From b382758242e540f59e03ce3f21e137b71d3c8103 Mon Sep 17 00:00:00 2001
From: Malte Woidt <m.woidt@tu-bs.de>
Date: Tue, 6 Dec 2022 19:40:19 +0100
Subject: [PATCH] =?UTF-8?q?=C3=9Cbung05?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Uebung05/NumPy-Grundlagen.ipynb | 818 ++++++++++++++++++++++++++++++++
 Uebung05/Uebung05.ipynb         | 712 +++++++++++++++++++++++++++
 2 files changed, 1530 insertions(+)
 create mode 100644 Uebung05/NumPy-Grundlagen.ipynb
 create mode 100644 Uebung05/Uebung05.ipynb

diff --git a/Uebung05/NumPy-Grundlagen.ipynb b/Uebung05/NumPy-Grundlagen.ipynb
new file mode 100644
index 0000000..c6aa5b9
--- /dev/null
+++ b/Uebung05/NumPy-Grundlagen.ipynb
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0b13807-b3b2-4f55-9581-9f8d4ffaa3b5",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>**Einleitendes Thema**</font>\n",
+    "Die Ingenieurswissenschaften versuchen LÃ¶sungen fÃ¼r immer komplizierter werdende Probleme zu finden. Da analytische Methoden hierbei hÃ¤ufig an ihre Grenzen stoÃŸen wird auf numerische LÃ¶sungen mit Hilfe von Computern zurÃ¼ckgegriffen. So wird zum Beispiel bei FEM oder CFD Berechnungen ein Problemen nahezu beliebter KomplexitÃ¤t mit einem linearen Gleichungssystem approximiert. Um die riesigen Matrizen die bei solchen Aufgaben gelÃ¶st werden sollen, kÃ¶nnen Bibliotheken wie NumPy verwendet werden, um die Computerressourcen effizient nutzen zu kÃ¶nnen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78480fdf-603c-48e5-b671-5b634c94fbaa",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>**Kompaktes Fallbeispiel - Hook'sches Gesetz und Tangens-Funktion**</font> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6e8da2d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Die Verzerrung betrÃ¤gt  0.105 % in x-Richtung.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mittelwert: \t\t -1.3134108454918585e-15\n",
+      "Standartabweichung: \t 17.1\n",
+      "Maximalwert: \t\t 12.068205279497743\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "    #Lineare Algebra\n",
+    "E = 70000          #[MPa] E-Modul\n",
+    "v = 2.5            #[]    Querkontraktionszahl\n",
+    "G = E/(2*(1+v))    #[MPa] Schubmodul\n",
+    "\n",
+    "#Hook'sches Gesetz\n",
+    "Steifigkeitsmatrix = np.array([[E, E/v, 0],\n",
+    "                               [E/v, E, 0],\n",
+    "                               [0,   0, G]])\n",
+    "Nachgiebigkeitsmatrix = np.linalg.inv(Steifigkeitsmatrix)\n",
+    "sigma = np.random.rand(3)*100                              #[MPa] zufÃ¤lliger Lastvektor\n",
+    "epsilon = np.matmul(Nachgiebigkeitsmatrix, sigma)*100      #[%]   Dehnungsvektor\n",
+    "\n",
+    "print('Die Verzerrung betrÃ¤gt ', round(epsilon[0], 3), '% in x-Richtung.\\n')\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    #Eine Funktion abtasten\n",
+    "import matplotlib.pyplot as plt\n",
+    "n_Stuetzstellen = 20\n",
+    "a, b = 0, np.pi       #Intervallgrenzen\n",
+    "\n",
+    "#StÃ¼tzstellen \n",
+    "x_i = np.linspace(a, b, n_Stuetzstellen)\n",
+    "#Funktionswerte\n",
+    "y_i = np.tan(x_i)\n",
+    "\n",
+    "#als Graphen ausgeben (mehr zu Matplotlib im eigenen Notebook)\n",
+    "plt.scatter(x_i, y_i)\n",
+    "plt.title('tan(x)')\n",
+    "plt.show()\n",
+    "\n",
+    "#Statistik zu den Werten\n",
+    "print('Mittelwert: \\t\\t', y_i.mean())\n",
+    "print('Standartabweichung: \\t', y_i.var())\n",
+    "print('Maximalwert: \\t\\t', y_i.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9837909c-0cc2-43bd-9692-bb149a7a87ba",
+   "metadata": {},
+   "source": [
+    "# <font color='blue'>**Ãœbersicht - Arrays**</font>\n",
+    "In diesem Notebook wird die NumPy Bibliothek vorgestellt. Sie ermÃ¶glicht dem Nutzer einen einfachen Umgang mit Vektoren und mehrdimensionalen Matrizen, den sogenannten Felder bzw. Arrays. Mit Hilfe von Arrays kÃ¶nnen Probleme der linearen Algebra gelÃ¶st und groÃŸe Mengen numerischer Daten effizient verarbeitet werden."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b34e520-f385-4530-b2c7-4b6df641d72b",
+   "metadata": {},
+   "source": [
+    "### <font color='blue'>**Lernziele des Notebooks**</font>\n",
+    "* EinfÃ¼hrung zu NumPy-Arrays\n",
+    "    * Erzeugen\n",
+    "        * aus Listen\n",
+    "        * nach Muster\n",
+    "        * mit Zufallswerten\n",
+    "    * Indexing\n",
+    "    * Manipulieren und verÃ¤ndern\n",
+    "    * Unterschied zwischen View und Kopie verstehen\n",
+    "* Rechnen mit Arrays\n",
+    "    * Scalare Opteratoren\n",
+    "    * Lineare Algebra\n",
+    "* Statistische Funktionen\n",
+    "* Daten speichern und laden mit NumPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d81e846-1817-4004-a833-623a60c897b8",
+   "metadata": {},
+   "source": [
+    "### <font color='red'>**Einordnung Lerninhalte in Kontext**</font>\n",
+    "Einordnung bezÃ¼glich der \n",
+    "* \"Ã¼bergeordneten Themen\"\n",
+    "* \"anderen Notebooks\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6012101-a726-4880-8bbc-ccfee49d9ceb",
+   "metadata": {},
+   "source": [
+    "# <font color='blue'>**NumPy**</font>"
+   ]
+  },
+  {
+   "attachments": {
+    "Indexing.png": {
+     "image/png": 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c4qqLvUP9Nugg1zlcWbKje8Hu/hWt/dXcYz9AxmnKrW5JC6/kd/z8C3/76U9/+mtf++rnP/95XAu+9KUv7dq5E430TxbJhHhk8+5dZ92x+d/OaTnqjI2fPmvbD6/qe3EZZa983NF4eLk4MH9d6yUPbvuPyzd/4VycEZs+e/bWYy/a9Zu7R9Ztxb6oUoycc8cIbpmEqqXR/jmVjnvLbXeU2+8odz5YHlhZEbp3IidaU3PLZz/7OfQdYwJOtE9+8pNXXnmldFwjHkNUmBPTPrO9SvCjPjOfSO4V4Epgy7tvY4YYSnE1KsearB1wb+fZ7URtoE7TeD0+9sDK3W+8ZPabL5/3rhsW9g6VHcFvae0kbLXotViVaW499ssXN732vBmv/+3MT9y1jOfVFFhptkgW4kp0eVsC0jWC+CKSewW4EtgHObzSBnUrFQGUAjlIDiFMtIntpmS7xutQTyFabWvKloVv4RSFl0q0GhspV99/8+I3XzYXOXbr4h3Q0IBo6gGeTGC3tg30jNP4+NbuoTf8bvYbLpn9+otmTd/UCR4/RMG1fOO1koA8xnsKO8amxp8O//nJp4499tjvf//755x77q23337dDTds2LABDkf5AyfO29Hq4PD2465df/hxLR85pflDJ7Z8+KTmI09Gcc8F99FHYGge7crB7tDDhBd8nB3DzbXWG0ttN5dap5Vabyq13VJqvbY0vLpM7+lreMWHeHzsiSefvOHGG6+94UZMa773ve/+9Gc/6+rqIhvvOu6mqxdBHCbCmyFzCStqg7qVigBKMZngrZ4vrJhpSnHXaFXUfOGlbQaua4QJuwaAbdvAgBhKcTUqx5qsHXvlVMGlrlQ5fNqiN19O2X73sl3Q8OU/VJDmcdl12zeeiRhLke0nPdPCY+msz9+3AnZOMcdnohMVrFG3pmuioMrY9g8Mnnra6d/59rd/9KMfIc9vuHHaw488Yi78eBM4NrxykyR58wdOaP7gCc1HnLzusP9su+xPuFLy0xTSBvLmXgGuROGl6wHAE+7evy5c/64fq8/384Iz6EMnNb33ZzuOv67WPwSitF3WW7dtv3HatFtuve3sX//mBz/4wTHHHHP//ffDq2sqk2T/WHHBw5m4DVKSlgA5MsFbPV9YMdOU6mevrSY8q4ntCpe9TJ9ceD20kiCuRuXInLMDmTbQWqWY7EzcBin5liTISBm66YZ5MMb/t1wxD+tVe/pgyjy2yoWw32jN++GJyvj9q/Zgbo1LwNuvXdA1WMKus3dXciU9JwDtGulIIhVKGIrHrrjyqm9+85vItEsvu+zmW265/Y47ent7mc0sOS9admz6zNlN7/tZy0dOxXnRciSdI00fOL7rsdl0ariTiOB2aOBUEjZgtLbt+5etf/dxTe87vulfftb8/p83v/fndEa8//iWI0/u/tNM+FRmcXgJ3kp9ofXo8GjXo+XW68ptt/Jl5eYyXVmuK/XNxLTYe0DPqMGt7e0333LrLbfe+psLL5ITbdq0adDT49IMcRrAJZ0TS4EYMQfOfatC8wjES3p0K9DVSqEnUYuE4NYoGdkyYFSFbTAcaPhGHVTjJR6wMSfG4PvGy+Ycdt0CzInpGJOdVmCC4msjM6HJJGIFB6JSq9TGfvXSJvLzuzmfuWcFaFgwvrt9aRtQgEPjhN4Posh7UWLAKzC8KDgVt0ELOaLpWgyNS4RQN2OkoroWOJGVWHgnrKSVsyoaCW9cxam4a4x8eDEOysHFujqKOXHlX25a/MZL52Aue9PicOcML1iFTGVuAYo80yW/zIF1rFytIcc2dQ1iqvEmzDZ+P+elTZ1wMgJ9VaqHxkqKwgmVVcNJ6BS6hZ7fKz/xlycxFuBd8oMPPoiiQAhgwhGK7Tf+Zf3hP9lw9JmYLmz7jyu2HnsxLuobPvGLpvf+FNd7EHh/2oaAgvBKC4bX1VqvL7VNo9lw++14Q1+BPLSW7n+PjYa7v84t3agAhoZHTjjhBEzif/KTn2BOTLsOHcrun2BUtg1iCFELEHdYge7tOeJUZ696c3bdn4BFoUYwu2qsa17FXWNI7kS8SQ4OGLhoEOXLM/J5YKT8nhsXIUVff9HMu5bR3SbOdkpjym25FhHGfVrKnwCaIRpLR099boOMpV96cFWFJ8pQxt81owC4M4i1fL8VNP70gs3UQF6jU+Pj/YODJ5966rHHHPPjH/8YV33KJ5PqcAQXg4ubmnjm2nLESRuOOh05j8t2+zWPUcj4PaRwtRm8IlA3CKwy4aU58Vjvk/Ob3nPcxk//ctv3Lt1x4g07T5626bPnYLYN55gWdNz2LJqhZxBDGgb85cknv8G47777UOQ5sXBonW0DgftMKm6DFrxjh33KXqVwZ2mLlwuimjzHg8oZndlV3BjhsjWu4lQmvHBCouEVZm9h1wSNtUGcYMVt0IIKHmiV5CRYyFsImBNj/H/jZXOxxpwYh5JOGXZEJ4WQQeeTCAlMp4W2l9bIep5gjGJOjJPiTZfNfecNC9vx7pN/G0D4tg3If1ayjjuG3UHDFFo5NrZ0Xlx2+RXHHnvMsd/85qpVqyjtGEojz3SObf/JNZiwbjjqDCzbj7t68+fOwbWg5aOn4hwpbW0lsvTBQurSbiMTF0a3fvfSpsN/tu07v9/5qzvbr30c6w2fPLPlY6dhzg23I5t2URvkfM84Ntlrjxr2gxr9cyqt15fb76Q5cdfjpc4Hym030Q3jtutLIxvpZKTmuAZJT4FZs2bjmoIT7cYbb0QxjFIMIps2vEZl2fDatgOQIlsMqQ4M1daI3UIFhYZDyYKokEe+QgwOHMeBCiyM37tiF+Yrb7x07mHXLeyROTG7ECuDWoN4agnDk3tjCahqfPy3s7a+/sKZb7hkzr/dt1JVDkolcsYtfKpGjlagZjFR1wSWJRtex26hgmLKwwtDzq1seJ3smtIIENO9t9pUoy3EyeTawLAVimFZsuG1d0uJwaBTl4/scKnyL9MWIbte/9uZMieWazazPODHZAK7IjhvQM9w5U2Xz8PA+obfzV6ys0e1DNsGVQmQXUGjw4GHnxPLdffee+/FQFyplGk4FiZVGK/19G/+4q8xTjV/4IQ9v72PvqnbN7j9pBvoXteHT9z+o2toMkFVqE5D4eWBeKS52vVwuX9uZWRTreN+fjd/I82JORRCC2vo0Fqgs7Pr+OOP/+Y3v3ncccfl58RapRBsLuTAwPu2LNnwOtk1pREgFvfe4aBlr3GiLKOJ3UKVa4NQXMEjznZC/3D5XTcs5GyfddcyurBRtps0JlBaKh/QizVDVePjZ7+0icbSi2d/7aHVqnIIbYowhlSgNSNzgUZnoOwfGDz5lFO/eeyxP/rRj/bs2TPKv0ERGPwB8dDyDVu/dfHuC+7tfXrR9p/9ge5dYU589WMw0fwlHwKAd4oXLT5IsqF2jA+t3tz90IzKrs7REbq4AKVtHVv//crmI0/GSbT9R1chuUMVbkulggnP6KOPPSbvV82cuAi0I66qhbDhdXyIFUojQExSIkTeTWUHcRK3QcCF4h3kK8QQE9b+AHhN1i1U+TZM3DXlugo5pNzKhtd2B0Rl4AiKsGRHD94l0u2MS+eu5Dmx5KdYFajn+EDypHiyuQMnBSbW77p+IbJXtQyiKdm6RWvgU90Gjx68x8suu+KYY7/xzW99a9GiRWhztcK3VMmqJ8XgoqbmD5244V9/gXTt+cs8NHVkw87NX/wNprBN7/t5+x/+TM5xdtgdYNfcFFl84ETADL3t6kd7n1kyOqTPEAP9Ly2nefa//gI+O+94lmhyZXFdy8DsDa2mUrV/tP3ucttt5bZpJUyOaWfDo91/pk8j224qdf+1TN7oBzt9ZT3RXnjxRVxTcKLJnDh7ojHZtyH9PDHBtIiRLTuwvsiYhYauUTTYBnesJEfL1dp187d97YFVR9+9/OdPNu3sLz2xru11F9Kc+B3X0bMTFBSusb1n+I/L95z4dMu3Hl79+XtXfO6+Fd99ZM1ls7Zs6x4ERw4DAnzD4l2/fHHzhXO2HX3PyjdcMptS9oZF57689VczeJm+ZUeve6x+bKx3pPJcS9cvX9r8g8fWfvH+FZ+5Z8XXH1p1xrMbZm3tZp+usdpkj2zZgfVFxiz2MbwFkBY37jnLTNaks5m2ddxGpv2TOaJv1DO9X8dBxKH8yZ/Xf/qPy79x/8qH1rRhBKE58e/nYE58s7lPPFiuzdjc/ZsZW//jsbVfeWDlp+5e9uUHVp7815bnN3SCg53izT2GkyW7+89+cfN5s7ad9NwmutPANxu+/8R6aM6ZsfXsF7c8vBa7oOyCU1RZ0zrwh4U7f/pk0zEPrUJ2feH+Ff/5+LrbF+8YKtMY6k4FCi/OfGjkPrHMiVGk4UA45I4I/TPX0PMSHzttw9Fnlra3QQO0T3sSI1cL3TY4fWTLHmg0vwVGZPhyMIxiNkIDLo2ZnQ/pnHh4HT87ka3O4Nh2dfeccMIJGL+S94kjFKizmPrsheYVmb0NMxn0CAwdkembu457Yv0n715+zAOrHlnXjnPg3XSfmObEd/KcWG5QbekeunPZ7hP+irF0zWfvWf6Ze5Z//9G1V87euqt3mA8iYkaH8tpFO3/54pYLZ2/7+N0raCz9/ZzDpy3+9YxtMpaeM33Lnv4SUkYuv3iX+HRT55kvboYrjKU4gzCWnvncxgU76B0jvLkOoWvUVJoTn0pz4h//+MdtbZTJ+eShH80q6cx151m3yz3j9qtoTuzuP3u4Ik6ZUnmsVMEa1VVpIe4os+mcGh2h76f2PjGXnB958rb/uJyu/fDPXIGcm4899jifmu4+sd6oNpD2ZztRjCwzXxOaBrI3wqGQvZNhAnXI1pRLj/qQlMORun/F7u/8ac3Rdy/790fWLN0z0NQ+8IbfzX7TZfPCfWIeLTsGy4+taTvtuY3ffXTNF+6jBD72T6t//eKmFfzMMSgypj68tv2XL9AE45hH19FJcdnct14177QXNp87c9u5uAS8sHnp7j6cFOJzqFKbs7Xngpnbfvj4uq8+uAo+v/TAyhOeanlyPV0mANcl2dJ5QfeJjzkGc+IlS5bQfu2MkFOu7bo/N7//+OYjTt7+Q3rcFhqst//kWrouHHHy1u/+3r2pc74ByHRG8ElR5k//LGCFCxn9a3SvG2sUt3zzd9hL8wdOaLv8YaLZawoQl6IynUBjwxuquIi031buuKuM+bG4759daTNKUtl6vN+Xpk/HNQUnWnpObDHq7xMXwFhJLDo55GA4eoFLR4phyemKZqckFHino4BloFT96gMrX3veDOQWlr8/9+WP3L7897O3yviLOXHPMA2I4CPDPnbHcnkuHnxeZmJag+Wwq+fN346s5VnL6Nh7b1rMX9Gb8YaLZ+G9oHy6DT409F2Tc15auJ3G6BGeYZz/8pbXXTATbtnhy0RD8bezXn/RrEtm0tPlNg0y4sEOL6HAe2NtSIPI9SvIWzvPCuS4U7YN0uzATMFYQ5U8GuwaDhwO3/0rd73xkllIEgyCOMo4spfN3nbEbUvf8DvMifXZiZEKqGM3L95F6aSZQItmwm9nnv7sBtBo6gzakl3/45cvUi5dMOONl/EHcJfNpVq/eRma/3HWi9/602rsuMSD18auoX+8aj48SGrJAp/w/PHblu7qG0Hr9e083faihAxz4vvcnJhBJPbZecdzTe87Hhfvrd+6hC7eY2MjLbvonsGRp2w46oymD54wMGMVVciNIyZSJJoiQ8JKNwJGOx/kx7+mlYbXFsyJMY7xtKa7cE6cr0PY6+wtYuYg5Lr0vc3e+jAMqZJGvmsJxC1xsGQjM1my/a5lOzHocbbPkey9fM62D9yyRLJd7hMj35DwH7p1KY+lkpNIThpIoXnXtQuW7e6HUyQCLo7vvH4hxlJw3nAJjaVIdZxHOk6i7q+nr9zdhwaUqpQMmCiQzwvCGYQi9vvGS2ZfO28775objNa7OfFJ8ZxYzwUL5B+SqlRFD3eccWvzB0+w94mV40B30cbGB+et3XrMb7f9++Vbv35Bz6OzoZHTJAC7h4ZvuXm0XvZw8xF0E3rXr+5AEa7o+uSA6S+Ujz3++DHfoHMz++zERJmT61URQpVCFGVvjIbSzMDslIR9chtHw8GS0xVdG9QaSHsZXjqGwKnPNmNkxnmBswA5+bZrFl43f/s/XIkJcXh2oszJ8O+PrwfTJzAyXBL4Lb+bff8KutFQwhEfH//WI2v/x1kvkPW3M+WkgB85lVAXptsW70ArhzGzHBt7aHUb5hJ8CdCTQk5MeD7uz02UZWgoXtIrPouL5sTggonE33nqTU0fPLHp/cfvufgB6MHpeXweZsktR53R8pFTNn767MqeLjprOIHpXBsbr+zs2P7Dy7d979Kt37gQE1w6KcjKDN01PZKBNZVgQ7E2RnPiI09uet/Pu+6bThyZ5scwGt4d/8lPNg4swPS33HZzqevRMpTwXG6r4srSdmu57XbSl3dVeXdEJriL4EsvRXPi6PHoHPg+cfbttC8YLYu6isgGQS+SK+s2mCVoHmzIuPUFo1UeIyITaP46Pn7FnG1Ipn+4aj6mrUjT99y4COkCAamGDH7HdQt6eU6MSzAOx3tuWIxMwhD/uXtWHP9k0/cfXfP2axa8+fK5yLlP3rW84g7YsQ+vfec18987bfE/Xj0fyYqU/Ycr5x5+4+J337AI/t957Xw5DTAnBvmM5zb9/TnTMfQffffyf39szc+fbPrgzYtlooMGrNrTj327kdr0gUVdGXWEoBfJlXUbzPsjvEDsVhiFmRPIrNNVAVm3ALvTcso+qTboFmBRVxHZIOhFcmXeSirs7B3+J06tt1wxD9dyHP1/oLyazQk2D7l002K6VGPwwhhx9fwdyIR//sOCT9y17DuPrDnhqaaP3b4M+UPM386Uu8XAo+va8R7sX6YtRs7QqMqpgkSF88ORXVfPP+u5Deg3zxLGmjuG3nAxfVJx5G1Lv3j/yhP+2vL1B1f9wxXz0B6k/clPN8Mh0pY7mbtPnP18Vr4MN956yYM0M/jQiTtOpu8fIJzbj7+u+UMnbvz0L1s+fjpGxu57XwLNzRhcWIKgopaNnQ4MBqCx0c6HKnqfmJ6dkEPgeK4agL0UzIkzR80XjJZFLUdkxX7LXgN2pwZvN8S4DWKZgq4Rgl4kV85WM11jsCHbBsn2bT3Db71ins32tyDbL5Fsn/u6C/U+MbjD5do/X7sQI+0Hb1ny+XtXINW/9+iat109n8bS387C6AqPoCGDvvLA6ndeuwBj6VuvoseEkPDwxmPpQvh/13ULmtsHce7QO8Dx8eOfavn7c6e/6wYaS3/4+Nqf/WX9++nBfar15kvnrG/vB0fGUrzQkv7+7JyYDjD3haBE7i5/N24nz4n9fWKX4QY0ZRnrf2Fp03t/1vyRU9e/5ycdtz5DR9EzvXPG4IL1vU8v7H1y/u7z/oi3lKjV8tFTh1dugslchqmOnJuPPfb4N/jcjJ6diH0KDnT2epjyKyV7dWu1ymNEZEUD4aUJBohPNdHjDTgj3nTZ3DddOudfpi3CGfHmy+a+mR97e+Pv6Tt2OJRlfs/z1QfXvPbXLx8+bdEn/7jix0+s+/Gf1yPPZSbw9mvmt/bTLQzQzn5x8zuvmvfemxa//VpMMGj8x/lFs4sbFh4+bfFhV817jD6N5JtuY+N3L9+Dk+Jt18z/1zuXfevh1Sf+tfkTdy5DY96MS8AFMx9eTVNt+aASQB9QvOwyfp44f5+YezRWrmz7wWX0nM8HTui441n0vtzavekzZ9PT9p/4RfNHT93w8dOGV2+mEJFbXDXopChtawWh+cMnrT/8pztPu5kiCCvbFSZ8mGxhvwMLm1qOPLnlqF/g4jKwtAUac58lVNMtwKKuqCNjvS9WWm+ir9b1PCffSxnrfqLcdlOp/c5yOz9QMby2qi1xkBOtYE6czhx5dkIKkR3BypSNPWtJAywKZNKtU2Ib7BkmkG5DHHuCxH+oXEViYRDHcvgNixbv7OscLF8zbxtlLd/cpfvE/OwE4otJxulPb8C7rsESjY+CNW0D/3QVpTvIK3dj/kq3IrqHKjt6hrsGy6c9u4FvmczGGN05UGrtL7X1l1r7RvCmEHuXj8JvWbTzdzO27OwLj9Ggpf9230o4fP1FMy+ZuQUcmWxHHQBCOWtJA6wDFV4HURqTillycTlrAUiVU+fa4FlOGYwihTJjirOXR5nxy+duowHxynlvuHjWpbO2dA6Ul+7qxXseJBhGSeSGPDsxUqX3sA+vbv3185s2dw9JGgj+43H6dOwNF83CdBZu4RUT6F29w3C1bGcvJd5lNP/487q2joHSnj4sIz3DdP7j2o/M2dw9/PO/NM3c1stjhOLBVa101+3Sue+5YVF/CWTuO41dlNh+TnyfPjtBY7uGl+/G7TrnDpoZfOhEuaHV+8TcdYf9sOPmp+mzsw+fRAPlzU9BH88YisLLbnPo4jlxm312AkucvTJ8R89OdNa/T6zdDGA5mb1qkbU3qpAkG4Ry1gKQKq22WsgSMacMRkih4DC58CYAVuHg4BA5M5L7FvzFM7cgpSXbr5q7DWPpoh2977uJs/1KynY/Jx4s1075a8sj69qHy2EsXbqr/x+umEtX68vmNrUPQIPTomuwspPH0p8+1YzpMlL98/etQPJjfiBjaYVmw5pr183fcfmsrbv76dczBRg5P3n3chpLfzvzmnnb0UW6d0YJTclT8OxE6JoCCr5S7vzFbdGcOFyhHXgWQs9BHnHShk+ehXOk887nSM/TAu8Ze6EdjY9v+/fL1h/+k+Yj6KzZ8Kmzdv3yzpH19DXEfBuS94mreoplyVwW5Ss4e5MnZhpgTZi9QCjn3TI5p57M4OA0sqGfe8cRHvvGQ6vecDH9htVbr5j7dFMHMvnRNa3Ic77ZEe4TyyX+4hlbbliwEycONIKe4eoHbtYvZD+0iuavOCn6Rqo7eoZAu2XxDpwUmBYfdv2CDe0D7TTHGNndN4KJDZ0UlGNjzzR3nvXshpauQTRGfAInPb0BlwCcp999dC30YLrm03mRmBNLeNlhdXBky9cuoB9j+eAJ8osQu869Cwnc88CMTZ/7FX0l7siTBhesw8704xG+EpW2t2389C83HHUGXTh+eTv7k31qxBz4ExIk/FB563d/TzeJ33/8jpNupF1TIzPkbGVTRkfGuv9KM2DMiftepJAOrqy0XlPqX1zpfqxCjxTfWBpcQV+zI+cOyTlxeFeQ2yEaTHNiVostZqguYyIhemSDEFkNhJxVU4kOh1WLGPMAVWT0VLRtqPEdr6W7+jBc0n24i2bds5zGa8mbz99HecypFr5jh6xlAt2T2NQ5iBnwsl39K3f1Usr+fg7G/UfW0NctcXlgFvn5zcv0Wd4bLpnz2XtXsNIDRGoMjrEqMOD2Dq9tHVi8o3dt28DxTzbRTOjiWf/55/XUWs5Gbf6hFd5Am2QbYqWMLawTkSFCzAx2tzEBybdByrZrLEgpJqsuYyJh8uGlRw1xlL/20Br+yGw2/xifDkmPrW1//UU8S7hQ58QyJHkCrvTr2gYW7+xd0zpw3kubkIpIhi/ev1J6gbXQdvWNyAdn8D+bnz73HnyDaWbM6B2pNncMLt/Vt2J3/7NNHf9wJd3Pe+tV85rawwcRct3N3Cd23zoSBhF2nDIN4xoGwdbf3lfrH9lw9C+2HHNRbXBk+39eJXPituseB43+KZGFtkg3DlT0rfW2zgd1Tjy0znzHTskKOXcK7xMzhdcGxg8j0Ooc4tiQIh/o7JVtTNZSrORina7pViHkLKjFTKzTNVwxUPELNGzOwnD3mXuW40AI8AYMoyvy7XUXzpLfnUC2+7F0pFLbQGNpH8bSpTt638O/UIF8fqqpHW6FBtdYn/H8RnjAifCVB8N34QnSArTGjqU9wzh3MJbiPPrPJ/hd5cWzT/hrE2g4MRFWIefnxHIicNdC7wh8aoQ5sTw7UaMMt+GlSQDcvrC0me/4Nh3+k87bnkbzR+lcIJq0lvcP4tjW711KP8qGs+bIUzZ++qy23z9U3t5OhtxsWy7V2eeJHa0oc4wgkDbkdbrOHOIJM8d5F3IGbIwvK07wCgdVZPRUPADZq1CF02vfaD2p8MoaLxx3vKM77LoFmEhg5vrLFzbikJF+fPwUulk2681X0PPE/jt2fqzGELa1e2j1nv6lO3vXtw18+YFVSGBcMi6auRme7QTj4bVtuIjA/ztvWNg3TE+lix5rAe9ONON4DwlvS3b0rm4duJKfDsWp+q93LXM/bMzN5jaknp0gq3S32je4+d/Obf7oaU3vO77vuSVDa7euf8d/tF3+cLWte8MnftHy0dPotu5s+iKs3F6lNX2LtHXDUafhvWLT4T/ddfrNaBcPfeQwCi+iAH6ttvOs23GuYeaNfWE+TSb98QdDBqTE63CI4ZnPr+4nMPct4zrSP79aGx6F3PVoebQ81vWnSitfXwaW8OeQMudnJJ+doAj4TPJ7ccCcWPfP8GYRyHO2BmBUQWSJVqryZdro1sNkNMOSEyCLWtMkegs1NvbU+naMv5hVvPXKeTt6hpGXI/x+7bLZ29zbr4Xy7AQf1vH5O3t/+Ojad9+w+G3XzH/rVfPfwr8viHkJf7o9646l+ktD8F3iwfUc/i22N/BvsUGLQ0pH3LUIDPA7hiq/fnHTEbcuffs1C/7xqvn0ufbl/DkLPZIxG+8yweHeY8mE1zvSLRBElmilKl+mjW499j68deHdKNmXaaNbLeczx5IJsZWLGRUjq6vXNW8RYerCyyckUumoO5fh6L/+tzMvmrEF/of5t6Pw5udt1yxA1mE4k2cn6GGysfHBSu3SWVuPun0ZZcLVnAl82wzZhfHrqDuX8oBF3uEZu9rSPeTmxHNe3twJDXKPs08agebQAPfouvav3b/qHdct/KdrFiDP4RY+UYuWS+dg2AUZl3LUkuuu+Y5ddN0lMGHnGTdj1GvCnPiSB9queGT9u/5zYGET9Nt/cFnLkSc1v//49hv/gqKZE0uLJg6vYHR8VO4Tm9+dcGcA22XL0YjnxPY+cc6tB1mKrQHOje7Pl83GCYdC9hIM2YkJlUq0UpUv00a3BrHGkgmSZuVK9SO3LeU7srMun70NdoylyPYtXcNIZhrN3PPE8ujk7O09P3hk7btuWOTG0rn0w4I67s16gG+JVXiiIL+Pyb/FNhMnwpceWIkidDSW8u6pBTgG4+N7BspnP7/xw7csxfn1j1dRqr9FfdJdj+89ugYcGX4lefLPE9P1mAmmh7wD/oQke59Yb9MqqALc4jRs7x2YsRLTgoHpK0o7MMclvXfr/NK24/Zndv/m7t3n3rnl6xfSOfXen2046vTBRS00fNjzjs48uqEuc+Kv2zlx4nAx7BlDYFHLXi3CZLLXW1RlSbmm1MveBMiiVkcy5CCyRCtV+TJtdAs4KWgIlpwAWYqtAfXDS3lLx3BdWz/dfcAQfcnslzd3QSmXgKfWd+BNGo3ev5+zajfNiWWmu6F76KS/Nr/vpiU4KTAZwATDJTCdPr94jr5Vwm/qRst8Ujy4mn6LDcP4O69f2DlYggZqrPlFzcGhxel27bxtR9+5/O3X8mXlSvJJH4NfhsvKnA/cvFg+qwGZms5zksSc2JnhsNo/RD8x8ZFT8F6u+/7pW//jig3/eka1dxCpvhFz4o+d1nzESQPz6FyjN6BUi5pRGyoNzlkzMGsVToqRddt4VOfFAkW+xLT+7kH6ZeKPnLrh6F/QYxh0OnhqRkhlL8oc/+4ny2030e979s+q9M+stP6hVGmj06qLv69Cc+KlfJ+YxxFxIhfBwu/YxXsSuDmxhSqocYJUxQzCe4PsFsjXz2pQlqBm4NxyWLIwKvpS0/j4fSt3v+FiSql337Cwiz+wQGrCeucy+S22OYe5350A5m7r+YfLaH6DKcjrLpz1ugtmYM6KrMLUGW/4oJ+2mEZ8Gif5ZwIhn/PSZrq3gTnxvctl974JELD0DJc/effy1543g2ZOF9E3n3jcp2c53nTFXLTtyw/Q+62QD0Cya1ns5/CmbDlVIDtbrpKqk5mTIytSbfBStlLDbaD+MqYivHy8xgdL9L85aJZw0axp/L855KGxvpHKe25kPf0WG82JkXWlau3Yh9e89jf8Xc+LORPoE4bZSAwMiBCOvG05jaa8Jyyota1nWIZLuJqxhaaDMgQJwMXZfsuSHfDDiUrf8OOvepBPnhPTreIFO/iTOx4QZDgwz07odTd45ZnB7t/chWkBxr7NXz6v+QMn7LmQaKMjpW38URc0nbfTD6zqjEErY6pKG4zYpo0J4L0B1v4+MT9PTNXESnAidNhLwbMTPnvt3v6ms9fZc4w8bBtcNY+oPneNNTHDh5cg+dE7zL8w+Ps5yN47+H9zyGUe4yo/+E7ZfiffNQCmb+p6E3/rDnwaS8+fKTeYMZZi3IP+7uXymCN5xhryac9vhB6cLz2wKto9ZO5G52D5o7cvjcdS+ogGu5A58bEPr4Uff45Anvh3J1wJb0Nhzd4ntudbgLsv5xAdExFVoQSg1jNAjyQdcTJmxtu+f+noiPxKVKgo5yY9OyHnpnyGU3ipnkzmOBhGjqx4BWYvI2bY9PHCFHeNB7/xRTt73nQZzxAun7uMfguC/wHN2Nj87T06CNN9YvpNCRzq7T1D77l+4evOn4GMpQQ+H+O2TgZ4gjHrpKd5Tkye5QOJ8YdWt9JJdNlcnGJd+g93tSUQsCsM7D/6y/rX/ob+rwcWd1mZo5eV3835l2lLBkp088LnGpwUP09MZsxutxzzW0x8Nxx1xhZcAt53fO+TC0Abadq+4RNnNH/0VEyXB5c0E91XRMTAMCBfPmYiQMHXl/brnqCv6330tJajTh9YRHdbqLeeJlCZp/J8ohijusNOep6u0Jz4tjL9E7sb6OfYoBwdGet8oCLf4R5aTXGkGq6+nGgNfcdOq/hnJ5wLQVxy8GHmbb5WIWy9XLXQfd4mOQG+DQwtuAfgHlvThkRBXr6L32bBInPiO5buRFLSfeLrFvbwXBkZ9pHbl2I+gbdZb7p07mnPtjy8tu2FDV3Pb+g6/Eb+rVma98icmDzI9eCc6fzsBN8n1lZye9AFIVw8czPOgbdePR9j9+fvXfHH5bufbOqYtbXn+4+ulST+8v38GYS2O0ZheJNsA1vPVBNMSXjrIbYmueHKxFtaJXmCdHSoShgUDbw3Xy1dfx/CCwaOWqlSo5zhK/SNi3hOzLnRO1x+9w3IGZoTyO9OAPev2CXf9QT/43csvW3prj+vp0w45ekWnsXOPuK2ZTQy8Plb47tcW3uG3sRzYlhf3kzTQbp9zK0jgf+rvjygieVt18y7eOaWR9a2T9/cfc+y3W+hB9roGx4L6PdS9CPjzJxYv2Nnb/eCMDbWfvWjGLPohyY+dOKmz51TbeM7zf3DcvOg6YMn9P55HtHdOMItopEH3gA9Kqnwig5bmRNjzBpeR89GR7+Y7uvl7xPb351wrjw/Ad8GhhaMLlE1ViUIQKprhYjbEHDwsjdsTTVB8sSEDmEfKlc+dAs/RXbxrNuX7oRFsh1z4sP4y6DI9jv52Qm8/fvALfpI/Vsum3Pmcxswlr64seuZpk6MwzKW3rUsfPWnwgea58Ryn5h+1QQGaQtWMpZeMH2TP4O+fP/Ke1fseYrH0m/+aQ2NpRfPOuZPbk7MlSEn7hPDqXMrUIGvyvK7E5gTt131KIp0qeZG6MuJtCE/BCkGiALvM4WPeQvkCp13pR3tG/71DPonBR89daSZhgWa1DgHOiem+8THRPeJHZTo+GnE1jTXt5m3tErzBAmbVJn67NVNXTi740b8cDh4m7PHyB+7ush448wdW72njx8dnvvmy+Ys3UX/r67M76PmbuvGmSJzYrlPDPzwiXWYLXACz/7uo2seXNX6dHPnzK09n757uVxETvyrzIkpKzDZhcxzYn52AnPiQZoTy36xAg3Fp5va4fMtV9L3vHF64nLz+Lr2WVt6znlhE04KKA+ftnigRKml3WW3hb/FJqRabfuPrsLgj0tA0+E/5Yd9aZeDyza2HHFS88dO2/CJM0c20rtijYJDdFJkw0uffmJfHbc90/SBE+ih5KNO75+/jhrG74yJHdeQEgiCxGVlfKx/Fv/s2h30Tbv2e8u1YdLWhuhHi9tvLUM5som7ZtqZnBNTBORcZKJj61a+Y5cBN8iwsc52We/NA3y6sJlfTvJ2I3m4sSHrNUL9NkAObaCUwhpv1yQ133L53E1dQ4gM/QIAhteXt/CcmH53opvvE+/oGfqna+aDCf3pz9JXIAXlSu2d1/MDQxfNvmmxPjsBzzJMnzudf2f+ktmfvXcFRkEM7shU+uBbPvWjX6hYjRkP2vC+mxYPjOgNaeCnTzZhR6ioc2ImAyJgnQ3EIRZe3waCngTMUYhEDwwYt6rMNiLlVmp4SUHns4oOphFUuQChDboubkNj4XWdwPUbBxGzgXNe3ITjKB+cbe4aeutV8zFP5fvEdPED8+SnW0CD8rA/LNjVQz/RKrjw5c10J+D3cz5yO+bElFTknvcSPzvRhcG2VKXsQn4J8/mWTuz9zVfQd57kUU7Bit19dP8MI7K7TyxjAqpCNs9O6G+xaefQJ54Z9Dwxj36KmH6w/cTevy4kU210ZNOelo+fTr878eGTBpfLp11uJGVUqpVyuYwXBFUF6B58YP3vTgyt5f+sDk+uEbIBEATspfDZCSCbObQVFdaeRYBcL3sp5gZiSmQv1pFboChzjKRoJHst3yO0QdfZNoRqjWVvBHBEmfNqI0YY/ey9K5FXyOoLX96Ci9UQZ3tLxyA9rsO/OyFPlzV3DNCdBX5G4pwX6DIv6B+pvN2NsXcv3w0NBkzsXD5z889OfOXBVXALJY2l6IILOZ1rPJYeeduSkvnq3vcfW0vvKi+Z/c2HzbMTnDyJ+8Rx0gLUAhq7qwhANCfGfqEkvo+DE9DvcmWsXOG1f0sZgB3hhQ6MVXHGVslPtVYbGNry1fObjzyZbrPNWQMaEQT06DKdm3yfODw7QfeJOURR5rhDTKDgWJuCug+9yxzZ4OXrKSbM3mDP78c7S9gCXBuETC69T0FRG9xx15qEIPkdUnR4a2yM7PlOW6Vm2NitbT7J3gyitSmwUzKMj+3sGX77tfQhCUbgZ1s6kXtDZcqYP61ug4ZGb/pqPt2VwLB45G3LUESiyldHoBR85h7MiWfjpND7xNw4mWDwsxP0PzswJ+4cxPBPehD4EkAtOW86XT6wo3/Sr44orl+wHecjKv4LzYnNsxN8XtB94sScmHfMebjn/D/SrdyPn77pM2eXNu9214X5Te87Hu/oNn/xvBrfSQxxAhCSUllOilH6YrcDe0WLwe95aKb8M+eWj5/WP4/ewdIzIugM5bmwCXpM4X50FNeUUqmEdY0f7heQmR8RHlxVab0Bc+Jy+830X+vEVu6gn2Oj3524pVTpQHe4De4w6py4wd8nprpmTmz765FUAqKntZylTsojp+PdAlyFBANb9nKW5CB6WvPHCujqZv3vuPTBxDVzt0EDwEq39/gzi8MwJx4sowlrW/vfKr+f9duZ963cjcr9POxO39SF5OZvVet9Yp0T89vB387Ygjkx/HzsjqVwzO4F1BLgC/et4HSf/fU/0Xg9jAQYGxup1PynkJn7xG6bhehpfWiE17dB1wrtti9bQMnmrFHKYc1SlsTIKDnNPTtboxEPHqJnN0XhzXaNRi689X98LcYyHMojbl0sJxtw46KdyBb51pGfE//wCb1yf/wu+n5SuSZTgbFP3L0cSsx6P3Lbcp7p0i44xeQ2MD+sdvHs55o72LdCkvBPq/agroy8a1r7kZID/H86Lp1Dv4ZBc+XfzZ3vn52gGS9Z889OUH+4azJylTbuwpv4lo+djsFrYIb+g8b2m//azO/vt3zl/Gr/EAVJAoQNh+L2228/8cQTTz31lMuvuAIXAGhgYscuaHSCyDky1sX/s4OenViPsRUmUrJPoQLkFtrUfWLH0C3By1ZpIXpac3NF0nWMbJkBpWZADCGLG1m7VRY5Jfx5dtbYmAeF6NlN6JqJJKBtN7rILFVE9LBl5A8i/91H1ki2f+S2pZIqwB8WbJdsx8h5+1LK9iU7eukpSX5E8rG17WiSjKVPNXeiuoyl7tkJ2oncWjv7xU00J/7dnE/+MXyBj0ENQH/87bQfPE63lzAjR7W+kSrde6axFHNiusqyS+JDLnp2gimug9C6vJT/2UG/T3wtfYtUQFkrZFRGU8fHB+eu2fLVC7Z+99LNXzqv56EZROCTiJwj/NXaSNM2zAy0vkP/C8tajjip5agzmo88ZWg5zX7oKsXARs5NmRPTuZm7TwxoG/QQa5mlgGyZAeV/zey1ayBLykPaoLK+LEwZ4nipUtV/z3TRrOOfXE+HmYH3adBgOoFZ6QqeE/cOl+lBu0vnYGRGqkMjY/X23mGaeNANFMyJ6Vt6uArAs5waj65tpzkxfQw4X+4TezBt7NRnWjCLwHnxvluWoPEV/qYTrF9+QG/G8bMTtCMOKbpH1uR9YrJjjTkxpr+Pz6XR/mOnbf7CudV2+Xr3+PYTrqNvi37whJ1n3Yaiv/nKMaPfJ6YfJ/7mJZu/cn7bJQ/QSYGk4ryiKS8i8Of5zR8+EVeQTZ89e2gF9TQDciUe2SdOvNbWtrN++cvTTz/9+BOOf+75F8AJVys+UJU9NVxH2m8vt99SKu1AN2m3/fMrbdNKmBZ3PYg5Ondb6jDkMp29T2w+qbRkkZP3iT3P821FlDiNYp1BxgCyagprJMDHlAXdJtwqYMCxQC8RuC/y3QXk3D9eMe+eFbsW7eg97i/rJV+RSYddt1DuE/eOVP/5DwvfdCk92XnMn1aPUL6Obe0Z/tc7acoivzQ0Ldwn1kcj7qRnMOjHiVDx7Bc2Prm+/emWzqeb2nuG6f8HgvCff2lCRczLD/vDgnVt9PND5UrtnBfppwbeQrf3Zn8puk9c0LVDL7wNQFk5tzFEneEmBMHedc2TC9ya8KbcZnTaBpkl/Hl9h1zmcSh/8pemxTt6712xB2/ZkVr0W2z0HTt9duLX/K9boHzrFXNnb6V/6YKQXztvB7Lrzfwdu4/cvqzGSQXvPMjQv5s5/MaFSF0MbZ+4a9kDK3c/s6Hzr03ta1r7mDC+eFe/JCf2fsGMLTIiLNzRd9i1C1CF7xPPdc8To8kY7mhqkr9PzP0S8DA2NrbjhOsx9tEtgc+f0/Pk/K57p2846gxczpve97PWSx+iRroBEaDhaWzs0ssu//KXv/z1r3/tjDPPLNn/Y0T9QdRGh1eXe56r9L1c7Z1eab+LnvfCcNb9WJk0L1WgrPagJRQWrccRzs+JPcHAq7wpw5ls9iomcCsl1eU5MflQyl7AGVKVsqCbUoj8n9bQT0xIvh3/VMuSHT13L9uNKzqSDYnt7xO3DpT/8eoFyFvQvvfYWn7IfmxT19CRty2ldOVHJ+2cWJ4nvnHhdnLOX2s+b/qmp5ran9/Q8UxLex/9wxsifPuxdagIt5gEb+ikHzQcLtdOf26j1ML6WHufmE+Q9LMTHqDiLJu+YtcZt+z+9d17zv/jli+f3/KRU1qOPGXrsRfvPu+Pu399166zbh1a1kIhrnFNnjH0Px//PjHraU2g5+xRHUv7DX/pfGBG/4vLBqYvh7zh6DPxPhMT4s1f+DW9qwS0MbTxc+JvfCP/7ATGBG22af3EcGRfKa4tJdXlOTH54GRvHspy5IJKos5wE4JgL8Mrl4Azn6fv2SMD3/i7OZfP3rpsZ99vZ2xBniNR6W6F+90J8I/+40qcEThZPnzrkjb+PUHME/AG7/UX0RObmEicLHNiSTROYPmgm53P/uETax+j5zk7cWrs7KUfDADh6gU76LJyJT2k9HQz/8I9PRq6G7V4hjOb7xNTammCcpsLn51AAOSUbO/Z9Nlf0dfpPnTitv+8qu/Zxa2/fwhyyyfObH7/8X0vLMWlRu9UUxU6KUpbW1s+fhomzesP/wn9PjFAp4w6HNnYitOKHhz6CJ1crb97YPc5d+D8ouW8u3f94taeh2dRjKjvxJcm7dix83vf/z4G/y9+8YuPPPY46f2JRizidz1ebruJ7rB03FceaaoOrqi031Fuu6Pcej39GAVdLlzPZZOeE0cXwQCpY+fEpFFPOZCe85lFWbSQglcrs4AmemusT6ZBTUVdAuTz5b/wT2rTOzaaItDPEiOH3nfTIpnIvuuGRT1D9OktmGc9v/G1571M+ff7OR+/Y9l3Hl3z9msXoO4/XDUP5DdePPumxfrNEqxlhNzSPfQPPBzLB4L0gTV9jDJzyU56awjC9M3deBsHJRbMv7/z6FpMfV53wYy3XDGXfF4y+6sPrgGPnUUgxf4Nb1gXk+uF18ErrZUqJtmA0YuoigK+V0MgOUWzHI8iMoH0E3fNw1uVSRseR/Gm/MO3LcWYSE+J4XrPnyC/7ep577huIeQ3XDTrFv7uHbCydeAtyI3fzcFY+bZrFnzr4TVH303/NBEJSan4+9kfvWM5J5U0Sz7oGDvhry2v/fXL/BPF9KkC6r7u/Bk/eYLukyEPhyu1z92zgtOJ0vsL9638xp9WUwZegvbMe/MVc5HhmBPDHcZZuPa/T0x3onhODBOGAzZiIfCHy+ODize0fJh+QEqGRXq27KgzMEve+Omzyzs7iceDCFVjQL7m2msx1f7Od7595i9/iTkxdsRqdkudGe2dXtpzLf8neryJv61Mb+5v52/a3VRqnTaCdXkPTlhyJk0Rt13d3ccffzzGr+OOOy75W2wi5bEfsze49esivldDIDlFsxyPIjKhsa55eKsyi9isF2NYO4mCPlSpvf/mJTQHxbSYZrf0yypvvwbZvgAysv0u/u4d2nbi0y2vPY/Tkt/OffuRtf90Nf23RT+W/hFz4jCWUqV17f0YP2nSYMfSS2ataaVPhEF4urlL7plBj2nxdx9dc8RtSzCYYz4hY+m3HlkLGr/9Q4tp8Kc58Sk0J/7Rj37U2kq/pOkufkyiCe545+3Prn/nfzZ/mL79hmzf8K+/wIJ3g5z2J2HW2/vkfDoV5WLM6/7pK3Bp3/jpX4LQdffzTs9BoreLtS1fvYAmzR84ofl9P2854uSWI0/GubPh45gQn9x0+E+6/zSbeuTOIIE07NHHHqPnib/+9fg7dp6VgddbwkHIXtZbYxGZcOCzN49cG5JMr7PGQJY5wLr2gTfh+v57+iK+vOXDEH34jQvfehX9hxqM2Kv20AiMo3nnst0w0VSEn6X83qNr33vTYozedFLgTLl41ilPbwRT5sRYYTNYrr532iIkOdIepwZOijddPud15798L/+wLAgbu4bfxpce7Ah7PPbh1Z+5ZwW/dZzDl5U5771pyUDZZxqN1aiIOfExx3wDc+LFixdDy7kHO++XysTpvPOF9e85Tv5xXcuHTsKy4RP0ePH2468b4weNyBv51G1pe9umz569AZPmI07a9as7vJ7OsvHxweXbcBbAA59cp9GpwZcVWjCNfveP8RbUn2Xsmtqwe/eeHx/34x/84Ae4Wj386GMw2fOXLgTjYyM7q/Q1u1vpmtKKK8s0vqxMK3XcU64NcQN8M3ktvz364vSXcE3BiZaaE7NzERmYE1Oy8BHxa0vxkgqy4f3aV4Arwa22Tq5qzJQ9CNTIa4KwU20ApKAqLTgSuyeNvFE5l34xbSaNsxfPxkj9wyfWY6aCqQbNd6+c28PfvQN9oFT9ziP0c7NgIlP//tzpSKmr5m5//82LkcrgXzuf7vnJOM7OKdEfXdP2wZsWv4Vv9aEi1T3/5UU7ekAAE4Rr5m3/B/5kBLt77W9mvPY3L//8qZaf/qUJAhrzybtXIDJwJZ1FQ/RU1T7pJnRNXwGuNLXhFahKNuJRBbUoXEnaIDxZ88uZnaRlLRCVRPcKMCVpLDbYsqj7CXDlCbJ3gvBipRaFKyXCi+zCWbx4Z++7/zBffvABh/ifrpz35Lr299y46HXnz0QKXTefntjh/zk3fs/y3W+7ij5wkEyA9duPrqN3Yr+hf/J5+I1L9NkJ+MYf76ZtsPy9R9Zi1MM8QBIYtf6df3NK/nN4S8fgR27FpJzSG+kE63tuWHTd/B0YEzGSYpmzDW/PaKihBnMVOyfGTjBGyL5c12g+DlrvUws3HH1m0/uOl5Gr6b0/3/LV84eW0We+lPqgRZEa/80FF2KUwVB76RVX0SybeiHu6J06BvDel0qt19CcGCNX642lthtokVGMijdiTkz3G+R3c2iLcIyOdnZ15ebEMPIBcG1wrwxUJRttighqUbjShNkr0LJsuIISnUVhStJYbLBlUfcTYMvOoazlJfCSCrJh7/zCSi0KV0pkL60CWY28JgjbtoGzfXze9p7Drp2PBJZsf/vV859a3/5O+jY9ZTv9GjcPjL3D1S8/sJoGPRoPkZYvv+WyudfM3/GeG+h79xhLb1myG42isZR3JGPp/Sv3vP/GxTS95mf0qe4FM1fzbTYZSy+dvfXN/AMXfAZh/Hz55Gc2/vDxdXCIk+gL99FnbpzNcEt8enbCzYn37NkDvWS7do0vwB23PbP+sB9idstPEh8fFhQ/eCKu1r1PLaCzkrJRPlke73tucdO7foSTYv07ftgx7UnsCK64H9Kb0Z2n30yzanjAuYPLPxZ2vuWYi3r/QjNsbiH/CdAWblhmTuwv1dxmbrZuA1zpYGWvGnlNEHbKrZdUkA3z+YWVWhSuNPns1TUTfTmCqpTp9u22Aa6UDq+cFLcv3fkmes6eviCEQfgTdy1/Yi19ooIsxXVh+S7+jjLXPv25jZLbSGwkLaznTt/ylftXUTL/+uUf/Zl+hMGcFOR84Y7eT9657K14F8oPYdIl4Jzpdy7diSwqyXi+rv2wa+jmHc4Luax86cHVF0ynL/fjrHz7NQv7+XliSl/45RPNzonRd8o92qHEgUloQ63Wds1jdGcE5wLSmP7n+c93nHBdtYO6A2aIA90nHi9t3UPfwHv/8XiHueOE66EhJyCwdXDZ1qZ3H6dnlpxccl7w5Hj9O3+0+zd3o2F0lqlLyvzmlg3f+d73/v0H3//6N46ZM38h9YL1xGBQe8fHhtdW2m7nawomxzeVWm8oddxbpqtJSBzpGr0qFfoM/8WX0nNioYeucdHdJ+YCrUXwcPsAsMcMMlxDIIt7sQ9fEODwq2TBOiUbZIpApAkFOcbo88wt3Re9vPmXL2y4b2UrBiRMNZ5p7nxmQ/f0zd38TDwxEUEwX9jYfdWcrb96ccN187Y1tQ9CM3t77zMbup5p6draM0xMcqwbydpSpbq6dWDWlu7nNnY9u7ELZPpPYzzw4QiDsK5z8OZFO3/90saLZ2x+YQNd1zd1DVEDWroW7aIfcLEtVqEBHPTwAs6FX+vWtcGw67ShHsiBe3mYAr1X9HB62WId1UIxU66HjFv3isIrCbC7b+TmhTvPen7DFbO3NndSzry8pRvZ9UxLp+YMfysC+i29w7cv2XXe9I0XTN/01PoO7GJ3f4lTsQtplm0dVwG29wwt2N7zEmfXs82dq1v72UgLrMPV0UfWtV8yc/OvXtx4x+Jd7QP0ucdz2PuGrmc3dMs/vRPP9v/YYTh44IEH2T1BuqZg11BWdnV2Pzir7YqH2658pOcvC2rdfMeORzqm0cInznhXd/ePjvvJd7/znW9957vrmugrqvE7b0Klo1baOjqynZbS9hovoyPbSCDlttroCPPYLV5wAgwMDp5wwgnHHnts9ncnDuHsNVpy4F4ephBlr4PYsY5qoZgp10NRG1zXuCCo0zUlKySHd/YOT1uw45fPt1w1Z9vGbsp2jKIyQm7vHUZGMo0y89kNXVfwWHrD/O0b+byYuVXG0s7tvSPYgU8ltAENA2G4Ul21px/DNY+l3fDQ535JSva+un3wpoU7zn1pwyUztszYTE86NnUO4qTA3pfu5vPC+YOpfzA8T9zdrY9Fsp14tMexsdKO9v6ZqwfmrhuYu9au+2m9rn/Wmkob3eAgtltjWjAwa83AvPWoWNpG/26AWq92bGiKPNKyo+fJBZ13PNf+h8fbr36s+/6X+2esGh2i/2aqrgykYcCf//IXeb9q5sSWHFV0R8ivdUvuIyKpXLctUrpIS17cy7fauE67ZYBliIRcr7MEg3pt8AVB/a4ZIiFTBCJNVGAXonF6XzJESUsk7RWztuKkwIW+b6RSqtSebul6hiYD3b0jFZwNnOB0oHHFv37+9nNe3HD5rK0L+UeBVrb244zAsqZtEA6Nb+ovCGhJc/vAnK09L2zseg4nRUsnzkEhyN539ZfuWrYL15TzXtr42Jo2XGw6hypyWZmxtYff+jkwn56dOPaYY7/5zRUr3P8aS0UM6uE125DDbZc+2H79n/tnrkJiE9dzWJADWxsqDcxfh7Omf9bqkXXbVO+Y1b5hnEoD80DIn2VrcR6NtNBP2SifJuTU6Geff+FrX//6t7/1rV/9+jy6v+tPMwMO0VilpzawojowpzowtzrcVB0dRjfJRGxTg7tKmDFzFq4pONFyzxO7w25qvQYy1EZjQWoNHonCSnCdKuwgQcqCd8s8IRdUMWrvNkX1OskbD2hUUkBBXLw4vhFkUutB7gg4D1WWe3tqNhArkNk7kNmL8gimG9Q1LoKdg1NxXWq319QByA2G1zVp4vA6simkuDECw1XPIoQXFKXn/Ybe8G6jQhJGTXSpkOA6FbcBBd+GFDIZAuTzTYDTXFUOcKsSQ3kM2SNal88fwDeIrLETIOvWk3mgkTnxD37wg5NOOunCCy8855xfrVpFvwjrf39CkesYipjbg2l54nPhokXf/va3McpceuU1YIZruQ0vOaDFCeIyaCTeAOpAvOuuu88555zzzjvvhz/84Xe/+92f8pyYrOzTtsFAqjpRkKKyzpFNIcWNERiuehZG7zMn5Zd1Qt7r7OViDDXIVAEF34Z6iNrAcF2IoG/yLZLZzqmUH/fS54VHlQhZ/5aIq7vqHIrONelM/8DgKaeehsxECv3m178+99xzb5o2TTLWQ2vWhVIZ8KtaBzVYqCUL6PnZJGLwmj+TGRtv2bDhN7/5zQUXXHDKKafgxMT71fvvvx/WTFM93IFiJ6bAYl0EhquegNP7zKnnl3fLBPFYwDXqum5ZBwa3AQVProeoDQzXhQgwOn1dp86Ta4MrFFXKnxQgq8AAR6rnB/PiBBYQgTY5iBnI7x1KlQTCIyBKFNnLr7gSYzUy7ayzzjr//PMvufjizk56Ik6GVgUKudbCLw/pxqWHUhSq9P5UXQ9+59jKrq+++mpMiI/55jfnLcw992zbQBGgBTr2xEa/axa4a+M7d+668IILcaKdccYZ//7v/4458bRp02D1nn0li/R37FKg6smMzakiRWzNV084LAaRkxUySoy2unCKIY0gVPhnTZThgGFXmLDyyM5MqhhnjII0UPsqsmZeYKOqunV7BAU0v4sU2HOqczlVpIit+eoJh/WRrJBTTsotkXMVTIoHRKw4/HGJkFPUA5GnKrxoCh9cWuOAQksHOnV8JfHiTKBPkLFAIxwgrkTZArJwfEUP3rsssKIuWb1Pl7QUXpz5GDAefvSxz33u88d+85tf+epXv/jFLx79yU/OmEHfnY8vwFyNXNP/4STvWMf7FbDPsVtvve1Tn/zkN775zeYWeiROXGXZKFN7sGZZBW4vVrxzEolCA+Ivz/7V0Ud/8ktf+hKmCF/96le/973vd3TSl0hcjxpCkppTTsKhkCXIBgclexOo7za25h0kXWYBkiQh1jLUiUwfSbID70USMsN0aSmULCgROJOFhjWHOoRXrJrt7AUU4csuGNQUpEpf/8BPfvqzL335y9845pgvfenLn/zUp0488cRqtYpLZziCoHIrXarDEa/dYo+VSqjCHPpXjnmrAG8xxS2YRJYzSI0eckouXrL0M5/97Be+8IWvfOUrODE/85nP3HXXXdDzAxXK9JhYUQ9E3pvs1a2ggUbVA5GTFeq7nWgfE9kD6jHrt6EIZmDXQQ4JSQvLFsxhJi/QgC/Z7q4A2Sr+pJCKdFLEDLN37zN5WUFFmj/+9qKLP//5fzvmmGNxanz+85/HALtrFz2gHF8CGNiXz174yjYthjuDopNCRWoct5IJniluSbBVSO4f6P/xj4/71Cc/de55F6Bh1HTW57KXAZ29rMQc0vGJ1ty84Ytf/PK//du/ffnLXzn22G9+9rOfveYadxOnuGt0n7gOjLU+UUAc4dVlB9oERAs6kRshB+cTsh1h0m61AjZheAmSO4q0VmYBjLU+URAc1mUH2gREi+Q4mYB1PgEm04bgFmutgM2hGF6CIzTqtkFI19x6AjhCIVHGlM2btzz3wgvTX54xHXPhmTNffGl6a1sroknWicKbgRtGxp997rlbb73lqafp/9sV7j4LIqZdc3nZihVoG5o4/eWXIcycNWtkhJ6ucO0pAlu5+fV5wKTC69AIMTjEWitgM8nwZmAYE5OF06BrR5i021AhdCjAdi0wJ0AjxOAQa62AjQmvpHq5XJ4zdy6SB9n+MlJ9+vTFi5donmu1AKPI2RIgjvAmZDtCgijt7OjseunllynVZ8xAU3F6trS0OCvXOujhjfAKz15uPqRQ4eCFN4W9Ca9BMZkt8L58+YrnX3gRyYacQ77Nmj17aEh+CIVWGThdI20gTia8lMKTDK87KTrvuffe2267bV1TM01boWT9RAgOLVt89vX1vTxzFnpNHZ8x47nnX1izhr6YK9YiyH3iDMWU8ga+CMZqh+AmXy1C5kLKpQzJFPPkggtx6lgkagsSbci6tUUv54UAqGwbHOMQCG+drk1MVuzH8Ca6liEToDp44Q3KyXUt9pYiE6DaT+HF+JJH1jEDqkwbmGSZ2AcV1Ut8E5cNtLIVAuqFl9qjHmMIpahrjMjEzjJkAlT7KbyEvcpegEkZpinGFirtZXjDBphc12JvnpyvYLvmrTEtlIraYNRW9HJeCIBKP0qNoWaFdatbAZVSXVNMWXjRnuJUx4q3sQ8q2m54a0wLpf0V3ljNpQzTFPPkTH2LQzF7Q5sYppQ3ZN1a0ct5IQCqRNeyTFPMk7NxCNAMi0EGrREqJtqQdWuLkYkLtMpXmCi8sEcnhfVB0uTCG6DuYoiJ2ZkqWuQ5MfzSXlll2pOpEcqWXAegKFMWy0aRN2IhKe1WjAFcplW6DUKXNQNiaEMxvLHhNhC8ZyJ7rQrmYB0C4QUa7hqt0m0QuqwZED2zDry94TYQfO+I7LUqHPDwWmhxf4VXtgAEz6wDb0+5pQ91q9UaLbUaNu6b+KF3RGYNQYX64R2lj7/IXa5eug0JgOIbwODGwamsUOBP7tSYduuNCi7TKt0GoU9xeLOVs+RDIXszUA1tvLHhrtEq3Qahm3dJEDyzDry94TYQfO+I7LWjyCLOIUl5ekVMR/QVFL6c7loOoHi3WaiGNt4Yu0WM5FTkZOdlVE8lys50G1hTJ7xen4HXp9xmK4WykKcye2mV7loOoCiTyRFUQxtvbLhrtEq3Qeg2vKZrVNRtFl7fcBsYQj5A4eWTguG2THPkCKqhjTdOqmtKlq55iLl+ePmkqLnLk8CLjbfBN4ADyd3lXvO4gH2YNsRunZ7mxJ5jgIru4mGbmOO6sterYHlmdwoqRioUMhSBuYBNjHpU2wYSxDGrnB5bJzKCPlJH5VzXomAJ8hqmOTdqjgoeruz1EVswpeFN0gR1TLYNHAFxzCqv161D0McWW97r8AY3ao4KHq7s9VkCcBDCm2MduuGNkFW5stcn6hz48OY7iTY4vBpej70Mbx6vhlfFCPsxvCz6ldk6FOuDJh3eSJUIOMO4CYws15UTVI+pDG9RYwn1bAb7NbxcjFSvpPDmwm1xoMJrFI2FN+uBMYo5MRvUakgkwrMTGRGL197EEvGdJlgSMYkV2jilZW3kNjQ/tjJEpW1lODqvtYapmBJdG7TgRQJJtmsTgt59OFhfGTU32XQtYvHamUSaivBmyFSaoGtimmR4le61f1PhjTEl4bU4dMIbubUOSJxseGVrzIKC8Hqta4NZe1BpUuG1ZN5qwbBIPATCK6UDHV5aBVBx0uF1sA4Ni8T9Fl4me2bUNesAotM5bd3wOkwQ3pT4Sg5vTsdkr301vIJDLrwMIyqi8KqYruDFV1p4gyWWGbGiTnjd706E+HrYPhQg1KlLzrlOaCzquTU1s07Cr1DBhppaKuoaa3Mmh2BIdc1b805CwagbCm+OEhSpNjCIkquX0FjUc2tqsmjK/4XC6y0k5OolNBbFbqOaLEaeQuGAhjdC0BjbVIaXtN4SFQD/fY1YHSEY6rYh7yQUSNJS3fAWIljzbTDWvJOgMbZXUHi9Ne8kFEjS0isxvCIGRTTu6YsRFQj7GF5jzTsJBZK01FDXcgh1Xg2vQygY9f4Ir0fOtdFMtg0RP6wJBeEVt8EE/BcNbw7jbk4cwzNF0MbHWlZGzXLkCJZBJqrk356oQsGSNQlEyraBYdqgBkcuhJpCqtA2vM9iobhrYlZ2HtGT6zl4fUbIdk1L2Ey+a1QpGd7MTtzWSdk2MOISIa/JQAnp8KrZdE0hdtcGz87iUAyvFvxOnNFBytk2MLLUlCYDJbwaXgcpZ9vAyFJTmgyU4MI7UdcUjiwbz44xSuGFrcAc9BlhojaoQTaelQeZqNKr4VWIMFEb1CAbz8qDTFzJcVihBb8TZ3SQsjNH1iw1pclACS54E3VNIXbH0q1lkOrV8HrCvmSv2+bDK24dLYu8XjQTtUENsvGsPMjElRyHFVrwO3FGByk7c2TNUlOaDJSwv8Lr2+e2BhmTnROTStXwypIUsY67bMG/Cs1msyKDAoqoFw7MybkNtf29Mi1iESkHIro2iIMskdrg3Fm3IuTYuoXkybLWl0fUtUDkV0QkOAtg28DrPNvhlRReaoh1K0KOrVtIzq1ZkV5BblUE2OI5LERwFsC2gdd5tgAn4CskvLShlXUrQo6tW0jOrVmRXqFuLTyHhQimtm0Dr/NswSs7vLwucksW59asyKBQtxZs9uQIpnbDbXgFhRcXM2pIw27J4tyaFRkU7DYGmz05gqndcBvS4bWAwrfBNoZ5eba+sG64DcBehFcAsdjt32Z4BZMOb2DniayNwyscrIvd7q/wNt61qQpvdgsp1wZjBOICefbsXBu0NrNybvNsh70IL4M5lsgItaVrYU6c4ier+21oD5edzcNbIiQbq0jVSLulLQkTtYGR0pKu4bjlHKQVU9c1XtH6IIeXSyTRuJsgM1Ja0tUJb6hCUs5BWnEgwyv9tVovZpgB2RqEHNkp6obXCwQuRBopcjgyeiUHLUlZToHi1fB6UDEKh7MLmdaiCZJBWvE3GF5CCG8abMmYqfhqeBNkp/Dh5e/gH4Tw5rSvjPDG5hzZKTxPw1sMJmb8UPFvK7weKR1DasTmHNkpPO8VGl73HbsYOVUmHLyXoFLJ7TlHdoSc3msSFq8qdqtgfdaYU6qc5eU0/m2QtUzYtSTYliDknWQ1h1x4s2CltaicJ2c1exXenJ7AyoQl7ySrcW2wHqYgvM6FdVLsVpHUszJhyauyGtc1a9mLrjkkLHknWY1rQ1jtVRucxm2dC+uk2K0iqWdlwpJTFXbNcveiaw4JS95JVuPaEFZ71YasxrmwTvaua6xMWPJOshrXNcvduzYwEpa8k6zGtSGsTGzy1aONQVZjuuZNE3YtCbYlCHknWY1pg2yACduQ03tN3pJXHYTwygaYsGtJsC1ByDvJavaqDTm91+QteVWqDQzWq9HpEtWjjUFGc4hNioJlwjbk9F6Tt6jK3ydGkTQJoocPLbVAifk6piRiTElIViA5UPKYRBtEhbUsWiakJIdA1o2BKYkYKEFy4JJXQchTYryiw2vvZaQkhynpGoFLXgWB5JgS49Xw6hYQMaYkyF4FgeSYEuNvMLwOU9w1Ape8CgLJMSXGIRdeRb4Ol0SFNS8HNLxesgLJgZLHq+HVLSBiTElIViA5UPI49MIrzHwdLnmjLFomBMnhFRZeUYV1oCQkK5CsZa9O4xUfXsyJzW4jk4Af9TCCMqjjOT6XnAp0zzU0p/NudQuwmGqDR0EbsI0ruZJpg2U4XaINADU31gCsyGmJTCuRYzgnvBOtnif+Fwqv2xxK4cU2ruRKpmv/RcIbm/N71KZGeDW8DFbktESmlcqyUeT3qE2N8EoIL28sXMm0wTKcLtMGt2kkvE4sDm+eTm5zHN+GdNewjau4kuna3254k12TTZ5Obo2OEcjprmEbV3El07VJhDfVtYADFt6YD3DJqCzXwelkk6eTT6NjBHK6a9jGVVzJdC3VhkzXCCSCajSCoIhNvg28sXClRsOb6RqEbDO4ZFSW6+B0BV3DKvLq7hOLCmt622kJCqPJGxW6S7K7FskGa3ZrABXvR3kGTJZtIYptvBdxm26DqRrZGRBdM40WMI2P3aZg20Ci32JdEN6AYpu2gQiv/PDGMNqC8BqGbQOJfov1IRPeeigmTKZrKsaauJkORjup8DqIzOu4DTkU23QPRHg1vIdgeLVdsTKFYoK6IIIjyRbr/RFeX9kwxmU/xq+CNXEbcii26R6IsA/hBSJlCsUEdtFgeBVGAzFupoPRFoQ3ALMK3k/exJpkGwKKbdoGIrxyw5uGafwrKLyeB6hGxQKopZjALg50eI0VgIr3k3fmyGJ5jSMaxHViv0BsNsELSOkYqQY5emSJabk2xJA2ZDxzMV0xw4xgapiuTdAAQmHXUDeySCFFZsSGQz68GTUhoRLAcKDCW4jYfAiGFwIvWE1VeBvAPodX9bH5EAyvYJ/Cy4wDHN4kpi68EfaiDZkqjYcXRdVIDRGwyvDqY3+GN1OljoeUKaGr4yGPvQyvRNJCaoiAVaJqMfYyvPlGRDhY4bWEZBwaz94AqeGQcSulOnVTJvEYWTI0FP2Sx1SElxEb6njIo/HwSqHQudRwmKLw+rXCFWhOPAG0MURMuw/tFWvEKfRv3DokuRy9qA0KjQzeW4m1xmv9RzBJVzlk3Wo5B98GbUnYh6mBopZMG0QiQJJF4AWtQ+WDHV5CkgSlUvYpvEXAIXRuuUrYR9ywfBtEqoeoDWm620lw61Ho37h1SHLBmzi8AFHIxmReKL2L2AJ1K9XqYO/DWwgYEU2ccSyEVwp13BZVcXWsPckFby+zN8mMYNpQlDmM/RBej6gNadRxW7gP49YhyQVPOqXWJAnKfBuSzAjCQE16HHZUR+8E9l94feYQuagC+UP7cDZSC5k1oW/j1iFZB7z9Ft6oa3XpReE1QDHfhonQWHh1k3BbuA/j1iHJBe9QCq9BpgKK+TbUA+xISNShhfafmSV61HFbuI+oa4IkFzwXXq4ipAxVvcRXiokRtSFdRSl727U6kGcnikJq1CxO4CxAiEX0rL4ht+iP5eFAQIPxtDKKBQqdGYeuiLBfuqbkqI7Gm+ENIlDSoHmyEJEkb2WwGPmrB+c2jay+IbdoD/MmInu7CIdEeCNigFGzWEDLQ93yOo+sviG3E4ZX83l0rDpWK4+OVkclVQr6ZrT13WZBRCVHdZLhFSniKbL5zE1lHyl2BkIpImb1DTjkBjBvIrK3i3Bws1ekiGdg9HV5OQixAbeMhtxObXhZremNVMfoLYk0kXdhKCsi78/wap7r+egSvgG3jEK3Fq7vE5G9XYT9mb0R9md4sxBiA24ZDbk9lMKbwiTD62c+VZ78uCtFoXuCt4lQRDV6Fot4EVzzlZypo6cPBDrNZRHfBc6NWngsNoAiorQva63j1v/uRMPg4SDtMXSzzh5jWyjUq2LBPMR3dKip0v1EpePecsc95e5HK4Orq944IdKUwpEOFbwpLwREKvMGB7lb6awNr6v2z6n0PFvpfa7S81y52l/D4YoeBgf2U3jrdC2PCcgTu0oz9lt4J4EDH97M8QVyZFAwapTbaz0vljvvRz6XOh8o982sVPtqdEbnHKR3NtXhTULUo5WxSnttcFW1b1YZ+UzLi+XaMLe2qOrBCy9gVMWuHNKMAxLeCcBtSFc9lMJb7M5jdHB1pevRcse9FRq9nygPN1cLk+eghpdSvaM2vLbaNxuDNi/PV6oDPHQrJSC92wMb3rQ11TXFgQ1vGnuRvR4HNrxpHJDwQg1neqV4odxxf7ndXSkqdKXgG8bKjVHgMIM0a1LhNT5EwpvecltteA2uFLhM8MXixfLoMM+Pix1nManw8jCSQVQ3FNIueU5MfsWcqhtvRNC5fn0QRWjZYyVHLwqL9i3mxWWVeYMRqvX6UttNWMpYt99car2uhHDLXfo4UFLwKkWwBJNlemX20LsibUwcvFb0Wh7lecLA3Co3tdR2Y7ltGta0VNoqlOC+nkKq+2IxiCK0BsKrYszLlxmuC0kjYPQkSjFLDpZgKmDGikmFl0AVpBR0gBbijQiTDm+MKQ7vKL3RH9lebbu9TImBTEaG3FJqvZ7e7OF6zJSUZ9V5kwg5ZqzY2/DSQkrueN/LFZfPJeTznhuowdXeKkxgHVrhzcLEkgQpZMnBEkwFzFixV+GljdUBUSGA1FMXXm8qYGYVhKLwunLd8GKLpTaGgbr1ujKluhvAMXr3zakIx38/HDhY4XWvsX5caG4akVTHidnKCV/pwNANAhhMMqA2HKzwWqguYhe7VUwc3syJGcIbIVtmFHUtAaIILUs2bfAmywz8bHi5WNQGV7bhhSSFLJmgusDGaz+Hl9qmV4rbypSHNyMVy2230pWiHVeKzvSVIgGiWM8Bpg3eZJmBn9lPpmsoQoKfvukVaicWf6W4tVTto7eUeZ8BxhuDhP0Q3mjjgTkxu8rqs2UH1htjEL1FVb5MG916mJAzLLkAYpW02FBFHrTdUWm7pUz3ie/jafGdGGdLgysrNM+k9GC2W2GTdx9rLJmQ4VNRVZElQ0M5NtNx6J1eaf2DzOBL7XeUMftpv71c7cAcInOguWA0QfQWVfkybXTrsdfhrU/iixUxLNnVyG6tRIjIQGzloqoiS4aGcqw5GNnrLQpLLoBzJRsPcY33zUhjyg0kxh3lrofKmGIiSTDwdT+Gt3lEcjWDh9iXOGKREVu5qKrIkqGhbDUomYpUIHF8rOeZcusfqMEY7NDg9tvKHXeX83e1g8gSrVTly7TRrUGsseQCOFeySaNu9jqEcmzJkjP1qKiqyJKhoRxrovA6ZMsOrDfGIHqLqnyZNro1iDWWXADnSjZpjNLNHMNgMa6BpmBwxhCNgRrDNZK8gz4VQSIh7Stt15dGNlZp9M5GiYuqiiwZGsqxZh/Cy67QmF5M332q316hofuOcrVL3v7RK+8u1nApT3JgH0EsRAPhteXYkiVneLRSlSXlutZQeGMEK0uGnLCoypdpo1uDWGPJCZBFrcUk4JAPr7BrQ6N0vmCKaa8Ud9CVoutxvVIIMhKtVOXLtNGtQayx5ATIotYciU/23qfdzIfbjNl8O64U/cnPP0M5tnDJqDI8WqnKkPJdS4Q3qxLQnFhFDyioxXjRiS+KiRDmdtktkK2fmQiihLIPkrPRzoVJ61CjNtr9RLmV33x0/7lcGxxFonQ/g0RBfpQ7HyqPVplLq1BJAQXmoamuuTbkke9aHdiusUS3zcb7Z1Qx+vfPrA4uq2AC0XYrJQfNibnTk3Kb3QLZ+rYNhInDm/eRQAiOa3KuEhT1wpvEvoU3XwkK7Ny34WCFl9BAeHlwwCyh7QaeEN9eHtlWG6uMDq3G+z1MN+l27MgOzRNaDoHw0kj3fKXzwVLfrMrAYmon8rn9Ln73Tw3Nw7Yh5zbaA3eNNV69T+ENsA3Lu2JAURDe4j3sU3gTRw0K6qtjFh9XA9sGV80jqs9dY41Xx+G1ECaFIm/LQSPGEHqukihKo50P0ECNxO55jj5FrfaPdj9Ol/bWm0rdfynLpcrVtl2bAHTvIZBISkej8fAyue9lDN2lvpmVwaWVtltLdFG/vURzYqQ6CL6+uKUNwasPaHj3Z/ZOFF5eY0V9bSy8itC19PESiFtH9upceE0FEonvVcVQtwyh5ypBcXAGB12P1mjfgyvkSkHvzUrb7ZWCTqiRne5KERC6dqDDS1XHe58rdz1Y7p9dHVhUadcrRblGc2LbXyFPaXizqgnCqyvq6zg9O2EpDCMaZN7YUPvTxByUlnhTAkR7Fq57ZYD6iGSlq4aBid5z3FQqbcVlmJTDTZVW5ArdOS6V99C1OexsyruW7odFllAbHBstUzurA2M0h8Ds57Zyxc2JBXsXXqIoLd2sbHjVbZIbt8GLGS48WBzs8FKB2+C06er7K7xZt5l4R8iElwp4jY91P8Xv8aaVel8qcz6Pj5ZG8X6aPhe7sYQBhYj8yRhVzOHAh7c2QI/Io6mVzlF6FggThTvpPrHP570Mb67KvoRXETv4L529uSqTcruP4UV1pEdpV03vct1aqrTp6D2wlB+Ek1uwPSGLEjgA4QVfGsA6Grol1ftGqeU0J+b7xIaDDW1VDjiQ4c1QPSbVhpQtrh8TqLBv4Q3grtn+kV05XD/DJ22k2pvwRoi98WviOpNpQ8oW148JVPDhxQtXiif5iSN3pYAmulLMcVcKrWOQCa90TTlTEN6oHy5uWGPCM8qPiFY63JXiLv4mFXuL6qQw1eGNQCY2O45uX5PwI6Ez7KjxQFQKPFmp0XHCYQjwDXVbQVwisEbU1AZ+ImJ4Q7V1Gt+ER3AHoB4fHeF7D/wxLmbGQ2v5uTTJjCxCc5wxJuWrsEbUWWOebLqmAm8oA1iudI0ifTEhpksCzYnp7cu+hDeFvQ2vRZ7MkBpqTHD2Jby+2Q55sumaCgkOg/ViPHTDS2vKXnr459aQukiK/rnuPd6N5e4nS2DxeBDg3KXcWrBGVlljntxYeEmHtMVmdAzvPzHStfO7f71PHFXhglmpMeJkENqgW0G+CmtE3UD2elWdNmSzd28yxyNbBkLXVEhwGKwX46GbvRG8qqANKPLoTR+J3FhqvZWemhgtE6naP9r+R7pz3H57BRf7ka24rkt6qRNZ8dogWwZC11RIcBisF2Oia5kskIzG0N1Bs3n6lO82MyfOweliW57JGlFPZfYa/b5kDj1JkEUuvEVgs3AmDi9Damg5rpGBM8akfBXWiHrKwyvGfQqvbi3qhpfeDYUrRRuuFOt4kjM+iisFrhF0n/jGcs9TcqXgKn5nKk3UNYYzxqR8FdaIeoLwwjc0o2Ol3XSlkPvE/OxE5kpxUMNrMRr97gSRQwWW6tcXq+fw3rI1Uk1gVYKMYtB6SYoEuk88PriUg3tLueuhirwFGZhPtxkwgaBRFe+W5ulcM8DKgC+SkLcFrZekWB+B6Kt54P0OAoF3S901+QAOi5sTF/oWg785kAxvXqGqBBnFoPWSFIsgFE8O1SwyCi46Yt4WtN7Ggi8pMoEJHFplyVmtJxZDrJMPLyNBRjFovSTFNCifx6pdo+13ViglbtbPPcp7qvQxk3wYcnOp68HyaMlVsKuEa6iCxdtZ8CVFKu88MW+LtRixMMWhpvKcWN6g9ulPBwiEfDDD6yieHKpZZBRczJEEqpaN5yRr7GN43aoQYn0lhNdpKdvH+2dXafS+udT9F72o0/ftbqBnIukT4RtLgyt5ulkLjljIuj1w4UWqo0AfifAdbqS6PE+MRha5IH3GhmLQekmKRRCKJ4dqFsZOYClHEqhaNp6TrHEgw0sCrXM1ilwkyCgGrZekWBemo75ahFjLUp7EULVsPCdZY9LhhS5zpdguVwp9n9YuV4qH8D5TKsj6kAjvaI0aUt5D94nlSsHPTgRHwQ9LoRhB1bLxnGSNSYfXaUWyc2JAKzinuiHo2CuQt5Gy8qIhRwg1E4zIrUBYvl+mEo2qYwPzKjwnLnU9WkbtcmsVE+Ke58s9z5Zbb6JfdeifkRywtOy7Bkpi58oTkndBB1UR1QkFTy0EGDwnbr+Fb3L754kFsdvQhgMZ3kQbCEqltSFnoSYfXrwSO1eekLw4FeElKMu2QZHoGq30oxkWSUgg3wbDjNwKxNpYePkrEeXWWrs8p4i3SXugGe16hAaO/gX8/OKtpY576KF5qi6XZwJt2J+WPbgsSi+a8EYI2qyXNJTlNvSBMkY6+ukAHun8sxNhxwc3vAyl0jrodBugmkmE13Yt0U4g37X6UJYjm0qJrtHqEAtvHXBOIE7jY70vlOlnVW4q9zxNH/6ObKm2Xlvum1Hp/jN9P731xtLAIhm92SOtDoHwcqrTnPgWnX9gToxaPkyAk40q0Wyx+npF5MmHVzZui1cyZmyXlRcP5ewNsF1TJJotVs8pIufCOwGUdDDDyw8T0wxY7pXcWirvRr3RrofDlaL9llLHvRX/M2f7Gt4Y3pfhGHLUx0T24vShObG/Ush9YoXSD2Z4HUs2mTlxEVyDaV8sQijem14WA7mQ6gzFvgLoOCOS/TPpZjB9ReOpCpRdj9E7pGr/aO+L9NEbrtA9L/CjNpFLFCZqg2tt/WdWGuuaKmMbD6w9mtbu2Qk1KTO4zVa22KvwFnIc0AbxRrIT8lA/sTukZqF/NUxl11RZ7CaDAxVeqlKEVHhpsBgv73afyeJaOzA6vK6658rS8LpKaVuN8hypQl/U5ax0+7MgHWyx2yJMKrz1gcZQPrfyhzZuTqyep6ANBGfA1lcpwj5mr0p5KNO6jStbTGF4DdKZU4TJhxfwVYqQb0PSrSqzNvHM18GeZ/mOxrQS5sGj1VFcxZHe9CVpnhPTHY259MNBOReExrs2GUwcXk31Tv88sXzHjiyTC28xdwrCmwDcMoLbfOWAvcqcCTFxeC1i8qTDW8Det/AW4gCGl8+d0i5MK0eQgVhwRRiSK8X6yshWvVJ03F2pDjC/wPdeh1cmRQXsicKLEhq/e7RtGt33wZXC3Ceug/0d3kIC5sTiRhh1+NGVg+20cq8AV+IemCoE2AJZJFuONcHiQHPi8f5ZVXoiDXPfZ8pDayp7rtbfX+t5mr51B33vizInRn3ZxI5QCgqRLCFo3CvAlLRrrGFiYj8oYyEeyagxPkpzYn6euO02NyfWQ38IhBcqcchrttOKGhiTnYnakO+2UYhkCdJbWrlXgCllw5vaD8pYiDeJrjHYTquCrgHUNVuFEJFFsuVYEyweufDi3T/eQO8epS/YYbC4szyysYYpAt79wzjSIg9glTr+WKr5kU4WhUihDJkLtHKvAFNqMLwA8dJdgw9MFPbUwrt/91tsbKfVQQ4vrwragBW1Id/tjILKoZ0HMLwRolixnVavsPDCAc2JyxiiW/nTPPpq3dWlkU308UL3o/yrUjeWBvycOOFWUD+8IsmaeFPQNW653CemVOdnJ7h/bBV4MsFVczgQ4QVyZCqLWwI5E4Z7BZhSVIVX+zm8Dq4EHq70WlBEZJFsOdYEi8crPLx8N5C+nyqDrVwp7ip3PUJXiuFmvVK0/7FcHQSfqhzo8ArYLRdopW3gxsuzE3Kl0DlxBkKOsF/DS0h2zd0n5gKtnFVhohf3Qrgx2ztRvVrdfFGLhNxB8TAkB69Cf8fHMGjqu6L76G2H/wHXnqf4/whMK/XNjOeavKUNCaolFLbBcDzUiVsFUDGKgxMjMrZYMLB2u4H1tnKlHe3UJ36Agx/eOm2Iqkl8ROXtOeIBDq+snEJhdtVY16xKDfsxvGwqt/Ij5pj73lnpvI8+9KAfTsFIt46evGyH/t5ybUgqm/4ciPA6cEGoAXwyoqn22QlSiY1evlqAUanhv3D2OnBBqBHMrhrrmlWp4RAKL/Y7Pt77Aj003H4r/b9GpHrPM+Xxcfp/5ngfiIERc+XBxTJ6swNB8ChaZ+Nd6T4ERsxAqBEaDy+neqVztJWfnaDPc2hOTD9tdEhlb8A+Za9SqGu85TUjVVtAhozRkBvrmlWpYTLhLeya4ICFN8tWJ27lrJMKr3w/FYMt/5xZqd1eKcbHhtfyM/q4UtxXHh3mrtp+OBiVGqYwvDHEKZuxgk++Uvi7J5gT8znuaBk/pg1xY6jCvoS3oGtMMsTXOB9mHUMcsaWYZGGoxTWgoqiIKbInyA6UGaODK6v0Rcvb6akaRFnSYnRcHqKg/wQ2sIh+kcT5dg6L3RoLxHTcrDbVaAtxErNQ4oNU6R7FLMffJwbT5JqtUAzLkk2iClSTDy8hX8EChkTXwjoFY4G4v8Ib1oWwFYphWbJJVIFqKsJL19rxas8ohgka6W6nfzpD/82Lw0E/RYn3frfQbePRCurYVAkwOuKoWIypCi99c2JsPDxPLL/FBn2ylYBxogyjiQHVVIQ3Cxga6pqFsUA8cOHNwVZIwThRltHEgCofXu5agmwRVcgBhsKu0ZbvXPTP4zkxRm++NVDp4jG9TA9R0BfqbyrhAk9+eBKQR+Rd1hGgyneNkSBb5CsEcEqPVzprMiduv71U6dR/2ZiqQmRaRy45vBMgqpBDyq1sCqtYC8SJ23BQwksQE9Y+ol6TBVSHZngnxj6El95PVnvo3nC4UszV7zQPLue7hLeUux+pjNE/Zyh4bgDaRsNLqe2gmvpINVoh/0CttHsU73jp9JG7J34XqSoCYykmGSip0C1UReHVraD4eeKs02zZIb+HOvDHpDHEZIxN6FNpO78ruoO+q9Hv/x1oebT9Xp4l31AabuEBq2BUJWTbkGwThw+o0+DIVLdrMKI99Fts7gtVt5Yq7VXWS8Wiyqyv4zlC3Tbk0SBZkrdxz1lmsuaUhjePLLmoMusb9TzV4SXCOFKX/4tBCVfcrgfp2Up5egyTY5oT31TqfrrEu6akJmTdJnezn8NbGxvF1EV+iw1z4lt5TtyLfB53+SyYqA0Rpjq8ginIXhd5hWgmchuZ9rFrRZVZ36jngx1eHpCH1+P6PYLRu21aGddyUo2OVftryB+Misil0i759Gwit5Ep37W6DWqUzHqkOqd0pd19xHd7GXNiaOJUn2CfWTRIPljZG2EfM6eo8qTaMBkmUIdsTRLexpGlo5wJr4B5njzBTiYOL9vHMd/FBYL/Nxn9xIS7Uoz2zdZvWHU/436LjSpwpYk8B2SZdWvWMVoTZmxoDl8p6LfY5EqBOTFdKXRGFCHRhgMR3gij/j6xhWmGsZJYRJYasnO3ziHunSNYopGjNng9PSxS7eP7ahibppWG19MHbVhGNlcp4vyfwOgff6N64Q2rANuGBDnXBt+1QI47JaV8HNBC2LDW/9nBc+JqD39KyHohmp46sMcitxGE5OAIlmhkQ87sNOGZkW/DhHAkqZKDbQO/s/VdC+S4U/k2BKZH1DWPeuTJugUcwRKNHLUhchZV4CEDR7/3ef7i0c2lnr/So/BYRis8UcbbJ/ncAxqMIMVtiPYhsG3Y2/AmwCSft+VOevdP+XxXqTZk85khZBat24RrX4XhCJZoZEO27YRkK1g01IY0Csi2DVMdXoFhSJUcmJx3W8T0cARLNHLUBnEoa94IYocNtQGqcf5SJt3oom/UjfDvSWGh/8XFWdTxR/6JFXBN/b0KbwFyXWMUVGGyT+lqX02f+7+9VO3jibtNdQPnyro0cq4Nk+yakhtDAdm2oSi8MWwbClm5rjFISFRgckNdM24BR7BEI+fa4N0GUuywoTYYGKslG7VtQxzeItg2JIgwQOe/okpXCnrsntKvOtb5AP3uBKbF9NwRuPyBjHi0bhvpGuAIlmwqiUdGxq2QaO050kJelztqeqW4uzQ6zM9O2NMncuuhnhlGzWSpMUF4jVsg4crCkfk+8biZPxY4F52uIrKB92KHNEBLQRk3VQwZt4FsIY57nqdnJCi+95RHNtdK22udeP+EueZNZfnZy1EJFSNuSyiENrBOV6GavhycbN1FZIXtmoa1OjqwvNI/rzqwsNo3s0qXBL5V3Du9Ag30g8ur/JGH4OCFV3mMiKyI3TqiIVj2gQivGg7x7HWy8hjeznPi4U30tVH+T87l/gXl8q5a38s89vFHzHiPR2MHVfHVDkR4AbYYtyyNlkcHllT751I+I4fRQv5Audw/k/N5bhVTHFfFuD5I4fWI3TqiIVj2wQkvCYbtoTxGRDY4lMLr3cXx4AL9Z37kNr3fw6CNoXtkc7XjHrrHgYt6D/93Lq5lanr5QIZXpMro4DI3dM/goZu/9d/3MmmQ6oMr+NNqs+ODFV7LDmTW6crbSQjkIGcyR2B0UxneDNHvOtOGbLUDGV5zWSFEBQXrdOXtJFiyk/c5vOPjo8MbazTzuYPuFvcvrOBKQSPwNH0YSa4UtJ/MroKT2KCloNzbzPGryD5aGh1YXOmfW/NXCpqz0ZVCTx++UkgFU80juN3b8Bo0njny7AQKskQoLifICYBCU9MkWTXx8UswE+XxsUpHtf02+olihBjTCLxtwmhF8rRSaUf8kC4JeMlSjGBMMItrWnJO8NtqrfOBUuv19JsYbTfRP9trv71C/3jmZvqssO2GUsd9FfqnDO4DQ67pvRUDlP0R3rDKGp3G6ElMMmMEe4JcXNmSc0KoJrRQFhSXE+QEQDlg4R0d7XmKMoSzgpP55nL7nRV6aGwm/eCg5SYQrLFbRnFVS84JoZrQuEwdHh8dqbX/sdR6A2cvtbPcxv8uh4o3US+6Hq7wDXDrJAf2FDxHUA1tgjHBTJbdKmt0mvw4mGfGCPYEubiyJeeEUE1ooSwoLifICYBy6IWXkmJ8bGR7FdNfmlxSnvMC+ZYyhnT6iI9pHkbMwHrOCaGa0EJZUFxWsrRhtFLruI9SmlO91CapjveukDnVOx+oyH9JMI1Wgb2ICEAKBUGy7FZZo9McEtlrDpDQQlmQLLtV1pgAKAc/e4WWZRaXGyAHWHJWSIbX27qfKuuVAjOf7JWCCMyrC1CmPLwMViKGBJ7C19rvlisFX9fo9KHJDxVx+lxXot/N4M8/fauLdwOp2GiR7VpWiCNkmQqaE7NODN7MVNXlTdSLULDbIAiEnFVTiTVGLWLMQ1EVEZFurTXTu3b6B0h8JaY3Tze7/4GU8cHlzLsE6EjLTHsemLVHtg3u/SOtfRwcQsBJ4ALmxDSwXkufbmAGjISmQRZryDeWWv9A/5QB76ioia7OwQuvpeXIioTexIGFAxNeBgtSsjrndkqyNwa5ZqJhi5irrgqvtzRa4yUNrPbTv+pAVtBgwT9WhSTp/mt5DIkBRqbf4tiIDBF8UZBtw96GlxaSUQdz4rtoRKPspXym/0NGWY2TEZpr+d8pRSOdbowgEH9ZUIuZaNgi5qqrwustLUdWhD4yqGTiwII4NiJDBC06bbYNexVe2VpdvmsCKnq3zuY5SXIW5JqJRV1zyLfB0nJkRd3wIjo8eg+uoEfdKH9k9L6Bpsj0PRAdA0MFJ7nNgQqvMGhOfE9pzx84sd3QHVIdQ/d9mBPT0O33TRVZ9vsQt27tkW+DpeXIirrhFUFKKkYmz3TayEobY89lDhuZoKwgeIUg3zWBFDM2z0mQpzJ7tW9pcobkkCGzYHSZNuxbeFnLBGUFQbdSna4UD+uVog2XiWk0l+jBlYL/U3q0D7sNgoCKUxhee2LqWubEd9LXAe3Mx58+e3Cl+FNF58S+lnGw/8MrolcIRjEntsQMnzxnahCMKogs0UpVvkwb3XqY7jIsOQGyqJVFHlirnbX+hdXeFyq9z1f651crrfqjdxmyl3VrEGssmZDhUzGjYmR11DVzRCCO0kcew2trw+uqQ+uqWA+vr6I4tI41a6qwjlVBdZ6MxyCyRCtV+TJtdOux9+GtC+LQuzBH5o2TdavlfOZYMiG2cjGjYmR1mfCKXUmem8xeJyZUKtFKVb5MG90axBpLToAsE1rR2DL9w47e6ZWe5yp9MzkrKNGV5RzI9gCFV/NISW7cq46OtNSGXD5zSht5TXVkM7fcOvduVOXLtNGtQayx5ATIUmwNIA7fQ1Ayb6xMEOEAhVftSvJcjTpLHnVUKtFKVb5MG90axBpLToAsxdYA7pohWrdeTST6pjQ/WIxBG0N37wvlgUVV+RII8Ww9B6/PIKub0vCSp9royIba0JoaRmxK77VYc6qTgDGcT1J5fBNI+dBSrLIgS7E1oOHwTi57vUVVlpTLnHrhNXAa2qpsVYogskQrVfkybXQLOCloCJacAFmKrQGvoPCCUh5FBuqVYkZ1xF8prHvdijThVdtr/FZgyQmQJWWVzo1WR/FGl2c+NNvRU4bPIDqJcKXYgj77XUxpeG3XBOI+gEtZEmnC704Eq0rh8OQr5uAn8VkyFfP1Ex7jQ6pwbrP94RzgZ7Tp1gIv/G1lZx2Lfosu1FVvbGQ5dmsreaS65qW4viVbm2+qWfhLGrykdxthP4c3WYNVRh91zTbZqGXNxmz1IkxBeIGcOjQw6yOBKQyvtU0mvJBMPoSssJkv0kEPLzUvZG9yUabCtiHnNlIUdQ16UVj1ZMJryBl6Vn2IZK8IuXEvBduGiE6FSDFheC0m7pqpM5muATx6Q/RrLLmLGDBxGwwm0wYRkuGNVROnOneIoV1zKw/o8/uZuGumzmS65hDx0ihsA62y9fdLeOM2RDYqRIpGwuuFwq4Rgkowma45ZH0kkGoDg4rZ+g23AQOUyT0VSG/WAQctvFLi5tkziH6r2C+yT4NQDm4LManwWk3exlD1uH92IkJOwwo/aEmJ/xqCrYeXfT8mfpykK/eKIWajR00UoaBF3RhzGkrItKHRrok5bYORDrOSAqhYk9/00bUItFfRiMPCNjh9EWw9vKYqvJ5kdPlqGSghzvX9Hl5GXnBgxYThzZ6fDrYeXlOXOVrwOlSF7BfkRg7KPejhtdlbo58EkrUu0gKtkPGXg7PTFq/9F95ITEMJmUxotA2MKQlvBFYUtiFHz8DWw+vQCS92hTItvFNZWC1CGnEbAvZHeElmgYZoFl2GRwJDuIdY9rKi0TYIEjZS/a1mr0G6qoES9k94xW3G7Iv5arRb2RmE+JMK0wanL4Kth9f+CC9OEBTkTGEhnDiicVBxf4W3wMRwgm7DfWLDwRxeS6LCOu4y4Ms4XdTMLy95RAWBV+VsRlG/DZBNP+nmTghfrrHBR9w1EcQYYMq5NlgjaspIIRCisxe1gVG/awRffiWFl4Jr3YqQZZuy7ZqRFK+GVyGKZHjzbFO2XTOSIg4vwzRCBQ+jqN81gi8fmuHNdFtMufDSuk54464ZSfE3Hd7oxHRt4EKusewDkXzFhDdkTiq8EYzCNcZUziAmc0lUWEeu9zm8Aog5ti9PHF7fIwfvLOfV1nRtEBXWWfYhGN5QN92GguzNs315ovAKIYJ3lm6DYsI27E14uWuMUNlJ2S2Qa4M1ouYhGV5bOQOj4Daknp0ISOkYYqC1xNq98shpXdcJWWPKRdotICGjFbVBbqQL2VfRwPiyAenYnDVaF86W5SQhB0oLuSopF4VuxUDrbNeyyGlfaeF1rzwy2gMb3nzXMgQgq8kz0jpGA+FVZMsE0mkTY1gXtHavPHLaV8PrQboJwouB3Esp5LR/U+ENltAGQaNu/0bDmxcU2TIhpRMEN6+GF/BiXlBI2a7znABvkTYEZKukXJDubyu8Hl6XNUrZrvOcAG/R8Iaybh1SLkh3cMPrvmMXw6kKHEkpUU+QN6imsEYCiKYKup1gf0mrVarsVPwGJg8xZ7gJwSNLzSFhcSpvijlSCrrYmi0CqskbijHZ8CZhLSo7VUElUWe4CcEjS80hYXEqb4o5UuJ1WAVkioBqnCFPyGPKw6uYwK2UVJfnxGRClppDwuJU3hRzpMTrsArIFAHVOEOekMer4Y1XAZkioBpnyBPymDi8clVioQgJi1N5U8yRkurynJhMyFJzSFicyptijpR4HVb1oATHK+CL2nFz5GytV8ProATHK+CLOsNNCIq/8fAyWIytCSjB8Qr4YQ4aNilB8QoJbwJCzj47UeSC9OENBs5qJRbwvZrIskmC9Y2T/Zs6Yvo2FMBa67v1gDglXbMcjyIygfT7N7zCJFnKeTQWXq+0VveONAWjh3hohbcA3kpk2STBejEKk2Qp57EfwzvFmWM5HkVkAulfDS/Bq4ksmxwsx6OITGisax7eSmTZJMF6MQqTZCnnkWtDEl5t7a+GV5gkSzmPV1Z4We+NELwygUMzvCmm11kjySkygfSvhpfgLcRUdhaW40Fyikwg/cEIL+bEtFtRyV5jir+fqiq3wTaQLJwW9YgE0B10qcD9coJYhUJwStkwT8xaQUtYsZIRkXnjBZaERmtWskRGMqueSH6lFN0wj2S2RmRRMoggW1UpheuQJHR9EbBlgtdLiVZsYBBJtqwSCpVFcjJRmMmSWKB146SWAVeTK+krAz3WaqEK/iUqhSuZQ8w6fak52wa3QRUStWhgNM6t37dWCnDlRrN377vmiLRSc/3wCpmKDlJQlRbUre+iWgGWsJI2iA1rcRvawIsIYQN3cRtYzYKXiCRrJtNCK2dRHm8YwhKabEkZ9FJydZjDJNmyisxSgTZBJgozWfIe1CpMhiuzXV8ZTHn2qhBvUEWJGRiNc+v3rZUCbFnJ+ufKhIOSvf6Vgapk49z6LqoVYAkraYPYHEMVUhIplGlN/shERfcSwUtOJrLKWImKoEZeE5iGsvwxWQFBZNVoQd3K1qsBqYnVBOHlRQSFq0kb9/KQgqrcBlsS3TbAlUwbhCkvNe+n7HUaQxc2MbKuvXRgslcZ7pVBog1mG+BKUxdeFLgbqhYZaxJ40UpsYaMyGaIUmmxJ6fVaohWLDCLJllViozLzhCZrEnixLHo5JoPLzBCtt4SCqrRAFZwjUqkVYEmK3iHWRHQK1qkUyrSWxinZqVnwkpOJrDJWoirMHJTlTy3uPrEni+Dhihl1Ei5RgFw1Ekb5VzlexX4HDrU5FjFwIDLHMlME8hoHditmR/IlFpwWUjwMKQrb5eDoUTVT8O+KCTEZ66hWrlgXE7mNfAlZgs0rkcPWrzxQh9bBIsWgdHx17Eqy8RVdWQRbU+VQw68MsuVXBDjYDD0WBpkikNc4sCMxO5IvseC0kCaRvXH7oi3DFKLsdRA71lGtXLEuCtugbo2v9ODAOiVnQYeAM4dEzSApOqUqeMuClpxSBHHCkghmrRrPMAgqkrQl9JIVm6O9KtTkoZRYqUW1MTKEGHanQeC1tsGbwtpDlQW7gLdk/AXG7Ei+lKk1JdmrJeNj/15WBI4kW6xJMDXrZC9giIRMEchrHNiFmB3JlzK13FmcUSdRP5sOWVDLc9mraZ7qkirVEhFMQURai39Cdh9Bo+zYLjtKtIGjTfBHzWwIDWfvayCDajRZqKPAmJibpMBWGhkZHBgYHBwcGhwYkvUArQe5qPoBZ/IcXySTF6CURZU5twAVsUdSssaQRSAzE0h2a5BZMzAwMjxcGh4qDQ2UhgZHZD3oBVqbZaA0ODAighbBjMlaBWtWKpMWEFAkft4t60MVuM20YTDwoS9X5H88ctAZIhYcNqhzR62Amkf9zMnB7CsCkrVe5ihSJvE4cRs8Q99GJtFAGxTKKJfK1Wq1VKZ1GXGvVEgolWtUFCW02FakWCqVarWaUyoHSpXZQbkCZY2UNa1ewarMnstlrk584rIX2jv7ZAIz4QRM5x8OyQlZeHcjw5RRJmd4idKM1pxjJs0ovT2TlLyWpaHslaStl72G7KsMDqC/Eu0UcCByR23iw0eQmo1xBWZfEYx+Mu6ARtvgGXWofsBvqA2hI3XpapREouylVObEQzpRjtXKPnspxVzKYV2jpIdMbKOUbGclZy8VayVJafGsTJ/SlL1k5KIwVWl3R06wJgec6ZWhoaHaKJ9rrBUOtVY9074Aqs5nsbiK2mDWpg3UWnS8VJI2kBJhQbGG3qlnUjNHz2I0lfxSBdsGmERJnJGRkcaOHQHEhjInQGokManMCWi0DZ5Rh7q/snc/dy3A7MuBq49ixNZJhZtgFExI1ErrRidF3sSLcpyel5xbgIq5NtDak5GKaDZnL685p13G0rmJbBcl1KSMzyAYpEjjA/NpzacAM90ZJHzrmU5wVQLqBLujs77mlWgSK6UWNBXWNHK4spmTrGOfJ64Pqu5vQ1vkVJGCC3hjNY6uvzRj1oJlKxcuW7lg+WpZL1i2at7SVQuWr4JAa11Wy0KcZVivwoLifE8mqwirFqxYs5A1kdvlq+YtWwV+7BbLGrKSWyoGMtzGzPnLVi9aufbF6TOfmjn/mUWrn1mwMrssNILIXpNfLNkrk4tlFpHz3haufHrBymcXrf7zjAXLVqzEuyb+nMGFvx6y536yQk45oVsLIucq2BHEy/FN4LjOVLQhUaW+26J9QI+WdnZ24tLb1dWNwaSnp6evrw/DSgeUw8NdXV3Dw0HZ3dU9PDzc0dE5PDTc093T19/X3z/Q1d09NARlB9bd3d0D/QPQdvf0wCcxyQk8D/b29vb09ELZ2dWF0QquaHe9fVgGee9QYncggAagCpSyu6HhoW7w+uC4H6ZdO3c+/vwM5AmyJaRQUZr5JZ9vRYtlZsh5J3XICyifsX5y3vKZ8xbwr1nSpx8a/QCbRYTk8copi45qEkTOZU52v4z9nb0JBKXuOmJNtI+J7GDwzRUkJ9IJ2T48NNTL6SSJisTr7KTEo+wdGEBKQ+AzAko6BZCK4CLhIYDJTqAcggtUJyWndGdHp+Q5nyzkbRhKTmnISF1O6S46rbi6KPsHBpDn2BEx+YwbGBjEmUV77Ondtm0brpTgQwnPtDucQdQGOlkGBgdAw/5ISafqcDe1lk9YOMYJS10mPp1BdMKiEQPSBZzgOIuxa3S4f4DiIOca1jijeXd92CN1Vs9itGGAlLI7VIeJT20ZH2R3u/fsoR/5ph/Tqn8QJz5qBkTOOGwoe3WrmIo2JBCU+yV7fX/qMXO2Cd1aEDkXXgWmw2jBlq1bX5g5d/HKNZiN0ITEzDFo0dkLph+TmhStQq36kyLRpCdFnsbLQiVTGxYuXz1n8fLpM2aWyyW6MPFZTycLXW7khOUrC1/45ITFeY8Mp2sQnx24BNEZNDCIc4HPNeJjBODxoV84PJLwGUQnbD/OLxou+Fyj3bESpwZ2h7MS/HaMD7Q7OoNgxWkolzycsNgL5vft7e2YLnPQ5YjUxwR2uk9cDGNMXJDyII7wLBsykqM0MrJwxeq2ynhbeay1PIZ1kFlQjV980RBsRbUWyBO7FZmLebetpbGu8fFFq9Y929Q6p3tsVjsvHWbh4kxe1JQhiMYvXmNNIvtFlO3sU4peKRqv9ybThtldYy9t6W1uaqKYm+Nlj0UhcIQa4hGpAaKjkdtG/Aa3WGsFbPywZiQ7BgUpAWPch+zNQWj0K4y4ANOk4ZUB+rCpfc+evyxpmdszNqtt1GaOz6VokTSTxWusSWS/iHLvstebvNwxNrtjbHrb6NwV68b5XyMUHBRWc/bWPWoEoQitLtkZD5Xs1W1dEEmIE7q1hDpkCSoyB9czXHIkjV4RQGtxzdbCfkHuo9t9BmYM/F9wXOhz2KvMqUtUEEd4WKcqvCKzl8F8yWNL3ofwumdSGXXDW4N1fBxz4jVbdnSOmQmJmWak5f02KUoIpgg/7dXxHf3lxUuX7Y8M338oE+jjRHsQHeRgk97a6hw5uU+cObahZH753oFVSY/jwU3GTsmBOfH8ZSt3D9d2DVV3DVdFwNot1d1ShHWINSpA6cmOD1kW0nNR3Xo/hhyUEVkICbcs7xqstFfHFqxc8+z6XXM6RmftqcxqrdKyh9dumZlS+mUm9LJ4ZYbpi7G+vlvSG5MjV2a3j764uXt9cwuyBIc0OgbZoxbExg8xkis/mMQ0LWFTx62zBEYqc0gIDAaKtgHOup+zV0tmgxeT8S65pp/ZINxQsZE26suLtCJRtyS5reiUpEW2y0aVXE2UJDKZtyw4SaBlkageW+hy27Z791OLm+Z0js7cUzaZE3LJLmSNMy3L9MVYvzfZa5c9oqzMbq1O312Zt2It9UW6ovCCPWoOqUOMYuo6mKFpiciZ+kDWbRD3LnsdMswg7qfsDW69UNw1zqHxgYHBKt+GQUnTyW2ZwBVI54q8dkVZqZJF0tGiAmvI7rTCcHbVsSBarsOybkUZCKVSqb+vXxT+JWRlcoEWJnklIS7zhiQuMoNtvOaS34iSRFdUvVTjAouscnrH6u3tpbGEUkRqBaCYGveCwNASkWMDofgQTyJ7MypChhnEBtoQsDfZ69FQ1xxSbUBxqi8rXOTPWLZu3bZq49a2ylg023EzjXhCwhMPFdxaiplJESlJn5i9MH+vJ0V7Rka39gwtWrKUukCJiY5MkL1pJSRWsooKYiKtEnirVrERnKg2JamaC07pCdiOjIxgUkxEB64VgGLiqKkPDxV5Tgy/VIMXkx2GDfiSnrck2lTKAxTXEMzSMCdesHzV7uFRiv5wdQ8dFZKxsKyClVlQTrSMkDIwR4yJis6tE0jOCjAVtgFr5AfPidc+s37XbJpD0Jx4Zlu4couMi7dVej0JbomKsQex2ipeEGXQxBxb0ZErsztGX9rcI3NiyRVeOaSOmrV7UAKmD7HQTSWIPnMK4U1pt5mars2eLGXPcuagAFNoUkXh7YzATrYhAVBC15gfwMXxsa7u7mrV39pxtOKuCSYOr68GYYrCi3xAVrTv2fPkkqY5XaMzW3VO7PNKFptmskRFQ4YsVlvFC6IMmphjK3qyLL4K8nlWG82J565YK/eJtSeHZHg99k/2ehR1LQdQCrumGhMI8cbFfBvGabqmlxyqU9QGocvAw4BwUMI7TnPi3t4+VhhmsVuHoq7lAMokwgu3jplsA/cKgwneYFOJVbSK2iD0AxVeXXtB4MxB0ZBbQXHm5ADKVGWvB5cnk73+qHlVHoGddzs6WsOQiznx6k3b2qrxnFimGTxv8RMPJ/vZS1B6jhNo9iKasOzzpAizZ5oT9w7znFj6wK/62csImaNxEMFDzFMZXoDLZBoeGvIDlKpp7QWBCqYN4o3Lxi3mxIFjAJ3Lusic5bpyNALkQNfjUomencCR26NvWaJDSItPAhKMlY6iK/ojHR3yUZiYXCX/rNS6dglVvPO0W2gwa++ojS9cufbZ9bvndIzRHd/W2kwsbbxuraEoi5dFEI5oZnmh7uIrWmWdJd8GXqqz28emb+pqbmqiOXF0GBKHhJE/akVMoP4h9jreSiGiZetEfIGKdarF3SJMlL0RcpX91lsStRI62evQ8LD7uDNVLeE2SRPs3/D6OfFTS5pnd43x5x6UNj6LfBIegOyldU6PJVZWZ7fVpu+pzlu+Nv8bOoypDK8D24QQ0bJ1XNnoVaxTzbRBcXCyN1al6hBIL7aR4ZFqtcpiEZINzoNJwoz42cqubPQq1qlGJdGgtcMjw74YA7pDJbwKFoeGhsyUIYP9Fl5vUcEws/vkUqQqam4yYlmuKx+g8BY2lrBfwitDLt0n3rS1vTq+e0hmFzTl0PlGPE+NJiR+cTMTO0UWoWj24pbiSZGZFpsqo7uHqq2lsW29w/TsBHch3zUHo1exiAnsl/DS1jmmL/blBqhsNSB2W9CsUZoTs8BFSyJRI+O1aZbA8Z3GkHhOXC6VaE7Mh4ru5+PAjNCR5uPkj58eZskA5AorOWk8mZVCdoJUUTIR1K0zGQ4J3lrQBloPRXNivoNlL9iJpYHLfFi83hImJGfkeKnO7hh7yc+JzQFXUQcFe1ykZI9aAmIy76OgMa60pnFAYj5zLJkQRMc3xkYgdF5rTeOARG5DiIMbFDMsgeN7sm6B1GAqqo6Ojpo/FfctvBb7I7x2TjzHzIn9ksyrCROykczMcxLMeKLMQnV2a42fnVhn5sSmhwB3TbcFyJsOneyN3RoHEKXk2gopyVJ4kkqWZGQDr3VtSNGcu67ubrkNkyelq/m1mg2LxP0WXkcapu/MdUMgz/kTE6LTOW3d8Do4uiWlK3jRdc3YPEjHXaNzkweTWg2CJRdV07WaDYvEqQpvThdXi7tmyBCdzmkPani5lERRNV2r2bBIbDS89B07d5+Y5sQyu5DZi0xvrEATD52Q0JzEzUNEcGSZveg8Z+8nRYGsHPZGxdaRaE6sfTokw0tw0uDgYKk0AsGYTAVBXC3OHEMeNb9PHKkJ2rhCwB7qTEBGcuh9YplxumMjR4XyQI4QH2MxKc0L8VsoCMokgY43EZQMJpEjJ5FbzyQNeRCZF2oDPzvh58SzO+k+ceZyHopteuOW9ObSTvfD3J22oGFZizmTXTJkkZMCcagBdJ/4xc0961s2ZObEESY4atksMGU3M+FKwU1R5hhtFhO2wVtZiDxRQRRGnehsyq1F3TZ4CwlaMNVFHKdLr3zcGTBh1zxYNOXgFlJwM0XhjebElM80J6b8qZO9oow1lGxWaUx2yZBFtiavIQ43wBfdOr5PLL/WlOqaQTZUUZCoED6DU9P+z94IpBGtsRW1IYKhAHXb4C0kaIE3EsNQMYcCt8P020z83s9facQfiwoqHOzwEtCG8WqlMjLM10hwJhteiJk2GKO1kKAF2bBbQ84i49YABf7QCWYmMS3yRIWGw1sH7N4hT86H1+yBIEWjnKrwciVrIUELhuHNeWTcZpHtWuSJCqIw6n0Lr79PzM9O0JzYLjITJQFrkmlCIrLORjBjwTLEE5Jo9mKEqZ0UYU7s7hNrHywOsfBasvxGmxYESjN7CMjtNsL4a5K1vE4EbbxqfQmbqA+ObDfCMPeJ9QC4p2GQDS4huMjrsPBR9LKSnYZlqWjX5M25FYc+UeziyKENrsgmzImr4wtXrH22affsrjF+zJGu1vnFX861mJsfyEKcjImnI5HGuGrQrV9oTtyBOXF3U3Mzz4lxAOgYuOMQH0MD0STNRWRF3ux14TpCW3clnTBz1FwEnurHdlPwYkbIutUSNtIGNcgmKsQgHV4cWrF3u0cAqcwq0VuIJtm1fCnSxGaB6iYf3jAnXsz3iVvDfeLCNDPy/s7esAS3PCeO7xNrRw3UINvYnC9FmtgsUJ3fn2xVWy+8fu02OdBULZe9Bl6fEbJd0xI2k89egNvAoG0qcyKIpqenuxR/hQWIqVSKNFlPBNXt//COjJR6e3pZn62XEeI2qGDaoAbZRIUYqpv4xIxgyONdXV2j5g12TKVSpInNAtW54MVds22YOLyWQbpDJXu9kbYNhjfbBka+FGlis0B1DWevzom3bVu90d0n5hkLCTLTcIvOXrzVTUiUtheTIn52Wd2qqaFJEc2JezL3iW3XIkg5joMiX4o0sVmgukkPDmQYHByU98CSE/nszYx7GWRU7j4xQasT4JUlz/Yu8+C7D1yXV6yilUJaSXNi9zwx34XlQ+WOOhY+YJwWfFDlWPL0lBY50rxmJZOlOjskQZi01HUrCeEW2wZJGlnvGqzIfeJn1u+e0znG35TXyzxdswuu8XZOoEIBGfo8WZhhL04fyV5jyLzwnHhTV5M+O4Gw8yoClUXnb0V6StEhJiK9PDvHg4k/8yPZuuVXsg2C+plDNYmgCGZPjmC0tg2MXBsURKzbBtKaNgQwp79/QLunVWgj4uTDywVa53jaBtZbtyJk6aFsu4Z26pyYn52YuadiMkcXrwmCS7PMsh+yV/XGLT87sac6b4V/npgg0oEIL6PILVlcLebE1UiZzxzPMTyF0TbcBiLWbQNps21gSA3LJHgXJry8HhwaqlSqoWUOKGgbtJq+InAb9Jrm3dKKkGuDx76Ed6xcrgwODlFJq9BGxYbbQER6cV1esdrAtsE2Rmpk6cFFCK8UaTXeP4DBxL3BZmTIvHY2D27DlIdX3MbwHMsTGG3DbSAivbgur1htYNtgGyM1svTgIhNeoKgNAMeBERxYiJb1k3BL4bXfsTPPTuhMQyYzNAmJJyTR7EXmOXb2wh6UKR54TUqet/jqSnaC07sq7FY9kEZ2F56d0E6GDhopFwcjQTQFIQd2ZGKIlvWTDK8QhVMqlXiAIgWv9jJ7pWt2TgxkKuTqOwVvZeVFZ/MwisycmA4AHxg5Nnyc6FDZw0M0NTHTK1nDgjmozipkFUjDx56KxPcehENuHZ9Mpg3u2Yk19DxxJ90n1gu5u8D7K71exV1RlwzZ6SEk+I6Z9cmmIDuNr2v5cp/YP0/sDkweovbGwqMW6pMk05IcuRDEzKYlg10YP0m3aUXSn0WmWqFb3srKi86msNMwhVMEQ29fr/mquCBTrdiLtyTC6wWPjIaKHI4sk8tGSaK8zJyY8jn+jp1LJF0kx0x2eSHBd8yJs1eYnpbiq0Cye3Yi3CcGTNcImWJQTFF4HZydt2HFa2fzSCsOuexNIWPlIv90rv4kvkMqvHWQcLtfw1sql9BmCNQ65Weqpb24ray86GyKKQ2vgH/cwz+I5cPLU79DLrwpZKoVuuWtrLzobIr9EF6B9xIESHvTtUbCO1pz94kxJ664OTHNLmS+gdkIT0jMhEdnL7wo001IwuzFmURQJXPErdb1ejL5SRFphCPWqA3mO3bUAQPpkOuoIC4BTnFgwstrvLCM53+LzYO5k2uD+45djJwqEw7eS6KeAOQcPfvshB4ws7hDTgeMNP6wYa1k6F0eCMHXoipy1MkkFQ1TlJorObdY1K16I0GenZDv2HXyd+zsBZsXaEQZ5gr2Ap8p5qrXWwrc5tugbmlNzxO/5J+dQNDDYcgfqtwBTR01QVLLSmtROU/OakLmJLi5NhRSAVYmLDlVYfYmKk+mDd3mMkYIp96Uh1eRV2U1BeHF3sOcWL5j5zLHZqyVo2I+8YoWMJmMur66CHWzN7/4Zyei+8SMAxbe3I6mLnsZCUtOlXM7Rdmb1SSyF85GMXb39fWVK5lLTqFbgJUJS07ld+QwReEtlUpoM4SEF9165NzWCy+UGbUW8+SsxnQta6Igj/X09Jibn3l/AWxLEHKq3I6mKLxOk7fkVTm3+z+8sjEo7FoSbEsQcqoJwoujSXPirdtWb+TfncDUws1eaO5ROHsx05JQdLOXepMiJRS41YpicgRDZmX6O3YQDr3wOkDGnHgYb4NVoUhQBU6Tt6jK3ydGkTQJoofEiRCikK9jSiLSmufEdJ94lzuodCREtoc5Okj+0EaHTRZ9Z8NWMXmC8iO3EGJv3mpohjAavmPXRN+xk2cnsGSu61q0Sp5kZPRhfgCNUYqQkUPRuI3IxgktWqQ58Qube9e1bES0TUZZgWRfTmASh1hUWPNipy0pyWHSmROUCbJXQSA5psTYh+wNSi/Rpzfjg4OD/HGnuIYtJudx8MIrA7Q+T0z5zPeJJXPiXEKm7VP2+km2cRuRjRNaRLZuVePvEyeenUhjisPrMLWZw+CSV0EgOabEmOI2eMkKJAeKA/Y8PDxcrVaRP27SphsgX4dLStPlQIaXP1atVCpoM1yjwewzJudx8MLrMSi/xYaWuCgD+TpcEhXWvBzQ7PWSFUgOlDwOuTYoM1+HS94oi5YJQXIo7lr22Qn5pxs663CCnXioKT97oQmJ4zhBaLxA4EVvJpI+nhTRErtl2Qm8iBzuE6MPIa+wlT65Uj0coPAq+IwplcvVagVsP0DpS5CQrEByoPCc2OyW/yLIkxssiaB23neWzSW3Aj1YeU4sv8WmB0APgxwkd4CtgHUQ3LGUtdAoCUTprUFgmvCVHLt1ZJZZ8DQR5HniFWv0eWL+jl107XeLKGUaEV37vWCv91jiO3NBH8ueLEW/ZAlBkO/Y6f/swAEKR0OgZaMRGEX6EGMbV+ISVuEQJ9y6hDEc2YTkDmBFWHkk04zhmsU7YTtpMsQJsxfbuAqXsDJdk0BGgIaU7R0d8nVXKjAr1wLVC/a+DRkGYXLhhVrnxO554iiFDn72hh1pUe4ThzkxdTfZNY+DGN5o5eHb4NrmETnRmjnHIXv3oWtMjpHtGkFrZslSvbu7x3/HThmma5lKTmXakGEQ9iW8jmDa4LwpRoZHurv5t9icP9q4eopDILxOT+js6AwfOpmuZSo5lWlDhgGYcS/euHBYsCKsLJe3eOV65AUVya3REUxkpLqWfdewjatwCSvTNePEoSi8hV0TpNvAGwunMm3IMIB64Q18OyduM89ORLMXTEISsxcmuKLwlWwFzze0RiZFtGTa4Jxkf59YukLrCcIbYerCK4KjRuG1GB4aLpWi+8Q2ex2KMod9Gt1rVJYNr0N7DVhjSCnoLg3Vc3lOHD9PHI6lHmbRiBLHzwmBTwsd15hpOBBEQ0wcb/tOKOs/7zYkBxb/HTv/7IS9YNsFF3V/XZ9wwYV/QrKdLoicqCITiD1aZII+T6zfsaPQ65F2B4HhCpEyIF/BAlnF2WFZsuF1ceYIILq0jGG1qUZbxt60gWErJKB7sCzZ8Nq20Nv5Z/blMqYavy0Amws5+zG8GCLyc2LJnER2FSz7K3vTbjPPTnDX8v2PwOZCzt909honyjKauN1Q5dogFFcIYK7cJ+ZKWiuFAx5eXzkwVFWpVPk+MRSOxNtkGxisLrABUxPeAuD6jdDJ/+yo08KDFt6AVBsEXEi2gZGvEGG/htchqpDDvoc3ixo/T7xl69ZV7tkJnWbYeQiWzMyE1zop8hwRJpgU6eyFCIYjgshcdIJTqsDNiOfEvv8ThzeVExZTH14FLOPmf3b4AYrXcbuZijWbhKVwZFHSsxNxTWeJKhVDBp0MOVcXyVEaKdH/dubQ75b/cygyDpJLCypaIRx7PuT+cJrj6jRxlujCbsm58y87kv0qIVQUt0ozv8Vm58T2kp+/DSZLxIkFu1glZL94pTf5tdVklDSH4N8nXqv3iTXyaYi1PscjeYgZ2cwRNOSW7mTTllcN+HG5nsMkszc2NJa9DqYNLHR2dmZ/FlEgtEI/MfZ/eGnr58T+t9gazt7kkklIv3ilN/m11WSUfjH83HfsfMdF8MX6mPrwgpENbwKRaaqyN0ayDYV1C9uQhdDEN6/NfWLnwrs61MLLFUdGhtFmLsUupBTrCjFhGzJo1K1ZK6iAwYTuEyNqKHnrXoY3bWSA0UB4IxzA7K2DRt2adRIweWsmPQwmn72mBruldzjj49u2b/e/O+GmGTr9SE1IVDO1kyKr0bmQF6CXHfG66HniAFHViQNM3tp4eCd0mwuvJeP95MgI/xZbwsWkxz13n3hijI7VRker1dFqDeuxas122LXXec0ByZG4T2wOJyvpOAWrF3C0AiFiCkcFnwFBiGvJsRcT03L64Nb+7sRs/j92dKnGNMLOJDJFv7Txra/YZIvRFEGYRWQR3B01LBm3oeIe97sTze4+MR0HeywSx4UPXaRPkoAJD3ExiFy3At5DOre8m0A2JwKQb0NdtxZCjum1MaQx8nlMsrref2nOgl2NDwwOoJqq0hX3PrxFzBwmJFN40WCdE9vv2GVySZbJZC8Vc34CWYRGsjfjlhqQ/i22GAcsvHXpe5u9DYPIRRXUrb3VlgcN3chzWvhHcPPkZEUeRsbHh4b4PnExDp3wsnW8XCkPDvFvsVGxCN5IQhGzofCmYcnJivBNepq4j48NDNjBJILrqFi5CksNQMh16UXhjZFvw0TwHBImcLs/w+utRd4nCC8Oipw4cgZFx0jIEd1D5sT6uxPueeJompGckISbwRHZzl7YOvWTol32PrEERXuW7KAJL5NdIYsJwlsPQk7TTQPHy/Q8cb0BKsYE+5fv2BWci7YyH2ALjJWF9bSit1Ld0shIuE/Mh4QWLcpCb3r0yDmlO2yeFgmSRiSoUhf4MUV6ZMcU3eKVMZlSbcR8x249/29n/x07c+WGjGt8doqQubqbRSYEtgoJonQakYmT0mcEkcUhPX9J94n9/3YOh6boIGXBvInI4ZjKuoBv1CwW0PIgopIL63iDCHvVBv5gK4MxfZ5P3fI6j6Dv6emh/+9FZ2cR2YApE/G8XYQpC68M0Don5v9j5zPH55KmU6wRJa2T2RsLIhPHTHa9PiOILA4tn4ok5L9jV7eXDcXB29VhAd+oWSyg5UFEJRfWCQaWinhGX5eXgxAdHVua1kagi5jr+kRuyd7X118pR7/FVgDvTISDEl5CaaTU29dX98Q0ehbr+rMQYgNuGQ27HevGYCIBK6zjDSLsz/AWItjr+jb6urwchNiAW0ajbpk3EdnbReDw8v9nzgDXDiZM4BZHE+To2QmdvWBNQmbS4qxhViPKvZ8UxXeOG5kUZZ8nnjBmBkotrOENIkxR9rKZfoutVPKz7xSybuq49b87kYBWow2lwuD8prZrH9993r2tVzzc+9yysQpmA/G0ON9N10pUx5x4/rKVfiYaHTB+E+OONJT+gMVk/15HyLomjhQ9XxdxG6UUkydqA631t9j094npPrFcts1NL11yl39aZAJhNHKZl/vNscCEjNuiKUVyX1hIr9+xW8f/21mPRTgguUND0LfnORSoFfWthEm7TZwgCXKkmuDuR5GV1PLLkeWdXV13Pb/7wvuxdN39QmVXN8XNj3dA6hxzZtr29Pbpx51pcHgn3bWENYMCRnFFzIj9nJifnfC/TzyZ7E0unMD0jjHO53hpIHvDImS9T2x/iy3bv0MovCoEJMiRqtiZoMBeXC3RBlbSvYvxgXnr2655bPf597Ve9qe+55eNVTNDd9It0hrnGJn6+wfk94mZlyAbFYkJhkGBtbjS5MJL23K53NffzycminXIGRSogXwb6g0OGSTV2ezt6e2tjaYHE1OfK4lYgAJrcaWJw0vFSFVcECRUhAI1sP/D65AgGxVXEhHgy0Sltbf73pd2//aB3Rfcj+tFaVs7XSaynlNu5T6x+d/OPANJTUi4yDfjWDDzEBFUWWdS5Mm6Nm6jSVH4oeIsmZ1n5sTcq0TXpia8KRRY61Qik/4+sVwnsplDuqh+KECKLAKeE4daMYOGRH5kolzZc/69Te8/vvkDJzTzuul9P9/246tL7fTMFocm4ZqhbpEcpejZCfnfzvZo6UJHiw8PHbmcVRdP4LTYNVTdNVjBWqx+yquLIwchWmKyc7truCb3iZ9Zn/0tNnfNji7h+YWYypEZQ3Vma3XG7vLLu0rTd468vLP08u4yNEKAAD9FrmTx1oI28O8Tb7L/29kcFU3f3GFSRfzdDibrRCTAFxP6LFkmrPyhSpi7ptvg7W5jisatCDCKPhhYUiuvHVQnXTN0Hul6n1my4RO/oGTmpel9x2/81Fn901f50DFIyMWBvXHXBgYHazKHTnfNKyYZXhaJxS/I+x7eMCeWZycy/7Mjt/gcyyhZ4GRu46TdXZF8ppTeVeKv7mm2E9/W9ULdM8jIDdwnVsXkwhsbUuTJh9frnXfaGLciwCj6YHASNkFHcPrJdM0AOtrb6GipsuvXf+RxW4fu5vf9fPvPr6t28L+0aCh76XG9inw0yT6xzbfBlb0gSJElCqwjUYwFbp3CbUwxHV4+auVyBW1WHW8cx0FLkwkvggnwhplJMsraBtqoSoQs0ymUyD/sSBJe+Ta4shcEVMqSpZRqQ2HXnOC8p8mmaw6GH8HpJxHeLJJklH0bnEkbbdcMFWnjXljTJtW1jKB3iPtnrN70mbPtZWLDUaf3/HURvaWkcyeql3Fb+Ftsk5yQYLGTIlVizhNPe4xbFPXusuc3MimKnp3QPviQ2DVDRbcxxUR4GbJVHUHkmOx0k8le+T92/EEWMZNklH3mOKiUZWJOzKqs2ZV5AtF2zWPr33Pchn/9RcuRp7R85JSWD58Euenwn+446Ua65SD9pPznataVk+HEPjuB6OvBcG+MaLFvkkTw74TMkaMirUneOVjBgslr99h456h8tVPm3MZJXtBd03smFgyBZRLiZydm7nFzVlzI3bU8s+iVHks0n6DJAZYZu0pYL+wbW14aW1Gm9eKBsVntozSTMNOI5OK9qZBuQ/R/7EI+EeyxSIAs7kjJJg3vxpKdrFstu2QIsGRCbOWiqiJLhoZyrOFStqIImTZAHB2tYj0+vHJT85EnNSOZP356yxEnI58hNB9x8oaPnzbStJ2o8hCFVo7OY4I7szs6Oqs1miu4MzENsqi1mATIGcS+cQQBVtKK1LLV8qTDKx/k+Tmx/90JvxRkb3KhRMVseMbuyrzusaXDlM9YIMztHuOZsWSy5nM2e4uXwKQ2uOeJl0/w7ARp1ZImKNiIlQulK5uNEw6F7CUYshMTKpVopSreyNB9+cNN7z6uxQ/dR+jQvfO0m+lJIZ+4WjELUff29MptmHoYpVtHxo1169UiSJ5rkgOsNKxIJFBRVZElQ0NZNCMjI136W2z5iqFSkLyYUEVN9fBXd1NDEWu4lCc5kIWtXV2d8bATY7LhZcnBkglB9BZVWZLroUe97E2ALGp1JEMOIku0UpUv00a3BrHGkhMgi1qLSUAcXhwIBLG0cRdmwM04ZeQygeWjpzV/5FRoBpe20M1iupoQ3a8EqnVz4lWb+NkJmRO7+YlOM0Tw8xA/M3Gm/KQIy47+0p7SWNfYOBb5lTc7KXJM0pgZlDEVtyHzHbt81zIgi1rTJCSRnC8WmkfZiiJMLntFHh52v8VmbKYGo+HsdXNiCyioYeN0F2F8rLRlDzKjBcnxoRN3nnnL4Jw1rZf9iRLlE7/A2ya6tYYaVXkQk+DcRW4RCPf7xOF4UAbIPXw/kWWNP0L8dsfxPZkPJN4hYTbcg3dy4+PLN2ybvWTlknUbMJGVilq90G0QbBu8hopmTizfsUte0VUZTyNychWzhKVDcFK7a/riX99832mXXHPmH+687omXZmzrXzI8NmN32ZELp8V+yTdDNXvkeWJ/n9geADmi0RGRQ5w+WoKsyr71EilXCQraD7kWGEaOrEi1wUvZSg23gV8EEwcSMVEYG9t58rTmD5zQ8tFTN3/xN33PLu59cv6mz/0K02Lk865f3oHouQeLGbk9eA1ORXPhEMGyJxdedkVDBubZOMOB6J/kAWTHem/CK2NT9B07nzkTZG9ObqvN2FNd0Du2uH/ssWVbfn/fn0+/9PrTf3/9Rff8+bGlG5aNjM3uGHW/f5zNZ3GSdJtT+mcn+HcntNe2m5MLr9GIlKsExd6GFwgML2UrNdwGz8yO40nYNmg1HrrHS5t2tXzsVBm6d/3ydgzde373oNzRaH7/8QOz8WZjnIdu6jZXtAhuR0ZcKnpWlt5w19BEN8WsVCqlkRKuC/nZl4PtmoOXspUCuVKtuO+hxwBhkuF1Ta329/d3d3d3dnb29fXBuejDlVfcOhi3ssv8fuKu0Q/e8f/skHKWHsjOlmWQol4bkojbIPBSttJk2uDsOUYetg2umocX63UNeq8wFZwYyHmWItc1fj+5+9d34YrQ8rHTN33m7N6nF/Y9vwTXi5aPndb0gRN2HH89DRR69LPuBHI/MfvshJ+H8HzDTFW5yNMP4eg8RMjKqe0YKO8ZGRseH8e0eP6KNXOWrmze3dFW0qmLVndkXvsZlN5YjNzGkyIsqWcn9iG8fI7gNO8fGKBzp6Ozt68XgwlpyUOoSlXqHeIkQhuwi8S1MoOgESnHgILbQM9OkDEiuMZWq+B03P5s0/uPpwnEl86r9g5wN8e3/ccVzUec3PTBE3b96k70nD9HqANKjvC7E3S339/wt2nBRRawlgOpNLfwe6narqFa99h43/j4rOWrv/3D4/6P/+O//8//7b+9413v3tY3jANMN6GxaGLpOu9WNGoVGi+QYYq+Y9c5lrm645otl3ASMKXwswqWlaN3czEhrsztHD33D7e+/fAPvCbG2w5790W3P7Sof2zmHkyLaRfqR6qLf6fJLN5EAjVAf4ttPT9P7PMtMy5Rwb0SsGnqUcBVRNZkfWhjkr4KkPZRCO/NV0vXh1sepErb2jYcfSbdIf7QSb1/XUjZPD7edd/0pvf9HMqNnz67sgdvv93HylQxsyVAxqmR/y02inZ694w64eXnPnFiw2dLS8vatWvXrFmDyzCUJnrp0DQSXnDgKtwn5mcnNHPqZW9YxAQm3sLN76ndPWPJ0V/+xv/yv/3vmsqM//7f//t3T/zFS9v75nTV3LQ4ckJLnNgkcANskZnu2Qlzn3jvw1uEyJoOb0ZNhTo+C3wUwXvz1dL1J9UGoEo3ujpueUaG7i1fu6A2MESJjqH7e5fS0P2BE3b/5o8ohjxPuUW44ae7p4fuEyf3Wb8ZGW9uNoz3e7t37163bt2atWuQ7TW01t09cq8CNBBeNHiYfost8z87FOn6yfCyDh3fuXPn+vXrV65cuZywbMWKFTg9d+zYQR/XYmcMrRJjUuNeR0cHTn/2Z0jFfELsPs2dTBuStjp0781z0uTi8E4Ax9EqMSYV3qLwJMDvJysdfZs/dw7dGP7gCV33T5e87fnrQpolH3X6hqNOH9m0m94TxVMx2wYZcsOzEzTTCPMZmYfohMRMZEngSY4KZCVBHmwYxGx4oHTzPQ+9873v/9/+1//1f/5//bffXXs9TmzMlcWDn8nQVFiry0557awiyx61DSAMVf2cWPtgsBfhxbmza+cuf+4sWxbOnXKlTNEL1QoOTwOHGKfM4OBgid8D552k3TK8N8fR7WtSlWgSD70c1J2n3tL8oRMxtu757YMoImNG1m3f+Olf4g1TyxEnb/7ahaMlfrpZMtTnaewWFfU+Mb9NwaRWDgO/R+ElpIW+jyGmZAYfYEem2Wp7ZXTm0uVf/fb3X/P/+J/0avya17zpH/9pa+9w68iYu1tc7JYFcit6IdPuiCwc+j928vvE/DwxzYnz365zi1zORfCyWzAzqMztHvvv/783oJH/3ze+5Wvf/4+fnXPhMT879f9+/Zuk5edce+uSIfrQeRY9oxnqBrcya6n7PTyaE9tnJ+g45BPKgA8xIGqTnQwU3HDvQHZ+pQ8xgzOHodeiCd2ynTkxE8iTA6dOG4q7xp9m9D6zhJ6M/+hpmz77q2pXPynL1Z2n3Iwk1/tns1ZTN8xHHzHgTf0NDg3p88S0k7i1RW0QGLLoa7Vqe3t7c3MzRg2MIJgQr1q1qq+vj42+6t6HFwcNWeG/Y6f/21kzJwg2/exi9dN3Dq+qjn3vrN8idf+X//P/+tinP3fcL8750VnnH/7RT0o+f/joz85uL/F7PFoayV5Z/LSbi/5/drjfJ7ZoOLwOzk7Zy7aogiAb3rpu2eLCq5JFRBZ4jhOydRioxXoxJtoQIDxa8yu4lcO948QbZehuvfgBGrpHR4dXbd34yTNp6P7wSVuOuYi+Jy0OsNjWhjbQBlPMqj0daM6gooPu2bYhgiuOjIxgionrIq6RqxlNTU3hjWVwyxV810SyQDnXBuiERv+zY0j+ZweXLULXZB17RsG5hQlB6+npWbp0KRq8ZcuW1tbWtra27du34/TEebphw4Z8yyVi2Ba5JeTaANWQe54YpUmHl6BXbQZt6nRNeWwPEm1lw0KuDc4Yu80g17WIjEKmDbzmV64NFq7W3oU3Qy4MLzPlwYmBuetw7rR8/PQN/3pGeWeH/ArbrnPvpsvEJ85sft/Pe55cQHvy5wVqBrfkUJ+d2LZt9UadE9MEgxeaeLh5iAh+CRMSXURJ6y19w5f/YdrbDnuXDLN/9/+kyc9vr7x2aGyM7x/L7EW9SRW/WLfCJGXcBgj07ESPuU8sgYm6lggvbx04vDL+9Pb22nOno70jde5I9ZC94nDizGG7cMp4exrfnIoaTPDeYrcZcBv8706AGvYBaNVyZdsPLms+8hS8Peq44znSVqvbf/aH9Yf/ZMNRZ7R85JSNnz67vKMdYaCP6yIHCvGDAMl94l0SfX4wXI8iv1/RI0SLPa7RAUYR75bkkYl3HXEUEuJ//3//nyeccurJZ5wF+R//+R3b+kb28OcIRBa34k3dssZ5U0EzSYvahnCf2P3uBD8iKVdrdx9LFztd8LJRVnimW/nSf/zsV9PueWl3aekgPXmJ5aWtHf/y0U+85u/+7v/zf/+PZzd2zumMbq3ZvXgZpoxVi7SW350I37HLJmt8iL0tImUzSayo6KYltPY1hO0HMwepY14GKAYtuVXBrQwyZSZ7YtaYyd5A9EBt/tpEx23P0IMTR5y89XuX0vA3Pt796Oymw3/S8tFTkdKYE3ffN534fAPAdC1yRn0eH+vq6qLfYlNEbfDsqFoyvDx8dHd342005sGbN2/u7urGRAGy3CcWZgB75IokxIAqWGx4ZZCyzxNTLvk7xCaXIiGapKrwMnJ4eOzMP9z5g5N/8cianYv7KZnxpm7Z8NgJF17xP/03GqkvuO1BKPlZ+QazVwUjp3+f2HVNEQxAQagKsldQnL0ewa2qZTOp7GUvnpgxRplD8ESPdNewdm0wDmiDEWCkvPU7v2/hobvrjy9QR8uV7T++ev3hP5Whe/Pnzqns7qQEo/d1qeyFiiVc2+Q+sZYduABV/fCqVjIQF0VcJnFd3LVrF+aXSHK8D6T7oybPnbO9Cy/Vw8y7t8d//xsGWUeIisnw0msc5yAm8aVSGS30QEBwpcd7146ODhSzOeQqG4ARtN6mAppMg0k3ff5rWsJW7k+o5qsKQngVLEWkbFx1s7fh1ZdB1DWPqJgML621a3EnXMOCiyARIrIgaoO3RaRcGwgUPmfwDnjk73l0VvMHT8DMZ8vXLqgODuMo9z2/tOlffoYTRy4THTc9RTkbPmZRQTZwG+bE7jt28axDJypeI7IIYVLEsxdMeQfGxx55eT6GVuDth73r2ptue/s73gH54quvHxzT+8SYvdAitaR61q06J/9m5kNKWsx9Yt8tAgpUdl1TRJQYdDjGx9y5ww9LOPhzp72dfsFDM1jAUuQ2HDVVyyYcNcbw0BAPUEgnshuPApBZxUYSAhKZ4+fEAq3ALBrCqr0Dm798XstHTsXbo57H5kDT/eDLyIydp92y8VNnNX/k1JaPnza8Zis1MHyIoE4cyIiKmf/ZwWuZrbpjoyZfpLXRqwaz1c7a+Ce+9I0f/OTn89fScwLX33IbkuOf3vFOzIlxUInJtag6+0+4ZaXoiZBrQ/7ZCbqQu2s5XbONTEt8maciL3SDGes9lbnto5g0zG7HlIJ+euKlHUNLR8au//PLnOSv+cOfZywesA8WqzfvNhSNxrYhc59YQp+CHn13kMzBiirRdw4ETnCJlYaaHIO2iUZoAgqL1/TZrM/wTKqHgnNbH8pyZFNJPPGlt/XSh2iw+9CJO066Abry7q6Wo07f9t1L6TPlI0/GYNd+3RMUQM3nfBt4yy3t7aPfYgt3FwnCaji8tCFfPT09mzZt0oclarXcnNjQ3ZYNTiEoDi8scKVzYsrncJ+YFpdIuex1BLewhlN6d2nJwNiC7rGXd1copXeO4L0fPL/lHe/6u7/7uy/84CfLRkZf3jXCt4pddXHritkzCIvVtOX+jx1Bu+bKXl8Q3oiR0ilU4wy0nVR4GTHZNChjKICyHNlUsl3jLwNJG1wFboOCBQzdY2PVrr7NX/w1fSXoQyf2PjkfR7/rnhdl6N7wyTOhp89/+RuluaE7hBeAK+RhpVKhVqghGd46IBa9xsdwjdyxY4dcJvv7+nBp9M9OOJ645PVehReGUrlM5w7NS+SYAfHGVgqeoA7hJfBFFm2T5pGBgeLWrdtWrFixdctWVvsaxq2FqF3XHCmQ4RBzBf8cizcERj1katHW9klxaGYvmQ25AI5RwBS17RrBkG0bJsxezkZMeeXWyfYfXoETpNozsOmzv9pyzEXbf3glXSY+cELrRfdTVuDccbulrWlDeJ7Y/T5xmJN4WSYktKQmJCxAuXOwgmx+Yvrsd7778BvuunfXYAkXhne9852YOVxyDT07sb0fc2LvluqyHLsVjXBEb9vAgv+OHffCIZ85BC8Uh5fPFIoSkeXUoVTfto3OnS1btjieX+k28icoyl6Q+T0w5sRUS+zGgSomgLJkY+fEpAkuZGDt7Nv0uV8181M1/S8uq/YN4X3Stv+8amTN1g3/egZ9BnfkyYNLW9Au9x1MAcu2jfGzE3QYeG0WPZC01iPni1pFCCQP1Ta2dfchcavjvbXaFddej+T4x38+zM6JxaGvRTnBGbCHk0OUIpgltIG+Y0e/T6xzYvoI2F+t3QKNXNdJyFlp0Wu83GOu2p+YmLmnPL+79tCiDa/5u79D4y/748NLhug3KELdlNvkXtxk3T1PzP/bmbJP0ohWsoisUh5K90ct5LZFgdJMWzJQ/QRuPYTjBT0JYiQ9kDJpAFTPg92e8+mnqZo+eOLO025Gcde5dzW957ihFZt2nn5LE+bKGOx+z88I0YdiXC/XBhTFwP/HTuYTynRSAvXDS7eIoGNUypX169f7OTFVrB9e+JwovMgHuMp+x44zJ526WDR7kwtNi/EWbgb5walBKY0Z8JLB0Q984rPI58989djF/fIezzykkXLosjdW0jr/W2yE4jhI91kkIUksUB6c7LVGklNkQmNd86Dn5rGptHZv/PQvaej+0IkDM1dVuwdajjhp+0+vHV61mb5Hj6H7I6cMr9qErNB7XezWutYhZHx8cGjQfDQZyDkUKE14q9UqpThdJukXebP3ifc5vGIoVypoc2DyOk8WTBjeeDd8YR8b27179/Lly/E+lpWonKvlV8ZtlsSQBgwMDIhnUuxteDNQfXHXYgjHC4nwClhvjSQXk9myN22gOBSxWS9GqULrYrJwWCQh6dbpKpSibVc90vz+45s/fNL2466h4pWPrD/sPwbmr99zAf8o7QdP2HUOfxtbn51QtywrZMi1zxP7+QYLmGzofEP0VhCaLFJsHRnd3DWwo7+EGfCe0tjWroHDDjvMz4n5PrFMeCblNkv294kpVNqr0LWoexGE44UQXpfSAAlS3LNnD86djRs3UpGy13MiqLZu5khdTIjp4X6FkhNsBuux8nYSLBlzYtmnGJwJWwyUuDy392z89FkyJ8bA2nb5o+vf+Z/Da7aWtrXSN5oxth558hD9Lkl4sMa5gNuwIxBS/9uZZax1tkoac5DM0WImCVJxuNZWGcebp+19I4NjY1dedwOS45/eEebExHE7ooo0yba7MFZDMwR3n3gF/29n//vEmEDE128U6fptlazRKz3pqaL+mCtZq5CheXl3aVF/7dYXFqHlmBbf9uJi+qad3CcWDyyoT50l8CJtMCa30Jz4hc2961o20uHQZAyHQKToEGdBCerhq5OjmOxKdIhd5jhEZJFCGWA7adwrwJRU5I2sQsMFrlyQvQovcdeqNEjtOudOJHPTB07Yc/49Q0s2rH/nj1ovoUkwpgvNHz6R58QPpfKZJS7QCu0ZH+/s6pLfYlMlrR3dbQ3qhZeOGMmkwelN94lXruofkPvEpvNMdhAplAG2k8a9FPAMV/o8sb9PLFlkcymbvbRIkmtCkpJTmqbFclLonHjm7sq8rtF//pcPIaO/dcIZK8p0n5hNDWSv35eXw31imROHA00wXXPYl+x1uhyZyrHbZHgBU1KRN7IKexDYcqINCi9NsmuUTuPl3V0bjz6z+WM0Jx6cu6b1kofWv+tHI83b6ccoPsK/QmjmxFIx0was5KKl37HjVGSzt086vHTvlgyk6e3tpfvEzS21Gs1C2K5k9wowJRV5I6uwB9KMj4+MjHR392Bn2FG+DQ5emmR40TfMQnbswHV927Zt0vJsG8Ka4MpOEywEKXV2dvlnJ1hDq+I2pMPrRYds11yVLNOUVOSNrPaua16abHila4Rgj8gJI6+dJlg8JtOGCn+c+PuHmt8vt05uGl6zrem9P9t55m3Q7/wFfb2q+UNuToxpkvPAzqggr+jZiUr4fWKeYziBFzu9CfOT3KSotTyG2fD2/hL4O/tGZE588dXX0Rfv+ul54oxb0hhZ3Ra1AUvmO3YuYtK/CcJrwAVapcJLlyF/7ohGSYHs6zSWveNjQ8PDOOV5gFKwXVah4a5avuw0vHX3iblAK2eVtKx29W/6wrn025ZHnb7r7NuRDXt+ex96Nbx6C/1A20dP+/9z9yYAlh1V3XgSdkSSsKogCorKLgiKgZBkkrAJKCD+EVEQmUwymSwkhFVFQGVHRREQ0E/lUyCoLInZl9mT2ffMksy+T+/9tu5+3f0/e51a7nuve2aS4ftNTd1zTv3q3LpVdc+td/u++7a/6n2N9fRZWf+I4DyZL4rR8i42GQAZBh0kttgohlnixhVmhtw8hgGmJ5L3j4yNTk9/8e//kdbE7tkJXgQrGat7GV25fbFbNwXRSK899veJ5YJtV+444ZU+thc4mPh+88Sd+5trmlNvfs98aPkvv+ilCw81Fh0Z93/RlurVbjOBnp2InicuoGAs8hBuECMOe2aTlWdE+jtDCaWGhbqYOQKricG2maIGQbIruh116JPfgoXvtldcte/Sv9v1e3+JL5oYGIGKtCa+EoqOfvG/gBbWxOAkcS2WqUad38UWPpM6kqJgYmBB1GuUw65lTRzuExPNEREz7F5oJ7jyzxNHU6i32YsCrY9jO94zvmt/a1V96h9+ePejHv0YmNL/cMPiFfosUNFtQdBlsagwn5PfsfOHbyiYGFiQ9RqA+4dNVn683eszR2A1MfgtgRSmRijtisBcqxYgJr5PfHRw5+s/AqF7xwXvh9C99Tcu5z+A1Nfev/38a7af9z58G/dm+tM/3ydOnRGoH1rN5gQ+I+QZKJXa0Kl7PaAlsCaGSb59u3+euJfuVQpxI9cgkjY+MYHXSDwxpZSpEWbVveyw1Wrxt+mHBofwDPWuKmdO4ozAJsjxXWzyYkeysWCigffEJiuP+gBxXLNXKHpQoiJKtRlcM0IlmblUGlPMFLWBDYaZH1oJ7JSKIw45oT9lHPnS/+B94ldetfc9X9jzR5+BUwa/aTc1tf/9/7QV1sQvx1sqOGndmpirm96mNTH/tjO/RThahNB6A2Veb9jCw4pAcHaWedlzsIVr4he6Zyf4PjHSrG7BLXkotkHl5D5xcmgpCiYGFuTdyzMKPl3zuTM4OIgdCDbHQbghjsc66l6B+qRv7LkLsUiO3PnEdMTTQA5U7wH+TU+3Rxu7fu+T+HMGF7x/628s2Pmmj0304Q8g1VZt2/7Kq7add82Oi65r7tiPPvCPv+QJnFjD1SEcfHqfOPysC44KJlbjJGRLvNhVdf/oGHxIytfEhaTDz6pNHUmuDbzHA6U1cTHBRV2u69lyIUv0UPJhWEA0VzfwYeKHPfJR0PLPf+emVbWpu+kLSVAaVgkqF9wmCwhU5Tt2923FNTH1fD4tEwUAuo4/0vMKAi0AcmAVZ04Cm6ZJDQeYrD20QSzsJG4DFwnB4HR2S5feI3/73xDs8GtGr3rf1pdeOnTzauyuiYk97/4szHMIdse+/r9kCQ+KZUAT7Pfo0WPxl2dzMug9HJrYIMOVK6+J12/YMGrPTpSqMHrpXrADDVwlz07gzLHZ1X324jTLOPRZ8eD44qPjiw/Xn0NvG3zt29+9ChfEsAu6f5xWKczeUhsK94mzfnCH3FP3AjmwQmeVqjB67F4TO7SBwOS4DUxOqzi9o1uxccAHBRPOoonh2q63fhwu6hi6f/3ynW/++MRADSi1e7Zse8WV2151zf2v/mBr10E89Xzo9tALy0D/AKwCScw4BLJBlh0aI69CLdQ1cbhPXILzlPRD7pZMwIb1ZV9/P+o2finZ6TPpXnAITd27d+/atWt37trJ/uN6oAE5cVk8OmayMHWsr48eoCow1VPkNvDIUkQPsxeg9o79QGAnvbTB6R3dis3NXrW4PAKYojYQQkMcmClihzYQmEwsesSu75s3y2Xi/Gu2vmTewHcW4xSF1eflX9r2m1fCZeLIF76HFhwyrp4iPE8c3sXmlhy80rAFibNj4gVJYtR0qDVVWhNrabVQTm5HYU0ceot7Joezd+xeK+Bzh28S79yp546bowni2ZvCTNCIWq0Gp7zo1f7CECesuEryHTsHqEvUfZd9CWYAvqbqnCuHbsD3uULR4E2r8EV99KsHE4OwLvXHVtgd1Go1W/I7dvBZBz/u6E1fGDOaKyJ7ISxSSdApZfb9tfEOa2KhYU6fhNhYz9wmfBb4eeJ1m27eGq2Jowt8fGm3VFgESMI/N991YOyewanvbz7wM/Qutj+6/H2r63xHrbyAYIu3V/in33bG54nDbzuno53AF3YgyuRlhdGzW0SRTLMTUO3JnRKAWENU1wSkhaTTrd+B7y7EVxFfeB3M6v3X/hN90J+cGGo88MY/33bu+2D1MHTjSiPHbuR0YsAB1Bt1+VpMujuCNxYJjLh7YezGJ/g+8fqREXwduBQYUldF12n3wk7AlTw7we8nnvHsjRLRaEF8ePzug61Vjak3/NGlMJ+f9UvPuW3HoSV9E/wgUM+zN0rsPP2OXXKgXk2KPE7A7E2GgC3Ei8ixkmiRCkj1CL2SKw4NzLBtT+59799sffmC7Re8HxbBQ7eswrkEofuH9+L8P/fqnb/zF+2RBhxK3jiE2HC1AtcbeUYoZ86yezusiYFxXN0LleGTaqNBv9nR6dAMJQ4iPjTsVGz2sWPH1q9fv2XLFroV7fbdq1uCLyQPdfmjU4xZdq8BdOtYBluIF5GrFXeIilSP0Cu5y6F1c5PqEXxhQoyKCj3OkX/olpVbf2PBdlj2/OYVey/9u8kx+r3eVnvX7/0Vv8tl4D8XIl3/nBgDvYJvmC3w8cneO1FYZkBuPzRGCxKWiUY5y07A91c0p/YPNXhNjO+dgIUmPTshTOcWE1nE6IR4UYTkA/WJ+HfskpnjMPPu5d7o6+srnzuMxBXqxTYQT8nAGMdXsYUAFbuJrtqEVI8wSfeJU1gz6MGaI5+/HhYKMA/2vPPTHLkgP/yp/8Q/QJ9z5d5Lv4QVpCmcIxK3UCV/ntgnMsoQ5vbkAxYlZOZrYvgIBaXiRNbc6tY5ZzvL8Qcy5EDy753A54n58n8k/gsvqfk1HiwpEy30J+aDY0v72ncfGP6Vl58HbX75b71t2cDEwiNj+Lds5OvKO1mv0LtdeUfpHkMb8vcTA5JxULiZVsEoww9xVyithzbQx1LmQS4VYOM4ANZ0jmMuTI/yoZFIH9wbG3Zuf+VVuFA4h3+iEzFKf/fAhyzPv6a5bT9Y7Dvggrgl7K9/cDB6b6shakOhjTmgBvNg13KfeP2GUXqeuKq+2ivKfRtwOuCxR/eJw8yJUsXsdbII/EePiTsPNNePTc392OdgPj/uiT/1b4s3rRihl20TB/OeZm9sR2b5XWyIiu6t6igA19DZy3mPqCDG3Ys5y1YBNo4D8G0wVoro0Ay9kJFjrifpXtfhT317268v2PbKq/a8+3OgQhHkhz7xLfg0CFf6fQu+DDoGisS9ucUSLBsYHGwlv9nhdg1gzR9a4jIB7BNa4tbE2FopYzhtRt3L5dDaQXq3t0RBg3crW0BCUkRkOYOGBrHNGzduHB3Fz6vYe4C4JequJ7ciQTAZGMR+ACPYYoes9d69DhVE34Zi92bI21CAdytbQC9k5FS6dkyAEmbsNlSIHbIWDo0uE82d9PO9518LJ8voXevAgsbt+3acfw1eJl5xZW0VvvNK7xPHII/8c/q79uy2907YGkOWGbI+4ffSZjeGs9WLFVXfJ5bVC8kTVEuqYC0WyosieiTD3yfWTknRrXvz2Ys8PneGh/mV5PJMoD85I7cGJ3ok/unmVPhDVhnoSt11ccv3iZPQoRqFqtHl+PJq/CPCuVcP/tdi+AxeW7F1x5zrYFWx9WXz+/7tNug+/IFQqBTcJHvFHmk1m8vXrJehTZan2WoVBoxVTDiKoqLdFZWfnbCnMphva19MiVuRoxkJdvrMRGtiej/xMfqOXbJOpat4eo2PkyvCxcHdh8YWH5tYeqx5/mvfCA1+8XkX33mghktk/CuzcCCJW9pdB+ecrA34/KX7bWc/pCSlIyLbDCWyIJuKiJgatOy80CzAqybnQiR6t2qumL0Z+CScbLR2v/2v8X0651yx/9qvtWvN9nB972X0F7FzroTVA/+QQdaQ4BbawOFgmN7FRm0KpQSvRkWkJOQIMHbJ88TF02qm3ctRSdbE9B07mznJjLJkRSxILmcBz9WJu/Y317amPvo3X4P5/JjHPOZLP1y4uj51J/36jM3nDsnaoG6DHapn752wI4oOTbYEUiKLIekxRkwN2ky7V7YFIRJn6DZCieyQTBJaE4/evTGE7h8sg9ANwZx+qB9Dd/9/3oXtwQ91iVvvCuXRWm2CvtZNBQlZ4A/N13cQDTd8jez07ISvmvtzpU4EF3yOj42NjYyOks0VIxI1gAqqS6nB4JMv6tByUHFfUiNUnOEQo8w6vtgR9kJyjt67FzCLNhByIRJn6DZCiexQCnEI0YJxtoeGKJEFaffC//GJPXO/iN+6fsVV+y770sRQDS4c+9+PX7CDs2nX2z45SX+IwMCqlRzIB82Z3bv3bLDfdpbVi6w0eKkjqw5eeAQ7FoX1Cee6ROny7ITuQv+uHvxw6rAoCmtiPg48kMKhKaIiUhIygvsBPkPyD1HxI/holXJGoSKj5NarKDebzXF+gXo1ep85siaewg+LZHJV8YyHlo9P7L3kb7a+jJ6tOe+aPe/89A76JSSYKw+87sPjR8Or0VOATd1CL8CaWJ6doN7XAcPxliGX0Qp2k2G6UC6jCzSuxWviL3R5dkJ2IXXVjh+kKtqAcvYuNlwTQ9Kf4wpX8fhyLmowyuKA7gSPLx9ov/Ftvw+t/aUXvPiWHYeWD0zefWDM3PKyg4V0lQAqp8TIMgr8PPHgFvptZx4RzHBLKA9xDNIxK5GVzjkBRGN2gJX33AZE5DkTzABS74dGH/MG/mPh1pfM23HhdfhrXm/75K63fmL7K6/aMecDW19yKSwdsPf4M564TT2xBXY1PDxCjwCSofc2wP8SGS10/2x8fCxaE3eF7aa6DRyYwnfs7Nuc8YwSNTHyhAwqLXbxe3XNNa2pT33jW4942MMe9ohHfPZb31vXmrpzP76TmGdmmM+gclKLGFlO7JzzfWL/HTs+kuPpXs55CwDBmB1g5T0PMSLynAlmAGl2bqNDS0FXnMnJ1jj+CP9vLIB18A4O3fir5tfAZ7+db/zT8X78Zohcm8ptkL6q1er6LjZil9tAFq2CACHqhAAgwWzkNfG2+Dt2mM2oHxCRALVhTQzXYNShveVDi2F6kUznTqPe4N8a6OvDHzrhHhZGBDHixsp7aAMEk0l+sWOJrPQHa/aiZEoq8JQhzCT2wv/yoWUAijCJHEEsuLHCE9AGpkv38p9Zhm5csfWll+Jl4hVX7nrLx3f/f3+Jl4kLP7D1xfP6/v12nLH+j4SZW/88Ma6J5bd73YKElh8syzrEL0isNFkUgR9YE5eenWBXyMFaIoSKmORuY2iDloKdfz7ar4m5U3rvXkRM5nOnVq9t3hKfO3hiYjmSOqDCLYMLwdBqtcb5Q3s437kQkApu9pbd4prYcUzEVuM/euq/uWXP/a/50NaXXobfq4PPSfRz+SCP3IbfT4r/yiyyN4EGtFb07ITcq48STwIYLRw2GrmEEBKWHqhPwMej0cnJL3wJ38X2y897we7BOlQ/UBuXr2c6MiY3tyqTtgHmh/+O3UL9HTu4kMv6QC/kduE3lS3IRI4siBceGlsxOvXGd+Ezlz/3nBf9YPP+5YNTd+5rgWcslSeJabVRkWxHFW3A+8R3ps9OGFjNjOlwFWmMolGgd/IAtGN2TDbbR4WHtA3acszdRGWEo8qF2KZunTcy0rfyW+MHPvD1rS++BL9shC/WwTvEsCA++LF/gzAH8znsptAG8g6n+hT+jh0/Z2k7o9yQHpoKCY2BZN4xrInpm7m4JsYCNAYPKLFjstk+HMdDyvnFQLwmXtwPn/HCd+xYMJUtOnujxNOMZHlxyue+c+PDH/no0047/U+/9n9BvWN/g6Y6JKAJ09UlodsZpEnvE6/N7xMz7NDNXqQxgjEvnsXsVSVtw2xnL2y9LT80gOcgVDFjVEp/5ZtqbNx1/8UfxAeIfeg+92r+c7CGbp8bUOXDgcXrmLz+E9SExgjGvLjQveoW18TbtvGzgH6ea4XZdC/Ym83m4OAQmYzPAoBk0cwI8BwEK+ABOqrZaN635b7169bZD9chAisAXYvNisQT5YaURj+K6dZYEULdxAsg714SOQtFsiWokpZKb5Fa7l6rQVAttoIqBm8HOaKpYsaolJG1AUiz7F49toTG0OO2YtiArd0+/PFvbX3JPLpM0JUCTp+XXLr/2q9NtsaAIFMAEbllRd7Fhmvi7NkJXvvOZEFiK2Nc3jTa+wbr4V1sk5N7R5phTYVVgCxLZLGn+1I1tmf3iQFyNJQb0u7VrohoYMRzp9mEy9m6devCD9chmROyKAeQC2ezfTgOQhUhQVPr9bo8oKyFpZkTajKUHFunJmFNTCaxWzHz8UvJeACwot196MBH/w98Wnrg9R/Z9dsf23vll/m1xHR4BOJb/cQtMMfcfWJYcbIAg02DYeOHCWWZCjJgKNjnHv481MRXFIO9OT39t1/+KkyOX3n+C/fRyyLAHt0tdgI7DM4r2oC5e54Yn53w337L1gqc/KU9lmHJO7a2PvWOy66Cdj7r2b/8g417VtXBZ3tp/xQsuJf2TcFyNtpFnMwOgsiFNhSfJ0bI2LPB2RX0WSk3K7gEJxlI4FktshXFzQOUhK5GsYruRAXNnNjESG3oqHKa8YZJ0gbZMYtu2xrr+8bNu9/xmZ1v+NOdv/XR3X/4mf5/ux0fDgM+EogTVYjAFnoXm17G2JRTu3VvAhg7fXZi/ah+x86d4e7Q3N7kKFl3omKaH24L94n1vROW4hkb5FLC74muaUx99Yd3POoxjznjjNP/7O+/uaGNExhmMiaY1XjKCL8we/NE8zkwcd2cv4stjKVDT7MXAVL17OUNCig+SLM3cqsbM5MURGOhQIoWZaqJNOitBw4e+PC/SOj+nY/tu+or9XX4CnNeEOeHFqBqo9nEn5oDKSF4Azii7lWQKLqZSeApPT09PDRMz0743+zADOD8AGbUvYiJ8XG+RqIuPWfcXrsXt9yB9OY1uKgfOYIXdSqBDYioKNfVcsZO3YugCYk7n27U6z5oM4LeY/eiiC2EbWKVjYgKlHz3hhJuVgT0ENqgbtngK4atdatuQy4WQKJGumuDSrA1ox1ayCNw94qSI5SAI9+9IEI10CfaA9+6Y/cffo4vE7ve8alj//S/k61x6GPrHmsDbkSkXNfEG+y9E5D4niCvQHTtAUsRkWVBQhxekMjihFcmk7CwgeXNkYnpQ/UJ/h27T/3dl1vT0wcak7QcEiaR2b+toHCnbJc2xByuG3/HDg/CMskjdOpePnfGx/BaRufOETpdALglGZeXrj734ExnL4LfxSbuBVm7xJFA3bLNlUy69xMHs0hhD3x4gPZwfWz/sYm+YVbdA+bhw6pzj2AVyBBZ6P3EYTxkBqCgIwQWlsNYKp+KtO7kgdrEjkN99+09dGhw8OOf+tzpp5/+Ky944eY9h8C4df+RPXTD2PjoJHLrnlcmIWkDqv47dsfwPjFfsJOLOqpwOadbX2jxl3aRJ+4+OH7P4OR7P/xJmMFnP/Wn//FHd925b+CHmw7cuPXIjfdhumHLoTv3jBI/3Cfmu3TgyvaYC17V54kH76NnJ2Cs44GI5ovC3WIQOJ1Ep3sy3iUVrWrmkByKKlGaOSalTqI2yBbQcfYqPB8/5uEEHm+PH+wfP9gHFlSpBHPMECiw4mqjTCp88G3L35SLmFn3wlnN35/lD9YbNm4YGhpio+xF2LPpXj6Fw/PEtCbGmdNx9nKSCSYWmM9j9wy2v3HHqsc/8SmnnXb6u6/96KKDIz+C+XzfYZ3Ph2/Z0beYJjNMS++EBZPNbkaX65p4Lb13ovCk5cy61ykoidZx9vaA4DbzkztJ2yAotiGtm+oOvg0oGRUE/gMuYGKIQne/hO54wZP3LQCMwEJa/8AAXHXQ5l07LT800fJDm5yyed4/0M/3ieHSwEb6s6THzLuXjI1Go9/exTbT7jWRjh0OfMf2HevXrz9w4MBEuw2N5MZzgyEnJmbUYyI7d4CK7o1JGEzs8FMnPXdveV9VqOxezIKJkbZBMOvuFfg2oGTlQRFTL91rJXkbXGHqpNOhwcky1Z4cP9Q/fqAP5i6qkKLTpww+9cKzE26ZgT+maysQWnLAmkQJtjIJpbLYbU7uHqxv3XcYFjmbd+3/lec+D5Y9n/jsFw8PDsFCaMfh/gP6CgutCDnuiFZBkFORLqjMrS2KILf7xHIImM2me3ko8dzZoecOnS/jE3jKQA4SfMxmOkLqznT2yuSz+8QMtFljDMHiyjxN5GldE8cwpgr0F2c4BgynCAw22R96vP8YODni33a2+/x8s1bGKaxiKfE4uYRkGHj4SLRlz8FnPvPnzz7rrCc96UmPeNSjYbl52umnn/2Es5/4xCeefdaZn/27fxiZogfPZdTFg7oVmQVrg6pYJN+xW7f55q343gm7tFviizcL4a/M7i/OSAD50PjiY5N37hz8icefCW084xGPfMJTfwrknzzrbE5nPuGJj3v8mR/87JdW1fHb+pFblVF1axRLEYHuE9N37Oz+PQ5IGBSSVE3Mug1mRKwhcksCIYRTBrchgJAgE9nBkzuEG1q1ZpUVZs8E2sZWbMMErIxlRmOD8baZkQrAMvgvdxOwLpyKchboydnt0EhUsIZVacG6f//+TZs2wWoYAMKmjZu2bNkC8ubNm3ft2mVkxMy7F4ywC7tPXP7J5Xz2st2pCw+P44smWlO/88734El32mlnPfmpMI0fd9ZZYT6feeY5F1y0mF60gi+48LtwKZ69Koc2FL5jN4vu9cgtCYQQd2+oJprpgkgPgxEDP4Uhs6I42DOBtrHVtUEE0wuAiQ2zlOY5AOc5NhJrdJ851OxGo6nPCAWk1JLFA5zDaQZXxO3bt/PEhnzjxo1+zttfV6UOIVYqdhK6F+Y5vpuJ3lcaXFm1TKBtbOV+4LPy2NFja9euhbZt3bqV22mA9sMlH44IwhLuB9LMT0zKMZjU6D6xLQpSasmSQAhhXUEGqyZa6ibW01IAmh7C2UtAAvyfVfdqcVSaUksWBkd4mAnYBZDaeP5wiWUI0YIbnj/4O3b32/uJk/VMvCDRUhRoQaJrVszxMdGp6U9+5nOwvHnyk5501llncfh9zGMf+6QnPxkWQs957nO3HjgKSyNYurAT8SxuKSe3HRZF5eeJs0NjqM7FUSloeO4c63DubOZzB3hcU+rHQ2yKaqYLaEdT42Py6TQtZgv8x5uEjJxiJtn6NbGxcYnAGpsgF5cwziChwjqAzmMqJlOQFKhAB/l3sR2Q58111CHRPKCPMjKWOFRUSkKYIrgmnpiCNfHP/vzPP/7Msx5/1tlnwUL4yU95wpOeTJfkJ4AR1sSjU1P7cU2cudW5gm5RCDOP5yKSQaiN0/uJN9/E37HT54ntyi1CnPxlXhMsVSfv3DXwyy940VlPevKZT3zyY3/yzMeddfZPnCnpJ8+GNcRZH/jM365qTCZrYnPilxSVbTgkz07A/IPexmFKxgERDHanl02QUw2HEFuZbMUpkWpjjvPGu2UhZQe9y8wBg28DZVqeefU1fRsoz9kCns8U9bgG5hE5agODHCKJnifG906o/1BzJt2LBaD5NTEMosURWRPjeUZ1zC0LiVu2Enz3YvWOz05AymcvTjyeZmGyTdx1sLW6PvmuK649+8lPeeJTfxo+4P3EmWc9LsznJ8B8/s3zL1xCr7YAfk+zl1LMpDUxPjuh7yd2mEn3hn4ggOTKEOIjnb2UZ91r6Bb38v0EZ7lXZziBbQDF2gAzgFbDdl2JiIhgCN1LLuVdbO5w4DpkyLo3ARpwp7CdjtbEAD/PeU1Mhw/U2XYvZU16nhgaye2PaqJb3FqBUBKIcXpgYGDzls3WSA84MXfcfz+viQ3mLPMaDKF7WaXcnieGnppF9/rZywCxfGiIU3X2RnBtUME4BbIi797Eddq9oW750HDutqEMtqxKQc5W4KG554llTYzrDV5jcHILEkvRooiWInx/F9bEtampT3z6s7C8gQUPLHue+OQnP/EpT8EFz1lng/GXfuU5tCaewrvFziEncot+eI/sNmkDCPjsxGC4T0xHrpIi715XCCADjS2cO3CmV5w7W3bcz2tiJs8y9jK9UW/gu9jCUOazVwoAmddg4EMLa2IqSfiV1XmLObeMJBQ8rARnYfx+YvgoAwINEo8HzAPMSeD1K9KkiIdQ7DKojfbe4eae4cae4ebuocbuwTrk8BEH03ADVsPCtOTdqjG0gZNrg9wn5nex6XfsMNkfmjWJSnYrQkEu8PIX5Dv21m/fW4N0x77GHfvqLqF698EWeZCVNyaunrtVo7cQh+4TP6DvYsOBSUckGyFAedRwSwJmKPGyhMs4D0h1BNpoWpbJwYpSymFDYqVpbra0SuYCkNnUwFvMK2evLMOcNSEg5F1sev4VGLlNDb578dBwtEBi4Lf8DKwgOwBV2WsM5gU27QD/QxiJ18Q2c2xGicxCaSFLAkxRetP2gdbte2qQbA6H+by3fuf+BtNgWsbVy7OX1Vi29xMX1sSKzK4G373Z7GVBQEpkYTXqXi3nLeap5ECGzHqqzd7UkjPAhlNzGn8mim/DBFIQQPLjk7ohnY1SRNOZJ7vMc3yVq4DKMWOqSQ5kyKzavZSNtVr4ezdyd4iyFJlNDby1epBBO9vRGUnvnq0+MQmJPdMRYmMfQ8PD+KGFrcbGsh6714Dq/6OzNxcErMfWlFOozd0bkFYhPTGi2kv3wqzBNfEeWhOPy3snbJmB612/INGlThCUyYlXKbCwwTUPLXJ42UNyE3JYDomf0grKuyWa+PRtgFVZ8uyEIRxaQKwB1GDnhJ4j0cnjwSzKDaj2Pnvh7IQA1YKP7O7hLi53mNns1e/YxVBThSPQ/F5U0iOxPgmgNbF/dkKHgZakPDY8PCQQQYvCmMFgO+ORsanDkFpTRyCRDDkLVJeYuuQNbimXuaKyusVaTNY1cXgXm7+JlaTCRV3JpOJKd/HRqSVH8bt0mI7hl5BA5YRfsDsyme8id9uhDbiGODZ1x87+bfTbzjgE2QA5hHlroybbDMUCMvoSkdVUrETmMHOME9qQVUupHmQslKjJimIOaNqGkHWavWJQe+APj8j7iXFByybBLLsXTm/kUx38XEP/UVGYW9kmbkEDutgCh2xuTYx/93DPToQpVJy9kUXTxOIjkzZ7ZW7TlIYc09HJGc5eSp5wxO4T27MThhM+ewXmVrYJB7SK7mUhZgNSaoZCiZqsKOaApm0IWZglvc7eAtSFcwJbFEdro+MT4zAZrbJM+Ap3ZCyUmAl2BbORJZvj/l6R4864e4EPF0h8F1s2bwBqsZKYApq2IWRg48ZxOyHnxjuoF90mALO6hY2R1Qsa4AO2rBKwc5XKmxhCyqCmipMCNNcG3nhyzAak1AyFEjVZUcwBTdsQMrIxYjZADGrPyhlg7nhoslUcZ/fKNuGAFrcBloK4Jt69x36zI1rSOJkXNrYgsSK/IKGEb2ELS52WW/OA3Joit0zmXXi37AcT+ycCCiyzkHzHzo4Gu9IdGqH7zEEoic4gUensgf9aRtA6wb9sGaABXWwpp0WLYnDr6qTUDIUSNtl9YlDRYgdaQCiCXhCPVIdFDzOBgDKtifE+8QG/JuYByyYKjSvKwnRVWMBEHPl5Dn7zGuTuFWzsBAWR+fNT5o3bYLWIgDKsiZP3E9OlGq/ubsVgl3m/egh2zCcWHQLLRJQOgTG20E21aC+x4BM61/06lb9jN3TfdvxSuZuo6ViYXoAMMTJtiBFBNIkFZGLy07EkKWY8cwKlIHkB5R5mLzIrDs1gJiTzRkASmTAbHMSfntJzFf7LphK9dS9pbIKcUufuDUdd2b1wuZU18cqwJk4mEqZ09uKbs9FiBJyiOEuT2QsJny9ysxpoVEumtKWK2esS30jmNXH2O3bumDL01r0EFpCJqfMOeuhehZlAiCkFyQsoB0qO7odmMBOSeSMoSF5AOVAUsGeY53IbBoHMiBYUk1hAJqbq7nUaMo+/e3Ge0zVyaGgY94stzcg5OnevGmhrJkmsIwqSF1AOFA+yDkAwmeSXewSfcZ3ITDml/wdnLzJZDpSC5AWUAyVH1AZhxnVMskJOocBJiuru5ZCbPjvBywxdnLCR7SLIEqiwKGJatM5BQRc/kJtbt6oxAWW/31Ibwn1iOIYwr2DLx9QVyBS2r6MSaWbiJDoiSIoeZi9Q3LvYOIspBckLKAcKrYndbrNi0LRYBCmlA4/mN4A0NQEdSQxaE/N7J/DHtW0kROBRBxmFMBs4Z7uRMcmMiVa3nFAFMi9/I7Ik4RDNVBSURoLeJ1636Sb+bWd+ntj/2Vcv5G65EMueQLIIuV0tIrNRLUmyNkDOFZWJa+Lb3G92RKMBEF0trkRRMcS4ZZNANRxilqKZoxubOfEmmzYAMnirkfk/Zx7qhHZCYtQGQbfZi1s2CVTDQ2OZL6skW8PRAhT8jh0GPrU5hm0dHsrupXa6Zyfcd+ws+SnXdfZiYruzRLVKqXr2SjIVVtXxmhgPNxxadohgegi7VzOBkfk/N8Qj0klEC1tDSWjDLA6N5eLsTQ4NkDB4ixkJ9UYDn5qlasIQt13awBK7NYU3SRt0Qz4TkMFZubG8JTmqhCcmf8cuPTGjdgAe4u41sMbBhESuAP+7tIEldmsKb5I26IZ8JiCDs0oj+NBY1GJjmSAiNxXFUBLID0r3kk9GKDHMpg0ssVtTeJO0QTfo05ox6Z8npmcneIERrV50pYsLkkSwdQstSFiOOD0vijDJokirZGRU3bMTeoAI7C13XAHBoIcsG9gSH87FuJJqvXYvC2pGn2kzSBvDN8DgH7KQhAbiimw1CjOHRWQwi1S9T8w0yFnI4MwVDDpQhXJ0G9bEOpAyDCaQPSRVzS5kGleuxUYUzKcKlCbtY5NxLJXcRm3A79jFz07YZZsu2EEWi18rdEt5dU7eLnJp3YDJrc4hkSzfsZP3E+vkSAHWygFUVBNoiLlYSaaRoFZAPKERILo54tBh5uSoJENeXUtwYtySPj119Ngxec7SUCBnqCZQG7hYSaaRoFbAzLoXYkS0JtZ3sfEseohnL9PSNuTvJ6ZDdAdcRjXh5HWvQumuWoKCCyZDXl1L0JNbJVW6tWuDB9lyMqnyHTtG6i1AXVjutLTWieje1KdiGl+o3D+g72JjwLaKb6gmdGpDWqu6ewHVu4Bgwt+xqwK5sL26LeSZW96fM4NYalfnQ0tRPDQCkAv8GNWETm1Ia82yewXVBHJhe3VbyLNavD9nzhj4mzkYcnft3r3h/t1HYE1sywxbY7BQXr2ExGRjIqewesF1sxlRqHJbbAPRQE7XxHhY8P9U7N4AemcLfseOYW5T9Dp7TwMZqJGH2F3mKC6ONUFmhMmR/LYzJpZhkEConhAq0wcdGn4zBsG7DQmNVITrYyQTLdoR75fawHYptWcntmbPTljSq7gZk8t/bocLf8LhxEafs8CJVUu5hVoi7yfeQu9i80PAYjQmxVGbOdKZ0wXAdbPJ1aycrB6lPbHHqKREC6j4pBChBwpz6o0GvpmHWj+DNvQG8HGiuhcAxx3WxPzsxKkze+Nammf3ie2IZtIpVeDOmoknrqE4SW3o7LaXnc6wYQV6MKEELWzib3bEn/26AWo+NN1LFSfGJxqNJvuLPM3arWGGHgr0Cg91/AGgXr0D76HpXodCG47f7Qw9FOjH3wbycfzdy7ch5Dt28i42WmDoWiUsSPLVCy+KkrWKJmfsaVHkLJDbHtW/21GyJubjIFExk06pAnfWTDxxDUVWc2wMX8YmSm9gH5EnVXBN3AXSGCTabWgPMGl7uTTisAKTI9wn5gdiQLA/HMTfgiTBjRaNH9nTUeRRB4dsZA4YSTC3aS11S+TcjkXh2Qn+zQ5eE+OtrPRuViHJdT1bc1Ql79ZqRYme6WS5TIAk72KT905geMWu92MRjYuCJmen+YYAo1DcZOgOR84quF2izOVxS+I6oElhJ7cZInKBHtxKacQpVCBwrWP9/XyfmJpa5AJrlt1bZEboRHa7RBmmg94nXpm+nzhJD83szZJ7dqLDeyfw0PyxFnlgFIpsij2WISIndLdLlLl0xrO3Oxy5WMG55fKIVayCKLShiov2waEh/Vp3gQam/NCq3AVE5IQuZQSQubSn7mWh2WoNDg6imjiOYA6RVCSa25PTvcDDNvDh98m72PBPfFzMAEXbwBssLbqLEJETupQpuLTTeQRq3oZuOFW6t4MnAJRpG3iD5E4VGBG5TOeQu2fvXlkT07MTuMzQBYmtXiC3ospFUbp6gbznRRGutYRcXhRRG2DNE75jJwfIKB4gMLR7HbmXuFfuL4+IXKYzhf3X7T4x7b7Sv3PbAeFdbDnym41dnBGYk9BhchTuE+uQaHITQo0ybIEWBCji4YdENLaTyrKU6oTQtTISyM6yr2huo/dOwJpY38XmL+pwpQc1WTSgMbZwQiPXpSrMMaNfWIiqsiVzawLL3AZcQ6S/7Sz9H4Yhg0wSAEjJmJWB5Y5YRVdar24RzIFcyLCJ2idwJ4kJBZzA2avgNqAdeheU0VqN+rnEJUTN70QU8KE5YhVdab25BYQ1sXsX2wmYvSwc9+yNqgvH3sWWv3dCkHQvkDr3w4y6F9Fz9wLMo5BhE7VP4EN8kDKc3NmrQtiUwJ0Kea1eGx8fB0tofDigAHdoLFT5dk5IrOJ5mEchwya0IUjcYGgtXCZJL/s+Cd3LQLsnVPEA1mjuNwom+uxEOKCAGXcvgMQqnod5LJGj7hWpo9tTq3tBonYHck/dW0bvh9amkMvPTsh9YllyQE7LDFmHYMIViy1mWJBlSVjFBjKp3RdF6JMEU0GoXhRByp6dqOyKqHupE4QKmwelexngFvbWGmuNj/PDXTmdW4N276+D2+KaOPDl85sHmYoew2+FpOU4OWBNvHzNehmS8BlIZgOOJdrDgLEgHMqllpElR44QwuRAMtsLZJs0zGTBtQFzeXZC3k+M94lLV/rOKbn8m9w96b7SWp3aQO8n1t92xkWQdD4hHbUg9j7EMLlKs91DKsGmB7dBLM2chIwA1TcgKVUEc++HBqicvaKFDfogzb2fGHVhIILo3CpKbQC1W98CpBKSk/qA1G0Q8RNSfJ+4+B27JPU0e2d0UvRG1n1lzxOXBugU6V6RgjEhI0CtaoNjBrGHNgTMbPYyTKg6NNiS25GRYb3klNsAau8nJpJzRlUbsMTkSHBkBKjog6wtej8xWdHkmEHsoQ0Bs+leQ0+Hhu8nbsOaGPvRNxgB6qnSvSU4cxB7aEPAg9C9glIbQO29ewE9toFDbv7bzrLMwBzWIW71kiyKeIGLdl2uHP+iiBIzUzI5Lzw7cap2r4DERqMxZs8T086SpoFWcEuVnVlEWhODA6kR1RMl3qCAe42YZQBFG4JrYv+bHQ26sZ+Mrg0VDY8ZMfFHnKAKQaaOm0xo1zkkkyl4S9xWtwF/s6PN94n9eyf4os7XbL4ZhnJypVe1fBPOkQNBXRk/YaIlUZ1Ryfx+4oH7wm87+5lRHrVUJ+hHurwws4NYmjkCsVlRRRtig6q8cXUFWhwMJ3v25mSxDA4O4u/YoccT2r1WAsIJ6t6wJqb7xPzsBM6ieF6d2NkbLPFe2G6cyK0KOJ9L72LzKFq7dq+bOafI7MWN4yAiJeAEzV6yRfOsYvZyZw0Pj9ia+Meie/FdbMPDqJ/K3Rsb7MWOUjsFW05098pW0L17SXRt6MktAys/dN0b10rJavHdC26ryGYL7LwN/r0TuCaO3zvBywwWxC6yLkh0zYNMEMIyhtPECV8UHaj6jl0P3asqb1Ky2U9g9wJIx6zZbLbG+NkJ+e/ITiDRtQGmGW8iwJo4OToG1w6iIuUG3SQRaKMyront2Ql8eAVSGAxJNgncbCA7MJVs8yCaEOFDEpBZENWnUIW9VbqlutGzE/w7dnz9xjxcuYPMAnPYki44KpJV9MYOKW8DJfyO3Z3uvRPc9QQvx7ASHSnelNGRrAptWSmWK1IdIKYZVes2eyNUOzNJBNqoMdAUvNdavca/hl8JqylC7smhI1kV2rJSLFckOkQjWxMv1vdOQLJZZJPwQZi9mGd2SLFR18RrbU2cHmMwiJARPHoik5FLovKUXKgsphlVe2hmb2wqlBu4rF6vp+9XyZG6KbolI5dE5T1UFlPHamoZn8B3scEFr+CHbYWC1BR0k0SgjRoDzRCZCuUJ8NyEYGIPYhWRlhSZZOSSqLy6skkiOGZ8CRFEpurmWkFgpNxCiQi0UWOgGSJToTwFUzr0LSAtLJLJKN4oF5Qrc8iFNfGGB+jZCVgT4+oClxyy3tB1Ki9YadVhgiZdmaSLourVi6bqRZHul2QVwFvhXWzpoRUglI7Mcg8lICOXROXVlXWK4nfs5OGugLQaIDLBhChQALgmxm3iDLoELdIzViiCJxuUr4WehJND30+MwwOfSGQ85NOMjZ8Ms8wSG0tiCjn8mcAEqcK/DI6EaHLIfGKOCFZaagPmyfPE8gMEM05VtczuCV3JiRwl+Y5d/1Z5njgMAIlwfWCDmnlsUfOjVgAXyVQgqG/KfU2QgYcWoVuhVPFkg/KLhTHiVlguNVX2bQj9oEFR9QjKN7JsASU+2/r6+sL38ZPuZaDW5dC0KBzayehevybG+8Tud+w6pK4TspeZmXMKzHj9TQR7dsK+Ywd5fIR0aLKtgBad3O6VbReENjA9dqsyt4GN2laQRFA9gpFEcqQi35mpigWHGFBG9oEB+c2OnKSW9NAk9xVA5kNTshXG/RBD+cVCD7yVpKRmszkwMAACtt+fmJDNonsVSnckEzsdWmX3yqFRKQYT+YAdyCqJWwLbKA9Ekju1ISYblF8s9PDdy3R1y1aWtQ1q4y1IIqhexMnqXtKK0CJxS2Ab5b4myJ3aEJMJ8tvOyW928FIkLEhUwIUHpdKCRMknaFHEO+WcOCxAXvodu1O0exFgJDt+x66pz04wcn6woKRu2cqytAGfnXAUBov+SBARBSDHwEjJCWByhGcn9IuWNtiq4jjZyJldhYgMQmDaeNPMICbNA17dsj1y61/9VmoDfQfT1sSL+6bod7nsao15EI7IjVskwKVdr+5sF1ktLIe6JFiRTwmZZS9YEQrYAHkXGz9PzGNNo1IcNTZ2HGISAxfIrpK4AVTNHLUS01EAoTKAyB7QdC3lo4grA9ggZtxUtcFhRm2wEhREcdXZl/xmR3yfOLpB37ENJGJmNhaoUmhd1aGplZiOAgiVAUQGg18T43yWZydw5jzIszf3Qw3wKjHj+8Ry6MfZveFvcGI+Qd0bkM3eDGyVMtxUtcFhRm2wEhREoQ1/HUZBPoIXROaWCNONRkM++9khsT8VEKxwJTNXHZpae2lDhG7di8XT0xP4mx1N+VZFVRscUgq3QWwa9wi+dSiIQhv7thGBmMYl+MqoCIAGCgUTKA71MDM+K1jJmbsdWvBlIJMiIhMcXYS4uqiurFsbACmF2yC2issKC6LQZrbdy0CylbI/EgWosEHMbHAcFiO3ndvAIdc/T+wTrUR17RHdqrOVCS1e67QgeXAWRe4+sRyDR8fu9ScmC1G/oMIGMbPBcVicQfcqGfTpcfzNjkKA8mwHVxaVszJ9WrGW2ViQlonV2gkbbSOJLIVCAjXc3SeWEdJlMcwGmhwyTpTCmMkociKmziQmQM5kyDkxEyccM2nUjSOlkRraICoX8Xfs1uH7ifFvzfFvdvhkl3NRs/UBJ8+xlJMjV16ucGuJ3ztx+073LjYbA0Q6uwQakeIhFiGvwWax58XOpoInJTMniGxO25ABiFAYkUpks7FQPDTa8Oz15Z0OTUFdSzhmv9kBpmKVGXQvamwWe1zMMJsKnmRtgE3avRyg7Tt2/j7xqTB7fVIyrYnT+8QxTnL3xm6T7tWipHGyKQCIUEjlSiqRxdbx0Ggz29mrgmwR/tAi8FzvHxhotcZojwRixVTUInNczDAbCyejexmwIO7v1/vEMUTvvXtVh8wrVdBCT6roXm0DYRqCif/NjpiKGlnUHBczEtvxdK+OdAB3L5VnfeogRadS91a0wQNNEanACTYWunYvh1x8P/H9ep9Y1iTJCkRVLFWBmGGJYuQeF0V02xGqcGJmL4siXBMPJveJT9HuFZEAH9r9fWI1e9ik7TR7Gf69E0iWCm6/gpInthmR9pbxyEpr4vAduwP08jwYJDcwOGAyachiRSHxeIdPP6iyQ0gmRG6BnLjVCSHJk10bDujv2N3E753Q54ndNbuQbB0QBCIX7CUj36Vj1e/FZKvlLZqna2KcR+lohBHCT90kBUpKRoBNiDyXSELBA4t0Hnq3/D+la4nkpJqNCwjJ9SwUYxYVEZx1podGWWiDB1j1yCIQsVbTn2OVesHFzLtX9Zzn+yFqMQoZPSkhFQYhfnai+C42k4Pw4M1eEZxcfhebHNqD0L2MjA5gmxHJQcpLZi8h1EAhgrPO9NAoK7YBrUDKweSUHlyE7qUcLjkTE/pdUgdQxMTmIDn4lvkWMzI6gG1GLB5ax+6ld7E1GqbyRsSe26CHFjIs8AArkBgmAJic0oOLZPYSdZqeJ6Y1MVtDaS45+Jb1fGiaY1Z5aClCDRQiOGvPbRAi754yLPAAq/WqCQAmp/TgosfgwIg5Oc9Zez40zqPfdqbniWm9QYsNWWbIIkQsrEYLkmz1okUhhUURrSQZLR4AAP/0SURBVF54UaSl2aKIVF0USV1mUrJnJ8JhCFAKNpLiQkOuWBYVEZx1ht3LGc9T+MQ+Ps4PMXIJszxCDRQiBCvPHL8mBiQV8voCKuDMxCoyTudkTYwjQSMdBgb/QMADpoNKI8dM4VASCwrObitdtAQ/MEWYzHaqi0yZK0wWb1hk5Oj9xPTsBF7IOfE1O76um11STPapwFdm6pOLTGZVyZCMjzKtie39xDowOdhshVWj5up3H+IcyMSBz2C+nMhqjuCgihEjYVVWogLOTKwi520IzMEh+qp4VJlFM7iSGB27N6+VWFCllqVM0tloIv93a2J9diKkjtPMp5M0eyV5y5Hib3a4Q0OYkOIEdW8K4oWMcpZynOqztwQuNQ4J09MjIyPjE/gVFkOpezsgKUb1pHZvq9UalvdOWGnCqnLLBZyZWEk2KKMzk0uN48jT+N4J+8KudW/8kbsKSTGq/0/M3gBldGZyqXEqyWn3dkHCQbWX7p2k33aW37Hj33aOVy9hQZKsXoDAqxcSzK7kUCS1YrfBXlgUoUV2kbQB1Hp47wQegEM4KskBJiSQ5zYBJ7V7KYf/kKYbzYb8qJDguGZvsiYWZI704GxfIFTuDchU5o6M1sT67IQOFQ2PJRlIzJkgw0aDyhyw03hz4pEOBOIzk3LHJFXnSuYWkrgVbyjL+4llTYzfscsu4WBhIywCbB0QFgSQ/H24fAXQIVW4LS4jsAjt+DzxHXifmN7FBp1eeT0A5LM1GzUV8sqAvHq0cRBLYeYUuFkbck5AVVlmr5y9JQ+VbcjJ6ZrYqpz47hXkdrF4t2KKuLD3sCbmZyd05tgcg+TlqKj32QvM2czePNmzE/m72B607tUddeteQt6GnNMdWZ3SoZGh5L2yDTk5tVgVO2qggAhr4tER/lp3QLVbQNEIyOy6o+zQSj4qDy1Ha0zXxJmfrE7uVkhF78fVvcpNi7CT6V1sVtJx5lSVZXZ1lx1ayUfeD9VU2RaQFeVuhVR0cpzdS8YCt8ptEVVlmb1L98KKENfEu/ds1N/s4BUIrzG6rV5CEiOvXjotioTQ1a2pKbn8HTsE30Al1WyGh6Z7FSBP47vY7P3EjO4zJy8RE6yJ7fAr/tBmCKXcC+hB/wc4jR2igdbEeJ/4gA4qjgTLfpijQYpGywRlqqyjbgRyos7ZjmT1lpQ6miPYfeJN+DyxvZ84u66L6o10mU/sYX0AFmeMZJcCObGw7JxgEhnXxLftHNqy/X7obZtRNiA2ZAInBvAQ06h1G2LIgAc5pfzP24ggKfT84bK43Glh5ijf6glIg4xbDEJcnIOJiBnP3mA0ibp3dHSUbu0wDyyB6MUAbgPRu7UBMuBBTum4u5cDtDxPbPeJeebEcwlm2nHNXltJaykkTyjPXu9KLHafuPR+4vzQAcfRvQExGRFKK7sX4LTjm70xU5C2AUn6P8BpHWdvkEQwnwInght+Fxv98YlbAm3AXCRHBmgRsylhFUVMRoTSE9G9MM/p2Yka/o4dtoFuVkW1C+A2kNseuxd3k1AKkggdutdAwYQOTW6vYXbKdW+QQGAeCKHYiwHcBqIbX/8HOO3Ed2/cBq5yMroXGfxfnp3g+8T07AQsMHSN4eREKKxecEGiHBOiWiTL3UaU40URpvKiSGVx4r5jB8egh9ZT9zIexO71gr2LDVnUBvpPZbZFmCRC8dB0TUwKMhwBwZOTwIKU4+FmbHHCGdBDKa2J+V1sMgAyDDxIPGCxALkXUOb7/0rjIuQXBHLuySpD7skkk2A0FuR54k3yPDF9x84u7bJoYNlZomu/Cf56D8nffqMUmLGMQu9u6dmJ2/E3O/B5YhigMBoOqQ5wpsohjqqZZxpiUSIGws0chjCkYTHEiWaGfJqpSEZItBMyYqalgl5mb1xFTXRoBG2v46EF1b7+/uS9rY6kcKbe2sD7g4zaIErEQMywe8Esa2J9ntjNnFNh9gaLqHyfOKyJ8XCLh2Y4ru6NOMiaUfdGmcG1QbxpebQ/Epka4aTNXnXrjIRUB1B1fhcbr4mFU9kG3h9kgY2biIMsbYNAynvqXqW4Nog3MoDcbDYHBwfJIGSmRngwu5dEpyvYTlUgmPBvdrAJs0I13h9k1AZR7CgVJ6J7/aGJt1DbBAHqic3tSo6JcrafhO51+zM4U29tYCeQURtEmVn3Gjv6zQ59dgLXG371AnlYtAQhEEhAo1q8gPLxLIq4SL2BJX0/MR8K5l26N8KJ614WpDzuXgFptVoN7xOjV8/FLeWE4swhoO5sp4nMG8qtQQzXLEfqAEc1Lq+J71m74QB9wW5/feJAbUIESiZ0SY3M0nuiPZZT5nb/6NiR8cl71m2+acuBxcfaCw+O4deSTrV00Ktji462b3+g/z59PzH2vh8LRqTkyCt4QAHOu4jFG8qTmcMQGgLEIiWylhrtGexkZm0g+ArV8CzeUB67lfJ6vQGBj5tCJhKDZFvIUeATFXIpgw1LIkspGnnjqSRYpgVYoppYcOsLQSFPIB05ePCGVVuX9MF8bsGcWaSTh37WjoSOaSFziJwUWXVzAgLejdY8SmqRKs6tVV8E8/nw+J0Hx5au3UzHQSMgx1UFOdwKsBPfk7qhvDhzhIYAsXpyKWLvilCPncysDQRfoQTnRFjOErsFU9YGpqiiEB2WmOPj422Y621KOOmdLDluIJctG1BmBacyyiQ6EspsoVI1soxJ/jFzgnhCYIFlTliIwtgYfseOLGmaYCNkzogVIzVOUZX2hAgu0U5JhP9C4zw02KfYIRtr8i62KkDRjGeO0BAgFikRIu+usoKdlOY4KdU7yCvE4CLIxbezpG7BlLeh+6EJVytkKLnlDeXFHQgN4UQFjClciHft3r1+x+7DY5P7a+MP2uplBsm5hTXYwWZ710BtxarVeER4yiLoaGiLiXT+z/9UZALpJDBRN5GNWKygqKWk0H/cULlwxCaUYMNK8A+iU7g5xVzKe5g5hJhcfp4YEVUCpLoi30MJ9Dt2Kzds7puc7puYPjYx3dfumIxQxfT2rmSfOnO0FFo4PD29auPWW7cfXT48taRvamk/piWUs7BkILKY7DlVQi5b8sYk93YWrBTSssGpu/bW+P3E3UcE0AsHIPOS5F6QMos1aXYCOriNimQq94qUXFWZ7L16dm0goa8Pb+1Ab6fAz4CaB6R6Z8yAXaTiOeeKSDh26NANa+5fOjy1qG9qcf8UPhRECQSWfZ4IueyNPiX2nJzIiTFY+qfuPja1fMPW6ano+/iCHkftxM9esPyYz95ewHSqMjQ0NKP5+JBjsj0xMjIiyo8JBgYGIJhwzwc8VLM3wqkwe2fCBHQg+yJbbPWIlF5VHe2wpoNh3bt379a9B+D8idY8VXLnZMyqKkW3vfgnDrSwf3L6YH1i1Zq1fPmAmUJzswcUiXwNStDBZVrEeocKgkaj3pR3seUjMuPZq/eJK+BKaZgr3MPBE7i0wIF2j4+N3Xz7nXev2rBo9caFqzcuWrMpSmRZqIJZQortnZje0pWcMylBCxev23zDbXd+787lP7hn4/eXrccEwvIN31+24X+Wrf/B8o2cc/r+UpGJuQFpUgtlsBsZLcvQwgJyQJaKJGsV4qDFkdmtVAS7dwv59Xfdu3rtWuhtHCkesYDy2CX2KlLXIa4GkjtXmJQn4oUVyLpXhm9Dfng5XGmokmOmhwYMcjV96PDhRgN/MaveaMA1eHhkpFavDw4Otlqt/oGBRqMxTKjVagODg81Wa5CYA0NDYAEAk39wq15vgDxaq43URmH9ARWB38C//w7V63Vwi8Z6o79/oNVsDQwMAp89gwDVcXf94Lk+NDQ8PALG+sDgEP/5GFxB3ZFRQA34e/fs/e9b7751zeabVmy4aeXGmyGt0JzTyo1shxyTlRqHjSu0yJFFjcksGDlhikyl7JZlUjdA/qPl625ftBQvL9L3OaKSKlo+xNUOEzC5I322s7czHIOrlDHT2evgyYWK0OnY8dPT+/cfuGfN+lUbNq9cD2kT5ZtXrd+0inKSfZEQyO5lLIqZJgRVHXJe5AS31IAK8rqNK9ZvXrFu88p1m0BYiQISVqyDIrSDwIQV60nAREVIRgGcBDLKRBBmB7eBzMyOZG7DpnvWbtx431bqber6bjOn52EOVSpRNXtjzHSauZ2icKLcOnhyVcXIbSAdX/e60lAlB6+JH9i588Y7Fi5et0XWPLbMiBce5dSVHNuPc1HELbzj3rW333X3+PgYXFPwSjQwAJcquKzApQWvU3BdoysLXYPgojcCpXh1azbxkgdFQ0N4sRkd5asbGPlKhJcgMA6BsdHX1w9GuDDBJa82WoPLVrMBV7d+vFoNYnW6uI2A2qe7wyK8uI3AdRBVum6CEXmjo9CACfw82eNHnC4suk/Mv/AQYIqzkih6RHYIdpZMx8A6Nd76p5sWX3rXvisW7l9wN6WF+y/H/ADLZqTcGxPCAaxlFmCKoAQvsx+zJPtK7JFx32WLDv/VDfe+6wcbX3froTfevO+Nt1C6ed+bbtn/xpv3vxFytqDgEhdhjqVvUkGT0jCBN873vSG4NU5Cdjv1bZCGofCGWw++6fvblq7bAqdi242oSMEAcENjILFEFsSRhBmVMyeQySYFEdngrOQuJbvyGbVBtgASRY/IDsHOkuppNSmFj6/HjvXBJz38s3J7AnL8+83EBBja7TbnaCMzkMg4NkHMCaIqc4wImCMbStttWOZCzi7Jx3h7Apnt9iQyeXe0XzJyERuhBu4dqlOORnQB1ok2hKCjhw+NDg4M9R+j1EeJhAGWSR3oGyZhsP/YMNj7rIhKIUeyVQ92Iw96u3gu74525I3BLXgb7Dt69OiR0OuCZCwIJIoekQUnbfY6zGz2cklySS1VEx4hIjsEO0uqp9XcoRHSckQgw9QC/op1G+b977bLFx2cf9e++Xdh0O49FflmTEoryBjwSeiydyy9a9+8hYc+cvPGK7+37OKbD7/+f/e+7qZ9r7tp/+tv2gcJBE6kigAEllmwIk1sRDkhs4WMbEGjq4tFpAqBq7tScftbN+2bc+OBL/zvPVPtsTYuUaPBYDzYs9fg9B+72StbAIkRK8aMule2ABJFj8gIfnZi9+7d192w7vLFh3RpERYkMGMLqxexp0bK/eolXsn0tCiyXWdtCPnB+bftumHRcjieFl1T8PKE1yu7ssDVB/IWKHq9aY/rhQxykOmi5S9MaEQ+1XDXrGCEixN5Bpmug+S4PTGh10EwQg525Ev18TG4opHDNiys8XliQDxmDjOYOfzsBCuJO/n8GBD0tKQMYGFDkIyzY6z5tdvXzF0zdtna1qVrqhOWjqXGPK1tzsMc5DIZSpFgqYPbvD1oab53ffsTt258yy17zlvenrOkOWdJC9IFkJZSHgQpmrMUBbDMUQILopqRqrBsddEe12IOFqlbJscc77Z5wbLxObceW77RfseO+j6MVZAU5SGmOJeTAWx0RSKm5Go9LQGgqWz21owVFJaiwhM7ex3spgpu4ZMurBqgt7EA/8SDNVQljRXWuEjMYmUKZ1zXiriQyaQimB+MnAVXmKS6OCEK2nALH6zhczaZf2wwWqvRKCSXKACNhTeSXJy9UsK5FYpQJDsEPS0BoKls9laQ4wkZlITJqJq9RXIJwMJRT8hZG4KeMAFQH598hXmzZtOWuUv7Lls/oeGxqeG0l0BdHXvXtgrXgpRMMtM8GWRRU89z17c/tOzQgps2vWpZ+0KMmU2IlhxmKaF6IViWNiEnQZPGVYv2HNKpFMmBCYntTOMkRvPvUtIGcsttAOGipc1XLh7/wl0bpyflhwcS0NjwAP0Yz97qy0oGYHWdvYCg526JnJlnEhzUEqNaz0rk2Yl91yzcd8n6Nqwo/IJknszz0nmRJyHHRk7RSVFcFJFMtGRRpG0IlkvXjb/3nuEbl67k6wccBcVgFElAsBFzLuWMjcTk3Bk1g5zGFSXMLYmRylFBGUEai7g1u1DYHQgtWL+PjZFv0OG/HwuQZzZ7cU1MZi6LGWJLilDInleNSh1Y5fvEza/dtmruyvr8VaOXraxhWgVp9FLIRa6LHVXg1NHIRWQEZgUZckfGxG7VGJjsluoGfqEN81eOwvL9k7es/92bdkKUvGjh6IWLanm6aFFujyxAKHEq04zInC5iAVq4pHHRzYfx+cvpafiQar1P0IFIIIbEjuqMhjglc2whWxbFY2Yo141T8zawDtPeCkhgLSaLLSlC4bgOjazgFdoAH0/b9NNTul7zOcIfC4kkpE1iKBfsVoK2hEy0UvdWumUabeDDNKyJUUEd/omQIDFQbTLqf2E4ESBC7laFxC5uEysgJtRkTYwHQbmDGBI7qh2GOC4okaEFlJtIYCFmhnLdOLXn2cvbmCxabCT1uGcv/qM2JBVTJoDvda3ZuHnu4sOXrWlybCzHXgjOrHYJ6d1jL6kW/0eFTPHfcjNSRa1LxktWtz645MAVN64/f8lYVdwuJgmkM0m9VOkS0hfWLl40eu7djc/fsQ7vE9Or5atmjhMYqOWz1/JkiLvOnO6zV2aOQIWEaYbEjmrXNiiKZNB7nr1iULtTy4cWCQzWKm2duxcuDXDu7Nmz9/137oaVKM7k4uzFfEYnhSN3WhTZGWRkcSt8S8Ht6GWrG3OX9t+4ZAW0H/sCjlC6xOcAO3TdKJs1yhEkWa+KM1NdDnBlIkpR3Abd0JblMfwdO/7NDjCgrTRz8H88e1lMmbAmJpPYHR8BHrIaAGcKIkmYicl02sDsoDXxe2mcIHryWF66qi7CyjomEmB4gjHIRmYmjTdalMBFiQze2K2Qqci7xWSqawOsideOf/LW9W+7edf5S8cweC3WKMYC5ItrF1GeGOeoioLmVFQngXMUmBkIYteEqpGD24xcJ3V0zpLmRbcc5vvE/tmJeCwKwJLq0gBzI2TTcSNb0fOZ48mIuJTUxERIbXaiCEgTk5WwkLRBxYJJJMzEZDpuZBsgBv6OHUpgyEgGLKkuDSCOI5uOG9mKPqvupR+jHh4exrigxQkNdG8RTUxWwsLJ614tAUxODgzgS7WcKQWWVJcGEAcy3Z/qbqPCqTB7EY6sYsEkEmZiMh03snWILZ6cYpKu62s2bpm76CisiTVa0lU2CqFij+SZxF5kauwNhBDzxU7XDrlAhDZwRSnCexkfWbTnihvWnQdrYliPLtQgiQEzxF4W0tgrpalaiL1SJG6FQCrI7Faccwp+UjI08pULG5+/c/305Li+jC0DzRw3Sn7UzMzCTGavlYjJk7KZ02n2FoAlUqokRw4iSZiJyXTcyBagUrAgPLkALKkuDTgJ3QuXYDh39u7Zd+1du+ethTUxzlibvTSBWQizVwRvEWOoRRaTA5nONTxBiKkETCarvXhiolzDNfGyfr5PHI7KHVoCLKkuDThps5fler2uz06Qjpj97NU1sYcYwslZqpggrMvTLQNmx5jcJ4Zh4PHjEcKJ4kZFZR4q5MBkyshJRSbztBAj1VJZCTyfqGKpDVSdZVkT30Jr4iW0Jl5E4Y+CmgoW7GhViiovT4EgHAmLYgycizQgGpOKSgKvvOVmg5FjAThAWOrXxH4AQITRjEcEkY+aQ2ryH71YyiqJuThzMrKg1AaT0ko9twGPl5CeCUV0nL15fbU0mg134WDBs0+l7qV8fHxcYgcjrfQgdG/mNqpPbsli5kazCbmqnn0qda9nmJRWmkn3annGyOHboNUMUf1C95II9ohHALf4jjAIJqs3bZm7+AjeJ6bIGQKmqhQwJZaSbKVp7CXZYi8TmIlqyW2DZErObZyYLFf6uWta1y07vODGDbImxjgpQXJmsZcTVklir+WR29gu5Or4b6X1ixfVXrmw+QW/Jk5HYyYzR+EYGVngZ47CpLTSTNqg5Rkjh2+DVjNE9asOzc9eE07aoSkcIyMT4MoA584eXhOvGfMnRZz87OXkT4T8pEBOiexlUdHizjt/BrnVsDLB7ermJbAmXrICWg5HRQd2inavd4tPH8c/FFCoFCwsZQwx2LMTMSGjE+zyT9u8VjX4PnHja7fympg+30BazeOhA6Z5NntkzHjNCkkJWkXs+KeBMOpu0kjORfxxDchA4DaEScMJ5WhNvLQl4UxjGcthYWqxjwUrFcEZKSBaoHTkmMMy5roLJLBD9WBVkIC06D4x3b6kri+OWsXIxR+uRKngCuLSIjcsHGmLWZHHiNsQ0MFMRVZeJla2oUwP8PUcF07fY8f6wqlIRdjbHfx1695C1dhUIICxp+7FvxvhN3/ptwwKhEIVhHmz8jLxRHcvAw5tYGCAJNJg08Hfyene6kMrIW5DQAczFVl5mXjSulclzWLOZLYmPs7YSxaKvatq8wLTirxsiYy4ayZj3KaLiNi9AM2DNfEHl+y/4sb1vCaO4qpESw6eIT6zhQQXe1nmJMG8OvYyrSL+cxEJ6oFUbMPC2sWLcU2M94nbuCbmEZBxiIcjRTJYso0xo9lbKuMqYbXiYN6sWqE+oNgG2XSElis34p+Yy0oFcm/l+tVtsPvE19y165K1LVp12Oy1EyeexjDzcVFETFkdaV5xUlB1T2DZ+WeVBL+SFoFrQU7nlDw7QfeJdQVROLQCHvTuNcDMrOFvdvhbJwHFKgwr0nLZnlaqQI8uOzZciNJDDk8t0+lCxfRfiYFPEswOd59YhoEGRlUaxTAPaN6oTIlVTULWIhlsVMPkSwUszVSW1ahtiNfEfJ/YQhvHSszDEnmR/kWMBU7ENLvImISDMVEs6hYjaZ3vZFhiWnALquwX7C7ILmm5NTF0ezZPfWDrPMQg+wfTdYgRVUOMd9j8zBFj2oiSW65hkgBjiogK1wisHPMNoQ2cH/fsjQAcodXrDX5uW2jpoeFWix/67gV1bGy8SbddyXDqdi8LrDQaDaH51nZuA8idupcchXKWCt07g0MLO1Cc8t2blqU9hlt+dmL1Rl4Ty3UdI3On2EtGyU1I7KyyrMYo/vN1QZgW0tEou5MiJtPVRCxzV7c+QM8TJ/eJu8dejKUaezViF0J6HnuNY7U4iVu0u5vTyKG9c16j+8QNf584mjk6xIjC7GVEs5c3s5m9oVz242DOCmUB2gYmn7DZqzuc2ewlLW0D7NY3H2UrBiKVeT5iNsGBz529e/fSfeLw7ETn2cuWsCgSu5SKPSaHJGeQ2N0dYn8GiaB1sTSoa+jZCb1PLIcK6Ni9SiIkce+kdS86kxpT+O6n6Oupsh8PraxtMHiN2oD3iRlcx3bLSFRDIGPL5SZ2egwEsYU1cYNmhg6DrIw58VChnUaOLZrjALNRks4PFjiBSrm5lVpS6iaE2ZUZEpbic2lrJ/A7doU1seb4ZFiIgBDvLOSZERMymUwCGeeYE1YpF4uQI89SZKpV98wlzQvd88TxVAXI+PgckJAMgRaGWKerQGaT6QLSIZOzKkLWBpISEiMx8nnAUl6D9axKGYFcOXvzQ4uKWR0cHJTniREnvHsFRR0yH6YUXbq30aiPjoxyP5pRcSp1L1VmcXBwiAXWfQ4wIUGgzbZ7S3Dd65wVyZnxpHYvSg4z6F6FOzQrg2OU79jJfWILkhZ7NZZacoG6S+wthHQrikN6sGOK3aosOd0n5ueJ/X3iKPaG4ElqEntBtYhKMq2qPZlolMitJiKb6i4BngwCGYVJKj87Yc8T28Cg4M4g1kkKSHVC0Yi+eEP/OdcsRWb8MZy9GakAboPI8t+j6KHCbdq94JvWxPrsBM/VjrNX8ui8CB8O3RlUZGI6vkVR7dLVjUuWDdCa2B/mcXYvETFLaxQ9VLhNuxf/K7XRaNC9Hmi0lMTodfZyHtbEHlrP6ptAgGU95iQXkBQQWdbEK2lNjMPMgxeNhw0VCipTzvMJcylie4FsbnVO+HmjFhGcWyvSNvB94k/cuo7WxPrsxKJwBxfipg+IGAT5b2EcAfEWMjCZLMtWV4UFiZJg924pD9FThKCqW1oNA5MTfgParYlxblQOEEBmmCOVRo3QyU2AsDK3MdiccAsCQ6dyVtAZSq5w62ZvyW1iy9sQCENDw7AmDoyAE969gi5uWRNbzKEMYketVtMG54fWE5RsleLaJ657GbXRURZcwanXvYiY/GPSvSWE7uXr+upNWy4Ja2KOkCREsbdjSDeLyh1ib+yWaFSEZLmui50ETiLzsxPXLT244MYN+N4JWhNTkgCrUbcQezmuclAVi90GzkI6+3Fkvn8s8R+MRiYO747roiBuSb14ce1ctybGfk9HrSdkQxzXZk1sOScmPzSzN4ewlFxRic0JtyAw4FNHSu0FSq5wy5rYcCNr4j17r8VnJ8bm4+y1iepnb5CPe1GkhJjsFkWhOuUsmKUO5ziuiZfimrjtjoaORxVBLjAepO4liAAL4vExeu+EGI5r9vo1MVqqXKCd5rNShFjBN7MKuCZu0ZqYBx6GTQYGxgkfLOMhCeNEeYiAahGyn16+1LtlI6krwy48Ry1RG7iWrImTZyc0OHIewlwIdmQkS4h6VMp2yJUAOYfRhIx25ZiARswtRlMp5qQKzT1PPElfsqPe5yEIuQ5JCqT7IS7dh3S1fWm4l5HD2VkUQwXfzCSU3cYcAcolMgLtpdlbASslobIN08MjI/wuNu+2REacxO7t8cScxu+r1ep1Uqj81O1ew6S+nxhl+V/CQ9+9CDOTcMK6Nz60SiLDSknofGhSGHJPhqUxX9flPvFavU98ImMvxvwo9oYLvDOSELs1u89RmLtm7EN3715g753oMfayinYJ6SnZBColMhqluqga6mcS/7Pv2NlwJ7DBiUapyxCfhNlLdl9oxgKOY/Z2OzQpDHk1OW7DCTsxO3WvWxO7+8Q8UatmbziDdJ3zoC2KQAaavouN7xPTgYRDY6kEKyHhQeleBWqtVkvfxQag8uOYvbAmxt3ipAm5p5gUTAC6k+D/B6gGbu1+A2jyLjb5jp0MBgs2MJERaNE0CjKqMLqhVL42V0zo2U0F/ONaB7eQYyl+V8OeJ97Ja+KLbOkZJYx93o6yRFUhaBEwjYzRkJh8O0HIQHDk3C3J4tYSVKH4C9Gf18T0fmJ62z4gjABLpSE2RKYwxJDFZNV4iJnHOf3XYpWCDqAKQoxLvMaNhw1sSZT9BKh+fLMXsqjEtDB7nVvXBiyc7h8YaLflMaaMTHqEyNRzG5jHOf3XYpWCDqAKQkxKwF+9Xh8ZGSGFym3fsp8Ar6tDzvk/w6RgAqBXJkIWlZjWrXuDNDk4hM9OVLfBEJl6bgPzOKf/WqyIdKrgyA5Oo53jBrYkyn4CvK4OOef/DJOCCUDe6T9kUYlpPXZvrhNRdcj4ui7fsZM18fHFXrlsc6nYWQU/HHvZIipasFRpLFeGdL5P/OHFe5PniYux1wdqKuI049hrZJZ9SCeOCK4NmFAgDlxZ5F1s7XF+P7HNnG5DTBzJ6b8WKyKdKjiyg9Nwn7hBG4myH4YWKlTHrf5nmBRMAPJO/yGLSkw7MbM3RmQ67u6VjRMQVEGIKPGamJ4npu/Y0fdQO87efJ4nxnBSxHZWO56YqedyG9aE79glBxn1QwoxCfNB6V4FSo0Gv4sN3EoJMXH/uh+GFipUJ6Lqep9YC0UwkFsG7jCG3xvA3ZpBSf8TYHaE79jRojMMiT4MHsIlGjXn0tgeWdQIqbtbS2jXSYM0kgOZnieOv2MH8YuDncYyFii0sd3CH5NF9kEQwqL7Ux0lZhKZCE5AppDZTkyt7vzLYtrfJ6aJGYMGEKxJQcaLLZGiLiyXLe6MBcwIEBFE8ijZAK6a+29win3gR6idt5BHtUBN9ARRaeJW/9uhkcIAE2mNhr6LDTPtG0dEJCogskSKurBctsfZvXZiwodpfhdb3DFOibpXweWQR7VATfQEUalzi3b9b4dGCiM6tEn6jp1CyA6JCogskUJu2aJ22p6o7mUH+t/glFOuew1kE3IAf3cef8fOvmOXx16+cVWIvTmZUxrqk9jri6g02FO3cs9MBfqyiqyJ/fuJNUiWYi8skcmo4RSEJPaa3XNYiN06vpKJIEYW0jagZdQ/Tyxdj4gGQ0fIctna7A04EbNXZ18ornBLAJYjIvLZGxkixblFu/63QyOF0fnQHBGRqIDIEinkgi1qNy0iglrdD8gVNq+J6V1s8t6JHmavTfUOiyIrDXY5Mc0hGUXovChybcCV8eqmfseODkMO5SHr3ryfkzUnaNNT0/i70PQCKDsXep692i5HPA1kMDuLB5rFEYrMKnDVFHaQknBN3Pra7SvlNzsw0aiAGkaXBRwzGkVOWMSDCmNGA8xzKxQlZEwFt1yXaGkbsEhVIeP9hsKaOERD+txvIQ/CototwrJF7Y6syTORBiom5nAeAmhK5iRkSfR+4mV8nxgHQIaBxyIdEQGYdaqEs5k3EcimZKeUuDECQ6ungMnaQxvC0dBuI6UIZ0Y6Vyhw1URtAMXa0A3Hjh3Td7GV/CLA3MOhiU3JTilxYwSGVk8R7LXR2hDfdq3sByafhO7liARKT90ru4V28rvYqEpeDyxyaKEwZ4Xq2gZVStwYgaHVUzh7r0M86+4lNYYUzKJ7I5d6CB7Q+3Rd5/vEl9LzxLOKvRxyhcaxl9Ry7AVB9yIWJmP8xz1yG4iJ97fIs7YKSmlNvM/fJ+bAS6FSwqnE1RBOMfBaqcVeia49xN7wV0QmCJMX3FgdSpUvexG3C7EuPjtxh3t2IoYOlA6xKikvR2Bo9QLUbjOnk1/aLRHYYwXXmZGuFTJIwcmYvViodnNa8q6etA2qlLgxAkOrO/C5E36zQ+dnh9mr9hksivgMwnPKOUG3ooqFmMVFEfuhMxTXxA19PzEeEh1f4dAIzm6jJpsIkROnlLgxAkOrO/jq9Xq92bD3Kcm2BNotEZhV5Ja/Y1dClZP8MhgZRME1sdwnnh9eF2IpHjwKfCiEIfRD7skwljiuIFPE9AlpMm/Uwm5jI9pdEqNbE9uzExzL+E9vmLNKkU6inkRGioPMsVLjiEreqAqXWgwlGTlqxyImY4QlD0pWghjpPrGuiXns8/GaOdBXghm5LZLTKU6ImPFeT0gb8iq9zV6B13AdX6/XJu137GaNB6t7wTQ21mrJu9iSvZ6MNgBm3b2qTU42m+5HRmaBE9C9FEkjPCSzN8dxdC+iyq0Aivk+sa2JJSxLmk3s9THWqcgPRg378SXcOGZE2dxqbMd3sbnf7OAIyfFTomVF7NXcxV7H6Rx7SQVmIf4bEwXxIBypG947Qe8nlt5nPFSzV7aMU2H25ui9DdXMk9K9AvAN584eep5Y30+ME1WnLsrJ7PVFmoJFOD0uikgOJ6bUQlqnRdEaWhPLsxPFeZIjPnyvnczuVeCFeHwM38aGd9Zoh3GFvHpnh3SfuANcaRdHBOQwL2XD7JDniRv4Bcwo4WDYnNDPN1bk1SwBWUY9GWmf0B7ckkC7S/m+Dbwm/gT/tjP9jh0FO4xlLNCNYQ2CJEguYZESWeQWchR/SUaOeFNB/wznOGFtHTxjUeoW0lL/3onyOOTozoiA9B6rKK2XNgS3kEsF2JTOSv1QirkwK+BKOxMZwWFXNhKmp/r7+yfaE51Dh32E7gXaD5x3gRJ68Q8uIcRN12s1fJ4YQwftpFv3doZj9NYG5XVlGwHaKe9iq6ijrUX04BYpmveIXojBIeRSATanevd2d9t27yemNTE/E0mpp9iLKYu9qnqOCeg2vk5DStfclphgbtEPPzuxAN/FNi7PTnCoxGgJuQbM2cZeEEohXYXgVixKJg8imFv2lj07cXxxz6EXYnAIeecKP16z15/pkIcKx9e9rrQTUdbE6fPEmHS20+zt8aQIpWVySHgGsSwnBRm1VBLayT8RuA3ACe9io2dqOnZEsXtx3fGgdK+AKPIuttJ+FX6/XdzyfWJZQCkq61ABZmVGcJOU4/XYP08cR0lWZbTCODmVJwcOMFcMA0yqCJmKNFbRTzaTTE3smOQ7dre69xOHwAehEKMhhDMJc6hqfEyeZ6Dwh4JVZ2bwZoJ5CG4heooc4rUEU/YDsu1uzpKGfcdOvmJHICkbkQqUyILilIupQUvIJbdOLcychIwAk3erjJM8e0VzRhAh0c/C4fuJsU1xlUiNikhJyAgwndzuBW16utVs4uO5hQYjkCKigEgJs1CRYeQyo+fu9W2AdvN7J4p1CFEJKTGXAKaT272CXAgA06nWvQAqiKsEFS7rtCbetGXuInyeGONkiNU+fmaqRXiN4Sxr8mpSlFhAFjXE/7TULHilKK2JLbRqFOWoO+vY6yyUiGlOgh2LlCyluCDmIrJAxYsXwZq48fnw7EQKGA9vDcMjW0bQqobY8b1oci4EgMk9eYwgLWEWKjJKZIeTMnsBUREqyTEQwFTq3g4xJwIVYOYYpMqzE/TeibX0287l2ZupdIp1WBRREvV4FkVWqusrfJ6Y3sVmv2MHB+IOC+HVqIiUhIwA08noXgG5aeJ7J8ZhN64gRe+zl9bE4BdrkMk1P6kR9BK5AKAEZrhPzJ9dwjDLXLFBUjuNFlt0OWsRWezGEQJPDrJgqnCr3qTWZauJGchc5J4npvvEEk8xllGuAsVN/InOKGha8CUmGIlGOX3ngwlSl4qIbLXU6N1i8uRYgERr4mXuPjFm1P2InocYs/IQM51zAojG7AAr77kNCCHz33DMqkIwnOzZq/wAXGJC7aFhfD8xG3pvA2blNjBdP1EDQDBmB1h5pzbgFt/FVsN3sdEBMPlU6N4EwQI7pzUxipR3bwNm5TYw/SR1ryIln7rdixsrLLdhsu1+s0OfnfBxkkMlC5aotCL2Og4KaewVAXPn1lV3BBVQdiG9dgn+tvMBfXaCnyfWYHtCYy/btdQbgSnejBOWwuqW7cCMnp3g+3PZWOhQPVizFyVKSDarCHhZYcxk9mJWIhcAFGESOYJYcGOFJ6ANTPfd6w6tA6w8cyv3ieV5YnoXW3n2+gVJKPVrYkqsCjPIq7Nlj8qlE9Ovc4yMuZD5eWJ77wQfXc/dixAyzxwDF5/I7gVwIRjcu9jMrfIlB4jgZm/ZLa6JjeMAFfUAgg9AylXd7CLEPGgorYlv1/vEMAzhYw0PjM0MGDYzhthK00XswoQiXE9DGmWB7J6MNLknjWQuZZUI6lyMLtF7J8ZgTYz3ie3ZCU5xTGSLyPS3ME5CcH8jC0wXauV3kkQOOaZQi+OpqJhrG4IAib5jJ79jJ785zACZ1XhYQBWD2ROCR4eiaKIijx1TDa2WVg92kXTj5lt0WiGiucjILWRLZq/PA1Q3e0oAZG1AEiSw07vYcE3s2+wwu+6VWlYZcAK6l/J6ozHs3sU2k+71JSBb6zK7gypmjEoZxe4NNScnh4aGtHuT6nkbEoJHKMoP8gR0r22c95PQvRFUN3tKAHTuXgIohYpslOeJ3e/YcewNf+jT4GmxNzOWY+88idvygiphOgLL9EAkCLBEQCMvGjSkSxHlogJh7uroO3YQLTWKhshJFo2ualQ7W6LYa/eMbWkLqhICDWWuaHuRUspRwFKys0PML6bv2MmzE7AYKaA4QIJ89pJomdsqMrsI3WdvKAcYO7KiKgYTAAnHdLOnBMBxzV4xa2nh5DN0KnNwPUC51kprs87PHenzxHifOJm9csr4yUyTXBPaA0eKSMBE507gyxmkZ5yS6YwgQc4gIctOKSkTOavpt51xTYyrRUJ6dIROcS9DWuanhcPMute2MEn0XWxgCsYEWBB5gt2xHllBhTUxmcTuilFEz7A1Kwqoh9ZbEUnWjwAngoxr4tbXbltF753gMbPBoHHiAcPbBrai5Y9KSmAL5jiQl9w7dOmK4ctW1y9b25y/buyyNQ2YIvPuHXFkqhgtlM1V4pbnDdu1DfI7dvF7JySuac7PiqFKD0iYXXOKelRKj6Dh82qLRzF6MmchxGtYENOvj1ri0pBQ1b1obgLbMdHC2q+Jo5EgBQYNxjOdnwD7rFQGF9kkc2TaiqI2AIr5zCERMmyDiAKUurQhRjbNRE/M1IZw/umR84ZyzOA/p6rZm/eY+qzX9V1sABD00GL01L3oEyS9IsIIoo1rkkhmAopiMmsv3QsYGxsvvYstQeiH4NZtEChKC9WqLoPFVSApqJHoZAWY1GrvYju+7hV4Mm1FcSwU8dBEJPTSvcHSBT12L21L3Wu5FmVqJKrMvijxlqFtKHUvlLXxuo73iRcdTX7HLoqZkMvF1RlLsXfePcPzVoyASnG7BT4h0s67dzhUD1fuJP7jVYN3yt5AljbYdZ1uuOBvdvj3TuCLHehZYY29PpxK7BU1C7YmhGBem7NwBF8GzyoWaUgvkSlx/FdO2Issr6GRr1zYcL/ZERAPSJg5UiJ6Yj5RszezKZkRZg5rBhV5xqnI25BrkcCpXiyYYWvWzrO3+tAEcVHH7sWM99Zr98KlAc6d8N6JePbyBGaBUsdFEdWCEweWPehnTXM+OKR3WeC5s4I+WOoZ5N3ivsKJCQLJ0YkpTJLrbk3sDuX4u1fJanOiYzEtiISq7kWoxO9iiz9OBpYgzEZENHN8yaR7P3HmI+wBL/wT7ckJ/skuN3WCFDWnAJgd9Dwxv4tNg50MDCWUeahonKyIP9N4C36auXp7e8Hm9iX3jr5n8bE/vmP/JcsH569vX7WlPY/nBySZYewweOO4ica8DbQjaUP4zY5d5y+h33bGhS/HNcrB4kKqBkG+SQDBTiIg3lcggSMjkSlELmlcfG/7wqWti5aNhe92qFthRomXzuqN2qD7QgLeEXHvJ6afsfNwA9Rp1FwlEp3un9bBd+eIVjVz0uoxQkFp5lhp7iQoztxx9io8fxL/KgwXIPnokJKNikLmGs4GSID+/j55dsIj8Du3wTIEDBkABGganN78OguywOnmaiHIbVw9RSiQNrChVqsNDQ2jT9bz+sHiynrrXjuEDN6InsxbpADslUzBPDkwMIiKtSHwC22QLYBEp3sFJdGqDi2zRgilpeO10txJsLiy3rpXtoZg8GS0WgkI+HuWMNUnJsgamKE2wnkIBWCUZyfkNzvo2QmOxu5SKvEzXr+ynZlqpLshV29rL8AoXXvPkr4/vnP/JcsG5q+fuPK+9qUravNWjkaBmgTn1nJePbhETYLEbZu7xr93ghevGnsloarh1CIqFXFOtzC0FuSw6uWQW79w4ehFy9sXLRvH0L2kJWFfa0HK4j+VBm9RwtKF7tkJWhPjCNAoRGMUFCvHTcRB5DMnQ6hTIltp5tpZXNlMZy+LwRBdVuQ/IVIQ5JYskdnvLhR0P7TISVCcGQ4Nzx27TABKbhMoF5db9uyE/I5d19nLyUpRlnNt5eiCje2rd7QvWzs+d/ngu+88+N6Fhy9ZWbvivvbl68dhuRyRtS5aimcQMlkli7VhdVN/x86WeSeiexVwfcCLhNbOODPoXiLLLSn8jh3d60naECFYXFlOm5rWNXGM1DMdBwAF+PRDttxbwb8cJc2O5HfsYGasDp9vcOTC4NHwqBAsyMEF8XuX9L3yw3//C29410/+wvMf+bgzz3jEIx/71J99+nm/8/ov/WjBpjZ8rponn4QwCtskiPywzG3Q/eIeTXZr4gui9xNTaMMYBwllCYuaQhC0yGilLC/E+woX3Dn41Ne+6/EvOf+J57/t3B/uuwh2gXbmuIr6JzZQdXkNOa+8iUCJ2wBrYnvvBD07gQOigyKhK6iKyBLMiFhD5JYEQgiBDreyAgOQkEdRT3bsFHwuVRWbPRNo66w4lRWgs1f+wNgB6AD+u4+EcCrqmti6N5QyVJdiD9FoMzo6emD//gceeGDrtm2bN2/eunUryMeOHsPFsbtqiDCb7sUMPk/Lu9hKnTzr7sW+m54eGBjYSRgeHsYWSnG8qQYS4L8u18WA72KDBottNt3rkFsSCCHu3lBNNNMFkV7qWAR2ETIrioM9E2gbW10bRDA9AU9yAO0eFrhRxTBGJBQOTb9jd4n9ZockDqEYLcPfZFXgy3AUezFu1+Faft6fffXZb3rPmb/0okc+/iyM2095+s+c+8bXfOG/Lt8wcdmaOj5Qwa44Gste1MLBWZz7UrtvAhxcE39wyf74d+w0WmrMhCVvuFsRBeosaLORwv6cu0Zes6r9wk//z1kvuxhC9y+9/6uvXjEx5+4RT+4e/2mhDHuXNlBj8nexpSNRsiQQQghjMqgC0VI3sS6aD4VoeohmrwEJ8D8+MbvPXr+NT8yUWrLwicNIqhPYLYkA0QINYhdUpN+xw9/smI9z22YvzWSZwGH2posio60cnb+u9Zb/uOdFf/T+p7781Y996tNOf+SjHvHYx531yy9+wbs+9K5b91wByx68G2h+sFZPJ6YmKMJzyv2OHVzY/DEdb/dSb4yOjO7atQsubX19fTTLQjUR4iEOjkQzPQC8jI+Py292qMWAFvgfX1YSJEV+TYwmMWNrWcLnYlp7jozcuvrIF/573/y/3zf/S3ve/bmjX/wfLILjxGNFgTTLsFDAjYH+GLPniWkwZJw4uomMD4yDsNrNBiuF4YS0YnTB+vG3/ej+0xQPP+tJP/FTzzjtEY9i9Tlvv+Ly9S10bhPCuVK3ZOSvd4Bgy3QuojbYmji8dyKENgxkkMufw1CGXNepsoo1uxilFgbWodeubT/78s9yg0975GPOuX7bxcvHJbambr0f8QCJbmOoW6P5+8T00dYmgiIMjt3pFRUSSxmQqEPMDjIihUxeXHq3LKTsoHefOehRQCXGISGClgB8GyiP2JNTEwMjtZX39/3LLfvf/429l/4dJJjY4wf6cW9A9WRQXBsY5BBJfX39E+227g2AW5Z77V7YGwULiBSrVq3asGHDli1bYEF83333rV+/ft26ddu3b4eFLBLpPDue7oX/tVptGO8TE8CKHj2MmHkNO8y7F7etVguW8tB+aDPEO35VJP8XryGD/4Zy9woHXU8ODg6QBpkrSdvArgtAoraBHWREbgOyskOrdIsl6tZlWCAAA3r0MA4JEVztntvQvXvb7dbeYyO3rT3y+f/ad/k/7LuMQveXfoBnq38xjSDU5u4FeZLvE7vniSlIUvAMMZOTt3gZ07yVowvWtd5x24HTTn8Eh72HP/5Jj3vqM0575GNZ/cU3/TGsiTEOQ9xG51KRUnClS2FKURssr81dPfahxbwmpvdOcJzEXONkHE4pbpdDugm4eF04euGS+gW39T8Smk346bfMf83q9py7hslPVfwnh+i2sg10nzg8O5GMSy+zF8YK55rOHAaIOVtxqs7eCK4NvjHEy9hB59mLAquQUnIAdZyxlUjbiaFabdX2gX+/HS4T++b9LZw+e9/7N637D2Fj6PSpdouHxp8n5dkJuk/cy+xlWRYkuIZB8rx7h6/a1n72Oz/AEw/WDPBh8lFP/CnWHv3kp/32vy65YtMEPZUUnGiKLEoIpfGiiJ6dcO9i00MMh5p3rysExAp1L5va7fa2bdvgsrZ27doDBw7QkoFrEytz27l7fXG9Xm/519hDEXqMgGyqkXm1Ejm0sCZ2fKnFgzp+ZHDnGz667eULtv3GAszPufK+F83dO//vsdfsbwq4VdlABrECW97Fhj+/CYMtK2OfcIVKAyPjJLLmmCi2jr3jlj2/8OZ553/sn9763bV/eNu+uUv6f/9/t//qH3+Yp8j5f/HNq+5ry7PF4pOdwITA3ByW2gA5MfH9xBO6JuZnJ3ySwAexUiMgCiYbjQWmYbp75NX3jJ3zfzec8eifeOQTf/r0hz/8jLOe/Ir/2nHxsvE5EHOFzMnqYmJZk+09YsKa2L+fOBuPaEAUGUsNXB8zlDDuFsgEZzURhWxaIojh2oZS0ASZgVD0J2C3JBoKbvEaPzV16OP/vvWll2075wqc0r95xVbIz7mitfMgrRXkAaHMo4kq2LvYtGFpDUSiBoN0L25QOnr06JEjR+Dc5kej4IPv0NAQLI5hibl7925kWh0ECrRXswhIr+xeWLk2Go1ixePpXphve/bs2bhxIzQYlsX9/f3kDLrb6iV1e+hewWS9Lr/tTM7SUtka1OD2DFkye01AkMKWiBB1h5YwGXO2BMkhMxBm370CNTAZc3eQKATg6Q+b8QN9D7z+wzDDfejef+U/QlPw7w9cXWG+AjCI4JqY3zuha2JOFkVDnoZTH3tX1i5f2/rDOw89+22Xv+pPv/zW76x65617IW7/wc33/9plnzztjDMgbp9z3d9q3OZATRUlD57DTTWy6zpDLHyf+MNL0u/YccDk5OXEQkyIrnnsrcPaF1bAT/udS6Gpj4Jl8RlnPP33r331KrtP7OM/qlQ9jf+qeiauic+N7xPbyNA4yslShBs1I6FwnLM3sz6os1dIpBBMTJgBXCMuzshqMJ50b4bJCWQc/Zvv0WXiSr5MwBm09WXzG+sfgA/8sDRiJsK8UYa5SHKf+Br37ESX2evmuRkhn7di9IpN46/45L89/53ve9PXb/u9H9z3J4uO/fHdh9/wzbue8Msvhgn5uGc8+71L+uhbVVwlnEGwi04npjBZ4N/s4PcTR90XDi0g1gBqyLuXu+Lw4cNwdYBlMeSHDh3Snme2eUOh99kLGTQVLprjE/RDAUzCzKMwxIa8hn7HLoaY6AsWYwf67r/wOpgNMC12nHfNjguvg8mx/+qvIUE+Rghd95z0HQEdteg+Mb+LTUbFxkNHyMZMZWe3CXTZ6saVW9pXbmtfsal9+boxUBdsmLh6W/uZF/z2aaef/tO/MeeKLRMcW311EswS5GIboueJ9dkJjHH0yZ4jmo9rqtJqVW4P6FLVBODcPfrqFeNnv+SCMx77uF+66ovQ2jMe/0RcEy+fwPsQUjf1TG7tBkbkFpiB7J+dgDEofchThDHqNGqEop2MvkRkM9k0TKvrxPUl1W3IqA5kLJSYKXcLSwHonAMf/MZ9vzoX5vP2c6/eccH7t19wLczqsd2HkV9x/yxsCCwPDg7BmhhrcSXdn+qI6kPzwJDhgCbYDA8PQ/jYtGlTq9UEFTxkbqNWIaBATKHEbLAgro2Oks6GjJqiUOJNHOygnfDR/9ixYzt37oRFPKyJ8Rh6DQ5iSO1ceRI+ePC72Dxm0L1FO/smMUJ3t9aVrvqMDy2gUGKmrm0IWU6madnadwTmdhK6D1z3Dfrsl3o1F8EJjC6Nrz47Ee4Tz8uiqAnlkE6Wy1bXMW5vbV+5GeJ269LVjcvXj19zf/uX3vDO004/40nPf9nlGyb02/Rana7fFPkxZyFxG8iQaE38/sX7F9yw/nx9doIjswRJCqQSTl1It4QWp3Ikn3P38KtXtl/61UWw/njaW+Y/5fw3gwBrYrxPfDffJ5a9YF6K/2kbOKeQfvFiXROH79hlo+NAZQWCmdLJYIACMYWSSnJOVaglLwmmrm0IWSDnHsWQ2WMLKLM6tCKgbHICTp/pw3/1H3iZePkCvkzgleLc9zU37UK/dH1NvcRt4HOH3k+865K1Y/Nx9tpE9bM3lUtnEH2qpCfvIcFqBx/uX12/Zufk7/77soc9Gv/Y8rov/eCqre1LVgxrXavOArqlM8jsaIyYcK6tbtJ37HBNrM9O4IZvoIqi6LF7oR8gr9cb69etP3DgwL59++BiAWtivEY4pkmVbkttYLSaLVgWU0UrqqAGS14iJrtPDCpaPJEPZvxg/553fWb/dV8fvH7RwT//N7zfAGviq75Klz1i+DqiRW4wAzY+T7ySv2OHY6ALXxwkeYwBZB4nTjxULJgsCZa88+7FrzBTDB2du7Tv6u1jvz7vz2By/NTzXrpgbYuiNn/fDsM0qZxweqlDcYv71TaIhX/Hzv22MyZ6FIwDGakS7OaE8MexT4RAxvhLdxrWtJ/75/8KjXzOB7720i/fBsLDznwKrYnH8evMQqYoTIJ5kzBKhNQttwFU9+xEuIUfxgIElE0vQOcjsGxFndchjU2QU/IfK0uSone3kgdKkBCkmQkElGNKDD406pbDf/0fe/7480e/euPAdxdCmNv+qmsg3o3torOUP0wExG5JQgZN/IGBAXx2QjoNzbypRMfuBZ96PonQarVgQbxx48Z6vR7qSj1AkBTV3UsOwQ/+jh1qoMYUTxbNTCCgHFNAR8PExMSWLVt27NgBPu+//35cEw/QmpiL4zqkeVNMySTwCQtuOihOjpKjY/caSGMT5JTKs1cxG7eSB0qQEKSZCQSUY0qMmbaBnnkY2390zx9+ev8Hvzn4vUUHPvovHLoPvP/rOs+VjAABZdMZ0P9AlvvEa+U+sV5WIcVh2Yd0MSaxl+L2Cozb9C62GlyAr9o29sr3fx4i4ROe+Zz5947K43N+F8GtOGG3KqNqMnDmrhn7yMJdV9ywLjxPzPFZgrPGak4uYlOO61QMpz720t2Ki5bWH/uLL3jEmU++4LajT3j5q6HBP/uO9/OaWJhMVgGdmEUDNduVKS3h79i5ZyfoxCHkQ0IamyCndIJnLyOmFCQvoBwoOU7wGWSSF1AOlBxRG4SZ1yENv5YK/KNf+p/df/iZo1/50eAPl+2Ycx1cJra/8mpYE7vTp9Oh8bkjv9kRP4tfNXttZYKEdFEEJ8Ionjj4khY8featAHV07pK+R5/95NNOO/01H//m1dsmsJTJUotS9xNT7fg8cbhPrAcpoqhqrUDoXgBdaBBwadi8eTNcLPbu3ZusiYO/ICm6zhzy32g08H1KuCAVq/xnFCQvoBwotCZ2u42KFO12u96CAwAc/af/xb8m/OYV+6/6Ch6SNkFAmprkgAXAdr9jB70PQ+JnCaawVJVksyGdQzTGUApz4hKYJfRyn2t3Tj7vze+GUPX0c1595ZYJiLwyFXRCWKp0i2Qpze8T01ciKJxFSSKdqMzREKwcSgtHLlrWPPdHex5+5pMf9bPPfs2qsRd9+nvQWnl2Yjl9x469YTK3Fs3lpoI4jxLXGpU1Mf2OHayJo9EAiO4sKeixHqtDDpAP27iSamGII7dq04llHN6AX0dmkCGzIhkzlmOoE9qJVM+JoQ0qINp1/NoWTskDfdteedX289yaWKa0OzSxENSGaXp6tDbK3az/IRNGCT12L+wQdWhMrVaDBTGEEnqkWP4gbvXt0OINkEQM4N1MT0PgoK+s6UFJGwhxpcgJc2MbAJxAIw8eOADrYGgqWGBlvH79ertPLMlBtS7da8WQQ7wTCTNHTtFj95rm2uAZaptR92oW4/i6F2zWhpkeGvuahE9sMNVhMKanj3z5hxy64zWxgrSkBaDwENt7JyychtCtAZPDqbNzOKXcjBhXUdUrOj7+eO3uyRf+/pWnnX76U1/08gUbxuF6TxcCImuVYvxHOWZSG9JnJzhCapC0cIp5eOEPFll4j2PvotoFdw2/dm37me/5cwjXv/Khf3rd+vZZL34VXmjoPvEFep8YE3mO3YoTcpu0QUI6fceuEX3HzoaYh8FBtZM3ex3XoDbe5HR062yEQJ7p7HVcx1BbcmgIFEuHFlDRBtp4eG2y0Zqix0cnhus7Lrh223nv237u1c2Nfk0sdN2gX2sGnzu8Jna/7dx59rozCDm4IBGyJiDQmmf00hUjV21r/9GP7nvk4x4Ps/EN//AjfPQI7xMTU52rW3WSnJi+DSCvDt+xk8MAoFTq3pgRYN1LW+iEvr4+WAfzdWHPnj1wvdA1sZAEIoYhTkYNhEIzpqfG8dGJCXBopTOZOZBFXvU+MZsgx4+dnoARFf5PtsZhcXzkH3+0jf4Sp2ti4TB0lwAtMAKwx/D9xLImDl9rg6QP2XBOSYeQRy7YlYM5BFaIoe/b1L58fftPlg++6kNfgpkBeO0Xv0dr4pEKt+bB2mAyzxVmFp6d4LsIIuttAA5z8qU3LSKyWihB6HzNmvZP/9afQAt/9fM/+q0t7Rf+5bdBftiZTz6H1sRz8HXFQgahk1uTQxvqF4Hg3k8czQkPMFNJRTGhuoyGmIuVZFo6c0SMLW6OOHSaORkqyZBnszcFl/F99Il2Y8cB+Ogv94l3Z/eJeUuGuN1UMD11rK+PTkWyMVztSlQToAEMkGEtuH379jVr1uzfvx8tVMtVFTG2xM1UWJ/gd+zkpRCuXoyCC+ZSbq444o+OjkKA27dvH8jw2VnuE/OaOIbTddfmttC9YoBYhU8nK5fJXVBNIKdcrCTTHoLZq2Ay5XEbMvTkVkmwhVHQ0H34b/9n26/Pz+4TG8gB2GIz/+7AGloTX8r3uqJwShEVLZiH665dg4uxd2V9wfqJ921uL1jfnnvv0JyPf/3000+HSHjhJ//lyvvwRpe4JT/i3Jyww7gNgUaXFVwTL9ob7hMvlHCKQZIjp1+2hpCui9c4pM+5e+Tie8de8e37znjUY8584SsuWta4aEnz7F+7ABr89He8/9W4Jh7xP02n3kQgVyJ4t8IBI74+eRTvE1f/tjNDR8hyp1XEPWcBsezZWZXO29wdrioqmkfkYhsCqss6taE6ODiopXoXgmoCubC9EvAyOgWXibGD/fjgRHaf2DlzooIjJL2LDd87gbO6h9mrRdWLotUNOHeu2ti+dG3rHT/c9LRfx6n45Oe+FJbI5ARX3rgvJotbc1J9YgINXw5Dz07IfWJeKcL/7t0rJTGBrzKtVmvjxo0PPPAAl8KaOLpPDMaeZm8VsHK9Xud7PWywXNvNAFNsMCiZq57GRFYEkUJu4ODGcBl+NF0Tx1RDbgY2vnfCPTsh42RPndPA6PA4C5J5IDGn6iDAR6Wrtoxf+IEv/MI5Fz/jnNf8xM/9MsyMs575nAs+8c/4pBp84smckIXktA0mKAFbZb/trM9OUKy0GBcniKfhfgALHAEl8N2Fj6O9+G9ugkY+5cLfe82K9sUrJn71U9eD+rCz8NmJi5aPX4ivgkc/tgvxIKrcvYAcbzAowciY6D7xMr5P7IaAxWhM4gGqmCkK9hVXYTWdOV0AXLcrV7NLAxDRJzkDe4xKSjQHKob5jH8dm24+cGh7uE98WKZ0pQdXQKdxo97g93uQwSH20OXoqHvx//RUs9XctWsXRI2dO3du3rx5y5YtBw8ehGU38boCfJS7F0F74d/swB5LSlO2gD1GZU6BXoRV+6ZNm8AtqLwmDveJiYF5gvKuAIUCcBCCnSEm9tK9qW8a5PTQuoBrKIqHVokyudCGmXktsb0NZBikcQzdR/7u+xy6ZU2sdQsuzAR13X3iS+l5YrmmcpCUS6yFTbWnsZdDOsrzKG6/9mNfwbj9itf+5LOeCzHw8c949rkf/fL8tQ0k840x4XN1F/8xd20gGgZz2SOWzl3d+sDSg/Z+YouQJEThVFa9wW6laIR1M37veeHoxcvHzv4NfFjiZd9YDnEbaE946RxQ7dkJq6LV0Y+4xaU27otLxag0Nl7kf9s5mRGznL1uogq4huJBmb15IyJUHNqMUKB39eAJFWcQWPHQABDep6fHDw/imvi8a7L7xAauoSC3fO7Qu9joNzt0fnaYvTrhy4siXLOuHZt7+55fvuCNzzzn4qe85FVnPPbM0x7xyGe99u3vvGE7LHvm4YIYybAL2gsn85yfmNIGFWqXranjbzvTmji8i80jOe4OoD6AHoDL2fp16+nxPwR0iL9PLOjiForT7hXQhZjexVb1cbLsmq1RmSq4Ju4C2BOQ6Nb00X+8IV8Tw39tDDuLXIoCbH12Yj4PRpzCROHBYznMFTd4lF9y79A129vPfd3/B7HJ8Nx3XnfZvYMLNvpXWJsHrC5uRbAiSZ6szxOvo9/s4OeJaYHL9xXspoLENQmCGGQjDgkLRy9aUp9z5+Cjn/aLD3vM415x/daLljZevXLiRX8ta+JzvrfjomX8LjaqIm5lF+hfn0UTh2kbJODKd+xkTcxrOz8W0bgoaHTdZCqSwCgUekNLBSuD1EFyxwr4RV4SpCVhH3HD8jaw1AlRGxydHrhsPnAQ7xPzmni3rokdqvyz/eixY7JgZfcFwO576l7YL+y8VqvBZ2hYVjLgs3WriY8t4WjmiA6tA7B7mTCKv9kxBAKGeyoxgCpaD0OMZIgGR49Ca/HNa9RCuIDImpieJ+Y9dHYbKR7aMCD0DwxS5SIXeLOcvUVmBKnD5A702c/e7ojaUEBnt2LCHwyGNfH/RGvi9LuksaYAFpBtTazxU4IkxmT626tea6NUjL2X3DN8zY72S35vHkdsxi+/bcF7Fx/Fl0nxF6P1noVzix4oscxGlHUvFtvxPrFbE3OExFApQRISR06LzyGcckR1sZeemnjOB78OjfyZN1/6mjXtOXcNXbS0dfZL8ebcz/7+ta9ZNaHvYnN+MPdueb+yAobEIV2q0J1j/5sd0vUKGJh8iMuj5RHNnI70qtnrAGqHaVYBOzGRXKzQ2W2xCsK5VRS5wOM2SGmRBMa8DYFJ87+wJpazh8mB7gEBEJjht53t2QmdqMXZ61NyBl26YnT++ok/uWErnTSCxzztWa//8i1Xb23Tgtg/nkEe7AxyZ6hzC8l2jWsefJ44+h07RvEAXfdSD4YeoZwPf2hoGFbA9OY16F8s9M9O4EWD+ClkSLCwqnuZwmW1er3VolsnetEpw7ntAHl2ooLlzBRYj37F1sT4HbvKQ5Ip4wqBLb/Zwe9iC0MVjxCPIltYMBWKAg1i8fw19T+46f63/ueKt37jjlf8+def+Nxfgyly9s//yh98f9PlG1r0p4RAVrfeIefcBmVSArLcJy7+tjMm/hMYRT1SSRAVQp6EP1bvHoHA+nN/iC8X/OVLPvaalRPn3Xrk4nubL/zL/wTLGWc++Zzv3nfh4gbGVg6jWpEFDNCg8l/0zI7Joqpaot92hs6X/q8YoxgwXdIxK8LKWaicAPK/V7cGrRg2DJnOAHeSlIgBzkxiRJM18SFbE7fC88TZ7I2AdvgPDRqt19r8Up4qrqGHfgBKu91u1BuQhoeH9+/fv4Ggt119VZTxfw9uPVpjrUYj/huTABwJfAwquuVg12w0oW07duyANmMcwO+ltO15YqYxP0NP3WuEeqNeyTX01g/ZzKlqoppn2L1MFHJUZwbdq3CFJHYke8TdC/uTNXF8n1jeOdjZLX4rAcirN+J7J2RNHC6uiRDUKFCnIb12+ZrGO27e+eZvLf+df7773I//81N+9RUQBh/3Uz/39m+vWrBxzP30QOowcUvJmJKghfjsxOI9C24I7ye2xSjFTwmnLIQie6pBYyw+NbG8de4Pd59x5hMfeeYTL/jRrjmLRi+4Y+DChbWzX3o+tPnpb38fhHG08C4keiduyeJV5bAATGgkPk/sn50I8yVgxrMXQGIFLQcSS+SHbvamQLsnVPEi5GdxT91LG14TH3JrYv8du5AVwOfOHr1P7G8F2jI0mb1CKC1IuBQWS5fcO/Q7/7Hirf+27LV/f8Nz//CaMx7xSJiKL/6j6y5fP8YfULMzNKjJCor2wp451S5d3dR3sdFHhKop5qGdKVTY4FUUtYlx/Po1YHxsnK4RGHN4TXzw4EHkFvw7C4k5wwNGDfaP72Ibx79VlujcPrR7fx3c2nsnuoH+AOfuE8t7J6SUEdRkj/BJIvmOnY4B5S5ocmKVikgNc4iZNOrz6O0kV2xuQ7p6W/uy1SNPo68DP+2c1y7YAPPDv9MHEy2+Oak3ksUetyF6nljuE/ughgnCmcY+/LgvpaKKDIH11SvGf/3rS0972MMf/8JzXrOu/dr17desbr9xR/slf4ePUjzs7Keed0vf6ze16afstFbwY3cXktsMwoE82Jc0wnfseCxsHORDcG/oQu7uqszo4LYwcwrkyOTDc+/gMx3+05q4cX9YE+t37MIraIqQMmrw4NAwLATprKuukpwjgFI/sAkcQRsMsLiEdeemTZvk+QGtV95ZD93baDRGR2tVzY2MFc54XkG+c+dOaBusjKWtBH6eeHBwkFVkUi1EYYgLSMsmp4aHR9RcqjiT7iWUnMQoM0707J0NbPbmqOpe0PjZib/9Pj5PnD07gag8NFgT43oa7xMvsvdOWJDkgKlhM8TPRM3IELfXUdze1L5qa/vy9c2fn4OvNnvKi16Bj09A0Ja4zdXDTS+VTSVLrMKVgtbE7jt2+LCvBWQXmb2qIV3tNXwKGUP3xE+95p3Qthd86vo33o9x+7Xr2q/f2H7ib74GjD/37j+FMP7qlW38fnNUPewFZXZeaIOE9PDbzm38+CJ97wDDo1YcqarRYpRLZzZzCsTIVHbUDTM7gwool/XkVlEiOxOKEYPXxF2fnSh1LwRJYMpvO68dw/Vot9mrKuYViyJ8nhhOHEhXbm5fs3Pyt770w9Mfhe9i+62//5G8WkCYyodcziDKS275viScO5fxmtj/jt2MujfuwP378evXQ0ND0A8Gfu/E4cP4V1kAM6v3QSh1r4DaANc1+ho6oXACxXWDUnZJa2L0y8Wlurzh+8TxsxOlZb4DFioB2P4+MY6NDhLKLODwYGKVBy8qkiphdbtylF7uMwIDCeH1jf902+mARzz67f+9ccFGmB/4pwSdW8EtVg+7oAaYhZn0iaqwJuYP95xYZTuHOVQpzHH4A3WhvOn9GX9w3WlnnHH2r1/4jD+49mlvnf/03738Z3//6ie98g0wlU971GN/+g3v+dnfu+Jl/3zPxfe08AmKSrdkhNySGYk5p9Pv2LEUdEaqE3i1CZdNVjM4O4qspuRQEooqmLFBVdy4NpiV7a4WSqyYCSFKvGEB3VLAwvvE5+p9Yv6OnfnFLctWV4BtINvA4CA9O4HNkZIYqU7o0r1wUhFYe+CBByCsHD16FE83VwUl0czIQuAwxBPl9UZjZGSUmov6TLqXEwJaUq/X+Rc6DhHgQz/nYAH7rl27QB4YGMDoZH4SoJ3LUga2QW2whQZXMwvIDi2BdS3XZyUlh5JQVMGMDaripufuxY1xGJESgObqQ3NACtMm8WWrhfvEvuHCpDwAdAgjQKZ3sR3m33Z2sVdiZrh4ZyE9IouRVYjb+MX5ucsH4NL+lm/dc/rDHn7aGQ8DAS729OSbuo3Wx+pW/JhD2hEXrapdkj1PzEtPfiKCZIurLpZa0GYyPkY8/sr/2fWwn3zCGY/9SQjaP/u2BRC3n/62yyFWP/Kn8KfsHvecl0EY/4XL/ppfGcQV1a3eCSaVG0DJ9qLCQnw/cXh2gu7P5UOsek+zFyG2iF3tVtB99tLfyUuzN0KqE7qdmA5IYVpK5jaIIKhgJgZXq7fuBQnotCauuk8c/KCQuJX7xP7ZiXj26gz3C5JwBlGpqUaAvA4nCCZY+dwzdOXW9jMufPNpp5/+i2/8oyv1J2+iHXU6g4JbOlWz907of3ekgqR7VaUNKWPj4/fddx9/JYYvEwz+zQ64roF87NgxJNMFKYL3hkChPGq0L3wX25j9jl1CFoFnTjx7o40B1sTESe2ks5HFcXxu0t47se+qr6CR/mqKxahoJgbTaYMzC3/b2X3HjhONBAkyKjJaGQGHVi0rRuetHIVx1ZiLv2932dqxd9+w9fSH42+Hvvlf7sbPTG5+OLcmaMqL0PPo3LVjn7zV/bYzRbdIsAQWNpJg69c5C/GJNFgT/wz9+lEB9IVrxq98+OuvxefVojf7eLcYRtmt+pciCcej/OzEPRu3Q2dHl7xoLGKY0UaqA8yNJ2uNaKuKQ2pPKKiKKSpJaKDHlm6zN0Kw43fs4Fy6354nvra16xDZuZr3EHUlgy21Wg3vE+PSz8gxzKiueJOArHJgmMF/PoP1W7oHDxxQhpESpPaEwuoYPjshPwvHSD3F3Qsaqs4EIhwuHDjENVisQ9s8Nm3atHnz5vXr169cuXLnzp148UAH4tS5Mh03si1ichKfnUCB1ARmVFe8KYMKMfPktEbRCEjtCQVVMUUlCQ302DLL2csIopWIyXTccOg+rPeJ91/7T2iFectDE5FTQHiHQYx/xw6DpMZeF0UjlWVkoqABlt6/xo88aoKovqb53jv2PvInz4QY+IYv//AqevVExKEkj0+ArN7Aom3gHAWM22vGPrJoT/R+YkxukcqJgqesVll1iR6cGD/n+u2nPeLRHJ9TaOh+2JOeNufuUVwE69snwCe7FeeQzD/tWgI4GvFmhzw7UfE8sWAS18pulIqjVjQCUnsQrURMnlQ4MWNLcfYSzBhKSXLkQomYTMeNbCvhyQVgibriTRkV3cuXg7HDA3CB4DVxY+NOLNFHjwhCNrDIa2L5bee1LZquNnttqRrN3lwIMxxfX8iPg4JFCJfcM/S+bWMv++PrYB4+/eUXwpoHPmfaokiTyHwGiQWbEU4lylG4jH/bGdfEdG82O7QEWCKlnoQHPjY2tmULXgvk2qDgt4vC5WPVqlVbt27lWUZxkTx4N4LUHkSVYF/8xR7PcnIRQEjDMQPXxCIawIDLafhP5yfNjMmxcXBy5MtuTQxHM9Gm2AqksC5PtwheE9u72HhIeDBoouDw8GihQBYaIR4qmT1sQcKCje3L149feu8wfE6CAHrJvcP47vf72r/9zbswVJ3xsN/5Pwuv3EL3G8LMY+fshxJPC0rSBrcLfj8x/WaH+46dxTUNavKlOgu4umwN5IW1i5Y2f/3/rHjBX3/7hZ/5rxd+9r8hveDT33vx39348+/+KEzlMx531nP+9Jsv+vwPXvHf9wMTbzmwK+eQ3hxkL9okt7wX5MiuMfpHzxOHASCRVyekMXBsedRQMnIOX4GhvrNKYJBvHAnZGFIlq6FtQBQKUwSy0rNKYICd+9lLZoAIsIGpCwuFycnGDlwTb3vV+/A+8c5DNKXRblyEEwnolvuyr6+vDXxuVNK9iF67F8YLABLIeKYSqFCeRoCP1OZsdt3LJbCW1e/YkZ5CyASWMh4YpqchAA0PDUMaGR4BDA+jAOr27dsh2B08eBBU/KKx/dFL3ehoqA5wIiE9tMGhQdwcR/cSxC0hd0UAw4M7e7U3HMAAO4fZyyWOIUJWI26DVjO02/gutslJeRcbr4lhesH8p9BNVcBD7heN8JEPZuZqWBMvwjWxi8wSOVnVcCoRVW5xudjr4jb++Y7jNiSI2xCof/fbK894xKNgmflbX77hqq1tWhNjRdoLvYwCVbwQ6H7FrSS5dkhIn7umdZ27T8zPTvhAzc8N04I1CadayuqSxgV39L/oc99/wV9/h+M2BvDP/BfE6sf90q9C6H7SnLdBGH/J399iq2F1BW7VMyaUMYaLZwvp0obkNzuk+wk6Kg/97MVbbIGEUjzZKIdsFrNXETskmIhueYMwczZ7XQXlmymHFoU2qI1yWPZM4EfHsYOwJpb7xI0NO/H0mZjQEA1UEtSXAcrh3JFnJ9bgsxPl2SspPoPYaIuiVbX5axpXbG5furo+7x48cfgmMaSrtrWf+erfhXPnZ15+0ZX34ZpYq6ArXWupJTsxxU4CkON3sfExzbx76XYshA6+KOA1AjICXCx27twJC+U9e/aAsVYbtSpQZ7azF1/kj48X+uakfCMDUIonG+WQgYfJaXx2wlMITgTQBZtx9Gv4mx1b6bedxQSdB6krgEfPE8t9Yh7s1X5IMKdFME+IwpihsLI2f13jrf+58p3/u/3Kre2r78cn0mA1/L7723PvHXzyyy487fQzHv1Tz3jPoqPz1zRt7NmteqAGQIL5AQRsA5QSgVolbYh+s6OFoQ2DGsc1ysnCdwIgjLIAdhQcjX/i7uJ7269d037NGswhvXpl+w1b2y/+4o14m+Gsp5530+HXbWhftKRFdakiC+wKclJDKebBQrumCOufndAxjAYWR1zHu4TyLEi4NNphyDvMHIWFD4a2oQKOHNczxN7oP+RqraoT7DRtEa19x/Q3O64dP4DvT2AYFQVRsrZMTzUbDf7egAFvN6S8gLyr2CksMRuNhuzb4dixY/CpGlaZ+EgWRoyy6967d3x8vNmkvzG5vWeIvdF/yM0KAp7zJfAKfsA9T0wIkyUBO2fRyQFwaPYuNiicafciIj5F65hWqhMh2eVDPnsBXdpg4GEgHPmHH259GYbuA9d9Q0xZ6M7c4ruogGa/2QGRU+422YXWC1wkRk4ae1eOzl/X/N3vrnnHj+678r4obl+2pvYz573ptNNPf/iZT/qj2/bSmzTta3aQxA85YVl3VNEGfu+EPU9M4ZECJi1GKZxSZMack4RToknslZC+uP6a1Ri3OXSjTI8UP+HlF0Po/rl3feS3trYvvqeNsRfdYkWuRQ5pL5Fz2TXKRl5YD89O0LvYYATCzImHIz2TTvjsLfhLZnHsjf5DrtaYq0iaKW1IuHRo/viwXDiVwUEkQudD8+TkkBSxN3f2TAyM4m92vOp9cLFobtsrVoSQDb4Ndp/4GvnNDp3Mnc8gWxTJygSXQ7CYec+iI7/zr0tAvWpH++odeO7AaviaXZMXffG/znjM42A2/tplnwQLfZ6kipFbvy9OfGKyPRDwPrE8OzEdXdjiQyug1L3URSmKzxNXue46e1Gbnhobl/vEmZuKoSZgERUrR7anZV4QzKY/HEw3N+068OFvHvzYvx7+xLd2v/PTuIY49+qdb/rzQ3/xb5AOfODrw7euwqbi18+xmvhTt3TQ9IkpeZ44JBqhMHI6PGHMJIEFPh5BMH3Wm+c+8nGPf/ab577yQ39z0We+ffEXvveb137hCc95KcwMwMuv+xv8QRd+cCJz0sGCe9Q2+DXxBfzshCQImrpOjZKsj52gMsS+haP4E6CaLrhj4NUrWi/66++e9rCHP+wJP3XO9VsvWtLEJ4ldRZPJGy151eJLOUkRv4ste++EINYQZGFzMvkKZALXkMISx2wqxKS8ClnYnBbm5DDFu7WB7FxohwZ1cKI2Wke/8L2DH/nnwx//9/3Xfm37q96Ha+Lzr91/zdfAcuAj/3z0S/8z1aIH9kvO0Y3a3bvY0v5DkIXN1d2LcbNer23atGnXrl1Hjx4dHByED9MDAwP79u0DIywxwS67dF5UqHKrkHqC0dFRexdbgWy9akKBQ8Da4IM2LNCNAXs/MVLQSImccEa5KBVQDpHASf/AgGoxyMJm2YchJwdTpzYEkuQxKdJICVlCzXVAODQRChyykZ0Lu7Wh3L24lp2aaqx/4MCHNHS/41MSun/7Yxa6R25fgxHa/1HJIB7lPvEluCaW67oscy1y6gXVkhKolCxwqb56e/s577z24Y993C++6d2v/OAXL/z0f8Dl/NwP/u1TX4TvnQC85LK/0JvE5kSq8yVDLLg7vq8hpWpkmdbESw7Ev2PngqTGZ3vhGoVTH1G9vUYRe+QCDt13DUEC4xN+/SII3T/7jvdDGL/gzkEmZ27FQykBmZyTSu8n7vzshA1PTzOHMZvZq5xwVyAgm70lYBEVM6f77FUJMq9UQQtjUl6FLGzu2AaGmbgNkE1PTbSPffkHBz6Ml4kDH/wGXCDwPvGr3rf/qn8Ey8GP/suRz36nPYLPoXH0i92iAnY4d+T9xGta88NSmM+g8uwVC5XKU78rRhdsmHj7j7ae9pifPPvZL3jp5X95/p9/5eK//f6cv/yX5779yoc98lFw7jzqqc941x37Ll/LrygWJ1I9uC2cmJrUwu9is/vEfFDRoRHIooVxcaSBA9Axl/9TU/Z+YiqWPQBQIIUtnd1aBoATxn6zg22lU8h2FPZYxmT03gkkhwrQKPxyxtTIHWvve8m8bb955bbfvGL7ue/bMec6/O3v868Fdds5V973ovce/dL3oRX4HEXsgCFNgNkR/7azfVpyS+QwVBryRMUEA7yyPm/F8JVbJl50yZ9yGE3w6Kf87Cs/8IXLN7TRufhnb+LKRVLKedJEbZAiep54AtbE4XliShjF5EM/32OgqEcWKOK7AhzpzI45Ma3unLuGX726/YJP/F9s9BkPP+f6bRcv5/cTC9OTkS9+XIQ1Cyc2xt+xS2cVIoyQSTnJgylGDtU8EgOpSszIzmplJKTMZPIGDmYpObUa0QM8Qs/UGjvf9LGtv4a/cwsTmOfzjgveDzJY7vu1y3b97ifwVz35K6T5fhhkx+eJ0ac1FTcsmiQlFeCKjUZjy5YtECnWrFkDuT2DtXHjRlgZ81tmdBfObdl1KLFyMkE23WqNyd+YMEJxoQeZiI1ChNhqRAZIOOXa27ZtW716tbyxWKpIgKLdhRqCzCDAylJGz2AAQA0uTGK1CkwxcqjmkRhIzUgRuNQ4xRonsntL4NLK7qU32A/fuuq+F8/jiR1C93nXWOg+9tUb8OIHZERwwRLkfJ84fp6YgzZHUY6WlqRUBcw59kLcvuq+iZdd/VmIdxSqIzzyCU99+VV/iS+T4oqYYp9eZv+2F0wsY34Z3ice09+xk3exJeFUg6eoVMRhnMOpldKyVVQiLByds7B28dLW4+n+y0+/eT6EcfsGiOyih/jvitBt8uyEjSIJXWdvbCUpI0XgUuMUazx4s5fJmGc1qlwUyc5qUk7K4A5Uq6EJzqr2xK53fOq+l1xKK58r4QIhp88rroLTZ+tL5z/wWx+Z6B+BuJd0FiuQw+UD18T27ATP1Y6zV1KyKML3a4394a27HvP0X+TzJcEzXvX6t16/ZsGm8Xn3jpZOTHUVn5hW6hZF+B07/c2O5GMRanZoLMWEIrhrqAZeceCD+vSuXbtWrVpFbyx2xYxELYFLjcMexsbGJ/BbE3rRKbggExWiECFYWfJrYoBUEKewJp6aHrl9zX2/Onfbyxds+40F2379cnyeGBIIoMIa4gV/cvTvvw80jMJlUNSGPsY18Uq6T4whNR48EmTM+GOTypSHKYLDWZ93z8jvfOve8z78pef90XXPeO07nn7BW37lD64597ov/sEtu6/ezi+vDnWDW0lB7tAGjK1rJz5x6zpaE/OzExDF7Jc58YM+xDhObAGCRD2KdMSkSOrIpOJt4wuXNV9x/dZf+eA/PvfP/uW8W49dtISeJBay+mE+C5FKbjHIRm2Y4+4Tuw9gCcLoE6poLjSHmVtFBkiR7hY3crmOQeVMMpHVHMFBFSOGsHwbIoCB18Rv/LOt8DGPp7TM5/kov3wBfPzb9ZaPT9Zb2APoIG+DbKGTBwcH8Tt2EbhUuUFI4CMOinBWDw0NHT16dP++fXv27IGQAXKtXoe9UBOEnnWvc0MgnY0migqeYPE9OjpKhwbG2XYvb6JKKIPPoeGhvv7+Jr8+nSy0kf8lVHYvA33SjW0Flxqn0m1gFNqQ1xLLDLp3NofWGcJScqVbKsCsQ/fiMnd6eviWlfe9aC6ugIuh+4V/cuwrP4LppWtiAPtRb7ADuq7rsxMtjZYcMJNAnYfTKPZC6bx7R978n6vO/+iXn/+uDzzjde98+nlv+qW3X/nK93/h92/cQXFbwjvUUlehOgkcn1UmOzHZQqo8O3Hgihs3nO/eO8ERkhPKYYVqIR1lYlIRpxDSAwfq/urnvw+h+6X/tOiiZU0M5mLXHGvpC+ypSHyaE3NL6sWLa+dGv2MXhtMNcQdIcTZ7UwRfp/bszeEPrRpcapwqchYcEqAN1jMTu97+1/f96iXRZQJPH7xMbH3JpQ+87sMT/cPQTeaDtpxhzufO3r17r9Hfdq6YvVUqCXwGwRmxuvFHt+1/9Rf/+zeu+Ktn/e78p5//2z/3unf+2qUfe+3ffn/+hvaCjfEfxuMzyLnlZCqRRYUqNfjc69/FZgiHJjnAhARduhcuQH19ffxtEzIIxw/xTGYvX9eaY2NjrtYsZy8jWRPHIOb4saGRuzeMLN48unjTyOJNkJswsmTTyF0bWrsPG5kATSbFHxnMjrHW126HNXEUxXi05oXhiYcqfKBBgSsimV7Rd9X29hX3tRdsbi/Y1L5iSxui6uXrxufdg396A6abByi4XbA9CMFtMCbPE8t94hDIKCcVox4KkiyeYhxUsnGwlAWwXLR8/LXr8em0C5c0jBY8Y84hlSuKt4JbViElzxPbrTbchuGpQDZqxYmpfmJ38IG/0r8UoDf1X83N2lBJBXQqi6CnG9eYmBhduW1k4UacvTSTKeH0xim9cCOU8m024Ve0ARYTA7gm5q+7MhOzCnTpXhiyIqgNdjewAHQKpUhQ/7RNADZwVm/UcU2sN3E7d2/P0O6FHlEU20CYwRBTweTwiL2fuAO6dK9C/MTuunUvAAnqP67sMINDmwlKh9a1DfTo3fjRQQ7dPMNtqkMOU334rg1NetcKVgKYfwX6ouv6Gnl2Qt874Va6GjCDwOGUZLa72Lu6DhdvCNRp3F4/bt+rE2Yki6sopFe3Ye6asQ8u3LPAvXcijb0YMDWKcoy1EKpRtxh7MeSSgE8Vr29fvAKfJCY7MyGFvRQCNZUqE1UmhPcTy7MTncIpIC3LBk7B48oEHeJKx/k0q6bKthfkbjvVj8k9XFYQWkWnfoYZHBpAyiYna2t2DC/cMBqfOHzu4GXi3q344gGiSqX40GRNTO9im7dmzP1mRy9nEJ81bGehNn9dC5/C39pesAXPHUj8UP6lK0Yvwa/Wld3OUwHcRmdQviiC5Ra/dyL6jh1m/CCvOziPmXUvXR8Qoleix9kLk2S61Wzyu9hCefeZU0mANTHvkxk5H8e1K4RL0Kp0BOF47HliGBgbFRqJKA8JP7UENS6FsV8xOu9e/PIyftGSEqj05QwjW+6nAj6jI18TCZxI5c9zdJ+Y18Q7w7vYJMZJULPvDrsAqimQo9hqERNK8SHju4bm8M/lR56hVJhExiRCcCt2agOTR2VNzL/ZYV+yU1QPsUEXBzRqVI4ZSjFZi3CI0/MhIrMUdOJj7v4HOE1mjpFtPwYtIp6UKSNmEvTQCDRnOwEYWsX5AtE0ahqsL+k+8QzbQHQqxwwl3hLIiiaxUVloPJaIqFLQsQLl7r+APbda8i42pnEuoiMDVJvZodERIJik2wDVgN2xe4M0yc9OoE+0qT0wDZ27N0CLsA0du5ehbhHOrfwPcJocmpFtPwbViCea2mImoaINkMVk1YjtPqJ0APGhIldVB7SFDLodOHKfWH+zgy6uIWx6WSOqxdUo9uJFlwI1/rLAitFLV46AgCp9qU4CPl7XJQ6LZ755TKpdxeOQHtpA94nHPozvYvPPE8exN4RTWpVGEZUiswu8kJSsFiDQs8X8WuKEbBzhh1rYBqzLqqt1EX3HTp6doL9Za/dXDTFkMHA6q2xuZGTUizNH/gc4TWavyJj52eurMc8x3dZB20B0Kses4tAAwINpJ4ogIsfVUGeL2tNywEzbgOATpAPQW2inc0v/+dzh54n5PnHF7I3OJp7qca4J3+qNL53AkwjXPKTij3QwAd16V7lbIHRpw5r4/cSEnrqX0VP30vUBC7Oy2IB6dfcKiNIaG5vg37EDFrUBZc6j/QSJUTw0vU9MCmZaKkCP+s61CUz4h7Y4x69oCDnOvC+YHf55YsyjIcFxkk85PtHgRfYSM6hFt6LGCZ3opEFa0obod+x0TWxrXxcEMSZq4KMIiyrRLBpqAiZVdKWQOzIHblTFLRRJadgL86M2qOzuE8Ogud4PKBj9KMmWgQUyaaJqYXa68oyYhTVFqWGhblKIqrhmqBiRTVGDwO3KNwbnM6aJMIc1oSqPQ0hd7MjYtXnt7+/Hd7E5BJLBmR7a7gXUa7URvO1qJNyIa4aKziQKUyO4XfV2aN4kBXn38qFZ8cAAvYsN0enQAA9t9yYOCKiKa4aKEY8UpkY4/u7F0C2TnOd5KsRHEztDgAMIJmv02QmMrnnslQA7s9jrVYrDRTI66RT/rQoLFNILv2MXL4hDOPUR1Var8lhFiL1pnsVevjmCfHIiqpC5CAUooopSijJxaE1M37HTZyd4KEpD3Gn2BhzX7BWKXkBERZRqM7AgKXTkWc5e2IATFB1vVnEP0Fsb2AmbJu2KYGeNWuicwstEVJmr22DwuUP3ifXZie6zN0z1ZEESp/SkKJyYwVXklmjuXPMCfLaM7xO74+vSvTG4KhVHnLh7RbEOU7ghTkYNiCmbtPAuNm1mcfZWzBwiOeJp6sPlMdgRlVSTPBw1cHFNjM9O2G92yHoUVBtLEXDMaBQtUanjyHQhCzA9udotCEjmijxLhBw4YWK5+8RhTSwJ41qIcRzmwkNpFhDZonZH1uSZRKM1NxqNWXYrZMh9G2ryfmL5bWfo9OK0lG0F8goeUIDzLmLxprKKLwGxPC29tdRoD3YyszYQfIVqeBZvClXE1Gg09dxTFMgeVFzJgYKT0r0Adjo2NtZq8bvYqjyyk5PQvc6JsJwlBpioDSQ3mnZju0j2oOJKDhScrO41xN5zsJNTo3s9mQ8tIeOr2MJ94kvp2YlC7BVLh9iL1+AZxF5wpT65ikuoBv/iLSRwS2vivf4+cRQkKYVlawinhdgrVXqJvWQPgjJtbR2YlsjtnIWwo1G8T3yHe3aigNKo8aZIJ7gSEGmIOyLyXnDLTkpzvED2yCvE4CLIxbezpABT3obuhybcgkNGyS1vKqv4kgKJ18T62868JqYZ23H2or28KErI4gETr6nklEFLcgaZW/VmboUsNFoT63fs8JDoqCCb4cwpgJ2cyO71GB+fkBdAASrdgimfOYSYXP08ceo01RX5HkrANbHcJ9YHa3hUZDxUpaQDZjODhq1IxoHkzzp+qpWYZvRMFtjOSexuTSzPTtiTEiHqSXTjqIelqmJOTMpdNCRBCKzGbv0f17huSoac/wYntXyi+8TLwpq4G3rhANhXj2RAyizWpNkJ6OA2KpKp3CtSclVlsvfq2bWBhGP9/RMTemcnR49uH5zuBWF6ahR/s2MYTkY0R/VOVPcmBaT26rnQBjAMDNp94hg9uj3x3QuWH/PZ2wuIztd1WxPbdd2CJMu9xV4Ueom9HeO/5ZSIwAsCbhvkl6xuXbfssP9tZw6PxXAq9iykM1mroMCxF9VC7FU/YS9E5pDuyFrq7fKbHfF9YsVDNXsjnAqzdyZMQAeyL+rpMumQ0quqo13uE7tnJ2jSdpq9boZbisiQSieFJSYXT0xPMyO7RVWYa2hNnH3HLkWHbvNFJ7N7fWGtXmvyvZ4CZjx79T6xhzsrXSmKBfdE5hpcWm5CWBM33MPmIemcgISDJKqMKOVhdH3COxAsuOEPqeAW+ew/5q+OyLwm/sSt6/A+sfyOHcW48Jc1DGd6L0ECHN7WDQmMGEA58nJOgroyP0RWt0wGb8IntxJhOXdkcYsqpKXxeyd4NAKcHA1xNF6lwUN0H+IMSuIqGXwbZJEmzEB2HEDehsA0RIdmCFUiEDl3W0Dslmm1ep3vE1MdVzEiO3tEipC3oYppUEIF2bdBv3AwNk7vYjM4DiBvQwHRoRm4SgYiz9QtwJMazWZaJ2pDVFjhvdCGKmaGUCWCb8NsZ28B0aEZuEoGIs/ULUBJnuxkR27r+4l1TWxvP8UgecJib0K2Ba65pYpK8Mm7FcLcNa0PhWcn8JdEJcyeuNjrQzqp6pAIlJMgVXjX5Ce44lL2k3zHLh2vfIj9yHVEqBLB+a+cvTF8GypZ3m3goFCoQOT80ApwbgFK8mQnZ20wt4EUO8zbEJgluNJQJTKTR7lPLGvisfjc4ZTM3o4nhZw4wGeByL0vihzTuYXk2gA5PjsxEN7FJseUHhoj6TEmYT6L7o3cGkKVyExk77Y11uL7xI5EiFuipSlLoGS6T0wrKAEKkSIgUbJSOSL24ooncU3svmNHQyWDCrPBPtZAKk0O+ZkPoMkYBz6R9RdfRCWByMzBhHsJpZxUpTY4OxbJeyduxfcTy292UGjTQEYRzRajoGKp5GC08KfGrDqovtTsJKtb5MhdYYmhWqrksC8QljTs2Ql+WTSDpGTUQmmA8AgRWVCaYG7mIEK1QCabZKFc/itU9u4issC3wc6Wkzt7ZZuUYkvsvRNojMpNAahMW1Gs3BFPcveiWm/U5Xli+idwxLgNXFLl1lUTHqFUjui5e5M2DA0NuRYw0akm01YUK3fEk9y9vHXGiCw4BbuXCirbIL/ZsWnLJfjbznSvK1xcKX5WxF4XooshXVV5piIp8hZU0/gflzo+MvnZiQX47AS9nzj/4WUMpyqIvXPsFVVKgeyiMRl1VY0W75bViBw4tFOoePGiUXyeODw7obBxiGeOjtbJnL0GZ/txm70AlYVHsHJHnFH3yhZAomRROf7n547kvRNrW/NxAneZvSLLGslo5UURC+GkSBZFIkQqkZmDiU4rLOXzCy2rw7vY9OBO0e4VgEjvGB3r8kzgDGYvPzsBCqcI1XqBXABQ8MMGMWF2hO/YxUMlAZSNnAJBoqGqFJE5GRNlmTRSykU4tyJVZG6AyKQSMyaDMTxPLPeJJWj66KYBNEQ9smCwE44mZVpCgpLJkgRNlp29mowJ74jwmti/n1i7nxAkhd4/NGilEhnARleEIvxPydV6D2QCGX2JySqEQpA4RajWC+QCgCKz15O5x+RcGh4eaYf3e3gaY7bda4UowP+UXK13ITebzXqNXuOQMkHmoKHGUJgwBakp4vcAYHXoXgXM4hF8oTKJnog45bo3hi8B+VTsXid6GgPI8jwx/2YHP0/sgqQETA2nokqqDule1tirBAzCIju3YOHULf5z3F7T+tCS/f537Oy+r6ZOsTcid4y9HNI5t6RMIVtpB7fRsxOwGCmDB+jBnL1WCMIJmL0h6wpgnYDZW0DqIYCNvntB4hShWk/JELDg3NHnicdwMdpt9vpSWryKrEWcJ8auJ4WoUiRkVpmMOZAx4fPEYU2sxxMdF+Gh714A6uBnGq9rY2P007OAdOaAEM8cK0FECgPXxK6CMbytUGRv6lBrVOrAqr9PrL/tHN0hwHEKQ4izQYwWJaUWGaPB9mTlRGSbMeyKVa5FH8iM6RPG1rVjsCbG3+yInp3Q5KOh/V1MHyPj3JiBoH+JA9mEwKQ7CsJEe3ArNM4tmZEJ/B07fj8xrdXcYOhAJBCD2T0tISccA6o2H6SUJ78TCSyYyrBy3bj55twypAwQC6wVbYWiGc3e/BoFVm4kvottkh67knpJ9dzOgs8NCceA6onq3marye9i426ZSfdyYqjNbRSoHnf3hmMCaXS0phWT6mYwu6cl5MCJC1CbRfeq1cp14+Zbz93L29gmmhkZqJ6o7q06NA8IIxBM6NmJw3yf2EVpyH3wNDkYU3JV7GW7pSj+i1AK6exQ7VKrNnd164P4287heWKJoicm9uptYLshIreBtXpgajIj0YBDFbUxC/E3O8KzE/w363QswkjFBajls9eJUVHXmaPecZPNHCokgrCCYAaFGBI7q0mZcQrk45q9YlC7OzEzsvVqoXsdmQRn69y9/NzRHrpPrM9ORLNX7++SKidFcVFEcnRSiFFv+hpTU0Q2giNLG9SVqPTeCXkXG81D/a+5gxh086B3L29ZjtbEQk5hbK4CUP9mEMCa2BMdH4Ge0xoAZwoiSZiJyXTa6JpY3jsh42Q5jYoOT5gWOlQkuyrE1ABNpZK8SjJ4Y7ee7N1iMtm1QZ+diN47IXFNc3xW2Kn8YJktiDXCco4WjJujGkNRZWYgiF0TqkYObjMyhOlR8Dwn+s0O6XqCH4sCsKS6NMDcCNl03MhW9HzmeDIiLiU1MRFSmztRCKSJyUpYSNqgYsEkEmZiMh03sg0QQ39//wQ/OwGUjGTAkurSAOI4sum4ka3os+pefGeNPjuh/hIa6N4iWspl4eR1r5YAJicH6Tt2BZICS6pLA4gDWXToXkawcCrMXoQjq1gwiYSZmEzHjWwdYosnp+BnJ/A+8aKj4Tc7irFXwywli6Ji7Bp7kamxNxAiMsp07ZALRGgDV5SiUffbznSfmJ+dyGIvCx1jL+ZJSA9kFjK3wOQEFnHOKfjR1TDVAhkaKe9iS56d8KCZ40bJj5qZWZjJ7LUSMXlSNnM6zd4CsERKleTIQSQJMzGZjhvZAlQKFoQnF4Al1aUBJ6F79dkJfe8ErnfD7KUJzILNXpPDfNa53XVRJCvs7MR0ZJOLJybX5XexRc9OpEfrgSXVpQEnbfay3Gg0Wk1+doJ0xOxnb3jvhPPGm3ByliomCB9Wy2SYHfTsBK2J3ccUGgk/KirzUCEnJ/NAZmT94CWyCCgrgecTVSy1garLLvzv2C2h33a2P35xBOS4lgj86R+ZwpGwiASyOKaWVnsLZEjy8nlRQxtEwDsi8e/YxQMBo5mPTMdRS02BDGVhcngwgQtJLrhNUWqDSWn9qA2yJWTmEzd7cxNYKN7Bqeh+G4UFzz6VuhdCBHwstXexMdL6M+/edJoV4fsho0eG9NDguux+ZMRtEbPvXtkmYPMpMntZOMndqwB7VhHdTsp9Ynk/Mb53gmNsRew12UrT2EtyMf57WVS0rGwoE9YHWur8U2KyXOnnroneO0HhkYLkTGKvfG0OE3BKsRcJavFJ7FJUHf8xcRv42Qn5zQ43Rg6VMydAzFSdUMHz8DNHYVJav7INmVmZxdmbmnwbMnpk6GX2mnDSDk2R+shAEVefnVhDz07wdE1mL01anb2c/ImQnxTImckZJDTMiyemMcEtPk/M72KTRxBO2e4Vt8QbHx+fGI9+KKBQP1gqfIt52p6diFCuZCtv2uIMLvNy6H3iW+U7djg2kPj7GTwndGbg2CSTxsYsjK63oweaCvS9Tq3LZB14yrlIHjMfxT1yG0puC7/tDOFMXyLBcliYWuxjwUoxoRyMEBndzQlPdt/ncNGTbypILSZALncapDoTYNdLwnsn7DnXMEgyauF/AfGHK1EquEUIN8xtRLjjQNvqmcPF5TIo5GftkmJTcyFCZRvK9ABfD/7z3zYJx/r6wm92kLn60AjdurdDVYYQZtO9aKrhu9iGUMk7+dTrXlIm+/v7RcN9FA9NcXK6t/rQSsg7lnEqdi949cWQpYcGi2JeE9NvO4d3rGrAnHHsJYFiL/+sVyn2mp/IGOI/qnQRETtYpGF4Ayx5nngkxFiOnxgtOXhqvMUwrjIFUhJUFn632Mu0ivjPRSSghcgs1C/E9xNH72LjEZBxcMMRj0wBQjie2VsqQ9PJmL0ol+kBvh78n+HsjWBkQk5MLJWEnrvX7hNfQ7/ZQa/bstnLyx6ewGH24szHRRHJ0bdXu54UsR1dOaPK2YlJe0QCFOE55Z6dwHexyaFkh1bAg969HvVavcXvU8pK0QD/45nDMJMKsg33iR2HfrSQNDZBHh8ywHQ4XaSY/qtk5SzB7Ei+YydjbCNEIxfmAQsmk2p1SZbZw6okVGmGBVWFkioTIncLsVXWxPg8Ma+JLWEMpaBGcY0sqGqU5FWs2S16+tApHPviBXFIXlzHZXdwGwQjm4WqWJCtXbikVf08McMZOg8xyH4a6RAjLBSFcpYmcd54tywEGsPpfuY4SYAxRUSFa4QIBmfofGgI03ubvTGwHIjT+CiC3CcWliN3bgPIXbs3gA3F7s3ZTneHhrFkGn8Ds9lsio9TuHsV+JptvE8s7fQlorEJ8qgNIHfqXnGn4KKsezHv0L3xoTlJcMp3b1bmDNQG0MElronpO3a6JqZoefyxl4VS7FUOJ2aKhcjFIsvteeLwHbs49opQiL34bk0IpyRrxGYhxF5Q89grHCdwYiYJ4WaKK2K3dJ+44e8TRzNHhxiRz0caNRgrP3sZNHwiK0yvmL2hPDtLgrPMa6jWw+wNJ2Zp9orCCJI1xkyujBGTSWMT5FEbYLf+0FC24vKhJd3LHMgztul4aPzcEb+fmO8Tu7OjcvayLCcFLWHj0kyd4aKIBJYtd+oa+x07GicKAgR3qFk/+EI47Aene7mYZw3eJ+abU0LJZ6+vnMAZqA2lZycCSjYCF2BugbiCLNawJqbv2OH8sPGwhGPGw1MaOc5D0vhoAhM414Szio0ouEmJpaU2QEIC/bbzhK6J+dmJOEFQow/6FgFBUFkDKCeNm5yzcQ4LFiItEY3cBifi1up6O7WB1QvdfWIYljCfFaURKo8agAswR1f8549gI8i8M90BbVScFnoXmOv/HImVzwNRsiolF2W3AC7APD00Q35oCQFV/G3ndluoGYNQshG4APPK7hWkOgJttl8P7wJz/c95rVYb1ueJ2epwSnUvQBwMDg4hO5/NiJKNwAXi4mR0L7dH/+fIrKdi93rkDPRPz07weyf0eWJMFns1nFpiC9E6xl4tMtksTGaBCSZQityaLKV0n3jsI+537DRIumDbJfaGiMoqF5mFVRQ41xTImNwlgOrGpSpTUXjvBK2Jo4EIQ8zoYdSgzv+zszcXBKz7POcUwG0IKLuNgbZeuhd805pYn53gKRqfFCqzYAuSYFEmWh6MRRG/d8J+21kRDk3zsO0A6V5jplVKLtDWS/fif5zpCHyeGJ8JlH2JNWBmbfD3iQOUV+GItdyZoFQga+KVtCbGTzY8wNF42FChoDLlgSxFaMc/vYmMKgvGtMmhbo0cagW3VqRt4PvE8psd+uwExEr5uI9Bzd0PwMT3DOgWL0Y6vt+AZGKaoHzhFNxSLuFSmZSLIG5ZACYntPhnJ2AQKgcIoLMpkCrZ1W58ichqqqjE5oRbEAwptRcoucIta5THBYzcJhYtCISRkRG/JnY4Id1bQBe3rIkt5lDG72LDBscco/YCJVuluDZrlMcFjNwmFi1ICfVajQVXcOp1LyImI1JqL1ByhVvWKI8LGLlNLFpQqpQidC9f1/XZifAdu86xN9gtpJtF5U5k/POdykyjIiTrXxfZTgInkfnZifcvObjgxg3n65qYkgRYjbqF2OvCNasWckUgJpNRIJqRyaE4p4CsZHJINNtR+Jodqhcvrp0b/2aHfQDsZbQM2RDHtVkTW86JyYiU2guUXOGWNcrjgioIS8kVldiccAuC4MHpXlkT43sndl2ydgyfnQing5+9Qe54UqhF80CWIkzdFkVssVwIIqzk54kHblyKa2L/7AQdjyqCXBA8+LO32dL3ToRKKbUXMDl9dqLKBdrl4g8iBE0hVvDNjGTc4pq4BWti/o4djoEGPhwn+ZOZDJ4bJFPVIuHYTy9fGuKp7IXtK22usOpkEnwbuJasid1vO1MscwktHAdrc0Kwo2hogjCFhkxKHCs1T8iQI58sGnDZCQmBiU9ZhOpod9+x47/pU9fzWPBAmLEAHNPSEMcwoy+N72XEcHYQZzxzSjTPMVSREWjvfmgGK0UybzKAbXp4ZFjexSbMKjLiJHZvjycmvcexVsf3EzOZNxnM5guryAi0n+DuNUzWcE3MzE7kh757EWZGMm8yeI6hiozo7dAMVopk3hRBdi5kJsqsM3BPdF2X+8Rr9T7xbGKvqU6mlMbecIEPHE6JWyUkOd4n/tDduxbYeyck5GqQlFRennaOvSgwk2hsRLcsCyfYiQmq1GJL6lbvE7tnJ/wgeJg9GqUHf/aS3QpBMGMBD9Xs9UjawJsMZvOFVWQE2qu7162Jd89LnsWvmr3hDKKTgtQHaVHE9jWNuUvDfWI6EN6iSGoRVtKB6TmGKjIC7d1nL2qtVmt8fJx1JvMmg9mtEAQzCmBNjLtlE+81pkxKq9SkG9gqUUwC1aAekgS4JrbniWVUolmSjA1NCyO4sYSEZLCE6vKkjpQ6GVUmm0Uf67FSL6OKpdDI7Dt2sACVcBYlDJ3BLoHPYigSpEjvFghZQ2T8nJkG4rJbls2hJa41Kmti+h07fNs+9nsYAdVxo/8TyFhLiQ2xbANUC0McuQ3FWhhtoAqK+j/AaerW9i2VAlTvdfbO/tCIhDJkUgwCPgLVP9DPv2OnDCxglf4neEi7F+RpvOc6PMy/Y0c227dUCvC6kGmr/xknrXtdtaGhYTOX2mA44d3LEF030kr9H+A0dWv7lkoBXhcybfU/42R3r+gAZidtgAyqQzCR907gmljC6exjr1y2uVTszASBqosdZPEWnpjkhOSqkM73iT+8ZG/yPHEPsdfibYfYq8aQCuQkpEuRtkFU55a+Y0fvYqPf7KD+x7532wDVOs/eMHPijUwC/R/gNHVr+5ZKKInF0ZldcmvSQzN7Y5zY4NBT9/K5w88TX7IW1sT8286dZi8VBZkSC2IMJwUKwc5ksITqqdsC2SzSBlg6u+/YyUHhYVHuDi2DmHQDWyLCJ5GYrNoJ6F4FkhuNxtgYPzshJcczc/Q+MdMgZ8GgKmy1oQEJ1xGwRP8TkjVxFOZgAYqDpJ9yxKg5l+Z2szi1u1tLaNdJgzSSA5meJ87fO6EBjmIZBzUKc3zL1oVCJJPgEjAhLIY/1XkmyRYxiQlCBdlbSAC3KLjf7IA5kYyOAKxJQc4r10TQEHOxkkwjQa0gxWFIkE+iBEjX/wan2Ad+hNp5C3lUK1M7onL2Qh4UgnYtfsdO7hMrhOyQqIDcojhJ3WvWMfqOHUpRNadE3avgcsjjnaVqRzi3WE3/Ux4Ugh4aYbLRoAYjKHY5IiJRAblFQW65WEmmkaBWkGbTvUzX/wannHLdayCbkAP4dwfWyLvY6F5XGk41ZhZib07mlIb6LPaSKjkntGNR4lbumalAX1bBNTE+T+zeT9wp9tbjWxgcTkGYQewNTCtSsoV0lTO3uCYe9c8TS9dn0BGy3GnxqJ2Y2SuVnY/yzCEAyxERiQrILYpObQgKocPsBTgiIlEBuUVBLrhYSaYltfQsTswMM/KamN7Fhu+dgJnZy+wVS3n1YnK6KNIzyCykstB5UeTagCvj1U39jh0diBxMRffmqCbMrnt1xwEJl/VWa2xiAu8To8YM4Tl6z3HvNJDB7CweaBZHgVHBRehOcwr08Vjra7evhDXx/FWjl62EhCOBw0CfUWjGaB4LTAtkqxssmshYditGnD3eUtWG+StH566h907ctPOCJa0LF/L9hk5J70nMInWqmLktky+CFi5pXnTzoWV8n9gNAYsVwwbmbNQqqAQlE6pnjkNguH1FgMlaPXMMoSg4YY8VlZzZxIpPCoQe2mAgzrG+Pv26K/mF/1w3eHA6rOh476GUgbrWpxy2mKmdU0CsBE0l3lJuGe+6NlobGho21YE4KmAOW24BbEmWPbEJESRkSqkzKqQabsyt2CQJUNICyuHf5OTAwIB6zwHmbNQqqIQeZ45DYHCNHM7e3R2gxzY4s4mV/qGnZt+GSkC303V99cbNlyw6AtfLQuz1wqxjLxstxwQXb7HI7oyAlw+8ogdyKAJh9JLVzQ8v2XfFjevPX9K6aOFIMWDiL+H3lHqKvceVFtJvdtzd+MId67o9O9HjzHEIDK5RhNq7uwP02AZnNrHS/8mZvVh4Mg7NITDcvhR87uCzE3fisxPz3ezVuepmr07g8hmUWZjmzqATsSgCJ6vreJ94yQpof5sCsBwkblWQPEiQseJtBmFwLgqON/rjRCa/EVhpbocMc3FYq9X0Xk/MTBENseUJyt+xK8E1IUZmigys4OwYa371ttXvWdWat6Y5d1VzLnwicQkCmVNbIiCt4WROXOrsVHRJ5hPSJeYKk5e1uku+DeDtPevaH791w5tv3v2qpRPnL2qcv7h5/uI4X9S8AIRFlFAAOxf5RBauHjG1KCRVFzUyt65UEqnBbeMCkJeOnX/L0WV6n5i6vjBeMbIzWbYRMmNXtx7Fdvj9mhzfBI7rnIg2FKp0dlu9DyiZbjQbHPhAhxwF3jDUyIBDsxwF2kqOGSNw1GdxwzAib8iWbEWT3Y6Pj+N3EdgkPC4JKht8ayGXwngjcJrrBxYoj9qgBDKKokLUBi1v+h8ZSYF+PIrj1XmIuwHJ2cxJ90s42bO3gGCUXUesbvvoVq7vWF29act7Fh+bu3ZcI+QJj70t8mxuhezc+uo+BTu3DfL3rJ24dtnRy27ceO6SCYyKIUhS5EzDaayWY2+SyNjFLSdVhWyXDEde1JyzuHHOwtbn8NmJsfiHBxDZIHUfNQckz2b2ylZwItpQQDCelNlrx9OJmZV1deuB5OJlBSBr4r37rrl77yXr25euaVJqqcAyq95oqpV6Y/PS1UGdt9o4JqBMdpKFXMVkNQiXrRube88QPzsBwReOAg6B47BtxYZAiTmSWw2l8gYzLLTc+AJhiM4M/a9WDzYZb2JC38VG6DaEXcrxPnE1fGG3HSGQw7yUDW0fb37z1nuuXX7sg/cc/QCmI5SOknrEjCx8MFhYlcQctlgp17JSqKsEk82t0IwcvN1rToi8/Mi1K/o/e9PK99y87U2LB96y8OibFx6D9BbMj75lEQqU0E5GsRANjGY/+tZFUgXzhcdAZab6DEx2S7Tg1kqZaar4wZzIdx998+L+t9y8a/m6LTBLcE2MCCPSBTj3ekHPDgOtF3pwC7lUgE1oVZA0BmEuzDJ8YUeiIDjsyuY2HDx8GJaYY+Nj7XabX5EIAhgsxzUo2YGD+djYRHsCTHwOQ832RNtXH5+A7ThwiAlGrAVmtDmf7ZiJRc4JtAI5wmceZoDh4eG+vj4uApVdKxNztDjPmLdasG8iUptpR1Cb5MAUJ7RrbAFUAI56Jp5VtzbjrnCP3AbKYc3OFvjPPvv7B7jPK4aFjDQ7ehw1zXtEL8TgEHKpAJsTNnt7QXDYtaYSQpUqwBBAMFm9YdO1d+364Io+iIoueEoI1bDJcofYa4KQKRk5CEZm2UpN1VqYWNDSI9DCa+7t++Td2z9ww6o3Lh58K0TFTrE3hNOOsRftTuWQHmKvqAUy7lT2bhwVPO31d/X9wx1rwrMTYb4E9DxzPHohBoeQlyr8uM5e4VDzQQrk4+jeuGoH4hR/zX337j3vv2nzvGV98xcfukzSYc3zZASWi5Y8GYeFIypT6RIr5WSqN3I6dNmSI/Pu2POjhcuh+U18IAFiNYRouYBoAOdQj1awYPyXAC6hHo0Y/5WJBLncEDNcL4iHuyCOxn/I1TnWAia1BFsQnEh1zkdGRqLfZ43AI+aH1csF8H3iypsahUek5V3OBbg/2Cfl8Bl0enJi7Cvfv/2y796z4HuYLneJLWxMSnP1CmdJStligiVTucgIiWoWlK9fPv97Kz71nzdf880b/uTrt7z3Gze/9xuQ54KpxZSU5uTEm6Wb50Yqp5zMAuVfx3zuV3+4ZOUauIzJV+wIKCUD6QaoaohzwOTKg0lemzcd3GpJYJRmDgqBQQDVN0BLT/LsFS1swAffAzh46FCj3hgaHm61mqOjo/V6rdlsDA0NwfkJebPZqtVq9Vqt0WBOC5akzSYz62CEM7nZaA4Sf3S0BkYAGptN5o8MjzSaTXAC4OqYj6CTGlQYHQVhZGR4bKw1MjIKRbi7eh2MwyMjsJYdHuLdjWAroHG12sjw8JEjR6nWCDDBAo0BVdpMzQM7GOGIuGHDQ0PQVLAIkzjQsGajwdUbjSYdFzePjg5MuAtsErcZ2oDHVq/LIUAObZZ+wGYocwxytsDhgZNhOBL6UiD3vCKIbtQQqCSjTgBTPnUBMVU0JOcFqdugzm72AoiQswRVbUgqMGY0ewUkYFZ5aDDNcZ6vXLPusv9714L/WgFR0QdJFixF8VOTqVzkCUU1KWKLCZAs/rPKRYFw/fLLv3fvR65f+Ml//f57vn4rxUYXJKNklqQoZ/qU1OpM9qnIRCOE+nd95aav/8+t+M1o7nfKDaCW4l5CEw3JeUE6c4I4g9kbm0jLWYKqNhQxm9lLQC11G9QkOCCInFc40ZcVUuncOXDgwMLl9y5fu2H5mvX3rN0AyQuQ7l0nQlJkAhDuWYuE5WvX36tFnHNdY8JevDejJWoiKEeMsH5Ys24dLFIh/lPAj+I/RmkK9SBjqKfLDVxNYG2Klwa9DnKox2sQBXy83NTqcK2EUg71XISXBL3kQU5MuKA0wDhKj0NQEV9MmyNwIRtFI1zXgDlEVzcgwMUCCLAyln4nuLFAgFoYtQILQWtiGH2sQcnNjqSGwsjxVMoBFGXi3YZ282Pfv+d5/3nk+d89DOl534FcZBPImKqQP+87xgylSTJytdsgODUlu9T/+99d9RtfuO3n/3r5Mz+99JmfXkYJBEvLnvkZFULOgpGTIktmIWbkZ2mqhkRkEczCAtRa/sy/unMZ3SfmNXF0WpdGzZcb8BNzeYiZ7iqBaDOnO8pu05qiuznJqkCLg8ExT8rsVX6A3FOAs7pNP2oAMuT4dxwWWDOQEcpIxow2juRLQIicwAYTVw87chA6biil1WlDRggiEG7Y4HICidJI5ZtmEoCLtCUCoYtR2iBGV5fARcoUlRTO6Z8I09P8ogzrc3StSEaF0X32WjUQojnWGWW3aU3R3ZxkVaDFweCY3Q6NOExODi0DUCoPTSxRQfnQJnl6r9iw5bn/vvP51x/LImSUeom9sVoVe6O6kMfxv1N63ncOPf+7fed/e+vb//GWn//reyiQ5qHSC1kML8deYhonGFlIVGJW+smEzy57xieWfPQ7d+ONI/kDXzoWOlxSjAChcohzzHD2hsQqQ4TS7O3ollB9YmYASuWhiSUq6PnQugaH0qGZqQMqDw0+54MrDGc/PoBVKbQcJc4xDpuAh2c5ETIjWJyqRgCrJBmTjJwpoHq0IUSMqGR6GpbIsCgHnzQ1fUIT5QAR3BBDI+A/6dgiAa6JjeMAFfXiEXwAUq7qSgakFABOiqmJ1sd/sPx53z70gu8eoHQQ0vMxsYoJZLBg0fWUUGamlLLKQqgoZCmV/HrzjH7EbUi+yLly8vOvP/rO7658xd/cCmvNZ31uybM+uxTT5yjPBVGXBHuSivycnFiKZM/xxs8tf/an7li6djPMEv5tZ0VhSAj5qFUxAZ2H2Gw0XViJaGkd48sWIGKHaq4NiswAAFuBmXNV7/HQAsACJ1F/f//EBL53AkMpkogYRHPrBMppExii2oZstMHEZlJUDRKLtBWRVd2IEci4gQ/r8e/YmVvPIkFyTGxhitlF4o2AOMoihWEsraXuZYMFVohbLkB5cnJoGL8USFCrwNxaSULwyMlF0J6ZENHSOqo7u4gdqrk2KDIDAGwFZs5VvcuhdfNUqoNAu6yJ129+/rfuf8H3Dlt4DAGzOvZSQkuIvSGkM9lirxjVLTEDOSp1brmIUmgD2I9c9O0t7/zqLRS3syCZ5JIovEeWCrLnJEbMIf7rlcJSKM1Uyn/x80t//q+W/Pl375qeak+UR6M4H3LMfPZaiQiOme6TtMhkp2mC4oRMuaqfxNkrYLGqsYiT071Er9OfBEGCUNaG/wQW4BKtguReBZjAcGrkLQcUcSnl2BAh4wMdAiVMtdGZ2acm2u2R4RGU8DiwCh0P/reNGkXEDfJpSzn95xwz8kIC5lIUNPlPuishGiZzTrlssJQKQYYeljWxcgBBMkQmqFmggB3WxFQgpY6EoiyezVpmMZSvFkeC3eMyrfUXP7jned/GEAZBjRNFTxKcMaTvxAKTUT4kdp+MPMNkbcCkTjC2wpr4O7Amvu2Zn14Owe6Zn12KCT79m+wT2r1gnCXlKkzTWhAZxW7pM66KOWc/RYcYYZf/4qdulzWxG3AR44klQM2PWgFc5D5HgcW5kprOAYr5zPFkRBCV7wp7AdMpl5rOAYrUhtAPcvwpi6F8I8sWoPUC1GetVsOgwji+7vU4Gd3LYQU+TkP4gM/XGmU6AAmxW1cFxZl2L29Z8iQnO5iVb1qgnnckaqdE98p2BkB6p+5l7eR3r7ahRIPrpVsTPx/XxBAbjyP2+khLiWOvBP/ciVl68U8ccnXkwm9v+YOv3PzMz9xDcbUq9oq9S+xFlQUXe0NAzpKS1S3UWubqamKZ8l/43NKf0zUx9Ld0PqE8KpZLsWOheKJmb2aLq8Uzx5FBVJtaO85eRZfZWxI7z95ZBAe1US7FjoXiTLqXXn85Tj8nkZIimRAMKJ3c7hUxqqC2SVzEw5LNbD11b4FTVU1yKXYsFGfSvdpm6uSJaalKyNvsq1nVUvfqeyfUO4FFv4cKhDpdyBhZJ1of/z7dJ6YbAPxxP09Y9B3KIVyGUFgiM63nuBmRJWVulUD3G47+wXdXniNrYgheFiizAJcmIBi5Y0InMbOTW0id2kBr4juWreM1cXEiEzqNWjoLnO7e4s3vUhHRc1gktyS6ohihoDRzrDR3EhRnjtrA6HhojGBIyVaCgiiuuu4uvIvNIxA7toFEp3vlxHcvG0bp0StUWc/rB4srO5Hdi1YrcQpt7Oswzjw4OMT1PZHQsQ3eCSMoKInWsXsrEUrzNrjS3EmwuLKHsHtzhAIgy7MT927Y/Lx/hzXxER9sZxR7MfUce0XoIf6HXAW5T/yfW94pa+LqOHycsdcnqvisz5kFCI4Tuc33uOQXPs9rYn52gkYgH6OgWDluHIfF0oRMEOqUyFZq+zAEMTI6hZG7zZwEQ3RZkf8Ep/BmprM3g5XmToLizMfbvYgR+saFKB5Cy/aAiIyxP8CJ6F43zXSDjxEA2u32sP5pTsoNQT8VuhfI4gIW8S3+jp2VihDYTiwaDdO6Jo5hTBZkz2I1DTbRMSjZb5hB94lxTXwPPe8FYYtiXAh5HZPcXSiFxWJCtx3J0e2KMvMF3wH70Xd+d4U8OyFRrOIOQWI0lYVilSjFbuPqHNajgFvh9lmfW4Zr4rX2PDGOgY5DPIa2VSkpZsQaIrLkxWYLZyJudXnedeZIcRXgmgF5VbnZEyF1KxpsuA1SwJtIiYE2+O++uNFoNqmfAf7QIiTFHrkWWwoQwmy6F7Px8fExjB1QX+p4PPTdCwjdi1v4ZNd0PzIi+3JgPW0DIddiSwFC0O6N3ZoGm+Ls9ZsMk9i9UFhdLkiE9NBEg80J6V6VSZCNA/9mh6yJrz+SBMlC6hrSOfbOKKTPLP4D7fD51297Bz87EUfIUuot9oq91/gvVcxerKVJ7xPzmlimVjwSNFgsVkMIOjfjmeOHuPvs9Qy0nSqz1wpx23X2sp62gZBrsaUAIfQeHEikNyXwzw6HBubdyyemlqfI7WxJD0002Myqe2kLIbfVwpB7qnevAnS8S+y+Y1fVvQTdOiRFfk2MJjGDV5KUbR1UAJ3FVJcyMmEmYCutif8Cnyfmv5dFAQ5vOUTpQBQ6JSZ2SPTnPE+TewbOgnu0nZJAfLqvwMYo0bMTx/7wO3SfOImtLtLRUpU/+i8To6ZiOH7W54DMf1aLbxhASt2WU+o2tGEJPzth94mx6/1AIGgsWOp5iJGI/42de8WQyaxo5jA5Y8sWJKzAJBFcIdVEgiAUE9kRGWRl0beBkLMZSOzYBrS6NgQQp4/uE+veAOZiFt1LCuYZUdpQ7N4CW7YgUS38DxmZ///23gTQrqq6G09CZHJCQUQEgSTgEALYqhUQh1r/tl8dam3r11paO1hlEAQcqn6iOAEyCDh18mvtgEQUGdQ6V2WQQUPCmAECZCDze8l7983v5X1r3mtP59778hICf3/Z2WfttX57nX322Xedfc/b59wW/rYz3XaF6CJuPYikNWKQlkXfBkLOZiDRtyGpBii0gcBkCoG9vb3giUTWkr3bNkg1/J8SpQ1Z9xJqbtFCtUgSIVRD5e7evZlbcyE9BrKsnaA58YJvwJw4j5ZZ7OUUTY67i72UyiE9S7kH0Gz4navuf7vMiUOYnf7YK4nd6gKJYlK3Rec4J/6Uzon1FAD8uSAJ/5tVQKcYzhWeZ2UaJ2Mb2oxedhvDOJ7HcNqO24BE/E91KSO1g2+DbwzXSOnBhfWYUWptAFA/EMhBRiTtFLpXaw0MDAwP4W2IiAwF26/AXOdOg7bzQ0Mi/qe6lJHawbfBCePj4xByzW26BSlrg/dNYSOAOs7YeWNJy6zMbc5WhNHLaLUG8D4xKgn+0Dx4bymClg8tuU+cVMjqq4K2nJmoNoNTRGsnXAgrzIZDnqaMzKkhsCYpcS4L47KpMykXrqH7xLaeWEMYRDeTO0nhDgFHTFtnRulCnUmXbyRUgiy3IaqCbmXtBK0nhpiRnQ9Goq6etVAfJf76lpGrQGZ1WHo/RbdlRdGfR1Kt6pa2nJmoNkG4I5FBK07iFBNvpEUNS6plXlTR2L0mGBINFmmvKZPKTokiFzEfGRkZGhyiD77jCGN36t6AicEBWk8szlKrbA2qmKbuVaidtiGjXG2GsmL37N4EsRWDCEbuO2ROvN7FSU6dxN4k6nJqir0R2U2FgRw9+1FLV+N6Yl07IeHRgmSImW4eHJQhdRF7nexS6rbahvg+MXc89T+dgcazyUjMWJzG0Ut4HI7enKyKtHvboOC2k+4F30Cz9cQCJcfYbbqXZg7ypzkH59GQeVHFruleymkHeDMe31KsyhzdtUGfsYuRqZLuoL2Udw8AcmqTOTE+Y4fridMv/SHSrZ1fvh/gkpBD3KwEVp8KynRH5vZqNNF9Ylw7kd0n9hGwEg3LqVMy7KvhXkWWgtt07UQ4Dfmpyk5o6awxilpSeovIOTnVhJFT4GZtqFIBpCxYMlV2sNqGQuUO2mAMehfbGH3iSBE+etkeC24FRS0pC5ZclWqaunf70BC+VDjnFKiCgiVTTXP3egCZXpSBRkfYZd1bPbRS9e4OjVCwZKrM7TR1b6oBtwI5arik8doJvE+Mz9jZ2okOYq8L6UFpqTH2RqYo0d8Ps/gfJay+4bVf4/dO2HrinR57O9hFlQB7DPeJq6crgGwFQqbaWaNXNbklV3U1ekGZqKWYk1PNlA6tCLIVCJmqo+6F+SW/EmHXdS+hULlU3W84Hx8fb/X3kwhQwu7avQxcTzzCv9lhpgo1aHKLqOw+MRRRUyAatLuBZb2Q13ElFolCc+KPZ/eJJYUAGsudJoyPzq2GS3OVxVnfBpHj/fKc+GR8xs7WEzdHvU7DLiQ3wy6nelxu2gvfJ76Z1k7QVzSGF+hccKmILk4xqyCn5L98liRF9yOHJESBbCoQUI4pMXZk9JKEUIm7t6e3FyKIegZNTM7xGHYvyfyCdGoGlGOKJ0vJVCCgHFNiTGv3mgSbbX38wAeIqAyUHNPcvYppPjQElUwFAsoxJcbO6d5YQDlQCPDVWubE/IxdYSaaxF5NXcXe9inbb+Q2WGk9Br134h++l6ydkCAZF/M0xdibaTpPvHbio2HtBJxiQX5KqMQqyCnt0tFrkhdQDpQcu10bhJnXoZIZOUkZESRFu0MbGpLXhKmCt55SlLyAcqDk6KJ7WRXyQJGak3BFg2tE0COg5MixLcMu7V6WYE6MnezI8p9RkLyAcqDQnNh78iYCr9wgiQVh4IFnfCppBvRgdWsnNJCVg5r8TS1K/IxzlhZUyKmGkiPrvhauLZKZqXPi/D5xSBI9k7+RhWIeQ02TCyG5fcXW9I9xLpFJ1xPLbzuHs8GQstMw/FniDeXhFMM2rqSlcIoLbtWb4/AmDO4AUoTMUBxmBFXSTqRmRms7emEbV6ISZO7QuCMjiKYf106grP8hy8hB8Rh3L2B4eDi82gxz3JScx0oSUZMQd1r3+kMbHBiwXWctMDeAx7h7Q2Z4PHSvQGpGZChEz9iF+8TNsTedv3Ybe9NUCenFiwU9G21rJ37B4ZECdfextyHe+iQ09VapFbchSnMvvuXwT98Y3Sf2Iyc6J1ZypzhhANzIiTedjF7HpS3lDJOdkkR064mAMPB2zeitHhqh0gbaeGjJtSFhAJq61/FxOzka1k6w3nNxSznDZKckEX16IiD0TFfdC3CHFpwEgBLfxeYrowjUjBwUscnaQBsPLXXavcmhgZDyaV+4QAUfZJRXZwCK3WtuZQsgEX063QyReUN5aK8DaRypBNmloxpX58S6diKLmy4lAdTCKCs5GYfuOqPJk+OAWw2peRu8W5gTb6L7xJVn7Cz2XXjLERfiT3WE4JiGRbeGLDH5IsthvTK6JUFzflAjVPFucV2y3CeW9xND15eGJal5W0KTDUBrZ2O3vKE8HjmRnQCiDssYXltqdMTopg0evkIBsgfP4g3lvoVmt3exSRkAUijkIFudsLO7l56xw9uuaTR09XZW9zonwnKauN2gsjZMbMFn7Bik4VplkK1OeCKPXudEWE4TtxtUWRuYAgUtI4AC1ye8T3yfmxPHsRfTtMfePKTHroKc/NVRfrOD1k7QemL6BQ0JoVOIvRaBOfaKFTVx7GUa+1FZipjCE3iiCW7RGj1jF58rhymMHKcBsezZayPvuTucVWRtYFCh2IaAum16Ri9AyyVEFXLscPfmQOVAqzWk72J7zLqXN94tbygXq8K/i40AJKVI5QJKjY6wE7o3oDUwwO/KQMRuHUCVtYGhZFbi2om0QVyIVHXwxTUhZ3XjtRN5QIxTWMZgzPJ9haYU8ddB0T2fkTYgvw+t94nvOPGyDtdOSIpDpyYNjs4JTaOjt1WoKYTmkMpu4yRz4kXJ2okS2NhICaif4uJQbnRro3TSuzVthMiPDOUcXY7e2NDZ6FWENkA92C8EO75PnIJ1VT8xdn73IkgYHRkdHh5Bt1CM/DR1bwSmVcipoX5oJRTaAA4G6YGPyMKFqp8Y09+9wMi6N0dkmq7ujbHD3VsG04ju104suHp9eYaax88s9pZCuqQ89pZS6ZJRagPl4T5xvHaiIfZqmmrsbZOC29Aec8vP2OnaCRqVdqamOHrTYUXoZvRG2B1Gb+duXV4EmMzKbUhAusbuLcJ1BrnlSzDeweT7JtW6u0v34hdgrDcxPDTMjS/QWdXoJ1iLx0y6tHu5UKIr0u5lMohgGMV3seEPypY8VLs3BdMm7D5xeyCx5l7bq14zZPeJkxkq/kqnFjE5KyR6tAIEVbrwZ7FShLhiGnDLe5Eq0gbJF+KcmN5PTPeJLypERtYkMZSUIfiavpZyt5qkLjuM3SaytEHfOxGesYvPRfHclQdnjranuA4kN1aA75DqlnYTyLpXRt6GRrceTC7Tp3RoSNu0SX+zg90XAL4jfc37Dh9aA7B7kTC5vdVvv9lBNdp1b8dAcq2CuPX3gjoBEaGdPT12nzjHLuveBvrUR2/HQHKtgrjttnsRnlyuGJ6x09/scEGyIfaiJg22SbEae6mu6kmZxn+fsr0AmefE9IxdNbpimmrsjdIU4r/joHP+zQ5bT5xjZ45eN3KS0Ruj+9FrHBTauN0poxd880GJteY9P7QaMwOTa3ScYva3Wvit3jWjiLwN7WAcFGoVxG3n3avvYpOioFgRfGv30m5q+8gPreiuBCaX6c7t5MDAAL0rQ3Vt0Gb//IwdXyFzODWJbZwFMDHQcU48Pnwevp9Y5sQWSaNkUS/nqMkibwjBKBiThKCxIiURzKTRNphE4PvEf56vnYjWM2ByMVFNyU+DllPsJ9xIzhNwYodJG+QmBIRXXE/Mz9j5+5ednjXitSObnYWdMnKEXK1jBhZ2+uiNgXr4D5+//lb/+DhFhE5cE6cd0ewsTPOh0XpiCtDguFonGBp9O30jLwMTO3BLoAXQ3A+Ne2jPAJidhcd29LJU4zl9Iy8DEztwS2h0a79jh2sn/PuJ28XeUlEEJkNejb1OMM+kwbXCbtdRMreQ0+/YVZ6xo5TGT9XnCThEs5Bejr2c1NRF/McU/Y6d9HwRdq7kFFfOnVOTWKHlQGI7crA3+nb6Rl4GJnbgltCpW+K1I5udhWnoXgi0EHLlt50beAGB1Ojb6Rt5CrOzUKHTpQzmxIND8sxJO7cRhFytYwYWpmv04oKTEQIXKc+R6hvc2nsnOkfdW/2069qJW/E3O0Ig4+TDWaJUmVc+YDQkPYdFK4aUFNslcktBlisG5zInXkjvJ/ZzYkxRyItNlHiVGMoaJc2UJllSJrLLC27JVNJLyn7Hzp+r4qmBj0AR9VOMaLYiKox6xcLIKZAjVZefWEVXbSjAkSbh+zS+d6LajaTv+tDaN6PCqFfUNgwODPb390Oz5D5xjEhVd9aI9m1oRkKCdm7b1ieFFLtd9zoUyJGq7qwRXbWhgEobinVxmOjaifv0XWw2kYVQ2RB7E1MpxbGXBA3saaL4bzLkRsv59Pe937G1E/g7HRQb41BJKY+9ImczaV34GynrqRzSrXq0L8jnXXzz4Z++ke8Tw1fsHE6HYvFsGSrWeqXH6egNKKqnHhyK7gwVa1GNc0x8TRj/7HBAgRypmltQRb1aZw6ZheuJt9J6YqlVrLw7dC8DTUNDQ6GTqWkJovqhUHZLc2I0sTkmqQ6jo5moI/zrXwhJ0SAudO2Eu0+Mq8HsuQpIFOzCnQASgjVJRI5TSvbhklaeoVsWInLWBko4J5bf7PhB9JsdcUr0UKS/ixUTTXNDuMQUk6MAGiubkrmVtRP03gmeE4ezIsO3dpr4FCvanOKCPiXzcCmNHOY7pNMzLeLGuWUBjKx3XlASK+UK1e3g6M3IUIY2o3Lr1m06J2ZOzU/SYBSbujeylMhM6ap7IacA3Wq1JsFd2gYhsbdS93Jy0FI3h5agSIayO6btMCeGAM3lmp+u2pCMtxKZ97/rRi9vgs5jJ3VvB4eGZf87dvouts5jLzGbYq9PiVssVpiFZG4pbtPaiei3ndvGXk7tg22SErdQbHCrjUkTVPH3iaHb81OsZRMYWMpHL+f5Ka67FWi5RraRo3D8Inb56HX8RBQ7bur9YAJD6lCuUF3b7uWGDgwM4s/po81iRYEMQP5j3L1YhCsaXCOCQRrtc4ITAY2HpsYgMLhU1rXtXtbCf2DqS6BBRD3835HuhTkxqVIzC7iPuIYnI4JoFlFZGTcYWcf5t535GbskAlLEpKAZ0tWlmKixNSS9hYzkxFTUSCq1wSVaT7zp7bZ2giOdhb84vLZJQK7yS1G42Xm9DfmcWOHPRQFo0TPFmzJwfVRGVllr8rabkUPAoqgiS0KDcqyhUlqRhekbvbx1YA2+iw3nxIScpECLuuJNGTu7e/272NSe0KDsNVJKuSzs9O5FTEy0BgZIoGIJaFFXvClDu1f3R5u0IgtT6l5RRZaEBuVYQ6W0YqDUyYiEh5morIwb2TrEGk9OEebEfJ+4fex1QTWPvSGkN8VeSR2GdC4imW8ng2d6PzGuJ6Y5cXM4TdJUY69LXYf0+HfscOwVMIFrQt1Z8mfN1Czs8AeTkX7dQ2OsoVJKCkBLsKbkgkVUVsaNbB1ijScXgBax1kmAndG9uJkcHrY/6wM8affrXijgemJ8F5toInIBaBFrnQTYmaMXAD0sC1S4jOike1MVQ+fEHqDAhoUPZ6ligjAvT7cEvk9cXjtBQc2UJGjElPCnfzuj2w9EWEdvuASa5iwgn+vK3V8hk4ypGE+9QAlqyX1iXjsR/7ZzFvWomIZL4xTJtOYM0gVQTAia/KK0EFK5ugohYZHXE98i64n9CQARzmZ0RuQUF88WI1X5r14sZZVAgU8cFUdORhaU2mBSWqnTNvB/RHnYJ2gcvXl90JByIz9jJ7WZ59mlQzOkKk9miXJPA3kHuhfQarV65Rk7KqeVPLnMQEW5ezHGg5yFIkDl0BgRn9yShjIoTvT29KBPoXn2FLtXbVklUOw+o1ftGSOHb4NWM0T1k+5lgIj9LKUAcCvrid0zdhAVKZzWAqlLFntJ4OBMzDRukwZzqwV6JatAfzzEFEI6XxQsMRnd2toJ+x27ECQxcThtWPWbmEL4JdOFN1JunCK5ZPVx27UhWk/M5yE9G52OHPyncIyMLPAjR2FSXAlvsaUknxNA3Emjl90qYoYfva6CioGcswS1Q3MARdfdi4D55RD/WT9hpZU6617KLXqrrRR0BV137/j4eO/WXjBRGRi7bfeaW3zGDtdOYG0l55WChiWfE0BED5O4dgLVzgSFqGSI1VmtBri1EwvtGbt6cjPgxgSuIDhSYgFyFMhq8VQT+HQxVMNuEChpLXr3+8a343sn4rUTFtd8oExSkwnmwT+HyevcS++ANOeS20n5c0dwZEz54jbmuDZoFVlPHN8nTuKSnrXKmSue+QpXEFnLIyf5SoyFBp9lH1WYN6tWrt9VGzyUltJpKUK4T0zA3m5w20n3Uliw2ODDRKV+F907OjKS/5Z9M8ybuUx88wHDkBOAhlUJrwalFavAoVmDwTgN3Zsgspa7N1FjocFnxUcN5s2qlet31QYPpRWrtBs59oxd+psdkngW2z5QY5LYa4Ea87hoZA7CuVvUx+FalIHM94l/+6r79XfsJDY2JQu5cTiNYi8oIUpfeNOcS26b+zkK3RfdesQFODnGPyG6KkyOlMFaTsmcODsldcTUMre7kVOwNdDNm3HKZN8GCw5lagzlSJUYu+ayophi947ga8LsycmCj7JbgnmLOf6KEBcAHXYvW4VDx2D/IeTyu36nsXspcPP1gYHnLnZfrt+uDVwaGR1xL4CKkCkCzJtyZDujVIn62bGh+RELCuFUBB5nwpSNQObE190Kc2IKXhTaijGOE8c4n8fMBQvXLLhqNeZxOhrSVRWflkoOk4Sxld7F1vA7dpDcmor8bZcWT9V04Y1zLv7FvM8vmfOZHx/6kWsgPe/c6+defMu8K+50dx3axlAXpoMSEr934se3uN/siE5CfEYQpGF14ykOQI6FoqgCIxkWuI1YUEjckpmziAnIyYHT0AYC6dm446M3Bh8//t+8ZcsYz4mhBKl6aJA3tEEAfosQMyJpN27buCWzZa3WQHh+IicHbypoOQXpMapN4GwYHNGbj/HPhPAlgTQM4Vkm/mRTRGgDb3p6eiBPWwpgjyK26wf1BsRwByRFqNOBW7K4LGICIjIjHJoIaR0F6dlYaENA4HEmxoiTIGsDAwpJa8kc/Y7d1dmcGJPMZWXK2xDSiQNxtRi3IdEt3kKVDsK1m08jbcNrvr70z2Q9sYurnSSeziZKSBfcOO/yO+d97vbnnXvDoR++5pAPf+uIT/1g3hWL51x8qwbkevyH1BjS+bedeU4M30Gg28MHSBBOmkjRyWNQHaefyuiVDQlZG9QYu81BZua0bwMBpaZDC1Bjg1sCmZmTt6HavcaMKjCoDunZ2OGhQcgNtyF8hawNaozdCkSH1wO8tONWgYXgutQGlIikhQRShy0QwOUaYVzYJK0lA1th/7RVlLoXYghgdBSvEQCYv7JG7AiSY7eRXygEuuORelAfZAxVsjaoKXabYyK8dwKoSA4VSGquz1a5xoAQO2BwE3hOTL/ZQdPNpgAHyZ5upuRlSAvXHvOtLQe8/h37zDl673nH7T13gaR5x4Bm3xe/9oX//tCCb0CUTN55zD9xJDLmjaGWyJv+fCGtJ77glvKcWCJdJewmcfDCG+deesfhn/rhM1//jr0POmwGYeYes/eac8wBf3ru3M/9Ev8kJ8y4YpzSlrg2xGsn5IQ6RKfYbAkpAVmhov6dAnOr4c+7A9dx/x2gGLRh5FjmkJSJbMTUON2jNz+02BmVIN5hN0PfUN0pdy942Lpt2/Llyx/IsHzZsoGBAQgz4WxKnaJrUAVL1r2TI8PhPjFzHLrpXgWMtN7e3pUrV95777133XXX3XffvWzZsrVr19rt8yl3rx0vNFjrysZygAlFkBVqF0evwLWBwHVIzCBq3mTdGyHz0E33GrEOtk65e1NN3AbL/doJ/y62hhRHXU5Qa+3RC9ccc13vs950CsbtI13cnnvM3nOO3ufoV7zgK8sWfHMD3tRIqifxP0p5e0CzEd/FJr/Z4UIlBkl62K4WsSEVY++FOCE++F2XP2X+CbP22odD95P2e9a+L3n98869fs4lt+HdYmR26VZTtJ7YnRgS4Lw0j95YS1JKEoiaNztx9BKSYgK22qHFBzE9oxeQkEqAirI75yCgmw+mqHlDh4biyMjIGP3ssK/mDo3RpntRNTkJM78VALkyBEDI3drb668RvOm4eyPAiMJrRGSFQnBhlohSBu4EAkh/f//DDz9833333U3XiPvvv3/VqlW8zBoIwQ9JFbei5k0YvVS2H0ZhZIfWpntxwwXKbU7MkArqVDZYhovc2Pj20fEJyMeg3+RMCfDbrRWdHoGt1/vE/v3E5TQ/3Gkoxl+Krdf27vOCl3Fs2mPGjFku7fHMg16Ec+L1eNch3VGDW0toxTZAaKb3E+PaCf+sRvRCStFI3hD4IGhefNth5313z0OO4mbvdeAh+xz2/D0PPHTmjBl7HrHgyC/cfcQFbgUFVsFaJMubgLwptEc0GJT9u9g4VurI8eCzYyfPnayIjIviGSJEpziHsnQL/4s7JzuTWCS3Qg0BihAK4rUNhOXbIPBuuQ1wtR8bnxgdmxiDNI7DW5DsKm+DbOFbbk9vj07+rBYL3XQv/nrQ5ObNmxcvXgzxgnHPPfewsGTx4r6+PiDwCc261/kHtOne7TC9Bm9UFayhQbGXGgqsdevWLVkCbVwMYe7BBx+EAA0tBw3EuzAI2wUHhhocAUP6BP/IiIKt3XRv2KQ6B2U5NvmLmW1Hb4T80JohLCW7Sv7Q6GkVtmoFaoPA1WLAIY1PQOimoU7R2zqvADaF7sXBGebE6yE2JjcanBynEHsl6s6/avWxN/Tt+xuv5QCYxO1Z++73gq8shTnxfLxbLFXUAyV22HjtkMS/2cG/7eznqTZztcjJQhJdRaO0C34+74rF+//h+2bNhFA9Y48999z3eUftfci82fs+FcoHnfIlmC5j6IZYnfgpui21YY67T8zrie0MiWDlMpTl2H7ICB4Ho7cKf2iCyK3fGKdCzoNDDWAen5BPjVwmoBOxUuSPUehevEa4W5hsYgQHXlsDXSPwaZC7CHxpYEC8vfPOOzdt2oSr7DTgyn/cQL32e7DuhQbjfeJt26LW5oeGcIRK94JfiB5btmyBNkMjYU7Mk3gQFi26E98KatcI5MoW/nfWvXJo1Mm4ntjddw4SV2gHYfHGz4lRU3ABjYW2xwBNQ3iV9mPDhMBzYnrGLvmF+iR6xnHQJas1/yqYE/c8+cWvmTFz1kEnn/vCf7n7yMtuPOqKW476/C1HXn7z879429FXJXca7HkOr5Qkbn0bSOa1EycX1k5YkAXhJnv/ZS3p4oqb5n7ujn3mnwBR9UnPOvTgd14y5/z/mXfRzXMv+J+DT/+HZ/3pR+deejtG1VCrjVtNURvStRNhGHFiWaQc6VlzY9tBlLENA2uRDRB9cItZDXkbSm5FF5uwVCIjRM9uKcDQKI6Ag5neDtpZG9C+ra8P58Q0dSMVVy+j1r2oh+l1Tw/MLFeuXAmfbfiazvkQZbqLMlAPPoNbzGoYHBqiu87AlTaU3JrOG1EOBgp20GaY/t57770QkfmxXzAMjwxv3qI/71dCh90LAD2k/lZLmJTXyWRxbjFPIcrY1tXorRERnR2a6bwR5RIZ0dmhCRwZCpXQDSNKeOwquOUyA2qH37GT9xNThNQgSXFSBDX5Iml0RQTE7WOv73vaCW+EuP2sPzr7hf9yz1EWt6+4+agv3Dr/a6sbpry0I9svuS2ScXf8mx30O3YUISlISswsL41wKYq9F9w497JFEKJn7TELZsBPP+mPDv8/12Dc/uzPjvjEfz/rbR8+5ANfoz/x8Uy3OQW3SRt47YT8jh2fhN139CJI740o18lkmUobsB9qbNKz0apIOUdnhyY6/WRgzh8ZDzTpDRSp0HhoEMDthiiiTRsQpPdGkPGDzHPiZcuW4XN7fG3gK8XwEK5GYOaOdS9YIDjANcLprQpLBRRPMU92oc0wcYdmr1u3DvoBlAC4WPRs2cJfFZBImxyij93mADu4cp0s5DI7cMyOgifDnJj3yQY1OQqMg4nB4YHFD2z97m2b/+MnW755U//P7xp9dDOODzfNN2gZ3AYTz4nl/cQWvyyo1UNhSPYeH/ob3L7HvRrml4ec+eUX/3BswTc2HPOtLTBRxvStLchkhz73giQK1uYWrETgopsT631iF9RKKbeqBu80LDnwrz4LDZ6179MP/eDXjvziPXRfASL1LRBz512+yE+IIdUDd1MbeE58M82JcehRx1P3IxpOsQLDq8Gqo6OYrCU8xcEhIyKz5AnsDDX6P8CVRKQNZ/l+uNxm9CrcoVFcG35oXd+PF21Z+PPN//XTvh/8aujeh6HTuN+YVPACCtGRNLm9j+bE+AUdy2jrtA1i5yr4EYMv0/BN+qGHHuJmRECOc6uiSqEMMtlRo/8VdFwQQwcGB+zYKOcs7IHhy+pQNeQKOmrFihUwJ960cRO1ERkAaTIrQKOVGFqyJjhEZNoHbSCqokD/XRtk67Ajo1d1GRnLwS07Q4b+D3AlEWnDWWg4w5fVoWpiJqH7Q0MbnovxgaGBRSt6v4OhuwdC9413j67bQidIOIxiGyCDr9ZAhjnx0fiM3Xo3K7UUz4NrsZfnxDf0PfX4N0IYfM47L4S4DQ5D3L4W4nZ2uyTMuXG/wVpvA8VtehcbPWOngRHy5shZlDFEz730jj3nHAttfvLxb4a4jU9F46N1N8+55DaI6nMuuVVDd8F/xW2a5tJvdvi1E9r9jac4jCrVZWQsT3X0Eki2/SBYysuqCRbD1D6YMSIyS76M8jS1gfTApiA2smpD30/u7Pn6jVv+83+2fv+OobtWcoDTBqZutbr+n8SQO8rvYuMauiGQ3KZ7RQM77e/vh3i7fPlyKFMrIhCN2ZhNoXtZyXPiQFdLaGWwGCIVFTCDRj3yyCOLFy9eu3Ytt5FNJMs1InQg2CI3gObuJdAddOzk0dHsyEjuoHuTQ9P7xFRgu4HnB5v+5Xsr3/TRFa85Z/nx71n6m6cse9lpy19xxoO/+6GNl10zvrUVtcOciBfVgwR9IHNiimgLebGvxTITwtMSIRryk8WWcO1Ez5OPexV8cT/41M8d992hBRBMr143/6rV87+2im4Sx2FaknlWa2MbdE7M7yfO1xO7CIh/BaMi/zksnt2iEubEl9/5tOPfNGPGzP1e/bZ5n19y+Pk/m3Pxbfh8xgU/P/z8n4aVxOTH7cu7yv42x2QOtWTS9cTy3onQ+w4FZZGHQIMMl4jDnlll9oxIg7WEUsNC3cSIxTBkASpGZCuoQpDsCm8DT7bufOCRky9Y8f99cPmJZyx76anLXnLq8hPOWPGqs1ef8cXBJQ/iZ1VqSV3sSBSDK7QTZ/PmzeNj4xqOEIFkKKgYaADXkGCnvT29EDtgTsw2dknPUwiN/QRn3XcvAOaX9BMYQGIV5uKaoaJTSYGpDGgwfB9YQjctaPUIGrDrQCKgEKoFOJUYkIyi49GhCWliorc3rJ1wJEVBxWCnZI44fOSsMntG3BWjV0EFpkYo7YrAXKsWEFRUd9M/fmflm86F0L2MQ/dvnQZj/sHf+/DGK64d7+O3Pkf1Y2eI6Bm76L0T/rE2Tkk49Qmf7oDIjPeJj38DzC8Pescnj/vvITRdvR6C9vwrV+ky4oLbNP6HVGoD3SfGtROynpgXlVnMhMRBFee7Tin6KPbSEyDP++CVs2bPnrnn3s/70NchaCPnktvmXHTLEef/7IgLfha8WcWK29KcWEI6eAtrJ/SjT//5VGKmaBq9ATs0eoXCn2IrIkq1GWhIjFUyc8kaU5xKDLXgUEL10Epgp2SOOHTQcJmY3D64bPUjf33xA6//++WvOBMuE0tfcgpcJpa/8qxV77qsdftS+FBo/3igKpwMIgyEZ+xYL9Zuuxf2aPeJ+ZuqmHifAShPtXtRgHiOy9VMJyg1rqASIHty+/Dw0D333gPQW8J4GJDhbmXXkIkQ4E5xfK7Ra8ZGwCQe9gVkOynF7o29BSDJEWeAHKjOgA2grl/97svvP/ady15++rLfOp3D6zIYHK866/4X/92q0z4/MTyCtaQF5AmL6kkd8pz449dlaycK8S6PdJSECSES58T7HvsqiK2HnvmlY65e9/wv3j7/q0uPubb32Bv6KLbGYbQaUvNku6YfFrl6k86J3doJiGI8N7UZ6oW3xBqdvEKuHIh6sw86HCfx77r8qH9cPufCnx127g2Hf/w7cz93x7wrlmAYxRTXQrdxeE0ItiNU4lPP8X1i7HXufz0JhKgAgLKef6TnFTyYHFjlkRNDRykgquEAg7WDNoiGncRtYJMQDK5Mbido/tp7zU33H/u3OJ7h291JZy0/6b0wsJe/6pylLzttxavPHrxrJbjEPy5TrZBHIBX+RtEATBqSHcWAcrtDgwhMT6rBnPjhhx+GD/PIyMgYLUWwwJdWceike/GI6LPMSzJoeNQ8spPm7kVpw4b1ixbduWbNGvAGGBuDJo9yxAA4soIjEujFtzrMmaiSNkA7+cf3oyoBzBQxNLoMJgeW1myo0mn3mjiVNjA5reLK7d2CidoABKDi2cUT8cjfXHr/cX+HQ/3lGrpPPBOu66BcfeaXJkbHiMpOtboHWcEP/rYzvncC1xP7IKlCnMqxl+bEN/Q99QScEx/8zs8u+OZGiNsv+telx3yr59gb+nHNg6yyILd50C67TdrA8/INr8W1EzQntploCJtOYwLLQaDYe8HP533+rmf9+Segwfsc+RvzLr9z7mW/OuKT3z/s3OvnnP8TMMk94/SNExq0zRvLicbpk2fsstPAAGU2chhFOqGD0QtQffthxk46aYMrt3cLJvIEBKEquVAFVFEbCKEhDswUsV0bmIxmvExsn+z74aL7f+NdcGmgy8R7V7zyLLpMnA0fJfhi2X/LvRCdcfmoeCz4xc6flJ+TwIiInzSiFbgMqgC5suyoQAUfQ5gTL1myBObEHGwt3gqv6hZADCCIbyXHVbBE7YRrhLOAaA3xcHp0SzWkGh4qNAsuanfeeefKlSuhkYCxsfHRkVEOKQAZnFKlgHj0VkBXT3CLq/Xa9AMYgBG3lBFXSZ6x8+AD277q3ZeteN3fb7z0m9t+8KvBJQ/2/eyu1adeAeF1xes+uPQ3T9n6vTvw8MLgAMR7IGA32NqJ9C9fhaQc+1Odm+bKfeJXz5g5c/Zz583e/zl77L3v3gc978kvef3hH7nymOu2IpmZQUiC7NrQBtJH03RKfJ8Yn7G7LF87IULxq3+qvPCmORfffvi5187ae99Zs2fP+di1B7ztw086eM4eT9pr9lP22/PI33z2//4wzIxp+hvPgOOU7SuQzSRz4kX2LjbseMoraDQGsKuI3JXbIplGJ6DBU2TKeQ01cyN9EmCUwuzzmhshuq394P/t/eaN/b9c1vrV8o1fuA4mxyte+/6lLz1t9Xu/jGxaWJzBIopgE/9mR60pZW0KckoPIixZsnTp0uXLl993330gPPjggxs3bqS7sMQypG6Luyl3b39/ix9Zs/isQEWMolvQgh6/w6xevXrRokU9W3pgng1T+fvvvx+avWLFinXr1rk+Ia+Rp7JbQtq9AGjW5i1b8HDyeg2ePAqjtxEpE8ocaw2syd3GNWNTh90r6JRc614N3X976QOv/9DGz12z7YeLMHT/dMmqd12+7AQI3R9Y+pJTQAmBAj8URdDZsLUT9t6JPFRCKoX0LPbK2ok3zJg5a/bBc2YfcPDsvfbZ68BD93nxaw/7wL8dc21vWouTxX/RZARN3AZ3n7jhXWxZlC7SaM3b/r/7VzAnfuZr337EJ78HTZ39lGfssefeex50+FOOf/NhH756zqW/8m+Xrz8HUtBrG27i9xPLeuJksD1WozfCLh69gnZu0nKEBqM3ZQeGZrp1AnNimP6uee8/9iz8Wf9tS1uLVmz65//Gv5a/5v1Lf+v0R/7mErydnHeMIKhhIjto72Iro7PupTnx3XffDWHWrhEPPPAAxttRfLVZVmmK3QtXnB78mSQoJyPHIa3sQCbMJrdv2LABrhF8RVi1ahVcI+69994Vy1esXbMWvipQm2NHqVsoF9tg+5DNwIB74V2K/LKSliNM0H3iFL4Z4+P9N90zumErHIBhvLd/5ZvOXf7Ks2BwrP/UlUwDP7xzdpi4zefELoximEtCrVo5AtrMmNJVa4+9rnffo14MoWrmzJl7PfPZs/baF2QszpjxjD8855hrt+pf4tq4dW0IiavQbzvzeyfcbzt3miwC3oR/gPvcrw4561/x2epnPPsZr307NHKPPffa+7Dnz37y07jNB/ze3+DDy2EFhdZN5VrAlaTP2Pnf7EjOg8Kd4gojBdfQU9xRJSV10AZelU9M+B8qxJ+IvA2BaSgfGolwgd++fWjZqgFaI+Gx7lNX4m2AV54NA3tsyza8B5B8XOOW4N7pPvEE3ifO2hG1odBGD9gRBIfNmzfDV2oIGUvvh1i39J577lm8ePGSJUtgZoz3A5CWtyHeJvBtwO5FGkSioSH3HsfYIZd89xbBLYF5MLRww/oN0FpoJz9QzM8Xg0DxQBC7ZTlD+dAA9kOjTucPzekLbhX5oTWQY1SIvg0yekn2FeKDyttQQHRohiZy7JZlwhiF7k3bZIgTxjZvXfn7H1n+qrOXvuy0DRcuRBpe2mHjduHaEJ6xo7UTFk7jyCnzVFS6AJvOnum9E0+mv+9B0NvrGQfO2ufJFLax+PTfe9cx3+qRv/IF/4X4H9oQFbUNSLY58a2VuN1ROKVXsC162stxAfTTT3rrk+gPfXs++3l7HTwHBMCe+x902Ee+OffSO3zopj2a54ZdhDZAFTcnJrhTAIhPcXK2mlEhOv/V0RvDt6HK8m4Dp4nc0aE5twAleKKTszaY20CKHaZtoAA3vPLR1i+X8afGsPGKb8EHZ/mrz3ng9R8cWbMRNPjxySH+0U/4OQnyW4NvA1VMgNeI/v5+CLlwaYDJJURduFhAEUBP3eFf//BaInzxmB5ayXUC8DE8AtcIT3SyHBoiccskFqAd0J5HH30ULgdr166FKwIIMJWHyxm/KAPaP8xPxXG1yK3BiR7RoaEEnTw+TjdikhrBLRrU2OQWwPeJkwlAVMC+hkRvJMH1jfSlZNW7P49/iXv56es/fSVaObAGL8le6W7DuK6diOIpJA18kAeTxDiTpRbeBli74Bsbn/0XHz/0rH96/j8sftFXlz3/n5Ycdu439nzh8RSpZhz2wa/iIoqrVgcPZbeYCsFdNDQn5vcT+7UTWZhzJk30FzG5tYxPLv/quWd+ZdasWTNm7QEzYwivR3ziv+dedOMRn/7Rfq/5M2jwrFl7HHrOV+deyq8ollhZcMsBVAWfWMnriW+m9xP7M0oSD11DcoICSmSBG7cBtZGTkEtufcHkXIhE71bVTaPXg/5ARx9W6B8YsTiexyeGR2H09l5/69LffDfMFVa++WNjm7bikDansg1uyQ0WN2/eMq7xLt6tL+SGmIzyZF9//8YNG3DVBGF4eBhCCUc9+IYNDTYqo9vuZZutJ47giO3dardAdOM3AUF042krnAaI2hCvoc0bNuAFA7lW1/ozcmeloIzaMDHRE97FVvQQeaNC0g+C5NAYoVGIUOi2e2VbECKxS7cRSmSHUvfSSMfgTKEbX8cGxUf+9jIO3TgnhgbhRb3WBlzQDlVuo7UT0fuJ/RNveZJVED6cYnHBNZsP+utPP/c9Xzjqy4te9G/LXvjPdx1+3nV7H4NPSwMOec8Xjv12fxS3tSLkuDtx2y5dvfF1tnYiDZW+iGvPcAqbrGdgDijxdzoWPeUlvwttm7nH7NlPecbB77p87oU/m/vZGw8585+f9KxDQP+033zdkfiEND51p87dkjbTSNErgzz3ougZuyL8yHGnRwRCKDwxRi9CSkE51UNDlMiCvHvpo0NXgTF6+ezIKAh9P7sbnz959TkP/u6Hhh/ZAAT8WHGlfFeEwcItTEd1YsOhUTYJfjasXw85XyPgYrFx48b77rtvyZIlK1asKPxF0VDSIUQfNtAG8NO3rc80mEXVfSE3YMYVoHNWr1591113wTUCLmR9fdv4Os1vWYZrBFixBoArVUCGehtIHBiQF941oPORI3NiTVFVoeB8l0Sk4IuKYXw89Lbzl5/03qUvOWXzf/wIa0RrJxSg0BtUPCfmZ+wqwdSF0UigxH99c+H1mOu2HvvtvgXf2nz0NzYu+ObGY7/TOvrKVXsdsWDGzJn7HH3iMddswfdcynqJSiQFb7xwwrlVge83uPvEIYpJIOPc3RWgIgs+yF7Ic+J/mbXHbIihex50OFShv7jdOOeSO+Zdetveh70Q9E856U+O/OI9h5//M66FHOe2ntxOsVZ4PzHfJ8YMtwwt5KfYQGXM9MTFQ4npnBNQVGYDgl3J7dpAUDIiE1IalTtxS2PYirh6bHJy45e/s/Slpy0/8cyH//Ii0pJNOsFqGkTT10/vYhMoc0rdC21QhCJEQI4pEAehyJ/CAoI+dQvwlYaGh1r4TDEomWnGTDCF0KgMblW/cuVKbhu/ZpKVIGzatIlvXcgItAoJQN2uexET21v9/C42/eZTOTQElTFrHr28BaCgzAYEu5LbtYGgZEQmpDQqd+U2OrQMQIFOwHGuNUGALhwaeeiPP7X8pLOWvuTULVf9FJcijvJnoeAWKsBJhHNq72LDqBiCJAtaDFHUUginknDZG8Tt1jHXblkAcfuaTTgJvnrDXi94GcTtveYet0CWLMte8ErBbiWSs8nyrAgzdZw3bwzP2Mlihsq70ihQpzHcing7406eEwMOfNuHjvrH5Tj9veDGI798/3P+8tP4Z8on7Xnoh8KtYtmLxX8R2LlatWjJr53gGVZ+LkSD54S2ABTgv5UrCHYl7/DodYGIaVRu51azlFwAUNoFB9wEozI7aUOHwQHOBMZ2KfNlYsuVP1v2slOXveK9K//oE7gcHw1WAQSS40OD0A2TVykY18iIVCh0L2ToFpqAABkzQm9PDwRhCMXbtuFfhMTHlLqXCxAkWq0WysGozHbdS8DWwiFAY9asWQMNu/uuu7dsoXfdoBHjyba+PtDDRBkmslDUyF6Cd8uFtA34B1vww39NRVV6aKkQ702Zzi3OiR3HRKio40N8YD4xiiOj9/pbcKX5a963/JXvHVy6Co7ePsrmQctSF3siWjthQa0pNd2KuGrN/KtW49/acO67dv5/PXzcd4ee++5LIHjNftoBL/iHRRBwbQVFktRtUxs4tr7dfrMDA2UhZaE2jXcYWD/3y0PP+bfZe+0Nbdv/d/+K3jvxUwiXkM/7/F3PeP1fg36v+SfOuwwnyml1l5raQDc8ZO2EvnfCep8gJ4Jyg57ioC/SGAnHgEX9LAJkx5ybSDDBI22DfkIwzz7aYgPkQqwrjF6CCQget6Obt6184//BPyi/5NSNX7we440sskRr1gbUsld5P3FA5ByLojA9CwmNgcdNBtxwJ8Dn/P7771+yZMnmzZuFwXtmprEFJnioU6pnc2Imd9O9sBURXMEAw5fsLFkM3/jZM+QsDA4OQowG8Dyea1AOMIGBxVL3umPajveeRcyri8L0LCQ0RuDEZixNYfRqIW0DdwIXp9a9IkvJlIyoqAVTFsjWBr6o93wDF9Nj6H7V2UMPrEUzfgS4YqH6uPsdO3rvRBwzdaqaBurC/JgS6K9ag3eCXdw+9jsDh579zxADZ+39lKMuv2nBt7ZY3G6K/5biNtCsnX+zg9cTc5CksMx3hUWThNMsbkOin6/b74Q34zXlKU8//GPXQ4DFEH3hjSAc9vHvPulpzwTTc075gvxsB9a6qeNXy2PiNkBe/B07B9N1NHpJ5CyYZEvQQmpV77gpj16rQdBSrIWiKDK9Q1a3QG4IDtmhZdVFoXo5toTGsF41gSElvEzADLl/cOUffwKXjL701PWf+Rp8LujywZyonqpgizd3bU7c2L2kFYJAS6EahRftAZBJ4Nuujz66FguOHG2DwMBi3gYAXNFoTpxUzKqLQjdybJLDBvrn0XWPLl68BC5h3APAYdrY2NjSpUthWkw/IGWuySYOOFNLEBBawC3L0Mmjo/rFg3SddS+XY+32CZgTk0r0ZmY+eoYtayfGMaoOPbgeX1Dy6nOWvuSUDZd+Q0cGk3CKLmzeqIyRFddO0JyYgxfEx/DtH+R1WPTKWmBFQvy48dX0Qx7XbZ1z7kKMrbNnH3npT/GhDQy+Qoj4kvI5MbUBhIUUW6/W33a22JreAHCpZsJn7G474rzvzN7vAGjbfm/9e/nJOqDhO9oWH/jWs0G/96FHzjn/p/iKH9CzB+8nyBBwNeZ6AqV8TsyQcx8UKcDi6GXIUIDTzQKCNirLVsrRyAHkbXAiwLuNkOrQEZEFJErZ1G70yo5Z5K1qQEFDd837/pkfyX/oreeN9dCnlLmBl0AUm7dsGbfZc0YygCV3UQBwuHt1B5A/+OCDEO82bqSlCOSJLFPoXlQMDAzou9hIndLYUeje2C1uzLR69eo7F+MzxSBLd2HzJ4eHRyAIQrxzc1mp5upbGTeydfCardu2oiInKcDSVfcql7ZSUJ0Iu2j05t1LCAcLWxON5XgKkjAzkmawgS1PiIeWr33gdR/g0L3x89fiiLIwIZsYeKdM58T8mx2l2At5JaRT7mNvkDX20gPT8z7z7ZmzZs2aOXPOZ75zzPXbIJgzuTAnTnbhks6JIaf3E+N94lvpfkE2SZXIKfrovobFXkj43oklB7zh3RCfZz7zIJr10ism8L3yvzji0z/a+zmHg+nAP/l7eneQPWmXu9UXE/nkNNF7J/gNDDngvGtwIJCYnjweHNXR6zQKlPzoDZbwqTagh3z0pizzAVsbXrrlDSKIJGHmSSrLFqASbE1ph1YDkuvWAHBU7F7oS6gPQX5yct15/7Xs5afjTeLf//DoWrpJQZcPa4NVVwHr4+Nfw7R2AtthFCy6AgE9dNG9APaBofjOOyGHj6qZAwul7BSrLFuASrh2gqaqADu0GsCSNxABysnJjRs2wpwYpr/QTry5jf6QDbt44IEH4Lom94/FA++t29GLgE7O1k5k7XIeAOo2ZYGa107EFUTCxhng9EPrRzf0PvQnn4RhsfS3Tl912ue3D42gU/QbvqwmO+EiVsb7xPIuNopcFhYpx4TRNphC+INiTL5qLcZNnLmaZjUE0yM+9B8QpPbYa5+jLr8RQq3EVg3igWwJbwazYBppA91vwPvEOCe+gOfEFltJCEEtjrmoB02knHPxrXscPA/a9sw/POfIL9yFL7bkmHvFkv3fcAro9z3qN+ZezBPi2FuUvFsS4jbQnPhHt9B6YrjYxSciOpsKd4tB4MokurInY+SWUj5yOKyTHExVlEaOSamTqA2yBbQbvQUAjSYK6y+5Gh+beOVZK15zzsDiB/AjKkvEEChwwVQAlidxrVh8nxjgeV10LwrBKBKcQ74HAHNiKrJ2St0LOb0YCL5SS9HygI66l5sBTYJADFN2VloOsem+++675557BuTZOIA/xShpyRVElR4a9MBQ8AMQHqGL7s0PTUqNo7cD+ENTmJQ66ah7CfmhNcC3ASWrygLfyxh9dMvKt56Hoftlp60580vbh/FFTjqkih8WUOJqSagb7hNjwKRw6mNvEDSchhRCOvHXaEwGGcnzIW5ft3XOedfMnDFj1h6z5134fXxxEC8p1vjPCzYWXC1hnFK9DfT3PfwdO5kT+3DqZQvUiSakw8/Hd7EdcPKnID7P2u/AI87/idyz+OxNEM8P/8T399z/OWA6+K/Pd/eJS/E/cpvsEZm8dkLnxNT3lNt5zEeOlIojpwtURy9mQcWYptGbmhpHLxdElQYHhT9ks5TaYJrUSeXQqCCL6/7xO/h671efs/wVZ/bfdA9o5FZgO0DIHRult4k4Oopp7fbdq5d032N4G4Ifd9blua4uotK9SV8jpIkYcnkSL5hK90JLodq2rduW0MuUxvipGyAQBy6acF0DU29vL6pVH++rLbgN8H9ydJTexYYlUalDh6BxNk8TeVLnxDGMyQKefphmbWs98lcX4R2149/z8DsuGuuFLxPRyPD+Y7i1EzDpxLsFefSsJ7m7oFUWroX5LiRcPvG1VfO/hj/VMf/KVS/+/uj+f3A6vqDt2c970VdXLPgG/3S+OvFJHHKqcDDCwpz4dlk7IVEsXitsKVFaEQRI9KzGfvgDTjOffvwbYR6MaycuuPHwz/wPzI+fSs817/Xi18n946Q6roTDMBr9pY8JEQ1Ttp4YT0j4TNE2KhCKZkZcQuSaBEKIPrb4MeMCa93HXBCbU6uB/xRfM5s+E2jrquEexnBIbvqn72Kke9XZy09677af30WRLpnjxl7gvz05NhnexYYgWrtDI1EhJfxsIVAE3/CfAJqBAVyHwH9jQgJouUr33YvKSVyb29u7FfZkNTw67F6oDI3p7++Hhlm8wxZTKIBd8FoxiFPcZqpTdWsG3ML/sGqaFBMTWyluMqbSvQ65JoEQ4u4NncUlMyqicrFnAXiWkVkxB30m0DbWujaIYOUEPCEe6+1/5OQLl51wxrKX42ukxra1oDUTYVF97lYAYQSql36zo/uEcRt/tQ7XvF21mn9iaf6Vj0DcPvDPPgyxcdbTD3zhP9+94JsbebockoVrm/7WE6+deM3Xl/5ZtHaiISWPfziBf7PjA/81a/aTIB36vq/O/dwvD//MzyB60295LJz1pD1xTnzWv+F7i/FFxVCRprz26J73lgguJXNiPgf5Cc01CYQQ5hWk6Gr0ZlYAqh6j0WsG3ML/7uNe2MYfzJRa0oBKVhxd9T+4WPTV58AnqPe7t4EG9MqJd+LcgASnotVqDfDP6YsuBaqIClnBTCA7XiaoALvEcAsADYRZCMK4vm4T/bQwNsjciFBzawbcqgwTVn5fZ2yGTIuK2BxZkUyvw4cLAa+jQyUBBPieAHq4TAwMtOSgzFuXsZcNrVb/EL3GPrejAv7Hl5UEicnPiY0t97kFdKmbGBzG1xK//HSYED/09vNH6dl8nkDQPUPiIDtIClLAofNvdkhcw6jH9w8snLmkL9/xOaeFaxd8c9O8C7935EU/PPaGvuP+e+jY77SO++4gCM/78FWz9n4KBKlnvv4vj/12C2Iu+bcEO9J9OYcxJyReO3Hywtuz905EcY2mqvzVP3lz+83hFgXdD372Oz6D9xv2ecoh53z1qC/fD1H1yC/dd9iHFs5+Ki5KO+At7513xV12syGaAccpj/LaBn7vxI/tPrF0fYSgsDu9rILcRo1AxirCTjEhJVJtzGnxv5QwYyFlh3KbkQMK3wbK1J559TV9GyiP2PDBGsMH8Huu/AlGOpgQv+LMrT/8FWgg0iHTk+MCA1WkHhwc5A+5sgK5q+4dHR3p78OVBtAGA8w1Vz64kr9ny91o8ODdspC4ZS0h6V74PzIyGv7GBCbXBoI5y7x6t5RDk5YvXw7N27Aen7828I958poKgWuDSYqowDAVt09ubCMCuavutX4ggJQctvhIRy/lqdsAuCKJmWuYJMj3E5zlXp1iGttAw2a8f3DVO+ldE8e/5+G//Ozolj4O3ZV+QHD3QhnOMpxTuU98dbaeGBPdso3WOeQcXDq84JpN8y760dxLfnrs9dswbn+7ddx3IG4PH37e9bOevB/EwKef9If5+4Jq8VkT78vfQgbNhtcuvP/tMifWIIwJZYqfXkkpDemcYLp809xLb9/z8KOheU99+RuO/MJdcy+7c+7nFh315fue+dv4yqA99z/48E/+YM7FfEM6cRsVG0I6rp2If8dO5wmIbPQmQAWcKz96GSBOfeSAwrWBYM5yr06hbWAV5DlbUWpDBKfQxpiqgcyjFwUuQorZafeGuhGRLxNbv30rfHDgMgF5z7duAs0EPZaau3XAQ9P53yj/AFPYCaLr7h2jJQ38eTQAdTW92wGmmPoHQKvdeffSyFFAs+EaEbwkW5DQLQlcNEkAcz2RAA899BDfw5YWE9avXw8XDv7xEeDAHqcee4kzYveJBXn3hjZmXoOCDy3MiTMqQi75o2Nrzv4n/E0XmBD/2WdGN/bCgU2MjqE1TL4yB6RgLc+J9b0TIWhmIY9NOicOGkxIvmr1sd8ZeNabT91j5swnn/DmA972gUPefcmBJ5/7lFf+8czZ+K19z4Pnvujfli34Jv+Zz5xwda8xOXkLveiJjO+dcGsnukz+3gDF1n1f8FJo4R5P2/9Zb3nvIad94Vl/8vezn30YaJ506PPnXkw/zc8LJ0r3EgopomFFWTshv2MH5yU9I9kZApR0BD7zmKEr/vrG5LRKWkagjoZlmRxy/R+BFImWhrnp0iqZC0BBByr+6t/7zZsw0p101rJXnLH1+7/EHhvBn/WSw5bj9S4Sb1jctHkTz1bZljAIJR1Buhc+3Xg/eODuu+9+gF7Avnnz5p6eXogaK5avgMAB8a63lz5u6Mq8oZB96hHMCLn+57y/v3/rVvltZ9Y6dNG9OCCgD3t7oYXQ8jVr1oBnAAdo0LRaLaLRXvS/Q9vuhTJVm5jo7aH7xPloRpR0BOlekrLRy4IgKghQ16Z7pX3yPwIpMu00jF4GGzBv6F41bh8eWX36FzB0v/z0h0++YHQTvmmeLuo21AWJCwT9tjjw76A5cfQuti4SVfnaKojbz/7TD86cuce+v/X7+//J+5/77kue/Rcfe+pr/vesvfbBGLj/wS/4pyX0YDTw3V4WhlsY7ebHluy3nWk9sYZHym2eakKcfFCFaEyvnjj4lC/MojcSP3nBKw9+50UH/92lTz3xLVieMePAP/8ELia2hRO1hG7zNkiK1xPH5zOMXkZ6lgpnjXSP79FLCqc1MRcEXPZ5zgmKIFD3BoQqfJno++Ei/G3nV5y17PjTe665ET87I2P4B7H4s8NgFebcvZS19F1sUsEMERq7l4Lt6Ogo/lrHsuVwjdi0aRPE3o0bNz744IMQb/HGxAZ6MZzUIBdddK9AaoyP5+/rZLLPw9ZgChLYG1wI4HIAjVy16hFwC0Vo/z309v2enh5qc+IHi52OXj0LgwOD1MnSjaIN6G706jN2MVjFRwX+Hv3Yvy996anLXnHmw++4aHQr3e6G/wT6ziUe9Ei0pQ4yJ+Zn7CDGJX8Fczdu9UYypEL8nU8/hnTwX396FsekGPse++rnf+mXvKwC+c6tS+q2uQ0QmuVdbD/QZzUohF2Y3am1MBpNUmMNvnnttsM/8b195p/ATeW3vgOedMiRh37gSnw5sfz1Tf0Hb1n4LrUBNOnaiXAaCidEVfjViAXZZigaSOktIquqWInUYbwYJ7Qhq5ZSPUhZsKjKTIED4Qw6p+9Hi5afeAb+dt1rztn2P4t5GBtsHCukeuZ2e19/P35xl1L4AADHyHZoss1Br5JZunQphIk777wTchNAyRNi9EcOsjbEbqEE+xNd4JhuaHhoYGAQizHHqBkKFq+C6MwBDuIyAJp977338pMTHD0M1g+ZR1GoPtoCm6fXMczJdI1egbmVbcKBUqV7WYjZgJSaoWBRlZliDpS0DSHLu5e2cA7WfuRf+SWDj/z1JWP9gzS8BRS6HUIpdC8AmOEZO4uWkgohWlIIp8jBdcPX9x165hf3mCmRzwIgYO8XHX/kZTfjU9HRm4JKzssh3SWM6vbeiXg9sYRNt1iCBRe3s9gL8o3zLlv07LefO+vJT4emQrO55TP3mL3f6/5i7qW3w9Q5dSsV4yInawPK/MQe3kIO94nlpOpp5E0MoWRQlZliDpR05HhOOnICUmqGgkVVZoo5UNI2hMwNvZgNEIXqMzsD1I2HJltFc/cC+DLRuvV+Xla3/JVn9Xz7VvnI4EcH4T4vBPAVt4FEXD+Ab13AEJ7szVHLiAhjY2P4XNoSvDTwRYK3MFHGJ7C5NUCNjlk8WEulHCAK3shUc2JioDUgOjPEh0aojpzQvTSb37p1KzQSWmvXCJglr1+/Pok/6iT4ly0DSqU2sAE6Gb4ziEKQUjMULKyy+8RQRI0dKB4QdM/I6PpPXbXspafC4Fj5lo8N3v/w2Kato2s3ja7fMroOEz+nn8H2Z24nce0Ev4stiWJR9KwGWV9r/n89MvfCHxx82hXP+dP3H/CGv9v/9//2oL/61BGfvB7fefmtLUfjqgn5ix7x+UXFZc/iNgq1yKS1ExtP/vrtJ0TriX3Cb/xdvHkHpsUX3wYx9KB3Xb7/7/7NU4//g/1f/46D3nH+Ebg6LUyIOVx27DZqg1874T6BLPCJoHNB5RLsOzMybWQjgmgSC8jEVL6XESQCMsMHRv4nMBWSeSMoSF5AOYzeHNBC+nNx30/voTCHwW7Lf/5ofGv/6JpNOJh5SK/vwRdPun7gjcAk7N5J+OI7jr9jxx9WTo6coty9vIG4CbEDohsEi7Vr18L3/t7erRAHMfjSqQxuc6nmNjABGJ/gy7TcwUVbTClIXkA5UACkgOZBPIJJ8KOPPgrN3rxlMxwIDj/jR3UYpgIhphSkib6+fmXJpoJpH72KjrqXYSok80ZQkLyAcqCk6OTQDKSCz//wyLrz/msphO5Xnv3QW88bWrZKQve6Hhzqj/rQjW65ovcHe0p+x87CYyWccuQkOYq9kiCozrv4x889/YsH/ekHD3jju/b/X39z0F+ed9jHrzn66+tknbEy8xRfNaohnf6+R++dwN/ssCUNECQlZjasYajG3gtunHfF4ud99Ppnve1D+73yj5920h8/+63nPPec/6AfH9XY6/nlFNwmbeC1E+537KDjBfEpidSUU3rijV5ishwoBYkFyDkFQ4akDcKM64jEl87+O1as+O330R8Sz9z4DzeMbW3BZWKMLhA888Ff8QiAKmn3wj/YJ4TckZHheP6HZN4ICpIXxPPo6Fh/f//mzZvh6gDBFq4UPVt6ON7qMYU2IJyoYJWQrYxgCa+S4/RKeJJxw0wUmVKCWZApbJHwGgGNhGvE+nXr16xZs2nTRugTvUYgkcmIICnajl4ShodgTkyvexNVRClJXkA5UGhOHM4W75X/81eloZUblx1/xvLXnANz4gf/10ceesvHV77hIyvf+FFMb/roA7/z/vXn/Qce3jgNJHKs3sEt6hg8J45+s8PFyjaJny9209YF31h3zHVbj/t269hv92tqHXvDNry5e9UaZPo5bjTf9SmK15iIyc1zc+L8NzuSRMHO7gqE2wNZbMV3W94EkRQi7LwrllBaTM8y0/ve02AN1TMP1YRMXjtxc1g7gR1P3U+QMm70fwKZUoqFRzTXiMlawlPMUuRWdTqwjMMb8pmAFF7LMo8qlCnzUCe0ExKjNgjiNtA93VXv+vzSl5224rffD+mht34ijOc3fvTB3//Iw390HsQ+dMuLgqCmb4N4Rg246untobUT9EcrMpXaYGjqXjhlOYjFzjin/7hBKTq0sAmtDMBaqBwYGICoigp2i7lISaWoTCJqWBssUMZPdoLQZtgGMkJL0HYi4QZJKIc2wKGpNDEhD3wgi/6RSP8TNHWvh5awDSxFblXXXfcagcDyjnevtaGrQ+PQPbh83bKXvwcGOd7OSEP3uSsgdH/6SjxZFLqplezR/mPGayd0TrweYyYGyey+BicOp42xl35rqXUcROwbNHTf0IcmukM8ZbeW6L3yG167UH6zg8MjhdY8lgZNOfb6G70X/HzOJbe5uL1k7ufi18mX3hfk3VbagGnuxe537OQcQNZ0infm6OWxQFyxWSVjmaAU+ectgdzV6O04OEgZ4Bn6P0HnbcA3r8GYX/O+/7v0JaeseO0HVrzmffCVcuUb/s/KN0KiywTIb/ro8PLVNBfF+uYWqnMzpLR9cmAw+Yk1qiBWEl0N3jhBKfgPp/XQsAxCUh+wJY+4ZZVAS/XuJS1c0bZt3QZldewZ5oQQCiLpBrbC50ujtNSBrWgjQf6rjk+WCapGn1ZRgZzBoSHq5Pqhkc7cyhZAIjKYRUW9T8w0yFkA0MV+aMXGZSeeueyEM3BVDf0iKOacTjjj/uPeufbsf6SGoEvdJUC92Ba6Qd7Fxr8bV7zNwMkemGBOst6X0kL8YxwlfAP8/K/Jg8xKrjnnUK7WbMGxS8DkOXF4P3EWN32iYCcxNA987leUgHPBz6OU8R2ZTZrTX9nERJpkpq7vYpO1E3YmI/hTXEOdQKeYzUqyEgmqBcQDGgGiGyMODSMnR5UMeb0WfTbwNZOr3v1F/Bnnk967HAY2LZG3hHPlV589vGoD7GPCHssvuJUyTDGRhlBGgZyhRpCPMJ85+kRBYjKJvFV0171Mg+/r+rJMKpdQcMFkyIu1+MPPDaemF9HNWbPgBZigH45WaoGcoU4gt+qKYSUSVAvYBaNXwWTI67UEHbmFIYnf/QbvfxRvZ5x4ZjV0f/Bf8PaVDGBykLUBfEEw4d92ztZO2MzYB+pqUBUr/dYSvnFCAjjF7SgUF93WUjI7BzLU4rUTdJ/4ojj2oqDx1k95NbmISjR5wEMjsI/b+MsdMbkgS6q0QUL6nItuSZ6xK0LPkOWuFJ81AHtxahDLnp1W6bzNfIKq2jggF/gx6oSmNqS1im1QXX0XgjqBXGCEoUfrtq95/78u/Y2/W37SWXiZ8J8duExA8YQzhu57GCrxawaKflkFIXc0evxLDaUabbuX2ocSh9oo3joxQTfdiz55AbQCGDvcvdZm2FqjeYMaEhx4f06dMQxkCQ8yAphbqNHp6J0BMlBzD9ByGBnDD29e+eaPY/qDj698y3mQHoL8D1FY+dZPPPh7H97w6a9RYI0dZO54Tqy/2VGNm5xo0szJmNk93XYpjpU4zy65lZSHXZoTb4I58YmX2doJDXZJMIUia/Iga5ogWMRkwb2twkyy5szVSlLBLea8duLmRXyfWHoewGJ+igkldYWaozhy6gBucVi6wdrgrtLSTtsAJLjAb9++9kNffeD1H8Ix/Acf58EM+UOQg+bNH3v4Tz8zunYzLs9KhnQCMm7ctEkfd62xS+pGxwZgTVf3WtZqtfQ3OzpyzB47ojI6cduFO/S3eUtPvUrJ0Jl/YE1T9zp04w7QdRs6oQIHz8Lk0IMbH3zTx1a+RUI3Dm8d5w9x6L5wIR4PkGtnDa9h7j4x/vByOXQn8bOr2NtZymplt4p5p5TbM3YQty3eQpBsiL1BmFrspeT2xangNuMkz9hRv1PvNwFPcCe8AK7RiG7cAbpuQyfU7ttQR5e+DHQ3cP2nvv7A6z+48g8/gZ8gnvnoJwg08AkafmAN3qOlt3LRvtLuRdWkvosNy921hz12UacTagec8fFxfm0wgOi1Ok7fya4dgN7doUmNKlr9/YODyWs32oB5RXZ470QZEBThqj86Lvno2ITknMZpACXtjXYk+6Y5sfxmRxbOCpNU43gyysh0MddXTEyiKafULSS5ISEe5Bm720+8LL9PLHGNNRLs1BTfym2fKjcbQpEIuduIzG2QOTGtncC//GPXF0+6Bw1Od/6KFUApFNm0dUtw5KyC22WQ45bEdXa8DYIxN3pJ4DyMc4f6PrA5A4MD7SaXtHt/ULKNgL5YmuqhZRXcLp08MhrexTbt3Vus0Oy2ug8gY020D0Y3LRKQU/GMKDpETyyV2lCFI/NnycHtMsg7d/QWKzS5haGp41wFjeE81OUWF6PcHr92gt47UYm9PpxKmnLsjUN6SqYgX9iduJK1EzgnpnexVaIx66cae6OUxH8nV5N3O+ci+c0Ot544QtMpbkA0cpIqYovRdBWYUhvMIbehgGa31X0UyEUu8TCregKArdgGfC+nfmqSOQ/m8AnST3jWvQpSj4zqb3Y4JBWgWGxDI3ZW98LhDA/BNcLbi1xyitnOjnvlKrpP3MModLG7cFf30VkbmufEVBm+7GAyIUsxeIeUh31jZB3n9cQ2J7bYR2GOZAt2GSdE0owTYiUlEhJOWoXJkIeYm7jltRPyLja5T0xJvvoXYx8rb8puSBQTr1dTQrjZkCfgFP0kdSHIRmsnrP/DacgQTh5IDbwAJBGR2bU6SuvULcI8Sg3YRO0TuA+JCQVMeiuJUgZPyQCWpPqY7sBtQD3bNm2m3+wITUsRLCDl/jLwoRGR2bU6SuvMLQDn7pPb+1stXJ6Lf0agXUXtE/gY1OA7795OwETK8zrcBtQbAf739vIL5AuIml+LnQ5ddS+C3HYI8yg1YBO1T7BLuzeMbUumlEY1+8bXqujaCZgT25/42sVeFKYce51gnnM/SRK3eB25esNrv8ZrJ37RGDmT+FlMwInJQU9CVFdDeofxX2WYHMf3if14CXAjx53nApy++ew6mMdSjcdo9BaAek+o8QDWaPwrJLU7kNt1L0rACZ+akCBT2Xssgdy0Wi1bilChh9bstO5FEhGpTbU6ZMT1xPoutgoPYY3mP/ICU8iwCQcUkI3eMqY2cloDAziPL+1XW4O+yJ84bfAtc+KYEUp4p4ZLKGiHggCimRTuD/axASrAWOLf7LBn7FwgC3mIdy4afn3t/IVNUTJP6f2GCjNz69tAc+KFd5yYPmMHsSyKdPF9AkoUKEnPTOGX7jfIkrJIQ3nBLZmc25CYzO9iu1nfOxGdg/AYGCOIZImRkgUwuPJRF9OkBJsO3AaxNHISMgKKvgF5HUIoldtA4xdZmFw3sYSZqQhSChs7jv7+Fj5jh21SlSCI0SoWRtoPCCjmfQuIaVJCclIfkLoNorYB3xFBy3MBCRkBxWIbAI4ZxA7aEFANDlIKm9jtRGtA3sXm1EHcnboXEAmOjIDiru5eULJerVBGpihJBageGn48IJjcHtYT60R258beJOHCOXUb0UrzY4zbr7O1E+GdPCFaaspjL6dS7E1Dekg1MiUJ5lzUfXkPKM+9KDxjx3+Kd+cCAUU/csLpkS1DSkiODYhpGb2JSuHUQeygDQHl0QuQUthM9dAUpTZAsdC9OPbpkwJlTK4SKz3qbRiR14TBZM+UCRkBxcKhEZw6iB30Q0DNs7JtQ18fJsajn9OvH9oOdW9KC6UODs2LKMN1DTuZJ9S0s6RpUCp1AqqcWkSaE4MDqRHfBpctw0rMoaI/1hxA0YYU7hPH64N9fCSZBEeIE0XGOCHZh8g8XIKGdgrMiufg1u4T45y4/psdGObcErFi0LTIiNbovkJIFC6F5vUlb2kit0jjtRPut539yCiftWB3wE98+RQz3VUC0UZOFWaqtCGuqkUjc9lIag4KYCrHuy1tlENFRy4AKOHQrDpDeqh361acE5Oi6DapxmjfvVYNhGnt3sGhIZjHk2hkNjsnvAkKYDKNqwjMHqN2aBmAUj20SIM3Lbg1nLdrQ9vudYfW0AaDY5fcBm8ELRqZy0ZSc1A4pncr2wS1Q8sAlHbdG42zklsoj9MrLOz9xHSfuBp7KajGL8Ekq2g6jr1WRHJzSHe1pA24dmJjWDvRaezlVAu21SCcuKX4r3LmVq2pt3jtBA+N9FyIBqy8BYCw46NXtoIweiU3gaHmoOjILaP+wcwAlB0fvUlVKnYTHIApSsqLCOyK2+2Dg/QuNoI6N7cmMNQcFJVDk22EaepefGtNq9XvTJU2xFXDoQmZBQObp7l7zYwvvJP3E4OO1V5giODawN6o7NzCnDhwHECnoy4yp9xQNkkEz5T7xDInxkCWRMBSqv2wZ/KwHRQhVrb/FVBMzKG8TRs4tr4d107QM3Yc4CpR1aVK9Gwfjl1qJtetMicOaycMXvaIAovflNFI1gJtuVC0K9IyQFRdVWs3eiPUnZkkQsRMq4GGPqS9vTIndp91j92oe7k4ODjI72Ize3M1OICUgADdzu1eX3MrzolF5o1iOrtXQUq2RPaUXKgsqq6qPebdCyjYCagPc2K+T5zFXpyzFqaqlRSi9A7E/yTxVBvJ6JNy+82O6r0MSHHs1bjdHOE7iP+F1xtDagzpye/YlZHqizxSsiWy1yubJIJjth+D5agX1QyMlFuwiBAx02qAdnaC07NYaywjNRbJpBRvlAtqlXELIde9xsExd9fuhStaX59/c3kRTl9ymyI1FsmkZEtkr1dWCTp5mL94uD5LqwEiFVALFNDjnJgEKhoJLvgoyuTZqiYsQBCVrxpHgt3jNC1fOxFuA5TDn4VgFpiMsr2vzaUkXnecolsRHGE5ttraCX+fGKNbEvWoaFFPBOPE64YtMU1rFWK3f0I5kG1fiUMs6npifsYunAAS7W83quZziyV/1gpgkwwFgvqmXGqqA+ChKHTVguDJMZRfNMaIW2G51FQHvg2hH/TjomVAEJVvZNkC8tCkPlutVpgNJ93LwFKbQ1NTOLSd0b3cuuHhkaGhQb+vGF6PFWK3sona0EX38pYlT3Kyg2nxqe1AiclYwmaUXRDUFA5tWro3iMp3xhpCG5je1L1c2vndS1X8H3YdJvL7xBAeu469DVNbjr1CCE50xmwa57/qTUO3e+8E/7ZzLfaKvk3sxSILpOTYK5pSUrK6hVq3uLqaWKbcz4mhv6XzCXpW0pEjuZgdC0UhqxYET0YEUfnOaEh0fqknSuqWtWrjNqhOtY2jV9E0euXQBCY2j976oQnUJD1GYB3lYnYsFIWsWhA8GeFEfBcbACWvRaRlp0Bp53aviMoit6rj11+SjLrduHtVkrUTHnmb42rqlrVqozboM3baIwQW/ZEwHAUgx8DIyRGi+8RyA6B8qwCjJK8eI0H1JTLTkENWFzcLRQ6XqTJzSwRsod4ndnNiC5RZgCOlu/FgzE5S7K3JrU+FNlTuExfPGisbTzGJrqwxkSqFk18bOZHWFQChMqDUBrXyQcSVAaxw6nQHgJJbj8Y2mAUFKUTVubB58xb/uCui7aEZSCw4pUrBzTR1LyuSd7HlcYPAWmfbZd0bTwigHrQTfxafJKQ0uvU28UeiAAvhb3Bi6qB7k0L7Nqh1St3LYuOhARrbYBYUpECbpHtRG7wgUrcTPCe+rXSfuCn2FlPgcNStxl5h5vG/uBdUBqvdJ357/i62QirF3hBUG2OvT1SFyGyKK0ZROm+PvHfC7hPbWYvODWvxBJodN47DYnqKjStIT3EC51GEuLoUnbLbNoDIbRBd5bLCghRoYw9zKdqN3gTpoUWVscAKp97R7kW0BgZq94n9RsHFiJbVaWyDORBd41XbCpjJUoZxeReb2AKiQyu1wazsj0QBFljh1DvavYE8MOBvxhOEFlcXOGVk58LkjGIt07EgjRetHQpsXBul8/VCENQAWztx6/yF6yiE0bMaxRhHie8fSBI5i6HFJG4byd55hUmxFd/FFr93ohJho6jnUhpkayl2a3wR0FqYcGdudT2xvovNzgEiGoAB+pGJT7EIeY1InZtNp25jUjJyFLU2ZIBrBhgjUolsOhaKh0YbHr2hDJkvlMFtgMvT5CR8n4ZJA+hwF8UqXXQvliJ1bGaIbgrdS7rREXoX2ySofEXB7tO9BNzCnHhIXjwp/yPs5O6N3SbdKwVH9psCuuvexkOjzTR0r8IfWgT+bWeZE1+d/GZHlhpDuiSOvd2GdBQ6ItOFY/2rr172Z+3WTmiaUuxNUhaKMYEy9VZOxfvE8ZnAEmlUnZ4ohOj0o78jo1d9BBRGbwli21mj14yeVBm9jW2IqViK1LGZIbqy21L34qyHfrMDH/+C7jMOMlJ01r2Cnda96HliYnhoSNkAf2gOj3n3AogM5dHRsbHRMetVEwLKca8MvU+MQLbU0P0GByVXrDMi7TbjsZbmxPSbHRy2ogBHt4190qebOXm5nLI/58k9A6+EPdpOSSC3vAotSXifGNuw6eSF9C622jN28tgyB0r3BnhOpThIq834z2qV8AqpMYCmLQltuCleOwHdbiPJEM4Qxl6SAiUlI0AnRB5UJKHggSZ1593yfyo6qEVyKpqODQQMIuBNEcxGjuC03R4aZaENHnEbHJC5efPmsfHwURQ/LHXbBrYGyYFaJvqoxShk9MRCRciogPeJt8p94qR7CaEGChGctttDo6y77mUy/duypYd1DHaxK7qXkdEBrgJmxUN7XHRvjOCCuxdkv56Y3k+cR8vSUgpI0eS4u9hLgnMrZC1GKfcAmg2/U7pPPP2xl5K61QUSxaRuo/vHmnBOHH7HLpwWORcFyUFOPOk7HjmaY9bxyAk1UIjgtB23QYi8e8rQ4OHb4BvD5JQeXHQYHBgxJ+exlvQdHxrnLPS3WrYUIWCq3Tu1q3aH3Uu1JsfHx7cWXn8Zqje3ITkxgYhZZCKwlvQdH5rmwTwwMMDv3RfVjnWvnxMDkgp5fQEZODOxSgbofWJ+xi6EsMJsOORpMnI22fXFkKKbzZjKzoveFuDv7/N7J+x37CjZLxJ1nThi2jqzOJVjcSXIchuiKuiW38XG752gKU8RrDZj9ayF+p2d4hjITIclwXw5kYtNaM9AJKxqJTJwZmKVbFAGsekeQH9/P95Iiw6UWeat6raxe/NaiQaLtNeUSWVWmsgCNpjfxZZXjApVJKxqJd4fZSZWyQZlJMyJgQF5eZz5cpyEHDBN3ZuCeCGjnKUmtGcgEla1Ehk4M7FKNiijmclW4yLcnHg9BsZStGxM5ajLqeKt6jYL6aV0NT9jZ+uJMTxakHQxU9MOx14nu5S6rbYBJsrJM3Y2eiWKa7GCxIzFX4/eBnLavW2QcLDYSfeCb6CNjIyMjeFrwmpIvFeQsKqVot2LWCUbmEEfdswHB+mH9xzMl/NWdbtrupdy3AG0eZRuxtMSOzI5ZIoiIlYyJxbknkVjjQWhujcgk80dmcyJ8Rk7XE88P/kTW4h09WhospADs4MwXXUbkrklge4T52snIPkIWImGLrX/u1uWYF/F2wmVFNymayfCV7b8VOWjNTtrKuSVAXn1aOOQasLIKXCzkVOlAopKQKbXg/WHRqqSh/ZtsL7r6e0dG+ff7EirGFmRuVUhYyKKSkCuTzXgVlQFH/B9Gt87gRy21ql1ZGQ9WH9opCq5zfpBWSUysCdKL5DfZd1bPbSSj+4OrYaMXDo0UpTcdtGGVOOqsAk6n9dO2DN2Gi07iL0dhPSQ4thbT0QwcmTShFcW/s2O79OcWGaiFiSb05Rjbwe7qBKi+8Tc9bK2s4yaLdPvrNGbawyZqavRC8qYrKycnGq6OrRd2L34SgS5hUnKKRyaQ2YqdS+h6ESUjW2Aj3xfX59qlFA6NMVj2b2MoaEh7mSHCjVocouoYE7M+4QiagpEgzXOeqFUx5X4DjZRaE788eQ+sUVAHwprYdG/miflYHw0a7iXwALkvJTC1fJkkcEaEXBOfLK9iw2DV3PU6zTsQnIz7JB8LK7H5aa98JzYfrODTgFABDpldC60XACfYjprET8mUwky4EFOyX9DK0mK7keOqQtkyLjFIKAipsRgImIKbVCoxB628vuJyXPUXWETg9tAbiN+TKYSZMCDnNJ0dC8AAjS+n5ibIRQlxWQqQcZUEFAhFNnEEJ+Aaehek8CVe3mcIwemA7eB3E5H9yp26NBUXSBDxq5BQIVQZBNjh9qgKEgiRN0VNtFvO9OcOJ+2prFXwqzRktsfxdgLKXWbpGgvSE7XZohM6zHoGbvK79jtrNhbsnY4veY5sf22M5wL6n49HXouGGqCsws5pV06ek0CgXkgoFIMZvfgNhC9+zYoCpII1gaBEwOiNnAV+x9AJciABzknVhKCpGC3iPKhDQ4NDg3jExGqeGy7l1Xl7iUZ1060WvoOe9mAEIheTCFt4Cr2P4BKkAEPck6sJARJwYeGqB0aSvLFg8nh0JQUkwki2KFZGWBzYgQyxKiAmCiS8LQyO4rZVNIM6MEq94lxTkzBCyJaOaiFZWQhAsqPGFkqkEPKQjCnBYGs1RfizyOpMuiZ6ebEPzziwluaIqn9jYyF8CezECWhOpFNkwshuX3F1vSPcZh8G3Q9sV87EU6BOzsxnKp6iuNqqgqnOBs3gDByGErlhsUgVcgMxWFGICUk2gnZmRqhk9EbV1GVO7RCe0XT3+pnq/5Xg4dTTb0NCQPRbffi/+ER/R07Jrk2ZN6YQSCRqRF2WveGxkxMDAwMIENrpnCqqbchYSCmefRm3hzTajodYqd3rzNpzQTRM3bhPrEPpy5JOLX4LKnb2JumSkgvxn96X5C9iw3vZUw99pbirU9p/OdUjNJpG6I056Jb7Hfs5D7xDo5eN3IYSu1k9CZczLMBY4KImDkdwu2Kq0u5q0MrtLc2equHxph6GxIGoLF7Ax+3kyPDw/IuNtF7Lm7hf3ZEJoiImdMh3OFO/dCckwB6F1t01CistLRdAAASRklEQVSWqKXaiK7akDAA9bgHQsqnfcF1bXQUOjncoPbdq2iMe043Q2TeUG4NYrhmOVIDHNW4ydoJFxxDONOURLoi0zgkBBOT44BbDal5Cm5pTrxJ58R+7YRGPYl9EDpvkfdZBg0LLj7mQl5kGXJRqluJpOgWAysTkGM7ojZgbKX7xLJ2Ans9OheMqJAjr+ABBhgOsVveUJ6MHIbQECAWKZG2+FlyDHbSXRsIvkIdnsUbyiO3Gh82bd7M72KTMiBIRZC5ygFD14cmNASIbY6+1Wpt3UrPT7hqiFCPneyE7nVOhOU0sVtQSRvgX09vL7UmtKoOMlc5kRNh8Yby4qEJDQFikRIh9q4I9dhJd20g+AolOCfCcprYLaiyNjBFCwK4sMD1Ce8T42870zN2SZBkgdO0xV7M05Aeuwpy2gYq8toJmhNfxOt3fXzW5IssQ44CkC2kx7HXmFnslSTVfZGF0iuK1Ql4ds/Y1YZA6azxhvJiNaEhQKx5Doi8u8oKdlIa41So7yCvEINNkItvp0ndgipvQ/tDE65WyFByyxvKizsQGsKJAagcaIV3sT1m3csAQnP3UnF8fNyWqxFAW9+zotRoD3Yyvd1LIIs9Y4eougVV1gZGTC6vJ0ZElQBpWZHvoYB47YSGufbJmMl9hfYpjpVJdE4bkJJRI/eJT7ysw7UTkiR0tk/8Dkv/torgP5qCs6YDtzInXpSsnaijAwqCXXVIBqTMYk0anYAGt5HJPs2dISXXKpO+U89pGwYGByboy0cZHbrdhd07MjI6PBTHDsF0dW9ioGKnnsttGExePGno0O30dy9ongijtw2I7tdOLLh6fR45OSXxcwdjbyl1eskgb3yf2P+2c5Ly2BvS1GJv29TgNv5tZx1ajMdq9EbYHUZvN0xAA9mbOrlKeqR0X4ZeNQT9KL4njN5hX93VbtS9ODLwXWxyjajWbnDrTTu1e50RH2QcrXVy192r94nbA4k199pethY4qKK1E0fTu9hgxskrzyCEpUKkVJqXWfj6o8ewLEWxotKn2K1YzZWXEw9YhDnx7Sdd9sM5F/5iLv5ycjmVTBhz8wTMBj9JmnvRTRm57BaSMn9xJL13As4HP2MXn4vCeSFE+hqp7SmuA8nNFfR94cIKZP9BKLUhMNuAyWX61A4Natl9YvrwRxWtkOxUC+VooeeMc0ORK1pfJWFawdrQP9Da2vQuNkTnbTBVTI6YUOCdJG6ZxMUcqAfLJHZTT09PlYeILDXaTho5gqmO3o6B5FqFHXSrKFek16pshznx0f+J752QIFmJvWmaeuzNCD41uj0GhfWvu+q+k//h+3MuvNVCaJexN9V0EHvbp1Ib0O28i28+nN47Af1N0STFYzN6Y3Q/zIyDwvS5NXhyrWLktkbaWd1L8bW/1S/3iTHgCjWpAgVuA+s1N0RkhnmIydFlBeSiW+aYhwCqzb/ZIWShZEyCd0sC5VETUNn9oRlClSITwNZWqzWE94lhV2VajDYcvk+cHwjDaUmUckQughmBN759cnJ86Nxrb33RlRvmL1w//6o4mSYRonxDZMoFyIMmJufMmokFlNfNX7j5T6765W9e/IPDPv2Lwz9zM6bzIb+JZM6TZCazlmjoBJKRTVNKskdHDoIVKQHz/F8c9skf3bz4Puxwd0ZLZy0qCEhXIgt4cCuYkYwcg1tx1c5tBHKXkl2tDtpQqkailCNyEcyo8fDQeI+47opupFGrcDtJObQyiFiiIuds4o0puTIdHDGYGSwIoRCkFjHhn9bXDfLwH24pQwaK8H16eGQkKFGQLfODOiiVJzrmhQJuUmcCUfEWDGSLdkcSZlqdLUrBDd0nrp1iB9KJoWTHfQQw47EdvWyxawqjVE14hIjsEPQsVXmlNiR0K+Dv2EGjb1ty3wv/feXRX9+EUTGPlkUBchMSfZI3CFZEuRTSi/yFm1955dK3fel7GLfPz4IkCi5+TkvslWSmhBynrA1HXHDz88678cNX6Xri0qnb1aPX4MqPt9HrQLqaU0BX3StbgHdbJtN94tExDGewF45uIbaJEssC4gSm2lHSzG/pvznhreRkFIDK2YLJMTEnKxzH8MiwZ1ojWOcKVKb/wCE9bkTrRDVQkYlcwJqckYkLCmayUioBSOd8oIS34pPfdk7Rxcipr53Iz3Iop5YygIUNobE1uX1ibOQr3/7Je65ddOZ1vzoD06L3UjrzukUgczrzeimCoEpkQs5MI5fk9m7PILdKRjlxi1bh/Oo91y3+7DU//sh/fP+Uf/vJqV/9MaTTKE9SUQmpps8TMLsiJxpIrDzt339y6lf++xeLlsAokb/pQxbOVZAUYU26QCpF1RxY6UxFVq4O5UIFVJXVXpuxkmicIbWGcnM9BbCSjxFBK8NcYXL16jX0Jod+mGsOEIaHh7nY198H0098HnZoCCairGy1OG8NoX4YBOC3+lsQN6HuEAEEqk4+iUCOB4eHR8AJMPk78SCo6GFbKIKyHzzD7gYGWtSGVgudWMOG8JcvYZ+Dvb1b16xZO4JO+oE2ODjEbRgYHEAn/aiEhg0ODIK3fjgEcoIEwsjIKDQMIibUAhN6tDYwc4QaRgZoCpigH+ToqA2ghIpUnQ5kaBj0XMRDIBM2YQjd4N77+jZu2sQdnp02Gr1eS3Jx9Iol5ISUJShWJhQqoKqs9tqMVS8wUpWUYVMgFwCsptFLiJzlbqE+hG58/fYvF9912tdvPvP6OzlscsC0mEnCNMVeE+L4D/rUbVYUt9dCvvjcb93y6f+84ZSvQtz+kY+TeaLYK+G9bWqIvUkit6mylt7z7z9+11d++JXrf4TvvqNulzOgoDIrcXSLOT9jhGJlQqECqspqr81Y9QIjVVG5flnJAKy2oxcQyrnbLDgQ2geH6ete8AQX4i1btmzbtg3DGoVNjJZxqLcoDWEPox6H+j4Mm8zHWApMUFKox4sIhnpRJqGeAzIIMBWHKwvtrgXxFEBhFi9Mw8DH2AvBF52zEnJkUjDfsGHDKATkPlJCG+jaAa6hYVC0KwUoAeS50AbeneXUhKEWXMIo8otncKL9AEX48sBM9gxW9oltpn7jCxPyR8PlBvjQyUCTfseR488FytG5iU5ZZGHgnJjUbIsZoktMKPjXvxAiq0MoQnwd7O/bPqhpwOT+oAwJlZNDnhb0FSEnW2IO5Cg0uvWpf6h/2/hQ/+TwQEgjTvByIiSpuRamVpBzQsRs0k8M9cMooe5OgiuLydkxRaLHYlenOCVzZCGdBRmtGDODXTeumLeB2f7QiM+lmCy6xITCDh0aavEftGGcAJ9GyOGbKnxVpe3o+MQ4BA5U4hfYMdyOUBFMY2gCFSpJYCbeTBgjJ8jX6lwX7zQQR5jAwxwwQRXHxyfQRNVJQHgnVJZv0ijqrkmHAjYM+SNExCpWnfwjj5UwnzYTaADUBjBNwLTYlADicB6UcJShc6h3yLcUIQeZ9o8ZF+l2AJ0CHRkBokj0WGw4xbGhROZBRjobnVIORYbZdeOKHY9e3DodIeg9sDhdo7fdoQXANWpiYJsPjJkwvbG3TMZixMF8ciitDprRgb6h/j4Kj3FcTSJnEj+9JheSYpGQkC0lZMgz5lCrj+9Mwv/SyEkEBpby0Wt5corbjhw3GottsJEj8PwIZT0W27ZBUSRD2dqQVPQ5QUTVuGLp0BKBwaWqLmlD8dAgiMFpxfgfLgoY4ijIUYizsIkhkBIqJRiKiZmSu1BvYVOdwH8skolyoaHgfGqo571BVHehe3QMvwpzqKerlZmwovg3Pl5u6BoRmGRAJrjh6wxfPeC/v3xgjkZicxE6iqjgEdSgZD4cA9LYC3mj3LVhHF+Pwx+fyrmAso0cged7wJyYVKIPfMrBQ1YD4FRBJAkzUVkZN7zFdtPNbkggeJkFKzZoWDCNN3HyReNwnjNNUxTweqw0Ts3FJHkryA3k3GSaBlNeBBnb7D6sBH8uCkCLWOskgLnxZJVlK+V85HgyIrZSUVSRJaFBOdZQKa3IQtIGFQsqkTATlZVxI1sHr4H+VoFEOwvYApR1Q//JhmyS4lzIUkSFmFRHRTagLFYycoYe2EwU0pKSCVhAQUwsaiXOKWGJilgSE+lipui8kiD1yC+bmEx2UitLzCSiWfhsZIuc8PwcKNAi1joJoG70dGrZbVTAfZuK4MmI2EpFUUWWhAblWEOltGKgOLKKBZVImInKyriRrUOs8eQCwIInxSWOMyZb0ctc9Lk3JZoGwYqJyXJLZoXEKJrylJtM01CLU1uCJWA2kMEEkB4vgkaOO0v+rJmahW5Gr1lE5UnZyNnxy4ojB5EkzERlZdzIFqBS0CA8uQC0iLVOAuy87qXTSieYRZbICwuoJYkTkZijNlJJCRmi46LLEVYP7WQiSQUWRUpEkmhL8E50g1sQOIMNJjVKjiwqiI6VTDYmUciqYlDCP97iRmqhTIUgcIY2VceY+ujVObGHKMKeShUThHl5ugWYitqJGbSPmwgqAmlShZBZSUUsc0H/Bxv8xwQltfA/zIiFQJnUlKPAG7HxRjPcsp32TAwSYwpnzCEjZqgl2RiUI9BKBq4iPDZzBTKKxGCBDGih+lxEDWUko4H/e0AZzmaqLZ01h1Tlv3qxlFUSdXHkZGRBqQ0mpZU6bgMeL4H6pB06Gr0BsYb2EH1JVajb0AbXmqBiVA7Nt1/VuBWjQHRWDqi2gSqg4NBFGwTOrVgC1eC7FyXvz4vAZLNmDBBt9Dp15DZDqqocmoeod4/Rq/aMkcO3QasZovp0aKSJGda9HplbjC8Sa3BjW7KIibZUgf5hJjyWSU05CrwRG2+kjAr1zB6IzjWNpAVlYhlLrEQb5+wBQVxUchVRqDls2cZAgaqIhf9hxixWC0dNvGE7MdiiCnGrHligLcEEQTcjR+EYGVngT7HCpLRSN21Qe8bIkQ0zXymqXzs0P3pN2GmHpnCMjCzI2yBb3MSVYg1LlPs+NLUKauKhpJsIcGESielSSSpQzuDn3J0RQGQ4CqcwsOjawBJmJggq3RvqGjss5gwmEkuHpiTvx5CrgkarJRDFpK6diAkZneB6V7IyL0NUL4XvVcn0fwFx30ihwhXE1iIX45FImhV5jPL5qVYxb2YvE3e/7hVUuILYWuQ+kbs3dVv8gCp+3b05fL2syu7Qvd0dWq17KzBvVq1cfxd1b4ETsHt3b5itOJg3q1auX2xDmRrD18v4j7/uLdlY9cTs3jpyb+X609S9RZi3JpeAKXQvW5UjVWL8/697ZTujVIluXDs2XIjSQ/bfl5CMZvqvxIQfQ69sWUf6T15zG0BOyESk/7U2UB2tFdqQ0MJ3lbwNKjHg0H0bEOas1gZC6vYJ0r3YuVqr4+7FIqmy1u6m3QscNqdendt2bQA5IQsRtrU2/Lp7ZYtobgPICVmIsAUi2TwfUejeLg4tb+0TunujQ0NZzdU2PG67l4j0v51bwmPcvc48le41Pgi+DQhzlnqNoG1gzuOse82t5yOi7uU27FD3AnwbEOYs9RohtIHzaene9IPp2Er3rc3aoCTCbtq9Zky9RkxqQ3jvBFOTCml9RSBjy8VrtjdEpqPdAqgyCgHiwOeAhGRwNGpDKFsNbRhvDFoOfy0I6K4NHnyiWMprcDnRpiRFID9uuxeyHezetOYu7d780GJnVJlFxbR3r6BYhszPfhVT797GkSPlRJuSFIH8eO7eElwb3IWgSM6UT6juZZD1idu97tB+3b2i48oxuJxoU5LCkR/j7kW4T1DJbblccbsrutdsKUnhaJ13bww0JMYd7F4iYpbWKJZTpWDXdW+YE3toPatvAgGm9ZiTHFAhW4PyGk2w89jgVtCZW2GZ21Dfg80JFwRhm4YxtUMzsvPv8YToXtkmYHXCBeEx6l5nqJALbUgIRUx79wrauOWS6Bzn193bGdq45ZLoHOex715DottZ3WtXpU7cGdq45ZLoHGc36t4cwlJypRKrlZuRk1q/7l6DsJRcqcTqhFsQGE/07iV05lZYmdsY3F8JtyAwHvfd6+fEqKm5QL3fvd6kqvBNzeQajfXeiHKdHHeBtqGCzt0aiDYNhxZzBCiXyAjU79zuDXmd3En3mtJbsWLdrYHE3al7KzArkxvdijHkdfJO695pHjkxR4ByiYxA/a+7F2FqJhdpMUeAcomMmGrcY3IoJyA9G0NeJydtIDGF1fZunsDd2+7QxBjyOvnx1L2k90ZTFrB7dm+JaTpvRLlERqD+192LMAsJj+vu3b79/wECClWGVO/udwAAAABJRU5ErkJggg=="
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+    "Lists-Array.png": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "799a4b32-983c-4a25-ace6-0b8038058ead",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'>Ãœbersicht</font>** \n",
+    "<p style='text-aling: center;'> <b>Indexing in NumPy</b> [Source: <a href=\"https://numpy.org/doc/stable/user/absolute_beginners.html\">numpy.org</a>]</p>\n",
+    "<div>\n",
+    "<img src=\"attachment:Indexing.png\" width=\"500\"/>\n",
+    "</div>\n",
+    "\n",
+    " **Unterschied zwischen Listen und Arrays im Speicher**\n",
+    "<div>\n",
+    "<img src=\"attachment:Lists-Array.png\" width=\"500\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a85106-b815-4d8b-bc87-ecd93aae5312",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'>Grundlagen</font>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5efbd04f",
+   "metadata": {},
+   "source": [
+    "### EinfÃ¼hrung zu Arrays\n",
+    "Arrays stellen eine Alternative zu Listen dar. Sie sind effizienter, da sie eine feste GrÃ¶ÃŸe besitzen und alle EintrÃ¤ge den selben Datentyp haben."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2baafb9",
+   "metadata": {},
+   "source": [
+    "##### Array erzeugen\n",
+    "Arrays kÃ¶nnen aus normalen Listen oder nach einem bestimmten Schema erstellt werden: <br>\n",
+    "``` empty ```, ``` zeros ```, ``` ones ```, ``` full ```, ``` eye ```, ``` arange ```, ``` linspace ```, etc.\n",
+    "> <font color='grey'>*Weitere Informationen zur Arrayerzeugung kÃ¶nnen in der [offiziellen NumPy Dokumentation](https://numpy.org/doc/stable/reference/routines.array-creation.html) gefunden werden*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18743435",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "# 1-dimensional\n",
+    "vector = np.array([4, 8, 15, 16, 23, 42])\n",
+    "# mehrdimensional\n",
+    "matrix = np.array([[4, 8, 15],\n",
+    "                 [16, 23, 42]])\n",
+    "np.eye(3, dtype='int32')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cede733b",
+   "metadata": {},
+   "source": [
+    "##### Zufallswerte\n",
+    "Auch fÃ¼r mit Zufallswerten gefÃ¼llte Arrays bietet NumPy Funktionen. Dabei kann sowohl der Datentyp, als auch der Wertebereich vorgegeben werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa9fadd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "print(np.random.rand(2, 3))\n",
+    "print(np.random.randint(-10, 10, [2, 3]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36cfdbaa",
+   "metadata": {},
+   "source": [
+    "##### Werte verÃ¤ndern\n",
+    "Auf die einzelnen Werte kann wie auch schon bei Listen mittels von $0$ aufwÃ¤rtszÃ¤hlenden Indizes zugegriffen werden. Die Dimensionen werden bei NumPy _Achsen_ genannt. Wenn ein Array einer neuen Variable zugeordnet wird, ist darauf zu achten, ob wirklich eine [\"Kopie\" oder nur ein \"View\"](https://numpy.org/doc/stable/user/basics.copies.html) erzeugt wurde. Das zugrundelegende Verhalten konnte auch schon bei Liten beobachtet werden, bei denen auch nicht automatisch eine Kopie der gesamten Liste erstellt wird, wenn Sie einer neuen Variable zugewiesen wird<br>\n",
+    "Auch die von Listen schon bekannten Operationen kÃ¶nnen auf Arrays angewandt werden: ``` delete```, ```insert```, ```append```, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c30f23a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "row1 = np.array([ 4,  8, 15])\n",
+    "row2 = np.array([16, 23, 42])\n",
+    "matrix = np.append([row1], [row2], axis = 0)\n",
+    "\n",
+    "#Kopie erzwungen\n",
+    "matrix_copy = matrix.copy()\n",
+    "#Erstellt nur ein View, auf das sich auch Ã„nderungen im Original auswirken\n",
+    "matrix_view = matrix\n",
+    "\n",
+    "matrix[0, 1] = 900\n",
+    "\n",
+    "print(matrix_copy, \", Kopie\")\n",
+    "print(matrix_view, \", View\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78943bc2",
+   "metadata": {},
+   "source": [
+    "#### Adavanced Indexing:\n",
+    "Als Index muss jedoch nicht zwangslÃ¤ufig ein ganzzahliger Wert verwendet werden. Es sind auch Zahlenbereiche, Index- oder Boolean-Arrays mÃ¶glich."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e40ba11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.array([[2,  8, 15],\n",
+    "                  [16, 23, 42]])\n",
+    "\n",
+    "print(matrix[1, 0:2])\n",
+    "print(matrix[matrix%2 == 0])\n",
+    "print(matrix[[1,0],[2,1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05d0e3e4",
+   "metadata": {},
+   "source": [
+    "##### Arrays rekonfigurieren\n",
+    "Neben den schon erwÃ¤hnten Befehlen zum AnhÃ¤ngen oder LÃ¶schen von Segmenten, kann auch nur die Ordnung bzw. der Aufbau verÃ¤ndert werden.<br>\n",
+    "> <font color='grey'>*Weitere Informationen zur Arrayrekonfigurationen kÃ¶nnen in der [offiziellen NumPy Dokumentation](https://numpy.org/doc/stable/reference/routines.array-manipulation.html) gefunden werden*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5cca97ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.arange(9).reshape([3,3]).transpose()\n",
+    "print(matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95dc5e96",
+   "metadata": {},
+   "source": [
+    "##### Attribute\n",
+    "Arrays besitzen auÃŸerdem eine Anzahl von Attributen, die ihren Aufbau und ihre Eigenschaften beschreibene, wie zum Beispiel ```shape```,```size```,```dtype```, ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d22c6c",
+   "metadata": {},
+   "source": [
+    "### Rechnen mit NumPy\n",
+    "#### Skalare Arithmetik\n",
+    "Werden auf Arrays skalare Operatoren und Funktionen angewandt, werden diese elementweise ausgefÃ¼hrt. Sollen zwei Arrays miteinander elementweise verrechnet werden, ist drauf zu achten, dass sie die gleiche ```shape```besitzen.<br>\n",
+    "```*```, ```/```, ```+```, ```-```, ```sin```, ```cos```, ```tan```,```exp```, ```log```, ```<```, ```<=```, ```==```, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91eef9d3",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "vector = np.arange(5)\n",
+    "print(((vector + vector) / 2 - 3) < 0)\n",
+    "\n",
+    "print(np.sin(np.linspace(0.0, np.pi, 8)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac021cd9",
+   "metadata": {},
+   "source": [
+    "#### Lineare Algebra\n",
+    "Ein wichtiges Feld fÃ¼rs Rechnen mit Vektoren und Matrizen ist die Lineare Algebra.\n",
+    "Neben Matrixmultiplikation und Inversen, kÃ¶nnen auch Eigenwerte und -vektoren, Normen, Determinanten und vieles mehr berechnet werden.<br>\n",
+    "> <font color='grey'>*Weitere Informationen zur Linearen Algebra kÃ¶nnen in der [offiziellen NumPy Dokumentation](https://numpy.org/doc/stable/reference/routines.linalg.html?highlight=linea) gefunden werden*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c176fbc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "matrix_A = np.full([3, 2], 2)\n",
+    "matrix_B = np.arange(6).reshape([2, 3])\n",
+    "\n",
+    "print(np.matmul(matrix_A, matrix_B))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbefade2",
+   "metadata": {},
+   "source": [
+    "### Arrays auswerten\n",
+    "NumPy bietet des Weiteren einige Funktionen zum Auswerten von Daten wie zum Beispiel zur Maximum-, Minimum- oder Mittelwertbestimmung."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86558403",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.arange(12).reshape([3,4])\n",
+    "\n",
+    "print(matrix)\n",
+    "print(matrix.sum(axis = 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a01b60c2",
+   "metadata": {},
+   "source": [
+    "### Daten speichern und laden\n",
+    "HÃ¤ufig mÃ¼ssen Daten aus Quellen auÃŸerhalb des eigenen Python Codes verwendet werden oder Werte aus der Berechnung gespeichert werden.<br>\n",
+    "> <font color='grey'>*Weitere Informationen zu I/O kÃ¶nnen in der [offiziellen NumPy Dokumentation](https://numpy.org/doc/stable/reference/routines.io.html) gefunden werden*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcabe0f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.random.randint(0, 10 , size=(3, 12))\n",
+    "\n",
+    "#Daten werden in Datei geschrieben\n",
+    "#Im Verzeichnis erscheint die Datei 'Data.csv'\n",
+    "np.savetxt('Data.csv', matrix, fmt='%i', delimiter=',')\n",
+    "\n",
+    "#Daten aus Programm geschlÃ¶scht\n",
+    "matrix = []\n",
+    "\n",
+    "#Daten wieder eingelesen\n",
+    "matrix = np.genfromtxt('Data.csv', dtype = 'int32', delimiter=',')\n",
+    "print(matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd476524-e4b3-4ff9-b30b-84140dccb612",
+   "metadata": {},
+   "source": [
+    "### **<font color='blue'>Erweiterungen</font>** "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a90b85",
+   "metadata": {},
+   "source": [
+    "#### Masked Array\n",
+    "Wenn ein Array fehlende oder ungÃ¼ltige Werte enthalten, kÃ¶nnen diese mit Masken ausgeschlossen werden.<br>\n",
+    "> <font color='grey'>*Weitere Informationen zu Masken kÃ¶nnen in der [offiziellen NumPy Dokumentation](https://numpy.org/doc/stable/reference/maskedarray.generic.html) gefunden werden*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b79ae67a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "x = np.array([0, 2, 5, -100, 3])\n",
+    "x_masked = np.ma.masked_array(x, x < 0)\n",
+    "\n",
+    "print(x_masked)\n",
+    "x_masked"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a8fef2e",
+   "metadata": {},
+   "source": [
+    "#### Konstanten\n",
+    "NumPy bietet ein auch einige Mathematische Konstanten an, die in einigen FÃ¤llen benÃ¶tigt werden: ```pi```,```e```,```inf```,```NINF```,```PZERO```,```NZERO```,```nan```, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "494b7d7f-0ce6-41cb-928e-39e211cddb1d",
+   "metadata": {},
+   "source": [
+    "# <font color='blue'>**Ãœbungsaufgaben**</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f7c666",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 1 (Arrays erzeugen)\n",
+    "Versuchen Sie mit mÃ¶glichst wenig Befehlen folgende Arrays zu erstellen:<br>\n",
+    "a)\n",
+    "$$\\begin{bmatrix}\n",
+    "    0&1&0&1&0&1&0&1\\\\\n",
+    "    1&0&1&0&1&0&1&0\\\\\n",
+    "    0&1&0&1&0&1&0&1\\\\\n",
+    "    1&0&1&0&1&0&1&0\\\\\n",
+    "    0&1&0&1&0&1&0&1\\\\\n",
+    "    1&0&1&0&1&0&1&0\\\\\n",
+    "    0&1&0&1&0&1&0&1\\\\\n",
+    "    1&0&1&0&1&0&1&0\\\\\n",
+    "\\end{bmatrix}$$\n",
+    "b)\n",
+    "$$\\begin{bmatrix}\n",
+    "    2&0&0&0&0&2\\\\\n",
+    "    0&2&0&0&2&0\\\\\n",
+    "    0&0&2&2&0&0\\\\\n",
+    "    0&0&2&2&0&0\\\\\n",
+    "    0&2&0&0&2&0\\\\\n",
+    "    2&0&0&0&0&2\\\\\n",
+    "\\end{bmatrix}$$\n",
+    "c)\n",
+    "$$\\begin{bmatrix}\n",
+    "    0&0&0&0&0&0&0\\\\\n",
+    "    0&1&1&1&1&1&0\\\\\n",
+    "    0&1&2&2&2&1&0\\\\\n",
+    "    0&1&2&3&2&1&0\\\\\n",
+    "    0&1&2&2&2&1&0\\\\\n",
+    "    0&1&1&1&1&1&0\\\\\n",
+    "    0&0&0&0&0&0&0\\\\\n",
+    "\\end{bmatrix}$$<br><br>\n",
+    "NÃ¼tzliche NumPy-Befehle:```tile```,```repeat```,```eye```,```flip```,```zeros```,```ones```,```full```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5df8d4d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#MÃ¶gliche LÃ¶sungen\n",
+    "#Aufgabe 1a)\n",
+    "a = np.tile([[0, 1], [1, 0]], (5, 5))\n",
+    "print(a)\n",
+    "\n",
+    "#Aufgabe 1b)\n",
+    "b = np.eye(6, dtype='int32')*2\n",
+    "b += np.flip(b, axis=1)\n",
+    "print(b)\n",
+    "\n",
+    "#Aufgabe 1c)\n",
+    "c = np.zeros([7, 7], dtype = 'int32')\n",
+    "c[1:6, 1:6] = np.ones([5, 5])\n",
+    "c[2:5, 2:5] = np.full([3, 3], 2)\n",
+    "c[3, 3] = 3\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e00c6b02",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 2 (Arrays manipulieren)\n",
+    "FÃ¼hren Sie die folgenden Arrayumwandlungen mit mÃ¶glichst wenigen Befehlen durch:<br> \n",
+    "a)\n",
+    "$$\\begin{bmatrix}\n",
+    "    0&1&2&3&4&5&...&20&21&22&23\n",
+    "\\end{bmatrix}$$\n",
+    "zu\n",
+    "$$\\begin{bmatrix}\n",
+    "    -1&1&2&3&4&5\\\\\n",
+    "    6&-1&8&9&0&-1\\\\\n",
+    "    0&0&-1&0&-1&0\\\\\n",
+    "    0&0&0&-1&0&0\n",
+    "\\end{bmatrix}$$\n",
+    "b)\n",
+    "$$\\begin{bmatrix} \n",
+    "    4&5&4\n",
+    "\\end{bmatrix}$$\n",
+    "zu\n",
+    "$$\\begin{bmatrix}\n",
+    "    4&5&4&4\\\\\n",
+    "    4&5&4&5\\\\\n",
+    "    4&5&4&4\\\\\n",
+    "\\end{bmatrix}$$\n",
+    "c)\n",
+    "$$\\begin{bmatrix}\n",
+    "    4&4&4&4&4\\\\\n",
+    "    4&4&4&4&4\\\\\n",
+    "    4&4&4&4&4\\\\\n",
+    "    4&4&4&4&4\\\\\n",
+    "\\end{bmatrix}$$\n",
+    "zu\n",
+    "$$\\begin{bmatrix}\n",
+    "    4&4&4&4\\\\\n",
+    "    4&8&8&4\\\\\n",
+    "    4&8&8&4\\\\\n",
+    "    4&4&4&4\\\\\n",
+    "\\end{bmatrix}$$\n",
+    "<br><br>NÃ¼tzliche Numpy-Befehle: ```reshape```,```transpose```,```append```,```insert```,```delete```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d9cc008",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.arange(24)\n",
+    "b = np.array([4, 5, 4])\n",
+    "c = np.full([4, 5], 4)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#MÃ¶gliche LÃ¶sungen\n",
+    "#Aufgabe 2a)\n",
+    "a = a.reshape([4, 6])\n",
+    "a[a > 9] = 0\n",
+    "a[[0, 1, 2, 3, 2, 1], [0, 1, 2, 3, 4, 5]] = -1\n",
+    "print(a)\n",
+    "\n",
+    "#Aufgabe 2b)\n",
+    "b_left = np.array([b, b, b])\n",
+    "b_right = np.array([b]).transpose()\n",
+    "b = np.append(b_left, b_right, axis=1)\n",
+    "print(b)\n",
+    "\n",
+    "#Aufgabe 2c)\n",
+    "c = np.delete(c, 1, 1)\n",
+    "c[1:3, 1:3] *= 2\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f6229f",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 3 (Statistik)\n",
+    "a) Erstellen Sie ein Array der Dimensionen 5x10 aus Zufallszahlen zwischen 0 und 100 mit zwei Nachkommastellen.<br>\n",
+    "b) Geben Sie die Maximalwerte jeder Spalte, den Durchschnittswert und Standartabweichung jeder Zeile und den kleinsten Wert des gesamten Arrays aus.<br>\n",
+    "c) Geben Sie aus wie viele Werte Ã¼ber 90 liegen.\n",
+    "<br><br> NÃ¼tzliche NumPy-Befehle: ```random.rand```,```round```,```max```,```mean```,```std```,```min```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "988937a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "#MÃ¶gliche LÃ¶sungen\n",
+    "#Aufgabe 3a)\n",
+    "data = np.round(np.random.rand(5, 10)*100, 2)\n",
+    "print(np.round(data, 2))\n",
+    "\n",
+    "#Aufgabe 3b)\n",
+    "print('Maximalwerte der Spalten: ', data.max(axis=1))\n",
+    "print('Durchschnittswerte der Zeilen: ', np.round(data.mean(axis=0),2))\n",
+    "print('Standartabweichung der Zeilen: ', np.round(data.std(axis=0),2))\n",
+    "print('Absolutes Minimum: ', data.min())\n",
+    "\n",
+    "#Aufgabe 3c)\n",
+    "print('Es existieren ', np.shape(data[data > 90])[0], ' Werte Ã¼ber 90.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905b21d4",
+   "metadata": {},
+   "source": [
+    "### Aufgabe 4 (Lineare Algebra)\n",
+    "Im 2-dimensionalen Raum werden die Ecken eines Dreieckes durch die Vektoren $\\vec{v}_1, \\vec{v}_2$ und $\\vec{v}_3$ vorgegeben. WÃ¤hrend $\\vec{v}_1$ und $\\vec{v}_2$ bekannt sind soll fÃ¼r $\\vec{v}_3$ der erste Eigenvektor der Matrix $\\underline{\\underline{A}}$ verwendet werden.\n",
+    "$$\n",
+    "\\vec{v}_1 = \\begin{pmatrix}3\\\\2 \\end{pmatrix},\n",
+    "\\vec{v}_2 = \\begin{pmatrix}1\\\\-3 \\end{pmatrix},\n",
+    "\\underline{\\underline{A}} = \\begin{bmatrix}\\vec{v}_1&\\vec{v}_2 \\end{bmatrix}\n",
+    "$$\n",
+    "a) Bestimmen Sie den Vektor $\\vec{v}_3$.<br>\n",
+    "b) Bestimmen Sie den Umfang des Dreiecks.<br>\n",
+    "c) Bestimmen Sie den Winkel in den drei Ecken des Dreiecks und deren Summe.<br>\n",
+    "**Tipp:** $\\varphi = \\mathrm{arccos}\\bigg(\\frac{\\vec{u}\\circ\\vec{v}}{|\\vec{u}|\\cdot|\\vec{v}|}\\bigg)$<br>\n",
+    "d) Transformieren Sie die Vektoren mit der Matrix $\\underline{\\underline{A}}$ und bereichnen Sie die Werte aus Aufgabenteil b) und c) neu. <br>\n",
+    "e) Zeichen Sie auch das Transformierte Dreieck in den Graphen ein und Ã¼berprÃ¼fen Sie qualitativ die PlausibilitÃ¤t ihrer Ergebnisse.<br><br>\n",
+    "NÃ¼tzliche NumPy-Befehle: ```transpose```,```linalg.eig```,```linalg.norm```,```dot```,```matmul```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ad752e90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "v3^t:  [0.95335371 0.30185542]\n",
+      "Umfang:  11.346758666467714\n",
+      "Winkel in Grad:  [ 28.52  22.61 128.87] und summieren sich zu:  180.0 Â°\n",
+      "\n",
+      "FÃ¼r das transformierte Dreieck:\n",
+      "v1:  [11  0] \n",
+      "v2:  [ 0 11] \n",
+      "v3:  [3.16191656 1.00114117]\n",
+      "Umfang:  33.9450022806811\n",
+      "Winkel in Grad:  [ 37.72  27.45 114.83] und summieren sich zu:  180.0 Â°\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#Eckpunkte des Dreiecks\n",
+    "#Vorsicht: Es handelt sich hierbei um Transponierte Vektoren!\n",
+    "v1 = np.array([3, 2])\n",
+    "v2 = np.array([1, -3])\n",
+    "v3 = np.array([0, 0])    #nur ein Platzhalter\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#MÃ¶gliche LÃ¶sungen\n",
+    "#Aufgabe 4a)\n",
+    "A = np.array([v1, v2]).transpose()\n",
+    "eigenValues, eigenVectors = np.linalg.eig(A)\n",
+    "v3 = eigenVectors[:, 0]\n",
+    "print('v3^t: ', v3)\n",
+    "\n",
+    "#Aufgabe 4b)\n",
+    "Umfang = np.linalg.norm(v1-v2)\n",
+    "Umfang += np.linalg.norm(v2-v3)\n",
+    "Umfang += np.linalg.norm(v3-v1)\n",
+    "print('Umfang: ', Umfang)\n",
+    "\n",
+    "#Aufgabe 4c)\n",
+    "phi = np.empty(3)\n",
+    "phi[0] = np.arccos(np.dot(v2-v1, v3-v1)/(np.linalg.norm(v2-v1)*np.linalg.norm(v3-v1)))\n",
+    "phi[1] = np.arccos(np.dot(v3-v2, v1-v2)/(np.linalg.norm(v3-v2)*np.linalg.norm(v1-v2)))\n",
+    "phi[2] = np.arccos(np.dot(v1-v3, v2-v3)/(np.linalg.norm(v1-v3)*np.linalg.norm(v2-v3)))\n",
+    "print('Winkel in Grad: ', np.round(phi*180/np.pi, 2), 'und summieren sich zu: ', round(phi.sum()*180/np.pi, 2), 'Â°')\n",
+    "\n",
+    "#Aufgabe 4d)\n",
+    "v1t, v2t, v3t = np.matmul(A, v1), np.matmul(A, v2), np.matmul(A, v3)\n",
+    "print('\\nFÃ¼r das transformierte Dreieck:\\nv1: ', v1t, '\\nv2: ', v2t, '\\nv3: ', v3t)\n",
+    "\n",
+    "Umfang_t = np.linalg.norm(v1t-v2t)\n",
+    "Umfang_t += np.linalg.norm(v2t-v3t)\n",
+    "Umfang_t += np.linalg.norm(v3t-v1t)\n",
+    "print('Umfang: ', Umfang_t)\n",
+    "\n",
+    "phi_t = np.empty(3)\n",
+    "phi_t[0] = np.arccos(np.dot(v2t-v1t, v3t-v1t)/(np.linalg.norm(v2t-v1t)*np.linalg.norm(v3t-v1t)))\n",
+    "phi_t[1] = np.arccos(np.dot(v3t-v2t, v1t-v2t)/(np.linalg.norm(v3t-v2t)*np.linalg.norm(v1t-v2t)))\n",
+    "phi_t[2] = np.arccos(np.dot(v1t-v3t, v2t-v3t)/(np.linalg.norm(v1t-v3t)*np.linalg.norm(v2t-v3t)))\n",
+    "print('Winkel in Grad: ', np.round(phi_t*180/np.pi, 2), 'und summieren sich zu: ', round(phi_t.sum()*180/np.pi, 2), 'Â°')\n",
+    "\n",
+    "#Aufgabe 4e)\n",
+    "plt.plot([v1t[0], v2t[0], v3t[0], v1t[0]], [v1t[1], v2t[1], v3t[1], v1t[1]], 'y')\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.plot([v1[0], v2[0], v3[0], v1[0]], [v1[1], v2[1], v3[1], v1[1]], 'r')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53f5fcd7-5668-400c-873b-1bd8c197abc2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Uebung05/Uebung05.ipynb b/Uebung05/Uebung05.ipynb
new file mode 100644
index 0000000..03f7b23
--- /dev/null
+++ b/Uebung05/Uebung05.ipynb
@@ -0,0 +1,712 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c716411b-f1c6-4aee-840f-7bae163f4251",
+   "metadata": {},
+   "source": [
+    "# <font color='blue'>**Ãœbung 5 - Berechnung eines statisch bestimmten Fachwerks**</font>\n",
+    "In dieser Ãœbung werden wir uns mit der Berechnung eines 2D Stabtragwerks befassen und damit erstmals eine praktische Aufgabe umsetzen. Im ersten Schritt soll in dieser Ãœbung eine Klasse *Fachwerk* erstellt werden, die das spÃ¤ter zu lÃ¶sende Fachwerk darstellt. In diesem Zuge soll die Ãœbung einige Grundlagen zur Objektorientierten Programmierung vorfÃ¼hren. AuÃŸerdem wird *matplotlib* zur visualisierung des Stabtragwerks eingesetzt. Das Packet ist ursprÃ¼nglich nicht dazu gedacht, es funktioniert aber trotzdem sehr gut.\n",
+    "## Grundlegende Ãœberlegungen\n",
+    "Die objektorientierte Programmierung ist ein Konzept, das darauf aufbaut, unsere Vorstellungen Ã¼ber die Funktionsweise eines Programms mÃ¶glichst einfach in ein Programm umzusetzen. Um ein Projekt, wie die Berechnung eines Fackwerks in ein Programm umzusetzen gibt es verschiedene Techniken. Als ersten Anhaltspunkt hilft es oft, sich den Ablauf des Programms grob zu skizzieren. Dabei soll der Funtkionsumfang des Programms umrissen werden. Am einfachsten gelingt das, indem man sich klar macht, was man eigentlich mit dem Programm tun mÃ¶chte. Ein Beispiel:\n",
+    "\n",
+    "1. Ich mÃ¶chte das Aussehen des Stabtragwerks definieren. Damit ich sicher sein kann, dass ich alles richtig gemacht habe sollte es mÃ¶glich sein, mir den aktuellen Stand visuell anzuschauen und ggf. anzupassen\n",
+    "2. Ich mÃ¶chte Auflager und KrÃ¤fte definieren. Auch hier mÃ¶chte ich, dass ich sehen kann, ob alles so aussieht, wie ich es mir vorstelle\n",
+    "3. Ich mÃ¶chte herausfinden, ob mein Stabtragwerk (potentiell) ein statisch bestimmtes Tragwerk ist, ansonsten brauche ich auch nicht versuchen, es zu berechnen bzw. muss es verÃ¤ndern\n",
+    "4. Ich mÃ¶chte die Auflagerreaktionen und die StabkrÃ¤fte berechnen\n",
+    "5. Ich mÃ¶chte mir das Ergebnis ausgeben\n",
+    "\n",
+    "Diese Punkte kann man grob in Kategorien eines Ablaufs einordnen. Punkte 1-3 kann man als Definitionsphase einstufen. In diesen Punkten soll interaktiv ein Tragwerk definiert und Ã¼berprÃ¼ft werden. Danach folgt die Berechnung des Tragwerkes. Dieser Schritt unterscheidet sich in so weit von den Punkten 1-3, dass die Berechnung keine Interaktion erfordert. Das definierte Tragwerk wird berechnet. Entweder funktioniert das, oder es funktioniert nicht. Falls nicht, muss etwas am Tragwerk geÃ¤ndert werden und anschlieÃŸend die Berechnung komplett neu gestartet werden. Die abschlieÃŸende Visualsierung stellt nur Ergebnisse dar, die Darstellung mag anpassbar sein, die Ergebnisdaten bleiben aber immer die Selben.\n",
+    "\n",
+    "In dieser Ãœbung wollen wir zunÃ¤chst die Definitionsphase genauer betrachten. Aus den Stichpunkten ist in etwa definiert, was das das Programm kÃ¶nnen soll. Ein Fachwerk aufbauen. Jetzt sollte man sich klar machen, was ein Fachwerk aus sicht der Aufgabenstellung eigentlich ist. Das funktioniert z.B. durch einen Freitext:\n",
+    "\n",
+    "```\n",
+    "Ein Fachwerk besteht aus beliebig vielen StÃ¤ben und Knotenpunkten. Ein Knotenpunkt hat eine Position. Ein Stab verbindet zwei Knotenpunkte eines Fachwerks. An einem Knotenpunkt kÃ¶nnen Lasten angreifen. Falls keine Lasten angreifen, kann man auch sagen, die Last ist null. AuÃŸerdem kÃ¶nnen Knotenpunkte durch Lager blockiert werden. Dabei kÃ¶nnen die Lager entweder eine verschiebung in x-Richtung, in y-Richtung oder in beide Richtungen verhindern. Die meisten Punkte haben keine Lagerbedingungen.\n",
+    "```\n",
+    "\n",
+    "Das ist natÃ¼rlich nur eine Sichtweise. Man kÃ¶nnte auch damit anfangen, dass ein Fachwerkproblem aus der Struktur des Fachwerks und Randbedingungen besteht etc. Bei grÃ¶ÃŸeren Projekten ist es oft hilfreich, sich mehrere alternative Sichtweisen zu verdeutlichen. FÃ¼r diese Ãœbung nehmen wir den Text erst einmal als Grundlage\n",
+    "\n",
+    "\n",
+    "## Schritt 1 Definition von Klassen:\n",
+    "\n",
+    "Als grober Anhaltspunkt sind alle Nomen aus dem Freitext potentielle Klassen sind. Gehen wir den erstellten Satz durch, haben wir die potentiellen Klassen Fachwerk, Stab, Knoten, Position, Kraft und Lagerbedingung.\n",
+    "\n",
+    "Der Text enthÃ¤lt auch ZusammenhÃ¤nge zwischen den Klassen. Man kÃ¶nnte sagen, sowohl Knoten als auch StÃ¤be gehÃ¶ren exklusiv zu einem Fachwerk. Das Fachwerk sollte die in ihm enthaltenen StÃ¤be und Knoten kennen. Ob auch die Knoten oder StÃ¤be wissen mÃ¼ssen, zu welchem Fachwerk sie gehÃ¶ren, ist erst einmal unbekannt. Die Lasten und Randbedingungen gehÃ¶ren zu einem Knoten und jeder Knoten hat eine Last und eine Lagerbedingung (die entweder frei, x-Achse, y-Achse oder beide Achsen sein kann). AuÃŸerdem hat jeder Knoten eine Position.\n",
+    "Wenn ein Zusammenhang zwischen Klassen besteht, z.B. ein Stab gehÃ¶rt zu einem Tragwerk, deutet das darauf hin, dass die StÃ¤be ein Attribut, also eine Variable der Klasse Tragwerg sind. Das gleiche gilt auch fÃ¼r die Verbindung zwishcen Knoten und Last etc.\n",
+    "\n",
+    "Als nÃ¤chstes versuchen wir potentielle Klassen zu finden, die wir durch bestehende Klassen oder Variablentypen in Python abbilden kÃ¶nnen.\n",
+    "Die Position eines Knotens ist ein Vektor im 2D-Raum. Die Last eines Knotens ist auch ein Vektor. DafÃ¼r kÃ¶nnen wir numpy-Arrays verwenden, die bereits Vektorrechnung unterstÃ¼tzt. Die Lagerbedingungen bestehen aus fest oder losgelagert in x- und y-Richtung. Man kÃ¶nnte sie als eigene Klasse definieren, wir wollen das hier vorerst nicht tun.\n",
+    "\n",
+    "Die anderen 3 Klassen mÃ¼ssen selbst implementieren werden. Dazu mÃ¼ssen wir erst einmal die Verbindungen der Klassen geeignet abbilden. Ein Fachwerk hat beliebig viele Knoten und beliebig viele StÃ¤be. Um diese zu speichern kÃ¶nnen wir Listen verwenden. Eine fÃ¼r die Knoten und eine fÃ¼r die StÃ¤be. AuÃŸerdem mÃ¼ssen StÃ¤be Knoten verbinden, die auch zum Fachwerk gehÃ¶ren. Offensichtlich sollte man also auf den Inhalt der Listen ein wenig aufpassen, bzw. die Fachwerkklasse sollte die Listen verwalten. Wenn das der Fall ist, sollte der Name der Listen mit einem _ beginnen, um Benutzern der Klassen mitzuteilen, dass diese Listen nicht direkt verÃ¤ndert werden sollten. AuÃŸerdem benÃ¶tigt die Klasse Knoten eine Koordinate und eine Last. Ohne Koordinate ist ein Knoten nicht besonders sinvoll und ein Standardwert bringt nicht viel, da Knoten nicht Ã¼bereinander liegen sollten. Daher sollte die Koordinate bei der Erstellung des Knotens ein Parameter der \\_\\_init\\_\\_ Methode sein. FÃ¼r die Last kÃ¶nnen wir als Standardwert ein Vektor mit zwei Nullen annehmen. Der Stab ist ersteinmal noch etwas unklar, da wir Ã¼berlegen mÃ¼ssen, wie wir sicherstellen, das der Stab zwei Knoten des selben Tragwerks verbindet. Wir lassen ihn erst einmal leer. Die Lagerbedingungen sind auch noch etwas unklar, auch sie lassen wir fÃ¼r den ersten Schritt weg.\n",
+    "\n",
+    "Jetzt wÃ¤re ein Zeitpunkt diese Klassen erst einmal nach den bereits bekannten Spezifikationen zu definieren\n",
+    "- Drei Klassen: Knoten, Stab, Fachwerk\n",
+    "- Die Klasse Knoten benÃ¶tigt eine Koordinate zur Erstellung, die als numpy Array gespeichert werden soll\n",
+    "- Die Klasse Knoten hat ein Attribut fÃ¼r die Kraft, ein 2D-Numpy Vektor, der bei der Erstellung auf null gestzt wird\n",
+    "- Die Klasse Fachwerk besitzt zei Attribute _knoten und _staebe, die nach erstellung leere Listen sind\n",
+    "\n",
+    "#### Hinweis:\n",
+    "Variablen die zu einem Objekt gehÃ¶ren werden in Python in der \\_\\_init\\_\\_ Methode einer Klasse angelegt\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5518cd95-6cdd-4e6d-a0c0-5792e4c88c4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "class Knoten:\n",
+    "    def __init__(self, coo):\n",
+    "        self._coo=np.array(coo)# wir sollten sicherstellen, das es sich bei der intern gespeicherten Koordinate um ein np.array handelt. Potentiell sollte noch der Datentyp auf FlieÃŸkomma gesetzt werden und die GrÃ¶ÃŸe Ã¼berprÃ¼ft werden\n",
+    "        self._kraft=np.zeros(2)\n",
+    "class Stab:\n",
+    "    pass #An den Stab haben wir noch keine Anforderungen definiert. wir lassen ihn zunÃ¤chst einfach einmal stehen\n",
+    "\n",
+    "class Fachwerk:\n",
+    "    def __init__(self):\n",
+    "        self._knoten=[]\n",
+    "        self._staebe=[]# Wir erstellen erst einmal leere Listen fÃ¼r die StÃ¤be und Knoten"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e1d018a-332d-41a3-ad14-4a45875b72d5",
+   "metadata": {},
+   "source": [
+    "## Schritt 2a Definition von weiteren Attributen und Methoden:\n",
+    "\n",
+    "Die Klassen sind grob definiert, kÃ¶nnen aber noch nicht viel. Im nÃ¤chsten Schritt sollte daher Ã¼berlegt werden, wie das Programm funktioniert. Es gibt verschiedene MÃ¶glichkeiten dazu. Eine MÃ¶glichkeit ist, aus Sicht des Benutzers verschiedene sogenannte User-Storys zu schreiben. Eine beschreibung was der Benutzer spÃ¤ter mit den Klassen machen mÃ¶chte. Bei komplexeren Programmen ist es auch eine gute Idee zu versuchen verschiedene Benutzerrollen zu identifizieren (z.B. bei einem Onlinestore gÃ¤be es einen Kunden, jemand der Artikel einstellt, einen EinkÃ¤ufer, etc.), was hier aber eher nicht der Fall ist. Man sollte die Einzeltexte so kurz wie mÃ¶glich halten. FÃ¼r unser Beispiel nehmen wir folgendes an:\n",
+    "\n",
+    "- Ein Fachwerk ist nach Erstellung erst einmal leer. Es soll nach und nach aufgebaut werden\n",
+    "- Ich mÃ¶chte dem Fachwerk Knotenpunkte hinzufÃ¼gen. Dazu sage ich dem Tragwerk, wo der neue Knoten hin soll\n",
+    "- Ich mÃ¶chte dem Fachwerk StÃ¤be hinzufÃ¼gen. Dazu sage ich dem Tragwerk, zwischen welchen Knoten der Stab liegen soll\n",
+    "- Ich mÃ¶chte zu einem Knoten eine Last zuweisen. Das ist ein Kraftvektor im 2D-Raum\n",
+    "- Ich mÃ¶chte Lagerbedingungen an einem Knoten definieren. Dazu muss ich dem Knoten mitteilen, ob er in x oder in y Richtung gelagert ist.\n",
+    "\n",
+    "\n",
+    "Bei komplizierteren Anforderungen, sollten zusÃ¤tzlich Akzeptanzkriterien definiert werden.\n",
+    "\n",
+    "Aus diesen kurzen Geschichten kann schrittweise festgelegt werden, welche Eigenschaften die Klassen besitzen sollen. Verben oder TÃ¤tigkeiten deuten auf Methoden von Klassen hin. Eigenschaften auf Attribute, also Variablen der Klassen. Wenn fÃ¼r die TÃ¤tigkeit etwas benÃ¶tigt wird, muss dem Objekt das benÃ¶tigte schon bekannt sein oder es ist ein Parameter. Das Ergebnis einer TÃ¤tigkeit kann ein RÃ¼ckgabewert sein. Bei jeder Story kann erst einmal gefragt werden, welche Klasse zustÃ¤ndig ist, welche Informationen zur DurchfÃ¼hrung benÃ¶tigt werden und was das Ergebnis eigentlich ist.\n",
+    "\n",
+    "Nehmen wir z.B. Knotenpunkt hinzufÃ¼gen. Dem Fachwerk soll ein Knotenpunkt hinzugegÃ¼gt werden. Es ist also ein Methode des Fachwerks. NÃ¤chste Frage ist, wer erstellt das Knoten Objekt. Soll der Benutzer ein Knotenobjekt erstellen und als Parameter Ã¼bergeben, oder eine Koordinate und das Fachwerk erstellt sich das Objekt selbst. Wenn wir kurz Ã¼ber den Knoten nachdenken kann man sagen, dass ein Knoten ohne Fachwerk nicht sinvoll ist. AuÃŸerdem sollte ein Knoten nur zu einem Fachwerk gehÃ¶ren. Das Fachwerk besitzt sozusagen den Knoten. Deswegen wÃ¤hlen wir die Variante, den Knoten innerhalb der Methode aus einer Koordinate zu erstellen (wie man diese Entscheidung trifft ist in der Tat viel Erfahrung. Prinzipiell sind beide Wege akzeptabel, wir werden aber spÃ¤ter sehen, dass diese Sichtweise spÃ¤ter Vorteile bietet). Das Ergebnis ist ein neuer Knoten. Die Frage ist, sollte der neu erstellte Knoten ein RÃ¼ckgabewert der Methode sein. Der Knoten gehÃ¶rt dem Fachwerk, er wird also in der Liste *\\_knoten* gespeichert. Es wÃ¤re nicht zwingend nÃ¶tig, ihn als RÃ¼ckgabewert zu liefern. Lassen wir die Entscheidung vorerst offen.\n",
+    "\n",
+    "Einem Knoten sollen Lasten oder Lagerbindungen zugewiesen werden. Beides sind Eigenschaften des Knotens. Um ihm diese Eigenschaften zuzuweisen, benÃ¶tigt der Knoten entsprechende Attribute oder Methoden. Viel wichtiger an diesem Punkt ist noch, dass das Knoten-Objekt benÃ¶tigt wird. Die interessante Frage ist also, wo kommt das Knoten-Objekt her. Es ist in der Liste des Fachwerks, die wir nicht direkt anfassen wollen. Es ist also vermutlich eine gute Idee, das Knotenobjekt bei Erstellung als RÃ¼ckgabewert zu erhalten. Damit wÃ¤re die Entscheidung, ob ein Knoten bei Erstellung zurÃ¼ckgegeben wird getroffen.\n",
+    "\n",
+    "FÃ¼r die Erstellung der StÃ¤be gilt im Endeffekt selbiges, wie fÃ¼r die Knoten. Sie werden vom Fachwerk erstellt, wir benÃ¶tigen zwei Knoten (die haben wir, da sie bei Erstellung zurÃ¼ckgegeben werden) usw.\n",
+    "\n",
+    "Den Knoten kÃ¶nnen Lasten bzw. KrÃ¤fte zugewiesen werden. Entweder betrachten wir die Kraft als Attribut, das wir direkt zuweisen oder wir schreiben eine Methode dafÃ¼r. Es ist fÃ¼r die spÃ¤tere Berechnung hilfreich, wenn die KrÃ¤fte als numpy-Arrays gespeichert sind. Da wir momentan nicht wissen, wie wir das sonst sicherstellen sollen, entscheiden wir uns fÃ¼r eine Methode.\n",
+    "\n",
+    "Die Lagerbedingungen sind momentan noch etwas unklar. Wir kÃ¶nnen den Knoten in X- und Y- Richtung festhalten. Der einfachheit halber mÃ¶chte ich die Lagerbedingungen als zwei Boolsche-Werte speichern. Festgehalten in x-Richtung und festgehalten in y-Richtung. Zum Setzen und Entfernen der Lagerbedingung sind dann Methoden definiert, die das Ã¼bernehmen.\n",
+    "\n",
+    "\n",
+    "Mit diesen Anforderungen lassen sich die definierten Klassen nun erweitern. \n",
+    "\n",
+    "- Die Klasse Fachwerk hat eine Methode addKnoten. Parameter ist die Koordinate des neuen Knoten. RÃ¼ckgabewert ist ein neuer Knoten. Der Knoten wird auÃŸerdem der Liste des Fachwerks hinzugefÃ¼gt.\n",
+    "- Die Klasse Fachwerk hat eine Methode addStab. Parameter sind zwei Knoten. Es wird Ã¼berprÃ¼ft ob die Knoten zum Fachwerk gehÃ¶ren. RÃ¼ckgabewert ist der neue Stab. Der Stab wird der Liste der SÃ¤be hinzugefÃ¼gt\n",
+    "- Die Klasse Knoten hat ein Attribut \\_kraft. Bei erstellung ist das ein 2D Vektor mit Nullen\n",
+    "- Die Klasse Knoten hat eine Methode setKraft. Die Methode setzt die EintrÃ¤ge des Kraftvektors\n",
+    "- Die Klasse Knoten hat ein Attribut \\_lager das ist ein Vektor der bei Erzeugung zwei mal false enthÃ¤lt\n",
+    "- Die Klasse Knoten hat Methoden setLager_x/y und removeLager_x/y.\n",
+    "- Die Klasse Stab hat die Attribute k1 und k2. Zwei Knoten, die bei Erstellung gesetzt werden.\n",
+    "\n",
+    "Diese Anforderungen kÃ¶nnen in der Form implementiert werden\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c948b483-90e2-4994-996d-0f205770a8dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class Knoten:\n",
+    "\n",
+    "    def __init__(self, coo):\n",
+    "        self._coo=np.array(coo)# wir sollten sicherstellen, das es sich bei der intern gespeicherten Koordinate um ein np.array handelt. Potentiell sollte noch der Datentyp auf FlieÃŸkomma gesetzt werden und die GrÃ¶ÃŸe Ã¼berprÃ¼ft werden\n",
+    "        self._kraft=np.zeros(2)\n",
+    "        self._lager=[False,False]\n",
+    "    def setKraft(self,kraft):\n",
+    "        self._kraft[:]=kraft\n",
+    "    def setLager_x(self):\n",
+    "        self._lager[0]=True\n",
+    "    def setLager_y(self):\n",
+    "        self._lager[1]=True\n",
+    "    def removeLager_x(self):\n",
+    "        self._lager[0]=False\n",
+    "    def removeLager_y(self):\n",
+    "        self._lager[1]=False\n",
+    "\n",
+    "class Stab:\n",
+    "    def __init__(self,k1,k2):\n",
+    "        self._k1=k1\n",
+    "        self._k2=k2\n",
+    "\n",
+    "class Fachwerk:\n",
+    "    def __init__(self):\n",
+    "        self._knoten=[]\n",
+    "        self._staebe=[]# Wir erstellen erst einmal leere Listen fÃ¼r die StÃ¤be und Knoten\n",
+    "        \n",
+    "    def addKnoten(self, coo):\n",
+    "        neuer_knoten=Knoten(coo)\n",
+    "        self._knoten.append(neuer_knoten)\n",
+    "        return neuer_knoten\n",
+    "    \n",
+    "    def addStab(self,k1,k2):\n",
+    "        if not k1 in self._knoten:\n",
+    "            return None\n",
+    "        if not k2 in self._knoten:\n",
+    "            return None\n",
+    "        neuer_stab=Stab(k1,k2)\n",
+    "        self._staebe.append(neuer_stab)\n",
+    "        return neuer_stab"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35bd4b01-16ea-40fa-b2eb-2fe7f2270e4a",
+   "metadata": {},
+   "source": [
+    "## Schritt 2b Definition von weiteren Attributen und Methoden:\n",
+    "Definieren wir eine weitere Nutzerstory, die etwas komplizierter umzusetzen ist\n",
+    "\n",
+    "- Ich mÃ¶chte das Fachwerk visualisieren kÃ¶nnen. Dabei erwarte ich alle definierten Knoten und alle StÃ¤be zu sehen\n",
+    "\n",
+    "Der Zweite Satz ist ein Akzeptanzkriterium; ich mÃ¶chte alle StÃ¤be und Knoten sehen.\n",
+    "\n",
+    "Das Visualisieren ist eine Methode des Fachwerks. Dazu muss es alle StÃ¤be und Knoten kennnen, was bereits der Fall ist. Das Ergebnis ist eine Ausgabe, die wir mit *matplotlib* erstellen.\n",
+    "\n",
+    "Wir haben in der letzten Ãœbung bereits mit *matplotlib* gearbeitet. Dabei haben wir gesehen, dass mit *matplotlib* einzelne Punkte im Raum als Scatter-Plot darstellbar sind. Das wÃ¤re z.B. gut fÃ¼r die Knoten des Fachwerks. Um einen Knoten darzustellen, benÃ¶tigen wir die Koordinate des Knotens. Die ist im Knoten als Attribut bekannt. Allerdings sollten wir auf Attribute mit Unterstrich nicht direkt zugreifen. Also definiren wir fÃ¼r die Klasse Punkt eine Methode getKoordinate, die uns den gewÃ¼nschten Wert liefert.\n",
+    "\n",
+    "Die StÃ¤be sind eine Verbindungslinie zwischen den Knoten. Wir kÃ¶nnen uns mit *matplotlib* mit der Methode plot behelfen, die eine Linie zwischen beliebig vielen Punkten zeichnet.\n",
+    "\n",
+    "So sollte es mÃ¶glich sein die Methode zu implementieren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3da3f717-b749-40d6-8ec5-0a3246808210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "class Knoten:\n",
+    "    def __init__(self, coo):\n",
+    "        self._coo=np.array(coo)# wir sollten sicherstellen, das es sich bei der intern gespeicherten Koordinate um ein np.array handelt. Potentiell sollte noch der Datentyp auf FlieÃŸkomma gesetzt werden und die GrÃ¶ÃŸe Ã¼berprÃ¼ft werden\n",
+    "        self._kraft=np.zeros(2)\n",
+    "        self._lager=[False,False]\n",
+    "    def setKraft(self,kraft):\n",
+    "        self._kraft[:]=kraft\n",
+    "    def setLager_x(self):\n",
+    "        self._lager[0]=True\n",
+    "    def setLager_y(self):\n",
+    "        self._lager[1]=True\n",
+    "    def removeLager_x(self):\n",
+    "        self._lager[0]=False\n",
+    "    def removeLager_y(self):\n",
+    "        self._lager[1]=False\n",
+    "\n",
+    "    def getKoordinate(self):\n",
+    "        return self._coo\n",
+    "\n",
+    "class Stab:\n",
+    "    def __init__(self,k1,k2):\n",
+    "        self.k1=k1\n",
+    "        self.k2=k2\n",
+    "\n",
+    "class Fachwerk:\n",
+    "    def __init__(self):\n",
+    "        self._knoten=[]\n",
+    "        self._staebe=[]# Wir erstellen erst einmal leere Listen fÃ¼r die StÃ¤be und Knoten\n",
+    "    def addKnoten(self, coo):\n",
+    "        neuer_knoten=Knoten(coo)\n",
+    "        self._knoten.append(neuer_knoten)\n",
+    "        return neuer_knoten\n",
+    "    def addStab(self,k1,k2):\n",
+    "        if not k1 in self._knoten:\n",
+    "            return None\n",
+    "        if not k2 in self._knoten:\n",
+    "            return None\n",
+    "        neuer_stab=Stab(k1,k2)\n",
+    "        self._staebe.append(neuer_stab)\n",
+    "        return neuer_stab\n",
+    "    \n",
+    "    \n",
+    "    ##############################################Neu in diesem Schritt#######################################################################################################################\n",
+    "    def plot(self):\n",
+    "        fig= plt.figure(figsize=(5, 5))\n",
+    "        ax = fig.subplots()\n",
+    "        #zunaechst alle Knoten darstellen. Matplotlib erwartet dafÃ¼r eine Darstellung in der Form [x_0,x_1,...],[y_0,y_1,..]\n",
+    "        x_werte=[]\n",
+    "        y_werte=[]\n",
+    "        for knoten in self._knoten:\n",
+    "            koordinate=knoten.getKoordinate()#die Koordinate holen\n",
+    "            x_werte.append(koordinate[0])\n",
+    "            y_werte.append(koordinate[1])\n",
+    "        ax.scatter(x_werte,y_werte)#Zeichnet alle Knoten als blaue Kreise\n",
+    "        #als nÃ¤chstes alle StÃ¤be\n",
+    "        for stab in self._staebe:\n",
+    "            koordinate1=stab.k1.getKoordinate()\n",
+    "            koordinate2=stab.k2.getKoordinate()\n",
+    "            ax.plot([koordinate1[0],koordinate2[0]],[koordinate1[1],koordinate2[1]],\"r\")#Zeichent einen Stab in rot. Durch die andere vorgesehene Nutzung von Matploltib mÃ¼ssen wir uns die x und y Koordinaten der Punkte etwas umstÃ¤ndlich umsortieren\n",
+    "    #####################################################################################################################################################################################                                          \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ae4e971-de59-482b-bc96-5f590e9e5f84",
+   "metadata": {},
+   "source": [
+    "Zeit fÃ¼r einen kleinen Akzeptanztest. Probieren wir alles aus, was wir bisher in den Userstorys gesehen haben"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c83bb13a-1670-44b3-9c35-0e2767f530af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t=Fachwerk()# Ein leeres Tragwerk erstellen\n",
+    "a=t.addKnoten([0.,0.])#Punkte hinzufÃ¼gen\n",
+    "b=t.addKnoten([10.,0.])\n",
+    "c=t.addKnoten([5.,5.])\n",
+    "d=t.addKnoten([15.,5.])\n",
+    "t.addStab(a,b)#StÃ¤be hinzufÃ¼gen\n",
+    "t.addStab(a,c)\n",
+    "t.addStab(b,c)\n",
+    "t.addStab(c,d)\n",
+    "t.addStab(b,d)\n",
+    "a.setKraft([-100.,0.])\n",
+    "b.setLager_x()\n",
+    "b.setLager_y()\n",
+    "c.setLager_x()\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a41947e1-00af-49a8-84a3-08181af74d77",
+   "metadata": {},
+   "source": [
+    "Die Randbedingungen und KrÃ¤fte werden noch nicht angezeigt, darum kÃ¼mmern wir uns spÃ¤ter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7b571b4-9284-4da4-93b8-ead316c6e68e",
+   "metadata": {},
+   "source": [
+    "## Schritt 3 Das erste Architektur-Problem\n",
+    "So weit scheint alles gut zu laufen. Diese und jene FunktionalitÃ¤t kann noch verbessert werden, aber das Ergebnis lÃ¤sst sich eigentlich sehen. Versuchen wir uns an zwei weiteren Features\n",
+    "\n",
+    "- Ich mÃ¶chte einen Stab lÃ¶schen.\n",
+    "- Ich mÃ¶chte einen Knoten lÃ¶schen. Alle StÃ¤be, die mit diesem Knoten verbunden sind, sollen dabei mit gelÃ¶scht werden.\n",
+    "\n",
+    "Das erste Feature ist einfach zu implementieren. Der Stab muss nur aus der Liste der StÃ¤be entfernt werden.\n",
+    "\n",
+    "Das zweite Feature kann auf verschiedene Arten umgestzt werden. Kritischer Punkt ist die Identifikation aller StÃ¤be, die mit einem Punkt verbunden sind. Wir kÃ¶nnten hier eine Methode des Tragwerks erstellen, die die Liste der StÃ¤be nach StÃ¤ben durchsucht, die mit einem bestimmten Knoten verbunden sind. Das wÃ¤re vermutlich das einfachste und unproblematischste. Ich mÃ¶chte allerdings einen anderen Weg zeigen. Die Frage, welche StÃ¤be an einem Knoten hÃ¤ngen werden wir bei der Berechnung noch Ã¶fter beantworten mÃ¼ssen. Wir kÃ¶nnten eine Liste in den Punkten fÃ¼hren, die angeschlossene StÃ¤be enthÃ¤lt. Jeder Stab wird bei Erstellung in die Liste der StÃ¤be seiner beiden Endpunkte eingetragen und wird ausgetragen, wenn er aus dem Fachwerk entfernt wird.\n",
+    "\n",
+    "Dazu gibt es die Methode \\_\\_del\\_\\_, die aufgerugen wird, wenn ein Objekt gelÃ¶scht wird. Man kÃ¶nnte meinen, dass das ein guter Ort ist, den Stab aus der Nachbarschaftsliste auszutragen. Er kÃ¶nnte sich selbststÃ¤ndig ein- und wieder austragen. Das stimmt allerdings nicht! Ein Objekt wird erst gelÃ¶scht, wenn es keine Variablennamen fÃ¼r dieses Objekt mehr gibt (man nennt das Referenzen). Wir geben allerdings beim Erzeugen der Objekte Referenzen zurÃ¼ck, die der Benutzer bearbeiten kann. Daher ist nicht sicher, ob ein Stab-Objekt wirklich gelÃ¶scht wird, wenn es aus den Listen des Fachwerks entfernt wird. Daher sollte das Tragwerk fÃ¼r das FÃ¼hren der Liste zustÃ¤ndig sein.\n",
+    "\n",
+    "- Die Klasse Knoten bekommt ein Attribut namens _staebe, die die staebe enthÃ¤lt, die an den Knoten angrenzen.\n",
+    "- Die Klasse Knoten bekommt die Methode _addStab und _removeStab, die ein Stabobjekt der Liste hinzufÃ¼gt bzw. eines entfernt\n",
+    "- Die Methode addStab der Klasse Fachwerk wird erweitert, sodass der neu erstellte Stab in der Nachbarschaftsliste der Endpunkte eingetragen wird\n",
+    "- Die Klasse Fachwerk bekommt eine Methode removeStab, die einen Stab aus der Nachbarschaftsliste seiner Endpunkte austrÃ¤gt und den Stab aus der Liste der StÃ¤be entfernt\n",
+    "- Die Klasse Fachwerk bekommt eine Methode removeKnoten, die zu erst alle StÃ¤be, die sich in der Nachbarschaftsliste des Knotens bedinden entfernt und anschlieÃŸend den Knoten selbst aus der Liste der Knoten entfernt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fa5263e-8e2f-4719-9662-2c15d0899f7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "class Knoten:\n",
+    "    def __init__(self, coo):\n",
+    "        self._coo=np.array(coo)# wir sollten sicherstellen, das es sich bei der intern gespeicherten Koordinate um ein np.array handelt. Potentiell sollte noch der Datentyp auf FlieÃŸkomma gesetzt werden und die GrÃ¶ÃŸe Ã¼berprÃ¼ft werden\n",
+    "        self._kraft=np.zeros(2)\n",
+    "        self._lager=[False,False]\n",
+    "        \n",
+    "       ##############################################Neu in diesem Schritt#######################################################################################################################\n",
+    "        self._staebe=[]\n",
+    "    def _addStab(self,stab):\n",
+    "        self._staebe.append(stab)\n",
+    "    def _removeStab(self,stab):\n",
+    "        self._staebe.remove(stab)\n",
+    "    ###############################################################################################################################################################################################\n",
+    "    \n",
+    "    def setKraft(self,kraft):\n",
+    "        self._kraft[:]=kraft\n",
+    "    def setLager_x(self):\n",
+    "        self._lager[0]=True\n",
+    "    def setLager_y(self):\n",
+    "        self._lager[1]=True\n",
+    "    def removeLager_x(self):\n",
+    "        self._lager[0]=False\n",
+    "    def removeLager_y(self):\n",
+    "        self._lager[1]=False\n",
+    "\n",
+    "    def getKoordinate(self):\n",
+    "        return self._coo\n",
+    "\n",
+    "class Stab:\n",
+    "    def __init__(self,k1,k2):\n",
+    "        self.k1=k1\n",
+    "        self.k2=k2\n",
+    "\n",
+    "class Fachwerk:\n",
+    "    #####################################################################Neu in diesem Schritt#########################################################################\n",
+    "    def removeStab(self,stab):\n",
+    "        if stab in self._staebe:\n",
+    "            stab.k1._removeStab(stab)\n",
+    "            stab.k2._removeStab(stab)\n",
+    "            self._staebe.remove(stab)\n",
+    "        \n",
+    "    def removeKnoten(self,knoten):\n",
+    "        if knoten in self._knoten:\n",
+    "            for stab in knoten._staebe:\n",
+    "                self.removeStab(stab)\n",
+    "            self._knoten.remove(knoten)\n",
+    "    ####################################################################################################################################################################\n",
+    "    def __init__(self):\n",
+    "        self._knoten=[]\n",
+    "        self._staebe=[]# Wir erstellen erst einmal leere Listen fÃ¼r die StÃ¤be und Knoten\n",
+    "    def addKnoten(self, coo):\n",
+    "        neuer_knoten=Knoten(coo)\n",
+    "        self._knoten.append(neuer_knoten)\n",
+    "        return neuer_knoten\n",
+    "    def addStab(self,k1,k2):\n",
+    "        if not k1 in self._knoten:\n",
+    "            return None\n",
+    "        if not k2 in self._knoten:\n",
+    "            return None\n",
+    "        neuer_stab=Stab(k1,k2)\n",
+    "        #####################################################################Neu in diesem Schritt#########################################################################\n",
+    "        k1._addStab(neuer_stab)\n",
+    "        k2._addStab(neuer_stab)\n",
+    "        ##############################################################################################################################################################\n",
+    "        self._staebe.append(neuer_stab)\n",
+    "        return neuer_stab\n",
+    "    def plot(self):\n",
+    "        fig= plt.figure(figsize=(5, 5))\n",
+    "        ax = fig.subplots()\n",
+    "        #zunaechst alle Knoten darstellen. Matplotlib erwartet dafÃ¼r eine Darstellung in der Form [x_0,x_1,...],[y_0,y_1,..]\n",
+    "        x_werte=[]\n",
+    "        y_werte=[]\n",
+    "        for knoten in self._knoten:\n",
+    "            koordinate=knoten.getKoordinate()#die Koordinate holen\n",
+    "            x_werte.append(koordinate[0])\n",
+    "            y_werte.append(koordinate[1])\n",
+    "        ax.scatter(x_werte,y_werte)#Zeichnet alle Knoten als blaue Kreise\n",
+    "        #als nÃ¤chstes alle StÃ¤be\n",
+    "        for stab in self._staebe:\n",
+    "            koordinate1=stab.k1.getKoordinate()\n",
+    "            koordinate2=stab.k2.getKoordinate()\n",
+    "            ax.plot([koordinate1[0],koordinate2[0]],[koordinate1[1],koordinate2[1]],\"r\")#Zeichent einen Stab in rot. Durch die andere vorgesehene Nutzung von Matploltib mÃ¼ssen wir uns die x und y Koordinaten der Punkte etwas umstÃ¤ndlich umsortieren"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ced278dd-1b13-47af-8c56-80cb6f333b57",
+   "metadata": {},
+   "source": [
+    "Probieren wir es aus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e2dba3f-36f1-4044-bbd1-e8b7306c0779",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t=Fachwerk()# Ein leeres Tragwerk erstellen\n",
+    "a=t.addKnoten([0.,0.])#Punkte hinzufÃ¼gen\n",
+    "b=t.addKnoten([10.,0.])\n",
+    "c=t.addKnoten([5.,5.])\n",
+    "unten=t.addStab(a,b)#StÃ¤be hinzufÃ¼gen\n",
+    "links=t.addStab(a,c)\n",
+    "rechts=t.addStab(b,c)\n",
+    "a.setKraft([-100.,0.])\n",
+    "b.setLager_x()\n",
+    "b.setLager_y()\n",
+    "c.setLager_x()\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb3d7fed-312e-42b0-9f44-fe02b0f3082b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.removeStab(links)\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a57552-1a97-48d6-8537-7d5d6bf5a9a3",
+   "metadata": {},
+   "source": [
+    "Das sieht gut aus. Jetzt probieren wir es mit einem Knoten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f29f40f2-c492-4c36-b060-9bb5f0776371",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t=Fachwerk()# Ein leeres Tragwerk erstellen\n",
+    "a=t.addKnoten([0.,0.])#Punkte hinzufÃ¼gen\n",
+    "b=t.addKnoten([10.,0.])\n",
+    "c=t.addKnoten([5.,5.])\n",
+    "unten=t.addStab(a,b)#StÃ¤be hinzufÃ¼gen\n",
+    "links=t.addStab(a,c)\n",
+    "rechts=t.addStab(b,c)\n",
+    "a.setKraft([-100.,0.])\n",
+    "b.setLager_x()\n",
+    "b.setLager_y()\n",
+    "c.setLager_x()\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7105c2e7-f430-44ff-ac20-628d24482e4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "t.removeKnoten(c)\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "017d5c24-3f1f-42b6-8aeb-5710e072d37a",
+   "metadata": {},
+   "source": [
+    "Das ist schlecht gelaufen. Ein Stab ist Ã¼brig geblieben. Was ist passiert?\n",
+    "\n",
+    "Das Problem liegt in der Schleife der Funktion *removeKnoten*. Die Liste, Ã¼ber die wir iterieren, ist die Liste der NachbarstÃ¤be des Punktes. Sobald ein Stab entfernt wird, wird dadurch die Liste verÃ¤ndert. Ein Element wird entnommen. Die for-Schleife bekommt davon allerdings nichts mit. Ãœber Listen zu iterieren, die sich in der Schleife verÃ¤ndern ist mÃ¶glich, man sollte aber sehr genau wissen was passiert. Die einfachste LÃ¶sung wÃ¤re, Ã¼ber eine Kopie der Liste zu iterieren. Das geht. Wird eine Liste kopiert, enthÃ¤lt die Kopie der Liste die gleichen Elemente wie die Originalliste, also die gleichen Objekte."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2edc249a-f719-4d4b-8fa2-5ac5fddfd887",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "class Knoten:\n",
+    "    def __init__(self, coo):\n",
+    "        self._coo=np.array(coo)# wir sollten sicherstellen, das es sich bei der intern gespeicherten Koordinate um ein np.array handelt. Potentiell sollte noch der Datentyp auf FlieÃŸkomma gesetzt werden und die GrÃ¶ÃŸe Ã¼berprÃ¼ft werden\n",
+    "        self._kraft=np.zeros(2)\n",
+    "        self._lager=[False,False]\n",
+    "        \n",
+    "        self._staebe=[]\n",
+    "    def _addStab(self,stab):\n",
+    "        self._staebe.append(stab)\n",
+    "    def _removeStab(self,stab):\n",
+    "        self._staebe.remove(stab)\n",
+    "    \n",
+    "    def setKraft(self,kraft):\n",
+    "        self._kraft[:]=kraft\n",
+    "    def setLager_x(self):\n",
+    "        self._lager[0]=True\n",
+    "    def setLager_y(self):\n",
+    "        self._lager[1]=True\n",
+    "    def removeLager_x(self):\n",
+    "        self._lager[0]=False\n",
+    "    def removeLager_y(self):\n",
+    "        self._lager[1]=False\n",
+    "\n",
+    "    def getKoordinate(self):\n",
+    "        return self._coo\n",
+    "\n",
+    "class Stab:\n",
+    "    def __init__(self,k1,k2):\n",
+    "        self.k1=k1\n",
+    "        self.k2=k2\n",
+    "\n",
+    "class Fachwerk:\n",
+    "    \n",
+    "    def removeStab(self,stab):\n",
+    "        if stab in self._staebe:\n",
+    "            stab.k1._removeStab(stab)\n",
+    "            stab.k2._removeStab(stab)\n",
+    "            self._staebe.remove(stab)\n",
+    "        \n",
+    "    def removeKnoten(self,knoten):\n",
+    "        if knoten in self._knoten:\n",
+    "            ############################################Die Korrektur####################################################################################\n",
+    "            for stab in knoten._staebe[:]:\n",
+    "                self.removeStab(stab)\n",
+    "            self._knoten.remove(knoten)\n",
+    "             ############################################Die Korrektur####################################################################################\n",
+    "    def __init__(self):\n",
+    "        self._knoten=[]\n",
+    "        self._staebe=[]# Wir erstellen erst einmal leere Listen fÃ¼r die StÃ¤be und Knoten\n",
+    "    def addKnoten(self, coo):\n",
+    "        neuer_knoten=Knoten(coo)\n",
+    "        self._knoten.append(neuer_knoten)\n",
+    "        return neuer_knoten\n",
+    "    def addStab(self,k1,k2):\n",
+    "        if not k1 in self._knoten:\n",
+    "            return None\n",
+    "        if not k2 in self._knoten:\n",
+    "            return None\n",
+    "        neuer_stab=Stab(k1,k2)\n",
+    "        k1._addStab(neuer_stab)\n",
+    "        k2._addStab(neuer_stab)\n",
+    "        self._staebe.append(neuer_stab)\n",
+    "        return neuer_stab\n",
+    "    def plot(self):\n",
+    "        fig= plt.figure(figsize=(5, 5))\n",
+    "        ax = fig.subplots()\n",
+    "        #zunaechst alle Knoten darstellen. Matplotlib erwartet dafÃ¼r eine Darstellung in der Form [x_0,x_1,...],[y_0,y_1,..]\n",
+    "        x_werte=[]\n",
+    "        y_werte=[]\n",
+    "        for knoten in self._knoten:\n",
+    "            koordinate=knoten.getKoordinate()#die Koordinate holen\n",
+    "            x_werte.append(koordinate[0])\n",
+    "            y_werte.append(koordinate[1])\n",
+    "        ax.scatter(x_werte,y_werte)#Zeichnet alle Knoten als blaue Kreise\n",
+    "        #als nÃ¤chstes alle StÃ¤be\n",
+    "        for stab in self._staebe:\n",
+    "            koordinate1=stab.k1.getKoordinate()\n",
+    "            koordinate2=stab.k2.getKoordinate()\n",
+    "            ax.plot([koordinate1[0],koordinate2[0]],[koordinate1[1],koordinate2[1]],\"r\")#Zeichent einen Stab in rot. Durch die andere vorgesehene Nutzung von Matploltib mÃ¼ssen wir uns die x und y Koordinaten der Punkte etwas umstÃ¤ndlich umsortieren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "633b5f2e-a32f-406b-bab3-e620c1e1aa25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t=Fachwerk()# Ein leeres Tragwerk erstellen\n",
+    "a=t.addKnoten([0.,0.])#Punkte hinzufÃ¼gen\n",
+    "b=t.addKnoten([10.,0.])\n",
+    "c=t.addKnoten([5.,5.])\n",
+    "unten=t.addStab(a,b)#StÃ¤be hinzufÃ¼gen\n",
+    "links=t.addStab(a,c)\n",
+    "rechts=t.addStab(b,c)\n",
+    "a.setKraft([-100.,0.])\n",
+    "b.setLager_x()\n",
+    "b.setLager_y()\n",
+    "c.setLager_x()\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe60fa93-f323-4e8c-b8a9-5acc3c83fee6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.removeKnoten(c)\n",
+    "print (\"Anzahl der StÃ¤be\",len(t._staebe))\n",
+    "print (\"Anzahl der Knoten\",len(t._knoten))\n",
+    "t.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad480a8c-408b-45f6-a475-2454fd90caed",
+   "metadata": {},
+   "source": [
+    "So funktioniert es!\n",
+    "\n",
+    "## Aufgabe zum zu Hause probieren\n",
+    "So weit ist uns das Bearbeiten und Darstellen eigentlich gut gelungen. Allerdings werden uns KrÃ¤fte und Lagerbedingungen nicht angezeigt. Das ist schade. Versuche die plot-Methode der Klasse Tragwerg so zu erweitern, dass auch diese Eigenschaften angezeigt werden.\n",
+    "KrÃ¤fte kÃ¶nnen als Pfeil in die entsprechende Richtung dargestellt werden. Auch Randbedingungen kannst du als Pfeile darstellen (vielleicht in einer anderen Farbe). Dazu kannst du die *annotate* Funktion aus dem Grundlagen-Notebook verwenden oder, wenn du eine schÃ¶nere LÃ¶sung haben mÃ¶chtest, dir diesen Link anschauen [https://matplotlib.org/stable/gallery/shapes_and_collections/arrow_guide.html#sphx-glr-gallery-shapes-and-collections-arrow-guide-py]\n",
+    "\n",
+    "AuÃŸerdem gibt es noch einige Details, die etwas Arbeit benÃ¶tigen. Versuche folgende Eigenschaften in das Programm einzubauen:\n",
+    "\n",
+    "- Neue Knoten werden nur erstellt, wenn an der angegebenen Koordinate noch kein Knoten existiert. Wenn ein neuer Knoten nÃ¤her als 10E-3 Einheiten an einem bestehenden Knoten liegt, werden diese als Ã¼bereinanderliegend angesehen\n",
+    "- Zwischen zwei Knoten kann nur ein Stab existieren! Wenn ich einen Stab zwischen zwei Knoten erzeuge, zwischen denen schon ein Stab existiert erwarte ich, dass die addStab Methode None ZurÃ¼ckgiebt\n",
+    "\n",
+    "Ein Vorschlag zu Punkt zwei:\n",
+    "Am einfachsten wÃ¤re es, wenn wir den *in* Operator der Listen verwenden kÃ¶nnten. Du kÃ¶nntest einen neuen Stab erzeugen und Ã¼berprÃ¼fen, ob er bereits in der Liste der StÃ¤be vorhanden ist. Dazu wird intern der == Operator der Klasse Stab verwendet. Diesen mÃ¼sstest du selbst implementieren und so anpassen, dass StÃ¤be gleich sind, wenn sie die gleichen Endpunkte besitzen. Allerdings unabhÃ¤ngig davon, in welcher Reihenfolge die Endpunkte angegeben sind. Der *in* Operator der Listen funktioniert fÃ¼r die Punkte bereits, da jede Klasse standardmÃ¤ÃŸig einen == Operator implementiert hat. Dieser Ã¼berprÃ¼ft, ob auf beiden Seiten das identische Objekt steht (ggf. unter anderem Namen). Dieser wird Ã¼berschrieben, wenn du einen eigenen Operator implementierst"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73f537ff-2565-427f-8e1f-37ca8dcebfc3",
+   "metadata": {},
+   "source": [
+    "## Wo ist das angesprochene Architektur-Problem der Anwendung (nicht prÃ¼fungsrelevant)?\n",
+    "\n",
+    "Wenn du der Ãœbung aufmerksam gefolgt bist, ist dir vielleicht ein Problem aufgefallen. Python lÃ¶scht ein Objekt erst, wenn es keien Referenzen auf dieses Objekt gibt. Das klingt einleuchtend. Was passiert aber im Fall der StÃ¤be und Knoten. Ein Stab besitzt Referenzen auf seine Endknoten, die Endknoten eine Referenz auf den Stab selbst. So eine zyklische Referenz wÃ¼rde bedeuten, dass die Objekte sich gegenseitig am Leben erhalten, auch wenn sie nicht mehr gebraucht werden. Das wÃ¼rde bedeuten, wenn wir das Programm lange genug Objekte anlegen lassen die wir dann nicht mehr benÃ¶tigen, wÃ¼rde irgendwann der Arbeitsspeicher des PCs aufgebraucht sein. Kann das passieren, bzw. mÃ¼ssen wir beim Python-Programmieren auf zyklische Referenzen besonders achten?\n",
+    "\n",
+    "Die Antwort ist Jein.\n",
+    "Python kann zyklische Referenzen entdecken, auf die von auÃŸerhalb des Zyklus keine Referenzen mehr existieren. Also toter Arbeitsspeicher. Python kann diesen Zyklus automatisch brechen und die Objekte lÃ¶schen. Allerdings gibt es eine Besonderheit. Wenn Objekte, die innerhalb der zyklischen Referenz liegen ein \\_\\_del\\_\\_ Methode besitzen wird diese evtl. nicht ausgefÃ¼hrt, obwohl das Objekt gelÃ¶scht wird. Das in der Ãœbung Ã¼ber Objektorientierte Programmierung gezeigte Beispiel mit dem ZÃ¤hler, wie viele Objekte gerade existieren, ist also nicht zwangsweise zuverlÃ¤ssig. Bei Python vor der Version 3.4. konnten die Objekte tatsÃ¤chlich nicht gelÃ¶scht werden!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4acb5039-db1e-436f-9329-85cb18bfcb9e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
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