{ "metadata": { "name": "", "signature": "sha256:f5b6f3b05751bbee69183c1d6b71c0c4f2916c0b8b58512cbdec48c935932a2f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Scale-Up of a Batch Reactor\n", "# (Escalado de un reactor discont\u00ednuo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Autor: Carlos Planelles ([CAChemE.org](http://CAChemE.org)) - Licencia: [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n", "\n", "#### Fuente original: [Rachael L. Baumann and John L. Falconer](http://demonstrations.wolfram.com/ScaleUpOfABatchReactor/)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esta simulaci\u00f3n muestra por qu\u00e9 un reactor discont\u00ednuo con una camisa de refrigeraci\u00f3n tiene un l\u00edmite en su escalado (aumento el volumen de reacci\u00f3n). Conforme incrementamos el di\u00e1metro y la altura del reactor, el \u00e1rea de la superficie de transferencia de calor no aumenta lo suficiente con respecto al volumen de reacci\u00f3n y, por lo tanto, el calor generado supera en gran medida al eliminado. Esto hace aumentar la temperatura del reactor llegando a alcanzar incluso la temperatura adib\u00e1tica del mismo ---como si el reactor se comportara de forma adiab\u00e1tico sin eliminaci\u00f3n de calor---. En la animaci\u00f3n siguiente se puede observar este efecto y el motivo por el que escalado de este tipo de reactores es limitado." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Este ejemplo es para una reacci\u00f3n de primer orden exot\u00e9rmica, en fase l\u00edquida. El di\u00e1metro y la altura del reactor son iguales (proporci\u00f3n H/D = 1). Una camisa de refrigeraci\u00f3n alrededor del exterior de las transferencias del reactor el calor de la reactor. Al aumentar el tama\u00f1o del reactor, la temperatura m\u00e1xima se aproxima al l\u00edmite adiab\u00e1tico. Comportamiento del reactor adiab\u00e1tico se muestra al seleccionar la casilla de verificaci\u00f3n adiab\u00e1tico. Esto es independiente del tama\u00f1o del reactor." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Escribimos las ecuaciones de balance de materia y de energ\u00eda" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "B.M.:\n", "$$ \\frac{dN_A}{dt} = r_AV $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "B.E.:\n", "$$ \\frac{dT}{dt} = \\frac{DH r_A V + U A (T_a - T)}{N_{A0} C_p} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* $ A $ = \u00e1rea de transferencia de calor, en $\\mathrm{cm^2}$.\n", "* $ h $ = altura (y di\u00e1metro del reactor) del reactor, en $\\mathrm{cm}$.\n", "* $ V $ = volumen del ractor, en $\\mathrm{cm^3}$.\n", "* $ r_A $ = velocidad de reacci\u00f3n, en $\\mathrm{ \\frac{mol}{cm\u00b3min} }$.\n", "* $ k_0 $ = factor pre-exponencial, en $\\mathrm{ \\frac{1}{min} }$.\n", "* $ E_A $ = energ\u00eda de activaci\u00f3n, en $\\mathrm{ \\frac{J}{mol} }$.\n", "* $ R $ = constante ideal de los gases, en $\\mathrm{ \\frac{J}{mol K} }$.\n", "* $ T $ = temperatura de la reacci\u00f3n, en $\\mathrm{K}$.\n", "* $ C_A $ = concentraci\u00f3n de A, en $\\mathrm{ \\frac{mol}{cm\u00b3} }$.\n", "* $ C_A0 $ = concentraci\u00f3n inicial de A, en $\\mathrm{ \\frac{mol}{cm\u00b3} }$.\n", "* $ N_A $ = moles de A, en $\\mathrm{mol}$.\n", "* $ N_A0 $ = moles iniciales de A, en $\\mathrm{mol}$.\n", "* $ C_p $ = capacidad calor\u00edfica de A, en $\\mathrm{ \\frac{J}{mol K} }$.\n", "* $ DH_A $ = entalp\u00eda de reacci\u00f3n, en $\\mathrm{ \\frac{J}{mol} }$.\n", "* $ U $ = coeficiente de transmisi\u00f3n t\u00e9rmica, en $\\mathrm{ \\frac{J}{cm K min} }$.\n", "* $ T_a $ = temperatura de enfriamiento del l\u00edquido, en $\\mathrm{K}$." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Importamos las librer\u00edas que vamos a usar:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline \n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "from IPython.html.widgets import interact\n", "from scipy.integrate import odeint\n", "\n", "plt.style.use('bmh')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Escribimos la clase reactor:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Reactor(object):\n", " \n", " def __init__(self, h, Ta, adiabatico = False):\n", " \n", " #condiciones iniciales y constantes\n", " self.h = h # [m]\n", " \n", " self.T0 = 323. # [K] T\u00aa inicial\n", " self.k0 = 3e3 # [min^-1]\n", " self.E = 30000. # [J/mol]\n", " self.R = 8.314 # [J\u00b7mol^-1\u00b7K^-1]\n", " self.Ca0 = 0.015 # [mol] inicial\n", " self.DH = -25000. # [J\u00b7mol^-1]\n", " \n", " if adiabatico == True:\n", " self.U = 0 # [J\u00b7(cm\u00b7K\u00b7min)^-1]\n", " else:\n", " self.U = 2.4 # [J\u00b7(cm\u00b7K\u00b7min)^-1]\n", " \n", " self.Ta = Ta # Cooling temperature [K]\n", " self.Cp = 125 # [J\u00b7mol^-1\u00b7K^-1]\n", " \n", " # La altura del reactor = al diametro interno\n", " # Volumen y \u00e1rea de reacci\u00f3n se pueden simplificar:\n", " \n", " def area(self):\n", " return (np.pi/4.*self.h**2) + (np.pi*self.h**2)\n", " \n", " def volumen(self):\n", " return np.pi/4.*self.h**3\n", " \n", " # Cin\u00e9tica\n", " def reac_veloc(self, T, na):\n", " \n", " # Arrhenius\n", " self.k = self.k0*np.exp(-1.*self.E/(self.R*T))\n", " \n", " # Orden 1\n", " self.r_a = -1.*self.k*self.concent(na)\n", " \n", " return self.r_a\n", " \n", " # Concentraci\u00f3n moles/volumen\n", " def concent(self, na):\n", " V = self.volumen()\n", " return na/V\n", " \n", " # Balance de energ\u00eda\n", " def dTdt(self, T, na):\n", " a = self.DH*self.reac_veloc(T,na)*self.volumen()\n", " b = self.U*self.area()*(self.Ta-T)\n", " c = self.Ca0*self.volumen()*self.Cp\n", " return (a+b)/c\n", " \n", " # Balance de mater\u00eda\n", " def dNadt(self, T, na):\n", " return self.reac_veloc(T,na)*self.volumen()\n", " \n", " # Sistema de ecuaciones diferenciales\n", " def ode(self, y, t):\n", " \n", " # Condiciones iniciales\n", " T = y[0]\n", " na = y[1]\n", " \n", " # Ecuaciones diferenciales\n", " dTdt = self.dTdt(T,na)\n", " dnadt = self.dNadt(T,na)\n", " \n", " return [dTdt,dnadt]\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Escribimos la funci\u00f3n sobre la que interactuaremos:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def batch_reactor_plot(height, T_cooling, adiabatic):\n", " \n", " # Llamamos a la clase\n", " reac = Reactor(height,T_cooling, adiabatic)\n", " \n", " # Vector con espacio temporal a simular\n", " t = np.linspace(0,60,200)\n", " \n", " # Condiciones inciales\n", " y0 = [reac.T0, reac.Ca0*reac.volumen()]\n", " \n", " # Integraci\u00f3n\n", " y_t = odeint(reac.ode, y0, t)\n", " \n", " \n", " # Representaci\u00f3n de resultados \n", " plt.plot(t,y_t[:,0]) \n", " plt.ylabel('Temperature / K')\n", " plt.xlabel('time / min')\n", " \n", " plt.title('Scale-up of a Bacth Reactor: \\n'+\n", " 'Reactor inner temperature during time')\n", " \n", " plt.ylim([300, 600])\n", "\n", " \n", " area_reactor = reac.area()\n", " volume_reactor = reac.volumen()\n", " \n", " area_to_volume = area_reactor/volume_reactor\n", " \n", " #Use 8 characters, give f decimal places\n", " a_reac_string = \"%8.0f\" % area_reactor\n", " volume_reactor_string = \"%8.0f\" % volume_reactor\n", " area2volume_string = \"%8.5f\" % area_to_volume\n", " \n", " # Adding these annotations to the graph\n", " \n", " plt.text(65, 323, #position (time, temperature)\n", " \n", " 'Area to volume:' + area2volume_string + \\\n", " ' $ \\mathrm{cm^{-1}} $ \\n'+ \\\n", " \n", " 'Surface area:' + a_reac_string + \\\n", " ' $ \\mathrm{cm^2} $ \\n'+ \\\n", " \n", " 'Reacting volume:' + volume_reactor_string + \\\n", " ' $ \\mathrm{cm^3} $ \\n \\n'+ \\\n", " \n", " '- CAChemE.org')\n", " \n", " #print(reac.h)\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Finalmente representamos los valores obtenidos mediante interact:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "interact(batch_reactor_plot,\n", " height = (10, 200, 0.5),\n", " T_cooling = (300, 350, 0.5),\n", " adiabatic = False)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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VI8/pcc65mvOcHleqanXLerwU9Wgd1cU555xzLm/qe3DCOuU5PcXN4yteSY4N\nkh+fc6WqqBs9zjnnnHO5KtjghHWhoeb0rFpTwaIVa8CgXcvGxNv0nXOuQfCcHleqcsrpibem/xg4\nAdjczAZI2hvoZGae05Pms6+X8bvnJzHvm9UA7NRlU87aoytdWzcvcM2cc8650pbr5a3fA6cBdwDd\n47LpwEX5qFSuGlpOz9QFy7nkudDg2bRZGc0aN+K96Yv56ePjefOLhTUuL+l5BR5f8UpybJD8+Jwr\nVbk2ek4BjjCzh4GKuGwysFVealWEzIw/vDCZhctXs3PXTXnke9tx//H92a9XW1atMa4Y+TmvTp5f\n6Go655xzJSunnB5JM4BeZvaNpPlm1lbSpsA4M+uW91pWoiHl9Hw0awnnP/0ZbVs05t7jt6V549Ce\nNDPueHsGj42dQ5NG4rrDe7Ntx00KXFvnXCnznB5XqnLt6XkWuF5Sc1ib43Ml8FS+KlZsnpkwF4CD\n+7Rf2+ABkMTpgztzZP8OrKowfj9iMnOWrCxUNZ1zzrmSlWuj53ygE2H6ic2AJUAPPKcHgMUrVvPq\n5+HS1aHbtN/gfUmcsVtXBnXelAXLV3Pp8M/5ZtWaastNel6Bx1e8khwbJD8+50pVtY0eSWXAd4ET\nCZOP7k641HW0mS3Kc/2KwkuT5rNyjTGo86ZssVmzrOuUNRK/HdqDLps14/N533Ddy19QUcTDBTjn\nnHPFJpcJR9cAN5jZcjObbWZvm9nMeqhbtQYOHFjoKgDw3rTFAAzt3bbK9TZt1pjfH7QVrZqW8foX\nC/nHmNlVrj9kyJA6q2ND5PEVryTHBsmPz7lSlevlrSclHZnXmhSpCjM+mr0EgAFbVJ+g3L1Ncy7e\nb0sE3PveTN6f7p1lzjnnXH3ItdHTAnhM0suSHpB0f3zcl8/KVach5PR8MX85i1esoUPLJnTapGlO\n2wzu1pqTBnWiwuCal76oNLE56XkFHl/xSnJskPz4nCtVuc6y/lF8ZCr5pJSxs9b18tRkuomTBnXi\nkzlLeW/6Yq56YTLDjtiaJmU+FZpzzjmXLz73Vi1d/eJkXv58AWfv0ZVv9d+8RtsuXL6aM58Yz1dL\nV3FU/w6ctUfBhjxyzpUQH6fHlapc597av7L3zOzFuqtOcTEzxs5aCuSWz5OpdfPG/G5oT3759Gf8\nZ9zX9C1vxdDe7eq6ms4555wj95yeu4C/pz2eBJ4H7sxTvXJS6JyeWYtXMnfZKjZtVkb3Nhs3oWjf\n8lacsXsPqWaZAAAgAElEQVRXAP4y6ksmz/tm7XtJzyvw+IpXkmOD5MfnXKnKqdFjZj3MrGfqAbQG\nrgJuzmvtGrjP5i4DoO/mrWhUg3yeTIf3bc8BvduyYnUFV74wmaUrqx+40DnnnHM1s1GZs3HsnquB\nX9dtdWqm0OP0TJ63HICt2reoVTmSOGdId3q2bc60hSsY9upUzCzxY4V4fMUrybFB8uNzrlTV5nah\nA4GS7pL4PF6K2qrdxl3aSte8cSMuPaAnLZs0YtSUBfxr7Jxal+mcc865dXJq9Ej6MuMxF/gncHF+\nq1e1Quf0pPJverarXU9PSpfWzblgny0BuPOdGdz7nxF1Um5DlfS8iSTHl+TYIPnxOVeqch2n5wcZ\nr5cCn5rZwjquT9FYunINsxavpEkj0bV17Xt6Uvbs0Ybjti/n0Q/n8ODomRxx4Crat2xSZ+U755xz\npSrXy1s7m9nLaY93zGyhpPPzWrtqFDKnZ8r80MvTvW1zGjeq2+EuTtm5MztssQl0HcAfXpzM6ori\nHUupKknPm0hyfEmODZIfn3OlKtdGz2WVLP9dXVWk2KxNYq6jS1vpyhqJS/brQbuWjflo1lLuemdG\nne/DOeecKzVVNnok7S9pKFAWn6c/TgcKOltmIXN6Pq/jfJ5MbVs24YhNZlEmeGzsHF6dPD8v+ymk\npOdNJDm+JMcGyY/PuVJVXU/PXYQBCJux/uCEdwKnAmfXZGeSyiSNlvRUfH25pGlx2WhJh6ate7Gk\nzySNl3RQTfZTHybX4Z1blenRrgU/2bULAMNenbrewIXOOeecq5kqE5nNrAeApPvNLDOZeWOcC4wD\nNk3tArjezK5PX0lSf+B4oD/QBRgpqY+ZVaSvV6icHjNjyvxweatH2/z09EDIKzAzxn+1jJcmzefS\n4Z/z16P60LZFMhKbk543keT4khwbJD8+50pVriMy17rBI6krcBihlyiV+au05+mOAh42s1VmNgWY\nCAyubR3qyrxlq1m6cg2bNiujbYtcb4DbOJI4f6/u9N28JbOXrOT3IyazcnVF9Rs655xzbj25jtPT\nWtINkt6X9EXaeD1Ta7CvG4ALgPRvbAPOljRG0t8ltYnLOwPT0tabRujxWU+hcnqmLgi9PN1aN0e1\nmH6iOqm8gmaNG3H5gVuxeasmjJuzlBtGhRGbi13S8yaSHF+SY4Pkx+dcqcq1m+JmoBtwBXA/Ydye\nC4B/5bKxpCOAOWY2WtK+aW/dGssEuBIYBpxWSTEbfMu/8sorvPvuu3Tv3h2A1q1bM2DAgLVd06k/\nXHX9el67bQCo+HIso0bNydv+xo4du/Z1u5ZNOKr1LG76eDovsD3d2zSn25KJeYmvvl6nx9cQ6uPx\n+eukvh41ahQPPfQQAN27d6e8vJyhQ4fiXKlRLj0Gkr4C+pnZ15IWmllrSV2Ap8xsxxy2v5rQUFoN\nNAc2A/5lZienrdMjljdA0kUAZnZtfO854DIzeyu93BdeeMF23LHa3de5m974kifHfc1PBnfmmO07\n1uu+3/hiAb8fMRkDLh3akyE921S7jXPOpXv//fcZOnRo/rqpnWugch2nR0Bq9OXF8TLUTGDrXDY2\ns0vMrFucof0E4EUzO1nSFmmrfRsYG58/CZwgqamknnE/b+dY17xLXd7q3jZ/d25VZo8t23DaLp0B\n+OPLU/hkztJ6r4NzzjlXjHJt9HwI7B2fjyJc7roNmLAR+xTrLlVdJ+lDSWOAfYBfAJjZOOBRwp1e\nzwJnWpYuqYLn9LTJb6OnsryCY7cv5+A+7Vixxrh0+OdMW7g8r/XIl6TnTSQ5viTHBsmPz7lSlWuj\n58fAlPj8XGA50Bo4ubINKhOnsTgyPv+BmW1vZjuY2dFmNjttvavNrLeZ9TWz52u6n3xZunIN85at\npmmZKG/VtCB1kMS5Q7qzS9fNWLh8NZc8N4l5y1YVpC7OOedcsai20SOpDPgRMB3AzGab2Wlmdnzs\nkSmYQozTk+rl6dq6OWV1POdWpqrGCmncSPx2aA+22bwlsxav5LfPT2LZyjV5rU9dS/pYKEmOL8mx\nQfLjc65UVdvoMbM1wJnAyvxXp+Fbm8/TplmBawItmpRxxUFb0Xmzpkyc+w1XvjCZVWt8DB/nnHMu\nm1wvb90HnJHPimyMQuT0fLm20ZP/JOZc8gratmjC1Yf0pnXzxrw3fTE3vDaViiIZwyfpeRNJji/J\nsUHy43OuVOXa6NkV+EscmHCUpNfi49V8Vq4hmlqPjZ5cdd6sGX84uBfNGzdi5MT53Prm9EQMXuic\nc87VpVzH6flRJW+Zmd1bpzWqgUKM0/OjR8cxY9EKbv9O37zNsL6x3pu2iEuHf86qCuOEHTpyary1\n3Tnn0vk4Pa5U5TQis5ndk+d6FIWVqyuYtXgFjQRdWhc+pyfTTl034zdDe3DFyMk8MmY2LZo04sSB\nnQpdLeecc65ByHXurUaSfiLpRUlj47K9JR2X3+pVrb5zeqYvWkGFQadNm9G0LNcrgxtvY/IK9tiy\nDRfuuyUC7n53Jk98NKfuK1ZHkp43keT4khwbJD8+50pVrt/cvyfMiXUH0D0umw5clI9KNVQN6c6t\nquzXqx3nDukGwK3/m85zE+YWuEbOOedc4eXa6DkFOMLMHmbdLOmTga3yUqsc1fc4PfV55xbUbqyQ\nw/p24Ge7hYnpb3htKsM/bXgNn6SPhZLk+JIcGyQ/PudKVa6NnkbAkoxlrYDFdVudhq0h3rlVle9s\nV84pO2+BAcNeneo9Ps4550paro2eZ4HrJTWHkOMDXAk8la+K5aK+c3qmLlgB5H/OrZS6yCs4cWAn\nTt0lNHyuf20qz4z/uvYVqyNJz5tIcnxJjg2SH59zpSrXRs/5QCdgAbAZodenByWU07OmwtZO7Fks\nPT0pJ+zQiR8PDrev/2XUlzz9ScNp+DjnnHP1JadxetauLHUkJDJ/aWaz8larHNXnOD0zF63gh4+O\no33LJjz8ve3qZZ917V9j53D7W9MBOGv3rhy17eYFrpFzrhB8nB5XqnK+71pSG+BAYD9gqKS2eatV\nA5TK5+nWwO/cqsp3B5Rz5u5dAbj5zWk8MmaWj9zsnHOuZOQ6Ts/+wBTgbGBn4BxgiqQD8le16tVn\nTs+U+aHR07Nt/Y3CnI+8gqO33Zxz9uyGgLvemckdb88oWMMn6XkTSY4vybFB8uNzrlTlNCIzcDPw\nEzN7NLVA0rHATUDffFSsoZky/xsAerQtrnyebI7o14FWTcu47uUpPDZ2DotXrOa8Id0pa+S93c45\n55Ir17m3FgDtzWxN2rImwFdm1iaP9atSfeb0nPHEeCbN/YYbj+xDv/JW9bLPfHvny0VcMfJzVqwx\n9tiyNZfs14OmjfM/0rRzrrA8p8eVqly/4e4Hfp6x7Iy4PPHWVFjRjdGTi126bca1h/Vmk6ZlvPHF\nQn7z/CSWrlxT/YbOOedcEcq10bMj8GdJ0yW9LWk6MAwYJOm1+Hg1f9XMrr5yemYsWsGqNUbHTZrS\nqmlZvewT6ievYNuOm/Dnw7emXYvGjJm5hF889SlzlqzM+34h+XkTSY4vybFB8uNzrlTlmtNzR3xU\nJbG3AaWSmJOQz5PNVu1bcMORffjd858zZf5yznlyAlce1IutO7QsdNWcc865OlOjcXoamvrK6bnv\nvZk8MHoWx29fzmmDu+R9f4WyeMVqrhg5mTEzl9C8cSN+s38Pdu3eutDVcs7VMc/pcaUq154eJO0N\nDCLMuQUgwMzs6nxUrCFZ29PTrv5uVy+ETZs15upDenHDa1MZOXE+l434nDN378qR/X0QQ+ecc8Uv\n13F6/g/4J7AX0C8++sZ/C6a+cnoKdbt6IfIKmpQ14oJ9tuT7gzpRYXDTG9O46Y0vWV1R9z2CSc+b\nSHJ8SY4Nkh+fc8VK0lGSOm/s9rkmMn8fGGRmx5jZD9IfG7vjYrF8dQUzFq2gkaBb62Tm9GSSxMk7\nbcEF+3SnSSPx5LivufjZiSz4ZlWhq+acK3KSjpZUIWmbPJXfWtIZ+Sg7Yz9L8r2PavZ/iKTxkj6T\ndGFN15E0RdKHkkZLejuH5RdL+ljSWEkPSWoWl28T1009Fko6J08xdwJ+SLjStFFybfR8CdTPLT01\nMHDgwLzvY/K8b6iw0MtT32PYDBkypF73l+nArdvz5yPW3dl19n8+ZdLcZXVWfqHjy7ckx5fk2CD5\n8RXYicDT8d/1KKpl+W2BM2tZRi4KlhArqYwwOPAhQH/gREn9ariOAfua2SAzG1zVckk9gNOBHc1s\nAFAGnABgZhPiuoOAnYBlwBN1HDJxX7OAMbUpI9dv8dOAOyQdK2nv9Edtdl4MPvs6fMn3al+adzL1\nK2/FTUdvwzabt2T2kpWc99RnvPr5/EJXyzlXhCRtAuxKGPft+Lish6QJku4FxgLdJH1f0lux5+A2\nSY3SynhC0ruSPpJ0epbdXAv0itv+MW5zfuyhGCvp3Cz1ukbSmWmvL5f0yxy37SFpbNrrX0m6TNKW\nsZfl7hjfg5IOkvS6pE8l7ZK2TaXxVmIwMNHMppjZKuAR4KiNWKeyBmbm8kXAKqClpMZAS2B6lu0O\nACaZ2ZfV1L9akjpLOjjtsXtty4TcGz07AYcBtwIPZjwKpj5yeibNDfk8vdvXfxJzQ8kr6NCqKcMO\n35oDtm7HitUVXPXiFP721vRa5/k0lPjyJcnxJTk2SH58BXQU8JyZTQW+kpS6/bY3cLOZbUe4WeY4\nYI/Ye1ABnJRWxqlmtjOwC3COpHYZ+7iQ8MU7yMwulLQT8CNCI2A34HRJmZcJ/hH3mXIs8Egl2+5Q\nTYzGul6gXsCfCTmw2wDHm9mewK+ASwBi70vWeCX9N17SydSFcAUmZVpcVpN1DBgZG5CnV7XczOYR\nxuabCswAFpjZyCz1OgF4KHOhpCaSrowdJ9fHZUfEBuBhki6SdEp8foOkzmY2w8yeT3u8Kak8Hsf9\nsuw7J7k2ev4AHGFmHcysW/pjY3dcLFI9Pb1LfMyapo0bccHe3fnZbl1oJHhs7Bwu+O9nfL20wV31\ndM41XCcSbooh/nsi4Uv2CzNL5Y8MJfzQflfSaGB/oGdaGedK+gB4E+gKbJ2xj8xeiiHA42b2jZkt\nBR4n3JSzlpl9AJRL2iI2auab2fRKts3lCkeqDpPN7GMLY8N8DKQaCh8BPaqL18wOj5d0MuXyi7O6\ndfaMjaxDgbMkpY7JkMzlknoB58U6dwY2kZTeEEVSU+BbrDu/6U4BppnZP4GJkmRmTwObAM8D/wKO\nNbNngHFUcozNbI6Zfc/MHqgu+Mrk2uhZCryysTtJkVQWu++eiq/bSRoRu/qGS2qTtu7FCslX4yUd\nlK28fOf0rFpTwRfxdvVeBbhdvaHlFUjiO9uVM+zwrenQsgkfz17KGU9M4L1pizaqvIYWX11LcnxJ\njg2SH18hxB6Z/YC/S5oMXEDoURHhOybdvak8ETPra2ZXxDL2JTQSdjOzgcAHQLNqdm2s3xAS2RsE\n/wSOIfS6PFKDbVez/ndp+pfFirTnFazLja1g/SFjssZbhelAeqdDN0JPTs7rmNnM+O9XhBycwfH1\njCzLdwLeMLO5Zraa0PjbI2N/hwLvxe0yDQImx3JvsXUDBM6Lc3quAmbHZSup/pxutFwbPZcCf4mt\n4Ebpjxru71xCKy4V8EXACDPrA7wQXyOpP+F6b39CEtYtG7GvWpu6YDmrKowumzWjZT1OP9HQbdtp\nE2759jbs2GVTFi5fzSXPTeK+92ayJg+3tTvnEuMY4D4z62FmPc2sOzAF6J6x3gvAMZI2h7U/jlPr\nbEbohVkuqS/hklOmxcCmaa9fA46W1EJSK+DouCzTPwg9T8ewrrcil21nE3qJ2inc0XQENUtyfrGK\neCvzLrB1zCdqSvi+fDLXdSS1lLRpfN4KOAgYW9lyYAKwWzwOIuTujMvY34nAw5XU90OgTyy3c2of\nlcjroJm5NiTuAn5GaDmuTnvkfA+zpK6EvKA7WRfUkcC98fm9hA8UhOu+D5vZKjObAkwktkLT5Tun\nZ2Iqn6dDYQYlbMh5BW1aNOEPB/fiBzuGy80PjJ7Fhc9MrNG8XQ05vrqQ5PiSHBskP74COYEN7+r5\nF+HH7tpGgpl9AvwWGC5pDDAcSOW1PAc0ljQOuIZwiWs9ZjYXeD0mHv/RzEYD9wBvA/8D7jCzDe4A\nMrNxhMst08xsdlxW1bYW11kFXBHXGc76jYHMxo9lPo/7zRpvZTk9sbfl54RLQ+OAf8TjtnabqtYB\nOgKvxcuEbwFPm1lqvxssjzHfR2hIfRjL+FuqPrGBdAChByib24Aukk4A9jazxZIOBfrFKzknEeby\nHEy4RPYtSZtVUlat5Doi81Z1sK8bCN2Z6YF0TH24CK3ljvF5Z8IHLCVbklbeTUzl85TonVvVKWsk\nfrDjFvQvb8V1r3zBh7OW8LPHx3PekG7svVXbQlfPOdeAmNn+WZb9H/B/WZY/CjyaZflKwo/n6vZ1\nUsbrGwjfQdVtt32WZVm3NbPN0p5njQPYPm2dU9KeT8l4r7J4D6+irs8Cz1a1TRXrTAY2yA8xs8+z\nLY/vXQdcV8l7S4EOVdTVgIuz1D/VoBtOyB0G+G5l5dSFnBo98QQRLzF1TF0LzJWkI4A5ZjY6XpPN\ntg+TVFWX4AbvTZw4kTPPPJPu3UNPYOvWrRkwYMDa6/GpX2sb+/qV10axaMFy+hzau07Kq+nr1LL6\n2l9tXt/+nb6cf9sTjJuzlKtWruHgaYvYYc0XNG/SKBHxbczrJMc3ZMiQBlUfj6/q16NGjeKhh8JN\nNd27d6e8vJyhQ4fiXKnJacJRSW2BmwnXOlebWUtJRwKDzey3OWx/NfADwiWx5oTenscJtxzua2az\nJG0BvGRmfSVdBGBm18btnwMuM7O30svN54SjK1ZXcPS9Y6gweOLk7T2nJwdmxtOffM3tb01n5Rqj\n82ZN+fU+PejfsVX1Gzvn6o1POOpKVa45PbcRBifaknXZ6G8SR2SsjpldEm9x7xm3edHCFBZPEoaU\nJv777/j8SeAESU0l9STckvh2Zrn5zOmZ+PUy1sSRmAvV4Cm2vAJJfKv/5tx89DZs1a4FMxat5Pyn\nP+Vvb01nxeqKDdYvtvhqKsnxJTk2SH58zpWqXBs9Q4Gz0y9rxdvSyjdyv6nupWuBAyV9Shib4NpY\n9jjC9c1xhOuRZ1ouXVJ16JM54Q7Kft5LUWNbtm3BX4/qwwk7hBStx8bO4Ywnxq89ps4551wh5Hp5\nayIh43qGpPlm1jbeUjfczPrmvZaVyOflrStGTmbUlAX8cu/uHNynfV72UQrGz1nKn175gi8Xhklb\nv7tdOSfvtAXN6nkeM+fcOn55y5WqKr95JKUmhLsTeEzS/kCjOAfGvcDtea5fwYxP9fSUe09PbfQt\nb8Wt3+7LcduHTsF/jp3DTx8fz+jpiwtcM+ecc6Wmup/bqfvwryMM3HQT0AS4G/gP8Jf8Va16+crp\nmbNkJV8vW8UmTcvo2jpvA0NWKyl5BU0bN+LHg7tww7f6sGWb5sxYtIILn53IWTc9xoJvch7qqegk\n5fxlk+TYIPnxOZdvknaWtI+kXxe6LulyusZgZhVmdqOZ9TezlnGY7L/Ud55NfRk3O/Ty9C1vSSN5\nD3Bd6Vfeilu+vQ2n7LwFTcvEe9MXc9pjn/D8p3NJ6EfJOZdG0m8UZkcfE6ck2mDQ2Wq2P0fSOEn3\n56uODYWk5gozr38QY74m4/27JM1W2gzvae+tN+VTXDZF0odx+dsZ67eR9JikT+K+so10XVM7EwY4\n7CBpkzoor05UN05PWbykVSkze7EO61Mj+Zp7a8zMcOllQKfCnqckzv/TpKwRJw7sxN492/LX1zdh\n9IzFDHt1Ks+On8uZe3SlT4Imdk3i+UtJcmyQ/PgKIaZFHA4MMrNVcS6unLrS49QHAs4AhqbmhyqE\nWBfy/aM/TrWxn5ktk9QYGCVpiJmluiHvJgyIeF+WzVNTPqVP92CEIWLmZVn/RuAZMzsm7qvWeR1m\ndpukMqCxmS2pbXl1pbqenubA36t5JM6YmeH8DOxc1fQgrja6tG7GtYf24sJ9t6RN88aMm7OUs/89\ngetfncr8BF/ycq6EdQK+jtM2YGbzzGxmnBtqbW+FpF9JukzSlpImSLqXMP/TnYTZAZ6TdG5c9wlJ\n78beo9PTyjg59iZ9ELdH0vdjz8loSbepkvkcs5UZ65hel26VlVdZnTaGmS2LT5sCZcC8tPdeA+Zn\nqX+2KZ/Wvp1l/dbAXmZ2Vyx3tZktrE290xwPXC2pSR2VV2vVNXqWxonhKn3USy0rkY+cnrlLVzFt\n4QpaNGnE1gXudUh6XsHrr7/O0N7tuPu4/hwzoJyyRuK5T+dyyqPjeGzsHFat2XBsn2KS5POX5Ngg\n+fEVyHBCY2GCpJsl7V3Jesa6YU16Azeb2XZmdiowg9BbcWN8/1Qz25kw0O05ktpK2hb4DbCfhZnY\nz5PUjzB7+h5mNogwy/l6U1Wk2aDMzLoQekIqKy/r9pXNo1UVhYm9PyBM0/RSHM6lOqkpnzL/gBow\nMjbI0htjPYGvJN0t6X1Jd0ha78tPUhNJV0o6VtL1cdkRkl6XdJikiySdEp/foDCp6A8Jw91ck6Uu\nBeP3DWdIXdraruMmNG7k+Tz1oVXTMn6yaxf+9t2+DO62GctWVfC3t6bz08fH886XiwpdPedcHYjz\nM+0E/AT4CvhH/GLMJvXH9wsz22Bg2jTnxkbBm0BXwkze+wOPpi7jmNl8wpfvTsC7kkbHdSr70Z5e\nZjfC4LiZdamqvGx1wswON7NZVcSygZhPOzCWs7cqmcYpRWlTPrFhr86esYF2KHCWpL3i8sbAjsAt\nZrYjsJQwCWy6UwgTsf4TmChJZvY0YYLW5wkTxx5rZs8QLqvtbWb3mtlpZna6ma2pSdz5VF1OT4P+\n1s9HTk/q0tYOWxQ+7yrpeQWZ8XVt3ZyrDu7F218u5Lb/TWfawhX85vlJDOq8Caft0oU+mxdXvk+S\nz1+SY4Pkx1coZlYBvAK8Ei9p/RAYyfo/wFukPa90RNPYABgK7BbzX14ipGQY2b+77jWzS6qqXxVl\nZqvLBuVVsn2tbwE2s4WS/ktIDn65ilX3AI6UdFis92aS7jOzk1ODC5vZV5KeAAYDrxEm9J5mZu/E\nMh5jw0bPIOCJuP0tacvnmdkaSasIvVEAK6mDmPOlyp4eMyv8N389S/X07NC55EJvMAZ3a83t3+nL\n6YM7s0nTMkbPWMLP/zOBK1+YzJcLlhe6es65jSCpj6St0xYNAqYQvizLJbWT1Aw4giwTTGexGTA/\nNi76ArvF7V4Ejo2J0qm5I18AjpG0eVzWTmGA3VzKzKay8nLdvlqSOkhqE5+3AA4ERle1TSVTPp0s\nqaWkTWNZrYCDCLlJxN6nLyX1icUcAHycUfSHxB6reOmqqoTXBt1ZUtSXt+o6p2fGohXMWLSSVk3L\n6N2+8L0KSc8rqCq+JmWNOHb7jtx7fH+O376cpmXitckLOP1fn3DDa1P5eunKeqzpxkny+UtybJD8\n+ApkE+AeSR9LGgP0BS43s9XAFYT5FYcTLo+kZDZ+0l8/BzSWNI6QN/ImrJ3G6A+E3qQPgGFm9gnw\nW2B43PdwQmJ1pqxlsn6eEVWUV9n2G5PTswXwYozhLeApM3shrbyHgTeAPpK+lHRKljJSde4EvJZW\n1tNmNjxtvbOBB2Ms2wNXZ5RzG9BF0gmES1eLJR0K9JN0ECGfaZDCEATfAr4labMaxFpvqru8VVJS\n+SM7d9mUMs/naRA2bdaY0wZ34ahtN+eB0bN4bsJcnp0wlxcmzuOo/ptzzPbltG3RYG4McM5Vwsze\nB/as5L3/I9x+nWn7jPW2Snu+knCXUrby7iPjVm4ze5Qwp2NVday0zCx1qay8yup0eFX7zrL+WEKu\nTWXvn1jZe/H9VwiXEjGzz4FK80HMbAwh8bqy9w24OGPZs6xrOA4nNDQBvltVvQotp7m3Gqq6nnvr\nN89N4p1pi/jV3t05yOfbapC+XLCce9+byauTFwDQrEwc1q8Dxw3oSPtW3vhxLhc+95YrVd7TEy1f\nXbE2n2eXrg2yV84B3do057dDe/Lp18t4cPQs3vxiIU989BVPf/I1h27TnuO270j5Jk0LXU3nnHMN\nkOf0RB/OXMzKNUafDi1p27Jh9BgkPa+gNvH16dCS3x+4Fbd+exuG9GjDqjXGk+O+5kePjuOG16Yy\nY9GKOqzpxkny+UtybJD8+JwrVd7TE701NeTzDO7mvTzFpFf7llx6QE+mzP+Ghz+YzcuT5vPshLk8\nN2Eue/ZozXcHlLNtR78TzznnnOf0AFBhxkkPf8zcZau46ahtim48GLfOlwuW848xs3lx0nxWV4TP\ndr/ylnx3u3L27NHGE9Sdw3N6XOnynh7gk9lLmbtsFR03acrWHVpUv4FrsLq1ac6v9tmSU3bpzJMf\nf8XT47/mkznLuOrFKXTatCnf3nZzDurTnlZNywpdVeecc/XMc3qAV6eEO4H26tkGqeH8+El6XkE+\n42vfsgmn7NKZB07Ylp/v0ZXOmzVl1uKV3Pq/6Zz40EfcOGoqk+Yuq76gWkjy+UtybJD8+JwrdpK2\nk7SvpMwxhapU1I2eulBhxmuT1zV6XLK0aFLGkf035+/H9OfSA3qyfadNWL66gv+On8sZT0zgvCc/\nZeRn81i5usHMh+dcYklaE2cl/1DS45LqLOFOUmtJZ2Qse72uyt+I+iwp1L4zSZoSj/loSW+nLW8j\n6TFJn0gaJ2lXSc0kva0wQ/04SddklHWIpPGSPpN0YRVl7VbV8jrQG/gMKK/JRiWf0zNu9lLOe+pT\nOrRqwgMnbEujBtTT4/Lji/nf8PQncxnx2VyWrQqNndbNG3PQ1u04eJv2dG/TvJoSnCtuhcrpkbTY\nzFLTIdwDjDWzYXVUdg/CqMUD6qK82kqPtdAkTQZ2Sk3Cmrb8XuAVM7tLUmOgVZznq6WZLYvLRgG/\nMtLhXJgAABzySURBVLNRksqACYSpKqYD7wAnmtknVZSVdXkdxdUX2MfMbs91m5Lv6XlxUvgM7NWz\njTd4SsSWbVtw1h5defh723HekG70at+ChctX88+xc/jxY59w7pMT+O/4r1m6ssFMDOxcEv0P6AUg\n6fuS3oo9EbdJWvvdJOkJSe9K+kjS6WnLT5Y0JvZI3EeY9qFXLOOPcZ0l8d8esafhb7Gc5yU1j+/9\nLvZcvCbpIUm/zKyopGsknZn2+vLUepLOlzQ2Ps7Nsm0PhclVU69/JekySVvG/d4taYKkByUdJOl1\nSZ9K2iVtm0qPTw2s9wUnqTWwl5ndBWBmq1ONETNLXftvCpQBqcbSYGCimU0xs1XAI8BRlZVV1T5q\nS9KvgS+B3lo3b1i1irrRU9ucnpVrKnhp0nwADtq6XV1UqU4lPa+g0PG1aFLGYX07cMvR23DjkX04\npE97WjRpxCdzlnHjqC85/sGxXPPSFN6fvoiKjegRLXR8+ZTk2CD58RVa7DE4EPhIUj/gOGAPMxsE\nVBDmcko51cx2JkyTcI6ktpK2BX4D7GdmA4FzCDODTzKzQWaWuuyS/h+3N3CTmW0HLAC+GxsW3yFM\nMXEoYRbzbP/Z/xHrmHIs8IiknYAfERoDuwGnS9qhmvDT5/HqBfyZMA/ZNsDxZrYn8CvgknisKj0+\nyn0+LwNGxsZjquHYE/gqNrrel3SHpJax3EYK83TNBl6K85kBdCE0NFKmxWU9Kimr0n2kk9RE0pWS\njpV0vaTDY+PvMEkXSTolPr9BUue42euEaTpWAF/kcAyAEr976+2pi1i8Yg1btWtOrwYwwagrDEn0\nK29Fv/JWnLF7F16fspDnP53LmJlLeGnSfF6aNJ8OLZuwz1Zt2K9XO7bu0KJBJbw7V0RaSBpN+KKc\nDNwOnAHsBLwb/1+1AGalbXOupKPj866E2b4HA4+mLteY2QLFGcmrMNnMPozP3yN8UXcA/h3n3Fop\n6SmyzBJuZh9IKpe0BSGHZL6ZTZd0DPC4mX0DIOlxYG9gTDV1Se1jspl9HLf9GBgZl38U6wcwlEqO\nTw3m89rTzGYqzAo/QtJ44BtCo+HnZvaOpL8QGo6XmlkF/H979x5dV13mf/z9yUlO7mmb3ugVam1t\ni6UtFGGYyt2OMIAOooCjKMw4js78YPkTZ9RhRgdn/dCFOPJzlJlRREBQi4CCygBys0UolzZtacul\n0Eta2jQ0be7JSU6e+WPvpKdpkia95GTv87zWysrZ9+9z0uQ8/e5nf78sDHtqHpV0tpk9Td8JIQS5\nRF/n+nV/1+h1/NXAdjO7T9I44HcEk54+SlC3c6uZXShpCsH7+3Mz667XWj7I96CnoZG1cGG/86cN\nyuNvBD12588amfNsLVmyJNtNOKZGYnzFBQnOn1XJ+bMq2dnYzuOv1/H4G3XUNKW4/5Va7n+llskV\nhZz9rtGcM3MMx4/pf4iDkRjf0RLn2CD+8WVRq5ktklRM8IF2Sbj+TjP7au+dJZ1N8KF/upm1SXoK\nKCL48B3q/zwyh2lPEyQP9DrPQOe8D7iMYJLNn4frerdDHJwYdHLgXZXMPxqZbeoCUhmvMz+f+3x/\nBsvMdobfayU9SJA0/pQg0Xgx3O2XBAlJ5nH1kn5L0AP2NEEdz7SMXaYR9PZs7+dc3z/UNUKLgAfD\na94GIKnOzNKSOgh6nCB4fwqH/AZkiPTtrSOxp6WDF6rryROcN3NMtpvjRqBJ5YVcdcok7rp8Hv9+\n8Sw+NG88Y4rzebuhnXuravjM/a/ytw9s5Kerd7G5rpUoPxTg3HAKe0auJZiZ+wngsrAXAkmVkqaH\nu1YQ9Kq0KShaPZ0gqXgS+KikyvCYMUAjMNTC4WeBixU8sVQG/Dn992b8AriSIPG5L1y3HPiwpGJJ\npcCHObjnoQaYEMZVCFw0wDX68iT9vz+HJKlEUnfxeCmwlKCAvAaozqiHOR9YL2lcd69ZmJx+AFgd\n7vMSMCusU0oClwMP9Xeu/tb30cy1BD14SJosqb+pEY64iz3SSc+R1PQ88toe0gZnHD9qxMy11Vvc\n6wqiEp8kTpxYxt+dMZV7r3wv37xgJh+cPZayZIK36tq46+WdfPaBV7n6vo3898odrK9posssMvEd\njjjHBvGPL4t6PuzNrArYBCwAbgAek7QGeIygNwXgf4B8SRsICpWfC4/dQJAwPRPWntxiZnuAZ8OC\n4m/1vh4HJxpmZi8BDxF86P4OWAf0WWgbXrOMoOeiJly3GvgJ8AJBYfYPzaz71paF+3QAN4b7PAZs\nyDxtf+9PxvEb+nt/BlnTMxFYHr5PK4HfmNlj4bb/A9wTnvckgltKk4AnM/Z/2MyeCNvSCfw9QS/d\nBuAXZrZxgHMNtD7TfwJTJF1BcPvqDGCupKUE9UuLJL0PuJggST3s+aIi/cj6LbfcYtdcc82Qj0t3\nGZ/8+XreaengWxe8m0VTRsRThQdZsWJFrLvZox5fKt3Fqh2NPLtlH89va6C+rbNn25jifCY1vM5f\nXvQBFkwuI5mI9P8vDhL1n92hxD0+n4ZiP0mlZtYcFtg+A3wmTMhcDEU66TnccXpWbN7HjU9sZuqo\nQm6/bK4Xpbojlu4y1tc088et+3h2Sz01TamebcUFeSycXM6pUytYPLWc48qP6Ja0c0fMk579JN0D\nzCOoFfqJmX3rEIe4CBuWQmYF4yE8Q1CAlAR+bWZfkfR14K+B2nDXr5rZI+ExXwGuISg4uzajO+6I\n/Wp9cLmL547zhMcdFYk8cdKkMk6aVMZnT5vCW3WtPLulnj9u3cdbdW08t7We57YGvebTRhWyeFoF\np06t4KTjykjmx6sXyLkoMbO/PPReLi6GJekJi9DOyRzhUdISgnuW3zGz72TuL2keQYHUPIJHG38v\naXb4GF2PqqoqhtrT8+ruZtbuaqKkII+ls0fmU1vd4t7FHtf4JDFzbAk7N67iqkuXUNuc4qXqBl7c\n3siqHQ1U17dTXV/Lg6/UUpgQ8yeVsWhyOQsmlzOzsjgSM8HH9WfXLe7xOZerhu2R9T5GeNwbLvf1\nF/5DwM/CArAtkjYRPGL3/JG2Y9na3QBcNHecz7TthsX40iQXzBnHBXPG0dllbNzdHCZBDWza08pL\n2xt5aXsjAGXJBPOPK2PB5DIWTCpjRmWxjxTunHNHybDV9CgYNnsVwQiUt5nZP0j6GsGgRPUEj8J9\nMRxk6nvA82Z2T3jsj4BHzOz+zHMOtaZnR30b19y3kfw8cdflJzK2dGQ+teVyR11LB6t2NLJmZyNr\ndjaxqzF1wPaKwgQnTSpj/nFlnDixjHeNLSY/Aj1BbmTzmh6Xq4azp+egER6B2wge5QP4BnAL8Ff9\nneJI23BvVQ0GnPfuSk943IhQWVLQMxgiQE1jijU7G6na2cSatxupbe5gxZZ6VmwJ6oEKE+I940s5\ncWIp8yYGo0hXFEV6jFHnnBs2w/7XMnOEx3BYa6CnN+fhcLH3qI9Tw3UHuPXWWyktLWX69GCcplGj\nRjF//vyee/HdY20sWbKE7fVtPPjokwBc+bGPH7R9JC7fdttt/cYTh2WPr+/lpUuWsHT2WJYvX05d\nSyfJE05iQ00TT/9hObXNHbSnF7J2VxMNbwZP1b73lNOZO6GErupXmDqqkI9ccC7JRN4xjS9zHJuR\n8n57fAPHc++99wIwffp0JkyYwHnnnYdzuWZYbm+Fc2l0hreuuocf/1eCERt3hft8ATjVzD4eFjLf\nS1DHM4VgPpJ3W6/GDmWcnm8+tYUn39zLBe8ZyxfeP+jBLLMq7sWUHt/Q1bd1sqGmmQ01Tazf3czr\ntS2k0gf+DufniRPGFDF7fAmzx5Uwa1wJJ4wpouAojhXkP7to89tbLlcNV9IzH7iTYAToPOBuM7tZ\n0l3AQoJbV5uBz3aPdCnpqwSPrHcC15nZo73PO9ianrf2tPK5B18lkSfu+Og8JpYnj1ZozmVVR7qL\nN/e0snF3M2+808Lr77RSva/toHvBBXniXWOLmTW2hBmVRbyrspgTKou9mD9HedLjctVwPbK+jmCm\n1d7rrxrgmP9H38NVD9kPX9iBAX8+Z5wnPC5WChJ5zJlQypwJpT3rWlJp3qxr5fXaFl5/p4U33mlh\ne307r9W28FptywHHTyxLMqOyiBljiplRWcyMyiKmjiqKxGPzzjk3VJGugBzMOD0vVjfw8o5GSpMJ\nPnHyoaYoGVni3sXu8R0bJeFj7/OPK+tZ15xKsylMgDbvbWNzXStb97VR05SipinF89saevYtSIjp\no4uYPrqIaaMKmTa6iGmjipgyqpDCcCBF/9k556Io0knPoXSku/ivlUH988cXTmSUP+XiclRpMsGC\ncADEbukuY0dDO5vrWnmrrpUtdW28VddKTVOKN/e08uae1gPOIWBieZJpo4ro2FZL/bh3mDaqkMkV\nhVSWFPh4Qs65ES/Wc28tW1PDj158m8kVhfz3pXN8uH/nBqE5lWbr3jaq69uo3tdG9b52quvbeLuh\nna5+/lwkE2JSeSHHlSeZXFHIpIpCJlckmVReyMTyZOwmXI06r+lxuSq2XR+7m1LcvXoXAH9/xlRP\neJwbpNJkgnnhOECZOtJd7GxMBYlQfZgM7WtjZ2OK+rZOtu5rY+u+toPOJ2BcaQGTK4KkaGJZkgll\nScaXJZlQmmR8aYH/fjrnhkWkk57+anrMjP//bDXtnV28f8ZoFk+tyELrjlzc6wo8vmgpSOT11Pqs\nWLGCK87aH1tzKs2uxnbebkixs6Gdtxvb2dmQYmdjO7ubUtQ2d1Db3MGanX2fe0xxfpAIlRb0JEPd\ny5UlwddwjkQdt5+dcy4Q6aSnP09s2ssL1Q2UJhN8/vSp2W6Oc7FXmkwwc2wJM8eWHLSts8uoaQwS\noF2NqTAJSrG7qYPdTSneaU6xt7WTva2dvFbb9/kFVBTlM7akgMqS7u8FB3wfW1LAmOL8ozoekXMu\nXmJX07OnuYO/eWAjje1pvnjmdP5shM+k7lyuS3cZe1s7epKg3c0papuCpKi2OUVdawf7Wjv7rSfq\nraIwwejiAkYX5TOqOJ9RRfnB6+6v4mB5dFE+FUX5Ofl4vtf0uFwVq56edJfxzae30NieZvHUcpaG\n8xk550auRJ4YV5pkXGnyoDqibukuY19bJ3UtHexp6Tjge11L5/7XrR00tKdpaE+zbRDXFlBWmDgg\nMaooyqe8MEFZYYLywnzKk8H3snBdRWE+JQV5yJ9Wcy5yIp309K7pWba2hjU7mxhdlM/1Zx4f+T9K\nca8r8Piia7hjS+Sp5xbWrAH2S3cZDW2d7GvrpL6tk32twff6cF3mcn1bJw1tnTS2p2lsT7O9vr3n\nPA1vVlExc2G/18kTlCUTlBUGCVJ5mCCVFiQoTeZRkkxQUpCgJJlHSUGC0mSCkmSC0oL9ywUJRf5v\n1NEi6X3At4EJQAvwMnCtmbWG238FTDSzP+l13PUEk1S3AR3A98zsbklbgJPNrO4ot/MnwJlAfbiq\n2czi+UseU5FOejJtqGnmzpeDKskvnXU8lSU+i7pzuSaRJ8aUFDBmkL//6S6joT1MgsKEqKE9zcv5\n1UyeO4HG9jRNqf2JUffr1o6unh6lw5WfJ0oK9idIpckEJQV5FBfkUZSfoKggj6L87uXwq9e2A7YX\nJCiMYCIlaSKwDLjczFaG6z4ClAOtkkYD7wXqJc0ws83hPn8LnEcwZ2OTpHLgw+FpjaAj72gz4Hoz\ne+BITiIp38w6j1Kb3BDEoqanqb2Tzz34GjVNKS6bP4G/OW1KtpvmnIuxzi6jsb2TpvY0Tak0je2d\nNLSlaelI05xK09LRRUsqWG5JdWWsT9McLnekj/7fXsEBCVF3opRMBK+T+XkU5udxfvmeEVPTI+lG\noMvMvt7P9muA+UANwWfWTeH6rcBZZralj2M2E8z3eDFQAHzUzF6TVAp8DzgxXP91M3tI0qcJEqYS\nYBZwC1AEfBxoBy40s72S7gB+Y2b3HyKmm4EPEiRJ/2ZmyySdDXwDqAPeA8wFvg+cA1QT9FT9+FDn\ndkcm8j096S7jW09vpaYpxaxxxVy9eFK2m+Sci7n8PDGmuIAxxYffo5xKdydG+xOk5lQXbZ1BT1Jb\nZxdt4fdgOX3AugO3B9tSaaO1I9h/IOcfep7m4XQi8JMBtl8B/AuwG/gVcJOkCqC8r4QnQ62ZnSLp\nc8D1wGeAfwKeMLNrwh6klZJ+n9GOhUAx8CbwJTM7WdJ3gKuAWwnyypsl3RAe84qZfTLzomEv1QLg\nJGA88KKkP4SbFwEnmtlWSZcBx5vZ3LC3ayNw+wDxuKMg0klPVVUVqzsnsrK6gfLCBP907oxYPa4a\n55oQ8PiiLM6xwfDEl0zkkSzOY3Tx0Ttnusto7zwwUWrvNNrTXbR3dpEKt9G09ehd9Ojos9cpTAbe\nbWbPh8spSScS9IwcSvctqFXApeHrpcDFYS0QQCEwnaBH5ikzawaaJe0DHg73WUeQwMDgbm/9KXCv\nBbdRdkt6BjgVaABeMLOtGfstAzCzGklPDSImd4QinfQA/GLtbvIEN5w3g8kVhdlujnPOZU0iT0GN\nUDIx4H6rVg1v0iPp8wQ9LUZwq2hXxub1wCnAQ30c+jGgMrxdBUGdz5VmdoOkpswanz50V6anOfCz\n7lIze6NX+07L2B+gK2O5i6F/VvZO4rrvZTYfYj93jEW6W2ThwuDpis//yVQWZUykGBdx/p80eHxR\nFufYIP7xDTcz+4GZLTKzk3slPAD/AXwqfIILAEl/IWkCcCXwZ2Y2w8xmAIsJbncB3AR8PyxgRlKZ\npE8ysEeBazOus6j75QDH9N52qERlOXC5pDxJ4wme9nqhj+OeBT6iwETg7EOc1x0FkU56AC6aM45L\n5o3PdjOcc84dBjPbTZDIfFvSq5I2ENyGGgtM636iK9x3C8FTXKea2W3AUwQ1M+uAPxD06hx0Cfb3\ntHwDKJC0VtIrwL/2sQ99vM5cvlnS6vBrlaQCSYsl/TBs44PAWmAN8ARBbdDuPs5zP7Ad2ADcTXAb\nrh53TEX66a1bbrnFrvr01cM6J89w8rqJaItzfHGODeIfn4/IPDJIKjWzZkljgZXAGWGC5I6RyNf0\nxDXhcc45F3u/CZ8iSwI3esJz7EW6p6evubecc84NzHt6XK6KfE2Pc84559xgRDrpqaqqynYTjqkV\nK1ZkuwnHlMcXXXGODeIfn3O5KtJJj3POOefcYHlNj3PO5Riv6XG5ynt6nHPOOZcTIp30eE1PtHl8\n0RXn2CD+8TmXqyKd9DjnnHPODZbX9DjnXI7xmh6Xq7ynxzn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