bspeice.github.io/profitability-using-the-investment-formula.html
2018-01-16 20:28:29 -05:00

7014 lines
529 KiB
HTML

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="description" content="Profitability using the Investment Formula¶I&#39;ve previously talked about crafting an Investment Formula that would guarantee making money if you could predict which direction the stock market was ...">
<meta name="keywords" content="algorithmic-trading, python">
<link rel="icon" href="https://bspeice.github.io/favicon.ico">
<title>Profitability using the Investment Formula - Bradlee Speice</title>
<!-- Stylesheets -->
<link href="https://bspeice.github.io/theme/css/bootstrap.min.css" rel="stylesheet">
<link href="https://bspeice.github.io/theme/css/fonts.css" rel="stylesheet">
<link href="https://bspeice.github.io/theme/css/nest.css" rel="stylesheet">
<link href="https://bspeice.github.io/theme/css/pygment.css" rel="stylesheet">
<!-- /Stylesheets -->
<!-- RSS Feeds -->
<link href="https://bspeice.github.io/feeds/all.atom.xml" type="application/atom+xml" rel="alternate" title="Bradlee Speice Full Atom Feed" />
<link href="https://bspeice.github.io/feeds/blog.atom.xml" type="application/atom+xml" rel="alternate" title="Bradlee Speice Categories Atom Feed" />
<!-- /RSS Feeds -->
<!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries -->
<!--[if lt IE 9]>
<script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js"></script>
<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
<![endif]-->
<!-- Google Analytics -->
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-74711362-1', 'auto');
ga('send', 'pageview');
</script>
<!-- /Google Analytics -->
</head>
<body>
<!-- Header -->
<div class="header-container gradient">
<!-- Static navbar -->
<div class="container">
<div class="header-nav">
<div class="header-logo">
<a class="pull-left" href="https://bspeice.github.io/"><img class="mr20" src="https://bspeice.github.io/images/logo.svg" alt="logo">Bradlee Speice</a>
</div>
<div class="nav pull-right">
</div>
</div>
</div>
<!-- /Static navbar -->
<!-- Header -->
<!-- Header -->
<div class="container header-wrapper">
<div class="row">
<div class="col-lg-12">
<div class="header-content">
<h1 class="header-title">Profitability using the Investment Formula</h1>
<p class="header-date"> <a href="https://bspeice.github.io/author/bradlee-speice.html">Bradlee Speice</a>, Fri 26 February 2016, <a href="https://bspeice.github.io/category/blog.html">Blog</a></p>
<div class="header-underline"></div>
<div class="clearfix"></div>
<p class="pull-right header-tags">
<span class="glyphicon glyphicon-tags mr5" aria-hidden="true"></span>
<a href="https://bspeice.github.io/tag/algorithmic-trading.html">algorithmic-trading</a>, <a href="https://bspeice.github.io/tag/python.html">python</a> </p>
</div>
</div>
</div>
</div>
<!-- /Header -->
<!-- /Header -->
</div>
<!-- /Header -->
<!-- Content -->
<div class="container content">
<p>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Profitability-using-the-Investment-Formula">Profitability using the Investment Formula<a class="anchor-link" href="#Profitability-using-the-Investment-Formula">&#182;</a></h1><p>I've previously talked about crafting an <a href="https://bspeice.github.io/guaranteed-money-maker.html">Investment Formula</a> that would guarantee making money if you could predict which direction the stock market was going to go. This is going to be the first in a series of posts trying to flesh out what an actual investment strategy based on this formula would look like.</p>
<p>But first, the formula doesn't take into account two very important things: <strong>leverage</strong>, and the <strong>number of days invested</strong>. That's why I want to set up what I'm going to call the <strong>Profitability Score</strong>.</p>
<p>The definition is going to be very simple:</p>
<ul>
<li>$p$: Profit made once you exit the investment</li>
<li>$i$: Initial investment into the asset</li>
<li>$m$: Maximum investment in the asset</li>
<li>$l = m / i$: The maximum leverage of an investment, as the ratio of maximum invested to initial investment</li>
<li>$d$: The number of days it takes to turn a profit</li>
</ul>
<p>$s = \frac{1000 p}{i(l + d)} = \frac{1000 p}{m + i\cdot d}$</p>
<p>Crazy, right? The score is simply the (normalized) profit you made divided by the leverage plus days invested. The $\cdot 1000$ is just to turn the number into something more reasonable - people don't like hearing something with a profitability score of .001 for example.</p>
<h1 id="Theoretical-Justification">Theoretical Justification<a class="anchor-link" href="#Theoretical-Justification">&#182;</a></h1><p>The formula itself is designed to be simple in principle: I like making a profit, and I want to penalize the leverage you incur and days you have to invest. Ideally, we want to have a stock that goes up all the time. However, the investment formula takes advantage of a different case: trying to profit from highly volatile assets. If we can make money when the investment only has one day up, let's do it!</p>
<p>Even so, there are two potential issues: First, stocks that trend upward will have a higher profitability score - both leverage and days invested will be 1. To protect against only investing in this trend, I can do things like taking $\log(d)$. I don't want to start biasing the scoring function until I have a practical reason to do so, so right now I'll leave it standing.</p>
<p>The second issue is how to penalize leverage and days invested relative to each other. As it currently stands, a leverage of 6x with only 1 day invested is the same as leveraging 2x with 3 days invested. In the future, I'd again want to look at making the impact of days invested smaller - I can get over an extra 3 days in the market if it means that I don't have to incur a highly leveraged position.</p>
<p>So there could be things about the scoring function we change in the future, but I want to run some actual tests before we start worrying about things like that!</p>
<h1 id="Running-a-simulation">Running a simulation<a class="anchor-link" href="#Running-a-simulation">&#182;</a></h1><p>This won't be an incredibly rigorous backtest, I just want to see some results from the work so far. Let's set up the simulation code again, and start looking into some random stocks. <strong>If you've read the last blog post, you can skip over the code.</strong> The only difference is that it's been ported to python to make the data-wrangling easier. Julia doesn't yet support some of the multi-index things I'm trying to do.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">Quandl</span> <span class="k">import</span> <span class="n">get</span> <span class="k">as</span> <span class="n">qget</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">api_key</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
<span class="n">profitability</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">p</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">d</span><span class="p">:</span> <span class="mi">1000</span><span class="o">*</span><span class="n">p</span> <span class="o">/</span> <span class="p">(</span><span class="n">m</span> <span class="o">+</span> <span class="n">i</span><span class="o">*</span><span class="n">d</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_profitable</span><span class="p">(</span><span class="n">current_price</span><span class="p">,</span> <span class="n">purchase_history</span><span class="p">,</span> <span class="n">open_history</span><span class="p">):</span>
<span class="n">shares</span> <span class="o">=</span> <span class="p">(</span><span class="n">purchase_history</span> <span class="o">/</span> <span class="n">open_history</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">current_price</span> <span class="o">*</span> <span class="n">shares</span> <span class="o">&gt;</span> <span class="nb">sum</span><span class="p">(</span><span class="n">purchase_history</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">daily_investment</span><span class="p">(</span><span class="n">current_open</span><span class="p">,</span> <span class="n">current_close</span><span class="p">,</span> <span class="n">purchase_history</span><span class="p">,</span> <span class="n">open_history</span><span class="p">):</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">current_close</span> <span class="o">/</span> <span class="n">current_open</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">t2</span> <span class="o">=</span> <span class="p">(</span><span class="n">purchase_history</span> <span class="o">-</span> <span class="n">purchase_history</span> <span class="o">*</span> <span class="n">current_close</span> <span class="o">/</span> <span class="n">open_history</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">t2</span> <span class="o">/</span> <span class="n">t1</span>
<span class="k">def</span> <span class="nf">simulate_day</span><span class="p">(</span><span class="n">open_vals</span><span class="p">,</span> <span class="n">close_vals</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">bias</span><span class="p">):</span>
<span class="n">invested</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">init</span><span class="p">])</span>
<span class="n">day</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">profitable</span> <span class="o">=</span> <span class="n">is_profitable</span><span class="p">(</span><span class="n">close_vals</span><span class="p">[</span><span class="n">day</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)])</span> \
<span class="ow">or</span> <span class="n">is_profitable</span><span class="p">(</span><span class="n">open_vals</span><span class="p">[</span><span class="n">day</span><span class="p">],</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)])</span>
<span class="k">while</span> <span class="ow">not</span> <span class="n">profitable</span><span class="p">:</span>
<span class="n">expected_close</span> <span class="o">=</span> <span class="n">open_vals</span><span class="p">[</span><span class="n">day</span><span class="p">]</span> <span class="o">*</span> <span class="n">expected</span>
<span class="n">todays_purchase</span> <span class="o">=</span> <span class="n">daily_investment</span><span class="p">(</span><span class="n">open_vals</span><span class="p">[</span><span class="n">day</span><span class="p">],</span> <span class="n">expected_close</span><span class="p">,</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">day</span><span class="p">])</span>
<span class="n">invested</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">invested</span><span class="p">,</span> <span class="n">todays_purchase</span> <span class="o">+</span> <span class="n">bias</span><span class="p">)</span>
<span class="c1"># expected_profit = expected_close * (invested / open_vals[0:len(invested)]).sum() - invested.sum()</span>
<span class="n">day</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">profitable</span> <span class="o">=</span> <span class="n">is_profitable</span><span class="p">(</span><span class="n">close_vals</span><span class="p">[</span><span class="n">day</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)])</span> \
<span class="ow">or</span> <span class="n">is_profitable</span><span class="p">(</span><span class="n">open_vals</span><span class="p">[</span><span class="n">day</span><span class="p">],</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)])</span>
<span class="n">shares</span> <span class="o">=</span> <span class="p">(</span><span class="n">invested</span> <span class="o">/</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c1"># Make sure we can&#39;t see into the future - we know either today&#39;s close or tomorrow&#39;s open</span>
<span class="c1"># will be profitable, but we need to check which one.</span>
<span class="k">if</span> <span class="n">is_profitable</span><span class="p">(</span><span class="n">close_vals</span><span class="p">[</span><span class="n">day</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">invested</span><span class="p">,</span> <span class="n">open_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">invested</span><span class="p">)]):</span>
<span class="n">ending_price</span> <span class="o">=</span> <span class="n">close_vals</span><span class="p">[</span><span class="n">day</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ending_price</span> <span class="o">=</span> <span class="n">open_vals</span><span class="p">[</span><span class="n">day</span><span class="p">]</span>
<span class="n">profit</span> <span class="o">=</span> <span class="n">shares</span> <span class="o">*</span> <span class="n">ending_price</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">invested</span><span class="p">)</span>
<span class="k">return</span> <span class="n">invested</span><span class="p">,</span> <span class="n">profit</span>
<span class="k">def</span> <span class="nf">simulate_ts</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">initial</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">bias</span><span class="p">):</span>
<span class="n">ticker_info</span> <span class="o">=</span> <span class="n">qget</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">trim_start</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">api_key</span><span class="o">=</span><span class="n">api_key</span><span class="p">)</span>
<span class="n">evaluation_times</span> <span class="o">=</span> <span class="n">ticker_info</span><span class="p">[:</span><span class="n">end</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="c1"># Handle Google vs. YFinance data</span>
<span class="k">if</span> <span class="s2">&quot;Adjusted Close&quot;</span> <span class="ow">in</span> <span class="n">ticker_info</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">close_column</span> <span class="o">=</span> <span class="s2">&quot;Adjusted Close&quot;</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">close_column</span> <span class="o">=</span> <span class="s2">&quot;Close&quot;</span>
<span class="n">sim</span> <span class="o">=</span> <span class="p">{</span><span class="n">d</span><span class="p">:</span> <span class="n">simulate_day</span><span class="p">(</span><span class="n">ticker_info</span><span class="p">[</span><span class="n">d</span><span class="p">:][</span><span class="s2">&quot;Open&quot;</span><span class="p">],</span> <span class="n">ticker_info</span><span class="p">[</span><span class="n">d</span><span class="p">:][</span><span class="n">close_column</span><span class="p">],</span>
<span class="mi">100</span><span class="p">,</span> <span class="mf">1.02</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">evaluation_times</span><span class="p">}</span>
<span class="n">sim_series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">sim</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
<span class="n">result</span><span class="p">[</span><span class="s2">&quot;profit&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sim_series</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">result</span><span class="p">[</span><span class="s2">&quot;max&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sim_series</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">result</span><span class="p">[</span><span class="s2">&quot;days&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sim_series</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">result</span><span class="p">[</span><span class="s2">&quot;score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sim_series</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">profitability</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
<span class="n">result</span><span class="p">[</span><span class="s2">&quot;investments&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sim_series</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">return</span> <span class="n">result</span>
<span class="k">def</span> <span class="nf">simulate_tickers</span><span class="p">(</span><span class="n">tickers</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">ticker</span> <span class="ow">in</span> <span class="n">tickers</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2015</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">results_df</span> <span class="o">=</span> <span class="n">simulate_ts</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">1.01</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">results</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span> <span class="o">=</span> <span class="n">results_df</span>
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">keys</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="And-now-the-interesting-part">And now the interesting part<a class="anchor-link" href="#And-now-the-interesting-part">&#182;</a></h1><p>Let's start looking into the data! FANG stocks have been big over the past year, let's see how they look:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">fang_df</span> <span class="o">=</span> <span class="n">simulate_tickers</span><span class="p">([</span><span class="s2">&quot;YAHOO/FB&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/AAPL&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/NFLX&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/GOOG&quot;</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">fang_df</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="s1">&#39;days&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">8</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Distribution of Days Until Profitability&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area"><div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBgAAAIICAYAAADE513IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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=
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">fang_df</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="s1">&#39;score&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Profitability score over time&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area"><div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBEAAAGECAYAAABgaLg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt41OWZ//HPPTlCSMIhBcJR1NZiWzxctoBuV/gJHsBq
WWURNFsXu7TqWqsLrW5bhdbK1l1W4VJ3V6hCFFcNUrUtBw3Ws9JdLVCUutoSREQwAsmEw0xm5vn9
MZMxh0kyk8N8h+T9uq65yDzzzPf7zAxovvfc9/2Yc04AAAAAAADt8Xm9AAAAAAAAcHwgiAAAAAAA
AJJCEAEAAAAAACSFIAIAAAAAAEgKQQQAAAAAAJAUgggAAAAAACApBBEAABnFohaa2Z/NrN7MwrHx
lWYW6aZzLjSziJmNSnJ+lZk932zsBTP7S7Oxblszjn+p/r0DACATEEQAACRkZufGLnAa3/xm9r9m
9j0z667/h1wt6TZJmyTNlXRVbNxJanJBbmaXmtntXXBOF7slK5JgfqLnd+eacRxo5/Nu8fcDAIBM
Z86l8jsTAKC3MLNzJf1O0qOS1kkyScMUvcgfK+kB59x3u+G8qyVd5Jwb2Gw8S1KWcy7YaOwhSX/n
nMvq5DlvVzRwMcY590ES83MkOedcqNHY7ySNds6dmI414/jQ1ucdC8RlN/77AQBApsv2egEAgIz3
lnPu0YY7ZvafknZI+raZ/cQ590miJ5lZtqIX0IEUzzdU0qHmg865sKRw89OkeOwu4ZyrT3JexqzZ
C2bWzzlX5/U6ulMSr7HVz9s5F5FEAAEAcFyhnAEAkBLnnF/S64peHJ0oNantPtXM/t3Mdks6Kml8
w/PM7Ntm9qaZHTGzQ2a20czOafT4ubH+AZMkndCohOLB2ONN+gvEvvn/u9jPDXPDZtYwdoqZ3W9m
282s1swOx0oxrmnj5fUzs2Vmtje2zjfM7P81n5SoJ0IiqazZzO6J3T8pwXGGmlnIzFa0cz4zs++b
2dbYa64xsz+Z2YpYVkTjuaebWYWZfWxmx8zsAzN71MzGNJvX5ufWaF7EzB40s/9nZi+bmV/SM40e
LzKzX5jZe7Hz7U90vnZe31fM7FdmVm1mR83sbTNb0Li0xsz+JbaWLyd4flHseWubjU+Jva6Dsce3
mtl3Ejy/ysyej713G8zskKStbay3vb+jLXoiNBobG/s78VHs726lmX0hNudvGn0mO83sH1o5f1Kv
CwCAVJCJAADoiM/H/qyO/dnQU2C1pCOS/i12f68kmdkvJC2QtFnSrZIKJc2T9Dszu8Q5t0HSO4r2
P/ixpEGSvq9ooOLPzc7R4A5Fg+F/JelKffaN72uxPyfFHvu1pJ2SCiTNlLTczEqcc79o9ppMUrmk
kKR/ia3xO5I2mNmFzrnGQYNkawFTWfObkr6naB+IHzU7ztWxucvbOd+PJS2S9LSk/1A0C2KMpEsk
5Sn62cjMLpa0RlJd7Jh/VjQD5AJJX1b0/Ur2c2vsq5Iuix1zZcOgmRUpGngaIelBSW9LKpV0naQ3
zOws59zutl6YmZ0l6UVJAUn3Ston6RuSfiFpnKSy2NRVkn6g6MX7D5odZpak3GZrmxd7r15X9PM5
LGmqpP8wsxOdcz9s9HwnabSk5yU9EXsP+7Wx7Pb+jibqxdEwtkqSX9LPJX1O0nxF/y7eFnvN90s6
KOkaSf9pZm875xqOm+rrAgAgec45bty4cePGrcVN0rmKNn1ruKgvUfRibXls/JVGc2+PjW2S5Gt2
nC8oejH7oqL13w3jpYpeBP1FsR49sfHfSfpLgvU8JCnc3lijx/q0Mv672HmzEqz/tWZrHK7ohdzb
zY6xU9LzCY77l06u+VVJHzZ+P2Lj70ransRn9mZ78yT1kfSJogGeoW3MS/Vzi8TmT05wrKWKXsR+
udn4SEk1kh5M4rW9qmjq/5eajT/e/LySft/K+/iypP0Nr0fRwMlRSQ8nON89kuolndDscw9L+vsU
/h219XnfHjveqAR/F59qNveG2PghScMajZfEXsPqRmMpvS5u3Lhx48YtlRvlDACA9ixS9KJzv6Qt
in4r/pSkGc3mOUn3uGidd2OXxv68yzVqROic26voBdZoSWd09aKdc0cbfjazPDMbaGaDJD0rqUjS
FxOs/+5ma9yjaHbFF83slK5eYwIPKHqRPq1hwMz+WtHMjzZLGWJqJA1PVG7QyAWKBoWWOOc+bmNe
Rz63rc653yU41hxJL0naa2aDGm6KXui+Ien8tl6UmX1O0kRJTzvn3m728M8V/Ya/8d/HVYq+j1Mb
HeMESWdLerTR65mpaGbCg43XFVvbbyRlSZrS7HwH1CiToZs4Scuajb0c+/Np59xH8YnOVSsaZPp8
o7kdeV0AACSFcgYAQHsekFSh6IXNYUn/55xr0fgw5r0EYw017+8keKzhgvBESW91ZpHNmVmBogGQ
mYp+492YkzQgwdP+lGCsYd0nKnqx1p0eV/Sb4msk/TY2do2iKfwPJ/H8f5b0K0kvmdleSS/EjrPG
fdYM8vOKvv4t7RyrI5/b/zWfGAsADFI0UJCoCadTy+aTqaxlh6Lf0J/YaOy/JS1RtKTh2djYt2J/
Nn4fv6hoAGJTK+d1koY0G/uzcy4dW1vtbHb/YOzPqgRzD0oa1eh+R14XAABJIYgAAGjPe65pP4C2
HOnWlaTmvxX9Rv+/FP0W91NFL1anK9pvIeOy8Zxzx8zsEUnzYhffxxTtMfC0c+7TJJ7/hkUbM14g
aXLsNkfSj83snDaCP10l0eff0AegUtFeE92+O4Vz7oCZrZP0TTMrcM4dVrTfxg7n3JvN1uYU7afQ
WlbGX5rdT9ff8dYCK62NW7OfU31dAAAkhSACAKC7NVysfEktv139UrM5qUr4jbCZFSsaLFjlnLu+
2WNtpc6PlfTHLl5jc+19i/2ApOsV/ea8VtEeBr9M+uDOHVE0G+FXkmRm10q6T9GMhiWKZguYpNMV
vbBvTVd9bp8oWsdf1EqpQzIazv+lBI+NVTQg1HwtqyR9U9JMM/s/SSepZaPFhsyZT1MIlKUqHVkL
zaXjdQEAeqmM+xYGANDjNGzzt8DM4sFrMytVtL9ClaQ/dPDYdbFj9W823vBtbZP/z8XO2doWjybp
JjPLaTR/hKTZkv7knOuqUobW1ixJcs79UdHGgNfEbh84555L5sCxmvfmGt7bgbE/n1V0V41/MrOh
bRyuSz63WOr/aklfM7PLWln359o5xieKNr38hpmd2uzhWxW9UF/bbPy3ir7Ov4vdwrF1NPaEos0a
F5lZfoJ1FZlZbltrS0Kbn3c3ScfrAgD0UmQiAAC6lXPu/8zsXxXdKvAlM3tc0caG/6DotouzO1Fj
/oai39r/h5n9VtGu828453aZ2bOSrjKzY5L+R9IJim5P+Bd9dkHdXLakl83sv2Nr/I6kfEW3Xuwq
ida82TlX1WjOA4o2UnSSFqZw7B1m9oaiWzJ+pGhzwXmK9lR4TIo2nDSzaxTtc7HdzFZIel/SYEX7
Fixxzv26iz+3Hyna1PBxM6uIvQdBRZszTpP0v4pubdmWGxXt8fCKmd2naJr+NxRtnrjaOfdC48nO
uVDsc/xHSWdJqow1hWw8Z08sU2N57L17WNIuRbdUHKfo1pinSvogydeZSDKfd5dK0+sCAPRSBBEA
AG1JtI996gdx7hYze0/SdZIWK3oB+YakK1yjve2bnbe19TT234qm5V8h6XJFMw/+XlK5pCsVrcG/
WNFvot9T9FvrsKQHWzn230n6rqQfSuovaaukv2slJTzRGpMZa23NVY3mPCbp3xW9WF+Z4Jit+TdF
L8pvkFSs6I4ar0v6l1iGQ3RBzv3azP5K0UaMcyUVStqn6A4Kjeel8rm1+nfFOVcb2zHinyT9raIX
sSFFt2F8RUnsPOGce9PMzla0Wea1ir43f1G0ROHfW3naKkWDCH1jPyc67koze1fSfEUDLv0VzWB4
V9HtTZv3FEj130Myn3ey2vr32GS8A68LAICkWHoaDAMAgGTFUs33KvqN9bT25gMAAKQLPREAAMg8
Vyn6zfEDXi8EAACgMTIRAADIEGZ2saK9G25XNBPhtE70iwAAAOhyBBEAAMgQZrZT0WaI/yvpH5xz
OzxeEgAAQBMEEQAAAAAAQFLoiQAAAAAAAJJCEAEAAAAAACSFIAIAAAAAAEgKQQQAAAAAAJAUgggA
AAAAACApBBEAAAAAAEBSCCIAAAAAAICkEEQAAAAAAABJIYgAAAAAAACSQhABAAAAAAAkhSACAAAA
AABISrtBBDP7pZntM7NtjcbuMrMdZrbFzJ40s6JGj91qZu/FHj+/uxYOAAAAAADSK5lMhIckXdBs
7FlJX3LOnS7pPUm3SpKZnSrpbyWNlXSRpPvNzLpuuQAAAAAAwCvtBhGcc69IOthsrNI5F4ndfUPS
iNjPl0h6zDkXcs5VKRpg+FrXLRcAAAAAAHilK3oizJW0LvbzcEm7Gz22JzYGAAAAAACOc9mdebKZ
/UhSvXPuvzvwXNeZcwMAAAAAgO7jnGvRnqDDmQhmdrWkaZLmNBreI2lko/sjYmOtLYhbF9xuv/12
z9dwPN5433hfM/3Ge8l7msk33kve1+PpxvvKe5npN95X3stMvLUm2SCCxW4NAYQLJS2QdIlzLtBo
3jOSrjCzXDMbI+lkSb9P8hwAAAAAACCDtVvOYGaPSpokaZCZfSDpdkn/LClX0nOxzRfecM5d55x7
x8yekPSOpHpJ17m2QhgAAAAAAOC40W4QwTk3J8HwQ23MXyxpcWcWhdRMmjTJ6yUcl3jfugfva9fh
vex6vKddh/eye/C+dg/e167De9k9eF+7Du9l9zOvEgXMjCQFAAAAAAAykJnJJWis2KndGQAAAAAA
x7cTTjhBu3bt8noZ8Mjo0aNVVVWV9HwyEQAAAACgF4t94+z1MuCR1j7/1jIROrzFIwAAAAAA6F0I
IgAAAAAAgKQQRAAAAAAAAEkhiAAAAAAAAJJCEAEAAAAAACSFIAIAAAAAIOOUlZVp7ty5TcZefPFF
lZSUaN++fZKklStXyufzqaKiosW8kSNHtjjm5MmT9eCDD8bv19TU6Nprr1Vpaan69eun0047TStX
rmzxvJUrV2rcuHEqKCjQsGHDdN1116mmpqbFvAsvvFCVlZVNnpdofQ2qqqqUlZWl66+/vsVjPp9P
hYWFKioq0siRI/VP//RP8V0UxowZo+effz7hMbsbQQQAAAAAQMZZunSpNmzYoE2bNkmSAoGA5s2b
p7vvvltDhgyRJJWXl2vQoEEqLy9v8XyzFrsTNlFfX6/zzjtPu3fv1ubNm1VTU6O77rpLt9xyi+65
5574vCVLlujWW2/VkiVLVFtbqzfeeEO7du3S1KlTFQqF4vOOHDmiN998U+eee258rK31NTw+cOBA
Pf7446qvr2+x/m3btqm2tlabNm3So48+quXLl7fzrnU/gggAAAAAgIwzcOBALVu2TPPmzdORI0e0
cOFCnXzyySorK5Mk7dq1Sy+99JIeeOABbdiwQfv370/p+OXl5frwww+1Zs0ajRo1SllZWbrgggu0
bNky/eQnP1FdXZ38fr8WLlyoe++9V1OnTlVWVpZGjRqlJ554QlVVVXrkkUfix9u0aZPOOecc5eTk
JL2+8vJy3XHHHcrJydGvf/3rJo855+KZB1/4whf09a9/Xdu3b0/pNXYHgggAAAAAgIx0+eWX68wz
z9Ts2bO1YsWKJt/El5eX66yzztKMGTM0duxYrV69OqVjV1ZW6qKLLlJ+fn6T8csuu0zHjh3T66+/
rtdee02BQEAzZsxoMqegoEDTpk3Tc889Fx9bt26dpk+fnvT6Xn75Ze3Zs0dXXHGFZs6cqVWrVrW6
1nfeeUcvv/yyzjzzzJReY3cgiAAAAAAAaJVZ19w66r777tPzzz+v22+/XcOGDYuPP/zww7ryyisl
SXPmzGlRMrBnzx4NHDgwfhswYIBeffXV+OPV1dUqLS1tcb6srCyVlJSourpa1dXVKikpkc/X8tK5
tLRU1dXV8fvr1q3TtGnTkl5feXm5pk2bpuLiYs2ZM0cbNmxocjxJOvPMMzVo0CBdeumlmjdvnq6+
+ur23q5uRxABAAAAANAq57rm1lGDBw9WSUmJTj311PjYq6++qp07d2rWrFmSpNmzZ2vbtm3atm1b
fM7w4cN14MCB+O3gwYM655xz4o+XlJRo7969Lc4XDofjwYOGYEIkEmkxb+/evSopKZEkbd++Xf37
99fw4cOTWt+xY8dUUVGhOXPmSJImTJigkSNH6tFHH21yjj/84Q/69NNP9d5772nRokWpv3ndgCAC
AAAAAOC40pD6f/rpp6u0tFQTJkyQmbVZEtDclClTtH79eh09erTJ+Jo1a5Sfn68JEyZo4sSJysvL
09q1a5vMqaur0/r16zVlyhRJLbMQ2lvf2rVrVVtbq+uuu06lpaUqLS3VRx991GL9rjPRl25CEAEA
AAAAcNwIBAKqqKjQ8uXLtWXLFm3dulVbt27VsmXLtHr16oRZA4mUlZVpxIgRmjlzpnbt2qVQKKSN
Gzfqxhtv1KJFi+LbK95222264YYbtHHjRoVCIVVVVWnWrFkaNWqUrrrqKklN+yG0tb5HH31U4XBY
q1at0jXXXKM//vGP8cdfeeUVbdmyRW+//XZS6w8GgwoEAvFbOBzu2BuaIoIIAAAAAICM1ni7xqee
ekp9+/ZVWVmZBg8eHL/NnTtX4XBYGzZsSOo4ubm5qqys1MiRIzV+/HgVFxdr/vz5Wrx4sW6++eb4
vAULFujOO+/U/PnzVVxcrIkTJ2r06NGqrKxUTk6OampqtGPHDp199tntri8UCmn16tX63e9+p5tu
uqnJ42eeeaYuuuiieDZCe1tUTp8+XX379lWfPn3Ut2/ftJU7mFfpEWbmMjE1AwAAAAB6EzPLyLT5
40VFRYWefPJJPfbYY14vpUNa+/xj4y0iGWQiAAAAAADQQQMGDNBNN93k9TLShkwEAAAAAOjFyETo
3chESKP3Pn1PG95vvd4GAAAAAICehCBCJzy/83k9+sdH258IAAAAAEAPQBChE/xBv8IuPdtoAAAA
AADgNYIInVAXrFM4QhABAAAAANA7EETohLpgHZkIAAAAAIBegyBCJ9QF6xRxEa+XAQAAAABAWhBE
6AR/0E85AwAAAACg1yCI0AmUMwAAAABA9ygrK9PcuXObjL344osqKSnRvn37JEkrV66Uz+dTRUVF
i3kjR45scczJkyfrwQcfjN+vqanRtddeq9LSUvXr10+nnXaaVq5c2eJ5K1eu1Lhx41RQUKBhw4bp
uuuuU01NTYt5F154oSorK7Vo0SL5fD6tWbMm/lg4HJbP59MHH3wgSbr66quVl5enoqIiFRYWqqio
KP46xowZo+eff77F8X/zm9+otLRUhw4dio89/fTTGjFihPx+f4v53YEgQidQzgAAAAAA3WPp0qXa
sGGDNm3aJEkKBAKaN2+e7r77bg0ZMkSSVF5erkGDBqm8vLzF882szePX19frvPPO0+7du7V582bV
1NTorrvu0i233KJ77rknPm/JkiW69dZbtWTJEtXW1uqNN97Qrl27NHXqVIVCofi8I0eO6M0339S5
554rSRo0aJBuv/12OecSrsnM9MMf/lC1tbXy+/2qra3VzJkz21zzxRdfrPPOO0/f//73JUmHDh3S
ddddp//6r/9SYWFhm8/tKgQROsEfoJwBAAAAALrDwIEDtWzZMs2bN09HjhzRwoULdfLJJ6usrEyS
tGvXLr300kt64IEHtGHDBu3fvz+l45eXl+vDDz/UmjVrNGrUKGVlZemCCy7QsmXL9JOf/ER1dXXy
+/1auHCh7r33Xk2dOlVZWVkaNWqUnnjiCVVVVemRRx6JH2/Tpk0655xzlJOTI0m64IILlJubq4cf
fjg+p3FAoaMagivPPvusbrrpJk2ePFnTp0/v9HGTRRChEyhnAAAAAIDuc/nll+vMM8/U7NmztWLF
Ci1fvjz+WHl5uc466yzNmDFDY8eO1erVq1M6dmVlpS666CLl5+c3Gb/ssst07Ngxvf7663rttdcU
CAQ0Y8aMJnMKCgo0bdo0Pffcc/GxdevWNbmY9/l8+tnPfqZFixYpHO6668ZBgwZp6dKluvLKK7Vu
3TotXbq0y46djOy0nq2HoZwBAAAAQE9ni9ouC0iWu71j38Lfd999Oumkk7R48WINGzYsPv7www/r
hhtukCTNmTNH5eXluummm+KP79mzRwMHDvzs/M7p8OHD8UyG6upqffWrX21xvqysLJWUlKi6ulqR
SEQlJSXy+Vp+/15aWqq33norfn/dunX68Y9/3GTOxRdfrDvuuEMrVqzQt7/97RbH+Nd//Vfde++9
cs4pJycn6WyK8ePHq6amRrNmzdKgQYOSek5XIYjQCXXBOsoZAAAAAPRoHb347yqDBw9WSUmJTj31
1PjYq6++qp07d2rWrFmSpNmzZ+tHP/qRtm3bpnHjxkmShg8fHm9i2GDy5Mnxn0tKSrR3794W5wuH
w6qurlZJSYkikUg8mNA8kLB3716VlJRIkrZv367+/ftr+PDhLY53xx13aO7cubrqqqtaPLZgwQL9
9Kc/TfatiJs3b56+9a1vae3atdq8ebPGjx+f8jE6inKGDnLORbd4pJwBAAAAANJq1apVkqTTTz9d
paWlmjBhgswsPp6MKVOmaP369Tp69GiT8TVr1ig/P18TJkzQxIkTlZeXp7Vr1zaZU1dXp/Xr12vK
lCmSolkI06ZNa/U8J598su6///52mz0m45e//KU+/PBD3X///fr5z3+ua665pkmDx+5GEKGDguGg
QpEQ5QwAAAAAkEaBQEAVFRVavny5tmzZoq1bt2rr1q1atmyZVq9erUgkuWu0srIyjRgxQjNnztSu
XbsUCoW0ceNG3XjjjVq0aFF828XbbrtNN9xwgzZu3KhQKKSqqirNmjVLo0aNimcXNO+H0Nwdd9yh
u+66K6XXGQwGFQgE4rdwOKyPPvpIP/jBD7RixQrl5OTou9/9rkpKSnTHHXekdOzOIIjQQXXBOkmi
nAEAAAAAulnjb/Cfeuop9e3bV2VlZRo8eHD8NnfuXIXDYW3YsCGp4+Tm5qqyslIjR47U+PHjVVxc
rPnz52vx4sW6+eab4/MWLFigO++8U/Pnz1dxcbEmTpyo0aNHq7KyUjk5OaqpqdGOHTt09tlnt3re
s88+W1/72tdSykSYPn26+vbtqz59+qhv375atGiRrr/+es2ZM6fJuR544AEtXbpUO3bsSPrYnWFd
scVEh05s5rw6d1eoOlSlMUvH6KxhZ+l//uF/vF4OAAAAAHSImXXJ1oO9VUVFhZ588kk99thjXi+l
Q1r7/GPjLaIeZCJ0EJkIAAAAAIABAwY02RWip2N3hg6qC9YpNyuXnggAAAAA0Is1NFfsLchE6KC6
YJ2K84rZnQEAAAAA0GsQROggf8Cv4vxiyhkAAAAAAL1Gu0EEM/ulme0zs22NxgaY2bNm9q6ZbTSz
4kaP3Wpm75nZDjM7v7sW7rWGTATKGQAAAAAAvUUymQgPSbqg2dgtkiqdc6dIel7SrZJkZqdK+ltJ
YyVdJOl+S2UPi+NIXbAumolAOQMAAAAAoJdoN4jgnHtF0sFmw5dKWhX7eZWkb8Z+vkTSY865kHOu
StJ7kr7WNUvNLP6gP9oTgXIGAAAAAEAv0dGeCIOdc/skyTn3saTBsfHhknY3mrcnNtbjNGQiUM4A
AAAAAOgtuqqxouui4xw32J0BAAAAANDbdDSIsM/MhkiSmQ2VtD82vkfSyEbzRsTGElq4cGH89sIL
L3RwKd6IBxEoZwAAAACALldWVqa5c+c2GXvxxRdVUlKiffv2SZJWrlwpn8+nioqKFvNGjhyp5iZP
nqwHH3wwfr+mpkbXXnutSktL1a9fP5122mlauXJli+etXLlS48aNU0FBgYYNG6brrrtONTU1LeZd
eOGFqqyslCS99957mj17tgYPHqz+/fvrlFNO0Y033qiPPvqoW87//vvvt3u+trzwwgtNrtFbk2wQ
wWK3Bs9Iujr287ckPd1o/AozyzWzMZJOlvT71g7aeIGTJk1KcimZwR/0U84AAAAAAN1k6dKl2rBh
gzZt2iRJCgQCmjdvnu6++24NGTJEklReXq5BgwapvLy8xfPb6/FfX1+v8847T7t379bmzZtVU1Oj
u+66S7fccovuueee+LwlS5bo1ltv1ZIlS1RbW6s33nhDu3bt0tSpUxUKheLzjhw5ojfffFPnnnuu
3n//fU2YMEEjRozQli1bdOjQIb366qs66aST9Morr3T5+d9//32NHz++zfO1Z9KkSUkFEeSca/Mm
6VFJH0kKSPpA0t9LGiCpUtK7kp6V1L/R/FslvS9ph6Tz2ziuO55d/OjFbsWbK1zJXSVeLwUAAAAA
OiyTr80qKirciSee6A4fPuxuueUWN23atPhjVVVVLisry61du9ZlZ2e7ffv2xR974YUX3MiRI1sc
b9KkSe6Xv/ylc865FStWuCFDhrijR482mfP444+7fv36Ob/f72pra12/fv3cmjVrmsypq6tzn/vc
59xDDz0UH3vmmWfcpZde6pxz7sorr3SXXHJJm6+tK8+fzPla09rnHxtvcS2fzO4Mc5xzw5xzec65
Uc65h5xzB51zU5xzpzjnznfOHWo0f7Fz7mTn3Fjn3LNJhTyOQ/EtHilnAAAAAIBucfnll+vMM8/U
7NmztWLFCi1fvjz+WHl5uc466yzNmDFDY8eO1erVq1M6dmVlpS666CLl5+c3Gb/ssst07Ngxvf76
63rttdcUCAQ0Y8aMJnMKCgo0bdo0Pffcc/GxdevWafr06ZKkTZs26bLLLkvb+ZM5X1fpqsaKvY4/
4KexIgAAAICez6xrbh1033336fnnn9ftt9+uYcOGxccffvhhXXnllZKkOXPmtChp2LNnjwYOHBi/
DRgwQK+++mr88erqapWWlrY4X1ZWlkpKSlRdXa3q6mqVlJTI52t56VxaWqrq6ur4/cZBhOrqag0d
OrTJaxhnWDyIAAAgAElEQVQwYIAKCwv1ne98p8vPn8z5ugpBhA5ii0cAAAAAvYJzXXProMGDB6uk
pESnnnpqfOzVV1/Vzp07NWvWLEnS7NmztW3bNm3bti0+Z/jw4Tpw4ED8dvDgQZ1zzjnxx0tKSrR3
794W5wuHw/GL94aL+Uik5XXf3r17VVJSIknavn27+vfvHw9yDBo0qMmxr7/+eh08eFDf//73VV9f
3+XnT+Z8XYUgQgexOwMAAAAAeGPVqlWSpNNPP12lpaWaMGGCzCw+nowpU6Zo/fr1Onr0aJPxNWvW
KD8/XxMmTNDEiROVl5entWvXNplTV1en9evXa8qUKZKiWQjTpk2LP37eeee1eE53nj+Z83UVgggd
1LA7A+UMAAAAAJA+gUBAFRUVWr58ubZs2aKtW7dq69atWrZsmVavXp3wW/tEysrKNGLECM2cOVO7
du1SKBTSxo0bdeONN2rRokUqLCxUUVGRbrvtNt1www3auHGjQqGQqqqqNGvWLI0aNUpXXXWVpKal
DFJ0J8KXX35Z8+fPj2+xWF1drR07dnTL+ZM5X1chiNABzjnVBetUlFdEOQMAAAAAdLPG2zU+9dRT
6tu3r8rKyjR48OD4be7cuQqHw9qwYUNSx8nNzVVlZaVGjhyp8ePHq7i4WPPnz9fixYt18803x+ct
WLBAd955p+bPn6/i4mJNnDhRo0ePVmVlpXJyclRTU6MdO3bo7LPPjj/n85//vDZv3qzdu3frtNNO
U3Fxsb7+9a9r+PDh+tnPftal50/2fF3FXCdqUzp1YjPn1bk762j9UQ34xQAd/ufDyvlZjiK3E0gA
AAAAcHwyMx2v12aZoKKiQk8++aQee+wxr5fSIa19/rHxFh0xyUTogLpgnQrzCuUzn5wc/+AAAAAA
oJcaMGCAbrrpJq+XkTbZXi/geOQP+tUvt5/MTD7zKeIiyrIsr5cFAAAAAEizhuaGvQWZCB1QF6xT
v9x+kiSf+WiuCAAAAADoFQgidEBdsE6FuYWSpCzLYptHAAAAAECvQBChA/wBfzwTIcuXxQ4NAAAA
AIBegSBCB1DOAAAAAADojQgidEDjIALlDAAAAACA3oIgQgc06YngyyITAQAAAADQKxBE6ICGLR6l
aCYCPREAAAAAAL0BQYQOaNETgXIGAAAAAOhSZWVlmjt3bpOxF198USUlJdq3b58kaeXKlfL5fKqo
qGgxb+TIkS2OOXnyZD344IPx+zU1Nbr22mtVWlqqfv366bTTTtPKlStbPG/lypUaN26cCgoKNGzY
MF133XWqqalpMe/CCy9UZWWlFi1apNzcXBUVFamwsFBFRUX6t3/7N0nSpEmT1KdPHxUVFWnAgAGa
NGmStm/fnvL74xWCCB1QF6xTYR7lDAAAAADQXZYuXaoNGzZo06ZNkqRAIKB58+bp7rvv1pAhQyRJ
5eXlGjRokMrLy1s838zaPH59fb3OO+887d69W5s3b1ZNTY3uuusu3XLLLbrnnnvi85YsWaJbb71V
S5YsUW1trd544w3t2rVLU6dOVSgUis87cuSI3nzzTZ177rmSpCuuuEK1tbXy+/2qra3V/Pnz4+u6
//77VVtbqwMHDujcc89VWVlZ596sNCKI0AFNtniknAEAAAAAutzAgQO1bNkyzZs3T0eOHNHChQt1
8sknxy+4d+3apZdeekkPPPCANmzYoP3796d0/PLycn344Ydas2aNRo0apaysLF1wwQVatmyZfvKT
n6iurk5+v18LFy7Uvffeq6lTpyorK0ujRo3SE088oaqqKj3yyCPx423atEnnnHOOcnJy2j23c05S
NKBwxRVXaMeOHSmt3UsEETqgrp5yBgAAAADobpdffrnOPPNMzZ49WytWrNDy5cvjj5WXl+uss87S
jBkzNHbsWK1evTqlY1dWVuqiiy5Sfn5+k/HLLrtMx44d0+uvv67XXntNgUBAM2bMaDKnoKBA06ZN
03PPPRcfW7dunaZPn57SGoLBoB555BFNmDAhped5KdvrBRyPmmzxSDkDAAAAgB7MXnihS47jJk3q
0PPuu+8+nXTSSVq8eLGGDRsWH3/44Yd1ww03SJLmzJmj8vJy3XTTTfHH9+zZo4EDB352fud0+PDh
eCZDdXW1vvrVr7Y4X1ZWlkpKSlRdXa1IJKKSkhL5fC2/fy8tLdVbb70Vv79u3Tr9+Mc/jt9//PHH
9Zvf/EbOOZmZ3nnnHQ0dOlSS9L3vfU/z58/XkSNH1KdPH61du7ZD740XCCJ0QJMtHilnAAAAANCD
dfTiv6sMHjxYJSUlOvXUU+Njr776qnbu3KlZs2ZJkmbPnq0f/ehH2rZtm8aNGydJGj58uD744IMm
x5o8eXL855KSEu3du7fF+cLhsKqrq1VSUqJIJBIPJjQPJOzdu1clJSWSpO3bt6t///4aPnx4/PFZ
s2Yl7NUgScuWLYs3jXzllVd0ySWX6KWXXtKXv/zlpN8Xr1DO0AGNeyJQzgAAAAAA6bVq1SpJ0umn
n67S0lJNmDBBZhYfT8aUKVO0fv16HT16tMn4mjVrlJ+frwkTJmjixInKy8trkSlQV1en9evXa8qU
KZKiWQjTpk3r0Gv5q7/6K5188sl69tlnO/T8dCOI0AGUMwAAAACANwKBgCoqKrR8+XJt2bJFW7du
1datW7Vs2TKtXr1akUhymeJlZWUaMWKEZs6cqV27dikUCmnjxo268cYbtWjRovjWjLfddptuuOEG
bdy4UaFQSFVVVZo1a5ZGjRqlq666SlLH+iE0eP3117Vjx47jIgtBIojQIU22eLQsMhEAAAAAoBs1
3q7xqaeeUt++fVVWVqbBgwfHb3PnzlU4HNaGDRuSOk5ubq4qKys1cuRIjR8/XsXFxZo/f74WL16s
m2++OT5vwYIFuvPOOzV//nwVFxdr4sSJGj16tCorK5WTk6Oamhrt2LFDZ599dtKv5x//8R9VVFSk
oqIifetb39LPf/5znX/++Sm+K96whq0l0n5iM+fVuTurcHGh9ty8R0V5RTr9P0/XQ5c+pDNKz/B6
WQAAAACQMjPT8XptlgkqKir05JNP6rHHHvN6KR3S2ucfG7fm42QipCjiIjocPKyCnAJJlDMAAAAA
QG82YMCAJrtC9HTszpCio/VHlZ+dryxfliTKGQAAAACgN2torthbkImQosb9EKRoJgJbPAIAAAAA
egOCCCnyBz/b3lGKbfFIOQMAAJ45cPSAbtrQe9JIAQDwEkGEFDXe3lGinAEAAK995P9IT737lNfL
AACgVyCIkKK6YJ0KcylnAAAgU4QiIR2tP+r1MgAA6BVorJgif4ByBgAAMkkoEtLREEEEAOio0aNH
y6zFTn7oJUaPHp3SfIIIKaKcAQCAzEImAgB0TlVVlddLwHGEcoYUtQgiUM4AAICnQpGQ6iP1BPUB
AEgDgggp8gf9TXoiUM4AAIC3QpGQJFHSAABAGhBESBHlDAAAZJZ4EIGSBgAAuh1BhBQlKmcgEwEA
AO+QiQAAQPoQREhRXbBOhXmNtng0eiIAAOAlMhEAAEgfgggp8gcTbPFIOQMAAJ5pCCIcCx3zeCUA
APR8BBFSRDkDAACZpSGYTzkDAADdjyBCihI1VqScAQAA71DOAABA+hBESJE/kGCLR8oZAADwDI0V
AQBIn04FEczsJjPbbmbbzGy1meWa2QAze9bM3jWzjWZW3FWLzQSUMwAAkFnIRAAAIH06HEQws2GS
bpB0pnNunKRsSbMl3SKp0jl3iqTnJd3aFQvNFJQzAACQWchEAAAgfTpbzpAlqcDMsiX1kbRH0qWS
VsUeXyXpm508R0ZpvsUj5QwAAHiLTAQAANKnw0EE59xHkpZI+kDR4EGNc65S0hDn3L7YnI8lDe6K
hWaK5ls8ZhnlDAAAeIlMBAAA0ie7o080s/6KZh2MllQjqcLMrpTkmk1tfj9u4cKF8Z8nTZqkSZMm
dXQ5aRGOhHW0/qj65vSNj2X5KGcAAMBLZCIAANB5L7zwgl544YV253U4iCBpiqS/OOcOSJKZ/UrS
2ZL2mdkQ59w+MxsqaX9rB2gcRDgeHKk/or45feWzzxI4KGcAAMBbZCIAANB5zb/YX7RoUcJ5nemJ
8IGkCWaWb2Ym6TxJ70h6RtLVsTnfkvR0J86RUfxBf5N+CBLlDAAAeI1MBAAA0qfDmQjOud+b2RpJ
f5BUH/vzAUmFkp4ws7mSdkn6265YaCZovjODFNvikUwEAAA8E4qE1Denr46Fjnm9FAAAerzOlDPI
ObdIUvMchwOKljr0OAmDCGzxCACAp0KRkApzCylnAAAgDTq7xWOvUhesU2Fu03IGn/koZwAAwEOh
SEiFeQQRAABIB4IIKfAH/JQzAACQYeKZCPREAACg2xFESAHlDAAAZB4yEQAASB+CCCmgnAEAgMxD
JgIAAOlDECEF/iDlDAAAZBoyEQAASB+CCCmgnAEAgMxDJgIAAOlDECEFiYIIlDMAAOAttnj03u92
/k6+RT75FvmU/dNsXfuba+Wc83pZAIBuQBAhBf6AX4V5TXsiUM4AAIC3Qi6kfrn9yETwUPWRav3N
2L9R6LaQDvzwgN76+C396Pkfeb0sAEA3IIiQgrp6yhkAAMg09ETwXigSUk5WjnzmU1FekX4757f6
1Z9+paVvLPV6aQCALkYQIQWUMwAAkHnoieC9UCSkbF92/H5J3xJtvGqjFr+yWNv3b/dwZQCArkYQ
IQWJtniknAEAAG81ZCIcCx3zeim9Vn2kvkkQQZJGFY/S+BHj9d6n73m0KgBAdyCIkAJ/IMEWj5ZF
JgIAAB4KRULqk91HklQfrvd4Nb1TKBJStmW3GB/Wb5j21u31YEUAgO5CECEFCbd49NETAQAALzWk
0vfJ6UNfBI809ERorrSwVHv9BBEAoCchiJCCVnsiUM4AAIBn4kGE7D70RfBI854IDUr7lZKJAAA9
DEGEFPiDCbZ4pJwBAABPkYngvfpwy54IUjQT4SP/Rx6sCADQXQgipIByBgAAMg+ZCN4jEwEAeg+C
CEkKRUIKhoPxxk0N2OIRAABvkYngvVAkpBwfPREAoDcgiJCkw8HD6pfbT2bWZDzL2OIRAAAvkYng
vdYyEQYXDNaBowcUioQ8WBUAoDsQREiSP9hye0eJcgYAALxGJoL3WgsiZPuyNajvIO0/vN+DVQEA
ugNBhCQl6ocgUc4AAIDXGi5g87PzyUTwSH0kcWNFKdYXgZIGAOgxCCIkqbUgAuUMAAB4q0k5A5kI
nghFQsrJatkTQWKHBgDoaQgiJMkf8Kswt7DFOOUMAAB4q3E5w7HQMa+X0yu1Vs4gsUMDAPQ0BBGS
1GYmAuUMAAB4hsaK3ms3iEA5AwD0GAQRktRmTwTKGQAA8AzlDN6rD7feE2FY4TAyEQCgByGIkKS6
YF2r5QxkIgAA4J0muzOQieCJNjMRCilnAICehCBCklrd4tHoiQAAgJdCkZCyfFlkIngo5ELK8bXS
WLEfjRUBoCchiJAkyhkAAMhMZCJ4r91MBHoiAECPQRAhSa02VqScAQAAT9ETwXtt9UQY2m+o9h/e
T+YmAPQQBBGS5A/4VZiXoCcC5QwAAHiKTATvtZWJkJuVq6K8IlUfqU7zqgAA3YEgQpLq6ilnAAAg
E5GJ4L1QJKScrMQ9EaTYDg2UNABAj0AQIUmUMwAAkJkaggj52fkEETzSViaCxA4NANCTEERIUqtb
PFLOAACApyhn8F59pPWeCBI7NABAT0IQIUn+QOItHilnAADAOxEXkcnkMx/lDB5qNxOhHzs0AEBP
QRAhSZQzAACQeRpfvPbJ6aNjoWMer6h3CkVCyvG13hOBcgYA6DkIIiSp1SCCZZGJAACAR5oEEbIp
Z/BKUpkIBBEAoEcgiJAkf7CVLR599EQAAMArzTMRKGfwRntBBHZnAICegyBCklrLRPCZj3IGAAA8
QiZCZqgPt9NYkXIGAOgxCCIkIRgOKhwJKy8rr8VjlDMAAOAdMhEyQ7KNFfmdCQCOfwQRklAXrFNh
XqHMrMVjlDMAAOAdMhEyQygSUk5W640V++T0UWlhqd4/8H4aVwUA6A4EEZLQWimDRDkDAABeSrQ7
g3PO41X1Pu1lIkjSGUPP0B8+/kOaVgQA6C4EEZLQVhCBcgYAALzT+OLVZz7lZOUoEA54vKrepz7S
dk8EKRZE2EsQAQAy1doda/Xcn59rd16ngghmVmxmFWa2w8zeNrPxZjbAzJ41s3fNbKOZFXfmHJmg
zSAC5QwAAHim+TfglDR4I6lMhFIyEQAgk7206yWte29du/M6m4mwVNI659xYSadJ+pOkWyRVOudO
kfS8pFs7eQ7P+QN+Fea23N5RopwBAAAvNb94zc/Op7miB0KRkHJ8rfdEkD4rZ6DcBAAyUygS0p8P
/rndeR0OIphZkaSvO+cekiTnXMg5VyPpUkmrYtNWSfpmR8+RKShnAAAgM7XIRMghE8ELyWQiDCsc
Jkna49+TjiUBAFJUH67XXw7+pd15nclEGCOp2sweMrO3zOwBM+sraYhzbp8kOec+ljS4E+fICJQz
AACQmRKWM5CJkHb14fZ7IpgZfREAIIPVR6JBhPYyxtr+r33bsiWdKel659z/mtndipYyND9jqytY
uHBh/OdJkyZp0qRJnVhO9/EHKWcAACATJcpEOBY65uGKeqdkMhGkaEnDlo+36BunfCMNqwIApGL3
tt06uumoFhxe0OqX6FLngggfStrtnPvf2P0nFQ0i7DOzIc65fWY2VNL+1g7QOIiQyShnAAAgM9FY
MTOEIiHlZLXdE0GKNld84u0n0rAiAECqBo0dJEWkGX8/Q+eMOkeLFi1KOK/D5QyxkoXdZvaF2NB5
kt6W9Iykq2Nj35L0dEfPkSnaK2cgEwEAAG8k7IlAOUPapZKJwA4NAJCZ6iP1ys/Ob7cvQmcyESTp
e5JWm1mOpL9I+ntJWZKeMLO5knZJ+ttOnsNzdcE6De03NOFjWUZPBAAAvEImgvcafg/yWfvfTX1+
0OdVfaRaB48e1IA+A7p7aQCAFNSH6/X5gZ/v3iCCc26rpK8meGhKZ46bafwBv04eeHLCx3zmo5wB
AACPkIngvWSaKjbwmU/jhozTlo+3aPKYyd28MgBAKkKRkE4pOaXdbR47sztDr1FX33o5g898cnLs
eQwAgAfIRPBesqUMDShpAIDMVB+p1xcGfqHdTASCCEloqyeCmclnPkoaAADwAFs8ei/ZpooNCCIA
QGaqD9friyVfJIjQFfyB1rd4lNjmEQAAryQsZyATIa1SzkQoPUN/2EsQAQAyTX2kXqP7j9aBowd0
pP5Iq/MIIiShrUwEiW0eAQDwCpkI3quPJN8TQZK+9Lkv6c8H/0ywBwAyTH24XnlZeTqh/wnaeXBn
q/MIIiSh3SCCjx0aAADwQvMgQn52PhenaZZqJkJedp5OGXSK/rj/j924KgBAquoj9crJytGJA05s
s6SBIEIS6oJ1KsyjnAEAgEzD7gzeC0VCyvEl3xNBoqQBADJRfbheOb4cnTTgJIIIneUP+ilnAAAg
AyUqZzgWOubhinqfVDMRJJorAkAmamiUe+KAE9vc5pEgQjucc5QzAACQochE8F59OLWeCBJBBADI
RA09bihn6KRgOCiTKTcrt9U5WZZFOQMAAB4gE8F7HclEOG3oadq+f7tCkVA3rQoAkKp4OcNAyhk6
xR/0t9kPQYr1RKCcAQCAtKOxovca0l9TUZRXpGGFw/Ru9bvdtCoAQKoaGiuO6T9GOw+xO0OHtVfK
IEXLGchEAAAg/RIFEchESK+OZCJIlDQAwH2/v0/L31zu9TLiGjIRCnILVJxX3Oo8ggjtSCqIYPRE
AADAC4l6IhBESK+GGtpUnTGUHRoA9G67ana1+Y1/ujVkIkjSV4Z8pdV5BBHaUResU2Eu5QzA8eyT
w5/oB8/9wOtlAOgGCcsZaKyYVh3ORCglEwHwUsRFFAgFvF5GrxYIBXSk/ojXy4hryESQpOfKnmt1
HkGEdvgDbW/vKFHOAGS6D2o+0FN/esrrZQDoBpQzeC8UCcV/6UzFGUPP0JaPt8g51w2rAtCeNe+s
0bd//W2vl9GrBcKBjOrjk2yPG4II7aCcATj+BcNBvpkEeih2Z/BeRzMRhvQbovzsfO2q2dUNqwLQ
nv2H99Pc1GPBcFBHQpmRieCcS7o8jSBCO5IJIlDOAGS2YDiYUaliALoOuzN4r/lnsGaN9B//kdxz
zyilLwLgldpAbUbV4/dGgXDmlDOEXVg+88ln7YcICCK0wx/0t9sTgXIGILNl0n+gAXQtyhm8Vx9u
+s3Vb38rff/70g9/KLVXqcAODYB3agO1qj5SrbpgnddL6bUCocwpZ2jcD6E9BBHaQTkDcPwLhoM6
FjrGv1OgB2J3Bu81/wz27ZOWL5deekm6+mqpvr715xJEALxTG6iVJFUdqvJ2Ib1YJmXLNt6ZoT0E
EdpBOQNw/AuGg5KUMZFeAF0nFAkpy7Li99mdIf2aN+Lat08aO1batEnauVO6777Wn0s5A+Adggje
C4QDGfP/LDIRupA/4FdhHuUMwPGsIYiQKZFeAF2n+bfgOb4chSNhhSIhD1fVuzT/DD7+WBo6VOrb
V/rP/5TuvFM6cCDxc8f0H6O6YJ0+OfxJmlYLoEFtoFbDC4dr50H6Inglk7Z4JBOhC1HOABz/CCIA
PVfzC1gzU5+cPux9nkaNu3lHItInn0iDB0cfO/VU6fLLpUWLEj/XzHT60NMpaQA8UBuo1bgh48hE
8FAwHMyYTNlUtusliNCOuvokggi+LMoZgAxGEAHouRJtL0hJQ3qFIiFlW/QzOHBA6tdPysv77PFF
i6TVq6V3W9lJ7oyhlDQAXqgN1Oq0IaexQ4OHMqn5d/MmuW0hiNCOpHsiUM4AZCyCCEDP1VoQgeaK
6dO4J8K+fdKQIU0f/9znojs1LFiQ+PlnlNJcEfACmQjeo5yhh/IHktji0chEADIZQQSg50oUROiT
zQ4N6dT4M0gURJCk731Pev116YMPWj7GDg2AN2oCNTpt6GkEETwUDAczJnOOxopdKKmeCD56IgCZ
jCAC0HO1Ws6QITWmvUHjFNiGporN5eVJ06dLzzzT8rEvlnxRu2t2s1c9kGa1gVqN6T9GwXBQNcdq
vF5OrxQIBxQMBzPiC2kyEboQ5QzA8Y8gAtBzUc7gvWQyESTp0kulp59uOZ6TlaMvDf6Stn68tRtX
CaCxQCgg55zys/M1ZsAYshE80tAEOBOyEchE6EL+YBJbPFLOAGQ0gghAz5WwnCGHcoZ0atzRu60g
wtSp0ubN0qFDLR+jpAFIr9pArYrzi2VmOqH/CTRX9EggHJDPfBnxOyqZCF2Icgbg+EcQAei5wi7M
7gweaxzIaa2cQYru2vDXfy1t2NDyMXZoANKrNlCrorwiSdKY/mQieCUYDqp/fv+M+B2VLR67iHNO
dcE6FeQUtDmPcgYgsxFEAHouyhm8Vx+pT6qcQWq9pIEdGoD0ahxEOKH/Cdp5kEwELwRCAQ3IH5AR
fXzY4rGLHAsdU44vp920DsoZgMwWDAdVkFNAEAHogdidwXvJZiJI0je+Ec1ECAabjn9l8Fe0o3pH
POgLoHs1DyJU1VR5u6BeKBwJK+IiKsoryojfUSln6CLJ9EOQKGcAMl0mpYoB6FrszuC9ZBsrStEA
wymnSC++2HS8ILdAJ/Q/Qe988k43rhRAg+blDGQipF8wHFRuVq765vTNiBI8Git2kWT6IUixTATK
GYCMFQgHCCIAPRTlDN4LRULKycpRJCJ98ok0ePBnj5Xt2KG3Dx9uMv/SSxNv9XjigBO1u2Z3N68W
gJQgE+FQlZxzHq+qdwmEA8rLzlOfnD4Z8TsqmQhdJNkggs98lDMAGYxMBKDnopzBew2fwaefSkVF
Um7uZ4+9ffiwfl9b22R+Q1+E5tcreVl5lDMAaVIbqFVRbjSIMKDPAPnMpwNHD3i8qt4lEAooLysv
momQAdlzZCJ0EX/Ar8JcyhmA410wHNSAPgN0JEQQAehpWi1nyIDU0N6ioRlXolKGQCSid440/W/v
2LFSXp60ZUvTublZuQQRgDRpnIkgfZaNgPRpKGfok00mQo9COQPQM5CJAPRclDN4r+EzSNRUMRCJ
6J1m5Qxm0iWXtNylIS+bTAQgXZoHEcYMGKOdh+iLkE4N5Qx9c/pmxO+oif5/2hqCCG2gnAHoGYLh
oPrn9c+IVDEAXSthOUMO5QzpFHLRvcUTZSIEndOOIy1/OU7UFyHXl6tAONCNKwXQoHkQ4fwTz9dP
X/wpJQ1p1KScIQOy5yhn6CJJZyL4yEQAMhmZCEDPxe4M3mv4DForZ6g6dkyHw01/Tzr7bGn3bumD
Dz4bo5wBSJ+aQE2TIMJ3z/quLjjpAl20+iL5A34PV9Z7ZGQ5A0GEzvMHk+yJYPREADIZQQSg56Kc
wXsNPRFaK2cYnpend5tlI2RnS9OnN81GyMvOUyBEJgKQDs0zEcxMd029S6cPOV2XPHYJgdg0aFzO
kAnvd32YnghdgnIGoGcgiAD0XK3uzhAmiJAubWUiBJ3TGf36JSxpaN4XgUwEIH1qA7Uqzi9uMmZm
un/6/RpWOEwzK2by77GbNZQzZNQWj2QidB6NFYGegSAC0HNRzuC9UCSknKzEPRECkYhO79evRXNF
STr/fGnzZqmmJnqfLR6B9GmeidAgy5ellZeuVJYvS2W/KuOL0m7UUM6QKY0VyUToIv6AX4V5bPEI
HO8IIgA9F+UM3mttd4ZQJPq70VcKClps8yhJ/fpJf/3X0vr10fu5WTRWBNKltSCCJOVk5ejxyx/X
p0c+1bxfz+M6p5s0lDP0ye6TGY0V05mJYGY+M3vLzJ6J3R9gZs+a2btmttHMits7RqainAHoGQgi
AD0XuzN4rz5Sn7CcIeic8nw+nVpQoB0JMhGk6C4NDSUNlDMA6dNWEEGKBmOfuuIp7ajeoZs33izn
XAn2P7kAACAASURBVBpX1zs03p0hE35HTfcWjzdKeqfR/VskVTrnTpH0vKRbu+Acnqirp5wB6AkI
IgA9V6vlDBnwrU5vEYqEZC5b1dXS4MGfjQciEeX5fPp8nz6qOnZMgUjLbzMvvljasEEKBqONFQki
AOnRXhBBkvrl9tO6K9fpxV0vauELC9OzsF4kGA5+1lgxA/6flbZyBjMbIWmapBWNhi+VtCr28ypJ
3+zMObxUF6xLbncGyhmAjBYMB1WQW6CIi6g+XO/1cgB0IcoZvBeKhHSkLlvFxVJOo98/A5GIcs2U
6/PphPx8vZegpKG0VDrlFOmll2LlDOzOAHS7+nC9guGg+mT3aXdu//z+2njVRj3+9uO67/f3pWF1
vUcgHIhu8dgLGyveLWmBpMb5LUOcc/skyTn3saTBiZ54PPAH/MlnIlDOAGSsYDgYTxfLhEgvgK7T
6u4MBBHSJhQJyV+T07KpYqycQVK0pCFBEEH6rKQhNytXwQiZCEB38wf9KsorkpklNX9wwWAtvXCp
ntzxZDevrHdpXM6QCc2A05KJYGbTJe1zzm2R1NbfwOO2gCalngiUMwAZK9O63wLoOuzO4L1QJKRD
B7KbNFWUpGCsnEGSTu3bN2FzRalxECGPTAQgDZIpZWhuUN9BOnTsUDetqHdq+JKrT/bxl4mQXOeE
xM6RdImZTZPUR1KhmT0s6WMzG+Kc22dmQyXtb+0ACxcujP88adIkTZo0qRPL6XpJb/FIOQOQ0RqC
CJmSLgaga0RcRBEXkc+afidCY8X0qg/X6+jhbPXv33S8oZxBksYWFOiZ6uqEzx87VsrNlT7aTWNF
IB06EkQozitWTaCmm1b0/9l788A26jvv/z0zug/ft+PEdhICJCEBAoGEkqSE9KAtW8ov9OZhu2y7
z7Pt04dne2xL92lp++tut9vzYbcXtLTb0lDaLZRCSzgSrgAJgRwOEMeOHd+SJVnnaEZzPH+MR9Yx
kkbS2FLk7+sfiDIaT2xZmu/7+36/P8sTNc5QLZtcCTGB4VeH8eVHv1zw2JJFBFmWvwDgCwBAUdQO
AP9bluWPUBT1TQD/DcC/ALgVwEO5zpEqIlQjYV7niEcSZyAQqhZJlpI7ldXyJk0gEIxBlESYaFOW
JZcUKy4tgiRA4E2w2dIfT4szOBz45xxOBIpS3AivHrGAv4CICATCYlOKiNBga0AwTkQEI+GE+RGP
5uoZ8bjhyg247dLbko995Stf0TzWiOkMmfwzgOspinoTwHXzfz4vIXEGAuH8R3UhUBRFRAQCocbI
NY6KFCsuLYIkQEqYYbWmP54aZ1jncGAkHoeH1xYJbrwROHzICk4kcQYCYbEJxoPFOxFsihOBjHo0
jtTOrmq4P13qEY+QZfmgLMvvmf9/vyzLu2VZXifL8h5Zls/L8IwkS4glYnCanQWPZWjiRCAQqhVV
RABQNW/SBALBGAqJCORmd2lQnQiZIgInSbDOu0QcDIO/7ujAV0dHNc+xbRvgnbYgFCVOBAJhsQlx
IdTb6ot6joWxwESbyH2UgaTGGaqhxychLdGIx1omlojBZrKBoZmCxzIU6UQgEKoVIiIQCLVLLhHB
RJtAUzQSEhnpuhQkpETOOIOFXrjVvHPVKtw/M6M56tFkAtZfaMVcmIgIBMJiE+JCqLMU50QASC+C
0STjDPPFipUWvhPi0o14rFn0RhkAEmcgEKoZ1SoGEBGBQKg18lkvSaRh6cjlREiNMwBAq8WC/93T
gy+ePat5nnqnBfEEiTMQCItNKZ0IAOlFMBr1HtXMmEFRVMWFb+JEMIBiRAQSZyAQqhfiRCAQapd8
IoLdRCY0LBWCJCDBZXcipMYZVP7nihV4IRjES6FQ1nnqXGQ6A4GwFJQqIqi9CARjUOMMgPKZVelI
A3EiGEBRIgKJMxAIVUuaiGAiIgKBUEsUciJU+oZsuSBIAgSucJwBULoR7urrw2eGhrKsu/VOK3hS
rEggLDoliwjWeszFz8u6u6qEE5U4A1AdG13EiWAAYS4Mt6XweEeAxBkIhGqGOBEIhNqFxBmqg4SY
QEJHnEHl1o4O+BMJ/NHnS3u83mVBQiJOBAJhsQnxZTgRSJzBMFIjt9Uw5pE4EQyAxBkIhNogU0Qg
O5MEQu2QN85gJnGGpUKQBPBxDSeCRpwBABiKwjdXr8bnhochSAtOznqXBYJMRAQCYbEpuRPB2kDi
DAbCCVxVbXQt+YjHWoTEGQiE2oA4EQiE2qVgnKHCuzqLQaXbu7UQJAGJuEYngkacQeUdTU3otFhw
7/R08rGmeisEkDgDgbDYlNWJQJwIhpEZZ6j0RheJMxhAmA/DbdUXZ2BohsQZCIQqhYgIBELtstzi
DLFEDP3f76/0ZWShFCvqjzMAAEVR+GZ/P74yMoKoqNxDNdZZIII4EQhLiCwDe/YA4vK6jyedCNVB
WpxhfsxjJSFxBgOI8BG4zEWMeCRxBgKhKiEiAoFQuyy36QwBNoCx4FilLyMNWZYhyiK4OKM7zqCy
pa4OOxoa8O0x5d/UREQEwlLDccD+/YDXW+krWVLIdIbqoNriDMSJYADFxhmIE4FAqE6yRASBiAgE
Qq2w3KYzRPgIRFlEQqzsLPFUBEkAQzHgOaqoOIPK1/v68N3xcczwPJoaLJAZriojG+czZwNnK30J
1Qs7/x4xOVnZ61hiSu5EsJFOBCNJjTOQYsUaodhiRdKJQCBUJ8SJQCDULuoCVotajDOE+TAAVNW/
SxVyOA7ZIkKeOINKn92Oj3Z04K6RETQ1MIDEQJCERbzi5celP7oUATZQ6cuoTlQRYWqqstexxATj
wZLjDKQTwThS4wzVcI9KnAgGEOb0dyKQEY8EQvVCRAQCoXZZbtMZInwEACq+W5WKIAkwM2bE48iK
M/AF4gwqd65ahX0eDzzWGCBawYsk0mAUoiQiyAWTrx1CBstQRBAlEazA6t4sTaXeRjoRjCQ1zmA3
2SvuniNOBAOIJIqMM5BOBAKhKiEiAoFQu+SNMzC1N50hzClOhErfaKaS14kgywWdCADQbDbjMytX
4pv+YUCwIC6QCQ1GobpXooloha+kSlmGIkKYD8NtcYPSIfBlUm8lnQhGkjmdodL3qGTEowGQOAOB
UBtUW2kNgUAwjuU2naEa4wwJKZE3zlCoE0HlU93dOBINA861CMeIE8EoVOt5lCcigibLUEQIcSHd
butMGmwNJM5gICTOUIOEOUWl0wOJMxAI1UuqE8Furvz4HAKBYBwkzlB51J9BOXEGALAzDLa43aDs
3fAFiRNBLyNzI/jqwa/m/PsQFwKAqvzsC3Eh/Ofx/6zsRcTn3yOWkYjgZ/1osjeV9FwyncFYOCGl
WNFEihVrgqKnM5A4A4FQlZA4A4FQuyy36QxqnKGaxBFBEmCmzWXFGVTsNA2KdiAQJE4EvRyZPII/
nv5jzr9XRYRqjDOcmDmBbz7/zcpeBMsCTueyExGa7c0lPbfOWocQFyIObIPgxOpyyxInggGQOAOB
UBsQEYFAqF2Wa5yhmsSRVCdCOXEGAHDQNGiTA/4QERH0MhYcy7t7Wc1OhGgiWvGdV7As0N+/rEQE
X8xXshPBRJtgN9lJUadBpMYZ7GZSrFgTFO1EIHEGAqEqISICgVC75I0zmEicYSlIiAudCOXEGQDA
wTCgGQcCIRJn0MtYaCzvwiPpRKjCToQIH6n8ZzLLAn19wPQ0IMuVvZYlopw4A0B6EYxCluXse1Sh
cr8PsixDlEVSrFguYb7IEY8kzkAgVCVpKq9J6USQl8mNAoFQ6xSMM1TRYtsIqjXOUO50BhXFiWBD
MEKcCHoZD43nfZ2r+fWKL9Y1iPCRiu+8gmWBxkbA4QD8/speyxLhY30lxxkA0otgFLzIw0ybk1My
HGZHRX8f1JJcvVM7iIiQAxJnIBBqg1SV18yYwVAMmUFOINQIyy3OEElEQIGq/MIrBUESYGbMxsQZ
GAY0YyciQhHodiJUYSdClK+SOIPdDnR2LptIQ7lOhHprPXEiGAAv8slSRWBho6tSFDPeESAigiai
JIJNsHCYHbqOJ3EGAqF6SRURABJpIBBqieU2nSHMhdHsaK6af1dUFHHnZBgMZQJNA6aMH0XRcQaa
Bm22IRghcQa96OlEoEBVbZwhLsQr6w5MFREmJyt3HUuIj/Wh2VG6E6HB1oC5+JyBV7Q84UQu6ZQF
Kn9/WkwfAkBEBE2iiSicFidoSt+3h8QZCITqhYgIBELtki+/WZNxBj6MVkdr1r/rLMviXNw4YcHr
1Xfc6VgMT4QFMDBluRCAEuIMDAOasSIUI04EPQiSAE/UA07gci7EQ1wIrc7WqvzcUzs+KiqKESdC
0ZA4gzFwApd2f2o3V3bEYzGTGQAiImhSTJQBUOIMxIlAIFQnREQgEGqXZRdn4CNodbZm2df/Y3IS
9xq0ADpzBrj2Wn3Hno3HEZcBwJxVqgiUNp2BMlkRjhEngh6mwlNodbbCzJjBidrfsyAXRKerszrj
DPPXVFGxj2WVRtCurmUlIpTViUDiDIaQGWeo9P0pcSIYQNEiAkU6EQiEaoWXiIhAINQqy206Q5hT
nAiZ/66IKCImGXMfEggAQZ3rg2F2fvFH2TWdCKVMZ6DMFkRY4kTQw1hoDD11PbCbco+GC3EhdLg6
qvJzLzltpJIdH8vQiVDOiEdgXkQgToSyyYwz5Ps9XgqIE8EAihURSJyBQKheiBOBQKhdCk5nqKIC
QiOI8BG0Oduydm4jooiYaMx9SDgMRHVuWp9VIxS0w5g4A02DYiyIxomIoIfx0DhW1K3Ia4MOcSF0
uqvTiVAVI0vj8WUnIhgx4pF0IpRPZpyh0venxIlgAGEuDLdF33hHgMQZCIRqhogIBELtstziDGon
wmI6ESIRIKbzLVIVESTKnjPOUIyIYGcYwGRBJE7iDHoYC+pzInS6Oqvyc484EZYeWZaN6UQgcYay
qbo4w/yIR70QEUEDEmcgEGoHLRGh1srWCITlynKaziDLMiJ8BC2OlqxFl9FOBEEAEonCx56Nx2GC
DInWjjNwkgRLkdMZwJjBcsSJoIex0Bh66nvyOhGC8flOhCqczqC6IyoqcCwzESHCR2BhLGmL12Ih
cQZjyIozVLhYUR3XqxciImhA4gwEQu1AnAgEQu1SMM5QQ4IhK7CwMla4LC7tOINBToRwWPlvoUiD
JMsYicfRbRIhwabdiVDCdAaJMSHGExFBD8k4QyEnQhXHGUy0qfLFiqkiQiXHTS4B5Y53BMh0BqPI
ms4w/3tcqZGnJM5gAGGexBkIhFqBiAgEQu2ynOIMYS4Ml8Wl6bAw0okQURzmBSMN0zyPOoZBHSVB
1ogzSLIMUZZhKtKJIDMmxBMkzqCHZLFioU6EKo4zaDlrlhRVRHC7AYpaUNFqlHKjDADpRDAKTuTS
HCEMzeSdtLLYkGJFAyBxBgKhdqi24hoCgWAcy2k6Q5gPw211azosFsOJUEhEOBuPo89mgxUSRDrb
iaBGGagipzOIlAnxBHEi6GEsOB9nyOFEECURrMCizdlWnXEGPqpZFLqkqCICsCwiDb6Yr6zxjgAZ
8WgUvMinxRmAyt6jEieCARQtItAMiTMQCFUKcSIQCLVLPhHBarIiLsQrZg01mggfgdvi1lwwFuNE
mOF5jMZziyuqE6FQnOEsy6LfboeZEiHCmiUiFBtlABQngkgxiCf4WneVl01CTGA2NosOV0dOJ4Lq
rHVanFX5uRfhI2h1tFaHEwFYFiKCEU4EEmcwhsxNLkARvysmIhAnQvmolkG90BRN4gwEQpVCRAQC
oXbJJyLQFA0LY6mYNdRo1HsTrZhGMU6Ef5+YwLfHxnJ/nSKdCBaIkChrVpyh2MkMgOJESFA0YOLA
1caPbdGYDE+i3dUOE23K6UQIxoOos9bBaXZWbSdCi6OlepwIXV1ERNABcSIYQ2acAZgv/66QqEac
CAYQ4SNwW4voRCBxBgKhaiEiAoFQu+QTEYDaijSo9yaZu86iLIOVJN1OhCGWRTTPsXpFhGFVRJAF
iFS2E6HYyQwAYKEoSKBgcSQQChX11GWH2ocA5G51D3Eh1Fnr4DA7EOWjVeXKESURnMih2d5ceSeC
qoB1dgKTk5W7liXAx5YfZ3Bb3YgmosSFXSZVF2cgIx7LJ5IgcQYCoVbQEhGqMRtKIBCKp5CIYDPZ
KrtAMZAwP1+smCGMROYFAb1OhOF4HNE8x0YiAE3rizP02e0wQYRIWbKcCKXEGSiKggUSzG6hbBHh
mdFn8NWDXy3vJFWMOpkBAGyM9us8xIVQb6uHmTGDpmgkJB1zO5eIWCIGu8le+bHLJM5QNDRFw21x
I8QRpa8cNOMMFRzzSEY8GkBJIx5JnIFAqEoyRYRGWyP8cX8Fr4hAIBiFHhGhWp0IgiQUdXyYU/Lt
mcJIRBTRaDLldRekoseJ0NKiP85glhMQabOmE6FYEQEALJQMxiEhWKZb+qmzT+HQ+KHyTlLFjAXT
nQhar3PViQBUn4Cu3mvbzbnHUy4J8fiyEhGMGPEIkF4EI+BELsuJ0NvQi3994V8RYANLfj0kzmAA
6ge1XhiKOBEIhGqFF/m0zFm7qx0zkZkKXhGBQDCKgnGGHIurauCS/7gEU2H9C5ZksWLGTlVEFNFi
NiMhyxAKuBEiggBPIpFXRIhEgPb2/CJCQpIwzfPosVrByAJEymJInAEArBTAOKSynQgD3oGaXuSM
hcaSTgS7SXv3MsgFkyJCtZUrRhPRpLOGOBGWDiOcCADpRTCCzPtTAPj5jT/HCvcKXPLDS/DE8BNL
ej2kWNEASpnOQDoRCITqJNOJ0O5sx0yUiAgEQi2gK85QyQVKHibDk5gIT+g+Xo0zZLorIqIIN8PA
QdNgC4gIw/NTGQo5Edrb88cZznEcuqxWmGkaJjkBgTIbEmcAFBGBsstliwgnPSdrepb9eGgcPfUp
nQg54gx1lnkRocrKFSN8BE6LE3azvjZ6T9SDd/zqHcbeb0sSwPMLnQhNTcBc7b5mAGXEoxEiQoOt
oaZ/v5YCTsh2ItjNdnzvHd/Dve+5F7c9dBs+/edPL5lThzgRDKBScYYXXyz7FAQCIYNMEaHN2Uac
CARCjXC+xhlkWUaEj2A2Nqv7OclixYwm/ogowsUwcDBMwV6EIZbFKppGTJ3jqIEqIuRzIgyzLPrm
F14mg+MMNgqgreWJCJzAYdA3WNM7pWnFijl289VOBKD6SoWTcYYckyUyOTZ9DH8+82ccHDmY9vjt
D9+O+0/cX9pFxOOA1Qqojpm6OtR6o6ef9ZddrAiQOIMRcGJ2J4LK9auvx7FPHMN0ZBqX//hyHJ06
uujXQ5wIBhDmw0VPZyg3zhCPA1dfDSSqp/OGQKgJMkWEVmcr/Ky/6DwygZCP4cBwpS9hWXK+TmeI
C3GIsghv1Kv7OZkjHtWm/aSIQNMFJzQMx+O4ZGoKUXUEgwZ64gxqHwIA0DIHkTYZFmew0RRgLW8t
96bvTXS4Omp6p3QsmBJnyOdESIkzVFMnQpRX4gx6ixVP+07DbXHjp6/+NPnYWHAMP3vtZxjwDpR2
EalRBmBZiAg+1hgnAokzlI9WnCGVJnsT7n/f/bjz2jvx9v98O77x7DcWNT5PnAgGUIk4Q2C+P6NQ
GzKBQNCPLMuKsprypmiiTWi0Nxa1A0gg5IMXeVzwgwuKyrcTjOF8nc4Q4RUnQDHvQ2Fe6WtiaAYm
2gRe5JVzFelE2DgygmgOh4AsK/chbW3570dSRQRG4iHSpqw4Q6lOBDtNQzbLZRUrDngGsHXFVsSF
eE0KxrzIw8/60eHqAJDfiZBWrFhtcQazU3cb/WnfaXxq66fwp9N/gp9VypF/8PIP0OpsLSoWlMYy
ExEkWUKADRgnIiwDJ8LBkYN4YOCBRTm3VpwhE4qi8MGNH8Qrf/sK9g/vx46f78C54LlFuR4y4tEA
KhFnUEWEPA5DAmHZI8syvvn8N3XPulYFBCpjN6zd2Q5P1LMYl0hYhvhiPoiyuOQlSITzN86gigje
mH4nghpnANLHgBXlRGBZbDx+HFGT9vcsFlPi4W53AScCy6J/fvFFSzxEmslyIpTaiWCnKcBMlbWW
G/AOYGPbRtRZ686b3VJJlnBi5oSuYydCE+h0d4KhGQC5x8KlFSuaq6tYsdg4w6B/EFu7t+Kda9+J
Xx3/FcJcGPe8eg/+z47/g4mQQSKC1ar0JHBcaeercsJcGA6zoyjLei4a7Y1JMaeW+dLTX8JjZx5b
lHPnizNk0lPfgyc++gQ2tG3Atw99e1GuR5AE4kQoB0ESwIs87CZ74YPnMSLOQEQEAqEws7FZfO6J
z+Hw5GFdx2dGGVTIhAaCkfhYHwDg8eHHK3wly4/zdTpDUkQoJs4wX6wIpIsjRTsRXnwRMZNJU4wN
hwGXC3A49DsRaImDyGSLCKU6EZwMA6lMEeGk5yTWt64/r3Lbdz51J7bfu13XsamTGQDkXIiHuBDq
rQudCFUVZ1CnMxThRLig+QL8zWV/g58c/QnuefUeXNd3Ha5ZeQ3GQ+OlXUSmiEBRihshT9znfMao
8Y4A0OnqxHRk2pBzVSvHpo/h2XPPIswtzuuhUJwhE5qicUXXFQhxi+OWSYhL1IlAUdQKiqKeoihq
gKKoExRFfWr+8UaKoh6nKOpNiqL+QlFUfalfoxKoymjmzmU+jIwz1Oj7FoFgCFMRxS7+4KkHdR2f
U0QgExoIBuKL+dBT14P9Q/vJpJ4lppCI4DBVl4VbJRlnYIssVpwfP526aNTrRBAkCeficayenIRZ
FBHXEBzCYcWF4HQWKFbMEBEkhtGMM5TSieCgGUhM+U6E9W3rNRvk73vtvqpzJ/z06E/xwMADiCai
ujalxkPjyVJFILcTIa0ToQqdCE6zU5cTgRd5jIfG0dfYh529OxFNRPHlA1/GHVffgW53d3lxhswX
bg1HGowa7wgAXe4uTIYnDTlXtXL34buxrWcbwvziLM44sXCcIRO31Z33esJcGF9/5uslXU9m/LcQ
5TgRBAB3yLK8HsDVAP4HRVEXAvg8gCdkWV4H4CkA/1jG11hyio0yAPNOBBJnIBAWncnwJLrcXXjw
1IO6Ig15RQTiRCAYhI/14bLOy1BnrdNtRyYYQyERwW11L9ouUjlE+AhsJltJxYpAusNCrxNhnOPQ
JkmwXXghnPG45pjHSGTBiZBLRIgIAqKiiHbL/HurGIdkog2LMzgYBhLDlNyJwCZYjIfGsbZprWb5
29ee/RoOjh7M8eylZ//QfnzxqS/i0Q89CpfFpWvBMhYcSxcR8jgR0ooVq0hQS8YZdDgRhgPD6Knv
gYWxgKZo3H7Z7djQtgFXrbgKDbYGJMREab/nmU4EoKZFBKPGOwK1LyIE2AB+e+q3uOOqOxbtM4QT
9McZVFwWV1KE1uKM/wz+6cA/lSSULpkTQZblaVmWX5v//wiA1wGsAHAjgPvmD7sPwF+V+jUqQSki
Ak3Racrxnl/uwdnA2aLOQUQEAqEwU+Ep7O7fDYqi8Or0qwWPzxtnIE4EgkH4Yj4025txff/1eHyI
RBqWkkIiQp21btGsn+UQ5sPobegtvlhxvhPBZrIV3YkwFI9jdTgMbN2qiAh5nAj54gxn43H02mwL
jk2Jg2SiYbGlC7ulxxlMkBi65HXc67OvY03TGpgZs6YTwRfz4fjM8dJOvgh8+i+fxs9u/BkuaL5A
d4dDVpwhVydCPJhWrFhtTgS9nQhqlEHls9s/i8c+pOTUKYrCiroVpbkRlpmIYNR4R6D2RYSfv/Zz
3LD2BqxpWrNoToRi4wwA4LbkF8b9rB+SLOG5c88VfT1L6URIQlFUL4DNAF4E0C7L8gygCA0A2oz4
GktFmAsn7YJ6oSkaMuTkzuixmWM4NnOsqHMQEYFAKMxkeBLd7m7cfNHN+N2p3xU8PpfKS+IMBCNR
c6Z7Vu/B/uH9lb6cZcX5KiJE+Ah6G3qLL1bMF2co4EQYZlmsnpkBtmyBMxZDlOezv8a8EyFfnOFs
PI7+FAu4JCVAiTJgTf/apcYZXCYTRJopeR034BnA+tb1ALJn2QuSgEA8UPQ92mIyGZ7E1u6tAJTG
ez2v1/HQOHrqF5wIuaaQhLgQ6m1Kqthprr4Rj06LMp2hkLhx2ncaFzQtiAg0RaeNYu+u6y6tXDEe
X1YiglHjHQGgw9UBT9SzqCMHK4UkS7j78N34+yv/flHdbKXEGQq5lQJxZUH59MjTRV9PsU4E/XMc
ckBRlAvAgwD+pyzLEYqiMj3GOT3HX/7yl5P/v3PnTuzcubPcyymbUpwIFEWBpmhIsgQaNPysH2f8
Z4o6BxERCITCTIYncWHLhbhqxVX40O8/hK+99Wt5+0tIsSJhKfDFfGh1tmJX3y58+L8+DDbBwm7W
X85LKJ2CcQZL/vxopYjwEaysW4n9Q/sL/htUUuMMqcWKYUHQ50RgWfQPDwPveQ+cp04hFg4rC6bU
r5HiRMglIgyzLPpSFl6CLIBOyJCcIgAm+XipcQYXY4ZIm0oXEbwD2NC2AQDQYE13IgRY5WarWpwI
vMgjzIXRaG8EoIheeoogx0IacQYdIx6LEa0Wm0gixYlQIM5w2ncamzs25/x74kTQh5FOBDNjRqO9
Ed6YNzlqtFb44ZEfos3Zhq3dWzEbm80bHyiHUuIMbqs77/X4WT8ubr24NBFhfsTjgQMHcODAgYLH
lyUiUBRlgiIg/FKW5YfmH56hKKpdluUZiqI6AOSco5YqIlQLpYgIwMKYx2giCkESMOgbLOr5gQDQ
0ECKFQmEfExFpvDWvrdiS9cWcCKHk56T2Ni+MefxuUSENmcbcSIQDMPH+nBhy4Wos9Zhc8dmPHvu
WexZvafSlwVAGYsqSIIhI72qkfPZiVBvq0eDrQF+1o82Z37TJicoI+dU66vmiMdCToR4HDedPAl8
5jNwHj+OaCgEdHenHaM3ztCX4kRIiAlQvAy5KV3AKDXO4DZZIFGliwgnPSfxsUs/BmDeiZASasv0
GgAAIABJREFUD5iNzWJ142qMBccQS8TgMDtK+yIGMRubRbOjGTSlfJ/qbfqcCGPB7DhD5hQSURLB
CiycZicApROhGuMMDrOjYJxh0D+Ivev35vz7bnd3aRMalpmI4Iv50NvQa9j51EhDLYkIr02/hi8f
+DJe+NgLoCiqYJFhOSxWnGFP/x7c8+o9CLCBpECpB3XEY+bG/le+8hXN48uNM9wL4JQsy99Leexh
AP9t/v9vBfBQ5pOqmVJFBHXMoy+mjPoa9BcvIvT0ECcCgZCPyfAkOt2doCgKN190c8EpDaRYkbAU
pI7N2tO/p6p6EZ4fex5/te+8qiYqCl3FilXqRHBZXGh1turqRci8N7GbNIoVCzkRQiH0+3xAczOc
koSoxg2H3jhDqoggSAIoXoZgzhYRSokzuM2KEyEYBHT052ahTmYAkNWJ4GN96HB1YF3LOpz0nCz+
5AbjjXrTBCQ9nQhxIY4gF0S7qz35mFavQIgLwW1xJ916DnN1TSqJ8lFlOsO8AJKvLDmzEyGTbneJ
cYZlJiL4437DRjwCypjHWupFCHNh7P3tXnz/Hd/HmqY1AAArY4UkS+DF7PhXuZQaZ8jnRAiwAXS4
OnDViqvwzOgzRZ17KUc8bgfwIQBvpSjqVYqijlIU9XYA/wLgeoqi3gRwHYB/LvVrVIIwX3wnArAw
5tHP+tFgaygpzrByJRERCIR8TEWm0OXuAgDcfPHNePD10kSENmcbvDEvGcdXBPccvacms49GoBYr
AsBbVr0Fh8YPVfiKFjgbOIup8FSlL2PRKMqJ8PjjQCKxRFeWHzWa0OJo0TWhIbVUEUjPwKsigjOP
E0GWZQxxHFa7lXM4ZBlRDauBnjiDlogADlkiQqlxhjqTFRJlBk0rkfViiPARzERmsLpxNQClYyA1
HqDu/G9q31QVkQZvzItWR2vyz3o6ESZCE+hydyXdC4B2sWJqlAGozhGPLosLNEXDzJjBiVzO4wJs
IM15kcmKuhUYDxfnRHh08FGwocDyEhEMHPEI1Fa5oizL+MSfPoGdvTvx/g3vTz5OUVTB3f9SKSXO
4DA7wImc8r6rgZ/1o9HeiF29u4qONCxZsaIsy8/LsszIsrxZluVLZVm+TJblP8uy7Jdlebcsy+tk
Wd4jy/Jc4bNVD6mZw2JQ4wx+1o/NHZvhiXoK2rNS0eVEePhhYLI2flkJhGKRZAnTkWl0ujoBAFtX
bEUwHsQp76mcz8klIlhNVrgsrmQ+lpAf9cO1Vm4WjCbVidDqaK2q19VMdEZXxvp8RbeIIIrA+94H
PP/8El5dbtSSxFZH6U4EzThDDidCQBAAUUTTqlUAACeAqIZKkDniMXNzWJZlnM3sRJAEgKM0nQil
xRnMAG1FU4uAWf3DKwAAp7ynsK5lHRha6WbIciLMC36XtF+CY9OVL1f0Rr1odS6ICHo6ETInMwDK
bmlCTKQJvamlisD8iMcqKlZMfU3nm9Aw6BvEmqY1aaJJJsUWK4qSiI/+10cx4RlcViKCkSMeAUVE
qBWR+t5X78XxmeP47tu/m/V3ekevFkspcQaKouA0O3O6EfxxRSja1VeiiLAUToRaJfVmsBiScQbW
hzZnG3obejEUGNL9fFVEyNuJ8G//Bjx0XqVDCATD8MV8cFlcyTdcmqLxvovel3dKQy4RASATGooh
lohBkARMR6YrfSlVSaoTod5WnzVSrpLMRGZKmhd9vqCrWJELA6dPKyvkl19ewqvLjVoq1+Jo0VV2
lzk5KrVYMTXOEM0hIgyxLPrDYVAXXggAcNI0omz2ok11IjAMYDYDXMbmsDeRgJWmUW9a+J4npATk
OJBgjIkzOGgatMmOjm4eU0WuT1InMwDZ0xlmY7NocbQoTgTP+elEGA+Np5UqAsrCIvU1AWQ7ERZj
xOOHT53C67nKM6B8duQSB6KJ6IKIkGNEJVA4ygAUX6z40sRL8LE+8JHQshIRjCxWBGrHiXDScxKf
f/LzeODmBzR7UhZrQkMpcQb1enKJCAE2gEZbIy7vvBwjcyPJmL0eEmIFRjzWEqk3g8WQGmdosjVh
bfPaoiINupwIXi9wrPLKOYFQCVKjDCqFIg15RQQyoUE36sggIiJkI8syAvFAcncn0z5daaaiHszB
njdvfD4jSAIYisn590knwpEjStC/WkQEtRNBpxMhM85gNxc34nE4Hsfq6WlAFREYRnPEoyoiANqR
hswoA6D8DGSWAs8YE2dwMAwoxo7WTq54ESFlMgOg3YmgOhGOzxyv+O+FN5ouIujpRBgLjmWJCED2
QlwrzmB0J8IzwSAeymEXOTxxGOv/fT3+6el/0vz7CB+B06KUPuZzIugREdqd7fDFfEiI+uJKfzr9
JwCAEF0+IoIkS5gIT2TdR5VDl7sLk5HzW0SI8lHc8uAt+Nb138JFrRdpHrNYU35KiTMkryeHqKFG
VsyMGdt7tuPg6EHd5yVOhDJRbSDFwlBMMs7QZG/CmsY1uic0cJwS02xvJyICgZCLyfBkMsqgsq1n
GzxRD077Tms+J59VjDgR9KPa86citWFbNJIgF4TD7Eh+8LosLsSFuO6b2cXmlOiCuO6zBUeona/o
KVYMcSHIhw8DH/kI8NJLS3h1uVFFBL2dCLniDJIsIyZJcBQoVhxiWfQPDS2ICCaTpoigxhkARXPJ
3GTOjDIAys9AYmlwtDFxBgdNA7QNrR3FOxFOek6mOxGs2dMZWhwtaHW2wm6yYyw0VvT1GYk3lh5n
qLfVI8TnX8BqxRmA7IV4kAtmORFKjTOIkpgV05JlGd5EAo8Hsh//vy//X7zz1+/EzRfdjGfOaZe7
pcUZ8jgRBv2DWNu0Nu/1MTSDNmeb7s+oRwYfwTUrr4EYjQAZohjc7qoUEQRJyJmD18Po3Cia7E1p
YmS51IIT4ZOPfRJburbg1s235jxmsZwIpcQZgPzliqmbGrt6d+Hps/ojDQkxoWvcsAoRETLwxUqL
M9AUnZzO0OxoxtrmtbonNAQCQGOj8r6VU0QQReXAkyeBPCOcCIRaZSqc7URgaAY3XXhTzkhDwTgD
cSLoQt3JI06EbDLdaxRFVdVYQY9AAdbWmo00FBIRLIwFJtoE6chhYO9eZWu9CrqF1P6lVmcrZlkd
ToQccYaYKMJO02AoKq8TYSgcxuqhIaC3FwDgNJsR0yiZLMmJIAqQozR4Ov1rc5IEawlxBjtNA4wV
Le0lxBlSJjMAOZwI8/d4qhuhkmTGGfQ4EcZD4+ip1+dEqLemdyKUGmf43eu/wxU/uSItLhGZF6wO
h8PJGE2IC+GWB2/BPa/eg0MfO4S7dt2Fk56TWV9XlETwIg+7SRGkynUiAPORBh29CGPBMUyEJvCO
Ne+AFIueN06Erz3zNXzj2W+U/PxT3lO4qEV7p71UzncR4ZfHfolD44dw9zvvznuc25I7PlAO5cQZ
cjkj1GJFAEX3IqgjHvVCRIQMVKtbsSTjDPNOhrVN+uMMqSJCzk4Evx+orweamoDh4aKvj0A435kM
T2ra8PJFGgrGGYgTQRckzpAbrR6daoo0zMEKWFoQqKKeBiPhRK6g/bLR5AZ1/Dhw+eXAlVcChw8v
0dXlJsJH4La6S3cizMcZIqIIN6PEOfI5EYbn5rAaUMoOADitVs3+hFQnQi4RoT9DRODFBCiORjRj
egsny7CUGGcAbUVTa3FxhmA8iAAbQG9Db/IxtRNBjS2oTgSgSkSEjGJFPZ0IY6EccYaMhbhWJ0Kp
cYaZyAyGAkP4txf+LfmYJ5FAVzCIy2gaz8zN4dj0MVz+48vRZG/CoY8dwpqmNbCb7djQtgGHJ9J/
56KJKBxmR3L8pN1s1xQ4ZFnGm743dYkI3XXdGA8VntDw6OCjePuat6PF0QIpfv6MeHxx/EVd/75c
vD77Oi5uvdjAK1ImXflivrIcEpXizdk3ccfjd2DfzfsKFuovxqhgWZZLjjPkciIkxATiQjwpOG/u
2Izx0Dg8UY+u85I4Q5mU6kRIjTM020tzIrhceZwIXi/Q2gps2lSTkYYjoRB+UgW7Q4TqRSvOACgj
9caCYxgOZItrvMjDQhMnQrkE2AAcZgcRETTQ6tGpt9VXxc6/JEuIMW6ANmEkWvnrMZqRuRFYGWtB
4X/znA1CZ4eyOLjySkN6EcJcuCy3SUmdCBpOBLUPAUB+JwLHob8+ZVfaZkNUow8g1YmgFWcY1ogz
8IIARsgudeTLiDPItBVNrTymi3jLGfAO4KLWi9Ja/C2MBWbanFygpv6+bmrfhGMzlb2f0nQiFJrO
EMwRZ9DRiVCqE8HP+vGRSz6C77z4HZwLngMAeHgebTMzuP611/BvbzyD3b/cjbt23oUfvuuHsJkW
hKbtPdvxwtgLaefLFMUcZodmnMHHKqVwqvCTj253t65yxT8N/gk3rL0BjbZGUGxlRIRiuzhkWcbR
qaO6XEu5eN37uuFOBBNtQouj5by7l2ITLPY+uBdff+vXcUn7JQWPX4wRj6Isgqbo5CSZYsh1PYF4
AA22hqQ4Z6JNeMuqt+DAyAFd5yXFimVSanNpapyhyd6EnroeeKNeXW/YRYsIxyvfKGw0zwSDeNin
v0GUsPzQKlYElDfJ9174Xs1IQz4nQpuzjTgRdBKIB+C4+IuYjBTeMV1uaDkRGmwNVeFE8LN+0LZO
QJZxljU+z1lpDowcwM7enckbplxcOUUhcsm6+T8YIyJ8+9C38fknPl/y89M6EfROZ0gtVpzvREgT
EXI4EThJgoei0LNiYeHpsNtzigiFnAiZcYaEKMDEM0l7e+rXLSXO4GAYyLQZDc3FxRkyJzOopE5o
qHongi2/E4FNsIjwkbTnqGR1IsTTOxEsjKXkXL2P9eHyzsvxySs/iX94/B8AKE6EttlZbL/3R3g+
HMeztz2LD2z8QNZzt/dsx/Nj6aNVo3w0u+NDI86gRhkK/Y4DSpyh0E49m2BxYOQA3rbmbWi0N4KK
c0suIjw6+Cj2Pri3qOdMhCfgjXl1CY65ODV7KmdxYDmcj5GG/zjyH+hv7Mftl92u6/jFKFbkBK6k
PoR816P28qVSTC8CcSKUAZtgIcqi5niPQqRNZ7A3gaEZ9DX2YchfeMxjUSLCJZfUpBNhkuPg1Sh5
IiwP9Kjyk+FJdLqznQgA8L6L36cZaSgUZ9Br8VruzMaDmG3YhkmuNsv5ykHTiWCtDifCTGQGlK0d
9YIP51hjR7tVAwdGDmDHqh0Fj9s8LsC/vl/5wxVXKHGGMruFJsOT2D+8v6TnyrKsNNObnUonQmy2
4HugZpwhU0TI4UQYicfREw7DtG5d8jGny4WoxsIsEsndiSDKMsY5Dqs0RASzaMpyIpQaZzBTFGRQ
sDfEFRHB7we+UTgLnjmZQUXtRRAlEXPxuWReeF3LOgz5hyDJlemZUq8n9f2jUCfCeGgc3XXdaW4L
FZvJlu5E4NM7EdT58qWUK6r3tp/d/lkcmTyC9+57Lz63/y40+2fRxs/CYWmHy9Wr+dztKxUnQur3
WX39q+QqVtTbhwDocyIcGDmATR2b0GRvUu7VtUQEpxNgWaWLTCd/fPOPeOT0I7qOHZ0bxZPDTxb1
ujs6dRQr6laULCLIsozXvcbHGYDzU0Q44z+D3X27dYlTgBIfMNqJUGofgno9WnGGABvQFBEOjB7Q
dV7iRCgDtQ9B74sqFTXOkLorpbcXQRURrFblPUuj66jm4wyTPA+v5j+cUOucDZzF2h9kNy9/5cBX
8J1D30n+OZcTAVDeJM/4zyRtlioFixWJE0EXE3GlTMvDxys+Eq3a0OrRyZxNXynGIjMQTW60ywFM
8FylL8dwDo4exM7enQWPu3iUxcyF8xnytjblA3dQX9wwF56YB2f8ZzA6N1r0czmRg4k2wcyYlVw4
qIJZ9VxxhrAOJ8IQy6J/aio5mQEAnE4nohkLfFnOH2cY5zi0ms1ZEQVBEmAWTFlOhJLiDI8+Csrr
BSMnYKnn4fEA0vGTwI9+VPCpmZMZVFRRby4+hzprXbKI08JYUG+rL2t3txx8rA8NtoY0O3OhToRc
kxmA9LGfQHacASi9XFEVEexmOx754CN430Xvw84Vb0NbQsD6f/gGdg8OYn/GlAaVDlcHGu2NeGP2
jeRjmtNGNJwIg75BXNCkT0TQU6yoRhkAoNHWCBOXyBYRaLrArt4CvMjjjr/cgZt/ezN+cewXuq7T
z/oRiAfSvh+FODp1FHv695T8Wp2OTMPMmHXFQorlfBQRih11uRidCPnuT3Vdj4ao4Wf9aLQ1pj22
qWMTPFEPpsKFbV0JiUxnKJlS+xAAJc4gSAICbCD5A1zbpK8XQRURKCrP+5bHo4gIq1crgkKw8jeo
RjLJcZglIsKyZN/APgwFhrLeEI/NHMO+gX0AlGz3VHgKHa4OzXOYGTNuXHcjfv/679Me58TcpTXt
LqUTgSyKCzPFK7+blLl+UWYln8/Mxma1ixWrwInwRmgWdimKZlrENK9/V+18YGRuBLFEDBe2XJj/
QJ7HqvEwxvtTbp63bi171KMn6sHK+pV48uyTRT9XncygoroR8j6HD2suulKdCHaaRkySst7ThmMx
ZTJDqhOhrg5RU/rNIs8r9yGW+bfMTCfCWZZFf+aCC0qxokU0IM7g8wG33AI8/TQYWQALHnV1QHjI
A1lH3DFzMoOK6kTQih51uDoq1vWSGWUAFHFIlEVwgrboNx4a1yxVBJTXROr0hNSWdpVSyxX9rD/5
vbu49WJ8+JIPw8o0okOSgZtvxvVPPIH9ebIn23q24flzC5GGXCNLMzntP421zfnHO6oUKlaUZRl/
GvwT3nXBuwAAjfZGmHkxW0QAdEUazgXPYcfPd2DQP4hf3/RrTWfjH974A3gx3WXrY32gQOG5c8/p
+FcpHJ06it39u+Fn/SU5Z16fNb4PQaXT1XnejX/OVdSdi2qLM+RyImjFGWiKxrWrrtXVi5AQSZyh
ZErtQwCUOINaPqb+ANY2r8WgT5+I0DT/M88pIqhOBIYB1q8HTpzIe87Z2Gzah0m1M8HzCIkiODK+
ctmxb2AfzLQZo8H0Hb3R4ChenngZ05Fp+GI+uK3utLKmTG6++GY8eCo90pBP6VV/V6tlFF81Mysq
i5IG10pSrpiBphPBWp82Vq5SDEZDaACHVhOF2fNEQxAlER/43QcgSvkv+ODIQV19CBgYgK+9Dn4m
5UbegF4ET9SDD2z4AJ4YfqLo56qTGVT0TGjIfI7dbM8qVjTRNMwUlfU5OuT1on9ubsFiAMBZX4+o
2azYD+ZJdSEA2SLCsEYfAqA4Eayiufw4w/e+p1gfZmbAyALCQgKdncC51wdARSLgYrlv4n0xH6J8
VHOBrTqDfDFf1k5sp6tT1w5dLh5+82H85JWflPTczFJFQIkc5HMjjAW1JzMA2QtxbzT7/KWWK/pY
X9bixBOPow0AnE5c39GB/T4fpByifGYvQjQRhdOSEWfI04mgh253NybDkzk3Bl6ffR2SLCXdKnXW
OlgTEgSrxqKpgIjw6OCjuPInV+KmC2/CQ+9/CBe2XKjpbPz4Ix/H697X0x7zsT5cteKqrJ6IfByd
OoqtK7bCZXGV9NlyyntqUaIMQPU7EbTWQkWLCFbjRzyWE2fIJWoE4oEsJwIA7Fy1U9eox4RE4gwl
o6VS64WhGHhj3rTnr2lagzMB/XEGoICI0Nam/L+OSMMnH/sk7j9xv97L181vfws89JCx55RlGZMc
BzfDEDfCMuO07zRmIjPY1bcLI3MjaX83MjeCXX278MjpR/JGGVSu67sOp7yn0uyMhexiJNKgD7+k
fFS4nOXdcNciWg62aokzjMRZtDIyOsym5M+w2hkODOM3J39TsGzwwKhGH4LW4uHIEUyt60q/4TJI
RPjgxg/iybPFZZuB7F1YPRMawlx2nCGzEwHQ7kUYnpvDanP6jaHTbkfMagXiCzfYqaWKQHacQatU
EVBEBLtkLi/OMDcH3H038NGPAh4PTLKAyLyIEBhRNmN+8dR3cj5ddSFoiUoNVsWJMBubzRL8Olwd
Ze2iPjn8JJ4b07+jnIqWEwHI//5RTJwhtURSxWF2lNWJkIonkUDbvJtl1d69aPb58FqOOeWZExr0
OBEkWcKgbxBrm/Q5EexmO5wWZ3KiQyaPnH4EN6y9IfkaoSkaDoFCkNLo48ohIgiSgC88+QV8/JGP
48G9D+Iz2z8DmqI1O5YESYA36s16L/PFfLhx3Y26nQieqAcRPoK+hj7d01wyWYzJDCpVKSLcdx/g
94NNsOj+djcS4sLaQpREzIS9+PPvtJ2tWizGdIZy4gwui0t3sSIA7OrbpUtEECSBOBFKxRfzocmW
/c3XA03R8Ea9aT+8tU1r8cbsGwXt0oEA4Krnsfr7q+GqE6D5Hqw6EQBdIsLI3IjhO4aSBHzhC8Aj
+rpjdBMSRTAUhV6bjYgIy4x9J/fh5otvRn9Df1q2OMSFEBfi+OvNf42H33w453jHVKwmK951wbvw
X2/8V/KxgiKCi4x51ENItsAEwGZvI06EDHI5EaohzjCVENFtMWGF1YoQSrtZWWpOeBSXXaGbUtWJ
gAMHgE99SnHoXXFF9oFHj2L2wlUIcSG858QJfGdsDNLmzcDJkwBXWk9EXIiDTbDY2LYR9dZ6nPSc
LOr5mQuoQhMa3px9E4P+wTSxSivOAGj3IgzxPFY3NKQ95mQYRO32tGhkaqkioB1nyBzvCACiJMAm
mxHNEC+KijP84AfADTcA27YBMzMwQUyKCOKMssj/1dPfy3kjP+AZwIbW7FJFYGHkqtZGUaerE9OR
achymp6imzOBMwVdJLnQciIAyg55LifCeGgcPfWFnQiSLMHHZjsvSulEkGQJwXgQDbb015BHktCm
ikrXXIM9AwN4PIdLdn3benhj3uRCO8JH4DJnFIVmOBEmQhNosDWkOXAKsap+lea4ZyC9D0HFkQAC
0CgM1hARJsOTuO4X1+GVqVdw9G+P4pqV1yT/rsnehBAXSluseqNeyJCzXh8+1oftK7djLj6nS5R/
depVXNZ5GSiKQoujpTQRYfb1RZnMAFSpiPCNbwCvvILx0Dj8rD+tcNMT9cAqNeHXv9S/WF6MToSy
pjPkcEbkEhE2tG3AXHyu4PQSUqxYBmU5Eeh5J4K9OWklWFm/Em6LGy9N5M9eBgKA4BjHcGAYlkZv
/jgDoExoKDDmcSw4Znjz/J//DIyPA2fPGnpaTHIcuiwWtJjNZELDEhBLxLDnl3sqfRkAlCjDLetv
QW9Db5oTYXRuFKvqV+Gda9+JAyMHMOQf0mU9y4w06HEinG9ZvkoQo+xYbbPAbGkiIkIG1exE8IoM
+uwOrLI7EaWKnzpUCdSRe1PhKbzxBnDzzdnHjM6NIsJHcJHUDNx4I9DVBXz3u8CQxjSk8XHwKzoQ
4sJ4IhDAbzwevG1oCJNbtpRcUuyJetDmbANFUdjdv7voSEMxToR9J/fhmp9dg7t23pW2k6gWKxZy
IsiyjGGTCX3d3WnnddI0ojab4gCYJ9OJkCUi5HAiJKQEHLBkdyLojTOEw8D3v6/sUrS1ATMzMENC
VBTR2QmY/coi7Pr6y/CdF7XdCLn6EICFToTZ2Cxa7BlxBrfirnrySaCnB3hev8McgFL8p2dEpxZa
cQMgvwg5FsoTZ0hZiM/F5+CyuLJ2FUvpRJiLz8FtdWcVrnlpGm3O+UgCRWFPRwcen9AuNqQpGlev
uDq5+x7lM+IMJnuWuFFMlEFlY/vGhbGdKSJAgA3g1alX8da+t6Ydb0sAfrmwiPDs6LPY8uMt2N23
G49+8NEsBwlN0Wi2N6e9FtTPyszfbT/rR4ujRemJ0BFpODp1FJd1XgYAJYsIlYgzyLJcOTF9Zgbw
epOL5tT7y8nwJCxcFw4d0i8cLoYToew4g8b1BOKBrB4UQHl97li1o+CoRzLisQy0RnXphaEYeKNe
rGbtwIoVwMmToCgKt266Ffe9dl/e5wYCQNSs7MKa6qf1iQgnT+YcPyNIAqYiUyV/sOXiBz8A/tf/
AkZGDD0tJjgOXVYrWs1mMqFhCRgLjmH/8P6KZ7YHPAMIckFc3XM1VjWswkhwJPl3o8FR9Db0otHe
iC1dW/CL478o6EQAgD2r9+C16deS7oJCIsJlnZfhpfHyCtaWAxzjwkaXC5SlgYgIGeR0IlSBiBCk
rFjrqEOXrQ4SaESE4ufDLzUnPCdQb63HZHgSAwPA734HnDqVfow6lYF64AHg3e8GPv954LrrFAE/
U4j2ekG1tcOb4GClaTx/6aV4S309Lv3Hf8Tv39Dfjp6KKiIASoyqWBEhsyRRqxOBEzj8/aN/jy88
9QX85cN/wce3fDzNqq814hHIdiJM8zzc8TjcF6Qvxqw0jQTDQEwRETKdCJlxhuF4HP0aIoIoC3DA
ktWJoDvO8O//DuzerUyPaG/PEhEcoQDYOgc+tvKv8P2Xvq+5iMo1mQFY+H3UEvw6XB2Yjk7jzTeB
vj7gve8FfvObwpcMKPdaZ+fOluxESH0dpZLPiTAWzBNnSHEi5BIoShnxqLW7KckyfGYzWuoXRkju
vOEGHHa7EY1qn//aVdfi4MhBANlCmsPsyIozDPoHixYRNrdvxrHpY0oZ+YoVyg02gMeHHse1q66F
3ZzipBEE0LIMv6Rx050hItz1zF34+lu/ji/t+FLaNI1UMiMN6melVpyhyd6Ea3qu0RVpODpdnogQ
YAOIJWLodncXPrgEWp2tmIvPZRVIvjj+Irb8ZMvSl1dznCKOejw5RQQqomxKvfhi4dMlJAkmk6Oq
pjMUU6yosqt3V8FyReJEKAN/3F+2E+H2+04qn7pvvgkA+Mimj+CBUw/kLTkMBIAQpYgIVN1Utogg
SUprccu8gl5frwgKWrsuUHZwJFky1IkwOAi88grwuc8BY2NFjc8tyCTPo3teRCBxhsVHVYwzOwiW
mn0D+7D34r2gKRq9Db1pcYaRuRH0NvQCAN6z7j14eeJlXU4Em8mG6/qvS85vL/QmfX3ix81sAAAg
AElEQVT/9SXPel8u8CIPydKEza46SIwT01EiIqjEhTgSYiLtZhhYsE9XGpauw4b6FjTY62EW5jBx
Hji9js8cx+7+3ZgMT2JiQukS/tnP0o85MHJAiTL8538CH/6w8iBNA83NwGzGDbbHA1N7JzyCjBVW
K0w0jX/q7cVDPh8+29CAj73xhqa4snt3bqOCJ+pBu6sdgJI1fe7cc1k30PnIciJkTGcYmRvBW372
FkyEJ/DK376SXECkojbxF3IiJMc7pkxmAJQCP0cigWhKnCFfsSIriggkEuiyZu+cibIAJ6WICOqC
QZZlJGQZlkJxhmgU+M53gC9+UfnzvIhgoSREJQGdnUBdNIRQfzc6eDP2rt+L7734vazTDHgHsKFN
O86Q5kTIUaw4OgrcdBPw5JPKfc7Xv65dsZHKueA51FnrSh67540V14kQ5aNgBTbnmL5UJ4LWvxUo
rVhRa2HiTyRQz3EwtSx8DVdvLy73ePDMfu3P1J29O3FwVFtEUEWxVEpxImzq2ITXZl5TlMdwOKkI
PTL4SFaUAfE4eAuDgNaGSoaIMBmexJauLXm/dpuzLS0emRQRUkQmSZYwF59Dk70J21duL8mJUKxo
pUYZShlfrwe1EyJzg+Hs3Fmc8Z/ByxPl9c8UjWd+7eP1Yiw0Bpqis0QEIdCFG24Ani5cE4CfTE3h
WzPRspwI/kQiS0xhE2zesvB85IpXpE4IzERPLwIZ8ViAb73wrZx5qXKcCDRFY/VzA1g5OgfcfnvS
87+yfiUu7bgUD7/5cM7nBgLArDACAJCd09mdCHNzypaAJWUxlCfSMBYag81kM1REuPtu4G/+RtEv
WlqAHI61kkiLMxglIhhd3FBDqNmwSooIsiwrUYYNtwBQcoyp1zMyN4JV9asAAO++4N0AoLtJd2v3
VhyeOAygsIhwedflGAuNkd31PMzF50BZWrDR5QJP28j3KgV1ZzPz5qzB1lBxJ4IkSRDMjdjU0IUG
WwMo3oeJEjsAloooH8VEaAI7e3cmRYQPfxj45S+B1I+GAyMHcL2wChgdVVb7Km1tCzeQKh4PzB3d
8IsMVqQsgK/atAmv3XknKACbjxzBSykLhnBYubk8dEj7OlN3kJvsTVjXsg4vjuvY0ponwkfSShJT
OxEeOf0Itv50K96/4f34/d7fZ+XQVWwmm65OhOFAAKvHx5Vd2QycoognX38CZ/xnkv9ul0txQfx2
4LdpIsJIPI6VNhtojYWIKAtwWiwwUxTi8wIGL8swU1ThhcuPfwxs3w5smBcAVBEBMlhJQmcn0ByL
gV/bD/h8eM+69+DQePoPxhP1QJTEnCOAk9MZNFxDarHi6CiwahWwcaOyO/mHPwC33ZZtbEll0DeI
zR2bIUiC5mSBQuTsRLBoOxHGQ+NYUbci5/c0zYmQQ6AoJc6gdW/sSSTQFoksOGTn2dPQgMcHtaeS
Xd55OYYDw/CzfmU6gzk9zpD5PTztO627VFFlU/smHJ85DunUANDfD9x7L0RJxJ/P/Bk3XJAhIrAs
BIsZgXgg+0QZIsJUeAqd7vxuyHZnthOhr6EvzYmgxkxMtAlburbglPdU3tb/ABuAJ+pJfh9KcSKc
8p5atFJFlS53V1qpNaC4ZuwmO3594teL+rWzmJkXcuadCJvaN6XdX06EJ8F6u/DBD+oTEYZYFpMJ
CWE+XJKrQpJlbDpyBMcydoinI9M537MK4bK4NEWNfE6Ei1ouQjQRTduwy4SMeCzAz1/7ec4Pe60R
NnpxxWV84ucDOPC5W4CLLgKGF4SKWzfdivuOaUcaeF65OZqKjaLF0QLRrhFnSI0yqOQpVxwLjmFT
+ybD4gyhsIT7Hgjg7/5O+XNfn7G9CJM8r8QZLBZjRIQzZxSLq4650suRanAiHJs5hoSYwBVdShFa
u6sdIS6UtFmqcQYAWN20Gpd2XIr+xn5d576i6wocntQnIphoE3b27sRTZ58q419T24xHfABtwmq7
HSzMRERIQWtRAlRHseKZiA+UFEeLzYV6az2kuLfqRYQB7wDWtazDyvqVmIwoIsJb3wqsXg089phy
zLngOUT4CNY89hLw/vcDppRdk9ZW5fNSJRYDBAHO5g7MyWb0pO6iX3wxXGfP4qft7fjm6tW48cQJ
3DUyAkGS8NJLigEwnxOhzbFgQ9/dV1wvglYnwlRkCp9/4vP4uz/9HX6/9/e44+o78i7AbSYbOJFD
OENEcDJMWsHh0OQkVvO84tTIwCmKOHTm+aTFVY0zHJs5hk8+9sm0OEOuPgRZliEiAbvVBBfDJHsR
eEkq3IfAssC//itw550pF+UEGAYOUQQrSuhs5uFKCKAvWAf4fNjQtiFZvqly0nMy52QGoIATwa0U
K6oiAgB0dip9ncEg8La3AX6/9uWf8Z/B2qa1aHW2lnS/lXc6g8b7R77JDED6bn7OOEMJxYqakxl4
Hm1zcwsO2Xn2XH01Hm9v19xpMjNmXN1zNZ4dfXbRnAjNjmbUWesQPnYY+MQngKkpnHzyfnS4OrCy
fmX6wSwLwWaGn9X4AaeICHEhjmgiWnCTsc3ZliUibGzfmLboT43U2Ew2XNpxad6d+uMzx7GxbWMy
QtHiaMEsW5yIsJiTGVR66nowFhpLe2w8NI7bNt+GB049UHBkr6HMzOCP27YhPKcUCV6z8pq0EeIj
/gnQkW687W3A0aPpvS9ajHEcpvgEKFBFOc5Ung8GMc5xOJfx+auKgqXgthRXrAgo7rOdvflHPZIR
jwUYC43lVGG08nJ6+dBfJvHcajNi116lqJ8pIsJNF92EF8Ze0GxhDQSAhgZl0XTViqvAWzXiDEWK
COOhcVzWeZnSDGtAFun//81+MB94H3rmu3yMFhEm5p0IrUYVK6p3mznU8POdk5EITuXIHOpBnY9b
SRFh38l92Lt+b9qopVUNq3AueA5AepwBAA7ffhiXdl6q69yXd12eFCn0ZM529+0+LyMNb8y+UXIW
txiGogFYxDCaTSaEJYqICCnk+syohmLF43PTsAhzyetJsJMYr3IR4cTMCVzSfkmyqGtiAujuBv76
r4F771WOOThyEDtWXQvqV79aiDKoZDoR5j873bY6hClbmhMBDANcdhlw5Ahuam3F0S1b8KjPhx9O
TuK554DLd03g1de0RzdmZtmLLVfUms7w8sTLeHX6VRz926PYvnJ7wXNQFAUrY0VYSOR1IgzNzaHf
ov0e6JRlRGPR5A6i6kSYCE3AE/XAYhOSN9hn43H0a0xmkGQJFCjYrDScKSKCrskM99wDXH45cGnG
e3t7O9xcAqwkocsyi1k7DXvHSsDnQ7e7G7zIpy3W8k1mABZEPa3ybLfFDVESMTweSYoIgKJlPPig
cnnbtmmnR8/4z2BN05qkxfyeqSkMFPHZXOx0hvHQeM5SRSB9Nz9XnKGUEY85xzvOzmaJCJe2tmKm
rQ3j+/ZpnmvHqh04OHpQe8RjihMhISZwLnhO9+ZBKps7NiN+8jXF3fLRjyLyox/gXWvflX0gy0Ky
WhFg8zsRpiPTaHe2F3TVtDnb0kZGT0ensbFtY5rAlCk8b+/ZnrcXYWRuBH2Nfck/FzviUZZl7B/e
j60rtup+TilklmMDynrruv7r0O3uLpjFN5SZGfzDJz+JQ3Y7xkJjuGblNenF3b5JNFu64HIppu5c
jjOVc/E4Jni+5AkNv/V6wQCYyljflCMiuCwuRBPRtDWeLMs5ixVVdvXmjzSQEY95CMaDCHGhNEUq
FT/rLznOsPZcBA+s5ZU32gwRwWlx4q8u/Cv86sSvsp4XCACNjcobxVXdVyFu0nAieDzZIkKBOMOa
pjWwm+2G3Mg+fXwQUsOZ5J97e40tVzS8E+GxxxRl5vTpkk/h4fmyFuqLye2nT2Pb0aPYdvQofjE9
XbRQNBGewPae7Tl/DxabZJRhvRJliIsifjI5mRZpyBQRchUZaVFnrcPK+pUY8A6AF/mCI3SuX309
nhh+QvP7ODeXXepWLXzs4Y9pvqcYzVk2DLsUQ5PZjDlRgjc2u7S7ClVMLieC0+wEJ3Bp476WmlMR
P5zzhWEWxgIm4ce5eGnvaYfGDi2JeHR85jguaVsQESYnFRFh715lZ3hmRoky3BLuBcxmZYWXSqYT
weMB2tpQZ60DS7vSnQgAcOWVwMvKLmCX1Yr/r60NQ/E4nn8e8F5/A16lf6zZ/zMTnUkTEbav3I4T
nhO63SdhLr1Y8YLmC/D7vb/XbH3Ph91sR1gQ8nYiDCcSWN2ofVPpgAyRE5IRN9WJMBGegAwZvNmT
FBGGWVbTiSBIAmiYYLMBLoZJlityspy/VJHjgH/5F+BLX8r+u/Z2uOMc4hJgC03B45TA1PcCPh8o
isKGtg0Y8AwkD883mQHI70SgKAodrk7MCdPozHCrMwzwrW8Bn/60krjInNww6B9UnAjzC7v7pqfx
9HyR31Nnn8obcZBkKdnSn0muYtaxYO7JDMB8xCU1zpCjWLFYJ4LW+5yH59Hq9WaJCAxF4TqHA/tP
nNAsldixagcOjBxQ4gyp0xkynAhn586iu667pBF4m9s3w3rmrFLUedttuPDxo3hXr8ZEKpaFbLcV
jDPoiTIA2nGGDW0b0sR+P5vevXbNymvy9iJkCkfFxhnUvpYdq3bofk4prKpflbVBqy6SP7DhA0sa
aZBnZjDe3IwJisJ4aBzberZhIjQBQVK6byZCk+hwKPHYXbsKRxrGOA6ziQRc1saiexEkWcaDXi9u
bm3FVKYTIVy6iMDQDKyMNe13OcJHYGWseTfOdvXuwtNnn9a835VkCZIsgaH0328vKxFBtdpoLZ5U
BafUOEObn8OwW1Ce39sLnDuX1j6oRhoyf3CBANDQKGIiPIEruq9AjJ7K7kTQciKsXq3Y9eeyC2FU
y1uro7XsXgRZBk6OjyAoTyRviA2PMxjZicCywHPPAbfeqt+JMJr9evjVzAzuMnoMhQEEBQEno1GM
X301/nHlSvzD0BDeKOTFymAyPIltPdsq5kQ4MnkEJtqEzR2bAQC/mJnB/xgcxKqGXowGRxHlo4jw
Ec3WaiB9jZCLK7quwOGJw7qcCGub1oIChdO+bNHpe99TSraqjbHgGF4YeyErg7gYnGNjcIODhaZh
o2k0OrsNn/xyvpKrR4eiKNRZ6yrqRhiKhtFILbyfOiUWI2xx7xWA8tn4kf/6CL745BeNvDxNTnhO
YGP7RrQ52+CL+TA+KaC7W1nYvve9So/igdED2PXCfFlC5s6glhNhXkTgTHXpTgQgTUQAgG6LBeNx
Di++JMMvD0Pa9s94YzD7MynTiWAz2XDViquSpXGFyNyFZWgG773ovUWJpYCyexsWhfxOBIsF/Rp9
CABgkUWYZFsy4qYWKyadCZgsGGdQRAQzrFakxRm4QnGG++4DLr5Y+Rlk0t6OepZDXJYRGjsDr8OE
Obo9GVHc0Joeacg3mQFQnDhz8bmcVt8GUwda+qa0Eh8AFFf8ffcpr8H77194XHUiqHGGMY7D2Xgc
sizj3fe/Gzt+vkPTgQooWXetEYxAbieCrjhDwvhOBE0nQjisdCJouFz2rFmDxy+6CHgpe/LRFd1X
YNA/iPHQeF4nQilRBpXL3BfAPhcFVq7EeLsdp5uBq45pxFtZFpTdnlNEiMViCAvKtDM906GynAiR
aaxvXQ8/64ckK8KeOplBZVvPNrw4/mJygZtJ5kjPYkWEuw/fjf9+xX83rFTx9GllmEomvQ29aRO2
gIVrv2XDLfjDm38AJyyNEy7g9yNmNmPcbEaYC6PL3YU2Z1vyfc0bn0RPgz4RISFJ8CYS6LJYYHN2
F+1EeD4YRIvZjB0NDYY6EYDscsV8UQaVC5ovgCAJmt2A6mSGYl4r55WI8PzzSmMuAPzZ58PRrNV2
fsaCY+hyd2nGGYJcEA6zoygbRyqtfg7jdUCzvRkjM3bIzc3A5MLc1GtXXYsIH8Gr06+mPS8QABzt
U2i2N6O3oRdhWWcnAk0rVi0NN4KqXLY528q2O584AVCNI5BkCRPhCezzeDDS5zFMRJBkGdM8j06j
RjwePKhEPa64Qp+IcOKE4hzJEBLOcVxVWn+fDgSwra4OLpMJ725pwRVuN06zxRU6VVpEUF0IFEVB
lGX869gYJFlGa/1ajMyNYDQ4ilX1qzTfyCIRZZb3lPZ9WRK1F0GPiKDOeteKNDxwagiD/4+9945u
s7z7/1/ay7LkbXnbcYYTMshihhFCAmEVCuQBmlA2LS19WmihdFJGoS0tLVAKtCUQCCSMsAIhhEBY
AbKH7XgveQ9Zkq0t3d8/LkuWbMkjCf2dPuf3PscHIsm3ZFu67+t6f95j9iSLw/8D2Fi+kRRtClan
9Rt/rjafjxSZ2BikKpWkm4r/f0vDEHrdvQmT0v+/bmho9nrJitqTJst8R5WJ8HXr1wSlIJuObIrU
ZX0TkCRJKBGy5qCUK0nVpoGhK9IWcN118NRLLbhcdlLf2gZXXz36IJmZo5UIGRkkqZMIqlLJHbnh
WbxYbHSGyP0cjYYam5ecYjsyGZilKTz+yQujnia6nSGMyeQiDPhjgxWPFlqllsFgKKESYSAQwKlU
Yiktjfv9ypAfnSIpRomQlDQcvuuQ2mPsDPFIBH/Ij1wSSoToPAbfWHYGvx9+/3v49a/j35+VRfKg
G48EA9Z6enV6OvzDzRuzs2ZzuOswIN43YzUzgJD+ugNu9Cp93OuBIWQhtWDsi8qKFaK54ec/h/vv
h0AwSGN/I1NSp5Chz6BzQGSONHg82Dw2lHIlF0+/mJP+eRL72veNOl4ipQAktkNZHVbyTePYGSaQ
iXDcSIQENV3npqay7cQTCY2sVUGook7KPYnq3uoxMxFqemuYlnp0JMJCp5G6dDkoFLxb8y4HVy5A
8dzzox/odiPTG+LbGYxGHpk7l183NgolwgRJhJFKhHxTPsma5EjuwkhVR5o+jVxjLoc6D406Hgxt
xE2xJMJE1/Xtznber3ufa+deG7nNdoxr7Of/1MXe378/6vZuVQ719uHPkDfgpd/TT6Yhk7zkPGZn
zmZL7ZZjeu6Jwjp00rKazRTpLJEGsMb+RnxBH4PBfgqH9lSnniqc4YmEx20+H5kqFYVaLSqdZdJK
hI3d3VyZkYFFrR5FIoxV1zoRjAxXHM/KAGK9e3Zx/KpHf2hyoYrwX0YibNsGmzeL/3+2o4NNI6uc
xkGzvTkSsDFSETCSHZwUXC603iDdetDLU5k7F3pNsZYGuUzOmjlreG5/bMCizQbK9CYKzYVYkiw4
QrEkQkUFdJWLacooJMhFaLGLk06G4diVCO+/D/qcRvQqPU39TWzu7aXaaDtuJEKP349RoUAjl5Om
UtHn9xM6lhyH996D88+HqVMnZme4804RzDVCs97s8dByHEmE4+UH+8BmY1mUNHWqTkfNJJQIkiTR
7mxnTtYc/EE//fGqjb5BhKQQG8s3RloZXu/uJlOlYnZSEvokYWcYaWWIRnOzUMEmsFtGsCh34iQC
iKrHkRuAykoILdqDVNg8oZ/tP4mNFRu5deGtx0WJUNNbw4OfPsifvvhT3Pu7/EHShvYoaSoV5qSC
cUmEBx5IfFE+Xni35t3/fP/0CIyVo5NIkvyfQrs/RJ5meEGQIg/S4Zv8AnL9ofVcN+861sxdw6Nf
Pno8X2IM2gfakclkZBnE5jxNnUNGyTARv2QJOFJ3cF3LdGTTpwvydyQyMmKVCEN2BoVMAZoMUhQj
Nj0FQ0FrLUKlmKvR0OL2ccJp4pp8gfFXbGh7cNSUcKQSASaXizBSiXC00Kl0DIRCGKPCJaOVCPWD
g5S0tSGbFrsZk6Qh3kTyYtSkxmQihO0MBaYC7MH2YSWC201xnEwET8CDDOVoJcJYdoYXXxSSxtMS
ZD9kZmJyuvBJMtytTdj0RqzutGElQlS4YvtAOyq5akwbiFwmJ1mTnNCuqvJmY8ganxidPVv4p998
E27+WTOZhky0Si3p+nSa3Hb8kkSjx0OLvYUCUwG/POOX/HnFn1n+wnI2VW6KOVaiUEUYW4kwZiaC
ShepEz+eFY/xQse7PB4yE5BEBVot6Xo9+/fsEerQETir6KzIa4m89uOoRMhtc1KRJmFz29hcs5mU
NTfDp59Cx4i/sduNQqdPGKxYaTZT43IJJcJE7AxJw3YGl9+FN+DFpDHFVLjGU6+dlp+46nGkhcWk
NTHoH5yQVe6Zvc+watYqbrvRFNk7nbBrF7WTVK+GEQxC6vrHubH1t6P+rH/s9tOAOXJNbnW2Ykmy
RNRVV51wFesP/2csDVa/H7kkYbVkMwtxng6TCB0DHehDWZxnfRb+9Cf0ehHJMtKqFEaLx0O+VkuO
Wo1ckzUpJUJIknitu5srMjOxaDQxJEK4rvVoLfQwOlxxIkoESJyL4A9Ort4R/stIhMOHh734jR4P
DZOcwLY4WpiVMQu1Qk2vO1balMjbOiFYrfSmaJHksPvTFPx+OOgojiERANbMXcNLh1+KSfe02QCT
qLMTiwqJfvfwm3TtWmjcFUeJAHFzEXxBHz2uHrKTssnUZx6z7HjrVvBoGyMe+iqXix6lh64usZk7
VrR5veQOSUxVcjlGpRJbnM7uCSOaRKipGbvoecsW4cu4/nqxY4xCs9dLm89H8DhsUnpcPZz93Nl0
DHRgsw0TYUeDD2w2zo0mEfR6aibxOeh192JQG9AqtRSZi8asevkm8KX1S4waIydknoAkSTzU3Mzd
BQWiv11nocneFFPvOBJNTcKq+OI4UQDzsudR1VOF3WufEImwtHgpHzd+HLNZeG6TC+tMI+25JgYH
478PQiHYuHH8PvHjiQZbAw22BlbPWX3Uk+EwcTDvH/M4Y+0ZVPZU8vjXj8d9bE8AstTiwpKqVJJk
yBmTRKioEGHr5eUJH3LMcPvdXLD+guNaYxsPLfaWMRfcY103vgklwmRIv56QgmKtPvLvdKUcW1Ai
EIofFhgPgVCADeUbuHr21fzklJ/w733/jj+1Ow4IhyqGFUhGWQ7JucMkgkwGead/zNJtntGBimGM
VCIM2RlsgQAyKYgUGMFsyWSxuQhqNf1yL3mzmyk0FXLJnDNhIJsNh4dZS0mS4k5552XPo3Owc0LE
3vEiETRKLe6QhCFqs65XKDjSGOL+++HhPzeT12njkqv0LFkCs2aJ5gGdTjgiO3s8KGRG+j39eAPe
mGDFhTkL6fMJJYLN70dCfP5HYkvtFiz+09BohpQI49kZgkF48MH4WQhhZGVhdrjwSjL8Ha0MGlNp
cqYK+2YoFMlEkCQp0swwHsxac0LVkOSwoEodR942BIsF3noLXvlQWBlAhN21eDwUajQ0eDwxtoPL
Z17Oe9e8x+1bbuf3n/4+sskaU4mQoN1lvMll9EZ8TDvDUQQrjiRLu3w+MhWJ7TfLMzPZeuGFsGnT
qPvC/vzoz4BepY8511b3VTM1bXL1jmHIj1TRX2zhq9av+KjhI5bN+ZbwoqxbF/tAtxulwZjQzlCd
lka9xzNpJYIkSXQOdJKdlI1MJotRD8QL9zy94PSE4YrRSgS/H6SQnFRd6qg9zEj4g36e3vM0V0+9
jQ0bhOh2IBCgzedjzyi588Tw0XaJK/0vUKJoipm7BUIh6lxu9MrsyOsaKdW/fOblbKndMulJ/tGg
RSZjtlKJNSOd6ZLYVIdJhDZnG2pfDqU9X8Jjj0EoNKalocXrpUCjEfsUTfqkXv/ndjsZKhXT9Xqh
RIjaNLU6W8esa50IRtoZbG4bKdqxlQgwTCKMHMJMtpkB/gtJhPZ2QWw2ejzUezyT+v4wixsvACRy
krTbx9dKjzpwC71pOkwaE69uVHLvvbCrtwTHgVgSYUrqFKanT+fdmncjt9lsEEgSdXYymYw0jQWb
b3iB3tgISlsCEiGOEqHd2U5WUhZKufKYlQguF+zc4yQoc7MwZyGNNkEiNHs95OaKqTBAMBRkybNL
+Mo62v82HsL1jmEcU0NDXZ0Iwpk7V6RVarXDfbEjEQgIFcIf/yjImJEkgseDHOg8Dm0RYdvAp02f
8uGH8IsJWoudTjHxCKPJ46E/EGBO0vCFd6pONykSodXRSo5ReMHipel+09hweDhQcZvNhjcU4oK0
NPI0GoLq1AkpEb79bbBaxxaaaJVaZqTPoN5WPyESISspi0JzIbtad0Vu+6zxMy7ZewBlKMihpvjv
gy++gFWr4B//GPcpqKuD558/dsJhY/lGLiu7jEJzIW3OtglP48PEwYlPnciSZ5fQ5mzjr+f9FeuP
ray9ZC1dg11xF5f9koJctfiMpqpU6HRZY5IIYQXrNxkpEh3A+U2htq+W0sdKSX04FcsjFk7792ms
3rSa33z0G9buX8snTZ/QbG8etSC0ejz8obn5uCsRWuwtTHts2oT/3k6ZjulGc+TfKRojRnmIzknI
Wbc3bKfQXEhpaikFpgIumn4RT+5+ctKvPRr72vfFJazCVWZhaP056LPaYh7jUW5nUW01rguuiNy2
pbd3WL02pERwu4eG1kNKhBavF1WgP/4CMIpE0MoVSB4F6gIrBaYCTjxRhuyTX/HgZw9Gfu/9nn70
Kv2owDeFXMHS4qUTqos9biSCKhmlDJTRJIJczoefBbFaQWVsI88V4LrrhDro5Zdh925RWfjqq+AO
OnEMqtFL2bQPtNMr90SUCAstC+n2tOPxQK1LWBniLXifO/AcU5zXRoIVoyse49oZNmwQZM9ZZyX+
wbKyMNmd+JAhdXXhM2fQ1qUUMon+flJ1qRg1RprsTeM2M4Rh0pgSqoa8vRZkSRO3aFksoEivJVst
NrkZhgza/AHmJSURkCSq+ltjpscLcxby5Q1f8lrla6x5Yw2egCeh3QDiKxGcXie+oG/MSeM3WfHY
KiXxUJTtszsUInNkxkgUlqemsvXUU8UkbAQW5y6myFxEsiY57muHY1MicOQIshkz+OtXf2VO1hzx
OwtXvESfP91uVIbkuMSoZDRSk5lJg8dD20SUCIEAWm8QrVKL3WunY6CD7KRsQPQ4VHUAACAASURB
VJBM4YFevAD30wpEQ8PIc/uAbwBPwBN5/IMPwi23TKyh4c2qNylJKeHgB7NRKsX1uGloE7tvklbw
ML56dCd6kxpzqI+KvcP7rzqPB79MxgmDeZG9VVgRHUaaPo0lBUt4s+rNUcc93rCqVJycnExrSipT
/MI2VjSUudXmbEM+kEvqQJOwnH/yybgkQv4QiRBUpcStVUyEjd3dXDG0d8tWq+mKUlofax4CCBLu
aJQIJSklyGVyavpiLd/+4P9hO4PHIz4EBQVwpDFIl99PwyRJhGZ7MwWmAgrNhaPCFcMSo44nnqDq
O98RlN9E0dJCb7qeFG0qmzfDmjWQcVIJzR+PDq4IByyGYbOBR9MUmbxm6rOxh4YvZg0NoBtMQCLM
ni3GfVG+tGi527FmInzyCcw4uZGilCKKzEVUOTsJITbYhUVSZJNQ3VvNoc5DXPzyxexp2zOp5wiH
KoZxTA0NW7bAeecN92GH1Qjx8O9/i1Thiy+GsrIYEsEdDNIfCHCCwXBcchEabML7saNpB/X1QqAy
kX3AU08J22/4bb7NZuOclBTkUQuzyZIIbc42co25wPBJ9T+FYCjIKxWvREiEh5qbuaugALlMRp5G
gxMtNreNqt6qhCRCU5NQMa9aBevHUcYtylkEMCESAWItDdXV0LGwnzX5OUxpbWdPbXwy7rXX4Jpr
hLX3yJHR97e3w6OPwkknCe/dz39+bEoUEJkSV866Er1Kj16lT7iY8HojKm1+vu3nnP7s6bQ6Wnl0
xaO0/qSVx1c+zplFZ6KQK1DIFZSmlsYNlxxAQ/7QRDtNpUKtSUtIIvj98NzGPk6+ajuNjd+cPKOh
X3ymvkkS4e2qt1k9ZzWuX7jYfdNuHjrnIZYVLwPE5vqeD++hzlbHlJQpMd/3ucPBk21tx12JUN5T
Tbf5tAnlUXxhtxOSgkxNGr5umDQmkvFP6py2/tB6rj5hOHvgZ6f+jL999beIZHqykCSJ2969jaf2
PDXqvkNdQokQhsKVgyJlmESwOqyceaCLivSzeX2HWFSv7+zk/EOHqA2fA4eCFf/9b7jiCiKZCFav
F23QGVciHk0iNDeD3KamW91DoamQwkIIVJ9L10BPRPUzspkhGsuKl7GtYXxLw8h2hqOFWp2MThb7
OdPJFdi9IR56CPQFduamafjWt+CMM8SSITcX9HrRbqkx2dGalLg7c3lmg5W9P9xFp6qHkBRiRvoM
Ogbb0emgyhHfytDY38ihzkNk2S8USgS5PL6dQZLEL/eVV+A3vxEqhLEmcFlZmPvs+JGj6O5FlpEj
ZjtpsZaGw12HJ6VESKQacrRl41VPbniUOrUGpX1YidAdkJGv1VKs1VLhtI2yHeQm5/LJdZ/gCXhY
+txSyrvLE9oZ4mUihPMQxppchpUILr8LCQm9Sj/qMUcTrNjr6qXCp+Sh5mZ8Q0qmLrmcTIMh4fec
aTKxS6tl8PDh4YnTEDRKDQ0/aogh4sKvXZIkBn2D9Lh6xrRujInKSkzzTmJL7RYumHqBuO2008Ra
OTrs0eNBaTDiDY5u0unSalEGg5gUClrcg+MqEewPP0zzXXdF1AijSIQxlAhTUqYQCAUiFdfOQIDf
NDRElCfhv/mXX4pBRLJy/HDFJ3Y9wW2LbuP55+Gmm4ZIBI8HjUzGvqNQIng8YNm2DsX1axhMycP6
xfDftHJwEGUggC6YErkmWx1W8oyxm+SrTriKlw6/xDcKvx+r0cj8tDTsej3ZPvEZiFYiBGw5GHub
YPVqWLeOk08WQ+p43ErzkJ0hV6PBr0yesJ0hGGVlAFDL5SQrFJH9zfEgEYxqYwwxPlESQSaTRVoa
ohEIBf7vKhGqqoT8bto02NXsoVSnoz8QiEkhHg9hZiyeEiEsS/2rVsuiO+7gs3/9a+IvzmqlL82A
wpfGwoWQlQUnXVWC/0g9I5WjV8y8go8aPoqcAGw2cCobKTQLEiE7KZsB2fDFrCq7iy1nlyGlx7nY
JCeLJ6sdrl+MZv8yDZl0uY5eifD++1B2SiPFyYVMkaVxxOXiBIMBk1JJ9kxfJBdhd9tuVpSu4OkL
n+aC9RdwoGN0TkMitI5QIhx1Q4MkweuvCytDGIlyEZxOsZB55BGxkAmTCGGG0OslT6OhQKs9LrkI
jf2NnF5weoREcDoj66CECAZFAm5yssiKBPigry/GygAgeTrp9vkm/Dloc7YlVCJ89RXs3TvhH2vS
+Kz5MzINmUxPn84uh4Nat5v/GTrB5ms0tPp85CXn8WnTp2MqEQoKBLny4otjkzGLcidHIiwrGd4A
/Pv1dlypCs656ELyuuzUtLWNenz4LXf33XDffYJM8PnEZ/qf/4RzzhHh4/v3w+9+B62tglC4//6j
VyPU9NbQ5myLyEHzkvMiIWgj8frrcNVVgjx9as9THLz1IE9c8ESEOBiJGekzONITy4RIkoRbbqDY
ICZGqUolcrU5ZiPr8DrYXL2ZO7feyYy/zKdvTRF7pl/I3tb4FbTHA+H3bZhM+CbwTs07XDTtIuQy
ObnJuSwpXMK1867l3rPv5flLn+ez6z+j6X+bRm1gat1umj0ekjTmSSkRttZt5Ymvn0h4/xe9Vpj6
I77srEz4GIA+v5+rKypIa/4X2cbsyO0mrYkkyTPhcEW3382bVW9G8ksAZmXOIsOQQWX32K8hETYd
2US/p5/avtpIWnkYh7sOx4TjBftzkAzD18IdjTu4udIA16zm2WfFgvh/a2uZptNRFfb4mkzg8VB7
2MNHH4HHKuwMVq8XQ8jF1k8cPPWUOHe88QZ88AF8HVpIcNceaquCvPsupIU01LscFJgKkMlg3lwZ
U7QiYwXi5yGEEc5FGE8tcryUCHm9cpKcsRuCwKAcuS6I2Qz1gUDCekeAvkAvPrmCM+fn8NjrHYR0
QWpkVnKNuViMFtqd7ej1UDMQP1Rx3YF1rJq1ioBXE8lEiGtnuOQSEXa8bh3cdhssj1O3F42sLMw9
Nvwo0PTZ0eYWYO0OxpAIszNFuGJ5d/mYzQxhmLSmYTtDT0/MSbi30cIAkwuLVWXV4rIKEiFdn04/
KvI1Goq0Wurcg3E3CHqVng2Xb+DcknN57OvHEioRjGojDq8j5n00XjMDDE/zwyqEaMKh/t57weul
0FRIs715wsrRYCiIw+ugPSDDHgyyta8PXyjEoFyO2WxO+H1JSiULjEZ23HqrGNqMA4VcgVKuxBf0
UdtXy5SUKZNuKwGEyrS+nsJF5wJwwbQhEkEmE+ms0a/F7Uam12PWmkdZGqr9fqa1tlKiVtMWkI2t
RPD7+UEoxM2zRbNM50BnLIkwIhNh5EZPJpPFWBruqKvjd01NlNti6x0PHBABn32tY5MI5V3lVPVU
USa7FKsVbrhBDGAaPR6WpaSwb2Bg0nlC773p49vSK5huvZpQXiG2/cN7qMrOTk6tqqInOY2+qv3A
6EBIgEtmXMJnzZ9Nql1i0ujqoiUnh0K9HtOgA6UkzrPhCvFWRyvuLgvqzmaxeHv9dXS4WbhQFLuN
RESJoFbjUSRN2M4QbWUIIzoXocXeMopkmSxGKhFsnonZGSB+LsL/6WDFw4dFGUFxMRzs9FCi1VKo
0dA4QTVCSApFmJ9CU3wlQqoulT16PTdIEpdaLGyfSDAfQEsLtnQDA92prBpab828sIT8YAOffBL7
UJPWxAXTLuClQ4KNs9mgXxpWIuQmW3DLxcVsYABcM/v4YvEMOkMJQoNGWBqi2b9jrXh8/33IntHI
yloZp9z2e1r8MF2vp1CrxTjVEyER9rTvYaFlIZfMuITHVz7OeS+eF0lOHg9tXm9MYvZRNzS89pqQ
Jl1yyfBt06bFVyI89BCce+5wx3hmplhQDHlpm71eCrRa8jSa46JEaOxv5LIZl9Fsb+ZIszh51o8W
qcRgyxaxXvrBD+Ddd0VAy4f9/SxLSaG+Xlh6NlVuYupjU8hVyamboBqh1dkqlAjB4CgS4ZlnhqXo
3wRePvxyRIXwcHMzd+TnoxpaZOZpNLR4PBSZi7B5bBFSbSSamqCwUKxHAXbtivswYPJKhCUFS9jb
vpcB3wBfdn7OpdUtKFJTybX7aXaNljvu3i28xbNmCYmhxQILF4qG1/feg+9/X7wl164VF36lEi67
TNh6t4+veI6LjeUbuXzm5ZHFVV5yXsJchKoq8Rrv/eh+bllwy6g0+ZEoSy+jsid2c2gPBEAKkqsX
F6Y0lQpJaeRQ1yHu+fAeTvnXKeQ8ksMjOx/BrDVj2fc4/yjpZb5pxSip3PFEg62B/OT8b0yJYPfY
2dW6i2Ulyyb9vbVOJyFArs2esBJBkiTu2nYXv/741wmn/AedIvxrc1fiHAxJkrixqopvpafjbN8a
CSkEoUTQhQYmTCK8U/0Oi3IWRRbCYUxNnXpUf1t/0M/d2+7m0fMeJU2XFpm6gdioVPVWUZZeFrnN
05WDVz1M3u3bs5npTQPM//WFHDgssWpfJXfk57MyLW2YRJDJICODnspuysrA0zxkZ/B40Hi9PPwX
J3v2iHPq2rXw8MPwo3tTaQlY+OGySu69F6amamj1eiLnoHnzwDS4KGJ1GotEKEkpQa1QjyLjRmLA
N4BRc+ztDFObfZi7ewV7OQR7pwJdiiBo6tRqpuTHn+b6g376/D0MKhSUZeew4mbx/mqil9zkXCxJ
FtoHBIlQ7xpNIkiSxPMHn2fN3DV4PCS2M9jtQivc1CTCBP73f8dWIYCwM3T3EUCBwTZA+6yZfHHT
LtF6FaVEONh5kIruiskrEU4+OTKRDgSgpymbXu/klAguXS0dFcN2hgGZgTyNhmKtFqsvkLBFQS6T
c+/Z9/Lu1e9yadmlcR+jUqhE80aUYiDcujUWwtP8kXkIAzYbZaecQsPevWQYMvjXxf/i8lcuT1g/
GQ27145RY6TB62VFSgobu7vp9vvJ8HiQpcfPmAhjeWoqW887D558ckJJu2ESpKavZnwrw9tvx/eG
NjSAxUJWzonkn7ae+7rl/Kyujn+0trL129+m5vPP8YUn8W436HSkaFNGhSvWuN1M6+qiSC7DLtMn
/MwDHN68mffnzePzggLS9Zb4SgRXlBIhjiImHK74Tk8PH/T2MrO/n339HZH3UVeXeLl/+Qs0VaRj
tSVWGf9919+5af5NbFiv5pprxAC2qQka3B5OM5mQYFRTwHiofvRdXMWzoKgIbVkRgdrGyH2Vvb2c
Z7XSkJtD+rtiYxpv0p6kTuL80vN5teLVST33pNDZiTUzk3yNBuNgL15JKKgKTAVYHVYabM2kdCaB
wSD2CAsXwltvJbQ0hEmEHI2GQZl+wkqEV7q7uXJEIH50LsJxUyJMsuIxjHBDQzSZFK54nAz+K0mE
KoeHIq2WYp1uwpaG7sFujBojepWeQnNhzAIGhnxK6hT2Wiz89JRTeKWhgf+preX9vjiprSPR0kJP
ahK91lQuHbomyCzZmLHz9F8G2blTLFpeeAH+9jeQH7yW+995jtWr4fMvJHr8zZEFS15KNh6VOLE3
NYGu1El1Xh6NHaOnAMAoEiGa/QvbGY7G/vT112JiHjA2MsfqR1dZSx96pum0FGm1qPI9MUqEBTli
Q375zMt5ZPkjLF+3fELTqlGZCGr15O0MfX1w++1c9I9/8Fn0+yGenaGlRRjYw12hIBY0M2dGGhqa
PR4KNBryhza2x4pGeyOlqaWcmn8qR9yfMm/e+CTCE0+Igc0FF4j3zoGBAVKVSvLUWs47D375zCfc
8s4tzM6cjZnBCVsa2pxtlASMkJdHkTF2E1ZVFV+SfzwQCAV4rfI1Vp2wiiqXi0/sdm6wDDP7YcKm
0FSIWqEetXEJI6xEkMnE5D+epWFDVxftXi+zMmeRa8yNK+mMB4PawMKchby08xOqT1Jww7wZAOT7
5LQrR78PXn1V5DPIZOJr7VphV2huFpzWt78tSIZoKBRwzz1CjXA0CNdjhpFrzE0Y5FZbIyHTH+a1
itf56Wk/HffY8ZQIbT4fCn8fZq2ZTz+FFKUSmcpErjEXuUzOA0sfoPun3Wy/djs3TP0lFe+fylVX
qpiRMRWrZ4Ik7FGgob+BXzQV0tR3fJQIDzQ18UHUuf79uvc5veB0DOrEct1EqG1vR+fz4VOlT1iJ
sL1hO96AlwWWBbxW8Vr843q8KEIevhpMvPj7e1sbTR4Pl2v7SNYkx0y7TVoTMlc/5b0TO6e9eOhF
rp49ukZxaupUanonTyI8s/cZisxFLJ+ynOnp02OsM032JtL16TEba0ebhQGGSYS0t7biumAFGrOO
Gb9qob0T7szPF0qE6PNfZiaOum4efEBC6+jCbxZ2Bnuzn5WXOnj66WElwrZtIm2/6MqTeO/er+no
gLNmqekKhCgwieaGuXPB37g4RokQTc5EQyaTjVv1KEkSA76BmGT6o4W5X8I4OEj0tKKvXY7GFCQQ
CtGSnEzRtPibMavDSrJKgzspiWJ5GgMmQcS0hAbINeaSnZRN50AnekOIRq+HkhEns53WnShkChbn
LsbrZXSwYtjO8Omnws8VR8mQEMnJGAZdBCUFyQ4vX+fmEsjwsGfKtBglwta6rSSpkya0cD49/3QW
5y4Wa4C6OtgnahdbW8Vaqc/TN6qFIxGCoSDd/gaqvixBkiBVl4pfZSb3n89QfOQIPSHVuBv+86ee
P6y4O+88RlZeJWuSIySkOxikxt427qYjPEVsd7bHqBw+PXIEn1rN10Oq1YunX8zN82/m2xu/jTcw
NqkY3pjUud38rKCAt3t7afZ4yBwYiG+zjcLylBQ+ADj9dCHPGwdhEmRCeQjPPRffG1hZCTNm8Htr
O+fkncjlmZmkKJXsGRjgDy4XKx54AOOuXRTs3MmyadNoNZlI0aWMykWodrmYZrOR7R1AZywZM7X+
Vx0d3C1JzGlqQiMvZfOOTvbXDpMI6fr0YRIhQaPP6QWn83HrPm6urmbtwYPM270ba1Nb5H104IA4
F5WWQmFGOtu+iD/Nd3gdvHT4JW488WbWrROK/aQkYWGqHto7zUtKYv8kLA12O8zY/QLJ3xOBtvqy
IvICjZHIsUqPhzO9Xga1Ooo+F++xFkcLsz+pEn/7qLD0Y7E0TCQQVOrspCUlRYR1u7pxSGKIpFFq
yDBk8LV1N7ODMuoXLKBicDBiaTjrLPj449HHi85EcKLGMQElQlCSeDUqDyGM6JpHq9OKU1vItZVH
p+qDoWDFSVY8hlFkLkKn0ok1n8sFDQ0od++hsH9yCpX/ShKh2TdEImi11E9w8xSdFVBgKqDZ1ihW
+UOsUK+7F61DiUqSyElN5axbbmHT3/7G6gMHeGe8KsmWFo4Ek7CY04gQs3I5FBcha2zgxz8WIaDv
vSf2tEXSOXhVHZSdeZi/PtNNkkYfWezlmSxIhg58PuFh8ue4qcnPoyGRv3hEQ0P0zxkOVpw1S2wQ
J4N77hG2xSZ7A0VNDmR2O3pNEVlyH4VaLaEMQSIEQ0H2d+xnvmV+5Huvnn01Dy17iHPXnRvXYx2G
zW1jf1/LqEyESSsRfvITpCuuYIdcxvroeq94JMI998D3vgcjpzNRuQjfhBKhyFzEkvwz6daLEJe6
usSPr6sTE/ZVq8RFw+WCV2r7OSclhS1b4DbrNTTuvJj1317PJdMvQenpnBSJMLOmHzo6mNIdjCER
qqu/ORLho4aPKDQXUpJSwh+bm/lBbi6GqGTn3AiJUEShqRC5bPSpKRAQGQN5Q+uoq68WQWHRZR5W
j4c1lZWs7+pCKVfS8uOWCZMIIDzNGz/+GLPHyYnnngNAsdZIpyl2ciZJw0RBGOnpwj5gMo39HFdd
JT7biSqFEqGyu5Jedy+nFQzXoo2lRMj/+lXeMqzgZPntE1pkxyURvF4kbw8fbk7hjDPA163CJSnY
unor9y+9n6XFS9GpxObi+efF78NggPmF0+ij5htrrehtreWWv36GbKJqsSG0e73cVVcXw753+3zc
39TEligS4e3qt7lo2kVH9dpqg0HO3L8ftyx5wkqEP37xR+489U5uXXhr3LwAgPaAnAVyG/XElxDv
dzr5bWMj62ZM4webb+FP5/4pRs5s0phor+7gvYrxQ9VsbhsfNX7EZWWXjbpvatrklQgOr4Pf7fgd
fzj3D/T5/aSknxRzbajorohRIQD0NubQ5xckgtVh5YKv+zHfeBv7nE4qZrcQ+F0ZMknGdL1+WIkA
hNIzCHV0sXKJk5BcyTvb9ZR3e3G1Spx0RoIF4OLFkcl0lkqBS6aPeKDnzYP23YvY3babkBQaU4kA
sbaoePAGvSjlyknLRuPB4JRQKWViKjuEnlYFKkMIa0cHmXY7Gkt8GXZjfyOZumQGjUYKZWaafUGm
63Q0eQPkGnPRKDUYNUbU5l6a/e5RSoTn9j/HtXOvRSaT4fEIEiFJJmNg6G/hDYUEibB9OyxdOrkf
TCZDbdBCSEG72czegBzT59m8VjRdWBGAsowyejNWklZ0xTgHE7hpwU2sKF0Bn35KdUEBvsNCLdnU
BEUFClINFta31fN2T8+4X880lWNKnYssoKe1VagL5NoscjdvpvCpp3BjmPiU0eUSvpoRsjqTxoTD
66Df72fRnj38WVrEV5r54w42dCodzfbmmCaKD3t6SLPb+doxnAnyizN+gcVo4Qfv/mBMabtQ6abT
7PFwanIycw0G1nV2ktnfD+npdHbGxHLF4ESjkU6fD+tddwn76DjT77ASYVwSweMRv7OqqtHHPHKE
ikWLeK27m0emTGFVZiY/LyzkmenT2TZvHvV+PwMPPMCOefNQ+/1sy8oiVZdKz+AIEsHtZqrTSYaz
F7WhKOFL2XXoELszMvjeueeyvLUVB7l8+FUX73/egS44bGfoHuzGG/DiC/owqkerkOZmzaU67UIu
MyVx5n33URoKYe92Rdbz+/eLcxHAiiXpfLanJ+7vfd2BdZxTcg61+3JJTRVbBBAKyboBsXc6MSlp
UrkIm1/sZxkfYPiu+KzJigqZk9zIwYOCFD2iUDAzJQWLRokbPTQ2oqhvpOQXfxJh51H28PNKhVK5
xd4y4ecHMSWf8rcpvHz45TEf19/djRJQEwB3J92yYfKnyFxEg6OaE/DxlwsuYOn+/bRdeCF89hkn
l3RRWSkIkzDcwSDOQIBMtRqDQoES6PWPvyf43G4nS6Vimj527RlDIjisdCjS+Kj/6GvWjzZYMYyz
i86m9e8PCTX22WeTd/uvuO+F0dbdsfBfRyIUFUG3XHwQSrTaCSsRmu3NkQl9oakQZW2DMA0PTfF7
3b3Y7XIWhOuhdDpO+9GPeOeBB7jhyBFe704sHQo0WdnWaGTx7Ng/nnJqCS/eV8+XXwoC4cUXBZlw
370KvnfqamwFz1G6sDFGum0xZqMwdTAwAIdbfMiUIQxeHwdbE5x8RyoRoiqA0vXp9Lp7abGGwqT7
hLBtm7iwXnedWGik17aCxYJcl4/e30ORVstAkiARjvQcwWK0YNbGLmzXzF3DvWfdy7Lnl1HXF3/H
/MSuJ7B6PaTIhy9g6ZNtZ/jgA9ixg8Zf/ZIBv5uX2luGk7pLS0VeRDiYYvdu+PBDuOuu0ceJJhGi
lAjHSiJIkiQqC82FTNeegaJkB2VlYysRnnxS/O51OjHhXrkStjY6WWQ08uijcJH2TVbtnsWykmXM
zpqNx1lLzQR7f1udrRRUism1uaIeb9ArFir9Yi3T0yNsNMcb4Ql6q9fL6z093JabG3O/QaFAr1CQ
bipJmIfQ1ibOdaqhtfe0aYILirYG/L65mRKdjk+GTsyTrc9ZVrIMp1bNJe0uZENPNMOSQUemLmah
dfCgWDideOKkDg+I13/XXfD730/u+zaWb+SKmVfEECy5yblYnfFJhCmdmzipux39nh9O6PjT06dT
21dLMBQV1Or1EPB08os7TZSVQW+jir44FaySJKym118v/j03bypSSrWosI2DZ58Vk+CjhaamieL1
68mq7ZuUt/Oehgb+bLXyane3GEE/+SRPtrWRrVZTPiS3DYaCvFfz3rCXdhIYCASwy+UsOXgQz4CM
fu/4C4SDnQc52HmQa2Zfw0XTLqKmr4aK7oqYx0iShF2WxI35pbhlWtpGnJcGAgFWVVTw19JSNh94
ikxD5igVgUlrYrDTSqdm/HPFqxWvsnzK8pj09DCOxs7wx8//yIrSFczJmss1lZV8bTidqp5hZruy
u5KZGTMj/w4EoM+agd1nwx/0s3/bi+R41HiXnME1lZU8NqOULLRs384oEmFAn8lUczdqezfBtEye
eQbKu7wsKpHjCcUJVoSYcEVVwIlGnxuxDM2cCd1N6RgUKdT01oxLIiwtXsqOxh0Jp9pHE6ro9YpL
nX0EJ9WWlInJLBckwtDnoLNJjkwXpK6ujhKnM6F1oMneRLbOzGBSErmhJLpDKlakptIZFBkggOh5
N7fRIYk1Vxhuv5tXKl5h9dzVkden1YLhgw9w7NgBkoQvFEItk4lr7mRJBECdpEdCydMXLOdbaWmc
0JHFO3l5ESWCX6ZGXrCKutSVfDaJhXjok09Y8uSTXDZ7Np5gkKYmyCkNMDjjN/zR2sHT7e0Jv55s
beFnhz/h9oMf4Z/5G+bPFzlCgVAISWkm/8ABzOm5pAazWHamYVzFISAWt6GQ+G8UkjXJdHv6+dbh
wyxLSeHkjn+SpDYyd/duvltZGTlfjYROqaPF0RKjRPgQuP3wYXZFqT7lMjlrL1nLTuvOMRtX+tx9
6JMKyVCr0SoUXJmZyfMdHWT09UF6OmeeCRdeSNxzvUImY1lKClvz82H69HHTkKOVCFNTx6h33L5d
rH0LC0dPyCor+enixfy8oIBUVRyi7uKLUR04QHF7O0u6ujiUnIxBnsLVN9iI4liEncHlwmxrA13i
PIRfVFbyy85OdDodK7xeanQ5dDq7MFo6+Oej2UjSsJ0hvMmLty55pdeGxjiV7775LMyZw9TZs7EH
NJF9S1iJALBwZjqK5J5R8ntJkmICFdesGb6vsBCsgSgSYRIS5c7HX8G2Xj7UhQAAIABJREFU4FwI
Z2AUFVGiaOLgQZEhluTzYS4spFhn4JWFFgLPr+Uf62zIfv1roRj57W8J/3I1Sg2XzriUDeUbEj9h
HGyr34ZGqeHH7/94lPUkGtb+fvK8XtqcbWhkbtqimmvCa8syBtmfm8sCo5ErGhvxXXwxmk0vs3ix
EE5FjjVUQR8OM09ThOiZgFjplahAxWhEZyJYHVbaQyJ3zXGUtfbHYmcAuOXLAPMee0Vc+xobqXj5
MUrbJqe+/q8gEZxO4QcqLhZfToOH5s5d+F0tE1ci2FsoSBbyxExDJrMah07AQwuHXlcvzV4F86P/
mOefz2KzmS1ffsltNTW8HKcu8LknBvA5vUxbouf0+SP+eCUlCXeL1867lhcPvUi9rT5m05SdlI3M
2M7AAOzvdZNvD1Dc3c9he4KFX3GxOHsPTdHCCb4gvOBamRG0tmixwpiQJDGsv+8+sdnp6WxA09WH
71vfYlCXSnCwhSKtlm65lxx7JY2vruMyT0ncKsUb5t/APUvu4ZznzxkVZOkJeHhs15OgTKarf3j6
maFScajFH1NtOCZeeAHuuov1nQeR28vxeXv4KnwlMBrFSa+1Vfxgd9wB994rbh+JEUqEfI1G+PSP
kUTocfWgVWqFtNixkGBqM87CtoSLC5dLyOK/973h21auhCOBAZK7kmg44KCwf5BFTYIImJ05m66e
vZNSIqTuq4KlS5Ht3y8aGvqbqK4Wm/JEWZTHAl/Qx6Yjm7hy1pX8paWF72Znkxbn4p6n0XBi0XIe
O/+xuMcJ5yFEI9rS0OLx8FJXFxtmzuRTu32YTJoEklxzqCpbxOVnzojcVnhCARqvL6bu87XX4PLL
QfbqK2I8MEl/4Zo1QokQJ68xLiRJGmVlgKFgxTh2ht5eODn0MdqQHPsO64QUAXqV8H1Gq1NqnQ5k
bgf33K3gssug9YiS3jhKoc8/F1aNk08W/56WNg3SamhqGvVQQOSr/fCHw80jk4HD6yDVm0mjxYJF
mjahtgKA3Q4H7/f18eqsWdxdX4/37bdxP/ccT7S28lhpKRVDG9EvrV+Sl5wXkbNPBnUDA5S0t1Nq
MmHzyiakRHhk5yP8cPEP0Sg1qBQqrp93PU/veTrmMX3uPkLaHFZ+Wo7cfpB3u2PfOLfV1HCaycSp
GjcPf/4wT17w5KiFqkljwtnXglfvo98zdhDr+sPruWb2NXHv87ZPzs7Q5mzj77v/zn1n38ffrFa6
fT660HEkioio6KmIIRE6OiAjTUGmIZOOgQ7kL71M48pTuauxkblJSVydmRlpbMtRq3GFQvQPvS97
ZRnMSO2Cri70RZl8sVPCY/Ry1ixV/HYGEKvzqipxAvZ2o9BmiYP39qLViiZgV80idrbsGpdEyDBk
UJxSHFMXG42JhipKkhBHfP/7olHhZz8T5+jHHhs63dhs7Jo6A2MWgnUZsuO11SuQNCHq29uZMsbi
tLG/kVxDGoN6PZagDofcyPLUVPplenKHcpUsRgsBSzsGSYk+Sjn2VtVbLMhZEBlYhO0Mp65dy47i
Yrq/+krYGbxeceJeuHDcn3ck1MlGAnI165dfwC25eZyWbKLBpKdjaPO8vrOTnEA730vq54qKChom
eA3c19CAWafD0NPDxYcPc6jdyxeXHiA1ZOfBFCdvz5496uu1mdM53/Mp+7ddwJyeDRw87TyCiiTK
Tvaxd6/wl2v9g3Sp07l5/+U4krNYOaWSH/5wAiG6Bw6IdcpIEkFr4jftLtJVKh4pLaXHVsG9BVnU
nXQSU/V6ztm/n4sOHeLT/v4YIlWr1NJsb45kIvT4fNSr1XwvP599OTkEosgHo8bIm//zJvfuuJdP
mkYEeA2hz92HQl9AyRCJ9O2MDNyhEJmdnTi1GTQ3C35g0aJRPwIAZ5jN7HQ4hNfv4YcZlTYehQkr
Ed54Q+Rfxak53+b3U2UwjBpURKDRCDngc88xu6eHQwYD9s4UBgI2du4UDwlJErVuN6WBAKauRjzK
VKzWiHA5go/a26kPBrl+5UoAFqam0qPSE0xzos/swNaSzVNPDQcrxmtmAKGg/HFtLVfL6in6+/Nw
991MLSuj15AeV4mQacjAmNXD7t2xx/m48WPkMjmLMs7kjTeEWjOM3ClBBgmQpVZPys7Q0QGLq18g
48ffGb6xqIgsj1AiVLpclLW3w5QplBqMfDjTguLe++hM1yH/4e1i0nLeeSKLbAhHY2l4ufxl7jjl
Di4vu5yfbk1sz2xxucgLhWixt2DUgDWK/CwyFaFCS7Gsi4NmM+vKykhTqbhzzRpYt46zz461NISt
DGFkKOX0hcbeNieyMsBwJoIn4MHpdVI34EEjSRyZ4BBwJEYFK7onGKwoSfDgg8zf8AkrbtISKhPr
3W3VyeBHbLgniP8KEqG8XOzxFAoxiQyke3hj3zN8XvPqhJUI0VkBMpmMs7oNeKZPiUgY+9x9VCl0
LBhZWfOXv3DiQw/xgdnMT+rqWNveQXm56GtdvBg2PtKCsjCPX3/r2uEqmTBKSkb53MKYkT6DfFM+
z+x9JhKqCIL5D+mFEqHa42LagIspA27qfQkukHK56G06dAhvwEufuy/Gr6kNZZAztZtDhyb0a2LT
JlHTduWVIlxsaqsHZs6kbv58su02Wh1NFGo0NHk9bGAV0/+8ljueOiRWN5s2jTrerQtv5Sen/ISl
zy+NkV2vO7COmXlnosfH/vbhSoAMlYq6Hj9XXy3aoMbF3r2weDEbG3exJD0Pee/nPN0cJckOWxre
eEMQLeFR6UiMVCIMVbq0+3wEj0GTHbYyALQ0qknKvJ4/qo8kJBFeeglOOUVwQ2GccnaQwWQPr/zJ
wE9Wfsy+XAVJ/kGcFS2UppZi6ztM9QROQv6gH4ejB/WBw3DrrbBvXyRcMUwizJhx/C0N2+q3MT1t
Oka9hWc7OvhxXnyZZ55GQ29QxvT06XHvD+chRGPVKnjzTRE49GBzMzdZLMxJSiJdpeLwBIKcRmLt
y58xtaWZI0nDG/Pk2YWUWls50Dv8GXzrjRC39/wa7rxT7ITjmenGgF4vQhbHq6kM43DXYQb9g5yc
d3LM7bnG3Lh2hvo9Nop83TSfs4D53p0jG7YSYkb6jJhwxc27BlG4PfzoR0IW2bA/vhIhrEII71uz
k7JB5aa8bnhC2O/pp7yrHEkSH9viYtFAMlk09jei14ogNUk/dULhipIk8b+1tdxXXMwl6enMNBj4
u0LBC9nZLEpKYmVaGr1+P45AgLer3+bCaRdO+nV1dsIH22op7rKhV+XRgWrcTASrw8rbVW9z68Jb
I7fdOP9GXjj4Am7/8PvtYE8dSrmWnOuuY2a/lbeiwhWf7+jga6eTv5WW8v3N3+eOU+5gSmps7SRA
0GVCUvaj6dGxpTzx+cLqsHKw8yDnl54/6j6/Hy45x8KA1zVhq8ZvPvoNN554I73yFB5obmbjrFnk
qFRUDAxPUCq6Y0mE1laxcc4x5tBmtzL3w8McuOF23ujp4e9TpyKTybj6apEX098vY5pOR/XQJrIt
kEmRQZAIiqwMfva7ADq1jEydIXEollYrElL37cPtshJSJMPNN4sgQOC734Xs4CL+8dYuOgc7SdNl
8q9/xbgIYrCseBm7vngV/vznUfeNF6rY3CzWF2Vlwq6bkwN79ggL/wcfCBv4iSeC/asKvjjhBDKl
HrjoosiLaamVE1AEqXM4mDJGDkFjfyMFSZkM6nRkdjjxqtJYmJSEJAUxGESDjyXJgjezj4xAbB7C
cweElSEMjwdMjQfIq6/nfwYH+dOePcLO0NoKS5aIVNlJQmM24dXoAIlTkpM5YYacqdV+NicnI0kS
T7W18eSJK/nDgsv5eUEBFx06NP5Er6uLLfn5rMzN5cWnniLb5+ORBV9SFjJxTvAwnYOxQYMhKcSL
B1+k7Iky3qp6i3eufocNl29gRvp0FhqN6Oc72btXTCxTnXb2KSw8+LwbmSzITc57aKiX4i2LYrF/
v1hwjVik9ZlOweoLsa6sDIVMJpq3kvNJUan4RWEhDSefzIVpaVx35Ain7tvHx0NSgJF2ho/6+zm9
tpaMWbPIHRigcv/+mOeZkjqFdZeuY9Wrq0ZlheFyMe3Pz6GR0pgylImRpVZzlslERk8Ph5pNzJol
Wod++1s4++zR67b54an32WfT7dLTvSFxqrBepcfqsOIP+eMSdbUuF7+trxfv9SgSwR4IsKGri9sq
Klh92WX8oaRkuBkkHq6/HtauZXZbG4c1GlprUzBb+iLRIi1eL2lKJUk6HZr2evwyNZd/J8iPfzx8
CEmS+MWBA9y7bx+qoiIAlNOnc2p1HcoZZjoGO3jxqSx+9SsY7E6ne7A7bjNDSJK4rqqK23Nzue2I
k06tH844g6llZTRm55DvVuPxCJvrzKFTZLo+HZm+hxF/Sp7Y9QTfX/R93nxTxsknQ3ZUtJSx1INh
UItcJmOaXk+HzyeCk8fBe082MkdZjubSlcM35uSgG+imYr+PIy4XZbW1UFJCkVZLx5RpHLzsNP56
0+zhBcH994vO8qGpwllFZ9HmbItRo40Ft9/NW1VvceWsK3nwnAf5oP6DUfWEYVj9fvIVCqwOK2aj
ltaoPV2RuQiDlEMo2U6qTEaqSsXzM2bwXlISLxQVcX7xkRh1R7jeMQyLWoldGjuoO5GVQXy/sDNY
HVYsxlwq3W5WHDggshmOAkbNsBLB7XfTNdg1dosICALhrrvgpZdQfb4TuyWV8q5yAA4cDnLYbCB4
YGLB+PBfQiKErQwA7lAQmSHI6Rvfx/TWRho8ngnJWZvtzTFBN4usIWquXBFRIvS4ezlgTmX+yA1O
fj6hn91NznU/YcX7c7lpZwNLHm6jvV1c6N98vAX1lHzOKDyDqWkj5FdjKBEArp17LdsbtseQCBmG
DEKaXvodQVoVLua1NzBVJqddOcYmcehE2upsxWK0xNTiyFyZnHNx14SUCMEg/PKX4ueSy4Xc8Sx7
CrK5c6kqLWV6WztN/U0UarU0ud3kBxu45Ppiyj94UcjLbr4Zvvhi1HFvP+l2vr/w+5z93Nm0OdsI
SSEe2fkIs8pu5gS1jz3te4SGvqWFZFS4VH62bhWTyjGJBJcL6uroKc6i0u3l8sIFXJWdx+vd3Vz2
bQm/H0EilJeLMc6f/iSYqCjUDTqwbH+D5nQz2GxIdnuEfVTL5aQqlTET6MkimkSorwdFzil0IKd9
0D9qeC1Jw4GKMceQDWDs07Npo5yTU96jZVoW+81n0bXxIxRyBTNNGdgCfgbGuSB0DnZypt2MbMoU
URq+bx9FyYUREmH6dEEiTDY/YzyEJ+h/b2vj4vT0mJNyNMbLoIinRAg3Ivx7s4eNXV38dCjr4gyT
iR1H4TX7QmNlQV9PjKdZlp3F9NZmdreITVN/P/xv5c3kVm8X54/rrjsqbf611wql30Q4qg3lG7hi
5hWjpsuJKh6dWz5nd64G9VnncL55Z7yPZVyUpZdFchHcbijvcpOt9yOXCxLhyG4lNr8/RuXhdAr+
cPXq4ePIZDJSpFL2Ng5Pm5/d9yw3vX0TDQ1CDPT3v4vBlCPBcDgRGmwNOMxTMLaEaMwtprW5fNzv
2dDVhSsU4rtDq6qHS0r4/cKF/GHVKu7wepHLZMzQ66l0uXi35t1JkwgOh/j8bP24CX2nnM2b8rFq
DfSPs9H+21d/Y83cNTFhSMUpxSzIWcBbVW9Fbvuit5m83m5kRiPf3VfPTqcgz6tcLu6oq2PDzJls
PvIaLY4W7jz1zrjP1dFkQmW0k+3Vs70u8fXk5cMvc9mMy2I63MPYvx88bhlmqXRClobyrnLeqn6L
20/9GVdXVvJoaSklOh0LTWa6ZMmRXvjK7sqYTIRoEqH1vY3UZxj5tTqdtTNmkDKkYkpNFa0nL78c
a2locmWQq+oWbTuZmVx4nZdig4ZkTXJiJQJELA12RwM+mQa/xRIJLJTJ4He3LmJP+y5q2rr44fWC
RLjhhvi1YOcUL+XU3/1bpMePUO7EUyJIkrA7Ll0qCIKWFmH5qaoS1+TCHD9897vMMTezZYv4uf/w
SB26oB91wBEhEQYHwdGlwEOQumCQktTE0tYmexNFxmwGc3JwPfgwcpQogwPIPR341GIDakmy4E4b
JM03fM7uGOhgp3Unl84YbhbweiF983OwejV3n346/ywowOp0om5qOiorA4AyMx1lIMC5Bz5GJpNR
Vgbmr1S8k5PDLqcTZzDIygyx3vlhbi5LzGauqqgYm/T/7DO2nHUWPVvSceaewNr2dua9NIef6KaQ
k2SJtBVIksQ71e9w4lMn8viux/nXxf9iy3e2xGQ/LU5OZjBftH20eL2ktfXjOzGNLk8LOqWXJinI
m8uf4Ec/it8/H8GBA0LWZrWKNQ3gD4WoSVrM5epOdAoFDq+DkBSKsY3qFApuycmh6qSTuMli4cah
C/dIO8OHNhvnfPEFTJvGosFBvm5sHPUSlk9Zzh2n3MGlGy7F5Y86N3z4ITPf+Jyy7VZKomRlj6Wk
sHr3bg4ckkck9t/5jmj0+ulPhegg7Nefk5REhcvF5g8k/tm8nPKnE4cBOXp1PLz2INPSpo261h0Z
HOTsAwf4S3Mze8IJg0Nr3yvKy3m6rY2Sjg42r1/PZSOnDSMxbx6kpVHwxhs45XLq69M5+SxbhESo
cbmYqtdDcjJ+Wzdp8gAHOj1s2jScZfRuby8Om43/Ofvs4eNOn87ynbvw5WSiU+qYO0vHjTfC43/R
Y/bJ6d/x4ahmhifb2nAEAtxdUMDsZzdz7yk+fCE/CsmDRAjf3nLKy8VSNjwUT9en41F0x9iUrQ4r
2xu2s3rO6lFWBgBVrgdVr/gcK2QyTjAYODABNYLn3+uxLbsCorLLUCohJwdXVQvltn7K6ushO5tC
jQZVUgEPXJlNiiVqEpabKxb099wjnl+uYNWsVRNWI7xX+x7zLfPJTsrGqDHy+MrHueWdW2KI9sjv
AcjT6UT7QUoKrampYjGDsGzqvcW0ZsqYN/TzmFUqXj/hBH58443IGjdRXT1szRmpRMjVaBiQjb4u
RmNjV9eoVoYwokmE9NTZZAwOctLu3VQk8n2OA6N6OFhxf8d+yjLK0CrHCLANBkWV2I4d4stiial6
rKrxcyjZSNvW/8MkQqPHg2YQvrsPrv3aDlIorrx2JGI6S30+iltdLN9eSqi1DX9PFy6ZHikYJG+G
kHW4XGIIcf31kPfHH9F30Mp5Fe/xasFckr/XxPS7rCxbBsoO6+iQvjBKSsQG9tVXRSPA/feLeqPV
q+H887n51meofgxO/XJ4E6CUK1EGUrHaugjprcz/Yhszz12K3ehOvNkYOpHGqwDy9mVwwuJuentH
+ylHYt06EQ533nni3439jSzsUcHcuVRlZjK7tp7G/gaMSiW6UIgjc8+k2ntIXFgXLBAHuOyyuLvQ
O069g+vnXc/S55byr73/wqgx8pXfyDWZmYJE+Mc/4LrraKtQg9nPqadKvP/+OETCoUMwYwYvVL2C
wXwCJ6dk8osTV+H0DbKpvJe9exFn3gcfFB03K1bEfLs/FOL0L7fSEVLxVMNemD6d3iNH0MrlGIcm
J/la7THlIjT2N1JkKgKgstWPKzkFg7uRtNMdo6TeX34pFhsjK7T3Dgwwz2DkpptA/f+4O+/oqMqu
7f9mJpMp6b333hNIQpeONCmKUkQEFQsqdgF7VwQVsaAgPBaKIGADBCmhhhIghBRI7733mZSZ7487
vSA+7/et9a5vr8UfZNqZM+fc997XvvZ1pVxCExpAgfd4dMfFjR9qG4yVREvGP9A5i+qLmFiiElQH
OztQqQjVmJFTk0NqqmAi+Pn932UiaNo0/JH6BzMD7mFjQQEvD3SvpKbC00//I4gwEBMBxEjDuuJc
HnV0xLpjYxhrbv6vQYTEhAqSgmx5aO40jmYe7QYnpVIcSxu4USQoXnFnNMzX/Yz0r0PiPM6ZIxaL
f8lYGT1aYGd9uwl9Q6/Xsyd5T79RBhC2Za3trf28i6UXYzjtpsVh8t2EN98+iNBTXHHvPj0Sn1ac
DeohLw+/R8bQnF+HkVTWq+O3Zw+MHStORc9wUvpyo6y70DyXf464ojjOxTWwwuFXgutiufPOAZu1
t4ycmhzy7d2IKrHlgn8gbRdurVDZ3N7OqqwsNnh7I+tITAPr6rj7wgVMDA0Z10GHDTQy4kJ1Kbm1
uUQ6/jv6dWysyEtdI2oYH25FRp0/0vZ2atoGp+7WaevYGr+VZ4c/2++xmT4zOZ59vOv/CXWVBOfk
wwcfcP+hazTpdNxsbGRBSgrvurvjatDGc0ee49uZ3w4q2JeXZgbKWgLURlypGLzzMZgrQ+f3dHQE
fcXtjTSsOraKNaPX8HZBBZEmJtzfcZFEGJtgajWEzOpMCusLMTI06gWk9AQRZDt38dzqN1hoa8sE
i95Uzc6Rhp4gQlqNLdZ6wUTA1pYCrRZnhaJX12bAGDYMLl2itCwTm5paSjZtEolWR0wNHYrMMYGy
pmKefsiWc+fEljdvnpDe6RnjT2QjralD5+7W7wYfCEQ4c0bgDStWiO++aZNYprvqqDffFB/WwYxY
uxYqrKtwLSgVifS4cZCURO7lctwdpDTpdGQplXjdopjKqcnBy8yJRqWSzLVrCcgvoijtCm1NedQg
umgOJg402MhwbujWxthxfQdz/Of0ci5p17RienAnLFmCi4sL87Oz2VJSgiIz878GESR29ky6coVR
eaID4u8P9UfNOeHmxsaCApY7OHTNKkskEjZ6e6PV6XjxFqrFNefPk2DvwP63zNhxPYTE7YnUn7bA
w12CvbE9JQ0lnMk9w5j/jGH1sdW8M+4dYh+KZZz7uH7vFW1iQrqsjuZmOBinxa2wAt0wNfl1+dgZ
6Ml5/318dr/HisAY3nprkAPS6YTATmSk2IA7RlJ+Ki3FQt+EbYsY1erUuhpoll4mkbDU3p6K1lbK
WlpQyVUU1hV2jTOcKC9nYlYWmJgQbWRE3CCF4wsjXsDf2p9H/3y0e+87fJij90RwftIIvP7zH3jj
DQAC6+txlsl6UewBhgwR+pAXLwpnqaoqUMtkuMqVPPR2I673Dkd+5cKgv09FiYpLeQlIqnqPMiQ1
NDAhIYF33d15JTWVLzor5NBQrtbVkdLYyF+hobxw4gRD/AZmMvaLZcuQlJfjUa/H2tcOS+dqrl4V
9WZaczO+KpUAEWoqsWiVYh/RzMaNsHw5NGv0vJqczHt79yLrmVd6eXHXqRh0lsHYdQizPvdQLU7b
3uWXfY4sthmO3LC7uE5rauLN7Gx+DAjA4PhxZNpW0kf4EV8cT35dPpZ1pWSkpHTrIRw/DidOYK22
pra1goKCbg2rzVc2syhkEQ1VJly82NvtHKDNRkN7YXeBGWFi8o8jDZkZeiYW/4Tjyw/0e0zq4U6k
dQ7XSqoJaGsDiQR3pZJ2QxvO5p3tsprvipdeEozNDvb3wuCF7EzceVtN4F1Ju1gYvLDr/7P8ZhFm
H8b7Z97v99x8uRwXExPy6/LxM7WmyNoafQc9f4zrGEKu/0GqnTFhVt1gToixMRstLbnP25Oocdpe
jJSeIIKrUk2TdHBXnXa9nn0VFQOOMtDU1KWJkF9bgMosgMDMbDxKa0gpub2RzL7Rc5whriiuy9Z8
wGhpEclyZqYQvrO0RK/XI3GYzut1jjybnsENbTs5zqbUnP3/HEQw0VbhUdnO6MxWTHX1tx5paGuD
xx9n0oEb3TOuiYmUWVtRoiyj0nUIDbEnsVOHMTQjg5s1DsydK2hAn30mbtxzl+R4/72J+ReeY7Zf
OyfDw/m0oIBP8vMhP59MX18+zsvjrw7Bn67w9hb8o127BBexuVlUQZMnw9NPY7DpW2689yxDPvxe
DFl3hKrNgaycHNSOJQQ88hChbnZIXJoGH1MJDYWEBG5W3OzlTazVQmOZLQqrMoKDO9hyg5wrrVbQ
0T78sDtxya7Oxr9QC6GhpEql+JaU0JQrNmj3ujqyJoVBvSOmig45+s65p6lThYx+n1gzZg0Lgxfy
6IFHuT96NQVaLcvcw0mtSKU95gScPUvi6RZkUmjS6QgLowtI2LNngIO+ehV9RARbrm6jSW5FgFrY
d1pU5sPcM6JD5Osrksn16/u9/J64w9Q1FjNal86BihIICCAvKwvXHovG/1QXoScT4ZpRBcMMrGit
vIhhdHE/ksqXXwothL4svPiGBhYMNeaLL8A0JROjYWPQjBiP1XUBIoTYhqBsrfxHXYTCukKi83Qw
cqT4Q0QEwYVtZFZn/j8bZziScYRQu1AON0gYaWZGQN9xoevXBQfyP//Bpbb2XzMRAIbOaCbPrZyH
jbuv/TvMzTldW9t7gyosFJ937pwY9OsTW389TnRKEZFBw5BJZaRWdoNhdjU6cppFAZK/9yIVdkFg
2pFY+/kJD6UrV27jjHSHVCrwxB9/vPXzrpVco03XNmBhK5FIBmQj2GQcJdXLHXlYBKaNRSSdvg2r
WnqPM2zc34xSocO9rRVmzUKSlMgip1MY6eRU9gARtm0THdm+4WXuQ3a9ENjQ6/Wcyz+Hs6kzB6/H
sjT/XdiyhbffFjPetyVA1hGlOckkeXqyapwPhTa2yK7d+oJdn59PtKkpd5h3d/GIj2fD1ascKi1F
0vG7BanVnCjNYKTLyFvaeQ0UZ84I1naGXo+Ppwc2Y/zxKCykTq8a9DVbrmxhiteUrvWhtlZ0oAHG
uo/lZM7JrucWl9XgV1gETzyBSXMbjlVpzEpKwlul4jFHR1YfW81sv9mMdBk56OdlJpvRKq1luKOa
bN3ATISU8hTKG8u5w+2OAR+PjYXnnoPqDB9SK24NIsRkx5BSnoK9+30cr67mKx/B1IuJgdqrxkhN
fEmtSO03ygDdIIKLwpY88whKXL1539Oz32dMmiSea1rbbfOYWGqLqbZcrPs2Nl0gwu0yEXwPX8JO
00ThmDEC1e34UUwUJnhauSFTNvPoAxZIJAKXfucdUTB1bf+FhSgbIe5BAAAgAElEQVRee5MvH4+g
INSDrkHrjqhv6S+suHevKE7mzRvACfH4cUFZ2rAB/v4bEHpF2igpLcnNZBc2ixdNnIh27594uUpp
0+tJs7HBy6v/WAsIy92i+iK8zZ1pbG8nY+xYVLoaHOc/zMjscnKuXIXUVBxNHGm0MsOpSuzxer2+
3ygDwIj6I+hcPcRaCKzx9RXjDHV1YtzyvwiJvT1/rV6NibE4IcbGYGBgQ3BuHnvKy1nWx3VCLpXy
S1AQhyor2TyI2Myx8nKCm+REBMqY/Xoo+Qevk5kp0jIHEwe+T/ieJb8t4bGhj5HweAKz/WcPKs4b
bWrKpfp6Iobo2R2jIbyikAxHOQV1BbgqDMk2NYUdO3g5fiGnvs8emA2anS30ECwsRJKblESbTsf7
ubksTYplxotCG6VXE2yAkEokDDM15UJdHSoDFe36dmzUNuRrNNS0thLSQa2Odnfn0gA0axB7yZa7
tpBSnsKn5zuQ3cOHuRBsRomFLZ4ffywo6QkJguVjbd1L7K8zbGzEZRoYKG6pxETQJpkQPLeBBZ8N
I7DhIjnZ/QtHvR6qy1TYhSaQfNqXCx1YQ0JDA5OvX2e9lxdLHRx4ZMsWfnd0FALczs6smzGD58zN
xfjCiRP80TCB23FmZ9EiMDREnSPBKcyMhrZqgoMFuTCtqUnQ0S0sMKioRlmtxCGymXnzRFq/ZFs5
hmVlzB4+vHfCplCgrDbEUFuPsWU46HTYzohikmsaC1YuxKs4n5Pq8VS1ttKm0/HAjRu85e6On1ot
cudVqxjpNpqzeWfJr83HSF9HelER165BZEAjbUuX0vb666jlavTo8Qtu4vp1oTu15eoWVkStYOdO
0cvr+zM3GmtoyFB29Tpux6Eh5pOrWBppMRgzov+D7u4Ms8vhZksrsgIl8+ZBc46SZpkJxQ3F/a9X
IyPRSH3+edDriXaKpl3fztWOceayMsFg6Rv12nr+zvy7n1PQxqkb2XxlM4mlvceACtRqnC0tKagr
wNPMCWVbG1UdwvgSiYTmfC2Jnh6Ed1npiVgYFcWMGzeonn+emJPiJOV3OLV1fWWVMVrZ4Ho2ZztG
GXz6nvxr12i1t0V66jhyiYTMumIMDJwJSEvnZmY4Kf8l27knMH656PLgIEJTE8ydK2rQgwfBxIRi
rZZZSUmc19nTnLaBr85/TPNj1fy+5DHkaf+fgwgOVekkOgfQ4u6KsiSDrMFAhJYWWLQI/aWLLDtV
i6NRx4DQpUvcsPJDaZ/LVXk0LefPYmnoz5DaWj5aK8HNTaztMTHwzDMd8+ljxsDEifD227irVJwK
D+fboiICwsMZGR1NZnMzT6enMz85mZLOQkilggMHBEDw7bfw/vviBlqyRKjlRUcz6/HPkB0+IloQ
+/eDXo+R3h7vHZ9RZGuF9+zZeKlU6Gw0ZGQP0tUKCaEtOZE3j77KY0Mf6/pzSgqYG9nxbosbzqMa
yT+WKnjEM2YI+nUPBsc334hzPKrbPY7cqmyccqsFiNDUhJ+hISYZeej1etwKC4kPtcSwcmhv4sHS
paKimDFjQA7fG2Pf4O/Ff1NhEs591rakJ6nxM/NCf+Y0eHpSe+AMpnQ7NHQCCStXDgAkXL1Kjqcl
DQbmuClVXcJPDtcCkY7UCJXVCRMEiNN5AXXEpuxEDtY08kfECJa5BnGz3Qh9QAB5xcW9Fo3/qc1j
Tm03iFDoVc4CB1si1IbU+xT2KpxKS8V877Jlfd6guZmr9fUMMTZG0tyEdUkd7iNnYHuHv0B+srMJ
sQuhtSH7H0GEovoiLCtlrA4KYviVK7QOHUpoYRunck+RmtXcxURIT4eKxirii+OJK4zjQsEFzuad
5WTOSY5lHeNwxmEOpB0Y1FqwZ+xO3s28wPmsz89nVV8WwuXLgnaxYQPMm4fztWu3BGwGYyJsrMrD
L92RE791d2DdlErUUmm3YM358wIhuf9+Qed65JF+73PKtplpKucBvd7tWxQUysX9Ij17mpbh3UVW
Y0ujgP1vWw20O5YsEboInbfiQKD87uTd3Bd036DJrJNpH12E5mY8KlNpixgFMhnS6CgsUi/clutG
gE0AN8pvkJqq56a6mmBlAy9/nSA6ZatWMVURg1xjQFXHAd+8KQCAaT3G51t1Otbm5eHn6EdZqyg0
s2uykSBhcchiCjJ/x7bqBhw/joe7ng8/FLhWT5eNW0V5WinGDc1MjlRgUtJMdc3g21ihVsvnBQWs
61uAxsejDA3FPiyMTnWqICMjEurrGOs29vYOpEecOQNjo5vJMDfHOyiIyHHGuJZWY95mQ2t7f6Zc
a3srGy5u4KWRQiCqqko0k8PDBYM+2DaYyuZKiupFMaSp0eFopgKZjOa7ZzHswila9Xq2+PoSmx/L
n2l/8uGkW9t93EhSIJPKmOAro8assS/LHoCdiTtZELyg10hcz4iNFcQbK4kPlzIHBxF0eh0vH3uZ
F8Z9xDOZ2ewMDCT3hgHTp8MTD7fw/YtyGuT2pFak9bN3PFZVxYax5/g8IoaDmba8+shjbPByFHaB
fUImE0z+/FjBRGhpgZRyG5S1ZV3jDPlaLS5K5T+DCL6+UFnJvD8ysLWxpKilRYx9dbalgCinKGzU
Nr0cUh59VNz+d98NWo1e6M08+SSeY+cQ66yD8+dp17Wj1+tpa9fz3GkZuY3e3edK1y3U2i/Ky8Ui
8cMPlN57L/pTp7oWi0t2VgzzVRB7qSP/WbECzx3vEOjSiJFEggywGKRgLKovwkZtg4VcSZNOR6ZG
g8ZZwcHJHgSVVJBZUAAjR2LeqKBVaYR5pQB/r5Vco05b1w9kurfpB9oWL+36v8vkyTx75Agutrb9
UfHbDKm9AAmkdt2D3bYBVtxz8iT32thg15Ne3REWcjkHQkJ4PTubmL4U4dpaDjs6YlLgxoQJ4DIj
lKmO13n/fVHfjHcfz9fTv+bmkzd5IOyBQe+BznBSKDCUSPAapUHh2ExAXi4pFm3k1+Xja2QimlsT
JyJ7dQ1H1HN4dnljf03BnlV4SAgkJbGzrAwXhYJFf57COy4DKioGZJn2jZFmZsTW1nZZ7lqrrTle
Xc342lqkHeBOWGgoN21saB5kvkItV/Pbgt9YF7uOG+f/BI2GBJt2ynVyvOztBWL29NNQXo7eypqk
pG4LwZ5hYCAYZm+9JVhqDfHG+E5vQOZkT7uRKSe39F878vJA36qiWJvJE/f5cu+9cCSnjinx8XxR
VsaiDz6AqCisamu5286OLcXFZGs0HB06lOUdLXn99ess+nIk339/y1MlwsoK3acbyLvigJGnmqrm
qq7bPb2TiTByJCE3q2jLMkPtpUEigc+/1PG7WQbvfPElkj4Jm14PKW1++GbHUW8xUiyYCgVWR36g
yGUYd//9FRMzklmUksL7eXmYGRiwwslJIBcZGbBwIaNdR3Mu/xz5dfkYmUpJ1+lIudLMXTc+Zs3z
z/PyyJFI0tOxVlvjFyF0Efbf2E+AdQCBNoEDjjIAFOs1yKuUXQBLuLExV28xZ6PXg3z3djT3LO7l
8HKwspIn09JocXcnxKeQVqkOucQOb2949XEFTchBIh/Y4nTJElHQ7tuHRCLpJbC4a5fAUfo4nfJ7
6u/c4XZHPy0JBxMH3pvwHsv/XN7lKNXS1Ea+uTkO1pbEl8Tja+WLU2MjhR22sADyolwSvH0IN+4P
BqwzNsZQUsZuuaAJ52s0vZgInkamtBmYD8qe+GWgUYbMTBqmjCPWspG4Ta9jb2hIekMVrVoTzApl
pBeGUCwzoGkwn9RbRC8mQmEce7+IpJ+RYF2dSNAsLARarVSyp6yM8MuXCTc25krUMBofj+F71SeM
/34CJW7hKHR56HW3x6z9Xw8iFBWJWqlTaDVHo8E77wbxJtNQz5yDU1Y6NxsG4OlrNMK0XKsl79DP
tCgMMDjbwemNi+NMSxSOgbn8VhFI6l8/YWgWRIRe1PwvvABWVv3fko8/Fm3DxERclEpiIyL49sAB
Clta+NbPj+tRUXgolYRevtzLcuofIyJCoEMrV4JSyeVvzuJQnoKyTo1cKkUhlaJuUhBXMDBY8vXN
n8g3aufYiE29aHcJCaCK8qdKr0QTVY7i8O9icO2++0Rn3t8f/vyT+nrBQHi/DzNIk5ZMq7kpWFiQ
2tREoLUNQWVQXVOMe2oqZ82lBFsO7WyOdMerrwqp3nnz+qnWSyQSJnlOYldZOc43bRk+HKbXuVNn
Ywbz52MdfxQHlZzyHhnuoEDC1avsNkxnpN9igjs63Ho9lPw0BQwNOV5zHb2pmfi+PeJGQy0rM3N4
zrSOiS6R3OcxgtZ2Ldf9fcmrre3FRHBRKP7Rl/lWkV2djbu5OwXVbbQG1LLIw4p7nAOotZD0AoW+
+05crl1sXb0eXn6ZlsBAbjQ1EWpsjObqJW7YgL9TKAGBEs7Jx0NMDCG2IVRVJfazedTr9ZRotRyt
quLT/Hw2V5oyZv2XNJmaotXrORYZiUlKOiGWkSiCD2BqKro9ltbtRH4bzdLfl7Li0AqeO/Ica46v
4e1Tb/PR2Y/YcGEDX1z6ghFbR/Rz3egZTa1ivtzAbhLuSiXDzcy6Hzx3TgBpmzeL32fKFJxPnuwN
2Jw/T2f7Rq8fmImQ3dzM/vJyXvF26SdS2GukYe9eIYKYmCje98wZerYrLp1OIc/RkseWCurtJM9J
HM062vW4k8KcYlMDWtv0uOWewnqeKDTTK9OxW29H+eRR/6yLoNd3zbx2hrc3ePq388IbbcyfL8aJ
Xnut50sGdmXoGX0dGvSX4kg2NSUqRIgwSkeNYJbN+X6b80DROUf75bYKHGfUMPHUSUw1OiFgMH48
4TUx6GrkXSNk27aJvKBTN61Fp2N+SgqvZ2fT4uKBhSYRRo7kfMZJRruOZpz7eKIKDqKZs0DM52Vm
8uijAkhZtEiwEv6J3VhdL8O1oAaJBMzqzKhR2w+q+L06K4vHHB1xV/VhBFy7xmWFC6tj6wTa2tJC
oJERRTr5vwYRtFohFBmmukqFmRnOpqaMGQPGJe3YyNwGFFf8OelnfK18GeIwhNpa0dGeMAF27xbL
5r69Uu5wu4NTOYJO32hggXeHqpbpg4/x5n9+5WJYEGqpnscOPMZnd37Wz2a3Z+h0YrLOXGmGm1EL
2Gm5ltT7nOn1enYm7hx0lCE/X3xXLy8Y5u1DcvHgIMLupN2AlD06Hx4ysOT83FPEDF/DlrQ7uFFm
yc2acNo1Uq5VF/RiIuwvL2dRSgofr/2Z46tXs1jeDDnvcbdb+KCfddddEPeriozmZrKy9cidbJFU
CCaC3taWC3V1eCiVveZHBwypFH1kJHWydtxdPSjUakUF1GOkIcoxakDBt48+Eh3YrZN2oc/JgVde
YZr3NN5pPUbO4Z8xeNeAB39fSuTONApslFy3GkNLuzj/588LfYd+LGy9XoDyS5bwZ3g4bqmpbL3/
frhwgaraWgosLLhjrDVNLc3CUWfSJG7a3MGiG6+jbm/Hq75+UOCxkyFnJJPR1N5ORnMzroYyPg2u
I3mUNZnR0fDgg1QfvIisPgtNo0gVf0j4gSVhS7pBlIYG2LiR8W1Hkczvsc/KZHwcEcEDY/89INcZ
Bg4i6TO07y5GPIPVPLXvN364xZiGj1rNrsBAFqSk9NoT9bGxHB4+nJJDNowfD/j7Y1CQw+pnxf5u
pbbiwfAHB9QCGSyiTU0JX1CPe2gtdhIdxa1V5NfmE2Ri3c2QXbkSy4lDeC1jKVu/67O4JSRAeDi5
Gg2HwsL4AXg3N5c3nJ3xuJRGhp8tlXt+4EDagYGLsh4xwtSU8x1MBJlEhoXKgmPV1UzMyAA/PzQa
UCqVBJaXc62Po0HPcDVzZc3oNZzZ/BrceSdlLRr0SISj0iOPQGMjfPUVtYY22NlBz229byxeLLba
T58w5rpG3HvaiOEU7e8eabje0MAd8fEcOdeKo5WSoYWwMu8KH4au5P5rJ/n2ww+Z9/33ogj47DO4
fp2nnZ3ZVFTEuvx8lhcUYJqQAGfPUu4WiZm9iu+/v73pwstRT2BQ40CV0oDq5uouECGtuRkflQpd
dBROla1oLxrQZiOaNDHyUoJ1FehvuqGz7N3NLi6GTAM/7rt0nDylHyW//goLFpBmUo1C28Ql+Q3W
/fAjGp2ODQUFbPPzEyM5a9eKwkMuZ7RrNxPB1VhBekAA7vG/4nbwS/ZERbF3/Hj027djo7bB1Dee
P28cZl3sOp6MepKEBKHZNGZM/++aq9XiKFPSKYkRYWxMoVZL7iD5bcKVNqbX7sJpVbcrw77ych66
eZO05mbmRkXR4tZISFMtox7w4oMPwNBAglGLFJS2A4NeMhl88okQ9tNqWRi8kJ+TfqaptYndu0Wf
9os+5lw/J/3MgqAFAx7jI0MewVBmyKbLm7h2DcaFlFNgY8vRhGO4mrkSbBuMk1ZLYYfwUns7KNtz
qVerelnWdobhwoX8+sYayoYVsjunsnucobgYkpNxVxmDwhpNW/9zNuAoQ0kJDeNG8f4dYPrVdzic
uoy5tJ2c5nqKJcZUp9nhtyAKr8Kif1czdkTnnlanrSOnOp/DPwbx8889nlBRIRKL4GD48UcqgYUp
KbyRnc2fISG86+GBoVSKXCYn5ZoJYwLsucvWml0TJpF+Iv+2juF/PYiwfbtgYXTuhTcaqhl1M48T
TfdhOGMW4zMqiC3vw4VtbBSZhZER7N1Lfks5f9/hRCc82Xb+EicqxlNrkMrhOz8gLKeVYnMXbOot
8PQcXOIAW1uBxD7xBOh0WBsackdsLAYdL1DLZHzk5cUqV1deusVc3oARGSmEdWpqWLH4Ae6Z8ghW
2m4FZxutqpc6PIik75Xjr7DhwgZsR0wiqLi3sN7lpHYqAm0JbYwlw74S75TfhZz9gw8KNahNm+Cl
lygZOoNFUem9aGl5Gg2XDSP47e65FGq1tOn12Hl7M7RWTcWpwzjq2ohrLOPuiAn9QQSJRCgEKhSC
o9lnNb9YV4eBRMLxTSbY2IDvGRkJ/uaUhk5mdPNRjA2aiCvvnaD2AxJaWtDfuMFX2jPY2EYT0gEi
pKWBkcKAKINqWmbH96Pma9rbGXvpBIHNV1k3XDg1GCuMsdHm8b2VnLyGBlx7ULX/J0wEvV7flaz9
lFGJSZYZZnID7vaeDC1lxNeKueS2NkFU6RJU1OkEBebECVIiIvDQalHLZJScPkSOhwWGMkO8veEv
zXjaj57A3tgemaaYS3U1bC0u5pn0dCZcu4ZtbCxBcXG8n5tLtkZDUHo8MV9/xEYfH5ba27Pb3h7i
4xlutAhZRHcFbjniT1pNx/LrgxeIWx7H+YfPc2bZGWIejOHYkmMcXnyYI4uP8OKIF5myfQrljX2h
TxGH0g8x1DGSTWW1vVkIJ06Im3r7dpg1S/xt8mSc//qLAq1WoLx6vbjP5s+HlhaqqkShamra+zPe
y81lhZMT902Vk5jYTQeH7pEG9HpR4M+ZIx4wMREMiP37u567LSaWUQmVGJmIBHKi58ReXu+Wrk6o
NC2cvNpAtO4iZtNHA/DT9Z/Q6XV8wSXRNezLy8/NFevO0qXg7i6oq33ABpNXMvjF5wZTpogEZt++
bp2AuKI45FI54faDF1HOJs69mAhNf5/lnKsEnX0oD9y4ASNGcIf8PG+8MaATa6+QSCT4Wwew/WgK
ZY7VLNm6l4OvLxCiSkOHYlGTTXupjqq2NlpbBabaaXjSotNxX3Iy7Xo93/r6kihTsSQnDf2FC7Tt
+IlRLqNwlY7groJ8JPdNFRnDcTH3P2GCKKa++07kqYPdcnq9ngaVLR71ogsZoHTjop8PupT+4ooX
6+o4UV3N6oEKjvh41uSeYV3JE+jcPSEpCTN9M61SNb62g5/rgSIuTuCxpRmJeDQ3I5NIiIiA9gI1
hnKHfi4Ger2e9efX8+KIF6mvF9Nfw4cLXHfSJKHA/9xzYFU/llO5p9DUV1Ni5cCQ8UKsRh4VjaFU
Ru2pP1kfux43czfuDbz3lseYmysuPXOVGc0tdRg3Kfgrsfd+crHwIgoDBRH2EQO+R2ysYIxIJDB9
uA/FLQOACHo9LcmJxH/wNGMrZtJ27hKrRo5gZtZGHn1agdOm15EUFWHywBzC0rKJLW7kRsUNAm0C
2V5SwpOpqRz++GOsr7Uj+/4U8xc+xNdjn+3V+e8bEyZAwgUDzGUGxGZqcPAxFotqTg4/mJlR1tLC
fFvbf2YiAHUL7+aD6ca4qowGZCJM95nOguD+Ca1UCj+uL2PBxef4bsQ2MDRkqONQrqyvx01qSf3S
dHY3u3KjrZS3WmIxrG7iudMC+Nu7dxAWwsaNUFHBrqefZnlqKl/6+PDq7NlUnjjB+Zs3ic7Px9TY
BBvnGnZ3WK5/4vQpAQm7UNfX43kLK730ynTczd2RSSQYSCQkNzbiozYiqSwJL5WKTI0GXnmF+OoG
TEuu0Nikp7W9lV1Ju1gStkR0d9asAXd39KdOM4ljKOz7WIstWiRGN//LkDuIolnt7N71t4BACQ2G
Vhj8A199goUF73p4MDMxkeoOwDMpLg65gZyMkypGjECsad7eXToE/01Em5iQKa+jTNKCnYUpZY1l
FNYXEmnpRE5ncSaRIPlmE8Oc8il/7oPencJr12gMC2Po5ctstLTkuJkZyx0cGJ+QQL2nE+uCa7n0
1RqcTZ1ZEbXilscyzNSUK/X1KORqrNRWaHR6DlZVcdfZs2TI/LCxEazeaI2GS4N573bEo0MfxeNS
GlnRvpS2y3BTGAhASiYTVd6ZMxRqrfuNMgwUQUFwl78x1xsbadfrsZ05DNusC13n4T8lJWQ1N/Oe
JIU1qYns+QUymg14/vnZRPzhwRn/g2LPfPllISSkUhFuYoK7Usm24mJWKpUCjDlxgnPy8bzyimi+
/pPWEAjy4JwwIzK17VRpqhk9Gi5c1pGv0eCpUlHZUstpbznhN69RZqBBq9Pxdk4OX2z/iv22T7C5
txMvycnQ5OJHdH4dQyUVrAeYP58fS0sJl5Zw2qUNh6Rs9vv4cDI8HGelUtD5zp7tYke6mLmgNFAS
kxODv5EpN53d2KB5jGurV2Eol6MwNib+9Gn8LX35reUpzunXM8ljErP9Z/PTT2JEciDyT45Gg5fS
EJtnhfWZXCJhno0NPw8yJ335o2No7N2Q+Al9ip2lpTyVns6R0FAOhYRgrlZz39ixBBQUgJcXUqko
KRozVKCwGxz0mjBBXBRffkmQbRB3et2J38YgElv+ZMcOYbzRmadUNVdxJu8Ms/xmDfhWUomUb2Z8
y+rDbzFhbj4rl5cg0etZ/cta7jR+GQAnnY7CjgK9rAxMAysIGwxgdXLC0dOTh3eU8WjWTdr1eswN
DERnZ+lSwX4yMKFK038fOVtbi72hYfcoQ20t9RNH81VAPQu+PEXEtGU4tBtRWRBPYUsrBWoTVMaB
jFrsgW92Pim36/fdIzrHGa4WX8W0OZRJEwz46aeOBwsLxf51553w5ZccrK4mNC4Oe0ND4iMjie6T
TF+9KqTtFtvZsXvaZDJ+u72Rhv/VIIJeD1u2CLpgZ6RWVxCWX8aR4gj0w4YzJLOQ1Opuqgp1dSIr
c3YWcsdyOXm1eSRNDhMLUXExZGdjPnQCzW3NWKiXU2HujLa9ndR0j646Y9BYvlx01zsHmfPz+6EO
Tzk5kdzYyPH/RnFTpUJj60+pjQHukm4qoptMTVpzN1LV0t7C0t+XciL7BLEPx2IUOYK+Q3eHDYvw
1kkxq4yhVNKIgaIY/bgeSrJTplAZc50dheNYd26EYBA0NrKrtJTIK1eQN7SyefwUfC5exF+tRhIU
RFCZntyDO8jU3iTAeRzLZw7h9OkBkn4DAyGb3Skv3SO2l5YyU2FH3CUJP/wALhfK+MtFw2lNNO6S
HG5mn+Sls1/wddzXvWhDPYGEY58nU+9kg6dzMHltsi4QoXM2+RWfIbQEmhNzpjewMivub5rqMjg5
aWWvRWSEsZyDtJMXFYXr5s1dnU2X/wGIUN5UjlquxkRhwh/V5XgVCoTSy9ILZWM2ySYiefnzT3EJ
RUQgPvfxxwXF+vhx4h95hCEd1k+aS7E0BIt2laEhZLqOR3ciBgkQYqxGqmvmdE0NLgoFq1xdSYiM
pGLUKE5GRPCFjw/zDu1D1fH6e21s+EOrRdvaimfuaOqsTlDdXE1tWxvpdxfR4LSQ6CtX+LygYFC1
62eGP8N9gfcxdcdUfkz4kbVn17Lm2BpO557u6qAH+S1DCtzZqRJ+6BAsWAC//ELjqCnExHTojb5v
g7GTEwY6HTVtbYLeV18vZok+/5y8vP4shMzmZn6vqOB5Z2cUCsHk2NVD7Hdsh0ODPjFRQNA9eZcL
F9IJ2erb2jjuqWaac3dGZGtki7u5e5fXu0mwG24FZcQeT6DCwgfMBaVt+/XtbJ21lW/iN9M2Yxps
3dpdWXt4iKHQw4dFlfj336ISe/TRLonnEq2WOKNyWn1rGb+omaAg8bTPPxdv0ymoOOCGV1QE+fk4
mTr10kTQHD9FTKCGLyrhr8pKzgYH41FxiYnj2hk6tFtderBQNfhjMjIb21YNLZYSZK7u4gG5HN3w
UZjkl1LZ2spff4kc3M9PAAj3JotC/pegIObZ2HChoYm5WQqyV77BiF/OM8plJBlnm4gqlnDOV9kL
RABxus6dE9oA48YNKKtCtaaaKhtvQs2FBd0UF0vi/Pyoiznc63mdlo7ve3h0iaR2RW0tuuJiTjge
Qy6Tc83CHq5c4Xz+OUzaq8nU/rNQb8/o0kPIzcW747MMDMBMYkWz2r4fE+Fo1lF0eh1jHKYyY4ZY
1zZu7AbKw8IEBnt+1zhO5pwk/a/9tMjlOHbSuiUSLo3xpPmzdWw49wlfTf9q0I5zZyQmCra0mcKM
Wm0t7hhxrqC3uOKO6ztYFLxo0PfqBBEA7hpvS2t7K+UNHcVcSorg9Nva0jxpLLaNQWzyH8GIi1E0
ZlXgnX0U5UdviaLS1BTZurWMKC/Cu1BCSlkKjhZ+PJOWxnR95ZkAACAASURBVLG33iLQO4jlLV/h
6KnEQmXB4tDFAx5PZ6jVgjBg2aDmUnEzPr4SsLUlt6GBl1pa+CkgAIVUionC5B9BhLSpUaSO8MXJ
0FAwEUJCREbboaHiaeHJS6NWDXwcq55G8eiDfHA0iu3bxd9Uhmokw4fz1tE02tpGEax5l+WvfMO5
919jR30SpSdPDQwixMfDe++xecsWXszJ4VhYGI84OnKfoSGvKhTElpUxsr6e0a6jaTRO4ofDYu+P
z7emas161NXVXZZ8PSOuMI5F+xbx0tGXmOMvkh0jmYykxkZCTC1p17fjY2xJTVsbjRYWnJs4kcdO
JHLwWC0PvHOYYcWO2D76PvrgYNGsuXQJ7Y69JBpG8g+X4L8OAysbWqVg6tw9+hEYCJVY9RCgGDwe
dXRkhpUV/pcuMSU+nqdsbAjTWjIsWtKtOxEa2i9v+jcRbWrK2dpaKiUSHN3dSatMw1Rhir+xGXka
TbeLjVKJ0eH9rJBuYsfCHr6gCQn86OXFGHNzDkdH8+O6dbxsbIzkjz9Q3z2f6c9+xdR8JV+OXSts
c28RZgYGeKhUNBk6YqO24Y+KCqJNTLCLu8LDH/sRFSXA6ShTU+L+YfRRpZMyNgdelcVQgwpPVY+x
mJEjYeVKEnTBvUQVbxXmcjm2cjnpTU3IxwxngtFF/vhDdG9/LivjSFgYNbU64oMjmbXqDhbMuIud
ERHsXh/Cr79JukCynvGijRsrTN1wCAqChAT0MTFsy5nAzJmiT3arkYb0dLENb9oED82TY2JgQKXO
gCZZIY6RGmwQ7lzFDcXE+tqwtOkw2RoN3xQVEdLUxKiyMp7ZNZzXXxe1WmckJ4Ms0A/fCj0v5uay
beJEMp2cOFRZyWTLFuqVUGjtjuWVK4R10uk//hieeqqXiEHnSMMQc1vSTczQKkzYP2sWd1tbM8fR
kd+jo9lp/yQZT+bTtu0Y749fCzoDduzo7ZLUGc3t7VS3tjK7MQbj1CtCqHX2bBZKJOwcoLOg04Hl
oe0YLBVv9p/iYl7KzORYWBjhJibIpVJ+dHVlyZkzTLlwQdDTEMuln4kJ9rr7sTO2I0+jYVVmZn82
78cfC/pWRQVbZ29lWttmpFNf4KXYJdx7L13gzL6Ufdzpdeeglrg1NfDGigBMUp4m4tUnCRxSjlN9
LY6uzXy5cgarV4O9XkphB5CYkQH4agi/BcDKAw/w8o1thCW64aNWI2lqEg2n/HxkCQnI2urJburP
LvylrKybhaDRUD9tAnvMCxn+7UHC7MNAKkU1+x7scrLJ05vjUlaG87RhjB4jwSRXx7W0nMGPaZAw
lBkiQcLJrLPUpkSxZYsoSTPOl4ukZOlS6t5+m0dSU3kqPZ0dAQF85u2Nqo9LnV4vJL2GDoXJFhYU
2ltxIzVjkE/tHf+rQYSTJ4WswLBh3X8r1bZjaG8EUinVDXKcXBypaZGh1+vRV1bSPmEc1T6unHz9
AXal7OHT85/y/bXvMXX1EZnGmjXkmYcyYbKKoueLeCbqZT6esYRpsbHsuurP3LmDHU1HyGRi5Vm9
Wggn6PX9+FwKqZSPvbx4PiPj1nZDg4SN0gHsDAgw7l5U/I1V5OvFwl+vrWfmzplUN1dz4kGh1Nro
HcbN3Qm8+654fkNbO1lReTzjYEFFYwnT6+v4cuwSckt6U/U++tSQ0iUvIUu6Tm1REfd/9hkvXYlF
kfIGa3f+wEmdnuzhw9kRGAiBgbgVNtJ6KoaRQ8fSIrfE0lL4WffRjhKhVsOff6L/5RdSt27l84IC
piYksL20lNa/7Fi0CCaObSOqLpmd5kWcuaTjmrcND2emYeL1EG8U1jB332JqNN0q+2FhYvLjwDtX
uWIv4f6Q+0lsbOwaZ+hM6O9yicBQU86Wkm5Z+k8yr3OiupoDQ8f1UgIHuN85gCy9MZleXriUlcG6
dcC/FFbU6wX1tYMv1slCKNFquSatZli7tUhG9+1jiEpJhWs+er0QVHzySUT3bOlSQac4cgTMzLjq
5UVEejpcuIA6MRVF1PCujzMK9UIjUUFMDBFW3izjGj8EBPCiqyt3WlriqFBQUFfAV5e+4rnXIom8
mI/ZBOH166hQEGJkxJF77kFyKRN/+WQ+T/qTkIux6NoLuOfAGGKHDGFfeTl3xMdzcxAf23fGv8Nc
/7kczTpKRVMFcpmcxw88jv9X/hzJOEKcgQ+rXV2RSCSUf7MPzcJlfDruD4Y+PxZbW4Fb1daKZl+S
0504NzQI0Obbb8Uuv3EjrF1L6dXCfnoI7+Xm8qSTU5fl26JF9Bpp8FKpMDMw4NDp04KF0DPLnTZN
rJolJRz7/SRaiZxl9/fuwE7y7NZFsB7qhl9OLt951LH0g5d46OZN9mSdRSVXsSB4AdFO0fwdZSky
lwMHBLPo0CHxe//8M40PPcCetgRWVf9C87bNYoA6JYWNhYXcb2vLQw4OfJWVBatW4VJ6mSNHYNVq
Hd+e28NU197jOGg0ghEVGgpDhrD03veZs/6gSAzWr8co4RxnRy9nhJkZ6728eK2qCuzteeveZDZ3
fPTnnw9M92xrg/TDUygZcoLwCzHsjlT2ulcM7xyPb302WRVtbN0qsBKtTse85GSkEgl7goIw7HA3
mdjayq9jxvFjZCQtulbCr5fTvPcg8R7ebC1L59CwYehjYnqNIhgbCzeWGTO6dO56RVZlJtkuXkwY
Llx05oQaU2zjSE1sb2RkZ1kZrTodS+wHSLwTEsixccS6PZLFHi+zz6AUrlzhVO4pPA2lJPe41m/H
R7sLRKitxbvHHJyHixuVZna91i+A9bHreWroC8yaJcHbW0yK9C3AZs4EbX4IRbVlJJ07hXV9OSUl
Ep58UvxGGQunUpV2jWs/GeNe9M80yE5dITOlGbWaWsIs1CTXd7+uTdfGnpQ9g44yQG8Qwc5OgrLJ
h0MXOtgIzz+PPiiYn9bEYPGwCa8/8i4fuYex/qchOLkNIFIplzNs8XyMFe5s+qGBn97dyKwjR7AJ
nkDYwQ959DHJLWnSfWPmTGjJFN/Jxwd0dnYse+klXnByIrQjWTc2NKa5rblrfnagyK3NxdXMFUeF
giKtVuz3o0ejOXOGH0tKiL5yhbC4OFr7JqG//grx8Ritf5uDB4X0USeB4ZrNKDZbSvnWZQRRianU
ttRy5rMH8TvZwCtHjjGdQ13+74AYEViwgPVbt/JhUxOnwsMJ7vgO70ZH87uvL9ulUkbJ5ZgqTHlj
3CsUBqwhIUHMlVs+fT9qU1O8Orzr23Xt7L+xnzH/GcO8X+Yx1GEo2c9kMy9QIBdqmYx2vZ4QM6FB
4GLqhIdSyY3GRuItLHjkzA2+HbaBJ3cs57tN2Ww65oN7awajrmxkxXpPvvmm23ru/2ZIZDKmL5Vj
6dGtlxEQAMVaK/QV/wwiAHzi5cX5IUN4LjubuXl5WF4LoacjHyEhHYrT/yK0WsGmO3qUSBMTLtfX
Y9/YiHFYJC3tLTibOqOSyfBVq7k3ObnbA97REflve1kc8zBxP96Amhr0lZVsbGnhGScnsQh0iCvy
xx+o71nA3JEPIRk2TADRg0R2ttAeWL4cIgxNqZI7YK22ZntpKfebm9NaUkH4LFd++UVcpsH2gRyw
tydyzx6e+/VX9iUldet4dcbZsxiEhHGu4QaNMjP8jfpQAD//nO0t82+LidAZQ0xMuNrQABERuDbf
5K99TcRUV+NkaIhTqxGzX0vh0MjhpEat4pegICZZivxy3z5RYyf3IJvp9bDlcUt+n+dOi08QZGSg
S7lJoVM0bm5ixG7Xrn7TtFy+DPfeK9YxBweRaoWFQaiRMWMClhD2TRjZD35Aa1ESxfXFFNcXc9bF
k7FlhzCVyXgzO5v3du2CFSsIDpHw+ONCIqIzkpPBfJgfHmUtzD2dwX2Njcy4fp2JFha4G4l99JBm
OG0xnfL/+aK52cfXe5SLECfzMXZBq5Vz+der/FpXx902Nsy2tub3sWPhp58wMRH90ps3heC+m9sA
Y1FAXoe47PTUzzkU9ooAKSMjGTNuHJUtLST1EUw6d6SBSdoDODw7n28KC3kzJ4eY8HCCeghjy1xc
+OyTT1i0f3+vDs+saCVNeXM5fUrKsxkZXKyrI/zyZZ5JT+++zvz9RTPp7bcBiN87mZ1jrnO+4DzD
F5zkm2+E9MvPyT8PyPwCkRsMGSJ+x5vfraZYm8GJrEuY1Jby1uSXuBYvJSsLrv6tIKVWz6JFgvja
FCgjfACAtSvuvhv3gjNIvjEkNiJCXICjRonm3nffYdheR1ZTPTcaG9lWXMx3RUXsKi1lb3m5ABHa
2mi45y6ON6dgtWUHYz3GdZ+zWbOZVKpDZhaJT3YxI++2R6kE82Zz4sv+wT5vkDBRmLDvagyeyijc
3UUeXPPEGpg9m5PLlxN2+TISiYTrkZGM6+Nu1BmFhWL5cXQEA6mUe+q1XHBvv62RoP9nIIJEIpkq
kUhuSiSSNIlEMjB0j+gY1WnrSK1IJSY7hp2JO/kk9hPejHmTL7+rZfny7gSrsb0djVSO7bBoPDzE
4hl0xyQa1VZEv+9BcpANX6iSiA6/yBun3uK31N/Iq81jgscElg15jJJly0g7fpzT2igmThQJVcg4
LdunRPH2Dz9SauxFQMBgR9ojhg4VrYOlS0ULeQAI/m5ra0wNDPj+X1h3NHcIazgZeYCtHA+z7vnN
IbZqKlVNFNcXM/b7sXiYe7B//n5kOjWffgpjV4ZhW5LA1q1iTfrgRiHyVDNmejpQ3ljOnFOnOD5x
WK/9srBQzDSvebWNjyrjcbh/Dmft6jj+3JMkfV/LqBJDJOHh2Bkaiq6GjQ1ypRF35hpw112PkKvR
oNfrmTKFfiMNDW1t/FFRwYrqajy3bmWiuTlJKSksd3QkM3o4v36tEr/ttXi0tq40yLz4K+t3fvcu
4t2bbaQOH8kc/zkct7wfv10PdXWEO0//dOcLHDQqYZrf3RS3tODdsSicPSsYbxKJhHEGEhLdBL89
vraa1dl5vGDewDiX/gqm09xHom8q4GpjI67vvScg+9hYnDosWW4JBrW2iup16FBxBy8Q8945NTm4
mbvzRHo6gelOBLnIxWaxYAEP64zRecg4d05sPPfMahWv7VRYNBHIa3xjI0OGDoW1a7EtqMJpdLeC
XUCghINj18FTTxFmGUBiWSJ6vZ7E0kTePfUukZsjWf5KMGMffZ/3fijAbt0m7Kbe0/X6+ba27B49
GkVKPOPd7+ejWlPcak+xuMWE7BQFvmo1J8PDWWRnx+j4eNbm5dHWJ3mWSCS8dsdr/DT3J9ZNWcc7
498heUUy22Z9z4KQraRX6Nj3jA1PW+1A9+RTvDLkMO1Rw9m4UTSTYmMFXvPFF/DuhSk4FRZSUFUl
sp1ly0Sr+4kn8Pj6xV5MhIymJv6sqOBZ527K3B13iPfsTDYkEgnrvLx4wdaW1r4UI5VKjDzt3ctP
N24SmdKOUtn7Pp7sOblLF8E4wIXPNm/khfX7uV9hgqtCwcO5dUwNWY5EIuG54c/xsu4I+oICMW+z
YgWNXq78krKXe3+5F8dPHdkWv42k8iQe0u5Gv24ddZMnszk9nefr6liRnMwPWVk0JibCsmX4e7Xy
3eELGLQbc/foYL77rsN3+/BhkfgmJAj+WWkpOVs/5aqDXqxDRUV8O2kWtT6TWevpyWI7O4pbWjhx
zz2wcSPToyu4cEGIvS9cSD+xxW3bwLN5PuMCHmX2uThOhpnha9XDbmv8eCIakrme3crp0zDrHh33
JCUhl0jYExgoFLI7r6+YGLZPnsYf+T9yaKYfBhs+x/7ib1yY8yD78Of52lqmvvUWyT3NrhFf47XX
BLg2Y0Zv94qL15NAD1FRgong5ijFoKaG3KxCOu1rGtvbWd1h6SgdqD0aH88pEy0LAx/kw0ULOeWa
S+O5C5zOPc0wC3tSOqiPf1VWYnH2LFMTEjhSVTWgmFJ7u7iGJzT8QYatLT6Ojl2PRY0PoNrUjIqm
bhDhWsk1ksqS2fvGIhwdBdOuL/U0ubGRpzLTmPJKNXMu+VCbXY6pYQvLlwty3caN4B80lpdWDcFu
xUviwv/gAwZUSuyInkyEGk0NY93VlCibul5yLOsYHuYeeFkOrObf1CTIBkOHdv/NWeXDkcvp6M6d
p+nqDaIPvsnLCT/iOOxtlvrb83TEADZXPSLC1ZVTQwI4H+zDx6PvpKliJcE7X2H1ajH6/G9i5kwo
vKAmu7UJG+8Wlixdilal4iWPbjs1qUSKkdyoS4hqoMipycHNzA0nhYLCjgokY8oUPI2N2VFayutu
bjgqFHzdk3paXS2qnK1bQaUiMFBsB/feK0ZUHmwfzrj8TB4ZZ8Z7caa8M0yL0sGO6U5z2BE5gRnu
XyNp7a529CtX8vqKFXxnZ8eZ8HC8e3QozY2MWHvhAvkqFcM6rrUnoh5H4XSDF786ia0tKFUS7H18
8HS24/MLn+PzhQ/rYtexMnolmSszeWHkC5gpuxEaI6kUT5UKVzOxljqZOuGlUrG7vBx/tZp90xwZ
ev4Hfg6vw7A4lVU1rxCfa8l77wktyoQE/rn58l+GxdQ5OJo6df3f2hpqZFbUZN4eiCCRSPBUqZj2
6ac8GxVFwgF1b8fJ22Ei6PXiBvr0U8FytbERfvf334/Z5cv4qtU4l5UhDw3HVGHaNQt+YcgQhpua
Mu7aNRZ36DMYTRxOzuNrsVk+m9Zjpzg6dy6GUilVJ83F8hUcLC4emYwuZGnuXLEf9oiGBoFXjxsn
wNaqKgHA/vmRKTlaW0yMXDldXUvua5XkKXxY96kMKyux5v9+NISS0FA26HTYZWTwn7//JuDYMXz+
+INlf/3FtrQ0Ms6cQTptGq+OeRVDY/f+avPQz97xn6LLDUCphKAgGk5dYXNWGYvs7Dh/Hta0fsHr
Ree5qzmGsT2cdCIixBj93LndNuXffSeIeF5e8O0PSvD0JNtuBFPuEmiWl5eoUw8dEj/fsWOCBDV3
rqgHs7NF/dop0B9ibMyE4Ec49FgmSocpyM5kE7IphI2XNpLV4oHO1h7P9nbuVCoJ379f5GqIJkhK
SvfPk5QEHiPsBdD0yy+sjowkU6NhiZ1dl+Vmntt4yvZ06Kx89pnIcyx7iwaOdhXjkhf+dsa4Rk1+
qI7atjaiTEwYYWZGkYkJOadOwe+/81X74zhND0P7/BpWTM8Z8NznaDS4t7djV5rAfsMFAvV74w2k
s2ezMCmJXX1GGtI//pUy39Fs0GhYm5/PyfBw4VbRM+RyUcE7OAhqbEf4miqJmKrhwc+ria9v4HBo
KCnR0UglEoLi4liVmSk0ld58E37+mfyjN8nNhcawevThX/JN0UZ8fGDDd8VcLb7KdJ/p3Z+p06Fr
1/PJJ2LNX79e5I5mxgo237WZ47lXUdSXsChkEfb2IhWbFGlEvtSAyEjxu2c6mxHeV/ywZxgbI71r
JsOyf6ahSiZutGXLxL9duzBqa+CxvHqmXb9OTE0N5+vq+L2igoV2dvioVDQ+8iDxmWep+uYz5gT2
dpRg0iS8UzJoU9tgkttNjvV29iRTfWsh187I12h4LSuLeUlJrEhLQ+e8iGRtKov/D3vnHR5Vtb3/
z0oPCSTUQOhVutJFQUCKCnZFvXawIdh7B0XlimLFci9iBxsKKqgUqdJFehcIQqgJgUAS0ub9/bHP
JJMA6v3+7iVg5n2eeWbOOfuc2bNnl7VXedfZLnvXra0XU3XtZO656WauWbuWkQ0bMuqUU470yAyA
3wvBLy7dUjeBBd1qsnmzWLfO2ayOCUn/9RdOOfEbUBsIB5YBjYuVUYPXGyjmuRiVea6MGrzeQGe9
f5auGneV7v3xXp374UUKu/Yipab65MevB1JV7+MPlbFssS6+WPryS0lJSao+bpwefPAB3fv+e+q/
dq0uWblSXX79VS0XLVKNefNUZtYshc2cqcpz5qjK+PFq+NRE5eS55968bp1aPDBNSTFN9NBD+utI
S5OqVJF69jxmkUUHDqj8nDm6bd06/Ziaquz8/GOWHbdnjyJmztTgzZv1wcf54odpqvbKKVq9Z7Uk
aVVKphg3S3VeraOhs4YqL8+njz+WateWOnacodUr86WyZbX4xxRV7rlDlb6ZqGGnD1dufq5inwzV
gYQERUyZpSeG5RZ855W3b1C7Jx5R7Ni7FD71W9258DMdzD4o5eZKI0dKHTtKxevcpYvUsKEkKW72
bKXm5Gj2bOm0Vj4tPXBQw7du1dlLlyp29mx1X7pUL27dqpUHD8q3aJFUubI0b54mTpTat/eeN3y4
Mm+6Q+FXXC8eLac3P7hfqlFD8rn/Z8a+fao6e5oiv3pOT899XT7vfHKT+up2zpn6MemAWi1e7M4l
SxUqFFZ51d7NYsp3mr7xgMr/9I1aT3is4H5JmjFjRpGflvj1YIXMmK7c/Hxp0iT3/y5cqISff1by
4cNH/mnp6dKIEVLNmq5dJk6U8vKkTp2kkSP1ws8vqM/UEWq6cKF69cnXwsGTpPr1pZEjldO+rZjy
nU7rlaanHj4sXXSRdMEFUlZWwePzfD7Fzp6ttNRU+cqW1aoE0/6s/QXXP/5YuqKvT+rTR0kPDVDl
4ZVV99W6qvNqHY149UqldGkvX61a0qhRUk7OEdXflZ2tuJ9+0vxyZ6jz9PmKHHO34obFacXGFFWt
WrTslsxM9Vi2TG0WL9aKgwcLzmdkSFddJV14oesyCxdK/frNUN26UswrK9Rp+HbN7TdKuQmJ8q1c
dWQbBuDKS7J1wyOP6d/vvCNdeWXhhYwMpZSro++u+qTg1PVr1mjw5s1HPOPBB6VHHy089m3dqp6v
vqrXtm498gsnTVJO69aq/PV4jR677YjLh7IPKea5GDcmJO0KS1QOYcrfvVdZuVmKGX25En6erdWH
Dsnn86nFWy00Ye0Efbn6S/X9oq/ihsWp18e99O6Sd5WSkSJJyszJ1GnvnKaRC0fqpRUrdMXnn+tg
tYpKqVlJ502brHe2b5fOOUcaPlx3fX+XbnjlBi1ZIl3c5nf9FH+psqrXl374oUg9d6TvUJUXqxQc
x374npp/8XnB8dhdu3T6vHny3XSTFB8v3Xyzsn5Zpf79pSZNpLVrXbkDB6SqVaUFv+Sr3NSp2nvX
XUe2WW6uRvW+SHHP/Krrb85T7+XLddnKlcopPk+kpelgQoLCpkxRyNCKGvLjo1K1atpQpaaqzJyt
yLe6KTXzgF4fMUKVp0zRqYsW6eZ16/ROcrKWpKcXzJWrVkkNGkj33uumpf7Dh+j0V14t8lVl3/1I
Ax6533VCn0+DN2/WlauO3dcmdOqmW8+NVmr6IUlSm3tuUmZ4uOKfLqMvdyXrvOXLtSc7W4lz52py
aqre37FDLRctUtOFC/Xv5GRl5uUVPGvpUqlj/d1S1arqPnOmJqemFvafQ1KdMWM0fOKbkqSJKSk6
deJwVXr5Q7UYlqShm5P0bFKShm7Zome2bNHTW7bonGXLVHXuXN0/b54qTZyo8c3bqf6IG9X0s7E6
9VRp9WqpYkUpKcmn3HxvLk9KcutQ69bSsmVH/c3Nm7u69p/QX6OWjNIv6emK/HiR+vWT7rhDOuWR
63TZ8Nc1c6a0b1/AjXl50tq1WjP4M/Vplax8n08f79ypL3fvVs9Rw1Su2w36OaaXhtb8l97+NEkx
716iRvPnKiOgjY6F3Px8hU6fotY/vq6LlqzSk0+6+eP/ivr/SBGfzVOFWT/rvjfeUHrz5keUSRyR
qKemP6WHpz6s/hP66/yx56vDqA6q+2pdxT4fq4ihEfpo2Ufal5OjcrNn61BenprPnKk3BwwoWJNW
HzqkSj//rL3Z2e6hN94oDRp0xHeNHi1Vq52nKjN+1i8tW0qzZslXu7a6j+6i8WvHa/9+Ka5Hiqp9
8612vur6dP7YsbrzySdV/803tdv//GLwvfKKFjRpIq1YUXBu6IQx4pb2OquLT8t3LdfdP9yjCi9U
UN8v+mre7/P+sN1aL16sPsuXKy8/T6FPh2rNnjW6e8MGVZ07V3dv2KCrxl2lM0afoSu/vPIPn3O8
MKHqbVp315t//YZly6Tq1bVvT66io2eoSLNu2+bmxClTCv5fSdKOHdJHH0nXXecmxXr1pNtuk776
qnCAfPml1KiRrl+2TFcMGSIdPqz6r9XXwIkDi3x9em6uhm7Zoopz5qj/2rXanJmp8bXv1uHIcjrv
ozFq8fAONWokVasmrbzlNSkiQtkD79G770qPPSatmrxdvvLllZ+VrZkzXXeLj3fiwldfSYcPywk9
WVl6b2qG7JsfFPb0y4p9fo1GnT1WGX0uL6jLhg1SpUpu3S6Az6f8lSu18p139Nbjj+vqIUNUefx4
vfLzz8rOy1bzuVM0JWBek6Tdu6WYmBlFmuzP8ENKis5eutQd3HmnfrzgJYVNmqOtGYf1Vr9F2hdX
W+kZadqbsfeo9w8c6MSkjRvdb1i1Slq+XEpIkLKvuEavVvunZs8uLD96tNSmjXs1biy9/750jCGl
D3fuVN9Vq9Rq8WKN2rpT7dtLNz+yTud90luRXUcoc+D9mvz669r2zDPSnXcWuXf2bKl6dbclKFdO
2rtXUtu2UufOkqRf0tOV5/Np3u/zxBA0/ZvdOmixytu+UypfXtq+vcjzZsyYobz8PP1zzgtq2lTq
Pm2NTlmwQAPXry8o02/tWr02dKjUtaumnfeShp8/S29G3KP8ChWl88938mvAHPyv5GTd9O672nHb
ELVsGfBlKSla0r696s6aVSAbZ2dL08N76olPv1WDBQu0NUAmPQKdO0tnn13k1PR9+9RxyRLFfrlQ
17yzp8i1bVlZGrB+vSrMmaOnNm/W/pdf1sZTeqvXs2tVbe5cPb7pN4VOGa+7v52hSn1eU/gV16t3
b2n405naPOAF5ceVl0C5hCo/MkqKjZXKl9ehmjU1o3t39f7nMN309vAibblk7ly1HDNGkpSVl6fo
H3/U4R07jv2bJOnHH7U+rq0mvbnFdTa//H/uuXrsnvZLhAAAIABJREFUns56b80PR70t88H7tKJ2
tF76cfAxHz311lvFjBkadON7BedWztyhyMmTlZV79HXT5/NpZlqaLlu6VOV/+kl3vvWWPuvXT69f
f71ufOgh1fxktJKSUqX8fOV36KAWz3yndjOWKeUocv/R8OST0uOPB3zfwYNq/MGHih20STEPb1D1
L5bKqQuO3O//Z8mw/zraAxslbQUws8+Ai4AiNHfzUi6mbHQckWFRWG4I7A+BAwYhIUyZXpva+W8w
7f2bubTzbayMiODF7WupvWc3ZVpeR7duThlc77PaPDnzJbZ07EjVTp1oEhZGhfBwyoeFUSHgc2xo
KGbG10/MYVCjMvTfsI77a9Tg25QULvV1pnPGj3z+Z3wIgYiPd4E7f0BQ065cORa3acPXe/fydFIS
V2Vm0qdCBS6pXJlzK1QgxotL+TYlhYEbNvBN8+aM2L6dj6v9CulRDD/7Sbp92I1LG1/K9KSZ0ORV
bq80mFbZN9KunVMEfvQRTJ8+k6bNu0LLluTbEvLvzOO+R3+k385phL3o44JtZUhPLEuV7EN8kj+W
pK+mM33jAnYkRJDQ6WlOi0vgyxZtqBrokzho0BEuVoAjRPHSpdWOiuLBTZvYVS6HVU8dotWkEMKX
ViBufQ1q74gnt0wYc+JhZTzEx7ejbY8PufScS/ikwSxuGej5XM2cSXT//jSftoM1eXO56epn4ckv
HJdC48Z0LV+ezWd05Z515Rm6M5nx3zzC1G73UmHTFuLPfoIRX2fQ/NzCUIZOnQote80q1SVy1wTO
JYSI9GVM6/1AkXjfmTNn0rVr14LjDpExTMk9DL4Qlzlg9Gg4/3xqfvEF27OzSfS3T3KyMwe++65T
b3/1lctG4ce//gVdurD95UuYXbMv0xo35ub12Zy24k7495vQqxfhn31G3a1bWF0ni1e2TODbOrXY
ec9d7N+9k9Q8Hym5uSRnZ5MQHk58hQrsu+ZSVi/9mmYBFqQmTWD4cIMJr1OrXTuGv3sHZ8Q0oeEb
Y7Blc52avH//Ai1xRoaLMPE3QUJEBK3jynPN0/dTbd1Sxsydx6yLL6N5/YocPOi0/n6X4jrR0Uxp
2ZL3du3i7OXLGZSYSP/4GvzjaqhWy0fnS/OY9Gsu/1yQRW7a19T4MJ7csCym/DKT6J9egjkzwcsT
fyw8OzyCIcNi2bZnkUvD6EeZMjzbYSLP/9gDvoxgQ58+TEpN5Td/nJPkSEl/+YX705awaswyVLkH
Nmgg9u23vLx5M2dv28Zl5auRkxLOzp3OAtGoRw+mjHiZCjuzuLr/kSRAMRExtKvejtlbZ9O7YW9S
Y2qRlV+BOlUqMXHNODqE7aNf/YZ0WrqUnuXL06b1U1w66QE6VW3OVY378Faft6hUpih7c3R4NOP6
jqPDBz3Jb9OIhMxPOOfphjSp0pTZG55lru7m98vKc8Gr/2bcbc1o9ltlUqLe4Qkbzd6LLqPJ7I/o
NLYMw091BgBw/A1pWWmk5WQxeOt2MirGcVtYQsF3Xlmliss7fv/9xN11F75Vq/B9PZbwZhWp07kl
rT6pwelnwsHdoZwyII6dtYy6a3ZS6eqjuLaHhREeX4uImL1suHY1NUNDGdOkCeHFzemffUZs587U
ycrjt8odOL1+FzbfWoHzatbggbq1mbTFx+erxnBrYgK3vvEGKz75hMUHD7IwPZ2RyclszsqieUwM
bcuW5e4fyjJ2cFnO7R1DSKc8Eg8UdfurmRfOuBaNeWruIjaNHcsbtWvza9u2x+xn05cuJvXS7lQo
6+aNe88fxKYxo7nM14JW5cqzevNWblm/nusSEujlWYhuqFqV6fv388q2bTyxZQu3JiYyKDGRObMj
eDPvVrjxRn6LiCjwiALH61s5JZVNO/fzZLVf+Th5O6fN30z9/FgatPiZzOUxEBuLxcRgsbFYmTL8
IyuLCSNGEDVvHu1feIFrh79IxuElRE0ux4IPnXHyrrvgnnuM8eO95bt2bRf+9P77jplx0CC3OEZE
QGYm+Y88zierZ9BkxSPEVy7HtgPbaJK+gfzqh9ja8HG2+uawM38VHTcP490xK4lZv4QOYb/SPvxX
GmQsJze+CkQ3YlT6o/xj3sdsioykRmQkyxu04VC1FWzuGc/jn93IJT/eixIG8GXzlgXpdv8IYSEh
1IsIYYU1571GtTn1j6wdfwGXNC3LiHXlmNKrNm2+HX9U2vi72t/FrkO7qBxTmQYVGlAlpgqVy1R2
7zGVKRtRFjNDErkS16xZQ9vKlbl90SLnWjBqFE3Ll+fqKlV4KimJt/y5oI/iEt+/PxzutYtJ+8rS
BuCmm7D772fyjQML0gfunliRYSurceX2bUyZN48B69axoWdPLp8yhSpHSWEIYL160eH++4vMp49e
cBVDp7/I0k4NOX9sDte1vI4lty4pSC/8R4gJDaVBdDShIaEM7jKYeuXrUT97L7tycjgzLo75sdX4
bNVnfH/193/pf/hfI7zqH3si5OY6i+OGDS72/ezP3iTunNuYPy2MatVmEhHRtbBwjRrOlHnvvW5h
7NrVhSRu3w7durn1ffDggpjvIrj8cvjqKy4ZNYo92dkQGUmlMpWoGVeUI6tsWBhP1KnDoOrVeXn7
dtouWcJ5Xwxgw8BsZpZL4Ob8KoxY5UJCn7uiBZ/m5HDZhxcSmuxc08+/rToTDjdiTtVnyIkqx6DG
e3irx26iD+yGZ/bAoN2Ohd2MGyMieODTT4ltXptRVVLplb0e8gr92xs2dJb4Dz90vMUAmBHSvDnN
mzenOXB7bi6/r11L18xMwnftJd1ij+DXWL4cKlWaiVlX/ipalS3L0kOHkISdfjqHopZRdncsH46I
pN33b7Ln8oGcUiaeo0e/O6N9166OXujxx50oCo437r68N/j6cDS/dyws37ev85C96irnxv5HmUZb
xMTQb906usTHc1PNBC6eBJ07n0KnlEnUSobox6bS67HHXL8I4PEBF8rWp48LoYiK8rwbWrd2LiJA
G8+rtFKZSsRGxNL1gipsjG5A9Ln9qHrBpYRXr17keX65tMm+h4iMhM71ohmydTdvNir0CLyoUiVe
O/987nriCXJ+gMcvgj59zmLgmOcc19NTTznvqAEDoH9/ktLSqLNgAVH3P0vSp05kMgMqVqRV//5E
7N7NwvR0OpSLY+pHycy9pi7j61Rm1mmnFcq9R0OdOlAsy0GdqCjmp6dzRo14fhhQiS29HN+RBLGH
o7gjtxEds2oyavVWRjRrR4erItlcfzmzr3mEBvHxhD9yA89VakjVfsn0T/gHHSZ+QqdXnmQ1rbgw
cz7nPdiQmx/NYPHBNOYfPMj8gwdZl51Ny4gIOkZEcF9APMfMmTMZePXVbDlwgGeSklibnk7D5GQi
e/U69m8C6N6dRN+NHHj1Mee+42+Dm2/mmicHMOi0F1ixdTIJsQlc3PhiGldqTM7LL7Ln47f5/JXr
GNpr8DEfXc1z52vaoPD/bHZWNWp+NIMZMzZwXs9Cd/iMvDzGLF/OyN27yc3IYNC4cbyRksXeej1J
rnM7VRvV4I0VF9FwSzgD0sby3a5dPN2zJ/tbVeP095qT1SCUvKqF2bOOhV9/hZtuKjy22FiemPg9
n/Y7ha7NEzk1tiLHajHT/yFm/89gZpcB50i61Tu+Fmgv6a6AMvrX869wMMfIyIVDeSFk5hlZ+ZDp
CyE9G8Kah7K2bAg7EmqQmJpCq7WruCw7jStedKwbn33mBKqHHxJndTEOH+YPX1lZLlz5upvz+ab9
ShYdPMhzdetSZ2kNbr3V7Q3/jymN/xJ2ZGfzTUoK41NSWJCeztnx8bQtW5bXk5P5vkUL2pYrh09i
wIxtfDkvi7QnTmHGlhks3rGY7nW702l2PlUm1yZ/dyT9b/PR7kwf2T4fY4cPp/d995H5+ecMq1OH
f8+ew5e7XuDG3ns4++kupGUfYEqvejx/3sWsPFiLsKWhlKtfi9wWPp5r5Ba3PyPmKsBGLwa2YUNG
79zJ74cPc1psLKfFxlIrIopDh4z9+znmq9Hc9zlnwRBib/kHEc0aucX7t99YuqssS9fto3/fRJeK
7623ICHBZcSoXBmqVGFRvbpc2awhYQd2cOfXE/npqdf4bvd+nq7QiKsqJfD8825heeCBwup2f+ND
pldPZWzimbSt2AGfj4LXyJFDGDBgCGlpblH9Yulkci95mqhlXTmzTXmaNoog4ZflzC7fgPSYaCql
HyAvNJSyGYfZVbk8yYkVyY6OJMQADEcgbhihJG5NYXtsFNFKpNcvPag0cggPX7SO0K+/dBVbs4YR
g59m+FWXE5N1gO0xB/HlpuHL2Ydy0gjNP0h4fgZl89I4JSaGSIWSEBbHmOu/KfhtGRluwdqwASq+
8xxRo0eS7wth7SWPMqfRzWxKjmLLFkfRkJTkylep4hbiTp3cxD5RO3grdwtbmyRQ7fHHCJk6Dbv8
cm6afT0qF0+TuB0k2k6q+nZQMXcn5bN2kB6WwWOX9mJG0yaE+kIoExNOfE4OlXfvJiEpidSpU3n4
hhvosGsX8R984Bbd4qyIx8Dt941hTfX9tK78Dzq1jCB1L0z6yce02T4+eHgTTYbexrPPPEPTkBCe
mjfPBTkuWeJWxLZtUes2PPhBMy7c/yG1D68nWxEMjXyOsbc1QwmHicmMJKRmFhlROYSV8WHRGfT8
KYJvXzxKTiTgudnP8d2G72ib2Jbe908ms2wUMwafxayts3jgjAe48bQb2X74MNPS0piWlsYvB9PZ
n5fPvrw8ws2oEB5eoMysEBZGZEgISw8dYmtWBpGpc3in0Slc0ewKzIz9Wftpungh+31hVMrI4IyN
m1j1/Q9Uu/pqUho2JDUkhJScXHLzwLc/jISocJrFR1PfV5ax6x8mr9ZVVMs/wOaFD7Jt4BZqVCmM
X1yTkcGUffswM0KAkPx8QpYsIWTaNPIycvg+qzsLypxGu34HWJSfyW1TpjBs2LCjhmr98MK/6N3h
FK4sW5ZPwsMJS0lxmSkCX5MmwdtvMzCsAm/nbaR+2Cn8TjY1F1fit8caM2njJIbOHsqOpJWsHZFD
/5ec+6b5RIhP+CyctMiqpEUnkhpTndQy1ckIK0dUTg63zV/BiGcfL6jPLZ9O5t2EMKJz8qi7cwe1
UjJptyeDcpGRxEdEEB8dQVxUBOWjwikfGcZ7N/al+hc/MvDycwAvvvbUKpybnE9Oq0to/ujVNMzO
Z+rOQ0SXieRwtpGZScFrvRmfVQ9jZo1Qmi3aS42w7fzapSMhZqxv356wgEWk1/PPs6JRI2rt2UvP
z/9NRng1XurVg7CUXY4vw0/at2uX80lOSHAs5LfcAtHRPLhiCy/t28ply8ox7p7WgPOUbdnS6dmq
V3fKvgMHHKcwyclcNvU24tN/Z1yt+7l267NsTWjPh1lX8lbis+w9sJN3a6VQPqIcT936Jk9NGccp
6XlEZvqI2JJERKVKhDVsRGa1xmyNbMayjKas2BTP2k0+4h76mcarF/F+q1ZEde3Kok1zePbi6/n1
9XeIi85kQ9Zhnm/WlQfrHD0k4mi4df16dubk8F2LFn/5nmNh2TJHKrZyJY64a8GCIhlY/lM0XLiQ
cqGh/NyqFdG5uS4t2YQJ8MorpDVuTOPUVF4ZOZJKN9+MPMHQL0/5parbN2zg06ZN6fjII47sY+tW
p10KQL5E7y+/ZEN+Pg1jYhjfpw8vDh3KkCFDjl4xPwtWMUXZwy+uQzE7+eftXf4wm0VxnLdiBb0r
VODOgNCw71NT6bNyJckdOzLml9d5ecHLbLt3G2Eh/yu701/HvL6vEPvNGDadeinlm1fncGQcv++O
ZOvOCLYkR/D77kjiq0RQrU4k9Wrnc/dXnbmsyVqmrapKt25DmDJlyJEP9fu8L1nilAdt2vy55A0u
fq55c7czHzeOiz+7mCuaXfGH3CIpOTkM37aN17Ymc0FODcadW6/g2sGkVHxnnEnWwpVUrRleULX1
L0yg8uyvqNAkAavqyUUJCUVlpPBwOHSI81evZsnBg2wbNIiw3393MVMBbHvz5jldY5kyLqovKqrw
3f85MhLSy2Sx4Kpl5JTN4c7vO9OhTQh16rhQinHjYMWKISxYcJS2/ANUnzeP9xs3pt6ePdw/cSLd
q9QlfeBGBmW8QNiW3yhb52i51QuRnOwoyZ55plBW//13F1Zz6aUckeb5r+Jwfj6NFi1iSsuWNPbG
57Zt7m/t0gU+HnXYhRx06OCUhsWwf79T8DZpEqBjKNipO+Tm5/LBsg+4pc0tbL/8HhK/ep1WkWvZ
HH4K1aq5ePRq1SApaQiXXTaEMWNcApT8LrsZtHEju884o0BZn5mfT9V58xjXrBk7U33ceEceN92d
R+2meezP815797J/+3YOZGTwW/XqvLNoEVc+NZi4OCf/7d7tuq/58und/XG+7nYmUbt8RJXJwFc5
mtmX9HHZCP4I773nOk2AwSHX56PG/PlMP+00vnktpoAzZccOF6WTmOjWrcREiGyYye7mu3nnvESq
h+A41O64gyd6JPJF557UVR02Va7C9ooVqR4VRc3ISNZnZhJiRsdy5TgjLo6O5crROjaWqKMorocM
GcJT993HA48/TnSbNtSU6PrBBzQOSNt7LKTeeD8VP3yZnhWWsKdGa6Kj4XB6DlM2Vudf9bsTWjaL
mLA91Nq/lHbJkGfG1Zefw6mRX1OpYgiVKkHFiu5VqZKzO+/dC1t+2cTt9dezrGFX6jUqDBE5+6V3
abAyh04JNahQO4eJufv4sl41Tl+zgc6z00la0ZYv9p5NhZoxNGniIp5374ZFp/TkvDatSa/ejfSk
JPY0acKE+h0ZcFUEmzY5/WLlyoXtHvjyn+vRAxYupCjv2EUXuUwa3bpBSAjWogWSjhAIS1SJUGbo
GspEGrGRRmx0COWijXIxRnxsCFXiQ2hXM5L9aasY/MNN3NW6P/d2vJdykUUJXrZscQq3PXsKJ8E/
esXGwsCBEFomn5HJydxTowahCmHLFhd+fbyQlpvLxNRUpqalMTAxkdMDrCbp6S62v3fvovec+8UW
1sbuo0aCER0aQlRICJEhIWx4+23a33knUUlJ9PzpJy5+9tlCzVlSklPJfvEFO6olcvrc5bQuH0OP
KnH0rliRen9EMPK/wtSpjhVlwwa3kr39dtHrktvx7nW5vgNfuXv38lT5Mkxr3Ih+Z3Rn71eV+fDV
CEJC3Jo/bpxb0/1YuiKbC25ZQWRKO0JCKPLau3cIVasOITLSGRVu6JfHjzs+YfH6ZGYtTGN3Sg4x
4WWpXjaasHIRhOfkEpKbx29xVcgIiUASPp8K333CJwgJyyE6Ip1H58/ltik/EZGXR065ykSu/tWN
WA8ZT71ASPI2ov/9mptdPfjkIzc/l+z8bA4cPsCmtE1sTN1Iq2qtaJtYVHA87zwX0pmbkUPv7PGs
qHMhifWjqVOHIq+6dd0a+NtvztAyf75bJLcli3Y9cvnoNW+xSE6Gjz8m+4Ox5GTkcbBcIgeiq5ES
kcjusER2qhq/5yWSlF2Ns5vvYUDdydj0n9zsc/nl0L07QwYOZEhEhBOYP/qoyG/+MyzfdYBb5m5g
W044+/JzCQ0TFcuGklghBCJ8HMzIIGbLFqaNH09cixZOkG7Txn2Ht2Dv3u0EiyorplFpwigOv/ke
2RUieGvPdhIjI2gUHU3l0EjmzwxhwuchPHJrJAEOKUWw+9BuvlzjFD+V5qwnt2JZ0ptWJyosimtb
XnvMnOKSyMh3yoR9ubkF7xk+H6fGxNA8JqbIZtMPP4txTTPo148hPh9DPv20iDCSmZ/PLxtyefrV
XDbmZJJX7yAZNXYQtWonuWt3USa0LNu/6//XGlyC2bPJGf4qIbOmE1alIr4qVbD778f6Hj1t4MEV
K3jzxRd5YOpUwipWdCtU4KtSJdcfLriAzXuyafnud7TY1YNzqsVzdd8QAgwqZOZmkt2jG7HL16CQ
UBQagkKcN5pCQlBoSMHntJhYllatRYenh1HljNYFz9i6O43L33wGn3K4aNkqrlmwnDwgz0Se+cgN
ce95Ie41IjuC8TvSCQstbP8JU9ex/r0dlFuzgcldffSavIJ6SbsI82UTFurmltAw9+4/PhQXxaTO
Lah1/UV0Oa0JjaKjj1DGPjH2XWbt3UWitlC+cgOGnH8bVeOKxr8WwE/iGLB5kcSds1cw7PTmlI0s
nCPmz4ehQ916FhfnXuXKee9lRfPlY2gweSRLez3CgqoX06ABXNHXS3W6ZAmEh3Nnw4ZMq1iR6NBQ
oiIiyI+OJteMHJ+PXM8SnyuR4/ORJzEgMZGhO3cS0rcvpKWhyEjuCc8n5MMBzC3TirjYGkxp0/mv
K6SBndnZhJlR+c+E1b+I3Fy3l2LWLEeOMvCP0+L9ET7atYsu8fHUDrS0TZzoYuO3b+fr2rV5++ab
scaO5NP/qwvezTg1JoZ/1q/vJLQtW5xZ9ChISU3l7cmTeejKK4kMDWXIkCHHViL8l/HvHTs4My6u
CGna74cPc+WaNcxv3ZpfdvzCqj2ruPG0G49Lff4Mvh272DriS/atSCZ703bK5B8kLiqb2MgcYsKy
iQrJISQn27HqZWe7tD0vvcThw/D880N45pkh/90KzZvnLFTdu7Pr0C7KR5U/5roQiH25ucSEhhL5
X7Zcjdqxg725uTxWo4ZjJ2/fvoBjyY/MTCdi+Y1r/nf/58OHnSPTvqgsJvl2ctqSeixe7GSGcuXc
48qXH8Lo0UP+o7o9sXkzE1NTOZifT/TOncyZNInU1PKM3daZp345ehq/v4I333ScL2ed9X9+hPOQ
KDZ3bdvmeG/q1MGlYLzkEud2cBRMm+Z0wdf+cTIZh7lzYdw49PIrpKe7DfbOne71wQdDaNFiCKGh
LoFBan4Os/bvp2+xOP6HNm1iQXo68WFhJK0Ko1OrMCpGhhEf5l5xoaHuc2Ym8d9/T73u3QmpX58p
U9z/XK2aW6pDQyF//UqWzvyaiLJRRJSJ4vQLLyQugE/mP0Wez0dYSAj5+U7Ur1DBbVjLHsvNJBB7
9rDj2osI27SFKiPegYsuIkdi6+HDbD18mIZlylArMvIvrTMF8+hXXzn3mY0bHVnG4GN7ChRgzRp8
jz/JrpHj2L3HGanLloXKc74mbMYUDuVGkZ4bzf7aTVhYJ51l8Xs5N/Zx9qdGkJpKwSslxb2npTnx
qG5daNdyJ/c9Wq3I1w0b/wP/jDTKZOYQdTCH+itzaLCjKYlNTqVJU6NxY+dJVMzxg1u/u5WLG19M
p7q9GLhhA0Pq1CnCo5Ob62Ti5OTC144dRY+johzXZpEmHT26kIFbwlavPq5KhNOBIZLO9Y4fwcVT
vBBQ5r//xUEEEUQQQQQRRBBBBBFEEEEEEcR/BcdTiRAKrAe6AzuBRcA/JK39r39ZEEEEEUQQQQQR
RBBBBBFEEEEEcVzwPwlwk5RvZncAU3CZGkYHFQhBBBFEEEEEEUQQQQQRRBBBBHFy43/iiRBEEEEE
EUQQQQQRRBBBBBFEEEH8/fA/zEUQxH8TZnaamVUu6XoEEUQQ/z2YWQszK/fnJYP4v8DsP6CpD+Ko
MLN4Myt5Wv4ggvgTmFlFM2ta0vX4uyA47v83MLPwkq7D3wXBtixZBAWsExxm1tzMJgDPEvy//iOY
2RVmdteflwziP4GZ9TOzbiVdj5MZZlbZzEYCHwAN/6R4EP8BzOxiM/vIzKIk+Uq6PicrzCzWzN4E
XgTi/qx8EH8NZlbJzN42s0tLui5/J5jZvcAM4IySrsvJDk+5/TZwWknX5e8Eb236HGj7p4WD+EME
2/LEQHBTegLDzM4HfgQWSzpf0m7v/F/PoVUKYWanmNnXwB3AmpKuz98FZnapmc0BzgFWlXR9TlaY
WXNgN7AD6CBpSQlX6W8BM2tsZt8DNwOfSjocnCv/bzCzPrgxfhi4W1JqCVfpbwEzexr4Bkj33oP4
/4SZRZtZEnA20F3SuyVcpZMWZhZhZq8BHwFrJP1S0nX6O8DMEsxsJjAQxxE3v4SrdNIi2JYnFoKu
Sic2snCCxkQAMzsHWI4TQDJLsF4nLLxNw6vAAUlneedCJeWXbM1ObphZK2AI8LWkISVbm5MbklaZ
2RaccjDPzDoDyZI2l3TdTnL0Ac6RFArOFVdSXgnX6WTFKTir7qOScsysjqSkEq7TSQ3PNfxJ4FpJ
Y71zwbXp/xOSsrxNRb6kvWbWDDAgSdKhkq3dSYfuQGvgKUnflXRl/kaoAZQHbpc0z8zKSArK8P83
BNvyBEKQWPEEgpmdC1SV9IF3bMB9QAegGfA7kAZkSrq5pOp5IsLMIiTleJ8vAy7Ftd0VQFVgNvCr
pL0lV8uTC2YW4ncHN7OKwHVAOPAx0A/YBGyRtDiwbBBFYWanAo2BHySle+d64pSDk4B4nFfYfGCk
pOSSquvJBjOLk3Qg4Hg98AgQC3QGkoA3JB0smRqeHDCz2oAk/e4dNwRu9C43AcoAa4GfJX0VHO9/
DWYWBxzyKwrM7B7gEtxcOgz4DdemU0uulicXzKwscDnwU0B/jQD2AHOBKsBmoBxwr6R1JVXXkwFm
VtkvF3n99S4gFTiI8zpcAWyQNKHkannywcyqStrlfY4BrgY6Ab8C5wOrgYWSPjUzU3AzdkwE2/LE
RTCc4QSAmYWb2YvAe8BzZtYYnEQHTAZ2Ag9KOg8nIDcws74lVuETCGbWx8ymAQP85yR9BYTiBIrO
uPa7BbgvSLT212BmjwLT/ceeO/NCXLzpapw2uBnwtZnVlOQLuo4fCTO7DlgK3A208p/3Ng2f4RRb
Z+P6b02gZUnU82SDmfU0s9+A24sRUz4CfIXzSvgA6Ao8ZGaJx72SJwHM4WlgPfC+/7ykjTilwZnA
LFyIyHLgKTOLDyoQ/hhmFmVmnwDfAqf6z0t6FagPLMJ5eqQAD3hehkH8CcysNS7MZjhwlplFAXgG
hIdwG98zJF2JU9BcUFJ1PdFhZrXMbDIw28x76E9WAAAgAElEQVTKAHgK2XnARTgjzHdANvCkmZ1Z
YpU9iWBmp5vZLlyKewAkZeDGe13cpvcRYDFO3k8IbnqPjmBbnvgIeiKcIDCza4GfcRr2DpL6Blwr
Lykt4PgVnBZ+4vGvacnDr2k0s7rAGGAjcAj4l6QVXpmGQEtPoYCZdcVZz4dI2lIyNT/x4SkC7gZ6
4ja0b0ka5l2LBM7FeR/423kkECOpXwlV+YSFOdbgM3CCbR/v9Ht+T4Pirsxm9i4uDvXl417Zkwhm
Vg14GKgO7AI+kzQ34HovSVO8zw1wyprLJG0tifqeyPCsuk8DX+Jc7T+V9LF3LQao5G83MwsFPgVe
C2zvIIrCC1u4BOcFlwPMAT73r+He2iRJv3nHrwMHJT0etKL9McysBc6VuQ7QDjefLj1G2ctwoSOX
HL8anjwws2dxHlvxwG5JD3vnDad8/VlSrnfuGSBE0hMlVN2TAmYWDQzCeQz3B96V9L53LQyo6ylo
/eW/A+ZK+mdJ1PdERrAtTw4ErbIlBDMbYGY3m1k779Q4L+b0Y6CmOWIr/0YjUIFwLW5jsul41/lE
gNceAvCUAdcBg3EWncv85SRt9CsQPKTiFszfj2N1TxqYI1TyC7CzcAJwD+Bhb6OBpGycS/6KgFvX
4zwUgsDxlpjZI2bW0BPA5kn6FZiAizNv423GKKZA6OpdX1wC1T7hYWahnvIA3Fge4Sla84AuZlbV
X9avQPA+/4bzRAo9nvU9kWFm7c2sgZnFemEeL8qRU40G7gronxnFFC89cXPoyuNf6xMfZlYLwOPh
+Bm4Eufd0ZEADyNvbfot4NZkYJ93LahACICZNTKzx8ysm7c+rZQ0G6cYjAI6mVn5o9xXH7gGZ7EM
woOZVfWPb+AdnOz0T+C8Yh6ws/wKBA/VccqwIIrBzMK8fhotKQvHGzUal1HtgQD5KQ/nHeO/LwI3
7meWQLVPSATb8uRDUIlwnGGOSfgd4B9AWWCsmbWRdBhALgPDv4BHvWN/LGUzM5uCWxgHSFpbIj+g
BGFmtwC/mNk/zUuPJWmTp3yZD1Q1F2tekMHC2xzfimMbXgwo6HZfCG/SHgV8giNORNJSbwOxHvge
eNtfVoW8E7GedeJmghkwADCzwcDruJjcYWY20C+ISVoJLMNZeBp75UM9oe5DnCA3QlJQUCsGM7sd
+AV418wuB2IlbfMuj8HF7Lf2PD/84WGhZjbIzJYA64BS74VgzsV+JPAFbn0ZAyBpp1dkAk45PaTY
fU3N7CNgKDBKHq9HEA5mVtOcW/gnZjbczFpI2inJJ2kazlumi5nV8MqHeK+m5lKU9cVlYQoiAN5a
PhvnefAw8KiZVYKC8IWvgTY4IkD/PeXMpXr8EZgv6fXjXvETEGbW2syWA6OA0ebS326XdECOM2IC
8IxX1rzwxHBPKT4ZqEAwI9MR8OTQHbjwmjHmQr02A0j6AdgAPOaVDfE8aOPM7CZgAY48fVnJ1P7E
QrAtT04ElQjHH/lAbeAaSa/gFAaPmVnNgDJfAKme1wFm1gQn3A2TdN6x3Pf+zvA8Nm4FbsMpAx4y
R0Tpxy+4SeYCKGLRuQ5HwnKTpH96gl3Q2oObiHHxZOHAA0BXM3siwOILLlb/fE/RlefdlwD8G0dY
2dWzDJVqmAv1SAR6SroPZ+U5w5xLrR9jccJYXc961kCOLOh9SafLI64KKrkK4bXT+cC1wEhcjP7D
/utyKcjWAd1wbrl4ipuO3rlbJT2oIAM+OB6TJpLqSLoJiDOze60wrjwfeBm42LPs+Pt1ORx3RztJ
40uq8icw+uLI587BxY/fZ2ZtAq6PxXkZtQKQ45OIxClgl0lqK2n18a3ySYFmwEOSHgSewCNK9F/0
NhZ7gJae8qCdp+CaC7SX9CIUrHOlDgGGFH+I4luSLsD1vZfNuYv78SZQ21womDyFbAWcG/n7ki5R
kPC3CMyFe10IXCDpYpzb/T3msoP48TBwlZlV8xQz0bh+XA+3Ng3wGxBLM4JtefKiVE6uxxtmdpk5
Eptw3AT+O67jI+klXNxkb395OeKQV4CPzGwP0FjSYUmlyjUvwO0OnHV3uqRFXpjCG94LAEkpONfF
g2b2gJkN86wWH0jq6rmUBxEAT5g9BZgjx3I9AGiEs5pFeGXScRbIl8yshZnd4XnL3C3pVklpxf6n
UgPPStMACkI9mgG9vMvzgR+AawI2aTsotPhswWO/lzTTe57fjbxUK7n8HgUeWgBx3iZrMvAhjlj2
/IAy/waicW09ycw6SvpZ0uWSlphDqVzrzMXf+yFgT8C5h3AhCs28siZpEY6YcqmZzcOR1C2QIwQs
PicH4dANF4ubhduMrQLu8F+UtBy3NnU0sxfN7N9e2QdVyDdT6tvVXJjNaVYYnlANOM/7vAwYD5xq
Zm0DbhuBy8S0GrdGRXkyQprnjWQqpSSg/nXEe88FdnuXBgANgHP986K3pj+HI1AcCtwhabekKyV9
dvxrf2LCAkh8PTm9CVDJOzUCtw51D1jLNwDvAu+b2cc4pdg2SY97CvBSi2Bb/j1QKgWr4wUz62tm
K3GEfq/iwhD8qcaaeNo3cFkZ+vsndDPriNsgfwG0Ko3WHzMbgnMJ928WcoCz/NcljQFSzOyBgNvW
4vgingCiJaUELZCFMLNEM3vJzPqbI6gClyKnjJnFyIXIzMFZcWsE3Po+0AW3Kd4GoMKUUCGlrY3N
7AxzGUEeAd40M78y63XgcnNhHxk4i9gOHLcEZlYBF4OaCXSX9Gjgc0tbOx4N5jIFfOy943m5hJvZ
Bd5mYAMuxOaKAAF4L9AWRwy4Ri6+3/+8EDmUqo2EmbU1F/422pyLfXscwSdAhQCFwTpciByeBbIp
zvMjA3giUHHt3VOq+6iZdTazH83s+YC16SfgJgDPs2gSEGVmvQNuPQTcD7THrfdIyvcUXKW6Xc2s
srlwmXdxngb+dJcjgRpm1srzgtuA4+vxhyyGA4/jFN8PSuoSaImUlF8aFbJmdq2nTH3GzDp4pw8B
EeZizQ8An+O8uwL3AJVxnl4tcd4zQQTAzJ4EppsLp73KOz0BaO6N4TU4j6SaQKDytjzQHdgm6enj
WukTFMG2/PsgqET4H8Fc5oB+OMXB+ThW68aehfdrHMt9Q2/DMZWi3gh7gf6SriptLmSeNWIJUAs3
iQw1sx5eG0WZ2Z0BxR8Cevut5rhYqlycy+49x7XiJzjMbACOdCYXaAoMNrMqOKVAPZwgBk64aIiz
AmFmp+JIrF6QVEPSN4HPLYWbs0q48JhPJXXDbR6uNLPquDjcHbg82+Bcbcvg2hzAcHnLO3kW8pDS
aiEvDnNEf/Nx4/55oI+ZveBdfg8n8CIpE5dqMBOo5e3BLsFZ2Vp4rs8FrrylrX8CmFkXXJjcaJyV
NhO4WNIeHDfEJTjPLnDK7UvMrKJ33A7n9txe0nR/O0Lp9pDxrNqP4Ta2H+GULx+ZYwn/BPCZ2UVe
8b048skE794EXJvf7m10FwT0T5Xydo3Aje1dklpKugGINrNr5HhPfsC54qMAgmkPYcBXkhL81vLS
7NFhZmXN8evcBLyE83rt73l2LMFlCEoAkCOra4jbkGFmZ+DSOnaTdJHnmRAEbvya2We49uqHG9t3
m5mfZDYBZ2QBp+RqjQtb9s/FwmUSeOx41/1EQ7At/34ICrD/I8hlDnhChamwluAsvFGSJuOYRa/G
uY6H4zYcq7x7f5NUWlnaDRgpqb+kT3Abs8u9a/fgyJXivOO9uDbzuz8/IKmXConCgqDAYlMVuNSz
fr+C23Q1xFl9wnHx+9XlwhfW4ll7cO3b128194Tm0ox0XKqh0eYyhWzHudnXxFl63wVuMbOW3oa3
IuAPDUlVQOiCHD9HqdvkHgMRwHBJ/eSyf9yMc7eNACbiNmkPemU34Nxx93sbsIme4LvDCl2YS+3G
DLfWDJH0uVyY11oKlYRv48b9eWYWLhfGNBtPFpD0oaT3oIBItTS3YyAicKmEe0kaK+kjHJnXdV4b
fwXc67VZKm7cR3r37pVzC/e3a2iwXR3kCBJ/wnkR+fEezioOLnyptpn5w0NigRjv3ixJs6BwXSrN
Hh2el+tS4BLPg+gDnLIwWi5tayRu3NfybvkGZ9lF0jxJF/rbM4giyAS+lXS9HEHyVNwaVA1YhMsM
1tvMKnryQBqF8+0cOU6e7SVR8RMQwbb8myGoRPgfQl4cvmd1CMVZgaK8yy/iNG+P4ixryQQZxMHF
Nn4aYKGdA+R7wtksXFzkK2Z2BY6ptZKc67g/Lj2IAHgbqlycZXI9gOfd4k/ndAAnTNTDhY+0Ak7H
S43luYQe8Kzm5rmVlhoEWmKhQOhd7n3ONxfX1wHY7hkV5+EUCQ+b2Wac0mFm8eeWZmH3GNiE20z4
idDKACsl5Xj9dTgw0MyuB17Dudv7YyX9ucxDVApdmC2AIM0bo4dwIR9+JOOUMFFyKRv/hWO1H2cu
3M6Hl2IwEKVtrP8R5DgMZknabS6jTRgu1egy7/pHuCwM73peX2fjDAMF3jBWGH4THPtFsarY2t0d
15b+teoxoKeZzcV5ax7hal/a+2rAOvVvSfs9eWktTpnlT3/7Gk6B+KKZPY7zAFlx5NOCCISnnPku
4JQPx9Wz3/Pu+gon14/1PEHqUSgjBI0EAQi25d8Ppd2q+F+BZ9HJPca1EDkm0aZAmDdQwA2aj82R
V2WWRuu5v20Cz3kCcCDOw23Q/ELCgzh3pxtwrvhBt6ZiCGxX/4bK3788YaMMbnOb5l2b6oWQPIbL
xzuuuEWitE7g/vY7Wpt6m4LqwMZA7bikEZ57Xk2VwlSsf4ajeQp4m4hs77PPU874/GUlLTWz/jj3
xgM4NubcYs8odX3UzB4FwsxsuKTsgPGeH9DOZwDJKkwjPMXMZuA8vHbK844JohDHWJv8a3e+JJlL
2Ri4eb0Zt8ntA9wvaXqx+0td/wyE54FxhAIlQMkSijNsRQDTvHPhkuab2ZVAQ896WephZuUUkGo1
YNxneu95ZtYIN6f6PVznm9lqHAdKQ+AcObK6II6BgPXnYMDpSrj5dC+AXCrsu7ywutrALZ6xoVTj
D8Z7sC3/Rgh6IvwfYWYVzYvPl5RrjrQuLuB68ZjcRsCXZlbJzN7HxZ8haVMpVSBYgPDQy4oysgfG
NtbGEVVhjgwwTC6103Wea9NRlTelGQHtempg+EHApqI6boO7xjvfSNI+nFfM+XKpR0ttqsHA320O
D+KI+4pc89q5GrDYzCqY2YfmpWWVdEjSWu/+4Dzrwduc/RVPgSuBqd5mratnWZshaYSke7w5tzTH
P/vH9c84pWrj4mUC2rkqMN6zoN9rZq0l5Ur6VMUyg5R2WCETuM/MyhytjNcnT8FlDVnpjf1W3pj/
QtIN8vgkSuscGojiHhjmiBT96QcL+p133R/amWWOg2K4d+2wX4FQ2vuqOf6Sh73P9T1lwdFQF7dB
yzGzJmbWSVK6pLcl3RdUIBwb5jIvlfXGemCqTIA6gN8oc4GZnQUgabykV4Ob3qIkvGbWvPiYDbbl
3wdB4fb/jtrApWZ2oZkNxks/5g2EgpjHgMHSCBgCTMHl3P6yJCp9osCbnBPM7FWcBbxOMYHLP3mn
4Qgox+GYmMO9+4PKg2PAzE43s9HAPwgY4wGbikbAQjPrYGazccRqBuR5/0tIsfKlCoG/2/vclEKl
X/E2uRy4DvgWx9FRJB2WZ8Uo1RZIKLKR8HlCxdNWmCEkMKe5v7+GArlmNhZ4mUKX3ILyR7NylBbI
88ySNAf4BehnZmWLl/PatS5wO7AQR1y16ijPK7VtGYgAwbcb8IWZXewdF5eVGgJzzGwQsBjHal8A
K8wKUirn0EAEKLU7mdk6XDjNR9614v2uOy47yCSgOS5lZvHnlcq+GqDgSgXqmtkGnPt302Ll/HJU
LSDUXOjCxzg+iVJrHPgPMYBCQs/AVJkAnXEk36NxnrGHj/qEUgxPjmxoZpNw8n3N4te9j8G2PMkR
VCL8BzAvLtw7XIdbCO/HWSRa4FzwzsVZ0Yq77bbDMQ13kvQGpQxH0UQmAPcB50rqKmljsc2bD5e/
/DpcFoaf5LJVpB7Pep/oOEq7NgPm4VzsHzmGJrcJblPxHPCcpBcCBd7StukNtBh6H1ua2RAz86cW
+hrHyxEeeI/3MQe3ibtW0gNybqRBIc1DMeVBlJmdh0uFWQ/HG3G7v6i/nHd8LvBPYIak1ipGplSa
N2deH61iZoPNpXB7ETgVF7ZQHAk4BZgP5711rDmhVKL4WDWzdma2HrfuVAD6mlmE138toHwz4E6c
h1IPSSMDn1Pa5tDi8K9L5ohOY83sRRwb+1045XY9c54GxRU05XA8E0MlXS3pt+B86hCg4GqAUwRW
BO6SNKFYOf/ceCFOyV0G6Crpx2LXSzWsMKW6mVmkmV0YcPl7IK248tDri81xSsPFks6SS5VbqnEU
OTQeZzSd6I3jpKPcE2zLvwGCSoS/CM+y4PM0bBXkYs+m4khA/MRWY3GMzS3MrIxX1u92eqakQd59
pQpe2/kXwD5mVl4uhdBkIN3MevjLFbt1P877oLukt49rpU9wBGzO8s2sjDmPmIqSVuOsE2d45SKP
8Yh7JPWQyxRytLYvFTDPa8jvGeMJWNtwVpuhZtYW5/1STc6FvriXxjA55vUkKySfDAppHoptpl7C
kXs9JOk63HzZ18yqeZs0v1BXFeeu20zSKO9cqXVhNrOXzewJ73Nlr3/txykIzvYUq58DN5hL2+q/
L0TSLuB0SddIWlNMEV7q4R+rAfNkD2CEpP648K4snMK1SHkco/hZctlEtgTb1aHYuhQpR3R6CJdt
oRmwXo7/5BZcFpu4wLEP/OhtJgpIVkvzfBq4LptZd3McWhfjFKzDgAe8a4Fhi/65cjRwhqTHdSTX
VKlFoGLbexfOa2OUmfU1sygcl0T7Yn3TX/Y9oKWkd45/7U9MBMj3FbxTYbhQBX8ocvhR7gm25d8A
pXLj8FdhZlW9CcVvSatlZhOBN83sKRzB13CgvplVlYsrz8FtODK9DYXf7TSjpH5HScDMuvg1u17b
dTOzWTjyqafN7DY54qlvgF7mCJR8gYKYpO2ShgUXwCOhQhfRy3D5dO8C3vcUMrcBPcysvqTsAKuQ
39r7oqTXvXOhgc8rDTCzaPPiSAOUMK8Ak8zsWdyi9gBOyfUYLg3W6Z6SpjjZ2m7vmf6UjaVW4C2O
YhbztsBQnMdBrFdkLvArzpsLXA5oJO2S9L6kjID+WSpdmD2Mx6UPPAW39vTwvAm+wK0953hCWBmg
T8CGwt+eiyDYR/0IUFb53y8HBnqXm+G8ZMD1zVm4tHjVAo0CcjG7P/ufU9rbNVBO8o7vxIV7PGlm
l+JclXOBiuY8O9bgslOdHXifpxArleuSH56ceY45clm/kqst8ATwtKSXvP72Ei4M9FI5D7i4wOdI
miBp6fH/BScmzAv3Cuij3c3sKzO7Dac0OBeXhv0FnFK2tV/B7ZX3y0/flDZ5/mgIlNW9tlyAy/Dl
9+JaghfGoMIMSnHeu398B9vyJEdQiXAUmHPBexpHWnWKd64STvv7HtAftyhejYuH/A3nogsuPV5O
abZImlllXIrAwWZWw5tsuuDCEm7FxZM+YC6k4TvcpuKykqrvyQBPCVM34DjKzG4CXgH6S+qBi8u/
GpcP+jlc7Ck4V+Yj3Bi9PlqqNmdmlgjswG3GojwN+Ws4PoPuQCLwnLcxeB833s/CeRuVP9ZzS1s7
Hg1/YjHv6SlcxlAYa3rAO+5pZqcdbb4s7e3q9cM5OC6dYTgvo+sB5EgRtwEXmVkEzvJ4I06AO2K8
l/a29CNgY+rnkIgEmplZR+AdoLmZVZdjET+MU87c6N17RCrB0rjR9cPbPEzHKa0jvHPXAC1xa3ou
rt/uxylkHgW6m1kXoAouFOwIlMa+as6b5QVcO90GfIhrO3BjercKPQf9njPP4WSpt4HvzWVtKHVt
dyx4iuxQM/sH8GBAH70eFwY2Eecl8wMujOYB4DTvWhLeXArBMBBw8pM5wu7AtMJn4pSw/8DJoCNw
HEaZOL6trmZW3sxGAX2hdI7vvyuCSoRiMLNzcGyhYUAXScu9S+E4ZUEV3ED5EvhE0u/AJ7hFdDou
PdHDpW3C8SZrv2Yy5f+1d+7BWpVVGP8t0bxRYmojmWMIKhheykwixEsNJOmY2UwMGsSRaZy88IeJ
Okxe8lbeUQsbmbxMIJCWjaRmKoipZVqak6KOCjh5iVEh8wIhT38873fOEc+N4+F833Gv31+wv/19
87LZ797v+6y1ngVcC7wKTC3X4hJgG+Be4FZcCnKepH8AzwOjopSA9P7oG5twithsbNw5pRxejSM5
HwOGlGN/wM7WoyWdDxwaEYe0d02reK0lvQQ8hGtvjysK+TnY32Q2Lk9ajVtdImkB9u5Yg+/fNKZq
n44i5kMiYgy+1oMi4sjynaeAKZIeq8+QG57aHD0BR23XAavKIhjsw3MEMEnSbcBEtbQiTGhTgN28
RMpnlENz8DtrNDbyfQI/aw/HafePAgPXj/RWmSLA/gxvYq+ntGQs7I/TmE/AdfmnlOfsxViIbcKb
jsskvdib425wpgCDgSGSvoU3tMeEMzp3BF4u2QmUDMOtJM3D/wdLgW+qVdvHpNnY+D38HN0GlyuB
yxd+WjLezsMeZ1cVQXAiXgOMpeX5W2mKEHM+nuc/xu/66eXjzXDW1tHAWXheLwauxnumU4FFwPOS
ZvXy0JONTIoIH2QlsL1cR/aviBgdESPxRBkNHAWcJamppNvuiSfQ8cDxsu/BG/Ubfu8TEePwQ3hS
OdQf31tzgJ3KZuK/wAHAJZJ+gTcP34uIEVhwmKYK+kV0kffwPXYTcFxETAaipClfQou6+yI2W6pF
zPeVtLAO420YSibM5WUOE26PtQRHecZGxG6yad9E3DVlAhYImyJiF2hOsb2bItZUUXzpjC5EzJfj
Wl6AK8o5tdZtf+31AfcRSgr9JnKp3OXYK+IWYFpE7IOv6ULceQFJy1LkaqEdAXYNziD8RER8tczn
24D9gM/gEqbb8HP1h8Bi3LlmVW+Pv4HZCfispBGSbgRWq8Ww81XgZmC5pJGSFkTE3jirYxbuvDJV
0vy6jLwBCZfJjAWulUvstpb0HC5hmITv1yF4zTSgzP0ZJYPr97I58or6/Qsai4g4PCL+XFLrwaLW
y8CBJRvh03g9WuMiYI9wW8dlwJnAdqXsptKUwOpL5a8HAuOxkDAtIg7G6/0JOGv7MEk/Kc/dkMvs
pmBD+Qs/8ONJnydFhPWQ9BfcU/vX4TrpS4H+JePgGbygeKUo8fPxguM9uT90VfvursAlCieU6M1a
fK1G4nSxyeW8PYAdykNpKHAB8LqkVSkgtE0pOViFI2T9sSP4V4DTy8JjDrBLRMyMiCNwTV/Nyb7W
V7vKm4oDcfr8uRGxVxEE+gEDcebGSeW8YcDTpbxhR+BxYC+AiDgUR9Qqv6DogK5GzKdIup4WQSHp
BLXUi5+DU28H0GJSuUbSxJLRVTs/Ra4W2hJgAR7G7/Ka0PUAzjJsAgZLmoHv5VE40vtoL4+70XkX
2LKkKo8BfhDuaDMOb9juxNFxIqIJuAoYLmkuThE/OtowW6sqpUxmDV5HgQ09kXQDbsk8CPgRzlSY
i1s2LswMrnb5N/AlYHpETMXi4G/xGurrONNwckQML+fviTsEvAkg6Z3anxNWAjuUwOpr+BXzIO6+
cCEu+34BdwV7swhct1NKlCW9nNfyo0uKCG1zHDAO2FLS/pLuKscvwovlq/CEWSLp2JKqV1lKJPHn
uHZ0C2Amjki+iFsR9SvCwXl4EXwF8CdJZ1VYeNlQfgNsJukRvME9DXt0vIH9OEbije6EkoLfVn/j
yiHpJvxC2w44ICJOwXXPWwF/x8ZUw7DB51gcMe+PU0MXlJ9ZgrM6nujt8fcVuhExf6bi4tYGES0O
4acBFxQhZoyk6et9nhQ6EGDPwGufecD2ETG9bH7fxpvf5eUnRmFB8WBJs3t7/A3OCryZvQEb0e2K
BduJOGX8Ijz378abidMk1YSYacBdVV83taY8C+8Ddgv7yayLYgSI31/DJT0maSouD9lb0py6DbjB
KVma1wCv4fl8E7ALLp0dgU3RzwXOjIjbcQeWxfUZbWNTAqu3RMR15VDNLP4ynJH0eSxw7YvXUdcB
V0q6og7DTXqZqPD+okMi4mycgvO1opivrW3GImJn4O2iyiU094VdhtXfJuAw4ElJ4yNiPI7qHJ4p
od0jIo7FIoFwb92L8cbsNaywHwC8I+mCsPNtpd3CWxMR+wH3APtgAXAt8Cyu6TsR+LKk75R7eKCk
p8r3+ikNgLpFRCzDGSADsCndA7UNb9I9ikizrmzMrpF0c8719ikigiLiKGBYeTaehMXsWbhWdyhO
zf04cIakv7X6/iaqsGliV4iIofi9v4WkN0rJyDBJp4TN/wZJWlLOzevZAWEvmROB51pvwCJiLjBT
0n11G1wfJCK2xffm7sAXgCPxhvdJYLGkX5Z3/kGSfle/kTY+pTzhBdwy9J+l3OatiLgReEilBXtE
7J6BwWqxaeenVBNJZ0fE0oj4dlmsbYadhmu150krJK2MiKuByyWNi4gngWEl5f5+HPldV1vY1XWw
fZMFOONgtqTPAYR7Rg/E1/d/wMnhVqOv1G+YjYekRyNiITbzGo9TwXfFqc53ArtHxCBJLwArS1So
cp0reoJWG4VaxHzPiJijUi+dG4nu0yo6+RaOqKXLdQe0es9sDewbEfOwADsVC7DX45TcYySthve1
cVPep51TEwgo6ffYN+qR8tlqnMWVgmwXkPR0iYqfHTZQfAy/s4TLQ5MNoIhaVwLzJB0UEYtwCW0T
fh7cI/sfpIDQCZJej4gZ2L9spFraMm6Ljapr5+V9WjEyE6EDSgT9Bkmbd3pyAkBELAdOlnRrRAyQ
tLLeY/ooUBa3lwJ3SPrj+ouyiOiP57bUDOoAAAICSURBVHPWnrVBUdKXAvuXxdpgSc9FxKZqo21b
0n0yYr7xiIhDgENwv/jclHWBEm18HguwJ5VjuwE7S7q31Xm50d1ASpBgZ+AbwHexYfKpSpO/bhM2
AR6NswvvlI2ok24SEUuxcff8UvY1CuuE99d3ZH2PkmE4CXgai7ArcJbxf/LdXk0yE6EDJM2NiE/l
AniDmIZrTTdPAaHHGQxsUbI53rfYlbtfJO1QlPTLsGv4XrLzdXPf94yQ9xwZMd+oLFLFO650g1V4
wXsHNIsFz+KSpmbyHt1wJK0tc31v7HuwCFpKSeo6uD6KbFr3YF7DHuN03IZ9fnnHp/dB95mG27Q/
DMxStmysPCkidIKkK+s9hr5ECi8bh1LbO1k2rku6QSlRGhFu8/h663szBYQe54vYAPTxeg/ko0Q+
T7tNuwJs8uGQO4N8H5oz5iKfpx+enOs9Q65Jew5J84po+CtJ79Z7PEn9yXKGJOljZIQiaXTyHk0a
iYj4ZAqwG5fM5kqSJKkWKSIkSVIpsvY5SapJiltJkiRJ0jOkiJAkSZIkSZIkSZIkSZfYpN4DSJIk
SZIkSZIkSZKkb5AiQpIkSZIkSZIkSZIkXSJFhCRJkiRJkiRJkiRJukSKCEmSJEmSJEmSJEmSdIkU
EZIkSZIkSZIkSZIk6RIpIiRJkiRJkiRJkiRJ0iX+D91jLOlExGw8AAAAAElFTkSuQmCC
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Let's think about these graphs. First, the histogram. What we like seeing is a lot of 1's - that means there were a lot of days that the stock went up and we didn't have to worry about actually implementing the strategy - we were able to close the trade at a profit.</p>
<p>Looking at the profitability score over time though is a bit more interesting. First off, stocks that are more volatile will tend to have a higher profitability score, no two ways about that. However, Netflix consistently outperformed on this metric. We know that 2015 was a good year for Netflix, so that's a (small) sign the strategy is performing as expected.</p>
<p>The final interesting note happens around the end of August 2015. Around this period, the markets were selling off in a big way due to issues in China (not unlike what's happening now). Even so, all of the FANG stocks saw an uptick in profitability around this time. This is another sign that the strategy being developed performs better during periods of volatility, rather than from riding markets up or down.</p>
<p>What about FANG vs. some cyclicals?</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">cyclic_df</span> <span class="o">=</span> <span class="n">simulate_tickers</span><span class="p">([</span><span class="s2">&quot;YAHOO/X&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/CAT&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/NFLX&quot;</span><span class="p">,</span> <span class="s2">&quot;YAHOO/GOOG&quot;</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span class="n">cyclic_df</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="s1">&#39;days&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">8</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Distribution of Days Until Profitability&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area"><div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABBgAAAIICAYAAADE513IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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