{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be27c3a6",
   "metadata": {},
   "source": [
    "This file is part of https://github.com/diehlpk/reusommer21.\n",
    "\n",
    "Copyright (c) 2021 Patrick Diehl\n",
    "\n",
    "This program is free software: you can redistribute it and/or modify  \n",
    "it under the terms of the GNU General Public License as published by  \n",
    "the Free Software Foundation, version 3.\n",
    "\n",
    "This program is distributed in the hope that it will be useful, but \n",
    "WITHOUT ANY WARRANTY; without even the implied warranty of \n",
    "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU \n",
    "General Public License for more details.\n",
    "\n",
    "You should have received a copy of the GNU General Public License along with this program. \n",
    "If not, see <http://www.gnu.org/licenses/>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5441b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import Symbol, simplify, lambdify\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import reduce\n",
    "import operator\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from matplotlib.ticker import FormatStrFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c18283b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolate_lagrange(x, x_values, y_values):\n",
    "    \"\"\"\n",
    "    x : value at which to evaluate y, should be between min and max x_values\n",
    "    x_values: list or numpy array containing values of x\n",
    "    y_values: list or numpy array contaning values of y\n",
    "    \"\"\"\n",
    "    def _basis(j):\n",
    "        p = [(x - x_values[m])/(x_values[j] - x_values[m]) for m in range(k) if m != j]\n",
    "        return reduce(operator.mul, p)\n",
    "    assert len(x_values) != 0 and (len(x_values) == len(y_values)), 'x and y cannot be empty and must have the same length'\n",
    "    k = len(x_values)\n",
    "    basis = []\n",
    "    for j in range(k):\n",
    "        basis.append(_basis(j))\n",
    "    #return sum(_basis(j)*y_values[j] for j in range(k)) \n",
    "    return basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c932c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = \"Quartic\"\n",
    "save_results = False\n",
    "g = -1\n",
    "con = []\n",
    "has_condition = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0102259b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################\n",
    "# Solve the system\n",
    "#############################################################################\n",
    "\n",
    "def solve(M,f):\n",
    "    return np.linalg.solve(M,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7c775d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################\n",
    "# Loading\n",
    "#############################################################################\n",
    "\n",
    "def f(x):\n",
    "    \n",
    "    global g \n",
    "\n",
    "    if example == \"Cubic\":\n",
    "        g = 27\n",
    "        return -6*x\n",
    "    elif example == \"Quartic\":\n",
    "        g = 108\n",
    "        return -12 * x*x\n",
    "    elif example == \"Quadratic\":\n",
    "        g = 6\n",
    "        return -2\n",
    "    elif example == \"Linear\":\n",
    "        g = 1\n",
    "        return 0\n",
    "    elif example == \"Linear-cubic\":\n",
    "        g = 31./4.\n",
    "        if x < 1.5:\n",
    "            return 0 \n",
    "        else:\n",
    "            return 9-6*x\n",
    "    else:\n",
    "        print(\"Error: Either provide Quadratic, Quartic, or Cubic\")\n",
    "        sys.exit()\n",
    "\n",
    "def forceFull(n,x):\n",
    "    \n",
    "    force = np.zeros(n)\n",
    "   \n",
    "    for i in range(1,n-1):\n",
    "        force[i] = f(x[i]) / exactSolution(x[n-1])\n",
    "    \n",
    "    force[n-1] = g / exactSolution(x[n-1])\n",
    "    \n",
    "    return force\n",
    "\n",
    "def forceCoupling(n,x):\n",
    "    \n",
    "    dim = n\n",
    "    \n",
    "    force = np.zeros(dim)\n",
    "   \n",
    "    for i in range(1,dim-1):\n",
    "        force[i] = f(x[i]) / exactSolution(x[dim-1])\n",
    "    \n",
    "    force[dim-1] = g / exactSolution(x[dim-1])\n",
    "    \n",
    "    return force"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c8dd79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################\n",
    "# Exact solution \n",
    "#############################################################################\n",
    "\n",
    "def exactSolution(x):\n",
    "    \n",
    "    if example == \"Cubic\":\n",
    "        return x * x * x\n",
    "    elif example == \"Quartic\":\n",
    "        return x * x * x * x\n",
    "    elif example == \"Quadratic\":\n",
    "        return x * x\n",
    "    elif example == \"Linear\":\n",
    "        return x\n",
    "    elif example == \"Linear-cubic\":\n",
    "        return np.where(x < 1.5, x, x + (x-1.5) * (x-1.5) * (x-1.5) )\n",
    "    else:\n",
    "        print(\"Error: Either provide Linear, Quadratic, Quartic, or Cubic\")\n",
    "        sys.exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ebed63f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################\n",
    "# Assemble the stiffness matrix for the finite difference model (FD)\n",
    "#############################################################################\n",
    "\n",
    "def FDM(n,h):\n",
    "\n",
    "    M = np.zeros([n,n])\n",
    "\n",
    "    M[0][0] = 1\n",
    "\n",
    "    for i in range(1,n-1):\n",
    "        M[i][i-1] = -2\n",
    "        M[i][i] = 4\n",
    "        M[i][i+1] = -2\n",
    "\n",
    "    M[n-1][n-1] = 11*h / 3\n",
    "    M[n-1][n-2] = -18*h / 3\n",
    "    M[n-1][n-3] = 9 * h / 3\n",
    "    M[n-1][n-4] = -2 * h / 3\n",
    "\n",
    "    M *= 1./(2.*h*h)\n",
    "\n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97eb7677",
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################\n",
    "# Assemble the stiffness matrix for the coupling of FDM - VHM - FDM\n",
    "#############################################################################\n",
    "\n",
    "def CouplingMDCM(n,h,nFD,hFD,xAll):\n",
    "\n",
    "    fVHM = 1./(8.*h*h)\n",
    "    fFDM = 1./(2.*hFD*hFD)\n",
    "    \n",
    "    dim = 2*nFD + n + 3\n",
    "    \n",
    "    M = np.zeros([dim,dim])\n",
    "    \n",
    "    M[0][0] = 1 \n",
    "\n",
    "    for i in range(1,nFD-1):\n",
    "        M[i][i-1] = -2 * fFDM\n",
    "        M[i][i] = 4 * fFDM\n",
    "        M[i][i+1] = -2 * fFDM \n",
    "    \n",
    "    # Interpolate the last FD node\n",
    "    weights = interpolate_lagrange(xAll[nFD-1], [xAll[nFD],xAll[nFD+1],xAll[nFD+2]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[nFD-1][nFD-1] = -1 # FM node at 1 \n",
    "    M[nFD-1][nFD] = weights[0]\n",
    "    M[nFD-1][nFD+1] = weights[1]\n",
    "    M[nFD-1][nFD+2] = weights[2]   \n",
    "                      \n",
    "    # Interpolate the first PD node\n",
    "    weights = interpolate_lagrange(xAll[nFD], [xAll[nFD-3],xAll[nFD-2],xAll[nFD-1]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[nFD][nFD] = -1\n",
    "    M[nFD][nFD-1] = weights[2] \n",
    "    M[nFD][nFD-2] = weights[1]\n",
    "    M[nFD][nFD-3] = weights[0]\n",
    "    \n",
    "    # Interpolate the second PD node\n",
    "    weights = interpolate_lagrange(xAll[nFD+1], [xAll[nFD-3],xAll[nFD-2],xAll[nFD-1]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[nFD+1][nFD+1] = -1\n",
    "    M[nFD+1][nFD-1] = weights[2] \n",
    "    M[nFD+1][nFD-2] = weights[1]\n",
    "    M[nFD+1][nFD-3] = weights[0]\n",
    "         \n",
    "    mid = nFD+n+1\n",
    "    \n",
    "    for i in range(nFD+2,mid):\n",
    "        M[i][i-2] = -1. * fVHM\n",
    "        M[i][i-1] = -4. * fVHM\n",
    "        M[i][i] = 10. * fVHM\n",
    "        M[i][i+1] =  -4. * fVHM\n",
    "        M[i][i+2] = -1. * fVHM\n",
    "    \n",
    "    # Interpolate the first PD node\n",
    "    weights = interpolate_lagrange(xAll[mid], [xAll[mid+2],xAll[mid+3],xAll[mid+4]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[mid][mid] = -1 \n",
    "    M[mid][mid+2] = weights[0]\n",
    "    M[mid][mid+3] = weights[1]\n",
    "    M[mid][mid+4] = weights[2]\n",
    "\n",
    "    # Interpolate the second PD node\n",
    "    weights = interpolate_lagrange(xAll[mid+1], [xAll[mid+2],xAll[mid+3],xAll[mid+4]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[mid+1][mid+1] = -1\n",
    "    M[mid+1][mid+2] = weights[0]\n",
    "    M[mid+1][mid+3] = weights[1]\n",
    "    M[mid+1][mid+4] = weights[2]\n",
    "    \n",
    "    # Same end node and start node\n",
    "    weights = interpolate_lagrange(xAll[mid+2], [xAll[mid-1],xAll[mid],xAll[mid+1]], [\"u1\",\"u2\",\"u3\"])\n",
    "    M[mid+2][mid+2] = -1\n",
    "    M[mid+2][mid-1] = weights[0]\n",
    "    M[mid+2][mid] = weights[1]\n",
    "    M[mid+2][mid+1] = weights[2]\n",
    "    \n",
    "    for i in range(mid+3,dim-1):\n",
    "        M[i][i-1] = -2 * fFDM\n",
    "        M[i][i] = 4 * fFDM\n",
    "        M[i][i+1] = -2 * fFDM\n",
    "\n",
    "    \n",
    "    M[dim-1][dim-1] = 11 *  hFD * fFDM / 3\n",
    "    M[dim-1][dim-2] =  -18 * hFD * fFDM  / 3\n",
    "    M[dim-1][dim-3] = 9 * hFD * fFDM / 3\n",
    "    M[dim-1][dim-4] = -2 * hFD * fFDM / 3\n",
    "    \n",
    "    if has_condition:\n",
    "        con.append(np.linalg.cond(M))\n",
    "        \n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "323c7f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute(amount):\n",
    "\n",
    "\n",
    "    hFD = 1./ amount\n",
    "    h = hFD / 5\n",
    "    nFD = int(1 / hFD)+1\n",
    "    n = int(1 / h)+1\n",
    "    \n",
    "    x1 = np.linspace(0,1,nFD)  \n",
    "    x2 = np.linspace(1-1.5*h,2+1.5*h,n+3)\n",
    "    x3 = np.linspace(2*1,3*1,nFD)\n",
    "    xFull = np.linspace(0,3*1,3*nFD-2)\n",
    "    xAll = np.concatenate([x1,x2,x3])\n",
    "\n",
    "    \n",
    "    forceFD = forceFull(len(xFull),xFull)\n",
    "    MFD = FDM(len(xFull),hFD)\n",
    "    \n",
    "    uFDM = solve(MFD,forceFD) \n",
    "\n",
    "    forceCoupled = forceCoupling(len(xAll),xAll)\n",
    "    \n",
    "\n",
    "    forceCoupled[nFD-1] = 0\n",
    "    forceCoupled[nFD] = 0\n",
    "    forceCoupled[nFD+1] = 0\n",
    "\n",
    "    forceCoupled[nFD+n+1] = 0\n",
    "    forceCoupled[nFD+n+2] = 0\n",
    "    forceCoupled[nFD+n+3] = 0    \n",
    "\n",
    "    MCoupling = CouplingMDCM(n,h,nFD,hFD,xAll)\n",
    "    np.savetxt(\"m.csv\", MCoupling, delimiter=\",\")\n",
    "    uCoupled = solve(MCoupling,forceCoupled) \n",
    "        \n",
    "    return xAll, xFull, uCoupled, uFDM , nFD, n\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2e388b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "xAll = []\n",
    "xFull = []\n",
    "uCoupled = []\n",
    "uFDM = []\n",
    "nFD = []\n",
    "n = []\n",
    "for i in range(4, 8):\n",
    "    \n",
    "    res = compute(i)\n",
    "    xAll.append(res[0])\n",
    "    xFull.append(res[1])\n",
    "    uCoupled.append(res[2])\n",
    "    uFDM.append(res[3])\n",
    "    nFD.append(res[4])\n",
    "    n.append(res[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52675529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x105f4d9d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xSlice = []\n",
    "uSlice = []\n",
    "\n",
    "for i in range(0, len(xAll)):\n",
    "    xSlice.append(np.concatenate([xAll[i][0:nFD[i]-1],xAll[i][nFD[i]:nFD[i]+2],[xAll[i][nFD[i]-1]],xAll[i][nFD[i]+2:nFD[i]+n[i]+1],[xAll[i][nFD[i]+n[i]+3]],xAll[i][nFD[i]+n[i]+1:nFD[i]+n[i]+3],xAll[i][nFD[i]+n[i]+4:]]))\n",
    "    uSlice.append(np.concatenate([uCoupled[i][0:nFD[i]-1],uCoupled[i][nFD[i]:nFD[i]+2],[uCoupled[i][nFD[i]-1]],uCoupled[i][nFD[i]+2:nFD[i]+n[i]+1],[uCoupled[i][nFD[i]+n[i]+3]],uCoupled[i][nFD[i]+n[i]+1:nFD[i]+n[i]+3],uCoupled[i][nFD[i]+n[i]+4:]]))\n",
    "    plt.plot(xSlice[i],uSlice[i],label=\"m=\"+str(i+4))\n",
    "    \n",
    "    \n",
    "plt.plot(xFull[0],uFDM[0],label=\"FDM\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.axvline(x=1,c=\"#536872\")\n",
    "plt.axvline(x=2,c=\"#536872\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e9fe4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cDgdXr16Fo6MjGjduzMzX1dXFmDFjcPPmzV/ephO10NBQsNlszJkzh2f+woULQQgp91m0s7PjqWVt27YtlJWV8ebNmyrv39zcHJaWlsw8RUVFuLq6Ijk5GU+ePOFZ39nZmed8EISamhr69OmD48ePA/hx27Bbt24wNDSsUsylta45OTkAwPzNlJSUhC6rd+/eUFdXx4kTJ0AIwYkTJzB69GiBt5eQkEC3bt2YGvWnT5/i69evWLx4MQghiI6OBvDjWtG6detqtfmr7t9+woQJPG0eLSwsmAcpyrKwsEBKSgpKSkp+Wd7P3x38pqNHjwrxLn+IjIyEt7c3czelPqG3NSmRMDc3F+iBADc3N5w4cQIxMTFYvXo1WrVqVW6dgwcPwt/fH8+ePUNxcTEzn9/ToD9/8ZYmDz/feiud/3PD3KZNm5b7sm7evDmAH+2Y+D3d8/LlSwA/vmAqkpWVVWEVfIcOHSAvL4+oqCg4ODggKioK3t7e0NHRQWBgIAoKCpgkreyXXVX8fHxKY8rIyOB5Sq2st2/fAgCaNWvGM19KSoonGRHWu3fv4OnpifPnz5f7O2RlZfG8lpSUhL6+fpX3VfpFU5UuDNzd3XHt2jWYm5ujadOm6N27N8aMGYPu3bsD+JH4ff/+HS1atCi3bcuWLcHlcpGSkgJTU9Mqxy+st2/fomHDhuUSi5YtWzLLy/r5vAB+nBtVbbj+9u1bvrdyy+6/7N+C32dZEGPGjMH48ePx7t07nD17FuvXr69SOQCYtpylx6z081CarAlDSkoKw4cPx7Fjx2Bubo6UlBTmFrigrKys4OXlhfz8fERFRUFXVxcdOnRAu3btEBUVBXt7e9y8eRMjRowQOr6yqvu3F+aay+VykZWVVent0saNG1frulKRZ8+eYciQIWjdujXzxG59QpMzqla9efOGSW4ePXpUbvmRI0cwceJEODo6ws3NDQ0aNACbzcaaNWvw+vXrcutX1Dakovnkp8bRVVFaK+bn51euDVypytpCSUlJwcLCApGRkXj16hXS0tJgZWUFbW1tFBcX4+7du4iKioKJiQlPW5GqqMnjAFRcC8XhcMq9tre3x7dv3+Du7g4TExMoKCjgw4cPmDhxYrmaRhkZmUqf3qtJLVu2xPPnz3Hx4kVcvnyZ6cLB09MT3t7e1S6/smMmTFun6qjp8+JXhK01KzVo0CDIyMjA2dkZhYWF1UpUEhMTAfyvux8TExMAP65LVemaZsyYMdi5cye8vLzQrl07vj88K2NpaYni4mJER0cztekAmBr1Z8+e4fPnz9WuTa/u317U19zSNmGC7FfQ62FKSgp69+4NFRUVhIaGVqk2VNzobU2q1nC5XEycOBHKyspYsmQJjh8/Xu7JyODgYDRu3BghISEYP348HBwcYGdnV6UGv4J49epVuYtH6dNbFfXAXnpLQFlZGXZ2dnynX3V1YGVlhZiYGFy7dg2ampowMTGBuro6TE1NERUVhaioqAob/ZYl6IgBwii9TVSaRJcqLi5GUlISz7zSmrifO7z8uabm0aNHePHiBfz9/eHu7o7BgwfDzs4ODRs2FCo2Qd9v6S/x0i9gYSkoKGDkyJHYv38/3r17h/79+2PVqlUoKCiAlpYW5OXl8fz583LbPXv2DBISEpU+NKGmpsa3g9Cfj5kwf1tDQ0N8/PixXK3Ps2fPmOU1ydDQsMLjIcr9y8nJwdHRETdu3IC9vX21GnkfPnwYLBaLefDH0tISampqOH78eLkfF4KwtLREo0aNcOPGDaFrzYAfdx+kpaWZz39pEmZtbY27d+8yT7r/6rpQE9eEmrRhwwbo6ur+cip9evlXvn79it69e6OwsBBXrlyBrq5uDb+DmkGTM6rWbNy4Ebdv30ZQUBB8fX3RrVs3TJ8+necpz9JfX2UTprt37zJtLkTt48ePOHPmDPM6Ozsbhw4dQvv27SvssLBjx45o0qQJNmzYwPcX3+fPn3+5XysrKxQWFmLz5s2wtLRkLqhWVlY4fPgwPn78KNAvZAUFBZH3BN6pUydoaWlh586dKCoqYuYfOHCg3L5KE9WyT59yOBwEBQXxrMfv70oIwZYtW4SKTUFBodwtUH60tLRgbW2Nffv24d27dzzLfvVL/uvXrzyvpaWl0apVKxBCUFxcDDabjd69e+PcuXM8XRSkp6fj2LFjsLS0rPCWMfDjmN25c4fn2F68eBEpKSk865X26ybI37dfv37gcDjYunUrz/xNmzaBxWKhb9++vyyjOvr164eYmBiez2leXh6CgoJgZGQkdC1SZf7++2+sWLECy5cvr3IZa9euxdWrVzFy5Ejm9r28vDzc3d3x9OlTuLu78z1Pjhw5gpiYGL5lslgsBAQEYMWKFRg/frzQMcnKyqJz5844fvw43r17x1Nzlp+fj4CAADRp0uSXyYYw501dIMo2Z3l5eejXrx8+fPiA0NDQck0z6hN6W5MSiX///Zf5lVxWt27d0LhxYzx9+hTLly/HxIkTMXDgQAA/vuzbt2+PGTNm4J9//gEADBgwACEhIRgyZAj69++PpKQk7Ny5E61atRKo6ltYzZs3x+TJkxEbGwttbW3s27cP6enp2L9/f4XbSEhIYM+ePejbty9MTU3h4uICPT09fPjwAREREVBWVsaFCxcq3W/Xrl0hKSmJ58+fM91gAD9+Fe/YsQMABErOOnbsiGvXrmHjxo1o2LAhjI2Nq92Ng5SUFFauXImpU6eiZ8+eGDlyJJKSkrB///5ybUNMTU3RpUsXeHh44Nu3b0yj6J8bAZuYmKBJkyb4+++/8eHDBygrK+P06dNCt3Hq2LEjTp48iQULFqBz585QVFRkzqefBQQEwNLSEh06dICrqyuMjY2RnJyMS5cuVTokUu/evaGjo4Pu3btDW1sbT58+xdatW9G/f3/m9sjKlSsRFhYGS0tLzJgxA5KSkti1axcKCwt/2Q7qr7/+QnBwMPr06YMRI0bg9evXOHLkSLluUJo0aQJVVVXs3LkTSkpKUFBQgIWFBd/2WgMHDkSPHj2wdOlSJCcno127drh69SrOnTuHefPmlStb1BYvXozjx4+jb9++mDNnDtTV1XHw4EEkJSXh9OnTIr1F3a5dO6brnV8pKSlhhlYqKCjA27dvcf78eTx8+BA9evQo9yPCzc0Njx8/hr+/PyIiIjBs2DDo6OggLS0NZ8+eRUxMDPMgDz+DBw/m6SRXWFZWVli7di1UVFTQpk0bAECDBg3QokULPH/+nG//eD/r2LEjgB+d5o4aNQpSUlIYOHBgpZ04i5Mo25yNHTsWMTExmDRpEp4+fcrTt5miomL9Gkml1p8PpX4rlXWlgf/vBqCkpIR07tyZ6Ovr83R7Qcj/Hl8/efIkIeTHI+SrV68mhoaGREZGhpiZmZGLFy+W6zqgtOsEPz8/nvLKPt7OL87Y2FhmnqGhIenfvz+5cuUKadu2LZGRkSEmJibltuXXHQIhPx7FHzp0KNHQ0CAyMjLE0NCQjBgxQuBHtjt37kwAkLt37zLz3r9/TwAQAwODcuvz60rj2bNnxNramsjJyREATBcN/LoWKHsc+D1m/7Pt27cTY2NjIiMjQzp16kQiIyPLdfdACCGvX78mdnZ2REZGhmhra5MlS5aQsLCwcsfsyZMnxM7OjigqKhJNTU0yZcoU5jH+st1FVNYFQm5uLhkzZgxRVVXlebyeX1cahBCSmJhIhgwZQlRVVYmsrCxp0aIFWb58eaXve9euXcTa2pr5uzZp0oS4ubmRrKwsnvXu379PHBwciKKiIpGXlyc9evQgt2/f5lmnonPH39+f6OnpERkZGdK9e3dy7949vsf23LlzpFWrVkRSUpLn/f38eSDkR9cV8+fPJw0bNiRSUlKkWbNmxM/Pr1zXIeDTFQYhFXfx8bOKtn/9+jUZNmwYc6zNzc3JxYsX+R6Pnz9jVdlfWRV1pVH2WiQvL0+MjIyIk5MTCQ4OZrr34Cc4OJj07t2bqKurE0lJSaKrq0tGjhxJbty4IfR7EaQrjVKXLl0iAMp1S/HXX38RAGTv3r3ltsFPXWkQ8qP7CD09PSIhIcHzea/O316YayshFV+DalJp90j8pqp2xSEuLEJqqQUoRdUxRkZGaN26NS5evCjuUOqNsj3YUxRFUTWDtjmjKIqiKIqqQ2hyRlEURVEUVYfQ5IyiKIqiKKoOoW3OKIqiKIqi6hBac0ZRFEVRFFWH0OSMoiiKoiiqDqGd0NZDXC4XHz9+hJKSUr0bqoOiKIqi/lSEEOTk5KBhw4aVds5Mk7N66OPHj5WO3UdRFEVRVN2VkpICfX39CpfT5KweKh1CJiUlpdIx/IRVXFyMq1evonfv3r8cuJuix0tQBYWFmLLYGwCw3XcplBTr5jAydQU9rwRDzyvh0PNKcDV5rLKzs2FgYMB8j1eEJmf1UOmtTGVlZZEnZ/Ly8lBWVqYfXgHQ4yUY6cJCSEnLAPhxztIv0crR80ow9LwSDj2vBFcbx+pXTZLoAwEURVEURVF1CE3OKIqiKIqi6hCanFEURVEURdUhtM0ZRVEURVEAAA6Hg+LiYnGHIVbFxcWQlJREQUEBOByOUNtKSUmBzWZXOwaanFEURVHUH44QgrS0NGRmZoo7FLEjhEBHRwcpKSlV6ktUVVUVOjo61eqHlCZnFEVRFPWHK03MGjRoAHl5+T+6g3Mul4vc3FwoKipW2lHszwgh+P79Oz59+gQA0NXVrXIMNDmjKIqiqD8Yh8NhEjMNDQ1xhyN2XC4XRUVFkJWVFSo5AwA5OTkAwKdPn9CgQYMq3+KkDwRQFEVR1B+stI2ZvLy8mCP5PZQex+q03aPJGUVRFEVRf/StTFESxXGktzX/cO/evcOXL1/A4XBw79493Lp1Cx8/fkSnTp3AZrOhqamJRo0aiTtMiqIoivpj1Mmas23btsHIyAiysrKwsLBATExMpeufOnUKJiYmkJWVRZs2bRAaGsqznBACT09P6OrqQk5ODnZ2dnj58iXPOt++fcPYsWOhrKwMVVVVTJ48Gbm5uTzrPHz4EFZWVpCVlYWBgQHWr19fI7HUlnfv3qFx48bo2LEjzM3NMWPGDBw9ehQzZsyAubk5OnbsiMaNG+Pdu3diiY+iKIqi/kR1Ljk7efIkFixYgBUrVuD+/fto164dHBwcmKcffnb79m2MHj0akydPRnx8PBwdHeHo6IjExERmnfXr1yMgIAA7d+7E3bt3oaCgAAcHBxQUFDDrjB07Fo8fP0ZYWBguXryIyMhIuLq6Msuzs7PRu3dvGBoaIi4uDn5+fvDy8kJQUJDIY6ktpTVmleFwOPjy5UstRURRFEXVN+/evcP9+/crnH6XH/gnTpwAi8WCo6Njze+M1DHm5uZk5syZzGsOh0MaNmxI1qxZw3f9ESNGkP79+/PMs7CwIFOnTiWEEMLlcomOjg7x8/NjlmdmZhIZGRly/PhxQgghT548IQBIbGwss86///5LWCwW+fDhAyGEkO3btxM1NTVSWFjIrOPu7k5atGgh0lgEkZWVRQCQrKwsgbfhJyYmhgD45RQTE1Ot/fyuioqKyNmzZ0lRUZG4Q6nT8gsKiNO0BcRp2gKSnZMr7nDqPHpeCYaeV8Kp7LzKz88nT548Ifn5+UKX+/btWyIrK1vpd4isrCx5+/atKN5GreBwOCQjI4NwOBxmXlJSEtHT0yNWVlZk8ODBlW5f2fEU9Pu7TrU5KyoqQlxcHDw8PJh5EhISsLOzQ3R0NN9toqOjsWDBAp55Dg4OOHv2LAAgKSkJaWlpsLOzY5arqKjAwsIC0dHRGDVqFKKjo6GqqopOnTox69jZ2UFCQgJ3797FkCFDEB0dDWtra0hLS/PsZ926dcjIyICamppIYuGnsLAQhYWFzOvs7GwAP54Eqc7TIPfu3RNove3bt2POnDlo1qwZZGRkqry/303psf/Te9P+leLiEub/JdU8Z/8E9LwSDD2vhFPZeVVcXAxCCLhcLrhcrlDlfvr06Zd3fgoKCvDp0yfo6+sLVbYgevbsidatW4PNZuPQoUOQlpaGj48PxowZg9mzZ+P06dPQ1tbGli1b0LdvX4HKJIQw/3K5XHA4HIwdOxYrVqzAzZs3kZmZWelx4nK5IISguLi4XFcagp6ndSo5K73Npq2tzTNfW1sbz54947tNWloa3/XT0tKY5aXzKlunQYMGPMslJSWhrq7Os46xsXG5MkqXqampiSQWftasWQNvb+9y869evVqtR59v3bol0HoHDhzAgQMHwGKxoKqqikaNGqFJkybQ09ODgYEB9PT0oKCgUOU46ruwsDBxh1CnFZf879Z5+PXrkJKs/tAmfwJ6XlWOnldVw++8kpSUhI6ODnJzc1FUVATgfx2q/oqgzV6+fPmC1NTUX64nbAe4JSUlOHToEObMmYNr167hzJkzmDlzJoKDgzFgwADMnj0b27dvx4QJE/Do0SPIy8v/MkkcPnw4Nm3ahJycHAA/voPV1NQwfPhwREREoKSkhKkk4aeoqAj5+fmIjIxESUkJzzJBjilQx5Izij8PDw+eGrns7GwYGBigd+/eUFZWrnK5Hz9+xNGjRwVenxCCjIwMZGRkICEhgWeZrq4uTExMyk3VHcKiLisuLkZYWBjs7e0hJSUl7nDqrILCIpy49uOHQK+ePaGo+Ocm8oKg55Vg6HklnMrOq4KCAqSkpEBRURGysrIAgLy8PJHWdAlaa5WdnS3Uj31JSUm0a9cOPj4+AIC2bdti8+bN0NHRwezZswEAvr6+2LdvH5KTk9GlSxfcv3+/0jKVlJSYf2/duoWjR4/i/v37UFZWhpSUFCQlJSv97i0oKICcnBysra2Z41n2/Qn0vgRaq5ZoamqCzWYjPT2dZ356ejp0dHT4bqOjo1Pp+qX/pqen8wylkJ6ejvbt2zPr/PzAQUlJCb59+8ZTDr/9lN2HKGLhR0ZGhu/tRCkpqWpdvMvexq2u1NRUpKamIiIigme+iooKTExM0KpVK7Ru3RqmpqZo3bo1GjZs+NskbdX9O/zuOGWq/yXpsRIYPa8qR8+rquF3XnE4HLBYLEhISDA94gvbM76olI1BUG3btuWJW0NDg2de6fftly9fICEhgebNm1daHpfLRXZ2NnJzc+Hs7Izdu3czd9dYLBZzrCp7DywWi++xFvQ8rVNPa0pLS6Njx44IDw9n5nG5XISHh6Nr1658t+natSvP+sCPatvS9Y2NjaGjo8OzTnZ2Nu7evcus07VrV2RmZiIuLo5Z5/r16+ByubCwsGDWiYyM5LlfHBYWhhYtWkBNTU1ksdQmQYeVKPsLQV1dXahysrKycPfuXezfvx8LFy5Enz59oK+vD3V1dVhaWmLatGnYunUrIiIi8PnzZ+HfBEVRFCVy8vLyyM3N/eV08+ZNgcq7efOmQOVVpanOzwlPaWJU9jUApp2YoqJipdP06dMBAK9fv0ZycjIGDhwISUlJSEpK4tChQzh//jwkJSXx+vVroWMVVJ2qOQOABQsWwNnZGZ06dYK5uTk2b96MvLw8uLi4AAAmTJgAPT09rFmzBgAwd+5c2NjYwN/fH/3798eJEydw7949posLFouFefPmYeXKlWjWrBmMjY2xfPlyNGzYkHkctmXLlujTpw+mTJmCnTt3ori4GLNmzcKoUaPQsGFDAMCYMWPg7e2NyZMnw93dHYmJidiyZQs2bdrExC6KWGpTaU1lZd1psNls3Lx5E/v27cPWrVvx7ds3sNlsdOvWDfn5+bh37x6zPYvFYo7Xx48fmUaVZcuSkZFBfn4+MjMzcevWrXLt3rS0tNC6dWueWjZTU1OoqqqK9s1TFEVRFWKxWALdXiwdS1KQ9epK2+QHDx5UulxRUREAYGJigkePHvEsW7ZsGXJycrBlyxYYGBjUVIh1LzkbOXIkPn/+DE9PT6SlpaF9+/a4fPky04j+3bt3PNWJ3bp1w7Fjx7Bs2TIsWbIEzZo1w9mzZ9G6dWtmnUWLFiEvLw+urq7IzMyEpaUlLl++zHMv+OjRo5g1axZ69eoFCQkJODk5ISAggFmuoqKCq1evYubMmejYsSM0NTXh6enJ0xeaqGKpLY0aNcKbN2/KjRDQvXv3ciMEbNq0CdOmTYObmxsuXLiAqKgoqKmpYcWKFZCVlUVwcDDi4uLw4cMHAD8SsQ4dOkBPTw85OTmIjY1FdnY2T2NIFosFbW1tKCgoIDc3F58+fcLnz58RERFR7vaonp5euYStVatWzIeIoiiKogTRtGnTSpeX3taUlZXl+f4GwFQU/Dxf1OpccgYAs2bNwqxZs/guu3HjRrl5w4cPx/Dhwyssj8ViwcfHh2kwyI+6ujqOHTtWaVxt27ZFVFRUpeuIIpba1KhRI2Z4pvbt26Nhw4bo168f3/viLVq0wPnz53Ht2jXMnz8fiYmJ8Pb2RosWLbBhwwa0aNECp06dwsmTJ/Hw4UPExsYiNjYW0tLS6NOnD7p16wZ5eXnExcUhMjKS6VqkrCZNmsDQ0JBJ2F6+fIn379/jw4cP+PDhA65cuVJu/Xbt2qFt27bMv0ZGRmJrL0FRFPUn0dTUhKysbKXdacjKykJTU7MWo6r/6mRyRtVtdnZ2iI+Px969e7F8+XI8f/4cAwcOhL29PTZu3IglS5bg2bNnOHnyJE6ePImnT5/i/PnzOH/+PGRlZdG/f3+sW7cO7dq1w7179xAZGYnIyEg8ffoUr1+/5rmP36xZM4wbNw5GRkaQk5NDWloaHj9+jMTERHz69IlZPyQkhNlGSUkJbdq04UnaWrduzTyBQ1EURYlGo0aN8Pz580q71KjJMZr5VdgkJyeXm/dzM5uqOnDggEjK+RWanFFVIikpialTp2LUqFFYvXo1Nm/ejLCwMLRr1w6urq7w8fHBihUr4OnpicTERCZRe/XqFU6fPo3Tp09DQUEBAwcOxMiRI7F582ZkZ2fj5s2bTLL24MEDvHz5kmfsUQMDA1hbW2PkyJEwNTXF9+/f8ejRIyQkJODhw4d48uQJcnJycPv2bdy+fZsn5iZNmvDUsLVr147WslEURVVT2TswlGjQ5IyqFhUVFaxbtw6urq5wd3fH6dOnsXPnThw7dgzLly/H7Nmz0aZNG7Rp0wa+vr6Ij4/HyZMn8c8//yA5ORknTpzAiRMnoKysjGHDhsHZ2RkbN26EhIQEMjMzcfv2bSZZi42NRUpKCo4ePcr0z6alpQVra2tYW1tj/vz5MDExwatXr/Dw4UMmYUtISEBqaipTy3bmzBkm/tJatrJJW9u2bWlbNoqiKEpsaHJGiUSTJk0QHByM//77D/Pnz0d8fDzc3Nywc+dO+Pn5wdHRESwWCx06dECHDh2wdu1axMTEMInahw8fsG/fPuzbtw/GxsaYMGECxo8fj379+qFfv34AfnSKeOfOHSZZu3PnDj5//szUxAE/GmtaWlrC2toaw4YNw+rVq8Fms/H582c8fPiQJ2l7/Pgx31o2FouFZs2aoUOHDjAzM2Mm2maCoiiKqg00OaNEysbGBrGxsTh06BCWLFmC169fY+jQobC1tcXGjRthZmYG4EcCZGFhAQsLC2zYsAFRUVE4dOgQTp06haSkJHh7e8Pb2xuWlpZwdnbG8OHDoaKigl69eqFXr14Afow5WrbN2q1bt5CZmYmLFy/i4sWLAH4ka7a2tujZsyd69eqFnj17Mn3eFBcX48WLF+Vq2T5+/IgXL17gxYsXOHHiBPPeDAwMmEStQ4cOaN26tcjaMVAURVFUKRah3y71TnZ2NlRUVJCVlVWt4Zt+VlxcjNDQ0Aqf1hRWbm4u1q5dC39/fxQUFIDFYsHFxQWrVq2qcMSH79+/4+zZszh48CDCwsKY5EdWVhaOjo5wdnaGvb09345vS0pKkJCQgMjISNy4cQP//fcfsrKyeNbR0dHhSdSMjIzKlfPp0yfEx8cjPj4e9+/fR3x8PF69esU3XiUlJVhYWDC1bB06dEDTpk1pO7YyCgoLMW7eEgDAfj8fKNFhdiol6s/h74qeV8Kp7LwqKChAUlISjI2NxdKtU11T2pWGsrJyla7llR1PQb+/aXJWD9WX5KzU27dvsXjxYqYWSlFRER4eHpg/f36lHRh++PABR44cwcGDB/H06VNmvq6uLsaNG4cJEyZU2tdMSUkJ7t+/j+vXryM8PBw3b94s97h348aNmWStR48e5QalL5WdnY2EhAQmWYuPj8fjx4/5duCrqKiIdu3aMcmamZkZWrVqBWlp6UqP0++KfokKhyZngqHnlXBociY4mpxRVVLfkrNSt2/fxvz58xETEwMAMDQ0xLp16zBixIhKx9kkhCAuLg4HDx7E8ePH8fXrV2ZZhw4d4OzsjNGjR0NLS6vS/RcWFiI6OppJ1mJiYlBSUsKzTuvWrZlkzcbGBioqKhWWl5OTg6CgICgoKCAhIQHx8fF4+PAh8vPzy60rLS2Ndu3aoVOnTszUqlUrSEr+/i0L6JeocGhyJhh6XgmHJmeCqwvJ2e//zUDVGd26dUN0dDSOHz+OxYsX4+3btxg1ahQCAwOxadMmdO7cme92LBaLSWj8/f0RGhqKgwcP4tKlS7h//z7u37+PhQsXol+/fnB2dkb//v35DhQvIyMDW1tb2NrawsfHBzk5OYiKikJ4eDiuX7+OBw8eIDExEYmJiQgICICEhAQ6derEJGvdu3fnqemTlZVF06ZNeS52JSUleP78OVO7VnprNCsri+mUt5ScnBzat2+PTp06oXPnzujUqROaN28u8JinFEVR1O+JJmdUrZKQkMDYsWMxZMgQbNiwAevWrcOtW7dgbm6O8ePHY/Xq1dDX169we2lpaTg6OsLR0RFfvnzBiRMncPDgQdy7d4/p6FZdXR2jR4/GhAkT0Llz5wpr5ZSUlHieBv3y5Qtu3LjBJGsvXrxATEwMYmJisHbtWkhLS6Nbt25Me7X27duXK1NSUhKmpqYwNTXFuHHjAPyo+Xvz5g3i4uJw79493Lt3D3FxccjOzkZ0dDSio6OZ7RUVFdGhQweeGramTZtWWrNIURRF/V7obc16qL7e1uTnw4cPWLJkCQ4dOgTgR22Su7s7/v77b6EGyX3y5AkOHTqEw4cP4+PHj8x8ExMTODs7Y9y4cZUmffykpKQgIiIC4eHhCA8PZ8YNLaWoqIiWLVti3Lhx6Nev3y/HayuLy+Xi1atXiI2NZRK2+/fv84w9WkpFRYUnWevUqRMMDQ3rTcJGbz8Jh97WFAw9r4RDb2sKri7c1qTJWT30OyVnpWJjYzF//nzcunULwI+BzteuXYsxY8YI9eHgcDgIDw/HwYMHcebMGab9F4vFQq9eveDs7IwhQ4YIlfgBP2q/Xr58ydSqRURE8LR9A348XODg4AAHBwf07NlT6OGiOBwOnj17xiRrsbGxePDgAQoLC8utq6GhwdwOLe2S5Fdt7sSFfokKhyZngqHnlXBqIznjcDiIiopCamoqdHV1YWVlVS+baZRNzg4dOgQXFxee5TIyMpWOJSqK5AyEqneysrIIAJKVlSXScouKisjZs2dJUVGRSMsVFJfLJSdPniSGhoYEAAFAOnfuTG7dulWl8rKyssjevXuJtbU1Ux4AoqioSFxcXEhERAThcDhVKpvD4ZC7d++S8ePHExsbGyIpKcmzD0lJSWJtbU1Wr15N4uLiqryfoqIiEh8fT3bv3k2mTp1KOnbsSKSkpHj2VToZGxuTUaNGkU2bNpHbt2+T/Pz8Ku1T1PILCojTtAXEadoCkp2TK+5w6jxxfw7rC3peCaey8yo/P588efKkWteM06dPE319fZ5rkr6+Pjl9+nR1whYLDodDMjIyCIfDIfv37yfKysokNTWVmdLS0irdvrLjKej3N03O6qHfNTkrlZ+fT9asWUMUFRWZD/moUaPIx48fq1zm69evibe3N2ncuDHPxcPIyIh4e3uT9+/fC11m2eOVnZ1Nzp07R2bMmEGaNGlSLnHS0tIiY8eOJYcOHfrlB/tX8vPzSUxMDNm2bRtxdnYmJiYmfJM1KSkp0qlTJzJz5kxy6NAh8vz5c8Llcqu17yrFS79EhVJXPod1HT2vhFOTydnp06cJi8Uqdw1isViExWLVaIJmY2NDZs2aRebOnUtUVVVJgwYNSFBQEMnNzSUTJ04kioqKpEmTJiQ0NFTgMn9OzlRUVISKiSZnf6jfPTkrlZqaSv766y/mQ6+iokJ27NhR5VooQn7UzkVFRZEpU6YQZWVl5iIiISFBBg0aRC5dukRKSkoEKquy4/Xq1Suybds2MmjQIJ4ks3Rq3749cXd3J9evXyeFhYVVfj+lMjIyyNWrV4mvry8ZMGAA0dLS4puwqampEQcHB+Lp6UkuXbpEPn/+XO19/wr9EhVOXfsc1lX0vBKOsMkZl8slubm5v5yysrKInp4e3+tNaYKmr69PsrKyBCpP2B+QNjY2RElJifj6+pIXL14QX19fwmazSd++fUlQUBB58eIFmT59OtHQ0CB5eXmEEEIUFBQqnVxdXXmSMzabTRo1akT09fXJoEGDSGJiYqUxiSI5o23O6qHfsc1ZZe7fv4+pU6fi3r17AH50ybFr165KO6AVRH5+PkJCQhAUFITIyEhmfqNGjfDXX39h8uTJaNiwYYXbC3q8ioqKEB0djStXruDy5cuIj4/nWa6oqIgePXow7dWEebCgIoQQJCcn4+7du8x0//59vu3XmjRpwrRbs7CwQPv27fl2RVJVtG2QcOrq57CuoeeVcIRtc5aXlwdFRcVajzM3N1eoNsG2trZMWzfgR7s3FRUVDB06lHnQLC0tDbq6uoiOjkaXLl0qHPGllKKiImRlZaGsrIy7d+/i5cuXaNu2LbKysrBhwwZERkbi8ePHFT5kRvs5o/4IHTp0wJ07d7Bt2zYsXboUt2/fhpmZGdzc3LB8+fJKRxmojJycHMaOHYuxY8fi2bNnCAoKwsGDB/Hu3Tt4enrC29sbAwYMwNSpU9G7d+8qN2yVlpaGjY0NbGxssHr1aqSnpyMsLAxXrlzB1atX8enTJ1y4cAEXLlwAUP0HC4AfD0AYGxvD2NgYo0aNAvAjSXz48CFPwvbixQu8fv0ar1+/xrFjxwD86A6kcePGMDExQYsWLXj+1dDQqNIxoCiKqilt27Zl/s9ms6GhoYE2bdow80pHfvn06RMA/PIHcOkDAQDQtWtXdO3alVnWrVs3tGzZErt27YKvr6/I3sPPaHJG1QtsNhtz5szBkCFDMHv2bJw7dw5r1qzByZMnsXPnTtjb21erfBMTE2zcuBGrV6/G6dOnsWvXLkRFReHcuXM4d+4cDA0N8ddff2HSpEmV1qYJQltbG+PGjcO4cePA5XKRkJCAK1eu4MqVK7h16xbevHmDHTt2YMeOHZCUlISVlRUGDBiA/v37o3nz5lXuQkNaWprpimPmzJkAgIyMDMTExPAkbF+/fmUGfv+ZhoYG36StcePGf8RoBxT1p5CXl0dubu4v14uMjGT6iqxMaGgorK2tBdqvsH6uCWSxWDzzSq+ZXC4XAH5ZIzh27FisW7euwn2ZmZn9svatuujVlKpXDAwMcPbsWZw9exazZs3Cmzdv0Lt3b4wdOxYbN25EgwYNqlW+rKwsU5v29OlTpjbt7du3WL58Oby8vDBw4EC4urqiR48e1X4/EhISMDMzg5mZGRYvXoycnBzcuHEDly9fxpUrV/D69WtEREQgIiICCxcuRNOmTdG/f38MGDAA1tbW1R6vU01NjamlA37cDv3w4QOeP3+OZ8+e8fz77t07fP36Fbdu3WK6PCklJSWFJk2aoE2bNhg6dCgGDRoEeXl5vH//Hk/KjItKUVT9wGKxBLq92Lt3b+jr6+PDhw/g10qKxWJBX1+/WncfRO3BgweVLq8seeNwOHj06JFACWm1CNXyjqoT/pQHAn4lKyuLzJkzh3lgQF1dnezdu1fkTyTm5+eTw4cPEysrK56GroaGhmTMmDEkOTlZpPsr6+XLl2Tz5s3E3t6+XBcaioqKZOjQoWTv3r0kNTW1xmIolZubS+Lj48nx48eJl5cXGTVqFDEzMyPy8vLlGgErKiqSLl26EBaLRdiSkrThthDq2+dQXOgDAcKpjac1f35is7ae1pw7dy7PPENDQ7Jp0yaeeQDImTNnBCqz7NOa3t7e5MqVK+T169ckLi6OjBo1isjKypLHjx9XuL0oHggQvutbiqojlJWVsWXLFty9exft2rXDt2/fMHnyZNja2uLZs2ci24+srCzGjRvHNAKdN28e1NTU8PbtWxw7dgxNmjTBkCFDcPnyZXA4HJHtF/jRNmLu3Lm4evUqvn79ipCQEEyaNAna2trIzc1FSEgIJk+eDF1dXZibm8Pb2xtxcXFM9b0oKSgooH379hg1ahRWrFiB48eP4/79+8jJycG7d+9w9epVLF26FEZGRsjNzcWdO3fK/ZL++NMoCxRF1X9Dhw5FcHAw9PT0eObr6+sjODgYQ4cOFVNk1ZeRkYEpU6agZcuW6NevH7Kzs3H79m20atWqZncsUBpJ1Sm05qy84uJi4ufnx9TiSEtLkxUrVtRYR6zfv38n+/fvJy1btixXm7Zy5cpq9ckmCA6HQ2JjY8mKFStIp06dytVc6ejokMmTJ5MzZ86QnJycGo3lZ1wulwQEBDCxlK05W+TuXq2uUP4E9flzWJtozZlwaroTWkIIKSkpIREREeTYsWMkIiJC4G6J6pqyNWdVQWvOKOr/SUpK4u+//8bjx4/Rt29fFBUVwdvbG+3atcONGzdEvr/SJz3XrFmDBw8eYO7cuVBVVcXbt2+xbNkyGBgYYOjQobhy5UqN1GJJSEigU6dO8PLyQmxsLD5+/Ii9e/cyQ1OlpaUxrzU0NODg4ICAgAC8fv1a5LH8jMViYciQIXyH3fL390enTp0QEhJSI8eFoijxYbPZsLW1xejRo2Fra1tn2pjVRzQ5o34rRkZGuHTpEk6ePAkdHR28ePECPXr0wKRJk8qNhSkqrVq1wubNm/Hx40ccOnQI3bt3B4fDwZkzZ9CnTx80adIEq1evRmpqao3sHwB0dXUxadIkhISE4OvXr7h69SrmzJmDxo0bo6ioCFevXsXcuXPRtGlTtGzZEm5ubrhx4waKi4trJB59fX0EBQWVuzgrKSohPj4eTk5OaNOmDY4ePYqSkpIaiYGiKKq+oskZ9dthsVgYMWIEnj59imnTpgEA9u/fDxMTExw+fJjvE0WiICcnh/Hjx+PmzZtITEzEnDlzoKqqiuTkZCxduhSNGjWCk5MTrl69WqO1RjIyMrC3t8eWLVvw6tUrPH36FBs2bICtrS0kJSXx7NkzbNiwAT169ICWlhZGjhyJnTt34sGDByJNlCZPnozk5GT8+++/zLwHCQ+wfPlyqKio4MmTJxg3bhxMTEywb9++GksUKYqi6huanFG/LVVVVezYsQO3bt2Cqakpvnz5ggkTJsDe3h4vX76s0X2bmppiy5Yt+PjxIw4ePIju3bujpKQEISEhzCgA69evx7dv32o0DhaLBRMTEyxcuBARERH4/PkzTp48ifHjx0NTUxNZWVn4559/MH36dJiZmUFFRQU2NjZwd3dHSEgIPn78WK396+vrw8rKinmtrq4BHx8fvH37FqtWrYKmpiZev36NyZMno1mzZti5cyffUQwoiqL+JDQ5o3573bp1w/3797F69WrIysoiPDwcbdq0wapVq1BUVFSj+5aTk8OECRNw8+ZNPHr0CLNnz4aKigqSkpLg7u4OfX19TJ06FYmJiTUaRylVVVWMGDEChw4dQlpaGm7fvo3ly5fDzs4OysrK+P79OyIjI7F+/Xo4OTlBT08PjRo1wvDhw+Hv74+bN28iPz+/2nGoqKhgyZIlSE5Ohr+/P3R0dPD27VtMnz4dTZo0QUBAgEj2Q1EUVR/R5Iz6I0hLS8PDwwOJiYmwt7dHYWEhli1bBjMzM9y8ebNWYmjdujUCAgLw8eNH7Nu3D+3atUN+fj6CgoLQpk0b9OrVC+fPnxd5dxwVYbPZ6Nq1K3x8fBAWFoaMjAw8fvwYe/fuhaurK9q2bQsJCQmkpKQgODgYf//9N6ysrKCsrMyMMnD48GG8fPmyyreKFRQUsGDBArx58wYBAQHQ09PDhw8fMHfuXBgbG2PDhg0C9VJOURT1O6HJGfVHadKkCa5cuYIjR45AS0sLT548gZWVFaZOnYqMjIxaiUFeXh4uLi6Ij49HZGQknJycICEhgevXr2Pw4MFo1qwZNm7ciMzMzFqJp5SEhARatWqFSZMmYdeuXUhISEBWVhYiIiKwZs0aODo6QltbGyUlJYiLi8P27dsxYcIENG/eHJqamnB0dMTEiRMrHW9u7do1fOfLyclh9uzZeP36NXbu3AlDQ0Okp6fDzc0NRkZGWL16NTPWHUVR1O+OJmfUH4fFYjGDnU+ePBkAEBQUhJYtW+LEiRM19sAAvzisrKwQHBzM3OZUU1NDUlISFi5cCH19fcycOVOkHeoKS1FREba2tli8eDHOnDmD1NRUJCcn4+TJk5g/fz66du0KGRkZfPv2DefOncPBgwfh6ekJZ2dnvsfxV4/Wy8jIYOrUqXj58iX27duHpk2b4uvXr1i6dCkMDQ3h5eVVa0k0RVGUuNDkjPpjqaurY8+ePfjvv/9gYmKC9PR0jB49Gv369UNSUlKtxtKoUSOsXbsW79+/R1BQEFq3bo28vDxs374dLVu2RJ8+fRAaGir2vsFYLBYMDQ0xYsQIbNy4Ebdv30Z2djbu3r2L6dOnM2N9Hjp0CAYGBrh48SLWrlnLbO/mtkig/UhJScHFxQVPnz7FkSNH0LJlS2RmZsLb2xuGhobw8PDA58+fa+Q9UhRFiRtNzqg/nrW1NR48eABvb29IS0vj8uXLMDU1xfr162u9ewd5eXlMmTIFDx8+RHh4OAYPHgwWi4UrV66gf//+MDExQWBgYJ26xSctLQ1zc3Ns374db968wbx58yApKYkPHz5g4MCBWLlqZZXLlpSUxNixY/Ho0SP8888/aNOmDXJycrB27VoYGRnh77//RlpamgjfDUVRlPjR5Iyi8ON2mqenJx4+fAhbW1vk5+fD3d0dnTp1wt27d2s9HhaLhZ49e+Ls2bN49eoVFixYABUVFbx8+RJz5syBvr4+5s6di1evXtV6bJXR09PDpk2b8P79e763MKt6y5jNZmP48OF48OABzpw5g44dO+L79+/w9/eHsbEx5syZg/fv31c3fIqiqHIyMzMxc+ZM6OrqQkZGBs2bN0doaGiN7pMmZxRVRosWLXD9+nXs378f6urqePjwIbp27YpZs2aJrbaqcePG8Pf3x/v377F9+3aYmJggJycHAQEBaN68OQYMGICrV6/WWls5QQQFBYHD4UBKSopn/qRJk6p1HCUkJODo6IjY2FhcunQJXbp0QUFBAQIDA9GkSRNMnz4db9++xfv37xEREUETNoqqBV5eXhU+COTr6wsvL6/aDUiEioqKYG9vj+TkZAQHB+P58+fYvXt3uUHeRY0mZxT1ExaLhYkTJ+LZs2cYP348CCHYtm0bWrZsiZCQELElQYqKipg+fToeP37M3OYkhODSpUtwcHCAqakpduzYgby8PLHEV8rX1xeenp7w8fFBUVERli1dxiw7e/ZHrdft27ertQ8Wi4V+/frh9u3buHbtGmxsbFBUVISdO3eicePGaNSoEXr27AlDQ0Ps3bu3um+JoqhKsNlseHp6lkvQSq8FNTnGpq2tLWbPno158+ZBTU0N2tra2L17N/Ly8uDi4gIlJSU0bdqUZ6QSYezbtw/fvn3D2bNn0b17dxgZGcHGxgbt2rUT8Tv5SZWGXK8hXC6XLF++nOjo6BBZWVnSq1cv8uLFi19ut3XrVmJoaEhkZGSIubk5uXv3Ls/y/Px8MmPGDKKurk4UFBTI0KFDSVpaGrP8wYMHZNSoUURfX5/IysoSExMTsnnz5nL7iYiIIGZmZkRaWpo0adKE7N+/X+SxCELQUe2FVVRURM6ePUuKiopEWm59d+3aNdK0aVMCgAAgAwcOJG/fvq0Tx+vFixdkzpw5RElJiYlPVVWVLFy4kLx586bW4/Hx8SEAiI+PDzMvv6CAOE1bQJymLSBq6hoEAGGxWGTu3LkkNzdXZPv+77//iKWlJXMcSicJCQmSkpIisv3UtLpwXtUHZc+r7BzRnUe/q8rOq/z8fPLkyROSn5/PzONyuSQ3N1fgadmyZQQAWbZsGd/Xgk5cLleo92VjY0OUlJSIr68vefHiBfH19SVsNpv07duXBAUFkRcvXpDp06cTDQ0NkpeXRwghREFBodLJ1dWVZGRkEA6HQ/r27UvGjh1LpkyZQho0aEBMTU3JqlWrSElJSYUx8TuepQT9/q5TydnatWuJiooKOXv2LElISCCDBg0ixsbGfN9gqRMnThBpaWmyb98+8vjxYzJlyhSiqqpK0tPTmXWmTZtGDAwMSHh4OLl37x7p0qUL6datG7N87969ZM6cOeTGjRvk9evX5PDhw0ROTo4EBgYy67x584bIy8uTBQsWkCdPnpDAwEDCZrPJ5cuXRRqLIGhyVvu+f/9Oli5dSqSkpAgAoqCgQPz8/Mjp06frxPHKysoiW7Zs4UkiJSQkiKOjI7l+/brQF7yqWrFiBU9iRgjvl+iChQuJi4sLE2Pjxo1JRESEyPZ//fr1cskZAGJra0sePnwosv3UJPo5FAxNzoQjbHKWm5vL97NU05OwP9hsbGyIpaUl87qkpIQoKCiQ8ePHM/NSU1MJABIdHU0IIeTly5eVTqmpqUxy1qJFCyIjI0MmTZpE7t27R06cOEHU1dWJl5dXhTH9VskZl8slOjo6xM/Pj5mXmZlJZGRkyPHjxyvcztzcnMycOZN5zeFwSMOGDcmaNWuYMqSkpMipU6eYdZ4+fcrzh+JnxowZpEePHszrRYsWEVNTU551Ro4cSRwcHGo8lp/R5Ex8EhMTSffu3ZkLSYsWLcjjx4/FHRaDw+GQS5cukd69e/Nc8Nq0aUN2797N/HKsTfy+RP/9919iYGDAxDd9+nSSnZ1d7X2lpKQQCQmJCi/8Q4YMIffv36/2fmoS/RwKhiZnwvmdk7MZM2bwzGvUqBFZv34985rL5RIA5Ny5cwKVyeFwmOSsWbNmxMDAgKemzN/fn+jo6FS4vSiSM8lK7njWqqSkJKSlpcHOzo6Zp6KiAgsLC0RHR2PUqFHltikqKkJcXBw8PDyYeRISErCzs0N0dDQAIC4uDsXFxTzlmpiYoFGjRoiOjkaXLl34xpOVlQV1dXXmdXR0NE8ZAODg4IB58+bVeCyFhYU8g0GXNqguLi4WaVcPpWXVdvcR9Unz5s0RHh6O/fv3w93dHc+fP0enTp2wdu1aTJ06FRIS4m/GaW9vD3t7ezx9+hQ7duzA4cOH8ejRI0yZMgXu7u4YOXIkhg4dCktLyxptC1KquLiE+X/J/5+zvXr1Qnx8PDw8PLB7927s2LEDoaGh2LVrF3r27FnlfWlra2PHjh2YMWMGOBwO2Gw2PDw88PTpU4SEhODMmTM4c+YM+vXrhyVLlsDc3FwUb1Gk6OdQMPzOK6pilZ1XxcXFIISAy+UyfSnKysoK/fDOunXrsGrVKkhLS6OoqAhLly6Fu7u7UGXIysoK3Z+jpKQkzzYsFqvcPAAoKSkBl8uFsrJypeWNHTsW69atAyEEurq6kJKSAovFYspr0aIF0tLSUFBQwPTtWBaXywUhBMXFxeWusYKep3UmOSvtq0hbW5tnvra2doX9GH358gUcDofvNqW9qqelpUFaWhqqqqoCl3v79m2cPHkSly5d4omP336ys7ORn5+PjIyMGokFANasWQNvb+9y869evQp5efkKt6uqsLAwkZf5u9HV1YW/vz8CAwPx8OFDzJ07F/v27cPs2bOhqakp7vAYffr0gaWlJcLDwxEaGor09HTs2LEDO3bsgIqKCrp06YJu3brB1NQUkpI1czkoLvnfWKHh169DSvJ/F6v+/fvDwMAAW7duxdu3b9GnTx84ODhg4sSJkJOTq9L+tLW1sWvXLqSmpkJXVxeampro1KkTbG1tcerUKdy8eROhoaEIDQ1F+/btMWLECLRq1ara71PU6OewcpWdV1TF+J1XkpKS0NHRQW5uLoqKiqpUrp+fH1avXo0lS5bAzc0Nfn5+WLVqFQghcHNzE7icnJwcofZbUlKCoqIinkSSy+WioKCgXHKZn5+P7OxsREZGVlqmkpISE0vHjh0RHByMzMxM5sf3o0ePoKOjg4KCAhQUFJTbvqioCPn5+YiMjERJSQnPsu/fvwv0vsSWnB09ehRTp05lXpdNhMQpMTERgwcPxooVK9C7d29xhwMA8PDwwIIFC5jX2dnZMDAwQO/evX/5C0AYxcXFCAsLg729fbkuEKjyiouLoaGhgTdv3mD58uVISEjAwoULsWXLFowePRosFkvcITJGjBgBDoeDsLAwnD59GhcuXMC3b99w5coVXLlyBerq6hg0aBCGDh2Knj178v01WFUFhUU4ce0WAKBXz55QVFTgWd6vXz/MmjULS5Yswc6dO3HlyhU8ffoUu3btQq9evUQWBwBmaKh169bh6NGjePDgAR48eAAbGxssXboUNjY2Yv+70c+hYH51XlG8KjuvCgoKkJKSAkVFRcjKygpd9sqVK7F69Wp4e3tj2bIfT2f7+vpCRkYGK1asgIyMDDNf1CQlJSEtLc3zXSghIQFZWdly349ycnJQVlZG+/btKy2TEIKcnBwoKSlh7ty52LNnDzw9PTFr1iy8fPkSmzZtwuzZsyv8/i0oKICcnBysra3LHU9BayPFlpwNGjQIFhYWzOvS23bp6enQ1dVl5qenp1d4IDU1NcFms5Gens4zPz09HTo6OgAAHR0dFBUVITMzk6fGquw6pZ48eYJevXrB1dW13Imko6PDdz/KysqQk5MDm80WaSxlycjIQEZGptx8KSmpGrl411S5vyMJCQnMmTMHAwYMwIQJExATE4OJEyfiwoUL2LlzZ52qRZOSksLAgQMxcOBAFBcX48aNGwgODsaZM2fw+fNnHDhwAAcOHICKigoGDRqEYcOGoXfv3lW6WJfFKXNrQbKCc0tNTQ07duzAiBEjMHnyZCQlJaFv375wdXWFn5+fSH+EtGrVCgcPHsSKFSuwdu1aHDhwAP/99x/+++8/dO/eHZ6enrC3txd7kkY/h5UT5LyiyuN3XnE4HLBYLEhISFSpaQaXy4WPjw+WL1/OM9/T0xMsFgscDqdGm3yUxv6reYK+v9Lbl6XD1V25cgXz589H+/btoaenh7lz58Ld3b3CsiQkJMBisfgea4HPU4Fax9WC0gcCNmzYwMzLysoS6IGAWbNmMa85HA7R09Mr1wg/ODiYWefZs2flGuEnJiaSBg0aEDc3N777WbRoEWndujXPvNGjR5d7IEAUsfwKfSCgbvj5eBUXFxMfHx8iKSlJABBtbW1y4cIFMUf5a8XFxSQiIoLMnDmT6Ojo8DTOVVRUJKNHjyanT5+u8sMEwjbczsnJITNnzmRiMDAwIFeuXKnSvgXx9u1bMnPmTCIjI8Ps09zcnFy4cKHWnnIti34OBUMfCBCOsA8E/MnKPhBQFb/V05qE/OhKQ1VVlZw7d448fPiQDB48uFxXGj179uTp4uLEiRNERkaGHDhwgDx58oS4uroSVVVVnr7Dpk2bRho1akSuX79O7t27R7p27Uq6du3KLH/06BHR0tIi48aNI6mpqcz06dMnZp3SrjTc3NzI06dPybZt2/h2pVHdWARBk7O6oaLjFRcXR1q1asV80U+ePFnkf6uawuFwSFRUFJk7dy7R19fnSdTk5eXJsGHDyIkTJ4R6srKqX6I3btwgjRs35jmOmZmZVXlbAvnw4QOZN28ekZOTY/ZpZmZGQkJCqnyRrgr6ORQMTc6EQ5MzwdHk7CelndBqa2sTGRkZ0qtXL/L8+XOedQwNDcmKFSt45gUGBpJGjRoRaWlpYm5uTu7cucOzvLTjVzU1NSIvL0+GDBlCUlNTmeUrVqzg+0ivoaEhTzkRERGkffv2RFpamjRu3JhvJ7TVjUUQNDmrG351sVu4cCFhsVgEADEyMiL//fefGKKsOg6HQ+7cuUP+/vtvYmRkxPPZkJGRIYMHDyaHDx/+ZcJUnS/R3NxcMmfOHGa/+vr6JDQ0tDpv65fS0tLIokWLiIKCAk9XJCdPnqy040lRoZ9DwdDkTDg0ORMcTc6oKqHJWd0gyPG6ceMGk9iwWCyycOHCenkB5HK5JC4ujnh4eJBmzZrxJGpSUlJkxowZPP0TlZSUkIiICHLs2DFyNSys2l+ikZGRPB3suri4kIyMDBG9O/4+f/5Mli5dyjP6QsuWLcmRI0dIcXFxje2Xfg4FQ5Mz4dDkTHB1ITkTf6dMFPUbs7GxwcOHD/HXX3+BEAJ/f3907NgR9+/fF3doQmGxWOjQoQNWr16N58+f4+HDh/D09ESrVq1QXFyM7du3o0OHDrh37x5CQkJgZGSEHj16YMyYMejbt2+1929lZYWEhATMmzcPLBYL+/fvR+vWrREaGiqCd8efpqYmVq5cibdv38LLywuqqqp4+vQpxo0bh1atWuHAgQO0by2KomoETc4oqoYpKSlh9+7duHDhArS1tfHkyRNYWFhg5cqV5frAqQ9YLBbatGkDb29vPH78GGFhYdDT08OLFy9gYWEBJycnvH//nu+2Fy6cr/J+5eXlsWnTJkRGRqJZs2b48OED+vfvDxcXF2RmZla53F9RU1PDihUr8PbtW6xatQoaGhp4+fIlXFxc0KJFC+zevbvKfUNRFEXxQ5MziqolAwYMQGJiIpycnFBSUoLly5eje/fueP78ubhDqxY7Ozs8fPgQw4YN+2XP3h4eHuBwOJWu8yuWlpZ48OABFixYABaLhQMHDsDAwADjxo3ju76vry+8vLyqtU8AUFZWxpIlS5CcnIz169ejQYMGSEpKgqurK5o2bYrt27fz7ZCSoihKWDQ5o6hapKmpiVOnTuHIkSNQUVFBTEwMzMzMEBgYKPSQJXWJuro6ZsyY8cv1Pnz4gKioqGrvT15eHv7+/oiKikLz5s2Rm5uLo0ePwszMDBkZGcx6vr6+8PT0FOkwVYqKinBzc0NSUhI2bdoEXV1dpKSkYObMmWjSpAm2bNnC9AL+/v17REREVFiTSFEUxQ9NziiqlrFYLIwdOxaJiYmws7NDfn4+5syZg969eyMlJUXc4VVZZUOQlZWamiqyfXbv3h0PHjzAwoULAQAPHjxAo0aNcOHCBSYx49c5pijIy8tj3rx5ePPmDbZu3Qp9fX18/PgR8+bNg7GxMUaMGAFDQ0P07NkThoaG2Lt3r8hjoCjq90STM4oSE319fVy5cgVbt26FnJwcwsPD0aZNGxw+fBiEEHGHJ7SyI3uIYj1BycnJYcOGDbh16xY0NDSQm5uLQYMGwdPTEx4eHjWSmJUlKyuLmTNn4tWrV9i5cycMDQ3x6dMnnDp1iqkN5XK5mDp1Kq1BoyhKIDQ5oygxkpCQwMyZM/HgwQN06dIFWVlZmDBhApycnPD582dxhycUKysr6OvrVzrskZ6eHqysrGpk/926dUNKSgrPLcz9+/fjwoULNbK/n8nIyDBjd/Ib6JnD4eDBgwe1EgtFUfUbTc4oqg5o3rw5oqKisGrVKkhKSuLMmTNo3bo1zp+v+tONtY3NZmPLli0AUGGC1qFDB5G2//rZhg0bwOFwmPHr0tLSMGjQIIwfPx7fvn2rsf2WJSUlhTlz5vAdd2/06NHw9PSstVgoiqoeW1tbsFisclP//v1rdL80OaOoOkJSUhJLlixBTEwMWrdujU+fPmHw4MGYNGkSsrOzxR2eQIYOHYrg4GDo6enxXX7hwgX4+/vXyL7LtjErKipibmeyWCwcOXIEpqamOHfuXI3s+2f6+voICgpiElEJCQk0bNgQubm58PX1haGhIZYsWYIvX77USjwURVVNSEgIUlNTmSkxMRFsNhvDhw+v0f0KnJyx2WyBJoqiqsfMzAyxsbFwc3NjOlxt27Ytbty4Ie7QBDJ06FAkJycjIiICx44dw7///suz/O+//8aePXtEuk9+jf99fHzg4+MDQgg0NTWRlpYGR0dHjBs3rlZqriZPnswch7dv3yIlJQWnTp1C27ZtkZubizVr1sDQ0BBubm5IT0+v8XgoqjbU9hPKtra2mD17NubNmwc1NTVoa2tj9+7dyMvLg4uLC5SUlNC0adNy1yFBqaurQ0dHh5nCwsIgLy9f48mZpKArEkJgaGgIZ2dnmJmZ1WRMFPXHk5WVxfr16zFw4EA4OzsjKSkJPXr0wPz587Fq1SrIycmJO8RKsdls2NraAgAKCgux6/SPC+OsWbOxZfMmTJkyBQUFBZg1a5ZI9sfhcPg+lVn6urCwEMXFxdiwYQOOHj2Ka9euYdeuXRg8eLBI9l8RfX196OvrM6+HDRuGoUOH4sKFC/Dx8cH9+/exYcMGbNu2DVOnToWbmxu0tLRqNCaKEgQhhOkSRlAHDx7E7NmzweVyISEhgcDAQDg7OwtVhry8fKXtViva76JFixATE4OTJ09i+vTpOHPmDIYMGYIlS5Zg06ZNGD9+PN69ewd5eXkoKipWWt7YsWOxbt06vsv27t2LUaNGQUFBQagYhSboWFGxsbFk2rRpRFVVlZiZmZHAwEDy7ds3ocabokSDjq1ZN9TW8crOziaurq484zvGxsbW6D5FqewYiFnZOWTu3LnMe1m9enWtxnLnzh3SsmVLZv9jxowhX758qdUYSnG5XHLp0iVibm7OM6D89OnTye7du+nn8Bfo2JrCEXZszdzcXJ4xdGtrKjtGryBsbGyIpaUl87qkpIQoKCiQ8ePHM/NSU1MJABIdHU0IIeTly5eVTqmpqXzH1rx79y4BQO7evVtpTLU6tmanTp2wY8cOpKamYsGCBThz5gz09fUxatQohIWFVSEtpChKEEpKSti1axcuXboEHR0dPH36FF26dIG3t3e9G9uRxWJh06ZNTI3WkiVL4OHhUWtdh1hYWOD+/ftYvHgxJCQkcOzYMZiamuLs2bO1sv+yWCwW+vXrhzt37uDKlSvo3r07CgsLsWPHDkyfPh0zZ85EcnJyrcdFUfVN27Ztmf+z2WxoaGigTZs2zDxtbW0AwKdPnwAATZs2rXRq0KAB3/3s3bsXbdq0gbm5eQ2+mx+EfiBAVlYW48aNQ3h4OBITE/Hp0yf06dOHPn1EUTWsX79+SExMxPDhw8HhcODl5YVu3brh2bNn4g5NKCwWCz4+Pli/fj0AYO3atZgzZ06tjZAgKyuLNWvWIDo6Gq1atUJ6ejqGDBmCsWPH4uvXr7USQ1ksFgu9e/dGVFQUrl+/DhsbG5SUlGD37t1o1qwZJk+ejNevX9d6XNSfTV5eHrm5uQJPz58/L/eEMpvNxvPnz4UqR15eXuhYS5/OLsVisXjmld4mLb3GKCoqVjpNnz693D7y8vJw4sQJTJ48Wej4qqJKT2u+f/8eK1euhL29PZ49ewY3NzcoKyuLOjaKon6ioaGBkydP4tixY1BVVcW9e/dgZmaGLVu21Lvhn9zc3LBjxw6wWCxs3boVkydPrtWB4M3NzREXF1cnatGAH18gPXr0QFhYGFatWgU7OzuUlJRg3759aNGiBZydnfHixQuxxEb9eVgsFhQUFASemjdvzvOEMpvNxq5du9C8eXOhyhG2vVlVPHjwoNLJ29u73DanTp1CYWFhhWP4iprAyVlRURFOnjyJ3r17o1mzZrh//z42b96MlJQUrF27FpKSAj9bQFFUNbBYLIwePRqJiYlwcHBAQUEB5s2bBzs7O7x7907c4Qll2rRpOHToENhsNg4cOIDRo0ejqKio1vZfWot2584dnlq0MWPGiKUWrZSpqSlCQ0Nx+/Zt9O3bFxwOB4cOHULLli0xduxYPHnyRGyxUVRFyj6hnJycXGu1TMKqym3NvXv3wtHRERoaGrUSo8DJma6uLtzd3dG1a1c8evQIBw4cgLW1NfLy8pCdnc1MFEXVDj09Pfz777/YsWMH5OXlERERgTZt2mD//v31qhZt3LhxCA4OhrS0NIKDgzF06FAUFBTUagydO3fmqUU7fvw4WrVqhTNnztRqHD/r2rUrQkNDERMTg4EDB4LL5eLYsWNo3bo1RowYgUePHok1Por6mb6+PmxtbXmeUq7vnj9/jps3b9ZqsilwcpaRkYF3797B19cXLVq0gJqaGs+kqqoKNTW1moyVoqifsFgsTJs2DQkJCejatSuys7MxadIkWFhYICIiQtzhCczR0RHnz5+HrKwsLl26hP79+yM3N7dWY/i5Fu3Tp08YOnQoxowZI/bOYjt37ozz58/j/v37GDJkCAghTJ9pTk5OdFgo6o9148YNbN68mWdecnIy5s2bxzOPEAJHR8cq7aNFixYghMDe3r5qQVaBwMlZREQEM12/fr3cVDqfoqja17RpU0RFRWHdunVQVFTEvXv30LNnT/Tv3x+JiYniDk8gDg4OuHz5MhQVFXH9+nU4ODggKyur1uPo3Lkz7t+/Dw8PD6YWzdTUFCEhIbUey8/MzMwQEhKChIQEDB8+HCwWCyEhITAzM8OgQYNw7949cYdIUZQICJyc2djYCDRRFCUebDYbixYtwqtXrzBz5kxISkoiNDQU7dq1w6RJk2qtx+7qsLGxwbVr16Cqqorbt2+jV69eYmn7JSMjg9WrV/PUojk5OWH06NFir0UDfnQd8M8//yAxMRFjxoyBhIQELly4gM6dO6N///64c+eOuEOkKKoahH5aMysrC8HBwdiwYQP8/f0REhJC25pRVB2ira2NrVu34smTJxg2bBi4XC7279+PZs2awcPDQyy1UcIovSWrqamJuLg42NraIi0tTSyx/FyLduLECZiamoq9LVqpVq1a4ejRo3jy5AkmTJgANpuN0NBQdO3aFQ4ODrh16xaA2h9Sh6Ko6hEqOTty5AgMDQ0xYsQILFq0CG5ubhg2bBgMDQ1x8uTJmoqRoqgqaNasGU6dOoXo6GhYWlqioKAAa9euRZMmTbBlyxYUFhaKO8QKtW/fHpGRkdDV1UViYiKsra2RkpIillj41aINHToUo0ePFusTnWW1aNECBw8exLNnzzBp0iRISkri6tWrsLS0RMuWLWFoaIiePXvC0NAQe/fuFXe4FEX9gsDJ2f379+Hi4gJHR0fEx8cjPz8f379/x7179zBw4ECMHz8eCQkJNRkrRVFV0KVLF0RGRuLcuXMwMTHB169fMW/ePLRs2RInTpyos092tmzZElFRUTA0NMTLly9hZWUl1s5Y+dWi1YUnOstq2rQp9u7di5cvX8LV1RWSkpJ49uwZ8zfmcrmYOnUqrUGj+KqtkTp+d6I4jgInZ4GBgXB0dMSBAwfQrl07yMjIQFZWFh06dMChQ4cwaNAgbNmypdoBURQleiwWC4MGDcKjR48QFBQEHR0dJCUlYfTo0TA3N6+zT3Y2adIEUVFRaNasGd6+fQtra2s8ffpUbPFUVIsm7n7RfmZkZIRdu3bh8OHD5ZZxOBwcO3aMfhFTjNLe9IUd6Jzir/Q4/jxygTAETs5u3bqFqVOnVrh82rRpuHnzZpUDoSiq5klKSmLKlCl49eoVfH19oaSkhLi4OPTs2RP9+vWrk/1mGRgYIDIyEq1bt8bHjx9hY2Mj9q4j+D3RWddq0QDA0tKy3JA6AODu7o4uXbrg0qVLNEmjwGazoaqqik+fPuHr16/Iz89HQUHBHz0VFRUJvU1+fj6+fv2KT58+QVVVlRktoSoE7tb/48ePaN68eYXLmzdvjg8fPlQ5EIqiao+CggKWLVsGV1dX+Pr6YufOnfj3339x+fJlODs7w8fHBwYGBuIOk6Gjo4MbN27AwcEBcXFx6NGjBy5fvgwLCwuxxVRaizZkyBBMnDgRT548wdChQ9G6dWsMGDAAa9asKbeNr68vMy5qbdDX10dQUBCmTp0KDocDNpuNnj174ubNm4iJicGAAQPQsWNHeHp6YuDAgbUydA5VN+no6AD43+DgfzJCCPLz8yEnJ1elz4SqqipzPKtK4OTs+/fvkJWVrXC5jIxMrffqTVFU9TRo0ACBgYGYM2cOli5dilOnTuHAgQM4ceIE5s2bB3d3d6iqqoo7TAA/xhUNDw9Hv379cPv2bdjZ2eHixYti78KntBbN29sb69atQ2JiIhITE/H27VscO3aMWc/X1xeenp7w8fGp1fgmT54MBwcHvHr1Ck2bNoW+vj7S09Ph7++Pbdu2IS4uDoMHD0a7du3g6ekJR0dHvrVt1O+NxWJBV1cXDRo0QHFxsbjDEavi4mJERkbC2tpa6FuTUlJS1aoxKyXUgJhXrlyBiooK32WZmZnVDoaiKPFo1qwZ/vnnH9y9exeLFi1CZGQk1q5di6CgICxfvhzTp0+HjIyMuMOEiooKrl69isGDByM8PBx9+vTBmTNn0KdPH7HGxa8W7fjx40hMTERERAS2b9/OJGbLly+v9fj09fV5htPR1tbG+vXr4ebmhk2bNiEwMBAJCQlwcnJCmzZtsHz5cjg5OdEk7Q/EZrNFklzUZ2w2GyUlJZCVla1Wu7FqIQJisVi/nCQkJAQtjqqGrKwsAoBkZWWJtNyioiJy9uxZUlRUJNJyf1e/6/Hicrnk/PnzpFWrVgQAAUCMjIzIsWPHCIfDEbq8/IIC4jRtAXGatoBk5+SKJMb8/HwyYMAAAoBISUmRM2fOEEIIKSkpIREREeTYsWMkIiKClJSUiGR/wigoKCAeHh6ExWIxxw8A8fHxEWh7cZxXX79+JcuWLSPKyspMvK1atSLHjh0TyzEURE2cV7+z3/V6VRNq8lgJ+v0t8M8iLpf7y4nD4Yg+e6QoqlaxWCwMHDgQCQkJ2L17N3R1dZGcnIwxY8bA3Ny8TgzTJisri9OnT2P48OEoLi7GsGHDMH/+fBgZGaFHjx4YM2YMevToASMjo1ofdqm0Fu3u3bs87VWePXtWp57oLEtdXR2+vr5ITk6Gl5cXVFVV8eTJE4wZMwampqY4cuQISkpKxB0mRf0xaJ01RVF8SUpK4q+//sLLly+xcuVK5snOXr161YknO6WlpXHs2DFMmDABHA4HmzdvLtd/14cPHzBs2DCxjIt5+fJlEEKYW0THjh2Dqakpzp07V+uxCEpNTQ0rVqxAcnIyfH19oa6ujufPn2P8+PFo2bIlDhw48Me3R6Ko2iBwcmZtbc3Truz8+fPIz8+viZgoiqpDFBQUsHTpUrx+/RqzZ8+GpKQk/v33X7Rr1w7bt28Xa2ySkpLYs2cPFBQU+C4n/99NxLx582q1Zr9s4/+SkhK4uroCANLT0+Ho6Ihx48bh27dvtRaPsFRUVLBs2TIkJydjzZo10NDQwKtXr+Di4gITExPs3bsXRUVF4g6Ton5bAidnN2/e5Pkwjhs3DqmpqTUSFEVRdY+WlhYCAgLw9OlTDBs2DIQQzJo1C//8849Y47p16xby8vIqXE4IQUpKCqKiomolnrKJWWnj/127dsHT0xPAj9vGR48ehampKc6fP18rMVWVkpISFi9ejOTkZPj5+aFBgwZ48+YN/vrrLzRv3hy7du2q08OAUVR9VeXbmoR2XEhRf6SmTZvin3/+wcyZM0EIwfjx48U6woCgPxJr68ckh8Ph+1Smt7c3fHx8MGnSJJiYmCAtLQ2DBw/G+PHj63QtGgAoKiri77//RlJSEjZu3AgdHR28ffsW06ZNQ7NmzbB9+3balRJFiRBtc0ZRlNBYLBa2bNkCJycnFBUVYfDgwWLrtV9XV1ek61WXl5dXhd1lLF++HHv27EF8fDwWLVoECQkJHDlyBKamprhw4UKtxFcd8vLymD9/Pt68eYMtW7agYcOGSElJwcyZM9GkSRMEBATQ5i4UJQJCJWdXrlzB+fPncf78eXC5XISHhzOvS6fqIITA09MTurq6kJOTg52dHV6+fPnL7bZt2wYjIyPIysrCwsICMTExPMsLCgowc+ZMaGhoQFFREU5OTkhPT+db1tevX6Gvrw8Wi1Wu77YbN26gQ4cOkJGRQdOmTXHgwIEajYWi6jI2m40jR47AxsYGOTk56N27Ny5cuID79+/zTA8eJNRoHFZWVsxntiIGBgawsrKq0TiEISsri3Xr1uHWrVtMLdqgQYMwYcIEZGRkiDu8X5KTk8OcOXPw+vVrbN26Ffr6+vj48SPmzp2Lxo0bY9OmTXScRoqqDkH75qiNfs7Wrl1LVFRUyNmzZ0lCQgIZNGgQMTY2Jvn5+RVuc+LECSItLU327dtHHj9+TKZMmUJUVVVJeno6s860adOIgYEBCQ8PJ/fu3SNdunQh3bp141ve4MGDSd++fQkAkpGRwcx/8+YNkZeXJwsWLCBPnjwhgYGBhM1mk8uXL9dYLBWh/ZzVDfR4/ZCRkUFatGjB06dX2YktKcn0R/XkydMaieH06dPMdYhfDNbW1qS4uLhG9l1d+fn5xM3NjUhISBAARFdXlyxdurRenVcFBQVkx44dpFGjRswxb9CgAfHz8yO5uTXTBxnt50w49HoluLrQz5nAyVlN43K5REdHh/j5+THzMjMziYyMDDl+/HiF25mbm5OZM2cyrzkcDmnYsCFZs2YNU4aUlBQ5deoUs87Tp08JABIdHc1T1vbt24mNjQ0JDw8vl5wtWrSImJqa8qw/cuRI4uDgUCOxVIYmZ3UDPV7/c/nyZYGSs6ibt2oshtOnTxN9fX2efWtoaDBJj5OTEyksLKyx/VdXdHQ0T5I7btw48u3bN3GHJZTCwkISFBREjIyMmPehqalJ1qxZQ7KzswkhhKSkpJDr16+TlJSUau2LJmfCodcrwdWF5Eyo4ZtqUlJSEtLS0mBnZ8fMU1FRgYWFBaKjozFq1Khy2xQVFSEuLg4eHh7MPAkJCdjZ2SE6OhoAEBcXh+LiYp5yTUxM0KhRI0RHR6NLly4AgCdPnsDHxwd3797Fmzdvyu0rOjqapwwAcHBwwLx580QeC0XVN1paWuIOAUOHDsXgwYMRFRWF1NRU6OrqwsrKChcvXsSIESNw+vRpDBkyBMHBwZCTkxN3uOV06dIF8fHxWLZsGTZt2oQjR47g+vXrCAoKQv/+/cUdnkCkpaUxZcoUTJw4EYcPH8aqVavw5s0beHh4YMOGDbC2tsa5c+fA5XIhISGBoKAgTJ48WdxhU78JLpeLgoICfP/+vdIpPz+/0uW5ublISUmBhIQEBg4cKJb3UmeSs7S0NAA/xnwrS1tbm1n2sy9fvoDD4fDd5tmzZ0y50tLS5QZvLltuYWEhRo8eDT8/PzRq1IhvcpaWlsZ3P9nZ2cjPz0dGRoZIYuGnsLCQ53H17OxsAD8GZxVlh5ClZdFOJgVDj9f/CNp7fEmJaM9Zfrp37878n8vlol+/fggJCcHw4cMRGhqK/v374/Tp01BUVKzROKpCUlISvr6+aNCgAfbu3YuXL19iwIABGD9+PPz9/evMIPSCGD9+PEaPHo1jx45h7dq1ePXqFc6cOcMs53K5mDp1Knr27Mkz7qegiov/d86ViPha+Duqa9crQggKCgqQm5uLnJwc5OTkMP/Pzc0tN7/s67y8PJ7/5+XlMUmXKCUlJYn8eAlantiSs6NHj2Lq1KnM60uXLokrFHh4eKBly5YYN26c2GKozJo1a+Dt7V1u/tWrVyEvLy/y/YWFhYm8zN8ZPV7A69evBVovNjYWWT89aFNbli1bBl9fX0RERKBbt25Yvnx5hZ3XipuJiQlWrlyJo0eP4sKFCzh8+DBCQ0MxY8YMdOzYUdzhCUVTUxPr1q3D8ePHERwczLOMw+Fgz549MDc3F7rc4pL/dSocfv06pCT/7MG6BSWK6xUhBIWFhUxSlJeXh9zcXOb/pf/+/P+8vDwUFBQgPz8f+fn54HK5InhH/ElLS0NGRqbcv/ymsst+/n9oaKhI4xL0QRmxJWeDBg2ChYUF87q0Zig9PZ3nkff09HS0b9+ebxmamppgs9nlnnZMT0+Hjo4OAEBHRwdFRUXIzMzk+dVZdp3r16/j0aNHzIWD/H8fbpqamli6dCm8vb2ho6PDdz/KysqQk5MDm80WSSz8eHh4YMGCBczr7OxsGBgYoHfv3lBWVq5wO2EVFxcjLCwM9vb2kJKSElm5vyt6vP4nPj5eoPU6d+6M7t261XA0/PXr1w82NjYYMGAAnj17Bn9/f1y6dAkaGhpiiacipefVgAEDMGTIENy+fRt//fUXXr16BV9fXzg7O2PDhg1QUVERd6hCMTMzQ0hISLkv5ICAAMyePRtz586Furq6wOUVFBbhxLVbAIBePXtCUbFuJtp1xc/XK0IIsrOz8fXrV2RkZODbt2/M/79+/YrMzExkZWUhMzMT2dnZzOvSeaIccUNBQQFKSkpQVFSEoqIilJSUyr0uO790/dL5CgoKkJeXZyZZWVlm2DRRHCtRKr3z9StiS85KD2wpQgh0dHQQHh7OJGPZ2dm4e/cupk+fzrcMaWlpdOzYEeHh4XB0dAQApouPWbNmAQA6duwIKSkphIeHw8nJCQDw/PlzvHv3Dl27dgUAnD59mqc6NDY2FpMmTUJUVBSaNGkCAOjatWu5DDosLIwpQ1Sx8FOaxf9MSkqqRpKCmir3d0WP14/bcYKtJ95j1b17d0RERMDe3h7379+Hvb09wsLCKv1xJC6l55WNjQ0SEhKwbNkybN68GQcPHkR4eDj27NkDBwcHcYcpMGNjYwQFBWHq1KngcDiQkJCAjo4OPn78iDVr1mDr1q2YPXs25s+fD01NzV+WxymT5En+wZ9BQgjy8vLw9etXJsHi9+/nz5/x5s0bLFq0iEnGqptgsdlsqKqqQlVVFSoqKjz/8vu/iooK891fNtGSkKibXa7WxLVd0PKETs4aN26M2NjYcr82MzMz0aFDB77ttQTBYrEwb948rFy5Es2aNYOxsTGWL1+Ohg0bMskOAPTq1QtDhgxhEp4FCxbA2dkZnTp1grm5OTZv3oy8vDy4uLgA+PFQweTJk7FgwQKoq6tDWVkZs2fPRteuXZkG+KUJWKkvX74AAFq2bMnUcE2bNg1bt27FokWLMGnSJFy/fh3//PMPz+1YUcRCUVTNat++Pf777z/Y2dkhMTERNjY2uHbtGgwMDMQdWoXk5eWxceNGDB06FC4uLnj16hX69OmDyZMnw9/fv97Uok2ePBkODg549eoVmjZtioYNG+LMmTPw9fVFQkICVq9ejS1btmDWrFlYuHBhnXjQpLYRQpCVlYVPnz7h8+fPzFTR6y9fvlRrnFN5eXmoq6tDXV0dGhoazL+qqqpQU1OrNOmSl5evtH9BquqETs6Sk5P5ZtuFhYX48OFDtYJZtGgR8vLy4OrqiszMTFhaWuLy5cuQlZVl1nn9+jWTPAHAyJEj8fnzZ3h6eiItLQ3t27fH5cuXeRrmb9q0CRISEnByckJhYSEcHByEHrDZ2NgYly5dwvz587Flyxbo6+uX++VaW7FQVF2jqakJWVnZXw7hoyHEbaua1KpVK0RGRqJXr1548eIFrKyscP36dTRu3FjcoVXK0tISCQkJWLJkCQICArB3715cuXIFe/fuRe/evcUdnkD09fV5HgBwcnLCkCFDcP78efj4+CA+Ph7r1q1DYGAgZs6cib///hsNGjQQY8TVQwhBbm4u0tLS8OnTJ54ki1/C9eXLlyo1QpeRkeFJrsr+q66uDlVVVSQlJcHe3h4NGjSAhoYG1NTU6uSTyxTAIkSwQTJLe/93dHTEwYMHeX6pcTgchIeHIywsDM+fP6+ZSClGdnY2VFRUkJWVJfI2Z6GhoejXr98fe4tAGPR48Xr37h3PDyfgxxeT2yJ3qDdvBwDYvcYTaqp1p5bn3bt36NWrF169eoWGDRsiPDwcJiYmYo1J0PMqKioKLi4uzMMYU6ZMwYYNG0R6TahthBBcvHgR3t7eiIuLA/CjZmf69Olwc3Pj+aFbUFiIcfOWAAD2+/lASYRtzt6/f4+XL1+iWbNmFT5JWlxcjPT0dKSlpVU6paamVmm0BCUlJWhpaaFBgwbQ0tIq9//S15qamtDU1IScnFyltVj0eiW4mjxWgn5/C1xzVnprkcViwdnZmWeZlJQUjIyM4O/vX7VoKYqq9xo1aoRGjRqVm3/48CHM9f1xbdi3by8Wlnm4RdwaNWqEyMhI2NnZ4cmTJ7C2tsa1a9fQtm1bcYf2S1ZWVkhISICHhwcCAwOxe/duphbt5z4Z6wsWi4WBAwdiwIABCA0Nhbe3N2JjY+Hv749t27bB1dUVbm5uVep6Q1C7d+/GtGnTwOVywWKxMGzYMBgaGpZLun7+IfIrioqK0NbW5pto/fz/0ppo6s8lcHJW+oSNsbExYmNjBWqwSVEUVbZ96qpVqzFu7Nhy/QGKk66uLv777z/07t0b8fHxsLW1xZUrV9C5c2dxh/ZLCgoKCAgIgJOTEyZNmoQ3b97A3t4eU6dOhZ+fH89DV/UJi8VC//790a9fP1y+fBne3t64e/cuAgICsGPHDri4uGDBwr9Ftr+UlBSEhYXh3LlzPGNEE0Jw6tSpCreTlJSEtrY2dHR0eCZdXV2e19ra2nWyXz2q7hK6zVlSUlJNxEFR1B8gOzsLHh4e2Ldvn7hD4aGpqYnr16+jb9++uHPnDnr16oXQ0FBYWlqKOzSBlD7RuXjxYmzbtg27du3C5cuXsW/fPvTs2VPc4VUZi8VC37590adPH4SHh2PlypX477//EBQUhGPHj8Nh7JQqlZubm4sbN24gLCwMV69eZToKr8jw4cPRuXPncgmYurp6nX3SkKrfRHZW3bt3D5GRkaIqjqKo39T+/fuZIc3qElVVVVy9ehW2trbIycmBg4MDrl27Ju6wBKaoqIitW7fi+vXrMDIywtu3b9GrVy9YWFggNze33Pq+vr7w8vKq/UCrgMViwc7ODjdu3EBkZCRsbGx4uj86deqfShvRczgcxMTEYNWqVbCxsYG6ujoGDhyIgIAAPHv2DBISErCwsMDcuXPLJVtsNhsbN26Em5sbxo8fD3t7e7Rp0waampo0MaNqjMjOrPHjx6NHjx6iKo6iqN/Q2LFjAQCzZs0SaSeWoqKkpIRLly6hT58++P79OwYMGCDW0UuqokePHnj06BHTP2RMTAwMDAxw48YNZh1fX194enpWq6NOcbGyskJERAS2b9/BzHN1dYWenh4GDhwILy8vnD9/HomJidi9ezeGDx8OLS0tWFhYYNmyZYiMjERxcTGMjY0xdepUBAcH48uXL7hz5w42b96MoKAg5riw2Wzs2rWrRtu4URRfwoymXlRURFxcXMibN2/KLfvw4QNJTk4WpjiqigQd1V5YRUVF5OzZs6SoqEik5f6u6PESTH5BAXGatoA4TVtAXr9JIqqqqgQACQwMFHdoFSooKCCOjo4EAJGUlCSnTp2qtX2L8ry6du0aUVFRIQAIADJr1iyybNkyAoD4+PiIIFrxKXte6ejqMu+xoklZWZkMGTKEbN++nbx69arSslNSUkhERARJSUmppXdT8+j1SnA1eawE/f4WquZMSkoKp0+f5rusYcOGMDQ0rFqGSFHUH0FLSwurVq0CACxevLjKnVbXNBkZGfzzzz8YPXo0SkpKMHLkSBw+fFjcYQmtV69eePfuHTp16gQA2Lp1K1auXIlJkyZh+fLlYo5OdBITE3Hr1i0EBATA2dkZrVu3hry8PLp37w4vLy/cvn0bX79+RUhICKZPn16u4/Gf6evrw9bWltaYUWIj9AMBjo6OOHv2LObPn18T8VAU9ZubNm0aTp06hRs3bsDFxQURERF1su2OlJQUDh8+DHl5eezduxfOzs7Iz8+Hq6uruEMTirKyMmJjYyEtLc20y9q/fz+UlJSwevVqyMvLiznC6pOWlkG3bt3QTUzjtlKUqAmdnDVr1gw+Pj64desWOnbsCAUF3o7/5syZI7LgKIr6/UhISGDfvn1o27YtIiMjERAQgHnz5ok7LL7YbDaCgoIgLy+PwMBATJ06Fd+/f6+z8VbE19cXxcXFkJaWRlFREQgh2LJlCy5duoT9+/fXm6dSKepPIfTP1b1790JVVRVxcXEICgrCpk2bmGnz5s01ECJFUb8bY2NjbNiwAQDg4eFRp0cWkZCQwJYtW+Du7g4AmD9/PnNrtj4obfzv4+ODwsJC+Pj4APjx8MOrV69gbW2NhQsX8jz9SFGUeAmdnCUlJVU41dX2IxRF1T2urq7o3bs3CgoK4OzsjJKSEnGHVCEWi4U1a9bA29sbALBs2TIsXboURLDR78SmbGJW2sZs+fLl8PHxQU5ODszMzEAIwcaNG9G+ffs62cUJRf2J6l5DD4qi/ggsFgt79uyBsrIy7t69y9Sk1VUsFguenp7w8/MDAKxevRoLFiyo0wkah8PhScxKlSZogwYNwqVLl9CwYUO8ePEClpaWWLRo0S8HsKcoqmaJLDk7d+4cDh06JKriKIr6AxgYGCAgIAAAsGLFCiQmJoo5ol/7+++/sW3bNgDA5s2bMX36dGZ4u7rGy8urwqcyly9fDi8vL/Tr1w+JiYmYMGECuFwu/Pz8YGZmhpiYmFqOlqKoUiJLztzd3eHi4iKq4iiK+kNMmDABAwcORFFRESZMmFBpT+91xYwZM7Bv3z5ISEhg165dmDhxYp2+LfsrampqOHjwIM6fPw8dHR08e/YMXbt2hYeHBwoLC8UdHkX9cYROziqq7n727Fmd7PGboqi6jcViISgoCOrq6oiPj8fq1avFHZJAXFxccPToUbDZbBw+fBijR49GUVGRuMOqloEDB+Lx48cYO3YsuFwu1q5di44dO+LevXviDo2i/ihCJ2eqqqqwtrbG8uXLER4eTp/woSiq2nR0dJhbhStXrsT9+/fFHJFgRo0aheDgYEhLSyM4OBhOTk71vr2Wuro6jhw5gpCQEDRo0ACPHz9Gly5dsHTpUlqLRlG1ROjk7Nq1a+jTpw/u3r2LwYMHQ01NDZaWlli6dCnCwsJqIkaKov4AI0eOxLBhw1BSUgJnZ+d6kwg4Ojri/PnzkJWVxcWLFzFw4EDk5eWJO6xqGzJkCB4/foxRo0aBw+Fg9erV6NSpU71JnCmqPhM6ObO0tMSSJUtw9epVZGZmIiIiAk2bNsX69evRp0+fmoiRoqg/AIvFwvbt29GgQQMkJiYy/XHVBw4ODvj333+hoKDA/IDNzs4Gh8PBjRs3cPz4cdy4caPeNf3Q1NTE8ePHERwcDC0tLSQmJsLc3BwrVqyo97dwKaouq9IDAS9evEBQUBAmTJgAJycnXLhwAQMGDMDGjRtFHR9FUX8QLS0t7NixAwCwbt06xMbGijkiwdna2iIsLAwqKiq4efMmOnTogEaNGqFHjx4YM2YMevToASMjI4SEhIg7VKE5OTnh8ePHGDZsGNM9h7m5ORISEsQdGkX9loROzvT09NClSxdcvnwZXbp0wb///osvX77gzJkzmDt3bk3ESFHUH2To0KEYPXo0OBwOJk6cWK/acHXt2hXXr1+HoqIiXr9+jY8fP/Is//DhA4YNG1YvEzQtLS2cOnUKJ0+ehIaGBhISEtCpUyf4+PjUiydsKao+ETo509LSwvfv35GWloa0tDSkp6fThwIoihKpwMBAaGtr48mTJ/Dy8hJ3OEJp165duTGHS5V2WDtv3rx6d4uz1IgRI/D48WM4OjqipKQEK1asQJcuXepFH3UUVV8InZw9ePAAaWlpWLx4MQoLC7FkyRJoamqiW7duWLp0aU3ESFHUH0ZDQwO7du0CAPj5+eHu3btijkhwUVFRSE9Pr3A5IQQpKSmIioqqxahES1tbGyEhITh69CjU1NRw//59dOjQAWvWrKnX/b1RVF1RpTZnqqqqGDRoEJYsWQIPDw8MGzYMsbGxWLt2rajjoyjqDzV48GCmv636dHszNTVVpOvVVSwWC2PGjMHjx48xcOBAFBcXY8mSJejWrRuePHki7vAoql4TOjkLCQnBnDlz0LZtW2hra2P69OnIzc2Fv78/fcSaoiiRCggIYHqs9/T0FHc4AtHV1RXpenWdrq4uzp07h4MHD0JVVRWxsbHo0KED/Pz86u2tW4oSN6GTs2nTpuHjx49wdXVFfHw8Pn36xCRs7dq1q4kYKYr6Q6mrqyMoKAgAsGHDBty+fVvMEf2alZUV9PX1wWKxKlxHW1sbVlZWtRhVzWKxWJgwYQISExPRr18/FBYWYtGiRbC0tMTz58/FHR5F1TuSwm7w6dOnmoiDoiiKr4EDB2L8+PE4fPgwXFxc8ODBA8jJyYk7rAqx2Wxs2bIFw4YNA4vFYh4CKCsnJwf37t2DhYWFGCKsOXp6erh48SIOHDiAefPm4c6dO2jfvj1WrVqFuXPngs1miztEigIAcLlc5ObmIjMzE5mZmcjKymL+//XrV8TGxkJPTw+dOnUSS3xCJ2cURVG1bcuWLbh27RpevHiBZcuWwd/fX9whVWro0KEIDg7G3Llz8f79e2a+np4eFBUV8fz5c9jZ2eHSpUuwtrYWY6Six2Kx4OLiAjs7O/z111+4evUqFi5ciDNnzsDMzAxaWlpYvnx5ue18fX3B4XDq3dO5lHhwOBxkZ2czCRW/JIvf69J5WVlZ4HK5le7DysqKJmcURVEVUVNTw+7duzFgwABs2rQJQ4YMgaWlpbjDqtTQoUMxePBgREVFITU1Fbq6urCyskJ+fj4GDRqEiIgI9OnTB+fOnYO9vb24wxU5AwMDXL58Gbt378bChQtx8+ZN3LlzByUlJSCE8LQh9PX1haenZ70aFYISHQ6Hg4yMDHz58gWfP3/Gly9fmInf669fvyInJ0ck+5aWloaqqirPpKSkhOzsbDRt2lQk+6gKmpxRFFUv9O/fHxMnTsSBAwfg4uKChIQEyMvLizusSrHZbNja2vLMU1RUxKVLl+Dk5IR///0XAwYMQHBwMAYOHCieIGsQi8WCq6srHBwcMGnSJFy/fh0AsGLFCmRkZGDTpk08iRm/GjWqfiGEIDc3t9Lk6ufX375943v7XxDy8vJQVVWFiopKuSTr53n81pGVlS1XZnFxMUJDQ2FjY1Pdw1FlNDmjKKre2LRpE8LCwvDq1SssWbIEmzdvFndIVSInJ4czZ85g9OjROHPmDIYOHYpjx45h+PDh4g6tRhgaGiIsLAw7d+6Em5sbvn//js2bN2Pr1q0oKSmhiVk9UFxcjE+fPiE1NZWZ0tLSyr1OT09HYWFhlfahqqoKLS0taGpqMhO/1+rq6lBTU4OKigqkpaVF/E7rBqGTs0mTJmHLli1QUlLimZ+Xl4fZs2dj3759IguOoiiqLFVVVezevRv9+vVDQEAAhg4dWm/bbMnIyODkyZNwdnbG8ePHMWrUKBQWFmLkyJHiDq1GSEhIYMaMGejTpw9cXFwQGRmJkpIS5klPSjzy8vIqTLTKvv7y5YtQtVuysrLQ0tISKNnS1NSEuro6pKSkavCd1i9CJ2cHDx7E2rVryyVn+fn5OHToEE3OKIqqUX379sXkyZOxd+9euLi44OHDhxUOl1TXSUlJ4fDhw5CTk8O+ffswYcIE5Obm/jZ9oPHTuHFj9OrVC5GRkQB+3AZr3rw5tm3bhsmTJ1faBQklOA6Hg7S0NLx79w4pKSlITk7GzZs3cfToUZ4aMGHabrHZbGhra0NXV5dn0tHR4fm/lpZWnW9yUNcJnJxlZ2eDEAJCCHJycnju03I4HISGhqJBgwY1EiRFUVRZ/v7+uHLlCt68eQMPDw8EBASIO6QqY7PZ2L17N+Tk5LBt2zZMnz4dkydPRr9+/cQdWo3w9fXFihUr4OPjg1GjRsHOzg7v3r3DlClTEBwcjD179kBfX1/cYdZphBB8/foVKSkpSElJYRKwsq8/fvwo8FBacnJy5RKun5MuXV1daGpqQkKiSgMLUUISODlTVVUFi8UCi8VC8+bNyy1nsVjw9vYWaXAURVH8qKioYO/evXBwcEBgYCCGDh1aruF9fSIhIYHAwEDIy8vDz88Pe/fuhbGx8W83XjG/xv9v3rzBgAEDcPnyZVy5cgWtW7fG5s2b4ezs/MfWouXm5vJNvMr+Pz8//5flsNls6OnpwcDAAHp6eigqKkK3bt2gr6/Pk3QpKSn9sce6rhI4OYuIiAAhBD179sTp06ehrq7OLJOWloahoSEaNmxYI0FSFEX9rHfv3nB1dUVQUBBcXFzw6NEjKCoqijusKmOxWFi3bh1kZGSwcuVKLFu2DIWFhfD29v5tvjg5HE65xv9sNhv//vsv5syZg5CQEHz48AEuLi4ICQnBrl27fstbvDk5OUhKSsKbN2+YKTk5mUm8MjIyBCqnQYMGaNSoEQwMDJip7GtdXV2m49/SJxD79etH23bVAwInZ6WPlCYlJcHAwIBWbVIUJXZ+fn64ePEikpOTMWnSJCxevLjcOpqammjUqJEYohMei8WCp6cn3r17h0OHDsHX1xf5+flYv379b5GgVdbBbEBAADZu3IgNGzZgxYoVuHDhAm7duoWtW7di1KhRtfr+vby8wGazq9xZbklJCd6/f88kXj8nYl++fPllDCoqKnwTrtLXenp6fLuBoH4PQj8QYGhoiMzMTMTExODTp0/letitzlM3hBCsWLECu3fvRmZmJrp3744dO3agWbNmlW63bds2+Pn5IS0tDe3atUNgYCDMzc2Z5QUFBVi4cCFOnDiBwsJCODg4YPv27dDW1uYp58CBA9i4cSNevHgBZWVlDB8+HNu2bWOWP3z4EDNnzkRsbCy0tLQwe/ZsLFq0iKeMU6dOYfny5UhOTkazZs2wbt06nrYjVX2PFEWVl5mZic+fPwP48dk7depUuXVkZWXx/PnzepOgAT86sDUzM8P8+fOxYcMGfP/+HYGBgb/9j2JJSUksXrwYAwYMwIQJExAfH48xY8bg1KlT2L59O3R0dGolDjabzXSSWzZBK70t6+3tjW/fvpVLukoTsbdv3/6yvZempiaMjY3RuHFjNG7cGEZGRjxJmLKyco2+R6qOI0I6f/48UVJSIiwWi6ioqBBVVVVmUlNTE7Y4HmvXriUqKirk7NmzJCEhgQwaNIgYGxuT/Pz8Crc5ceIEkZaWJvv27SOPHz8mU6ZMIaqqqiQ9PZ1ZZ9q0acTAwICEh4eTe/fukS5dupBu3brxlOPv708aNmxIjh49Sl69ekUSEhLIuXPnmOVZWVlEW1ubjB07liQmJpLjx48TOTk5smvXLmadW7duETabTdavX0+ePHlCli1bRqSkpMijR4+q9R5/lpWVRQCQrKwsgbcRRFFRETl79iwpKioSabm/K3q8BJNfUECcpi0gTtMWkOycXJGWHRcXRwD8coqLixPpfmtS2fMqKCiIsFgsAoC4uLiQkpIScYdXa4qKioiXlxeRlJQkAIi6ujo5cuQI4XK5hJCaPa8IIcTHx4cAINOnTyfbt28n3bt3JwCIrq4uUVFR+eU5Jy0tTVq0aEH69u1LZs6cSfz9/UlISAh58OCByK/dgqDXK8HV5LES9Ptb6OSsWbNmZO7cuSQvL6/KwfHD5XKJjo4O8fPzY+ZlZmYSGRkZcvz48Qq3Mzc3JzNnzmReczgc0rBhQ7JmzRqmDCkpKXLq1ClmnadPnxIAJDo6mhBCyLdv34icnBy5du1ahfvZvn07UVNTI4WFhcw8d3d30qJFC+b1iBEjSP/+/Xm2s7CwIFOnTq3We/wZTc7qBnq8BEOTM+H8fF4dPnyYSEhIEABk1KhRf9z5Fh8fT9q3b8/8LQcNGkQ+fvxYo+fVp0+fyKZNm4iOjk6l55Wuri7p3r07GTduHPH09CQHDhwgkZGR5P3794TD4Yg0puqi1yvB1YXkTOjbmh8+fMCcOXNE3odJUlIS0tLSYGdnx8xTUVGBhYUFoqOjMWrUqHLbFBUVIS4uDh4eHsw8CQkJ2NnZITo6GgAQFxeH4uJinnJNTEzQqFEjREdHo0uXLggLCwOXy8WHDx/QsmVL5OTkoFu3bvD394eBgQEAIDo6GtbW1jy9ETs4OGDdunXIyMiAmpoaoqOjsWDBAp4YHRwccPbs2Sq/RwAoLCzk6XE5OzsbwI8GnsXFxZUfWCGUliXKMn9n9HgJprj4f7d3SkR8zgraVUBJSUm9+Tv9fF6NHDkSkpKSGD9+PE6cOIH8/HwcOXIEMjIy4gyz1piamuLWrVvw8/PDqlWrcP78eURGRmLnrl3MOqI4r4qLi3H58mUcPHgQoaGh5c4tCQkJ+Pv7w9jYGEZGRjAyMqr0e5DD4YDD4VQrJlGi1yvB1eSxErRMoZMzBwcH3Lt3D40bNxY6qMqkpaUBQLl2YNra2syyn3358gUcDofvNs+ePWPKLR3YtKJy37x5Ay6Xi9WrV2PLli1QUVHBsmXLYG9vj4cPH0JaWhppaWkwNjYuV0bpPtTU1JCWllZp/FV5jwCwZs0avt2UXL16tUY6+gsLCxN5mb8zerwqV1zyvy+o8OvXISXJFlnZr1+/Fmi9mzdvIjU1VWT7rQ1lzys5OTm4u7tj3bp1OHfuHGxsbODu7v7HJGgA0L59e/j5+SEwMBD/196dx0VV/f8Dfw27C5uRIAKKG+6iqIgLbiwmpahkmqapCRqW6KdcE1w/5FJmZpF+TM3ccsMNUURMM3IBXHChxDUUFBUolXXO7w+/3J8TqDM4wx3g9Xw85hFz77ln3vd0HN7cc8+5qampGD58OPqP+QjAq/WrGzduIDY2Fr/88guys7Ol7Y0bN4alpSVOnz4NIyMjFBYW4uTJk3B2dsb169dx/fp1bZxWueP3lfp00VaPHz9Wq5zGyZmfnx8+/fRTXLx4Ea1atSoxJbdfv35q1bNhwwYEBQVJ7/ft26dpKFqjVCpRUFCAr7/+Gj4+PgCATZs2wc7ODnFxcfD19ZUtNgCYPn26yhW5nJwcODo6wsfHR6s3jRYUFCAmJgbe3t6caq0Gtpd6cvPysfnQcQBA7169ULOm9lbzT0pKUqtc165d0bZtW619ri49r1/17dsXXbp0waBBg5CYmIiIiAjs2LGjwj4doawCAwPx8ccfY83atdK2zp098LqNjdp1PHjwAFu2bMG6deuQmJgobbe1tcWwYcPw3nvvITIyEnPmzEFYWBhmzpyJBQsWYM6cOWjSpEmFXH+O31fq02VbFY98vYzGydnYsWMBAHPnzi2xT6FQqH0Zt1+/fnB3d5feFw/bZWRkqKxrk5GRAVdX11LrsLGxgaGhITIyMlS2Z2RkSLN67OzskJ+fj6ysLJWrZ8+WKf685s2bS/uLn/d18+ZNqZ7SPqd434vKPLtf03MEnj6Dr7S/kI2NjXXyj0xX9VZWbK8XK3pmRreRltvKyEi9rzAjI6MK9/+otH71xhtvIDo6Gn5+foiLi8Obb76JqKioKjWzz9jYGCtXrkR95wZIuvV0SYoe3Xvg66+XoU+fPs89rrCwEAcPHsTatWuxa9cu5OfnS/W99dZbGDVqFHx9fWFsbIx58+Zhzpw5KmuyFS+vERoa+txlNioCfl+pTxdtpW59Gs/LViqVz31pMr5ubm6ORo0aSa/mzZvDzs4OsbGxUpmcnBycOHECHh4epdZhYmICNzc3lWOUSiViY2OlY9zc3GBsbKxSJiUlBTdv3pTKdOnSRdpe7MGDB8jMzES9evUAAB4eHjh69KjKeHFMTAxcXFxgbW0tlXn2c4rLFH+Os7OzxudIRPQsT09PHDp0CFZWVjh+/Di8vLzw4MEDucMqVwqFAv/5z/8fTbh67SreeOMNNG7cGB9++CF27NghLeR6+fJlTJs2DU5OTvDz88PWrVuRn58PV1dXLFu2DLdv38b27dvx5ptvSr84S1ssF3i6rMbcuXP16l4yqqReZdaBJss/qOPzzz8XVlZWYteuXeLcuXOif//+JZaZ6NWrl1i+fLn0fvPmzcLU1FSsXbtWXLx4UQQGBgorKyuRnp4ulRk3bpxwcnIShw8fFqdPnxYeHh7Cw8ND5bP79+8vWrRoIY4fPy7Onz8v3nzzTdG8eXNptkZWVpawtbUV7733nkhOThabN28W1atXL7GUhpGRkViyZIm4dOmSCAsLK3UpjZed48twtqZ+YHupR5ez6m7cuCHMzMxeOKPO0NBQ3LhxQ6ufq0vq9qvExETx2muvCQCidevWKssHVQXP9quJIZOEsbGxyv93AwMD0aBBA5VtNjY2YuLEiSIpKUnu8Msdv6/Upw+zNTVOzgoLC8XcuXOFvb29MDQ0FKmpqUIIIT777DPxv//9r2zR/h+lUilmzZolbG1thampqejdu7dISUlRKVOvXj0RFhamsm358uXCyclJmJiYiI4dO4rff/9dZf+TJ0/Ehx9+KKytrUX16tXFgAEDxJ07d1TKZGdni9GjRwsrKytRq1YtMWDAAHHz5k2VMmfPnhVdu3YVpqamom7duuLzzz8vcQ4///yzaNKkiTAxMREtWrQQ+/bt0/gcX4bJmX5ge6lH1+tR3bhxQyQkJJR4rVq1SvqlHBUVpfXP1RVN+lVycrKwtbUVAESzZs1EWlpaOUSoH/7dr7KyssSuXbvERx99JJo1a6aSnL/11ltix44dKkshVTX8vlJfhUzO5syZIxo0aCB++uknUa1aNSk527x5s+jUqVPZoiWNMDnTD2wv9eg6OXuRkJAQAUDUrVtXPHz4sFw/u6w07VcpKSnCwcFBABANGzasUFcJX8XL+tVff/0l9u7dqzKKUpXx+0p9+pCcaXzP2Y8//oiVK1di2LBh0gNVAaBNmzbS8hVERPpgwYIFaNy4MdLS0jBp0iS5w9GJJk2a4OjRo3B2dkZqaio8PT3VXmKkMqtbty78/PxKLF1EVBFonJylpaWhUaNGJbYXL0dBRKQvqlevjjVr1kChUGDt2rWyLtmjS87Ozjh69CiaNGmCGzduwNPTk38sE1VgGidnzZs3x7Fjx0ps37ZtW4VZR4iIqo4uXbpIV83Gjh0rzeKrbBwcHPDLL7+gRYsWuH37Njw9PXHu3Dm5wyKiMtA4OQsNDcWECROwcOFCKJVK7NixA2PHjsWCBQsQGhqqixiJiF7J/Pnz0aRJE9y5cwchISFyh6MzdnZ2OHLkCNq2bYt79+6hR48eOH36tNxhEZGGNE7O+vfvjz179uDQoUOoUaMGQkNDcenSJezZswfe3t66iJGI6JVUq1YNa9euhYGBAX788Ufs2bNH7pB0xsbGBocPH0anTp3w8OFD9O7dG7/99pvcYRGRBjROzgCgW7duiImJwd27d/H48WP8+uuv0mOPiIj0kYeHh/QYtMDAwEq9cKuVlRUOHjwIT09P5OTkwMfHB0eOHEFRURGOHDmCTZs2Se+JSP+UKTkr9s8//yAnJ0flRUSkr+bOnYumTZsiPT0dEydOlDscnTI3N8f+/fvh7e2NR48ewcfHB3Z2dujZsyfeffdd9OzZE/Xr18eOHTvkDpWI/kXj5OzatWvw8/NDjRo1YGlpCWtra1hbW8PKykp6jBERkT56dnjzp59+wu7du+UOSaeqV6+O3bt3w83NDQUFBcjMzFTZn5aWhoCAACZoRHpG4wefDx8+HEII/PDDD7C1tYVCodBFXEREOuHu7o5PPvkEixYtQlBQELp06YLXXntN7rB0xtjYGOnp6aXuE0JAoVAgJCQE/fv3V1m7kojko3FydvbsWSQkJMDFxUUX8RAR6dycOXOwZ88eXLp0CR9//DE2bNggd0g6c+zYMaSlpT13vxACt27dwrFjx9CjR4/yC4yInkvjYc0OHTrg1q1buoiFiKhcmJmZScObGzduRGRkpNwh6cydO3e0Wo6IdE/jK2f/+9//MG7cOKSlpaFly5YwNjZW2d+6dWutBUdEpCsdO3bElClT8Pnnn2PcuHHo1q1bpRzerFOnjlbLEZHuaZyc3bt3D6mpqRg1apS0TaFQSPcucGo2EVUUs2fPxu7du3Hx4kV89NFH2Lhxo9whaV23bt3g4OCAtLQ0CCFKLWNpaYlu3bqVc2RE9DwaD2uOHj0abdu2RXx8PK5evYpr166p/JeIqKIwNTXF2rVrYWhoiE2bNlXKWYuGhoZYtmwZADx3Ald2djbmzZv33OSNiMqXxsnZjRs3sHDhQri7u6N+/fqoV6+eyouIqCLp0KEDpk6dCgAYN25cieUmKoOBAwdi27ZtqFu3rsp2R0dHvPvuuwCeTpKYNm0aEzQiPaBxctarVy+cPXtWF7EQEckiNDQULVu2xL179zBhwgS5w9GJgQMH4vr164iLi8PGjRsRFxeHa9euYcOGDVi6dCkAYNGiRZg4cSKUSqXM0RJVbRrfc/bWW29h0qRJOH/+PFq1alViQkC/fv20FhwRUXkoHt50d3fHli1bEBAQgICAALnD0jpDQ8NSl8sICQlBtWrVMG7cOCxfvhy5ubmIiIiAgcErPUSGiMpI4+Rs3LhxAJ4+BuXfOCGAiCoqNzc3TJ8+HfPnz8eHH36I7t274/XXX5c7rHITFBQEMzMzjB49GqtWrUJubi5++OEHGBlp/GuCiF6Rxn8WKZXK576YmBFRRTZr1iy0atUK9+7dQ3BwsNzhlLuRI0di48aNMDQ0xPr16/Huu++ioKBA7rCIqpxXumadm5urrTiIiGRnYmIizd7cunUrtm7dKndI5e6dd97Btm3bYGxsjK1btyIgIAB5eXlyh0VUpWicnBUVFWHevHmoW7cuatasKS2fMWvWLKxevVrrARIRlad27dphxowZAIAPP/wQd+/elTmi8ufv74/du3fDzMwMu3fvRr9+/fD48WO5wyKqMjROzhYsWIC1a9di0aJFMDExkba3bNkS//vf/7QaHBGRHD777DO0bt0amZmZ+PDDD6vk8hJ9+vTBvn37UL16dRw8eBB+fn74559/5A6LqErQODn78ccfsXLlSgwbNgyGhobS9jZt2uDy5ctaDY6ISA7Fw5tGRkbYvn07fv75Z7lDkkWvXr1w8OBBmJub48iRI/Dx8UF2drbcYRFVehonZ2lpaWjUqFGJ7UqlkjeOElGl0bZtW2l4Mzg4GBkZGTJHJI8uXbrg0KFDsLKyQnx8PLy8vPDgwQO5wyKq1DROzpo3b45jx46V2L5t2za0bdtWK0EREemDmTNnok2bNrh//36VHd4Enj4kPi4uDjY2Njh9+jR69uxZJe/FIyovGidnoaGhmDBhAhYuXAilUokdO3Zg7NixWLBgAUJDQ3URIxGRLJ4d3tyxYwe2bNkid0iycXV1xZEjR2BnZ4dz586hR48euH37ttxhEVVKGidn/fv3x549e3Do0CHUqFEDoaGhuHTpEvbs2QNvb29dxEhEJBtXV1d89tlnAIAJEyZU2eFNAGjRogWOHj0KBwcHXLp0Cd27d8fNmzflDouo0inTOmfdunVDTEwM7t69i8ePH+PXX3+Fj4+PtmMjItILM2bMgKurK+7fv4/x48dX2eFNAGjcuDGOHj0KZ2dnXLlyBZ6entKSSkSkHRonZ6dOncKJEydKbD9x4gROnz6tlaCIiPSJsbGxNLy5c+dObN68We6QZOXs7IyjR4+icePGuHHjBjw9PZGSkiJ3WESVhsbJWXBwMG7dulVie1paWpV83AkRVQ1t2rTBrFmzADwd3kxPT5c5Ink5ODjgl19+QfPmzZGWlobu3bsjOTlZ7rCIKgWNk7OLFy+iXbt2Jba3bdsWFy9e1EpQRET6aPr06Wjbti0ePHhQ5Yc3AaBOnTo4cuQIXF1dkZGRgR49eiApKUnusIgqPI2TM1NT01JviL1z5w6MjIy0EhQRkT4qHt40NjZGZGQkNm3aJHdIsnv99ddx+PBhdOjQAffv30evXr1KvfWFiNSncXLm4+OD6dOnq6wSnZWVhRkzZnC2JhFVeq1bt5aGNz/66KMqP7wJANbW1jh06BC6dOmCrKwseHt7l7oeJhGpR+PkbMmSJbh16xbq1auHnj17omfPnnB2dkZ6ejq++OILXcRIRKRXpk2bhnbt2uHBgwcYN25clR/eBAALCwtER0ejZ8+e+Pvvv9GnTx8cPnxY7rCI1FJYWIi//voLJ06cwM6dO7F3715ZJ7loPA5Zt25dnDt3Dhs2bMDZs2dRrVo1jBo1CkOHDoWxsbEuYiQi0ivFw5tubm7YtWsXNm7ciGHDhskdluxq1qyJffv2YcCAAThw4AD8/Pywc+dO9OnTR+7QqAr7559/kJaWpvL666+/VN6np6dDqVSqHNehQwe0bNlSlpjLtM5ZjRo1EBgYiBUrVmDJkiUYMWKEVhIzIQRCQ0NRp04dVKtWDV5eXvjzzz9fetyKFStQv359mJmZwd3dHSdPnlTZn5ubi+DgYLz22muoWbMmBg0aVOK+uVOnTqF3796wsrKCtbU1fH19cfbsWZUy586dQ7du3WBmZgZHR0csWrSoRCxbt25F06ZNYWZmhlatWiEqKkor50hE+qVVq1YICwsD8HR4886dOzJHpB+qVauGXbt2oV+/fsjNzUX//v2xa9cuucOiSkipVOLu3btISkrC3r17ERERgVmzZmH06NHw8fFBixYtYGVlBXNzczRt2hS9e/fGiBEjMH36dKxYsQKRkZE4deoUbt++DaVSCUNDQzg4OMDd3R0eHh6wt7eX7dzKfAf/xYsXcfPmTeTn56ts79evX5mDWbRoEb7++musW7cOzs7OmDVrFnx9fXHx4kWYmZmVesyWLVswefJkREREwN3dHV999RV8fX2RkpKC2rVrAwAmTZqEffv2YevWrbC0tMSECRMwcOBAHD9+HMDTrLpPnz7o168fvv32WxQWFiIsLAy+vr64desWjI2NkZOTAx8fH3h5eSEiIgLnz5/H6NGjYWVlhcDAQADAb7/9hqFDhyI8PBxvvvkmNm7cCH9/fyQmJkrZd1nOkYj009SpU7Fz504kJCQgKCgIu3btgkKhkDss2ZmammLbtm0YNmwYtm7dioCAAGzcuBFvv/223KFRBaFUKnHnzh1cu3btuVe70tLSUFBQoFZ9NWvWRN26dVG3bl04ODhIPz/7vnbt2jA0NERBQQGioqLg6+ur47N8AaGh1NRU0bp1a6FQKISBgYFQKBTSzwYGBppWJ1EqlcLOzk4sXrxY2paVlSVMTU3Fpk2bnntcx44dRXBwsPS+qKhI2Nvbi/DwcKkOY2NjsXXrVqnMpUuXBAARHx8vhBDi1KlTAoC4efOmVObcuXMCgPjzzz+FEEJ8++23wtraWuTl5Ullpk6dKlxcXKT3gwcPFn5+firxubu7i6CgoFc6x3/Lzs4WAER2drbax6gjPz9fREZGivz8fK3WW1mxvdTzJDdXDBo3WQwaN1nk/P2P3OFo3fnz54WJiYkAINavX//K9VWmflVQUCCGDx8uAAgDAwPx448/aq3uyt6vtE0f+1VeXp5ISUkRUVFRYvny5SIkJES89dZbonnz5sLMzEwAUOtla2sr2rVrJ9566y0xbtw4MX/+fLFmzRpx8OBBceHCBY1/V+qyrdT9/a3xlbOJEyfC2dkZsbGxcHZ2xsmTJ3H//n385z//wZIlS8qcJF67dg3p6enw8vKStllaWsLd3R3x8fEYMmRIiWPy8/ORkJCA6dOnS9sMDAzg5eWF+Ph4AEBCQgIKCgpU6m3atCmcnJwQHx+PTp06wcXFBa+99hpWr16NGTNmoKioCKtXr0azZs1Qv359AEB8fDw8PT1hYmIi1ePr64uFCxfi4cOHsLa2Rnx8PCZPnqwSo6+vLyIjI8t8jgCQl5eHvLw86X1OTg4AoKCgQO2/GtRRXJc266zM2F7qKSgolH4u1HKf1QcuLi747LPPEBoaio8//hienp6oU6dOmeurbP1q1apVMDExwQ8//ICRI0fi0aNHGDNmzCvXW9n7lbbJ1a9ycnKQmpqKq1evlnjdunWrxH1ezzI0NISjoyMcHBxgb2+PunXrSv8t/rlOnToqv5efR5Pz1mVbqVunxslZfHw8Dh8+DBsbGxgYGMDAwABdu3ZFeHg4Pv744zIvQFg8Hd3W1lZlu62t7XOnqmdmZqKoqKjUYy5fvizVa2JiAisrq+fWa25ujiNHjsDf3x/z5s0D8PT5cQcOHJDWbktPT4ezs3OJOor3WVtbIz09/YXxl+UcASA8PBxz5swpsf3gwYOoXr36c48rq5iYGK3XWZmxvV6soLBI+jn28GEYGxnKGI1utGjRAg0bNkRqaioCAgIwY8aMVx7erEz96s0330R6ejqioqIwfvx4JCYmws/PD0VFRbh48aL0B27z5s1haKhe/6gK/UoXtN2vhBDIyspCenp6idedO3ekiwnPY2pqCjs7u1Jfr7/++nPXT3348CEePnyICxcuaPV8nqWLf4OPHz9Wq5zGyVlRURHMzc0BADY2Nrh9+zZcXFxQr149jaadbtiwAUFBQdL7ffv2aRqK1jx58gRjxoxBly5dsGnTJhQVFWHJkiXw8/PDqVOnUK1aNdliA56uSv7sFbmcnBw4OjrCx8cHFhYWWvucgoICxMTEwNvbmzNv1cD2Uk9uXj42H3p6f2fvXr1Qs2YNmSPSDWdnZ3Tq1AmnTp3Cw4cPMXz48DLVU1n7lZ+fH6ZPn44vv/wSq1atwpMnTxAXF4e0tDSpTN26dfHll19iwIABL62vqvQrbXmVflVUVITr16+rXPUqvhp27do1PHr06IXH29jYoEGDBiqvhg0bomHDhrC1tdW7+zR1+W/wZclqMY2Ts5YtW+Ls2bNwdnaGu7s7Fi1aBBMTE6xcuRINGjRQu55+/frB3d1del88bJeRkaEyJJCRkQFXV9dS67CxsYGhoWGJmZcZGRmws7MDANjZ2SE/Px9ZWVkqV8+eLbNx40Zcv34d8fHxMDAwkLZZW1tj165dGDJkCOzs7Er9nOLPKP7vy2LR9ByBp39ZmJqalthubGysky9vXdVbWbG9XqzomWELo0rcVm3btkVYWBhmzpyJyZMnw9fX95Vme1XGfrVkyRLUqFED8+bNw08//VRi/+3btzFkyBBs27YNAwcOfGFdVaVfaduL+lVRURGuXr2KCxcuSK+LFy/i8uXLKrfW/JuBgQEcHR2lhOvfL21eRChPuvg3qG59Gidnn332mZQlz507F2+++Sa6deuG1157DVu2bFG7HnNzc+kKHPD00qidnR1iY2OlRCUnJwcnTpzA+PHjS63DxMQEbm5uiI2Nhb+/P4CnMzxiY2MxYcIEAICbmxuMjY0RGxuLQYMGAQBSUlJw8+ZNeHh4AHh6mdHAwEAley9+Xzwe7uHhgZkzZ6KgoEBq3JiYGLi4uMDa2loqExsbi5CQEKmemJgY6XOcnZ01PkciqjimTJmCnTt34vTp0wgKCsLu3bv17qqAnBQKBcLCwrBs2bJSryAIIaBQKBASEoL+/furPcRJmikqKsK1a9dKTcJyc3NLPcbMzEzlitezr/r166t13xdpQBuzD+7fvy+USuUr1/P5558LKysrsWvXLnHu3DnRv39/4ezsLJ48eSKV6dWrl1i+fLn0fvPmzcLU1FSsXbtWXLx4UQQGBgorKyuRnp4ulRk3bpxwcnIShw8fFqdPnxYeHh7Cw8ND2n/p0iVhamoqxo8fLy5evCiSk5PF8OHDhaWlpbh9+7YQ4umsSltbW/Hee++J5ORksXnzZlG9enXx/fffS/UcP35cGBkZiSVLlohLly6JsLAwYWxsLM6fP6/ROb4MZ2vqB7aXeqrarLrk5GRp9ua6des0Pr6y96u4uDi1ZuDFxcW9sJ6q1q/KoqioSFy5ckXs2rVLzJs3T3Tv3l24urq+cCZktWrVRLt27cTw4cNFeHi42L17t0hNTRVFRUVyn065qZCzNUtTq1YtbVSDKVOm4NGjRwgMDERWVha6du2K6OholfW/UlNTkZmZKb1/5513cO/ePYSGhiI9PR2urq6Ijo5Wuel+6dKlMDAwwKBBg5CXlwdfX198++230v6mTZtiz549mDNnDjw8PGBgYIC2bdsiOjpaGn60tLTEwYMHERwcDDc3N9jY2CA0NFRa4wwAOnfujI0bN+Kzzz7DjBkz0LhxY0RGRqqsMKzOORJRxdWiRQvMnj0bM2bMQHBwMGxtbfH666+XKGdjYwMnJycZIpSXuov1clFf9SmVSly/fr3ElbBLly7hyZMnpR5jZmaGZs2aoXnz5mjRooX0ql+/Pq9Y6gGFEC9/KNzLxv6ftWPHjlcKiF4uJycHlpaWyM7O1vqEgKioKPTt25f3b6iB7aWe3Lw8DA+ZAQBYs3guzKvAjdtXr15Fo0aNXvjMTTMzM6SkpJRI0Cp7vzpy5Ah69uz50nJxcXHo0aPHc/dXxX6lVCpx48YNlQTswoULuHTp0nNnAZqamqJZs2Zo1qwZDAwMMGDAALRp0wbOzs5Mwp5Dl/8G1f39rdaVM0tLS60FRkRU2WVlZb30Yei5ubnIzMysclfPunXrBgcHB6SlpT23jRwcHNCtW7dyjky/KJVK/PHHH0hMTERCQgISEhKQlJT03Nl+pqamaNq0KVq0aKFyNaxBgwYqq95X1qS/slErOVuzZo2u4yAioirA0NAQy5YtQ0BAABQKRakJWr169aRnHVYFRUVFuHz5skoidubMGfzzzz8lypqYmEhJ2LOJWIMGDZ67JhhVPGX+P3n37l1pXTMXFxfpOZZEREQvMnDgQGzbtg0TJ07EX3/9JW23sbHBw4cPcfz4cQwePBibN28udRkhORUVFeHYsWO4c+cO6tSpg27dummURBYWFuLixYtSIpaYmIgzZ86UOixZvXp1uLq6ol27dnBzc4ObmxuaNm3KK19VgMbJWU5ODoKDg7F582YUFT1dodnQ0BDvvPMOVqxYwSFQIiJ6qYEDB6J///4lEp3o6GgMGjQIkZGRUhIn90LgxXbs2FEioXRwcMCyZctKvTc7Pz8fFy5cUEnEzp49W+pyFTVr1kTbtm2lRKxdu3Zo2rRplbl6SKo0Ts7Gjh2LpKQk7N27V1q/Kz4+HhMnTkRQUBA2b96s9SCJiKjyMTQ0LHHTv5+fH/bs2YP+/fsjKioKfn5+2L17N2rWrClPkP9nx44dCAgIKDEMm5aWhoCAAGzatAkNGzZUScTOnTuH/Pz8EnVZWFigbdu20tWwdu3aoXHjxkzESKJxcrZ3714cOHAAXbt2lbb5+vpi1apV6NOnj1aDIyKiqsfb2xvR0dHw8/NDXFwcfHx8EBUVVeIZyeWlqKgIEydOLPX+uOJtQ4YMKfVYKysrlWHJdu3aoWHDhtLTaIhKo3Fy9tprr5U6dGlpaSmtlE9ERPQqPD09ERsbiz59+iA+Ph49e/bEwYMHS10zTteOHTumMpT5PObm5ujUqZOUhLm5ucHZ2ZlPiSCNlenxTZMnT8b69eulZ0Wmp6fj008/xaxZs7QeIBFRRWNjYwMzM7PnPgoHeLr0gY2NTTlGVfF07NgRR44cgbe3N86cOYPu3bsjJiYGdevWLdc41F0QNyIiAu+++66Oo6GqQOPk7LvvvsOVK1fg5OQkrc9z8+ZNmJqa4t69e/j++++lsomJidqLlIiognByckJKSorK00yApzP1Ro0ahYsXL6Jz585wdHSUKcKKo3Xr1jh27Bh69+6NS5cuSVfU7P7v6S269scff2DFihVqlX2VB90TPUvj5Kz4AeNERPR8z/4B+6ytW7eibdu2iIuLw48//oiRI0fKEF3F0qRJExw7dgxeXl5ITU2Fp6cnjv36q04/8/79+5gzZw6+++47FBYWvrCsQqHgwrmkVRonZ2FhYbqIg4ioSmjevDnmzJmD6dOnY+LEifDy8ir3YbqKqH79+jh69Ch69eqFlJQUDB/+HmxbtNf65+Tl5eGbb77B/PnzkZWVBeDpDFIfHx+EhIQAgMrEgOL7yb766ivOtiSt0Xi6yK1bt1RujDx58iRCQkKwcuVKrQZGRFRZffLJJ+jQoQOys7MRGBj40kc90VP29vbYvXs3LCwsEB//m1brFkJg69ataN68OT755BNkZWWhTZs2OHToEPbu3YuPP/4Y27ZtK5FIOzg4YNu2bRo9g5roZTROzt59913ExcUBeDoRwMvLCydPnsTMmTMxd+5crQdIRFTZGBkZYe3atTAxMUFUVBR+/PFHuUOqMJo0aYL169drtc4TJ06ga9euGDx4MK5evYo6depg9erVSEhIQO/evaVyAwcOxPXr1xEXF4eNGzciLi4O165dY2JGWqdxcpacnIyOHTsCAH7++We0atUKv/32GzZs2IC1a9dqOz4iokqpeHgTACZOnIi0tDSZI6o4+vXrh+nTp0vvd+7cUaZ6rl+/jqFDh6JTp0747bffUL16dYSFheGPP/7A6NGjSx2mLF44d+jQoejRoweHMkknNE7OCgoKpGedHTp0CP369QMANG3aVO3pxkRE9HR4s2PHjhzeLIPp02dIP48ePRpffPGF2u2XnZ2NqVOnomnTpti8eTMUCgVGjRqFP/74A7Nnz5b9aQREGidnLVq0QEREBI4dO4aYmBjpqQC3b9/Ga6+9pvUAiYgqKyMjI6xZs0Ya3ly3bp3cIVUYBgaqC7t+8sknGDt2LP7888/nHlNYWIhvv/0WjRo1wqJFi5CXl4devXohMTERP/zwAydmkN7QODlbuHAhvv/+e+mybps2bQAAu3fvloY7iYhIPc8Ob4aEhHB4swzmz58PAFi9ejWaNGmCrl27YvXq1fj7778BPL3Zf+/evWjVqhWCg4ORmZmJpk2bYs+ePTh06BBcXV1ljJ6oJI2Tsx49eiAzMxOZmZn44YcfpO2BgYGIiIjQanBERFXBs8ObH374IYc3NRQcPAExMTHo27cvDAwMcPz4cXzwwQews7PDiBEj4O3tjbfeeguXL1+GjY0NvvnmG5w7dw5vvvkmH61EeqlMT141NDQs8RzN+vXro3bt2loJioioKnl2eHP//v04fPiw3CFVOF5eXti3bx9u3bqFzz//HC4uLnj8+DHWr1+P2NhYmJiYYMqUKbhy5QqCg4NhbGwsd8hEz6XWIrTt2rVDbGwsrK2t0bZt2xf+pcFHNhERae7ZxWlXr16NyZMno379+nKHVeHY29tj6tSpmDJlCn7//Xf89NNPMDAwwH/+8x+2J1UYaiVn/fv3l2Zo8vFNRES68cknn2DHjh04deoUPvzwQ+zbt4/DbmWkUCjg4eEBDw8PuUMh0phaydmzj2zi45uIiHTDyMgIq1atQvv27bF//36sW7cO77//vtxhEVE5K9M9Z0REpBvNmzfH0KFDAXD2JlFVpdaVM2tra7UvrT948OCVAiIiqur8/f1x+fJlnDp1CoGBgdi7dy+HN4mqELWSs6+++kr6+f79+5g/fz58fX2lsfz4+HgcOHAAs2bN0kmQRERViaGhIVatWoWOHTtKi9NyeJOo6lArORs5cqT086BBgzB37lxMmDBB2vbxxx/jm2++waFDhzBp0iTtR0lEVMU8O3szJCQE3t7eXMGeqIrQ+J6zAwcOSI9selafPn1w6NAhrQRFRERPZ2926NAB2dnZCAoK4uK0RFWExsnZa6+9hl27dpXYvmvXLj5bk4hIi4yMjLB27VqYmJhg3759+PHHH+UOiYjKgVrDms+aM2cOPvjgAxw5cgTu7u4AgBMnTiA6OhqrVq3SeoBERFXZs8ObEydOhJeXF4c3iSo5ja+cvf/++zh+/DgsLCywY8cO7NixAxYWFvj11195wyoRkQ5weJOoatH4yhkAuLu7Y8OGDdqOhYiISlH87M127dph3759WL9+PUaMGCF3WESkI1yEloioAmjRogVmz54NAJg4cSJu374tb0BEpDNMzoiIKohPP/0U7du3R1ZWFgIDAzm8SVRJMTkjIqog/j17c/369XKHREQ6wOSMiKgC4fAmUeWnV8mZEAKhoaGoU6cOqlWrBi8vL/z5558vPW7FihWoX78+zMzM4O7ujpMnT6rsX7lyJXr06AELCwsoFApkZWWVqOPBgwcYNmwYLCwsYGVlhTFjxuCff/5RKXPu3Dl069YNZmZmcHR0xKJFi0rUs3XrVjRt2hRmZmZo1aoVoqKitHKORETFnh3e5OxNospH4+Ts0aNHmDVrFjp37oxGjRqhQYMGKq9XsWjRInz99deIiIjAiRMnUKNGDfj6+iI3N/e5x2zZsgWTJ09GWFgYEhMT0aZNG/j6+uLu3btSmcePH6NPnz6YMWPGc+sZNmwYLly4gJiYGOzduxdHjx5FYGCgtD8nJwc+Pj6oV68eEhISsHjxYsyePRsrV66Uyvz2228YOnQoxowZg6SkJPj7+8Pf3x/JycmvdI5ERM8qnr1pYmKCvXv3cniTqLIRGhoyZIioU6eOmDJlili6dKn46quvVF5lpVQqhZ2dnVi8eLG0LSsrS5iamopNmzY997iOHTuK4OBg6X1RUZGwt7cX4eHhJcrGxcUJAOLhw4cq2y9evCgAiFOnTknb9u/fLxQKhUhLSxNCCPHtt98Ka2trkZeXJ5WZOnWqcHFxkd4PHjxY+Pn5qdTt7u4ugoKCXukc/y07O1sAENnZ2Wofo478/HwRGRkp8vPztVpvZcX2Us+T3FwxaNxkMWjcZJHz9z9yh6P3NOlXCxYsEACElZWVuH37djlEpz/YrzTD7yv16bKt1P39rfE6Z/v378e+ffvQpUsXrSaJ165dQ3p6Ory8vKRtlpaWcHd3R3x8PIYMGVLimPz8fCQkJGD69OnSNgMDA3h5eSE+Pl7tz46Pj4eVlRXat28vbfPy8oKBgQFOnDiBAQMGID4+Hp6enjAxMZHK+Pr6YuHChXj48CGsra0RHx+PyZMnq9Tt6+uLyMjIMp8jAOTl5SEvL096n5OTAwAoKChAQUGB2uf5MsV1abPOyoztpZ6CgkLp50It99nKSJN+NWnSJGzfvh2JiYkYO3YsduzYAYVCoesQ9QL7lWb4faU+XbaVunVqnJxZW1ujVq1aGgf0Munp6QAAW1tble22trbSvn/LzMxEUVFRqcdcvnxZo8+uXbu2yjYjIyPUqlVL+uz09HQ4OzuX+JzifdbW1khPT39h/GU5RwAIDw/HnDlzSmw/ePAgqlevrs4paiQmJkbrdVZmbK8XKygskn6OPXwYxkaGMkZTcajbr0aOHIlz585h3759mDp1Knr06KHbwPQE+1XZ8PtKfbpoq8ePH6tVTuPkbN68eQgNDcW6deteKTHYsGEDgoKCpPf79u0rc12V3fTp01WuyOXk5MDR0RE+Pj6wsLDQ2ucUFBQgJiYG3t7eMDY21lq9lRXbSz25efnYfOg4AKB3r16oWbOGzBHpt7L0q6ysLISFhWHdunWYNGkS6tSpo+Mo5cd+pRl+X6lPl21VPPL1MhonZ1988QVSU1Nha2uL+vXrlwg8MTFRrXr69esnPTgdgDRsl5GRofLFkpGRAVdX11LrsLGxgaGhITIyMlS2Z2RkwM7OTq04AMDOzk5lAgEAFBYW4sGDB1I9dnZ2pX5O8b4XlXl2v6bnCACmpqYwNTUtsd3Y2Fgn/8h0VW9lxfZ6sSKlUvrZiG2lNk361YwZM7B7924kJCTgo48+QmRkZKUf3mS/Kht+X6lPF22lbn0aJ2f+/v6aHlIqc3NzmJubS++FELCzs0NsbKyUqOTk5ODEiRMYP358qXWYmJjAzc0NsbGxUlxKpRKxsbGYMGGC2rF4eHggKysLCQkJcHNzAwAcPnwYSqVSSiA9PDwwc+ZMFBQUSI0bExMDFxcXWFtbS2ViY2MREhIi1R0TEwMPDw8AgLOzs8bnSET0MsWzN93c3LB7925s3LgRw4YNkzssIiojjZOzsLAwXcQBhUKBkJAQzJ8/H40bN4azszNmzZoFe3t7lYSwd+/eGDBggJR8TZ48GSNHjkT79u3RsWNHfPXVV3j06BFGjRolHZOeno709HRcuXIFAHD+/HmYm5vDyckJtWrVQrNmzdCnTx+MHTsWERERKCgowIQJEzBkyBDY29sDAN59913MmTMHY8aMwdSpU5GcnIxly5Zh6dKl0udMnDgR3bt3xxdffAE/Pz9s3rwZp0+flpbbUPcciYg01apVK4SGhmLWrFn46KOP0Lt3b41GEIhIf2icnOnSlClT8OjRIwQGBiIrKwtdu3ZFdHQ0zMzMpDKpqanIzMyU3r/zzju4d+8eQkNDkZ6eDldXV0RHR6vcdB8REaFyQ72npycAYM2aNXj//fcBPL0HbsKECejduzcMDAwwaNAgfP3119IxlpaWOHjwIIKDg+Hm5gYbGxuEhoaqrIXWuXNnbNy4EZ999hlmzJiBxo0bIzIyEi1bttToHImIymLq1KnYuXMnEhMTMW7cOOzcubPSD28SVUYKIV6+tHStWrXwxx9/wMbGBtbW1i/8x/7gwQOtBkgl5eTkwNLSEtnZ2VqfEBAVFYW+ffvyngQ1sL3Uk5uXh+EhTxeAXrN4Lsx54/YLvWq/On/+PNzc3FBQUIANGzbg3Xff1UGU8mO/0gy/r9Sny7ZS9/e3WlfOli5dKt0f9tVXX2klQCIi0r5/D2/26tWLw5tEFYxaydnIkSNL/ZmIiPTP1KlTsWPHDiQlJWH8+PFVanFaospArx58TkREr87Y2Bhr166FsbExIiMjsXnzZrlDIiINMDkjIqqEWrdujVmzZgEAJkyYUGINRiLSX0zOiIgqqWnTpsHV1RUPHjzA+PHjocb8LyLSA0zOiIgqqeLhTSMjI+zcuRNbtmyROyQiUoNGyVlBQQGMjIyQnJysq3iIiEiL2rRpg88++wwAhzeJKgqNkjNjY2M4OTmhqKhIV/EQEZGWzZgxA66urrh//z4+/PBDDm8S6TmNnxAwc+ZMzJgxA+vXr0etWrV0ERMREWmRsbEx1qxZg/bt22PHjh1YuHAhfHx8SpSzsbGBk5OTDBES0bM0Ts6++eYbXLlyBfb29qhXrx5q1FBdlTkxMVFrwRERkXbUqlVLWuts+vTpmD59eokyZmZmSElJYYJGJDONkzM+oJuIqOLJzMxEYWHhC8vk5uYiMzOTyRmRzDROzsLCwnQRBxERERGhDMlZsYSEBFy6dAkA0KJFC7Rt21ZrQRERERFVVRonZ3fv3sWQIUNw5MgRWFlZAQCysrLQs2dPbN68Ga+//rq2YyQiIiKqMjRehPajjz7C33//jQsXLuDBgwd48OABkpOTkZOTg48//lgXMRIRERFVGRpfOYuOjsahQ4fQrFkzaVvz5s2xYsWKUqdmExEREZH6NL5yplQqYWxsXGK7sbExlEqlVoIiIiIiqqo0Ts569eqFiRMn4vbt29K2tLQ0TJo0Cb1799ZqcEREpB02NjYwMzN7YRljY2PY2NiUU0RE9DxlWoS2X79+qF+/PhwdHQEAt27dQsuWLfHTTz9pPUAiInp1Tk5OSElJQWZmZol9K1aswA8//AALCwtUr15dhuiI6FkaJ2eOjo5ITEzEoUOHcPnyZQBAs2bN4OXlpfXgiIhIe5ycnEpdYPbbb7/F77//josXL+Ljjz/Gxo0bZYiOiIpplJwVFBSgWrVqOHPmDLy9veHt7a2ruIiIqJyYmppi7dq16NSpEzZt2oS3334bAwYMkDssoipLo3vOjI2N4eTkhKKiIl3FQ0REMujQoQOmTJkCABg/fjzu378vc0REVZfGEwJmzpyJGTNm4MGDB7qIh4iIZBIWFoZmzZohIyMDEydOlDscoiqrTBMCrly5Ant7e9SrVw81atRQ2Z+YmKi14IiIqPyYmZlhzZo16Ny5MzZs2IC3334b/fv3lzssoipH4+TM399fB2EQEZE+cHd3xyeffIJFixZh3Lhx6NatG2rVqiV3WERVikbJWWFhIRQKBUaPHg0HBwddxURERDKaM2cOdu/ejcuXLyMkJAQ//vij3CERVSka3XNmZGSExYsXo7CwUFfxEBGRzIqHNw0MDLB+/Xrs2bNH7pCIqpQyPSHgl19+0UUsRESkJzp16oTJkycDAIKCgvDw4UOZIyKqOjS+5+yNN97AtGnTcP78ebi5uZWYENCvXz+tBUdERPKZO3cudu/ejT/++AOTJk3C2rVr5Q6JqErQODn78MMPAQBffvlliX0KhYJroBERVRLVqlXDmjVr0LVrV6xbtw5vv/02/Pz85A6LqNLTeFhTqVQ+98XEjIiocuncuTMmTZoEAAgMDERWVpa8ARFVARonZ0REVLXMmzcPjRs3xu3bt6X70IhId9ROzvr27Yvs7Gzp/eeff67yF9T9+/fRvHlzrQZHRETyq169On744QcoFAqsWbMG+/fvlzskokpN7eTswIEDyMvLk97/97//VXmEU2FhIVJSUrQbHRER6YWuXbtKj3QaO3asyh/rRKRdaidnQogXviciosptwYIFaNiwIdLS0vCf//xH7nCIKi29uudMCIHQ0FDUqVMH1apVg5eXF/7888+XHrdixQrUr18fZmZmcHd3x8mTJ1X2r1y5Ej169ICFhQUUCkWJG1qvX7+OMWPGwNnZGdWqVUPDhg0RFhaG/Px8lXLnzp1Dt27dYGZmBkdHRyxatKhELFu3bkXTpk1hZmaGVq1aISoqSivnSEQkt+rVq2PNmjVQKBRYvXo1Dhw4IHdIRJWS2smZQqGAQqEosU2bFi1ahK+//hoRERE4ceIEatSoAV9fX+Tm5j73mC1btmDy5MkICwtDYmIi2rRpA19fX9y9e1cq8/jxY/Tp0wczZswotY7Lly9DqVTi+++/x4ULF7B06VJERESolM/JyYGPjw/q1auHhIQELF68GLNnz8bKlSulMr/99huGDh2KMWPGICkpCf7+/vD390dycvIrnSMRkb7o1q0bPvroIwDABx98gJycHJkjIqqEhJoUCoXo27evGDBggBgwYIAwMjISPj4+0vu+ffsKAwMDdasrQalUCjs7O7F48WJpW1ZWljA1NRWbNm167nEdO3YUwcHB0vuioiJhb28vwsPDS5SNi4sTAMTDhw9fGs+iRYuEs7Oz9P7bb78V1tbWIi8vT9o2depU4eLiIr0fPHiw8PPzU6nH3d1dBAUFvdI5/lt2drYAILKzs9U+Rh35+fkiMjJS5Ofna7XeyortpZ4nubli0LjJYtC4ySLn73/kDkfvVYR+9c8//4gGDRoIACIwMFCWGNivNFMR+pW+0GVbqfv7W+0rZyNHjkTt2rVhaWkJS0tLDB8+HPb29tL72rVrY8SIEWVOEq9du4b09HR4eXlJ2ywtLeHu7o74+PhSj8nPz0dCQoLKMQYGBvDy8nruMerKzs5GrVq1pPfx8fHw9PSEiYmJtM3X1xcpKSnSY03i4+NVYikuUxxLWc6RiEjf1KhRA6tXrwbw9LaRQ4cOyRwRUeWi9hMC1qxZo8s4kJ6eDgCwtbVV2W5rayvt+7fMzEwUFRWVeszly5fLHMuVK1ewfPlyLFmyRCU+Z2fnEp9TvM/a2hrp6ekvjL8s5wgAeXl5KjNli4cRCgoKUFBQoOnpPVdxXdqsszJje6mnoKBQ+rlQy322Mqoo/apLly4YP348vvvuO+lWDnNz83L7fPYrzVSUfqUPdNlW6tap8eObtGXDhg0ICgqS3u/bt0+uUFSkpaWhT58+ePvttzF27Fi5wwEAhIeHY86cOSW2Hzx4ENWrV9f658XExGi9zsqM7fViBYX//8khsYcPw9jIUMZoKo6K0K88PT2xfft23Lx5E8OGDcO4cePK7bPZr8qmIvQrfaGLtnr8+LFa5WRLzvr16wd3d3fpffGVoYyMDNSpU0fanpGRAVdX11LrsLGxgaGhITIyMlS2Z2RkwM7OTuOYbt++jZ49e6Jz584qN/oDgJ2dXamfU7zvRWWe3V+8Td1zBIDp06errMqdk5MDR0dH+Pj4wMLCQsOzfL6CggLExMTA29sbxsbGWqu3smJ7qSc3Lx+bDx0HAPTu1Qs1a9aQOSL9VtH6Va1ateDr64vo6GhMmjQJPXv2LJfPZb/STEXrV3LSZVupO4FGtuTM3Nxc5RK4EAJ2dnaIjY2VEpWcnBycOHEC48ePL7UOExMTuLm5ITY2Fv7+/gCePvszNjYWEyZM0CietLQ09OzZE25ublizZg0MDFRvx/Pw8MDMmTNRUFAg/c+KiYmBi4sLrK2tpTKxsbEICQmRjouJiYGHhwcAwNnZWeNzBABTU1OYmpqW2G5sbKyTf2S6qreyYnu9WJFSKf1sxLZSW0XpVz4+Phg3bhwiIiIQFBSE8+fPo2bNmjr/XParsqko/UpOd+7cwYkTJ+Dm5gYHBwet1q1u2+vNOmcKhQIhISGYP38+du/ejfPnz2PEiBGwt7eXEi8A6N27N7755hvp/eTJk7Fq1SqsW7cOly5dwvjx4/Ho0SOMGjVKKpOeno4zZ87gypUrAIDz58/jzJkz0hMO0tLS0KNHDzg5OWHJkiW4d+8e0tPTVe4De/fdd2FiYoIxY8bgwoUL2LJlC5YtW6ZyRWvixImIjo7GF198gcuXL2P27Nk4ffq0lCiqe45ERBXJokWL4OTkhOvXr2PatGlyh0OktqysLBw6dAj//e9/MWDAADg4OKBevXoIDw+XdaKLbFfOSjNlyhQ8evQIgYGByMrKQteuXREdHQ0zMzOpTGpqKjIzM6X377zzDu7du4fQ0FCkp6fD1dUV0dHRKjfdR0REqNyz5enpCeDpJIf3338fMTExuHLlCq5cuVIiSxb/9yQES0tLHDx4EMHBwXBzc4ONjQ1CQ0MRGBgole3cuTM2btyIzz77DDNmzEDjxo0RGRmJli1banSOREQVibm5OVavXg1vb2+sWLECAQEB6NGjh9xhEal48uQJkpKScOrUKZw8eRKnTp0qdRF4hUIBR0dHra/lqgmFEHwOU0WTk5MDS0tLZGdna/2es6ioKPTt25eXvdXA9lJPbl4ehoc8XdB5zeK5MOe9QS9UkftVUFAQVq5ciQYNGuDcuXOoUUN3/6/ZrzRTkftVWRQWFiI5ORmnTp2SkrHk5GQUFRWVKOvs7IwOHTqgQ4cO6NixI1q1aoWjR4/qpK3U/f2tV1fOiIio4lq8eDH279+Pq1evYvr06fj666/lDomqACEErly5Il0NO3XqFJKSkvDkyZMSZW1tbVUSsfbt28PGxkaljD4sN8LkjIiItMLCwgKrVq1Cnz59sHz5cgQEBEi3kRBpS1pamkoidvr06RLPzAae9sf27durJGMODg6yDleqi8kZERFpja+vL8aMGYPVq1dj9OjROHfunE7WY6Sq4cGDBzh9+rTKfWJ37twpUc7U1BRt27aVErEOHTqgSZMmJVZeqCiYnBERkVZ98cUXOHDgAFJTUzFz5kwsXbpU7pCoAnj06JF0w35xMpaamlqinIGBAVq2bKmSiLVq1apS3UvH5IyIiLTK0tISK1euRN++fbFs2TIEBASgS5cucodFeubq1auIjY3FiRMncOrUKSQnJ0P5zPp1xRo1aqSSiLVt21ank030AZMzIiLSujfeeAOjRo3CmjVrMGrUKJw9exbVqlWTOyyS0f379xEXF4eYmBgcOnQIV69eLVGmTp066Nixo5SItW/fHrVq1ZIhWnkxOSMiIp348ssvceDAAfz555+YNWsWlixZIndIVI5yc3Nx/PhxHDp0CDExMUhMTMSzq3cZGRmhU6dO6Natm5SQ1a1bV8aI9QeTMyIi0gkrKyusXLkSb775Jr788ksMHDgQnTt3ljss0hGlUomzZ89KydixY8eQm5urUqZFixbw8vKCt7c3PD09VR7jSP8fkzMiItIZPz8/jBgxAj/++CNGjx6NpKQkDm9WIjdu3JCGKWNjY1We4AM8HaYsTsZ69+4Ne3t7mSKtWJicERGRTn311VeIiYlBSkoKZs+ejYULF8odEpXRw4cPERcXJ10dK35mdbGaNWuiR48eUkLWrFmzCrGumL5hckZERDplbW2N77//Hv369cOSJUswcOBAuLu7yx0WqSEvLw/x8fHS1bHTp0+rzKg0NDSEu7u7lIy5u7tXqiUt5MLkjIiIdO6tt97C8OHD8dNPP2HUqFFITEyEmZmZ3GHRvyiVSiQnJ0vJ2NGjR/H48WOVMk2bNoW3tze8vLzQvXt3WFpayhRt5cXkjIiIysWyZcsQExODS5cuYc6cOQgPD5c7JAJw69YtaZgyNjYWd+/eVdlva2sLLy8v6eXg4CBTpFUHkzMiIioXtWrVQkREBAYMGIDFixdj0KBBaN++vdxhVTnZ2dnSfWOHDh1CSkqKyv7q1auje/fu0lBly5Yted9YOWNyRkRE5cbf3x9DhgzB5s2bMWrUKJw+fRqmpqZyh1WpKZVKnDhxAhs3bkR4eDhOnTqlct+YgYEBOnToIA1Venh4wMTERMaIickZERGVq+XLlyM2NhbJyclYsGAB5s6dK3dIlY5SqcRvv/2Gbdu2Yfv27fjrr79U9jdp0kQapuzZsyesrKzkCZRKxeSMiIjKlY2NDVasWIHBgwcjPDwcAwcOhKurq9xhVXiFhYU4evQotm/fjh07diA9PV3aV7NmTbRu3RojR46Er68v6tWrJ2Ok9DJMzoiIqNwFBARg4MCB2LFjB0aNGoWTJ09yCYYyKCgowOHDh7F9+3bs3LlTZRFYS0tL9OvXDwEBAejZsycOHz6Mvn37sp0rACZnRERU7hQKBb799lscOXIEZ86cweeff45Zs2bJHVaFkJeXh5iYGGzfvh27du3Cw4cPpX21atWCv78/AgIC0Lt3b+nesYKCArnCpTJgckZERLKwtbXF119/jeHDh2Pu3Llo0qQJGjduXKKcjY0NnJycZIhQfzx58gTR0dHYvn079uzZg5ycHGlf7dq1MWDAAAQEBKB79+68MlYJMDkjIiLZdO3aFQYGBigsLMSQIUNKLWNmZoaUlJQql6D9888/iIqKwvbt27Fv3z48evRI2mdvb4+BAwciICAAXbt2haGhoYyRkrYxOSMiItncv39fZVmH0uTm5iIzM7NKJGfZ2dnYu3cvtm/fjv379yM3N1fa5+TkhEGDBiEgIACdOnWCgYGBjJGSLjE5IyIiktGDBw+we/dubN++HQcPHkR+fr60r2HDhlJC1r59ey4GW0UwOSMiInpFN2/eVJkp+W//vm/u3r17iIyMxPbt2xEbG4vCwkJpn4uLC95++20MGjQIbdq0YUJWBTE5IyIiegU3b96Ei4uLyhDkv5mZmeHo0aM4deoUtm/fjiNHjqgM57Zq1QoBAQEYNGgQmjdvzoSsimNyRkRE9AoyMzNfmJgBT++b69ixo8q2du3aSQlZkyZNdBkiVTBMzoiIiMqJu7u7tABvgwYN5A6H9BSTMyIionKwb98+9O3bV+4wqALgPFwiIpKNjY0NzMzMXljGxMQENjY25RSR7tjZ2ckdAlUQvHJGRESycXJyQkpKSomZjkIIfPrpp4iLi0PDhg31OrF5dqYlkTYwOSMiIlk5OTmVusDspk2b0KJFC1y6dAnz58/H3LlzZYjuxR4/foxPPvlE7jCokuGwJhER6SVbW1t89913AID//ve/OH36tMwRqXr48CF8fHxw7NgxuUOhSobJGRER6a23334bgwcPRlFREUaOHIm8vDy5QwIApKWloVu3bjh+/DgsLCxgYmLywvJmZmaV4r45Kh8c1iQiIr22YsUKHDlyBBcvXkRYWBg+//xzWeNJSUmBj48Pbt68CXt7e0RHR8PS0lKjJwQQvQiTMyIi0ms2NjZYuXIl/P39sXjxYgwePBjNW7SQJZZTp06hb9++yMzMROPGjXHw4EHUr18fAJh8kdZwWJOIiPRe//79MWTIECiVSsyaNUuWGGJiYtCzZ09kZmbCzc0Nv/76q5SYEWmTXiVnQgiEhoaiTp06qFatGry8vPDnn3++9LgVK1agfv36MDMzg7u7O06ePKmyf+XKlejRowcsLCygUCiQlZX13Lry8vLg6uoKhUKBM2fOqOw7d+4cunXrBjMzMzg6OmLRokUljt+6dSuaNm0KMzMztGrVClFRUVo5RyKiqm7evHkwNDREVFQUTp48Va6fvWXLFvj5+eHRo0fw8vJCXFwcateuXa4xUNWhV8nZokWL8PXXXyMiIgInTpxAjRo14Ovr+8Jnlm3ZsgWTJ09GWFgYEhMT0aZNG/j6+uLu3btSmcePH6NPnz6YMWPGS2OYMmUK7O3tS2zPycmBj48P6tWrh4SEBCxevBizZ8/GypUrpTK//fYbhg4dijFjxiApKQn+/v7w9/dHcnLyK50jEREBjRo1wogRIwAA//3vgnL73G+++QZDhw5FQUEB3nnnHezduxfm5ubl9vlUBQk9oVQqhZ2dnVi8eLG0LSsrS5iamopNmzY997iOHTuK4OBg6X1RUZGwt7cX4eHhJcrGxcUJAOLhw4el1hUVFSWaNm0qLly4IACIpKQkad+3334rrK2tRV5enrRt6tSpwsXFRXo/ePBg4efnp1Knu7u7CAoKeqVz/Lfs7GwBQGRnZ6t9jDry8/NFZGSkyM/P12q9lRXbSz1PcnPFoHGTxaBxk0XO3//IHY7eY796sdTUVGFkZCQMjYx03q+USqUIDQ0VAAQAERwcLAoLC3XyWbrGfqU+XbaVur+/9WZCwLVr15Ceng4vLy9pm6WlJdzd3REfH48hQ4aUOCY/Px8JCQmYPn26tM3AwABeXl6Ij4/X6PMzMjIwduxYREZGonr16iX2x8fHw9PTU2W6tK+vLxYuXIiHDx/C2toa8fHxmDx5sspxvr6+iIyMLPM5Ak+HWp+dPp6TkwMAKCgoQEFBgUbn+SLFdWmzzsqM7aWegoL/v3p6oZb7bGXEfvVijo6OGDFiBNb9+KO0TRf9qqioCB9//DFWrVoFAAgNDcXMmTOhVCqhVCq1+lnlgf1KfbpsK3Xr1JvkLD09HcDTRQefZWtrK+37t8zMTBQVFZV6zOXLl9X+bCEE3n//fYwbNw7t27fH9evXS43P2dm5xOcU77O2tkZ6evoL4y/LOQJAeHg45syZU2L7wYMHS00kX1VMTIzW66zM2F4vVlBYJP0ce/gwjI0MZYym4mC/ej53d3ds2LhReq/tfpWfn4+lS5ciPj4eCoUCQUFBaNeuHfbv36+1z5AL+5X6dNFWjx8/VqucbMnZhg0bEBQUJL3ft2+fXKFg+fLl+Pvvv1WuwOmT6dOnq1yRy8nJgaOjI3x8fGBhYaG1zykoKEBMTAy8vb1hbGystXorK7aXenLz8rH50HEAQO9evVCzZg2ZI9Jv7FfqOXX6NB7+38+9evaEuXlNrdSbk5ODgIAAxMfHw8TEBOvWrcOgQYO0Urec2K/Up8u2Kh75ehnZkrN+/frB3d1del88bJeRkYE6depI2zMyMuDq6lpqHTY2NjA0NERGRobK9oyMDI0eknv48GHEx8fD1NRUZXv79u0xbNgwrFu3DnZ2dqV+DgDps55X5tn9mp4jAJiampaIDQCMjY118o9MV/VWVmyvFyt6ZgjIiG2lNvarF5syZSqmf/EtAGDLzz9j/LiglxzxchkZGXjjjTeQlJQEc3NzREZGolevXq9crz5hv1KfLtpK3fpkm61pbm6ORo0aSa/mzZvDzs4OsbGxUpmcnBycOHECHh4epdZhYmICNzc3lWOUSiViY2Ofe0xpvv76a5w9exZnzpzBmTNnpOUvtmzZggULns4I8vDwwNGjR1XGi2NiYuDi4gJra2upzLOxFJcpjsXZ2VnjcyQiopIcHR2kn6dOnYJLly69Un1Xr15Fly5dkJSUhNq1a+PIkSOVLjGjikNvltJQKBQICQnB/PnzsXv3bpw/fx4jRoyAvb09/P39pXK9e/fGN998I72fPHkyVq1ahXXr1uHSpUsYP348Hj16hFGjRkll0tPTcebMGVy5cgUAcP78eZw5cwYPHjwA8HRV55YtW0qvJk2aAAAaNmwIB4enXwDvvvsuTExMMGbMGFy4cAFbtmzBsmXLVIYbJ06ciOjoaHzxxRe4fPkyZs+ejdOnT2PChAkanSMREanv8ePHGDJkSJmXJDp79iy6dOmC1NRUODs74/jx42jXrp2WoyRSn95MCACerjH26NEjBAYGIisrC127dkV0dDTMzMykMqmpqSrPL3vnnXdw7949hIaGIj09Ha6uroiOjla56T4iIkLlhnpPT08AwJo1a/D++++rFZulpSUOHjyI4OBguLm5wcbGBqGhoQgMDJTKdO7cGRs3bsRnn32GGTNmoHHjxoiMjETLli01OkciIlKfjc3rOHfuHD799FMsX75co2OPHj2Kt956Czk5OWjdujWio6NVbjshkoNCCCHkDoI0k5OTA0tLS2RnZ2t9QkBUVBT69u3LexLUwPZST25eHoaHPF0Aes3iuTDnhIAXYr9Sz7P96l2fbhg0cAAA4Oeff8bbb7+tVh2RkZEYMmQI8vLy4OnpiV27dsHKykpXIcuK/Up9umwrdX9/682wJhERUVl4e3tLt5gMHjwYw4cPx19//fXCY1avXo1BgwYhLy8P/v7+iI6OrrSJGVU8TM6IiKjCCw8Px9ixY6FQKLBhwwa4uLhg3rx5ePLkiUo5IQTCw8PxwQcfQKlUYsyYMdi6dSuqVasmU+REJTE5IyKiCs/ExAQrV67EqVOn0KVLFzx+/BihoaFo1qwZtm7dCiEElEolJk2aJD1nefr06Vi1ahWMjPTq9msi/ZoQQERE9Crc3Nxw7Ngx/Pzzz/j0009x48YNDB48GN26dYOtrS22bdsGAPjqq68wceJEmaMlKh2vnBERUaWiUCjwzjvvSEsaVatWDceOHcO2bdtgZGSEn376iYkZ6TUmZ0REVClVr14dYWFhSElJwbBhw+Ds7Iw9e/Zg2LBhcodG9EIc1iQiokrN0dERP/30k9xhEKmNV86IiIiI9AiTMyIiIiI9wuSMiIiISI8wOSMiIiLSI0zOiIiIiPQIkzMiIiIiPcLkjIiIiEiPMDkjIiIi0iNMzoiIiIj0CJMzIiIiIj3C5IyIiIhIjzA5IyIiItIjTM6IiIiI9AiTMyIiIiI9YiR3AKQ5IQQAICcnR6v1FhQU4PHjx8jJyYGxsbFW666M2F7qyc3LQ0F+HoCnfVYoi2SOSL+xX6mH/Uoz7Ffq02VbFf/eLv49/jwK8bISpHf++usvODo6yh0GERERlcGtW7fg4ODw3P1MziogpVKJ27dvw9zcHAqFQmv15uTkwNHREbdu3YKFhYXW6q2s2F7qY1upj22lPraV+thW6tNlWwkh8Pfff8Pe3h4GBs+/s4zDmhWQgYHBCzPuV2VhYcF/vBpge6mPbaU+tpX62FbqY1upT1dtZWlp+dIynBBAREREpEeYnBERERHpESZnJDE1NUVYWBhMTU3lDqVCYHupj22lPraV+thW6mNbqU8f2ooTAoiIiIj0CK+cEREREekRJmdEREREeoTJGREREZEeYXJGREREpEeYnFUxK1asQP369WFmZgZ3d3ecPHnyheW3bt2Kpk2bwszMDK1atUJUVFQ5RaofNGmvtWvXQqFQqLzMzMzKMVp5HD16FG+99Rbs7e2hUCgQGRn50mOOHDmCdu3awdTUFI0aNcLatWt1Hqc+0LStjhw5UqJPKRQKpKenl0/AMgoPD0eHDh1gbm6O2rVrw9/fHykpKS89rip+Z5Wlrarq99V3332H1q1bSwvMenh4YP/+/S88Ro4+xeSsCtmyZQsmT56MsLAwJCYmok2bNvD19cXdu3dLLf/bb79h6NChGDNmDJKSkuDv7w9/f38kJyeXc+Ty0LS9gKcrSt+5c0d63bhxoxwjlsejR4/Qpk0brFixQq3y165dg5+fH3r27IkzZ84gJCQEH3zwAQ4cOKDjSOWnaVsVS0lJUelXtWvX1lGE+uOXX35BcHAwfv/9d8TExKCgoAA+Pj549OjRc4+pqt9ZZWkroGp+Xzk4OODzzz9HQkICTp8+jV69eqF///64cOFCqeVl61OCqoyOHTuK4OBg6X1RUZGwt7cX4eHhpZYfPHiw8PPzU9nm7u4ugoKCdBqnvtC0vdasWSMsLS3LKTr9BEDs3LnzhWWmTJkiWrRoobLtnXfeEb6+vjqMTP+o01ZxcXECgHj48GG5xKTP7t69KwCIX3755bllqvp3VjF12orfV/+ftbW1+N///lfqPrn6FK+cVRH5+flISEiAl5eXtM3AwABeXl6Ij48v9Zj4+HiV8gDg6+v73PKVSVnaCwD++ecf1KtXD46Oji/8a6wqq8r9qqxcXV1Rp04deHt74/jx43KHI4vs7GwAQK1atZ5bhn3rKXXaCuD3VVFRETZv3oxHjx7Bw8Oj1DJy9SkmZ1VEZmYmioqKYGtrq7Ld1tb2ufevpKena1S+MilLe7m4uOCHH37Arl278NNPP0GpVKJz587466+/yiPkCuN5/SonJwdPnjyRKSr9VKdOHURERGD79u3Yvn07HB0d0aNHDyQmJsodWrlSKpUICQlBly5d0LJly+eWq8rfWcXUbauq/H11/vx51KxZE6amphg3bhx27tyJ5s2bl1pWrj5lpNPaiaoQDw8Plb++OnfujGbNmuH777/HvHnzZIyMKioXFxe4uLhI7zt37ozU1FQsXboU69evlzGy8hUcHIzk5GT8+uuvcoei99Rtq6r8feXi4oIzZ84gOzsb27Ztw8iRI/HLL788N0GTA6+cVRE2NjYwNDRERkaGyvaMjAzY2dmVeoydnZ1G5SuTsrTXvxkbG6Nt27a4cuWKLkKssJ7XrywsLFCtWjWZoqo4OnbsWKX61IQJE7B3717ExcXBwcHhhWWr8ncWoFlb/VtV+r4yMTFBo0aN4ObmhvDwcLRp0wbLli0rtaxcfYrJWRVhYmICNzc3xMbGStuUSiViY2OfO9bu4eGhUh4AYmJinlu+MilLe/1bUVERzp8/jzp16ugqzAqpKvcrbThz5kyV6FNCCEyYMAE7d+7E4cOH4ezs/NJjqmrfKktb/VtV/r5SKpXIy8srdZ9sfUqn0w1Ir2zevFmYmpqKtWvXiosXL4rAwEBhZWUl0tPThRBCvPfee2LatGlS+ePHjwsjIyOxZMkScenSJREWFiaMjY3F+fPn5TqFcqVpe82ZM0ccOHBApKamioSEBDFkyBBhZmYmLly4INcplIu///5bJCUliaSkJAFAfPnllyIpKUncuHFDCCHEtGnTxHvvvSeVv3r1qqhevbr49NNPxaVLl8SKFSuEoaGhiI6OlusUyo2mbbV06VIRGRkp/vzzT3H+/HkxceJEYWBgIA4dOiTXKZSb8ePHC0tLS3HkyBFx584d6fX48WOpDL+znipLW1XV76tp06aJX375RVy7dk2cO3dOTJs2TSgUCnHw4EEhhP70KSZnVczy5cuFk5OTMDExER07dhS///67tK979+5i5MiRKuV//vln0aRJE2FiYiJatGgh9u3bV84Ry0uT9goJCZHK2trair59+4rExEQZoi5fxcs9/PtV3DYjR44U3bt3L3GMq6urMDExEQ0aNBBr1qwp97jloGlbLVy4UDRs2FCYmZmJWrVqiR49eojDhw/LE3w5K62dAKj0FX5nPVWWtqqq31ejR48W9erVEyYmJuL1118XvXv3lhIzIfSnTymEEEK31+aIiIiISF2854yIiIhIjzA5IyIiItIjTM6IiIiI9AiTMyIiIiI9wuSMiIiISI8wOSMiIiLSI0zOiIiIiPQIkzMiIiIiPcLkjIiIiEiPMDkjIiIi0iNMzoiI9MCmTZtQrVo13LlzR9o2atQotG7dGtnZ2TJGRkTljc/WJCLSA0IIuLq6wtPTE8uXL0dYWBh++OEH/P7776hbt67c4RFROTKSOwAiIgIUCgUWLFiAgIAA2NnZYfny5Th27BgTM6IqiFfOiIj0SLt27XDhwgUcPHgQ3bt3lzscIpIB7zkjItIT0dHRuHz5MoqKimBrayt3OEQkE145IyLSA4mJiejRowe+//57rF27FhYWFti6davcYRGRDHjPGRGRzK5fvw4/Pz/MmDEDQ4cORYMGDeDh4YHExES0a9dO7vCIqJzxyhkRkYwePHiAzp07o0ePHoiIiJC2+/n5oaioCNHR0TJGR0RyYHJGREREpEc4IYCIiIhIjzA5IyIiItIjTM6IiIiI9AiTMyIiIiI9wuSMiIiISI8wOSMiIiLSI0zOiIiIiPQIkzMiIiIiPcLkjIiIiEiPMDkjIiIi0iNMzoiIiIj0CJMzIiIiIj3y/wCAlhduZpyOpAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "markers = ['s','o','x','.']\n",
    "level = [5,6,7,8]\n",
    "\n",
    "for i in range(0, len(xAll)):\n",
    "    plt.plot(xSlice[i],uSlice[i]-(exactSolution(xSlice[i])/exactSolution(xSlice[i][len(xSlice[i])-1])),color=\"black\",marker=markers[i],markevery=level[i],label=\"m=\"+str(i+4))\n",
    "    \n",
    "plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%0.6f')) \n",
    "plt.xlabel(r\"$x$\")\n",
    "plt.ylabel(r\"Error in displacement w.r.t FDM\")\n",
    "plt.title(r\"Example with \" + str(example).lower() + \" solution for MDCM with m = 2\")\n",
    "plt.grid()\n",
    "plt.axvline(x=1,c=\"#536872\")\n",
    "plt.axvline(x=2,c=\"#536872\")\n",
    "plt.legend()\n",
    "plt.savefig(\"MDCM-adaptive-endpoint-non-matching-\"+str(example).lower()+\".pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea5fb565",
   "metadata": {},
   "outputs": [],
   "source": [
    "if has_condition :\n",
    "    np.savetxt(\"con_mdcm_n-quadratic-non-matching.csv\", con, delimiter=\",\")"
   ]
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}