From 667407441bfcfd34fd60e1c6221db7b37cb20105 Mon Sep 17 00:00:00 2001
From: irustam <49302555+irustam@users.noreply.github.com>
Date: Tue, 1 Oct 2019 17:00:02 +0300
Subject: [PATCH] Lesson8

---
 homework8.ipynb | 1072 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1072 insertions(+)
 create mode 100644 homework8.ipynb

diff --git a/homework8.ipynb b/homework8.ipynb
new file mode 100644
index 0000000..059ff82
--- /dev/null
+++ b/homework8.ipynb
@@ -0,0 +1,1072 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy\n",
+    "import scipy.linalg\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Введение в высшую математику\n",
+    "#### 1. Решите линейную систему:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1, 2, 3],\n",
+    "             [4, 0, 6],\n",
+    "             [7, 8, 9]])\n",
+    "B = np.array([12, 2, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59.999999999999986"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.det(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A1 = np.linalg.inv(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-9.2       ,  0.9       ,  6.46666667])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(A1, B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-9.2       ,  0.9       ,  6.46666667])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.solve(A, B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Найдите псевдорешение:\n",
+    "x + 2y – z = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3x – 4y = 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "8x – 5y + 2z = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2x – 5z = 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "11x +4y – 7z = 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1,2,-1],\n",
+    "             [3,-4,0],\n",
+    "             [8,-5,2],\n",
+    "             [2,0,-5],\n",
+    "             [11,4,-7]])\n",
+    "B = np.array([1,7,12,7,15])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1.13919353, -0.90498444, -0.9009803 ]),\n",
+       " array([0.71523211]),\n",
+       " 3,\n",
+       " array([15.2817306 ,  9.59852942,  3.65197794]))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.lstsq(A, B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.76979505,  0.03751835, -0.16349016, -0.21671144,  0.21805317])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(A, [ 1.13919353, -0.90498444, -0.9009803 ]) - B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3. Сколько решений имеет линейная система:\n",
+    "Если ноль – то измените вектор правой части так, чтобы система стала совместной, и решите ее."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1,2,3],\n",
+    "             [4,5,6],\n",
+    "             [7,8,9]])\n",
+    "B = np.array([[12,2,1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  3, 12],\n",
+       "       [ 4,  5,  6,  2],\n",
+       "       [ 7,  8,  9,  1]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = np.concatenate((A,B.T), axis=1)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.matrix_rank(A, 0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.matrix_rank(C, 0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.66133814775094e-16"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.det(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Определитель равен нулю, а ранг расширенной матрицы больше ранга матрицы А, значит система не имеет решений, векторы параллельны. Изменим правую часть:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = np.array([[0,0,0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 0],\n",
+       "       [4, 5, 6, 0],\n",
+       "       [7, 8, 9, 0]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = np.concatenate((A,B.T), axis=1)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.matrix_rank(C, 0.0001)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ранг расширенной матрицы стал равен рангу матрицы А и меньше кол-ва переменных, значит система имеет бесконечное множество решений."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Вычислите LU-разложение матрицы:\n",
+    "После этого придумайте вектор правых частей и решите полученную линейную систему трех уравнений с данной матрицей."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1,2,3],\n",
+    "             [2,16,21],\n",
+    "             [4,28,73]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 1. 0.]\n",
+      " [0. 0. 1.]\n",
+      " [1. 0. 0.]]\n",
+      "[[ 1.    0.    0.  ]\n",
+      " [ 0.25  1.    0.  ]\n",
+      " [ 0.5  -0.4   1.  ]]\n",
+      "[[  4.    28.    73.  ]\n",
+      " [  0.    -5.   -15.25]\n",
+      " [  0.     0.   -21.6 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "P,L,U = scipy.linalg.lu(A)\n",
+    "print(P)\n",
+    "print(L)\n",
+    "print(U)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4., 28., 73.],\n",
+       "       [ 1.,  2.,  3.],\n",
+       "       [ 2., 16., 21.]])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = np.dot(L,U)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = np.array([15,3,12])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.09722222,  0.74305556, -0.19444444])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.solve(C, B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Найдите нормальное псевдорешение недоопределенной системы:\n",
+    "#### x + 2y – z = 1\n",
+    "#### 8x – 5y + 2z = 12\n",
+    "Для этого определите функцию Q(x,y,z), равную норме решения, и найдите ее минимум. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from pylab import figure\n",
+    "from pylab import show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1,2,-1],\n",
+    "             [8,-5,2]])\n",
+    "B = np.array([1,12])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Q(x,y,z):\n",
+    "    return (x**2+y**2+z**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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diOh+mSC63e6zBHF8fPwsQRSZH0Thu045H8E738HRgYDAey0D/NNRF9/Y1YTV5SNWoyArTsO31yzE6Q1w+PQI9xckc6BlgJsXxBIZJuMPH59kRbSTISfocpdQnhXDnonRY/6AwNtNJh4uSuHgySFWpEcTrVawvbKf9Bg1hvEAr9QMoFHKSItR8fPNBbi8Aap6LEGTS9MADxQkIQjwTvMg6wsS+fDkMKsWxKCSS9lbb2JjcTIV3WMUpUURJpfwQafzol3vi03IMZqYmEh2djYFBQWUlZVRUlJCRkYGYWFhWCwW2traqKqqoqamhpMnT9LX14fZbMbjmX9Rh6D4XI3CNxuzzTGVSCT4/f5JQTSZTLS3t2OxWM4SxCv5QeVaQRS+64xQD975VHjnOjjaHxA40Gzi3v88zjd3N+EJBP8P/NDyFL5yYwbVPWPEahTkJoQHq7YSHS5vgO2fnqAsEY72OXjsrjLC5bC7xsiW0uBUlgGri5XZMeypDQ6c9gUE9jcYyUsK54PWQd5sNCGVQElaBN9dk0v/qAu5TEpuQji7aoImF4vTR4vRxs05MeyrN7GhMBlfQOBg2wj3TJhc1uYnIJPAeyeGuHtRDJ/1OrG5fBfnwl8mQg7T5OTkMxymhYWFpKamIpPJGBkZoaWlhcrKSmpra2lra8Pj8cyLw1QQhCt6qfNiMNNg79BkmumCOD4+jsVimRREl8slCuIlQBS+64QvCn/9Mr5M+PwBgTcbjax74Rj/fU8z4xNi8TdF4dyQFc2+BgMPFaYgk0rYUxvsxWvot6Lv6yEjUsIHHXb+ft0KvH6BD1qHuSlVzkcnBynJiCZao2DXhEj2jTrpHXWSGavmPz/poqJ7DIDNK1K5J0dDo8HOzTlxaJQydtXoebQ0aHKJVMnIiFGxp9bIIytSGRz30DfqoiRDy946I5tWpOD0BqjoHuOOvHherzeSER2G2w9vNQ1chKs//8jlcqKiokhNTWXhwoUUFxdPOkxDPYkDAwM0NjZOOkzb29sxGo2X1GE63xWfTCabl2P7/f7J/MMvqhBdLtekII6NjZ0hiOIc07kjCt81TkjwHA4HFRUVc+rBk8lkM974fP4Ab9QbWPuLY3z7tRbMDi9qhZQ3vr6S8DAZh/s8PFyYiNHi5tTAOLcviuf1OgOLNXbkEvi4x83TqxfRYXbjF2C5LordNXpWp8nw+gXeaTKxoTCFj1qHWJYSiVoh5Zu7m+gxO/ELAs/dlUNJhpaK7lHWLIzEGxD4sHWIB5Ync6B5gNvzElAppOyZMK/U9llJ1YaRFKlkd62BR1ak0Dfqwub0kp8cwc4aA/lJ4Vhcfn51zIhCCrtqDNf0jUWhUBATE4NSqSQvL4+SkhLKysrIz88nJiYGr9d7lsO0s7OTgYGBi+Iwnc+Kb6qrcz6OPZvozhb9FNpDDAni+Pj45BxTURDPD1H4rlGmV3gymQyv1zunp+vpFZ/XH2BvrZ61vzjGd18/gcniYllqJL/YUoDTG+BI+wgPFCTzud5DkS6C2HAFO6v6uDlZwOzw0mGVcM+yJA52jnN3fiJqhZTdNXq2lOroGLLj8EFJRjS7a/Q8VBRcktz0YhVObwC3L8CPNixFq5bTqLfyaFkavWYnNo9AQYomWOmV6HD7Ahw8OcS9S5N4s8FAkseAXAp//OQUa3MjON41Rm68iliNgl21RkoztLQPOfjF4R5kUgk6rZKnlofTOeKkps9yMf9prngkEglhYWHExcWRkZFxlsM0KioKp9M56TCtqqqiubmZ7u5uhoaGzsthOp/mliulleJcmS6IoeHe0yvEqYIoZiHOjCh81xjnk3Z+rkw29/oC7K7Ws+bnx/j/97eiH3OxpSSVp2/K4ITRRnashqy4oPhsKdXhDcCB5gFuy1DxyekR8lO0pGpVfNjlZEtpGjaXj8/aR7ivIJm3m0zcmhtPRJiMT/p8bFyRQo/ZydN/qQPA7Qvwg/X5ABgtLjYW6zh0apiitOBy6Hunx1m/JJbuEQcWl4/luiheOd5NvnIEl09gTJ3KXXlxHO51ckuGCpkEfvdRMwuj/Hxy2szLlXokwJIkDd+8LQu9xUN6pIxIlZxdNcaL8C9z9TPVYZqVlTXpMC0pKSE7OxuNRoPNZqOjo4Oqqiqqq6s5ceIEvb29jIyMzOgwnc92Bpi/+KkvqvjOly8SRAiGA7tcrjOSLvR6PRaL5boVRFH4rhHmknZ+zu+NlN21Rtb8/Cj/9FYrRquLH6xfzOLkCBr0Vjav0BEQ4LV6I4+U6KjttYDfR1aUhN01BjYsD05dOT4gsLkkleOdZhIjlCyI17CrOiiSTm+AD08OcX9BMhUmPz8/2DnxuQT+ZlUmHn+ApKgwbsiOYU9tsAUi6PQ08nBRKsf7HCxLVqNVy/njp6cpjXbSa/GwYOHiYFxR/QBbSlKxewL0e8NZka7lw14/FaZgJVuapmFdbjhtQw7iXQaUUvig3cad2Ro+PDmE3my7Zm8MF/q5pjpMFyxYQEFBweTItvT0dBQKBaOjo5w6deoMh2l/f/9kduP1xsUUvtmYbclUKpXS29s7mXk4PQvxeqgQReG7ypke/noxBc/l9fPy57387YFBfnyoF4vLx482LAEB+kZdbClN46RpnDGnl5tzYnmt1sDaxbHIpfCr9xu5M0uFwS4gj4jhxgWx7KkxsKEoddLksqVER32/BYVUQn5yBL/9rIuDJ4cICCCTSliTn4DN5WNreRqx4Qp2Vet5tCwN/ZiLPrOTmxbEsrtGz6biFAIC7K83ckMiHO22sfnWIrRqOXvrB9halkb7kANBgPQYFf/3o06qei34AwL3Lkng5gUxdI/6ePb2PHwBGFCmsDY/jspBgbtytfgD8LuDJybnbLa1tU0+MV+PN+1zRSaTERkZSUpKCrm5uRQWFk46TFNSUpBKpZMzMysrK6mrq6OtrQ2DwXDNX9v5NNaElkUVCsVZgggzV4ghQTx06BBms3lezvtiIgrfVcpM4a8XS/CcHj9/Pt7LXT87yg/ebUMmlbClMB6Hx096rIbb8uLZV2fgnqXB/bldNXo2LE/EYHHx1pF6bsmOonIQbs2JRqOQTlZ1BouLtgmTy756A/cVJKOQSfjhu20YLS5MVjdatYKUcAkKmYR/uDMHvwBvNpjYWJzKoVNDFKREkhChZEdVP4+W6TBa3NS268mPgQ867PztPUX4BXi3dYQNRal82DrIDdkxaBRS/nH/KfpGXdg9fh4tSSEjVkXfmIvHynQM2z10DjtYmRXN7lojGwsT8fih16XgpgUxfGYQKC4pnXRBhtLDGxoazhgrdrFMH5eTy73XJZfL0Wq1pKamolarJ1suli5dSkJCAoFA4Ixr29DQQHt7+zUVCnw5Kr4vwufzIZefGc7zRRVi6DUvvPACJpNpPk75oiIK31VGaCrEXAXvi27IDo+fPxzt4fbnj/Dv77WRGBlGUXoUHr/AkyUJhIfJ2DPRFzc07qGye5R7liTwVoMRxWg3MWo5jfZInrolF4vLR/2Ah7sXRfNuywClGdHEhSvZUd3Po2VpmO1efvFxB3KphMqeMVKj1YQppCxLjeT+BQp6zE4GbR5WZsewq0bPphU6BOD1BiMbV6TySdswsVIn0WES9jUOsbEwEbNLoN/iYVVOsBLcvCLY8/c32+txeAMMjnt4slyHRiHF7vHzWKmOJoONGI2ctGgVO2uMPFqSitHqZmjcS26MjF01RraUpDA47uFQ28ikCzItLY3FixdPuiAXLVqEVqs9y/TR0tJCd3c3w8PDOJ3OK3LZ6EoxmCiVyhmvbV5eHjExMXg8Hvr6+iYdpk1NTXR2djI4OIjdbr+qHjbmW/jOx1wzVRBtNhsxMTGX+OwuPaLwXQWEssVCsxq7u7vn3JYw081h3O3jxc+6ue0nn/HjD06TmxBOmFzK4uRIvnZLNmOuANX94zxQEGwTKEzTkhSp5I+H2yjQWHD7BUY0mWwuSeOTtmEyY9RkxWl49/Q465fE4PYFeKd5gE0rUvnk1DCtRisyaXD/LzU6ODx6S0kqDxel8m7LIItjpGjVcnZU9bN1Ymmze8TBqpw4dtcYWJ2mAOCNhgEeLUunzuQiJ05NvEbO9qp+tpanYbK6+ac3WwHoHHKwZUUKANEaBeuXJ/PeiSFuyYlBPdHusKUkhZpeC7poFUmRSvbUD7ImK4wesxOFRIJOG8bOasOM11UikaBSqWY0fWRmZqJWq7FYLJw+ffqsPa7R0dHrOpboy8wtoWsbcpguWbJk0mGak5NDZGQkDoeDrq6u83rYmO8HkPkWPpibscdqtRIdHX0JzubyIgrfFUxI8EJLmqGbRKjSO1+m9+ONu3z8+nAXt/3kCD/5qJ2U6OBA56+uymL98mQONJsoTo8mIVzOmy0jbCkNtgn8/sN6bkwSqDe5WVVazJKUSHZOVFgCsLfOwKOlOlqH3CAIFKVp2VHVR7hShgD85KMOEiKUAPx88zIWJUWwq8bAoyWpuH0BqgcDbCgMjiZbrouaXNpclxvOoM1NQ88Ity6M5eMeN5tK0pFKJLx72sa9i6M42mFmb01QoOr7LdyzJBEBuHFBLDdMLGNuWpEcbJY/Ocz9y5I40DLE7QvjCJNLJ/r7UqnstZIWKSE2XMHuOiNbSlKp7rXQNmg/5+s9W07f1D2uoaGhs2KJLnXT+HSulIrvfAiFAickJJCVlcWyZcvOetiwWq2TDxvV1dW0trbS29uL2WyenJoyX1wJwjcX3G43KpVqvk/jghGF7wpkahae2+0+I/w1lCM2F0LCZ3V6+eUnndz608/42aEO1Eop4UoZrzxdQnyEkp3V/WwpTcPpDfBOk4l787TU6B0Y+7rJ1ko52O3mHx4oD5pUJkSubWCcEYeHVTlx7Kk18MDE/t3rLSPkJGjoHnHy04MdRITJiFLL2fnVUhQyCbtrg68/YbTh8QsUp2v5uM/L5hXB/r399UbW5Wk53DZMjMxJclQYnxph28oMRuwe6vst3LU4gQMnx+gccQPwWfsIJRnRBAT4+9sXkKINY2eNni0lKZisbvrNLsozteyuMfLIihTcvgCH2ka4d2kC7zQPsiY/HoVMwse9XjYVpXD4tJmMWDUyqYTtVfoL/vedusc1UyzR9KbxpqamS5rCMJ/CBxe3pWDqw8aCBQtmDAU2m82cOnUKu91+Rijw5ay+51P45vr9me8q+WIiCt8VxGxp51Oz8EJ5bXPB4YP//LSH2356hBc+7sTp9XPrwjj+78Zl2D1+Pjo5xKbiVA63DRMfoWRpSiQ7qvoojnYhlcARg8BXb82je9SN3uLi9kXxvFZnYO2SRMLDZOys0vNomY4Bq5ua3jFy48J4p9XCa3VGZBIozYzmRxuWYnX6aNLbWJOfyOv1Ru7OT0SjlAWXKcvSMNkF9KNOytIjePl4FzcmgUQCdZZwHi1N43inmdRoFRmxav58vBen18+4J8DnfXay4jSoFFJ+vGFpUFhr9GwpSePzrjEyY9QkR4Wxo8bA1lIdRmvwc5RkaNk1Mc7M6Q3wabuZu/Pi+Kzfw8pMLQD/fe8JEATeahqYjD26mEyNJZreNJ6Tk0NERAR2u53Ozk6qq6vP6pELPSBdyPGvZaaGAufm5rJkyRKio6PPCAUOVd8VFRXU1dVx+vTpyVDgS+EwvVpHtV0L3xVR+K4AZgp/nS3tfLbxYV/EqMPD8wfb+dv3zPz+uB67x8//XLeIR0rSON41ysLEcLIneuo2lwRNJNsrerglVcLpIQduWTgr0zS81zbGXfmJRKrk7KjST5pUjnWaWb88hQMtAyxLiSJKJefb+1poHXQhANvK03h8ZToN/RYKdFGkaMPYUdXPY+XBJvbDbcMTS6sDlGfFEKGA3xxs5fZ0OWaXQCAqhTvyEthbp+fBwhQUMgl/PNpDnEZJs8HKsQ4zEUopGVoFP1ifz7jbz/EuM2uXJLGvzsD9BUkoZBL21AUrvIruMTJj1SRFKtlZY2BraSr6MRfD4x6K0qLYWWNgzeJYXD746x1NIAG5TMLPNy/F4xfYV3/5XG1Tl/Sys7Mnl/Sm98i1trbOud3iWnqSP1dC2wYzhQKHHKZxcXH4/X4MBsNZ7t0LDQWez2s+k6PzXPB4POMlQ8kAACAASURBVCgUiktwRpcfUfjmkS+KBprtqep8hG9k3MN/fHCaO356lN982o0/IPC7zYtIiFBytNPMo6U6PL4AbzaY2FKio67PwrBlnKIkJbur+1lfnEa4UsahXg/3LIzAbPdytGOEhwpTeP/EAPnJEaTFqNlR1c/mFal4fAEe/HUFVpcPty/Ac7ekkBWtoLZ3LDjJxS/w+kST+7FOM7EaBYsSw4OtCRP7h785UMVNKRIahvysLV9KclQY2ydE0mz3cujUILpoFa/VGWkyWJFKYN3SJJ4tT6Jz1ItSLmFRUgSvVvbxWJmOcbefIx1m1uYn8GbTIPdMLGPurg3u233eNUZ2rJrESCXbq/U8uDyJvlEX395/GgC1UsZ/bFiM1y8wYHVTmqFlZ40BX2B+xWJ6j1xRUdHk0OnExEQEQcBkMp3REjDbDXu+lzrngy8y1YSq79jYWNLT02d077rdbnp7e6/KUODQCMPzxWKxEBUVdQnO6PIjCt88cCHhr+cifEM2Nz96v407f3aE3x/tISFSyb8+sBhvAPotLjZOLGdq1QqK07XsrNGzZpEWhRR+e7CFR1akMOYW6BqXs74whY/bLSyIlpE+IXJTRWxzcSqV3WP83Y4GABweHz98MB+pBAw2L/flRdJqGsfm8nPjglh2Vet5uDgVuVTCzmo9j5am0WK0UdfYzOI4BUdMcG+eloAA++oMbCnVcbTDTJhcSpRKzg8OtNFrDubkfeP2bDYUpfLRyUFuWaBFLZewvVLP4+VptJrGCQiwJCWS7ZV9bC1Nxe7xc6RzjHVLEnizaYB7liSglEnYXWfkoeXJHO8a48cfdgCgVcnZkqdk3O1Ho5CRnxTB9moDj5WmYrC4+aRt5EK+ApeMUAWTlpZ2xtDpvLy8s1LcQw7Ivr6+yZicK/mGfTGZy6i0qe7dzMxMlixZQllZGWVlZSxYsGAyFLizs5OqqqrJ6xsKBZ56fefbWDOXiu9acXSCKHyXlamCV1VVBZx/2vkXCd+A1c2/vXuKO54/yh+P9VKaGcMdefEM2z2sW5ZEcoSc/c0jk8uZe2r0PFSQQNewgw8qW7hjYQwVJoE7l2cG98Kq9WwtS8PjFzjYaWdLqY6qnjEkQGmGlj8e7WF3bdDs4fYF+MoN6Xj8Aila1YThZJQb09SEh8mCS5tlaRgsLloMNtYsSWRfnYFEjx6VXEKtNZy/uj2P/jE3gw6BmxYEh1TfvigeqQS+8udabC4fAvDClgIWJ0dwoHmQx8qC484Od1q5PTucAy0DrMqJI0oVbId4fGU6bYN23L4Ay1Iigp+pNBWHx8/h02buyk/gtToT26uDnyMuXMnW0hSG7V6WxkmJD1eyvdrAtvJUOoYdqBUyUrVhvHoRTC6Xi+k37KntFllZWahUKnw+31ntFn19fddsu8XFnJwSWo4OhQIvW7aM8vLySYdpKBR4qsPU5XKdEQp8OR845rrUabVa0Wq1l+CMLj+i8F0GZgp/dblcF6UlAcBkcfGv75zkzp8d4eXP+1i7JAG1QkpcuJK/WpWF3e3n3eZB7suLot4QFIGbsqPZXtFDktdIZJiMxvFInr4lB7vHz3stg2wu0XGkfQS1Qsry1Ajeb7ezoTAFuRT+7d02us1OzA4vMomE0sxo3D4/X1udRbRGMbF/l47F5ada7+ChwuD+X1G6lsTIMF461sWKyHFsbj+D8kQeLtbx/skRyjKjiY9Q8n6nk/uWxDE07mHL76sJrSq+/PQK1AopB08N83h5OqcGxnF6AxSna3m9aYR7cjV4fAHebgpOenn/xCBlE5l+r1T282hJCl0jTqwuPwWpkfz2SA9H2s34AgKJkUpuzY1lxO5hW5ku6Ors8/FISQpHOkbJT55ImZjYE6zutdBqGr/g78Z8EpqxGRcXR0RExFntFjKZjKGhIZqbmydHip0+ffqyt1tcCi5HJNFsocAFBQWEhYVNhgKfOHH2OLxLGQo8V0fp2NiYKHwiX85UwTvf8NfZkEqlk0+H+jEn//JWK3f+7Cjbq/opStMiAJtKdDxUlMq7LQNkx2lYlBTBjup+1i7SIpfCC+/WUxbjZtQVwBOzgI0rdHzQOkRatIq8pAh2VPWzqTgYHLuzWs+moiSM437+dLwHpVw2sT+nJDJMzoIEDf/fXbnYPQE+ah1mU3EqB08NkxWrJjMmjANt48Gq0Rdg5+ed3JICFT1WivNzWJQYzmsNQ2wtC+417m8w8eDyFGqMHn7wQRcAaoWMHz+8FF9AoGPIwfrCFN5pGmBVbixatZxXKvt4fGU6/RY3Q/bgcuqO6n4eKUnFLwi8VtvPnVkqPm4zIx3tIVIB//b2CbqH7Yw5fSRGKFiYoMHjE/j6LZk4vQE+aTNz79JEjhl83Ls0IWiMqTXxSHEKh9vNlGZoUSukvFLZf8HfkSuB6Xt809stQqG1IcOHz+c7q91i6gSVq2G5dL5TIcLCwiZDgWfanx0YGDijv/NihgKLFZ8ofJeEc007n+sNYsgR4H/uP8HdPz/G3loDMRoFiZFhvLgtOJh5Z5V+0iyyv8E42SfX3GVkRYKEI/0eHr+7PNjfVh38W19AYG+tka1lwf0xk9XDnXnx7K3V4/IJSIDfH+2dbDx/vDyNbeVpfHp6hKRIJXlJEbxa1ceWEh0BQWB3rYGNhYm0mb2MjFrIj5Oxu9bIM7flI5dKeKt1jK1l6bQYbdg9fkoyovndkW521wSXEGM1Cp6+MYMxp5eFCeEsSYmcMKyk4fYFeLtpgI3FOj5sHaJQF0WcRs47p8d5fGUaRoubk0Yrpalqtlf28mBBAhLgXVM4EpmMXpufxAgFEQoJaomXO5O99I+5qD/VxbIkFa9W9rO5KBG3Hz5uG+HepYnsbzSxbkIE9zcO8uDyZN49McTwuGfO35OrjamGj+ntFpGRkdjtdrq6ui5Ju8XFZj6Fb7aKa/r+bKi/Mz8/n9jY2BlDgecyH3auwmexWEThEzmb88nCmx7uei70jDj4H6+38I9HXLzRYMQfEHhiZTrfW7cIk9VNde/Y5GDmuHAlxelatlf1kS0ZQiWDY4MyNhUlY3P7eb91cNJdCcHJJrtr9Ny3LInwMBmvVvaRGafB4vTxww86iVRKkErgz08WB6vC6n4eKdEBsLvGwGNlwaSGYbuH1blx7KnRc0uGGqUU/nSkgyduzGLIEaBvXJjc31uzJIFwpZT//fYpTg7YsLp8ZMapKUgKw+L08fSNGagUUl6t6mdbeRptg3YsTi/lWdGTTtCAILC3zsDDhYnUGt1kaMNIipDzm0Mn2VgQh9UD73d5UCtlfNYxSkJEGFIJrFwQy1M3ZdA05GPNDctJilTyYa+PB/OjMNo8VDS1kRsl8Odj3dySFAjGJrWYWLckgf2NJh5cnojXL7CrZuYxZlcTF+LqPNd2i5MnT04u5506dQq9Xs/o6Oi8ieGVKHwzEQoFjo2NnTEUeKb5sCGH6WyhwHNd6rRYLNfEnE4Qhe+iMJfw1/NpRO8atvOdfc2se+EYbzaaCAiw45lSbs6J5d2WAW5bFE9cuJKdEykIvoDAjopubkoM0GN2YVPE8mCxjk97HKRFSlmYGM6OKj2bV+iQSyXsqtaztSyYnlDZPcry1CjebDTxuyM9KGQSsmJV/PNN4UFDTJ2RxyaqwgGbm9sWxbOnVs89SxOJCAs2oW9YFtyf+6ipj5vTlBw3+Lh1cSoJkUperQyKmNXl498OtOEXoNVkY0lyJLEaBVq1gq0FWmxuP5+2j7B+eQpvN5lYlRMX3Kur6OOJlRnox1y0DQaTHnbX6Fm3OBaZBH5+oJb1+VpOjQawokYpk/BqZT9adfAJ97HSVO5ZksD+xkHuXZqIUiaZ2LfTUdM/TmFOGqnaMI6NqFibrWTEKTBOGAVJYeyoMVKkGcPpDfCz906glEl4pbIfq911Mb5G88alaGeYLZIo1DAeWs5zOp1f2m5xKbhahG82viwUODw8fNZQYJvNRiAQOO+HDqvVKrYziFxY+Ou5jB7rGLLz3N5m7v3lcd5qNKGLVvPqMyVIgI/bhnm0NDiM+XjnKJtWpPLxqSH8bifLEhTsrO7n4fIFRKsVvHPKymMT7syPOsbZWppGs8GK0eri7vxE9tUbKM+MIUol51uvNXO8axSAB5cn8627cuk2B6N8QpXcumVBkQuKWDpmu5fP2s3cvzSeA00mFOMmdNowjg0pWLtAhdsX4M1GE4+WpvFZ+whHO8zIpRIOtAyQlxQBBCvOJ2/I4GiHmVi1nAVxKl6t7GNbeRoub9CwsnlFKh+dHCI/OZJUrYqXK4K/N9u9vFXRSnmyjGNGgbz0ZCQS+OF7pwkPCwreD9cvZnFSODtqDDxepsM+4eq8b1kibzUNsGZxHGqFlB3Veh4r1VHTayFWJSEtWsUbrVb+anUuI84AHYF4wuRSKowekiPkjHsC/P6jhjPMH6FpH1eT+eNy2eunLufl5uYSGRk52W4RHR19VrtFc3Mz3d3dDA0NXdR2i/kWvkt17C8LBVYqlTidTvR6/YwOXo9n9qV7q9UqVnzXMyHBa2lpmXP46xdVfG0D4/z3PU3c98vjHGg2sSY/kW1l6fSPOUmOUlGQIGNPrZ7VC2NJjAxjZ3U/9y2ORhDgdx+3srUsjRGnQJsFHi4ODnuO0SgpSAnn3dPjPLA8GY1Sxo7K4HKhxenj/l99jtXlw+kN8M/35lGaoaWmz8JDRSmoFVI+6vGwrTydoXEPRztG2VCUyvsnBliUFE5GjIrffnySEq0DvwCn/Qk8vjKDun4rgYBAaWY0r1T2oZQFv26/OtyFbmIg9nfWLGRVTiw7q/VsKA5OZXmnbZwNy2JpNY0z7vZRnhXN9qrg/FCA3TXBfcmKrlH6T58gU6vg+HAYBUkqHN4A39rXglohC5pznikNtjZUG9hWmkr7kAOb209RWhSvVunZOpH+/sHJEdYXJPFO8yC3LYpFo5TxUa+fx8t11Pdb6R5xoJBJeLXKQFx4cHrF12/NJj85gk+MUkrLys6Y9jHTrM2LffO+WMzX+UydUBRKYJjebpGdnY1arcZms501cPpcbtazMZ9BsPNx7FAFnpycTHh4OIsXLz7LwTs8PExLS8sZocB6vZ6enh7MZvMFm1ueeeYZEhMTWbZs2eTP6uvrueGGGygqKqK0tJTKysqL8XG/FFH4zoPpaeehJOK5tiVMr/hOmmz8t12NPPCrz3n/xCDP3JRBpEqOLyDwlZsyEAje9O/MUjFo8/BZu5kHl8Xz2ekR2ts7uClbyzGjwLqiTBIiJ8JaS9PwBQT21P6XO7PJYOX+giTebDTxz28Fo3tsLh/ffyDYeN4/5mTbynT6R5009Ft5YHkKFUY/Bboo0mLUvFrZx9ayNLx+gV8eqGV1Cpw2+8hZlM9NC2LZWd3P+sJkVHIp73U6yU0IRz/m4icftROtVgT37Z4pCboyK4K9doM2N7W9Fu5dlsRHHTZuTA8nSiXnL5//19LmKZONuxYnsLOqj1SvHqVMQqMrlnsKdJwcdPBivR25VEKMRsErT5XgDwgcODHA5hIdH50cojAtkthwBS9X9vN4uY7+MRcGi4uVWdHsqA4Osfb4BQ40D7GhMIlKk58olRypBH56qIsIZbB6/MEDi8iKU/NypYEnV+roGnFypN38heaP0NLTlRhNNF+TW76s6gpVL7MNnJ7pZn2uFfflaGeYjblOTrmYxw+ZW6Y6eBcuXHiGgzcUuHzw4EHWr1/PoUOH+Na3vsV3v/tdXn75Zdra2s7ruE899RTvvffeGT/7zne+w7/8y79QX1/P97//fb7zne9ctM/5RYjCdw7MlnYul8vnPLx26mtbDFb+bkcDD/66gsOnh7kjLx5/QOCWhfE8UqLj0KkhFFIJq3Pj2F2rpyhJSWKEgt8ebGV5uA2pBFq9sXzlpmyGxj0cPj3CIyt0fNY+glQCq3Lj2FWt5/ZFcUQo4KcftXOsI9i/5vD42VQcjAPKT47g7vxEXqszsConjoQI5eRyoycA+xuC+3vVPWO0tJ5kabyMj3u9/N39ZagVUl6tDIqYyeqmqnuUAl0kn/b72FmtRy6VsCQlkl9tXY7LG+DD1mE2Fuv4oHWQRYkRpMeoebmilydWZuD0CXzSaWXTiqBg5acElzZ//2kHJZE2rG4/1vA0bs9L4PUGE7890o0ESIuU8oMH8xl1eBkcd7MqN47tVf08MhGXtK9+gEdXpHKkY5QFcRpSosJ4pVLPE+U6BmweTg85WJ0by85aA8XpWvwCfO/NU6jk0qCrdVsBWrWc7VVGnijXccI0Tly4kqRIJX+umLm1YWpz89Sbd+hJe+pw5MrKShwOx2Xd64L5E765Hjc0cHqmm/VsFff0wNr5XOqcz2oTzk14p4YCP/PMMxw5coTc3FxeeOEF7rjjDgYHByeHcJwrq1evJjY29oyfSSQSrFYrEDTPpKamnt+HmSOi8M3C1PDX2dLOL0T4ZDIZzUYbf/1KHQ//tpKK7lESI8NIjlLx/KZlk43gW0rTEIBdNcEpKkM2D0e7LKxOldA05GVh/lLuXJzAnloDZZnR6KJV7KjqY0upDqlEEtyzKktj0Obmt8f0gIRmgw2FTMqC+GCSwXfXLpxMR9hWnobF6eOD1kG2lOr4tH0EjVLGwmgpr1T0kqc0o5DCkQEpX79zKYPjXqp6xlhfGDShFOgiidEo+MfXT1DVYwHg4aIUvr46ixNGG9EaBUtTI3llYpZmQBDYXRMcM1bba0EqgSWJKl5vNvNYmQ5BEPjzp6dZlRygzmCneNliFsRreP5QFx+cGCQgBPcHt5WlorcFKE7TkhwVxp+P9/KVG9IZsnlo0Fu5a3ECrzWYuL8gEYVMwq4aA4+VBRvR48IVZMWq+UtFP3cuisVs9/Ktfa3IJBAml/LSk0XIpBLeaBzgkeIUDp4apjhNS4xGwauVep4oT6Oqx0KL0XbO//6hJ+2pw5HLyspQqVQz7nWFRl9dia0Bc+Vii89sFXdoLzHUblFTU4PRaKS3t3derul8Z/HNVXjHx8dZvHgxa9eu5bnnnmPbtm0XfC4/+9nP+Pa3v016ejrf+ta3+Pd///cLfs9zQRS+acwU/jrbHt5cha++z8I/Hxzga691UtM7BsA3bsvmH+7MoXvEQaPBxsbioJFDKQtWeruq+1Fbe4hXSzk6IOWZ2/JRyCSTU1LGHF4+ODnEo6VpVHaPYXX5uGtxAntr9FgcHuTS4P5UhDL4GdYsSeDvb1tA36iLun4LDxam8E7zALkJ4SxMDA/2sq3QIZNIeOlYF7ckB+gfc2OWaFlfmML7p0YpmRDaVyr6J/vrHvp1JaMOLy5fgH9cu5CF0RIqe8bYXBKchvJqZT9PrEynY8hO36iTO/IS2FWj576CJDRKGS9X9LFhWSx6q5e6ThPFiXL2Nw/xzB0FhMklfGd/G90jwX26dcuSKM2MpnPYzmNlOiQS2FVjYFt5Op93jRIfoWRBvIaXjvfy5Mo0bC4/RzpGuX9ZIvsbB7hzUTwapYxXqwzcvyyRFuM4/+vddqQSiNHI+WaxArcvQF2/lTX58eyrN/FAQRJymYRdtQa2lqRyuN1MSYaWiDAZf/78whraQ9+zmfa6QqOvprcGtLW1YTAYsFgsczbTXG0V3/kQcj9Ob7eIj48nOTl5xmsaare4VNNT5lv4YG7bM5eiUv31r3/N888/T19fH88//zzPPvvsRX3/2RCFb4Lp4a9fJHghzjcUtrpnjKdfqmXL76toNDlRyiS8/40byUuKYF+9kXVLEoNLaZX9bJ3oUfvL0U7KY92M2L3ohVievCmblmEfYw4Pd0/k2RXqosiO17C9so9NK1JRyqW8UtFHToIGi8vH/9jfOmnn/0ZxGLfnxbO31shti+KDy5kT7kiPL8BrdUa2ladzwmija9BCeWoY++qMLE+UkxCh5M1WC4+vTMfl/a/m+MruUf5hdxMAFqeX/31/HmFyCZ3DDu7OVNA/6qTJYOXeZUm8Xm9kdW4cseEK/lLRxxMr0xl1ePm03cyGomDVuDBaQnQY/KWij6/dvgibR+Bf3u3E6xfoHnGwoSiFGLUCp8fPV2/OZMDqptkwTlmynD21QRFVK6S8XNHHV27IoMVowx8QKEiN5OXKfraV6XD5ArzXOsS6JcHw2d8c6QUgLVrFP96dw6jDh4CE5bpI/lLRzxPladg9fj4+PRIUzobgkOswuZQ9tUY2F6fwQesQ/WPOC/wmns3U0VfTWwMSEhIIBAIYjcYZl/bOJSlgPvf45jP5fbZrmpSUBHDG9JSGhgba29sxmUyT7QBzZb6Fby6V7aWqhl966SUefvhhADZv3iyaWy4Xs4W/notL81wrvsruUb7y5xq2/bGaz7vM3LQghu+vCQ50PtJhnmz+PjkwPtmA7nE5WJ4gZ2+dkfXli9BFq3ijZZRNK3TIpfBawyCPlQX74d5pHuCxsjQa+q30jzkp1EWxq0bPrw53o5RJSI9R8+bf3oBCJuFgj5fHy9MZsXs4eGqIR0qCy5lhcikrs2PYUdXPHblaNAoJvz3YymNlOhw+qB+W8khJcN9Qo5RTnB7FH4728E7zAABD426eWJmOLyAQFxHG+uUpvNloZFG0lOSoMF6p6OfJGzJwePy83TTAlpI0PmkbJlUbxqLEcP7yeS8P5kfj9QvsbxzkvkVRNA16OXByFAlQ0T3KmvxEAHTRaratTOPjtmEyYtVkxWl4paqftVlKbC4fh04O8VBRKm81mliVG0u0WsFLFf08UZ5K36gL/Vgwgf13R3t5u3kQAVgQr2FraQr9oy5KM7UkRih5r9vHUzeknWGCebVKf4ZwPrQ8ibeaB1i3NBGpRMJfKi7f8GqFQjG5D7N48eIZJ6lM7eO6UCfkxUYQhCtun21qPt/U6SmLFy8mJiYGt9tNX1/fWXFE5+PYvZrT1y/2g0pqaiqHDx8G4NChQyxcuPCivv9sXLfCFxK8gYGBLw1/nY0vEj5BEDjeaebxP1bzxJ9qqOoZ45t3LODGBbGcHrKzMlOLLkrO9qp+HliePJlgcF9eFL6AwJ8On+LJm7IZcwvUDHgnljBHGXV4uCUrgndPBgcnL0oM59XKPh6YCFt99i91VPWMIQiwsSiF76xZSN+oE8OYi3uXJXHM4GO5LoqsOA3bK4N7iDJJcMn0keIkDBYXew7Xc19+HNUDfgoXpLA4OYKP+nxsLk5GLoX/+OA0ZruXoXEPQzYPpZnReP0BvrY6m1Stir983jdZFR4z+tlWns7xzmC0UHG6llcqg3uQMkmwAXxzUSInTeM0ne7mpiwtH+sD2CdWmF6vN1KUHrRQbynVccuEYWVjcerk0umTK9Np1NvwB4IDq1/6PFhJev0C++qMPFqm4+NTwyxMCCcpUsn/+bCDJoMNpzdAXlI4q3JiMFpcPF6mQy6TsLPGyGNlOlrNAVKjVKTHqHjp836eXKlj0Obh1ICd1bmx7Kg28EhJCv6AwJ46I8lRYbxWZ2TUMX8OzemTVKb2cc3khKyvr6e/vx+Hw3HBlcz5Mp85gOfj6gxNTwktQYfiiEpLS89oFm9vbz/rISOUvjD92FdbK4XD4SA8PPyCjr1161ZuvPFGTp06RVpaGn/4wx/43e9+x3PPPUdhYSHf+973ePHFFy/oGOfKdSd809POW1tbz1vwQswkfIIgcKR9hK1/qOapl2ppNdn4q1WZ+AMCSpmUJ1YGDRfH++zck6OhYaJH7N78OA40mzCb+lmZGcWnhgB3L0tDF61ie2X/5BLm9qp+HloSg8Mb4K2mgcnZmpterMLrF7C5fPzrA4tZmhJJvd7KQ4UpaJSy4DDn8nRcfnir0cS28jTq+iwM2tzckRfHrqo+1GM9JEYoqBrT8NXbFuENCOyuMfB4eTr9tgDvtgwSpVLw0ckhXN4AkWFyFiWF80/35uH2CUHXZ3lQoAHKs2I42Ovj4aIUwuRSXv68jydvSKfX7KTVZOPuvDj2VPeTEhhGq5Lx2bCKpGgNY04/b5y0khatQiGT8LPNBcSFK/nz8V6eujGD4XEPld2jPFCQzL46A7fnJRClkvN+t5ev3JhB36iTzmE7t+fFs30iJFcmhe+/247N7cdocZOfHEFGrBqnN8DXVmVgc/s53G7mgWVJ7G8c4K68OFQyeLlKz5PlaTQabIQrZeQmaPjT5308dUMao45g474uWsWeWiODNjcev8CO6isvsmgmJ2RoDmRkZCSBQGCykpnaOD48PIzL5bokS13z7ay80GN/UbN46CFjZGTkrHaL8fFxHA7HvAw4mM9khh07dmA0GvF6vfT39/Pss8+yatUqampqaGhooKKigpKSkgs6xrly3QjfbOGvMPfyfarwCYLA4bZhNr1YybMv19E/6qQgNQpBEPj66mxKM6PZWa1nVU5cMEG8cYjV6WGo5FJ+fqCeG+Lc+ALQ5ovnyZuyGLC6OXx6hK1lQSEZsXu4d2lwjyw7TkVOjJLffto1mRAw5vTy3N25CIDV5eOJG4IGkmajjQ1FKRxoHkAXrSY7SsrLlX08VBhsTP/PD5opibRh9woMaTJ54oZMPu8axecPsHphHDur+0mMDEMmgR992EHo9vfsTRk8e3MmxztHUciklGVG82plHw8Xp6Ka2F978oZ0RlwCNX1jrF+ezP5GI2WZ0SRFKvnVR62Ux/w/9t47vun7XPt/a1mWvOQlT0leYDzwxJuRCdmDDCAECJDRpOv0PO1pz3POc57T9tee30l7etqmTdKQAAGyCRlkQhII4IkHNsMYG1uW5L1k2bKs/fzxlXXcBBJWIEl7/8cLWV8Jf/lc3+u+7+u6prC5vHSLYsiKD2X/qWHeONKHQiYmIUTKU/fl+gNv7ytK5NP2EWJD5cxRB/m3Nm0+V5cVCxJoGHCTERtCgiqQrdUGNpTrGJty8sS+TqRiEU0mC0mqAAKlYtQhch4q19A+aGXS0dX4ogAAIABJREFU7qZAE8qOuh5WFwvm3u8eH2RxopQPjg9SnCRscG6pNrG+VEPH0BTDkw7UwQH8YZ+eXrNgWfZAaSJXzRGY4JTj6+/aMsNkQkJCCAsL8zOZ2cLx8fFx2tra/NrDmcWP8fHxC95onqkryfi+StD9IrlFVFQUHo+HoaGhvzKb7uzsZGBgwC+3+Krq78kMQn3rge/L0s5FItEF32hSqRSn08knbUMs/0sdD79whKEJoa2xPD+ef7lpLlaHcDCvLk7EOGajqmuUlQsSaTBO0NE7THmCjJo+J4uK8ij1zdgWp0URHxbIjjojd+X7mJ7P43LK4eYvtUOYp930WexMOVwsSotg2ulmeV4cRTpBjL0sUy0IuWuNrC4W2n6vNvRwnU5K1/AU79UcpzRWzCGjjVsWF5ERG8wLdUbuzhfY2bZaIxWpEQxNOnj4hSNIxSACXtxQyPyEUF5q6OEeHwsVQE4QmTcazNyWE8fuln7yEsOIUojYVi38/bTTw9MfNrM4Do4OOpifnYUmPFDQFXYKs7ybstT8YFEiPRMuxm0uKlIjeOGwkbt819rmW1hp7Z9kzOaiPCWC7bVGVhQmIhbBC3XCteq7zTR2jxEgEfFmcz/RSuFWz42WcI1Wwp7WIQIsJsIDxfzlwGlW5EbRO27n9JDVD15LEiWIREIk0cwGZ1KkgmC5hH9+u43BSQde4MfXpVCSpOKN5gHWFiditrl4o7n/Ym/dy1afZXOzheOpqan+xY/c3Fy/z2Z/fz/Nzc2fSwk4n4P7m874zrdmtHEBAQGkp6f/ldl0SEgINpvNL7f4rITlUrHuCxXPf5uSGeBbDnxfBHgzdaGSBI/HyyH9BD94r49HX2zGMDqFVCLije8Uc/XcKHY19ZIZG+IDFBPXpkcTHRzA1kOdpIoGkIqhalDKD2/Kw+7y8nqjsIbfOz7NgY5hVhUlUts1xqif6fXSNTxFoFTMuyfNyKUilDIJ8+ND+Zcb03F54JX6HiGbbsxGTdeYX/weKBNTnhLBi4eNZIY6CA2AD7um+fFtC3C6vexs6vMnlZ8aslKRGsFrDT38xwftiEUQFybnP68JRySC15v6WFOioWt4ihP9E9wyP4Y3j/SywCdt2FZjYG2pBrvLw66mXq7Tyqg3mDnVcZrMSDEfdk7z3VuKkElErN3aiHFsGrcXfnhNCjfPj+XTjhEWp6gICRALrK5My9CEg1r9mMAam/tY6NsKfd7X+hycsNNoNFMSJ+G1hh5UCsFt5b8/6fTLN352YyYVKeHs0dv57rI8xCIRTZOh3JsXTVOvDffkCLFKEX/6uI0lMU7MNhfHhpwsnRfJ60f6uCEzSpihvtDCpN2N2+PlF7fMISlSwdstg2wsS2Ro0oFhzEaBJpStNSac7ss3L7uYOlfmJZVK/yo2p7Cw8HMpAbMP7hlT5DPNuc7nul9FXen54gz4zJZbJCUl+eUWsyUsZrP5c6zbZDJdkNzC7Xb/zUcSwbcc+GbqizY0ZTLZeQOfecrJ7U/V8rN3Ouk2u1gyJ5Ktawtwub3sbulnVbFwCH7cNsRqX1J41ckeKuJEVOstJOhSuDk7lsoeJ4kqBYVawYvyqrmRxIbK/2qmt6PWSJpaic3p4Z/eOE5IoHDTPlwYxqriRD5uGyZQJmFhWiQv1ZtYPCeSmFA5O2oFWzGAHTUGrtGIhcWMcSmrirVUGyaRS8VUpEbw0mETyzLVhARK+fHOY3zSNozHC4vTIvnxdWn0jduZ9oi4Ki2c1xp7uNong9hea2RtiRab08MbRwQZRJ3ejMvtpSI1gh21RoqiXMgl8KHewfeWZjNkdXLvJmEeaXO4+c87s4gPk1PdOcoDZVqsdjd728dYmqrg47YhtOEKUqOD2FrVzbpSYWHmzSN9rC7SsO/UMPGqQNKig9hS1U26SozN6eGnb5xALhEhArZvKCY+LJAtNUbWlSQwYhUE9zdmRfPW0UHuKNASFCDh4FAAj16dhmHCS3ikmozoQPYYPJSGW7E5Pdy3uQGn28u008P/uU5DaKCU/adG2VCqoXVgErfHS2ZsMJurjWwoE9xr3jk6eFH37eWqi40lmp0SMHNwFxQU+GOJzjTn6u3tPSepxVdZVwr4zuXan01vn826Y2NjEYlEZ5Rb9PX1feGS0t+z+IT6VgPfuSyszLQrz6dqu0Yxjk3xr8tSKYyV0tJjYW5MsC//zkRFSgTaCAUv1JkoS5ChlMK2mm4euTYLsVjEWyfMQuqAW7ABu79EaINWd46xckEiladHGbE6yEsM5eWGHn679zRyqZio4AA++H4ZKoWUd33J5h6vl5cPm/xLM/tPDbNyQQKVp0exTNkpTVTySr2RkqRwElWBfNLr5Z78OCSi/xGT91vsbNzexMS0i6FJB+tKNRRowjg9bOXuwgSC5BLea7dxb1404zYX7x8fYGWREEIrl4kpTlKxo9bIHbmxKGRitlTpuV4nZXDSwclxMXfmxvJxxzj/9VEHAOM2F/9yw1y8CL6ga0q01OnNiEWwQKfilaYBrk8KFET3dSbWlwmtzdEpJwvTIoUW8Ezrs8bI0kw1rf2TbDnhRCqC4AAxu75T6k+QX1eqob7bjEImJiM2mK21JtaVJGJzenjv+BB358ex58QQ+ZowooICeLllhEeWpDAyDc8cFw7mKRf8sCIWEdDYNcRV8fDJqRECrP1EKiU8faCLtUWxGMammXK4yYgRQNDt+eY7rFxIzY4lOput2MjICH19fd8II++vS812/DmT3MLpdJ5VbjE1NYXT6bzgGZ9KpfoKvtGVqW818J1LXUirUxshMLBpt5frdHLGppy8f2KQ+4oT6R61UdU5ym2Z4dR3m2loM3Jnbix1vQ6Cg4ME66ymHubGhJAcKoSsXpseTXSIwKDu9m0grtnSQJ1ekCXcnhPLz2+dx/CkgyMmC3fmqKnvtSMCIY+usYeSJBXaCAXba40sz41BKob/freRu3IisTqheUwqtDNH3QxPTLMsU82rDT28caQPgKM9FtaUaBCLQCIW80C5MLOr049xV348lUYbscEyMuNC2FZjZKXPiWVbjTBz6x2fpqZzhGtTgnn36ADp6iCSIhTs7vLQaLLg9sDwhIM7c+OwuzxkJYSyZE4kL9aZuM2XFrGl2sD6Mi19FgdtI05umR/LzsYelsyNIiJIxpYqAxvLha3OytMj/oT4Jz/tAiBGCb9fkcOkw0Nzz7j/56/PUBMil7KtrocHShPRj9gwmadZmBrOC/U93FMgPEG/XN/L/cUJ1HSZebZSELT3W+xsLEvE4wVlUBC3zFfzicHBQ0vzCJSJ+XRAyj3zI2jum2JyuNfXLj3FzSlS9KM2djcZv/bxRJez7TfbViwuLg6tVvuNMPL+OtdsuYVWq/UvKRUVFX0um6+rqwuj0fiFcosz1d+B7xtU5/Kf+UJanRlxIRRow3i1oY85YR6So5S8UGtkWYaacIWUP+85RkG4A7lUTP24knUVKbg8Pk/KEg3jNhfvHO3nWq2U00NWGo3jrPCZSq/d2oDbI2xp/uLWeeQkhNDcY+HGzBgigwLYUWtkRWE8IhG86GN6o1YnH54YZGVhPI2GcT6pbuTq1FBq+2Hx/BTSY4IFQMyLQy6BZyqNTNhdWB1uPmkboiw5HI8X7siLY1lmDDsbeyhLifBr8taUaPB4YVfLEOtKNXQMWWkbFOKN3jzSS25CCLHBUp78+CR3ZqlweeG1k9N4gX6rh5FJJ/NigkEE/7RsDmEKKc9XG1hfrmPE6uDTjhHuLojn/WMDZMSGoFEFsrt9igd8W5tvNAnzz/2nhlGHyEmNVvL/f3CKD31enblxQXxnoY6BKdCEK5gbE8zmym7Wlwtt2Ldb+llVlMDHbSNkxASTECZnS7WJDWXCv11tl5mbstW8fqSPep+FXNugleJYMS6Pl0JtGHmJoTxfa2JtcYKPKQ5yd14ce06OckOuFpVCyoGhQL57zRwMEx4iVaEkhEjZVGWgsbHR//T9VUsELqSudDrD+Rp5X+7Q2ktdl+v3LhKJPie3SEhIICUlhYSEBKRS6efa0LMt8Gafi+Pj438Hvm9TXUirE2B1sQbDmI1jwx7uK0qkpcfCrk9quVoXQPOgC21qOrfmxPJ2Sx8qpYxFaYI0IDch1C86L4oRo1JI+d1HHXzYKsyDhicdfO+qZLxecLq9rCnVoh+Z4rDBzIoFCexvH8aDiAWxUnY29pCXGEZKlJJNn3aQ6DASKBXRMh3Oo9fMY8rpZtcRYRmlbWCSgx0jRCklfNI+Sm3XGJFBAcSFBfKHe3NQBkj8iymWaRe7W/q5v0RDnX4Mq91NiUbJW8dHuCY9mqhgQVO3plhoF/7h7Vpuywijw+xhyBtMhFLGriN9TNrdBIhhflww/3JTOuM2F3tbB1lRmMje1kESVIGkxwSzpaqbtSUaPF4vLx42cX9xPB1jLqxODxWpEWyvM3JPQTxyqYgfvtKCfljw6izThbAwNYLucQcrizQESGBrtcAKTw1aGbDYWTwnku21Rp+WT8QLh3tYU5JIc48FsQiy40LYVGXEandhd3mp1ZvJSwzF5fZya4oUdUgAm6tNbCzT0Dtup31oiqvmRPLC4V7unWGKDX2sLkrgQMcoc9RBxIUGsKluGI9IgsHiYTI02S92/qxEYLbfpsVi+dqA4eWoLwPcsxl5ny209ptg5H0lN1lBmPHJZDJCQ0PP2IaOjo7G7XbT19dHc3Mzf/7zn7n55ps5cuSIP8X9fInCmXL4AJ544gnS09PJysq6bHFEM/WtBr5z3VS7kK3OpRlqooIC+KjbgdbVS6BURIMliB/eVIBYLPLLD6adwnbj/b4Z3EcnhYWX1v5J9hpcSMRiWnosTDmEcFS3x8OGch3zE0LZUWcUrhMsML2VPpeVVxr7uVYjZdzmYvO+Y1REO+kYdRCZks3y/ATePTZIXFggBdowdtQaKdCGESAV8+PXjzNgFdpua0oS+emyORhGbbT0WrgrP96n9QtkfkIo22oEhqgMkPB8jYE7M1WMT7sFZrkggQPtIxw/2kJOjJwD/RIWz09CIoafvXECh2+b8btLkrl5jpIDnWPEhgSQFRfC1moD9xUlIBGL2F5rZH25lvZBK/pRG9dnqHmloYfr50URLBOxpaqbDeU6hiYc/NvuVlxuLx3DUyxKDiE6OACXSMqjS1IwTwmSksUJUna39FOoVREbKufZSj0PVgissvL0KLfNV/NGcz9L0iJQKaQ8c8hAdHAA/RY7+9tHSVQFIpeK+fWt6cgkIj4yuFlbnEC9YZxQhZQ56iCeqzKysVywivu0Y5Tb5sew60gfN2RGEyyX8PtPupDLJHSOTOFwe4hQythUZfQ/fX9WIjDjtzkTpTM1NXXZ2eE3yavzi0Jrz9XI+0oC4pXMAZy5/tlmfDNyC41Gw7x58ygsLOTRRx/ld7/7HXK5nNHRUX75y19SWlrKT37yk3O+5ply+Pbt28dbb71FS0sLx48f58c//vFFfa/zrW818J1LXQjweb1exsdGWBgHx0a8xCXPZXl+Ah+eHCFAKvbP8ZIjgyjQhvFCnYmFqcLCy7Yagz/cdGe7S8h6E8HSTDU/u0HQ/b3V3O+XDBzuNrOiMIFP24eZdrpZlqlmV1MvapkdTYiI3a0WfnBbKaGBUnbUCa1Uh8sjhMHmxGEcs3Hbk7W43B68wK+uVVOiDeGtln6um+eTWFQbuN/nsyksg2h9KREWv3G0LlxOSoScZw92kubtQyaGZlsEdxUlMzBhZ83mBsQiEWIRvLxxAfNig3nxsIkb5wQhEonYVmtifbmOzuEp2gat3JQdw87GXhalRRIdEsCWqm42VuiYmHbx4ckRrk0KYG/rEIc6hhGLYP+pEQriFQDMS4xkfbmO2q4x5FIReYlhbK42cJ1WghehBfxAmbAwEygVk5MQyuYqgaE63V5eaehlbkwwVV1m9rePECSXkBSh4D9uT2fS7uaT9hHuyI2lssfNolQVKoWUzVVGHizXcHp4ipFJJyVJKrbWmFhTkiBYzNWYUAcHUKM3MzLpIChAQlKkgu8tSeJo7wTVXWNnvJdm/DZnonSCgoL8s5kvY4eXanZ4JdMZLhUInK+Rt81mOy8j70tVX4csvvNZbhGLxaSnp+PxePjJT37CSy+9RH19PY8//vg5v8eZcvieeuopfvaznyGXywFQq9Xn/H6Xov7mgU8mk51zq9Pr9TI0NERtbS0DAwM8ujQHsQh2HR32pxu89pk53poSDcYxGwfahynWqThisvCjnccICpD6wk2zWZohgFm6OtifVXdDpsD0ttcYWFmUiFQsYketget0UibsbuoG4ZGr5qI3OzgxYOXugnj2tA6iDJBQnBTOMwf1/McHQkJyhFLGSxsXIBWLqDLauCcnys8+7ytO5FDHCG6Pl6vmRvFyvYmr50aiDpHzfLVBMJ52e9l1dIjFaif6MTuBcWlcl6Fm15E+fv7uSURAZHAArz60AIA3mvtZX6alY8iKacLD9XPDeb2pl/KUcGJD5Wz2Mbkph5tdTX2sLdFSeVrw8izUqthWY0SKwBq3VBvRqWQAPHR1OkszonnpsIlb5scQLJfwXKWBBxfqMI3Z0Fu83Jil5uX6Hm7IVBMaKOXZym4eXKjDMGajtX+S9Jggnq/t8W+RLkqL4KfXpXJ6eIpJu5siXRjbakzcX5SAB9jZNMDqogRBvB6hQBseyDOVBh4q1zBidbLv1Aia8EB2HemnZ9yOVAwVqeE8tlhHg8GCNiKQmJAAnj5oOOfD9cvYocfj+VzQ6sVuRH5TGN/51pmMvPPz81EqlVfEyPtKJzNcjI4vPDzc/+eL/b2dOnWKgwcPUlJSwpIlS8471PZi61sNfJeq1en1ehkcHPQDXk5ODtnZ2STFqFgQK2VXUy/xKgVlKRG8eNhEfmIoc2OC2VFn5Nr0KMIUUn78+nF2NvUhAgq1Yex8uAiAnT5BuACUA6z1ZdXVG8ysXJDAp+0jTNgEY+pX601oQqVkx4ewvxduyYkjXCkIuVcXa/B4vPzw1RaOGMexOT3MTwhlY7mOwUkHgTIJyzLV7D09ybxoIaduW42RFYUJPlmAgXWlWkatTj44Mcj9xYKsYnB4hFy1hPfbrSzLjiNCKePn77ezt3UItxdyE8P4x+vSGJ50MOXwsCwzhlcbhE3M6JAA3mqbZEVeFFMON6839bG2VEtt1xhuj5cyn+vKnb6W6nOVerLiQuizOHi93YkqUIxMImLr+hKhdXmom40VSYzbXHxwfJBVRYl8eGKA1KggkiKVvK93sbFcSIB4q6Wf+4oT2XtyiKRIJVHBAfzbu6c4OWDFC6wojOP+YkH2kacJISYkgGerjDxYrmVw0kGDcZzSOCk7j/Rzc5aaoACJMOsr13CifxLLtJOYkACe2K/HOCZYlt2dH8t9RQnsbR2mLFlFRJDM1xrV0mSyUKs3X9T9PPsQnx20eraNyNn2Yl/EDq9U6+9KpTN4vV4CAgLOy8j7XDRy51JXGvgu1LnFbrejUCgu6ecYGxujpqaG3/zmN9x7772X9T78VgMffDn4fVnCwgzgDQ4O+gFPqVT6X3NDqgLLtIu3fXq8foudT04Nc78vauj2p+oYtwkblD+6NpV7CxM42jtBqEJGYZycXUcGyEkIZV5sMNvrjNyYJWxvbq8xcm9hPFIx/G53PbemhzDthiNmOWtLtfRZvdQbxlmxIIFP2oZ5tlKPWCyi2WTh2nlRJKgCmXZ6eGihjkCZoHdbV6Zlyunlg5NjrC3VcqzXQteIjdtyYnmzuY/0mCDSY4J5vtrA0lQlAWJhWeSxa+Yx4fDyeOUI49Mu+sbtXJcRTZFOhWnMxooFCagUgtxgQ7mWiWkXbzX3s7ZES3O/Hbz/A3J35MYRJJew2ffawQk7BzsENvx2ywDbao3IxKBWwAsbivzbsELrcgyxWND6banuZlVRIhKxiOdrDDxYoaPb4mFk0sGitEi21fgWWkQi1mxpYHjSgdPt5ftLdJQmhfHxyRFWFsYjEol4oa6XB0o1NBjGkUtFZMUF81yVkZuSZdicHt5sGWDlgnj2tA6RHhNESKCUf3qzjYEJwbLssUU6rp8XxVstA9yVF4tUImZ7XQ8PlCRS3WUmLVqJOiSApw+dO+s71/qijcgZe7HPZvV9lh1+m4Noz1Rnm7N9kZH3ZzVyF2rkfaWB70Ku/1XcI4mJiSxfvhyRSERxcTFisZjh4eFL9v5fVt964PuyOlvCwsDAADU1NQwNDZGbm/s5wJupLHUg6WolL9QZuWpOFPFhcp7Y18nLDYJDf79lmn++YQ5SMQxO2FlbKszgXqnv4db0YMZ8gvA1JRpODUzSZBrn3sJ49p8aprK2noW6IKr7YXHuXPISw9hRJ7ishAXAc4f0TDuFp8+XDvewQCusG5elRLCxQucDtinuyI1j99F+NOEKMtUKdh0b45b5sT5ZQTfrfD6arzb0cm9uFKcGrRw83s2t89UcNNo5pLcgEkFT3zRLM6IJkIoIlkv5zuJkwaHm5BArixL4uG1IAHStim01Bu7KjyNQKuKlpiE/yB3oGGZFYQLvHx8gOVJBgiqQf999kv3tIwAsTg3nX2+ax6ANhqxOrk2P5oU6EzdnxxASKOXZQ908WKGjb9zO4W4zd+TGsetIHxWpEajkIjZVdvPwoiRGrA7Wb2vC5fEybnPxz8vSiA6WUd1l5qEKHcNWB1WdY9yeE8Mbzf0sTgsnXCljU6WRhyq0mMzTmKxwzZwIXqrv4fYcNVKxiA07WpiYduHyePnfS1PJigvmzZYBNpZrmLS7+bB1mLvzY3m7ZYCFqcJ7PldlZGOZAKx13RfH+s61ZtuLfTar77Ps0GAw0N/ff8nMp8+1rtSG4/lc92wauQs18r7SwHexLj2Xqu644w4++eQTQGh7OhwOoqKiLtn7f1l964HvfBjfbMAbGRkhLy+PrKysL6T4MpmMe/LUnBq08lxlNx4vtA9a/czD7vKwNCOGW+bH8caRPmJCAwWbsHoTOXFKksLlbKsxckt2DOFKGX/Zd4o5on4kYjjuiOK712didbjZdaSXtaUa9CNT7GsbIiFEQnXXGFurDcSGyoWkhZU5foH5HblxhAZK2VptYG2pFofLw0uHTdydE0HvhJNa/SgrChP56OQQCpmYUl0oWw51kiw1E6mUsa9PSpBCjtPtZVuNkQy1APrL8+NZnhfP2y39pMcEMTcmmC3VBlb75pBCKoIgfq/Vj7FsTigfd5iZqxZeu7lKmBvi9XLf5gZ6zNPY3V5WFcRyU3YMDSYL186LJjQAnj2k58GFOsw2J+8fF4J397QOkhSpZK46iGcP6VlfrsPpFh4kbkyRU6s38/uPBYcY05iN712VjFgEPWa7f0NTJoGc+BC2VBtZ61tOea2pnzXFCVR2jqEOCSAtWsnuDjvrS+OZsLt5cMdRnB4vUw4P/+eGNKKCZHzUNswjC3WYzNN0DFm5em4k2+t6uDc/zied6OWB0kQqO8eYow5CHRzAkwe6r1hr8WzsMD4+nrCwMD87nDGfns0Ov4oFkCutH7yYulAj79HRUdxu9zdKe3ih7dGZOlMO34YNG+js7CQ7O5uVK1fy/PPPX9Z74VsPfF9WYrEYj8dDf3+/H/Dy8/PJzMw8p562RCLhmtRQQgOl/O7j04hEImQSERVpEfz81nkAvHTYxJpSjW/O1cuaEg0DFju1PXaWz4/geN8E+1o6qYjxUt09ybys+dyYHcsbzf2kRAaRrwljW42RspQIguUSfvz6cU6MuBEByzLVPLEyB5vTw64jfX6BeaNxnHsLE9jbOohcKmbxHCHAtSI5jOggCc9XG7i/JBGRSMR/vdPI4lg3ZruXXmksmfEhVHWOsq3GSGRQACGBUv5411wiFGK2VAnG0E63h5cO97C+TMupgUnaBq3clhPHG0d6yU0MIylSyXOVBpZnqfB6YVutiQ3lwmu//3Izbi8MTjj4Tnk8MSEBnB618/DCJKx2N28c6eN6nYxDsxZetlYbWFWUgEwifIYHFybRPmjFMDrF0gw122uNnDYLc6xmk4Vbc2LxeAWXnaXpEbxcbyTG2UewDH73wQluSQ2gZ9xOY/coN2RG80pDLzdmRhMil7C5ysRDFVp6Jj38/P3TAAxZHfzjNcnIxHCif5IN5RrquscJkUuYFxPEpkojD1cIbd49J4e5Kz+Ot1oGWORjfc9UGsjXhNJotFBzkbO+S10zh/gMO5wxn05NTSU4ONjv+nEmk+SLYYdXasb3VTLNLzPynp6exmw2n7OR99ehLBYLoaGhF/zzZ8rhCwgIYMeOHRw7dozGxkauueaaS/iJv7z+poFv5qnMarUyOjrqB7zAwMBzfg+pVIoEQcQuArauzWd5fjzvHh1AIZNw3bxoXm3oITUqyK+rW5gWiS5CwdsnLRRGuAmSwmvNQ/zo1kIkYkGnt65UMGze2dTLqiLBy3PpH6qYtLtxebz8YEEQt81X82n7MNpwpQCOtUaWZcUQHRzgAzYNIpGIHbVGf4Drp10T3DI3mDq9marGY5TGSfnU4OCOxQVEBwfw7++0cbBjFLEIrk6P4r/vyWZi2sXH7WZuTFVS1TmK3eXh6rlRf2W3trmymw3lQsv0lQYBEI/1Whia8rA4KYSXDhv56MQAIADH3TlCWyMwUMH6ch11+jGcbkGw/nyNgUXxEoLkEp6r7OahhTp6x6ep05tZnhfHriO9FPnSIP60vxOJGKwON/X9TnLiQ3B5vDyyKImUSAV/2NvKtfFuHG5od6t5oEzHkUEXuqgQklQyNh3qpjjEjM3p4bl9J7h1XigftQ3zli9WqHPExn0L4vF6wQvclR/PWy0DVKSEExkk4+lDBr6zSEf3qI2uEUHcvq2uh5UFcYhFAuAvTA2npsvMh63DyKUi/vzplWN9Z6ozMa/ZSe6zQ1Y/a5I8m9GcrzzgcmwdjmnrAAAgAElEQVR1nu26lxNwZxt5zywnnauR96WUrVzo9/62GVTD3wDwnek/1kxLp6amhrGxMZRK5XkD3kzNtEpXFSUiFot4pUFgdHaXh1cbelhTqsVsc7K7pZ+1pUI6+IFTw9w6L5QTg9O0D1m5tyiRaqMNkUTKDVkxvN7US3KUkjxNKE992sXje9p93wU2rRY8Io8Nu1lVEOObzQnLH6YxG4c6RrivOJGDHSNY7S5uzFLzamMPOfHCpunLjYPkh9kIlMAnvSL+4aY8ppwebniimqFJBy6Pl58tm8O9hQkc6hghJUpJVnwILzX0c5VW5tu+FDR35ikn7xwTlliqOkdxugVJxAt1Rm7IUhMRJOOFpmHsLjdTDg/7To2QnxCExwsrS1NYlCa4qtyWE+uXHjy0MInhSQdNQx5WFCby3rEBUqKE1uamQ3rWl2lxe7xsrjKQEhXEsd4JPjg+SEyInCAZ/OftcwmUiXl8dxM3aKFnwoNSreP6edG83NDLrT4ZxOsnJ/nu1Wn0TnoIjk/jmrkRvNcxxYAvT7FGb2Z+BHi8kBLkoCgxiK3VRlYVxgGwo66X9WUaavVmVAop82KC+MshAw8v1Pq0iMMsmRPJmy2D7D46iFgEc9VB/OTaFJp7LBw6fWZd39e9PmuSPJvRfFYeMNtr80zs8Jvc6rzQmr1Ycy5G3pdStnIxyQzfJrsy+BsAvtk1A3jV1dWYzWby8/PJyMhAKpVe8FPVDPDFhgWyNEPNzsYe4sMC/dKGvMRQMmKD2VZr4Nr0SNTBMv645xgV8RKUMjH7e2BdWZKPmZlYVyYsSfzLWyfQD9sw25yoFDJWLkjAMu0iOkTOHblxHDRMExYopiwlgh11RpbMjfQnj69ckIhc+j95dla7m1cOG1iqldI5aqfXJuPeBYl8dGqMf9x5DIAph5snVswnQimjyhcR5PJ4eaGuhw3lOvSj05wcdnJ3gc/hJSyQnIRQtlYbuLsgXgBEnwh91OpkZ1Mv2nAFTX02ak1TRCmlqJQBPLm6kGC5RHBVWSi4quxpHfLP72JCAsiOD+V9vZO1pRokYhGbZ7U22wYn/d6jBztGkElE5GlU/OauLCYcsLOqjcUJEiqNdu5YlI82QsFTB/Q8skjHhN3Nu8cHWVUYz97WYVKjlCRFKnj6QDfhygCmnF4+6bSSHiOA8+rsYOJDZbxyfII75wYyZnPxwr5mFidKeaO5j3mhLsIVUp4+OIv1DU9RkqTimUPd7D0pbKnlJITwo6uTOTVoRRuhICFMzp8+1X9tWN/FAtDsTLnZ8oDZXptnYodTU1NXxFrsSgPfl83LZht5n4ts5VyNvC8mff1iWp1fx/rWA59IJMLr9dLb20t1dTXj4+MUFhaSkZHhZ3gXalv22Z+d8bl8q7mfdaXCHG9v6xBrSjS0D1rZ9n4NN6QqODnqQRmt4dasSA52WxGLBAu0VxtMHDVZCJCI+OD4IEmRCiKDZKgUUv7XdWl++7B1ZVqcHnj9yAAPlGn915lJHjeZbdyRG8dbLf1EKyVkRgewuVLPzblxRCikvHJymoOnR/F4YdLu4pFFSbg8XqZdHu4v0XCgfQSXx8s16YJYfPGcSOJC5bzTYeOBMi1ehBbexgodhlEbdXoh9Pa9YwOoAgV/y9/u6aDZJPhhliQq+M+75jNidbDvlBCyu+fEILEhcuYnhLK5qpv7ihORScRsrjLw8CIdg1Nemozj/q3NQq3goPK/dh6jtX8SgBWFCXz/qhQaDWZG+ntIDoHdHdP85PYFiMUiNld18/DCJI71WhizuViYGs72uh5h21Qm5tlKA9lxIXQMT/H6kX6igmQEySX8/q5MFDIxuzsdrC9JoH3YTlhULKXJKj7qFfHd6zMAEa8fHeWWVMGtpU/fTmKImF99cIrD3WZcHligDWPVgjiO9U5QmhKOOjjA3xo90T/Jx20jl+IWv+j6qpjXl7FDl8uF0Wj8K/H45UhiuJKblRfq3HIpjLwv9HtbLJa/tzq/aTU2NkZ1dTUWi4XCwkLmzZvnt8mZqQs1qp752Rngy9eEMT8hVJhR+eZ4zx08TdRUN2FyMbVmBd+7MR+FTMzzNQZW5AsLGNtrjSRHKpm0u/nFe23EhgmA/GCFjgcrkqg3jGMcs7HcZx8WLJeyIF7B6y3DlCSpSI5SsrXawF15cQTLJWytMrC6KB6Hy8N/725gVb6akWkvb7bZkErFdI27sTs95CaG4nC5ebBCR0qUki1VBlYtSCBQJvZp8nSz2rSJtI26GZ50cEOmmlcaTJQmCzZsz1V2s6IwHo/Hy13P1DHo07f9dFkqy+dHUWeykRKlJCM2mGcP6VlTokE6A3ILkzCM2mgwmLkrP543m/uYHx9KrFLEMwe72FiuxenycNczdZhtLpxuLz9dmsbVc6P44MQAhZFuFFLYddLKiuxQBq0uGgzjLM+L5/WmXspSIogNlfP0gS4eKtcwNuVk78khinQq3jsxxDvHhOWfpAgFf7wni0m7mw9ah1hRGE+VyUFWrJIEVSBPHuzm0YWCwL9aP8ldebHsPW3l7ooMIpRStreLGZzyMuX0khUVQF60mKM94+QFCQ8yT37SztqiGBqNFiKUMpIiFfzpU/3XIq/vcrYcZ7NDhUJBRkbGX4nHxWLxGQ/wgYEBrFbrJWGHX3fGdz51Pkbex44dY3x8/LyNvP/e6vwGlkKhOCvgzdSFRBPN1GzgE4lEfsnBe42dLIxxc3zARmBsGveX6jjQMcao1cEdefHsbulHKZeRrBJmZk8e6EIhExMdHMA7j5WQoArk+Roj9xQm+LPq1pbOtB+NLM9WMWZz8e6xAdaXaTneO8HJgUnuzo/j/eMDtB1voUQTxME+iFVHIZeK+ctBPWJAIoLiZBX/elM6Vodgor2hXMeJvgnaBq0+uUIfmvBAchND2VJlYHlePAop/vme1e7m1YYe1pZoaemxsPLZw3gBl8fLM/floItQ8O6xIVblq/ECz9cYeXBhEp3DU7T0WLgzV1hSyUkMJTlKyTMH9Wws1+LxwrYaIzenyGjtn+TRl1vwAuO+mKbIIBkHO0ZZnhHEuM3F/k4La0t1HOqeJCZExpxoJU8f0LOhQud/qHiwQkeDYRyXx8u8mCD+sE/PgY5RREBpkor/vTQV/ahNCLpNDef5GhMrCuKQSWB7fT+PVGg53jfJpN1NabKKzdVGVhcnAF7+/d127C4vveN25seHMCc6iBGHiH+9PQ+HG45PhbI8O4L9nROo3GaiAkX85oNW7kyVcXp4itcO6y+bbu7rVrMBd7Z4/LMHeFhYGDabja6urjNai53vQ+u3CfjOVGcz8k5JSUGlUp2Tkffs+jvwfQNLoVCcFfBm6mJbnTM3itfrZYFajEouYsfhXh67IZ+QQCkvNw2wqijRH9x6f4lglrxqWwudZrew7FGYwK9uz2Bo0sGh02PcX6zhcLeZ7tEpf1ZdoEzMNenRvFzfQ1aMgtQIwU/ztpxYVAoZf/qolSzZEAAnXGqW5SQyOuXkoR1HkEmEX/Wvbp/HVRoZ7x4dIDY0kAU6Fc/XGLkxW01kUADP+XLs3B4vO3ztTOOYjequMa7RSNnTOkiQXEppcjjPHOxic6UQAutye/j5zXPxeOF4/yQbynUc7bHQN+lkkVbBqw09lCSHowlX8MxBPRt819heIwBTa/8kXSNT3JQdw0uHTVT2Cr8P05iN7y4RYppGJh3ckxMpbJbabBTrwnizbZL7ihMJlIp546SV9cWx6EemON5r4db5sbxcL2yeqhRSfvR6KycHrLg8Xm7JVrNqQTyHu83ka0JJUAXy1MFuvrNQi9nm4oPWIZYlK/jw5CjZ8cEkqAL584FuHlukY3TKyaZKA4oAKU0mC7oIBeFKKW4v/OO1yfSO22kyWrg5W82rTQOsKE4iWC7hoz4Z/3D9HPQWD6pwFakRAWyq7qWh6Yh/ecFut1/2FPKvq3PLzAEeFRVFUlIS2dnZn7MW+yJ2eDat3Lcd+M5WXq8XpVJ5zkbe7733Hj/5yU84ceIEU1NTF7wHcbZYIoDf/va3iESiy+raAn8DwHcudbHA53Q6GRoaoq6ujpGhQVYXa2gecDDmEHFPQTwfnhjE5fFyc7YQ8vrDV44CMG5z8Z3cQLLiQqjtHuP6DDXxYYFsre7mnsIEguQStlQJTM/jFYBofZlWiOHptLI8S8WpQSvvHT7FojgvtcYpsnLyqEiNYFutkV+824ZELCJCKePDH5ShUsp48XAvS3USXD7Q2VghSAX2tY1wf4mwDWpzerg+Q81L9UI7Uxeh4LkqA9dppUhE8Mv3TtI9YmXC7sbudHFTZhRWp5d8XQRLfPl3N2b7TLbrB7gjXcmUw80rh3vYWKGjpcdC/4SdG7JieKnexNVzo4gNlfOHT04zPuVk2uXhtNnDNXMjcbq9lKdGUJEcxnOHuiiMdBEaKOXdbg+PXZXK0ISDj9uEueEh4zSpUYGkRQfx9IEuHl6oY9rp4cEdTZhtLizTLlYtiCdfE0qt3syaYiEeaUuNiYcrNBzvm2Tc5qIiRWB9S1MCCZCIea7axCMLtZzon6TRIGj3dh8dRKWQIhWLSI1S8N3FSRwxWfB4vBRoQnmm0sCGskRcHi8vNfSxvkzD/vZRtOEKUqOUbDk8SL42gkGrm9PE+ZcXxGLxXy0vfFWJDLPrm7Zdea7ssKGh4Yzs8G8V+M623HImI++ioiLy8/PJy8ujr6+PnTt3UlRURFlZGb/+9a/P67pniiUCMBqN7N27F61We8Hf6ULrWw9855rCfiEzPq/Xi9lsxmw209fXx/z585k/fz73lyUhl4p97E6D1+vl1++3cbTHgsPtZcTq4OGFQivO4fawvlxH1/AUVZ1jvvBXM4bRKe4pSOCD4wPIxCKuz1Dzcr2JjNhgsuNDef3YGPNVDsLk8NrRUX58ezFSiYjvvNjCwY5R3B5YlBbJL26dx+iUk5YeC6uLEvnk1DBur7BM83K9iSKdipQoJZurulm1IBFlgITNvkSDiWmXvw16tMdCTZ+b0EAphzpGcbtcxIfKCVHK+T+3ZKIMkPDsIT0PL0pi1OrknZYB1pVqqe224PDp/rb7ZA5RwQE8c1DPI4sEwfpzVd3EhQVyrHeCWv0YSZFKZGL4txvTCFdIefydZm7QeJl0eulwhLOuTMu+tmFUCin5mjCePdTNmhINEhFsPzzAo0uEDcr/3NOOSASnh6dYXZSASiFFPzLFdxfrGJp0cKBjlHsK4th9dJD8xFASwuQCq1usw2xzUWmyc1duFO8dGyRcIUMhE/P7/XokYuGeunW+mtVFCbx7fIi8WT///SVJfuZ+Z24sO5v6uGpOBBFBMv64v4tFaeHoR6fZeaSf0EApTx/qxub0oFQqkclkf7W88Nm8vqamJr9P5Nc9dPXL6lIC7pnY4czhnZiY6DeePnr0KCaTCb1eT0dHx2VPcf86At+ZSiQSERcXx+rVq4mNjeW//uu/aGxs5OOPP2b16tXndd0zxRIB/OhHP+Lxxx+/Ig9d33rgO5e6EMY3MjLC4cOH6e/vRy6Xk5OT4/fyjAgK4PbcON480kuzaZyQQCl7WoeYdnlIiVIik4j4/tUpzI0J5kO9k2WZ0cSEytlabeDeWTO9Nb5U8m2+wFbLtItdR/q4MyME07iDhl4b68uTaeqb5ufvncLp9mIYs7G2VEOBJoz2wUluyo4hPiyQ5yq7WV2sIUAi5kO9k40VOizTLl5v6vPP91r7J7i7IJ53jvYTHSynOCmcLdUGkiIVSMUiXjnl9DOO2/M1/HjpHPQjwmLKvYUJvHtsgNhQOfmaMJ6r6uaegniCAyTsOmnl4UVJmKecvNXczwNlQgzR2JQTTXggz1UaaOmxIJeKyUsM4zfLs5hywaaPjnK9TkLzoIvMjHmUpUSwuaqbewviCZJLePqgnkcXJ9M7Pk3l6VGWpQXzXusIR4zjiIBP20dYmiHkfAUHythQpqG6y0yARESBJpTnqo3cX5SAWITA+nysbtTqZHFaBLvbbSxJCUMiFvGD1477F1F+cn0qV82JZHttD3fnxxIok/BMpYFHFwvbmmM2JxUp4b73F5Lft9aYuDlLTb3BwtaaHuRSMSqFlN/flcHYlIvna0xnvM8+m9e3YMECv0/kzKymtbWVw4cP09TUdEGH+de11XkpakYrN2M8XVBQQExMDDqdjvDw8M+luH/VTirfRLZpsVj8Mz6lUolOp7voz/L222+TkJBAbm7uRb/XhdTfgY/zA77R0VHq6uowmUxkZmaSm5t7xptpXakGh9vLj1475p+vrSvV8tNlcxmccPDhiSHWl2npmfRSpzezpkRDdecopjEb9/hmelKxyB/zkxYdRFaMgmf2t5MbCbEhMt7rcmM02wHYf2qYO3IFcXWIXMoji5Ppt9jZ0zrEurJZMoe8OCp73cSHCfO9rdXd3JQdQ3RIAJsOdfvlClurDdyUrWbAYmfd802IRcKhv2nNApZlCknpFakR/pndA6UaAUCqDDy8KIke8zQHOka4tzCOul4HKqWM4iQVm6u6uTk7BrlUzIPbmzD5In0eqtDy2JJkavVjdHfrmaeC9zrt/OCWIoEVHeji0cUCk9rTOsT9xRo+PDFIgiqQrPgQnj7QRahc7F9oyYoPAeC2HMEDdHutkWUZUUQoZTx10MBji3UMTvwP63u7ZYD8xFASVYE8eaCbDaUJWJ1eHnu9A7fXixf4071ZpEQpea7KyGOLBV3g2y0D3F+UwJ7WYdKigkiJUvKnTwXWOG5z8d6JIW7JVvP20UG21/UgFkFsiJw/3pOJ2ebiaN8k16VHsbXWxIj13A7aGYuxmVlNXl6eX/h8tsP8ixZBrhTwwZXLATxbivuZnFRmYokuBTu8kv/WF6Pju5TLLVNTU/zqV7/iF7/4xSV7z/Otbz3wXapW59jYmN/JPiMjg9zcXIKDg8/6+jR1MIvSBG/NPT8oJ18Txo46IxWpET7pgAAAYQGw5a+YXrd/pret1sj6Mg0T0y5+90Y1N6cFMmTzcnI6lPgwOe2jTt5s7mNuTBAi4EfXpnJ1umAlVqRTkRYdxHOV3dyVLxhWP3tIz4ZyHW4P7PBtO/aN2/no5BDrSgX3FfOUk6vmCnO6n7/ThghQKaS8/70yAiWwrVYIfrVMu9jZ2Ouf2elHbdyWE8drjb1kx4cwVx3EMwf13F8kRCttOqhnY0USAxY7tz1Vi93lweXx8ocV8ylOUglaPZUdhRTeap9mRXYoYzY37x8fZG2phr2tQ4T5kh82HdKzujgRhUzCk592UZIUjsk8zYtHLUQqpUjE8Pt7hM3SP+3v5NHFSdgcbl5r6md9mRAVJBOLKNSG8WyVwPqkEjHPVpt4qEJD68Ak33/tBCAcVM+smk+IXMJLDX18d7GQIt8+aOXGzGh2HO7h1vlC6O2fDuj53hLh708PT3HVnAierTLwVotg1ZYUoeD/3jSH/gk7Y1OCrvDZKiMbyhKxO908dbD7nO/rM1VAQMDnDvMZW6yzLYIMDg7idDq/ka3SC62zsS6xWPw5J5XZsUSXgh1eKdCDCzebvtQ6vtOnT9PV1UVubi5JSUmYTCYKCgro7++/ZNf4svrWAx9cXCaf2Wymvr4evV7PvHnzyMvLIyQk5Jyuu6FcJ+jCTgzyQJkWw6iN/aeGeaBMy/G+CZp7LFynk3GoY4QBi527CwSPT4HpqXn5sJFRfSsZUTL29Yq4rTSL0EAp/7a7lSM9k0hFUJ4SwdP35YFIJEgGfFZibzb3sbFCx6mBSY6YLKwqEkJZAQrUEl48bKJQp2KOOsinw0tAESDmH15tYX/bMG6Pl+xYJf/35nTMNhcnB61crZUJInWFjLKUCLZWG7g5W020b2b30EIdDreHHbUmHl6UTMeQlaN9VpYkSnmzuY9fvnsSALvLw59X5qAMkLD3xCB3pisZnHBQ1+fggfIkDnZNEBooJT8xhGcru1npmz0+daCLx5YITHZfm2AJ9u6xATZXGZBLxUQoxDxxRzJikYhnK/U8tiSZ1v5JDKM2bsxS82J9L0szookIkvHkQYN/1re/fZR7C2LZ3TLAM4cMADg9Xr5fFIrHC7V6M+tKE9l3aoSYkAAyYoN58mA3Dy8UUi9ebuhlY7mGQ6fHUCmkZMQG8fje09R1j+P2gC5CwYNliehHbWgjFKSrg3jiUz3fW5LE5LTLF2UUx+tN/fRbL+2saXar70yLIFarlfHxcU6ePEl9fb3fBeRyxhNd7rrQWKLLzQ4vdV1o+rrL5UImk12yzzF//nwGBwfR6/Xo9XoSExNpbGwkNjb2kl3jy+pvAvi+rM4EfOPj4zQ0NNDZ2cncuXPJz88/K+DNJDx8tspSIkiPCWZrVTfXpgvhsJururk9V0hO31zVzdUaYWliS5WQi+fxetn0aTulKitWh4dT7mgeuiqdPoud6/5YhWXahccLv7xlDjelBVJ5egS3x8uNWYKofK462K+9uzE7BnWI3C8al0nEbK7s5qYUmTDf8zG2toFJ/vWtEzicHgxj0yxJCaU0WYXJ4uSWnFgSVIFsOtjNsiQZErGI5yq7eWSR0HZ8//gg68oEtjhhd7EsU80LPmabqArkvz/u5OSo2+8S8/AiHQ6XhymHm1vmhfHO0QGiFSJyEkJ4/YSF1cUalAES3mibYmNJPAMWOx+fFNxvPjwhWJrNUQfx6w9O8f7xQUAwDvjt8ixGbR6O9k1xd0ECrzf1UqgNRxeh4Il9nSyfp8TmcPPUnqPcnRFCrd6M2+2mJEnFkwf1NBgseAGzzcW6kgSmnR6kEjHXpIXxwuEebsxUE66U8acD3fzgKqGVW989zh25sbza2MeStAiig2T8+7sdDFgcTNrdxIbKuX5eJN2jNm7yPSD8/hM9/3BNMj3maRqN49yWE8OL9T3ckRtLgFTMa+1fnWPJTM1eBElOTiYiIoKsrCzy8vL8LiCzI3Uu1iPy61aXKpboTD6bmZmZRERE4HA4MBgMfnZ4/PhxDAYDLpcLu91+ib7J+dWFtDovxe/6TLFEV7r+JoDvyxjf7FbnDOB1dHSQlpZGQUHBl/rUnY0xikQi1pdrOTVopUZvZl2plkbDOCf7J1ldnMi+tmGmPGLuyI1l99F+bNYJiuJk7GoZ5OoFmRRqw9hUbeSX77UJn1Ms5pUHFxCulLH35AjX62R+L8sN5YKo/OX6Hh6sSMI4ZmNfm8Aua7rGGJiwc2duHG8096EOkrJAKyyg6IetiIAPW4co1QYhFUN0eAjfvzqVMd8yysZyHUdM4wxNi7g9J4ZdR/pIiVIyPyGUZyuFJZbQQCmbfJuak3Y3v//4NDKJGP2oDYvDywKdiimHm/uLE0mOkPP7PSe4ITkAmUTEBwYvjy1Jocc8zcGOEe4rSqTKNE10kJS8xDCe8bU25VIxD7/QTPugFbvLw41ZataWamg2jZOmDiI1IoBtDUNsLBfWo5851MWagihODkxyqt/CsoxIPui0sygpmPBAMb9+9wRj5nEmpt10DVsp0QRjdbi5OVvNXHUQLx+bYE1BNA6Xhxfre3iwXENNlxmpGAo0ofzlkIF1pUIqx+N7TyOViDGM2VAGSMiKC2bE6uD7S5KRSUQ8U2nku4t1NPdYsDkEIfxfDhl4oDQRsUjE9joTD5Qk0jTood5weWOLZg63s1mMnckj8nLILL6q+ioXTGZ8Nj8bWqvT6fya39bW1r9KYejr62NiYuIrZ4cXmr4OF9eiPVMs0ezS6/WXNYQW/kaA78tKIpHgcDhobGz0A15hYeE597W/qFV6c3asP7bnroJ4QgKlgjdlkQa5VMwHXU7uyAjF5fayaf8pHr16DlNOL//fHj0dQ1Ympt1EBwfwyMIkJuwubE4Pq4sT2d8+yqTdze25cexq6iXOZ4y9rVawS0uKVPJspZ57fdfcdEhwM3G6PXzQ5UQXHsjQhIOnD3ajVQltjO9fn8Hy/ATeONKHNvz/sXfegU0dVtv/aVqy5C3vIW9sAwYMNpg9AgFCBtnN3oOMJmk60vbt2zZtmrRpkqZNSvZmNGRAyICwMeAFHhgweEiWLcnblrX398fF/khCEjAktM37/Gv7Xkm+uueec56hFNiZe9q4aEICMSo5H7V4uGlqMj5/gDfK27lrlmA3tru5n2tLUtl8pIdOi5sYlZy1+40MOr2EhUiIDxXx2MUF+AJBHv+gkotzFBhtQeyKOK6cnMyHtWZy4lQUJIbxwm49N0xLRSoW8VqlmRVzhe7oZ+8fwuMLYLK4uLIoiYLEMOqNQ9xcqkUuFfPPXXqumxiNcchDtWGQi8fF8t4BE3ESO+nRSj5ocnP37HQ8/gAbWjyka9TohoK02USkRoYgEcOtE1SESuGxDXVckBrAbPNT0drD0oIY/nXAzOxswf7s7zva+PHcdHrtHtbuNxEXFsJe3SAefwCNSoZcIuLXi3OwOH1sONjF9SUpfHa4h+xYFVmaUJ7ZruPHc9MZcvrYUN/F9SVJfHKoh/dqzSNFNPA9d1Vfd2P7Oo/Ir5NZHDp06Asyi39HfN/MSrFYjFqtJjY2FpVK9QUyUkxMDF6vl/b29i90h9+FVGU0xBqXy3VKuaT/afjBFz6r1UptbS0ul4vMzMzTKnjD+KbCJ5eKub4klb3HGZtXTxHCYR0eH8vGatjV7qbLaGBeThTbDF5aB/3IJSI2H+khK1ZFUoQCsVjEijnpaNRyXj4uS1BIxXzS6uaW6VrcvgBvV7Rz+0wtPVYPGw8KXdohk5UGs5UfTRFMoQOBIAUJYXzS6ua9ui4UUhFJESG8d/d0IpUyXtyt57YZ2pHCduesdEwWF1sbe7ipNI36bi+DDs+Iu0pRmkCgeWG3joX5GiRiEXevrsPhET6Lm0vTuG9eFk2DQcr211OaLGd7u58rZheSHhPKP3fpuHWGFpEIXt5jYMWcDPR9Dsp1AyzJUbGpsZ/dTb2IRXS7CxcAACAASURBVFCuG+CCcfEopCIc3gAPzM/COOgSYpiKU9h4sJO0yBCyouU8vbmRGTFOJGIRO7sV3Dc/i6ZuO3UdQ2THqni3ppN6k5VQmZjMGCV/WZ6PzROkxhLCLTO01PYEKByTQU6UhH8dsjI/0UcwGOSJDTVcki2j3mTliLGfhHA571SZ6Hd4kUtE5MWr+eXibFr7nBzrtrF0bCxvVRpZOi6W6FAZz2zX8cC8dAwDLuqMVi4sjOetyg726YQOz+L0sVAr4UinnY8bus/ouj4djOaG+HUyC61W+wWZRUVFxb/d3uvEaKDv+7wndlzf1B2eTKpyYnc4mg57NF2bxWL5r0tmgB9I4TvZP9xms1FbW0tjYyPp6emoVKpRU3YlEsk3EgGuLhbIGa/tNXD91DTEIhFPbqxhaqQdfwAO2MIZlxKF1e3n9x8fJSlSeMK6cnIy98zNoLHTxv52CzdMTaWsuY9uq5vLipLYa/KjDpGwIC+Wdyo7mJgSQUFiGK/sbePCwng0ajkvlemFpHVEXP5iBYfMVoLARWNjeOySsZgsbip0A1w3NYVtR3tx+wIjjipFqRGMiVfzYplAfgmViXhln0BccXj8rKrs4NqSFJq67Vz5UvXIk+kbN06mJD2KtyrayZb2EyYLsqlDzC8vLsLpFcgvd81O57BZ8Be9dFIS6w4YGZsYxph4Nc/taCVcLiEIvFXRwYQU4UFkakY0101N45OGTpIjBR/Rf+7SccO0NEKkYl4uN7IkxU+XPYBdncJVU1L4sM7MmDiVELL7SRPHuoXR7vl5Gn6+KIsjXXa6bV4W5ml4o8LIBWPjiA6VsXKviRsmRdHnDGAMxnBNcTJ7zX4mZSYQHiLmiW0ddB/P7pueKOHqceHsaR1AKQ5QmBzGczvbuH2GsLN9fV8Hd8/SUm2wEAwi7BV36rE4vPgC0NRtZ+nYWEHnGS5mbKKaZ7brcHi+n/Hh2aLYD3c2J8ospk6delJWZHV1NU6nc9R+m2fjtX7fOJVR48k+wxO7w+FEixM7bL1e/50ZGZxtKcO/C34Qhe9E2Gw26urqOHz4MGlpaRQXFxMVFQWMfpH7bTrACKVsRBje2NjItEQJ29tczCwpIjtGzptVnTy7vRWlTExUqJT1d5eQE6fi1b0GLhyfMKKxGy6gL+9p4+ZSwdB5hMnp9LLugInbZwouMGUt/dwwNZU9Lf1c90oV/mAQuyfA/y7JYnycjD26IebmxpAapWTlbv0IqeTFsv/vqLKqysids9Jp6bFToR9gabaKHU0DSMUiZufE8GKZnr983gyAOkTK2tumIJeKWVvdzgXpYnpsHhqsISxOl1HRNoTd4+f8gjjeqjAwO1s493M7dNw+Q0swCC+X6SnNjKKt38lb9RZiVVLEInj8kgLGJwtF7vqpqShkAsPz3rmZmCwu1u09wtxkMeUmH+Mzkyg8/rs3lwrawqterqbH5iEQhDtmpHJFUSKbG3uZnBYhkF926lkxW4vL62f1fhO3z0ijQj9IIAhTUlS8tNfAlZMSBeOBD1oYcgcIAitma7mkMJ6dHV7m5UQRo5Twl83NLEl00W3z8K+9R7k4P4L19V2MTVSRHqPkyW2tJISHMOT2s7uln0kp4Xj8Qa4qSiQ3TsW6Jh8Pzs+k2+rh1X3to7oe/91wMlbkxIkTkcvlX5FZjCbJ/T8FZ9JpnpjRV1BQMNJhp6eno1QqGRwcPKnp9PD+dbRp9//X8f0HQyQSYbfbqa+v5/Dhw6SkpFBSUvIFGx2JRHLGYbRfB7fbzQyNh0AwyDZjkJ9dVITbF+SSlRU09Qk35OUTEvjr5eMYcPjYdLhnRIpQoR/gxmlp7Gvtp33AyVXHc+9EIhElCRLWVHeQHatiijaS1/a1MX9MLGnRSp7e0sznhwVdTJfVzQNz0hAB7RYfV4yNoM/hZUN9F7fNEOzIGjutXD1FOLZKLmFurobXyw3MztGgjVaycpeOC8eokEtE/Pz9Buo6LLh9AZIiFNw/L5NBp5c+m5tFWSo+rOskNzGKSakRrK7tZXaKlEiljOd2tLJiTgY2t59VVR3cOUvIymvtdTAjK5pVVUZe39eOXCIiRinh+eUZyCRiVu7Wc99cgfyyq6mP60pS+fhgFzgGyIoQs6bews+Wl6CQiXmrpp/7j49Bb3yjBq8/iN3j59dLcsmPV7GxoZtbSlOQiP8/4aSp205jl40Lx8ezptrE7JwoEsNDeKvOwi1TNFicPq5/sxa3L4jHH+TX52cxITmctfvN3DojFbFIxLpGBw8syKLV4keTmsXc7Cg+POpgYaYKlUzEo+vryVB6MPS7WF/fRWK4HLEI/mdJNnFqOX/ZouPh8zLpdQU5aBxiydhYXi/vwGRxjeqaPB2cC1G1WCxGKpV+RWbxdUnuR48exWg0/kfLLM62XdmwkUF8fDxZWVlfMZ3+8v7V6XSi1+vp7e3F5XKdciTRf1sWH/xACl9nZycNDQ0kJSVRXFxMTEzMV37nbIXRngiPx8PRo0fZv38/eamxLB4bz4eH+vmfjwQ9W6/Nw/2lsYyNU1DZNsis7BiyY1W8vKeNpWPjiQ8PGdGxqUMkvFSm56bSNMSi484qGTLsbj+rqzq4fWY6Zoubl/fokR73p9T1OpimDcfth8WFySwdFy8UyugQ8uNDeXmPMBKNDZOzcreem0q1iEXw0nG5wqDDy3sHjNw+M51DZisfNNqRSkQcNFlJjwmlMDmcAYeXa0uSiVVJefLTQ1w9IQaxSMRHzS5WzMnAbHGzvyvATaVp7DwecHteXixvlLezYIyGWLWch9YdZGdTH0GgNCOKJ5aPpc/pp8bk4EfFKayvM5MWrRwZbZ6fFUKIBF6v7uGRZePodfjY2tjLFYUxlLXZeWm3HgDjoJOfLMgmVC6mQj/Aj+cJyQm7mge4enISGw92kR0bSl686vhoMpUg8PKedu6Zo6W538tfdpoBGHT4eGRRJhqVjI2Henhw/nEXmcO9XFeSzMcN3WRrQsmNU/HMdj33z8vE6w/yucHP4nHxHBkIsr3Dj1ouRiUT8fNpYUhE8If1tVyaI6XBbKXV3MfEWDEv7jFwQ0kyIuDJLa2juiZPB+ei8J3snN+U5J6QkEAwGMRsNlNbW/sFmcXp3MjPJb4vn86T7V/Hjx+PWq1GqVRisVg4evToKbFz/xsjieAHUvji4uIoKSlBo9F87Rd8tEbV8NXC5/V6R2jfarWa0tJSEhISuG2GkBbQ0mPnpuOjSsRiriyMxjjo+kKnV64f4KZpaVToBtD12UdSy92+ABcWJvDuASMxoRJmZkXzRnk7cWoZ6hAJf9+uo2vISahMzNjkSP565QRCpGJeKmvjjlnpODx+Pm2xc90kDR0DTrYe7eWWUi0VugHMFheXTkri/RoTyZEKpmZE8fKeNlRyCVKxiA8bbcSpZUjFInJiVfxsUTZ9dg/PfFjO5WMjaB4M4FNGs3xSEu8eMJIbp2J8cjgbdV6uLk4mQinluR2t3DM3A6vLx41v1tBjEzRvlxclcenERKoNg0xMjSAzSs4b1d3cXCrs757bqeOW4nhMFhef1Zu5YVoqewwOYtQKStIj+edOHZ3Hd27VbUKobSAIYjHcOj2dz4/0EK6QMjk1nBfK2ri2OIlQuYR/7Gzj/rkZdAy62Ns6yNWTk/iwrovPjwgxKW2DHm6bLoxMm3ocrDiewjDo9DE/N4ZX97WzfEI8UaEynt6u46HjGr09rf0szNOwrraTdTWdSMUi4tRyXrxmPA5vkP2DCm6fqeVAd4CxmcnkxSp4obyTJSl+vL4Af9/UwMW5Sj5v7GXHEfM5J4ScbZwOs3JYZvHl9IBhmcXX3ci/D4nA6WC06etnA36/H4VCcUrdYWVlJWVlZfzyl7+kuroav98/qoeKk8UR/fSnPyUvL4/CwkKWL1/O4OD3K90Zxg+i8EkkkjNyb/k2DP+tz+ejpaWFyspKFAoFpaWlJCcnj5x7bFI4pRlRKGRiHlqQxYSUcNYd7GdSgpysWBUv79Fzwbh4EsJDeLmsjSunJI/o426cloZUIuaVPW3cNiMdty/AFoOPi8bH0Wf3cOkLVbi9wtPab5eNZcXcTCr0A5gtbq4oEoJlI5Qy5uVqWH9kiMIEBblxKl7YpeeKyUlEhsqElPKZ6QSCQuDsgjGx9Ng8PLiugXCFIHy9d3oCV05O4sO6TrpajzAuTs5Wo4hb5o8lNkzO8ztbuWNWurCz22Pg3rmZ9DqDbDnSzY3T0th2tJeXy9oQAce6bFw/NYXE8BAaO22sOJ6792JZG9cXxWAc8rJP18+Vk+LZWN+JZ7CLiclhrG92c32plnCFlKe2tZASFUqfw8uWJgtjNHL8wSBXTE5iZnYML5UJOsOoUBnP7tRz/9x0+uxePm7o5qZpKWw71keYQsKUtAie363H4xP2d7tb+pmeqiQQhLiwEK6eksT7tZ1MTAknUxPKM9t03DtH2Au+VWnkrplpVLZZ8PqDlGgjeHa7ns8OC045GTFK/rw8j26bh3qTjUsnJrC62sScnBiSIkL4+24Tv1wyhgFngLpBGTeXprLX5KO2x49IBH/a3EJFVfUXInYGBwfP2sjv36XjOx2cKLP4uhv510kEzlUxPNfJDCc798m6w+LiYnJzc8nNzUWn07FhwwaKioqYM2cOjz766Cmf82RxRAsXLqShoYH6+npyc3P505/+dMbvbTT4QRS+U8GZFD6xWEx/fz8VFRVIpVJKS0tJTU096RPt7bPS6bV52VDfyW0z0jENednTZuPWGVoaO4Wd3s2laVTqB2jutnNNiWA1NuTysXyikFoeppAyKzuGjS1ufrmhERGglInZdH8pqVFK3qps5+rJQtF8YbeOW46TR17Z08ads9MZcgfYeHhgxFasXDfAjdNS2X6sF5vLx+xswavzsc+OIRWLiFBI+eTeaUQrJbxR1UlxmAWCQSqsETy8ZBzdNg8fHezi9hnpVOoHMVtcXDwhkbX7jeTFq8mIEPP8Th3O44X544YuFuTFAgLx5565mTSYhmjusXPZcYZnbqzyuDThKEXqIZQyMdu6lTy0MIduq5uP6ruYoo1kd1Mf79eY0KjkKKViHpmlIUYl56ktLTwwX9g9rt0vkHTKdYN4/EHm5kTz6r52LhofT4xKxtPbdBQkqBl0+Fh7QGCBBoJwcX44hQlKVu5u47piwUv12R1CV6fvd1LVZuGKoiTWHTAzJS2CtGgF/7PxKHVGK95AkNx4FffN0aLrcyKXiAU25642bihJJkQq5tnteh5akMmxbjtN3XYuGh/HJp0Pfb8TgKZeFxeNj8dkC3CMpC8EsHZ3d5/UWWU0I79zVfi+C2bliTfyk0kE+vv7cblcX7EX+6bg2rOFc1n4TseuTCQSERcXx0033URBQQG//e1vqampYf369Vx22WWnfM6TxREtWrRo5HVMmzaNjo6Tp5J81/hBFL5TNao+3cIXCAQwGAwcOnSIQCDAtGnT0Gq13/iFnp4ZPSI5mJerIS0yhHcbLCwbJ+z0XioTQmgjlFJeLNNzw9Q05BIxL+/Rc+t0LT5/kHtX11He2o8vAJnRch69MA+HN0CVYYjbjptGHzQJ9l+fH+nB4fGPjEfTokKZmBTKuwf7mT8mBm20kn/u0nFtcQqhcjF3r65lx7FeAkEo0Uby9BXjsbh8fFzXwaI0MfVdbsLi07i0KJkP6rrIiBGE7i/u1rN8YiKxajnPHTeG9geCvLhbT2akBJPFzUtlbWRphOimu+dksDA/ltf3GZg/RkNqlGAtdtfsdABe2Wfk/CQfXXY/Q6pUbizV8umhLsIVUnLj1Tz5eRPbjvYiFYsYlxTOC9dNxO4N8GmTjbtmp1OpH8DiFCzUXttn4PwCoZP+2w49980R3GXeqTIyNyeGA+1DvFlpJCpUhkIq5pnLC4hTy3ntwCC3TYmh3+Hlg7oubj0eJquUiSnRRrCyzMD1JckoZGIe2XAUi8PHoNNHeoySC8bG0txtZ3Z2NOnRSp7c2spD8zOwuX2s3m/mrllp7G7pRyEVU6yN4G87dEQoZPiBLY29zMiMIhCEcYlqZmZF8c9dbfQ5fF8IYD2Zs8qJI79h3de/g3buyxgty3A0OFEikJGRQVhY2FfMp78puPZs4Vx3fGeazBAZGUlBQcFZe02vvvoqS5YsOWvHOx38IArfqWA4Sf1UEAgE6OjoYN++fXi9XiZNmoRSqTyli1okEo1IDrYf6+W64kRaB7xUtQ2OdHpN3XauLUlla2MPAw4PlxclsaHOzLv7O5CIoc44xJTkUHKjZfQ5gywrTCA3Xs1Lu/VcPCFhhKxyw7RUlDIxL+4WAmLdvgBvlBu4YXIc/U4hUueOWekcMln5+YeHcXkDmC2CRnBhfiwNZivj4kPIjJTywm49l06IJ1Ih4bXKzi+MM++bl0nnkJuPGwSWaPnxfeHktAjeqepga5uXUJkYjVrOO7dOIUIp5e/bW7l3biY2t5+3Kjq4Z24Gh8xWtte1MitJzE6Dm6LsxOOEFkGLGCqXcMPrBzjWZSMQhAvHJ/DwwmwaTENYXT7mZUeyvtHKwrw4kiMVPLWlmR/Py8TtC/Dq3jZWzNLSYLLSNuBkZlYUb1YYea+2E5lYRGJ4CK9dOx6vP8DrFR3cM0dLY6+bbruPJQWxvFnRwcL8WBLCQ3hyq46HFmQy6PDy7A4doXIpx7rtRKtkFCSo6Rxyc9csLaFyCc9s1/OTBRno+5xUGyxcUSQkWBRrI0mPVvLnz1uYnhHFkMvPW1VGklQigsBVRYnCyHRnG3fPEhx3ntz6VaLLl51Vhkd+48aNG3EFOTFR4MSb+vCD3n/iqHO0GN4tftl8+sTg2uGu+mzLLM514RvNub8rVucf//hHpFLpaYfani38X+E7jlMZdQaDQUwmE+Xl5TgcDkpKSsjKyiIkJOS0usVF+XGkRSt5qUzPhePiiFKIealM/4VO7/qpQtF6YZee6FAZvgC8tMfAGE0IAMXZ8VxbGE6f3cuHdZ3cOSud5h47ZS39I2QVQ7+Tq6ek8HGDkOK+uCCetyvbGRMXyhiNnBd269D1OhAB24/2cuH4BBQyMS5vgLtmpuHw+Hn641pun55CtyNITW+QSwvC2dPST6/NPTLOzNKomJQawQu7dSyfJPh23rWqjkq9sLguipfxp4ty6LV5+PxwD7dM17LjmCCWP78gjjfLDeSofcSHinjjQC+/urQYiVjE6vpBfjw/C5PFxS1v1uDw+Bly+bh9ppa5uTHsaOpl6bgEEiNCeGpLM3eUJuHxB3l1Xxv3z8vkkNnK0W5hfLqmuoOitHDSohT8+qNj7G4ZIAhMSA7jDxcJJuD1ZhuXT0pk3QEzhcnhaCNlvFLdxz2ztfiDQV7cY+D+uekc6bSxr3WAuDA5m470IhJBdKgMsQj+94JcrC4fa/abuGuWlj2tAwDMyIxi5e42rpmShFoh5amtrVxRlIBhwMXfduiJVApP47eMlZGlCeWJLa38ZEEmTo+ffx0wc+v0VD491MM+3cApXWMnuoKcGFF04k19eFRqtVoxGAyjHpWOBucqkPXbziuRSL7QVZ+OzOLb5FD/KaPOEzE0NDSicz5beOONN9i4cSPvvPPOOYtp+kEUvlP5cL+p8AWDQTo7O9m3bx9DQ0NMmTKF3NzckaiO090PSiVibp0ujCQbuhwszQyhXDdAa4+d6453et1WN0WpkWw42Mnfd+iIUIiRS0S8fNNU5uZqeLO8nfw4xYgs4bw8DWnRSlbu0nPlcbLKP3fpuHm6VtCslbVx12xBmP6v+j5Sw6XH5Q9t5CcKqRPLJyXxoynJfNLQydHDDczKCGN7R5AF49PIS1Dz1v4eFmWphJSCHbqRcebLe9q4d24mZoubH71SzZDLh93j5/aZWi6blMjBHi/ZGuWIsPyqyckC2WR7KzdMisbhEbqdBxfm0jbopdZo49LxGra12lm33wgIRJjbZmiJDpVRbxziwQXZ2Nw+3qowcM+cTOqNQxgG3cxLV7K6qoNJqULk0t+2tXDnrHREwJ1v19Ax6MLh8TM9I4JrpiRSb7SSGaNkfFIY/9ih5+bSFEJkEp7doee2KTGYrV52tfRzXXEyH9V3EaOSERUq5W879FjdfmHUmhjGr5dk09Lr5KBxiMsnJbKm2kSJNkIQrW/V8eD8DJzeAG+UG7mhJJnKNgt/2aJDLhEhk4h4+dpColUyVh318sj5gg5x27E+rp+azPr6LorSIkiLUvDHz5px+0Y3tvzyTX14VKpQKEZGpceOHfteRqXnuuM7HXyTzCI+Pn5EZjHMiGxoaDipXu5cd3yjKXxnW87w2Wef8cQTT7BhwwZCQ0PP2nFPFz+Iwgenl9AwjGAwSE9PD+Xl5fT391NUVEReXh5yufwLvycWi0/7KXn5xEQ0ajmvlRuZnSIhQimME6+bmoJcIuaaV6rY09oPwJzsaFbdWoI3EOTN4x6aAw4vW1qdXH9clvDZ4R7umCkIwg+0W7hxWio7jvXSZ3dz2aQkPqg1oQqRkhev5s2qLra0OlDJJSSEh/D2zUXEhcn566YjFMp7kIlFVNiieHhxAVa3TxhFzsmkw+KhosPBLdO1lDX30Wf3csmERNZWd7CmWlhSt/bYuX9eJhq1jNp2C3fPziAQhFfLTdx/vHv79FA3109JoKy5j2aDmcUFGj5rcTI9O5bc48UKIAh8eqibRfmxBEHoJGdnUKEboM/uYdn4BN6qaGd6VgwZmlBW7jFyeZ4KsUjEcztb+cl52ej7HPxq3X4CwSBGq5/SNDU50XIOmYaYpOgjVAZ//PgwN02MGCHp3FqayvZjfYRIxUxKVLJyt4GLC+MJkYm5e00DQ07hIefKogTunJnGtmN9RCiExIt/7NRzw9RkQkOkPLVNx88XZtHW72RP6wBXFiXyfl0nK8sMiIBQuZjXr5uAWCTixT0GfjI/k1ZLkPYBFxeMi+PVfe0sLYglMTyEJza3cP/cdNr6nby8x3Ba19o3QSQSIZFIiI2N/YoJ9fCo9MR4nZONSkeD74rc8m04m52mVColMjLyKzKLjIyMk+rlBgcH6evrOycyi9EWXYfDgUqlGtU5TxZHdO+992K1Wlm4cCETJ07krrvuGtWxzxQ/mML3bfhy19bX10dlZSVdXV1MnDiRgoICFArFWTtfiExyPPV8ANOQj2uKU9jS2MO1r1Th8QewewI8sjCDSycmUq4fJEIpY2FeLG9XtpMTp6YkPYp1hyxMSAghN17NC7t0XFiYQEJ4CCt36bmuJBV1iISVu/TcNiMNfyDIpSsraOyyEQQWpCv486Vj6Rxys25fEwuSghzsdBKlzePq4hQ2HuwiVC74gL5ebmBqRhTZGiVrGyxcXSx0bE9tbSYYFNxMtjX2snRcPEEgUinjzlkZVLUN0jHoYn5GKOsP9pARo2RiShjPbj1GXsgA0aFSNhpEPLAgB68/yAu79BSlRWLod7KmtoeUcClB4M7ZGczL1QjC/nHxJEUo+OuWZu6fm4nPH+SF3ToeWpCNrt9FXZeb66amsr6uk4rGdqQiKO9wMTktCnWIBL9Iym8vKsDiDtIqSuDOmVpqO9043F6KE+W8vMdAaqCTGKWYleVdXDNOidXl49rXa3F5AwSC8LOFWSzK17Cm2sySsUJh+vPnrfz0vEyGXD5WVZm4Z7aWfbpB3L4Ac7Kj+cdOPZ8eFoynQ2US/nppPg5PgM+P9XLb9FQ2H+klRiUlN0rMM9t13DZdMCJ/cquOny3MpLnHwa83HgOEkXdrr+OsXYsn675kMtlXDJSLiopISUkZGZXW19ePmlX6fZJbvnze77LrEolEJ3VTGTduHDKZjEAg8BWZxemmuI8Go83iO5MHlJPFETU3N9Pe3k5tbS21tbWsXLlyVMc+U0h++9vfftPPv/GH/0kIBALf+IUMBAJ0dXWhVCppaGjAbreTl5dHSkrKKaUPt7e3k5qaelqvaUy8mtXVHRgsPkxWL11WDw6Pj/vnpFFtsJAcrebWGVreqmhHJBJxdXEKb1W0ow6RcMH4eNbsN6EJlbBwfAprqo3kJ4SRnxDGmmojs3M0RIfKWVNtpLy1n167F7cvwB8vzsfl8VJjcnBniYadTX3UmBw8fU0J6+u7aOl18IvFubxd0Y7F5eX2mVreLG9HIZMwLyucDw72oY0OxeLwsk83QGOXjYyYUKxuHy9cM4EGs5WtR3v5/YV5bKjv5LDZyk2FKj5pstPR3c+sOB9bDF7G52iZmhHNmmoj88Zo6La6+eRQNw0mK6FyCeoQCU8siGGnwUNrj52fLMzmzYp2pGIxi8fFsbrKyGRtJFGhMv6138S9czOoax+kvN3B+TlqdrUMUWN2khqtxOIUpCAzMqNYe8DMojwNXn+Q9fVd/PL8HHY09XO4z8cfLhnHmv1mVJExLMuP5sPDg9SYXTh84PUHuXNCKD0uqOmw8JvFWayr66bX7uGa4iRW7zeTn6AmPiyEdw+YuW9uBjUdFjYf6cHpC9Bv96KQirm5NIWy1gGmpkcRGSpjXU0nDy/IpLJtkLKWAa7PFbHV4MPh8XNRYTyrq03UdAg+p/5gkMcuzKWyzcJB0xAXF8afleJhMplISkr61mOJxWJCQkIICwsjJiaGxMREkpKSCA8PJxgMYrPZMJvNGAwGurq6sNlseDweRCIRUqn0C8d3Op04nc6Tuih9l3A4HOfkvBKJhK6uLrKzs0lISCA5OZnExERUKhU+nw+LxYLRaKS9vZ2enh5sNhterxeRSIRMJjvj/7PZbCY2Nva0i9/rr7/O3XfffUbnPof43df94P86vuNwOBwMDAyg1+vJy8tjwoQJp93in+64U62QcuWkeBoHArT22JmcEoY3ABdMTOXSSUKKuCpEyuKx8ayq6iAlUsms7Bhe22dgQkoE+XFK1tYPMH9M7EjMz2WTBEnBnzc3sd8gECEMA05+cl4W7FuRnAAAIABJREFUYtFx0fgkDVZPkHfK27h/QQ4mq48ynSCFqNAN0Nbn4Oopyayv60Qllwpd3z4DE5PDiVaI+d+PjrCntR+ZRMSE5Ahevn4SAC+UCaSSbqubD2o7uXt2BjXtFo52DjErScTWVjvzpk2iNDOaF8v0XDA+AY1Kzn1rD7L3OAlkWmYU/7hqPAMOH7vaHNwxK52yln4GnV6WjU/gzQoDU9OjyIlT8czWFm6fmU6IVMwz21pZNiacfleA/93cQWiI8FT/v8vyOL8gjlf3GjgvT0NqlIK/btNx3xwtXr9AWHlgXgZN3XZq2i1cPTmJD+q6+OyYBYB+V5CfzM8gVCamYUjGitI4TENeVpcdYWGqiE8P9eAb6qUwQcmz2/VcX5KEKkTKo58eQyWX0Gv30mfzsDAvhkGnj8mpEUxOE+QLt5QKxuB/3tLCI4uEkWhtb4Abp6Wwvr6LDfVdgGBt97PzMlFKxWxo6Oan52VyoH2IdQfMp3W9fRcY3n+dLK/vm0alQ0ND5+T1nitSzfC5T+w2h2UWJ6a4nyizcDqd6HQ6qqv/v3lBR0fHqMwLRjPqPFdd+feBH3zhs1qtHDhwAJ1OR0hICJMmTSIsLOy0jzOcrnyqcLlcNDQ0UKQaRCaCuWPiePqqiUjEIl4u03P7TCEX77W9gm+mw+Pn7Yp27p6TwYDDy7sHTNxYHE+XzccnDV3cNTudxk4bH9R2EqOSc9A4xGGzjakZUbi9Ac7Lj2NJgYbVVe0EbT2MjRHzcaub+QVCKsDzO1u5fHIyGrWcf+xo5baZ6UjFIiEFYY5gMXbl6wfpdwXwB+GuWek8tCCb2g4LZotrRHieEqVkWkYUL+7WMTE6gEYh4r1jbu6Zn02IVMw/duh4cEEW/XYv1722n167B4fHzzXFydwwLZVK3QAJEUpK0sJ574iNSyYK49u/fi6MNv2BICt36Xno+P5ux7FeLi2MY9Phbp7YYUIsArlExHt3lJAcqeDJz5t5cEEWXn+AF8oM/HhuOk3ddqoNFq4tTubDui7SoxVMSA7nbzt02Nw+gkC1wcKS3HACQbC6/QJDU2chIjKKuTkxfKzzc+f5E4lTy3ml1srNE8MYcvl4Yn0NSaFBDppsHOm0ka1R4vL6WTE7neRIBY9tbuHnCzOxu/28Xt7OA/MyqGqz0O/wsqQglo0tPszHjakPmq1cOyUJEXC0286D8zMp1w0CQaZlRPLUNh2dQ2ce9vpdEE2+bVQ6ODhId3f3WRHgnw7OdeH7tnOfKLNIT0//isxCJBLR1dU1wsgdlln09PTgdDq/9rMbzajTarWO6l74n4AfzKhzeF49DJvNxuHDh+nu7iY7O5vMzEyMRuNpjyuH0dXVRUxMzLdeXB6Ph6amJnQ6HampqYzLH0Ojrp3NzTauLUnB4fHzfq2JW6an02vzsL7OzIo5mej7HHzS0MVPzhOKzbajPTwwK5ndLf1Ut1u5Y2Y679eY+LyxB7vHh1QiojAlnKcvH887Ve3ojD2cn+Jns95LbFwcxVFuNus8RCplLMiLZXWVkTHxavLj1aypNrJgTCyqECn/2m+kpt1Cn92D1x/g7klK7ChoMFv53UV5fFhr5li3jUfOz+Xtyg4sTi+XFkTwbl0vMrxcMEnL+wd7GZ8QijYukjXVRqxuHy09dvodXm6boaXP4aHT4ubRi/JZXW3ENOjkttJk1tR0o5RJRl5fSXoUUaFCuvvds9NpMFpYX2fmkNmKPwixahkPTVGys10Yr108IZFVlR2MT4kgPjyEtfuN3D49laPdDrYe7eP3F+Sw4WAXjV128hPUVLZZONplZ2JKOOYhN9cXxRAM+Pn4SD8/X5TFnpZ+9uoG+N0FOazZb8Li8nFNcRJrD3QxOTMBm9tPpclFnytIuEJCiBQeLlaxrc1FQ1sXy3OVbDxmI0YhIj8pnDX7zdxUmoKuz8Gnh7qZlhFFdfsQTT0OZmZFYeh3MSMzigkp4ayuNnFtSRImi4uNDT38flku6+u7aOp2sHRs7BkVLpPJRHJy8qj//lRx4qhULBajVCopKCj41lEpcFbGfSB87/1+/zkxXj6Tz3n4swsPD0ej0ZCUlERSUhIRERGIRCKsViudnZ0YDAY6OzuxWq243cJDkVQqxWQykZKSclrn7OrqoqysjGuuuWZUr/nfAF876vzBFL7hHZ/D4aCxsRGTyURGRgbZ2dkjpJXR7OmG0dPTQ0RExFcYn8MY9vFsamoiISGB/Px81Go1IpEI0ZCZLQYvTq+f22el81ZFO/5AgBunpR3fa4m4aoqw3wtTSFk2PoFVlR3EhoWQqQ6wudnG6qoOfH7BZ/LxSwooSArnX/tNjFE5CHrdbDd4eHDZFKxuHx/WdnJJlpQhcTifH+nhV0vHsONoD+W6AX53YT7v15io7bAw5PLRPuDC4fGxYk4G5boBEtUSrp81hncqO0iLCmVCagRrqo3MydUgJ8B7tZ0sSgErCnbr7fxmWT6bG0zsN9rJjA2jpt1CS4+dJePiaeq2U5QWySUTEllV1UFuvJrsWDWrq41cOiEeY+8gm44O8ssluZQ197GnpZ8/XJTP6qoOyo510T7gxOEDbUwo1xansqOpj8kJclIT4nj3gJEH5mdR02Fh+9EeHru4gPdqTLT0OXlgfgZvVxkJkYqICwth27E+DpqsxKrluH1+XvjROCr0FioMVu4vieazZjtdVje3lqayqtpESpSS/Hg1q/ebua44kYMmGxsbujEPuZGKReTGqXj8kjxWVXeSEKdhXq6GDxr6WVaYiNPt5ePGfi5L81Db46esqZd5WiV7DA4OtA+RECrC5oXbZqQRKpfw7gEzD5+XSVWbhR1N/Ty6LJd3D5gZdPq4ZEKC8HoilYyJV4/2q/G9Fb4TcWIBkslkqFQqoqKiiI+PJzk5GY1Gg0QiweVy0dvbS1tbGyaTCYvFgsvlIhAIIJVKT3t8Z7VaCQaD5yRq52x/zsP7vy9/dnFxccjlcjweD729vRgMBux2OzabDYfDMSJm/zYPY4PBQH19/WnZlP2b4f92fC6Xi0OHDlFfX09CQgIlJSUnXXCf7TBav9+PTqejoqKCkJAQSktLv0IkiFXJuKQwnvdqTMglIi4qTGDtfiNRoXIW5cfxdmU7adFKZufE8OreNgqTwylMDmflHiOv1Ai7EoVMzIYV00iOVPBmhYFpMR7UMniv0cmvL5+GXCrmhd167p6dgccf4BO9l3vnZdJn97C22sg9czPR9Tr4134j8eEhNJis1BuHKNZG4vIKqeyLC+LYrHMzPimMCSnhPL+rleUTE4kPk/PHjw4yS+NALhGz2SzjJwtzsDh9vL7PwLh4Je2DHl7Z00ZOrIogcMO0VC4qFOQIE1MiGJsUxrPbWrmmOJlolYy/7TRwxZgQ3L4AL+7W8+B5Wej7HDz1WQMyUYCWfg8JkSpK0iPpHHJzWVEiGTFK1hx2cvfsdGQSMU9vbeEX5+ditrj5oNbMnbO07G0dwOryMS0jklf2dvBhfRdyiYhYtYw3b5iATCLm2R1t/Oy8TMFHtd3BLcdZl0q5hJlZghB9+cQEokOl3L3mEC29DgJBmJ4Zxf8syaaxy46+z8mlExJ4u7KD4nRBU/jMbhO/WJKPRCxmU5eKO2dl0mH18+L+AVQy4Xq4TOsnN1rCnzc3sXyMCpVcwmObmvnNkhyMgy4+OtjN3bO1fN7Yy6YjPYiAxzY1020985Hn94lvG/t93ag0NTX1pK4qw6PSbxr3ncp5/xtwosxi2HhaqVR+QWYxHFpbU1Mzotf8sszivzWLD35Ahc/hcKDRaJg6dSqxsScfDZ2JUbVEIvnC3w7TlsvLywHBkDUtLe2kXzqpVMoNxYkjqQjD9mKv7zOMBLe+VdHOiuP7vV98cIi2fgc2j5+YUDE/XZSDze3nSKeVK8aGU2+0crTfxx2zM6lot2G2uLmmOIUN9WZEIhEXFiaw3eBDG61kZlY0L+/RMzYpjKhQGY9vaqKp206oXEJ2nIq/XTkepVzCcztauW9eJm6/YFP24/lZmC1uVn5Ww5I0aB7wIYnN5IZpaXzc0IUIRjw8Pz02hEIqIlYt5+1bJo+YSN8/L5NAMMjzO3U8fF42JouL9fWdrJidQbXBQrc9wOVFSaytNmIbGiJUCh8eGkStDCFMITA/H70wH48vIOz9FmRhtgfYfrSXW2do2XS4G7FIxHl5sbxYpmdRvobEcDk/ff8I5bpBgghemE9fVkCPzcvnjb3cOj2Vzxt7kUnElKap+VfDEMvGxZEcEcJjm5p5+LwsXF4/d64+yIDDh8MbYFG+hmunJLGvdYAsTSgTU8J5apuOW6enEKaQ8vimZn69OJsuq4f3aju5pTSVXc39PLGlFalYhEQs4pXrJpASqWBdm4TfLM3D7YPXqzq5Nl9OvdHK1v2NLM4K5e0qI82dQ4iA2o4hLi6Mw+cP8LtPmv7t8+hOxGj2ihKJhLCwsJO6qqjVaqxW60gc2IkC/BNv6Oeq8J3L/83wez6ZzGLs2LEjJKRhmcWePXu4+uqrWbt2LRaLhd7e3lGd92SxRP39/SxcuJCcnBwWLlzIwMCpORGdbfxgCl9MTAzx8d9M/z4b0UQn2pq5XC5KSkrIyMj4xpGMRCIhIUw20ulFKGQsHSvYiyWEK5g/RsPre9to7bETIhWz+UgPmTEqMmOUOL0BrpmSjDYqhKc+O8zsNAWJESG8e9jONSWpRIbK+PuOVm6doUUuFfP8zlZWzMnEF4SXytq4fWY6/XYvS/++j0GnIOB/cEEWDy/Mpr5jiCOdNm6YmsrHDV34AkFKkySsqmxHZu9hTJSY9486WLFsGtpoJU9vbeGW6WkoZYKnZk27hSAwLU3Fr+fF02Pz8FF9JyvmZFCpH0DX5+DqKSmsqzGREKFgRlY0K3cJbM+USAVrjji5YEwEgWCQn3+sRyQWPsNLJiTx80W51HYMcaTLxo+KU3j3gJHUKCV50RL+vqOVK4uSiQ8P4fFNx/jJedl4fH7uWdNAt82LzeNnRmYUt5Sm0GC2oVZImZ0dzQtlBpaNiyc5IoQ/bW7m7tJ4vIEgz+9q46cLs2jucfDklhZARPuAi/MLNExODWdf6wA/mpJErFrOHze18Mvzsxlyenl1XwcPLcikpmMIfZ+DSyfG82ZFB6+XC2J/uUTMK9cVopSJeWqbjt8syabLEWRbyxB3zdJSZnCSk6llTnY07zV5SdOoEYvg4yP95EeLkQDm3kFuKopiV3M/79Wce5bnqeJsEWpOdFX5MqtUo9F84YZeVVWF2WxmYGDgrBtQfxvOJUPym+zKTrS2G+6sS0pKeOCBB5DL5RgMBq666ioKCwtPe9d3sliixx9/nAULFtDU1MSCBQt4/PHHR/2+zgQ/mB0f8K1uCb29vYSHhxMSEnLaxx4aGmJoaIiWlhbEYjFjx44lLi7ulHYQ/f39hIaGMi5NsCILAtdNTeXNcmG/lxuvZktjL1uP9pIcocDi8nFTaRpLx8bxbm0X7n4TxSkqNrU6mZafRmFyOKurjRSnR5EWFcqaaiOLx8ajlEn4134j15Sk0Gow8elRCzuaenF6hS/lmluncLjTSlXbIL9blsdH9Z3UG4f4zdIxrKnuwDjgZFGCi63tATwBEfcsLOCdKiMRoTIW5MWxuqqDCt0AxkEXbl+A22Zq0UaHsrNpgGsnRGJ2Sth8pIffXZjHZ4e72W8Y5PcXCjs706CTH8/P4o3ydiQiEbMzw9lwuJ8NDUKmXRBYec1E3L4AG+o7eWRxLntb+tjV1MdjFxew7oCJ1j4HF6UF2KQXyC3LxsfzTmUHNreP1l4H3VYPs7KiiA6V09hl5/fLctl8pIf97UP8ZnE2q6qHCSvJrK42kRoZQrIaPjw0QFqUgoMmK7o+J9MyonB6A7i8AX5/4RhWV/9/osuqahNZsaFkalSs3m/i5mnJtPU7+bCui36HjyGXD7lEzKPLctnc2ItYJOLiQmFXNzktAp/DwsYjg9w7N53DZiubDvdya2kKmxt7qe6wkxKlYMjlY/H4JGZnR/N+Qz8LstQMOr181NBLst+MxzaI0+kcueF92zV4LnZ8FotlpIP7LiCRSFAqlURERBAbGzuim7NarUilUhwOBx0dHbS3t9PX1zey+xKLxV/RHJ4N+P1+ent7SUxMPKvHPRV4PB4GBweJj48/pd+XSCSkpKSg0+koLi7m2Wef5a677mLu3LmnZTOm1Wpxu92sWrWKFStWAHDvvffyzDPPEBYWRlZWFr/4xS+47777RvW+TgH/t+M7FZxOQsOJ6Ovrw2AwYLFYmDhx4kltzb4JEokEv99PhkbF0nHxrK7qQKOWMzU9ihfL9Pzx02MopGJCZRLeu6uEqRlRvLRbj2xAhzYMNneIuG7eRLJjVTy3o5WLJiSSFKHgHzuEndmwL+ZtM9KRS0T8av1hjg0G8QWCSMUi/nBRPr5AkN0t/dw/L4uOAedIZ3bQOMR+wyBXTYhh69FerO4Al05K5LOmIRIilMzJieGF3Tr2tvQB0GAa4s5Z6cSoZNS1W7h/XiYiEbxdO8DDC4XE9rcrOnhgfhaNnTb26fq5dbqWzUd68PiCLBkbyyt79CNJBDKJmHV3FKNRy3luZysPHZcm/GNHKz87P5f2AScbDwqvdXdzP0OeIMsnJPLGvjYcHj9yiZj1dZ0UJIYRppBic/v51WLB5/O1cqEjO9Jp40DHENeXCL6YsWqZsAOs6iYjUopMLOLFPe2kRCmRiAVD6l+fn82xbjv7Wge4aVoKHx3sJjpUxvTMKP6+Q88VkwQZxq8+OoY/EMTpDTDo8HLztBTsHj99di/Xl6TwXm0nadEKpqRF8NetrSzWSolWyfn9J038eG46fXYPD3/YiEImFK+rJydx1eRE3q40Mj45nAnJ4fyzaoBHlo5FKpWwVq8gTZs+kj136NAhKisrqaurG0kX+LY92PeBc+HVKRaLkclkaDSaUY9KR4tzbVA92mSGYfarSCRCo9Gc8Wvp6uoaKf6JiYl0d3ef8TFHgx9M4TtTo+qTwWKxUFVVRUdHB1qtltjYWJRK5Wm/thPPu2JOJg6Pnx+9UkWFfoBAEGZnx/DqDZNweP28saeV85P99Dm8HPHEsDwnBJPFzcaDndwzVwiW3dbYw12z06nrGGK/wcKtMwRvzU8OdRKmkHHAYEEsgtLMKPrtHmZmx7AgL5bX9rYxITmcSakRPL9Lx9JxcaRGhvD4xw3MSQwSpRQsxu6bl4VIJOKZrc0kRiiwuvx8WGdmTk4MQSAxQsE9czKpahukqcfOZYWC4bRSJmFRfiyv7G1jWkYUBYlhPLO1heumphATKuOn79ZR1dqDPwgKuYwb8mW4fAH2tAxw/7xMDhgsHO22cW1JKu/XmIhVy5mVHcPzu3QsGRtPWrSSNY0eFhXEEQjC7z4+ikYtPIBMTY/iofmC8FvX5+TKoiTW7jeRpQmlKDWcZ3foR8aVj21q4capKTg8Af6yd3BEDH/5xARuKU1lY0M3YQoJs7KieW5XGxcXxpMcqeAPnzXzs/My8QWC/GVLK9mxobQPuGjssjM9IxK7x0+xNmLEwuziwjhSIhX8/pMmHjk/C68/yLvHvPxioRBOe/+6wwxftv+zJJs52dE8u0PPZRMTSI1S8NuPj/GrxVn4/AH+sUvPI+dnU9MxxJq6PhISEr4gih4zZsxIusCXb+5er/d7z+z7d0lnON1R6eHDh0eV1fefaFB9Yhbffxt+MIXvVHCqhc9ms1FTU0NzczNjxoxhwoQJqNXqMyLGDIvfs2JVLC6Iw9Dv5I5ZWuaN0VDTYSElXEpRgpzX93UwpzCTqRlRvFllJi9KNJJ6MH9MLDlxKv5xvOtLjlTw7PZWpmgjkUlE/OGTY4hEIJeKSAuX8LulOYhEIp7fqeP+eUI23mv7BOJK15CbpzdUcXGWlHZrgG5pPHfOzuBQX4CmHjtT0iL46GAXa6qNJISHCMe/KJ+itAie29nKssIEUqOU/PXzZq6fEk+oTMRTW5p58Lxs3L4AK3fpeHhhNsZBF3/59BASfBgsHiRSOeflaeize8mKlDBvjIaVu3XMzRXe25OfN3PbDC3hChlPbGriZ4uycXj8vFSm544ZWkz2IHetqiNEKlzav78on2Xj43ltn4EkyRAZkVL+vLmJaybGCMSTzS38YlEWFqeX18o7uGe2lsOdNu5de4jh++OTl+QxIzOK53a1sbxQ2AH+cVMLP10oCOqf3aHnV+dno+sTzMJLtBHsbO5nb+sAqZEKAoEAP12YRaYmlEc/beah+RmIRSKe+LyV3y7NwTDgYkN9N/fM0VLXG+CPmwSTbp8/yF+W55MXr+IvW1r58bwMFDIxj37azO+WCVFKq6pMPHyeIGx/5bh59fO72mgwWUeuL5FIhEKhGEkX+LK7SjAYHMnsO9Eh5FSidkaLf/d0huEk9xN3X5MnT/5aVum3ici/68L3ZZ3yiTiTwne2WZ3x8fGYzcIu2mw2ExcXd1aPf6r4Qe34vu1LbLfb8fl8X/uU43A4OHLkCGazmaysLDIzM0f2gV6vl8HBwVH9I7983qxYFauqOihOj+KqSQm8XWVkoLebq0u0vN/QjyZcxSUTEnm7sgO1XMRVM8awqrKDhHAFc3I0rK4ykqlRkaVR8WGdmfcOmJBJxPgCQf525XgSI5SsP9TPBQUa5DIp7x4wcfP0NPrsgmB+abKXpj43B7oDPHXtNHY29bGnpY/fLhvDqoo2PjrYjb7v/7H31nFyFub693dsZ91n3d3iCSHuTgQnuLS4UyBQ3NtyqnBaOD20EA8JIURJSEhCdC3rWdeRHZfdcXv/mGR/KYQoLT1vuf7aTzbzjOwzz/3c932JHaEAxmZG895NI1h1XI7d7eOOCZmn/ETFzC1JYE2FnDxZKEnBXrY0mZhfmohEJGBDlZLZeZEcbjdwQmkjSCwmXCoiNEjMezeN4LMTKvosHl5ZOoyVx+XYXB5uG5/OquNyEiKkjM+OYU2FnKn58UjFQtZXKjjUocfnB6lYyKb7ruBAm47ybiMPjY1iS5MRvRN+Nj6JTfUGnLYBhsX42NVhJzXITmyYlM31Oqp6zbi8fsQiAX9alsOxbjM1ikFeWVTAuiol2kE390xMZ3WFkoTwIMZkRLG2Usn8knj6LU62NWjoNToIlQiJkIr56y3D2VKnpql/kKdn57C6QgkCAYtKE1hTqWR0ehSycClrK5WY7B60gy6cHh8vLcynUTVIvXKA164qZF2VEt2gi7uuTGNVhZLUqIDbzJrKgFtNj8GO0e7hxtHJaAZd7GvVs2x4IkHic2fPhYSEDBmxp6amkpiYSHBwMC6Xa2iEL5fLMRqNF7U3PB+MRiPBwcH/8mgajUZDdHT0Ra0iTuO0q8q3vUrPJiJXq9UMDAwMCfDdbjdOp/Of4hGqMju47eNaegx2JufGfuf3FosFn8930d3b+vXrWbRo0WWNOE0m0z/s+Hp7e2ltbWXy5Mm8//77ZGZmMmfOnEs+/nnw047vckadDoeDpqYm6urqSElJYdy4cd8JZ7xYy7IzcWbHB1CQGM68YhkfH+1B1dHE5KwI9vb5GZ2XyrT8OD460ktxUgRXZsewo8vN2IxoRqVH8ZeDXUwviCdXFsYbO1r4/b4OBEBMmIQvH5lAQoSUPx/s5s4r0wmTCHjvYA/3Tc0OXOT3dbAkW4jT42Nnj4+Xrx6F0e5h5fE+Hp+VS6/BzjUfVGB1g9Pj474pmTw0LZDAYLS5uX5MKhuqFMjCg5hZGM//HOrmyuwYSpMj+PCoktnZISRFSvnN7jauGx6P3+/n4Y0t2E+97auGJ/H60hJ6DHa+bNTwwLRAdyk3OrhpbODYyVHBTM6L4/0DXcwvSSArLoQXtpxkX7MWPxAVIuHx0UG4vT7WVsi5/8qASP5wt4X7pmZxqHuQ4NBwFpbK2Nbu4IZpIylODOOjmgFOKG34AbfHy33DxHh9fjadUHFbaQgtmkDA788mZrCzSYtYJGB2YTx/OdTLrMI40mOCeeqzk5yQWxAKIE8Wygc3D8Ngc7OqQslTs3Ko7DXTobOxfGwKayuVZMeHMi4zit981cGA040faFYPsjBbhEQoYF+LnpcX5tOutfHlSS0PTc1iT7MOt8/PorIE/vJNDz6/H5FQwP42A9ML4ogNlVDZa+b1xYUoTA7e2NV+0bu805l9qampQ1E7Y8eOJTs7+wfdG/67jDovF+calcpksqFRaWtrK2q1+pJHpWeD1+fn3a86WPTnCjp0Nmrklu/tNn+MLL6zxRKtWLGCPXv2kJ+fz549e1ixYsUlH/9y8B9T+OD8xe/bhc/lctHS0kJ1dTWxsbGMHz+e+Pj4sx7n2zq+i8GZz+vz+ejt7WVStAWH20+jN5GnFwRy8f5+tJeHZ+RgsrtZebyPR2fmYnHBmvI+Hp2RQ7/FyeMb6lGY7FgcHoanRvHc/AIMVjfVcgsPTM2mqtdErcLCssIwDnWa6DPYWFwYydZ6NeIgKVePTObzRgMJEcHMKpLxP990s64iQL9XmOzcWiIlKzaYvc06bp+QQXx4EO/uaePBqVlIRAJ+v6+DJ2efHj/28PTcfFQWF3s7bdw3KYM6hYWbP67F5w8wNX93/TCuGZXMukoFefGhjM+O4U/7O1kyPImEUAG/2t3GA1OzCJOKeefLNlbMy8fq9PDi1mZcHj96q4vQIBF3jE9HO+jC54erhwc6zRC3hSsyo1jbYOHa0WmkxwTzzp5OHpuehUgo4NXtrUjEQixOHwMuP8vHpuDwQnJ6FjeOlPFVlx2p38nweBF//LqTAqmJ1Agxr21v5ZGpaYgEcO+aeuQmBy6vn7EZUTw/L492rY0OrY1bxqWyrkpJVlwI47Oi+e3eLm4aExhBv7K9lWEpEVhdPnY16ZhZEIvPDx4fPD4zh286DOitLq4dmcTfj8oZnhLB6PRI3vpbGHsIAAAgAElEQVSynQnZ0QHt31E5efGhSMUCrE4Pby4ppENnY/dJLfdPyWRbg4bPTxldXw5Oa8AuZm94vgDbH2vU+a/atX17VJqTk0NaWhrp6emIxeKLHpV+G1W9ZpZ9UMnHxxW4vX5y4kL46NYRZ/1MTzu1XCwud8d3tliiuLg49u7dS1tbG3v37iU29rsd6r8C/1GF73w4HUZ72l6soqKC8PBwJkyYQFJS0j9NA3i6aKpUKo4dO4bT6eTa2ROYX5rIylOjvXklCXx8rJeMmFBmFMbz0ZEeChLCGRYv4n8OddOqGUQiEvB1q47xWTHkycLotzi4YUwqebIw/vR1B1ePClx0f7+vg0UFYUQHC3lt8wmuKYkgTCpiXaOVh6cHiCu/2dNKkEiAze3jcIeB60anBC7MfiGPnCLR7G7S8OCprq+pf5A7J2Syo0GN3e3l6pEprC4PMCEnZEayvnGQP33dBoBAIGTV3WOQRQTx18M9PDYjF7FQwG/3dvLs3HxMdjd/PdzDjQUSOrRWdjdpeXhaNofa9ZR3GUmIkLK/VYfP76c4KUDlv2dSJtlxIaw56WJqnJXwIBGfdsKLi4oYdHr588EuVszNp1Nn4/M6DQUJYQFfTI2VYSkR2FxerhuVxKi0SH7/dTd3TswmIVzC2jY/b1w3CgRCNncJeGxyEgqLixWb6nB7fagHXIxIlLKgMIqqXjMFCYFO7r/2dgaKXJSUV7a38dzcXHx+P7/a08l1I5PoNTr46Kic7LgAGWpybhw3jklmT4+X/IRQrsiM4ld7Orl5XAppMcG8uK2F+ydnYHf7eGFrK2FSEQKgIDGMX87Lp7zHzMn+Qe6akMbGE/1kxoZwRWYUb+1qp11rvaTz8lw4397Q7Xafc2/4/4cg2ovB6a4rIiKC5OTk77BKIyIivsMqbW1t/Q6r1O7y8Naudu5cWUu3wQ5ApFTEX5YPIzTo7MXtUjs+l8t1SdKu/wv4j9rxnS+T73SEikKhIDo6mtLS0qH5/fkgEAjo7e29aK/P0ynvvb29SCSSIf2fUCgkVxbGJ8f7EAoE3Do+nY9P6fpuuSKNT471IRFBGHaq+t0cajeQJwtDb3Vz09g0lo5MZnW5nIRIKTMK41lToSArLtBRra1QECe0kxcfzN4eFwtH55ISFczaSgWziuJp0wxyqN1Al95GdlwoFoeH928aQZfOyv7OAZ6amckJxSD7TuXufdmkobzbyOtLitl0Qklz/yAvLixgdbmcmh49CoMVkyvA1HxwajaHOgwUJIQzNS+eNRVyRqRFkRUXypoKOctGJuPz+fm0WsHSU36iOxrVPDIjh231/exp1iIQCBAKoDApgreWlrDyuBy5xsC0JC9f9XkpyclgakE8a8rljM+OJSE8iDUVcpaPTeFEr5k9LTq0gy4igsVEnLpobKlX06Ac4Jfz81hdETDSvm5YLJ81GEmOlDImI4r11f1kyCJp0VhRDfoYkRpJhFSI3OzmkbERHOmzsb9Zw13FIr7qcdKutvDA5HTWVqsJkYgYlR7J53VqjnebCJeKcHn9/Nc1xagHXHxep+blhXnsO9nPgQ4Tby8tYkttP/XKQX4xO4e1lUp2NGoQCAT4/HDb+FTGZkazplLJ3OJ4gkRC1lUqeXhaFt16O5tr+7l3Ujpft+k51GHg6hFJSERnv+D/kDq+M/VzCQkJ37s3NJlM2Gw2nE7nD7Y3vBAolUqSk5P/5cXPZDKdVbf4fX6b8fHxSCQSHA4HWq2Wnp4edtf18ujWXqrl/4+4JBYK+PCW4eQnfH+EmlarJSIi4qKCtP1+P3//+9+5//77/y9HE/204zsXfD4fcrmc2tpavF4v48ePJzMz86K+HJdycphMJiorK9HpdERFRVFcXPwPd1j5CeEsKktk5fFe4sOCWFCayCfHA24uYzOi+PPBbtafDBApQiRCVt01hkm5gay7kWmRjMmI5s8Hu5iSGxfwwtzbQaJTTmqEiB19QpZfGYjK+d3eDm65Io3QICH3rDxBrdyCSAjjs6OHsvb++HUnT83Jx+b289ExBU/PzUdtcbK2QsETswOatj0nNTw0LYfybiPba3qJCvJTrbBi94kojBUx4HCzsCyRyXlx/PeBLmYUxlOYGM67e9q49Yp0EiKkvL2rlcdm5iIRCfm01cNTs3Ix2dxc+0E5dnfgrveeiZk8PSef411GKlr7mJkuZHf7IBNGD2NUgoi/fNPN9Px4ipLCeWdXK3dPzCRcKuaulbV0GewICPhq/v66EvoHXHx6QsUTM7Kp7jvtVJPG5lo1USESxiYH8d8He8iNCyFEIuR/j8qJD5MQLhXh8fl5e2kxNrePLd1+XltSgtLqp8MdzT3jEjnaO0hzt4KJyUL+dkzOX4/0IRSARCTgo1vKSIsO5qVT3aBQAK/vbOeeUgn9Fid/OybnuXm51Mgt/OKzk/gBrx+enJnNorIE/vdIH+OzohmVFsnru9q5e0I6KdHBPLelmcdnZOH2+nh+aysSkZA+o4NXtrf+aNq9s+0N4+LiSElJQSqVYjQa/2V6wx+z47uYwn56VJqenk5yVj4fdQTz22o3g65//DzuLhEjNHSfc1R6qaxOuLTr2v8F/Ed3fH6/n/7+fhoaGpBIJJSUlKDRaMjMzLyk419ousPg4CCNjY0YDAYKCwtJSUmhv7//rHfd+QnhfHKsD5/fz10TM/n4aC+7GjU0qwfxA6OSgnjjqgI+rdUgEQtZPjaNT473ERIk4rrRKaw8LidUDMMinexot1KQnsjCkemsr+4nJULC1KIk1lYq+KKuH5Pdg88Pz88vYERaFJuqVSwsSyIkSMT6Sjm3XJGGQqNjV4uZh6bl0GOwsa2+n+fm5VPRbWJfi46fj09ka72a/R0W/AgRi4TkycL4eamIr/u8KM0OnpiVxyfHenF5/Nx+ZQYrj/cRFSJhdrGM1eVySlMiyJWF8nmjga+atTg8Pvx++ODmkZjsbnY2qnl4QgKH2nQc67Pyh5vHsq1RS43cwk25Pvb1eem3OHlkRsAJZmejGoPVjc8Pt4xNYXJuLJtq+llYGmDgbjzRzyPTs2hRW9nZqOHVqwrY16LjWI+Fm0uk7O1xsqNRS7BEiMvrZ25JPDeNSWVNpZLM2BCGpwRigxaUJCAQwKYaNU/NyadFPchXXXb0zgBpKDRIyOszZezvslLbreWGPAG7Op2YBwa5ZngC62s0JIUJmFycxpoKJT0GOwabG6fHxzNzAvrOXU1aXl1UwOFOI3uadbyztIit9Rqqes28vCCfNZVKtjVo8Jw6z+cVyZhfKmN1hZLIYAnDUyO/c479GM4tGo2GhIQEYmNjiY2NHWJInt4pWa3WC05zvxj8GO8VAu5MISEhF8Vi9fv9bKhWce/aerr19u/8fnGZjOeWjDwvq9RsNhMfH39RTFaXy8XatWu59957L/gx/4b4KZYI/p/Wxe/3o9PpqKurA6C0tJTExETEYvFlRROd77EOh4Pm5mYUCgU5OTnk5uYOdXhyufyseVkxoUHIjXY2VSvRDDhp11qxODzcPzULWbiU8t4B7rwiCYMTttT28/D0HLoNNrbW9fPw1AxqurXsbtbzi9l5tOjd7G838ssFhew/qeKb7gHkJgdqixOX18e715ZRrzDTbbDz6lVFfFaj4qRqgBcXFLCuUkGvwc7y4mB2tNswWAPpDp8c68XjCxBK1lep2FSjxn2KuPL4rFxmFMpYV6kgP1rEqPw01lQoWDw8CZFAwIYqBT+fknXKnFrF8/MLqOg2saNBjdLswGANsN7+cH0ZBzsM9A84eXxqGmsrVagMFh6aWcD6ExoiQ6TML01gVbmczEgRVxZlsLpcjtLsQG60Y3V6eWR6Nj6fn6NdJl5amM+BNj2HOoy8ubiQzbVqmlSD/HLeKdsyu4dbx6Wwrrqfg31O/Kfez4q5uaTHhLCuSsWtV6RgsLn5vLafFXNzqeqzsPuklreWFrG9UcOBNj1Orw+91U2wRMgzs7P5qsVAUnw0i4clsalez8SSDLKiJXzWYGR8gg+X28O+Xg9ijwP5gBeDzc3dV6aitjipVw3y9tJCttRrqOmz8MqiQJHrt7h4aFomK8sVfN2mx+724fPD8jEpTMiOZl21ivkl8QgFAtZWKhibGUVK1D+OvH6swhcbG4tEIhn6t9NF7VwRRRqNht7eXlQqFWazGafTid/vRyKRXFAn92MVPp1OR3h4+AUbXPQabNzxSS1b6jX4ztL0LiiR8fbSogselRoMhqFYJ7vdft5YJ4PBwJ49e7j99tsv523/2Php1HkaBoOBiooK+vv7GTly5HfGi5cDoVB4VhbbmezQhIQErrjiin9gM33f407joWnZeHx+dp/UcMOYFEQCGHR4eHxWLm6fn7+Xq3h0RiA14INvunh0ehZWp5c3P6vg3gnJWN3wRauNp2bnoRt08fu9HUhEQox2L+0aK8vHpeL2+hl0enhidh5NqgH2t+qHiCu1Cgv3Ts5iX4sOnd3P9SNlbKlT4fX5WTYiiU+O9fLMZw1AoECsums0YzKi+ehILwtKE8iThbG+xclt4zOGxpmPzMhBKhHy6y/beHZuPk63j9e3tyARCTDa3PSbnczNDDi3aK1uHpwScJ/5pqaFG0YlsL/XSWxUGAtKE/nwUDejT0k61re4MNtdCIBjnQZuuSKNCKmIih4TLy4swOby8t7BHn45P58eQ2AX9sTMQBpEY/8gd4wP2JadFpF7ffCH60oZnhrBH/d3c8u4lCHCyjNzchAJBby5q53XFuWjt7r47d5O0qKC6TU6MFrdLC5LwGz3ECwRc9OYFFaVK8iICWZqXix/2N/D1aPTKUwI46+1dsbkp+FHwDGli2lZ4UhFcKhFxW0FflRmB7/f3cwvpibToBpgd7OOp2blsL9Nz58PBoTrJruH5WOSWVASz7oqJaPTI5mQHc1bX3Zw67hU0mNDeGrTyaGE9x8TFzNyPDOiqLS09DuJ5CqVihMnTlBeXk5DQwM9PT3o9fohDd2/Ay501Onx+fnNng6WflBF51m6PICxGVG8uaTwnF3vmaNSqVTK2LFjhwT4EonknKxSn8/3g0YS/e53v6O0tJSysjKWL1+Ow/Hjn3//UR3faeJKUVHR0AnwbVxOx9ff349MJhs6wU9n8bW2tpKQkEBJSclQ+OyZEAgE53zeqBAJarODZvUAf7hxOHaXj40nlNx+ZQZqvYmdLWbumpiBxe5h0wklo0J0eEQhHOxz8dT8MlQWB1/U9XPLuHQOtuk41GHA4vQiCxXjRcCfl4+kssfEV81aXlpYxIE2HQfadLy2uJgvmzSnAmqL2FLXT7PWwTMz0tl20sjhVg2dGgsDLgiWiHlzSTF7W3QIhULunZI1RMa5bkwq66r6kYUHBYg25XKKkiIoTY5gdYWcMRnRdOlsHO82YXF4yJWFoR10ct+IYIy+ULbWqVicbKXZIqbRKODd64bzRW0/Vb1mXj9lct2jtzEmI5pDnYFCPS4zGoXZwdziBKYXylhVLmd0ejSp0VLWVam4ZmQSbq9vaMzZprHyRb0auyvA1HR7fLw4L4uKHjO9RgcvLcxnTUVARH7flExWVSgIloiYXyJjTaWSnPhQBpwejnaZMNo95MaHoBlw8fLCfLr0NrbUqXllYT5Hu0zsbtbx9tIitjdoqOg2c9PYZL48qaOy10x6uACLC8ZmxbJ8XCobanUMy0ljbFoYmxoMFEZDTJCPzY0m/M5BFIM+tIMuFhbHESoVc7jTyIsL8qlVWPiiXsObiwv5psPAV806npuTxxcNao52mlg8LHGI7PJjdEGnvyuXuns6WyJ5cnIy4eHheL1eLBbLkAm1TqfDZrPhdrsxGo1DBfNfCbVaTWxs7DnHjdW9Jn6+pp5vOoxn7fIAcuJD+estwwmRXPi+UKFQkJaWdsEC/Ouvv55NmzYNmQwAxMbGXtLfSqFQcN9991FbW8tjjz3Ghg0bcDqdjBw58qKPdQn4qeMDSEtLY9SoUYSHf39atUAguGTPwtOShtNavGPHjiESiZgwYcJlf9kenJ6DQCDgvf2dPDgtG4FAwPv7O7llZCwI4N1dTUyMMuH3+zlijuH5xcPx+vy8f6CT+yZnBSQGHxyn/1Rg6fzCGJ6dEo/Z7uF/jwT0dtoBFyuP9/H0nEDo6afVSp6cnUu71squRg2PzcylVe9iQ6WcEKGXNr0TN2LmlcgwOzykxoQEIoKqFIRIRCwensRHR3vJjQ+lLE7I+we6mJIXR1lKJP/1VTuLhycTFSLmyU0NtGkGkYqF5MpC+cvyEYiFAtY22VmUYsfq8nHcGserS0qRmxysqwyQa+oVFg53GLhqWBL7WnS8t7+LqCABAmDF/ALmFMt4/0AnE3NiGZ4aya92t3HT6CRSo4N5fWcbj8/IJlgi4uXtrcSESgKZhupBbhmXgtvnp1Pn4LbSEE7ILVT2mLlvckDA7nB7uWZEEn8/1kdefAi58SH85qtOahUDRASLCZOI+N21JcSFBfHi9lZeXliAzw9v7GrnjSWFaAecfHCol0emZXJSPcgrO9qJDA5cVCamiLhnYjqbavqRiAQsGZbAh4d7GZ0Vx8ScGD46YWFEXhoSkYAjCjfDk0KRhQo50mng6jQnQUI/T22s5xeTZPh8fl7a1so7SwrRW908trERr89Pq9bKC1tb8J3D5uqfjX8GyeRMvWFeXt5Z9YZOp/Oi9IY/FM7V8dlcXl7d3sodK+voM35/NxQfJuF/bxlOuPTSbhbOhrMJ8L/55htWrFhBUVERFouFP/7xj8yaNeuSDTo8Hg92ux2Px4PNZiMlJeUHe/2Xiv+ojg/OH03U399/wXFC34ZWq8XlctHc3IxUKqWsrIzY2NgLKnjn6zQjgsWYbG42VCm4YWwqQoGA9VUKZmeH4rDb2N1h4/45w5BIJGyoVnLTuDQ8Xh/rqxR82aTB7g4QRD66bRRCgYDtTTquKY7AKwnj8xoVD03LoddoZ0udiqdm59HcP8COBjXPzcunstfEnmYNt4+WBboUpROEIkKDRMSEBfH+8hF8XquiVm7m1cXFbKhS0Ka18sKCQlYd76Pf4mRBmofdPW5sLi93T8rgk2N9fFqtYMAR+DLdOzmLxcOTWFepICdGQjRWvur1cN2VecRGBKKVbr8yHbPdzcZqJb+YnUd1n4lNNUoalAOIhQKiQyW8NjGYCq2Ayh4Tby4tYV2lgk6djRXz8vnkmByry8tdV6azqkJJiEREWJCQo10megx2hqdGoDQ7uX9KJlKxgI01apbkS3GJw9hc28/z8/Ko6jWzq0nHm0sK2FavYWOtGt2gOyCvSAzjt9eWBOzFrG7unxLYvUlEApYOS2RVhZKcuFBKksNZV6XicKcRAYHx8LtXF2F3e/my3cqTs3Jo11r5vFbNK4sKKO82saNRyyPTstjdrONQh5GUqGDcXh/hwRLeWVbCploNBkJZMTubT2u1qMxOri8M4otWK182qXF6A88zJSeKa0Yms7pSic8H4zKj6O/v/5dfjFQqFUlJSf90huWZe8Po6Gi0Wi1XXHHFD7o3vBAolUqSkpK+c135ulXPLX87Qd0Z3qpng0go4OPbR5ARe/Em+JfS0Z88eRKJRMKzzz7LsmXLuPvuuy/ps4iMjEQkErF48WL+8Ic/UFpayjPPPHPRx7lE/NTxXSguVYiu0+nQ6XSYzWbGjBlDfn7+RY0GLqTTvG9KoEP5w74ObhuTQJBIwP+Wq7l1tIyQIBF/Pizn/inZSMVCntvcxOFOA6dv6P+8fARSiZCNJ1Q8OjMXgFW1Jh6flYvH5+dPX3fw1Ow8HG4f/32gi6fn5GN2uPnwUA/3TUpDO+Dipo/rcZ666bt7YiavLymhQ2tjV6OGJ2blUSu3cKTTwEPTczjUrqdVPcg9kwKidpvbz/KxqaytkPPKtmYArE4v715TyuTcWNZUyhmfHkZ2tJjffd3Fz2eVkhAq5L/29XL/1KxAUdvewrNz8xEK4M5PqjnZP4jH66csJYI/3zwCg9XN3j4PT8/OoUFpYX+rjkdn5LC/VUev0c7tV6axqUZNsETIqLRIPjzcy1cteqJDxISIhbyztIjc+FBe29HKA1MyiQmV8D+1Np6fFxD1v76rndcXF2C0ubh7VR1GuweXx8fUvFheWZhPs9pKVa+ZB6ZksqtJi8Xh4cYxyawsV5AUGcTMglh+u6+TTSf6h/6mf1leRk58KK/uaOfxmdlEBMFzW5qHDMRf2NrKiwvyMdpcPPJpIyJhoKPNk4Xym6uLaVZbWV+t4uVF+VT3WTjYaeG5uXlUKh181hH449s8MDMvkpuGRXKw00xvXx9TUkV8eLiXjw+24PV6/2lm1N+HH8O55czn/FfvDb/d8ekGXdz4UTWPftqIw3Pu771YJGDdXSMpTPz+SdX34VI7+h/KoNpoNLJlyxa6urpQKpVYrVZWrVp12ce9XPxHdXx+v/+8xUWv1xMeHn7BYk+z2Ux9fT02m43IyEiSkpIu6YRRqVQkJiaes9MMDRJhc7hYX6UiR2wkIzGOHa0DTM0KJztZxtpKBdnxIdQpLLRrrYRLxUzOjaVeOcCdEzOICpawpkLOkuFJiPxevmgyce3olKHucfm4NNw+PxuqFNwxIQOjzcmGKiVft2hx+UAogN8vzkBndfNVq4Fn5uZRq7Cws1HNSwuLONxpYF+LlteXFLP7pJZDHXreXFrCF3X91GtcSCRBdOttDDo8PD0nn8oeIx6fnydm5rDquJwuhZoHpmazsd6ANCiIiYk+trVZiQsLYnZRgLWpt7ro1Now2NxMyollYm4s+1p03HJFGi6Pj+3NZh6ckkGf2cnntSpeWFhEebeBXY0a7i4L4mC3lc9q1SjMToSCwM7kg5uHsb5aRZ/RwRMzs1lVrsTp8XHT6EQ+rdURFSJmdlE8ayqVqC1O5CYnZoeHucXxDEsJZ/dJHXdcmYbJ7mHjCRVPzcqhWT3IF/UaXl0U2Otta9AgQIBm0IXX5+f1xYUc6zLSqrENGWB36+0sy/Kxqytw/J9NDHSmXzZp8fj8+IEbRicP7RXzE8IYlRbJ6gol47NiyIoLYXWFEqlYSK/Bjt7qZkZBLMVJ4expMXL7pEAqxLbWQR6ckY/J5mRzg4nMMC/+Qd0Q68/hcOD3+xGLxf+0jkyhUPzLd21erxeNRvO9YbCXuje8kPDa0+8X4L0D3Tz7eTMqs/O8r1kshL/dNpKylO/KUC4EHo/nkgJwDxw4QEJCwmXv4rZt24Zer2f58uWIRCJcLhdHjx5l0aJFl3XcC8RPHR/8sJl8Z4smCgkJ+UH8Os8Gj8dDe3s7w4K0RAaL2KMO4aHZRUQHi/n7CSPTC+ORiAS88EUzHp+f0CARKVHBvL64mJhQCe/uaeeeSZlEh0r4zZ527roylVCJgN/saefBadmEScW8u6edR2fkIBEJeHxdNcfbtfj8EBQk4b0bhyEUCtjRYuFnY+Owubz894EuVswLeIF+eKib5+YXoDI7+eRYHyvm5dOls7GmXE5BQhiKQT9HOvRMz4/D5fWTEhPMfVOy2X1Sy4HyGq4ujeEbhZe4uBiuGZnMx8d6SY4QMyU3mvf2d+Lz+ZGKhWyrV1OWGkl6TAhdehuPTM8hKVLKy1ubeXJ2HsHiQGf20sJC3F4/7+xq4aErYtAOunjlgBGzE1xeGJsSzM+Gh9Chs7HhQB03FIeyr1VPt8bCLeNS2FCtIjRIxMQUCR8e6sVscxMkEnCww8iV2dHkxYdS2WPm3smZpEQH88utrTw9O/A5vrC1hdcWFeDx+nhxWys5caEYbR7atFZuG5eCy+sP6O4WFVCnHGD3SR3PzMnlcKeRDrOPh6ZmsbNRy7tfBcJ4HR4fD07N5NqRgaT21Cgp84pl/Gl/NyPSIpiWF8uv93QQGyohWCJkT7OOsuRwypLDOdxh5IZRyYxMi+TFra3cNCaZ4amRvLSjnRmFCUjEQv7SCBHpxf8Qu6NWq6mtrf2HjsdgMFy2sfKZ+Fd3fJeyV7yQveGF+pQ2qAa5+W8n+PBw35AZw7kgEgr4y/JhjEy7tKIHl+7TOTAw8INk8WVkZHDs2DFsNht+v5+9e/dSXFx82ce9XPxHdXxw/mgik8mERCL5XgLMubR4p0/2S+n4vi8uxefz0dfXR1NTEzExMYwcXkaQWMTaCgUTc2KJDPKzo9nExmoFIoEArx9+fU0Jw1OjArqtrBgKE8JZW6FgXFYM+bJw1lbIuSIrhnDfINtaLEzKjSUjJmAZFi32cFI1QK/ZQ1SolEm5cTQoB7hrYiahQSI21qiZkxtGVFQU66sU3D4+Hbvby6fVCu6bkoVmwMnmGuUpazI9u5o0yI12IoJAKpHw9ztH8027nq+a1CxLsXJCBx3WIN69YRRbalVU95p5Y2kJ66sUdBvdLB+TzI6Teva26EiKlGJ1epiUE8tD03NOsUaFXD8mlZXH+5CFB5Ef5mRb6yDFyRFkR4nYUKPB5vQgt3gwObwsKIknNz6Uoz0DPHvVMOQmB/u67Tw5PYN61SBfNhu4IdNJnc7HvlY9s9OgSuvnaLeZrLhQHG4vMaFBvLqogDWVShQmB0/OzGZVuQK728edp2KDpCIh8eFSDnUa6THYGZUeicLkZOmIRPJkYaypVDKnSEZUiJjVFUpuHJ2Mzw/bWgaRiAX0Gh0MOL3cOykDn9/HnmYdz83Lo1k9yOZaNa8syqdGPsDntWoenZ7JvhY9hzqNxIcHESIRYnJ4+PWyYo50GdnWqOHtpUUc7jCyvUHDM3Ny+KpFx4E2A1HBYgR+H7tO6pldFI8sKoyIiIihjiclJWWo4zGZTPT19dHX14der8dmsw2N8EQi0UUVsh+DSepyuTCZTCQmJl7WcS5Wb6g3mljfMMA7+1VoBi9sVCoA3r+xjIk5l2fi7HQ6GRgYQCaTXdTjtm7dypgxY8jOzr6s509LS0Oj0UDablUAACAASURBVPDoo4/ywQcfkJSUxKuvvnrJbN6LxE8dH1x4x3e2u1q32z2kxZPJZN/R4p1+7A/V8fn9ftRq9ZBp9Zk2ajePSyM5SsqKzY2srAzsi6JCJOx6dCJpMSH86esurh2dQkZsCO/uaee6MamBn3e3ccPYVNJjQvj9/h5mpItJiwnh17vbGJsiJUgEvz2oxCMQEREsJi48iDeXFBMfHsRbu1q5f0oWkcEiPqww8Mj0bEKDRLzzZRtPzMpDIhLy691t/GJOHm6vn2UflNOlD9iDzSlO4MkxwZjsbv70VSvX50D/gJt6l4xXlpTSqbOxuUbF03PzqVNY+LpFx88mZVKncfHUlnakp2j3Ly4q4o4rM9hQrUQsEnDNyGQ+OtJDRkwIMwvjeW9/J2UyCSNSwnhz+0k89gDpZX+3jQk5sSRGBNHUP8jj0zOICA6wOZ+fG2DL/umYlneuLsPugb36KJ6dX4DW5uP9Oi9SUeC8KYpwctfwMKp6zew/2c/DUzPZ16qn1+jgnonpfFbbj98Pk3Nj+Lhcwa6TWuLCJAgIWI1NzInh13s6mV8iY3hqBK/uaOXmsakUJITx/BfNpEQFbqAOdRhZNjyRxIggttareXFBAeFSMc98fpJXFxUgEQlZ8XkzLy/Ix+nx8siGJrx+CBILCQsS8sfrS3F7fPxyawu/WlqE2+vn+S9aeGtJITaXl4fWN+Ly+AkWCwkSC3h4dAgur4/71taj+9aFWSAQ/EMyw5nGymFhYZjNZpqbm6moqKCmpoaOjg40Gs3QHf6/E/7ZdmVn2xsiy+W+nUa291zcZ/HsnJyzZutdLP4d0tdfffVVmpubaWhoYOXKlf8Wxtc/dXzfwrdDYb1eL93d3bS0tCCTyb5XiweBbtBms11S2OSZlkZGo5G6ujrcbveQq8yZX1ixSEhEsIQttf0UJ4UxJ0NMudLJsNQoJubEsqZCTnZcKJPzApq5zNhQpubHs7pcTnpMCNNOGTjHBwuYVpLKpho1m+u0CAgYIK+Yn8+sQhlrKhTkJYQzLjMQ+lqcHEF+rIQtjUZGpkdTlBTOmgo5E3JiyYwLZXW5nGNdBnSDLtxeP8/OyycvIZzPapRMTxMjlkjYdtLEAzMLsHsFfFbbzyPTc+gz2tlco+LZuXnUyy1sOqGkqteExxe4OG95YDyHOgwcbNPx9rIStjeoOdpp5O2rS9h0QkWN3MybS0pYW6mgy+BgUqKfY/0+qtVu0mNCsNg9jM+K5p6JGayqUBIsEbF0eAJrKlXEh0mYmhvD2qp+8uODKUoKZ321iv1txoDLD/Cba4oJFovY3jrAXRPSMQw6+aLJwNxEG/1WP1ub9NwzMoJGjZONNWq6DXbEQgERUjEr7xjJ1216vmrR8+ulRexo1HCow8g7S4v4vE5NRY+ZG8eksKdZxwm5hdwoAVaPgJAgES8vLGBtlZJuvY3n5uWxpkKJ3OTg6dnZrKoIWJM5PX4EwJiMKJ6fm8uaCiUqi5OnZ+cO/f9n5+SwqkLBjqaA/ZtQEAg7/tWyIj6r6afN6OWNpcV8VtPP/jY984tlBJ9DJ3amW0hsbCxJSUmkpqYSExODQCBgcHAQtVpNT0/P99qN/Rgd36V2P5eCAYeHRzY08N7BXlzeCy96AuD1OSksHpZ4WdZsp2G1WnG5XBcd/7N69Wquueaa7+SO/h/DT5Zlp3E+covdbsfhcBAdHY1cLqexsZGoqChKS0uJjo4+54nocrmwWCyX9MUymUx4vV46OzsxmUwUFxeTlpb2vXdrhYnhfNmkxubyck+ZhNYBCQfadLy4sJBD7Qa+btXywoICjnQa2Nui5YX5BRzvMvJVs5ZfLijgcIeOA912jnYZcfsC3cLm+8fToLTwdauelxYVcbhDz75mLS8vKuKbdh1ft+h4dnYmX7foONxl4o0lAYH7vhYtAB06G2a7h+fmFdCiHkBhdPDqokI2Vctp0bt4fk4mu1oHaDc4eeWqQtZWBKQGv1xQwMrjfVT2mOgzObA4PCRESHloXCQHum0IhIKAT+mxPiRiIdePTuWT433EhQUxtySBVcflRAocxIscfKP0UanxESIJJM6/cVUh8RFBrKlUcv3oAGV/Q5WKn03Kwmh3s6mmn8dn5dKhHeSzOg09ejtWlxcB8Mer86jpNXKg08ybVxWwt1XPke4B3r22jO2NWtqtQfzm2jI21/Szv8uKZtCN1w8JIQKemRDBni47HZoBnpmdw5rKgMThkVP2YjaXl6tHJrKxpp8D7QbiwiTY3T5GJQi5e2oBK8sVhAeLWDY8kZXlSmLCJCwqS2BVuZKqXjMWhxevz8+CEhk3jgmE20aHSlg6PImV5QrCpCKuH5XEynIl1X2B/+/x+RmRGsGzc/JYV6VEM+DiiRkZbKrT0a238/TsHNZXqTjUYWR+aQLSc6S3nw1isZjQ0NALGv85HI6hc/ufSaI5Ew6HA6vVelmJ4heC1RUKHt/URLvWdlGPEwDvLEinLE74g/mUno40utju7aOPPuKuu+66YIu1f1P8VPhO43wdn8PhoL+/n56eHqRSKaWlpcTFxV3QF9Pj8WA0Gi96h+BwOOjo6MBoNFJYWEhOTs55DWWFAgEpUcGsrlAQFQS3TCli1XE5MSESlo1MZuVxOWFSMdePSeGTY32ESETcODaVT4710dSrpV1rZdANSVEhPD+/gC+bNMSESrj5ivRA5JFQwM2n4o+kYmHgscf7CA0SMTlZwNaWQP4fCKiRm+kx2JlbnECbxsrU/DjmFAXcUtwmFZNyYtnZbqMsPZ4rcuJYUx6IIipJjmR1uRxZhJQ2jZUOnQ1ZeBCTcuOo7jOzuCiS8NBgPj3Rz22ndokbqhTcPzUb9YCTTdVK7hwVTYPcwJ4OK52WQCq1VCxg48/HUNFjZk+zlreWlLC3Rcu+Vj2/WlbMtgY1R7qM/GppCZ/VqjjSaUQoEKI0O/H5/fxyfi6H2o30akw8MT2DzQ1GNINOHpyczupKFTanhzuvTGP1KSNp7aAbsyOQo3f3lensaTVSkBLLuJRgNjeaCHWbyI8WsLV1kPRQD4UJYXxaq+VwhxHhqZihZ+fkkB4TwpaTFmYXxZMQIWV1hZL5JQnEhUlYVaHE74c+o50Bp5eryhIYkRbBljoNswvjSY0OZlWFgsm5seTJQllVocTt86M0Bxiio9MjuWF0MlvqNUSHiLl+dDKflCtwe/wsyoRtrVb2txtwef0YbW4quk3MLZZ9p/hdrAzhbDFFCQkJqNVqIiMj0el0Q45K/2xGqd1ux263X9JE5kLQpbPy3BctrK0MsIIvBiIB/PXW4UwvTv5BfUrNZjNCoZDIyIsjyHzwwQc8/PDDP0qSxQ+InwrfaZwrk0+n09HS0oLb7WbcuHEXLWT3er1otdoLpg673W46Ojro6OggKioKmUxGUlLSBT9fVlwox7qMHO6x8uz8IpqUFrY3qHlydh7tGitf1Kl4bGYuPXobn9eqKIsTcrzXQrvBTWpMKDKpF9Wgl+cXFKAecPLZKVKK2nLq52k5qMwOPqtR8tD0HJQmO1vqNSzLl9IxKGZno4ZOnZXESCken4//uWUkrZpBttSomCuz0GsVUqHx89Z1ozh4UsnX7WZeuaqIo50GvmzScv/UTLY3qDnQpic0SESIRIhEJOC9m0awo0FNtcLGczPT2NNuobzHyNvLStlYraRWbmHFrAw2VKs40mXC4hUx6PQREyrh8fGRHOyxozIM8NCUNNZVqxl0ebhvShafHJfj98Nt49NYeTyQKi8SCKhVWDDb3cwtkXGyf5AU4QBjMyLY2WFnQkESJckRrKlUMiUvnvSYYNZUqShICKNNY6NNayMlSsqwlHBq5APccWUKbq+fjTUaHpyRh8XhYWeblWcWltFvsvHFSTPdBgdOrx+/H568IpRBj5DN9TqemJ5BY5+OLxoMPDM7h26DnQ1VSibkRFMjH6DbYGdaXiyxoWKOdpl4cmYOmgEnayuVPDg1gwGnh9UVCsZnRdPUP0iHzkZhYhhj06P4psPIjII48hPCWFWhJE8WxszCeFZVKOk0e7G6Az6R+bJQVszJYV2VimPdJuacpfhd7vhNKAx0NMXFxchksiHZQEhICG63G4PBQF9fH3K5HKPROOQfeZpEc6k4nf/3Q6d++/x+1lUpeWRDIz2Gi/ehFAAr7xjB6IzvdmXnyzc0GAz09vb+w2d1Zr7h+ch634ePPvqIBx544P96LNFPhe80zlb4ztTi5eTkYLfbz5qUcD74/X5UKtV5dxc+n4+enh6am5uJj4+npKQEr9eLx+O5qJm6QCAgXxa4kIkEAu6YkMHHx/rw+uDnk7P45HgfdpeXadlhbGvUs6/DQmSIJCC6Lohjabqbr3o96AfdPD4rl1XlcjQDTp6ak8eq43LUFge/mJPP6vLAz0/OzmPlsV72dTtQDwSMoCfnxfG764ex6ngfarOdBWlednbYkYTH8MyCUj453seA3cPCTAHb223Y3F5uuzKQnrCxOtDFeP1ww5iUU69ZTohExLWjU1hTqSRCKuTasRmsPC4nKTKYaXkxrK5QYDHqMXskKCxuokOCWFiWQHmPicWjM0mNELG5wUhBmJMIsY8vTloYEe0iKlTKpzUarhmRSKfezp5mHUqzg/TYEEw2N3cWCTA4/BxUeHhxyTA6dTY21fTz/Lx8mlQBBuXD07PZ16LjQLuREImIyGAxNreX/7qmmCOdJnY2aXl9UT4HO4x8eVLHm1fls7/dwM5GLUESEeoBF14/vLWkkHrlAA06H7+cmcyBTgu7T2q4I89LjUHAl01abhkZy8GuAcp7zOTGhyISgGbQzW9OPdcX9RreWlJIVZ+ZTTX9PDQ1i0MdBo50mYgJlZASLaVDa+fRGdk43D5Wliu4flQSsac6SLEAlBYnAy4/RYlhPDA1k89r1ZgdXh6dnjXkLjO7KH7IG/KHuhB+e8d3po/k9zFKT2vozmSUCoXCC2aUWq1W3G73D7q3atNYWf63E+xs0nIpVB6JSMDau0dflE7vYvSGpxPvTz/uQkalfr+fv/3tb//XQ2jhp8L3/+A/w5/QarXS1NSEXq+noKCAzMxMxGLxBRWvs+F8ZtOnC2N9fT1hYWGUlpYOEQIudQyTGBlMRXMPu1pN3Ds5C7PdzafVAQG63mJjY00/e1qMiIQC/H74+M4xhEnFrKtUMClFTG5GKmsq5CwenkT0KYH7wrIkYkIDqeULShOJCw9idbmcyh4TmgEXHh88NTuPYSkRfFajYm5RHDgG+bzJxLVjM5BFh7O2UsmyEcmESISsqZQzKzuU6MhwNlT3s69FOzQK+vDWkYiFAj6tVnLPpEwsDjfrqxQ8OC2brn4jO1vMPD4zlx6DnU3VCsZGWGg0CqjTepCIhMjCpZjtbn59dQlVvSZ2Nel4+5oyjnQaOa5y8YdbxrK/Vc8xhYMnJsSzt8PCpho16gEXEiHIwsS8MCGcA712WsxC/rB8NJtr1JR3m3j76mI2nVBR1WvmtcUFrKlU8FmNCo/Xj1AgoDgpnF9fU8q6SiVtWjuvXFXImgol3QYHL8wPRBy1aW2kRUtpUFkx2NzcOi6FOsUANpeX5+bmsq5ahdou4MWFhXx6Qk2/Q8RTc/L4vF7H/s4BQsTg80Ok2MuTE+LY3TFIebeRd5YW8kW9hm/ajbxxVSCuaGt9IKE9PEiE1+fj3atLqFcO8OkJFSvm5aK2uFhdoWBafhwn+wdp09pIj5YyLlFEucKBLDyIm8cFEiSMdjePzchifZWSvS0BqUPYD+gReSHkFoFAQFBQEOHh4d8xVfb7/VgslqFdmFarHSKmfd8ubGBgAL/f/4M4kjg9Pp7/opm3v2xnwHlprjcxIWK23DeOrLgLz+j7Ppz5WZ2ZbzgwMEBYWBgul+uC94an09cfeOCBy35dPzJ+Knyn4fP5sNvt36vFO1/xOhfO9Vi9Xj+U/1dWVoZMJvuH+bnD4WBwcPCSFu9Cs4Kvej0Yrac6t2N97GtSUq8cxOOH5Khg1v5sHNvq++nU2XhhQQEbq5V0mTy8ec1wNteoqJNbePWqIjbXqjjRZ+a1xcVsqVNxtMuASCCgTWPFaHPz1MwcTiqNqAZcvLa4mM0nFHzTouL52Vl83TVIrcrGm0tL2FLbT0WPkbeWlfB5rYoKhR2N1YPJ7gkIc28eyTcdelr6rby5tJhPT40w31hSzIYqJQ1KC4+Mj2N7i4XaPgNXpdjZ2+vlkNKHxxcYD43JjOaNxYWsqVSiNDt5dm4+q8rlqAecPDUrj5XH5Rhtbh6dkcPK4wqOyB3obZ6AZ2VuDLcPC2V7W6ALmJMpZmenE4vJyNKSGDbW6wkWC7l2VMBybEfDKb9T4JZxqVw3OlAgEiKkzCtJYGW5nKQoKbOLZKwqV5AcHUxsaBD72wx06e2MzYhCbnIwMSeGOUVxrK5UERcmZkFxPKsrVUQFC7mqLIGN9Qb2tQbc+f3AvJIE7puczvoaLU6/kOVl4Ww5aaGpV8NtxUHs6LCzpV6N0+NDJICUaCl/vL6UnU1a9jTrTmn5TGyu6eex6dkc6TLxTYeBcKmYwsQwWjQ2JqYFMT4/mVUVSiKDxdwxPo1V5QoOdhhwe/3YXF52n9QyJS+O6JDvJppcLPx+/yWzOs/HKLVarUM7+m9f4O12OwKB4KL3Xd/GzkYND29ooEY+cEldHkC+LJSNPx9DbNiFh8NeLAQCATqdjqSkJBITEy9ob7h79276+vqoq6vj7rvvvuzXYDKZuPXWW3nppZd4//33h0wS/kX4qfCdxmkxeFpaGgUFBd9JRL6cwnf6+Gc+1mKxUF9fz+DgICUlJaSkpJx1T+F2uzGZTCQkJFz0cxr65UTLkllTocBtt3BS40Bv9zEiPZp5JQl8065nTnECufFhrKmQMyYzmrKUSDbVaSlJCmdMRkCuUJAYwYTsGFaXy0mJkuL0+KjoMdGlszEtP55OnY25JQlkBw2ws8OOw6Bken4M29us5KfGM+cUwzIpMpi5JTJWHZcjEgjQDjjpMgQW8TeMTaOqx8TI9ChmFMpYdbyP9NhQphbEs+p4H9nxoUzKjWNVuZy0CCEZwU52dzmp1AoCSezAL2bnMj4rhv+PvfeMkqu8s71/lTvnnHNu5YASAiRQjggTjDFgwOAwJgw2YJscDAaHGfvOO9e+M0YZZQklBCIjqXPOrY6Vc87hfih1j8hqibHnrpe9Vq/VS6o6p1Sqc3Y9/2eH3U1KZufFU5ERy44GBVcVJlKcEsWOBgVLSpLITYxkR4MCo9PLiNGF3RNgy8xMKtMiONlr5JqCKDJTEjjcY+Xe5TVEiIUc7DSyuiyOoN/H/k4j/QojZk8Ijz/IT5dkkRYXyYFWNXfMz8EXCLGzQc6dV+XiCwTZ2aDgzqtyLuyXqhnQOUi6oNZ8ek05AgHsbAgHiMvEInY0Ktk8I5PYCBE7G1X0qm04vEECIbhnQQ6zcuPY3ayiKiOGBUWJ7GrWUJqdwtradPa2G+i3CrF5AgRDUJIg5PuVYt4Z9dI2auDRqzM52W/hkyETL64r52inlsMdWvzBEEnREiwuH4/dUIrb6+NIn52lJUnMzI1je4OSXk247NgfDJERJ+XFdeWc6jVwsE3N7Lx40uOuzIcVCoW+8WDsCUVpQkLCl97g1Wo1Vqt1sqVh4nmXKuDQ2jw8f3KAP384hsN7+dmmlRkx7Lp75pSqhS4XarV6spR2Al+1b9jS0sKxY8dobW1l586dvP/++5w/f54ZM2ZMqcF9Aj/84Q9ZtmwZ//Ef/8F9991HQkLCJcdBfgP4lvgmIJPJyMnJITY29kvn198E8blcLnp6etBoNJSVlVFQUPCF/X8T8Pv9GAyGKYlbLj7njNwEjnQbaVK6KcuIxRcIERcp5uVNVbzZoaZ+JNyp91a3lo8HjTy1tpzjrWN8Mmzh6XUVfHzewDu9Oh5fVcbJTg1vdmoY0jtJjJIgFgrYeucs2hUW3uxQszbbh9Yn45wmyPObZ9KttvNmu5qfX19Kn8bOoTYV9y4q4L1+Pe8PGHD7AqRECXH6gvz+ptrJxzy2opQetY3D7WoeX1FKlyr8+yPLCmga0vL2eQfWgBiTO4g/EOSPN9WgtLo53afn16vKaB63cLRTy1Nry2kcNXOsU8Mza8s5N2LmWIeaufkJNIyYGTK4WFuTjtcfoEdl4a4qMb1mAWcUHl7aWMVHg0ZOdul4aWMlHwwa+HDEQVlWIj1qOxZPiB/MS0dt8dCqsHF7sZ82XYDjXVoeWZhMo9LN8S4dL2+q5P0+A280qxgxuhAJBURJROy8eyYNoxYOtal5YX0FzXIrB1rUPLO2nC6VjT3NKmJlQsbNHhzeIPcvykYmEXG0S8fdC7Lx+kPsaFSxqTaVWJmYHY0qAoEQCnM42WVBYQK3zcnizW4jGemp3Dkviz1tOpQWD3dUyTgx6ORQuxq3P4REGM57/eOWKnrUDnY1Kbl3QRYOh4O97QYiJUI0Ni8mp4/sBBlPryrlrR499aMWnllTSt2ImZ0NCoqSoyhOjb6s6wO+PjPzm8Jnb/AQ7pVLSkrC4/FcsqI0FApxokvLD3a006NxXNFr2jgtjf91cw2iv5Na8staIT6LiX3D2bNnU15ejlar5eTJk9TW1uJ0OqmpqZmyEd5qtfL444/z+uuvIxAIEIlEf0/Sg2+J778gFAq/NlHiSohvbGwMp9PJyMgI+fn5lJSUXNJ/djAYRKPRTPlb8MTFGxsdSV5WOh+dN/LU2grmFoRXbsVp0VxdmsL2ejmZcRGsqk5nW904iVESZsZ7ODboIlIi4ra5Obx+bpz9zWF/F8Da2nSeXVfJtrpx7C4v12cHOTbgxIeIp26cEx4lunw8eF0x2+rG0dm9/PTaIrbVjbOnSYnd40cggCUlKfxoThzH+u3hkeSKMrbXy5GbXTy+spxtdeOMm1w8dkNYVNN0XklcVCQjZi8OX4h/ujbcBO8LBnnshhJ2NChQmN08vjI82pSb3Dy+ooTt9XIUFjerq9I50a3j3LCZqsxYjA4vKREhNhcEeWcsgF8Wd2EsqkBpcfPz6y881+xidl4CHw0a6VHbub4ihRGDk4SYSH69ppwdjSo80nieXFPO7mY1o2Yvd9VGcrTfwYl2JXqnH28gRHqMhH+/rZYjHVpaxq28vDFstD83YubVzVUcalPzwYCeqwoTaRq3MG72sKEmGZsnSIfKwSubqqgfMXOoXcuz6yvp19rZ06xmaVky7QobwwYXM3JimZYVwweDJpYUJ1CVGcuOBiWZCZFsnp7BrhYtfRYmV4QZsRKevCaFj0YcHO3Q8P1KIQo77O8wMDdDhNIpoF/rJDVayh1X5fB+vwGV1cNTq0t5q0fHWz167piXTdO4hWNdOqQiATNz4y5L/DBV9fM3BZPJhEwmIzk5mbi4uEtSlI7obDywb4B9rRqm4EP/HATAKxvLuW9xwd9VMHI5YeDnz59neHiY9evXk5qaSm1t7WWlv/T09PDuu+9y5swZnnnmGRobG1m2bNllrRwvE98S38X4OhO7QqEgMzNzSh6WiYSXiW+y1dXVREdHX/IHbqr7HjabjY6ODhwOB2KxmIqKCqbnJXGyS8Mn5408uaacjwYNnO7V8cTKMprHzBzv0vDzG8KrssNtKm6uisYhiORAq4ohvROlxY3bF+TJ1eUkR0s51qnhltmZWKxWDnYauXVeLnHRUbzZY2HTzCyipGJ21F8QxkRK2NEg53SvDvuFm+3zGyqpzIhld6OCq3KjyE2KYk+rlqtLk8lPimRHvZzFxUkUXkh9iXFr8QgktGv9aOw+pmdGobD62FSTTEVWAjsblMwvTKQsLYYdDQrmFiRQnn7h9/xE0mKl7G8Jp4/ERYjx+IPcOyeZFLGbU8MeVs0upSorjh31CmbnJVCdGcv2egUzcuJJjpJwpENL87iFnIQIrG4/t8/LYV5BAtvrFZSlxbCwKInt9XIKU6K5tiyFXc1qImPi0Ni8GFwBchJk3FybyIcjduwmPdfliDg66MRstXHXVZnsatJgdvq4viKFE9062hRWqtMisLgDREglvLShkn0tSs4Nm3j1xiqOdWl5u0fHQ9cV8naPnrPDZrITIkiOkjBkcPGL60uwun3saFCxsTaV9FgZOxpVuHwBtDYvVneAgqRInl5dypEOLUOWcI7r6X4j59QBfrIki8ZxGy0aP0KCVCYJGTL7iREFuHt+Jgc79DSNWfjlilLe7Tfw/oARmVhIbVYsh9o1DBtcLC5Ommxyn8q1crnTjSuB0WgkIiLiC7c3PqsoTcvI5K+NRn77kRqj68oqmyJE8NfN+Vxd9fdNqoH/al+fCjo7OzEajVx//fVXdG65XM5zzz3Hv/3bv/Hcc89x+vRpGhoauO66667ouFPAt1mdU8FUMjdDoRByuZxz584hEAhISkoiLS1tyt/qRCLRJXWiud1uOjo66OnpobS0lOnTpyOVSgkEAuEMxxVljBic7GqQ88TKctRWD//nk1GeWFWG2eXjT+8P8diKMty+IFvbHYgE4PGHaJVb+Mk1RURKhHx83sAjy0uQiQU8tqeJu+amkRAl4V/PaPjptUXESeGZY708sLSAjDgZvzjYRcOoGQCz08e/3lxLbmIkf/l4lDsX5FGQHMWf6wxsrE4Id88d7eW783IpSY3mqTd7KBFqSIoU8pdOH21qN7EyMbEyMa9trqIoUcKLp4bIDygoiBPy1JEeFmcJqUqP5rnj/WyYnkFVRgxPHO5hZ4MCoQBkYiF/uamU4gQhf/xEzT3LpzMrN54XTg6yqjqNWbnxPHe8nxsqU5meHceTR/t4o1mFVCQI+whvrmFxcRIvnxrkqsJElpYm88rbg8zMiWN5RQq/Pz1EjExESoyUNzu0REiELCpOZMzkYUZxJrfNzeatUT8FRcXcMSeDE/1W+sZ1rCwQc7xLy+/eHSb2wpfe/woedAAAIABJREFUWQXJvLSxiuZxC/95bpzfbwlnl758apDfbqpEa/Pw4zc6EQoFRElFeP0BXttSTXZCBD/b18V9iwuYm5/A0yeGSImVERch5tyIhYw4GXdflc2I0cXRTg2/3VjGiNHJU8f6+e2mckLAE8fHsHqCpEYJcflhaVUO912VwXtDdt5oUvKjWhEaq5t/2tuFze0nPUaCwxugPC2any7ND1su/tbCiGFqCSXBYPAfIpO/1KzOs0MmbvprM3vadEzRh/45pMVIeP3GHHIvo0D2m8Dl5KV+UzmdOTk55OTkMH/+fAC2bNlCc3PzFR/3m8C3xPcF+LKg6osRCoXQarWcPXsWh8PBvHnzKCwsRCKRXFZQ9deNYP1+P/39/TQ3N5Oens7cuXMnZdkXk+bSshSWlCTz5w+GKUyOYnVNOn/9ZJT4SAk3zcpmR70cty9AbXYcH8s9vDtgZHp2HL5AiPmFifz4miLe7dOz7716vlsbR7cxiNwXzSPLS2ges3C6T8fN5VI6FFZ2Xsj+HDW46NPYuW1uDt5AiGG9k6fWVjBicPK3s2M8vbYClc3PrlYDz62vRGF286/vDnBXjRSNzcuvz3gwuoIEQ3BdeQp/vX0aFpePP30s57c3Tcfhg/dMibyyZRoOX4h/+UjJnVUirE4fd/2fc4wbnXj8QRKjxPz7zVWEQkFeODnAbzZWEUTAs28N8cKGCgKhEL860stLGyoRAHdta6VXYycQDJEaI2XffXPCYdAHe3h6TRmxEWL++UA3v1pdSnK0lIf3d/GTpQVESoT8/GAPLm+A5GgJbm+AX64spSozhl8c7OHm2VnMyo3nyaN9rJ2RzcKiRP7aYuPtUT/CC/f7f1qUweqSKLbVK2jr7ufmqiiOdmj4pF/Dr1YWc2bIxAO7O/AHw1mcFRkx/Mft07G5Azyyv5tXN1cRGyHmx290cNfCPGIjxPzpg1HEQgFz8uIZM7mJjZTy6PIi3ukzcqhDx+82VzJmcnHvzk4sLj8ysQChAO6oiWZ1VSp/+nAMi0/As2vL6NH7+LeOAJ5A2GsmANaVyLghX8yuJhXvdCp4cEEK40YXN/21meNd2kv+rP8jSmiBSRP8l8Hm9vPKqUHu29XBef3UyPyLsLYkku3fKUDg9/w/lYBisVi+EctHRkYGubm59PX1AXD69Gmqqqqu+LjfBP5/Oer8upXV15XRms1mOjo68Hg8VFdXf2rz2GAwEB0dfVmbuF+0tzhRS9TV1UVycvIXhmRPnHMiV686K45t58axuv08uKyYHfXjyE1ufn5DKdvrx9nbFN4jkwihIDGCHT8IWx0+HtSzJc9Dg8pPr0XEa7fO5sMBA2/36HhqTTkNo2aOd2rYUCik2yrhVI8Ovd1DVkIETm+AP9xUi8LsYn9LOAHG6PTyRpOCexYVoDNZOdpj5rvzcrDabOxvN2ALiFFavbh9Qe5fkk91ZgyH2jSsrkkjJUbGjgYFy8tTJqPZrilLpSg1ij2tOioKMhk2OFHY/KTFSLimIJJmpZtYt4bqVAnvjPnJjhGytCyFXU0qsuPDStNtdXIMDg9Kiwe9w0tBUiQ/u7aQ411apGIRd16Vy+t18gsdeOEQAKPDx0PLCtlWJ2d/iwq3Lxz0XJMVy++31LCnOez1e2VzFQdb1Xw4aOC1LdWc7NJyqF2N0e7B6QshFAr499umM2J0cazHxFPra9HaPBwbcHDXwnwEoQD7Ogz0Kk3YLySpXFcUzW2zM9jTosXq9vPzG0rY2aCgTWHlxfWVHGxVcahNfUGtKcXi8vHEijIEQgHb6uTMzI3n+soUttUpaBm3YnX7CQYhLkLECzfk0KOy8tawhy0z0ilMiWJno4p+jR2bJ4AvGCJSIuS3myqxevwc6rEyvSCNW+dkcqjDwFm5E38Q4qTwZqeerhE1xdF+RILQZHLIF8Hr9WKxWC5LwXwl0Gq1xMfHf2E7wJkhI9/9WytN49YrPo9IALu+X8vKyiRcLhd6vR6DwYBGo8FqtV6WovRycKmBGp/Fe++9R25uLrW1tVf8GmbNmsWdd97Jn/70JzweD6+99trfM//z2z2+i/F1xGcymb5wL+Biw3tFRQW5ubmfU2p+2T7CpeBi4ptYUXZ0dCCTyaitrZ30Kn0WRqMRmUxGdHRYaTdxA9zZKGfD9AySo8Nm9Hd6dRgc4SDlh5YVMy9DyPF+G9lxEipifRzts1GYlcZtCwrZWidHKhbxvatyef3sOP4g3Lckn9fPjvO+IoDZFRauXFOWwqtbatnZIGfE4OTJNeXsbJDTp7bz9NoK9jYraVdY+dmCFE70mfmgW0FuYiS9Og8qq5e7F+ShtLoYM7p4YUMF7/Tqea9fzwvrK3m/X8+pHh3Prw+3ux/v0vKjqws51aPlw0EjUdLwuNHo8HJHuQCNR8IZDTyzcTpDOhtHu02syvGjc/g53GVkWmKAHr2PTpWd9DgZ07LjaJNb+f5VuUTLRGyvl7O6Jo2MuAi218tZVJxEeXoM2+vl1I+Ysbr9BEJw65wsbpmTzY4GBVESEbfPz+H1c3JcvgA/vaaQ7XUK+jQ2JAIYN3sIBkM8vbqEhjErTeMWXruxilO9Oo51anh5U3jMeaBdS3F6HAPacIbqTbMyKEuJ4ESfheLYEJWJcLjXhsVs4vaZSezvNPFmpwaXL4hEKCA+Usy/3TqNdoWNnY0KfnJ1AVKxkG11cgSA3u7F6PSREi3h5Q1lvN+n551BK0+vLcfmCbK9UUVKjBS9w4ve4SNGJuKldSV0qZ3saVaxeXoa1Zkx7GxU0TBqwe0PIhaFc0ZvmZvD7Nw4jnSb+GDEQYrYg8eonBSITKSsTJDhlYS5Xwm0Wi2JiYmfElcYHV7u3dnOX8/I8V2JeuUCchNkfPDwQtLjIycVpTabjZKSEvLy8pDJZFNSlF4JLldEdOLECaqrqyktLb3i15CRkcF9993HAw88wC233PL3Dr3+lvgmIBAIvpb4LBYLYrF4Mt/O4/HQ39/P+Pg4hYWFnzK8f91zp4Lx8XFycnImI9S+rJboUs45PSeefS1K6kdM2Nx+Ro0uHJ4AL2yoZMzgpFVu5d7ZCbQrnbzVo+eha/OR2+FYt45/uq4YtcXD3mYl9yzKx+0LsLtBTt2ICZMzTJxPry6lKiuePU1KritPJT8pLFCZU5BAbVY82+vDNUZXFSaxvU6O1GslWiqkTRekV+dhbn4CCoubRcWJ3DI7i231CoQIuGtBHq+fk+PxB3hgScEFE7qfB67OZ0e9gn0tSoLBcF3Q9KwYvl8W4p1RH8ZQNC/fWMveJhVtChsvb67mQKuGQbuI5zdN40Crmo/GwnVBUiEE/T7urxXRoQ9yvEvLUyuKaJbbOdim5qnVZfSo7expUhIpFTGsd2Jz+3lgST4pMVKOtGu4dU42UTIR2+oVLK8Il9tuq1dQmhaN1x+gYcyKyeVjdVUKPRonsZESHl5WzLY6OQNaB89vqGRfs5KPB42srUnjkyET/VoH11ekIhUJqBux8Oj1pTh9AfZ3mrh5QSkV6dHs6zBw3uTF4g7iD4bIjIKH58XysdzDyW4tL60vo0fjZHu9gtU1afRrHfSo7cRFiLl/ST4fDhpoHTHw9MpCWtVu9rVo2TIrk/N6Jx1KG1KRkJ9cW0jLuJUPz5v55cpSXL4gu5rU2Nx+bO4Abn8QqUjAo9cVkBIrY9cFQc09C/P48LyJt4dcSGJTWDu/kqSEOILBIBaLZTJGy2Qy4fF4kMlkiESiv1cpKRqNhuTkZCQSCaFQiP/v4zEePdiL3Dz1fM0vwtOrinlhQ+XnvpxOEK5MJiMiIuKSFaWfzd2cKrxe72WF5h8+fJiFCxf+PY3m/134lvguxtepOiea1KOjoxkaGmJgYICsrCwqKiq+diVns9kALisdYmxsDIPB8KkV5aXcFKxW6+cSKWQSEfEREt5oUqKxelhTk06P2s78giRunBVubHDYbGyqjOPEkBskkfzsgi1BbXXz2IpSdjbIaZdb8QWCDBtc2D1+nl1bQfOoniGjhxc2VvJWl5b3+/U8t76SDwYMvNWt5ek1FdSPmjjarmZTfoB2rZePFAFGrEHiIsSEQiH+/dZpGB1e9jSr+MGiPHyBELsaFXxndjYREhE76hWsq00nKVrCzgYF7/UZcF0wsD+6vJCS2CBv9lqZU5LJtZUZ7GhQkBUfwcrqcIpKXISYzTMz2V4fjhnzXSDLlVVpPLGqjF3NarySWP5pUQYHOo20jur4bkmQ98Z9nBnQMjMrimalg2G9k5tmZWJ0+OjV2HllUxVnh00cbFPx7NpyetV23mhU8tCyIrpUNo60a1BaPOTFSzG5A9y1MJ+qzFi21cnJSYhgw/RMXq+T4/UH2Twjk4Ntas4NmylJjcYTCOLwBvjDlho+OW9kX4uSJ1eXobF62HpunMz4CIYMTsyuALVZsfzsmkLe7DZgDsp49OoMTvVbON6p4aaiAN2GEO8OmAgFQ8wvSKBP68DnsPLD+am8O+LmzKidp9aUUzdi4q3ucITcNWXJ9GscGOxenl9XSfO4hV2NCmbkxKG1eVFYPMjEQp5aXYrB4WNvq4b8pEi+NzeD41163hswErhQffT+gIHD7RqykmKYWZj2qcgxsVg8WVQrl8snP/dOpxO/339Zje6XArVaTWpqKgM6F48c6OHNDu03sspLiRbzySMLqMn+4n2xifN+0bX8dRmlF+duGgyGyVi2S3mP3G73ZfUP7tmzh5UrV/7dR9H/DfiW+C7G16347HY7Wq2W4eFhEhMTqa6uJi7u0jxLny2yvRR4vV4GBgYwGo0UFxdTVlY2pZbiic6tz25IV2bE8m6fHl8gyF9uDxvND7UqWZxgwREQ84Hcz61zMoiOjmFno5yNMzJJigrncs7JT0BpdtM4ZkZt9bCsPIUBrYNrylOojHZzdMBBXISEm+eE/X8CgYB7FuXzt3Nj2N1+NpVIOdht4ZzKj8IWFvsUJUjYftds9rao6FTZeG59uAi1fsTMixsqONap5cNBAy9tqODtXj3HO7WYnX60di++YIg/bqlmSGvl7V49Dy3NRm4PcbhTz0PLClFaPLzRpOD+JQU4vAF21itoV9qwXRhPPrmmjLK0aHY1KrmqMJGaC763afkpXFOWwhutOqqKcqnOjOHtfjPtaidFCSJM7hCxeLh3djxv9tnoUlp5aUMl+1vUnBky8tqN1bzZoWZXgxKNzYtUJEAmFvLX781g2OBkR4OC+xfnEULAtno511emUpAUyc5GJR8MGIiWivAFQszKjefXq8p4o0lB/aiZ126s4u1eHYfbNXx3bjbnRkx0qeyUpUWzsDiJjwaN5CZGcvu8HHbUK1Hag/xmUxVHOrR8KA/gDoRzSC2eALMTPFQlhnhXARq7n19cl8M7Axb2NauweQJkx0dgdvkpTYvhwWVFHGnXcLJbyw8X59OvtVM/aiEYgnsX5TFicHGiS8eNMzKZV5jAniYlH5034wmESIqS4PQFiZKK+NHiXMbMbnY1qXivz0BZaiRpseHPtN/vx+fzUVJSQmZmJtnZ2ZPXi81mQ6lUMjo6il6vn7yeLjVg+aswrlBydMjHY4f7UVk9l32ci/HLFYW8urnmKycyl2oin8CX5W7Gx8cjEAgm36OxsTG0Wu1k+PZn3yOXy4XT6ZxyDOLWrVu55ZZbrjja7X8AviW+i/FlxBcKhdBoNAwMDCAUCpk3bx5JSUlTutgmimwvpfokGAxOtrunp6cTDAbJzs7+yoSXL8KXpc4LBAKqMmN5/dw4Lo+PZRk+jg+6EETE89TG6eysH2fI4Oa5DTUcbFXRNGbmyTXl7GtWcrBVhdLsJj5STIRExN/umEWr3MKRdjW3VkdhJ4IDbWruX1KAzeNnd6OC2+Zm43W72NOqxe2HEbMXuzfIutp0lpclcKrfTKRbz8ysKA73WEiNErKmNoPt9QpiZGJunp3F1jo5Dk8AoVAQTk5x+/nh4nyaRs1o9UZ+ND+Zt847GbeFeG5dBftaVDSOWfjNxgqOdGg43acjQixi1OTC6Q3w3LpyxowuPug38My6CjoUVva3qHj0hhLkJhe7m5TctzgPk8vHG01KmsetxMrEeANBfrGynGnZcexrN1CcFseCbCkHuszotBrWFct4s9/J2z1q9PYwueYkyNh610yOdmh5py8scPnkvJH9rWqeWVvGsN7Btno5/VrHZCv3T68p5JqyZLbWyREIBPxkaSHb6+V0q2w8tqKEw20a3unTkxAlIT1WyrDeyf1LCkiJkbGtXk5KjJS7FuSyrU7OsS4tDm8AiUiAWCTkn5ek4nS6OC0PMqMkm1tnprG/Xcfpfgt2bxCxAILAxvIoFhYmsLdNS7/GwbNry/n4vJGjneHjLSxORGF2M6hz8IsbSgABOxsVDOmcOL0BBIJwAPrCokR+sDA/nJzTpScuQoLV7cfs9LGvTcOgzk5RUgR47Ph8vsmwaWAyfzMxMZGMjAxycnI+1eg+EbD8VTf6r0K7wsqPD49xZuTKxSsASZFi6n+xmJqsr1c/Xo6J/LO4OKN04j3Kzs4mKSkJkUj0uYxSm802ubeanJw8pXP/5S9/4f7775/yfeh/IL4lvovxRdVERqORjo4OAoEAeXl5BIPBKc/GIbwf+HVh0xNZhRMtDTU1NcTHx6PX679UdfZVcDqdX0q2SZEiBhR6jnQZ+e6CAlLjY9jVpGRZeSppESEOdZuoyY5jQWHYnH2gRYXR6SME3DI3m8duKGNr3ThOb4CfXhseheqdQR5bUcL+Ni29ajvPrKtkX5OC97vlVKVIaNV4GDJ5WVWdhjcQYNTo5vn1lTSNWfhY7uWfrwuvJN7sMnB1ogWrV8CRbiMriiIYNvt5b8CI0uymJjMWpcXD0nQfefESTo14WVKVy1WFSWyrl5MdH8HqmnS2XcgEjYuU0DRmQWlxc9OsLLpVNkRCIb9aVcbuRgWdChsvbaxkf2u4fPbVzVUc69RwoEXFeb2TEBAhFrL7nln0ax3sbVLxs+sKMbv87G3VcvfVZSRESTjUHZZ7jxqdWNxBiuIEbCoR88G4D6XWzE8WZXKw00Cb3Mormyp5s0PD0Q4tQqEAvd2HPxjipQ2VBIFdDQrWT88I7xHWyUmKlnL73Gy21ck52qEBQCoSIhML+ON3aujTONhWp+B787PJio9kW72cYb0To9OHNxAiOVrCa+uKaBwxcrTPzg+vLaUgObz/OGLyYPcE8ARCyMRCXt5UCaEgB7rMSARBbiqT8u6wk4NtGuyeACnRYhzeIHEREp5aXUa70squRiVJURKsHj9mly9sh5ifw5KSJA61afjovIHFxYmobZ7JVdX6aRksKEzkZI+eHY0qOpVWZpfmkJEQNXktBoPByd8vJsPPNrpPlEJ/URi1z+ebJIiJG73D4+dn+7r4w3sjeK/QkzeB/31rDb9eXXbJj78cE/ml4ssySiUSCWazefJLg0qlumRF6V/+8hd+8pOf/L9eSQTfEt+ncTHx2e12Ojs7sVgsVFRUkJOTQygUwmAwXBbx+Xy+r5RqG41G2tvbCYVC1NbWfqql4bO2hEuF2+3G4XB8imyDwSBjY2P09PSwpDKLE31Wzpu8PL22gqMd4THdI0uz+XjIxFu9etRWNwqzG7c/wG82VSEVCTjaoeHOBXkQgh0NcjZMzyQtRsa+dj1zc2OZWZDM9jo5MreR5Ag4p/TTrPZQlBqNyeljaWkS9y3KZ1u9HKvHz8PLi9lRr0DjCvHMuir2tqjQBWN4aVMVe1vUHO0xo7H5kAohVgoPzRDSqQ9Qpw7wys2z6VTaONCi4p+XF6Ewh0ebP1iYx6DOwfEuLYM6B8UpURgcPu5dnEdVRjiZpTQ1hmUVKWytkxMjE/Hdebm8fm6cHo0dvcOLxeUnOVrKn2+p5WS3lsZRC6/eWMXxTi2nenS8urmas8MmDrSquGlWJh+fN9KjcVCQIGFWXiLNSie3LC6nIi2Kve16YiVBlmaLONxnp3dcQ1q0mH69B7PLx4PXFjJicPHhoIEXNlRyXu9ke72c71+VQ1K0lG114fqniSaI2uw4frelmiPtGk50afnNxkrO6x1sPSenMiOGEYMTldVDZryMJ1eV8HavjvcHjLy4oQK13c+2egWFKVHo7V4UZjcRYiG/2VjBoM7J7iYl66dlsKg4ib2tOjqNQZzeECJhWK15VU4EKwvEvD/i4ESXlqtzIzC5Q/RpnQSDIe5bnE9arIy9zSpUFg/3LMqjT+OgadyKPxBky6xMSlJjONKhpltlpSQerD4BKkeQgx16WuQ2EqIjyE+ORiL+r/2qryLDrwqjdjqdaDQaRkdHUavVHGrX8ItjwwzoXFO+jr8IxckRfPjwQnITp3Z9Xm4TxeViIqPU7/cTExNDZWUl6enpX6konTD3i0Sib7SLLxAIMHv2bI4ePcptt932DfzrpoRvie9ihEKhT1UTlZSUUFhYOClzDgaDlx2i+2Vh0xcTbFVVFdnZ2Z+b+X/WlnCp8Hg8k/LwUCiETqejvb2diIgIampqyEhJIjYiHC9WlBrN9ZVpbD03jlAowOH2MmjworN7uXdxPq3jZiQiIU+sKmdPk4IOpY3n1lVyuD1Mli9sqORYm5wPzlu4o1pKw5iN98c8dOv9yMRChMDrd0zH6QvyRpOSm2ZnExshYXu9gmvLUibbE2qy4lhwIQLs/QETeoePYAhWV6fxw7lJHO6x4hVIuGdOMkf6bLQMKvlehZh3x7w0DBt4bm0phzq07GlSIje7kYoExEWK2Xn3LJrGLBxsVfPYihJGjS52Nyn44eICvIEgO+oVzMqNo1/roE/jICFSwpaZWZwZMpEVH8GtFzJLbW4//7w8XM7bo7bx6PXF7G9R8Va3jhgJREdIcPhC/PbGaga0DnY2KHjg6kJEQgF72w2snl2IVCTkkxE7Cquf+TkRKCx+jGYr902X8dG4l5NdWl5YV0qnysGOegUysRCF2Y3LF2RVdSo/WJjHzgZF2OqxvpKjnRqOdWr48dICzo2YaByzEB8pYdP0dM4MmRnTmPjl8nw+GXNypFPPfRf26OpGzIRCIe5ZlEeXysZHgyaeWFlCIAjb6hWYXT48/iA2dwChAB68toja7Fj2t+tRe8Q8vqKELpWds+NOnL4gS3Ik+PwB3h20EC8O8L1ZqdSN2Xinz4DDE+DqkiSiZCI+GDDi8flZlClE7QwxZgvhD8KioiSWlibRPG5lb7OKXY1KrG4/aXGRpMVFIJFIEAqFCIXCC2PU0ORPMBgkEAh8jgwjIyNJSEggLS0NSVwKvztr4kC3Fa//ysUrAEcfmMP9Swou67l/b+KbgMlkmmxfFwqFX6koNZvNPPvss/zqV7+arHJyOp3ExcVN+X50Mf7whz/g8/nweDzfEt8/GqOjo/T09JCbm0tZWdkXrrAu98P62bBpj8dDX1/fFxLsZ3G5Vgifz4fJZCIyMnLSWF9TU/MpG0RVZiwfDRo42aXlp9cW8eGAnvcGzajsfsrSY9DZPDy+sozUWBnb6+VcVZTEtOw4ttfLyU+OYmV1OlvPjRMpEbIg2cPx825a1R7GrQH8QShJjWbr92dwsE1Nh9LG8xsqONKu4ZMhEy+sr+TdvrAn75m1FTSNmTnYpkIsFNCvdWBx+7l/ST6pUUJO9hhYmiclPzOZ/R0GVs0soDYrjn0dBmaX5bAwL4p9HUY+7NWgdQQIBEPUpEl5ekU+h7qMnNc5eXZdOXubVTSMmnllczXHO7W83avjV6tKON6l5VSPHqFAQFZ8BGqrh8duKMEXDLGtXs6amvQLo0E5ZWnRLK8Iq0SPtKmZmI7PyE3gtS3V7G9R88GAgdduDDc87G9V8fSaMrrVNt5oCo9PM+Jk2D0Bbpydy6aZmexu0eGXxPDQkkyOdht5u0dDRbyfAVMQudnN8pI4yjNiOdGtpzw9JtwHWCdnSO/kmbXlHGpTc7RTi1AAxalRjJnc5Ee4WVMez8khN30GH7/ZUMm7/XqOtGuwe/zMyY9n1OjG7Q/y/LoKGsbCLQsLihIwu/wM6pwEQyF+cUMJoRC80awkRibm4eXFnO7Vcahdi8XtpzIjBpcvwIg1wHfm5LGgMI4TvSY+GLbh8gVJuZDZMKh3UZEi5do8GS1KJx36AB5/iAVFiczNT+CjQQNN41YiJSJc3gACoGnMwu4mJcc61QzpnETJxKTHRyAVixFf+Pk6MgwEg+xqVPLA7k5GTd+MRWFpUTxHfzT/inoI/1HEZzAYiIiI+FLiulhRmpyczJo1a/j+97/Pnj17WLt2LZ988gl/+tOfmDNnzmV5LuVyOa+88goPPvggZ8+e/Zb4/tGQSqXk5uZ+LgFlAkKhkLGxscv2sUyEXA8PD9Pf3092djbl5eVfO8K0WsMb71NVUzkcDkZHR7FarZSXl5OXl/c56bRAIKAmK5a/nR3jjSYFWpsXoQAqkiVsvXseh9pU1I+YeG5dJe/06jjdq+OptRU0j5k50qbm4eUljOps7G1WkSCFIUsQncPP4uJkrq9M5Z1ePXPy4sOtEA0KMuIi2DAtg611coQCAT9YmMe2Ojk6u4fStBjODZsZ0Dq4cWYGCrMLpcHKPbUSmnTQqPLx4oZKzg2bONKu4YlVpQxqHexvUZOfGk+n0obJE2J1dRpz8mJ5d9BCRaKQotggR3ptBOxGVpTFs7/ThNfn58dLw4b8PU0qPP4gYqGAvKQo/v22Wk506zjZo+XlDeE9yL3NSn5xQwk6m5ftdXI6FGacF8aODyzJY920DLbVKwgRFqZsq5PTq7bz8sZKDrWreKNJhcLsRiwUIBII+PPNNfiDIbbVyVlYlMSSkqTw80VSNs3I5GSPgUFziBnZsYgEIXq1bjYWQSgY5ECniQShh83TUtnTpuNIhwa3P6yaDAZD3DUtCokgwKkRH2lJ8Tx4XRH7mlXsbw2rNRMixbh8QRaXJHPPojyXcd0gAAAgAElEQVQOtKh4p1fPo8uLGdA6eH/AiN0T4JY52ehsHk5161lTk8a15Snsa1byVk84cDxGJsIbCCERCXj4uiIixEJ2NSnpUjkmPX2BUHjEdtvcbApj4fR5G81qL54AZEULSI2W0KFyMGZwMiMnDrPLh9kVVvuGQuG4vPL0GM7rnTSNh2ucXj83zrlhE91KG25/EIlYSGykBMkXkOGQ3sntr7dzskd/2eWwn7pegL235LB+WuYVmcqvpHT3SqHX64mJiZnS1onVauXUqVO8+uqr3HDDDdxxxx2XHTRw99138+yzz+Lz+Thz5sy3xPePhkgk+spczCstoz1//jwqlYr4+Hhqamq+svvvYtjtdgKBwCVbIfx+P0NDQwwPDyMWi5k/f/5XRqWlxsqwun00jpr59eoyZufEcqTbSHV2HEtKUibrim6Zk8Pfzo7h9Qcna4Y6R1TMSoE6lY8eY4DytEhCAiFOb4CX1pdzZsjEiW4tj68ooV/r4ECrivuvLsTu8bO7UcmmGRnYPQGOdYYbEIpTojA6fVyTK2FGop9To36yMzK4Z1EeW+vk6O0+HlsRjufqUtpYNy2Dd3r1tMitXFWYSCAYQmH28PKmKlrlVt4asPHM5pnhCLBeK1tmpCHBz/4OIx/2qXH6IRiCG6clc8+ifHY0KLF5/Pz8+vA5OpQ2Xt4UHume7NYSKRYgt3hw+YI8cX0hYrGIA60abp6TRXqsjG11ckrTolk3LZ3Xz8lpVVgwOHy4fAGSoiRs+/5MPhk2caBVzdNrytDavGytk7O6Jp3C5Ch2Nyn5cNBIcrQEfyBEpFTEv9w8jXMjZk4NuXlsTTVSIezvNNGrdWL3hgiGIDlCwC/mR9Oj93ByyM2di4upzY5nW52cumETVneYTERCAb9aXUphcjTb6uVorR6eW1fBe/0G9reqMTl91GbFYrB7kZvdPLGiFJFIwI6GsFrTGwhOjgln5yXw0LJi2hVW9rWoEAkF+ANBbJ6wOrokLYYfXR0OOjjYrmXA4MEfhGipCKlIiNEdJCVGyqKcCDR2H/06F95AiKQIIdcUx1GYEk2T3EqvxoHHHyQpSsKs3DiSYqT0qO00jlk42a1lR72C//3RKNsbFHzYr+ejQSOfDJn47dvn+csZ+eTruVLcNS+TP99YiiAY/JSp3Gw2T+6HXaqp/HLrxr4JaLVaEhISpiSWU6vVnD17lltuueWKzn306FGUSiX33nsvIyMj3xLf/wRMjEe+CpdDfDqdbjJxZeHChVOWETudTjwez9daIUKhEAqFgq6uLhITE6moqECj0VyScmxmXgJH2lQMah38enUp73QqOd1v5LEVpQzoHOxvUXLfkoJwu3j9ODPivYR8Hj4Y91Gn9JEQJcHtC7KmWMZ3ZmXzRquWQAh+vDS8+lFaPPxyVSl7mlS0KSy8uCGcJ7mvRUWvxoFEJCBaKuJP63PpUlo4PezmiY2zcPvDDeYbZ2SSfiEybFZuPIUpURxsU3O6T0/6hbHhouIkHlke7tAb1Dl5fn0Fe5vDSs2w/03POwMWEmOjGTG4cPvhwaW5RIhCnOg1UxPjIFkW4nCvjRSxl+srUtjVrMHjD5GdIKNxzIrS4uHWmWnILR56tS5+e2MldSNm9jQr+fn1JVjcPrbWySlPj2bY4GLU6CIlRsqjy4t5u1fPgM7BK5sqOd6l5c0OLS9urGRI52BrnZxOlS08pgNWVqfxyPJidjcqOTNk4nc3VtMwZmZ7vZyC5GiG9E7s3iBVGTH8ankeJ3qN1Kt8PLQwBbXNyxutenzOcGGuxR0gRiriX79Tg9LqYVudgtqsWL47L5s9zSoOt6uxuv2TK7i8xEieXB0u9N3ZqCA/OQpfIIjC4iEQDHHTrEzW1KRztEPDqR4dyytScHkD9GvDK71rSpO4ZU42DaMmjnVqw6QgEDBRTi4AbqhKY0lxIu1KOy0qF05/iKKUKK4rTSIYCvHJiI1BvQuvP0ScFKpTZURIhHSqHIyb3HgDIYRAdoKMuIhwO4TTG8AXCDGsd9Aqt2JxTz0U/osQIYL6RxewuDT1c6byzMxMoqOj8fv9mEymTyWsOJ3OLyXDKymYvlJcnFRzqRgZGaGnp4eNGzde0bn/8z//k7179/Iv//Iv7N69m97eXnp6eti8efMVHXeK+Jb4PotLIb6cnJxLIi6r1UpHRwdOp5Pq6mr0ej0FBQVTfk0ThtPk5OQvfYzBYKCtrQ2RSERtbS1JSUlTGs1KxULykqLYWjdOTISYBcluTgy5sbj8PHpDOK2lV23jh7NiOd5j5INRF/2m8HsVJRVx6IdzGNTZOTVoZ2UeOD0+DnebqY1zkZ0Yxb52PdOz41hYFLYctIxbGDY4wybtvHheWFnAgXZduHpnywwOdmhpGLXw0oZKTvXoeatHy3PrK2gYMfNGk5KGUQuiC/8H/+vWacRfEMosKEpkVl54pZMWI+Xm2Vm8XifH7PRRlBJF/aiFUaOL2+dlIze5GTa5+cN3pnNmyMgH415+853ZjBkcHO4ycW2OCLPLy7uDVvo0DkoSRRjdISoyYnnk+rAStV1h49XNYaXn8U4tP7++mPf6DXw4aCRCImRufgJdKjszc+PZPCOT18/JkZvdPLe+gsNtao60q/EFQphdfoLBEE+sLJlUnYpFQh5aVswbTUo+HDRw69xszgyZ6NU4qM6KZXV1Km/3GlDoLTyzppz3h6ycHnZy56JiulU2hsw+oiQi7pkVT6fWzdEODRsKBaTFRbC3TU+XKrwHN5FSctvcHG6cGU6NOdWj4+6FuRgcXuqGzdg9fm6enUVBciQH2zSMmdz87LpCFGY37/UbJtW6CwoTOdmt49ywiYqEEP6QEI0zLF7JTojg9rnZZMVHcLJbQ8OoBYc3QHqslHkFCQzrXbTIrejsPoKELSQlqVE4/SGGTT50jrAvUiqEnFgBKdFidI4ABodvcozp9AYmvZDfBJ5dXcQrmyoBJtWkF98jhEIhUql0cj/s4oSVYDCI2WxmfHyc8fFxjEbjpIcuGAxis9kuSyF+pVAqlaSnp08p7mxCj7Bq1aorOvfy5ct5+OGHefDBB5kzZw4qlYp9+/Zd0TEvA98S38W4lBXfRNrCV8323W43PT09qNVqysrKyM/PRyKRXPaY1Ov1fmnEkN1up6OjA5vNRnV1NZmZmZMf6KmOZotSounX2NjXomJ5nojc7Cy218tZVpFKToyAPS0aNFY3cnsQmydIeXoML26o4FCbGqPDx+MrS9nTrELplvK7W2dxrENDi8bHzxYk0zRu580ODdF+C32mIEqLh+vKEpmRHcd7/UbKopzMLklnf6eJ0vRYVlWnT+4D3r+kgG3nxjk3bGbc7MLpDZIYKWHXD2bxfr+R0316XtxQQdOYhQOtKh5ZVoze4WVng5ItszJxevwc69LSqbRRnRmD1uZlbn5i+Lh1coYN4dXhvmYVH5838vstNbzdq+fYgA2VPUSEWEAwCD+/Nps4cTgjU+zUc11RFAd7rKhMDn65qoxdjQr2tarwB0LER0rw+AK8uLESpzfA1jo5C4oSWXrBlK61eUiNkdKncWB3+3lsRbg89mCbhtvmZlGUEs3WOjmBUIh7FuZyoDVcpJsWJyMnIYJutZ3SSBcrKlM42m+nX+/m2bXlvN2r41SPjhAhllWkhM3+ATGvbK6mR+3gzX4HeYkRWFx+tHYfhELcURNBelwE+9v1qMxunlpTSsu4jTc7NOjsXhYVJSIUCvj4vIn8pCjuX1LA2SEjR9o16O1eytOjSYuV0Thmwebxs740EoPdS5chiN0XojozhptmZaKxeiftJf4LnsHMOBkqq4cRg4vUGClefxBfMESMTITTF0Tv8BEIhAiFwmPa/MRIEqMkqO0BtI7ANxIt9kVIixHT8NhSqrISJvfyLr6uLrZVTIhoLoZMJiMmJuZTkWyxsbFAWKymVCqx2WyYzeb/9ki2z0Iul0/ZON/e3o7D4eDaa6/9xl7Ht6PO/yG4FOLTaDSkpKR8Yb6e3+9ncHCQ8+fPk5eXR2lp6af21hQKBVlZWVP+YE+oMy/+duj1eunr60Mul0+qQr9odDFVsp2dl8juRjlDZj+vbJnB0XYVb3UomJEUoFnjZ9DopTIzlrK0aNrkVr43P4eESCk7GxVcVZjIrLwEttfLSYuVsWVmJtvqFciiYlhWlc6pXgPdxiDVGVE4PX6cbjebM210mYU0aAL8ZEEqI2Y/B1rDyS8ef5Cd9QrK0qLp1dgZMbhIj5Vx18I83us3ECUV88DV+WyrkzNqdPHs2rBq8+ywid9uruJEl5bdTUoGdE4iJeEvKn++ZRpCIWyrk7OkNJmZueHVYZxMzF0L89h6dpzTvXqUFjfBECRFidl/31zOjpg53mvhmU3TMDl9vNlrZ/2MHIoSROzrMHKiU43bHx7jFSZK+f2GEt4/b+Vwm5pn11agsnrYei4cTUYoxHv9BuQmFxumZzBmdDKgc/DbzdX0qGxsq1Nw0+wsytOi2V6v4O1eHSKhAKlYGBavVInwI+KtES/FmQncuzif3Y1KDrapcPmCZMbJMLn8zMpN4EdLCzjUpuZYp5b7lxQwrHfQrHDgvzCGNrv8vDPspDojmvVlUbx73srBNi0ml4+kSBGBUAiNzcsd83OZm5/AgRYlb/fosLrD4haRUIDa6mVuXjzrKhNoGjVxRu7B4g2rS68uSaJp3MqZIRNR0vDrd/uDiAQCfMEQERIh8wsScHmDKK0e/MEwkYmEQgqSInD5gngCYSKEEAaHD6PTP/k4CL/n3yR23j2Tx1aUf+rPBAIBAoFgkpwuJsOJv5u4f1ysKL0YUqmU6OhokpKSiI6OJhQKUVJSAvCpuDGdTveNRrJ9FnK5fMrG+fr6ekQiEQsXLvzGXkdBQcE/gvTgW+L7PL4ur1On05GQkPAp68GldOPBf4XSTjVRPRAIoNfrycjI+FScWVZW1teqQqdKfDEyMQmREva26RB7LBRGe/lQ7ueMwktKjBS3L0hlegzPry3nUHs4T/O5dRW8PxC2RDy+soRBrYN9zSruWZSP1e1nb7OK9/oNxEeK8fiD3DErhdp4D2+PBcjPz+NHVxewq1kTHkGWizg96uaDXhU3V0TywaiLd/sNRElFZMVHoDS7+PkNpQRDIbbXy1lekUplRgzb6hVkJUSwZWZ4nPjxeSNysxt/MEROwv9l77zj2yrM9f/Vnpb33tuOneWEQCaBQCijhE3ZI1DaCxQodNBSKJSyOm5LewstFAoJIQQIu+xRCCNObMd7b8myJUuy9tb5/XEskYSQRbi39/54Ph9/4JNxpBPpnOe87/u8z6PmmfUNvNlt5Y0uK/eeUcMuo4stTRNcd2ypqK5sNKJXyhiwerD5IpSmqbjj1GpeabfQb/Vy3xm1vNw+xRudVh44aw4dJhdbWqaQyJWYnAHCMVg3L5tLFmXzfNs0gxYXl1VL+MgY4tX2SX64LJ0eW5hnWyYZtvkpTtPM+mDq+Nm3qniu2czbPdM8cNYcBqxe/vHZOEM20fpLAOrykrj56GQ+HfPywXiUm0+qJnNWTPPpsANvKIpMKkEhlXDbqVUUpWrY2GjE5Ajwq9Or+aDPxsvtUzgDEY6tTGfSGaB9ws11q0soTNXwTMsUndYwgYgAs1ZjuQYF6xtScPpCvNBho9fsRIDErE6vkrF+eTHVmRpxRWXEjS8CyRo5+ckqhqb9mGYCHFeVTiQmMGITxSsyKSwvT2dJSQo2b5jmcReekLgrODdPT1W2Hm8oypgjkCC4UFQgOvtMKpGIDyQKmYTgEdrHA5ibq+ODH64gx3BwmZlxwosT4MGSYXxfOBgMkp2dvYfdWF5eXsIO8UhYsu0Lcau0Q8FHH31ERkYGCxcuPKzX/DfDN8S3Nw5EfHa7Ha1Wi0ajOaRsPPg8huRQve5isRhmsxmZTEZbWxt6vZ76+vqDMsg+VOKLxWIkRWbYPmjjvZEgzVbx6VwQ4KEL5lKcKhop1+UZOL46kw2NRlRyKVctL2ZjoxGzM8hts6bKr7ZPzQo2xJvk5kvq2DU6zRv9bn767flEBHiq0cRp83IpzdCxZZeFY+aUsHZONlt2WXh7yEtMENtcpUkxrlug4iNTmA96rdx9aiWfjYry9p+dVIlpJsCmHUbyUjR0md1MuUM0FCbz3eXFvNI+hYCEG44r46lGEx0T7oRl2JtdVtYvK+K9XivNRhc5ejlHlaTSbHSzoiKd1VUZYhxSOMaP11bw9E4Tnw7bWVKcSvO4E5MzwHcW55GXrOafnRaOr81mVUU6T7dYUBnSuf2UKp5vneK1XhdTnjByCQjAlfO0VGfr2dJqxR8Wk9pfaDXzWruF7CQl444AvlCUdfOyObsuhRfap+m1hXnwOwtoNbl58jMjepUc00wAXzhKpl7JXy+YR9+sUKahMJkLj8rn2eYJXmidxB2MkKFX4gtFMajl3HV6DX1TXp7aYUIhkxKNCdh8n5sFrF9WxEeDM7zZ76a+MA0JYHaHCcegMk3B2VVKpr0RXuu20zfpRpBIiCGmsvtCMYrTtZw5PwenP8LHQ2JsVZpWwWn12ZRn6mkam2HnmBOHT8z4O7okhfIMHWOOAJ1mD55ZJaZSKuZIRqIxogIY1HJStXKmPeEjSnq/X6VgXW1ywrprd3uzQ8G+yDBOiPFfD4fDjI6OotVq0el0X7BKPBKWbPvD4axRvPvuu5SUlPzbJKV/RXxDfLvjYDL5HA4HKpWKcDh8SNl4IO7PJCUlHbLnpsvlYmRkBJlMRn19/R52ZgdCvK1xMBfE9PQ0bW1tqJRK8tVBPjCKN9Snr2zgnR4rnw47+OVpNWwfmeGV9il+uKYcizs468SSR7pOyVM7TAQiUfotXhz+CNXZem7/VjmvdFgYNtu4/bRaXumy02x0c8+6Wt7usfJml5U7Tq2ia8LNMzsnaB4XRQ+CAHedVsPSslSeb7dRWZjDaTUGnmuzMzhh5ezSeFyQlfPnp/P+oJPG0RmqsnTkGFR0mT18b1UxGoWMDY1GFhWlJIyfxdlZMU/vNPFK+xQqGWiUMmJI+c9z6hic9vFUo4kLFotijCe3G8k1qFlYmMI/Oy20GF0sLUvFF4wyavdz77paRm0+Nmw3cvq8HOrzDDy53UjPlBe7V6ykDGo5G69ooNfi4eUeF9+qMlCkh62dMwxPTFGXpaR1Msi4I8CZ87MpSFbxetc0SbIIP1hTxYvtVt7tnebchlyaxp0MTfsoStPws5MqeL/PxuudVn6ytgKdSs6GRiOtRheuYAQJItme15DLhUcVJDxCz12Yi8Mfoc3kwh+OcvGSfBoKk9naMknT2AzrlxXiDUX5ZJa4lpencdaCHD4ddfGxMUS6GhQyKTNBgaggqh9X5EqZl62kyxLkX4MOrJ4QGTolJ9SkI0gkvNdno3fKgz8cQymXMjcviRSNghajk8FpP+5gFJkUStM0FKVpCEYFpr1hkEjQyCV4QrEEKR4JnFaXwYvfP5rSwnxUKhWBQACr1crIyAgmkwmXy0UgIC69x51jDgdx0rPb7XR3d1NQUJBwadpXZfhVLNniDisSieQLleHh7g++9tprNDQ0UFZWdljn/2+Gb4hvbxxoxme1WjGbzQkPz4PNxgNReRmvFg8GPp+Pzs5O7HY7giCwePHiQw7nPBgxjtfrpb29HY/HQ11dHTk5OdgnjVSWFfNGl5V5eUmcXJfFk9tNAPzHqhKeajQxZPNxx6lVvNQ6ybZBO2ctyOXtbiu7jC6qsvWUZ2hpM7loSHJRkpXKq30eFpeKcT8bthtRyCQJgUmHyU0wIkrmw7EYfzy3nsFpL+/0TPOTteWYnUG2tExy2fJysgwqnm+3s3p+BUuKknih3SYmPMghKkCaMsrPVmXy0ZiP17us3L2ulp5JN0/vnOCKpYVolTI2Npp4q9tCZPbjPmFOFr86rYYtzWY+HnLwn+fUsW3QzrPNE/zohHJs3hBP75ygcXSGdJ0CfzjG/HwDd55WzXPN4gL4b8+eQ8eEm42NJo4uTqHP4mHU7iddr+SuU6t4r9fGh4N2fntWHT1THp5rt3HB0gpyDGre6nfSb49Ql6UmGo3SZfZwYpaPTIOafw4G8IXCXLe6jFfaJ2f3/JSsrEijacyJ0x/hnnXiA8mGRiOFqWqm3GKyukou5e7TaxIPJWMOP788tYpWk4vXu6zYvSG+VZeJSi7jnZ5p1AoZPzqhnPYJNy+3TzHpCjIvP4mabD0fDdrpnfJwYomaSDRC34yAJyxQlKbhimMK0asVvDfkpdsWxRcWUMqg2CDF6o3QNeXF5Qshk4hL7VlJSoLhKCZnkCl3CEEAqQSK0zSUpGmw+cIMT/vxhcUPSBAgfITMpAFkEmi+dQUn14v2g3HrruTkZLKysigoKCA3Nxe1Wk0wGNyDDJ1O5x6V4cGQYSgUoru7G5fLxdy5c0lJSUks2e9dGcarQziwP+nulmz5+flkZWUhl8vx+/1YLBbGxsYwm8243W6CwSDRaJSZmZlD3h/cunUrxx577P/I3uHXgG+Ib298WcUXCoXo6+vDYrGQlpbG/PnzD7lym5mZSQy494dwOMzAwAAjIyOUlpZSUVGByWQ6LEXo/sQ44XCYvr4+xsbGqKyspLS0FLlcTiwWw2q1kiH10OMQeK1rmu+vKMQTivH0ThOnz8uhMFXDUztEo+clpak812zmlfYpUjQKQtEYxSkKLioL8dmkQK9bwf3nzmfHyAwvtk7yg+PKcAcibNph4tjKdIwzfnaOOXH6w5zTkEur0Y1BI+fHJ1ayaYeJlnEX959Vy5tdFl7vtHDXadV0md08vWOCHWNuorO7bz8/uYq1tZls3mUFiYzzajW82utmW7eJ/1ioYftEiBdbJ7G4AswEREu1u06tpDbXwKYdJnIMKi45ukBMQ5+d673SPskzTWaGZ1MaAO5ZV8uc3KSE6vTmNWVs3jnBtkE7vzilkre6rXzQb0OjkLGiPJ02k4tkjYIfnVjOs80TvNMzzQNnzqHX4mVjo5H2CRcps/PPVK2cq+tkDHpkvG8SOGtBLtWpMl7onOHNLisSIEUtZdobYW11GmctzOOZpgk+6LPxH6uKaTW5aDO5UUgl3HpSBeOOAM80TbCg0MCVS4t4tX2K51rMOHxhitM0hCIxBq1+zlyQwwk1mbzaPsXLbVPYfGFStQq0Chkjdj+5yWoumJ/G8JSLj00hbAFx7+6MeTkMWL283TON1RNCJpUQigrolDICEQFPBBYUJoMADn8EAbH69IaiGJRiJRSJQbJaTqpOwcRMALMrhDd05Kq6vXHT8SU8eslC5AeYtR+IDKenp/cgw0AggCAIXyDDqakpurq6KCgooLy8fL8Pr7u3SXefGx4sGcaNqOPvOT8/n+zsbJRKJcFgEIvFgsvlSvw3GAzu8z3vjU2bNrFu3bqDilX7X4BviG9v7E18e2fjxXfp9s64Oxi4XC6kUmlC1rw34iKZ7u5uMjIymDNnToIkD3cVIj5X3FuME09oyM7Opra2NjGzjA/fc3JySEtLoypdLkbpDFs4szDA9imBD3qt/HBlDq1mP8+1iMGpIEEA7j+9ggypl9f6fcyvKOA7S4p4YrsRdyDCj9dWsHmniZZxJ3d9u5oXd02ydZcZqzuY8K6887QadEoZGxpNLCpK5thKMT1BgoTrVpeysdHEJ0N27L6wGH8jlfDEZQsYdwR4rsXM1SuKUSmkPNNi4bi5Raydk8XmFgthmQalNMaQI4Q7GOWiGhm2oIT3+hxctzyXQFTChkYTKyvSWVGexhPbjQxafcz4wzj8EdQKGY9fuoA+i5dnmia4enmR6MTSaCRVq+TqFUVsbDTxfIuZaFQgVavAHYjwoxPLyU1Ws2G7kZgAt55UyXPNE2zdNYnVHUqIN77TkM2afIE3Bv30uRX87rx6Rm1+nm6ZYngmSiASQyqBFK2Cu04qwRsI8VybDYfDzoU1Sj42hni9SwwXXlubyaBVXOL+0Qnlsz6rJj4ZtOOZzeWLCpChU/Lzk6uIxGJs3jlBp9lFKCqIc1XEqujCo/I5ptjAW91TfDDkZiYEqVoFc3L09Ex5aDO5mV9gQCGTYHYGEzt0lZk6VleJ18ouoyvhnpKXrGRufjIgMOWJEBNEIgxGxBZm/OFCp5CgUcqO6BxPr5TQ8rPVLCo+9Gs3jt3JMDMzk4KCAvLy8tBoNIRCIaanpxkdHU0kow8PD+Pz+RJV3uHgSJBh3Ihar9cTCoWYO3cuarWaUCiEzWb7QiqDIAh7WLL94x//4NJLL0Wr1R72v92/Eb4hvr2x+5fGbDbT3t6eEJMkJycTCAQOuEz+ZfB4PPtMRBcE4fP5mkpFfX39F0Qyh0t8VqsVg8GQqE73TmiIX4y7y6/jF5pCoSA/IxmNUs5zbdMsn1fBybXpbG6x0G50MmIXHTd0Sgm/PzmXnSYv7/XauOPUakacEba2TnHF0iLUCikbG00sKUlheXkaG7YbealtErsvjARYVJzCIxfP5+W2Kd7r+3wn7/kWM9etLiUcExWcc3KTMM0E6J7yIAjwg9WlfDrsYGImwH1nihl6r3dZ+PW3a+ie9PD0ThMXHZWPacbPh4MzTHoirC5PZsgeJCs9ndtOLOa1Lhvv9E5zUVmYUbfAc7ssLMpRMOYM0znpQSGT8oPjSvlkyEHvlJffn1PHv/ptbGma4IdrypBJxQT1T4bsiSqlJEPLoxctYPuoaPp8+dJCitNEg2vTTICYIGBxhxCA+9bVQDjA1g47GclJ3HpyNS+1TfFS2ySZehXGGdFEen6BGEP0fp+NF9ptXL68hCUlqWxtt/GZWczSy9DK8IRiqGIBrqzX0D8TY3PzJEop+MJRXAGRWNbNz2H9siI+HLDxbLOZgmRRxTjpEom4JlvHzSeUE4zGeLZ5kl1GF1FBfLDRKmW4AuKDwPkNuSCR8OmwA7svjFYp47T6bIe+aLMAACAASURBVBYUGhhz+Nk26MAymz5/VHEqc/MMeEJRWk0unHE/TiBJLQdBnBPqlTLStTJm/DFRYXqE8Mez53DfWfVfy45c3NR594QDqVSacEiRy+WJJfaZmRn8fv9BVVkHes39keGXJVfEzTCysrIS73l3F5p4KoPdbmdsbIxHHnmEP//5z9hsNubNm0d6evp+7Q/3h/Hxcc444wzuv/9+HnroISKRCMccc8xhHesr4kuJT7I/z0o4In6v/5YIh8NMT0/T19eHwWCgoqJij2rJ4XBgNpsPS91kNpvx+/17DIjdbje9vb0olUoqKyu/dP73ySefsHTp0kO+cLu6usjNzUWhUNDb24tcLk8kT+z+hAifK9L2RkwQuPyJFnqmPNy7rpZfvtaLzSu2ytZUpfPYZ0bOKZdQm6nm19v9LMmRsb4hlVvedZCdpGTD5Q1csbGVcbufBYXJfDRgB+D6Y0vQKmXc//YgP11bQU2Onis37OLkumxuOr6Ucx5pIitJyUMXzOP8vzcx7Qmhlkspz9TRaXbzp/PrsXvD3PFqL1ctK2LtnEwu+UcLc/OS+N3ZdZzzyA5ss7FGqWoZM4Eof/nOXEbsfu5/a4DLjynkW3VZXP5EC5VZOn797SoueWIXzkAUlQzydRKGXAJXLjRQnJHEne+I5H3nadWs39DKjD9MQ2EKHw7YADi1PotT67O54dkOKjJ1/P7sOm7e2knflJdfnlbFMzsnaJ9wo5SJCtMN28dw+MLcuiqTiZCGv308xvKyVGpyknjs0zEEAVZXpbOoMJn/fG+Isgwdd59ezR/fG+bTYQf5KWpMMwHRwUYicMuacpQKKb95axCVXMrFDRlihJAnglQC6ypURKUKXu3zkKJR8N0VRbzWYaF9wg3A6so0ji5J5bFPx7F6QpQlS/BFpUx6REJXyCScPjebglQN/+yYot/qA8QK8LiqdOzeMNsGbYm5qUImYW5eEmq5lE6zZw8LsbxkJek6FVPuIBa32CLVK6V4Q9HE3z8SKExR8OYPVhy5Ax4AcfMKlUpFZWXlHgruWCyG1+vF5XLhdrtxuVzEYjF0Oh1JSUkYDAaSkpKOaML57pVgfPc3JSWFvLw8BEFIEGWcSPdGKBSira2N66+/nuOPP55du3bhcrl48MEHWbVq1SG9l3jwbUNDA263m0WLFvHiiy/+TyhFv/Qm+v8t8XV3d+N0OqmqqtrnLM7j8TA4OMj8+fMP+dhWqxWHw0FVVRXBYJD+/n58Ph/V1dVfqAL3xvbt21m0aNEhi1u6uroIBAKEw2Gqq6tJSUk5aMLbHUaHn3UPNxIIx0jVKZAAUgTuOFrBk70CLZNBtly1mHd7rPz5XyPcsbYYSTTIL9+d5PgCKUilvDcWQSIRzX7f6XMQiMR4/urF3PFaHx/229hw+UI+HXLw4AfD3H5KFfnJKq55uh21QkogHEMhk1CYombDFYu4ZlMrw9M+nrlq0WzCwgS/OWsOggA/fqGLXIMSsyuEBMg1qNi8fhHXPN3GqN3Pk5ctYOuuSTbtMPHzb1WSbVByw5ZOlHIpwUgMtUKKViFj4xUN/PbtAd7rs3HTsgwCfj8PtXhZkCmjIkPL891uBODsBTn4wjFe77Rw/epS6nKT+MGWDkrSNfz2rDr+Y3MbxpkAUgksLU3lkyEHFalyrl6g5e+dEQasfm4/tQqjw88jH48BMC/fAAi0mdxcenQBy8vT+PELXUSiMU6szeK1jinCsy3V3541h42NJt7vm2ZpaSrrlxXy85d7mJollLPm59BqctFn8bK0OIk1xSr+3mTH7BU//7lZKpK1SraNuDGo5ZxQoqRjKkifQyS8ghQ15zXkMWL382r7ZKKdqZZLqc9LYtAqKniVMvF7FIyIS/SBSBSH73OykwCFqWpyDComXSFMM36+JuMVAF74bgPVOfu/ro4U4j65RqORysrKg+4IxWIxfD4fLpcrQYjRaBStVovBYDhiZGixWBgcHKSsrCyRz7k7Ke6O+P0gToaCILBq1SpaWloSyvdoNPqlMWoHi3Xr1nHddddx4oknfqXjHAa+Ib69EVdrfRkCgQCdnZ0sWrTokI/tcDgwmUyo1WosFgvl5eVkZWUdVBXX1NREXV3dQbcZ4nO8oaEhcnNzqampSbRA4m2PgyG83fF8ywS/eKWX61YWkit1cfuHTtZUpfGLU2s48287SdEo2HRlA9/b1EbvlIfnv7uYu/7ZxydDDgDqcrR0Tvo4q1rNvNQYv24MUZ+l4pffKuH7L4wgAM9etZhbX+rm0yE7lVl6uic9AFx+TCHLy9P47lOtooHzmjLO/3sTSWo5Gy5fyI3PdtJucvHtuVm80DpFTIBlJQYuOrqIH2zpZGVFGredUsnFj7eIy++XN3DvmwO83zdNdbaO3ikvACvK0/jxieVc8kQLBrWcv1+8gDte7WX7iIPfnl1Hq9HJE58ZAZiTqcToDImzu2N0vDMu8P6Ij/VL81lals61m9tnF77FVQZXIMKVi9LQRNw80hEhL0XN78+u54G3+/lseAYgIXJRyqT89myxrbqx0cjiomTWzcvm128MEIjEyEtW872VRfzx/RHcgQg3Hl+KWiHjvjf7CcfE3ceKTB1jdh9KuZRrjy0lHI3xlw9HCM7GKWXplYSjMRz+CAtyVDSkx3h3NMSoR/xOlKSqWFOTwbt9dkZsfpJUMmLCrDBl9nykEpiXl4TNF2bcEUAmlRCdnVsa1HKigoA3GBXFK1oFU24x2eLrxNwMGT8+WkcoFEoQSLyi+qo3633B5/PR3d2NXq+noqLikA0q9oYgCF+oDCORyB6VocFgOCgyDIfD9Pb2Eo1Gqa2t/dLz350E9ybDQCDAypUrGRwc/ErntTtGRkZYtWoVHR0dhxy3dgTwpTe9/29nfMB+o4lAdD44VMuf+P6M0WgkIyODurq6g44lgs+jRA504e6etB6XOuv1epKSkr4wxzvUtmlVpobmYSuvdNq4aEkhhVnJbNpppjpbz2n12Tyx3YgnGBUXvXeYeKbJxJDNj0ouRSOXsuGKRTj9EV7snOHi1fXU5ejY0jqNLBbh5GIJ/+z380nvBCkqCYP2MDZviFtOKEMulfBah4VzGnITs7IMvZIrlhYlcu9Orc/ivT4bXZNeji7UUpKh59MRJ6fUZdNQlMwT242EowI/ObGcLc0TvN9royZHR+eEm2lvmIuX5LO8PI2tuyZRymXceHwZm5sm+NeAjd+fXUfzuJON243sMrowqEVyWlCUyv1n1fFG1zQfGiPcdGweoVCY59psNA1bcATFyKAklZTfnlqE1eHm1X4flYU5/PCECp5vmeC5FjPTnlDCc3JRcQr3nzGHj4fsPNVoYu2cTE6oyWBL0wTv9tnQq2QsKU2hY8LNhDPA3afXYPeGeGqHiY4JF55gFJVcXEgvy9Dy81OqmHQF2bxzggGrl0B8PQCQSeGypUUsKtDzXr+DxskYzpCEdK2C6gwVfdMBdhnd5GsiKOVSpn2fG1oXp2k4oToDpVxKq8mdmNmla+UsKExGLpVgmRXvxMUrM/7PrcZ0Stms8vPIkaAE2HbzMi5dXk5eXh4FBQUYDIaEWfTY2Bijo6PY7Xb8fj/RaPSgY4T2BUEQGB8fZ2BggKqqKgoKCg57ZrfHeUgkCePrjIwM8vPzKSgoICkpiWg0isPh+MK57CsFYnp6mo6ODvLz8w9bTdrW1sall17K4sWLOfPMM7/yuYHYNTv55JO55557WLBgwRE55iHiG3HL3jiQX6dEIjnkMFq73U5bW1viCz1nzpxDJp348vv+Kj632017ezt+v5/6+nqysrISWX5xb8D4F/xQXl8QBKampujs7GRVTQ7vDnvZYRK9MZvHRRHK1SuKkUulbGw00m/xMmLzEY4JrChL5cHz5rKl2UzLuJN7z6jl/b5pXm2f4qcnVWHzhtnabuP8FTWEYzE+HfUw4gizokjDhCvC9IyTa+Yp2TEZ4dUOCz89sQybL8LG7UZOrMmkOF3D8y2TvN9nI0cvIxKToFEr+cO59TSOikkO319Vgk4lLrHnJauoydHzTu80HRNuvjUnk3BUoGXcyU3Hl6GUS9mw3UiuQSXm8zUaea3TwpQ7RCQqIAHuXVfLgsJkntxuxOoJcc8ZtbzRZeGVTgcrq7PpMLtxBARK0tTctjqbj4ZcvNYzwznlUlJ0KrZ2OhiZdqNWyJl0BQlHBa47toRv1WWxeaeJ7SMO7lk3B4cvNKtidRATwKCR4/RHOKEmkyuWFvJ6p4UtTROUpWsxu4K4AlFkErhmZREn12XzWoeFl1onqcvR4w5GsbhDRAWBxcXJ3LSmHIc3zHMtZtpNLiIx8UFIoxDFKzqNkgsW5YFEwi5zAFdIQCOXcGyxhsoUCWZngE9GPZhdQWRSCYuLkllQmEwkBq1GJ3bf5+IVvVKGVCIhEhPXHHINKlyBCN7QkSO9CxpyeObqo9AoP7+5x91M4mbRB0OGB5up5/V6aW1tRaFQUF9f/7WrHfcmw/i5xMlw93OJK0tnZmaoq6s75Bg0ECvFBx54gEceeYTHH3+ca6655oicRzgc5owzzuDMM8/kyiuvPCLHPAx8Q3x742CI72AVll6vl46ODpxOJ3PmzCErK4vJycnDSl3e3/J7KBSip6cHk8lEdXU1xcXFyGSyRNtieHiYiYkJXC4XoVAIOHhLJpfLRUdHB9FolLq6OvKyMqjMEpMDvCGxunu2eYLtww7q8pJoHJlh3BHgnIW5VOfoeat7mlWV6SwrS+PJ7WKL8Ibjynh6h4nmcRf3nVHL650Wnmky0WfxJWy1fnpyLUvL03hmlxW9IZmrFqfzcpeD97vNXFoRpssBz7SIXqEyCcSAG4+v4NxF+Ty5fZyhaR/3nlHDPzstvNw2xR2nVjFi87Ol2UzTmJOCFDWuQISiNC0PnFmbIIlfnFyFPyymKWiVUqa9YSZdQVRyKX+9cB6D0z6e3mni3IY8FhSI5Gd0+LlmZTGvd1lpHJ2hJE3DSbWZbBtyYHZ4ufOUUpongrw1GuachgLc/hC7Jnw4fGHWlcmQy2S82m0nUyPhxjXlvNZhZUuziXBsNq5IgHS9gt+cVYdKLmNjo5GhaR9XLS+madRJ95QXlVzKbSdXIpVK2Nxkxjjj5wfHlTFg9bJjTHTCWTcvm2/NyeKdHvHBQ08IqUyKc7YyVcmlnDE/l7U1mfROeXizexrTTCBhNZamV/LRsJt+RxRXCORSCXOy1OTppfRafHRMerF6RLVqtl5BRaYOmVSC3RcmJggY1DJCkRh2X+SIzfaUUmj66QqOrz24eJ8vI8N4R+TLyFChUCSuqZGREYaHh6mpqUkoOP8nsDsZxs9Fo9EwOTlJWloaGo0Gs9nM6OgoNpstkQIRX5b/MnR1dXHRRRdRXV3NY489dsQyAwVB4IorrqCoqIg77/xS7vnvwDeqzr0Ri8UIh8P7/TOffPLJfl3KQ6EQg4ODCZFMfOkzFouxfft2li5desjvq7+/P7GUuvt7HR0dZWJigvLycrKzs790jhfP/3K5XDidTjweDzKZLDEvMBgMaLXaBBkGg0EGBgYIBAJUVVV9Yffwvjf7eXK7kf86fy6tJid/2yaKMhYXJ9M54aYmW8/DF87j0id2MeUK8vx3F/PwR6M82zzBf50/VyTNF7ooSdcyYvMlhA+b1zewfmMb4w4/z6xfzKadRp5qNHHvuloMajnXPtPOvLwkRm1enMEYKhn8fImK5wcitE9HuXttPo6wjN+8N8YFi/P5zuI8Ln68WTT9DUaQSiTEBIE/nTcXsyvA3a/3s25eDlevKOKyJ1qQS6X87uw5/PiFbiacAQxqOecvyuOxT8aozU3i92fXccvWLrrMYjK71R3ivrcGANGcWaOQYXEHuWquCrlKw8M7Z6jNSeLeddXc+GwnQzY/ACsr0mgem0Ell/Grk0t5r8fC850zlCdLyNJK+dQstqTn5uq44fhy7vxnH6aZAOuXFVGZpeX2V/oIzApxTp+bzTs9Vpx+0XasPi+Je98cwO4Tv8fHVqbhDUbZOeYkL1nFcQVyPjX6GXKKD3gVmTrOXZhD+4SH1zunEqQUF6/0WTy4AlHUcikSCfjDMXIMKoK7iVfitmj5yUqydDIs7hCTnujXKl657VvlXLik6Gs5tiAICdFJ/LoJBoOEQiEMBgNFRUWkpKQcUQXmV0EkEqG/v59AIEBtbe0enaH4GkP8POIPwPHdPkEQkMlkFBUV8ec//5mtW7fy8MMPH5aOYX/Ytm0bK1euZO7cuYmHhXvuuYdTTjnliL7OQeAbccveOFji29dqQVxQYjKZKC0tJTc39wt/5kCk+WUYHh5GrVaTm5ubMMceHBwkOzubkpISZDLZHmrN+Ovur6oLh8O43W6cTiculwufz4dSqUQQBAKBAKWlpV8aoxSKxPjOY00MT/sIRmLoVTI8wSgPXzAPTzDCLVu7uHJpIec05HHuIzupyNTxt4vnc/kTLRgdfs5aKGb9RWMCa2szWFubxS1buzhrQS7fX1nMeX9vIlWrYMNlC/nh8500j4thqw++P8ikO0yaRspNayq4+40BarL1/PGcWq5+SlRP/nJlMh8Me3h9JMKyQjXtlhDuYIw0rYLHLp3Pz17qYcjq45GLRTeZBz8Y5oLF+Zy9IIeLn2ghGI4hk0ooTNUwbPNx60kVFKZquOHZDsozdfzxnDpufamblnEXOpUM7+xydnmmlusb1Dy8w0m3PcoP15RRkanjpuc6EQSBUFQgebZdubY2k6tXFPHjrd2M2f38cE0pFneIJ7cbEYCjCpMoTJLyQpeTTK2Ey+uUNFrgg9EgcqnYNixK1TDm8FOWoeWm48v4aNDOlqYJFFIx8ic7SYXTHyYUjbFuXjZVKRL+sWOSKXELgfq8JI4uTeGVtiks7hDpOgWhSAx3MJr4PBUyCQ2FBqbcIUZsfmQSEmSWrlMQjgq4AhGSVDJStEos7sARXTrfF9I1cj760cqv9TV2RywWY3h4mOnpacrKyohEIglCDIfDeygwD1Z0ciRht9vp7e2lqKjooGPP4te4y+Xi008/5fe//z1GoxGdTsf555/P0UcfzTHHHPM/EpT734BvxC37woGMqs1m8x4JxvEZWHt7O1qtNrHsvq8v4OEuorvd7kQF197eTigUShhWxyu6vau8A10AcXujuAu8SqXCarWi0+lITk7Gbrfv0fKJuznIZGIO25LiVDbvnCA3WcVL3z+KjwftvNQ6xY1ryvCHY2xsNLGsLI1Vs8bQ/lCUlZXpvN0zzS6ji2Mr09EqZLSaXHx3RTEps04oxekaLjoqnw3bjfRZPPzspEpe2GXmjS4rCAI1OXpGHEFOqc/mpDmZPPmZkQlXSFxi77TwsSnMNcfX8MmgnX5biDSNjNMrVOyYCNI5MsXNS9NoNAd5rnmS61eXisrQRiNv90zjCYrxPhk6BX+7aD5T7iBPbheX5y9ZUsCmHSbe6ZlGKZcy5Q4RigpcuDif79Qn8XKnnaapCP95/gLcgQhPbhdbklZPiGhMQCGVcPfpNcwvMLCx0UjTmJP7z6jFOONnc5OZVpOLojQNyWo53VM+FpVmcv3qEv41OMNbQ0HCyHAHo4nq6pgcCWfVGmgyB3i2ZZJAOIo7EBEXlxH9MG9cU0aGVs6LrVN8NOLBGxZ/vThNQ6fZQ8+khxXlqUiQYJwJJFYV6nKTWFuTgVQioWncmajs8lNULCpKRiGVYnIGCM2KU0KzBBiPDkpSyTBo5ARmFaRHCo9dPJfbT6s9gkfcP5xOJ21tbSQlJSXixr5szra76MThcODz+fZokx5pRKNR+vr6sFqtzJs3LxFndDCIt3y1Wi3btm2jpaWFxx9/nBtvvBGpVEpzczPhcJja2v++f+v/RnzT6twbgiAk5mBfhubm5oTNl9PppLe3F61WS2Vl5QH9Ow+34hsZGUlEE9XU1CRaFIe6j7cveDwe+vr6UKlUVFRU7HEO8TZJvCp0uVxEo1H0ej0Gg4GPJyL86q1RvreymG/PzeHcR3cm2pyXPdGCaSbAs1cv5r/+NczLbVMAidTtb8/N5uY1ZZz7aBNqhZSn1y/itpd6+GjAzqMXz2dw2svdr/eLFluzOW5FaVqeuGwhNz7bQavJxd8vXkD7hIvfvD3IZUcXsHaOuJAejgmkaOSk65QMT/v44/n1OP0Rbnu5h2PLDFxcr+XWt6fwhgVq0uS0WMQb++rKNL63spjvbmpHr5Lz6EXzePCDEd7osvDd5UWMOvyiZ6YE/mNlCb2TTt7pc3BqpY4LllVww3NdhKMCVy4r5O8fj+EORknXKfjt2XP4z3eHaTO5uGZFMYuKkvnR1i58YTGFQiKBaEwgK0nJ3afX8kGfuMZQmq6hoTCFF1vNogG3TsFdp1Xz4YBY3WXo5Kwq1vJWvxtPWBTfnFimYUFhClvaHYzYA6QowRsRY4MkQCQmsLQ0leXlqbzdPU2ryQWIi+in1mWBBLYN2Bmxi21ZlVzKkuIUUrUKBqd9dE+62S0Hlgy9gkAohicURaOQkp2kZModOqJrC+VpSl76j6X/bfO0aDTK4OAgLpeL2traA/rr7o7d26Txn8NdR/gyOBwOent7EykPh3Pdm0wmrr32WiorK3nggQcO6Rz/l+ObVufeOBjia2trIy8vD5PJlFgM/zL/zb1xqA4s0Wg04f2XnJzM/Pnzv/I+XhzhcJjBwUHcbjdVVVUHXKKPI+5AESfDP3w6zcemKLetTCUmV/Pr981cdkwBFx5VwDl/24FCJhVdOwQBqUTCpisb+HDAzp8+GOanayuYl2/gsidbWFyUwu/OmcPFj7cw5QqSqZMz4hD3Kr+3oojFxalcs6mNpWWp3LOuhsue2IXNG+KpKxayeecET+0woVFICURiSICabD0PXTiP6za30zvl5eEL59E35eG+twY4tT6L/BQNj2wbRQBOqU4mHA7z9pCPtcUyji3Rcd+nHjRKGQ99p5773h5ix6gTgOOr0mkam0EQBG5sUNEXSGJzi4UTazI5tyGXG5/rxBcSI53OmJ/DE58ZyUpS8tuz6tjSPMHWXWbqcvU4fBEmnGLkzan1WZy3KJ9fvNLDuN3PFUsLMWjk/OmDEaIxgeI0DeuXFfHXbaOYZgKsm5fDsrIU7nljAGdAXB6/clkREzN+Xmm3oJJLmJsq0O+Emdmvc6ZOzjkLcxAkMp5tnsDmFVv62Ukqjq1KY8Dio3ncmficFTIJ9XlJqGRSWk2uBJFJgKJUNTnJ4iL6uMOPIIh2ZsFwlCPd6Xz87EL0BPF4PEgkkj3IQ6fTHXEyjJNKXl4ehYWFR8TmbH9kuPue4YHIMBqNMjAwgMfjYc6cOQed9LI7YrEYmzdv5k9/+hO/+c1vOPHEE78WK7d/Y3xDfPvC/pbYI5EIjY2NRKNRampqyMzMPKRjNzY20tDQcEAHlnj7dHBwkLy8PHQ6XcL15VDmePtCLBZLuEyUlJSQk5Pzlb74/nCU8x/dybQ7yIOn5fNUk4U3hwMcXyRnuzmKNyxQmKLiT+fN5epNbYnq7o5Xevmgz8bfLpqH2RXktpd7OHN+Dg5vkA8GHEgl8LO1Zbzf72D7yAwPXzAPkzPAHa/2cm5DLuuXFXHR481IkJChV9IzJS67/+C4UiqzdNywpYOjS1K594wacf7nCPDoxfN5YZeZ51rMADQUJjM8K6557JL5bGk2s2mHiXMXZLGmVMMtr47hj4g+kgalBFdIYHWJluNywjzUAc5glN+dXcfwtI/fvDOIRAJKmZQcg0pMlF9SwElzMrn5+U5cfnHR/J8dFtom3EglcP3qUrzBKH//ZIzidA2/PLWa51vEpAsQjaRrcvRsG7STl6zmlhPK6TK7eOzTcWKzUT4rytPonnRj9YRZVprC3JQIL/b6mPKJRLW0JJnFeRre7XfQZQ0iRbyAlTIJRakqhmwBogKk65QEI1E8s1WqJxAhONv6lEogJogCpFyDmil3EKMjQPQAO69fBWsqUvnThXvueUWj0T1EGh6PB6lU+gWR1uGQYSQSYWBgAJ/Pl+jofJ2IL6rvfj77c22ZmZmhp6cnsdd3ONesxWLhhhtuIDU1lT/84Q+HbZz9vxzfEN++EAqFvrDEHieLsbExVCoVhYWFhzX4bW5uZs6cOfvdx4u3T3U6XaL1GJ8f1NTU7OGvd6iw2WwMDAyQnp5OaWnpEZs9DE/7OPfRnVRm6rhxTSnXPt2OLxyjIl1NeaqMNwe8XFgtpypTzd2feFiQp+d3Z9exflMHFneQjVcs5M7XemkacyGVwJrKFN7rn2FxcQq/O6eOK5/cxYQzwMbLG3i908Jft41y1bIihm0+3u2dRiaVcOvaCt7qttA05uLB8+pxBsL87KUejq/O4OffquSyJ1qYdAWJxISEeOOsBTlcuayIKzfsIhQReOSiebzWYeEfn41TmaVjeNonzufkEn69Np+Pe8y8OBSlLl3KJXNUPNIRZsgRIVUrx+4TnUxStQoePG8ub3ZZeHK76Lry05MquPn5LkbtfqQSOGN+Dk1jTsbsfq5cVsTRJSn84pUe0dRZLiUSE1DLRe/K7yzOY1VFOr97d5BBq48klTjrS9EqmPGFyUpSsn5ZIZNWB5ta7cRzWo8uSSFDr+StbivhqEB1tp5JZwBnIJIQyKSopdSkShhzxZjwfv6dl0sllKZrsPvC2LyiAbVWKWXa80Xhl1omIRQT9mh/fhVIgc9+shy96uBcViKRyB7k4fV6E4rleCWl0+n2e73YbDb6+/spLCw8aIHI14F9kWEkEkmsJsUtxw61TSoIAi+//DL33nsvd911F+vWrfv/rcrbHd8Q376wN/FZrdYEWZSVlTE+Po5KpTqsUMa2tjbKysrQ6/Vf+L1AIJCQJNfU1JCUlPQFg9n4RZ2cnExycjIGgwG1Wn3AL7HP56Ovrw+pVLpfM+yvgje7LNz0XCcgfBwF9gAAIABJREFUKv6C4RgZeiVPr1/ErS92s23Qzl/Oq6XP7OC3/zKztkTB8QVS7vwsSDiGmMumkuIOxnj4wnnYvGFufambM+bncN2xJVw4W91tuHwBt2ztps00S5LVmbzXa2V+QTJ/OLeOa3drbQ5avfz6jX5qsvWMOfz4QlGUMgmPXjyfT4ccPPTRKGfMz+Hq5UVc9VQrbn+E9cuL+PsnY3iCUXINKn5zZg0/faGTSXeEX55cikSu4o5Xe0XfSb2CT0fFGVl9upRzqlX8pTWEIxDjJyeUkKRRc/srPUQEcYYXN5YuTddy12nVvNw+xbPNExSnqlHIpQzMGj/nJ6u57ZRKPh50sGmHkWS1nJqcJD4bcSSuvjMX5LCiIp0nPhmldcKDQiqGtWbplYRjMRy+CKXpWo6vSuP9fjtD0+KxNQopJ9RkkpWk5LNhB51msVKWSmBOlpo0lcCwI4TRLXzhQo9XfskaOdl6JXZfWExIP0L47vJCblxT8ZWPE1csx8nD5/Ptc30nEonQ19dHOBympqbmsJMHvi44nU66u7tJS0tDr9cnzulQ/DwdDge33HILkUiEv/zlL4fcpfo/iG+Ib1+IE188OUGhUCQSDQCMRiPRaJTi4uJDPnZXVxd5eXl7tBii0SgjIyNMTU1RUVGRUGp+2RwvHA4n9vHi+VnxjLA4GcYvgkgkwvDwMA6Hg8rKysPKETwUPPDWAP/4bJw7TqmkPFNMW1hZkcY962q56PFmHL4wW65axBOfGdnYaKQqS0ufRbwhFyfL+NEiJfc3BpgOwB9OK2THZJhHPzNz7bElnFCTyYWPNRETRAusZI0clz/CH86tJxITuOX5TpaXp3H36TVc/VQrRoefm08o568fjWL1hMjQK3ngjFpuf7UXpz/C3y6ax8eDdv78rxFOrsvinIW5XPtMO4FwjPwUNcdVpbOx0URVioSfnVDEg9tnaJ51eDE7A2xumgDgzPk5pGjkPP6ZkQX5Sdy0Moc//GucFnOAHK2EKZ+AZJYwrjg6j2UVGdz2ci82b4j1y4rot3h4r09MeFhVmcZ5C3O5761BjDMBTpubTVGqmkc+HiMcFUhWy/nhCeUMWL1s3mkiFhMoSpIw7hFTNESLNBlnLcglK0lMXZ9wiq37sgwtJ9RkMOEM8n7vdCJGKUkl4+jSVJQyKZ1mN6Ozohbx96Skz1azrmAMhRTSNFKiggSH/8jt6WnlErb/ZOXXon6MI37d7P4TDodJTU0lNzcXg8GARqP5t6iEYrEYQ0NDOByOPXI549jdz3Nf5tZ9fX00NDTQ0tLCL37xC37yk59w4YUX/luc278BviG+fSGucvR4PIlEg90xOTmJ1+ulvLz8kI/d29tLeno6GRkZCILA5OQkQ0ND5OfnU1RUlHBDP5Q53u47OXHBSTgcRiaT4ff7ycvLS6Srf92IxGJcvbGVlnEXG69YSKvRxa/f6OeaFcWsm5/Dd/4uRg2tKk/j8c/EnbVvz0lncWk6d7zWxzkLc/nu8kIufLwFhBi/WmVgY5uLjyeiVKcr6LWJ1UVxmoaNly/k+i0ddJrdPHTBPCZmAtz+ai8n1mRyzcoiLntiF95QlAydkmNKU3m1Y4pT67P5wXElXLWxDZs3xMMXzGPn2AwPvj+MRAIauQydSobdG+KaBRqUCgX/1eSmMFXDn86r51ev9/PZsGi6vaDAwKQriM0b4hcnV6FTybjt5R60Shlnzc9l4w4j/nAMg1rGL08o4J9d07wz5KXEIOHyuTq29IXosorKk9WV6ehUMl7rsFCQoubmNeXsGJ3h6Z0mBCBNq+DcRbn8s8PCuCNAfY6WUm2Q940Cnlnrr9WV6ayuSueTIQfvdFuJayoLUtTMyzewfcSBzRtGLhWX+GVSCTXZetyBMCP2QOIzlEklVGRqydQpMDmDjNr9xITPl9SPNO47vZrTFxx69+RwEe+eCIJAeXk5fr8/QSB+vx+lUrlHZXgwHZUjCZfLlQiJLi4uPujXjpPhzMwMt99+O83NzVgsFlavXs2KFStYsmTJIUcJ/R/FN8S3L3R2dqLX6xNOKHtjenoam81GdXX1IR97cHAQnU6HWq2mt7eXpKSkRObfkVpPmJmZScwIk5KSEjODuCIuXhnu7tRyJOHwhTj3kSaiMYFnrmrgzx+M8FyLmQfOrGXE5uMvH44CsKxIh8kdxeGPsOnKBl5pm+Kv20a54bhSjq3M4JJ/NJOXrOLk+iz+61+jRGMCywo1LMiU8lCzl7mZcm5ekcVdH9oxuUI8ctF8Okwu7n97ELlUggTQKGVEYzEevXgBjSMz/Od7Q6ytzeTmE8r5/tNtjNv9JGsUTHtFAqrP1XPDUXp+t81Cjz3GD9eUMTcvieu3dBCOxghGRL9JbyjKwgIDv/p2Dfe82c8nQw7ObchlWWkqt77UQyASIztJxVXLi3j4o1HcgQg3rSkjx6Di9ld6EmnjmVopvlAMfxROLlNzVHEyjzc7GJ0JIZWIs7bidC39Fi/pOgWXHpWHecrCC31+glFReXnWghw0ChnPt0ziDkZI0yqY8YdRyqRolDIcPvH/a3N0jNj9OP0RNAppQqWpV8kIRQRC0Rh5ySrStEpMzgAO3+G2MAX2c29JICdJwXs3/fdl5cUFY8PDw4lklH0hGAzuURUGAoFEaGv8R6VSHfFrJ74ob7fbqa2t3ec45EAQBIFt27bxk5/8hGuvvZbLL7+cvr4+mpqaGB8f5+c///kRfc//S/EN8e0LkUhkv0vsTqeT8fFx6uvrD/nYAwMDTE9PI5fLqampQa/XHzHCi88II5HIPvMEo9HoHlWh1+tFqVQm2qPJyckH3EM8WHRPurn48Waqs/U8ctF8Ln+yhS6zBwFIVUtxBGJcenQBFy8p4ILHmtAoZGy6soEH3h7k1fYp7jujFrs3xANvi1EoS0pSmPGFGbH5+euF8xhz+Lnj1V5Wlxm4bJ6OW9+ewu6PkaqRMjWbM7eqIpVfnFLN+o2t2DwhHrpgHh0TLh54e5CjS1KQzqaHA3x3RTF52hh3vz1OnkHBXy5cwF8+HOWfnRYWFSXTN+XBHYwilcDtp1STpJbx85d6SNYo+P3Zc3inx8rjs3FFepWMknQtHRNujipO4cdrK/jLv4Z5v89GfooaqztINCYqRWuydfzoxAre6LLwXLMZvUqKUgo2fyzhkrK2MonjqzN4eqeZ1slA4qo9qiQFszPAuCNAdpKKhYUGPhly4JoNfJVJ4JjSVMoydGwbtDNs8yU+n8JUNaXpWvqtXszOIAqZhEj0izM9g1qGXiVnxh/GFxIzEZNUMpz+6FdSdD5+QQ1LKr6amvhQEAwG6enpSQQxH6o4JN5Ric/Y4uOFvcnwcOF2u+nu7iYzM5Pi4uLDUqX6/X7uvPNOOjs7efTRRyktLT3s9/N/HN8Q375wIOLzer309/cfUqRGNBpleHgYk8lEenp6gjSPxD5efEZotVqpqKggIyPjoP9u/Ok2TobBYBCtVpsgQ4PBcNgt0rjYpSJDy7DNR0wQhRUvfG8JGxuNbNhu5LaTK6nPMyRS0P964Xyu3dzGLqMLAUjRiNXL8VXp/PK0aq7c0MqEM8BjlyygeWyGB94eZG1tBjEB3umZRgJctSQTn8/PUx0eluTIuGy+gXs/8zDtjfK7s2t5ftck7/XakErgymVFNI3aaTF6uHSujhV1Jdz8Qi8qhZQfnVDO794dxOIOkaSS8cCZc/jHZ+NsH5nh4iUF4gL+851MOAMkqeQ4AxEUMglyqYTbTq4EJPz6jX6kEjh/UR4vt01h9YiV3PplRVTn6LnvzQFsnhCnz8vB4Qvx4Ww6fX6Kmh8eX8qO4Wmeb7USiYFBCc4QaBUSfGEBjULKt+dmk2NQs2mHKVG11mTrOa4qgxl/iDe7rAnPzlStnKWlaShkEton3AmxSxypWjm+UIxgJEaKRkF2koIZfwSLJwQC6JRSZFIJrkD0sG8A9dkq7l+bu4dF3tfZVhQEIWHUXFlZeUjXxoGOu3dlGAwG0Wg0e5zPgWLE4n67VquV2trag94H3hs7d+7kpptu4pJLLuH666//Wmel/wfwDfHtCwcivmAwSHt7O4sXLz7gseIX3vDwMAUFBahUKjweD+Xl5V95H2/31k18t+erLvPuvmgbJ8NYLJZokRoMBvR6/UG9jiAI3P9qG/+PvfMOj6rA2vjvTslMyqQXUggppNFLAgiKKCKK2Bu6Lrro6qdE2EVUXNTFhrq2VdkFRFDUVWyrICpiQ4okIXRIJwnpPZlkJtPnfn9M5jqBAOmwkvd5fB5JJpM7k7n33HPOW97b30B8oJqlVyUyf8NhQn3UrJ87lqWbcvglv47XbxmBDIHUjw8TFeBOVbMJo8WOXAZv3zGG/BoHM/PaUYNYeGk0d67fj9Zg5e07RrNiWxHbCxoQBJgzPoxf8utpMdocPpzHm3jlh2NcHOvLPeN9eGhzKTVt2rY4fyWFjRYivBX83wgZ31W5s+2Ylj9MCOfKYcHc9+Eh9GaHZ+WVw4P54kAlg7zV/PPmEWw8VMX76WUMD9XgpZKTXuwIkR0b4cMTs+JYviWfzBItlycFcfPYUJZszKZeb0GlkPHgtGj2ljTxc149CSGeLLo0hvczytl5zFHwxg32ZvbIENb+Wkp5k5GEACUahci+GqskF7gwSsOYQW4cLGtmV7lF2r+NDvVgWJg3vxZpJdcVcMgrxg32QWuwcKCsWcrEU8gE4oI88HFXUtxgoKrZ4QPqpZJjsNh63XPzp79cwCDv9qxJs9nc7rN24lixJ1MIg8FATk4OarWauLi4Pt9xu+7aXc2gT+XlqdPpJMZmdHR0t85ds9nMCy+8wM6dO1mzZk2vWIzNmzePzZs3ExwczJEjRwCHH+itt95KcXExUVFRfPLJJ/j5+SGKIgsXLuSbb77Bw8ODd999l3HjxgGwfv16nn32WQAef/xx7rzzzh4fWy9hoPB1BJvNhtVqPe33MzIyzpiy4HSA8PHxITY2Fjc3N+rq6qiqqiI+Pr7Tnpodobm5mby8PDw9PaXn7iucLtnBWQxPZMM1NTWRl5eHn58fqw4a+TarjlduHIavu5L7PjxEyhBfXr1pOPf+5yD5NXoWTY/hrV0l1OnMhGhUvHrTMJZ8mYPWYOH9u8byQ04db7aZSf/pggjmrN2H1mDB1iaqLm00Ogyupw5h3vsHaNBbWHX7KLIqdTz/XT4Bnkqp+JitdlInBSC0NrL6qB1PJSxKdietRsbmPL009vNv+5n7p0Zx0VB//vLpEZoNVh6fFc+vxxr45mgN4NDkxQV58tpPhfh5KHlmdgK5NTpe/7lIsiObGOXL/tJmBAHuv2gI4X7uPL8lX5ICRAd44O+pZG+JlmCNG7eM9OdoSQ3by+0O8bxawT1TImkxWvl0XyVNBsdrMVkdezm5IFLaZtESoIZGk+PsDvBUUtOmvXOSU5QygbgQTzQqOSUNRqqaTX16Ql87Mojnr+/cWqA3OilRFCkrK6O8vLxdOsrZgNPy70QmKThYpjExMYSEhHTLvuzIkSOkpqZy7bXX8uijj/ZaYd++fTteXl7MnTtXKnyPPPII/v7+LFmyhBdeeIHGxkZefPFFvvnmG958802++eYb0tPTWbhwIenp6TQ0NJCcnExmZiaCIDB+/Hj27t3b56zyTmKg8HWEMxU+OL3npsFgIC8vD6vVSkJCQrs9ntFoJDc3VxrzOIkmPj4+nSpeZ4oL6i+4UsO1Wi0GgwG1Wo2npyfNzc0IgkBSUhIeHh6YrXbmvX+AIxUtrP3jaEobDfxtYw7XjAph7sQI5q4/QKvZxiBvFcmRvmw+Us3ciRH8ISWcP67fj12E9XPH8Pn+StbtLiUm0IPCulZkAngo5bx/1xi25tSzcnsx14wK4cFp0dz7n0NUag1cNWIQXx2uwmwTGaRRsWpOIk9tymJ/tYU/T47g8mEh3L/hMK0mK2HebhTUO9iNgWp4ZJIXW0tFfjimZ+pQPx6ZEcfi/2ZJDjEpQ3yp15sorHM4tFw1IpjHv8rhWG2rVGg93OS0mm1MiwvgzxdG8vauUn7Oq8PXXUGLyREcaxcdF8g5KeGMDvVg1S/HKGxydKY+agVXDA8ivbiJ4noDAZ5KwnzUHK1skcJdAcZH+hDmo2JHQQNNht8+uxqVjCA1lOvsmGzgrZJhEzkpBFYpA5VSjsHskCioFDKUcoFWs63bwnSFAHuWXIiqh2kFzk7K1S/WYrFIdl+uOrbW1lays7Px8vJi6NCh59zIT6/XS+Q5X1/fdrq8E+3LTlXIrFYrb7zxBps2beKtt97qkxTz4uJiZs+eLRW+hIQEtm3bRmhoKJWVlUybNo3c3Fzuu+8+pk2bxm233dbucc7/Vq9eDXDS484yTln4+p73fg6juzsGp2autraW+Ph4aZ/g3OPZ7XZUKhWjR48GHEVMq9VK4Zdms1lKRvDx8UGj0UgnrjPyqLKyktjYWEnrd7agVCoJCAggICAAcNwsFBYWUlVVhbe3NxaLhYMHD0pm1s9dEcn9nxcwf8NhPpw3jgemRvHv7cV8c6TGUcDc5NjtIgsuicbPU8l76WV4qRSsvWMMc9fvZ977B7hhbChKuUBhXSujwjX8fVY89284zLwPDrH2jjEoZAJvbivCZLUz/+Io/rYxm88PVDI8VMMNYwbxwncF3P/hIV6+No7Ps1pY82sZ5c1mpscH8vG+CgrqjVwxLIgbRg9i8RfZPJtm4G/TQonxVfD23kbSCzMw2X/bsdXrzSy/Zhibj1TzQUYZO4/VkxSiobC2lXq9BW+1gmVXxVPZbGLFtmLSihqZOSyIIC83anWO7mxUhDd/vnAIP+XW8WFGORsEh5xALkCIt4oKrYmvDtdw7agQJkX78eWBKg5XtAAwfJAnU4YGoDVY+OZoDXtLHD6bAZ5KJkX5IZMJZB5volD7mwVfs8mOTPjtzPd3F/BVy6kziGiNNhQygUBPR6qCri0JwimM7woemh7N3VOievIRkyAIAu7u7ri7u0uhqK4j+draWo4dOyaFxg4aNOici9MRRZGSkhKqqqpITEw8yRfXVZfnjBxzNYOvrq4mISGB6upqUlNTueiii9ixY0evkdHOhOrqakJDQwEIDQ2lpsYx7SgvL2+XNhMREUF5efkpv36u47wufF2FKIqUl5dz/PhxBg8ezKRJkzrU4zmtxpxQqVQEBwdLtGrnh1+r1VJRUUFLi+MC5+bmhk6nIzg4mJSUlH7R43UFTrunkJAQJk+e3C6uyfl6tI013D9c5Ll0K3es3YNa6XiM1S7y5ymRzBoRwp3r9/Pn/xzk3TvHoDNZ+ff2Ytzd5Dx0WQxPfpXLyu3HSY70IcRbxddHath0uIZ3545h3vsHueu9/az5w2isdjsrtx/nu6xaQr1VeLjJyapsYYyPkWemB/PcjnoWbirm37eOxGS18fURxwl8WWIgBrOdLVm1eLgp+OBPY3n4v1ks2VLGxUMDcG+TMAjAnFH+hLnbeDOjiTve2cstwzy5fbQfGw41UlxvID7YkwXTonljWxGLPs9iWlwAL12fxLNb8tl4qBq5TODOSYMJ9VaxeudxHthwmAQ/GRHeCkqbrdhsIjJgdIQP90/1Y0d+PR/vrZBS0q8fNYhAjRs/59Wxasdx6e8wNMiDUeHe1LSY+DGvDmNbtZILEB3ogVopp7i+FV1b3p5vmwFAYaNVEiDY7KJkS6YQQJAJWLqgUvdWy9j98NQ+vykTBAFPT09JstPc3ExoaCjBwcHodDoqKyvJy8uT9tOunWF/J6a3traSlZWFj48PycnJHXahgiDg5eWFl5eX5AjlNINvbm7mgw8+4JdffqGmpobJkycTFhbG/v37SU5OPqvXg44mg07zjY6+fq7j3Lqy9jM68wdyZuA5d1m+vr5MmDABpVLZrsNzPrazz+n88IeHh6PT6SShbWhoKHq9nj179nRrRNoXcI50BUFgzJgxJ9k9nfh6hg2DIXFN3PXeAfQWGwuTPfm11MCaXSWoLS0snxnBw9+UcO9/DrHujjE0t1p45QeHnCHAU4neZKNCa2T5tYn4uCtZn1aKxWbn3bljuPv9A9zx7j7kMkHyofT3VDB/tIq395v5T46Fu/1VfHDXWP78n4PMaXOAGeStol5v5mhlC6/fPIKR4RpW7ThObrWO+y8awrKv8/mloB6NWsG/5ozgiwNVrNtbx6RoP965K5lXfzjGh0cbAT0hnjJCPeBAjZ6nNmczf/Ig6k1+rPq1nG35DhbprOHBFDe0sj7NMbKdk+TOT0U2chvsgJ1of3fumjyYwrpWPt9XybdHa6QObUKUL00GC18cqgIcez8AP3clId5uFNS2SpZnABqVghFhXsgEGXk1unbf05kcZtSuOPFSZRWhK9YsK24ZzqWJHWvj+gKujEhnVBeAj48P4eHh0mOc48SysjIp4cHLy0vaT/dFwgO03zUmJiZ22RBaJpOh0WhoamqisLCQK664gqeffpqioiIyMzNZt24dI0aM6Jber6sICQmhsrJSGnU6b9YjIiIoLS2VHldWViblFG7btq3d16dNm9bnx9lTnNc7vs5EE6WlpUkFJyEhAU9Pz17T450pLsg5InXuPE43Iu0LOOUTdXV1xMXFdZk8sLuwgfs+PMjIMG/+fdsoHvrsCGnFTSyeHIDMZuSldD0+KgGDFYxWh7bsoemxTIz25Z4PDuKpkrPujjF8vLeCd9NKuSjWn7Imo5SysPTKoQjGFp7/pZpQbzfe+sNY3ksv46PMcoYGeVDSYMDaZqp8/0VDmJYQyMJPjtDYauHvV8WjkAks3ZSDxSaiUcmZOSyYzYerUCsVPDU7nsZWKy98ly/t5gQBZIKAxWbnnilDGBvhxYtbCyhqMOGhgFYr+KgEtCbH8901MQxPmY23dlfQ0DaFHBmuYXKMPxsPVlHVbCJY44YAVLeYJUNtAccuTyETyCzRYhNFnKepp5tMEp7LBIEIXzVGq42q5pM/x06/TXCMMe1il+rbSRjiq+SbB6f06x29U/cWGBhIVFRUlwpXRwkPcrn8pLijHiWWGAxkZWX1aNdot9v54IMPWLlyJa+++iqXXnppv73HJ+74Hn74YQICAiRyS0NDA//4xz/4+uuvWbFihURuWbBgARkZGTQ0NDB+/Hj27dsHwLhx49i7d+9ZJRq5YIDc0hFOV/isVivHjh2jtLSUpKQkwsPDpbbe2eWdONLsLLobF9RupKjV9plLiyiK1NbWUlhYKN3VdfdOeWt2DYs+O8oF0X68fONwUj8+zIHSZlKnRfHZ/krKm4x4KGU8OdWPT480sbfaxtyRnkyM9udvWytwU8h5+cZhPL8ln5xqPSqFjKVXDGXDnlKyqw3cl+LHBUmRPPjJURQygfsuimTVjhIaWy1o1HL+PWckn+2rYuOhKqYnBLL4slj+vjmHjONa1G1sSV8PJY2tFuYkh3HTuDCe/CqHrEodU2L8Od7QSlmTgwgzdrAPj1wey0d7ytl0qJpB3ioSQjwdmry2M+XakUFMHuzOR/uq2F9lRiE4OipftQwPNzkVzRb8PZRcMzKE3Bq9JKwHmDDEh+QhfpQ2trI1uw5TW+p5iMaNsYN9MFrs7ClpQm/qWIKjlAuEeqtQyh2p6UaLHQ83GRqVgiaDFZPVLkUVdfXEfvPKYPwVZkmG4Kr/7Iv9k6uHZXfdTTqC1Wptx7zU6/Uolcp2TNLO+Hg61x5lZWUkJCR0m8VYVVXFggULCAkJ4dVXX+10VmZv4LbbbmPbtm3U1dUREhLCU089xXXXXcctt9xCSUkJkZGRfPrpp/j7+yOKIqmpqWzZsgUPDw/eeecdSea1bt06li9fDsDSpUv505/+1G+v4QwYKHynwomZfM6xhfMP39TURGRkJN7e3j3W40H7uKCoqKgez+1dXVq0Wm23WaRO6PV6cnNzO0xp7y7+e6CSxzflcEl8IH+dHs2d7x6g0WDBz0PJTWNDWfdrCYmDNKy8bSTPfZvPd9m13DnOn5H+Io//3IjR5thfjQ71YF9FK0mBSu4freaTIjk7C5u4e3IkFw7144GPDmOw2AnwdGPO+DDeTStFrZTz2k3DyKrU8dL3xwjxdiPA000ijkT4qvnnzcP56nAN69NKiQ7w4OEZMbz+cxG51XqH+P2CSMJ81bz64zHMVpG7p0QiIPLWzhKsdpEATyVLr4hjf1mzZCgd6yujpEXE1NbJquRwSZQ7Ye42tpVYKGx2nFoBHgpuGBuKzQ4/5NZS0ualqZAJpAzxJdJfTVmjkT3HtZhtjkIoEyDSXw2ijLImg5To7qVWUNNspsVkRQBUCgFRRMraUysce7yudH1Tor1Z88fx0r+dMgRX5uXpNGzdgVarJScnh0GDBhEZGdnn3Y/ZbKalpUV6TU4fzxOLu/M4jEYjWVlZeHh4EBcX160uTxRF/vvf//LSSy+xfPlyrrrqql59nbm5udx6663SvwsLC3n66adpampizZo1UnLD8uXLmTVrFgDPP/88a9euRS6X88YbbzBz5sxeO56zhIHCdyq4RhPV19eTl5eHv78/sbGxKBQKaczinNt3d6zZH3FBTnRnRGq1WiksLKSpqYmEhIRev/P8aE85z3ybh1LuuBj7eShp0Jt545aRyARY8OkRYgI9WX37KF798RibDlW30+Qhivw1xZOqxhbezxMY5CnjyUuC2XzMxFdZjchlAjIBgrzcqNCauGdyJLNHhrDw0yOUNxlZeEk0BbV6Nh5yBL9eOSyYa0aF8PhXObQYbfx1egyxQR4s/jyLZqMVQYDZI0LIrdaRV6PnkvgA7p0yhLd2HefntoSFEI2KWSOC2XSomnq9mSnR3gxxM/B1sQ2tyVGkLo4L4KoRwew61sjXR6poa+Lwc1cwNlRNdo2BSp1jvKloY1XGBLjjpVZytKK5XZEK81ERH+xJq9lOVlULujZrtc7IELpqPC2dSsYMAAAgAElEQVQAOxdPwc/jzDdNHaWOO2n7zuLRmbG8zWaTRv9OiczZQkc+nk63GZ1OR1xcXLcZpfX19Tz00EPI5XJWrFghMab7CjabjfDwcNLT03nnnXfw8vJi8eLF7R6TlZXFbbfdRkZGBhUVFVx22WXk5eWdczKRLmKg8J0KZrNZ6nLAscfz8PCQ9nhVVVUUFRVJ4x3nidzZLqq/44I6wulGpE5JQkVFBZGRkYSHh/fZHfb6tFJe3FrAhbF+PH/dMB746BDZVTpevnEYnm4KHvz4MCHeKoaFavi2TTQ+YYgvj02PYOGnWZTr7Cy7Kp4IPw8e/OQwAuClhEqdY/QX7iVj2WWhbC4wsvFIPZNj/Pj7VfE88t9sDpY7svSmxQXQaraRcbyJSxMC+csl0bz2UyE/59VLptSBnm7U6c0M9nPnsZlDKaxrZcW2Iiw2554P/DzcqG4xkRDiybxJ4WTklrIxz4DV7hCO39gmyfjvgSr0ZhthPirq9BZsNjsatcOeTSkXGD/Yh+oWM0X1rSjlvzEr1XJQyqHFDF4qGZF+7mgNVsq1pg7fWyd6I1nhjuRQ/jYrsUfP4WQqOm++nMxl1/2aqzNQQ0MDeXl5PUod70sYDAaOHj0qEWZ0Ol2XO11RFNmyZQvLli1j6dKl3Hrrrf3yOrdu3cpTTz3Frl27WLZsWYeF7/nnnwfgscceA2DmzJksW7bsjOYd5zgGCt+pkJWVRU1NDQkJCdIsG07e47nGAWm1WqxWazvG2In0aVfvwMGDB/dpQekObDabVNTBwSxzLe59xSJ9d3cJ//j+GDMSg3jyqngWfHyYQ+UtPHNNAvk1et7Z7WCOXT96EL5qOe+klzMmWM5z143gme9LSCtq5K5JgylrMvBDTh0AcydGcEG0Lw9/kY1cgL9M8KakXs97WWbkApjtjg5La7QSrFHxwnVJZFW28NpPhWhUcsZE+LAtvx5RdGjrHpwWxbBQDc9tyae43kDyEB+qm02UNjrGkL7uCuZfHIWnm4IV245R0WxBIXME7I4I1UiJB3HBnkyO9mNrdi2VzY6CpZQJTIsPIHGQF78WNrKvRCudZNEB7gwP1VDaZORIebPUyZ14ErorBEK8lJjtDlKMTXQck5tcoL7Vgs1OpztBV6jlkPbIhbj1UIh+KnRENnGypgHJfeVcOk9cz+P4+Ph23ZnTrcV17Gu1WiWButOGzcPDg+bmZpYsWUJDQwOrV6+WtHL9gXnz5jFu3DhSU1NZtmwZ7777Lt7e3iQnJ/PKK6/g5+dHamoqkyZN4o477gDg7rvv5sorr+Smm27qt+PsAwwUvlOhvr5eYnZ1JR/P9Y7W2UU57b2USiU1NTX4+fkRExPTo31HX8BisVBQUEBra2s7V5j+YpG+n17K898VcHFcAMuvTeS+/xzkSFs6+LBBXpQ1GREQSR0lp0Xhy4rdNQwL1fDqTcN45L/ZHChrRgBuGR9KbrWeA2XNzLtgMNeNGcRfPj1KUV0rF8b6k3G8CZPVjgDclOhOvLeNNUcs1LaK3DzKn1A/L/61oxSrXSTCV80zVyfwQUY5P+bWMTTIk/lTh7B2dylH2vaBlyYEcFtyBKt3FJNZosVXDb4qBcVaq9StRfiquWVcKDKZjHd2l1DfZlU2KkzDjKQgypuMfHu0Bm1bsoK3WsHFcQF4uMnJrmrhSEWLCxNTIMJPjd5so6bFjJtcINxH5ZhEtJgxte0+VTKwiI4xqUxwaACNFnuXTt4nrxzKnJTBZ35gL6Kuro68vDyCgoJQKBT9ZmjdWZhMJrKzs3FzcyM+Pr5T+/gTg2MXLVpEWVkZra2tXHLJJcyfP59x48b1WwK82WwmLCyMo0ePEhISQnV1NYGBgQiCwBNPPEFlZSXr1q1j/vz5XHDBBe0K36xZs7jxxhv75Tj7CAOF71SwWCxSd9dTeYJOpyMnJweTyYRKpcJiseDu7t5uRHq2Rajl5eWUlpYSHR19yhxC18efWNwB6W62JyzSj/eW8/TXeYT5qqloMqKUC5htIn+aMIgk9xZeyTSiNYm8fONwBGDR50ewi0jFpazJyLBQL165cTjr00rZkFnBxChfbhkfzrLNObSYbPh5KHnlxmF82qaTS4704dEZMaz65Rg/FjhGn0HuMDzIjZ1lZtQKGQumRRPio2bZ13k0tloQgCuGBSOXwddHavBUyZk11J3KJgO7Khw2Xx5KOXdOiiDcV81/D1Sxr1Qrvc7R4d5EBXqwLa8OrcGKsk17qFLIGB3hTWOrhbwavfR4uUwgPtiTcF8VlVozeTU6LDZR0iyeCV0ddQZ5KvjloYu68BM9h8ViIS8vD4vFQlJS0kkEKrPZ3K6LOjEaqK81ra6m8D1JemhtbeXvf/87eXl5LFy4kMrKSvbs2cOBAwf44YcfJD1iX2Ljxo3861//YuvWrSd9z1XKMDDqbI/ffeH79ddfiYmJkcStvRkX5DoKcZ7Ioii2kx/0VEfUWbiaSfckpf10WX9dHZFuPlzNki+y8PVQsu6Okaz4IYcfClu5engAf70snoWfHuFwRQvjBvuwv0yLgKMwvHT9MJRyGY9+mYVSLuPlG4ZxrFbPC1sLsIuOFPMZSUF8caASjVrB07MT0BqtPPtNHha7KI003eQCLSYbN48J4ZIod1b+WsnhGhMqOZhsEOghR2e2Y7XDDWNDmRSmYvXOYnIbHadFuK+auyYO5peCenYea0AhgyAvFZXNJrzVCqx2kVazDX8PJYmDvDhc3kKLyYpaKZPcVvw8FBjMdoxWOxG+agK93ChtNEidYnfQ2eL37h9HMiG6d+J7OgunTVdnbrycOF0aQm/EarnCbDaTnZ3d7Tw/JzIyMli0aBF/+tOfmD9/fr+7yDgxZ84cZs6cKUkMnOJ0gNdee4309HQ2bNjA0aNHuf322yVyy/Tp08nPzx8gt/weIYoiCxYsIC0tDYAxY8aQnJxMSkoK8fHxZ/ywdicuyOkw4SyGTh1RX+3WTCYT+fn5WCyWDkNre+t3OAuhVqvt0oh0W14df/3sCD5KeGHWYDJqBFbtOM4F0b5cmhDES98XYLaJxAZ58NINw1i2OY9D5c3ce+EQZo8M4S+fHqaozoBKIcNiE/FSydEardx74RBmDgvibxuzya3Wc3FcAIV1emlPNzJMwxOz4tl0qJoP95Tho1YwOsKHHccasNsdEoQJEe5cGa1iR3EL20ptkv5No5ITG+jBwfIW5DKB6QmBqBQyvs2qkcgpIRo3ZiQF466UsflwtbTjkwswOsKbcB81+8uaKWsynrKbC/BUEuihpLbVQoPeglwmEOChwGwT0Roc9mPd2eVFaeCJSep2ery+7qLMZjM5OTkIgkBCQkKPf9eJsVotLS3tPC87yyR1RXV1NYWFhQwdOlSi+3cVJpOJ5cuXk56ezpo1a0hISOjW8/QGWltbGTx4MIWFhRJL+49//CMHDhxAEASioqLa7Rufe+451q1bh0Kh4J///CdXXnnlWTv2XsJA4TsdnCO9zMxM0tPTSU9PJz8/n9DQUJKTk5kwYQLJycn4+flJd6hVVVWUlZX1SlyQc7Tj/O/EwuHt7d3lO0an2XVVVRWxsbHSXL8/0FmhfWtrK7m5uRzXy3gpzfGYf906krTiRt78uQgRiA/2ZESYhv8eqGLcYB9euD6J1TuO8/n+SkaHa7DYRbLa9oOjwr154dok1vx6nC8OVDEiTMNDl8WwfEsB+TUOTd69UyIJ93Pnpe+P0Wq2cdcFgwnwVPLaT0WYrXa81Qoeu3wotXozb+8qodloJcZboM4k0GyyS93U2CAZE8PcyGkS2F5ikPw1rx0ZTGSAJ3uKGx1FtO0MGhrkyQXRvujNNnYXNVLpws70VssJ1qioajahM9nwUSsI91XTbLRS2exIcVcrZCjkAgaLHZtdRCEDhUyG0do1V+kt85OJDNCcUo/nSszQaDQ97qJcbw5jY2MlC6y+wJmYpM4Jy4nnkmtRTkxM7HaXd+jQIR588EFuvPFGFi9e3OtrjaioKKmYKxQKMjMzu5Wfdx5hoPB1FU6X9bS0NNLS0tizZw96vZ6YmBjq6upQKpWsX7++T+QJpyocrru107lLOM2kg4ODGTJkyDkxrjhRaN/U1ITdbicoKIiQkBCabG6kfppNpdaIXRRxV8qxiSJucoFXbxqB1mDh8U05eKkUPHtNIuvTSvm1sBEB+NMFg4kKcOf57wpwU8hYdlUCIiJLN+ZisNgk78ysKh2Fda1cnhTEn6dE8vauEr7LrgXA31PJjWNC+eZoDeVNRsZFaBjta2JLsU2SS4wO9+YPE8IpqNXzcWaFRFBRyQXGhyhoNFjJaXQK1gVMNpFQbxVDgz3JqtRRr//NJUijkjMq3Bt3pZzcah2lTUaENq/OvtDlXZHoz6u3jD7tY5xdlGsxdDV/9vHx6XQ4MTiE3jk5OSiVyh6NDXuCU9mWObtCq9VKaWkpQ4cO7XZRtlgsvPbaa2zZsoW33nqLUaNG9fKrcCAqKorMzMx2O8eu5uedZxgofD2F2Wzm1VdfZd26dVx88cVS3pa3tzcpKSmkpKQwYcKETtuPdRVOqyVn4XDm4rl2hVarVTKT7muRfHdRU1MjWaEFBwe3y/qr15n492EreQ02bh0TxB8vGMJfPsumsE7PXy6N4cIYP+796DB1bVE/MxIDyanWUdZo5O4pDsH645tyOFLRgl+bDZmvu0MzlxDixeNXDCWzRMuq7cWONHNBwC6K+HsoqdGZiQv25L4pg8kqruDDQ80YbbQVzRCiAtzZsLeCOp2ZYC83jFY7zUYrgZ5KKWR2ZJgGGSKHK3WOwiSCHZABfu5yGgw2FHKBuCBPRESO1RokN5aOIJeB3X7qk7AzxU8uwO6Hp+Cl7t5EoqNwYplM1i6c+ESCkyiKVFRUUFJScpIE4FyAxWKhsbGRwsJCzGYzCoWiwzT4zpzHubm5pKamcumll/LEE0/06bi4o8LX1fy8/pRRnAMYKHw9RXFxMR999BF//etfJSqyKIrU1dVJXWF6ejrV1dXEx8dLI9IxY8b0SQFyXfg3NjZSW1uL2WzGz8+P4ODgU451zhYMBoN09x8XF9ehFZooijQ1t/DMt/lsyWtmdJCMucNUfH5M5NcyI37ujq5Ko5LTYrIxOcaPJ2bFs3ZXCZ/tryQuyJPEQV5sPlyNCHi6yXlyVjxqpYzlWwqobjFxaXwgJY0GCmodTMoATyULpkXjoZLz+o/HKNM6ZANmm8jQIE+ajRZqWswM8XfniuHB7MivJ6vKMVqVAZNj/UkZ4ktejY6t2bXSji/Yy43kKF+MZhvpxY3ozfYO93EKmUC4rwqVQk55kxG92Ya7UoafuxKt0YrebJNSG7pqMH3/1CE8OC2mi3+pM8PV79LVJs/pc1lVVSWZNp9r0VoAtbW1FBQUEB0dLeX+uTq1aLVaTCaTxCTtyLTCZrOxatUqNmzYwMqVK5kwYUKfH3d0dLS0brnvvvu499578fX1pampSXqMn58fjY2NzJ49myVLlnDhhRcCMH36dF588UXJX/M8wUDh6y/YbDays7PZvXs36enpHDhwALlcztixY6WuMDo6ulcKkquZdGhoKOHh4dKoynl3rlAo2hEY+ks/5ITdbpcYr/Hx8Z0aDYuiyIbMCp7/Lp9gjRsTBnvy1dEGbCJ4KGDheDVa0Z21+5pQK+Q8eVU8OdU61u4qwS5C4iAvHrksltd+KuRwRQuTov2478IhvPxDAUfb9oEzk4K4aVwoK7YVc7C8mSAPgUB3Odn1VuQyAVubtm9OcjheKjlv7yqRzKqHBnlw09gwanQmvjlSQ1UbccVNIWNqrD8RfmoK61tJL2rEZHWcQjIBhvirkQkyjjc4/DUDPeV4yO3UG0R0FsdZ6iZ3dKJOj0+1QsAugrkLVc/TTSDt4Yv6dcRtNBopLCyktrYWtVqN3W6XpDy94d/ZG3DKKKxWK4mJiaf1oXW9sXSOfhsbG3nzzTdJSEggPT2diRMn8vLLL/fbZKWiooKwsDBqamqYMWMGb775Jtdcc02Hhe+qq67isccea1f4/vGPfzB+/PhTPf3vEQOF72xBFEVaWlrYs2ePVAyLioqIjIyUCuH48ePRaDRdGpF21kzabDa3G5GaTCaJBu68KPXVBbKuro6CggLJbLirxX5/qZa/fnqEWp2ZpEFezJ8WxQvfFVDeZOSOcUGkhMj4x44aSlscH9MwjYJwX3f2lLYQFeDO0plxHG8w8MqPxzC0yQemJwRittnZUdBAoJcbNyZ5UVil5acym6Owusm5Z/JgQn3VfLq3kn2lWmmkGBPowYgwDTsLGmhoteDW5jtqE0WGh2qwi6JUWJ2I8ncnMcSLFpOVwxUtNBut3WJidhYv35DErBGD+ubJTwG9Xk92djbe3t7ExsYil8s7dDXpKeuyJ3D68HYlDeVEWCwW/vnPf/L9998THBxMdXU1BoOBqVOn8s9//rMPjvrUcFqPrVmzZmDUeWoMFL5zCXa7naKiImlEmpmZiclkYtSoUVIxTExM7PCi4Or9GR8f3+XQS1cCg/OiBL0jSnfCaDSSm5uLIAjEx8f3qMts0Jt5+Issdhc2ckl8IEsuj2XljuN8ebCKYC83tEYrNruIXRTxUArMHaVBZTfyn2wLNQYRfw85Da02fNpSyD1Vcv50QSSxfo7RZlHzb/KD21PC2VHQQGaJI7JoiL87+bV6lHJHmI/JKhLk5UZKlC+H2qQIrv6aod5umG0i9XrHbjHCT02V1ijtAF0hFxyjS0FwEGFsbeL8jtCZXV6En4qtD07u9vvcHYiiyPHjx6muriYxMfGMxuanYl267tZ6W9fq3HubTCaGDRvW7bSRyspKUlNTiYyM5OWXX5bcjsxmMyUlJQwdOrTXjrkj6PV6iWik1+uZMWMGTz75JD/++GOX8vPOMwwUvnMdRqORvXv3SsUwLy+PgIAAxo8fL3WFX3zxBXa7nWuvvbZXvT87ijbqjim3q4QiLi6u10gNdlHkg/QyXvnxGN5qBXdMjOCTzAoqm00IwO0p4dwwNpTlW/LZW6JldLiGxBAvPj9QhdUuIhPg0ggZF0So+a7ETka5UQpmVcplDPJRUVxvwMNNzrWjBuHpJuPDPeW0tnWJQ/zduWJYEO5uCjYdrKKw3pFwrpQLpET6EBXgwdHKFg5XtCCK7U8aZ3fnrVYwxN8dnclGaaMjINfPQ4G7Uk6dzozZJqKUOX62iwoFXp4RQHLsIMneqz+g0+nIzs7G39+/R6N7J+vSWQx1Op2Uj+f87HXXsqyhoYHc3FyGDBlCaGhot55DFEU++eQTXnvtNV544QWuvPLKs2KfVlhYyPXXXw84ivntt9/O0qVLqa+v73J+3nmEgcL3vwZRFKmqqiItLY0vv/ySr776iqioKGJjY5k4cSIpKSmMGjWqT0JAgS6bcjc0NJCfn09QUFCXk7I7i7xqHUs2ZpNTpcNdKWPxZbEcLG9m06FqKUnhQJmWtbtKsIkwyFvF41fG8WNuHRsPVqGSC4wKECnQCtQbHR9tNxlcEe/NBbEB/HysmR9y6yVN3nWjQogM8ODn3DoyS36zIRvsp+bitpSHtKJGKlw0eaHebkT4eVCpNVLWZMRNLhDmq0a0iw5/zTbvUFnbKWkTHWenWiHDbLN3icByUawvb948vJ15gMlk6lObPOfOtq6ujqSkJKnz6U04x/PO1+RqWdaZGzGr1Sp50Q4bNqzbNwO1tbUsWrQId3d33njjjV5NFS8tLWXu3LlUVVUhk8m49957WbhwIcuWLTuf8vL6GgOF738Vq1ev5uOPP+a1115j+PDhHDlyhN27d5OWlsahQ4dQq9USg3TChAmEh4f3SdE5lSm3p6enpI3qjww1s83Ov38pYu2uErzUChZfFkuoj5qnvs6VXFkCPJWMifBme0EDAgI3jwlmkKyZ944aqW11FJ5p8QHcPC6Urw5WsjWnHpv4WzEaGexGsxmKGs0IQEBbTJFGrWCIn5pjda3SzhAcRXJ0uDeBXm4U1ukpqG3tlK9mR+jMWFOjkvPdgkn4up988T/dbs1ZDLuixXNFc3MzOTk5BAUFMWTIkH5jDJ9oWabVarFYLO3E9s5ddWNjI7m5uURERHR7KiKKIl9//TXPPPMMf//737nxxht7vcurrKyksrKScePG0dLSwvjx4/nyyy/55JNPzqe8vL7GQOH7X0Vzc/MpiS+iKNLY2EhGRga7d+8mIyOD0tJSYmJiJG3huHHj+sQP1G63c/z4ccrLy/Hx8cFqtWI0GvvNlLugVs+yzbnsK9USG+hBjc6MzmRFIROw2URuHBfKtSNDeGdHPj8WOqQLguBgc8plAt9l1WKzi4yP9KG6xRE5pFLIMFntuCtlpISpsVotZFZaMLuMHgM8HASa/Fo9BoudMB8VNlGkutnc7vjkAniqFRjMNiw2EZVCwFOlQGe0domh6QoBeOrqeG4aG96ln7Pb7eh0una7NVct3pkMEWw2mxRSPGzYsD6xvesqXFMQnK/LaDQiCAIREREEBgZ2q8A3NTXx6KOPotPpWLVqVbfDZruKa6+9ltTUVHbt2nU+5eX1NQYK3/kCu91Ofn6+1BXu27cPURS77EN6OjgNr537HeddZ3+bcouiyKZD1bz0QwENegsXxvqROi2Grw5X8XFmBaJdxNddTr3BYRStNVgQgcsSg7g0IYCP9lRIAbVeKjnXjQohOtCTzONNfJ9TJ3VtSSGejAhWo201sKfMQKOp/WmhVsowWx0xQIN91QRp3ChvMlHdbEImOFxhRBEaWi3YRXBXClhtIpYu7PJuSw5j6RVxvdZluRoiOOOAnCJuV8/YpqYmcnJyCAsLY/DgwedUVp4TzmMMDQ3Fx8dH2hme6NLSkdjeCVEU+fnnn/nb3/7G4sWLueOOO/qtoy0uLmbq1KkcOXKEV1999XzKy+trDBS+8xVOFmdmZqYksj+TD+mpYDabyc/Px2QykZCQ0Kk7f1fLqKamph6lOZwKepOVt3eVsD7Nka13caQKs9XGrnJHoRGAK4cHcdXIQewr0fLhnjJpVDlskBezR4awr1TLtrw6iViikAlcEONHhK+aA2Vasqt+iw7ydVeQGOSO1WYjt9ZAi1nEXe5IStdbRFotpz5t3NpYoJ09sSYO8WXFbSPxdOt7IfiJe11nUGxYWBiBgYF9Kn3pDmw2G8eOHaOlpeWUY3aLxdKOPOMU2/v4+NDc3ExAQAABAQE88cQTFBcX8/bbbzN4cP/lEup0Oi6++GKWLl3KDTfccL7l5fU1BgrfAH6Dqw9peno6GRkZ6HQ6hg8fLskphg8fLgmObTYbRUVF1NbWEhMTQ3BwcI/u/J0uGSeacvv6+kp35l292xZFkaOFZazaXsT2MhtW0REbtOjSaA5V6PhsXwV6sw1vtcJhNeblhr+HUsrCiwn0oLHVQmOrhWCNG3qTDb3Z4dHpdHIJ0agYEa7BYLKRVdVCk8Ha7ffgTLu8xBAP3rxlFOF+/W8719DQQF5eHuHh4fj5+bVLQHDt4L29vfHy8jorXaBWqyU7O7tbnajTnPuLL77g/fffp6SkhIiICG699VYmTJhASkpKv2TlWSwWZs+ezcyZM1m0aNFJ3z8P8vL6GgOFbwCnh9ls5uDBg5LI/ujRo2g0GskfcO7cuTzwwAN94r7RU1Pu1tZWcnJyUKvVxMXFUW+w8daO42w8VI3BYmPCEF/CfNV8e7RGGkkqZQIXxwcwMkzD1uzadsLzAE83Rkd4U91sJKtSh0Iu4K6U0Wy0tfu9bnKBEI0bNhGqW8zY7CJ+7kpUSoF6vQWLTeySmbQAXDUimIcvH0qQV9+wdU8HV81bUlJSh2xI1w7eGaulUCh6RX7QGdjtdgoLC2lsbOzRvtFoNPLcc8+RmZnJmjVrkMlkZGRksGfPHi699FKuvvrqXj7y9hBFkTvvvBN/f/924vfzLC+vrzFQ+JyYN28emzdvJjg4mCNHjpz0/f/85z+8+OKLAHh5ebFy5UpGj3a42m/ZsoWFCxdis9m45557WLJkSb8ee3+ioaGBxYsXs3fvXi688EJyc3P7zYcUOmfKLZfLJTu0hISEk8T8WoOFz/ZVsGFvBeVtuXdXDAtiQpQfOdUtfHWomhaTo5iFaFRcnhSISiHnQJmW/aXadtKCQRoVFruder0FtUJGbJAnMhkU1bWiM9lwkwt4qxUYLHbJX9NDKcdgdcQInQ5eSpgcpWHJ5UMJ8fc5Kx1UXV0d+fn53dK8uboDuSamu/6teuOGqbm5mezsbMkJqLvv0/79+1mwYAFz5sxh0aJFZ6V47Ny5k4suuoiRI0dK043ly5fz0UcfnU95eX2NgcLnxPbt2/Hy8mLu3LkdFr5ff/2VpKQk/Pz8+Pbbb1m2bBnp6enYbDbi4+P5/vvviYiIICUlhY8++ohhw4adhVfR9/jkk0+wWCzcfvvt0gXG6UPqHJHu378fmUzGuHHjet2H9EQ4Ke3OQtjQ0EBrayteXl6Ehobi5+d3SlNum11kR0E9Gw9WsS2/HpPVLhldB2vcSBniy7G6VnKqfuv63OQCoyO8GaRRUdJoJKuyBYtdxF0paydlOBXO1OkJwCUJAfwhOZwEfxktLh2UcwflmhTQV7BYLOTm5mKz2c7oX9lZnPi3OtGuzJn119nPidPpqKGhoUddnsVi4aWXXuKnn37irbfeYsSIEd16nq7ifLphPscwUPhc4To7Px0aGxsZMWIE5eXl7N69m2XLlvHdd98BJ9OLz0ecyYc0JSWF8ePH4+3t3WtdjNlsJi8vT0qUd+0MXU25XcdurtCZrGzLq2drVg3bC+ox2xzOLjGBHpJ5dHywF95qOQW1rTS0nmw35oRccJBgzHaRjk4jp1Dd2Tl6KGVMiw9gQrQfV48chLuy407jVDvQEzVrPYUzcTwmJqbPafsd6UCdkgrn6+qIcdnS0kJ2dnaPtaEL95wAABgQSURBVINZWVmkpqZyxRVXsHTp0n4zzD7fbpjPMZzyonPuZYacQ1i7dq00TigvL2/H9oqIiDgfgx3bwbmHmz59OtOnTwd+c/bYvXs33333Hc8991ynfUhPB9eMtxMJNj4+PtLfxnXsVlZW1qEp9+yRIcweGYLZZudQWTNpRY3sK9VS0uBIUs+pdnR+CpmATIAwHxWh3irqW60U1rWikDl2e0q5QEmjEVEEd6UMuyhKaQzgkEiMCvcmSOPGLePCGRGuQdGJC7dKpSIoKEhy73D1V62qqiI/P79HMhGTyURubi4ymYzx48f3aYacEzKZDI1Gg0ajISIiAmgfb+R0WnF2uxqNRgosHjZsGF5eXt36vTabjRUrVvD555+zevXqfk8nyMjIYOjQocTEOOKh5syZw8aNGwcK31nGQOE7BX7++WfWrl3Lzp07AcfF50Sci5qmsw2ZTEZMTAwxMTH84Q9/AH7zIU1PT+ell14iJyeHgIAAaVeYkpJCUFDQKd9PnU5HTk4OGo2GlJSU04ri3dzcCAwMlMI6TywazqBeZ9FICvRhfGSUFEpb2mggv0ZPUV0rOVUt1OvNtJhslGvNtJgsyGWOLq6h1YpKIcPTTU6wRkW4rxp30Yi7YGVC4mBSYoII8+kdkocgCHh6euLp6UlYWBjQ3uOysLCwUyNSpw1ecXExQ4cOlQrr2YJCocDf37+dFZjJZKK6uloqzHK5nGPHjrWLN+qsKUJRUREPPPAAEyZMYOfOnf0eyQUDN8znKgYKXwc4dOgQ99xzD99++61ktBwREUFpaan0mLKyMukiNIDTQ61WM2XKFKZMmQK09yHdvXs3//rXv2hsbCQxMVEqhqNGjcJisfDBBx8wbtw4EhMTu0UxP1XRcHaFzk7D1ZT74lhfLkvsfFFwBptGRkYSFhbWLzdEcrkcX1/fdoQe1xFpaWlpuxGpu7s75eXlksXd2c7G6wh2u52Kigpqa2sZO3YsGo2m3Y1LTU0NBQUFUkqBa7frOgK12+2sW7eOd955hzfeeIOLLrrorL2mgRvmcxMDhe8ElJSUcMMNN/D+++8THx8vfT0lJYX8/HyKiooIDw9nw4YNfPjhh2fxSP93IQgCoaGhXH/99e0c550+pOvWrWPnzp3odDouuOACgoKCCA0N7bbH5ImQy+X4+fm1C8V1kjEaGhooLi5uZ8p9Kn9Ls9lMTk4OAOPGjetTEkpn0NGIVK/XU1xcTHFxMUqlEovFQn5+fp846fQEer2erKws/P39SU5Olt7rjm5c7Ha71O0eP34cnU5HdXU133zzDUlJSXz77beMGDGCHTt2dHtE2lsYuGE+N3HekVtuu+02tm3bRl1dHSEhITz11FNYLA4Cw//93/9xzz338PnnnzNkyBDAMY7JzMwE4JtvvuEvf/kLNpuNefPmsXTp0k79zp5IKKKioqTATtdj+b3CYDAwb948rFYry5Yto6ysTCLOlJWVER0d3ec+pPAbGaOpqUnyt3TaX/n4+GA0GqmoqCAuLu6sjwxPBYPBQFZWFp6engwdOhSFQtFuROrKInXVTPZnAXeaKVRVVZGUlNRt4XhjYyOvv/46P/30E0qlEr1eT2RkJDNnzuTBBx/s5aPuPKxWK/Hx8fz444+Eh4eTkpLChx9+yPDhw8/aMZ1HGGB1nk10V0IBSAJy587q9w5RFNm9ezeTJ58cqnqiD+n+/fux2+2MGTNGyi2Mi4vrM12WxWKhpqaGoqIiRFFELpefRJzpK1PurkAURUpLS6moqCAxMfGMYcX9xSI9Ea2trWRlZeHj40NsbGy3u/mamhoWLlyIj48Pr7/+On5+ftJ7UFpaKo3Yzxa6e8M8gB5joPCdbXRHQgHnX+HrCnrTh/RMcKZR1NTUSGL5/jbl7gz0ej3Z2dn4+PgQExPTrYLlulfryKqsp6/LtTAnJSWdMbn9dM+zadMmli9fztNPP8111113ToxtB3DOYKDwnW10tvC9/PLL5OTk8PbbbwMQHR0tXbjvu+8+7r333v443P9ZOC+qziT7M/mQdgbOHLrAwMAzhuyeapTYm6bcHcG1MCcmJna7mJwKvTUibW1tJTs7G41GQ2xsbLc7ycbGRh5++GHMZjMrV67s03Hzww8/zFdffYWbmxuxsbG88847+Pr6UlxcTFJSEgkJCQBMmjSJVatWAbB3717uuusuDAYDs2bN4vXXXx8oyv2PgcJ3ttGZwvfzzz/zwAMPsHPnTolNWlFRQVhYGDU1NcyYMYM333yTqVOn9tdh/y7QkQ+pt7c348ePJyUlhYkTJzJo0KCTLkw2m42CggLJ/b+7jiFOU2RnV2g2m9sRZ7riYtIRnCLvgICAPnPO6Qhms1l6XWcakYqiSFlZGeXl5Z0av54Koijyww8/8MQTT/Doo4+2cxbqK2zdupVLL70UhULBo48+CsCLL7542nN6woQJvP7660yaNIlZs2axYMGCAYux/seAgP1cR0cSCkBigAUHB3P99deTkZExUPi6CDc3N4kQA46LZ11dHenp6RKL1NWHNCUlhdLSUnbs2MGSJUuIj4/v0cVVpVIRHBxMcHCw9PudxJny8nKam5u7FAzrhNPKq76+nqSkJDQaTbePsTtwc3PrlNDew8ODlpYWNBoNycnJ3d6DtrS0sHTpUioqKvj2228JD+9aIG93cfnll0v/P2nSJD777LPTPr6yspLm5mYpOWHu3Ll8+eWXA4XvHMJA4TsHcCoJhV6vlzRLer2erVu38uSTT57FI/19QBAEgoKCmD17NrNnzwYc3V1OTg5bt27l/vvvx2azERoayquvvtrrPqSCIODl5dWOau9qvVZdXd2hKbfreFar1ZKTk0NISEg7+v/ZxInSA2eXV1JSgr+/PxaLhczMTJRKZZdHpDt27OCRRx5h/vz53HPPPWft9a5bt45bb71V+ndRURFjx47F29ubZ599losuuojy8nLJnQYckgbnzn4A5wYGCl8/wFVCERERcZKE4umnn6a+vp4HHngA+E1CUV1d3U7ndvvtt3PFFVd0+veeSUaxceNGnnjiCWQymeT4fuGFFwKwfv16nn32WQAef/xx7rzzzh69B+c65HI5Pj4+vPvuu/zjH//guuuua+dDunTpUoqKioiIiJDcZnrTh/REFxNXo+e6ujoKCwslo2ez2YzZbGbEiBFnXad2KhiNRrKysvDw8GDixIntujzXEalTaH8iO9Y5IjUYDDz99NMcOnSIL774QrL+6m1cdtllVFVVnfT15557jmuvvVb6f4VCITkShYaGUlJSQkBAAHv37uW6667j6NGjA6L1/wEM7Ph+xziTjEKn00nsvEOHDnHLLbeQk5NDQ0MDycnJZGZmIggC48ePZ+/eve0E379HOEeQpyomrj6kaWlp7N27F6PRKPmQpqSkkJiY2GeShoaGBrKzsyUxvTMLz5U4c7ZF9KIoUllZyfHjx0lISGhnR3a6n3FlkZaVlbFkyRJiYmLIzs7m5ptv5tlnnz2rbjPr169n1apV/Pjjjx0mvQNMmzaNl19+mfDwcC655BLJ3OCjjz5i27ZtrF69uj8PeQADO77zE1OnTqW4uPiU33e9wOv1eumu9LvvvmPGjBnSRWvGjBls2bKF2267rU+P92zDOYI8FU7lQ7pv3z7S0tIkRm5XfEg7A6vVKlmrjRs3rl0Gomv35DTldhJMfH19JfOD/oDJZCIrKwuVSnVGT1VXnDgijY2NZfbs2WRmZnLzzTdz/PhxUlJSCA8PZ/Pmzf3ePW3ZsoUXX3yRX375pV3Rq62txd/fH7lcTmFhIfn5+cTExODv749GoyEtLY2JEyfy3nvvnVUR/QBOxkDhO8/xxRdf8Nhjj1FTU8PXX38NdGysO7Cj6BhqtZrJkydLgntXH9K0tDTJhzQhIUHaFY4aNarTnVl9fT35+fkMHjyYhISEky76pyOYVFZWkpubC9Bup9ZR/E9P4Gp+HRcX1yPN6dGjR0lNTeXqq69m69at7bq8xsbGszIyTE1NxWQyMWPGDOA32cL27dt58sknUSgUyOVyVq1aJd0srly5UpIzXHnllQPElnMMA6PO3zk6qx/cvn07Tz/9ND/88AMvvfQSJpOJxx9/HIBnnnkGDw8PHnroof445N8dXH1I09LSOHz4MGq1WnKbSUlJISIioh1hw2w2U1BQgMlkIikpqUfJAq6m3Fqt9iRTbh8fn26PEc1mM9nZ2SgUCuLj47v9PFarlTfeeINNmzaxevVqxo4d263nGcAAXDAw6hzA6TF16lSOHTsmEXC2bdsmfa+srIxp06adtWP7X4dCoWDMmDGMGTOG+++/H1EUaWpqIiMjg927d/Phhx+28yG12Wx89dVXbNiwoVfSHs5kyl1UVCQRZ05nyn0inEG2PY04ys/PJzU1lSlTprBjx44+3VMuW7aMNWvWSMe7fPlyZs2aBTjCpdeuXYtcLueNN95g5syZwECC+u8RAx3f7xyn6/gKCgqIjY1FEAT27dvH1VdfTVlZGY2NjYwfP559+/YBjuSBvXv3doqo4ERPGKVyuZyRI0cCEBkZyaZNm7rz0v+nYLfbycjIYNGiRTQ0NBAYGIjRaGTMmDHSvrAvfUjtdjs6nU7qCnU6ncR0dSXOCIIgpVLIZDISEhK63eXZ7XbWrFnDe++9x4oVK/rFU3PZsmV4eXmxePHidl/PysritttuIyMjg4qKCi677DLy8vIABhLU/3cx0PGdjziTjOLzzz/nvffeQ6lU4u7uzscff4wgCPj7+/PEE09Igu8nn3yyS0UP4K677iI1NZW5c+d2+P3p06dzzTXXnMQoBXB3d+fAgQM9eOX/exAEgWeffZbFixdzww03nORD+uyzz/aZDykgCei9vb2l/a7FYqG5uZmmpiYqKiowGo3I5XIMBgODBw8mMjKy2wzW0tJS5s+fT1JSErt27TolU7K/sHHjRubMmYNKpSI6OpqhQ4eSkZEBMJCg/jvEQMc3gD5DZ/eLu3fvZt68eWRnZwMOtqlOp+uPQzynIIriaYtYX/iQdhYWi4Xs7GysVitBQUHo9fpumXLb7XY++OADVq5cySuvvML06dP7lbCybNky3n33Xby9vUlOTuaVV17Bz8+P1NRUJk2axB133AHA3XffLRFStmzZInnnvv/++6Snp7NixYp+O+YBdBsDHd8Azj10xCgFx/7JaW21ZMkSrrvuurN4lP2HMxUAQRCIjIwkMjKSW265BfjNhzQtLY1///vfHDlyRLIGO50PaVfgTJiPiYkhJCSk3fdczasLCwtPa8pdVVXFggULCA4OZvv27b1upO3E6cTo999/P//f3r2HNPm2cQD/7lCGSBlGasySVDRnWplkkZIdsOMQEq3+KLOiMi1N0JLk7cCLWVCBWSEZHahpnirCXIlIUppbKJSLHDn/KCybB+yPH+q25/3D3262N+c0dUuf6wPBuJ3sGUgXz/3c1/fKzs6GQCBAdnY20tPTcefOHatN50ajcdh1MrVR4SMOY5rA/vr1a2RnZ6O6uhrAUITbggUL0NbWhvXr12Pp0qXw8fFx8NX+ncxzSFNSUobNIe3s7ISfnx8rhsuXL7foBbRmcHAQra2t0Ov1CA0NHXaqhEgkgqurq0XotCmUu7e3F7W1tbhw4QI8PDzQ1taGtLQ0pKSkjOuUqi2mvyNbDh06xCLrRpqUThPUpx/a6iSTZrRbncDQ+CWlUvlbD1hCQgK2b9+O2NjYybrMac+UQ2qaTtHU1AShUIgVK1ZYzSHt6OhAe3s7vL29x3XH2NXVhZMnT8JgMCAiIgJqtRrNzc1YuHAhysrKJuorjlpHRwc8PT0BAFevXsW7d+9QVFSElpYW7Nmzhx1u2bBhAwvZpgnqUxZtdZK/y/+fKB0YGICbmxt6enrg7OwMJycn6HQ6vHnzBhkZGY6+3ClNJBJBKpVCKpXi4MGD4DgOv379gkqlYjmkbW1t8PLyQkhICJqamuDl5YVLly798Z0Zx3FQKBQ4e/YssrKyEB8fb1E8BwYGJurrjUlGRgaam5shEAjg7e3NYsSkUini4uIQGBgIsViM/Px8doL2+vXriI6OZhPUqehNfXTHRyaF+YlSd3f3306U5ubmWpwovXz5MtauXYu3b9/i8OHDEAqFMBqNSE1NxYEDB8b02bZaKUyUSiXCw8NRXFzM7ij5Fs5tYjQa8fDhQ5w5cwbBwcHQ6XTo7+9HcHAwO0U62hzSvr4+nD59GjqdDgUFBewOixA7o0G0hD9shXMDQ9t/mzZtwqxZs5CYmIjY2FjehnMDQ89Vk5OTcfPmTTbnzjyHtKGhwWYOKcdxqKurQ2ZmJo4fP479+/dP+vig+Ph4FsvW29sLV1dXNDc303R0AtBWJ+ETW+HcAJCXl4edO3dCqVSyNb6GcwPDBwUMl0P648cPNDQ0oL6+Hvn5+eju7kZAQACWLVuGjx8/orOzE8+ePcOiRYvsct3FxcXsdXp6usVJUR8fn2H7QY8ePYqCggI2Hb2qqoqyNHmGCh/hnW/fvqGiogI1NTUWhY/CuUcmEAjg4eGBmJgY1mJiyiGtrKyEs7MzFAqF3aZBmOM4Do8fP0ZNTc2I76Pp6ASgwkd4KDU1Fbm5ub/9B00DRMfOPIfUkerq6uDu7g4/Pz+2RtPRiTVU+AjvqFQq7Nq1CwCg0+lQWVkJsVhM4dx/qdFMR5fL5RZb0jQdnYyECh/hHa1Wy16b+gRjYmLQ3d2NrKws9PT0AABevnyJnJwcR10m+ZethnS9Xo/y8nK8f/+erTk5ObEpD6GhofDx8UFrayskEgm+fv3K3kcN6fw0uUeuCHGA3bt3Y/Xq1fj8+TMkEgkKCwtx69YtdqrPGvNw7rCwsD8K505MTMT8+fMRFBQ04vuUSiVEIhFKS0vZmkgkYtuGMplsTJ/LZ9XV1QgICLDYwvz58ycMBgMAWExH9/T0ZNPROY7D/fv32V0j4Q9qZyBkAv1pKwXA33Du8UpISEB4eDiOHDnC1srKyiymo587dw47duwAMLTVbT4dPS8vj7Y7pyfq4yPEXmxFtV27dg0zZsyAUqm0iGOjwkfIhLJa+GirkxA7MrVSmN+dmJimUoSHh+PJkycOuDr7KykpgVQqhVAohEqlsvhZTk4OfH194e/vD4VCwdarqqrg7+8PX19fXLx4ka1rtVqsWrUKfn5+iI+Pd1gsGvn7UeEjxI6stVIAQ+kpKpUKjx49QmpqKr58+eKAK7SvoKAglJeXIzIy0mJdrVaz8OiqqiokJSXBYDDAYDDg2LFjePHiBdRqNeRyOdRqNQAgMzMTaWlp0Gg0mDt3LgoLCx3xlcgUQIWPEDsytVJ4e3ujtLQUSUlJ7O7OdLpw8eLFWLduHZqamhx5qXZhHitmztpE9MbGRjYRfebMmWwiOsdxqKmpYdvG+/bt481dMxk7KnyE2JFWq0V7ezva29sRGxuLGzduICYmBj09Pejv7wcANpUiMDDQwVfrONZSdKytd3V1wdXVlYVoU2M6GQkVPkIm0J+2Unz69AkrV65ESEgIoqKicOrUqT8qfLbaKWprazFnzhzWNnH+/Hn2M2vPzsZr48aNCAoK+u3f06dPrf6OtUbzsa4TMhxqYCdkAsnl8lG/9+7du+z1mjVr8OHDh3F/fkJCApKTk7F3716r74mIiMDz588t1kzPzl69egWJRIKwsDDIZLIJuesc7UR0c2OdiD5v3jz09vZCr9dDLBZTYzoZEd3xETKNREZGjrnpHoDVZ2eOIpPJUFRUhP7+fmi1Wmg0GjYKSaPRQKvVYmBgAEVFRZDJZBAIBIiKimKBAPfu3aPGdGIVFT5CeKa+vh4hISHYsmULWlpaADhuMkVFRQUkEgnq6+uxbds2REdHA7CciL5582Y2EV0sFrOJ6EuWLEFcXBybiJ6bm4srV67A19cXXV1dYx5gTPiDGtgJmWZGaqDv6+uDUCiEi4sLKisrceLECWg0GpSUlEChUOD27dsAgAcPHqCxsRF5eXn2vnxCJgo1sBNCgNmzZ8PFxQUAsHXrVgwODkKn0434TI2Q6YYKHyE88v37d3YCsrGxEUajEW5ublafnREyHdna6iSETCECgUAOYB2AeQB+APgPgBkAwHHcLYFAkAzgKAA9gH8AnOQ47u2/v7sVwDUAIgB3OI77r92/ACF2QIWPEEIIr9BWJyGEEF6hwkcIIYRXqPARQgjhFSp8hBBCeIUKHyGEEF75H8/nfA48qj4/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = figure()\n",
+    "ax = Axes3D(fig)\n",
+    "\n",
+    "X = np.arange(1.2,1.5, 0.001)\n",
+    "Y = np.arange(-1000,1000, 1)\n",
+    "X, Y = np.meshgrid(X, Y)\n",
+    "Z = Q(X,(10*X-14),(21*X-29))\n",
+    "\n",
+    "\n",
+    "ax.plot_wireframe(X, Y, Z)\n",
+    "\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Судя по графику Х в районе 1.35 - 1.40. Подставим в формулы среднее значение - 1.375:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=1.375"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.375 -0.25 -0.125\n"
+     ]
+    }
+   ],
+   "source": [
+    "z=21*x-29\n",
+    "y=10*x-14\n",
+    "print(x,y,z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Решение близко к решению через numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1.38191882, -0.18081181,  0.0202952 ]),\n",
+       " array([], dtype=float64),\n",
+       " 2,\n",
+       " array([9.65316119, 2.41173777]))"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.lstsq(A,B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6. Найдите одно из псевдорешений вырожденной системы:\n",
+    "Попробуйте также отыскать и нормальное псевдорешение."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1,2,3],\n",
+    "             [4,5,6],\n",
+    "             [7,8,9]])\n",
+    "B = np.array([2,5,11])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 1. 0.]\n",
+      " [0. 0. 1.]\n",
+      " [1. 0. 0.]]\n",
+      "[[1.         0.         0.        ]\n",
+      " [0.14285714 1.         0.        ]\n",
+      " [0.57142857 0.5        1.        ]]\n",
+      "[[7.00000000e+00 8.00000000e+00 9.00000000e+00]\n",
+      " [0.00000000e+00 8.57142857e-01 1.71428571e+00]\n",
+      " [0.00000000e+00 0.00000000e+00 1.11022302e-16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "P,L,U = scipy.linalg.lu(A)\n",
+    "print(P)\n",
+    "print(L)\n",
+    "print(U)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[7., 8., 9.],\n",
+       "       [1., 2., 3.],\n",
+       "       [4., 5., 6.]])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C = np.dot(L,U)\n",
+    "C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6.75539944e+16, -1.35107989e+17,  6.75539944e+16])"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.solve(C, B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1.25,  0.5 , -0.25]),\n",
+       " array([], dtype=float64),\n",
+       " 2,\n",
+       " array([1.68481034e+01, 1.06836951e+00, 1.47280825e-16]))"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.lstsq(A,B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.12309149  0.90453403  0.40824829]\n",
+      " [-0.49236596  0.30151134 -0.81649658]\n",
+      " [-0.86164044 -0.30151134  0.40824829]]\n",
+      "[[-8.12403840e+00 -9.60113630e+00 -1.10782342e+01]\n",
+      " [ 0.00000000e+00  9.04534034e-01  1.80906807e+00]\n",
+      " [ 0.00000000e+00  0.00000000e+00 -1.77635684e-15]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Q,R = np.linalg.qr(A)\n",
+    "print(Q)\n",
+    "print(R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2., 3.],\n",
+       "       [4., 5., 6.],\n",
+       "       [7., 8., 9.]])"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(Q,R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-8.1240384 , -9.6011363 ],\n",
+       "       [ 0.        ,  0.90453403]])"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R1 = R[:2, :2]\n",
+    "R1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.21860576e+01,  8.43769499e-15])"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B1 = np.dot(np.transpose(Q), B)[:2]\n",
+    "B1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.50000000e+00, 9.32822279e-15])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X1 = np.linalg.solve(R1, B1)\n",
+    "X1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.50000000e+00, 9.32822279e-15, 0.00000000e+00])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.append(X1, 0)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4999999999999896"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2247448713915894"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(np.dot(A,X)-B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.3693063937629153"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.array([ 1.25,  0.5 , -0.25])\n",
+    "np.linalg.norm(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.224744871391589"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.norm(np.dot(A,X)-B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}