQqGP9OxBPQSYp9Tv3v2wjxpmaJLjkUnYzxWi0PN6IhIvVA1+Js/ZOvrY64mSesxm4mOyRP62bVs8OfEaF97k4NOdk74TPTxD3OJ/1WRRlU9NmzovQMo6llSfCX/0ZQTFJ7CxcJpHueq3yJYKE4vW+lLE95kE+kSt8hiLYEiKXimgSpOwAlx9JdqOTUzG5liTfMj4Rio0kY5t6Ne/OqXiK/7OePEEYkNqcknMMJ5IYB/GFnRKhXfYKem0KOoU7lPrk262Cxce3g7Vm1++qHeB8pDxg/Qi1mde/Ez/jsgLur/x9W/qAeTr20UXPVOP0lrU7rzjDj9o/6lPe2q7T6dgeQ/pJr0z9IQTTvRr1ebqpbPg8E6HdJDPGBjx3nn7nX4p91133aWH5T/Yzjv3XL9JYaoePO97NdV36+67v31f70flwdEsWuBYrbccnKbHjT37559t29euXdse0FmRDesf0Ntz7tLbai7Qa4xOd/wz8fJzwg9/8MP2tGc8zW9e+MbXvyHM69pKvRT8mc98hn1WfnNfaNCVDx/lW9ujCdw+o3+VmUg+E9D70f2idvvYPZ++grz8Tp44SrelZ6gjQUMs8hc90TW5eJR++GuswAD2wfcI6+01VtwnYBI+/usAILxZBP3YRs1Bhd9iJBd6xoexdfOpK3+BJYteMKhJqWOXPMaA6SXBeUbCBA1zHfwlD9rSnb6QbzQfYQseytoz8riujy4wcgUF3okTLEv5aLQRuS1CzTolRTOD0/TaUDEsiOa1GNXFAWzLeCYa0gCcfNcTwW4e2ovOtV1PKMzoL2CXCOetAyDRJeWmKwwqSGl3nvHSCaHBJleFSB2L8zRpIUNt3HJQyiroLU5tXsxUcDAw0SkP/gooBjZ1Q2c5cKpzQgtWghQaB71obAc/oCtHyh5Q8nzTuQlAyVAZnWDZrles/NvH/10vxzy6rVi+fNj7HvpKaKDlwbtcws6jmVh02DtjYvnJrT9p1157Xbtv3bp21x13+T2Rv/prLxPdGV6o2AO/9tpr9Rvklnb0sUe3BzY80L7xtW9qoExtv/2G3/KA+Y5epMxk9OuveoUnG+RyCu6aH13TvqBXch2jU2+//upX+JUvYL733nv1wtGrvL3rrnvaLr1G5hd+8bntYl1MwSrPYKiUoLbFqkq9d2zkBfb8ZdpQj2Oq/+nK+BceiLJ1OyyqS+TK1/SFPh7M9J8wTMZkPSNWDeZDJ9n0O3X8M9g1EPtOleOJ+KAjlEzRv9h4rxo5fUHyJCLBjp+IMxfywEkKdnBDoH8DoSXt5MgH16NtCA104oNdWegQYXpVcWTE/4GpnCad1njX6Gydaj9+2fE6fXp0+1//+BG9mPXeduLJes+c3mowZcoc8R5o87XAsRB+Ty/Efsc7/sIyeKwWb4Z5zGNP0hvUv9Xe/Rd/2b53+eXtbX/0f3uBRWcl7JuqnTpeJ8Q7Qk9csULP0DzFL5P98If+sb3p997cnvvc57TXvu41WpgPUexc3f787e/y63+OPuZo4bypffazn/eLuolx3t335S9+1TH6eL0xfcXyFVr41ra3vPmt7Td+4zXtRS9+gbBc3d7x5+/0uDhGLwu/Ri8h/6JeNs6rihYKu/tXuPB1/Bk/2XGqc7+IyH1iU7pT4VB9fCt+TeCU8Tfbahv7obeLT6Tmwy9FV9v4CkUQwWMiT++Zc1TXF4viKdyUa16K3zu/hSKoUtka+4hngOcMENhCV4tm5qOMl1ECuW6T6KMv9lAfmd0/mrsZc+DrXHA6j17bIRq20YmcUPI9+FiVtYOHD6Z63s8ZKnQWhw9+JJcdci2GarA0DMwEEMp0uirpM3D4S2LNQq0dS7UTCsQjulyxRwmarjqIh0nGLMgWmx1LhXkjI9JMpS/cEVlZMHTYrMnJnSke/Tl5QjQjNWSyKB4QKD6kzDM4ihJf+k9TL6uocsQIixh8FGKagRBmYeDQPtZidxa/XPhiCarDvrRJJwedxJLqPDFq6703VeU3I6TWAifdEMNkNHQ+hPKFMvEHeIxUWyZT7d3obek/vunmdvttt7fn6bVY0zmi06Oa0AcWjMMeArDkc+aHgOEZhzfe8GNdODFTb52+2BfV3HrLre2///c/b+/ShPY/3v/exkTDizg/9rGPtz/9sz/xO/hOWHGC37jwgfd/sG3ctNFYPvj+v9WptC16WeeLpTLe36cF9xMf/4Qmpa+0V/z6r0m/AMmE23RkukUTzgUXnK8Xfz5T7zDb3N7znvdpYnq3X3j7DO2lc8RhU5Fus/GTJkzhxo76nW5yMBhI//IgEl+2iQdJkEicUqLxavyZcGUhCk8X443jXgQ9pG0fEoIjAxVC4oidQOhdNnDpdkcQi9GNysnT/5lQoheZ7MFWLBd+951qjRb/6h88/Jde2kZcLnjCLr2ms33IZ0xQCB0TEqcPf3Kr+kZvbeH9nsTGl/Qb9B/8X29pPKt31VlntD982x9oYXpr+8f/+RFdjHV626XXny1durS/Vm2KXsa6QhdxzfSZgsfoodBHH32UrTlfCxKnND/57//HL2z9nTf+puoBjw34Uk8E0c4ZLz3myOwlL/kV28UZA/Tz/r1/+d//6tOub3rzG9uyZcdpx26u3uy+UUd+G/Xm9ZfqhbDPalu0Y7h8+XHtO5d9Ry+H/Wp7+zv+m1/ZhqnQ8Ib3//G+D7Rzzj3br3vjNXLrpW+L3gr/6te8ov3iLz1XZyu2a6E/Sv4YT917PCHEKbGA/+zCjp8m7HAU9DFncr7EmrgID47PjtN4mjHyug6JkRbLy9GO6BnT1sk4yPhHHTFLim7ijIkjZbeLgHmr6omHyOlzDZBN72/JCdbSVWYz/kglx9QirnaXiXHFO3XWqTmzcBuf6/WlSTKnSsGAraJ3brSjF+OHYexItlmKmm3+K85LEvqoK9zIy441OfzJtjsUYh/yavix0Nh42knYpA11vUgJ1ol68bs1X8mLhiJf/b9kxMsRXHLM6SqIcaK2+s+mLxCI6gxQldKxUxKclk/AQIChiHIgqChbh87o7QzGTGgEDwwdm3Nq6x90E5D8l3621oMOsFWZjMSYVh1uuk4DGQsqBLEHfdioLf8kT/TkIxM5TFLpHyZaVjJTmga21Tp1xcUwvNkaPjoZ7GIKobIg4WiNiZejHBZI5B6nPeJT9fbrBYce6tOUvI/s53R66M4779L7H6/X5DNN7za7od180y1trV7Smd9yHmkrTlje3vA7v9UOmnNQO37ZsvZYvUuSyQ6N9pdWXN6IzV75TB1ZeCdDjRyRHnLIIXrJ86rGW0GgOfkxy/Xushf6vZXfuuTb+q2TRSsDOgYkiLEV2fgAX9J3k1vaREKD3QldftPIb7vspECThAByVFKPx1hk7Sz3X9FRV/8QZ3CxZSIjlUz1jXxetJbl1mo3cdcnm3TkNRw1QCds9DewjAUfwKqKWjC7BKhtezdCJGAcj0otS30MBu9EdlspMwkhF1OZqNiihvF02OGHtiP1kGTedHD2uau8yNkOETz88F4vIi968fN1GvQaXwnKadPligV08ALuw49YqP492LbN0O+Q/I64W1cz83b1X37+L7bDDj1ML8y+Qi/RfljxYlDWzU7cmjX3ti98/stazJb5yI5YIUY50nvpy15suV/96jf0GqJ79VPAoS4j4Ti9B4+3tBNPj3nMY2zzJz/xKd2bemi7b+26dpNi92Z2GH9yp14i/gS/1JuXfPPu1EN15MoR8LJlx/tK7EWLFrcTTzzBvy/iW3yKX/BB+gq/obXa2MYO+ohxRao650U7WVch6Hgd/E8P5IglfcLcSoxJnpoeFbdQWohnsEG2MQqL5wjX5ov6WgCpid7YQL6OzBAZudiUeKCcutiEr7q5Fh7ZiSEvRphh30T+fp2Kxnx8hz3JZ9yWLirNFk7Lpc3+BJRScOAL8Ez6OP4vWaEbMYe9pFsSIjzOplsBwvXJgFMDNE4jk1QYYBSzt8ogjUG1LQtGLswSjSqqLt0FOBTwRXs6IBjkKQDYw2RC62BSXYzsnJT1ofOQVG0SKeNUoVq6wF/SEZoUGaxjp4sOfSLgd0C2FEkO5jRR0r/OmytgvYCojH384Rfrhw8R+k3Ichg4cGnRY2ITkenQh+/ZFm51t/IIo146tFgxUYHFeBVA1ifBHhQdERpKBhP81j072v65U9qDOjJjIE2dpgsFFIR7tKjMmqJTXuK3N9SWyTb+m6nJas7hc4xpl96UzZWb23dsH2Rv1F43WPi9aMOGDe0P3/o2n8ZkwTzppJM88ezWkQGGc6sGW/t12HJ/Yy7MUKPlMrEceeThXjiZELdt29Z+oqNE9M7Rb1HslfNbou3D/fJfeBmcmcRrci8f5JQ2v0X221fkdvTgi/KjaWVMxYD7wpLxReIGXyOLQV2n/iGhP0kdimVTxje0TvYpldQbo2lUIPbYQ6mkLPjcJ6pznGiLHKiILeKB5C1xlAbxVCq9iSlqK0awMXZHhp/EwmRkZnaGlHFeCFwZbOywsDgcrt8IOSpaolfpHHfscW2P+oN/mLQ+tZe9/GU6bX5D++eP/ovi4ASdUjxHvxdrwdMpa2QjMhIrHrgoZ7+OEpfqTMNRbdPGzW2n4o3+IeEv3ll6q07Xb9RRnk99e+gYpOPhhBOXt2UrljfOXPA7H0ej2EvczdcOFXbkHaacgl/nF9G+SqflV5x4vI5y9XDqrmiFjlx/9eUvNT0LNTJm6kh29qxcdc3i6/5jp4Y+A4Jkp68y7hLjCKSpfIgd6hP5cBhjvc2LqHfuEJb4+C8xo6boMImmBGKGsZwjm0fRo1ipcExuJ2Mn4ONf9GZBlGNtVOIHXuylHVssC7MhE13aoos2x4vpaReFedKOjMi208yb+DUpljvj8eWzbEVXsRlfSqR4c11B+RdfVCp7y881VoKFhTZygweuCcBVFI2eTZrkQ1QFQnWAzQgKLOQPRCaOcMrhtVIVqt6kblJdOMSa9pJbTnVniZfBGmeLzrvsqEsHlVyDsIBH6wV1JjoWk+pAXI3DRoeS96QjVcYDOtsmGiYEJ2R13L0tnRY5DiDxuQO0cKG7R4r946VPajOJ9lMC3a9ALz9wNBFTJFcfFlgq7AbReSKjzMrKEYq+/bxOcqaDn9qaaHVaSZPPnTs3tO8duKnNPHtuu2rXj9v7Pv+hNkf3Gm7csqlNO3Npe8LxZ7cnTFuuxTE2OHAkhgmSvXEWwKuuvNovaOZ3kmP0u9D8+fPtHzDznrInXviE9os6Bfs17ZV/+MP/1D7xiU+2lSvPbL/0y7/gCxJYhHALCYT0LZOf+7HcrHqyTAx79FviVZdf4d+Hlixe3JYevUST6Y5OLyr6g5hAGFjlTztqkBVbEkMRXH4OE32nRYKP+CtG8LX7seMAo7GrbHQGrxx9BaMTSonXLDqRmdimvuQnZimnLVtkgAUbiMUYEDzRU9isH2oRQ8VRpx8jFq+pJikS8ztLjkyDQVBAY6JI6LIkryaSjIVgIPzq9G2wRz6ntrkYBZ+zMMyeM8cXQdFK7GALC+ZFF/2cniBzY/v0p/6zPV47R3NmH2Qe44dIKYsdmIKMIzzOQOzYsUMPgJg4wuC0mqi4QGv//r1tzb1rHD/pg+wIzp9/sI8Yb9ERHslzo0Rja34Hiu+p2aGjPt7uvn79/b7w68Cs+CU+Mnt8Ipy9BYiqy4Lhsap+NGrFIWUTSLZ9qDI1WVzwy9jnSDcngUFeMml3DKslPLHJZ1niqohn/hIb/NVnLg0gLVEyQxM3C4tkjH0bYhBahtqqTwLJClTHadO6olPyqNZXxo3mH+cBN2KJDdENEhJ08JIqjiZ5aSoTac+OH/Kjw4wTX5GXOENuYrTm+UlbutIJXnyH3ZFRPg8Buks28/dUncXimja3Dqd44lEp7VK9lVT+qg0F+thYtVPNRIRwEt+wxRl0TgIUGuprEMFnumJg0qfCOyuiJegoK0UWma5XWeuzStUVIcQkWDWAcy9gAhD6YEZ2HAopg7SrQahtcTu12IQObTmNBX8FgXJuRgZJMSmOfBx44nuUXzsmfCdBfSHVEQx7ReLNIBt96YUKwUq082g35AqJ8mCmVt/2q/yvS9dWT32ozTh7UZuj+8S2HHioffr7n2kf+/a/tS9d8aV27Y9/0C4/cEt74MDONq1jsWz5gtMdq+9Z3f74j/7MR2OPf/x5PtV5yCEL2nq9jZ3EQOaS9IO1OL7lrW9u73v/e9prX/saX0jxfd2I/Wd/9nb9nniNFlWuywLbmBIb8Q21uJXFmwnv3e/8q/bd73y3nfe4c9tpZ5zaFi1epCsD1/l3KnSGPltxipf+xoWZpBOL9qr7Mqdu6GsGeA1eYVc5/Q8zGBIP9BH9nR2qETf97H6UzuJDC9jhjU2G5zz09CG0tKE7gz36oAxfePAGPCUzutJWk0Mt8O53fGHdbPAH/4lfysSnE9U2IzgEx5jQhU5S+ZV8emXEOmkXOwdFi13ZCeDlrfW2jantFp12XHXWme0Vr3hZ+56u5vyPT35afcviFh+xtV9Kt8rIylmPKf49bt68g73oggdEmMAFOLN12v2mH9+snbSHHKO2XI308RwdBS48cmHj98JaAJkfbL76moTeBQsW6MWwC/S74XfbHbffqZ8PZrkNSk7lC45+k941YSf+yjisGMD/tl/y3BdiSp1EKY+NtMNHsg9VwF+Dz6XH6JjXlLyAYysA9JfYSUyjF3chD7mk2kJOKp291MvUI8uT6EDn2PZOTU7ljxea9fjxjk30wDTqYlaLLuN0CTvB7YK/aBvb+3gZm+0rj4eJ8UFcOb6sG51ghncU3E3vNkXnqEt0/BkIjkq+sHFQM6b4GVpiByzkoxMq9WuIcQIGBkzqLNvZ6swJjFCLviiNKXU2Jg1FM0FmoeVouB0cFiQq/SkUBqHwE0wOqK7MGG0E+us/AQT/+D8udvDkHwIcEBXjohw5riVQ3R5n5betroeOtITq7C5IdWDpMR7hYGPy0b/xq1/I90bTY3vZ4FNXEkcdH3tUMorGLbLbOxQYgOpO78Uc/ao4dqpehDlvUTvg1U63QEyf2WbpNgj26DfdeE/beNPqvijAbgHth7qKk1ORn9QR3n26wu4pevYnpzMZ7PwuuFWnLqfrNx4SfvyJLs7hKOqss89qv/n617b3f+B97Xd/97f1qK4D7UdX/8i3diTQ4jdbJOz8PpojL8DnVNi3v3Vp+6puA3nK057im+o5Ctm2dYcnP26tKP8AtZInZ5Vxg52AJcSu6+QH/G6GTMIumziDY5QZIZEjUcIY36teeUsfGl2CyPUW7xyaDESTaXaHqHbcKiAG29XWRRo3GGpSjUDFAp+JIIIGW4uvxkEtkJkE4BYmiKCnZPjIGxODnzipCQI98QNjQ5TyX8lzfaT6GxsKK6fSeUM6txxwGpTTmFt0McuKE1a0F73khb6I5l/++WM6bXq9TlnmPj8m3gce0K0Md92l3+FyATvydu7crt8Kd+hU+zN97yuLAZ3IN4stv/edfvqp7TbFGxfA8Hs1BrKIckpz7X33tWddfLGekLOk7duj05nicxBUTnYx5hYvXaTfBi/Qb4urdbHMB31xGfq5tQf8X9eV0DyYgqNTJwSVL+XXwU8gU1vK6dswxP1uVEUWokLiCOhkXZZK8TXl5I29zw+eqOn3PmGXjnHbLYWZpM2AqQcL9lWiLWiqpoqRM1FrOYAax1BaPb7cN8RN1zfBWDGKrtj/aP30bfklsQSG4Dc+HwUhEPk9JuVsaG2b6hPi8MSPUDumbats0R8iw0Nr+TkxVT4Bh/0PPTRaBtHhEzXG5JVOStwckJZGjQgQkAk7ZZPlywjg8J4NdRp0uJkw8B8FCGjSxog7rw1VfaYvnBAeWw6NdA+3dxhol9vlwV8p2GERXv27bN0jjfXL8Xw4lRk6yVRDTTIU6t4ZGRW45rBE5WICWPGLMagytpAJfxnvTlAwTNEeCXDRw95JYaeciY5Tibr8F7mcVpS3Sj4BwgLkoxgHCx1KwEmXMLAPsUdXgx6lZ3a+fM6FbdWxZzoo4kfsm9J26yhs6xW3tbn7MtA4TXXD9T9ul116mU4fzfS9YdwbeNeddwrr1LZeE8Tll3/PV5CCj8RN05d9+zLfz8UCuUsX6nDxwkte+uK26uyVyi9wHy9cuNA3WfMb4Cz9/sLEw1Wt9+u3RvtH/kAkj4pjYuP3oX06JcYVrUy0YIMvg9CqxZet/Yx3NFmQ8CkJG+3f3mc1kNI4sfhAp8rOZj7HqpjVE0jqjaoVLalvJD9jAUr8XkfvlMMUejNRIyPps7EfhVvCwO7+F5vtoQ+VL5scD6owLvpYcoKh24x+GNTGUZHly6c14MWgONLvVZYbOl7Vg3+sD1v1D332zrNYEpdc2cviwEUl5Pfs2e1/rujlvr33/vX7tAP1Q9+a8LWvfr2dfPJJnhuOOOLI9rrX/4Z3nt6pq49vu+0Oyc5vtcTVe//6/bpd4Trp5UKYPe2aq69rT3/6U9tFF/+cLvg6oAtm1vjKVa4I3aorWOfNm9fe9PtvbKeeeoqfevOVL39diyQvDd/Trrrq6nbKKY9tr3zVr3nsbN+xrfGbNqf5WSRZgLGNscVO2Ct//RW+UOZSxfprXv2b7bd+87fb7/3um9p/+9O3M101ro7dvHmzxsAG2bylbdYVzfgC/lxohqfxff6R7esLqFWV+1KtLGTwJeH3TM7MjdDVP/Xlf/KOXURLHjrpWmLA/ep+Dk3Pii79D58XT20jO32ceLNAtyMXWraVQp9SLSqmYf5G3sADTfioc2loD66fXuzGcRucKYcXux2XXZbb7KpgBxc+BEP8Gn/ELYnb2Eee+ZexkfiFt/pi0uZhJ7DrBi++tI2eR/QzExXA5TvmI4wiX9ROJBRN1qjRpCKphf1nnUqFBjeEFgkwVk2X7yowqAlCkmngo6LTu04OMAFrgILdB7gI6LSdPB0nh3ZuWGpSJ+8jKreiIdTQ8tscwT7JF0xQqVbBgsNxfDpMPOQRqsTFKixoliG62guH152Vec+0wWNBEpu9qdgLH1deSU6XjE5jRjcfdSITYTCpLGj7DuxrKw49uv3pC97a3vmZ97Xv3fJ9HRnOlGZ4mCC0CEv/Lt2E/8Mrr2r/80Mfbuecc7aOHGfrSsFV7Qe64fhd7/pLP3Zr+7at7fkvfL5xcd8fe9YcQc4/eH77ez00e6Z+Y1ylo0O8zj1bi/Tw5idqD5y9+nPPO6f9x3982vd6LdX9Z/t11LhFF/QwwfLb02rdm/bww7vbqpWr2uG6ovBjH/3Xdvcdd6p/Wjtr1UrVn+HTpOvXbdARxE7fpL1B9zPCz2nbRbpS0AGNQ0GAn5WnyEBSEVhO7itZj4NqEEJHPX0JMaReiLhLWn9Jqu8yMpnJ073C/CKyPFhUUW0Io506RZmKCLEG07hexRwlR1NoM5naLlXTx07SyaKLb6ayI4copciNHutn4aNeBF1bMFAwTzK1gBetJXUdQF6nHZTzzjvX8u+6625vwbRTR4S33nqrT2dyVuAq3dvHbRc5ZZo3kp988sneMeLZt9dec31boqM2fLRkyeJ27rnn6PaGr3uhO0wXYR2i3wv5fRE8xBW3Szz3ec9pc7UjxII4V6fjTznllPZXf/2X7bP/+Vmdgv2+6PboNPpinYadrgXu13wGg52SjQ9s8o3xPC+Vh4xzwU+MzpNqjjnmGN9W8Xm9FePGG3/sK1qPOPII3cv6PN2Uf5p9ul434p9z7lm+H3bzpgeNiYWUkPDQ1Jj2xC0n4e8pfRKOP1m40jfudveDPez+YMFKfPQ+tVcTj+47dQ397ThwX9HXiR0WR1HaZnwJ3UR0GHtks3PTZaBaXI4bAseJ9sxdQ6yqntuS0BG5jgbE94RNyElFFp4slt4xh3/wAyzFGF+4pi82NFXrkHNd5D3aP8IhvebHDz1fPgEPtlSyX1TnAwNt41PmSxTkoKJ2VKAFO3MhbWhhHp5yww83ij+DqJwxIIbK6HtHGlsA0j2kgUf5aq42gE8uPtDSZh4Zl09khCeuync6ZZSPUaWEbTrIfCjGaCRS7wkoewuq6m2wqCCyWlDUkjZ4CBIvPuoY8+ir83bJxk3QOOBUcpAq0GRmJmDlHVQwQN07IWrovO5HQES4+Nh7R1cWeLCR3DnkA8YLom2TjBoQOYXL4GCRAw97SfjlkXbQzDntg1/5aPs/P/iU9871TuA2c9fctnz2Ce1Zjz/fp4SYfJYsXeyb7nmKCA/w/uIXvujf/Q7VER73Gq48a5WfIPLNb17iPfWLn32xjyKv1EJ6jybKRYuPbEfo0nsWOO5BXLr0qH5f45T2Hf0O+K1LLtVvjPO00GqBkyyOMr7ypa/ovrOZvmVj454H25e+8uV2+Xcv19HB7rbynFXt6c94Wlt88BHt5qtvblu0ID/zmT/n52ReeeXVsu+Abr4+1Xv5+JDBTtdnIkoUZS9QbfILrmZnhKvw6H58VD5ONxAzhEH6i3hjoOQILf1FG6n0sEUvvo6/0255nU5R1sU7iERXvz8KT+fjaPnuu+9uH/zA37Y/+uO36UKUQ/sZgfCgE1pSTWKTMZV4CvayBSRwYHNgUyOs1E7IyrikNiMM2t5sPrBRx+PSWKikxTHGjgYXWiGfMxXQcGq7/IcMrgSlDHaO+l/9qtd6gfnwP31IR/yzdcZgt/pjho78dZWzFq34MHagl/j2YgZ0y5sumTzObZd/2+OMBA+gR78JhA0ZPPUIn+zVKVMWDzATF2AED9hn6OZ7YhC50/QTAnzELglv+E0u3aZcbyBC257+9oVx9L0+wJtsw3akoItEHJESo9SJq9NQX/mK4ZShrzaokkYfBQe11NWCgM+GfI9xYg58pA7J/gC75+bOU30VQn1LLlhiR+wn73lH26L/WZg8D8nnEoJWb0vWKA/ZzHuZr+gX6NNX45iLfcxridLoU7s+SGZ8k59Mnjdp6wbXOC2ajEPt4Eh30dBm2df9YMOjpAV+Z51skXA7tw6BettPg4mjLN5CrIRcB2fsUpL6GFZ1HE2ZHxMlHxa2qPIEpwJ8JcsTl8rwGPdEG49W8ukf8bpvQjDKhK/jGpyCIie1ZVeQNcqLnZtQXSQdHHsXnmA1CDk8xB80MUGAmdM1HlTk9e8FVELrh3t4HBRawGLFYJ404Q/sz/1Ge/dm4pg5Uwf0jnP6JIAmbzrn2Ypf+9qX2oc+97/alrl72vx9h7SFjxzT5u45WEdhC9prf+cF2lM/wgMCfgY4fmWA8Fi1GsRI9o3Gso0JCD8xuZE4XYqNTJTsjcEHXmLEPlCeiSX29sFn0HqqiWQxcO+6f3V7/Xt/u23ZvsX+yLNCmx4Dt6NdeMYT2vvf8B7feL1HRwPIZrIj7dXk5bdNUIt/ug9qMqgJqxY+xw6+VyA47sTDIGFHo3wOZvpCTcqxg5HJHwrHHIqVbJtk8anJxjELCP1LhGnwpXVZTs8Dtif6PIvhPe1vPvA37f+pxVD1GajI0b8nc7BEhnGqPkihyeKOemh8O45izsyiQxbU4WDyTLyGHw2xzzZIFj4iZjm9Ca/5u70Wg41dmjZDig8ywdgVokIGMfKqV77GcfN3//A3upDlMMcbAGWV+CXPhkav8+mEjl0I0a86tuw8koLNWRokBiFsYm/5K3XyUo89+mWIEy2m5JGbSRQ5RuS68PrbfUkudtLPwmv8qSPmgO24glDJO1VgowV8yg/8KhOXeLNDH9pYUMyjb2IoMTv2k6qdKi6RaeC9ng3jzvKlFnm2seLfc1VkF4vb5aM6WxE/0orsbIgZ+9GAE5M0Fb4xj6kV87S7RV+TOLtc2y+snLHqtkIdvCzwGbeUk694TrzjP/4qQUdK3CvGBhypxy7qIh+9GcfE/XQzB61lxs4RdOmxcyh4ApGhfcLAQZUfO1b8nZF2EnKdE9jUuFp5+CHIF0OkOrJkeMggABoxYy+xTUCamyaEqsHOMZnyVkoDAeWNvsbUxbmiOjTo0gHw1aRW7RDDh0AkExzpREpUR1cGRfaeDVr1fpapuGyz+LInpI5xh5jdXxnIfTA13vCue6XufqBdefmtbee2ne24Exa18598mutZiKBHFoDoT+StXbOlzdl+RDtiyqFt/nTdx6cz4nun6v4w/Sbn3yNFzrsNTW/G2LJHe9+s/hncyMqEtn+/9qZjoje7WRRlE8Gk2TP6FRPunE7IwsnECuMe/UaELrBSzynW29be3jY9tNET/v5pmkz60ds0yb3j3tvbfRvXt8WH6HSoysQdRxTOKp8rzgh2YZzsd3QIPwk/ZxJk2qKUMjK4rcHmWJaJA11ZFiA+Ux7RAE1vIy558QKCeLNeykoDhl5GeGGLngn+sLi913ZZkivZ8FEP3kn9VMs6t2FDaOJf8kwAvqK0xyQxmNhgAoHBAvJoKmpKhgRbp4yiP/EVsgrLMKGIhzDLBKJCT4arPPKGyUblyEB3EmX+u2LBYZEyCOsnhvnUTjH28+d/bdave6itXb1R9yTu1VH0QW3FSUv0kAfdDxjFg4zgiy9jw3hKGv05oo2t8TUqYjPlspstqWiqLzI+00Y7MUYddlGbRYixwaTbj4DKFHytj28VglkM+M+6JMcF4vendEMKjsJHGeaxLmVRud47gMpNtpdddKLRizRjBTma03lubudhfGWxdlXw2QZhkF2RAH0wlc+jP/agm5Qd9T4fu4a6ojGF48L0XQe19qnl11EcurLIpk0yHF59HhJPFvHojQuz+CHv0W1gL3u5h1FnNHyOdaxOTjSIGwJV+YhHoApMekosgm5RHccDGJMAYhHpeVsNsdyHELbIiDeUIRtai1ajTDZN7zJDLpqO37oZNNYnuYkjZKPbUo2Zic2Z2lLoOpRJJ4DBwewa64asJlV00q6xq7YELBixOVcgyX7Jd6dbD51mw4yLQMmpJOTAaQgoy0BSB0OPjTt37GrXXHG7nv4yo51+1oo2bdZUXV25q33/kps1GWxqCw6foSvg7m+33HhIO3XVsRbkIyEPvJrEDujU5MJ2xNzl/r0QPfyOCXaO5Pbs1tGc0UtnXGU/sHfGrRM6HjROHPGI9p7pB05lEIgkJhR53vjLHwSn+0L2eys7xW77E4SJj9rRgf6me27RRTP7faRoB+BD1XPUvHnrQ23dpvXtqMMW6xSb9uCDVNpBw0DBj+BJmeAaJiXVQjRgUpE2OPKUkwxm8MUDxMyEbeTlGMc/RMp7o/6hj/hHQdppE79wQ0gM4Cd0oZO2qFENAwBWpcJGe69SrSkt3zs3KkdudMBX4yBbatI24kIKWNRv9JNwlQyUB4/0oKrDoT/pX3yATfyDnwQ9xPSha2W7q9yaL8rpY2ID+6N3pm6xuf2OO/xWFPqZt6PwfFJo6kgAPWUTOMDqfsQGQaCf+W35e5f+pN143RqdJtWN8eCTLx975rr2rOee7R0riTTWxCnjjVO4kc22EnnHL3XMH7YNzPFt0cVu6acvYdcXn0o5jR6dae5jTwR4Si1iYUzIFyLgt0f7vjuPfGKl7BcLusQqk2UjmiKDnDF0/QM2jdeyp2gppx0BkQ1/3hzTy7YFmd1+K6NPwosu/Gg5xomFY8Jn+AK6bk7fQpeYRzb6c5YpeqtvkUS7aUTGm9j6KOlygyO+Q05SZIY2/qMNH0FfNJmH6J8cuRO70SfPKk9BtLIZiLnmIlh8072ykWTRoS3hNHS3dsAIFz0sbJ0iIaCCioCzNgRZmGjTZDlZWCIZEcZoo1QHqSpsPEVQmzeGkbcu8WVbDpFOZHkyIGMmbQqnm/1V8hFNiv4uP1XGSRYS20OuGFxPGV7pAROf3k4Q1ATrdgRBRuAR7T3BI61uIxi3PfSwLhbR8xX1VqVlJx3VFs6Z33Zs260QO9Ce+gvLdHpySrvu+w+09Wsfaqeddbx0M9gqeMAvWZLJpJ/JOD4BFgHCaU+/bkd04VO9GrPQgQ3uTESGKDrbAUQWVKRr0JLADX44MnnT57ThFDZgIIMuAtFV5tmuJ47cfPdNaoMFKbKBANWHiZIj1DvW3dHOPWmVCLomcCKkT2J+tBP81OtDIrg9qAFgfZFLc+GtiRt6w9O2JkiJsjx9m7544EZPePB5X55T5Xq+yqfmA5f+h/6Xa2yj6tDHhBNUyDa3y9QxccAXnTIEPCrTB+RJtBXuYQFxS/hpK/yRJ8kYLPaSD7n9Jd9nEo9c6w0pmqRLeCw7iONxCdJf6ShZGANCdO3YvrO94AXP91SxY3sepDD6BHvgQkL0UiLejFN5ppHvfuuWduUVd/m+VN7IMlNPUdp3YG/71LWfa6sPurG94aJXg9BS4qP4s/S4Tq22CT19/Flr128b1UYR35CQqcfLt4d1mh5f4h/inDM8LgFONPh+177dbbfOfhykx8Adf8gS1ceG8nPtmCCXNIltLDtnHeGTfAEqX9A6JBysVG1lG1jgoU+zYxLCyIM+Oxz4vXgLKzFYtmPvmPAKYtnCKM90H7pMbToSMiW1J6Bdos0xFqVqw7+RaXFwQE8VG9HxT53jTlj8E0rViwgsWBaacRwYB3qsQwSmYgdv9KV3SoXfZ9U6FvhyNSmTTcdGAMRoOUZgnK9G0QRv6GNAUUuvAsVgDJKRH8D+7rwlwRitS8QCitya2DHAOCSPVI5xJ+ABVXuSteNM4i87zjrlHJB33LCQaiGiuovJ5NQnHk1xUEV7d2YFRR3pIpPBqudymA4scIEnaFVmMBljAgpc9HV17vibHLKYEFkAtFFasHCe3gCw3Deuz583R4uX3gBwyCzT3XL1JuHVPVvr9rSV5y4MdmsPb+yNDf6tUhMnQeB+EglbdO/cuUv6pkm2HuBtxcJOwEHTgxwpiM5iJnOwM6LVEB9xRJoJnarYDw068ZftNXW+oEAOFzGsfXB9u/f+e63fgwOXyV3w+1SftjetvlV46ZUotu+FF///66WfbFfc8kNfzPOG57yuLV+8zBfumNJuJ3bJyGZ8oMlpiA8p8eBQfLmvDH9Sh3h9JK0Gkprsl+6E+Dl968ZuVyYcbEwbuO2L3rmUsc/7ZkN8uJYW+wt1+Bw0SZmA4ZtMxqC60ikIoAgcCJWl4JgXHX4MbtVOCHOfiJg6zGPSgtd+Vx3+gS+mBwRY7T8VyUdXVJc/KXFhCrdFrNSVwcjnrER2mpAzyjQc6TR+YPdEnKy5d1O74dp7dfYgPxfgvPX7V7c799/QHnjk7nbf93/cHnfCWe38E8/1TwCw2gZwTaTCmZh8dBu2DfbZVsZlbPz4LV9p1973Y/c/Nvs2Fp0OYrTsl334n8Vx7yP72h5dyf2YI05qb1v1Sv32Ft+VH71DhxKS1E/iwP70OH2UmE1fEcOdR0zUmc/xZIdFnM3hyxnR0H/Vp8UfnfKO42tyh8P+n/BX6SksHp/g8N8oFyzQVgxmAZbdWk8cH+qrOgMAthqP59oD6wAAQABJREFUoEwcBFPpGyFEBsaBgbkMmtBR122XIDBUiq9VR8x6AbUAfUED7kyylkNdx08ZG3JkOPhLDF4xEY9BbFGOOL4IKRyQCQohJZhOKzEGqAJl10GnPLzIQKA0VavylChnESnqqrfT1epUOu0EZGXiyQSDDDlaNJlZ5RQ+KhuTeMphwSU4OA56J2jh1147ZdG7TZX7CW7IpMJHH9YvNSoHu3BUnfXh+OiGIjrkUw0YVdvPBAp5MD24eYePehYcPl+nf45VPYONU126MnTurPbUZ5/efnjZbW3Prn3twouOb8tP0ulDDUZ3MPI82WOnWbVIpeMxA5tQBE7cVUeGDAj7lsARRQU1MpCb3xbVIiMTRzbWE4EkmZ42x4OY7AeR146D9YnF9ltDaOiTtRvXtc3b9VQRXVF4QI94MxZJgBbSaTrFtuaB+9qO3Tv90ID0IbLZM9/frrn9mnbZ9d9pc+fMbS++8AXthMUr7ONgrxjAjsgs20CDP4xVLqp65OcMggHDyJ+xRAZZxb14wWe/IUxpaJeMcZCjp/oDJtiyRUbxUEWtaU2ltk5r/3Xaogd75IrKvD3GlPdFYzrFjB7oHBt+9VrF36h3mKRFh++TmIzJib/b7rzqSic42KFiywde30qEs0wXGfad2uHjilS2pPhbfhQ5+KjPxK1GRKjMPxMr/+vWbNa9rHu1A6Wb9SXioUc2tmv2X9J2P7JVC850xcfD7bq7b/RiGJ1WIxnIj73YShp9GCyFaWxLvW1TduuBXe22aQ+0bQtzMZCiUjI0ZiWYndgZchv34+pysDZLA2u/xvbyQ09sM9nRZEcMEEqxCdtThd+wZegDE+ET+mHkoRos5RPKsKrGGe802cbMc6UPGbbVtPpSKh+4YPZOo4qSn50IVYiFOmxlpxBe2sKGvOhHluspKtWOM3lOURJDxCKJ09b0fTfddXyVfT5AsF9z4OAuE3/GQGIC3sxzI4bCbrySL5jB0Uncl/Jr2U+5/E7sMtfbV8IyHR6VsJ6cCLtlUoshBuMWGtNWQUx1jKlgo7dLhpTo47+Sb4DdMJilE4mudo5KqgsD4sZ8Wjs9vPqH18YUDyrRp86woXQKHxkNiaHYDqETmWUoYy1dFdzVeeSpti09V6hcJ6FeSrQdcZOPLrEO9QkqJgD8JZ0i4rFVt1y3ul3+zRvbvAXT23lPPKWdcPIx/i2NU0IcGW7bvF2XpM9qF//yWYPckl+yhNgDFLzYxZWcPO/Uk5rs9U6O9NK/HBmi2/7RkSb96d5ON6qsUl8g3UZZ9Jyi5NFxkHkCJLjxifTZdhT34Kq4CE5QqUfU7qBU5TV3XKP7zR5scw6e1/briGH6VF2tKsHoPaD7FAWr3Xf/2rZx2+Z2zOFHSed+3Ru2t11z5eq2Qfegbbh/k5+5Ol2nzPZqEkpQBw860OvBZ80dn/LQ1e83YDQeaJQHJXbwcGlKmJNEpk9WaiceLEeTHvTogrZ2CuwQUaE/9sow5FsHns4AREf6b5RpP5suWOAh5WyCODTa/VF9Yc8kRjk8HK3TFl7GpOzpclSQNElQGT54qm0c1+Fh5ccT/uv87ufOz6Ie+yZ8IN9l0ns0Pq6cZSfS6hHoBBb+SSww+KawkocOH6UdfLfvv7HteWSbLwhDN/GyVw+0D7zIDT1mSx9y1Q/BjZzgoy2+lq2wYQt+coFY1RmYPdvazll628qsBTRaFjuzjKHpujXICR4WCmzbP62dcNAS7zj6iLybhn8HTCjSH3X1Xz5NFIAbAjAhIDusVBgzselxh/b4HVrjpkNgMQ9bU6QtRTisF9HoyW/oNCLfzKYMZvxHYodFFMKciwCTd4tY/DZ6YcoCGJ8PstRO3yRGLSx6qGN+Qqdt7f1vDB2HNoU38xf2Vv+BCVCROY51/UQkub4AUIprDIK90vB7qCom631kiJMHP2qgZsUEkJQPMpRhcJTDVBzyEFXZdnRjunboqGEyhT35bCExNcHmMZiABLvjj1YTdGHeqFGVDA4EZgEXkZhyWog9VznajL1zBJAYglY544WGSQ2ydH4PFMRbA7oTtCnre+gADXZ9UlYY9KM8oIKdPRRnhI/TKVR4yVHZwSxCOnOvyqvv3NBOOO0QHQ1NabffvL4dt3yJj+weuH9r+/5lN7YN923SqYIpbcXJR7XHabHkqfpO0iF2Bxr4CUpsmaonj+TtEPEPODKQ9OSP/Xv0G+T2jg1+Akrthout8Yn9qNp6HisT1W4tSHc+8kA7euqCNv8R3YKBLeIr3gLlPUTaxFN+rQmJMhdTEGPnn3p+mzlXb7cQ7O17drbV69a0ZUuPabOn6bSwjgTmTJ9tWuKHifR737mtXftD3ay/+8G2cYd8og5lctqt+8aQBxLrdP9iE/FG/1cCb3wx1Lg9jqxJRhBtFL4ljwx05aKM1Ok7sdZ1Up5MxWv71YBePvzRxuTJjlAq0ofwG3+nsXLVYb+5hcG83SbjVMukLmR4kjAfthMX+IWcPvZTZMJHio+QE/9RFxxiUvL4AhO26pNJTLsnnT5lRwGcliNS31qB4t266IUXTs+cPVMPbNDLf4sGiaJzbJCnBZX6Agt6l5+wuC084s62dfPutvbAPW3D/jvEPfptho6ET1yyQiz0f3DDO6bgYjK1MnF7EcWHXY/xSzd/NenStmnf1rZ7/y6dPhMvtBrvM6bObHP1ZKSt+x5O/0tXukNvu5h2UFsy81DFyYR+7FHKjkftoAAF2+NvxzFCOlv5Hn1jYhGIsOKDzkl8HvsqUxP7iRX0YVMtqMrzwRbpVrV9nB1EaDKGMp920d0nOdvT+0q8Q5J85jWwMx8iy7e0CSsxDiWmoRO/ux8MEsyxz3hECSZ04w9fjGg13U+1EGkNGuJbPKxb8Z8y/E1gi7zs9GA5/hsvrII2VuAjLnDMDWtUqqacBEiDV30uWEBPOL3tQlCQSTMgelfgbrmlG4EzpBVHF9DQBUgXK1HjZEaLMSBJ9WPndMUCjz+xhg/GcIoL+dblVoILQlOGVmV/elB5UIgfXR6QiDdWBIIBJfIFfMCTrEG+6Ej17Y7u7W5CdY9lw0CePp60wyRx2NfaYQvnt/vW7Gg7tuxvp6xc5JuWd+iq0ku/fp1+H3ywzdaVpQT29VdrIhCQJz1jpY4YuZBCtsuW0s3RA3YAapaeC8kCmr0Znerwuwv3tv17HtZTQx70Iqfb/cSbHQdw2DfKHNAjN3TAJR4hxglq40WrX3vohvbFDZe187Yf316+8jlSk0ATCn2IGRZSjOsGwkgbMPrAoIwrX/n0X22veNpLVdRrnXS66+qH7mgfX31J+90TfqEdd9CR2klgYcc+xY0ced/aB9tNN67VjdISpotrGPyeuPbppmnZxg/snvSBK5LoTHzUZJ369KEgmIah7MEEPvQBGdzJ9DpX0tATNkmOPgxG+CYTbZM+cGnwC/vPeAseZJDANHKUuOKLFUKqhg7L+ukbyqlDEvEfPOz9goMdD1L5gDx48598xSF6+MtYGGUhP5hMAVz/M0FmfCaGPI48UEIHhvvXP9i+/vmr2ob1m3Wqc2p78kWr2mkrV/RJiUjoMdQnRlk02MTPBIcdPq8987mntvd++iPt+k1X6WkwejWUdHCEws8ETz3jSe2JJz9u+L0Q+0geExob2O2+GmzGZ8ENnSdnMkrUV5qi3wXv2q2r2KRniuKTeY7Lx174mMe3C5ec2N591Rfamq0bPPHjS4b6wunz26HT9fooxNAdJG3xC6kWM5xXMQPOjFkYElc1niknpS19QI30sbDga+vqyiCwvdp0VqpKV20jQfU9fqpMJw+x0PNgEyrpw3oJReBkQqWUFWbPb7Qzb4gem2uNiP3dRvxqjCnDQtkX/onXeq2O+MDiyENmaGn03wAJ/kl46U98jS7iOWMdGtrKH0hGlo4MJQFCynRaKN1YylIIiNTFaQMRwpwsNpMS5B0cnYZD+ARg9CELsl7yxBI5+c7kBk9fSG1t2uwgJkyMUhUdZtndI5MGp33UkyCQZivvmMQwOgiZsQW5CW6VJ2gsE136R4xFlY1AhJx6BYPfSgEgZLFRsq9hlwmnnXVsm7/gIF8kcOyKI9x+3+rNuqJ0Wzt6+cJ23IrDNP/vazdfd1+7+/b721mP2+kneVi39BOI2fvXBGjupvex5ejxEV11t097sfv27lQfaG9e9xauv29tu/vOe00JL7hicb6ZovZpIeTlwEuXHOlbH+55YE372Cc+3NZuWN2WnvmMNuUsUYkm9rtn7R8CzJMki5jyNUgILdZpL5bCzOO0HKRGIdQHzWjzjj+8TRFunuE6oz8WDX9x8/aauzfZB7zpYq8WdP/2Kl407+G3U9GBnpRFAivSN+kIWvA/NBVT1I0YPWm5s/EJkygmlcwsEtaYKkv3oiYfekKjP5mglMZYcnGoSym4KsYkyLqwAf8gq3A6jwFdZ+CIwRWS4HrJE9gMetrGyQlNk/LQj2zLl0LOaBRWthELMvkISZYbTMXLloRuJhn0mV6xnsQixAU0+9sPvn2zTmPubMsee4jOcGxrl371unb0sYv06qa5PtoX5cCDRvgAkTHKjvCBtmL50e11L/rl9u+XT2s333uLfkPcpVOXc9uFp1zQXvakF7SDZul1UZ7w4gNsY9x5p0P8FglG442v0IFt4452h2G79VOCLi67Z88DXnixz44R/7rtm9t1m+5tW/TOUPtLMvg8ogtojpm1sM2bNrtjKf/0LYsCrrLe+LU0YuPYR6m1PZYbnOa0jYnrUMlPkonPIrtLFNwad4w54oKDBfsBWi9UxHP44nNrEF30AbVk2JeKE2JzjLHoSnz0hVlVjqu+hVY3SsVPJq/4KPvtkIFHrtEfOjQm4e1jCVYoE16xmZqyG3r8V3ihJ+/kGGc8sWPfxyn+0O8w6ErvxTd+hLxkCV2A2TiEoYDO0Md/tOsPh3pCoyiaYaESj2mDQG29PNBFrQH0OkmLQKtXC8JhtM/ICzQkKFYqR/vSWHj6Ium9u5CYv3B1QXB2rBht4YiLPIIEL8NfARPwtmegV5u6SETwx3lWyReTlwMsIPINvgRbhCNff2VT72iwTtdVcyedqsuxlacP6TSe94imR/aDTff/qawrt3VkpDVIvyPS+OCm7Q7o+QfPti/MLx30zyy9TX7q1AN6OPd2ydsjHQfJzuk6VaUBvW5n+7sP/FvstxKpkC6+MZ0g47TBYQsPbhe/8II2dda09oWrv66HGN/j+yqPX3QcHggPDE70LHlNMLJxKh1nKktt+1j1TarFTKc2H9FDuYmv+3UhxDf23ti27Num2yl2tk9PvaIt3Xt4u2jGqRpGGsnCwpHA1u2cCmWxZ6DotLN9C175SvIymcQY/J49QbbUKUk3WRYvsFeqoyvKsYlBVhN0eGCu8eAjDU8wsduSmHFIrooi3OL+VyWxAT6TdH/Rx0PMDD7sPEN5XNTMjArZG/sir+zL4I9d7oeuv/R6zIqf50jq23jgrcXAcYkS8WU/ZMJ3qkY+4yT6a7EODZOq22EXdmMSFGJ44wNb2yEL57QjFs3zGZCrL1urnbwt7VAd8XF63GdL1M/+mUH88OY9j12/yuzEnb3sjHbmcafoKuT7tRjubgfPmae3sxyuBZVT6ByZYnt0My9QNhbLlK+Yv2R64SsXY48iXjppU1a+4SzIxr1b29b92yUnft4v/vmz57XVO7a2Nbu2+8jUvDraUMAonqe2JbMObzNEv4e+td7INDYLB2P6CE1gqaMl8qT6bXgyBkVpbIWFcmy0EPOhAxcghrhKOT6gzu6B0vlgqHm8aIMNXeLnI9NMg9/8kIoIQjc09rvp5D/8zRzYzYu++K5sq639EdT+zhdRC94Ra+glUL732TnrTfzSljEvXyvv+JNS9FImVvkgMHGrecR1wh5YbodWf/bX9MlBACjVmwibCFRKTp0ph0kCzSfEzncq8xuAmQjsDBSUBikOprE4lNXESfjg0OogkOAc4+idG+FpKXaLQrYzVmpcGbTRY+dIULY4LJgGB8o7Pu2meo8N0ImeDkBsxlF+57Ii6qUTOahGjjMwQc8/bRT0lQGHXurVExDAozynAZmQeNQa9vMBA6dOZ+iG+y0P6re0OzbrodW6WV5Hh0uPXdAOmjdb9xlubt/7ll6ToyOqJz59lfe02RMHF5PHTE6TCvgBHRnOnLNAp0VnKZ8rw5hAtm/X0RUXHoBRiyTYyiYwAHL1mvvan3zkHW3/PH5r3KdTGNBN9QUtkPy/ZL3pk57XeeZ3GugNaKCxEgBBEgDBBdwgkqI0I1uSzcnYUrxkZI0nLtdkslWSqql8SKWSD6lK/ot88KRSqVRl4pSrPCo7HsX2WLIpydo3LqK4giQIkACIhVh6RXcDjVy/6zr3877gnLf7fc65z72f+yzPeZbXuspAdwRRoLdbDdulh5peSBPthcXX2qtrZx28lLnWZNlCWNd9dzfuXLdEBoDzK+fbhc0L7Z0tHzpQbi6utpUri2327Ym2d32/zhy3Kajzajg0IG3obNe+dgk7kim/d7AHTvLpHLSfTJf/ORaNbVHBnKlQwibq0Zvt2LR7OuN4PbRmJiA4pMTQqLMaKH6RGz8hzDqlEir3m9H1DXihQ3SmDTKJjwabkgeLjm18l8cNFK0c2+vIY6v0EE40hiIJeaSBt4qVdxx3vxjPrMSjM0E/FnkH793dXn3xdLvy0U3tNEzqmuFc23fPTuuPD3wGpDjgjB8ari8uLt5sq8vrvouUrfp1vWuU7sIt9rMzU/odwznF1nS7Oak3GSnOpzR5MRCzUVE+Uqbno5C36aV/BtBRG8aO2JWxMH64vLGo5wZXbT9bsif2HWn/+WNfaI/Oa/dCn9dvXGx/9Iu/axe5dq1l26T60P6p/GKLJBsHe8p/JQf9cGtik3x5nViQAVDKiXVmCD345XfKpDRp7GD8wa6kkczBF53neNl6SD8+7FyRqj7yIpOxb+QzIUmwJQgJP1gv5Rm3sSR6wS104uq/3HgT/MEW7OQSh2WIWn/uQ/Ko9aNCib5BAkYqGdE3dXy7HK38XcjGsBr6kkGRhwz6Afyh1TapCRCgD44h+agsR6BuHA90ru44EmHGNGAUMRc7S87ptHe4/qR6O8s8VYDUNNCFHsjQ+Ts/5JbBaGZnmFZfUdXO82Qk+jhDiK7sOOCTlbUmUY/xdSisV7J2qqcysnpQgd9xuqjYIGasrhO2cICQowYw/ZMoWqY7Q8HkEfmCdi05bnjTdh7YK1puFtl/YL4997mH24///vV2/WP9nqBebLzv4E7dbfqYfidu2pPjtjndSbmmiXRdS2zrYWam53VnvAmEFtyyVY8veM+feuyKDyYYYWTj5GR+Kglz6bT4nc+mzkg3tArfnOWuM7Y1ddRdgTxYjDg6LDCf0cMXu/lgh+BMutS9t36+nV3/oM1oEjOhcBicSAxwDCYkbk0PrR41uX1ZZ7Vr7eYv9SDzmdm2dUGv3dLr5CbvzGgy1KQsfLF2m6z5oWikj1olmvBti8HGVPNHUbcBUC9GoktkC9gtIVcDTcYaFlHICR/kw5M0+MHx3CsQjgY+kk/Cx8YXMfzMgi/wdMzt7DW4hTeyQhefm5OAfrn8JwTAm5Sj+GvArcGy6oygL2xWA/iHoQ27S1faMhM5daDqu/PttIJM6V25XL+j/yLHd44qj28/9+uP6VckVtpHHy540vr1Lz/Z9mmhhxuZLG/pmu+1qze0db/Qzn9w3a9cW9JLJnjZtt9Bi3+ES59w3EoHHhvi/by7ds+1Awd3acLdpRfE72n7D87rudPEaQbnrittIuXRf2gv4q/bil9ph2oXFmUXNvSrFX0HYs/23e1fPvmP2oM7dFdpTyd3H2pfffiz7X979Zuivd3mts61Q1Pzaj74xPe1DYmPE1sZS1UrLgjnP/bh50w6iUVQ4FXtZrFBN121bfSOnZZNLQoogYNNXuB1OYEFp+46xifxbXQKOXpFb84AzQO13f5j/SWKUtHNKdkAIOCfca/rKGFeAAiNHR9mItWAbB0IxpoToqsQR2zgpKLsMk/aLTslNZZSk3EAsuBhX2zNOOnJVf5GLz+C5rzitrdcVyCG2BkoT8TaCFR1Fo0tpENUxOExB2pqSShLolSrBoPQTL5kmP73k6gFpsb0ZFyOicZP5UBaWwKpqxUVugoRZzulUUvLTJ6R4RUYPpK6yDZOVI9dwHE4CPzJVtPr6KCzfsARJOowMS9095mxjgBcpwECXvFZBkNkmr85REvseuJTR9teraKvfHTDA84Dxw60ea2sWT0ffmBPu7lyVNuh03pF25wHj2w1IUyN79U2cuQDdEQvMkpW1fro1QEbultuapuh2GCbjBW8bEmbgXls6uyTW9mLUwVusAUVj9rqiZ2AtPJ30GeiJUa8IGFA0t+EfuVATu4+VFmD3a01bX3+crqdOP1g2962tc35vsCSX2oSRk38FHn4F3v5x15FmOoYTDzIWSa6IF3tIiS3IzfijNnteJbvejNKz1BgAym45qDBn9iHpz6scpw4Ak/ZfsItKmawM8S+NJ6wQ9FpVFCu14cjOlkfs0yd7aR6iHH4Fl0ERudwBxXL/QAzbpfvUdmy5CdrFdWskG3qAwaLXuRBgzdIjitnWnvv7QvtlZ++o237hbZtbro99ezx9vjJo74Ba4+em/3qv/hiW1nSmaEeD5rSj/tiC5Pb2VMX2mu/ONPOntazhDoJ8+JJcL8sWrop2+3WQCVlb3MWhS+0qLy5ojfDrNxoFy8s+KF8fvj38P272+NP3d9OPH7YC0ZuHsKObh3Gxl061iUZBkmbJPvsU8mR2PbBzYt+vIcbCD9/34m7JkLMJp3QqwLndb3yhrZN57fuaPv1v6GJg8b24AySfJe+RwH1y3+jdkw8Mw3QBxizUFT1tEtfXJkupNZTCOYHrvUGnw/8+TNdUJjMSEIZEvWhSywglzL0HhPNH94hYbxm2UoqPMsSgnFEh3qxm1z0gdx4OlpnAWgTkheCxJQZCBayUT9RGVzjm6TrQz9XO0GX8ZUxgFajjLzyBX0uec83wieE0LF0QmtYU9Y2KRqgh0VaHxoBiD9WyCj6EhkdBI49QRX8UMAA6hgY9OpTtE9cRn2tAOCl/66HKqyYzLPSdjxU3dkWa1zogEeWG1eN5cFLwJE1nbXQTWHaoul4Jd66W/sQSReWsLHH4tTBe7B7UlMHplY8YeuGwBr5hwVADX62QR0YffmPz0dnB2nY6gTC0Nmrt+Nkz+H772n36R9qePruQB0ntQLm3aQEAdcQJ9gqQgkh8hM63E3K6hkdbutxCkaBtIkQECXbtugi8oa2UdGJ9iA41ZhU2o8g+tk/gbmzb0o31Bz/0mfb9OG9bats5MYWvek94YBpPVkNwdEZ//DheqXlI5eHeaV39BWWCIaVucQja+ODzfbg+0fb3OaMnivTC7/lPz63tCXadA10Ri9oXrq1LAlECu3Yg7wHm33OdQ7Jc6VdE1nEsE1VhSy0fHQkBV029Q9gYLHJKNIVGH4i9XZTzjEqxJwRjDnEdXT+dHx4+9ojjDtd9anEBvxTR/14nnrbhmI9udyLtDcpg0OsKHpkeoBVfQ0SYiYP0Idpj9hK+8DeZ+9Sw3TIxVdKqAZPVtWn3/mo/fkff1/Pri63mTnF0EU9fnPqnCar9fbcrz6mtmR7fYu28XdYYxap5z/8uP3ou2/p5fNXtcWd3Qb0USO3mxN6k4vamx8mjklpGQZi+8a+V7vpTmkmTTWxt6/X9GD+u+9cbu+f/tjvMP3y73yqHTi0y7sm6V/YRLszrrjVZQSP9Ky2dV2/9mMiso1dh5XNtXZ++WK74+dfdUOXLlH/6Pxpx/oaZ8Bco5Ye569ea0uXrwl/qe3Yc6Rt109C8RYa5DDZM9Hat7ZEdsSgtJ8LwOJ/+5hIFtzt5aNZ2d92vH0vfPneC0Ad7Tfxp71hSZ3+VA5FTYo+yu44FRngRBbR7ohHF5phiGkV+BPfmkCsdtcjY25YMn7QhIlf+KnAP3Yoh+kcyYBLhk+wwHatv+0DIKgL4ZCCbVr4miSUWdwQHOGNHPsmbM0I38Dbk71ZKV77bgYi+jYpjIM4yB3LoDTJfJWlQ5UiKHYXLUhY4EYm2+sBgwsX/QGHazfFPChJVUGTwHYCNyqkiHDzVVEtlbMX9NdgI7lwhkvxjnPTuQYdRE8A4hgix2cqphRrlalTUXppEw9xDrboZlxMBKHjMFF5USDd8Ad8HaCdN6agjwNQupkWa4xHIGWghI8T1zHpHOpb4EBLp0aesvYf26Nue8tM1ZrOqK5eXm4fnr3W7j34VNu7Wyvom4vt0pUPffs5252lwxauvUmef/aH5yzMWsyRJfkMNLzJnpd1I/eBL5xsx/RDqDvXZ9ol3VWXiU5Bp8EOvZkcvfXR+QC/JfsXry60lbXFNq0t1qiftvLCQi4tf8px7bYGtZsLy23y3HRb27LSlm9fkf43qFJwsy283h57/FC7uHRvu6g7a/ELZ8TwzUDH3Wu9o6uu2gib7Fps0sf+BI/2V6oVrbdw4Ka6ivPEMFgC8q0DbUwi5uBLxxv1A/LUoRVU6BE5BnQd3AfACVqO8NZgBHcPYNBbFvqDGLmuJ7ZUB3SQpTI2jeQHXyhOpWN0VruJh5HFJepGTsmEqPTEbx60QVS/Y1v05Z+8q+c8N9rxx/e1fYe2eZJ665XL7UW9bP6JZx7U7w4yxKCT8DWRvPyTd9rPfvReW16+pUctptuM3rWba8h6qYK2wU8de6utzWiRc0te881jVkCXtfV40bp05SRHBwZsdNx3/WA7uHDI/WBK8Udz3ri+3FZ0qnn6nZvtyIP8CDRtpIruZ48T6ji31Ln+1at/3t74+N02pcsQPLrCCx9uC75yS3df40e16//11vsEiniz8MxvO+6c0UR76nrbdmRv27Z7qt23QzcESR+PRTpyE9B4m6hgfXHdAKctodF/YD3PSs1VKctit6nHJOUH3B4LMYt2lo18Wxb+cRHRytd4Bj3wTKRD+xu5YkDVGketg75o91qsMQQVDXGKLuhvUchJzoI9gcIXeR0vuqMncAhM2Q/KU+z2mxvlzt9taHQYKqZ6g7okOL06/CExosvu45YlH4BHzCtTP0oc+zQZOkNDm5UwxcQwfYeSY7IMoojMHw3lrARmsDamcNK4UhUE/fnmFDmO5IYi09l6kJCinDjhyIDFw5OalEacHdkNcVADRL7wheIreMjq9OajCq+6PFCmM7jxkA2qUq2uLAO9AfIl9ji67HCMmf+oHiQPOpYpj401LDebWAcmagUhzmfAD38mROxiYMGG0SQNC+Dxv1TpMjNZdBlCQT+ex6GjcoPByrJ+Jfz6Srv4oSY9Pai/uKBrLnrF2YzenDE5dVuvc9uts8Tt7ewHr9v/dCpWrjvmdIffszvaGzfOaBBjEtEQowlHF0vallvcSn27zWzdoy2qDa/C1xfX21t//eP2R3de0vU72SD5WzTY8aYcRqOtOiN1XjagO2cp+OjKgh6L0IuMhwkDN8oZ+IyzYHwFL/7v6KwQ+zb3bWmn9v/CvvNg1refXLf7drv8sl7l5rNhDaro7CR6ePZ4pjHjQjyPczkq4X61DfoIXYmjtTAKceHBQzWuh0kYmQZ96Au1nQfvxGjkZRAl3ru8MOm8YNVjwPqBEzzHkNV09HW9iF1okBHassUrX9HaP6qMPbEfX5YNYiRa+KRNYOS2IS6lS/LAwBzThaKSOBqvW1NAP36zcG21ze+e0Q1f29va6m1thU7rZ5W2teUFXfPTwmbbtvyALi/s/tbf/Ly99doVxaS2S6d1xiVO3FhB8oClxd38of3t1gNzmgyxA51pLCEI2YtNFTZ1sxnATcXg6jm97/RnQcA/tMvxRw+4D3zj66+2/+j3n9O7fg974haF3JIFAM3JjSO/99AX2m8eea6dv3yh/ek3/007pZ8O26prmVt1o84WHYmKtAte4IeAdYlCb0T6n/75/9D2P7+r3VHMs+ib1cP49eyvxyZUdjvLd9aW7/JuctTDs5d8BCPtGr9TDY4nf3Qhj/Kyo/RKGRixQH0fa+lbAhOnxAjjRS3woAlu9EJ4vEg5WrlHCFjxSpyQhz3jsxfnwoYiE44yxB1jvYDIQi4JWiujfLhHT7e/eNJ2wKsfjefFSLQZJ0G0LejBZKwPwtznhJPrm9EzMR/ZwUO4DbIuZJ2c0aUceJmdFVJOBriRDOzI1AnupLwNNVV4A6fWhvR8NfRdAiGFXthlhApWDChMqB7RGERsdbgQEGQeqApBjhorhmQZOI8AkCOZhoJLYEZAzOllWEJNFRXiPwyUKjLZ8laX6B4x6A+vgslDkdEdW3CrZaM6f9VXnTmpzrqbLnXWtdsGXxRz4Es/OjCdb01nSFcvL7UL5xbapQtLGnzW27q2dWyfGWsVq5U7WkK7Y8deDVI72uLqouSrsys4b2qL9PKcbh/X9ThfwNfJ4RZNnojc1CoeX+yc2IeDzXFjebndUrCvMhHLv2x/scu6hVU8g4K2XdlS8o1QUNAG+B+9eVhebPjNQkXsEE9cH0xzRs8tmlhFZv48bE9iIPAEBkshX1j5qF1bue529YlCdTjVOa4konYI7EbB3fHFRu5wQv/qeLD3GbBqaoESLIkVb/yduBB3BaNbxOY4J9Qcw08lCe3N55gwXFjozocy8UXZg4jorRY85evUd67CQelcFyUu0bu0gxc+Er+uo9vROEJyYCEHnYoHerBISaxZB9kYlsBwErTR1ZJcRBal6E6Oxc+sJr/FRf3wsia3uXndSKNF2IrO+vjxZ26QIa0rVr/59Z+2t9/8SNv3uktaZ3l1HcuDshhzY9amnh+cuKFJ6EEWk25Jq1L+Qzz6btmu2rhFsTTVbk3r7ul16W2d7+hB/bl2/eqK9Fj3L14c1Hbpjp3cJNa5QquEr5/Yd7wtqE/863/7x+3tD0+1SU2CVLMbQswYz4anf/O7nE8feao9vO8B90W0pJojbQlP2oCErjBLyVmFMW0dBbDLlDo6HlXHbgx0pnG9MMwzNEx01WYgWYa4ZIJQ/DLpKLkP4g94fYJPYGBZio7wRqssjlJTdfQJxRyKKaUthB91xIGPErYqIbf6kGMbVOqGAbrzFYico0x17O5k3AifxHnnrnajjm1ny7cPui7YyACjm/Bq4i17zR3R3QfxDZOp5ImHT6KsRJTJNUMBLNZ6KjccaTjV0Rg6wjyYLgzC3YHcoK62wt3/QexUxRcHwMsdUpLTAQlwK4L2XYcYDBMc645jSiAo2bXC2ZBahwSSJchJoUuAYCNw7DE1jgQivDQybOQowRJMGoYVXKwPxxuKFQnJYmEofuiC0x2U4sEQRb2T9Io8BjICLkdg2ERt6c6ZnumMkw4oAdpi1EpaFfz00XtvfNwu6M67G/q5p3WtkgkSBtd6vdctnYUtL11pczt0rVE6xM/Sx0FDh8k1O37od1HPIW6d1+SogWsLE5rq8IIDRwqaBiYoyxeBqIHXq1fBHWjgSwe7RYFr/9DYqudmB/QXUIlybKVk5TjQgfHRuDzBTKL62tbAz3jWElQPPv/8dE7oqQ9bjlYX3dGPNiYnAvdLgS2PajcWsSEcIVmuvrDNnVJyybtGB0/OxWusk0PbqXvfwD/4m7MRpu3Uhte4HwSnbUSPfta15+3fMDYv6pNCT5F4Qju3gY6FkyP4xCtHsKoeQnwIH/qW8lKWsm0GbprgVz6g8JrUwuXpzz7Y/vrPLrUzp676Zq6F62t+ddrzv/Wo4k83Pmmi/d4LL7e39ZrBWS3GJtmOL79atjWyf27rUYbZi1NtmRdEpFGsNloTc7wWjZu3iAASPtvksR8t5rZd0WAIjXhzNrquF3izWPvowg1dQzzbfuXXTqidmaxE7djpbapB/v/+/tfaj0//pM1snx18R7uRaDcmQLb/9+7c27783Jfaf/ZrfyAxun7N9cGOSNkfFnuVDAuKTLXrXMuXy/iR+t4+FJX1YwjgiD4TIVi0UxA4elyjH48xpo3QtybM7qah/1PvdoZX/0S2uMsv4GdxRVYFxTYTB7bb18Sw7XWtdUC+449+oDqPG6gLOR/sUMpitOJLR8FCG4TkgaW/UE6/YfkgHRhfoOp8nVc5sjM+I6dsL97En8dkfKk4NwN9AyOBbzvFy5MhSkNcKfle7g2Vkr75G8M1c5VdHyQQUu4MYy7fnZ5jgIKV7CIGLTAHmI0IP680Oj46e5CHVXncEuQgfUhqxxJpHPDDqY59kOqTBP6pgQCe2JlJkUZlNZ2BOOK4BpKOVeJrAo14yejyKcMP+XaB+NDZHYiGgRhdzUtf9rHo0AdrfnnujfbdUz9sFy5daUcWP912TOxSJUGiAIRIfDyY6VTt6tV3NTDqoXtdB/ED6uLAlie3v4tIqMiGzj3PW5tMhJaNHujLV/eLK4CBoH/jKeskHOywvr2v2He9PgN0Jl9wPOjTCjKZQd8DE4zEVGOXjnzFH8anzg2pKiGwrUpChvVQnrMMOlu1D9TWRzbQ0r6Ow7ErbhFdhjgN/ghfvpVQ0DLK0BFYCqkeX0SO21EFNZXlpuOXP0NfEz7XScmjyzBwiLWssYDSUZxSjzEaaHxTS1TQd/kvMrA1Mkd+s3lR0fjxV48rVVablTz3Jdtr7paE7zy4EAdhHZs7HOCjT97vgfSHf/9Gu3Z5TVvys+23/ukT7Un91iZ+fu/Nc+2Vn5/VRKlnAxm8xTmWWkT/4m01ulas63TbFva1xTX5ZnokkIHruLYm/6vHf639xekX208+fMNnkm5O7WSs79LvDV7Sdqz9r2uGN1b9kD+CwHn91fN6BdwR7Y7wdhiQgOP/ifbuexfaj195Q1vtmrh14w7tzhkkbcT1wT3aUfmVx/5B+/RDT7fH73+kHdt/xDpney/q15iRUiy0bt062gZdsvClzfBs7FMp/Ywxj4GbuHMSXIkbhQJK2UDgvZ9wAkH741u/+5lJSbzT3mCrX3FQgg9y0+Ydhh4dDs+aDOFCTPqVjjq6L9qoHkPyD3esAzdfCGDUU+SrXjYl3um3Paa7TGsjkviPMTHJ/hLT+jWU4gW8dEdmxTA6SMUkZUtXcFkQVdkxDq4SbQ8/PvzBS5tXxQUmAK2iCagzsp2gyoxDqoNGZX/n6LyydfYWZgq8qhiJGciLi5U1oiDGEyMdy8joiBxXWnHks4s2ck4c7QlThjAxJPiYCLTy1QeDWTlB4yBEpvJ87CjrwJf0JqjgI1rkmsbBAqwHG4OE/TDSq1w10la48BPNMOAA0VYlPD2Ai68Us68dGOLKJFd3o333zR+3/+fbX2uXr11ud+Zbm9/1UNt9S2/faPw0Dqyj422tYC9feqvdWD7fjh/9ggJRtvrD69U2/HC6WDthA4Hqa3b9mpt1pnMRdIzsJJtoR8un8Zp9bkODghLxBMroj7owUxUZFXunMCK2C2aaXm8c42lla9kw0r/yngTJd1yLtu9lnY615UZb+h/94aWPso4D9LFMvqgTL+zg3+4TLL4ftW/pXvhZRBAb4Q0c/4JH5yo8+0cFOpsHDFcM6rutsctg+QXd9GddDHQF+FEaGQZJUWIYqejCJJGBBOqO7xz8BqbUCB/+0BIrObPnLUP2pSB1VBZGwdXRtokucSpKDS5LC6v61y+f6DOv1wg++ezRdvThg368h7NFJkRifUUvdvj5j95S7Gk7lW1QXoHFdhdb4Qi02rF/Y0NvddGZ4cy6hu5V3Xm8Tdwddtiuh/EVv7yCL7s61FGp2NDl4rV57paOrhzffv2CJr/PtAcf2t9Ov3tFP5a92N5/91J7+rkHdSOyeLAQlQ/WdbPZD771Tnto8bl27+yj7dKtc+3irTNtY2pBd5leb88cOdn+u6/8y3bi8EO++euW2pNnH6M7fky71FmGTWKRhMb2N0eMJB4ZS12jL/IjmPugiCtuoOGTCW+Mh1inDc3I7Z+cvmE++LRo4FkYpRd654wydV2XAfFuPeCbO9uJOXh1u1Wgb3XBlkObe2GOtRJnfGVsD3WasPGVRVGfJlS9xiHVYxt204biILnwH6XEOrjdVNMECXyS6XWsfjGaQMWTBYf0h54Fj/FV8PgppXIDDUy6BB+NhsAYgggTjh1LOPDRRIIwJQuAnlJgHkicRxNwiisEJJTrQOsZZQF5ggMDL1ImmI2qL+BdRvD6xKaqOguhPo1ALjzwhfM6OpjLVhqoy0kjWrClxC4arfvFDsW9osF/4giN3G3eNAi+IZHnIff4WTjIcQ2VwUlDAr3TrupVa9wVeu3acnvj1MftxNYvtvknP2jLhz7WTx/p1WVndPu56CxPvHxWdedaO3h0tu1be1osteXDB/nSd1XXRdZ5LEF3kAIzXPX8oaPv+esDNH7zuNk1RLvS1b7phTrDsRmCuU1AHuwUULxx54gB2PKTBsXokLIVoQYdTIZu/AN0S6modveko8EUf+qfoGYby4jqne64KC+6iJUs4OLVWZsfUi0fnsjTxzHSJzXHhHjbF6o3jtsbStqTtg4PfGJ8wenQlXeHNDZfiZsU0YezAIIAeyXHNhNJJIxOzDiauh/se8E5Vn7kQ3ia7K4646GfJj7rbHqaqLeLy0yS2cYV9/jF+uLfHrOU5cBfvnS6ff+FN9qitujFpe3ZP6eXb59sjzz+QNv0zTJaeGnCYMv+vVPn24dnrio/qy3TWZ1FHvRk+trL5/yeWRqIRSona6urH5vfVl2DnryqHY39uv5d7Si8S/rty3/15g905+qq4fiO696Kgra+T8/LTqkdtfGBjRe1NfrSj8+2L/2Tk+3f/ulL1uH99660p55+QLQS06//nz1zpZ3T7yXObt3W5rbMtb1bD7ZHp55un/+dY+0HL/6sffaRJ9pTR04ovvSD2LrumTbEJzjajHSkPeInrAGnBuLEHP6s8afojCZMfAofUcke26v8v9e2FiVswkN9nnqPuY5BlYlTcPgykqZ6gl0JvoETH9BGR3hYvnGiB7DITl/ATMpMyonp2IZslyXO+Obb8z1+GN9wEXjmMXQ+dOp6oLNdQibwwXcaTGhbqknlH3Qq/49sjG3g4QIE12UJSqRhUuw2VdsAB5/P8JxhAMB7ZxBOTUIww3GFw6rKThYD0mATWEIDM0CoirJ4UDlK4aByMaHKPCIPhjgzDig8eIqgUChRtODgw6a2OAMH0pOQLa4ra8eoCnLyvAGEhI3efujB7oYHjgNhUAkaAeiIPDhcKzoCwUHLKaxwPNCbxopaHisp66IjW2jLa3oTx62r7Tvfe7PturTbjbh1+1y79NAbbXnuuk7l9ToqnR1ubtVZ38ZEm90x5S2hAwe3twP3npD8ifaDF87orR83Bx2xaWn5enS0LdiogEYvLcTLv7EP2+TvsrHrF7XRe8xw8QgukT+WgCvZLh1THMWVAeKPDiBFLp00fBxbYRGYOruLdGTR+CaRzP5w7y8BCAHfJVfZri1tEh4RWcyB0XIdU5nqvAGhY+rS8UZ0UTy0Ym28+DGTDp3M10vVqWOf2hkdFFMZ5IkJcWEwxCaLIYbCi0r6nyUWjbDCy9opX0cyKeDTyOEIr85bGMOAoHzhlW680b8G9ZroU5eBkJ2Kd9883775F6+oX+kO3mmNARoHLl+83v7y3/y0/cf/hX6+6L69PW50bU8T4hu/+EBne5Pt/mN72pd/7wm/NWlaN6jM6rVs3/873bWprXl0uq2t/TU9/rNtZt430WzXZLigvsP+ARFxYOe+9l8//uvt0V16TEL2fP+j99qfvvMjbacu+3nXO/N6VeGM7nzekPNk9FbF9Us/fl/vP93evvIHz7ava0I8q8mQa+x+QThb7fLNe6cuaeK+o0c8iIL8LuKjjxxqJ+4/1l78mw/bgs44fY1SOsanUqYnyuXP8lP5lGP+hexGSvwhMxHUv9WuPNkPCs8Jk2pcDc9qPxASq/BFeUGodI6zdU9OlMf6d1BGsV8TTcV4bBINXU8pvLvKcAYuccBDy8II6Uq2y0QU9E/gxq9UIYM4GreDMSXlcXh4ioHZFN2mHunifbAseDKG0ndop/AoXekjSC9brJ8Ari8dO+8Uu7GCjbcf+L4nPU0jpXyzA5RJJdgt6FUxEFK+OTpXX70hQh0s8+iTJ0048EQzGSLThG7tYwAla91hSNCfYcgxOo7vjgVmFH3Jca5WFs4i0rcgnLIo2alAtE0XRoFTR45/62i9kreT1bBleXDFw8hpIGDg4VyYlI2gkFxHgetF+nifX7hecQkbnRmsb+rGlz/6xv/R1rYL787htqfpPYgStDi1qLs7J9qRDx9pn3n8kXby6LF2dceKJ95779OdcjumfQ2F63Mv/eh9b2N5rxxlaBNtLy0v62eb3OH6qg595Ebu7qQzeaLG713pYeIGdleCJ9tcAVZQcrSv6ihbByRQq2fJl06izyo1aAR1JpySJzniJbAQ9K88ydtjZKSAO5crAaA6NKDSKdM2Ju95fE9KhyNW3FKh0zeqYQMCOYQfkxfbSibt9cgAwL/aXXkvakxLnHWlEyQmDF9xNB98lUGQ59pIrledY8jlcIcAvflE28SxiYBiGx9E6sjWel0KQLfS0fZEuEmjj7NjX1bOOlS7BpmXPdxpL2uCWV+73Z77wuE2qecDsXNJdzG/8oMLmnzebYd/f6/00ZmddPjwg6vt7PtX9Hakne23/9lT+p3OK+27f/tOe/KZe9vjnzrkmIufFfc3r3mLdHJuv8TpZrEbevG8fij3jkYn3dbVfv/hf9g+s/+BQc/fOfJE2zkz2/7Xl/5a/UJ9RdcX17bfatsWdceo5DO5brkz3V746zfar/6jh9s//Refad/5xpvtgs4Cd++d83WkRW31fqAH9OknDLgojk+OPbK/fXjuSrt2/Vr7tePHpSevjNMujNur+7v8SIwo78mFgZ826nUc8QUpCx7q3EiGEevVFfADZIU/xKcA4QMjEPgj79EtIBUtX/VwH004FmObXC/9wzeLdqsJKyXrzbErwEmE4cgnY1tA5j+xi8vQrd7CNG6bOQoVn1UsmrW+atyET9kWGYIwfhrODYGccAXH44KqeByMCTZ6YHcuqRRe6Y8u2Fe4nNxQ5/GfAIGFEngZg/CJdxmosTpGMNJY2YIACm0cizx1hsKfxnVjj2MN1VYm2F0TjE718J2GVtFpxKeMxRhEFoc+93hykQAUskMJlThGHAPujjaK8+FZsrCCQYejQkpENbjZRphQJ/5eTQwagKq6Xo98sqax+tCFlg7nBqEBBfM1QzVseOoxCDX0jpkd7dPHnmnvnn6zLW2/1lZ3r7T7H97dnnrwgTa3Pt2eP/FU+8cPP9v2aQV9/NF72okn9DqoXXmVGvpd0wut33/n47t0QA0mwmVtk6pnWx/0IMy5g3RKr8iyimUDjJTAiR3xh2GywbjVqcFxAKsW3xccehDdHn0AUD44cFJCh75ypIpOgjw+/JFMwoghvsipf+rxqrGNxEIkK0RIC14mUSeHp8bCyGOfDh5CXLTfANLJ0AUbyg/uOKbpyokE+sgNP7jZZ7ATiLpxn1Bf/NBSUaDYLT2oHU9iIP7E4WAtuiEUiOAeJCgIpHEnMvGV66lAH32JBWw8wLjWNXx1G2Kvbe58fZNHl8Wi7ZbuWl68oWdFtXji5fC79m53DPPgPI9FLOisa0MTEzK4hvv6K6f1+M+d9tkvPKjHHG7qjtJ3dSfp1vbYyYOalK5r8kMmbXtHb6u5prPCndKGeNI1vQVtkeqRCdpxXs/HPrPvfus6/vWrBx7UYxEP+O0xTbsk6/N6KwzbHOLHtUX8ganff+Gd9sNvv6uX2T+il9zrnaj9Ouk5/UTaNT1+wTVd+1i4HGdm9QYa/ULKzOyWdkhvWsp1VZjFh45RyeCIX2gHkmNTMHw9So4g61QwIIVUtKlD7wzOBbeMPm6AY9ouz5yVtz72VGRBO9Bbl8RXxSEqWs1Oa57yHMCyDXrfr9DNKX4CdxyuV6tSqc7+6MsVj/FNHAFt4Oqj8j1zBPWemGBoq6IF/GqRCA6JA77NfQHxETyiE/0evY0KtnnD03efuiK48EOKMIwcf0RH8tR7m3TEDDwG9Di50w0MzMUOQUmYg14MEdLpPLIZ2yx8q7CxQ2M/jhsQVMjNz/roKxxhG75jJCCaqnQvM4vWTgWrEzFZm98nbROwGtsTHYOmYEj3BC9labPwAVYrDPQa1EjGeBBDDeOOo4xK5mtbOmEaMvxB5mH9L518Xo84TLS/Wvhhe23XT9rq/JG2tLnSJnS2+MQjR/1TSLVy4+0uihQHGx3p3bcuazv0pjs4/NAb+PXrF6RSAq68Kql6KH9ad9FNtbWlZfHQ2Z5+fDVE2InCSijuPMYkOacVswdXHFNOtl0KPvkIUHiQ57kx0AJ0XnrZ37SHzjpAcBvJ35ypkhgwUcCdSfXQ+6F8mW1aY+k5Nn61YtChA1FcNqv7yCTxUzFb2PKDcKtD+XoLbW4F0m65+xRIb2uRkyfU5W4V4I0c2g7aJOsn3lmRAqOzRj51QY09lOngaEiqSc6F+hJr7HQScfVL+0NgeJMCt2IeOMS010W3wQ4VPYCLJnceMwgwoLEKN6v42IVOS/vow6Qxpfd/snuUrc7pNn1oSr9RuCxZgunVf/zyBIl3lL71y7OaTPa0Yw/vb3/5tdfarl2z7fn/8FHZ2XQWec4xAu4tvSpwQ78NOKs3uqDCxm1dE1zURHl1vd3eq0eH1ra2t8+ebefmttn/k9J1RndJ83KaA9v26AxQPtYgu7F3Tc/A8t5c/fCzbizTVcRMdHLGKz87q5eAX2uPPbG3Pf2ZR9ukHqHYOjXRDh6e9yR+Sw/7e6GNC/U/qwmRVyDu2qM7tuUL/Fu+pk2HuBd6tUnFH74GRhpgKYJtOF+JixGvcZrKj/DSbvgncRQ/q9RlqY0SmGCId4812QKN40XH0jVQ6aLKjAeJg8i1FMshV6nspy/FrlBiL4njuA68VcjPRKu9SNTRL6AtG4BZJ2mBy4pX/DQuHQbR31jdv+Wnkjvi22WgE4RKHgN8jM2eH+jH8OptrI2IEsqR/yhMNjX5Bg5EdE7WxzhpkDgodfWNIuCbEsEkD1CiQY4lKBfWFhjdIBK8hIksjSDnmZs52anOQaQ/4iEOR064h3nK4KJFBYV5IbsnOxe6IBGx1iNOxi+wVQCXPAdgiLHIzDlWVrigAh8GX1WGn9DgpQBRKAoaf5D/8lP/uN1453Z74eL32i+vvemB6w+P/64u8m/zb/d5ktZWRlZXkb9446ZuFLjm32JDPv8MfNyuvqRXp5G3DlSpLXjUYofeNbplz7529prOGtkiv6mVvd/6r8BlBhTIemmy4owAd9RERY3bXAMR7sJItjDZ69+qSRXXMAdRBs+TrXggO04xB1NywgoPn7gyoKK/aPygvrOZ8FkhbrKtK/7IINEmy/y+nCcGEQKAni/+YNxTdTwq0i7ipzpepkOZiKkOBskwAPR2rDITAq0lp9Kw1t2+QJ4ZR420M4zjA1QpufAnz+SAksOZBRVOquBPtB58QRR+VVnfXm+9lbe9HccRNejS7VJdqmk7LC+GsgdbJHD8VzCGvqSaKV3rO/bIPXqp9uX2+kuXdEPMtPsBZ32c8T2qF2T7+o543rh2Tb8osaM9/vR9rnv85CFNint1Y8uSt0p5ZSC85UBNdotaxc/IF9nGY4tzemOmbby91Jb26tcsPjjd/ts/+U7bc/JePW6hu0zlD34V4w79Rvlbwnc73Lqg6+q6Y9VbYrKM4NNAvHfhQHvk44fblUvL7e8vXNAvW+xqDzx4X9u5a7p99Z8/qxfVb7Zl/ToKP56NS+7TT6SRHjiiNy8hxz5m0DZYx/ipYrdngAkAAEAASURBVIl+RarJLVjIJ8bSR2gamMOrJtI6Bp/4MFJwJMJxIVDkOtqCqu+Kq9CEjrasNoUm/uRSBe5BRwvoOsEv5YHaNAWrCQ8+sQH6sgkaJ9GgMFTWRZn0qLIzMVZ6po/Qd8DHH/g1nR8uFQOB44Pow1jEuOO+gJ3Qg98/IrR8t7nqEFCyUtC3XMBiGNmkYcGuPFBk9hto0qCRHQVAMSOQlfUgpkxqbY5qSAySQAtfR2fRiIAIl9Qy8GdCs0uotNAgMSBUqskDR1QevkXvgPCKFoeLDYZWw0WYoGnIDCY4XxUdx4O9GHpCEZ47p9RAZxYzVkvophFerreFH+xFokodNIKnYSM/JpXdwreMyEY/EuMA9gsavQNAC9df/fiGfrFhtj20/JTfHkvDbZva2Tb26YFjAsgxJg5SkgBAR24GWFniR3zZCpWfPElN6AXKH7crN05p4tvTZhq/A4cMLLjZVm4stamr8+3a2Q+0waQ3QOincyZ4ubd+zNdvglEAZpCULOngf9nAZIz9XK+BlX0kHTjGRAwkLtBVMOOLzjCVbXcmTo9AKAyhdKt2yK3zgsHX2yvCEb/bGkjv6PlOXqJsvlIhE6HaUrJGz2WhD2wzyCIb3viYNs/qufsPfYRLM6S90VW0Eom2JMd4si4bATcKUdSGEVPEgjgFR9/wY52B7MgYMQlV5FgMQoUUP8EnEyFH/ofO7jL8ulxiwDDEjuDEil8ZJzTHjWJbf6JjEFLeNNhOn+xbfj0+LFH+hI7+Qzw99/mH9TKHVV2Xfk/bptGTB9y/8BuP6VcqHvBgs76ux3s+Wmif/eIj7eHHDvmaIrZ98+tvtov6mSYGNXyRMYOzt63eIl3XYxUSpW3JDV2ju9kOnzmkuJxtb+u3LRfXr7Wbb+a3BT0OSR+8SPv7mp5igsdvjm4/2Xbc1lboFj0kr+1V6ratzdnmSe22LOiNNO++/b7otrW/+vMX/Vaavft3eGLnzJYt0SVtBR99ZI92JWQ3A7BirmICP5Ao4z8+lcYnt+Cnvcq34KV5Q1ttV7xTTxukTSXE+MihTVOP3xTwvZxY6zroUGNB6rWYlP3DAk78kIWebk/b5YFEdMR7bxP46BNboSF2M8aAk5ixOtbC4yfx13mjkycx0UGryHNdxW76dWIt926gN/Ep1M4j3KOv9XJ8Ri+Q41PokqcGOdXXoUc2unLiwpHYIhcaYTM2mV5w2cfNizozDKMoUN+2QshhypHUoUMuxgIFIUg4MsIjGK3ogBDb9WaK8b0hqAoj04EHfThibKTW0fU+u4BOPFTdWSLKdJEX+TE4SOStCjoho+vFIA8t/Jj0lI3CPqiEswgeWqwT5a364uKtrugZ20GBMTYqcJFJuWRxVHmwh3oqkc+Xsu+/f7ld/2ClHZzkBhomW539zGsrUHUecMG0Ktobl+4Leubrg/d1g4w6LyzYRgVXJ1Ht4xuntX11p+2bP6jnv/hhUhpezxzqjHFVv+Z97r2z2iZdElS/XfjxStOPDuZ9ozpDzERoU/qXNMVXCOGPC+3yjcQYZjtsq3OmEVo609jRytMGVMrn8MNOc45hglOPkPx7wpM4JsLbGtTYSp7as920FQcoAg0k6JR26O0MJk7pg021kcAg6ov2gg5toGHCN0PBQVLSIYMEhdRZkOnCGzrIMkgo4wQ9A2kmYY7cuEGt40rVvqZsjNigrDsp6ibsEjPoDbdaOJaNVscjCpRgwJPYSd4AVIQenwseXSm71nBy6OQBUD3W9oqOrsYP6/7G7z7THnrssLZH9YPM4nXfkX0609pvfkw+Vy5daj/49tv6VZGd+tWIve3H3z3dXn/5vH+6iYUKE2EtXhRA2iJd0ovYr+sHfFd8o8rU9FzbtmNn2z6xW7sgm3q+UC9pV1vfvqn4p7ujLH+q29RZHfFA/ZbZrW3H6q52z6putZar5+bukY952418zqm/HISP337zHd2wo19C0S7IVd2NevGjRQ+Y+I9djLkdM23fgZNt715t2yIn4tym8aUAQo7/4kv86DoalFr7OP2wHvqGJnDiEB7B82Kj6GVYYtDVYFg+BkObAZw4Qq/ABl48n6I/eh4wRIhER9p6xAceJPgpwIxvgGNGPMUk9EycxavHgRBZFHlcsA0jXeDhyzfiyye6xiGMqTWJe4fIesWPkS3V0Ud0g10qAbPPlI89sQsaL3zVtkxk8UHRUkteOss+J7G2PmhmnpFt3vhHTifOczepJNk5g9MhigIRFJ75BhfmXQ422JHgywl2Unc2VdRLtgeYbizgEuAHtEHpOuDIzi5GWhHqJZA8PMIQLgJR5qhB3jwMBhJ4FceO4IuhaWy3AQBxormZlkAdBhRkI14pKKOAIzgmtPwf8R3xYSvUPG2WMBiQhWl7lMPe+CwNy3bNh9qK8sSmkzwvGkRyUHeNoiuNal+hK6z0f+7sda3A17ytw088UU8gbOi1VksLF9vBvQ+3ffse1daifu1eEyHyvR11c6Gd3fjIwe0tW51Vsv1EgCGL1XaM0sH+ok4CJdiTIr4nEASiE6VO7JXg4SN1dDr8p+SVmniZrrfpKGiFowFJlcKUDEh6QNNt3bEln06KTJKwBEdNvhhMEsvAgKIXqWQAh6+PMOmsKm4pjzrmaPUIPiZZnvh6oUMJX4jhwFMdlMcU1HKRAf+qRBHLox3pgPA0AjVqG+4sNYL5lSzsYJC0XjpyNzL21MAGzdA+klUDUcHsN/GoTu+JTnxI6IF++A95yPIEqFwGQ7ijE3cOMsBM6JnCe9ujT9xnmZ6U1Fz2t9qNZwuXVzf8nlKablELNb/qT3aCkwFT7FS5tHixnb/wiq+Vz++6v+3bc6xNzfJr8dqlkB47J/a2+c2D7fKd9wxDN/dzFmFopUmw6a7WSb0X9dCWh9uByfv0KrcPdS18zrfl6y6c+BFbMU3l9999V7+duEOPU+gxDsXulLbb7Uvx1lyuujVdez/fDj1/TzcKv7vhkWgb8Kv5ma9g9IEugmP6NnFKHMSn5V/TAiUmXJsj+Zwt6yj+jgvqaRuVh7YWOvoAQw0b2O2rBRIiTUfQOOkIzH0hdIkRPBpc/CrJQe804R869w8BsMl937x77HQxuYMdfdVvOg/0wNTyBXpnPBjca1siP+KDYyMMgB7/BS59GPgME5yTEele/qQC+eW/cDR616FkZJEQ/5IftknFuSc7QHmE8zeeUmf32UDKgXV6FVA8ouN0KwlMBpgn1aIrAz2iq95k8PuE0JS7TGhZDbjoLmoHwQxHoUy9/QQ6mrfYQUIStb4R2DnLCbZBPKgByoeSGyhA849YhVTHTZMrUDVImAZckoV1nVBC5XB3rQel4AfHqxsGJX2uXV3S66RWPBmyBUvDT+uXIe45sNOdzgGPHP3jWya/S9p+oj/iAz6uU9CuLt/wO0337Dmu1fY+193RgMubavzrFLpjbot+jqkrqHox4ZUelWxHFbo99BfJta8RapvxlTNBVt4dQsfqgCAObdQHD8vtMnygg9kG2GAQssISfiS3vQeZThirRCb++gxaoE/pBFvixrXCojMpmUP/yvYU8P6PHgkMY9KZKQ9n+zC4K+ED1Idh11Vl7zQQY8rTltgRLVkEwQB5bjXRRwZHL4Icm/GtB2Q44wdkYA/+LRthpYT8gueuWASzS6DVedcLQOk60KMLqvhrVB97wtNtIMJcB86ii3oGHvzDj06//+5H6h+6mUbPEOJg7kIFZ4hbSeBOan6X8srV93TGuLMdvufxNj3LzSr4hEmXt8ToCoHejHT/Vl3v2zhjtSaYrWSDuoX0n2ozE3NtbmK+Hd7yUDu69YRfFH9dv3E5O8svzqt3it4miSd89ad/FhFcC6QVkKedBv3bHypvSK/VFd1l6p86q3ZM7Lp/CKdioXyHrhgL/2GbU4LNX7FuPFfiR3DDr+hroosvPWoZp3haVxNGhsjDE1YkOkWXB435E1wd5slCUGxl4kbu6B+f8F+8o2NtYYKP3cQx/MCrs0P6NjxHNnVddMjZoGhRxzqJvMuNLFd0WNkcHOprIcZNZuyGQUsb4lP6MizxdfjLtj6mmA41wOuVtBeXKhy/ijEv2ilD44kUHX1miPEQAqgUYTFCVE44EKSUC79oEUSedHcemlI69YNhxu+CqVLWnR1TVMYgFMOogTf6I0SJUDVcdDbcPMBNR6xVLzhjqps2WwcJdgDQowmDDk1jx1uQfKMP9DiVhOOTT2cyGjp0+eakfJlHuQYD60udB8bI5c00ORtruu38Wru1nptHbuu5w9XVhbZ3bq7t1GuvoOXh50o8I7iydNu3iAOzntiqDxP2+tpS27/nIW39HMACEDxAbNGddOyRTyvAp7iFvWhU5pVrts1ixBGakcihUIFme+ANEu3UfUSQUTZvDZZhQZzRQWgjwfQv9v4vm7xiNQ8o8FuPKwexQJxhIIrtMXgIF+6I94rZE6rwaDD9uRPAQx86Sk04FqpyCZdathsf0vZe8MDUZbW3YLaptnNVU6na1mfzHYhdIrCtzlodfwG27rWosm6WJWn4jL4EkpWDU/zGkQ+quJ+IxpOkdeu2dN7QGgy18OpsMnB49gS+PhXT1a5V7YHEs7awxKfOaKMnWPKXfDelmLpw/kr76Pyi7jrlhdeq6jZ5oNJNNtjFD+Pe0WKMRygOHTqpu1Dzo7883C6gxbr/dr8f2qJ3im69py3ePi92k23bFm2FThzV/31t99b9bVYT4uQmk6TuKtadqGu6IWe3zjJJtAsq2H4rpGcYNelOTeaF3KpyXW6skI80oty+veKt39CIh+yAB3HBHzFU6a44HqurR3M8lhCf9ltsE5q8HZ7wMW8BwaFi8D9IxLzggYWy5BecOmCctZsfMa4M7WY606tOR3+Y1JSybZnxjLLt05EJzmXxtU7A0G08WbdOIbXIgUIcuOsaxhjZY9V9GAYjnsXOcdd3oMB3rKiSrXT8x86cdcVHncgxrwK7DExs6Gdat7F0EF35h+NIf+FpzCSG47dcRsiYoDNDJFARPjg8xhtm91EPEAtTbyR9uZRKg8Z5BMeEyqJQhwjJjaQi+Al88j1wxwKAlSwN6BTk5KGNdCthKQgA1XhGsFPAw8EZNEsOzrJWcb7yvJmdQRB6+j5juRlaRzqK9APUU6nDUOykQ+ySsz2YwQDNkkJfgU2DpZGq8ZG7rlu8ufmAxt+4qZ+gWbmqX5X4uM3svK+dev0j1euVUNoh4sZxddu2vnSt3Vi80VZWtbVEkCPO6uR31wjsHfP3CcbNAWzBVUK2bpnRSnt2RmeGulToLVE3EitBrt+R+Jbm0s3BBcR2Uqe8I59M2tfvLgUm/MFyHKU2NUTZcAQlGCN+IznmTUWv9ApOQLMSmVembKdaF/jQZuGHHD7ccj/Uw4ckFM4C8D0pCyQz6bYIgQFZ9ebGl6rpXOirfqeU1a63igSzikKinsGXtvdiotNCARNkqtZ5EykHCitfdKfGMnUkgw3uxFYSYI8X9HMalZUrUPRBF5goeRVMhqLQyk/QWPcOT15U4j/eJyknKSY8yKQ0whvJPq0fW76tZx6YGDd0gxMC2dWAw7q27Dc28iq1GV0X3Kp/7ONxIl9DlM4kuDnW3An1dpg22w62B9pqu94e2PJ4e3DyyTav64mggwvtxh393ibXvLkOvnZDOki29E5baMAzY9qIX7TQDgl9ne01wfGT84Zt6qzyVjt8/0HBuj5CqjxsKm47cTigCw6EW4S5GnzSQCNoxV43t+NDJHodBlzGwc4P3vaJiDyWWZZZG36XfvabpZp3TZTuq+KfxY1aZOCBX9LG6EQeGnNAra5D2jtwNLOyOlhL48k2un4fa8fpjWsa5cQfOWWP/aayF3eyGfs8kcNAifo6yyRvfOmEzTXh1yUL99M40bTU+/nEHsLQlt+TT/81svSb9Ko2JdgIJEGdoky3MTaYnDL6s2KDgQZgqTmBE//eLWxo6C6PwzAZGIZ0Ur7lBpcoMrZ1sPVLW+Y0vZB8y7vwavBMo3XH9yCJDplkLcUOSseRUWGlb2gdZBqMjIcOpUDPVyeCqEg5ymp9CVsHWyQgnHFPnyvi4+4vSxPCip4R5BoLgTu1dbuezZrTGzOO+XrkT79/yvIvzF9sH8/rOt+0+F282Xae3tIevv+LHvS8l25HSXcNCDPaLpqa0t10+rD6TerBqMIUK229ySMBqojh4nj/xXvrRKfA1k6LFZW3QTAsthpMCD6MHAIau2GhAB/SYHMIa8vSzhFSJpnC7vEDX4ZU/hRY+BLBsCIOzV0FtxmBJxjcaWtw8Sd0xDVlt4sHP/jDJEsa/ORVommhV8fUJxOKeCKQpAPPRmIv9gUkPSybaj5Khe9s9AKMSMsCLt9aRyqUhgWGaamLgOq8mVTRhXbs+kBnm2EcPaADZkmgKeu209FQ0eIPr7JNz4TPREgt/RL7yIuf8pHHwNT7pLkY1Vv6y3pW9fSpC1rJ68eetcja0LptXde/d+jnwVjUzej3DWf0UolJPUZhP+Eh/aEDZqAvavJPm9jnrphoh27peuLmnnZw6rh0Ec1WfKkj+NhAO6idbq4yEeqsEy5qU26YIdlP8FLiTJSH8mvcsb3YrP91vff0hB4H2b2XyZZYiC/QygsEimLjOutrbQ20DAT0BG3aODxc7jpYP5GiN3rU4oNrbtGLtpMcGahWcn3xh0/xio5WXe2oM6jefsC7KGkTfPzLyYXPpuAh+XKR8BJH8By1bVmBjuELQ3jGJo6qg4FyRWcdFTPRq/REDoKIqcQXskh1NB0Axa77H7p1/5Y8cKG3j9BFH+OMDIVDT2jX9dCRhI7IuVsHcRGs9OC1ryE0SYzEagQlUCOY76go/G4MJIMhru9ljDEaCgHjn9BVUkWEp67wAkNGFExODaUPdWg50gQcmI01hErhBTwJ3fJPXZyjnGHYRoozGPDoUEgh4TTVw1CJ76IPvnjoE51T17HMT1Uuhp9wPZgATIVFK8uRxq/V4vWPl3W9gl+EV8OhjeoIWuimBPt4/9V2/eAH7dnjh/XE8Xq79fhmW//UrNlWUNMh6EBMMjPTXDvhGgw3HMT/BIOfJZNwsfQvk6OIdUQUA5EMZgLzJKY62yFgnWXAkz8n4N1PAO0f649tsd2GmiY6jPBHtPgi9KEzjWEoMyYPNLZVhkR7cnYlfYWKrvFZ+EQHdULhu43pwFLDA4fZIAR/paMaEd6SOVwnkS/QDV7+x3HK40t8y6LIdkMMO/1bnrPxSfhSScKnaiXswncdmSx8nCgMqTNMhb6DU350ewhaExvHqhuOCMlfdBV/PsirfxAsXnijs4PEQ4keZMkH9oW5NO1oXGsf6w1IvI0G+NqabkQ59b6297f4naPbt+3W9Ws916rBcqsNHbdV3G2ufI5s1fvuWsm4valXoemmlj1b9V5SPVNEG6Jj95JjshYrs3NTbVpvxMEe9ynw1Pc4e+QFFRO6u3Riq36RXm1mO9FdsuhviNf5bDumZxDreqH7FJJUWT7iSLq7HBi8SjPzVTsUPrFSddDDMwOzsqaDJ/lun/JwpUwqeeAWz8D5JpoSn5Rsj/CKbx0TF8H3BEUWRfjuMQOfkmW46qEvPSrGoApldKMPFD5H90cZZNpBBjXhH+tCG/5w6zYoix4kSbY+uIjYsfvxE3UClq2JV2jsNdeZXjjjNkUWNdAzzjpjPrpbQgMBAgXD7b2tzQCRRka0EfgSjMDqQkptXwPr2ELqCZUJgjgTB8lLaDGS062zYjgP/sBEWrA0fugQUQ0C3MZBQwXi9MXBDuhll4Q74Hf5DEZGEX6CtfMOA3NxwLrju6iv7ooepC53PZIXMROKvQkEO2Cof6koVMxPcllfSlxv4pcqNvSuyqn+rGDeUCJOItjQQPDe0i/a9D0TbVF3j09cQoJWs4dutVXdSbp9Q48ZqAHhb0EMGqIjSFkR0j5ecVs4g/gtvcptvu2c3S4cFOBmB22l6jGMrSyR4KX3Q8ZBUlgvNB51IITEngxOqiOwdLbkNkaFSlYo+LUAQUk+jiHynkwgEE/7htpkhZVJmbgRricfnivTOzL1c8dGSttFn3KuY1Sr2vhEXGS/z3bwCROfPgnF8Ed/6tNW0a8705rQN0g2xzGtgnh50EW01RM9o7nyCY/Ou3fidGbxdlsQa8LDUPCxRf+elJWjGP9wlG+lW/oBlamjvnwIE3RwGYbwNb/AKNFvKs5d61gI3xGfxJWg5gHPshF+5sshjoCNZX5w5nLbWNts2+aAMAHp0YXLt9sX9QziKz+9qm1/XStUXOXMNTjwGyYt8wHe9cVmnV4uLZzX2aS2QdSW3BATtRSwrHyUin5DuM//B19ob70+3d5/W+/tnVXc6/oQ19Lcj+xPfsVeLdkXfPi8xgQmzLm5LbpeeNB+xFT0w+/4hkEX32XRNESDccADxxQ64juPG8rfBZfPQLNMBDgPFW0A7+BnYFc1vNyoRhl4Bb8v1qiCrz7I9afGZzdh2hGaJPREHqUcR3JG+lquMKo/Uw4MevAyZo7swyclwxgqaeRhbBEYvIznAEijfhi+aG6wcRkrxu8Ujb7RHdqaBE0rMP4zOYrBW/HnsYlOrsQYyPVHlMm4TxuxaMpCiBjRbVUizt+gjAX7C8ZRkpz59oEEKCjAbS25yA1kVOn6KoIycqpRB3wa1H89CAoPWZET/KFceF0w+K6TgdVZwk8dC2fZUV1b8sLHYdxMguA680EKeQe06mEfzjEQXTJ4ssJMYEBDIiSpqwBDJ3dY1cEDDFJ0zZHrlXQ63uZvLGi01cSATsNtSsbanZvt6rXzvjayLtiUtp14MH5jY7ndWPmozU0/ZBtVpSQZ+EY5D4C6vogOnox49rHrcOwRvSfyis4sQZe99+y5R3ed6lqOXru1qQGD0CCI8I+DkzL6UNZnZE1854EdqH0imCZ4h5pA6Qys+HOtEpvJUz/ZYVOTesWXtmkndeTMAN0drNKdj/0o9Sc1MV+6crG9du1tydI1I/1CAp0W3MQMXsz1ODQgDx5tJcZpH2DYrX+2mMjbPwFbX3hRzxd+dWwJkTxw10HoBFA4vY4jFMRCjqEzkvFUJ32MJgx4RZ4yLsjDjCTdJjr/yDZRQShf0G6llyDGiS/kMfEJXwRQm1SxmRKMqIwm4FuuyuCN5AYPGAldUrep5/bW2qk3z0gXbKWeV/1ta+fOLGi79HZ7SO/Rfe0lPWuoX7rImbQWI9LbrLp4YsdZgLJ58/ZaW1w8p1ggHvTibr02UEIR7VS+Ykza1M1UM7o2+eCD97ajD365vbj3l3oP6RE/cnFTd7jyaxVXryzp7PWGbkjTz0up3/isPxJ9p+2G5B08Mt/23bM3/V42xH/Sx3LjD/d7bLfy2JuUyU+qS3c8QJuUjuWz4UgcgtQxAycuiZHYCHvvToEmEHWjtohuhQsKrBhDSOWmkuf46LypgzdM0TWTcIepj4hzeAnRfMAVTdlCgQ9/IAJHL0+a5g2sty+9m3HEyPlGuGUIRn8lGd+6SKfOEx3Td4wy4CGLZN3gnIzLWUSM+RA8/eOH8GXCZBxJHBYvAV0/WYM+AECQu1HMBqEoKAVQTnn7AFQM18cQOjXRbHIQqR8ww0MKwclLYVeN6mGH8ZbVBwjrIJTBWKghMaICxVkDIE9CBgFhxF7HQakwB37YNeBbuLA0YehTDRLbVWLAEd8MtpkEsjU24pzOgOF9VdIbGgzX0Yilu32aIgMIjU7nOnr8Xl13uaQHl/V6NL3Pk5Q9e02Uen/M/i2H2/nrb7Xbb+qt/HRovUNx9/T+duW9l9rsvVv18zRHzSttEj1pByYdbJUiWiXn5piHHz3QTuhXyn/0XU188g6T73//n/yP7bH79DNQKrNSIsB9VJ6tJfCYoLnp4Zauu8A7sYKeTDvpsPjJ8iQTu6ijSTh6ApQ+U5KH3UyK2OjJTxPVpG5pDw166iO14yM8Kx9K5uz0TPvuGz9s/8v/+T9LP1qMawm5VnVHb0ZxoimU3IZigu5sz4k8uqmcP03uyjAxUcnBQiFGtj7QuOBM+Jfd1MhRIUGO9KkFEvY63mBKDMmfyKTTEqa0U6S7aQRX7MkfRVM+tAjxhV+UtmLGs7roCJK+KXsBIF3xHSoP/CBXGmCqpz2yjU4N/oYDiWO4umS9Estuz24r1wcXb1zXZHNdW6T9Zdt9UF3Uz4i9+uKH7TOfO6ZfrbjkXQ8WHIiA+9BNDcN3APUrFqvX9XjRGW3z71S7zsh1LBJHNloffd3SQ/c752fbyWcf0LOPh9rBA7pTdGpfW15c0Q/6rnoy3K0XM9x/ZK9ev7bNv7jx9uvn20s/PavHjnhbU+KUOOca+2OP672lU1OawHm0Qx/p5T4vWTXRxD9qtd4egy/tqzgYupEf4/+CmSf9QXbG7+FV+PgW3qSKH48f+Jv46QkfIttfKhR/quFVtPTX8eRr5PhStPBILCYSCX5yxDz+hg98ScXPecUCyTK9qBGuaNGveFZ9WHVlLRPeXXf7DD3Cb/CB5SbW4DeuEzJRCb1LN/ihH/TxHTrHRuSP2iqxBz5w8GkDjvxPluNVEvM4LkJwiYjtIIaLEDMguXHghprSjMGqaMXGCXx6vJ/vgjfl7tjiVeXIjtGgJYUn/EIWHnwzkIwuNgfbODSGdDGCwPi4izSSdQDOhwGJQMEp1Nr+BAKOsV3IVlU6AnVGBNm51EKKM7tfrHAaF7zyi5iIRg2mbwZ+BvH1m7oVfH1N70fMc1XYdvzRnWqgRb1AmJdP82sBejMHLx/euNnu3djZFqZ3taXVJdVpm3Bxtu1c4W3rt9qZc99pKzcf0YPLx7VKnpcM/ZyNjGcKs8/kNKRv3lnRpKmzwEO325n33mo39V7SbRPb9MaPbe2W3tZ//eJl4aRdWZXjTmzbrgebsXPnLgYcbVspYRsw7LTZonNQWiKdvM5C4zv7qMeY6fW1eWtDfHTGKO3YZuU5Lzow27j1ywb8WGxeLSU+EsRPBK1evdH2Tu9py1v0i+srt/Ww91ttx9wObdPpVnvpPaO7ZNl6rvanzd3+tLnanoQ+tK0HCNuBJWlVsE2DLwyLbGrRAb7mElYqg59OmT6F7PiRjhvcINcZKJxdAzMl6wMfM9IXugnOdjcqI5dtv8jvWsl3YW4WroNBFm4ZaKmHDwne0APhzMN92cVgoHMl9K4EHL3hS6Jsu4SzsLCsduPxCtncvYUgfq/wjVcvtJOfvr+dfO7+9tPvnTbM8lXvD0d8Kb6T2tZcXLqo2H+/bd9+j/jlF1no08SV7fCgKwWk+5Ofuq/9yvOP6F2oK/45pnsP7nWb79i5q/3VX7ym+EFXaSQBO+e3tfsf2NP+4Rcf9sT5ja+/qtfD6Rqn7nxdX1tt8/MT6ntHNSlKGbuAduh9urdfuYbFWrUhtsSftDV+yZiCf1TV/Rv9gQVO/Kge/9J+SuHR6aUAYcqpP/DEPv0YuuxGIQv+1fzwU0lA4k7E+rM8wUJHy5AnRTeTDDBX2M+J3+gKjidC4ZHQZ9wG+qxPhHod9ZnkMyEjK3LKT+jl5afGKPmRR0JEg8ya0GJLl4stMMBY7O3ZjD2JxejFd9DwndH1Zdu1yPS2qcoey8PKeqCf41rjz/BohVdpMCt3SWo330c3lgAFFQsJHHWMKImjrJO/TDPGD2C3aYREDr5FmNayoqHvCJ0CmVukrPEHfTpR8eIISB3G6vhLuumDfIDDKgs7VfbZnzMjXaJSBRA8451xvUwiuESFMTjKo5+DUsKyJQOMViJYwaWxuQaiyVADP8GwrpsOeNLhqWfu052l+kkcPbC8oV/Y3uQmAg3s7c7R9rk7T7ePNq61pTVNard1tsibNMSX31yztfyW28SC8DUZaNWPHlNa7SKTW5An9L+5uabXt51yIB6dPND+y2f+wGcIq5evtQ/1flJeUHxL7x2dmeWGB93Zqolw2/bt+tebPaTnhPSFL37Q35DHJnRwsJc/gBmXWgYXJsikmizhwe/6mVaO9EJh7abs12vjtFhYW121f5gUuTbEmc+MFgr/zef/07xDVexuXL3ms8yt2j7GVt9IoWtUqOFWAyhBHlQFA+4OxSCbKh2kCDiqoz3Ri+RDz4ND1gNiuHT78EWQxv0CPfZnIhnVF5w6JJSsoQxUQJeFkjGzfB44pNgwPN8ogG3Qd/iER2Qp9mzZqI96kIOm/1s7K4IcxamdAHVsiPYM1NX38QWPBOlmLrWNwsa+iV7iocFuWQus73/7nfabv/uEtk2vt/N6jnZKcYn/zBcfqnxLZ2bXrr6jO0+vtd16HGjrpDoCtmnLPH1esno881zu555/VGeE97ef/fC99qJ+Z/GZ5456YuMh/yPHDrRnP/tQ+5neoZoJp+na48322i/O+WX2v/lPnmpf+cNPtz/745/5x4nX1ZeOnjzc5vVycU8EUo0oKB+iIoalfbEfAD4BFt+Q8ZagGsqWeUB1znQDL4HsP2ExfJq1aEsugIofYONnQB4/pJdDNiqIC7ElPPHwZQCV0SA8gI3aV+CuM/2AOqyIndGvMxU0tlJPCk+iq/gZZptjY9nH0bQYB6VYQscJAH7j4z6oCiZC+gVX8qBxbXW84gOTQY5k4VfGf094I30FjF+F60Ua/DoP4kdFlYlb5JipdcvYLH4C6pohsjBIDlRDSkW0p2gEamyOOehrWIpE+V7bg87M4OQGwjh3KisFvwg1c9cJRh+1gXDq0rosqwV4LOFQ6yscjMaZyEPvHFNvFqr3y6xFb110RB9K0THHBGV0Bw/HcBZVcjjSgAzSpnXAS6aFaLuGU21qMAYUpcixccbzoEUUO+Vi7oReJcVE421AwSugwliTgvi6PWAr3pwVcH0NfTY0APGgMnyZILiOx1bnLcGZzNjeRCnqeUcj24gol20BdQB9pvV6NhJaTekRC/hO8k5SOQR7bb94IFOFkX6iVdFlXFBnC8ijA49sRdfgmZcQWOUmQZk7/4TSdaVNpQ1vIt+hh7GFUtu08GTVDj/aDjwmAfJ1DYjQhAhZ+BT8miiA4V+3iPIePETrBNPemByg9ZYq3heAFSyC07mQ6SgRTcjd+eBFubP0GScDDh/xKPk1OJc/4M8H/rZdLDwwhrVp3T+Q2gdh1A29yYY84jMwJsbhaT3Mm4VRTWKYG73KJuqS0JU2EI4HJrIMmAzMxASxE9zw4LlCYgYrckYPBRMXdnA59q3XLvj3N7/0lSfa1/71zzVB3hSNziBUz48GLy1fbh/ptWy39bLu/fse0RAz6ZdN8J7SKTWqHygXf96+hsznv/R4O3psf/vzP/m5HvK/7rY8ovejWjfOnCT/M597qJ05rZcAXLghWekzxMmqXhX3V1/7RfutrzzVfvurT7Y//t9fUEwutc/+6knZpy1jLQLxLf+J5TSyxKo+MPxUC834r3sOpBofBaIOXwQngeEzdsY716s2YB/RX1FufMe39Amt8G27uUmvu9uIPpBxkHpirmRbSpeBING5DamPAtEk/Y42Rl5kEkMj/YC5PcUGPaN3lHcd8WFgxo7wSP3oO9Kydauo0ljA+DySI927bxLf1Ima2DST6IaB9KNqn4xt+CS+Dr9IHfyiOr//uOMQrXaUkN3uGksnLc5wQgiZEmJE1yiXo0FCwN5xoRBhuIMALXCoucCNcvim8MlvmAU7fKo+UqG1QDgSKHQuywu4jw0qwAZ5GKwDKjtBR6X+TVuBaUzIoAiO9YZIAzaDHybpQ3Bu0VmInWrhwSkn23bBUzY303nwEDyqIKMHF+SSMaaiV0gOJMQ60EHRgKxtJjiiRba9etAKMq2zNlXZRfFL7IhcdSLRQGsrShiBZj+hg3yDnUq8qotJB19V22Jq8kLocsC1PmoXD9y97eyDoIFiW9N0CB7xrAE1alHXFRs6F9QVD9AhRwPtlmzRon50KTq0GSV7FV7YhS1yuTseAwCkGNWTpaOkEnDaj5QVdp80APW6cHARoPj67hVI3GZ+4YBL/avL8s1REhsfiU48qUJmRxEBCA5Rxzi+BR+dGIDLTelZ8IoM6J3XV3mCI7TmD1/98VVtlDpgwamzHOhKL2qJKRI+n9AZdm1T04bjuqPKvv172vYd23QGr+vInB4qxoh33wTmbbA77Vv/7s32e3/4bPutr55s/9/XXtbPimkHQpPo1snFdnPtlLbud7T5HQ/roffdmiC1uFN8T+uXJ7byOIYWc9xUpdbRL2EcaI8/eW/75l++pp2UjXbw0G5tqU75SN/Bd+i+S29s+vyvPdr+8v992bscvHkEP9C3iYkX/t3r7Td++3i794HJdvDgk+3gvQc8EQrJvsJP8RlHUdrX+CR+Kjx8QT3+JLEY4Tqsi6YJvMYvI6GKwPiSMawWk24L6iSj/O/2lwwWu7QRikRm+IKb1Jkaq7et+JMKI3jIhTa8UH7ET7boQ/8JTkfrhKN2NxaMeo38br3US+yP+E7ZotQxuLQROS5zFS5IsKpxjnLZij9r7ASeJB4xrfOgkAUb9cj1mBjkLlkYig1StSt5ePvEAYnf+dt3pIaou4OiKmhJ42UriCWg51BkARSNHYzgYOETK4CWXU5HvfsgdA9GY1CjD9JCXmywrVZJGEVwVSPGmTF2JBN9elLWjUFxDJwGNLUHI3TH3vBLkCZ4K4iqHsSReejBQJKBTRXigW00EuIiBwL+wiv5UR36uVF19GRYCKavgBWDrqQP4DhJ4wBGEAEADT7qCPaDgxT/obN4q8wqEu2iR/hSR5lUfGJjBklXuC58HMx4Dxp80nkiJ2ywLfy8oFAWXGsacLGEq/OWp5xjBZ6mpw5i6BULmqx8zdCckG/SsS/RdXwppZzo0ckDATxE0Hm7fcCGh/mEWcE7sNOoYwoRescAEgcaMugbIAPCwsJC+/DD8+3EiUcBhjY5fcde6wU/0UHKSjZxhA0kBhawGRQiGzg+HiUKwR6HpR2ia3TLgAHOqF3Dq9o7fS0TDzwZ+7/1Nz9pf/93r+mNM/x0EteauSmKm7NEq5jnpQ+7dN3ud//Z0yKZaN/727fbiScOt2MP6Z2k0pzNssS4dgN8jVh6aJEG7ZZ+UxV42+dm3S/X9XgNZeRPa1L1Dw93e8v3hNovXjzTXvjG63ruUSt/D8Ci0tnjyvKC3verX+H47WfbseMP+2XifrNRGse84SO1RTA+IJcf64g58asXXsqbjviTAvHZyO+gZsxKPFnl/jVqC7yWBK/yt+Op8w/fkQ6UkUequBRlH+ijI7JHMSEskbuvW37kmb4jIbtk5hj66IRNiji1T1JsdP9hLCl9gqZyYhNc6McT+tZJQNmK7sALt+TT3vzh68KFV/k6OmccQz+XVV8LJXA9T0BjX6aNgPXJEAz/WQEIPHCXU5CuZNeDhz4usBLKqsAra8GsfMfpQ4wpYYWBdycQO79eFwwaNQ4NPgFBDbBPcICvjZLzGMCFNkKhUIEczkXtkpBxcjGtrQ/LERd1c8tEc5JxBXfQ9TMsN7o6WfEITsmKDayqgafOrPoXdplxdEZ/46Jz5yE6NzoYzpcu3dfo2RcCMJUU00b3BCAwUuxL8IbL2HcUkYjQeKIxFZNV+GaCTCvCEXsg03eXGdnxlzFQOf4yr+gcP1Df5VuAymIGv0joBGAFGABo5MATPDoA6bTGpSxE9NMAUfzKD7226zWmR1UUX/uSiUYV1HVONSnFjoFI/Ixk0eDbdtF667IPjBmwiAX4cSKVu2C5XmwafY1P8rZPeNEh/Kvjjwai1NeK3pPK2ODjjq4YqYEveiN9ZDv50smyVJvJSfEgsTVRhEI6dnvgzYP2XOP+sz/5Vjv11gVdW9abj7jWJx6b2A8LFTbWN9uhe+f1o7qf1Vngtn52EP8i02OI9GCywAf2h/s0nPBnnMa2ad7RGzioyKj6wmWxAcUH+km0X758tl2+oJ+K0nXoO7oGPz272U4+85CuLT7VF0CJ3fAY+cUxY7GjyQbFjGcV0SH4wNAlahY/tMF+8KhL/xpisQeT7fcAHzw7rxqEAn/iwQc5XG9HKm004g0kugADz3oKxhhRKTqCB8/RjlvGEWi0AOkLrqIREGTzq7YfycUmUvogqMSmL4lgjvUwwqCrsVFEKT4xWs9HTo2bRooLYIUYm5m+BF1s4Ridym706cgmMid/oR91xYP7Nia+/c1TbkEq0rnCEKcnOCPYHIRjgQNPlXF0oUQnlQuuXO801h68jpMyA1UlDOh5HenY2W4ZpyEfJPQlOzI2tIbBFcYEigDQaCjwGyyIFWj4KKOOkbNJO8Xl7lDosE0waDKg5cI/GvCPAhbjgGLbLLhUgeGgUWc2EhKpFk0tIIKXBrHv0ZMOrCPJOjqXFRj8ErzaMvHAa4aGgSYs5n7L8RmY+MArvgrPavzAKoihwZ/dXstEV9WjvuViqfjZnuRDE3m2VfjmAX6Mti7CsG7VccFxZ1GHQ2eQgk+Ack1RQrtcdAC/Jpq8VBySLgG9CWw+kgshdaRxe4yvel4UXXoAY1CJT0IjIvMgdhiQwak4sO2q5QXp6BcdoYvvwke6ihYay1cdqSaq6AYMu7od6Ks8MQavT94pDU1NDvAqeeEVO5GFKHTAF7E92DbJWZQmjWLOtkGBnR2RI/4eDYiiEywfdKkYhV9o0XlpaaW98Dc/a6++9L7wdT3YovDfFj3QPt0eeuRQe+4fHG8HDu7WFikLRBCqvUbyR/0DHwoFd+mYMQEYNH1wBwH/KSUGzdRlbCLOKnZW9SabjXW9x1TtN6tt2Cn9PmP9qgYE8C/bMkCju/hZRGTAE3FM2OgRXxGzabvy4/iR6/RuFxGCTzJfHZGHjuBXIk9cYiP5lBUv+qhktPhAWehQ0T6BP3gC6z/HT7Y1JOHrMUd4Htvwp4jCH7vAG+crxJ5K19iStii7qCvdbKoVKY3QKe2NnPBhLEBebcXjCwT5yxLHeUeF8Ct/RN6Yb/GF549gWyckdz+jVxaG8CElTia+7W1SlMF4MUTZjqCslQRGF6O5SZEzriwGqIP0gZ8a+CWFpzsznOHJl9IgJ4iW5caIN1KGJONlxypnQ81/eJX+RhpkhwRn+fqF9KsGgNZOpo4BRPrXytR+wBbxBo4I+wW9RiKL+SCSwO5AHRJWBFpkdh7yZPFNh+uY+F66WLaPcJIw5e1/1wvSfUNtOBqLim6PshERFH2bJ7YUBB7dpnGdPXBQNy4DN3TC8QCCZ+maRYuC2noziYRGxbtYpW2jRA14sSd+LrE+A4W/GZXSOVY7xLuxzfrhJ8uPXoN+MdSKEIPQkxKPDATxuQfBqgNBuKWj+aOc7YE/zRLHVF+gPAx4HoDVCMLzQNf5xdai7bpbZm/7AQ9aaeoYTMfFXt/JK+FMUigTe5GBDegMT/ph+iPsSMjNnbu9TG+W4rGh6tO7S0cwsScDZmwlXlOPsPgNodEjkzg6nnnvfHv7jXN69lDPywr1wME97bEnj7RDh/fqLFL4FhWdcCM8x+XSRi6rEsl8jeNED4J85Evy+CA20Z9DM7IRNuGLL5ELH3wXWmwKDUfoyi7KpFEZGvBDS77051iTDPjeKo7B3Ya01zg/dIUH+HfDo1PgI1mRW/qil8kGevMTbKDo/MM77cyLJrp3TedFqXSgX3jME/In/QNf7EMeecej8GJ79BmXARIfNAmNaNFKBWIrNtMGORMd2Rl/uu+kg1kGMmMrlkVe+Rod4InOozTSqXQITuKg5NlPYtzPDEVuyHCwEeUsmEeJiLHx2GQ7xaQUg2FQYrTzVRveUhlu4jeiG5mmqsFijIvjzcYKjDlJeIAGdJNGeiblLlcgcjEcerjF2DwcLUBn5Ib2wEKjFW/l6DAitMMZbISP0/HPeFBFYueNGJKKaXhhmw+s4FV8xn2GroN21nsMEBuwR7TgWV/7BZr4M/ZhEpDwA29I5DtNYKITDBA2jQfLYE+vh9TkEIrA9QCcik8C3e1rOsq9rYTHIDwewOERDtFTesNbtPlPvIBB2R0EmeLjrXpXRBfqscPVNZCjM96ymvoSwnD2TduwxU0SHNqhPHiPNlZbCQW+TsiQ/DBFtkJE/zWhpkbf/VoCepmBvsInegKkKnorrw6Vu+m6zaozTxSzTKtp/JEvwto2opEYIqVk4kuSWA06l3+BV33BTNfh4zD0dLt1W4Jna2DjRPwQm2z9ogNnYaDzaA+0PgMQnBT9yGNrzmbcANJ06C/F3iRpH2grnsiPJ3tXuBXD9kMa3vLKVptgniPq0EiPT8DRs+iCDUINvPEz9fFV2iKLObATw+TGeQQ3dQOd2JZPxyfoDO7gUs/iBPnVxxR1quADn+xysFAypHsa6Wk7+PPvBbp1G8ccwfEF7VX+sw9UsiTpUT6KvpTjN3SkjqMXmV2GpXTZ6O44Ul2lkoMNfIh5Up2xAq8Eb9tg/dQOAsAPGHVJQ8ZwYFkMBgd2ZWNo4peMqmALGjdHMEo56VDKFCxKqyFsOVisK6QQWbjrD4NiVPjYkcbo5dI86FAmiWcZZvbCi9PTyUFyueg7GYfSEx2o9oDV9YfGrhZT+KKkYZQ77UDvWsFxMh8PfND0W/11AR5TCE6nMEiQAUO4ZVCbxic4vHgIKAEhmG1hYPdKydxgrcZSUJNBvu1JwBs/Gttet4WCMQnbYo91gBpdlATux2TAo86DrTsYuNhji03gVoWfcaknqFQGHzYqh3v4g0eq7QgX9GUadBSyB0zBBpu6oyhb87AoUvOqQvFHOPMM+MAQC1nqwyCPkOC7LDqMIBwGUvIDr26bZXek0g17o5eZW04NVNgYqeH1/5P2tj33bVd537bP/xyKjR18bNPExkQxkFCQgxGJSdqkjdI0ohFJFKmV8ip9E6VSFbV9U1Xqq36D9kU/QFWUNupDMKaVykMExobULTERz05IANuAscHG2PHjsd3r97vGmGvdByJV6tz3XmvOMce4xjXGfFhr73vf+y6PzItg70Yirxx2A3MOpA03FMFvDGDkSfp7h5kKdeQ5WI3m+AR/N4AmVRXnn3ignjGdeKPScSge9jjoZsvmGj0k8eczfWwYmwv8bB0fFHK7sj1jy98c7u8Ymcf8js8ckM88fFVubGAUq6/UVZCT+fCgK7zpizNi/N3L5gnOLTi4NOhfG17RHJ1U6OuYTA6mnzjp47G2PSNv/vYVN37htX6o3wkgb9nzNEcLHxTU8LHjgawxwW3HZG+kFekTPXLDD3pzexGszkcwN2XNNb7W6+Q2vg/P2LmHm8/lRtzU295z5wG2Oy5cVmY9o42fPJlzzI0tcBIvfXBzvmE5cs4Uz6kuDkGiSxzao5P5i/+WO8dKxBYPTLjwNjQ++2c3aD1bANJHAFsqX/TI/UGjQZ7+qCSU3tVaS5sN80C1Qtp5JAyxUr2KKuCmC+CwdLEr6cCjQvD7tiNJaGxlTR3TMZ8kRehPsffivHow37K+PXu3Oj2D21ZjKA6+0IZHlIhsNt69o8Gmg2SG9OaAJtct2KVII4fgkTfxJUkXutXzd3cSZODrG/Nu+NGbjR48KQEdvfIL9my04CDvhri6lx7u2A+6aODFD/Z44wCfPq+7TNrV1TnKIeHoZMOjWc6Io5v2NZbcWW7+ZhlHZfbJzif91u3mWfxMp2789bE3Lo0ZSsSVcwonLkjKkit00jobt7QMfDZUbF1kFwa/G+OVD+X4sPX0IDa+iINDqsn6UWLMEDY/FTtW+Bu9xd+8obW4nF/ez58A4YpCbi34D6Zzko1tKBjrRUdV/MghLf8GLErYkV/0R2nmRZuu09PJJmV6ixMbPrbOn+5Q9gItycnNZY+DJdT5fY8V+8bbce6rIPTogV9fzXw53zbiVhxO6qefNaS7iYGcF6t+yPeuaeTemObsPpY+858Q2FD9oAV9AdwxLO+bv4lNZlMH1/ziy/VHFprXqsyeF73aedKH/ttMeypYYxgMkaynlnVEPMSov3RfF4LMi8xvY8ziKv/6FSvY2Kkz8z4g6uNG3NnjymI50CJ+NPCNz34egBu4oTZ8gwNlNFPx5ljMyjh27l5xmbvhbb8XMvyxhuJ5eO+lhRuvxgi/yz+yxl8O8N2bZrjkYoihmAaSQ4qHCjluv1FMciJs4O2kTkFlNxAFo5f0tD96Dtj44eLoQGg/KASOHu9rB7D9MTdofMSYnxDHe/lTA5TncLptCGuMCHs3zaiSIAuYFM2TwDxMNu08tUnyz98ggoGtZvWNDslnMW7ZgXETHuF9M0c01kN7JthAoHsmsMrtNwfYLv+gENu2sdF2cxBdY8oZLqsXwqnvGBRb13M48MapwxyCJeluIstlz+Zt+gM+OdIKd/qGC5CO4Mjqi0nOpM2n5bJp0F/NLmriIqcscIB9xTH2+or+luVTUeK0I5gxXS34bGl+wIVzLyby86rc34nxNW/w48nXj72UL4mWw4L8fzz//7W/uwHrDyqGNgGqU4GqjBGypDMlkTMPsgEyKF4EaBNnHpt/O2vtsYduMB0PJOSmPbgGci/My5N+RgM1sEtxx7SyiC1rQ6PjiR7zlDnLxs65fb3IcOlj/+j80D6TtZz2XGzwjDvaxpi2eHWWegPZc9checNfMTqTmoPyGzn9a48odXzk9QwBmpfaNn5UR30A5gQnLp489Nmb2F7grjVBl/FHrzzBaw6MM3L2A2KRh32T0w5E+qmQW0anMbJXcANI15P47IeTpNKqf4LTX9YvIPbmjF9977qCx4yfxvcD/h1XzvjdC3ga8uvarG90ioXyRWfztX469p2n5SxPfZVb448DElXm51R6sDFACMEF8IClHgjrp726qtUBRFUOxkkIjrLB7ICqEtk1WOWwdwhi6Lf+bbPK4JWkM2DXgov4ZQV+cuWOIXYu+ujI+6ZLiuhjMrHbg9k6XLnrzaTIZI7XPGrPQIFTLKQpULNszBHJXyt7mOAm/8ZdDLhOzoDRf8HOcfPBWdzpEU/fYMATv90sUDmTPXngjgh/3gXnjHL/Y7TVRJmxLQIo+jF3zBMKNukn99RoI6q/5s1xSS7pJ/9hYA578SCfnahBiUJzKKrYM7fA1jr24hug3F307cYxPykiyHcEY9d8xKWFcSvnzbdNefjWV/TMM3Ap+Oqm0Hnw2c9+5vHe9/zE43d/N9+A4oW5ejsmtLSv+BzhiPzz+bq5n/zJf/T42Mc+Zh7u+vT3W1OO2b8Cq8H01eqMy5jcF73YyWHnaTOE7Jpb9Bm6me5H2juvl+89ls715gs/9/j3LeBJ27DZjbLzKJ7kYmccrP3idkKBf6FQL4fLlrmL7M4NrN1I1zn964Powdq3yYiP0vk5F7UhsLjoP+VSm5VvHsHafULMAWe+u6HHC/7hsjdQPRMb82Jyib/hGWV9r/+9MJQBY0iOupaQofc0XnA71uV57bPLe8IVB4ryI0dZI80nvFuw0YfN+MqCuueJPKLd9RWsiWnjIcaW8sR20qS4WIxxrzdtd0yJFf9gFKfzdnPJkmaPw3bzNc48kSl4oC9udDsW1ec/jI1KaokC5RIgiU1C8mKAHHESDRRb0KnaIdBmdEGewdE2bTbILb2IgdfSc5ODb9Oac32ilwf2KDoJsCO5i4A/ZBx4YptjkmeCeLtk7exAd0oNbXiBiP2Ti3Nw/GowMp4iPziK3RiIB854vpdyQBdG0x/hxoUsw1J/TP4Yty8VjKYoGzsCo82EXRVzq3N8zSjgM/W1BYquXogGwwsW3DtRj2+xZBNOnUT4ha2Ft2TSpnC8Y658dbHHL+ousDGCmx9CSoe/y8l5dY1B/BhhfCvg76JwYWCnLj5mU9uxjp19gRF9xhD1M1qBd2ETW+Q8sekNEQIoND9QeSlfMP693/v38+cE+bJnv92k36qBnP+kzld/uSFoR1/fKuR3aeX+lcf3f9///vhdDey2AABAAElEQVSt3/povmmluHytHnaf+9xnHj/0Q//w8fGP/865K+f7P7noeeHzzxLyu7l8n+cL+Xdbv/mbv/H4kR/5sXwtX/5W8VbuuScg+G/u4bDPnSs30/SxxJYr40xvz9iBx3jvRWVtwS9ubHk4PztPbEe2mzJ6vUFrrpcPZ+fFwWr+kVM8MUyprAw+V52xioK6jGFv/tzII4M3uah+9VROFTued2x4Vy6iqhxWRszmAj9zsTDPg4UeuWvp3FS24pyJCZ/mBjv0saMjxRcrg1Ex8Y4N8zn18gGUi1/NiRO59sfPtYdhuFzxj1/zEryz/0XecawP8MkJ/mUafNpw79hGOmvFPM463PkkPpbYBYSLk3oAplxx0I+k/dvnXp6OnVvV6NGv7hN7biw0ujTIDnOO8VI6Pqn73aRMWxwSGIXAIOTmqqS9MONBizQ0FXuBa3CoGyzd0d9JB969dCERUIOnj+C6mKKrbbHYpCyMITj5YaC8EHgtbD/2lH5KtJN93Z5XQPbDf/2Wg7hKeyivutMhLjST8NEkH/igywFNnYtK84SQDSV8s2DoKQQTkK9A6y+Tm5tOpp0IjQj/M9HMTTZUwCiRq0N/RNQZxx1DJqWyo34fny4O3k4SyvzWo2MHfJ7E0N8zdobs4gDbT10Ge/WNPUTQgQXWllTtQ8JXSs3ivC7chlJVEjlxFbcYYDZIYiUO8jkXPHzmIsFNTufaxBk58WUrmDmVWKDlHCo7Msu4sIE5PsST28NdKLk6xoAPxVy5gyjcuLjxbSj7JeiM04c/9GE3g/f/43/yeNOb//Djg7/2oXxp9Dc8vuVbvunxwz/8Y4/v+Z6/8vjZn/n5x9v+5Lf5bShf4/evfuXxv/zP73x88zd/c+w/9PiXn/nM40+/4089/sH/9n2Pt7zlzfmK1tc+PvKRj+TfGn328YEPfODx7W9/++PdP/bjjz//b/+5x0/9Pz/1eMMb3xAuzx4/8P0/8PiO7/iT+e8L/eo6eJ5CGsNvc9bRbd73foGcotY11A209iSthbjRqz37Q+eP88EPyuT3g35pRDUuLDZnbMeJnprTQKZ4EFsdJHREXZOjUy74cNNVp+sDxZ2fy3N9esEtcZydsnr6xFd61hUynug4/4az60oaaDI/yhUq+99okOEO2+prgFjZwSYneZAn0NznlkB7goMP1mGfAPNAhg9Zxwb/FPTax/qYuTx99DPPuYHZ35Vh71cxgpmf2qNZPuaZur4Ue8ANMvfgGDZX+6qtysuZPgLkz0wSRc/yxO7yCbe7TbHx0zyLE+/obF96oW25X6+Wt46HH5rqxGBvvDHcG1X2Cku/2zPVsJNQxfiyEAQPypKiD92W2xkbs8WJgc7DAC777D8WJuodrwHV1wYnn8HDqFhs9hDghwe8WMQ81UJCRfwuzPpfn+DwNVH4r43qY4Oh8Lb3FSUbalHwyeBf/PuKsnzwLD9ADjiTX1gnpBrhe+LJZFi8xoOz+DmLZrmy8McveaHQ5oGv9adOu+t168TNJxpnrAPRHF+mgypfFj0PAt9JVn02inIsbyYtEdYaPkdvXdMr513M7cBqS8cKPdCKsTE1tG4y9LqAkp8WzskXm3QA+V7Q3UzsT7czpWr2sWG0b3nXn3lk7mbAjVGlO8vyAzHD9vjtj3308a53/R/5N0Fvenzrt/3xx9/73v/p8Tsf//jj/f/4p/NK74uPH3/3ex7/4l/86uNNb/ojcwGtXxB/5md+Tl5/9t/6M4//6x+9LxvVVx5f94Y3PN74xtc9fvr973/83M/+XL7/88XHx3/nE49f//CHxXzxxdc9vvVbv+Xxo3lF+NrXvCZfdfbafL/na81HA4LfxBaBs3UWnfI4pteoo7eq5IsxRqfz6YaDzShyVscBASVYHRzre6ho5uUK61X9ZbimbHLFjbKdHeP7p/5Wp+wHNLq+cxPuXFSguXyWK+edv9wYnJseHPGTZ0svIuhvuV7xNZbF3v2musyVruF+gnY2uYBc+rVnPC78xki+KTe3hy+6Ypz8NEbf0gfLu7zdH9hTuu8KOMkFf9cW8sOJkY5O+xvzaefXF9ThxFO5bfiXLJjktWsNvSu2/mpBFto2P8Xhgtx3Fjbu+iYaMGU03NdH83C/OEbXeVkf7Adw5DwS1y92HfvxkXVPjT272HwxQgwNVgIEDsQmDVKVSaw4ibZuDDr63tFoKLz2gchPHFWVxmBPsvTHnUVIJvAwXlBt1k7EExiuk/jbQK+exkJoMc46GRvD4B90Jnzf3sA2NCxnoU08yHcCVGOVgzevGPaiXQ/kjph2YpK5LaPBYlWUY/R4sNjddONXPDB2U4rMQY5N89WLmfmPrGhF9HeBTsz62s2eAadgcxU4JgezgBty5wM6Mh/fBNHpQwc6xZKrhlhszJ2I6psHXsWnhWtD5tB6WaYeDLkhx3Pa5MMYIyMX+krvPS8AoUMc4mOeot06yTwEj7IYznntcM08veUnund/2JG/zSFtys6L/Bnd4xOf+MTjl3/5l+XwLf/Gn8h/TH/94xve8g2PD37wQ3n78qXHt3/72x4/9qPvfrz4+hf1Rz7J73N5W/S1uZDxwZxXvSr/jzH/Kot/XRUlf2/4S7/0gccX83H0r//6f/3xt//O33p84zf90cerX/M1eXv0hbxqfE2+kzPfosJ/Wsic4h8j3wshd9PfMTZb5kaiyeukRbPNUddFxjD2DsfkuPOEsSXfObKhTNLJlzJ1m6tujjOGIEWXHOonyh3bbqTiFMD+csH7MLgPbqTEi7pF3Jue1eVWjOITb43gS9FlVJA3luLsq0p0zEfmyHJB1/V4bDpfa4/F2OyepqS49RPfN5+9KLPeuUEtp+5DNkmb/Op33m6fNb434L1hS8w8zDEf9uu7Z/AiTpzCAhzafNJz97vqoNS9ixsLcLgJrn7ztjGuzDEGD/gp2KFHjuAuf7jEH3JuFNYf9o6le2J4sibMdfNQ+8hjz4X1Xjq/iIOoOtc5ww38+kDS+JFbzGf00YstL4gor9xkGE1zEXE7e2wLHK7AcWXvatQpUgiguz2tl0DJYU0vOuiD6XM7OEMwQNrhk8cM/GKrhr88ilicTQr2dtFrUpp87LYL/zs4KJeLDsUsNlr0NqYTywAx2L7cpp1S7OrSNrdigwLHnPPjxlGJxIs7+gFZPc4+owsHHuWp8Tnwe6i96OFp8dQ/7R0DNbwAAsDEhWfr9V37WBNQSvOEbjk0p3ZBjI5pPI1Be2KYXjbTNirRig0D3Dzxi0w9YPPk4iVjAo9EzEh4dNE1XphRxBg+nMq1dtRZAEcH11pdR/Gj14sm8vZxal408MukP5OvH/ts3r787fyX94/mnyJ/VS5Q//f73pf/0v7Jx9ve9rbHd/2bf1rzT33qk48/82e/K2+bfjAYjZMN4VP5fePnPvs5f+/ov+TKRfBTn/qUOi/luxJ/8Rf+2eMbv/GtXkTf9a4ffLz//T/7+OTvfvrxe8HngsnvET/9Lz+d/zv5gj5/+Z/9c8eqDHtk7Hy6QV3hzEAc/WvjKj/ahm4uyRkbG3nsW93aMySUcyah5IuLBZv25E5J+kbPoYzM/6+pLhtu8SO24KsyIMvJsaE3sKx1xsNHbLmg7PisHn66YU7fRUfdXTPGWrfSx/7C6D7RbuZc+SStlvWJr7sdc5PSC1370N05u/N131bGdrGav+ZEbmDzGEx8nWSGT2Ps+VpjBKshyhZjOvrp8ye5mdzJQZu5YYlVOYFFfWPc/aLnHYONH5vGih1xdQ/RX+oUYlkd7BlTnl6EtSH+4nCOQX7KA/vdIwiiekgLvnrI3bvoOXsPesIBWUzyGt3n/qO/9Z/91wxYB6dO6ZB47eZYR21QLzFUKRIeYraXWBpgFxJcWh34IsAIuYrVW1Bxu4HuQGFD8upPBZyXjqenk0o94XHA+8O9W7GRA1JjD8bq6itt5EzG42uMuD9hArlhJqNdTLXfhcVgYSdWdA9BbAebwRCbFOzisS9KnWcoeId3Bm5i38nQqJgc9Q8eE+CUUegEKY+oWPx9IMCUJtZTBVGKYnMBvzR5sHBUGJAxVIq+d1nzqm5BhUIfvHIzT/rueGFvvqPmQpk28fQfG0Ogc6F3wdADGDLRoW7Z8aAR7N3U5YLmy8ez+UUbDqDs3Ta65KDJyd8h5SUgd9vIeAXOW5WvyauzT37y037X5b/3l//d/IPaT2aOPf94+9vflrcu/9Djj731jz7e/Oa3PF7/+jc8vvmPf1POrzeXvFp89atf/Xjz17/58ca8JfpHfPuUV4mvebz1rX9MOePzju96R77c+g/nbdjfDtZbfbv0da97Xfrf5N87vv4Nr/ft0hdffFE8XnluYewcN2JIZfP1ZG7ada0Z8x19xmBt08uy7dhnDByf6JAn54dZyxzMeW12TXT+kEPS1v7KuIAxFh1T+0eHesvFayU46OYdu/jLpR521jnu+jv6t8qdC2J0ly99KwN35xOpo6xtbeBFPKbVfm4o5TH6G+vmc/FQViZnTdseR8Vnjdw4HN3KoMo03CIPGvhmnOzoETx9b1+U9eF6mDo5jZx9gzH1b0yPT/LStUl8B4u1Jef6wTFNZM1pc8NY2V5b0KJTPS6I6BWr8vaDh925mSC07C1tDw/jhB82HqlZJxbi9NpGTqJgfOMb7c0NfZRX/MMf/sBXWBz7ChEFWLCnlXCJ+pIyBhAs+Z5FgQB9AM75NKrQTuqQQQdllThThhD4eUgwvNSVDBrVUV37miEvXCzB3z4UqcfsiS3ylMbXum1xYhBye7elPDlhIHiIZ38mEhtDHvvqxUmm79jjNMVNJWfz16Dr187l0EEmr6js4NDgsViYNC/Bti+CnLvho1d6e/6DbK+Jx93a2uiUrT8OOMIjfU4SvLcdARqNbOs6I2gmPR5rt7lBLWKxxkSS6KGjrxx30p6Nlr7lEAxsyyTKKeWG38XOZJ8LLXrrq3UjGFnBkOs7PpzzacKTi51RpOH/ItQDMeVfEeetTN6epNDmzx9eyis828ZPHL0L5kJGPp7lf1LyO8Ct83eJ6seeT47yCpFPo64OmF/84v5Xhtwpx5YbOHBfeikEwczfN34x/9GdHNAHJn5Y7OhT8MddNnFSNx+Yp4+5TTviPNmQ0siPndqm2aExN+aDNRiF5j1VWz08ka0vAauFn2NbAggsYOOauegcSH3HppvXbOaREwcFf1Amd9S77tqnwu1Af99+5KaHOLkA9mLTvHRuiDuca7PjBLv63jkKL8r6JQbqKyeP5djcr5/uKWbzvLIVKIf1yXn1wbw+6FIO9FNA4YfDuaEYv0ibO3R3zK6LAf3rg372L2/y6EgOThwxv18bGJfGsDdKGsyhvEVYjs4FJhIFcpwg3RiQUpaLM8GbiggzTv19IvqDYW5ykWP+5uHcOfkCaXPX+bTj7M3zcEKLfYLcQokDMRGznyY1scgHrGR3MlTOwurmRTsWs9ivwWlQQR4nqglF/KtHECUAzqy49MuLnnRnelcHlRSDjmx5gtfLZPu9GAEb32xgZ1McKvoeB55mA3BcNoH4kRoaDDoJjR8WUETe4WSjQYVOtL5EUlM32WkzqNjuhXMXjxB2FYtqJ8VOWhZobWMsvirx0nzRCpfxW108pSALUWJELkoGemURryv70euzbsDANfnUOo3K6rtv6zEe8IJv5MFfHShoAUiEyClynAYciMsvUZBhLOCLZc6ONtwhHwRkNrCrMU7CQE9Up9TGMcodkPlJJ767aLHHFAvBIr+sBV0eGXz87sbpeOPP/uaW/7P35Xl1iI8vfKG/41k24M0+GVH97MUPnXudWLmgUfbtOuqL6YUPQQq/E7yX1YHD/p5psVavOShvUsjblvyDWMpuEnA015Exz7J0TDv98CMEcJrDnFLdjeQao724gLyFXEef3GF+GwPy2s2WC0i/VAM9xxCjGPBgs+r6gUvHc/Fog+uGhjaNupxTfSDnbchyzQ0HayuyzpPYxHbnCfEXF3mLdqmufMd25bVHoeu3ftIeLqDILX7Yo5jn90IfNs335gybatV2MdayseuLvEZ39TYGNdNH/rhhoKBP7kMlpX6R7dxbn0/6nAfR3ayqz00YeNkDcr7KNsCuGyT7VjhJqUbzTIP2E7+059n9Io342fjYdXbO7I3eK3MTSD9zavtoO64SqSNyjMw+fGQfW9xX5FuLKPlP9828yUVJoAa7dbYhikRh708TegV59TNJ1xF2csIOQ5I4WA04uoMv1mY4m9PeoZQb9uNtNjQRg+tFOmcfu9nFiS7xpU7wcI/zJ9Oycch3DNDZSeLmbwD4TrnhwgsOyJurTmzV0yEGnVn01SGPYZnT6jQ2FnwnF29RUNQPT3AXuxf9TirsbEeXfJ87SIBHVlubOXSCIKNwIl94kovSwc6iec4FzmSOXcaHHmzdgHL2xmFwdMkhP+AObM/i5pB++eAVnG3PJMW8m9/YAxInzI0znwBPrCZQRvisQy6p3knSDwTiUiqPxHSfRy4OZEC6sLNxcFPBg00D/7N4hAog/fyJA39OcRUs0G5+6ywCAgLCUyrW0YTWWtisDMVb2XFCVN7trHx9rYEMbazd3QaPxAuZ9ezYY8bYJk4Isj6+wuYZPXLiTSWyxO6rKnHwXa4djiiIW5kf4CIXepJS9eeCIY+I8bnr+1KdeQGbgPPYDQ4k3CLnLAd4E1cKXGpTvVK89pD05iesxNAk9Wt9pffgwhG8LfjiWd/FZK7u3tA6PC4dbLHZuctMLkb1Vk7s5OriUh7IF8O5CnH10GXzl3HqanneHOjX/OKTfWXAYtKLxiXbPuTazd5tLicJyM+TNZMH/eXXHMKpNjOf4nIYH1ttYrdYnZPCGLRzLjyYd/hw7MmbSIwv49JYyB+cmW8tV+6fzC07a4N/fEO9s4v5tvtF3nFBt3ERTAdiA8WkIYmYfohF6mH7Ykey6TNp6NL3rypddFeAf7AuZC2N44CVW3v79uB0RW8HaYOGB5skpEEz+ZPMTpJesLoJ33kARj54slkOW9ceC5QJgN8c+hP9EnWTN0tzR5u6kzlnB0Kz4W++ICeYeJ2UnayNNf65QMqlnICg4JHn6nWgB3v64LX9nJ1E+OOp645b1OsDO3OUTq4U+ekC6iRSb3SvzaW8uIg4uViswTdrEEzB9/IjVXJxM+pm5jipmLyRF2zy6Ff3hcSTMjFBEbAUwmHxuJCc4PW3G1ZvMdBk8reP2ODhq53Ii4WvGav0MZ48yQFYfICkG9HlO2yA6uKdzblY4DQO8oIvWnBAzpPCXDSAEVSvfe0fxRHR/4pXZMPgwh+41dcX8Cn9YEbHxXFOmjdXh0tgGwtG+FBJXmJG1P+iUXrIcImfzQG+drSs0SlUc2KT2FEk/gh4gFU8ctExoVs9MDGchtV04vPKa/PJPHPNiTcbMb6SY9MTOfbazkZe4Bk1fYKx9BgzAlgmyIkbWXnvRowXCnODTVqzijxiBxZnbDs3cVQlM3Qbl/qtP3WiWhn5C1bMitWY6kToyHtGh7J+F7PS4QN4fpava29eHaEnL/snF9QTP6+mil8c4sndU3LH+DX+/j/SonC93rdf4b35sHcIMwqWnIwxpPwu27TN13iskpbh3bHfV7qNteuTsQGJ2DIs3M9aNg89x1P64F3b6mce3QWEuoNaBdoD9/Q0mXRwJh42TyZdJ20sN9tDDgBzYAJMuaTnYDJ69Tczug4b/aJtQqWYFHIGLGeSVtzxWZPAJhOpi4FCnlCF16EWCa8oSGLlTF4GufqptI67xFYeiKIDMpMkNfTkF1kvft2Q7AlWB1Y69QOx2HA0T8MZMHM62CjQ9lOrkM6zCxNkEFo6CexWwCbBc3104MEitl6ojzFazpzGg13jLHo3z+EZXfLJw4tlzmg5MXPuWBAXPJNTLz4E0Vde9Zl2ilwcCLyFE5tK5MbHvOQRjJOPyFqPkhDkGhyeWHqZUZCt0AtpxxVejSmd6gGw+Tmi6DAW8Bo4cdnszHv66GfzvXDAIz5Eiz0uECJi3kyfJ9RnnXS9dDyYh8gtnFD2XJFuU3XzSR+YyOTkJtDckT8/lZf+jkF5g+JcK5zHzQ9Y+RGPM23nDGNAfBRPfeuNbGK7vy4xv64JYmu+1pevFiOjzaO8U2PtxI/5rQfb7a/AnMiLtcyc6m1NL0AzZ4LRHPQt0c5B/MVk5w/cJ66Nj7YcR7587SdcADCLANm90LXrsL5pcwGe3XeUHU94jH+A7ljNIbI+m4vfnxfgVqd7dv14QzR9ja+O0aV0LlPn2XjUo3PGwk8Ip854dw6yt1Vb/sHqO0+Kp6f9myPwT33c9Calcxs+e4MGysYCza7DnFn/uzeBBz9xq1/vAy4L5sO2qTMfe1GEHe3tRX4vcOV5+cufJjFIEGoHBAgrZ4CZ1NwtRupg0kdOJQgQdQo6GZwIqrd9DPANe0GiCwmv8MECfxyj3GrOJMxgSso+g0rfbtDlgnn1YUPRFgzqebiwSZyk50Khaz2or2EO5IKB0WYIgEeRykkssiwAddzOYaHeJML6xiEEF2jyRWzJMZshxcGhkg9T8IIM1o0KVfLKGASdCZOzJXJVaQteHGxHw80VHbyqyzkVsKrXvIlNXNxlpK/9Y5S2rOEBcn7MB2dQB3jzrCB95DCnOM/RH1vFXjL0C1iYvctfTMlKuAYgWMthN1NC4cnC5Uzx5iMNHo037AgvfSwg3tYxJmzSJh7z7F1WlDQCKQXsPEQLJtgI4crwmQsV4dFMQ4Nxqu8sytS3oL8X1G7WGdN0dpOLjS0BIqOjlmBR+D2M9jWKznDXd7m6PhanZh4vrtiUO5sGOelGDI867JyI3i2vcEGOSvms7zpB7roONnpsdMh+f+maZLKDwyb8cr+DaL55tdCx7EWVOLAz33FQFz1yI0S5YoVv+jCYnHScqrPx0Md63L1pFw25Aat6+KDOHhZd6jjLsZzw0djXP2PhWgiHYtHvzqHNXhCx23r94Wnyl3nJ8NZH519Dgm991qbzkxwsBnqU6pELW0Hecs05Yhw4+9smxsa3FusTOWVfAFG3L+D1s/6qt2sN3BawO15Xfhs38w6OzUH1t22e6Jkgilfu5Pgq17wWaQxwD0+a2DJGZRFQzTMxUdIRG1kafWI4xtnI6xh37eeMoxaRdIQMvMUcBU93jOqBNXRi0AsxPX1gVCx0dsDAZ6OJTNNyWGwvnHAQo/ZSFaHc2VR4KX8lEIwsVG4GsIULm5/O0zVo9KuTvt6J0hfLSbYoOWAPNmf1ktfA2gZL2ORo7SJxA+GVIB94QG48KC42O3BB7GvGwVycVNJAi3L1158LUox2HvyjGZz4x519ifHUBbuQyd+VO1mNDZ4FqJDmMOokJ7stxOjFK82+wthRzzl9qwcX6nLCFFKeU5Vvb3JOvyprbUpIy2x4tBnDbLKDQx/e7vbAEzLjQoE7IIz9XjxkiC1cJ1d6TRvMPwgPuF4I0Zk5rG+c5Km7xnvmgF2NBxng+C59a3KCo/nEOVDoRWn50ndKVJzfw7VYjYVXA9iAXL4XTm/i2AuKr0KUat/8yDGO9kxgVy46FxvNYWN/adNfHXK/GMuP/DdHOsihsaK3PlZXHF3UG230FpN2L4LEwnNuXobc5m1ZLi5tuO0rG9rFZY2vL+Yb66e81rb5IyPXuBCBe8TowsO1EqhyZCwOtJzxuWVj8kYoY4wNGyO+9JemaQsuzAEzj23Zll+6aouvzqtiX765qMEPI/ooa0NOKMtnP/VrHtGtujrFuOZV92yM7Z6bRrASTx7ERtEnUMc3/q941jdnYoSbz7lQNv9P18ErFwzfhLADu4EJGmbmVZ0NNAH4ijCWFaW3pBeT9pKgvgUs5CdwAAzeg/UdJB1HLA9cYRug8msS09mkLMmbIyfUYO+ktolL7fJL2NyV+uEJsIOxTzb5DiCA9dHY6nemySEFryVovUFFxIV1y1gxiMtbsy6q4revWOQ5BV30GFhmdLJQH40DlS3yT6M5ejpBChbbxNYcTw7VN8rU9DQ+U5+cIO6F7CingtpwC6PmVLF27QGP0tyKLiaAtJhfiYn8qzdi8LadBa1q2h1HZt/Yxwi9LtBpjF0Bb7YB8WYAzphHz2cackBGB/KcWQ8snE6tRjMGkZWti225xhRxX/kJU3X53xafTtu/voj+/O4JnP0a/eEDLqPexd1NQfCBoQ9Oh386e5PQzXjXVFmjy+90eGsxG2Z8jBvR6KOIlbr7wsTWPhS60a4evsuNPgKs/frdNn3d9FHIM+rmniqmKYwx2cb/ylDGh32RszHa5/oCCH3eJrvyTP/q44gYeS5X9b1w6DYHYhgStKyuXf2jCa/qwaM53xuQ5nNukp07HRf6Ly7lypg98Zc23HhS9txxWFn9b7866drc177zA5i2mZO8WsS2N9mpyac+BluvPcBNH3BKTtA7Y2kOp/cJ3/pj7MBfG/kP2vrt/o8e4zVjG4I7ftgWBcNyQQAv+vZJb+OqNvb0gcnz8l0RbeLBBrXq8kXdKkcpZQFKssrIMaLQ32odSQ9SedyDR3dMrPUqrDZdlicE09U7bojLSF/GI/JOwvTPPE/KOglBwyaTrgXdfdVmR4jfMcvDTUID2KcQ2wTKB1b4VheszAn2gfKuZOp485N32KaQ3BwGo1jmqwGJAz52e3fj4km7btMj9sYKaJ6UM7joRk8D9GdDFBVlJkgXf8dS69noilv/0dS2WLIyf9dmxubnoo5vF/vk4o7Lq/Hqxc/oc64sWB1A+7oQ8U78V67b7hf4EhZRcM5xcoWkmx4S4hd3MHxXFzvbnMdSMBuApWALpx2jynC2r9B2PMqimDVlgXbzy5sncmB2uIlFHoQOFT5nkc00VdecQcyILtxyNlg5wKVrT69uCvxHjy7cInbc6G8uqtm44U98blhx56Ygt2te9AvWu15lBOcpy2fXAW25p7/1o5lKud516F3+YLgOzffa9Yy8vokptckpWNAhVT5Rj+6W8kKhkrabe+zyo8/i0IJPxwldJGczt29ySo70DefmZtAcd2QUMHajbh1pOVPbdU09LZ3LInZggMJ87V6HRnRoV6lxpb4c0IOv3KuUPvrJScefPvmJETFOUogWj6e/4vZFl374TmhyqG4gIFlNj3KPIv1bnGvhvzb2nO4Le2+isFscPmSznwLtjSb7Tn3CGkyeHauM0fA98JLYNdn5sdz0QXRyLab5iY2ck8+dA+dFRWJjXEOhRNZgzyXvMRopvoWK9i1RZPIJw7S3Ox1gNbCoSW5kY1RfRMrQEAAJ65M6Nuh00IIVGizm3byKCQV6tqDHhjxvKURML8+NTTscTfE6wCEi+vZuWkF01EdOPQ85rrH9vfNmI7hQa7cxLlfMimQlWB1M5CbAyiQhaOB10Ip3dc8kuJvZ2Zy1SkyMbzefxWeRMfjkmMLFBZ7EtTmgurGisxy0syfGYw/LeEFN+/4D5FjHT+PumIB3yaBWv24wUMX5cmkrzeAylowP/qKyvMedF9/KUJj4DQaN6m9/x4PoCsdZ/zlvn/G4Mm56uAc+pflKxU1jCMF7FvS6Jt4W4p6NPwI2gH3i88yvxMm8hVm54HPvcuFSXWTYKMGvDnveeUYM/p5tyKDDGCJfbJMpRzaeS47J6ugnAs683WUYBKUOFeLp7/s23uapeJWRBzZOtCOBO+RT8IN4/SkEPM+NpflAp3OMGOgrzgKRq91Ai4Kd+dTvFR96yLsRc0EYjDHjhqfj09yf/qid+up6Qan9cuK8BWieHbNyBIOHe1R0dwtYO3SvC1WxsHEdr/I4qA06zQfJRHbm6BKJP4q+xz/tnYfUwYjGnDNieddgLxxgYrscZ0fBKLLNCxyYm80tWKBhV/fDcWSXXTG8iY7i3lTokzU2ORYHh8a6Zzhj3zUlJvOcGDHNgRjoN6clMjxrp5/onh0MAU+BB9x2lEB18NiQIELS8ESTfs1GjiZ9KQQnpM2p0xH9u7878QI2EAZ0KGE1YVjVff0wSAk+AaPQDYlK9XqMjpVyrL8mqRMsnawATvrswE8YnYTsycZTjD0yoRePHCGH9N5cNYdXTnTCAbpsnnGyfoxj/GuHHm0wU8wHF7FzkUm3KvdgZ0gxELt3PcsRbm4Q9J8CjzbwpD+Bmwd61oOcGRR/bv1R2LtAFpg2cB+uveBE35hNkWPrRS7K+OxCGls25zzYaPGOXeCStt7ZnfzQRwf2+Bp/SjI2lI0d2ufCB4+0a9vzckmGxdF47J0nUzdOYuPB4l8f4UJxnmSc4F9B47MeO7C6WTVW5Npk0tQi2CHb+ZCFDFbs4N4cTUws8rEFY7wNVrGvPAUvOowND/xTyMme8UHffnFx81Zu6Gzbs2jrkbHvHXx1FrsyfOCOvlZEA9GYtg/fW8wHGkJtPmhcOuA1T91AyWtzFKQd+/gwB2N6xl/+jW1l9Z31ER9gyY/xFSt+Ewi6+PVLo4O5fZtn+cxcNV4YS9lAmgOQg7EXPOZddTuXwegrJ/JTn3DbmDZG1oQxR//cKJiwy46cgodtz1f+4LU3QfgHA1kquDvlbo+e7emtHYnYONesfuVv/sCMLYp54qIxVw8rZv/6ok1BG1KeHTPykWcwuVij/6V8j6/9QxtZx7SC1sEYFH13H2EMmtfk8ks4yrMDU7KQKlEIlhE6DYK+p4H4t0gRnuB0imVs1B19LgACto2+qGBrX2fI+7VY5OEKghqBdYMUPWYEE3ufyEQkgiVexfSTxCXQC0Jl6LpVYIp9TEUB09LJCvedPCjx6KJZrdGPHt45lE+10TVHUWs+I1cR5XCZRYfO6u5AoUHZV2jHvp6KSz++B/ScNWyuUd+Fg5hSComGvnAjrqaBHoRzobOKXrCSm+YdVdpcrDvAZIG4y7G64AmEnHFIsR9MFrWc0xj5WT9pG0dMdjgubJSLih08DSKncyO2cs5Rbk7KofWnYygGvtR2Vpx4N2+yDBnzlQPtvpuAVfn6dqStYm1OCcJxNoejkBMx8anQlp7NsUyal/NtKmgPDud7TMTdOWmWrFuLnCLnGa/LdryChU6e+ha7m4ZC1W44gMmb6MrxskM50ojva4RwVgcN53n0jJ9zfPIkJh6ppnAAn5ubSycNC/p7gUCXdvWHE1reF22uNKtvY2hbtbGFD0YgwEVE5reSxqAwEkw6BpV3LqysfKPWom6Qs48VajgGZDGMPWKolcdiFaJ6AYoNsXqTzNkLeO3Ma/g6LuKUGwjYtx/fPFvg5Kesp797D5FTNu+9GWAfxHd17FareOAX11hkMTiemBfsFcUdQ3l17CpZDNaLcSQbfuCNxERiDoXo/sEo3XNn9mILR/yBARbXgeVNfhOGeX62iYyeG0gTja8IosXiqgPaiDCn9Kha6jjisb3iqtekQR+TvetyQxA7ScmZJ4hNRgjPi9aT9PQxgPR3UyIIZvgkFQydFGOT+or5tKAJmQHCj1gOPqHOpIE9VJiogCEnbH0musHfVyvLh34XY+zJwZZmZCYNfTgOCJHuH5NrJw/wGSwwKMXyrjG86duYpJaD7XG3/RfX9isPGssYO1LWV1lX/44xCvUfRXFJBoaUGPLKxSRck/3oreLw7xhh3sVTDPwH8HBG2olcDujT2blg/ibuwDIcdFnIPcV4xZi5gX2U2z/Kc6rj06jt4O9Ygm+Iix2n66OLkn5yV9/M83OPdUFrA7/NF6DOwcj4PSAA7SsOuujkhwoH+3d94i+a+jW3EqhmN7HqM8c6B/hQDPO4OuY1sOTUOfcy//gFcu+28VXcjjWMdlQqJ//F10cpDz8lHFKuedYW8SenecCFcvFsezequ235kZ/okIvjj5xsLtvRqVG/4s/66g0/kvYRB+X6+zez5Bh0THGCbt3h58wT+O/cgBKuU+7zDv1LRm32U9/dWH7lrCIa2rRPf0x68cfBKNrKoReMixc2PH3lGkPHKmdzPTdbO67kkqc+46PcN5Z9q7Py5mq59lzbjF/y6K+vGJsp9ZEGoqgTFly7L8yYwcrYVFG1b+WDD271vMl20FmLdhkP8wTO9You31e730hTPfXlUKwc54sA+veqGzv5MdNOsCix8AocckyUNIrTQRR+FApCA5sK5ZkDffYvcRJebsVLCxtkOyB0b6BrL4ZyNINJcpilKSYV3PVFVlNIEAE3prbN+Oiuk9qBOgFhyyN4cuLCmwfdQB/92WB24uh0IE4eCAR/eYDV33MWCBzKnGrOgDLp20Vv/WEyF8LFKx8uIGxG5YWPxR0hPVPlbCS2V1f9kKAX+s1bY61tepgC6WReUNi0eGqFrGKMjXNj6oTXxMlKrryp6Q5jrPAAgKNfKKB62ovprh7hgLqQon3iNKz6xYjm8il2BfVCvbFcHNfR5HpikIBgMw+CDSee+N7nHc/8xKbZtEfatNUvuxmzK4bFgu9yhh++WoqIrLr0TY5G5257r99fqRpOAMWYC8Plb28cysvxjUM2InhUD5+7EfXi6CZ8eILNHXg99VT7IBgK/czlM0aMZR74QyMeGrK5qgS8bqDND/PIGNR52ofx+h0aw72fmL2vVzGiZGwJcjd62O8rDxiA41hIp3Fje88zdfSIj9K+rk0FHpoL8YhUm1lH6V9u101pcTQ1Lc0rY1D89MCNE6BPcldbYqJvse96YFCQ7bM5AGr5FQdd+2KinbbovCxGc9DxZFzXRzT1tTzYVMADpjr1hxZcmBXp0cZ2q0oVpgZW7cHArk/6wbywk9t8xoRPTBer+2Z1Lo6A03/bgS8CBGNAQ6oORwrX47Sh7gIKpI8qtIUtZU6Z/NGBfQRY7yTSHzKDaYI0jO5iUOnbhLHjYs0CPHeng4svMepf3+s8Z/zEqdDehUTXhYrOxNZTF9vencC5evO2kQhAMbBJIyTHz75FBg9KJya6HYCVcyYfW8wBWXHSZ+E7oa/e5ivtvEIj8sYJfzBuOHJdu56rc5dFP/RWbgw3LieYqO346m+CbEzhIN/hQLjGfHHxl6dpCm06tKga6lzs87jH0FQ2dyoentGkE/g5YffkmU58MS7lXXUi3/AYUxf4yApJ9lPMORt0fVVaLvqOypX38YPNYC0XMDNTQPQGLhFa56A/ySyPjmX7yMWqXn7XJz34kB8Xl5TtUx5w2vzOGj8W9JHnwQ+bCRsE5+WLHvrcgxTv2qTp2zWCDvzc0ABLQT9Hz970MAaRNA7GowHVV23Ti4WPxWO8IAEvL4KJj77ig3dh4Y8/icrJ/vVR3bue1HJYPatPMCvJMb76t5XwbTz2wSEV5g0FH/C6+FReDq0LFt3VWVvsO/d2Y74uKvhonqlN0U991m/HkvzsXoqPrkfG7LItH3CW0+IsePmBy8Mgg0UOsN1cbr86A8W7Y45RbHllrXkg7i+o7ny3vnkgf5TluL7WL+fNUzWri3yfIejaaj98d85eOSD04pAFbMGpjFwj3dKL4bGdBKSfwPdOhW4Wtpt06gYmMBtOLhpeDOwoLqym4HibSJvQkiKBFBeBeun1QoC0dwAYg6FE8tZ6UcRvQAwS5NHVXw76Sq8DN7pYfznf67hJb4p4dZIJkAcY3sFGTx0MIuvdyBUX4mLAYScOqtXhD7npJ92Vtb1+s97rC6CUDkzr4G1pTOULj8KnP9hw2nLlyEQqxlexsGv2BUBeiJGHCxYTXvOQJpulikzyefUqMtyrvhecNccnXlHwPPWeGEsuHNzI1H7fNmURaRv7zrOSgTeP4h3EucCAairKB44TREbMhWkOLjMX8L7SBbs5DH503EciS+CHi/zGXi7mpE5tD4ZmmOq/Y01u0AGcTaC54QxrQtp51XGkLd/0bS6q2HYXdfIzPuhbvWLecojWxCGr8UXdDZ9xMNfowRGsPK1FizhvPLHrM8fog13ta+4z8sRpXIyFesXWVvz27zrfzd+NFc7yaI5sjfnGCb3VWT70KaTTihq27LN2z9XFmc8mwLPjw2ptqd3TMSNN1b30GmPXxoUD/j3+iw/o6G3BD+N67TnX5r06qO+GTs4dp4Tcmwb8LGbzj51jF3njWH/YMu6LLJr2SJr94l4a2E6+4gjui7FjbTyObf3e8Zd7f7V05Z14rrGZPMcQaubj+CrZ7j+NA7vNITd9tKvFOLLmmo/VoX3mlxC175cB9MaLePNH98RaMOsDRCcgc8g5iuhGKBlSl4m0G3Kd1Yn+6I+uGGN68F1obIjRBzJPDgb1ssHCKRh9Dl7U0R0zPEUwSZi7SfsRl7TntfGLYBMPA4RsN2JUz8AOPgjw7KKl2hjxjcx+GtFrvLOhhXD/oPlS2Th26IpVn+YyCiyK8rz8DPzhtq8meyEavdiSkeUAn930td8DcakbO2Qbz8lVus0nXRMTnHjMKyDM1CGgKfgGmoLUGPRFvZuHG4TYxUN3cyEi8cNtxpK+3QTA9LmHOaPPRQxX+jwYkEmHejlFjjcL4mnjy9i0rxzNmg6fG8uNa8eAXKrO5pdHscgtvIiFHHbDikgKyFw3+NyLJAGklNfwEPqSt29fERRLG4Dzc99E1K2pmPhsLhHuE+u00ty5TDhwM5Z08HhShuclk2R5R3X9rr/Gf8W1mxpyyu4f1Dd2+FHHNzzgvXheNOJy+/bXJmu/uCZE/I4B/RRwiwHve2z97kziV6cVLEgOpimDpSgMyA/zx/7VAXXfCuRmBz/FXH87TsixxdXOg/XNGVv3Vf7WNO36mbzEZmVrCz9l5C36PO4Fmn0u95xfdlHSDkJPSn3Du3OovMpnY7hsNB8MUxOs5S6s3KmRs80bnIzgSQxonTgXDKvUjTB+zFPWGDIeXKQt+oE7LfT2elWui0tfnNBf5eF+cSscXreGmzRnYnLG6RBIM1V0m7g1QtRvvk8lTvBTPZrRjaJW1qlVzrkLVAlSKtElaeVRyyJ0UQm+qtVPd/1QUeRhF6V8IkkqUCyfibnJpTOG87wS+JQroOsHNw5Kk2KdfjjK1pwhmQiSRyeaOeXCcSNqtby8uwoPqOiB/KexmIjhYPGMLm1yVr7tpF3ftNnYnUDA4VsgoVVHBg6byMD5ar4Lo4g97gUYi/Gn3wDij3wM9najuZyxeLK5DfSOg3qZ9JC/x3MuTAsaO3jedVrnQsJEhUYYSlEGjS9GlY1jcSBcLOIFFx5c6Gj3BsHgvDlEtzxPBhDFJYSsTntiAJ5nIcYWjOreYzh5mvg1lRAQ5LE2dRT8cWjM0yV3b0avfnz0RiWWqTsXExsF+d500aa/+vamn5xVZhzaHCK9uauh/HbO1GYwOK2xZ3KXx2y+hpiD8eMr2qxfrSZo4pc345uynLoxEp/i66DejTs96owML/lpzukAoxzwtXWMLp3Ch6IyOPSbreAG62KjtfzYf5G/vGx+wKJOWRv8V7Z5Lwdk5EDECXhtuz/Cq56W/8qRwrHZLf9qbtxt9eLceMBYfOq7pxIPbuirH24Gy3H1GjMyxrGk6ItR7UbGiXftiAs8HqhF0XZO2uuL/YlH4dJ/v06gp1Xt0Ms6uEo+jEMCliiOR310IsgP/sLDoq6b4k6aE0p55UiCl5BGeS3LL6bXt0Gng0V2fKdNIBgatA4h1OBWxgKhLEbYoxKvfdDHfS0/1ln4PLBLDODgtwu8b/O6ofEvcWKwi7UxgIP/nBkIJ7TVTroNCHwwZyLWM3pwbX6RnRzqCWxkIZVXqY0nAjDT5nEvLhpEmMEpejuYEZ1XIWfSECuPhZmKd8UDvDncCU7+5cOiIFZs8/x9m2GETnwDqJ9CZpFMzLolvgHxQor+FHzLjyO+0A1H9TNfShd9kbSSW2oMh/bwxFTY6jWfYLfQ5ZOc4TMd+hCnHNQg5+FOH4/lt/Nh4EQrz3JAj8IF+StZtF3c3ZTs8OAszfyYdyKYkxAcTjR4HGapdmNCUvxioVO/fEuSFgK1l+POw94gFHM5giZuxOVZLP3K4fKFjaGBn5/OkeifOY+smwn5cL3gX1YyiX3wsM2Gl5O+6aHQh53ztSLbVF03g+m8Tw46rsIlz7vJ1bDxMWb1hVY/TTnA6SDu8iAe2FwFzv7rIeadTMOPx3B0CDRL79jWZzG6zlsnD/jZgn5tesbu4KrUnKNzx2ykUMVu9pHoL1Y/9dsxWB3jn4vr6q2/67yvjhZ7mc6YJE/XnIcTc3Vj2DFs/jaL6PO886dOLrBdXtQX+z53Kp/85B8VbOzY+e7ajAXt4pHf5g0ZRXmrOeIbZeTE1Ru6XX9wQz8T6XBkD3LUJJP/4t55wDQQL+eVTaJoBkQgVADlETnGWG4/kw9SBV2bYY5ubPtAbXyKgUX19++uNrGcKT3rFFMLfi/0tUeZ7vF3u4jbscbRQa2cgpvGiTFyBxBdlFJI9C7+StqBiqxSkSPnKHTx0jOcZ5LIWdzBC+7qOlaxKE5NvZAVwpyfmMfv+lTFQal/jpvTIFlkHB22/9LKGS4UOY1vmyDUiTm69dtGh/5zptUJe+WxHNaFclxLds+xi8Jy5VzQ4SUHxhb9GVO6VFt+WCBoP8zOK8eIN46DLVZvlLBsPOsPFDNEV7ll0VPgoGsuoHm2XruNWcU5wIgLyW4C5Uws1w3h6bsZDqISuFE8h8D6IRYfcQI9Fn91yae3hrGCAX21N84djLDHHh3ndXTAJkYKutT3iaz9Owb0o4yvcozVyC4c7NblQCN6gqXg2NZnZSrmwFrpx+LlpRge5Yluee74Fz9HH+W38ZRFZfDsBRNc8mCM5AVAHYB+4Z1QKzVPywNv+PTvpWPPOGxZfNr4oG0sU69e84h82/x+jLIXn7Z6Qdw6VMmR3G94tFt6rt/N2eVr/5a19hNrumu+OS1frNDbG6XLx3jSqP56E0aOe2G/6zYfYK6fi89ePJsHRoobm2vNECuFfnXS5Iy/ax2gRX/nZ2+wuDBHp1SNL2PeBapCehzEATYWwXHQJ7LcRAuijBp2Kq8OCbgUweSxV2Z1o4/Wl3YjGVKBCtxigstmE93bplEZvpqA++/GkC0XScfWCzNk9dkE7KA0gbGJ39zfGAuNMiahPGsrr9FTtpzpD+fAl1NacEC/Q185b5kg51/w1GPl+HPQIZHiqy4x0xhZDFI89A4XZyk9Vt64Gx/m6wOdsxjTOJMkSvjy1RxKg9nYsEYZLykFbB0h+inNU88csYUHD8bFWKLvBNau/fpIbjvvGMe6wGE/3h604JjznLnRA5+CrfFgA2aeUEeGn76q1eDoo2BfJMbFObL7vFIZIPq8metYCF6pRw+8ksyDzcNXAnDJk1ye/A5fIQdXpQCwN24c5cG8KB/alj3NORaV77HAxoGITzF3oa8CFrVpbgBq/jdPjkNkXUPdRPhgwXLg7Dd85LybCOisZT90FvnqEj91sLe49pIHZOw1pwxe/a/0wirPlacFuNEkBsOYuNLwEVl9Vbe5hHNyQrInbnCQiRcIztez8+d6J6RrCX19zJyAVeiXx9Y99xUSHfhEZ03WR9TM0bY3X5yRWXLqHtdxaN4Q9sLXd9mayyv3jH1vElAsFmN9jUV1O/7o0N7n+r7vi8wd4uZnqZVgRJMA5MaZQCtr39YxFIOxM7zOMWToMC82F47R4Cz/i1f1mYPNR8eueQrBFPCYx7VBVhv6FofzmScZ167X9ptRQHgSPHx7Tk3yF1BB057JFBN1arV6t8mWfjaVYsqIvFr27RZs1VEpRKNB0iSfczdLwmqwGNPXoDpZhkYHQ2Np6Ye49m5HW+wT9SY0RvGfAYHlkMsQRauNsm/LRRJ5pl2O5SkmfIPJwLjJILR0cKwSC9+rCr9Awwvf1CngUdyU0KHAjcnsedrRY6EbJn7z6E1G9CL04pPxoYeyG+zlQfEcymE3cxkEo1zw3SfU5Bsr8ySHxOrG37GAdyOAVLW8+OMpIsZRXG8uyku06MptfYGd8WDC2jVxpPuUJ/xEXd1uQpoEFow14/wK/lckZzcqJNG/54pUHy+px/7iSEfGL3GcMQQf3unZi6fRDMheFNFfJvQznrsg/RuIsHI+8wfDBKoSPLeCuB95d36hQxFXhlGIruJyOrZVNKdnzkcRaHm5WZKH60MayNee89rhljpPYxveYFHgRqktmMWB+x2DOb02O6/M4+CvPTpbv3DZI3JhPWM4cz7t+ghI7Lb/PkfBqp/JURT3LdfK4W8ScZd4OBJcfdBXvp2j3YgbM5ot+IimY8Qrw1sO2CRS6HPN4mtibKztYy41z1zc6otz7S6Ozc3YiLkXFt1M7q7+HQN6e0G9vcJ2HnQM2TMo8Wgeqe9chkMYxb55WflyqW5xkCVzAMkdXW8eWXP66N4M5v5udX+Nsy+cGL+dR2Bjhv76A7OlMvq7RsaFNsTDs33U5a20c8J8V6mK0SBqjY5DwJm8SmPIg8ThFQecNasGatoq1KiqqXrVVhTdmDWhMU9id3M4QZLGBAoWTw0w+n32wyy81Rp9eB7HsTGsw7O6qmCeTnNqf5I7ypzwfcXfdnNQXqoKhBlgyGexpy0PxeSwC0Ne6E4/OqjoF5kxRJ8JN3oosN+hV1H1/H2juJ0U5sp8wQRl9OnD+/AHn76UrWEidmROFJygNL9HaR9HZEVjfERNu/bDSXwWEmMYmRcVDI+H2uW4dvg5E5v5lYsm+sYDl3vBPzENx50ztm96dDPHmhPQbjjwGt4E1I28goEVybEZgTiDv295gmFCOEWvvGjsouwixbc88ZsHQ4st+wILkb7nnntWe8K/U908AIoPzinE9pxjS2xBLZl2Brv+rvPhqcbGX367+XTDNChAxbjDaqoAu91cllFdN+eNr5Ie4Vd89Ot//e0YNgb8Ty5TQ7ZPkDZfl+7G3liSCvXR88b1SQCrC1LqUr/xN79wY6PuBVbfgMoJ3dtNEdLYxPMt38jSkXLf1+JNGfo+Y3OV1MeouYADF77qNtZqb9x784G0F+bir67244tY0QebNbmlvu5ztWu6vpvH4gd7sdgT6so8rb/FpO1jrhMd42LZl37jDx7tcupePykKVDmW38W39ot1vYPBuoOT+lFH70l6J4+L500Qdzs1IrTHMwaL97XPK4hg+OlPsaIBj9h82Tt6vDDwU6jQP6cG1vbK2DScBDc8A0JhEsHZTTPnDQLSDnDUGgAGFHQyHXOXz5k2k5PzbnwkXx9VyCTgbUl4axA8+tlxYpaEoE8e0PEtTDJjQRK1bDqeUweXiyNQxNa81RYTsDHnraZ0yxH+ysceMOOjzQTVEE58AKN36NxReRGhMz6NiWqeiJqT5siLZkOTHxw3Z0ztNGJBGetZZEjKd+KLmraDtRh6pA99bXLMj4srFR7NbfNgPsm9PznEP01p5Mw4Yd+yozK+yRWJi7EqqjIuyefGgYzOYFHkGQeMKba18who+6O3eVmYmtOPP0p8Z573zhge8B4ndqee+e/NEX4CtL3lkxaVCdZ+FTJfUty8Zr3RJnLvE+TOhwViC5dX8v4Ivsq9vDuvsMMFOecYhLSbr6MvLvJAxIGZlCDWEMJWYlOfscyrQ3LY9bNdcMBnba7NHZ8ru3EQGV6xS65cO7EldmTXf9Ko/40tZqdsyunr2pzcoQEXx6B/cI8APZ7Uy7N8lMODteSc39iZg8jSzrPjdJ11E87IlzcXVX3HP3bNf/mgTz75F2HGfeYTusuFjbua4FA3g/FRbvGVz22kmVK/1Hkeu3YNiGq3Ohc5ECkY8tPcKElfsUrCuYime1tsHfv06ZPYFwvrqzDfKNiDhx31zSe54aYuG2D6yQf8m1vsmo/iI+ehvfwCmAJuD8GPvPjEMnnL2CFb+33R5NirhHn0M/fhFavqEl5dpIKnFM6R85Z/9pe08uQ824iGTYrqJswakVHmrK1tQmoHiwU+bBh7oVh9tOhT08lJcBHcOHQjXKbpd7dAb+1w30QU+fSomgAAQABJREFUh476FystJoEPcFNISL8VhlhR6KRjc+3mmTP1eWoUvXJLK87Bg4M8hrJ6wFkpQVxit7nhTHsHT1VkeXB84lNJdJMb+9kcqS0muGnbr48r1sM1us2hnqINBMeLtD4jYcQpzZ7VcwBPDlDgeZ2qH7iz+PBp7gYvtuTctyVZbGPfvMjEnNQH8AB0nMoNgxqV+eYzjIglP+bzzA20ho/V5hwZKvrFyyxwMdCH45Kz//K5N2dgWNp1eCLb3wGvQllEBUx5IlnulberuW1f+eGv4zpzm0EnUHBSsDt8FaPHP6Zmc0Knea42FpVxbM6qRw7A6nj1bH6Qg5JzqTPXxjbnLc1Xx4H6crowBwdrX91j2Rygv7kvXsekmDdfOJ6y+txA6Su4xA1H5g22ta/RxoI5c8QyPNzwzUv5F7sZO9zanPVae/wUFx/lAa72UeHsXkI+zN/MU5330LxfMRDAWdckb3htPD13ftqd/o2fvt68zwWd/MqHXy/w3ZwJAvw84U6bunzVm5jnQhiROTzreXReblP75lsuWT/INnfljOvgh+PeHEChOuxrqY8dfvUp3Zn3cg2GC7dYtR3cUsc0mNnXmbeEuDHqoWsFjsijOTzLo/rY0HMdnuEIA4+pc+a4crCoo2aM9CWJJgqgJ4XBTl9kYGDAAu9E6UZZcsXENEOUw0w6uUwfzlII6FwQ01bFjYJeJojMI2/gmIk5x+WKthsccqFjhymT2cUCbwarg8tContfqXWjKjc5BcSJEJ3JXmotV74aQzW62e2Ew5abBV6VQ4QFLr/IyN0ueN6qgQvMjDWQJ465GGDf8QqU8cMdjMYii9Sxo6SqPjFhd/RUjAKpYVHzqHJxz+YmjHr4o4C8ODFWhimFJn00GWm+nUcD6nkeu4irRX8xjGuamDm/qCDbxRxVtfeQ80yphRHX7tjNLXkqLcY4dRd50NDlSI627NgBmqypwwH7zsNqsulG2FhmrhqHBAitOUfbTW1irfWMGfHNGFzjNuNB/KDn1L7awJi5wp9dUMqr6wefclBO3479niEH7sUNeyT3Ncg7J+SBeHuxqY1+GY+JbUMSIzjoZ3eQUyAntvLAJ77xI2aa5J1XkWsPHvXGsXtJZcVb/rWFeOcKvfWvPfHROVidDFmL5O1snmEUvlGxbEzYr17ji0+4xpaNH9nyxLZ45YrtVYjjGpeVV5/WNQfWzldbMBemvuJ58gFXOOCfPBBjizFHdo+hPC9e68P1tIaaX/4G7unp+ENvdUktm2pUTcaYSIsZkJK+5dV8wu/i403UYKtXkxyj57h0TcYjEuO2Lz4aC84o5dQPQVbCse9u4BOKGOUc3DCYxI99T1dwBNbB7mRtUiGDHWC9e6mr2rkTJTkDiZIkmzCyJE1tzwYT7d7tbH+J4q9+2u5kKGcDkUMnpG2wOytctARKmxi4oJA8NhCeXGTBXl6c2//c41k2tOKVTzpMGMx5Lq9epJLGAJFQ/QSXM4X8uTnSjzP54qV49Z1JrbadWA1+84adfBt8egkK7PLlDEDjqR90sOsZZ+EWPdtMKP1xoFbecGA8yo46WGxgqhyfyC12tNp5EN004WFc5mA8TWhOzPin4Gd9pVJOYOMXAeeYrztEW1ZGPPzszUr7V5H8UMgjOGmx5lLXzA0Erui0mKPcXW9OsTX/3JSxyLeA1+WjxL8x1NvEHge6GR8THSTUN77Y06woXOGWhuM2dmg7zmMHqjcxeFeXC8g133xXIAHt/INE8bpGjlyu6xyf0opdX1k4frF1fXJ2zOLdsc2sz7lFhtojAcfcBb95hTF8yqm41Pmw0JVPdDfO+xwUb+zHofG0HhwxmOtIOt+JOWi2rdluHmh7Tzcy9BjX5oh6Y8AcWe0nhnSOSPn2cV5797Ane2JVN+/l2zFuTzdncyWn8mkO67c5Iz8XFjz2AkIM1z5czsi4uO4FdnMLAvXyGUBhU7e59py3vnlWEYSteIbXxYXYek3o3sekrsWikYPl1RwzV9ZHeLsQNAvWy70hHyQTQuxZC+xphCA1sKg0z2xDvgMYPXSrX16Ml4iRPxMg2pCRYIjwe6xV4F8g4QHwLkJdlE9wlMnttsvEONgcM/FCMg43eKSU9AQWlsB3kmEjucxWzMszOsalooFolH7seOKMZHQigt7SiZd6+nkEcS5o+KlvdcLRm0J1cFc+vo8BFKbc+VnvwFGFl3Ggn2b77ZHn8uli2+Rz4VPTzczQNKlsFyY6DNrGWJvqVB0J1pScww9YbCqhD17VYuPcCbK8qokfLNoiH92MJkf0RGFxxXO8oo8c19HZxQ4WNwWWhqwCmOaVDgwsvT0ZAnvSl3MCTk7yxlFUHAAAE2Lro62IZ07IIwdiLn+7MLNNPs4n2EYGEi9dN17mLT6dY3hkkedMaQixGHw0vUCYkG6ucIC/+hi4CcyrNhHa1/GIfX7P7KYS8b7VDX4XOmtUKN9NoM6a2g0HvR1Xs6VzuCegcCMmbE5sA9YczhobvW5s14ZVr3sk5hDM2Dqmg8OJfQNvJ0maEGPHwCYt8wB36jnEyrcwU1++fOqQOWV3NIgV7hTkVOnbjXNzOAPT3ESX+FqYC8y3GVMUcS0OlTYWF5v6ae7A4fdKOG4uO/a7CaO/Od4Ylm/7NtbozZy49iw4sr/ye7YklmeCw56HvNNHvItJvNjvHMAH9eastmdugjXrSFn0oHB00zZ8QFJBp7hg9rtsdz5uvHC5l47jJVmel7wGx26GRT6Jc88grC38Ou6k44qpY9hxiPb0Yaf1RUKsxuBa3hxMjG4jN22zEDdxyiItQ448IdAnk64DMyGlv1oQJ0EZsmGz1peXHfyAnUCxA9uLDJsMkdDWPH1WOKInmQm2uPI6I4hR3RezOhzh3bciuymA1U2wH/M1qpqDoCGTuRMfx7Mocl6fnM/CIo4M2ph63lCefAvG2LPICcQL04SLj6D7gAGcmQRKVycd9DVPrYPjxMBhRrZjoln1InHc0CPwPLtQGQcuThMTfRRgxGwTKXrrUxV9LZ6Mqhw5XYCINljapi7P6TNSfZYfvffCIhhyLtj2VSeZ14c8h7csNh56WemU9Os/Vc5ST90FrYNmrLkW5ejLQV0ugN0szKGwcIBP8PE1wI217Y2o/odP9HpDElMLWvBKf6qsQepnjOil63ZBa3+t+98FNjbyzkPIHMoA7tjsc+V7ISOmZqE4RS4X+q6YwQ1aOJqvwO9mOsOgj+Lhpblf/AsLXslDNvOVwY0y7I8cP4wVZ3R7UehFAC6BOYUmOhR1vZFJZLOOsDVfkTeOrq9JkzL8byzgHF4RgknO1g/t9g/3YwhK810+cEK3/ojnKtUzcmkx1wxEf42DubaxNb7mZXKStYLr1dXnzQVxn/UQHDizXy039ijUN67U5Mr4lBf5ztvVjNfkZ/21vzcm91zd185YoVoncQZ/5455EUVe0bD4gccQkmv4d/5Hb3LsehSwsspZN20TC/teixlNtfGAefoyFo2JPm2qvI4UA5SnAUbgxW4mJJiSFCaJwE1kX0HfRNaOOh07+ODvxMQxyRB/SCLTZ1E68cDIjxjDCbUuwvoufkIKPjw0iK7+0uLcxKfOI8k1GSMvbP2gkZpl/zYRG3naQaAmYLSKX5qDIQmJxG8XgflyUMsFYzigdXKpHQwaX2M0JcqIAz/Lx3N0laMhGCfufie36kcJvTzJP/5cmMGDAxOTsnfYOknbRWQ2yBkaOUwOPCGKzAcKiU+JZ2w0iqgksEGXot8m3ouDfdFfE+ZOeTFx4dcx8AIOjSHAIkePmK45Mf5WKdbN/3KaVyTwyEYAmK/o0MuTNvo0uHA17ziFRZ5DkpPVecfA+X5uNdHs/ASS0rwXG65XyWjhR+coji4XYAr8cA+nW2Esd+wWj6+uouw7Oxj6+/CX2TamCwx8b9KwNa7GvbH2IhTOdE7Bt/tCsC9u1JtTZLuJbb80blyQt6/jR4TYIyOmjimENid1vhyvC3ovCItHLHAzL7Pu4sm5VN7NrVSMqbntxa6cnFdxtzmQJzecGOUH+eY9Qok1T9EJ4e0DvvtAce83ARhhuTGDvf7wT70xNX6dBBv56okRu+WlvqikLQ8vaqlzLs2DiY8K66dtENfv9Mdu+eg37XJFt/EtJ2LpnkyOWAPzIEdSgAT4eC5vzvcSNeNzD0Jx+psnNJffYl32V0zoocluSL52PbP2+k8UIoaBsRFPpswF5OTFCi1LL2ZKJqmob6+Wkg2oSjlgz2DVU1A6CbVzE+gmzKu1EiAhHfj1CQ/eMpBa4DrAbaJTl90kegczOhKSiBMfO5TlErtrwwckT3iiTjXxrd61maAmaMNCMXhIvJi50K6NrVj0Cp4z4CzUDj4DJSd7IuOMUX52IWJbEbbXoDcH5AoraBBXJ6myiMXSPpGQ6yjYh//oEuN5ZYK+CIsVZX6cwCAjb19diqDF+kkvqOre9eFvX85ocMAvmSvvsbFfDfsWy4U3uOYOAMvYpb3cUUPqhZGNOC03pcR632S2zni4ITHePCELfCawddvwDFbkYtOdZxQ4nnlELOQGLjwoxNjS8UGMxLi1Z03N3J9z+9FZe/pp55zHbi4DjJJcXa8YDy/7408mcksMtDMXzCOgKXDphaY3Ta635ALl5aaiyrrTprX2mKtR2tht1kWgIgUybfxRJnzrK3t6Jp+d/3DhSRvz6l3jjv+NCc6bI3RXvtj6Bq807C//GaMonPuY6MET76YE45TVb6N89gbijDkuhuvdd2Ooc/aNLepMmzo+6udpDMUiD3NxWoAhA19R4U3DGIrlWhqFwzMa3TfBLC4q6xu0rUPPC2ls2KN27eDj4qWFJHJBsWDPE/vGdu2TKGZWH9/FkfVgECf+4GF1Dh2b4sEX8Ti8q01d3OisxuJ1vpdP+yZX2KXaI+QHaE84JAnoEBiqhMLDWk4bzHSKhj06e2FRX4DamSzu/tTDP/oiWPeAr2PDAHKR3PeRg5g+AsTuOD3Ws/EPRi+E9YNyYYuxbZDA5Pll/sHlgO7ZiJyQsSBXgPTnco+tccXKi2UJ7Z2iNmpDbOLLGR9i5uzbGumGrXbBZFJTzFt8b14VAlQ4UIgCYHFoUzau9MgLXMbWDXX7uTUypmsD1r+xYthF0gvLZesCD7AXPLiCMX6lAn4ecHFhlZL8mg8VyhGD6KEvDM1bYdNTFIzGsLqRDnc07ps6C1is4OAajOst7Flw6Vgu4PP07/60KLZzLdX7HCyZKFvA4p2Ha6w2BscFubGnRfLHRq4BQrTjQi7NfXR8myrtYzNc92LJfAHfD6WE3+LBDf87Z3ybMHi0Ox/KExxigltjjFEKOowX/XCrrGcxZ38j3jM/5wqy+o0XrMlJBCfPwS8PYq+DPV862LWfWDq3nvLEEvO1AWPnxvIgoo1t+4j5+D95Ir6A0RfXi7ljcecnTnQXB0u48sRu/VBfu/MOQLSQ7XN1RbjlRSpNzeG6Opzrj33xKKnHePRfyF3rRW2SOGV9M3caZzGo780F+B3b6qwcnSJFHl852nYepVZs5vPGiNP1E8u9+YxsYweB+Ua7fJZreSHfcvatsy/2ukB/bVezeSaGjbc5q58oY+EYPsPNhoU5BoLJg4k45OhEkziUKqCbnwgHCQUGxp/cac7HvFcMtht5zuIwIayDjQRzghY153rTLi0Z+dZUeOEHW11WHz2eJO5sSrEDj9jUt+kU0NcuEhBECR7sbIcLZvyRvxhcRPKgpS9qKCBFD/8zQEyM5hPd3lAAih2RODjgiFUY38qMhNsE5PBFj1iL1YlaBijouW+zzcYFPBzhA4bswsV8wINJlQ7xRcA3WlexmQMbsQjq1wJsimEMCvmyGiFay5XJzaO5GiNUNW4fQLVarvFrP9Layg8ZMXVaxszdSr90sfCIsX8qg2oe0Wc48CcniKcgXw66iqztS5ekEotOGzJalQRAbH7ngkg8ujquWo5PO2PHuyFshvAAzk008l4oJk44Mz7R6SYz629J6jfWOZ8LEGBpU/BLHJ3/xNx4MMfPxnjGRybxBQTcbNNqWf2e4djf9+CJPOOv67V+HafjszkHqfTT5jFczxmf2tx9lsNugOoGZF/B0fbdo9j1b4h7I1GECH3lIWxFHssHLuBmJRGG3Dp38Fm/4BtzlL3BmTUNMkltX7ww324xmUFiyQ1KLxyDR4Ap2IHX2MkZk/kqyOl3rMgtFw2vAbW/NMs76qfIKYL7zZ+d5G206je8mYv6AoAIKFfsNkf67Fk+Xf/sWXC7xp57Fl5Za1/4wkuNgzUeLP1rs3tUUAIMOn3k44pvGeXsz/RFd//c7OLAfN5A62u5orO+HdNg1Qf5KWb3MORqy4cG/fmxsObm7wxFxLo9nI/hOEsgyNAorR7rkMVbWzZtgx7ylbLw63yJu+AzILwRxE8DSQU9Hi5cvsjAmSDxHcCEKE83TKtYVCaYHBjs8mW60bv2Tpb4PjaJdzemXmCZ5GDG//AGYdOCHOS9Y0eRCYvcaYVjW/V5euDF76nS3ag0yIES60z85zLxvvQSnyqrFF1TLyYcuIsnR1HfCc3Chi/tyPUuQLSia87FL+bmYTx46rgE07x3ElE/SYy9mx5+cF4nhZi6YnmCUwxxE0CzCW+UuwEg0zQS9CkLbR7tRZaH48U86s0JJNBdLqJGB2L2pPPMNToXKzFVrnAAxm9E2Uo6OINfS3JYHZ1yME7OaNCkkvmMiE8dzu/v7I+sr96i4sDXiPzWjrtacpFOujIPXuk3EdmIYPHp4ycPAk+385a6P9fFThvUI6ezGyxn5lDP9FAc51afbKSMAX348EIubQ7lxepFhxbrZdewdmilY301zjrZsS4OiiHplJDsIMK6cYJrCV69odeYvMGLIzlkbX3ZT+QWpzlKX5rLQ47JNWugGFj2d0jObx2VGSgX7+BsPLLopt1NGn7lun64WETEj9rEMg1xxo05640fko5N+7jY47P8Vp/zrl/6ymks1I0b1u3Bqg4aVyyZN+wZTtbqt6++zFuJP55//rnHr/3ahx4/+H/+0OPDH/51P4T0dW984+Mv/MV/5/G2t31bcNg7Ac/teyrN63K2Ayqd/zSTBKCRUfrf5q/56M2FfVcurhjJYZ4RdF4HIHVvOpjTGfvujdc6MBbVuEmYdaZ91x7YJZM5ARgXJpKBoYPp5Bu26KYQgvlBXwRDamdUa/vKx7O8EkTz+Reez37QTyF5l5QB+uIXvuiievbs+Rh4GeRkYC+88EITabzl8Vsf+ejj85//fL/eB09J+HJggZJQWHKBSQTBiv9cTJZ50gNxn8/lzka/2qBRLXmnhRJ1BnMvk2hgvgMMxr04oRgEoTpAzZIstWte64vfMXz4wx9+vOud73y89MUvxh/Bhwk5zQO/73n3ux/v/fF39wMNS2Ccwq8F7dpV0iNSVFSLguOUNvxreumJE50LE0n7BVehWEw29ewmQtpr2/h3I6GvRYb1raDjw7sA5p7xS1n9zYHnmV9d1I4iweSnF30NQ7JZAI0+nnPHnbo3NTf82gADRnKSAzHtE2HlzRX1zTF0Nk/Ls/pRyoWLPueIfsMB47LSX6MkT2xAHY/OqeKCSSFe/bSJRDmHcuC8Y5yPvPtR/MqqaCbU2Zu5k4dwao5uuYnsiovxaCzl0zqhRMtPYNe+PPHXG5OQzQ9roaVjMQ3xiXv79QemObq44+OU4LVVLHR5grM95I+Nszi19gIkyOo3HxcyNxmja44bYzHxVQZg3i/+lUM6P+nr2FU/2Ty0txIVc11PmPVGgfz15iWy8KeHteVe5my7MR0yja/znjrPHR+RcQaxFKKl2jle3e2jH9XLXgPb9olxx888zbth73znDzz+07/7nz/e+56feHzt1/6hx9d93Rsfv/Dzv/j4L/+L/+rx3/43/93js5/93KwlPp1PTI1djqHjzYL0mlzkcGBebty0z1jeOHZ+iWjMjn/swPBJxODkIsx15/nnnw925gUX+c1J5gw6W/AVJWXWp4O6u/tetEgnwjXlLKzgNEhWzirMxFJWIU5/8ef+6eN9P/m+x6te/arH9/yNv/r4mq959eOTv/d7j0/8zscfn/7Upx+/8s9/5fH27/yOx1u+4c2Pz37u848P/PwvPX7qfT/1ePH1Lz7+/b/2Vx5f9VUvCP+LP/sLj9e9+OLjVa/6ah3il6B/4B98/+OFF77q8d1/9bsfzz3/7PGR3/jI4/v/1+97/Lm/8OcfX/u61z0+9KsffHznd/0pB6j8O8Af/JVfffzGr//m4zvf8Z2zcd3iSZT7rSjEgC8SS+WlL3zBDew9P/rex68G4z/4m//h41Vf/dUmk1eGP/KDP/T4xMc/8fhrf+Ovh/u/5p9kkA2NPHdywP35XPA/9tGPPn7ivT/x+Mvf/d3pjS8yjMPUnz3/wuOfvP+nE9/zj7/4l/6Sd03eqBRwFmjHiCS5sLTHHz8du1FvDpwIUVaYRZUHE9HJN+NKzLs5U+fBpmH+2L1T43eo8IQqOnIf3tSxr/9OUIzdeGK+386B3lUkFMnIhpM8U6cXBmC4BXJGBy/h3TtvfHWDNIZwBc+LKBhuOMMneBsj/dw8xVg6znn0jWcYpov09C18yBB7/HLGVD+RT32AauwFMm8vg795GZ57A4WcfnPpGay2WY/mLr74PSAwOPLv+6jHVv+IU8qbDnLUzZmxcIwj7XiNXtTIWWVzMSHZefmkrIk3v75ySBc4+b8CqfWihz9zgWDwyA96FPNW0uOnmz42xZq4wdHC9KZWDvAwzPGDCnD6HNzKGDNqlFZ2jJFQ7/zAvoG94pX4pu0MCB9iod3xXx9yiF43VtDSz1EieMscyppGvx3Lrxcu7HZuYr246uKTfPMg0AjBhS/zAzn6QFM2r/qmrd74TnvlZlM8LhDlgf0W7ldekX9ivjk6nFBgzkqOBheXZ4//8e/9/cf/8N9/7+M/+bv/8eMd/29d5wF92VWd92O1kVBHI426NBqNCmpIoAqIJkA4eNESL7xMEgyiGQMGAsZxvLJWHMfBBkLAGGNYy86yA8lK6EiiSAiNegEVBKiOekNtpJlRmRmJfL/v2/veN8ryefPePfecvb/97X3qve++/2hOvefu+3VrdMN445veMO684/ZxwQUXa47Spn6L5/iqbJutl0w3RJ7W31nduPFp317l6lKUJTt0cbNhbL1k67GluKibO23YoL9qpIWUmCEHjY0bn9EFwyb/Scclmg9Jm3THLH/zmYWPnyEJExzpnH/+hdAerzztxeKYmNB/uVOAHr7ZX8N37DuGbglukwrJLSKkaiCUjAyAG5UIp+GoMahI6Ap5bEEj1iB4bN1jWsB2HS877WXjc5/83Fi6+1LnLzxv1TjxlJPG8oOWj6ee3DD+/m+/PN71gfeM7bSoLNtr2Tj5JSePL3z6b8ZuS3cbr/kXrxlXXHrlWLd+/TjmuOfLsaeqYcfYdtttx6NrHtWisbUWDC2asr2NFsTrtaC+VJftd99+l3Sv8GK77fZLHAQCzJ8yuuvOO8dVP7l6HHf8cQrkVg4qwYuv3A5VXrKUMUDuvP2O8ctf/EKd4MSx6267jb332Xt89R//aey1157jta/7zbFx06bx6KNrxrnnnCuZE7T4by9MNWo1wNa+x55bB9jYsr5z3GmnneT3to6nZRlQerH4cjW9yy67+GqYiHshUO9gQXTM5S/t5UEmzC08wJF0//GRttysJCcWkCl1HIYxPW5ONHcGI4MSURXoX7Bk03YpUz1chAEf3+IABlkHU5Mr9qyLeNtRZ4mQ9ZDFhrtg+WTR0sMOMp7AlCUOM6/kiR2YLJDOKU7dfhTAj/KOBhjmyKfBsC9f/EoNcfVCSQy84uFn6ogBXmA3yDpRig9tb24fSxMLZLw4ehm3TYcI3nDEcS9g0Y2CtCW0hSfugCSW0XEwYGGf8Av7W7j/AUceH+c2DYYXSuOiEz3HItUoTtHg8bGaq7zBY5IibKS2C4JjWHpgmVJxaw6zTuTBsV1VLC6OaauOS+zlj9ezaINCf4pf8Pdv5FTBogQe52D3QgBPzpMqdvY7Cw/l4VH9KyGxOJi09awfPuYhzPApZAmlj6qHVAUcyDdkuOA4xel7ynnzgtT0lYsEiEnLw49NnfFizpyn+NEwSpx3Gbi94EWFIOALmyDs825miXoqDOTN+OrVq8dXvvK/xnvee8bgluhHPvRH42Ft+rfRhp05+V3vPmN8/N9/zAvW05oLt9Kcetfdd43LL79yLNHcfNLJJ47ddIHz4IMPj8svu9xXkEcffeRYecjB42fX/lxz3Vbjjjvu0nrw1HjJqS/SvP7YePzxx2Xj4XH//b/SnHr82GefvaT/0Lj00ss9l7/whcdqHtZFlOQuuvCn41cPPDCOOeaosUxXq9/QRRI8lmy3ZGy35Dljhx2fY5zdl+4+DjjwgMSGMPBWwv9545h+qCvDBHGKDQMJDYImJcZpdthARJbCyFeAK/g77riDJ3Mad48997D+tVdfq6u3+8fueyzVar9xHH/SC8cVl1w+fnDmD8bb3vm2sbMmfxbBXZc+14MZ588/98fjFa955fjlz3+hK8j9xjZcLUJJH9tqIZm+EKXDagCwSPI3RJ8rjINWrvBicuH5F4w9tXDdpfvdG7XDWHn4IePgQ1aaw4U/viC4arSfXHHlOFCL9PEnnTAe/NUDbpSHH3po3Hj9jWPf/ffVd3h8cbxp7LX3nuON//LN45wfnONFeo899vCVLovXrroi5fbsmkceGWsefXSse2ztuOnGG8fJp5wsDnuNdevWjQcfeEg4T4+LLrjAt0jZ/a9X+WNcNT+yZtx/333jZS9/eTopwS1/cZyG82BLIU3pRYkOL7FK9HD+6TU1pmEWztN+xNGbHGkjHy3VKZ7oUmIwH5mYKc8EW5WqYSenzqE6VRrHi0kJ+Na1/DWafWERzYIAK3OAr3Cjb2PU6DQMXFLZFKW/UcQyRlqUhQoTSf4CvfhqEQq9LJqCdhytap+EoaO8cDlHy7PQKhP9TE7myKImDPibgzFUrzsE5oFOTYKIUUa7EWtP2LKEf715ZDA6IpKhvCdxLGBDzSGMTHbU9YIuhBCVDDYSSyZAsdL5nKiLKHL8d0/angFtfD7hxcMRzotPNgSZTNGZOckPXVWAR7k+Xcd5Lxopx5P4CC9eLU+kmU8yUeuM73jS/MY1CeFaHhPo2x41aXvnbB8utCs9ITq9KCKjohw282Euo37q78p3/2ZDS4JDxx4/OnW+i5DLQtiLsFqUBcoco4WMeRanxJRm0AJOm9jPxIl5DBsdd2IFlGPiWKY/cO740mYyA1biStiIG4FzhfL0bDDAKhJUIcOxxr3vtugcmVWrLhoHHLDf+K3X/5YWnyfG7/7u72hR2Vfz7zbjolWXjs98+rO+ZXrsccda/p677xl/8Z8/MQ4/4tCxfv3jY5Xm3w/84R+Mz3/uC7pjt4vWhZ3Gpz75mfGhj3xgfO1r3/Cid8opJ4wfay6+5eZbtPDtPb6nu2yvfOXLxzVaMy7U3bOPfuzD4/N//QXV7aMFeqvxV3/56fHxP/7Y+L//52tj9S2rx1HHHDH+8hOfHG94wxt81+xJrS9PPfH4+OH3zxn33HPP2HPZsvF63bHD5QzLCogCM81VxErn9GG1YwVHHat3/HQuIxAYBpgSgUOWN1/uuwEc5JR5sNakcO+99zrAx7zg+ePOW++oQSycwjjsiMO0QN7rhQm8u++8Z2yn26Ev1KX4lbqye1QLCh1s1bmrxlf+4X/6gRJP/DSe/q1b+5j17xXGr+57QIvoJu8K775TV4a6RcsO4bwf/si3TPc/6IBx6OGHjbvvuHNcfvElqTvnRwrWvWPFyoN19br7+Pr//tp4TFec3z/z+w7KCu1ebrj++rH3vvuMndSIdJIN+r6TBfPAgw4cZ3/3LO1YHhzb6xYwV3TQov6HakyuCJ935BHuQF/+2y+pY6wf5/7wnLFh44Zx8MqV6kDL3NH5LvS8c38ke1uO5cuXj59eeeW47JJLPTERcU8wjrzD5onZ7UDTSSD1tMKcmGQ7Tj0gun2NiSJJhy53QOWAkVyvnOrR70kOGTSp5kjyAMPxxlSNbZqf8vQ+A5WS5Ki3Pcr1zzaRUp35qN74FmNAZ6JrXI7U9yRlPQMVrurdtQvP8vBTNbJZTIoWZbyoVwLBeU7haVvU1yYACRfDCyuRF6r1KLOeyklBRYVFDWklHVhMLZsSQCzcIhQvTlq2RSVikk1d+gCyjpHLia8LKJ5sdDxcJgLxsdmJfY1vyBGPQCSPTuaE2PG5bDUn5NPfGi92jVMOKTrVF2Ib++j7yksb2ciC3P7N+J7cDV0xFzn0BIGwbbct90cV4499lGzzBLvzxKNlLUddlRWscXscOSD6QLblY2Meg+WqF0FkmAvRw2YnyrFDClb5XtjNO3WxNXEuGOpIwZ05EQNeLW8hC04523Q8S44NozkVNnVtmw3BXbpqO/ywwzy/cdfrxJOOH1/+0v8Y/+GP/0z5E8dhhx06fqALA/rHNttsOX6kOXUH3fX60EfePz704Q+M1/6m7vBd8dOx7vH148Mf+eB4/wfeZb0zv/s9x+fAA/cfb3/HW8Y73vF746abbvJXadx1+7dve8t43/vf4wuNVbr1edutt4/9dUG01157+07cd7793XH1VdeM9/7+u8d73/v2ccY73z5WrDhwHHLYIePw5x0muy/1erF06dLxJ3/68XHEEUfkDlZ2sfIxfqoVJn8dKrWV7nLOQWVtYxLrRotQdhUOa7et2oTg+3KeCreRKtVReTDk4lUXj9Nee9rYTzsL6h5bu9YTQl/u833i1nqIBmLsOi6/5LLxuje8TleIS8cNv7zBV1rP10L6xt9+k89X37zai0Q66NDC+RxfTT5XV5S77LqzOy+9b/dle/hynYV1D+V31WX6cS98wTjmBcd4d4JNdHfVTmXnnXfybcmDVhzkoKxbt36s1g7lIV0V8iXxHtpVPKEdjr9LJCIKDld/b/rtN4/rrv3ZuOWmm82BJ5gov+3WW8eNN1w/dtfiCs9Xnf4qL5iXXHzxuO6aa3XLYDcPVK5On7PDDtrZ3DJWr17tq1oWyt9561vHUUcf7avjdOp5YCtMSRkLiqk6vt49OCimU6LXO8ru2PCmvJM3N5wvtHtPeDXW5K1NIGJf2K327pUGRc62lelBTDs3H2zS1lmc53L6jCceW+jycGHnHJ7xo/U9sZhP+xsM/AEPPQi5b6isfeWqzjKqi38maPuYhzcJKe/kfVYTrOq4sm2f6KexRllwEDcC5+Q58CHdxIEC+o3aRJPLoh+JAdV4AGd9KKGXPp4x15DAIgmW8TTOIh9exCC36Oc7BcFCC5Rux8QLjPYZ3+A2XRFZvrWkKFNFTyeZ6M2DUsXYWIUfQXukktiswKgqfdMV+gg/NtUzelFtEbdtrqLjQ/vEgojaYkxRMq+CQzbnPX+lIjrCK33Os3GjiA1g/txcdCVk02CFVnCxlTc4SdJVLMORTanaUhw4J2WuTHtFPp+ZnONL2+S4mDgPDvqdght/kE9bcER2fhNrNnS0Hf0wiyAtnCvB2HIc2i7hUd7PkpRt5sebNefdfNMtupu1buzy3F3GWt0Bs/+S564fdxh0t9TPTuyzz74JndqK3zuqSnPdEl8cwJy7aXy3x1dluaD4tebkXYSDf1sKY4kvXDgyx3N1eeqpLx3LNK+zkFNOaPdctufYccedxgZ9F+lYVogPOfRQrwWJD/4kxot+9hyAE/YXqXQ4SCQwatLqAxyFruLsqgk05zl6QIkRCwIYW2hRuE73g1cetnK8+KUvHhtFcMXBK3RVdrdvQRIsGulJLYDLVyy3Q7/42c/H8487ZrxQV100FLc8H3v0sfHkk09qMeMW6lLfToEEuupj/nKX7wx52Gbb7Z7jcurssbwgj/McN+gKjEBxTkowIkoAejLl9utpp582VquxueQ/7TWnjcOPPFxXdBuDpckd3b102/Md7z5jHHbE89KZaNmaeO++655xn253QmWHHXYcy/ZcZh+5H85DNjS6xSXA5PMr3RtnIWUB3f+A/XXLeCfbQF8eCF+3bKzQh3RyCDUOPrkd7J8+NDHCk7frBOZ4CM8LjsqZcJ0QrxcWpgUPLALmoBF3JjZwKLLSZN8i5oqSUGwPfJA1+CQAV/cfxYkeRYpccRPnPqeafNvWSdkUPsV6kehv7sC2p3INOtSmhFgVuM9yatsGsS524OUNoP3KhB0uHSNwm6dA9c/jJY4bBx5g9wITij05MjHmz4N5I2KbjLCk0Aw+bZZFKefhjSToJPFV2zk+XSI87HFIObFgPOZ2G+UkOHqTAlbpzOXwTzzti+rhhQlkGZck337W7X3qiDd8jQUF4mFbc7yon/AMCF5k6U/mowLKVOp/6WcWSpHjDGa4YGTqv6hVgscsg68x2PEyT8l2v8E2yRslxoNJMAYydqI3xwQyFrGW2rbaoXGxxx0q9CnDfG7b9mLc/MtU4fjgwBFnZOENf3GVDcdP9Rzh3H6hl3xsIW8ZlSfuyAoPOCcyc+wim7YqAfMmv7XmJOau2267TRi568WDM3/1qf8y/vErX1Q/2DSu0EOPL3nJiyStB2U2PKMH/l7phwO//KV/Gp/77389PvvZz2tzf6QXyM985rPjS3/31XHOD88dr9dtV+ZUFk8SD7fwNdYm3c2jjOUFzPW6onye5l+eQbnxhhvH7bfdOi655BL/nOMo3Xn7uy9+eXz1q18ff/qn/3HcceftXlz5bvJb3zxHa8dTgpBvIOFAJZ7mT9vEb6oYY/maQ+sKJ4sKBCwoORgHVBUmuD0AVFjBZhLgf7fgqdDrrrluHHTw8nH2t8/y054v0VOeRx971PieLo/f/JY346eC+cx40ctePPhe76brbxoH6Nbjd77+bS18zx2nnHqKb43ec9e9vvW5z357jwNWHKgFkZ3Kr/X92/rsJMrBjWokri75IpbHfB9f/4QXv3W6GuWLXnd2yfLgznp98Yr847p1+eQTTzgwT2nRXS9Zrmj5Xm+tnnj9yeU/8VXfA/c/oO8+l7lTrtNuCLzn6grPV3CaIIylRlsvTgcuP3ActGKFny5969v+zXj4oQfH83SJfvwJJ4yrrvzp+NY3vqGFcetxrxbLtVrsDzjgAO1odhhf/Jsv+LtF4rfykEP0fecTbkx1ZcfcoVeOCY44a0ioKZi6MkCop12YLt2O1M2jVrUMKuos6XOJWt5/sFw55LFnW8q3ZMP43J1KdeqsMCPZps9aAy6mp1p6tSQE0vKc+x8yepmvK5VfWMzQsRUZy1O+nAeHKt6ZdIiHfOcKy3a6k8fiHIcaGHxfBhRodSTfkw5aLpYTQYivnBHv1uk26Hg3FOf45SSAZ/QdW/yMjakiGcfdvB3bWYb2ImVyhGsmMcQ8wcJOIkiVqsuH7JE6fh6vbocsXGjgK0f/YF25cFZGxbGadmHi6CjEp/IN0+ITG9FxX1U5KfMJx8hRBk72i2BX2/06ExNylAUzfNBQicvgV2fmaiwp0V/MfeIS9vE5PoBJDINdC4vKMh6wQvtAMBaMR+w55cObt8pi0+XUBb8EhZ+YmrPqvOmU8KJdtJLgjVXqZ06cp20ilU1B+gQbyvTl8OwFd2oXxyCbdbTnWIIaHcribOH7PH603cSavjLGcXri/5vf/Pa45pqr/Z3dH330TzRn7eun/ledf9E4VfP6q3X3a9OmbB6WLz9Qt0g/OH7wvR/oGY8l4yP/7g/HgcsPGB/56IfG2Wd9T89E3D9+/33vGcc8/6jxyJo1vjO3QYvonnqA8o1ver3nW+bwTXqKlLt2/0p34FasOGh88IN/MM4++/vjoQfv863Rg3Rxdca7zhhnnXmWrlJXj3e/553jpJNOkuzBmjufHGvWrBunn/7qwW3STZvSBvjViTh0v3Ecp6Aomhf8aLUu7OrSmX4gPQfQR0HQbn0ggBIIGB1gDja7ossuumys0RUQSps2bRz7Kxh8D8iKf9WVV41tFSRuZfJd2xJdMl+koPKwCYmnM1fqvu/zjz1mXK9bpY9rgdmZ25m6WuISmXUXnpdddKl/onC8cLmMf1hPK1128aX64vZ5Xqy4x3ysGvKan1499tdTRNxLJt0ozNtuu328QLdNr7nqaj+VdIR2Lrfqi9ifX3vdOPnFL9LxZ/qu8AZ30vXr1nrH8q73vVtf/u7qK8Z169d6gSNGLLJPaEG9WF80767bqse94Djf9+aBHK70eDCIJ5m44nxkzSPjgh+v0iK+jb43PFi7nNv8lCq3z8477zx96fvkOObYY+X/ofre8BIvfCeeeFJu1WGLiUm+267aIK/EngHFgGew0NDdlr56sec0mUCU0CN1Z+iBMi8ac50F64Nmt7YyPdl4cE1Che/6DHJsMdnAEl0oMA/nkXIdA6rjbLOvInOFJRxjcAwK5tCzrvCYg/DFf6BaQp74IsSnU8zYgckmgJR4Equ8Tuyby7Uy+DYrNqh33YznzZ9OzVd16BjzWXIsXI4tMhZCkHgwCTJJE5NcwaWcmKHD2EIuSomVzjWJutTFiq58LwnJRjyxQYqUxS99wsFKaZGBQ6wkpuj24sGTmTwtHD7hRdvBa7IqeZ9RXpyac/pYYuSrMUnSCZh4n1ZceKrZtzthWXw4mpPlOi6p7z6MA2Dkll/wKEs8O669yFATzs2rbYEXT7DZ8Qp2nzNZ0rfAbv1eOIgTsUhKnDt2lEVHGXxrsQjb3441RfEbId4OpNWocx/y5ihthN8spoaVeIUO0YWEZ0pV33PE5DsVFoi/iDYHR0WubaW/MvMJPZzyE23k/+K//pkvOC7S/M6dtmN1J2/lIStUd814+StO9d05kfJDLoxxwslCRP+hH+VJ+i7nayX5qH8sfGSoR4cNAD/HgMtWW22hK8hNfgYDTBKUN+pnGGCCgQ7NwPMaKcvXOchimz9e0u3Vbcp559PX4rsvmlb96BZHzh1DJFrAbeKIA63uby9xqDsBDRJgA9DBdXnN05eqye5IZZu0SEKANz+692+olPfjuMgqT/P7yT3Js3Dy4/j+DsODUk4RCHjw+xd6AAtJGlfBlF3/JkXl3Irkkp4ydo9cfpOYdIKlAGtRIiBcGSnjW5ksglfpC9+3/t6/VoCf8QM11+q7viOPPsr3tW1fH7mCMWNPRtiDHP6wm8ZP7HOEHxMWP+Dl5x1w6XJk8IXvpmhUZKn3pbwiwmaCjmLujnN2YMQcWf2zXi9k+ONGVjlanuiVc3tBnnIpIWMMFfQOlgHPTwngsZimSUwOGls63rEa161ucbiQwE+iBxCWdPbZbpWXHcS7Dr3W50jcspAEsVyYZGb5LB5NHX97IwBDOjntAyHw4r+OKXJ7gMW0754oGbgTGxLtxRs94uNJXJnwzgCmAnnkMIS+kzKcxVbihc9TpWo8hlTAFTBrCjaQSbuGb/OmD8dOypElwQWd2MkE6rJUOpYxW/akGB7oBctA+qDvM06wxRgkno1vrjaKDuWKCz7wEo65qSyLQniZohxzX7BdfrCBfcnLFkfS1C6S4YXP1NmcNYRUthd10O1z8o2tUudzDD61wazACY92h/9iCqXEBXnecHX7SnS+KjWiVRv32cf4kjGLj2xMjFMUjICf5hEXHQtx8hPZxLn4UR75+ONz+FV9Y6lWnNnMbH6RY+VnfcQ3boPHz+bPPLRWDyp+6pP/bVx7zc/0YMrp4+hjjh7baM7+pebKs848Wxv+ncef//l/8h29G264aVx80SV+2OWEE1+gC487dVGwdGyv50OY+7ir9vBDa8ZOO+0wrteT+tfrAcWTTtbP7XTBxDMJ69ev00ON94299dQ+z3zw1db2enCH8kcfXev+tlR35ZZsu52+s3zU/nFRJVc1j2quX/CL/GKLKkRqP/Upj8/EBnHa1LHu+F943mq1LYsC1dUBvBNJwCnzbQUJpDEw0+YyCRB4jQSJhgKH9FsymRBBb8PkScFDLXr2wDDhYT6y5XoB9gCMNp9pwM1xxQkuxRHdbuBo1GeRRJfF9+Ybbxx//6V/GKe8+BTfL99hxx0Hl/l8UduLWC+EwcNCJt5p0ZBZc1H8eM2DHf/CyxMbFJQ8mXhSwV9K0lAVjWAh50ZUrf1S7Sxg3yYcIFQZKH+mXqWchQUy5Jm8ZE95OklzJd/tYjkJwK3bFN+7IHFHaipSLrtk2qrr+f4qKZzIw5lbdcaDBVyqrTCGPpsIeHHeKfJYSaLr2QvrT1KqR0IDoDYbU42KwbCPZS88xVt1XoQ7BshKEa4cHcPSDdcq1KEn1Ux05Se8xcv4wuR0so2q2z6LbccfIWzZbx17AGPc7Yy3ACkxgfafH8NPl9FYjkXscRq7qUemseNDx0IVxRUZFkM2SBlLlCR1rISqArgEF6xuK9eYg3I6up/hV/FexHJeYng9Y89zTffHuQ7IxLfxus7lhTXbyPzzz+GwWfK41pFxnNjM+GXKvm7uMxxjhYNQplhhC1eJHceOEfzCnVg0Q47BCsd5kwE+YvGzrHhcoV/GidwCWGIBZsez9Rfno5k7tk3GhyYVbBaxjXq47/zzV43vfPtMP+OAPA+svOrVp43TXv2KsYseeuEq7Mkn1o/vfvdsb+Y3aRG+XXfh+NkZiyFzKYvbrbfeOl6n32gv3X238e1vnenfoT/y8BpdnGw19tVPK266+RZ/98fvB/ffb19frHA38W49+c9vC1cefPBYq5+j8eAOP7dbpp/v8XXToYce4juLYY/fanNcUFwS/8SCqDimKqSdiTeCaUetA+ikMA91EP0OrjsHwCpk0PHneajjrwvQ+OSzC9cZ99exjLQPGKmOSCfjv2RyJYRkVf9aPhVmr2wTj60sAGBRX6myvppUeTiUjOr8fUev+lLJ02KQkgwO8rmAxy3e5SsOGu987zv9+xd2IDxNmh/2z7tXqzaKfNL1ngMJKk4b3b4rXvI9/rnAclK1XHb6nBRnsgLPQolz0WFCYjORDq56NR75Ps+f/bJybLVPHAURFOCUc5k40gmqnfqYSTnSi3HufA/m8J13qmXZ8DGRHT1ytAlXOrarc2LB7R3HSMw0daisJlLEeOG6ElioeiLWibHpSkq+nWGq4PUExKKnQumDweaNDHCNaV2AVAoXXpaQQNoJ+wa2XsrCxSDWASXcgh9u+MEfn4BP+vwsZ1viE8sp59N9lvbkVRx0MCsc9hVtiYfrdKIMnKWrydY4Asc14gUWS3fzV6nkE6tCmA5wdZuU/VS0LrFliQcbBsHArs8xqBTuzvrDUUXJmnyGa/hlAqIfgw0O+vqwtPOCdd/Rw1fYJMVe+SdJ+q2vkmuMI2ZdZHkVbiY8MIITfGLUmLJf/QpMxiwy6JOQyxHeuV2aslSEdcv0PJo69117hY/xlX4pykqJSTRjB5OTDz6Rv6UXuYqV/aOk/BTWot4Uq/KDuviE0/i9uV/GUUW4zVeI6HBXjwXxdH03+FJ9R7j20fVY0+K2/dheV2+5FZp5nau0/fbdbzz0yMPjaT2FnzWFq7b8gZSluy3V934P+ftEnhrdd999zYsHCLkLBu7ee+/ln7rdqZ/B8UP56/RwJYshP1fbqDebKn52x1Op++k34FfoB/5HHXmkceyno5L2Ay9+E3P1ZWJJPeNmIa5pD2Ki+ot+fKvGlALFuwLlzqBzNysfasQsKEargFanoV4RBiyBFZQ105WzK1SVwCl3Y3jQopaVebGBrBUho0STjop8OdqDnknPLrZNziirczvgU9uadwMpS1DEwb6rQ2hC3XKLrTzJ5BYRRoNp/8WLl/0zH52prHeWtutBzq4a2IqJTmgQyixTunMe2fhm/MpTzyDFjVJtqj62DnLh5aaaZCj14sfkQ2ODUiSsiyKlC7wyKDTh1MLbE4P9tD9SgT99QjzT6fGPAW80x8eYWlyspxKbcHsjVzE3FHVFQEqVKyPRB1WOOA7+ftAEEjMJ2gZHJzCIn/hbwYBEsDZJqqPCB9Vl4k3sN4snDtg/ALJogUEyJHaU6cWBtkbfNpGRfeo7zdjELYsCddbR0XEG0wqJKzHFGosb44gNhDdIlnGVaqgjNiwS8Zky91FEiid2oEfqiWrqEyqb+RELJgyOQTED8tYvhgUGfo+rxki/T7yzKKAT48hM9uXf4jncHAfY2778aU98TuyQWpTr847lQtAtR3l87j6aY+T6R+5sGoAmdnDYfOzSZ2fcmaMVIs8EtZAW5fEFvO4rcyyi0HWcLerNPBP7UEgsw5X4oNO+R3/RHnnLMFcWReLfscDjRV9bHh3y8On2Ih9sgMRJn/SN8NeT+/rqh99Pc84ix0aLBxO30xP/ElRev6VQhtugyElgPCUd/mINP9TfXj85w9bGjU/pjpz+tKWuMPllwY477WgePKPBX8Hh6yv+zCfyJ+tWazgJWtgdI75bXeTevMEnb2n84aVzz+EshtQx0CKTwYAwFX31wKUwDk2NhVXVt3GDCCUBixxlvIKVMml5YGNRM4ZCSQ4ZamIzg7EXEpuhBlcj48/I9o4uV1FlDUdEzC8cLh7RRjlBczmm8cOTlySQrTIOXGV2WXxVqRrZE0lNPp6klAeDoKLPopKYxJ6UbJcjvGDKcfJb573wgdEdEG0neJUO8SHBxzbEJ3Vd2HUW80f4UL4QD1xRrW3ZaerTuRM/8IOlT9ub9FWRvgDePFm0b91vwLfPjlXArFf2fJAfvuJDmDwa4NdmJ3Eq/4mzfViwWT7NnKvfqlz/EiNjpl2UDQbxpN2hVYME+Sm5XGdV7yhD2PbUN9Dh5SJNC7YleWOEdfxB02rmgrzvslCo1BvGxT7MwpbvlwBFqojVYYqP6tz2lCuPH5EvXguTYPfHtBugAeO8N2tpy/Qvc5QMekyepO57lPHuycVxBg/nSs5ldZ6yLJzko0v79OQUB8It9ZQkNs0zfRPIRWx0Jj2cNwX4MZGHP/X4hs/Yjk7oLvoMNwCAwGr0UpaSLrMRKizT8Xn2OLOAPowolebZ5f/csfl6gyMiaZ/EgbmH5DodG7v9MvEFW12eBTAzLjLpoe3p7IdtEwGqlMIlMQNrMeGv1wYV+nkH4mzdyKHb3JkC+FWAipTiS3M1DhvqKmcTT1+mf/mnPVKiK4PV34ViL7axGL6MoY5d200fTdtjuX1ADi55ql5XsXQ2Ji7/sBoCMBYwR4LXrm/eqVRKHQT16sZp3Xa0dRM/zhIASHTebKiyEDwioxJJRydFKpcMYkySCZJ4Sg4ORmRyQoYyf4ciBPGEO4HpTtHBoTPhYdukPpN4MJgkjcdkr5cgXK8xpiOLpMLAH77Vy9gYdmJjkQmlsbuGAUoZfkRcn5BXW3GOZfg5SYgOke+FZFN58xARYiLXjEWT+Um/wvWOHlg5kMlGuD7HbgaS45XA2jCc0ub1PR5k6H0wgofrwxvCYJMykcSnKrCsPmJUhUYRWWNIzdE1JpVqS7UVV3O0RQ9wCLs/Ia88IOhTjmXyzYu2pY1J4VmmjWHrkp05YsNxJnCACbz9sKlAlT1ETGLSsW2z7biAQJvBs3gDC1j+Rbf6pxd5cfZEX20tLXPEFlzyljJ/S198vFEyVhYmcxAGMej+QhljtmNoz91X4rvjCCGn+MyZe1XFM7HQQ1+MH/DlD26QwLdDlTcHVfpoR2lBECqAynfCHztiCEnp6PCo3Hwnldle0IIYnCIy+QAbItcJUHDTHzoelnL7z3XEHkp2qREayPTUosTT/iHHlQ7cEv+OeVt+9jELTwOq1rrgUDb7lHO04zf4lOXNHGyHVN99lLGiPBiCajvIo2tsHVgUGiv2sK0c8a4+h9XFBFZkEYxsbxIp6MXF4SIO2NMfqG686MfHqb2Z2I8ukGUAABZZSURBVMxdfok3KeM1Rxfog/FJ6rWEfC4seIgL25qXNMbTt/A1T4zit2M0xSxxoL7j2LzwwbiqJBYds1jWE6z8bbwQh7LekkeFshhKgLg9ZVCAqNdHhj8KekNGLwdICC7CsvBrnlIZyIhKUlk6e9/GoyY6NHTJwLiwqxAp//MCwRkytsuiVJMBHcGTgKpUBkTb9qSiRscGyQFxJ1CeTlKyuR3nUyHgqfKlh0k6QHMAIylB9vRSZdjNYowt6oUlcTTaNcvrjzKTsNVcfdStAiaLXuAoC7RiSBBJOlDmOk4LuAewfVR7Uoxg2o2sYsNgE4BVdJx8KY6qtE7wOSmbYoqdaSIztoeoJxEAXYQcEHY7+TImW4nrJAsf6dGPiG8Gbh6+cXuqvJoCIhNXZImnO1VbdUFzVR+0KWKGHP8sYAx89u5Qx94MdRwcS7DBBU6duQcsGI6beec7LsdUgjQNtlDhSMb9pRdtCBEj/LUAQmy6kPen6mmzTCYeVyrGXlG3nPshY5UB6X+JISc2Ue3LeftEPn7F3+QBVg229Y+x7nwZnDjZo5KTPPzQRywbko7sTNT49GFzELZ0EmebFKJkY744UlKY7vvxO3ZK0FiLNkzG2HCNT+AnvpOW6ja/ksFXZOajAyB8Yt428c15iEq283nIK+fps/Gp+y9xARslMDqO3a59THnmCqTB7zJ0wegFA/uexyYZNEh4mTEFbseBUlMoHCQ7wc8cEFA+bYgGvOmjznJmDgZy2f/fdyKZzUKPbY7xkVpiCC+V6QwfE6fJyHTu+NJPGNvwqJSY5IQ60oQhH3wVqTL86jZDhv40+0JJ7HecseH/3Je1wA2GvCdsFpV0kDaEEsk7BRQ47YFtZTGpgJKhKoGWYHqDne8G4pgJYw54iVlctdbHpi+ZCYgAYwLjNE6C23l0fKuJ2gV5cP3ny0oHTGMJkknkGeOALTxNSO2zd/B2WwD6l+An792IpKOjY01uaSyUrKij5PlsG17A4M7k4CZSHpx0mnQQ/FLZgg5oLDy0j/H0ob4SePqEiplkbF96sZr6cEIoC2JvIGifnpSw57aXpicr84GDQ2U+AlAKL3IeOKFTbqbdqUPOC6Xz+ItI8YJc+RY/6fDxOe1CwxhimpTNU2VMUMZRHv6khF4n+tc4GdTYBSjl5PNdAue5DUn7u50RAkLi4NMy3ihIBz1HT7F3X/QgjHH4k3LLNJyqiQByaplnc/FtUPGgX9Gf3AaQUNoSu1wxGwQ+tZlRXdosvCSWfiEeWcDiG1gZw4lt10EKC9Q3H+xxTswo67rU4wTcLOW4kSMhl6MP1rW0ipn0aCkbq8JnCIz+ZRxFZ3OMxIAa/O7JDDOqcRmcvCkv4Mgk1Prx0eRT800NPAQCD73ncZ2ri+YTbH76EG7zZ2LScTEOcARfifHLlbRNuEyVLqcsvwvs+kV/EZ15WsXn+Ep/crCM03YiM8dg5lA1PrQ/iRM2yvfC6gUpOq2Zts8ZfYNc/J554E98oy4yaTMvzmik0HJTPqD6RDZ63tyqv4EHX47BBj9cuizlwQbec7uk2r+GtxyuSoh+4XrJ+2ICX9xnqJ8x2jfmBf9t0pCh84aQwaTheMBNb0/+KsuVH+ZDOBNe8sjZsBRRAxeUXH1AkAamUnY0+Fm4mHh5k7DXbxwjwCwyYKBjXrpP7IXEHiHPlacefGFx8VslOno3IjXnG1dyGaCwk64aBp+BCjfXWpoBY1pl17zExY3ORgGpmjyMxbmAeHeSqrGF7qKp3ub5KB9dW5ykbj3b0vOqipFQEA3X0nFHoVBvT77ymSe/HDPwTKP8sQ+WNI7jCM8iiB/4n7asmAk0MeE8fvVkaXjK9HLbGT8y9hHz1RbwhAqyTpgl0zqLMVQd+mB6wLZWqaLGJAH1xvdkyOZMyWKF4XMKEOaAVeGC7zPZxz/6V/seUfGFE2hRtf/g2GbxJe8+CoYQudrI4hMl26F/mZqOspe+Rp5C2j79wmNLJeaDHeX5SD3MxUmy1Pv2JdXmk81BxOHnsHLqepl0chuoMqeJQ2qQBzucqec8KWVMKP49rgqRo74fLsukSt9J/8GP2Zd81w6WkaRrzzAvjPn9rHnHbSJJYVoXPfhJj6Pbym3W3w0VL+xUjKxYH+jM77QVcQWnEz6yyWEgdR93a02x8Fl4oFT87Y+8ktsU6Z1+O6m1gajYZ4raD2LQbS71uV6Y1BHLHKt/E4dKc3xSgBzVYNsfzquveoy4D6UvNQY6pAlW/neZSl3nOEkA3Mh2eTgGYQaJftoJefMCy/EJx+bnmFtm0b+Mf2Sw7f4kdczTCoyzxHmRa8bOYrti120pfLCSwpY1rDlUhW3pC4kE8Df0HRcGE2QyOKLHZqXvxuM7MpVRAFgbgiCpg+UTHLccJNIoArO+FxMvDpIokuhG30ZtE8coQyZXe+U8OEqucx5L6ZY9OM1TpjlnN2rTwnPDRD361jTDYEqRan/6O7/FILYVAh++oaJYiGcajgkRHeoN6Y/wgnPtgCyRyY4rEeRtmBw+A5wKKl2NLqIczRibyFJPChkJqF5+a7grH987Qtb3JEAu7eL/L87tEbu2req0h2TKEUeYchtLho7qhbLige+WR0h8HEvq0DIOfpXf2FcZr/gLH7HWE72ZUOOZr7gl2z5gvrl5kOM3OPWGg9tDcfDGzdXEiYy1+XA/T1upRv+P0Ga85Ve4QVcvxzY+A6MRAILbPGC4Kx0McOSAIFKcVB6Zvo0TH1MPPGKJX6QpAwMfwAjXyOnT9sDjJSaS4Z3vibDrctOJPmWMKfoi+tizj+ZHn87iYD9UxpGEjPuZTqdbybST3uzyzUGy85iIXnRjD7mCl7xh/RFb1Z+nMRWB3iAHhxgmWcf+sxGQLwJkEwhPfGt8UATp8tJMLKVLdHCbNnCbNbyUwgmf6T89IadPpQ0i3PFym4Eow2auD7AbR2c67zjkIRPqSI1B3qgoKqWNqk9SN5VHL/NJxnc0U05+tiscz0XoezYwdj4Sq/i+ON5kP0Tij3j2PNzK5iI+6avhS11zdH91fLHRWviac+TwO+Ng9rFjItamgGp8gVLsCMExblmweHOeOz7ILZYJS3bTf6NbEibWOBzhRH+xY9Qa3Ka7MruvJmMZ1XO10mUOFhUKfIODCQHv4mXEBPQBPicZuCo3UerT+ZCDlOWAnKLJAEY1jvtEH1wN+gkmlxc+4cI5bHWqvAPHRMZ5Ybu9utGs0gHPxI7NiJeQZBKnOrqj8+eH+Es3bRPZ6KGbdyabNHawFDHHrP0Kv8IVQpjUedl1aeAN7FjBobDslwyCLQG9SQtxc1EhS4T2S0KeSSKdFfVuz8bhfBrYXjDSXgVgP43hAkfO8varJi5TalpYdDuYiPUZCn6JVzZmgBUn43IagAxITqVRZT6oGv9THnEmNnDpF+Yj/rEdrDl+GBEfbSQ8iag/KjMPKtWZcvlgDMQxrPeEExSXU+d+7dGGTPyGDy/HDH2X85lScs3X+BRY3pm0sfTSJuWPacz6wDaneSGMLdcoLlzp9eSGnfjCQpxxh79ue9lmWsUXzl22YD/yibuZmkv6V2R7bBdXCVFOarxwpF8Vj4U6ODrhU7UnXClvfOrxOfGKn7GhuPcmB4GOY0QmHWLJItl9C7z46xwfTs2bk9iirTIfiZ35cew67JFvP9FbzCNrSuLTfaV1megTl8QKjp06XshaT8fWQ67z0cRo7NpYyaLXSdoTL3TpO1Yi123uNkvgkCd1G3jeRU9qzccCC/5z3twmTOvEWvuX/hQ+cKiuYjiJT/GkgLrG4gh381e++w1cp3gsgMETmelH9wAENEfVypuQdtWk3AEinGKk8jbqsCAH0Xp1YxEkkLHjOQEhSnQS23VuqUxmLHaIZbchcQaI4RmoCk6rUMU9e3di7rdLjrdedOy5qSVYHYSwO6CqzURZdGqgqyWlDWOxBIMNAD7gn32UcThExLevqANzui9eda3HACeoxMSdBudkQ94EWzkGFPWoBkuYvHyCdMp1mJJjS6yal/gijwoBtK6O5keJZPGZMSDRCR8mNlB+mGsKzMv4Os/gqfi4iRJ76vk7qraKfeBURjtBBa/QMiQ8yPOvfOPcu1lzRwNvSeSTOv7xr1qo+Uov8Ysf/g60XMrAbJQgxw95SZvrZfvkwNPbduuAb+6HBM01czx9Dmdn0g7mhz/Soz059+64dNsnBqH7LjYl42Sd8JBDjiOMXW07IcXChP4zv8ETwPBCZ4JQBjzewXbslG+/E4XiZcWWo2+kjzgWxhaSoPzHM/QEoWB0jj36a7hhCXu56g2RySeVs+haQkAJIzgAudgfjDP3f3Ua7Llax8XJE0w2wZnkZmX3afdt8RGafWAeUzJHjz94VdsJByx54bbFGOczZ/RqbKouVzexh0yuRtIfsGEsCJdDiTfyYKR9NpfjLIn6yMumitoutb47QmHh4rdtUaRyeHXZzIFKcODFOz5TFj/ks3FYMFPvcTDFoHWJTwERJ+YWzv0R3amajJKrdEy/iN32Bx6d7G8RXJT13C05vyZ5HBH3ipPDHGa2g09tOYuool5t335hFzst2zHJXFgP0JiUgNNgUJASMxnGIKMOwUSmv+5Z5SWjMoOLYJyswYgqIEqe/IRBMCi0M1RwXg5jxwOLYurkWP9FjxRQWHwKOItJl3MEHZzCKA75IjqBISjwxHkWuFKq4BRCNQ44mKQj/lqDmFPePcAcSM7BU9BR66B3sBkEnRxjBjgFgNuATGCD0jrPIR1IiMZnsNp9/LP6PHDQdb3KJeV4e9ypHD9tr8rNxrYjp+I2C4nQpVNV20Au4lT2JFptXFbdvq4Fq3kLv+zgNxw9mcGHcnzmqIzFQjh9xOWqKdsSsgw6U37yCtrCAYpEMH0SbOsUHnlDuF9bi+havONnUT7KljH9EQn6Apr05UUR8hMPYqfEhihA+KhavaEXv3Ju2l5Q6D+URRY095fmskipYggjZBhPiAWf8TXbwL7L1dd5RYbvAPOEsonCHA7mnBI+PUZki8Wp+3zjeeJ2366FII5YudsNX+FFyriAVy/a4dvjxsYll/kHjVJ0HIgnHCknJdoc7X9WVeclZQmO1PUE7Lzks3kgDhbTseJMHYV6Z4HAXoTwo+33It88cwzGjGUY68QK54kTWM0JeMfI8M0jGmC1L+VSxrI5MQ6RNytv+tHquQdfOxmndODQdeGxwEsKzT/84Iv/mdfIUp8yO2GfZh3qVa0UG5w0j/S7XNCUUMlZVoryRiWpA7MXsearyhgQcVYi+ny3E3awHbfB0TlYKiAmnab4qD6y6eOJdGzz98NlSIHidxyg6k1AXAzNzKAxyABgkAutx3rv5NoZ+6T67gCLOziuGrDHnz9rcm2Hjobz/CFvduXK2eYmDSBPQXDUK0kuiGcW1uiB+2vJKEzW05lKIpegU6JXLYK+0vPMYXcsiwS8mAjwsRsbH6yrMhLn3uXqlF2M6+FcOhLWv8QQFcLqDlxWwHCsdewGb103zsTLVhFPAmghNT98Im9MidimGCBtmWYjmbZHHd70poLyLOIoEWuO6OutE3dm26CDudb4XnCJOwOUeKdK+WSmdhYOrRM/gW4uzlk+XBM3S/AdWOFgDEls8yKPPPwjQ51SlTXHFEqifYiIi9NGwnFn9tCXWmIJjvmoL2CLf7EsIOWn3ug+lxpA8br7vtUQp0/Vbt4oCAmXA9+Rpk2sqXL8QImjpadTtys8OkO98vRlhEpFeSaUugrbQvHEF0oNKS7a3P1aP+Vx/Gg3mXMcxSl9IO2ky3zLwD0cwWi+xEegQqb/8cp5DLlKWeaSvlOScYKOYj2Nw9gCn0V5SgJIzGITktTCtRfV5oIudvjusPPI0UrEhXf71yRbF6mk9gt84dFeajfkOqUvVyxUjNw0ZohDjNq3GR+/5rslYM2Y9AHwkamFGByfd6wZe7EJfst5vNnH8Gh7/dTqoh37U9woJ+FL82jervDYJ55h1rhwmsYy85NS47as5w3phRsaNJYldZRO1IItf1vPxVMdfne72Yz1IkM/JkccUpfYhi/9BY5VM/lXBT70T+aYv8HoGFDpB2jw7RkRoBKCThxtsYMSgtRy+/JpNZBJQU9ySLkI71uPcuNFRic+ZxHDwxJzpwPXA0eDxD+Yl2PIeLJWJRMXgz7/WwUDSJxZ+PTCJPreMWCDgYZTZU9VruPoBiJDAoNOryw2syCz81CJ3n4ZJgYINDIZpGhhAsu88SdlLJDEwo3tAZ7YtvwkpwL/xXXgjYMEnV8xwL7i5EFpbKzQCRQ7zClhbZaNfbiREEk86lzCtJI7C7Z8LqFQRsXJPMo2TlCdtSKCWfyUF0QvhMSZhF8OgT6oSzu7asKRhArKPsLK4pdl0VctLhjHmFCkVHK2k7Pu9LEc25YBUG+kyEVT/Yg2qRJ3DYTVNuaMrDuvtGTYE6frEQlCTqmPrTycQJ36gwz56kqy6INFO4WDrFabGIM27fhLlvYjwc/tbR5tN3VxIrbQZZImYYuUuMxHOCU+HYHgQAPZjl10mWhnrF6kgMZMaZpb9622Ky3ppn9Fsvq5AINpetNH25n4qgYPsGXfsda6RK/84+jNWCE5ttbBfgpZcOc2nOcy2tztIMHeNMxX7QVYfoBLbODXY7y5Ikm+bbsc8lN5+EYeLiHmtlKWOaHj53ETzyNHH6uNQPN1PBRbYOBvcdlqXrQTmJslnbaNScECkWtO6Wub63bdZN8G2wf5vtB34I8dYuU2ElT0TdZ5t6k2I82nmtJs2lb3Q1tZiK3QJrz0AbjSJsSfsTy3b8t2+RwPxmXafsEVYeTuRNo5mwx0eW8FMd6cJIBMTMqXAM64HjoMdg0QSImOdpc0E66gq08GDw2rc548YzCJuqASVGQsbVuRZUEToB3kr7oQaBaz5LmdIzyAxJF3AqirMZUjCyKdwpMpMnXuDjSpyUfZwCd8YVBw+zS3ixIIuGVChjNJn5iWU35s3rYMURgCx5Yxo4FW4slkGFwobeEf1CMDq+JNXCVjnzDkyICQMoVAC6VsuMOjRQ3fp2TH7w6hsv5/4fCcNkk7wjO+Ng/gp8GDXSdw505mI4WBWa60Z0kswCGvTPjJuxz5zujoiUfOO94VC1frI0icwSmN5PlANcSr29MDrrkKP1xks3wDKZ2aGhQjAysbiULkuwBOqocDfZMMXwlk4KSUdnGOD1LB9S13b1CIjcqnAcpJ8Wp+7rvWLyJYZpAIkDEBd08s/KlDrhB5Vcwy6Sa+E57kqe9E3yRWGaNZgFOHHQ8r2RDupJLyxCzjGnnwzYXhpJQ/qi+ASQ/O4RsuCPbkHp8SBxTwrWOTMedxBS59XuOIc9qbOwUk+KU/+dRc+vvFcI3tyDYp/Gp7uTsDx24P/IeLN8bYUuzCneLWI2aMqfjTcXafddzAy1huHqBix/hyonUX28WxFzf85A8tWJcB3cnmaW1RkTC6doV6ce0Y0ye9MZOux64UqGsdAIJCud7mk1g51qrt2BsaIUzAi/mwknX1AY++cmpOHOmFzDuocHUP2b4CdT264PIqGylP3MmT5uNiX6UinFo3dzXgil3q0sd0T9G8ibl5ACrlMmn8uZ1gk0Qua4XOVchdUJQ6NvDi/f8A9wzswQ8LpLYAAAAASUVORK5CYII=" alt="pastedGraphic.png" width="626" height="352" /></p>
<p>&nbsp;</p>
<p>How are employers responding to this?</p>
<p>Despite the trends, using <a href="https://thehrworld.co.uk/talent-acquisition/ta-after-ai-josh-bersin-describes-how-ai-is-changing-the-role-of-talent-professionals/">AI to assess</a> applications is still too fraught with risk for organisations to use these tools. Instead we are seeing practices such as:</p>
<ul>
<li>Closing for applications early: &#8220;sorry if you are someone who likes to consider and research before you apply”!</li>
<li>Raising benchmarks on employment tests because these are largely a tool to reduce numbers. So more candidates are getting the message: &#8220;sorry if in behaviour and attitude you are a great fit!”</li>
<li>Adding in extra steps or stages. These include costly second round interviews or time consuming video interviews used purely to reduce numbers. This time, it’s “sorry” to the recruiters behind the scenes who need to spend more time reviewing and assessing.</li>
</ul>
<p>So, what can be done, that is both fair AND ensures you have the best suited candidates in your recruitment process?Our pre-application process (and there&#8217;s a difference already!) <a href="https://talent-tech.connectr.com/insights/a-new-way-to-tackle-high-volume-applications-and-low-visibility-of-quality-talent" target="_blank" rel="noopener">IntentSignals</a> are significantly reducing volumes of poor fit candidates (by as much as 60%) and improving the conversion of those in process (by 2-4x).</p>
<blockquote><p>It is about&#8230; being straight with potential candidates and empowering them to make decisions based on accurate and authentic information.&#8221;</p></blockquote>
<p>By being ‘positively honest’ with potential candidates about the role they are considering or wanting to apply for, and encouraging self-reflection on the role, everyone can make a well informed self-select ‘in’ or ‘out’ decision – BEFORE they apply. If the role is not right for them, they don’t have to waste time applying and going through the recruitment process; and where the role is right for them their ‘intent’ grows and they ‘stay the course’ through your recruitment process.</p>
<p>The results from this earlier intervention are impressive:</p>
<ul>
<li>As much as a 60% reduction in poor fit candidates</li>
<li>Those in the process are 3-4x more likely to progress to final stages</li>
<li>88% reduction in incomplete applications</li>
<li>36% reduction in withdrawals</li>
<li>Record breaking video interview scores</li>
<li>More top scoring candidates at final stage</li>
</ul>
<p>Crucially, there has been no adverse impact on candidates when it comes to diversity.</p>
<h3>Addressing the issue sooner</h3>
<p>Addressing application volumes is not about interventions post application – at that point the horse has already bolted! Instead, the innovation is in addressing this issue pre-application. It is about being bold and not succumbing to vanity metrics (application volumes) but instead being straight with potential candidates and empowering them to make decisions based on accurate and authentic information.<span class="Apple-converted-space"> </span></p>
<p>And this approach does not just drive efficiencies in your talent acquisition, with great, well-suited hires, but also strengthens your employer brand.</p>
<p>&nbsp;</p>
<p>Want to learn more? Contact <a href="mailto:simon@connectr.com">simon@connectr.com</a><span class="Apple-converted-space"> </span></p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>Going Beyond the CV – New Ways to Identify Vital Talent</title>
		<link>https://thehrworld.co.uk/hr-event/going-beyond-the-cv-new-ways-to-identify-vital-talent/</link>
		
		<dc:creator><![CDATA[Malaya Plummer]]></dc:creator>
		<pubDate>Thu, 12 Feb 2026 10:01:32 +0000</pubDate>
				<category><![CDATA[On Demand]]></category>
		<category><![CDATA[Talent Acquisition]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?post_type=event&#038;p=65146</guid>

					<description><![CDATA[As the candidate pool becomes more diverse and challenging to engage with, it’s time to ensure your organisation is taking the best approach to finding the talent it needs. In this round table discussion we will discuss the options that now exist when it comes to engaging with talent, presenting candidate ability and assessing potential for employment. Whether you’re looking for newcomers to your business, early or later talent, or assessing internally, we’ll be discussing how can HR make the most of the options that exist to ensure no individual is overlooked.]]></description>
										<content:encoded><![CDATA[<h2 class="v1elementToProof">Are the days of the CV numbered? Is the once necessary document now unnecessary and, if anything, more of a problem than a cure for finding the talent an organisation needs?</h2>
<p>&nbsp;</p>
<p class="v1elementToProof">At a virtual round table, held by The HR World and sponsored by Vizzy, these and other aspects of the CV came under debate among HR professionals and experts. With lead speakers, Michelle Rajkumar, Director, Talent, People Team, Railpen; Chris Bleakley, Director of Resourcing, Spire Healthcare Group and Jess Woodward-Jones, Co-Founder of Vizzy, the round table became a lively debate featuring a sharing of ideas and practices used within the talent search arena.</p>
<p class="v1elementToProof">While some people in the discussion still saw the use and value of the CV, there was a general acceptance that AI-generated CVs and cover letters, as well as an increase in application volume and a challenging jobs market, were problematic. For example, CV screening followed by in person or online interviews is generally being used to verify and backup first impressions.</p>
<p class="v1elementToProof">While there was some openness toward other formats there was also some reservation &#8211; video CVs were given as an example of a format that didn&#8217;t always feel right for employers. There was a general acknowledgement however that the CV did not always capture the skills and aptitudes of individuals, and that Generation Z in particular could feel constrained by the format. In some professions the concept of the CV no longer exists, while questions were also raised over whether the document worked when considering internal talent.</p>
<p class="v1elementToProof">Whichever format was chosen to package and communicate talent the discussion made it clear that anyone making recruitment selections in this way should undergo training in order to understand how to interpret and use the format effectively. When better insights are uncovered, talent acquisition teams and hiring managers can make more informed decisions as long as teams have a clear understanding of the skillsets &#8211; soft and hard &#8211; that are desired.</p>
<p class="v1elementToProof">With experience and ideas being shared enthusiastically, the event demonstrated how methods and tools used to assess talent were evolving and adapting to current challenges. This aspect of talent acquisition will be interesting to follow in the future as new generations of employees enter the workplace and employers continue to try and find the skills their organisations need.</p>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>Gen Z Doesn’t Do CVs — Are You Ready?</title>
		<link>https://thehrworld.co.uk/hr-event/gen-z-doesnt-do-cvs-are-you-ready/</link>
		
		<dc:creator><![CDATA[HR World]]></dc:creator>
		<pubDate>Wed, 28 Jan 2026 12:00:46 +0000</pubDate>
				<category><![CDATA[On Demand]]></category>
		<category><![CDATA[Talent Acquisition]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?post_type=event&#038;p=53429</guid>

					<description><![CDATA[Has the CV had its day? Is it now impossible to put an accurate picture of a life and an individual’s work potential on to a few sheets of A4? More to the point, does your talent actually want to create a CV? Do they find it frustrating when they have to approach your business in a way that doesn’t match what they think they can offer? These questions and more were discussed in The HR World webinar, Gen Z Doesn’t Do CVs — Are You Ready? sponsored by Vizzy.]]></description>
										<content:encoded><![CDATA[<div style="padding: 56.25% 0 0 0; position: relative;"><iframe style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;" title="Gen Z Doesn’t Do CVs - Are You Ready?" src="https://player.vimeo.com/video/1159241257?badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0"></iframe></div>
<p><script src="https://player.vimeo.com/api/player.js"></script></p>
<h2>Has the CV had its day? Is it now impossible to put an accurate picture of a life and an individual’s work potential on to a few sheets of A4? More to the point, does your talent actually want to create a CV? Do they find it frustrating when they have to approach your business in a way that doesn’t match what they think they can offer?</h2>
<p>These questions and more were discussed in The HR World webinar, Gen Z Doesn’t Do CVs — Are You Ready? sponsored by Vizzy, the company that has reimagined the CV and is already revolutionising how employers approach, assess and select talent.</p>
<p>On the call were <a title="" href="https://www.linkedin.com/in/chriswoodward-jones/" target="_blank" rel="noopener">Chris Woodward-Jones</a>, CEO and Co-founder of Vizzy; <a title="" href="https://www.linkedin.com/in/lynnecutts/" target="_blank" rel="noopener">Lynne Cutts</a>, Talent Acquisition Specialist, Footasylum &amp; SEVENSTORE, Tobi Ogundipe, Founder and CEO of JABARI (formally DIVERSE) and <a title="" href="https://www.linkedin.com/in/lisandraradu/" target="_blank" rel="noopener">Lisandra Radu</a>, L&amp;D Senior Coordinator EMEA, VML.</p>
<p>Together they explored this area of recruitment with a particular emphasis on early careers. It became clear that while the CV sometimes has its place, when it comes to understanding more about their candidates, employers are becoming more open to receiving diverse material, illustrating what candidates can do and what they’re like. Indeed, Lynne Cutts said that having used Vizzy she and her team felt as if they knew their prospective candidates well before they’d met them in person.</p>
<p>Changing processes to go beyond the CV alone may require time and effort but it could be exactly what you need if today’s processes aren’t giving you the results you want. From the panel, it was clear that there is a strong likelihood that better hiring signals can lead to better hires, faster. Is your process giving you the signals you need to understand candidates and make informed hiring decisions?</p>
<p>This webinar is well worth a view – packed as it is with insights, inspiration, and first-hand experience. Check it out – it might change your view on CVs forever.</p>
<p>If you’d like to get in touch with the sponsor, Vizzy, who help brands like Burberry, Virgin, Montagu Evans, and Tiffany and Co. find top talent fast, please reach out to<a href="mailto:jess@vizzy.com"> jess@vizzy.com</a></p>
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			<media:title type="plain">Gen Z Doesn’t Do CVs - Are You Ready?</media:title>
			<media:description type="html"><![CDATA[Has the CV had its day? Is it now impossible to put an accurate picture of a life and an individual’s work potential on to a few sheets of A4? More to the point, does your talent actually want to create a CV? Do they find it frustrating when they have to approach your business in a way that doesn’t match what they think they can offer?

These questions and more were discussed in The HR World webinar, Gen Z Doesn’t Do CVs — Are You Ready? sponsored by Vizzy, the company that has reimagined the CV and is already revolutionising how employers approach, assess and select talent.

On the call were Chris Woodward-Jones, CEO and Co-founder of Vizzy; Lynne Cutts, Talent Acquisition Specialist, Footasylum &amp; SEVENSTORE, Tobi Ogundipe, Founder and CEO of JABARI (formally DIVERSE) and Lisandra Radu, L&amp;D Senior Coordinator EMEA, VML.

Together they explored this area of recruitment with a particular emphasis on early careers. It became clear that while the CV sometimes has its place, when it comes to understanding more about their candidates employers are becoming more open to receiving diverse material, illustrating what candidates can do and what they’re like. Indeed, Lynne Cutts said that having used Vizzy she and her team felt as if they knew their prospective candidates thoroughly before they’d met them in person.
However, moving the CV forward and opening your processes to more input than a CV document alone can mean having to put more time and effort into recruitment. That said, the likelihood is that better hires will be made in the first place and that these techniques can help organisations as they pursue skills-led recruitment and planning, and look to develop and place talent later in their careers.
This webinar is well worth a view – packed as it is with insights, inspiration and first-hand experience. 

Check it out – it might change your view on CVs forever.


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The HR World
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Through original content and bespoke, immersive and outcome based events senior HRs collaborate and problem solve to support their business and personal ambitions.

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		<title>Maximising Talent: Think Strategy Before You Post</title>
		<link>https://thehrworld.co.uk/feature/maximising-talent-think-strategy-before-you-post/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Mon, 19 Jan 2026 11:10:31 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[growth]]></category>
		<category><![CDATA[Recruitment]]></category>
		<category><![CDATA[succession]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?post_type=feature&#038;p=60115</guid>

					<description><![CDATA[Colin Minto, Chief of Staff, Cognisess believes that in 2026, the Best Talent Strategy Is the One That Starts From Within.]]></description>
										<content:encoded><![CDATA[<h2><a href="https://www.linkedin.com/in/colinminto/?originalSubdomain=uk" target="_blank" rel="noopener">Colin Minto</a>, Chief of Staff, <a href="https://thehrworld.co.uk/hr-directory/cognisses/">Cognisess</a> believes that in 2026, the best talent strategy is the one that starts f<b>rom within.</b></h2>
<p>For decades, the talent strategy Genesis has<span class="Apple-converted-space"> </span>been recruitment. When capability gaps appear organisations recruit. When performance lags, the assumption is that the ‘right people’ aren’t in place. When the business changes direction, the reflex is to go back to the market in search of new skills.</p>
<p>But it’s starting to feel like that logic needs a reset.</p>
<p>What if the most effective <a href="https://thehrworld.co.uk/talent-acquisition/talent-winning-strategy-perfecting-the-hunt-for-great-candidates/" data-wpil-monitor-id="707">talent strategies</a> don’t start with a job requisition – but with a much deeper understanding of the people already inside the organisation?</p>
<p>This question is becoming increasingly hard for senior HR leaders to ignore. Not because it’s intellectually interesting, but because the operating environment has shifted. Hiring budgets are tighter. Productivity increases are being demanded. Headcount growth is constrained. And HR is under growing pressure to justify talent decisions with evidence, not narrative.</p>
<p>Ironically, HR functions are sitting on more data about their people than ever before &#8211; yet many still struggle to access it, connect it, or turn it into insight that genuinely informs decisions.</p>
<h3>When HR Has Data – But Loses Sight of the Human Story</h3>
<p>Most HR teams are not short of information. Applicant tracking systems hold years of hiring history. HRIS platforms contain job, tenure and movement data. Performance systems record appraisals. Engagement surveys capture sentiment. Learning platforms log development activity. Exit interviews explain why people leave.</p>
<p>The issue isn’t data volume. It’s data fragmentation.</p>
<p>These data sources rarely talk to each other. And even when they do, few teams have the analytical frameworks or confidence to extract insight that goes beyond reporting what already happened.</p>
<p>This isn’t a critique of HR capability. It simply reflects how HR technology and operating models evolved over the last twenty years. But in 2026, those limitations are no longer benign. They’re becoming real liabilities.</p>
<h3>It’s No Biggie: Other Functions Made This Shift Years Ago and HR Is Simply Catching Up</h3>
<p>There is a reassuring perspective here. Other business functions have already navigated this transition. Marketing no longer operates on disconnected customer data. Finance doesn’t allocate capital without integrated forecasting and modelling. Supply chain relies on real-time data and predictive insight to manage risk. Customer service analyses every interaction to improve experience.</p>
<p>HR, by contrast, has been slower to unify its data and embed analytics into everyday decision-making. As a result, some of the most expensive and strategically consequential decisions organisations make – people decisions – are still too often driven by intuition and incomplete information.<span class="Apple-converted-space"> </span></p>
<p>That gap now matters more than ever: when hiring was cheap and growth unconstrained, inefficiency could be absorbed. When talent markets were buoyant, attrition felt like a nuisance rather than a risk. Mobility, engagement and role fit mattered less &#8211; because talent was treated as replaceable.</p>
<p>The reality of 2026 is that none of those conditions apply anymore.</p>
<h3>Why Hiring First Is No Longer a Sustainable Strategy</h3>
<p>When HR lacks a joined-up understanding of its workforce, hiring becomes a blunt instrument. Organisations recruit externally without knowing whether capability already exists internally. They assess candidates without understanding which traits truly predict success in a specific role context. They repeat hiring patterns that generate churn, mis-fit or burnout – because outcomes are never connected back to long-term performance, mobility and retention data.</p>
<p>Graduate recruitment assesses potential through CVs and assessment centres. Succession planning relies heavily on managerial opinion. Leadership development builds capability that is rarely tracked or redeployed. Redeployment decisions are made without real insight into who can adapt, who can grow, and who is at risk against new role demands. HR isn’t short of processes – it’s short of corroborating evidence.</p>
<p>Without a unified, data-driven view of capability, hiring becomes the default response to uncertainty rather than the final lever in a well-understood talent strategy.</p>
<p>The most effective organisations we’re seeing today are starting to invert this logic. They begin by deeply, continuously and predictively understanding their workforce – and only then deciding where hiring, redeployment or development is genuinely required.</p>
<h3>The Role of Integrated Talent Analytics</h3>
<p>This is where HR has the opportunity to move from a reporting function to vital strategic infrastructure. By bringing together data across the talent lifecycle – recruitment, performance, engagement, learning, mobility and attrition – into analytical frameworks designed to answer real workforce questions.</p>
<p>It’s about seeing patterns across systems and enabling different conversations: which hiring profiles correlate with long-term performance; where internal mobility is failing silently; where burnout or attrition risk is emerging; where leadership potential actually shows up; and which gaps reflect true skill shortages versus misalignment between people, roles and work.</p>
<p>This level of insight allows HR to act earlier, intervene more precisely and align talent decisions directly <b>to business outcomes</b>.</p>
<h3>The Journey from Cost Centre to Growth Enabler</h3>
<p>One of the most persistent frustrations for senior HR leaders is being viewed as a cost centre rather than a value creator. That perception won’t change through narrative alone – it can only changes through evidence.</p>
<p>When HR can demonstrate how talent decisions affect productivity, risk, retention and performance, the conversation shifts. Talent investment starts to look like capital investment. Workforce planning becomes predictive. Hiring becomes a strategic lever – not a default response.</p>
<p>This isn’t about making HR more technically literate. It’s about equipping HR with the same analytical discipline other functions already rely on, while preserving the human judgement and ethical context that make people decisions nuanced.</p>
<h3>AI Is Raising the Stakes – and the Opportunity</h3>
<p>AI makes this inflection point even more critical. Not because it will replace HR, but because it will expose what HR truly understands about its people. AI won’t fix poor data – it will amplify it. Shallow, siloed or biased inputs will simply produce faster, more seemingly confident wrong decisions.</p>
<p>But when AI is applied to a strong foundation of integrated talent data, grounded in human science and behavioural insight, it becomes a powerful multiplier. It can surface patterns humans miss, predict risk earlier and support fairer, more objective decisions.</p>
<h3>HR Transformation that’s a Natural Evolution &#8211; Not a Leap of Faith</h3>
<p>For many HR leaders, embracing Talent Analytics can feel daunting. Concerns around capability, data quality and change are real. But this isn’t about a big-bang transformation. <span class="Apple-converted-space">       </span></p>
<p>See it as a journey: some organisations start with hiring or mobility analytics. Others focus on attrition, burnout or leadership readiness. Many begin with targeted diagnostics before scaling data integration. The important point is this: there are now mature, proven providers like Cognisess who can support HR at every stage of their journey.</p>
<h3>Why 2026 Will Be a Turning Point</h3>
<p>The pressures facing HR aren’t going to ease. Organisations want fewer mis-hires, higher productivity, stronger leadership pipelines and better use of internal talent – whilst controlling cost and risk.</p>
<p>These outcomes won’t come from incremental improvement or better storytelling. They require a step change in how HR understands and manages its most valuable asset: its talent data.</p>
<p>2026 won’t be the year human judgement disappears. It will be the year it is finally augmented with clarity, confidence and evidence. And yes, the best talent strategies will still hire – but the smart work will have started way before the job is ever posted.</p>
<p>&nbsp;</p>
<h5>Colin Minto has 20+ years’ experience as a HR Leader in Talent Acquisition and HR Tech within some of the UKs biggest employers. He is currently is Chief of Staff at Cognisess – a pioneer in Talent Analytics and AI who work with some of the world’s biggest employers. From this unique perspective, Colin has observed that 2026 will be the year that separates out those organisations that manage talent from those that truly understand it. <span class="Apple-converted-space">         </span></h5>
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		<title>Q&#038;A: Two Sides of the Same Hire</title>
		<link>https://thehrworld.co.uk/qa-two-sides-of-the-same-hire/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Thu, 27 Nov 2025 10:43:10 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[candidates]]></category>
		<category><![CDATA[Culture]]></category>
		<category><![CDATA[Hiring]]></category>
		<category><![CDATA[trasnsparency]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?p=53541</guid>

					<description><![CDATA[In a webinar hosted by The HR World and sponsored by Morgan McKinley Talent Solutions, Niamh McCarthy, Client Services Managing Director for EMEA, Morgan McKinley Talent Solutions; Justine Friedmann, People &#038; Culture Director, IFX Payments and Ed Gairdner, Chief of Staff, iplicit gave their insight on the challenges and solutions to achieving a smooth hiring process that benefits both employers and candidate alike.]]></description>
										<content:encoded><![CDATA[<h2>In a <a href="https://thehrworld.co.uk/hr-event/webinar/how-impress-candidates-empower-hiring-managers">webinar</a> hosted by The HR World and sponsored by Morgan McKinley Talent Solutions, <a href="https://www.linkedin.com/in/niamhmccarthy" target="_blank" rel="noopener">Niamh McCarthy, Client Services Managing Director for EMEA,</a> Morgan McKinley Talent Solutions; <a href="https://www.linkedin.com/in/justine-friedmann-2a772626" target="_blank" rel="noopener">Justine Friedmann, People &amp; Culture Director</a>, IFX Payments and <a href="https://www.linkedin.com/in/edward-gairdner" target="_blank" rel="noopener">Ed Gairdner, Chief of Staff</a>, iplicit gave their insight on the challenges and solutions to achieving a smooth hiring process that benefits both employers and candidate alike.</h2>
<p>The discussion was wide-ranging and full of useful, practical insights. Here we pick up on a couple of extra questions with the panel:</p>
<h3>Q: What is the balance between speed to hire and ensuring cultural fit – especially if you really need those people?</h3>
<p><strong>Niamh McCarthy:</strong> I find balance is achieved by taking a partnership, data-driven and talent advisory approach in guiding the hiring managers toward better long-term outcomes, rather than just rapid fulfilment. We counsel our clients to define a ‘quality hire’ jointly with the Hiring Manager before the search begins. This moves TA from simply providing a service to being a strategic partner accountable for the outcome.</p>
<p><strong>Justine Friedmann:</strong> To balance both speed and culture fit, I would clearly define the key cultural attributes that are non-negotiable for success within your team, and design a process that is quick yet is still able to assess the most important criteria. The ability to clearly articulate the culture expectations will be helpful to ensure these can be communicated to the candidates during the process. A wrong hire can be difficult, time consuming and sometimes costly to manage so it’s important to strike the right balance. Ultimately, it is all about assessing the risks, and being comfortable with these.</p>
<p><strong>Ed Gairdner:</strong> Cultural fit is absolutely non-negotiable for us, especially as a remote-first company. We need people who are not only excellent at their job but who also demonstrate the self-motivation and behaviours that align with our values and way of working.</p>
<p>The pressure to hire quickly is always there – particularly when you&#8217;re scaling at pace – but we&#8217;ve learned that hiring fast at the expense of cultural fit creates far bigger problems down the line. A bad hire doesn&#8217;t just underperform; they can impact team morale, drain management time, and ultimately cost far more than leaving a role open for a few extra weeks.</p>
<p>Our approach is to be disciplined about the process while still moving efficiently. Having our CEO involved in first-round interviews – regardless of role – ensures we&#8217;re assessing cultural fit from the outset, not as an afterthought. And because our agency partners deeply understand our culture after growing with us from 30 to 190 people, they&#8217;re pre-filtering for fit before candidates even reach us.</p>
<p>So yes, speed matters – but not at the cost of getting the right person. We&#8217;d rather wait and get it right than rush and regret it.</p>
<h3>Q: Some hiring managers can want to go with their &#8216;gut instinct&#8217; on a candidate rather than following set procedures – how do you allow for personal preference or the set ideas of a hiring manager especially if it might go against the &#8216;official line’?</h3>
<p><strong>Niamh McCarthy:</strong> This speaks directly to the need for TA to evolve from a transactional role to a true strategic partner who influences outcomes. When Hiring Managers are experts in their specialism but fall short in engaging candidates at interview, that&#8217;s precisely where we step in. We support this partnership by providing the necessary structure and tools that professionalise the process whether that&#8217;s hiring manager interview training, interview guides, supporting interviews, ultimately, the goal is to shift the dynamic from TA serving the HM to TA coaching the HM.</p>
<p><strong>Ed Gairdner:</strong> Transparency and honest debate are vital ingredients in our hiring process &#8211; and we actively encourage them. Gut instinct absolutely has a place, but it shouldn&#8217;t operate in isolation.</p>
<p>Because our hiring involves multiple touchpoints – from agency partners who&#8217;ve been embedded with us since we were 30 people, through to our CEO who interviews for most roles – we create natural checkpoints for discussion and challenge. When a Hiring Manager has a strong instinct about a candidate, we don&#8217;t dismiss it; we explore it. What are they seeing? What&#8217;s driving that feeling? Does it align with what others observed?</p>
<p>This collaborative approach means gut instinct gets tested and validated rather than overruled or blindly followed. Sometimes a Hiring Manager spots something others missed – that&#8217;s valuable. Other times, what feels like instinct is actually unconscious bias or a sense of urgency to fill a gap quickly – and having multiple perspectives helps surface (and avoid) that.</p>
<p>Ultimately, cultural fit is so critical to us that we&#8217;ve built in safeguards – but we&#8217;ve also built in space for debate. The goal isn&#8217;t rigid adherence to process for process&#8217;s sake; it&#8217;s making sure we&#8217;re all aligned on bringing in people who&#8217;ll thrive here and strengthen the team.</p>
<p><strong>Justine Friedmann:</strong> The manager’s position would need to be discussed with them to ensure that it does have sound and objective basis rather than be based on bias or subjective views.</p>
<p>Training managers on best hiring practices and unconscious bias will help ensure that the benefits of a good process (both for the business and the candidate) and the potential risks of deviating from them are understood.</p>
<p>In terms of process, these should be able to be adapted in certain circumstances but I would recommend an interview with another manager so that the business can be satisfied that an assessment has taken place but also make sure that the candidate receives the experience and opportunity to be confident in their decision making.</p>
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		<title>Talent Winning Strategy: Perfecting the hunt for great candidates</title>
		<link>https://thehrworld.co.uk/talent-winning-strategy-perfecting-the-hunt-for-great-candidates/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Thu, 20 Nov 2025 11:44:57 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[behaviour]]></category>
		<category><![CDATA[CV]]></category>
		<category><![CDATA[Potential]]></category>
		<category><![CDATA[skills]]></category>
		<category><![CDATA[Skills gap]]></category>
		<category><![CDATA[SWP]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?p=53440</guid>

					<description><![CDATA[Creating and delivering an effective talent acquisition strategy is no mean feat in the face of pressure for new employees. But, says Simon Kent, its well worth taking time to get it right.]]></description>
										<content:encoded><![CDATA[<h2>Creating and delivering an effective talent acquisition strategy is no mean feat in the face of pressure for new employees. But, says <a href="https://www.linkedin.com/in/simon-kent-4581b71/?originalSubdomain=uk" target="_blank" rel="noopener">Simon Kent</a>, its well worth taking time to get it right.</h2>
<p>Identifying and bringing talent into an organisation has never been more complicated. The diverse challenges that face businesses as they adapt to customer demand, alongside addressing the needs of their talent pool means organisations cannot – must not – rely on only one avenue for their new hires.</p>
<p>When a talent need arises, employers should not simply perform a knee-jerk reaction and send for the next pile of application forms. Constantly creating more job descriptions, processing yet more applications makes for an admin heavy HR function, hiking up costs and taking time from the function that could be used in as more valuable way. But laying the ground rules for this isn’t always easy.</p>
<blockquote><p>Any strong TA lead can build an effective strategy, the challenge lies in empowering them to work in partnership with the rest of the business.”</p></blockquote>
<p>“We often hear CEOs and board members publicly praising the importance of their people, yet in practice, Talent Acquisition is still treated as the poor relation within HR, let alone the wider business,” says <a href="https://uk.linkedin.com/in/natashapreocanin" target="_blank" rel="noopener">Natasha Preocanin</a>, Managing Director, <a href="https://thehrworld.co.uk/hr-directory/ihr/">IHR</a>. “HR and leadership teams need to recognise that giving TA a real voice and access to the company’s broader strategy is essential. When TA understands the bigger picture, they can align their own plans and deliver real value.</p>
<p>“Workforce planning is often seen as complicated or even futile, but that’s usually because TA isn’t given the information needed to forecast properly,” Preocanin adds. “Any strong TA lead can build an effective strategy, the challenge lies in empowering them to work in partnership with the rest of the business.”</p>
<h3>The heart of the search for talent</h3>
<p><a href="https://uk.linkedin.com/in/professor-nick-kemsley-9a61841" target="_blank" rel="noopener">Professor Nick Kemsley</a> of the HR &amp; Organisational Capability Centre, <a href="https://uk.linkedin.com/school/henley-business-school/" target="_blank" rel="noopener">Henley Business School </a>agrees, stating that strategic workforce planning (SWP) should sit at the heart of any candidate hunt because it offers the chance of a thoughtful and talent-centric approach to attraction rather than relying on something that’s generic, reactive and ad-hoc. “A sustainable talent sourcing strategy takes into account the availability, preferences and affordability of specific talent segments and weighs it up against the employee value proposition, financial health and TA maturity of the organisation targeting the talent,” he explains. “Taking this approach makes it clear where we may require external TA support – for example with new skills or sectors, higher volumes, niche skills, seniority – or where it falls within our core in-house recruitment capability. Without this basis in rationale, TA efforts can be directionless and unfocused.”</p>
<p>Kemsley also recognises that the right approach for an employer might be a combination of methods: “Frequently hybrid approaches can make sense, for example, automated primary screening only,” he says. “New technologies like AI are increasingly allowing us to match skills profiles to work and to find and analyse talent market data ourselves. Research also shows that AI is likely to play a pivotal role in the future of SWP, even creating potential sourcing strategies for key skills segments for us.”</p>
<p><a href="https://www.linkedin.com/in/claire-carlin-cc/?originalSubdomain=uk" target="_blank" rel="noopener">Claire Carlin</a>, People Acquisition Specialist at global digital media company<a href="https://uk.linkedin.com/company/komigroup?trk=public_profile_topcard-current-company" target="_blank" rel="noopener"> KOMI Group </a>says her business is always on the look out for “passionate, hungry and driven candidates” and they have a range of tactics for finding such people, recognising that candidates will not all be in the same place. The company uses an applicant tracking system, diverse job boards, referrals from within the team, and LinkedIn. “Having conversations with both active and passive candidates – whilst working on our employer branding every week – keeps us on top of our sector’s brightest people,” she adds.</p>
<h3>Diverse candidates, consistent approach</h3>
<p>While her talent search may stretch over diverse areas, the business has sought to create a consistent recruitment process for candidates and hiring managers alike. The process varies according to role and seniority, but nevertheless gives certainty to everyone involved in finding new talent.</p>
<p>The process begins with a video call with Carlin, and includes an in-person interview where hiring managers get to meet the candidate, assess their experience and skillset and see if they are right for the role.</p>
<p>Interestingly, Carlin says the recruitment process has been brought in-house over the past few years, a shift which has prioritised candidate experience and enabled the business to pool talent ready for future hires.</p>
<p>“We continuously track and discuss acquisition data to understand what’s working, what’s not and where we can improve,” Carlin says. “Support goes both ways too, and all of KOMI’s hiring managers have to go through recruitment training before hiring, giving them confidence before the process even begins.”</p>
<p>Technology and AI has been introduced to the process but Carlin is clear that the human element will never be entirely removed from recruitment: “Every CV and application is read with attention and care, whilst AI helps with admin and efficiency so we can put more energy into our conversations with new talent,” she says.</p>
<h3>Exploring the options</h3>
<p>Taking a thorough view over available talent doesn’t just mean addressing external talent pools, as advised by <a href="https://www.linkedin.com/in/imranakhtar01/" target="_blank" rel="noopener">Imran Akhtar</a>, Head of Academy at talent and training partner <a href="https://uk.linkedin.com/company/mthree" target="_blank" rel="noopener">mthree</a>: “Before filling a vacancy, it’s critical to ask what kind of work is required, how long it’s needed for, and whether those skills already exist in the business,” he says. “Sometimes the answer is to <a href="https://mthree.com/services/reskill-and-staff-training/" target="_blank" rel="noopener">upskill or reskill</a> current employees, in other cases, bringing in new talent with fresh ideas and tailored training adds the most value.</p>
<blockquote><p>Approaches like this help organisations stay agile, close skill gaps faster, and build capability that lasts.&#8221;</p></blockquote>
<p>“Finding the right people works best when hiring teams build long-term partnerships rather than relying only on open job boards,” he continues. “Collaborating with universities and trusted training providers creates a steady pipeline of candidates who already have the foundations and mindset to grow. That makes hiring more focused and ensures people are ready to contribute sooner once in post.”</p>
<p>Akhtar believes a model such as his company’s <a href="https://mthree.com/services/hire-train-deploy/" target="_blank" rel="noopener">Hire Train Deploy</a> shows how employers can bring people into their organisation on the basis of their potential, provide tailored training and place them so they can contribute from day one. “Approaches like this help organisations stay agile, close skill gaps faster, and build capability that lasts,” he says.</p>
<h3>Not on the CV</h3>
<p>According to <a href="https://ch.linkedin.com/in/mrashleymills" target="_blank" rel="noopener">Ashley Mills,</a> Senior Partner at <a href="https://www.linkedin.com/company/kornferry?trk=public_profile_topcard-current-company" target="_blank" rel="noopener">Korn Ferry</a> and author of <a href="https://foreshorepublishing.com/product/built-without-a-blueprint/" target="_blank" rel="noopener"><em>Built Without a Blueprint</em></a>, one challenge faced by employers is that some of the talent they are currently seeking simply doesn’t show up on a routine scan of a CV: “The most sought-after leaders today aren’t just strategic, they’re emotionally intelligent, adaptable and calm under pressure,” he says. Rather than being usefully listed on a CV these facets are generally developed through experience, adversity and more unconventional paths.</p>
<p>In answer to this, says Mills, employers need to study behaviours, patterns and moments of growth – a task which can be done through leadership diagnostics, deeper referencing and even exploring how a candidate handles failure. “Increasingly, it’s less about prestige and more about potential and the deeper story behind it,” says Mills.</p>
<h3>Stay human</h3>
<p>Even with this, perhaps unconventional approach to finding talent, Mills still highlights the importance of in-person interviews at the final stage, for alignment, stakeholder buy-in and cultural fit. Overall, he says, the hiring process is becoming more collaborative: “Companies are co-designing hiring journeys with their search partners, blending objective insight with internal priorities,” he explains. “AI may help shortlist, but it’s still human judgment that makes the final call.”</p>
<blockquote><p>Talent Acquisition is, at its core, about people, so some parts of the process should stay personal.&#8221;</p></blockquote>
<p>“There’s now a huge and growing range of technology providers in the TA space,” agrees IHR&#8217;s Preocanin. “Some of the tools available are excellent, but it’s easy to be distracted by shiny new platforms. Talent Acquisition is, at its core, about people, so some parts of the process should stay personal – interviews and offers widely being seen as the most important.”</p>
<p>Emerging best practice therefore should acknowledge the multiplicity of tools and techniques employers can now use to access the talent they need. It requires a different view on finding and assessing talent, one which acknowledges the potential of people as much as it does their achievements. And yet at the same time being certain of the right talent still comes down to a human being making a judgement on the suitability of another person for the task required. Ultimately the way to make a talent acquisition strategy work is to ensure the balance is always right and that decisions are made in the right way at the right time.</p>
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		<title>Skills, speed, scale: achieving talent advantage through workforce agility</title>
		<link>https://thehrworld.co.uk/hr-event/skills-speed-scale-achieving-talent-advantage-through-workforce-agility/</link>
		
		<dc:creator><![CDATA[Nic Thomas]]></dc:creator>
		<pubDate>Fri, 07 Nov 2025 11:00:17 +0000</pubDate>
				<category><![CDATA[On Demand]]></category>
		<category><![CDATA[Talent Acquisition]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?post_type=event&#038;p=52848</guid>

					<description><![CDATA[Hiring challenges aren’t limited to peak season anymore - skills shortages, rising costs, and shifting demand are now year-round concerns. This virtual round table, sponsored by Indeed Flex, brought together leading HR professionals from diverse industries.]]></description>
										<content:encoded><![CDATA[<h2>The HR World hosted a virtual round table, sponsored by Indeed Flex, which brought together leading HR professionals from diverse industries. In each case the HR function was dealing with the challenge of creating a flexible workforce, able to respond to business needs swiftly and cost effectively.</h2>
<p>The conversation, cemented the need for an agile workforce in today’s organisations as well as highlighting crucial aspects of how to manage such a workforce to maximise return. The discussion determined that with the right approach, from recruitment to management, companies can maximise productivity and loyalty from their flexible and contingent workforce. At a time when companies are under huge pressure to react to external and internal challenges, securing an agile workforce which brings the right skills to the right part of the organisation at the right time has never been more important.</p>
<p>The discussion was chaired by HR World’s Managing Director, Tom Bright and featured input from Michael Farrier, Solutions Director at Indeed Flex.</p>
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		<title>TA After AI: Josh Bersin describes how AI is changing the role of talent professionals</title>
		<link>https://thehrworld.co.uk/ta-after-ai-josh-bersin-describes-how-ai-is-changing-the-role-of-talent-professionals/</link>
		
		<dc:creator><![CDATA[Simon Kent]]></dc:creator>
		<pubDate>Mon, 27 Oct 2025 09:51:36 +0000</pubDate>
				<category><![CDATA[Talent Acquisition]]></category>
		<category><![CDATA[automation]]></category>
		<category><![CDATA[candidate experience]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[volume hiring]]></category>
		<guid isPermaLink="false">https://www.thehrworld.co.uk/?p=53112</guid>

					<description><![CDATA[Despite all technology progress Josh Bersin is adamant that people will remain at the heart of the talent world. Here he discusses the current and future impact of AI on the function with The HR World's Head of Content Simon Kent.]]></description>
										<content:encoded><![CDATA[<h2>Despite all technology progress <a href="https://www.linkedin.com/in/bersin" target="_blank" rel="noopener">Josh Bersin</a> is adamant that people will remain at the heart of the talent world. Here he discusses the current and future impact of AI on the TA function with The HR World&#8217;s Head of Content, <a href="https://uk.linkedin.com/in/simon-kent-4581b71" target="_blank" rel="noopener">Simon Kent</a>.</h2>
<p>In<a href="https://www.linkedin.com/company/joshbersin?trk=public_profile_topcard-current-company" target="_blank" rel="noopener"> The Josh Bersin Company</a>’s recent report <a href="https://joshbersin.com/talent-acquisition-revolution/" target="_blank" rel="noopener">‘The Talent Acquisition Revolution: How AI is Transforming Recruiting’</a> the TA function is clearly positioned as becoming AI first – with professionals using the technology to create the very best experience for businesses and candidates alike. Traditional recruiting isn’t working, says the report, noting only 17% of applicants received interviews in 2024 and 60% abandoned slow application processes. AI, on the other hand, was found to drive 2–3x faster hiring, stronger candidate quality and sharper targeting. Companies that are joining this AI revolution, says the Company, hire with greater accuracy and efficiency than their peers, despite the job market slowdown.<img decoding="async" class=" wp-image-53128 alignright" src="https://thehrworld.co.uk/wp-content/uploads/2025/10/BersinWebinar-5334-1-200x300.jpeg" alt="bersinwebinar 5334 (1)" width="364" height="547" /></p>
<p>Bersin himself doesn’t see this development as a remarkable stride forward, characterising it instead as “a continuum from the hand crafted, face-to-face relationship to more and more precision recruiting.” The TA function has always been home to copious amounts of data and information and there is little surprise that technology has spotted an opportunity to work with this part of the HR process. Bersin emphasises the remarkable amount of data that now exists around bringing jobs and candidates together, at one point noting: “There’s so much people data now that’s been consolidated, that an AI system knows all about you before it even meets you.”<span class="Apple-converted-space"> </span></p>
<p>But while talent acquisition may be being led by technology it certainly doesn’t mean technology is going to replace human professional. It does, however, mean those professionals need to evolve their roles to make the most of the opportunities the technology offers.</p>
<h3>A challenging market</h3>
<p>Bersin is nothing if not pragmatic. He believes some aspects of the technology are being oversold and this can make it difficult for HR to understand what they can trust and what technology they should take on board.<span class="Apple-converted-space"> </span></p>
<blockquote><p>All sorts of entrepreneurs who don’t know anything about HR love TA</p></blockquote>
<p>The other issue is that the technology industry is, says Bersin “brutally competitive”. He estimates that for every 100 vendors who enter the market with a great new idea, only a few can still be found trading 5-10 years later – the rest have been bought out or have bitten the dust. “All sorts of entrepreneurs who don’t know anything about HR love TA,” says Bersin, “and they always think they have a great idea. So the TA guys are being barraged by a flood of ideas.”</p>
<p>That’s not to say some of the ideas aren’t very good indeed. “There is clear benefit to sectors such as hospitality and those where candidates are required in high volume,” says Bersin, “But there’s also a place for the technology to find specifically skilled individuals – if you need to find a specifically skilled engineer for example, AI can help you find that precise person.” Not only that, but the technology can swiftly make you a specialist in your field. The right search will flag similar job opportunities in the current market as well as the people already carrying out those jobs. In a short space of time users can understand the talent landscape in any particular field and know where to go and who to approach if they want someone who can do the job.</p>
<h3>The strategic TA function</h3>
<p>Armed with this kind of technology, the TA function can become very strategic and increase the value they bring to their organisations. Bersin is aware that currently TA is viewed as a ‘fulfilment centre’ and as such is usually identified as a cost centre. His company’s research found as many as 75% of TA functions within businesses are involved in workplace planning, a sign, he says, of wasted opportunity. With a more engaged and informed TA function employers can be far more efficient in their <a href="https://thehrworld.co.uk/talent-acquisition/talent-winning-strategy-perfecting-the-hunt-for-great-candidates/" data-wpil-monitor-id="708">talent use and hiring strategies</a>. After all, the talent may already exist within their business, or perhaps the job they are trying to fill will actually be redesigned (through the impact of AI?) therefore making it less than desirable to fill right now. Use of the right tools – AI or not – and the right analytical approach, will give organisations the insight they need into how to manage their talent most appropriately, and getting that right means creating and supporting effective TA.</p>
<p>And while there’s a clear benefit to businesses from taking this more focussed approach, Bersin expects a better deal for candidates too. “At the moment 60-70% of candidates never get anything back from their recruitment experience,” he says. “AI means you can get something.” Bersin notes how AI and related technology enable candidates to be screened and given feedback as soon as they apply: “In some of the more sophisticated processes the assessment is so highly tuned that during the application process you can do a simulation of the job itself and then you know what you’re qualified for and if you’re a good fit,” notes Josh.<span class="Apple-converted-space"> </span></p>
<blockquote><p>how organisations hire people is focussed on – or should be focussed on – bigger issues than the availability of technology.</p></blockquote>
<p>Of course the same technology that it helping recruiters is also generating a headache in some areas. The ease with which candidates can apply for positions – and use AI to help them – means employers can be inundated with applications, and those candidates don’t always have relevance to the vacancies they’re trying to fill. At least with technology on their side, recruiters can even offer initial automated interviews for those who are genuinely interested in working for their organisations – and that can be offered at any time if conducted online.</p>
<h3>The necessary recruiter</h3>
<p>In the midst of this automation and AI, Bersin sees the recruiter still standing, providing a crucial service to recognise the needs of their employers. He believes individual recruiters are very much switched on to the possibilities while the organisations they work for are less ready to take things forward in a productive way.<span class="Apple-converted-space"> </span></p>
<p>Moreover, how the TA function adapts to the technology and behaves in the future may be as individual as each company. He cites the diversity found among two similar businesses where one was focussed on getting cultural fit right for their new hires compared to another which was focussed purely on reducing cost of hire. Both companies have laudable intentions, both were clearly using AI in different ways, but trying to compare the worth of each AI use is practically impossible as they have radically different intentions. Ultimately, says Bersin, how organisations hire people is focussed on – or should be focussed on – bigger issues than the availability of technology.</p>
<p>Perhaps because of this Bersin doesn’t believe technology will take further ground from recruiters or TA. Despite the current debate around AGI he doesn’t believe AI will “become a person” or replace the need for human input.</p>
<p>“These are just tools,” he says, “and once you’ve figured out they’re tools you need to figure out how to use them effectively.”</p>
<p>The shifting nature of the recruitment and TA function is embodied, perhaps in the example Bersin gives of Mastercard where their recruiters are now referred to a career advisers. “The recruiter decides where that person should be working,” he says. “In a really high growth company the recruiter wants to find the right person, but they need to be sure the position they go to is right as well.”</p>
<p>Even as AI and technology continues to make inroads into the function, the need for a human being to make these kind of human decisions is more important than ever.</p>
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