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    <title>MaplePrimes - Maple 2026 Posts and Questions</title>
    <link>http://www.mapleprimes.com/products/Maple/Maple 2026</link>
    <language>en-us</language>
    <copyright>2026 Maplesoft, A Division of Waterloo Maple Inc.</copyright>
    <generator>Maplesoft Document System</generator>
    <lastBuildDate>Mon, 22 Jun 2026 13:21:27 GMT</lastBuildDate>
    <pubDate>Mon, 22 Jun 2026 13:21:27 GMT</pubDate>
    <itunes:subtitle />
    <itunes:summary />
    <description>Maple 2026 Questions and Posts on MaplePrimes</description>
    <image>
      <url>http://www.mapleprimes.com/images/mapleprimeswhite.jpg</url>
      <title>MaplePrimes - Maple 2026 Posts and Questions</title>
      <link>http://www.mapleprimes.com/products/Maple/Maple 2026</link>
    </image>
    <item>
      <title>How to get consistent output from dsolve for system of ode&amp;#39;s?</title>
      <link>http://www.mapleprimes.com/questions/243653-How-To-Get-Consistent-Output-From-Dsolve?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;Consider these two output, both for solving system of 2 first order different equations.&lt;/p&gt;

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"&gt;&lt;/p&gt;

&lt;p&gt;Why is the first result is put in a list, then each solution is in a set inside the list, while the second one is just a set of the two solutions?&lt;/p&gt;

&lt;p&gt;My guess is that because the first system is non-linear.&amp;nbsp; Is this why?&lt;/p&gt;

&lt;p&gt;This makes it little harder to parse the result later on, as it can change each time.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Is there a way to get same output for the first example as in the second example?&lt;/p&gt;

&lt;p&gt;Mapkle 2026.1&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=diff(x(t),t) = x(t)^2, diff(y(t),t) = exp(t);
sol:=dsolve([ode],[x(t),y(t)])

ode:=diff(x(t),t) = x(t), diff(y(t),t) = t;
sol:=dsolve([ode],[x(t),y(t)])
&lt;/pre&gt;

&lt;p&gt;ps. the ode&amp;#39;s are not even coupled in these example. So each can be solved on its own if needed.&lt;/p&gt;

&lt;p&gt;And if there is one ode with multiple solutions, now dsolve returns expression sequence. No set, no list.&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=2*x*diff(y(x),x)*diff(diff(y(x),x),x) = -1+diff(y(x),x)^2; 
dsolve(ode,y(x));&lt;/pre&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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"&gt;&lt;/p&gt;

&lt;p&gt;This whole thing is a mess.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;There should be one consistent way to return solutions for all cases.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Regadless if it is one ode with one solution, or one ode with mutliple solutions, or coupled systems of odes, linear or not and so on.&lt;/p&gt;

&lt;p&gt;The output should be the same form in all cases. A list of lists or list of sets or whatever it is decided on.&lt;/p&gt;

&lt;p&gt;But it should not change.&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Consider these two output, both for solving system of 2 first order different equations.&lt;/p&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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" /&gt;&lt;/p&gt;

&lt;p&gt;Why is the first result is put in a list, then each solution is in a set inside the list, while the second one is just a set of the two solutions?&lt;/p&gt;

&lt;p&gt;My guess is that because the first system is non-linear.&amp;nbsp; Is this why?&lt;/p&gt;

&lt;p&gt;This makes it little harder to parse the result later on, as it can change each time.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Is there a way to get same output for the first example as in the second example?&lt;/p&gt;

&lt;p&gt;Mapkle 2026.1&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=diff(x(t),t) = x(t)^2, diff(y(t),t) = exp(t);
sol:=dsolve([ode],[x(t),y(t)])

ode:=diff(x(t),t) = x(t), diff(y(t),t) = t;
sol:=dsolve([ode],[x(t),y(t)])
&lt;/pre&gt;

&lt;p&gt;ps. the ode&amp;#39;s are not even coupled in these example. So each can be solved on its own if needed.&lt;/p&gt;

&lt;p&gt;And if there is one ode with multiple solutions, now dsolve returns expression sequence. No set, no list.&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=2*x*diff(y(x),x)*diff(diff(y(x),x),x) = -1+diff(y(x),x)^2; 
dsolve(ode,y(x));&lt;/pre&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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" /&gt;&lt;/p&gt;

&lt;p&gt;This whole thing is a mess.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;There should be one consistent way to return solutions for all cases.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Regadless if it is one ode with one solution, or one ode with mutliple solutions, or coupled systems of odes, linear or not and so on.&lt;/p&gt;

&lt;p&gt;The output should be the same form in all cases. A list of lists or list of sets or whatever it is decided on.&lt;/p&gt;

&lt;p&gt;But it should not change.&lt;/p&gt;
</description>
      <guid>243653</guid>
      <pubDate>Mon, 22 Jun 2026 06:41:21 Z</pubDate>
      <itunes:author>nm</itunes:author>
      <author>nm</author>
    </item>
    <item>
      <title>How to make debugger not show intermediate results?</title>
      <link>http://www.mapleprimes.com/questions/243651-How-To-Make-Debugger-Not-Show-Intermediate?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;I was trying to use the debugger into a proc that has this call&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
P:=plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):
&lt;/pre&gt;

&lt;p&gt;Even though the proc has : at its end, and the above call to plots also ends with :, the debugger insists in printing to the debugger window the contour cuves lines. i.e the value of P&lt;/p&gt;

&lt;p&gt;Is there a way to tell the debugger not to do this? i.e. not show the value of P. It seems it does that automatically.&lt;/p&gt;

&lt;p&gt;Here is the worksheet. Simply evaluate the call foo(); this will open a debugger windows. Then click on &lt;strong&gt;next button&lt;/strong&gt; and now debugger will print&amp;nbsp; the output of&amp;nbsp;&lt;strong&gt;plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):&amp;nbsp;&lt;/strong&gt;&lt;/p&gt;

&lt;form name="worksheet_form"&gt;&lt;input name="md.ref" type="hidden" value="5C600FF731666F925B3E90619BFB4A05"&gt;
&lt;table align="center" width="768"&gt;
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				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;restart;&lt;/span&gt;&lt;/p&gt;
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			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;kernelopts(&amp;#39;assertlevel&amp;#39;=2):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;kernelopts(numcpus=1);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="32" height="23" src="/view.aspx?sf=243651_question/1cf25b557d0ae6626fadb4ff66b96ed9.gif" style="vertical-align:-6px" width="21"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;interface(version);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="`Standard Worksheet Interface, Maple 2026.1, Windows 10, April 28 2026 Build ID 2011354`" height="23" src="/view.aspx?sf=243651_question/486a0ed15a8ff238395266f57ddbc632.gif" style="vertical-align:-6px" width="560"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;foo:=proc()&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local L := [$ -4 .. 4]:&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local RHS:=y/tan(x):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local P,T:&lt;/span&gt;&lt;br&gt;
						&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;DEBUG();&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;P:=plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;T:= timelimit(60,plottools:-getdata(P,&amp;#39;rangesonly&amp;#39;)):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;end proc:&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;
			&amp;nbsp;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;foo(); &amp;nbsp;#this will open a debugger window&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="left" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;span style="color:#0000ff;font-size: 100%;font-family: monospace,monospace;font-weight:normal;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
	&lt;/tbody&gt;
&lt;/table&gt;
&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243651_question/hang_maple_2026_1_on_timelimit.mw"&gt;Download hang_maple_2026_1_on_timelimit.mw&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is screen shot&lt;/p&gt;

&lt;p&gt;&lt;img 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AIIIIBArQL8QYEAAggggAACCCCAAAIIINARAlyl1B6hv4bVkowRQAABBBBAAAEEEEAAAQQQQCD9ArQQAQQQQAABBBCoX4CQUv2G5IAAAggggEBjBcgdAQQQQAABBBBAAAEEEEAAAQTSL0ALEWh5AUJKLd9FVBABBBBAAAEEEEAAAQRaX4AaIoAAAggggAACCCCAAALtLPCqxn8VIaWffuMbmRDoRAFGPgIIIIAAAggggAACCCCAAAIIpF+AE18IIIAAAgggECigEw6rCCnpbEAaBBBAAAEEEEBgLQQoEwEEEEAAAQQQQAABBBBAAAEE0i9AC1tZgJBSK/cOdUMAAQQQQAABBBBAAAEE2kmAuiKAAAIIIIAAAggggECKBQgppbhzaRoC8QRIjQACCCCAAAIIIIAAAggggAAC6ReghQgggAACCGgLfOfKiw987k//6DOfeeBP//Tll75DSElbjoQIIIAAAggggMCaC1ABBBBAAAEEEEAAAQQQQAABBBBIv8Dat3D16tUL/+E//Pt//1fbtn3wN/bv3w+0HgAAEABJREFU/+A/2vr4439FSGntO4YaIIAAAggggAACCCCAAAIIpEiApiCAAAIIIIAAAggg0PYCFy9c/N4rr3zq7k++55d+6R/8g3/wS7/4i5/6558kpNT2/UoDEEAgUQEyQwABBBBAAAEEEEAAAQQQQACB9AvQQgQQQACBEIEXr1y5vHL5ruE7f+qNP/X6173u9a9/nUx87bXXElKSDkwIIIAAAggggAAC7SRAXRFAAAEEEEAAAQQQQAABBBBAoHECX/u//++e973v2muvqyoi4ZDS8vTg0PRiVRnuj8wjgAACCCCAAAIIIIAAAggggED6BWghAggggAACCCCAQNsKXHnhytve9jZv9RMNKZXmHpwXA7f3eYtZ+yWludzQ6NlSbRVZnB4anF6qbVu2QgABBNpQgCojgAACCCCAAAIIIIAAAggggED6BWghAggg4C/w/R98/41vfKN3XZIhpeW548XsvrFeTylLU0O5Oc/SZi4onZ08XhzYvT1TW6F9w3uy80emSrVtzVYIIIAAAggggAACCCQvQI4IIIAAAggggAACCCCAAAIINETg6tWr69b5xI98FtVYvnmJ0s7hGjdv6GZLD50oZvcO1375VGbH7gFxfvJMUkGlhraWzBFAAAEEEEAAAQQQQAABBBBAoCUEqAQCCCCAAAIIIJAmgcRCSoGXKK29Vuns6fOi9kuUzAb09Q+I4smHiCmZHLwigAACnSBAGxFAAAEEEEAAAQQQQAABBBBAIP0CtBABBLQFqkNKpTOjQ0ODxuR68pB6EJG5cDDne6VOwCVKVm5HzovicSNPK5OhoallTxVLS3PTOaf0wdz0XGX8Rj3QSGWSmxOlxemcnVVuKuIJSaWnLhZ9nvBk1U011m7p0pTKXy3xaWbP7duEOD/HE5U8HccCBBBAAAEEEEAAAQTWTICCEUAAAQQQQAABBBBAAAEEmiVQHVLK7JiZ3ZMV2X2zhZnyk4cyw/nCsQEhsnuO5Xf4PI+o9PSFYnbfsOcpSjK3QuFc4eA2maGakfPWNN5T2UIZ4Bk5ckH07561EhzbJS6M5NxRpb4xuUpmJS7kJk+JncfMDGf7L50YsWNClXman1TdxLZ+37rN7stms4dm7Zb2jhdm75VLBg6e82lmb78UmH9y0cyW1wQEyAIBBBBAAAEEEEAAAQQQQAABBNIvQAsRQAABBBBAICUC1SEl2azMjgMDxeNVl+OUzhydF9sm/OJJQizOnSwO7Bz2iTXJ7PQmI/g0M7ajz84k0zO2e6B4YbHySiWVWVFs2jkz1msllBseGihefNqbTqWVP1eeL4ps93o5550yw/mJ3Q9O2hGp0lxu5NSWCZm5N6lc0tedFeLyipxjQgABBBBAoGMEaCgCCCCAAAIIIIAAAggggAACCKRfgBYioCPgE1ISIjO8Jzt/2n2FUEkGjbJ77rLCOJUZl86cmve7RKkyVeSn0vL0aM646Zx597lc7tRl342yW8c8lxwVnw8MKa0YufjWXGWf6ZNRpZXJ0enpqdzkyi7niiW1rvqne1P1Ej4jgAACCCCAAAIIIIAAAmsuQAUQQAABBBBAAAEEEEAAgSYI+IaUhHmh0qT92KRGX6IkxOJ0bv/hy5t3zVq3sysUzk1MbG1SBCfTN9wv5ufPi/67egJDT03oC4roUAGajQACCCCAAAIIIIAAAggggAAC6ReghQgggAACCLS/gH9IybxQqXjyIePan4ZfoqSucxL7Cvnxnkw5pJPJdCfCa15aZDTEPz/1GKeFrbOFY1sWjubsKJpvUvOCJ99VLEQAAQQQQACBNAvQNgQQQAABBBBAAAEEEEAAAQQQSL8ALYwQCAopqQuV9mbPT54pNf4SJaEed7SpOoBkxJkiaq+zev3GrCiuXAlIujw9Ovn87tn8cEZktudndj1/NDe9GJB2caUohKeeAYlZjAACCCCAAAIIIIAAAggg0EwBykIAAQQQQAABBBBAAIHGCgSGlIQMsezcVjx5dPJkceDgePnqIVd9VNRH5ylKG7qzxePTS9aVQqXS4tnp0aGh0bPWAtFz+zYxf8qdYDo3OrIgsq6yap7NdG8W4vzCkjeD0tnc6INid36sz2ldz9jMxMZTuZz7OVL2hksL80IM3N5nf+YdAQQSFCArBBBAAAEEEEAAAQQQQAABBBBIvwAtRAABBBBoa4GQkJIQvXftzRaLgUGjxTkZbdo57MRjAiEyw/nZfeL0/qGhQTmNjJxa2bh7dnZmu7Nl7/jswa2XnQSTp0T/7kJ+9yZRPDEyOGRdNrQ4LTc/cl4Uj8tMps0QUWkuNzR4WIZ65u9zx6gqaqJaIeafrLr2SOa2/0SxWFQbDlq5LU3JnEdOyqXHR1wRLzO35SfPC7FtuNf8xCsCCCCAAAIIINBxAjQYAQQQQAABBBBAAAEEEEAAAQTSLxDcwtCQkhArRTGwc9gJ/bjz0b1EydwmMzyWP1comNPM2I4+11OTVIpM73DeSZBXCYToGzPTj5kXBtkfjYVjZmgnM5w3Pho5u2JUKkvnJ7N9p7oKyrkoyljhl1vvuJGPVclyxEttsLgwL7J77sqoeX4QQAABBBBAAAEEEEAAAQQQaEkBKoUAAggggAACCCCAQMMEwkJK6ilK9V+i1LCqx8hYXahUPDFXdaFSjAxU/Exsm9hBRCkGGkkRQCC2ABsggAACCCCAAAIIIIAAAggggED6BWghAggg0K4CISGlUuh97dRVPtalQm3QdvNCpYVl++lNMats3OIv4IFSMbMiOQIIIIAAAggggEBbC1B5BBBAAAEEEEAAAQQQQAABBDpUoDqkVDozOjSknng0NLRfPaLoiJyfWk4Bjrqp3XhPjVcZtVf8LAW9RRMQQAABBBBAAAEEEEAAAQQQqF2ALRFAAAEEEEAAAQQaIVAdUsrsmHE9T8h8qtB4TyNKJk8EEEAAAQT8BFiGAAIIIIAAAggggAACCCCAAALpF6CFCCDQhgLVIaU2bAJVRgABBBBAAAEEEEAAgeYKUBoCCCCAAAIIIIAAAggggEDnCRBS6rw+p8UIIIAAAggggAACCCCAAAIIIJB+AVqIAAIIIIAAAggkLEBIKWFQskMAAQQQQCAJAfJAAAEEEEAAAQQQQAABBBBAAIH0C9BCBNpLgJBSe/UXtUUAAQQQQAABBBBAAIFWEaAeCCCAAAIIIIAAAggggEBHCRBS6qjuprFlAeYQQAABBBBAAAEEEEAAAQQQQCD9ArQQAQQQQAABBJITIKSUnCU5IYAAAggggECyAuSGAAIIIIAAAggggAACCCCAAALpF6CFbSNASKltuoqKIoAAAggggAACCCCAAAKtJ0CNEEAAAQQQQAABBBBAoFMECCl1Sk/TTgT8BFiGAAIIIIAAAggggAACCCCAAALpF6CFCCCAAAIIJCJASCkRRjJBAAEEEEAAAQQaJUC+CCCAAAIIIIAAAggggAACCCCQfoF2aCEhpXboJeqIAAIIIIAAAggggAACCCDQygLUDQEEEEAAAQQQQACBDhAgpNQBnUwTEUAgXIC1CCCAAAIIIIAAAggggAACCCCQfgFaiAACCCBQr0B4SKl0dnp0aGjQnkZzublSvSWyPQIIIIAAAggggAACcQVIjwACCCCAAAIIIIAAAggggAACaywQFlI6m9t/4vLW2cK5gjXN5PPDmdgVZgMEEEAAAQQQQAABBBBAAAEEEEi/AC1EAAEEEEAAAQQQSLdAcEipNHexmN07QQwp3QOA1iGAAAKWAG8IIIAAAggggAACCCCAAAIIIJB+AVqIAAII1CEQHFKqI1M2RQABBBBAAAEEEEAAgeQFyBEBBBBAAAEEEEAAAQQQQACBtRPwCSktTxsPTxo5XhTFEyPGvPk4pdxcdT1Li9O58sOWcrmpZd9HLWkmU7mXzuaMEtP30CbVOn4QQAABBBBAAAEEEEAAAQQQQCDdArQOAQQQQAABBBBIrYBPSKlnzHh40uy+rMjunTXmzWcp5YfdDKWlqdzkyvDEjP2kpXP5if6FydHpJXcqoZnM2qb01MWiMVu8sOgbnTJW8oIAAggggEBjBMgVAQQQQAABBBBAAAEEEEAAAQTSL0ALEUCgNgGfkJJWRqW5ySNiV344k3Elz/SN5XeLI6NnnWiQZjInj8xtW7LGh+zWPnfOxjJeEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE1kSgxpBS6ekLxYH+Hp8q9/UPFC8+bcWUNJO5sslszxvXRclglWsps6kRoCEIIIAAAggggAACCCCAAAIIIJB+AVqIAAIIIIAAAmkUqDGkdOX5YnZj4GVExeetkJJmsjTC0iYEEEAAAQTaVoCKI4AAAggggAACCCCAAAIIIIBA+gVoIQKxBWoMKa3fmHXiRt4ynWiTZjJvDixBAAEEEEAAAQQQQAABBBAIFmANAggggAACCCCAAAIIINBsgRpDSpkPbM3OLyz71HZxYT675QPWBUyayVzZlM7mBoeGBodyc9aFTq51zCKQFgHagQACCCCAAAIIIIAAAggggAAC6ReghQgggAACCKRMoMaQksgMTxwUD+amlkvl0E+ptDidOyUOzmy3IkpCN5mDWnrqYtH4ULywWM7YWMILAggggAACCCDQRAGKQgABBBBAAAEEEEAAAQQQQACB9AvQwjgC3pCSfZ3QyPGiKJ4YMa4ZGjJepxfdOWd6x/MT/QuTR9VFRUaCycmF/omZsV53KqGZzNomc9uWrDGb3drnxKWMBbwggAACCCCAAAIIIIAAAgggUCHABwQQQAABBBBAAAEEEGiigDeklNmeP1co+E1jfdUVy/SN5WecxPn8eI9vHEgzmcrdLj0/7JuTSsIPAgikQ4BWIIAAAggggAACCCCAAAIIIIBA+gVoIQIIIIBAegS8IaX0tI2WIIAAAggggAACCNQnwNYIIIAAAggggAACCCCAAAIIIJB+Ac0WElLShCIZAggggAACCCCAAAIIIIAAAq0oQJ0QQAABBBBAAAEEEGiOACGl5jhTCgIIIOAvwFIEEEAAAQQQQAABBBBAAAEEEEi/AC1EAAEEUiFASCkV3UgjEEAAAQQQQAABBBonQM4IIIAAAggggAACCCCAAAIIICBE2kNK9DECCCCAAAIIIIAAAgggULvA/NWbb3nt5jtXL9WeRa1b6hd9afWBe67e88BqrSXpbadfH738Gp3q0vzqPXca3Sd78JbX7rzz6gPzDSZqdJPIP1yAtQgggAACCCCAAAINFyCk1HBiCkAAAQQQiBJgPQIIIIAAAokKXHrACCQExIHm73ktLErUboETKXfpidXff3T1kanVB9Yg8CXLb8Xp0gNX7/jNq488uyre1XXru7pkFb/27Orv/+bVO+8hqiQxmBBAAAEEEFgrAcpFAIF2FyCk1O49SP0RQAABBBBAAAEEEGiGQDuVsTlr1PbZ1Sd8QiyrxW+ErBXzjxshh3eKzaI5/60+cEefkKcAABAASURBVKcKcd0zX0dxZnvf1ZVtVqXrqGvzNr31o+se+8trnnt43cMPr3vu69c8Nm4Elh69Whd186pPSQgggAACCCCAAAIItKAAIaUW7JTkq0SOCCCAAAIIIIAAAgh0ksBA18dVc1fPPWHEh9S8/XNJnHvWnPdbK1Yff1St/fiHVfhBzbXDz+YBFTJ57uGugXaobXPquPnudQ/f37V5c7k0ueRPPqo+PmJGDdUsPwikUIAmIYAAAggggAACjRQgpNRIXfJGAAEEEEBAX4CUCCCAAALJCXR92AgefO2b1VleemL1a/Yy71pxSRiXMHV9mOCMSOF/Ax82IoXfED5Xrwn+QwABBBBAAAEEmiVAOQi0sQAhpTbuPKqOAAIIIIAAAggggAAC/gJW8ODR1ar7yX37m+q6pY+Pr7tVbudZawWcPup/uc+l+at3Gjepu/mW1+68Z9UVlrBuXnfnA6sy18rJWuV3szVz1dXfN66aeuQ31e3vZM43+z0CKrhoozTf5z9dWr3nTuOZUreonO+88+oDVRbGphUv2ptE1MfM9NLqA/dcvdMo3WjX1XseWHWhCeuhVvesmsntV5PlNQ/m6j1GVvc4rdCurZ2z/f7s6rftWd4RQAABBBBAAAEEEEAgjgAhpThapG1vAWqPAAIIIIAAAggg0DkCb+tSQSMhiu4ghnVfu64P3y3eqShWH3fiE+qjMANOt77D+FD58sQ9V+/4zdWvPdt167vUiq89evWOW67aW3d9aFBd/vK1c6KiNCGEdZ+9rne8TXj/y75T5qY2NFap+Vvf1XWrUTNjifUSWrSVpvpNBpk+cvWRZ1dlhh//aJecxLOr54pVwZvKjbQ30arP/NU7P3L19x9V14TJOtwq0Z5dfWTKQLONgsJ+3zRibNXXkFVdQKZdW3cjLxWNT+/q8usNYxUvCCCQEgGagQACCCCAAAINEiCk1CBYskUAAQQQQACBWgTYBgEEEEhIYLMYlGEMUfnAJDMsoSIK1p3xvlERZTEfpNQ1+CEnzGNX5lkVHbl1fN1zX1/38MPXPPeX6z6u1qzOPGAFaTZ/yIhgPbv6hB0vUetlRMm8z967uj7keqiPuUqIroH7ZW5d/1LVU3z8j+W8Md3fVZE2qmjh89/qAzOqYh//42sefnjd/fer6eGvX/Pw3V0+aa1F2pvo1OfS6p0y/CbErR9d95gs92HZLgNNtXT1N/7FqoVkPfKqMuw3v/qIWaXKa8gqLyDTrq2Zlfl6afVfTK3K2VsHRYWw4D8EEEAAAQQQQACBNRCgyPYUIKTUnv1GrRFAAAEEEEAAAQQQQCBUwOfKITMsYUYUzEtkKq4rMoMZ/uEfI+TjhGQ2d/36uArPlDf3jWCJ1SfOJRDD+LiMNoUU7adgXOjjf2mUX3K1TH+Tj//xunJ0ykshxPxnr6oHVr1r3R+4w2Obu+7/A+N+g89e/ax9edc7jCDTuSeUkqqE3PZxOd91q1peEWoyLyD7uPkwJCH0a3tpfvWee66qewB+xK7V3arvzOJ4RQABBBBAAAEEEEAAgTgChJTiaJEWgRQI0AQEEEAAAQQQQKBDBJwrh5wH55hhiXdmjYiCeWc813VF5l3RbvW9hOVd6359wNjKttucteesd58IlrDvejdaTwwjumjh/c8K1Xx21boeyJvCs0R3k+j6mBd7iY+PdlVfDGQF3oR9cZgXbbX4DSHe1fUHo12i4gozM89ykEy3tkJ8+/Grjzy6+sizqzLbj4+ve+zhrupaeShYgAACCKREgGYggAACCCCQvEBLhpRKc7mhwaGhweml5BtMjmECtrzEHxoaPVsKS8u6ThRokRGiqtGO43Nx2jiyDeXm2m/wKHN1WG7OwaF0ZrQ5BQV3RIt3VunstElkdspoLjcX+4Dd3D4Npg5Z0+K9EFLzRFa1VyZt3Vmq8vzO2V4DLk5tN1c9MMkKS3x4wMjECm+sftOKOFlXFFkBJyNJrBcnguXc+868KEp8tMssUDTvv667VUhGmA98uucBncBSDZsEtMe8u6Aoh39c6bqyxpOinOckOWhWJ5hBuHeKzUbAr3wRWPUFZLFr+/E/vua5h9fdfzfxJFdvMIsAAggggAACCCCw9gJtV4NWDCmdnTxeHLh3b/U/+1srW3WiwTi5aZ65s19zU8ultapSw8rNDOcL5wpymt2XDP/SVFuePW8YsGh3kJYYISXjELF7e6bcT2dz9o5pxmzMV89OujxdTmadPZQ9YiaWr9OL5RzlXGlxOlc+a5+bnivJxHVFgzLd5n61qVtm32ZT4l0f2v7MjplC4d6mn3xz16mlO+tsbv+Jy1tn5bHammby+WHXDuFuSPB8c/s0uB4ha1q6F0LqHW9VvQeWeKU1LHXLdZbP90LgAbxveE92/shU+n6ta1h3t1nG1gOTHlH3UhPCDHWoBykJ4z/rEhlrrX0vNSvgZKSI97K5a/Sjcgvn6U1WjMq5V5tc17xpYJ164JO6fdzqI1NX77jltTvviQos1bBJ/Pa87R1dFRvZgb3HzVvhfXv1a0IoMXP5s6tmqMnnArKm1Laiqj4fWIQAAggggAACCCCAQKcJtFxIqXRm9ERx26Gxvpbpib6xwrnZPVkxcK+KtVjn787N7hQPjrTjdRIt40pFEKhNYOmhE8Xs3uGKQ8T2/LnCwW0iu8+9kxYmuqt20p6x8r481msU3zsuNzk0ILJ7jhXch53SXG7yVPfOA3KtMR3bJS6MHDlvbFPzS+xz/jWXxIZ1C7RwZ5XmLspdYCJ+DKlulKZn0MK90HSLli+w5TpLfS/I39nUP5HJ7p01/rlMfjiIMbNj94A4P3mGoFKQUJsvNx+YJL4hLsmI0hMqXOG+r51187pHV1U4w7wOpr4riszirGtrzAtuRFftMSpR33+bu+5/+BoZWPqXH1VRHHXF0j2rETnWsElEjtWrzXsPupZWBPbmH5c1NC9vMq9nWn1c9Y0ZnOsa/JBqSHnbxte2XBZzCCCAAAJtK0DFEUAAAQQSFViXaG71Z7Y4d7I4cHC8p/6cGpxDpnc8f3Dzicm5BpdD9ggg4BYonT19XgxUXKLkXl0xnxme2CNOzFVee1SRIvDD8txx0X9ge69zkjTTMzYjI0+BG+it6N6k0mU3Otmqj/y0pgCd1Qr9Qi+0Qi9o1qFRnaVZfL3J+voHRPHkQ8SU6oVs0e0Huj4ua2Y8MMkIZlSGJcy1QhQvCSOYYVwfI9PXPJkZGsWt3V3vKmu/uevu+9c998dGMMYMnlWu9/lUwybuXOz7Ddp3FHSvMx6VJMSt7ygvtAJ73xCXhLH2XV0fMh52ZMbnzGvIvvmsesCSuVxU/RdV24H7r3nu69fcv6bXP1dVmY8IIIAAAggggAACCLStQGNCSrVyLE/fNz9wr3X1QK2ZNHW74spyjeWVrFuy1PAAjDglllyPJCktTeWG7Bt/5UbPLtVx5qTqnmCeO4xZ5R45L4rHK28bOOUVKy3Nle8wlhudPrO4PD2a8//Hwsm5lUqqlEoQ2SnW/dAc5IiW2vdFzM0JldLhnap6ElUsECOrUcctV8Vbsh42NuS6UZusuZXetVA2omq5pM5Z94gbNVtqVcx4dFbLjhDZEGsqPXWxKAZur7hEyVoV9HZ5JWhN3OU9t2+Lu0lV+vUb1Z3vNnWHh5RabZBXNSLqo9oRgkevsXXksDRSWS9y0E5bg3ZwKODAZaRxFTrt82ChWIXKsvU6SyZs3mTduXHkeFEUT4zYRxt5EJPHn8pa6IBUbhH2qSK3wEO0OqJaRyFZpaFRda/IOr5kzArp9UKr7TJt/71g4stXva6XCdWk11kqZWv+GEf483MaT/GUvzyooS6/mluzJdTKV+Adxs3fzj1hPkhJZI1whZ3SvDOeulVd8RtymXl9jJypeSpn+MS5VZmLuoebfNOYvlFU6TUS1pTEjHUZwTPd7WvYxMraRBBmNMhaZr7ZV25VXG9kFmTE4c7J0NE7hdVF5nIZajIuIHNfXia8/5mJYzXQmwlLahNgKwQQQAABBBBAAIEOEmilkNLS1OH5lrrlXcQ4KK1cEtnunohUAauNM+NqXfHCYvVJN/tslDor5zppWP44WhWuUPkE/DiPJFmZGx05LXbNHjPu4nWukD/Q/eTRXGX4ISCP6sUq8DC5MjwxY2VVOJef6F+YtEIUZmqjXL97kRU8l6AtTY0cuVC+w1j+wLBYeHC+WHy+2kXlHOam1uv+yKjMyNGFjQcmCsbNcArnZid2i9P7TxQrMtBoqbovorrlmriQmzwldlq8s/2XTlTeF1EfJLpQ4wko6maMrsqad/jxXkZjLjcSr5zNjU4+2T0xYTT54Faxoi7fMSqmHlrTuiPE1czS0xeKYlu/ec861/Kg2SvPF7P9twWtDVkuzy0WT+6XO0ip5BqHveOF4PsmheTmrMp0yxMk2e4NzgK/maYOchE93vzqGLRMM7fIYenkL4fl5OmVbmfPmti9cnq/O5gqU8qwqDyGiP7d9rOFjLsUeqL1+oXKPOWk1VkyXeCU5JHcLKRnzNh53TfyMo9glcNSE8TMM/pV7xC9PL3/8OXNh5yvmNkDuzauTE6O1vnQQa1eaLldpu2/F6xRodf1VmIhtDrLSe0zk/wu41NIyKLe/gEh5p9U34whqYRYXJg3flconl/QiD+FZsXK5glY91X72tTVR2ShnvvamY/2kWt/XwYz7OtjZMKaJ/PaGitDrbvemXd4E1/7Zs1leja8tHrnnVfnL606Ky49sKqaL6oias56IWrYxLV11ayJIB69eucDq5ecdZdW7/nI1a/Jjx/tulv+UiRnrMkMQa1+87PqzoTuIJwKBz67+tnHV2XCd2aNC63knJxi1Hb1gTtfu/mW12RN5HZMCCCAAAIIdKoA7UYAAQSSEmidkNLi9JHzbXHLO4O+VFqamjwp9k4E3pffSBb8krltS9ZYm93aV33FgnE2yjxR6P86s716EyOrsJfi/OWts/nxnoyzpbqR1y5xn3mpStimVetKc5NHxK78cDknmSDTN5bfLY7EiHXJjVzT5m7XHcYyO8Z3DYis783Bwtxc+UXMls5OHt90cGZsR7kRmUzf9vyxvWanmJvHamlRbNo5M2a3QsZpDg0ULz7tikaYeUa+xio0Mjd3gvkLKzsP5Mf6rDb3Do/tcF/r0+IjxGqJDBGJbPd661Pom3EV2uHL+yZ2OGPeTj9/n/oH5uUY7eDheXuV8947Xpg9tkWcmpzcbyRWl1zUeXJc5S2zLUTtvy07yFUDQn9qGL2Rw3J+8678uHMHQrmfjsn99PJ97qsY5e5WKMjduc/uaXlw2z3gE623Kx9RqJ1Mp7PstH7vjTiS+5XjWRYbxJODZ0HUIXppSu5rFV8xmUzPjvH8zs2H67w9rE4vtOwu0/bfC3IgRHW9TOJMOp3lJPaZWbNdxq5LX7f8HSD6wta+/gGZTp6Uj/HvG+wian9ny3oFNn+o61Y7j1sqZJofAAAQAElEQVRd91szl1WsHRQVkQ5R03/25TJq4492yXClmgn9cQIwN99y9c47X7v5ztVyGCZ0w7CVz67+xkeuGhlevfOW1+6YWpWJP/7H68LqU8MmMlPfaWDdY+MqAiRDa3fIcM6dqg43f8SI6r1r3WP3q1Xu7UyBRx6VlXRfKGaGA1cfeVSm9TySSru26qZ5QlgPuJI5MSGAAAIIIIAAAggggEDtAq0SUmqDW95VnIber672mI04NRzaLRnzn8wXZGwmNF1SKwd2DtvnW8tZ9ty+bf70XPmzxpy6TGSg3+/aLHmSpaYgSu/47MHuhZxz06rRXG5KDJ/LeyMBqnqhbktTxtl/30u7BsvBM/VP2n3PBKnMnfsuxmtpdquzoaqm8eN/oZWxKuglXqFBufguH9jtRLz817fsCHFVd+Wy+uAdyWqp/HHfZdG4Cs1//xq417nAzpzxXt0lMxOZzPaxmXzevC5EXXLx4MigO5Kh0jTkR41DVTHfg8MaDvKoxtYyeiOHpc+j9TJ9/dniwlOu6qhbruVcMcJc7pQxUlxJ3LNRhbrTtud8TJDwRmocopefPJ/tv81nx+y9S8b29C+rDa9I8NpW3WXa/XtBo+uDO6Ut15iPg4qses+YcZV23nPVdeSWJFhLgc1iUN37Tlah8kFKcoGcwtfKBLGnrl83oilyO/cFN/Jj4CQDMH9sxr1Wv/asiLjDW2AurhWbu/7gL9d9/F0yciMzVJf+3Pqurj/5y9DnCW2Ov4mrQO/s5rvXPWbVQXztWVUH8a4uGdN67uEun7idE4ervFBsc9ZkEaIqOBentupSJ5GEquA/BMIEWIcAAggggAACCHSGQKuElNSZ4oqYzeCJopg/MjhU053ZGtJ3laeh82OVl+kkWWQj7v2SjbjXlnb9rzxf9L9+yMihhiCK3C7TOzyWN07QFOTp+xnzNnq1nIVU/0Ra5uA/lUM+L6wUNe5Y2IiWysaGT2tSqFGllh4hRg01XrL7VCRGjaJjMkp0+Xn3Tes0Ng9JYl5yMbtPnHzI+zCwkO2SX9XCg7xpozfT7T4RtTidU7dcK9/Vs3BuYmLrplj0DUnciCO5TkWTB4k8RMtv8OLJ/X4R/f3zorjygk61G5amhXcZzTY3bc/y1iey672b1LGkibtMaS7nuTdmHTVn0/YQ6Lr74Wue+7qc1lXeb82sffhaIQbWqW29gZCg5UJsNq5mE+9a9+th1wSZpVuvmwfWPaxqKCt5zcN3d1lLg4rwLvcs2by56/6HjZob2T788LqBzVauQW+bIzfxlGJlFbC8KsPnHl53/4DdNGtL563rfqOe1QGngS6L5f7qDasyD26g1b9lVadMZhBAAAEEEECg0wRoLwIIJCDQKiEl65IddS7YigfszYqBg+cKY+57cyXQ4HbIohH3fknspN76jdmQuFFItCmGfKZvbOfmEyNT8W8bp11IcSUyMNCMlnrquyaFGrVojxFi/hNyjYGR6Rmbmdh4arK2mLQ82zg05TNCMrL8SysaxRuka/3S9EHetNGrnmNn65bOnJoX+wp59109RSbTba9fw/dGHMmjm9MMEM8hWu4Y2T3Wk+TssK71VS4/lsP50dVf0xRN32U0W9u0PSu6Pp6uj94kRoom7jIvrBSLK1c8dZPBUc8yFrSUQFtVZt549s+tidxGr60aTmURQAABBBBAAAEEEEAg7QLr0t7Alm1f6WzOuD9bs/6d7PzpOe+Z8OUnzw/sjPc4qMwHtmbnF3zOtqsHVme3fMB766PwLlD/Krl8S7rwtGptEm69dw2I8wtLKruqn9KZ0SG7R5JuaVVR/h/XpFCzKq06QszaWa/y1KrwOw9ora58y+w4sOVy/KeFWZn4jZCSPN+4uc8zxtWwMe66FmckW8X4vbXrIG/E6J0/4o3tlRYXis6B68rzRbGpu4rRCKtULeuUj0mDaB2ie4bjXcDHLhNrODZiz9KogFbXa+TTRkkWV4rCezypboB999Gm3Ae1unA+t4/ApdUZ49k/gx+qvrCmfdpATRFAAIHaBNgKAQQQQAABBFIvQEhpjbpYPc7HKLp4YdEb6jHWJPqS3du/MpmbWi7fCUw9bONBcW/sfz+eGZ44KB6syEqUSovTuVPi4Mz2qrPtG7qzxePTS1YLZbKz06NDQ6NVN7WbPzIq01iJZLNlbkfOZ/fcVZWZXCOSccuMze67bBSq8jR+Sktz07nBkZNi74T90KlYLTUyiX6JBNEuVN37a35h2VErLUrbB+ejqxCQoqVHiFNn1eqAcKCTxjWT2T4hO9obk3AlCZ5Vw/JMee+Uo3d68sKmg+PeYbm4IE9DCpHdN9wbnJ3+mvYd5NqjVxsjOzBw6XBu6qzrGDKd239xU/nA1XP7NjF/Sh5AhPGf6qbc6MiCMO83ZCzrrJd6QIKk1L6wVP7yEN5DtOz6PZfk94LsKeeYJESptDSV8xzwZSnsMhKhPLXu94KI7vpyM9p/bmlBfoUO3B5xcfzyk+dVU7P7Jvyf+KhW8oOAuPSE9dygD21GAwEEEEAAAQQQQACBlhOgQgjUJ9CCISX1D2OHhlrnWUqqPiMni8L1qKcErkXI3LbFPOWZ3eq96KG+Pg3Y+gPjEzu7Fybtx13kjq7cfiDvvq+gutmXceHU0ODQyPGiKJ4YsT9WnhPM9I4bjzs6KrvJnCYnF/onZnyiU5nh/Ow+cdoqdGTk1MrG3bOzVZGn7N6DB/pXHpo0L9uSpU8udB88lvc9WZOUm6rYge4nj+ZkccY0cvqC6L93tlBRN42WquExdOS8KB6XFNbAMCQPy1NTasxUxs9UuREgGoUaXdw7PrtHPGj3UU6iDc/sGhDGQLULtf8xddUA9kb1VI6tO0JU7ayf3rv2ZsX8k4vWR/tNXfNn98KQ+551meFdA+cPDw2qf05u9ItDYXWWQSQ7y3gSjO2mst12aHa3WDg1YgwP2blykHfvPOAzyEtzF42I0sDOYW+0SeUU96flBrkMDcw5e0oCBwfDXJI6fSHnjansb1zFcp/ch7qHZ2Z3dq84x5DJU907j1UcuOSOcHDrZVcC0b+7kN+9SRhHMOfOhxqFxu2opqe3L1+rPj5XPnRQE8TYHQz2iAO+DJbqHKIzO9Rj8FZOH3V2maHc0bmV/onqA74aTmnfZVLzvRDn27np+0Nkgep7QR7AvfuLXCi/LzzbG7GibVH/MmBRxZ1EtvyvTzz5sAABKbD6xLlV+dagu97JnJkQQAABBBBAAAEEEEAAgbUTaMGQUt9YofwMhhZ4llJlfYy6+ZxWjt2FGevxUfmETkNrVMB4zrZtm5/Z3lt5AjwznDdaV/B5rQi0qKIyfWP5GSdlXj3IRC32+ckMj+XtQgszYzv6MhXFSl5Vk54d43knWX68um7lfJNzy2S2j82Um5w36lYuyJ6LaKmsv9O6c9bAqJC06ez8RASIkS6iUCONEBmJZneW2QU9Y2Zl7EJ7x50+qpyxE1g5WW8tOUKsutlvme071VUpVRe6bXcGjwIZ77FTy3fTREUoK/rF7qwKIptFpRzv8fSC/7B8Qd0qSQyUr5uRhdY3tdogl0NtuLynKGFzmJmvNprTZo9bT8UuL0SFuZmJ+VrOSsYnjBGrlkQMS1m73mHX0UPtyELYO6YTNdco1GlBq87YA6O6C6ofOijFokHUIDfZva+K3UaQktqH6MquL5gH1Yydk/Oe/l1GopVV2/Z7QbZCu+udzm2lme0V3wvlHjGOLfJjxTeFECpW5H95tKtR5k01s3sOVF+T7UrDLAJSoOvuh6957uvXPHw3d72TGkwIIIBARwrQaAQQQAABBNIs0IIhpTRz0zYEEKhXQF2oVDwxV32hUr3Z1rq98Q/bs3uHI+6VVGv2bIdA2gRafpdJGzjt0REwYkXbIu9lp55Vxi3vdEBJgwACCCCAAAIIIIAAAgi0vAAVrFmAkFLNdGyIAAJrImBeqOR6jtSa1MIqtLRyWYiB3fyLdcuDNwQiBNhlIoBYvRYCi3MniwMHfR6VV1UZddlrEy8uryqdjwgg4BZgHgEEEEAAAQQQQAABBNZKgJDSWsk3qVz7kSTGY0WGBiseM9OkKlBMSws0eYQkY6FuYjZefTu1ZLKOm4txLzLn7mpxtyY9Ah0nwC7TcV3eDg3uGyvYd0Nth+pSRwQQQAABBBBAoD0EqCUCCCCAQEoFCCmltGPtZtmPJDkX8PAAOx3vnSrACOnUnqfdCCCAQLAAaxBAAAEEEEAAAQQQQAABBBBAIP0CtbSQkFItamyDAAIIIIAAAggggAACCCCAwNoJUDICCCCAAAIIIIAAAmsgQEhpDdApEgEEOluA1iOAAAIIIIAAAggggAACCCCAQPoFaCECCCCQPgFCSunrU1qEAAIIIIAAAgggUK8A23eywGs33xI0dTILbUcAAQQQQAABBBBAAAEEUhhSolMRQAABBBBAAAEEEEAAgbgCThjpmue+7jvFzZD0CCDQcAEKQAABBBBAAAEEEGiuACGl5npTGgIIIICAKcArAggggAACLSDghJHkjBNG8q2XTOC7nIUIIIAAAggggAACoQKsRACBVAkQUkpVd9IYBBBAAAEEEEAAAQSSE0hhTjIy5J6cMJKciWytTprITEiAAAIIIIAAAggggAACCLSvACGl9u278JqzFgEEEEAAAQQQQACBThdwR4/MeRkWck+dDkT7EUAgFQI0AgEEEEAAAQQQaJoAIaWmUVMQAggggAAC1QJ8RgABBBBokIBvAEkGk+opTuZZz+ZsiwACCCCAAAIIINC5ArQcgbQIEFJKS0/SDgQQQAABBBBAAAEEEHAJ1BlAKufEHAIIIIAAAggggAACCCCAgCFASMlg4CWtArQLAQQQQAABBBBAoCMFko0nvXbzLTJDOcmZjuSk0QgggEDrC1BDBBBAAAEEEGiGACGlZihTBgIIIIAAAggEC7AGAQQQQAABBBBAAAEEEEAAAQTSL0ALUyBASCkFnUgTEEAAAQQQQAABBBBAoBkCnXyhUjN8KQMBBBBAAAEEEEAAAQRaW4CQUmv3D7VDIAkB8kAAAQQQQAABBBBAAAEEEEAAgfQL0EIEEEAAAQQaLEBIqcHAZI8AAggggAACCOgIkAYBBNpEgAuV2qSjqCYCCCCAAAIIIIAAAq0p0N61IqTU3v1H7RFAAAEEEEAAAQQQQKChAq/dfIsMI7mLkB/lQvcS5jtGgIYigAACCCCAAAIIINDRAoSUOrr7aTwCnSRAWxFAAAEEEEAAgcQEiColRklGCCCAAAIIJC1AfggggAACjRMgpNQ4W3JGAAEEEEAAAQQQiCdAagRaTeA1zyVKrVZD6oMAAggggAACCCCAAAIINE0gsZBS02pMQQgggAACCCCAAAIIIIDAmgtwodKadwEVWDMBCkYAAQQQQAABBBDoVAFCSp3a87QbAQQ6U4BWI4AAAggggAACCCCAAAIIIIBA+gVoIQIIINAQAUJKT0b0+wAAEABJREFUDWElUwQQQAABBBBAAAEEahVgu1YRiLzrHRcqtUpXUQ8EEEAAAQQQQAABBBBoigAhpWSZyQ0BBBBAAAEEEEAAAQQ6SICoUgd1Nk1FoFKATwgggAACCCCAQAcKEFLqwE6nyQgggECnC9B+BBBAAAEEEEAAAQQQQAABBBBIvwAtRACBpAUIKSUtSn4IIIAAAggggAACCCBQv8Ba5xB51zunguaFSjK9s4QZBBBAAAEEEEAAAQQQQCCVAoSUUtmta90oykcAAQQQQAABBBBAoJMEZFSpk5pLWxFAAAFHgBkEEEAAAQQQ6CwBQkqd1d+0FgEEEEAAAVuAdwQQQACBQIHXbr4lbpRIppdbBebICgQQQAABBBBAAAEE1kiAYhFIUICQUoKYZIUAAggggAACCCCAAAJtLyAjQzI+VFsz5La1bRi0FcsRQAABBBBAAAEEEEAAgdYRIKTUOn1BTdImQHsQQAABBBBAAAEE2k5AxoRqjifVvGHbKVFhBBBAAIEKAT4ggAACCCDQMQKElDqmq2koAggggAACCHgFWIIAAgi4BOqJJ5nZEFUyHXhFAAEEEEAAAQQQQKDFBKhOMgKElJJxJBcEEEAAAQQQQACBxAXk+f3UTInjkCECnSRAWxFAAAEEEEAAAQQQQKAlBAgptUQ3tH4lUnM2h4Y0XeAWSlwrgdY/sFBDBBBAwBEIOlRe89zXUzMFtTHWckeMGQQQQAABBBBAoNUEqA8CCCCAQCcIEFLqhF7WamP46YzUnM2hIQh0jkD4Tl3bWq2jCYkQQKANBZpZZd/jT9DBuZkVa3RZQW2MtdxXT39ho9tI/ggggAACCCCAAAIIIIAAAi0tUHflCCnVTdieGXhPPYSfzmjPVlJrBDpaIHynrm2t99BhLuloaBqPAAJ+AubBwffV9/jjlwfLfAR89fQXenvEp4wOXiR9JGYHA9D0lheggggggAACCCCAAAIIrLUAIaW17oHGly//NvZO8q/lqqnxFaEEBDpYIC1NrzpuOB+9B5lElqSFjXYg0AYCieyz7kyc44N3pg040ltFb3e4e622+fRq0TIEEEAAAQQQQKAGATZBAAEEUi5ASCmFHVx1OsB77kAuSWGzaRICCKydgDyqNGKqOpp5P65diykZgTQIuPepxHfh9gTqxFrX3/XugVTbfIu4y8pLjRapDNVAAAEEEEAAAQQQQAABBFpTIB0hpda0XYNayb+E5ST/GHZPa1APikQAAQSSEHAfynzn5RHPd0qicPJAoBUFfAd8zQvdu1UrtpY6tYmAeyDVNh85hpsgIesgK9+EgigCAQTqFyAHBBBAAAEEEEAAgTUUIKS0hvh1FS3/7vVO8i9hOdWVLxsjgAACDRNIPGN5xPOdvIfHkCWJ14oMEUhQoGro+g74mhcmWE+yQqAegcgxXLUjOB/rKdS9rcxQ1sG9hHkEEEAAAQQQQACBugTYGAEE0itASKkt+9b8u1f+6Vs1tWVjqDQCCCCQtEDVsTH8ozyi+k5JV4r8EPAR8B177oVVo9cnCxYhkLhA62VYtSM4H907i5yvreJyQ5lhbduyFQIIIIAAAggggAACCCDQaQKElNqpx+VfvObk/3dvOzWFuiKAAAKtIiCPqL6TebzlFYGGCviOPffCVtlPqAcCLSng3lnkvM7eWtUOuYncsGohHxFAAIF2EKCOCCCAAAIIIIDA2ggQUlob91ilyr91zUn+xWtOsTYnMQIIIIBADQLm8bYBr18nTwQcgRpGJpsggECQgLNnhcyYv1Q7rzIrZ75qRq5iQgABBBBAAAEEEECgHgG2RSCVAoSUWrpbzb9snb+KW7quVA4BBBBAAAEEEEAAgdYWcH6vljOypvI1aDJ/Dw96ldsyIYAAAggggAACCCCAAAIdKEBIqXU7Xf4Fa/6J27pVbMmaUSkEEEAAAQQQQAABBMIFzN+0Q9KYv4cHvcrNw6eQnFmFAAIIIJCYABkhgAACCCCAQNMFPCGlpamhocHqKTc6vVRy1W1x2ptGLRmdPuNOZm9RKi1Pj+Zyo1a2uamzS3LJYM43sb1RW75LvdxcIjWXf6PKv2ATyYpMEEAAAQQQaDkBKoQAAgisnUD9v2nLX9TDJ1lEyLR2TadkBBBAAAEEEEAAAQSaLEBxaRPwhJR6xwuz+7Iiu3f2XKFgT/kD3U/uH5petFvfN1Y4N7tHptpXTiOXHNx6+eT+6kBRaS43cnTl9gP5/IyZeHaiXzx59PC8nZkQJRmJGaqKWpXXdtqc+cen/Bu10xpOexFAAAEEEEAAAQQQSIeA/GU+ZDJ/4Q96bSUB6oIAAggggAACCCCAAAIIVAh4QkoVa50Pme1jx/Zevq86XOSsN2YyvcP5g9uKJx9yXam0OD1yfNPBme29GSOJeslk+raPzRwaUPPmT0bFsXaL0/uHclPLro3NtR31Kv+wlO2Vf3/KVyYE6hBgUwQQQAABBBBAAAEfAfn79pr/si0rEDLJGnonn5awCAEEEEAAAUOAFwQQQAABBJopoBlSklXKbN9ZFS6SCyOm0plT8wP3jvX6JOsZO5ffUY4ziUzfWP7c7M7uhcnB3PRcqfbAkn1TvtycKC1O5+yb+OWmzvrkWVqetm/HZ9y4z1t06YyTYNTKQV1TZWXrxNisZEfOi+Jx6/5+KkOZbGrZp/U+i8y/G82/LX1WswgBBBBAAAEEUidAgxBAoJkCzu/bzSy0hrLMvwiqXs3KO681ZMsmCCCAAAIIIIAAAgggsGYCKSpYP6QkRG//gLi06BOYsTxKS1O5I+cHDo47kaLFhWJ2o/PJShbylukdloGlXRtXJkdGp88499kL2cK7St2Ur3BwmxAXcpOnxM5j1t32+i+dGLFjQvZGy9P7D1/efGjWSlOYPaCKnhx1XymV2TGj7gSYzR6andluNkVdU3VvNpsdOFiOiqlkhXOq3GzFzQALhfEeu7iQd/nHoflHY0gaViGAAAIIIIAAAggggEBtAu3++7b5x4LzKpvjTLWBsFUMAZIigAACCCCAAAIIIICALRAnpKS2Ka68oN6sn8orckaOnC8O3DtceU3Spm4zDmNtofPWs2M8XzjQ//ypodzU2aXgEFZ4XkWxaefMmH3DPRnyOTRQvPi0K7elqcOX983mx3sydg0zGVX0zs2HJ+cq8s4M5yd2Pzg5al2lpJ4OdWrLhMy8IlXNH+Rfg/KPw5o3Z0MEEAgUYAUCCCCAAAIIICBE+n7fln8+OJNsnXei2xFAAAEEEOgwAZqLAAIIINAkgbghpcpqVV2RM3vskDg1MlRxlU/FBq5bxpl3h5teqljv+pDpGZsp7BQXj+zPTYdcGeXaomo2u9V7w73i8+WQ0vKT57P9t9nRJNfGvXcNFC9Y0SNncaZPRpVWJkenp6dykyu7nCuWnAQ1zZh/+8m/Bmvamo0QQAABBBBAAIEUCNAEBBorIH/lTvfv27J13km22ndqrDW5I4AAAggggAACCCCAQNoF4oaUst0bHBLPTEbFgWb3CPdVPpdXylEc0Ttu3oZOvh4aENk9x7xRHztT4ylHp8WWg8fyY30+gR87Xc3vK5dF8eR+M7JV+bp/XlRejGWWkekb7hfz8+dF/109dVfI/APP/NvPzJ5XBBBAAAEEEEAAAQQQSFxA/sqdeJ6tn6Fste9k/hkS/tr6raOGayJAoQgggAACCCCAAAIISIE4IaWlhXmxOTK8k+neLCMyyzJvIfr6s+4Lg4xl0S/LZ6ZyQ0cXNu4u5Me323eui94sZoruTSqmJYNbvpM31lU6MzqysHW2cGzLwtHcGVecLGa5Mrn8E878A0/OMyGAAAKNFiB/BBBAAAEEEEDAFDD/DAl/lX+tMLW1gNnXvCKAAAIIdKIAbUYAAQQaLxAjpKTuFLfnrkxUnUorl0S2u8dIltmxe2D+VPVN5IxVvi+lpbnp3OCDz3dPzM6M7ejzTZPUwp7hfeLkQ2boKzLP5enRyed3z+aHMyKzPT+z6/mjuenFyK2qEjh/lsg/4apW8REBBBBAAAEEEECgowVoPAItIyD/WmFqawHnD8+QmZYZblQEAQQQQAABBBBAoM0E9EJKpdLZ6dEHxb35HRERpdLS1OTJ4sDOYZuhb2x268XJ0bNLlZf1lBYX5u0k5ntpUQaTRk6v9E+cy48NZyLKMbep7zUzPLHn0oO5KVk3V+VKsgm5oVFXGKx0Nifbvtt9/72esZmJjadyuTm1obsWG7qzxePTdmNLpUXpNjQ0av4q7/xZ4t6CeQQQQAABBBBAAAEEEEAAAQSSEnD+8AyZMf9EDXlNqjKpy4cGIYAAAggggAACnS7gCSktTQ2NHC+K4omRwfIThiaPrtx+QMZUbK3F6aHBkZMy1fFymiGVfsR4+lHFXeMyw/mJA+LJo+6UucmF/tljToBKRnGGRk6JnccK+fG6H1Ok6jZ05LwoqrpNLxlVLs3lhgYPyyDW/H0ywOOEizI7ZvIT/Sunj44YlVc1zB2dk1Gt2ZntVkxL5rb/RLFYVBsOWrlJIqP5xeLxkYrgkxCysbP7xGnrEU0jI6dWNu5+9BtPmL/KG1XhBQEEEEBgTQQoFAEEEEAAAQQQQMASMP9EDXkNiTY1aJVVM94QQAABBBCoWyCVGTTo+3cNs01lN3VIozwhpd5xn2cL5WfGKp5p1DdWOOeTTC70ffpRJrN9bMadPq9CR1bQRkJnVKFVRcjFtU0VdbOCWzLSI+tmTU64yMg/0zeWd9VNtnRHn+saKb/cVG2d5lfmJrPMDI/lzz36jW8b0xO5g5+Qv6bLxUwIIIAAAggggAACCCAQLsBaBBBoEQH5Z2yTp5BTWi1iQjUQQCAFAiGHGlYh0OICTf5ebkJxvuApOM50QhM8IaVOaHSD2yj3B1mCs+PJ+U6YaCMCCCCAAAIIIIAAAggggAACtQk4f0F7Z+Sf2L5TbQWxVQICZIFAmwh4Dx3eIwxLEGgXgTbZ7WJU01feu9sGLYlREkmTFiCklKSoOcTN/SHJfMkLAQQQQACBRATIBAEEEEAAAQQQaDcB809s76v5B7jva7s1kfoi0BECvntrQxd6jxsdAU0jEbAE2vLNu9sGLfEePdqywe1ZaUJKCfSbM4LNIZ5AjmSBAAIIIIAAAggggAACHSpAsxFAQEvA/APc99X5Iz1oRqsAEiGAQE0CQfud797a0IU1VZ+NEECgPQS8Rw/3wac92tC2tSSkVHvXOcPUGcG158WWKRGgGQgggAACCCCAAAIIIIAAAmss4PyRHjTj/DkfPrPGzaD41hbo5NqF7zhB+10ni9F2BBBogoD74BNymGpCTVJfRERIKUS/QataWbyqyc4wbeU6UzcEEEAAAQQQqBLgIwIIIIAAAgh0uIDz53z4TNVJgKqPHW5I81tcoGq4JvsxfMdpcRmqhwACnSBQPkw99/Wqed/jYSeYJNjGwJCSiStLqkJv9EdZYotMpoD7tartLVJPqoEAAlxwZu0AABAASURBVAgggAACCCCAAAIIpE2A9iDQAgJVJwGqPrpPF7jnW6DiVKFTBNwDr2q+argm+7FTfGknAgikUcD3eFh1CE1ju5Nsk09IyRR0cJMsrTIvs6CWfXUEnJnK6vMJAQQCBFiMAAIIIIAAAggggAACCKRdwDlXUDUTdJYj7R60r1ECQSNKLq8ae+6PjapNdb58RgABBNIg4D5+ynl5gDWnNLStAW3wCSnJUiScfG30JEtp5anRzSd/BBBAAAEEEEBg7QQoGQEEEEAAAQQaIhB0osM8OeV9bUglyLQNBbxjw1wSNKLk8jZsJVVGAAEE2kBAHmDNyTwOt0GNo6qY7PrqkJJkSrYAckMAAQQQQAABBBBAAAEEEEAAgRoE2CRNAubJKe+rPA8TPqUJgbY4At5O944Nc4mzCTMIIIAAAk0WMI/D5hG7yUW3cnHVISVZVyklX5kQQAABBOoRYFsEEEAAAQQQQAABBBBAIFJAnoQJn8wzWfqvkSWSoHEC+t3k7fTG1YqcGy5AAQggkGoB84htHuFT3VDdxlWElKSL7nakQwABBBBAAAEEEECg3QWoPwIIIIAAAi0vYJ7J0n+V53aY1kpAv5taftxRQQQQQACBCgHzCC+/XyqWduSHckjpDe95nxSQNPK1DSaqiAACCCCAAAIIIIAAAggggAAC6ReghfEE5IkdprUSiNdVpEYAAQQQQKANBcohJVl5+Y0rX5kQQAABBBBISIBsEEAAAQQQQAABBBBAAAEEEEAg/QK0EAEEOkSgIqTUIW2mmQgggAACCCCAAAIIIOAIMIMAAggggAACCCCAAAIIIBApcM1zX+fed4SUIsdJSyegcggggAACCCCAAAIIIIAAAgggkH4BWogAAggggAACLSBAVMkKKb3hPe/7wZefaYEeiV+F0lxuaPRsKf6Gad0iHEStHRwaMqbcXKCBZrLA7atXlJbmpnOjVrmyv5aqE2h+Li1N5czKDw3mphf9u71UOjs9apWVGz275J/KKTGwbm4Eu1Az29wZT566hZaWnboNjU775CPHs9E7lSXKcn0KtdsQ2AQ7gfVut2g6wD9Z3tKZ6M6SNS93lurTuZKH1qy8Tm5myia/6jdBs2JavWDnJUsP37NKpUVDOPQgqTl6NZPZdRNR481KGJEsapexchERI8QuRe5KVZPvnhWRmyp0cdqzkw4NTS2rVdU/Kjc7cZ0HruVp5/gQ2qfVVUjis9sw5OtDu6jI0auZk9YuE3P0BtbNjWD3qTmifAaSbqFRgzxWobZaYBPsBNa7nTnfCxZIEm8S3zjwWntrbjrwqy1eaVGdZeYmS4/4Xkjy2zmh0Ws3zdyb3K+ePUvz2KuZTJlpfVEKkeyhRiM33SZofi9oJlMi+j/ujgv5XtBMpl1u5CDXzEmjF2TH82dFAKfdrXx9BADVtFj3N4d4mYfuMrqHGpmJ5leb1p5lt0BmG/Gdxd8ytpXxHvW1ayRqyAHfyDn6xT4yqK/ykO+F6IysFJEjxEoX9aY1LGPugIF1cyPw94LTNdq8On87S3zdI5LG771OHROdichMvwkRGdmrtQZ5OXHEGVqtX481+1QzmV03Ye9BQb9gWAkjkmkdLWVWEePNLkUd0yJ3Z/lrY/R40/3aja6bTCEnPV7N7wV3Mpl35GSFlFzpfJs3OJSbWnad6j2bs/5OLrOGfGFYvwdYm3j/tPbJzdVVnj8mXZUVpbOTx4sDu7dn3AuNeYM1Z+eTM+ovdczcSmdGyzWXJ7KNM/tVC83h6183IzejGPUis3Xl5pzmq5RUPpVL7LrZ23rORZYWXb9gyQjKnDxGDKl8VKEBP8Eg5Q2ye2fPFQrnCvnh8jKfOc1kPltWLipJw5HTK/0TB1ShstzCzPbeyiSan5amJk93T6gcVP13iVOT04vVm8odfmT/RbF71kg2u3PzxSP7zU6vTqk+h9Yt071J2AhGbmb992bVlhU/uoXK4o4+uHG3mU+hcKD/+f1DVU3QL9SqgcxzUJPX2Fnu9am/mVWivMvTgyMnL205eMxo7LGJ2xcmc3MlsyD7VaU5vdI9bA+M2WNbxIWREc+OIIRKGZWbnWtT31XF9JqgWy2dXrDyiuj65TOj8ri3+LToG9s9YG3i96Y5ejWTuUo4OykPzsHjzU4Zmky2MWqXMfJRHRE+QuLsWdG5GYUKn+PDeI+1qvymlZs2b8+YOvoVZvd5j0PlIhs4Zx8SI74+Imsge1b3wBWRl84uo81rlBVaN/2BpFuoLC5qkOsXajRA/iqr/7UbugMKkSivzr6g0ugdVFXK8L3e0mj2m6qYXhPi1iyis1R2cjhF7FmqelFuKo1WE2RxCY3eeIPcPhCVfzfzOfbqHKKXfb4olaPPT6L7gtaepSqh1VLN7wXNZKrYeD92JSO+FzSTRZYtR13EII/MwkqQbJ+qTEPrpj/I+fpQmBU/6qCU2IGrdf+sUCfRYvwZW0EU/CF0WFqb2btn8EFVdYHW94Lebw5WuRF10z1Ea+4ymsmsuqm3sy31t4yqkRSL+tpVyUTDDvhG7tEv9oiK+F6IzEi2lwO+qSQporpe/1vGzFKeSs3p8kbsC4l+n6qjTQcc8FUzNQ+qVn9Fven0gpWHHE5hXb+s+eux5kFVM5lVN/V2tqWOvXH2LNWtUaNXtbCFzyPF/frwhpT6xgrnZvdkxcC9xrlg4+yVXLJTPDjiBEvE9ryxXJ3VivrCWJrKjdznnOIvzB7bpc4al7MyQMXAQSND+XvMwW1CbDskZ4wp8PS3udnSQyeK2b3Dfean8qsctZNHVzbunjBjJ4VzEzu7H5wcfXDeSpLZMSNbJzM3y83vUCEpc6Fxqk5VYMwIe6iWyipl98n05Wmi262h0BWF2qog4yUqM1mQkiwUjsmTuaoU9YVqLPHmJluqNpebuCbVhFPdO+2z7QXD7ch5Vwq/2SAQv7RNWSaPVkdXdh4r5Md7MpZLjeVKkCNiV37YyaVnbGbL5fuMyF85y+U5dRY7P9ZnJsv0jucPbiuefMjv0oHIusn+mqmOVpbmLhazWz5gZm+Vq1lo6czRE2JrfoczXDM9Y/cOzJ+qvMZOt1Cj7MgmGKmMFxkxPVHcdmjMKd1Y6rwky1uae3BeHhlk7NCEyqiO2LkyacRunTLVjuMeGJnM9rGZvdnzD1Ymk39f6eTmZNvMGd0maNZJrxeMzKK7vmfHTD4/vn2HtS8YW/m8aI5ezWROARHjzU4Xnkxvl5Hnz3XGW99Y+eDsFO+zOyc83vT2hbi8dgPa9D169Oo2TG+XicMbWTfdgaRZqN4g1y3UcItsgpHKeAnfAeW+kEvwa1dvX9A9qOrlZrSy2S+6TYhZr4jOUrlpdL2em2YTEh29sQa5am0iP5pflInvCzp7ViINTF0mGoNcs818fVT+LWOwxeCNOCIly5vogUvuzi37Z4Xmbw5GZ2m+xOjT8Bw1vxckr/bxLbpu4Ydop8KabprJnGwjBrmdLjyZ3hel5t8yQjc3u25t/h49QnQbmOwRSZUaWTfd32o0h6Ve1+sWqlqg4km6p+nCB7lIlrdjDvi6B1Wjt6Jf9HrByCdy9AqOvZXnLbX3LL3Ra/SCxotebprHEI3yakyyTm73Bo273qkzwgc3n5gMvlWazMg7qZF9ftPBY84pfpGRp9FnZveIE37XIngzCF1SOnv6vPC5RGlxeuTClomZMXku1TyhLUSmdzif370pNLsYKzPDE3vEiTnXJTKZ27Z4T4XLHJcemhfbho3olPwUPq13rZYjQ/Qf2N5rN0Aot0MyPOVK45kNAvEkbNYC+eV3sf/AWLkVtRdcevpCcaC/6gqAvv7s/JOuXhCLCzKSURVi7O0fEJdWStVF11Y3VY3s1sqT9LqFXnm+mO2/rbIefd3Z4soLlcs8n/wKVYniNGFx7mRx4KDvP+M1skqW94WVYnZrdTRuQ7dYeMrTD6r0iJ9kc4sobC1Xq46OHuSqhnG6XqUP/tEcvZrJnHIixpudLiKZ7i5T6whR4NW7sxC15mY3qvJdK7e4vJVFtNUnWdnkRq9QPRi9y8Tgra1uqhrVA0m3UN1BLuEqJ79CVYo4TYjYAVURCfJq7QuqCVo/yeamVeTaJoroLFk5ra5P1C3x0Stb4Z7UCKzes9zrmzevapLcvqCXW/Na10YlaQ1yvfbo9YLukVyWWVvdVDWqB7luoYnvgHGaEHFEUu1KbpdZi1/MZJc2f9Ltev2axelT/VzDUup1vcohubppumkmU3UzfiIGuZFGvkQk091P9b6ddXOT9Wr/KbkRwt8LVSev1OCIwxsxyPX2eu0dUG9fUG3Q+Uk2N50S1yiNXi+oysXpepU++EezTxcXtM/QGmVFjDcjjXyJSKZ7tKx1hCjw6t/fOvQ8kgopyQ7RnYorfld7BG4t4yLFgXu9EYXMjgM+1yIEZhOwovTUxaIYuL36qovSmVPzPnEmmUmfjC3It8SmyyuurDIyvFFceMq1RM0uP3k+u+cuJy6kFrl+ZMB/yLzvWWY4XxgPSuZs0XP7NmfeZyYAxCdlkxaV5k6Kqgt6ai5ZHRQ2VgtlujeLil7wDSCvXPYptca6LS4UxabuynroFrp+Y7b4fFVIpSRjL90bfOrnXuRXqFwfownL0/fNqz1RbuU/JctbWrnkX0zxQsDzr8zk6m6nJ4rbdhlXDZqL5GutuclNmz/5N0GzHnq9IDOL0fUydeikOXo1k1lFRY43M11kMs1dpuYR4rtnxcpt5em5aec2sJU3RDXbqJdbPF4z57Z9TXD0Cr1dRp+3xrr5DSTdQjUHube7/QqVqWI0IXIHTJZXb1+QTaia/A+qteZWlXlzPvo3IVbZkZ0lhFbX1+rm34SkR281ScAgV8kij70qkRCayczEIa/J7gt6uVnVSaoJVnb6b62YUmuQa1Zcrxd0j+SaO6C3bn6DXLfQpHfAGLyRR6RkeZM9cNWam7f3El+i2/XaBcfo0/iHGv/vBb2uly2IUTeZOnTSdNNMZhUVOcjNdJHJNPdTzWGpmZtZtzZ/TXCE8PdC1ckrOTRi8EYOcr29XncH1NwXZBsqJ/8jUq25VebdpE/+TdAsXK8XZGYxul6mDp00+1QzmVVU5Hgz00Um0zxa1jxC/H5/E7Fyi/za1cstHq+pl+xrjJCSalK2u+oykdDayLilT8jH2MQ/AGOsqnjp3uQ5gW+vV4FBsa3fcwGQ7N2sJ/ZgbtQzds68x535sZ5XtdNWXnGS+cDWbPFCxU3MjEvVaoupyOhR8eR+9SD3kisI0Tse8gCkIJB6mlnXtqWnLovN8hts+cxUznpw1Oh01T3N6irA2LjovQDJWG69qIdLiT0Hqq+Yqa1u6q53QUPaKs948y9URVIvn5peKndo6czRy966GVmUX4IK9TZhKIB3aerwfPAt78ol+c3VxOuzL8i/tp++UBRF30uyzlon5fcfvrx1tlB9KVXc3Pya0fBl4U2ot/hi5SDX7/pIUKjTAAAQAElEQVRaCvYfvZ6cgpNpjjeNZJq7TI0jJGDPipNb8fLzon/CumvrbOXtYU2xOLmZW5ivwbzm+vZ9bezotV2qdhl7sf0ewFtb3QIGkl2W8+5fqOYgd3KxZoIK1W+Cxg5oleV9q4k37r4QflCNm5u3EU1YEt6EGBXQ6Sy9ro/rFt6EhEdvlUjQIFfJoo+9KpXQTGakre2lpn0hsCif3BrfhMDatN4KvUFeb719esGdpf+RXNRWt7BBHl1owjugfhN0jkju6rvna+JN+MDl/YM99I8Ud/WbPh8w3jTrod+ncY6W4d8LgVWr6voYdQvMMniFpltwMs1BrpFMcz/VHOR+uWmcTAiWat01jR0hdrurhqW92H4PGCG11Y0Dvs1qv/vzau4LdiYi/IgUNzcn22bOhDeh3ppUDfLaRq9uJfz71LN1cDKNg6rKTSOZ5tGyxhESsDvHyS36N/w4uSkV+yeY106R7LtmSKlUWpqaPCn2TgzHKL60cllkg67AMICsa56258+ZDy7yZi6DQEGrZFBHZu++WZyzefVlJM6KRGZKKoB8+PK+icprKURmeNdA8eLTroCBPIfuvf2XrEPx+JARYhk5WZSf/CcZPZo9tkWcmpzcbyQezU3PLZfz9tkoBMQndRMWvbAim7cwPXj45KUtB4+pJ1HNHuh//qiMkyVb+JXA7Epnc/tPbLrXJ45YW93UVpG3MQwuVGS2T2y9fHp/TkVQRnO50ZGFrRM+tzKvbE9QoWq50OBdnD5yPuSWd5WF+XyqhdfYF9TNLc0RW1qczg2OLIisT/ZqkTwCyOExe3DfgDg+OT1nbqRWmD8xczM3avJrRBPqrk1FL+h2fQ2lhoxed24hyTTHm2YyvV2mthGiGP12Z93c+sYK5/Jjwz32RYuZ3vHqG6JKM93cZFJnCuF10rTtjGLXOXDV28CKXaYis2De2uqmtvIbSGGFutfpDXL3FnI+qFC1XIdXcweUJflPtfDG3BciDqoxc/NvRoOXRjRBt3S9ztLs+phuUU1IdPRWgagW+e5ZesdeoZmsqtTYH2vZF4ILqcytSU0Irk6LrVFDQuf4Vm+1K3vBnRtfH1JD74gkEwZMtfAme+CKmVtAO5qwOHi8aRauu8vEO9REfS8EVq6i63XrFphb8ApNt5BkmoNcM5neF6XusNTLLVinbdY0cIRUGFQMy4o1wSOktrqprXx/q3GXGlwo55HcTq75iCOS7p7lyrHpsxFNqLs+FYNcjcMG/SoVMnrdbQhJpnlQ1Uymd7SsbYQoRr/dOTQ3l4Le165ubq6M1TPSAk6Au1MlOh8cUpq/z4hkDBqv+yef7J6Ynam+1CPRqsTNTAas5Cb22Tw528jJDgIpjZGjCxsPFPLD3qKNS4sesk+Il9T1UpVXMllVzO6TJ9DlNLsn6By7kTCT2T42k88b/wR+9sCujSsPjgzmgq/yaRLI0pRCMEJi3pnpJaPm5Zfz8/PbDhVmrIdCGU/S2iVOVVzLVU5c45xvWFEI62hVGKu+NaJdTOy6LT95Xgz0e/vdzlC+hxZamsuNrOzKnzP6VPbsTGHnysjIlD1g5OY+U2ih0U2IvCbUp8jKRbXxymDw7B4hR6waJCOnLm+6dzbqYWaZ3uGx/LEtl49XPg1P1aaG3NRmNf/EG+TlYkKaUE5U05ynF6K7Pn45oaO3nF1YMs3xpplMaO8yNYyQkD2rhtxMHnUrzvmFqj06Zm5hvGYprfgab5dpxOitVvHsMmaCSN7YdQsZSGaREV9G2oPczk29hxYa3QTdHVAV5f9TG2/MfUEVHXJQrSE3lWPNP/EGebmYkCaUEwXPxems6K6X5dTgFtiE5EevrKA1hQ5yK43z5nvsddY6M5rJnPQ6M7XtC0E5B+RWTt6IJpRzX5O5eHuW1iCvsx3uXnBlxdeHwohzRFLpvT+18SZ54BKihty8DYmxJN4gNzOOHG9mssjXGneZyENN4PdCcI08XV9j3YJLkGs03cKSaQ5yzWQJ/y1T09eudGmVKd6+0IgRUi3hGZZmgrARYqSIXTeN32pCC62p60MLjW6C7iA3RHxfauOt4RAdckSqITfftugujDfIy7mGNKGcqKY5Ty9Ed338ckJHbzm7sGSa400zWcLH3nIT1FzInlXzePP92o2ZWxivqndjfoJDSgP3ypiHM+XHhjOhp9LL1ZPHu5xxnUGme1PAfa5kYnWXtmys2+jJjXSny5W3idLdLCSdHQQqFI4dGhCXn3ffjc61We9dA+L8nBlWWXrI+1QYV9KYszIYs2M8P7tPnHwo1uOsYhajkbx33BkV3hnvJWXZPXd5bpZY9L37mUbZfkmy3X4DU+1OFzfdGxxPUlnFrFvIjRxVbuZ5w5BCjUeL9Vdo9I7vzdoDxsyj+jWi0MgmqEBjRXh48ERRzB8ZtB7iVV2c53MdvBk5YgtGQLQwkx/rM7op8LJFu+DM9p3biieduKy9WAj93Mrb1DwXc5BXlhPYhMpkcT759UJk18cpQKbV2mUiB7nmeNNMFmuXiTlCIvasmLlJQGPa0J0VouKfAhmLtXPT7AUj05Z6ibnLJD16PRZ+u0zk6DVziVm3iIEUWWisQW7WUIiIQiOboLkD2sV53uvg1d4X3IUGHlRrys2dc5z5mIO8MuvAJlQm8/kUq7Miu94soCY3nyY0YvSaNYwc5HYy+z3g2Guvtt81k9nJo9/r2Bd8MvfPrTJh4k2ozH4NPsXcszQHee0N8e8FrW/nmHWLOJLz9VHViUkduMxsa8rN3DT+a8xBHtn1sWoQc1jaeWsdany+F+ztPe9+e1aNdfPkbS/Q2k8jeTW/djWTxfqijByWsXKzWVrpPea+EGOE1NZKv2EZOULMomLWjQO+ySZftfbTyH1BZuSZAo9INeXmyV5zQcxBXplrYBMqk8X55DfIY47eyOK0+jRyz9I8qGomi3W0jDlCKnbn126+5Zrnvu42ipmbvWnA1652bpq9YBeX3HtwSKnmMl5YKRZXjHNqff0DYv7JRb+cgq/g8Uvtu0wGrOTyqn8PLpf09WeLC0/JmRonWX8hPLFcJ7NMz9jMxMZTk9O+7co4TTZDlxXxAycPeyazY8aJeZzNDZYv8ZFhuaEpn9BRRjb6UlC8TK6TGXtB5MK1mXolRmIlqwesebq1tHJJbOquLkPdZm3/xf5jMoxRvcr5XEPdlhbmhd/ljWaeOoUK4X3Kl+y1sAhoSKF6TTAvpHUH//ZmxcDBc87AM6ufMK+ZaeVr6cyped/7QFYmMz8ZBxBz1v81Vm7+WTR4aWQTvOXr9oJe13vzD1yiN3qFRjLN8aaZTFY49i4jtzGmiBESsmcZm1e9ROTmpJZfHxr/VMI/Nw1ep5w2nkl09OruMtJLh7eGuoUPJJ1CO/l7Qf5twfeCHJyhk+7RsobRa5frf0Sy11a9V321xT5Eh+8yTmGayZz0esdeoZnMydaeSfZQEyM3uwLl91qbUM6hfefqGOTeRsfoBZ0jeQ11Cx/kOoXy9ZHKPyv0ut47pH2W1DAsnVziHGrc3wu6e1Y9dXMq6Z7RdNNIpvm1q5lM1jH2F6Xcxph8v51rzs3Isn1eEh0husNS8miMEFFD3TrygP/oF/61PNvumf7xx77w7enPfsKz/JbQJZ/IfeOJj30oPM0tr+154NFvPHDTzVHJbtbLrTIfOTqaNrkPqpqF6g7yGkZveA10dhmZg0YyzYOqZjJZZs1HS99jr8zQmsJ3ZytR+S0iNyeh3teuf24avE45ic+si5djzNQ9w/uy8/dNL1WHOUpnjtZ/BY/cbYQVu6qolnoSlzjuLVQmKp0ZHTKvoJIfhJDBJ2/EywwFZYwEQS+yiC2XZbt81htNPnVWhszmxcDtQbdc89nQs+j8gnm1k3tFSQZlN5uXe7gXm/NBIObatXjtG96TLVZfcSLb4HupigyrDqqbpOXmfGJp6iKV3QPFC5V3zCvNnSxWI8to3Mh9l2U8qepJV9Xtj1U3tXHYwNArVHWQyqn6J+TRX2GFithNqC7Y9TmzI1leV9bmbGlO72FspbOnz4uBymu5zBzcr8G5qX3cuCvjtHf3cefQwPmQJiQ0yBPteuOiYI1dRm+QJ+tawy5jVSB4hJgJQvcsM4nr1Zub1Bga9Ywx1fVZ35udujKT4D77gsxQ68DlzihivgX2Bd8axjpwJbTL6PLGqptqXdhA0iu0hkEeVmiiBwe+F1QfJ/OjDg4BX20Rg1y7/Nij18rZe3yzVlS9+TQh6dFbLjFwkMvdSufYq5msXGDEXLL7glZuSTchooWVq/n6KHvIjtD6do69AwYOclm2XqFJ74AhTZB1ijdpDXKZpV5LZcLqqY4DV3VW8nNwbs3eF+KBRH59aPSpLFHnoCqVfCaf7wXdrk/01xX5q3VOZz+VjdVJ5tPS2hfVsJ9ahfkNy9pzszL1eWv2IPepgu8ijdFb3i5iX9AdlrojJFbdVC077YAvg0M3HXwif/Lr1zznmp74X8fe+baxI39VsdCdIGhebfihP3/ClZV/yr/6819920d//fMR+Qfn9uKRD330nW+TU3XNjeJkoyIn1dl1/fgcVO38EhrkHHt9b3FlK6t3v2OvWm7/OLuzOR7keLPX+L17c5PHmZq/dr25yTJlhk3/apPFOlNjQ0oiM5w/uO3ykf256UUrrFQqLU+PjpwUe2fHw6/gcaoYNKPuNij84i4isz1/rzh9NHfGLlRmUVo8mxuU5R6aKD8DSX27XL6vXDdRWj4zenh+26HAp+/IjMwps31i3+UjvhcS3bYlW7w4eWo+5IoWM4+o1/kjo9OuJpRk7HHywqaD40HhrmCQqJIatl7G3vZmz4+Ue39xOrf/xKbdPg/lKj11sWjUo3jcJ5am1vSNHdx8cdK4p6L6KJanj14euLfiVntLc7kRSXQuXxFPWpweGq2MRantY9RNJa+4vFEtcH60C5XjbdPCUVewU423B0VlE5xs1UxwoWqtiNkEY5vAl2R5S+5bQ5aWpnKTK7tmZyr6XY7noVG19zlJ5RI5PIR3B9TIzW7X4oIxjLL7hnvtRY17lxXWbYJRiUQGuZGTRtcb6SJfNEevZrLI4mIm0N5lYowQowrhe5ZGbpnhiT3qBpJqABs5mpdwnRD7JioOPnKdRm6N4W3qviAbqj3FGL2J7DJxeGPUTbU3eCBpF6o9yFV5xk9wocbqmE0wtgl84XshkCZwRfLfC4FFVa3Q63qNI5J2ExIfvXaLgge55rFXM5ldnsZ7svuCRm7JN0GjlXYSvj4sCe0juUyvtwPKhOYUPMi1C018B4zZBLMhQa8ag1y7pUIkeeDSys1uVlP3hRggRv2if0fS+FNR81Cj/b0ghEbXG9VPbLxpumkmM+qW4Iv2fqoxyI1/YhvzZEJ0U5o6yKOrU04RY4RE7wsawzLOCIlRN9Wg5h3wVWnWT3ChRoLIJpiny6teP/aFb+f2eK8Bqr7o50u//qGxX/984AGiigAAEABJREFUrafpNPaFGEckjdwMEPkSvi/IyEHkVMUV/6O6hOvRz/pewmWs+sa3H/3Cv36vcfmUrHDlpDHIjQ0iu95IpfGiuctoJtMoMFaSZI+9rqKtPUt2rjkeXKvkrMZ40/zalZnp/PKzRryqdvaPN6Qkz7/L0EtRuJ6/4vm32EKGZ9QFJSPHi6J4YsS4uMS4MkAtPHLeztx47x3Pz967RZwaMROM7H9QbJ2tOrksE8rYmplAbX7+sDmfm5NrAqfeu/Zmg26s1zeWn9klFiZzdt1GTq303ztbmOnJuPOTyY7tcuo2tP/B57fOFipiXaqlskrF46pp7pvRZYZ3Dah65s5YwTI730xff7ZYLGb33FVRlFqtbIfKudl1Mxp7wjgTrlJZP9sOze4WC7bb0ODk5EL3zgMVh2Yrpf0WBmKnafa7DO8dO+QIj5wS/ceqbrlm1SgjQ3HGbHZff1AwoHd8YufKpME1NDT64MbdVbe2K61ckANy/kgF7NDQffNGxp4X7brJLZcC73oXp1A53g50P3nUGEuykkcXRHUTZFHlKbhQO02cJghj+A0FP0spQd7SU5P2YSGXm1oUd+Xz45W7nvyluG+scGDXxoXJyf0WyMipy5vunZUp7eZZ7zq5mUlLc2ZgcmBnOXJsrmnIa0a7CWbxSQxyMyfJtz0fsWctT8sxZk5yF3AdqHPluKzm6NVMZtfNfI8ab2aqiGGpt8vojxCz0PA9Sy+3zI6Zwuy9my7bh2hr9HoGnkZuNfGaLQl+bfK+EFwRvzXaB64kdpmYvNp1kw0LHkhxCtUb5LI4cwou1Fyvc3CwU8r3qP2U7wWJFGuK/72wJWsUEPLLj7FeRBwtZSKN0atxRBIxmpD46JWtECJ0kGseezWT6XxRGnUSIsF9QeYYlZtMotkEmTLhia8PGzTOkVxuo7EDylTmFDzI4xSa+A4YpwmRR6SoQR6jpckeuHRyM7upuftCDBCzejq/I4noPtU61MT4Xog+WprV1/l1RecQremmmcyum/ke9TuSmSpiX9DbT3WHpV5uVsU03po7yDUq5E4SPXqt1Dr7QoJHJFWqdt1k4pY54MuT4K7pH8v4kIxeWLeJ+9Ancl/4tvzoJDBPl9uvn88bl+981Hz1uxLoxZN3j5lr3/mhuk7T6ewL+kckndxkH8kpiX3B5oq8oMpM8FdfOnL32K+q66IM2A/JONyLxhVRfvkY12BJ4V/9X79kpHF6qjyz54HpL/xrq0NvvuW9n33CucGgbJ9rih69HHtdXJWzxu4sA6uVS+1PeuNN62tXZqmRW01fbTLrJCdvSEmeJD3nfvKKnPeGMbx3MJTJXFNFVEb+dbp9bMZZmx8bzniCLep6pkJ1uYX8cGhb1ePLxPwp7wUo5lY9O8bzeSfPmbEdvneMy/RU1c3c2H6tbKnVLnNlz5jKvPKCGLVGDZFC1YUyarkQPrYOi5wpO2eG8zKyJY+VeZdbfnx7b8bMKOA1AiRgq0YvdgvLXghqgjy0KU/Z6SGXr2VkhNIaJzP5HdX3FTTlpaRnmqm4PqbcYs26qV+OCwX/68NiFppx7QtSo7oJ5arJOfVwP/9C5Up70m5C1fDzuxQvMV41gI3elDtC2LjNVO2k+TG/nVQ3N6EekyBdqq5dk0saOOk1wapAAoPcykm9RXS9eYDy7Avn5C7m7ISao1czmapU+afycOc33oy0kck0dhn9EWIUKc8MBu3Oar1+bhWH6JmaR29NvKqmYT8vrKh/pdDUfSGsOp51EaPXTp/ALhOfV7NufC+oXorBq7tn6R1UdXNLyfeCstb4Epdn6Fy/08rfMZyDvZGBfNF10+sFmaE8X1n+LVqWWP9vNWF7lipQ/ugce/WS6XxRypzMKbHfkYzswnMzksj+7Bsr/yEQ8C1jJU3uja8P2zLG8c3ahK8PC8J8Cx/kMXiTPXDp5ub5+jBb1bDXGCBWHbR+R5LHkYjvBZmb1kFV/3tBhHe9LNCeInYZnUO0pptmMrti5nvkHymayZL9W0YjN7NeOq8c8G2l+CMkYvTaGYf9VhOzUP+uL0cUjItXrI9+TxXyC1SYsQ2f13L91Vz0vhC+18doqe4hWu+IpJtbsw/4ClXoNcFIKo/k280z2/lh82RprN60RkV5hPxjGXB69BvfNqYnxnZU/73AsddS9771jj/6jQdMfO9Kd1AjH3YWVPanxm/4GqM3xp7lU91kFnlDSsnk26Rc1HU5xRNzi00qrvWLAaT1+4gaNkxA3dhUZPcOh57SaljpZNyKAp1aJ/aFTu152l0twL5QLcJnBEIF2GVCeVjZQQLsCx3U2Z3aVAZ5W/S8Jx5Qces58+y2zmtbNDaJStaSR7r3hfDhET7A6llbS0+05DZuBInZknVcs0q1eUhJmNflLCyX1kywxQrWAbFvgRV+X0HnloYRyVoMgOp0rkBp5bIQA7sDrkjrXBdaniYB6zp0ddfZkGY1bl/Q/PoIqRurEGimQOP2hWa2ohPKoo21C+h9Lwi9ZDXsMprfC5rJandgSwQSFahhX0i0fDJDoA6Bhh3wNevEAd8fyn1iWn9ensIOmfxLYmk8gY4+4IeMrjpX6Q9y35Tx+jCh1L41cTskVM4aZKP3vaD590K5/u0eUrKuJK1+TEu5gZ03Z9wtLRDEdfVcIeS+glrJOs+WFre0gHH/h8AbrLV01akcApoCruvQg+7nKXNqzL7A94KkZWozgcbsC22GkKrq+v6l1+EL3/tZ89Yl8vWJj33olqD+bsjXh+b3gmayoKqzHIE1EODrwx+dpW0h0JADvmbLOeDbUN5fTtwnpvXn7fx4b5wAB/yG2OoPct+U3j2oCUskhLcycmEKJr3vBeFOptNqFVL6wZefecN73qeTmjQIIIAAAggg0F4C1BYBBBBoL4GgPxq9f+axpEogiE4ub68xQG0RQAABBBBoZQH5xRoyVX07y4+t3BbqljaB9m+P3GXWZGp/uaa2QIWUmloghSGAAAIIIIAAAggggEDLC4ScK2noqqC/IVserL4KJrF1EJ1cHtRlSRRLHggggAACCHSKgPl9Kr9YQ6ZOsaCdCCDQwQJWSIkLlTp4DND0+gTYGgEEEEAAAQQQSJGAea5EvoacK2noqhRZtlBTgrpMdrQ5tVBdqQoCCCDQugLULCUC5ndfDa/m92lKFGgGAgggUKuAFVIyN5cHU3OGVwQQQAABBBBAIEUCNAWB9AjI39gbOpnnSuRreshoSbCA7GhzqmdQBWfPGgQQQAABBJonoP9dZn731fDavMZQEgII1CHApo0WKIeUfvDlZ2Rh8vgrX5kSFJCkdU4JVoasEEAAAQQQQAABBNpCIOgXyBpOf8TapC1wqGTiArEGSVXioLFaw/LITRJvOBkigAACCLSCQOTxXydB1ddTyMdWaDJ1QAABBNpXoBxSkm2QR1v5Kg/T8pUploBEC5qkap2Tb86xqkdiBBotQP4IIIAAAgggEFfA93c8Z2HQL5BxSyE9Ao0WCBqrjVju7CDMOAKN7l/yRwCBxgk4O3Kbzdx8S+IVTuQro3E9Rc4IIIAAAm6BipCSXGEexOV3g5xn8gpIGd/JdPN99WYSd4lvtr7VqGFh3MqQHgEEEEAAAQTaVYB6N10g/Hcz39/xnIVNrywFItAGAs4OwowjEH6cSWptGwwOqohAawjE2umcHZmZ1ug9aoEAAgikSaCBbakOKZlFyS8z97eguTDdr+72hsxLGd+p+Ti+1ahhYUhjw1c1v8mUiAACCCCAAAIIrKFA+K9GvmvDfzdbw7ZQNAIItLBAvKqFH2eSWus+xMWrH6kRaB8B9ziveT7WTtc+NtQUAQQQQACBsoB/SEmud38LVn2VyrVtPVU1x/zobm/IfFs33LfyIY0NX2W6ab76Fs1CBFInQIMQQAABBNIm4P5VJ/xXI9+1aeOgPQgg0KkC7kOc+8Done9UIdqdsIB3aDVhiXuc1zyfMATZtbAAVUMAAQQ6ViAwpOQWqfoq9f0id6dvwXl3nauaY35swTq3eJVMN81Xt3/IfIs3meohgAACCCCAQFsIhPyy8drNt8Ra6/5Vpy3aTiURQACBRgu4D4ze+VjHWG/iRlee/FtZwD0evEOrCUtaGYe6IYAAAggg0DoCWiGlqur6fpG7v/sbNF9Ptu46VzWHj00QcPuHzLu7uAm1oggEEEAAAQQQaJqA+1u+0fMhv2zEXdU0HwpCAAEE0iEQ9zBblb78BXHzLXHn0wGY7laE96l7MKTbgdYhgAACCCDQ1gLhIaXS2enRoaFBexrN5eZKAc11f/e34HxArVncWgLukRP+u2b9a1ur5dQGgTQJ0BYEEGg3gfq/VXVycH/LN3q+3XqA+iKAAAIIWAL1fEHofBmRZm0FwvvXGgS8IYBAOwlQVwQQ6ESBsJDS2dz+E5e3zhbOFaxpJp8fznSiEm1eA4Hw3zXrX5v4b9JrYESRCCCAAAII1Crg/h6s/1tVJ4daa8p2DRIgWwQQQCBtAjpfRqRZW4G0jTnagwACCCCAQEcKBIeUSnMXi9m9E8SQWm1cUJ9kBBL/Tdp9bq7++WQaSS4IIIAAAqkTqP8rxszB/T2YOiQahAACCCCAQEoEaAYCCCCAAAIIINBqAsEhpVarKfVBoLUF3Ofm6p83z/c5r63ddGqHAAI+AixCIK6Ac8wPn6n/K8bMIW71SI8AAggggAACCCCAAAIIIOAjwCIEOkzAJ6S0PG08PGnkeFEUT4wY8+bjlHJz1Tilxelc+WFLudzUsu+jljSTqdxLZ3NGicEPbVKp+EEg7QLm+T7nNfz0YpPXpt2e9iGAQIcKNPlY6i3OOeaHz3Ro99BsBBokQLYIIIAAAggggAACCCCAAAJxBHxCSj1jxsOTZvdlRXbvrDFvPkspP+zOubQ0lZtcGZ6YsZ+0dC4/0b8wOTq95E4lNJNZ25Seulg0ZosXFn2jU8ZKXhAQHUYQfnqxyWu9p0Fbf0mHjReaiwAC0QLeA1eTj6Xe4qIrTQoEEEAAAQQQQKATBWgzAggggAACCLSQgE9ISat2pbnJI2JXfjiTcSXP9I3ld4sjo2edaJBmMiePzG1bssaH7NY+d87GMl4QQKAVBLynQVt/iffcMUsQQKApAre0bCneA1crHGCpAwIIIIAAAggggAACCCCAAALtKECdO0egxpBS6ekLxYH+Hh+nvv6B4sWnrZiSZjJXNpnteeO6KBmsci1lFgEEEKhHwHvumCUIINDhAvUcUtgWAQQQSJkAzUEAAQQQQAABBBBAAAEENAVqDCldeb6Y3Rh4GVHxeSukpJlMs64kQwCBKgE+IoAAAggggAACCCCAAAIIIIBA+gVoIQIIIIAAAq0hUGNIaf3GrBM38jbEiTZpJvPmwBIEEEAAAQQQQCAlAjQDAQQQQAABBBBAAAEEEEAAAQTSL9ARLawxpJT5wNbs/MKyD9Hiwnx2ywesC5g0k7myKZ3NDQ4NDQ7l5o5EK/cAABAASURBVKwLnVzrmEUAAQQQQAABBBBAAAEEEECgAQJkiQACCCCAAAIIIIAAAtECNYaURGZ44qB4MDe1XCqHfkqlxencKXFwZrsVURK6yZx6lp66WDQ+FC8sljM2lvCCAAII+AuwFAEEEEAAAQQQQAABBBBAAAEE0i9ACxFAAAEE1lzAG1KyrxMaOV4UxRMjxjVDQ8br9KK7upne8fxE/8LkUXVRkZFgcnKhf2JmrNedSmgms7bJ3LYla8xmt/Y5cSljAS8IIIAAAggggAAC7StAzRFAAAEEEEAAAQQQQAABBBBAoN0FvCGlzPb8uUKhPJXnx/qqW5vpG8vPOAny+fEe3ziQZjKVu116ftg3J5WEHwQQQAABBBBAAAEEEEAAAQQQSFyADBFAAAEEEEAAAQQQCBXwhpRCk7MSAQQQQKA1BagVAggggAACCCCAAAIIIIAAAgikX4AWIoAAAmspQEhpLfUpGwEEEEAAAQQQQKCTBGgrAggggAACCCCAAAIIIIAAAm0sQEhJs/NIhgACCCCAAAIIIIAAAggggAAC6ReghQgggAACCCCAAAJBAoSUgmRYjgACCCDQfgLUGAEEEEAAAQQQQAABBBBAAAEE0i9ACxFAoMEC69atu3r1qrcQQkpeE5YggAACCCCAAAIIIIBAwwTIGAEEEEAAAQQQQAABBBBAoLUFfuoNP/X3f/99bx0JKXlNWBIswBoEEEAAAQQQQAABBBBAAAEEEEi/AC1EAAEEEEAAgY4WuGnD+m99+1teAkJKXhOWIIAAAggg0NYCVB4BBBBAAAEEEEAAAQQQQAABBNIvQAsRaJzAL/zCLzzzzDP//f955Sc/lv//5Mc//olZFiEl04FXBBBAAAEEEEAAAQQQQKB5ApSEAAIIIIAAAggggAACCLSswE3r13d3b/qLwv/16quvOpWU84SUHA1mENAVIB0CCCCAAAIIIIAAAggggAACCKRfgBYigAACCCDQwQJbt2694Yb/6dgDDywuf/HKd1788lf+42c/9wAhpQ4eETQdAQQQQACBFAvQNAQQQAABBBBAAAEEEEAAAQQQSL8ALWyUQNe6df/ogx/8lV/5xwsLC3/2Z5+/cOHCr/zKrxBSahQ3+SKAAAIIIIAAAggggAACCIQKsBIBBBBAAAEEEEAAAQRaWuCm9evv/tSnfuvTn5avcp6QUkv3FpVDoIUFqBoCCCCAAAIIIIAAAggggAACCKRfgBYigAACCCDgCBBSciiYQQABBBBAAAEE0iZAexBAAAEEEEAAAQQQQAABBBBAIP0CzWohIaVmSVMOAggggAACCCCAAAIIIIAAAl4BliCAAAIIIIAAAggg0CYChJTapKOoJgIItKYAtUIAAQQQQAABBBBAAAEEEEAAgfQL0EIEEEAAASXQOiGlxemhwaHqaWpZVZKfZguUzowODTULvzSXK/f76NlSRGOTrVuyuZWrrhoV1pbS0tx0btQe8KNnl8qbmnOlpamczZKbXoxSMTeq41VV2NkBw2peRxmtsWnntLRW77M5cyQ06wgQq56q+5ozPu2vpNxcrAqSOEJA9aA5wORrUl0Z2VkqwbTnMOupqmYyz3YsaA8BaokAAggggAACCCCAAAIIIIAAAgkItE5ISTYmu3f2XKHgnsZ75OI1neyzq/Lkl8+UO9PwU/2JtX5paqg1z41mhvNmp8/uyybW2uQyqsWtdHbyeHFg9/aMbzVKclCNnF7pnzhgj/aZ7b2VKZemJk93T5gshXO7xKnJ6cXKFEl/SrwXNN00kyXY3MRbmmDdEs+qJt7t+XOFenbGmgrVa7rvnqXCAHZ01nWUzk0tl/Ry9U+V6TaPR5u6/deztDaBhuyAkZ3VN7wnO39kKmpEaCarreVshQACCCCAAALRAqRAAAEEEEAAAQQQaH2BlgoptSjXwEE7ynVwmxDbDtkn+veaJxxbtNZ1VSuzY6ZQWPt4nm8bkq1bsrlZFV566EQxu3e4z/pY8SbjSUdXdh4r5Md7Mv4RJ1Gayx0Ru/LDzuqesZktl+/T+Cf2FSXxAYFmCjSjLP89q2+scG52T1YM3GvHaNURe3aneHCknotgnP2vGS2jjPoEojsrs2P3gDg/GfWvQDST1VdbtkYAAQQQQAABBBBAAAEE2lmAuiOAQMcLrOt4AQAQSFagdPb0eRFwiVLpzNGL/QfGesNOgJaevlAc6K+6Pq+vPzv/ZIMvVEqWgdwQSFggbM/yFpXpHc8f3Hxisvbb1nVvUrlmN4btrSoFPy0goNFZff0DonjyocgLlfSStUCbqQICtQmwFQIIIIAAAggggAACCCCAAAL1CbRWSGnl6blp6zEeg0P13raoPpiGb106a7Y0Nxd1hkuI+ipjPC5ocOjIeVE8Xnl7KM+zUkqhjzVy1k7PnZ0eNbJSFwE4T/3JTVe1pbRsJTPvRjWqEtTcWqd09YQhVW6FirNWs25Oet/cjKxjuBnprZfSUxeLYuB2/0uU5k6KLR+IOEN95fmi9yx2pnuzuLxiFaH3ZtV/SFk5faR6LTd6dqnmbhCitOh6CtRgzrOfWuVGjTfNZFZbowqNl8xKHfFW8byr3Oj0mUU5ngPudZnY7uzuKdN2edrcfdx7a/SeFYtXtjRnHo7k7hA8PGSyiq6v3t9F3ELDc6vunrA9qzpt+XNxpeYH8q3fqC5E3dTtv8NaB3ApZtyUUuLYhqNVFxTKVaEttW/cl5szdi6zu9U3oO+D5ZIaISZRRd1CBnnEDhivCWbRIa/WQJK2xuHLSuk6aHt3w/DOMnPouX2bEOfnop6opJnMzJNXBBBAAAEEEECgAQJkiQACCCCAAAIItLRAS4WUipefF/0T6p5FhcK5um9b1HD37k0i6GxjdNnG6VGVrHhhsfr0vn16Tp1Qs08vVsyP+p5qVLn5/Rj3djtXOLhNZPdJWNfkubVd+FMujLWHBoSYP35R7J6VfbRHnBgZHDlyacvBY4XZezfNH3ffVmh5ev/hy5sPzR6zSpw9sGvjyuTkaI0PODFKV1n5PuXFWKtfN2GkD8zNYIzhZqQ3X9Q1RmJbf9Wzkax1T10Wm7szYvnMVM7qUBmoqO5+M63Pa3FFO6na2qj/vbK7VuZGR06LXU5H5A90P3k0Z5wHV+ni/KjT2ZMrwxMzis64A2R+on9hsuIculFu9HjTTCZrp1OofjKZUmtamho5cqF7p/28q/yBYbHw4Hyx+LxfJ4TtzlqlmYmWpwdHTndP2I+Um9jZ/eDI6IOXRXaP3InKe6vOnqXPaxQqJpwD78TuldP7c3PV8Ut5ll+CiH614xu9f2yXuDBSGRHXL1QnN9PEeQ3bs5xElTOllUsi2111wV9lkrBPKo4rt9/gn0Y9cUodA2XYaeVsbnTyyW7DUI78rWKlfEGhRkvVjfvU8VlcyE2eEjtlX6svwdn+Syc8N+4zOiuZEaJapTfINXbAGE1Q5Ub9yIGk7mSY3TdbmCk/kU4dtI/JA5rcHfI7quN84Z1lFdjbLzePvtxTM5mVKW8IIIAAAggggAACCCCAAAKdKECbEehkgdYJKalzUvmx4R77TFGmd3xCRizmyufm1qSf5HnDMd/wgBA9Y+eCVkVXNXPbFnkqUqbLbu2zmyw/GZOiME7aqhOLfjOus2zGBs1+Gbg3P6ZqnfnA1qzI7p2d2d6bERnjtkLOCfelqcOX9826nxiUyfTsGM/v3Hy4jltRRbdUp27RudSe4srzRXkWer1vBi+sFIVYmB48fNIIwsmQzOyB/udjRHeu+GYbsbA4f3lrRUeITM/YzC4R/+FMpblJ8yFP7hGb6RvL7xZHRmOFOSOq7F6tWahmMnfO0fObu+XAtpJlMjvGdw0I7wVkan3Y7qzWa/1Yu8xwxubN9A7nZ7cKOWjc21vJXM/iqmPPkgEPuZ8W8q5CVYce23L5fFWx8ix/oTAztkPt+EZ15CjaPVD0RsSNlVEvNeQWtmf5FFeSgZDJk2LvxLDPSs1FveOyyeWQRtBW8xdWdh5Qh0Sz43qHpZKTNkZLi2LTzhnnrphyw0MDxYtPu0KYVte7OiuBERI1yGPtWZFNcFyiZjI7DgwUj1ddUVQ6c3RebJvwxJNUZlqd1dctv3ajL/fUTKaK5QcBBNpUgGojgAACCCCAAAIIIIAAAgjULNA6ISWfJqh/dzy/4Dqj5pOmbRdlZLBKhYvkydy2bUNIxZefPJ/tv808xVqRrPeugeKF2sIPFfm06oeVy6pmPg1Xi+XP+fn5bYcKRhBOfpLBABXdOaUJsl5uEn8a2DnsrU/P7dvmT8/FykxdJuJ5yJORg4wmVp77NpYm8qJZqGayGFXqHZ892L2QG1W3ClSXlI3mclNi+Jz38ggjz9DdeWnKzsTnokPnJmn+u0xm2Io9G8XIF/9kckVNe9biQlEOD7l15ZTZvnNb5RL5Sd1tL6corFbkcqeMwS5X1TDFzi1qzxJi/j6X83512ZCMdnuHfg2VDd9kYLcTB/JLqN3S7Fbvv1FwXxXn3/X1jBCNQR5vz4pqgp9P0LLM8J6sPEa5fgEozZ0sZvfcVU+XdhvPxwoq0lmumcxJzwwCCCCAAAIIIIBA8gLkiAACCCCAAAKtKtDSISWxQf2T4pquzGhVb616JXzjO60yk04kz/8WT+53neG1TkMPDe2fF8WVF5Iur33yk6dEPXfi0gPJdtd0LjXbvSEZnSt+D3lysnaf+3YW1j+jWahmslj1yfQOj+WdW/zNmLf40wz+VRSkrp8IuuKwfKWj3GU2+fVw1dltmSy5PWtRXTdXUVf7g3HstT/I98XpnLqPZfn2iYVzExNb9c7Py82rpmRzszMfuFcF6QsWdX5s2Lney07R/PckWyq7PvEREjnIa9yzkpA2L1RybqYadolSEsWRBwIIIIAAAggggAACCCCAAAIIxBQgeYcKtHZI6YWVYh1PwmjbLm35G99pyMrz4Fn1ABjr9K77VK+c9/5jfI0s2yOJbLisqOtf1stP9mQ8osP+EPi+fmO2uPBU1Wr1VJhN3VUL9T7qxas08lIVc25s6Envf0c4T7K4CzQL1UwWt/SK9Jm+sZ2bT4xM+XduRdJaPsiRc9nvaVkykODOTiZLbs8ybvDlzt2Zl8deZ16I0plT82JfIe+6254QmUxtA7LG3GTDZY0ahC9zbsSUrJsUaPgI8QzyZuxZgfTqQqXiyYeMTk/gEiVZTtXeJJf4TprJfLdlIQIIINA+AtQUAQQQQAABBBBAAAEEEKhFoDVCSqW53NCocwMoux2ls6fVzdPsjyl7L53NGRfu5OaM82Upa53oGd4nTj60nLZmRbdHnoEVxZWAS+v6hvdk7TOkTlallcsVFxJldqhH1FReDaNOpw7c3mdsU/FSOjNqXgrm2X3KyeZP+4yx5SfP+93xrLyVd049Omt+wa9PFxfms1s+UNM1VN5iKpdoFqqZrDLvkE/qSsEQUs+WCezOPbdvk6HE6qNBae5i5UMhRDc9AAAQAElEQVSNkt2z+vrVvcV8mnP6vHvhleeLwhPRNOIl7lS687XlFrpn6RYt0+nsMjJZIlNtLQ0qOukRojXIk96zghrnvzyz48De7PnJM6WELlEyLsvzjGRP2ZrJPNuxAAEEEEAAAQQQQACBpAXIDwEEEEAAgRYUaI2QUmZ4Yo+YPzKYm160zqiW1P2CToh9Ezsacp567Xui9JR1prh4wW5zgyu1oTtbPD69ZAOXFs9Oy2jEaGXoIrk6qD699GBu6uxSySpS5V0qLU3J8GGjClVFJP0T0009AEycX1jyr4Z5hnSkapxv2r29Ypj3jR3cfHGyHAdanj56eeBe30u7FheMgEN233Cvf4lCZPf2r0zmppbL/aAe7vKg8M8wKBe1XPbpQSH71JWVKKld9ZQ4OFPZBKHuWqkz3iJ5NQvVTKaaofszf0TGuctqQrb0yPms76NcEtmde8cPbTo+ImPM9g6jdpaRCyJbWWHZ0j3ae1YUb2bHjCx0yF2obGZu/0WRdRcrgxli/pT76DGdGx1ZqK6aVdGoQuPlZmUqwvcsO1X0u8YuE52JZoraWhqYeQNGSPQgl+NNf68PrHrNK9RjvYonj06eLA4cHK84TNaQ5dLCvBC+sfmKzDSTVWzj/4GlCCCAAAIIIIAAAggggAACCCCQfoHOa2FrhJSEkGc2C7P3brp8amTIuHZn5NTlTffO5ofrPYWUSI+qi6iMWh05L8T5w2YNc3N15Z25bYt5yja7ta85jcwM52f3idPW841GRk6tbNw9O+uKBDjNlA0cOV4UxRMjRqvlxyEj8mQkOCzPyc3fN2QuUQRGsulFNSt/XKtkn+Yn+ldOH7X6VOaTOzq30j8Rq1CZp5yMos3LcYbqr1tkbrJEZ4p0c1KaM7137c2K+SdtEHNh+TWzPX/skCiPc9F/rDDmufyod3xi58qkFFPT6IMbd+e9aWSe9iUsAzuHwwbRB2Ru3QuTVtcP5Y6u3H6gIkNtkEzvuOzThcmjVl8MDU5OLvRPzPiEuzJR403WX04ayTQL1Uqm3VIVijt4oH/loUnzakLZEZML3QeP5Xf4SSe0O/eMnZuV/W7vd5Onxa7ZmV2eBxZp7VnSVk4avEahYnLS2tlHJk917zyWn9hqHutyZ4wAV+/47MGtl52jx+Qp0b+7kN+9yTxKOLu/LFFOkYXGyk1maE6Be5a62mbkZFGog4/VisArzDR3GbPEkNelKXMXqCrXdWA0NtZqqar/kPxyKR6XeVo1Nwaq52ArjM5amUxshGT3agxyjT1LuwlGu2Qz1eR7JDfYKl5UvxeLYVHziuQhH5aflN/g24Kj79ammsms1LwhgAACCLS7APVHAAEEEEAAAQQQQACBmAKtElJS1c70jeVnCgXz6Tsz8pS337lblbDZP/IMqVUrs27Ga364vmrI0IKVT/OamRkeyxuFqubMjO3oy7jL9m2mSik3MSJPFQkqlxgBD3m60+g+Y5WpU9Gn5wr5+IVa+QznrZrIylRNRnGx6laR2C83s1DnNdzNSWbNqH9WL+ZPBV+JlekZK4/zMd8QhQyyyuCN1eSZ/A5PzMks64UVdY1SwAVMZhLzNdPr6vr8zPZed8fLwqJ4zVzM18o+zavn65grPK8ZV6EFT9c7yXWSeQutbIGVWWQy3a7vGysopZ4d43lnl8mPV7tZpcq3xHZndfre6vdzobbuo6XfniUrZU4avHJ4uJqpGi5sKCeEVpVGHj2EkErG7mPs/mZp1mtUofFyszPdvnOb355lV8N2k0chnxinmYn2LmMmD3ztHZel+E3G4ci1mUZLK+pv1dz2N4qoyDO5ESLLVX2tNcgj9iyZlTESjC4Ia0JFu8qbeJvpIhTyMBcRNXenDpxfXJgX/lcZVmyimaxiGz4ggAACCCCAAAIIIIBAIwXIGwEEEECgtQRaKaTUWjLUBoFaBYx/Vn9iLuhCpVqz9Wxn/Gv67N7hgICTJz0L2lFg5XI71roxda57z0rlLpPuEaKeopTEJUrGo7+2Rd5KVzNZY8Z3WnOlXQgggAACCCCAAAIIIIAAAgggkCoB35BSqlpIYxBouoB5odLCsnHPsIaVXlKnkgeqnsPUsNLIeA0ESstnRk+I6PPga1C1tSmyzj0rfbtM6kdIae5kMZFLlFQ+0U9jWtRLtjajn1IRQAABBBBonAA5I4AAAggggAACCCCgL0BISd+KlAhoC6j7Yo33ZLTT15LQuNma955jdlalM6NDQ/fNm0+7GRocGppatlfx3roC9kN61MNmhvY/+PzW2fx4yDhq3YY0qGZ17VkRu0yDqpxwth0wQoxjlzxkyWm/enjfETlT5+FL3ZrPuiNfWH9oJgvLgnUIIIAAAggggAACCCCAQEMEyBQBBBBoGQFCSi3TFVQEgUQFMjucJzaZzywZ70k0fzJriIAKmZj9pV7zY8PEkxri3L6ZdsAI8Ry75L7A4at9hyw1NwR4QQABBBBAAAEEEEAAAQQQQCAtAoSUgnuSNQgggAACCCCAAAIIIIAAAgggkH4BWogAAggggAACCCCgJUBISYuJRAgggAACrSpAvRBAAAEEEEAAAQQQQAABBBBAIP0CtBABBFpBgJBSK/QCdUAAAQQQQAABBBBAIM0CtA0BBBBAAAEEEEAAAQQQQCAFAoSUUtCJjW0CuSOAAAIIIIAAAggggAACCCCAQPoFaCECCCCAAAIIIBAlQEgpSoj1CCCAAAIItL4ANUQAAQQQQAABBBBAAAEEEEAAgfQL0EIE1liAkNIadwDFI4AAAggggAACCCCAQGcI0EoEEEAAAQQQQAABBBBAoL0FCCm1d/9R+2YJUA4CCCCAAAIIIIAAAggggAACCKRfgBYigAACCCCAQIgAIaUQHFYhgAACCCCAQDsJUFcEEEAAAQQQQAABBBBAAAEEEEi/AC1cOwFCSmtnT8kIIIAAAggggAACCCCAQKcJ0F4EEEAAAQQQQAABBBBoWwFCSm3bdVQcgeYLUCICCCCAAAIIIIAAAggggAACCKRfgBYigAACCCDgL0BIyd+FpQgggAACCCCAQHsKUGsEEEAAAQQQQAABBBBAAAEEEEi/wJq0kJDSmrBTKAIIIIAAAggggAACCCCAQOcK0HIEEEAAAQQQQAABBNpRgJBSO/YadUYAgbUUoGwEEEAAAQQQQAABBBBAAAEEEEi/AC1EAAEEEPAIEFLykLAAAQQQQAABBBBAoN0FqD8CCCCAAAIIIIAAAggggAACCCQt0HohpaRbSH4IIIAAAggggAACCCCAAAIIINB6AtQIAQQQQAABBBBAoM0ECCm1WYdRXQQQQKA1BKgFAggggAACCCCAAAIIIIAAAgikX4AWIoAAAm6BipDSf/vv/50JAQQQQAABBBBAAAEE0iFAKxBAAAEEEEAAAQQQQAABBBDQFHCHjoLmK0JKQYlY3nwBSkQAAQQQQAABBBBAAAEEEEAAgfQL0EIEEEAAAQQQQKB9BAgptU9fUVMEEEAAgVYToD4IIIAAAggggAACCCCAAAIIIJB+AVqIAAKWACElC4I3BBBAAAEEEEAAAQQQSKMAbUIAAQQQQAABBBBAAAEEEEhGICKk9N/+2387f/6Jf3vi+B995jNMCDRdgFGHAAKpFTh54sRfPf74Cy+8kMy3GbkggAACCCCAAAIIIIBAOwtQdwQQQAABBBBoC4GwkNKz//lrf/HII//gZ95y58e3j/32bzMhgAACCCCQlMDwnXd2ZzJ//cQTj33hCz/+8Y/b4iuTSgYJsBwBBBBAAAEEEEAAAQQQQAABBNIvQAsRECIwpPStbxW//MyX/vndnxj44JaNmZt++o1vYEIAAQQQQCApgQ0bbrrtA72/9Zu/8VNveMOfP/IwUSV+J0EAAQQQQKCxAuSOAAIIIIAAAggggAACCNQt4B9SevVHr178D39958c/9j//zM+Iri4mBBBYSwH2QQTSK3DN6173TwY/ctPP3XT+iSfq/kYjAwQQQAABBBBAAAEEEECgrQWoPAIIIIAAAq0u4B9S+i/PPfe2t21ev379NV3rmBBAAAEEEGicwLp1637tI7/68ssv81ylVv+VgfpFCLAaAQQQQAABBBBAAAEEEEAAAQTSL9DhLfQPKS0uLd36rn8oBNcnIYAAAggg0GAB0XXNNa97/3vf94Vz5wT/IYAAAggggAACjRQgbwQQQAABBBBAAAEEEKhHwD+k9MMf/uBn3vzmH//kJ9//wQ+ZEEAAgVYQoA5pFZDfNXLa2L3xhz/8YT3fZ2yLAAIIIIAAAggggAACCCCQBgHagAACCCDQwgL+IaXrr3/Dd77znXXrupgQQAABBBBogsC3vvWt66+/voW/LqkaAgjoCZAKAQQQQAABBBBAAAEEEEAAAQTSK2CHlCpb+MEPfvCLzzxzzbqu112zjgkBBBBAAIGGCnzvlf/xxS9+8WN33ln5XcQnBBBAAAEEEEAAgUYIkCcCCCCAAAIIIIAAAjUK+IeUsu94e9e6rn9//vy6dV3XXLOOCQEEEECgNQRSeEB+6W9f+renTvd+oO/Nb35zjV9lbIYAAggggAACCCCAAAIIIIBAugRoDQIIINCaAv4hpa6urg/f8atXrrz4+eMnv/71v/nB979/zbp1TAgggAACCCQlIL9ZvvXNbz38539x+vT/+YH+/n/4D29tza9JaoUAAgjUIMAmCCCAAAIIIIAAAggggAACCKRSwD+kJJt6/fXX/5N/+s/e9Qu/8MUvffmP/uRzh/7/hzthoo0IIIAAAs0RmPmTYxeefPpn3vyWnbt3v/3tb5ffO0wIIIAAAggggAACCDRPgJIQQAABBBBAAAEE4gsEhpRkVl1dXW9/+zs+8mv/ZO8nPvFbn/40EwIIIIAAAkkJ7PvEJ//JP/2nv/Dud7/+2mvlN068idQIIIAAAggggAACCCCAAAIIIJB+AVqIAAItJxAWUmq5ylIhBBBAAAEEEEAAAQQQaA8BaokAAggggAACCCCAAAIIIJA2AUJKaevRJNpDHggggAACCCCAAAIIIIAAAgggkH4BWogAAggggAACCMQSIKQUi4vECCCAAAIItIoA9UAAAQQQQAABBBBAAAEEEEAAgfQL0EIEWkmgIqR0Lf8hgAACCCCAAAIIIIAAAggkJUA+CCCAAAIIIIAAAggggECKBCpCSq0U66IuCKy1AOUjgAACCCCAAAIIIIAAAggggED6BWghAggggAACCOgKEFLSlSIdAggggAACCLSeADVCAAEEEEAAAQQQQAABBBBAAIH0C9DCFhEgpNQiHUE1EEAAAQQQQAABBBBAAIF0CtAqBBBAAAEEEEAAAQQQSIcAIaV09COtQKBRAuSLAAIIIIAAAggggAACCCCAAALpF6CFCCCAAAIIaAgQUtJAIgkCCCCAAAIIINDKAtQNAQQQQAABBBBAAAEEEEAAAQTSL7D2LSSktPZ9QA0QQAABBBBAAAEEEEAAAQTSdV5odgAAEABJREFULkD7EEAAAQQQQAABBBBoewFCSm3fhTQAAQQaL0AJCCCAAAIIIIAAAggggAACCCCQfgFaiAACCCAQLtDGIaWXHr9/9OhjL4W176XHjo7e/3hokrDNWacroNEXulmRDgEEEEAAAQQQqE2ArRBAA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"&gt;&lt;/p&gt;

&lt;p&gt;In my main actual code, the output was so large that it hanged Maple UI when stepping into the debugger and hitting that line.&amp;nbsp; I had to kill Maple from task manager as Java UI got stuck due to large output.&lt;/p&gt;

&lt;p&gt;Why is it showing value of P when I have : at the end? Is there an option to turn automatic display of variables in debugger as one steps in?&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;I was trying to use the debugger into a proc that has this call&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
P:=plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):
&lt;/pre&gt;

&lt;p&gt;Even though the proc has : at its end, and the above call to plots also ends with :, the debugger insists in printing to the debugger window the contour cuves lines. i.e the value of P&lt;/p&gt;

&lt;p&gt;Is there a way to tell the debugger not to do this? i.e. not show the value of P. It seems it does that automatically.&lt;/p&gt;

&lt;p&gt;Here is the worksheet. Simply evaluate the call foo(); this will open a debugger windows. Then click on &lt;strong&gt;next button&lt;/strong&gt; and now debugger will print&amp;nbsp; the output of&amp;nbsp;&lt;strong&gt;plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):&amp;nbsp;&lt;/strong&gt;&lt;/p&gt;

&lt;form name="worksheet_form"&gt;&lt;input name="md.ref" type="hidden" value="5C600FF731666F925B3E90619BFB4A05"&gt;
&lt;table align="center" width="768"&gt;
	&lt;tbody&gt;
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			&lt;td&gt;
			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;restart;&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;kernelopts(&amp;#39;assertlevel&amp;#39;=2):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;kernelopts(numcpus=1);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="32" height="23" src="/view.aspx?sf=243651_question/1cf25b557d0ae6626fadb4ff66b96ed9.gif" style="vertical-align:-6px" width="21"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;interface(version);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="`Standard Worksheet Interface, Maple 2026.1, Windows 10, April 28 2026 Build ID 2011354`" height="23" src="/view.aspx?sf=243651_question/486a0ed15a8ff238395266f57ddbc632.gif" style="vertical-align:-6px" width="560"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;foo:=proc()&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local L := [$ -4 .. 4]:&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local RHS:=y/tan(x):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;local P,T:&lt;/span&gt;&lt;br&gt;
						&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;DEBUG();&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;P:=plots:-contourplot(RHS,&amp;#39;:-colorbar&amp;#39; = false,&amp;#39;:-contours&amp;#39; = L):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;T:= timelimit(60,plottools:-getdata(P,&amp;#39;rangesonly&amp;#39;)):&lt;/span&gt;&lt;br&gt;
						&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;end proc:&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;
			&amp;nbsp;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;foo(); &amp;nbsp;#this will open a debugger window&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="left" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;span style="color:#0000ff;font-size: 100%;font-family: monospace,monospace;font-weight:normal;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
	&lt;/tbody&gt;
&lt;/table&gt;
&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243651_question/hang_maple_2026_1_on_timelimit.mw"&gt;Download hang_maple_2026_1_on_timelimit.mw&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is screen shot&lt;/p&gt;

&lt;p&gt;&lt;img 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Lb/+KDWOp/Pv/g//5ckycimFMqu8hpSLWlJqfbex/ueecalR9a0ugG0rrlPJpUOvDHRe59XdGVqXI0PHNi795su9Q+Mq4mpK/rihUHV07nEHRauLVS4qc1cm/YXs9PLl2rW292obxWfhyklc/H4QgABBBBAAAEEEEAAAQQQQCCRAnQaAQQQQAABBBCotcC/+9M//cs//3Np9de/+pUkycimFEqmfKp4YqJpKprxFO9Ma65NdmoV7JWtnq3PfOc7kfSomW6S8rBSUFtLcbihY5uyKzxCNoo2q5tzYUpJLhgJAQQQWCABTosAAggggLMiLCUAABAASURBVAACCCCAAAIIIIAAAukXYIQIJE/gP/z7v/y3/+Zfu35LRjZdvvy6pUUnK8m0jdJKF+n26FuvT27YercaOHL2ih1Ua65DDY5ctPlo/eLlSms1OTUdaJgPIynlNrXWSv5zjRQ/fM5ZXOVmWJe/A9iLAAIIIIAAAggggEC2BRg9AggggAACCCCAAAIIIJA5Aa31f/rrv/7c5/6VJMnIZiUEWrckLcmwZHAF3b5y5vnDExs2r122bvMGdfzImWkZV25d7wp1/vCbo5I36cqZN8uUt3Uu7xg78Z5XefTN1waVMkeZE+Xa2tTYlExUGatSzfqVTZ1myqf7U0qKBQEEEEAAAQQQQAABBBBAAAEEUi/AABFAAAEEEECg5gK/+7u/u+Mb35AkmQob14lbZGBjx599YudOPx0d1aNHn31HbdyyNiejya29+4tSQUq1Xnbfcw+uOP+qV/PZoVxXTmpoU757w0RBeW7tlo0dfuXh7t0bOpSytWW17C5v14H3pu3hRZuVik2YmFJSLAgggAACCyvA2RFAAAEEEEAAAQQQQAABBBBAIP0CjDCBAp/6g09JqrzjLUlb2u564kD/gWja3NnSufnAgZ13tXlj6dwsFaTUbnbKLtm06YmgSktLtB2/vO2unX7Lmzvb5ER+I6a62+WfpVSz9pxNtqr8bqAmAggggAACCCCAAAIIZFSAYSOAAAIIIIAAAggggAAC8wk04adqSnXpsR2PFU2l6lPuBPiU0nw/BCnYzxAQQAABBBBAAAEEEEAAAQQQQCD9AowQAQQQQACBBRZosg/UlOtOKalyx7BPBErBUY4AAggggAACDRTgVAgggAACCCCAAAIIIIAAAgggkH6BdI/QfZAlEeuDLx4smhLR+QXsJJ9SSvePMKNDAAEEEEAAAQQQQACBmgnQEAIIIIAAAggggAACCJQRWMCpDk7dGAGmlMrc/+xKlQCDQQABBBBAAAEEEEAAAQQQQACB9AswQgQQQACBBRLQLS1aqRaWdAss0N3FaRFAAAEEEEAAgTkCFCCAAAIIIIAAAggggAACCCCAQDIFfu+Tn/z1b35d2WdlqJVUAT6llMyfTnqNAAIIIIAAAggggAACCCyMAGdFAAEEEEAAAQQQQKCIwGc+84eXLl1O90d0GB1TSkVufYoQSK8AI0MAAQQQQAABBBBAAAEEEEAAgfQLMEIEEECg8QIrvrhqcOj8Ly5dyufzSf0MDv2eTyA2pfSJT/yL//bf/wcJAQQQQAABBBBAYAEFODUCCCCAAAIIIIAAAggggAACiRCQOYVg7qq1tXV978ahocFXX3v1le++QppfIIFKsSml//xf/8tjjz9OQgABBBBAAAEEEEAAAQQQQACBcgK8dkYAAQQQQAABBBB4/HGZUwimlCTz2c9+7v7NWx/b8QQprQKxKSW55CQEEEAg/QKMEAEEEEAAAQQQQAABBBBAAAEE0i/ACBFAAAEEaizAlFKNQWkOAQQQQAABBBBAoBYCtIEAAggggAACCCCAAAIIIIAAAs0lUI8ppeYaIb1BAAEEEEAAAQQQQAABBBBAAIF6CNAmAggggAACCCCAQKYEmFLK1OVmsAgggEAoQA4BBBBAAAEEEEAAAQQQQAABBNIvwAgRQACB2gkwpVQ7S1pCAAEEEEAAAQQQQKC2ArSGAAIIIIAAAggggAACCCCAQNMIMKVUt0tBwwgggAACCCCAAAIIIIAAAgggkH4BRogAAggggAACCGRFgCmlrFxpxokAAgggUEyAMgQQQAABBBBAAAEEEEAAAQQQSL8AI0QAgZoIMKVUE0YaQQABBBBAAAEEEEAAgXoJ0C4CCCCAAAIIIIAAAggggEAzCDCl1AxXIc19YGwIIIAAAggggAACCCCAAAIIIJB+AUaIAAIIIIAAAhkQYEqp6EWeHHhme9/JyaL7KKyHwOTJvu3PDCBeD1vaRAABBOYXoAYCCCCAAAIIIIAAAggggAACCKRfgBEiUK1AqSml4cMPb98epsPD1Z5oYY43ExXhKMyIUjdRlJIrtTD3B2dFAAEEEEAAAQQQQCAxAnQUAQQQQAABBBBAAAEEEFhggSJTSnYa5iX10KFDB7305CY1ntzPjyze9KQ/kEMHt3Uce2r7w0mdISu4WdJ2pQqGl6pNBoMAAggggAACCCCAAAIIIIAAAukXYIQIIIAAAgikW2DOlNLw4aeOqU3fOrS1Kxx4bv3W3ly4meRc19aDT25afO6lFEwqpfxKJfkuo+8IIIAAAskUoNcIIIAAAggggAACCCCAAAIIIJB+AUZYhUDBlNLkwDvnFm96sMwEkv1kjPkNcubX4kUmZoYPb99+eDjca3eFm5EPBlVeU8YVacG0LyUuSSN9JyfDvdfwZ3hyvRtXqfd/GPzdnrCRh/uCQneWyC47HlNq/sxSsCEFpk5kWzpmZB7evv2ZgeH43wcqs8s0IoeYFPbBFMq45DBTHjmHnFU12ZWaHOgzndxuxx52Vfpe5jLJXlvfWMU+BRdtTQTMePlCAAEEEEAAAQQQQAABBJTCAAEEEEAAAQQQQAABBBZOID6lNDl0/vLiFctLfiJJ5gCeOtaxzfs9ck9uGn9JppHCzr//0vfUg/bX5W1b9f5LMlvgb875YFBlNcuf7vKxp/z2t62SjXAiI+xR8VyubbG6PGEnMcwpzq/wfjPeQx3Hvh22Ik367R/atvLcSxXMbUhrL72/yvP5uvrhsctBB8rveqpEH5R0YrDHkkY/NqZUk12pyQ8mVnzL+zWJxipUNCPwGWOXqTTI8OFvH+vwf+/ithUBIZnkCzACBBBAAAEEEEAAAQQQQAABBBBIvwAjRAABBBBIrUB8Sqn8MCcHfvi+WvVQMLeR6/36psWRj/uoxZseXO+mo7p6Vkpbq+7xNnPdKxar8cjfY6qk5rynW7ltr9d+192b4u3LycukXHuH22tOsXjT1/0PZZlmzg0Ou31Khe0rs+fy+SE7C+Xvnvt9eDDqk+t9UHrl1Sq9q3wf1KptgbfXVAXfTJsNvFJKRX81YlfPKvV+qBhhNIr+bVAGZHxMLW5z95FSXev9q1PBuKmCAAIIIIAAAlUL0AACCCCAAAIIIIAAAggggAACCKRf4PpGeE1TShOX1aqeyN9YUmZuxvu4jzl9e3vOfDNfufbFkXkBlWvrMKXBVyU1J+c53eLwbLb9SEeC8xTPTPqTFuYUl4992/26Nlk/deyyGnMfX1Iq2r7KySzUfCcYHjxX4BOcvsyusn1Q4dxK0FYFGdNmA6+U7ZH5NX3ud9+9fM4WeKsoo7kNnGIZkFzvPSvtRangY2HeOfiGAAIIIIAAAggggAACCGRJgLEigAACCCCAAAIIILAgAvEpJTtxcv6DeT6MsyAdreVJzYxLhz8h5f+eOu+3+R3yP/lUyxPO19a196G5rtTw4Ye3R35336r5xjvP/q6t5nfobWs/9pTMUUV+h948h7EbgYQI0E0EEEAAAQQQQAABBBBAAAEEEEi/ACNEAAEE0igQn1Jyv+Dt2Ingd7/FhpxrW6wivxdO9gUf95F8zVO9Tjd8+OVzizfdbT5tNfcUkVFcjvyiPhUfafBJJqk+Oe7/wSTT2lj8oAp3xVWl0fmT+SVyl5vkStmPHG3b4/2GusmJsfm7X8bKP9hMLH0r/psV/V18RwABBBBAAAEE6itA6wgggAACCCCAAAIIIIAAAgggMEegYEpJ5dY/uGnxuZce7huIfFRp8uRhs2l+I5k693LwsZHJgVePXV55jzeTMKfpaguu73Tlzzo50PfwS+eCP5I09xR2oF4b77/kDzU6UvN3ocK5nOHDL73vVVduz6uGyhRVuqv3npVx1WgfTEPFv5rpSkXmGicHvnfMn0gr3nFbWsZqcuDwSf/mM58ns/VZIYAAAggggAACCCCAAAIIIBATYAMBBBBAAAEEEECg0QKFU0pK5Xr3HDr0UEfkLwxtf+p8W7f9K0ldWw89uWnspYe3bzfJ/KqzQ1vNp33q1OvanO6y/f1ppsPbt3/7/IpvHYr2WU6xbaVMofkjancDNQNavGlb2zte+bH2bcFR/lyO3TXY8+Smxaa2+RK6Jzcp/3SV7lJl+mBaLfklp2uOK5XrNfOQL1uQV9WDD1Xyi++k86Ws1Nixp+wNtn37y2ObvrW3XnOWJWHZgQACDRDgFAgggAACCCCAAAIIIIAAAgggkH4BRogAAikTmDulZAcosxz+3xY6JBn/d5rJvtz6vaZECg/G/uyQHBFMunjVIkeZaRN/s/KaXjv2XHLS6F85kkaim6b9g0Vmt6K9lRYOHSwyPyFN2V3m7/f4bcqEh4yuy8yuubPH2jZ7vUO2dplThHtju6T/kVRml+m+12BE1bTso0XaiWel966Hbh2pbw53hZE25WA5ouZXKjyXdEBO4F8LyfqkcmZlxunv8iYvXQ8doxwrtXK9e12hWRe5XlKFhAACCCCAAAIIINAAAU6BAAIIIIAAAggggAACCCCAQFSgxJRStAr56xSYHDp/efGK8GNPkWbK7IrUqiLLoQgggAACCCCAAAIIIIAAAgggkH4BRogAAggggAACCDRQgCmlGmJPDjzj//UlpSZPfu+YzCgtz9kTlNll97NCAAEEEMigAENGAAEEEEAAAQQQQAABBBBAAIH0CzBCBNIjwJRSDa9lrntF8Iemtj91rGNb+Hv2yuyqYQdoCgEEEEAAAQQQQAABBGorQGsIIIAAAggggAACCCCAAAKeAFNKHkRNvoV/Usj8HaDwLyxJ42V2yV5SfQRoFQEEEEAAAQQQQAABBBBAAAEE0i/ACBFAAAEEEECgMQJMKTXGmbMggAACCCCAQHEBShFAAAEEEEAAAQQQQAABBBBAIP0CjDAVAkwppeIyMggEEEAAAQQQQAABBBBAoH4CtIwAAggggAACCCCAAAIIKMWUEncBAmkXYHwIIIAAAggggAACCCCAAAIIIJB+AUaIAAIIIIBA3QWYUqo7MSdAAAEEEEAAAQTmE2A/AggggAACCCCAAAIIIIAAAgikXyDpI2RKKelXkP4jgAACCCCAAAIIIIAAAgg0QoBzIIAAAggggAACCCCQcQGmlDJ+AzB8BLIiwDgRQAABBBBAAAEEEEAAAQQQQCD9AowQAQQQQKCeAkwp1VOXthFAAAEEEEAAAQQqF6AmAggggAACCCCAAAIIIIAAAgg0sUCNppSaeIR0DQEEEEAAAQQQQAABBBBAAAEEaiRAMwgggAACCCCAAALZFfCmlPIzMz/5yY9f+94rzx/Yf6B/HwkBBBCoXuCVl//38Xf+dmpqSkIsQaZ6z1q0QHhHIFUCBBnCAgII1FWAIFNXXhpHAAGCDPcAAgjUVYAgU1deGkcgywLelNI//PTvL1+61N29YuOGjfdsvIeEAAIIVC+wcuXKG2/8nR/84K2f//yfCDLVe9ICAggUCBBkCkDYTI0AA2kSAYJMk1wIuoFAWgUuMok5AAAQAElEQVQIMmm9sowLgSYRIMg0yYWgGwikT8CbUvrZxdE7/vgLt976qZtvvvkPWK5XgOMQQCAqcOunb/3C57+wvHP5j3/0dwSZqAx5BBCoiQBBpiaMNIIAAqUECDKlZChHAAEjUPUXQaZqQhpAAIFyAgSZcjrsQwCBKgS8KaWPPvropk/cpFgQQACBWgvcdtttV6/+kiBTa1faq0KAQ9MlQJBJ1/VkNAg0nQBBpukuCR1CIF0CBJl0XU9Gg0DTCWQ+yDTdFaFDCKRAwJtSyufzWusb7XITCwIIIFALARtRbmxpaZmdlRhDkKmFKW0ggEBEgCATwSCLQPoEFn5EBJmFvwb0AIFUCxBkUn15GRwCCy9AkFn4a0APEEipgDelJJNjMqUkaxICVQvQAAIxgSC2BJnYbjYQQACB6gSC2BJkqmuPoxFAAIGYQBBbgkxsNxsIIIBAdQJBbAky1bXX6KM5HwIINLlAEFuCTJN3mO4hgEDzC4RTStLXfH5W0gwLAgggUAsBiSeSJLYESTYl1aJt2kAAgWoFUnC8xBNJQYSRjGxKSsHQGAICCDSDgMQTSRJbgiSbkpqhb/QBAQRSICDxRFIQYSQjm5JSMDSGgAACzSAg8USSxJYgyaakZugbfWi0AOdDoKYCkSklrZVWJikWBBBAoBYCLqRIbHGNScaVuE3WCCCAQJUCLqRIbHHtSMaVuE3WCCCAQJUCLqRIbHHtSMaVuM3GrDkLAgikWMCFFIktboyScSVukzUCCCBQpYALKRJbXDuScSVukzUCCCBwvQLhlJJEFfMZpXz+499+TEIAgWoF+Dn67cezefPJR4ktLkBJxpVgiwACCNREwIUUiS0EmZp40ggCCBQIEGQKQNhEAIHaChBkauu5oK3xNhoCzShAkCEsIIBAnQTCKSX3dgxrBBBAAAEEEEAgMwIMFAEEEEAAAQQQQAABBBBAAAEE0i/ACGslEE4paa3zdvl45mMSAgggUL2AjSh5iS0uYEnGlVTfMi0ggAACIuBCisQWgoxokAKBD77/6KP7B8Z4oEWgagGCTPBjteCZxHRgfODZHY8+uuPR14d5TY3A/AIEmcT8aFf9D0qdRsozT51gU9MsQSY1l7J+AzFh5PsfuPZNnpdRzRrw3TVqnnU4peTejmGNAAII1FCAphBAAAEEEEAAAQQQyIbAh0f2HW/7q/379+2/b2k2RswoEUAAgagAeQQQQACBbAjEppTc9PUMCwK1EJh497md/e9O1KIp2kiogAsp0VjqShI6HLpdVwEiRl1509q4CykEmRpc33Q18Sdf+c53Hll923yDIuzMJ8T+GYIMN0F5gcIwMjE+odpb540+5Rtlb5YECDJZutq1GWtB2Knwmac256aVBAoQZBJ40Rrd5fysUrN5d9Z5Q0pBCHJHsU6gQA26HE4paRYEai2gtKp1k7SXSAH3hm8iu06nGyigiBgN1E7ZqQgyKbugDRsOYadh1Ek/EUEm6Vewfv2PhxG5U+r6NFO/cdDyAgvIrSNpgTvB6RMiEA87Cek03VxoAYkwkha6F5y/KQXkzlCq8p4pnnQqx0p1zXBKSZlZyfxs3puZrMF0FU0kU+DC0V0HTk9MnD6wa/cuk54/Hf2kUVi+W2qZEdqSIxdMVr4mTj+/a9fRH8l634kxNXZinzQSb0EqkTIiMJs3IUViS5BcSUaGHxkm2biARA2JDC6Z+GDixtyIIbVMCDLVvGgzM2NqykZkVxB84qdgKxsCLqQEEUYyriQbo2eUJQXkSWbX0RG3W/KxoGFijuwxwaRs2JGHGa8FqU3KrIALKRJbguRKMguS7oFHni6iEcCEi6P+Sx0RMNVMhDHl0TAyKq+e9tvXP/vlNZR5PjE1JeZcOGKfZ0yJd7h5tpE60bPMSLCSwGUOcXvNKeS5x39Ftts7XFogpUnAhZQgwkjGlaRpjIzFE/BCgf3Zl8hgS8MfefnBtz/1ttgEhAqfXlzoKHuU7DTxqlgck10mRbohpzUlfFUp0DyHu5AisSVIrqR5ekhPFlwg+imlaEgxTyESmlwyUctEkuiTT/S94gUfBR1ovEBsSikIMWQyLjB+sv+o+lrf0319T2/pGR/of3vUgVx8e2//SNcOU97XtyU3cOCti0otWv3IA91Dp85OmzoX3xtQvTu+8qerH+7bsb5dtfeayg+vXmT28YUAAgiIwOjbBwbatkh4MemBTilZNDdiFI02UlVSJED1SfB5/aCLPrKHhAACCBQRiASN4KmmeNjpP5l7wD3kPL2jd/KNvf7zT5FGKUIAgXQJmAePa4sAhWHkjtWP9D3W2y6vfx6TJ5yv3ul85JXUSKd9VWVK5jnL0BvhS7ChN/Z+c6+/uaO3feh1IpIjZY1AAgWmz764943JXhMcJD7s6M2ZMZQPCBU+vZiGIl/FjorsLpY13ZjzJk+xipQhgECmBCp63yZTIgy2QCCcUtLafHRNybqgStNv0sHaC3RvecSbBlqyRmaGJqfMfNH02VND7b33ejvUnV+S1zYjMqek1J1retXJo2enR9+W56SgQu27RYtJE5B4os3i+i05E2G0dpusMyowPWX+zMDt3ujvXO2HFK/AfisdbczuMECZ4NM+PvyhiVBmD1+ZE5B4os3iBi45goyjYB0TCING5KkmVkMpE3ZUzxbznq/ds2j1vfKY8y5T1lYjwyutJaporR2B1lo2TXLbrFMjUMcI0P3AV5Z4TvOepb33a95j0ZLObjmo+y5vc9HSznblXpFJMSlNAjaqaK3dmLTWJsLI2m2zTonA6JmT4z3BWyxq0eqvrF40b0Co5Ollrs+1HmW60V70TZ65bVOSSAGJJ9osrvOSI8g4CtbzCFTyvs08TbA75QKxKSUJLiQE5JZvz7UGDq25NjU+dUW2r0yNq/GBA3v3ftOl/oFxNWH36NY19/WqgQNvTPTetyY8VFqSf63kSFLWBcytoLgZsn4baAfQuuaubhtJDp41gcUVmrWSxXyXr9LRRupEA5QOI5QcRsqugNwYkrI7fkYeF5CbQfn/5iilokEjHjNkp1/PhJ2eziWRhuJVIzvIZlHA3CvKv1uyCJD2MZeNAMosUQHZDjYlH70xzGZk2wSgRUHdec+SC1+DLcq1S/AKjjUBSUUaDtokkxYBZZe0jIZxxAUuXhhU8WcM2T9fQKjo6UXr6I0j+VJHyS4VCyFKuU3TDfvSbO6bPJolVQLKLo0dEmdLhkD01gjzlbxvk4zx0ct6CTClVC/ZJLertIp234QUuy2Znq3PfOc7kfSoP4Pkvc6JHijVo5u2CVbZFDD3guJuyObFLzLqpfeaMLK1beC5vXv2vH3Rq6Fk8bJaS75EtFFKKx1dlCzRbfKZFJC7QFImh86giwqoSJxQ8aChZPGOkZzyslrHNrRdihTZ8rSvGF8RAXMzqPB2KVKDomQLKFkKRiAl3iWXXHRfdFPyXiW/hopsK1n8cvNdNiN7TUk09KjYPiWLreGtCja9Ur6lRkAusKTUDIeBxAXk2sZ+vu3eIoWRIqWVreWtonsk75XabypSUZU7ytb2VkqpMFfiZZdXgW8pEVB2SclgGEaNBZQfEKTdMF/B+zZSn5RdAaaUUnPtazcQpVTsSUTJYlpvzXWowRH/7V9TEn6NvvX65N1b71YDR85EPncQbyesTS5rAnILScraqBlveYGl9z777M67OwZPBUEjjBglo42SOmNT02HLVyYnVUf4//SGO8hlS0DZJVtjZrRlBKL3g+SLPtXYwyWk2O9azw07hBePhm9GQO4jSSbHVyoFykUAJYFiMvLsYf6ffinzHWSvn5XvShVsR+NPubNorcyiI0tBS/HNSD2yqRAwl1+paxoKlRMjMPdnX7o+tzD64KGU0kpq+UnJ4udlR2SX7FD+plJKdgb1tJLFbikpLx7H5nbDHsAqfQLKLukbFyOqgUD03ojmbdPl3rexFVhlViCcUmphQcAKaHny0DbnVnbbFOTW9a5Q5w+/edGVt1w5+9aZKyZ/5ewLhyc2bF63bN3mDeqETCqZwpZcW5sam7IV7DarDAvYf5VUhgEYekTgypk3z/qB4crUmFIt2uyNRYyS0abF/KM1GIShK2ePnBhb0bsuZ1pIyhf9rIcAQaYeqslt0z65eN2P5k2R3bZRJ/6gMjfsEF6MF1+eAEHGg0jrt3IRoK1recfYife8V0AX33xtUCkXRFriYURwtFZKuQcbf0tryblU7iwt5tCwqt1UOiywu8NN1yDrFAnInSMpRQNiKBGBuT/78kbK3MLIg4f9iY+0YLddBIi9aGqxscLtiOfNweFRpePY3G5I38zBfKVNQCKMpLSNKhHjafpO2lDh9TLMV/K+jXcQ37IqIDHFJc2CgBMwN4TLeWulvMyy+557cMX5V5/YudOkZ4dyXTmtR48++47auGWtZHVu7d1fHDv+7NFRc8CyuzZ22MoH3ot8psDs4StrAsouWRs14y0lMHH8WRtDdu58bWLjrsdt8NA6HjGKRhtpUCnVseHBtpNeFHqn7cHn7lsm5aSMC8iNISnjCAw/FJC7wX900dG8raGCXXPCzu4NE+FDzvLdhBcLltVVfNzKLvEytlIlIA8epSJAbu0W70WNvAIa7t69oUOVCCMiomSRby5JPqhpS8qcpTBYzTlWxZuy7bFKj4CyS3rGw0jiAoU/+/aNlMLC6IOHuR9iTaggAsSfXmKhQ8lS/KgycUy6MfdNnlgrbKRCQG4OSakYCoOotYDcGUGEieQred+m1l2hvSQJmP/h29wwKrh9ktR7+loPgWX39+9clwtblu3n7g/espWt/uf6XXLVTMnjwQHL7pe9XvXcusdtzXBv2Gq9crTblALKLk3ZNTrVcIHcup0SJbzkoojrQ2HEMLHFqxYJSkr+tWr1Yovs9aKNa4F1dgWUXbI7fkYeFzDRww8Oko8GGi3b4VNNYdiR+OSecGQdOyrePlsZFLAxRv4ByuDQMzTk0hHAjxX2wcNU8yOM1v4u93pH9j0XBg/Z6nflEUVTKO3YFFbVJjj1h81Kuztjx0rsmtNUpFWyiRcgyFzvJUzMcUV/9osWypDkJz4aH0yAKPH0IjWD0CH5+Y+S4HP/MnPeSMCRA+XJx6VYC9IVUloECDJpuZK1H4eJAH5ACPMSJiRceCkaGOJPPrXvDi0mRiCcUsrqx7QYNwII1F3APb7U/TScIAMCWivl/26HDAw3xUOs8dCUXWrcKM0hgAACvoCNMfwKX5+D7wggUGsBgkytRWkPAQRiAgSZGAcbDRfghCkUcGFF1poFAQQQqI+ARBhJ9WmbVjMlIPeRUlplaswMthIBZZdKalIHAQQQuA4BG2Oy+K/PdVhxCAIIXIcAQeY60DgEAQQqFyDIVG5FTQQQqEQg/JRSJbWpgwACiRBotk7y+NJsVyTB/TE3U4K7T9frJGDuC8W7vXXSpVkEENDKLkAggAACdRKwMYYnmTrpZqBZhojA04gWRAAAEABJREFUfAIEmfmE2I8AAtcmEE4ppfATWAwJAQSaQ8A9vjRHX+hFogXae/cc2tvbnugx0Pl6CCQ1yNTDgjYRQKAOAgSZOqDSJAIIhAIEmdCCHAII1EGAIFMHVJpE4FoF0lXfhRVZX9tUFLURQACBigUkwkiquDoVEUAAgWsTkAgj6dqOoTYCCCBQsYBEGEkVV6diygQYDgJ1F5AII6nup+EECCCQVQGJMJKyOnrGjQACtRfwPqWkW1rU7Gztm6dFBBDIvEA+n7/hhhsWIshknh4ABLIhQJDJxnVmlAgsmABBZsHoOTEC2RAgyGTjOjPKugtwglICBJlSMpQjgMB1C3hTSr/3yU/+5je/SdfnrxgNAgg0hcAvf/nLRYtaCTJNcTHoBAJpFCDIJP2q0n8EmlyAINPkF4juIZB0AYJM0q8g/UegyQUIMk1+gegeAokUUHb5zGf+8BeXLl3TxBSVEUAAgXkFZmdn3//Hf1yydBlBZl4rKiCAwHUIEGSuA41DEECgcgGCTOVW1Ey5AMOrjwBBpj6utIoAAp4AQcaD4BsCCNRUwPuU0oovrhocGpRZpXw+X9P2aQwBBDIqIMFkamrqnePHb7nlluXLuwkyC3YfcGIEUipAkEnphWVYCDSLAEGmWa4E/UAgpQIEmZReWIaFwMIKhGcnyIQW5BBAoNYC3pRSa2vr+t4NQ0ODr7726ivffYWEAAIIVCnw+huv//TcuaXLOr+86S+UUgSZKj05HAEECgQIMgUgbCZcgMfvphMgyPAzhQACdRUgyNSVl8YRQIAgwz2AAAL1E/CmlPIzM5OTE7/9/7+VKSt585dUsQAVEUCguMDMzMyvfvX/xi5fmpqakhoEGUEgIYBADQUIMjXEpCkEEJgrQJCZa0IJAgjUUIAgU0NMmkIAgbkCBJm5JpQggECtBLwppX/46d9fvnSpu3vFxg0b79l4DwkBBBCoXmDlypU33vg7P/jBWz//+T8RZKr3pIVqBDg2lQIEmVReVgaFQPMIEGSa51rQEwRSKUCQSeVlZVAINI9AdoMM72wjgECdBbwppZ9dHL3jj79w662fuvnmm/+ABQEEEKiFwK2fvvULn//C8s7lP/7R3xFkaiFKGwggEBMgyMQ42EAgBQJNNgSCTJNdELqDQNoECDJpu6KMB4EmEyDINNkFoTsIpEfAm1L66KOPbvrETbX66BPtZE6AASNQWuC22267evWXBJnSQuxBAIGqBAgyVfFxMAIIzCdAkJlPiP0IIFCVQAKDTFXj5WAEEGiwAEGmweCcDoEsCHhTSvl8Xmt9o11uYkEAAQRqIWAjyo0tLS2zsxJjCDK1MKUNBKoSSNvBBJm0XVHGg0CTCRBkmuyC0B0E0iZAkEnbFWU8CDSZAEGmyS5Io7vD+RCon4A3pSSzZzKlJGsSAgggUFuBILYEmdq2T2sIIJBxgSC2BJmMgzB8BBCorUAQW4JMbduf2xolCCCQKYEgtgSZTA2fwSKAQL0FgtgSZOp9RtpHAIHUC4RTSjLUfH5W0gwLAghclwAHFQhIPJEksSVIsimpoBqbCCCAwPUJSDyRFEQYycimpOtrjaMQQACBAgGJJ5IktgRJNiUVVGMTAQQQuD4BiSeSgggjGdmUdH2tcVSjBTgfAk0vIPFEksSWIMmmpKbvOB1EAIFmFwinlGSyWmslKQg0ZBBAAIFqBCSe2KRdI9osBBmHwRoBBGogoLUJKVpr15Y2iylxmyXX7EAAAQQqE9DahBSttauuzWJK3CZrBBBAoEoBrU1I0Vq7drRZTInbZI0AAghUKaC1CSlaa9eONospcZusEciGAKOsi0A4pSTN5+3y8W8/JiGAAALVC9iIkpfYEiRXUn3LtIAAAgiIgAspQYSRjCuRXSQEEECgegEXUiS2BMmVVN8yLVQgwGtSBNIv4EJKEGEk40r4AUEAAQRqIuBCisSWILmSmjROIwggkGWB2JRSEGLIpFvgwt88uu/0VLrHyOgWToAzI1BO4Drijxzy6N9cKNco+xBAAIHSAsSQ0jbsQQABBBBAAIGUCJgHnudONf6NnpTwMQwEEEAAgWsRCKeUtNbeZPXMxx+TUi0wM6tm81xlBOoukLeLxBYXlCRjC/JEmCwLXEf8kUPU7EyW0Rh7KQEXUiS2EGRKEZUpz84uYkh2rnXNR0qQqTkpDSKAQFSAIBPVIF+lgH3g4bV23d/lqPIyNfhwgkyDwTkdAs0rMFPj8BhOKbm3Y1gjUHOBqff6dx6Y/1NRFVarefdoEAEEEEAAAQQQQAABBBCoRqBer2Wq6RPHIoBAlgSWfm3//sfWttohE5EsAysEEEAAgXoJxKaU3PT1DEvaBfKzSuXzDRtlXs43Ozvv+fKVVWtYtzlR9QJ5u0Sjly2Y916o/sxN0AJdKCEgP+jXGn/MIbPcNiVAs12ctwtBJtt3wfyjJ4bMb0SNEgI2xhT5q5AlqlOcaYG8xJoKXvJk2ojBzxHI24UnmTkwFFQrkCciVUt4rcc3af28XQgyTXp56BYCSRYIp5Q0SyoFrpx5Yc/u3S69eOaKG6P8e6LVlbMveOV73rzoyu06Ur5791ujtkxffCvMS4nZDFrTkb3x02k9febF3fsHxtX4if3SB3dItI536mLV5DSktAjIHScpLaNhHNUJmFuhkvjzgh+wtJZDlNJmMbHihbPTkTAVC1+mCl+ZFFB2yeTQGfQcgYtv+o83u3e7B4/47REJIC+cueIfXuThxOwylaURr00CjjHJ5pe9idy/RNkEyO6oiwYBUygvbUxyjyvm+ST+kseUvBV5iWUO8V9YmXw8sMhrq9jjjezNLnlGR06QyeiFLz1sCQve88yLZy7KWzdeWCgXW8whJs6YOvGIVPo07MmMAEEmM5e6goF6L23se8VebNHm4cQ82NhCE0lcOyaexB5RvHdxI+8Du4ratuC35pfxPc0C4ZSSxJfZfF5SEmbI6GOFAiNH95/Ibenre9qkB5bNusPys2rsxP7v5++15Vt61OBrR0fcrgtHd+070fqArd/39I7eicO77K47lnWrwQ8uuEozIx8MKjX2wfCE2zabPcv+ZGam8HQTM7f92fa+HevbVXvvDmlz+5/dOjMzMTzR+Zjpj5z9gW536iLVXNOsEy0g8USSxJYgyaakRA+KzlcpME/8+aDTxAoJF1taT+w/4mKOHKJm3aeU8rOx8NVnYsjzp71QVGXPODyBAhJPJAURRjKyKSmBQ6HLtRGYOH1g1+GJXu8xY0dvq3nyicSQGfOcUyzOFHs4sV2Sg8dO7PtgmTy09D39lTtsGavsCEg8kSSxJUiyKSk7Aox0Zk4QKBZG5r6WMU8s3sOLQ5R2gm3JxwOLLYi8OpO99iWYO5T1zMxMWhEknkgKIoxkZFNSWsfLuCoUkDjz2mC397bMV2cHToypWfNIMyMhaVYFscS0JuHD35as3Tc3IpmKfGVTQOKJJIktQZJNSdnUYNQiUPTlksScom8Fu5gTeQM5fAcm/i6xNDzy7omxnrXmXV/ZIGVBIJxSSvPEWWbHdmVqQrW3LvLGv2TNmlabNf+WdD/wDW9r6Zd629XklPkA05Wzp4ZUz5Z7l9hqWreu+Vpv+9C7Z2Xfkq4eNTTi/le7ixcG23t7u8eHR2WHzETLWXo65ZgSp9P2IwbaX1rX3OudWeslndLqBdeqjlfTLCkSMLec4v/tTdEVrXgocysqWUrGn/ber/nhY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ClJZnY2Xxpy+PDD27eHqf7zMMOHI6czp67/KUuPvj57Jk/2xcfYx5RRfaRpFQEEEEAAgdQIMBAEEEAAAQQQQAABBBBAAAEEEEi/QF1HWDilVNuT2ZmPl9RDhw4d9NKTm9R4Az4vs3jTk/4ZDz206tzL9f1AkRlm4z8GFB3jwb29uZKXrmvroUN7vP0L09WSXWMHAggggAACCCCAAAIIIIBAKEAOAQQQQAABBBBAAIFmFvhnAAAA///PuFt/AAAABklEQVQDAMs0wdkZosNaAAAAAElFTkSuQmCC"&gt;&lt;/p&gt;

&lt;p&gt;In my main actual code, the output was so large that it hanged Maple UI when stepping into the debugger and hitting that line.&amp;nbsp; I had to kill Maple from task manager as Java UI got stuck due to large output.&lt;/p&gt;

&lt;p&gt;Why is it showing value of P when I have : at the end? Is there an option to turn automatic display of variables in debugger as one steps in?&lt;/p&gt;
</description>
      <guid>243651</guid>
      <pubDate>Sun, 21 Jun 2026 05:08:54 Z</pubDate>
      <itunes:author>nm</itunes:author>
      <author>nm</author>
    </item>
    <item>
      <title>Calculation of the integral and Green&amp;#39;s integral theorem</title>
      <link>http://www.mapleprimes.com/questions/243649-Calculation-Of-The-Integral-And-Green39s?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;The integral shown in the attached file &amp;quot;test&amp;quot; was posted on another forum for calculation. I unsuccessfully attempted to apply Green&amp;#39;s theorem in Maple and&amp;mdash;as befits a Maple beginner&amp;mdash;failed. Does Maple offer a sequence of commands to carry this out? I would appreciate some advice. If this is possible, I would then tackle the line integral using the residue theorem.&lt;/p&gt;

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</itunes:summary>
      <description>&lt;p&gt;The integral shown in the attached file &amp;quot;test&amp;quot; was posted on another forum for calculation. I unsuccessfully attempted to apply Green&amp;#39;s theorem in Maple and&amp;mdash;as befits a Maple beginner&amp;mdash;failed. Does Maple offer a sequence of commands to carry this out? I would appreciate some advice. If this is possible, I would then tackle the line integral using the residue theorem.&lt;/p&gt;

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</description>
      <guid>243649</guid>
      <pubDate>Sat, 20 Jun 2026 14:37:16 Z</pubDate>
      <itunes:author>Alfred_F</itunes:author>
      <author>Alfred_F</author>
    </item>
    <item>
      <title>plot3d fails with &amp;quot;invalid input: expects numeric&amp;quot;</title>
      <link>http://www.mapleprimes.com/questions/243646-Plot3d-Fails-With-quotinvalid-Input?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;I am implementing the Fokas (unified transform) method for the heat equation on a finite interval&amp;nbsp;&lt;code&gt;[0,1]&lt;/code&gt;. The solution is expressed as a contour integral in the complex&amp;nbsp;&lt;code&gt;k&lt;/code&gt;-plane and I evaluate it numerically in Maple.&lt;/p&gt;

&lt;p&gt;When I call&amp;nbsp;&lt;code&gt;plot3d&lt;/code&gt;&amp;nbsp;I get the following error, even though&amp;nbsp;&lt;code&gt;approx_u(0.5, 0.1)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Is there a way to make&amp;nbsp;&lt;code&gt;plot3d&lt;/code&gt;&amp;nbsp;work?&amp;nbsp;Any help appreciated.&lt;/p&gt;

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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="V := exp(-k^2*t)*((-4*k*(sin(k*x)/exp(1)+sin(k*(1-x)))*(k^2-1))*(1/((k^2+1)*(k^4+1))))/((2*Pi)*(exp(I*k)-exp(-I*k)))" height="65" src="/view.aspx?sf=243646_question/8b59d5202052dfccdf27573c613869e6.gif" style="vertical-align:-20px" width="531"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="integrand1 := Re((eval(V, k = k1(r)))*dk1(r)-(eval(V, k = k2(r)))*dk2(r)); integrand3 := Re((eval(V, k = k3(s)))*dk3(s)); integrand2 := simplify(evalc(integrand1)); integrand4 := simplify(evalc(integrand3))" height="77" src="/view.aspx?sf=243646_question/61962cecbecb9536e630c9aae4f8ea66.gif" style="vertical-align:-60px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="approx_u := proc (x, t) local temp1, temp2; temp1 := Int(eval(integrand2, [:-x = x, :-t = t]), r = 0 .. infinity, method = _d01amc); temp2 := Int(eval(integrand4, [:-x = x, :-t = t]), s = -L .. L, method = _d01ajc); evalf(temp1+temp2) end proc" height="148" src="/view.aspx?sf=243646_question/0c75087a3653abc2d81311b0c8cbf19e.gif" style="vertical-align:-131px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="forget(`evalf/int`); forget(evalf); CodeTools:-Usage(plot3d(approx_u, 0 .. 1, 0 .. 2*Pi, grid = [10, 10], axes = boxed, labels = [&amp;quot;x&amp;quot;, &amp;quot;t&amp;quot;, &amp;quot;u(x,t)&amp;quot;], title = &amp;quot;Fokas Method of solution&amp;quot;, shading = zhue))" height="76" src="/view.aspx?sf=243646_question/0c99cb7937771138ff6668ddafe384cc.gif" style="vertical-align:-59px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;a href="http://www.maplesoft.com/support/help/errors/view.aspx?path=Warning,%20unable%20to%20evaluate%20the%20function%20to%20numeric%20values%20in%20the%20region%3B%20see%20the%20plotting%20command's%20help%20page%20to%20ensure%20the%20calling%20sequence%20is%20correct"&gt;&lt;span style="color:#0000ff;font-size: 100%;font-family: monospace,monospace;font-weight:normal;font-style:normal;"&gt;&lt;u&gt;Warning, unable to evaluate the function to numeric values in the region; see the plotting command&amp;#39;s help page to ensure the calling sequence is correct&lt;/u&gt;&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;
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&lt;p&gt;&lt;a href="/view.aspx?sf=243646_question/heat_equation_on_finte_interval.mw"&gt;Download heat_equation_on_finte_interval.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;I am implementing the Fokas (unified transform) method for the heat equation on a finite interval&amp;nbsp;&lt;code&gt;[0,1]&lt;/code&gt;. The solution is expressed as a contour integral in the complex&amp;nbsp;&lt;code&gt;k&lt;/code&gt;-plane and I evaluate it numerically in Maple.&lt;/p&gt;

&lt;p&gt;When I call&amp;nbsp;&lt;code&gt;plot3d&lt;/code&gt;&amp;nbsp;I get the following error, even though&amp;nbsp;&lt;code&gt;approx_u(0.5, 0.1)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Is there a way to make&amp;nbsp;&lt;code&gt;plot3d&lt;/code&gt;&amp;nbsp;work?&amp;nbsp;Any help appreciated.&lt;/p&gt;

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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="L := 3/4; k1 := proc (r) options operator, arrow; L+(3/4)*I+r*exp(((1/6)*I)*Pi) end proc; k2 := proc (r) options operator, arrow; -L+(3/4)*I+r*exp(((5/6)*I)*Pi) end proc; k3 := proc (s) options operator, arrow; s+(3/4)*I end proc; dk1 := D(k1); dk2 := D(k2); dk3 := D(k3)" height="165" src="/view.aspx?sf=243646_question/79eeead87ec24c54c8455789974656ef.gif" style="vertical-align:-148px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="integrand1 := Re((eval(V, k = k1(r)))*dk1(r)-(eval(V, k = k2(r)))*dk2(r)); integrand3 := Re((eval(V, k = k3(s)))*dk3(s)); integrand2 := simplify(evalc(integrand1)); integrand4 := simplify(evalc(integrand3))" height="77" src="/view.aspx?sf=243646_question/61962cecbecb9536e630c9aae4f8ea66.gif" style="vertical-align:-60px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="approx_u := proc (x, t) local temp1, temp2; temp1 := Int(eval(integrand2, [:-x = x, :-t = t]), r = 0 .. infinity, method = _d01amc); temp2 := Int(eval(integrand4, [:-x = x, :-t = t]), s = -L .. L, method = _d01ajc); evalf(temp1+temp2) end proc" height="148" src="/view.aspx?sf=243646_question/0c75087a3653abc2d81311b0c8cbf19e.gif" style="vertical-align:-131px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="forget(`evalf/int`); forget(evalf); CodeTools:-Usage(plot3d(approx_u, 0 .. 1, 0 .. 2*Pi, grid = [10, 10], axes = boxed, labels = [&amp;quot;x&amp;quot;, &amp;quot;t&amp;quot;, &amp;quot;u(x,t)&amp;quot;], title = &amp;quot;Fokas Method of solution&amp;quot;, shading = zhue))" height="76" src="/view.aspx?sf=243646_question/0c99cb7937771138ff6668ddafe384cc.gif" style="vertical-align:-59px" width="768"&gt;&lt;/p&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;a href="http://www.maplesoft.com/support/help/errors/view.aspx?path=Warning,%20unable%20to%20evaluate%20the%20function%20to%20numeric%20values%20in%20the%20region%3B%20see%20the%20plotting%20command's%20help%20page%20to%20ensure%20the%20calling%20sequence%20is%20correct"&gt;&lt;span style="color:#0000ff;font-size: 100%;font-family: monospace,monospace;font-weight:normal;font-style:normal;"&gt;&lt;u&gt;Warning, unable to evaluate the function to numeric values in the region; see the plotting command&amp;#39;s help page to ensure the calling sequence is correct&lt;/u&gt;&lt;/span&gt;&lt;/a&gt;&lt;/p&gt;
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&lt;p&gt;&lt;a href="/view.aspx?sf=243646_question/heat_equation_on_finte_interval.mw"&gt;Download heat_equation_on_finte_interval.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>243646</guid>
      <pubDate>Fri, 19 Jun 2026 22:28:52 Z</pubDate>
      <itunes:author>Paras31</itunes:author>
      <author>Paras31</author>
    </item>
    <item>
      <title>Can dsolve obtain this AI solution to this first order ode?</title>
      <link>http://www.mapleprimes.com/questions/243645-Can-Dsolve-Obtain-This-AI-Solution-To?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;Any one knows a trick to help Maple obtain this much simpler solution to this ode obtained using AI?&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode := 4*(-1 + sqrt(1 - 1/x^2)*x^2)*sec(4 + 4*x + 4*arccsc(x))^2 - sqrt(1 - 1/x^2)*x^2*diff(f(x), x) = 0&lt;/pre&gt;

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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;ode:= 4*(-1 + sqrt(1 - 1/x^2)*x^2)*sec(4 + 4*x + 4*arccsc(x))^2 - sqrt(1 - 1/x^2)*x^2*diff(f(x), x) = 0;&lt;/span&gt;&lt;/p&gt;
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			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="4*(-1+(1-1/x^2)^(1/2)*x^2)*sec(4+4*x+4*arccsc(x))^2-(1-1/x^2)^(1/2)*x^2*(diff(f(x), x)) = 0" height="50" src="/view.aspx?sf=243645_question/e96eff781bc0879738c297a419a9396a.gif" style="vertical-align:-20px" width="586"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;maple_sol:=dsolve(ode);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="f(x) = c__1+Int(8*(-1+(1-1/x^2)^(1/2)*x^2)*x^6/((1-1/x^2)^(1/2)*(-8*((x^2-1)/x^2)^(1/2)*x^7*sin(8+8*x)+x^8*cos(8+8*x)+x^8+80*((x^2-1)/x^2)^(1/2)*x^5*sin(8+8*x)-32*x^6*cos(8+8*x)-192*((x^2-1)/x^2)^(1/2)*x^3*sin(8+8*x)+160*x^4*cos(8+8*x)+128*((x^2-1)/x^2)^(1/2)*x*sin(8+8*x)-256*cos(8+8*x)*x^2+128*cos(8+8*x))), x)" height="153" src="/view.aspx?sf=243645_question/d7245292a3e8aeb36e2fce35eb786721.gif" style="vertical-align:-119px" width="768"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;odetest(maple_sol,ode);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="0" height="23" src="/view.aspx?sf=243645_question/f293eba4d4e0c4dafdc71f746a7154a1.gif" style="vertical-align:-6px" width="13"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;AI_sol:=f(x)=_C1+tan(4*(1+x+arccsc(x)));&lt;/span&gt;&lt;/p&gt;
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			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="f(x) = c__1+tan(4+4*x+4*arccsc(x))" height="28" src="/view.aspx?sf=243645_question/7b19ee9ff6da0426af66c144097a9d59.gif" style="vertical-align:-11px" width="307"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;odetest(AI_sol,ode)&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="0" height="23" src="/view.aspx?sf=243645_question/ebb278e8bd7d4238737d725c6bd1ee9a.gif" style="vertical-align:-6px" width="13"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp; &lt;/span&gt;&lt;/p&gt;
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&lt;/table&gt;
&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243645_question/AI_sol.mw"&gt;Download AI_sol.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Any one knows a trick to help Maple obtain this much simpler solution to this ode obtained using AI?&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode := 4*(-1 + sqrt(1 - 1/x^2)*x^2)*sec(4 + 4*x + 4*arccsc(x))^2 - sqrt(1 - 1/x^2)*x^2*diff(f(x), x) = 0&lt;/pre&gt;

&lt;form name="worksheet_form"&gt;&lt;input name="md.ref" type="hidden" value="50A03749670412361B21F21CF743DD6F"&gt;
&lt;table align="center" width="768"&gt;
	&lt;tbody&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;ode:= 4*(-1 + sqrt(1 - 1/x^2)*x^2)*sec(4 + 4*x + 4*arccsc(x))^2 - sqrt(1 - 1/x^2)*x^2*diff(f(x), x) = 0;&lt;/span&gt;&lt;/p&gt;
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			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="4*(-1+(1-1/x^2)^(1/2)*x^2)*sec(4+4*x+4*arccsc(x))^2-(1-1/x^2)^(1/2)*x^2*(diff(f(x), x)) = 0" height="50" src="/view.aspx?sf=243645_question/e96eff781bc0879738c297a419a9396a.gif" style="vertical-align:-20px" width="586"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;maple_sol:=dsolve(ode);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="f(x) = c__1+Int(8*(-1+(1-1/x^2)^(1/2)*x^2)*x^6/((1-1/x^2)^(1/2)*(-8*((x^2-1)/x^2)^(1/2)*x^7*sin(8+8*x)+x^8*cos(8+8*x)+x^8+80*((x^2-1)/x^2)^(1/2)*x^5*sin(8+8*x)-32*x^6*cos(8+8*x)-192*((x^2-1)/x^2)^(1/2)*x^3*sin(8+8*x)+160*x^4*cos(8+8*x)+128*((x^2-1)/x^2)^(1/2)*x*sin(8+8*x)-256*cos(8+8*x)*x^2+128*cos(8+8*x))), x)" height="153" src="/view.aspx?sf=243645_question/d7245292a3e8aeb36e2fce35eb786721.gif" style="vertical-align:-119px" width="768"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;odetest(maple_sol,ode);&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="0" height="23" src="/view.aspx?sf=243645_question/f293eba4d4e0c4dafdc71f746a7154a1.gif" style="vertical-align:-6px" width="13"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;AI_sol:=f(x)=_C1+tan(4*(1+x+arccsc(x)));&lt;/span&gt;&lt;/p&gt;
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			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="f(x) = c__1+tan(4+4*x+4*arccsc(x))" height="28" src="/view.aspx?sf=243645_question/7b19ee9ff6da0426af66c144097a9d59.gif" style="vertical-align:-11px" width="307"&gt;&lt;/p&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;odetest(AI_sol,ode)&lt;/span&gt;&lt;/p&gt;
						&lt;/td&gt;
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				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="0" height="23" src="/view.aspx?sf=243645_question/ebb278e8bd7d4238737d725c6bd1ee9a.gif" style="vertical-align:-6px" width="13"&gt;&lt;/p&gt;

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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp; &lt;/span&gt;&lt;/p&gt;
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			&lt;/td&gt;
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&lt;/table&gt;
&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=243645_question/AI_sol.mw"&gt;Download AI_sol.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>243645</guid>
      <pubDate>Fri, 19 Jun 2026 16:48:27 Z</pubDate>
      <itunes:author>nm</itunes:author>
      <author>nm</author>
    </item>
    <item>
      <title>Should dsolve return this?</title>
      <link>http://www.mapleprimes.com/questions/243642-Should-Dsolve-Return-This?ref=Feed:MaplePrimes:Version Maple 2026</link>
      <itunes:summary>&lt;p&gt;given&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=2*x^(1/2)*diff(y(x),x)-y(x) = -sin(x^(1/2))-cos(x^(1/2)); 
ic:=y(infinity) = y__0; 
sol:=dsolve([ode,ic]);
&lt;/pre&gt;

&lt;p&gt;It gives&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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"&gt;&lt;/p&gt;

&lt;p&gt;This solution satisfies the ode itself. Now cos(sqrt(x)) when x=infinity is&amp;nbsp; -1..+1&lt;/p&gt;

&lt;p&gt;But IC says y(infinity)=y0&amp;nbsp; so odetest do not verify the IC and gives this&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
odetest(sol,[ode,ic]);&lt;/pre&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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"&gt;&lt;/p&gt;

&lt;p&gt;I think dsolve should not have returned a solution at all.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;What do the experts here think of this result?&lt;/p&gt;

&lt;p&gt;Maple 2026.1 on windows 10&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;given&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
ode:=2*x^(1/2)*diff(y(x),x)-y(x) = -sin(x^(1/2))-cos(x^(1/2)); 
ic:=y(infinity) = y__0; 
sol:=dsolve([ode,ic]);
&lt;/pre&gt;

&lt;p&gt;It gives&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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" /&gt;&lt;/p&gt;

&lt;p&gt;This solution satisfies the ode itself. Now cos(sqrt(x)) when x=infinity is&amp;nbsp; -1..+1&lt;/p&gt;

&lt;p&gt;But IC says y(infinity)=y0&amp;nbsp; so odetest do not verify the IC and gives this&lt;/p&gt;

&lt;pre class="prettyprint"&gt;
odetest(sol,[ode,ic]);&lt;/pre&gt;

&lt;p&gt;&lt;img src="data:image/png;base64,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" /&gt;&lt;/p&gt;

&lt;p&gt;I think dsolve should not have returned a solution at all.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;What do the experts here think of this result?&lt;/p&gt;

&lt;p&gt;Maple 2026.1 on windows 10&lt;/p&gt;
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      <guid>243642</guid>
      <pubDate>Wed, 17 Jun 2026 04:31:03 Z</pubDate>
      <itunes:author>nm</itunes:author>
      <author>nm</author>
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