From mboxrd@z Thu Jan 1 00:00:00 1970 Path: news.gmane.io!.POSTED.ciao.gmane.io!not-for-mail From: Giovanni Bono Newsgroups: gmane.emacs.help Subject: Re: Emacs as a translator's tool Date: Fri, 29 May 2020 17:02:29 +0200 Message-ID: <87tuzybx8a.fsf@cb001.local.net> References: <871rn35lqc.fsf@mbork.pl> Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="=-=-=" Injection-Info: ciao.gmane.io; posting-host="ciao.gmane.io:159.69.161.202"; logging-data="62007"; mail-complaints-to="usenet@ciao.gmane.io" User-Agent: Gnus/5.13 (Gnus v5.13) Emacs/26.3 (gnu/linux) To: help-gnu-emacs@gnu.org Cancel-Lock: sha1:AubUDgnexZ4b2Nv1wjiNa3b+7ls= Original-X-From: help-gnu-emacs-bounces+geh-help-gnu-emacs=m.gmane-mx.org@gnu.org Fri May 29 22:54:15 2020 Return-path: Envelope-to: geh-help-gnu-emacs@m.gmane-mx.org Original-Received: from lists.gnu.org ([209.51.188.17]) by ciao.gmane.io with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.92) (envelope-from ) id 1jem18-000G4u-38 for geh-help-gnu-emacs@m.gmane-mx.org; Fri, 29 May 2020 22:54:14 +0200 Original-Received: from localhost ([::1]:42750 helo=lists1p.gnu.org) by lists.gnu.org with esmtp (Exim 4.90_1) (envelope-from ) id 1jem16-0003im-UK for geh-help-gnu-emacs@m.gmane-mx.org; Fri, 29 May 2020 16:54:13 -0400 Original-Received: from eggs.gnu.org ([2001:470:142:3::10]:53348) by lists.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1jehQZ-0006EE-OF for help-gnu-emacs@gnu.org; Fri, 29 May 2020 12:00:11 -0400 Original-Received: from ciao.gmane.io ([159.69.161.202]:36856) by eggs.gnu.org with esmtps (TLS1.2:ECDHE_RSA_AES_256_GCM_SHA384:256) (Exim 4.90_1) (envelope-from ) id 1jehQT-00069t-AM for help-gnu-emacs@gnu.org; Fri, 29 May 2020 12:00:11 -0400 Original-Received: from list by ciao.gmane.io with local (Exim 4.92) (envelope-from ) id 1jehQQ-000Fsn-5o for help-gnu-emacs@gnu.org; Fri, 29 May 2020 18:00:02 +0200 X-Injected-Via-Gmane: http://gmane.org/ Received-SPF: pass client-ip=159.69.161.202; envelope-from=geh-help-gnu-emacs@m.gmane-mx.org; helo=ciao.gmane.io X-detected-operating-system: by eggs.gnu.org: First seen = 2020/05/29 10:40:03 X-ACL-Warn: Detected OS = Linux 2.2.x-3.x [generic] [fuzzy] X-Spam_score_int: -16 X-Spam_score: -1.7 X-Spam_bar: - X-Spam_report: (-1.7 / 5.0 requ) BAYES_00=-1.9, HEADER_FROM_DIFFERENT_DOMAINS=0.249, SPF_PASS=-0.001, URIBL_BLOCKED=0.001 autolearn=_AUTOLEARN X-Spam_action: no action X-Mailman-Approved-At: Fri, 29 May 2020 16:49:13 -0400 X-BeenThere: help-gnu-emacs@gnu.org X-Mailman-Version: 2.1.23 Precedence: list List-Id: Users list for the GNU Emacs text editor List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: help-gnu-emacs-bounces+geh-help-gnu-emacs=m.gmane-mx.org@gnu.org Original-Sender: "help-gnu-emacs" Xref: news.gmane.io gmane.emacs.help:123184 Archived-At: --=-=-= Content-Type: text/plain Marcin Borkowski writes: > Hi all, > > does anyone here perform translations within Emacs? Do you know of any > tools facilitating that? There exist a few CATs, or Computer Aided > Translation systems, but - AFAIK - they are all proprietary and closed > source. Emacs seems capable of implementing at least a simple CAT, but > I could not find any existing solutions for that. (I skimmed through > the answers here: > https://www.reddit.com/r/emacs/comments/a35bs2/emacs_for_translations/, > but did not find anything useful.) > > The first thing I would need is a way to highlight the "currently > translated sentence" in the other window, where I would keep the > original text, with an easy way to highlight the next/previous one - > this seems very easy to do, but did anyone actually code anything like > this? > > TIA, hello Marcin, I translated a few books, a few years ago, using Emacs as a simple CAT. Here is a screenshot of the last iteration: --=-=-= Content-Type: image/png Content-Disposition: inline; filename=20200529.png Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAB4AAAAQ4CAYAAADo08FDAAAgAElEQVR4nOzdeZyN5f/H8dc5Z86Z 3ZjFbPYQ2ZWUb6JfpUWUpSI7JYUQiSh7KKIiIUuyFKL1W3z7tpL0jUpCyDKYGQazmBmznDnn/P4Y DmOWc8+C0vv5eHg8uK/r+tyf677vc+ZyX3Pdt+nAgQMuRERERERERERERERERETkb898pRMQERER EREREREREREREZGyoQlgEREREREREREREREREZGrhCaARURERERERERERERERESuEpoAFhERERER ERERERERERG5SmgCWERERERERERERERERETkKqEJYBERERERERERERERERGRq4TXlU6guFwuF9u2 beP333+nSpUq3H777cVq73A4WLZsGbt27SI8PJzu3bsTGRkJQFJSEu+8806e+k2aNKFly5bufzud TjZu3MiePXt4/PHH88Xfvn07n3/+OQBt2rShYcOGxe2iiIiIiIiIiIiIiIiIiEiJ/K1WAGdlZdG+ fXu+/PJLKleuzA8//MDw4cMNt8/OzqZNmzbs37+fNm3aEBISwqRJk9zlsbGxvPfee5QrV879x8fH x12elpZG+/btmTRpEvPnz88Xf9++ffTo0YN69epRr149unfvzr59+0rXaRERERERERERERERERER g0wHDhxwXbghLi6ONWvWcOrUKRo1akTHjh0xmUykpaXx8ccfYzKZ+OOPP3juueeYM2cOVquVwYMH YzKZANi8eTNbt24lLi6Opk2b8uCDD+bZod1uZ8WKFezbt4/g4OA8K3ABNmzYwJYtW7BYLNSvX592 7dphsVgAGDBgAG3atKFt27bu+vfeey9r167Fz8+vyPwBZs6cya5du1i4cGGBB+Ozzz7j448/Zt68 eUUetNjYWO6//362bduWZ/s999zDc889R6tWrQD49ttvmTp1KuvXry8ynoiIiIiIiIiIiIiIiIhI WcizAviHH36gTZs2REVFcccdd7BhwwZGjhwJQGpqKpMnT+bIkSPs2rWL3r17Ex0dzeLFi92rXNeu Xcs777xDdHQ0LVq04NVXX83zSOUTJ07QvHlzDhw4wG233UZISAh9+vRxl7/zzjvMnDmTO+64g5tv vplt27bxn//8B8hdvfvTTz/lmfzNzMykbt26JCUlecwfYPXq1UyaNAmn08m+ffvIzMzMczBiYmKI iopiw4YNbNiwwR3XqEOHDnHjjTeSmppKamoqN954I4cOHSpWDBERERERERERERERERGRksrzDuBB gwaxePFirr/+egBuueUW6tWrx+TJkwGwWq08++yzfPLJJ6xYsYKuXbuybNkydu/ezbXXXkunTp3o 1KmTO15YWBjDhg2jZ8+eAPTv35927doxbtw4d52+ffu6//7rr79SrVo1mjdvjsVi4c4773SXJSQk uN+nu2/fPiZPnoyvry/79+/H4XB4zN9ms3H69Gk+//xzPvvsMyIjI9m0aROTJk2iXbt2APj7+7N3 717CwsJISkpi2LBhvP7669xxxx0eD6TD4SAnJwc/Pz/3O4O/++47cnJycDgc7lXMIiIiIiIiIiIi IiIiIiKXinsCOCsri6SkJCZNmoTZfH5hsNPpJD09HcA9ienj44Ovr697W05ODpA7CfrWW2/x7bff YrVa8fPzy7PKdu/evcyZM6fQZKZMmcKoUaNo2rQpjRo14tZbb6V79+54e3uTkZFB7dq1AejSpQsb NmwgLCyMIUOGGMrfZrPhcrnIycnh/fffB+D48eO0atWKNm3aYLFY6Nmzp3uyGqBZs2aMHDmSrVu3 ejyQZrPZHf/chPm543JhPiIiIiIiIiIiIiIiIiIil4p7AtjLywuLxcKcOXMoV65cnkoBAQEcO3bM Y7CePXvStGlTli9fjsViIT4+nvvuu89d7u3tTWJiItHR0QW29/Hx4dVXX8XhcLBt2zZWrlzJ3Xff zTfffIPNZiM5ORnIndT19vbm6NGjbN++3VD+AOXLl+fee+91b4+IiMDpdJKVleV+h/CFGjduTEZG hsd+A5hMJsqXL8/JkyfdK4CPHTtG+fLl3e8gFhERERERERERERERERG5lNxLUy0WC7fddhvTpk3D z8+PwMBAAgMDiY2NNTyB+fvvv/PAAw9gsVhwOBwsX748T3n37t3p37+/eyLX5XKxevVqd/nSpUtJ TU3FYrHQrFkznn32WeLj4wEIDg7mxx9/BGDatGn06dOH+fPnM2nSJHx9fQ3lP2jQIIYPH47T6QTg p59+wt/fHz8/P3Jycpg1a5b7cdJOp5Np06a5HydtRM+ePfM83nrs2LF5VhSLiIiIiIiIiIiIiIiI iFxKed4BPHfuXF544QVatGjBNddcw7Fjx7j99tsZM2aMoWDPPfccDzzwAA0bNiQ9PT3f5OeQIUM4 ffo0t956KzVr1uTYsWPcc8897nfkZmZm0r59eyIiIvDx8WH37t1MmzYNgHLlypGVlUVqaip33303 d999d779e8q/a9euHD16lDvvvJPIyEiOHz/OqlWrgNwVvFlZWTRv3pwqVaoQFxdHo0aNWLBggTv+ Qw89xOHDh3E4HKSkpHDTTTcBsHHjRmw2G4MGDeLJJ5/krrvuwuVyUb16dQYOHGj0XIiIiIiIiIiI iIiIiIiIlIrpwIEDros3OhwO4uPjqVixYrEfX5yRkUFqairh4eGF1nG5XMTFxREVFVXg+3HT09NJ TU0lIiIiz/7/+OMPhgwZwvLly6lQoQIOh4Nff/2VG264oVj5OxwOTpw4QWRkZL4yp9NJfHw8wcHB BT4W2ogzZ84AlLi9iIiIiIiIiIiIiIiIiEhJFDgB/Fe2detWFi5cSFpaGl5eXjz00EN53jMsIiIi IiIiIiIiIiIiIvJP9bebABYRERERERERERERERERkYLlf/6yiIiIiIiIiIiIiIiIiIj8LWkCWERE RERERERERERERETkKqEJYBERERERERERERERERGRq4TXub+0aNGiyIrp6emXPBkRERERAX9//yLL 4+LiAIiOji6ynsZvIiIiIlIQjTdFRERE/p48jeM2bdoEaAWwiIiIiIiIiIiIiIiIiMhVQxPAIiIi IiIiIiIiIiIiIiJXics6Abxi9WrWrHmfjz76iHBr/l17KpeiVe0wkTdfbnnF49fsPY2Zz9Qrs3pX i44z5zPqhrArncZfmskSQHh4yGXZ16W+/v6K1/flOL5+0bcybsYbvL30HebPvL9YbS/n+S9rl+Pn V2mPj5H2f8XrVkRERERERERERESK57LOsnZ7+GG69hqLj48PZlPxy6VoXgFhVAj1ueLx04/sYuf+ VI/1bIGhVAi2lUVqfwuBFcIJtlmudBp/aX7h3Vg8//nLsq9Lff39Fa/vy3F8204cSuSOlQx/eigj J/ynWG0v5/kva5fj51dpj4+R9ka/v0VERERERERERETkr6tEE8DewRWp36gxNatG5A9oDaR2/UbU r1MN26WYXjZZqVq7Ho0b1CHQK/8O/MIqUbdBY+rVroK1gJvw1nLRNGjcgIrlrFh8Agjw83KX+QSV J+CClVsWn3KUD7IWa/9mr3LUrt+IhvVqEx5gzVdeaLcsgYSE+BIQVZPrqpbHZLZRp2Ejoi7Iz0j/ 8sYMICwsLG89D/kXx8XxzdYgwsLCsG//jA++ii+wjbVcFA0aN6TSxccV8AsJIchmxlquIg0aN6Ri uQKOXyH5my2BhIaUz1c9JDSUAEsxZmMKiW8y+xAWFpbnA2My++XbBkV/PoAi+1fU+TVyfIq6vovq n1HFuf4KUtjnw2S2ERYWRmiwD5ishIWFERYWRvBFn6HSH5/Crz8j/fMODiHIx4LJ7Efteg2pXS20 WPGL4h8aQpDVjH9kDRo3qEtQAatIS5qf0ePrSVHf734hIYSFhdEk2MbRn49iMpsxG/zsGc3P0/Ev 7fVR1Pe3kfPjSUnzK+3nw0h7I9/fns6/x+9vEREREREREREREbksvDxXyat1/4kMbluLvTsPYAqq QmjKUnqN+i8A/hVvZ9brwyDmd9L8q1HNup+RA8axLzOnTJL1Dm3K5FfGUDEzhsPp3tStAi8PfJpN CZkANBj9Ji828OXPI3G4/CpxTcAxXhg0ht/T7ACENnyEuVO6cnznDsyR0Rw6bqORfTo9xvwCQOc5 S6g79wlG/nAcgGoPTWPqrSt4+PHvDe3fJ6wls+ePwBmzk0SnL1VrVWNOj4fYfDrbY98CogeybE51 9u1JpUb9Omz5JZZgXyvXVoylfecXcBro34UstmiGz36d0G1zGbngK0P5F0dB8QOrdmP80Lr4hFfF e99Y93E9J6RJV96c9AjxO3fgXbEyh497wQW7fui1RdzyzddYb6pJUnYQ11WzMa1XH749lekxf4t3 VZaueJkXHnqAX84eD1u5f7Fi+SgGdehImsPzNejp+PSYNp/w/4zhudV/AHD/2Pl0sL9H7xf/7Y5R 1OcDILhuZxb0Kbh/ns6vp+Pj6fou7fkvzvVXkKI+H1a/howf3xuzNQyLty/jx48H4NjGGUxedahM jo+n689I/1rNWED7rxeT0aY3QaknsQZXZsewR5hxNM1jfE96zVnCjf/bjs91AcSklaN+1Rwm9xvC lsSsUuf3enIdj8fXE0/f77cPHc09IT5E+3qRM2gk4+1OMhM/4ZmxGzzGNnL+i+rfjKNppb4+PH1/ ezo/npQmv9J+Poy09/T97en8ezq+IiIiIiIiIiIiInL5FGsCOLTJMIbeF82zPXqxMzn3pnjVxpXc 5T2nPoXz83E8sXAbYKbLqyt5fuQN9JrwY5kk+/gro/H57iW6Lt4CQO0OM5gytQubHn0bgMMfzKDT 1P3YXbn175y8jGcG16H3lB2AmWfHdeXPN55izOeHMFsr8OqaJbCz7PZf67E++O2bRrdRuRPGZmsI 5VzGJ78dWUcZ+uwkbp+3mm7Jc3h0zE6WfvIpDfytbE+3e+jfeV4+VXnujZlYv57OyOVbDOdvVGHx U/6cy6BBUPept3gu8uJWZkY/35U/5wxkzPoYzLYo3ljzFvyet1bITWk80n8wdhd0eGM1PXrW4NtZ Oz3mbz/zO4sOptGvQxUGLNsPQPWHe5KybwH7Df4CQlHxXc5M3nh6IguWT6XNlh78du2T9K19kD49 Pne39/T58NQ/I+e38Paer+/Snn+j119hivp8ZKdtZdCgrfhH9WfV3NoMGjSsRPsv6vh4uv6M9q/y /Z2YMKQXW+MzMJn9qWbLMhTfiKBGx+jcZy52F7QcsZih41vTZfCnpc4vO9Pz8fXE0/f7p2Of4VNg 4vufkDzpWWYeTTMc28j5L6p/UNrrw9j3d1Hnx5PS5Ffaz4eR9kV/fxv7+V7U8RURERERERERERGR y6dYE8D1+jYj4Ycp7sktgJhfjwK5N8vvD/fl9XXnbvY6+WLhHrpN6ACUfgLY6t+AtlH+vPpjEjVq 1ADAtH8z/tF98TYvJcvpImX3QWpd34J610TjZ/MiyGEioEYUsANbYDMaB9gY8HVuvk77Cd75I5kh Zbj/M7FnCGxyL62aJvLbzn0kZSSSXIw+5mQdBCDpeBbpB1MBJ7HZDip6W9iebi+yf+d4+dZg7IIB VP3zVXpdMDlrJH8jCovviS2wGQ0CrAz4JhYAZ3Y8S/ac5qmL6h1e94V7AmPHTyd5pHmg4fy/mb2Z vpP7YV42CqfJQv82lfh+1CZD+RmJn526nWcm/5slMyeQaqvEzMd6k5jjdMco6vPhqX+AofNbWHtP 13dZnH8j+RWltJ+P0h4fT9ef0f6d2j6brfEZALic6RzMBFvgzYaub09i3v/Unf+25VsIeqsr8Gmp 8yutS/39XhyF9a801wcYuz6LOj+elDa/sohfUkbPf2nyFxEREREREREREZGyU6wJ4OhyNtI2phZY ZrFGYjaZOJrlcG/LSkrGYmtcugzPxfepBkCLrj1pccH2bT9vx9dsIsvpouP4hXSNPMJHX23ndHoW tmwHJrM3AGZr7vtY47LP55eRkAl5XyFZqv0fXDmaxbZHad9vFCOrVCB259dMGD2Toxfss0iu3HpO pxNXTu5d9BwXnHuTYlH9O8cnpC1n3vuGsE59aVxuM7+efXypkfyNKCy+JwUd//RjZ/Idf3vK+Xgu B2CyGM4/ec9cDnuto0flANaZHuZaSwyj/jQ2xWj0+CT+soYDlhVUOrGO7xIy8sQo6vPhqX9g7PwW 1t7T9V0W599IfkUp7eejrI/Pxdef0f6d3p3/mjJ6fXuSEXfG/XdHdhxmayg2M2Q7S5dfaV3q7/fi KKx/pbk+wNj1WdT58aS0+ZVF/JIyev5Lk7+IiIiIiIiIiIiIlJ1iTQAfOZVJUL1Q4GC+Mkd2LDku FzV9LOxIz32noU9EGI6smDz1XM7cR2p6m0wF7qOwcseZvQAsmTSOPwt4pK+XT236N6/IgAcedz/y t171drQ71z5jX+42Py+2nnsncDV/uGC+zu5yYfE9f8PaO9RmeP8ATkcKHy6ayYeLwDukBiPnzeKZ e1cz9KOYAusXh6f+nZMe9ybT3v6UTUHzeWHGEzzyxGyynS5D+RtRWHxPHFmHAKju48WuM7nHv3wl P8gootGF7Q3k73JmM+ezWMY8cT2HLHcQu/5FQxMzRuMDtB4+i5Dti/hftZ5M6LiRsev2ucuK+nx4 YvT8Fpq/h+u7tOe/tPmBwc+Hywmm/F9LpT4+Hq6/4sR3FXC9l/b6PiegWgD8fDI3J98qOLKPk+0s fX4XFBZ4fD0x+v1eagbyK6h/l+v6LOz8nM+t4J9fZZHf2R2U7vPxVz//IiIiIiIiIiIiIlImzMWp vOutb6lww3BuqRrgbt6gZe7jZJ05Kaw6kkbbPi0wASazL52eqE3Clg/yxMjJOkh8toNOTSsWuI/C yu0Ze1h16DQj+t2BzZx7c91sDeam2+oB4HJl4nS5uCYkd8WTLaQeg/8v6oL2u/goPp3H+7bCagLf qBt5onpQnn0ciM8g+u7cFU1mWwTdWkQY3j9AaLPrCbbmHtLs5KOcsDtxZBqcgfTAU//O18tdgbV5 zgi2B9zB9D5NDOdvLI+C43tiT9/OxuQsHmt7LQDWwNr0q1Xe8H6N5r9/5RKCGw1iYIMQ3l7xZ5nG r3THcAbdkMDwFz/i1WEvUavPdNpWO/+I06I+H54YPb+F51/09V3a81/a/MDY5yMn8wBe3tWpH2LL s73Ux8fD9Xep4xtV9aHOBHmZATP/1685yX8sK5P8zins+Hpi9Pu9tEqa3+W6Pgs7P+78C/n5danP n9H4f4XzP2X5Kt6Zc3ux24mIiIiIiIiIiIiIccVaCpS0ez4TVgQybM679IuPwxQYScrvMxj83X4A Vo+aTqOZo1iztAtpvhF4xW1k1Ou/5Q3isjNm5lpeHDqHz5+3cvSLZ+g3c6eh8mXDxxI5cRTrVnUn LsVJeGR5DvzwJj9+sxNHVgwT125jzFvv8NDhkwQGZvDhB0d4uNX50IuHT+WFKcP48MMBJB/byerv E+jkd778t1ffwbVgCKuXduSMK5UN3x6nVoPz5UXtHyCyZQ9eGjuGE3EJEFgRc/x3jPgqtjiHuFBG +nchp+M0059+lRWLJ9J5c3dW7U72mH9xFBT/+UXLqO9vxcuvHP7m53nvPTtOewJdewwG4LXn5jJr xkssvysOAlxs/i2J5sXYp5H8s1O38O4JCx3Na/jB4OOpjcT3Cb+VV4beytyB3UmwOyFxC8Ne+ZYF r0xg+yMjOJLt8Pj5KEpxz29BPF3fpTn/ZZGfkc9HVvKXLPhva8YuWYOXw8HRDc8xeP6eMtl/Udff pY5v1PEvHcxbvpj07EDCXPt5fuA3ZZYfFH58jTD0/V5KJc3vcl2fhZ0ft0J+fl3q82c0flHH19P3 d1mdfx9/f/yyir8KWURERERERERERESMMx04cMAF0KJFiyIrpqenu/9utgZQMTqUzOTjnEjJvKim mfBKlbHlpHD0WNm/ixIgICyKUF84dfw4aRc949cnJIoIf4g7Go+9kCehWqwWHHYHN09fSc+dYxnw 9vmVohbvYCpF+nH8SByZhTxKtaj9W3yDiAwPxnXmFHEnin4fbEkY6Z8nReV/qZks/lSqFErS0aOk OUq276LzN/P86g/xmvUY439IuATxPSv681G0sji/RV3fULr+lTa/0n4+Srt/T9ffpY5flAHvfkTF KX0Ytx8qhvkQeySOnItyKIvro3Qu/fd7aVzK69PI+bnU+V3p+H/183+18ff3L7I8Li4OgOjo6CLr XTh+ExERERE5R+NNERERkb8nT+O4TZs2AcVcAXyO057GkZi0wkpJOHpp3wuYdjKewvaemRhPTGLB ZeXrPkzLiEP89mc8AZWvZ0gdP96aeiRPHUdWEjExSSXevyMjhdiYlKI7UApF9c+oovK/1FyOdI7E lO4/B4XlX7VBI+o06sjN3kfp9tPJMo9vVNGfj6KV5vwaub6hdP0r7fVX2s9Haffv6fq71PGNyDmT SMzhgsvK4vNfOpf++700Lsf1WdT58eRSn79Lf338tc+/iIiIiIiIiIiIiOT6Rz2HMSctiVod7uO2 B8qTfTqOpWMf56vErCudlpSRpm3upy6JTB08nZScy7uy+a9A1/ffW8zPP5Gaar/SaUghdH5ERERE RERERERE5O+iRI+AFhEREZFLR4/kExEREZFLSeNNERERkb8no4+ANl+OZERERERERERERERERERE 5NLTBLCIiIiIiIiIiIiIiIiIyFVCE8AiIiIiIiIiIiIiIiIiIlcJryudgIiIiIhcWmZrBZYunglA 4u6XGTJlx4WlNPy/+2nZuBY+znRiD+5h81ffEZNmB6DeAw/SyN+WJ54j+xir3v8KgNsf7EykzZJv n3s/XsPWNDu12nXixkDvPGXJf6zns58T3f+u3rwN991cD6s9iZ++WMumPUln8w6ny0N3kn70v3z0 XQIAtsCb6dQmnHdXfWwoP4Aery/grmBf7Gf20rf/JPf2cjVb07ZZBY6sX8fGxEwAQm64j3tqB7H7 4zX8cvYYXGkmSwAVQm0kJCQWWqdm72kMCFvGsBk7L2NmIiIiIpfPP3W8c+2/WnPTdTWJDAskIyme 7z9fxy9HSvbu5VuefoXrN05l9taT7m0h1Rtx+y1NqRwVgj0pjm//vY4d8Rl52kVd35oOrRrjnXWS jZ+sZutF+6/bewodTszjxX8fLlFeIiIiUva0AlhERETkameyEB4ezvRB/Xlmet4bZrcMmcOkx1uS fGQv++JSiL6hPQNvCHOXN+zUlU6tahMREeH+Ex4e7C4PDg/PU1a55h307NmTSO/cSWHfsNzymx7q Sqfb6xAREUFo4PkJ2+g7R/DGmK5kxO4lNj2YkbOW0r5KAAAW72h69epFv6GjsJpy69uC/kWP7p0M 5wewcvhAhjz/U77tQbXvpUePh3n0yXrubZ2HPUbnHj1oFph3UvlK8gvvxuL5zxdZJ/3ILnbuT71M GYmIiIhcfv/U8c5dne4kzJbNkf0HyAq4jinzlnJPpF+x43gH38bIljYW/Xwqz/Y5r42hQZQfJ2IO 4ahwEy+/tYTbw33c5SH1e7Fw0uNkH97F4cwoJry5kJuD8o6V9615j2aPjyfcqlvNIiIifxVaASwi IiLyD5Fjt2O3O93/NltDeO6ea1jQsz0fn8hdAcuaFVgvunFz8peVzJq3p8CYa+fOvuBfZvrPX0X8 1y/z6anceL8teZPfgI6NWnDv9veYNXtXnvaDn2zJ7rkDWPTZEQBiqzdgyKjb+HDAp+46f2TX5Ina wcz+I6nAHIrKD8Bht5N9Qb8vlHlyLa4mj2E1bQP/ptzlXE+C/f68lUxWql57LcE2B/t37yU1J28s 7+AQfDJSOJ3tzbXX1YT0WPYcOnVRnYrUqlKBzOR4/ow5nqfML6wS1aLCMGUnsnfvYeyus7s12wgN KYdfsA+YrISF5U7MOzJTSDq7OtlsDSIkyIp9+2d8YE8p9BiIiIiIXEpmr3LUqlMdb1cmx2IOkHDx k1SKGE9Zg0IJ8cl7i9KRlcTJ5Ozc2AbGO2ZrILVqX4M1J4W9ew+RfcFwzS8kBGtaMmd8oqhzTSiJ B3YTe9p4flfanOEjL/jX+5xqsJbOvWqw/qUdhbYpSJOn+hC/fipnnK4824d360Z86rnjsYacFR/S s18tvnoxN36HZx/g6EfPs3DtLuDf5Nywiieeqs+WyT+7Y9jTf2NJfADP3FGRZ9cfKUEvRUREpKxp AlhERETkH8pkLofVbMLnohtu9kImSz2pcu9Y2lU4Qt9B3xiqb/WrR5MAG69vOeHetnvNYQKmPAyc nwBeP/tHHh96J7OfWFOivIridKSzcG8gPSsF8OVNPTm4cjqBT56fAPYObcrkV8ZQMTOGw+ne1K0C Lw98mk0Jme46rWYsoP3Xi8lo05ug1JNYgyuzY9gjzDiaBkDr/hMZ3LYWe3cewBRUhdCUpfQa9V8A Gox+kxcb+PLnkThcfpW4JuAYLwwaw+9pdqx+DRk/vjdmaxgWb1/Gjx8PwLGNM5i86hAAgVW7MX5o XXzCq+K9byw9xvxS5sdIREREpCg+YS2ZPX8EzpidJDp9qVqrGnN6PMTm07kTuJ7GU1UeGMwzzcPd 8QIqV4Vfx9Dj+dxxjafxjn/F25n1+jCI+Z00/2pUs+5n5IBx7MvMAeCh1xZxyzdfY72pJknZQVxX zca0Xn349uwvLBoZ75WFgZOmEZG6lrEv/1SKKGbCrBayTmYVr5nJyhM3VuCLR/M/ovn85O/Zf2c5 wJT7+B2T2Y8OFfz45JMYd/m2j2J59LF7gZ/ztPtx5SF69bsL1i8qXm4iIiJySWgCWEREROQfypF1 iGW/JdJ77pvU/OI7ftv5Gz9u2sqJLEeeeqGNOzN4cLL731mJG5i/PO+KW2tAA6YNbMaqZ7uRYHAC 2eJbB4A/M3Lc27JPxmGx3Uiwl5m0s9tO7XEUTuIAACAASURBVJjNqagVNAv4iF0FxDGSX1F+X7iZ x/teR3Cdyix69BhDnzxf9vgro/H57iW6Lt4CQO0OM5gytQubHn07T4zK93diwpBebI3PwGT2p5ot 96ZcaJNhDL0vmmd79GLn2VUsVRtXcrc7/MEMOk3d7171e+fkZTwzuA69p+wgO20rgwZtxT+qP6vm 1mbQoGH5ck/5cy6DBkHdp97iuUjDXRYREREpM7Ue64Pfvml0G/U9kPuUmXKu8+M7T+Op/e+M48l3 cusGVG7Nkrn9mPfmXnd7T+OdnlOfwvn5OJ5YuA0w0+XVlTw/8gZ6TfjRXSfkpjQe6T8Yuws6vLGa Hj1r8O2snYbyKys16tenSuJXJWpb4eZHeKRZJBWuuZ7y+79k4rL9xWpvK3crUTYLnycVPaltC2pK nwhfVow9AICXby2sZhN7zthp0ukxah37hA1/pmL1q5evbdKun/AJ647NvJjsi1YZi4iIyOWnFzOI iIiI/IOtGNmHiW98iiO0Fl0GvMDba5bQsV7ed+XmnEnm5MmT5/8k579x1HnSGDK/m86yXQU/prkg JosvAFkX3B9yuXInTr0vGKW6nBm89mkcj/WsVWAcI/kV5fShxTgaP82/7J+w88z5FRBW/wa0jfLn sx+TqFGjBjVq1MC0fzP+0Q/ibTbliXFq+2y2xmeczTedg2dXnNTr24yEH153T/4CxPx61P33lN0H qdakBe0ffJiuXbtSy2EioEZUsfIXERERuZLOxJ4hsPq9tGp6HcG+XjjtiSSffYRyccZTXj7Vmfjq EL6bMZQvY9MN7dtsDeH+cF8+Wrfz7BYnXyzcQ0jDDnnqHV73hfsX7nb8dJJydQKLnV9pvTxkIE+P 3VyitjmpJzmecJwTJ04QUTGaSD9Lsdrb/OrgtJ8isYhf1DRbQxn22mgOrR3LurNPsjGZc8frmS5o 82A7HmhXF2eOHcze+do7Mv/EbPbhGm+tNxIREfkr0E9kERERkX8wlzOTLetXsWX9KkxmH+56ag4D XniMdV2mu+uk7N3AypWFr6iNbDmCLlXj6Pvst8Xat9Oe+y7cEC8Th85uM1uDcTlzSMxxge183f3v ziNqxRACPt2bL46n/DxxOTOZ8cEmrt/7aZ7tFp9qALTo2pMWF2zf9vN2fM0msi5Y2XB6dzIFiS5n I21jaqH77jh+IV0jj/DRV9s5nZ6FLduBqYAbaiIiIiJ/VQdXjmax7VHa9xvFyCoViN35NRNGz+Ro tsPweMpk9qbfjJew/vdFZn8bZ3jfFmskZpOJoxc8wSYrKRmLrXGeevaU87+M53IAptwJ1OKM90rr 2OEYz5UKkbTzC1btBFjJL8+/zbMTb6fL4PWG27twgKnw28Amsy+PTZtDjd1L6b94q3u7034MgApW M1N7PILFcYbA+nfisCcUECX3mOa4tPpXRETkr0ATwCIiIiIC5E6Efr/6Z4a0vsVwGy+fGkwZcRvv jexu+NHP59hTt5HtdHFTeW9+TstdeVu+YTT21P+R7XRhvbBu2i8sji3P4CZhxdqHUbvemZvv8dKO M7mTzUsmjePPzJz8jS7gKuTm4JFTmQTVCwUO5ivz8qlN/+YVGfDA4+w/t2K4ejva5Q9e5A07ERER kSvJ6Ujhw0Uz+XAReIfUYOS8WTxz72qGfhRjeDx168BZtMr5gh7zfijWvh3ZseS4XNT0sbAjPXc8 6RMRhiPL2GRrccZ7fxUxvybh270+YHwC2J62HbNXRyraLMRm533dCyYrncfO49aMz3h0+kdcOKLP yTzE4awcboj245NTiTiB8BYVyEr6Jt8+rH51ceakcDDr73EcRURErnZ6BLSIiIjIP9jAh28nzDd3 ctFk9uX/etxIxsm87yaz2AIICgrK8+ectmMnYv1lAZ/EOvOU+1tzh5m2gECCgoLwMZswe/sRFBRE gHfu6gBnTiJL/kzh1kFtsJlzHzvXo3sNjv7nvQJz/WrWNzR5rH6+7UXlVxr2jD2sOnSaEf3uwHb2 EYBmazA33Zb/nWeF2fXWt1S4YTi3VA04u8VMg5Y1AHC5MnG6XFwTkrvi1xZSj8H/l//xzzmZB/Dy rk79EFu+MhEREZErLbTZ9QSfHftlJx/lhN2JIzN3GtHIeKrSHU8zouUZnh252P2YZqOcOSmsOpJG 2z4tMJE7nu30RG0StnxgqH1ZjPeMmrJ8Fe/Mub1Ybcy2KO65ua47N1v56vR45BpSdm8sVpzs1P+x N8PO/RG+F5WYaPfMHB6M+p2RMz/GNzB3LB0YcH7cufCHE9R/6iF8zCYs3tE83jqaP5bnf/JPWLMm nDn2Pg4tABYREflL0FICERERkX+wiJsf4Z0+I0hLOgH+oZhP7GTmqJV56lS+bwqr78vb7u677waT lSdvCAMGsHr1gDzlO6b05plv47l79tsMij47+dn6RVa3hkMfDKb/vNxHNn/6/IvcOHMca997gDM+ wZzZ/TnD3t5XYK4pfy5gZ3Yb6l40D1pofmVg2fCxRE4cxbpV3YlLcRIeWZ4DP7zJj9/s9NwYSNo9 nwkrAhk25136xcdhCowk5fcZDP5uP46sGCau3caYt97hocMnCQzM4MMPjvBwq7wxspK/ZMF/WzN2 yRq8HA6ObniOwfNzj9/zi5ZR39+Kl185/M3P8957dpz2BLr2GFwm/RcRERHxJLJlD14aO4YTcQkQ WBFz/HeM+CrWXe5pPNWkewu8/GzMXr3O3ebEz2N5bMJ2wPN4Z/Wo6TSaOYo1S7uQ5huBV9xGRr3+ m+H8SzveM8rH3x+/rOLdijWb/eg4cBJDx9lITkonMKQ8sb9uYMzUrZ4b5+Fk3pfxjOhRmzenbHNv NVn8GXRnNaAaS9690709LW4Onfp8AsDWV17gl1kvs3pFc9L9KpC87T1GXXB+z2n9cFW2z51azLxE RETkUjEdOHDABdCiRYsiK6anp1+WhERERET+6fz9/Yssj4vLfS9adHR0kfXOjd/Mtkg+/2QpM4YN Zm/SYWLiMvLUs/oHER4WDBnJxCYU/C7bS8tMeMXKWO1JxCacLvPooVWrERXZjWmjQmnbYViJYgSE RRHqC6eOHyctu3iPugYwWwOoGB1KZvJxTqRk5inzCYkiwh/ijsYXe9WLiIiISEmU9XjT4htEZHgw rjOniDuRWmDd0o6nimYmvFJlbDkpHD1WsvHspc2vdAKCwwgJ8icz5TgJSZmeGxTA6t+Ad1eOYMCD vYv96haAsOgq2OyJxJ1Iy1fmE3ona95qQ5cHh5Nehu9NFhERkfw8jeM2bdoEaAWwiIiIyNXPmcXW rVu5rWtPrj/4Ni8tzLvC1p6eQmx6yhVKDsBJQqyx97SVRPMuvWhezsa2X/aUOEbayXjy3+oyzmlP 40hMwREyE+OJSSxFcBEREZErzJGRQmxM0ePJ0o6niuYk4WjpxpOXNr/SSUs6SVrSyVLFsKfv4IX3 fqNd/WAW/XKq2O1Pxh0utKzq3Y35cMoUTf6KiIj8hWgFsIiIiMhfTFmvyBARERERuZDGmyIiIiJ/ T0ZXAJsvRzIiIiIiIiIiIiIiIiIiInLpaQJYREREREREREREREREROQqoQlgERERERERERERERER EZGrhCaARURERERERERERERERESuEl7FbRAQPZg3X7oRgJyMg/R5fKy7rHHLe2h83TWEBQVgTz3J L5s+47sdx4wFNlmof1MrmtS7lojQQDKTj/Hjlx/z0/4UY+UGVbnpHtr9qwE2eyI/fv4+my9ob7ZF s3TRS+5/z3iiL9vT7YbilqvZmrbNKnBk/To2JmYCEHLDfdxTO4jdH6/hlzRjcYpStcNERjX/L08+ +12JY5gsAVQItZGQkFjstitWr8ZmMmOzWen3YAcS7M4S5yEiIiIiIiIiIle/v8P9pNLcL7vUQqo3 4vZbmlI5KgR7Uhzf/nsdO+IzCqxb856O3BiUw7urPi7Rvm55+hWu3ziV2VtPGo5ftW0nbinnnafe hjXvceqC81y39xQ6nJjHi/8+XKK8REREpPiKvQLYbC1PeHg4qRveZeHbH+QpG/jMUzzS8QFuu6Ul bdp3ZvT0JTz1r3CDcSvwyoSRdLn/Nq6r05D7OnZj0pzlPFI7yFC5EeH/GsRbE5+m3V0tuavtQ7ww +23ujvB1l7tykliwYAGfHTETHh6Ot9lkOHZQ7Xvp0eNhHn2ynntb52GP0blHD5oF2gzHKYpXQBgV Qn1KFcMvvBuL5z9forbdHn6Yrr3G4uPjQzEOjYiIiIiIiIiI/EP9He4nleZ+2aU257UxNIjy40TM IRwVbuLlt5Zwe3j++4O+4XcwvV9fenTvVKL9eAffxsiWNhb9fKrA8sLiX9O+C+1vrklERIT7j+2i 87xvzXs0e3w84VY9jFJERORyKfFP3aTtW/h+8y95ti2ZOoaundrR9oG29HxmCSaTmdsea24soOMM C14eRacOj/Bo3x70HfdfTGYb7Qc3MVZuQL+hd+FyZjOm+4N0G7EGs8WPXiP/5S53OTPYuHEj2+PP GI55ocyTa3E1eQyrCawBTbnLuT7fbzX6hVWiboPG1KtdBWsBg17v4BCCfCyYzH7UrteQ2tVCC92f yRJAWFhY3jgmK1Vr16NxgzoEep0/vSazjbCwMEKDfcBkJSwsjLCwMIIDrO46/qEhBFnN+EfWoHGD ugSVYFDmqX+5faxI/UaNqVk1ooBOFZz/OWavctSu34iG9WoTfkHuIiIiIiIiIiJyhXi4n1PS9iZL ICEhvgRE1eS6quUxmW3UadiIKD8vQ+0B/EJCCLKZsZarSIPGDalY7vz9JCP3ywDM1kBq129E/TrV sF3mOczh3boxbvpslq9ew5tTB7MuxYee/WrlrWTy4vGp/Vnx0u8l3k+Tp/oQv/4Nzjhd+Qs9xD++ aRmzZs1y/4nPzns/1J7+G0viA3jmjoolzk9ERESKp9iPgC7K5h9+df89I/U0AJkJBT8y5GJOx2nW fnl+Qjn5QO4jQVxOp6FyTyzeVWgZ5E3mqc/YdioLkpZxOudBytXoCHxpKIbnPqSzcG8gPSsF8OVN PTm4cjqBT97vLm8w+k1ebODLn0ficPlV4pqAY7wwaAy/X/B46FYzFtD+68VktOlNUOpJrMGV2THs EWYcTcvbH1s0w2e/Tui2uYxc8BUA3qFNmfzKGCpmxnA43Zu6VeDlgU+zKSETq19Dxo/vjdkahsXb l/HjxwNwbOMMJq86BECvOUu48X/b8bkugJi0ctSvmsPkfkPYkphlqP9G+te6/0QGt63F3p0HMAVV ITRlKb1G/ddj/gA+YS2ZPX8EzpidJDp9qVqrGnN6PMTm09nFO1EiIiIiIiIiIlImPN3PKU37gOiB LJtTnX17UqlRvw5bfokl2NfKtRVjad/5BZwG9v/Qa4u45Zuvsd5Uk6TsIK6rZmNarz58e8rY/TL/ ircz6/VhEPM7af7VqGbdz8gB49iXmVOs4zRw0jQiUtcy9uWfitUuPjXva+XisxxgyrvqomrbcTTY NYslx1vTt1jRzzJZeeLGCnzxaMGPaPYU3+ofTr0GAWQmxrI/tuDHaP+48hC9+t0F6xeVJEMREREp pjKdAAao3nkqLz5Qg/Ihgez/aT2zpvyvBFHMdBjZHoANb/xWgvL8vPzqAmDP3EvV29pT6/jXxGQ5 aOBfE6sJ7AX8cltJ/L5wM4/3vY7gOpVZ9Ogxhj55vuzwBzPoNHW/e193Tl7GM4Pr0HvKjjwxKt/f iQlDerE1PgOT2Z9qtrwTsF4+VXnujZlYv57OyOVb3Nsff2U0Pt+9RNfFudtqd5jBlKld2PTo22Sn bWXQoK34R/Vn1dzaDBo0rMD8gxodo3Ofudhd0HLEYoaOb02XwZ8a6run/oU2GcbQ+6J5tkcvdibn TtpWbVzJUP4AtR7rg9++aXQb9T0AZmsI5VzFG2yLiIiIiIiIiEjZ8XQ/p7TtHVlHGfrsJG6ft5pu yXN4dMxOln7yKQ38rWxPtxvaf8hNaTzSfzB2F3R4YzU9etbg21k7Dd0v6zn1KZyfj+OJhdsAM11e XcnzI2+g14Qfi3WcatSvT5XEr4rV5mK2oKb0ifBlxdgD7m1W//pM7h3OyEf+B1GtSxa33K1E2Sx8 npR/0t5I/Ii7n2LIv+xEVYoiftsHDBu3gDRH3putSbt+wiesOzbzYrILWmUsIiIiZarMJ4Bz0k9x 7Jg/poAAKteuR/3qAezbmVSsGLf2n0GvBqHsXDuRJX8kF7u8ICaTN5D7mOe7ezxG80MxHDk72PA2 m7A7ymbgcfrQYhyNF/Ov1E+YcSbvb+il7D5IretbUO+aaPxsXgQ5TATUiALyTgCf2j6brfEZZ/NN 5+AFYy8v3xqMXTCAqn++Sq8LJn+t/g1oG+XPqz8mUaNGjdw+79+Mf3RfvM1LyTI4sIp5/1P3BO62 5VsIeqsrYGwC2FP/6vVtRsIPU9yTvwAxvx41nP+Z2DMENrmXVk0T+W3nPpIyEjF29kVERERERERE pKyV9n6Up/YAOVkHAUg6nkX6wVTASWy2g4reFnZRx9D+D6/7wn2/a8dPJ3mkeaCh/pmtIdwf7svr 63ae3eLki4V76DahA1C8CeCXhwzE6ij4/brGcgll2GujObR2LOsueFJgxwmj+eON4cRlOyhXwtg2 vzo47adItOd/0qKn+D9NfJIOh3OfAOlToQGvLJzKlIe/YPC7B/LUc2T+idnswzXeXvyRYS8gkoiI iJSlMp8APvLpDIZ9Clb/GixdNYe+457ig4cnGm7fqMsEnu9Yj32fzWL4gu+LXV4YZ04CAGavUBb0 6cJCl505H5lwOs6QXkaTvwAuZyYzPtjE9XvzT5p2HL+QrpFH+Oir7ZxOz8KW7cBk9s5X7/Tuwqc1 fULacua9bwjr1JfG5Tbz69nHH1t8qgHQomtPWlxQf9vP2/E1mwxPAGfEnX//sSM7DrM1FJsZsg08 adtT/6LL2UjbmFpgWyP5H1w5msW2R2nfbxQjq1QgdufXTBg9k6PZDkN9ExERERERERGRslPa+1Ge 2jsAXLn3fZxOJ66c3Hg5LrAWY//2lPOLEVwOwGQx1j9rJGaTiaNZ5+89ZSUlY7E1NtT+QscOxxS7 zTkmsy+PTZtDjd1L6b94q3t7QKXH6FErmRfPVOXmm6viGxaMyeTNzTffzIFt/yOhgAndgrhwgCn/ bWIj8dMOn3/9X+aJHcz95hgT774BLpoAhtxjnuPS6l8REZHLocwngM+xp+/nUFYOTfzrG25Tq+0z vNT7JvZ89ipDX1vPxcMBT+VF5pP2C6k5TnzK34bJtRaTd1Wqe3uRnfR1seIYseuduey6aJuXT236 N6/IgAceZ//Zd4TUq96OdgW0dxUxOE6Pe5Npb3/KpqD5vDDjCR55YjbZTheOM3sBWDJpHH8W9Q4S l7PAAd05AdUC4OfcgZuXbxUc2cfzTP66nLmxvS9614iR/h05lUlQvVDgYL79Gsnf6Ujhw0Uz+XAR eIfUYOS8WTxz72qGflTyAbSIiIiIiIiIiJSM0ftRhd1P8tTe0zpdw/fDPCnkfpkjO5Ycl4uaPhZ2 pOeuWvWJCMORdRnvRZmsdB47j1szPuPR6R+RZ0rXlMPvu0/zQPvcV+V5+URjsvjRvn17Ptz1Mwn2 7AJDXsyeth2zV0cq2izEXrjQogTxzVYzuPKv8LX61cWZk8LBLL3OTURE5HIwl1Ugn9C2zJ8+jv6P 9qBTh048Ofo1bgiwcebY58bah9zHa4PuxOVM57h/E0aNHs3o0aMZPuBfhso9cTkzmb8jCavvtUzu 35nez4zDbDIR88kHJe5zcbhcmThdLq4JyV0Rawupx+D/iypBnNyB1eY5I9gecAfT+zQBwJ6xh1WH TjOi3x3YzLmDabM1mJtuq5enfU7mAby8q1M/xFZg/KoPdSbIywyY+b9+zUn+Y1ne9lkHic920Klp xWL3b9db31LhhuHcUjXg7BYzDVrWMJx/aLPrCbbmXrLZyUc5YXfiyDT2m4wiIiIiIiIiIlK2DN+P KuR+ktH2pd2/J4XdL3PmpLDqSBpt+7TARO5K3E5P1CZhS/HvJ05Zvop35txezFYm2j0zhwejfmfk zI/xDQwiKCiIwIDcPNOOvM2oUaPcfybO2okzJ5FRo0ax5bSxyV+A7NT/sTfDzv0Rvnm2e4pvtoZw Z9OaWM/O6wdVu5khLSLYt3Zbvn2ENWvCmWPvU4YPYhQREZEilNkKYKc9Ea+K19Ox4fkJ2cQDPzLz hXcNtbd4V8JiMoElgJatWrm3px8/yCtzN3ssN+KbiRO49dVJ3NSxL02BI1s/ZMyq/KtRLwVHVgwT 125jzFvv8NDhkwQGZvDhB0d4uJXntgVxOk4z/elXWbF4Ip03d2fV7mSWDR9L5MRRrFvVnbgUJ+GR 5Tnww5v8+M1Od7us5C9Z8N/WjF2yBi+Hg6MbnmPw/D3u8uNfOpi3fDHp2YGEufbz/MBv8u7YZWfM zLW8OHQOnz9v5egXz9Bv5k5D/UvaPZ8JKwIZNudd+sXHYQqMJOX3GQz+bj+Ax/wjW/bgpbFjOBGX AIEVMcd/x4ivYkt2AEVEREREREREpNSM3I8q7H6S4fal3b8HRd0vWz1qOo1mjmLN0i6k+UbgFbeR Ua//ZvwAneXj749fVvFuxZos/gy6sxpQjSXv3unenhY3h059Pil2DoVzMu/LeEb0qM2bU/JP3haa n8mfR8e+xnAvB8mnswkO8uHX/yzmhX8fyVe39cNV2T53ahnmLCIiIkUxHThwwAXQokWLIiump6cD UK7qWNYsuIXY9av5T2ws761en6deuZAKlC/njz31JPGn0i5R2qVhIrxiZaz2JGIT8r6P1mwJokvn +6hwc3va1A7ihQfb8b9U478tZ4RPSBQR/hB3NB77JfqNt4CwKEJ94dTx46QZeXnvWQPe/YiKU/ow bj9UDPMh9kgcOcXM0Uj/zNYAKkaHkpl8nBMpmcXK3+IbRGR4MK4zp4g7UfD7hEVERP7u/P39iyyP i4sDIDo6ush658ZvIiIiIiIXuhTjzZLej/qrtC+amfBKlbHlpHD0WHIZx/5rsPo34N2VIxjwYG/D 7w4GwGwjPDICP5uZ1IRYTp3J/4hnn9A7WfNWG7o8OJx0D++FFhERkaJ5Gsdt2rQJKMEKYHvGfrZu 9Yawa6hbzidf+enEE5xOPFHcsJeRi4TYwwUXmW3Uq1cPUvexdSukFGewY1BmYjwxiWUeNo+0k/GU Zuo950wiMYUcIk+M9M9pT+NITOEZFpW/IyOF2JiUkiUnIiIiIiIiIiKXRGnvR13p9kVzknD0Mr73 9wqwp+/ghfd+o139YBb9csp4Q2c2CXH5V/xeqOrdjflwyhRN/oqIiFxGxZ4AzkhYwZgxlyKVK89p P8GYq7VzBsT8/BOpqfYrnYaIiIiIiIiIiIhcZrvfncHuSxB3z8oZ7PFcTURERMpQmb0DWP7+/j19 8pVOQURERERERERERERERERKwXylExARERERERERERERERERkbKhCWARERERERERERERERERkauE JoBFRERERERE5Opi8qLVXXdT2896pTMRERERERG57PQOYBEREZGrnNlagaWLZwKQuPtlhkzZ4S6r 98CDNPK3AZCTmc7R/dvZvP1QvhjtHn4En6zfWfPR+bZmr2C6PHwvAC6Xg/Sk4/zy/fccSbUban+O T1hd2rVtRdUKPpyK289Xn3xOzGl7mcQPaXQv99QLzlf/o/feJd3pAqB68zbcd3M9rPYkfvpiLZv2 JOWp2+P1BdwV7Iv9zF769p+Up6xWu07cGOidZ1vyH+v57OdEAFasXo3NZMZms9LvwQ4k2J35crkc avaexoCwZQybsfOK7L8wj7y6gMqLnuPlHaeKrFea/E2WACqE2khISCxReWn3X5YKy+Ovkt/fkZHz X1Z0/i4zVw4Hcq5l5qxr6fbkHLLPfucXl1/0rYwY1oXqFQLJOvU+/Yd9XMaJivz9GR3v6PuuZK79 V2tuuq4mkWGBZCTF8/3n6/jlSHqJYt3y9Ctcv3Eqs7eezLPdO7QuD9zfiiphfqTEH+Sr9z9mf2aO u7xu7yl0ODGPF/99uFR9ERERkctHK4BFRERErnYmC+Hh4Uwf1J9npue94dawU1c63HoNwcHBVKzR iCEvvsncEffkqWMLasmAXp3o2280ARaTe7vZK4RevXpxfVQFQkLCuaF1b+avXEzrKD9D7QECqt7D 4ren0yQ4i31/xOATcSPTZtxSZvFDb7ifrp1bEBERkeePlym3XvSdI3hjTFcyYvcSmx7MyFlLaV8l IE+MlcMHMuT5nwgPzz+R7BsWTkREBDc91JVOt9chIiKC0ECbu7zbww/TtddYfHx8MJvyNb9s0o/s Yuf+1CuXQCECKoQT7O35vySlyd8vvBuL5z9f4vLS7r8s2QJDqRBsy7f9r5Lf35GR819WdP4uvyNf zWbKpmhe6nN9iWO0nTiUyB0rGf70UEZO+E8ZZidy9TA63tH3Xcnc1elOwmzZHNl/gKyA65gybyn3 RPp5bngR7+DbGNnSxqKf8/7inU/oLbz19gyaR1s4fDAWc1h9bg3K+/Nq35r3aPb4eMKtupUsIiLy d6EVwCIiIiL/EDl2O/YCVmQkbl/DG/P2ABDy2ZO8O/Nprn/zS35Oy11pG/V/nUjZ/yZ7oobSu3Ig cw6dztN+45L5fJSYCZjoteR9+o5syhdDv3OXF9X+yalPkvbZOEbP3Xp2yzqWVQ4os/gA9tM/MmvW 0gKPyeAnW7J77gAWfXYEgNjqDRgy6jY+HPCpu47Dbie7kJUsvy15k9+Ajo1acO/295g1e1eB9UrK PzQEr9PJ5IRWp1YFbw7+8QcpF+XiHRyCT0YKp7O9ufa6mpAey55DuTf2zNYgQoKs2Lf/P3v3HR5V lT9+/D2TTEknhRASegwdldXV1UVweTMDHAAAIABJREFUdd1dXVSKgqAIqAgriIgoCEhTARUBBVFA ioIFrKz8LF9XsSCigooYEaSF9AAppM9kZn5/JAQCmXvvzJ2ZhPB5PY/Pg7lzzj3nc9otc+98yHv2 ojrpDEERREdVYbMk0dp8jN/Ty+jUvQtF+1PJLjv1xIfRFEFKpw6YqorYt+8wNg8fYtaSPiwhmZR4 K0d+30P+aR9QKv+pipho27Ej0WYHB/bso7iqOr3BaCY2JpLQaCsYTMTFxQHgqCiioMSuul1t/8ag CKKjDBzPP72/GYiLi+VE/vHapw31xs8U2ZLOHZpTcGjPWds0xUeBNaoZwWUnKKnpU0HWSCIs5RQW Vdc/NCYGU0khZdaWdO4QS/7BPWSeOPsJ/Ppo7V/u2u8kpf596jNJpLRpTkVhNvvTcutsC41rRbuW cRhs+ezbdwR7zUOgWtofGnf7ARiDI0np3B6Lq4KctIPknVZ2tfY9SSl+qttV2k+pfJq264w/BhP5 36/nFbODiGDjWeVTEhoTQ6jRSM9oMxk/ZmAwGjFS986Wu/51kpb+K8T5QG2+07TeqMw3auPxXLb0 ocmn/d/bHO/xDoOHJ/PxU2e/WUdJz/tHkv3xPMrOeCPC32feT9WX83lwwVduUoK99BfWZIcz6dok Hvk43aP9CiGEEKJhyA1gIYQQQghRq/jwVqAfHUOCa28A/31gGw4s3c3bVx9j0p0pMGenm9Qufskq 46ak5nX+6i69Oeoq/h5r5enX6168Kkkv8Un+akyh3egZbub57Udr/7bnrSOEzx0EbHafMICGL13D n7/fhbVLOGklkXRvW8UTox5ge35l7Wf6LFhBvy2rKb9hBFHFxzBFt2b3xCEsyCghou3tzJrQFWt8 Wyx/zGDYtJ9q04UnjmXd0vb8sbeY5O6d2f5TJtEhJjomZdJv8GM4gbCka1j0/ERI+5WSsHa0Mx1g 8n0z+eO0VwIq0ZK+ZZ9xvNw5krTicLq3N/D06Pv5Kq8cQLH8AJbYS3ni2WkkVaRxpNRC1zbw9NgH 2ZpXgSn0QmbNGoHRFEeQJYRZs2YBkPP1Ap7YcFh1u9r+g0O7sv61qdx78wDSbQ4AQuNv5dVVfRnU bzg2H8QvpudQXnx8CNmpu7EkteZIbjBUnNquFh81g5euoeuyMUz+tvqmXrtb5zPvqtcYdO83ANz6 3Cr++sUWTJdfQIEtii7tzMwfPpIvj1coZQto619K7XeSUv8GuG70HMb3TWFf6kEMUW2ILXqF4VP+ B0CPqS/yZI8Q9qdn4QptRYfwHB4bN41fS+ya2r+xt581rjdLlj+MMy2VfGcIbVPasXTYrWw7YQPU 21ctfmrb1dpPrXxq2/XGX0v/UnLNhKn8K8ZKYkgwVeMmM8vupCL/AybN+ARQ7l8nqfVfIc4XavOd 2nqjNp61jMfGYOzj82lR/A4znv5BRy5G4kxBVB6rVP/o6Qwmxvy5OZ/efcYrnI1mRl4Qxauzviem bQpJYXBo7wFKHGd/Yea71w8zfNQ/4ONVOsovhBBCiECRG8BCCCGEEKKGkYtuvBOno4StRdUX4IOt HRjQPIQpvxaQlvsjsYtHYGQnp18SMoeFEW4zEd2qB/d2jyZ15fe125TSWyKvBOCbmov97nib/0mm yMt58MGY2v8vO/o+y9cfIiikMwD7y0/dTLAdyyLI/Geig40UePCkmD9FXZTD4JHLsLug98OrmTDr Om4bX/cGdeubBjL7geHsyC7HYAyjnbn6omDR/mWMGwdd71/Jowln5+2ozGDCI49zzUsbub1wKXdP S+WVDzbTI8zErlI7d867H+dHMxnz8k7AyG2LX2f65EsYPvs7TWXXkj7q4uMMHj4TmxOufPBlHnzi Zr66901N5b/32alYv3qKoau3A9Cp/wLmzruNrXevxVayg3HjdhDWcjQblnVi3LiJddKqbVfbv634 O/573MXoPzdn+jc5AKQM/xfHf3qBEodLc/3dMzJ1+lD2Lx3LtI/TMJpb8sJbK+FXbeXzlZjLSxgy ejx2F/R/YSPD7kzmy0XafrtRrX8ptd/p3PXv2J4TmfDvRB4ZNpzUwup5pO3FrWrTHXlvAQPnHah9 CuzvT6xj0vjOjJi7W1P7N/b2S7lnJKF/zOf2KdU3dI2mGCJd2m6Ognr81LartZ9a+dS2651/tPYv dzbPmMRmYM7bH1D4+CMsPOOmrVL/Op27/ivE+UTLfKe03qiNZ63jsaEld+9Om/zPvUrb/C9DGHJZ As07/IlmBz5jzroDHqU3R15FS3MQHxXU/RKMydqR8CAjwcPn8UJnIznOWC5oXsqz9z/IF9lldT5b 8NsPWOPuwGxc7fXvqgshhBAicOSHG4QQQgghznNJ181g7dq1rH/zPWYPSeCdRQ+RUfNEY8xFI3Cc +JrdpXZKMtZhM19Av1hrnfT3vPw677zzNisXT+PIuhnM2HzqtXBK6Y2mUFwuJxU1F5C63P8Ma9eu Ze3atZgM+vM/yVVVTFZWVu1/OXnVF98NQSEAVJ52/crlqt6m4WdpAybt7c21FzR3rt9OVIehZ33m +K4l7MiufmrW5SzlkMYn5KoqDwFQkFtJ6aFiwEmmzUGSJQijKYab4kPY9O7Jm31OPn15LzEX9teU t9b0R97dVPta159f30p4q8EEa/i9ZFNYD/q2DOPD7wpITk4mOTkZw4FthCXegiVAP7j8wbr9dLn7 mur/MZgY3Suez1dW11dv/MwRl9Ej3MTqLzKrU9uyWbP3hEoq3zvy7qe1/W/3D8eI7ByhOa1S//Kk /dz17253XUbet8/X3pwESPs5o/bfRXsO0a5nL/rdMoihQ4eS4jAQntxSU9nPhfYryywjov319Lm0 C9EhwTjt+RR68MUVtfgpbdfSfmrlU9quN/6BmB+09i9v52chzjfu1hst41nPfB9ITz8wlgdnbPMq bVXxMXLzcjl69CgtkhJJCA3yKL05tDNO+3Hyz/gpEUNQ9U+v3BzxCUNGP8CD/7mDxT9ZGPf4zWfl 4ajYj9FopYNFnicSQgghzgWyYgshhBBCnOeOfr+aJ94+TJWtjNzMnNobsgCX3NkJZ+WP3HHHHQBk 2hz8o18b3l21r/Yzy4bczH+LXFx0/XieHDWGdpvv43DNBW6l9FVlGRgMfyHRbCTL5uTQxiU8ve16 Fs0dgMEALp35n1RV9hsbNmw4q95Oe/VrUWOCDRyu+ZvRFI3LWUV+VeN5qqE869TTFw5bFkZTLGYj dX4L88SeQu8yd1Xf6Hc6nbhq6lzlAhMQZErAaDCQUemo/XhlQSFB5os1Za01fXlWee2/HbZMjEGh RAYbz7pAeVb+1nYA9Bp6J71O+/vOH3cRYjRQGYAnU3K+fAnLhMV0tG4kO344bVwHGV/zlKDe+BlN LQDIsp1KX5pTBrE+rIAG9qJTN/9cDsDgwQVnpf7lQfu569+JkWZKvi52u/sBs15maEI6mz7fxYnS Ssw2BwajRVPRz4X2O/T6VFab76bfqClMbtOczNQtzJ66sPYLPGrU4qe0XUv7qZVPabvu+ScA84PW /uX1/CzEecbdeqNlPOuZ7wMp50ia12kLUj9lQyrA6/w0fS2PzLmG28Z/rDm9CwcYzr4M7LRX/xTK ztVba//2w/o9THrhJuCNMz5d3SZVrsZznCyEEEII9+QGsBBCCCHEec5WlMGBA2e/Rs4YFMXIdpHs /n+FWCzVF9F+//YY//hHP1j1dJ3PuhyV/Lz5Gd666W2m/+ci7lm0UzV9ef4HlDkGcHNSBC8eKqIi 9wgHHcfrLaM3+auxF+/E5nRxeTNL7e8dN7swEXvx9z59rZ3LWX2z2mLw7qmz8Hbh8OMxAIJD2uCw 5da5+Vu9D99fiHPYMqlyubjAGsTu0ur4WFvE4ajUdvFSa/rw9uHwXR4AJmt7nPZCClVu/gI4yqpv 8q95fCb7lZ6ocznrveCpebuCqor9vJpezr29E3i7Vx9ytszn5HcHdMev8jAA7a3B/FZW0z9bhUK5 QiIP2V0ugkJO3dC1xJp9l7kKze2H+/6dfryCqG6xwKGztgVbOzH6iiTuu/leDpx8Yrj9jdx4dub1 tv+50H5ORxHvr1rI+6vAEpPM5JcWMen6jUzYVF1GtfZVip/adi3tp1Y+pe264+9B//KG5v6Ff+Zn IRojvcc77qiNZ0/GY1OR9nMBIXd0B7TfALaX7MIYPIAkcxCZp31RqKriIDk2B0GnvR3BYAzG5Tr7 95NNoV1xVhVxqFLeZCCEEEKcCxrRy+2EEEIIIURjEtFuBBGuAua+tIJVq1axatUqVr60Gmuza7k0 3FRvmnfmbiLpuql0CTGppnfacnjms0yumzmGtjX5WZpFKZbJk/zVOKvyWbO/iKvG3YDZCEZTLMPu SCbj/97UHCNzeARRUVFYjQaMllCioqIIt9R9QrKq8hDZNgcDL03SnO/p2t46mKhgI2Dkb6OuoPD3 dV7l4ylnVREb0kvoO7IXBsBgDGHgmE7kbX/Pp+lb9xtCdE39eo/qRcG+tWh5ia29fC8bDp/g4VHX Yq65aGk0RXP51d3qfK6q4iDBlvZ0j6n/5qbadjVbVv5KyoiB3Nczlk2n/R6f3vjZS3fxdWEl9/Tt CIApohOjUpp5VUZ3DmaXk/jP6icqjeYW3N6rhU/zV6K1/ZT8tvJLml/yEH9tG17zFyM9eicD4HJV 4HS56BBT/eUQc0w3xv/t7NeBumv/c6H9Yi/7E9Gm6lN6W2EGR+1OHBWnRo9a+yrFT227lvZTK5/S dt3x90H/UqK1fwlxPtF7vOOO2ng+l8bj3PUbeHXpNR6lMZpb8q+/dK2tu7lZe4YN6UDRnq89ysdW /D37yu3c1CLkjC0uVv10nEvv+2f1T7AYgrj6ni4UH37nrDziLutJWc7bOOR7LUIIIcQ5QZ4AFkII IYQQ9eo0/BKKM1+j/LSnl2xFX/FzycMM+WsLfv7y7DTFh9fxwdH+PPSfi1jRTDn9jk8y+HbxBN6d NIcXNr5NwdETNIuL5LvNy7E7T75kTl/+ajZPf5I/L5zJO2/eTJk1mrI9HzFx7R+aY/TPJWsZl1hz c+S6J9l4HRx+bzyjX9p76kMuO9MWvsOTE5by0XQTGZ9OYtTC1PozrEfuZw5eWr+aUlsEca4DTB/7 hea001eto3uYieDQSMKM03nzTTtOex5Dh43XlH7jlGe4aOEU3nrlNkpCWhCc9TVTnv9F8/61pM/9 vIwXXltDWUUYcYZDPDb2M83lX/fQDBLmTOHdDXeQVeQkPqEZB799ke++OBXfysLPWPG/65ix5i2C HQ4yPnmU8cv3atquJX7Hf16GPepVoku/5YP8Cp/G77lHl7FowVOs/0cWhLvY9ksBV5y2XW/7/rL4 VVwrHmDjKwMocxXzyZe5pPTQXDzdtLSfkoI9y5n9WgQTl77BqOwsDBEJFP26gPFfHcBRmcacd3Yy beWr3HrkGBER5bz/XjqD+tTNQ6n9G3v7JfQexlMzpnE0Kw8ikjBmf8XDn2fWbldrX6X4admu1n5q 5VPbrjf+evuXEq39S4jzisLxjt75Tmk8n0vj0RoWRmilZ5dijcZQBox9nAkzzRQWlBIR04zMnz9h 2rwdHu7dyUufZfPwsE68OHdnnS3b5s7k6oVP8tb66znuiqFZ5V4en/jRWTlcN6gtu5bN83C/Qggh hGgohoMHD7oAevXqpfjB0tLSgBRICCGEEOJ8FxYWprg9KysLgMTERMXPnTx+M5oT+OiDV1gwcTz7 Co6QluXDd5D6iCkilsTYMIpyMims0Pb7lb5jJD6pNSZ7AZl5J87aGtu2HS0Tbmf+lFj69p8Y0JLd 98YmkuaOZOYBSIqzkpmeReB/nthIfKvWmKuKyMjx5rcs1dMHh8SQ1NxKVnoWdi/qFx7XktgQOJ6b S8mZ78ducPriZwgKo1WrWAoyMihx+L5uQZZoWiWEkpueVef3vwNJb/sZTeEkJcZSUZjL0aK6N+Gt MS1pEQZZGdle9a1G334hUSTER+MqO07W0bN/r1dL+yrFT8t2pfZTLZ/Kdv3zj3/nB/39S4iG4+vj zUBQGs9NfTyGR8cRExVGRVEueQVnz8VamMJ68MbrD3PfLSPIq+fnNmJbtibcWEF65tGz3sZijf07 b628gdtueYhSebW9EEII0aDUjuO2bt0KyA1gIYQQQohGx+c3gIOjeXz2JABOHFrLUy9rf8JVQN/J M7ki0kxV5RFmzlke0H2fvAE8bXd+QPcrhBBCiKbtXLwBLPTrMmQSV/6+hlU/HfcoXaehk+i1by2r dhzzU8mEEEIIoZXWG8DyCmghhBBCiCbOWVXAtGnTGroY56zNT81mcwPtO+3HHygutjfQ3oUQQggh RFOy540F7PEi3d7XF7BX/WNCCCGEaETkBrAQQgghhBCN1P975omGLoIQQgghhBBCCCGEOMcYG7oA QgghhBBCCCGEEEIIIYQQQgghfENuAAshhBBCCCGEEEIIIYQQQgghRBMhN4CFEEIIIYQQQgghhBBC CCGEEKKJ8Pg3gMMTx/PiU38GoKr8ECPvnVG77eLe/+LiLh2IiwrHXnyMn7Z+yFe7c7RlbAii++V9 6NmtIy1iI6gozOG7z/7LDweKfJN/jTaX/4sbr+yB2Z7Pdx+9zbbT8jeaE3ll1VO1/79gzF3sKrVr yrdD34H8JdJS77bC3z/mwx/zGbBwOR1fm8b8ncc8KrOnQhOv4uGJt9G+eQSVx99m9MT/1tl+wYj5 3Be3jokLUn2+b0NQOM1jzeTl5TdIeiVt+89hyhX/4z+PfOXzvE+nFn+hj7fxfW3jRswGI2aziVG3 9CfP7vRzST3ji/6pZ/w09vic5K/5a8jiFbRe9ShP7z6uKx9/zq+ByF80bY25//hz/T/X+Wp+Er7h bhyd7+OrIeuvpX56y6cnfVOf3wIRfz38HX+1/Jt6+wsRaI15vW1QhiAuvW4AV17YDnNVCb9u/4iP tx9u6FIJIYQQooF5fAPYaGpGfHw8B9Y9x2sHs+tsGzvpftpYgrFX2DFZTVx/861cNGc4S7blaci3 Oc/OnkxVRQE5x+0kJl5L3/6DeGXCHbyxt0h3/gDxV45j5cwbcTltuAwm/nFDXxaPHMonueUAuKoK WLFiBa2uH8OIS+KwGA2a4xISF0+LaCsArXtfS7v8nXz9ayEAQZlmACKaxxNtDtKcp7f6zplAwtcL eeiDPTgcZWdtL03/jdSiYr/sOzT+dlYv60Tf/hMbJL2S4PA4msdafZ7vmdTiL/TxNr63DxqEKbQr m99bhAdDO2B80T/1jJ/GHp+T/DV/hTePJ9qi/6UY/pxfA5G/aNoac//x5/p/rvPV/CR8wxwRS/No 81l/P9/HV0PWX0v99JZPT/qmPr8FIv56+Dv+avk39fYXItAa83rbkC4dt4SZvStYs+pDykJacef0 F+nx7F08syVbPbEQQgghmiyPbwCfVLBrO9/srvst1jXzprF3928cL7HRosdtvLpgJFffcwVLtm1S z9BRxoqnp/D/tvxMhdNFy8snsXbOdfQb35M3xn6hP39g1IR/4HLamHbHLRxKvIM3Fgxi+OQr+WTi ZwC4nOV8/fXXdL34To9iAZC69kVOfv/wxgt70W/PRhYtqv8biabIJDp3iCX/4B4yT5zxhLHBRNuO HYk2OziwZx/FVdqfwguNiSHUaKRntJmMHzMwGI0YOXUnx2iKIibKhH3Xh7xnL6o3D0t0DNbyIk7Y LHTscgGUZrL38KknTozBkaR0bo/FVUFO2kHySqrLbzCaiY2JJDTaCgYTcXFxADgqiigoUX+KWmv6 0LhWtGsZh8GWz759R7C76ubjrnz17jMonNhoK0XHj52VjztGUwQpnTpgqipi377D2E5rHrX4a6FW P9XyKdTfGtWM4LITlNQ82RlkjSTCUk5hUd0YWaKTSGnTnIrCbPan5Z61D8XtKv1XrX2UtqvFV2v9 vGEIiiA6qgqbJYnW5mP8nl5Gp+5dKNqfSnZZ1akyqrSf2viqu896+qeb+GoZP2GxMQSfKKQqtj0p zS0c+v13ijx8yldL/9TTP5Romb88Gf/uhCUkkxJv5cjve8i31S2fUvuplS80JgZTSSFl1pZu539T ZGLNtt/JsVkIMVZQUtO//J2/aNp80X9AeXyrrY/u8te6/qvNn7rnXx3zk9r6ozW+atzNT1rWP2/b Rwu19Erl07q+qrWP6vGrQv21MEW2pHOH5hQc2nPWNi3rkxIt67O3/Vvz8bmG/u9u/KvVX2/99M4f ettHz/zpq/jrnb/0HH+D+/YJRPyV9q/G3+uLWv6+Or/15/olhC/5Yj3z9/mQ3ustDUktvqP+3pZf Hx/BuzuOApCRfBWz77wctrzfQCUWQgghRGPg9Q3g+mz79ufaf5cXnwCgIk/b646djhO889lPtf9f ePAIAC7nqQMaPfkHWdrQO8pCxfEP2Xm8EgrWcaLqFiKTBwCfacrDF6K7DmbFyAsosEXRpZ2Z+cNH 8uXxCgAssZfyxLPTSKpI40ipha5t4OmxD7I1r0JT3tdMmMq/YqwkhgRTNW4ys+xOKvI/YNKMTwCI aHs7syZ0xRrfFssfMxg27aez8uizYAX9tqym/IYRRBUfwxTdmt0Th7AgowRrXG+WLH8YZ1oq+c4Q 2qa0Y+mwW9l2woYp9EJmzRqB0RRHkCWEWbNmAZDz9QKe2HBYtexa0veY+iJP9ghhf3oWrtBWdAjP 4bFx0/i15gRaqXxnCjIn8tCS54nduYzJKz7XFN+wpGtY9PxESPuVkrB2tDMdYPJ9M/mjokpT/NWo 1U+NWv0HL11D12VjmPxt9UW7drfOZ95VrzHo3m9q87hu9BzG901hX+pBDFFtiC16heFT/qdpu1r/ VSuf2na1+Gqpn7fCE8eybml7/thbTHL3zmz/KZPoEBMdkzLpN/gxnGhrP6Xxdbr6+qdSfLWMn+FL 1/Dn73dh7RJOWkkk3dtW8cSoB9ieX6kpBlrqp6d/qFGbvzwZ/+607DOOlztHklYcTvf2Bp4efT9f 5ZXXbldqP7Xy3frcKv76xRZMl9c//8deOIRlc4eSm7obY0Iih3PNXGR/pjYff+cvmja9/QeUx7fa +qiUv9bjB6Xxp3f+1Ts/qa0/WuKrRml+Utu/nvbRQi29Uvm0rK9a2kepfdXqryam51BefHwI2am7 sSS15khuMJwWGi3H10rU1mc9/fv5ws6q40tLfJXGv1r99dZP7/yht330zJ9ayqe3f/v7+FupfQIR fz3nR/5eX9Ty98X5rd72FyKQ9M73/j4f0nu9xVfGPj6fFsXvMOPpHzxKpxbfg+VVxDc79YaS0NBg Ko8e9WnZhRBCCHHu8ekNYID2g+fx5M3JNIuJ4MAPH7No7vde5GKk/+R+AHzywi8+yT84tCsA9op9 tL26Hym5W0irdNAj7AJMBgL2zb+Yy0sYMno8dhf0f2Ejw+5M5suaJ4XvfXYq1q+eYujq7QB06r+A ufNuY+vdazXlvXnGJDYDc97+gMLHH2HhGTeVivYvY9w46Hr/Sh5NcJ9P65sGMvuB4ezILsdgDKOd ufqAMuWekYT+MZ/bp1RfUDSaYoh0VV88s5XsYNy4HYS1HM2GZZ0YN86zV1xpSX/kvQUMnHegtq3+ /sQ6Jo3vzIi5u1XLd7pga1sefWEhpi3PMHn9ds1lvHPe/Tg/msmYl3cCRm5b/DrTJ1/C8NnfAerx V6NWPzVa6+9ObM+JTPh3Io8MG05qYfVFn7YXt9K8Xa3/qpVPbbve+OrlqMxgwiOPc81LG7m9cCl3 T0vllQ820yPMxK5Su+b2cze+TnLXP5Xiq3X8RV2Uw+CRy7C7oPfDq5kw6zpuG79ZU/3V6qe3f6hR m7/09n+AqIuPM3j4TGxOuPLBl3nwiZv56t4363zGXftpmV/dz/9GHpk5lP0v3M+0jw5jNDVn8Vtr 4LSXSPg7f9G06es/6uNbbX1Uyt+T4wd340/v/Kt3ftJCKb5aaJmf3NHTPoGon9r6qrV93LWvlvq7 Z2Tq9KHsXzqWaR+nYTS35IW3VsKvpz6h9fhaidL6rKd/2yrUx5dafNXGv5b6662fnvlDb/vomT+1 lE9v//b38bdS+wQi/nrOj/y9vqjl74vzW7XyBWL9EsIT3s/3/j4f0n+9xVeSu3enTb62hxDOpBTf F6e9wMwpM3jwgu8ptyZxacx+nnzMm+uxQgghhGhKfP6DYlWlx8nJyaHI5qJ1p250bx/ucR5XjV7A 8B6xpL4zhzW/F/okf4PBAlS/5vmfw4Zx+y0dKHNWH/l58lu/eh1599PaA87dPxwjsnMEAKawHvRt GcaH3xWQnJxMcnIyhgPbCEu8JaDlAzi+awk7smt+F9lZyqGaJyTKMsuIaH89fS7tQnRIME57PoUB fMVU0Z5DtOvZi363DGLo0KGkOAyEJ7es3a6lfMEhycxY8RwXpC1hhgc3f42mGG6KD2HTuyfPQJx8 +vJeYi7s74uqAer1U6O3fbrddRl53z5fe3EPIO3nDE3btfRftfI1dP9SU1V5CICC3EpKDxUDTjJt DpIs1b/rrbX93I0vcN8/fTU/pL29uXb+2bl+O1EdhmpOq1Y/vf1DL1/0nyPvbqp9LejPr28lvNVg gs8onlL7qedf//xvjriMi8PNrN5SHS+n/SivnrH2NYb8RdPmrv+A8vjWuj4q5a+Vu/GnZ/4N1PGX 3vprmZ/qE6j20ZNeaX31pH3qa1+9x2/miMvoEW5i9ReZ1alt2azZe0Jz3bRSWp99cXzhjpb4qh0f BqJ+vpg//Mnb8unt34E4/tZ7fqJXoPavd33x1rmwfgnhCW/ne3+fD6ntP5CefmAsD87Y5lVapfhG tk4kKhSqnE6cDgfWZjEkxYf4oshCCCGEOIf5/Ang9M0LmLgZTGHJvLJhKXfNvJ/3Bs3RnP6i22Yz fUA3/vhwEQ+tOPvVrd7m76yo3s4PAAAgAElEQVTKA8AYHMuKkbfxssvO0k0GnI4ySh2B++EPe9Gp iycuB2CovnkUZG0HQK+hd9LrtM/v/HEXIUYDlc7AlfHEnvoPtA+9PpXV5rvpN2oKk9s0JzN1C7On LiTD5ghIuQbMepmhCels+nwXJ0orMdscGIwWj8pnjelL2ZtfEDfwLi6O3MbPGl8PG2RKwGgwkFF5 Kq/KgkKCzBcHrH5q9LZPYqSZkq+Lvdqupf+qla+h+5cqV3U5nE4nrqrq8VjlAlPNZq3t5258gfv+ 6av5oTyrrPbfDlsWRlMsZiOafgtRrX56+4devug/5VmnXvfssGViDAolMthI/mm/raTUfmrczf9G UwsAsk4ra3leBcQ2rvxF0+au/4DK+Na4Pirlr5W78adn/g3U8Zfe+muZn+oTqPbRlV5hffWkfept X53Hb/XNn6U5ZT6fP5XWZ18cX7ijJb5qx4da6K2fL+YPf/K2fLr7dwCOv/Wen+gVqP3rXV+8dS6s X0J4wtv53t/nQ9Dw89lJOUfSvE7rLr5VxmienTSYLybexov7qn8f+f8GPM/zc8fyf4Pn6S6zEEII Ic5dPr8BfJK99ACHK6voGdZdc5qUvpN4asTl7P1wMROe+xilUxZP87eX/ERxlRNrs6sxuN7BYGlL e0swtoItivsJFEfZPgDWPD6T/R48UeYPLjcni05HEe+vWsj7q8ASk8zklxYx6fqNTNiUdnpiMOjo Vm7SB1s7MfqKJO67+V4O1MSnW/sbudHD8pVmvcj8tZvZGrWcxxaMYciYJdg0nBw7bJlUuVxcYA1i d2nNbw63iMNR6f3Bu6f1U6NWf7vLRVDIqRMgS6y5Tvr04xVEdYsFDtWbv9J2Lf1XrXya+pcCtfoB uJzVZbMYfPuteE/az934Avf9U/P8oDL+wtuFw4/Vv5seHNIGhy23zs1fd/HRUj+9/UMvvf0HILx9 OHxX/WUhk7U9TnshhWfcXFFqP285yv8AoFtoMDtO/gZWuzDQd709YPmLpk9xfPtqfdRw/FDf+NM7 //piftKy/uilND8p7d/fxy9a6ImPJ+1Tb/vqrL+j8jAA7a3B/FZWnb5Zq1AoV0jkBXfrs6+OL9yN Ly3xVTs+1MIX9VOk9/zD33TE/1QW3s1feo6/NbePn+IfyP6ha31Ry1/H+a1S+RrT9QMhTvJ2vvf3 +YrP5pMG5i6+5vCuNDMZ+Tnn1AFK/s4cTKP+0lBFFUIIIUQj4bNXQFtj+7L8mZmMvnsYA/sP5D9T n+OScDNlOR9pSx/zb54b93dczlJyw3oyZepUpk6dykP3XemT/F3OCpbvLsAU0pEnRg9mxKSZGA0G 0j54z+s6+5K9fC8bDp/g4VHXYq55ZZPRFM3lV3dr4JKdEnvZn4g2VXcZW2EGR+1OHBV1b45UVRwk 2NKe7jHeXfx0l97lqsDpctEhpvobmuaYboz/W93X9Wgpn8tV/Y3QbUsfZlf4tTwzsqemcjmritiQ XkLfkb0wAAZjCAPHdCJvu2/6j5b6qVGr/8HschL/Wf3Ei9Hcgtt7taiT/reVX9L8kof4a9uTr1U3 0qN3sqbtWvqvWvm0tJ8StfpB9Wsms20OBl6apDlfLXzRftX51N8/tc4PauOv7a2DiQo2Akb+NuoK Cn9fVze9m/hoqZ/e/qGX3v4D0LrfEKJr4tN7VC8K9q0lEC8ht5f/xqbsUu69qw8mA4S0/DNj2ked M/mLpk9pfPtqffT2+EHv/OuL+UnL+qOX0vyktH9/H79ooSc+ettHb/3tpbv4urCSe/p2BMAU0YlR Kc00l18rd+uzr44v3I0vLfFVOz5szPVrLPTEX4m/j7+1to+/4t/Q/cNX9ddzfqvEk/4zZsU61i2/ Q3PeQnjL2/ne3+crvppPfGHu+g28uvQar9K6i6+99BfybA4G/rNT9QcNQVw1vDOV+Z/5qNRCCCGE OFf57Ku6Tns+wUl/YsCFV9b+Lf/gdyx87A1N6YMsrQgyGCAonN59+tT+vTT3EM8u26Y7f4Av5szm qsWPc/mAu7gUSN/xPtM2eP9tdl9b99AMEuZM4d0Nd5BV5CQ+oRkHv32R775IVU+swfRV6+geZiI4 NJIw43TefNOO057H0GHjNaVP6D2Mp2ZM42hWHkQkYcz+ioc/z6zzmcrCz1jxv+uYseYtgh0OMj55 lPHL92ouo7v0jso05ryzk2krX+XWI8eIiCjn/ffSGXSqq2gq30lOxwmeeXAxr62ew+Btd7BBw2vz Nk55hosWTuGtV26jJKQFwVlfM+X5XzTXTYmW+qlRq/8vi1/FteIBNr4ygDJXMZ98mUtKj1PpC/Ys Z/ZrEUxc+gajsrMwRCRQ9OsCxn91QNN2tf6rVj5P2q8+avUDwGVn2sJ3eHLCUj6abiLj00mMWqh/ fPmi/U5XX//UMj+ojb/czxy8tH41pbYI4lwHmD72i7o7dhMfLfXT2z/UqM1fevsPQO7nZbzw2hrK KsKIMxzisbHaT5j1zq+rH5rHY3Mn8v7791GYk8rGb/IYGBq4/EXTprf/qI1vX6yP3h4/+GL+1Ts/ aVp/dFKan9T278/jFy30xkdv++it/3OPLmPRgqdY/48sCHex7ZcCrjhtu97xBe7XZ18dXyiNL7X4 qo1/LfVvyPrpbR9ftK+e+Kvx5/G31vbxV/wD0T98sX+1/PWc36rR2n8SY5physzXnrEQXtIz3/vz fMjX5+t6WMPCCK307lKsu/i6HMVMenI98x+ex5s3ZFFmiSe66iCLp6zyYcmFEEIIcS4yHDx40AXQ q1cvxQ+WlpYCENl2Bm+t+CuZH2/k/zIzeXPjx3U+FxnTnGaRYdiLj5F9vMTnBdafv4H4pNaY7AVk 5tV9n4wxKIrbBv+b5n/pxw2donjslhv5vljbb8T6UnhcS2JD4HhuLiVafpwzgIJCokiIj8ZVdpys o4F/f6g1piUtwiArIxt7PW+683/5jMS3ao25qoiMHO9/C9QdtfqpUat/kCWaVgmh5KZnUeHmVYFG UzhJibFUFOZytKjC4+1K/Ve1fDrbT0v9/Elv+2nh7fxw3xubSJo7kpkHICnOSmZ6FlUellFL/fT0 D718Mf6DQ2JIam4lKz3Lb22oJMgUhMPu4C/PvM6dqTO4b+3+cyp/4TthYWGK27OysgBITExU/NzJ 4zdfUB7f/l0f1fhi/tUzPwVi/VGan9T337Dt44v46Fs/9NXfEBRGq1axFGRkUOLw7dqlZX1uDMcX auu7O42lfo2d3uMjfx5/N3T7NPX9+3v9CramsPn9JSwbMYD/5pS5yUGcr3x5vOmr+d6f5ysNPZ/o oSW+BqOZhFaJmKpKyMg6FpC3WQkhhBCiYagdx23duhXw4glge/kBduywQFwHukZaz9p+Iv8oJ/KP epqtZvrzd5GXeaT+TUYz3bp1g+I/2LEDiuwNc7hUciwb39869w1HeRGZaUUNtv+K/GzSFL687P/y OcnL8N/v5qnVT41a/R2VBaSlFSjm4bSXkJ7mvgeqbVfqv6rl09l+WurnT3rbTwu980NVWT5pbqZA NVrqp6d/6OWL8V9V7n189GjWdRC9Wxzml/3ZhLf+Ew90DmXlvPRzJn9xflAe3/5dH9X4Yv7VMz8F Yv1Rmp/U99+w7eOL+OhbP/TV3+UoJT3Nd1+mqI/S+twYji/U1nc1DV2/xk7v8ZE/j78bun2a+v79 vX6FtOjDb98tlZu/ImC8ne8Dcb7S0POJLyjF1+W0kX3kcEDLI4QQQojGzeMbwOV5rzFtmj+K0vCc 9qNMa6qVE0Kc19J+/IHiYntDF0O4UVVSQEr/f3P1zc2wncjilRn38nl+5TmTvxBCCO809fW5qddP iMauOO1lJs5s6FKI84He+V7OV5TJeiqEEEIIb3j8CmghhBBCCOFfjfEV0EIIIYQQoumQ400hhBBC iHOT1ldAGwNRGCGEEEIIIYQQQgghhBBCCCGEEP4nN4CFEEIIIYQQQgghhBBCCCGEEKKJkBvAQggh hBBCnKMMQeFc969/kWgOauiiCCGEEEIIIYQQQohGwuMbwIagcOLjY/xRFt37H7BwOVMuiQtwicTp tPSPC0bMZ+Gkbl7vQ296PRqy/zeF/q03fv6Of0PPb/52rtdP5v+G07b/HF58uneDluFc77+NQWji Vcxc8AJrX3mV5Qtv8nn+7tbn1zZu5K233mbTpk3Em9wfeiqt74Fs/4Y8zvCGy1FCVsRfeGb2rRi8 SK+1fbzl7/wD5VzrF97ytJ5q7dtY2t/b9vPF/OULevI/H9bPxhx/Ic4ngbgeJJo2f5+vCCGEEOcb j8/CQ+NvZ/Xy6f4oi+79RzSPJ1qefmhQWvpHafpvpB4o9nofetPr0ZD9vyn0b73x83f8G3p+87dz vX4y/zec4PA4msdaG7QM53r/bQz6zplAwu7XeejBCUye/X8+z9/d+nz7oEEMHT4Dq9WKUeEOpdL6 Hsj2N0fE0jzaHJB9+UrqW7NYk9ObGTcne5xWa/t4y9/5B0pDHn8Gkqf9X619G0v7e9t+vpi/fEFP /ufD+tmY4y/E+SQQ14NE0+bv8xUhhBDifBOs9YMGo5nYmEhCo61gMBEXV/2klaOiiIISe+3njKYI Ujp1wFRVxL59h7E56+Zjikykc4dY8g/+To7NQoixgpKyqtN2ZKJtx45Emx0c2LOP4iqnR/uv3kdS zT72kHmi7jZ3+Z9kiY7BWl7ECZuFjl0ugNJM9h4+rh6foAiio6qwWZJobT7G7+lldOrehaL9qWSf Vr/QuFa0axmHwZbPvn1HsLtO5REaE4OppJAya8uzym8MiiA6Kojj+YV19hsTG4utMJ8Shws1YbEx BJ8opCq2PSnNLRz6/XeK7HXrr1Q+pfhoaR+jKYqYKBP2XR/ynr3IbTkt0UmktGlORWE2+9Nya/+u ll5v/ZTir7n/B0eS0rk9FlcFOWkHyTujb7oTExtLSUE+NmfdgBuDImjWzEX+8ZLavyn1b7Xxp3f/ /o6fO76af/TmrzY/eBuf2vKr9B+18VldxrPHj974a6VWfn+3D3g3//tifj3J3fwF+vuHGtX5R2X9 U91++keDwomNtlJ0/Fi9/bDe8im0vzWqGcFlJyipmbODrJFEWMopLLIHrP82ZaExMYQajfSMNpPx YwYGoxHjGc+Keju/gPb13R2l9FrbX+/4MkW2pHOH5hQc2uOmjN7NX6dTmh/U8lca3yfj9/NrM/jR i/hroaV/6KFWf7X1V2/7KOWv9/gV8Mn5h7frixZq/d+T9cErXuav5fxLS/upnp8q0JK/nv6tZ35t Cse3pqhYYqx1L1k4Kgs4VmjTHB+l+KuVX+/6JsT5whfXg7Rcz/HF8di5yp/X87SmV1sv9a4naucr 53P7CyGEEN7SfAPYFHohs2aNwGiKI8gSwqxZswDI+XoBT2w4DEBY0jUsen4ipP1KSVg72pkOMPm+ mfxRUX1AEHvhEJbNHUpu6m6MCYkczjVzkf0Zhk37CQBL7KU88ew0kirSOFJqoWsbeHrsg2zNq9C0 f4DoroNZMfICCmxRdGlnZv7wkXx5vEI1/5P6LFhBvy2rKb9hBFHFxzBFt2b3xCEsyDh1A64+4Ylj Wbe0PX/sLSa5e2e2/5RJdIiJjkmZ9Bv8GE6gx9QXebJHCPvTs3CFtqJDeA6PjZvGrzUHxLc+t4q/ frEF0+Vnlz/I0pZXXnuax269mZ9qPm+OvJLX1k9hXP8BlDjUL1IMX7qGP3+/C2uXcNJKIunetoon Rj3A9vxK0FA+pfg8X9hZtX0i2t7OrAldsca3xfLHjNp2P911o+cwvm8K+1IPYohqQ2zRKwyf8j9N 6fXWTyn+WvqfNa43S5Y/jDMtlXxnCG1T2rF02K1sO2FTbZsJy9eS++AQXkiv289a/XsOC/71IYPu +xRQ7t9q40/v/v0dPyW+mH/05g/K84Oe+IB6/9EyPt2NH73x10Kt/IFoH2/nf1/Mr6A8f+ntH3rj r7b+aVkfTwoyJ/LQkueJ3bmMySs+11Q+tfYfvHQNXZeNYfK31Tc12t06n3lXvcage78JSP9t6q6Z MJV/xVhJDAmmatxkZtmdVOR/wKQZnwD65hfQtr4rUUqvpf31jq+YnkN58fEhZKfuxpLUmiO5wXBa 19czf2mJn1r+auNbb/zVaOkfemiJr9L664v2Ucpf7/GrL84/9KwvatT6vyfrgzf05K/l/Eut/dTO T9Wo5a+3f+sZ303h+LbNzeOZdEV8bVnDW7eFn6cxbPpPPom/Wvn9Pb8K0VRomW/0Xs/xxXrfGIx9 fD4tit9hxtM/eJTOn9fztMz3WtZLPfmrna80lfYXQgghAk3zDWBbyQ7GjdtBWMvRbFjWiXHjJp71 mTvn3Y/zo5mMeXknYOS2xa8zffIlDJ/9HWDkkZlD2f/C/Uz76DBGU3MWv7UGUk+lv/fZqVi/eoqh q7cD0Kn/AubOu42td6/VtH+AmMtLGDJ6PHYX9H9hI8PuTObLRamq+Z+u9U0Dmf3AcHZkl2MwhtHO XKkpRo7KDCY88jjXvLSR2wuXcve0VF75YDM9wkzsKrVz5L0FDJx3oPZbbn9/Yh2TxndmxNzdquW3 l/3KqkMljOrfhvvWHQCg/aA7KfpjBQc8OOCJuiiHwSOXYXdB74dXM2HWddw2fjOApvK5i4+tQr19 ivYvY9w46Hr/Sh5NOLtssT0nMuHfiTwybDipNd/qbntxK83pfVE/d/HX0v9S7hlJ6B/zuX3KNwAY TTFEurS1zf/bX8Twq1vAuhKMwSaMTjtVTrjgH4lkfPC7avlAbfzp37+/46dE//yjP/+T3M0PeuID 6v1HLX+l8aM3/lqolT8Q7ePt/O+L+VVt/tLbP9SoxV9t/dO6PgZb2/LoCwsxbXmGyeu3ayobBG58 ivptnjGJzcCctz+g8PFHWHjGl9r0zC+gbX1WopReS/vrG19Gpk4fyv6lY5n2cRpGc0teeGsl/Hoq fz39F9Tjp5a/2vjWG381Wo8PvaU1vu7WX73to5a/3uNXvecfvlhf3FPv/1rL7y29+audfym3n/r5 qRq1/qG3f+sZ303h+PbAqzP5z6vV28JbX8eaZaN46cV9tel9EX+l8vt7fhWiqdAy3+i9nuOr9b6h JXfvTpt8bV+iPZO/ruepp9e+Xnq7nqidrzSV9hdCCCECzePfAHabkSmGm+JD2PTuySMAJ5++vJeY C/sDYI64jIvDzazeklG91X6UV38/9bpNU1gP+rYM48PvCkhOTiY5ORnDgW2EJd6CxYMfjDry7qe1 BxS7fzhGZOcIj/M/vmsJO7LLAXA5Szmk8QZAVeUhAApyKyk9VAw4ybQ5SLJU/y5l0Z5DtOvZi363 DGLo0KGkOAyEJ7fUVH6AL5Zso3W/UdWNZghi9A2t+GbpVs2xAUh7e3Nt/jvXbyeqw9DabVrKB97H R023uy4j79vnay9uAaT9nOFRHnrrpxR/NWWZZUS0v54+l3YhOiQYpz2fQo2vsDv0XhrxfboCcOvK N3lpfDcA+iaGsfX7o6rlUxt/vti/v+Onh976e8Jd/9cbH7X+o5a/L8aPHkrlD1T76Jn/9c6vavFv yPlHrf5a18fgkGRmrHiOC9KWMMODm7+BHJ/CO419flGjZ3yZIy6jR7iJ1V9kAuC0ZbNm74nadL7o v0rx05K/nuMLX9B6fOgNT+Jb3/rry/nF2+Nbpfb1xfmHL9YXd9T6v6/Oz9zxRf5q519K1M5P9dLb vwOpsR7fnhRsbc+cxQ/w1YIJfJZZqqlOWuPfUOcvQoizubue05TOJ55+YCwPztjmVVp/Xs9TSu/J eqlnPXGnKbW/EEIIEWianwBWE2RKwGgwkFHpqP1bZUEhQeaLATCaWgCQZTu1vTyvAmJr0lvbAdBr 6J30Oi3fnT/uIsRooPKM3yZ1x1506uKIywEYgjzO/8QeL0/8XdV1czqduKqq86tygalm84BZLzM0 IZ1Nn+/iRGklZpsDg9GiqfwAhXuXcST4XYa1DuddwyA6BqUxZb9nZS3PKqv9t8OWhdEUi9kINqe2 8oGO+KhIjDRT8nWxrjz01k8p/moOvT6V1ea76TdqCpPbNCczdQuzpy4k47Q+707h7x8TEj8cY/BW brZ8g+2yvphCquhiLuXRglNPoLvt3yrjzxf793f89NBbf0+46/9646PWf9Ty98X40UOp/IFqHz3z v975VS3+DTn/qNW/QuP6aI3pS9mbXxA38C4ujtzGzxpebw+BHZ/CO419flGjZ3zVd3xamlN26vjU B/1XKX5a8tdzfOELWo8PveFJfOtbf305v3h7fKvYvj44//DF+uKOav/30fmZOz7JX+X8S4na+ale evt3IDXW41sAg9HCqAVPYfrfkyz5MktznbTGv6HOX4QQZ3N3PcfVhM4nco6keZ3Wn9fzlNJ7sl7q WU/ckfNJIYQQwnue3wB2OcFwdjKHLZMql4sLrEHsLq3+DQdrizgcldUHN47yPwDoFhrMjpO/IdEu DGquZzjKql/ltObxmexX+sazm/2r0Zw/4NJ5MaM+wdZOjL4iiftuvrf2laLd2t/IjR7k4XLaWPph JtPG/InDQdeS+fGT2Dx8ACS8XTj8eKy6TCFtcNhysTk9K59ifLxsH4D04xVEdYsFDnmVHnxTP0UK 9XM6inh/1ULeXwWWmGQmv7SISddvZMIm9QN8W9E3ZDGFa7vcReFH77Pp8rlcfkEQZcfe1XTxS238 6d1/IOKnJ73e+ntSvvr6vy/io9R/tOSvafzojb+X5Q9k+9RHy/yvd35Vir/Pxo8Cxfir1N+kcX0s zXqR+Ws3szVqOY8tGMOQMUuw+Wh+srtcBIWcuuBqiTWfnZEf++/5zGfziwqXszpvi8HLpwbdtL/e 8eWoPAxAe2swv5VV989mrUKhvGa7D+YvpfhpyV/P8YVW7trHV/OXu/w9iW9966/P1hc3+Wuh2L4+ OP/w5/qi2v81ll9tfLttfw/i4w9q56cneTt/6e3fPnMOH98CXDV2EX2qPmXYS996sGffzg9CCI10 Hq+7u55jlPEM+O96nlp6reult/mrkflcCCGE8J7Hr4CuqjhIsKU93WPqXpx1VhWxIb2EviN7YQAM xhAGjulE3vb3ALCX/8am7FLuvasPJgOEtPwzY9pH1aa3l+9lw+ETPDzqWsw1r/wymqK5/Opumvav Rmv+/uJyVeB0uegQU/0NN3NMN8b/zfPX5x14fQ3RF41jbI8Y1r623+P0bW8dTFSwETDyt1FXUPj7 Op+Wz9v2Afht5Zc0v+Qh/to2vOYvRnr0TvYoj4asX+xlfyLaVD2kbIUZHLU7cVRovYPk5O2sUu4e eyX/+zCTn9bv5/7/XELulp3aUquMP737bwz9Qym9/vrrK58v4qPUf7Tkr2X8aKnfmBXrWLf8Do/K rlb+hm4frfO/nvlVKf6+Gj9KlOKvVn+t8XG5qp+Q2bb0YXaFX8szI3tqKpuW9j+YXU7iP2veGGJu we29WpyVj975Q9TPV/OLmqrKQ2TbHAy8NMmrcrprf73jy166i68LK7mnb0cATBGdGJXSrHa7L+Yv pfhpyV/f8YU27trHZ+u/m/z1xtdX64seSu3ri/MPf64vav1f8/mZyvh2t72hz8/Uzk/Vyq+mMfRP OLePb1td+yAP9y7jkcmra197qlUg4z93/QZeXXqNz/MV4lyj93jd3fWcxjKf+oKe+cJf17vU0mtd L73NX01Tan8hhBAi0Dz+al5l4Wes+N91zFjzFsEOBxmfPMr45XsB2DjlGS5aOIW3XrmNkpAWBGd9 zZTnf6lNu/qheTw2dyLvv38fhTmpbPwmj4Ghp/Je99AMEuZM4d0Nd5BV5CQ+oRkHv32R775I1bR/ NVry9xdHZRpz3tnJtJWvcuuRY0RElPP+e+kM6uNZPrbi7bxxNIgBxrf4VuPrN0+X+5mDl9avptQW QZzrANPHfuHT8im1z/RV6+geZiI4NJIw43TefNOO057H0GHjASjYs5zZr0UwcekbjMrOwhCRQNGv Cxj/1QFN6Ru6fgm9h/HUjGkczcqDiCSM2V/x8OeZmvPe82EmEXeHsim/AuOudUR2WMyWJ3M0p1cb f3r2H4j46U2vt/56yueL+Cj1Hy35q40frfVLjGmGKTNfe8E1lB8atn1A2/yvZ35Vir+vxo8Stfir 1d+T9dHpOMEzDy7mtdVzGLztDjZoeG2lWvv/svhVXCseYOMrAyhzFfPJl7mk9Kibh975Q9TPF/OL lvUZl51pC9/hyQlL+Wi6iYxPJzFqYarm9O7a3xfj67lHl7FowVOs/0cWhLvY9ksBV5y2Xe/8pRY/ tfzVxrem+Ktx0z4+m78U2l9vfH2xvijRe/yq9/zD3+uLWv/XVH6F9lXb7u/zM7X2Uzs/VSu/Wv56 +6cvxve5fHzb845eBIeaWbLx3dq/Hf1xBvfM3gU0nvhbw8IIrZS3lAih53oQuL+eA/5f7wNFz3zh r+tdWtJrWi915K+mqbS/EEIIEWiGgwcPugB69eql+MHS0lKNWRqJb9Uac1URGTn1XxQOMgXhsDv4 yzOvc2fqDO5bW/dJq/C4lsSGwPHcXEo8fcexBv7OX4k1piUtwiArI9vjbzFXMzJ94/sEL7qHWd/m eZTyvjc2kTR3JDMPQFKclcz0LKrOKIP+8ulnNIWTlBhLRWEuR4sqNKdrDPULCokiIT4aV9lxso42 xO8lqo8/PRpD/1Dm3/qr0Rsftf6jJX9vxw9AsDWFze8vYdmIAfw3p0w9gYflb+j2AbX53/v5tTYH hfg3hvlHbf3z7/qo3P5BlmhaJYSSm55FhT9fhXmOCAsLU9yelVX9O4iJiYmKn9N6/Obv+cXf9I4v Q1AYrVrFUpCRQYmjvr6vf/5Sjp/K+Gjg4wv/r/9649vw64va+NA7v/pzfVHv/037/AzUz0/1afj+ qUdjOL7V59yOvxD+FFuXHgoAACAASURBVOjjTSVaruecz+M5ENe7tKTXs1764nro+dr+QgghxJnU juO2bt0KePMbwKqc5GXU/zsMzboOoneLw/yyP5vw1n/igc6hrJyXftbnSo5lU+L7ggUsfyUV+dmk ef5wHQBte1xE54sG8BdLBrf/cMzrMlSV5ZN2xPfl8xWnvYT0NO9bqCHr5ygvIjOtyH87UOV+/PlC Y+gfyvxbfzV646PWf7Tkr2f8hLTow2/fLfXq5i9o6f8N2z7gfv731fyqFP/GMP+orX/+XR+V299R WUBaWoHf9i6U+Xt+8Te948vlKCU9Tenipf75Szl+KuOjgY8v/L/+641vw68vauND7/zqz/VFvf83 3fMzreen+jR8/9SjMRzf6nNux1+I843S9RwZz/693qWU3hfrpf75XtpfCCGE8FRA31NUVVJASv9/ c/XNzbCdyOKVGffyeX5lIItwTrv0hpvoSj7zxj9DUZXn34xP+/EHiovtfihZ49DU6yeEvxWnvczE mQ1dioahd34VQgghxLlHzk+FEKJxkOs5yho6PrJeCiGEEOcmP7wCWgghhBBC6NGYXsknhBBCCCGa HjneFEIIIYQ4N2l9BbQxEIURQgghhBBCCCGEEEIIIYQQQgjhf3IDWAghhBBCCCGEEEIIIYQQQggh mgi5ASyEEEIIIYQQQgghhBBCCCGEEE2ExzeADUHhxMfH1LttwMLlTLkkTneh3BmyeAWP9Ij1W/6g XL9ApG/stNTvghHzWTipW4BKJIQQQgghhBBCCCGEEEIIIYQ4yeMbwKHxt7N6+fR6t0U0jyfaHKS7 UO6EN48n2uLfh5aV6heI9I2dlvqVpv9G6oHiAJVICCGEEEIIIYQQQgghhBBCCHFSsNYPGoxmYmMi CY22gsFEXFz1k76OiiIKSux1PmuKTKJzh1jyD+4h84T9jIxMtO3YkWizgwN79lFc5fS40GEJyaTE Wzny+x7ybWekV8nfGBxJSuf2WFwV5KQdJK+m7J7Urz5a01uiY7CWF3HCZqFjlwugNJO9h4/Xbg+N a0W7lnEYbPns23cEu+vUPkJjYjCVFFJmbek2vu7qpyX/U2VMIqVNcyoKs9mflqu5fkZTFDFRJuy7 PuQ9e1G9cTKaIkjp1AFTVRH79h3m9ObzRf2EEEIIIYQQQgghhBBCCCGEOJ9pvgFsCr2QWbNGYDTF EWQJYdasWQDkfL2AJzYcrv1cdNfBrBh5AQW2KLq0MzN/+Ei+PF4BgCX2Up54dhpJFWkcKbXQtQ08 PfZBtuZVaC5wyz7jeLlzJGnF4XRvb+Dp0ffzVV65pvytcb1ZsvxhnGmp5DtDaJvSjqXDbmXbCZvm +umNT58FK+i3ZTXlN4wgqvgYpujW7J44hAUZJfSY+iJP9ghhf3oWrtBWdAjP4bFx0/i15ibnrc+t 4q9fbMF0ef3xVaofoJo/wHWj5zC+bwr7Ug9iiGpDbNErDJ/yP031i2h7O7MmdMUa3xbLHzMYNu2n OjEKS7qGRc9PhLRfKQlrRzvTASbfN5M/Kqp8Uj8hhBBCCCGEEEIIIYQQQgghzneabwDbSnYwbtwO wlqOZsOyTowbN7Hez8VcXsKQ0eOxu6D/CxsZdmcyXy5KBeDeZ6di/eophq7eDkCn/guYO+82tt69 VnOBoy4+zuDhM7E54coHX+bBJ27mq3vf1JR/yj0jCf1jPrdP+QYAoymGSFeVR/XTGx+A1jcNZPYD w9mRXY7BGEY7cyUAR95bwMB5B2qfyv37E+uYNL4zI+burk2rFF+l+mnJP7bnRCb8O5FHhg0ntbD6 pmrbi1tprl/R/mWMGwdd71/Jowln1/vOeffj/GgmY17eCRi5bfHrTJ98CcNnf+eT+gkhhBBCCCGE EEIIIYQQQghxvvP5D+oeeffT2huMu384RmTnCABMYT3o2zKMD78rIDk5meTkZAwHthGWeAsWo8GD /DfVvjb459e3Et5qMMEGbfmXZZYR0f56+lzaheiQYJz2fAq9eAW1Xsd3LWFHdvVTyy5nKYdqnoAt 2nOIdj170e+WQQwdOpQUh4Hw5JZ10rqLL6jXTy3/bnddRt63z9fe/AVI+znDJ3U2mmK4KT6ETe+m 1vzFyacv7yXmwv4+q58QQgghhBBCCCGEEEIIIYQQ5zvNTwBrZS86dfPQ5QAMQQAEWdsB0GvonfQ6 7fM7f9xFiNFApbOeH6OtR3lWee2/HbZMjEGhRAYbKdOQ/6HXp7LafDf9Rk1hcpvmZKZuYfbUhWTY HF7U1Hsn9hTW+/cBs15maEI6mz7fxYnSSsw2Bwajpc5n3MUXUK2fWv6JkWZKvi72YU1PCTIlYDQY yKg8FevKgkKCzBf7rH5CCCGEEEIIIYQQQgghhBBCnO88vwHscoLB82SOsn0ArHl8JvsrvH9tb3j7 cPguDwCTtT1OeyGFdidBGvJ3Oop4f9VC3l8FlphkJr+0iEnXb2TCprRTH/Kyfp6kd9VzszvY2onR VyRx3833cqCm/N3a38iNHuxaqX5a8k8/XkFUt1jgkK761cdhy6TK5eICaxC7S6t/c9jaIg5HZZpK Sm31E0IIIYQQQgghhBBCCCGEEEJ48QroqoqDBFva0z3G7FE6e/leNhw+wcOjrsVc80pmoymay6/u 5lE+rfsNITrYCBjpPaoXBfvW4tSYf+xlfyLaVF1lW2EGR+1OHBV1XyHsbf30pne5KnC6XHSIqX4i 1xzTjfF/a6mSqi6l+mnJ/7eVX9L8kof4a9vwmr8Y6dE72Sf1c1YVsSG9hL4je2EADMYQBo7pRN72 93xSv9PNXb+BV5de41H5hBBCCCGEEEIIIYQQQgghhGgKPH6Us7LwM1b87zpmrHmLYIeDjE8eZfzy vZrSrntoBglzpvDuhjvIKnISn9CMg9++yHdfpKonrpH7eRkvvLaGsoow4gyHeGzsZ5rzT+g9jKdm TONoVh5EJGHM/oqHP8/0Wf30pHdUpjHnnZ1MW/kqtx45RkREOe+/l86gPpp3rVg/LfkX7FnO7Nci mLj0DUZlZ2GISKDo1wWM/+qApvpNX7WO7mEmgkMjCTNO58037TjteQwdNh7+P3t3HhZVvT9w/D0H ZhgYFmEQBNyV3FOz9WZa3by3xUrzumGKmltqaqZpam6VWXq13EorzUxNLcvy13ItrVzS0syM3Bdk U5R9n2Fmfn+MogjMHJgBjD6v5/F5ZM75fs7nu5zDYb5nATZOnkfbBZPZtLoP2d6heCbuZPKi391S v2vpDQZ8Ctz+dHMhhBBCCCGEEEIIIYQQQgghbnia06dP2wA6duzocMWcnBy3bdQ3OAyjN6RcuEC2 qeQdnM54egcRUVtPYlwi5lJeHewovod3AHVCArHlppB4sXLed+sKfVAYoQZIjE8qtW7OOKufmviK 1peIcCP56Re4mJFf/iQcUgipWw9dYQbx50t/F7IjN3r/CSGEEO5gMBgcLk9MTAQgPDzc4XruPH8T QgghhBA1h5xvCiGEEEL8NTk7j9u1axdQkXcAu0H2pSSyXShfmJdK7LmKxbfkZZAQm+HC1itXfmoS sakVL++sfmriW83ZxMW60kMOo5McX/F39t7o/SeEEEIIIYQQQgghhBBCCCFEdSr3O4CFEEIIIYQQ QgghhBBCCCGEEELcmGQCWAghhBBCCCGEEEIIIYQQQgghagiZABZCCCGEEEIIIYQQQgghhBBCiBpC JoCFEEIIIYQQQgghhBBCCCGEEKKGkAlgIYQQQgghhBBCCCGEEEIIIYSoITyrOwEhhBBCCFE5NIqB vn0e5+CnmziSZwbAJ+x+Hu3owYZN26o5u6qj8fCltlFHcnJqdadyw1m7cSM6jYJOp2Xof7qTbLaW WEdN+zUdOJeRwWsYPz+mMtOtkKro/+qs/43QP+6KX1ac6mxfRVub1SsXAJB65HXGzjl83XIjDzzW lVaNQijIvMih3V+zO+Z80fJGdz3MI3e2QmtO45dtn7DrWJq9nGcgfXo9BIDNZiEn7QIHd+8mLst8 bXRuvu8xOrWLRG/NIeHMMfZs/5HYbPs6kY/24DY/r2L5pB/9mi9/TaX2nV3pHHqGj7cUb7NHevUh b9fnbE/MdZjfFf0XreBfgd6Yc48zePhLDtvqRjwOOOq/oLYP8WCrwBJltny0nhyrzWH7KtoQ+vR8 gJz4b9nyYzIAOr876fFwCOs3fA5Aq8f/Q1uDrlh5i+k8Gz7e7tY6CnEjafPE0wx6oD1BBj2W/Bie Gv5adackhBBCCPG3JXcACyGEEELUUBpPP6Kjo2ll8Cj6zBDRhf5RD1VjVlXPJ6QfK5dPq+40bkj9 evUiKno6er0eRVP6OmraLyfuT2JOZVVChq6riv6vzvrfCP3jrvg6PyO1A3UlPq/W8aXxICQkhHmj hzNhXvGJTQ+vBry8eiVRt4UQe/womVY/+k/oWbQ8/IGJLJ0aRV7CcRJyApm0cDXd6vsCoHgGER0d zS1htQkKCqFDl4EsX7eSLmE+ReXvHruEl4Z1Ij3uOCcSMwjv0I1RHYKLlnsHhxAaGsodPaPocX9z QkNDMfrZ28+S05bBQybiec1+rTXczJinBqHkWpzmd8W650YxdtovhISUnCi9Xln9V60c9J+xw2NE 9e5IaGhosX+eGnujOWpfD69woqOjGTpuMtrLbawL+Af9n+xRFP/mHlH06NysWGw17SjEX5WHrh5z hz5Os7oK+/bsYveeP6o7JSGEEEKIvzW5A1gIIYQQ4m/MJygIbXY6ufowmjc2knr6CAmZZucFAY2H H4EBhZi8Iqinu8TRuFyatW5BxskYknILr1lRS4ObbiJQZ+HUkeNkFRa/y9QrMAh9XgaZJi9uatEU chI4djblunUiiKxfm/z0JE7GXiheh+C6NAwLRmNK5fjxc5htlzer6DAG+eMTqAeNluBg+8SJJT+D tOyrdVS0fkQ2a4y2MIPjx89iKnkTrFOO8nMUvyrav6L1U9N+ijaAoAAt5kNf8qk5Q13gaxiMQXhm plNobERkbS/OHD1KxnV3IZfVv1eUNX7U9r+z8WnfRun966z+rtbP0fioiv4BUDz9iWzeCC9bPudj T5NcbN9xPb7WP4zmjWuTduZIyW2rjO9o/1PTv2oUms2Yr+u79mNn0Nz0Db1fWFbUb+tW64uWj3m6 E0eWjeS9L+MASGjUhrGT7+WzkVuL1tm5ajlbUvMBDdGrPmbwpFvZNu5HFG0QLzzYmBUDuvH5xXz7 ypvWotVevYb791Vv8TvwRNuOPHToIxYu/rNoWcax1Vg936VroJ7PUu3lA1v1xJzzB9+mF6jOz2I2 YyrlyQBXOOo/wGH7qzn+ORp/oP74Vlr/AZgz97Fw4epSyzhq3yuOmpoyolkgi4+mlQwAXDq4joVv Hys9KSFqkIAgI4Za7fBUNGSnfsGmT3Zjs16zvyo6goP8sVqySMuApi2a4ZF3kaOnk66uovUj8qZG 6Mjn7PGTZF23zwY3bEG4Tz5//HmGWkYjGlshKan23w1B1/6s0RJsDMBqzSb18vHPWfxaQUY8NFZS 03K4qWULrGmnOZFw9cIjX2MQek3xe2hsVhMpqZmq8xdCCCGEqGoyASyEEEII8TfW8833uPv7HWjv aEqaKYAWDXXMjR7EDyn5Tsv6ho9izZJGnDiWRZPWzdl7MIFAby03RSTQrfeLWAEv4628/N+pROTH ci7Hi5b14fVRz7Ir+Wr8zvNX0G3HSvIeHkhA1iW0gfU4PL4v8+OzAegyfDZjukZyPOY0moD6GDNW Ez35WwDaTHmLV9p4czIuEZtPXRr7nufF0VP5I9uM1udmZs4ciKINxsPLm5kzZwJwfud8Xt5wFgBD xP0sXDQeYv8g29CQhtpTTBo5gxP5hajlKD9n8Su7/V2pn5r282vQj5njWqIPaYDXien0n3pQdbsB RC9ZxW0/H0LfwpfYbH9aNyjk5aFj2Ztqn6By1L9XlDV+FqU3d5q/mvHpqH+d1d/V+jkaH1XRP/rg TixePhFrbAypVm8aRDZkSf+e7Mk0uSV+UPso3nqpL0kxh/GKqMe5C55wzdBXE99R/6jpX1cM7xjK idc2F5u0t5rtsbU+rWjvq2PR3otFy45sOofvnF7AVkqy8XtiLo9F1AZAo/ijVTTo9cX/ZC9tErM0 FlM8H1/K4/576/DZ5rMANO/TlEu/vl7B/Epy1n/O2t/Z8c/Z+HPH8dtVXy/ex7BxD7B4xKYq26YQ N6IZ762mlY8WAN+wkaxdOxJT9m882mMSADq/21m79kVyk9cTY3mc2y4/7eDbp3owLz6boPY9mD9j EBHe9hjm3ETem/4cnx62v97grhHzmdm9DQAXfl5F4K0DIfdQUfzVH64t+llraMXata+RnbScHgM3 AziN/8qq1TTySOOrWC+6Ng3AZitk8wsDWHHQfkHi0CWreDDo6gU+AKasfTz6n+mq4gshhBBCVAeZ ABZCCCGE+JsLuiObvsPHYLZB96Ub6T+gCT8sVPcOR0tBPOOef4n7395Iv/QlPDU1htVfbKWNQcuh HDPD/jsF/Y+vEbVyLwDNus9nzqt92PXU+8Xi1HusB7PGRrM/KQ+NYqChzj5BZmw/nnGPhPN8/2hi 0u1f+jdoV7eo3LlP59Pj1VNFEzAPvLyGCWOaM3DOYUzZ+xk9ej+GsOFsWNaM0aPHl8h/wKvPYP1q BiPePQAo9HljHdMmdSB61j5V9XeWn5r4ldn+rtRPTftlnFzG6NHQ8pl3eKGOqpRLCGh7nt6D7HdQ dpq4knEzu9BnjH0CylH/Xqu08WPKd56/s/HprH/V1N/V+pU1PqqifyKHDMLnxFz6Td4NgKINwt92 dXLNtfgKU6ZFcXLJKKZ+HYuiC2PppnfgmieGOovvrH/UHn8qwsOrPvW9PNlbxqOpPbybA3Ay72p7 mS4l4qG7jUBPhSuldAYDviYtgXXbMKx1IDHv/AyApeAsa35PZeCyt2i67Ud+j/mdfbv2c7HAojrH 7z+N49FHb4fNZwGFqCZ+HFh8WlV+aU7vlHbef2ra39Hxz9n4c/X4DaD1v4Nnnw0q+jn34mcs//CM 6vIphxeTEraW2323UPL+YDC2682YMelFPxekfsPyD+WOYFHzrJz7EkH+7Zg64QnyLm3m9aWHsVhK PrnBJ6Qvhv+tY847x9EFNyG4wIKiDWHB7KcItp7l1effJNEWyviXnuepl1/hf91HYvJpx4vdWlNY EMfi198lqNMwohUNJpW5OYufY7X/EvbQhmD84TVe/t+dTBvZmS4j72TF0P8DYPPcmezU2l+p0vjB UTx1TzjpR38tV3whhBBCiKom7wAWQgghhPibO7d5W9EE1OFfLuHf3E912cIC+xflaRcKyDmTBVhJ MFmI8PJAa2hD1zADX+5Lo0mTJjRp0gTNqT0Ywv+D13UvnE05tJj9SXkA2Kw5nLl8B1erwbeT/NOi oskdgNjf4ov+n3HkDA3bd6Tbf3oRFRVFpEWDb5MwVbkr2iAeC/Fmy+Yrk61Wtr17jKCbu6uuv6P8 1MavrPZ3R/2qQuzHW4vqf+DDvQQ0jipaprZ/yxo/jqgZn87GX1XUz5Xx4archFz8Gj1E51tbEOjt idWcSnoFH6F8PZ3f7bTx1bLy+wQArKYkVh3LdFKqOEf9U57jT0Uoiv1dubmW0ttD4+ENQME13/vb bPYLW7yu+St8yLvr+OSTj3nnjamcWzOd6VvjipatnTSI2Uu3YjFG0mfki7y/aRVPtFL/DtmkHV/g E9obPw8NXoFdaOCZzwdxWeXKryzO+k9t+zsa347Gn7uOb7bCLBITE4v+nU8uKF95ax5vbk1kyIDI UpcX5qZz6dKlq//S3XP3uRA3mj/27WPfLycAMOcdZ8+ePezbV/JitoKMH3n2v6v5YfdPbNvyIesv 5uFXbyBhOg+yTu2BwDDCgxT2ns9Dq2/Mw0F6fOs+jodGQ9KOhXy9ay8bF7xSrtycxb/Cas3nlU3b 2f3VKgC0hvpFy2IPHWT//v2cpTkD7g4jO+47xs78vFzxhRBCCCGqmtwBLIQQQghRU9nKuFPMVnzC wpxxdfLEZgE0HuXehtVqxVZo/xa/0AZawEPfEICOUQPoeE2RA78ewlvRUHDNHRGZR9IpTbi/juyd pd9hB/DEzHeJqhPHlu2HyMwpQGeyoFG8VKXuoa2DotEQf80ddQVp6Xjo2qkq7yw/tfErrf3dUL+q kJeYW/R/iykRRWtEp4DJqr5/yxo/jqgZn87Gnxqu1s+l8eGiM+umsFL3FN2GTmZS/dokxOxg1pQF xJvU34VaFkUbCkDiNbFyzueCUX0Mh/tfOY4/FWExxVNosxHhXfqf1Faz/V3EQZ4azl7+TNEGYrMW klpoA539s2V9H+fzDBttHxrDK0NH0HDrSM5evoDBZs1n79cb2Pv1BjSKnn89s4SRLw5hc595qnIs SNvGn/lj6V3HwDdt/012whoyLk+gOs3PCWf9p7b9HY1vR+PPXce3wtw/2bBhQ7nKXO/U+rcJWzsW 363HSyzLOP4N69bJHb9CXGHK/rXEZ/oQ+4EjqFV/XmhVfFldLw90RvsFK1mXn7hQmH8Kq039MdxZ /CtshemYbaBcvhhGoyl+sZBvgy4sndkPW/YfPP/MAlIvH0/VxhdCCCGEqGoyASyEEEIIUUPZLBlY bDa013yBpWgVrIVpVbJ9S679y/BVL83gpJM7Mm1lTMbEpeQT0MoIlHwkp6e+GcPvimDk48M4deWO 4UaP8mjJ4KApedprMSVQaLPRVO/B4Rz7O1f1ocFYCmIdV0xlfu6I7wq127dZ7W3npSnjrsgy2s9d fBv6wq+XAPD0ro/FdAGTtRz9S9nj5/LC0vtfxfh01L9quaN+DlVi/1gtGXz23gI+ew+8gpow6e2F THhoI+O2uD6GLQVnAWik9+TPXPv4rFXXB/LUx3C4/5Xj+FMRVksm/0stoFXXerCs5MN/zVkHMFlt 3FHLi18vv9O51s3hmLN+xmS1FftD3GYp4Let89j02MdMe7otQxYeKBHPZs1n98ZfGdvl7nLl+eGv lxjxaAQXb6nHmfU/q87PGWf95472dzT+qvv4ei1z9kFWJtRiTPvgKt+2EH85pVycmH/e/i7yuK8n M/qtI9csUbAU5GIwZAMQ2MYIn59D69sO5bpzFis2PDzs7xX20IWXK74aulrtmL9oLH62C8weOY1T 1zw+3x3xhRBCCCEqgzwCWgghhBCihrJZTfyQUcDd/7bfjqBRdHTqVo/sc7urZPvmvGNsOJvJxKH/ RHf5kZ+KNpA77m3lpORVf77zA7U7PMfdDXwvf6LQplMTAGy2fKw2G42D7HdM6oJaMea+ko8HLsw/ jadXI1oH6Yp9bi3MYENcNl0HdUQDaBRveoxoRvLeT92Snzviu0Lt9gsLzpBkstDj1ohS45TVfu7S oGdvAjwVQOG+oXeRfnQNoL5/nSkrfzXj01H/3uj1cwfj7bcQqLX/yWhKj+ei2Yol3z2PgDbnHGJn egFDut4EgNavGUMja5UrhqP+ccfxx5mPlu6i7sNTuS/S/g5ZxdPAPx7+BwDWwlRWnczgntEPo1NA 0Rrp/2QT4v/3UZnxPpmzhYguU2jhrQVgVK/7Cb58h7FG8ea+/reRd2l70fo6Xz8CAgLQKxoULx8C AgLwve5us9NrD1Cnc1f6hvuy/pdLRZ9XJL9rOes/d7S/o/FXFcdXNe17xfaF39N+SOsSn3vofAkI CCj2TwhRXFbCSmLzCwn/53P0/FdH2rbrwAMP92Tmovcx2yAn7mNMVhuhd03iyW5dGT7j+RIxjuUW ovW+ieHdHqTfuKhyxVdj8tJZNNJrOfPt19TqcC8PPvggXf7ZwW3xhRBCCCEqg9wBLIQQQghRg701 ZRlzXnqRzx7JJFcXiObCfl6eVDUTwABrnptOndmT2bzhSRIzrITUqcXpn95i3/cl3wtXmrQjy5m1 1o/xS9YzNCkRjV8dMv6Yz5gfT2EpiGX2JweY+s4H9Dx3CT+/PD77NI5enYvHKEj/jhXfdmH6qk14 WizEf/MCY5bbH8m5cfI82i6YzKbVfcj2DsUzcSeTF/2uun6O8nNHfFep2r7NzNQFn/DKuCV8NU1L /LYJDF1wtX8ctd+099bQ2qDF08cfgzKNjz4yYzUnE9V/jOocL3xn4e0PV5Jj8iPYdoppo74HUN2/ zjjK39n4dNa/aupfnfVztX/qdOrPa9OncjExGfwiUJJ+ZOL2hKLlrsZ/84VlLJz/Gh/+KxF8bez5 PY27rlnuLL6z/nH1+OPMhd3zmL1uAuMXfsiw9BQMxmCSf1vJni/ty7dOe4XbFszgk48eJ1cfSO6R rxj//oky42WdXcMXF7vz3NNtGbJgP6F39uWDQRPJTrsIBiPKxRgWTF5XtP6/F7/P6PDLk99dXmFj Fzj76RiGv331kcNZcR9g9v8In+wfOXj5Tt8rypvf9Zz1n6vt72z8VfbxVU37XpFxcgUxpodped11 GPUemcPGR66L++9/uy1HIWoCqzmVic+/yYwXhvPkqIkA2Gw2Lp75AwBz3hGmrP6RV6M70f/pZzi+ 7Q1M1rHFYry1Zi/LRtxD9xHj+POrDUAf1fHVaBto37mbPjyIZx+2f2bK2se27w64Jb4QQgghRGXQ nD592gbQsWNHhyvm5ORUSUJCCCGEEH93BoPB4fLExEQAwsPDHa5XdP6m8aRO3Qi05kzizlfN45+v 5xschtEbUi5cINtU/jsIFa0vEeFG8tMvcDEjv9gyfVAYoQZIjE+q4J0WCiF166ErzCD+fPnfJess P3fEd011b79sI9dvIWLOIGacgohgPQlxiVz/+lHX+9c5Z+PTcf+W7Uapnys8vAOoExKILTeFxIuu vQ+5NBoPA3XrGkmLjyfbUrG7i531jyvHH0VXh6++WM388WM4nnaO2MSSz6hWtL6EhxsxZaeQnJJ9 /VJCIuqhNaeR2r9GYwAAIABJREFUkJxZrm0DaA0BhAQHQl46CcmVsf86zs/YoCFhdfoxd7KRrt3H l1iupv9caX/n48/x8U1N/wnxd+X28003CAyNIEAPmamXSM0qKLZMVyuUOt5mziWl8sVXX0PuIR7t MalouXdwBEYlg/jk64/D6uJXdv5CCCGEEO7i7Dxu165dgNwBLIQQQghR89kKOR9X9e9FvFb2pSTK /jrOOas5m7jY0iPkpyYRm+pCcKwkx7vWPo7yc0d811T39p0rzE0l9lzpy1zvX+ecjU/H/etcddfP FZa8DBJiMyotvs2SQ1ysa5MHzvrHpeOPtYD9+/dzb9QAbjnzPq+9W/IOWas5m3hH+39Cxfc/c04G CTmV1/7O8rurTzR3+es4cLDkXa+grv9caX/n48/J8U1F/wkhbhxpFxIo61JFU/oFzjm4DibvUgLx LsR3h8qOL4QQQghRHjIBLIQQQgghhKgWsb/+QlaW2fmKf1E1vX5/B9bCNKZOnVrdaVSbra/NYmt1 J+GCv3v/CVFT/fLLPsg/U91pCCGEEELc0OQR0EIIIYQQN5gb8ZF8QgghhBCi5pDzTSGEEEKIvya1 j4BWqiIZIYQQQgghhBBCCCGEEEIIIYQQlU8mgIUQQgghhBBCCCGEEEIIIYQQooaQCWAhhBBCCCGE EEIIIYQQQgghhKghPMtbwDd8DG+9dhsAhXlnGDRsetGydp0epF2LxgQH+GLOusTBXV/y4+Hz6gJr PGh9R2fat7qJUKMf+enn2ffd5/xyKsM98QEPfS2at76ZlpERaDUavtn0ESlma9FyRRfO6vdeK/p5 /ojBHMoxq4qtaEPo0/MBdn68gTiTBQBdwD30eMjI+o8+U52jGk0HzmVk8BrGz49xa9zKtnbjRnQa BZ1Oy9D/dCf5mrZXS+PhS22jjuTk1ErI0M4n/B4mju9Do9p+FKR8zPDxn1fatspLTf1dHR/VOb6q on8rqkH32Uy+61uefv7H6k7lhuSO9nG1/x2Vf2LBcm5aO5W5By45jFHR8S/jQ9zoFK2RBx7rSqtG IRRkXuTQ7q/ZHXP1HKrRXQ/zyJ2t0JrT+GXbJ+w6lmYv5xlIn14PAWCzWchJu8DB3buJy7r2/Ejh 5vseo1O7SPTWHBLOHGPP9h+JzbavE/loD27z8yqWT/rRr/ny11Rq39mVzqFn+HhL8X3ukV59yNv1 OdsTcx3md0X/RSv4V6A35tzjDB7+klvaTAghhBBCCCGEEEKIv6py3wGsaGsREhJC1jfreff9T4st GzXhGfo+8Tj33t2Jh7v1Zsq8VTzzjxCVcWvz31mT6PPYvbRofjOPPNGPl5Z8SN9mAW6JD3DbguUs eGUqQwYOJDo6mlCtR7HltsI0VqxYwZdxCiEhIXgpGtWxPbzCiY6OpoH31Zj6Wvcx4MleqmOolRP3 JzGnstwet7L169WLqOjp6PV6ytG0xfiE9GPl8mnuTew6XWePo87hdTz37DgmzfpfpW6rvNTU39Xx UZ3jqyr6t6I8fYOpbdRXdxo3LHe0j6v976i8X+0QAnUepS67VkXHv4wPcSPz8GrAy6tXEnVbCLHH j5Jp9aP/hJ5Fy8MfmMjSqVHkJRwnISeQSQtX062+LwCKZxDR0dHcElaboKAQOnQZyPJ1K+kS5lNU /u6xS3hpWCfS445zIjGD8A7dGNUhuGi5d3AIoaGh3NEzih73Nyc0NBSjnw4AS05bBg+ZiOc15wVa w82MeWoQSq7FaX5XrHtuFGOn/UJISKDb208IIYQQQgghhBBCiL+act8BfEXaob3sPlz8LqtVr07l 2OE/Sck2EdqmDx/MH8S9Q+5i8Z4tzgNaclnx+mT+b8dv5FtthN0xgfdnd6HbmPasH/W96/GBi3v/ x4qtpwnuN5Yngr1LLLdZ89i5cyct2w1QFa8ifILr0jAsGI0plePHz2G22T9XPPwIDNCQkpp5zdoa goONZKamYLLaULQBBAVoMR/6kk/NGSVjBwWhzU4nVx9G88ZGUk8fISGz+B3MWv/wy8uOct7khbeS T3Zuocv5u4ui9SOyWWO0hRkcP34W0+WbhDWKDmOQPz6BetBoCQ62f7Fsyc8gLVvdXdqO4oO9/XwU hfaBOuJ/jUejKCion6k2GIPwzEyn0NiIyNpenDl6lIzr7nJ21n5egUHo8zLINHlxU4umkJPAsbMp qurvbHxc3UYEkfVrk5+exMnYC9e0jePyrtbP0fhU27+Kpz+RzRvhZcvnfOxpklX2vcbDj8CAQkxe EdTTXeJoXC7NWrcg42QMSdeM//KMb42HL8ZAPRkpl66up9HS4KabCNRZOHXkOFmFxdunovlXJL/K qH9Z47P0bapvH1f37/KU1/pHlHp8dHX/UVV/IapJ+7EzaG76ht4vLCsaj+tWX71gYczTnTiybCTv fRkHQEKjNoydfC+fjdxatM7OVcvZkpoPaIhe9TGDJ93KtnE/omiDeOHBxqwY0I3PL+bbV960Fq32 6jWGv696i9+BJ9p25KFDH7Fw8Z9FyzKOrcbq+S5dA/V8lmovH9iqJ+acP/g2vUB1fhazGVMFniwi hBBCCCGEEEIIIURNVOEJ4NLs+em3ov/nZdknMvOTHT9u8wqrJZNPvjtY9HP66XMA2KxXv8xzJT7A qQ/e4xTwaM/Rqsu4U5spb/FKG29OxiVi86lLY9/zvDh6Kn9km/H0acmHa6cw7PEnih4h7RPSkw/e 60qvbtGYAL8G/Zg5riX6kAZ4nZhO/6kHi8Xv+eZ73P39DrR3NCXNFECLhjrmRg/ihxT7F6rGm/uy bE4UF2IOo9QJ5+wFHW3N80rEqUj+7mCIuJ+Fi8ZD7B9kGxrSUHuKSSNncCK/EK3PzcycORBFG4yH lzczZ84E4PzO+by84azL8QHuHzeFB4P0hHt7Ujh6EjPNVvJTv2DC9G9UxY9esorbfj6EvoUvsdn+ tG5QyMtDx7I31f4Ftpr26zx/Bd12rCTv4YEEZF1CG1iPw+P7sii9udP6OxsfAF2Gz2ZM10iOx5xG E1AfY8Zqoid/q6q8q/VzND7V9K8+uBOLl0/EGhtDqtWbBpENWdK/J3syTU77xjd8FGuWNOLEsSya tG7O3oMJBHpruSkigW69X8Sqsn+u8NCF89ziRRgPLGPSiu0AeBlv5eX/TiUiP5ZzOV60rA+vj3qW Xcn5Luevpn2rov5ljc/58dkutY+r+7fa8oEte7NiUOnHR1f3H2f1F6I6De8YyonXNhe7GMFqto99 rU8r2vvqWLT3YtGyI5vO4TunF7CVkmz8npjLYxG1AdAo/mgVDXp98VNKs8rJWIspno8v5XH/vXX4 bPNZAJr3acqlX1+vYH5CCCGEEEIIIYQQQgi3TgADNOr9Kq883oRaQX6c+uVrFs75uQJRFLpP6gbA N0t/r4T4lefhoaO45co7gP2aFFt27tP59Hj1VNEXsA+8vIYJY5ozcM5hTFn7+DzFxvDbajNtt/2d fJHRD5JycCnZFnuBjJPLGD0aWj7zDi/UKX37QXdk03f4GMw26L50I/0HNOGHhTGAwvMzoji59Bmm fnUWRVubNzatgnK85tJR/u4w4NVnsH41gxHvHgAU+ryxjmmTOhA9ax+m7P2MHr0fQ9hwNixrxujR 490aH2Dr9AlsBWZ//AXpLz3PgusmtdQIaHue3oPsd1h1mriScTO70GeM/Qtqte1X77EezBobzf6k PDSKgYa6Akz5zuvvbHwY249n3CPhPN8/mph0+6Rjg3Z1VZd3R/3KGp9q+jdyyCB8Tsyl3+TdACja IPxt6u9etxTEM+75l7j/7Y30S1/CU1NjWP3FVtoYtBzKMavuH099A15YugDtjnlM+nBv0efD/jsF /Y+vEbXS/lmz7vOZ82ofdj31vlvyd3X/c1f9SxufrraPq/u32vJlHx9d33+c1V+I6uLhVZ/6Xp7s LePR5h7ezQE4mXf1eGS6lIiH7jYCPRWulNIZDPiatATWbcOw1oHEvGM//7IUnGXN76kMXPYWTbf9 yO8xv7Nv134uFlhU5/j9p3E8+ujtsPksoBDVxI8Di0+ryi+tUO76FUIIIYQQQgghhBDiem6fAC7M SeH8eQMaX1/qNWtF60a+nIhJK1eMe4bPJ7qNkZhPZrPqaLrb41emzJSLXCqwfxmpMxcA2qJlGUfO EHlLR1o1DsdH50mARYNvkzDAPsHyxZqTvPnU/bB7HWi0DO8YwvbR5ZihBc5t3lY0gXP4l0v0vcvP novf7bTz1TFyRzwAVvNFPjiazthyxHaWvysUbRCPhXizaPOV+lrZ9u4x+s3qDuy74eNfEfvx1qL2 P/DhXgLeieLKHUpq2y/l0GL2J+UBYLPmcCbfPbm1Gnw7yT/NKZq8Aoj9Lb5cMVytX1njU43chFz8 2j9E51tT+T3mBGl5qaQ7L1aksOAMAGkXCsg5kwVYSTBZiPDy4FCOWVX+nt5NmL5iJA1OvkH0NZN7 WkMbuoYZeGNfGk2a2C/80JzagyF8MF7KagqsNpfzd3X/c0f9wfH4dKV9qoIr40/N/lNW/YWoTopi f1durqX0iVKNh/2VGAXX7IY2m/3CDi+FogngIe+uYwhgsxXyw7sv8urWuKL1104axMl/PU7nuzrQ Z+TjPD02lfdeGMtmlednSTu+wGfICPw8NmHy70IDz3wmxWWpyk8IIYQQQgghhBBCCFGS2yeA47bO Z/xW0BqasHrDEgbPeIZPe81WXb5tn1lMe6IVJ75cyHMrdrs9fmXbtXkjuzLsEwT+DZrS+5HmRcue mPkuUXXi2LL9EJk5BehMFjSKV9Hy8z+8jde4N7hJv5GkkGjq204zppx3oZozrk5O2CyAxgMARRsK QKLp6h05ecn5YFQf21n+rvDQ1kHRaIi/5o6hgrR0PHTt/hLxr8hLzC36v8WUiKI1olPAZFXffplH yjMtqF64v47snaXfAaaWq/Ura3yqcWbdFFbqnqLb0MlMql+bhJgdzJqygHiTyrvMbPb1rFYrtkL7 TEKh7eolGmry1wd1Jfej7wnuMZh2/nv47fLjmz30DQHoGDWAjtesf+DXQ3grGgqsNpfzd3n/c0P9 wfH4dKV9qoIr40/N/lNW/YWoThZTPIU2GxHepZ/yWc32d1kHeWo4e/kzRRuIzVpIaqENdPbPlvV9 nM8zbLR9aAyvDB1Bw60jOXv5FQo2az57v97A3q83oFH0/OuZJYx8cQib+8xTlWNB2jb+zB9L7zoG vmn7b7IT1pBx+c5ep/kJIYQQQgghhBBCCCFKcPsE8BXmnFOcLSikvaG16jKRXSfw2sA7OPblG4x7 82scfa1XkfjVyVPfjOF3RTDy8WGcuvyFaatGj/LoNesU5p/kg7g8hnWqw8cdO3N+x1zc9d2mJe+E fZs+nuy//E5PY0PD1Vt73JC/GjarvayXRlM8P1MChTYbTfUeHM6x56cPDcZSEHt9ANCUf9iqju8i 34a+8Kv9vdSe3vWxmC5gspav/WyOJsMqWH+AuJR8AloZgTMVKg/uqZ9DDupntWTw2XsL+Ow98Apq wqS3FzLhoY2M2+J6H6rNPyfxLea+v5VdAct5cf4I+o5YjMlqw5J7HIBVL83gZH7pj3V2JX+3ta8b 4jsan660z+XgFR7fbinvgJr9p6z6C1GdrJZM/pdaQKuu9WDZnyWWm7MOYLLauKOWF79ePj+odXM4 5qyfMVltxU4UbZYCfts6j02Pfcy0p9syZOGBEvFs1nx2b/yVsV3uLleeH/56iRGPRnDxlnqcWX/1 9R7O8hNCCCGEEEIIIYQQQpTktofn6Y1dWT5vBsOf6k+P7j14esqbdPDVkXv+K3Xlgx7hzdEPYLPm cMHQnslTpjBlyhSeG/kPt8QHeHjMBKZMmcK/Au13tQ147nmmTJlCB1+tk5Kus9nysdpsNA6yb1sX 1Iox94WVWG/HO38QObAHI9sb2bLmlNu2b877ky1JOQwb3BmtBrzDbmNEowC35+9MYcEZkkwWetwa Uexza2EGG+Ky6TqoIxpAo3jTY0Qzkvd+Wrx8/mk8vRrROkhXru2qje+qBj17E+CpAAr3Db2L9KNr ADe2XwXrD/DnOz9Qu8Nz3N3A9/InCm06NXFY5nrVWT/j7bcQqLUfskzp8Vw0W7Hku+fdj2rzt9ns d3TuWTKRQ77/ZN6g9gCY846x4WwmE4f+E51iv7hB0QZyx72t3JK/u9q3suO70j7g2vh2R3lH1Ow/ ZdX/WnM+3MAHS+53e35COPLR0l3UfXgq90UGAaB4GvjHw/bzK2thKqtOZnDP6IfRKaBojfR/sgnx //uozHifzNlCRJcptPC2nz+N6nU/wZfvMNYo3tzX/zbyLm0vWl/n60dAQAB6RYPi5UNAQAC+XsXv wD+99gB1Onelb7gv63+5VPR5RfIri+x/QgghhBBCCCGEEOLvwm23SlnNqXhG3MITN/+j6LPU0/tY 8OJ6VeU9vOriodGAhy+dOncu+jznwhn+u2yPy/EBmt11D52D9EU/t+/YCYCYtxdwALPqOBVhKYhl 9icHmPrOB/Q8dwk/vzw++zSOXp2Lr5fy2zLMAR8QmPMTX6QWf/nrtPfW0NqgxdPHH4MyjY8+MmM1 JxPVf4yqHFY+9yovzhnPZ5+NJP18DBt3J9PDx735O2UzM3XBJ7wybglfTdMSv20CQxfY38u7cfI8 2i6YzKbVfcj2DsUzcSeTF/1erHhB+nes+LYL01dtwtNiIf6bFxiz/JiqTauJ76oL31l4+8OV5Jj8 CLadYtqo7wH3tZ+j+jsbH2lHljNrrR/jl6xnaFIiGr86ZPwxnzE/nlJVvrrrV6dTf16bPpWLicng F4GS9CMTtyeUbwNlKG/+Vksm8559g7UrZ9N7z5NsOJLOmuemU2f2ZDZveJLEDCshdWpx+qe32Pd9 jMv5u23/q6L4FWkfcG3/drW8q/uPs/pfoTcY8CmotIdvCFGqC7vnMXvdBMYv/JBh6SkYjMEk/7aS PV/al2+d9gq3LZjBJx89Tq4+kNwjXzH+/RNlxss6u4YvLnbnuafbMmTBfkLv7MsHgyaSnXYRDEaU izEsmLyuaP1/L36f0eGXL57o8gobu8DZT8cw/O2r+2dW3AeY/T/CJ/tHDmYXPycrb35lkf1PCCGE EEIIIYQQQvxdaE6fPm0D6Nixo8MVc3JyAPBvMJ1NK+4m4euN/C8hgY82fl1sPf+g2tTyN2DOukRS SvneX6tGZcZXPALo0/sRat/ZjYebBfDifx7l5yz3vsNRHxRGqAES45MwV9OTCz20HljMFu6ct44B MdMZ+f5J1WUrP3+FkLr10BVmEH++Mt6FW3nxR67fQsScQcw4BRHBehLiEks8wvtG6H9F60tEuJH8 9AtczMh3XuCyG6F+Ht4B1AkJxJabQuJF195nXBp35O8bHIbRG1IuXCDbVPwOX1fzr+z2rYrx6ah9 /goquv8IUV4Gg8Hh8sTERADCw8Mdrnfl/A3s4zc83IgpO4XkEudQCiER9dCa00hIzix3vlpDACHB gZCXTkJyJf3+dpCfsUFDwur0Y+5kI127j6+E7QshhBBC1CyVcb4phBBCCCEqn7PzuF27dgEVuAPY nHeK/fu9ILgxLf31JZZnpl4kM/ViecOqVqnxFR2tWrWCrBPs3w8ZZvdPTuSnJhGb6vawqtRq2YtO oWf5/WQSvvVuYWxzH955Na5cMSo/fyvJ8e59L2/VxofC3FRiz5W+rDr7/wqrOZu42IpfPFGd9bPk ZZAQm1Fp8d2Rf/alJMpqXVfzr+z2rYrx6ah9/gpc3X+EqE5WczbxZY5fK8kJFf/9aM7JICGn8o7P zvK7q080d/nrOHBQ/VMDhBBCCCGEEEIIIYSoqco9AZyXvJapUysjlepnNV9kak2tHFCYnUZk90e4 9/FamDITWT19GNtTC6o7rRoj9tdfyMqq3EeJV6eaXj8hhBB/XVtfm8XW6k5CCCGEEEIIIYQQQogb RLkfAS2EEEIIISqXPJJPCCGEEEJUJjnfFEIIIYT4a1L7CGilKpIRQgghhBBCCCGEEEIIIYQQQghR +WQCWAghhBBCCCGEEEIIIYQQQgghagiZABZCCCGEEEIIIYQQQgghhBBCiBpCJoCFEEIIIYQQQggh hBDib0rj4UtISFClxG7QfTZvvd6pUmKroui4+7Eonp3wPGOeHkCHeo7fmyiEEELUFDIBLIQQQggh hBBCCCGEEH9TPiH9WLl8WqXE9vQNprZRXymx1Xhg8ltM7NmKM38cJLEggllvvcudAbpqy0cIIYSo Kp7VnYAQQgghhBBCCCGEEEKIyqF4+hPZvBFetnzOx54mOdsMgEbRYQzyxydQDxotwcHBAFjyM0i7 vA6AovUjslljtIUZHD9+FpO15Da8AiOIrF+b/PQkTsZeKDMXjYcvxkA9GSmXMNvcW8/rKbownr0n gtWDx/BZUg6wjfSW6xk+siV7X/2tcjcuhBBCVDOZABZCCCGEEEIIIYQQQogaSB/cicXLJ2KNjSHV 6k2DyIYs6d+TPZkmtD43M3PmQBRtMB5e3sycOROA8zvn8/KGswAYIu5n4aLxEPsH2YaGNNSeYtLI GZzILyzaRpfhsxnTNZLjMafRBNTHmLGa6MnflsjFQxfOc4sXYTywjEkrtperHqNemkto1idMf/0X 1WW03i3wVDT8lJJf9Nmpny8R1K0zIBPAQgghajaZABZCCCGEEEIIIYQQQogaKHLIIHxOzKXf5N0A KNog/G32yVtT9n5Gj96PIWw4G5Y1Y/To8SXKD3j1GaxfzWDEuwcAhT5vrGPapA5Ez9oHgLH9eMY9 Es7z/aOJSTcB0KBd3RJxPPUNeGHpArQ75jHpw73lrkeT1q2pn1q+SWNLwVkAmvl4EmeyAFC7ZQAe hpvKvX0hhBDir0YmgIUQQgghhBBCCCGEEKIGyk3Ixa/9Q3S+NZXfY06QlpdKusqyijaIx0K8WbQ5 5vInVra9e4x+s7oD9gngVoNvJ/mnOUWTvwCxv8UXi+Pp3YTpK0bS4OQbRFdg8hfg9bGj0FpSylWm MP80a46lM3jqYFLf34E27FbGNdGi0WgrlIMQQgjxV6JUdwJCCCGEEEIIIYQQQggh3O/Muims/F8q 3YZOZu3mz3ln/kTq6jxUlfXQ1kHRaIgvsBR9VpCWjocurOjncH8d2aeyHMbRB3Uld/tugm8bTDt/ XYXqcf5cLHEJ2eUut27COL4448+TQ4bzSHsf5s79E2t+QoVyEEIIIf5KZAJYCCGEEEIIIYQQQggh aiCrJYPP3lvAs8Oj6d7vGeLq38OEh657RLPNCpqSD4q0mBIotNloqr86YawPDcZSEFv0c1xKPgGt jA5zyEl8i7nvz+PVb/N4cf4IdIrGtUqVg9WUxPplrzH+2WeZPm8F1gcakB23o8q2L4QQQlQXmQAW QgghhBBCCCGEEEKIGsh4+y0Eau1fAZvS47lotmLJtxZbpzD/NJ5ejWgdVPzuXGthBhvisuk6qCMa QKN402NEM5L3flq0zp/v/EDtDs9xdwPfy58otOnUpFgcm83+eOg9SyZyyPefzBvUvtz1mPPhBj5Y cn+5y/nUbYrh8oRzaKsHmdolnM8XHyx3HCGEEOKvRt4BLIQQQgghhBBCCCGEEDVQnU79eW36VC4m JoNfBErSj0zcXvwRyAXp37Hi2y5MX7UJT4uF+G9eYMzyYwBsnDyPtgsms2l1H7K9Q/FM3MnkRb8X lU07spxZa/0Yv2Q9Q5MS0fjVIeOP+Yz58VSJXKyWTOY9+wZrV86m954n2XBE7duIQW8w4FNQ/q+y a3cYxNsr2pCRbSbAx8JXKyex/qzjR1YLIYQQNYHm9OnTNoCOHTs6XDEnJ6dKEhJCCCGE+LszGAwO lycmJgIQHh7ucD05fxNCCCGEEKWR882/Fw/vAOqEBGLLTSHxYkUmPxVC6tZDV5hB/PnSJ20VrS8R 4Uby0y9wMSPftYTdTB8UQqifF6mJCWSZrc4LCCGEEDcwZ+dxu3btAuQOYCGEEEIIIYQQQgghhKix LHkZJMRmuBDBSnJ8rOM1zNnExWa7sI3Kk5+aTGxqdWchhBBCVC15B7AQQgghhBBCCCGEEEIIIYQQ QtQQMgEshBBCCCGEEEIIIYQQQgghhBA1hEwACyGEEEIIIYQQQgghhBBCCCFEDSETwEIIIYQQQggh hBBCCCGEEEIIUUPIBLAQQgghhCiTxsOXLg8+SLjOo7pTEUIIIYQQQgghhBBCqFDuCWDf8DGsWbOG NWvWsGrF7GLL2nV6kIHDRzLh+ecZ+/RgOrWpoz6wxoPWd95P/6dGMOH5iYwe1p/bmgRUaXxFF15U tzVr1tDWoFUd3r9pF6KiorgnSF/0WVCHR4iKiqK9r7o4DbrP5q3XOzldr+nAuSyY0Ep1bn8XatvP EY2HLyEhQZVS/okFy5ncIdhpjIr2b3XX3x3bd6ey2tFZ+/qE38OM+Ut5f/UHLF/wWLmXVydXxy/c 2PVTo6qOjzK+xF+NojXyrx7RPDthIiOHDeTuViXPocoavzZLNol+dzJvVk80lZCb2t+PfxVynuZ+ VXn8qYn9V5nnt+6Ir0ZN7JeKqKx2uJHPP8qjoudnQghR063duJFNmz5my5YthGir/l6g6t5+UKO2 /OfJp3h24kRGD+lHmzDvYss1ihf1m7XlwW696Nu3V5XnJ4QQQlQWz/IWULS1CAkJ4dSaN1l7OqnY slETnqG+lyfmfDNavZaHHu9J29nRLN6TrCJubf47axKF+WmcTzETHv5PunbvxepxT7L+WEaVxLcV prFixQrqPjSCgR2C8VLUf80Z0Owh+vdvzIVGx9j5ygEAeo8fwoNBer7csYWD2WanMTx9g6lt1Dtd LyfuT2IyslTn9nehtv0c8Qnpx8plzejafbzby/vVDiFQxd1TFe3f6q6/O7bvTjo/I7UDdSU+d9a+ XWePo86Aftu5AAAgAElEQVTOBTz3xREsltxyL69Oro5fuLHrp0ZVHR9lfIm/Eg+vBry0ahHh53bx +Z6j+IQ0pP+EnuwetLjYeo7Gb8ymmawKX8b0x39h1pZTbs1P7e/Hv4qyjg+i4qry+FMT+68yz2/d EV8N+fvHrrLG5418/lEeFT0/E0KImq5fr15ofVqy9dOFlOOrzhqz/SVvTuXEzp2ciD2LX5N7eP2d R5k3eCDbk/MBiPjnqywbGcbpMzZuauHH+vUbqz5JIYQQohKUewL4irRDe9l9OLXYZ6tencqxw3+S km0itE0fPpg/iHuH3MXiPVucB7TksuL1yfzfjt/It9oIu2MC78/uQrcx7Vk/6vsqiW+z5rFz505a thtQztawy7/0Cbb2Q9BqDoDhVv5l/Zpkc/ErqH2C69IwLBiNKZXjx89htpUdT+PhizFQT0bKJcw2 ULQBBAVoMR/6kk/NGSXW9wkKQpudTq4+jOaNjaSePkJCZvGJZ61/+OVlRzlv8sJbySc7t1B1HcuT f/G6+BEYUIjJK4J6ukscjculWesWZJyMIema7TuL7xUYhD4vg0yTFze1aAo5CRw7m1LGNou3n/1D LQ1uuolAnYVTR46TVWi1f6zoMAb54xOoB42W4GD7nUiW/AzSVEzel6e81j+i1P5x1r9X2yCCyPq1 yU9P4mTshbJzKq3+Lubvyvi1f1h6+xe1gac/kc0b4WXL53zsaZJVtP21tP5hNG9cm7QzR0osU7P/ +CgK7QN1xP8aj0ZRUK65183ZcjX1czp+HZR3tH+7On7V1k/R+hHZrDHawgyOHz+LqXj1yqyf2v3f lf53x/HRGRlfFR9fonq1HzuD5qZv6P3CsqLj8brVVy/YUfP7R9H6Effdcj4ptKBTKLH/q+Hs91dZ vx8Bp+Pf+bYrZ/+4mnvZxwdX89cH1MIzN5Nss72Mh94fP6880jPMqvMri9rjs8vHHxz3f1nnF2qP P85+PznjSv+paX9nv99czb8satrP0fhyVt7V80fFw4/AAA0pqZnXZk1wsJHM1BRMVpuq41NF/z5R w2AMwjMznUJjIyJre3Hm6FEyzFXz+/eKyjy+3Ajnf64e3yt6fqZm/KnJz1n/Oz2/dbH+lTn+hRB/ L2rORypyPncjeK5fP5Kyrhx/N1G49jMGDI1k+yuHAUjaOZPHtmXiU3c8H79zX/UlKoQQQrhZhSeA S7Pnp9+K/p+XZf9DKj/5kqqyVksmn3x3sOjn9NPnALBZr55xVHZ8V1ktObx73I8BdX357o4BnFk3 D7+nr04At5nyFq+08eZkXCI2n7o09j3Pi6On8kcpX6B76MJ5bvEijAeWMWnFdgD8GvRj5riW6EMa 4HViOv2nHixWpueb73H39zvQ3tGUNFMALRrqmBs9iB9S7Fe0GW/uy7I5UVyIOYxSJ5yzF3S0Nc8r Eacs5cn/er7ho1izpBEnjmXRpHVz9h5MINBby00RCXTr/SJWlfE7z19Btx0ryXt4IAFZl9AG1uPw +L7Mj8922n5exlt5+b9TiciP5VyOFy3rw+ujnmVXcj5an5uZOXMgijYYDy9vZs6cCcD5nfN5ecNZ p/VTWz6wZW9WDCq9f5z1L0CX4bMZ0zWS4zGn0QTUx5ixmujJ35ZYr7T6u5q/q+PXUfsD6IM7sXj5 RKyxMaRavWkQ2ZAl/XuyJ9PkNH+AoPZRvPVSX5JiDuMVUY9zFzwh/+pyZ+17/7gpPBikJ9zbk8LR k5hptpKf+gUTpn+jarmz+oHj8eusvKP929Xxq6Z+hoj7WbhoPMT+QbahIQ21p5g0cgYn8q9+gVdW /ZZrnO//rva/q8dHZ2R8uTa+RPUa3jGUE69tLvYljNV8dew4G79q9n9nnP3+cvT7Uc34d6ay9g9w fnxwNf/eS1bRctkIJv1k/5KtYc+5vHrPWnoN260qP0fUnJ+5evwBx/3v6PxCzfHH1fHpav85a39n v9/csX+VRU37ORpfzsq7ev7o6dOSD9dOYdjjTxBnsgDgE9KTD97rSq9u0Zhwfnxy5e8TNaKXrOK2 nw+hb+FLbLY/rRsU8vLQsexNLSha5698fKnu8z9X83fl/EzN+HP1+Ods/3e1/pU9/oUQfx9qjvcV PZ9zp1EvzSU06xOmv/5Lucpdnfy9/HOBBTRXL3iy5GdeX0QIIYSoEdw6AQzQqPervPJ4E2oF+XHq l69ZOOfnCkRR6D6pGwDfLP29SuO76o939zBscAsCm9fjvafOM+7pq8vOfTqfHq+eKvoC9oGX1zBh THMGzjlcLIanvgEvLF2Adsc8Jn24t+jzjJPLGD0aWj7zDi+U8frjoDuy6Tt8DGYbdF+6kf4DmvDD whhA4fkZUZxc+gxTvzqLoq3NG5tWQYz6uqnNvyyWgnjGPf8S97+9kX7pS3hqagyrv9hKG4OWQzlm 1fHrPdaDWWOj2Z+Uh0Yx0FBXUGx5We037L9T0P/4GlEr7Z816z6fOa/2YddT72PK3s/o0fsxhA1n w7JmjB5dvkfYqS1fdv84719j+/GMeySc5/tHE5Nu/9KgQbu6JdYrq/6u5u/q+HXU/gCRQwbhc2Iu /Sbbv9BWtEH429R++akwZVoUJ5eMYurXsSi6MJZuegf+uLqGs/bdOn0CW4HZH39B+kvPs+C6iwqc LXdWvyvKGr9qypc1flwdv2rqN+DVZ7B+NYMR7x4AFPq8sY5pkzoQPWuf8/rVdr7/u9b/rh4fnZHx 5er4EtXHw6s+9b082Xuq7EdfOhu/avf/sqj5/eVo/1Q7/p2pjP1DzfHBXfk7UvHjm/Pjs6vHH2f9 7+j8Qs3xx7Xx6Z7+c9T+zn6/ubp/OVLZ57eunj+asvbxeYqN4bfVZtru8wBERj9IysGlZFvsBZwd n1z9+0SNgLbn6T3I/gSFThNXMm5mF/qM2Vpsnb/q8aW6z/9cy9+18zM148/V45+z/d/V/quK8S+E +Htwdrx35XzOnZq0bk39VOc3OTiiC7iVQaHerJ1+2k1ZCSGEEDcuxd0BC3NSOH/+PBkmG/WataJ1 I99yx7hn+Hyi2xiJ+WQ2q46mV2l8V2WeXYml3bP8w/wFMbnFrzDLOHKGhu070u0/vYiKiiLSosG3 SVixdTy9mzB9xZs0jV3MdJWTd9c6t3lb0QnX4V8u4d/cDwCd3+2089Wxckc8AFbzRT4oZ93V5O9I YcEZANIuFJBzJguwkmCyEOHlUa74KYcWsz8pDwCbNYcz11yRWFb7aQ1t6Bpm4Mt9aTRp0oQmTZqg ObUHQ/h/yvWuZ1eV1T9qtBp8O8k/LSo62QaI/S2+2Dqujh9HXBm/ato/NyEXv0YP0fnWFgR6e2I1 p5Ku8hFoOr/baeOrZeX3CQBYTUmsOlZ1V3CWZ3yVNn7Vlndl/LhC0QbxWIg3WzZfmUywsu3dYwTd 3L3EumXtn872f1f6X62Ktp+ML/FXpij286RcS8X2p/Ls/2VR8/urrPHnzt/flbF/ODs+VNX5hyv7 r6Pjs6vHH3De/66cX7o6Pt3Vf47a39HvN3fsXzc6Z/37xZqTtHjqfvsPGi3DO4aw/R31V6i6+veJ GrEfby3q3wMf7iWgcVSJdWri8aWyz/9czd8d52eOxp87jn+O9n939F9VjH8hRM2n5nhfmedz5fH6 2FE8O31PhcsrWiPj35zC2U+ms/m6i56EEEKImsjtdwDHbZ3P+K2gNTRh9YYlDJ7xDJ/2mq26fNs+ s5j2RCtOfLmQ51bsrvL4rrJZ85n/6S5uOb61xLInZr5LVJ04tmw/RGZOATqTBY3iVWwdfVBXcj/6 nuAeg2nnv4ffVD7+9ApzxtX1bRZAY59cUbShACRefrwVQF5yPhjVx1aTv0M2+7atViu2Qvu3HIU2 0JYzfuaRsieuy2o/D31DADpGDaDjNesf+PUQ3oqGgivveKpkZfWPGuH+OrJ3ln0HF7g+fhxxZfyq af8z66b8P3v3HR9FmT9w/LOb7GbTSaEkoUeKlLOenh7K2e5356GCnCIgICqCgIgUQcDQFFAQVCII KoiISlSEk7OcdzYQ8Q4LQkSQFkgjQAqk7mZ3f3+EBCLZmdnMTJrf9+vl64WZeZ555qnfmdmdZZX9 PvqOmMqUts3JSP2M2dMWk35On/Wlpv5dlF3sV//Ww5/+VVP/1ZpeT//RI8DWCqvFQnrZ2foty8sn wH7xefv6HJ8q419P+2tV2/qT/iUaM7cznXKvl4Tg2oV8/ox/X7SsX776n5HrtxnjQ21+qKv4Q9f4 VZif9c4/oN7+euJLvf3TqPZTqn+l9c2I8dXQqbVv9hcvEjT+WTo7UshqMYy23oOM8+OGrO7rEw1K Mour/u12ZmK1xZz3W+hNcX4xO/7TW34j4jOl/mfE/Kc4/g1ov7ro/0KIpk/LfG9mPOeP7CNptU5r sQZz/4JkEvesYeSqHQaWSgghhGi4DH8AXMlVdIDDZeVcEtpDc5pOfSbx1D1XsveDZxn/3EcoXfKY nb8eP722jJ9+9bdARxdGXpXA6Nse4EDlNyI63MItv9qvKHM5C17dzNbIFTy+aBQDRy3FacDNQXfJ LxXHDAlkx5nf4IhpHwrK92P9Ln9t+ZO/V6E+fNWfu3gfAKvnzmS/0m+qeT1g0TEs9KZXcPRkKZHd Y4BDPvfR3X98lF9v/9VS/x53ARtfWczGVyAoOpEpLy5h0l9TGL9JPcB3lx0GoIMjkJ/OfPO+WesQ KNF43jpp7l/U3H/9Sa+SuSn9z+3MoNzr5QJHALuKKurX0TIWd9n5baM0PpXoaX+zSf+qyty0+U2Y x+M+xb9yy+jepw0s+3V0os6f8e+LlvXL5/GN6r+YMz7U5gcjyu/yegkIPvtAMSjGXqt8akPv/APK 7a85/vMx/+jtn3XRfkrrmxHjSxOF+VtT/1Kb/3XEj+Wl+3ntaAkPXNuKd3r1JvuzBZRrDCXMvj6p FNY+DL47UXHM4La4nceqPfyFxju/KB7f5PjP7PrRQqn/GTH/KY5/nedfV/1fCNF0eD0Vc0WQpfpb BrTM90bEc76OXycsNgYkvcg1JR9w38JNGPuuMSGEEKLhMuwV0I6YPqxYOJOR9w2hf7/+PDjtOS4L s1Oc/aG29NF/47mxN+L1FHEs9BKmTpvGtGnTmDj66jrJ32xebyker5eO0RWfgLNHd2fcdee/DsXr rfgE+LbkyewMu4GFwy8x5Piukp/YlFXEA/f2xmaB4LjfM6pDpOHlry2j8vdVf66Svaw/fIrJI27A fuaVWlZbFFf+qXu19OWlBwkM6kCP6NrdXNWbXslPL31B88sm8sd2la89t9Lz2sRq++jtP77Kr7f/ aqn/mCsuJcpWMSU589M57vLgLtUWlruKdrIlv4z7+3QGwBbehRGdmvlz6rpo7V9mpa9kVv/zlBew /mghfYb3wkLFJ2f7j+pCzvb3DDuGnvY3m/SvCmbOb8Jcb72wldY3T+e6TtEAWANDufpmbfGPEeNf y/rli1H916z81eYHI8p/MKuE+P+r+AaG1d6Swb1aak6rlxHlV2p/rfGFr/lHb/+si/ZTWt/8Kf+o lWtZu+Juzcc9l9L8raV/qc3/euPHz17aTad7+jP6khg2rT2g+bz8uX7QU3/t7hhAZKAVsHLdiKvI /3mtpnSNYX5RYnb8Z3b9aOWr/5k9/vXmb/b1uRCi6SkvO0SW003/yxOq/V3LfG9IPOfj+P6Y9/p6 Xku+3s9UFm6ZlMzf43YzZfE/CA6PJDIykvCws3GLxeogMjKSiLCKd5RFRkYSEeH/Tw4KIYQQDY1h X+XxuHIJTLiU23939oZi7sFvWPz4m5rSBwS1JsBigYAwru3du+rvRccO8cyybabnbzZ3WRpz3v2W 6S+9xh1HThAeXsLG945yZ++a9/e4T7HwkWdZt2oOA7bdzfo9+cx4ZS09Qm0EhkQQap3BW2+58Lhy GDRknKYyrJo4n8fnTWDjxtHkZ6eS8lUO/UPMKb+/jM6/pvpbOzGJVnOmsmH93WQWeGjRqhkHv17O N5+f/Z2xsvz/sPLfN5G0+m0C3W7SP36McSv2aj6unvRq7Zu3ZwWz14UzIflNRmRlYglvRcHuRYz7 8vwbZTWdv57yG9F/1eq/1bVDeCppOsczcyA8AWvWl0z+NENTuQGee2wZSxY9xet/zoQwL9t+zOMq P+pXLy39y8z0oL//KkmZupCLFk/l7TV3URjcksDMLUx9/kdD8gb97W92+0r/Mrd/CXMd+2ohc96Y xIQlr/NA/klCY2LJ+WEV2z6o2K7Wf/WOf3/Wr5oY0X/NzF9tftCb/4/PvoZ35cOkrLmdYu9pPv7i GJ161uJEa0lv+ZXaX2t8oTT/6O2fZref2vqmtfzx0c2wZeRqPq9zKdWflv6lNv/rjR9P/rAMV+Rr RBV9zfu5pdW2Kc1P/sSneurv2H/cvPj6Koqc4cR6DzBjzOea0zb0+UWN2fGf2fWjJT5T6n9mj389 +Zt9fS6EaIK8LqYvfpcnxyfz4Qwb6Z9MYsTiivlGbb43Ip5TOr5WjtBQQsr8u5VtCQhl7I3tgfas fvPGqr8XZibTf/j7AIS2up+U1We/s5ySkoLbmc3Ntwzz61hCCCFEQ2M5ePCgF6BXr16KOxYVFQEQ 0S6Jt1f+kYyPUvhXRgZvpXxUbb+I6OY0iwjFdfoEWSe1/36TVmbmbw2I5K4Bf6P5H/pyc5dIHv/7 Lfz3tHG/oQrgiI6jZShkpmfhqpufnT1PgC0At8vNHxa+wdDUJEa/ul9zWrPLXxf1ExYbR0wwnDx2 jMJfv7+tEbDawkiIj6E0/xjHC0rVExjIiPZRqv+A4EhatYjCW3ySzOMa309+DktAKK1bx5CXnk6h u37aVm//atj900qL1m2wlxeQnq3tQwX+0Nv+ZpP+JepSaGio4vbMzEwA4uPjFferjN+gYv2Ij4/B WXiSHL9jKP3jX+/6ZXb/1ZO/lvlBT/4BQVG0bhXCsaOZlBr0u8H+0lv/Su2vP77Q1z9Nbz/V9U25 /IGOTmzeuJRl99zOP7KLa0ivj9n9q76vH/TU3+g3N5EwbzgzD0BCrIOMo5maX1F9roY8v6gzN/4D 8+unPsunJb7Vk39DuL8gGi8z4k3RmKnP9+bGc0IIIYTQSi2O27p1K1CLB8DBLQYz4+FuALidmSTN fkF3YRsKq605c2eNr/r/1+bOZK8Jv6dUX5p1u5NrWx7mx/1ZhLW5lOmPDeelYQP4NLesvosmhBBC iHPIDTkhRKXwdvcz+95sJszcXN9FaZT01F/lA+Dpu2r37WEhhGjIJN4UQgghhGicTHsALBqvsLY3 MXJILxKaN8N5KpPP31vNR9/n1HexhBBCCPErckNOCCHq398mzyD67edYe7jhvZlECCH0knhTCCGE EKJx0voA2LDfABYNX+GRT3jmyU/quxhCCCGEEEII0eD9c+ET9V0EIYQQQgghhBCiVqz1XQAhhBBC CCGEEEIIIYQQQgghhBDGkAfAQgghhBBCCCGEEEIIIYQQQgjRRMgDYCGEEEIIIYQQQgghhBBCCCGE aCIa1G8At+s3h6lX/ZsHH/2y1nlYAsJoHmMnJyfXwJIJIYQQQoi6cME9Cxgdu5YJi1IbRP7rUlKw W6zY7TZG/L0fOS6PKeWqTxI/CyGEEEKIpspiDaJNp650u7ALUcHw5pspVdusthbcdceN56UpLz1C yoatZzII4PKbbufq37XHXl7I7u0f8tH2w3VUeiGEEKL2GtQ3gAPDYmke49CVR0iLwaxaMcOgEgkh hBBCiLpkD4+heZS9weQ/+M47GTQsCYfDgdViWrHqlcTPQgghhBCiqUq4YT7LFkzl5mv7MmzokGrb LJYgWrZsWe2/3/UdSL9eoVX7XD52KTNHXEX67u/ZnVbC0BnLmXxdXF2fhhBCCOE3zd8AtgSEExVZ jjMogTb2E/x8tJguPS6kYH8qWcXlVfuFxLamfVwsFmcu+/YdweWtnk9QVDSOkgJOOYPofOEFUJTB 3sMnfRwzjJgoBwUnT5zNx2KjXefORNndHNizj9PlFd/CsFjtxERHEBLlAIuN2NhYANylBeQVuqry tAZG0KlrB4K8pWSnHSTnnG1CCCGEEE2RavzjI746V1BUAp3aNqc0P4v9aceq528Lp1OXjtjKC9i3 7zDOc5KHREdjK8yn2BFH144x5B7cQ8ap6se3RcTRtWNz8g7tqbH8avGlGrX8tZx/bWiNn9WOryV+ VmofX/WnOX5WaF8hhBBCCNH06b6eMCne1iJryyxu/eQUIa0n8M5L11Xb5nYeZcmSJWeLaQ3ihT/d wJZl26v+NuLGduyeew8bdhwHID3xGmYPvRI+21gn5RdCCCFqS/MD4LD4MaxN7sAve0+T2KMr27/P ICrYRueEDPoOeBwP0HPacp7sGcz+o5l4Q1rTMSybx8dOZ/c5QUHvRSvp+9kqSm6+h8jTJ7BFtWHX hIEsSi+sdrwAezwTlz5PzLfLmLLyUwCCYi7niWemk1CaxpGiILq1hafHPMLWnFJsIb9j1qx7sNpi CQgKZtasWQBkb1nEE+sPA+CIvZalKybjSUsl1xNMu07tSR5yB9tOOfXVohBCCCFEA6UW/yjFV5Vu GjmHcX06sS/1IJbItsQUrGHY1H8DEJpwPUuenwBpuykMbU972wGmjJ7JL6UVDzjveO4V/vj5Z9iu vIA8ZyQXtrezYNhwvjhZkX/0JYNYPncgWam7CEpow5FjgXD20JriSyVq+Ws5/9rSEj9rOb5a/KzU Pkr1pyV+VmtfIYQQQgjRtOm9njAq3h4zdwEtT79L0tP/8yudu/SU5n2bdR1NW+9hHj5QUPW3gyXl tGh29g1CISGBlB0/7lcZhBBCiPrg128Au8vSGf/oXK5/MYXB+cncNz2VNe9vpmeojZ1FLo68t4j+ 8w9UfavgxifWMmlcV+6Zt6taPm1u7c/sh4exI6sEizWU9vay6oVytOOxFxZj+2whU14/+4mrB56Z huPLpxi0quJvXfotYt78u9h636s4C3cwduwOQuNGsn5ZF8aOnXBe+TvdP5yQXxYweOpXAFht0UR4 5eaVEEIIIZoutfhHKb4CiLlkAuP/Fs+jQ4aRml9xk6fdxa2r0g+d/xCeD2cy6uVvASt3PfsGM6Zc xrDZ31TtE31lIQNHjsPlhX4vpDBkaCJfLEkFrEybMYj9yWOY/lEaVnscL7z9Euw+W36t8WXN1PNX O3+91OJnrcf3FT+rtY9S/WmJn7W0rxBCCCGEaLr0Xk8YFW8n9uhB29xP9Z+QguseuorMj2dVe+PQ 8ukvMHNqEo9c8F9KHAlcHr2fJx//r6nlEEIIIYzg128Al5cdAiDvWBlFh04DHjKcbhKCAgAo2HOI 9pf0ou/f72TQoEF0clsISzz/NxFO7lzKjqwSALyeIg6d8w2CwOBEklY+xwVpS0k65+GvLbQnfeJC +eCbPBITE0lMTMRyYBuh8X8nSOMPshVnFBPe4a/0vvxCooID8bhyya/DV44IIYQQQtQ1pfhHS3zV /d4ryPn6+aqHiwBpP6QDFTd/bm0RzKYNqWe2ePjk5b1E/65ftTIc2fBJ1U2UXf87QUTXcADs4VfQ M8zGqs8zKlI7s1i9t/on9LXGlzVRy9+I+FKNUvzsz/F9xc9K7QP66k9r+wohhBBCiKZLz/WEkfH2 0w+P4ZGkbWacIgC2kB7c1z6C19/YX+3vEW3iiQyBco8Hj9uNo1k0CS2CTSuHEEIIYRS/vgGM1w2A x+PBW15xF6/cC7Yzm2+f9TKDWh1l06c7OVVUht3pxmINOi+bU3vyfR7CEd2H4rc+J7b/vVwcsY0f zrxOJMDRHoBeg4bS65z9v/1uJ8FWC2Ue9R+DO/TGNFbZ76PviKlMaducjNTPmD1tMelOt/q5CyGE EEI0Qkrxj5b4Kj7CTuGW0zXmHWBrhdViIb3sbCxVlpdPgP3iavu5Cs4+nPS6AUvFhwettpYAZJ4T ixVlF0PM2bRa48uaqOVvRHypSiF+9uf4vuJnpfYBffWntX2FEEIIIUTTped6otTAeDv7SJruc1HS tv+DOE+8x5fnXLtYA6N4ZtIAPp9wF8v3VbwW+l+3P8/z88bwrwHzTS2PEEIIoZd/D4CVMnJ0YeRV CYy+7QEOVH4jocMt3FLDvl6Fxb0oczkLXt3M1sgVPL5oFANHLcXp8eIu3gfA6rkz2a/0m2NeD1hq Pi2Pu4CNryxm4ysQFJ3IlBeXMOmvKYzfZG4AIYQQQghRX5TiHy3x1dGTpUR2jwEOnbfN7cyg3Ovl AkcAu4oqfpPX0TIWd5m22MpddhiADo5AfiquSN+sdQhUfNHVr/iyNvlrjS+9noptQRZjvhVcVT6t 8S2+42el9tFcfz7iZ73tK4QQQgghGj891xM2P+LdemUJ4MF+7UhdMrfanwNDutHMZuWH7JKqv+V+ m41txB/quoRCCCGE3/x6BbQSr7cUj9dLx+iKbxTYo7sz7jptr5ernk/Fp6y2JU9mZ9gNLBx+CQCu kr2sP3yKySNuwH7mFSFWWxRX/ql7tfTlpQcJDOpAj2j7eXnHXHEpUbaKU3bmp3Pc5cFdev4roOe9 vp7Xkq/3u+xCCCGEEA2NUvyjJb766aUvaH7ZRP7YLuzMX6z0vDYRAE95AeuPFtJneC8sgMUaTP9R XcjZ/p6msrmKdrIlv4z7+3QGwBbehRGdmlVt1xtfquWvOb4sO0SW003/yxM0H1tT+TQeX4lS+2it P1/xsz/tK/GzEEIIIUTTpOd6woh4t1Jt402L1UFkZCQRYRXvsIyMjCQiIqzaPhHt76d7UDHPb8+p 9ndX0Y/kON30/78uZzIL4JphXSnL/Y/f5RBCCCHqmmHfAHaXpTHn3W+Z/tJr3HHkBOHhJWx87yh3 9q5dfh73KRY+8izrVs1hwLa7Wb8nn7UTk2g1Zyob1t9NZoGHFq2acfDr5XzzeWpVurL8/7Dy3zeR tIR6nXIAACAASURBVPptAt1u0j9+jHEr9gLQ6tohPJU0neOZORCegDXrSyZ/mnHesR2hoYSUGVY1 QgghhBD1Ri3+UYuv8vasYPa6cCYkv8mIrEws4a0o2L2IcV8eACBl6kIuWjyVt9fcRWFwSwIztzD1 +R81l++5x5axZNFTvP7nTAjzsu3HPK46s82I+FIpfy3nD4DXxfTF7/Lk+GQ+nGEj/ZNJjFicet6x akPT8RUotY/W+lOKn7W2r8TPQgghhBBNk97rCb3xbqXaxpuhre4nZfXZd+CkpKTgdmZz8y3Dqv52 9UN/4uSPyeS4qn9RyOs+zaQnX2fB5Pm8dXMmxUEtiCo/yLNTX/G7HEIIIURdsxw8eNAL0KtXL8Ud i4qKNGXoiI6jZShkpmfhMuBn02oSFhtHTDCcPHaMQuf53+BVEhAcSasWUXiLT5J53PfvpQkhhBBC 1JfQ0FDF7ZmZmQDEx8cr7lcZv2mJf9TiK6stjIT4GErzj3G8oPTXW2nRug328gLSs2v+rVolloBQ WreOIS89nUL3+cfWG1+q5Q/64ksj6D2+Uvvoj8/1ta8QQgghGh6j403RtBlxPVHf8bYeFqudVq3j sZUXkp55gsZVeiGEEE2NWhy3detWwIQHwEIIIYQQQh+5ISeEEEIIIcwk8aYQQgghROOk9QGwYb8B LIQQQgghhBBCCCGEEEIIIYQQon7JA2AhhBBCCCGEEEIIIYQQQgghhGgi5AGwEEIIIYQQQgghhBBC CCGEEEI0EfIAWAghhBBCCCGEEEIIIYQQQgghmgh5ACyEEEIIIYQQQgghhBBCCCGEEE1EYH0XQAgh hBBCCCGEEEIIIUTthcRfw+QJd9GheThlJ99h5IR/1HeRGgSLNYg2nbrS7cIuRAXDm2+mVG2z2lpw 1x03npemvPQIKRu21mUxhRBCCMPJA2AhhBBCCCGEEEIIIYRoxPrMGU+rLYuZ+P4e3O7i+i5Og5Fw w3yWjY7j4CEvnS8Mr/YA2GIJomXLltX2b3nV9bRLTyZlQ12XVAghhDCWPAAWQgghhBBCCCGEEEKI RigkOpoQq5VLouykf5eOxWrFiqX6PrGtaR8Xi8WZy759R3B5q+cRFBWNo6SAU84gOl94ARRlsPfw SSwB4URFluMMSqCN/QQ/Hy2mS48LKdifSlZx+dkMLDbade5MlN3NgT37OF3u0ZR/XcjaMotbPzlF SOsJvPPSddW2uZ1HWbJkydnTsAbxwp9uYMuy7XVSNiGEEMJM8gBYCCGEEEIIIYQQQgghGqHrx0/j L9EO4oMDKR87hVkuD6W57zMp6WMAek5bzpM9g9l/NBNvSGs6hmXz+Njp7C50VeXRe9FK+n62ipKb 7yHy9AlsUW3YNWEgKyxjWJvcgV/2niaxR1e2f59BVLCNzgkZ9B3wOB4gKOZynnhmOgmlaRwpCqJb W3h6zCNszSlVzX9ReqHm8xwzdwEtT79L0tP/86t+3KWnNO/brOto2noP8/CBAr+OIYQQQjRE8gBY CCGEEEIIIYQQQgghGqHNSZPYDMx5533y5z7K4l89VD3y3iL6zz9Q9a3fG59Yy6RxXbln3q5q+7W5 tT+zHx7GjqwSLNZQ2tvLoDm4y9IZ/+hcrn8xhcH5ydw3PZU172+mZ6iNnUUuHnhmGo4vn2LQqopv zXbpt4h58+9i632vqufvh8QePWib+6lfafx13UNXkfnxrPO+IS2EEEI0RvIAWAghhBBCCCGEEEII IZqggj2H6HRpL7p3jCfEHkik20JYYhxQ/QHwyZ1L2ZFVAoDXU8ShUggHyssOAZB3rIyiQ6cBDxlO NwlBAfxEV/rEhfLsN3kkJiYCYDmwjdD4ewmyrqHM41XM3x9PPzwGm9u810bbQnpwX/sInpq637Rj CCGEEHVJHgALIYQQQgghhBBCCCFEE3T7rJcZ1Ooomz7dyamiMuxONxZr0Hn7ndqTX3MGXjcAHo8H b3nFA91yL9iAAEd7AHoNGkqvc5J8+91Ogq2Wag+AfeavUfaRNF3p1bTt/yDOE+/xZYHT1OMIIYQQ dUUeAAshhBBCCCGEEEIIIUQTE+jowsirEhh92wMcKC0HoHuHW7ilhn29Hv/fe+wu3gfA6rkz2X8m f19qk3+dsQTwYL92pC6ZW98lEUIIIQxjre8CCCGEEEIIIYQQQgghhDCW11uKx+ulY3TFN37t0d0Z d12cYfm7Svay/vApJo+4AbvVAoDVFsWVf+pu2DEqzXt9Pa8lX+93OovVQWRkJBFhNoCKf0eEVdsn ov39dA8q5vntOYaUVQghhGgI5BvAQgghhBBCCCGEEEII0cS4y9KY8+63TH/pNe44coLw8BI2vneU O3sbd4y1E5NoNWcqG9bfTWaBhxatmnHw6+V883mqcQcBHKGhhJT5fys7tNX9pKw++53nlJQU3M5s br5lWNXfrn7oT5z8MZkcl8eQsgohhBANgeXgwYNegF69einuWFRUVCcFEkIIIYT4rQsNDVXcnpmZ CUB8fLzifhK/CSGEEEKImki8+dviiI6jZShkpmfhMulNzGGxccQEw8ljxyh0yoNUIYQQwixqcdzW rVsB+QawEEIIIYQQQgghhBBCNFmluVmk5Zp7jMITWRSaewghhBBC+EF+A1gIIYQQQgghhBBCCCGE EEIIIZoIeQAshBBCCCGEEEIIIYQQQgghhBBNhDwAFkIIIYQQQgghhBBCCCGEEEKIJkIeAAshhBBC CCGEEEIIIYQQQgghRBMhD4CFEEIIIYQQQgghhBBCCCGEEKKJCPQ3QVj8OJY/9XsAyksOMfyBpKpt F1/7Fy6+sCOxkWG4Tp/g+60f8OWubG0ZWwLocWVvLunemZYx4ZTmZ/PNf/7B/w4UGJO/hvRWezxr Xnmq6v8XjbqXnUUuTXl37NOfP0QE1bgt/+eP+OC7XM3l9GXgsytp88pjPL3rpK58LrhnAaNj1zJh UWq1v69LScFusWK32xjx937kuDy1yqe2/D2+WYw+r0q3L15B53XTWfDtCUPzbWhC4q9h8oS76NA8 nLKT7zBywj/q5Ljt+s1h6lX/5sFHv1TcT2v7mtUPGirpn79tDWX+FebodEt/fh9ePUY5NzZpKO3/ W5t3Gwq19m8o/cMXreVr6P2roZevkr/lbAz9yxIQRvMYOzk5+q/X6puv9mks/auxkvqtWUMY30KI 2qnNetKQ19PoDhdx/R8vp01cNK68TL745wZ2ZZVUbe989U1ceeEFtIoNpyQvi68+3MD3R4vqscRC CCGEMfx+AGy1NaNFixYcWPsc6w5mVds2ZtJDtA0KxFXqwuaw8dfb7uCiOcNYui1HQ77NeWb2FMpL 88g+6SI+/gb69LuTNePv5s29Bbrz15LeW57HypUraf3XUdxzWSxBVovmegmObUHLKAcAba69gfa5 37Jldz4AARl2zfkoCWvegqgg/V/aLjr6E6kFp8/7++A778QW0o3N7y1By6nbw2NoHmXMudXm+Gbx VT96hTdvQZQ9wPB8G5o+c8bTastiJr6/B7e7uM6OGxgWS/MYh+p+WtvX6P7d0En//G1rKPOvMEdw bAtaNnPQ/tobaH3yO7am5lWLTRpK+5u1/gplau3fUPqHL1rL19D7V0MvXyV/46PG0L9CWgxm1bIu 9Ok3oX4KYCBf7dNY+ldjJfVbs4YwvoUQtVOb9aQhr6fJz03nly1b+CXtMOGJ1/D0S7ew8N57+DSn FIA/978R2/59HD1wkPC2lzLvxTU8d99QPsqWewZCCCEaN78fAFfK27mdr3ZV/1TX6vnT2bvrJ04W OmnZ8y5eWzScP91/FUu3bVLP0F3Myqen8s/PfqDU4yXuykm8Oucm+o67hDfHfK4/fw3pvZ4StmzZ QreLh/pVFwCpry6n8vNvt/yuF333pLBkyfmfiLPawunUpSO28gL27TuMsxYfgg1tlUinFg6O/LyH 3HMycEQ2I7D4FIVnPlkb4IggPKiE/ALXmWNHEh1pw7XzA95zFdSYtxpbRBxdOzYn79CeWqUHCIpK oFPb5pTmZ7E/7Zh/iS022nXuTJTdzYE9+zhdXr0CQ2Jb0z4uFoszl337juDy/vrY0ThKCjjlDKLz hRdAUQZ7D1d8o1qtfkKio7EV5lPsiKNrxxhyD+4h41T1b4jbIuLPbPuZbGcQwdZSCovLf7VPgs/0 Sv1DrX0BrIERdOragSBvKdlpB8kp/NU32FXqTzW9gpDoaEKsVi6JspP+XToWqxUr1a/01fq/Uvv4 yxIQRkyUg4KTJ3B5tfV/pf6tpf191a81IJyoyABO5uZX2z06JgZnfi6F7l91VJ8nVXP+FquDmOgw ck+coLJKLdYQYqJDqv0N1MefUv9UGl+GjA+V/qlHg+ifOtvPV/1bAsKJiizHGZRAG/sJfj5aTJce F1KwP5WsM/UbGhNN4Kl8ymM60Kl5EId+/pkCf7+FYVL7RMfEUJiXi9NTfRxYA8Jp1sxL7slCU4// W/Dj6uX8CNx+US/+uvMtliz9ydD8tfSv2q6/Wvp3RR61j6+0lF8tvlBbP1W36yi/nvXJKGr1o0Zt ftWTv9r6b4uMIdpR/ZLIXZbHiXynpu1q5VNLryU+UeofddH+qvG/2fOzzvx9jT+L1U5MdAQhUQ6w 2IiNjQXAXVpA3jljVE/8o6X/mNk+Rlz/gXL86Kt/GhafKNSPlv6vNv825fGrt37B2Ouzmhhxf0SI xsSM+dSoeLm264naemrY/RAdJg4eTNbpyvn1bcrXbWToiE58+uQuAJInTjln73c42fNdBgxL5KOn dpleNiGEEMJMtX4AXJNtX/9Q9e+S06cAKM3R9jpRj/sU7/7n+6r/zz94BACv52xEoid/I9LrFZpw PUuenwBpuykMbU972wGmjJ7JL6Xl6onPiOs9lpe7RpB2OoweHSw8PfIhvsypeG3JgOTVdFs2iilf VwSR7e9YwPxr1nHnA18BEN5uMLPGd8PRoh1BvyQxZPr3Po9Tk+hLBrF87kCyUncRlNCGI8cCodSv LLhp5BzG9enEvtSDWCLbElOwhmFT/60pbVDM5TzxzHQSStM4UhREt7bw9JhH2HrmE3s9py3nyZ7B 7D+aiTekNR3Dsnl87HR2n3OR33vRSvp+toqSm+8h8vQJbFFt2DVhIIvSC1Xr547nXuGPn3+G7coL yHNGcmF7OwuGDeeLkxXHj/ndQJbNG8Sx1F1YW8Vz+Jidi1wLq+UT1W0AK4fXnF6tf6i1ryP2Wpau mIwnLZVcTzDtOrUnecgdbDvl1FR/aunVXD9+Gn+JdhAfHEj52CnMcnkozX2fSUkfazo/tfbxR4A9 nolLnyfm22VMWfkpoN7/1fq3Wvsr1W9AUDvWrHuax++4je/P9Ed7xNWse30qY/vdTqFbfQ5Qa78h C1bQ4l/TeSzlZwBuTVpBP9db3PPkP6vyUBt/Sv1TbXzpHR9q56dXffdPve2nVP9h8WNYm9yBX/ae JrFHV7Z/n0FUsI3OCRn0HfA4HmBY8mp+/9+dOC4MI60wgh7tynlixMNszy3TVH9mts/4Fa9y7JGB vHC0ej22/tscFv3lA+4c/Ynp/UPoo6V/1Xb91dK/9cZXauVXm//U1k+17XrLr2d9MoKW+EuNUv/Q m7/a+t/2tnFMuqpF1f+HtWkHP0xnyIzvNW1XK59aerXyqfUPs9tfLT4yu3/pzV9p/NlCfsesWfdg tcUSEBTMrFmzAMjesogn1h8G9Mc/au1vdvvovf4D5fhRqX8aEZ+o1Y9a/avNv019/OqtXzDu+qwm RtwfEaIxMWs+DTUgXtaznqitp0bcD6k0Zu4CWp5+l6Sn/+dX3Z99+Hvm/8vcYPH1agIrsbYAyk5o u1YWQgghGjJDHwADdBgwnydvS6RZdDgH/vcRS+b9txa5WOk3pS8AH7/wo6H5G1O+2hk6/yE8H85k 1MvfAlbuevYNZky5jGGzv9GcR+TFJxkwbCZOD1z9yMs88sRtfPnAW5rSFuxfxtix0O2hl3islb+l tzJtxiD2J49h+kdpWO1xvPD2S7Bbew4xl0xg/N/ieXTIMFLPfGq63cWtNad/4JlpOL58ikGrtgPQ pd8i5s2/i633vQrAkfcW0X/+gapPbd/4xFomjevKPfOqf2Kvza39mf3wMHZklWCxhtLeXhHUaamf 6CsLGThyHC4v9HshhSFDE/liSSpg5dGZg9j/wkNM//AwVltznn17NaRqTa+/f3S6fzghvyxg8NSK B8JWWzQR3rOBtFr9qaVXszlpEpuBOe+8T/7cR1n8q5sCWs/PV/toFehox2MvLMb22UKmvL696u/K 7autfyu1n1L9uop388qhQkb0a8votQcA6HDnUAp+WckBjTc4lPL3ekp54ZE5rHx9PjdvH8KPnR/k 3i6HGD7kw6r0Wsaf0vlpGV96xoda/9Srvvun3vZTq393WTrjH53L9S+mMDg/mfump7Lm/c30DLVV /ZZ95EXZDBi+DJcXrp28ivGzbuKucZt1l1+vf+4vYNifWsLaQqyBNqweF+UeuODP8aS//7PpxxfG 0NK/arv+qvVvQ+IrhfKrjT+19VNtuxHlr+36ZASt8ZcaX/1Db/5q/evAazN58LWKf4e1uYnVy0bw 4vJ9mrerlU8tvVr5tPQP89pfPT4yu3/pzV9p/DkLdzB27A5C40ayflkXxo49/5WV+uIf9fY3u330 Xf+px49q/VNvfKKlfpTqX23+bdrjF0PqF/Rfn/lixPonRGNh5nx6UGW7erysbz1RW0+NuB9SKbFH D9rmfupXml+zR17O8JbBrEs6WO3vzf8wkIFXtKJ5x0tpduA/zDlTViGEEKIx0/+Dsr9SXnSS7Oxs Cpxe2nTpTo8OYX7ncc3IRQzrGUPqu3NY/XP1V4Tozd+I8tWG1RbNrS2C2bSh8omHh09e3kv07/r5 lc+RDZuqXtPywxtbCWs9gMA6+D0de/gV9AyzserzDAA8zixW7z3lVx7d772CnK+frwp2AdJ+SNeU 1hbakz5xoXzwTR6JiYkkJiZiObCN0Pi/V/1Wc8GeQ7S/pBd9/34ngwYNopPbQlhi3Hl5ndy5lB1Z Fd+a9nqKOORHwHlkwydVNwh2/e8EEV3DgYr6uTjMzqrPKs7H4zrOa7/qu0rpjegfxRnFhHf4K70v v5Co4EA8rlzyz7zCS0v9KaXXy5/z09M+gcGJJK18jgvSlpJ0zsNfNVr7t6/201K/ny/dRpu+Iyom XUsAI29uzVfJWzWVT0v+ztM7mfTEP3lw8WyeHnc5ix+eT+457adl/Pk6P9A2vmo7PrScn5nM7p9G tJ9a/ZeXHQIg71gZRYdOAx4ynG4Sgs7+rnPaO5ur2ufb17cT2XGQpvoxu30OvZdGi97dALjjpbd4 cVx3APrEh7L1v8frvX8IbbT0r9rO70r926j4Sqn8auNPbf1U2m5cfFj79UkvrfGXGl/9w6j81QQ6 OjDn2Yf5ctF4/pNRpHm71vKp5V8Trf3DrPZXi4/M7l9G5K83vtUT/5yrpvY3u32MoBQ/aumfeuIT rfWjVP9q7d+Ux28lvfUL+q7PfDFq/ROisTB7PtUTL9fFeqLnfsi5nn54DI8kbat1Oay2GCY8N43D 7yax4VcfCi8/fYJjOcc4fvw4LRPiaRUS4CMXIYQQovEw/BvARzcvYsJmsIUmsmZ9MvfOfIj37pyj Of1Fd81mxu3d+eWDJUxc+ZXh+etNX1sBtlZYLRbSy9xVfyvLyyfAfrFf+ZRkllT92+3MwBoQQkSg lVx/f8vRT1ZbSwAynWfLX5RdDDHa84iPsFO45XStjh/gaA9Ar0FD6XXO37/9bifBVgtlHi+3z3qZ Qa2OsunTnZwqKsPudGOxBp2X16k95z+Y1cpVcDZY97oBS0VAWFP9lOSUnlc/vtIb0T8OvTGNVfb7 6DtiKlPaNicj9TNmT1tMutOtqf6U0uvlz/npaR9HdB+K3/qc2P73cnHENn7Q+Ppqrf3bZ/tpqN/8 vcs4EriBIW3C2GC5k84BaUzdr+1cteQPkPv92xwMWEfr4xuqXg1fScv483V+gKbxVdvxofX8zGJ2 /zSi/VTr31tRdo/Hg7e8Ir9yL9jOyaMks7jq325nJlZbDHYrqr+1Znb75P/8EcEthmEN3MptQV/h vKIPtuByLrQX8VheGQFR5h5fGENL/6r1/K7Qv42Lr3yXX238qa2fiuuzQeXXsz7ppTX+UuOrfxiV vxKLNYgRi57C9u8nWfpFpl/btZRPLX9ftPYPs9pfLT4yu38Zkb/e+FZP/FPJV/ub3T5GUIofNfVP HfGJVWP9KNW/Wvs35fFbSW/9gr7rM1+MWv+EaCxMn091xMt1sZ7ouR9yruwjabUug8UazP0Lkknc s4aRq3actz0v9RPWpwK8wfczXuXROddz17iPan08IYQQoiEw/AFwJVfRAQ6XlXNJaA/NaTr1mcRT 91zJ3g+eZfxzH6F0SVOb/I1M7y+3M4Nyr5cLHAHsOvO6K0fLWNxl/gUvYR3C4JscAGyODnhc+eSf efjr8noJCD57wRsUY/e7nF5Pxad5g371WxjussMAdHAE8lNxRfmbtQ6B6s8oFB09WUpk9xjgkP/H L6541dbquTPZX8MnjgMdXRh5VQKjb3ug6hUy3Tvcwi01HsP4hwXukl8qjhkSyI4zv2kS0z4UND7v 1tI/1NrX4y5g4yuL2fgKBEUnMuXFJUz6awrjN6Wp1p9aer386f962qcoczkLXt3M1sgVPL5oFANH LcWpIT+9/VtL/Xo9TpI/yGD6qEs5HHADGR89qfrgzZ/8AW6auITona/w3/ZDmX37FpI2/FK1Tcv4 88Wf8VVj+VXGh9bzM4vZ/VNv++mt/0ph7cPgu4rfvQ8MbovbeaxaH6zt/KuXs+ArMpnKDRfeS/6H G9l05TyuvCCA4hMbKPN4sdVz//it8NX+Wqn1r4pjmLD+GhVf+Si/lvGntn4qrs8Gld8XreNXrf19 bTdqfqo4xvn9Q2v+evvvNWOW0Lv8E4a8+LVf27WWTy1/X/T2D73zt1p8ZHb/MmL90RTfej1gOf+y 2Kj+7av9zW4fIyjFj2bPv0as/0rt39THbyUj6lfP+u1zfJu8/gnR0NTFfOqLWv6GrSc+1tOKTbW/ H2IIi40BSS9yTckH3LdwE2qHTvshj+C7ewDyAFgIIUTjZtgroB0xfVixcCYj7xtC/379eXDac1wW Zqc4+0P1xIAj+m88N/ZGvJ4ijoVewtRp05g2bRoTR19tTP460+vlKS9g/dFC+gzvhYWKT571H9WF nO3v+ZVPm74DiQq0AlauHdGLvH2vVgUuB7NKiP+/M5/gs7dkcK+WfpezvOwQWU43/S9PqPZ3V9FO tuSXcX+fzgDYwrswolMzv/L+6aUvaH7ZRP7YrvK121Z6Xpuo7fgle1l/+BSTR9yA/cwrqay2KK78 U8WrQr3eUjxeLx2jKz6xbY/uzrjrjH89oC+ukp/YlFXEA/f2xmaB4LjfM6pDpOb0WvqHWvvGXHEp UbaKIe3MT+e4y4O79MyHA1TqTy29Xkb1fzVeb8Un6LclT2Zn2A0sHH6JpnR6+7eW+gU48MZqoi4a y5ie0by6br+h+be+YSJjL8th4pObeHbCU3QavpA+7c++Ak/L+PNF7/hSGx9a6w9g1Mq1rF1xt+Zj a2F2/9TbfkbNb+3uGEDkmfXjuhFXkf/z2mrbazv/nqt27ePhncwi7htzNf/+IIPvX9/PQw9exrHP vvX7+KJm9rBwIiMjcVgtWINCiIyMJCyo+jfUfLW/Vmr9yyxGjV9f5dcy/tTWT6XtDWH+AfX297Xd 7PhLa/56+m/rGx5h8rXFPDplVdVrUrVu11I+tfyV6O0feudPtfjI7P5lxPyvJb4tLz1IYFAHekRX /3ClEf1bqf3Nbh8jKMWPZs+/Zrd/Ux+/lcysXy18je+6uj4UoqGoi/nUF7X8jVpPfK2nlWp7P+Rc 815fz2vJ1/uZysItk5L5e9xupiz+B8HhkURGRhIeVlFOqz2Ov/yhW9VcaG/WgSEDO1KwZ0utyiiE EEI0JIZ9A9jjyiUw4VJu/93VVX/LPfgNix9/U1P6gKDWBFgsEBDGtb17V/296Nghnlm2TXf+etMb IWXqQi5aPJW319xFYXBLAjO3MPX5H/3K49inxbywbjXFpaHEWg7x+Jj/VG378dnX8K58mJQ1t1Ps Pc3HXxyjU8+zaWe8spYeoTYCQyIItc7grbdceFw5DBoy7uxOXhfTF7/Lk+OT+XCGjfRPJjFiccXv hDz32DKWLHqK1/+cCWFetv2Yx1V+lD1vzwpmrwtnQvKbjMjKxBLeioLdixj35QFNx187MYlWc6ay Yf3dZBZ4aNGqGQe/Xs43n6fiLktjzrvfMv2l17jjyAnCw0vY+N5R7uztozA10FQ/ClZNnM/j8yaw ceNo8rNTSfkqh/4h2o+v1j/U2rfVtUN4Kmk6xzNzIDwBa9aXTP40o2q7Uv1pSa+XEf1fK4/7FAsf eZZ1q+YwYNvdrN+Tr9q+evu3Wv0COE9v583jAdxufZuvNb6eWkv+jhbX8Mz4a1g25m5yXB7I3c6E Z75g5TOz2TlwMkedbm3jzwcjxpfa+NBSfwDx0c2wZeRqP7BGZvdPXe1nQP0DHPuPmxdfX0WRM5xY 7wFmjPm8+g61nH/PVdv22fNBBuH3hbAptxTrzrVEdHyWz57M1lR/Qt3/LX2VsfFnbjbd9CQpN8Hh 98Yx8sW9Z3dSaH8tVPuXAr3rryHxlY/ya5n/1NZPte31Of9UUWt/H9uNWB+UaM5fofxq/euSu3sR GGJnacqGquyOf5fE/bN3qm7XUj61/NXKp7d/6J0/1eIjM/uXEeXXEt+W5f+Hlf++iaTVbxPodpP+ 8WOMW7HXkP6t1v5mt4/e+VUtfjRz/gVz2/+3MH7B3PrVRGF81+X1oRD1rS7mUyVq+RuxnvhaBz2V IgAAIABJREFUTyvpuR9SyREaSkiZf7eyLQGhjL2xPdCe1W/eWPX3wsxk+g9/H6s1hNvHzGX8TDv5 eUWERzcj44ePmT7//NdECyGEEI2N5eDBg16AXr16Ke5YVFQEQES7JN5e+UcyPkrhXxkZvJVS/XUY EdHNaRYRiuv0CbJOFhpeYL35K6W3BkRy14C/0fwPfbm5SySP//0W/nu6dkGJb1ZatG6DvbyA9Oza /ZZOYHA0Cc0dZB7NPO+TyAFBUbRuFcKxo5mUmvCqRUtAKK1bx5CXnk6hu3bfDrXawkiIj6E0/xjH C0r9Th8WG0dMMJw8dozCX70zxhEdR8tQyEzP8vtT2kYJsAXgdrn5w8I3GJqaxOhX/flko3L/UGvf gOBIWrWIwlt8kszjNb9/Wqn+tKTXR3//N5MR/VupfsHKjJSNBC65n1lf55iQvzo948+I8aU2PpTO L9DRic0bl7Lsntv5R3YxxjO/f+ppPz31P/rNTSTMG87MA5AQ6yDjaCbltWjD+m0f/f2/MQkNDVXc nplZ8TuE8fHxivtVxm9mMqp/6VP78aul/GrjT239VF9fG/b8o8bs+KshxHdKzC+f/v6hp/21xEdm z8968tcb39ZF/zO7ffRSjh/NnX/B3PZvyuO3LupXv4Z9fdjUNaZ4s6kwaz7VeHTF/M1fT/TfDzFT WFQs0ZGhlBYcIyfP/3uVQgghRF1Si+O2bt0K1OIbwK6SA+zYEQSxHekW4Thv+6nc45zKPe5vtprp zV8xvdVO9+7d4fQv7NgBBS4zAh4POen6fsejvCSXtCM1b3OX5ZGWlqcrfyVedxFH0/QF9x5XIUfT av/hgMITWfhKXZqbRZrxXw7UpFm3O7m25WF+3J9FWJtLebhrCC/NP+pnLsr9Q6193SUFZKQVKB5B qf60pNdHf/83kxH921f9tut5EV0vup0/BKUz+H8nDM9fKz3jT8/40jo+lM4vuGVvfvom2bSHi3XR P/W0nxHzW3mx7/VDi/ptH/39X5hLb//Sx4D4SqH8auNPbf1UX18b9vyjxuz4qz7jOy3ML5/+/qGn /bXER2bPz3ry1xvf1kX/M7t99FKOH82df8Hc9m/q4xfMrV/9Gvb1oRBGM3s+VTm6Yv5mridG3Q8x U2HeCQrzGmbZhBBCiNry+wFwSc46pk83oyj1z+M6zvSmenLCdOWFeXTq9zf+dFsznKcyWZP0AJ/m ltV3sUQDcfnNt9KNXOaPW0hBedP+5mJNjBgfp9NeZsJMkwrYxKV99z9On3aZegxpn9+uuuhfZmrs 5RdCiMZK5l9zSf0KIRqK3/r9ECGEEKK++P0KaCGEEEIIYS55JZ8QQgghhDCTxJtCCCGEEI2T1ldA W+uiMEIIIYQQQgghhBBCCCGEEEIIIcwnD4CFEEIIIYQQQgghhBBCCCGEEKKJkAfAQgghhBBCCFEf LIH0/vP/0SXEVt8lEUIIIYQQQgghRBNSqwfAIfHXMHPRC7y65jVWLL71vO0X3LOAxZO66y5cTSwB YbRoEV2rtLcvXsHUy2INK0ttzlNP+c3Wrt8clj99ranHUDp/o9unrhlRf3r7hxH1W9vxq/f8Bz67 kkd7xtQ6fV3l76t+1qWk8Pbb77Bp0yZa2NSnVl/5mDl/+qOu5zez27+hUFs/zaSlfRrq+i0aN63j u67mv4Y+//rSUMrXUMphtHo7L285B8s788SSUditFlMOYWZ8+Vvnb/xX1+oqvjJq/Jg9P5sV3zbV eVEIUXfq+35SQ1/PdLPa+eOtg3hk0qOMe3Aol7Wp/ruJ0R0u4u9338cjkycz9v7B9IwLrqeCCiGE EMaq1areZ854Wu16g4mPjGfK7H+dt73o6E+kHjitu3A1CWkxmFUrZtQqbXjzFkTZAwwriz08huZR dr/S6Cm/2QLDYmke4zD1GErnb3T71DUj6k9v/zCifms7fvWef1jzFkQFmXehYVT+vupn8J13MmhY Eg6HAy33b33NH2bOn/6o6/nN7PZvKNTWTzNpaZ+Gun6Lxk3r+K6r+a+hz7++NJTy1WZ9aAzqs36P frqUeVvjeWr4pabkb2Z8+Vvnb/xX1+oqvjJq/Jg9P5sV3zaU+VkI0XjV9/2khr6e6XXj1OVMvqM7 h3Z/T2ZZArOXv8wfIs+uB8nPTadnXAjH0w7jbn4lT7+0mutbmHt/VAghhKgLgf7sHBIdTYjVyiVR dtK/S8ditWLlbGRgtUUSHWnDtfMD3nMV1JhHUFQ0jpICTjmD6HzhBVCUwd7DJ8/mERhBp64dCPKW kp12kJxCFwAWq52Y6AhCohxgsREbW/FNRndpAXln9tHKFpFA144x5B7cQ8ap6mlDYlvTPi4WizOX ffuO4PL+Om0cXTs2J+/QHr+OqbX8Vls4nbp0xFZewL59h3F6NOYfEE5UZDnOoATa2E/w89FiuvS4 kIL9qWQVl2s+v+p5hhET5aDg5Imz+1lstOvcmSi7mwN79nG6XFsB/Wk/pfap9fENqh+1/lv9mNrr T2//NqJ+tYzfijpIoFPb5pTmZ7E/7ZjvMtV0/hqEtkqkUwsHR37eQ+6ZAWANCCcqMoCTufnV9o2O icGZn0uhW/sBasofwBHZjMDiUxS6Kv4W4IggPKiE/IKKOtJaP0qU5g+1/EOio7EV5lPsiPM5PmwR 8We2/Uy2M4hgaymF5/RvPeUD3+NDa//TMv/4ah8t6X2tH1VU5g/V9CqU5m+19VNNaEw0gafyKY/p QKfmQRz6+WcKXNrqR0v7aOrfCvWn1D81r386618o01K/SvO7z/6lcX0F3+NbS/+rbXxUSc/8q4Vq fFDL8WNU+dSYFf9W0Tv/+oqfNPQ/tfmzLvqfKouN3P++zhq7m/BAq+b4tqp8Oq+f9K7veutHS3yg Fn/62q4U32npP2rxoRHnp2f+0Mqs+Kq+42O9+UMdxE8qlOrXiP4nhNCvKdxP0nI9qUb3em3AelYb Vnscj1yTwJp7x7Exqwj4hPxubzJydDe2z/8BgImDB5N1unJufZvydRsZOqITnz65q07KKIQQQpjF rwfA14+fxl+iHcQHB1I+dgqzXB5Kc99nUtLHAIS3G8ys8d1wtGhH0C9JDJn+/Xl59F60kr6fraLk 5nuIPH0CW1Qbdk0YyKL0Qhyx17J0xWQ8aankeoJp16k9yUPuYNspJ7aQ3zFr1j1YbbEEBAUza9Ys ALK3LOKJ9Yc1n0NUtwGsHH4Bec5ILmxvZ8Gw4XxxshSAntOW82TPYPYfzcQb0pqOYdk8PnY6u88E TNGXDGL53IFkpe4iKKENR44FQqm242opf2jC9Sx5fgKk7aYwtD3tbQeYMnomv5SqP8AJix/D2uQO /LL3NIk9urL9+wyigm10Tsig74DH8Wg4v3MF2OOZuPR5Yr5dxpSVnwIQFHM5TzwznYTSNI4UBdGt LTw95hG25qhXgtb2U2ofPcc3qn6U+q+e+tPbv42oXy3j96aRcxjXpxP7Ug9iiWxLTMEahk3993n7 1XT+WsT1HsvLXSNIOx1Gjw4Wnh75EF/mlBAQ1I41657m8Ttu4/sz7WGPuJp1r09lbL/bKXRre8jp K3+AAcmr6bZsFFO+rrhIaX/HAuZfs447H/hKc/0oUZs/1PK/47lX+OPnn2G7sub2i/ndQJbNG8Sx 1F1YW8Vz+Jidi1wLNZdTrXxK40NL/9MyvpTaRy290voB6vOHWno1avO32vqpZljyan7/3504Lgwj rTCCHu3KeWLEw2zPLTOkfdT6n1r9KfVPLcfXW/9CmZb6VZrflfqXlvUVlMe3Wv/TEx+B/vlXC6X4 QM/4Map8SsyMf0H//KuUXkv/U5s/ze5/eutHjd7rJ73rh9760RIfqMWfStuV4jst/UctPjTi/PTM H1qYGV/Vd3ysN3+z4yc1avWrt/8JIYzRFO4nqcVDavSu10asZwBj5i6g5el3SXr6f5rT2IIvJNBq 4euTZ4914L8niO7bG6h4AHz24W+FrDI3WJrgV6GFEEL85vj1AHhz0iQ2A3PeeZ/8uY+y+FdBSsH+ ZYwdC90eeonHWvnOp82t/Zn98DB2ZJVgsYbS3l4RcHS6fzghvyxg8NSKCxqrLZoIb8XNA2fhDsaO 3UFo3EjWL+vC2LET/Cl6legrCxk4chwuL/R7IYUhQxP5YkkqAEfeW0T/+QeqPsV24xNrmTSuK/fM 2wVYmTZjEPuTxzD9ozSs9jheePsl2K3tuFrKP3T+Q3g+nMmol78FrNz17BvMmHIZw2Z/o+kY7rJ0 xj86l+tfTGFwfjL3TU9lzfub6RlqY2eRS+X8zgp0tOOxFxZj+2whU17fXvX3B56ZhuPLpxi0quJv XfotYt78u9h636uGnD8ot4+e4xtZP776b6Xa1J/e/m1E/aqN35hLJjD+b/E8OmQYqfkVNyXaXdz6 vP18nb8WkRefZMCwmTg9cPUjL/PIE7fx5QNv4SrezSuHChnRry2j1x4AoMOdQyn4ZSUH/LgB6yt/ LbTObzVTnz+05O+7/aw8OnMQ+194iOkfHsZqa86zb6+GVOPKpzQ+tPQ/LeNLqX3U0iutH6A+f6il V6M2f6utn1pEXpTNgOHLcHnh2smrGD/rJu4at1m1frS0j1r/0zL/+uqfWo6vt/6FMrX6VZvf1caf 2voKyuNbrf/pi4+MmX+18BUf6Bk/RpbPFzPjX9A//6ql19T/FOZPc/uf/vpRo/f6Se/6obd+1OYX tflJa3zqi5b+o4fe6wu9/QPMja8aQnysJ3+z4yc1Ev8I0Xg0hftJSvGQGr3rtRHrGUBijx60zdX+ JQMAd9nhimOGBHLU6QagebdIAkI717i/PfJyhrcMZl3SQb+OI4QQQjREfj0ANsrJnUvZkVXxqWOv p4hDZz6EVZxRTPglf6X35bn8mPoLeSW55CvkUxtHNnxSFbDs+t8JBl4VXrWtYM8hOl3ai+4d4wmx BxLpthCWGAfswh5+BT3DbIz+PAMAjzOL1XtP8ZBB5bLaorm1RTDPb6h8YuPhk5f3Mnh2P0DbDZTy skMA5B0ro+jQacBDhtNNQlAAO4tciudXKTA4kaSVo2m3/1mGnRNs2kJ70iculGe/ySMxMREAy4Ft hMbfS5B1DWUeP97xq8BX+xhxfCPqB3z3X6j/+lOj1P/VdL/3CnK+nlcVzAOk/ZBebR9f56+9fJuq Xhv4wxtbCVs9gEDLW5R74fOl27j3iRFY107FYwlg5M2t+WrqVsPyN5NR84ev9rOHX8HFYXZGf1bR Hh7XcV77OZ+HDSyf1vHhi5b0Su2jll5p/dAy/vSsP0bM31qkvbO5qv2/fX07kS8NAs48wNDZPkq0 zl965pe6WP9/y9TqV21+V+tfausr1H7+1Tu+zI7fzlVTfFAX40cvM+NfvfOvWnrQ1v+U5k8lZs/v RsSHeudPPeuHEfWjdny1+UlLfKpES//RQ8/1hVHXD2bFV3qZPT/XRXyrl8Q/QjQeTeF+Um3jIdC3 Xht5P+zph8dgc9f82mxfyksPsnZvPvdOv5fcVz/DFnc54xNtWCy28/a12mKY8Nw0Dr+bxIZafGhb CCGEaGjq5QHwqT01X9YcemMaq+z30XfEVKa0bU5G6mfMnraY9DOf0DKCq+BsMOJ1A5aAqv+/fdbL DGp1lE2f7uRUURl2pxuLNQgAq60lAJnnlKUouxhijClXgK0VVouF9LKz+Zfl5RNgv1h7Jt6KtB6P B++ZO6rlXqgMaZTOr5Ijug/Fb31ObP97uThiGz+cef1UgKM9AL0GDaXXOft/+91Ogq0Wwx5g+mof Q45vQP2A7/4L9V9/apT6v5r4CDuFW04r7uPr/LUqySyp+rfbmYE1IISIQCu5Lg/5e5dxJHADQ9qE scFyJ50D0pi6379bJEr5m8mo+cNX+9WUf0lOqeb8tZRP6/jwRUt6pfZRS6+0fmgZf3rWH0Pmbw1K Mour/u12ZmK1xWC3gtOjv32UaJ2/9MwvdbH+/5ap1a/a/K7av1TWV6j9/Kt3fJkdv52rpvigLsaP XmbGv3rnX7X0btDY/3zPn4rlN3l+NyI+1Dt/6lk/jKgfteOrzU9a4lNFGvqPHnquL4y6fjArvtLL 7Pm5LuJbvST+EaIRaQL3k2obD2k5P6X12Mj7YdlH0jTve643Jo3Hff9Q7r5/JIWZe1iw4CeemGmt to/FGsz9C5JJ3LOGkat21Oo4QgghRENTLw+AvT4Wd4+7gI2vLGbjKxAUnciUF5cw6a8pjN+Udm5i sBhf7EBHF0ZelcDo2x6oeqVs9w63cMuZ7ZWvDOngCOSn4opPgzdrHQIlNWSmxEf53c4Myr1eLnAE sOvMp80dLWNxl9UuuPk1tfOrVJS5nAWvbmZr5AoeXzSKgaOW4vR4cRfvA2D13Jns1/ObZ7VsP8OO 74PW+gHf/RcMqD+9/duk8QFw9GQpkd1jgEM+9/F1/lqFdQiDb3IAsDk64HHlk3/m4YDX4yT5gwym j7qUwwE3kPHRk5ouVLTm7/J6CQg+e8M9KMbuX+aA11PRtkG/+q0Yw+YPH9wlvwDQPSSQHWd+gyem fShovB+qVj7N48NH/9Oa3lf7aEmvtH5oGX+a1h9f9Wfy/F0prH0YfHcCgMDgtridx3B69LePmrqY //XUv1CnVr9K87s/66MSpflXid7xZfb8e66a4gOz4xe9zI5/9c6/aum1fk/a1/ypWn6T53cj+oee 6ye964fe+tFyfLX4U2273vhOS3pf8Z/e6wuj5g+z4iu9TI+PTY5vjaBWv0ZcnwghzNdY7iepxUN6 1jOl9bghxMMeZxZvLnuKN8/8f8+HX6bw6Gtnd7DYGJD0IteUfMB9Czdh7lcEhBBCiLpjVd+l7sRc cSlRtooiOfPTOe7y4C6tvuyWlx4kMKgDPaKNvfjxekvxeL10jK74BJs9ujvjrour2u4q2smW/DLu 71PxGxG28C6M6NTM7+P4Kr+n/P/Zu+/wKKr1gePf3WQ3m04KgSRAaKFjr1cEG16vojSlKQIqqICA FEFCVwEFwYIoIE1sgA3lZ7leOyIqqIiRXkJIIUB63c3u/v5IgUh2ZjazmwR4P8+T54GdOWfOOXPO mXdmdmZzWJ+cT49hXTBQ9s2zvo+0JWPbhzWv1BnU6nd6vbJvGG5dMomdQTezYNilANiK9rL+SC6T ht+M2VgWDBpNYVx9Q0e3ylHT/eep7buitX3U89HXfnr7t7fGB8DfK76j4eUTuC4uqPwTI527tqqy jqv6a9W010DCfI2Aka7Du5C1b02VwPvg26sJu3g0ozqHs+atA27XQSn/Q2lFxPy77IkVo7kR93Zp 5Hb+pSWHSbPa6XtFbJXPPTV/uGIr+ptNaQWMeKAbJgP4R1/JIy1CtadXKZ/W8eGq/2lN72r/aEmv dPzQMv60HH9c8fb8XSHunv6ElrfPjcOvJXvPOkD//lFTG/O/nvYX6tTaV2l+99TxUW1+d0Xv+PL2 /Ku6fS/HL2d6ZPk61i27z6003o5/9c6/nmo/V/OnGnf6X03a3xP103P+pPf4oXd8atm+Wvyptlxv fKclvav4T+/86an+7634Si+vx8dejm89Qa19PXF+IoTwvnPlepJaPKTneKZ0PPZkPDz3zfW8seQm t9MFNGlNYPm2G3W8jYTuMXz88u/lSw3cOXEJd0f/xeRFH+MfHEpoaCjBQfKlGyGEEOc+j36VddrK dXQKNOEbEEKgcRrvvmvDYctg0OAxmtI37jqYZ2ckcCI1A4JjMaZ9z6SvU6qsU5L9Fcv/150Zqzfi a7dz7IsnGbNsr+6y20uSmPP+DhJWvME9R08SHFzERx8m06/b6XVefHIpixc+y5u3pkKQk61/ZnGt m9tRKv+GKQu4eNEUNq4dQL5/I3xTf2DKS3/qrpvW+p3JYc9lweMv8NaqOfTfeh/rd2ezbsIMGs+Z wgfr7yM1x0FU4wYc+ulVfv42sfpMqqFn/3li+6642z5qatp+evu3nvRq4zdr9zJmvxXM+CXvMDwt FUNwY3L+WsiY7w9qqr8Wx78u5JW3VlNYHEik4TDTR31VZbk1bxvvnPChj3EjP7n5emm1/P984Q2c y8eyYW0fCp15fPHdceI7a28fAJw2Eha9zzPjlvDZNBPHvpzI8EVl+1dt/tA7f66aMI/pc8fz0Ucj yU5PZMOPGfQN0N42SuXTOj5c9T+t6V3tHy3p1Y4fauNPy/FHiTfn7wrHv7Lz2purKLAGE+k8yLRR 3wL69w+o9z9vz/96218oU2tfpfndU8dHpflXrf/pHV/enn/V6B0/WssXE94AU0qmW2WrjfhX7/zr ifnH1fwJnut/NWl/T9RPz/mTJ44fesanlu2rxZ9qy9XiOzWa0ruI/zwxf3qk/3sxvtI7f3p7fvZm fOuJ8qm1r97+K4SoHefK9SSleAjQdTxTOx576nqeJTCQgBL3L2U3vHwYry3vTE6+jdAAO5+tmsw7 R8pemWbwCWT0Lc2B5qx+55bKNPmpS+g77BO3tyWEEELUJ4ZDhw45Abp06aK4YkFBQa0UyMc/lMZR YTgLT5F6QsfvOdWQJTyaRoGQeiwNWzVvZjH4BNKkSQRZx46Rb/fG00lGopo0xVyaw7F0937fVAu1 +mkRFBlNhD+cOn6cfHffwesB3ty+J9pHTV23n15GUxCxMREUZx/nRE6xx/P39Q8ntqGF1OTUavaB kWkbPsJ38UPM+inD4/n7+IXRpHEAx5NTKfbC7zJ7f/4AH5MPdpudaxa8zf2JMxi5RvuT0mrl0zs+ tKRX2j9q6bUcP5TGn/7jj/fm75HvbCJ27jBmHoTYSAspyamUutk+nuDN+auuj//1TWBgoOLy1NRU AGJiYhTXq4jftLSv0vzuif6lPL+r0Te+amP+VePN8eNriWfzRy+zdGgfPk4vVE/wD7UR/+qdf2va flrmT3XK/U9v+4O+/qF3/tQ/vvWNTy3bV4s/lZbrje/0pq8P51/ejq/08Pb87O34Vi+19vX2+Ym4 sHk63rzQ1efzMU/EQ544Xtfl9TBLeBSNgv3ITE0hT8NP0QghhBD1mVoct2XLFqAe3gAWQojqxHW+ mHYX9+Gxexpyb9/R5JRKwH6mBh360bXREf48kEZQ08tIeHIYK4b05+vMkroumvCAihP2hF3uP10m zk1yQU64IzjuIWY/kM74mZvruij1Tm3Mn9L+QgghzkUSb1445HxSCCGEOL9ovQHs0VdACyGEt1xx +110IJN5YxbIzd9qlOZnEd/7Dm7o2QBrbiprZ4yQm7/nkaTffiUvz1bXxRBC1FN5Sa8zfmZdl6J+ qo35U9pfCCGEEPWZnE8KIYQQFyZ5AlgIIYQQop6RJzKEEEIIIYQ3SbwphBBCCHFu0voEsLE2CiOE EEIIIYQQQgghhBBCCCGEEML75AawEEIIIYQQQgghhBBCCCGEEEKcJ+QGsBBCCCGEEEIIIUQ9YvAJ ovtttxFj9qnrogghhBBCCCHOQXV6A7j10PksmtixztILIYQQQgjP8GZcZvAJIioqvM627466KIeW 9tHL2/WqL/tPzYXYDrXRv+rS+V4/oaw+7H9X495pzyc1+BoWzL4Hgxe3I4Q4//VZtIwpl0fWdTHq jMHoR7O2F3Nbr34MHNjvrOVxPfoyaNCgKn8RJqPm5UIIIUR9VadHK3NwBA3DzHWWXgghhBBCeEZB 8t8kHszzSt4BUfeyatm0Otu+O+oiPtXSPnp5u33ry/5T4+39Wx/Pb2qjf9Wl871+Qll92P9K81/i xlmsTu/KjJ6tdG+nPs4vQojaEdwwirAL+G0CsTfPY+n8KdzetRdD7h981vKWvQbQ65rWNGrUqPLP bNC+XAghhKivfN1NEBDZhObRkRismezbdxSb84xl4eGY8rMptETTrmUEmYd2k5Jrq5LeFBJNu5YN yTq8u0YFVktvNAUT37YlptIc9u07gtXhXvmU0gP4hYVjKcoh1+pHm/atoSCFvUdOnU7vG0J8uxb4 OYtJTzpERn7V/IUQQgghalNgRDi+udmURrQgvqEfh/fsIcdWNcBRiu9MoRGEW6qGjPaSLE5mWwEw mkIJDzVh2/kpH9pyqi+EwURcmzaEme0c3L2PvNLT21eKzwxGMxHhIQSEWcBgIjKy7MkFe3EOWeUx lqbtq1Eonxbejk9d7R8t7aO2fVCObz3SvgrU8tfSPq4YfIIJCy3F6hdLU/NJ9iQX0rZTe3IOJJJW WHpGGZTbR8/+1UItf6XxqUbL+HdVfq39S238qJ0/aeEXFkt8s4YUZ6dxIOm4pvKD/vkF1NtftX46 5xel80tLaAN8C3PJL9+nPpYQgv2KyM7RNkbU0tfG+PNE/1HqH96eP9WYQmLK224P6VY//I3F5JfX X8v8GhDZhNSvl/Oe1YbJgFvjv2z7Ktdfanh8rqB2/UNv+wlxQXExHg1GCxHhQWSePEnFEDIYA4gI D6jyGSjPhwCmkFi3403Qer3X9XynVL/akPbDLO76MpeAJuN5b8WN1a5zfMs6Fm847DIPteVCCCFE feTWDeDOU1/lmc7+HEhOxRnQhJZB6UwfncBf5UH+PS+u5Lpvv8F0dWuyrKG0b25m/pBhfHeqGIDw Swfx6lMDSUvchV9sU44e94Vi7dtXSx8YexOLXxoPSX+RH9ic5qaDTB45k/3FpZrKp5YeoNvC5fT6 ZhVFtw8lNO8kprCm7Bo/kIXH8rFEduXlZZNwJCWS6fAnLr45Swbfw9ZcqzvNLIQQQgjhMUOWrObK X3ZiaR9EUn4IneJKeXr4WLZllgDq8V2znmOYeG1UZX5BTePgjwQGT/sdgOC4e5k1rgOWqDj89s9g cMLvVbbvF3EFTz+fQGxxEkcL/OjQDJ4b9ThbMsriL6X4zBRwEbNmDcVoisTHz59Zs2YBkP7DQp5e f0TT9tWolU+Nt+NTpf2jpX30xrd621eNWv5q7aMkKGYU65a0YP/ePFp1ase231MI8ze2CGDbAAAg AElEQVTRJjaFXv2n49DQPnr3rxq1/NXGpxq18a9Ufi39S8v4UepfWnR/eA5jesSzL/EQhtBmROSs ZciU/6mWH/TPL1raX6l+eucXtfPL/ktW02HpI0z+qewif/N75jPv+rfoN+JHTfmrpff2+PNE/1Hq H7UxfyqJuGggS+cO4njiLoyNYzhy3MzFtgWV85za/Kd3/KvNL3qOz6DeP/W2nxAXErXxOHj+MqL+ m8CTG/YAcNeMZfS2vcvQZ/6vMg+l+RAgrEN/lg9zP94E9flAbb7TezysMOqp+TTKe58Zz/3qVjp7 ca7qOqbAKDp2DqI4M4WDKZluLxdCCCHqI7duAB/9cCF95x2s/BbYLU+vY+KYdgydu6tynfCr8xn4 8BhsTuj9ygYG39+K7xYnAkamThvEgSWjSPg8CaM5mlc2roC/tG5dPf398x7D8dlMHnl9B2BkwAtv M23y5QyZ/bOG8mlLD9D0rr7MHjuE7WlFGIyBNDeXXUCJf2gYAfvnc++UshNmoymcEKec3AghhBCi boVenE7/YUuxOaHrpFWMm9WdAWM2A+rx3cE3ZvLoG2XLgpp2Z/XS4bz26r7KvHMOLGX0aOjw2Aqe bHz2tkc8PxXL988yaNU2ANr2XsjceQPY8uCaynVcxWfW/O2MHr2dwOiHWb+0LaNHjz8rf7Xtq9FS Pte8H58q7R8t7aM3vtXbvmq05K/UPmrsJccY98RT3PTaBu7NXsKDCYms/WQznQNN7CywqbSPZ/av a+r5azn/UqM0/pXKr6V/aR0/rvqXmohLxzPujhieGDyExPK3DsRd0qRyuZ7xpaV+WtvfVf30zS/1 4/zSm+NPb/9R6x+1NX9Wz8gTMwdx4JXHSPjsCEZTQ17YuBrOaDq1+U/f+FefX/Qcn0G9f+prPyEu LErj0eko5pXH57D8zXncvm0wf7Z5lAfaHmbY4M8q06vNh1DzeFM9vfp8p/d4WKFVp040y/zarTRa Nfr3Y4z9l43oJtGk7fiQ8TOXk293al4uhBBC1Edu/QZwzu7DNL+0C73u7segQYOItxsIahVdZZ2j H3xZGTDs+vUkIe2CATAHX0XnIBOrvk0BwGFNY/Ve9W9gVVBLbzSFc1eUP5s+qIgwHHz5+l7CL+qt qXxa0wOc2vky29OKAHA6Cjhc/g3WwpRCglv8h25XtCfM3xeHLZPsWnyliRBCCCFEdZLe21wZ/+x4 cxuhLQdVLtMS3wH4Wlow54WxfL9wHF+lFGjarimwMz2iA/n05yxatWpFq1atMBzcSmDM3fgZT/9w lqv4zNu0ls8Vb8enoH3/VMcT8W19oKd/lJaUvaov63gJBYfzAAcpVjuxfj6q7eOp/euKlvMjPfu/ gqvxr7f87oyfmvavjg9cRcZPL1VezAZI+uOYW+XX03+0tn919dM7v0D9OL/01vjzRP9R6h9Qe/Nn dczBV3FJkJlV35SVx2E7wRt7sjWl9UT51eYXTxyflfqn3vYT4kKiZTxa83Yy8en/49FFs3luzBUs GjuPzDOOB2rzIeiPN5Wu9yrNd544HlZ4buwoHp+x1a00Wvw651F6D7iPEcOHcc/9k7B1vpO5/Vpo Xi6EEELUV249Adxn1usMapzMpq93kltQgtlqx2D0q7KOLed0sOG0AwYfAIymRgCkWu2VywvSCyFC 27bV0vuYGmM0GDhWcnp5SVY2PuZLNJVPa3qA3N3Vn7gdfnsqq8wP0mv4FCY3a0hK4jfMnrqIY2eU WQghhBCithWlFlb+225NxWiKwGwEq0NbfGcw+jF84bOY/vcML3+Xqnm7PpbmAHQZdD9dzvh8x287 8TcaKHGUXUVyFZ95m9byueLt+BS07R+X9fNAfFsf6OofzrK6OxwOnKVl+7PUCSbU28dT+9cVLedH evZ/BVfj36mz/O6Mn5r2r5gQM/k/5FW/fQ+MLzVa27+6+umdX6B+nF96bfx5oP8o9Q+ovfmzOtWN 76KMYs3XP/SWX3X+8sDxWal/6m0/IS4kWsdj5u8bOeTzFk1OfMD3GUVV8lCbD0F/vOnO9d4z5ztP HA8rpB9N0ryuO/KPnqz8d/GJXSz9Np05/74c3jmkabkQQghRX2m+AexracvD18YysucIDlZ847bF ndypMb295AgALSy+/F1Y9hsSDZoEQJFCIjfS260plDqdtLb4sKugbLmlUST2Em3BgTvpnS6CE4c9 h49WLuKjleAX3orJry1m4n82MG6TdwIUIYQQQggtgpoHwW9lFy58/Zthtx7H6tAe310/ajHdSr9k 8Gs/ubVde2HZq6JXPzWTA3qeKHU6wODW9xY10Vs+b8enmuNvF+3jifj2fKbWPl4//1DJX+/5VwVX 49+otfyu+pcb46em/Sv5VDGhHSOAw2dvX2f7n1G4auvnTvtXVz9PzH9q55c2pxMf/9MX8P0izG7l rze9Hp7oP0r9ozbnz+rYi/aXbTPAl+3lv6EZ0TwQlO/PuF9+V9tXm7+83D89Nj6FuABoHY/dJywm fOdKfml+P7P7/MCMD/ZXLlOaD9Xonm9U5juPnQ/UIqPJCE7Xv7eutlwIIYSoLzS/AtrpLMbhdNIy vOwbYObwjoy5Ufvrx2wFO/khu4SHerQBwBTcluHxDTyW3lGaw/rkfHoM64IBMBj96ftIWzK2fagp f73pASKuuowwU1mTWrOPccLmwF589iu65r65njeW3KQ5XyGEEEIIPeLu6U+orxEwcuPwa8nesw7Q Ft81uflxJnUt5InJqypf+6aVrWgv64/kMmn4zZjLX/FmNIVx9Q0d3cqntPgQvn4t6BTu2ZsTesvn 7fhUa/ztqn08Ed9q9cjydaxbdp/H8/Umtfbx9v5Vy1/v+VcFV+Nfa/ld9S9PjW8lf6/4joaXT+C6 uKDyT4x07trKrfKrcVU/3ee/HmgftfPLQ2lFxPy7/Il1cyPu7dJIc96eSK+HJ9pHqX/U9fxpK/qb TWkFjHigGyYD+EdfySMtQjXXzdvXX7zdP91pP7k+Ii50WsZjk5snMPryDCY8s4kXxj9L/LAF9Gh+ +hXOSvOhGv3HO+X5zpPxQk3nC4PRQmhoKCFBJoCyf4cElZclnFuuaI2p/G3Uoc2vYWyXRux/f4em 5UIIIUR9pvlRCntJEnPe30HCije45+hJgoOL+OjDZPp1076xF59cyuKFz/LmrakQ5GTrn1lc60Zh 1dJvmLKAixdNYePaAeT7N8I39QemvPSn5vz1pm/cdTDPzkjgRGoGBMdiTPueSV+nnLWeJTCQgBLP P8UihBBCCFGd41/Zee3NVRRYg4l0HmTaqG8BbfHdpfd1wTfAzMsbPqj87MRvM3ho9k4Apq1cR6dA E74BIQQap/HuuzYctgwGDR4DwLoJM2g8ZwofrL+P1BwHUY0bcOinV/n520S0Ksn+iuX/686M1Rvx tds59sWTjFm2V9P21egtnzfjU63xt1L76I1vtbZvTHgDTCmZmvN1N39vUWsfb59/KOXvifMvcD3+ tZZfqX95Ynwrydq9jNlvBTN+yTsMT0vFENyYnL8WMub7g5rLr8ZV/TzR/nrbR+388s8X3sC5fCwb 1vah0JnHF98dJ76z9vLpTa+X3vZR6h/1Yf5cNWEe0+eO56OPRpKdnsiGHzPoG3B6udL8VxvXX7zd P7W2n1wfEUJ5PFqiruf5cdezdNR9ZNgckLmN8c9/x/LnZ7Nz4CSSrXbV46UST8w3avOdp+KFms4X gY0fYsPq0880b9iwAbs1ndvvHILBEMiDM15kgq+d7FwrYaEW/vjvKqb/XzKA6nIhhBCiPjMcOnTI CdClSxfFFQsKCgCwhEfTKBBSj6W5/RQIgMEnkCZNIsg6dox8+9lPx+pPbySqSVPMpTkcS6/Jb03p S+/jH0rjqDCchadIPaHx/U5CCCGEEGcIDAxUXJ6aWvY7vDExMYrrFRQUMPKdTcTOHcbMgxAbaSEl OZXSf8RweuM7LYIio4nwh1PHj5NvdT8G9DY95fN2fKp//+iNj5X5WuLZ/NHLLB3ah4/TC9UT1DvK 7ePt/auWv579r2X8e6J/eHt8G01BxMZEUJx9nBM5xf9c6tX+7Yn5UU/7qJ1f+viF0aRxAMeTUymu wau29ab3BL39R6l/1If508fkg91m55oFb3N/4gxGrjmgOW1tXH/xZv/09vgU+ngy3hSe4c35UI0n jndq8129PR8wmolq3IgAs5G8jBROFZa6t1wIIYSoZWpx3JYtW4Aa3AAWQgghhBDe5Y0bwAm73H86 UwgtguMeYvYD6YyfubmuiyL+Qca/EHWnQYd+dG10hD8PpBHU9DISnhzGiiH9+TqzpK6LJgQgN4CF 58h8J4QQQtQurTeA5T07QgghhBDnsaTffiUvz1bXxRDnsbyk1xk/s65LIaoj41+IulOan0V87zu4 oWcDrLmprJ0xQm6GCCHOSzLfCSGEEPWTPAEshBBCCFHPyBMZQgghhBDCmyTeFEIIIYQ4N2l9AthY G4URQgghhBBCCCGEEEIIIYQQQgjhfXIDWAghhBBCCCGEEEIIIYQQQgghzhNyA1gIIYQQQgghhBBC CCGEEEIIIc4TvnVdACGEEEIIIYQQQgghhBD1T1zvOUy59n88+sT3Nc7D4BNEwwgzGRmZHiyZdn4R Heh5VzeaRQaQk3aYr9/7mIPFpWet1/q2PlwZWso76z+u/Mxg9KNpfDs6tG9LmD+8886G2iy6EEII UWPyBLAQQgghhBBCCCGEEEKIs/gGRdIwwqIrj4Coe1m1bJqHSuQeS8R1rFizkGtjfDh6OAVjZCeu DzWftZ5/1M0sGP4Ag+/rW+Xz2JvnsXT+FG7v2osh9w+urWILIYQQuskTwEIIIYQQQgghhBBCCHGO MfgEExZaitUvlqbmk+xJLqRtp/bkHEgkrfD0E64BkU1oHh2JwZrJvn1HsTmr5uMXFo6lKIdcqx9t 2reGghT2HjnlYptBRIRZyDl18nQ+BhNxbdoQZrZzcPc+8kodZR8bzUSEhxAQZgGDicjISADsxTlk 5ds83h7VuWXmY5R+N5/HFyo8wWzwZcS8h3nr2b94YHp0lUVpP8ziri9zCWgynvdW3Ojl0gohhBCe IzeAhRBCCCGEEEIIIYQQ4hwTFDOKdUtasH9vHq06tWPb7ymE+ZtoE5tCr/7TcQCdp77KM539OZCc ijOgCS2D0pk+OoG/zrgB223hcnp9s4qi24cSmncSU1hTdo0fyMJj+VW252OOYcLLLxGxYymTl38N gF/EFTz9fAKxxUkcLfCjQzN4btTjbMkoxhRwEbNmDcVoisTHz59Zs2YBkP7DQp5ef8Stuo56aj6N 8t5nxnO/ak9kNDOsdShvzPqF8Lh4YgPh8N6D5NsdVVaL6zGTzn8vZvXx7jzwjyzsxblulVMIIYSo L+QGsBBCCCGEEEIIIYQQQpyD7CXHGPfEU9z02gbuzV7CgwmJrP1kM50DTewssHH0w4X0nXew8mnd W55ex8Qx7Rg6d1eVfJre1ZfZY4ewPa0IgzGQ5uaSKst9LXE8+coiTN8sYPKb2yo/H/H8VCzfP8ug VWWfte29kLnzBrDlwTVY87czevR2AqMfZv3StowePb7G9WzVqRPNMr92K43J0oYgHyO+Q+bxSjsj 6Y4IWjcs4PnHHufbtMKydQI78fTQKCYP/AWiu9e4fEIIIUR9IzeAhRBCCCGEEEIIIYQQ4hxUWnIY gKzjJRQczgMcpFjtxPr5sLPARs7uw8Rf1oWOLWMIMPsSajcQ1CoaqHoD+NTOl9meVgSA01HA4eLT y3z9WzFj+UjiDrzAkDNu/poCO9MjOpAXfs6iVatWABgObiUw5gH8jGspcfzjXdM6PDd2FCZ79a+l dsXgEwRAz+AvGPjw5wDcnLCG0U/15NuH3gGgz+yp7HllAqlWOyEeK60QQghR9+QGsBBCCCGEEEII IYQQQpyLnHYAHA4HztKyG66lTjCVL+4z63UGNU5m09c7yS0owWy1YzD6nZVN7u5sl5uwhPeg8N1v iez7AJeEbOWPXCsAPpbmAHQZdD9dzlh/x2878TcaPHoDOP1okttpHLYTZeVZtaXys1/f3M3EV+4C 3iGoyUMMjs/mmcI4rrkmDv/IMAwGP6655hoO7fiFDJvDRc5CCCFE/Sc3gIUQQgghhBBCCCGEEOI8 42tpy8PXxjKy5wgOFpcC0LHFndxZzbpOhZu1BamvMn/NZraELmP6wkcY+MjLWB1O7IX7AFj91EwO lOdfLacDDLV/Gbq0+BDpVjs+RkPlZwajL05n+e8fG0r5a3cuPXv1AsDXEoPBJ4BevXrx0d+/kWGz 1nqZhRBCCE8x1nUBhBBCCCGEEEIIIYQQQniW01mMw+mkZXjZE7/m8I6MuTG6BvmU3QjdumQSO4Nu ZsGwSwGwFe1l/ZFcJg2/GXP5TVajKYyrb+hYJX1p8SF8/VrQKdxc47rMfXM9byy5yd2Ss/L3U1wx 8t+YDIDBhxseak/ekfcByE9ew5QpUyr/5ixOxFGayZQpU9hW/pSzwWghNDSUkKCyZ6pDQ0MJCQmq cT2EEEKI2iJPAAshhBBCCCGEEEIIIcR5xl6SxJz3d5Cw4g3uOXqS4OAiPvowmX7dapafw57Lgsdf 4K1Vc+i/9T7W785m3YQZNJ4zhQ/W30dqjoOoxg049NOr/PxtYmW6kuyvWP6/7sxYvRFfu51jXzzJ mGV73dq2JTCQgBL3L2VvnTuTGxY9w8Y3/8MpZzgNSvby1PjPNKcPbPwQG1affmZ6w4YN2K3p3H7n ELfLIoQQQtQmw6FDh5wAXbp0UVyxoKCgVgokhBBCCHGhCwwMVFyempoKQExMjOJ6Er8JIYQQQojq SLx5YbGER9MoEFKPpWHz3M/yVhEUGU2EP5w6fpx8a/377dyI6KYEGYtJTjlB/SudEEIIoZ1aHLdl yxZAngAWQgghhBBCCCGEEEKI81ZxZhpJmd7dRv7JNPK9uwldTqUlc6quCyGEEELUIvkNYCGEEEII IYQQQgghhBBCCCGEOE/IDWAhhBBCCCGEEEIIIYQQQgghhDhPyA1gIYQQQgghhBBCCCGEEEIIIYQ4 T8gNYCGEEEIIIYQQQgghhBBCCCGEOE/IDWAhhBBCCCGEEEIIUcngE0T3224jxuxT10URQgghhBBC 1ICvuwmCYsbw6rNXAlBadJhhI2ZULruk621c0r4lkaFB2PJO8vuWT/l+V7q2jA0+dLq6G5d2bEOj iGCKs9P5+auP+fVgTjUrG7mtXz/CfY38+sEG9heXai5/s6tv485/dcZsy+Tnz95j6xn5G80xrF35 bOX/Fz7yADsLbJrz7tjzbi4ONFf5zG5NZ/17X1f5LCDmeiaNH0CLhsGUnHqPh8d/7NZyPQw+QTSM MJORkemxPGsr/7jec5hy7f949InvPZ73ucAT9a/L/d9n0TLavJXA/B0nFfNoPXQ+IyPXMX5holvb rq3+oTQ+vd2+Qpkn+p+3tl+hpv3bU+m9zZvHL6GP0dSQtasWAZC5+znGzt1VuezM+KW0uIBjB3ey deeRs/K4s99ALCV/sXHT6bRG3zAG9PsPAE6nnYKs4/z+448k550dP1WXvoIlsgN39uhGXEMLp1IP 8vUnn5GUa/NI/uEX/4fbOoadtf6md9+hwOEEoMW1t3PHNR0x2bL49cv32bI3q8q6g19azq1h/tgK 9/HAw0/9IycjF914F10vicfiKCDl8F62fv09SfnaY8jz2VsbNmA2GDGbTQy/uzcZNke169Xl/Hiu H79r4/hzoZP2Oz9pnZ88pa76kdJ2leYPpz2f1OBrWDC7Afc9+S5OL5VDxpcQQgghhBDe4fYTwEZT A6Kiosj74h1eX/NhlWWjJj7GwD49ueG6rtzeqz9TF6zmsX9Facy3Ic/PnsyAu26gfbuLuKPPvTy1 5E0Gtg09a92Ym6fw+IPDGDJkCB0CtN/DjvrXaFbMeZw7b+3KrT3uYfrLa/h3I//K5c7SLJYvX86n yUaioqLwMxo05w1wUd9B9O3WlkaNGlX+RUWdfcGxx5xxNN71NhMeH8fk2f91e7keAVH3smrZNI/m WVv5+wZF0jDC4pW8zwWeqH9d7v/ghlGEafj2eEHy3yQezHN727XVP5TGp7fbVyjzRP/z1vYr1LR/ eyq9t3nz+CV0MvgQFRXFgtEPM3FB1QusF/UdRO/rWxIWFkZsq4sZ+8yrLJ10W5V1zKFdGTmkLw8M n0qQz+n4yOgbzpAhQ7gsuiHh4VFc3n0oy95eRffoAE3pAYLibmPVmgVcGlbC/j1JWBpdyfyF13ks /4jL72JQ/y5V4rNGjRrhayhbL+aWSbySMIiilH2kFIQxefFaejULqpLH2xNGMXbar9XGddeNXcJT I7qSnbyP/ak5xFzei1GXR7rcFReae/v1Y9CQGVgsFpRC67qcH8/143dtHH8udNJ+5yet85OnmIMj aBhmVl/Rw5T6r9r8kbhxFqvTuzKjZyvd5XBVfxlfQgghhBBCeIfbTwBXyNq5jR93Vf2W6Op5Cezd 9Ten8q006jyANxYO44aHruXlrZvUM7QXsvy5KfzfN39Q7HASffVE1szpTq8xl/LOqG9PF9gSz9xx XcgvsBEUaHKrzMPH3YrTYSXhvrs5HHMf7yzsx5DJ/+KL8V8B4HQU8cMPP9DhkvvdyvdMJ39/m8Wv 7a12WUB4OAFGI5eGmTn22zEMRiNGDJqXAxhNwcS3bYmpNId9+45g/ceXlP3CwrEU5ZBr9aNN+9ZQ kMLeI6cwGM1EhIcQEGYBg4nIyLILk/biHLLOeELF6BtCfLsW+DmLSU86RIbGp1c0569SfncYfIKI CLOQc+oktoqvIxtMxLVpQ5jZzsHd+8gr/ccGFJYHRoTjm5tNaUQL4hv6cXjPHnI0fgvc4BNMWGgp Vr9YmppPsie5kLad2pNzIJG0wtNPqAdENqF5dCQGayb79h09Xe5yrvaf3vrXl/0PYAqJpV3LCDIP 7SYl98y+EUp4qAnbzk/50Fbdk/8VbRRLfLOGFGencSDpuOsyVdc+KpT6p9L41Fx/hf4XEB6OKT+b Qkt0te2jlVL7qNVPy/YV219l/Cn1bz3190T/01J+PdtX6t9a5g+18aGl/UwhMeXL9pBu9cPfWEx+ ofY3aNR0fIj6pdRmw1bNsS1z50ZeKY9fwj99lHcWPc5lr37Fb+V9OPrGvuQcfJW90eMY2jSYJUdy q6T/YfUyNmUWAwaGrH6PByZfwZfjTr+RQSn9o/MeJf/TmUxdur38kw9Y1zTIY/kD2HJ/ZvHitdW2 yZhHu7J76UhWfpoMQEqLzoydcgMfjdxcuY7dZsNaTbsZTeE8eVtLlt/fi49PFJd9uPEtTKbT37HU FF+ozD9qx2fV5R6Mv6qjZ37RevyvaXql+dETx29wfXwz+gQTFmrgVOaZ/dFAZGQEuZmnsJY/gV7T +EDv8UcLTcdnDcdPPfGD3v6vJ/7W235atq9Ey/yhev6gs3293f91nx/VMH7TSs/+AzCFRNOuZUOy Du+ufgWd5wdK+08x/tQ4/wVENiH16+W8Z7VhMuDR+ntifAkhhBBCCCFcq/EN4Ops/emPyn8X5ZWd 6BVnaHvdpsOey/tf/V75/+xDRwFwOqqewN0xfSYNsr9j3uGOzLm6keay+fg1o2uoH8WnPmXHqRLI Wkdu6d2EtOoDfKU5Hz1uGjeV28ItxPj7Ujp6MrNsDoozP2HijC80LQ+MvYnFL42HpL/ID2xOc9NB Jo+cWeUV2N0WLqfXN6soun0ooXknMYU1Zdf4gbyU3Y5Zs4ZiNEXi4+fPrFmzAEj/YSFPrz8CgCWy Ky8vm4QjKZFMhz9x8c1ZMvgetuZaVetmCrhINX8t5dfKxxzDhJdfImLHUiYvL3vFtl/EFTz9fAKx xUkcLfCjQzN4btTjbMko1rR8yJLVXPnLTiztg0jKD6FTXClPDx/LtswS1fIExYxi3ZIW7N+bR6tO 7dj2ewph/ibaxKbQq/90HEDnqa/yTGd/DiSn4gxoQsugdKaPTuCvM06wXe2/hcfyddVfy/7x9v4H COvQn+XDWpNlDaV9czPzhwzju1Nl7R8cdy+zxnXAEhWH3/4ZDE74/aztdH94DmN6xLMv8RCG0GZE 5KxlyJT/nbVede2jRq1/Ko1PLfVX63/3vLiS6779BtPV1bePFkrto1Y/LdtXyl+tfqDcv/XU3xP9 T0v59WxfqX9rmT/Uxoda+0VcNJClcwdxPHEXxsYxHDlu5mLbgmrHWXX0jA9x7sk7sgXoRRt/38ob wLf0bcbBJbt474aTTLw/HubscJHayZ+phdwV27DKp67Sm0Ov55YIC8+9XfW1zfnJVY97Nc1fjSmg I5cGmXlp24nKz3ZvPErQ3H7AZtcJyxmMIZiMBiyWqiH1mTfZ1eILtflH7fisttyT8Vd19M4vWo7/ etIrzY+eOH6DQvyd04E335rKiJ59SLbaAQiIuoc3VvagX68hWDXkr7f8ettX7fiipX30xA96+7/e +Ftv+2nZvhIt5ydK5dfbvt7u/3r3j574TQu9+y/80kG8+tRA0hJ34RfblKPHfeGMouk9P1Dbf0r9 V8v84e366x1fQgghhBBCCGUevQEM0KL/PJ7p2YoG4cEc/PVzFs/9pQa5GOk9uRcAX7zyZ+Wn4ZeM 4NHLGvDig0soHr7UrRx9AzoAYCveR9wNvYg//g1JJXY6B7au0TdZXYm4pD9jxmRX/r8k8wuWvVn2 RM3mGRPZDMx57xOyn3qCRf+4qae2/P55j+H4bCaPvL4DMDLghbeZNvlyhsz+ucp6Te/qy+yxQ9ie VoTBGEhzcwnW4u2MHr2dwOiHWb+0LaNHjz+r7PEPDSNg/3zunfIjUPZUS4hT28VBa756/lrLr8bX EseTryzC9M0CJr+5rfLzEc9PxfL9swxaVfZZ294LmTtvAFseXKNpOUDoxen0H2QGAZkAACAASURB VLYUmxO6TlrFuFndGTBG/QIwgL3kGOOeeIqbXtvAvdlLeDAhkbWfbKZzoImdBTaOfriQvvMOVva1 W55ex8Qx7Rg6t+pF7+r2n976a9k/3t7/AOFX5zPw4THYnND7lQ0Mvr8V3y0uexVpzoGljB4NHR5b wZONz04bcel4xt0RwxODh5CYXXZRI+6SJmet56p91Kj1T6XxqaX+WvqfUvuoUWsfLeNPaftq+Wup Hyj375rW3xP9T2v5a7p9tf6tNn+opVeun5EnZg7iwCuPkfDZEYymhrywcTW48TNresaHONcYufjO +3HY89mSUzbWfS0t6dPQnyl/ZZF0/DciXhiKkR2c+RU9c2AgQVYTYU06M6JTGIkrTsd/Sun9Qv4F wI8qXzaqaf4VTCFX8/jj4ZX/LzzxEcvePIyPfzsADhSdPt5ZT6biY76SMF8jWSpPktlLjrDuz0yG Ln2V1l9+z5+Jf/Lzlu2cKLFXWU8pvlCbf9SOz2rLPRV/VU///KJlftOb3tX86KnjN7iKv3/m41NO Hr6yIdN+TAcgfshtnPr9FfLtTs356ym/3vZV2r6W8uuNH/T2f73xt97207p9JVrOT1yVX2/7erv/ 690/euI3LfTtPyNTpw3iwJJRJHyehNEczSsbV8Bfp9fQe36gtv+U+q+W+cPb9ffE/CSEEEIIIYRw ze3fAFZTWnCK9PR0cqxOmrbtSKcWQeqJ/uH6hxcypHMEie/PYfWespupRlNDps+8iyOfzOKz1AK3 8zQY/ICy1zz/e/Bg7r27JYXlr51y97d+lZQWZnPy5MnTf9me+fax0RTOXVH+bPqg4oqagy9f30v4 Rb3PWvfUzpfZnlYEgNNRwGGNT3gUphQS3OI/dLuiPWH+vjhsmWR76BVa7pRfia9/K2Ysf5HWSS8z 44ybe6bAzvSIDuTTn7No1aoVrVq1wnBwK4Exd+NnNKgur5D03ubKE9wdb24jtOUgzWUrLTkMQNbx EgoO5wEOUqx2Yv3Kfnc0Z/dhml/ahV5392PQoEHE2w0EtYo+Kx+l/VfT+mvhzf1f4egHX1a2765f TxLSLlhz2o4PXEXGTy9VXjwESPrjWJV1XLWPGk/1T1e07h9vtY/W+iltXyl/d/qfUv/WU38tXOXv ifGjl9r8oYWr+pmDr+KSIDOrvinbXw7bCd7Yk+0qm7N4e3yI+iG2+wzWrFnDm+9+yOyBjXl/8QSO lT+xFX7xUOy5P7CrwEb+sXVYza3p9Y/fXH/o9bd5//33WPFCAkfXzWDG5uTKZUrpjaYAnE4HxeUx WfvHFrBmzRrWrFmDyaA//wrO0jxSU1Mr/9Izym4eGHz8ASg544uATmf5k7kao+S3Jg9jziubsUfE M2DkdNZsXE2fjlV/K9hVfKFl/lE7Pist9/b41Tu/1JaaHl88cXz7ZN0B2j94U9lKBhMPd4ni6xWJ buXv7eOjGj3HT73xg57+D56Jv/XQun0lWs5Pqiu/3vb1dv93p31qWj+99Ow/c/BVdA4yserbFAAc 1jRW7z39OmxPjH9vn795s/5CCCGEEEII7/P4E8DJmxcyfjOYAluxdv0SHpj5GB/2m6M5/cUDZjOt T0f2f7qYCct/rPw8tOVoOgSY2Bn5b6ZOvZUGbRoA0H3MJLIXP8t3OcpPjjhKMwAw+kawfNgAXnfa WLLJgMNeSIHdQ4//Ajn7vuDtt6v/DWA9fEyNMRoMHDvjiZKSrGx8zJectW7u7ppdeDv89lRWmR+k 1/ApTG7WkJTEb5g9dVHlBWA93Cm/Ekt4Dwrf/ZbIvg9wSchW/ih/YsjH0hyALoPup8sZ6+/4bSf+ RgPFKstLyi88F6UWVi6zW1MxmiIwG9H2W3nOsro5HA6cpWX5lTqh4peq+8x6nUGNk9n09U5yC0ow W+0YjH5nZaO0/2pa/4r6KfHm/q9gO2OcOu2AQfvNrZgQM/k/5Cmu46p91Hiqf7rMX+P+8Vb7aK2f 0vYV83ej/yn1bz3118JV/p4YP7qpzB9auKqf0VT2cwmpZ4zlooxiiNCWr7fHh6gfTvyyiqffO0Kp tZDjKemVN2QBLr+/LY6S37jvvvsASLHaubVXMz5Yua9ynaUDe/JxjpOL/zOGZ4Y/QvPNIzlSfgNA KX1p4TEMhmuIMRtJtTo4vOFlntv6HxbP7YPBAE6d+VcoLfyb9evXn1Vvh63st0jDfQ0cKf/MaArD 6Sgls1Tb2Hc6itn2+Xq2fb4eg9HCrY8tYeT0h/hgwILKdVzFF0YN84/a8VlpubfHr975pbbU9Pji ieNb+nev4TfuBdpYNpAWNYRmzkOMKX9LQm3EB56g5/ipN37Q0//BM/G3Hlq3r0TL+Ul15dfbvt7u /6Bv/9RG/KZn/1U3PxakF1bOj54Y/94+f/Nm/YUQQgghhBDe5/EbwBVsBQc5UlLKpYGdNKeJ7zGR Z4dezd5PX2Dci59T9ZTNTnFxMW0vvwoAo8kMQPPLrqCpny+gfKPHlv87eaUOLA1uwOB8H4NfHC38 fLFmfUMtXNrXzW5NodTppLXFh10FZb+5Y2kUib0k6ax1nUonu04HGKrf7Q57Dh+tXMRHK8EvvBWT X1vMxP9sYNyms7fhbv7ulF9JQeqrzF+zmS2hy5i+8BEGPvIyVocTe2HZRd7VT83kQDXf2DepLK8Q 1DwIfiv73Wpf/2bYrce13fxV4Wtpy8PXxjKy5wgOlm+/Y4s7ubOadZX2X03rf0bmdbL/PSH5VDGh HSOAwy7XcdU+ajzVP132f637Rwel9vFE/RTzd6N+ivOTXjXsfx7bP17s/3rYi/YD0DHAl+3lv9kW 0TwQlL9PcTq9p8aHqNesOcc4ePDgWZ8bfUIZ1jyEXf+XjZ9f2UXfPT+d5NZbe8HK56qs67SX8Mfm BWy86z2mPXoxDy3eoZq+KPMTCu196BkbzKuHcyg+fpRD9lPVlrEm+aux5e3A6nBydQO/yt87bnBR DLa8XzQdP84qo6OYHzf8xtju11X53FV8oSU+UTs+Ky339vjVOr84HWV18zPUzlsV3OaB47er41tp 8QHeSC5iRNfGvNelG+nfzKfiuwXn+vFHS/n1xg96+r+n4u+acmf7SrScn1RXfr3t6+3+r3f/eGr8 uJqf9O4/e8kRAFpYfPm7sPz40iQAijxXfm+ev3m7/kIIIYQQQgjv89groC0RPVi2YCYPPziYvr37 8ujUF7k8yExh+mfa0offwYujb8HpKOB44KVMmTqVqVOnMmFk2W/DZe2dQ8+ePSv/pv1c9kTviqH9 eTOjUClroOyC3LJdWZj82/D0w/0ZOnEmRoOBpE8+rHmlq+FjDiI0NLTKnyc4SnNYn5xPj2FdMAAG oz99H2lLxjb3yl9afAhfvxZ0CjeftSziqssIM5V1CWv2MU7YHNiL3bv76Sp/T5Xf6Sy70b91ySR2 Bt3MgmGXAmAr2sv6I7lMGn4z5vJXZhlNYVx9Q0dNyyvE3dOfUF8jYOTG4deSvWedW+VzXe5iHE4n LcPLLk6bwzsy5kb3Xv9Wlk/N6l+hrva/J/y94jsaXj6B6+IqXitvpHPXVlXWcdU+arw9vrTuHz2U 2scT9VPKvzbqp0VN+5+nyu/N/q+HrehvNqUVMOKBbpgM4B99JY+00H5s8tT4EOem4OZDCXZmMfe1 5axcuZKVK1ey4rVVWBrczBVB1T+j/v7cTcR2n0p7f5Nqeoc1nQVfpdB95iPElefn10C5f7qTvxpH aSarD+Rw/ejby57INUUw+L5WHPvvu5rbaFS/m4j0L7t4bjD6c+PgKyk6+XWVdVzFF1rmH7Xjs9Jy d8bvI8vXsW7ZfZrrXVZ+bfNLaclh0qx2+l4R61b+tcXbx+9vVvxF/NC+jLw0gk3rTn/R4lw//mgp v974QU//91T8XVOe2n5Nz0/0tq+3+7/e9vHY+HExP+kuX8FOfsgu4aEebQAwBbdleHwDj5bfm+dv 3q6/EEIIIYQQwvs89lVxhy0T39jL6HPRvyo/yzz0M4umv6MpvY9fE3wMBvAJomu3bpWfFxw/zPNL t3qkjN/Omc31LzzF1X0e4AogeftHJKx3/TRhTTS9Yy4b7qj62b///W+P5L1hygIuXjSFjWsHkO/f CN/UH5jy0p9u5VGS/RXL/9edGas34mu3c+yLJxmzrOyV1Y27DubZGQmcSM2A4FiMad8z6esUj+Xv ifJXcNhzWfD4C7y1ag79t97H+t3ZrJswg8ZzpvDB+vtIzXEQ1bgBh356lZ+/LfudKbXlAMe/svPa m6sosAYT6TzItFHf1qh8/2QvSWLO+ztIWPEG9xw9SXBwER99mEy/buppq1OT+kPd7n8101auo1Og Cd+AEAKN03j3XRsOWwaDBo8BIGv3Mma/Fcz4Je8wPC0VQ3Bjcv5ayJjvz35irbr2UePt8aVl/+ih 1j5666eWv7frp4We/ueJ8ittX61/q9GbftWEeUyfO56PPhpJdnoiG37MoG+A5qp5dP4W55a2Qy4n L+Utis54+sqa8z1/5E9i4HWN+OO7s9PkHVnHJyd6M+HRi1neQDn99i+O8dML4/hg4hxe2fAeWSdy aRAZws+bl2FzQHUvunU3fzWbpz3DlYtm8v67PSm0hFG4+zPGr9mvuY0aXTOQN4ZNIj/rBARGYDyR yKIpb1dZRym+UJt/1I7Pasu1jt+Y8AaYUjI117uCpvnFaSNh0fs8M24Jn00zcezLiQxfVFa/up4f wfvH71N/LMUW+gZhBT/xSWZxlWX1/fijRq38euMHPf3fE/G3nvbzVPyv5/xEb/t6s/97on08En+6 mJ88Ub4Xn1zK4oXP8uatqRDkZOufWVzrwfKr7T8t/dfV/FEb9ff2/CSEEEIIIcSFznDo0CEnQJcu XRRXLCgoACAkbgYbl19Hyucb+G9KCu9u+LzKeiHhDWkQEogt7yRpp/Kry6qOGYiKbYrJlkVKRtX3 0xl9QhnQ/w4aXtOL29uGMv3uO/klT9tviNYeI1FNmmIuzeFYuud/q8rHP5TGUWE4C0+RekLj+0Hd 4t3yAwRFRhPhD6eOHye/mvc3u1o+8p1NxM4dxsyDEBtpISU5FY0//6eZJTyaRoGQeiwNm5fehKtW fyXe3//6GU1BxMZEUJx9nBM5xeoJ3Mu9zvunXsrto79+au3v7fp527lefjU+Jh/sNjvXLHib+xNn MHLNATdSe398iNMCAwMVl6empgIQExOjuF5F/GY0N+azT9aycPwY9mUdJSm1/r2D0RQcQUxEIDnp KWQXe+7357UxnhEf5p61NCKuOdGN72X+lAh69B5/1nJTYChRkWFQlE1KRtXxoTW+UJp/1I7P6sdv 5fHra4ln80cvs3RoHz5OV3+zTnX0zS/1n7ePD+f68Uet/HriB739vzbibyV6tu+p8xM97auWXi9P 7J/6XD6DTyBNmkSQdewY+fbqy1afz99qo/6i7ng63hRCCCGEELVDLY7bsmULUIMngG1FB9m+3Q8i W9IhxHLW8tzME+RmnnA321rkJCPlaPWLjGY6duwIefvZvh1ybPXxBMVBxjHv/e6ivSiHlKQcr+Xv 7fID5J9MQ+mrB2rLSwszSXLRRfQqzkwjyf2Ha9yiVj8l3t//+jls+SQneevLJXXfP/VSbh/99VNr f2/Xz9vO9fK70qBDP7o2OsKfB9IIanoZY9sFsGJespu5eH98CC9ylLB9+3ZuGHQ/lx1ew7Ova3/C tbbY8k6RlFf97/96n4OMFNf9+9oBQ7g2xMyO36t/q4CtIIeUAuXjp1p8oTT/qB2f1Y/fyuPXv1E3 /v55SY1u/npmfqn/vH18ONePP2rl1xM/6O3/tRF/K/HE9vWen+hpX7X0enmifepz+Zz2ApKTlG+O 1efzt9qovxBCCCGEEMI73L4BXJTxFgkJ3ihK3XPYTpBwvlZOqEr67Vfy8mx1XQwhhPC40vws4nvf wQ09G2DNTWXtjBF8nVlS18UStchRmiUxjg6bn53N5hqmPRfii7yk1xk/s2ZpZX4RwnvOhflDCCGE EEIIIUT95PYroIUQQgghhHfJK/mEEEIIIYQ3SbwphBBCCHFu0voKaGNtFEYIIYQQQgghhBBCCCGE EEIIIYT3yQ1gIYQQQgghhBBCCCGEEEIIIYQ4T8gNYCGEEEIIIYQQQgghhC7X3Pcgjz76KA18vXO5 0dv5CyGEEEKcT9yOmAw+QURFhXujLPWClvq1HjqfRRM71lKJhBBCCCGEHvUhfj1X4sf6Vs63Nmxg 48b32LRpE1Gmc+9ir5by12b/rOn+1dMvzqfx524+avu/Nvp3fWh/b/P2vFXf5kUh6rPO/7mTXr16 EeJj0JXP62vfYOWyyV7LXwghhBDiQuD2WWZA1L2sWjbNG2WpF7TUryD5bxIP5tVSiYQQQgghhB71 IX49V+JHc3AEDcPMdV2MSvf268egITOwWCwYz8FrvVrKX5v9s6b7V0//PZ/Gn7vtp7b/a6N/14f2 9zZvz6/nyvwtRH2w55sv+fzzz8l3OHXl0ygqiqjIs7+84qn8hRBCCCEuBL5aVzQYzUSEhxAQZgGD icjISADsxTlk5dsq1/MLC8dSlEOu1Y827VtDQQp7j5yqXB4Q2YTm0ZEYrJns23cU2xkxW0B4OKb8 bAot0bRrGUHmod2k5J7OG8DoG0J8uxb4OYtJTzpERn7V5Ur5ny5jLPHNGlKcncaBpOOa62c0hRIe asK281M+tOVU205GUzDxbVtiKs1h374jWB2erZ8QQgghhDssoQ3wLcwl31YWlPhYQgj2KyI7pyzG 0BKfuGLwCSYstBSrXyxNzSfZk1xI207tyTmQSFph6Rkrmohr04Yws52Du/eRV+qoko9a/Fi2ztnx WwVX8Z/W+FUpftNCqfxa4kdTSEx52+8h3eqHv7GY/PL2U9t/SvXX2r6mkGjatWxI1uHd1ZZPd/yu s33VaIn/lWiJv5X6n1r/dkVr/9RbP7X9q1R+Lf1Xb/1cbV/L/BIYEY5vbjalES2Ib+jH4T17yLF5 pvwV9LSfR3i5f9Xn83dTaAThlqqXLOwlWZzMtpbl7eXzc0/0HyEuFKbgcEL9jOz+aD27gZx/zFUN wiPwMTjIzCqgTYf2OLIOsT+l6hcrwuPa0iTQxl9/H3I7/wphsa1pHh1M3okUDiRlVFlmNAUT36YF Zoo5su8AeTYPz9dCCCGEEPWM5hvApoCLmDVrKEZTJD5+/syaNQuA9B8W8vT6I5XrdVu4nF7frKLo 9qGE5p3EFNaUXeMHsvBYPp2nvsoznf05kJyKM6AJLYPSmT46gb/KTwLveXEl1337DaarW5NlDaV9 czPzhwzju1PFAFgiu/Lyskk4khLJdPgTF9+cJYPvYWtu2QmgWv4A3R+ew5ge8exLPIQhtBkROWsZ MuV/muoXHHcvs8Z1wBIVh9/+GQxO+L1KGwXG3sTil8ZD0l/kBzanuekgk0fOZH9xqUfqJ4QQQgjh rv5LVtNh6SNM/qnsplXze+Yz7/q36DfiR0A9PlESFDOKdUtasH9vHq06tWPb7ymE+ZtoE5tCr/7T cQB+EVfw9PMJxBYncbTAjw7N4LlRj7Ml43T+SvEjuI7fQDn+0xLfqcVvWiiVXy1+jLhoIEvnDuJ4 4i6MjWM4ctzMxbYFleup7T8t8a9S+cIvHcSrTw0kLXEXfrFNOXrcF87Y9Xrjd0+0rxIt9VeiJf5W 6n9a+rcrWvqn3vqp7V+18qv1X731U9q+lvllyJLVXPnLTiztg0jKD6FTXClPDx/LtswS3eX3RPvp 5e3+BfX7/L1ZzzFMvDaqsqxBTePgjwQGTyvbj94+P9fbf4S4kHQYv5jn/tW48v/D77qdoyX2yv8/ s3otLXyy+CzJjx6tQ3E6S/ngyftZ/nvZF07+9cgCZva+CID0X9ZiAJxu5G/wCeKBGQvpd02Lys/S dy5kyBNfAhB+aV8WzhxGrL8JAFthKitnTODDXZkeawMhhBBCiPpG8w1ga/52Ro/eTmD0w6xf2pbR o8e7XLfpXX2ZPXYI29OKMBgDaW4uOwE/+uFC+s47WPmt3lueXsfEMe0YOndXZdrwq/MZ+PAYbE7o /coGBt/fiu8WJwIQ/9AwAvbP594pZRe8jKZwQpynLx6p5R9x6XjG3RHDE4OHkFj+reG4S5porl/O gaWMHg0dHlvBk43PWsz98x7D8dlMHnl9B2BkwAtvM23y5QyZ/bNH6ieEEEII4Q1K8Ykae8kxxj3x FDe9toF7s5fwYEIiaz/ZTOdAEzsLbIx4fiqW759l0KptALTtvZC58waw5cE1VfJxFT8qxW+gHP9p ie+0xG9auCq/cvxo5ImZgzjwymMkfHYEo6khL2xcDdqaXrX+6uUzMnXaIA4sGUXC50kYzdG8snEF /OVe/kr9x1Ptq7f+rqjF32r9T2v/ro6W/qmvfur7V638auc/euuntn21+QUg9OJ0+g9bis0JXSet Ytys7gwYs1l3+T3Rfnp5u39VqK/n7wffmMmjb5QtC2randVLh/Paq/sq03v7/Fxf/xHiwnLk3ReZ /aWF7uOe5F+h1b8q38cURcR3z/L0f69h2shudB95DcuH/x+moMuY3qszdmsaS59fToOuIxhsNGB1 I/82Q56l3zUtyNy1mZff+QFDg1iu/VfZ5GI0RbFozoNEOo4w74kXSXU2YvxTT/Dg08/w394jKZDX SQshhBDiPKX5BrA7Tu18me1pRQA4HQUcLv+Ccs7uw8Rf1oWOLWMIMPsSajcQ1CoaOH0CefSDLytP AHf9epKB1wZXLitMKST40v/Q7YpM/kzcT1ZRJtlnbFct/44PXEXGT3MrL94AJP1xzCN1NprCuSvK n5c+qLhi5+DL1/dy7+zewOkTTD31E0IIIYTwBqX4RE1pyWEAso6XUHA4D3CQYrUT6+fD37SjR3Qg L/ycRatWrQAwHNxKYMwD+BnXUnLGBTdX8aNa/KYlvnRFa/ymhavyKzEHX8UlQWZGflNWH4ftBG/s yWasG9vVWv/qymcOvobOQSZGfptStn1rGqv35vKYm/m76j+ebF+99XdFLf5W6n+mwM6a+3dd1M8c fJXi/q2N8itR2z4ozy8VN4CT3ttc2f92vLmN0BWDgM26y1fX7Veb+6e+nr9X8LW0YM4LY/l+4Qi+ SinQVCdPnJ8LIbTL2fsbW4GOo+wu13E4inlm49fYTbthZDdMgc0ACGpyF0aDgWPfLWTzt3/h+/Mp Bl/3klv5331bU5xOJ3Omv8buIhvwBz9+VbYsuOlQos0+ZCZuhbBoYoBt6UUMbN6S28MtbDxZpL8B hBBCCCHqIa/cAM7dXf1tyz6zXmdQ42Q2fb2T3P9n777Do6j2P46/d5NNDyGFEJqAiCBFxXpVBK/t 2kG9gEQRUBEuIIKAIF1ULCA2REEFMVas/PSKXgsWxIYFFRGkhZDQUyB9s7u/PxYCkezObGaXhPh5 PY/PI5mZM99z5syc78zZnS0qI6Lchc0eWWUdZ8HBhyseF2ALq/z3ppcnsCDiZnoOGs+4YxqRvXoZ d0+YzdZyl6nymzaIoPDLqr8xEixhjjRvwnrIK2jK8vIJizg5aPUTERERCQV/+YkhjzdPcbvdeCq8 T9ErPOAAwqJaAdA1/Ua6HrLJDz+uItpuqzKB4St/NMrfzOSXvpjN38zwFb8/dkdjAHIOyfVKdpZC svkyzNa/uviq23/R9uIq+7eSvwezfX2xcvzBOP/21/8C6d81ZaV+Rsf3SMTvj9H+XeD3+nJASU5x 5f+7ynOwO5KJsGP5t6Zru/2O5PGpq/fvADZ7JINmPYjj4/t44vMc03UKxv25iASXpyIfpwfsHu9b Bmw2GwARydEA7F23F4CKkrW4PYFd41pGhuFxF+2f/K0qKtV74U7q2I+7OlZd1jxS57yIiIjUX4FP AHvcYPO/maeam9HwqHYMPqsZQ3vcyob9v7nTsfWVXBnArt2uAt55bjbvPAeRSW0Y9/QjjLl0MSOX ZJoqP2tPKQkdk4FNlupXHVd5NhUeD8dFhfHr/k+jRzVOwVWWGZT6iYiIiNSE0+MhLPrgw63I5Opf yxcKrmLvqzoX3jOV9Qa/+Vpd/gj+8zfT+aWP/C4Y+ZtR/P64Sv4EoGNMOCv3/+ZlcqtYOGS+0d/x CyS/ri4+V9lmAFpHhfN7sXf/DZvHQEng5VdbvyC1r8ft3Xfk/gfFB4T6/gL89z+z/dtX/IesUG3/ tNz+Bsc3kPPTEl/nn8H+zX4PM65VHPy4G4Dw6GNwle+wPPkLwWs/o+Pva3nQjs9RfP8OcO6wR+he 8RH9nv46gD0H9/ouIqFVtqsQgIYdkuD/tuCI7Yzd15jpw7qSClpGxXFanKMyp2L/LwmXbt8FQNYH 4xn+1JpDtrLjKiv+a1EiIiIi9YY90A0qSjcSHtmaTkmBPTz0eEpxezwcm+T9RG9EUkdG/LNJQGUk n3EKiQ5vyOX5W9nldOMqdZsu//dnPqfRqaM5p2Xc/r/Y6dytTVDq564o4LWsQq4Y2BUbYLNHc+2Q duz85u2g1O9QM158jRfmnB9QfCIiIvL3tHFbCU3/5f3Gkz2iMdd3bXzE9u0sWctrm/cydtAFRNi9 D/LsjkTOPK+jwZYH+cvfzOaXvvK7YORvVjhLfmfJtiJuvak7DhtENzmdIa0Tqqzj7/hZza+dRav4 Mr+MW644HgBHfDsGtW0YtPKD1b4VZZvYVu7i2tOaVfl7qO8vwH//M9u/fcVfudxH/wz18Q3G+WmG r/oFa/8te/UhIdwO2PnnoLPI/yMjKHEHq/0Mj7+P5cFqn6P5/r35BaMY262YO8ctqHxNs1lH8vqu +3P5u7OHJ9CjRw969OhBmyjvh9b+efmV9OjRg3+kRhluX5T1BuVuD2ld7yT9yku4ZfJdAZf/5lve D9XdNfsurrr4Qq769w3cN/tyAPZlLyCztIKmF4ym18VdOenkU7nwsl5MA5dGQwAAIABJREFUe/z5 gK8tIiIiIkeTgL/qWpb/CfM/vogpC18n3OVi64d3MWLeWsPtXGWZTH/zByY+8wK9tuwmPr6Ed97O ond38/tO69aPB6dMZFfOTohvhn3bF4z9NNt0+Xlr5nH3S/HcMecVBm3LwRafRsFvsxjxxQZT9Zv0 XAadYh2ExzQg1j6JV1914nbuJL3fCAAWj5/JSbPH8/qi6yiMbkx4zpeMf/yXoNTvUFGxscSUheTt 3SIiIlLP/PLoC3jm387iRddQ7NnHh5/voG3nI7f/jNFTSJs+nrdeu4GcAjepaQ3Z+PVTfPvZauON 8Z+/mc0v/eV3VvM3I0b544LR9zN5xh28885Q8revZvFXO7k25uD2/o5fMPLrx+6ayyOzHuTFi3Mg zsOKX/I4i+CVH5T29TiZOPtN7hs5h6WTHGz9aAyDZq8O+f0FGN8/mOrfPuI/wFf/DPXxNRO/Uf81 w9/5Z/X6ALDjExdPv7iAovJ4UjwbmDTss8plVuO32n6A4fH3tzwY7XM03793uaEr4TERPLH4rcq/ 7fpxCrfcvQoI/f252f6j+3P5u7OHpzB06NAqf0sf/B8AfsxewTc7S/1u7yxZw8SMr7j/xnPoP3wU 6z6aTbl7VEDlb3pjAvObTWfAxWczbPQ5eDxu1n4xAwC3M5exdz7G1LsGc8OwsQB4PB52bfrNWsVF RERE6jjbxo0bPQBdu3b1u2JRUVFQdhiV1ITGsZCzdVuNPmkXFp1AWmoinuI95Ow6/Pe4zJRvd8TR rGkypfk72FXgPxENnJ3U5i2IqChg6/bAfwvOqH4iIiJS/8XGxvpdnpPj/R3Epk2b+l3vQP4WFplI 87QYdmTlUBri3/X0JS6lCcnRsGfHDgpr8H5Wf/mb1fzSav4WDGGOMFxOF/+Y+TI3rp7C0OfXH1xm cPys1t8WFkvz5snkbd1KoevwY1PX2zfU9xdgfP9gtX/7E+rjC6GN34ya7n/oK0toNmMgUzdAs5Qo srNyqAjyJa4utF9tHp+6cP9uTe1f30XqqmDnm1ZFJqWRGllK1raan6vhMck0S42nJH8bO/PLDlue 2LgZCVGwN3c3ufsOXy4iIiJyNDDK45YvXw7UwgSwiIiIiPhX1x7ISeg07NCbbo0388v6bcS1OIWJ dw3kmf59+DRXDyVFjByYAJ74a25thyIictRRvikiIiJydDI7Aaz3FImIiIiI1JKKwjzaXn055/Vo SPneHBZNuVWTvyImZf74Pfv2OWs7DBEREREREZE6RxPAIiIiIiK1pHDLRzx830e1HYbIUem/M++t 7RBERERERERE6iR7bQcgIiIiIiIiIiIiIiIiIiLBoQlgEREREREREREREREREZF6QhPAIiIiIiIi IiIiIiIiIiL1hCaARURERERERERERERERETqCU0Ai4iIiIiIiIiIiIiIiIjUE5oAFhERERERERER ERERERGpJzQBLCIiIiIiIiIiIiIiIiJST2gCWERERERERERERERERESkntAEsIiIiIiIiIiIiIiI iIhIPaEJYBERERERERERERERERGRekITwCIiIiIiIiIiIiIiIiIi9YQmgEVERERERERERERERERE 6glNAIuIiIiIiIiIiIiIiIiI1BOaABYRERERERERERERERERqSc0ASwiIiIiIiIiIiIiIiIiUk9o AlhEREREREREREREREREpJ7QBLCIiIiIiIiIiIiIiIiISD2hCWARERERERERERERERERkXpCE8Ai IiIiIiIiIiJSZz39/CL+0ySOFldMZ/7j/Wo7nL8dtb+IiMjRJ7y2AxARERGRvzFbON0vuoDtyz9l bbGztqMRERERkTrorcWL2bWvjPw1H/JWaX5th/O3o/YXERE5+gT8DeC4piPIyMggIyODhfOnV1l2 crdLGDB4KGPuvJPb/3MT3TqnmS/YFkanf5xPv5uHMObOsQy/tR+nt0nwGfYlva8jPT2dtlHm57DD ohrS8bRu9Orbl/T0dJIdVatvj2haWbeMjAxOinWYLvua6/pyWtzh6zdocxG9e57qXWf2PMafmmK6 zCMt1PG9tHgxr7/+BkuWLCHVYdz1jhvwALPHdAx6HDFNz2XqrCd5ftELzJt9VcDLa0vfR+dzZ+fk 2g7DtECPn80eRZd/9mDQ0FGMHTWUy85sFbrgRET+ZlqfdRnDR41l1PBb6NouMahlBzq+H8ZTwcaK 47n3kSFE2G01jsPXuGM5vqOALSyO1NSkWo0hVHkb1I36hcrfoX9adSSOfyj7bzD5ijPU8dfm/ZOV 46/7C5H6wWaPJT09naxl/+OnQif7NnzFF6sb06fXRbUdWlAcqN8J0QefKcY0Of+w+vm7Hra8ejpP PdTNUhx2R6PK56GPTeh8WHxq/9C2f7/H55ORkcGCeZMtlSMiInKogJ8y2B0NSU1NZd+Hr/Ds829X WTZszG30vaYH553Tjct69mHCzIXcdnaqyXIb8fDd47juqvM4of2JXH7N9dwz50X6tjt8ErjpBeMZ dfNA+vfvT4cY8xPAp8+ex+z7JnLLgAH079+fxo6wKss9FXnMnz+f97PspKamEhnAQ8iTrk2nR8sG h8f6r178+xJvHeIbpZIYEXbYOnVFqOO7vndv0vtPISoqCjNNGxGfTKPEiKDHccX0kaT9+jKjR41k 3N3/C3h5bYlrlEpi5NHzYDDQ49f8XzO47dITKN25keyihgyb9hRDOtXPh70iIkdS0wvH8uTEdEqy 15FdlMi4RxbR85i4oJUf6PhenaxPn2DG8qY8OPCUGsdRlPU7qzfsC0l8dV1M6vUsmDepVmPw1f7B UBfqFyp/h/5p1ZE4/qHsv8HkK78Odfy1ef9k5fjr/kKkfrCFx9O/f386xh58XhXb7CL6pV9ai1EF j9n6+bsehsel0Cg5ymIgYaSmpjJz+GDGzFwdcHxHq7rS/i+PHsbtk74nNTW4H9YVEZG/txq/Ajpv 1Td89Wtulb8tvH8ia3/9nT2F5TTufB0vzBrIebecxRMrlhgX6Cpm/kPj+e+ynyl1e2hy5hien34R PUd04ZVhnx0MOKotM0Z2pbDISVwA39AF2PXN/5j/3kZSrr+da1KiD1vucZfw5Zdf0uHkGwMqF+Dr gjIu79gAVu+p8vfEExqQ93N2lb85GjSj/bHJ5G5cQ/bev7zq0Oag5fHHkxjhYsOadeyrcFcuiklK wlGYT3FUE9/bG7CHN6Bt+9ZEekrZnrmRnYWHb+8vPrsjnrbtjsVRUcC6dZspPxgeUQkNCS/eS6HT +8ewqAbER5aQX2A+RkeDJrQ/thF5m9YEVC8z8cUkJRFjt9MlMYKtP27FZrdjx2Z6OeD3+ABEJiYR VVLA3vJIjj/hOCjKZu3mg33CTPsbiU1rQ9vUKLb8sYbc8qr7N4rPF1tYPIkJFZRHNqNFxG7+yCqm XacTKFi/mm3FFZXrxaQ0p1WTFGzluaxbtwWnp2o5hsfPT3zbl9/LTUsPXFOWUHb2m1zZoxlP/5Zb fVkiImLKiP90Y83coTz3fhYA2a07c/v483hn6HuAufHb6Ppvmc1B7ncvsijCRXy43fT4BWB3JJCU 4MC56n3edhYENyyT46Ov/MPs9jXN/2z2CJKTGhCTGAU2Bykp3je5uEoLyDskx/CXH5nhaNB0/77/ YHt5JNH2Ugr3x2/U/mb6l6/86EjULzY5ifC9+VQkt6Zto0g2/fEHBc7A8iuj/M8qK+efmfoZxm9Q f8P8NsT921/7OBKSSfrLG6NcZXnszi/3xm7i+mF0f2F4f1bD/LyyDn7y62Bc/+rq/ZPZ46/7C5G6 zxHfCNe+XQSYfgTE3/XYHhZPYoKNPbl7D9nCRkpKMntz91Du3j9w1PJ474vp6+Gh24TFkZwYRcGe 3Tg9gT2vq3A6cf41FzKg9v/LNjVof5fTSXmA7S4iImIkqL8BvOLrnyv/v2Sfd2Av3bnb1LZu117e /OSnyn/nb9wCgMdddfC7fPJUGuZ/zv2bOjL9zMYBxbfhhefYAFzZa3hA25mx5bd8Ejp6v+k7+qln ab3zGYZP/ZYTEiPZ/c3BNkjs0If5A48jrzyBE1pF8ED/gXy+pxSAyOTTuPfhiTQrzWRLUSQdjoGH ho1i+U7v8l6PPcc5ny3DcWb12xuJSunGE/PG4s5cTa47mpZtWzGnXy9W7C03FV9ss/N55PE7IPM3 CmNb0cqxgXFDp/JnqfcBYJ85C+kwdwjjvt4BQKteD3D/uS/R+9avTMWX1CWdp+7py7bVvxLZrAVb doSDuaqZiu/8kRO4JCmKptHhVAwfxzSnm9Lcdxkz5UNTy42OD0D3WfPpuWwBJZcNIGHfbhyJLfj1 jr7M2lpoqv2NNOk+nGfbNyBzXxydWtt4aPBtfLGzxHR8vsQ1HUbGnNb8uXYfbTq155ufskmMdnB8 s2x69pmMG+g84Snu6xzN+qwcPDHNOTZuO5OHT+S3/Qmv0fEzis+57+CDGEd8By5NiuK3j7abbhsR ETmcI6YjXeIiePybXZV/W/P6FuJm9Aa8E8BG47fR9d8qK+MXQHzL65k2sgNRqS2J/HMK/Sb+ZLyR SWbGR3/5h5ntreR/jpgTmTZtAHZHCmGR0UybNg2A7V/O4t7XNoNBfGYkn9iXuTPS2bH6V+xpTdm8 I4KTnDMr29mo/Y36l7/86EjUr/+chZz+3SqiTogjs7ABnVpWcO+g2/kmtwxMHB/wn/9ZZfX8M6qf UfxG9TfKb0Pdv43a55geIxhz1sE3UsW1aAk/T6TfJHP916h/Gd2fWb2+GeXXVq9/dfn+6fH89obH X/cXIkeHRqeP48mBkfxv6Qcs/eAjNueafwZilr/rcXhMB158aQK39riGrHIXADGpvXjhuSvo3bM/ 5dT+eO+PmfHwUGERTRn9xOMk/zCXcfM/Baw/rzOi9j+oNtpfRETEl6BOAAO07nM/9/VoQ8OkeDZ8 /wGPzPiuBqXYuXpcTwA+fPKXyr8mnXwr/zmlIY/dPIfSQXODFHFw7P56N7FDWmB3bOO8pi7cqT2B b+kU6+C77OLK9ZLOLKTv4BE4PXD1k4vpd2MbPn/E+2qVWx+eQNQXD5K+4BsA2l09ixn3X8fym583 tb2RtrcMJObPB7h+vDfBsDuSaOCp+nDMX/k33n8b7qVTGfLsD4Cd6x59mUnjTqX/3d/WsNUOZWfC pHTWzxnGxA8ysUc04cnXn4HfzJdgFN97U8bwHjD9jXfJv+dOZv8lSTRabub4ALS46lruvr0/K7eV YLPH0irC+4DNTPsbSTh5D336T6XcDWePepZR9/bgi1tfDSg+X1xlWxl55z2c//Rirs+fw80TV7Po 3ffoHOtgVZGTLW/P4tr7N1R+q+LCezMYM6I9A2b8ipnjZzY+R8zx3DP/fvL/O4tZ3+1CRERqLiy6 PQDrSw6ON+W7cwiLOJ3EcDt5Jr6J5v/6b53V8atg/VyGD4cOtz3DXWlBCakKo/HRKP8w2t5K/lde uJLhw1cS22Qwr81tx/DhdxwWv7X8zc6dU9NZ/+RtTFy6GbujEY++vhAOST2ttr+//Cj09fNKOGk7 fQbOxemBbmMXMHLaRVw3wvsBCav5n1XBOP/81c8ofqP6G+W3oe7fRu2z4YWp/OcF77K4FhexcO4g nn5qXeX2Rv3XTP+yen/nm3F+bfX8q8v3T+Wlxsdf9xciR4ecT+9kdF5XLr7oYh5aNJicnz5j6X+X 8ul3a4L6Vhmf48m+b/m/PR4Gn96ISV95PwTStv8l7PnpSQpd3gBqe7z3x8x4eEB4VEvuenI2jmUz GffiNyGP7VBq/9ptfxERkeoE/QdFK4r2sH37dgrKPbRo15FOrQP/jblzB8+if+dkVr85nYV/5HsD dTRi8tSr2PzuNJbmFAU7bMsKN6/FEduZmJSe7NvwAt+4OtI6Oobjo8L5+pBveG5566PKBPfX73fT oH08AI7YzlzRJJb3v82jTZs2tGnTBtuGFcQ2/XeV3yL2tb0ZxdnFxLe+lO6nnUBidDhuZy75f3nw 66t8uyOJq1KjWfLWgSd+bj56di1JJ14dQCv5FhF/Bp3jHCz4zPu6bHf5Nhau3Wuw1UGhjs/s8QHY s+oJVm7zfivX4y5i0/5P0JtpfyNb3lpS+Vq2n19eTlzzPoTbAovPl4qyTQDk7SijaNM+wE12uYtm kd7fQSlYs4lWXbrS89+9SU9Pp63LRlybJoDx8QskvvPumUbab08xZt6ygNpGREQOZwvz/uRF2SEP 1zwe74OSaJNZoL/rv1XBGL9Czd/4aCb/8Ld9qPM/q/lRRPwZnBwXwYJlW71bO3fxwv7cPFis5EfB yv8y33ivsn1/ePEbEo5NB4KT/1kVjPPPV/0OVV38Zurv7/gdifsbs+0THtWa6Y/ezhezRvJJtrl7 SbP9y+r9nS9W74+CVb+aCvX5o/sLkbonIi6ehIQEEhISiIsKO2SJm40/fcHTD02i778HsHjFVs7u M443X57HBQ0jg7Z/f+PJuxnrOeHm873/sDkY3DWVT5/xXv/qwngfDOHRbZgy/zGOy3yCKbUw+aj2 r932FxERqU7QvwGc9d4s7ngPHLFtWPTaHG6aehtv955uevuTrrubSdd05M/3H2H0/IOvwkg4djgd YhysSvkXEyZcTMPjGwJw0Yix5D/yIJ8XBP8VMoFw7l2BI2YqKV13k/1//8eH5+fz79anYavIIbPM dXC9Q+L0uACbNykOi2oFQNf0G+l6SLk//LiKaLuNsv2/ieFrezM2vTyBBRE303PQeMYd04js1cu4 e8JstpabiM+Rht1mY+shdSnLyycs4mTT+/fH7vC+zjvnkFiKthdDsrntQx2f2eMDsHdN9Q9GzbS/ kZKcksr/d5VnYw+LoUG4neIA4vPJ443D7XbjqfCuX+GBA7+0fc20Z0lPy2LJp6vYW1RGRLkLm917 s2Z0/AJpv7NbxJP5lJJlEZFgcDu9rxlLCrexef/f7I5EPB43uRXmvnLh7/pvVSDjQ63xMz6ayj/8 bR/i/M9qflTd+F6ys9R0fmaGlfwoWPlfSc7Bt/W4ynOwO5KJsIM9CPmfVcE4/3zV79Dfeq0ufjP9 09/xOxL3N2bax2aPZNCsB3F8fB9PfJ5jumyz/cvq/Z0vVu+PjBwN90/BKl/3FyJHxqUPz+emtBgA sj+5i6GP/37YOrGN0khLSyM1tSE7cn6kwMyHvjw+cgJP1W39jSfbP3+ayJGPcnzUYral9ucYz0ZG 7H9rQaivV4ZM1s9IVNIVFL/6GSnX3sTJDVbwcwA/N+aX2t+UkLW/iIiIBUGfAD7AWbSBzWUVdInt ZHqbtleM4cEBZ7L2/UcZ+dgHVL0ldlFaWkq7U88AwO6IAKDVKafRIjIcqOUJ4JJ15JPIPy+LZNno PLYUbKLTVRfi3LvC1PauYu+ryBbeM5X1IfoEm9tVwDvPzead5yAyqQ3jnn6EMZcuZuSSTOP4yrOp 8Hg4LiqMX4u8v6kV1TgFV9nBbZ0eD2HRBxO8yOSIw8rxuL11i7RV/RSfq2wzAK2jwvm92Ft+w+Yx UIIpZuKzIpDj4/HxMMdK+x8Q1zoOvt0JgCOqNW5nPvlON2Eh7j/hUe0YfFYzhva4lQ37y+/Y+kqu 3L/c6PgF0n4fv7gAdhX7XUdERMxx7vueUrebMxtG8uP+38RseGJTnIXfU3pg8sXP+G10/T/A1/hu 5EjkP1Dz+IxYzT+CVn+PG2yHp/WW4yv5E4COMeGs3N9/klvFwj7zoRnlh6byoxDV74C4VnHw424A wqOPwVW+g3I3OIKQ/5nhq3+aPf+M+Kpf1RgOj99M//R3/ELdv822z7nDHqF7xUf0e/rrgHZb2+e3 1fsjw/KPgvun/QurP/91fyFS5ywZ3Jcl1fw9LLIR51x0IRdeeBFdWtj54n9LmXPXjazOMvdWA4+r AJfHg+OQcdLusOOuyDMdW0Xpel7IKuHWbmm80bU725c9wIHPQgbtelVDpuvn43p4QFHOUzzw/Hss T5jH5FlD6DvkCcpN5PtBi88PtX/N219ERMSKoL0COir5CubNnMrgm/tx7dXX8p8Jj3FqXATF25ea 2z7pch4bfiEedxE7YrswfsIEJkyYwOihZwOQt3Y6PXr0qPxv0v5JsGcG9OHFneZu5i4bMYYJEyZw caL3U+E3jr6TCRMmcGqcw2BLMzys2FvOtY3K+SivlL3rl9K4++kUb1tlamtnyVpe27yXsYMuIGL/ K07sjkTOPK9jEGLzSj7jFBId3kNenr+VXU43rlJzn2hzVxTwWlYhVwzsig2w2aO5dkg7dn7zduU6 G7eV0PRf3k+M2yMac33XxoeVU1G2iW3lLq49rVmVvzuLVvFlfhm3XHE8AI74dgxq29B03czEZ0Uw jo+V9j+gRc++JIbbATvdBnUlb93zuIMUnz8eTyluj4djk7znTkRSR0b88+Dr9YyOXyDxtWrRgtR4 JcMiIsHgrshnwR/5nDv8Mu83Gh3J9LuhDTkfv1q5jr/x2+j6f4Cv8d3Ikch/rMRnxGr+Eaz6V5Ru JDyyNZ2S/jK5ajm+31myrYhbb+qOwwbRTU5nSOuEgGIzyg/N5Eehqt8BLXv1IWF/fvXPQWeR/0cG UPv90+z5Z8RX/YyYqb+/4xfq/m2mfZpfMIqx3Yq5c9yCgH9nsrbPb6v3R0aOhvsn8H38dX8hcvRI 6zaO9HNb88M7T9C710BmznvN9OQvgMddzucFZZzzL+/5bbNH0K1nCwq3fGWwZVXLnvmNtgOuZWiX ZJZkbKj8ezDH+yHzM8iYd0NA25itn6/rYWU5Hu8XY1bMGcuquAuYObBL5TIzz+usxmdE7V+z9hcR EbEiaN8AdjtzCW92CteceHbl33I3fsvsya+Y2j4ssjlhNhuExdGte/fKvxft2MTDc819i9ZIu7PO pXtSVOW/u3TtBsDqp2fzA07L5f+wq5jzyt/2PlzY9w0by11UfLvL9PYZo6eQNn08b712AzkFblLT GrLx66f49rPVxhubkNatHw9OmciunJ0Q3wz7ti8Y+2m26e0Xj5/JSbPH8/qi6yiMbkx4zpeMf/yX yuW/PPoCnvm3s3jRNRR79vHh5zto2/kvhXicTJz9JveNnMPSSQ62fjSGQbO99Xvsrrk8MutBXrw4 B+I8rPglj7MCqJ9RfFZZPT5W2x9gx6fFPPnSQopLY0mxbWLysE+CFp8/rrJMpr/5AxOfeYFeW3YT H1/CO29n0fvgqWp4/MzGd/bFF5P/xYu8vdVy2CIiArw/+V7OePRu3ny1B8VRibDhI4Y+90flcn/j t5nrP+B3fDdidfya9FwGnWIdhMc0INY+iVdfdeJ27iS934igxGfEav4RjPG7LP8T5n98EVMWvk64 y8XWD+9ixLy1QYlvwej7mTzjDt55Zyj521ez+KudXBtzcLlR+xvlh2byo1DWD2DHJy6efnEBReXx pHg2MGnYZ5XLQp2fAz77p+nzz0L9jBjV3+j4hbJ/m2mfLjd0JTwmgicWv1X5t10/TuGWu70f0jXq v7V9fhvl16auf37U9fsn8H/+6/5C5OiQs2wcQz4y/9NX1Xlqwlxm3DOZdy7fS3FEIrYdK7l3XGAT kHt+nosz4QUSi77m3dzSKsuCNd43TWqIIzs3oG3AXP38XQ8P5XbtZeaoR3lpwXT6rLiB19bkm3te ZzE+I2r/mre/iIhITdk2btzoAejatavfFYuKigBo0HIKr88/h+wPFvO/7GxeXfxBlfUaJDWiYYNY nPt2s21PYYjCDg17WALX9bmcRv/oyWXtEpj87yv5bt+Rf7V0XEoTkqNhz44dFP71/WwWhUUnkJaa iKd4Dzm7Anh/XyU7qc1bEFFRwNbt1fxWWGQizdNi2JGVU/lqyUDYwmJp3jyZvK1bKXTVpO7+4wsG K8fHevtDeHQSzRpFkZOVU+03GULZf6KSmtA4FnK2bqt232aOXyjjExGpL2JjY/0uz8nx/o5l06ZN /a53IH8DO6nNWtDhkmGMusDDpGGT+TXvYI5jNH4bXf+D4egeH6znH6Gtv/X4whxhuJwu/jHzZW5c PYWhz683v61B/wp1furP0FeW0GzGQKZugGYpUWRn5VDdz2PXZv+0cv6ZrZ8Rf/U3c/xqMz+1rnbP b+v3R0bq9v1TXShfpL4Kfr4ZYrZw0po3w+HcS9Z2868fDoSV60l4VFvee+cJ5g64hv/bXoPXzoe4 fkb5mD0ijaXvLmLWHSNYl7eFzJy//OaA2t8So/ZPbtmKJmnX88D4ZK64+o6g719EROoXozxu+fLl QA2+Aews2cDKlZGQciwdGkQdtnxv7i725pr/1mudYo+gY8eOsO9PVq6EAmft3DwW7t5GqKbOXSUF ZGcWWCjBzc6tvn8XylWWR2ZmzRMlj6uIrEwrNw/+4wsGK8fHevtDRUkumVt8Lw9l/ynN3Uamnw9T mjl+oYxPRER8cbMzO5Odz92J2zWEqJhwOGQC2Gj8Nrr+B8PRPT5Yzz9CW/+ax9ewQ2+6Nd7ML+u3 EdfiFG5vH8Mz92cFVIZR/wp1fmpGRXHt5VdGgnH+GdXPiL/6mzl+tZmfWle757f1+yMjdfv+qS6U LyJ1hKeC7Vl193oV3bg7v387p2aTjxDy+hk+r3OXsXLlSs5Lv5FTNj3Pg8/+eUTjg793+591XX/O ahDBDz8d/q1iERGRmgr4G8AiIiIiElpH3TcypN6KO+YiBvfrSrNGDSnfm8Nnby/kg5921nZYQXP5 2Ekkvf4YGZtr9maWuq6+109ERGpO+aaIiIjI0cnsN4A1ASwiIiJSx+iBnIiIiIiEkvJNERERkaOT 2Qlg+5EIRkREREREREREREREREREQk8TwCIiIiIiIiIiIiIiIiIi9YQmgEVERERERERERERERERE 6glNAIuIiIiIiIiIiIiIiIiI1BOaABYRERERERERERERERERqScIeumLAAAgAElEQVQ0ASwiIiIi IiIiIiIiIiIiUk9oAlhEREREREREREREREREpJ7QBLCIiIiIiIiIiIiIiIiISD2hCWARERERERER ERERERERkXpCE8AiIiIiIiIiIiIiIiIiIvWEJoBFREREREREREREREREROoJTQCLiIiIiIiIiIiI iIiIiNQTmgAWEREREREREREREREREaknNAEsIiIiIiIiIiIiIiIiIlJPaAJYRERERERERERERERE RKSe0ASwiIiIiIiIiIiIiIiIiEg9oQlgEREREREREREREREREZF6QhPAIiIiIiIiIiIiIiIiIiL1 hCaARURERERERERERERERETqCU0Ai4iIiIgcrWzhdL/4X7SLcdR2JCIiIiIicpSxhcVx0SWX0DQi rLZDERGRIAt4Ajiu6QgyMjLIyMhg4fzpVZad3O0SBgweypg77+T2/9xEt85p5gu2hdHpH+fT7+Yh jLlzLMNv7cfpbRJ8hn1J7+tIT0+nbVR40Mq3RzStrFtGRgYnxZp/kGZ3pJKenk6LQwbLiIRz6Xtd T9NlWPXS4sW8/vobLFmyhFTH0Te3f83seYw/NSVk5QfaPscNeIDZYzoGtA9bWBypqUnVLgt1/Yz0 fXQ+d3ZODln5wep/MU3PZeqsJ3l+0QvMm31VlWX+2reus9mj6PLPHgwaOoqxo4Zy2ZmtajskEfkb aX3WZQwfNZZRw2+ha7vEI77/unz9PtrzJzwVbKw4nnsfGUKE3Vbb0YREXe4/ZtUkrzwamD1/zNY/ 0HZSfiUiIsHQ8urpPPVQN0tlWM1XgvE8qab5RjDqX59U1452R6PK58WPTegc8PZHgr/9+utfHlch OfH/YObdvQjG3USg9T/q78dMsHp96Pf4fDIyMlgwb3IQowoeM/Wzel7U9fspf8+zRWpTwFdVu6Mh qamp7PvwFZ59/u0qy4aNuY2+1/TgvHO6cVnPPkyYuZDbzk41WW4jHr57HNdddR4ntD+Ry6+5nnvm vEjfdodPAje9YDyjbh5I//796RBjbgLYTPmeijzmz5/P+1l2UlNTiQzgIVpYZFP69+9Py+iDE8BR Df/JjTf0Nl2GVdf37k16/ylERUVxND7/i2+USmIIP20WaPtExCfTKDEioH3EpF7PgnmTql0W6voZ iWuUSmJk6BKpYPW/K6aPJO3Xlxk9aiTj7v5flWX+2reua/6vGdx26QmU7txIdlFDhk17iiGdju6H 2SJydGh64VienJhOSfY6sosSGffIInoeE3dEY6jL1++jPX8CyPr0CWYsb8qDA0+p7VBCoi73H7OK sn5n9YZ9tR1G0Jk9f8zWP9D8W/mViIgEQ3hcCo2SoyyVYTVfCcbzpJrmG8Gof31SbT5iCyM1NZWZ wwczZubqwLc/Avwdf6P+ufr1aSzc3o0pPdpYjiPQ+teH+zEjVq8PL48exu2Tvic19ch/mNsMM/Wz ej9U1++n/D3PFqlNJr8+e7i8Vd/w1a+5Vf628P6JrP31d/YUltO483W8MGsg591yFk+sWGJcoKuY +Q+N57/LfqbU7aHJmWN4fvpF9BzRhVeGfXYw4Ki2zBjZlcIiJ3EBfEPXTPkedwlffvklHU6+0Xy5 AbI74mnb7lgcFQWsW7eZcrf370nJyRTm5VLu9lRdPyyehg095O4pDOn+AWKSknAU5lMc1YT2xyaT u3EN2XudVbZ3NGi6f9kfbC+PJNpeSmFxhfn9hzegbfvWRHpK2Z65kZ2FzsPWcTRo5nP//uKPSmhI ePFeCp3eP4ZFNSA+soT8gsP34YujQRPaH9uIvE1rTG8DYLNHkJzUgJjEKLA5SEnxfjLTVVpAXuFf 29B3/bA5aHn88SRGuNiwZh37Krx1sYfFk5gQxp7c/CqrJyUnU56fS6Grar/xJzatDW1To9jyxxpy D21AP/sPGj/lxyQlEWO30yUxgq0/bsVmt2Pf/9nDQNrXH3/9JzY5ifC9+VQkt6Zto0g2/fEHBU7z 7WN0/mxffi83LT1wzVxC2dlvcmWPZjz9W9XrqIhIsI34TzfWzB3Kc+9nAZDdujO3jz+Pd4a+B5gb P2NSmtOqSQq28lzWrduC85Bhx9/1z+z129/12Qx/8Vll1D7ByJ+s1h+bg9zvXmRRhIv4cHvA47eV 8dHM+GlUv8jEJKJKCthbHsnxJxwHRdms3bzHVP8JxvGx2v7+8lu7I4GkBAfOVe/ztrOg2u1ren5Z jd+bX9rYk7v3kL/aSElJZm/unsr7Eivnl5n6G+bffvIv5VciInWfI74Rrn27qOnTBVtYPIkJFZRH NqNFxG7+yCqmXacTKFi/mm2H5FNG45WvfKP6fcaRnBhFwZ7dB8vxMR5ZfV4RjOdJZsZbbxs0o+0x jSjN38b6zB2+Y/pL/c3kI0bPk4yeB5p5XuiP1XzK7PPACqcT51+fFZnZ3sLzJKh5vmm2f8WkNCfn 0/m8Ue7EYSPg+ykr9bfC7PXBaP9mrg/+zh9f/S9Y98Mup5PyavpdTdns0SSFlbHHYplm6ue3f5o4 fkbXtyMyn2Ewn+LrebZIXVDjCeDqrPj658r/L9nnfZBQunO3qW3drr28+clPlf/O37gFAI+76oXo 8slTaZj/Ofdv6sj0Mxubjs1s+aEU2+x8Hnn8Dsj8jcLYVrRybGDc0Kn8WVrByHnPs2NUX57MqjrR 2/zy6cy65H16D/0opPsH6PXYc5zz2TIcZx5HXnkCJ7SK4IH+A/l8TykAySf2Ze6MdHas/hV7WlM2 74jgJOdM+k38yd9uK0WldOOJeWNxZ64m1x1Ny7atmNOvFyv2lleuk9ihD/MHVr9/o/j7zFlIh7lD GPe1dxBu1esB7j/3JXrf+pWp+JK6pPPUPX3ZtvpXIpu1YMuOcCg117aOmBOZNm0AdkcKYZHRTJs2 DYDtX87i3tc2m6pfZPJp3PvwRJqVZrKlKJIOx8BDw0axfGcpYZEtWfTSQ0zu1YOf9g+gEQ3O5qUX xzP86msodJkbtJp0H86z7RuQuS+OTq1tPDT4Nr7YWWK4/2AwKv/8kRO4JCmKptHhVAwfxzSnm9Lc dxkz5UPT7euPUf/pP2chp3+3iqgT4sgsbECnlhXcO+h2vsktMxW/0fnj3HfwQaQjvgOXJkXx20fb g9G0IiI+OWI60iUugse/2VX5tzWvbyFuRm/AOwFsNH52nvAU93WOZn1WDp6Y5hwbt53Jwyfy2/7x yN/1z8z12+j6bMQoPquM2sdq/mS1/lbHb6vjo9FyM/XrPms+PZctoOSyASTs240jsQW/3tGXx/Pb G/Yfq8fHavsb5bfxLa9n2sgORKW2JPLPKYflzVbOL6vxh8d04MWXJnBrj2vIKncBEJPaixeeu4Le PftTbiI+I0b1N8q/jfq38isRkbqv0enjeHJgJP9b+gFLP/iIzbnlxhsdIq7pMDLmtObPtfto06k9 3/yUTWK0g+ObZdOzz2TcmBuvfOUbs7ZWfQ4XFtGU0U88TvIPcxk3/1PA/3hk9XlFMJ4nGY23ABcN ns6IK9qybvVGbAnHkFywiP7jPz5sverqb5SPGI3XRvmSmeeF/ljNp6w8DzSzvdXnSVbyTTP9y2q+ Z7X+Vpi5PpjZv9H1wd/546/9jsT9cCASWnTikssu5ZKLu/Pl0N4s2FFsqTwz9fPXP80cP6PrW6jn M4yOj7/n2SJ1QVAngAFa97mf+3q0oWFSPBu+/4BHZnxXg1LsXD3O+9u5Hz75S+Vfk06+lf+c0pDH bp5D6aC5FqKsvvxguGzQME7Z/wAlIr7qazNuvP823EunMuTZHwA71z36MpPGnUr/u7/lv+sL6H9e Y8goxB7uwO52UuGG4y5uytZ3/whKbP72f0DSmYX0HTwCpweufnIx/W5sw+ePrAbs3Dk1nfVP3sbE pZuxOxrx6OsLwf9bT6poe8tAYv58gOvHex/I2R1JNPBUHcx8799c/DVnZ8KkdNbPGcbEDzKxRzTh ydefgd/MbV1euJLhw1cS22Qwr81tx/Dhd1S7nr/63frwBKK+eJD0Bd8A0O7qWcy4/zqW3/w8zuLf eG5TIYOuPoahGRsAaN37Rgr+nM+GABKChJP30Kf/VMrdcPaoZxl1bw++uPVVw/0Hg1H5700Zw3vA 9DfeJf+eO5l9yE2Y2fb1x0z/SThpO30GzsXpgW5jFzBy2kVcN+I9U/GD/+N7gCPmeO6Zfz/5/53F rO92ISISSmHR7QFYX3JwrCjfnUNYxOkkhtvJM/HJ6y1vz+La+zdUfor5wnszGDOiPQNm/Fq5jq/r n5nrt9Xx3Ux8oWYlf7Jaf6vjt9Xx0Wi52fq1uOpa7r69Pyu3lWCzx9IqoozyUuvjP4Q2vzTKbwvW z2X4cOhw2zPclXb49lbOL6vxl+/7lv/b42Hw6Y2Y9JV30rRt/0vY89OTlW+XsXp++a+/cf5ttn8r vxIRqbtyPr2T0Xldufiii3lo0WByfvqMpf9dyqffrTH9LUNX2VZG3nkP5z+9mOvz53DzxNUsevc9 Osc6WFXkND1eVZdvHCo8qiV3PTkbx7KZjHvxm8q/+xuPrD6vCMbzJKN8I7nLHYy8vCl39uvP6nzv pGHLk5sftp6v+hvt32i8NsqXzDwv9MdaPmXteWCw8hl/7Wsl3zTTv6zle8HL52rK6Ppgdv++rg9G 54+/9jsS98NG7OHxnHbexVx62aWc1jKcrz75hCfG3cSPFid/wVz/Mro+GR0/o+0htPMZRsfH3/Ns kbog6D8IWlG0h+3bt1NQ7qFFu450ah34b8ydO3gW/Tsns/rN6Sz8w/vKW7ujEZOnXsXmd6exNKfI UozVlR8se/fsYvfu3d7/cg8mknZHElelRrPkrQNXGDcfPbuWpBOvBmDT25mkdu8AQK9nXuXpEd4f Nb+iaSzLg/AQw2j/B2x566PKAevX73fToH08ABHxZ3ByXAQLlm31bu3cxQsBtl1xdjHxrS+l+2kn kBgdjtuZS/5fHjz72r/Z+GsqIv4MOsc5WPBZtrf08m0sXLvXYKvA+aqfI7YzVzSJ5f1v82jTpg1t 2rTBtmEFsU3/Xflb1J89sYIWPQd5T1pbGIMva85Xc5YHuP8lla+p+Pnl5cQ170O4zdz+rQh1+UbM 9p/MN96rPD4/vPgNCcemBxS/r+N7qPPumUbab08xZt6yoNdTROSvbGHRAJQd8nDN4/HmJ9Ems8CC NZto1aUrPf/dm/T0dNq6bMS1aVJlHTPXv+oEY3w3E1+o1TR/slp/q+Or1fHRaHkg9duz6glWbvO+ lcTjLmJTED/xHsr80kx+64+V8ysY8b+bsZ4Tbj7f+w+bg8FdU/n0mYNPREJ5fhnl34H0b+VXIiK1 LyIunoSEBBISEoiLOvT3at1s/OkLnn5oEn3/PYDFK7Zydp9xvPnyPC5oGGmq7IqyTQDk7SijaNM+ wE12uYtmkd79mB2v/OUb4dFtmDL/MY7LfIIph0x+1vbzjANqmm8DdLzpDHZ+/Xjl5BVA5s9bq6zj q/5G+zfTPkb5Um3mU1afBwYrn/F3fK22jxEr+V4w87ma8nd9CGT/vq4PRuePlfYL9fPu5M638MKb L5PerTUr336c3r0G8sDcDH5cv7PKer6v36FndH03I1TzGaE+PiJHQtC/AZz13izueA8csW1Y9Noc bpp6G2/3nm56+5Ouu5tJ13Tkz/cfYfT8g6/uTTh2OB1iHKxK+RcTJlxMw+MbAnDRiLHkP/IgnxeY ey2Ir/KDZflbi1m+P5YGLY+jz+Xeb96EOdKw22xsLXNVrluWl09YxMkA5P/xAdGp/bGHL6dH5FeU n3EFjugKTogo4q68ssN3FCCj/R/gPKQdPS7A5r3Y2h3e123nlB/cvmRnKSSbj2HTyxNYEHEzPQeN Z9wxjchevYy7J8xm6yFl+tq/2fhrqrr6FW0vDqh+ZvisX1QrALqm30jXQ9b/4cdVRNttlLk95K+d y5bwt+jXIo63bL05PiyT8esDm4QvySmp/H9XeTb2sBgahNspNrF/K8zUL5TM9p+SnIOffnOV52B3 JBNhB7vJ+H0d30Od3SKezKcOv5kSEQkFt9P7WtykcBub9//N7kjE43GTW2Hu2nvNtGdJT8tiyaer 2FtURkS5C5u96sM6M9e/6gRjfDcTX6jVNH+yWn+r46vV8fHAh8p8LfcEUL+9a4L7ocxDhTK/NJPf +mPl/ApG/Ns/f5rIkY9yfNRitqX25xjPRkYc8qn1UJ5fRvl3IP1b+ZWISO279OH53JQWA0D2J3cx 9PHfD1sntlEaaWlppKY2ZEfOjxSYncTyeMcKt9uNZ38OW+EBx/7FZscrf/lGVNIVFL/6GSnX3sTJ DVbw8/7X69b284wDappvAzRtEEHhl/v8ruOr/kb7N9M+RvlSbeZTVp8HBiuf8Xd8rbaPESv5XjDz uRrzc30IZP++rg9G54+V9gv1825n8S5ythfSovH+a2/CH2TmHT6HYub6HTIG13czQjWfEerjI3Ik BH0C+ABn0QY2l1XQJbaT6W3aXjGGBwecydr3H2XkYx9QdQhwUVpaSrtTzwDA7ogAoNUpp9EiMhww ngD2X35oucqzqfB4OC4qjF+LvL+hENU4BVdZJgDlBV+Rw3guOOEm8pe+w5IzZ3DmcWEU734roMHQ 4/Z+OinSVvVTTEb7N4y/5E8AOsaEs3L/b0Akt4oF//ljFW5XAe88N5t3noPIpDaMe/oRxly6mJFL jGMwE7/T4yEs+mCCFJkccVg5PtunbDMAraPC+b3YW37D5jFQQmA8brAFflq5itcBsPCeqaz38Y0X j7ucOe9nM3HIKWwOu4DsD+6r8qPzZsS1joNvvZ/yckS1xu3MJ9/pJszE/s3w2b5BKr/G7Wuy/8e1 ioMfvb9bHh59DK7yHZS7wRGs+IGPX1wAu6y/ZkVExAznvu8pdbs5s2EkP+4fvxue2BRn4feUHnjY 4Gf8DI9qx+CzmjG0x62VPznQsfWVXBloID6u31bzk2DF52v8AnP5hS9G+ZPl/Mzi+GR1fDRabg+g fh5/+a6f8d/S8bHY/mAtv7Xaf4MRf0Xpel7IKuHWbmm80bU725c9wIHPhpiNz9/54zd+g/w7kP6t /EpEpPYtGdyXJdX8PSyyEedcdCEXXngRXVrY+eJ/S5lz142szgrOW9cCGU/95RtFOU/xwPPvsTxh HpNnDaHvkCcod3vMj0c1fF4RtO39yNpTSkLHZGCTz3V81d+ImfYxypdqNZ+y+DwwmPmML1bap5KP /hXq9jNb/5rmk4bxBdD+vq4P/s4f0+0XovthI3s3LOHOwUto2uFMLr30Mh564TZ2/PIlH3/0EZ98 8TNF++vs6/ptWgivX1ZYnc8I9fERORKC9groqOQrmDdzKoNv7se1V1/LfyY8xqlxERRvX2pu+6TL eWz4hXjcReyI7cL4CROYMGECo4eeDUDe2un06NGj8r9J+yexnhnQhxd3Gt/sG5Ufau6KAl7LKuSK gV2xATZ7NNcOacfOb94+sAZv5BRx87Cz+fj9bH56cT23/edUdiz7IaD9VJRtYlu5i2tPaxbg/v1z lvzOkm1F3HpTdxw2iG5yOkNaJwQUW/IZp5Do8Ha58vyt7HK6cZWam8E0E//GbSU0/Zf3Ezj2iMZc 37XxYeX4ah9n0Sq+zC/jliuOB8AR345BbRsGVD+AitKNhEe2plOS+YePAM6Stby2eS9jB11AxP5X kNgdiZx5Xscq6214eSGJJw1nWOcknn9pfcDxtejZl8RwO2Cn26Cu5K17HncA+zfis32DVX4N29ds /2/Zqw8J+9vnn4POIv+PjKDGD9CqRQtS4wOLX0SkptwV+Sz4I59zh1/mfaOBI5l+N7Qh5+NXK9fx N356PKW4PR6OTfJ+gjkiqSMj/hn46199Xb+t5idBi8/H+AXm8gtfjPIn6/mZtfHJ6vhotNxq/Q7w N/5bOT7BiM9Kfmu1/warfZc98xttB1zL0C7JLMnYEHB8/s4ff4zy70D6t/IrEZG6K63bONLPbc0P 7zxB714DmTnvtaBN/kLw8kGPx/vFkhVzxrIq7gJmDuwCmB+Pavq8Iljb+/P7M5/T6NTRnNPywM/0 2encrU2VdXzV34iZ9jHKl2ozn7L6PDCY+YwvVtrnAF/9K9TtZ/r8qWE+aRhfENrf3/ljOl8O0f2w WTm/f8tzD08l/d8DeP3rbM7qPYYbGscErfxQXr+ssDqfcaSOj0goBe2jGW5nLuHNTuGaEw9OqOZu /JbZk18xtX1YZHPCbDYIi6Nb9+6Vfy/asYmH566wHF+oyzdj8fiZnDR7PK8vuo7C6MaE53zJ+Md/ qVy+5v1s4m+OYUluKfZVGTQ49lGW3bc9sJ14nEyc/Sb3jZzD0kkOtn40hkGzV5vav5EFo+9n8ow7 eOedoeRvX83ir3ZybQBjRVq3fjw4ZSK7cnZCfDPs275g7KfZprc3iv+XR1/AM/92Fi+6hmLPPj78 fAdtO/+lED/t89hdc3lk1oO8eHEOxHlY8UseZ5mvHgBl+Z8w/+OLmLLwdcJdLrZ+eBcj5q01tW3G 6CmkTR/PW6/dQE6Bm9S0hmz8+im+/ezg77CV7/uGV3aFcY39db7ea/yt97/a8WkxT760kOLSWFJs m5g87JOA9m/IT/sGo3wr7Wum/+/4xMXTLy6gqDyeFM8GJg37rHJZUNoHOPvii8n/4kXe3mq8rohI MLw/+V7OePRu3ny1B8VRibDhI4Y+90flcn/jp6ssk+lv/sDEZ16g15bdxMeX8M7bWfTu7mNnPvi7 flvJT4IVn7/xy1R+4YdR/mQ1P7M6PlkdH42WW60f+O8/Vo+P1fiM8ttJz2XQKdZBeEwDYu2TePVV J27nTtL7jQhK/w1G++75eS7OhBdILPqad3NLK/9uOj4/54+/+oNx/m22fyu/EhGpu3KWjWPIR8F5 VW11gpYP7ud27WXmqEd5acF0+qy4gdfW5Jsaj6w8r7C6vdF4m7dmHne/FM8dc15h0LYcbPFpFPw2 ixFfbDisrOrqb8SofYzyJSvPC4Nx/K0+DwxWPuOLlXzzAF/960i0n6n6+8knrbLa/v7OH7PtF6r7 4UC5yvbw5Xsv8eV7L+EI4pet/dXPTP/0x+r2VuczjuTxEQkF28aNGz0AXbt29btiUVERAA1aTuH1 +eeQ/cFi/pedzauLP6iyXoOkRjRsEItz32627Smsrqg6yx6WwHV9LqfRP3pyWbsEJv/7Sr7bF/gk m8FeSG3egoiKArZuD91vnYVy/2GOMFxOF/+Y+TI3rp7C0OfNfxM1LDqBtNREPMV7yNkVwPujK/mP PywykeZpMezIyql8tWUgbGGxNG+eTN7WrRS6Any/cpDEpTQhORr27NhB4WHveLYzafE7hD9yC9O+ 3lmj8sOjk2jWKIqcrByc1TSR//1bF+ry/fPdf4a+soRmMwYydQM0S4kiOyuH6n4es3bjF5G/i9jY WL/Lc3JyAGjatKnf9Q7kb2AntVkLOlwyjFEXeJg0bDK/HvLbP0bjZ1RSExrHQs7WbdWOHdZZy09C HZ/V/AKM8ifr+Zm18anm46O58TO0+a/142MtPqv5rfX+G9r2DfX5ZSb/Vv4lIhJ8wc83a1fo89Wj fzyyO+Jo1jSZ0vwd7CooNd4gQP7axyhfqu18yigfsUeksfTdRcy6YwTr8raQmVMS0PZgrf9Yf57q X6jbD2r//LG6f3/nT6jz+eSWrWiSdj0PjE/miqvvqMkO/vaszGfU/nyOyOGM8rjly5cDNfgGsLNk AytXRkLKsXRoEHXY8r25u9ibuyvQYusGewQdO3aEfX+yciUUOEMxGLnZubU23xNf8/037NCbbo03 88v6bcS1OIXb28fwzP1ZAZXhKikgO7OgRvv38h+/qyyPzMy8GpfucRWRlVm7Ny+Fu7dR3UcnWnY+ ifYnXcM/Irdy/fe7a1x+RUkumVsC33+whLp8/4z7f0Vx7baPiEhouNmZncnO5+7E7RpCVEw4HDIB bDR+luZuIzM3xPFZyI9CHZ+V/MJc/mQ9P7Q2PlkfH/0vD23+azX/sxqf1fzWev8NbfuG+vwyk38r /xIRESOhz1eP/vHI7SwkKzN0NfDXPkb5Um3nU4b5iLuMlStXcl76jZyy6XkefPbPwLbHWv+x/jzV v5C3H7V//ljdv7/zJ9T5/FnX9eesBhH88JP5twqIVzDmM2p/Pkek5gKeAC7Z+RITJ4YilNrndu5i Yn2tXBBUFObR9urLOa9HQ8r35rBoyq18mltW22H9bZx22VV0IJf7R8ykoOLo+6RpXZf54/fs2+es 7TBERELui+efru0Q/laO9vzJaHzU+CkiIiIi9Z27Ik/PjKXWvPfg3bxX20EcpY72+3ERqwJ+BbSI iIiIhFZ9eyWfiIiIiNQtyjdFREREjk5mXwFtPxLBiIiIiIiIiIiIiIiIiIhI6GkCWERERERERERE RERERESkntAEsIiIiIiIiIiIiIiIiIhIPaEJYBEREREREREREamzbGFxpKYm1XYYIWOmfscNeIDZ YzoeoYiqsjsakZGRQUZGBo9N6HxE9/3S4sW8/vobLFmyhFRH9Y+yj2T/8HUcrByf+t6//TE6vmaO v1V/h/YP9fXDX/n9Hp9PRkYGC+ZNDtn+RaR6mgAWERERERERERGROism9XoWzJtU22GEjJn6FWX9 zuoN+45QRH9hCyM1NZWZwwczZubqI7rr63v3Jr3/FKKiorDbql/nSPaPiPhkGiVGHPZ3K8envvdv f4yOr5njb9Xfof1Dff3wV/7Lo4dx+6TvSU1NDNn+RaR64bUdgIiIiIiIiIiIiMhf2ewRJCc1ICYx CmwOUlJSAHCVFpBX6KxcLzIxiaiSAvaWR3L8CcdBUTZrN++pXB6T0pxWTVKwleeybt0WnJ6D+4hJ SsJRmE9xVBPaH5tM7sY1ZO89WDaAPbwBbdu3JtJTyvbMjXwpABkAACAASURBVOwsrLrcX/kHY2xG 22MaUZq/jfWZO0zXz+5IICnBgXPV+7ztLKi2neyOeNq2OxZHRQHr1m2m3B3c+h1Q4XTidLoP+7uZ 7aurfyWbg5bHH09ihIsNa9axr+LwfVTHbP8wc3z8cTRoQvtjG5G3ac1hy8wcH6vx+2ofW1g8iQkV lEc2o0XEbv7IKqZdpxMoWL+abcUVAMQmJxG+N5+K5Na0bRTJpj/+oOAvx9Bf/zFipn/V9PiaFuL+ U5evL46EZJKiqk7xuMry2J1f7i07xNcPM+W7nE7Kq7luiEjoaQJYRERERERERERE6hxHzIlMmzYA uyOFsMhopk2bBsD2L2dx72ubK9frPms+PZctoOSyASTs240jsQW/3tGXWVsL6TzhKe7rHM36rBw8 Mc05Nm47k4dP5Lf9kyy9HnuOcz5bhuPM48grT+CEVhE80H8gn+8pBSAqpRtPzBuLO3M1ue5oWrZt xZx+vVix1zvBYlQ+wEWDpzPiirasW70RW8IxJBcsov/4j03VL77l9Uwb2YGo1JZE/jmFfhN/qtJG sc3O55HH74DM3yiMbUUrxwbGDZ3Kn6UVQamfETPb+6o/QGTyadz78ESalWaypSiSDsfAQ8NGsXxn qeG+/5+9+w6Pqsr/OP6emcykE1IICUU6qIBlXRUV9aeLrgVFQZAiAipVRKQjHURFEUQQBQUp4ipY V3ZhRV0BQdgVWQRE6SUJPYX0MjO/PyYEIpmZm8yEhPh5PY8+mnvvud9Tbjtnzr1Gys9I/XgSdW1X 3prShaM7txNYuy6HjwfAeaF5qx9f4/dUPmG1nmbpnAbs+S2dRi0uZ9PWRCKDrTStnchDj47DAfSY 8x7X/2cbQVeEcSijGi3qFfBC72fZlJwLeG8/3nhrX77UrxHl3X6gcp9fLms3iGE3xRbFGla3Hvxv DN3HutpheZ8/fGn/IlL+NAAsIiIiIiIiIiIilU5exo8MHPgjofF9+WhuMwYOHOJ23boPdmDSsz34 8Wg2JnMo9W2uAa7Dn02nw0v7imbNtXlhKcMGXU7PF7cXbRt1YwZd+g4i3wkPv7mc7o83Yu1M16uO mzzVi5A9L9Nt1AYAzNYoqjnPDY55Sz/62iEMvr8WI7r3YGfhrLx619QxnL+0vXMZOBCufOYdRsdd mO/HX3oGx6oJ9Ht3C2Cm8+sfMHbkdfSYtNkv+fPG2/ae8g/Q57XnCVo3ja4LNwHQ7OHpvPhSZ75/ cpHXfRspPyP1756Z58d2Ze+cpxmz+hBmWzxvrngHdpxbw1v9+Bq/t/Kx5yYweMQU7nx7Od1S5/Dk mJ0s/nIlLUOtbMt0DRJGXH2MR3vNJd8Jtw1fyOCJd9F50ErAWPvxxlP78qV+jSjv9nNWZT2/7Fsy gf5LXMvC6t7Fe3N78/Zbu4u2L+/zhy/tX0TKn74BLCIiIiIiIiIiIpe009tm8+PRbACcjkwOFM5g S9t1gPrXtuahRzrRtWtXmthNhDWKL7bt4U/XFA2wbP/vKapdHl60LCsxi/AG93L7n68gMjgAR34y qee9YtZb+s2fuIETP7xRNPgJcOh/CX7Js9kaxYOxwXzx6dnv8jpY8+5vRF31sN/y54237T3l3xra krbxofxzcwqNGjWiUaNGmPZtJLTWIwT66YOvRurfHVv4DbQMs7Lwu0QAHHlHee+3M36Jywgj5VOQ ewCAlOO5ZB5IBxwk5tmpHWgpSufQxyuL6n/L+5uIaNgVMN5+vHHXvsq7fi9G+zmrsp5fzgoIasDk 159l3fTBfJOYaShP/jh/iEjlphnAIiIiIiIiIiIickk7syu1xL+3n/guXeOO8MW32ziTmYstz47J HFhsnfy0c4OTTjtgOjd4duCD51loe5KHeo9i5GU1SNz5byY9P4OEPLuh9GtVs5GxPt2POT3HYo3D bDKRkGsv+ltuSioW2zV+y5833rb3lH9LUH0AWnd9nNbn/X3LT9sINpvIdZTyY70lMFL/7pitNQFI Oq8sMo9lQbTPYRnirXzsUFih4HA4cBa4yqvACdbz1s9Oyir6b3teEmZrNDYzOA22H2/cta/yrt+L 0X7OqqznFwCTOZDe06dh/Xoqs9cmGc6TP84fIlK5aQBYREREREREREREKi+nA0yeuzGdJQz2BAQ1 o+9NtRnQrg/7CmfsNW/wAA+UYtcOexqfL5jB5wsgMKoRI9+eybB7lzP4i0OG0j9yOoeI5tHAAZ/y VxJ7XiIFTieNgyxsL3zdb1DNGOy5h/ySP39s7yn/9izXq2rfmzKBvR6+Oet0uJYFmtzM6nRTfr7W vz33IAANggL4JctVvtXrhEC2wQSMchO/t/IxOg8zrH4Y/HQKgIDgy7DnHSfPAWY/tB9P/FW/7pYb Td+rS/j8AnDr0zO5vWAN3d/+oRR79s/5Q0QqN70CWkRERERERERERCqtgpz9BAQ2oEWUrVTbOZ05 OJxOGka5ZszZopoz6A5jr/89K/qGPxFpdXWh5qUmcDLfgT3HYTj9X95ZS43rhnJLvbDCv5hpeVsj v+TPUZDGR0cyaNurNSbAZA6mQ79mnNj0mV/y54/tPeU/P/s3Pjp4huG9/4Kt8JW9ZmskN/5f82L7 KMg9wNE8Ox3+XLvEGNyVn6/1n5+5jfWpuTzVtikA1vBm9G5S3fD2RrmL32j5eFOv46NEBJgBM3f0 vonUX5cC/mk/nvitft0s91f5XMrnlzp/eY7ht2UxYuTCotc0G1Xe9S8iFU8zgEVERERERERERKTS yk39hvlf38X491YQYLeT8K/RDJr3m9ft7LmHmPzJFsa8s4SOh08RHp7N558dodPtxvcdd1t3po0f w8mkExBeG/PRdQz/NtFw+im75jFpWThD5vyN3keTMIXHkbZjOoPW7TOUv7ELltIi1EpASDVCzWP5 8MN8HPkn6Np9EADLR73K1TNGsWJxZzKCaxKQtJ5Rb/zsl/z5Y3tv+V86dDxxk0fx6UePkZTmIDau Ovt/eIvN3+08txNnPmNmfMLUwXNYNdZKwpph9J5xbrm78vNH/c8aPZeZ06fx/t1JEOZk488p3HTe cm/1Y4Sn+jdUPl4c/8bO2+8vJDMvnBjnPsY+/V3RMl/bjzf+qF9Py/1RPpfy+eXax1oTEGJj9vJP i/528qfxPDVpG1D+5w9/tH8RKT+m/fv3OwFat27tccXMTGMfDxcRERER34SGhnpcnpTk+q5PrVq1 PK6n+zcRERERKckf7X4zKCqemqGQlHC01LPkACzBEcTFRuLMOk3SyQu/Z2skfbM1jNq1oslJPc7J tJzSB+GRmdg6dbEVpJFwrORvlXriLX9mWxyrvlzM9CGD2J1ymENJ2aXaHrznPywmnuhgOH38OBl5 xmcgG+Fr/ZssodSpE01KQgIZdv/GZlRZy2fA376g9ou9mLAPascEkXgkiYILysC39mNEedbvxUjf k8pwfvFN+dZ/dL36xMd14+VR0bR9eIjf0xf5I/J2H/f9998DmgEsIiIiIiIiIiIiVVhO8lEOJZd9 e3t2GomH0nxK35GfwZFDGWUPwnPqnEgo+3c7veUPRy4//vgj/9f1cf50YBHT3t1Tuu3xnv+MU0cp r9Lxtf6d9kyOHKrYHzv4Wj4FWckcOuxuqW/tx4jyrN+Lkb4nleH84pvyrf+bOvfgpmo2tmz1Pqta RPyraAZww4YNKzoWERERERERERERqSSqygxgkT+q+4ePJWrFLJYeLHlmtoiIXHo0A1hERERERERE RERE5A/qH6++UNEhiIhIBSkaAL7uuusqMg4RERERMWjLli2A7t9EREREpHycvd8UERERkUuTuaID EBERERERERERERERERER/9AAsIiIiIiIiIiIiIiIiIhIFaEBYBERERERERERERERERGRKkIDwCIi IiIiIiIiIiIiIiIiVYQGgEVEREREREREREREREREqggNAIuIiIiIiIiIiIiIiIiIVBEaABYRERER ERERERERERERqSI0ACwiIiIiIiIiIiIiIiIiUkVoAFhEREREREREREREREREpIrQALCIiIiIiIiI iIiIiIiISBWhAWARERERERERERERERERkSpCA8AiIiIiIiIiIiIiIiIiIlWEBoBFRERERERERERE RERERKoIDQCLiIiIiIiIiIiIiIiIiFQRGgAWEREREREREREREREREakiNAAsIiIiIiIiIiIiIiIi IlJFBJz9j6NHj3pcMTMzs9yDEREREREIDQ01tJ7u30RERKS0UlNTPS6vXr36RYpEKpLR+00RERER uTRpBrCIiIiIiIiIiIiIiIiISBWhAWARETGs1WNP0r9/f6oHlO3y4ev2IiIiZaHrl4iIiIiIiIj8 kRjuwah9Rzs6tL38gr/f06kzdzWq5tegxD2TJYzY2KgSl7WfMY9R18WUW/pVReOeLzNjWPOKDqPU xs1byOJFb/uURkBINDVr1iTSVrrOy9hWz7N06dKif/rHh/kUR0UKqXUrE6a/yaLFS5g348GKDqfS enfxEhbMG3nB31ve+wAPPfQQ1SymC5YZaV+etq8MqmL7qOjjt6L3L6Lr5x9LVbp+VXT78ef+W7bv z4y581nkpn6kbCry/FYZ0vdH/svT5PnvsWjBaxUdhkiV4Y/+sLL2B9V7eDJvvXJbqbe7WPwRn6/9 gWZrjaJ7hlnPt/QplopgJP/+6k+sqH5JT/vt/sZ8li5dysJ54y5yVCIiUp4MP4Wl7Mrjiadf4aYI W9Hfoq4eyKDu97InQd+Xu1hCYruxcN7YEpeF14gl0mYpt/Sriswjv7BzX3pFh1FqkbGxxMbW8CmN um0nsmTJEoY1K903nXJO/sz69evZkRFEbGwsEQGVc/DOiLaTBxO3/QOGPjeYkZO+quhwKq2asbHE xlz48PPrv9ewevVqMhzOC5YZaV+etq8MqmL7qOjjt6L3L6Lr5x9LVbp+VXT78df+Lba6vNy7Hc3q mNm88Xs2bNzh50j/uCry/FYZ0vdH/stTdM0axMb69gNtETnHH/1hZe0PCgiLoUZ0UKm3u1j8EZ/P /YEmC7Gxsbw6sC/DXt3pUywVwUj+/dWfaAuPpkakzfuKfuYp/g+GPs2zY/9LbGzkRY5KRETKU4DR FbOOreKV9Y8yaNx9/DDsc0zmIAaP+Stb5w/kYK69aD2zNZwmzRpiLUhj9+6D5DnOpREUUZ2ArDNk 5Lv+aAmqRnhgNqlp+aUKOjCyNk0uq0FO6lH2HjpebFlITB3qx8dgyktm9+7D5J/XRxMSFYU1I5Ws oHgubxhN8v5dJJ4p3b4DQmNp2rAWFns2JxIPcDwtr9hyszWcJk0bYCOHg7v3kp7vKNVyd0xmG9FR 1QiJDAKTlZgY14OkPSeNlIziebBWq+02f+7KpzTpew7USr2mTYm02dm3azfpBa78mS3hREaYOJ18 5vyViYmJ5kzyafLOdqa52R58rz+zNYKoCCv52/7JZ/lpFyz3JX1LUHWqhzg5kx9C0zg7O/eepkmL FuQn7eLg6XNtJLxmXerWiCaAHBL37+V0VsF5AdqIiaqGw55OSho0vqIZluyT/Lr/aIn7rBYdjc1k Iv9MMmmFB5q79mWyhBEdGURkqOuQt1WPJibGjMOZQ/LpDMBMTEwUDmcOKSlOLm/emNwTe9h/PAuA M/tWMn8fXPnM9dzZ0P2Mf4/5KxRZuzH148NJP5nI3kMnii3zdnx4O/48CYmKIsRs5tpIGwk/JWAy mzFTvCPT0/kLIDAyiqDsNM7kBdL0isaQmchvB08bjsF7/qsV7j+12P69tS+j7c9d+mdF1WtGndB8 dvyy/4K4reFRRASa2fX5R+wC0s47Nr23L8/bn4uv/OrfGyPtw1v9u2s/Jks4kREF5AXWpq7tFL8e yaJZiytI27uToyUcI25j9HB988To8eutfYDn9uvu+De6f6lYFXV/o+ungfwVqqjrp7H96/pVHuVf Fc7fEVHRhFa/hgCziYzkL1nxyQacjuL312U9vo23Hw/1Y+D8Udb6u/TPb8a4Kx+j6fvafr3l3xt3 5WO2RRBVzYrDkUVysuucHhkdjcVkIjP5NNnn/RjEW3wms42mV16BI2U/exKLd7x7qv/qUdFYTA6S UzLdbh9RtxmNoizs2r6LwOpRWExw+vTv7k/LcH0W8SdreA3s6Scpa+vzR3+Yt/6gszz1NxaLqfAc l3b6lNfnMqPPg96e90rTH1FifG7624yWrzmgGk0ub0CgM4djh/Zzwk1fYUF+Pvn5599vGXwe9tAf aDT/ZekvNpJ/o+3HE2u1eC5vWIOUA7tKXsHH/lBP9WMkfnt+Pnm6RoiIVDmGB4ABNsyYQu+PZ9Ox 3tdsunI0V9v/Q8d/HC5aHlr7Tma+MQQO7SAjtD71rfsYOWACe3JcF/RH57zHlXP7MfIH10W4fseX eenWZXTqs8FwDHf1ncygtk3YvXM/pojLiE5bTI9RXwPQ8vm3mNoymL1HknCG1KFh2DHGDRzDjsKL XsdZC7jlu39jvbExKXkRXFHfxss9erH2dI6hfcff0Zc5wx8izOKaOO10Ovh7r47MPep6gI26tgPT J/SidrAVgPysJBaMH8pn25MNLffEGnIVEyf2xGyNwRIYzMSJEwE4tn46L3x0sGi9yCsfZX6vkvPn qXyMpu9JYPSfeeG1MdTOOcThzECuvAxeefo5vj+RQ0DIlby/7Hn6tGvPkTzXDwZCYjuyZEFbOj3U gzwv24Pv9RderxsTB19JUGw9AveMp/uYrcWW+5L+ZQ9O4c3u4Zwilpo2Cz9vOspVreKx5x2jZ/te nMh3cMtLCxj/pzpF2zjsGXwx7WneXnsMAFv4DSxbNo6sE39jp70d18eHAPD1kx14NaF4J0zdOwcw f8SDpO9fw8BnZwKe21dofB+WLfhr0fZXjZ3DMiAneTXtuszEbK3OsmXLyEvbyI9Z13BzfAhORz6r ZvRj1poEQ+XrLX8mSxhPjJ9Op1YNitY5tm06PUas8Ro/eD/+vLlz8PPcExVEreAACgaOZGK+g5zk Lxk2/l+A9/MXwO3T5/PQvxeSfV9PItJPYY2sy/YhXZie4D0Gr/lv8RCvvfAUtQrzn318B1MHj+G/ yTle21eogfbnKX2Am/u9yoSHr3LF9Z/FmIDznzevHDKTV26OK/r/3g/ex+HCH/94a1/etofyr39v vLUP8Fz/ntpPWK2nWTqnAXt+S6dRi8vZtDWRyGArTWsn8tCj4wx1hHi7vvnKW/vw1n69Hf9SuVXk /Y2un5X/+qnrV+W+flX28/eEBYtpHuKKLSx+AMuWDSAv43880GGkof17Or4/vtlA+/FSP97OH77U 36V+fjPCU/kYSd/X9ust/954Kh+zOYapC97gsoBMhjzSjYSaD/PhvCfJOvYtXXu9Yjw+k5mBs9+n beMInM4CPh39OPO3ugYsvNX/1PcW08CSwqpDgSVuf/kjY5j51K2YTSYyjqznTM2biTOlcm/brl7z J3Ix1bh+JG/2CuSrVatZtXoNB5NL90M0f/SHeesPAs/9jeez2GoxdPYbRG+Zy8j533qN38jzoJHn PaP9ESXF56m/zUj5BsXcxux5w3Ec2kmyI5h6Teozp3tHNp7xXpdG8u+tP9BI/svaX2wk/0bajydR 13blrSldOLpzO4G163L4eACc19Xoa3+ot/rxNX4REbl0lWoAuCBnH+MW7mDG5L7cFXkNHw/rem7m JvD4S8/gWDWBfu9uAcx0fv0Dxo68jh6TNvsl2OhrhzD4/lqM6N6Dnamui1i9a849MB3+bDodXtpX 9CuuNi8sZdigy+n54vaidaJuzKBL30HkO+HhN5fT/fFGrJ1p7NUkffq3Jdh+nKH9RnKsIJjGLW4k Js81OGS2xjJj8pPEOA7y0ohZJDlrMmTKCJ58YSpfPTyAbEsNj8szvbxOLi/jRwYO/JHQ+L58NLcZ AwcOKXE9T/nzVD5G0/dYPq89T9C6aXRduAmAZg9P58WXOvP9k4vIS9/M30876Xt9DcZucD3QNulx D6e3vkmG3el1eyP58yZt71wGDoQrn3mH0XElr+NL+hZbPB8OeoZHps+kadNtPDNhJ7MnteHeyCAW n8giK2kLr698k12HEigIqsfUmZO4f2B/3l47oVg6IbFdCP3qA158Zze2mEbEnNfJCFDjz92YO/xB 0vf+iwHPzeJUvsNr+8s59QWTJm0i9pbe9G9Ti32LZvD+oXQcecV/EWmLuBnryllMPh7LqMGduXvQ JOZ98xQ5Bl536C1/TXtMo1OrBiRvX8nsv63HVL02N93sStdb/JkOp8fjz4iV44exEpj88ZekThnB jN89JBk9f9V9sAOTnu3Bj0ezMZlDqW/LNbR/j/kPiODlqb2JC8hkwbQZnKp+EyP6/JXRM56kfc83 Ac/ta52X5UuTrR7Tt4b9iXEPtcSed5S5r82n+m196G42cf6j3MEPZzFpTRB3DR7NzRHFX1VkpH15 2v5i1L833trHWe7q31v7secmMHjEFO58ezndUufw5JidLP5yJS1DrWzL9D6Ia+T6VlZG2p+n9gve j3+p3Cry/gZ0/azs109dvyrv9etSOH8vfHkKUdWuYcyw9mSf+pRX3tyO3X5u5omvx7fH9pMSZvj8 5C59X+uvKpzfPPFUPt7S90f79ZR/b7wevzn7eH78Ct6f1plRw25la+MuOArSmTF0VtHsXyPxWayx 1Ph+Oi99cyOj+97KXQNaMb/3PwBj7d9ijSV67TRe+KoVYwfcXrS9JbAeLz3RGgqSmTttJuZretCv rYWzE+y95U/kYkr6dgRDU1pz911388riviRt/Y5V/1jFt//ZZeitRv7oD/PWH+Stv/GsgKB6jH5z BtZ/v8rI9zcZLgNvz4NGn/e89Ue4i89jf52B8m3yVC9C9rxMt1GuCTxmaxTVnMavh97yb6Q/0FP+ fekvNpJ/I/2J7pl5fmxX9s55mjGrD2G2xfPminfgvC9i+Nof6q1+fItfREQuZaUaAAY4+PkEfuu2 nAYH32Pp7nMP72ZrFA/GBvPGp2cHyxysefc3uk16GPDPAHDzJ27gxA8vFl3MAQ7979zsirRdB2jy p9Y0b1iLEFsAEXYTYY3igXM3TIc/XVN0wd/+31N0uSnc8P5T7Q7M1po82qEtazdv4afvVrCp8NVS 4XV7Em+zkLxzI0TGUwvYdCybLvUbcl9UEF9V87x8xalsX4rGUP6MlE9ZWUNb0jY+lNc3p9CoUSMA TPs2ElrrCQLNi8l1OPly6V5mPXknbPgATFb6to7l24E7DW/vLX/+4Gv6m/fv5aosO39K/y+Hd4cD bagV6PoOza8fr6fpPbfQsfU9BBV+m8YafgMBJig476EnN20dz722uPD/fiiWvskUxNxJj2EuSKL/ c7M4nW+s/a04tY+NG/fRoE4XAFJ2/JeNJfzyuyDnIOOX/BMH8G6ntgyoU4fW1Wx8nep9kNNb/h65 py5Op5PJ495mV3Y+8D82fIPB+LM9Hn++Ks356/S22fx41HW8Oh2ZHDA2Ad1j/kNqdKNeUADHN73C 8m9/BDbRsMPtdIx/kMuD53O29D21L0/LvaV/vM6DmE0mEtZOZ+V3OwjYfJrut7xRLP60335iI9D8 6eIdjuD6cZC39uVp+4qu/9Ioqf6NtJ+C3AMApBzPJfNAOuAgMc9O7UCLoQHg8jx/e2sfv2bne2y/ YPz8JpVTZbi/0fWz8l4/df2qvNevS+H8vWPzZgKrhwKQn72bjRs3Flvuy/F9ds6lu/ZjpH48pQ+e z49GXernN088lY+348sf7ddT/r0x0j5Ob1vE1FU3Me6+kdwLbHqzL+tPnbv5NxKfw5HDlA/XYLfu YHTfW7GGXla0zEj9Oxw5TF3xLXbrLhhwe9H2IbEdCbGYObrhVb5YvxXzxl/pcd9yAg3mT6Q82MLC CS58I4A9N4OMnLPXTgf7t67j7a3reGdWFDfe0Ya/PjqSpwfnMqv/IL4xcL9khC/9Od76GwECghsx fv4A6u19nR6lGPwF78+DRp/3PPVHuIvPaH+bJ1mJWYRfey+3/zmZn3fuISU7mVQ/5f8XLjccn7v8 +6O/uLzYwm+gZZiVAd8lAuDIO8p7v53hmcLl/ugP9bV+RESk6ir1ALDTkc1/0vOI3vNLsb9brHGu Dpjzfo2cm5KKxXaN71EWqlXNRsb6kj9WD9B+4rt0jTvCF99u40xmLrY8OyZzYLF18s/7ZpPTDpgs GLVwzGziR/Tihns6ccM9nSjIPcE7Qwfy+Z40gmKjAYhq3p3RzYtvVyfQ4nW5v3jKn5HyKStLUH0A Wnd9nNbn/X3LT9sINpvIdTg5tvZtAge/TtOg5RyN7cFlzv0MKpxlZ2R7b/nzB1/TdwLOwn+ffQGh 1WzCGtqShe+8QmQA7N++hcSULJyAyWQmyGwqmgUNkJfxk9v0TeYALElnCKhTmyduiuXVda7Z1P5q XwXZe4teR5twKhfqhFPTZva6nZH81Qu04HRkFnaOFGckfk/Hn69Kc/46s6tst9Ge8m+NcP0EM313 StHffsnMg5hg6gWZ2V34N3ftCy/LvaWfEh3syttu1ze6C7J/w+G8eKN2FV3/pVFS/RtqP07XMofD gbOwR6/ACVaD+y3P87e39vFrtpf2W4rzm1ROleH+RtfPynv91PXLvYq+fl3q529/Hd/u2k9pjm93 6fuj/i7185snvpSPr+33LHf598Zo+Wz9+J9w3wAcjhw++Kr4YJCR+JwFqeQ7wex0DXCZTK5zn9H6 d799BABZh13nP4c9nZP5DuqYS5c/EX+697X5PBHnelV94jejGfDGLxesE1ojjri4OGJjq3M86SfS Cvz3o15f+nO89TcCBEW1JevD74jp8ATXVNvI/wy8/rh4QO6fB40+73nqj3AXn9H+Nk8OfPA8C21P 8lDvUYy8rAaJO//NpOdnkJB34Q/kSuQh/6WJz13+pWQ6NQAAIABJREFU/dFfXF7M1poAJJ1XVpnH ssB1mvZLf6jP9SMiIlVWqQeA3bHnJVLgdNI4yML2wtlMQTVjsOceKlon3+nEEnzuAhUYbbsgHU+O nM4honk0cOCCZQFBzeh7U20GtOvDvsJvdjZv8AAPlCEv7qTv/5pR/b4mOKou1996H2MGtKfb8Jv5 vM8qco6ddMW4ehQD39p13lZm7LlZhFg9LzfM6QBT6avNcPmUMX17lquL770pE9ibU/JrYApy9rLk SDZ9bovj49a3c+zfLxf9stnI9peyao07E2U1c3rniwwYvhaA1z9bSbythAcSp/sbNIcjh579ezP+ w/f5v2EvsOI//TiYU+C1/RUlXdgpagoouVPaGtocmxnyHNA0ztWpmnjeoFZBduE386oVP3aN5G93 dgH1gsL4c5iVH4u+Y+P6Up+R+D0df74ycv46y2ng4agknvKfl3oYuJGIFjWBfQBcF+Z6GNmT7fsN u7f0c0+6fohR/coo+PthrKEtMZtMblJzz1v7cqei6780Sqr/0rSfsvDX9c3d8Wuk/Xlqv0bPb+72 LxWvUtzfuKHrZ8VfP3X9cu9iXb+q6vnbX8e3O0aPb0/pl+f9x6VyfvPESPm4S9/X9ust/94YKR+z pRpDX32CgtwjOKx1mPhiF7oNWVr0gx8j8blTqvovQW7KQeAGoq6/Apbuwxragjo2CxQYy58+0iHl 4Yu+XfiihL9bAmtwy11taNPmLq6ta2bdV6uYM/pxdh45U7odlLG/yghP/Y1nZSa9xcuLVvJ9xDzG Te9Hl36zi30Wr6xK87znqT/CXXyG+9s8lK/DnsbnC2bw+QIIjGrEyLdnMuze5Qz+wvdn3tL0B7rL v1/6i8upfdlzDwLQICiAX7Jc14rqdUKg8EUk/ugPLc/6ERGRS1vpn/LccBSk8dGRDNr2ao0JMJmD 6dCvGSc2fVa0zv6j2dT6q2tGlNlWk26ta5ZqH7+8s5Ya1w3llnphhX8x0/I21+sxnM4cHE4nDaNc D422qOYMuiPe53yd75Exw2h35800iLaRevIYTqcTp8P1cJqeuJBDOQXU+stQOt7dmquvuY4293Vk 4huLyHd6X25UQc5+AgIb0CKqdB0wRsunrOnnZ//GRwfPMLz3X7AVzugwWyO58f+K/9z43+/soEnP Dgy4Npovlu4r9faXqvxU17euQmL/TMPadWjVfiRXhBid+3ceZwFn8lJ5eeq/CAisy5TRdwHG21fm AdcDVuNePXioXTvuv/vqYslbbPHMePYx2nUbxuM1Q8jP3s2G837VemrzEQCaD+3Pw+3a8UDbNobz 98mnewAYPWM0D97dhgcfeYypM+43HL+n489XRs5fvvKU/6wTy/glM5/oa0bQp/09tO81kbbRQaQd +ID9fvhBhLf0M498TJ7DSVzrEXR94B6eGje62PbmgAjatWtHu3btaBTk6pS64/4HaNeuHa1iz71C zl378ra9P+u/3/ylLJ33mM9lVhrl3X78dX1zd/waaX+e2q/R85u7/UvFqwz3N+7o+lnx109dvy7O 9cuTqnr+9tvx7YY/zk/lef95qZzfPDFSPu7S97X9esu/N0bK55aB07klOoiNM8cx+Z+HiWr+GOPa 1i9Kw1B8bvha/1knl7EzM5/IZs/wyujnmPLmlFLnT+RiibttJF1vbcCWz2fTqWMvXp33UekHfyl7 f5URnvobz3I6Xfd2G+cMZ1vYX3i117V+2be/nvfcxWe0v81T+Ubf8Ccira4u5LzUBE7mO7Dn+Gf2 tj/6A/3RX1xe7Ss/cxvrU3N5qm1TAKzhzejdpPq55X7If3nWj4iIXNr8+tOm5aNe5eoZo1ixuDMZ wTUJSFrPqDd+Llr+8+tLcM5/luWL25PlTOdfa4/TpKXx9FN2zWPSsnCGzPkbvY8mYQqPI23HdAat 24c99xCTP9nCmHeW0PHwKcLDs/n8syN0ut1/+TOFN6XPiDYEFM4syEs7yMKXXN+xcuQnM3zELCaM 7stjTw8HXL92Pnlgh6HlRuWmfsP8r+9i/HsrCLDbSfjXaAbN+83rdkbLp6zpAywdOp64yaP49KPH SEpzEBtXnf0/vMXm73YWrXP6f3PJj1hCZOYPfJmcU+rtfTF2wVJahFoJCKlGqHksH36YjyP/BF27 D/JL+p6cOfwWH25qQedWd/PWwrtJ2beWLRl5XBdWthvLU1tm8+H+W+ncajA9m29i0U5j7ev0tlms /nECrVveQf8Bd5OTvJp/fLWtaHlu2jp+q3s/A+6JwlGQxoqXJxfrIEjZ+Rp/WxfNA61upt+AW7Hn HePLlV8byt+Bj59nfu3J9Lz7Zp4eegtOp4Pf1r0IGDs+PB1//uDt/OUrT/l3OrIZP/R1XpjyDB36 PgfA6T0beGHUB37Zt7f087N3MWbpBl56/BZ6DHyO3WtmkOd4rmh7c0AMAwYMKJZm1779AfgpcSOb TriOZXfty/v2J/1W/7WiqmNNLN336/yhPNuPv65v7o5fI+3PU/s1en5zt3+peJXh/sYdXT8r/vqp 69fFuX55UlXP3/4+vn/PH+en8rz/vFTOb54YKR936fvafr3nP6XE9c7y1j6iru7JmHsvI+vYal7+ 7ijODRM42OZdWvWfzi0/PMaG0zmliu/3fK1/pyOHcc9OY8Rzj9Hoyqv4df1cDtz/LPUtdkP5E7mY kv49kn5rfP/xjC/9Vd76gzz1N/6ew36GV597nWULJ/Poxsf4qIyfiTrL3/2ZJcVnpL/NU/nG3dad aePHcDLpBITXxnx0HcO/TfQp3+fztT/QH/3FnvLva3/irNFzmTl9Gu/fnQRhTjb+nMJNfsy/t/qp yP5QERGpWKb9+/c7AVq3bu1xxczMTINJmomtUxdbQRoJx0r4VmJgJHXiQjh+JImcMr4qxWwNo3at aHJSj3MyrfggYlBUPDVDISnhaLn8sjUgJIIa0dWxOXNISjxe4j4ia9YmIgjOJJ8iOT231MvLU3mX D0BYTDzRwXD6+HEy8kr/izNft6/MwmPrEGXN5XDiSQMvBiubsrQvszWKVSv/Rk7yatp1mUV83brk nkggObd0D2lG8hcQEk3t2HCyU49yIrV0x4eR4883ns9f/uAx/yYLsbXrYMtPI+F4OezfS/qBUXHE BuZw5Gj55N0IX+o/IKgJKz+fzdye7fn7Md9fPVt65dt+yv38baD9eWq/F+P89kcSGhrqcXlSUhIA tWrV8rie0fu3yn5/o+tnxV8/df3yrELvXy7x83dlPb7PKu/6q+z598bn8vGx/fqDr+XjS3y+1H+1 Zg3I3n2AfCdYw67k0xUzIH0jD3SaXGy9kvKXmur5fFm9enWPy6VquNj3m5cCT/2N5a2y99dZgiOI i43EmXWapJMXfm/XbItj1ZeLmT5kELtTDnMoKfuixgcV21/sjckSSp060aQkJJBhLzlv5Vk/3kTX q098XDdeHhVN24eHlHp7ERG5uLzdx33//fdAuQwAi8ilpngH9syKDkekTMLrPcWkJ44xZMLKig5F xGfqkLs06PopIiIV5YbX/8b4upB0PIXwuvWJslnYvngAwz64cMbi72kAWED3m+Jf5oBIpkwaBsCZ A4uY9u6eCo5ISqPtyAncVM1GQe5hJkyeV9HhiIiIF0YHgP3/dXsRueQ4HXls3ryZ/Iz9FR2KSJml H3qXIRMqOgoR+SPR9VNERCrKgQ8X8+X1VxJdPQz7/l3s2bqez7/xPvgrIlIeHAUpjBkzpqLDkDJa OW0S+im9iEjVoxnAIiIiIpWMZmSIiIhIedEMYAHdb4qIiIhcqjQDWEREREREREREitEAr4iIiIhI 1Weu6ABERERERERERERERERERMQ/NAAsIiIiIiIiIiIiIiIiIlJFlGkAuGX7/syYO59Fi5ewYN5I f8dU4eJuHcvSpUv5c5itokMpZty8hSxe9LZPaQSERFOzZk0ibWUb+zdZwoiNjfIphorka/498Uf9 lKfJ899j0YLXKjoMERERERERERERERERKUelHgWz2Orycu92NKtjZvPG79mwcUd5xFWhgmrGUiOm OikFjooOpZjI2FhiY2v4lEbdthNZsmQJw5qV7Zs/IbHdWDhvrE8xVCRf8++JP+qnPEXXrEFsbExF hyEiIiIiIiIiIiIiIiLlKKA0K0dERRNa/RoCzCYykr9kxScbcDryi60TXrMudWtEE0AOifv3cjqr 4NxCs42YqGo47OmkpEHjK5phyT7Jr/uPYgmqTvUQJ2fyQ2gaZ2fn3tM0adGC/KRdHDyddy4JazhN mjbARg4Hd+8lPd9hKP2iDIfG0rRhLSz2bE4kHuB42rm0z7rq7niOrX+ZfTkFFyxzx2j8ZS2fklSL jsZmMpF/Jpm0PIfH8jFZwoiODCIy1FXlturRxMSYcThzSD6d4TV/JrON6KhqhEQGgclKTIxrINGe k0ZKxrk2YLaG06RZQ6wFaezefZC8Uo6hh8TUoX58DKa8ZHbvPky+s3Tbu6tfo/k3W6sVxp/qNv7I 2o2pHx9O+slE9h464TaWkurHG3f1Z7ZFEFXNisORRXJyliuO6GgsJhOZyafJdpwrKG/xmcw2ml55 BY6U/exJTC+2zFP7rB4VjcXkIDkl0+32EXWb0SjKwq7tuwisHoXFBKdPn/aaPxERERERERERERER EfGPUg0AT1iwmOYhVgDC4gewbNkA8jL+xwMdXK+BvuWlBYz/U52i9R32DL6Y9jRvrz0GgC38BpYt G0fWib+x096O6+NDAPj6yQ58fPMU3uweziliqWmz8POmo1zVKh573jF6tu/FiXwHUdd2YPqEXtQO dsWQn5XEgvFD+Wx7stf0X03IIP6OvswZ/hBhFtfEZ6fTwd97dWTu0fMHQM3cRgqvzfpPqQrysge9 x+9L+byaUHyQtu6dA5g/4kHS969h4LMzATyWT2h8H5Yt+GvR9leNncMyICd5Ne26zPSaP2vIVUyc 2BOzNQZLYDATJ04E4Nj66bzw0UEAQmvfycw3hsChHWSE1qe+dR8jB0xgj8GB9JbPv8XUlsHsPZKE M6QODcOOMW7gGHZk5HvfGDzWr5H8R7V4iNdeeIpaheWXfXwHUweP4b/JOYBrEPmJ8dPp1KpBUTrH tk2nx4g1F8RSUv1446n+zOYYpi54g8sCMhnySDcSaj7Mh/OeJOvYt3Tt9Yrx+ExmBs5+n7aNI3A6 C/h09OPM3+oaoPXWPqe+t5gGlhRWHQoscfvLHxnDzKduxWwykXFkPWdq3kycKZV723b1mj8RERER ERERERERERHxj1INAC98eQpR1a5hzLD2ZJ/6lFfe3I7dnla0PCtpC6+vfJNdhxIoCKrH1JmTuH9g f95eO6FYOiGxXQj96gNefGc3tphGxOTaAbDY4vlw0DM8Mn0mTZtu45kJO5k9qQ33RgaxNCWMGZOf JMZxkJdGzCLJWZMhU0bw5AtT+erhAWSeNwPSXfp9+rcl2H6cof1GcqwgmMYtbiQm7/eDkw6G9elT mmIp4in+xSeyfC6fs2r8uRtzhz9I+t5/MeC5WZzKd2C2xnosn5xTXzBp0iZib+lN/za12LdoBu8f SseRd9xQ3vIyfmTgwB8Jje/LR3ObMXDgkAvWefylZ3CsmkC/d7cAZjq//gFjR15Hj0mbDe3j8GfT 6fDSvqJZv21eWMqwQZfT88Xthrb3VL/e8m8OiODlqb2JC8hkwbQZnKp+EyP6/JXRM56kfc83AWja YxqdWjUgeftKZv9tPabqtbnp5gunKJdUP954q7/MnH08P34F70/rzKhht7K1cRccBenMGDqraPav kfgs1lhqfD+dl765kdF9b+WuAa2Y3/sfgLHj12KNJXrtNF74qhVjB9xetL0lsB4vPdEaCpKZO20m 5mt60K+thbMvCPCaP0cpp3qLiIiIiIiIiIiIiIhIiUo1ALxj82YCq4cCkJ+9m40bNxZb/uvH62l6 zy10bH0PQTYLANbwGwgwQcF54zu5aet47rXFhf/3AwBn5yxu3r+Xq7Ls/Cn9vxzeHQ60oVaghfC6 PYm3WUjeuREi46kFbDqWTZf6DbkvKogVp7I9pg+Qandgttbk0Q5tWbt5Cz99t4JNpX1HsRfu4ve1 fM4ymYKYO+kxzAVJ9H9uFqcLBxe9l88+Nm7cR4M6XQBI2fFfNvpx5qXZGsWDscG88enOwr84WPPu b3Sb9DBgbAA4bdcBmvypNc0b1iLEFkCE3URYo3jA2ACwp/otyPGc/5Aa3agXFMDxTa+w/NsfgU00 7HA7HeMf5PLg+fyanc8j99TF6XQyedzb7MrOB/7Hhm+Kx+Cufrwx0r5Pb1vE1FU3Me6+kdwLbHqz L+tP5RSlYSQ+hyOHKR+uwW7dwei+t2INvaxomZH26XDkMHXFt9itu2DA7UXbh8R2JMRi5uiGV/li /VbMG3+lx33LCSxF/kRERERERERERERERMR3pRoA9sQa2pKF77xCZADs376FxJQsnIDJZCbIbCLD fm6EMy/jJ7fpOAFn4b9d/4DVbCIoNhqAqObdGd28+DZ1CgdYvaW/cMxs4kf04oZ7OnHDPZ0oyD3B O0MH8vmetBLXLwt38furfEzmACxJZwioU5snborl1XWu1/OWpnzKg8Uah9lkIuG82cq5KalYbNcY TqP9xHfpGneEL77dxpnMXGx5dkzmQO8bFvKlfq0RcQCk704p+tsvmXkQE0y9IDO/ZkO9QAtOR2bh 4GrJ3NWPN0brb+vH/4T7BuBw5PDBVwnF1jMSn7MglXwnmJ25rnhNJsD48et++wgAsg6fAcBhT+dk voM65tLlT0RERERERERERERERHzjtwHgao07E2U1c3rniwwYvhaA1z9bSbythMEdp/3Cv3mRc+wk AEdWj2LgW7vOW2LGnptlKP30/V8zqt/XBEfV5fpb72PMgPZ0G34zn/dZVep4Sstf5eNw5NCzf2/G f/g+/zfsBVb8px8HcwoMl4/T6RrIMwWYy5YRpwNMFzYbe14iBU4njYMsbM90DUAG1YzBnnvIULIB Qc3oe1NtBrTrw77CbwY3b/AAD5QiNCP16y7/eamHgRuJaFET2AfAdWGuwec92a762J1dQL2gMP4c ZuXHou8Smzg70A/u68cbI/VntlRj6KtPUJB7BIe1DhNf7EK3IUs5O8fYSHzulKp9liA35SBwA1HX XwFL92ENbUEdmwUKjOdPREREREREREREREREfFfGUcAL5ae6vqUaEvtnGtauQ6v2I7kixOqv5ElP XMihnAJq/WUoHe9uzdXXXEeb+zoy8Y1FRd+M9eaRMcNod+fNNIi2kXryGE6nE6ej9IPRZeG38nEW cCYvlZen/ouAwLpMGX0XYLx8Mg+4Zmg27tWDh9q14/67ry7V7gty9hMQ2IAWUbZif3cUpPHRkQza 9mqNCTCZg+nQrxknNn1mLFvOHBxOJw2jXIOutqjmDLojvlSxGalfd/nPOrGMXzLzib5mBH3a30P7 XhNpGx1E2oEP2F84gPvJp3sAGD1jNA/e3YYHH3mMqTPu/11GSq4fb4zU3y0Dp3NLdBAbZ45j8j8P E9X8Mca1rV+UhqH43PC1fWadXMbOzHwimz3DK6OfY8qbU0qdv7NefP8jlsy50/C+RURERERERERE RERE5By/zQA+c/gtPtzUgs6t7uathXeTsm8tWzLyuC7M5n1jAxz5yQwfMYsJo/vy2NPDAddszpMH dhhOwxTelD4j2hBQ+NravLSDLHxpo5et/MPf5XNqy2w+3H8rnVsNpmfzTSzaaax8Tm+bxeofJ9C6 5R30H3A3Ocmr+cdX2wzvNzf1G+Z/fRfj31tBgN1Owr9GM2jebwAsH/UqV88YxYrFnckIrklA0npG vfGzoXTtuYeY/MkWxryzhI6HTxEens3nnx2h0+2GQzNUv+7y73RkM37o67ww5Rk69H3Ote6eDbww 6oOibQ98/Dzza0+m59038/TQW3A6Hfy27sUSY7mwflJKXO8sb+076uqejLn3MrKOrebl747i3DCB g23epVX/6dzyw2NsOJ1Tqvh+z9f26XTkMO7ZaYx47jEaXXkVv66fy4H7n6W+xW4of+cLCg0lJNdv pyYREREREREREREREZE/FNP+/fudAK1bt/a4YmZmpqEEw2PrEGXN5XDiSQMvni2byJq1iQiCM8mn SE7PLdW2ASER1Iiujs2ZQ1LiccOzh/2lspeP78zE1qmLrSCNhGOppd46KCqemqGQlHC0THXjc/2a LMTWroMtP42E4yXHHxASTe3YcLJTj3Ii1f/l62v9+RKfL+2zWrMGZO8+QL4TrGFX8umKGZC+kQc6 TS62XsW2TxGRS0NoaKjH5UlJSQDUqlXL43pG799ERERE5I9F95siIiIilyZv93Hff/894McZwGel n0gg3d+J/k7K8UQ8z6d0ryArjaNZaX6NpzQqe/n4zsGJBGPf/S1JTvJRDiWXfe8+16/T7jX+gqzT HDp4uuz78MLX+vMlPl/a5+X9X2R8XUg6nkJ43frYzCa2f77sgvUqtn2KiIiIiIiIiIiIiIhUbXrP qoj4xYEPF/Pl9VcSXT0M+/5d7Nm6ns+/2VfRYYmIiIiIiIiIiIiIiPyhaABYRPzi5KbVzNu0uqLD EBERERERERERERER+UMzV3QAIiIiIiIiIiIiIiIiIiLiHxoAFhERERERERERERERERGpIjQALCIi IiIiIiIiIiIiIiJSRegbwCIiIiIiIiIiIlJJmbnqjge57ZomBDkySTzwGxu/XcehjPyiNd58dyE/ j3uGf147jDFt9tFvyPsVGK//NGzbgVbVAktclvrrav75U7Lf9hVS61aGD+lMgxrh5J7+mL5D/u63 tM3WGixeOAOA5F2v8OyL289fqvotwaVUv93fmM/dkcHkZ+3mib5T/JauiIj4RgPAIiIiIiIiIiIi Uind8uwcRrTKYcUna0k3hdH4uod4Ou0XRqw9WrTOqpV/51h6Pmf2ruMfjtMVGK1/BcfEUjMyCIC6 t/2F+slbWL8jFQBLos2v+2o7eTBx62cw9Mtd2O1Zfk0bk4XY2FiGd3qYXVk5xRapfi/9+v1g6NP8 o1Z/lrxe36/pioiIbzQALCIiIiIiIiIiIn5nDa+BPf0kjjJub7ZGMfqehsx//CH+frJw4HDFMqzW 4l+1++c/vqFJsyuoW3CAf311sNiykKgorBmpZAXFc3nDaJL37yLxjGt2qckSTmREAXmBtalrO8Wv R7Jo1uIK0vbu5GhWwblETFbqNW1KpM3Ovl27SS84lyNrRDRRQcW7WO25KZxKzStjrs/Zuegtdhb+ 9wNXteahXcuZOXPnBeuFxNShfnwMprxkdu8+TL7T9XezJZzICAunk1OLrR8VHU1eajIZdichUVGE mM1cG2kj4acETGYzZkxF6wZFVCcg6wwZ+a48W4KqER6YTWqaqww9le/vFeTnk59/ruxUv1Wjfu35 +eTll/UoFxGR8qIBYBEREREREREREfG7GteP5M1egXy1ajWrVq/hYHLpBs1M5mpYzSaCfjcAd/4g YmjtO5n5xhA4tIOM0PrUt+5j5IAJ7MlxDfB1nLWAW777N9YbG5OSF8EV9W283KMXa0/nEFbraZbO acCe39Jp1OJyNm1NJDLYStPaiTz06DgcQGD0n3nhtTHUzjnE4cxArrwMXnn6Ob4/4RqwvKzdIIbd FFsUT1jdevC/MXQfu7WMpVY6LZ9/i6ktg9l7JAlnSB0ahh1j3MAx7MjIxxJYj8XLXmFcx3ZsLXyl sq3azSx7fxQDH25Phr2AOwc/zz1RQdQKDqBg4Egm5jvISf6SYeP/BcCjc97jyrn9GPnDcQDqd3yZ l25dRqc+G7yWrzeqX+8u5foVEZGKpQFgERERERERERER8bukb0cwNKU1d991N68s7kvS1u9Y9Y9V fPufXUWzGD2x5x5k6c/J9Jz7Fo3XrOPnnT+z+fsfOZlrL1rn8ZeewbFqAv3e3QKY6fz6B4wdeR09 Jm0uWifqxgy69B1EvhMefnM53R9vxNrCmZb23AQGj5jCnW8vp1vqHJ4cs5PFX66kZaiVbZn59Hnt eYLWTaPrwk0ANHt4Oi++1Jnvn1wEwL4lE+i/xLWfsLp38d7c3rz91m6/lJ8Rhz+bToeX9hWVZ5sX ljJs0OX0fHE7+Vk7WHAgg94PX8aApfsAaNDpcdL2zGdf4QDqyvHDWAlM/vhLUqeMYEZCRqlj8FS+ nqh+vbuU61dERCqW2fsqIiIiIiIiIiIiIiWzhYUTERFBREQEYUGW85Y42L91HW+/MpYuj/Rk+cYE bn50JJ98MI+/VA80lPaykb2Y/OZK7NFN6DxgHItWvEf75pGA6xXCD8YG88WnZwejHKx59zeirnq4 WBqHP11TNIC2/b+nqHZ5eNGygtwDAKQczyXzQDrgIDHPTu1AC9bQlrSND+Wfm1No1KgRjRo1wrRv I6G1HiHQbCq2j4CgBkx+/VnWTR/MN4mZhsvOV2m7DlD/2tY89EgnunbtShO7ibBG8UXLv5u9kboP 9XZ1Apss9L2vDhvmfO/XGDyVrzeqX88u9foVEZGKoxnAIiIiIiIiIiIiUmb3vjafJ+JCAEj8ZjQD 3vjlgnVCa8QRFxdHbGx1jif9RFqBsW+GOh05bFr9EZtWf4TJHMTdz8xhwLin+LTzq1iscZhNJhLO mzGam5KKxXZNsTTy0869etppB0znDVI7Xds6HA6cBa5RrgInWAFLUH0AWnd9nNbnpbflp20Em03k Olzrm8yB9J4+DevXU5m9NslQvvyl/cR36Rp3hC++3caZzFxseXZM5nOD66m/zeVwwKd0rxvGp6ZO NLUcYtTeVA8plp7H8vVC9evZpV6/IiJScTQALCIiIiIiIiIiImX2Rd8ufFHC3y2BNbjlrja0aXMX 19Y1s+6rVcwZ/Tg7j5wp036cjhw2LP+JZ+8CLf0bAAAgAElEQVS6BQB7XiIFTieNgyxsz3R9AzWo Zgz23ENlzUox9izXq37fmzKBvYWv1C3JrU/P5PaCNXR/+we/7NeogKBm9L2pNgPa9Sl65W/zBg/w wHnrOB15zPlnImP6/YmDlr+QuHoqecbG3gHIdzqxBJ8b8AuMtvkp+gupfouravUrIiIXl14BLSIi IiIiIiIiIn4Xd9tIut7agC2fz6ZTx168Ou+jUg/+Pt3pTmKCXXNYTOZg7uh+PdmnvgXAUZDGR0cy aNurNabC5R36NePEps/8En9+9m98dPAMw3v/BVvhK4HN1khu/L/mRevU+ctzDL8tixEjF3r8rnG/ +UtZOu8xv8R1ltOZg8PppGGUa0aoLao5g+6Iv2C9fR+8R+TVA3m6ZRSLlu0t1T72H82m1l9dM27N tpp0a13T98DPo/p1ryrUr4iIVBzNABYRERERERERERG/S/r3SPqtsXtf0YOarbqwpNdwMlJOQmg0 5pM7mTHqg6Lly0e9ytUzRrFicWcygmsSkLSeUW/87GvoRZYOHU/c5FF8+tFjJKU5iI2rzv4f3mLz d67v0l77WGsCQmzMXv5p0TYnfxrPU5O2FUunVlR1rInJfosLwJ57iMmfbGHMO0voePgU4eHZfP7Z ETrdXny9vPRN/O2khfbmFfxwJq/kxNz4+fUlOOc/y/LF7clypvOvtcdp0tJ/eVD9ulcV6ldERCqO af/+/U6A1q1be1wxM/PifdxeRERE5I8sNDTU4/KkJNd3p2rVquVxPd2/iYiIiEhJLrX7TWtoBLEx kZCdSuKJkr5vaia2Tl1sBWkkHPPv90/PCouJJzoYTh8/TkZp3rELBAQ1YeXns5nbsz1/P5bl99iC ouKpGQpJCUfdzFI1M3b55wTMfIqJP5wodfqWwEjqxIVw/EgSOQ4P02DdMNviWPXlYqYPGcTulMMc Ssoutlz161llr9/oevWJj+vGy6OiafvwkFJvLyIipePtPu77778HNANYREREREREREREKrH8zDQS M9M8rOHgRIJ/vgvrTsapo2SUcdvgmrfzy+Y55TI4CJCTfJRDbiaf1mt5NZdf3Z5WgQl0+++pMqVv z03h0KGUsgfoyOXHH3/k/7o+zp8OLGLau3uKLVb9elbZ6/emzj24qZqNLVt/K3MaIiLif5oBLCIi IlLJXGozMkRERETk0qL7zT+ODiPHcSVn+PbDBWw4VNYhTqmsVL8iIn88mgEsIiIiIiIiIiIi8gf2 ybQpfFLRQUi5Uf2KiIg75ooOQERERERERERERERERERE/EMDwCIiIiIiIiIiIiIiIiIiVYQGgEVE REREREREREREREREqggNAIuIiIiIiIiIiIiIiIiIVBEaABYRERERERERERERERERqSI0ACwiIiIi IiIiIiIiIiIiUkVoAFhEREREREREREREREREpIrQALCIiIiIiIiIiIiIiIiISBWhAWAREfl/9u48 Pqrqjvv4dyaZyZ6QlZCALAFBkCrValUetFV81NIqIiChCKgsD0RERFnCJu6AqIALqChNoQKK8KoV WxQVEbXVItXIvgSSsAlJIAnJTGbm+SMQCEnm3mRmAOPn/ZfOufec3/mdc695zc97BwAAAAAAAAAA NBIUgAEAAAAAAAAAAACgkaAADAAAAAAAAAAAAACNBAVgAAAAAAAAAAAAAGgkKAADAAAAAAAAAAAA QCNBARgAAAAAAAAAAAAAGgkKwAAAAAAAAAAAAADQSFAABgAAAAAAAAAAAIBGggIwAAAAAAAAAAAA ADQSFIABAAAAAAAAAAAAoJGgAAwAAAAAAAAAAAAAjQQFYAAAAAAAAAAAAD+yBEWq+y23KMUedL5D AfALRAEYAAAAAAAAwAXHaktSenq6bu+WVPWZPeq36tf3T+dk/NTf3a5ePTrU+PyWPnere1q0X8Zo 2XO6XpnRzac+rLZ43dxroB4a+4hGDB2k6zol1zim7aBnNHtsp1rPtwRFKikpzqcYAuXO2fM1/oqE gPW/eNkyLV/+jlatWqUk24X5VXmg18dqS1RWVpaysrL04sTOXo/1to9qY5Tfc5H/QF8f3to9rmLl R/1WMx/rLUvDwjcVZ33XReL+esqAOQuUlZWlhfMn+2XMCwn3T7AqAAAAAAAAAC44QSEpGjhwoIaM Hi/byeqJPeZaDfhzr3MyfsFmh+4dOUPXxNirPou7LEOjBtyq7bklfhkjODJBifGhDT4/KKSlnli0 UOm/SVLOti065o7SgLG9axxXsu9HZe88Xmsf4Un9tXD+pAbHEEhRiUmKDeDTk/379FH6wCkKDQ2V 1R8VugAI+PpYgpSUlKSZGcM0dma210PtUfFKjLV7PeZMRvkNdP7PxfVh1J69fJrePNBNU25Pq/8E zlJX/r3FXxfur5WWPDxSD076j5KSYv0y5oWE+yeCz3cAAAAAAAAAABofW1SiXMcPy+1jP1scbTW8 fazmbimo/QCLTS0vvlixdpd2bt6m4xWVI8bFx6u44Kgcbk+1w61BUWrSxKOjR4q9jlt6YLVmfN5X oybfpi/HrpTFGqrRmf9XGxdkaE+563R/tii1a99Gtooibdu2R46zJhwSG6fQE0U65gjRxZe0lUry tHXPkdqnEhSp+NhQFR35SU5PrYdU0+XBqerg+Kf6Tni56vgli04XPKy2GMXF2OTc9IHecxZVH8tq V3xctMJjQyWLTQkJlU+KucqKVFDslDUoSrExQTpytLDaeXHx8XIUHlWxy0SAkqzB0WrXobVCPGU6 kLNLh4qdNY4JiU1Vu4sSVVa4XztyDtZot0WnqkObeB3dtVl5x6qfb5R/o3Zf1TU/s/mr63yj9alS x/63BEUpNqZCjpBUtbD/pC37StX+0ktUtCNb+0srasyjwumU01kzObboZurQJlEFuzfXnoA6xveX 8ITmatUsQRbHUW3bttfUdXFKIK8Ps+sTntBc+WsX6B2HUzaL6hW/5D3/3uI365d+f3U5nXLUsu8b ymINU1xQuY74qc/Gfv9EYFEABgAAAAAAAOB3ib8Zp5cGh+hfqz/U6g/XaM9RR4P6+XDu1xo6+ibN Hb68RltI/JV64rlMpZblaG9JiDpeJM0Y+ZDWHyrT6Plv6eBD/fTSvuqFiOZ/mK5Zt3ygPiPWGI79 xezHNeSduerd8iN91XGCLnP9W73/sbeqPSL193p+zhgp5wcVR7RSK9tOjRsxVdvLThfYrp+1QHd8 slAnbhukmOM/yRbbQt+P6adZudXjCrKn6OG5cxT/7csat2CtqdwM69pU259dUa2o5HaWVf1zVMv+ mja6o0KTWipk+xQNyNxY1WYL/5WmTRskqy1BQSFhmjZtmiTpwOez9MTSPQoKaalFi2docu/btfFk 0cEefa0W/3W8MnreqWJXzSLi2UITumnu/EfkzsnWUXeYWrZrpXkDemvDsdN7ofuw6RrVo522Ze+S JeYixRct0sDxH1W1x3bsqwWD26rAEaNLWtn1zMDB+uxI5RyN8m9mfXzhbX5m8uftfKP1kbzv/8iU kcqa11rbtx5X2qUd9NXGPMWG2XRxap7u6DvZ1P+YEdclXa883k/7s79XSGoL7T0YLJ3eXl7H94fO E1/Rk53DtGNfvjzhzdUm8oAmZ2Tqh1qKYLUJ5PVhZn18jd8o/97iN4v7q3/EtLhUt9x2q265+Xp9 PqKPFh4s9bnPxn7/ROBRAAYAAAAAAADgd/lrH9XDBV11c/ebNWPRMOVv/FSr/7Faa/+9uV5PwR35 fq6ONFusqyJX6cez2oY+N1Gh655V+sKvJEnte87SU0/frfX3vaV/7CjSwBuaSlnFsgbbZHU7VeGW 2t6coty/bzE1dkXZTk1e+INmTx+m7rGX652x6dWeeLvn6QfkXj1Vw1//VpJVd7+wRJPGXaGBj31d rZ8Wf+qlxx4cqG/2n5DFGqFW9vJq7cGhLTXhpdmyfTJT4/76lanYgkIu0kUhwfrKy6tfi3a8rIwM qeMDr2nCWT996ij+RhkZ3yii2TAtfbm9MjLGVGt3lv6gN3YXa0jPizQia6ckqXWfe1S0fYF2miwA tLt/sMK3P6P+47+QJFltcYr2nD43vssYjf5Dih4dMFDZhZVFjZaXN6/WR9zVxeo3bJScHqnnS8s0 4J40ffZ85auKjfJvdn0aytv8zOTP2/lG6yN53/+S5CrP1ehHH9fvX12m/oXzdF9mthb9/X11jrBp U4lREdKqiZPStWPeSGV+mCOrvZleWv6a9IP58X21971Z6vX0zqr7xU1PZGnsqA4a9NT3hucG+vow sz6+xG8m/97iN4v7a8NZg6N05Q0369bbbtWVLYP1xccfa+64e/VfPxR/pcZ//0Tg8RvAAAAAAAAA ABrMHhmlmJgYxcTEKDL0zN8bdGvXxnV6dcYk9btrkJZtyNW1fcfp3SXzdWOTENP9e9wn9OL7+br/ nnbVPrdFdFaPZhH64OsCpaWlKS0tTZadGxSRcpdCrBbtfi9HSdd3lCT1fu1tvTqqkySpR0qE1v/7 sOnx96ycqq2RNyhmz5vK2nb6NatWW5z+lBSmVStO/W6qW2te36q4X/Ws0ceRTXP1zf4TJ+dTot1n FFCDw9I0ZcGLapszV1PqUZywWiMlSaWuwL2T89O5G9TijiGVXyJbgjTstub6Yt560+eX5pUqqvWt uv7KSxQbFiy386gKz3hFcKd7r9KhL+dUFS8kKee73Gp97F2xpqqA9v1/flJ0hyhJxvmvz/o0lNH8 jPJndL43RvtfkirKd0uSCg6Wq2T3cUlu5TlcSg0x/l1Qe9RV6hxp08JP8yRJbsd+vbn1WL3G91XR 5t1q1aWr7rirj9LT09XOZVFkWjNT556L68OIL/Eb5d9fuL82THzn+/WXd5covVtrffPeHPXpPVjP vJyl/+44VO24uv/7aKyx3z8ReDwBDAAAAAAAAKDBbn1uge5NDpck5X08QSPmnP0cmRSRmKzk5GQl JTXRwfz/qqievxO682+vqtniBxX5/raqz4JCW0mSuqbfo65nHPvtfzcpzGpR4ZYPFZY0UNbg9bo9 5As5ruohW1iFLrGXaEJB9SfEvPG4T+jfxx2K3159XkG2ZFktFuWe8XuV5QWFCrJfXqOPY5sLa3x2 SmhcD5W+/akSet2ry6M36Ltj5l6V7XLkqsLjUWpY4L7iLdz6svYGr9CAFpFaYemji4NyNH5H3XM5 2+4lE7XQfp/uGDJe4y5KVF72J3ps4mzlOipzlhJtV/HndT+hKUnOotP58LgkWSqLKEb5r8/6NJTR /IzyZ3S+N0b73yWdTJjkdrvlqaisAlV4JJuJuVltTSVJ+WfEUnKgVIo3N365u54/dluLO6e9rvTk fVq1dpOOlZTL7nDJYjX3P4+ci+vDiC/xG+Xfn7i/1p+z9LDyDxSrRdOT/22L2aKcgpp9m/nvY10a +/0TgUcBGAAAAAAAAECDrRrWT6tq+TwoJFHXdb9JN93UXV1aWLXuX6s1b8I9yt5X/6fYnMUbtTCv iUZ1Saj6zFVaWax48/Gp2lHbK4mLvlC+xuvGS+5V4eqVWnX1U7q6bZBKf1rhl+KUy5GnCo9HbUOD 9P3J1+mGNk2QqzynxrEeL+OV5L+iZ956X+tj5mvyrOHqN3xutdeg1sXtOqZ/HS1Xpx4tpJfNFxVq CU6y1P41scft0LwP8pQ5/NfaE3Sj8j58Uo561O7driKtfGO2Vr4hhcSladyrz2vsrcs0elVljvYd KVNMp3hJu+sdtlH+za6Px125d0Is9X9q1Wh+RvkzOv9kJ7Wuj9H+j6r3bM7qv3yPJKl1aLB+LK3M X5Pm4dIJc+OfYpTfutqDQ9tr2DWpGnH70KpXZndq/Uf90WT85+L68Nbua/xG+fcn7q/1d2znKj06 bJVSOl6tW2+9TTP+8oAO/u9zfbRmjT5e951KTo5R138fzWjs908EHq+ABgAAAAAAAOB3yd3GKf3/ tNa3K+eqT+/Bmjl/aYOKv6esff5Tdbn/0qp/d57YqqV7jumRITfKfvKVs1ZbrK6+odPJI9x6J79E 9428Vh99kKeNf92hB/7fFTr4ybe+TKuKu6JIS/cVq8fgrrJIsljD1Gt4ex366r169ePxVD6htWHe I9oUeaNmDu5i+ty3X1qv5rdl6nft4iRJ1uAIXXvbtfUav6Jsl4JDWuvSOHut7TuXvKnYyzI0snOc 3lq8o159x1/1a8XaKr+CdhTm6rDTLVfZ6Qroj699psQrHtZ1LSNPfmJV525ppvo2yr/Z9ako3639 Dpd6XZlar7mZmZ/kPX9mzq9rfYz3v2+cJZv0eWG57u9xsSTJFtVeQ9o1qff4Rvmtq93jKZPb41Gb uMonZu1xnTTqd+Zen3zKubg+6mr3NX6j/Psb99eGyf/xa73x3FSl3zVIy7/M0zV9xurPTcP90ndj v38i8HgCGAAAAAAAAIDf5X8yTsPXGL/K1qyiHQuU7bhNHc+os2Q9PEXJ08drxdI/K7/IraTkJtr1 5Sv6+tPK3y3c/EGeou4L16qjZbJuylJ0mxf0yZMH/BbTsvEzddns8Vq+6G4VhzVVcP7nGj/nfw3q y+06ppkPvaDFC6er74Y/a6mX15qecvCLmZq+ZKzGPP9XDS08ooj4BB36bqE2fFDZPumNLF0aYVNw eLQirJP09ttOuZ2HlD5gVFUf5YUfa8FH3TXlzeUKdrmU+88JGjV/a1W74/hX+tvhIN1pXa4v6/n6 1ORuA/TslEwdzj8kRaXKun+dHlmbV9VesHm+HlscpTHz/qYh+/NliUpW0Q+zNGrdTlP9G+Xf1Pp4 nMqc/a6eHD1PqyfZlLtmrIbMzpYZRvOTvOfPzPne1sdo//vqxQkv6/lZz+qvN+dLkR5t+F+Brjmj 3dT4Rvmto91VnqPp736rzNf+ot57f1JU1AmtfG+f+lxvPv5zcX3U1e6P+I3ybyZ+s7i/+sZVfkSf v79Yn7+/WDY/PQzb2O+fCDzLrl27PJLUtWtXrweWlJSck4AAAAB+6SIiIry25+fnS5JSUlK8Hsff bwAAAKhNY/x7MzKhmeLDpCMHD6q4Pu8o9gurkpq3kL2iSLkH/FtUMB2BLVIpKfFyFB/RoSPF/u5d k5atVPDz92val4fqfXZQWIySk2LlKT2i/MO1/16l1Rap1JR4lRUe1OGisnrH5z3/gV0f4/l5z5+Z /BjxZf9b7cla/fdFmjVmlLYV7FVOfvV3DFuCItS8ebwKcnNV7Kq970Bef6FxzdQ0QsrP3S9nA9/c G9jrwztf4zeT/0BrzPfX+Jat1Cy5v54ZH68ePcf4vX9fNfb7JxrG6O+49evXS6IADAAAcMFpjF/I AQAA4MLB35swq2Xny9Thsjv1QO9E9e+VoaKK81OA+rn6OeTPGhyrxx8bK0k6tvstPfv69vMcEXDu 9Bg3VddE21VRvldTp88/3+EAppgtAPMKaAAAAAAAAABADVfe9id11FE9PWrmBVm8vND9HPLnrihQ Zmbm+Q4DOC/ef/YxvX++gwAChAIwAAAAAAAAAKCGd599XO+e7yB+xsgfAOB8sZ7vAAAAAAAAAAAA AAAA/kEBGAAAAAAAAAAAAAAaCQrAAAAAAAAAAAAAANBIUAAGAAAAAAAA0GhZgiKVlBTXoHPvnD1f 469IMDyu7aBnNHtspwaNYcRqS1RWVpaysrL04sTO9Tq33wsL9Gjn+Aa3nxLI+QWaL+t/vi1etkzL l7+jVatWKckWmK/yzeQnkOs/YM4CZWVlaeH8yQHpHwB+qSgAAwAAAAAAAGi0wpP6a+H8SQ06Nyox SbH2IMPjSvb9qOydxxs0hiFLkJKSkjQzY5jGzsyu16mRiUmKDan7K2Cj9lMCOr8A82X9z7f+ffoo feAUhYaGymoJzBhm8hPI9V/y8Eg9OOk/SkqKDUj/APBLFXy+AwAAAAAAAACAs1mCohQbUyFHSKpa 2H/Sln2lan/pJSraka39pRVVx4UnNFerZgmyOI5q27a9cnpOnm+1Kz4uWuGxoZLFpoSEyid5XWVF Kih2VhsrJDZV7S5KVFnhfu3IOVgjFlt0qjq0idfRXZuVd+z0uVZbjOJibHJu+kDvOYtqnBceFydb caFKQ5vVen5l3ykn27bogCNEYdYyFZ8xv1MqnE45ne6aY9Qx/zNFJKepXVKo9m7ZrKOOmn3U1e6P +dW5PibXVxabWl58sWLtLu3cvE3HK9ymxje7/lZblNq1byNbRZG2bdujWtLjlTU4Wu06tFaIp0wH cnbp0Fl7S/K+v3wd32j964rPTH6M1t8f83M5nXLUsq8BAL6hAAwAAAAAAADgghOZMlJZ81pr+9bj Sru0g77amKfYMJsuTs3THX0nyy2p88RX9GTnMO3Yly9PeHO1iTygyRmZ+qHYKVv4rzRt2iBZbQkK CgnTtGnTJEkHPp+lJ5buqRqn+7DpGtWjnbZl75Il5iLFFy3SwPEfVbXHduyrBYPbqsARo0ta2fXM wMH67EiZJCmqZX9NG91RoUktFbJ9igZkbqw2h94vvqHrPv1EtqtrPz/+V/308lPpOpj9vazJKdpz 0K7LnDNr9FMXb/M/pdn1GXq9Q7Ryjkfq0tYWzRj2gNYdOmGq3df5eYvPzPqGxF+pJ57LVGpZjvaW hKjjRdKMkQ9p/aEyw/HNrH9E6u/1/JwxUs4PKo5opVa2nRo3Yqq2l9UswNcmNKGb5s5/RO6cbB11 h6llu1aaN6C3NhxzVB3jbX/5Or7R+nuLz0x+jNY/0PMDADQcBWAAAAAAAAAAFyRXea5GP/q4fv/q MvUvnKf7MrO16O/vq3OETZtKnNr73iz1enpn1VOPNz2RpbGjOmjQU9/LUfyNMjK+UUSzYVr6cntl ZIyp0X98lzEa/YcUPTpgoLILK4t2LS9vXu2YuKuL1W/YKDk9Us+XlmnAPWn67PnKVzEX7XhZGRlS xwde04Tk2udQ9/lWPTo1XTteekCZq/fIakvUC8vflOrxlmdv8z8l5vIj6jtwqhxu6dqHXtdDT9yu dUPfNtXu2/yM4zNa36HPTVToumeVvvArSVL7nrP01NN3a/19bxmOb2b973n6AblXT9Xw17+VZNXd LyzRpHFXaOBjX5vKf7v7Byt8+zPqP/4LSZLVFqdoz+niptH+8nV8o/x6i89MfozWP9DzAwA0HL8B DAAAAAAAAOCCVFG+W5JUcLBcJbuPS3Irz+FSakjl7/IWbd6tVl266o67+ig9PV3tXBZFpjUz3X+n e6/SoS/nVBWvJCnnu9xqx+xdsaaqwPb9f35SdIeoes2hrvPtUVfp8ki7Fn5SOZ7beVh/2VJYr77N zH/vilVVr939bsl6RTbvq2CL+faGzs9MfN7W1xbRWT2aReiDrwuUlpamtLQ0WXZuUETKXQo54wdx G7o+Vluc/pQUplUrTlXc3Vrz+lbF/aqn6bmX5pUqqvWtuv7KSxQbFiy386gKz3hFtbf95Y/xjfJr FJ+vAj0/AEDD8QQwAAAAAAAAgAuTxyVJcrvd8lRUVvkqPJLtZPOd015XevI+rVq7ScdKymV3uGSx hpjuPiXaruLPj3s9xll0urjlcUmyBNVrCnWdb7U1lSTlO1xV7ScOlUnx5vs2M/8T+adf9+xy5Mka FK7oYKuOnvzdVaP2hs7PVHxe1jcotJUkqWv6Pep6xnjf/neTwqwWlbs9huN7E2RLltViUW756fyX FxQqyH65qfMlafeSiVpov093DBmvcRclKi/7Ez02cbZyT66pt/3lj/GN8msUn68CPT8AQMNRAAYA AAAAAADwsxMc2l7DrknViNuHaufJ3xTt1PqP+uPZB3rckqX2r0H3HSlTTKd4SbsDGmttXCe2S5I6 hQfrm5O/2RrfKkLyXo+uYnb+ka0jpa8PSZJsoa3ldhaq8IzirlF7Q5lenzq4SrdJkt58fKp2+PKb sXWsv8uRpwqPR21Dg/R9ycnfzG2aIFd5jumu3a4irXxjtla+IYXEpWncq89r7K3LNHpVZR/e9pfZ 8T3uyrmHWKo/lm0mv0bxecuPGf6YHwAgMHgFNAAAAAAAAICfHY+nTG6PR23iKp94tMd10qjf1Xz9 c0XZLgWHtNalcfYabT++9pkSr3hY17WMPPmJVZ27pQUy7CrOEz9q1f4SDb33etksUliz32h46xjT 55udf4s7+ik22CrJqm5Duqpg21ty16O9oczGVxfnia1auueYHhlyo+wnX/lstcXq6hs61SuOutbf XVGkpfuK1WNwV1kkWaxh6jW8vQ599Z7pvuOv+rVibZVfsTsKc3XY6Zar7HT2vO0vs+NXlO/WfodL va5Mrfa5mfwaxectP2b4Y34AgMDgCWAAAAAAAAAAPzuu8hxNf/dbZb72F/Xe+5Oiok5o5Xv71Of6 6seVF36sBR9115Q3lyvY5VLuPydo1PytkqSCzfP12OIojZn3Nw3Zny9LVLKKfpilUet2moph0htZ ujTCpuDwaEVYJ+ntt51yOw8pfcAoU+cvfPhpTX5qjFauHKHCA9la9sUh9Qr37/wPri3VS4vfVGlZ hBIsuzV55Mem232Zn9n4vMl6eIqSp4/XiqV/Vn6RW0nJTbTry1f09afZxief5G39l42fqctmj9fy RXerOKypgvM/1/g5/zPdd3K3AXp2SqYO5x+SolJl3b9Oj6zNq2o32l+mxvc4lTn7XT05ep5WT7Ip d81YDZmdbSq/RvEZ5cdo/f0yPwBAQFh27drlkaSuXbt6PbCkpOScBAQAAPBLFxER4bU9Pz9fkpSS kuL1OP5+AwAAQG0a29+boXHN1DRCys/dL6enYX1YbZFKTYlXWeFBHS4q82+AJgTZguRyuvTbmUt0 T/YUjXhrx+nY7Mla/fdFmjVmlLYV7FXOGb/ZK5mbf3BYnFITQ5W/L7/WY4zafeGP9YlMaKb4MOnI wYMqdvjj+eQzWZXUvIXsFUXKPVBY7yXnIUIAACAASURBVLODwmKUnBQrT+kR5R+u/f3d3veXb+Mb 5ddMfL7yZX7xLVupWXJ/PTM+Xj16jglIfADQmBj9Hbd+/XpJPAEMAAAAAAAA4Ges7Oh+5Rz1rQ+3 s1j7cor9E1A9NOnYR92a7tH/duxXZItf68EO4Xrt6X1nBVeub775Rjek36Nf735Lz76+vVqzmflX nDiqnL0Nb/eFP9an+Kf9CtzquHUot+G/S+s6UaS8nCLvI3jdX76Nb5RfM/H5ypf5XXP3QF0Tbde3 G7cGJjgA+IWiAAwAAAAAAAAA50FFcYHa9fyDbri9iRzH8rVoylCtPVpe7Rh3RYEyMzPPU4RAYL3/ 7GN6/3wHAQCNEAVgAAAAAAAAADgPiveu0XNPrjnfYQAAgEbGer4DAAAAAAAAAAAAAAD4BwVgAAAA AAAAAAAAAGgkKAADAAAAAAAAAAAAQCNBARgAAAAAAAAAAAAAGgkKwAAAAAAAAAAAAADQSFAABgAA AAAAAAAAAIBGggIwAAAAAAAAAAAAADQSFIABAAAAAAAAAAAAoJGgAAwAAAAAAAAAAAAAjQQFYAAA AAAAAAAAAABoJCgAAwAAAAAAAAAAAEAjQQEYAAAAAAAAAAAAABoJCsAAAAAAAAAAAAAA0EhQAAYA AAAAAAAAAACARoICMAAAAAAAAAAAAAA0EhSAAQAAAAAAAAAAAKCRoAAMAAAAAAAAAAAAAI0EBWAA AAAAAAAAAAAAaCQoAAMAAAAAAAAAAABAI0EBGAAAAAAAAAAAAAAaCQrAAAAAAAAAABqlO2fP1/gr EgLW/+Jly7R8+TtatWqVkmzn56tWS1CkkpLiGnSu2fy0HfSMZo/t1KAxjFhticrKylJWVpZenNi5 Xuf2e2GBHu0c3+D2UwI5v0DzZf3Pt3Nx/ZjJTyDXf8CcBcrKytLC+ZMD0j8A1IUCMAAAAAAAAIBG KSoxSbH2oID1379PH6UPnKLQ0FBZLQEbxqvwpP5aOH9Sg841m5+SfT8qe+fxBo1hyBKkpKQkzcwY prEzs+t1amRikmJD6v6K26j9lIDOL8B8Wf/z7VxcP2byE8j1X/LwSD046T9KSooNSP8AUJfg8x0A AAAAAAAAgMbHFpUo1/HDcvvYjzU4Wu06tFaIp0wHcnbpULGzxjEhsalqd1Giygr3a0fOwZqxRKeq Q5t4Hd21WXnHqp9vtUWpXfs2slUUadu2PXKcFbBRe0NZgqIUG1MhR0iqWth/0pZ9pWp/6SUq2pGt /aUVVceFJzRXq2YJsjiOatu2vXJ6Tp5vtSs+LlrhsaGSxaaEhMoneV1lRSo4K0cNzY/VFqO4GJuc mz7Qe86iGueFx8XJVlyo0tBmdebXFp1ysm2LDjhCFGYtU/EZ8zulwumU01kzuXXN/0wRyWlqlxSq vVs262gtC1RXuz/mV+f6mFxfWWxqefHFirW7tHPzNh2vcJsa3+z6+7p/fb3+fB3faP3ris9MfozW 3x/zczmdctSyrwEg0CgAAwAAAAAAAPC7xN+M00uDQ/Sv1R9q9YdrtOeoo959hCZ009z5j8idk62j 7jC1bNdK8wb01oZjp/vqPmy6RvVop23Zu2SJuUjxRYs0cPxHVe2xHftqweC2KnDE6JJWdj0zcLA+ O1ImSYpI/b2enzNGyvlBxRGt1Mq2U+NGTNX2sgpT7b6ITBmprHmttX3rcaVd2kFfbcxTbJhNF6fm 6Y6+k+WW1HniK3qyc5h27MuXJ7y52kQe0OSMTP1Q7JQt/FeaNm2QrLYEBYWEadq0aZKkA5/P0hNL 9/glP1Et+2va6I4KTWqpkO1TNCBzY7U59H7xDV336SeyXV37+fG/6qeXn0rXwezvZU1O0Z6Ddl3m nFmjn7p4m/8pza7P0OsdopVzPFKXtrZoxrAHtO7QCVPtvs7PW3xm1jck/ko98VymUstytLckRB0v kmaMfEjrD5UZjm9m/X3dv75ef76Ob7T+3uIzkx+j9Q/0/AAgkCgAAwAAAAAAAPC7/LWP6uGCrrq5 +82asWiY8jd+qtX/WK21/95c61OctWl3/2CFb39G/cd/IUmy2uIU7TldXInvMkaj/5CiRwcMVHZh ZVGq5eXNq/URd3Wx+g0bJadH6vnSMg24J02fPV/5quF7nn5A7tVTNfz1byVZdfcLSzRp3BUa+NjX ptp95SrP1ehHH9fvX12m/oXzdF9mthb9/X11jrBpU4lTe9+bpV5P76zK101PZGnsqA4a9NT3chR/ o4yMbxTRbJiWvtxeGRljavTva36KdrysjAyp4wOvaUJy7XOo+3yrHp2arh0vPaDM1XtktSXqheVv SvV4y7O3+Z8Sc/kR9R04VQ63dO1Dr+uhJ27XuqFvm2r3bX7G8Rmt79DnJip03bNKX/iVJKl9z1l6 6um7tf6+twzHN7P+vu5fX68/X8c3yq+3+Mzkx2j9Az0/AAgkfgMYAAAAAAAAQIPZI6MUExOjmJgY RYae+Xuybu3auE6vzpikfncN0rINubq27zi9u2S+bmwSYqrv0rxSRbW+VddfeYliw4Lldh5V4Rmv yO1071U69OWcquKMJOV8l1utj70r1lQVkL7/z0+K7hAlqbJY9KekMK1acaoi6daa17cq7lc9TbX7 Q0X5bklSwcFylew+LsmtPIdLqSGVeSzavFutunTVHXf1UXp6utq5LIpMa2a6f1/yY1Zd59ujrtLl kXYt/KRyPLfzsP6ypbBefZuZ/94Vq6peu/vdkvWKbN5XwRbz7Q2dn5n4vK2vLaKzejSL0AdfFygt LU1paWmy7NygiJS7FHLGD+I2dH38sX99uf78Mb5Rfo3i81Wg5wcAgcQTwAAAAAAAAAAa7NbnFuje 5HBJUt7HEzRizo81jolITFZycrKSkproYP5/VWSySLN7yUQttN+nO4aM17iLEpWX/YkemzhbuQ6X JCkl2q7iz4977cNZdLp443FJslQWV4NsybJaLMotd1W1lxcUKsh+ual2v/BU9u12u+WpqKzyVXgk 28nmO6e9rvTkfVq1dpOOlZTL7nDJYjVXPJd8y49ZdZ1vtTWVJOU7TufvxKEyKd5832bmfyL/9Oue XY48WYPCFR1s1dGTv7tq1N7Q+ZmKz8v6BoW2kiR1Tb9HXc8Y79v/blKY1aJyt8dwfG/8sX99uf78 Mb5Rfo3i81Wg5wcAgUQBGAAAAAAAAECDrRrWT6tq+TwoJFHXdb9JN93UXV1aWLXuX6s1b8I9yt53 zHTfbleRVr4xWyvfkELi0jTu1ec19tZlGr0qR5K070iZYjrFS9pd77hdjjxVeDxqGxqk70tO/qZo 0wS5ynNMtZ/icVe+cjbEUo/HSk0IDm2vYdekasTtQ7Xz5G+Kdmr9R/3x7AM9bslS+9e8vuTHV64T 2yVJncKD9c3J32yNbxUhea9HVzE7/8jWkdLXhyRJttDWcjsLVXhGcdeovaFMr08dXKXbJElvPj5V O3z5zdg61t/s/vXGl+vP1+vHTH6N4vOWHzP8MT8AOF94BTQAAAAAAAAAv0vuNk7p/6e1vl05V316 D9bM+UvrVfyVpPirfq1YW+VXmI7CXB12uuUqO128+/G1z5R4xcO6rmXkyU+s6twtzVTf7ooiLd1X rB6Du8oiyWINU6/h7XXoq/dMtZ9SUb5b+x0u9boytV5zM+LxlMnt8ahNXOUTj/a4Thr1u5qvf64o 26XgkNa6NM5eo82X/PjKeeJHrdpfoqH3Xi+bRQpr9hsNbx1j+nyz829xRz/FBlslWdVtSFcVbHtL 7nq0N5TZ+OriPLFVS/cc0yNDbpT95CufrbZYXX1Dp3rFUdf6m92/3vhy/fl6/ZjJr1F83vJjhj/m BwDnC08AAwAAAAAAAPC7/E/Gafga317FmtxtgJ6dkqnD+YekqFRZ96/TI2vzqtoLNs/XY4ujNGbe 3zRkf74sUckq+mGWRq3baar/ZeNn6rLZ47V80d0qDmuq4PzPNX7O/0y3S5I8TmXOfldPjp6n1ZNs yl0zVkNmZ8tXrvIcTX/3W2W+9hf13vuToqJOaOV7+9Tn+urHlRd+rAUfddeUN5cr2OVS7j8naNT8 rZJ8z8+kN7J0aYRNweHRirBO0ttvO+V2HlL6gFGmzl/48NOa/NQYrVw5QoUHsrXsi0PqFe7f+R9c W6qXFr+p0rIIJVh2a/LIj023+zI/s/F5k/XwFCVPH68VS/+s/CK3kpKbaNeXr+jrT83vH2/rb2r/ euHr9efL9WMmv0bxGeXHaP39Mj8AOE8su3bt8khS165dvR5YUlJyTgICAAD4pYuIiPDanp+fL0lK SUnxehx/vwEAAKA2P7e/N4PCYpScFCtP6RHlH679/cFWW6RSU+JVVnhQh4vK6jmCVUnNW8heUaTc A4UNaA+s0Lhmahoh5eful9PTsD58y4/vgmxBcjld+u3MJbone4pGvLXjdGz2ZK3++yLNGjNK2wr2 KueM3+yVzM0/OCxOqYmhyt+XX+sxRu2+8Mf6RCY0U3yYdOTgQRU7/PF88pl827++X3++jW+UXzPx +cqX+cW3bKVmyf31zPh49eg5JiDxAfhlMfo7bv369ZIoAAMAAFxwfm5fyAEAAODnhb83ca406dhH 3Zru0f927Fdki18rc8JgvTawr9YeLa86xhocq8cfGytJOrb7LT37+vbzFS7gdz3GTdU10XZVlO/V 1Onzz3c4ABoBswVgXgENAAAAAAAAAPC7iuICtev5B91wexM5juVr0ZSh1Yq/kuSuKFBmZuZ5ihAI rPeffUzvn+8gAPwiUQAGAAAAAAAAAPhd8d41eu7JNec7DAAAfnGs5zsAAAAAAAAAAAAAAIB/UAAG AAAAAAAAAAAAgEaCAjAAAAAAAAAAAAAANBIUgAEAAAAAAAAAAACgkaAADAAAAAAAAAAAAACNBAVg AAAAAAAAAAAAAGgkKAADAAAAAAAAAAAAQCNBARgAAAAAAAAAAAAAGgkKwAAAAAAAAAAAAADQSFAA BgAAAAAAAAAAAIBGggIwAAAAAAAAAAAAADQSFIABAAAAAAAAAAAAoJGgAAwAAAAAAAAAAAAAjQQF YAAAAAAAAAAAAABoJCgAAwAAAAAAAAAAAEAjQQEYAAAAAAAAAAAAABoJCsAAAAAAAAAAAAAA0EhQ AAYAAAAAAAAAAACARoICMAAAAAAAAAAAAAA0EhSAAQAAAAAAAAAAAKCRoAAMAAAAAAAA4IJjtSUp PT1dt3dLqvrMHvVb9ev7p3MyfurvblevHh1qfH5Ln7vVPS3aL2O07Dldr8zo5lMfVlu8bu41UA+N fUQjhg7SdZ2SaxzTdtAzmj22U63nW4IilZQU51MMgXLn7Pkaf0VCwPpfvGyZli9/R6tWrVKS7cL8 qjzQ62O1JSorK0tZWVl6cWLnBvXhbX8ZuZD339nqmqcv8/em3wsL9Gjn+Aa3nxKo+M6Fn9P+ONu5 uL+Yyc+Fsv71jcMofz+H+/f5RlYAAAAAAAAAXHCCQlI0cOBADRk9XjZL5Wf2mGs14M+9zsn4BZsd unfkDF0TY6/6LO6yDI0acKu255b4ZYzgyAQlxoc2+PygkJZ6YtFCpf8mSTnbtuiYO0oDxvaucVzJ vh+VvfN4rX2EJ/XXwvmTGhxDIEUlJinWHhSw/vv36aP0gVMUGhoqqyVgw/gk4OtjCVJSUpJmZgzT 2JnZDerC2/4yciHvv7PZo+KVGGuv8bkv8/cmMjFJsSF1l3CM2k8JVHznws9pf5ztXNxfzOTnQln/ uq6fuhjl7+dw/z7fgs93AAAAAAAAAAAaH1tUolzHD8vtYz9bHG01vH2s5m4pqP0Ai00tL75YsXaX dm7epuMVlSPGxceruOCoHG5PtcOtQVFq0sSjo0eKvY5bemC1ZnzeV6Mm36Yvx66UxRqq0Zn/VxsX ZGhPuet0f7YotWvfRraKIm3btkeOsyYcEhun0BNFOuYI0cWXtJVK8rR1z5HapxIUqfjYUBUd+UlO T62HVNPlwanq4Pin+k54uer4JYtOF5StthjFxdjk3PSB3nMWVR/Lald8XLTCY0Mli00JCZVP2rrK ilRQ7JQ1KEqxMUE6crSw2nlx8fFyFB5VsctEgJKswdFq16G1QjxlOpCzS4eKnTWOCYlNVbuLElVW uF87cg7WaLdFp6pDm3gd3bVZeceqn2+Uf6N2X9U1P7P5q+t8o/WpUsf+twRFKTamQo6QVLWw/6Qt +0rV/tJLVLQjW/tLK2rMo8LplNNZMzne1s/b/jI6/1zNLzyhuVo1S5DFcVTbtu01dV2dyRbdTB3a JKpg9+aaczMxfyNm4otITlO7pFDt3bJZR2vZwHW1G8UXHhcnW3GhSkOb1Xl91RWf6f1Vx/oZjW92 f/h6fft6f/J1fKP19+X6Mbs/Azk/b9dP5UTq3h/+4Ov1/3NHARgAAAAAAACA3yX+ZpxeGhyif63+ UKs/XKM9Rx0N6ufDuV9r6OibNHf48hptIfFX6onnMpValqO9JSHqeJE0Y+RDWn+oTKPnv6WDD/XT S/uqF3qb/2G6Zt3ygfqMWGM49hezH9eQd+aqd8uP9FXHCbrM9W/1/sfeqvaI1N/r+TljpJwfVBzR Sq1sOzVuxFRtLztdALl+1gLd8clCnbhtkGKO/yRbbAt9P6afZuVWjyvInqKH585R/Lcva9yCtaZy M6xrU21/dkW1L7XdzrKqf45q2V/TRndUaFJLhWyfogGZG6vabOG/0rRpg2S1JSgoJEzTpk2TJB34 fJaeWLpHQSEttWjxDE3ufbs2niwo2KOv1eK/jldGzztV7KpZRDxbaEI3zZ3/iNw52TrqDlPLdq00 b0BvbTh2ei90HzZdo3q007bsXbLEXKT4okUaOP6jqvbYjn21YHBbFThidEkru54ZOFifHamco1H+ zayPL7zNz0z+vJ1vtD6S9/0fmTJSWfNaa/vW40q7tIO+2pin2DCbLk7N0x19J5v6HzOM1s/b/jI6 /1zMr/PEV/Rk5zDt2JcvT3hztYk8oMkZmfqhliJfbeK6pOuVx/tpf/b3Ckltob0Hg6XTl5fh/I2Y ia/Z9Rl6vUO0co5H6tLWFs0Y9oDWHTphqt0ovt4vvqHrPv1Etqtrv768xWcm/97Wz2h8M/vD1+vb 1/uTr+Mbrb+v14+Z/RnI+RldP0b7w1e+Xv+NAQVgAAAAAAAAAH6Xv/ZRPVzQVTd3v1kzFg1T/sZP tfofq7X235vr9RTOke/n6kizxboqcpV+PKtt6HMTFbruWaUv/EqS1L7nLD319N1af99b+seOIg28 oamUVSxrsE1Wt1MVbqntzSnK/fsWU2NXlO3U5IU/aPb0Yeoee7neGZte7Ynie55+QO7VUzX89W8l WXX3C0s0adwVGvjY19X6afGnXnrswYH6Zv8JWawRamUvr9YeHNpSE16aLdsnMzXur1+Zii0o5CJd FBKsr7y82rNox8vKyJA6PvCaJpz108CO4m+UkfGNIpoN09KX2ysjY0y1dmfpD3pjd7GG9LxII7J2 SpJa97lHRdsXaKfJAkC7+wcrfPsz6j/+C0mS1RanaM/pc+O7jNHoP6To0QEDlV1YWXRpeXnzan3E XV2sfsNGyemRer60TAPuSdNnz1e+qtgo/2bXp6G8zc9M/rydb7Q+kvf9L0mu8lyNfvRx/f7VZepf OE/3ZWZr0d/fV+cImzaVGBdBjNbP2/66EOa3971Z6vX0zqr7zU1PZGnsqA4a9NT3hnOXrJo4KV07 5o1U5oc5stqb6aXlr0k/nD7CaP5GzMQXc/kR9R04VQ63dO1Dr+uhJ27XuqFvm2o3E5+368soPqP8 G62ft/HN7A9fr29f70++jm+UX1+vH6P1D+z8jK8fM/vDF75d/40DvwEMAAAAAAAAoMHskVGKiYlR TEyMIkPP/L1Wt3ZtXKdXZ0xSv7sGadmGXF3bd5zeXTJfNzYJMd2/x31CL76fr/vvaVftc1tEZ/Vo FqEPvi5QWlqa0tLSZNm5QREpdynEatHu93KUdH1HSVLv197Wq6M6SZJ6pERo/b8Pmx5/z8qp2hp5 g2L2vKmsbadfo2m1xelPSWFateLU76a6teb1rYr7Vc8afRzZNFff7D9xcj4l2n1GATU4LE1TFryo tjlzNcVk8VeSrNZISVKpy8/vND7Dp3M3qMUdQyq/RLYEadhtzfXFvPWmzy/NK1VU61t1/ZWXKDYs WG7nURWe8YrPTvdepUNfzqkqPkhSzne51frYu2JN1Rf43//nJ0V3iJJknP/6rE9DGc3PKH9G53tj tP8lqaJ8tySp4GC5SnYfl+RWnsOl1BBzv6vsS3wXwvyKNu9Wqy5ddcddfZSenq52Losi05qZGt8e dZU6R9q08NM8SZLbsV9vbj1meu5mmIlv74pVVa/d/W7JekU276tgi/l2I3VdX2bi85Z/M+tnNL43 /ri+fbk/+WN8o/z6ev0ZCeT8jK4fs/vDF75c/40FTwADAAAAAAAAaLBbn1uge5PDJUl5H0/QiDln P6crRSQmKzk5WUlJTXQw/78qqueX2Dv/9qqaLX5Qke9vq/osKLSVJKlr+j3qesax3/53k8KsFhVu +VBhSQNlDV6v20O+kOOqHrKFVegSe4kmFFR/Atcbj/uE/n3cofjt1ecVZEuW1WJR7hm/B1xeUKgg ++U1+ji2ubDGZ6eExvVQ6dufKqHXvbo8eoO+O2buVdkuR64qPB6lhgXuK97CrS9rb/AKDWgRqRWW Pro4KEfjd9Q9l7PtXjJRC+336Y4h4zXuokTlZX+ixybOVq6jMmcp0XYVf173E8yS5Cw6nQ+PS5Kl srhnlP/6rE9DGc3PKH9G53tjtP9d0smESW63W56KyipbhUey+Wl+gTzfH/O7c9rrSk/ep1VrN+lY SbnsDpcsVnP/84nV1lSSlH9GrCUHSqV4U6ebYia+E/mnX/fscuTJGhSu6GCrjp78vWajdiN1XV+m 4vOSf6P1Kz/5JgVv43vjj+vbl/uTP8Y3yq+v15+RQM7P6Poxuz984cv131hQAAYAAAAAAADQYKuG 9dOqWj4PCknUdd1v0k03dVeXFlat+9dqzZtwj7L31f8pOmfxRi3Ma6JRXRKqPnOVVhaD33x8qnbU 9krioi+Ur/G68ZJ7Vbh6pVZd/ZSubhuk0p9W+OXLZZcjTxUej9qGBun7k6/TDW2aIFd5To1jPV7G K8l/Rc+89b7Wx8zX5FnD1W/43Gqvma6L23VM/zpark49Wkgv1yy6m+ZxS5bavyb2uB2a90GeMof/ WnuCblTeh09WPW1ohttVpJVvzNbKN6SQuDSNe/V5jb11mUavqszRviNliukUL2l3vcM2yr/Z9fG4 K/dOiKX+T50Zzc8of0bnn+yk1vUx2v/mnqP0bX5+OT9A8wsOba9h16RqxO1Dq1653an1H/VHU5FL rvI9kqTWocH6sbRy/zRpHi6d8HJSPZiNL7J1pPT1IUmSLbS13M5CFZ5R3DVqD3R8dTG8P5tV1/6o x/23Lr7cn3y9v5jJry/Xjxn+mF9djK4fs/vD6P7sS35/CXgFNAAAAAAAAAC/S+42Tun/p7W+XTlX fXoP1sz5SxtU/D1l7fOfqsv9l1b9u/PEVi3dc0yPDLlR9pOvjLTaYnX1DZ1OHuHWO/klum/ktfro gzxt/OsOPfD/rtDBT771ZVpV3BVFWrqvWD0Gd5VFksUapl7D2+vQV+/Vqx+Pp/IJuA3zHtGmyBs1 c3AX0+e+/dJ6Nb8tU79rFydJsgZH6Nrbrq3X+BVluxQc0lqXxtlrbd+55E3FXpahkZ3j9NbiHfXq O/6qXyvWVvkVtKMwV4edbrnKThenfnztMyVe8bCuaxl58hOrOndLM9W3Uf7Nrk9F+W7td7jU68rU es3NzPwk7/kzc35d62O8/31nJj5fzw/U/DyeMrk9HrWJq3zizx7XSaN+Z/71r86STfq8sFz397hY kmSLaq8h7ZqYPt9f8bW4o59ig62SrOo2pKsKtr0ldz3aAx1fXfy1P+vaH/64//pyf/L1/mImv75c P2b4Y351Mbp+zO4Po/uzL/n9JeAJYAAAAAAAAAB+l//JOA1f459XVUpS0Y4Fynbcpo5nfM+d9fAU JU8frxVL/6z8IreSkpto15ev6OtPK3+3cPMHeYq6L1yrjpbJuilL0W1e0CdPHvBbTMvGz9Rls8dr +aK7VRzWVMH5n2v8nP81qC+365hmPvSCFi+crr4b/qylXl4bfcrBL2Zq+pKxGvP8XzW08Igi4hN0 6LuF2vBBZfukN7J0aYRNweHRirBO0ttvO+V2HlL6gFFVfZQXfqwFH3XXlDeXK9jlUu4/J2jU/K1V 7Y7jX+lvh4N0p3W5vjT5eupTkrsN0LNTMnU4/5AUlSrr/nV6ZG1eVXvB5vl6bHGUxsz7m4bsz5cl KllFP8zSqHU7TfVvlH9T6+NxKnP2u3py9DytnmRT7pqxGjI7W2YYzU/ynj8z53tbH6P97yuj+Iz2 1/mcn6s8R9Pf/VaZr/1Fvff+pKioE1r53j71ud78/F+c8LKen/Ws/npzvhTp0Yb/FeiaM9rNXF++ xndwbaleWvymSssilGDZrckjPzbdfi7i88Yf+9Pb/vD1/uvr/cmX+4uZ/Pp6/Ritv1/m54XR9WNq fxjdn33I7y+BZdeuXR5J6tq1q9cDS0pKzklAAAAAv3QRERFe2/Pz8yVJKSkpXo/j7zcAAADUpjH+ vRmZ0EzxYdKRgwdVXJ93FPuFVUnNW8heUaTcA+Z/H9evEdgilZISL0fxER06Uuzv3jVp2UoFP3+/ pn15qN5nB4XFKDkpVp7SI8o/XPvvTVptkUpNiVdZ4UEdLiqrd3ze8x/Y9TGen/f8mcmPEV/2v9We rNV/X6RZY0ZpW8Fe5eRXf8exr/Gd7/mFxjVT0wgpP3e/nA1487slKELNm8erIDdXxS7/31vMxBcc FqfUxFDl78uv9Rij9kDHZySwHMt65QAAIABJREFU92ffrm/f70++jW+UX39cP0YCOT8z108g94c/ 9u+FyOjvuPXr10uiAAwAAHDBaYxfyAEAAODCwd+bMKtl58vU4bI79UDvRPXvlaGiinNdXP95+znk zxocq8cfGytJOrb7LT37+vbzHBEAwBuzBWBeAQ0AAAAAAAAAqOHK2/6kjjqqp0fNvCCLlxe6n0P+ 3BUFyszMPN9hAAD8jAIwAAAAAAAAAKCGd599XO+e7yB+xsgfAOB8sZ7vAAAAAAAAAAAAAAAA/kEB GAAAAAAAAAAAAAAaCQrAAAAAAAAAAFALS1Ckut9yi1LsQec7FAAAANMoAAMAAAAAAAC4YFlt8bq5 10A9NPYRjRg6SNd1Sq5xTNtBz2j22E61nm8JilRSUlyd/Xtr97iKlR/1W818rLcsDQvfVJze4q+L 1Zak9PR03d4tqeoze9Rv1a/vn3yO04zU392uXj061Pj8lj53q3tatF/GaNlzul6Z0c3rMQPmLFBW VpYWzp/slzEBAGgMKAADAAAAAAAAuCAFhbTUE4sWKv03ScrZtkXH3FEaMLZ3jeNK9v2o7J3Ha+0j PKm/Fs6fVOcYRu3Zy6fpzQPdNOX2tPpP4Cz2qHglxtprfO4t/roEhaRo4MCBGjJ6vGwnq9P2mGs1 4M+9fI7TjILNDt07coauiTk9n7jLMjRqwK3anlvilzGCIxOUGB/q9ZglD4/Ug5P+o6SkWL+MCQBA YxB8vgMAAAAAAAAAgNp0eXCqOjj+qb4TXpbTU/nZkkWnC4JWW4ziYmxybvpA7zmLqp1rsdoVHxet 8NhQyWJTQkKCJMlVVqSCYqdh+ynhCc2Vv3aB3nE4ZbOoKg6zbNHN1KFNogp2b67R5i1+s7Y42mp4 +1jN3VJQ+wEWm1pefLFi7S7t3LxNxyvckqS4+HgVFxyVw119QtagKDVp4tHRI8Vexy09sFozPu+r UZNv05djV8piDdXozP+rjQsytKfcdcYco9SufRvZKoq0bdseOdzV+wmJjVPoiSIdc4To4kvaSiV5 2rrnSO1TCYpUfGyoio78VLUOLqdTDqe71uMBAPilogAMAAAAAAAA4II0rGtTbX92RbWiq9tZVvXP US37a9rojgpNaqmQ7VM0IHNjVZst/FeaNm2QrLYEBYWEadq0aZKkA5/P0hNL9xi2S1Lnia/oyc5h 2rEvX57w5moTeUCTMzL1wxkFYm/iuqTrlcf7aX/29wpJbaG9B4Ol0+F7jd+sD+d+raGjb9Lc4ctr tIXEX6knnstUalmO9paEqONF0oyRD2n9oTKNnv+WDj7UTy/tq17obf6H6Zp1ywfqM2KN4dhfzH5c Q96Zq94tP9JXHSfoMte/1fsfe6vaI1J/r+fnjJFyflBxRCu1su3UuBFTtb2souqY62ct0B2fLNSJ 2wYp5vhPssW20Pdj+mnW/2fvzuNsqh8/jr/unbl3dmNmzGCQnUJJfdu1fL+tv9KCUsiWLCFkCRFR URGVpRAlUSiljfaFpL6VryRLdjNjnY3Z78y9vz9mjBnm3nPu3DsGvZ+Ph8ejzvLZzufzmXPP5/M5 J6F0ugLs8Qyd/goxv81ixJxvvC0mERGRfxQNAIuIiIiIiIiIiMgZJyDoPM4LCmSdh1cjp2+fxYAB 0OzRuYw66dPAeRm/MmDAr4TV7MOSWU0ZMGCIV/sB9n4whfaTdhQPQN/0zEKGDTyf7hM3msiBlSfG dGL7jP6MXrUHq70mM5fNhT/Npd+s5I3TSa65iMvDV/DXSft6v/gEwT88T6f56wBo2nYKEyc9wJqe b/Lp9nS63VAdFmZgDbRhdTrId0KjW+JJ+HiLqbjzc3bw5Pw/mTqhDzdHXcx7wzqVWlHcddKjOFeO o+/rvwFWHnhpMWNGXEq38T+XCqfOXe0ZP6gbv+7PxmINo549t9T+wOC6jJo5Fdu3kxnx9jqvy0hE ROSfRt8AFhERERERERERkTOO1RoOQFZB5b3eN33zLuq1as0993agU6dONC6wEN6wpqlz7RGXc2G4 jfnfJQLgzNvPG1uP+j2NLmc2L3+SxMNdG5fabgu7kDY1w/js51QaNmxIw4YNsexYS1j8vQRZLez6 YA9x1zcD4L657/LawOYAtIkPY80vh03Hv/vDcWwNv4HI3W+wcNuJ11hbbdHcFRfCiuWbirY4+fL1 rURf1PaUMJI3TOfX/dlF+clkV4kVwoEhDRk752Ua7ZnOWA3+ioiImKIVwCIiIiIiIiIiInLGKchL IN/lolZI5T3CbPfU63SqsY8V32zgaGYu9rwCLNYgU+dabdUBSMo78T3czANZEOP/dO545zVqLhpE +CfbircFBNcDoHWnrrQucexvv28gxGohbcsqQuK6YQ1cw91BP5J3eRtsIflcYM9kVGrpFbieuJzZ /HIsj5i/S68/DrDVwGqxkFDie8C5qWkE2C8+JYyjm9Pchh8c3Yasd7+jWvuHuLjKWv53NM902kRE RP6pNAAsIiIiIiIiIiIiZxxnwVG+SMmleZs6MOvklxt7weUEi4fHoG72BwY3pc9Vteh3d292FK1I bV7/Tu40GW1B7m4A6gcH8ldW4TeDq9YOhWxvEm+OI2M98xOrMrBVtRPxZxUOBr/x9Di2l1hRWyz9 R5IYyY0XPETayg9ZccVErmgUQNaR5eSWeI1zeRXkJZLvctEoOICNmYX5D65ejYLcPacc6/IQX2bS qzz35iesiZzNk1P60rHv9FKvmRYREZFT6RXQIiIiIiIiIiIickZ6d+Yaat8+mn83jgbAGhjG1bdf 7VUY+Tk7CQyqT4tou1f7Xa4cnC4XDaILV/zao5sz8N/mXv8M4MjcwOq0XB5u0wQAW0RTejWu6lXa vfHNtO9o9XCLE/Fnb2XJ7qMM73UjdqsFAKstiituaF50hJP3kjLp2f9qvvoskfVvb+fRRy7l4Le/ +SU9zvx0luzLoE2P1lgAizWE9n2bcmjdB16F43IVrvhdO2M4G8JvZHKPVn5Jn4iIyLlMK4BFRERE RERERETkjHTwx8lMWDyMIdPepndaMmEx1Tj0v/ms/axw/5h5C2kRZiMwtAph1jG8+64Dp+MQnboM LA4jN+1r5nx1M2PfWEZgQQEJn49i4OythvsLcvcw4f3fGD33Le7be4SIiGw+/GAfHa43n/6XR81i 2pTnefuWJAh3sfaPVK4qsd9M+s1K3z6HTXm306zEOPbCoWOpMWEky5c8SFK6k7gaVdn506v8/F3h d3k3f5ZIRM9QVqTkYN2wkCoNXuLbZw94Hbc7S0dOpuXUkSxb8AAZIdUJTFrNyFf+KFdYzoKjTH7s JRbNn8D9ax9kiYfXRouIiPzTWXbu3OkCaN26tccDMzMzT0uCRERERP7pwsLCPO5PSkoCID4+3uNx un8TERERkbKcjfebVls48fEx5GUkcyg547TFCxAcXZPqYZCUsB9HOd48bAkIo3btGFITEsgocPo/ gSaEV6tJTAgkHzxIRt7pToOVuNp1sOenk3DA/4O2MXXrUbNGZ54bGUObtkP8Hr6IiMiZxOg+bs2a NYBWAIuIiIiIiIiIiMgZzunIIGHP6R34PS4nZT97Usp/vqsgk317KndyZsaR/VRO6QE4OZRw6nd/ /eWqB7pxVRU7v63fanywiIjIP4QGgEVERERERERERETkrPTJ8+P5pLITISIicoaxVnYCRERERERE RERERERERETEPzQALCIiIiIiIiIiIiIiIiJyjtAAsIiIiIiIiIiIiIiIiIjIOUIDwCIiIiIiIiIi IiIiIiIi5wgNAIuIiIiIiIiIiIiIiIiInCM0ACwiIiIiIiIiIiIiIiIico7QALCIiIiIiIiIiIiI iIiIyDlCA8AiIiIiIiIiIiIiIiIiIucIDQCLiIiIiIiIiIiIiIiIiJwjNAAsIiIiIiIiIiIiIiIi InKO0ACwiIiIiIiIiIiIiIiIiMg5QgPAIiIiIiIiIiIiIiIiIiLnCA0Ai4iIiIiIiIiIiIiIiIic IzQALCIiIiIiIiIiIiIiIiJyjtAAsIiIiIiIiIiIiIiIiIjIOUIDwCIiIiIiIiIiIiIiIiIi5wgN AIuIiIiIiIiIiIiIiIiInCM0ACwiIiIiIiIiIiIiIiIico4IPP4fSUlJlZkOERERESmSnp5u6jjd v4mIiIhIeZi93xQRERGRs5NWAIuIiIiIiIiIiIiIiIiInCOKVwDHx8d7PDAzM7PCEyMiIiIiEBYW 5nH/8ZW/un8TERERkfIwe78pIiIiImenQONDRERERERERERERE4vqy2OB+67icyEr1jxwyEA7BFX 0v72ON5Z8tFpSUPzu++lZZi91LaCvAMsee8bv4S/aOlS7BYrdruNXve25ZDD6Zdw3WnU/Tn6VVvI kCmbTB1fMv/5OZkk7NjA2g27KzCFZ6bQ+GsZPuQB6sdGkJv8Hn2GVHz9O17/V7+3hH15BUXboml3 762k/vopX/99tPhYs9fV2+tf0Sq6fbWbOpsmi0bz3G9H/BJeZbrmsRe5ZPUkpv9amBcz/aNR+Xpq 32bqn5nr16z7RNoefo1nP91bZr6stlgWzJ8KQMrmFxg0caPp/FU09f/q/892egW0iIiIiIiIiIiI nHECguLp1q0bvQaPxGYp3GaPvJouD7Y/bWm4qH0n2l/flOrVqxf/i4uL8lv4nTt0oFO3sQQHB2O1 +C1Yt+wRMcRG2Y0PLHJR+060vbYBUVFR1GrYkkHPvsqs4bdVYArPTG0mDKbGxsUMfWwwI8Z/cVri PF7/64YEAGAJCKf38zNp2zKYH3ccK3Vs5r6/2HTStrJ4e/0rWkW3r4jYOKLsAX4Lr7IERd3AiOvs zPs9uXibmf7RqHw9tW8z9c/M9ft72btc3vsp4mxuhqIsAcTFxTF5QB+GTT4xMKn+3//U///zaAWw iIiIiIiIiIiI+J0tIpaCY4fxdU3TlrxG9G0axfQtqWUfYLFRt0kTouwF7Ni8jWP5hTFGx8SQkZpC ntNV6nBrQARVq7pISc4wFf+R9YuZ9trWMveFRkdjy0gjK7gm5zeIIWXnZhKPOkodY6sSX7RvCwfy ggix5pCRlW8qboDQarWpV7MalrwUtm3bi6NEdszFX5PzG8SSumuz6ThLStmwjJlF+Y/+7BHemfoY l7z6Nb9nFMYTFBVNcHY6R/OCaHJBI8hMZOvuEwNVVlsEjZs2wJafzrZtu8k7qUIYlY9R+O7KxxIQ QVRkPnlBtahjP8KWfVk0bXEB6ds3sd9k+YdGRxNqtdIqyk7C7wlYrFaslB6pMcqfUfrNsFiD6DLh NW6wrKbX6AXkFNVpqy2S6Egbjg2f8YEjvcxzPV1/M/XHXfuyBkQQFRlAckpaqcOjY2LIS0sho6B0 u3PHXfuyWIOJiQ4n5ciR4j7EYg0lJjq01DaAoKhaND4vlpy0/Wzfc7CMMqjlNn++ty+D9u2m/LzR 6tEe7F81iSznqWVq1D966r/AffveWOIYd/XPTPiOzD94Y384w26sxeOr9rk9Lt/hwFHGClj1/+r/ K6v/PxdoAFhERERERERERET8LvayEczsEcQXK1exctWX7E7JK1c4q6b/TO/BNzG977JT9gXF/Itn XhxNrZw97M0Motl58EL/x1hzKIfBs9/k4GMdmbmv9IP+2ndMYMptn9Gh35flSk9J9708j2u++xbb FY1IzYvkgnp2nuvWg++TcwCIuagjsyZ24uCmjVhrxLP7oJ2Wjsl0Gb3eVPgXPvEqz14YwvZ9SbhC a9Mg/ABPDhjNn0UP343ij27ViVef7sj+TRsJqlWHvQcDIaf8+T22ew1wD01CAosHAK6fMod7vp1P 9u3diTx2BFtUHTYO6ciUhAzCav2Haa8MgT1/khFWj3q2HYzoN46/c/JNl4+n8D2VT3h8fxbOqM/f W4/RsMX5rFufSFSIjSa1Ernn/idNTUz4z+AnuC06mPiQQPIHjOAph5OclI8ZNvZzAMP8GaXfnEDu fWIGd0avp8/A1zhWcCLlEXU789TgZgTH1SXo77Gn1Cuj629Ufzy1r4CguixY9AJP3nc364vqgr3K 1Sx6eyQD2rYjo8D3QZYuz80m7ovRjFq6BYC7xs6mreNduj/7afExN/eZwMA2jdm2aSeWyPOISV9A t5FfFe+PanY/c3qUnT9f25dR/fVUfqZZbPS9LJYve5b9CmVP/aO3SrbvjdnHt7qvf2b9vHg33Xrd AqvmeX2u+n/1/5XV/58LNAAsIiIiIiIiIiIifpf0zeMMTW3NLTffwgsL+pC0/jtWfrqSb37ZXGoV k5HkjdNJrrmIy8NX8NdJ+3q/+ATBPzxPp/nrAGjadgoTJz3Amp5v8un2dLrdUB0WZmANtGF1Osh3 QqNb4kn4eIvp+GMuvp+BA0+scsxN+ZzZb59YERZ9RQYd+wzE4YK2M5fSpWtDvp+2CbDy+LhObJ/5 KKNX7sZqi+WlZW+AF59f3fvBFNpP2lFcXjc9s5BhA8+n+8QT6/M8xf/EmE5sn9Gf0av2YLXXZOay ufCn+fhLs9Lyzq44CzJYk156ML/OXe0ZP6gbv+7PxmINo549F4Cukx7FuXIcfV//DbDywEuLGTPi UrqN/9mr8nEXvlH5FOQmMPjxp/nPa0vpnDaDnqM3seDjT7gwzMaGTMepEZ3kk7HD+ASY8N7HpD39 OFNPGrT1nD/j9Jtxw6BXuPaaWkzp2I+Uk1ZIpm+fxYAB0OzRuYyqcfKZ5q6/+/rjuX05sv5k3q4M erU9j34LdwBQv0NX0v+ew44c84O/7tqXy5nDzMcmMOftSdy+rgt/NHmEh5ruokeXlSfObTWEwXfE 83iXbmxKK6yTdS+ubTp/vrYvo/rrqfzMsle5lpr2AFamlj1y56l/BOP+64ST2nfRm4I91T+z4af+ 9V+Cqz2I3Tr/lBW5RtT/q/+vrP7/XKBvAIuIiIiIiIiIiEi52cMjiIyMJDIykvDgkt/bdLJz/Q+8 9sIYOt7bnaVrE7j6/hG8v3g2N1YNMh2+y5nNy58k8XDXxqW228IupE3NMD77OZWGDRvSsGFDLDvW EhZ/L0FWC7s+2EPc9c0AuG/uu7w2sDkAbeLDWPPLYdPx52elceTIkRP/0koPxOxd/mXxA+iN/z1C lfMjCssl4nIuDrcz/9uEwtJwHOatLaVfl2skffMu6rVqzT33dqBTp040LrAQ3rCm6fgvDLcx/7vE wvjz9vPG1qNexQ9Q6+axvPnmm7z97geM71iD96cNJSGvoNQxyRum8+v+wiWDLmcmu3LysdqiuSsu hBXLjz/Rd/Ll61uJvqhtcfrMlk9Z4Zspn/zcXQCkHswlc9cxwEliXgG1gnz/LqxR/syk34xLGyWw +M9keo67H28+E2r2+rurP0btC+C76Wupc0+vwkEGSwB9bq/NjzPWeJFKz+0r79gGhj3zKY9MHc8L A//F1EGTSCnxCuXmD13OoZ9eKR78BdjzvwRT+QPf25en+mum/Mywh56P05Fc5uAruO8fjzPqv4za t1H9MwofoCBnO1ZrMA2CvF+PqP5f/f+Z2P+fLbQCWERERERERERERMrt/16cw0M1QgFI/HoU/V45 dR1aWGwNatSoQVxcVQ4m/U66l9/B3PHOa9RcNIjwT7YVbwsIrgdA605daV3i2N9+30CI1ULallWE xHXDGriGu4N+JO/yNthC8rnAnsmoVPMrMNO3fc7ixR6+cVliNZSrALAUPly22qoDkFTiYXn2oRyI MR017Z56nU419rHimw0czczFnleAxVp68Nyb+DMPZHkVP8DhX+bzzHu7yc/L4mDigVLf/zzu6OZT H9wH2GpgtVhIyD0Rf25qGgH2i92mz135lBU+mCgfV2HYTqcTV35huvNdYDPIsxlG+TOTfjPmPvYc X2bV48alUxlz2zc87eE7qiWZvf7u6o9R+8p1ukjbOou9gcvpUiec5ZYONAnYw8jtXg5yGbSvlPXL 2BmwiNqHl/PDoexS++Kr2MlYfcxj+O7yB/5vXyXrr5nyM8NFAVg8D+OU1T8eZ1S+Ru3bqP4ZhV+o sMzyXd6t/j1O/b/6/7JUZv9/ttAAsIiIiIiIiIiIiJTbij4dWVHG9oCgWK65+SZuuulmWtWx8sMX K5kxqiub9nm/CsmRsZ75iVUZ2Kpa8baCrMLBgDeeHsf2slZUpv9IEiO58YKHSFv5ISuumMgVjQLI OrLc9OCLLwqy/wageWggvxZ9LzGmXhicNF7lchamPchSen1dYHBT+lxVi3539y5+pW7z+ndyp9n4 c3cDUD84kL+yCuOvWjsUsj2cVIa89AR27Njh8RhXGeVZkJdIvstFo+AANha9bjO4ejUKcvcU7jdZ Pu7C97V8fGWUv5LKSr9ZR/OdOLK3MPLlNcwb/Dyt1nZn/VHj72n7ev0N2xfgcuYx47NERve9hN0B N5K46lny/PxxzZuHTiN6wzx+qdeV8e1WM3b538X79iXnENk8Btjldbg+ty+D+mum/MxwZGzAGtiO WvYAEk9aeXnimFP7R7OM2nd5619JttBmOPPT2ZVbvnJQ/6/+/2SV3f+fLfQKaBEREREREREREfG7 GteNoNO19fntw+l0uK8Hk2cvKdfg73HfTPuOVg+3KP5/R/ZWluw+yvBeN2IveqWq1RbFFTc0LzrC yXtJmfTsfzVffZbI+re38+gjl3Lw29+8ijfAHl78iuvj/8xwZP/Fiv2Z9H7oemwWCKl5GX3rn3pu fu4u9ucV0P5ftUptd7lycLpcNIguXNFkj27OwH/XPOV8t/FnbmB1Wi4Pt2kCgC2iKb0aVzV9vq+c +eks2ZdBmx6tsQAWawjt+zbl0LoPCtNnsnzc8bV8fGWUP39L+moSS3YFMur5HgSYeIOwr9ffuH0V 2rH4DaJaDqD/hdG8uWi7+QwV8dS+at84lAGXHmLosyt4acjzNO4xmTb1TrzC+a+53xN76VCuqRte tMXKhdc1NBWvz+3LoP6aLT8jecd+YVu2g7uqh3g87uT+8bjy9l8nc1f/zIRf7fJWZB14jwIfxl3V /6v/L6my+/+zhVYAi4iIiIiIiIiIiN8lfTuCvl+WvWKtPNK3z2FT3u00s5/YtnDoWGpMGMnyJQ+S lO4krkZVdv70Kj9/V/jdwc2fJRLRM5QVKTlYNyykSoOX+PbZA17FW+eOiSy9o/S2W2+91dS584dO 4smJQ/jww36kHdjE0h8P0T70pINcDkZPfZ9nB89g5RgbCV8Oo9fUTRTk7mHC+78xeu5b3Lf3CBER 2Xz4wT46XG8+7S+PmsW0Kc/z9i1JEO5i7R+pXGX+dJ8tHTmZllNHsmzBA2SEVCcwaTUjX/mjeL+p 8nHDH+XjK6P8+ZeLxaMncdviiTx11xc8uWIXY+YtpEWYjcDQKoRZx/Duuw6cjkN06jIQ8P36G7Uv gLxj63jncADtrMv4ycuVoeC+fQXHXcuLg69lVv8HOeRwQso6hrz4PXNeHM+GjsPZl1dA6ubZjF8U wZAZ79BrfxKWiBqk/zmFgT94XrEI/qk/RvXXTPkZc/La1/sZ3qUpr050P3hZVv8IvvVfpZ1a/8yG f3OHumyYNakccZ6g/l/9f0lnQv9/NrDs3LnTBdC6dWuPB2ZmZp6WBImIiIj804WFhXncn5SUBEB8 fLzH43T/JiIiIiJlORfvN8Or1SQmBJIPHiTD3++g9YMAWwAFjgKunLyYrpvG0u9N8yslg6NrUj0M khL24yjHCjpLQBi1a8eQmpBARkFllI2VuNp1sOenk3Cg7G85Vmb5+M44f5XJH9ffc/uyMmbphwRO e5infjrke4LLwWoLp1Z8DDlpBzmcnuPVuf6oP0b119f+yRZ2Ie8sHk6/e7sXDoafRYJjbmLZ3Nt5 4N6hZJbxKl+rvQYrP17AlCED2Za6lz1JXr6jGPX/nqj/PzcZ3cetWbMG0ApgEREREREREREROctl HNlPRmUnogxVm3Xguuq7+WP7fsLrXMKg80OZO2mfV2HkpOxnT0r50+AqyGTfnsocrHdyKOHU7+LC mVE+vnOfvzOBP66/u/ZV98KWnN+yHVcGJdD5v0d8isMXTkcG+/aUrwfwpf6Yrb++9k+OzI08+e4f 3Nkiinnrk30I6fSre+vFfDhxYpmDvwA4c/n111+5oVNXLtn1Js+//nfZx3mg/t899f//bBoAFhER EREREREREakA+RmpNG57BzfcXZW8o0ksGNubb1JyKztZZwyVz9ntX7ffRTNSmDRwMun5Z97Ky4p2 Ouvv5nemsLlCQq5YWxdPYauH/c78VEaPHn3a0nM6qX/zTOVT8fQKaBEREZEzzLn4Sj4REREROXPo flNERETk7GT2FdDW05EYERERERERERERERERERGpeBoAFhERERERERERERERERE5R2gAWERERERE RERERERERETkHKEBYBERERERERERETmjhcZfy7gpM3lzwVvMnnrXaYnTaoujU6dO1LEHlNgWzb0d O3Jj4yqljm3U/TmmDmtuGKbZ406X5nffS6dOnUr9u//e//gt/HZTZzPy0mp+C68yXfPYizz6r9J5 KVl+HdrdzdUt61VO4qRcFi1dyrJl77FixQribBUxVGLlon/fw4DHhjNsUD863nUjdcNtFRBP2Tq+ NIfHL4wxPO509Uvu4vEl/vpX3c6Ax4bz2ICHad00ytckVgh/la+34fhSv4///bv7urjibfaIK+l4 /+n5+yv+oQFgEREREREREREROaO1mTCYGhsXM/SxwYwY/8VpiTMgKJ5u3bpRN6RwANgSEE7v52fS tmUwP+44VurYzH1/semkbWWxR8QQG2WvkPSWx0XtO9H++qZUr169+F9cnP8GUSJi44gqMYB+tgqK uoER19mZ93tyqe0Xte9E22sbEBUVRa2GLRn07KvMGn5bJaVSvNW5Qwc6dRtLcHAwVov/w79m0Aye 7n0dafu28XdSOvGX3kMTF5hAAAAgAElEQVT/0zghIjw2jqgg4yEgs/2Xr9z1f+WNP/6m4cwc3Yns xG0kZkYxYtoC7jkv3B9J9St/la+3fz98qd/H//71GjwSW9G59sir6fJge+8CkkoV6O0J4fEDefX5 ywDIz95Fj95ji/ddfN1tXHxBA6pFhuM4doT1az7jh40HzAVsCaDFFdfTqnkTqsdEkJN2gJ+//oj/ 7kgv42Art3XoQHSglf8uX8rfOfle5qLs8632eBbMe774qCl9H2JDpsNUiA3atOfKKkFl7kvbsorP fk+h3dTZNFk0mud+O+Jler0TGn8tw4c8QP3YCHKT36PPkI8qNL7Trca1Y5jcuykvP9KTXzPyKjs5 xeq2ncDIq77ikcd/KHcYloBwYmPsHDqU4seUCRTOeLJbrNjtNnrd25ZDDqfH4xt1f45+1RYyZMom U+GfrvYt7p0J7ceo//W2XpVXeeIxU36nK/0iIiIi4jurLZYF86cCkLL5BQZN3FjJKTrVmXx/WfOS m2l7/cUE5R5h9cdL+XVfpttjI8+/hZsb7+W9j7eYPr/LK3O4JSoER9Y2HurzdIXl41wQGh1NqNVK qyg7Cb8nYLFasVL6SbbVFkHjpg2w5aezbdtu8k76yR8UFU1wdjpH84JockEjyExk6+7SA3lGLNYg ukx4jRssq+k1egE5TldR3JFER9pwbPiMDxxlPccEW5WanN8gltRdm8vMny0jjazgmpzfIIaUnZtJ PHrS80iLjbpNmhBlL2DH5m0cyy/MoDUggqjIAJJT0kodHh0TQ15aChkFLlN5O7J+MdNe21pGnoOJ iQ4n5cgRnMXbQomJDi21DSAoqhaNz4slJ20/2/ccLKMMarnNX2i12tSrWQ1LXgrbtu3FUSLZZsrH ViW+aN8WDuQFEWLNISOrxPNiN+XnjVaP9mD/qklkOU8t05QNy5hZVH7Rnz3CO1Mf45JXv+b3DEdR 2Xiuf0b11yh/RuG7K19LQARRkfnkBdWijv0IW/Zl0bTFBaRv38T+rHzD/UbhmxEWE03g0TTyY+rT ODaIXVu2kH7yMzuD6+eP9u2J0fVxf140o25rwJyu9/DR4ZzCjcsWYStjJaan9lPe61dSWI2GNI4L Zu+WzaSUyICZ/qu8+T/OU/9nJn5PBj5yHZtn9WPeZ/sASKx/IYNG3sCH/T4pPsaofhi1L1/6J1/z V5g+9+UH+KV/82RLXiP6No1i+pZUr+KPjokhIzWFvJP6TGtABFWrukhJzvBrOqVsXg8AW21ViYuL Y8fCl1m0c3+pff2HPcp5QYE4chzYgm3839330XJCN6avPWQi3FheHD+C/JxUDiQ7iI+/kTZtO7Bg 8IO8s7V044i/cSSP9bwegMxVy70eAHZ3vis/lTlz5lD7//rS/dJqBHkxLSKkWhzVo4IBqHPdjdRL +Y3VfxbefAUkFs7KOF0z3tpMGEyN1VMZ+vFmCgqyKjy+0y24ehyx1aqS6ufOzFeB4dWIjQn2KYzQ uM7Mn9WUNm2H+ClVclznDh2whTbjkw+mmZrx5O2MqnNlRuvZ7ExoP0b9b+a+v9iUXnkzKj0xU36n K/0iIiIi4geWAOLi4hjeoS2bs3IqOzVlOlPvL6NbdOP1p9vwwfw3ORjZkvGvvs7TnbuxLr2sSegW eo3rT+iM3l6dv3hofz6Nf4S3XqpX8Rk6y/1n8BPcFh1MfEgg+QNG8JTDSU7Kxwwb+zkAYbX+w7RX hsCeP8kIq0c92w5G9BtX6nnh9VPmcM+388m+vTuRx45gi6rDxiEdmZJg9gF0IPc+MYM7o9fTZ+Br HCs48Uwqom5nnhrcjOC4ugT9PZYuo9eXOjO6VSdefboj+zdtJKhWHfYeDIQSTfK+l+dxzXffYrui Eal5kVxQz85z3XrwfXLhQUEx/+KZF0dTK2cPezODaHYevND/MdYcyiEgqC4LFr3Ak/fdzfqiwUZ7 latZ9PZIBrRtR0aBt4tmTtXludnEfTGaUUsLJzjcNXY2bR3v0v3ZT4uPubnPBAa2acy2TTuxRJ5H TPoCuo38qnh/VLP7mdOj7Pxd+MSrPHthCNv3JeEKrU2D8AM8OWA0fxblx6h8Yi7qyKyJnTi4aSPW GvHsPminpWNy8XXwVH6mWWz0vSyWL3vuNTz02O41wD00CQksHgD2VP+M6q9R/ozC91S+4fH9WTij Pn9vPUbDFuezbn0iUSE2mtRK5J77nyTMYL/TxPUz0m3GG1z2ywaCLwhnT0YVWtTN55leg1iXkmv6 +vnevt0z07+4Y7FWwWa1EBxcegjGcdIAt6f248v1Ox5LzesH8Pr5VdhzLJwW9S280OdRfjiUDRj3 X77kH4z7P6P4PbGFNqdVuJ1X1h0u3rZ52V7CJ3YATgwAe6ofRu3L1/7Jl/yZKT+/9G8GVk3/md6D b2J632Wn7PMU/+DZb3LwsY7M3Fe6Hda+YwJTbvuMDv2+9FsaxT2vB4CPS92wjh83ll4l9Mak0Wzd +BfJGXlUv/AB3prSgxsevorpa1cYB1iQxZwXRvLpt/8jx+mi5hXDeHPCzdwzsBXv9P/uRIKDGzNx cGsyMh2Eh3n/vnxP57uc2axevZpmF3f1OtxNb77K8fmqd17Umns2L2XatLJnsHqa8ebLjA0zMyKN ZmS5mxFjekaRjzOyrIFVaHx+fYJcORzYs5NDZdwsXHRLTQ6sfo4dXgz8+2vGmjczyiwB4cREBZOe fOREOG7Kx2K1ExNdhdCoYLDYqFat8FUgBTnppJq8YfJlxmhF5L90WXguf3/NWK3IGWnmw3Dfvt2l z1/ty4in8vF1xqWZ+memfXvidsajH9qPp/BNnWvQ/3qa8Wd6RrXB9S9v/TVTfh7Tf5rqr4iInJ0C w+Jo0iCegIJsDiXu4uBJgydWWwSNm9THTg67t23n2En3H0b7RcRYvsNxysPm4zytOPJ1RaWn+39P 95eFvw8tJKccLbHVQrVqMRxNSS5eSeLr7z9P2j5+NwkrxvD6+38Bn5J/6RL6PtqCdc/8fsqxkY0f 5obgndz30yGvzi9wOMhTn2bKJ2OH8Qkw4b2PSXv6caaeNKjTddKjOFeOo+/rvwFWHnhpMWNGXEq3 8T+XOq7OXe0ZP6gbv+7PxmINo54913Qabhj0CtdeU4spHfuRctJ1S98+iwEDoNmjcxlV4+QzrTwx phPbZ/Rn9Ko9WO01mblsLvxZ+qjoKzLo2GcgDhe0nbmULl0b8n3Rc8XeLz5B8A/P02n+OgCatp3C xEkPsKbnmziy/mTergx6tT2Pfgt3AFC/Q1fS/57j1XOzmIvvZ+DAE89kclM+Z/bbW3E5c5j52ATm vD2J29d14Y8mj/BQ01306LLyxLmthjD4jnge79KNTWmFf+frXlzbdP72fjCF9pN2FP8Gv+mZhQwb eD7dS7y1wP35Vh4f14ntMx9l9MrdWG2xvLTsDSjxSNZT+Zllr3ItNe0BrEw1GlSx0vLOrjgLMlhz 0j2Pu/rnuf4a588ofKPyLchNYPDjT/Of15bSOW0GPUdvYsHHn3BhmI2dBvs3ZDpMXT8jkS0PcH+P WThccN3w+Qx+6mYeGFg4gGf2+vnSvj0x27+UpSB3Nwv/SKH7rFdp9OUP/LHpD35e8yuHcwuKjzFq P75cv+NvNo28OJn7u40jzwlXP/Y6jz1zNz/0fhcw6r98y7+Z/s8ofk8CQs4HYHv2ib4u70gSAfbL iAq0llo8Vnb9MG5fvvVPvuXPTPn5o38zkrxxOsk1F3F5+Ar+Ommfp/g/3Z5Otxuqw8IMrIE2rE4H +U5odEs8CSXemCIVq9wDwGVZ+9P/iv87+1jhjXrOIXOvQ3UWHOX9r0/MgEjbWTijyuUsfVN1x5Pj qJr2PZN2NWfCFdW9TqOv5/vK04w3X2dsGM2INDMjy92MmNkW4xlFvs7ICq52HdNnD8e5ZxMpzhDq Nq7HjC73sfZoyRsmK9eRyosv/+JVuZuZEeVL+Zw8oyzAHs/Q6a8Q89ssRsz5xvD62kIv4qmnumO1 VSMgKISnnnoKgAOrp/DMkt2m8ujLjFH8nH9vy98fM1YrekaaGZ7at6f0mamfvvYPRuXj64xLo/pn rn2756l++qP9+Dpj1aj/NZrxZzSj2qj8fam/ZsrPU/pPR/0VEZGzU81/92HG8HsIDyh8zZ3L5eSj Hvcxa3/hvWN0q/ZMGdeDWiGFE3MdWUnMGzuUD4omGhvtFxHfeFpx5OuKSqP7f0/3l4GhzXh70RP0 vrsd+/IKH5KHxt3HW/Pa0OGebuSZTB9A/6efo/qx9xn7wn9Nl4vFGkrb2FA+/nhP8bbfViTS8+H/ A04dAG474ja2vDmE7KKBaW/PF99YbdHcFRfCK8uPP7F38uXrW+k8vi1QeoAiecN0ft1fuOrN5cxk lxc/Ry5tlMDiP0PpOe5+vhq0ALPzhe0Rl3NhuI1+3yUWpi5vP29sPcqjJx23d/mXxQMMG/97hI5X RQBgC7uQNjXDeOnnVBo2bAiAZcdawuIfIsi6gFyni++mr+WhZ3phXTgSpyWAPrfX5seRa8xnDsjP SuPIkRPPcHOPniicvGMbGPbMp7wxdTzH7LWZ+nB3UkoMrDR/6HIO/TSxePAKYM//EkzlDyB98y4a X9Ka5g3iCbUHEllgIbxhTWCj4fn2iMu5ONxOv28L43M6DvPWljQGFZ1npvzMsIeej9ORfMrg/3G1 bh7Lm1c6CAyOIirkKO9PG0pCXkGpY8qqf0b11yh/RuGbKd/83F0ApB7MJXPXMcBJYl4BtYIC2Gmw f0Omw9T1M7LnvU+Kr+9vb68jcm4n4BOvrp8v7dsdb/oXdxaN6MH2W+7m+qsu5YF+d/PIoBTmjRrE 8k2Fr9M1aj++XL/jA8B7l68oniT1v8VrCH/jfgIt75JvUP19zb/Z/q+8LAEhAOSWyIfLVfQc86S3 bJdVP+wRVxq2L1/6J18ZlZ+/+jcjLmc2L3+SxJCujSn5pTuj+Hd9sIe4Xs1g4Q7um/suN28Yy8Mv baJNfBhrfjnsJjbxN78OAAPUv38Sz97dkKrREez47yqmTfRuoK6QlbYj7gHg85l/FG+Nvrg3j1xS lZd7ziCn1yyvQ/X1fH8o74w+M4xmRJqdkVXmjJhY4xlFvs7IavxwD0L/fo7OI38ECv/IVHGdPHjn ZFjv3pSHv2asGc0oCwyuy6iZU7F9O5kRb68r3u6pfPIyfmXAgF8Jq9mHJbOaMmBA+V5h60v98lf+ 3fFc/r7PWK3oGWlmeCp/o/T5q335Uj6+zrj0lH9z7ds9T/XTH+3H1xmrRv2vpxl/ZmZUey5/3+qv mfIzmrFY0fVXRETOTr0faUNIwUGG9h3BgfwQGrW4gmp5hX//rbY4pk7oSTXnbiY9/jJJruoMefpx ej7zLF+07Ud2QKzH/Zl+eqAh8k9ltOLI1xWVRvf/nu4v8479zEfJLvpcFsuYHw8UhtftNpLXzyx+ O5TZ9DVs0YLzUr7xqmwCQxpjs1rYmuWgVfuHaXzgYz7ffgxbaPNTjg2t2ZYO1Y/Rpejbg96eL74L sNXAarGQUGJFXW5qGgH2i0859ujmtFO2mTX3sef4MqseNy6dypjbvuHpVfuMTwKstsLFJ0klBgMz D2RBTOnjHCVWi7oKAEvhJ6YCgusB0LpTV1qXOP633zcQYrWQ63SRtnUWewOX06VOOMstHWgSsIeR 273La/q2z1m8+NRvAB+Xsn4ZOwMWUfvw8uJXxx4XX8VOxmrPr3J3lz+Adk+9Tqca+1jxzQaOZuZi zyvAYg0ydX5Z5Zt9KKe4fM2UnxkuCsDi/jH64V/m88x7u8nPy+Jg4oHi70OXVFb9M6q/RvkzCh9M lK+rMGyn04mraEQw3wU2k/vNXD8j2UknPqFVkJeE1RaD3QpWL66fL+3bHW/6F3dczhzWrVrCulVL sFiDueXRGfR78mGWPzAZMG4/Pl8/IDvpRJstyEvEGhBKlUCr2wkNx/maf7P9X3k5HYVvLokOtLC7 OM4oXM58Uk4a3S6rfphpX770T74yKj9/9W9m7HjnNWouGkT4J9uKtxnFn7ZlFSFx3bAGruHuoB/J u7wNtpB8LrBnMirVPyv0xZjfB4DzM5M5cCAMS3g4dZo2p0X9cP7e5OYD0W5c22cK3S6MYdP7E3hj S2HjtNpieXLcXez++ClWJmXS0st0+Xq+v/gyo89XZmdklTUjJgLPM4r+4nyfZ2RlJWYR0er/uP5f Kfyx6W9Ss1Pw559uf81Y8zSjLDCkIWPn9KPu9pfoVmLw93TNyPGlfvkj/54Ylb8vM1bPlBlp7srf TPr81b7KYrZ8fJ1x6WnGm6/t2x8zSiszfCOeZlQblb8rrGJnVJpRkfVXRETOXmkFTqy26tzfvg3f //wbv3+3jHVFyw8i6nSnpj2AlE1rIaom8cC6A9l0rNeA26OD+aKK5/3LjmR7jFtEPPO04sgfKyp9 vf//eOF2Xu75H/hxMVhs9GkdxzcDNnmdvhcG9cdWYPzZopIs1sIVRTkuuP3eO2m2ZwcrZzmgjAGN mx7vwJ4Px5Nc4iG6N+eL7wryEsl3uWgUHMDGotVuwdWrUZC755RjXT789jia78SRvYWRL69h3uDn abW2O+tNvNGqIHc3APWDA/krqzB9VWuHgsk/YwVZhQ/b33h6HNvdTJB3OfOY8Vkio/tewu6AG0lc 9axfX4kOcPPQaURvmMcv9boyvt1qxi7/u3jfvuQcIpvHALu8DjcwuCl9rqpFv7t7Fy8AaF7/Tu40 eX5BdmE6mocG8mvRG7xi6oVB0XiamfIzw5GxAWtgO2rZA0g8aWUvQF56Ajt27PAYRln1z6j+GuXP KHxfy9eIv8IPrxcOvxeuQA8MOY+CvIPkOcHmxfXzpX27nIVhB1lKf0rRm/7FXDw5/Lj0dwbdfE3x Nk/tx2/lWz8cfi78TIEtuD5ORxppJj5B4Gv+fe3/jDiO/Uae08UVVYOKv7dd9aJ4HMd+Kf5cxHFl tj+D9lXR7ceIUfmZ7d/c1W9vODLWMz+xKgNbVTuRPqP4038kiZHceMFDpK38kBVXTOSKRgFkHVmu Z4GnkdX4EO/s+2QKQ4YMpGvHRzkWUouHxnn3CLrlA+MZ0645f382jaFzfizeHtlgAM1CbRytditP PPEEnZtUBeDmgcO5PtJuGK6v5/uLmRl9Dz30EA899BBd7r+0eMaEP7R76nUm9b6ZCGs+mZmZZLuZ keV2xpSHGUXepN9d+LsWP8H8L1K4p9dIFi3/iLlThlPb7p8ZM0bpBz+UDxAc3Yasb36k2mUPcXGV E/XqdFxf8K1++SP/HhmUf+GM1RZ0qRNORJ1uNAnYw2yTM1bdz0iraep8tzOqvOS2/M2kz0/tqyxm y8fdjEuz8Xua8eZr+zZbP8urosM3o3BGdVOCDn9eaka1Ufn7q/76pALrr4iInL3mj57O/3ancPlt HRg+7nkWvreAexpHAhAcVzh1Pbp5F0aNGsWoUaPoWK8KALWDAgz3i4hv4qvYydhR9oojb35fVdTv +wPfv0ZQzU40CQ4kok43znPtZGHRW3a8Sd+BvXvYl+j5k0UnczoKVx3H2qxM6tKR7qO+xRYZQoHj UKnj7JHX0adJIC8s2l6u88U/nPnpLNmXQZserbFQOADfvm9TDq37oELiS/pqEkt2BTLq+R4EmPg5 48jcwOq0XB5u0wQAW0RTejWuajo+R/ZWluw+yvBeN2Iv+v1ktUVxxQ2lV5TvWPwGUS0H0P/CaN48 qU6aEWAPJzIystS/42rfOJQBlx5i6LMreGnI8zTuMZk29U5M+P5r7vfEXjqUa+qGF22xcuF1DU3F 63Ll4HS5aBBd+PvbHt2cgf829ywHwJH9Fyv2Z9L7oeuxWSCk5mX0rR9ZYr+58jOSd+wXtmU7uKt6 iFfnGTGqv0b5M+Jr+Z6u8Ovedz+RgVbAyr97XUXaloWA/66fkfzcXezPK6D9v2qV2u6P/qV/h/9Q LaRwDZ7FGsK/u1xG9pETb6bw1H78Vb517ulIVFH5XterNanb3sTMHBFf8+9r/2ecvhTe2J7OtQNu L1wxbouhy4MNSfjiXXPpM2hfFd1+DNNnUH5m24e7+u2tb6Z9R6uHW3gRv5P3kjLp2f9qvvoskfVv b+fRRy7l4Le/+ZQO8Y7fVwAf58jcwe7cfFqFtTA+uEjjNsN4vvsVbP3sJQa/vOqk72kUkJOTQ9NL LwfAaiscXKt3yb+oExQIGM268/X8iuWvGWnueDNjpTwzprxJv7vwnQXpfDhvKh/Og6Dohox4bRrD /m8pg1eUb1aVN/xVPplJr/Lcm5+wJnI2T07pS8e+08lzusyXj8vp8ZUy5WUUf0XXDzN8mbFqdkaa 2xl9FTwjzecZcz72D2bj98eMS3d8ad+m62c5209lz+g7zt2MaqPrb/NX/a2k/kdERM5dx3Z+xci+ XxESXYfLrr2d0f3a0Xn41XzYeyU5Bwq/+7Rv1UgGvLq5xFlWCnKzCLV53i8ivvG04sgfKyp9/X2f n7Odt/Zl0/u6GrzX+noOfPtc8bcK/b0i69S4d7M3N59L40P5ODkFJxDXOpbc1O9KHXflkIc5+N0L 7DrpHtfs+eI/S0dOpuXUkSxb8AAZIdUJTFrNyFf+MD6xXFwsHj2J2xZP5Km7vuDJFbsYM28hLcJs BIZWIcw6hnffdeB0HKJTl4EAvDxqFtOmPM/btyRBuIu1f6RylRcxLhw6lhoTRrJ8yYMkpTuJq1GV nT+9ys/fbSo+Ju/YOt45HEA76zJ+MrEy+WR17pjI0jtKb7v11lsJjruWFwdfy6z+D3LI4YSUdQx5 8XvmvDieDR2Hsy+vgNTNsxm/KIIhM96h1/4kLBE1SP9zCgN/8LwiFqAgdw8T3v+N0XPf4r69R4iI yObDD/bR4XrzaZ8/dBJPThzChx/2I+3AJpb+eIj2oSf2myk/Y05e+3o/w7s05dWJ/h28MKq/Rvnz xB/lezrCP/h1Aa+9PZ/MvAiquXYwpv93xfv8c/0MuByMnvo+zw6ewcoxNhK+HEavqYXh+9q/VL+y I2/1GE5G6mEIi8F6eBNTRy4u3u+p/fitfL/JYuaiN8jKCaOaZRdP9v+6eJ9R/+Vr/o36P6P4jXwy 5lkumzqO99+9m6zgKLI2r2TIm38bn1jEU/vyR/n7mj+j8jPVPjzUb2+kb5/DprzbaVZiLaVR/Js/ SySiZygrUnKwblhIlQYv8e2zB7yOW8rPb096g2Pa8PLIS/l9y06OpGQRd8F1XBpuJyNhubnzo+/g 5QE34XJmcjCsFSOfaAVAbtp3vDhrLalbJ3D33SeObzluAS9cXYO53e9nRYrxO2h9Pb+ilZwx8ejM L8hzurDaorjsmni//EErOWNlR1L+iRkrKX5IPP5Jf8zll+Bc/z9SHU7y0hI47HASmePnd9a44a/y cbkKb7LXzhjOfxYuZHKPNQya97vp8snP2Ulg0F20iLbzZ4r/JiUYxV/R9cOsHYvfIOqdIfS3RDBt ovkZqyVnpH045SsonpH2bKnjSs54mvrj3uLtJWdUDVm66cSMKj/9XjSbPnd8bV9m46973/1EfjSZ 9HyKZly+5Zf4wbf2bbZ+lrf9nAn1//iM6p6dV5AesZ83F0ymze9d+GT3McPy91f9raz+p6S+cxZy jetzuvR522/xi4hI5bl39DAcP63l730HSTt8AJfLhctZuGLvWOJ89uT8m9o3DuW+HW+y7VA2sfEN aH3bnYzs3cFwv4j45q+53xM7eSjX1O3Fj3syKFxxVJ+NP+zw+fcL+Of3/bdz/6TzY+3pFxHDsm4n BpK8Sd/Et5dQO202XQd49x3g1386zPBH7yO47xwctpr0vjmeLbO+L95vC23O0H9FMeH+/5XrfCmf sfeWPU03J/lnhnZrT1ztOtjz00k4cOrK9C963ssX5YjTkfE/br311lLb8o6u54E2/1f8/8/07OIx jGM7V9HrvtXUrh1DakICGQVOZpXYv6BL21LH71jYjw4LS6ZhMxOHdCO8Wk1iQiD54EEyTpkxb6Ve mI0/pn3iTfYAeKdrO95xsy/n0Gruv3N1qW37v5vGnd+VPm7dO1N44L3XqBUfQ07aQQ6nn3jWapS/ n+aO5t73a1I9DJIS9uNwwZI3MX1+TvJ/Gd2nIwG2AAocBVw5eTHHNp14w4G58jO2bf4rhC8eTpyt e+FgeBFP5Xecp/pnVH+N8mcUvqfyPbbvOdp1LfzvDeO7MajonOK2lmKw3yB8sw7/PJ9+i6FWtWAS 9yVR8vOtZq5fedt3SYnfzqP7t/NO2W6mf/Fk7JBe2MIiiasWBdlpJB469XxP7cen6wfM7XwPc4HA hdHUig0maV9S8efbwLj/8jX/Rv2fUfxG8tL/YFTP+4irVQebI5XEQ0dPOcZz+/Pcvnztn3zNn1H5 me3f3NVvT075++fKZ1j7Nicd4zn+vR89xh0fFf53QfYW/u+227xKg/jObwPATkcKgbUuod1FVxdv S9n5M1OfNPoTWCggqDYBFgsEhHPd9SemUWQe3MWLs9b6K5lntIqc0VTRM77A9/TXuK4Lz48dzeGk QxBRC+v+Hxj+TaL/EuiBv8vHWXCUyY+9xKL5E7h/7YMs2Zxmqnxy075mzlc3M/aNZQQWFJDw+SgG zt7qlzx6iv901A8zfJmxampGmocZT77OyPVL+jzwtX2Zib8iZ1z60r7N1s/ytp/TUf89zfgzM6Pa qPz9UX89lZ+vMxbN1p/46KrYEk/zzBMREakwlogm9H78JgKL3r6Sl76b+ZMKf9s5HSkMf/xlxo3q w4P9hwPgcrk4vOtPU/tFxDdGK/Z8/f1idP9v5v4y+X+zcES+RVTmT3x80sR9s+kLDgsjNNf7R1+/ vvgk66e9wNJFV5EZGkvab+8yskT6mz88mGMbXua3om8Genu+VAQnhxIq/g1y5eUqyGTfnkyfwsg4 sp+yXmhe98KWnICoS08AACAASURBVN+yHVcGJdD5v0d8isMXTkcG+/Z498r143JS9rOnnD8Fqzbr wHXVd/PH9v2E17mEQeeHMnfSvlOOc1d+ZjkyN/Lku39wZ4so5q337tvixtzXX7P588SX8j1d4edn pbBnr/v9vl4/3/jWvzgy00nMTPccg4f245fyzfZcvp75ln9/9H+eOTmUWL70mWlfFd1+jJgpv8pt H5Ufv7hn2blzpwugdevWHg/MzCysZFXqjmXZnGtIXLWULxITeXfpqlLHVYmOpWqVMBzHjrA/+ey6 7NaASB64/w5ir7yH25tG8uS9d/LLsdP/amhfZ6R5EhxdesZKRfAl/QEhkdSIi8KVlUzS4bK/R1SR zvTyqej4T0f+PbMyZumHBE57mKd+Ks/3kazlnpEGYAkIKzWjyv98Sx/4Wn/cx9/vnRXUmtiDcTvK nnHpj/h9bd8VXT8rv/4b81T+FV9/fecp/YHBjfnkw+nM6t6Oj073N4zPQGFhYR73JyUlARAfH+/x uOP3byIilSUwNJLYmKrYXTkkJR4s829sVPVaRAbD0ZQjpBzL9Xq/iJTNaq/Byo8XMGXIQLal7mVP 0qnfCLHawstccVS016ffLxX/+97331dGqsWfh92RQtLhE8+3Aux1eHv5bN7o1o4vkj2/Ua6s84+L qVuPmjU689zIGNq0HeL3tBvR/ea5o/2IJ2nGUb55d17Riv5/lvDzbqZPl9bUiq1K3tEkvvvgDVat P3e+uX2u5++O4WOIXvYyC3ef/ufAIud6+5Jzl9F93Jo1a4ByDACHxHVmzKBmABTkJTF2/EyfE3um sNpiefqpwcX//9bT49iq7xWKnBbHZ6w+el8sndsPID3/zBzAOlcdHwAevVGrL6VyRNR9mPEPHWDI OO9fWXYu0gM5ERER8ZU1MIqnxw8D4OiuN3n+dfPfxBP3rPYaNKhjZ/uOci+lAqDNiHFcVcVOfu5e xk2Y7afUmaf7TREREZGzU4UNAIuIVIR/+ozVyqYZlyJnFj2QExEREZGKpPtNERERkbOT2QFgv30D WETEF+8//zTvV3Yi/sE+nfxMZSdBRERERERERERERET8wFrZCRAREREREREREREREREREf/QCmAR ERERERERERE5IzW/+15ahtlLbSvIO8CS977xS/iLli7FbrFit9vodW9bDjmcfgnXnUbdn6NftYUM mbLJ57CstjgeuO8mMhO+YsUPhwCwR1xJ+9vjeGfJR4Bx+ZXcn5+TScKODazdsLtU+KvfW8K+vIKi bdG0u/dWUn/9lK//Pmrq+jTrPpG2h1/j2U/L/nZ2l1fmcEtUCI6sbTzU52kfS0VERERAK4BFRERE RERERETkDHVR+060v74p1atXL/4XFxflt/A7d+hAp25jCQ4OxmrxW7Bu2SNiiI2yGx9oQkBQPN26 daPX4JHYitJuj7yaLg+2Lz7GqPwuat+Jttc2ICoqiloNWzLo2VeZNfy2UuHXDQkAwBIQTu/nZ9K2 ZTA/7jhmKnyAv5e9y+W9nyLOVvaj6MVD+zNozH/9el1FRET+6Sp1BXB5Z7wZnRcafy3DhzxA/dgI cpPfo8+Qj/yR3ArXbupsmiwazXO/HanspFQYS0A4sTF2Dh1KqeyknHU6vjSHOvNG8cLGZI/HeWof Z3L5n+4Zt5XNnzN+/eF09T8VWT8run6fze3nn9a+RERERKTiWW2xLJg/FYCUzS8waOLG0xr/mXx/ boZWPHrnyPrFTHtta5n7QqOjsWWkkRVck/MbxJCyczOJRx2ljrFViS/at4UDeUGEWHPIyMo3HX9o tdrUq1kNS14K27btxeHyNv6anN8gltRdm81n2gtb8hrRt2kU07eklrnfU/kBpGxYxsyi/dGfPcI7 Ux/jkle/pmSrtliD6DLhNW6wrKbX6AXkOE8UglH4jsw/eGN/OMNurMXjq/adsr/A4SBPv1NFRET8 qlIHgMs74y1z319sSj/mdn+bCYOpsXoqQz/eTEFBli9JPK0iYuOIsgdUdjIqVGhcZ+bPakqbtkMq OylnnfDYOKKCjBfte2ofZ3L5d+7QAVtoMz75YNppmXFb2fw549cfTlf/U5H1s6Lr99ncfv5p7UtE RERETgNLAHFxcQzv0JbNWTmnPfoz+f7cjMVD+/Np/CO89VK9yk7KWe++l+dxzXffYruiEal5kVxQ z85z3XrwfXJhvYy5qCOzJnbi4KaNWGvEs/ugnZaOyXQZvd5U+Bc+8SrPXhjC9n1JuEJr0yD8AE8O GM2fGQ5T8Ue36sSrT3dk/6aNBNWqw96DgeDnJrNq+s/0HnwT0/su8zmsY7vXAPfQJCSQjdnHtwZy 7xMzuDN6PX0GvsaxAu8Ha39evJtuvW6BVfN8TqOIiIgY83oAuDJnvFltkURH2nBs+IwPHOmnpi06 mlCrlVZRdhJ+T8BitWLF3JNua0AEUZEBJKekldoeHRNDXloKGQWuojRE0LhpA2z56Wzbtpu8Evc7 wZFVCcw6SkbRjLWA4CpEBGWTll66DIzYqtRyW36eyj8sJprAo2nkx9SncWwQu7ZsIb3E7Dmj/QBY bNRt0oQoewE7Nm/jWP6J/WaurzsWq52Y6CqERgWDxUa1atUAKMhJJzXjRBieytesoKhaND4vlpy0 /Wzfc7DUPnfhWwIiiIrMJy+oFnXsR9iyL4umLS4gffsm9pecEeqhfMzwdP3MCqvRkMZxwezdspmU EgXkqX2YLX9DBvk3yl9QVDTB2ekczQuiyQWNIDORrbs9r2gG8+2zvArDt5CccrTEVgvVqsVwNCWZ vOOzWn1sH0b9n1H9Nyo/a2AVGp9fnyBXDgf27OSQN9e2OI3u+x+j6+8pfn/UT3f1y3T9Lmf7PV39 l+H187H/Ka8z5e+jUfn44++HiMiZyKh/M+wfffz74u7+w+z9k8/3h5X0909EzMt3OHCUsXLP8PeL Uf/m7ve7v37fGvD5/txgv1Y8eifm4vsZOPDEb5LclM+Z/faJFafRV2TQsc9AHC5oO3MpXbo25Ptp mwArj4/rxPaZjzJ65W6stlheWvYGePEyrr0fTKH9pB3Ff8NuemYhwwaeT/cSq949xf/EmE5sn9Gf 0av2YLXXZOayufCnjwVykuSN00muuYjLw1fwVxn7jcrvBCst7+yKsyCDNel5UDRv/YZBr3DtNbWY 0rEfKWXUWzPhp/71X4KrPYjdOv/EcxYRERGpMF4NAFf2jLeIup15anAzguPqEvT32FNm6v1n8BP/ z959h0dVJXwc/84kM+kJKYSQAAERQYqAurrusrLr2kUFlL6ADWEBEbGAgICoIIpgQRRQEBEUVJSV 17I2ROwFERFRAoQUipBCeiYz8/4RCMQkc+9kJgX293kenofMuffc0+6Zc+ecey+XxwSTGBJI2diJ zHC4KM56i7umvWcYd0BQMstXPsJ9/a5l89H82CP/wsqXJjG2T1/ynWWEJV3E/CcnQOpP5Ie1prUt hYmjp/NbcfkE4YAFy+i4cBQTvyifdGzd72Fm/20l/W/9zHQeozsOYPGN1ZefUfkPX7CMP329heAz w0nNj6RzchkPjridL7NKTIUHxZ7Lg49NIak4lb0FQXRsBY+MuYNNB8uPb1S/nthCz2LGjBuw2uII CAphxowZAOz/dC4Prt4DYFi+ZlwycibjerXj1227sES1IjZ3OcMnfWAYf3jiGFYsaMNvO/Jo27kD X27OIDrExhlJGfQecB8uE+VjxKj+zGjecyzPdYgkNS+czm0sPDLyNjYeLF+O6en8MFP+RozybyZ/ PecupvfHSym68gai8g5hi27J1gmDmJue7/HYZs5PXwSGduSllZO59dq+pJU6AQiN78eLz/eif+/h lJrIv6/9n5n276n8guMu5KlFd+NK3UaWK4Tkdq1ZMLQfnx8pNV0Onvofo/wbHd/X9umpfZnZ35fz tz76L6Py87X/8UVj+H40Kh9/fH+IiDRGRv2bUf/oj++XmsYfT+Yaj598HR825PefiPjO0/lt1L95 Cvfm+nbMAw/TLO91pj3yjVdp97X/VP/lf2WFORw6dPyVRSVHKpfl3rXvV0zQbv3mEIMuiADAHnEe 3cLtjP44HQCX43de/CWH2704du723bQ7uwedTksk1B5IlNNCeNvmcMIDkj0dv0u4jdEbMsqPX7qP ZTuOcJsXxzfD7SriifWZTBjWjurehGdUfkmXTOOFPzsIDI4mOuQIr8+/k/RSJ7ajE8DnnJ7Oqp9C uXn6AD64fTl/nL41ih/AWbwTqzWY04IC+aXIf4s1REREpHpeTQA39Iq33J0LGTsWOt62hHsTqoav n3YX64GZr71FzgP3MM9gUulEjsKfeH53PiP6tGL0ihQA2vQfRu5vi0k5egEybPZtuN6ZzqjnvgOs DHx8FVMnnsPw+78ynwkDNZefufKP6rqfATcuxOGGC+9eyvgZlzBw3HpT4bc+NpngjXMYvPRLANr3 mcus2QPZdPMLptLnSWn+t4wd+y1hzUeyemF7xo6t+ogmX8s3tvsExl+VyD1Dh7Mtp/yiLLlbC9Px O0vSGX/PA1z07BqG5Czg5inbWP7WerqE2dhS4DBVPp6YqT8jUd0OM2D4dEpd8Jc7nuOOB69l462v AJ7PDzPlb8Qo/2bz1/Ka67j/9uF8u68IizWM1vYSw2ObOT99UZr3Ff857Gbkn5oy9bP9ALQbfjmH Nz9dcXejb+eHcf9ntv3XVH7tbrmR0N8eZsik8gk1qy2GSLd3ZePp/DbKv9HxfW2fntqXmf19OX/r o/8yKj9f+x9fNIbvR6PyqY/vZxGRhmDUvxn1j/76fqlu/FFabDx+8nV82JDffyLiHzWd30b9m6dw b65v23buTKusj7xOt6/9p/ov/8v99T1WrfLwjtnc44uf3U7AUv6KI6utGQCZRxcrARQdLIZY88fu O+M5Biekse6jLRwpKMFe6sRiDar18Qv2F3p1fLNSXn6W5itvJ3z9r1XCjMrv96+X8uBreygrLeRA xv5K7/cFWHLHw7xf2Jp/rpnH1Ms/4oE/vMfXKP5y5WVS5tbdvyIiIvXB+IWiJ8jdvpvW3XvQ+/r+ DB48mHYVK96O++OKt8gOlVe8Lf3DirfGZMNTn9Oy94jyQrEEMPLKFny2YBNQPti/Jj6EdWuPTXa6 eP+5HcSc1cevaaip/MBc+ae+tr5i/+9e+pKo0wabCreFdaFX8zDe/iqbtm3b0rZtWywpnxOWeD1B J7ww0lP6fOGP8u1003kc/OLJislfgNQf0k3HX1ayG4DsAyUU7M4DXGSUOkkKCjBdPp6YqT8je9eu q3js1Q+rNhHeYgCB9fA+TzP5N5u/w1ue4tt95Xctu10F7DY5gevp/PSHt1bs5MybLyr/w2JjZI94 PlpS3l58PT+M+j9v2n9N5VeYUUhEmyvoee6ZRIcE4nJkkePlIxJrSr+Z/Pvj+J74cv744/z1xB/9 l6fyq+v0m9HQ34+eyqe+vp9FROqbmf7N6PvXX98vNY0/PI2fwLfxYWP4/hMR31V3fhv1b/4c3z1y +xjumPa51/v50n+q/2pcnEW/AdAp9Pg9MLGtw6ps53aVf7cFWSrXUWBwe0ZekMTE8Q+wYs1a1q1b xzcF5u9edZbsAaBN8PHjN2kRanp/bzjyN7M0ownjusd5vW9pbjopKSmkpu2rMvkLcKTMhaPoFyY9 sYm/jJ1D90i718ewhXbEVZbL7hI9qUlERKQ+eHUH8Mmy4q22cnYsZG/gWoa2DGetpT9nBKQyaWf5 +ysCbAlYLRbSS46nvyQ7hwB7N7+moabyA3PlX5RZWPF/Z2kmVlssdisVk4Y1hVuDWwPQY/AwepwQ 33ffbyHEaqHk6ODPU/p84Y/yTYy0k/9pXu3jd5eHuVwu3GXl+S1zgw0IMFk+npipPyNFmUUV/3eW ZmANCCUy0Frt+1f8yUz+zebvyPacKp+Z4en89If9nzxL0PjHOSN4Dfvih9PKvYtxR58iYLb+a9v/ edP+ayq/3asms9R+M71HTGJiq6ZkbPuY+yfPI/2EYxqpKf1m8u+P43viy/njj/PXY/x+6L88lV9d p9+Mhv5+9Fg+9fT9LCJS38z0b0bfv/76fqlp/OFp/ATmv7+ri78xfP+JiO+qPb8N+jd/ju/2702t Rap96z+L1X/ViQB7OFFRUZU+y83NNdzPUfQz6/YVcOtNPdny1AcEJvyJUW2icP5Yebuykt3sK3Vy 3blJzPtsb8XnbncxLreb02KCSMkswx7TiXH/aA5Z5tLtKNjCpzkl3NLrDCas2YYtoj0j2jWBH6tu O2rxCv7qfo+hI18yF3k1Ppq/gVvnXYmbw5U+r235/VHmB7NZfe1q7p1zI4NGL+LoQz9MxR93XncK 979WsY+IiIjULdMTwMdWvI2+9taKRz52anM1V5vc/8QVbz8Xlq+Ua9IiFIo87FTP3K5SFrydwZRR Z7Mn4J9kvPtQxcSpszSDMreb04MD2Hp0pV9wszicJccvJhxuNwEhxydEg2K9Xw1XE7PlH946HL4v f+dGYEgrnKUHKvLgKdxWWP54mGUPTGdnXb4z0e0CS9VmZ6Z8jaQdLiaqUyyw2+/xO30sH1/Pn2PC 24TDVwcBsAW3weXIIcebyd8ayt+IUf69yZ/bw8VuTStuy8NqPj/9oax4Jy+mFXHrhQm81qMn+z9+ mKPrAHyuf6P+z5v2WVP5uZy5vPn8PN58HoJi2jLx2fncdcUaxq+r3Q8eldJvIv9+OX4N7dN0+6qp f/FX/1aH/Zen8jObfk/nj5lwTxr6+9Fj+fih/EVEGiMz/ZvR968/vl+g5vGHp/GTr+NDv31/i0iD qvb8NujfTI/vanl9a4Yv/We9/b7yP6blVbNYc1Xlzy677DJT+y69czb3zZrAm2+OJmf/NtZ8dpDr /ngTrtvBlHmv89D4Bbwz1Ub6+3cxYt42nCWpzHz9O6YseZF+ew8REVHEm2+k0b+n+bQ/ce9C5s+d w0uXZkK4m89/zOaCarZLjGmCLcPkzHINcncuZlvplXT8wyWXL+VXmZtVU2Zz+apZzLjmv9y3brfp +C/pn8yWhbNrcUwRERGpDdOPgD5xxRtwfMWbSSeueAOOr3hrZFJWLSO661jGdInhhZU7Kz53leWy Oi2fXjf2wAJYrCFcN6o9B798o2KbXfuKSLysfEWq1d6MIT2a+S1dZss/ud8AogKtgJV/jLiAnF9W mAp3FO1g9Z4j3D3in9iPPpLIaovm/L938lseAMqKdxEY1IbOMZVHombK18jPSz6h6Tl38tfk8KOf WOlyYVu/xO9r+fh6/hzTsvcgoo/W34UjepD96wt4MwdaU/kbMcq/v/J34orb6tR0fp5o1OIVrFj0 L6+PDfDxkp9od8N1jO4ey7qj7zoF3+vfqP/zR/uPPe9som3lXXppTjq/O1w4i/0zQ24m//44fk3t 02z7qml/f/Vvddl/eSo/s+k3On+Mwo005Pejp/LxR/mLiDRGZvo3o+9ff3y/GKlp/OTz9WM9XZ+I SP0z6t/Mju/MXN/Oemk1Ly64yOs0+tJ/qv/yv5eH9eWyyy6r8u+Y5UP7MPGLAxV/p6wYTf9bP6v4 u/jwN0wZOYhrru/HkJH3sT/WTt6uqk+Qy/j4eW7odw1XXHEFI+Ydf6XBF0umcP3QsTw0awbDho5n 9QujuO7Gt0wfP2/Xu4zoN4B7H3iQUUP+zcIpQxg6ZXOlYwcGt+O8cBurZm/wqmwc+T9w2WWX8UP+ 0cdSu8u467peXHn18IptjMrv5WF9Gfls9e/vPRb/phOeGFZ6ZDMDe11RMflrFD9AcOzF9A7fw2Nf /17tcWKTW9O6ZVS1YSIiIlI7ppdK1ueKt5pMfX4FncNsBIZGEmadyiuvOHA5DjJ46DgvYvGsNO9L Xv49gL7WV/niSGmlsDWTHqXrvEm8unwg+SHNCMz8lElPHn9my4+Pv4h78e2sWd6XQnce731ygHZd /JMus+V/4EMnz760lILSCOLcKUwds8F0+Io7p5EwcxJrV/+LzFwX8QlN2PXFM3y1YRv+UpLzIYs/ uIRpy14l0Okk/b17GbeofJBpVL5Gsrcv4v6VEUxY8DIj9mViiUgg96e5jNuY4pf4fSkff5w/AAc+ KuTplcsoLA4jzrKb+8Z8WBFm5vzwVP6+5N9f+atpxe0xns7PY3xZMXv4h4U4ol4kuuAL3soqrhTm 6/lh1P/52j4TLhzKnGlT+D3zIEQkYd23kbs/yjC9vxGj/Bsd35f2abZ9eWrf/ujf6rL/Mio/U+k3 OH8Mww005PejUfn4Wv4iIo2VUf9m1D/65fvFQE3jJ3+MD+vj+kREGoZR/2ZmfGfm+jY4LIzQEu/v Eva1/1T/1bg06difC5vt4ced+whveTa3dwhlyew0r+IoztpHqg8357qdBaSlFtQYHtKsJz9/tYD/ 7C+scZuTWfJl3Xhz1iwKaniqyAUDh3NBpJ3vNpv7jUpERESMWXbt2uUG6NGjh8cNCwrKBynBMc1p FgaZ6ftw1OKdDZaAMFq0iCU7PZ18Z92+t7R2rExd8yaB829hxhcHqw2Pb9ESe1ku6fureZdNUDQt EkI5kJZJcR2818VT+Y9+eR1Js25kegokxQWTkZZZ8Qg2M+HHhMc1JzYEDh84QL4/n7FriufyNRWD LZykxFiKcw7we27xH0N9jt+X8vH1/AEIDIkhqWkwmWmZtY7DF57y74/8eeb5/AwMbsf6N59i4Q19 6+yiyZf6N+7/fGufASFRJMRH4y48TObv1b8P21ee8l/Xx/dH+6rb/q3u66+h++eG/H40Lh/f+/fG JCwszGN4ZmYmAImJiR63OzZ+E5GTmUH/atA/NvT3S+P//haR2rLaE3jnreXMnTCOX7P3kprp7Tu+ jMZvDTu+80f/6Sk8Nrk1zROG8PCkWHr1mVAnefDkf2m8Gd7qEkYO7UFS0yaUHslkwxvLeHdzddc0 IiIiIo2f0Thu06ZNQC0mgE9lyV260qFrX27r15Qh140lt+zk+nHh2ATvlK3VL0k0ChdpzMycnxHJ t3D/TfuZMH19A6RQ5NR1sn8/noz+l36QExERkZOTNTCaB+6/C4Aju19gznO/NXCKTi69Jk7ngkg7 ZSV7mT5zUb0fX+NNERERkZOT2Qlg75+Dcwo798pr6EgWs8c9elL+uJ36/Tfk5TlqHS7SmJk5P/NS n2PC9HpOmMj/gJP9+1FERERE/M9Vls2UKVMaOhknrfVz7kdLl0VERESkrugOYBEREZFGRndkiIiI iEhd0nhTRERE5ORk9g5ga30kRkRERERERERERERERERE6p4mgEVEREREREREREREREREThGaABYR EREREREREREREREROUU0qgngvvMWMemcuIZOxilr0OOLuadLbEMno9b8lf7QxL8xfe7TvLD8RRbN u8YPKTOnvtv36Tc8zLy7OtXb8URERETkuKZ/7sX111Ydi13VfyAXJYbWKs6axneNddyn6zsRERER ERGRhtGoJoAjmsYTbQ9o6GScssKbxhMd1Kiq3Cv+Sn+vmeNJ2LqKO+8Yz8T7/+uHlJlT3+3bHhFL 02h7vR1PRERERI5zFnTlplvuJtBy/DNb2FmMu/lGrIXOWsVZ0/iuIO1ntqXk1TapdUbXdyIiIiIi IiINw/RsmiUggpiYEMKbn86ZyU2wWO10OKsrzUMDK7YJjmpCuO14lAHBkTSJslWJKyg6ic5du3F6 crNqj2WLTKJLt7NIiqy6r5HQuBZ07NKNTu1bYbNUDbcGRtK+c1fO6tSe+HD/x28kKDqGqOAALNZQ 2nc6i/atK9/R6kv6w2JjiLJZCUtoS7cuHYmyVV+9YQlt6XZWJ2Ls1YRbbCS370S3Lh2ICKwabpT+ msKtARHExjSpEl9MbCzhAd4VZG3THxoTQ1xcHN2j7aR/n47FasX6h2NbbRG079yVzh1aU130Rvk3 Kj/w3L491X9oTAxRdqvH/W2RzenS7SxaVHPeiYiIiEj9yd2xHFdgAr2igys+i+7UD0fBT3yQU3J8 Q4Pxo6fxndUWRVxcHI4tb/PGR/tqlU5fx5/g4/WdifGziIiIiIiIiHgn0HiTcuGJY1ixoA2/7cij becOfLk5g+gQG2ckZdB7wH24gAELltFx4SgmfnEAgNb9Hmb231bS/9bPKuK5ZORMxvVqx6/bdmGJ akVs7nKGT/qgIjy64wAW33g62aVRnNnazsPDb+STw8Wm0thl8jM81CWEnWmZuENbcFr4fu4bO4Wf 8h0ABMddyFOL7saVuo0sVwjJ7VqzYGg/Pj9S6pf4zeg5dzG9P15K0ZU3EJV3CFt0S7ZOGMTc9Hyf 0z98wTL+9PUWgs8MJzU/ks7JZTw44na+zDr+A1PznmN5rkMkqXnhdG5j4ZGRt7HxYBEAQbHn8uBj U0gqTmVvQRAdW8EjY+5g08FiU+n3FP5EVjLLVz7Cff2uZfPR/Ngj/8LKlyYxtk9f8p1lpsrPl/Rf NH4yl8cEkxgSSNnYicxwuCjOeou7pr0HQFjSRcx/cgKk/kR+WGta21KYOHo6vxUfT5un/JspP0/t 26j++z3xPH/d8DG286vfP6b7YJ55YBD7tm0lKKklew8EgrlTR0RERET8zFmazmuHirjo7wm8uXYP AB0Gns6h7x+p2MZo/Gg0votIHsKM8R0Jjk8m6LdpDJ2y2as0+jr+BN+u78yMnwHGPPAwzfJeZ9oj 33iVPxEREREREZH/VaYngAGcJemMv+cBLnp2DUNyFnDzlG0sf2s9XcJsbCkwngSN7T6B8Vclcs/Q 4WzLKZ+0TO7WotI2MefnM2jkOBxu6PP0GoYOa8sn87eZSt/eN+Zy3ewUHO7yvy9+cAV3jevADbO2 AtDulhsJ/e1hhkwqn5C22mKIdJubeDQTv1ktr7mO+28fzrf7irBYw2htL/Fb+qO67mfAjQtxuOHC u5cyfsYlCB0F2gAAIABJREFUDBy3/nh4t8MMGD6dUhf85Y7nuOPBa9l46ysA3PrYZII3zmHw0i8B aN9nLrNmD2TTzS+YSr+ncEfxTzy/O58RfVoxekUKAG36DyP3t8WkFJuvA1/Sv37aXawHZr72FjkP 3MO8o5PWxwybfRuud6Yz6rnvACsDH1/F1InnMPz+r0zl30z5eWrfZtpXzftbmTx1MDsXjGHKu6lY 7c15+tUl8JPpohURERERP9vwRhpXX30erN0DWBncNoLvntpVEe55/Gg8vsvduZCxY6HjbUu4N8H7 9Pk2/vT9+s7s9Ufbzp1plfWR9xkUERERERER+R/l1TO2ykp2A5B9oISC3XmAi4xSJ0lB5t7r1Omm 8zj4xZMVPw4ApP6QXmmbvWvfr/gBYus3h4jsEGE6fbnbd9O6ew96X9+fwYMH085pIbxt84rwwoxC ItpcQc9zzyQ6JBCXI4ucMpff4jfr8Jan+HZf+V2rblcBu49OgPoj/amvra8ov+9e+pKo0wZXCt+7 dh2lR3f5YdUmwlsMINACtrAu9GoexttfZdO2bVvatm2LJeVzwhKvJ8ha+VlwNaXfKHzDU5/TsveI 8kZnCWDklS34bMEmr8rOH+mvjtUWwzXxIaxbe2yxgYv3n9tBzFl9qmxbXf7MHt9T+zbTvmra3x5x Hl3CbSzdkFGe+tJ9LNtxxDDfIiIiIlJ39n38FqHNBhARYCEo+hKSA4t5Ma38Xb1G48f6GN/5Mv4E 367vvBm/P3L7GO6Y9rm/si0iIiIiIiJyyvPqDmDcTgBcLhfusvKr+DI3mH3baGKknfxP8zxu48g9 /uOB2wlYzE0uA/Sd8RyDE9JY99EWjhSUYC91YrEGVYTvXjWZpfab6T1iEhNbNSVj28fcP3ke6aVO v8Rv1pHtOXWW/qLMwor/O0szsdpisVupmDQtyiw6ITwDa0AokYFWCoNbA9Bj8DB6nJCm777fQojV QonLbZh+o/CcHQvZG7iWoS3DWWvpzxkBqUza6TmuP/JH+qsTYEvAarGQXnK8LEuycwiwd6uybXX5 CzB5fE/t20z7qml/q638fWuZJ7SFgv2F8IdXFIuIiIhI/SnJfp+fi29nQEIY73W9jPyMFeQeXcBp NH4srofxnS/jT/Dt+s7s+Blg/95U7zImIiIiIiIi8j/OuwlgAw63m4CQ4z8IBMXaK4WnHS4mqlMs sNufhwUgMLg9Iy9IYvS1t1Y8UrhTm6u5+oRtXM5c3nx+Hm8+D0ExbZn47HzuumIN49cZ/6BgJn6z 3NVMRvor/eGtw+H7Q+VxhrTCWXqgYvIXILxNOHx1EABbcBtcjhxyHC4CCn8FYNkD09lp8Ejm6tJv JtztKmXB2xlMGXU2ewL+Sca7D1VKmxn+SH91nKUZlLndnB4cwNajjzMPbhaHs6Rq26guf04fj+9r +3KW7AGgTXAgPxeWp79Ji1Ao8rCTiIiIiNS5l74/xKirk/j97Jbsfvnris+Nxo+2Oh7f+eP6xpfr O1/HzyIiIiIiIiJSM68eAW1k174iEi8rv2PSam/GkB7NKoX/vOQTmp5zJ39NDq84fJcL2/rl2G53 MS63m9Niyles22M6Me4flR9fFnve2UTbyrNcmpPO7w4XzmJzM5Bm4m8M6U/uN4CoQCtg5R8jLiDn lxWVwlv2HkT00fALR/Qg+9cXcAGOoh2s3nOEu0f8E/vRR65ZbdGc//dOfssjQMqqZUR3HcuYLjG8 sHKn1/vXVfpdZbmsTsun1409sAAWawjXjWrPwS/fMLW/r8f3tX05CrbwaU4Jt/Q6AwBbRHtGtGtS 7bazXlrNiwsuMh23iIiIiNTerpXfkdCzF4MSw3n5m0MVnxuNH70Z39WGP65vfLm+82b8rPGriIiI iIiIiHf8egfwj4+/iHvx7axZ3pdCdx7vfXKAdl2Oh2dvX8T9KyOYsOBlRuzLxBKRQO5Pcxm3McXn YztLUpn5+ndMWfIi/fYeIiKiiDffSKN/z+PbJFw4lDnTpvB75kGISMK6byN3f5Tht/gbQ/oPfOjk 2ZeWUlAaQZw7haljNlQO/6iQp1cuo7A4jDjLbu4b82FF2Io7p5EwcxJrV/+LzFwX8QlN2PXFM3y1 YRv+Upr3JS//HkBf66t8caTUeIc/qMv0r5n0KF3nTeLV5QPJD2lGYOanTHryR9Np8+X4/mhfT9y7 kPlz5/DSpZkQ7ubzH7O5oJrtgsPCCC3x66kvIiIiIjXIS3sRR+QrhOZvZHO+o1KY0fjRaHw39fkV dA6zERgaSZh1Kq+84sDlOMjgoeMM0+WP8aev13dmx88av4qIiIiIiIh4x7Jr1y43QI8ePTxuWFBQ YCrCgKBoWiSEciAtk+IaHgVstYWTlBhLcc4Bfs8t9jLJngXHNKdZGGSm78NRzeEDQqJIiI/GXXiY zN89v6+qNvH7ypf0j355HUmzbmR6CiTFBZORlklZNXEEhsSQ1DSYzLTMao8RHtec2BA4fOAA+d4+ o9mQlalr3iRw/i3M+OJgrWKo2/RbiW/REntZLun7vXs/sT+O72v7sgSE0aJFLNnp6eQ7/V13IiJS X8LCwjyGZ2ZmApCYmOhxO7PjNxFpWJ7Gj3U9vvPH9Y2v13d1e/0hIiLV0XhTRERE5ORkNI7btGkT 4Oc7gAGcJdmkpmZ73MblyCctNd/fhwagOGsfqVk1hzuLcslIza2z+H3lj/SXFWaRutdDeJHn8PxD +6iL2knu0pUOXfvy56B0hpzw+Dtv1W36XRxMN34ntCe+HN/X9uV2FpCWqosvERERkZOJp/FjXY/v /HF94+v1XV1df4iIiIiIiIj8r9JztE4hqd9/Q16ew3jDBnLuldfQkSxmj3uU3DKt7BcRERERERER ERERERHxN00An0L+79EHGzoJHr0+5wFeb+hEiIiIiIiIiIiIiIiIiJzCrA2dABERERERERERERER ERER8Q/dASwiIiIiIiIiIiKNjtUWz8B+F1OQ/gHrNh4EwB7xZ667Mp6XV/8HgE7XXk/XMHul/Zyl +1n92kdVwsuKC0hP2cLnW/ZUiv/T11aTVuo8+lkMfa+/jOxv/48PfztiGH/5Pk1ZvnQeAFnbH+H2 WVtPqfwZGfrkYi6NDsFR+Cs3jXzA9H4iIiJSd3QHsIiIiIiIiIiIiDQ6AUGJDB8+nBHjJ2GzlH9m j/oLQ/91XcU2Z103mOt6tqdZs2YV/+LjoyuF9/nbaURHR5PUtiu3P/QMC+++vFL8ySEBAFgCwrl1 ztP06RrMZyl5puI/uiPx8fE8OnYkdz267dTLn4FVd47h9qnfeL2fiIiI1B3dASwiIiIiIiKVNP1z L3o2281r6yr/iH1V/4EUbfoPH2UWeh3n6Tc8zOi4FUyYu83U5yIicvKzRTTFmfc7Lh/j+aX0dEa1 j+apX7KrDT+0eRXzn91R4/5ZW17l6aPhMW//m5fn3cHZz3zIiffpWqxBDJ35LH+3fMqIKcspdrlN x39MmcOBw+F9bk+W/NXE6XBQWot8i4iISN3RHcAiIiIiIiJSibOgKzfdcjeBluOf2cLOYtzNN2It dNYqTntELE2j7VU+L0j7mW1H70ISEZFTS9M/TeT1FU/x78FX0Tqm6neAWe8+9RV/G3+xX9KUt2cT AGeEnHhfTCDXT36Gq2M2M/qeZ8lz1u9k5qmePxEREal/ugNYREREREREKsndsRxX4HP0ig7mzaxi AKI79cNR8BMf5JQc39BiI/mMM4i2O0nZ/it5ZZV/ULZFNqfDaU3J3r29yjGstihiomw4trzNG47c Os2PiIg0jMyP7uHO7B5cesmlPLJ8JJmbN/DO/73DR19vx+E23v+Yw1uf4nDzlZwXvo6fqwmP7TaA ceNyKv4uyXqPRS9Vd0erla5XD8PlzGdTbikcnZP+++1P8re/JjF30GiyqrmT1Xz8tXOq509ERETq nyaARUREREREpBJnaTqvHSrior8n8ObaPQB0GHg6h75/pGKboNhzefCxKSQVp7K3IIiOreCRMXew 6WD5hHFM98E888Ag9m3bSlBSS/YeCITi48eISB7CjPEdCY5PJui3aQydsrnatIx54GGa5b3OtEe+ qbP8ioiIb+zhEYQElD9o0FmST37xsadFuNi1eSPPbt7IkidiOP8fF3PZgImMGV/CE/8ex4cnLiry wO0q4on1mUwY1o4J/6kaXlaYw6FDhyr+LjlSXCk86ZJpvPBnB4HB0USHHOH1+XeSXurEdnSC9JzT 01n1Uyg3Tx/AB7cv549z00bx++pUz5+IiIjUP00Ai4iIiIiISBUb3kjj6qvPg7V7ACuD20bw3VO7 KsJvfWwywRvnMHjplwC07zOXWbMHsunmFwArk6cOZueCMUx5NxWrvTlPv7oEfjoef+7OhYwdCx1v W8K9CTWno23nzrTK+qgusigiIn5yxWOLuSkhFICMD+9l9JNV72MNa5pAQkIC8fFNOJD5Pbll3j2G OOXlZ2m+8nbC1/9aJSz31/dYtarmO1Z//3opD762h7LSQg5k7K/0/luAJXc8zPuFrfnnmnlMvfwj Hng3zav4/eFUz5+IiIjUL00Ai4iIiIiISBX7Pn6L0FtGERHwKqWRl5AcWMzEtPJ39drCutCreRiP f5VN27ZtAbCkfE5Y4k0EWZfjDjuPLuE2Rm/IAMBVuo9lO45wWy3S8cjtY7A5D/srWyIiUgfWjRzE umo+Dwhqyl8vuZiLL76E7i2tbPzvOyy4dxjb0o54fQxH/maWZjRhXPc4r/ctzU0nJSWlxvAjZS4c Rb8w6YlNPD9+Dt0/v4HNR0q9Po4vTvX8iYiISP3SBLCIiIiIiIhUUZL9Pj8X386AhDDe63oZ+Rkr Ku7WCghuDUCPwcPoccI+332/hRCrhWJbMwAyS50VYQX7CyHW+3Ts35ta2yyIiEgDS7hwIoP/lsU7 bz7F7I0/UOTy4sW/1fho/gZunXclbiovDAqwhxMVFVXps9xc798vn/nBbFZfu5p759zIoNGLcLr9 G7+RUz1/IiIiUn80ASwiIiIiIiLVeun7Q4y6Oonfz27J7pe/rvjcWVj+eMplD0xnZ3FZlf1sJXsA aBMcyM+FDgCatAiForpPs4iINB6ZH09k1PtO4w1Nyt25mG2lV9LRXvnzllfNYs1VlT+77LLLanEE N6umzObyVbOYcc1/uW/dbj/H79mpnj8RERGpP5Zdu3a5AXr06OFxw4KCgnpJkIiIiMj/urCwMI/h mZmZACQmJnrcTuM3EfFVVJvbeHGWnaKIi5kz8Bo25zsqwm5a9Crn//Qctz39X0pdbqy2aP7010S+ 2rANgKmr/0PM6/cyYc02bBHtWbJqPgE/TmHolM2VjlH+DuCFVT4/ZtZLq2mRs4hhY/UeYBERf9F4 07+s9gTeeWs5cyeM49fsvaRm/m+teIpNbk3zhCE8PCmWXn0mNHRyRERETmlG47hNmzYBugNYRERE REREapCX9iKOyFcIzd9YafIXYMWd00iYOYm1q/9FZq6L+IQm7PrimYoJ4CfuXcj8uXN46dJMCHfz +Y/ZXHDC/lOfX0HnMBuBoZGEWafyyisOXI6DDB46rtJxgsPCCC3RpauIiDRirhK+/fZb/j54GGfv foE5z/3W0CmqVxcMHM4FkXa+27yjoZMiIiIiR+kOYBEREZFGRndkiMjJJDyuObEhcPjAAfJLXZXC LAFhtGgRS3Z6OvlOVw0xiIhIfdN4U0REROTkpDuARUREREREpM7lH9pHfg1hbmcBaamaHBARERER ERGpT9aGToCIiIiIiIiIiIiIiIiIiPiHJoBFRERERERERERERERERE4RmgAWERERERERERERERER ETlFaAJYREREREREREREREREROQUoQlgEREREREREREREREREZFTRKC3O4QnjuOZOX8CoKxoNzfe Oq0irNuFl9PtzNOIiwrHkXeIzZveZuPW/eYitgTQ+fyedO90Bs1iIyjO2c9XH/6Hb1Jyq9nYyuX9 +xMTaOWbtWv4rbjM1CG69+7HmaG2Sp/t+2AtHx8sLo/Vnsjy5+dUhM0ddRNbChzm0g+0u/o6/hQR VOmznF/e5e3vsyp9Fpr4N+6eMJA2TSMoOfwaIyf8x/QxLAHhNI21c/BgVpWwvvMWccbKKTz83SHT 8dWl0294mNFxK5gwd5upz83u74uTqfz8beWaNdgtVux2GyOu78NBh8vj9rUt/7qot/pyqrePxpC/ xt7/1XX7rW3+jc5fb89vERERERERERERETl1eT0BbLU1IT4+npQVT7By175KYWPuuo1WQYE4ih3Y gm1ccW0/us4czlOfHzQRb1Meu38iZcXZ7D/sIDHxn/Tq05/l4//FyzsqTwIn/nMSd9zcE4CCd9ea ngA+t98Qro8LqfTZt1vfrZgAdpdls3jxYlpcMYobzokjyGoxFe8xIXHxNGsSTOsL/0mLw9+zaVs2 ARn2Ktv1mjmehE/ncedb23E6C706Rmj8EJYubE+vPhOqhEU0jSfaHuBVfHXJHhFL0+iq+S9I+5lt uXm13t8XJ1P5+duQ/v2xhXZk/RvzMdO0a1v+Zuu3MTrV20djyF9j7//quv3WNv9G56+357eIiIiI iIiIiIiInLq8ngA+JnvLl3y2tfJdWMtmT2HH1p85nF9Ksy4DeXHujfz9lgt46vN1xhE6C1n8yCT+ 7+MfKHa5aX7+Xbww8xJ6j+vOy2M2HE9wcDtmje9BfoGD8DBbzfHVoOjwm4yf/E7F34X7jk8uu11F fPrpp3TsNszreAF+XPYMPwJ9u/bgii2vMP+pnyuFh8bEEGq10j3aTvr36VisVqyY+6XeYrUTGxNJ aHQwWGzExcUB4CzOJTu/8l3KtsgkOpwWS9au7WQc+cMdzBYbyWecQbTdScr2X8krK79LzBoQQXRU AIezciptHhMbS2lOFvlOt6l02iKb0+G0pmTv3l4lzGqLIibKhmPL27zhqO7Obs/7+6Kxl19MbCz5 2VmUuipvZw2IoEkTN1mH88v/tkXQrv1p2Mpy+fXXPZSecJNfcFQTAguPkH/0zr+A4EgigorIyTV/ F7sv5W9Uv6ExMdjycygMbl5z+QJB0Um0a9WU4px97Ew94FX+gqJjCC7K5UhpEGeceToUZLBjz2HD tNd1+zBitv6N8hca14LWzeOwlGbx6697cbj9nD8DntpnY+7/ytNu3D/Vtn2Zzn8t249ZNdWP2fYn IiIiIiIiIiIiIieHWk8AV+fzL36o+H9R3hEAig+aexyny3mE1z/cXPF3zq69ALhdlX8Av+q+6TTJ +YTZuzsx8/xmXqfR7SoBWxAU/c6e9KqPEa1LF42fzOUxwSSGBFI2diIzHC6Ks97irmnvGe5rCz2L GTNuwGqLIyAohBkzZgCw/9O5PLh6T8V20R0HsPjG08kujeLM1nYeHn4jnxwuv8M5KPZcHnxsCknF qewtCKJjK3hkzB1sOlhMQFAyy1c+wn39rmXz0QkVe+RfWPnSJMb26Uu+0/gu65jug3nmgUHs27aV oKSW7D0QCMXHwyOShzBjfEeC45MJ+m0aQ6ds9mp/XzT28hu/6AUO3DGIp9MqT7S0uGomcy9/m/6j 3ycs6SLmPzkBUn8iP6w1rW0pTBw9veIO+AELltFx4SgmflE+adq638PM/ttK+t/6maky8rX8jeq3 3xPP89cNH2M7v/ryBbhk5EzG9WrHr9t2YYlqRWzucoZP+sB0/nrOXUzvj5dSdOUNROUdwhbdkq0T BjE33fMEVl23DyNm6t8of10mP8NDXULYmZaJO7QFp4Xv576xU/gp3+GX/Bkxap+Nuf8D4/ZrVP5G jPLvS/sxw1P9mG1/IiIiIiIiIiIiInJy8OsEMECbAbN56Nq2NImJIOWbd5k/6+taxGKlz8TeALz3 9I8Vn8Z0u5V/n92EJ25eQPGIhbVKX2jTASxaMACAA9v+y72THiej1FmruLy1ftpdrAdmvvYWOQ/c wzwTkwbHlOZ/y9ix3xLWfCSrF7Zn7Niqj0AFiDk/n0Ejx+FwQ5+n1zB0WFs+mV/+LstbH5tM8MY5 DF76JQDt+8xl1uyBbLr5BRyFP/H87nxG9GnF6BUpALTpP4zc3xaTYuoR21YmTx3MzgVjmPJuKlZ7 c55+dQn8dHyL3J0LGTsWOt62hHsTvN/fF429/P5vZy7D/94MVuRjDbRhdTkoc8HplyaS/tYvAAyb fRuud6Yz6rnvACsDH1/F1InnMPz+r/xQQr6Xv+f6LeepfGO7T2D8VYncM3Q423JKAUju1sLrnLS8 5jruv3043+4rwmINo7W9xHCfum4fRszUv1H+9r4xl+tmp1Tc9Xvxgyu4a1wHbpi11S/5M2LUPhtz /wfm2i/Urn2Zyb8v7ccMT/XjTfsTERERERERERERkcbP6u8IywoOs3//fnJL3bRs34nObcK9juNv I+cyvEss216fybJfyh+pa7U15b7p17DnrRm8k1lQq7Slb/6Y5xY+zpxHH+ed7w/QrNOlzJr051rF 1VjtXft+xQTQ1m8OEdkhAgBbWBd6NQ/j7a+yadu2LW3btsWS8jlhiddXvOt4w1Of07L3iPJGYQlg 5JUt+GzBJlPHtUecR5dwG0s3ZADgKt3Hsh1HTKfb1/39paHKb/cbqcT37AhAvyWv8Oy4TgD0Sgxj 09e/Y7XFcE18COvWHpuMc/H+czuIOauPX/JdX+VfU/kCdLrpPA5+8WTF5C9A6g/pXh/j8Jan+HZf EQBuVwG7Tb4j3Axf2ocnRvV/opryl7t9N62796D39f0ZPHgw7ZwWwts290v+jNR1+zSrrurnRHXR vvyZvuoY1Y837U9EREREREREREREGj+/3wGctn4uE9aDLawty1cv4Kbpt/FG/5mm9+868H6m9u3E b2/P587Fxx/tGnXaWDqG2tgSdxmTJ19KkzOaAHDJuLvJmT+HT3JLa4qywjtzn6j4/8cfbeCcda8T 2/16wNwjck8GjhPKwe0ELAEABAS3BqDH4GH0OGH7777fQojVQonLTc6OhewNXMvQluGstfTnjIBU Ju2s/E7bmlht5Y/jzjzhbuqC/YUQay7dvu7vLw1Vfjm/vEtI/HCsgZu4NugzSs/rhS2kjDPtBdyb XUJA8GlYLRbSS46XT0l2DgH2br5mGai/8q+pfAESI+3kf5rn8zGObDdX5rXhS/vwxKj+T1RT/vrO eI7BCWms+2gLRwpKsJc6sViD/JI/IwG2hDptn2bVVf2cqC7alz/TV238BvXjTfsTERERERERERER kcbP7xPAxzgKUthTUkb3sM6m92nX6y7m3HA+O95+nPFPvEvln7ydFBcX0/6c8wCw2uwAtD77XFoG BQLGE8CVWAIIsFhwu/x3d2C9cLvA4n21OQt/BWDZA9PZWcMda25XKQvezmDKqLPZE/BPMt59iFJX tZtWjb9kDwBtggP5ubD8HbhNWoRCUf3sb1ojLb/S3M/IZBL/PPMmct55k3Xnz+L80wMoPLSWEpcb a2kGZW43pwcHsLWgvHyCm8XhLEmtiMPhdhMQcnzCLijWXk0ay9MeZKl8V2G9lb8HaYeLieoUC+yu NtxM/gDcvkyW1WH78MSo/isnsWr+AoPbM/KCJEZfe2vFI8c7tbmaq/+4YS3zZ8Rpon36RQPVT+Uk +DYZWx2z6avp/DUKN6ofb9qfiIiIiIiIiIiIiDR+fnsEdHBsLxY9Op2RNw/luj7X8e/JT3BOuJ3C /e+Y2z/mKp4YezFuVwEHwrozafJkJk+ezJ2j/wJA9o6ZXHvttRX/pn51EIAlNwzgpYOFhvEHBCUz b8YEBl3fm6uu7svdDy8i1mblwOev1D7Tf2APjyAqKopgqwVrUChRUVGEB5m7g86ssuJdBAa1oXNM 9ZNfNXEU7WD1niPcPeKf2I8+UtRqi+b8v3eqtF3KqmVEdx3LmC4xvLByp/n4C7bwaU4Jt/Q6AwBb RHtGtGtSJ/uPWryCFYv+ZTruEzXW8gMXr2UWcPOYv/DB2xlsfmknt/37HA58/F15aFkuq9Py6XVj DyyAxRrCdaPac/DLNypi2LWviMTLyu/os9qbMaRHs6r5L9nNvlIn152bVDl/PtafP/y85BOannMn f00+9th4K10ubFsRbiZ/vqrr9lEzz/VvxO0uxuV2c1pM+R2/9phOjPtH1cc/1zZ/hqk30T79oeHq p26ZTV9N569RuHH9+Nb+RERERERERERERKRx8dutYC5HFoFJZ9P3rL9UfJa16yvm3feyqf0DgloQ YLFAQDgX9uxZ8XnBgd08tvBz3xPodhHX6R/ccMFl5X+6y/h54yvc/8Rm3+M+6rKnXmBs4tHJq0se Ys0lsOeNcYx8doffjlGS8yGLP7iEacteJdDpJP29exm3yFz8K+6cRsLMSaxd/S8yc13EJzRh1xfP 8NWGbRXblOZ9ycu/B9DX+ipfHPHuruon7l3I/LlzeOnSTAh38/mP2VxwQvjU51fQOcxGYGgkYdap vPKKA5fjIIOHjjO1/zGJMU2wZWR5lbZjGnP5bX87g4ibQ1mXVYx1ywoiT3ucjx/aXxG+ZtKjdJ03 iVeXDyQ/pBmBmZ8y6ckfK8J/fPxF3ItvZ83yvhS683jvkwO06/KHg7gdTJn3Og+NX8A7U22kv38X I+aVp99s+dfEqH6NZG9fxP0rI5iw4GVG7MvEEpFA7k9zGbcxxXz+fFTX7cMTo/r3xFmSyszXv2PK khfpt/cQERFFvPlGGv17Vt7Ol/wZMWqf/lCX9eNr+/WVqfbj4fw1CjeqH1/an4iIiIiIiIiIiIg0 LpZdu3a5AXr06OFxw4KCAgAik6fx6uK/kvHuGv6bkcEra96ttF1kTFOaRIbhyDvEvsP5dZTs2rIS G9+MiFA7eYcyOZzvqBwaEMXAAVfR9M+9ubJ9FPddfzVf53n5aOmTQHhcc2JD4PCBA+RXeUaxlalr 3iQtViuwAAAgAElEQVRw/i3M+OKg13FbAsJo0SKW7PR08p0mn3/sxf6Bwe1Y/+ZTLLyhL//Zb3zn d12oy/IzZiW+RUvsZbmk76/6LtKAoGhaJIRyIC2T4lo8utXX+vMHqy2cpMRYinMO8HtucaUwX/NX Hzy3j7oVHNOcZmGQmb4PR4MUj+f22Rg0ZP2YUbfpa/z1I8eFhYV5DM/MzAQgMTHR43bHxm8iIiIi IifSeFNERETk5GQ0jtu0aRNQizuAHUUpfPttEMSdRsfI4CrhR7J+50jW795GW09cHD64j8M1BVvt dOrUCfJ+49tvIdfR+CYH/CH/0D6qm5pP7tKVDl378uegdIZ8c6hWcbudBaSl1n7wb7R/SLOe/PzV ggab/IW6LT9jLg6m1/xeVWdJNqmp2bWO3df68weXI5+01OoXj/iav/pQU/uoD8VZ+0it3c3xfuK5 fTYGDVk/ZtRt+hp//YiIiIiIiIiIiIiI77yeAC46uJIpU+oiKQ3P5fidKadq5kw498pr6EgWs8c9 Sm5Z45z8zkt9jgnTGzoV1TsZyk9ERERERERERERERERObX57B7Cc/F6f8wCvN3QiTmIqPxERERER EREREREREWlo1oZOgIiIiIiIiIiIiIiIiIiI+IcmgEVERERERERERKTRsgSEEx8fU21Y33mLmHRO nGEcp9/wMPPu6uT1sZP7zOSZRy70er/6ZLU1ZcWKFaxYsYInJnfxuG1ty8FXno479MnFrFixgqWL 7qvnVDU8f7QvT+dHY9NQ7a8mDV3+K9es4dVXX2PdunXE2xrnVI03/UtjZKZ+Glu7PNWo/284jbNX EREREREREREREQFC44ewdNHUasMimsYTbQ8wjKMg7We2peR5fezA8DiaxgZ7vV+9sgQQHx/Po2NH ctej2zxuao+IpWm0vZ4Sdpyn8l915xhun/oN8fHR9ZyqhueP9uXp/GhsGqr91aShy39I//4MHj6N 4OBgrBafklF3vOhfGiMz9VPb7wcxR/1/wzmpJoDNrujzRWNeMVUf+T/Z+WNFaG3jN8toRVFo4t+Y PvdpXlj+IovmXePTsf6XnYwrt+q6//Fn/LUp38bcvxqtuPR1ReagxxdzT5dYn9PZmPuHuu5/RURE RETk5GOLaOrTj48Wq524uDhio4PBYiMuLo64uDiiw21VjxWZRJduZ5EUWTnMaosiLi4Ox5a3eeOj fTUeKyg6ic5du3F6cjPPaQoIJy4uDlsjnKwpczhwOFxVPrdFNqdLt7NoEVW13ACw2Ehu34luXToQ EVi5xkJjYoiyW2ssXwBrYCTtO3flrE7tiQ/3vvydDgel1aS7tizWEGL9cDelJSCCmJgQwpufzpnJ TbBY7XQ4qyvNQwMrbRca14KOXbrRqX2rattFUHQMUcEBWKyhtO90Fu1b1/z7QLXtq4b6MXt+eKof Mzzlz0z7MGx/NTgVyj8sNoYom5WwhLZ069KRqFq0S6P8lefRQ//l4fwG8+3jj/2L2foxOr6Z+vGU v5rKx0z9mP1+8Mggf2Z4yp/VFkH7zl3p3KE19hOiNyr/xlA/DdH/S2WBxps0HmZX9PkiNH4ISxe2 p1efCXV6nNqoj/yf7DzVnz/Kzx/toyDtZ7bl1ryiqNfM8SR8Oo8739qO01lY6+P8r2tsKwrNqOv+ x5/x16Z8G3P/OqR/f2yhHVn/xvxqV1wahRsJbxpPdJDvF5+NuX+o6/5XREREREROPk3/NJGnbwzi v++8yzvvvs+erFKv9reFnsWMGTdgtcUREBTCjBkzANj/6VweXL2nYrvojgNYfOPpZJdGcWZrOw8P v5FPDhcDEJE8hBnjOxIcn0zQb9MYOmVzleNcMnIm43q149dtu7BEtSI2dznDJ31QZbsAeyJ3PvUk sd8tZOLij7zKS0OJ6T6YZx4YxL5tWwlKasneA4FQfDw8KPZcHnxsCknFqewtCKJjK3hkzB1sOli+ Ub8nnuevGz7Gdn715RscdyFPLbobV+o2slwhJLdrzYKh/fj8SHldmyl/f4lq2ZnLr7yCyy/tyaej +7P0gG/XzeGJY1ixoA2/7cijbecOfLk5g+gQG2ckZdB7wH24gC6Tn+GhLiHsTMvEHdqC08L3c9/Y KfyU76iIp+fcxfT+eClFV95AVN4hbNEt2TphEHPT8ysdr7r25al+zJwfRvVjxCh/Ru3DqP2d6uU/ fMEy/vT1FoLPDCc1P5LOyWU8OOJ2vswq8Uv5g+f+y+j89qV9mKkfo+ObqR9P+fNUPmbqx9f+yUz+ jHjKX1jSRcx/cgKk/kR+WGta21KYOHo6vxWXGZZ/WCOon/rs/6V6pieALQERREeVURqUREv7IX5J K6R95zPJ3bmNfYVlJ2xoI/mMM4i2O0nZ/it5ZZVn74OiYwguyuVIaRBnnHk6FGSwY8/hP2yTRLtW TSnO2cfO1ANV0mKLTKLDabFk7dpOxhFHpbDQuBa0bh6HpTSLX3/di8N9QlhMDLb8HAqDm1fZ32K1 ExsTSegJK0IAnMW5ZJ/QoVoDI2nXoQ1B7mL2p+7iYH7l49dWTGws+dlZlLrclT63BkTQpImbrMPH v5A85d9qi6Bd+9OwleXy6697KDW5eMLs8Y3iN6rfmsKtARFERwVwOCunSrpKc7LId1ZO1x+ZrT+o XfvxJv6aWG1RxETZyle8OHKrhIfGxBBqtdI92k769+lYrFaseDfTZNQ+a6o/f53fNaYrIILoKAuH s46c8KmFuLhYjmQdPt7uPMTv6fw9xhbZnA6nNSV79/bq0+Fj+/X1/K9pf7Pty1P/5in9ptuvQf0a lW9NTPevtey/KvY3qp9atl9/CktoS7v4YPb+sp2sP2bQoP0b9Q++tu/alo+/+t/GUD8iIiIiIuJf mR/dw53ZPbj0kkt5ZPlIMjdv4J3/e4ePvt5e5Zq2OqX53zJ27LeENR/J6oXtGTu2+gXFMefnM2jk OBxu6PP0GoYOa8sn88sfVZq7cyFjx0LH25Zwb0LVfWO7T2D8VYncM3Q423LKJz2Su7Wosl1gcDL3 Pj0P28ePMvGlL80XQoOyMnnqYHYuGMOUd1Ox2pvz9KtL4KfjW9z62GSCN85h8NLyPLXvM5dZswey 6eYXKrbxVL7tbrmR0N8eZsikz8qPaIsh0n38tySj8vc5h4ERnPv3S7niyis4NzmQzz78kKcm3sT3 Pk7+HuMsSWf8PQ9w0bNrGJKzgJunbGP5W+vpEmZjS4GDvW/M5brZKRXt+eIHV3DXuA7cMGtrpXha XnMd998+nG/3FWGxhtHaXnkCsKb25al+zJwfRvVjxEz+am4fxu3PyMle/gBRXfcz4MaFONxw4d1L GT/jEgaOW28q/0b5M+q/jM5vX9uHUf2Y6V+g5voxyp+n8jFTP772T2bzVxOj/A2bfRuud6Yz6rnv ACsDH1/F1InnMPz+rwDP5b/LILw+6qeu+38xZnoCuDGs6ADPK/p8WZFUHyumPBm/6AUO3DGIp9Mq rzxqcdVM5l7+Nv1Hv2+Yf08rQvxxfDPxG9VvTeFPZCWzfOUj3NfvWjYfrS975F9Y+dIkxvbpS77T cx78sSLU1xVDRoxWvFw0fjKXxwSTGBJI2diJzHC4KM56i7umvWcqfqP26cuKIbPnd00CQzvy0srJ 3HptX9JKnQCExvfjxed70b/3cEpNxO/rikJf26+v57+n/c20L19WND6Z08EwfqPy92XFppn8+dJ/ GZWvmfzVh+Y9x/Jch0hS88Lp3MbCIyNvY+PBIlPpM+offG3fvpSPP/rfxlA/IiIiIiJSe/bwCEIC yp965CzJJ7/YeTTExa7NG3l280aWPBHD+f+4mMsGTGTM+BKe+Pc4Pswxdxeckb1r36+YANj6zSEG XRBhet9ON53HwS9mVfx4DZD6Q3qlbQJD2jJt8WiSdz7O8JNm8hfsEefRJdzG6A0ZALhK97FsxxFu OxpuC+tCr+ZhPP5VNm3btgXAkvI5YYk3EWRdTsnRBfueyrcwo5CI7lfQ89wsftz2G9lFWVS+vaPu xHa5hScevJZDWz7h/Tee5JHPtlLkqrqyoOb2aaysZDcA2QdKKNidB7jIKHWSFBTAlgIHudt30+7s HnQ6LZFQeyBRTgvhbZsDlScgD295im/3lf8G4HYVsPuEy92a2pfZ+vHE1/oxk7+a2odR+zPjZC9/ gNTX1leUz3cvfUnUksGAuQlgo/x56r/MpN/X9uGpfn6mg+nyq6l+jPpns/VfF/zRPjzlz2qL4Zr4 EJ5ce+y9yy7ef24HQ+7vA5RPAHsq/10G4fVRP9LwvHoEdEOvGADPK858WZFUHyumPPm/nbkM/3sz WJGPNdCG1eWgzAWnX5pI+lu/mMq/0YoQX49vNn6jFVXVhTuKf+L53fmM6NOK0StSAGjTfxi5vy0m xcQEkD9WhPq6YsiI0YqX9dPuYj0w87W3yHngHub94TEkRozapy8rhrw5v6tTmvcV/znsZuSfmjL1 s/3l6R1+OYc3P11xd7dvK06NVxT62n59Pf897W+mffmyorG02Dh+z+Xv24pNM/nzpf8C4/rxdUWe P0R1O8yA4dMpdcFf7niOOx68lo23vmIqfUb9g6/t26fz2w/9b2OoHxERERERqb0rHlvMTQmhAGR8 eC+jn/y5yjZhTRNISEggPr4JBzK/J9ePT/1x5B7/8dntBCzmX0GTGGkn/9OaX9UFEBzTi8JXNhB3 3U10i/ycH/xwM0h9sNrK38eYWXp8wrNgfyEcfYVjQHBrAHoMHkaPE/b77vsthFgtFRMAnsp396rJ LLXfTO8Rk5jYqikZ2z7m/snzSC81P8laW47C38ncn0/LZkfbVtQvpGZXrRsz7bNG7vJ8uFwu3GXl 5VHmhmNvEe074zkGJ6Sx7qMtHCkowV7qxGL9f/buNDCKKl34+L876c6ekIUQEjAgIsgiuL9eGZzR ce7oMCogICAiIMsFRAQVFATEXRBUFgUEhAAKKMLIjI4LoCzCjIqIEZE1ZGHPAtk73f1+SMgC6arT qWoSwvP7BKmuszzn1Omkz1PVARcUc2aP5201T/NLdXy0GB0flf55mh9680/JJR5/gIKMirvRncUZ WG3R2K0oPflOr39a65dK+w1fvxrj4038PI2P3vqsOv6+YMb80Bw/WxxWi4W0ooqxKMrKxs/eseJF OtdHbY+PqH1ebQDXhYwBrYwzIxlJKnyZ0XbokxRiB7eBpAP0WPAhd+2axKNvJtMlPoQt/zmp236V jBAj9XtTvlZGldbxTbO2MfDFwViTxuOy+DH0niZsHb9FNYRKjM6fukxrfhrNGPLm+vbk06T9vDXo Dti6Aiw2hnaKZcPI0vYYzTjVyyg0Y/4avf4vRsalVvu16MXfHWI8Y1OL0fULtONrVsamUUfWrCv/ 5f6nFVsIXdwLf8uHWIKNtc/o/L5Y8fF0/daV8RFCCCGEEELU3LqhvVlXzc/9Ahpy211/5s9/vovr mlr59ovPmP3MwySnnqnm1RrcLrB49TGmstTThUS0jQYOeXxNXsY7vPr+erZEzOO56cPoPWzWBV+j Vhc5iw4D0DzQn1/zS58g1qBJMBSUHc//HYDFL0xmv+ITuM7ncuawduEM1i6EgKgWjHt3Jk/evYrR 61IMt1/PmQPreHroOuLb3MLdd9/D60sf4/jPm/nqyy/5+tufyCsbI0/z0yj/wFYMvTWB4fcNKb+B pW3zv/P3al7r1pgvnuaX8vhoXB9Gxseb/lVHb/4ZdSnEHyC0WSj8eKq0zUFX4Cw+XmXz1+0qLTvA UvWrvlT6p7V+qbTfl9evN+uLp/HR6p/y+Pvo/cOM9VNz/IrTKXG7uSrQj915pddPYKMYnEXmrK2+ Hh9RN1i9erVixsDAgQMZOHAg/XrdUJ4xUJlmxsAB7YwBrYyzblPe45UhdxFmLSEvL48CLzKSVBxa 8SyLvsjk/sHjWb7mHyyY/hRN7Orna8n+7XOCYu/D6h/JfQFb8b+5C7agVlxjz+OfWRV30Hpqv+eM kMam1O9N+VoZVVrHs/fO5Yh/O/o1DSWsaX+u9kth3n5zHxpjdP7UZVrzU2n8TLq+PTn2zbsENO7D 1YH+hDXtzxXugySV3cWoWr7XGYVlzJi/Rq9/o+erzk+96686evHXi69RRtcv0Jn/JsxfMxRkVPyF 4yxOx+oXTLi/1XD7jM7vixUfj+9fdWR8hBBCCCGEEOaL6zyOPn9ozg9rZ9GzxwCmzVvp/eYvUFJ4 EP+A5rSLspvexl8XfEPDG8ZyW2Jo2U+stO/cospr3O7Sv2e2zX6KXaF3Mm3AdReUM2x+EknzHqpx O15etpKls++o8fnVceTtYnN2EY92uRoAW1grBrdsUHG8YC8rD5/hqcF3Yi/7+8tqi+SWP7ZVriP6 5uuJtJV+xFycncZJhwtnoXl3d6vI+HUHC9+YTJ8HHmH1d+nc2vNJHmoU7PN63e5CXG43V0aVfj5j j2rLqD+pf5ZRUU7180t1fLSuDyPjY7R/evPPqEsh/gCJPXoR4W8FrPxp8K1k/5ZU9fyiQxwtdtL9 xgSv+6e1fqm035fXrxnri1b/VMffV+8fvu6fqySHlam5dBnQCQtgsQbRfVgrTmz/5JJov6gbTEt9 qO2MAaMZSZUa55OMKT3FOVvJYDx3XjOQ7M/Wsu6Wl7nlKj/yT61RuvvJaEaIXv1WL8rXyqjSOu52 FTP7X+lMGHY9h/3uJP3zl5QehXFeITXK6KntjCEzaM1Po/PDjIymksL9LE0tYEjnOD7qdDvHNr5K 2T6z4fJ1M1pNmL9Gr3+l8z3ML7MyGj2Vrxd/m1kZm57qNyGjTXP+K84vTxmXqsf1hDYPhR0nALAF NsflyCbb4cLP6Pw3OL/NuL7LCq/R+mha/UIIIYQQQog6J2PjOIZ9afxRwEXZXzP/q7uYtHg1/k4n af9+hlHz9iqdO3FhEu1CbPgHhxNinciHHzpwOU7Qp98oALL2zOP55WGMmf0Bg49mYAmLI+eX6Yz6 9sAFZbmcZ5j2xJssXzSVXtseYmWlJNv4qAbY0jNr3MfAkBCCi8z/zOmtZ+Yyc/prLPtLBoS62fZz FrdWOp40dhJxU8ezZuVDZOS4iI1rwMHv3mHHpmSPZVYW17kfr02awMmMExCWgPXotzy1Ib38uF78 zeQsOs3m9cvZvH45touQT+wsSmHqxz8wYcFSehw5RVhYAWs/SaXn7TUrr7r5pTI+WteH3vj4un96 88+ISyH+AMe/dvLuskXkFYcR4z7AxBGbqlbsdjBhxse8NHo2n020kfblkwyekazUP731S6/9RuaH CqPri1b/VMdfa3yMrk++7B/AqvHT6DBjPKuXPEhuUCP8MzYz/u2flcquC+2/mOu/qJ5pv1VUzhh4 bM4XFLvcWG2R3HRbvPKE+XXBNzScNpbbEgezNSWX0oyB5uyu5heu81XO+DiQUVKR8eHl712lGSH3 0i7Kzi+ZVb8zIvrm63Ht/Iksh6s8IyaimoyYYfOTuM39b/oNXeZFzS4+yshj0Ij/YdWEOezct59Z /3cDxzeuUju7UkbI2ulfQXlGyEum1G+8fDUHViwm8oMxjLCEMfPl/V6frzV+WlTnT03Lvxi05qfR 8TPj+gbYuOAX+j7RneFh0azuX3FdGy2/ckbhmFXJFRmFZe+HZsxf1evfyPme5pev1ze9+OvF12j9 vh4f1flVOeNyxtYjF7Zf57iepvf3JvKjV8gqgc6DO5H1+/u4AJfB+V9Xru+aro9m1S+EEEIIIYSo e9wl5n0P7Jo3xrPmjQt/vqRf1yr/P5A0nJ6VbrB7cVA/3bK3fzCdBz96l4T4aAqzj3Myp+L7lA4k DeeBSuUVHN9It79trHK+f2BLbg61MfeVTUp9qc6Yrl1qfO45Cc2vJC/rCCmVnkB19uDnDO6xmSZN oslKSyPX6WJupXMcuXt4eUx/QmMaEx0Ep48fJ7fSHRl68U2e/gT3zYkgLjYSd/5pMk5WfbqjSvyj E5vROC7Cu87qcJjwhO6zqa/S7eHSf+96vj+Pl/180gMVKfnfLZjAAx83plEIZKQdxeGGle9XLeeL QQ/whYc69OaX3vic4+n60BsfPXr905sfevNPS32IP8DJHYsYvgISYgJJT80ovyGmsvSNC3lk48IL fq7SP631S6/93syP89cXlfFRiZ/W+Oj1TyU+4Hl8VNYnLarzQ4tW/wpP72Bs/+7ENmmKvSSHtGMV SUe68c+s/fGprfVfVDA1rczXGQNazMr4MSNjqqYZf3v+lU7YoGDWZRZi3ZVE+JVvsvGlY8rnG80I 0avf1xknAMVnt/PBST+6WVfz3RnvN1hrmhFqRsaQHl9nvOjNT6PjZ/T6Bjj901wcEUuJzPuOTzOr fjmt0fL1MgqN9t9oRpzK+Z7m18VY3/Tib0bGplb9vh4fpfnlIeNS+biO4xvymbN8MfmFIcRYDvHc iK+9a5+GunB9G1kfzahfCCGEEEIIIYxwOXJJTcmt0blBjW7n1x2z+YeJX5fkFVcR33//PX/s8zDX H3qf197bV+Ww25lHakqeZhG5p45Ss96DsyCH9JScGp4Ntz7Yn1vD7fywU+1vyLqmMPMoKTW/+VtJ bY6P0f6pzD8j6nr8AUryM0nx/l4CQK1/euuXVvt154fO+qLCaPy0+ncxxl+PL/sHLk6k+fY71X3b fm2X+vpf11kOHjzoBujUqZPmC/Py1BdpIxkPAFZbaLUZAyoCo6pmfJjNL0g7I8Y/sCXr185i7iPd aumXPmu1GSGXUvkTV63Ff+ajTPnuhA/K1+br+eNrevPTjPEzen37snyLX0iVjMILGeu/fnx9e/7F mJ9a8dePr1G+Hx9fz189/kFRJDQMJCM1o9oxNNa+un991/X665KQkBDN4xkZGQDEx8drvs6b39+E EEIIIcTlQ37fFEII3/vbUxOJWv0WSYe9/xxQCCE80fs9bsuWLYCPNoAvZ2GJj/L8wGOMmby+tpty yUls34HWHbrxWI+G9O0+kpySy/vDfyGEEJcv+UBOCCGEEEL4kvy+KYQQQghxaVLdADb1EdACzqa8 x5jJtd2KS9ON99xLGzJ5ZdQ02fwVQgghhBBCCCGEEEIIIYQQogZkA1jUGR+/9gIf13YjhBBCCCGE EEIIIYQQQgghhLiEWWu7AUIIIYQQQgghhBBCCCGEEEIIIcwhG8BCCCGEEEIIIYQQQgghhBBCCFFP yAawEEIIIYQQQgghhBBCCCGEEELUE3VqAzix61Teeb2zoTIsfqHExkaZ1KLqBcf/gcnT5/D+kqXM m3Gv18eveuRVZjzZtsb11/T85atWsXr1R6xbt45Y24VDr3f8cmN0nMSFLsb1WV95e33K/PWOanwv l7heLv0UQgghhBBCCCGEEEKI+si/thtQmX9oDA2jAw2VERzbl0VzW9Gl6xiTWnWhLlNHE7d5BmM/ 3YPTme/18bzUX0nOOVvj+mt6ft+ePbEFt2H9JzOxWrw/frmxh0XTMNJe282oVy7G9VlfeXt9yvz1 jmp8ja7flwqZP0IIIYQQQgghhBBCCHHpUr7N0+IXRlRUEKGNr+KaxAZYrHZaX9uBxsFV95CDY5rQ pn1H2ra6Als1H6IHREYREeiHxRpMq7bX0qpZtEadocTExFQtx2IjsVVbOrZvTZh/RfMtVjsxMTFE RwaCxUZMTAwxMTFEhtpUuwiA1RZGq3YdaNe6GfbzohMcFUVMTAzXRdpJ+zENi9WK1c+ifNxqiyAm JgbHrn/xyYajHtsQEJlAuw4duSqx0Xlt0z9fL/41ZfULIzqqwQU/j4qOJtRPvSKt+IZERxFhsxIS 14KO7dsQUd1deB7GH0rjH2G3YgtPoH3Ha0kIr2bsNc4HsIU3pn3Ha2kS4d28UWX1D6dVuw5c27YV sdXMTU/xUb3+9PqnwtP8C4xoQGilMfELDKfBeXHy1D/V69PX/ddbf/TGx5Oo6Gjs1ewaWv3CiIoO 1e0fqMVXjxnz19P4Qx2Ynzrnm/n+cz6l9dvg/DNKq/8q66PR+WO0fiGEEEIIIYQQQgghhBDmUL4D ODR+BEmzm7Nv71latGvN9p3pRAbZuDohnft7PYcLaP/sO7zUPoj9qRm4g5twZegxnhs5gV9yHeXl 3D59PvdvXETBPY8QcfYUtsim7B7Tm+lpuVXq87PHM3bW20T/MJdx8zcAEBB9Iy++MYGEwhSO5AXQ 5gp4fcQTbDlRiC34WqZMeQSrLQa/gCCmTJkCwLHN03lx5WGlPoYk3MHMt8dAyi/khjSjme0A44ZP Zl9hCQB3jH6Wv0YFEh/kT8nIcUxxuCjM/JQnJ/1b6XhYYl+mjG5DYGwiAfsm0W/CzgvacNfQqYzq 0pLfkw9iibiC6Jwl9B//ldL5KvGvKb+ARJYsf53netzHzrLy7OH/w/Jl4xnZtRu5zhLdMvTi23/2 Ym76zy4CrwklJTecdoklvDj4cbZnFgHa4w/Q462F3LZpI7ZbriKrOIJrmtl5tf8AvjldqHR+1HV9 eOeF3hxN3k1AQlOOHPeHQsOhKxcY05lZ857ClZJMpiuIxJbNmN2vB9vOFOvGR+X60+ufCq3512v2 YtrMHca4744D0KzHq7zyh+X0HLJVt38q1+fF6L/W+qM3PlpGz3uf40/0Zk5q1XWsyd+mMv2v/6Ln 8C91579efPWYMX+1xr+256fe+Wa+/1RHb/01Ov+M0uu/3vpodP4YrV8IIYQQQgghhBBCCCGEebx6 BLSzKI3RT7/AHe+uom/2bAZNSGbJp+tpH2JjV56DI59Mp/srB3C4S1//5xeTeHJUax55eXeVcpre 253nH+/P90cLsFhDaGYvqtqowESemTMD28ZpjFu2vfznQ954lsBvX6PPotKfteo6nZdfeZAtg96n OPd7Ro78npDGQ1k5txUjR3r/iNmHX3kM12eTGfbeD4CVB99cwcRxN9D/+R0ArJ/0JOuBqR99Sgg2 /mgAACAASURBVPYLTzPjvA/t9Y7n7J/LyJHQ5rEFPBN3Yf3R141h9N/iebpff5KzSzedEjs2UT5f Nf414cj/hYWHchnc9QqGJx0AoHnPh8nZN58Dhfqbv6AfX4CIDsfoNWAuDjd0fmoRo6fcxYOj1gPa 439O1C259B46Cocbus5ZRb+HW/DNzGSF8608O7EP+2ePYMLnKVjtjZmzegH8YixulbV8dADB+16l 7/jSDT2rLYpwd0Xs9OKjd/2pxEeL3vwz0j+V6/Ni9d/T+qM3Plr+uT+H/n9sBEm5WP1tWF0OSlxw 1V/iSfv0N6X+GWN8/uqNf23PT73zzXr/8URv/TU6/4xS6b/n9dH4/DFWvxBCCCGEEEIIIYQQQggz efUMzpKiQwBkHS8i79BZwEV6sZOEAD8AcvYcotl1nbj/gZ706dOHlk4LoS0aX1DO6V2z+P5oAQBu Vx6HKm0g+ge1YNL8t7gqZRaTKm3+2kLa06VxCP/akUWLFi1o0aIFlgPbCIl/gAATvrDWaovi3tgg 1q0592G0iy/f20vUtV0Nl62q7cCbOfHd2+WbLwApP6Upn68a/5raNGsbTe8fXDppLH4MvacJW2dv UTpXNb4pH60v30D4Ydl2Iq7sA6iP/5E1X5afv/u/pwhvHaZ0vj3sZtqH2li0Kb20dcVHWbz3TI3i 5El+ej5hze/m9huvITLIH5cjk+wSl3J8tK4/M64Po/NPq396Lmb/Pa0/Rtp/6JMUYm9vA0CPBR/y 7qi2AHSJD2HLf076fH0xY/5qjX9tz0+V8814/6kpM+afUSr997Q+mjF/jNQvhBBCCCGEEEIIIYQQ wlxe3QGM2wmAy+XCXVL6KW6JG859k1+3Ke/RJy6VdRt2cSavCHuxE4s14IJizuzJ9lhFYFQX8j/c REz3gXQM38ZPZY9f9QtsBkCnPg/TqdLrf/hxF0FWC0Uut1ddOZ+fLQ6rxUJakbP8Z0VZ2fjZOxoq 1xvx4XZyN5+t8fmq8a+p7L1zOeK/hn5NQ1lj6cnVfimM3+95LCtTjW9BRn75v53FGVht0ditYFUc f0dOxeaV2wlYSpMT9OZPoa30+04ziival3csH0z8is5DK55lkX0Q9w8ez7grGpKevJHnn51BWrFT LT4a158Z14fR+afVPz0Xs/+e1h8j7c/+7XOCYvtj9d/CfQFbKb65C7agEq6x5/FMVhF+gVf6dH2x mjB/tca/tuenyvlmvP/UlBnzzyiV/ntaH82YP0bqF0IIIYQQQgghhBBCCGEu7zaAtQoKbMXQWxMY ft+Q8kcCt23+d/5ezWvdGh/252W8w6vvr2dLxDyemz6M3sNmUexy48z/HYDFL0xmv9YdU24XWLzv lrM4nRK3m6sC/didV/qdhYGNYnAWpXhdVk2lni4kom00cMjrc1Xj73aVHguwVH/XndZxt6uY2f9K Z8Kw6znsdyfpn79EsdoNksrxDW0WCj+eKu1T0BU4i49T7AKb6vh7ql/nfFvRYQCaB/rza35p+xo0 CYYCr6vyyOXMYe3CGaxdCAFRLRj37kyevHsVo9elGJ5/yteHBr3553C78Quq2LAJiLZXOa7Vv3Ie rs+L2X9P649S+z0oztlKBuO585qBZH+2lnW3vMwtV/mRf2oNRS43VoX+6cW3tO3VX59OE+av1vjX 9vzUO9+s9x+99bGm7VOtv6a86X91jM4fo/ULIYQQQgghhBBCCCGEMJdXj4DW4nYX4nK7uTKq9I4f e1RbRv3J+8cPu92ldwhtm/0Uu0LvZNqA6wBwFOxl5eEzPDX4Tuxlj9S02iK55Y9tq5xfUngQ/4Dm tIu6cPNEi6skh5WpuXQZ0AkLYLEG0X1YK05s/8TrPtTUrwu+oeENY7ktMbTsJ1bad26hdK5q/EuK DnG02En3GxOqLUfv+IEVi4nsMJIR7aN4f/l+pbaBenwTe/Qiwt8KWPnT4FvJ/i0JUB9/T/TOd+Tt YnN2EY92uRoAW1grBrdsUG1Zw+YnkTTvIeW+nxN98/VE2kovueLsNE46XDgLS3fQjc4/o/EB/fl3 8GgB8f9besen1d6Ivp0aKffvHE/XZ13ov0r7PXPxUUYeg0b8D1/9K52dy/bz2P/dwPGNPyj3Ty++ 4Pn69Gb+eqI1/rU9Pnrnm/X+o7f+1bR93qjJ+mK0/0bnj1nxF0IIIYQQQgghhBBCCGEO0+4Adhal MPXjH5iwYCk9jpwiLKyAtZ+k0vP2mpXncp5h2hNvsnzRVHpte4iVe7JJGjuJuKnjWbPyITJyXMTG NeDgd++wY1Ny+XlF2V8z/6u7mLR4Nf5OJ2n/foZR8/Yq1blq/DQ6zBjP6iUPkhvUCP+MzYx/++ea daAaExcm0S7Ehn9wOCHWiXz4oQOX4wR9+o0CIGvPPJ5fHsaY2R8w+GgGlrA4cn6ZzqhvD+ierxx/ t4MJMz7mpdGz+WyijbQvn2TwjGTl48Vnt/PBST+6WVfz3ZlivKES3+NfO3l32SLyisOIcR9g4ohN 5cdUxl+L3vlvPTOXmdNfY9lfMiDUzbafs7i1mnLioxpgS8/0qu8AcZ378dqkCZzMOAFhCViPfstT G9K9io+R/unRm38/v7kU9/zHWbWkG/nus/z7m+O0bK/eP9C+Pmu7/yrt17LnX+mEDQpmXWYh1l1J hF/5JhtfOlZ+XK9/evEFNK9P1fnrid741/b4aJ1v2vuPRnz11m+j/TunJuuLGf03Mn/Mfv8XQggh hBBCCCGEEEIIYYzl4MGDboBOnTppvjAvL0+pwMCoxjQKgYy0ozjMf9IlAKExjYkOgtPHj5Or+gxi ZVZimzTFXpJD2jHffFejbgtsoSTER1OYfZyTOYVenev7+FuZuGot/jMfZcp3J2p0vqf4Dv9gHQkv D2DyAUiICSQ9NYOSavpgdPy1zrf4hdCkSTRZaWnkOi8s2z+wJevXzmLuI934x7H8C47r8QuKIC42 Enf+aTJOVvd9q8bnn9H4aM0/v4BImsQFczw1g8JqHmWr3z/d2mu1/8bbr0e7f3rx1aM3f5VaqLn+ 1P781Dr/Yrz/GGmfHqPri9H+G50/dSH+9UlISIjm8YyMDADi4+M1X6f6+5sQQgghhLi8yO+bQggh hBCXJr3f47Zs2QL4YANY1F+J7TvQukM3HuvRkL7dR5JTYu7m+7kN4Am7vb+79mIJS3yU5wceY8zk 9bXdFCFEPSPri6hMPpATQgghhBC+JL9vCiGEEEJcmlQ3gE17BLSo/268517akMkro6aZvvkLkPLj fzl71mF6uWY6m/IeYybXdiuEEPWRrC9CCCGEEEIIIYQQQgghzCAbwELZx6+9wMc+LP+f0170YelC CCGEEEIIIYQQQgghhBBC1H/W2m6AEEIIIYQQQgghhBBCCCGEEEIIc8gGsBBCCCGEEEIIIYQQ4rLU bcY8xt8QU9vNED4WHP8HJk+fw/tLljJvxr2mlm21NSQpKYmkpCTeera9qWXXF1c98ioznmzrs3LM Kv9i6/3mfJ5uH637uku1fwD93p5PUlISi+Y9V9tNuUBi16m883pn3depxv9SHidRP8kjoIUQQggh hBBCCCGEEJelsIaxRNr9arsZwse6TB1N3OYZjP10D05nvrmFW/yIjY3lqZ5d2ZNfaG7Z9URe6q8k 55w1XI49LJqGkXaflX+xhTaMJTJA/x69S7V/ACvGjuCf8f/H0jeb1XZTLuAfGkPD6EDd16nG39P8 FKK2+OQOYK2MKotfKLGxUb6o9rKnmrGipS6Pz/JVq1i9+iPWrVtHrE1/6l5uGTfexqc6vhx/1Yw2 UTN642/G/Kisvl1f9X39NIvRca9v80YIIYQQQgihzRbW0JwPHy02Elu1pWP71oT5V5RosQYSExNT pQ6LNfiCnwEERCbQrkNHrkpsVH1bwxNo3/FaEsJtFxwLjmlCm/YdadvqCmyW845FRRFht2qebwuP p33H9iSE2/ALDCU0+Lx7cjz0T0VIdBQRNishcS3o2L4NEdX8zR8QGUVEoB8WazCt2l5Lq2ZVP5+x 2sJo1a4D7Vo3w15N9Xrt1yvfU/wsfmFERQUR2vgqrklsgMVqp/W1HWh8XvlW/3BatevAtW1bERt6 YXy1BEdFERMTw3WRdtJ+TMNitWL1qzqIev3X6985JQ4HDoerys/q8/iojJ/VFkFMTAyOXf/ikw1H q42bHlt4Y9p3vJYmEReOvRnlq8wvrfXD6PwGSufHtW2JOm+AVfqnNz+0qMxPvfVJb345HQ6Kz7su jLBYg4g24bPNasv2CyUmJqZ8HFXirzU/Vd4fPMXX6hdGdFSDC14eFR1N6HlrmBCe+OQOYK2MquDY viya24ouXcf4ourLmmrGipa6PD59e/bEFtyG9Z/MxKqwxl1uGTfexqc6vhx/1Yw2UTN642/G/Kis vl1f9X39NIvRjNNLOWNVCCGEEEII4b2GN41jzoAAvvjscz77/EsOZxZ7XUZA9I28+MYEEgpTOJIX QJsr4PURT7DlROmdlv1enUfsFxN4ZtVvANw7aR5dHR/yyEv/LC/jrqFTGdWlJb8nH8QScQXROUvo P/6r8uORbXoxf8BVZBVHcE0zO6/2H8A3p0vLb//sO7zUPoj9qRm4g5twZegxnhs5gV9yHQD0eGsh t23aiO2W6s+PvrY3c1/uw/Hk3Vjj4jl83E4HxzT6Tdip1D89/Wcv5qb/7CLwmlBScsNpl1jCi4Mf Z3tmUflrbp8+n/s3LqLgnkeIOHsKW2RTdo/pzfS0XEIS7mDm22Mg5RdyQ5rRzHaAccMns6+wRKn9 euVrxS80fgRJs5uzb+9ZWrRrzfad6UQG2bg6IZ37ez2HCwiM6cyseU/hSkkm0xVEYstmzO7Xg21n 1ObSHaOf5a9RgcQH+VMychxTHC4KMz/lyUn/BtDtv17/LufxCVEYv7DEvkwZ3YbA2EQC9k2q0i4V Udf14Z0XenM0eTcBCU05ctwfKl0aRstXmV9a64fR+Q3Q+PaRvNc6nJSzobRrbuH1oY/x7YkCpf6p zF8tevNTZX0ycn14I6JpO/56z9389S+3s3l4TxYdN/dOfj97PGNnvU30D3MZN38DoB9/vfmp9/6g FV+/gESWLH+d53rcx86y9xt7+P+wfNl4RnbtRq5TbYzF5c3rDeDgmCY0axyDpTiT338/gsNd6VhU FMFWa9WMKkp3GixWO9FR4QRHBoLFRkxM6XdrOAtzyCqbwKUvtJF49dVE2p0c2PM7Z0uqZocEREYR WJDDmeIArr7mKshLZ+/h0+X123KzyQ9sTOsro8k8uIf0M44q51ttYbRsdSW2khx+//0wxecln2gd VynfE4tfGJERJRQHJNDUforfUvNp1e4acvYnczS/4mLViq9e/y+sM5ToyEByTp+qKMdDfJXHR0FA ZAItr2hIYfZR9qccr3JMK76BEQ3wzz9DbllGkF9gOGEBBWTnqNdvC29M6ysbknVoj1dtVqlfZfz1 xsfT+Fr9woiMsHA680yl0izExERzJvM0xa6yF+pcHzWlOv5614+KkLgWtIwN5Mhve8g8vwCF/tVk fqlef0bjq7c+1nT9UCn/YtC7vrTntx+nM7OrvD4qOpri7ExyndoduZzWTy267deZv1ZbA1q2jOdM 2j6OF9oJsRdztqx9euuf1RZBVIStNOPRkeN1+/TOV7k+bOHxZcd+41hxAEHWQnLz5ZddIYQQQggh 6rKMDU8zNqsTf7nrL7y+ZCgZOzfx2T8/Y8N/9ij/TTvkjWcJ/PY1+izaDkCrrtN5+ZUH2TLofdyu QuY8MZX5y17hnu39+Pnq/2Ngq0MM6PdZ+fnR141h9N/iebpff5KzSzd1Ejs2qVJH1C259B46Cocb us5ZRb+HW/DNzGQAjnwyne6vHChv759fTOLJUa155OXdCudbeXpyH/bPeYwJnx3GamvIm6sXQ7Ja /1RFdDhGrwFzcbih81OLGD3lLh4ctb7Ka5re253nH+/P90cLsFhDaGYv3eB5+JXHcH02mWHv/QBY efDNFUwcdwP9n9+h1H698vXi5yxKY/TTL3DHu6vomz2bQROSWfLpetqH2NiV56DlowMI3vcqfcdv BcBqiyLcrf634PpJT7IemPrRp2S/8DQzztuU0u6/fv9U1NfxOahzfFeeg5z9cxk5Eto8toBn4pRD VsbKsxP7sH/2CCZ8noLV3pg5qxfALxWvMFY+uvNLb/0wOr8BIjqeplf/yRS74H+eeI8nXryPb4d8 qNQ/1fmrRWt+qq5PRq4PLVb/MG7841+4+567uTHRn61ff82scQP50eTNX//ARJ6ZMwPbxmmMW7a9 /Ofa8defn6D9/qIVX0f+Lyw8lMvgrlcwPOkAAM17PkzOvvkcUNzgF8KrDWC9jDetjCpb8LVMmfII VlsMfgFBTJkyBYBjm6fz4srDgPGMEr2MCr2MGL3jeuVrUcn40YuvXv8rqy5jRSu+KuOjQisjSi++ vWYvps3cYYz7rnRTr1mPV3nlD8vpOWSrUt16GTd69OpXGf+aZtT5B7dh2fJnGXJfN1KLnQAEx/Zg 6cIu9Ly/P8UYzwjVojL+RjPKQDujTaV/NZ1fKtef0fgazQg2Wr6v6V1fWu0zmrF2uayferTarzd/ Q5r8mbdnj8Z9ZC8lkXEc3ufixsQkHhj0BaC//qlk1Gq1T+98oxnzQgghhBBCiNplDw0jyK/0qV/O olxyC51lR1wc3Pkt7+78lgVvRXHLn/7M//Yax4jRRbz1f6P4Olt7k8AW0p4ujUN4c0cWLVq0AMBy YBsh8QMJsC6hyOWm+Owunnzxnyye8Txn7U2Y8egjZFZKiG078GZOfPdy+eYNQMpPaVXqObLmy/IN nN3/PUXvW8PKj+XsOUTL6zvR9sp4gu3+RDgthLZoDOzWPd8edjMdQ+0M31han8txkqW/ZfO4F/1T kfLR+vL6f1i2nYgFfYCqG4ynd83i+6Oln8G4XXkcKizd7Lo3Noi315zbMXTx5Xt76ft8V2CHbvv1 yleJX0nRIQCyjheRd+gs4CK92ElCgB+78hzkp+cTdt3d3H5jJj8n7yOrIJPsauqvCb3+q/RPRX0d n4M6x89tcNaUPexm2ofaGL4pvbR/xUdZvPcMjxkqtSq9+aW3fhid3wBH1qwrv4nlpxVbCF3cC3/L h5ToXP7ezF8tnuanN+uTkevDk+j2j/LWi/dxatc3fPnJ27y+dTcF1ayJnt9/1PgHtWDS/OEk7n+T /pU2f/Wozk9P7w8q8d00axsDXxyMNWk8LosfQ+9pwtbxW7zqn7i8ebUBrJfRopVRVZz7PSNHfk9I 46GsnNuKkSMvfESmGRklWhkVehkxKhkzWuXr0cv4Ucko1Os/eM5Y0Yqvyvjo0cuIMiMjyTO1jBuj VMa/Jhl1xWd38I/Tbobe1JCJW48B0LL/Xzm9c0753ZFmZIR6ojL+pmSUaWS06fXP6PzSu/6MxtdY RrA55fuO/vWl1T4zMtbq+/qpylP79ebvQy8Ox/HpcwxbuBOrLYqXP1yCN38xq2bUemqfyvlGMuaF EEIIIYQQtevuN+YzMC4YgPSvn2H4279e8JqQhnHExcURG9uA4xk/kqPw1C2/wGYAdOrzMJ0q/fyH H3cRZLWUb0Bk7lzNQb/lNDm5pjzR/Jz4cDu5m7W/isaRU7G543YCFr/y/3eb8h594lJZt2EXZ/KK sBc7sVgDlM632kq/LzSjuGJDouBEIUR71z89BRkVd8M5izOw2qKxW6ny5LYzey78I9DPFofVYiGt qKJ9RVnZ+Nk7KrW/surKB4X4uUvLdrlcuMt2vErccO6bMg+teJZF9kHcP3g8465oSHryRp5/dgZp xd5t8lRHr/8q/VNRn8dH97gB1fUv71h+tf2rKb35pbd+GI4fUJBRsWY5i9Ox+gUT7m8lU+d7c72Z v1o8zU+rF+uTkevDE0f+STKO5dK0Udl7R8RvpGRd+Oh3lfcfLYFRXcj/cBMx3QfSMXwbPyk+Xl51 fnp6f1BZ/7P3zuWI/xr6NQ1ljaUnV/ulMH6/+bEW9ZdXG8AqGW81ZVZGiaeMCr2MGNWMGa2MQD16 GT+q8dXqv6eMFbMyCrVoZUSZlZHkycXICAO18a9pRt2nSft5a9AdsHUFWGwM7RTLhpGl8boY46fF rPHzlNFmCdbvn9H5pXX9/Uprw/E1khGswpfr7zmeMuZUri+99hnNWKvv66eq6tqv1z6HNZL744KZ 9Unpo7tdjkyW7TjJlFYXp32qapoxL4QQQgghhKh964b2Zl01P/cLaMhtd/2ZP//5Lq5rauXbLz5j 9jMPk5x6pppXX8iZ/zsAi1+YzH6NBOK7xs4katdC/tPsYZ7vtplJa/aVH0s9XUhE22jgkDddAsA/ sBVDb01g+H1DyhOY2zb/O39XPN9ZUNqOtsH+fF/2hKroZiFQtp+k2j89oc1C4cdTpW0OugJn8fEL vrbLXc3frs7idErcbq4K9GN32d2IgY1icBalKLVfr3yj8QNwOXNYu3AGaxdCQFQLxr07kyfvXsXo dSlelFI9vf5XVl3/VNXn8fElZ9FhAJoH+vNrfmn/GjQJhgKNk7ykN7+01g+z4hfaPBR2nADAFtgc lyObbJ3NX/Bu/mrW72F+2rxYn4xcH56cObCOp4euI77NLdx99z28vvQxjv+8ma++/JKvv/2JvLI6 Pb3/qMrLeIdX31/Ploh5PDd9GL2Hzar4OkYNRuenyvrvdhUz+1/pTBh2PYf97iT985dq9JWM4vJl 9ebF3aa8xytD7iLMWkJeXh4F1WS81VTljIeBAwcycOBA+vW6oTzjoTKtjBKPGRUeM2IaKx3XK1+J TsaPany1+h8Y1YX8DVuJuWkgHcPt5T/3Jr41FR9uJ/dA9RlRqvGtKY8ZNyZTGX+tjDqt8T32zbsE NO7D1YH+hDXtzxXugySV3UV/McZPi1nj5ymjTaV/hueXxvVnRnxVrl8j64cv199z7n5jPkuXLmXp 0qW8PqRid1Dl+tJrX2nGWjv6NQ0lrGl/rvZLYZ43GWv1fP1UVW1GsE77/Oyl10dqpeujIN3Ev5Z0 2qfK64x5IYQQQgghRJ0X13kcff7QnB/WzqJnjwFMm7dSefMXwFGwl5WHz/DU4Duxl/39ZbVFcssf 25a/psmdYxl5wwnGvrSON8e8RssB0+jSrCLh+tcF39DwhrHclhha9hMr7Tu3UKrf7S7E5XZzZVTp 35f2qLaM+pP6ZyGOgl9ZdzSPIQNvx2aBoMY3Max5hFf9U5HYoxcR/lbAyp8G30r2b0lK57lKcliZ mkuXAZ2wABZrEN2HteLE9k+U2q/HaPwAom++nkhb6UfYxdlpnHS4cBaaswOi13+z1Ofx8SVH3i42 ZxfxaJerAbCFtWJwywam1qE3v7TWD7Pi1/T+3kSWzY/OgzuR9fv7qMxws+avp/lp1vpkVMavO1j4 xmT6PPAIq79L59aeT/JQo2DTyne7Sz+P2jb7KXaF3sm0AdcpnWd0fqrG98CKxUR2GMmI9lG8v3y/ cvlCgBd3AJuWEeR2geXCar3JeKtJRoluxpRJGTM15U18tfrvKWNFOb4exkeFVkaUSnwdbjd+QRUb YgHR9gvKcbtK2x5gqbrpYkZGmEr9KmqaUVdSuJ+lqQUM6RzHR51u59jGV8u/60F1/DzFx4vGV399 mpVR5iGjzU+hf0bnlxajGbe+zphULV9v/PWOe8qY07u+VNrny4y1+rB+qqo2I1infVaO4nK7Sax0 fQQ3qfqLsi/XP6O8yWgWQgghhBBC1C0ZG8cx7Etjj+pNGjuJuKnjWbPyITJyXMTGNeDgd++wY1My gbF/4I3Rf2DuiIc44XBB5nbGvPEN8994nl29nyK12EnWnnk8vzyMMbM/YPDRDCxhceT8Mp1R3x7Q rdtZlMLUj39gwoKl9DhyirCwAtZ+kkrP29Xbv2jsKzz38hjWrh1O9rFkVm09QfdKf5Jp9U/V8a+d vLtsEXnFYcS4DzBxxCblc1eNn0aHGeNZveRBcoMa4Z+xmfFv/6zcfi1mxC+ucz9emzSBkxknICwB 69FveWpDunoBOvT6b4b6PD56Ji5Mol2IDf/gcEKsE/nwQwcuxwn69BuldP5bz8xl5vTXWPaXDAh1 s+3nLG41sXy9+aW1fpgVv+Mb8pmzfDH5hSHEWA7x3IivlftnxvzVmp9mrE9mcRadZvP65Wxevxyb D+7HcDnPMO2JN1m+aCq9tj3Eyj3ZuvHXm596VOJbfHY7H5z0o5t1Nd8pPp5aiHOUP6munNFyIKOk IqMl07sKSwoP4h9wL+2i7PySWTFhK2c8PDbnC4pdbqy2SG66Ld6UBaVyRsza6V9BeUbMS0rHfc2s +FbOWLkjKYlpA7bw+MIflePraXxU/LrgGxpOG8ttiYPZmpJLaUZUc3Z/e0ApvgePFtDlfzvChgys 9kb07dTogv6XFB3iaLGT7jcmMGPrkfKfV864GbMquSLjxov3O5X6a0p1fDcu+IW+T3RneFg0q/tX /CGiPH4e4qPK0/ibdX00vb83kR+9QlYJVTLaXAr9Mzq/tBhdf8y6fo2Wrzf+NZ0feteXavsOrFhM 5AdjGGEJY+bL5mWsXUrr57D5Sdzm/jf9hi6rcX/Pp9c+V0kWH2XkcW/Pjqx/ZztWWyyP3NIQsirK 8OX6Z1TljOZds77CP640o9lp7t/jQgghhBBCCB9wlxj/nlZH7h5eHtOf0JjGRAfB6ePHyS3LKC48 sZlef99c5fVHN83k75uqlrH9g+k8+NG7JMRHU5h9nJM5FU8VWtKva5XXHkgaTs9KN2h+t2ACD3zc mEYhkJF2FIcbVr6P8vmFp//LhKG98bP54XQ4+X/TVnA2uSKjVat/qk7uWMTwFZAQE0h6akb5DQXn fDHoAb7wcG7h6R2M7d+d2CZNsZfkkHYs+7zj2u3XK18rfmdTX6Xbw6X/3vV8//Kv+pn051n+sAAA IABJREFUQEVKd/L0J7hvTgRxsZG480+TcbJm2cCVy6zaP+3+6/WvsoTmV5KXdYSUjKp3pNTb8cnU H78XB/XzULOaswc/Z3CPzTRpEk1WWhq5ThdzKx03Wr7K/NJaP4zO7wV972cB4J8URULDQDJSM8q/ HkulfyrzV4/W/FRZn/Suj+jEZjSOU78zXYXDpPsPDiQN54FK63XB8Y10+9vG8v/rxV9vfuq9P6it /1aahdj4eeZ6L3snhBcbwGZltBRlf838r+5i0uLV+DudpP37GUbN2wv4PqNELyPmYmR8eWJ2xlV1 GSsq8dUaHz16GZV68f35zaW45z/OqiXdyHef5d/fHKdl+/MqcTuYMONjXho9m88m2kj78kkGzyht v9GMG6X6a0h1fE//NBdHxFIi877j08yqjzhVuj404qNCa/xNySjTyGjT65/R+aXHyPrj64xJ5fL1 xt/A/NC6vlTb56uMtUtp/YyPaoAt3fydVb32fTDhLW56ewLLrkulKCyYX/57msSrKs7XW/+MZtQa Pd9IRrMQQgghhBCifsg9dZRcA+e7HLmkptSshMLMo6TU8E+5Bm160rnRYX7ef5TQptfzeOtgFryS esHrjPavJD+TFO/vBSjj4kRa9U9RU22/FiPxA3AW5JCeklPzAnR57r/a6UV8//33/LHPw1x/6H1e e2/fBS+pz+Pja25nHqkpeT4rX2V+aa0fZsSvpMA380O5fp35aWR9uvXB/twabueHnWp7DJcaM+an p/gmtu9A6w7d+H8BafT97ylDdYjLk+XgwYNugE6dOmm+MC+vdBIHRlXNaPEFIxlv+qw6GTF6x33r 0o8vWG2h1WZElR3VjK9fQCRN4oI5nppBYQ0eJWrxC6mSceMto/XrMWN8fT1+2oxfH/5B1We0naPX PyPzS4WR+Pr6+r0Y64MWvetLv31WJq5ai//MR5ny3QnT21fX10//wJasXzuLuY904x8++I5yvfZZ rMFckdiI3IwjhN/3JtP+91MeGFSRo+nr9c8MlTOaH06exPD36+93n4SEhGgez8jIACA+Pl7zded+ fxNCCCGEEKIy+X3z4gm94i6G9utEQsMGFJ/JYNMni/l8p3l/E//tqYlErX6LpMO++Z4cX7e/vpPx EXWZr+enMKb7uOdowxk2fLiw7ImYQpTS+z1uy5YtQA02gIUQQghvnctYe6xHQ/p2H0lOycVOXqh9 YYmP8vzAY4yZXPuPbGnec9YFG8B12fkZzROeGcCC/r3YkFlU203zGflATgghhBBC+JL8vimEEEII cWlS3QBWfgS0EEIIUVM33nMvbcjklVHTLsvNX4CzKe8xZnJtt6JU4Ym97N5Th5/vdJ6S3Cxadv0b f7yvNKN5yaQh9XrzVwghhBBCCCGEEEIIIYyQO4CFEEIIIeoYuSNDCCGEEEL4kvy+KYQQQghxaVK9 A9h6MRojhBBCCCGEEEIIIYQQQgghhBDC92QDWAghhBBCCCGEEEIIUWdZ/EKJjY2q7WbUyPJVq1i9 +iPWrVtHrM03H8UajY/V1pCkpCSSkpJ469n2ppdvhqseeZUZT7b1Sdkq/TOrfk/l+LJ/vtbv7fkk JSWxaN5ztd0UIYQQlcgGsBBCCCGEEEIIIYQQos4Kju3LonkTa7sZNdK3Z0/69J9EYGAgVotv6jAc H4sfsbGxTBs5lCenJZtfvgnyUn8l+cBZn5St0j+z6reHRdMw0u6z8mvDirEjeHzif4mNjaztpggh hKjE6w1grYyobjPmMf6GGMONqivqYuZVXci4u1jqYvyNupgZhRebXkarasZrbfe/tuvXcuOoGUy+ KwGAv06dwxNtqs4lM9bnutp/izWYpKQkXh9yNbbgNiQlJTHlf5vUdrPKJXadyjuvdzZUxuW0vgsh hBBCCCGECovVTkxMDNGRgWCxERMTQ0xMDJGhtiqvC4iMIiLQD4s1mFZtr6VVs+gqx4NjmtCmfUfa troC23mbsMFRUUTYrdjCE2jf8VoSwquWDWD1D6dVuw5c27YVsefVHRIdRYTNSkhcCzq2b0NEDe7y rWn7VOOj1f7KShwOHA6X1+Ubib9e/Ky2CGJiYnDs+hefbDh60eOnUr8eW3hj2ne8liYR1cwtE8rX iz8WG4mt2tKxfWvC/KvGV2X+a50P4HQ4KK40b4QQQtQN/t6eEBzbl0VzW9Gl65gLjoU1jCXS7mdK w+oCTxlZtUkr/vVNXYy/USrjl5f6K8k5l17GX9+ePbEFt2H9JzOrzWjVO35Obfe/tuv3xBZ6Hc/d fQ3LVmQSFPtXHr+5Bc++XrWdZqzPdbX/Vv8oYmNjycp2YLE1JjY2lkNnimu7WeX8Q2NoGB1oqIzL aX0XQgghhBBCCBW24GuZMuURrLYY/AKCmDJlCgDHNk/nxZWHy193+/T53L9xEQX3PELE2VPYIpuy e0xvpqfl0v7Zd3ipfRD7UzNwBzfhytBjPDdyAr/kOgDo8dZCbtu0EdstV5FVHME1zey82n8A35wu BCAwpjOz5j2FKyWZTFcQiS2bMbtfD7aV/U3af/ZibvrPLgKvCSUlN5x2iSW8OPhxtmcWKfXRSPtU 4qPX/tqOv178whL7MmV0GwJjEwnYN4l+E3Ze1Pjp1a8n6ro+vPNCb44m7yYgoSlHjvtDYcVxo+Xr xT8g+kZefGMCCYUpHMkLoM0V8PqIJ9hyolA3PoDu+UIIIeou5Q1gi9VOdFQ4wZUyogCchTlklb2h nmMLT6D1ldFkHtxD+hnHeQXZSLz6aiLtTg7s+Z2zJWrZQVa/MCIjLJzOPFO5MGJiojmTeZpil1u3 /OCoKGy52eQHNvbYPlt4Y1pf2ZCsQ3uqb4ctjJatrsRWksPvvx+m+LzmB0RGEViQw5niAK6+5irI S2fv4dMV5/uH07J1cwLchRxLOciJ82LniWr89dqnR6t9IdFR+J/JpiS6OS0bBnDot9/IOT+7y8fx 98TiF0ZkRAnFAQk0tZ/it9R8WrW7hpz9yRzNL6non8Hxq+n8VRk/qy2CqAhbacafI6dG/atp+8rj ozc/DZavWbdG/0F1/sSXHfuNY8UBBFkLya0cHx/Xryc4pgnNGsdgKc7k99+P4HCrn9v6sVHY3YWs yyzgf6b1x1n4OzvLxseM9dlo/5XiozN/tK6/0IQeOItP8PraI0TeMARHfjKv7TipFDvV60dvfHTX hyp1hhIdGUjO6VMV5XjovzfjJ4QQQgghhBCXk+Lc7xk58ntCGg9l5dxWjBzpOWG26b3def7x/nx/ tACLNYRm9tINxCOfTKf7KwfK/zb784tJPDmqNY+8vLv83Khbcuk9dBQON3Sds4p+D7fgm5mlj0Ju +egAgve9St/xWwGw2qIId1f9rCGiwzF6DZiLww2dn1rE6Cl38eCo9Up9NNI+lfiotN+TixV/rfjl 7J/LyJHQ5rEFPBN38eOnV782K89O7MP+2SOY8HkKVntj5qxeAL+YVX4FT/Ef8sazBH77Gn0WbQeg VdfpvPzKg2wZ9H75uVrzX+V8IYQQdZPyBrBqxldkm17MH2B+xpB/cBuWLX+WIfd1I7XYCUBwbA+W LuxCz/v7U6xQvl5Gk15GVkjCHcx8ewyk/EJuSDOa2Q4wbvhk9hVW/NKklXHl64w7lfZpMZrR6Ov4 awmNH0HS7Obs23uWFu1as31nOpFBNq5OSOf+Xs/hUoyPkYw5LUYzClX6ZzQjT2/8fZ3xp5fxqDd/ oq/tzdyX+3A8eTfWuHgOH7fTwTFNOXPSaP169DJS9fgFnSUjPZtiFwQ4s8hIO1R+zIz12Wj/zcgY 1br+Yv4Qww8rppBW7OS6zgF8+9Y08lxqO+gq14/K+Gi1r8pY2eMZO+tton+Yy7j5G3T7rzp+Qggh hBBCCCE8O71rFt8fLQDA7crjUNmfmzl7DtHy+k60vTKeYLs/EU4LoS0aAxUbhEfWfFm+gbj7v6fo fWtY+bH89HzCrrub22/M5OfkfWQVZJJ9Xt0pH60vP/+HZduJWNAHUNsANto+PSrtN4OR+Nfl+Blh D7uZ9qE2hm9KB8BVfJTFe8/wmA/qqi7+tpD2dGkcwps7smjRogUAlgPbCIkfSIB1CUVln6t4io/q +UIIIeom5Q1g1YwvX2UMFZ/dwT9Ouxl6U0Mmbj0GQMv+f+X0zjnkOt3K5Xtun35G1sOvPIbrs8kM e+8HwMqDb65g4rgb6P/8jipt9ZRx5euMO9X2eWI0o9HX8dfjLEpj9NMvcMe7q+ibPZtBE5JZ8ul6 2ofY2JXnMDx+huavCRmFev0zmpGnN/6+zvhTyXjUmj9PT+7D/jmPMeGzw1htDXlz9WJIvlj161PJ SNXy06RRDCr797+fHs6/Kx0zY302o/9mrP+err8DSyfwXNlrdr42Bm8fiKR3/aiOj6f2neMfmMgz c2Zg2ziNccu2l/9cq//eZFQLIYQQQgghhKjemT3Vb2t2m/IefeJSWbdhF2fyirAXO7FYA6q8xpFT cXOG2wlYKr5C6dCKZ1lkH8T9g8cz7oqGpCdv5PlnZ5BWdoMKQEFGfvm/ncUZWG3R2K0oPZnPaPv0 qLTfDEbiX5fjZ4TV1giAjEqxzjuWD9Gezqi56uLvF9gMgE59HqZTpZ//8OMugqyW8g1cT/FRPV8I IUTd5PV3AOvxZcbQp0n7eWvQHbB1BVhsDO0Uy4aRyV6V76l9ehlZVlsU98YG8faac5sdLr58by99 n+8KVN1A9JTx5suMO2/a54mRjEZfx19FSVHpHZFZx4vIO3QWcJFe7CQhwI/dxWGGxq8uZLxp9e9X Whtun9b414X+g/b86RhqZ/jGNABcjpMs/S2bxy9S/SpUMlJ9zWjGq975Zqz/ntZPo7Sun115DuXx 0Wqff1ALJs0fTuL+N+lfafO3rlw/QgghhBBCCHHJcrvAov0xpruav638A1sx9NYEht83hANlT4Br 2/zv/N2Lql3OHNYunMHahRAQ1YJx787kybtXMXpdSvlrQpuFwo+nSusMugJn8fEqm5duV2ndARaL 6e0rq8BjfFTab6T8ipfUPP568fPkYsTPCGfRYQCaB/rza37pE8YaNAmGAtOrqjb+zvzfAVj8wmT2 Kz4h0szzhRBC1C7T39l8mTF07Jt3CRj9JlcHruJobH+ucB9kVNmjN41mNOllZPnZ4rBaLKQVVRwv ysrGz97xgnZ6ynjzZcadN+3zxEhGo9XH8VfiLj3X5XLhLimtr8QNNoyPX53IeNPqnwnt0xr/OtF/ vJs/BScKTc+oNJIxqpKR6mtGM171zjdj/fe0fhqmcf2A+vhotS8wqgv5H24ipvtAOoZv46eyx6fX letHCCGEEEIIIS5VJYUH8Q+4l3ZRdn7J1P8qtXPc7kJcbjdXRgVwIKMEe1RbRv2pMWSq1x198/W4 dv5ElsNFcXYaJx0uIgqr7k4m9uhFxD+mkVMCfxp8K9m/La3a/qJDHC120v3GBGZsPWJq+0A7Pirt N1K+FtX+6cXPaPl6ato/PY68XWzOLuLRLlczZlUytrBWDG7ZAH42rQrt+gv2svLwGZ4afCePzfmC Ypcbqy2Sm26LZ8cm/SfaGT1fCCFE7fJ+A7iGGVFmZAyVFO5naWoBQzrH8VGn2zm28VXKPsc3ntGk k5HlLE6nxO3mqkA/dueVHg9sFIOz6MJsueoyrsC3GXfetM8TIxmNNh/H3yij42daxpuvMgpNaJ/W +KuW7ymjVfV4TTkL9gHQNtif78u+szW6WQicNbWaGjMtI1WPj+aXUd7MT0/rpy95Mz5a7cvLeIdX 31/Ploh5PDd9GL2HzaLY5Vbvfx0dPyGEEEIIIYSobUXZXzP/q7uYtHg1/k4naf9+hlHz9uqe5yxK YerHPzBhwVJ6HDlFWFgBaz9Jpeft6nXHde7Ha5MmcDLjBIQlYD36LU9tSK/ymuNfO3l32SLyisOI cR9g4ohNVQtxO5gw42NeGj2bzybaSPvySQbPSDalfaAdH5X2Gylfi2r/tOI3cWES7UJs+AeHE2Kd yIcfOnA5TtCn36iLEj+t+lW89cxcZk5/jWV/yYBQN9t+zuLWSseNlq8naewk4qaOZ83Kh8jIcREb 14CD372jvIFr9HwhhBC1x+tPmmuaEWVWxtDGBb/Q94nuDA+LZnX/A6aVr5eR5SrJYWVqLl0GdGLt 9K/AGkT3Ya04sf0l5bb7MuPuYrXPU0aer+NvlNH4mDV/fZZRaEL7tMZftXxPGa2qx2vKUfAr647m MWTg7eya9RX+cTcxrHkEzouUUanHrIxUPb6aX0bV9YxRs8bH7S6N+bbZT3FHUhLTBmzh8YU/ql8/ CuP38rKVNMmex8MjN9S4v0IIIYQQQghxKVrzxnjWvFH9sS8GPcAXHs77bsEEHvi4MY1CICPtKA43 rHy/4viSfl2rvP5A0nB6JlX8P3n6E9w3J4K42Ejc+afJOHlhtvnJHYsYvgISYgJJT80ov2GlsvSN C3lk40LT23eOp/iotP+chOZXkpd1hJSMC+/I8FX8QTt+Lw7q57G9KuUbjZ9e/XrOHvycwT0206RJ NFlpaeQ6Xcw1sXzQjr8jdw8vj+lPaExjooPg9PHj5FZ6vrZefPTOB4hObEbjuAjD/RBCCGEurzeA a5rxBeZkDJ3+aS6OiKVE5n3Hp5lVvxzSaPl6GVmrxk+jw4zxrF7yILlBjfDP2Mz4t9V3mHydcXcx 2qeVkefr+BtlND5mzF9fZhQabZ/e+CuV7yGjVeW40f4vGvsKz708hrVrh5N9LJlVW0/QPVjpVFPq 12JWRqoeI+tzXc849SWzx8flPMO0J95k+aKp9Nr2ECv3ZCv1X2X8AkNCCC6Su4SFEEIIIYQQwhuF mUdJMZCE7SzIIT0lR/M1JfmZpNQw191o+/Tott9VxPfff88f+zzM9Yfe57X39plav0r/6nL8jHI7 80hNyavVNuSeOkquj86/9cH+3Bpu54edap9BCSGEuDgsBw8edAN06tRJ84V5eea9SWllDNV2+Ra/ kCoZWReyEtukKfaSHNKOef9dlX5Bahl3Nee79g3/YB0JLw9g8gHtjEbfxt8oY/EB389fo4y0T2V+ 1vX++9n8cDqc/L9pK3g4eRLD399f200qFxhVNSP1clSX58/FGJ+63H9Rt4SEhGgez8jIACA+Pl7z dWb+/iaEEEIIIeoP+X3TPH97aiJRq98i6XAd+R6qS4zETwghhPCO3u9xW7ZsAWppA1hcms5tAE/Y XYdT6sRlq0GbnnRudJif9x8ltOn1THhmAAv692JDZlFtN00IIbwmH8gJIYQQQghfkt83hRBCCCEu TaobwPIcSaEs5cf/cvaso7abIUS1SnKzaNn1b/zxvgYUn8lgyaQhsvkrhBBCCCGEEEIIIYQQQojL jmwAC2X/nPZibTdBCI9yj3zJGy99WdvNEEIIIYQQQgghhBBCCCGEqFXW2m6AEEIIIYQQQgghhBBC CCGEEEIIc8gGsBBCCCGEEEIIIYQQQgghhBBC1BOyASyEEEIIIYQQQgghhBBCCCGEEPWEbAALIYQQ QgghhBBCCCGEEEIIIUQ9IRvAQgghhBBCCCGEEEIIIYQQQghRT8gGsBBCCCGEEEIIIYQQQgghhBBC 1BOyASyEEEIIIYQQQgghhBBCCCGEEPWEbAALIYQQQgghhBBCCCGEEEIIIUQ9IRvAQgghhBBCCCGE EEIIIYQQQghRT8gGsBBCCCGEEEIIIYQQQgghhBBC1BOyASyEEEIIIYQQQgghhBBCCCGEEPWEbAAL IYQQQgghhBBCCCGEEEIIIUQ9IRvA4v+zd+fhTVX5H8ffSZs03ekKtOwV2RXU0XFEHLcZnUEFlUUQ EBVhpCIiCoICooIKggqioIKIqKCD8pNxGUdRBERFEbGyL4W27F2ga9Ikvz8KhUKbe9ukgPh5PY+P kHvPOd9zzvcmIecuIiIiIiIiIiIiIiIiInKW0AKwiIiIiIiIiIiIiIiIiMhZQgvAIiIiIiIiIiIi IiIiIiJnCS0Ai4iIiIiIiIiIiIiIiIicJbQALCIiIiIiIiIiIiIiIiJyltACsIiIiIiIiIiIiIiI iIjIWSL4dAcgIiIiIiIiIiIiUhVHfGtu6HwFjRMcHMzaypcffUL6IdfpDitg6l/dhSvrhpX/ffG7 71Dg8Z7GiCqy2hKYO3sKANnrn+X+CesqbNf8nF59XpzF32JCcRVu4s6BT5zucERE5AyhK4BFRERE RERERETkjBTR+DpmvzGJDjElbN6QjqPun3h68mWnO6yAssckULduXZIb/Zl+/foRGWw53SFVZAki MTGRSakDGT4prcImzc/p9/aDg7n/0R9ITIw53aGIiMgZpNpXAEckDeHlZ/4EQGnRdvrfM6Z8W/tO 19G+VTPioyNwHT7AmuUfs2zdHnMVW4Joe8kVdGhzLnXjIinO3cN3X/wfP2zNq2RnK9d1705ssJUf Fi1kc3Gp6fhtkU24rvNVpNSPoeTQXlZ//iE/pOeX1WpPYu7rz5TvO3nQnawtMHe2WrPOt/DnqJBK t+Vu+JSPf8rm5ikzOXf+aJ7+8YDpeE9kCYogIc7Ovn3ZVe5T7/JHmXRPC174112sznfWuC2paP7C hdgtVux2GwNu7co+l+d0h3QSX/kRiPyTM5uZ94eaatx1PCMv/R//enhZwOsOlLM5/wMx/rWZHyIi IiIiIpWxRSbgPrwff35B+dfEf5H/8VhGzVh95JVFzGsYUWGfkJhYHEV5HHKGcG6rc6Agk407Dh7b wWKj8bnnEmN3s3X9Jg6XnhCRj+1hsbHY8nMpdNSnZbM4sretJzPAV7emv/8qUwFH/K1cc2WLk3cw iN+o/1VttwZFEhMdxMHs3Ar1xcbF4czNJt9d8SrXUpcL1wm/h2l+jOOv7flxu1w4z8DfKUVE5PSq 9gKw1VaHxMREts57gfnbdlfYNnj4fTQKCcZV7MLmsHH9Td04f3w/pq3cZ6LeBJ57fASlxTnsOegi KelqOnftztyht/POxoqLwElXj+SBu64AoODTRaYXgEPiLuWlOY/SMCSYQwcP4oiJoX3wD/zwykYA vKU5zJo1iwbXD+KOC+MJsZo/mys0PpG6MQ4AGna6mibZP/LNr2UfzkGZdgAiExKJsQeZrrMyYYm9 mT2jBZ27DqtyH0fdRBLi65Bz4pcl8Uvv7t2xhbVmyQdTqUZqnFK+8iMQ+SdnNjPvDzUVHBFPQpwj 4PUG0tmc/4EY/9rMDxERERERkcok/GkEL/UP4b+ffMonn37OjuzqXahgj76ca+IcPPt2xVsO5+/K r/D3KybPosvS2RT94w6iDx/AFtOQdcNuY3JGPiFxF/Hkc6NJLk5nZ0EIrRvBs4MfYPm+YgDD7d1e eJ3LvlqK7ZJzyHFG06qJnaf79efrg8V+jIx5RvEZ9d/X9heyGzN3/rM81u0m1uSXLZrao/7C/LdG ktr1ZvLdvn9z1fyc2fMjIiJ/bDV+BnDO2lWsWFfxKqI5E0ezcd1vHMx3UrddT96c3J+/3n0p01Yu Nq7QXcisZ0fyn6U/U+zxUv+S4bwx/lq6DOnAO4O/OhawozkThnYkv8BFRLitWjH/fez9NLB5eeWh vnzwy16stljaNXOXb/d6ivjmm29o3b5vteoFSHvjZY7eAOWG8zrSZf1Cpk5Nq3RfW1RylWekhcU3 oEn9eCzObDZt2onryIl2FquduNgowmIcYLERHx8PgLs4j5z8inWc97f67PnmabZWsjBuDY6iecum hHiL2ZO+jX355s6IswRFEhNdijMkmYb2A2zYVUiLtq3I25LG7sJj7VhtkTRv0QxbaR6bNu3AecIa tN9n/Bnwu31/GcRvNP41np9q5Iev/PN7/A3irzK/TeaXv/H5m39VxW+2/77y058zVs3Ov9HxUR2W oAjiYhzkHTxwbBz8OGM3PC6W4EO5lMY1pXlCCNs3bCDP5Nmrpzv/zeavUf5U5/2pOuNfnfERERER EREJpKwvH+bBnI787dq/8ezcgWSt+YpP/vMJX36//qR/E1UmJOovAKw4ZLxw3PDGW3j8/n6s3l2E xRpOE3sJAPc8NwrHsmfoNXsVAC26TmbCxJ4sv+sNU9sBYi/J57aBQ3B5oetLC+nTN4Wvq/jdL9DM xAdV99/Xdlfxr7y+PZ8BXRtx77ytADTt3pe8zbMq/V3xRJqfM3t+RETkj63GC8CVWfntz+V/Ljp8 CIDifeZut+lxH+LfX6wp/3vutp0AeD0Vf4D/52NjqZP7NRO3t2H8JXVNx2axOuh/TjQFe17jP7uC ad2mFQfTt7B246n98TumdQ9m9a/8jLR2o17mqXahbNmVhTesAc0i9vBY6mh+zXdhCzuPcePuwGqL JygklHHjxgGw55vJPLlgx3EtWOlEDs+98P1JbTviOzFt5kN40tPI9oTSuHkTpvfpxkoTX9IikgYz b3pTNm88TErblqxak0lMqI1zkzPp0uMxPEB48lVMfXEYpP9KfngTmti2MuLesRWu0PbnjD8j/rbv L6P4jcbfn/kxmx++8s/f8TeK31d+m8kvf+MD//LPV/xm+m+Un/6csWpm/s0cH2YF2ZN4cNqLxP04 gxGzvgSM58doe7/pc/jT92txtIogPT+Kto1LeXLA/azKLqkyjur0H2ov/83kr1H+gPn3p+qOv/nP DxERERERkZqxR0QSGmQFwF2ST37x0QsuPGxbs4xX1izj1RdiueTKa/h7jxEMHlrCC/8awhe5vv/N Z7WF4fV6KPaUrRa3um8SIy5MAGBA/zsqLCIfXDuN1buLAPB6CtheDLbwdnSuH87z3+WQkpICgGXr SsKT7iTEOhdPaFuf20uOtLtz0eflba374QC3XRrp95iZYRT/0fiq6v/xqtr+1bSV3PnkAKzzRuKx BDHwHw1YMXK5qfg0P2f2/IiIyB9bQBeAAZr2mMhTN6VQJzaSrT98ytQJJy9EGrMIUu/9AAAgAElE QVTSdUQXAD576ZfyV2Pb38O/LqjDC3dNp3jAjGrVGBzWirAgK8W2S3nv7btxWK14Sg/z/oTBvL5i bw1irBlfZ6Tt/GAyt0zcWv6F5Zon5zF8SEvumLAOZ/5qUlNXE15/IAtmtCA1tapbeHoYfs89lW5p fnd/wjY/Te+RKwCw2mKJ8ppf/HGXZDD04Se46pWF9M6dzl2j05j70RLahdtYW+Ci78T78HwylkGv /QhY6fn82zw64kL6Pf5dhXpqesafEX/b95dR/Ebj78/8mM0PX/nn7/gbxe8rv8E4v/yN76ia5p9R /Eb9N5OfNT1j1cz8mz0+jAQ7GvPIS1OwLZ3EiLdWlb8eiDN2o8/fQ4/+M3B5odNDsxk67lp6DlkS kP5D7ea/Uf4a5c9RRu9PNRl/858fIiIiIiIiNXP9c7O4s14YAJlfPMK9L/520j7hCfWoV68eiYl1 2Jv1E3km7rpUWpiBxfJnkuxWspweti+cxrMrr2fqhJuxWIDjFhgPrc89qXyQowkAHXv1peNxr//4 01pCrRaKDbYfXcBz5R07Od/rBiy1+4gh75F+GcV//AJjZf0/XlXbczfOYGfwIvo0jGCRpTvnBqUz covvuo7S/JiLD07P/IiIyB9bwBeASwsOsmdPOJaICBq2aEPbphFsTsupVh2XD5xMv3ZxpP17PHM2 lH2gWW0JPDb2RnZ8NI5Psgo4v5pxWYIiAHAktOPzmc+yPC+REQ/2pevDY5lz072cqqfl+jojLW/9 dppf0JE2zZIIswcT7bYQkVIfWFd5ZdVUmFlIZIfrueKibH5J20xOUTbV+bpQWrIdgJy9JRRsPwx4 yHS6SQ4JYp0zkhsTQ3lx0dHFKg+fv7aR3o93BSouMNXkjL/jvzBVxmqL9at9f5mJ32j8/Z0fM6rK P3/H30z8RvntK79+o6Xf8R1V0/wzit9X/83mZ1XzExRU9T9cvF4PngAeH74Eh6YwZta9NN7yPP2O W3wM1Bm76e8vKe//j2+tIvrVXoDxArBZtZn/vvJ3bYHL9Pu7r/enmo5/dY4PERERERGRmlg88DYq ewBcUEgCl117Dddccy0dGlpZ9t9PmP5IX9J2HTJVb1H2RxS6b+am5Ehe3p5H8d6dbHNX/qgcbyX/ 9nEXbgJgzhNj2VLJHbBsBttPNWtQJF6vl3x3WV+M4j9eZf03s93rcTL940xGD7qAHUFXk/npU6Yf GaX5ObPnR0RE/tgCvgC8a8lkhi0BW3gKcxdM586x9/FB9/Gmy5/f83EevbkNmz+eyoOzVpS/Ht0s ldZhNtbG/51Ro/5GnXPrAHDtkIfInfoMX+f5vk2u11X25cNVkMbkRV8AMPv2W0lNSuEcRzCbTtGX CF9npN087jV61dvF4i/XcqigBLvTjcUaErC2t789itn2u+gyYCQjGiWQmbaUx0dNIcPpNi5cHjB4 PB68pWVfSkq9YAOCbPWwWixklByrqyQnlyB7+5OqqckZf0YLGP627y8z8RuNv9/zY0JV+efv+JuJ 3zC/feVXAOI7qqb5ZxS/r/6bzc+q5ueNN96osj8leSu4O/UVn32uzvHhiyO2M4XvfkX8LXfSPmol Px+5vXWgztgtyios3+Z2ZmG1xWG3ErB/2NRm/vvKXzD//u7r/amm468FYBEREREROV3qdRpBr8uz +eTDaUxc9jNF1fz3ice5h0lfZDJ87CA+Tp1Cer6LkDrRpsu7ijayYMchHhpwNfe99F+cHi9WWwx/ uiyJ775KM9xeHYNmzeMy72f0GfiW6TJhiX3p2nY17331G04PtLjuYlyHv6fg6JWtAYzPl61vzyHm nWEMtkQydcIW0+U0P2f2/IiIyB9bwBeAj3IVbGVHSSkdwtuaLtO883CeueMSNn78PENf+JSKXwnd FBcX0+LCiwGw2uwANLngIhqGBAO+F4BdRRs56PIQZT3W5WBL2f/zz4Afx4MdLRh4aTL33nQPW48s RrdpegM3nLij1wOWmk2bx53Hh69P4cPXISQ2hRGvTGX49QsZujjdv+ABtzOTUq+XcxxBrCs48kzU uvG4S06uuyZn/NV2+2Z5PWWxhVgsFds3Eb/R+AdkfmqYH/6OP/iO33R+12J8R9Uk/8zE76v/1cnP yvTp08ds5yqdf3/bP6og62WefmMJy6Nn8tjkQdw2aBpOjzdgZ+xGNImAn8qeGx8c2gi3c2/1Fn9P Y/77Up389/X+VNPxP67yGn9+iIiIiIiI1ETW0hEM+ty/E9u/fX4oi4aP56WF75Oz/xB14qP4bslM XCb/vTjvwTHUGz+SRQtuJyvPQ2K9Omz79uXyBTqj7WYlxdbBlpldrTKe0kN0unsitw9zc+CQl7hI JwueHFOt+APBeXgV7+wP4mbre3x7yPdvrCfS/JzZ8yMiIn9cAfsl2BHXmRdGXshPG7ZxILuQxFad uDDCTn7GInPlY//JC6nX4PUUsDe8AyNHdQCgJPcrnpuxkpyN47nppmP7nz92Ls/+pR6v3tGDxdkm 7uHrdfP6uoM8fEELHr39BlYdSuTOuuEUH/ycrABeYVlTXm8xHq+XZrEhbM0qxR7bhiFX1ocTvpeU Fm8jOORG2sba+TW7eh/4cRdfgGfNz+S4PDhzM9jv8hBdHJhL6zyleSzYlU/n/h35cPL/wBrKLYNa sG/VU6bK+3vGnL/tm1Vasp3dTje3XJTMlBU7qxW/0fgHYn5qmh+BOGPRV/xm87s24/OnfjPx++r/ KcvPKuY/UO17vWV1rpz+EFfNm8ek/su5//WfAnbGbuNuPYj+v0nklcKVAy4ld8ObAem/kdrOL3/z /1g9NRv/o/z5/BAREREREakJb6n/v7l53fm89cwwFsyIIykunLw9meQWV6z3v3fdyn+rKO/KX8+E Yf2IiK9PXCgc3LuX/OPONjbaPrdP1wr1bZ13L93nVWwj2NGciyNszJj4VbX6Vpz9IQN7/R91EupS JyyY/ZmZFJzwbGSj+Iz6b2Y7WGkSbuOXqb4fw5TctBkFOTtJzyoqf03zc/rnJ65xE+rXM3/ltYiI /DEEbAHY48omOPkCbj7vL+WvZW/7jimPvWOqfFBIA4IsFgiKoNMVV5S/XrB3O8/NWBmQGJc9MZ7L n3uCy/ukcjlQkr2ZF0b4vnXqqeIuSWf8v39k9Ktv0m3nASIji/jwg110v6LifiW5XzDrf9cyZs57 BLvdZHz2CENmbjTVRr1OfXhmzGj2Z+2DyGSsu5fx0JeZAevDwpGTOH/KSN6b25P80LoEZ33DyBd/ MV3e3zPm/G3fFK+L0VP+zVNDp/PJozYyPh/OgCnmzkg0Gv9AzI8/+eHv+PuK32x+12Z8/tRvJn6j +TsV+elr/gPZvsd9iEkPPM/82ePpsfJ2FqzPDcgZu3u/cPPKW7MpcEYS793Ko4O/Clj/jdRmfgUi /49Xk/EH/8ZHRERERETkdHMdPkj64cqfL2tG/oHd5Pux3ZfQulfw23fT+b89hcY7n8RD7v7dGD2w zJ/4fGnc7nxann8zfw7JoPcPByrfyVPC6tWr+Wuvvlyw/Q2eeW3zSbtofk7f/Fzasx+XRtn5cY3+ jS8iIsdYtm3b5gXo2LGjzx0LCgoAiGo8hvdmXUbmpwv5b2Ym7y78tMJ+UbEJ1IkKx3X4ALsP1sbH nv9ikxpTx1bKrp2ZuI6726Y1KJqePf5Jwp+78I8W0Tx26w18f/jUXiXliK1P3XDIythdIbZACQqN pl5iDN7Cg2TtPxz4BrCS2KAh9tI8MvbU7Fm7vs6YOxXt+8tX/EbjX/vzY8yf8TeKPxD57V9++Fe/ UfzG83e687P22zean6q23/vOYpIn9GfsVkiOd5C5K4vS03B3/trMr9p+f4faPz7k1AkPD/e5PSsr C4CkpCSf+x39/iYiIiIicjx935SjbhnxGK05xJfvvs6K9DPzt9w/Ms2PiIicyOh73PLly4EaLACH Jvbm0ftbA+B2ZjHm8Zf8DvZMYbUl8MS4oeV/f/OJsWyshedBiohIRUcXgEevq+Z9kUXOUvpBTkRE RERqk75vioiIiPw+mV0ArvYtoIv2zWf06JoFdabzuPYz+mztnIjIGSz9px84fNh1usMQERERERER EREREfndC9gzgEVERGrqP5OePN0hiIiIiIiIiIiIiIicFaynOwAREREREREREREREREREQkMLQCL iIiIiIiIiIiIiIiIiJwltAAsIiIiIiIiIiIiIiIiInKWqPYCsCUogsTE2Eq33TxlJiMvjDes45w7 nmbK8DbVbZrGXcfz8rOdql3ueIGIv6b1+1s+EPFVxWpLpFevXtzUKbH8NXvkn7mtx4210t6Jkq+8 iVs6tzzp9eu69+TalKiAtOF//lg578oupD7wEMPvv5fbbryaxhG2Cnv4O/+++Bt/m5tupVevXhX+ 63HrVQGLrzbzU8wLS7qcsZNf4o25bzJzyqk5fgFue34WD7eLq/V2avr58Xvwe/38EBERERERERER EZEzS7UXgMMSezN75qOVbotMSCTGHmRYR8Gu30jberi6TRMcEU9CnKPa5Y4XiPhrWr+/5QMRX1WC QpLo168fA4aOxGYpe80e/Rf63H5LrbR3opz1Tu4c/CyXRtvLX4s9P5Uhfa5nc0ZBQNrwN38uu386 T9zTidxdm9iclUfShV0YfMKCir/z74u/8Z93Sy9uuaIFdevWLf8vMTEmYPHVZn6KeZ3HD6Xeurd5 8IGhjHj8v6es3YiERGJCav+mEjX9/Pg9+L1+foiIiIiIiIiIiIjImSXY7I4Wq5242CjCYhxgsREf X7bw5S7OIyffVWFfW1QyLZvFkb1tPZmHjm2z2qKJjbbhWvsxH7jyqmwrJCaZ5o0SKM7dzZb0vVXH FBRBXIyDvIMHcHlrP36AsPgGNKkfj8WZzaZNO8vbrU79tRkfFhuNzz2XGLubres3cbjUY9j28TY4 z2FQiximbcipItDK64+NiyM/Jxunp+JEWIMiqVPHS/bBfJ/tFu75hGe/6cGQx/7Bt8M/xGJ1MHT0 31kzK5UdJe5j9dkiad6iGbbSPDZt2oHzhO6FxMTiKMrjkDOEc1udAwWZbNxxsPKuVCN/rLZYHrmu GbP6duH/9heXvfjefGy2sgUvs/NnFH9ZHwKf/0cdWPM2U1/ZeHJdVgdxsRFkHziAp/y1MOJiwyq8 Zia+mhw/AGGxsdjycyl01K+yvC0q6ci2DexxhhBqLSa/sPS4jviX/77mJzwuluBDuZTGNaV5Qgjb N2wgz3VC/T7aN9M/a3AUzVs2JcRbzJ70bewz8d5xfP1hVisdYuxk/JSBxWrFisV0/6B6x09Vwuul 0DzRwc4N68k+rgFHdB2CCw+Rf2TMghxRRIYUkZt33PHho/9Gnx9mxtcoP4zG35/5gbP/88NofKrs f1AkMdGlOEOSaWg/wIZdhbRo24q8LWnsDuDxLSIiIiIiIiIiIvJHYXoB2BZ2HuPG3YHVFk9QSCjj xo0DYM83k3lywY7y/WJa92BW/3PIcUbTqomdp/v15+uDZQtmkY17M25oaxyJjQnZPIY+o9ec1M61 A8czpHNzNqVtwxLdiLi8ufQb+b+T9guyJ/HgtBeJ+3EGI2Z9eUribzfqZZ5qF8qWXVl4wxrQLGIP j6WO5td8l+n6azO+kLiLePK50SQXp7OzIITWjeDZwQ+wfF+xYftHfTrtO+4Zeg3TBr130jZf9Q+d +QZ7H7iNl3ZVXOht8M/xTL7uY7rf+7lh2yumPMGA96fRrfH/WNX6Ec53f0+3/+ws3x6efBVTXxwG 6b+SH96EJratjLh3LJuLjy0QXDF5Fl2WzqboH3cQffgAtpiGrBt2G5MzKsZV3fyxWKOwWS04HBUP GdeRxSwz82cm/trKfzP6PD2TxP+O5pGFGwC4ccxMurre5Y6n/mM6vpoePwDdXnidy75aiu2SysvH nXcbMyb0Ym/aOqz1ktix1875rknl7yP+5r/R/PSbPoc/fb8WR6sI0vOjaNu4lCcH3M+q7BJT7Rv1 zxHfiWkzH8KTnka2J5TGzZswvU83Vh5ymor/qqGjuC7WQVJoMKWpIxjn8lCc/RHDx3xmqn9g/vip Sv0rUnmtZRTphyNo29TCswPvY9m+IgB6TJ9D6xmDGPFt2UkDTbo9zcTL59P9nhWm+m/0+WE0vkbz Y9S+v/Nztn9+GI2Pr/5HJA1m3vSmbN54mJS2LVm1JpOYUBvnJmfSpcdjeAIQn4iIiIiIiIiIiMgf iekFYGf+alJTVxNefyALZrQgNXVYpfvFXpLPbQOH4PJC15cW0qdvCl9PTQMgb8sMUlOh9X2v8ki9 k8vGdRjG0H8m8XCffqTllv1o3Lh9g5ODdjTmkZemYFs6iRFvrTpl8e/8YDK3TNxaftXSNU/OY/iQ ltwxYZ3p+mszvnueG4Vj2TP0ml02Ji26TmbCxJ4sv+sN03EcXDeNg/Xnc3HEYn47YZuv+v+zJY9+ f60L8/KxBtuwelyUeuCcvyWR8dEGU22XFm/lsdm/MmX8QK6Nac/7w3tVuKK478T78HwylkGv/QhY 6fn82zw64kL6Pf5dhXoa3ngLj9/fj9W7i7BYw2liL6mwvSb54y7Zwbxfsrljxsuc8/kyfkn7he+W r2b/kauTzcyfUfy1mf9HxbXvwZAhueV/L8n+jJlvbcTrKealB8Yz662J/GNVH34591/c2WI7/ft8 cqysifhqevwYl7fy8NhebHnpPkZ/sgOrLYHn35sDacfa9jf/zeRX9Pl76NF/Bi4vdHpoNkPHXUvP IUtMt+9rfJrf3Z+wzU/Te2TZgqjVFkuU97irHw0sGTOcJcD49z8i94mHmXLCom2gjh9fotsfpEe/ sTg98JcHXuOBJ29i2T3vmipr1H+jzw/w7/3RqH1/5+ds//wwGh+j499dksHQh5/gqlcW0jt3OneN TmPuR0toF25jbYErIJ9vIiIiIiIiIiIiIn8UAX9g485Fn5f/wLvuhwNEtYw0XbbNnRez79sXyxeX ANJ/zqiwT3BoCmNmvcA56dMYc9ziV1BQUJX/Wa0Vb4Na0/jz1m+nSYeOdLm1O7169aK520JESn3T dQdCVfHZwtvRuX44H3+XQ0pKCikpKVi2riQ86VZCqtF/r6eIF5ZkcXff5hVeN6p/+wfpJF7RGoBu r77LK0PaANA5KZzl3+833f6OD8eyMeKvRO+Yw7xNx27zarXFcmNiKIsXHV3x8/D5axuJPa/rSXUc XDuN1buLjvSngO3HXeFYVf6YMX9Ef8a/tAR3XHN63vsYb7w3h5vbmHuGrpn4/cl/s0oLczlw4MCx /3KPXT3nPLyW4U/+h39NeZxnh1zElPsnkn3cLVbNxOfv8VNVeXvkxbSPsDN7aVl7Htd+3txwbCHb 3/w3m1/p7y8pj+/Ht1YR3axXtdr3NT6FmYVENr2eKy5qRUxoMB5XNrkBusVtoI4fIzsXLS6/rfTP by8nokEPgk2+/QSi//68Pxq17298Z/vnh9H4GPW/tGQ7ADl7SyjYfhjwkOl0kxwSFLDPNxERERER EREREZE/CtNXAJvlyju2OOR1A5Yg02WTouzkf3PY5z6O2M4UvvsV8bfcSfuolfx85PaSb7zxRpVl SvJWcHfqK6Zi8BX/zeNeo1e9XSz+ci2HCkqwO91YrCGm6g2UquILcjQBoGOvvnQ8bv8ff1pLqNVC icfkQ2KBre+8Qv359xOxZFP5a0b15274lNDEfliDl3NTyAqcF3fGFlpKK3sBj+SYv4LQ6yni+8NO 4jZXvP44yFYPq8VCxnHPAy7JySXI3v6kOg6tzz3ptaOqyh9zsRWz6tMFrPp0ARarg7/dN517H7ub RT0nGZY1E78/+W9W3qbPePvtk58BfFT2mvfYFjSfBvsXld+6tzrx+Xv8VFXeaqsLQJbz2PgV7SuG uLI/+5v/ZvOrKKuw/M9uZxZWWxx2K1hNtu9rfLa/PYrZ9rvoMmAkIxolkJm2lMdHTSHjuD7XVKCO HyNFWcdyxu3MxBoURlSwlewTn5VciUD035/3R6P2/Y3vbP/8MBofw/57y/bzeDx4S8vaK/WCLUDx iYiIiIiIiIiIiPyRVH8B2OsBS8DXjQHYdbCY6DZxwPYq9ynIepmn31jC8uiZPDZ5ELcNmobT46VP nz7mGqlh/MGOFgy8NJl7b7qHrUeuiGvT9AZuCFD9/pZ3F5Yt1s55YixbqnHFXmVc+WuYnVmHIR3i zdeft4IsRnJ1qzvJ/eRDFl8ygUvOCaLwwKKA/DjvdmZS6vVyjiOIdQVlz4x11I3HXZJ+0r5eH+1V lT/V5fUUs2LhT9x/7WUnbqh0/szE70/+B8q1D04ldu3rfN+kL4/f/A1jFm2uVnxVMX38VMFdVBZH m7BgVh95ZnBck3A4sh7tb/6bza+IJhHw04GyPoU2wu3ci9MDtgAcfx53Hh++PoUPX4eQ2BRGvDKV 4dcvZOjik3O8ugJ1/BiJaBoB3+0DwOZoiseVS+6RxV+X10tQ6LEF75A4e4Wytdp/E/Nj1L4/8f0R Pj98jY/fx38AP99ERERERERERERE/giqfQvo0uJtBIc0pW2s3Xjnavrt1a9JuPBBLmscceQVK+06 pVTYx+stu4Jp5fSHWBtxNZP6d6hWGzWN3+stxuP10iy27Iole2wbhlx58u07/R2fmpZ3FW1kwY5D PDTgauxHbolptcVwyV/b1CiOL6d+RYe721ajfg/vZxVw1+C/8L+PM1nz1hbu+9eF7F36Y43aP5Gn NI8Fu/Lp3L8jFsBiDeWWQS3Yt+qDatXjT/4M7n4V8aFliysWayhX9vkTRQe+rLBPVfNnJv5Tkf9B 9giio6Mr/HdUg6sfJPXCfTz41GKeH/YMzftPonOTY7coNhNfVcweP1VxFf3G4t0F3HPnFdgsEFr/ TwxqGn3cdv/y32x+Ne7Wg+hgK2DlygGXkrthXkDaB4i7+AJibGVvyc7cDPa7PLiLA3ML6EAdP0Ya drmNmCPj02lAR3I2vcHRHmzbXUTS38uuOLba69K7Y90KZWuz/2bmx6h9f+L7I3x++Bof/4//wH6+ iYiIiIiIiIiIiJztqn2pUEnuF8z637WMmfMewW43GZ89wpCZVd9S9niPvj6PtuE2gsOiCLc+yrvv uvC49tGrzxAActbP5PH5kQyb/g4DdmdhiaxH3q+TGbJs60l1edyHmPTA88yfPZ4eK29ngcnbltY0 fndJOuP//SOjX32TbjsPEBlZxIcf7KL7FYGpPxDl5z04hnrjR7Jowe1k5XlIrFeHbd++zHdfpRkX PkHellmkOf9B6+PWEYzqX/9xJpF3hbE4uxjr2nlENXuepU/tqXbbVVk4chLnTxnJe3N7kh9al+Cs bxj54i81qqsm+VP3z7fxZv+HyM/ZD+FxWPenMWXk2xX28TV/RvGfivxv+M8JLPxnxdf+/ve/40i8 nOeGXs6Mwbezz+WB7FUMe+5rZj33OGtve4hdTne14juR2ePHl9kPTuSxCcP48MN7yd2TxsIV+7gl 7Nh2f/PfTH7t/cLNK2/NpsAZSbx3K48O/ipg7dfr1Idnxoxmf9Y+iEzGunsZD32ZaapsoPrnr71f FvLS/DkUFocTb9nOY4O/KN/2y/Nv4p11Pwvn3kyh9zCffb2X5u2OlTXqv9HnhxGj+TFq35/5+SN8 fvgan0Ac/4H8fBMRERERERERERE521m2bdvmBejYsaPPHQsKCk5JQABWWwTJSXEU5+5lf17xKWvX DEdsfeqGQ1bGblxn6GMHI+LrExcKB/fuJd8ZmCvoTmX9vllJbNAQe2keGXtq/qzSmrKFR5MYHwNF uWTuq0n7xvGfyfkP/sUXiOMnyBaE2+Xmz5Pepm/aGO59Y0uF7f7lZ9Xzc+87i0me0J+xWyE53kHm rixKK+mDP+0HhUZTLzEGb+FBsvb7ft5yzdT+8RMcGktygoOsXVknzXFQSAwN6oWxd1cWxZXcarr2 ++97foza9ze+s/3zw2h8AtH/0/v5c2qFh4f73J6VlQVAUlKSz/1O5fc3EREREfn90PdNERERkd8n o+9xy5cvB87QBWARkRPVad2dTnV38MuW3UQ0vIDRj/Tn1X49+DK75JS0f3QBePS67FPSnoj8sekH ORERERGpTfq+KSIiIvL7ZHYBuNq3gBYROR1K83No3vWf/PWmOjgPZTF3zD2nbPEXIP2nHzh82HXK 2hMREREREREREREREakJLQCLyO9C/s7Pee6pz09b+/+Z9ORpa1tERERERERERERERMQs6+kOQERE REREREREREREREREAkMLwCIiIiIiIiIiIiIiIiIiZwktAIuIiIiIiIiIiIiIiIiInCWqvQBsCYog MTHW5z7n3PE0U4a3qXFQZ6qbp8xk5IXxAavv9z5OgY5//sKFvPfe+yxevJhE2+/33AR79BW8Ofe1 WqnbzPH3R1ab42OUn2dL/oqIiIiIiIiIiIiIyO9btVcpwhJ7M3vmoz73Kdj1G2lbD9c4qDNVZEIi MfaggNVnj4wjIcYesPpOtUDH37t7d3r1G4PD4cBqCVi1p5zXdYA1a9bWSt1mjr8/stocH6P8PFvy V0REREREREREREREft+Cze5osdqJi40iLMYBFhvx8WVXwrqL88jJdwFgtUUTG23DtfZjPnDlVSwf FElMdCnOkGQa2g+wYVchLdq2Im9LGrsLS4/b0Ubjc88lxu5m6/pNHC71VK9HBuXD4hvQpH48Fmc2 mzbtxOU9uYqQmGSaN0qgOHc3W9L3nrTdFpVMy2ZxZG9bT+YhV7XCs0XVp2WzBHK2r690e1XxWYMi iYkO4mB2boX9Y+PicOZmk++upCPVqB8gLDYWW34uhY76VfbPKH4zjMbXF6stkuYtmmErzWPTph04 T0gPa3AUzVs2JcRbzJ70bezLP2F+DPLDsLwPFquDuNgIYC9vzpt/0nZHdNoB9OYAACAASURBVB2C Cw+R7yprM8gRRWRIEbl5x9qoqn0zx58ZRvnvT//D42IJPpRLaVxTmieEsH3DBvJcx8bXqP9m8g8q zx8z42Nm/M28P9REoI5fERERERERERERERERI6YXgG1h5zFu3B1YbfEEhYQybtw4APZ8M5knF+wA ILJxb8YNbY0jsTEhm8fQZ/Sa8vIRSYOZN70pmzceJqVtS1atySQm1Ma5yZl06fEYHiAk7iKefG40 ycXp7CwIoXUjeHbwAyzfV2wqRqPy7Ua9zFPtQtmyKwtvWAOaRezhsdTR/HrcIte1A8czpHNzNqVt wxLdiLi8ufQb+b/y7TGtezCr/znkOKNp1cTO0/368/VBc/HFdujFy0/cxu60dYQkN2Tn3mA4rqiv +IJCGjN3/rM81u0m1hyJ1x71F+a/NZLUrjeT7y6tolVz9QN0e+F1LvtqKbZLKu+fUfxmGI2vL+HJ VzH1xWGQ/iv54U1oYtvKiHvHsrm4rO+O+E5Mm/kQnvQ0sj2hNG7ehOl9urHykBMwzg+j8kZskRcz blx3rEHRNGtSh+uuv6HC9h7T59B6xiBGfFu2aNmk29NMvHw+3e9ZYdi+mePPiNH8+9v/ftPn8Kfv 1+JoFUF6fhRtG5fy5ID7WZVdYqr/RvkHVeePmfExat/M+0NNBeL4FRERERERERERERERMcP0ArAz fzWpqasJrz+QBTNakJo67KR98rbMIDUVWt/3Ko/UO7kOd0kGQx9+gqteWUjv3OncNTqNuR8toV24 jbUFLu55bhSOZc/Qa/YqAFp0ncyEiT1ZftcbpmI0Kr/zg8ncMnFr+VV91zw5j+FDWnLHhHUAxHUY xtB/JvFwn36k5ZYtejVu36BCG7GX5HPbwCG4vND1pYX06ZvC11PTTERnZdSjvdgyfTCjP03Haq/P S++9Cr8e28NXfK7CX3l9ez4Dujbi3nlbAWjavS95m2extdjc4pFR/333zzh+I2bG15e+E+/D88lY Br32I2Cl5/Nv8+iIC+n3+HcANL+7P2Gbn6b3yLIFPastlijvsbExyg+j8kacectITV2GI/Y6Ppw/ 2HS5o3y1b+b4M2I0//72HyD6/D306D8Dlxc6PTSboeOupeeQJabL+zq+fOXPqRgffwTi+BURERER ERERERERETGj2s8A9kdpyXYAcvaWULD9MOAh0+kmOSQIW3g7OtcP5+PvckhJSSElJQXL1pWEJ91K iIkHapopn7d+O006dKTLrd3p1asXzd0WIlLql9fR5s6L2ffti+WLSwDpP2dUaGfnos/LF4jW/XCA qJaRpvpuj7yYdhE2Zn+VCYDHuZs5Gw9V2Mcovq+mraRhlwFlk2YJYuA/GrBi+nJT7Zup31f/zMRv xMz4VsVqi+XGxFAWLzq62O7h89c2Ente1/J9CjMLiWx6PVdc1IqY0GA8rmxyj9zi2Ux++Cp/KtR2 +0bzH4j2099fUp4/P761iuhmvapV3tfx5U/+mGHm+PCHv8eviIiIiIiIiIiIiIiIGaavAA4IrxsA j8eDt7RslafUCzYgyNEEgI69+tLxuCI//rSWUKuFEo/vZ2SaKX/zuNfoVW8Xi79cy6GCEuxONxZr SPm+SVF28r857LMdV96xxSevG7AE+dz/KKutLgBZTnf5awV7CiHu2D5G8eVunMHO4EX0aRjBIkt3 zg1KZ+SWis8U9cWofl/9MxO/ETPjW5UgWz2sFgsZJcfaL8nJJcjevvzv298exWz7XXQZMJIRjRLI TFvK46OmkOF0m8oPX+VPhdpu32j+A9F+UVZh+Z/dziystjjsVk56VnNVfB1f/uSPGWaOD3/4e/yK iIiIiIiIiIiIiIiYUf0FYK8HLIFfN3YXbgJgzhNj2VKDW6IalQ92tGDgpcnce9M95bdcbdP0Bo5/ Suuug8VEt4kDtle7fcP4SnYA0NQRzG+FZc8ArdMgDIrMx+f1OJn+cSajB13AjqCryfz0KdMLa2bq 9yd+M8yMr9dTFluIpeJV325nJqVeL+c4glhXcOSZtXXjcZekl+/jcefx4etT+PB1CIlNYcQrUxl+ /UKGLk43lV++ygeCy+slKPTYgmZInL367dfw+DMz/4Hof0STCPjpQFmboY1wO/eW56hR/42YOj59 jI+v9s0eH1Xlp5nt/hy/IiIiIiIiIiIiIiIiZlX7FtClxdsIDmlK29jqLd4YcRVtZMGOQzw04Grs R27Ja7XFcMlf2wSkvNdbjMfrpVls2RV99tg2DLmy4u1df3v1axIufJDLGkccecVKu04pAegduArW 8k1uCXd3PhcAW2QLBjSvU77dTHwAW9+eQ8z5qQxuF8sb87eYbt9s/TWN3wwz41tasp3dTje3XJRc 4XVPaR4LduXTuX9HLIDFGsotg1qwb9UH5fvEXXwBMbaylHbmZrDf5cFdXLbCZia/fJUPhG27i0j6 e9kVy1Z7XXp3rFthu5n2a3r8mZn/QPS/cbceRAdbAStXDriU3A3zyrcZ9d+IqfzxMT6+2jd7fFSV n2a3mzl+J7y1gDenX1XpNhERERERERERERERESPVvpSwJPcLZv3vWsbMeY9gt5uMzx5hyMyNADz6 +jzahtsIDosi3Poo777rwuPaR68+Q0zVPe/BMdQbP5JFC24nK89DYr06bPv2Zb77Ks24sEF5d0k6 4//9I6NffZNuOw8QGVnEhx/sovsVx8rnrJ/J4/MjGTb9HQbszsISWY+8XyczZNnW6g5TpV54ZAZT Jz/DW3/LgggvK3/J4dIj28zEB+A8vIp39gdxs/U9vj3kPKmNqpitv6bxm2FqfL0uRk/5N08Nnc4n j9rI+Hw4A6aUzf/CkZM4f8pI3pvbk/zQugRnfcPIF38pL1qvUx+eGTOa/Vn7IDIZ6+5lPPRlZvl2 o/wyKu+vX55/E++s+1k492YKvYf57Ou9NG93bLuZ9n0df76Ymf9A9H/vF25eeWs2Bc5I4r1beXTw V6b7b8RM/vgaH1/tmz4+fOSnme1mjl9HeDhhJaf27vwiIiIiIiIiIiIiInL2sGzbts0L0LFjR587 FhQUnJKAACLi6xMXCgf37iW/BvdI9VXeEVufuuGQlbEbVxWPFbbaIkhOiqM4dy/784pr0oUqWYLC adAgjpyMDPLdJ/fNOD4rjy78kOCpdzPu233Vbt9M//2J3wz/xtdKYoOG2EvzyNhz8vNTg0KjqZcY g7fwIFn7K39erK/8MFPeSGjCbSyacwPXd+51cnwhMTSoF8beXVkUV/Jc60C074vR/PvT/r3vLCZ5 Qn/GboXkeAeZu7IoPaENo/6b4U/+GLXv7/FhzL/jV0T+OMLDw31uz8rKAiApKcnnfqfy+5uIiIiI /H7o+6aIiIjI75PR97jly5cDNXkG8CmQf2A3+bVUvjh7N+nZvst7XPnsSvcngqp53QXsSq/6y7Gv +Bq3O5+W59/Mn0My6P3DgRq1b6b/vhjFb4Z/4+thX0bVz6R1F+WRmZ7nswZf+WGmfFUan9+BBHsI bTv/g7xt71def0kO6ek5VdbhT/tmGM1/INovLcwmfWcV9Rv03wx/8seofX+PD18CcfyKiIiIiIiI iIiIiIgYOSMXgKVyF/3jRlqTzcQhk8grDdyzaSUw2v+9MxeE2yg48AUjn/6/0x3OKZf+0w8cPuw6 3WGcsXT8ioiIiIiIiIiIiIjIqXBG3gJaRERE5I9Mt+QTERERkdqk75siIiIiv09mbwFtPRXBiIiI iIiIiIiIiIiIiIhI7dMCsIiIiIiIiIiIiIiIiIjIWUILwCIiIiIiIiIiIiIiIiIiZwktAIuIiIiI iIiIiIiIiIiInCW0ACwiIiIiIiIiIiIiIiIicpbQArCIiIiIiIiIiIiIiIiIyFlCC8AiIiIiIiIi IiIiIiIiImcJLQCLiIiIiIiIiIiIiIiIiJwltAAsIiIiIiIiIiIiIiIiInKW0AKwiIiIiIiIiIiI iIiIiMhZQgvAIiIiIiIiIiIiIiIiIiJnCS0Ai4iIiIiIiIiIiIiIiIicJbQALCIiIiIiIiIiIiIi IiJyltACsIiIiIiIiIiIiIiIiIjIWUILwCIiIiIiIiIiIiIiIiIiZwktAIuIiIiIiIiIiIiIiIiI nCW0ACwiIiIiIiIiIiIiIiIicpbQArCIiIiIiIiIiIiIiIiIyFlCC8AiIiIiIiIiIiIiIiIiImcJ LQCLiIiIiIiIiIiIiIiIiJwlgqtbICJpCC8/8ycASou20/+eMeXb2ne6jvatmhEfHYHr8AHWLP+Y Zev2mKvYEkTbS66gQ5tzqRsXSXHuHr774v/4YWte+S4dunSjVZitQrHd/1vE0n3FpuNvdMl13PCX dthd2Xz3yfusPK5+qz2Jua8/U/73yYPuZG2By3TdAFZbHNfc2Jk2TRMpObSftSs+ZUWayTE4A1iC IkiIs7NvX3aV+5xzx9PcGz+PYZPTTmFkZ077p8ttz8+i4euP8Oy6g5Vun79wIXaLFbvdxoBbu7LP 5TnFEfrOn5unzOTc+aN5+scDtVL/6da463hGXvo//vXwshrX4U//zoT5PxuEJV3OQ8N60jQhkpKD 7zNw2P+d7pBERERERERERERERKql2gvAVlsdEhMT2TrvBeZv211h2+Dh99EoJBhXsQubw8b1N3Xj /PH9mLZyn4l6E3ju8RGUFuew56CLpKSr6dy1O3OH3s47G8sWaS/q1ptb40MrlFu97lPTC8CJf0nl 1bE34PU48Vps/O0fnXm+fy8+21sEgLc0h1mzZtHg+kHccWE8IVaLqXqPCgppzBNzXiRp53L+b+UG whKb0Gd4N1b0n1atek6nsMTezJ7Rgs5dh1W5T8Gu30jLO3wKozqz2j9dIhISiQmp+qL93t27Ywtr zZIPplLN1A0YX/kTmZBIjD2o1uo/3YIj4kmIc/hVhz/9OxPm/2zQefxQ6n0zhQc/Wo/bXXi6wxER ERERERERERERqbZqLwAflbN2FSvWVbxKbc7E0Wxc9xsH853UbdeTNyf35693X8q0lYuNK3QXMuvZ kfxn6c8Ue7zUv2Q4b4y/li5DOvDO4K/Kdys6+CFDR31S/vfC3XmVVFa5AUP/htfjZPTtt7I96Xbe mdydfiP+wmfDvgDA6ynim2++oXX7vqbrPF6H+8fS0vkZPR6Zgctb9trbcysuCFltkTRv0QxbaR6b Nu3AedxFemGxsdjycyl01Kdlsziyt60n81DFK5BtUUlHtm1gjzOEUGsx+YWlx3aw2Gh87rnE2N1s Xb+Jw6UVrwK0BkfRvGVTQrzF7Enfxr78svotVjtxsVGExTjAYiM+Ph4Ad3EeOUf2sdqiiY224Vr7 MR+4Kh93f/vni6n2q+ifWWHxDWhSPx6LM5tNm3aWz6MZ4XGxBB/KpTSuKc0TQti+YQN5J1yFGRIT i6Moj0POEM5tdQ4UZLJxx7Eren2NX3k79VJonuhg54b1ZFe2gy9V5IclKJKY6FKcIck0tB9gw65C WrRtRd6WNHYfn19VVWsif46yRSVXOf9VjX916q80PpP9M5p/o/mr2GYEcTEO8g4eOFZPVeNvon9m 8suImfwOiUmmeaMEinN3syV97wmdqtn7S3X4at9X/P6+v4TFxhJmtdIhxk7GTxlYrFasVFxJr+3x MxKI99fajE9EREREREREREREzgw1XgCuzMpvfy7/c9HhQwAU7zN3u1eP+xD//mJN+d9zt+0EwOup +AO011MCthAo2s+ODPO3SQ0KaUSn6BCKD37MjwdLIGceh0pvJSrlZuAL0/X4MrBjXTY/s6jCooDH dezq5PDkq5j64jBI/5X88CY0sW1lxL1j2VxctgDV7YXXueyrpdguOYccZzStmth5ul9/vj5YVkfc ebcxY0Iv9qatw1oviR177ZzvmkSf0WXjFhJ3EU8+N5rk4nR2FoTQuhE8O/gBlh+5QtoR34lpMx/C k55GtieUxs2bML1PN1YecmILO49x4+7AaosnKCSUcePGAbDnm8k8uWAHAJGNezNuaGsciY0J2Tym vN1A9c+IUfu++mdGu1Ev81S7ULbsysIb1oBmEXt4LHU0v5pcxOo3fQ5/+n4tjlYRpOdH0bZxKU8O uJ9V2SXl+1wxeRZdls6m6B93EH34ALaYhqwbdhuTM/INxw+g/hWpvNYyivTDEbRtauHZgfexbF+R qfh85UdE0mDmTW/K5o2HSWnbklVrMokJtXFuciZdejyG0TKQmfwBiGndg1n9K59/X+Nvtv6qmOmf mfn3NX/HC7In8eC0F4n7cQYjZn1pOP5m+mcmv3wx079rB45nSOfmbErbhiW6EXF5c+k38n+G8YP/ x59R+0bx+/v+ctXQUVwX6yApNJjS1BGMc3kozv6I4WM+OyXjZyQQ76+1GZ+IiIiIiIiIiIiInDkC ugAM0LTHRJ66KYU6sZFs/eFTpk74vga1WOk6ogsAn730S4UtYQk9mDm9BwB70/7LIyOfJ9PpNqwx OKw1AK7iTTT+axea711KeombduHnYLNQrSs9KxMU0ohGIcGs2lr1rYn7TrwPzydjGfTaj4CVns+/ zaMjLqTf49+V7xN7ST63DRyCywtdX1pIn74pfD01DbDy8NhebHnpPkZ/sgOrLYHn35sDxz0G957n RuFY9gy9Zq8CoEXXyUyY2JPld70BQPO7+xO2+Wl6j1wBgNUWS5S3bPHAmb+a1NTVhNcfyIIZLUhN PfkWtHlbZpCaCq3ve5VH6gW6f8aM2vfVPzN2fjCZWyZuLc+Fa56cx/AhLbljwjrTdUSfv4ce/cuu AO/00GyGjruWnkOWVNin4Y238Pj9/Vi9uwiLNZwm9rIFPDPjF93+ID36jcXpgb888BoPPHkTy+55 11RsRvnhLslg6MNPcNUrC+mdO527Rqcx96MltAu3GT4L20z+gO/59zX+Zuv3xah/Zue/qvk7KtjR mEdemoJt6SRGvLWq/HVf42+2f2byqypG/YvrMIyh/0zi4T79SMstW7Rt3L6BqfjB/+PPqH0z8+PP +8uSMcNZAox//yNyn3iYKScs6tf2+Bnx9/21tuMTERERERERERERkTNH1Q8UraHSgoPs2bOHPKeX hi3a0LZpRLXruHzgZPq1iyPt3+OZsyG3/PWMNUt5bcbzPDPpeT75aS912/yNCSP/bKpOiyUEKLvN 89/79KH3rc0o9JT9kl/dZ/1Wxmot62ehu/JrJa22WG5MDGXxoqOLER4+f20jsed1rbDfzkWfly8w rPvhAFEtIwGwR15M+wg7s5dmlJV27efN48bGFt6OzvXD+fi7HFJSUkhJScGydSXhSbeW968ws5DI ptdzxUWtiAkNxuPKJjdAt/j0t3+B4G//8tZvp0mHjnS5tTu9evWiudtCREr9asWQ/v6S8v79+NYq opv1Ommfg2unsXr3kedOewrYXlxajfFbXH7b15/fXk5Egx4Em0hfM/lRWrIdgJy9JRRsPwx4yHS6 SQ7x77m9FeOvev4DMf6+GPXPbPuVzd9RwaEpjJn1AuekT2PMcYu/ZsbfDDP5VRWj/rW582L2ffti +eIgQPrPGabj9/f489W+mfihdt9fanv8fAnE+2ttxiciIiIiIiIiIiIiZ5aAXwG8a8lkhi0BW3gK cxdM586x9/FB9/Gmy5/f83EevbkNmz+eyoOzVlTY9snkF8r/vPTLr7hw8b+J63ArsAIjntJ9AFiD 45jVvyeveV1MX2zB4y6kwO3n5b+A25lBqddLcmjlQxpkq4fVYiGj5NjVyiU5uQTZ21fYz5V37Md5 rxuwlC1OWW11Acg67mrnon3FEHekfkcTADr26kvH4+r78ae1hFotlHi8bH97FLPtd9FlwEhGNEog M20pj4+aQoaJK6iN+Nu/QPC3fzePe41e9Xax+Mu1HCoowe50Y7GGVCuGoqzC8j+7nVlYbXHYrVR4 Vueh9bknlTM7fkVZx2737HZmYg0KIyrYSrbBs2CN8sMNRyYEPB4P3tKyY6LUCzafNVePr/kPxPj7 ZNA/s+1XNn9HOWI7U/juV8Tfcifto1by85HbH5s5Ps0wk19VMepfUpSd/G8qv4PBqXh/8dW+mfih dt9fanv8fAnE+2ttxiciIiIiIiIiIiIiZ5aALwAf5SrYyo6SUjqEtzVdpnnn4TxzxyVs/Ph5hr7w KT5/crYEEWSx4PWYu8WoK38Nh0s9OOr8FYv331hCGtM0JBhnzlLf7ZjkcR/iv9kltOncEGb8dtJ2 tzOTUq+XcxxBrDtyO11H3XjcJemm6ncXbQagTVgwq488czKuSTgc+T3fXbgJgDlPjGVLceVj4nHn 8eHrU/jwdQiJTWHEK1MZfv1Chi4+LgavByzVTwt/+xcIpvpXhWBHCwZemsy9N93D1iPj16bpDdxQ zRgimkTAT2XPvQ4ObYTbufekxTlvJYspZscvomkEfFd2MoPN0RSPK5fc4xZ/jx4PIZaKV+0Z5UfA rpOsYf6YHv8a1h+w9ql8/o4qyHqZp99YwvLomTw2eRC3DZqG0+M1dXweqdxn/4zyq6r5N9O/XQeL iW4TB2w/qd2Avb/44Kv9QB2fNXUqxs+XQLy/1mZ8IiIiIiIiIiIiInJmCdgtoB1xnZk5aSwD7+rD LV1v4V+jXuDCCDuFez4xVz72n7yQeg1eTwF7wzswctSo/2fvzuOjqu7/j79mkpnsCckkARIkQAzI pqBWa6XQam1rxa8sAooioAX8KlKKWJAgIm6oiKCIgoILUgE3qP7UtiqKiNqiiBgRhEDIRgLZyJ5J Zn5/BAKRZO6dzAQw3/fzL5Jzzzmf87nn3plHDudeZs2axZ23/QqAgKAkFs6dxvXXDuGqq4dx1/xl OGxW8raYe/+p21XFsh1F2EK688CkUYybfi9Wi4WMt99q8Zh/as3Tm+n0p1R+mxIDgDUwjF/9qT5+ V20JazPLGDx+ABbAYg1h+K09yP/CXP/Oyu/ZkFvOxJsHYbNASMdfcGvXqBPKd7F2/xHumnA59qOP 7LTaorn4N70bjnFcdD7RtvpTXlOcxSGni7qqxquTtVXpBAZ1pU+M3aux+zo+fzAzvua43VW43G66 xdTv6LPH9GbKb71//HDSiFFEBVoBK7+dcAnFP6wyVc9s/s4acj3RR9sfOGEARbtf5MQR1lbvI7em juEXJjaqZ2Z++ENL54/Z/Le0fX/1b9xO/Q7MLUvuYnv45Tw2vj9gPv9G4zOaX82dfzPj+/65T4i7 4E4uTTr22H4rfQcmm47fl+vPqH9/nZ+WOhX588Qf99fWjE9EREREREREREREzix+20rnchYSmHg+ w879VcPvCtO/ZOE9r5qqHxDUiQCLBQLCGThoUMPvy/P28fjSLeB2Edv7t4y75A8AuN21fL9pDfct 3mY6xo/n3cevF93PxcNu5kIgc+t6UteevBuqpfI+e4x5f5/OtCdeYWJxAWGOWPK/WcmWd+vL1818 jPMWzuS1l66jLKQ9gTmfMvPJb023v/LOh7nnoWmsX38bxQfTWPdZPsNDj5evunMOHebN5M21N5JT 4iK+QzvSP3+GLz+uf29kh4FjeGROKody8iEiEWvuJu76KLtRH9XFH7L8gyuY88JrBNbVkfXPu5my bBcAs1esok+YjcDQSMKss1mzxonLmc/oMVP8Mj4jRv2bGV9z6qozmPfGV6Q+9zIjDhwmIqKS9W9l MnKQcd0T5X1Yx7OvrKS8JoJY915m3/6x6bpm8pf3UQVPr36BiqowYi37uOf2Dxs34naSuvANHpy6 hPdm28j693QmLKw//0bzwx88zR9PzOa/pe37q3+zXHVHeOyvi1i9ch6jttzI2p3FpvJvND7D+dXM +TczvqKdy7hvdQTTlrzKhNwcLBEdKPluAVM27QX8c3/xxFP//j4/3joV+TPi6/21teMTERERERER ERERkTOHJT093Q0wYMAAjweWl5cDEJk0h9eWX0r2++v4V3Y2a9a93+i4yJg42kWG4Sw9TG5BmZ/D teKIb09EqJ3SwzkUHH0UsncsxCeehc1ZRHZ+4/chWgOiuG7UVcT9cgh/6hHFPddezX9Ka5ppx0OU tnASEhzUlBWQf1IOrMR3Ogt7bQlZB5t/l6gnAbYA6px1/PKxv3NT2hxue3FPo/Lw2I44QqAgL4+y nzx/OCAkig7x0bgrCsg51Pz7NlvO9/H5wtfxBcd0pH0Y5GTl4vTy2eC3vbqBxIfGc+9eSIwNJjsz h1qvny9unL/AkBgS44LJyczxOkbwPD9ON1/y/3Ppv6X598f8MjM+qy2cxAQHVcV5HCqp8ip+f9xf PPX/c5gfvuTPmO/319aNT/wpLCzMY3lOTg4ACQkJHo879v1NRERERORE+r4pIiIi8vNk9D1u8+bN QAt2ADsr97J1axDEdqNXZPBJ5UcKD3Gk8JC3zZrkoiA/lwKf2nCTn32g6SKrnd69e0Ppj2zdCiXO lv3x2+UsIyujucVvF/lZLXsvbrteIxnYfj/f7skl/Kzz+cs5oTz3cOZJx5UdzqW53usqS8jOKGlR /+a0fHz+4Ov4qgpzySj0LYbaikIymplixozzV1vpS/ue58fp5o/8n+n9+5p/X+aXmfG5nGVkNnv/ av37i6f+fw7zw5f8GfP9/tq68YmIiIiIiIiIiIjImcDrBeDK/NWkprZGKKefy3mI1DN4cLVlRaQM vYrfXNOOmiM5vDRnIh8VVp/usOSojK//S2lpS3alixjT/BIRERERERERERERETO8fgS0iIiIiLQu PZJPRERERFqTvm+KiIiI/DyZfQS09VQEIyIiIiIiIiIiIiIiIiIirU8LwCIiIiIiIiIiIiIiIiIi bYQWgEVERERERERERERERERE2givF4AtAeHEx8c0WTZs4TJmXhDrc1BGOvx6NqtWreLCcHur9+VP ZvNz9rj5LJze+xRE5P84/DE/Wjr+pKHzeObRgV7XExERERERERERw67s8AAAIABJREFUEREREWkr vF4ADo2/gZXLZjdZFhEXT7Q9wOegjAS3jycuth1Fta5W78ufzOanPPN70vaWnoKIPLNHOIiL9m6R 3R/zo6XjDwyPJc4R7HU9ERERERERERERERERkbbC9AKwxWonNjYWR3QwWGzExsYSGxtLdLjtpGNt kYn07XcuiZEnl2GxkdSjN/36nkNE4PHurQEROGLanXR4jMNBeICl0e/O/X1HDn46n71VtT+pH/nT zoiNjcVuPaF+M/2bFRrbiV59+9G7R2dslqaPCYpOpM95/Tg7qX2T5c3lx2qLIjY2Fuf2d3nro9ym G/eYP9/Hb4vsSN9+59Ipqolz54E/5oep8WOc34aYAsKJjY1tOE+hMTFE2a0tmp8NMQZG0qPPeZzb uwfxTYzNqFxERERERERERERERESkNQWaPdAWei5z547DaoslICiEuXPnAnDw0wU8sHZ/w3HRvUax fPzZFNVE0bOLnfljx/NJQRUAQY4LeeDxVBKrMjhQHkSvzvDo7X9lc34VAUFJvLT6Ue4ZcQ3bypwA 2CN/xepXZjJ56DDK6o4t9loZSBGPL/5P44GE9uKV1bOYeM0wMmvqAAiNH8HLKwYzcshYagz6N6Pv rGd4sG8IezJzcId2olv4Qe6ZnMp3R+MFuGLSPKYMTmF3WjqWqM44Sl5i7MwPTOUnIukG5k7tRXB8 EkE/zmFM6rZG/XuK3x/jj+k/mmfuv57ctB0EJZ7FgbxAMJcav8wPo/Gbye8xAfYE7nzqSRxfLWXG 8o8AGLF4BZd+vBHbxd7PT4Dg2IE8tewuXBlpFLpCSErpwpIxI9hypMZUuYiIiIiIiIiIiIiIiEhr M70AXFO2lcmTtxLWcRJrl/Zg8uRpTR4Xc3EZ10+agtMNQ59ex5ibkvnkiTQAJj4+i+BNjzB65RcA 9Bi6gIcevo7Nt7yIs+I7VuwrY8LQzty2ai8AXUfeRMmPyxvt9AUX0ydOPDm+0i/5R4GbSb+IY/Zn BwFIGftHCrY9TVmd27B/Mw68tYDhD+/FWd8cv3tgFdOnnMO4h3YA4Og/jalXJfC3MWNJK65f9Evq 18l0fkr2LGXyZOh1x3Pc3eHk/j3F7/v4rcyaPZo9S24n9f0MrPaOPP3ac/CdqdT4ZX4Yjd9MfgEC g5O4++mF2DY+xoxXvjDdv9H8SPnzeEJ/nM8NMz8DwGqLIdJ9fG4alYuIiIiIiIiIiIiIiIi0Nu+f gWzgwJv/blgg3fHfw0SeEwGALawvgzuG8e6XRSQnJ5OcnIxl7xbCEq4l6Ogjij9+agtnDZlQH5Ql gEl/6sRnSzab7vvtVXvoectl9T9YbEwaEM9Hz6WZ7t9Iyc59dOk/gCHXjmT06NGk1FkIT+7YUN77 5ovI//zJhsVJgIxvskzlx4iZ+H0Zvz3iIvqG21j5cTYArppcXth1xFRs3mjp+MFcfgNDkpmzfDFn ZzzFnJ8s/nrq30x+K7IriOh6JYMu7El0SCAuZyHFJ7yH2qhcREREREREREREREREpLWZ3gFslrPk +OKcuw6wBAAQENwFgAGjb2LACcd/9fV2QqwWql1uinct5UDgm4w5K5w3LSPpHpDBzD3Fpvs++Mmz BE1dRPfgdeTGj6WzO50pWWWm+zcybO7zjO6QyYaPtnOkvBp7TR0Wa1BDeUKknbJPSz220Vx+jJiJ 35fxV9nq36ebc/Tx0QDlByvAYSo801o6fjCX3+CYwVSs+ZjY4TfTL3IL3/zk8cu+zM99f5/FSvst DJkwkxmd48hO28h9sxaSdTRnRuUiIiIiIiIiIiIiIiIirc37BWC3CyzeV6ur2A3AC/ffy56qph+L 63bVsOTdbFJvPZ/9AZeT/f6D1HixgbK2ag8vZ1YycWAHXh8wiIMb51PrNt+/J4HBPZh0SSK3XTOx 4ZHUvbtezdUnHJNZUEVUbwewz+v2jZiJ35fx26r3A9A1OJDvK+rfadyuUyhUehloC+eHGWbyW57z DPNffIfNUcu4Z8GtXH/rU9SYWNw3k19XXQnrVyxk/QoIiklmxrNPMP3KdUzdkGGqXERERERERERE RERERKS1ef0I6NqqdAKDutInxu5VPWflLtbuP8JdEy7HfvSRulZbNBf/pnej4/b+/QWiz5vM7X1j eHH1Hm/DY+Nz35Eybji39Xew4ei7hL3pvzludxUut5tuMfU7fu0xvZny246Njvn+uU+Iu+BOLk0K P/obK30HJns9hqaYjb+l43eWb+fT4mr+PLg7ALaIHkxIaed1nC2dH2aYya/bXb/Dd8uSu9gefjmP je9vqm0z+XVcdD7RtvpLpqY4i0NOF3VVLtPlxzz0ylpeXnKZuUGLiIiIiIiIiIiIiIiIeMHrrZrV xR+y/IMrmPPCawTW1ZH1z7uZsmyXqbqr7pxDh3kzeXPtjeSUuIjv0I70z5/hy4/TGo6pKf2CVw8F MMz6Gp//5PG9ZhR8sxRn1MtEl3/O24VVXvffnLrqDOa98RWpz73MiAOHiYioZP1bmYwcdPyYop3L uG91BNOWvMqE3BwsER0o+W4BUzbtbb7hE8xesYo+YTYCQyMJs85mzRonLmc+o8dMMR2/L+NffPdS nljwCK/8PgfC3Wz5tohLTEV+nC/zw2j83uTXVXeEx/66iNUr5zFqy42s3Wn8KHGj/HQYOIZH5qRy KCcfIhKx5m7iro+yG+oblR8THBZGaHXr7JIWERERERERERERERGR/9ss6enpboABAwZ4PLC8vNxv nYbHdsQRAgV5eZSd9IxnK7PXrSfwiT8z9/N8v/Vpvn/PgmM60j4McrJycTbzZGGrLZzEBAdVxXkc Kqlq+iAf+BK/UX1LQBidOjkoysqirM77tk+F05nfgJAoOsRH464oIOfQye8jNioXERExIywszGN5 Tk4OAAkJCR6P8+f3NxERERFpO/R9U0REROTnyeh73ObNm4GWvAPYD8oO51LWxO+T+p7HOecN45dB Wdzw38OnvH8zqgpzySj0fIzLWUZmRkt7MOZL/Eb13XXlZGac2V/eT2d+6ypLyM4oabauUbmIiIiI iIiIiIiIiIhIazqjnkN74Z/+h14U8vCUxyipPTN3n4qIiIiIiIiIiIiIiIiInKnOqAXgNx65nzdO dxAiIiIiIiIiIiIiIiIiIj9T1tMdgIiIiIiIiIiIiIiIiIiI+IcWgEVERERERERERERERERE2ggt AIuIiIiIiIiIiIiIiIiItBFaABYRERERERERERERERERaSO0ACwiIiIiIiIiIiIiIiIi0kZoAVhE REREREREREREREREpI3QArCIiIiIiIiIiIiIiIiISBuhBWARERERERERERERERERkTZCC8AiIiIi IiIiIiIiIiIiIm2EFoBFRERERERERERERERERNoILQCLiIiIiIiIiIiIiIiIiLQRWgAWERERERER EREREREREWkjtAAsIiIiIiIiIiIiIiIiItJGaAFYRERERERERERERERERKSN0AKwiIiIiIiIiIiI iIiIiEgboQVgEREREREREREREREREZE2QgvAIiIiIiIiIiIiIiIiIiJthBaARURERERERERERERE RETaCC0Ai4iIiIiIiIiIiIiIiIi0EVoAFhERERERERERERERERFpIwK9rRCeMIVnHvkFALWV+xg/ cU5DWb+Bf6Rfz27ERoXjLD3Mts3vsmnHQXMNWwLoc/Eg+vfuTntHBFXFB/nyw3/w370lDYf0HzKC nqG2RtVyP3iTjflVproICG7HOX3OpVdKIjaLhX++toYCp6uh3GpP4KUVjzT8vODWm9le7jQXP5By 9XB+ERHU6HfFP7zPu18XnjDMcOIcdvLzC39avUGHX8/msYk9WPy/t7C1rMZ0/2Z46n/YwmV0X53K /K8O+7XPtubscfO5LXYV0xakne5QWqS14l+9bh12ixW73caEa4eSf8K15S9mrh9fhSb8mrumXUfX uAiqC15n0rR/tFpfP3UmjM+ovLXnvz1qEM8/OYabxv65VdqX1nU6rx8RERERERERERERqef1ArDV 1o74+Hj2rlrM6vTcRmW3T7+DzkGBOKuc2IJtXHnNCM6bN5antuSbaDeOx++bQW1VEQcLnCQkXM7g oSN5aeqNvLqrfhH4whE3cG1sSKN6W3e8b3oB+BcLl3FfcruGn79Z/1qjBWB3bRHLly+n05W3Mu6C WIKsFlPtHhMSG0/7dsF0GXg5nQq+ZnNaEQHZ9kbHhMbfwMqlPRg8dFqz7QS3jycuth1Ftf5fQPPU f0RcPNH2AL/32daUZ35PWknp6Q6jxVor/htGjsQW2ot33noCLy8d08xcP74aPG8qHT5dyJ1v76Su rqLV+mnKmTA+o/LWnv9u52G2bdveau1L6zqd14+IiIiIiIiIiIiI1PN6AfiYou1f8NmOxrvUXng4 lV07vqegrIb2fa/j5QXj+c2fL+GpLRuMG6yrYPmjM/l/G7+hyuWm48XTeXHeFQyZ0p9Xb/+44bDK gvVMnfVew88VuSVNNNa0Q1/8i+XvpBN7w18Y9pOFZAC3q5JPP/2UXv1uMt3mib594Rm+BYadN4Ar t6/hiae+byizWO04YiIJjQ4Gi43Y2FgA6qpKKCprvMv43N935OCn89lbVXtSH9bASFLO6UqQu4qD Genkl5nboexN/7bIRM7p5qAwfSfZR37SvsVGUvfuRNvr2LtzN6XeLlKbqB8UnUhK5ziqinPZk5HX qMxqiyClRzdstSXs3r2fmhOqh8bEYCsrpiK4Y7PxN1ffEhBBdFQtNUGJnGU/zA+ZFfTo05OSPWnk VtQerRtFTJQN5/Z3ecvZ/LzzFL+v+bHa2pGSksCRrB/Jq7ITZq+h9Oj5C45qR2DFEcqO/qeGgOBI IoIqKS5xGsZvDYggOspCQeGRE4MhNtbBkcICalzu+hzHdqJLx1gsNYXs3n0Ap9u74Xkan6fz5838 9cRo/oRarfSPtpP1dRYWqxUr3q1ktzQ/ZscXFB1DcGUJR2qC6N7zbCjPZtf+AlP9G43PqLy157/F GowjJhzI4+VVq5s8prnxmbl+/cHn+U/z+TG6fuvrej7/hp8PPt6/W/v6ERERERERERERERH/aPEC cFO2fP5Nw78rS+sXkqryzT1O2FV3hDc+3Nbwc3H6AQDcrsZ/oHa7qsEWBJWH2J/l3WNS9768gr3A 1SMme1XPH2yh5zJ37jistlgCgkKYO3cuAAc/XcADa/efcKSVgRTx+OL/nNRGcOxAnlp2F66MNApd ISSldGHJmBFsOWL8mGiz/Uf3GsXy8WdTVBNFzy525o8dzycF9TusgxwX8sDjqSRWZXCgPIheneHR 2//KZpM7sM3Uv2LSPKYMTmF3WjqWqM44Sl5i7MwPAAhLvIwnnpwGGd9RFtaFLra9zLjtXn48ulA+ YvEKLv14I7aLm47fU/3whNtZtaQrP+4qJbnPOXyxLZvoEBvdE7MZMuoeXEBE0g3MndqL4Pgkgn6c w5jUbSeN0VP8vuYnrNPveHLJVNwHdlEb3YH9P7q4MGkV197yLwBGLXmBXktvZcbn9YtKXUbM5+Ff r2bkxM/AIP7A0F68snoWE68ZRmZNHQCh8SN4ecVgRg4ZSw3Qd9YzPNg3hD2ZObhDO9Et/CD3TE7l O5MLsEbj83T+zF8/zTOaP5dNncUfY4JJCAmkdvIM5jpdVBW+zfQ5/zTVvi/5MTu+QQuWM2TjSir/ NI6o0sPYos9ix7TrWZBVZti/0fiMylt7/tsiLmLu3JFYA6Lo1qUdf7zyatP5NXP9+srX+Q+e82N0 /YLn82/0+eDr/bu1rx8RERERERERERER8R+/LgADdB31MA9ek0y7mAj2/vd9nnjo5IVMY1aGzhgC wD+f/rZRSWjcKJYtGQVAXtq/uHvmIrKPLlidyWrKtjJ58lbCOk5i7dIeTJ7c3CNeXUyfOLHJkpQ/ jyf0x/ncMLN+QcBqiyHSbW53m9n+Yy4u4/pJU3C6YejT6xhzUzKfPFH/rs+Jj88ieNMjjF75BQA9 hi7goYevY/MtL5qKwai+o/80pl6VwN/GjCWtuH7RIqlfp4b6Nz18B6737uXW578CrFy36O/MnnEB Y+/70lT8RvXrqrOY+rf7uezZddxQvIRbUtN46e136BtmY3u5k5I9S5k8GXrd8Rx3dzh5fEbx+5qf Gx+4Defb93Drim1YbTE8tOYlKDbdvMf4a0q/5B8Fbib9Io7Zn9W/tztl7B8p2PY0ZXX12xwPvLWA 4Q/vbdj1+LsHVjF9yjmMe2iHX8YHzZ8/89dP84zO/ztzpvMOMO/1tym+/28szCrzqn1f8uPN+M76 n+Hc95exbM2txGINo4u92lT/RuMzKm/t+V9TsonJkzcRHPNH1q++/aRyo/EZXb++8nX++5qfY5o7 /0afD77ev1v7+hERERERERERERER/7H6u8Ha8gIOHjxISY2bs3r0pk/XcK/b+PWkBYzt6yDtjXm8 8MPxFa6sbRt5fukiHnlsEe99nUf73r/noZm/9Gf4Z7SK7Aoiul7JoAt7Eh0SiMtZSLGf3xN84M1/ Nyxw7PjvYSLPiQDAFtaXwR3DePfLIpKTk0lOTsaydwthCdeaeleymfq9b76I/M+fbFgcAcj4Jguo X8z4n/gQNryZdrTExb+f30XMuUNNxW+mfm31PgCK8qop31cKuMiuqSMxyNx7kT3Fb8QoP9bAGIZ0 COUfb+2sj95ZyCtfHjLVtllvr9pDz1suq//BYmPSgHg+ei6tobxk5z669B/AkGtHMnr0aFLqLIQn d/TL+I5p7vz5yuz88YUv+fFGwfan2JpbCYDbVc6+ozswT1X/zfFl/pthND5fr19f+zfir/w0d/49 fT74ev8+FdePiIiIiIiIiIiIiPiP33cAZ76zgGnvgC0smZfWLuHme+/grZHzTNc/77r7mD2sNz++ +wR3Lv+sUdl7CxY3/HvjRx9zwYY3cPS/FviM/wv2/X0WK+23MGTCTGZ0jiM7bSP3zVpIlh93QDtL ji9OuOsAS/3iSUBwFwAGjL6JAScc/9XX2wmxWqh2eX4Zppn6CZF2yj4tbbq+rQNWi4Ws6uNjrS4q JsDez1z8Zuq768tcLhfu2vrx1LrB5nFkx3mK34hRflz2+vgzT4i/MrsSerSouyYd/ORZgqYuonvw OnLjx9LZnc6UE3bxDZv7PKM7ZLLho+0cKa/GXlOHxRpkqm2z86e58+crs/PHF77kxxtHdja97ftU 9d8cX+a/GYbj8/H69bl/A/7KT3Pn39Png8/371Nw/YiIiIiIiIiIiIiI//h9AfgYZ/le9lfX0j+s j+k6KYOn88i4i9n17iKmLn4fj3+StgQQYLHgdpl7BPIZw+0CS8vS7qorYf2KhaxfAUExycx49gmm X7mOqRsyWr3/uordALxw/73sqfI+52bqZxZUEdXbAew7uX5NNrVuN2cHB7Dj6ONcg9vHUldtbuy+ 1jfDU/yG8Rnkx0ouLrebpBPiD+0U2ugYp9tNQMjxBdMgh92rGGqr9vByZiUTB3bg9QGDOLhxPkfX 0QgM7sGkSxK57ZqJ7D0aX++uV3P1T9o4dj0GWRrvKvR1/pzQQcvmbyuff7P5MWRifO4mFuv81r8P fJn/Rk73+PzRv1F+zF6/TZ1/8Pz54PP9+xTcP0VERERERERERETEf/z2COhgx2CWPXYvk24Zw/Ch w/nfWYu5INxOxcH3zNWPuYrFk3+H21VOXlh/Zs6axaxZs7jztl8BEBCUxMK507j+2iFcdfUw7pq/ DIfNSt6WNaZj/NOU6cyaNYvfR9fv2rrpzr8xa9YsLgj3zx4xe3gEUVFRBFstWINCiYqKIvwnjx+t rUonMKgrfWK8W5wDcFx0PtG2+lNWU5zFIaeLuirvHgHd0v6dlbtYu/8Id024HPvRR4ZabdFc/Jve fqv//XOfEHfBnVyadOyx4Vb6DkwGwFVbwtrMMgaPH4AFsFhDGH5rD/K/eMtU/77WN8NT/EaM8uOq LeL1nHL+Z2S/o2XxjLs4rlEb6bmVJPzhaLm9PTcMaO/1GDY+9x0p44ZzW38HG1btbfi9212Fy+2m W0z9tWOP6c2U3578+Nva6n3k1tQx/MJEr8ZnVkvnb2uff7P5MdLS8fmrf1/4Mv+N+HN8ty5fxapl N57y/o3y4+v16+nzwdfr71TcP0VERERERERERETEf/y2A9jlLCQw8XyGnfurht8Vpn/JwnteNVU/ IKgTARYLBIQzcNCght+X5+3j8aVbwO0itvdvGXfJHwBwu2v5ftMa7lu8zXSMPS75NYNight+7j9g IABpzy7kK5ym22nOH556kckJR/+4f8WDrLsC9r81hUnP7mo4prr4Q5Z/cAVzXniNwLo6sv55N1OW 7WqmxcY6DBzDI3NSOZSTDxGJWHM3cddH2V7F6Ev/q+6cQ4d5M3lz7Y3klLiI79CO9M+f4cuP04wr m6hftHMZ962OYNqSV5mQm4MlogMl3y1gyqb6hch1Mx/jvIUzee2l6ygLaU9gzqfMfPJb02P3tf7s FavoE2YjMDSSMOts1qxx4nLmM3rMFFPx+5qfV1MX84snU3mlfybVEaF8998Cks4+Xv/bRS/jXv4X 1r00jAp3Kf/8JI+UvubjByj4ZinOqJeJLv+ctwurGn5fV53BvDe+IvW5lxlx4DAREZWsfyuTkccv 1XpuJ6kL3+DBqUt4b7aNrH9PZ8LCNFPjM8OX+evr+ffEdH4MtHR8/urfk9ae/574c3wJMe2wZRee 8v6N8mN0/Rox+nzw9fprzetHRERERERERERERPzLkp6e7gYYMGCAxwPLy8sBiEyaw2vLLyX7/XX8 KzubNeveb3RcZEwc7SLDcJYeJregrKmmfGDFEd+eiFA7pYdzKCjzfdG2UesBUVw36irifjmEP/WI 4p5rr+Y/pTXGFU+hgJAoOsRH464oIOdQ671v05Pw2I44QqAgL4+yGu92IJupb7WFk5jgoKo4j0Ml VT8tJb7TWdhrS8g62PS7MD3ztb6JHjzGb8xTfizWUDontacs5wCR1yzisT+8zbW3/KuhPCAomk4d QsnLzKHK4L2eLREc05H2YZCTlYuzhc37On9807rn3x/5+Tn3D77P/5C463nzhau5cvDok8p8HV9g cArvrH+KpeOG8Y+DFV7X90d+PeXH1+vXzOeDb9df698/5biwsDCP5Tk5OQAkJCR4PO7Y9zcRERER kRPp+6aIiIjIz5PR97jNmzcDLdgB7Kzcy9atQRDbjV6RwSeVHyk8xJHCQ942a5KLgvxcClqpdax2 evfuDaU/snUrlDhP9eKUsbrKErIzSk5rDGWHc/Flad+ovstZRmZGc0e4yM/y5b2TvtY30YPH+I15 yo/bVUHGvvp3iEY2UV5XXURGRlGL+zZSVZhLhnebJ0/i6/zxTeuef3/k5+fcP7R8/ied1584exB9 Bv+JkvTXmzzG1/GFtB/E918uadHirz/6B8/58fX6NfP54Nv11/r3TxERERERERERERHxndcLwJX5 q0lNbY1QTj+X8xCpbXVw0uZU5e9ix87TvNon4if9/jCY88NslB/+kJnz/9EqfZRmPM+0e1ulaRER ERERERERERGRM4bf3gEsIqdW7sdLuO/j0x2FiH9sePR+NpzuIERERERERERERERE2gDr6Q5ARERE RERERERERERERET8QwvAIiIiIiIiIiIiIiIiIiJthBaARURERERERERERERERETaCC0Ai4iIiIiI iIiIiIiIiIi0EVoAFhERERERERERERERERFpIwK9rWANjCTlnK4Euas4mJFOfpmzoSw4qh2BFUco c7oACAiOJCKokuISZ6M2gqITSekcR1VxLnsy8k7qw2O5xUZS9+5E2+vYu3M3pbUu0/GZKRcRERER ERERERERERER+bnyagE4OHYgTy27C1dGGoWuEJJSurBkzAi2HKkBYNSSF+i19FZmfF6/aNtlxHwe /vVqRk78rKGNKybNY8rgFHanpWOJ6oyj5CXGzvzAVHmQ40IeeDyVxKoMDpQH0aszPHr7X9mcX2Uq PqNyEREREREREREREREREZGfM68WgFP+PJ7QH+dzw8z6BV2rLYZId63p+o7+05h6VQJ/GzOWtOL6 Rdekfp1Ml098fBbBmx5h9MovAOgxdAEPPXwdm2950VR8vsYvIiIiIiIiIiIiIiIiInIm8+odwBXZ FUR0vZJBF/YkOiQQl7OQ4p88gtmT3jdfRP7nTzYs7gJkfJNlqtwW1pfBHcN498sikpOTSU5OxrJ3 C2EJ1xJktZiKz9f4RURERERERERERERERETOZF7tAN7391mstN/CkAkzmdE5juy0jdw3ayFZNXWm 6idE2in7tLRF5QHBXQAYMPomBpzw+6++3k6I1UK1y20Yn6/xi4iIiIiIiIiIiIiIiIicybxaAHbV lbB+xULWr4CgmGRmPPsE069cx9QNGQA43W4CQgIajg9y2BvVzyyoIqq3A9jXZPueyusqdgPwwv33 sqeq6cc2G8VnVC4iIiIiIiIiIiIiIiIi8nPm1SOgHRedT7StvkpNcRaHnC7qqo4/Qjk9t5KEP/Sr b9jenhsGtG9U//vnPiHugju5NCm8ofu+A5NNlTsrd7F2/xHumnA59qOPfLbaorn4N71Nx2dUfsxD r6zl5SWXeZMaEREREREREREREREREZHTzqsdwB0GjuGROakcysmHiESsuZu466PshvJvF72Me/lf WPfSMCrcpfzzkzxS+h6vX7RzGfetjmDakleZkJuDJaIDJd8tYMqmvabKV905hw7zZvLm2hvJKXER 36Ed6Z8/w5cfp5mKz6j8mOCwMEKrvUqNiIiIiIiIiIiIiIiIiMhpZ0lPT3cDDBgwwOOB5eXlAASE RNEhPhp3RQE5h05+X29AUDSdOoSSl5lDlcvdZFtWWziJCQ7heqPeAAAgAElEQVSqivM4VFLldXl4 bEccIVCQl0dZTeMdvIbxGZSLiIiInG5hYWEey3NycgBISEjweNyx728iIiIiIifS900RERGRnyej 73GbN28GvNwBDFBXWUJ2Rknz5dVFZGQUeWzD5SwjM6OsxeVlh3NprtQwPoNyERERERERERERERER EZGfK6/eASwiIiIiIiIiIiIiIiIiImcuLQCLiIiIiIiIiIiIiIiIiLQRWgAWERERERERERERERER EWkjtAAsIiIiIiIiIiIiIiIiItJGaAFYRERERERERERERERERKSN0AKwiIiIiIiIiIiIiIiIiEgb oQVgEREREREREREREREREZE2QgvAIiIiIiIiIiIiIiIiIiJthBaARURERERERERERERERETaCC0A i4iIiIiIiIiIiIiIiIi0EVoAFhERERERERERERERERFpI7QALCIiIiIiIiIiIiIiIiLSRmgBWERE RERERERERERERESkjdACsIiIiIiIiIiIiIiIiIhIG6EFYBERERERERERERERERGRNkILwCIiIiIi IiIiIiIiIiIibYQWgEVERERERERERERERERE2ggtAIuIiIiIiIiIiIiIiIiItBFaABYRERERERER ERERERERaSO0ACwiIiIiIiIiIiIiIiIi0kZoAVhEREREREREREREREREpI3QArCIiIiIiIiIiIiI iIiISBsR6G2F8IQpPPPILwCordzH+IlzGsr6Dfwj/Xp2IzYqHGfpYbZtfpdNOw6aa9gSQJ+LB9G/ d3faOyKoKj7Ilx/+g//uLWk4pP+QEfQMtTWqlvvBm2zMr/JL+1Z7Ai+teKTh5wW33sz2cqep8LsN Hs4vI4OaLCv+4X3e/bqQYQuX0X11KvO/OmyqTWlaaMKvuWvadXSNi6C64HUmTfvHKevbEhBOnMNO fn5hs8ecPW4+t8WuYtqCtFMWlzfO5Pg85dfs9dPS8SUNncfMSz7gf/+2yat6p4o/4jMzf89U1y9a zlkr7ubRHQWnO5TTwtfPj9Xr1mG3WLHbbUy4dij5TleL2jmT7x8iIiIiIiIiIiIiUs/rBWCrrR3x 8fHsXbWY1em5jcpun34HnYMCcVY5sQXbuPKaEZw3byxPbck30W4cj983g9qqIg4WOElIuJzBQ0fy 0tQbeXVX/SLthSNu4NrYkEb1tu5439QCsJn23bVFLF++nE5X3sq4C2IJslrMpoWQ2HjaRwcDcNbA y+lS+BWfflcMQEC2HYCIuHii7QGm25SmDZ43lQ6fLuTOt3dSV1dxSvsOjb+BlUt7MHjotGaPKc/8 nrSS0lMYlXfO5Pg85dfs9dPS8QWGxxLnCPa63qnij/jMzN8zVXhcPNFB/3cfWuHr58cNI0diC+3F O289gRcfbSc5k+8fIiIiIiIiIiIiIlLP6wXgY4q2f8FnOxrvInvh4VR27fiegrIa2ve9jpcXjOc3 f76Ep7ZsMG6wroLlj87k/238hiqXm44XT+fFeVcwZEp/Xr3944bDKgvWM3XWew0/V+SWNNFYy9p3 uyr59NNP6dXvJnNtniDtxWc4th/q6nMHMGTnOp54oukdUrbIRM7p5qAwfSfZR36yw9hiI6l7d6Lt dezduZvSWu92aQVFxxBcWcKRmiC69zwbyrPZtf/4jrnQ2E506RiLpaaQ3bsP4HQfrxsaE4OtrJiK 4I7NxmeLTDha9gMHa4IIsVZRVlHrt/ittghSenTDVlvC7t37qTmhemhMDKFWK/2j7WR9nYXFasWK dysZntoPc8QQeKSYWkdXUuKC2PfDD5Qc3SVnsdpxxEQSGh0MFhuxsbEA1FWVUFTmPNp2FDFRNpzb 3+UtZ9Pz0mh8Rvm3BkaSck5XgtxVHMxIJ7/M3A51M/GZ6d+Ip/nliZn8HtPc9WMm/wBB0YmkdI6j qjiXPRl5zccUEI4jOpiSgsOG47AERBAdVUtNUCJn2Q/zQ2YFPfr0pGRPGrknXB9G+TG6fg3ja+b6 8ya/Rjzlz9f7ixlhHZJJiQ/mwA87Kaz5yf2lle4/1oAIoqMsFBQeObEzYmMdHCksoMblNtV/c+c3 xuGgrKjweDvH4gmIoF07N4UFZQ2/a43PDzPz1x/3D8PPDxERERERERERERHxixYvADdly+ffNPy7 srT+D+VV+eYeV+mqO8IbH25r+Lk4/QAAblfjP2C7XdVgC4LKQ+zPMv8YU7Ptt7boXqNYPv5simqi 6NnFzvyx4/mkoH4Hc5DjQh54PJXEqgwOlAfRqzM8evtf2WzmEddHDVqwnCEbV1L5p3FElR7GFn0W O6Zdz4KsMvrOeoYH+4awJzMHd2gnuoUf5J7JqXx3dAFoxOIVXPrxRmwXNx2f49zrWfrQaPLSdmDt kMD+PDvnOR9jTOo2v8QflngZTzw5DTK+oyysC11se5lx2738WFW/QHDZ1Fn8MSaYhJBAaifPYK7T RVXh20yf80+/tD92yQv84j/bCe4ZTkZZJH2Sanlgwl/4orAaW+i5zJ07DqstloCgEObOnQvAwU8X 8MDa/QBEJN3A3Km9CI5PIujHOQ15Mdu/Uf6DYwfy1LK7cGWkUegKISmlC0vGjGDLkRpT4zeKz6h/ I0bzyxMz+QXP14/R+ACumDSPKYNT2J2WjiWqM46Slxg784OTjguwJ3DnU0/i+GopM5Z/ZBh/eMLt rFrSlR93lZLc5xy+2JZNdIiN7onZDBl1Dy6T+fF0/RrF5+n6M5tfI57y5+v9xYyOgybz/DmRZJSG 06erhUcn3cGm/ErD8Zvh6foMDO3FK6tnMfGaYWTW1AEQGj+Cl1cMZuSQsdSY7L+58xu56EXy/no9 T2c2Ps+drprHgj++y8jb/g203ueHmfnr6/3D6PNDRERERERERERERPzHrwvAAF1HPcyD1yTTLiaC vf99nyce+k8LWrEydMYQAP759LeNSkLjRrFsySgA8tL+xd0zF5F99A/y/mi/tcVcXMb1k6bgdMPQ p9cx5qZkPjm6U3ji47MI3vQIo1d+AUCPoQt46OHr2HzLi171cdb/DOe+v4xla24lFmsYXezVABx4 awHDH97bsCvvdw+sYvqUcxj30A4T8Vn5272j2fP0HaS+tx+rLY5Fr70AJ2xy9jX+mx6+A9d793Lr 818BVq5b9Hdmz7iAsfd9CcA7c6bzDjDv9bcpvv9vLPzJopiv7QNEnXeQUeOX4nTDwLtWMnXuFVw3 5R1qyrYyefJWwjpOYu3SHkyefPIjdEv2LGXyZOh1x3Pc3aFl/XuaHyl/Hk/oj/O5YeZnAFhtMUS6 ze+eM4rPqH8jZuZXc8zk1yg+o/E5+k9j6lUJ/G3MWNKK6xfNk/p1Oum4wOAk7n56IbaNjzHjlS9M jR2grjqLqX+7n8ueXccNxUu4JTWNl95+h75hNraXO03np7nr1yg+T9ef2fx6YpQ/3+4v5kT1K2DU 2HupccGv/vo8f33gGjZNXGM4fjM8XZ81pV/yjwI3k34Rx+zP6t9rnzL2jxRse5qyOrdX/Td1fuP3 lDD2N+1hVRnWQBtWl5NaF5z9+wSy3v7BVP58Hb/R/PXt/mH8+SEiIiIiIiIiIiIi/uP3FyrWlhdw 8OBBSmrcnNWjN326hnvdxq8nLWBsXwdpb8zjhR+KG36ftW0jzy9dxCOPLeK9r/No3/v3PDTzl35r /1Q48Oa/GxZIdvz3MJHnRABgC+vL4I5hvPtlEcnJySQnJ2PZu4WwhGu9ehcxQMH2p9iaW78rzu0q Z9/RHaYlO/fRpf8Ahlw7ktGjR5NSZyE8uaOp+OwRF9Ev3M7KjVkAuJyHePmE3Pkav9UWw//Eh7Dh zWMrAi7+/fwuYs4d6tXYfW0/4/V3Gsb/1StfENVt9Cntv7n8A1RkVxDR9UoGXdiT6JBAXM5Cir18 xK0RT/0bMTO/Tmd8vW++iPzPn2xYvATI+Car0TGBIcnMWb6YszOeYo4Xi78AtdX7ACjKq6Z8Xyng IrumjsSg+ve2ms1Pc9evp/j8ef9ojlH+fLm/mHXgzQ0Nj2X+5u+bCe80ikDLqbn/vL1qDz1vuaz+ B4uNSQPi+ei5+uO96b+p87vvrQziB/UCYMRza3h2Sm8ABieEsfk/hwzz54/zbzR/zWjp54eIiIiI iIiIiIiI+JffdwBnvrOAae+ALSyZl9Yu4eZ77+CtkfNM1z/vuvuYPaw3P777BHcu/6xR2XsLFjf8 e+NHH3PBhjdw9L8W+AyzPLV/KjhLji+euOsAS/0f1wOCuwAwYPRNDDjh+K++3k6I1UL1T94N6cmR nU3/YX3Y3OcZ3SGTDR9t50h5NfaaOizWIFPxWW3tAcg5Ybd1ZX4VOPBL/AG2DlgtFrKqj7dfXVRM gL2fx3pmmW2/Mqei4d91NTlYbQ7sVvjpq0Zbq//m8g+w7++zWGm/hSETZjKjcxzZaRu5b9ZCsrze Ad88T/0bMTO/Tmd8CZF2yj4t9XhMcMxgKtZ8TOzwm+kXuYVvTD5e+3hA4HK5cNfWz/daN9iOFpvN T3PXr6f4/Hn/aI5R/ny5v5hVmVPZ8O+6mmysAaFEBlqpOAX3n4OfPEvQ1EV0D15HbvxYOrvTmXL0 KQTe5L+p81v8w/uExI/FGriZa4I+o+aiwdhCaulpL+fuouM7wFv188Ng/prR0s8PERERERERERER EfEvvy8AH+Ms38v+6lr6h/UxXSdl8HQeGXcxu95dxNTF7+PxT9aWAAIsFtwu84/A9ar9U6yuYjcA L9x/L3uqzI+pKe4m/tgfGNyDSZckcts1E9l7tP3eXa/marPxVf5YXyc0kK1H3+np6BIGpf6Jv64m m1q3m7ODA9hRXt9+cPtY6qozvG7Ll/bDu4TD1/XvrQ4M6UxdTV7jxV+3CyzeXzb+GJ+rroT1Kxay fgUExSQz49knmH7lOqZu8E+OfOHr/GrQwvyakVlQRVRvB7Cv2WPKc55h/ovvsDlqGfcsuJXrb32K Gj8snnqTn6auX6P4TF9/PuTXU/78dv4NhHcNhy/zAbAFd8XlLKbY6SLgFNx/aqv28HJmJRMHduD1 AYM4uHE+R9dJvbr/NXV+a0o+I4eZXN7zZorfW8+Gix/i4rMDqDj8pqnFW7P9H/u8DLL4Z1e4WUaf HyIiIiIiIiIiIiLiX357BHSwYzDLHruXSbeMYfjQ4fzvrMVcEG6n4uB75urHXMXiyb/D7SonL6w/ M2fNYtasWdx5268ACAhKYuHcaVx/7RCuunoYd81fhsNmJW/LGr+0f7o5K3exdv8R7ppwOfajj+y0 2qK5+De9/dK+212Fy+2mW0z9jjx7TG+m/Nb843mdld+zIbeciTcPwmaBkI6/4NauUX6L31VbwtrM MgaPH4AFsFhDGH5rD/K/eMv8IP3QftKIUUQFWgErv51wCcU/rGpUXluVTmBQV/rE2Fulf08cF51P tK3+kq0pzuKQ00VdlX8fAd1Svs6vY1qaXzO+f+4T4i64k0uTjj2W3krfgcmNjnG763cwbllyF9vD L+ex8f390re/8tNcfGavP1/y6yl//hqfkbOGXE/00etz4IQBFO1+ERen7v6z8bnvSBk3nNv6O9iw am/D732/f7t4PaecW27/FR+8m822V/Zwx/9eQN7Gr0zVNn3+q/eRW1PH8AsTTcblH0afHye6dfkq Vi278ZTGJyIiIiIiIiIiItLW+G2rnctZSGDi+Qw79/iCamH6lyy851VT9QOCOhFgsUBAOAMHDWr4 fXnePh5fugXcLmJ7/5Zxl/wBALe7lu83reG+xdv80/4ZYNWdc+gwbyZvrr2RnBIX8R3akf75M3z5 cZpxZQN11RnMe+MrUp97mREHDhMRUcn6tzIZOci47jEr73yYex6axvr1t1F8MI11n+UzPNR/8a+b +RjnLZzJay9dR1lIewJzPmXmk996OVLf2s/7sI5nX1lJeU0Ese69zL7940bl1cUfsvyDK5jzwmsE 1tWR9c+7mbJsFwCzV6yiT5iNwNBIwqyzWbPGicuZz+gxU/wyvg4Dx/DInFQO5eRDRCLW3E3c9VG2 6fpG8fnCH/MLPOfXiNH4inYu477VEUxb8ioTcnOwRHSg5LsFTNm096S2XHVHeOyvi1i9ch6jttzI Wg+PZTbDX/nxFJ+Z68+X/HrKn7/H15y8jyp4evULVFSFEWvZxz23f9hQdiruPwXfLMUZ9TLR5Z/z dmFVozJf+9/5bjYRt4SyobAK6/ZVRHZbxMYHD5qqa7p/t5PUhW/w4NQlvDfbRta/pzNhobn4fL1/ GH1+HJMQ0w5bdqGpNkVERERERERERESkaZb09HQ3wIABAzweWF5eDkBk0hxeW34p2e+v41/Z2axZ 936j4yJj4mgXGYaz9DC5BWV+DteKI749EaF2Sg/nUHD0UZJ+az0giutGXUXcL4fwpx5R3HPt1fyn 1It3gPpJeGxHHCFQkJdHma8vn/2J4JiOtA+DnKxcnC18sm2ALYA6Zx2/fOzv3JQ2h9te3NOo3Lf4 rcR3Ogt7bQlZB31bdPO2/dte3UDiQ+O5dy8kxgaTnZnT8IjXU9G/GQEhUXSIj8ZdUUDOoTPv+an+ mF+tzWoLJzHBQVVxHodKqowr+NGpyE9r3j/Ac/5OxfgCQ2JIjAsmJzOnyT5O9/2ntfN/pvdvxNPn R2BwCu+sf4ql44bxj4MVHlr5vyEsLMxjeU5ODgAJCQkejzv2/U1ERERE5ET6vikiIiLy82T0PW7z 5s1AC3YAOyv3snVrEMR2o1dk8EnlRwoPcaTwkLfNmuSiID+XglZqHaud3r17Q+mPbN0KJc7T88fz ssO5+Hvp/JiqwlwyWri5ql2vkQxsv59v9+QSftb5/OWcUJ57OPOk43yL30V+Vmu+09a4/dqKQjIO nL7+PamrLCE7o8SP8fiXL/PrVHE5y8jMaK0rzLNTkZ/WvH+A5/ydivHVVnq+Pk/3/ae183+m998c M58fIe0H8f2XS7T4KyIiIiIiIiIiIuIjrxeAK/NXk5raGqGcfi7nIVLb6uD8oLasiJShV/Gba9pR cySHl+ZM5KPC6tMdlt9kfP1fSkv9u6tcRETMfX6UZjzPtHtPU4AiIiIiIiIiIiIibYjXj4AWERER kdalR/KJiIiISGvS900RERGRnyezj4C2nopgRERERERERERERERERESk9WkBWERERERERERERERE RESkjdACsIiIiIiIiIiIiIiIiIhIG3FaF4DPHjefhdN7+7WeJSCc+PgYX0M7bXyJP2noPJ55dKCf I2rMTHwtPa/+8HM+/6vXreO1115nw4YNxNta59L8OefHjNYeX2vOf3+ff39fh6difra2tj7/Tzfl V0REREREREREROTMcFr/im+PcBAXbfe6Xnnm96TtLW2yLDT+BlYum+1raKeNL/EHhscS5wj2c0SN mYnP0/lpbT/n83/DyJGMHjuH4OBgrJbW6ePnnB8zWnt8rTn//X3+W3p/bc6pmJ+tra3P/9NN+RUR ERERERERERE5MwR6WyE0thNdOsZiqSlk9+4DON0nlMXEYCsrpiK4I+d0c1CYvpPsI85G9W2RHTmn WxxF+3Z6HazVFkVMlA3n9nd5y1nSqMxiteOIiSQ0OhgsNmJjYwGoqyqhqMzZVHON6wdEEB1VS01Q ImfZD/NDZgU9+vSkZE8auRW1huO3BkQQHRVAQWFxo3ZjHA5qigspq3Pjidn4PeX/5DGF44gOpqTg 8PHjLDaSuncn2l7H3p27Ka11GebGbHwez4/J/LZmfPUxRpDSoxu22hJ2795PjbnmDeOrP/8WCgqP nHgwsbEOjhQWUOOqPwHenL+fCo5qR2DFEcqc9X0GBEcSEVRJcYnx/DB9fbQw/0b9mxHmiCHwSDG1 jq6kxAWx74cfKHE27r+58+eP8Xm6f/k6//2RHzMM768G57e14zNidH0GRccQXFnCkZoguvc8G8qz 2bW/wLDdU3Z/AIKiE0npHEdVcS57MvJMj8/M56c1MJKUc7oS5K7iYEY6+SfE7un+YHT/9fXz71Td X0RERERERERERETEHK8WgPvOeoYH+4awJzMHd2gnuoUf5J7JqXx39A+8Ixav4NKPN2K7+GyKaqLo 2cXO/LHj+aSgCoCY/qN55v7ryU3bQVDiWRzIC4Qq8/1HJN3A3Km9CI5PIujHOYxJ3dZQZgs9l7lz x2G1xRIQFMLcuXMBOPjpAh5Yu9+w7fCE21m1pCs/7ioluc85fLEtm+gQG90Tsxky6h5cBuMPCEri pdWPcs+Ia9h2NB/2yF+x+pWZTB46jLK6Wo/9m4nfKP8nCrAncOdTT+L4aikzln8EQJDjQh54PJXE qgwOlAfRqzM8evtf2ZxvfBLMxOfp/JjJb2vHF5Z4GU88OQ0yvqMsrAtdbHuZcdu9/Fjl+dwc4ym+ wNBevLJ6FhOvGUZmTR0AofEjeHnFYEYOGUsN3p2/poxa8gK9lt7KjM/rF5W6jJjP/2/vzuOjqu7/ j79mkpkskxCzEJIAsokoS9Vqq/6aaqvSxS8qS0UFEVAWvxiR4gIIsrgAFhSsiAIKCqISKopYW79W xYpbCypiUBTClgWQJSF7JjPz+2PCBEwy905mJgF5Px8PHo9kztxzPudz7pyTxxzuvbN+vZKBoz4C g/rN5CeY/Bu1b8bQBcv4xX82E31uHLtLW9GzQw0Pj7yLTw9XAf7HLxT98zd/BXv+hyI/RozmV6P+ hzs+I2Y+n5fPXUzf95dScfUwEkoOYktsz5bxNzE3r9Rv3c0xPwD0Hv0gY/t05bucXCwJZ5Jc/AJD J/7LVP1G62d0ymU8uehe3LtzOOyOoUPXjiwYcj0fH60G/M8PRvOvI8j1rznmFxEREREREREREREx L6AN4D2vzWXArB2+q36uengF94w9h2Ezt/jek3RxKTeNHovTA/2eymbILV34YF4OYOX+KYPYvuAO Jv9zN1Z7Ok+tXgJfm2+/ePtCsrKg+51LmJR2Yll16UaysjbiSB/NqoXdyMoaH0jXAHBV5THuvoe4 4plsBhct4LbJObyw7k16OWxsLnP67b+z/Gue21nKyH5nMmbFDgA6DbyF4u8Xs8PEBoKZ+M3kHyAy ugOTnnoc2/tzmPDip77XRz12P9H/fpRBS72vdes3l5mzbmTDbc+HJD5/4wPG+Q13fLfMuhP3P6Zx +7ObACs3zn+JKRMuZOiMzwzrB//5qy75jDcOeRj9i9ZM+WgfAF2H/oFDXzzlu/rb7Pg1lb/6zeQn mPwbtW9Wwnn7uGH4QpweuOzepYyb3psbx74J+B+/UPWvsfkrFOd/eMffeH416n+4z08jZj+f7a8d wIy7hrKxsAKL1UFHe5Vh3c0xPyRfMJ5x/5PBfUOGklPk3ZTtcH67gOpvfP2EriOGE/v9bAZP9P6H D6stiVYe85vT/ubfXINyo/WvOeYXERERERERERERETEvoA3g4m920vXnmfTonEGsPZIEl4W4LulA 3QbBnjXv+L4g3vLfg9x0aTwA9vhf0ivOxpj1+QC4qwtZtu0od4amHyFRU7UTgCP7qyjbWQK4ya92 0TYqgs1lTsP+r3/yY259eCTWFRNxWyIYfXU7Ppq4IWTxmcl/ZEwXpi4eQ4ft8xl63OavzdGLPukO 5n92hC5dugBg2fExjoxbibK+QJU7/Pd69ZffrZwT1vistiSuTY3hr2tyal9x886z2xg8ox9gvMFj Jn/rVmzniduugI9eAouN0ZmpvJeV46vDzPgFI5j6Q3F+hKJ/u//2pm/+2PTipyQsGQS82SzjB43P X6EQzvE3ml/N9D/c56c/gYzvoc1PsrGwAgCPu4ydIbiANNjzC6DHrb/kwCczfZu/ALu/zAuofn/n X3l+OfEX/JHLLzrMVznfc6TiMCc+cMA/f/NvrkG5mfXPn5Nh/RERERERERERERE5nQS0Adx/+rMM StvL2vc2c7SsCnu1C4s16oT3OIvrvvz2uABLBABWWxsACmpvjwtQtq8ckpsaehh4vLG53W48Nd4v pGs8YKstNup/0baF7Ilcw5D2cayxDOTsiN1M3B7IV/T+mcl/dFIfyl9ZT8qAWzm/1cd8WXt70Ijo jgBkDrqFzOPev+nzzcRYLc3zBbyf/IY7vghbGlaLhbyquvOv6kgREfbzzR1vIr59HzxD1Lj5nB2d TWHqUM705DL2uFvTmhm/YARTfyjyH4r+VRSU+352VRdgtSVjt4KnGcYPGp+/QiGc4280v5rpf7jP T38C+Xwe/SZ0c2pT2m9MRis7pR+WBFW/v/Nv50v3s9R+G31HTmTCma3Jz3mfGfc/Tt5xY+6XwfoW 7Prnz0mx/oiIiIiIiIiIiIicRkxvAEdGd2P0pW0Zc90o3y2Ne3S6hmtMHu+q2gVAp+hItpZ7nyl5 RrtYqAgoXmMeN1gC2tc2xUz/Pe5qFryVz+Tbf86uiCvJ/+cjVLsDbKiR+M3mv6zgaWY//yYbEhbx wNzbuen2J6l2e3CVfwfAsoemsT2AZ1qajS9Y4Y7PVZ1PjcfDWdERbCnznn/RbVJwVe0OWXw1ldtZ vreCUZel8bfMy9n3/mxq91FMj5/H7S2Lsljq1e/0eIiIqdsQikq2+342/flsLD9B5j/Y+eGYuI5x 8PlBb50xZ+Kq3k+1G6xmxy9M/TOq30goxt8fo/nVqP/hjs8w/gA+n55gNgvDND8A7D1USUKPZGBn WOp3u4p5/bnHef05iErqwoRn5nHPH7MZt9Zbh7/5IVgtPb+IiIiIiIiIiIiISGCsZt/o8VTi9njo nOS94see1IOxv0033ZCzbDMfFlUxos/ZANjiuzGy6xkBhmuspjKXyKhO9EwK3ZffYL7/O15aRuJ5 WdzRK4nnV24PuJ3G4jfbvsfjvYLs4wX3sjnuSuYMvwAAZ8U2Vu06yr0jr8Ru9W7eWG2JXPybHiGJ L1jhjs9dU8yqvaX0GZ6JBbBYYxhwezcOfPpaSON7fyjP0HAAABd/SURBVMnXdB02gDEXJLO29lnQ YH78aqp2UljtYsBFbeuV5RZWkPF77xWDVnsbBme2Cbz+RvITbP6DnR+O6XD9DSREWgErvx15KUXf rgDMj1+4+mdUv5FQjL8/RvOrUf9DGd/ti1ewYtHNAcUf7OfTrHDNDwBbl3xA6wvv5lcd4mpfsdLr si4hqz/5lz8n0eZdsquL8vjB6cZVWfc/jPzND8Fq6flFRERERERERERERAJj+lI2V9VuHnx1E5OX LOf6PQeJj6/g9df2MvBy8409MWkh8+Y+you/K4A4Dx9/dYRLAwh2ynMr6OmwERnbCod1Cq+84sTt PMCgIWN976kqepfF/+rN1GWriXS5yHt7EmMXbQuglYaZ7X91yae8/EME/a2r+eRodcOV+dFY/IHm 3+06ypw/z2fl0ge54eObWfVNESvunkragxNZs+pmCordpKadQe4nT/PZ+pyGKwkgPjA3Pv6EO77s iXM47/GJrH7hRkpj2hBZ8CET//qV6brNxHfoy4U4E5aTWPYJ6w7XPZzU9Ph5nEx+/FUeGbeAf0yx kffOPYx83Fv/V/OX41l8F9kv9KfcU8LbH+yna6/A6veXn2DyH4r5AWD/uy6eeXEpZdXxpHh2MOWO 9b4yM+MXrv6Zqd/f+R+K8TdiNL/6638o48tIOgNb/mHTOT0m2M+nGeGcH458s4gZK+MZv+BlRhYW YIlPo/jruYz9946Q1J922RAenTqZHwoOQHxbrIX/5t738n3l/uaHYLX0/CIiIiIiIiIiIiIigbHk 5uZ6ADIzM/2+saysDIDopHTaOKAgrxBnE+7EaYlw0K5dMkfy8ih1BXp/5JZn3H8rU7JfJ3LeCKZ/ cqAF2jcWl5JOcgwc2r+f0oDvUR1+4Y3PSmq79thrisnb17RniQYTX7DjFxGVSLu0WPbvLaCygVvh tvT5EUz7Y15eS9uZw5m2A9qmRJO/t8B3C+06LTt+wQrF+PhjZn711/9g44uM7sqbrz/JwmH9eWNf ufEB9QQ/vsEJvn2rLY62GclUFu3nh+LKH5cGVX9ETAJpqYl4yg9R8EP95w0bzQ/Baun55XTjcDj8 lhcUFACQkZHh933H/n4TERERETme/t4UEREROTUZ/R23YcMGoAkbwNK4Dr3O45zz+nPn9a0ZPCCL 4hp9uS1i1rEN4MlbAr96VE4O8R1GMOPWfYyf9mZLhyJyytMXciIiIiISTvp7U0REROTUZHYD2PQt oMXYRVdfS3cOM2vsHG3+igRo9+f/paTE2dJhSBBKdj/L+GktHYWIiIiIiIiIiIiIyOlNG8Ah9Oqj D/FqSwchcor6+5yHWzoEERERERERERERERGRU561pQMQEREREREREREREREREZHQ0AawiIiIiIiI iIiIiIiIiMhPhDaARURERERERERERERERER+Ipq0AXzWsNk8fk+PBsssEXGkpiYFFZSIiIiIiIiI iIiIiIiIiASuSRvAZXu3krOjpMGy2NTBLF00JaigREREREREREREREREREQkcJGBvNlqSyApwYZz 81u85iw+ocxitZOc1IrYxGiw2EhJSQHAVVnMkVJn6CIWEREREREREREREREREZEGBbQBHN9hMNPH dSc6tQNR309lyOQvfGW22J8xffowrLYUIqJimD59OgD7PpzLw6t2hTJmERERERERERERERERERFp QEAbwMXbF5KVBd3vXMKktBPLqks3kpW1EUf6aFYt7EZW1vhQxikiIiIiIiIiIiIiIiIiIgaa9Axg ERERERERERERERERERE5+WgDWERERERERERERERERETkJyL0G8AeN1gCurO0iIiIiIiIiIiIiIiI iIiEQMg3gGsqc4mM6kTPJHuj75n54iqWL7gi1E2LiIiIiIiIiIiIiIiIiJzWArpUd8pzK+jpsBEZ 2wqHdQqvvOLE7TzAoCFjfe+pKnqXxf/qzdRlq4l0uch7exJjF207oZ5oh4PYKl0lLCIiIiIiIiIi IiIiIiISSpbc3FwPQGZmpt83lpWVNUtAIiIiIqc7h8Pht7ygoACAjIwMv+/T328iIiIi0hD9vSki IiJyajL6O27Dhg1AOJ4BLCIiIiIiIiIiIiIiIiIiLcJ3BXDnzp1bOhYREREREREREREREREREWmC 3NxcQFcAi4iIiIiIiIiIiIiIiIj8ZEQe+0HP9BAREREREREREREREREROTkZPQP4GF0BLCIiIiIi IiIiIiIiIiLyE6ENYBERERERERERERERERGRn4hI47fUSe/dj0sj/sOaf+aHK54TnDVsNmNSVjB+ bo6p15tbwjm/o3fXPfxt3bcA9H98EWevnMzsTQdbNK5jfhyfWSdLfpvKbPw/zk9Sp/O44lcX0T49 CeeRAj74+xq2FFaccEz6z3vT7/Lziao6yIfrstm4t+7W6MEebxSf0fEd+gzgV62iTqjj7dWvcMjp bpb+W6xRtO96Dt3P7UZiDLz8cvYJx5qJz2x+zvpDf36RUMPLq95osDxcLBFxtE62c+DA4XplwX7+ T4b8H6+p84c/p1P+GuKv/6EWrnn8VF8fzDpd+ikiIiIiIiIiIiI/TQFdAdz+fwZyw7XtwxVLPfb4 ZFon2uu9XrZ3Kzk7SpotjoZZGDntDrofPuJ7Jb51Kon2iBaM6Xj14zPr5Mhv05mLv35+FjwxmV7p sfywexeu1hfzlyXLuCI12lee1HMozz40iuo9W9lTmc6Mp5/lkgR7yI43is/o+M59b6TvJWfRpk0b 3z+7pfn63/bKWSycPZGrL+vL0FuG1GvRKD6z+YlJvZI5I29lyM0DGutc2MSmDmbpoikNlgX7+W/p /J+o6fOHP6dP/hrmr/+h1tj6GaxTfX0wK1z5ExEREREREREREWkOAV0BbIbVFk/Xbp2x1RTz3Xe7 qP7RxVGxKe3omJ6Cpfow3323B6fnxHJbq3TO6dyaIzu/aaDuBJISbDg3v8VrzuJ65bFJSdhKiyiP Tueczskczv2G/KPOH9WfUVv2Lfuqo4ixVlJaXhNwPxO6juA30blc/8mBemW2Vm0bbb+x/Fgi4klM qKE6qi3t7Qf5dm853XqeS/H2HAqPj89io8PZZ5Nod7Hjm+8oqWnk6r1G4vPXf6P8+ovfGhFPYoKF Q4ePHvduCykpyRw9fIhqt3eg/Y2/0fgZlZuJ319+7h48mMKSY/Wtpmbl69wysivvPbIFgH73XUfe 2ik8++pW4O/UXLiK2+/syacPfx6S443iM3P8/g0rmJe902/fw9X/wg+nc+07R4ltN56/Lfltg+36 i89UfiyRjJo1mpWPfs2tD6Qb9vPHohKTiK4o5mh1FGefexaU5bNt1yFfeaOfT6ud5KRWxCZGg8VG SkoKAK7KYo6U/niOafzz709L5/94jc0fyp+5/P2Y2f4b5TeY9TM64Qwiy49SWnvFckR0K+KjKigq 9rZvS0gmKfrEPwlcVUc4WFQNBLc+gLn12ez6Fi7+8genfv9ERERERERERETk9BHSDWBH2yuY99fx sPtrSh0d6WjbwYQx0/i+0rvB2Ov+p3mkVwzb9xbgiW1H57h9PJA1ma9rvwBPumAQTz90E4U5W4hq 2549+yOhsq7++A6DmT6uO9GpHYj6fipDJn9xQvvXP/Ecv1r/PraLz+JIdQLndrQze+hwPjjkrST5 ZzexcOYg9udswZqWwa79ds5zzqlXjxn9JvyBb58fT4X7xG/gE7vfwOLhDbfvLz9xGXewYkEnvt9W Qpee5/DpF/kkxtg4u20+fW94ADcQlXwRDz82mbaVu9lTFkX3M+Evd/yZDQcqTcVn1H+j/PqLPzK2 Oy+uvJ9R1/Vnb7ULgNjU61n+XB8G9h1KtYnxNxo/o3Kj+I3yU7d5VPt7lQss3kv8LNZY+rWOZd26 3b7yTWvzuW3EH4HPQ3K8v/jMHm9zpNKjVxyVh/PZkd/4bWbD0X9X5VGMNBaf2f516DONXlvnsWx/ b241bK2+y+cupu/7S6m4ehgJJQexJbZny/ibmJtX6vf8tsX+jOnTh2G1pRARFcP06dMB2PfhXB5e tctXv7/Pv5GWzP+PNTa/KX/m8lfvOJP995ffYNfPGxYso/vC25nwyX4AOl4/m1m/XsnAUR8BcOZ1 Y7nn0lTf++Pad4AvJzNkSvDrAxjP34Gsb+FglL9TvX8iIiIiIiIiIiJyegnpBvAts+7E/Y9p3P7s JsDKjfNfYsqECxk64zMA9rw2lwGzdviuWrrq4RXcM/Ychs3cAli5f8ogti+4g8n/3I3Vns5Tq5fA 13X1F29fSFYWdL9zCZPSGo4h6eJSbho9FqcH+j2VzZBbuvDBvBzAyn3TBrH9qTuZ/I9dWG2tmb96 GTTh8X6x6f0Y2KaEIW/tDaB94/y4qvIYd99DXPFMNoOLFnDb5BxeWPcmvRw2Npc5GfXY/UT/+1EG Lf0UgG795jJz1o1suO15E/EZ998ov/7iry75jDcOeRj9i9ZM+WgfAF2H/oFDXzxFqcs74P7H3zh/ RuVmzg+j8TvGnnARw9vEsHJqLgCRMV2xWS1sK3dywYARdN23jre3l2CL7RHy4xuKz+zxbX5/J3f9 Pyfp7dIp3PQa46ct9uW/OfvfmMbiM1O/zdGTh4elMuGm/0B674DaPV77awcw466hbCyswGJ10NFe BRic36UbycraiCN9NKsWdiMra3yDdRudv2Y1d/6PZ3R+KH/+89cQs/2HxvMb7PppZMfyafzvcu/P ce17s2zhSJ55+jtfeTDrwzH+xtfs+hYexvk7tfsnIiIiIiIiIiIip5uAngHstyJbEtemxrB2zbEv 69288+w2kn7Wz/ee4m920vGCTPr+aSCDBg2iq8tCXBfvbVzt8b+kV5yNpevzvUdXF7Jsm/EVWT+2 Z807vi/It/z3IK3OiffVf36cnaXv53nrd/7A8m+LmtTXq+4byO7XZ3PIWf/2jY21byY/NVXeW4se 2V9F2c4SwE1+tYu2URHYHL3ok+7grc+O0KVLF7p06YJlx8c4Mv5ElPXEB1E2FF+w/TcT/7oV2zn3 tiu8v1hsjM5M5b0ldZs3/sbfKH9my83wN37eviYz/on72fXqVNbklXq7Y40BoNIDV//pGq67pjvu GidYo0J+fEPxmTn+vw/+L/1uvJlRI4dz/S334ux1DTMHdmr2/jfGX3xm6u8/436+fWo6BbVXmDfE HhdPQkICCQkJxEU3/DzZQ5ufZGNhBQAedxk7K2tMnd9mhOL8bIn8H8/o/FD+/OcvWA3lF5pn/QSI jO7Eg/Pv4t9zx/FufpmpY8yOf2PjG8j6Fg5G+TvV+yciIiIiIiIiIiKnn5BdARxhS8NqsZBXVbc5 U3WkiAj7+b7f+09/lkFpe1n73maOllVhr3Zhqf0C3mprA3DC5k7ZvnJIDiwOZ3G172ePC7BENFp/ xYHKgOu3J1zG6LMjyZq0PaD2zeTHewC43W48Nd5vkWs8YAMiojsCkDnoFjKPa2/T55uJsVqoqr1V a2PxBdt/M/Hv++AZosbN5+zobApTh3KmJ5extRsw4H/8j2ksf2bLjRiNn8Uaw4jZC+jyzQuMXrrR 97rb6b2qubXNyqwhNxHhKie+51W4nAdCenxj8Zk5vnTPQd/PlT9sYeH6fTz4+wvh5dxm678//uIz qj+u3QiGdC3ikfIOXHJJB2JSErFYorjkkkvI3fQfDtRuVv7xscXcmhYLQP67kxjz16314jj6Tf3/ +GDq82lCsOdnS+X/GKPzA5Q/aDx/odBQfqF51k+LNYqRcx/F9q9HePKDAtPHmR3/RtdHk+tbuBjl 71Tvn4iIiIiIiIiIiJx+QrYB7KrOp8bj4azoCLaUeZ9JGN0mBVeV95mLkdHdGH1pW8ZcN4odtVc0 9eh0DdccO75qFwCdoiPZWu49/ox2sVARovgqvve2GRvJxtpnJiZ3dEBJYPVcMn4E+9f/xXdVlun2 DfJjeHy591acyx6axnY/bTcWX7D9NxN/TeV2lu+tYNRlafwt83L2vT+b2n1sw/FvLn7Hz2LjhqnP 8OuKt7htzlqOv/6xpnIXe6pquDAjlnWHDuMGUjNbU3VkfeiO9xOf2eOPZ7VZwXPic1HD2v8AHR+f Yf2WGr7+5ijX9e0LQGR0BpaIWPr27cvrWz/ngNO78bJ29E2sNWjX08Bmi+nPp8cNlpDeOb9OC+b/ GDPzm/Ln1VD+DJnof0P5DcX66fR4iIip21CPSrbXa+fXd8zj8pp3GPLMJwF0qvnWt3Axyt+p3j8R ERERERERERE5/QR8C2hLRKzvFqvH/tmtFtw1xazaW0qf4ZlY8F6JNeD2bhz49DUAPJ5K3B4PnZO8 VyzZk3ow9rd1t/91lm3mw6IqRvQ5GwBbfDdGdj0jBF2srb9iK2sLyxh16+XYLBCT/gtu75QQUB22 2B7cfVEiTz39ZcDtG+XHOP5trNp1lHtHXom99paRVlsiF//muGek+okv2P6bjf/9JV/TddgAxlyQ zNoVO3yvG41/c/A/fhauuWcBf0r/mgmPv0FMvPfcjo+r2yR59pMf6Hnn9URbLUREZTCqdwbfvvhB iI43Pr/8HW+1JXHVRWdhq72baELHS7grsw3fv7qpmfoPFms0CQkJtIqzeWNISKBVqzjT8fmrv3Tv 80ycONH378F5ObhrDjNx4kQ+PVp31V1TmT2/aypziYzqRM+k+ptnwWn5/DfH/PZTzt8xM19cxfIF VzQYZVP7H4r1M7ewgozfe69YtdrbMDizzQnl7a78M/deVs59E5b6bmNsVnOsb8f4y29Ty43y15z9 ExEREREREREREQmFgC/FSug4gezsE197YUhfXjpQQfbEOZz3+ERWv3AjpTFtiCz4kIl//QoAV9Vu Hnx1E5OXLOf6PQeJj6/g9df2MvDyunqemLSQeXMf5cXfFUCch4+/OsKlx7Uz5bkV9HTYiIxthcM6 hVdeceJ2HmDQkLGmYl969ywemDme118fQ9G+HLI/OsCAWPN97zFiHCWbn2BTaYBXfdXylx8zVtw9 lbQHJ7Jm1c0UFLtJTTuD3E+e5rP1OabiM+q/UX7NxH/oy4U4E5aTWPYJ6w5X+l43M/7BMorfX34s EQ6yruoIdGTZy1f5Xi8tWMCA4esA2PjYA3wx7y9kr7yUstjWFG16hYnv5YfkeKP4DNu3OLht6hPc Hemi6Gg1iQnRfPl/S3ng73tN1R+K+B1pI8heVndNd3Z2Nq7qfVx9zVBT8RnVH25mzu+qondZ/K/e TF22mkiXi7y3JzF20bag2z4Z8t8c89tPOX/HRDscxFY1vLQ2tf+hWD+/mr8cz+K7yH6hP+WeEt7+ YD9de9WVX3BzJpGxdp7MXuN77YfPpzJixmYgNOuDP0br2zH+8htMuVH+mqt/IiIiIiIiIiIiIqFg yc3N9QBkZmb6fWNZWZnJKq2ktmuPvaaYvH31n2UYnZROGwcU5BU2eJWRJcJBu3bJHMnLo9Tlrv+G EIiwReByurhkzkvckjOVMc83/rxL3zH29ry4ZhHLhvbn/w5VGr6/cf7zY0ZcSjrJMXBo/35Kq90B x9eU/ocqfqPxD5fQjR+kZJyJ3XmYgh9Kjd9s8vhA4mu0faud1LQ2xNqtlBzI51B53a1GT4r++4kv JPWHRPCfz3ALR/5Ppvkt3MJ9/oZTsOtnRFQi7dJi2b+3gMqwPHc2POtbczH+++PU7p+IiIiIiIiI iIic+hwOh9/yDRs2AGHZAD55ndF9IJe12cVX2wuJa/9zJk8azpKhN/De4SrDY632NDq3t7N9x55m iDRwZuILpv+nup/C+J3M9cupTeeHiIiIiIiIiIiIiMjJTxvADYg7szejh2TStvUZVB8tYP1ry/jn FwdaOqxmc7r3X6Sl9OvXr9EyV9Ve3nhrYzNGc+pR/kREREREREREREREtAEsInLSiIuLa7TM466m rLy6GaM59Sh/IiIiIiIiIiIiIiJN2AAWEREREREREREREREREZFTU6dOnQCwtnAcIiIiIiIiIiIi IiIiIiISAhaLhf8P5iwryAIpA6UAAAAASUVORK5CYII= --=-=-= Content-Type: text/plain On the left there are three windows with translated, current, an next sentences from the source text. Central windows are for translated and current sentences, and the bottom central window is for current word. The right window is for statistics, and (not shown here) Wordnet (/usr/bin/wn) lookup. The idea is to have some words (in bold in the sreenshot) that are controlled, so that while translating them you can keep trace of all other occurencies and prior translations. So every word in the source material need to be indexed and referenced to a (possibly empty) word in the ongoing translation. Work happens in the very central frame, where words are presented untranslated at first, and you can move them around or substitute them with prior or new (including empty) translations. After a while, it gets fast. I am attaching the code. Most of it is a painful and messy tratment of the publisher markup, and all of it is intended for personal use and for the particular book I was translating. But maybe you can adapt some of it to your needs. Regards, Giovanni --=-=-= Content-Type: application/emacs-lisp Content-Disposition: attachment; filename=roth_2015.el Content-Transfer-Encoding: quoted-printable ;; -*- mode: emacs-lisp; lexical-binding: t; -*- ;;; files ;; (add-to-list 'backup-directory-alist (cons "roth_2015" "backups/")) (defconst gi/roth/basedir "~/2020/05/201504-roth_2015/") (defconst gi/roth/roth (concat gi/roth/basedir "roth_2015.roth")) (defconst gi/roth/glossary (concat gi/roth/basedir "roth_2015.glossary")) (defconst gi/roth/rglossary (concat gi/roth/basedir "roth_2015.rglossary")) (defconst gi/roth/traduzione (concat gi/roth/basedir "roth_2015.traduzione"= )) (defvar gi/roth/errors ())=20 ;;; tables (defconst gi/roth/elt-tables=20 '(("gi/roth/elt" . 1900) ; (chk elt) -> len ("gi/roth/elt-type" . 1900) ; (chk elt) -> str ("gi/roth/lne-text" . 4200) ; (chk elt lne) -> str ("gi/roth/lne-pageskip" . 250) ; (chk elt lne) -> ((point . num) ...) ("gi/roth/lne-italic" . 300) ; (chk elt lne) -> ((start . end) .= ..) ("gi/roth/lne-notesref" . 200) ; (chk elt lne) -> ((point . num) ...) ("gi/roth/lne-indexref" . 800) ; (chk elt lne) -> ((point . num) ...) ("gi/roth/lne-link" . 15) ; (chk elt lne) -> ((point . url)) )) (defconst gi/roth/elt-ttables=20 '(("gi/roth/elt-pageskip" . 250) ; (chk elt) -> ((point . num) ...) ("gi/roth/elt-italic" . 300) ; (chk elt) -> ((start . end) ...) ("gi/roth/elt-bold" . 10) ; (chk elt) -> ((start . end) ...) ("gi/roth/elt-notesref" . 200) ; (chk elt) -> ((point . num) ...) ("gi/roth/elt-indexref" . 800) ; (chk elt) -> ((point . num) ...) ("gi/roth/elt-link" . 15) ; (chk elt) -> ((point . url) ...) ("gi/roth/lne" . 4200) ; (chk elt lne) -> (start . end) )) (defconst gi/roth/gls-tables '(("gi/roth/gls" . 2200) ; rstr -> ; (((chk elt lne strt end) strt end) ...) ("gi/roth/gls-index" . 200) ; (chk elt) -> ; (((chk elt lne strt end) strt end) ...) ("gi/roth/gls-names" . 400) ; str -> ; (((chk elt lne strt end) strt end) ...) )) (defconst gi/roth/gls-rtables '(("gi/roth/gls-r" . 3100) ; (chk elt lne) -> (rstr ...) ("gi/roth/gls-index-r" . 700) ; (chk elt lne) -> ((chk elt) ...) ("gi/roth/gls-names-r" . 700) ; (chk elt lne) -> (str ...) )) (defconst gi/roth/trd-tables=20 '(("gi/roth/trd-italic" . 300) ; (chk elt lne) -> ((start . end) .= ..) ("gi/roth/trd-notesref" . 200) ; (chk elt lne) -> ((point . num) ...) ("gi/roth/trd-indexref" . 800) ; (chk elt lne) -> ((point . num) ...) ("gi/roth/trd-link" . 15) ; (chk elt lne) -> ((point . url)) ("gi/roth/trd-note" . 15) ; (chk elt lne) -> ((point . note)) ("gi/roth/trd-text" . 4200) ; (chk elt lne) -> str )) ;;;; xhtml (defun gi/roth/xhtml () (let ((order '("front02.xhtml" "front03.xhtml" "c01.xhtml" "c02.xhtml" "c03.xhtml" "c04.xhtml" "c05.xhtml" "c06.xhtml" "c07.xhtml" "c08.xhtml" "c09.xhtml" "c10.xhtml" "c11.xhtml" "c12.xhtml" "c13.xhtml" "c14.xhtml" "c15.xhtml" "c16.xhtml" "back01.xhtml" "back02.xhtml" "back03.xhtml"))) (dotimes (x (length order)) (with-temp-buffer (insert-file-contents (concat gi/roth/basedir "xhtml/" (nth x order))) (goto-char (point-min)) (while (re-search-forward ".=C2=A0.=C2=A0." nil t) (replace-match "[dots]")) (goto-char (point-min)) (while (re-search-forward "=C2=A0" nil t) (replace-match " ")) (gi/roth/sxhtml (cdr (assoc 'body (libxml-parse-html-region (point-min) (point-max)))) x))) (maphash (lambda (k v) (puthash (list (car k) 1) "parte" gi/roth/elt-ty= pe)) gi/roth/elt-bold) (maphash (lambda (k v) (mapcar (lambda (x) (mapcar (lambda (y) (let ((z (gethash x gi/roth/lne))) (when (and (>=3D (car y) (car z)) (< (cdr y) (cdr z))) (puthash x (cons (cons (- (car y) (car z)) (- (cdr y) (car z))) (gethash x gi/roth/lne-italic)) gi/roth/lne-italic)))) v)) (gi/roth/elt-lines k))) gi/roth/elt-italic) (cl-mapc (lambda (a b) (maphash (lambda (k v) (mapcar (lambda (x) (mapcar (lambda (y) (let ((z (gethash x gi/roth/lne))) (when (and (>=3D (car y) (car z)) (<=3D (car y) (cdr z))) (puthash x (cons (cons (- (car y) (car z)) (cdr y)) (gethash x b)) b)))) v)) (gi/roth/elt-lines k))) a)) (list gi/roth/elt-pageskip gi/roth/elt-notesref gi/roth/elt-indexref gi/roth/elt-link) (list gi/roth/lne-pageskip gi/roth/lne-notesref gi/roth/lne-indexref gi/roth/lne-link)))) (defun gi/roth/sxhtml (lst chk-idx) (let ((elt-idx 0)) (dolist (x lst) (let* ((elt (list chk-idx elt-idx)) (a (cond ((=3D 18 chk-idx) (gi/roth/sxhtml-match-notes x)) ((=3D 19 chk-idx) (gi/roth/sxhtml-match-index x)) (t (gi/roth/sxhtml-match x)))) (cat (nth 0 a)) (key (nth 1 a)) (txt (nth 2 a))) (pcase cat (:n nil) (:a (puthash elt (cons (cons 0 txt) (gethash elt key)) key)) (:b (progn (puthash elt key gi/roth/elt-type) (gi/roth/sxhtml-elt txt elt) (setq elt-idx (1+ elt-idx)))) (:c (dolist (z txt) ; non recursiveness bites here (pcase z (`(li ,_ (p . (((class . "left ")) . ,y))) (progn (puthash elt (concat "elemento " key) gi/roth/elt-type) (gi/roth/sxhtml-elt y elt) (setq elt-idx (1+ elt-idx)) (setq elt (list chk-idx elt-idx)))) (_ (push (list :list z) gi/roth/errors)))))))))) (defun gi/roth/sxhtml-match (x) (pcase x ((pred null) (list :n)) ((pred (lambda (z) (and (stringp z) (string-match-p "^[ \n\t]*$" z)))) (list :n)) (`(div . (((class . "timebreak ")) . ,_)) (list :n)) (`(a . (((id . ,y)))) (list :a gi/roth/elt-pageskip y)) (`(p . (((class . "center sink")) . ,y)) (list :b "extra" y)) (`(p . (((class . "left ")) . ,y)) (list :b "paragrafo" y)) (`(p . (((class . "follow")) . ,y)) (list :b "paragrafo" y)) (`(p . (((class . "center ")) ,_ (b nil ,y) ,_)) (if (=3D (length y) 1) (list :b "extra" (list y)) (list :b "sezione" (list y)))) (`(blockquote ,_ ,_ (p . (((class . "left ")) . ,y)) ,_) (list :b "citazione" y)) (`(blockquote ,_ ,_ (p . (((class . "right ")) . ,y)) ,_) (list :b "attribuzione" y)) (`(blockquote ,_ ,_ (p . (((class . "follow")) . ,y)) ,_) (list :b "citazione" y)) (`(h1 . (((class . "fronttitle ")) . ,y)) (list :b "titolo" y)) (`(h1 . (((class . "chaptertitle ")) . ,y)) (list :b "capitolo" y)) (`(h1 . (((class . "backtitle ")) . ,y)) (list :b "titolo" y)) (`(ol . ,y) (list :c "lista ordinata" y)) (`(ul . ,y) (list :c "lista non ordinata" y)) (_ (push (list :1 x) gi/roth/errors)))) (defun gi/roth/sxhtml-match-notes (x) (pcase x ((pred null) (list :n)) ((pred (lambda (z) (and (stringp z) (string-match-p "^[ \n\t]*$" z)))) (list :n)) (`(div . (((class . "timebreak ")) . ,_)) (list :n)) (`(a . (((id . ,y)))) (list :a gi/roth/elt-pageskip y)) (`(p . (((class . "center ")) . (,_ . ((b . (,_ . (,_ . ((span ((class . "smallcaps")) ,y) . ,_)))) . ,_)))) (progn (string-match "^[0-9]*" y) (list :b "sezione" (list (substring y 0 (match-end 0)))))) (`(p . (((class . "hang ")) . ,y)) (list :b "nota" y)) (`(h1 . (((class . "backtitle ")) . ,y)) (list :b "titolo" y)) (_ (push (list :1n x) gi/roth/errors)))) (defun gi/roth/sxhtml-match-index (x) (pcase x ((pred null) (list :n)) ((pred (lambda (z) (and (stringp z) (string-match-p "^[ \n\t]*$" z)))) (list :n)) (`(div . (((class . "timebreak ")) . ,_)) (list :n)) (`(a . (((id . ,y)))) (list :a gi/roth/elt-pageskip y)) (`(p . (((class . "index")) . ,y)) (list :b "voce indice" y)) (`(p . (((class . "index-2")) . ,y)) (list :b "sottovoce indice" y)) (`(p . (((class . "index-2") . ,y) . (,z . ,w))) (list :b "sottovoce indice" (append w (cons (cons 'a y) (list z))) gi/roth/errors)) (`(h1 . (((class . "backtitle ")) . ,y)) (list :b "titolo" y)) (_ (push (list :1n x) gi/roth/errors)))) (defun gi/roth/sxhtml-elt (lst elt) (let ((lne-idx 0) (str-idx 0) (str "")) (dolist (x lst) (let* ((a (gi/roth/sxhtml-match-elt x)) (cat (nth 0 a)) (key (nth 1 a)) (txt (nth 2 a))) (pcase cat (:n nil) (:a (setq str-idx (+ str-idx (length txt)) str (concat str txt))) (:b (let ((upd-idx (+ str-idx (length txt)))) (puthash elt (cons (cons str-idx upd-idx) (gethash elt key)) key) (setq str-idx upd-idx str (concat str txt)))) (:c (let ((key (cond ((string-match "^p[0-9]+$" txt) gi/roth/elt-pageskip) ((string-match "^http" txt) gi/roth/elt-link) ((string-match "px[0-9]+$" txt) gi/roth/elt-notesref) (t gi/roth/elt-indexref)))) (puthash elt (cons (cons str-idx txt) (gethash elt key)) key)))))) (puthash elt (length str) gi/roth/elt) (let ((sentence-end-double-space nil) (m '(0))) (if (not (string-match (sentence-end) str)) (setq m (cons (length str) m)) (while (string-match (sentence-end) (substring str (car m))) (setq m (cons (+ (car m) (match-end 0)) m)))) ;; non sentence-end terminating strings (when (not (=3D (car m) (length str))) (setq m (cons (length str) m))) ;; sentence-end exceptions (dolist (x (butlast (cdr m))) (let ((s (substring str 0 x))) (dolist (y gi/roth/sentence-end-exceptions) (when (and (>=3D (length s) (length y)) (string-suffix-p (concat y " ") s)) (setq m (remq x m)))))) (dolist (x (reverse (cl-mapcar (lambda (a b) (cons b a)) (butlast m) (cdr m)))) (let ((lne (append elt (list lne-idx)))) (puthash lne x gi/roth/lne) (puthash lne (substring str (car x) (cdr x)) gi/roth/lne-text) (setq lne-idx (1+ lne-idx))))))) (defun gi/roth/sxhtml-match-elt (x) (pcase x ((pred null) (list :n)) ((pred (lambda (z) (and (stringp z) (string-match-p "^[ \n\t]*$" z)))) (list :n)) ((pred stringp) (list :a nil x)) (`(span . (((class . "smallcaps")) ,y)) (list :a nil (downcase y))) (`(em ,_ (span ,_ ,y)) (list :b gi/roth/elt-italic (downcase y))) (`(em ,_ ,y) (list :b gi/roth/elt-italic y)) (`(b ,_ ,y) (list :b gi/roth/elt-bold y)) (`(a . (((id . ,y)))) (list :c nil y)) (`(a . (((href . ,y)) . ,_)) (list :c nil y)) (`(a . ((href . ,y))) (list :c nil y)) (_ (push (list :2 x) gi/roth/errors)))) (defun gi/roth/elt-lines (elt) (let ((lne 0) (lnes ())) (while (gethash (append elt (list lne)) gi/roth/lne) (setq lnes (cons (append elt (list lne)) lnes) lne (1+ lne))) lnes)) ;;;; exceptions (defconst gi/roth/sentence-end-exceptions '("Ph.D." "Dr." "D.C." "a.m." "U.S." " no." " No." " ed." " bk." " vol." " chap." "para." " A." " B." " C." " D." " E." " F." " G." " H." " I." " J." " K." " L." " M." " N." " O." " P." " Q." " R." " S." " T." " U." " V." " W." " X." " Y." " Z.")) (defconst gi/roth/titles16 '("The Kidney Donor=E2=80=99s Diet and Exercise Regime: You, Too, Can Qua= lify")) (defconst gi/roth/titles18 '("=E2=80=9CPrice Coherence and Adverse Intermediation=E2=80=9D" "Charlie=E2=80=99s Place: The Saga of an American Frontier Homestead" "=E2=80=9CJumping the Gun.=E2=80=9D" "=E2=80=9CThe High-Frequency Trading Arms Race: Frequent Batch Auctions= as a Market Design Response=E2=80=9D" "=E2=80=9CInformation Frictions and the Law of One Price: =E2=80=98When= the States and the Kingdom Became United=E2=80=99=E2=80=9D" "Judge John D. Bates to All United States Judges" "Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis" "=E2=80=9CFrom Boston to Chinese Parallel to Deferred Acceptance: Theor= y and Experiments on a Family of School Choice Mechanisms=E2=80=9D" "The Early Admissions Game" "=E2=80=9CPropose with a Rose?" "Signaling in Internet Dating Markets" "The Handicap Principle: A Missing Piece of Darwin=E2=80=99s Puzzle" "The Histories" "Putting Auction Theory to Work" "=E2=80=9CMore Money, More Problems?" "Can High Pay Be Coercive and Repugnant?" "The Kidney Sellers: A Journey of Discovery in Iran" "When Altruism Isn=E2=80=99t Enough: The Case for Compensating Kidney D= onors" "The Collected Works of F. A. Hayek" "The Road to Serfdom: Text and Documents=E2=80=94The Definitive Edition" "New Studies in Philosophy, Politics, Economics and the History of Idea= s")) (defconst gi/roth/common '("be" "a" "in" "it" "have" "as" "t" "s" "but" "are" "on" "can" "at" "one" "by" "make" "i" "or" "so" "who" "an" "more" "other" "some" "about" "he" "not" "many" "even" "also" "might" "get" "there" "all" "first" "just" "do" "only" "time" "each" "people" "hi" "like" "before" "new" "most" "way" "take" "may" "want" "will" "good" "such" "no" "high" "out" "use" "too" "after" "need" "early" "know" "any" "much" "up" "help" "place" "very" "kind" "don" "well" "another" "find" "come" "still" "try" "now" "hard" "making" "become" "go" "say" "safe" "then" "while" "give" "enough" "re" "allow" "better" "why" "d" "through" "being" "isn" "part" "often" "turn" "start" "big" "best" "long" "lot" "wait" "look" "over" "begin" "already" "think" "case" "less" "play" "fact" "us" "tell" "let" "later" "someone" "me" "own" "waiting" "consider" "happen" "between" "able" "little" "fail" "apply" "things" "instead" "true" "move" "possible" "once" "found" "earlier" "top" "bad" "back" "provide" "every" "old" "few" "almost" "getting" "down" "lots" "ways" "ad" "yet" "keep" "going" "notice" "longer" "least" "must" "last" "show" "set" "using" "put" "care" "easy" "thus" "run" "mean" "close" "looking" "soon" "though" "whole" "next" "given" "small" "clear" "again" "always" "under" "suppose" "suggest" "rather" "either" "near" "far" "around" "side" "together" "here" "off" "bring" "appear" "real" "thing" "never" "slow" "prove" "eventually" "higher" "quite" "agree" "away" "m" "seem" "really" "saw" "starting" "living" "p" "maybe" "late" "felt" "sent" "finding" "further" "main" "full" "strong" "feel" "taking" "certain" "thinking" "indeed" "parts" "pretty" "ahead" "ever" "sure" "lead" "led" "fall" "left" "ago" "ready" "various" "giving" "badly")) ;;; glossary (defun gi/roth/glossary () (gi/roth/gls :titles16 16) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :titles18 18) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :nprefixes 18 0) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :titles 18) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls-index) (gi/roth/gls-rev 'gi/roth/gls-index) (gi/roth/gls :specials) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :acronyms) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :numbers) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :names) (gi/roth/gls-rev 'gi/roth/gls-names) (gi/roth/gls :words)=20 (maphash (lambda (k v) (when (=3D (length v) 1) (remhash k gi/roth/gls))) gi/roth/gls) (mapc (lambda (x) (remhash x gi/roth/gls)) gi/roth/common) (gi/roth/gls-rev 'gi/roth/gls)) (defun gi/roth/gls-index-regexps () (let ((case-fold-search nil) (lst ())) (maphash (lambda (k v) (when (equal v "voce indice") (let* ((lne (append k (list 0))) (x (gethash lne gi/roth/lne-text)) (y (if (string-match "^\\(?1:.+?\\)[., ]+$" x) (match-string 1 x) x))) (push (cons k (list y)) lst)))) gi/roth/elt-type) (mapcar (lambda (x) (let ((a (cadr x)) (b ())) (cond ((string-match "=E2=80=9C[^=E2=80=9C=E2=80=9D]+=E2=80=9D" a) (setq b (list (concat (substring (match-string 0 a) 0 -1) "\\([.,:]\\)*=E2=80=9D")))) ((string-match "^\\(?1:[^=E2=80=9C=E2=80=9D]+\\), \\(?2:[^=E2=80=9C=E2= =80=9D]+\\)(.+)$" a)=20 (setq b (list (concat (match-string 2 a) (match-string 1 a))))) ((string-match "^\\(?1:[^=E2=80=9C=E2=80=9D]+\\) (.+)$" a) (setq b (list (match-string 1 a)))) ((string-match "^\\(?1:[^=E2=80=9C=E2=80=9D]+\\), \\(?2:[^=E2=80=9C=E2= =80=9D]+\\)$" a) (setq b (list (concat (match-string 2 a) " " (match-string 1 a)) (match-string 1 a)))) (t (setq b nil))) (when (string-match "[[:lower:]]" (substring a 0 1)) (setq b (cons (concat (upcase (substring a 0 1)) (substring a 1 nil)) b))) (cons (car x) (mapcar (lambda (y) (concat "\\(^\\|[^[:word:]]\\)\\(?1:" y "\\)\\([^[:word:]]\\|$\\)")) (cons a b))))) lst))) (defconst gi/roth/gls-regexps `((:nprefixes . "\\(?1:^.+?: \\).+$") (:titles16 . ,(concat "\\(?1:" (mapconcat #'regexp-quote gi/roth/titles16 "") "\\)")) (:titles18 . ,(concat "\\(?1:" (mapconcat #'regexp-quote gi/roth/titles18 "\\|") "\\)")) (:titles . "=E2=80=9C\\(?1:[^=E2=80=9C=E2=80=9D]+\\),=E2=80=9D") (:specials . ,(concat "\\([[:blank:]]\\|^\\)\\(?1:\\(" "Ph\\.D\\.\\|Dr\\.\\|D\\.C\\.\\|a\\.m\\.\\|" "U\\.S\\.\\|no\\.\\|No\\.\\|ed\\.\\|bk\\.\\|" "vol\\.\\|chap\\.\\|para\\." "\\)\\)\\([^[:word:]]\\|$\\)")) (:names . ,(concat "[[:blank:]]" "\\(?1:\\([[:upper:]]\\([.]\\|[[:lower:]]+\\)\\)+" "\\([-[:blank:]]" "[[:upper:]]\\([.]\\|[[:lower:]]+\\)\\)*\\)" "\\([^[:word:]]\\|$\\)")) (:acronyms . ,(concat "\\([[:blank:]]\\|^\\)\\(?1:\\(" ; and roman nums "[[:upper:]]+[[:upper:]&]*[[:upper:]]+" "\\)\\)\\([^[:word:]]\\|$\\)")) (:numbers . ,(concat "\\([[:blank:]]\\|^\\)" "\\(?1:[[:digit:]:,$]*[-]*[[:digit:]]+s?\\)" "\\([^[:word:]]\\|$\\)")) (:words . "\\(?1:[[:word:]-]+\\)"))) (defun gi/roth/gls-index () (let ((case-fold-search nil)) (maphash (lambda (k v) (let ((mtc ()) (flt ()) ; (((chk elt) strt end) ...) for line k (b (gi/roth/protect k 'gi/roth/gls-names-r))) (mapc (lambda (x) (mapc (lambda (y) (let ((a (gi/roth/match v (apply-partially #'gi/roth/matcher y) b))) (mapc (lambda (z) (push (cons (car x) z) mtc)) a))) (cdr x))) (gi/roth/gls-index-regexps)) (dolist (y mtc) (dolist (z (cdr (memq y mtc))) (when (and (not (member y flt)) (gi/roth/overlap-p (nth 1 y) (nth 2 y) (nth 1 z) (nth 2 z))) ;; tiebreak is quasi random (if (<=3D (- (nth 2 y) (nth 1 y)) (- (nth 2 z) (nth 1 z))) (push y flt) (push z flt))))) (mapc (lambda (x) (puthash (car x) (cons (list (append k (cdr x)) ()) (gethash (car x) gi/roth/gls-index)) gi/roth/gls-index)) (cl-set-difference mtc flt :test #'equal)))) gi/roth/lne-text))) (defun gi/roth/gls (typ &optional chk lne) (maphash (lambda (k v) (when (and (not (member (gethash (butlast k) gi/roth/elt-type) (pcase typ ((or :words :numbers) ()) (_ '("titolo" "sezione" "capitolo" "parte"))))) (if chk (=3D (car k) chk) t) (if lne (=3D (nth 2 k) lne) t)) (let ((mtc (gi/roth/match v (apply-partially #'gi/roth/matcher (cdr (assoc typ gi/roth/gls-regexps))) (apply (apply-partially #'gi/roth/protect k) (pcase typ (:nprefixes nil) (:titles '(gi/roth/gls-names-r)) (_ '(gi/roth/gls-index-r gi/roth/gls-names-r))))))) (mapc (lambda (x) (let ((key (pcase typ (:words (gi/roth/root (substring v (car x) (cadr x)))) (_ (substring v (car x) (cadr x))))) (tbl (pcase typ (:words gi/roth/gls) (_ gi/roth/gls-names)))) (when key (puthash key (cons (list (append k x) ()) (gethash key tbl)) tbl)))) (remq nil mtc))))) gi/roth/lne-text)) ;; [str fun ((strt end) ...)] -> ((strt end) ...) ;; fun : str -> ((strt end) ...) (defun gi/roth/match (str fun exc) (let ((mtc ())) (cl-mapc (lambda (x y) (let ((a (funcall fun (substring str (cadr x) (car y))))) (mapc (lambda (z) (when a (push (list (+ (cadr x) (car z)) (+ (cadr x) (cadr z))) mtc))) a))) (cons '(nil 0) exc) (append exc `((,(length str))))) mtc)) (defun gi/roth/matcher (rgx str) (let ((case-fold-search nil) (adj 0) (mtc ())) (while (string-match rgx str) (push (list (+ adj (match-beginning 1)) (+ adj (match-end 1))) mtc) (setq str (substring str (match-end 0)) adj (+ adj (match-end 0)))) mtc)) ;; (lne (tbl ...)) -> ((strt end) ...) (defun gi/roth/protect (lne &rest rtbls) (let ((nts ())) (mapc (lambda (x) (let ((tbl (symbol-value (intern-soft (substring (symbol-name x) 0 -2))))) (mapc (lambda (y) (mapc (lambda (z) (when (equal lne (butlast (car z) 2)) (push z nts))) (gethash y tbl))) (gethash lne (symbol-value x))))) rtbls) (when (car nts) (sort (delete-dups (mapcar (lambda (x) (nthcdr 3 (car x= ))) nts)) (lambda (a b) (< (car a) (car b))))))) (defun gi/roth/overlap-p (ba ea bb eb) "True if right-open integers interval BA-EA overlaps BB-EB" (or (and (>=3D ba bb) (< ba eb)) (and (> ea bb) (<=3D ea eb)) (and (< ba bb) (> ea eb)))) (defun gi/roth/root (str) "Get wordnet root of STR" (let ((wn (with-temp-buffer (apply 'call-process "wn" nil t nil `(,str "-over")) (buffer-string)))) (when (and wn (string> wn "")) (string-match "Overview.+ \\([[:word:]-]+\\)" wn) (match-string 1 wn)))) ; ((key val)) -> (lne (key)) (defun gi/roth/gls-rev (tbl) (let ((rtbl (symbol-value (intern-soft (concat (symbol-name tbl) "-r"))))) (clrhash rtbl) (maphash (lambda (key val) (mapc (lambda (x) (puthash (butlast (car x) 2) (cons key (gethash (butlast (car x) 2) rtbl)) rtbl)) val)) (symbol-value tbl)))) ;;; unused (defun gi/roth/key<=3D (a b) (cond ((< (nth 0 a) (nth 0 b)) t) ((=3D (nth 0 a) (nth 0 b)) (cond ((< (nth 1 a) (nth 1 b)) t) ((=3D (nth 1 a) (nth 1 b)) (or (=3D (length a) 2) (cond ((<=3D (nth 2 a) (nth 2 b)) t) (t nil)))) (t nil))) (t nil))) ;;; complete setup (defun gi/roth/setup () (let* ((files (list gi/roth/roth gi/roth/glossary gi/roth/rglossary gi/roth/traduzione)) (tables (list gi/roth/elt-ttables gi/roth/elt-tables gi/roth/gls-tables gi/roth/gls-rtables gi/roth/trd-tables)) (printout (cdr tables))) (setq gi/roth/errors ()) (mapc (lambda (x) (unless (file-exists-p x) (write-region "" nil x))) f= iles) (mapc (lambda (x) (mapc #'gi/roth/hash x)) tables) (mapc (lambda (x) (mapc (lambda (y) (clrhash (symbol-value (intern-soft (car= y))))) x)) tables) (gi/roth/xhtml) (gi/roth/fix-repeated-pageskips) (gi/roth/glossary)=20 (cl-mapc #'gi/roth/print printout files))) (defun gi/roth/hash (alst) (intern (car alst)) (set (intern-soft (car alst)) (make-hash-table :test 'equal :size (cdr alst)))) (defun gi/roth/print (tbls fle) (let ((tblsyms (mapcar (lambda (x) (intern-soft (car x))) tbls))) (with-current-buffer (find-file-noselect fle) (erase-buffer) (cl-mapc (lambda (tab sym) (insert "(setq " (prin1-to-string sym) " " (prin1-to-string tab) ")\n\n")) (mapcar #'symbol-value tblsyms) tblsyms) (save-buffer)))) (defun gi/roth/printt (ntbls tbls fle) (with-current-buffer (find-file-noselect fle) (erase-buffer) (cl-mapc (lambda (tab ssym) (insert "(setq " ssym " " (prin1-to-string tab) ")\n\n")) ntbls (mapcar #'car tbls)) (save-buffer))) (defun gi/roth/load () (let ((files (list gi/roth/roth gi/roth/glossary gi/roth/rglossary gi/roth/traduzione))) (mapc #'load-file files))) ;;; ugly (defun gi/roth/fix-repeated-pageskips () (let ((rem ())) (maphash (lambda (k v) (let ((a (gethash (gi/roth/following k) gi/roth/lne-page= skip))) (when (equal (cdar v) (cdar a)) (push (gi/roth/following k) rem)))) gi/roth/lne-pageskip) (mapc (lambda (x) (remhash x gi/roth/lne-pageskip)) rem))) ;;; order (defun gi/roth/following (lne) (let ((nlne (list (nth 0 lne) (nth 1 lne) (1+ (nth 2 lne)))) (nelt (list (nth 0 lne) (1+ (nth 1 lne)) 0)) (nchk (list (1+ (nth 0 lne)) 0 0))) (or (and (gethash nlne gi/roth/lne-text) nlne) (and (gethash nelt gi/roth/lne-text) nelt) (and (gethash nchk gi/roth/lne-text) nchk)))) (defun gi/roth/previous (lne) (cond ((> (nth 2 lne) 0) (append (butlast lne) (list (- (nth 2 lne) 1)))) ((> (nth 1 lne) 0) (let ((x (list (nth 0 lne) (- (nth 1 lne) 1) 0))) (while (not (equal (gi/roth/following x) lne)) (setq x (gi/roth/following x))) x)) ((> (nth 0 lne) 0) (let ((x (list (- (nth 0 lne) 1) 0 0))) (while (not (equal (gi/roth/following x) lne)) (setq x (gi/roth/following x))) x)) (t '(0 0 0)))) ;;; glossario ;; by construction, glossary match intervals do not overlap (defvar gi/roth/g) ; (num ((strt end) tstr altstr ...) ...) (defvar gi/roth/w) ; num ;; -> (((strt end) strt end) ...) | nil (defun gi/roth/g-init (lne) (let* ((a (apply #'append (mapcar (lambda (x) (gi/roth/g-init-2 lne (car x))) gi/roth/gls-rtables))) (trd (mapcar (lambda (x) (let ((b (car x))) (when (cadr b) (cons (nthcdr 3 (car b)) (cdr b))))) a))) (setq gi/roth/g (cons 0 (sort (copy-sequence (mapcar (lambda (x) (gi/roth/g-init-1 x)) a)) (lambda (a b) (< (caar a) (caar b))))) gi/roth/w (length (gethash lne gi/roth/gls-r))) (if (and (car trd) (not (=3D (length (cdr gi/roth/g)) (length trd)))) (error "incomplete translation") trd))) ;; -> (lst ...) (defun gi/roth/g-init-1 (lst) (cons (nthcdr 3 (caar lst)) (cons (or (gi/roth/g-init-trad (car lst)) (gi/roth/g-init-word (car lst))) (remq nil (mapcar (lambda (x) (gi/roth/g-init-trad x)) (cdr lst)))))) ;; -> ((((chk elt lne strt end) strt end) ...) ...) (defun gi/roth/g-init-2 (lne tblstr) (let* ((rtbl (symbol-value (intern-soft tblstr))) (tbl (symbol-value (intern-soft (substring tblstr 0 -2)))) (mtc (mapcar (lambda (x) (gethash x tbl)) (gethash lne rtbl))) (otc (mapcar (lambda (x) (mapcar (lambda (y) (when (equal (butlast (car y) 2) lne) (cons y (remq y x)))) x)) mtc))) (apply #'append (cl-remove-duplicates ; multiple occurrencies (mapcar (lambda (x) (remq nil x)) otc) :test #'equal)))) ;; -> ((chk elt lne strt end) strt end) -> str (defun gi/roth/g-init-word (ent) (substring (gethash (butlast (car ent) 2) gi/roth/lne-text) (nth 3 (car ent)) (nth 4 (car ent)))) ;; -> ((chk elt lne strt end) strt end) -> str (defun gi/roth/g-init-trad (ent) (when (cadr ent) (substring (gethash (butlast (car ent) 2) gi/roth/trd-text) (cadr ent) (car (cddr ent))))) (defun gi/roth/g (kyw) (let* ((a (nth (car gi/roth/g) (cdr gi/roth/g)))) (pcase kyw (:current (cadr a)) (:current-id (car a)) (:raw a) (:list (cdr a)) (:word? (< (car gi/roth/g) gi/roth/w))))) ;;; edizione (defvar gi/roth/current-line) (defvar gi/roth/buffers-windows) ;; faces: ;; highlight -> current glossary ;; bold -> non current glossary ;; warning -> unmatched refs ;; success -> matched refs ;; add underline -> italic ;; match -> temp overlays (defconst gi/roth/color-borders "black") (defun gi/roth/split () (let* ((names '("edit" "curr" "xtra" "trsl" "glos" "prev" "succ")) (w1 (selected-window)) (w2 (split-window w1 -78 'left)) (w3 (split-window w1 -78 'right)) (w4 (split-window w1 -24 'above)) (w5 (split-window w1 -24 'below)) (w6 (split-window w2 -24 'above)) (w7 (split-window w2 -24 'below))) (setq cursor-in-non-selected-windows nil mode-line-format nil ;max-mini-window-height 0 ) (set-frame-parameter nil 'right-divider-width 2) (set-frame-parameter nil 'bottom-divider-width 2) (set-face-attribute 'window-divider nil :foreground gi/roth/color-borde= rs) (setq gi/roth/buffers-windows (cl-mapcar (lambda (x y) (set-window-buffer x y) (with-current-buffer y (setq cursor-in-non-selected-windows nil mode-line-format nil max-mini-window-height 0)) (cons x y)) (list w1 w2 w3 w4 w5 w6 w7) (mapcar (lambda (x) (let* ((bsym (intern (concat "gi/roth/" x "-buf"))) (bnme (concat "roth-" x))) (set bsym (get-buffer-create bnme)) (symbol-value bsym))) names))))) (defun gi/roth/buffers (lne) (setq gi/roth/current-line lne gi/roth/edit-italic () gi/roth/edit-notesref () gi/roth/edit-indexref () gi/roth/edit-link ()) (let ((inhibit-read-only t) (trd (gi/roth/g-init lne)) (str (gethash lne gi/roth/trd-text))) (gi/roth/line lne) (gi/roth/xtra-toggle) (with-selected-window (car (rassoc gi/roth/trsl-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/trsl-buf (erase-buffer) (let ((l (gi/roth/previous lne)) (ll nil)) (dotimes (x 10) (goto-char (point-min)) (if (not (equal l ll)) (insert (concat "\n\n" (gi/roth/out l))) (insert "\n\n")) (setq ll l l (gi/roth/previous l))) (fill-region (point-min) (point-max)) (goto-char (- (point-max) 1)) (recenter -1)))) (gi/roth/word) (with-selected-window (car (rassoc gi/roth/prev-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/prev-buf (erase-buffer) (let ((l (gi/roth/previous lne))) (dotimes (x 10) (goto-char (point-min)) (when (not (equal l lne)) (insert (concat "\n[" (number-to-string (nth 0 l)) " " " " (number-to-string (nth 1 l)) " " " " (number-to-string (nth 2 l)) "] " (gethash l gi/roth/lne-text) "\n")) (setq l (gi/roth/previous l)))) (fill-region (point-min) (point-max)) (goto-char (- (point-max) 1)) (recenter -1) (font-lock-add-keywords nil '(("\\[.*\\]" . 'bold))) (font-lock-fontify-buffer)))) (with-selected-window (car (rassoc gi/roth/succ-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/succ-buf (erase-buffer) (let ((l (gi/roth/following lne))) (dotimes (x 10) (goto-char (point-max)) (when l (insert (concat "\n[" (number-to-string (nth 0 l)) " " " " (number-to-string (nth 1 l)) " " " " (number-to-string (nth 2 l)) "] " (gethash l gi/roth/lne-text) "\n")) (setq l (gi/roth/following l)))) (fill-region (point-min) (point-max)) (goto-char (point-min)) (recenter 1) (font-lock-add-keywords nil '(("\\[.*\\]" . 'bold))) (font-lock-fontify-buffer)))) (with-selected-window (car (rassoc gi/roth/edit-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/edit-buf (setq header-line-format "") (gi/roth/keys) (use-local-map gi/roth/keys) (erase-buffer) (if str (let* ((ita (gi/roth/str-buf (gethash lne gi/roth/trd-italic) :buf)) (nt0 (gi/roth/str-buf (gethash lne gi/roth/lne-notesref) :buf)) (nte (when nt0 (mapcar (lambda (x) (cons :note x)) nt0))) (id0 (gi/roth/str-buf (gethash lne gi/roth/lne-indexref) :buf)) (idx (when id0 (mapcar (lambda (x) (cons :indx x)) id0))) (ln0 (gi/roth/str-buf (gethash lne gi/roth/lne-link) :buf)) (lnk (when ln0 (mapcar (lambda (x) (cons :link x)) ln0))) (ref (append nte idx lnk))) (dolist (x trd) (setq str (gi/roth/propertize str (cadr x) (car (cddr x)) (car x) :glossary))) (insert str) ;; insert markers (when (car ita) (dolist (x ita) (gi/roth/edit-italic x))) (when (car ref) (dolist (y ref) (gi/roth/edit-refs (car y) (cdr y))))) (mapc (lambda (x) (insert (concat " " (gi/roth/propertize (cadr x) 0 (length (cadr x)) (car x) :glossary)))) (sort (cdr (copy-sequence gi/roth/g)) (lambda (a b) (< (caar a) (caar b)))))))))) (defun gi/roth/line (lne) (let ((inhibit-read-only t) (str (gethash lne gi/roth/lne-text)) (lst (apply #'append (mapcar (lambda (y) (gethash lne y)) (list gi/roth/lne-pageskip gi/roth/lne-indexref gi/roth/lne-notesref gi/roth/lne-link)))) (adj 0) (ovs ())) (dolist (x (cdr gi/roth/g)) (setq str (gi/roth/propertize str (caar x) (car (cdar x)) (car x) :glossary))) (dolist (x (gethash lne gi/roth/lne-italic)) (put-text-property (car x) (cdr x) :corsivo t str)) (dolist (x lst) (setq str (concat (substring str 0 (+ adj (car x))) (gi/roth/propertize (concat "[" (cdr x) "]") 0 (+ 2 (length (cdr x))) (car x) :ref) (substring str (+ adj (car x)) nil)) adj (+ 2 (length (cdr x)) adj))) (with-selected-window (car (rassoc gi/roth/curr-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/curr-buf (setq header-line-format "") (erase-buffer) (insert str) (mapc (lambda (x) (overlay-put x 'face 'match)) (gi/roth/get-text-property-corsivo)) (fill-region (point-min) (point-max)))))) (defun gi/roth/line-page (lne) (or (cdr (gi/roth/line-pageskip lne)) (let ((x (gi/roth/previous lne))) (while (not (gi/roth/line-pageskip x)) (setq x (gi/roth/previous x))) (cdr (gi/roth/line-pageskip x))))) ;; no line contains more than two pageskips, and when a line contains ;; two pageskips, one refers to a blank page (defun gi/roth/line-pageskip (lne) (let ((skps (gethash lne gi/roth/lne-pageskip))) (or (and skps (> (length skps) 1) (string> (cddr skps) (cdar skps)) (cdr skps)) (car skps)))) (defun gi/roth/info (lne) (let* ((i (gi/roth/following lne)) (nelt) (nlne) (pge (gi/roth/line-page lne)) (frq (gi/roth/words-occurrencies lne)) (ind (gi/roth/page-index pge))) (while i (cond ((and (not nlne) (not (=3D (nth 1 i) (nth 1 lne)))) (setq nlne (max (nth 2 (gi/roth/previous i)) (nth 2 lne)) i (gi/roth/following i))) ((and nlne (not nelt) (not (=3D (nth 0 i) (nth 0 lne)))) (setq nelt (max (nth 1 (gi/roth/previous i)) (nth 1 lne)) i (gi/roth/following i))) ((and nlne nelt) (setq i nil)) ((not (gi/roth/following i)) (setq nelt (nth 1 i) nlne (nth 2 i))) (t (setq i (gi/roth/following i))))) (when (equal lne '(20 1 1)) (setq nlne 1 nelt 1)) (concat "\n" (format "sezione : %d (20)\n" (nth 0 lne)) (format "elemento : %d (%d)\n" (nth 1 lne) nelt) (format "linea : %s (%d)\n" (nth 2 lne) nlne) (format "pagina : %s\n" pge) (format "tipo : %s\n" (gethash (butlast lne) gi/roth/elt-type)) "\nfrequenze\n\n" (if frq (mapconcat (lambda (x) (format "%18s : %d" (car x) (cdr x))) frq "\n") "...") "\n\nindice\n\n" (if ind (mapconcat (lambda (a) (concat "-> " (nth 0 a) (when (nth 1 a) (concat "[" (nth 1 a) "]")) (when (nth 2 a) (concat " <- " (nth 2 a))) (when (nth 3 a) (concat "[" (nth 3 a) "]")))) (mapcar (lambda (x) (mapcar (lambda (y) (and (stringp y) (replace-regexp-in-string "\\(, \\)*$" "" y))) x)) ind) "\n\n") "...")))) (defun gi/roth/words-occurrencies (lne) (mapcar (lambda (x) (cons x (length (gethash x gi/roth/gls)))) (gethash lne gi/roth/gls-r))) (defun gi/roth/page-index (ref) (let ((ind ())) (maphash (lambda (k v) (when (cl-some (lambda (x) (string-match (concat "#" ref "$") (cdr x= ))) v) (push k ind))) gi/roth/lne-indexref) (mapcar (lambda (x) (append (list (gethash x gi/roth/lne-text) (gethash x gi/roth/trd-text)) (if (equal (gethash (butlast x) gi/roth/elt-type) "sottovoce indice") (let ((i (gi/roth/previous x))) (while (and i (equal (gethash (butlast i) gi/roth/elt-type) "sottovoce indice")) (setq i (gi/roth/previous i))) (list (gethash i gi/roth/lne-text) (gethash i gi/roth/trd-text))) ()))) ind))) (defun gi/roth/word () (with-selected-window (car (rassoc gi/roth/glos-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/glos-buf (erase-buffer) (insert "\n") (let* ((inhibit-read-only t) (str "") (lst (delete-dups (gi/roth/g :list))) (top (nth 0 lst)) (len (length lst)) (lim (> len 20)) (ita (format "[corsivo: %d/%d]" (length gi/roth/edit-italic) (length (gethash gi/roth/current-line gi/roth/lne-italic)))) (cid (gi/roth/g :current-id)) (roo (if (gi/roth/g :word?)=20 (format "[root: %s]" (gi/roth/root (substring (gethash gi/roth/current-line gi/roth/lne-text) (car cid) (cadr cid)))) ""))) (dotimes (x (if lim 20 (max 2 len))) (setq str (cond ((=3D x 0) (concat (format "%39s" (gi/roth/propertize top 0 (length top) nil :glossary)) (format "%39s\n" ita))) ((=3D x 1) (concat (format "%39s" (or (nth 1 lst) "")) (format "%39s\n" roo))) (t (format "%39s\n" (or (nth x lst) ""))))) (setq str (concat str (if lim (format "%39s\n" "...") ""))) (insert str)))))) (defvar gi/roth/xtra-toggle 0) (defun gi/roth/xtra-toggle () (let ((gi/roth/xtra-toggle (if (not (boundp 'gi/roth/xtra-toggle)) 0 gi/roth/xtra-toggle))) (interactive) (with-selected-window (car (rassoc gi/roth/xtra-buf gi/roth/buffers-windows)) (with-current-buffer gi/roth/xtra-buf (erase-buffer) (pcase gi/roth/xtra-toggle (0 (progn (insert (gi/roth/info gi/roth/current-line)) (goto-char (point-min)) (fill-region (re-search-forward "^indice") (point-max)) (goto-char (point-min)) (recenter 1) (font-lock-add-keywords nil '(("^indice" . 'bold))) (font-lock-add-keywords nil '(("^frequenze" . 'bold))) (font-lock-fontify-buffer) (setq gi/roth/xtra-toggle (1+ gi/roth/xtra-toggle)))) (1 (progn (if (gi/roth/g :word?) (with-temp-buffer (apply 'call-process "wn" nil t nil `(,(gi/roth/g :current) "-over")) (buffer-string)) (message "no wordnet root in glossary")) (fill-region (point-min) (point-max)) (setq gi/roth/xtra-toggle (1+ gi/roth/xtra-toggle)))) (2 (let ((str "") (occ (mapcar (lambda (k v) (cons k (list (gethash (butlast (car v) 2) gi/roth/lne-text) (gethash (butlast (car v) 2) gi/roth/trd-text)))) (gethash (gi/roth/g :current) gi/roth/gls)))) (dotimes (x (length occ)) (setq str (concat str (car x) " -->[ " (or (cdr x) "...") " ]\n\n"))) (fill-region (point-min) (point-max)) (setq gi/roth/xtra-toggle (1+ gi/roth/xtra-toggle))))))))) ;; types <- :glossary :ref (defun gi/roth/propertize (str beg end ref typ) (let ((str (copy-sequence str))) (if (equal typ :glossary) (put-text-property beg end 'face 'bold str) (put-text-property beg end 'face 'warning str)) (put-text-property beg end :roth ref str) (put-text-property beg end 'front-sticky t str) (put-text-property beg end 'rear-nonsticky t str) (put-text-property beg end 'read-only t str) str)) (defun gi/roth/edit-glossary (&optional back) (interactive) (let ((inhibit-read-only t) (f (gi/roth/g :current-id)) (l (length (cdr gi/roth/g))) (n (car gi/roth/g))) (if back (setcar gi/roth/g (if (=3D n 0) (- l 1) (- n 1))) (setcar gi/roth/g (if (=3D n (- l 1)) 0 (1+ n)))) (gi/roth/update-bufs f (gi/roth/g :current-id)) (gi/roth/word))) (defun gi/roth/choose-word (&optional up) (interactive) (let* ((inhibit-read-only t) (s (gi/roth/g :list)) (new (if up (cons (car (last s)) (butlast s)) (append (cdr s) (list (car s)))))) (setcdr (nth (car gi/roth/g) (cdr gi/roth/g)) new) (gi/roth/word))) (defun gi/roth/edit-word (&optional new) (interactive) (when new (setcdr (nth (car gi/roth/g) (cdr gi/roth/g)) (cons (completing-read "-> " (gi/roth/g :list)) (gi/roth/g :list)))) (let ((inhibit-read-only t) (str (gi/roth/g :current)) (x 1)) (with-current-buffer gi/roth/edit-buf (while (not (equal (gi/roth/g :current-id) (get-text-property x :roth))) (setq x (1+ x))) (goto-char x) (delete-region x (next-single-property-change x :roth nil (point-max)= )) (insert (gi/roth/propertize str 0 (length str) (gi/roth/g :current-id) :glossary)) (gi/roth/update-bufs (gi/roth/g :current-id) (gi/roth/g :current-id)) (gi/roth/word)))) (defun gi/roth/rm-current-word () (interactive) (let* ((a (append gi/roth/current-line (gi/roth/g :current-id))) (b (catch 'rm (dolist (x (mapcar #'car gi/roth/gls-rtables)) (dolist (y (gethash gi/roth/current-line (symbol-value (intern-soft x)))) (dolist (z (gethash y (symbol-value (intern-soft (substring x 0 -2))))) (when (equal (car z) a) (throw 'rm (cons (or (and (stringp y) y) (prin1-to-string y)) x))))))))) (when (y-or-n-p (format "elimina %s da %s" (car b) (substring (cdr b) 0 -2))) (remhash (car b) (symbol-value (intern-soft (substring (cdr b) 0 -2))= )) (gi/roth/gls-rev (intern-soft (substring (cdr b) 0 -2))) (cl-mapc #'gi/roth/print (list gi/roth/gls-tables gi/roth/gls-rtables) (list gi/roth/glossary gi/roth/rglossary)) (message nil) (gi/roth/buffers gi/roth/current-line)))) (defun gi/roth/guess-words (&optional revert) ; fixme (interactive) (let* ((a ; which glossary items are words? ()) (b ; are there any already translated literal matches? ()) (c ; are the translations different? ()) (d ; map the most frequent translation, if any, to word items ())) ())) =09=20=20 (defun gi/roth/update-bufs (a b) (let ((inhibit-read-only t)) (mapc (lambda (x) (with-current-buffer x (let ((from (gi/roth/get-text-property a)) (to (gi/roth/get-text-property b))) (put-text-property (car from) (cadr from) 'face 'bold) (put-text-property (car to) (cadr to) 'face 'highlight)))) (list gi/roth/curr-buf gi/roth/edit-buf)))) (defun gi/roth/update-buf-refs (pnt) (with-current-buffer gi/roth/curr-buf (let ((inhibit-read-only t) (a (gi/roth/get-text-property pnt))) (put-text-property (car a) (cadr a) 'face 'success)))) (defun gi/roth/get-text-property (val &optional str) (let ((a (if str 0 1)) (b (if str 0 1))) (while (not (equal (get-text-property a :roth (when str str)) val)) (setq a (1+ a))) (setq b (next-single-property-change a :roth (when str str) (if str (length str) (point-max)))) (list a b))) (defun gi/roth/get-text-property-corsivo (&optional str) (let ((a 1) (b 1) (ovs ())) (goto-char (point-min)) (when (get-text-property a :corsivo) (setq b (next-single-property-change a :corsivo nil (point-max))) (push (make-overlay a b) ovs) (setq a b)) (while (< a (point-max)) (while (and (< a (point-max)) (not (get-text-property a :corsivo))) (setq a (1+ a))) (setq b (next-single-property-change a :corsivo nil (point-max))) (unless (=3D a b) (push (make-overlay a b) ovs)) (setq a b)) ovs)) (defun gi/roth/str-buf (lst sym) (let ((adj (if (equal sym :buf) 1 -1))) (mapcar (lambda (x) (cons (+ (car x) adj) (or (and (numberp (cdr x)) (+ (cdr x) adj)) (cdr x)))) lst))) ;;;; markers (defvar gi/roth/edit-italic ()) (defun gi/roth/edit-italic (a &optional b) (interactive "r") (if (>=3D (length gi/roth/edit-italic) (length (gethash gi/roth/current-line gi/roth/lne-italic))) (message "fully italicized") (let* ((inhibit-read-only t) (beg (if b a (car a))) (end (if b b (cdr a))) (begm (make-marker)) (endm (make-marker)) (ovr (make-overlay beg end))) (set-marker begm beg) (set-marker endm end) (push (cons beg end) gi/roth/edit-italic) (overlay-put ovr 'face 'match)))) (defvar gi/roth/edit-notesref ()) (defvar gi/roth/edit-link ()) (defvar gi/roth/edit-indexref ()) (defun gi/roth/edit-refs (kyw &optional obj) (interactive) (let* ((m (make-marker)) (a (cond ((equal kyw :note) gi/roth/lne-notesref) ((equal kyw :link) gi/roth/lne-link) (t gi/roth/lne-indexref))) (b (cond ((equal kyw :note) (symbol-name 'gi/roth/edit-notesref)) ((equal kyw :link) (symbol-name 'gi/roth/edit-link)) (t (symbol-name 'gi/roth/edit-indexref)))) (c (if obj (cdr obj) (completing-read "-> " (remq nil (mapcar (lambda (x) (unless (cl-some (lambda (y) (equal (cdr x) (cdr y))) (symbol-value (intern-soft b))) (cdr x))) (gethash gi/roth/current-line a))) nil t)))) (set-marker m (if obj (car obj) (point))) (set (intern-soft b) (cons (cons m c) (symbol-value (intern-soft b)))) (gi/roth/update-buf-refs (car (rassoc c (gethash gi/roth/current-line a)))))) (defun gi/roth/rm-markers () (interactive) (mapc (lambda (x) (set-marker (car x) nil)) (append gi/roth/edit-notesref gi/roth/edit-indexref gi/roth/edit-link)) (mapc (lambda (x) (set x nil)) '(gi/roth/edit-notesref gi/roth/edit-indexref gi/roth/edit-link))) ;;;; ndt (defun gi/roth/ndt () (interactive) (let ((a (read-from-minibuffer "-> "))) (puthash gi/roth/current-line (cons a (gethash gi/roth/current-line gi/trd-note)) gi/roth/trd-note))) ;;; produzione (defun gi/roth/out (lne) (let ((trd (gethash lne gi/roth/trd-text))) (if (not trd) (format " ...[%d %d %d]... " (nth 0 lne) (nth 1 lne) (nth 2 lne)) (gi/roth/format-frase lne)))) (defconst gi/roth/format=20 '((gi/roth/elt-type . (lambda (x) (format "[%s]" (upcase x)))) (gi/roth/lne-pageskip . (lambda (x) (format "[PAGINA: %s]" x))) (gi/roth/trd-italic . (lambda () (cons "[CORSIVO]" "[FINE CORSIVO]"))) (gi/roth/trd-notesref . (lambda (x) (format "[NOTA: %s]" x))) (gi/roth/trd-indexref . (lambda (x) (format "[RIFERIMENTO: %s]" x))) (gi/roth/trd-note . (lambda (x) (format "[NDT: %s]" x))) (gi/roth/trd-link . (lambda (x) (format "[URL: %s]" x))))) (defun gi/roth/format-frase (lne) (let* ((str (gethash lne gi/roth/trd-text)) (elts ()) (len (length str)) (adj 0)) (mapc (lambda (x) (let* ((tblsym (car x)) (tbl (symbol-value tblsym)) (fun (cdr x)) (lst (or (and (=3D (nth 2 lne) 0) (equal tblsym 'gi/roth/elt-type) (list (gethash (butlast lne) tbl))) (gethash lne tbl)))) (mapc ; -> (precedence pnt str) (lambda (y) (pcase tblsym (`gi/roth/elt-type (push (list 1.0 0 (funcall fun y)) elts)) (`gi/roth/lne-pageskip (let ((arg (progn (string-match "p\\(?1:[[:digit:]iv]+$\\)" (cdr y)) (match-string 1 (cdr y))))) (push (list (or (and (=3D (car y) 0) (/ (string-to-number arg) 1000.0)) 9.0) (or (and (=3D (car y) 0) 0) (length str)) (funcall fun arg)) elts))) (`gi/roth/trd-italic (let ((lab (funcall fun))) (push (list 8.0 (car y) (car lab)) elts) (push (list 2.0 (cdr y) (cdr lab)) elts))) (`gi/roth/trd-notesref (let ((arg (progn (string-match "px\\(?1:[[:digit:]]+$\\)" (cdr y)) (match-string 1 (cdr y))))) (push (list 3.0 (car y) (funcall fun arg)) elts))) (`gi/roth/trd-indexref (let ((arg (gi/roth/fixed-indexref lne (cdr y)) ;; (progn ;; (string-match ;; "#p\\(?1:[[:digit:]]+$\\)" (cdr y)) ;; (match-string 1 (cdr y))) )) (push (list 4.0 (car y) (funcall fun arg)) elts))) (`gi/roth/trd-note (push (list 6.0 (car y) (funcall fun (cdr y))) elts)) (`gi/roth/trd-link (push (list 5.0 (car y) (funcall fun (cdr y))) elts)))) lst))) gi/roth/format) (let ((ord (mapcar #'cdr (sort elts (lambda (a b) (cond ((< (nth 1 a) (nth 1 b)) t) ((< (nth 0 a) (nth 0 b)) t) (t nil))))))) (dolist (x ord) (setq str (concat (substring str 0 (+ adj (car x))) (cadr x) (substring str (+ adj (car x)) nil)) adj (- (length str) len)))) str)) ;;; progresso (defun gi/roth/line-trd (lne) (with-current-buffer gi/roth/edit-buf (let ((inhibit-read-only t) (str (buffer-string)) (lnestr (mapconcat #'number-to-string lne " ")) (ita (gi/roth/str-buf gi/roth/edit-italic :str)) (nte (mapcar (lambda (x) (cons (- (marker-position (car x)) 1) (cdr x))) gi/roth/edit-notesref)) (idx (mapcar (lambda (x) (cons (- (marker-position (car x)) 1) (cdr x))) gi/roth/edit-indexref)) (lnk (mapcar (lambda (x) (cons (- (marker-position (car x)) 1) (cdr x))) gi/roth/edit-link))) (if (and (and (=3D (length ita) (length (gethash lne gi/roth/lne-ital= ic))) (message "controllo linea [%s]: corsivo" lnestr)) (and (=3D (length nte) (length (gethash lne gi/roth/lne-notesref))) (cl-every (lambda (x) (rassoc (cdr x) nte)) (gethash lne gi/roth/lne-notesref)) (message "controllo linea [%s]: note" lnestr)) (and (=3D (length idx) (length (gethash lne gi/roth/lne-indexref))) (cl-every (lambda (x) (rassoc (cdr x) idx)) (gethash lne gi/roth/lne-indexref)) (message "controllo linea [%s]: indice" lnestr)) (and (=3D (length lnk) (length (gethash lne gi/roth/lne-link))) (cl-every (lambda (x) (rassoc (cdr x) lnk)) (gethash lne gi/roth/lne-link)) (message "controllo linea [%s]: url" lnestr)) (and (mapc (lambda (x) (progn (remhash lne (cdr x)) (mapc (lambda (y) (puthash lne (cons y (gethash lne (cdr x))) (cdr x))) (car x)))) (list (cons ita gi/roth/trd-italic) (cons nte gi/roth/trd-notesref) (cons idx gi/roth/trd-indexref) (cons lnk gi/roth/trd-link))) (message "linea [%s]: elementi" lnestr)) (and (gi/roth/glossary-trd lne str) (message "linea [%s]: glossario" lnestr)) (and (puthash lne (substring-no-properties str) gi/roth/trd-text) (message "linea [%s]: testo" lnestr))) (message "linea [%s] completa" lnestr) nil)))) (defun gi/roth/glossary-trd (lne str) (catch 'err (mapc (lambda (x) (let ((rtbl (gethash lne (symbol-value (intern-soft x)))) (tbl (symbol-value (intern-soft (substring x 0 -2))))) (dolist (key rtbl) (let ((y (gethash key tbl)) (strt 0) (end 0)) (dolist (val y) (when (equal (butlast (car val) 2) lne) (let ((old (cdr val)) (oldtxt (gethash key gi/roth/trd-text)) (new (gi/roth/get-text-property (nthcdr 3 (car val)) str))) (or (and new (puthash key (cons (cons (car val) new) (remove val (gethash key tbl))) tbl) (message "glossario: %s [%s] per %s in %s --da %s [%s]" new (substring str (car new) (cadr new)) (nthcdr 3 (car val)) (substring x 0 -2) old (and oldtxt (substring oldtxt (car old) (cadr old))))) (throw 'err nil))))))))) (mapcar #'car gi/roth/gls-rtables)))) (defun gi/roth/next (lne) (let ((go (gi/roth/following lne))) (while (gethash go gi/roth/trd-text) (setq go (gi/roth/following go))) (gi/roth/buffers go))) (defun gi/roth/writeout () (interactive) (let ((ctrd (mapcar (lambda (x) (symbol-value (intern-soft (car x)))) gi/roth/trd-tables)) (cgls (mapcar (lambda (x) (symbol-value (intern-soft (car x)))) gi/roth/gls-tables)) (new ())) (load gi/roth/traduzione) (load gi/roth/glossary) (cl-mapcar (lambda (x y) (maphash (lambda (k v) (when (not (equal v (gethash k (symbol-value (intern-soft (car y)))))) (push (cons (car y) (prin1-to-string k)) new))) x)) (append cgls ctrd) (append gi/roth/gls-tables gi/roth/trd-tables)) (if (car new) (let ((max-mini-window-height (+ 1 (length new))) (msg (mapconcat #'identity (mapcar (lambda (x) (format " %s: %s" (car x) (cdr x))) new) "\n"))) (when (y-or-n-p (concat "cambiamenti:\n" msg "? ")) (gi/roth/printt ctrd gi/roth/trd-tables gi/roth/traduzione) (gi/roth/printt cgls gi/roth/gls-tables gi/roth/glossary) (load gi/roth/traduzione) (load gi/roth/glossary))) (message "nessun cambiamento!")))) ;;; chords (defvar gi/roth/keys) (defun gi/roth/keys () (let ((km (make-sparse-keymap))) (mapc (lambda (x) (define-key km (kbd (car x)) (cdr x))) '(("" ; glossary left . (lambda () (interactive) (gi/roth/edit-glossary 'back))) ("" ; glossary right . gi/roth/edit-glossary) ("" ; glossary up . (lambda () (interactive) (gi/roth/choose-word 'up))) ("" ; glossary down . gi/roth/choose-word) ("M-" ; kill word . (lambda () (interactive) (let ((inhibit-read-only t)) (kill-word nil)))) ("M-" ; yank . (lambda () (interactive) (let ((inhibit-read-only t)) (yank)))) ("M-" ; transpose forward . (lambda () (interactive) (let ((inhibit-read-only t)) (transpose-words -1)))) ("M-" ; transpose backward . (lambda () (interactive) (let ((inhibit-read-only t)) (transpose-words 1)))) ("C-" ; choose translation . gi/roth/edit-word) ("M-" ; new translation . (lambda () (interactive) (gi/roth/edit-word 'new))) ("C-c C-r" ; remove from glossary . gi/roth/rm-current-word) ("C-c C-n" ; next untranslated line . (lambda () (interactive) (gi/roth/next gi/roth/current-line))) ("C-c M-n" ; next line . (lambda () (interactive) (gi/roth/buffers (gi/roth/following gi/roth/current-line)))) ("C-c C-p" ; previous line . (lambda () (interactive) (gi/roth/buffers (gi/roth/previous gi/roth/current-line)))) ("C-c C-i" ; italicize region . gi/roth/edit-italic) ("C-c C-f" ; insert footnote ref . (lambda () (interactive) (gi/roth/edit-refs :note))) ("C-c C-l" ; insert link . (lambda () (interactive) (gi/roth/edit-refs :link))) ("C-c C-d" ; insert index ref . (lambda () (interactive) (gi/roth/edit-refs :index))) ("C-c C-a" ; nota di traduzione . gi/roth/ndt) ("C-c C-c" ; format line . (lambda () (interactive) (gi/roth/line-trd gi/roth/current-line))) ("C-c C-w" ; save changes . (lambda () (interactive) (gi/roth/writeout))))) (setq gi/roth/keys km))) ;;; startup (defun gi/roth/startup () (interactive) (let ((go '(0 0 0))) (gi/roth/load) (gi/roth/split) (gi/roth/next go))) ;;; index fixups ;;;; so so ugly... (defvar gi/roth/fix-indexref ()) (defun gi/roth/fix-indexref () ; (gi/roth/fix-indexref) (let ((a ()) (b ()) (c ()) (l '(19 0 0)) (e ()) (z ())) (with-temp-buffer (insert-file-contents (concat gi/roth/basedir "xhtml/" "back02.xhtml"= )) (setq a (cl-remove-if-not (lambda (x) (and (listp x) (equal (car x) '= p))) (cdr (assoc 'body (libxml-parse-html-region (point-min) (point-max))))))) (dolist (x a) (dolist (y x) (when (and (listp y) (equal (car y) 'a)) (push y z))) (push z b) (setq z ())) (dolist (x b) (dolist (y x) (let ((lnk (cdar (nth 1 y))) (lab (nth 2 y))) (push (cons lnk lab) z))) (push z c) (setq z ())) (setq d (copy-sequence c)) (while d (while l (when (or (equal "voce indice" (gethash (butlast l) gi/roth/elt-type)) (equal "sottovoce indice" (gethash (butlast l) gi/roth/elt-type))) (let ((lab (car d)) (ref (gethash l gi/roth/lne-indexref))) (when (and (equal (length lab) (length ref)) (cl-every (lambda (x y) (equal (car x) (cdr y))) lab ref)) (push (cons l (pop d)) e)))) (setq l (gi/roth/following l))) (setq d (cdr d) l (gi/roth/following (caar e)))) (setq gi/roth/fix-indexrefs e))) (defun gi/roth/fixed-indexref (lne str) (or (cdr (assoc str (cdr (assoc lne gi/roth/fix-indexrefs)))) (progn (string-match "#p\\(?1:[[:digit:]]+$\\)" str) (match-string 1 str)))) (defvar gi/roth/indice ()) (defun gi/roth/indice () ; (gi/roth/indice) (let ((l '(19 1 0)) (e '(20 0 0)) (a ()) (b1 ()) (b2 ()) (b3 ()) (c ())) (while (not (equal l e)) (cond ((cl-some (lambda (x) (equal l x)) '((19 363 0) (19 363 1) ; job/labor market (19 392 0) ; national (19 503 0) (19 518 0) (19 769 0) ; acronimi ridondanti ))) ((cl-some (lambda (x) (equal l x)) '((19 326 0) (19 327 0) (19 328 0) (19 329 0) (19 330 0) (19 331 0))) (push l b3)) ((equal "sottovoce indice" (gethash (butlast l) gi/roth/elt-type)) (progn (push l c) (when (not (equal (nth 1 (gi/roth/following l)) (nth 1 l))) (setcdr (assoc (caar b1) b2) (cons (sort c #'gi/roth/key<=3D) (cdr (assoc (caar b1) b2)))) (setq c ())))) (t (progn (push l c) (when (not (equal (nth 1 (gi/roth/following l)) (nth 1 l))) (push (sort c #'gi/roth/key<=3D) b1) (push (cons (caar b1) nil) b2) (setq c ()))))) (setq l (gi/roth/following l))) (setq b2 (cl-remove-if #'null (mapcar (lambda (x) (when (> (length x) 1= ) x)) b2)) b3 (list '(19 235 0) b3)) (cl-flet ((lx (lambda (x y) (string-collate-lessp (gethash (car x) gi/roth/trd-text) (gethash (car y) gi/roth/trd-text) "it_IT.UTF-8" t))))=20 (setq b1 (sort b1 #'lx)) (dolist (x b1) (dolist (y x) (push y a)) (when (car (assoc (car x) b2)) (dolist (y (sort (cdr (assoc (car x) b2)) #'lx)) (dolist (z y) (push z a)) (when (car (assoc (car y) b3)) (dolist (w (sort (cdr (assoc (car y) b3)) #'lx)) (push w a))))))) (setq gi/roth/indice (reverse a)))) --=-=-=--