Lab06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Epidemic Models on Networks\n",
    "In this section we consider a special kind of continuous time Markov chain, as presented in Lab04, which is used to simulate the spread of epidemics over a network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Theory recap: Pairwise interacting network systems\n",
    "\n",
    "Consider a finite population of agents, identified with the nodes of a graph $\\mathcal G = (\\mathcal V, \\mathcal E, W)$. \n",
    "Each agent $i \\in \\mathcal V$ has a state $x_i \\in \\mathcal A$ and all states are collected in the configuration vector $x \\in \\mathcal X = \\mathcal A^{\\mathcal V}$.\n",
    "\n",
    "We consider an **evolution process** for the state vector $X(t)$ which is _caused by pairwise interactions between neighbouring nodes in the graph and by spontaneous mutation_.\n",
    "- Each node is equipped with a Poisson clock with rate $1$. When the clock ticks, the agent is activated and it undergoes a **spontaneous mutation**.The probabilities of spontaneous mutations (conditional on the ticking of the clock) are described by the **mutation kernels**\n",
    "\t$$\n",
    "\t\\psi^{(i)}\\in\\mathbb{R}^{\\mathcal A\\times\\mathcal A}\\,,\\qquad  i\\in\\mathcal V\n",
    "\t$$\n",
    "- Each directed link has a Poisson clock with rate $\\beta W_{ij}$. When the clock ticks, the link is activated, agent $i$ meets agent $j$ and as a consequence $i$ makes a **transition**.  The transition probabilities (conditional on the ticking of the clock) are described by the **pairwise interaction kernels**\n",
    "\t$$\n",
    "\t\\varphi^{(i,j)}(c)\\in\\mathbb{R}^{\\mathcal A\\times\\mathcal A}\\,,\\qquad (i,j)\\in\\mathcal E\\,,\\qquad c\\in\\mathcal A\n",
    "\t$$\n",
    "    \n",
    "Then, pairwise interacting network systems are **continuous time Markov chains** with state space $\\mathcal X = \\mathcal A^{\\mathcal V}$ and transition rates\n",
    "$$\n",
    "\\Lambda_{x,y}=\\begin{cases}\n",
    "\\psi^{(i)}_{x_i,y_i}+\\beta\\sum_jW_{ij}\\varphi_{x_iy_i}^{(i,j)}(x_j) \\quad \\text{  if  } x_i\\ne y_i\\text{ and }x_{-i}=y_{-i}\\\\\n",
    "0 \\quad \\text{  if  } x\\text{ and }y\\text{ differ in more than }1\\text{ element}\\,,\n",
    "\\end{cases}$$\n",
    "\n",
    "\n",
    "The main models you studied during the lectures are:\n",
    "$$\\textbf{SI:}\\qquad\\psi=\\left[\\begin{array}{cc}1&0\\\\0&1\\end{array}\\right]\\qquad\\varphi(S)=\\left[\\begin{array}{cc}1&0\\\\0&1\\end{array}\\right]\\qquad\\varphi(I)=\\left[\\begin{array}{cc}0&1\\\\0&1\\end{array}\\right] $$\n",
    "\n",
    "\n",
    "$$\\textbf{SIS:}\\qquad\\psi=\\left[\\begin{array}{cc}1&0\\\\1&0\\end{array}\\right]\\qquad\\varphi(S)=\\left[\\begin{array}{cc}1&0\\\\0&1\\end{array}\\right]\\qquad\\varphi(I)=\\left[\\begin{array}{cc}0&1\\\\0&1\\end{array}\\right] $$\n",
    "\n",
    "\n",
    "$$\\textbf{SIR:}\\qquad\\psi=\\left[\\begin{array}{ccc}1&0&0\\\\0&0&1\\\\0&0&1\\end{array}\\right]\\qquad\\varphi(S)=\\varphi(R)=\\left[\\begin{array}{ccc}1&0&0\\\\0&1&0\\\\0&0&1\\end{array}\\right]\\qquad\\varphi(I)=\\left[\\begin{array}{ccc}0&1&0\\\\0&1&0\\\\0&0&1\\end{array}\\right]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SI model\n",
    "The SI model is a pairwise interacting network system characterized by the following interaction kernels:\n",
    "$$\n",
    "\\phi(0) = \\begin{pmatrix} 1 & 0 \\\\ 0 & 1 \\end{pmatrix}, \\quad\n",
    "\\phi(1) = \\begin{pmatrix} 0 & 1 \\\\ 0 & 1 \\end{pmatrix}\n",
    "$$\n",
    "\n",
    "In the SI model, each node can be either in a susceptible (denoted S or 0) or infected (denoted I or 1) state. When a susceptible node makes contact with an infected one, the contagion spreads and the susceptible node becomes infected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: SI model on a grid\n",
    "In this example we consider the SI model on a $3 \\times 3$ grid. We simulate the evolution of the epidemics starting from a configuration where only one node is infected and all the rest are susceptible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct the graph describing interactions among agents\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "size = 3\n",
    "# Create Grid Graph\n",
    "G = nx.grid_2d_graph(size,size)\n",
    "\n",
    "# Make node relabelling\n",
    "f = dict()\n",
    "for v in G.nodes:\n",
    "    f[v] = v[0]*size+v[1]\n",
    "\n",
    "G = nx.relabel_nodes(G, f)\n",
    "nx.draw_spectral(G, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Markov chain by constructing its transition matrix Lambda\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "# Number of agents\n",
    "n_agents = len(G)\n",
    "\n",
    "# Pairwise interaction kernels\n",
    "phi = np.array([[[1,0],\n",
    "                 [0,1]], \n",
    "                [[0,1],\n",
    "                 [0,1]]])\n",
    "# Links activation rate \n",
    "beta = 1\n",
    "\n",
    "# Adjacency matrix\n",
    "W = nx.convert_matrix.to_numpy_matrix(G)\n",
    "\n",
    "# Initialize transition rates matrix\n",
    "n_config = 2**n_agents\n",
    "Lambda = sp.sparse.lil_matrix((n_config,n_config))\n",
    "\n",
    "# Number of states for each agent\n",
    "n_states =tuple(2 for _ in range(len(G)))\n",
    "\n",
    "# Fill transition rates matrix\n",
    "# For each configuration x, we consider all configurations y that differ only\n",
    "# in the state of a single agent and we compute the rate for the admissble \n",
    "# transition x->y\n",
    "for x_id in range(n_config):\n",
    "    x = np.unravel_index(x_id,shape=n_states)\n",
    "    x = np.array(x)\n",
    "    for agent in range(n_agents):\n",
    "        y = np.array(x)\n",
    "        y[agent] = 1-y[agent]\n",
    "        y_id = np.ravel_multi_index(tuple(y), dims = n_states)\n",
    "        for other_agent in range(n_agents):\n",
    "            Lambda[x_id, y_id] += beta*W[agent,other_agent]*phi[x[other_agent], x[agent], y[agent]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate the evolution of the epidemics\n",
    "\n",
    "# Simulate the Markov chain with local clocks method\n",
    "w = np.sum(Lambda, axis=1)\n",
    "w = np.array(w.T)[0]\n",
    "for x, weight in enumerate(w):\n",
    "    if weight == 0:\n",
    "        Lambda[x,x] = 1\n",
    "        w[x] = 1\n",
    "D = np.diag(w)\n",
    "P = np.linalg.inv(D) @ Lambda\n",
    "\n",
    "n_steps = 12\n",
    "\n",
    "# store the ids of visited configurations, describing the state of each agent\n",
    "states = np.zeros(n_steps, dtype=int)\n",
    "# in the initial configuration, agent 0 is infected\n",
    "x0 = np.zeros(n_agents, dtype=int)\n",
    "x0[0]=1\n",
    "states[0] = np.ravel_multi_index(tuple(x0), dims = n_states)\n",
    "\n",
    "transition_times = np.zeros(n_steps)\n",
    "t_next = -np.log(np.random.rand())/w[0]\n",
    "\n",
    "P_cum = np.cumsum(P, axis=1)\n",
    "\n",
    "for i in range(1,n_steps):\n",
    "    states[i] = np.argwhere(P_cum[states[i-1]] > np.random.rand())[0]\n",
    "    transition_times[i] = transition_times[i-1] + t_next\n",
    "    t_next = -np.log(np.random.rand())/w[states[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAI+CAYAAABZpVCsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxM9/4/8NfnzCSZyZ4QW2SxB01QS+xL7OQiSpWgclUtpcXFF62i1JJbpVxRt74tpUotja9Kcy2XkFqSqmv9uaoqXDRyNU0jIuLk/ftjTJrEJJkkc+Yzc+bzfDzmUZ2ZzHkd8ZqTT+acz4cREUEQBEEQBEEQBEGoEIl3AEEQBEEQBEEQBHskBlOCIAiCIAiCIAiVIAZTgiAIgiAIgiAIlSAGU4IgCIIgCIIgCJUgBlOCIAiCIAiCIAiVIAZTgiAIgiAIgiAIleAQg6nmzZvj2LFjvGMIgqqJngmCdYiuCYLyRM8EcznEYOry5cvo3r077xgmHTt2DHXr1uUd4zlfffUVOnbsCFdXV5v9uxNsi+hZxc2aNQuNGjWCh4cHQkJC8Pnnn/OOJNgB0bWKmzNnDgICAuDp6YmgoCC8//77vCMJNk70rPJ+/fVX+Pn5oXPnzryjWIVDDKaEivP19cX06dMxd+5c3lEEQbXc3Nywf/9+ZGVlYcuWLXjrrbdw8uRJ3rEEQXXGjx+Pq1ev4vfff8fJkyexfft27N27l3csQVCl//mf/0HTpk15x7AahxhMBQcH4/Dhwxg3bhzeeeedwvtLjuyDg4Px17/+FWFhYXBzc8P48eORnp6O/v37w8PDA7169UJmZiYA4ObNm2CM4e9//zvq1KmD2rVrY9WqVaVmSEhIQLNmzeDh4QF/f3988MEHyMnJQf/+/XH37l24u7vD3d0dd+/eRUFBAVasWIEGDRqgWrVqePnll/Hrr79WaruV1atXL7z88suoU6eOxV9bUCfRs4pbvHgxQkJCIEkSwsPD0aVLF5w6dcri2xHURXSt4po0aQI3N7fC/5ckCdevX7f4dgT1ED2rnFOnTuHSpUuIiYlR5PVtkUMMpipiz549OHToEK5du4b9+/ejf//+WLZsGf773/+ioKAAa9euLfb8o0eP4scff8TBgwexYsUKHD582OTrjh8/Hhs3bkR2djYuXbqEiIgIuLm54dtvv0WdOnXw8OFDPHz4EHXq1MHatWsRHx+PpKQk3L17Fz4+PnjjjTcqtd0VK1bA29u71Jsg8CB69rzc3FykpqaiefPmZj1fEMwhulb8a93d3VG3bl3k5ORg1KhRFfibFITSiZ4ZyLKMN954A3/729/AGKvg36L9EoOpEqZNm4aaNWvC398fXbp0QXh4OFq1agUXFxdERUXh3LlzxZ6/cOFCuLm5ITQ0FDExMfjyyy9Nvq6TkxOuXLmC33//HT4+PnjxxRdLzbBx40a8//77qFu3LlxcXLBo0SLs3r0bT58+rfB2586di99++63UmyDwIHr2vEmTJqFFixbo27evWc8XBHOIrhX/2uzsbPzwww8YM2YMvLy8yny+IJhL9Mxg7dq1CA8PR+vWrcv661IdMZgqoWbNmoV/1uv1z/3/w4cPiz0/ICCg8M9BQUG4e/euydfds2cPEhISEBQUhG7dupV5Kk9aWhqioqIKfwPQtGlTaDQapKenV3i7gmCLRM+Kmz17Ni5duoSvvvrKoX6bJyhPdK04xhhatWoFvV6PhQsXKrINwfGIngF3797F2rVrHXJyF4caTLm5ueHRo0eF///LL79U+TVv375d+Odbt26Veo1R27ZtsW/fPty/fx9DhgzByy+/DAAmf3AKCAjAt99+W+y3AI8fP4a/v3+Ft7ts2bLCc2pN3QTB0kTPKtazhQsX4ttvv8XBgwfh6elZ5nMFoSjRtcof054+fYqffvrJ7OcLjkv0zLyepaSk4N69e2jWrBlq1aqFt956CykpKahVqxZkWS7/L8WOOdRgqmXLlkhISMCvv/6KX375BWvWrKnyay5ZsgSPHj3C5cuX8dlnn2HEiBHPPefJkyf44osvkJWVBScnJ3h6ekKj0QAw/DbjwYMHyMrKKnz+pEmT8PbbbyMtLQ0AkJGRgX379lV4uwAwf/78wnNqTd1KI8syHj9+jKdPn6KgoACPHz9Gfn5+hf9+BMcjemZ+z5YvX47t27fj0KFDqFatWoX/XgTHJrpmXtcKCgqwceNGZGZmgoiQkpKC9evXo2fPnpX6OxIci+iZeT3r378/bt68iX/961/417/+hffeew+tWrXCv/71r8LcauVQg6kxY8agRYsWCA4ORp8+fUr9R1QR3bp1Q8OGDdGzZ0/MmjULffr0Mfm8rVu3Ijg4GJ6envj444+xbds2AEBISAhGjhyJ+vXrw9vbG3fv3sVbb72FQYMGoU+fPvDw8ED79u1x5syZSm23srZu3Qq9Xo/JkyfjxIkT0Ov1mDBhgkW3IaiT6Jn55s+fj1u3bqFRo0aFv/FbtmyZRbchqJfomvm+/vprNGjQAB4eHhg9ejSmTZuGadOmWXQbgjqJnpnHxcUFtWrVKrx5eXnByckJtWrVstg2bBUjIuIdQmmBgYHYtm0bunbtarHXvHnzJurVq4f8/HxotVqLva6tblcQyiN6JgjWIbomCMoTPRPMpfpPpjIyMpCRkYHg4GDeUQRBtUTPBME6RNcEQXmiZ0JFqHowlZqaikaNGmHatGkIDAzkHUcQVEn0TBCsQ3RNEJQneiZUlEOc5icIgiAIgiAIgmBpqv5kShAEQRAEQRAEQSliMCUIgiAIgiAIglAJYjBlbRs2AG5ugCQBjJV+kyTD8zZs4J1YEOyT6JogKE7UTBCUJ3pm2zSLFi1axDuEw9iwAZg1CyiyknaZ8vOBo0cBX1+gbVtlswmCmoiuCYLiRM0EQXmiZ7ZPTEBhLampQPfuxdrwO4A2AG4AkAHoAcwF8G7Jr3V1BZKSgDZtrJNVEOyZ6JogKM5EzWBu00TNBME8omf2QZzmZy3LlwO5ucXuegygDoBjAPIBzAKwEEByya/NzTV8vSAI5RNdEwTFmagZzG2aqJkgmEf0zD6IT6as4f59ICgIePy43KfqAUwF8NeSD+h0wK1bgJ+fAgEFQSVE1wRBcRWoGUprmqiZIJRN9Mx+iE+mrGHzZrOedgmG3zf0MPUgY2a/jiA4LNE1QVCc+fUovWmiZoJQNtEz+yEGU9Zw4UK5v1p4BKArgBAAA0w9ITcXuHjR8tkEQU1E1wRBcWbUDOU1TdRMEMomemY/xGDKGrKyynz4KQw10AI4W9YTMzMtl0kQ1Eh0TRAUV07NYG7TRM0EoXSiZ/ZDyzuAQ/DyKvWhAgBNAWQD+AmAa1mv4+Nj0ViCoDqia4KguDJqhoo0TdRMEEonemY/xCdT1hAWZrgK0IQXANwD8P8A+Jb1Gno9EBpq+WyCoCaia4KguDJqBnObJmomCGUTPbMfYjBlBQVjx+Lp06fP3f8dDDXIAVAbAHt2m2LqRYiAceOUCykIKnCmaVPk5eU9d7/omiBYTlRUFp48eWLiEfObJmomCGUbO7bA5M+Oome2RwymFFRQUIClS5fCq1EjfCPLKCjxeCcAZOIWV/KFGAMGDBBzWwpCKY4fP46QkBB0GDwYp318QIwVe1x0TRCq7tdff8XQoUMREuILJ6fDYKxyRzVRM0EoXUFBAVatWoUmTXwgy98AlfzpUfTMesRgSgEFBQV477334OnpiaVLl+LPf/4zBiYnQ3It8yqN0un1wLx5lg0pCCpw7NgxNGnSBN27d0f16tXx73//G90SE8H0+sq9oOiaIDzn119/RVRUFPz8/HDq1Cl8/vnnOHFiAPT6yv0IIWomCM8rKCjAX//6V/j4+GDevHkYOXIkjh/vD1dX0TObR4LFyLJMixYtIjc3N3JxcaHp06dTfn7+H0+IiyNydSUyfPJq3s3V1fB1giAUOnr0KDVq1IgYY9SlSxe6fv168SeIrglClWVkZNDgwYNJkiSqXbs27dixo9jjomaCUHWyLNPy5cvJw8ODnJ2dafLkyZSXl1f4eOV6ViB6ZkViMGUBsizTggULyNXVlVxcXGjmzJnFB1FFGVvBWJlNKGCMHgL0v23bWndnBMGGHT58mBo2bEiMMeratSvduHGj9Ceb2bWnAOUA9OSjj6y3I4Jgw9LT0ykyMpIkSaI6derQzp07S32umTUjxmQCHtIbb1y04p4Igu2SZZmWLVtG7u7u5OzsTFOmTCk2iCrK/J4VEPCQwsLESMqaxGCqCmRZpnfeeYdcXV1Jp9PRrFmzSh9EFZWaSjR0KJFOR6TXF2+CXm+4f+hQOrl2LTHG6JNPPlF+ZwTBhh08eJAaNGhAjDHq1q1b2YOooszo2tPBgynC05Pail9cCA4uPT2dBg4cSJIkkb+/P+3atcusrzPzkEYDBrxLzs7OdOfOHYX3RBBslyzLtGTJksJB1NSpU0sdRBVlbs82bfoXSZJEq1atssLeCEREjIiI96mG9qagoAALFizA6tWrQUSYNm0ali1bBq22gst2ZWQAmzcblqfOzDQsBhAaaph65dkVg2+//TZWrlyJH374AWFhYRbfF0GwZf/4xz8wZcoU/Pzzz+jevTs+++wzBAUFVfyFyunajz/+iGbNmmHq1KlYvXq1pXdDEGza/fv3ERMTg8TERNSpUwdr1qzBSy+9VOHXMeOQhqZNmyIrKwu3bt2q+DFTEOyYcVKy2NhYPH36FBMnTsRf//pXODs7V+h1zOlZbGws5s2bh+TkZHTo0MHi+yKUwHkwZ1dkWaa5c+eSXq8nvV5Pc+fONe+TqCrq1q0beXp6UnZ2tuLbEgRbkJCQQPXq1SPGGEVERFBaWpri29y+fTsxxmj37t2Kb0sQbMG9e/eoX79+xBijgIAA2rt3r+LbzMrKIjc3N+rdu7fi2xIEW1Du9fQKGTBgALm6ulJmZqbi23J0YjBlhvz8fJozZw7pdDrS6/U0b948kmXZqtuvUaMGhYaGWm2bgsDDgQMHKDg4mBhj1LNnT6sMooqaNGkSabVa808jFAQ7dOfOHerbt2/hICo+Pt6q209NTSVJkmjJkiVW3a4gWFPJ6+lnzJhhlUFU0e0HBARQo0aNrPozqyNS72l+9+8bPge9cAHIygK8vAzLScfEmD3p/tOnTzF//nysW7cOjDHMmDEDS5YsgSRZf0b5tLQ0NGrUCOPGjcPf//53q29fEEyxQM0AAN988w2mTp2KW7duoVevXvj0009Rt25dxXKXpWXLlrh79y7u3r0rTkMSbIYlunb37l3ExMTg0KFDCAwMxNq1azFo0CBFc5fmb3/7G958803885//RPfu3blkEISSLNGzgoICLFy4EB9++CEKCgowdepULF++nMvx5P79+wgMDERUVBS+/PJLq2/fYfAezVlcSgpRVJThSjydzvQVelFRhueVIj8/n2bOnEk6nY5cXV1pwYIFNjGqj4+PJ8YYbdu2jXcUwcFZoGZERLRv3z4KDAwkxhj16dPHJi5Mz8nJIS8vL+rSpQvvKIJgka7dvn2bevXqRYwxCgoKov3791tvB8rw0ksvkU6no/T0dN5RBAdniZ7Jskzz588nvV5POp2OZs+ebdVPokpz+PBhYozRxx9/zDuKaqlrMGX+3JEmF7vIz8+nGTNmkIuLC7m6utLChQttYhBV1IwZM0ij0dDVq1d5RxEcVBVrRkSGXwwEBAQQY4z69u1rE4Oooi5evEgajYbmzZvHO4rgwKratbS0NOrZsycxxig4OJgOHDjAZ0dKIcsy1a9fn4KCgmzuWCs4jqr2rOT19HPmzLGJQVRRixYtIkmS6OzZs7yjqJJ6BlNVWD0wPz+fpk+fTi4uLuTm5kaLFi2y6Tf28PBw8vHxodzcXN5RBAdT1UU69+7dS3Xr1iXGGPXv35/u3bvHd4fKsGnTJmKMUUJCAu8oggOqStfS0tIoIiKCGGNUv359m/43/ODBA9Lr9TRo0CDeUQQHVJWeGa+nNw6irH09fUVFRESQh4eHmMxMAeoYTKWkPNeGYQC5AgSAGpTRisdaLXVwciI3NzdasmSJTRfBKC8vj3x9fcW6OIJVmajZs9swAlwJAAENTFbNxSWf/Pz6kyRJNGDAAJseRBU1duxYcnZ2ptu3b/OOIjgQ010rv2cAkSTlEtCG6tevT4mJibx3xSzJycliXRzB6qrSM602j5ydO5Jer6f58+fbxc+O+fn5VKtWLWrevDnvKKqjjgkohg4F4uMN/8afmQNAA+D/AOQBuF7Kl8oAfmzWDI0vXuQysURlGdfFmTJlCj766CPecQQHYKJmz5jTNhm1ap3B+fMNUaNGDYWTWpZYF0ewNtNdM/+o1qnTf5GcXFPhlJYl1sURrK2qPQsJuYrLl5va1c+Ot27dQqNGjRAdHY1PP/2UdxzVsP/B1P37QFAQ8PixyYc7A/gFpdcBAKDTAbduVWz6MRvw5ZdfIjo6Grt27arUAouCYK5yavZM2W2z05rh999/h7+/Pzp06ICDBw/yjiOoXPldK/+oZq9dGzhwII4dO4Y7d+7A29ubdxxBxRy5Z9988w0GDRqEzZs3Y+zYsbzjqIL9DKdLs3lz1V+DMcu8jpWNHDkSEydOxCuvvIKff/6ZdxxBxRy4ZvD09MTRo0dx5MgRLF26lHccQeUcuWv79+9HtWrV0K5dOxQUFPCOI6iYI/csMjISs2fPxp///GdcvnyZdxxVsP/B1IUL5f26vHy5ucDFi5bJY2UbNmxA8+bNER4ejqdPn/KOI6iUg9cMbdq0wUcffYR3330Xx44d4x1HUDFH7pokSUhJScGtW7cQHR3NO46gYo7cMwBYuXIlwsPD0blzZzyu6l+EoILBVFaWZV4nM9Myr8PByZMn8eTJE0RERPCOIqiUqBkwdepUvPTSS+jfvz/u37/PO46gUo7etVq1auHAgQPYuXMnNmzYwDuOoFKO3jMAOHr0KDQaDTp16sQ7it2z/8GUl5dlXsfHxzKvw4GrqyuSk5Nx8uRJzJ8/n3ccQYVEzQx27tyJOnXqiNOQBMWIrgE9e/bEwoULMXXqVPzwww+84wgqJHoGODs748yZM7hw4QLeeOMN3nHsmv0PpsLCQDrdc3c/BvAbDLP1FTz7c6kfZOr1QGioUgmt4oUXXsAnn3yCFStWICEhgXccQWXCwgwX25pmXttUUDNIkoTU1FTcv38fUVFRvOMIKhQWBri4mBqom39UU0PXFi5ciO7du6N79+54+PAh7ziCyhiOaabmX3OsnjVo0ABffvklNmzYgK+++op3HLtl14OpR48e4c0ffjB5vmc/AD4ATgP4+dmf+5X2QkTAuHEKpbSemJgYjBkzBlFRUfjPf/7DO46gIi4uX5ZxXrV5bVNJzeDr64tDhw7hm2++wYcffsg7jqAi/+///T/s2NEPeXl5Jh41/6imlq794x//gJubG9q3b887iqAijx8/xuXLs0s5pjlez4YNG4apU6ciOjoaP/30E+84dskuB1OPHj3Cq6++Ci8vL2w7eBB3w8IM06oUcQzPVuwtcjtm6sUYAwYMsL+5LUuxZcsW1K9fH+3atRMTUghVtmnTJvj5+eEvfxkDf/8LYMzUb/KOoby2qaxm6NSpE5YvX47Zs2fj1KlTvOMIdu7y5cto27Ytmjdvjvz8O+jWLbfkIQ3mHtXU1DWtVoszZ87g2rVriImJ4R1HsHOPHz/G66+/Dk9PT8TH/x0vvPAfE8e0Y3C0ngHA2rVr0aJFC4SHh+PJkye849gfvmsGV0x2djaNGTOGtFot+fr60vr16w0PmF7G2rybqytRairfHbOwrKwscnd3p969e/OOItipjRs3UrVq1Uij0dDIkSMpKytL1MyEgQMHkqurK2VmZvKOItihixcvUuvWrYkxRqGhoZSSkkJE4pBW0v79+4kxRps3b+YdRbBDubm59Nprr5GTkxN5eXnRqlWrSJZl0bMScnNzydvbmzp06MA7it2xi8FUdnY2RUdHk0ajoWrVqtHHH3/8/JPi4ireCldXw9ep0NmzZ0mSJFqyZAnvKIId+fjjjwsHUdHR0ZSdnV3s8crUTKeT1VozkmWZAgICqFGjRiTLMu84gp04f/68yUFUUeKQVtycOXNIo9HQpUuXeEcR7EROTg7FxMSQVqslb29vWr169XPPqVzPClTbsytXrpBGo6HZs2fzjmJXbHowlZWVRSNHjiwcRG3cuLHsLzC2grEym1DAGD0EaHN4uHV2hJP169cTY4yOHDnCO4pg49avX0++vr6k1WppzJgxzw2iijKzZsQYkSQ9IlfXv1BeXp4V98a60tPTycXFhV555RXeUQQbd+7cOWrVqhUxxqhFixZ09uzZMp9vftdkAh7StGlXrLQnfHTs2JG8vb0pNzeXdxTBhuXk5NC4ceMKB1Fr1qwp8/nm96yAgIfUsqWJX+iryJYtW4gxRvv27eMdxW7Y5GAqKyuLXnnlFdJoNFS9enX65JNPzP/i1FSioUOJdDoivb54E/R6w/1Dh1LymjXEGKNPP/1UuR2xAcOGDSOdTkfp6em8owg2aN26deTj40NarZbGjh1b5iCqKDNrRsnJj8nHx4fat2+v8J7wdfjwYWKMUZxaf10pVMm5c+eoZcuWxBijli1bljuIKsrcrvXpM59cXFzo3r17Cu4JX3l5eVStWjV68cUXeUcRbFBOTg69+uqrpNVqycfHh9atW2f215rbs40bDWf9rF27VsE94W/8+PHk5OREaWlpvKPYBZsaTGVlZdGIESNIo9GQn58fbdq0qfIvdv8+UWws0ZgxRJGRhv/Gxhruf2bu3LmqP21AlmWqX78+BQYGitOQhEJr164tHESNGzeOcnJyKvU6ZtSs8LSBWbNmWSi9bVq0aBFJklShH5QFdTt79iy1aNGCGGPUqlUrOnfuXKVfq7yuybJMjRs3Jn9/f1W/11+/fp20Wi1NmTKFdxTBRpR6PX0lmHNMW7ZsGUmSZPL0XDV54YUXqFatWpSfn887is2zicFUZmYmDR8+nDQaDdWoUcOqnxZ17tyZvLy8Kv3DpD148OAB6fV6GjRoEO8oAmdr1qwhb29v0mq1FBMTY7V/91u3bnWI0wYiIiLIw8PD7E/4BHVKTU2lsLAwYozRiy++SOfPn7fKdjMzM8nNzY369+9vle3xsnPnTmKM0c6dO3lHETgqej29r6+vVc8M6Nu3L7m5uVFWVpbVtmlt2dnZ5OHhQREREbyj2Dyug6nMzEx66aWXSJIkqlGjBpeZevLy8sjPz49atWpl9W1bU3JyMkmSRKtWreIdReBg9erV5OXlRVqtlsaPH8/llwfG2ZTUfNpAfn4+1apVi5o3b847isBBSkoKhYaGEmOMWrduTRcvXrR6htOnT5MkSbR8+XKrb9uapk6dSlqtlq5fv847imBl2dnZNGrUKPOvp1eALMvk7+9PISEhVt+2NZ07d44kSaKFCxfyjmLTKjyYSk8nWrmSKDra8BFodLTh/4t+BFqeBw8e0NChQ0mSJKpZsyZt3bq1ojEs6saNGw5x2sAHH3xAkiTRyZMneUcRzFDVrsmyTB988AF5eXmRk5MTTZgwgfuF26GhoVSzZk1VnzZw+/ZtcnZ2ppiYGN5RBDNY4ph2+vRpeuGFF4gxRm3atOEyiCpqzZo1JEkSnThxgmsOpbVu3ZqqVaum6glu1KSqXavS9fQKuHPnDrm4uNCYMWO45lDaxx9/TIwxOnjwIO8oNsvswVRKClFUlOEiPJ3O9MV5UVGG55XmwYMHFBUVRZIkUa1atWjbtm2W2AeL2L17NzHGaMeOHbyjKMq4Ls6DBw94RxFKUdWuybJMsbGx5OnpSU5OTjRx4kTugygj42kDPXr04B1FUWJdHNtniWPa6dOnqXnz5sQYo7Zt29rU9bdDhgwhvV5PGRkZvKMoRqyLYx+q2jWLXk9vYYmJicQY4z6wU9orr7yi+gluqsKswVRFpkI2tc5FRkYGDR48mCRJotq1a9P27duV2JcqmzZtGmm1Wrp27RrvKIoxrovTsGFDVV+kbK+q0jVZlmnlypXk4eFBTk5ONGnSJJsZRBV1/vx5kiSJFixYwDuKosS6OLarqse0kydPUrNmzYgxRuHh4XTliu1NSS7LMgUFBVG9evVU/V4v1sWxbVXpGs/r6SvinXfeIY1GY7VrI3mQZZkaNWpEAQEBqn4/qaxyB1OmFzS7TkBtAkCAhoApJhcOzMjIoEGDBhUOouzhU582bdqo/rQB47o4I0aM4B1FKKKyXVu/XqZly5aRh4cHOTs705QpU2z+3+/GjRsd4rQBsS6O7anKMS05OZlCQkKIMUbt27enq1ev8t6dMmVkZJBOp6OhQ4fyjqIosS6ObXq+a2X3zNi1VatyuF9PX1HdunUjT09PVU9mZpzgZsCAAbyj2JwyB1MpKaWtDB1IQAAB9whY/6wY8cWeo9HkEmNtyd/f365m3MnNzRXr4ghWV5WuAQ9Jq21PU6dOtflBVFGjRo1S/WkDeXl5VL16dbEujo2oSs8Ye0RAG+rQoYPND6KKSkpKEuviCFZnumvmHM8MxzRf3z60ZcsW3rthtvz8fKpRowaFhYXxjqIoR5ngpqLKHExFRZn6aDb9WQESi9xXj4DwEs97SuHht621HxZ19epVh1gXZ/HixWJdHBtRta7JNGTIU967UGGOsi6Oo0xwYw+qekzr08c+p7x3pHVx1D7Bjb14vmvm9oyIMZns8cPUmzdvFl6nrGarV692iAluKqLMwVTJCwUNt+3PClH0voEE1HjuuTpdxWZEsiXGdXHi4+N5R1FUz549xbo4NsBRu+Yo6+Ls2rVLrItjAxy1Z0SOtS6O2ie4sXXp6aa6Zn7P7Llr8fHxxBizqQnWlDB48GDS6/ViMrNnJFTYAwAlv8wXQN5zz2QM2Ly54luwBaNHj8b48eMxfPhwpKWl8Y6jmMTERLi5uaF9+/a8owjPUX/XvL29ceTIEfzjH//AihUreMdRzLBhwzB16lRER0fjp59+4h1HKEb9PQOAhIQEeHt7o127dryjKMbd3R3Hjx9HUlISFi1axDuOwzLdEfN7Bthv1wYPHobZltcAACAASURBVIwZM2bg1Vdfxb///W/ecRSzd+9e1KhRA23btkVBQQHvONyVOZh6/NjUvdUAlPyLywTg8twzc3OBixcrG42/Tz75BCEhIWjXrh2ePn3KO44itFotUlNT8eOPPyImJoZ3HIflyF0LDw/Hhx9+iLfffhvHjx/nHUcxa9euRYsWLRAeHo4nT57wjuOQHLlnkiQhJSUFN2/exOjRo3nHUUzLli0RFxeH9957D4cOHeIdxyFduGCqa+b3DLDvrq1atQpt2rRBhw4d8Nj0m47dkyQJ33//Pe7evYuXX36ZdxzuKvHJVM9n/y36JnUFQD2Tz87MrPgWbMnJkyeRm5uL3r17846imLp162LPnj3YsmULtmzZwjuOUMhxuvbWW29h0KBB6NevH/773//yjqOY5ORkyLKM7t27844iFHKcntWpUwf79u3D9u3bsWnTJt5xFDNx4kSMGDECf/rTn/DLL7/wjuNwsrJM3VuxngH23bXjx4+DMYauXbvyjqKY6tWr49tvv8XevXuxbt063nG4qsRgqgaAAACvAbgPYAOAGwDmmXy2j0+ls9kEd3d3JCcn4/jx43j33Xd5x1FMZGQkZs+ejfHjx+Py5cu84wgAHK1re/bsQc2aNdGuXTvVnjag0+lw8uRJpKSkYM6cObzjCAAcrWd9+/bF22+/jUmTJuHChQu84yjmiy++QGBgINq0aaPa9xNb5eVl6t6K9Qyw7645Ozvj9OnTOHfuHGbMmME7jmK6d++OJUuWYPr06UhNTeUdhxttWQ/qdKWdFnEUQGcANQFoAEwBMPi5Z+n1QGho1UPyFhYWhg0bNmDSpEno0qWLaj+lWrlyJb777jt07twZ9+7dg06nMzxw/77h5OULFwy/cvLyAsLCgJgYwM+Pa2a1EF0znDaQmpqKwMBADB8+HHv27OEdSRFNmzbF5s2bMXbsWHTu3BmDBg0yPCB6pjjRM4MlS5bgxIkT6NKlC+7duwdXV1fekSzOeFpj3bp1ERkZiYSEBMMDomeKCwsD9uwx1TXzegaoo2uNGjXC559/jujoaHTt2hVRUVG8IynCeIp+jx49cPfuXXh6ehoecKSulTU7hemZj8y/2etsLKUxrotz584d3lEUU2xdnJQUw/ymOt3z/xj0esN9UVGG5wlVIrr2B+O6OGvWrOEdRVHGdXHu/d//iZ5ZiejZHxxtXZzPpkwRPbMS07P5OW7XJk2aRE5OTnTjxg3eURQjyzLVqVOHQkJCHPJnxzIHU6bX5DDvxhjZ5ToBZXGkdXGmMEaPtdry/wEwZliZTyz+WyWia8U5yro479WuTTkAFYieWYXoWXHGdXEmTJjAO4qiDg8bRg9Fz6xKdK24li1bkp+fn6rXQLtz5w69odHQY43G4X52LHMwVfpq8eXfXF2JUlOttRvWY1wXp1+/fryjKCcujvKdnYt9Q70AQpGbk6lvuEpKwYPo2vNUvy5OXBwV6PWiZ1YkevY81a+LExf33Ddd9Ex5omvF5eTkkJeXF3Xp0oV3FOXExdFTFxeH7FqZgykik+9DZvxW4ZEa/m5KZTxtYPny5byjWF4p74BeAI11xHdAK6pM13Q6WbVdk2WZ/P39qUmTJryjWJ7oGTeV6ZmTU55qe0ZENGPGDNJoNHT16lXeUSxL9IyrynTN1bVAtV27ePEiaTQamjdvHu8olufgXSt3MEX0RyHM+dROr5dJkqbQ+PHjlc7O1Zo1a0iSJEpKSuIdxbJK+WzerEKo8bN5K6tI1xh7RG5uf6G8vDzesRVz584dcnFxoejoaN5RLEv0jKuK9MzZ+QkBk+jw4cO8YysqPDycfHx8KDc3l3cUyxE94878rhUQ8JBatfo778iK+vTTT4kxRgkJCbyjWJaDd82swRSRYdA4dKjhurESZ6YUXk82dKjhefv27SPGGG3ZskXJ7NxFRUWRXq+njIwM3lEso4yrRr0AYs9uHgCtLq0UarpqlBNzu3biRC55eXlRp06deEdWVGJiIjHGaOPGjbyjWIbomU2oyDFt+PDhpNPpKD09nXdsxeTl5ZGvry+1bduWdxTLED2zGeZ2LS4uhRhjtH79et6RFTV27Fhydnam27dv845iGaJr5g+mjO7fJ4qNJRozhigy0vDf2Njn/w5mzZpFGo2Grly5YqmsNkeWZQoODqbg4GB1TEixcmWphdgE0B2AsgB6DYZzX4+YKoReb/gHIVSZOV27dOkSaTQamjt3Lr+gVrBgwQKSJInOnz/PO0rViZ7ZFHN6JssyNWjQgAICAtTxXl+Ka9eukVarpTfffJN3lKoTPbM55nRt8eLFJEkSnT17ll9QK2jatCnVrl1bHRNSiK5VfDBVEe3bt1ffaQMlZGRkkF6vp6ioKN5Rqi46uuyPYovcqgH0UmmPjxnDe08civG0gQMHDvCOoqhu3bqRp6cnZWdn845SNaJndunBgwek1+spMjKSdxRFbd++nRhjtHv3bt5Rqkb0zG717NmT3N3d7f+9vgxZWVnk7u5OvXv35h2l6kTXSFJyDaukpCRIkoTOnTsruRmuqlevjsTERMTHx+Ojjz7iHadqsrLMfiqD4VcMJmVmWiKNYKaYmBiMHTsWUVFR+M9//sM7jmIOHz4MnU6Hjh078o5SNaJndsnX1xeHDh1CQkICVq1axTuOYkaOHImJEyfilVdewc8//8w7TuWJntmtxMREuLu7Izw8nHcUxXh6eiIpKQlHjhzB0qVLecepGtE1KDqYcnZ2xpkzZ3D+/HlMmzZNyU1x1bVrV7z//vuYOXMmUlNTecepPC8vk3enAXgfwG8AHsOwZvl/AbxW2uv4+CgQTijL5s2b0bBhQ7Rt2xZPnz7lHUcRWq0WKSkpuHr1Kl5//XXecSpP9MxuderUCbGxsZgzZw6+++473nEUs2HDBjRv3hzh4eH2+34iema3tFotUlNTcf36dYwbN453HMW8+OKLWLduHd59910cO3aMd5zKE11TdjAFAA0aNMAXX3yB9evXY/fu3Upvjpt58+ahT58+6NGjB37//XfecSolMyAAedLz/yQeAVgGwAeAHsDnAJYD6G/qRfR6IDRUwZRCac6cOYOHDx+iX79+vKMoJigoCLt27cKmTZvwxRdf8I5TKfILLyBfo3nuftEz+/CXv/wFAwYMQO/evfHrr7/yjqOYkydP4smTJ4iIiOAdpXLCwiA7Oz93t+iZfahbty6+/vprfP755/jss894x1HMlClT8NJLL6F///64f/8+7ziVkhUcLH52tNb5hFOmTCGtVkvXr1+31iatzrguTuPGje3qIuWbN29S9+7dqQZAuZVdstx4s/MZWezd2bNnSZIkWrx4Me8oipo5c6bdrYuTn59Ps2bNogAXF8qtSsdEz7iTZZkCAwOpQYMGdvVeX1H2ui7O/v376cW6demR6Jndmzt3Lmk0Grp06RLvKIqRZZnq169PQUFBdvV+cvv2berdu7fhZ0cH75rVBlNERK1ataLq1as7xLo4o0aN4h2lXDdu3KCuXbsSY4waNmxoWEellLUCzLqpYK0ANVi/fj0xxsS6ODYiPz+fZs6cSTqdjlxdXWnBggVUMGSI6JmdS09PJ51OR8OHD+cdRVH2NMHNvn37KDAwkBhj1LdvX3rUr5/omQp06tSJvLy8KCcnh3cUxRgnuBk0aBDvKOW6ffs29erVixhjFBwcbHhvcPCfHa06mMrJyRHr4tiA69evU5cuXYgxRo0aNaIjR4788WApq1ibdVPBKtZq4Ujr4rRp04Z3FJPy8/Np+vTp5OLiQm5ubrRo0aI/fusoeqYKR44cEevi2IC9e/dSQEAAMcaoX79+dO/ePcMDomeqkJeXR9WrV6dWrVrxjqKo5ORkkiSJVq1axTuKSWlpaRQREUGMMapXr17xhYcdvGtWHUwR/bEuzpw5c6y9aauyxXVxrl+/Tp07dybGGDVu3JiOHj1q+onGJcsrUIYCV1fD1wk2QayLw09eXh69+eabhYOoxYsXm/4eVKJnT3U60TMbY1wXJ9XOfxgoT0hIiM2ti7N3716qW7cuMcaof//+fwyiiqpEzx5rtaJnNubGjRuk1Wpp8uTJvKMo6oMPPiBJkujkyZO8oxRKS0ujHj16EGOM6tevT4mJiaaf6MA/O1p9MEVEtHnzZmKM0f79+3ls3mpsZV2ca9euUceOHYkxRk2aNKGkpKTyv8hYinI+ti1gjB4CtLZZM+V3RKiQBw8ekKurKw0cOJB3FEXZyro4eXl5NG3aNHJ2diY3NzdasmRJ+QNZM3tGjFGuJNGbzs6UlZVlnR0SzNarVy9yd3dX9ffGltbF2b17N/n7+5MkSTRw4MDyP4GvQM/yXVxoEkDbtm2zzs4IZtu1axcxxmjHjh28oyhq4MCB5OrqSpmZmVxzGK+nNw6iDh48WP4XVeBnxxyAPmjYUPkdsQIugykionHjxtn0aQOWkJ+fTzVq1KDQ0FAu27969Sp16NCBGGMUEhJCJ06cqNgLpKYazmPV6QyrUxctg15vuH/oULr42WckSRItX75cmR0RKu3kyZMkSRLF2vHK4uaYNGkSabVaunHjhtW3nZeXR1OnTiVnZ2dyd3enpUuXVuzTQDN7Jp85Q/7+/hQSEqLczgiVkp+fT7Vr16amTZvyjqKo1NRUkiSJlixZwmX7O3fupDp16pAkSRQZGVmx05jN7BmlptKMGTPsboIbRzFt2jTSarV07do13lEUI8syBQQEUMOGDbmcWXLz5k3q1q1b8evpK8LMrv24Y4ddTnBjCrfBFBFRs2bNqFatWjZ12oClpaWlkZOTE7322mtW2+bVq1epffv2xBijpk2bUnJyctVe8P59othYw+rUkZGG/8bGFpt5Zc2aNSRJknmfeglWZTxtoMr/Dmxcy5Ytyc/Pz2oT3OTl5dHkyZPJ2dmZPDw8aPny5VU78JnRM+MEN9HR0RbYA8GSbt++Tc7OzvTqq6/yjqIo4wQ3xa61VdiOHTuodu3aJEkSDRo0iDIyMir/Ymb0jIioXbt2djHBjSNq3bo1VatWTdWTmRknuBkxYoTVtnnjxo3Sr6evDDO6tmnTJmKMFb/+yg5xHUxlZ2eTu7s79ezZk2cMxcXHxxNjjLZu3arodq5cuULt2rUjxhg1a9bM6ufcDhkyhPR6fdUOdIIiIiMjSa/X04MHD3hHUYxxgpvOnTsrup3c3FyaNGkSOTk5kYeHB61cudKqvz00TnDzySefWG2bgnkOHDhAjDH69NNPeUdR1LBhw6wywc327dsLB1FDhgyx6rHFOMFN27ZtrbZNwTy5ubnk4+ND7du35x1FUcYJbuIUvqao6PX0jRo1Kv16eoUYJ7i5c+eOVbdrSVwHU0R/rIuzaNEi3lEUZVwX58qVK3/cmZ5OtHIlUXS0YdQeHW34/wrOtX/p0iVq27YtMcaoefPm3C5clGWZgoKCqF69eqqe9MAeGdfFqV+/vqq/N8Z1cebOnVv8AQt0LTc3lyZOnEhOTk7k6elJsbGx3P4uFyxYQBqNxqYmuBEMjOviXLx4kXcUxRjXxQkMDCzeAQsd07Zt20a1atUiSZIoKiqK2y+BjBPcTJ8+ncv2hdJduXKFNBoNzZw5k3cURRknuDl79uwfd1qoZ9euXaNOnTpV7Hp6hdjiBDcVwX0wRUQUFxfnUOviPD5xwjAnv05nuJk6nzQqyjDVZBkuXbpEbdq0IcYYvfDCC3T69Gkr7UnpMjIySKfT0VA7XzNAjYynDQwbNox3FEUVWxcnJaXKXcvNzaXXXnuNnJycyMvLi1atWmUTA1LjBDdqXnvFXnXu3Nmx1sWxQM+IiLZu3Uo1a9YkSZJo6NChNvFJuq1McCM8b+vWrcQYo/j4eN5RFNWzZ0/y8PCgnGPHLNKzKl9PrwBbmuCmMmxiMEVENGLECHJxcTE9talK5OXl0UxXV3okSWbNKkSlTBl5/vx5at26NTHGKDQ0lFLKKY61JSUlkSRJtHbtWt5RhBKMpw2sW7eOdxRFjR07lt7QaEjW6yvdtdzcXBo/fjxptVry8vKi1atXc9ob04wT3ISFhfGOIpSQl5dHfn5+1LJlS95RFJWcnEyTAHri5FSlY9qWLVuoRo0aJEkSDRs2jPssZiXxnOBGKJvxF103b97kHUUx+fn5NMfDgx4xVqWeFb2e3lYGUUXxnuCmKmxmMCXLMjVs2FDd6+LExZFc8rcJ5d2KlOL8+fP04osvEmOMwsLCbHpdk2XLlpEkSTY30BOIlixZov51ceLiDAeeSnQtJyeHYmJiSKvVkre3N61Zs4b33pTq5s2b5OTkRBMmTOAdRSjBuC7OpEmTeEdRTlycYSBVyWPap59+Sn5+fqTRaGj48OE2N4gqqkWLFuTn52e3pyGpWWhoKNWoUUO935u4OMMvBivZsytXrlB4eHjh9fS2PBnVunXriDFm9eu2qspmBlNERJmZmepdF6ec1aEPAgSAgk089lSno1FNmhBjjFq0aFH83Fkb1rdvX3Jzc1P12iv2StXr4pTRtbJ6RgA91mgoXKMhHx8fu/lk1TjBjVgXx/bs3r1bveviVOGY9sTZmXp5e5NGo6ERI0bYxfuQcYKbLl268I4ilJCdnU0eHh7UvXt33lEsrwo9k3U6GtusGffr6SvKWhPcWJJNDaaIVLwuTlRUmR/P+gLkWdpgCqBDXl52M4gykmWZ/P39qUmTJryjCCWoel2cMrpWVs+MXfuxRQvee1BhxgluxLo4tke16+JU8Zh2pm5duxhEFWWc4EYN6+Kozfnz50mSJFqwYAHvKJZVxZ4d9PS0ievpK8I4wU1QUJDdnKlmc4MpIqJVq1apa12c9PTnLxYscpsGUF2AupXxQx7pdBWeqcUWiHVxbJdxXZyxY8fyjmI5ZXTNrJ7ZcdeME9yIdXFsT5s2bcjX11c96+I48DGt2AQ3gk3ZuHEjMcYoMTGRdxTLcOCeFZvgxg5IsEEzZ85EZGQkevfujV9//ZV3nKrbvLnUh/4D4GMAu8t7DcbKfB1bVadOHezbtw/bt2/Hpk2beMcRiqhbty7i4+OxdetW/O///i/vOJZRSkfM7hlgt107fvw4GGPo2rUr7yhCCSdOnAARqed748DHtJiYGIwZMwZRUVH4z3/+wzuOUMTrr7+OkSNHYvDgwbh79y7vOFXnwD3z9fXFoUOH8M033+DDDz/kHadcNjmYAoCvv/4afn5+aNu2LQoKCnjHqZoLF4DHj00+FAmgJ4Dw8l4jNxe4eNHCwayjb9++eOeddzBp0iRcuHCBdxyhiP79+2Pu3LmYOHEiLl26xDtO1ZXSNbN7Btht15ydnXH69GmcO3cOM2bM4B1HKEKn0+HUqVP4/vvv8Ze//IV3nKpz8GPali1bUL9+fbRr1w5Pnz7lHUcoYuvWrQgKCkK7du3Ez46AXfesU6dOWL58OWbPno1Tp07xjlMmmx1MSZKE1NRU3L17FyNGjOAdp2qyskzevRPAVQC7zH2dzEwLBbK+9957D507d0aXLl3w8OFD3nGEIpYtW4YOHTqgc+fOePToEe84VWOiaxXuGWC3XWvUqBE+//xzfPTRR9izZw/vOEIRTZo0webNm7F69Wrs27ePd5yqEcc0nDlzBtnZ2RgwYADvKEIRkiThzJkz+O233zBw4EDecapG9Axz5sxBv3790KtXL/z222+845TKZgdTAFCjRg0cOHAAe/bswd/+9jfecSrPy8vk3TsA5AHwAqABkATgJgDX0l7Hx8fy2azo8OHD0Ol06NixI+8oQglHjhyBs7MzOnXqxDtK1ZjoWoV7Bth110aOHImJEyfilVdewc8//8w7jlDE6NGjMX78eAwfPhxpaWm841SeOKbB09MTR48exZEjR7B06VLecYQivL29ceTIERw8eBArVqzgHafyRM8AAPv370e1atVs+tNGmx5MAUBERATee+89vPXWW/j+++95x6mcsDBAp3vu7k8AnAdw7tmtNQB/AGdNvYZeD4SGKhhSeVqtFikpKbh69Spef/113nGEIpydnXHmzBlcunQJkydP5h2n8sLCQCW6VqGeAaro2oYNG/DCCy8gPDxcnIZkYz755BOEhITY9yli4pgGAGjTpg3WrVuHd999F//85z95xxGKCA8Px4cffoi3334bx48f5x2nckTPABg+bfz+++9x69YtREdH845jGu8ZMMzVu3dvu10X5/T//R89NmMBUTXOyGKKWBfHdhnXxdm+fTvvKBWWmZlJMQMH0qOq9ExFXRPr4tiu7Oxs8vT0tMt1cWRZprhFiyi3nJ450jHNHtfFcRRRUVGk1+spIyODd5QK+yExUfzsWMThw4eJMUZxzxYjtiV2M5iSZZlq165NISEhvKOYLSkpiZo8W2z3mK8vFZhRCpM3xoiGDuW9OxYl1sWxXdOnT7erdXEePHhAUVFRJEkS1apVi9Jaty5zXQ5H6ppYF8d2nT9/njQaDb3zzju8o5hFlmX64IMPyNPTk5ycnOhsUJA4pj1jXBcnMDDQbtbFcRSyLFNwcDAFBwfbzffm9OnT1Lx5c2KM0T+9vUXPili0aBFJkmRz667azWCKyH7WxTl69Cg1btyYGGPUqVMnww+l5axiXebN1ZUoNZX3blmcWBfHdtnDujgPHjygIUOGFA6iCj/pFF0rRqyLY7vsYV0cWZZp5cqVhYOoiRMnGt6zRc+Ksbd1cRxJRkYG6fV6ioqK4h2lTCdPnqRmzZoRY4zatWtHly5dEj0zISIigjw8PCg7O5t3lEJ2NZgiIkpISCDGGG3atIl3lOccOXKEGjVqRIwx6tKlC12/fr34E+LiKlyKAr3e8HUqlJeXR76+vtSmTRveUYQScnNzycfHh8LDw3lHeU5GRgYNGjSIJEmi2rVr044dO55/UiW6Rq6uqu3aq6++Ss7OznT79m3eUYQSRo0aRS4uLnTnzh3eUYqRZZmWL19OHh4e5OzsTJMnT37+lyuiZ8UkJyeTJEm0atUq3lGEEpKSkogxRmvWrOEd5TnJycnUtGlTYoxReHg4XblypfgTKtEzWcU/O+bn51OtWrWoefPmvKMUsrvBFBHRvHnzSKPR0MWLF3lHISLDeZwNGzYkxhh17dqVbty4UfqTjaUo52PbAsYohzGa5+1tNx9NV8a1a9dIq9XSm2++yTuKUMLVq1dJo9HQzJkzeUchIqL09HSKjIwkSZKoTp06tHPnzrK/wMyuyYzRQ4Buzp1rnR3hpGnTplS7dm3Kz8/nHUUoQpZlaty4Mfn7+9vEe70sy7R06VJyd3cnZ2dnmjJlStmfUFfgmPYQoO1du1pvZzj44IMPSJIkOnnyJO8oQgnLli0jSZIoJSWFdxQiIjpx4gSFhIQQY4w6dOhQ9mUPZvaMGKNHjNFsDw9Vv9cbz1QbN24c7yhEZKeDKSKiLl26kJeXF+Xk5HDLcPDgQapfvz4xxqhbt25lD6KKSk01nMeq0xHp9cWLoNcb7h86lH47fJj0ej1FRkYquyOc7dixgxhjtHv3bt5RhBK2bdtGjDGKj4/nliE9PZ0GDhxIkiSRv78/7dq1y/wvNrNrk9u2JXd3d5s6bcDSsrKyyN3dnXr37s07ilBCZmYmubm5Ub9+/bhlkGWZlixZUjiImjp1qvmn+ZrZs8MrVhBjzPSnySoycOBAcnV1pQcPHvCOIpTQr18/cnNzo8zMTG4Zil5P37FjR/OvTzazZzlJSeTh4UE9evRQdkc4279/PzHGaPPmzbyj2O9gKj8/n/z8/KhFixZW33ZiYmLhIKpHjx508+bNyr3Q/ftEsbFEY8YQRUYa/hsbW2zmFeNpAx988IGF0tumyZMnk1arNX9AKljNhAkTyMnJqfL/zivp3r171L9/f5IkierWrVu1wXY5XTOeNtCsWTMLpbdNZ8+eJUmSaMmSJbyjCCWkpKSQJEm0bNkyq25XlmVavHgxubm5kYuLC7355puVv1bSjGPatGnT7GqCm8qQZZkCAgKoYcOGNvFpo/AHWZbJ39+fGjdubPXvjcnr6SvDjJ6dP3+eJEmiBQsWWCi9bZozZw5pNBrD9WUc2e1giojoxo0bpNVqadKkSVbZXkJCAtWrV48YYxQREUFpaWlW2a7xtIHk5GSrbI+Xli1bkp+fn01PeuCoQkNDqUaNGlY5beDevXvUr18/YoxRQEAA7d27V/FtEv1x2sCrr75qle3xsn79emKM0ZEjR3hHEUpYs2YNMcYoKSlJ8W3JskwLFy4sHERNnz7daqcFtW7dmqpVq6bq9/r09HRycXGhESNG8I4ilHDnzh1ycXGhUaNGWWV75V5PrxDjBDcHDx60yvZ46dixI3l7e3OdzMyuB1NERHv37lV8XZwDBw5QUFAQMcaoZ8+eVhtEFRUZGUl6vV7Vpw0Y18Xp3Lkz7yhCCTk5OeTp6UndunVTbBt37tyhPn36FA6ieJxaeODAAWKM0aeffmr1bVvT8OHDxbo4NioqKop0Op1i6+LIskwLFiwgV1dXcnFxoRkzZlj92grjBDft27e36natzZbXxXF0iYmJxBijjRs3KraNCl1PrxDjBDf37t2z+ratJS8vj6pXr04vvvgitwx2P5giKmVdnPR0opUriaKjDR+DRkcb/r8Ci5ft37+/cBDVu3dvrjNhybJMgYGB1KBBA1WfNmBcF2euyicDsEcm18WxQM9u375NvXv3JsYYBQUF0b59+xRIb765c+faxGkDSpJlmRo0aCDWxbFBpa6LU8WuybJM77zzDrm6upJOp6NZs2ZxvUD9ypUrpNFoaNasWdwyWMPixYttcl0cgWjBggUkSRKdP3++8D4LHNIqfz29AmxtghulGM9UmzJlCpftq2IwRfTHujhPvvuOKCrKcCGeTmf6Ar2oKMPc/aXYt28fBQYGEmOM+vTpYzNT1qanp5NOp6Phw4fzjqIosS6O7TKeNnDyo4+q3LPbt29TsLLTfAAAIABJREFUr169CgdR+/fvt+KelK1Tp07cJ7hRmlgXx3YVWxcnJaVKXZNlmebNm0d6vZ50Oh3Nnj3bZmb52rp1KzHGuP8CRWk9e/ZU/QQ39qpbt27k6elJx47lVPWQZrnr6S3MOMFN//79eUdR1K5du4gxVv5MvwpQzWAqNzeXZuj19EiSzJo60tRaF3v37qWAgABijFHfvn1tZhBV1JEjR4gxRuvXr+cdRVFiXRzb9b9t29LDZ1MdV6ZnaWlpFBERQYwxCg4OtslBs/G0gVatWvGOoiixLo7tSkpKokkAPXFyqtQxTZZlmjt3Lun1etLr9TRnzhybGUQVNX78eHJycuJy+ry1OMoEN/YoPz+f3N1nEWOPKvujI7fr6Svi9OnTJEkSLV++nHcURRknuLHWdWlGqhlMUVwcySV+nRAMkAQQAHICaGwpiwfu3r2b6tatS4wx6t+/v82fW2o8bSBVhStbFyXWxbFBJhYPNLdnN2/epB49ehBjjOrXr08JCQm896ZMxtMGJk+ezDuKosS6ODYqLs4wkKpg157+7W80e/Zs0ul0pNfrad68eTZ/eo81J7jhxdbWxREM4uKI9Hq5xMApmACJABDgRMBYk+tO28L19BWxZs0akiTJKhPc8MRjght1DKZSUkyuDh0PUNazPx94dhDaWuI5OYxRW8ZowIABNj+IKqpXr17k7u5OWVlZvKMoxrguTq9evXhHEYiq1LNcSaI2ANWvX58SExN574nZdu/e7RDr4kRGRop1cWxJFbr2EKBOLi40f/58mx9EGWVnZ5OHhwd1796ddxRFGSe4sYV1cYRSa0ZAPAFZz/584NnAamuJT6lyCGjD/Xr6ihoyZAjp9XrFJrixBcYJbjp06GC1bapjMBUVVe5pEAnPDjzTS9wvA5Q7cCDvPaiw/Px8ql27NjVt2pR3FEUZ18VZvHgx7yhCFXr2FKBfOnXivQeV4ijr4gQGBop1cWxFFY9pBVFRvPegwhxlXRxHmODGXphRMwISng2mppe4X6b+/R/x3oUKM05wU69ePVW/1xsnuJk9e7ZVtmf/g6n09OevFixya/7slAgApAfonqnn6XQVm6rFRoh1cQSrceCeETnOujg6nU6si8ObA3fNUdbFcYQJbmxdOTUjoHlhzwA9AffUUrPCCW6GDh3KO4qirDnBjQR7t3lzmQ9fApAHYD2AjgA8TT2JsXJfxxbVrVsXX3/9NT7//HN89tlnvOMoZsqUKRg2bBgGDhyI+/fv847jmBy4ZwCQnJyMgoICdOvWjXcUxdSoUQMHDhzAV199hQ0bNvCO47gcuGuvv/46XnnlFfzpT3/C3bt3ecdRzD//+U84OTmhc+fOvKM4rPLrUX7T7LRmqF69OhITExEfH4+PPvqIdxzFjB49Gn/+858xbNgw3Lp1S9Ft2f9g6sIF4PHjMp/iDGAKgLsAxph6Qm4ucPGi5bNZwYABA/A///M/mDBhAi5fvsw7jmJ27NgBf39/tG3bFgUFBbzjOB4H75lOp8N3332H1NRUzJ49m3ccxURERGDRokWYOnUqfvjhB95xHJODd23btm0ICgpCu3btVPte7+zsjJSUFFy8eBFvvPEG7zgOyYyaobym2XHN0LVrVyxduhQzZ85Eamoq7ziK2bRpE5o0aYJ27drh6dOnim3H/gdTWVlmP1UG8FNpD2ZmWiINF8uXL0eHDh3QqVMnPHr0iHccRUiShJSUFGRkZGDw4MG84zge0TM0bdoUmzdvxqpVq7Bv3z7ecRTz7rvvIiIiAt26dcPDhw95x3E8Dt41SZJw5swZ/Pbbbxg4cCDvOIqpV68evvzyS2zYsAE7d+7kHcfhVKBmKKtpdlozAMC8efPQu3dv9OjRA7///jvvOIo5deoUHj16hD59+ii2DfsfTHl5mbz7MoA3AfwC4AmA9wFcAzCgtNfx8VEgnPUcOXIEzs7O6NSpE+8oivH19cWhQ4eQkJCAVatW8Y7jWETPABhOGxg/fjyGDx+OtLQ03nEU8+2338LDwwPh4eG8ozge0TV4e3vjyJEjOHjwIFasWME7jmKGDRuGqVOnYvTo0fjpp1KHxYICSqkZKto0O64ZACAhIQHe3t5o164d7yiKcXd3R3JyMpKSkrBo0SJFtmH/g6mwMECne+5uCcDnAGoDcAGwBIYPaZeZeg29HggNVTCk8pydnXHmzBlcunQJkydP5h1HMZ06dUJsbCzmzJmDU6dO/fHA/ftAbCwwejTwpz8Z/hsbC2Rk8AurJqJnhT755BOEhIQoftoAT1qtFikpKbh+/TpiYmL+eED0THmiawCA8PBwfPjhh3j77bdx/Phx3nEUs3btWrRo0QLh4eF48uTJHw+IrimqlJqhIk1TQc0Kz/q5efMmRo8ezTuOYsLCwrBhwwa89957OHTo0B8PWKpnik9xobTyp2Qp/2avU7KY4Cjr4gwcOJBcXV3pt0OHDPOb6nTP/zvQ6w33RUUZFpQQKk/0rBhHWxdn/7vvip5Zi+haMQ63Lk5KiuiaFYiaFZeYmEiMMdq4cSPvKIoaOXIkubi4UMa331q0Z/Y/mCIyd7EA0zfGiFQ2PeSbb77pEOvizPP2pkeMUUF533vG/liyXKg80bNiHGVdnK/79KGHgOiZNYmuFTKuixMcHKz6dXEmM0Z5Wm3533vRNYsQNStuwYIFJEkSnT9/nncUxciyTAv8/CjHwj87qmMwVfoy1uXfXF2JUlN574HFtWnTRt3r4sTFUYFeX+x7eQIgP4AYDItZhgKUW/J7LQ4+lSd69hzVr4sTF/fc91z0zApE14oxrosTZYeLEZstLo7ynZ1F16xI1Ox53bp1I09PT8rOzuYdRRkK/eyojsEUkcmDfnm3Ar1etW9ExtMG2rdvzzuK5ZXyDugHUAOAMgE6D5ALQEMd5R3QWirRsyfOzqrtGRHRqFGjyMXFhe7cucM7imWJnvFVia6p+YfrpKQkYozRmjVreEexPNE1bipTM71eVmvNKD8/n2rUqEGhoaG8o1iegj1Tz2CK6I9WlPPRXQFjlMMYzff1VfVpA1evXiWNRkMzZ87kHcWySvls3hmgRUX+vw1AISW//2r8bN7azOwZMUZ5Tk40CaBz587xTq0YWZapcePG5O/vr673E9Ez/ipwTHsI0A6VX8O3bNkykiSJUtR2vZDoGlcVOKQRY4/I03MO5efn846tmJs3b5KTkxNNmDCBdxTLUrBn6hpMERlGjkOHGi4eK/FRXuFFZUOHUuahQ6TX62nIkCG8Eytq69atxBij+Ph43lEso4yrRkcCVB+gDIBSn/12YY6pd0Sdjvde2D8ze0apqdS9e3fy8PBQ72kDRJSZmUlubm7Ur18/3lEsw1I9U8vV2TyZ2bV/vP8+McZo165dvBMrqm/fvuTm5kaZmZm8o1iGOKbZBHMPaUePZpO7uzv16tWLd2RFxcfHE2OMtm7dyjuKZSjcM/UNpozu3yeKjSUaM4YoMtLw39jYYgf3EydOkCRJtHr1ao5BlTdhwgRycnKimzdv8o5SdStXllqI/QDpAcKzWwOAZFOF0Ot574V6mNGz/Px8qlmzpjpPGyji9OnTJEkSLV++nHeUqrNUz2Jjee+JepjRtSlTppBWq6Xr169zDKosWZbJ39+fGjdurI5PgsUxzaaYUTM6e/YsSZJEixcv5hfUCmbOnEkajYauXLnCO0rVKdwz9Q6mzLRy5UqSJIlOnz7NO4qiQkNDqUaNGvb/0XR0tMky5AOkAagXQFkAXQOoFkBtTRUCDv/P3urS0tLIycmJxo8fzzuKotasWUOSJFFSUhLvKFVjqZ6NGcN7TxxOq1atqHr16uqdfIiI7ty5Qy4uLjRq1CjeUapOHNPs0vr164kxRocPH+YdRVHh4eHk4+NDubm5vKNUjcI9s/9Fe6tozpw56Nu3LyIiIvDbb7/xjqOY06dP4/Hjx+jduzfvKP+fvTsPi6rs/wf+vs8MMAMom7ig4r4Lmhsa5q65pUGoKa6puaRlbl8tLUuz3MosUdMUH/35WC5p5pJKapKa5FMumZmhZlpISoiIgMPn98cEuQwww8yZe5bP67q4zJnDnPch3zNzM+fct3XS003e/CsAA4CPAZQGUAvAUACn7JWLFSk0NBSbN2/G6tWr8Z///Ed2HNW89NJL6N27N7p27Yq//vpLdpySs1XP0tJsn40VKTExEbm5uejQoYPsKKoJCQnB9u3b8d///hcfffSR7DjW4dc0pzR27FjExMSgZ8+euH79uuw4qvn6668hhMATTzwhO4p1VO6Z2w+mAOCLL75AYGAgWrRogby8PNlxVOHt7Y3Dhw/j8OHDmDlzpuw4JefnZ/LmOgC0AJ4HcBfAZQBrAZS3WzBWnF69emHSpEl47rnn8NNPP8mOo5rNmzejXLlyaN68ufM+n9iqZwEBKoRjRfH29sY333yDY8eOYfr06bLjqObJJ5/EjBkzMGbMGJw65cRDDH5Nc1obN25ExYoV0axZM+d9ri+Gp6cnjh07hh9++AEvvfSS7Dglp3LPeDAFQFEUJCUl4bfffsPAgQNlx1FNeHg44uLi8NZbb+HLL7+UHadkwsNxz8PD5F3rARwD4A2gGoz/uHea2lCvVysdK8aCBQvQvHlzREZG4u7du7LjqCL/+SQlJQUxMTGy45RMeDjyvLxM3mVRz8LC1ErIitCgQQOsXLkS8+bNw65du2THUc2bb76JJ554Ak888QRu374tO06J5NavjxyNxuR9/Jrm2BRFwfHjx3Hjxg307t1bdhzV1KpVC+vXr8cHH3yALVu2yI5TMmq/d7TjGYsOb+/evSSEoBUrVsiOoqrY2FinXBdnxYoVVCcggO4Udi6ruV8885FU2dnZFBQURE2bNpUdRVWHDh0iRVGcbl2cM2fOUOdGjWzTM57NT6ohQ4aQp6cnXblyRXYU1TjrujhZWVk0YsQICtFqH1wglF/TnM6RI0dIURRauHCh7CiqGjNmDGm1WkpOTpYdxSLr1q2jBsHBqr535MHUQ15//XVSFMWl18UhIqpTp47TrIuzfPlyCgoKIo1GQwMGDKCcnj2LXxCisC9ek8MhXLhwgbRaLY0fP152FFW9/fbbTrMuzsmTJ6lp06YkhKCwsDC62a4d98wF1K9fnypUqOD8kw8VIX+CmxEjRsiOUqysrCwaPnw4abVa8vf3N84mXMj6N9w157Fw4UJSFIUSExNlR1FV48aNKTg42CkmuFm7di2VLVuWFEWhmJgYyu7RQ7We8WDKhPbt27v8ujjp6ekOvy5OXFwcBQYGkkajoUGDBv37/6OQVazN+uLV4h3GJ5984hbr4nTt2pV8fHwoPT1ddhSTTp48SU2aNCEhBIWHh1NSfj+4Zy4hI8O4Lk7Hjh1lR1GVo6+Lk5mZSUOHDi0YRD3wiTV3zSX07NmT9Ho93bhxQ3YU1WRmZpKfnx+1bt1adpRCrV69moKDg0mj0VCfPn3+XZNOxZ7xYMqE3NxcKl++PDVs2FB2FFUdP36cFEWhuXPnyo7ygKVLl1JAQABptVoaPHiw6UFt/pLllpYhLs7+B8QKxeviyPP999/TY489RkIIatSoEZ04ceLRjbhnLoHXxZEnMzOThgwZQlqtlgICAmjJkiWmN+SuOT2DwUChoaFUo0YNh3qut7XTp0+TRqOhadOmyY7ygFWrVhUMovr162f6F5gq9YwHU4Vwp3VxhBAOsS7OkiVLCgZRQ4YMoczMzKK/Ib8UxX1sKwS/6DgwXhfHvk6cOEGNGjUiIQQ99thjxZ/SzD1zCbwujn1lZGTQ4MGDSavVUmBgIC1durT4b+KuOb2UlBTS6XTUp08f2VFUtXr1ahJC0M6dO2VHoZUrV1KZMmVIo9FQ//79iz8LRIWe8WCqCNu3bychBK1du1Z2FFVFR0eTTqej1NRUKftfvHgx+fv7k1arpWHDhhU/iLpfUpLxPFadzrg69f1F0OuNt0dH82kQDiz/tIHIyEjZUVQle4KbpKQkCg8PJyEENWnShE6ePGnJN3PPXECfPn1Ip9NRSkqK7CiqkT3BTUZGBsXGxpJGo6HAwECKs3TAw11zegkJCSSEMG8A7cRkT3Dz8PX0Fp1Kb+Oe8WCqGFOmTHG40wZszWAwULVq1ahq1ap2/Wj6vffeIz8/P9JqtTR8+HDLBlEPu36daP58okGDiHr2NP45fz7PJuYkzpw5QxqNhqZOnSo7iqpmzpxJGo3GsoGMlZKSkigsLIyEENS0aVPr9s09c2oGg4Fq1qxJlStXdunTkM6fP2/3CW4yMjJowIABpNFoKCgoiJYvX27dA3LXnNrs2bNJUZR/r0F1UfXq1bP7BDf3X08fGxtr3fwGNuoZD6bM0KpVK/L395d+2oCaUlNTSa/XU1RUlKr7MRgMtGjRIvLz8yuYfcmVf67MfPHx8SSEoB07dsiOoqp27dpR6dKlVZ/g5tixY9SwYUMSQlCzZs3o9OnTqu6POYe0tDTy9vamHj16yI6iqo0bN9plgpv09HTq378/aTQaKlOmjMsvrcLM16lTJ/L19XXYyYdsIT09nXx9falTp06q72vp0qUUGBhY9PX0kvBgygyyTxuwl8OHD5teFyclhWjePKLYWOPIPTbW+HcLRu4Gg4Hmz59PpUuXJg8PDxo1ahQPotgjhg4d6hbr4pQrV+7RdXFs0DMi4yCqQYMGJISg5s2b05kzZ2yYnrmC/HVx5s+fLzuKqkyui2OjnqWnp1O/fv1Io9FQcHAwrVq1ysbpmbPLzc2lChUqUL169WRHUVWhE9zYqGsWX08vAQ+mzJS/Ls64ceNkR1FV/ro4x44dM04jGRVlPHdUpzN9TmlUlHG7QhgMBpo3b17BIGr06NE8iGJFql+/PpUvX9591sWxQc+IjG+Q69evT0IIatGihUufmsys527r4uR8841NepaWlkZ9+vQpGEStXr3aTkfCnNGVK1fI09OThgwZIjuKqvInuElISLDZa9r919MPHTrUIQdR+XgwZQF3WRenW7du9KKnJ+Xp9SWe7cRgMNDbb79NpUqVIk9PTxozZoxLz9bGbMdd1sXZvn07jQYo19PTqlmFEhMTqV69eiSEoJYtW9K5c+ckHA1zRu6yLs4EnY6yFMWqnqWlpVFMTAxpNBoqW7YsxcfHSzga5ox27dpFQgiXH3j36dOHxmm1Vr13JDJeT58/iLL6eno74cGUhdxiXZylSynT0lWi/ymFwWCgOXPmkK+vL3l6etK4ceN4EMUsln/awKxZs2RHUU9cHGVrtSVe7+Lw4cNUt25dEkJQq1ateBDFLJa/Lk716tVdd0KKuDgyPPzbcQt6duPGDYqOjiZFUahcuXIuP7svU8f06dNJo9G49LWrhqVL6Y4V7x2d+Xp6HkyVQJMmTSgoKMg1BwkmVoiGia8wE6XI9vCgJ/R68vT0pPHjx7vmz4fZTVxcnOuui2NFz+55edEzVaqQEIIef/xxOn/+vOyjYU4sf12cmJgY2VFsz4qe5en1NLldO1IUhcqXL0/r1q2TfTTMybVu3Zr8/Pyc4pMWi1nRtRwPD2rr40MeHh40cuRIpxpE5RNERGAWuXv3LipUqID69evjm2++kR3HtqKjgW3bjP/ETbgOoByAJQDGP3SfAcDpGjVQ/+xZeHp6qpuTuYVnn30W27Ztw6VLl1C+fHnZcWzHyp4dDgpC5W+/RY0aNdTNydzCV199hU6dOmHJkiUYN26c7Di2Y2XP9nh54daaNejfv7+6OZlbyMnJQaVKlVCxYkV8//33suPYlpVdO1WtGuqdPQudTqduTpXwYKqEfvzxRzRq1AiTJk3CvHnzZMexjevXgSpVgLt3C91kJIB4ANkAFFMb6HTAb78BwcGqRGTuJS8vD3Xq1EF2djYuXboERTH5r865cM+YA5ozZw5ef/11fPvtt2jWrJnsONbjnjEHdPHiRdSuXRsjRozAsmXLZMexDe6a6WNixWvQoAE+/vhjLFiwAF988YXsOLYRH1/sJpsBRKKIfzhCmPU4jJlDURQkJSXhxo0b6NWrl+w4tsE9Yw5oxowZ6NChA9q3b49bt27JjmM97hlzQNWqVcOnn36KFStW4JNPPpEdxza4azyYssaQIUMwZMgQPPPMM/j9999lx7HeqVNF/mbhKIC/AbxZ1GNkZQGnT9s4GHNn/v7+2L9/P3bv3o0FCxbIjmM97hlzULt370apUqXQsmVL2VGsxz1jDioqKgovvfQSBg4ciF9++UV2HOtx13gwZa01a9agVq1aaN68Oe7duyc7jnXS04u8eyYAPwBtinuctDQbBWLMqFWrVliwYAGmTZvm/Ncpcs+Yg9JqtTh+/Dh+/fVXDBkyRHYc63DPmAN777330LhxY7Rs2RI5OTmy41iHu8aDKVs4duwYMjMz8eSTT8qOYh0/vyLv/hpAlDmPExBgizSMPWDixIno2bMnOnfujJs3b8qOU3LcM+bAKlWqhG3btmHdunX4+OOPZccpOe4Zc3CHDx8GEaFNm2KHGY6Nu8aDKVvw9fXFwYMHcfDgQbzxxhuy45RceDiokJlUVgDIBfBWcY+h1wNhYTYOxpjRZ599huDgYDRv3hx5eXmy45RMeLjxYlsTuGfMEXTr1g3Tpk3DqFGjcObMGdlxSoZ7xhycTqfD0aNH8d1332HSpEmy45Qcv3fkwZStNGnSBB9++CHeeOMNJCQkyI5jsXv37mHWpUvILuS818UAqgEIKe6BiIChQ20bjrF/5E9Ice3aNfTr1092nBLZGxKCu9wz5uDmzp2LVq1aoXXr1rhz547sOBa71qVLoadPcc+Yo6hTpw7Wrl2L9957D9u3b5cdx2J5eXl4588/3f69Iw+mbGjMmDHo27cvevTogT///FN2HLPcu3cPkyZNQqlSpbBg7Vok16kDEuKR7X4CkFzcgwkBdO/utFNbMudQtmxZ7Ny5E1u2bMGHH34oO47ZPv/8c1SpUgVdBw/GibJluWfM4SUkJMDT0xORkZGyo5jt999/R+fOnVGpSRN85eWFPO4Zc3CxsbEYMWIE+vTpg8uXL8uOY5a8vDy8+uqr8PX1xRvLluGXmjXd+zVN6pLBLshgMFDNmjWpcuXKZDAYZMcpVG5uLr388svk5eVFPj4+9PrrrxvzmljF2uwvb2+ipCTZh8bcxOzZs0lRFEpy8H9z27Zto8qVK5MQgrp27UpXr17lnjGnkZycTFqtlkaPHi07SpEuX75MHTt2JCEEVa1alXbu3Mk9Y04lLCyMypYtS7m5ubKjFMpgMNC0adNIr9eTXq+nadOmGfO6edd4MKWCtLQ08vb2pu7du8uO8ojc3FyaMGFCwSBq1qxZjw764uIsL4W3t/H7GLOjzp07k4+PD6Wnp8uO8oitW7dSpUqVSAhB3bp1oz/++OPBDbhnzEls3ryZhBC0YcMG2VEecfnyZerQoQMJIah69eq0a9euBzfgnjEnkZmZSaVLl6a2bdvKjvKI3Nxcmjp1asEgavr06fze8T48mFLJkSNHSFEUmjdvnuwoRESUnZ1N48ePLxhEzZ49u+hPzvJLIUTRRRDCZcrAnI/BYKAKFSpQ3bp1ZUcpsHnzZqpYsSIpikLdu3d/dBB1P+4ZcxITJkwgrVZL58+flx2FiIguXbpE7dq1KxhE7dmzp/CNuWfMSZw8eZI0Gg3NmDFDdhQiMg6ipkyZQjqdjvR6Pb3yyiv83tEEHkypaNGiRaQoCh0+fFhahuzsbBo3bhx5enqSr68vzZkzx/zTD5OSiKKjiXQ6Ir3+wSLo9cbbo6Od/uNZ5tyuXLlCnp6eNHjwYKk5Nm3aVDCI6tmzJ6WkpJj3jdwz5iSaNWtGgYGBlJ2dLS3DpUuXqG3btiSEoBo1atDevXvN+0buGXMSK1euJCFE0b8gUFlubi5NnDiRdDodeXt708yZM/m9YxEEEZHs67ZcWe/evbFv3z78/vvvCAwMtNt+c3Jy8PLLL2PVqlXw8vLC9OnT8X//939QlBLMOZKaCsTHG1enTkszrgUQFmacecWZLxhkLmP37t3o0aMHVq5cieHDh9t135988glefvllpKSkoGfPnvj4449RpkwZyx+Ie8Yc3N27dxESEoLatWvj2LFjdt33xYsXMXToUBw+fBg1a9bEsmXL0LFjR8sfiHvGnMDAgQOxefNmJCcnIySk2LnwbObevXuYOnUq4uLioNFoMGXKFLz22mv83rE4skdzrs5gMFBoaChVr17dLhNSZGVl0ejRo8nT05NKlSpF8+bNc+iJMBizlenTp5NGo6HTp0/bZX8bNmygChUqkKIo1Lt3b0pNTbXLfhmT6dy5c6TRaGjixIl22d+FCxeodevWJISgWrVqUUJCgl32y5hsderUoYoVK9rlPZxZ19OzQvFgyg5SUlJIp9NRTEyMavvIysqiUaNGkYeHB5UuXZrmz5/PRWBup3Xr1uTn50eZmZmq7WP9+vVUvnx5UhSFoqKi6MaNG6rtizFHtG7dOhJC0LZt21Tbx/nz5ykyMpKEEFS7dm06cOCAavtizBGlp6eTj48Pde3aVbV9WHw9PTOJT/Ozk6+++gqdOnXCkiVLMG7cOOON168bPwI9dQpITwf8/Iyrtg8bZvZHoHfv3sWLL76I+Ph4eHt7Y+bMmXj55ZdL9pEsY04uJycHlSpVQkhICH744Yd/77BB19avX4/JkycjNTUVTz/9NFauXGnXU3cZcyTPP/884uPj8csvv6BKlSrGG23Qs19++QVDhw7F0aNHUadOHaxYsQJt2rRR70AYc2BJSUlo2bIl5syZg+nTpxtvtEHPcnJyMGnSJHz00Ufw9PTEtGnTMH36dH7vWFKyR3PuJH9dnB/j44mioowX4el0pi/Oi4oyzttfiKysLBo+fDh5eHiQn58fvffee3Y8EsYc1wPr4hw/bnXX1q5dS2XLliVFUeiZZ56htLQ0Ox4NY47v7iYDAAAgAElEQVSrYF2cI0es7tm5c+eoZcuWJISgunXrSp24iTFHsmTJEhJC0Inly63uWXZ2No0dO7ZgUrK5c+fyJ1E2wIMpO1tcty5lApRXwmkjMzMzadiwYaTVasnf358HUYyZsHXrVhoNUK6nZ4mnaI2Pjy8YRMXExPAgirGHZGZm0gQvL8pSlBL37OzZsxQREUFCCKpXrx4lJiZKOhrGHNfyxo3pthXvHbOzs2nMmDEF19O//fbbPIiyIR5M2VNcHOWZWNBsPECeAAEgLUAfmFjQLDMzk4YOHUparZYCAgJoyZIlso+GMccVF0fZWm2JurZ69WoKDg4mjUZD/fr140EUY4WJiyPDw78hN7NnZ8+epRYtWpAQgho0aEBHjhyRfTSMOSYr3jvy9fT2wddM2UtSEtCuHXDnzgM3vwNgBoDlAIYCyL/Ko9l922RrNGgL4Hzp0njzzTf/veaKMfYoK7p2B0B7RUH1vn2xYsUKlC5dWv28jDkjK3qWpShok5eHuw0bYtWqVYiIiLBDYMackDXvHbVatCXCTz4+eO211/h6ehXxYMpeoqOBbduMvzO4TykAzwCIL+JbDQAuNWqEGvdfUM8YM82KruUBMDz1FDw+/1y9fIy5Aitf09LbtUPggQPq5WPMFVjZs+SwMNT44QceRKmMf7r2cP06sHv3I2XIAXAbwJ8APABoAIQDuPnQt2sA1Pj5Z+MCaIyxwlnZNQWAx7593DXGimKD17TAY8e4Z4wVxQY9q/XLL1Bu3LBDWPfGgyl7iI83efOZf/48DCAJwFkAlwA8ZWpjIQp9HMbYP7hrjKmPe8aY+rhnToMHU/Zw6hRw9+4jNwf88+cAAI0B1AEwCsD3ph4jKws4fVqthIy5Bu4aY+rjnjGmPu6Z0+DBlD2kp5u8uRqMH8MKcx8nLc1GgRhzUdw1xtTHPWNMfdwzp8GDKXvw8yv0rscB/D8APwK4COAjAE0L2zggoLB7GGMAd40xe+CeMaY+7pnT4MGUPYSHAzqdybv2wPhbhoYAagKoCmCHqQ31eiAsTKWAjLkI7hpj6uOeMaY+7pnT4KnR7eH6daBKFZPnvppNpwN++w0IDrZdLsZcDXeNMfVxzxhTH/fMafAnU/ZQtizQrZtxVpWSEALo3p3LwFhxuGuMqY97xpj6uGdOgz+ZspdCVrE2i7c3cOgQ0KxZ8dsy5u64a4ypj3vGmPq4Z06BP5myl+bNgYULjf+4LeHtbfw+LgNj5uGuMaY+7hlj6uOeOQXNrFmzZskO4TaaNwcCA4EDB4B794reVoh/yzBmjH3yMeYquGuMqY97xpj6uGcOj0/zY4wxxhhjjLES4NP8GGOMMcYYY6wEeDDFGGOMMcYYYyXAgynGGGOMMcYYKwEeTDHGGGOMMcZYCfBgijHGGGOMMcZKgAdTjDHGGGOMMVYCPJhijDHGGGOMsRLgwRRjjDHGGGOMlQAPphhjjDHGGGOsBHgwxRhjjDHGGGMlwIMpxhhjjDHGGCsBHkwxxhhjjDHGWAnwYIoxxhhjjDHGSoAHU4wxxhhjjDFWAm4xmGrQoAEOHjwoOwZjLo17xph9cNcYUx/3jJnLLQZTP/74I9q1ayc7hkkHDx5EpUqVZMd4xNChQ+Hp6QlfX9+CL4PBIDsWc2Dcs5LZv38/mjRpAh8fH1SuXBmffvqp7EjMwXHXLNegQYMHXs+0Wi2eeuop2bGYA+OeWe7mzZvo168fypQpgzJlyiA2Nha3bt2SHUt1bjGYYiUzdepU3L59u+BLo9HIjsSYSzl79iwGDBiAt956C+np6fjhhx/QtGlT2bEYczk//vhjwWtZRkYGQkND0adPH9mxGHMpM2bMQFpaGpKTk/Hrr78iJSUFs2bNkh1LdW4xmKpatSr279+PoUOHYsaMGQW3Pzyyr1q1KhYsWIDw8HD4+Phg+PDhSElJQbdu3VCqVCl06tQJaWlpAIBLly5BCIGPPvoIISEhqFChAhYtWlRohl27dqF+/fooVaoUKlasiIULFyIzMxPdunXDtWvXCn5bdu3aNeTl5eGdd95BjRo1EBQUhL59++LmzZsl2i9j9sI9s9ycOXMwatQodOvWDVqtFkFBQahRo4bN98NcC3fNOl9//TWuX7+OZ555RtX9MOfGPbPcxYsX8fTTT6N06dLw8/NDVFQUfvzxR5vvx9G4xWDKElu2bMG+fftw/vx57NixA926dcPcuXPx119/IS8vD0uWLHlg+wMHDuCXX37B3r178c4772D//v0mH3f48OFYsWIFMjIycObMGXTo0AE+Pj7YvXs3QkJCCn5jFhISgiVLlmDbtm04dOgQrl27hoCAALzwwgsl2u8777wDf3//Qr+KEhcXh8DAQDRt2hRbtmyx4KfIWNG4Z0bHjh0DAISFhaFChQoYOHBgwYsfY7bAXXvU2rVrERMTAx8fH7O2Z6w43DOjF154AV988QXS0tKQlpaGLVu2oFu3bhb+NJ0QuYEqVarQvn37aMiQIfTqq68W3H7gwAGqWLHiA9utX7++4O/R0dE0evTogr8vWbKEevfuTUREFy9eJAD0008/Fdw/ZcoUeu6550xmqFy5Mi1fvpzS09MfuP3hDEREdevWpf379xf8/dq1a6TVaik3N9fi/ZbUiRMn6K+//qLc3FzauXMn+fr6UmJiok33wVwL98xyHh4eVKVKFfr5558pIyODoqOjacCAATbdB3M93LWSy8zMpFKlStGBAwdUeXzmOrhnlrt69Sp17NiRhBAkhKBOnTpRdna2TffhiPiTqYeUK1eu4L/1ev0jf799+/YD21euXLngv6tUqYJr166ZfNwtW7Zg165dqFKlCtq2bYujR48WmuHy5cuIiooq+A1AvXr1oNFokJKSYvF+S6pJkyYICgqCVqtF9+7dERsbi61bt9p0H8x9cc+M9Ho9hg0bhtq1a8PX1xevvPIKdu3aZdN9MPfGXXvQ1q1bERgYiLZt26ry+Mw9cc+M+vTpg9q1ayMjIwO3bt1CjRo1MHDgQJvuwxG51WDKx8cHd+7cKfj7n3/+afVjXrlypeC/f/vtN4SEhJjcrnnz5ti+fTuuX7+Op59+Gn379gUACCEe2bZy5crYvXs3/v7774Kvu3fvomLFihbvd+7cuQ/MYPTwl7mEECAis7dn7ot7Zn7PwsPDTWZjzBzcNctf09auXYvBgwdz75jZuGfm9+zkyZMYNWoUfHx84Ovri9GjR7vFLwjdajDVuHFj7Nq1Czdv3sSff/6JxYsXW/2Ys2fPxp07d/Djjz9izZo16Nev3yPb5OTk4P/9v/+H9PR0eHh4oHTp0gUz45UrVw43btxAenp6wfajR4/Gq6++isuXLwMAUlNTsX37dov3CwCvvPLKAzPyPfxVmM2bN+P27dvIy8vD3r17sX79evTq1cvinw9zP9wz83s2bNgwrFmzBsnJybhz5w7mzZuHnj17WvzzYe6Ju2Z+1wDg999/x4EDBzBkyBCLfibMvXHPzO9Z8+bNsWrVKmRlZSErKwsfffQRGjVqZPHPx9loZQewp0GDBmH//v2oWrUqqlatimHDhlk9m0nbtm1Rs2ZN5OXlYfLkyejSpYvJ7datW4dx48bBYDCgTp06WL9+PQCgbt266N+/P6pXrw6DwYCzZ8/ipZdeAhGhS5cuuHbtGsqWLYt+/fqhd+/eFu+3pN5//30MHz4cRIRq1aph5cqVDrveAnMs3DPzPffcc7h8+TIiIiIAAF27dn3kQmXGCsNds8y6devQqlUrnjGTWYR7Zr7Vq1fjxRdfRKVKlUBEaNGiBeLj4226D0ckyA3O3QoNDcX69evRpk0bmz3mpUuXUK1aNeTm5kKrtd+YVNZ+GSsO94wx++CuMaY+7hkzl8uf5peamorU1FRUrVpVdhTGXBb3jDH74K4xpj7uGbOESw+mkpKSUKtWLYwfPx6hoaGy4zDmkrhnjNkHd40x9XHPmKXc4jQ/xhhjjDHGGLM1l/5kijHGGGOMMcbUwoMpxhhjjDHGGCsBHkzZ2bJlgI8PoCiAEIV/KYpxu2XLZCdmzDlx1xhTH/eMMfVxzxybZtasWbNkh3AXy5YBkycD9y2kXaTcXODAASAwEGjeXN1sjLkS7hpj6uOeMaY+7pnj4wko7CQpCWjX7uEy3ALQDEAyAAMAPYBpAF574Hu9vYFDh4BmzeyTlTFnxl1jTH3cM8bUxz1zDnyan528/TaQlfXwrXcBhAA4CCAXwGQArwNIfGCrrCzj9zPGisddY0x93DPG1Mc9cw78yZQdXL8OVKkC3L1rztZ6AOMALHjgVp0O+O03IDhYhYCMuQjuGmPq454xpj7umfPgT6bsID7e3C3PwPgbh/aP3COEJY/DmHvirjGmPu4ZY+rjnjkPHkzZwalT5vxm4Q6ANgDqAuj+yL1ZWcDp07bPxpgr4a4xpj7uGWPq4545Dx5M2UF6enFb3IOxCFoAJwrdKi3NdpkYc0XcNcbUxz1jTH3cM+ehlR3AHfj5FXVvHoB6ADIA/ArAu9AtAwJsGosxl8NdY0x93DPG1Mc9cx78yZQdhIcbLwI0rSGAPwD8BCCw0MfQ64GwMNtnY8yVcNcYUx/3jDH1cc+cBw+m7CAqKh05OTkm7vkGxiJkAqgAQPzzNfaRLYmAoUNVDMmYC8jJ+Qh3TZ5kzl1jzFYaN/4B2dnZJu7hnjFmK336ZPJ7RyfBgykV/f333+jTpw/q1QuCVrsPQuQ9tEUkADLxFffAVkIA3bvz1JaMFWbx4sUICAjAG2+8gNDQHyHEwys+cNcYs9Z3332H8PBwdO3aBH5+R7hnjKng9u3bGDhwIGrV8oNGs5ffOzoBHkyp4O+//0ZMTAyCgoLw9ddfY/Xq1UhM7AG9vmQ/br0emD7dxiEZc3J5eXl499134e/vjylTpiAmJgYZGRnYvLkp9HpRosfkrjH2qKSkJISHh6NFixbw9PTEqVOnsHdve+4ZYzZ069YtDBgwAP7+/tizZw/i4uLwzTc9+b2jE+DBlA3dvHkT0dHRCAoKwjfffIO1a9ciJSUFgwcPRvPmwMKFgHfh1wia5O1NWLgQaNZMncyMOZu8vDwsWrQIAQEBmDZtGvr27YuMjAysXLkSOp3Oiq6Bu8bYfb799ls0bNgQERER8PLywqlTp/Ddd9+hYcOG3DPGbOTWrVvo378/AgMDsW/fPixfvhx//fUXnn/+eX7v6CyIWe3GjRv09NNPk6IoVL58eVq/fn2h28bFEXl7EwlBZDyb1fSXEHkEZFLNmgvteCSMOS6DwUDz58+n0qVLk4eHB40aNYqysrIK3d7crgEGAm7TvHnpdjwaxhzXkSNHqH79+iSEoBYtWtCZM2cK3db81zRjz5599qAdj4Qxx5Wenk79+vUjjUZDwcHBtGrVqkK3tfS9Y+XKb9nxSBgPpqyQmppKvXv3JkVRqEKFCrRhwwazvi8piSg6mkinI9LrHyyCXm+8PTqaaOPGX0ij0dC0adNUPhLGHJfBYKC3336bSpUqRR4eHjRmzBjKzs4263vN6VpUVB5VrNibQkNDyWAwqHw0jDmuxMREqlevHgkhKCIigs6ePWvW95n7mjZy5ApSFIVOnDih8pEw5rjS0tIoJiaGNBoNlS1bllavXm3W95nbs88+u0JarZbGjh2r8pGwfIKIHr6ClBXj+vXrGD58OHbt2oXy5cvj3XffRb9+/Sx+nNRUID7euDp1WppxLYCwMOPMK/kXDK5ZswbDhw/HF198ge7dH13dmjFXlZeXh3nz5mHu3LnIycnBiBEj8N5778HT09Pixyquazdv3kSlSpXQuXNnbN++3ebHwpgjS0xMxMiRI/Hzzz8jIiIC8fHxqFOnjsWPY85rWseOHZGUlIRr167B19fXpsfBmCP7+++/MWLECHz22WcoU6YM5s+fjyFDhlj8OOb0bPPmzejbty82btyIvn372vQ4mAmyR3POJCUlhXr06EGKolDFihVp06ZNdtnv4MGDydPTk65cuWKX/TEmk8FgoDlz5pCvry95enrSuHHjzP4kyhqJiYmkKAotWrRI9X0x5ggOHTpEderUISEEPf7443T+/HnV95mbm0vly5enBg0aqL4vxhzBjRs3KDo6uuBSkHXr1tllv+PHjyetVksXLlywy/7cGQ+mzJCSkkLdu3cnRVGoUqVKtHnzZrtnqFu3LlWoUIFyc3Ptvm/G7MFgMNAbb7xBPj4+5OXlRS+++KJdBlH3mzdvHimKQkeOHLHrfhmzpwMHDlDt2rVJCEGRkZF2GUTd7/Lly+Th4UFDhw61634ZsydLrqdXS9OmTSkoKMjur6XuxmVP87t+3fgx6KlTQHo64OdnXE162DDz59z/888/MWzYMHz55ZeoVKkS3n//fURFRamauzC3bt1CxYoV0apVK+zdu1dKBsYeZoue5eXlYfbs2ViwYAHu3buHMWPGYMGCBdBqtapmL0zPnj1x4MABXL16Ff7+/lIyMPYwW3Tt4MGDeP7553HhwgW0bt0aa9asQY0aNVTNXZgvvvgCvXr1wpo1a0p0qhNjarBFz/766y+MGDECO3bsQLly5bBo0SL0799f1dyFuXv3LipUqIB69erhyJEjUjK4BdmjOVs7fpwoKsp4IZ5OV9gF58btCnP16lV68sknSQhBlStXpm3bttnvAIpw4sQJUhSFZs+eLTsKc3O26JnBYKDXX3+dvL29ycvLi15++WWH+OTVYDBQ5cqVqWbNmjwhBZPOFl3bv38/1axZk4QQ1KZNG0pOTrbfARRh6tSppNFoipwtkDF7sEXPUlNTqVevXqQoCoWEhNDGjRvtdwBFOHv2LGk0GpoyZYrsKC7LpQZT5k8dadwuLu7B77969Sp16dKFhBBUpUoV2r59u5wDKcLSpUtJCEEJCQmyozA3ZW3PDAYDzZgxg7y9vUmn09HkyZMdYhB1v5SUFPLy8qJ+/frJjsLcmLVd279/P9WoUYOEENS2bVuHGUTd7/HHHyd/f/8ilzlgTE3W9kzW9fSWWLt2LQkhHPJ9rStwmcFUfhn+/YcfQ4A3ASCghsli5JfiypUr1KlTp4JB1I4dO2QfTpFiYmJIp9NRSkqK7CjMzTzas+K7lt8zg8FAr7zyCun1etLpdDRlyhSHG0Tdb//+/SSEoLiHXzkZswNrXtP27t1L1atXJyEEtW/fni5duiT7cAqVnZ1NQUFB1KRJE9lRmBuypmf3X09fsWJFKdfTW2L48OHk4eFBly9flh3F5bjENVNJSUC7dsCdO/ffOhWABsDnALIBXDD5vYpyF3l5T6Bq1b+wdOlSp5h+PC8vD7Vq1cK9e/dw8eJFKIoiOxJzA6Z7BpjTNa02BxpNByjK/zB+/Hi89dZb0q6JssQbb7yBN998E0lJSWjSpInsOMxNWPOaJsQdELVFhw6lsWbNGoSGhqod12q//vor6tati+effx5Lly6VHYe5CVu8d6xcOUXq9fSWCgsLw19//YUrV644xWuws3CJwVR0NLBtm/F3Bo9qDeBPFFYIwIDHH0/FN9+UVy2fGnhdHGZvRfcMKLprBtSu/RN++qm+0w3+eV0cZm/WvabloWvXu9i921u1fGr49NNP8eyzz/K6OMxurH3v2LJlCo4eDVEtnxpu376NkJAQNG/eHAkJCbLjuAzneldjwvXrwO7dRb3BK44G//tfeaSm2jKV+gIDA7Fv3z588cUXePfdd2XHYS7OFj377beGuHHD+Z5yvvzyS/j4+KBly5ayozA3YH3XFBw86O10r2l9+/bFCy+8gNjYWPz666+y4zAXZ4vXtB9+CHG6nvn6+uLrr7/GwYMHMWvWLNlxXIbzvbN5SHy89Y8hhG0ex94iIyMxf/58TJkyBUePHpUdh7kwd+6ZVqtFUlISfvnlFwwbNkx2HObi3LlrH3zwARo1aoSIiAjk5OTIjsNcmDv3rHHjxoiLi8Obb76Jffv2yY7jEpx+MHXqFHD3rnWPkZUFnD5tmzz2NmnSJHTr1g2dOnXCzZs3ZcdhLsrde1apUiVs2bIFa9euxdq1a2XHYS7M3buWmJgIg8GAdu3ayY7CXJi792zUqFF49tln8dRTT+HPP/+UHcfpOf1gKj3dNo+Tlmabx5Hh888/R1BQECIiIpCXlyc7DnNB3DPjYr5TpkzB8OHD8eOPP8qOw1yUu3dNp9PhyJEjOH78OKZOnSo7DnNR7t4zAFi/fj1CQ0PRokULfu9oJacfTPn5FXbPXQB/AzAAyPvnvwv/NURAgK2T2Y+iKPjuu+9w5coVDBgwQHYc5oIK7xlgSdecuWcAMG/ePERERKB169a4a+2vNRkzgV/TgHr16mH16tVYuHAhPv/8c9lxmAvinhnfOx4/fhw3b97EU089JTuOU3P6wVR4OODlZWpE3RVAAIBjAC7+899dTT6GXg+EhakW0S7Kli2LnTt34tNPP8WyZctkx2EuJjwc0OkKu1LXvK65Qs8A4MCBA9BqtYiMjJQdhbmg8HDAw+OeiXvc6zVt8ODBeO655xATE4PffvtNdhzmYvi9o5G/vz8SEhKwZ88evPPOO7LjOC2nHkz9/PPP2LSpB7Kzs03cexAAPfR10OTjEAFDh6qT0Z46duyIWbNmYdy4cfjf//4nOw5zETk5Ofjpp/8r4pOYgzCna67SM09PTxw/fhynTp3CCy+8IDsOcyGffvop3n03HLm5uSbuPQh3e01btWoV6tSpgxYtWuDePVMDTMYsd/HiRXz2WW9+7/iPiIgILFq0CK+++ioSExNlx3FKTjmY+umnn9CyZUvUq1cPmZkX0abNHQhRsscSAujeHQgOtm1GWV577TW0b98ebdu2xe3bt2XHYU4sJycHY8eORalSpbBlyzLUr/8bhCjZPLKu1rNq1arhv//9L5YtW4ZPP/1Udhzm5D755BOEhISgf//+aNmyOrp3V/g17R9Hjx7FnTt30KVLF9lRmJP79ddf0aZNG9SoUQM3bvyEyMgM7tk/JkyYgKeeegpdunThycxKgpzI2bNnqUWLFiSEoPr169ORI0eIiOj4cSJvbyLj7wks+/L2JkpKknxgNpabm0vly5en+vXry47CnFBWVhaNGjWKPDw8qHTp0jR//nwyGAzcMxPGjx9PWq2WLly4IDsKc0IbNmyg8uXLk6Io9PTTT1NqaioR8Wvaw77//ntSFIVef/112VGYE7pw4QJFRkaSEIJq165NBw4cICLu2cMMBgNVqVKFqlevTgaDQXYcp+IUg6kzZ85Q8+bNSQhBDRs2pGPHjj2yTVyc5aXw9s6juDgJB2QHV65cIU9PTxo6dKjsKMxJZGVl0ciRIwsGUQsXLnzkCbUkPfPyuueyPSMiatq0KQUFBVF2drbsKMxJrF+/vmAQFRUVRTdu3Hhkm5K9ppHLdm358uUkhKC9e/fKjsKcxPnz5+nxxx8nIQTVqVOHDh069Mg2/N7xQampqaTT6eiZZ56RHcWpOPRg6vTp09S0aVMSQlBYWBgdP368yO3zSyFE0UUQIo+ATKpVa5GdjkSOHTt2kBCC4uPjZUdhDiwrK4tGjBhBHh4e5OfnR4sWLSryt1Lm94xIo8kirXZ8wW/cXVFWVhb5+/tTq1atZEdhDm7dunVUrlw5UhSFnnnmGZODqPuZ3zUDAbcpNvawnY5EjmeffZa8vLzojz/+kB2FObBz585Rq1atSAhBdevWpcOHi+6Fpe8dq1R5205HIseBAwdICEFLliyRHcVpOORg6uTJkxYNou6XlEQUHU2k0xHp9Q8WQa833h4dTbRhw3nSaDQ0c+ZMFY9EvqlTp5JGo6EzZ87IjsIcTGZmJg0bNoy0Wi35+/vTe++9Z/b3mtuzb781UNWqValatWoufdrA2bNnSaPR0JQpU2RHYQ4oPj6eypYtS4qiUExMDKWlpZn9veZ2bfjwZaTRaOjkyZMqHolcBoOBatWqRRUrVnTp5xNWMmfPnqWIiAgSQlC9evUoMTHR7O81t2dbtlwmrVZLEyZMUPFI5JszZw4pimLR+2935lCDqZMnT1KTJk1ICEHh4eGUZMUJqdevE82fTzRoEFHPnsY/58833p5vxYoVJISgPXv22CC944qMjCR/f3/KysqSHYU5gMzMTBo6dGjBIGrx4sUlfixzepaamkp6vZ6io6NtkN5xrVu3joQQtH37dtlRmINYvXo1BQcHk0ajoT59+lg0iHqYOV1r27YtlS5dmjIyMmyQ3jGlpaWRj48PdevWTXYU5iAKu56+JMzp2YYNG0gIQZs3b7ZBesfVpUsX8vHxofT0dNlRHJ5DDKa+//57aty4MQkhqHHjxnTixAm77XvAgAHk5eVFV69etds+7S07O5vKlClDTZo0kR2FSZSZmUlDhgwhrVZLAQEBdv0I/9ChQySEsGrg5gyGDx9OHh4edPnyZdlRmESrVq0qGEQ9++yzdnszkpubS2XLlqWwsDC77E+WY8eOkaIo9Pbbrn26FSva/dfTN2jQwKpBlKVGjx5NWq2WkpOT7bZPezMYDBQSEkJ169aVHcXhWTyYSkkhmjePKDbWOGqPjTX+/f5Ru7lOnDhBjRo1IiEEPfbYY/T9999b/iBWMhgMVLt2bZc/bSA5OZm0Wi2NHTtWdhRmJlt1LSMjgwYPHkxarZYCAwNp6dKl6gQuxty5c93itIGGDRtSuXLlKDc3V3YUZgZbvqatWLGCgoKCSKPRUP/+/aX8RvfSpUvk4eFBI0eOtPu+7em9994jRVGKvR6GOQ5bde3MmTPUrFmzIicls4fGjRtTcHCwSz/XX716lTw9PWnQoEGyozg0swdTx48TRUUZzxvV6UyfTxoVZdyuOElJSRQeHk5CCGrSpIn0c7zzTxvo2rWr1Bxq27RpEwkhaOPGjbKjsCLYqmsZGRkUGxtLGo2GAgMDKc4Bph/q2rWry582kJGRQaVKlaL27dvLjsKKYMvXtOXLl16K7/sAACAASURBVBcMogYMGCD93/e2bdtICEHr16+XmkNtvXv3Jr1e79IT3LgCW3XNmuvp1ZCZmUl+fn70xBNPSM2htj179pAQglauXCk7isMyazBlyexdRU3Nevz4cQoLCyMhBDVt2pROnz5ty2OxyvHjx93itAFeF8ex2aJrGRkZNGDAANJoNBQUFETLly+3/4EUwmAwUMWKFalOnTqyo6iK18VxbLZ6TYuLi6PAwEDSaDQ0aNAgh7pWaeLEiaTRaOjcuXOyo6gmf10cV5/gxpnZomu2vJ7e1k6fPk0ajYamT58uO4qqXnnlFZef4MYaxQ6mbLHWxbFjx6hhw4YkhKBmzZo51CDqfosXLyZFUUyuReBKeF0cx2Rt19LT06l///6k0WioTJkytGLFCrkHVIirV6+Sl5cXxcbGyo6iqvwJbnhdHMdii9e0pUuXUkBAAGm1Who8eLBDDaLuFxERQQEBAS49+VD+ujiuPsGNM7K2a99//z099thjJISgRo0a2fV6ekusXr2ahBC0c+dO2VFU1aZNGypdujRlZmbKjuJwihxMFb469AUCKhAAAjQEjDVZiDVrTlODBg1ICEHNmzd3ium5o6KiSKfTufRpA1lZWRQQEMDr4jgQa7qm1+dR587TSaPRUHBwMK1atUr24RQr/7QBRx3w2Ur//v15XRwHYu1r2uTJGwsGUUOGDHH4NxXZ2dkUGBhIzZo1kx1FVbwujuMx3bXiewYQ6XT3qFat/lKvp7fU4MGDydPTk65cuSI7imryJ7gJDw+XHcXhFDmYiooq7KPZUAIqE/AHAUv/Kca2h7a5R8BmioiIoLNnz9rreKxmMBjXxalatapLnzaQvy7O5MmTZUdhZH3XPD130OrVq2UfhkVmzpxJiqK49GkD+eviVKpUyaWfT5yFtT0TYgsNGzbM4QdR9zt//jxptVp68cUXZUdRFa+L41hMd82cnhm75ue33+leG+rVq0cVKlRw6Qkp8ie4GTVqlOwoDqXIwdTDFwoav1L+KcCe+26rRkDEI9t6ehpKNCOSbPnr4kRFRcmOoipeF8dxWNs1na5ks4/JxuviMHuytmdeXnlO2TNeF4fZU0qKqa6Z3zNnfU1LT08nX19f6ty5s+woqtq6datbTHBjCQUWS/jnzyfvu60+gIuPbKnRKIiPt3wPspUpUwZ79uzB9u3b8f7778uOo5qBAwfiueeeQ0xMDH777TfZcdgjzO+aEHDKru3fvx86nQ6PP/647Ciq8ff3R0JCAr788ku88847suOwR5jfM0URTtmz/v37Y9SoUXj22Wdx8eKjx+Uqdu/eDT8/P7Ro0UJ2FLdmuiPm9wxwzte00qVL49ChQ0hISMCcOXNkx1FNVFQUJkyYgCFDhuDnn3+WHcchFDmYunvX1K03THxbIIDsR7bMygJOny5pNLnatGmDt956CxMnTsS3334rO45qVq1ahbp166JFixa4d++e7Dhuy127ptVqkZSUhHPnzmHkyJGy46gmIiICixYtwquvvorExETZcdyWu/YMAJYtW4aGDRsiIiICOTk5suOoQlEUJCUl4eLFixg4cKDsOG7r1ClTXTO/Z4Dzdq1Jkyb44IMP8Nprr+Grr76SHUc17777Lpo2bYpWrVrhruknVrdSgk+mggDkPXRbGgAvk1unpVm+B0cxbdo0dOnSBR07dsTff/8tO45qjhw5gjt37qBLly6yo7AHuEfXQkNDsWnTJnz88cdYv3697DiqmTBhAnr16oUuXbrg5s2bsuOwAu7RMwD45ptvkJOTg44dO8qOopqQkBB8/vnn2LBhA1atWiU7jltKTzd1q2U9A5y3a2PHjsUzzzyDHj164Pr167LjqObQoUMQQqBNmzayo0hXgsFU/pPwvvtuOwugmsmtAwIs34Mj2blzJ/z9/REREYG8vIefCFyDr68vEhMTcejQIcyaNUt2HFbAfbrWu3dvvPzyyxg6dCh++ukn2XFUs2XLFpQtWxbNmzd32ecT5+M+PfP29kZiYiKOHj2K6dOny46jmieffBLTp0/H6NGjcerUKdlx3I6fn6lbLesZ4Nxd++STTxASEuLSz/U6nQ7Hjh3D999/j5dffll2HKmKHEzpdKZuLQugMoARAK4DWAYgGcCjT8x6PRAWZnVGqRRFwfHjx3H58mUMGjRIdhzVhIeHY9myZXjzzTexb9++B+67fh2YPx8YOBB46injn/PnA6mpksK6IO4asGjRIjRr1gyRkZEue9qAoij47rvvcO3aNfTt2/eB+7hn6uOeAQ0bNsTKlSsxb9487Nq1S3Yc1bz11luIjIzEE088gTt37hTczj1TX3i4qa6Z3zPA+buWf8ppamoqoqKiZMdRTa1atfCf//wH77//Pj777LMH7nOrrhU1O4XpmY/y1woo/8/MLEWtFeB8s7EUxh3XxTl+3Di9qU736L8Fvd54W1SUcT0JZh3umpE7rovDPbMf7tm/3G1dHO6Z/Ziezc/8nrlS1xITE0lRFFq0aJHsKKoaNWoUeXh4UHJyslt2rYTrTBX/JQSRqy1Inr8ujjMsIFdSBoOBateuTX5+/0fe3nnF/v8X4sEVy1nJcNf+5S7r4sydO5eA0aTT3eOe2Qn37EHusi6OoowlrfYu98yOuGv/WrhwISmKQkeOHJEdRVWNGjUiX9/JbvnescjBVOGrxRf/5e1NlJRkr8Own3bt2lGpUqVcel2cRYsyCbht8f9vVymFDNy1B23cuJGEELRp0ybZUVQTF0ekKFncMzvinj0of12cTp06yY6imrg4Ii+vXO6ZnXHXHtSjRw/y9vamGzduyI6imsWL7xKQ6ZZdK3IwRWQ8SEsLodcbXOKHY0pubi6VK1eOwsLCZEdRReFPgH5k/Gg+/8vDLZ4A7akkXXOVJyJTxowZQ1qtlpKTk2VHsTnumTzcswedOHGCFEWhN954Q3YUm+OeycVd+5fBYKDKlStTzZo1yWAwyI5jc+7etWIHU0T/FsKcj+2EuEOlSk116dMGLl++TB4eHjRixAjZUWyu8I/m/QgYXOz/f1f6aF4G87tmIOA2TZr0i+zIqmrcuDEFBwdTdna27Cg2xT2Ty/ye5RFwmyIi4mVHVtXSpUtJCEEJCQmyo9gU90w+c7sG3CMgkz74wHXfO6akpJBOp6N+/frJjmJz7t41swZTRMZRY3S08cIxvf7BH0T+BWXR0USHDmWSr68vdezYUc3c0m3bto2EELRu3TrZUWym8ItGzSsE4DoXjcpkbtdat55Aer3epU8byMzMJD8/P2rdurXsKDbDPXMM5vZs8eJEEkLQ6tWrZUdWVUxMDOl0OkpJSZEdxSa4Z47DnK717n2PSpVqTy1btpQdV1UJCQkkhKA4F/r4jbtmwWAq3/XrRPPnEw0aRNSzp/HP+fMf/CHknzYwa9YsW2Z1OBMnTiSNRkNnz56VHcUm5s0rrhDin69SBLxncju93vjvgVmvuK4ZDAYKDQ2l6tWru+RpA/lOnz5NGo2Gpk2bJjuKTXDPHIs5r2nTpk0jjUZDp0+flhdUZQaDgapXr06hoaEu8XzCPXM8xXXt7NmzpNFoaOLEiXKDquyNN94gRVHoxIkTsqPYBHetBIMpc8XFxZEQgvbv36/WLhxCy5YtKSAggLKysmRHsVpsbFG/OVhFwFUC0gkYQcZzXxNMbjtokOwjcR/5pw3ExMTIjqKq1atXkxCCdu7cKTuK1bhnzql169bk5+dHmZmZsqOo5saNG6TX66lnz56yo1iNe+ac1q1bR0II2rZtm+woqurYsSP5+vq6xGRm3DWiIhfttcaYMWPQt29f9OjRA3/++adau5Hu0KFDUBQFrVu3lh3FaunpRd07HEAIgNIAVgIIAhBncsu0NFsnY4UpW7Ysdu7ciS1btuDDDz+UHUc1w4YNw+DBgxEVFYXff/9ddhyrcM+cU0JCAjw9PREZGSk7imoCAwOxb98+7Nq1C4sWLZIdxyrcM+c0cOBADB8+HH369MHly5dlx1HNnj174Ovri4iICNlRrMZdA1QbTAHAhg0bULlyZbRo0QJ5eXlq7koaT09PHD16FCdPnsSLL74oO45V/Pws2VoAIJP3BATYIg0zV4cOHfDmm2/ipZdewnfffSc7jmri4+NRo0YNtGjRAvfu3ZMdp8S4Z87J09MT3377Lc6cOYMxY8bIjqOayMhIzJ8/H1OnTsXRo0dlxykx7pnzWrlyJerWrev0z/VF0Wq1SEpKwoULFzBs2DDZcazCXVN5MKUoCpKSknDjxg306tVLzV1JVatWLaxfvx4ffvghNm/eLDtOiYWHAzqdqXsuA3gLwN8A7gIYC+AvACMe2VKvB8LCVAzJTJoxYwY6dOiA9u3b49atW7LjqObYsWPIyMhAt27dZEcpMe6Z86pWrRo2btyIFStW4JNPPpEdRzWTJk1Ct27d0KlTJ9y8eVN2nBLhnjm3I0eOICsrC507d5YdRTWVKlXCZ599hrVr12Lt2rWy45QYdw1Q7Zqp+x05coQURaH5znx1mRny18W5cOGC7CglsnHjVySEqUVEzxLg/c+5riDAh4C3TZ7z6uwzsjiz3NxcqlChAtWrV092FFU5+7o4p0+nkKJkc8+c2IsvvkharZbOnz8vO4pq8tfFqVGjhlNOSPHHHwbSanO4Z07s5MmTpCgKzZw5U3YUVeVPcHPmzBnZUUpk69ZEt3/vaJfBFBHRokWLSFEUSkxMtNcupWjcuDGVKVPGqdbF2bVrF1WrVo2EEBQcfPiftVWKnsbS1JcrrBXg7K5cuUKenp40ePBg2VFUlb8ujjNNcPPHH39Q165dSQhBOt2uf9YK4545q2bNmlFgYKBTPddbKiUlhby8vKhPnz6yo5jNYDDQ66+/Tj4+PqQo2wjgnjmzFStWkBCC9uzZIzuKqiIjI51ugpuEhASqVasWCSEoKOiQW793tNtgioioV69ebrMuTmRkpOwoxdq5cydVqVKFhBDUqVMnunLlShGrWBf/5QqrWLuCXbt2kRCCVq1aJTuKqvr06eMU6+JcvXqVunTpQkIICg0NpW3btnHPXEBWVhYFBARQRESE7Ciq2r9/PwkhaOnSpbKjFMlgMNDMmTPJ29ubvLy8aOLEiXTkSC73zAUMGDCAvLy86OrVq7KjqCY7O5vKlClDjz32mOwoxdq/fz/VrFmThBDUpk0bSk5OdvvXNLsOptxlXZwzZ8449Lo4O3bsKBhEde7cma5cufLA/fkrlltaBhdag87pTZ8+3S3WxalRowZVrlzZIZ9Prly5Qp07dyYhBFWpUoW2b9/+wP3cM+d37tw5XhdHMoPBQK+88gp5e3uTTqejyZMnU25ubsH9JetZHvfMgRgMBqpduzZVrFjRIZ/rbSU5OZm0Wi2NHTtWdhST9u7dS9WrVychBLVr144uXbr0wP3u/Jpm18EUEa+LI9O2bduocuXKJISgJ598ssjf8uSXQojiymAgIJMWL75rxyNh5uB1ceS4cuUKderUqWAQtWPHjkK3NbdnxlMCb9Pzz//PjkfCzMHr4shhMBho+vTppNfrSafT0dSpUx8YRN3P/J7lEXCb6tZdbOejYcVJS0sjHx8f6tq1q+woqtq0aRMJIWjjxo2yoxTYs2dPwSCqffv2jwyi7mfZe8fbtGiRa7w/sftgish4nqUQgj744AMZu7ebIUOGkKen5yOf/Njb1q1bCwZRXbt2Nfuj8qQk43msOp1xder7i6DXG29/+mkDBQR0pvDwcJWPglkqOzubgoODqXHjxrKjqCoxMZEURaGFCxdKzXH58mXq0KEDCSGoWrVqtGvXLrO+z5yeRUcTRUfPJQ8PD0pOTlb5SJilRo4cSR4eHkW+yXB2ubm5VL58eapfv77UHAaDgaZNm0Z6vZ70ej1Nmzat0EHU/czt2dq1P5KiKDR79mw7HA2zxLFjx0hRFHr77bdlR1HV+PHjHWKCm/uvp+/QoQNdvnzZrO8zp2tRUXlUrlwPqlmzpkt82ihlMEVENHv2bFIUhZKc/UTJYtSrV4/Kly9v1pO9rW3evJkqVapEQgjq1q0b/fHHHyV6nOvXiebPN65O3bOn8c/58/+deeXSpUvk4eFBI0eOtGF6Zgv5pw2MHj1adhRVLVy4UNoEN5cuXaL27duTEIKqV69e4guli+sZkXGCm+DgYCnPJ6xoYWFhVLZsWZf+f5M/wc2QIUPsvu/c3FyaMmUK6XQ60uv1NH369BK9CTOnZx988AEJISghIcGGR8BsYfHixaQoCh06dEh2FFU1bdqUgoKCpExwY+p6+pIormt//PEHeXl5Ub9+/WyYXg5pgykios6dO5OPjw+lp6fLjKGq9PR08vX1pY4dO9ptn5s2baKKFSuSoijUvXv3Eg+iLLFt2zYSQtD69etV3xezzNatW0kIQRs2bJAdRVU9evSw6wQ3ly5donbt2lk9iLJE/gQ3TzzxhOr7YpbJzMyk0qVLU9u2bWVHUdWOHTtICEGrV6+2y/5yc3Np8uTJBYOoV155xS6/yY6JiXGKCW7cUVRUFOl0OkpNTZUdRTX5E9y0bNnSbvss7np6NeRPcBPn5BdOSR1MGQwGqlChAtWtW1dmDNXZa12cTz75hEJCQkhRFOrZs6fdXwQmTpxIGo2Gzp07Z9f9suJNmDDBIU4bUJO91sVJTk6mNm3akBCCatasaffp2U+fPk0ajYamT59u1/2y4p08eZI0Gg3NmDFDdhRVTZ06VfV1cXJzc2nixImk0+nI29ubZs6cadfTgQwGA1WvXp1CQ0Nd4jQkV2IwGKhq1apUtWpVl/5/c/bsWdJoNDR58mRV97N9+3YKDQ0lIQR16dLF7rMmzpo1y2EnuDGX1MEUkZuvi5OSQjRvHlFsrPEz0NhY498tXLls48aNVKFCBVIUhXr16iX1tzUREREUEBBAWVlZ0jIw09xlXRydTvfoujg26NqFCxfoiSeeICEE1apVS+opQPkT3Jh7XRazH7deF8cGPcvNzaUJEyaQl5cX+fj40Ouvvy7tDXP+BDe9evWSsn9WuNTUVNLr9RQVFSU7iqryJ7h5YDZYG713LOn19Gro0KEDlSpVymEmuLGU9MEUkXuti+Pl5UWpu3cTRUUZr8LT6Qq7Oo/o+PEiH2/Dhg1Uvnx5UhSFevfu7RAfeWdnZ1NgYCA1b95cdhT2EHdZFyd/gpulS5caO2Rl1y5cuECRkZEOMYi63+DBgx1ighv2qNjYWPdaF8cGPcvO/v/s3XlcVOX+B/DPOTMMMyyyKC64opmaghuIimvuSyqmlbtetZRSu2Zd15vmlpbpT5P0akZXMrtqyS33vKSSC2QqLpmZqeWCaIiIgDh8f3+MECADw8yceWb5vl+veWXDcM7niB9mnplznieHJk+eXDCImjt3rl186pA/wc2yZctER2HFHDhwgGRZphUrnHv2xXHjxpGbmxvd+O9/rfLa0VrX01tT/gQ3jRs3Fh3FLHYxmCIimjlzpkusizO7UiXKlCTKK3uOVqMT8MfGxhYMoiIjI+1uEeQLFy6QWq2myZMni47CinGldXEmAPRIqzVlPuQSu3bhwgVq27YtSZJEDRo0sMsLnhs1akTVqlVz6kkPHFWDBg1cYl2cKEmibLXa7J7l5OTQpEmTSKPRkKenJ82fP9/u/s6WLl1KsizT4cOHRUdhxSxevJhkWabEMgYQju6dwEDKBCx67SjievryuHLlCmk0Gho9erToKOVmN4MpIqL27ds797o40dGUV3yeyMe3vQABoDqlrGi2ceNGqlKlCsmyTAMHDrS7QVRhmzdvJkmSaOvWraKjsGJiY2Odf12c6GjKkmXTe1aoa+fPn6c2bdqQJEnUsGFDOnTokOijMSp/gptu3bqJjsKKSU9Pd/51caKjKVejMes5LScnh6Kiokij0ZCXlxctWLDA7gZRhfXp04c8PDzs+nnXVfXs2ZM8PT0pLS1NdBRlWPjaUfT19OWRP8FNTEyM6CjlYleDqdzcXAoICKCmTZuKjmJ9iYmlLg3tD1AFIy/ycjUa6ubnR7Is0/PPP+8wvzAmTpxIarWa18WxQ069Lk4pXSutZwTQA1mmUMDuB1GF5U9ww+vi2J/ExESSZZkWLVokOor1WfCclq1WU2u1mry9vWnRokV2PYjKlz/BjbOsi+NM9Ho9Va9enZ5++mnn+9lY+NqxR8WKdnE9fXnYYoIba7OrwRSRE6+LExlp9DSISQDVAKijkUI8AuhI9eoOM4gqjNfFsV9Ouy6Oka6V1bP8rqU64NTW+RPcxMfHi47CilmxYgVJkmSXp4laxMLntPONGzvcC9+UlBSnWRfH2Vy7do3c3d1p6NChoqNYl4U9O1ytmsMMogpr27Yt+fr6OsxkZnY3mCJywnVxUlKevFjw8e13gNwAOlrGizzSass9U4s94HVx7JdTrotjpGsm98yBu8br4tivgQMHOte6OC78nOYs6+I4o71795IkSbR27VrRUazDhXuWk5NDFStWpBYtWoiOYhIZdigyMhJTpkzByJEj8csvv4iOY7mYGKNf6gugC4DwsrYhSaVux155eHggISEBhw8fxowZM0THYYV4eHjg0KFDSEhIwJw5c0THsQ4jHTG5Z4DDdu2LL75A9erVERYWhry8PNFxWCFbtmxBtWrVnOdn48LPaV26dMHbb7+N1157DT/++KPoOKyQbt26Yfbs2YiKikJycrLoOJZz4Z5pNBocO3YMycnJePXVV0XHKZNdDqYAYPny5WjevDlat26N7Oxs0XEsk5wMlHAMXwA4D2CLKdvIygJOn7ZyMNto0qQJ1q1bhyVLlmDnzp2i47BCQkJCsGbNGixcuBB79uwRHcdyJXStXD0DHLZrsiwjMTERqampiIyMFB2HFZL/s0lJScGgQYNEx7Gciz+nvf322+jcuTM6duyI+/fvi47DCnnnnXfQvn17tG/f3vF/Ni7es3r16uHzzz/HRx99hP/85z+i45TKbgdTAHDw4EEQETp27Cg6imXS00u8ezOAHAA+AFQADgC4DMDD2HbS0qyfzUbGjBmDkSNHIjIyEn/88YfoOKyQcePGYejQoejXrx+uX78uOo5lSuhauXsGOGzX/P39sW/fPnzzzTf44IMPRMdhhVSqVAl79+5FXFwcVqxYITqOZfg5Dbt374aXlxfCw036vJvZ0L59+6DT6dC2bVvRUSzDPcOgQYPw2muvYdiwYfj1119FxzHKrgdTWq0WR44cwfHjxzF16lTRcczn41Pi3esAnAJw4vGtJYDqAI4b246fnwLhbCcmJgb16tVDq1at8OjRI9FxWCGxsbEICgpCq1atHPs0pBK6Vu6eAQ7dtYiICCxduhRvvvkmjhw5IjoOK6Rdu3ZYuHAh3njjDRw7dkx0HPPxcxrUajWSkpJw8eJFjBkzRnQcVoharUZiYiLOnz+P8ePHi45jPu4ZAGDlypVo2rQpwsPD8fDhQ9FxSmTXgykAaNCgAT799FOsWLECX331leg45gkJAbTaJ+6uBCCk0M0LgBuARiVtQ6cDgoMVDGkbR48eRUZGBnr37i06CismMTERd+/edeyfTUgI8tzdi9xVrp4BTtG1N954A7169ULXrl3x559/io7DCpk+fTp69OiBLl264O7du6LjmCckBMTPaahRowa2bduGTz/9FJ9++qnoOKyQWrVqYevWrfj4448RGxsrOo55+LVjgYSEBOj1enTq1El0lJKJngHDVK+88orDrouzdv58yipt9jBTbg46I0tJ8tfFmTdvnugorBhHXhcnKSmJOj3zDD3grhERr4tjz/R6PdWoUcMh18XJyMigic8/zz0rxBHXxXEV06ZNI5VKRefOnRMdpdxili7l146FnDt3jlQqFb355puiozzBYQZTREQhISEOtS7O8uXLycfHh9RqNf1QqxblGVkroMybJBENHCj6cKwqf12c/fv3i47Cilm5cqVDrYuTmJhIwcHBJEkStWzZku4++6zRdTlcrWspKSmk1Wp5XRw7dOPGDYdaFycjI4OGDh1KKpWKKlasSJeaNeOeFRIREUE+Pj6UmZkpOgorpnXr1uTn5+cwaxZFR0eTv78/qVQqSqxRg187FrJx40aSJIni4uJERynCoQZTjrAujl6vp/fff79gEDVu3DhDgctYxbrUm4cHUVKS6EOzusGDB/O6OHbKEdbFOXr0KDVp0qRgEHX69GnDF7hrRezfv5/XxbFTjrAuTnp6Or300ksFg6iCrNyzInJycqhSpUrUvHlz0VFYMflrFoWGhoqOUqpVq1aRn58fqdVqGjFiBGVkZHDPSjB27Fhyc3OjK1euiI5SwKEGU0REp06dIpVKRTNnzhQdpQi9Xk9Lly6lChUqkJubG40fP/7Jd0Gio8tdCr1Wa/g+J6TX66levXpUq1YthzvVxdnp9XoKCgqi2rVr293P5ujRo9S4cWOSJInCwsJKPrXGjK6Rh4fTdm3evHkkyzIdP35cdBRWzJw5c0iWZTpx4oToKEWkp6fTiy++SCqVigICAmjdunVPPsiMnuU5cc8uXbpEarWaoqKiREdhxVy4cIHUajVNmjRJdJQnrFy5smAQNWrUqCc/3TSjZ4+c+LUjEVGTJk2oSpUqdnOmmsMNpoiI1q1bR5Ik0c6dO0VHIb1eT0uWLCFvb29yc3OjCRMmlP5Rcn4pyvrYVpLogSzT61qtU582cOfOHdLpdNSvXz/RUVgxqamppNPpaMCAAaKjEBHR4cOH6ZlnniFJkqhVq1ZlnwNvYtf0kkT3ATr5yiu2ORBBunTpQl5eXoZ3O5ld6dy5M3l7e9vFzyYtLY0GDx5cMIjasGFD6d9gYs/yHvds5TPP2OZABNmyZQtJkkSbN28WHYUVs3nzZpIkibZs2SI6ChERrVixgnx9fUmtVtPo0aNLf61XjteOWbJMkzUaSk9Pt93B2FhGRgZ5e3tT586dRUch1GPptAAAIABJREFUIgcdTBERjRgxgjQaDV27dk3I/vV6PS1atIi8vb1Jo9HQxIkTKScnx7RvTkoynMeq1RLpdEWLoNMZ7h84kB4ePkwBAQHUrFkzZQ9GsISEBJJlmd5//33RUVgxhw4dIlmWafny5cIyJCQkUMOGDUmSJAoPDy/fhcQmdm1+//4OO8GNqXJzc6latWr0jJO/mHVEubm5VLVqVQoODhaWIS0tjZ5//nmSZZkqV65c9iCqMBN7dvqTTxx2gpvymDRpEqnVarp48aLoKKyYqKgoUqvVdOnSJWEZCl9PP2bMGNPfMDexZ/pjx6h69eoOOcFNeZw4cYJkWaa3335bdBTHHUwRETVs2JCqV69u038ser2eFixYQF5eXqTRaCgqKsr0QVRxt24RLV1KNGIEUd++hv8uXVpk5pX80wYmTJhgpSOwT++//z7JskyHDx8WHYUVs2TJEpJlmY4ePWrT/R46dKhgENWmTRs6f/68+RszoWvBwcEONcGNOX7//XfSaDQ0evRo0VFYMVeuXCE3NzcaN26cTfd7584dGjhwIMmyTFWqVKFPP/3U/I2Z0LMVK1Y41AQ35mrZsiVVrFjR/NcHTDHNmzengIAAm/5s9Ho9LVu27Mnr6c1hQs+uXbvmUBPcmGvt2rUkSRLt3btXaA6HHkylp6eTp6cn9ejRQ/F96fV6mj9/Pnl6epJGo6FJkybZrIhbt24lSZJo06ZNNtmfKH369CEPDw+6c+eO6CismF69epGnpyelpaUpvq8DBw5QgwYNSJIkatu2LV24cEHxfRL9ddqAPU9wYw07duwgSZIoJiZGdBRWTFxcHEmSRBs3blR8X3fu3KHIyEiSZZmqVq1qk33mGzBggN1PcGOprKws8vPzozZt2oiOworJzMwkHx8fateuneL7Mul6eoXs3r3b7ie4sYYhQ4aQu7s73bhxQ1gGhx5MEf21Ls6CBQsU2b5er6e5c+eSp6cnubu70+TJk4W805R/2oCtXliKwOvi2K/8dXHq16+v2M8mPj6e6tevT5IkUbt27YScInPq1CmSZZlmz55t833b0vTp03ldHDul9Lo4qamp1L9/f5JlmapVqybkTTq9Xk+1a9emOnXqOPXv+vx1caZNmyY6CivmzJkzpFKpaPr06Ypsv9zX0yskf4KbU6dO2XzftqLX66l+/fo2P1OtMIcfTBH9tS5OfHx8wX0pKURLlhANG2b4FHTYMMP/m7p2mV6vp7fffrtgEPX3v/9d+Ok/oaGh5O/v79SnDfC6OPYrf12cIUOGFNxnac+IDFN35w+i2rdvL/w6g/zTBnbv3i00h9J4XRz71aZNmyfWxbG0a6mpqdSvX7+CQZToCRJSU1NJq9VSZGSk0BxKs9d1cRhRTEwMSZJEO3bsKLjPGq8dFy9ebN719Arp2LEjVahQwS4muFFKWloaeXp6Uq9evYTs3ykGU0REzz//PGm1Wtq7N40iIw3X4Wm1JV+fFxlpmLq/JHq9nubMmUMeHh7k7u5OU6dOFT6Iypd/2kB4eLjoKIrKXxdn9erVoqOwYr799luSJIlmzPjSop7lb+upp54iSZKoQ4cOQi8ILm7o0KHk7u4ubIIbW8jJyaGAgABeF8cO5a+L07JlS0pMJIu6lpKSQn379iVZlikwMJC++OIL2x5MKQ4cOECSJNGKFStER1GUPa6LwwxGjx5NGo2Gvv76psWvHa12Pb2V5ebmUuXKlYVOcGMLR48eJVmWafHixTbft9MMpvR6PVWsOJskKZMkKa+smSOfWFJGr9fTzJkzycPDg7RaLU2bNs1uBlGFnT9/nlQqFU2dOlV0FEXxujj2q0+frwm4b1bPiAwLldatW5ckSaJOnTrZ5Qx6er2enn76aaGnDdgCr4tjvy5evEiSFEVqdY4psyE/0bWUlBTq3bs3ybJM1atXt5vpoItbtGiRkAlubC04ONiu1sVhf6ladS4B5r92FHU9fXmImuDG1lasWEGyLNOhQ4dsul+nGUxFRxPpdMWLUIcAmQAQ4EbAyCfW6Fy9Wk8zZswgnU5HWq2W3nrrLbv/ZZd/2sD27dtFR1EUr4tjf0peO7DsnkVHGy6GzR9Ede7c2S4HUYXlnzbQs2dP0VEUlT/BjejTvlhR0dFEGk1uubv27rt3qVevXiTLMtWoUYO2bt0q+lDK1KNHD5tNcCOKva2Lwwwsee04b9484dfTl8f27dttNsGNSAMGDCCdTmfTCW6cYjCVmGhscejtBKQ//vOOx+XYWOwx98ndPYKmT59u94OowsaNG8fr4jCbsqRnkpRJQCh16dLFoU51yT9tgNfFYbZk6XNalSp96MsvvxR9GCbT6/UusS5O/gQ3c+bMER2FkeU9c3NrYxfX05fH1KlTFZ3gxh7kT3ATFBRks98nTjGYiowse1FoYOfjQrxe7H49RUbmiT4Es7jSujijRo0SHcXlWdqzXr0cc6IDXheH2ZolXZMkPQ0cKPoIyo/XxWG2Zulz2oABjjnwDw8Pf2KCG2eTP8HNQBv9MnT4wVRKypMXCxa9NX78US0I0BFw44nHaLXlm33MXmRmZlKFChVcZl2cDRs2iI7isly5Z0REkZGRLrMuTuvWrUVHcWmu3DVXWRcnf4IbkeviuDpX7llOTg75+/tTaGio6CiKOnDgAMmyTCtXrlR8XzIcXExMWY84AyAHwGoAbQFUeOIRkmTKduyPh4cHDh06hISEBMyZM0d0HMX07t0b//jHPzB+/HicPXtWdByX5Mo9A4CtW7eiatWqCAsLQ15enug4itBqtThy5AiSkpLw5ptvio7jsly5az169MDs2bMxceJEnDx5UnQcxWzcuBG1a9dGaGio0/4+sXeu3DONRoOjR4/i5MmTmDJliug4iunQoQMWLFiA119/HUlJSYruy+EHU8nJQHZ2WY/SAIgCcB3AiCe+mpUFnD5t/Wy2EBISgujoaCxcuBB79uwRHUcxixcvRuvWrREREYEHDx6IjuNyXL1nsiwjKSkJKSkpGDRokOg4imnQoAFiYmKwbNky/Pe//xUdxyW5etfeeecdtG/fHh06dMD9+/dFx1GELMs4duwY7t69i759+4qO45JcvWf169dHbGwsVq1aha1bt4qOo5gZM2agW7du6Ny5M+7du6fYfhx+MJWeXp5H6wH8WuJX0tKskUaMl19+GUOGDEH//v1x/fp10XEU87///Q9ubm5o166d6Cguh3sGVKpUCbt378b27dvxf//3f6LjKGb48OEYO3YsBg0ahKtXr4qO43K4a8C3334LnU6Htm3bio6iGF9fX+zfvx979uzBu+++KzqOy+GeAS+++CImTJiAIUOG4LfffhMdRzE7d+6Er68vwsPDFduHww+mfHyMfeUsgMkAbgJ4CGAhgAsAepf4aD8/BcLZ0MaNG1GnTh20atXKaU8b0Gg0SExMxOnTpxEVFSU6jkvhnhl06NABixYtwtSpU3Hs2DHRcRSzbt06NGzYEK1atcKjR49Ex3Ep3DVArVYjMTER58+fx/jx40XHUUx4eDg++OADzJo1CwcPHhQdx6Vwzwyio6PRpEkThIeH4+HDh6LjKEKWZSQmJuK3337D8OHDldmHIlu1oZAQQKst6SsygH8DqAbAHcB8GD6mXfTEI3U6IDhYwZA2kP+P5e7du+jdu+TSO4OgoCBs3rwZa9aswRdffPHXF27dApYuBYYPB557zvDfpUuB1FRxYZ0I9+wv06dPR/fu3dGlSxfcvXtXdBzFHD58GFlZWejWrdtfd3LPFMddM6hduza2bNmCjz/+GLGxsaLjKGbKlCno378/evbsidu3bxfcz1VTFvfsL99//z0ePnyILl26iI6imMDAQMTFxWHTpk1Yv359wf1W65niU1worOwZWcq+OeqMLCVJTEx0qXVxrmzdapjfVKt98h+CTme4LzLSsKAEMxv3rChXWhdHpVLRR2PHcs9shLtWlKusi1OnTh0KCgqio0f1XDUb4J4Vdfr0aVKpVDR9+nTRURQ1Z84cUqlU9NlnF6zaM4cfTBGZulZAyTdJIodck6M0rrIuzqJatSgToLyyfviSZFiZLzpadGSHxj0rylXWxTk4dCjdB0jPPbMZ7lpRrrIujlo9iVSqrDJ/9lw16+CeFbVhwwaSJIl27NghOoqi6tdfRkAmSVKe1XrmFIMp46tYl33z8CBKShJ9BNbn9OviREdTXrEf+iGAAgCSAJIBCgYoq/gPm599zMY9e5LTr4sTHf3ED517pjzuWlH56+K0bNlSdBTFREcTubs/KvbzPERAAAESGRaODSYgi6tmJdyzJ40aNYo0Gg39/vvvoqMowvCUVnwQZXnPnGIwRVTic36ZN61W77S/iPR6PQUFBVGdOnWc7zQkI78BAwCqB1AaQKcAcgdooKv8BrQRc3qmVuc4bc+IDKcNyLJMJ06cEB3FurhnQpnTNQ+PPKft2oULF0itVtOkSZNER7E64y/qAwioR0AaAacIcCdgIFfNisx77fjIaXtGRNSoUSOqVq0a5ebmio5iVUr2zGkGU0R/lcKUj8hl+QHpdH93+tMGdDodDRgwQHQU6zLy2bwGoLmF/j8UoIYl/fCd7bN5GytPzzSaXAIm0Ndffy06tqI6depE3t7elJGRITqK9XDPhDO9a3kE3KfGjT8UHVlRmzdvJkmSaMuWLaKjWJXx0800BMwt9P+hBDTkqllZ+V47ZpFGM5nS09NFx1ZMeno6eXl5UdeuXUVHsSole+ZUgykiw8hx4EDDxWM6XdG/sPyLygYOJDp8+CFVrFjRqU8bICI6dOgQybJMy5cvFx3FOkq5anQIQHUBSgUo6fE75m+V/LaS6KNweKb2LCmJaMyYMU592gARUW5uLlWpUoWCg4NFR7EOa/XMWa7OFsjUrn388SmSZZmWLl0qOrKiJk6cSGq1mi5evCg6ilWUPhHCEALqEpBKQNLjd8zf4qc0BZjas2PH9FStWjVq1KiR6MiKOn78OMmyTPPmzRMdxSqU7pnTDaby3bpFtHQp0YgRRH37Gv67dGnR5/aLFy+SWq2m1157TVxQG1i8eDHJskxHjx4VHcVyS5YYbcTXAOkAwuNbPYD0JTVHpxN9FE7DlJ4RETVu3JiqVq3qdKcNFHblyhVyc3OjcePGiY5iOWv1zMlf2NuSKV1btmwZybJMCQkJ4oLaQLNmzahSpUqUk5MjOorFSqkaAV8ToCvomuFUJD0/pSnIlJ79/vvvpNFoaNSoUcJy2sLq1atJkiT69ttvRUexmNI9c9rBlKm++OILpzxtoLiePXuSp6cnpaWliY5imWHDSmxDLkAqgLoClA7QBYCqAhRWcnNEH4XLycjIIG9vb+rSpYvoKIravn07SZJEGzduFB3FMtbq2YgRoo/E5fTr1490Oh3duXNHdBTFZGZmko+PD0VERIiOYjEjVSMglwAVAV0JSCfgAgFVCQjjpzQ7sHPnTpIkiTZs2CA6iqIGDx5MWq2WUlJSREexiNI9c/hFey31wgsv4NVXX8WQIUPw66+/io6jmB07dsDX1xfh4eHIy8sTHcd86ekl3v0rAD2AjwFUAFAfwGgAybbKxUrl5eWF7777DvHx8Zg3b57oOIrp378/3njjDYwePRo//fST6Djms1bP0tKsn42V6quvvkJAQADCwsIc+3d9KTw8PJCQkICjR49ixowZouNYxEjVwM9q9q1Xr16YPn06xo8fjzNnzoiOo5jNmzejevXqCA0NdejfJ0r3zOUHUwCwatUqhISEIDw8HA8fPhQdRxGyLOOHH37AlStXMGLECNFxzOfjU+LdDQCoAbwMIBvAFQCfAqhqs2CsLC1atMCHH36IefPmYf/+/aLjKOa9995DWFgYIiIikJ2dLTqOeazVMz8/BcKx0siyjKSkJFy/fh0vvvii6DiKadKkCdatW4clS5Zg586douOYzUjVwM9q9m/RokVo06YN2rVrhwcPHoiOowhZlpGYmIjbt2+jf//+ouOYTeme8WDqse+//x56vR6dO3cWHUUxVatWxddff43PP/8c//rXv0THMcujxo3xUKUq8WuxAI4C8AAQBMM/7h0lPVCnUyoeK8PEiRPxwgsvoE+fPrh586boOIo5cOAAZFlGu3btREcxT0gIHrm5lfilcvUsOFiphKwUlStXxo4dO7Bt2zZ8+OGHouMoZsyYMRg5ciQiIyPxxx9/iI5jlho17kCSjL3pYlrb+ClNnP3790Oj0SAiIkJ0FMX4+/tj37592LlzJ5YtWyY6jlmaNNFDpTL2YYkVema7Mxbt35kzZ0ilUtFbb70lOoqiHHFdnKysLJowYQIFqtVFFwg158ZTHwml1+upfv36VLNmTedbA60QR10XZ9OmTdSkcmV6YI2e8Wx+Qs2fP59kWaYkJ1+IyBHXxbl48SK1a9eOgMokSVn8lObALl26RGq1miZMmCA6iqLef/99h5vgJicnhyZPnkwaTQ0qvBCvtXvGg6liPv30U5IkidfFsRNZWVn0yiuvkJubG1WoUIGWLl1KeQMGlL0ghLEbL8phF9LS0sjDw4P69OkjOoqiHGldnNjYWKpatSrJskyRkZGU06cP98wJdOvWjby8vFxiXRxHmODmwoULFBERQZIkUYMGDejAgQOlrH/DVXMUX375JUmSRJs3bxYdRVF9+/Z1iAlucnJyaNKkSeTu7k6enp40f/58GjAgT7Ge8WCqBLwujnhZWVk0btw4cnNzIx8fH1q2bNlfn2IYX8a67BsvF283Dh8+7BLr4kRFRZFaraZLly6JjlKijRs3UpUqVUiWZRo4cOBfT5LcM6eg1/O6OPbg/Pnz1KZNG5IkiRo2bEiHDh0q+BpXzTm8/vrrpFar6cKFC6KjKEav11OtWrWoXr16dnlmSU5ODr322muk0WjIy8uLFixYUJBTyZ7xYMoIXhdHjKysLBo7diyp1Wry8fExvthw/pLl5W1DdLRtD4iVylXWxWnevDkFBATY1bo4n376KVWuXJlkWaZBgwaVvGwC98wp5K+LM3LkSNFRFGWP6+KcP3+eWrduXeIgqjCumnMIDQ0lf39/u/pdb20pKSmk1Wpp8ODBoqMUyMnJoYkTJ5JGoyFvb29avHhxiYM9pXrGgykj8tfFefbZZ0VHUVRcXJxdrIuTmZlJY8aMIbVaTb6+vrRixYqyvym/FWV9bitJ/Kxjx1xpXZx27dqJjkIbNmyggIAAUqlUNHjw4LLXnuOeOYX8dXHWr18vOoqi7GVdnHPnzlF4eDhJkkTPPPOMSW8YcdUcX1ZWFvn5+VF4eLjoKIrav38/SZJEq1evFpoj/3r6/EHUkiVLyvzETIme8WCqFPmnDcydO1d0FEVNmzaNVCoVnTt3zub7zszMpFGjRpFarSY/Pz9auXJl+TaQlGQ4kVWrNSxPXbgJOp3h/oED+TwIO5Z/2kDdunXt8rQBa8mf4Gb69OlC9r9+/fqCQdSLL75YvmtouGdOYcaMGaRSqej06dOioyhGr9dTvXr1hE1wc+bMGQoLCyNJkqhx48Z0+PDhcn0/V83xnT9/nlQqFU2dOlV0FEXNmzdP2AQ3JV1PX56+W7tnPJgqQ3R0tN2dNqCE1q1bk5+fH2VlZdlkfxkZGTRy5MiCQdSqVass2+CtW0RLlxKNGEHUt6/hv0uX8mxiDiL/tIFBgwaJjqKoDRs2kCRJtGPHDpvtc926dVSpUiVSqVQ0ZMgQyyYi4J45vPbt25OPjw9lZmaKjqKYO3fukE6ns+kEN2fOnKHQ0FCSJImaNGlCR48etWh7XDXHFhsbS5Ik0fbt20VHUVTXrl1tOsFNqdfTm8FaPePBlAleeuklcnd3pxs3boiOopicnByqWLEitWzZUtH9ZGRk0IgRI0ilUpG/vz9F83kK7LH4+HiSJMnygbWdGz16tE0muFm7di1VrFiRVCoVDR061Klnc2Omy83NpYCAAGrWrJnoKIpKSEiwyQQ3p0+fppYtW5IkSRQcHEyJiYmK7o85jvHjx5ObmxtdvnxZdBTF5Obm2mSCG5OvpxeEB1MmcJV1cS5evFjyujgpKURLlhANG2YYug8bZvj/cgzdMzIyaOjQoaRSqahixYq0Zs0aK6dnzsBV1sV55plnnlwXxwo9IzJ8mu7v708qlYqGDRtm98sfMNtz6XVxrNSzU6dOUYsWLUiSJAoJCXH631nMPMHBwVS5cmWnnswsf4KbUaNGFbnfGlUz63p6AXgwZaL8dXF69+4tOoqivvjii7/WxUlMJIqMNJw8qtWWfFJpZKThcUakp6fTkCFDCgZRa9euteHRMEfUrVs38vT0dOpPUjIyMsjLy4u6du1qlZ4RGWYy8/f3J7VaTSNHjuRBFCtV/ro4mzZtEh1FUfnr4tzdt88qPTtx4gQ1b96cJEmipk2b0vHjx210JMwRZWZmUoUKFahjx46ioyhqx44dJEkSbdiwwSpPaRZfT29jPJgqh/x1cZYsWSI6iqKioqIoSpJIr9WaPd1Jeno6vfTSS6RSqSggIIDWrVsn6GiYo8lfF6dhw4aioyjq+PHjNAGgHDc3i6YVWrVqFfn5+ZFaraZRo0Y59bUwzLpcZV2cGX5+9ECSKM+Cnp04cYKaNWtGkiRRs2bNeBDFTHbq1ClSqVQ0e/Zs0VEUNX36dJKkiaTV6s1+SsvMzLTu9fQ2woOpcspfF8fYWhFOITqaHpR3mejHrUhLS6PBgwcXDKI2bNgg+miYA3KJdXGio+mhm5vZC16sWLGCfH19Sa1W0+jRo3kQxczi9OviREdTXvHpusrRs6SkJAoJCSFJkqhFixZ04sQJwQfEHNG6detIkiTavXu36CiKiY4mkuUHZlUt/3p6tVrtkNfT82DKDP3793fedXFKWCIaJdyCS2hFlkpFrWSZKleuTDExMaKPhDk4p14Xx4KePXRzo05eXqRWq2ns2LE8iGIWcep1cSzo2SOtll6sV48kSaKWLVvSqVOnRB8Nc3DDhg0jd3d3unbtmugoVldC1UrsGhD8xIBKpcomWW7l0NfTS0REYOWSl5eHunXrQqVS4ZdffoEsy6IjWc/AgcD27Y978KRbAKoAWAlgUrGv6QH80bIlav/wg7IZmcuYNWsWlixZgpMnT6JJkyai41iPhT07WacOGv/0E7RarbI5mUv4+eef0bhxY0yZMgXLli0THcd6LOzZAV9fVD50yLl+9zChGjZsiPv37+Pq1atO9dqxjKqhrLY1b34VP/4YpGRERfFgyky3b99GzZo10bdvX2zZskV0HOu4dQuoXRvIzjb6kPEAYgDkACjx14BWC1y9CgQEKBKRuZ4OHTogOTkZ169fh4eHh+g4luOeMTv02WefYcSIEfjqq6/Qv39/0XEsxz1jdujevXsIDAxE+/btsWvXLtFxrMKEqqGstjl61ZxnWGxjlSpVwq5du7Bt2zZ8+OGHouNYR0xMmQ/ZCiACpfzDkSSTtsOYqf73v/9Bo9Ggbdu2oqNYB/eM2aFhw4Zh3LhxGDx4MK5cuSI6juW4Z8wOVahQAfHx8di7dy8WL14sOo5VmFaR0tvm6FXjwZQFOnXqhHfeeQdTpkzBD85waltycqlvLRwBcBfAO6VtIysLOH3aysGYK1Or1Th27BjOnj2LiRMnio5jOe4Zs1P/+te/0LBhQ7Rq1QqPHj0SHccy3DNmp8LCwrBixQrMmjULBw8eFB3HYmVUDaa0zdGrxoMpC82ePRtdunRBp06dcO/ePdFxLJOeXuqX5wDwAdChrO2kpVkpEGMGQUFB+M9//oO1a9fi888/Fx3HMtwzZseOHj2K7OxsdO3aVXQUy3DPmB2bNGkSIiMj0aNHD9y+fVt0HIuUUTWY2jZHrhoPpqxg9+7d8PHxQXh4uOgolvHxKfXLBwFEmrIdPz9rpGGsiMjISEyZMgUjR47EL7/8IjqO+bhnzI55eHjg0KFDSEhIwJw5c0THMR/3jNm5LVu2oFq1aggLC0NeXp7oOGYro2owtW2OXDUeTFmBLMtISkrCpUuXMHLkSNFxzBcSgjwjs4OtBZALYGFZ29DpgOBgKwdjzGD58uVo3rw5WrdujezSzyuwXyEhIO4Zs2MhISFYs2YNFi5ciD179oiOY56QEMNV7SXgnjF7IMsyEhMTkZKSgkGDBomOYzZD1YzNZWda2xy9ajyYspLAwEBs374dsbGx+Pjjj0XHKbebN2/ipd27kWPkBeoKAEEAAsvaEBEwerR1wzFWyMGDB0FE6Nixo+go5ZaXl4f3UlO5Z8zujRs3DkOHDkW/fv1w/fp10XHK7fv69ZGdk1Pi17hnzF5UqlQJe/fuRVxcHFasWCE6Trndvn0b8fGjSnlz07S2OXrVeDBlRb169cKMGTPwyiuv4MyZM6LjmOT69evo2bMnAgMDcfjiRaSFhxumVSnmJwCXytqYJAG9ezvu3JbMIWi1Whw5cgTHjx/H1KlTRccxSV5eHv75z3/C29sbc1atwi9PPQXinjE7Fxsbi6CgILRq1cphTkPav38/6tevj/bPP49Ef3/uGbN77dq1w+LFi/HGG2/g2LFjouOY5NatW3juuedQpUoVJCd/i7CwOyW9dIQpbXOKqgldMthJtW/fnipUqECZmZmioxh17do16t69O0mSRLVr16a4uDjDF0pextq0m4cHUVKS2ANjLiM2NpYkSaIvv/xSdBSj9Ho9zZ49mzw8PEir1dK0adMoNzeXe8YcRnp6Onl6elKPHj1ERynVt99+S/Xq1SNJkqhjx4506dIl7hlzKL169SJPT09KS0sTHcWolJQU6tOnD8myTNWrV6ctW7YQEVeNB1MKyM3NpYCAAGratKnoKE/4/fffqWvXrgWDqK+//vrJB0VHl78VHh6G72PMhsaPH09ubm50+fJl0VGK0Ov1NHPmTNLpdKTVaunNN980DKIK454xB5GYmEiyLNOiRYtER3nC3r17qW4eKvLrAAAgAElEQVTduiRJEnXu3PnJ3wXcM+Yg9Ho91ahRg55++mnS6/Wi4xSRkpJCvXv3LhhEbd269YnHuHLVeDClkMuXL5ObmxtNmDBBdBQiMgyiunTpQpIkUZ06dWjHjh2lf0N+KySp9CZIkvO0gTmkkJAQqly58pODFQH0ej3NmDGDdDod6XQ6euutt0rPxT1jDmLlypUkSRIdOHBAdBQiItq5cycFBQWRJEn07LPP0pUrV4w/mHvGHMSNGzfI3d2dhg4dKjoKERny9OrViyRJopo1a5Z5JoirVo0HUwr68ssvSZIk2rRpk7AMV65coWeffZYkSaK6devSzp07Tf/mpCSigQOJtFoina5oE3Q6w/0DBzr+57PMoWVmZlKFChWoY8eOwjLk5ubSW2+9VTCImjFjhunvLHLPmIMYOHAgabVaSk1NFZZhx44dVKdOHZIkibp06VL6IKow7hlzEHv37iVJkmjt2rXCMly7do169OhRMIjavn27yd/rilWTiIhEX7flzP7+97/jww8/xJkzZ9CgQQOb7ffKlSsYPXo0Dhw4gKCgIERHR6NHjx7mbSw1FYiJMSxPnZZmWAwgONgw9YpDXzHInEVycjJatGiBf/zjH1i4sMwJj63m0aNHmDlzJlatWgVJkvD3v/8d8+fPhyybMbcP94zZuby8PDz11FPIy8vDpUuXzPt3bqZvvvkGr732Gq5evYquXbtiw4YNqFGjRvk3xD1jDmDu3LmYP38+jh8/jmbNmtlsv9evX8eYMWOwb98+1KxZE6tWrUK/fv3M2pZLVU30aM4VhIWFkb+/P2VlZSm+r8uXL1PHjh1JkiSqV68e7d27V/F9MmYP1q1bR5Ikle/TVzPl5ubStGnTSKvVkoeHB82ePdvuznFnTAmpqamk0+lowIABNtlfXFwc1apViyRJou7du9O1a9dssl/GROvcuTN5e3tTRkaG4vv6/fffqVu3bqVfT8+M4qnRbcAW6+L89ttv6NixI4KCgnD9+nXs27cPFy9eRLdu3RTbJ2P2JH9dnAEDBii2Ls6jR48wdepUeHl5ITo6Gv/4xz+QkZFh/qdRjDmY/HVx/vvf/yq6Lk5cXBxq1aqFAQMGoFGjRvjjjz+wZ88eBAaWuToUY05h79698PT0RNu2bRXbxx9//IGuXbuiVq1a+OWXX/DNN9/g8uXL6Nu3r2L7dEZ8mp+N/Pzzz2jcuDEmT56MDz74wHDnrVuGz0CTk4H0dMDHx7CU9JgxJn8G+uuvv2L06NH4/vvv8dRTT2HNmjV49tlnlTsQxuxcw4YNcf/+fVy9erVggGNp1R49eoQ333wTH330EdRqNd58803MmTOHB1DMZb377ruYNWsWDh8+jPDwcABWeUrDV199hSlTpuCPP/5Az549sWHDBlStWlW5A2HMjl29ehVPPfUURo0ahXXr1gGwTs+uXr2KMWPGID4+HnXq1MHq1avRq1cv5Q7E2Qn+ZMyl5K+L878lS4giIw1X4Wm1JV+dFxlpmLjfiIsXL1JERARJkkRPP/00xcfH2+5AGLNjhdfFSUy0rGo5OTk0efJkcnd3J09PT5o3bx6fzsfYY/nr4uzff8/SpzTaunUrVa9enWRZpt69e9ONGzdsdyCM2bG4uDiSJInmzdtpcc8uX75MnTt3LpiUbPfu3bY7ECfGgykbi23Xju4DlGfmvJEXLlygtm3bkiRJ1KBBA7uZppYxe5KYmEjABHJzyzFritacnBx67bXXSKPRkJeXFy1YsIAHUYwVo9frydd3OknSA5KkPLOmQt6yZUvBIKpPnz6UkpIi5mAYs2Pdum0j4L7ZPePr6ZXFgylbMrKi2SSANAABIDVAq0pY0ez8+fPUpk0bkiSJGjZsSIcOHRJ9NIzZrehoIje3hyU82UwiQEMACFATsKpI1VatyqWoqKiCQdSiRYt4EMWYEdHRRDqdvtw9i44m+uKLLygwMJBkWaa+ffvyIIoxI4wvhlt2zy5dukQdOnQgSZLoqaeeom+//Vb04TglvmbKVpKSgE6dgAcPitz9LoDZANYAGA3g5OP7Qws9JkuW0SEvD5mNGmHdunWIiIiwQWDGHJORqsG0tmXCw6M35szphbfeeouviWLMCMt69gCS1An9+gVi/fr1qFSpktJxGXNIlvRMlrORl9ce9eun8/X0CuPBlK0MHAhs325406AQbwDPA4gp5Vv1AO527IiK332nWDzGnIWRqsGUtklSHgYMkPDll5JyARlzApb0DMhDnz65+OYbd6XiMeYULOuZHu3b/4mDB51tUSf7w2+72sKtW8CuXU+04SGA+wBuAnADoAIQAuDPYt+uAlDx2DHDCmiMMaOMVA2mto1Ixq5dEleNsVJY2jNAxv797twzxkphec9USEoK4J7ZAA+mbCEmpsS7zzz+7yEASQDOAbgM4LmSHixJRrfDGDMwXhHT28ZVY6x03DPGlMc9cxw8mLKF5GQgO/uJu/0e/3cogGYAGgB4BcCJkraRlQWcPq1UQsacgpGqoTxt46oxVjruGWPK4545Dh5M2UJ6eol3B8Hw4azJV2ekpVkpEGPOyUjVUN62cdUYM457xpjyuGeOgwdTtuDjY/RLbQF8BuAsgN8A/AtAS2MP9vMz9hXGGEqtGsrTNq4aY8ZxzxhTHvfMcfBgyhZCQgCttsQv7YbhPYYmAJ4CUAfA1yU9UKcDgoMVCsiYcyilajC1bVw1xkrHPWNMedwzx8FTo9vCrVtA7drGTn41jVYLXL0KBPAUl4wZw1VjTHncM8aUxz1zHPzJlC1Urgz06mWYVsUckgT07s1tYKwMXDXGlMc9Y0x53DPHwZ9M2YrxZazL5uEBHDgAhIaW/VjGXBxXjTHlcc8YUx73zDHwJ1O2EhYGvP++4V93eXh4GL6P28CYSbhqjCmPe8aY8rhnjkE1d+7cuaJDuIywMMDfH4iPBx49Kv2xkvRXGyZOtE0+xpwEV40x5XHPGFMe98z+8Wl+jDHGGGOMMWYGPs2PMcYYY4wxxszAgynGGGOMMcYYMwMPphhjjDHGGGPMDDyYYowxxhhjjDEz8GCKMcYYY4wxxszAgynGGGOMMcYYMwMPphhjjDHGGGPMDDyYYowxxhhjjDEz8GCKMcYYY4wxxszAgynGGGOMMcYYMwMPphhjjDHGGGPMDDyYYowxxhhjjDEz8GCKMcYYY4wxxszAgynGGGOMMcYYM4NLDKYaN26M7777TnQMxpwOd4sx5XHPGFMe94yZyyUGU2fPnkWnTp1ExyjRd999hxo1aoiO8YRr166hf//+8Pf3R40aNbBmzRrRkZgd4m6V33/+8x+0bdsWHh4eJf7dnTx5Ei1btoSHhwdatmyJkydP2j4ksyvcs/Irq2cvv/wyGjRoAFmWERMTY/N8zP5wz8qvtJ5duHAB/fv3R0BAAPz9/dGjRw/8/PPPYoIqzCUGU6z8hg8fjqCgIKSkpGDHjh2YOXMm4uPjRcdizOH5+/vj9ddfx/Tp05/42sOHD9G/f38MHz4caWlpGDVqFPr374+HDx8KSMqY4yqtZwDQtGlTREdHo0WLFjZOxpjzKK1nd+/eRb9+/fDzzz8jJSUFrVq1Qv/+/QWktAFyAbVr16Z9+/bRqFGjaNasWQX3x8fHU/Xq1Ys8bunSpRQcHEweHh70t7/9jW7evEk9e/YkLy8v6tKlC/35559ERPTbb78RAFq7di1Vq1aNqlatSu+//77RDDt27KBGjRqRl5cXBQYG0nvvvUf3798nrVZLkiSRp6cneXp60rVr10iv19PixYupbt265O/vT4MHD6Y7d+6YtV9zZGRkEAC6detWwX3jx4+n4cOHW3U/zPFxt8y3bt066tixY5H79uzZQ4GBgZSXl1dwX82aNWnXrl2K5WD2j3tmvpJ6VlhERAR98skniu2fOQ7umfnK6hkR0Z07dwgA3b59W7EcovAnU8Vs27YN+/btw4ULF/D111+jV69eWLRoEW7fvo28vDysXLmyyOPj4+Pxyy+/YO/evXj33Xfx7bfflrjdsWPHYu3atcjIyMCZM2fw7LPPwtPTE7t27UJgYCDu37+P+/fvIzAwECtXrsT27dtx4MABXL9+HX5+fnj11VfN2u+7774LX19fo7eSEFGR/+b/+cyZMyb/PTJWHHerbGfPnkVISAgkSSq4LyQkBGfPnjVre8z1cM8YUx73rPwOHjyIqlWromLFilbZnj3hwVQxkyZNQpUqVVC9enW0b98e4eHhaN68Odzd3REZGYkTJ04Uefzbb78NT09PBAcHY8yYMfj8889L3K6bmxvOnTuHe/fuwc/Pr9RTC9auXYuFCxeiRo0acHd3x9y5c7F161Y8evSo3PudPn067t69a/RWEm9vb0RERGD+/PnIzs7Gjz/+iG3btuHBgwdl/fUxZhR3q2z379+Hj49Pkft8fHyQkZFh1vaY6+GeMaY87ln5/PHHH3j11VfxwQcfWLwte8SDqWKqVKlS8GedTvfE/9+/f7/I42vWrFnw59q1a+P69eslbnfbtm3YuXMnateujY4dO+LIkSNGM1y5cgWRkZEF7wg0atQIKpUKKSkp5d6vuT777DP89ttvqFmzJiZOnIhhw4bZ5cWPzHFwt8rm5eWFe/fuFbnv3r178Pb2tlkG5ti4Z4wpj3tmutTUVHTv3h1RUVEYMmSIzfdvCy41mPL09Czy6crNmzct3ubvv/9e8OerV68iMDCwxMeFhYUhLi4Ot27dwoABA/DCCy8AQJHTefLVrFkTu3btKvKuQHZ2NqpXr17u/S5atAheXl5Gb8bUrl0b33zzDVJTU3Hs2DHcuXMHrVq1Kv0vg7ks7pbp3SpN48aNkZycXOQU2+TkZDRu3Nis7THnwj2zTs8YKw33zHo9S0tLQ/fu3dGvXz/MmjXL7O3YO5caTDVr1gw7d+7En3/+iZs3b2LFihUWb3P+/Pl48OABzp49i08++QQvvvjiE495+PAhPvvsM6Snp8PNzQ0VKlSASqUCYHh3486dO0hPTy94/IQJEzBr1ixcuXIFgGFUHxcXV+79AsDMmTMLzrEt6WbMTz/9hIyMDDx8+BCxsbHYu3cvpk6dWu6/H+YauFumd0uv1yM7OxuPHj1CXl4esrOzkZubCwDo1KkTVCoVVq5ciZycHHz44YcAgGeffbYcf3PMWXHPrNOz/GPKzs4GESE3NxfZ2dnIy8sz/S+OOS3umXV6du/ePfTo0QMRERF49913y/cX5mBcajA1YsQING3aFHXq1EH37t2N/qMqj44dO+Kpp55Cly5dMG3aNHTv3r3Ex23cuBF16tRBhQoVsGbNGsTGxgIAGjZsiCFDhqBu3brw9fXF9evXMWXKFPTr1w/du3eHt7c3WrdujWPHjpm1X3Pt2bMHdevWhZ+fH9asWYPdu3cjICDAqvtgzoO7ZbqNGzdCp9Nh4sSJOHToEHQ6HcaPHw8A0Gg02L59O/7973/D19cXGzZswPbt26HRaKyagTkm7pnpSusZAHTv3h06nQ6HDx/Gyy+/DJ1Oh4MHD1o1A3NM3DPTldazr776CklJSfjkk0+KfMp19epVq2awBxIVPp/ESdWqVQuxsbHo0KGD1bZ5+fJlBAUFITc3F2q12mrbtdf9MlYS7hZjyuOeMaY87hkzl9N/MpWamorU1FTUqVNHdBTGnAp3izHlcc8YUx73jFnCqQdTSUlJqF+/PiZNmoRatWqJjsOY0+BuMaY87hljyuOeMUu5xGl+jDHGGGOMMWZtTv3JFGOMMcYYY4wphQdTjDHGGGOMMWYGHkzZ2EcfAZ6egCwDkmT8JsuGx330kejEjDkm7hpjyuOeMaY87pl9U82dO3eu6BCu4qOPgGnTgEILa5cqNxeIjwf8/YGwMGWzMeZMuGuMKY97xpjyuGf2jyegsJGkJKBTp+JluAcgFMAlAHoAOgDTAfyzyPd6eAAHDgChobbJypgj464xpjzuGWPK4545Bj7Nz0YWLwaysorfmw0gEMB3AHIBTAPwNoCEIo/KyjJ8P2OsbNw1xpTHPWNMedwzx8CfTNnArVtA7dpAdrYpj9YBeA3Ae0Xu1WqBq1eBgAAFAjLmJLhrjCmPe8aY8rhnjoM/mbKBmBhTH3kGhnccOj/xFUkqz3YYc03cNcaUxz1jTHncM8fBgykbSE425Z2FBwA6AGgIoPcTX83KAk6ftn42xpwJd40x5XHPGFMe98xx8GDKBtLTy3rEIxiKoAZw3Oij0tKsl4kxZ8RdY0x53DPGlMc9cxxq0QFcgY9PaV/NA9AIQAaAXwF4GH2kn59VYzHmdLhrjCmPe8aY8rhnjoM/mbKBkBDDRYAlawLgBoCfAPgb3YZOBwQHWz8bY87E0DVjc+pw1xizBn5OY0x53DPHwYMpGwgNPYOcnJwSvvI9DEXIBFANgPT4FvXEI4mA0aMVDMmYg3vw4AF+/HEysks8yZy7xpi1uLt/zj1jTGFt217g144OggdTCjp58iSaN2+Orl1DUKHC95Ck4u+YRwCgEm7RRR4lSUDv3jy1JWMlefDgAUaNGgUfHx/s3RuLkJDrkKTij+KuMWap9evXIyAgAG+8MQLVqyfzcxpjCjh79izCwsLQoUNDeHkd4p45AB5MKeDHH39Es2bN0KJFC0iShB9//BH79j0Lne6JV3gm0emAGTOsHJIxB3f//n2MHDkSPj4++Oabb/B///d/+PPPP7F+fT3odOZtk7vG2JP+9a9/oVKlSpgwYQK6deuGP//8E1991Yqf0xizojNnziA0NBTBwcHIycnBsWPHsH9/V+6ZA+DBlBX98MMPCAkJQWhoKNRqNU6ePFkwsAoLA95/H/Awfo1giXQ6wvvvA6GhymRmzNHcv38fw4cPh6+vL3bu3IkPP/wQd+7cQVSU4RQHc7vm4QHuGmOFrF27FpUqVUJUVBR69uyJu3fvYtOmTahQoQL3jDErSU5ORmhoKEJCQvDw4UMcO3YMycnJCAsLs+C1Yx73zJaIWSwxMZGCg4NJkiRq2bIlnT592uhjo6OJPDyIJInIcDZryTdJyiNJyqSAgDmk1+tteDSM2af09HQaMmQIqVQqqlixIq1du7bUx5vaNUBPwH2aPfuajY6EMfu2evVq8vf3J7VaTSNGjKCMjAyjjy3Pcxpwn7p122bDI2HMfp04cYKaN29OkiRR06ZN6fjx40YfW77Xjg/I13cGv3a0IR5MWeDo0aPUpEkTkiSJQkNDSx1EFZaURDRwIJFWS6TTFS2CTme4f+BAoj177pC7uzu9+OKLCh8JY/YrPT2dXnrpJVKpVFSpUiVat26dyd9ratdCQv5GPj4+lJmZqeCRMGbfVq1aRX5+fqRWq2nkyJGlDqIKM7Vnc+d+Q5IkUVxcnMJHwpj9OnHiBDVr1owkSaJmzZqVOogqzNSe/e9/98jT05N69+6t8JGwfBIRGZtHmBlx5MgRjB8/HufOnUNYWBg2bNiAxo0bl3s7qalATIxhdeq0NMNaAMHBhplX8i8Y3L9/P7p164YPP/yw4DQmxlzBvXv38PLLL2Pr1q3w9/fH4sWLMXbsWLO2VVbXHj58iOrVq6NmzZr48ccfrXocjNm7VatW4e2330ZGRgaGDx+O1atXw6O85xXBtOe0cePG4d///jcuXryIWrVqWfU4GLNnP/74I/72t78hOTkZzZo1w4YNG9CsWbNyb8eUnh07dgxt27bFwoULMX36dKseByuB6NGcI0lISKBGjRqRJEkUHh5O586ds8l+582bR7Ism/zuBWOOLC0tjQYNGkQqlYoqV65MGzZssMl+L126RGq1mqKiomyyP8ZEW758Ofn6+pJaraYxY8bY7JPZJk2aUJUqVSg3N9cm+2NMpKSkpCKXgpw6dcom+12+fDnJskyHDh2yyf5cGQ+mTJCQkEANGzYkSZKodevWdP78eZtn6NKlC3l5eZl82gVjjiYtLY2ef/55kmWZKleuTDExMTbPsGXLFpIkiTZv3mzzfTNmK8uXLycfHx9Sq9U0duxYm5/empGRQd7e3tS5c2eb7pcxWyrP9fRKGTBgAOl0Orpz547N9+1KnPY0v1u3DB+DJicD6emAj49hNekxY0yfcz8hIQHjx4/Hzz//jDZt2iAmJgb169dXNLcxjx49Qs2aNeHv74+zZ88KycBYcdbo2Z9//onx48dj+/btqFy5Mt577z0MHz5c0dylmTx5Mj766COcP38e9erVE5aDscIs7VpeXh6WL1+O+fPn48GDBxg9ejRWrlwJrVarePaSnDx5Ei1btsScOXMwd+5cIRkYK84az2nHjh3DuHHjcPbsWbRs2RKffPIJmjRpomhuY/Ly8lC3bl2oVCr88ssvkGWexFsRokdz1paYSBQZabgQT6st+QK9yEjD44yJj4+np59+miRJooiICLpw4YLtDqAUv//+O2k0Gho9erToKMzFWaNnd+7coQEDBpAsy1S1alWKjY213QGUoWXLllSxYkXKyckRHYW5OEu7ptfraenSpVShQgVyc3OjV155hbKysmx7EEasWbOGJEmivXv3io7CXJw1ntMOHz5MjRs3JkmSqFWrVnTmzBnbHUApUlNTSavV0vPPPy86itNyqsGU6VNHGh4XHV30++Pj46l+/fokSRK1a9eOLl68KOZASvH111+TJEk2u46EseIs7Vlqair179+fZFmmatWq0aZNm8QcSCmysrLI19eX2rRpIzoKc2GWdE2v19OSJUvI29ub3NzcaMKECXYziCrspZdeInd3d7px44boKMxFWfqcJup6+vKIj48nSZJo5cqVoqM4JacZTOWX4a9/+IMI8CAABNQrsRj5pdi/f3/BIKpDhw506dIl0YdTqrfeeotUKpXdvOvBXMeTPSu7a/k9S01NpX79+pEsyxQYGGj31yWdO3eOVCoVTZs2TXQU5oLMfU5bvVpPixYtIm9vb9JoNBQVFWXXn7Dq9XqqX78+Va9endfFYTZnyWtHe7ievjwWLFhAsixTYmkfrzGzOMU1U0lJQKdOwIMHhe99C4AKwH8B5AC4WOL3StIDEHVEx46e+OSTTxAUFKR0XKto164dzpw5g+vXr5s1hS1j5VVyzwBTuqZSZSMvrwMCA69jxYoVGDRokNJxrSI2NhYjR47E9u3b0a9fP9FxmIuw5DkNyIRa3RUTJoRi2bJl0Gg0yoa1grt376JGjRro0KEDdu7cKToOcxGWvXbMAlEHtG2rEXo9fXn17NkTCQkJuH79OipUqCA6jtNwisHUwIHA9u2G9wye1A7ATRh/4slDz55Z2LXLU7F8SuB1cZitld4zoPSu6REefgNHj9ZQLJ9SeF0cZmuWPqcNGED46iuVYvmUwOviMFuzrGd6dO+ehT17vBTLp4S8vDzUqlUL3t7e+Omnn0THcRoOP63HrVvArl2lvcAri4zvvvNEaqo1UylPo9EgMTERp0+fxquvvio6DnNylvdMhVOnajhczwBg/fr1aNCgAVq1aoVHjx6JjsOcnDWe03bvVjlc18LDw7Fs2TLMmjULBw8eFB2HOTlrPKcdPOjlcD2TZRmJiYn47bffhM6a62wcfjAVE2P5NiTJOtuxtaCgIHz++ef46KOP8MUXX4iOw5yYK/cMAI4cOYIHDx6ge/fuoqMwJ+fKXXv99dfx3HPPoWfPnrh9+7boOMyJuXLPAgMDERcXh02bNmH9+vWi4zgFhx9MJScD2dmWbSMrCzh92jp5bG3QoEF47bXXMHz4cPzyyy+i4zAn5eo98/LywsGDB3HgwAH885//FB2HOTFX79qXX36JypUro1WrVsjLyxMdhzkpV+9Zjx49MGPGDEyYMAHJycmi4zg8hx9MpadbZztpadbZjggrV65E06ZN0aZNGzx8+FB0HOaEuGdAs2bN8NFHH2HBggXYt2+f6DjMSbl612RZxg8//IAbN25g8ODBouMwJ+XqPQOAhQsXIiIiAu3bt8eDJ2eWYuXg8IMpHx9jX8kGcBeAHkDe4z8bfxvCz8/ayWwrISEBeXl56Nixo+gozAkZ7xlQnq45es9efvllvPTSS3juuedw8+ZN0XGYE+LnNKBSpUrYtWsXvvrqK6xatUp0HOaEuGcG+/fvh1arRZs2bURHcWgOP5gKCQE0mpIuCu8JwA/AUQC/Pf5zzxK3odMBwcGKRbQJrVaL77//HklJSXjzzTdFx2FOJiQE0GqNnXJjWtecoWeAYbr0WrVqITQ0lE9DYlYXHJwHtTq3hK+41nNap06dMH/+fLz++utISkoSHYc5GcNrR30JX3GtnqnVaiQmJuKnn37Cyy+/LDqOw3LoqdG3bduGSZPm48aNIwB0Zm9HqwWuXgUCAqyXTRReF4dZ25UrVzBs2N/x/fefgXtmwOviMGt79OgRZs6ciZUrNyMn5wIArdnbcqau9ejRA99//z2vi8OsZufOnXjllTn4448E8HOaQVxcHCIjI7Fx40YMGzZMdByH45CfTG3duhU1atTACy+8gBYtaqBXLwmSZN62JAno3ds5ygAAw4cPx9/+9jcMGjQIV69eFR2HObArV66gc+fOCAoKws2byWjb9h737DFf3/9n78zjojrP9n8/Z4Zhhh0X3BHFXSExIi7gvitVB7XG3VSTiBVjjEk1r8Q1Wom+Uhei0Vqs1h+J1MobMcZqjJG4QGxCtNZaYySGWKSGUEIQx8P1+2McAjjAwMyZZ5bn+/nMh2TmMM91HK5zzj3nee4rgE6fPk0ffvgh/fa3v+UtR+DEPHr0iF599VXy9fWlHTt20KuvzqGJEz2F1x7zwQcfkL+/P0VGRvKWInByjh07RiEhIRQTE0Nduzam0aNJ+OwxEyZMoCVLltCcOXPon//8J285zgeciHfffRctW7aEJEmIiYlBfn4+ACArC/DyAoyJAfV7eHkB2dmcd0wBwsLCEBQUBIPBwFuKwMm4desWBg4cCMYYOnTogBUV+UgAACAASURBVFOnTgEQPjPH1q1bIUkSzp49y1uKwMkwGAxYunQptFotvLy8kJCQAFmWAQivVScvLw8ajQYzZszgLUXghKSnpyM4OBiMMYwaNQp5eXkAhM/MERkZicDAQJSWlvKW4lQ4RTGVmpqKFi1aQJIkTJgwAQUFBU9sk5xcf1PodOVITuawQ3aguLgYvr6+GDx4MG8pAifh5s2bGDBgABhj6NixI06fPv3ENg3xmUZjcFmfAcDEiROh0+nMHpcEguoYDAYsWbIEnp6e8Pb2xurVqyuKqMo0xGteXnBZr504cQKMMezZs4e3FIGTcOTIEbRp0waMMYwePRp37959Yhtx7ViV0tJSNGrUCL179+Ytxalw6GLq0KFDaN68OSRJwsSJE+u8WDGZgrHajcBYORgrQVDQG2ZPYq5CTk4OJElCQkICbykCB+bmzZuIjo4GYwydOnXCmTNnat3ecp8BanUZGIvDtWvX7LMzHJBlGW3btkW7du1c+ngisI6ysjIsXry4oohas2ZNnX8v9TmnEf2IkSP/Yqe94cPrr78OlUqFnJwc3lIEDsyRI0fQunVrMMYwZswYs0VUZep77RgYuMKlj/U3btyAWq3GkiVLeEtxGhyymDp48GBFEaXX63H//n2Lfzc7G4iNBbRaQKer/m2C8fnYWOCDDwrg6emJ6dOnK7gn/Nm9ezcYYzh58iRvKQIH48aNG+jfvz8YY+jcuXO9pqpZ6rPsbKBv374uP22goKAAWq0WsbGxvKUIHIyysjLEx8dDo9HAx8cH69atq9eFmKVeS0hIB2MMGRkZCu4NfwYOHAg/Pz8UFxfzliJwMNLS0tCqVStIkoRx48ZVLAWxBEt9durUD9DpdBg/fryCe8KfQ4cOgTGGtLQ03lKcAocqpg4cOIBmzZpBkiRMmjSpXkVUde7dAxITgVmzgJgY48/EROPzJk6ePAnGGHbv3m0D9Y7LtGnT4OnpWee3MwL34Pr16+jXrx8YY+jSpQvOnTvX4PeyxGdlZWVo3LgxIiIibKDecTlz5gwYY9i2bRtvKQIHoKysDAsXLqwoojZs2GDVt9mWeG3OnDnQaDS4c+eODfbAMTEYDAgKCkJYWBhvKQIHoab19A3BEp9lZmZCkiRs2bLFBuodlwULFkCtVuPWrVu8pTg8DlFMpaSkICgoCJIkYfLkySgsLLTb2AkJCS4/bUCWZXTq1AmtWrVy6VvTgtq5fv06+vbtC8YYunbtiszMTLuNbZo2sHjxYruNyYMNGzZAkiRkZWXxliLgRFlZGeLi4qDRaODr64uNGzfa9bjbtWtXtGjRwqWbD92+fRseHh54/vnneUsRcKTyevrx48fbdd3q5s2bIUkSzp8/b7cxefDUU0+hadOmLn08sQX1Lqby84FNm4AZM4xV+4wZxv+vXLVbyr59+9C0aVOoVCpMmTLFrkVUZQYPHuzy0wYKCwvh7e2N0aNH85YisBBbee3atWuIjIwEYwzdunXjdvBPTU11i2kDo0aNgre3N4qKinhLEViArXxWWlqKBQsWwMPDA76+vti0aROXL6+Kiorg4+ODESNG2H1se3L06FEwxnDw4EHeUgQWYiuvHTp0qKKIsmQ9vVKMGzcOXl5eVs2icnRKSkrg7++PAQMG8Jbi0FhcTGVlAXq9cd6oVmt+Pqleb9yuLvbu3VtRRE2dOpX7RYfBYECzZs1cftrAxYsXIUkSNm7cyFuKoBZs5bWrV6+id+/eYIyhe/fuDvENWlxcnMtPG5BlGa1atULnzp15SxHUgq18VlpaihdffBEeHh7w8/NDYmIi9xkAly9fhiRJWLNmDVcdSvPyyy9DpVLh+vXrvKUIasFWXrNmPb0SyLKMNm3aoEOHDtw9ryRXrlyBSqXCihUreEtxWCwqpurTvau21qx79uxBkyZNoFKpMG3aNO5FVGVyc3Ph4eGB+fPn85aiKElJSSIXx4GxhdeuXr2KiIgIMMbQo0cPXLx40f47Ugs9e/ZE06ZNUVZWxluKYuTl5cHT01Pk4jgotvBZaWkp5s+fDw8PD/j7+2PLli0OdUG1c+dOMMbMRhy4EiIXx7Gxhdcqr6ePjY3lXkRVJj8/H1qtFlOnTuUtRVH27t3rFg1uGkqdxZQtsi527dqFxo0bQ6VSYfr06Q5VRFUmPd3YDenAgQO8pSiKyMVxTKz1Wk5ODnr16gXGGMLCwhx23Y5p2kB0dDRvKYoicnEcE2t9Vlpainnz5kGtVsPf3x9bt27lu0O1MGXKFGi1WqsW5Ds6plwcV29w44xY67X9+/dzW09fH06fPg3GGJJdNXzqMbNnz3b5BjcNpdZiquZ06JsgagEiApEKRAvNGuI3vzmMRo0aQaVSYcaMGU6xJmnZsmVQqVQun4sTEhKCkJAQh/om1Z2xxmtarYzOnWeAMYbw8HBkO0Es+9WrV6FSqbB8+XLeUhRl5cqVLt/gxpmw7pxWjl/8Yg3UajUCAgKQlJTEe3fqRJZlhIaGIjg42KWP9e7S4MaZMO+1un1GZAx6DwgYzn09fX1Ys2YNJEnC5cuXeUtRlC5durh8g5uGUGsxpdfXdGs2GERtQHQXRDsfG+NotW0egbE/Y/bs2U5RRFXGXXJxdDod9Ho9bykCWO81P7+/Ot1BfN++fW4xbWDQoEEu3+DGWbDWZx4e/+d0re/v378vcnEEdse81yzxmdFrrVpddNhZTDUxbNgw+Pj4uPSx3l0a3NSXWoup6gsFjY/8xwY4Uem5diDq88S2np7lDeryxxtTLk6vXr14S1GUs2fPgjHmFN+wujrWek2rbVhHTd6IXByBPXFXn5lycTZv3sxbiqKIXBzHID/fnNcs95mzes1gMKBFixbo1q0bbymKkp2dDUmSsG7dOt5SHAaJ6s3pxz9HVXquGxF9/cSWksQoJaX+I/BGo9HQhQsXKCcnhxYvXsxbjmIMHDiQ3nzzTVq6dCllZ2fzliN4Asu9xhg5pddSUlIoNDSUIiMj6dGjR7zlKIJaraasrCy6fv06vfDCC7zlCJ7A9X0WFRVFiYmJ9Nprr9GFCxd4y1GMt99+m7p37059+vShhw8f8pbjtpj3iOU+I3JOr5mO9Tdv3qTnnnuOtxzFiIiIoN/97nf0xhtv0EcffcRbjkNQazH14IG5Z++b+bVGRFT2xJalpURXrjRUGl86duxIBw8epB07dlBaWhpvOYqxYsUKGjFiBA0ZMoT++9//8pbjtriz1y5evEjFxcU0ZswY3lIUo23btnT48GHau3cv/elPf+Itx21xZ5+98sorNHbsWBo+fDh9//33vOUoxvnz5+nhw4c0bNgw3lLcli+/NOc1y31G5Lxea926Nf3lL3+h/fv30/79+3nLUYxFixZRbGwsjRs3ju7du8dbDncacGeqMRGVV3uukIg8zW5dWFj/ERyFqVOnUlxcHE2bNo2+/tr8tyeuwPHjxykgIIAiIyN5SxFUwT285ufnR2fPnqWPPvqI1q5dy1uOYkyYMIGWLFlCc+bMoX/+85+85QgqcA+fERGlp6dTkyZNqE+fPlReXn2fXQMvLy/KzMykCxcu0IoVK3jLcUuKisw9Wz+fETmv18aOHUu/+c1vaN68efT3v/+dtxzFeO+996hly5bUu3dvlz2eWEoDiinTtz1/rfTcNSJqZ3brwMD6j+BI7Ny5k8LCwlx62oAkSZSVlUW3b9+mmTNn8pYjqMB9vPbMM8/Q9u3bafXq1S49beB///d/KSIigvr160cPzN8mEdgd9/GZJEmUnZ1N3377LU2fPp23HMXo0aMH7d69mzZt2kTHjx/nLcft8Pc392z9fEbk3F7buHEj9e3bl6Kiouinn37iLUcRTMeTgoIC0uv1vOVwpdZiSqs192wQEbUhovlEdI+I3iaiW0T05DdAOh1RWJjVGrmTmZlJDx8+pKFDh/KWohgtW7ak9PR0OnToEL3zzjtVXrt3jygxkWjmTKJf/ML4MzGRqKCAk1gXRHiNaOHChTR58mSXnzbwySefEGOMBgwYUOV54TPlET4jCgoKooyMDHrvvfcoOTmZtxzFmDdvHs2aNYv0ej19++23Fc8LnylPeLg5r1nuMyLX8NpHH31EGo2GoqOjeUtRjEaNGtFf//pXOnbsGG3ZsqXKa27ltdq6U5jvfGTKCmj+uDNLzVkBztiNpSbcJRcnISEBkiQhJycHWVnG9qZa7ZN/Czqd8Tm93pgnIbAO4TUjplycNm3auE0ujvCZ/RA++xl3y8U5f94gfGYnzHfzs9xnruS1W7duQa1WY+HChbylKMqmTZsgSRLOnz/vlue0BuZM1f1gDIiNtddu2IeUlBS3ycXx9FwCna68zs+fsaqJ5YKGIbz2M/fv34eXlxdiYmJ4S1GUQ4cOgWgBNBqD8JmdED6ryvDhw90iF0ejWQxJKhU+syPCaz+TlpYGxhhSU1N5S1GUsWPHwsMj3i2vHWstpmpOi6/74eUFZGfbazfsx9y5c10+F2fHjkcgKqn35+0qpuCB8FpVzp8/7/K5OMnJgFr9QPjMjgifVcUdcnGSkwGt9pHwmZ0RXqtKfHw81Go1bt68yVuKYuzcKYMx97x2rLWYAow7WV9DaLWPXOIfpya6deuGFi1awGAw8JZic2o+APrDeGve9PBwiwOgPWmI11zlQGSOzZs3Q5IkZGZm8pZic4TP+NEwn5W7rM/u3LkDjUaDOXPm8JZic4TP+CLOaVXp1asXGjdujLKyMt5SbI67e63OYgr42RCW3LaTpFJoNItRVFSktHZuFBcXw8fHB8OGDeMtxebUfGveH0Sz6/z8XenWPA8s95oMoh/x3HMuNOnYDDExMdDpdLh//z5vKTZF+IwvlvusHEQl6NhxC2/JipKRkQHGGPbt28dbik0RPuOPpV4jMp7TEhP/y1uyYpSWliIwMBB9+/blLcXmuLvXLCqmAGPVGBtrXDim01X9hzAtKIuNBS5dktGiRQt06dJFSd3cuXz5MiRJwpo1a3hLsRk1Lxq1zBBErrNolCeWem3atC1Qq9W4ceMGb8mKIcsygoODERoa6jINKYTPHANLfXbo0A2oVCqsXLmSt2RFWb58OVQqFa5evcpbik0QPnMcLPGaXl+OFi1+gZCQEJc51pvj+vXrUKlUWLZsGW8pNkN4rR7FlIl794DERGDWLCAmxvgzMbHqP4Jp2sDs2bNtqdXh2LlzJxhjOHXqFG8pNmHTproMwR4/fEG01ex2Op3x70FgPZZ4LSIiAo0aNXLJaQMm8vPzodVqMWXKFN5SbILwmWNhic92794NxhhOnDjBT6gdiIqKgr+/P0pKSnhLsRrhM8ejLq8VFBRAp9NBr9fzFaowBw4cAGMM6enpvKXYBOG1BhRTlnL8+HEwxrB3716lhnAIpk6dCq1Wi/z8fN5SrGbGjNq+OdgLojwQFYFoPoxzX0+b3XbWLN574j6Ypg306dOHtxRFOX36NBhj2LlzJ28pViN85pxMnz4dnp6eyMvL4y1FMcrKytC0aVP07NmTtxSrET5zTs6ePQvGGJKSknhLUZT58+fDw8MDubm5vKVYjfAaUGtorzWMGTOGli9fTi+++CJdvXpVqWG4c+jQIWrdujVFRERQeXk5bzlWUVRU26vziKglEfkR0R4iakxE5gMfCwttrUxQE1qtli5cuECfffYZvfLKK7zlKMbQoUNp7dq1FB8fT5999hlvOVYhfOacHDhwgEJCQigyMtLpj/U1odFo6NKlS3TlyhVauHAhbzlWIXzmnAwcOJA2bNhAS5cupezsbN5yFGPPnj3UpUsXioyMpEePHvGWYxXCa0SKFVNERBs2bKD+/ftTdHQ0/fTTT0oOxQ1Jkig7O5vu379P48eP5y3HKvz967M1IyKYfSUw0BZqBJbSuXNn2r9/P23dupXS09N5y1GMlStX0tChQ2nIkCH03//+l7ecBiN85pxIkkRZWVn0ww8/0Lhx43jLUYx27dpRamoq7dq1i959913echqM8Jnzsnz5cho5ciQNGTKEfvjhB95yFOP8+fNUWlpKI0aM4C3FKoTXFC6miIg++ugj0mg01L9/f6WH4kZAQACdOnWKPvjgA3rrrbd4y2kw4eFEWq25P/JcInqTiH4gogdEtJCI/kNE85/YUqcjCgtTUqXAHDNmzKD58+fTlClTKDc3l7ccxfjggw/I19eX+vbty1tKgzH6zNwrwmeOjp+fH505c4ZOnjxJGzdu5C1HMSZNmkSLFi2imTNn0r/+9S/echqE8Jlzk5GRQQEBAdSnTx+XvRPs4+ND586do3PnztEbb7zBW06DEdeORIqtmarMrVu3oFarsWDBAnsMxw1nzsWRZRnLl/8viErNzGW9BiKvx3NdCUTeINpods6rs3dkcXbCwsIQFBTkkhloJpw9F+fo0fNgzFxYr/CZs5CUlATGGM6ePctbiqI4cy7O9evfQ5LKhM+cmLy8PHh6emL69Om8pSiKqcHNyZMneUupN7IsY9WqnW5/7WiXYgoAjhw5AsYYDh06ZK8hueBsuTiyLGPNmjXw9vaGRqNB+/ZfPM5Wqb2NpbmHK2QFODslJSXw8/PDoEGDeEtRFGfMxTlz5gw6deoExhgaNfpY+MzJ0ev10Gq1KCgo4C1FMZwxF+f+/fuYOHEiJEmCp2fG40w+4TNn5cSJE2CMYffu3bylKIqpwc3du3d5S7EIWZaRmJgIPz8/eHh4oG3by259TrNbMQUAS5YscZtcnPbt2zt0VoIsy1i9ejW8vb3h6emJxYsXo6ysrJYU67ofrpBi7Qrk5OS4VS7OlStXeEuplTNnzqBjx45gjCE6Oho3b94UPnMBZFlGu3btXD4X59q1a1CpVFi6dClvKbVSUFCACRMmQJIktGjRAocOHRI+cxESEhKgUqmQk5PDW4piyLKMTp06oVWrVg59PJFlGRs3boSvry88PDywYMEClJaWur3X7FpMAUDv3r3RqFEjlJaW2ntou2HKxZk8eTJvKU9gvCW7Cl5eXvD09MTLL7/8xJQwU2J5fc2QnMxppwRPsGfPHrfIxYmOjnbYXJxTp06hQ4cOYIxhwIABuHnzZpXXhc+cH3fLxTl69ChvKU9QUFCA8ePHVxRRqampVV5viM90unLhMwdj8ODB8PPzQ3FxMW8pilFYWAhvb2+MHj2at5QnkGUZGzZsgK+vLzQaDeLi4p6Y/uvO5zS7F1OmaQORkZH2HtqumHJxtm/fzlsKAKMRVq5cCS8vL2i1WixdurTWdTUmUzBWlxlkEP2IN9/83o57I7CEGTNmQKPRuEUuztNPP81bSgWnTp1CaGgoGGMYNGgQbt26VeO2lvrMOH3iR8TGOt+celfn3LlzkCQJW7du5S1FUUy5OLdv3+YtBYDxS8tx48ZBkiS0bNkS7777bo3b1s9nJWjefLUd90RgCQaDAc2aNUNYWBhvKYqSlZUFSZKwceNG3lIAGK8d169fDx8fH2g0GixatKjWNZT1vXZcvfrfdtwb5bB7MQUA169fh0qlwssvv8xjeLuxbt06SJKEbI73L2VZxuuvvw6dTgetVotly5ZZ3JwgO9s4j1WrNaZTV/3mzvh8bCwQEjIZLVq0cOmmB85K586dHX7agLU4SoObkydPon379mCMYfDgwRZfdFrqs5deOgBJknD+/HmF90RQXzZu3AhJknDx4kXeUhTFERrc5OfnY+zYsZAkCa1atcLhw4ct+j1LfXbs2L+dusGNK5ObmwsPDw/Mnz+ftxRFSUpKgiRJXBvcVF9PHx8fb3EjGku91qXLTKdtcFMdLsUUABw8eBCMMRw5coSXBLswfPhw+Pj4oKioyK7jyrKMFStWVBRRr732WoNPgPfuAYmJxnTqmBjjz8TEnzuvFBUVwcfHB8OHD7fhHghsQVFREby9vTFq1CjeUhQlLS0NjLEnpvjYg+PHj6Ndu3ZgjGHIkCEN/ua+Lp8BwLhx4+Dl5eU0DW7cidGjR8Pb2xuFhYW8pShGcXExfH19MXjwYLuPfffuXYwZMwaMMbRu3RppaWkNeh9LfPb+++87XYMbd+Ho0aNgjOHAgQO8pSiKXq+HTqeze4ObmtbTN4S6vFZaWoqAgACnanBTE9yKKQB48cUXHWragBIYDAa0aNECXbt2tdt4r732GnQ6HXQ6HZYvX26XbxEvX74MSZKwZs0axccS1A/TtIENGzbwlqIoixcvtmuDm4yMDISEhIAxhmHDhiE3N1fxMWVZRps2bdChQweXvtvojMiyjFatWqFTp04u/dnk5ORAkiQkJCTYZby8vDyMGjUKjDG0adPGbl/Avvbaa1CpVLh69apdxhNYzrJly6BSqXDt2jXeUhRDlmWEhITYrcGNaT29qYgyt55eCUwNbpYtW6b4WErCtZgCgPDwcO7TBpTGlIsze/ZsxcYwGAx49dVXodVqodPpsGLFCruf0Hfu3AnGGE6fPm3XcQV1s23bNrfIxYmIiECjRo0UnTaQkZGBtm3bgjGG4cOH486dO4qNZQ5Tg5upU6fadVxB3dy9e9etcnGUbHCTl5eHkSNHgjGG4OBgLs0voqKiHLbBjbvTt29fBAYGunQzM3s0uJFlGQkJCRVNyepaT68E+/fvB2MM6enpdh3XlnAvptwlF+f48eNgjGHv3r0Vz+XnA5s2ATNmGG+Bzphh/P/6BJcZDAYsW7asoohauXIl129Fp0yZAq1Wi/z8fG4aBOaJjY11m1ycPn36VHneFl57//33K4qoESNG2L2Iqoypwc3OnTu5aRCY5+TJk26Vi1O5wY0tfHbnzh2MGDECjDG0bduW6wVWWVkZmjRpgp49e3LTIDBPWVkZGjdujIiICN5SFMXU4CYpKaniOVv4zLSe3tSUrD7r6ZVg3rx58PDwsMsMDyXgXkwBP+fivP7667ylKMqKFSugUqnw//7fTej1xkV4Wq35xXl6PZCVVfN7GQwGvPzyy/D09ISXlxcSEhIcYmqJLMsIDQ1FmzZtHEKP4GdMuTht27Z16c/G1OBm6dKlyMqC1V47evQo2rRpA8YYRo0a5TDdEdesWQNJknD58mXeUgTVSEhIgCRJ+Pzzz3lLUYzKuTgXL8pW++zOnTsYNmwYGGMICQlBRkaG/XamFkwNbuLi4nhLEVTjxo0bUKvViI+P5y1FUUwNblJS/m61z2y5nt7W9OjRA82aNXMYPfXBIYop4OdcnOPHj/OWoiihoW+BqKTOpGjGzPffNxgMWLJkCTw9PeHt7Y3Vq1c73IXx/fv3odPpEBMTw1uKoBqmaQMTJ07kLUVRDhw4AKIF8PQ0WNAO2bzXjhw5UlFEjR492iGT6YcNGwYfHx+Xzl5xVgYPHgxfX1+X/mwKCwuh0SyGJJU22Ge5ubkYOnQoGGNo166dQ14DHD58mFuDG0HtpKamgjFmcVdHZ6Vbt21WXTvKsozly5fbfT19feDZ4MZaHKaYAlw/F8fYf78mI5wEEYEoxGygWVlZGRYvXlxRRK1Zs8bhiqjKZGZmQpIkbN68mbcUQTXcIRcnORlQqR5Y7LPKXktLS0Pr1q3BGMOYMWMcsogyYTAY0Lx5c3Tr1o23FEE13CEXJzkZ0GofNeicdvv2bQwZMgSMMbRv397hA8bj4+Pt2uBGYDlxcXFQq9W1Zvo5M9ZcOzrCevr68Pnnn9u1wY2tcKhiCgC6dOnikrk4WVl1JUM3ApGf2Ys8tfoB1Oq+8PHxwbp165zm32bz5s0iF8dBceVcnNq9VrPPjN/qlYCx3hg3bpzTrPszNbiZO3cubymCarhyLo415zRJKgVRBEJDQ3HypPMEUffq1ctlcnFcjZ49e6Jp06Yu99lYd+1YBo2mv0Osp68Pu3btAmPMqY4NDldMuWoujl5fWyJ0PIhag2hQDRd5j9C9+z+cxgiViYmJEbk4DsqYMWNcMhenZq/V5TOASMa4cc7XHSojI0Pk4jgo6enpLpmLY+05LTraOb6sqIypwY0r5OK4GiUlJfD390d0dDRvKTbFWp916XLVKa8dp02bBk9PT4eeGVIZhyumgJ9zcdavX89bik3Iz39yseDPjzsg8gDRxVov8rTa+nVqcRRkWUZwcDBCQ0Od0tCujCzLaN26NTp27Ogyn03NXrPMZ87steXLl4tcHAfF1XJx3Pmc5iq5OK7I1atXoVKpsHz5ct5SbII7+0yWZXTs2NFpZqpJ5ID07t2bkpKSKCEhgT7++GPecqwmJaW2V2OIaBgR9an1PRir630cE0mSKDs7m/Ly8ujZZ5/lLUdQCdNn880339DMmTN5y7EJNXvEMp8ROa/XNm7cSH379qWoqCj66aefeMsRVOKtt96i3r17U1RUFD148IC3HKtx53Na165dKSUlhbZs2ULp6em85Qgq0b17d9qzZw9t2rSJjh8/zluO1bizzyRJoqysLPrhhx8oJiaGt5w6cchiiogoPj6e9Ho9jRkzhv7zn//wlmMVX35JZP78+S4RXSeiw3W+R2kp0ZUrNhZmJ4KCgigjI4PS0tIoOTmZtxxBJZo3b07vv/8+paam0jvvvMNbjtWY95rlPiNybq999NFH5OHhQdHR0bylCKpx9uxZkiTJJT4bdz+nzZw5k371q1/RlClTKDc3l7ccQSWee+45mj17Nun1evr22295y7EKd/dZQEAAnT59mj788EP67W9/y1tOrThsMUVEdPjwYWrZsiVFRERQeXk5bzkNpqiopldSiaiMiPyJSEVEZ4noNhF5md26sND22uzF0KFDafXq1RQfH09/+9vfeMsRVGLEiBH0xhtvUFxcHH3xxRe85ViFea/Vz2dEzus1jUZDWVlZdOXKFfr1r3/NW46gEhqNhi5dukQ5OTm0ePFi3nKsQpzTiPbu3UtdunShyMhIevToEW85gkqkpKRQhw4dnP6zET4j6tOnD23ZsoX+53/+hz755BPecmrEoYsp0zSke/fu0aRJk3jLaTD+SJCvOQAAIABJREFU/jW9soeIcojo88ePXkTUiogum906MFABcXbkjTfeoCFDhtCgQYPoxx9/5C1HUInVq1fToEGDaODAgU792Zj3Wv18RuTcXmvXrh2lpqbS22+/Te+++y5vOYJKhIaG0p/+9CfasWMHpaWl8ZbTYMQ5zcj58+eptLSURowYwVuKoBqXLl2i4uJiGjNmDG8pDUb4zMiSJUto/PjxNHr0aIedqebQxRQRUaNGjejkyZP0f//3f5SUlMRbToMIDyfSamHmlSZEFF7p4UNEHkTU9YktdTqisDAlVdqHEydOkK+vL/XpU/faFYF9OXnyJHl7e1P//v15S2kw4eFEHh7Vv4m03GdEruG1SZMm0aJFi2jmzJn0r3/9i7ccQSV++ctfUlxcHE2bNo2++uor3nIaRHg4kaenudki7nVO8/HxoczMTPrkk0/ojTfe4C1HUAkfHx86e/YsffTRR7R27VrechqEuHb8mT//+c8UFBREkZGRjjlTjXcHDEvZtGmTU+bilJSU4Je/XASin2rsHmbJw1k7spjDlIszZ84c3lIE1TDl4sybN4+3lHqzd+9eNG7cVXitEiIXx3Hp2bMnmjRp4nSfzdWrV/H00yOFzyqxe/dup8vFcRd27twJxhhOnTrFW0q9KC0txcyZS4XPKlFQUACtVovY2FjeUp7AaYopwJiL4+Xl5RS5OMXFxZg9ezbUajUaNWqEp576qpasgNofjAEO+LdjFSIXx3Ex5eLs37+ftxSL2L17Nxo3bgyVSoVp06YhJuah8NpjRC6O42LKxYmKiuItxSKuXr2KiIgIMMbQo0cPDBp0X/isEqZcnLy8PN5SBNWYMmUKtFqtUwSxl5aWYv78+fDw8IC/vz969LgBxsqFzx5z5swZMMaQlJTEW0oVnKqYcoZcnOLiYsyYMQMqlQqNGjVCcnIyAEtSrGt+eHkB2dmcd0wBRC6O4/Lqq686fC7Orl27Koqo6dOno6ioCIDwWnVELo7j4gy5ODk5OejVqxcYYwgLC0NWVhYA4bPqyLKMTp06OU0ujjshyzI6dOiANm3aOOxnU1painnz5kGtViMgIABbt24FIHxmjg0bNkCSpIpjkSPgVMUUANy9exeenp6YNm0abylVKC4uxvTp06FSqdC4cWPs2rXriW2Sk+tvCk9PAx7XYy5JVFQU/P39UVJSwluKoBr9+vVDYGAgSktLeUupQnJyMho1agSVSoVZs2ahuLjYzDb195qXF1zWawcOHABjDOnp6bylCKqRkpICxhgyMjJ4S6lCTk4OnnnmGTDGEB4ejmwzV2UN8ZlOV+6yPissLIS3tzdGjx7NW4qgGoWFhfDy8kJMTAxvKVUoKSnB3LlzK4ooc3dcGuIzjca1rx1HjRoFb2/vii9ReeN0xRQAnDp1CowxswWLvSkqKsK0adOgUqnQpEkT7N69u9btTaaoa3oEY4BK9QAq1a9detpAWVkZmjRpgp49e/KWIqhGWVkZGjdujF69evGWAsA49z0wMBBqtRqzZ882W0RVxnKvlYPoR0ye7Fxz6uuLaepIbm4ubymCasydOxcajQZ37tzhLQWff/45evbsCcYYnnrqKVy+fLnW7evnsxK0bLnWTnvCh4sXL0KSJGzcuJG3FEE1zp8/D0mSsHnzZt5SUFJSgjlz5kCtViMwMBDbtm2rdfv6XDuq1Q/A2ELcunXLTntjf2RZRqtWrdC5c2feUgA4aTEFAKtWrYIkSfj888+5jF9UVISpU6dCpVKhadOm2Lt3r8W/m51tnMeq1QI6XfVv7YzPx8Yat+vcubPLTxu4desW1Go1FixYwFuKoBo3b96EWq3GokWLuGnYtm1bRRE1Z86cet3FtNRrv/71H5yywU19CQsLQ1BQEAwGA28pgmp069YNzZs35/bZXL58GU899RQYY+jZs2e9zq2W+uz99+/Cw8MD8+fPV3BP+JOUlARJknD27FneUgTV2Lx5MyRJQmZmJpfxq6+n37lzp8W/W59rx549e6Jp06ZO1+CmPuTl5cHT0xMzZszgLcV5iykAGDJkCHx9fev8htqWFBYWYsqUKRVFlDUNFO7dAxITgVmzgJgY48/ExKqdV4qKiuDt7Y1Ro0bZQL3jkpaWBsYYUlNTeUsRVOPdd98FYwyHDx+267hJSUkICAiAWq3Gc889Z9VUUEu8Nnr0aHh7eztFg5uGUlxcDF9fXwwePJi3FEE1iouL4ePjg2HDhtl13OzsbISHh4MxhmeeeQY5OTkNfi9LfHb06FEwxnDgwAEbqHdcJk6cCJ1Oh4KCAt5SBNWIiYmBTqfD/fv37TZmTevpG4IlPjM1uImOjraBesflxIkTYIxhz549XHU4dTFlMBjQvHlz9OjRQ/GxCgsLMXnyZEiShKCgIKSkpCg+pomsrCxIkoQNGzbYbUwexMfHQ61W48aNG7ylCKqxcOFCqNVq3Lx5U/Gxtm7dCn9/f6jVasybN89u6+lM0wY6derk0neCc3JyIEkSEhISeEsRVOPy5cuQJAlr1qxRfKzs7GyEhYWBMYZevXpZVUTVl6VLlzp8gxtrkWUZISEhCAkJcenjiTMiyzKCg4MRGhqq+GdjyXp6pXCGBje2YOXKlVCpVHY9hlXHqYspwJiLo9FonsjFyc8HNm0CZswwVu4zZhj/v7799u/fv4/Y2FhIkoRmzZpxaxe9bds2MMZw5swZLuPbC5GL47g888wzT+Ti2Mpnsixjy5Yt8Pf3r5gGxKPxhanBzfTp0+0+tj0RuTiOS3JystlcHFt57eLFi+jRowcYY4iIiMCVK1dsqN5y+vbt65ANbmxJQUEBdDod9Ho9bymCauTn50Or1WLKlCnVnreNz+q7nl4p9u3b55ANbmzNoEGD4OfnZ9eZapVx+mIKAN5///2KXJysLECvN84d1WrNzynV643tJmvj/v370Ov1kCQJzZs3d4gpCbGxsdBqtS49bUDk4jgupaWlCAgIQFRUlM18JssyEhMT4efnBw8PD7z44ovcL65OnjwJxhi3k5+9mD59usjFcVCmTp1akYtjK69dvHgR3bt3B2MMvXv35h5J4WgNbpTi7NmzDpmLIwBOnz4Nxhh27txpM59Zs55eKRypwY1SGAwGBAUFISwsjMv4LlFMAcZcHMbioNXKFnU7qakNckFBASZMmABJktCiRQscOnTI/jtTA7Iso127dmjbtq1LTxsw5eIsXbqUtxRBNa5evQrG4qBWl1nlM1mWsWnTJvj6+sLDwwMLFizgXkRVJiEhgWuDG3sgcnEcF1MuTkDACnh5lVvltfPnz6Nbt25gjCEyMtKhptbduHEDarUa8fHxvKUoiiPm4giMrFu3DkQLoNU+sspntlxPrwTdunVDixYtXLr50O3bt+Hh4YHnn3/e7mO7TDGVnAxI0k8NzpUpKCjA+PHjK4ooR22EYJo2MHHiRN5SFMWUi3P06FHeUgSVSE425lc01GeyLGPjxo3w9fWFRqNBXFycw07pHDx4sN0b3NgbkYvjuGzZUgKiHxvstczMTHTp0gWMMfTt2xfXr1/nu0M1kJqayqXBjb1xtFwcgRHjtWNpg33Gcz19fTA1uBk+fDhvKYpianBz8OBBu47rEsVUzQnRISCSQEQg8gDR7Ce20enKER29BJIkoWXLlnj33Xd5706dnDt3DpIkVSRkuyqmXJzbt2/zliKAdT7z8ipHXNzv4ePjA41Gg0WLFjlsEWXCYDCgWbNm3KYN2AuRi+N4WOM1T89HaNt2Ehhj6Nevn8MWUZWJi4uzW4MbXjhaLo7A+mvHIUNe5b6evj7Ys8ENT0wNbux57HOJYkqvrynI7CiIih7/d8Zjcxyots0jaLUZTvet2MaNG0UujsCuWOszxo4gPj7e4YuoyuTm5opcHIHdsdZrjRqdcbquqD179nyiwY2r4Ui5OALrfebpecwh1tPXh507d5ptcONq9OnTx64Nbpy+mMrPf3KxoPnH8ceGWPLEa1pt/Tu1OAJjxowRuTgCu2Abn5U7pc/S09PdIhdHr9eLXBwHwF3PaaZcnKioKN5SFMWUi+PqDW4cHXf1GQBMmTKlosGNq1JWVoZGjRohIiLCLuNJ5OSkpNS1RQ8iYkQ0log8ieg3T2zBmCXv43gcO3aMAgMDKTIyksrLy3nLUQQfHx/KzMykTz75hN544w3ectwW2/iMOaXPxo8fT6+88grNnTuX/vGPf/CWoxhpaWnUrFkz6t27t8seT5wBdz2neXl50aeffkoXL16kFStW8JajGKNGjaKVK1dSXFwcffnll7zluC3u6jMiotTUVGrdujVFRES47LFeo9HQxYsX6YsvvqCXXnpJ8fGcvpj68kuiBw9q2+IqEZUR0U4i6k9Efk9sUVpKdOWKIvIURZIkys7Opm+++YZmzpzJW45ihIeH09tvv03r16+nDz/8kLcct8SdfUZE9NZbb1Hv3r0pKiqKHtT+D+G0mI4n+fn5NHnyZN5y3BZ39lr37t1pz549tGnTJjp+/DhvOYqxdu1aGjBgAA0YMIB+/PFH3nLcEnf2melYf//+fZowYQJvOYrRsWNH+uMf/0jbt2+nP//5z4qO5fTFVFGRJVtpiGghEX1HRLPMblFYaDtN9qR58+b0/vvvU2pqKr3zzju85SjGCy+8QNOmTaMJEybQd999x1uO2+HuPiMiOnv2LEmSRNHR0bylKEaTJk3oxIkTlJ6eTr/73e94y3FL3N1rzz33HM2ePZv0ej19++23vOUoxqlTp0ir1VL//v15S3FL3N1nAQEBdOrUKTp+/Dht2bKFtxzFmDZtGr344ov07LPP0tdff63YOE5fTPn712drmYi+MvtKYKAt1PBhxIgRFdMGvvjiC95yFOPAgQPUtm1bl57W6KgInxmnDVy4cIFycnJo8eLFvOUoxsCBA+nNN9+kpUuXUnZ2Nm85bofwGlFKSgqFhoZSZGQkPXr0iLccRVCr1ZSdnU3Xr1+nF154gbcct0P4jKhfv36UmJhIr732Gn366ae85SjG22+/TT169KA+ffoodjxx+mIqPJxIqzX3yt+JaDER/ZuIHhLRm0R0g4zzX6ui0xGFhSko0g6sXbuWBg0aRAMHDnTZaQOSJNGlS5fohx9+oHHjxlV57d49osREopkziX7xC+PPxESiggJOYl0M4TMjHTt2pIMHD9KOHTsoLS2NtxzFWL58OY0cOZKGDBlC//3vfyueFz5THuE1IxcvXqTi4mIaPXo0bymKERwcTIcPH6a9e/fSwYMHq7wmvKYswmdGXnnlFRo7diyNGDGCvv/+e95yFOPTTz+lhw8f0tChQ6s8bzOf2aXNhYLU3JHlGoj8QUSPH54gmmW2W4uzdmSpjsFgQPPmzd0mF2fDhg3IyjK2N9Vqn/w70OmMz+n1xjwJQcMRPqvKwoUL3SYXp1OnTrh4URY+sxPCaz/jbrk4165dE+c0OyF89jOyLCM4OBihoaGQZZm3HMW4cuUKVCoVli9fbnOfOX0xBdSWFVD3gzEgNpb3HtgOUy7OvHnzeEtRlKSkJBAtgKfnozo/e8aqJpYLGobwWVXcJRdHpfo1VKoHwmd2RHjtZ9wpF0enexleXuXCa3ZC+Oxn8vPzodVqMWXKFN5SFGXfvn2Prx0NNvWZSxRTNadY1/3w8gKys3nvgW0x5eI4QyJ3Q0lOBlSq0np/1uLk03CEz6riDrk4ycmAp+cj4TM7I7xWFXfIxdm+3QCiEuE1OyJ8VpXTp0+DMYadO3fylqIYxmvHBzb3mUsUU4BxR+trClc+EC1btqxi2oCrUfMB8ByImoKIwRiyFwai0ic+c1c7ANqThvms3GV9dvXq1YppA66G8BlfxDntZ2RZRmhoKNq0aeOS05CE1/ghfFaVdevWQZIkZLvgH5WSPnOZYgr42RR137qTQfQjXnrpH7wlK0q/fv0QGBiI0tJS3lJsSs235puCKBREhSDKgXGuc2y1z961bs3zwHKflYPoR/TsuZu3ZEVJSUkBYwwZGRm8pdgU4TP+WOo1okcgKkFS0gPekhXj/v378PLywrhx43hLsTnCa3yp77Xjiy9+wVuyogwfPhw+Pj4oKiriLcWmKOkzlyqmAGPlGBtrXDym01X9BzMtKouNBUaOfB2enp64e/cub8mKUVZWhsaNG6NXr168pdiMmheNAkQaEK2u9P8RIOryxHZaLe+9cH4s9dmuXZ+BMYbt27fzlqwoc+fOhUajwZ07d3hLsQm28pkrLM7mjSVemzhRRkDAcDz99NO85SrK+fPnIUkSEhMTeUuxGeKc5hhYek6bOHE9PDw8cPv2bd6SFcNgMKBFixbo2rUrbyk2Q2mfMQCwvumg41FQQJSSYkynLiw0ZgGEhRHNnUvUtClReXk5denShR48eEC3b98mSXL6LvFm+eqrr6hLly4UFxdH27Zt4y3HahITiVatqim5fDoRXXr8uE1E0UT0EhFtqrKVTkf000/K6nQX6vIZEdH69etp1apVdOnSJYqIiOApV1G6d+9OhYWF9M0335BareYtxyps5bM1a4hefVVZre5CXV77+uuvqVOnTjR//nx6++23ectVjC1bttBrr71Gn3zyCUVFRfGWYzXinOZYWHJOCw8Pp/z8fMrLy3P6Y31NfPvttxQaGkrTpk2jlJQU3nKsRnGf2asqdEQKCwvh5eWFsWPH8paiKO+++y4YYzh8+DBvKVYzY0Ztt+HfB5EORKaWpqEgks1uK7AvI0aMgLe3t8tNG6hMcXExfHx8MGzYMN5SrMZWPps1i/eeuBdHjhwBYwyHDh3iLUVRYmJioNPpcP/+fd5SrEac05yPkpIS+Pn5YdCgQbylKEpGRgYYY9i3bx9vKVajtM9c83aMhQQEBNCpU6foxIkTlJiYyFuOYvzyl7+kuLg4mjZtGn31lfkUb2ehqKimVx4R0UQiiiKiIjKG7JUQUV/7CBPUyokTJ8jPz4/69OnDW4pi+Pj40NmzZ+nMmTO0du1a3nKswlY+KyxUQJygRvR6Pb300ks0e/Zs+te//sVbjmKkp6dT06ZNKTIyksrLy3nLsQpxTnM+vLy86Ny5c5SZmUkJCQm85SjG2LFj6Te/+Q09//zz9Pe//523HKtQ2mduXUwREfXr14/eeustWrFiBWVmZvKWoxg7d+6ksLAw6tu3Lz18+JC3nAbj71/TK18RkUxEvyciPyLqSERziehLu+gS1I4kSfTZZ5/RrVu3aM6cObzlKMYzzzxD27dvp9WrV9Pp06d5y2kwtvJZYKDttQlqZ+vWrdSzZ0+nP9bXhiRJlJ2dTXl5efTss8/ylmMV4pzmnISHh9OuXbvozTffpA8//JC3HMXYuHEj9evXj6KiougnJ55LqrTP3L6YIiJaunQp/eIXv6CRI0fS999/z1uOYmRmZtKjR49o6NChvKU0mPBwIq3W3CudiUhNRC8Q0QMiyiWi/UTU/IktdToFBQpqpGXLlnT06FE6cOAA/f73v+ctRzEWLlxIv/zlLykmJobu3bvHW06DsJXPwsIUFCmokU8++YQA0MCBA3lLUYygoCDKyMigtLQ02rFjB285DUac05yX+fPn0/Tp02nChAn03Xff8ZajGKdPnyaNRuPUaxQV95ndJiw6OLIso23btmjfvr1L5liYcPZcnM8++waM1RS4lgoifxizAhiIWoHoquh85GC8/vrrUKlUuHLlCm8piiHLMjp06OC0uTh5eQaoVA+t9pno5seP69evQ6VSYenSpbylKIqz5+IcOPAhqmfaiHOac9G5c2e0atXKKY/1lnLr1i2o1WosWLCAt5QGkZNzF5JUppjPRDFViYKCAmi1WkyePJm3FEVxxlyc27dvY9CgQWCMwdv7w8d5D7UtKDT/EJkcjsGAAQPg7++PkpIS3lIUw9TgJiYmhrcUizEYDFi6dCm0Wi0k6WiNi3CFz5yDgwcPgjGGo0eP8paiKM6Yi5Oeno7g4GAwxhAUlPk4l094zRkpKiqCt7c3Ro8ezVuKoqSlpYExhtTUVN5SLObOnTsYPnw4GGPQ6T5Q7NpRFFPVOHPmjMjFcSBu3bqFAQMGgDGGjh074vTp07WkWNf9EGnxjoHBYEDTpk3x1FNP8ZaiKKZcnM2bN/OWUisGgwEvv/wyPD094e3tjVWrVuHiRVn4zAV4/vnnRS6OA3H06FG0adMGjDGMHj0aeXl54pzmAmRlZUGSJGzYsIG3FEWJj4+HWq3GjRs3eEupldzcXAwbNgyMMYSEhCAjI0NRn4liygzOPm3AUrp164bmzZvDYDDwlvIEN2/eRHR0dJUiqjKmxPL6miE5mdMOCZ7A2acNWMrmzZshSRIyMzN5S3kCg8GAJUuWVBRRq1evrjJVRfjMNQgLC0NQUJBDHuttxZ07d6DRaDB79mzeUsxy5MgRtG7dGowxjBkzBnfv3q3yuvCa87Nt2zYwxnD27FneUhQlIiICjRo1QllZGW8pT5Cbm4uhQ4eCMYb27dvj+PHjVV5XymeimKqBkSNHukUujq+vr0Pl4ty4cQNRUVFgjKFz5861HpRMpmCs7tuz4qTjmLhLLs748eMdKhenrKwM8fHxFUXUunXrapzvL3zm/LhbLs7evXt5S6kgLS0NrVq1giRJGDt27BNFVGWE15yf2NhYaLVaFBQU8JaiGKWlpQgMDESfPn14S6ng9u3bGDx4cEURdeLEiRq3VcJnopiqAVmW0bJlS3Tp0oW3FEW5fPkyJEnCmjVruOq4fv06+vXrB8YYunTpgnPnzln0e9nZxnmsWi2g01U1gk5nfD42VkyDcGSWLFniFNMGrEGWZQQHByM0NJTrIuWysjIsWrQIGo0GPj4+WL9+vUV6hM+cn5ycHKhUKqxcuZK3FEVZsWKFQzS4OXz4cEURFRMTg/z8fIt+T3jNuZFlGe3atUNISIhLN6RwlAY3ldfTh4aG4uTJkxb9nq19JoqpWsjLy4NGo8GsWbN4S1GU5ORkMMZw6tQpu499/fp19O3bt95FVHXu3QMSE4FZs4CYGOPPxETRTcxZ6N27Nxo1aoTS0lLeUhQjPz8fWq0WU6ZMsfvYZWVlWLhwITQaDXx9fbFhw4YGneiFz5ybPXv2gDFW67e2rkB0dDS3Bjepqalo0aIFJEnC+PHjG3yHQnjNeSkoKIBOp4Ner+ctRVEOHDjArcHNrVu3MHDgwIqlIA29frWVz0QxVQfHjx93uGkDSjB16lRotVqLvz2zlmvXrqFPnz5gjKFbt24OuZ5EYD9M0wYiIyN5S1GU06dPgzGGnTt32mW80tJSLFiwoKKI2rRpk0t/WyqomxkzZkCj0SAvL4+3FMUoKytD06ZN8fTTT9ttzEOHDlUUURMmTHDpaV6Cujl37hwkScLWrVt5S1GU+fPn27XBTV3r6XkhiikLELk4tuPq1avo3bs3GGPo3r07zp8/r9hYAufCNG3g5Zdf5i1FUezR4Ka0tBQvvvgiPDw84Ofnh8TERFFECSoQuTi24+DBg2jevDkkSYJer3eYdZEC/mzatAmSJOHixYu8pSiKPRrcVF5P36lTJ5w5c0axsRqCKKYsZMCAAfDz83OLXJxx48ZVeT4/H9i0CZgxw3gbdMYM4//X5zbo1atXERERAcYYevTo4fIHF0HDMOXiHDlyhLcURTGXi2MLn5WWlla0wvb398fmzZtd+oJZ0DBMuTijRo3iLUVRTLk4lRvc2MJngHGKU7NmzSBJEmJjY0URJTDLmDFj4O3tjcLCQt5SFKO4uBh+fn4YPHhwledt4bUbN26gf//+FUtBHLVToiimLMTdcnESExORlQXo9caFeFqt+QV6ej2QlVXz+125cgW9evUCYwxhYWHIqm1jgQCouKPiLrk4tvBZaWkp5s2bV1FEufrUEoH1uEsuzuLFi6FWq5GWlmu1zwBg//79CAoKgiRJmDRpkktfJAusR5ZltG7dGp06dXLpL7YqN7ixxTnNVuvp7YUopurB7du34eHh4fK5OFu2bAHRAnh6Pmpw68icnBw888wzYIwhPDzc5TO7BLYlPDzcLXJxVKpfQ6V60GCflZSU4LnnnoNarUZAQIAoogT1wl1ycYKDN4CoBIyVN7gVckpKSkURNWXKFFFECSzm7t278PT0xPTp03lLUZTdu3dbfe1YeT19165dnWY9vSim6ok75OIkJwMqVWmlP3wy8wgzG2r2+eefo2fPnmCM4amnnsLly5d5747ACXGHXJzkZMDT81GDfFZSUoK5c+dCrVYjMDAQ27Zt4707AifF1XNxjJky5Q3yGQDs3bsXTZs2hUqlwtSpU0URJWgQJ0+eBGMMu3fv5i1FMYzXjg8a5LVr164hMjLSadfTi2KqAZhyca5fv85bis3JyqorHTr/sSG2mfmm4ScQReDpp58WRZTAakzTBl5//XXeUmyONT5TqR5ApeqDwMBAbN++nfeuCJwcUy5O27ZtXW4akjU+02gewt9/GFQqFZ599tkq6xsFgoawatUqSJKEzz//nLcUm2ON1yTJeO3ozOvpGQCQoN5ERkbSV199RXl5eaTVannLsRmxsURHjz7+TsEszxNRChGVEZFU7TWZhg4tptOnAxRUKHAn9u7dSy+88AJlZGTQmDFjeMuxGdb67KmnbtMXX4QqqFDgTvznP/+h4OBgGjVqFP3lL3/hLcdmWOuz1q0v09//3oX8/PwUVClwJ4YOHUqfffYZfffdd+Tj48Nbjs2w1muDBxfRmTONFFSoLKKYaiAPHjygli1bUseOHenSpUu85diEe/eI2rYlevCgtq0CiegpIvrY7KtaLdE33xA1bWp7fQL3ZObMmXT48GH6+uuvqWXLlrzlWI3wmcARyczMpEGDBtGWLVtoyZIlvOVYjfCZwBF59OgRtWnThpo2bUpffvklbzk2QXjtyfJQYCFarZYuXLhAf/vb32jp0qW85diElJS6trhARD8Q0doat2Asw4A9AAAM3UlEQVTMkvcRCCzn4MGD1L59e4qMjKTy8nLecqxG+EzgiERHR9Obb75Jr7zyikt8QSh8JnBE1Go1Xbp0ia5fv07PP/88bzk2QXhNFFNW0blzZ/rjH/9ISUlJLjE14ssv6/pmIYGI/IloYI1blJYSXbliY2ECt+fSpUv0ww8/0NixY3lLsRrhM4Gjsnz5cho1ahQNGzaMfvjhB95yrEL4TOCoBAcHU1paGv3+97+ngwcP8pZjNcJropiymmnTptELL7xAU6dOpdzcXN5yrKKoqK4tPiEifZ3vU1hoCzUCwc/4+fnRmTNn6K9//Su9+eabvOVYhfCZwJE5duwYBQYGOv2dYOEzgSMzfvx4WrZsGc2dO5f+8Y9/8JZjFcJropiyCbt27aKuXbtS79696dGjR7zlNBh//9pe3U1EBiKq+0I2MNBGggSCSvTu3ZuSkpIoISGBPv74Y95yGozwmcCRkSSJsrOz6ZtvvqGZM2fyltNghM8Ejk5iYiJFRkZSVFQUPaj91o5DI7wmiimbceHCBSorK6Nhw4bxltJgwsOJNBq5hleTiKgdEdXeAECnIwoLs7UygcBIfHw86fV6GjNmDP3nP//hLadBhIcTabU19f0RPhPwp3nz5vT+++9TamoqvfPOO7zlNAjhM4Ez8PHHH5MkSRQdHc1bSoMxXjvWdBfbPbwmiikb4eXlRefOnaNPP/2U/ud//oe3nHrzySef0Dvv9KeHDx/WsMU/iOhWne8DEM2da0tlAkFVDh8+TC1atKCIiAinm4b0/fff09mzz9XyLaTwmcAxGDFiBK1cuZLi4uLoiy++4C2nXpSXl9NPPyXTgwdlNWwhfCZwDDQaDV26dIlycnJo8eLFvOXUm0uXLtEf/jCIHj50b6+JYsqGhIeH065du2jjxo30wQcf8JZjER9//DF17tyZBg8eTM2bSzRihEyMNey9GCMaO9Z5W1sKnANJkigrK4vu3btHkyZN4i3HIr7//nvS6/XUtGlT+tvfTlCvXgXCZwKHZ+3atTRo0CAaOHAg/fjjj7zl1El5eTm99dZbFBgYSBs2LKGQkH8QYw1LfxE+E9iL0NBQ+tOf/kQ7duygtLQ03nIs4sKFC9SjRw/q168f+fk9oGHDDO59TuObGeyazJo1CxqNBnl5ebyl1MiZM2fQsWNHMMYwYMAA3Lx5E4AlKdY1P7y8gOxszjsmcBvOnTsHSZKwdetW3lJqpKCgABMmTIAkSWjRogUOHToEQPhM4DwYDAY0b94cYWFhvKXUiCzL2LhxI3x9feHh4YG4uDiUlZUJnwmcioULF0KtVldcjzkimZmZ6Nq1Kxhj6NOnD65duwZAnNNEMaUQXbp0QcuWLSHLMm8pVTh16hQ6dOgAxhgGDhyIW7duPbFNcnL9TeHlZfw9gcCebNq0CZIk4eLFi7ylVKGgoADjx4+HJElo2bIlUlNTn9hG+EzgLOTm5sLDwwPz5s3jLaUKsixjw4YN8PX1hUajwcKFC1FWVlZlG+EzgTPxzDPPoEmTJk/8HfMmMzMTXbp0AWMMffv2xfXr15/Yxp29JoophSgqKoK3tzdGjhzJWwoAYxEVGhoKxhgGDRpktoiqjMkUjNVuBMZcxwwC52TMmDHw8vJCYWEhbynIz8/HuHHjIEkSWrVqhcOHD9e6vfCZwFlIT08HYwz79+/nLQWyLGP9+vXw8fGBRqPBokWLar34FD4TOAulpaUICAhAVFQUbykAjDNATEVU//79cePGjVq3d1eviWJKQbKysiBJEtavX89Nw8mTJ9G+fXswxjB48GDcvn3b4t/NzgZiYwGtFtDpqhpBpzM+Hxvr/LdnBc6NLMto3bo1OnbsyO1OcH5+PsaOHWtxEVUZ4TOBs7Bs2TKoVKqKqT32RpZlrFu3Dt7e3vD09ER8fLzF3+ALnwmchatXr0KlUmH58uXcNJw9exadOnUCYwxRUVF1FlGVcUevMQDgvW7Lldm+fTu99NJL9NFHH9HgwYPtNu6HH35ICxcupK+//pqGDBlCf/jDHyg4OLhB71VQQJSSYkynLiw0ZgGEhRk7rzj1gkGBy/Dvf/+bQkJCKDY2lg4dOmTXcX/1q1/Rhx9+SK1ataLf/e53pNfXHU5oDuEzgTPQr18/+uc//0nfffcdabVau4xZXl5O69ato7feeosePXpEcXFx9NZbb5Fara73ewmfCZyB/fv303PPPUfHjh2jsWPH2m3cjz/+mF544QW6efMmRUVFUUpKCoWGhjbovdzKa7yrOXdg0qRJ0Gq1yM/PV3ysjIwMhISEgDGGYcOGITc3V/ExBQJH4OTJk2CMYdeuXYqPlZeXh1GjRoExhjZt2uDo0aOKjykQOAJlZWVo3LgxevXqpfhYsixj1apVFXeiXn75ZRgMBsXHFQgcgblz50Kj0eDOnTuKj3X69OmKpmQ1racX1IwopuyALMto37492rZtq9g0pIyMDLRt2xaMMQwfPtwu5hMIHI1Vq1ZBkiR8/vnnirx/Xl4eRo4cCcYYgoODkZ6ersg4AoEjc/PmTajVaixatEiR95dlGQkJCfDy8oJWq8XSpUtFESVwS7p3747mzZsr9vdf3/X0AvOIaX524vvvv6fWrVvTyJEj6ejRo0REdO+e8Rbol18SFRUR+fsbk6Sfe87yW6DHjh2jRYsW0TfffEMjRoygP/zhD9SyZe1J0wKBKzN06FD67LPP6LvvviMfHx8ist5r3377Lf3qV7+iU6dOUXBwMO3YsYNiYmKU3RGBwIF577336Nlnn6X33nuPJk+eTETW+6y8vJwSEhIoKSmJysvLKT4+njZs2NCg6XwCgSvw448/UsuWLSkyMpJOnTpFRLa5dvzrX/9KCxYsoK+//poGDRpEKSkp1LZtW+V2xNXhXc25E6ZcnJdeOgi93rgIT6s1vzhPrzf27a+Jo0ePok2bNmCMYdSoUQ6daSUQ2BNTLk6PHj2QlQWrvHbnzh0MGzYMjDGEhIQgIyPDvjsjEDgwplycI0fuWOUzWZaxYsUK6HQ6aLVavPbaa+JOlEDwmMuXL0OSJLzwwh6rrx1PnDhR0ZRs6NChYimIjRDFlJ2ZOPEEiH4EY+UNaht55MiRiiJq9OjRuHv3Lp8dEQgcmNzcXEjSQqhUDxrUojU3NxdDhw4FYwzt2rXD8ePH+e2MQODAtG79JohKGnROk2UZy5cvh06ng06nw/Lly0URJRCY4dlnP3587Sg36Nrx+PHjYj29gohiyo7UHGgWDyINiAhEahBtfyLQLC0tDa1btwZjDGPHjhVFlEBQC8nJgKenod5eW7/+PxgyZAgYY2jfvj1OnDjBe1cEAoclORnQ6cwVUbX7bMeOR3j11Veh1Wqh0+nw+uuvO1zAvUDgKFhz7SjW09sHsWbKTmRnEw0eTPTTT9Vf+S0RrSSiXUQ0l4i+ePx8RMUWjP1ERINp7Ngg2rdvHwUFBSmuVyBwVqzxGlEJtWo1g/7wh1/TiBEjlJYqEDgt1vrM03MUvfrqEFqzZg1JkqS0XIHAKbH22hEYRCNGBNK+ffuodevWygt2U0QxZSdiY4mOHjV+Z1AVXyKaREQptfx2OY0b95COHbNPpodA4MxY4zXGykmvl+jPf1ZOn0DgClh7TtPrGR05wpSSJxC4BP+/vftJbSIMAzD+TIuQZFNSaLdVENzYINhsvEEKXsALSG+RA/QEXXXjCYTETQnFpQuhVbdKV5KCpSC0lUK7SEAwf53m/XSmz28TyHwJ3yIP4c0Mk7t21mpd0elUo7anIX8OSqDfh253XAy/gJ/Ad+ABsAw0gB9/rFvi4KDC6Wn4VqVCu2trNzdLdDrYmjTFIr7Tut3MzqQpFtFZr1e1swQcphLY35905NPw8T3wAfgCfANejqzMsmnvIwlsTUrBzqR4dlYcDlMJHB3B5eW4I/Xh4yvgGfAEeA18HFl5cQHHx1E7lMrB1qR4dibFs7PicJhK4Px80pFHDE7Pznfd+NnZgjYklZStSfHsTIpnZ8XhMJXAysq0oy+AN8Bn4CuwBzwfu7JeH/u0pCFbk+LZmRTPzorDYSqBRgMqE2/E947BrwxPgcfAQ+DtyKpqFTY3gzYolYStSfHsTIpnZ8XhrdET6PdhY2PSta/zqVTg5ATW1ha3L6lsbE2KZ2dSPDsrDs9MJbC+Dq3W4K4qeWQZbG8bgzSLrUnx7EyKZ2fF4ZmpRCb/i/VstRocHsLW1uy10n1na1I8O5Pi2VkxeGYqkWYTdncHH+6/UasNXmcM0nxsTYpnZ1I8OyuG5Xa73f7Xm7gvmk1YXYVeD66vp6/Nst8x7Oyk2Z9UFrYmxbMzKZ6d/f+8zE+SJEmScvAyP0mSJEnKwWFKkiRJknJwmJIkSZKkHBymJEmSJCkHhylJkiRJyuEWY7Qq+s6/qx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the infection spread\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pos = nx.spectral_layout(G)\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "for t in range(0,n_steps):\n",
    "    plt.subplot(3,4,t+1)\n",
    "    x_id = states[t]\n",
    "    x = np.array(np.unravel_index(x_id, shape = n_states))\n",
    "    nx.draw(G,\n",
    "        pos = pos,\n",
    "        with_labels=True,\n",
    "        nodelist=np.argwhere(x==0).T[0].tolist(),\n",
    "        node_color = 'r')\n",
    "    nx.draw(G,\n",
    "        pos = pos,\n",
    "        with_labels=True,\n",
    "        nodelist=np.argwhere(x==1).T[0].tolist(),\n",
    "        node_color = 'b')\n",
    "    plt.title('jump step = {0}'.format(t+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remark**: we simulated the evolution of the epidemics by exploiting the general simulation method for continuous time Markov chains viewd in Lab04. This is totally correct, and it is the easiest way to understand and learn the process, but it requires **precomputing the transition matrix** Lambda, which is then used to perform the simulation. Computing the whole Lambda has a large computational cost, and it becomes practically unfeasable when the number of agents grows. To have a better idea, try to repeat the previous simulation on a $4 \\times 4$ or $5 \\times 5$ grid, and see how the computational time blows up.\n",
    "\n",
    "To overcome this issue, a more efficient simulation method is proposed below, which may turn usefull if you need to deal with larger models. Instead of precomputing the transition matrix Lambda, it computes its rows along the way when needed. In particular, when the process is at some configuration $x$, row $x$ of $\\Lambda$ is computed, used to compute the probabilities of transitions from $x$ and to pick the next visited configuration.\n",
    "\n",
    "Try again to simulate the SI model on a $4 \\times 4$ grid with this method to appreciate the computational saving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAI+CAYAAABZpVCsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxM9/4/8NfnzCSZyZ4QW2SxB01QS+xL7OQiSpWgclUtpcXFF62i1JJbpVxRt74tpUotja9Kcy2XkFqSqmv9uaoqXDRyNU0jIuLk/ftjTJrEJJkkc+Yzc+bzfDzmUZ2ZzHkd8ZqZz8w5nw8jIoIgCIIgCIIgCIJQIRLvAIIgCIIgCIIgCPZIDKYEQRAEQRAEQRAqQQymBEEQBEEQBEEQKkEMpgRBEARBEARBECpBDKYEQRAEQRAEQRAqQQymBEEQBEEQBEEQKsEhBlPNmzfHsWPHeMcQBFUTPRME6xBdEwTliZ4J5nKIwdTly5fRvXt33jFMOnbsGOrWrcs7xnO++uordOzYEa6urjb7dyfYFtGzips1axYaNWoEDw8PhISE4PPPP+cdSbADomsVN2fOHAQEBMDT0xNBQUF4//33eUcSbJzoWeX9+uuv8PPzQ+fOnXlHsQqHGEwJFefr64vp06dj7ty5vKMIgmq5ublh//79yMrKwpYtW/DWW2/h5MmTvGMJguqMHz8eV69exe+//46TJ09i+/bt2Lt3L+9YgqBK//M//4OmTZvyjmE1DjGYCg4OxuHDhzFu3Di88847hdeXHNkHBwfjr3/9K8LCwuDm5obx48cjPT0d/fv3h4eHB3r16oXMzEwAwM2bN8EYw9///nfUqVMHtWvXxqpVq0rNkJCQgGbNmsHDwwP+/v744IMPkJOTg/79++Pu3btwd3eHu7s77t69i4KCAqxYsQINGjRAtWrV8PLLL+PXX3+t1HYrq1evXnj55ZdRp04diz+2oE6iZxW3ePFihISEQJIkhIeHo0uXLjh16pTFtyOoi+haxTVp0gRubm6F/y9JEq5fv27x7QjqIXpWOadOncKlS5cQExOjyOPbIocYTFXEnj17cOjQIVy7dg379+9H//79sWzZMvz3v/9FQUEB1q5dW+z+R48exY8//oiDBw9ixYoVOHz4sMnHHT9+PDZu3Ijs7GxcunQJERERcHNzw7fffos6derg4cOHePjwIerUqYO1a9ciPj4eSUlJuHv3Lnx8fPDGG29UarsrVqyAt7d3qRdB4EH07Hm5ublITU1F8+bNzbq/IJhDdK34z7q7u6Nu3brIycnBqFGjKvA3KQilEz0zkGUZb7zxBv72t7+BMVbBv0X7JQZTJUybNg01a9aEv78/unTpgvDwcLRq1QouLi6IiorCuXPnit1/4cKFcHNzQ2hoKGJiYvDll1+afFwnJydcuXIFv//+O3x8fPDiiy+WmmHjxo14//33UbduXbi4uGDRokXYvXs3nj59WuHtzp07F7/99lupF0HgQfTseZMmTUKLFi3Qt29fs+4vCOYQXSv+s9nZ2fjhhx8wZswYeHl5lXl/QTCX6JnB2rVrER4ejtatW5f116U6YjBVQs2aNQv/rNfrn/v/hw8fFrt/QEBA4Z+DgoJw9+5dk4+7Z88eJCQkICgoCN26dSvzUJ60tDRERUUVfgLQtGlTaDQapKenV3i7gmCLRM+Kmz17Ni5duoSvvvrKoT7NE5QnulYcYwytWrWCXq/HwoULFdmG4HhEz4C7d+9i7dq1Djm5i0MNptzc3PDo0aPC///ll1+q/Ji3b98u/POtW7dKPceobdu22LdvH+7fv48hQ4bg5ZdfBgCTb5wCAgLw7bffFvsU4PHjx/D396/wdpctW1Z4TK2piyBYmuhZxXq2cOFCfPvttzh48CA8PT3LvK8gFCW6VvnXtKdPn+Knn34y+/6C4xI9M69nKSkpuHfvHpo1a4ZatWrhrbfeQkpKCmrVqgVZlsv/S7FjDjWYatmyJRISEvDrr7/il19+wZo1a6r8mEuWLMGjR49w+fJlfPbZZxgxYsRz93ny5Am++OILZGVlwcnJCZ6entBoNAAMn2Y8ePAAWVlZhfefNGkS3n77baSlpQEAMjIysG/fvgpvFwDmz59feEytqUtpZFnG48eP8fTpUxQUFODx48fIz8+v8N+P4HhEz8zv2fLly7F9+3YcOnQI1apVq/Dfi+DYRNfM61pBQQE2btyIzMxMEBFSUlKwfv169OzZs1J/R4JjET0zr2f9+/fHzZs38a9//Qv/+te/8N5776FVq1b417/+VZhbrRxqMDVmzBi0aNECwcHB6NOnT6n/iCqiW7duaNiwIXr27IlZs2ahT58+Ju+3detWBAcHw9PTEx9//DG2bdsGAAgJCcHIkSNRv359eHt74+7du3jrrbcwaNAg9OnTBx4eHmjfvj3OnDlTqe1W1tatW6HX6zF58mScOHECer0eEyZMsOg2BHUSPTPf/PnzcevWLTRq1KjwE79ly5ZZdBuCeomume/rr79GgwYN4OHhgdGjR2PatGmYNm2aRbchqJPomXlcXFxQq1atwouXlxecnJxQq1Yti23DVjEiIt4hlBYYGIht27aha9euFnvMmzdvol69esjPz4dWq7XY49rqdgWhPKJngmAdomuCoDzRM8Fcqv9mKiMjAxkZGQgODuYdRRBUS/RMEKxDdE0QlCd6JlSEqgdTqampaNSoEaZNm4bAwEDecQRBlUTPBME6RNcEQXmiZ0JFOcRhfoIgCIIgCIIgCJam6m+mBEEQBEEQBEEQlCIGU4IgCIIgCIIgCJUgBlPWtmED4OYGSBLAWOkXSTLcb8MG3okFwT6JrgmC8kTPBEFxoma2TbNo0aJFvEM4jA0bgFmzgCIraZcpPx84ehTw9QXatlU2myCoieiaIChP9EwQFCdqZvvEBBTWkpoKdO9erA2/A2gD4AYAGYAewFwA75b8WVdXICkJaNPGOlkFwZ6JrgmC8kTPBEFxJmoGc5smamY94jA/a1m+HMjNLXbVYwB1ABwDkA9gFoCFAJJL/mxuruHnBUEon+iaIChP9EwQFGeiZjC3aaJm1iO+mbKG+/eBoCDg8eNy76oHMBXAX0veoNMBt24Bfn4KBBQElRBdEwTliZ4JguIqUDOU1jRRM+sQ30xZw+bNZt3tEgyfN/QwdSNjZj+OIDgs0TVBUJ7omSAozvx6lN40UTPrEIMpa7hwodyPFh4B6AogBMAAU3fIzQUuXrR8NkFQE9E1QVCe6JkgKM6MmqG8pomaWYcYTFlDVlaZNz+FoQZaAGfLumNmpuUyCYIaia4JgvJEzwRBceXUDOY2TdRMeVreARyCl1epNxUAaAogG8BPAFzLehwfH4vGEgTVEV0TBOWJngmC4sqoGSrSNFEz5YlvpqwhLMxwFqAJLwC4B+D/AfAt6zH0eiA01PLZBEFNRNcEQXmiZ4KguDJqBnObJmpmHWIwZQUFY8fi6dOnz13/HQw1yAFQGwB7dpli6kGIgHHjlAspCCpwpmlT5OXlPXe96JogWE5WVBSePHny3PWiZ4JgOWPHFph871iRpomaWYcYTCmooKAAS5cuhVejRvhGllFQ4vZOAMjEJa7kAzEGDBgg5rYUhFIcP34cISEh6DB4ME77+IAYK3a76JogVN2vv/6KoUOHwjckBIednFAgeiYIFldQUIBVq1ahSRMfyPI3QCXfPYqaWY8YTCmgoKAA7733Hjw9PbF06VL8+c9/xsDkZEiuZR49Xjq9Hpg3z7IhBUEFjh07hiZNmqB79+6oXr06/v3vf6NbYiKYXl+5BxRdE4Tn/Prrr4iKioKfnx9OnTqFzz//HANOnIAkeiYIFlNQUIC//vWv8PHxwbx58zBy5EgcP94frq6Ve6suamZFJFiMLMu0aNEicnNzIxcXF5o+fTrl5+f/cYe4OCJXVyLDN6/mXVxdDT8nCEKho0ePUqNGjYgxRl26dKHr168Xv4PomiBUWUZGBg0ePJgkSaLatWvTjh07it9B9EwQqkyWZVq+fDl5eHiQs7MzTZ48mfLy8gpvr1zNCkTNrEgMpixAlmVasGABubq6kouLC82cObP4IKooYysYK7MJBYzRQ4D+t21b6+6MINiww4cPU8OGDYkxRl27dqUbN26Ufmczu/YUoByAnnz0kfV2RBBsWHp6OkVGRpIkSVSnTh3auXNn6Xc2s2fys9e0i2+8Yb0dEQQbJssyLVu2jNzd3cnZ2ZmmTJlSbBBVlJk1I8YKCHhIYWFiJGVNYjBVBbIs0zvvvEOurq6k0+lo1qxZpQ+iikpNJRo6lEinI9LrizdBrzdcP3QonVy7lhhj9Mknnyi/M4Jgww4ePEgNGjQgxhh169at7EFUUWZ07engwRTh6UltxQcXgoNLT0+ngQMHkiRJ5O/vT7t27TLvB818TXt3wABydnamO3fuKLsjgmDDZFmmJUuWFA6ipk6dWuogqigza0abNv2LJEmiVatWWWFvBCIiRkTE+1BDe1NQUIAFCxZg9erVICJMmzYNy5Ytg1ZbwWW7MjKAzZsNy1NnZhoWAwgNNUy98uyMwbfffhsrV67EDz/8gLCwMIvviyDYsn/84x+YMmUKfv75Z3Tv3h2fffYZgoKCKv5A5XTtxx9/RLNmzTB16lSsXr3a0rshCDbt/v37iImJQWJiIurUqYM1a9bgpZdeqvgDmfGa1rRpU2RlZeHWrVsVf80UBDtmnJQsNjYWT58+xcSJE/HXv/4Vzs7OFXocM2qG2NhYzJs3D8nJyejQoYPF90UogfNgzq7Iskxz584lvV5Per2e5s6da943UVXUrVs38vT0pOzsbMW3JQi2ICEhgerVq0eMMYqIiKC0tDTFt7l9+3ZijNHu3bsV35Yg2IJ79+5Rv379iDFGAQEBtHfvXsW3mZWVRW5ubtS7d2/FtyUItqDc8+kVMmDAAHJ1daXMzEzFt+XoxGDKDPn5+TRnzhzS6XSk1+tp3rx5JMuyVbdfo0YNCg0Ntdo2BYGHAwcOUHBwMDHGqGfPnlYZRBU1adIk0mq15h9GKAh26M6dO9S3b9/CQVR8fLxVt5+amkqSJNGSJUusul1BsKaS59PPmDHDKoOootsPCAigRo0aWfU9qyNS72F+9+8bvge9cAHIygK8vAzLScfEmD3p/tOnTzF//nysW7cOjDHMmDEDS5YsgSRZf0b5tLQ0NGrUCOPGjcPf//53q29fEEyxQM0AAN988w2mTp2KW7duoVevXvj0009Rt25dxXKXpWXLlrh79y7u3r0rDkMSbIYlunb37l3ExMTg0KFDCAwMxNq1azFo0CBFc5fmb3/7G958803885//RPfu3blkEISSLNGzgoICLFy4EB9++CEKCgowdepULF++nMvryf379xEYGIioqCh8+eWXVt++w+A9mrO4lBSiqCjDmXg6nekz9KKiDPcrRX5+Ps2cOZN0Oh25urrSggULbGJUHx8fT4wx2rZtG+8ogoOzQM2IiGjfvn0UGBhIjDHq06ePTZyYnpOTQ15eXtSlSxfeUQTBIl27ffs29erVixhjFBQURPv377feDpThpZdeIp1OR+np6byjCA7OEj2TZZnmz59Per2edDodzZ4926rfRJXm8OHDxBijjz/+mHcU1VLXYMr8uSNNrnWRn59PM2bMIBcXF3J1daWFCxfaxCCqqBkzZpBGo6GrV6/yjiI4qCrWjIgMHwwEBAQQY4z69u1rE4Oooi5evEgajYbmzZvHO4rgwKratbS0NOrZsycxxig4OJgOHDjAZ0dKIcsy1a9fn4KCgmzutVZwHFXtWcnz6efMmWMTg6iiFi1aRJIk0dmzZ3lHUSX1DKaqsHhgfn4+TZ8+nVxcXMjNzY0WLVpk00/s4eHh5OPjQ7m5ubyjCA6mqmt07t27l+rWrUuMMerfvz/du3eP7w6VYdOmTcQYo4SEBN5RBAdUla6lpaVRREQEMcaofv36Nv1v+MGDB6TX62nQoEG8owgOqCo9M55PbxxEWft8+oqKiIggDw8PMZmZAtQxmEpJea4NwwByBQgANSijFY+1Wurg5ERubm60ZMkSmy6CUV5eHvn6+op1cQSrMlGzZ5dhBLgSAAIamKyai0s++fn1J0mSaMCAATY9iCpq7Nix5OzsTLdv3+YdRXAgprtWfs8AIknKJaAN1a9fnxITE3nvilmSk5PFujiC1VWlZ1ptHjk7dyS9Xk/z58+3i/eO+fn5VKtWLWrevDnvKKqjjgkohg4F4uMN/8afmQNAA+D/AOQBuF7Kj8oAfmzWDI0vXuQysURlGdfFmTJlCj766CPecQQHYKJmz5jTNhm1ap3B+fMNUaNGDYWTWpZYF0ewNtNdM/9VrVOn/yI5uabCKS1LrIsjWFtVexYSchWXLze1q/eOt27dQqNGjRAdHY1PP/2UdxzVsP/B1P37QFAQ8PixyZs7A/gFpdcBAKDTAbduVWz6MRvw5ZdfIjo6Grt27arcAouCYKZyavZM2W2z05rh999/h7+/Pzp06ICDBw/yjiOoXPldK/9VzV67NnDgQBw7dgx37tyBt7c37ziCijlyz7755hsMGjQImzdvxtixY3nHUQX7GU6XZvPmqj8GY5Z5HCsbOXIkJk6ciFdeeQU///wz7ziCijlwzeDp6YmjR4/iyJEjWLp0Ke84gso5ctf279+PatWqoV27digoKOAdR1AxR+5ZZGQkZs+ejT//+c+4fPky7ziqYP+DqQsXyvu4vHy5ucDFi5bJY2UbNmxA8+bNER4ejqdPn/KOI6iUg9cMbdq0wUcffYR3330Xx44d4x1HUDFH7pokSUhJScGtW7cQHR3NO46gYo7cMwBYuXIlwsPD0blzZzyu6l+EoILBVFaWZR4nM9Myj8PByZMn8eTJE0RERPCOIqiUqBkwdepUvPTSS+jfvz/u37/PO46gUo7etVq1auHAgQPYuXMnNmzYwDuOoFKO3jMAOHr0KDQaDTp16sQ7it2z/8GUl5dlHsfHxzKPw4GrqyuSk5Nx8uRJzJ8/n3ccQYVEzQx27tyJOnXqiMOQBMWIrgE9e/bEwoULMXXqVPzwww+84wgqJHoGODs748yZM7hw4QLeeOMN3nHsmv0PpsLCQDrdc1c/BvAbDLP1FTz7c6lfZOr1QGioUgmt4oUXXsAnn3yCFStWICEhgXccQWXCwgwn25pmXttUUDNIkoTU1FTcv38fUVFRvOMIKhQWBri4mBqom/+qpoauLVy4EN27d0f37t3x8OFD3nEElTG8ppmaf82xetagQQN8+eWX2LBhA7766iveceyWXQ+mHj16hDd/+MHk8Z79APgAOA3g52d/7lfaAxEB48YplNJ6YmJiMGbMGERFReE///kP7ziCiri4fFnGcdXmtU0lNYOvry8OHTqEb775Bh9++CHvOIKK/L//9/+wY0c/5OXlmbjV/Fc1tXTtH//4B9zc3NC+fXveUQQVefz4MS5fnl3Ka5rj9WzYsGGYOnUqoqOj8dNPP/GOY5fscjD16NEjvPrqq/Dy8sK2gwdxNyzMMK1KEcfwbMXeIpdjph6MMWDAAPub27IUW7ZsQf369dGuXTsxIYVQZZs2bYKfnx/+8pcx8Pe/AMZMfZJ3DOW1TWU1Q6dOnbB8+XLMnj0bp06d4h1HsHOXL19G27Zt0bx5c+Tn30G3brklX9Jg7quamrqm1Wpx5swZXLt2DTExMbzjCHbu8ePHeP311+Hp6Yn4+L/jhRf+Y+I17RgcrWcAsHbtWrRo0QLh4eF48uQJ7zj2h++awRWTnZ1NY8aMIa1WS76+vrR+/XrDDaaXsTbv4upKlJrKd8csLCsri9zd3al37968owh2auPGjVStWjXSaDQ0cuRIysrKEjUzYeDAgeTq6kqZmZm8owh26OLFi9S6dWtijFFoaCilpKQQkXhJK2n//v3EGKPNmzfzjiLYodzcXHrttdfIycmJvLy8aNWqVSTLsuhZCbm5ueTt7U0dOnTgHcXu2MVgKjs7m6Kjo0mj0VC1atXo448/fv5OcXEVb4Wrq+HnVOjs2bMkSRItWbKEdxTBjnz88ceFg6jo6GjKzs4udntlaqbTyWqtGcmyTAEBAdSoUSOSZZl3HMFOnD9/3uQgqijxklbcnDlzSKPR0KVLl3hHEexETk4OxcTEkFarJW9vb1q9evVz96lczwpU27MrV66QRqOh2bNn845iV2x6MJWVlUUjR44sHERt3Lix7B8wtoKxMptQwBg9BGhzeLh1doST9evXE2OMjhw5wjuKYOPWr19Pvr6+pNVqacyYMc8Noooys2bEGJEkPSJX179QXl6eFffGutLT08nFxYVeeeUV3lEEG3fu3Dlq1aoVMcaoRYsWdPbs2TLvb37XZAIe0rRpV6y0J3x07NiRvL29KTc3l3cUwYbl5OTQuHHjCgdRa9asKfP+5vesgICH1LKliQ/0VWTLli3EGKN9+/bxjmI3bHIwlZWVRa+88gppNBqqXr06ffLJJ+b/cGoq0dChRDodkV5fvAl6veH6oUMpec0aYozRp59+qtyO2IBhw4aRTqej9PR03lEEG7Ru3Try8fEhrVZLY8eOLXMQVZSZNaPk5Mfk4+ND7du3V3hP+Dp8+DAxxihOrR9XClVy7tw5atmyJTHGqGXLluUOoooyt2t9+swnFxcXunfvnoJ7wldeXh5Vq1aNXnzxRd5RBBuUk5NDr776Kmm1WvLx8aF169aZ/bPm9mzjRsNRP2vXrlVwT/gbP348OTk5UVpaGu8odsGmBlNZWVk0YsQI0mg05OfnR5s2bar8g92/TxQbSzRmDFFkpOG/sbGG65+ZO3eu6g8bkGWZ6tevT4GBgeIwJKHQ2rVrCwdR48aNo5ycnEo9jhk1KzxsYNasWRZKb5sWLVpEkiRV6I2yoG5nz56lFi1aEGOMWrVqRefOnav0Y5XXNVmWqXHjxuTv76/q5/rr16+TVqulKVOm8I4i2IhSz6evBHNe05YtW0aSJJk8PFdNXnjhBapVqxbl5+fzjmLzbGIwlZmZScOHDyeNRkM1atSw6rdFnTt3Ji8vr0q/mbQHDx48IL1eT4MGDeIdReBszZo15O3tTVqtlmJiYqz2737r1q0OcdhAREQEeXh4mP0Nn6BOqampFBYWRowxevHFF+n8+fNW2W5mZia5ublR//79rbI9Xnbu3EmMMdq5cyfvKAJHRc+n9/X1teqRAX379iU3NzfKysqy2jatLTs7mzw8PCgiIoJ3FJvHdTCVmZlJL730EkmSRDVq1OAyU09eXh75+flRq1atrL5ta0pOTiZJkmjVqlW8owgcrF69mry8vEir1dL48eO5fHhgnE1JzYcN5OfnU61atah58+a8owgcpKSkUGhoKDHGqHXr1nTx4kWrZzh9+jRJkkTLly+3+rataerUqaTVaun69eu8owhWlp2dTaNGjTL/fHoFyLJM/v7+FBISYvVtW9O5c+dIkiRauHAh7yg2rcKDqfR0opUriaKjDV+BRkcb/r/oV6DlefDgAQ0dOpQkSaKaNWvS1q1bKxrDom7cuOEQhw188MEHJEkSnTx5kncUwQxV7Zosy/TBBx+Ql5cXOTk50YQJE7ifuB0aGko1a9ZU9WEDt2/fJmdnZ4qJieEdRTCDJV7TTp8+TS+88AIxxqhNmzZcBlFFrVmzhiRJohMnTnDNobTWrVtTtWrVVD3BjZpUtWtVOp9eAXfu3CEXFxcaM2YM1xxK+/jjj4kxRgcPHuQdxWaZPZhKSSGKijKchKfTmT45LyrKcL/SPHjwgKKiokiSJKpVqxZt27bNEvtgEbt37ybGGO3YsYN3FEUZ18V58OAB7yhCKaraNVmWKTY2ljw9PcnJyYkmTpzIfRBlZDxsoEePHryjKEqsi2P7LPGadvr0aWrevDkxxqht27Y2df7tkCFDSK/XU0ZGBu8oihHr4tiHqnbNoufTW1hiYiIxxrgP7JT2yiuvqH6Cm6owazBVkamQTa1zkZGRQYMHDyZJkqh27dq0fft2JfalyqZNm0ZarZauXbvGO4pijOviNGzYUNUnKdurqnRNlmVauXIleXh4kJOTE02aNMlmBlFFnT9/niRJogULFvCOoiixLo7tqupr2smTJ6lZs2bEGKPw8HC6csX2piSXZZmCgoKoXr16qn6uF+vi2LaqdI3n+fQV8c4775BGo7HauZE8yLJMjRo1ooCAAFU/n1RWuYMp0wuaXSegNgEgQEPAFJMLB2ZkZNCgQYMKB1H28K1PmzZtVH/YgHFdnBEjRvCOIhRR2a6tXy/TsmXLyMPDg5ydnWnKlCk2/+9348aNDnHYgFgXx/ZU5TUtOTmZQkJCiDFG7du3p6tXr/LenTJlZGSQTqejoUOH8o6iKLEujm16vmtl98zYtVWrcrifT19R3bp1I09PT1VPZmac4GbAgAG8o9icMgdTKSmlrQwdSEAAAfcIWP+sGPHF7qPR5BJjbcnf39+uZtzJzc0V6+IIVleVrgEPSattT1OnTrX5QVRRo0aNUv1hA3l5eVS9enWxLo6NqErPGHtEQBvq0KGDzQ+iikpKShLr4ghWZ7pr5ryeGV7TfH370JYtW3jvhtny8/OpRo0aFBYWxjuKohxlgpuKKnMwFRVl6qvZ9GcFSCxyXT0Cwkvc7ymFh9+21n5Y1NWrVx1iXZzFixeLdXFsRNW6JtOQIU9570KFOcq6OI4ywY09qOprWp8+9jnlvSOti6P2CW7sxfNdM7dnRIzJZI9fpt68ebPwPGU1W716tUNMcFMRZQ6mSp4oaLhsf1aIotcNJKDGc/fV6So2I5ItMa6LEx8fzzuKonr27CnWxbEBjto1R1kXZ9euXWJdHBvgqD0jcqx1cdQ+wY2tS0831TXze2bPXYuPjyfGmE1NsKaEwYMHk16vF5OZPSOhwh4AKPljvgDynrsnY8DmzRXfgi0YPXo0xo8fj+HDhyMtLY13HMUkJibCzc0N7du35x1FeI76u+bt7Y0jR47gH//4B1asWME7jmKGDRuGqVOnIjo6Gj/99BPvOEIx6u8ZACQkJMDb2xvt2rXjHUUx7u7uOH78OJKSkrBo0SLecRyW6Y6Y3zPAfrs2ePBgzJgxAyJ+rIYAACAASURBVK+++ir+/e9/846jmL1796JGjRpo27YtCgoKeMfhrszB1OPHpq6tBqDkX1wmAJfn7pmbC1y8WNlo/H3yyScICQlBu3bt8PTpU95xFKHVapGamooff/wRMTExvOM4LEfuWnh4OD788EO8/fbbOH78OO84ilm7di1atGiB8PBwPHnyhHcch+TIPZMkCSkpKbh58yZGjx7NO45iWrZsibi4OLz33ns4dOgQ7zgO6cIFU10zv2eAfXdt1apVaNOmDTp06IDHpp907J4kSfj+++9x9+5dvPzyy7zjcFeJb6Z6Pvtv0SepKwDqmbx3ZmbFt2BLTp48idzcXPTu3Zt3FMXUrVsXe/bswZYtW7BlyxbecYRCjtO1t956C4MGDUK/fv3w3//+l3ccxSQnJ0OWZXTv3p13FKGQ4/SsTp062LdvH7Zv345NmzbxjqOYiRMnYsSIEfjTn/6EX375hXcch5OVZeraivUMsO+uHT9+HIwxdO3alXcUxVSvXh3ffvst9u7di3Xr1vGOw1UlBlM1AAQAeA3AfQAbANwAMM/kvX18Kp3NJri7uyM5ORnHjx/Hu+++yzuOYiIjIzF79myMHz8ely9f5h1HAOBoXduzZw9q1qyJdu3aqfawAZ1Oh5MnTyIlJQVz5szhHUcA4Gg969u3L95++21MmjQJFy5c4B1HMV988QUCAwPRpk0b1T6f2CovL1PXVqxngH13zdnZGadPn8a5c+cwY8YM3nEU0717dyxZsgTTp09Hamoq7zjcaMu6Uacr7bCIowA6A6gJQANgCoDBz91LrwdCQ6sekrewsDBs2LABkyZNQpcuXVT7LdXKlSvx3XffoXPnzrh37x50Op3hhvv3DQcvX7hg+MjJywsICwNiYgA/P66Z1UJ0zXDYQGpqKgIDAzF8+HDs2bOHdyRFNG3aFJs3b8bYsWPRuXNnDBo0yHCD6JniRM8MlixZghMnTqBLly64d+8eXF1deUeyOONhjXXr1kVkZCQSEhIMN4ieKS4sDNizx1TXzOsZoI6uNWrUCJ9//jmio6PRtWtXREVF8Y6kCOMh+j169MDdu3fh6elpuMGRulbW7BSmZz4y/2Kvs7GUxrguzp07d3hHUUyxdXFSUgzzm+p0z/9j0OsN10VFGe4nVIno2h+M6+KsWbOGdxRFGdfFufd//yd6ZiWiZ39wtHVxPpsyRfTMSkzP5ue4XZs0aRI5OTnRjRs3eEdRjCzLVKdOHQoJCXHI945lDqZMr8lh3oUxsst1AsriSOviTGGMHmu15f8DYMywMp9Y/LdKRNeKc5R1cd6rXZtyACoQPbMK0bPijOviTJgwgXcURR0eNoweip5ZlehacS1btiQ/Pz9Vr4F2584dekOjoccajcO9dyxzMFX6avHlX1xdiVJTrbUb1mNcF6dfv368oygnLo7ynZ2L/UK9AEKRi5OpX7hKSsGD6NrzVL8uTlwcFej1omdWJHr2PNWvixMX99wvXfRMeaJrxeXk5JCXlxd16dKFdxTlxMXRUxcXh+xamYMpIpPPQ2Z8qvBIDX83pTIeNrB8+XLeUSyvlGdAL4DGOuIzoBVVpms6nazarsmyTP7+/tSkSRPeUSxP9IybyvTMySlPtT0jIpoxYwZpNBq6evUq7yiWJXrGVWW65upaoNquXbx4kTQaDc2bN493FMtz8K6VO5gi+qMQ5nxrp9fLJElTaPz48Upn52rNmjUkSRIlJSXxjmJZpXw3b1Yh1PjdvJVVpGuMPSI3t79QXl4e79iKuXPnDrm4uFB0dDTvKJYlesZVRXrm7PyEgEl0+PBh3rEVFR4eTj4+PpSbm8s7iuWInnFnftcKCHhIrVr9nXdkRX366afEGKOEhATeUSzLwbtm1mCKyDBoHDrUcN5YiSNTCs8nGzrUcL99+/YRY4y2bNmiZHbuoqKiSK/XU0ZGBu8ollHGWaNeALFnFw+AVpdWCjWdNcqJuV07cSKXvLy8qFOnTrwjKyoxMZEYY7Rx40beUSxD9MwmVOQ1bfjw4aTT6Sg9PZ13bMXk5eWRr68vtW3blncUyxA9sxnmdi0uLoUYY7R+/XrekRU1duxYcnZ2ptu3b/OOYhmia+YPpozu3yeKjSUaM4YoMtLw39jY5/8OZs2aRRqNhq5cuWKprDZHlmUKDg6m4OBgdUxIsXJlqYXYBNAdgLIAeg2GY1+PmCqEXm/4ByFUmTldu3TpEmk0Gpo7dy6/oFawYMECkiSJzp8/zztK1Yme2RRzeibLMjVo0IACAgLU8VxfimvXrpFWq6U333yTd5SqEz2zOeZ0bfHixSRJEp09e5ZfUCto2rQp1a5dWx0TUoiuVXwwVRHt27dX32EDJWRkZJBer6eoqCjeUaouOrrsr2KLXKoB9FJpt48Zw3tPHIrxsIEDBw7wjqKobt26kaenJ2VnZ/OOUjWiZ3bpwYMHpNfrKTIykncURW3fvp0YY7R7927eUapG9Mxu9ezZk9zd3e3/ub4MWVlZ5O7uTr179+YdpepE10hScg2rpKQkSJKEzp07K7kZrqpXr47ExETEx8fjo48+4h2narKyzL4rg+EjBpMyMy2RRjBTTEwMxo4di6ioKPznP//hHUcxhw8fhk6nQ8eOHXlHqRrRM7vk6+uLQ4cOISEhAatWreIdRzEjR47ExIkT8corr+Dnn3/mHafyRM/sVmJiItzd3REeHs47imI8PT2RlJSEI0eOYOnSpbzjVI3oGhQdTDk7O+PMmTM4f/48pk2bpuSmuOratSvef/99zJw5E6mpqbzjVJ6Xl8mr0wC8D+A3AI9hWLP8vwBeK+1xfHwUCCeUZfPmzWjYsCHatm2Lp0+f8o6jCK1Wi5SUFFy9ehWvv/467ziVJ3pmtzp16oTY2FjMmTMH3333He84itmwYQOaN2+O8PBw+30+ET2zW1qtFqmpqbh+/TrGjRvHO45iXnzxRaxbtw7vvvsujh07xjtO5YmuKTuYAoAGDRrgiy++wPr167F7926lN8fNvHnz0KdPH/To0QO///477ziVkhkQgDzp+X8SjwAsA+ADQA/gcwDLAfQ39SB6PRAaqmBKoTRnzpzBw4cP0a9fP95RFBMUFIRdu3Zh06ZN+OKLL3jHqRT5hReQr9E8d73omX34y1/+ggEDBqB379749ddfecdRzMmTJ/HkyRNERETwjlI5YWGQnZ2fu1r0zD7UrVsXX3/9NT7//HN89tlnvOMoZsqUKXjppZfQv39/3L9/n3ecSskKDhbvHa11POGUKVNIq9XS9evXrbVJqzOui9O4cWO7Okn55s2b1L17d6oBUG5llyw3Xux8RhZ7d/bsWZIkiRYvXsw7iqJmzpxpd+vi5Ofn06xZsyjAxYVyq9Ix0TPuZFmmwMBAatCggV0911eUva6Ls3//fnqxbl16JHpm9+bOnUsajYYuXbrEO4piZFmm+vXrU1BQkF09n9y+fZt69+5teO/o4F2z2mCKiKhVq1ZUvXp1h1gXZ9SoUbyjlOvGjRvUtWtXYoxRw4YNDeuolLJWgFkXFawVoAbr168nxphYF8dG5Ofn08yZM0mn05GrqystWLCACoYMET2zc+np6aTT6Wj48OG8oyjKnia42bdvHwUGBhJjjPr27UuP+vUTPVOBTp06kZeXF+Xk5PCOohjjBDeDBg3iHaVct2/fpl69ehFjjIKDgw3PDQ7+3tGqg6mcnByxLo4NuH79OnXp0oUYY9SoUSM6cuTIHzeWsoq1WRcVrGKtFo60Lk6bNm14RzEpPz+fpk+fTi4uLuTm5kaLFi3641NH0TNVOHLkiFgXxwbs3buXAgICiDFG/fr1o3v37hluED1Thby8PKpevTq1atWKdxRFJScnkyRJtGrVKt5RTEpLS6OIiAhijFG9evWKLzzs4F2z6mCK6I91cebMmWPtTVuVLa6Lc/36dercuTMxxqhx48Z09OhR03c0LllegTIUuLoafk6wCWJdHH7y8vLozTffLBxELV682PTvoBI9e6rTiZ7ZGOO6OKl2/magPCEhITa3Ls7evXupbt26xBij/v37/zGIKqoSPXus1Yqe2ZgbN26QVqulyZMn846iqA8++IAkSaKTJ0/yjlIoLS2NevToQYwxql+/PiUmJpq+owO/d7T6YIqIaPPmzcQYo/379/PYvNXYyro4165do44dOxJjjJo0aUJJSUnl/5CxFOV8bVvAGD0EaG2zZsrviFAhDx48IFdXVxo4cCDvKIqylXVx8vLyaNq0aeTs7Exubm60ZMmS8geyZvaMGKNcSaI3nZ0pKyvLOjskmK1Xr17k7u6u6t+NLa2Ls3v3bvL39ydJkmjgwIHlfwNfgZ7lu7jQJIC2bdtmnZ0RzLZr1y5ijNGOHTt4R1HUwIEDydXVlTIzM7nmMJ5PbxxEHTx4sPwfqsB7xxyAPmjYUPkdsQIugykionHjxtn0YQOWkJ+fTzVq1KDQ0FAu27969Sp16NCBGGMUEhJCJ06cqNgDpKYajmPV6QyrUxctg15vuH7oULr42WckSRItX75cmR0RKu3kyZMkSRLF2vHK4uaYNGkSabVaunHjhtW3nZeXR1OnTiVnZ2dyd3enpUuXVuzbQDN7Jp85Q/7+/hQSEqLczgiVkp+fT7Vr16amTZvyjqKo1NRUkiSJlixZwmX7O3fupDp16pAkSRQZGVmxw5jN7BmlptKMGTPsboIbRzFt2jTSarV07do13lEUI8syBQQEUMOGDbkcWXLz5k3q1q1b8fPpK8LMrv24Y4ddTnBjCrfBFBFRs2bNqFatWjZ12IClpaWlkZOTE7322mtW2+bVq1epffv2xBijpk2bUnJyctUe8P59othYw+rUkZGG/8bGFpt5Zc2aNSRJknnfeglWZTxsoMr/Dmxcy5Ytyc/Pz2oT3OTl5dHkyZPJ2dmZPDw8aPny5VV74TOjZ8YJbqKjoy2wB4Il3b59m5ydnenVV1/lHUVRxgluip1rq7AdO3ZQ7dq1SZIkGjRoEGVkZFT+wczoGRFRu3bt7GKCG0fUunVrqlatmqonMzNOcDNixAirbfPGjRuln09fGWZ0bdOmTcQYK37+lR3iOpjKzs4md3d36tmzJ88YiouPjyfGGG3dulXR7Vy5coXatWtHjDFq1qyZ1Y+5HTJkCOn1+qq90AmKiIyMJL1eTw8ePOAdRTHGCW46d+6s6HZyc3Np0qRJ5OTkRB4eHrRy5UqrfnponODmk08+sdo2BfMcOHCAGGP06aef8o6iqGHDhlllgpvt27cXDqKGDBli1dcW4wQ3bdu2tdo2BfPk5uaSj48PtW/fnncURRknuIlT+JyioufTN2rUqPTz6RVinODmzp07Vt2uJXEdTBH9sS7OokWLeEdRlHFdnCtXrvxxZXo60cqVRNHRhlF7dLTh/ys41/6lS5eobdu2xBij5s2bcztxUZZlCgoKonr16ql60gN7ZFwXp379+qr+3RjXxZk7d27xGyzQtdzcXJo4cSI5OTmRp6cnxcbGcvu7XLBgAWk0Gpua4EYwMK6Lc/HiRd5RFGNcFycwMLB4Byz0mrZt2zaqVasWSZJEUVFR3D4EMk5wM336dC7bF0p35coV0mg0NHPmTN5RFGWc4Obs2bN/XGmhnl27do06depUsfPpFWKLE9xUBPfBFBFRXFycQ62L8/jECcOc/Dqd4WLqeNKoKMNUk2W4dOkStWnThhhj9MILL9Dp06ettCely8jIIJ1OR0PtfM0ANTIeNjBs2DDeURRVbF2clJQqdy03N5dee+01cnJyIi8vL1q1apVNDEiNE9yoee0Ve9W5c2fHWhfHAj0jItq6dSvVrFmTJEmioUOH2sQ36bYywY3wvK1btxJjjOLj43lHUVTPnj3Jw8ODco4ds0jPqnw+vQJsaYKbyrCJwRQR0YgRI8jFxcX01KYqkZeXRzNdXemRJJk1qxCVMmXk+fPnqXXr1sQYo9DQUEoppzjWlpSURJIk0dq1a3lHEUowHjawbt063lEUNXbsWHpDoyFZr69013Jzc2n8+PGk1WrJy8uLVq9ezWlvTDNOcBMWFsY7ilBCXl4e+fn5UcuWLXlHUVRycjJNAuiJk1OVXtO2bNlCNWrUIEmSaNiwYdxnMSuJ5wQ3QtmMH3TdvHmTdxTF5Ofn0xwPD3rEWJV6VvR8elsZRBXFe4KbqrCZwZQsy9SwYUN1r4sTF0dyyU8TyrsUKcX58+fpxRdfJMYYhYWF2fS6JsuWLSNJkmxuoCcQLVmyRP3r4sTFGV54KtG1nJwciomJIa1WS97e3rRmzRree1OqmzdvkpOTE02YMIF3FKEE47o4kyZN4h1FOXFxhoFUJV/TPv30U/Lz8yONRkPDhw+3uUFUUS1atCA/Pz+7PQxJzUJDQ6lGjRrq/d3ExRk+GKxkz65cuULh4eGF59Pb8mRU69atI8aY1c/bqiqbGUwREWVmZqp3XZxyVoc+CBAACjZx21OdjkY1aUKMMWrRokXxY2dtWN++fcnNzU3Va6/YK1Wvi1NG18rqGQH0WKOhcI2GfHx87OabVeMEN2JdHNuze/du9a6LU4XXtCfOztTL25s0Gg2NGDHCLp6HjBPcdOnShXcUoYTs7Gzy8PCg7t27845ieVXomazT0dhmzbifT19R1prgxpJsajBFpOJ1caKiyvx61hcgz9IGUwAd8vKym0GUkSzL5O/vT02aNOEdRShB1evilNG1snpm7NqPLVrw3oMKM05wI9bFsT2qXReniq9pZ+rWtYtBVFHGCW7UsC6O2pw/f54kSaIFCxbwjmJZVezZQU9PmzifviKME9wEBQXZzZFqNjeYIiJatWqVutbFSU9//mTBIpdpANUFqFsZb/JIp6vwTC22QKyLY7uM6+KMHTuWdxTLKaNrZvXMjrtmnOBGrItje9q0aUO+vr7qWRfHgV/Tik1wI9iUjRs3EmOMEhMTeUexDAfuWbEJbuyABBs0c+ZMREZGonfv3vj11195x6m6zZtLvek/AD4GsLu8x2CszMexVXXq1MG+ffuwfft2bNq0iXccoYi6desiPj4eW7duxf/+7//yjmMZpXTE7J4Bdtu148ePgzGGrl278o4ilHDixAkQkXp+Nw78mhYTE4MxY8YgKioK//nPf3jHEYp4/fXXMXLkSAwePBh3797lHafqHLhnvr6+OHToEL755ht8+OGHvOOUyyYHUwDw9ddfw8/PD23btkVBQQHvOFVz4QLw+LHJmyIB9AQQXt5j5OYCFy9aOJh19O3bF++88w4mTZqECxcu8I4jFNG/f3/MnTsXEydOxKVLl3jHqbpSumZ2zwC77ZqzszNOnz6Nc+fOYcaMGbzjCEXodDqcOnUK33//Pf7yl7/wjlN1Dv6atmXLFtSvXx/t2rXD06dPeccRiti6dSuCgoLQrl078d4RsOuederUCcuXL8fs2bNx6tQp3nHKZLODKUmSkJqairt372LEiBG841RNVpbJq3cCuApgl7mPk5lpoUDW995776Fz587o0qULHj58yDuOUMSyZcvQoUMHdO7cGY8ePeIdp2pMdK3CPQPstmuNGjXC559/jo8++gh79uzhHUcookmTJti8eTNWr16Nffv28Y5TNeI1DWfOnEF2djYGDBjAO4pQhCRJOHPmDH777TcMHDiQd5yqET3DnDlz0K9fP/Tq1Qu//fYb7zilstnBFADUqFEDBw4cwJ49e/C3v/2Nd5zK8/IyefUOAHkAvABoACQBuAnAtbTH8fGxfDYrOnz4MHQ6HTp27Mg7ilDCkSNH4OzsjE6dOvGOUjUmulbhngF23bWRI0di4sSJeOWVV/Dzzz/zjiMUMXr0aIwfPx7Dhw9HWloa7ziVJ17T4OnpiaNHj+LIkSNYunQp7zhCEd7e3jhy5AgOHjyIFStW8I5TeaJnAID9+/ejWrVqNv1to00PpgAgIiIC7733Ht566y18//33vONUTlgYoNM9d/UnAM4DOPfs0hqAP4Czph5DrwdCQxUMqTytVouUlBRcvXoVr7/+Ou84QhHOzs44c+YMLl26hMmTJ/OOU3lhYaASXatQzwBVdG3Dhg144YUXEB4eLg5DsjGffPIJQkJC7PsQMfGaBgBo06YN1q1bh3fffRf//Oc/eccRiggPD8eHH36It99+G8ePH+cdp3JEzwAYvm38/vvvcevWLURHR/OOYxrvGTDM1bt3b7tdF+f0//0fPTZjAVE1zshiilgXx3YZ18XZvn077ygVlpmZSTEDB9KjqvRMRV0T6+LYruzsbPL09LTLdXFkWaa4RYsot5yeOdJrmj2ui+MooqKiSK/XU0ZGBu8oFfZDYqJ471jE4cOHiTFGcc8WI7YldjOYkmWZateuTSEhIbyjmC0pKYmaPFts95ivLxWYUQqTF8aIhg7lvTsWJdbFsV3Tp0+3q3VxHjx4QFFRUSRJEtWqVYvSWrcuc10OR+qaWBfHdp0/f540Gg298847vKOYRZZl+uCDD8jT05OcnJzobFCQeE17xrguTmBgoN2si+MoZFmm4OBgCg4OtpvfzenTp6l58+bEGKN/enuLnhWxaNEikiTJ5tZdtZvBFJH9rItz9OhRaty4MTHGqFOnToY3peWsYl3mxdWVKDWV925ZnFgXx3bZw7o4Dx48oCFDhhQOogq/6RRdK0asi2O77GFdHFmWaeXKlYWDqIkTJxqes0XPirG3dXEcSUZGBun1eoqKiuIdpUwnT56kZs2aEWOM2rVrR5cuXRI9MyEiIoI8PDwoOzubd5RCdjWYIiJKSEggxhht2rSJd5TnHDlyhBo1akSMMerSpQtdv369+B3i4ipcigK93vBzKpSXl0e+vr7Upk0b3lGEEnJzc8nHx4fCw8N5R3lORkYGDRo0iCRJotq1a9OOHTuev1Mlukaurqrt2quvvkrOzs50+/Zt3lGEEkaNGkUuLi50584d3lGKkWWZli9fTh4eHuTs7EyTJ09+/sMV0bNikpOTSZIkWrVqFe8oQglJSUnEGKM1a9bwjvKc5ORkatq0KTHGKDw8nK5cuVL8DpXomazi9475+flUq1Ytat68Oe8ohexuMEVENG/ePNJoNHTx4kXeUYjIcBxnw4YNiTFGXbt2pRs3bpR+Z2MpyvnatoAxymGM5nl7281X05Vx7do10mq19Oabb/KOIpRw9epV0mg0NHPmTN5RiIgoPT2dIiMjSZIkqlOnDu3cubPsHzCzazJj9BCgm3PnWmdHOGnatCnVrl2b8vPzeUcRipBlmRo3bkz+/v428VwvyzItXbqU3N3dydnZmaZMmVL2N9QVeE17CND2rl2ttzMcfPDBByRJEp08eZJ3FKGEZcuWkSRJlJKSwjsKERGdOHGCQkJCiDFGHTp0KPu0BzN7RozRI8ZotoeHqp/rjUeqjRs3jncUIrLTwRQRUZcuXcjLy4tycnK4ZTh48CDVr1+fGGPUrVu3sgdRRaWmGo5j1emI9PriRdDrDdcPHUq/HT5Mer2eIiMjld0Rznbs2EGMMdq9ezfvKEIJ27ZtI8YYxcfHc8uQnp5OAwcOJEmSyN/fn3bt2mX+D5vZtclt25K7u7tNHTZgaVlZWeTu7k69e/fmHUUoITMzk9zc3Khfv37cMsiyTEuWLCkcRE2dOtX8w3zN7NnhFSuIMWb622QVGThwILm6utKDBw94RxFK6NevH7m5uVFmZia3DEXPp+/YsaP55yeb2bOcpCTy8PCgHj16KLsjnO3fv58YY7R582beUex3MJWfn09+fn7UokULq287MTGxcBDVo0cPunnzZuUe6P59othYojFjiCIjDf+NjS0284rxsIEPPvjAQult0+TJk0mr1Zo/IBWsZsKECeTk5FT5f+eVdO/ePerfvz9JkkR169at2mC7nK4ZDxto1qyZhdLbprNnz5IkSbRkyRLeUYQSUlJSSJIkWrZsmVW3K8syLV68mNzc3MjFxYXefPPNyp8racZr2rRp0+xqgpvKkGWZAgICqGHDhjbxbaPwB1mWyd/fnxo3bmz1343J8+krw4yenT9/niRJogULFlgovW2aM2cOaTQaw/llHNntYIqI6MaNG6TVamnSpElW2V5CQgLVq1ePGGMUERFBaWlpVtmu8bCB5ORkq2yPl5YtW5Kfn59NT3rgqEJDQ6lGjRpWOWzg3r171K9fP2KMUUBAAO3du1fxbRL9cdjAq6++apXt8bJ+/XpijNGRI0d4RxFKWLNmDTHGKCkpSfFtybJMCxcuLBxETZ8+3WqHBbVu3ZqqVaum6uf69PR0cnFxoREjRvCOIpRw584dcnFxoVGjRllle+WeT68Q4wQ3Bw8etMr2eOnYsSN5e3tznczMrgdTRER79+5VfF2cAwcOUFBQEDHGqGfPnlYbRBUVGRlJer1e1YcNGNfF6dy5M+8oQgk5OTnk6elJ3bp1U2wbd+7coT59+hQOongcWnjgwAFijNGnn35q9W1b0/Dhw8W6ODYqKiqKdDqdYuviyLJMCxYsIFdXV3JxcaEZM2ZY/dwK4wQ37du3t+p2rc2W18VxdImJicQYo40bNyq2jQqdT68Q4wQ39+7ds/q2rSUvL4+qV69OL774IrcMdj+YIiplXZz0dKKVK4miow1fg0ZHG/6/AouX7d+/v3AQ1bt3b64zYcmyTIGBgdSgQQNVHzZgXBdnrsonA7BHJtfFsUDPbt++Tb179ybGGAUFBdG+ffsUSG++uXPn2sRhA0qSZZkaNGgg1sWxQaWti1PVqsmyTO+88w65urqSTqejWbNmcT1B/cqVK6TRaGjWrFncMljD4sWLbXJdHIFowYIFJEkSnT9/vvA6C7ykVf58egXY2gQ3SjEeqTZlyhQu21fFYIroj3Vxnnz3HVFUlOFEPJ3O9Al6UVGGuftLsW/fPgoMDCTGGPXp08dmpqxNT08nnU5Hw4cP5x1FUWJdHNtlPGzg5EcfVblnt2/fpl69ehUOzsP7DQAAIABJREFUovbv32/FPSlbp06duE9wozSxLo7tKrouTkpK1aomyzLNmzeP9Ho96XQ6mj17ts3M8rV161ZijHH/AEVpPXv2VP0EN/aqW7du5OnpSceO5VT1Jc1y59NbmHGCm/79+/OOoqhdu3YRY6z8mX4VoJrBVG5uLs3Q6+mRJJk1daSptS727t1LAQEBxBijvn372swgqqgjR44QY4zWr1/PO4qixLo4tut/27alh8+mOq5Mz9LS0igiIoIYYxQcHGyTg2bjYQOtWrXiHUVRYl0c25WUlETAJHJyelKplzRZlmnu3Lmk1+tJr9fTnDlzbGYQVdT48ePJycmJy+Hz1uIoE9zYo/z8fHJ3n0WMParsW0du59NXxOnTp0mSJFq+fDnvKIoyTnBjrfPSjFQzmKK4OJJLfJwQDJAEEAByAmhsKYsH7t69m+rWrUuMMerfv7/NH1tqPGwgVYUrWxcl1sWxQSYWDzS3Zzdv3qQePXoQY4zq169PCQkJvPemTMbDBiZPnsw7iqLEuji2KS6OyMnpSYk3dMEESASAACcCxj5Xtb/97SnNnj2bdDod6fV6mjdvns0f3mPNCW54sbV1cQSDuDgivV6ucM/i4mzjfPqKWLNmDUmSZJUJbnjiMcGNOgZTKSkmV4eOByjr2Z8PPHvDt7XEfXIYo7aM0YABA2x+EFVUr169yN3dnbKysnhHUYxxXZxevXrxjiIQValnuZJEbQCqX78+JSYm8t4Ts+3evdsh1sWJjIwU6+LYkFKqRkA8AVnP/nzg2Ru+rSXu85BcXDrR/PnzbX4QZZSdnU0eHh7UvXt33lEUZZzgxhbWxRGq1jPGcghow/18+ooaMmQI6fV6xSa4sQXGCW46dOhgtW2qYzAVFVXuoX0Jz97kTS9xvQxQ7sCBvPegwvLz86l27drUtGlT3lEUZVwXZ/HixbyjCFXo2VOAfunUifceVIqjrIsTGBgo1sWxEWZUjYCEZ2/yppe4XqaoqALeu1BhjrIujiNMcGMvqtqz/v0f8d6FCjNOcFOvXj1VP9cbJ7iZPXu2VbZn/4Op9PTnzxYscmn+7PAjAKQH6J6p++l0FZuqxUaIdXEEq3HgnhE5zro4Op1OrIvDWTlVI6B5YdcAPQH3VFM1R1kXxxEmuLF1jtwz4wQ3Q4cO5R1FUdac4EaCvdu8ucybLwHIA7AeQEcAnqbuxFi5j2OL6tati6+//hqff/45PvvsM95xFDNlyhQMGzYMAwcOxP3793nHcUwO3DMASE5ORkFBAbp168Y7imJq1KiBAwcO4KuvvsKGDRt4x3FY5Vek/LbZa9Vef/11vPLKK/jTn/6Eu3fv8o6jmH/+859wcnJC586deUdxWI7cs+rVqyMxMRHx8fH46KOPeMdRzOjRo/HnP/8Zw4YNw61btxTdlv0Ppi5cAB4/LvMuzgCmALgLYIypO+TmAhcvWj6bFQwYMAD/8z//gwkTJuDy5cu84yhmx44d8Pf3R9u2bVFQUMA7juNx8J7pdDp89913SE1NxezZs3nHUUxERAQWLVqEqVOn4ocffuAdxyGZUTWU1zY7rhq2bduGoKAgtGvXTrXP9c7OzkhJScHFixfxxhtv8I7jkBy9Z127dsXSpUsxc+ZMpKam8o6jmE2bNqFJkyZo164dnj59qth27H8wlZVl9l1lAD+VdmNmpiXScLF8+XJ06NABnTp1wqNHj3jHUYQkSUhJSUFGRgYGDx7MO47jET1D06ZNsXnzZqxatQr79u3jHUcx7777LiIiItCtWzc8fPiQdxyHU4Gqoay22WvVJEnCmTNn8Ntvv2HgwIG84yimXr16+PLLL7Fhwwbs3LmTdxyH4+g9A4B58+ahd+/e6NGjB37//XfecRRz6tQpPHr0CH369FFsG/Y/mPLyMnn1ZQBvAvgFwBMA7wO4BmBAaY/j46NAOOs5cuQInJ2d0alTJ95RFOPr64tDhw4hISEBq1at4h3HsYieATAcNjB+/HgMHz4caWlpvOMo5ttvv4WHhwfCw8N5R3E4pVQNFW2bPVfN29sbR44cwcGDB7FixQrecRQzbNgwTJ06FaNHj8ZPP5X6EZSgANEzg4SEBHh7e6Ndu3a8oyjG3d0dycnJSEpKwqJFixTZhv0PpsLCAJ3uuaslAJ8DqA3ABcASGL6kXWbqMfR6IDRUwZDKc3Z2xpkzZ3Dp0iVMnjyZdxzFdOrUCbGxsZgzZw5OnTr1xw337wOxscDo0cCf/mT4b2wskJHBL6yaiJ4V+uSTTxASEqL4YQM8abVapKSk4Pr164iJifnjBtEzxZVSNVSkbWqoWnh4OD788EO8/fbbOH78OO84ilm7di1atGiB8PBwPHny5I8bRNcUJXpmYDzq5+bNmxg9ejTvOIoJCwvDhg0b8N577+HQoUN/3GCpnik+xYXSyp+SpfyLvU7JYoKjrIszcOBAcnV1pd8OHTLMb6rTPf/vQK83XBcVZVhQQqg80bNiHG1dnP3vvit6ZiWiasU53Lo4KSmia1YgelZcYmIiMcZo48aNvKMoauTIkeTi4kIZ335r0Z7Z/2CKyNzFAkxfGCNS2fSQb775pkOsizPP25seMUYF5f3uGftjyXKh8kTPinGUdXG+7tOHHgKiZ1YkqvYH47o4wcHBql8XZzJjlKfVlv/LF12zCNGz4hYsWECSJNH58+d5R1GMLMu0wM+Pciz83lEdg6nSl7Eu/+LqSpSaynsPLK5NmzbqXhcnLo4K9Ppiv8sTAPkBxGBYODYUoNySv2vx4lN5omfPUf26OHFxz/3ORc+UJ6pWnHFdnKioKN5RlBMXR/nOzqJrViR69rxu3bqRp6cnZWdn846iDIXeO6pjMEVk8kW/vEuBXq/aJyLjYQPt27fnHcXySnkG9AOoAUCZAJ0HyAWgoY7yDGgtlejZE2dn1faMiGjUqFHk4uJCd+7c4R3FskTPuKpE1VT93jopKYkYY7RmzRreUSxPdI2byvRMr5dV27P8/HyqUaMGhYaG8o5ieQr2TD2DKaI/WlHOV3cFjFEOYzTf11fVhw1cvXqVNBoNzZw5k3cUyyrlu3lngBYV+f82AIWU/P2r8bt5azOzZ8QY5Tk50SSAzp07xzu1YmRZpsaNG5O/v7+6nk9Ez7gzv2oFBDyk7t3Vfa7ssmXLSJIkSlHb+UKia1xV4CWNGHtEnp5zKD8/n3dsxdy8eZOcnJxowoQJvKNYloI9U9dgisgwchw61HDyWImv8gpPKhs6lDIPHSK9Xk9DhgzhnVhRW7duJcYYxcfH845iGWWcNToSoPoAZQCU+uzThTmmnhF1Ot57Yf/M7BmlplL37t3Jw8NDvYcNEFFmZia5ublRv379eEexDEv1TC1nZ3NkbtXef/8fxBijXbt28Y6sqL59+5KbmxtlZmbyjmIZ4jXNJpjbs6NHs8nd3Z169erFO7Ki4uPjiTFGW7du5R3FMhTumfoGU0b37xPFxhKNGUMUGWn4b2xssRf3EydOkCRJtHr1ao5BlTdhwgRycnKimzdv8o5SdStXllqI/QDpAcKzSwOAZFOF0Ot574V6mNGz/Px8qlmzpjoPGyji9OnTJEkSLV++nHeUqrNUz2Jjee+JaphRNZoyZQpptVq6fv06v6AKk2WZ/P39qXHjxur4Jli8ptkUc3p29uxZkiSJFi9ezC+oFcycOZM0Gg1duXKFd5SqU7hn6h1MmWnlypUkSRKdPn2adxRFhYaGUo0aNez/q+noaJNlyAdIA1AvgLIAugZQLYDamioEHP6fvdWlpaWRk5MTjR8/nncURa1Zs4YkSaKkpCTeUarGUj0bM4b3njicVq1aUfXq1dU7+RAR3blzh1xcXGjUqFG8o1SdeE2zS+vXryfGGB0+fJh3FEWFh4eTj48P5ebm8o5SNQr3zP4X7a2iOXPmoG/fvoiIiMBvv/3GO45iTp8+jcePH6N37968o1RNVpbJq38C8P/Zu/OwqMr+f+Dv+8wAM4CyiQsK7ruguaHhvuWWBqHmkktqLmmZqV8tLUuz3NIsUdMUH/35WGlp5pJKapKa5FMukZmhZlpISoiIgMPn98cEuQwww8yZe5bP67q4zJnDnPch3zNzM+fctwHAhwDKAqgNYDiAU/bKxYoVFhaGLVu2YO3atfjPf/4jO45qXnjhBfTt2xfdu3fHX3/9JTtO6dmqZ+npts/GipWYmIi8vDx06tRJdhTVhISEYPv27fjvf/+LDz74QHYc6/BrmlMaP348YmNj0bt3b1y7dk12HNV8/fXXEEKgbdu2sqNYR+Weuf1gCgC++OILBAYGomXLlsjPz5cdRxXe3t44fPgwDh8+jFmzZsmOU3p+fiZvrgtAC+BZAHcAXAKwHkBFuwVjJenTpw9eeuklPPPMM/jpp59kx1HNli1bUKFCBbRo0cJ5n09s1bOAABXCseJ4e3vjm2++wbFjxzBjxgzZcVTz2GOPYebMmRg3bhxOnXLiIQa/pjmtzZs3o3LlymjevLnzPteXwNPTE8eOHcMPP/yAF154QXac0lO5ZzyYAqAoCpKSkvDbb79hyJAhsuOoJiIiAnFxcXjzzTfx5Zdfyo5TOhERuOvhYfKujQCOAfAGUB3Gf9w7TW2o16uVjpVg4cKFaNGiBaKionDnzh3ZcVRR8HySmpqK2NhY2XFKJyIC+V5eJu+yqGfh4WolZMVo2LAhVq9ejfnz52PXrl2y46jmjTfeQNu2bdG2bVvcunVLdpxSyWvQALkajcn7+DXNsSmKguPHj+P69evo27ev7DiqqV27NjZu3Ij33nsPW7dulR2ndNR+72jHMxYd3t69e0kIQatWrZIdRVWDBw92ynVxVq1aRXUDAuh2UeeymvvFMx9JlZOTQ0FBQdSsWTPZUVR16NAhUhTF6dbFOXPmDHVt3Ng2PePZ/KQaNmwYeXp60uXLl2VHUY2zrouTnZ1No0aNohCt9v4FQvk1zekcOXKEFEWhRYsWyY6iqnHjxpFWq6WUlBTZUSyyYcMGahgcrOp7Rx5MPeC1114jRVFcel0cIqK6des6zbo4K1eupKCgINJoNDRo0CDK7d275AUhivriNTkcwvnz50mr1dLEiRNlR1HVW2+95TTr4pw8eZKaNWtGQggKDw+nGx06cM9cQIMGDahSpUrOP/lQMQomuBk1apTsKCXKzs6mkSNHklarJX9/f+NswkWsf8Ndcx6LFi0iRVEoMTFRdhRVNWnShIKDg51igpv169dT+fLlSVEUio2NpZxevVTrGQ+mTOjYsaPLr4uTkZHh8OvixMXFUWBgIGk0Gnr66af//f9RxCrWZn3xavEO46OPPnKLdXG6d+9OPj4+lJGRITuKSSdPnqSmTZuSEIIiIiIoqaAf3DOXkJlpXBenc+fOsqOoytHXxcnKyqLhw4cXDqLu+8Sau+YSevfuTXq9nq5fvy47imqysrLIz8+P2rRpIztKkdauXUvBwcGk0WioX79+/65Jp2LPeDBlQl5eHlWsWJEaNWokO4qqjh8/Toqi0Lx582RHuc/y5cspICCAtFotDR061PSgtmDJckvLEBdn/wNiReJ1ceT5/vvv6ZFHHiEhBDVu3JhOnDjx8EbcM5fA6+LIk5WVRcOGDSOtVksBAQG0bNky0xty15yewWCgsLAwqlmzpkM919va6dOnSaPR0PTp02VHuc+aNWsKB1EDBgww/QtMlXrGg6kiuNO6OEIIh1gXZ9myZYWDqGHDhlFWVlbx31BQipI+thWCX3QcGK+LY18nTpygxo0bkxCCHnnkkZJPaeaeuQReF8e+MjMzaejQoaTVaikwMJCWL19e8jdx15xeamoq6XQ66tevn+woqlq7di0JIWjnzp2yo9Dq1aupXLlypNFoaODAgSWfBaJCz3gwVYzt27eTEILWr18vO4qqYmJiSKfTUVpampT9L126lPz9/Umr1dKIESNKHkTdKynJeB6rTmdcnfreIuj1xttjYvg0CAdWcNpAVFSU7Ciqkj3BTVJSEkVERJAQgpo2bUonT5605Ju5Zy6gX79+pNPpKDU1VXYU1cie4CYzM5MGDx5MGo2GAgMDKc7SAQ93zeklJCSQEMK8AbQTkz3BzYPX01t0Kr2Ne8aDqRJMnTrV4U4bsDWDwUDVq1enatWq2fWj6SVLlpCfnx9ptVoaOXKkZYOoB127RrRgAdHTTxP17m38c8ECnk3MSZw5c4Y0Gg1NmzZNdhRVzZo1izQajWUDGSslJSVReHg4CSGoWbNm1u2be+bUDAYD1apVi0JDQ136NKRz587ZfYKbzMxMGjRoEGk0GgoKCqKVK1da94DcNac2Z84cUhTl32tQXVT9+vXtPsHNvdfTDx482Lr5DWzUMx5MmaF169bk7+8v/bQBNaWlpZFer6fo6GhV92MwGGjx4sXk5+dXOPuSK/9cmfni4+NJCEE7duyQHUVVHTp0oLJly6o+wc2xY8eoUaNGJISg5s2b0+nTp1XdH3MO6enp5O3tTb169ZIdRVWbN2+2ywQ3GRkZNHDgQNJoNFSuXDmXX1qFma9Lly7k6+vrsJMP2UJGRgb5+vpSly5dVN/X8uXLKTAwsPjr6SXhwZQZZJ82YC+HDx82vS5OairR/PlEgwcbR+6DBxv/bsHI3WAw0IIFC6hs2bLk4eFBY8aM4UEUe8jw4cPdYl2cChUqPLwujg16RmQcRDVs2JCEENSiRQs6c+aMDdMzV1CwLs6CBQtkR1GVyXVxbNSzjIwMGjBgAGk0GgoODqY1a9bYOD1zdnl5eVSpUiWqX7++7CiqKnKCGxt1zeLr6SXgwZSZCtbFmTBhguwoqipYF+fYsWPGaSSjo43njup0ps8pjY42blcEg8FA8+fPLxxEjR07lgdRrFgNGjSgihUrus+6ODboGZHxDXKDBg1ICEEtW7Z06VOTmfXcbV2c3G++sUnP0tPTqV+/foWDqLVr19rpSJgzunz5Mnl6etKwYcNkR1FVwQQ3CQkJNntNu/d6+uHDhzvkIKoAD6Ys4C7r4vTo0YOe9/SkfL2+1LOdGAwGeuutt6hMmTLk6elJ48aNc+nZ2pjtuMu6ONu3b6exAOV5elo1q1BiYiLVr1+fhBDUqlUrOnv2rISjYc7IXdbFmaTTUbaiWNWz9PR0io2NJY1GQ+XLl6f4+HgJR8Oc0a5du0gI4fID7379+tEErdaq945ExuvpCwZRVl9Pbyc8mLKQW6yLs3w5ZVm6SvQ/pTAYDDR37lzy9fUlT09PmjBhAg+imMUKThuYPXu27CjqiYujHK221OtdHD58mOrVq0dCCGrdujUPopjFCtbFqVGjhutOSBEXR4YHfztuQc+uX79OMTExpCgKVahQweVn92XqmDFjBmk0Gpe+dtWwfDndtuK9ozNfT8+DqVJo2rQpBQUFueYgwcQK0TDxFW6iFDkeHtRWrydPT0+aOHGia/58mN3ExcW57ro4VvTsrpcXPVm1Kgkh6NFHH6Vz587JPhrmxArWxYmNjZUdxfas6Fm+Xk9TOnQgRVGoYsWKtGHDBtlHw5xcmzZtyM/Pzyk+abGYFV3L9fCg9j4+5OHhQaNHj3aqQVQBQUQEZpE7d+6gUqVKaNCgAb755hvZcWwrJgbYts34T9yEawAqAFgGYOID9xkAnK5ZEw2Sk+Hp6aluTuYWnnrqKWzbtg0XL15ExYoVZcexHSt7djgoCKHffouaNWuqm5O5ha+++gpdunTBsmXLMGHCBNlxbMfKnu3x8sLNdeswcOBAdXMyt5Cbm4sqVaqgcuXK+P7772XHsS0ru3aqenXUT06GTqdTN6dKeDBVSj/++CMaN26Ml156CfPnz5cdxzauXQOqVgXu3Clyk9EA4gHkAFBMbaDTAb/9BgQHqxKRuZf8/HzUrVsXOTk5uHjxIhTF5L8658I9Yw5o7ty5eO211/Dtt9+iefPmsuNYj3vGHNCFCxdQp04djBo1CitWrJAdxza4a6aPiZWsYcOG+PDDD7Fw4UJ88cUXsuPYRnx8iZtsARCFYv7hCGHW4zBmDkVRkJSUhOvXr6NPnz6y49gG94w5oJkzZ6JTp07o2LEjbt68KTuO9bhnzAFVr14dH3/8MVatWoWPPvpIdhzb4K7xYMoaw4YNw7Bhw/Dkk0/i999/lx3HeqdOFfubhaMA/gbwRnGPkZ0NnD5t42DMnfn7+2P//v3YvXs3Fi5cKDuO9bhnzEHt3r0bZcqUQatWrWRHsR73jDmo6OhovPDCCxgyZAh++eUX2XGsx13jwZS11q1bh9q1a6NFixa4e/eu7DjWycgo9u5ZAPwAtCvpcdLTbRSIMaPWrVtj4cKFmD59uvNfp8g9Yw5Kq9Xi+PHj+PXXXzFs2DDZcazDPWMObMmSJWjSpAlatWqF3Nxc2XGsw13jwZQtHDt2DFlZWXjsscdkR7GOn1+xd38NINqcxwkIsEUaxu4zefJk9O7dG127dsWNGzdkxyk97hlzYFWqVMG2bduwYcMGfPjhh7LjlB73jDm4w4cPg4jQrl2JwwzHxl3jwZQt+Pr64uDBgzh48CBef/112XFKLyICVMRMKqsA5AF4s6TH0OuB8HAbB2PM6LPPPkNwcDBatGiB/Px82XFKJyLCeLGtCdwz5gh69OiB6dOnY8yYMThz5ozsOKXDPWMOTqfT4ejRo/juu+/w0ksvyY5TevzekQdTttK0aVO8//77eP3115GQkCA7jsXu3r2L2RcvIqeI816XAqgOIKSkByIChg+3bTjG/lEwIcXVq1cxYMAA2XFKZW9ICO5wz5iDmzdvHlq3bo02bdrg9u3bsuNY7Gq3bkWePsU9Y46ibt26WL9+PZYsWYLt27fLjmOx/Px8vP3nn27/3pEHUzY0btw49O/fH7169cKff/4pO45Z7t69i5deegllypTBwvXrkVK3LkiIh7b7CUBKSQ8mBNCzp9NObcmcQ/ny5bFz505s3boV77//vuw4Zvv8889RtWpVdB86FCfKl+eeMYeXkJAAT09PREVFyY5itt9//x1du3ZFlaZN8ZWXF/K5Z8zBDR48GKNGjUK/fv1w6dIl2XHMkp+fj1deeQW+vr54fcUK/FKrlnu/pkldMtgFGQwGqlWrFoWGhpLBYJAdp0h5eXn04osvkpeXF/n4+NBrr71mzGtiFWuzv7y9iZKSZB8acxNz5swhRVEoycH/zW3bto1CQ0NJCEHdu3enK1eucM+Y00hJSSGtVktjx46VHaVYly5dos6dO5MQgqpVq0Y7d+7knjGnEh4eTuXLl6e8vDzZUYpkMBho+vTppNfrSa/X0/Tp04153bxrPJhSQXp6Onl7e1PPnj1lR3lIXl4eTZo0qXAQNXv27IcHfXFxlpfC29v4fYzZUdeuXcnHx4cyMjJkR3nIp59+SlWqVCEhBPXo0YP++OOP+zfgnjEnsWXLFhJC0KZNm2RHecilS5eoU6dOJISgGjVq0K5du+7fgHvGnERWVhaVLVuW2rdvLzvKQ/Ly8mjatGmFg6gZM2bwe8d78GBKJUeOHCFFUWj+/PmyoxARUU5ODk2cOLFwEDVnzpziPzkrKIUQxRdBCJcpA3M+BoOBKlWqRPXq1ZMdpdCWLVuocuXKpCgK9ezZ8+FB1L24Z8xJTJo0ibRaLZ07d052FCIiunjxInXo0KFwELVnz56iN+aeMSdx8uRJ0mg0NHPmTNlRiMg4iJo6dSrpdDrS6/X08ssv83tHE3gwpaLFixeToih0+PBhaRlycnJowoQJ5OnpSb6+vjR37lzzTz9MSiKKiSHS6Yj0+vuLoNcbb4+JcfqPZ5lzu3z5Mnl6etLQoUOl5vjkk08KB1G9e/em1NRU876Re8acRPPmzSkwMJBycnKkZbh48SK1b9+ehBBUs2ZN2rt3r3nfyD1jTmL16tUkhCj+FwQqy8vLo8mTJ5NOpyNvb2+aNWsWv3cshiAikn3dlivr27cv9u3bh99//x2BgYF2229ubi5efPFFrFmzBl5eXpgxYwb+7//+D4pSijlH0tKA+Hjj6tTp6ca1AMLDjTOvOPMFg8xl7N69G7169cLq1asxcuRIu+77o48+wosvvojU1FT07t0bH374IcqVK2f5A3HPmIO7c+cOQkJCUKdOHRw7dsyu+75w4QKGDx+Ow4cPo1atWlixYgU6d+5s+QNxz5gTGDJkCLZs2YKUlBSEhJQ4F57N3L17F9OmTUNcXBw0Gg2mTp2KV199ld87lkT2aM7VGQwGCgsLoxo1athlQors7GwaO3YseXp6UpkyZWj+/PkOPREGY7YyY8YM0mg0dPr0abvsb9OmTVSpUiVSFIX69u1LaWlpdtkvYzKdPXuWNBoNTZ482S77O3/+PLVp04aEEFS7dm1KSEiwy34Zk61u3bpUuXJlu7yHM+t6elYkHkzZQWpqKul0OoqNjVVtH9nZ2TRmzBjy8PCgsmXL0oIFC7gIzO20adOG/Pz8KCsrS7V9bNy4kSpWrEiKolB0dDRdv35dtX0x5og2bNhAQgjatm2bavs4d+4cRUVFkRCC6tSpQwcOHFBtX4w5ooyMDPLx8aHu3burtg+Lr6dnJvFpfnby1VdfoUuXLli2bBkmTJhgvPHaNeNHoKdOARkZgJ+fcdX2ESPM/gj0zp07eP755xEfHw9vb2/MmjULL774Yuk+kmXMyeXm5qJKlSoICQnBDz/88O8dNujaxo0bMWXKFKSlpeGJJ57A6tWr7XrqLmOO5Nlnn0V8fDx++eUXVK1a1XijDXr2yy+/YPjw4Th69Cjq1q2LVatWoV27duodCGMOLCkpCa1atcLcuXMxY8YM44026Flubi5eeuklfPDBB/D09MT06dMxY8YMfu9YWrJHc+6kYF2cH+PjiaKjjRfh6XSmL86LjjbO21+E7OxsGjlyJHl4eJCfnx8tWbLEjkfCmOO6b12c48et7tr69eupfPnypCgKPfnkk5Senm7Ho2HMcRUQHba3AAAgAElEQVSui3PkiNU9O3v2LLVq1YqEEFSvXj2pEzcx5kiWLVtGQgg6sXKl1T3Lycmh8ePHF05KNm/ePP4kygZ4MGVnS+vVoyyA8ks5bWRWVhaNGDGCtFot+fv78yCKMRM+/fRTGgtQnqdnqadojY+PLxxExcbG8iCKsQdkZWXRJC8vylaUUvcsOTmZIiMjSQhB9evXp8TERElHw5jjWtmkCd2y4r1jTk4OjRs3rvB6+rfeeosHUTbEgyl7ioujfBMLmk0EyBMgAKQF6D0TC5plZWXR8OHDSavVUkBAAC1btkz20TDmuOLiKEerLVXX1q5dS8HBwaTRaGjAgAE8iGKsKHFxZHjwN+Rm9iw5OZlatmxJQghq2LAhHTlyRPbRMOaYrHjvyNfT2wdfM2UvSUlAhw7A7dv33fw2gJkAVgIYDqDgKo/m92yTo9GgPYBzZcvijTfe+PeaK8bYw6zo2m0AHRUFNfr3x6pVq1C2bFn18zLmjKzoWbaioF1+Pu40aoQ1a9YgMjLSDoEZc0LWvHfUatGeCD/5+ODVV1/l6+lVxIMpe4mJAbZtM/7O4B5lADwJIL6YbzUAuNi4MWree0E9Y8w0K7qWD8Dw+OPw+Pxz9fIx5gqsfE3L6NABgQcOqJePMVdgZc9SwsNR84cfeBClMv7p2sO1a8Du3Q+VIRfALQB/AvAAoAEQAeDGA9+uAVDz55+NC6AxxopmZdcUAB779nHXGCuODV7TAo8d454xVhwb9Kz2L79AuX7dDmHdGw+m7CE+3uTNZ/758zCAJADJAC4CeNzUxkIU+TiMsX9w1xhTH/eMMfVxz5wGD6bs4dQp4M6dh24O+OfPQQCaAKgLYAyA7009RnY2cPq0WgkZcw3cNcbUxz1jTH3cM6fBgyl7yMgweXN1GD+GFeY+Tnq6jQIx5qK4a4ypj3vGmPq4Z06DB1P24OdX5F2PAvh/AH4EcAHABwCaFbVxQEBR9zDGAO4aY/bAPWNMfdwzp8GDKXuIiAB0OpN37YHxtwyNANQCUA3ADlMb6vVAeLhKARlzEdw1xtTHPWNMfdwzp8FTo9vDtWtA1aomz301m04H/PYbEBxsu1yMuRruGmPq454xpj7umdPgT6bsoXx5oEcP46wqpSEE0LMnl4GxknDXGFMf94wx9XHPnAZ/MmUvRaxibRZvb+DQIaB585K3ZczdcdcYUx/3jDH1cc+cAn8yZS8tWgCLFhn/cVvC29v4fVwGxszDXWNMfdwzxtTHPXMKmtmzZ8+WHcJttGgBBAYCBw4Ad+8Wv60Q/5Zh3Dj75GPMVXDXGFMf94wx9XHPHB6f5scYY4wxxhhjpcCn+THGGGOMMcZYKfBgijHGGGOMMcZKgQdTjDHGGGOMMVYKPJhijDHGGGOMsVLgwRRjjDHGGGOMlQIPphhjjDHGGGOsFHgwxRhjjDHGGGOlwIMpxhhjjDHGGCsFHkwxxhhjjDHGWCnwYIoxxhhjjDHGSoEHU4wxxhhjjDFWCjyYYowxxhhjjLFS4MEUY4wxxhhjjJUCD6YYY4wxxhhjrBTcYjDVsGFDHDx4UHYMxlwa94wx++CuMaY+7hkzl1sMpn788Ud06NBBdgyTDh48iCpVqsiO8ZDhw4fD09MTvr6+hV8Gg0F2LObAuGels3//fjRt2hQ+Pj4IDQ3Fxx9/LDsSc3DcNcs1bNjwvtczrVaLxx9/XHYs5sC4Z5a7ceMGBgwYgHLlyqFcuXIYPHgwbt68KTuW6txiMMVKZ9q0abh161bhl0ajkR2JMZeSnJyMQYMG4c0330RGRgZ++OEHNGvWTHYsxlzOjz/+WPhalpmZibCwMPTr1092LMZcysyZM5Geno6UlBT8+uuvSE1NxezZs2XHUp1bDKaqVauG/fv3Y/jw4Zg5c2bh7Q+O7KtVq4aFCxciIiICPj4+GDlyJFJTU9GjRw+UKVMGXbp0QXp6OgDg4sWLEELggw8+QEhICCpVqoTFixcXmWHXrl1o0KABypQpg8qVK2PRokXIyspCjx49cPXq1cLfll29ehX5+fl4++23UbNmTQQFBaF///64ceNGqfbLmL1wzyw3d+5cjBkzBj169IBWq0VQUBBq1qxp8/0w18Jds87XX3+Na9eu4cknn1R1P8y5cc8sd+HCBTzxxBMoW7Ys/Pz8EB0djR9//NHm+3E0bjGYssTWrVuxb98+nDt3Djt27ECPHj0wb948/PXXX8jPz8eyZcvu2/7AgQP45ZdfsHfvXrz99tvYv3+/yccdOXIkVq1ahczMTJw5cwadOnWCj48Pdu/ejZCQkMLfmIWEhGDZsmXYtm0bDh06hKtXryIgIADPPfdcqfb79ttvw9/fv8iv4sTFxSEwMBDNmjXD1q1bLfgpMlY87pnRsWPHAADh4eGoVKkShgwZUvjix5gtcNcetn79esTGxsLHx8es7RkrCffM6LnnnsMXX3yB9PR0pKenY+vWrejRo4eFP00nRG6gatWqtG/fPho2bBi98sorhbcfOHCAKleufN92GzduLPx7TEwMjR07tvDvy5Yto759+xIR0YULFwgA/fTTT4X3T506lZ555hmTGUJDQ2nlypWUkZFx3+0PZiAiqlevHu3fv7/w71evXiWtVkt5eXkW77e0Tpw4QX/99Rfl5eXRzp07ydfXlxITE226D+ZauGeW8/DwoKpVq9LPP/9MmZmZFBMTQ4MGDbLpPpjr4a6VXlZWFpUpU4YOHDigyuMz18E9s9yVK1eoc+fOJIQgIQR16dKFcnJybLoPR8SfTD2gQoUKhf+t1+sf+vutW7fu2z40NLTwv6tWrYqrV6+afNytW7di165dqFq1Ktq3b4+jR48WmeHSpUuIjo4u/A1A/fr1odFokJqaavF+S6tp06YICgqCVqtFz549MXjwYHz66ac23QdzX9wzI71ejxEjRqBOnTrw9fXFyy+/jF27dtl0H8y9cdfu9+mnnyIwMBDt27dX5fGZe+KeGfXr1w916tRBZmYmbt68iZo1a2LIkCE23YcjcqvBlI+PD27fvl349z///NPqx7x8+XLhf//2228ICQkxuV2LFi2wfft2XLt2DU888QT69+8PABBCPLRtaGgodu/ejb///rvw686dO6hcubLF+503b959Mxg9+GUuIQSIyOztmfvinpnfs4iICJPZGDMHd83y17T169dj6NCh3DtmNu6Z+T07efIkxowZAx8fH/j6+mLs2LFu8QtCtxpMNWnSBLt27cKNGzfw559/YunSpVY/5pw5c3D79m38+OOPWLduHQYMGPDQNrm5ufh//+//ISMjAx4eHihbtmzhzHgVKlTA9evXkZGRUbj92LFj8corr+DSpUsAgLS0NGzfvt3i/QLAyy+/fN+MfA9+FWXLli24desW8vPzsXfvXmzcuBF9+vSx+OfD3A/3zPyejRgxAuvWrUNKSgpu376N+fPno3fv3hb/fJh74q6Z3zUA+P3333HgwAEMGzbMop8Jc2/cM/N71qJFC6xZswbZ2dnIzs7GBx98gMaNG1v883E2WtkB7Onpp5/G/v37Ua1aNVSrVg0jRoywejaT9u3bo1atWsjPz8eUKVPQrVs3k9tt2LABEyZMgMFgQN26dbFx40YAQL169TBw4EDUqFEDBoMBycnJeOGFF0BE6NatG65evYry5ctjwIAB6Nu3r8X7La13330XI0eOBBGhevXqWL16tcOut8AcC/fMfM888wwuXbqEyMhIAED37t0fulCZsaJw1yyzYcMGtG7dmmfMZBbhnplv7dq1eP7551GlShUQEVq2bIn4+Hib7sMRCXKDc7fCwsKwceNGtGvXzmaPefHiRVSvXh15eXnQau03JpW1X8ZKwj1jzD64a4ypj3vGzOXyp/mlpaUhLS0N1apVkx2FMZfFPWPMPrhrjKmPe8Ys4dKDqaSkJNSuXRsTJ05EWFiY7DiMuSTuGWP2wV1jTH3cM2YptzjNjzHGGGOMMcZszaU/mWKMMcYYY4wxtfBgijHGGGOMMcZKgQdTdrZiBeDjAygKIETRX4pi3G7FCtmJGXNO3DXG1Mc9Y0x93DPHppk9e/Zs2SHcxYoVwJQpwD0LaRcrLw84cAAIDARatFA3G2OuhLvGmPq4Z4ypj3vm+HgCCjtJSgI6dHiwDDcBNAeQAsAAQA9gOoBX7/teb2/g0CGgeXP7ZGXMmXHXGFMf94wx9XHPnAOf5mcnb70FZGc/eOsdACEADgLIAzAFwGsAEu/bKjvb+P2MsZJx1xhTH/eMMfVxz5wDfzJlB9euAVWrAnfumLO1HsAEAAvvu1WnA377DQgOViEgYy6Cu8aY+rhnjKmPe+Y8+JMpO4iPN3fLMzD+xqHjQ/cIYcnjMOaeuGuMqY97xpj6uGfOgwdTdnDqlDm/WbgNoB2AegB6PnRvdjZw+rTtszHmSrhrjKmPe8aY+rhnzoMHU3aQkVHSFndhLIIWwIkit0pPt10mxlwRd40x9XHPGFMf98x5aGUHcAd+fsXdmw+gPoBMAL8C8C5yy4AAm8ZizOVw1xhTH/eMMfVxz5wHfzJlBxERxosATWsE4A8APwEILPIx9HogPNz22RhzJdw1xtTHPWNMfdwz58GDKTuIjs5Abm6uiXu+gbEIWQAqARD/fI1/aEsiYPhwFUMy5gJycz/AHZMnmXPXGLOVJk1+QE5Ojol7uGeM2Uq/fln83tFJ8GBKRX///Tf69euH+vWDoNXugxD5D2wRBYBMfMXdt5UQQM+ePLUlY0VZunQpAgIC8PrrzyEs7EcI8eCKD9w1xqz13XffISIiAt27N4Wf3xHuGWMquHXrFoYMGYLatf2g0ezl945OgAdTKvj7778RGxuLoKAgfP3111i7di0SE3tBry/dj1uvB2bMsHFIxpxcfn4+3nnnHfj7+2Pq1KmIjY1FZmYmtmxpBr1elOoxuWuMPSwpKQkRERFo2bIlPD09cerUKezd25F7xpgN3bx5E4MGDYK/vz/27NmDuLg4fPNNb37v6AR4MGVDN27cQExMDIKCgvDNN99g/fr1SE1NxdChQ9GiBbBoEeBd9DWCJnl7ExYtApo3VyczY84mPz8fixcvRkBAAKZPn47+/fsjMzMTq1evhk6ns6Jr4K4xdo9vv/0WjRo1QmRkJLy8vHDq1Cl89913aNSoEfeMMRu5efMmBg4ciMDAQOzbtw8rV67EX3/9hWeffZbfOzoLYla7fv06PfHEE6QoClWsWJE2btxY5LZxcUTe3kRCEBnPZjX9JUQ+AVlUq9YiOx4JY47LYDDQggULqGzZsuTh4UFjxoyh7OzsIrc3t2uAgYBbNH9+hh2PhjHHdeTIEWrQoAEJIahly5Z05syZIrc1/zXN2LOnnjpoxyNhzHFlZGTQgAEDSKPRUHBwMK1Zs6bIbS197xga+qYdj4TxYMoKaWlp1LdvX1IUhSpVqkSbNm0y6/uSkohiYoh0OiK9/v4i6PXG22NiiDZv/oU0Gg1Nnz5d5SNhzHEZDAZ66623qEyZMuTh4UHjxo2jnJwcs77XnK5FR+dT5cp9KSwsjAwGg8pHw5jjSkxMpPr165MQgiIjIyk5Odms7zP3NW306FWkKAqdOHFC5SNhzHGlp6dTbGwsaTQaKl++PK1du9as7zO3Z599dpm0Wi2NHz9e5SNhBQQRPXgFKSvBtWvXMHLkSOzatQsVK1bEO++8gwEDBlj8OGlpQHy8cXXq9HTjWgDh4caZVwouGFy3bh1GjhyJL774Aj17Pry6NWOuKj8/H/Pnz8e8efOQm5uLUaNGYcmSJfD09LT4sUrq2o0bN1ClShV07doV27dvt/mxMObIEhMTMXr0aPz888+IjIxEfHw86tata/HjmPOa1rlzZyQlJeHq1avw9fW16XEw5sj+/vtvjBo1Cp999hnKlSuHBQsWYNiwYRY/jjk927JlC/r374/Nmzejf//+Nj0OZoLs0ZwzSU1NpV69epGiKFS5cmX65JNP7LLfoUOHkqenJ12+fNku+2NMJoPBQHPnziVfX1/y9PSkCRMmmP1JlDUSExNJURRavHix6vtizBEcOnSI6tatS0IIevTRR+ncuXOq7zMvL48qVqxIDRs2VH1fjDmC69evU0xMTOGlIBs2bLDLfidOnEharZbOnz9vl/25Mx5MmSE1NZV69uxJiqJQlSpVaMuWLXbPUK9ePapUqRLl5eXZfd+M2YPBYKDXX3+dfHx8yMvLi55//nm7DKLuNX/+fFIUhY4cOWLX/TJmTwcOHKA6deqQEIKioqLsMoi616VLl8jDw4OGDx9u1/0yZk+WXE+vlmbNmlFQUJDdX0vdjcue5nftmvFj0FOngIwMwM/PuJr0iBHmz7n/559/YsSIEfjyyy9RpUoVvPvuu4iOjlY1d1Fu3ryJypUro3Xr1ti7d6+UDIw9yBY9y8/Px5w5c7Bw4ULcvXsX48aNw8KFC6HValXNXpTevXvjwIEDuHLlCvz9/aVkYOxBtujawYMH8eyzz+L8+fNo06YN1q1bh5o1a6qauyhffPEF+vTpg3Xr1pXqVCfG1GCLnv31118YNWoUduzYgQoVKmDx4sUYOHCgqrmLcufOHVSqVAn169fHkSNHpGRwC7JHc7Z2/DhRdLTxQjydrqgLzo3bFeXKlSv02GOPkRCCQkNDadu2bfY7gGKcOHGCFEWhOXPmyI7C3JwtemYwGOi1114jb29v8vLyohdffNEhPnk1GAwUGhpKtWrV4gkpmHS26Nr+/fupVq1aJISgdu3aUUpKiv0OoBjTpk0jjUZT7GyBjNmDLXqWlpZGffr0IUVRKCQkhDZv3my/AyhGcnIyaTQamjp1quwoLsulBlPmTx1p3C4u7v7vv3LlCnXr1o2EEFS1alXavn27nAMpxvLly0kIQQkJCbKjMDdlbc8MBgPNnDmTvL29SafT0ZQpUxxiEHWv1NRU8vLyogEDBsiOwtyYtV3bv38/1axZk4QQ1L59e4cZRN3r0UcfJX9//2KXOWBMTdb2TNb19JZYv349CSEc8n2tK3CZwVRBGf79hx9LgDcBIKCmyWIUlOLy5cvUpUuXwkHUjh07ZB9OsWJjY0mn01FqaqrsKMzNPNyzkrtW0DODwUAvv/wy6fV60ul0NHXqVIcbRN1r//79JISguAdfORmzA2te0/bu3Us1atQgIQR17NiRLl68KPtwipSTk0NBQUHUtGlT2VGYG7KmZ/deT1+5cmUp19NbYuTIkeTh4UGXLl2SHcXluMQ1U0lJQIcOwO3b9946DYAGwOcAcgCcN/m9inIH+fltUa3aX1i+fLlTTD+en5+P2rVr4+7du7hw4QIURZEdibkB0z0DzOmaVpsLjaYTFOV/mDhxIt58801p10RZ4vXXX8cbb7yBpKQkNG3aVHYc5iaseU0T4jaI2qNTp7JYt24dwsLC1I5rtV9//RX16tXDs88+i+XLl8uOw9yELd47hoamSr2e3lLh4eH466+/cPnyZad4DXYWLjGYiokBtm0z/s7gYW0A/ImiCgEY8Oijafjmm4qq5VMDr4vD7K34ngHFd82AOnV+wk8/NXC6wT+vi8PszbrXtHx0734Hu3d7q5ZPDR9//DGeeuopXheH2Y217x1btUrF0aMhquVTw61btxASEoIWLVogISFBdhyX4Vzvaky4dg3Yvbu4N3gl0eB//6uItDRbplJfYGAg9u3bhy+++ALvvPOO7DjMxdmiZ7/91gjXrzvfU86XX34JHx8ftGrVSnYU5gas75qCgwe9ne41rX///njuuecwePBg/Prrr7LjMBdni9e0H34Icbqe+fr64uuvv8bBgwcxe/Zs2XFchvO9s3lAfLz1jyGEbR7H3qKiorBgwQJMnToVR48elR2HuTB37plWq0VSUhJ++eUXjBgxQnYc5uLcuWvvvfceGjdujMjISOTm5sqOw1yYO/esSZMmiIuLwxtvvIF9+/bJjuMSnH4wdeoUcOeOdY+RnQ2cPm2bPPb20ksvoUePHujSpQtu3LghOw5zUe7esypVqmDr1q1Yv3491q9fLzsOc2Hu3rXExEQYDAZ06NBBdhTmwty9Z2PGjMFTTz2Fxx9/HH/++afsOE7P6QdTGRm2eZz0dNs8jgyff/45goKCEBkZifz8fNlxmAvinhkX8506dSpGjhyJH3/8UXYc5qLcvWs6nQ5HjhzB8ePHMW3aNNlxmIty954BwMaNGxEWFoaWLVvye0crOf1gys+vqHvuAPgbgAFA/j//XfSvIQICbJ3MfhRFwXfffYfLly9j0KBBsuMwF1R0zwBLuubMPQOA+fPnIzIyEm3atMEda3+tyZgJ/JoG1K9fH2vXrsWiRYvw+eefy47DXBD3zPje8fjx47hx4wYef/xx2XGcmtMPpiIiAC8vUyPq7gACABwDcOGf/+5u8jH0eiA8XLWIdlG+fHns3LkTH3/8MVasWCE7DnMxERGATlfUlbrmdc0VegYABw4cgFarRVRUlOwozAVFRAAeHndN3ONer2lDhw7FM888g9jYWPz222+y4zAXw+8djfz9/ZGQkIA9e/bg7bfflh3HaTn1YOrnn3/GJ5/0Qk5Ojol7DwKgB74OmnwcImD4cHUy2lPnzp0xe/ZsTJgwAf/73/9kx2EuIjc3Fz/99H/FfBJzEOZ0zVV65unpiePHj+PUqVN47rnnZMdhLuTjjz/GO+9EIC8vz8S9B+Fur2lr1qxB3bp10bJlS9y9a2qAyZjlLly4gM8+68vvHf8RGRmJxYsX45VXXkFiYqLsOE7JKQdTP/30E1q1aoX69esjK+sC2rW7DSFK91hCAD17AsHBts0oy6uvvoqOHTuiffv2uHXrluw4zInl5uZi/PjxKFOmDLZuXYEGDX6DEKWbR9bVela9enX897//xYoVK/Dxxx/LjsOc3EcffYSQkBAMHDgQrVrVQM+eCr+m/ePo0aO4ffs2unXrJjsKc3K//vor2rVrh5o1a+L69Z8QFZXJPfvHpEmT8Pjjj6Nbt248mVlpkBNJTk6mli1bkhCCGjRoQEeOHCEiouPHiby9iYy/J7Dsy9ubKClJ8oHZWF5eHlWsWJEaNGggOwpzQtnZ2TRmzBjy8PCgsmXL0oIFC8hgMHDPTJg4cSJptVo6f/687CjMCW3atIkqVqxIiqLQE088QWlpaUTEr2kP+v7770lRFHrttddkR2FO6Pz58xQVFUVCCKpTpw4dOHCAiLhnDzIYDFS1alWqUaMGGQwG2XGcilMMps6cOUMtWrQgIQQ1atSIjh079tA2cXGWl8LbO5/i4iQckB1cvnyZPD09afjw4bKjMCeRnZ1No0ePLhxELVq06KEn1NL0zMvrrsv2jIioWbNmFBQURDk5ObKjMCexcePGwkFUdHQ0Xb9+/aFtSveaRi7btZUrV5IQgvbu3Ss7CnMS586do0cffZSEEFS3bl06dOjQQ9vwe8f7paWlkU6noyeffFJ2FKfi0IOp06dPU7NmzUgIQeHh4XT8+PFity8ohRDFF0GIfAKyqHbtxXY6Ejl27NhBQgiKj4+XHYU5sOzsbBo1ahR5eHiQn58fLV68uNjfSpnfMyKNJpu02omFv3F3RdnZ2eTv70+tW7eWHYU5uA0bNlCFChVIURR68sknTQ6i7mV+1wwE3KLBgw/b6UjkeOqpp8jLy4v++OMP2VGYAzt79iy1bt2ahBBUr149Ony4+F5Y+t6xatW37HQkchw4cICEELRs2TLZUZyGQw6mTp48adEg6l5JSUQxMUQ6HZFef38R9Hrj7TExRJs2nSONRkOzZs1S8UjkmzZtGmk0Gjpz5ozsKMzBZGVl0YgRI0ir1ZK/vz8tWbLE7O81t2fffmugatWqUfXq1V36tIHk5GTSaDQ0depU2VGYA4qPj6fy5cuToigUGxtL6enpZn+vuV0bOXIFaTQaOnnypIpHIpfBYKDatWtT5cqVXfr5hJVOcnIyRUZGkhCC6tevT4mJiWZ/r7k927r1Emm1Wpo0aZKKRyLf3LlzSVEUi95/uzOHGkydPHmSmjZtSkIIioiIoCQrTki9do1owQKip58m6t3b+OeCBcbbC6xatYqEELRnzx4bpHdcUVFR5O/vT9nZ2bKjMAeQlZVFw4cPLxxELV26tNSPZU7P0tLSSK/XU0xMjA3SO64NGzaQEIK2b98uOwpzEGvXrqXg4GDSaDTUr18/iwZRDzKna+3bt6eyZctSZmamDdI7pvT0dPLx8aEePXrIjsIcRFHX05eGOT3btGkTCSFoy5YtNkjvuLp160Y+Pj6UkZEhO4rDc4jB1Pfff09NmjQhIQQ1adKETpw4Ybd9Dxo0iLy8vOjKlSt226e95eTkULly5ahp06ayozCJsrKyaNiwYaTVaikgIMCuH+EfOnSIhBBWDdycwciRI8nDw4MuXbokOwqTaM2aNYWDqKeeespub0by8vKofPnyFB4ebpf9yXLs2DFSFIXeesu1T7dixbv3evqGDRtaNYiy1NixY0mr1VJKSord9mlvBoOBQkJCqF69erKjODyLB1OpqUTz5xMNHmwctQ8ebPz7vaN2c504cYIaN25MQgh65JFH6Pvvv7f8QaxkMBioTp06Ln/aQEpKCmm1Who/frzsKMxMtupaZmYmDR06lLRaLQUGBtLy5cvVCVyCefPmucVpA40aNaIKFSpQXl6e7CjMDLZ8TVu1ahUFBQWRRqOhgQMHSvmN7sWLF8nDw4NGjx5t933b05IlS0hRlBKvh2GOw1ZdO3PmDDVv3rzYScnsoUmTJhQcHOzSz/VXrlwhT09Pevrpp2VHcWhmD6aOHyeKjjaeN6rTmT6fNDrauF1JkpKSKCIigoQQ1LRpU+nneBecNtC9e3epOdT2ySefkBCCNm/eLDsKK4atupaZmUmDBw8mjUZDgYGBFOcA0w91797d5U8byMzMpDJlylDHjh1lR2HFsOVr2sqVKwsHURZ6ZPgAACAASURBVIMGDZL+73vbtm0khKCNGzdKzaG2vn37kl6vd+kJblyBrbpmzfX0asjKyiI/Pz9q27at1Bxq27NnDwkhaPXq1bKjOCyzBlOWzN5V3NSsx48fp/DwcBJCULNmzej06dO2PBarHD9+3C1OG+B1cRybLbqWmZlJgwYNIo1GQ0FBQbRy5Ur7H0gRDAYDVa5cmerWrSs7iqp4XRzHZqvXtLi4OAoMDCSNRkNPP/20Q12rNHnyZNJoNHT27FnZUVRTsC6Oq09w48xs0TVbXk9va6dPnyaNRkMzZsyQHUVVL7/8sstPcGONEgdTtljr4tixY9SoUSMSQlDz5s0dahB1r6VLl5KiKCbXInAlvC6OY7K2axkZGTRw4EDSaDRUrlw5WrVqldwDKsKVK1fIy8uLBg8eLDuKqgomuOF1cRyLLV7Tli9fTgEBAaTVamno0KEONYi6V2RkJAUEBLj05EMF6+K4+gQ3zsjarn3//ff0yCOPkBCCGjdubNfr6S2xdu1aEkLQzp07ZUdRVbt27ahs2bKUlZUlO4rDKXYwVfTq0OcJqEQACNAQMN5kIdatO00NGzYkIQS1aNHCKabnjo6OJp1O59KnDWRnZ1NAQACvi+NArOmaXp9PXbvOII1GQ8HBwbRmzRrZh1OigtMGHHXAZysDBw7kdXEciLWvaVOmbC4cRA0bNszh31Tk5ORQYGAgNW/eXHYUVfG6OI7HdNdK7hlApNPdpdq1B0q9nt5SQ4cOJU9PT7p8+bLsKKopmOAmIiJCdhSHU+xgKjq6qI9mwwgIJeAPApb/U4xtD2xzl4AtFBkZScnJyfY6HqsZDMZ1capVq+bSpw0UrIszZcoU2VEYWd81T88dtHbtWtmHYZFZs2aRoigufdpAwbo4VapUcennE2dhbc+E2EojRoxw+EHUvc6dO0darZaef/552VFUxeviOBbTXTOnZ8au+fntd7rXhvr161OlSpVcekKKggluxowZIzuKQyl2MPXghYLGr9R/CrDnntuqExD50LaenoZSzYgkW8G6ONHR0bKjqIrXxXEc1nZNpyvd7GOy8bo4zJ6s7ZmXV75T9ozXxWH2lJpqqmvm98xZX9MyMjLI19eXunbtKjuKqj799FO3mODGEgoslvDPn4/dc1sDABce2lKjURAfb/keZCtXrhz27NmD7du3491335UdRzVDhgzBM888g9jYWPz222+y47CHmN81IeCUXdu/fz90Oh0effRR2VFU4+/vj4SEBHz55Zd4++23ZcdhDzG/Z4oinLJnAwcOxJgxY/DUU0/hwoWHj8tV7N69G35+fmjZsqXsKG7NdEfM7xngnK9pZcuWxaFDh5CQkIC5c+fKjqOa6OhoTJo0CcOGDcPPP/8sO45DKHYwdeeOqVuvm/i2QAA5D22ZnQ2cPl3aaHK1a9cOb775JiZPnoxvv/1WdhzVrFmzBvXq1UPLli1x9+5d2XHclrt2TavVIikpCWfPnsXo0aNlx1FNZGQkFi9ejFdeeQWJiYmy47gtd+0ZAKxYsQKNGjVCZGQkcnNzZcdRhaIoSEpKwoULFzBkyBDZcdzWqVOmumZ+zwDn7VrTpk3x3nvv4dVXX8VXX30lO45q3nnnHTRr1gytW7fGHdNPrG6lFJ9MBQHIf+C2dABeJrdOT7d8D45i+vTp6NatGzp37oy///5bdhzVHDlyBLdv30a3bt1kR2H3cY+uhYWF4ZNPPsGHH36IjRs3yo6jmkmTJqFPnz7o1q0bbty4ITsOK+QePQOAb775Brm5uejcubPsKKoJCQnB559/jk2bNmHNmjWy47iljAxTt1rWM8B5uzZ+/Hg8+eST6NWrF65duyY7jmoOHToEIQTatWsnO4p0pRhMFTwJ77vntmQA1U1uHRBg+R4cyc6dO+Hv74/IyEjk5z/4ROAafH19kZiYiEOHDmH27Nmy47BC7tO1vn374sUXX8Tw4cPx008/yY6jmq1bt6J8+fJo0aKFyz6fOB/36Zm3tzcSExNx9OhRzJgxQ3Yc1Tz22GOYMWMGxo4di1OnTsmO43b8/EzdalnPAOfu2kcffYSQkBCXfq7X6XQ4duwYvv/+e7z44ouy40hV7GBKpzN1a3kAoQBGAbgGYAWAFAAPPzHr9UB4uNUZpVIUBcePH8elS5fw9NNPy46jmoiICKxYsQJvvPEG9u3bd/+d164BCxYAQ4YAjz9u/HPBAiAtTU5YF8RdAxYvXozmzZsjKirKZU8bUBQF3333Ha5evYr+/fvffyf3THXcM6BRo0ZYvXo15s+fj127dsmOo5o333wTUVFRaNu2LW7fvl14O9dMfRERprpmfs8A5+9awSmnaWlpiI6Olh1HNbVr18Z//vMfvPvuu/jss8/uu8+tulbc7BSmZz4qWCug4j8zsxS3VoDzzcZSFLdcF+f4ceP8pjrdw/8Y9HrjbdHRxu2YVbhrRm65Lg73zG64Z/9yt3VxuGb2Y3o2P/N75kpdS0xMJEVRaPHixbKjqGrMmDHk4eFBKSkpbtm1Uq4zVfKXEESutiB5wbo4zrCAXGkZDAaqU6cO/Z+fH+V7e5f8D0CI+5csZ6XCXfuXu6yLM2/ePBoL0F2djntmJ9yz+7nLujiKMp602jtcMzvirv1r0aJFpCgKHTlyRHYUVTVu3Jh8faeQt3e+23Wt2MFU0avFl/zl7U2UlGSvw7CfDh06UJkyZVx6XZysxYvpVmn+h7tKKyTgrt1v8+bNJISgTz75RHYU9cTFUbaicM/siHt2v4J1cbp06SI7imri4oi8vPK4ZnbGXbtfr169yNvbm65fvy47imqWLr1DQJZbdq3YwRSR8SAtLYReb3CJH44peXl5VKFCBQoPD5cdRR1FPAP6AYR7vjzc5RnQjkrTNVd5IjJl3LhxpNVqKSUlRXYU2+OeScM9u9+JEydIURR6/fXXZUexuaLf0Pvd1zPAg2umAu7avwwGA4WGhlKtWrXIYDDIjmNz7t61EgdTRP8WwpyP7YS4TWXKTHPp0wYuXbpEHh4eNGrUKNlRbK+Iz+b9ABpa0rOgq302L4H5XTMQcIteeukX2ZFV1aRJEwoODqacnBzZUWyLeyaV+T3LJ+AWRUbGy46squXLl5MQghISEmRHsamiTzXzI2Ao18wOzO0acJeALHrvPdd975iamko6nY4GDBggO4rNuXvXzBpMERlHjTExxgvH9Pr7fxAFF5TFxBAdOpRFvr6+1LlzZzVzS7dt2zYSQtCGDRtkR7Gdoq8aNe9NHuA6V41KZG7X2rSZRHq93qVPG8jKyiI/Pz9q06aN7Ci2wz1zCOb2bOnSRBJC0Nq1a2VHVlVsbCzpdDpKTU2VHcUmiqmZWW/wuGa2Y07X+va9S2XKdKRWrVrJjquqhIQEEkJQnAt9/MZds2AwVeDaNaIFC4iefpqod2/jnwsW3P9DKDhtYPbs2bbM6nAmT55MGo2GkpOTZUexjfnzi32TJ/75KgPQkqIaodcb/0Ewq5XUNYPBQGFhYVSjRg2XPG2gwOnTp0mj0dD06dNlR7EN7plDMec1bfr06aTRaOj06dPygqrMYDBQjRo1KCwszCWeT4qp2T9v8MQ/X2UIWMI1s4OSupacnEwajYYmT54sN6jKXn/9dVIUhU6cOCE7ik1w10oxmDJXXFwcCSFo//79au3CIbRq1YoCAgIoOztbdhTrDR5c5K8N1gB0BaAMgEbBeO5rQlFv9J5+WvaRuI2C0wZiY2NlR1HV2rVrSQhBO3fulB3Fetwzp9SmTRvy8/OjrKws2VFUc/36ddLr9dS7d2/ZUaxWTM0IWEPAFQIyCBhFxms5ErhmDmDDhg0khKBt27bJjqKqzp07k6+vr0tMZsZdIyp20V5rjBs3Dv3790evXr3w559/qrUb6Q4dOgRFUdCmTRvZUayXkVHkXSMBhAAoC2A1gCAAcUVtnJ5u62SsCOXLl8fOnTuxdetWvP/++7LjqGbEiBEYOnQooqOj8fvvv8uOYx3umVNKSEiAp6cnoqKiZEdRTWBgIPbt24ddu3Zh8eLFsuNYpZiawZKmcc3sa8iQIRg5ciT69euHS5cuyY6jmj179sDX1xeRkZGyo1iNuwaoNpgCgE2bNiE0NBQtW7ZEfn6+mruSxtPTE0ePHsXJkyfx/PPPy45jHT8/szcVMP7a3KSAAFukYWbq1KkT3njjDbzwwgv47rvvZMdRTXx8PGrWrImWLVvi7t27suOUHvfMKXl6euLbb7/FmTNnMG7cONlxVBMVFYUFCxZg2rRpOHr0qOw4pWZBzVBc07hm9rd69WrUq1fP+Z/ri6HVapGUlITz589jxIgRsuNYhbum8mBKURQkJSXh+vXr6NOnj5q7kqp27drYuHEj3n//fWzZskV2nNKLiAB0uoduvgTgTQB/A7gDYDyAvwCMMvUYej0QHq5iSGbKzJkz0alTJ3Ts2BE3b96UHUc1x44dQ2ZmJnr06CE7Sulxz5xW9erVsXnzZqxatQofffSR7Diqeemll9CjRw906dIFN27ckB2nVIqoGSxpGtdMniNHjiA7Oxtdu3aVHUU1VapUwWeffYb169dj/fr1suOUGncNUO2aqXsdOXKEFEWhBc58dZkZCtbFOX/+vOwopfLV5s2UbWJuy2SAvPHvOgE+AL3lqlOyOLG8vDyqVKkS1a9fX3YUVTn7ujipp09TjonFerlnzuP5558nrVZL586dkx1FNQXr4tSsWdMpJ6T44w8DabW5JuqTTIB3Yc8AHwLe4po5oJMnT5KiKDRr1izZUVRVMMHNmTNnZEcplU8/TSQhst26a3YZTBERLV68mBRFocTERHvtUoomTZpQuXLlnGpdnF27dlH16tVJCEGHg4Mpv+QFIUx/ucJiAU7u8uXL5OnpSUOHDpUdRVUF6+I40wQ3f/zxB3Xv3p2EELRLpyMD98ypNW/enAIDA53qud5Sqamp5OXlRf369ZMdxWwGg4Fee+018vHxIUXZRoCBa+bEVq1aRUII2rNnj+woqoqKinK6CW4SEhKodu3aJISgoKBD/6zL555ds9tgioioT58+brMuTlRUlOwoJdq5cydVrVqVhBDUpUsXunz5cnHLWJf85QrLWLuAXbt2kRCC1qxZIzuKqvr16+cU6+JcuXKFunXrRkIICgsLM85SxT1zetnZ2RQQEECRkZGyo6hq//79JISg5cuXy45SLIPBQLNmzSJvb2/y8vKiyZMn05EjeVwzFzBo0CDy8vKiK1euyI6impycHCpXrhw98sgjsqOUaP/+/VSrVi0SQlC7du0oJSXF7V/S7DqYcpd1cc6cOePQ6+Ls2LGjcBDVtWtX4yDqXgVLllvaBhdahM7ZzZgxwy3WxalZsyaFhoY65PPJ5cuXqWvXriSEoKpVq9L27dvv34B75vTOnj3L6+JIZjAY6OWXXyZvb2/S6XQ0ZcoUysvLK7y/dDXL55o5EIPBQHXq1KHKlSs75HO9raSkpJBWq6Xx48fLjmLS3r17qUaNGiSEoA4dOtDFixfvu9+dX9LsOpgi4nVxZNq2bRuFhoaSEIIee+yx4n/LU9CKEk5FMgCUBdCdpUvtdyDMLLwujhyXL1+mLl26FA6iduzYUfTG5vZMCLoF0P+efdZ+B8LMwuviyGEwGGjGjBmk1+tJp9PRtGnT7htE3cvMmv1zmtItqlePX88cTXp6Ovn4+FD37t1lR1HVJ598QkII2rx5s+wohfbs2VM4iOrYseNDg6h7mds14+m3t2jxYtd4f2L3wRSR8TxLIQS99957MnZvN8OGDSNPT8+HP/mxs08//bRwENW9e3fzPypPSjKeyKrTGZenvrcJej2RTkeGJ56grgEBFBERoe5BMIvl5ORQcHAwNWnSRHYUVSUmJpKiKLRo0SKpOS5dukSdOnUiIQRVr16ddu3aZd43mtEziomheTEx5OHhQSkpKeoeCLPY6NGjycPDo9g3Gc4uLy+PKlasSA0aNJCaw2Aw0PTp00mv15Ner6fp06cXOYi6l5k1o/XrfyRFUWjOnDl2OBpmiWPHjpGiKPTWW2/JjqKqiRMnOsQEN/deT9+pUye6dOmSWd9nTteio/OpQoVeVKtWLZf4tFHKYIqIaM6cOaQoCiU5+4mSJahfvz5VrFjRrCd7W9uyZQtVqVKFhBDUo0cP+uOPP0r3QNeuES1YYFyeundv458LFhROvXLx4kXy8PCg0aNH2zA9s4WC0wbGjh0rO4qqFi1aJG2Cm4sXL1LHjh1JCEE1atQo/YXSJfSMyDjBTXBwsJTnE1a88PBwKl++vEv/vymY4GbYsGF233deXh5NnTqVdDod6fV6mjFjRqnehJlRM3rvvfdICEEJCQk2PAJmC0uXLiVFUejQoUOyo6iqWbNmFBQUJGWCG5PX05dCSV37448/yMvLiwYMGGDD9HJIG0wREXXt2pV8fHwoIyNDZgxVZWRkkK+vL3Xu3Nlu+/zkk0+ocuXKpCgK9ezZs/SDKAts27aNhBC0ceNG1ffFLPPpp5+SEII2bdokO4qqevXqZdcJbi5evEgdOnSwfhBlgYIJbtq2bav6vphlsrKyqGzZstS+fXvZUVS1Y8cOEkLQ2rVr7bK/vLw8mjJlSuEg6uWXX7bLb7JjY2OdYoIbdxQdHU06nY7S0tJkR1FNwQQ3rVq1sts+S7yeXgUFE9zEOfmFU1IHUwaDgSpVqkT16tWTGUN19loX56OPPqKQkBBSFIV69+5t9xeByZMnk0ajobNnz9p1v6xkkyZNcojTBtRkr3VxUlJSqF27diSEoFq1atl9evbTp0+TRqOhGTNm2HW/rGQnT54kjUZDM2fOlB1FVdOmTVN9XZy8vDyaPHky6XQ68vb2plmzZtn1dCCDwUA1atSgsLAwlzgNyZUYDAaqVq0aVatWzaX/3yQnJ5NGo6EpU6aoup/t27dTWFgYCSGoW7dudp81cfbs2Q47wY25pA6miNx8XZzUVKL584kGDzZ+Bjp4sPHvFq5ctnnzZqpUqRIpikJ9+vSR+tuayMhICggIoOzsbGkZmGnusi6OTqd7eF0cG3Tt/Pnz1LZtWxJCUO3ataWeAlQwwY3Z12Uxu3HrdXFs0LO8vDyaNGkSeXl5kY+PD7322mvS3jAXTHDTp08fKftnRUtLSyO9Xk/R0dGyo6iqYIKb+2aDtdF7x1JfT6+CTp06UZkyZRxmghtLSR9MEbnXujheXl6Utns3UXS08So8na6oq/OMa9EUY9P/Z+/O42M69z+Af86ZyWQmiyzEEmuooiS2RBCE2pciSls7Fy1p0avaa72lttIqPyrlUk2vVPWildva66ZILUkVsVRVFa0lQiMikojJ9/fHSJo9k5k588zyfb9e86rOTM75HPHJzJM553k2b6bq1auTLMs0YMAAm/jIOzs7m3x9fSkkJER0FFaEs6yLkzfBzZo1awwdMrNrly5dorCwMJsYRBU0atQom5jghhU3fPhw51oXxwI9y87OpilTpuQPoubNm2cTnzrkTXCzfPly0VFYEQcPHiRZlmmlg88mPH78eHJxcaGb//2vRd47Wux6egvKm+CmadOmoqOYxCYGU0REs2bNcop1ceZUqUIZkkS55c/RWuoE/DExMfmDqIiICJtbBPnixYukVqtpypQpoqOwIpxpXZyJAD3Wao2ZD7nErl28eJHat29PkiRRo0aNbPKC5yZNmlCNGjUcetIDe9WoUSOnWBcnUpIoS602uWfZ2dk0efJk0mg05O7uTgsWLLC5v7Nly5aRLMt05MgR0VFYEUuWLCFZlimhnAGEvXvH358yALPeO4q4nr4irl69ShqNhsaMGSM6SoXZzGCKiKhjx46OvS5OVBTlFp0n8sltH0AAqF4ZK5pt2rSJqlWrRrIs06BBg2xuEFXQli1bSJIk2rZtm+gorIiYmBjHXxcnKooyZdn4nhXo2oULF6hdu3YkSRI1btyYDh8+LPpoSpU3wU337t1FR2FFpKWlOf66OFFRlKPRmPSalp2dTZGRkaTRaMjDw4MWLlxoc4Oogvr27Utubm42/brrrHr16kXu7u6UmpoqOooyzHzvKPp6+orIm+AmOjpadJQKsanBVE5ODvn5+VHz5s1FR7G8hIQyl4b2BahSKW/ycjQa6u7jQ7Is0/PPP283PzAmTZpEarWa18WxQQ69Lk4ZXSurZwTQQ1mmYMDmB1EF5U1ww+vi2J6EhASSZZkWL14sOorlmfGalqVWU1u1mjw9PWnx4sU2PYjKkzfBjaOsi+NI9Ho91axZk55++mnH+96Y+d6xZ+XKNnE9fUVYY4IbS7OpwRSRA6+LExFR6mkQkwGqBVB4KYV4DNDRmjXtZhBVEK+LY7scdl2cUrpWXs/yupZih1Nb501wExcXJzoKK2LlypUkSZJNniZqFjNf0y40bWp3b3yTk5MdZl0cR3P9+nVydXWlYcOGiY5iWWb27EiNGnYziCqoffv25O3tbTeTmdncYIrIAdfFSU4ufrHgk9vvALkAdKycN3mk1VZ4phZbwOvi2C6HXBenlK4Z3TM77hqvi2O7Bg0a5Fjr4jjxa5qjrIvjiPbt20eSJNG6detER7EMJ+5ZdnY2Va5cmVq1aiU6ilFk2KCIiAhMnToVo0aNwi+//CI6jvmio0t9qB+ArgBCy9uGJJW5HVvl5uaG+Ph4HDlyBDNnzhQdhxXg5uaGw4cPIz4+HnPnzhUdxzJK6YjRPQPstmtffPEFatasiZCQEOTm5oqOwwrYunUratSo4TjfGyd+TevatSvefvttvPbaa/jxxx9Fx2EFdO/eHXPmzEFkZCSSkpJExzGfE/dMo9Hg+PHjSEpKwquvvio6TrlscjAFACtWrEDLli3Rtm1bZGVliY5jnqQkoIRj+ALABQBbjdlGZiZw5oyFg1lHs2bNsH79eixduhS7du0SHYcVEBQUhLVr12LRokXYu3ev6DjmK6FrFeoZYLddk2UZCQkJSElJQUREhOg4rIC8701ycjIGDx4sOo75nPw17e2330aXLl0QHh6OBw8eiI7DCnjnnXfQsWNHdOzY0f6/N07eswYNGuDzzz/HRx99hP/85z+i45TJZgdTAHDo0CEQEcLDw0VHMU9aWol3bwGQDcALgArAQQBXALiVtp3UVMtns5KxY8di1KhRiIiIwB9//CE6Ditg/PjxGDZsGPr3748bN26IjmOeErpW4Z4Bdts1X19f7N+/H9988w0++OAD0XFYAVWqVMG+ffsQGxuLlStXio5jHn5Nw549e+Dh4YHQUKM+72ZWtH//fuh0OrRv3150FPNwzzB48GC89tprGD58OH799VfRcUpl04MprVaLo0eP4sSJE5g2bZroOKbz8irx7vUATgM4+eTWGkBNACdK246PjwLhrCc6OhoNGjRAmzZt8PjxY9FxWAExMTEICAhAmzZt7Ps0pBK6VuGeAXbdtbCwMCxbtgxvvvkmjh49KjoOK6BDhw5YtGgR3njjDRw/flx0HNPxaxrUajUSExNx6dIljB07VnQcVoBarUZCQgIuXLiACRMmiI5jOu4ZAGDVqlVo3rw5QkND8ejRI9FxSmTTgykAaNSoET799FOsXLkSX331leg4pgkKArTaYndXARBU4OYBwAVAk5K2odMBgYEKhrSOY8eOIT09HX369BEdhRWRkJCAe/fu2ff3JigIua6uhe6qUM8Ah+jaG2+8gd69e6Nbt274888/RcdhBcyYMQM9e/ZE165dce/ePdFxTBMUBOLXNNSqVQvbt2/Hp59+ik8//VR0HFZAnTp1sG3bNnz88ceIiYkRHcc0/N4xX3x8PPR6PTp37iw6SslEz4BhrFdeecVu18VZt2ABZZY1e5gxNzudkaUkeevizJ8/X3QUVoQ9r4uTmJhInZ95hh5y14iI18WxZXq9nmrVqmWX6+Kkp6fTpOef554VYI/r4jiL6dOnk0qlovPnz4uOUmHRy5bxe8cCzp8/TyqVit58803RUYqxm8EUEVFQUJBdrYuzYsUK8vLyIrVaTT/UqUO5pawVUO5NkogGDRJ9OBaVty7OgQMHREdhRaxatcqu1sVJSEigwMBAkiSJWrduTfeefbbUdTmcrWvJycmk1Wp5XRwbdPPmTbtaFyc9PZ2GDRtGKpWKKleuTJdbtOCeFRAWFkZeXl6UkZEhOgorom3btuTj42M3axZFRUWRr68vqVQqSqhVi987FrBp0yaSJIliY2NFRynErgZT9rAujl6vp/fffz9/EDV+/HhDgctZxbrMm5sbUWKi6EOzuCFDhvC6ODbKHtbFOXbsGDVr1ix/EHXmzBnDA9y1Qg4cOMDr4tgoe1gXJy0tjV566aX8QVR+Vu5ZIdnZ2VSlShVq2bKl6CisiLw1i4KDg0VHKdPq1avJx8eH1Go1jRw5ktLT07lnJRg3bhy5uLjQ1atXRUfJZ1eDKSKi06dPk0qlolmzZomOUoher6dly5ZRpUqVyMXFhSZMmFD8tyBRURUuhV6rNXydA9Lr9dSgQQOqU6eO3Z3q4uj0ej0FBARQ3bp1be57c+zYMWratClJkkQhISEln1pjQtfIzc1huzZ//nySZZlOnDghOgorYu7cuSTLMp08eVJ0lELS0tLoxRdfJJVKRX5+frR+/friTzKhZ7kO3LPLly+TWq2myMhI0VFYERcvXiS1Wk2TJ08WHaWYVatW5Q+iRo8eXfzTTRN69tiB3zsSETVr1oyqVatmM2eq2d1gioho/fr1JEkS7dq1S3QU0uv1tHTpUvL09CQXFxeaOHFi2R8l55WivI9tJYkeyjK9rtU69GkDd+/eJZ1OR/379xcdhRWRkpJCOp2OBg4cKDoKEREdOXKEnnnmGZIkidq0aVP+OfBGdk0vSfQAoFOvvGKdAxGka9eu5OHhYfhtJ7MpXbp0IU9PT5v43qSmptKQIUPyB1EbN24s+wuM7Fnuk56teuYZ6xyIIFu3hoXzzgAAIABJREFUbiVJkmjLli2io7AitmzZQpIk0datW0VHISKilStXkre3N6nVahozZkzZ7/Uq8N4xU5ZpikZDaWlp1jsYK0tPTydPT0/q0qWL6ChEZKeDKSKikSNHkkajoevXrwvZv16vp8WLF5OnpydpNBqaNGkSZWdnG/fFiYmG81i1WiKdrnARdDrD/YMG0aMjR8jPz49atGih7MEIFh8fT7Is0/vvvy86Civi8OHDJMsyrVixQliG+Ph4aty4MUmSRKGhoRW7kNjIri0YMMBuJ7gxVk5ODtWoUYOecfA3s/YoJyeHqlevToGBgcIypKam0vPPP0+yLFPVqlXLH0QVZGTPznzyid1OcFMRkydPJrVaTZcuXRIdhRURGRlJarWaLl++LCxDwevpx44da/wvzI3smf74capZs6ZdTnBTESdPniRZluntt98WHcV+B1NERI0bN6aaNWta9R+LXq+nhQsXkoeHB2k0GoqMjDR+EFXU7dtEy5YRjRxJ1K+f4b/LlhWaeSXvtIGJEyda6Ahs0/vvv0+yLNORI0dER2FFLF26lGRZpmPHjll1v4cPH84fRLVr144uXLhg+saM6FpgYKBdTXBjit9//500Gg2NGTNGdBRWxNWrV8nFxYXGjx9v1f3evXuXBg0aRLIsU7Vq1ejTTz81fWNG9GzlypV2NcGNqVq3bk2VK1c2/f0BU0zLli3Jz8/Pqt8bvV5Py5cvL349vSmM6Nn169ftaoIbU61bt44kSaJ9+/YJzWHXg6m0tDRyd3ennj17Kr4vvV5PCxYsIHd3d9JoNDR58mSrFXHbtm0kSRJt3rzZKvsTpW/fvuTm5kZ3794VHYUV0bt3b3J3d6fU1FTF93Xw4EFq1KgRSZJE7du3p4sXLyq+T6K/Thuw5QluLGHnzp0kSRJFR0eLjsKKiI2NJUmSaNOmTYrv6+7duxQREUGyLFP16tWtss88AwcOtPkJbsyVmZlJPj4+1K5dO9FRWBEZGRnk5eVFHTp0UHxfRl1Pr5A9e/bY/AQ3ljB06FBydXWlmzdvCstg14Mpor/WxVm4cKEi29fr9TRv3jxyd3cnV1dXmjJlipDfNOWdNmCtN5Yi8Lo4titvXZyGDRsq9r2Ji4ujhg0bkiRJ1KFDByGnyJw+fZpkWaY5c+ZYfd/WNGPGDF4Xx0YpvS5OSkoKDRgwgGRZpho1agj5JZ1er6e6detSvXr1HPpnfd66ONOnTxcdhRVx9uxZUqlUNGPGDEW2X+Hr6RWSN8HN6dOnrb5va9Hr9dSwYUOrn6lWkN0Ppoj+WhcnLi4u/77kZKKlS4mGDzd8Cjp8uOH/jV27TK/X09tvv50/iPr73/8u/PSf4OBg8vX1dejTBnhdHNuVty7O0KFD8+8zt2dEhqm78wZRHTt2FH6dQd5pA3v27BGaQ2m8Lo7tateuXbF1ccztWkpKCvXv3z9/ECV6goSUlBTSarUUEREhNIfSbHVdHEYUHR1NkiTRzp078++zxHvHJUuWmHY9vULCw8OpUqVKNjHBjVJSU1PJ3d2devfuLWT/DjGYIiJ6/vnnSavV0r59qRQRYbgOT6st+fq8iAjD1P0l0ev1NHfuXHJzcyNXV1eaNm2a8EFUnrzTBkJDQ0VHUVTeujhr1qwRHYUV8e2335IkSTRz5pdm9SxvW0899RRJkkSdOnUSekFwUcOGDSNXV1dhE9xYQ3Z2Nvn5+fG6ODYob12c1q1bU0ICmdW15ORk6tevH8myTP7+/vTFF19Y92DKcPDgQZIkiVauXCk6iqJscV0cZjBmzBjSaDT09de3zH7vaLHr6S0sJyeHqlatKnSCG2s4duwYybJMS5Yssfq+HWYwpdfrqXLlOSRJGSRJueXNHFlsSRm9Xk+zZs0iNzc30mq1NH36dJsZRBV04cIFUqlUNG3aNNFRFMXr4tiuvn2/JuCBST0jMixUWr9+fZIkiTp37myTM+jp9Xp6+umnhZ42YA28Lo7tunTpEklSJKnV2cbMhlysa8nJydSnTx+SZZlq1qxpM9NBF7V48WIhE9xYW2BgoE2ti8P+Ur36PAJMf+8o6nr6ihA1wY21rVy5kmRZpsOHD1t1vw4zmIqKItLpihahHgEyASDAhYBRxdboXLNGTzNnziSdTkdarZbeeustm/9hl3fawI4dO0RHURSvi2N7Sl47sPyeRUUZLobNG0R16dLFJgdRBeWdNtCrVy/RURSVN8GN6NO+WGFRUUQaTU6Fu/buu/eod+/eJMsy1apVi7Zt2yb6UMrVs2dPq01wI4qtrYvDDMx57zh//nzh19NXxI4dO6w2wY1IAwcOJJ1OZ9UJbhxiMJWQUNri0DsISHvy551PyrGpyHMekKtrGM2YMcPmB1EFjR8/ntfFYVZlTs8kKYOAYOratatdneqSd9oAr4vDrMnc17Rq1frSl19+KfowjKbX651iXZy8CW7mzp0rOgoj83vm4tLOJq6nr4hp06YpOsGNLcib4CYgIMBqP08cYjAVEVH+otDArieFeL3I/XqKiMgVfQgmcaZ1cUaPHi06itMzt2e9e9vnRAe8Lg6zNnO6Jkl6GjRI9BFUHK+Lw6zN3Ne0gQPtc+AfGhpabIIbR5M3wc0gK/0wtPvBVHJy8YsFC9+aPvmoFgToCLhZ7DlabcVmH7MVGRkZVKlSJadZF2fjxo2iozgtZ+4ZEVFERITTrIvTtm1b0VGcmjN3zVnWxcmb4EbkujjOzpl7lp2dTb6+vhQcHCw6iqIOHjxIsizTqlWrFN+XDDsXHV3eM84CyAawBkB7AJWKPUOSjNmO7XFzc8Phw4cRHx+PuXPnio6jmD59+uAf//gHJkyYgHPnzomO45ScuWcAsG3bNlSvXh0hISHIzc0VHUcRWq0WR48eRWJiIt58803RcZyWM3etZ8+emDNnDiZNmoRTp06JjqOYTZs2oW7duggODnbYnye2zpl7ptFocOzYMZw6dQpTp04VHUcxnTp1wsKFC/H6668jMTFR0X3Z/WAqKQnIyirvWRoAkQBuABhZ7NHMTODMGctns4agoCBERUVh0aJF2Lt3r+g4ilmyZAnatm2LsLAwPHz4UHQcp+PsPZNlGYmJiUhOTsbgwYNFx1FMo0aNEB0djeXLl+O///2v6DhOydm79s4776Bjx47o1KkTHjx4IDqOImRZxvHjx3Hv3j3069dPdByn5Ow9a9iwIWJiYrB69Wps27ZNdBzFzJw5E927d0eXLl1w//59xfZj94OptLSKPFsP4NcSH0lNtUQaMV5++WUMHToUAwYMwI0bN0THUcz//vc/uLi4oEOHDqKjOB3uGVClShXs2bMHO3bswP/93/+JjqOYESNGYNy4cRg8eDCuXbsmOo7T4a4B3377LXQ6Hdq3by86imK8vb1x4MAB7N27F++++67oOE6Hewa8+OKLmDhxIoYOHYrffvtNdBzF7Nq1C97e3ggNDVVsH3Y/mPLyKu2RcwCmALgF4BGARQAuAuhT4rN9fBQIZ0WbNm1CvXr10KZNG4c9bUCj0SAhIQFnzpxBZGSk6DhOhXtm0KlTJyxevBjTpk3D8ePHRcdRzPr169G4cWO0adMGjx8/Fh3HqXDXALVajYSEBFy4cAETJkwQHUcxoaGh+OCDDzB79mwcOnRIdBynwj0ziIqKQrNmzRAaGopHjx6JjqMIWZaRkJCA3377DSNGjFBmH4ps1YqCggCttqRHZAD/BlADgCuABTB8TLu42DN1OiAwUMGQVpD3j+XevXvo06fk0juCgIAAbNmyBWvXrsUXX3zx1wO3bwPLlgEjRgDPPWf477JlQEqKuLAOhHv2lxkzZqBHjx7o2rUr7t27JzqOYo4cOYLMzEx07979rzu5Z4rjrhnUrVsXW7duxccff4yYmBjRcRQzdepUDBgwAL169cKdO3f+eoC7piju2V++//57PHr0CF27dhUdRTH+/v6IjY3F5s2bsWHDhvz7LVYzxae4UFj5M7KUf7PXGVlKkpCQ4FTr4lzdts0wv6lWW/wfgk5nuC8iwrCgBDMZ96wwZ1oXR6VS0UfjxnHPrIS7VpizrItTr149w7o4x45x16yAe1bYmTNnSKVS0YwZM0RHUdTcuXNJpVLRZ59dtGjN7H4wRWTsWgEl3ySJ7HJNjrI4y7o4i+vUoQyAcsv75kuSYWW+qCjRke0a96wwZ1kX59CwYfQAID33zGq4a4U5y7o4k9VqylSpyv/mc9csgntW2MaNG0mSJNq5c6foKIpq2HA5ARkkSbkWq5lDDKZKX8W6/JubG1FiougjsDyHXxcnKopyi3zTDwPkB5AEkAxQIECZRb/Z/OJjMu5ZcQ6/Lk5UVLFvOvdMedy1wvLWxWndurXoKMqJiqLHrq7cNSvinhU3evRo0mg09Pvvv4uOogjDS1rRQdRhAvwIkMiwQHMgAZkVqplDDKaISnzNL/em1eod9ueQXq+ngIAAqlevnuOdhlTKT0A/gBoAlArQaYBcARrkLD8BrcSUnqnV2Q7bMyLDaQOyLNPJkydFR7Es7plQpnTNzS3XYbt28eJFUqvVNHnyZNFRLI+7Joxp7x0fO2zPiIiaNGlCNWrUoJycHNFRLKr0wbMfAQ0ISCXgNAGuBAyqUM0cZjBF9FcpjPmEXJYfkk73d4c/bUCn09HAgQNFR7GsUj6b1wA0r8D/BwPUuKRvvqN9Nm9lFemZRpNDwET6+uuvRcdWVOfOncnT05PS09NFR7Ec7plwxnctl4AH1LTph6IjK2rLli0kSRJt3bpVdBTL4q4JVbH3jpmk0UyhtLQ00bEVk5aWRh4eHtStWzfRUSyq9NM6NQTMK/D/wQQ0rlDNHGowRWQYOQ4aZLh4TKcr/BeWd1HZoEFER448osqVKzv2aQNEdPjwYZJlmVasWCE6imWUcdXoUIDqA5QCUOKT3+K9VfKvlUQfhd0ztmeJiURjx4516NMGiIhycnKoWrVqFBgYKDqKZViqZ45ydbZAxnbt449PkyzLtGzZMtGRFTVp0iRSq9V06dIl0VEsg1/TbIKxPTt+XE81atSgJk2aiI6sqBMnTpAsyzR//nzRUSyi7AlHhhJQn4AUAhKffDL1VoVq5nCDqTy3bxMtW0Y0ciRRv36G/y5bVvi1/dKlS6RWq+m1114TF9QKlixZQrIs07Fjx0RHMd/SpaU24muAdADhya0BQPqSmqPTiT4Kh2FMz4iImjZtStWrV3e40wYKunr1Krm4uND48eNFRzGfpXrm4G/srcmYri1fvpxkWab4+HhxQa2gRYsWVKVKFcrOzhYdxXz8mmZTjOnZ77//ThqNhkaPHi0spzWsWbOGJEmib7/9VnQUs5VRMwK+JkCX3zPDKX/6CtXMYQdTxvriiy8c87SBInr16kXu7u6UmpoqOop5hg8vsQ05AKkA6gZQGkAXAaoOUEjJzRF9FE4nPT2dPD09qWvXrqKjKGrHjh0kSRJt2rRJdBTzWKpnI0eKPhKn079/f9LpdHT37l3RURSTkZFBXl5eFBYWJjqK+fg1zS7t2rWLJEmijRs3io6iqCFDhpBWq6Xk5GTRUcxSSs0IyCFARUA3AtIIuEhAdQJCKlQzu1+011wvvPACXn31VQwdOhS//vqr6DiK2blzJ7y9vREaGorc3FzRcUyXllbi3b8C0AP4GEAlAA0BjAGQZK1crEweHh747rvvEBcXh/nz54uOo5gBAwbgjTfewJgxY/DTTz+JjmM6S/UsNdXy2ViZvvrqK/j5+SEkJMS+f9aXwc3NDfHx8Th27BhmzpwpOo55+DXNLvXu3RszZszAhAkTcPbsWdFxFLNlyxbUrFkTwcHBdv3zpJSawVJNc/rBFACsXr0aQUFBCA0NxaNHj0THUYQsy/jhhx9w9epVjBw5UnQc03l5lXh3IwBqAC8DyAJwFcCnAKpbLRgrT6tWrfDhhx9i/vz5OHDggOg4innvvfcQEhKCsLAwZGVliY5jGkv1zMdHgXCsLLIsIzExETdu3MCLL74oOo5imjVrhvXr12Pp0qXYtWuX6Dim49c0u7V48WK0a9cOHTp0wMOHD0XHUYQsy0hISMCdO3cwYMAA0XFMVkrNYKmm8WDqie+//x56vR5dunQRHUUx1atXx9dff43PP/8c//rXv0THMcnjpk3xSKUq8bEYAMcAuAEIgOEf986SnqjTKRWPlWPSpEl44YUX0LdvX9y6dUt0HMUcPHgQsiyjQ4cOoqOYJigIj11cSnyoQj0LDFQqIStD1apVsXPnTmzfvh0ffvih6DiKGTt2LEaNGoWIiAj88ccfouOY5G6tWsiSpBIf49c023fgwAFoNBqEhYWJjqIYX19f7N+/H7t27cLy5ctFxzFJs2Z6qFSlfVhiXNPKrJn1zli0fWfPniWVSkVvvfWW6CiKssd1cTIzM2nixInkr1YXXrTQlBvPfCSUXq+nhg0bUu3atR1vDbQC7HVdnM2bN1OzqlXpoSV6xrP5CbVgwQKSZZkSHXwdIntcF+fSpUvUoUMHqgpQZnlzcvNrmk27fPkyqdVqmjhxougoinr//fftboKb7OxsmjJlCmk0tajgQryWrhkPpor49NNPSZIkXhfHRmRmZtIrr7xCLi4uVKlSJVq2bBnlDhxY/oIQpd14TQ6bkJqaSm5ubtS3b1/RURRlT+vixMTEUPXq1UmWZYqIiKDsvn25Zw6ge/fu5OHh4RTr4tjDBDcXL16ksLAwkiSJGjVqRAcPHixrARzump348ssvSZIk2rJli+goiurXr59dTHCTnZ1NkydPJldXV3J3d6cFCxbQwIG5itWMB1Ml4HVxxMvMzKTx48eTi4sLeXl50fLly//6FKP0ZazLv/Fq8TbjyJEjTrEuTmRkJKnVarp8+bLoKCXatGkTVatWjWRZpkGDBv31Isk9cwh6Pa+LYwsuXLhA7dq1I0mSqHHjxnT48OG/HuSuOYTXX3+d1Go1Xbx4UXQUxej1eqpTpw41aNDAJs8syc7Optdee400Gg15eHjQwoUL83MqWTMeTJWC18URIzMzk8aNG0dqtZq8vLxKX2w4b8nyirYhKsq6B8TK5Czr4rRs2ZL8/Pxsal2cTz/9lKpWrUqyLNPgwYNLXjaBe+YQ8tbFGTVqlOgoirLFdXEuXLhAbdu2LXkQVRB3zSEEBweTr6+vTf2st7Tk5GTSarU0ZMgQ0VHyZWdn06RJk0ij0ZCnpyctWbKkxMGeUjXjwVQp8tbFefbZZ0VHUVRsbKxNrIuTkZFBY8eOJbVaTd7e3rRy5cryvyivFeV9bitJ/KJjw5xpXZwOHTqIjkIbN24kPz8/UqlUNGTIkPLXnuOeOYS8dXE2bNggOoqibGVdnPPnz1NoaChJkkTPPPOMcb8w4q7ZvczMTPLx8aHQ0FDRURR14MABkiSJ1qxZIzRH3vX0eYOopUuXlvuJmRI148FUGfJOG5g3b57oKIqaPn06qVQqOn/+vNX3nZGRQaNHjya1Wk0+Pj60atWqim0gMdFwIqtWa1ieumATdDrD/YMG8WkQNizvtIH69evb5GkDlpI3wc2MGTOE7H/Dhg35g6gXX3yxYtfQcM8cwsyZM0mlUtGZM2dER1GMXq+nBg0aCJvg5uzZsxQSEkKSJFHTpk3pyJEjFdsAd83uXbhwgVQqFU2bNk10FEXNnz9f2AQ3JV1PX5G+W7pmPJgqR1RUlM2dNqCEtm3bko+PD2VmZlplf+np6TRq1Kj8QdTq1avN2+Dt20TLlhGNHEnUr5/hv8uW8WxidiLvtIHBgweLjqKojRs3kiRJtHPnTqvtc/369VSlShVSqVQ0dOhQ8yYi4J7ZvY4dO5KXlxdlZGSIjqKYu3fvkk6ns+oEN2fPnqXg4GCSJImaNWtGx44dM2+D3DW7FhMTQ5Ik0Y4dO0RHUVS3bt2sOsFNmdfTm8BSNePBlBFeeuklcnV1pZs3b4qOopjs7GyqXLkytW7dWtH9pKen08iRI0mlUpGvry9F8WkK7Im4uDiSJMn8gbWNGzNmjFUmuFm3bh1VrlyZVCoVDRs2zKFnc2PGy8nJIT8/P2rRooXoKIqKj4+3ygQ3Z86codatW5MkSRQYGEgJCQmK7o/ZjwkTJpCLiwtduXJFdBTF5OTkWGWCG6OvpxeEB1NGcJZ1cS5dulTyujjJyURLlxINH24Yug8fbvj/Cgzd09PTadiwYaRSqahy5cq0du1aC6dnjsBZ1sV55plniq2LY4GaEZHh03RfX19SqVQ0fPhwm1/+gFmfM6+LY6menT59mlq1akWSJFFQUJDD/8xipgkMDKSqVas69GRmeRPcjB49utD9luiaSdfTC8CDKSPlrYvTp08f0VEU9cUXX/y1Lk5CgmH9C63WcCvppNKICMPzSpGWlkZDhw7NH0StW7fOikfD7FH37t3J3d3doT9JSU9PJw8PD+rWrZslakZEhpnMfH19Sa1W06hRo3gQxcqUty7O5s2bRUdRVN66OPv337NIz06ePEktW7YkSZKoefPmdOLECescCLNLGRkZVKlSJQoPDxcdRVE7d+4kSZJo48aNFnlNM/t6eivjwVQF5K2Ls3TpUtFRFBUZGUmRkkR6rdbk6U7S0tLopZdeIpVKRX5+frR+/XpBR8PsTd66OI0bNxYdRVEnTpwgYCK5uGSbNavQ6tWrycfHh9RqNY0ePdqhr4VhluUs6+L4+MwkSXpIkpRrcs9OnjxJLVq0IEmSqEWLFjyIYkY7ffo0qVQqmjNnjugoipoxYwZJ0iTSavUmv6ZlZGRY9np6K+HBVAXlrYtT6loRjiAqih5WdJnoJ61ITU2lIUOG5A+iNm7cKPpomB1yhnVxoqKIXFwembzexcqVK8nb25vUajWNGTOGB1HMJI6+Lk5UFJFOV/YgqqyeJSYmUlBQEEmSRK1ataKTJ0+KPSBml9avX0+SJNGePXtER1FMVBSRLD80qWt519Or1Wq7vJ6eB1MmGDBggOOui1PCEtEo4RZYQisyVSpqI8tUtWpVio6OFn0kzM458ro4Ja/EXlLXAou9+Li4PCIPj86kVqtp3LhxPIhiZnHkdXHM6ZlW+5gaNHiRJEmi1q1b0+nTp0UfDrNzw4cPJ1dXV7p+/broKBZnTtdUqiyS5TZ2fT29REQEViG5ubmoX78+VCoVfvnlF8iyLDqS5QwaBOzY8aQHxd0GUA3AKgCTizymB/BH69ao+8MPymZkTmP27NlYunQpTp06hWbNmomOYzHl1AzlNa1evVP46aem0Gq1SsZkTuLnn39G06ZNMXXqVCxfvlx0HIsxt2fe3gdx+HBVh/rZw8Rq3LgxHjx4gGvXrjnUe0dzu9ay5TX8+GOAkhEVxYMpE925cwe1a9dGv379sHXrVtFxLOP2baBuXSArq9SnTAAQDSAbQIk/BrRa4No1wM9PkYjM+XTq1AlJSUm4ceMG3NzcRMcxmxE1Q3lN45oxS/vss88wcuRIfPXVVxgwYIDoOGbjnjFbdP/+ffj7+6Njx47YvXu36DgWwV0r5f0wK1+VKlWwe/dubN++HR9++KHoOJYRHV3uU7YBCEMZ/3AkyajtMGas//3vf9BoNGjfvr3oKBZhXD3KbhrXjFna8OHDMX78eAwZMgRXr14VHcds3DNmiypVqoS4uDjs27cPS5YsER3HIrhrPJgyS+fOnfHOO+9g6tSp+MERTm1LSirzVwtHAdwD8E5Z28jMBM6csXAw5szUajWOHz+Oc+fOYdKkSaLjmK2cmsGYpnHNmBL+9a9/oXHjxmjTpg0eP34sOo5ZuGfMVoWEhGDlypWYPXs2Dh06JDqO2bhrPJgy25w5c9C1a1d07twZ9+/fFx3HPGlpZT48F4AXgE7lbSc11UKBGDMICAjAf/7zH6xbtw6ff/656DhmKadmMLZpXDOmhGPHjiErKwvdunUTHcUs3DNmyyZPnoyIiAj07NkTd+7cER3HLNw1HkxZxJ49e+Dl5YXQ0FDRUczj5VXmw4cARBizHR8fS6RhrJCIiAhMnToVo0aNwi+//CI6jsnKqRmMbRrXjCnBzc0Nhw8fRnx8PObOnSs6jsm4Z8zWbd26FTVq1EBISAhyc3NFxzEZd40HUxYhyzISExNx+fJljBo1SnQc0wUFIbeU2cHWAcgBsKi8beh0QGCghYMxZrBixQq0bNkSbdu2RVbZ5xXYrKAgQKstbd4f45rGNWNKCgoKwtq1a7Fo0SLs3btXdByTGHpW2qPcMyaeLMtISEhAcnIyBg8eLDqOyfg1jQdTFuPv748dO3YgJiYGH3/8seg4FXbr1i28tGcPskt5g7oSQAAA//I2RASMGWPZcIwVcOjQIRARwsPDRUepsNzcXKSkvIesrOxSnmFc07hmTGnjx4/HsGHD0L9/f9y4cUN0nApr2PB7ZGeX9gsX7hmzDVWqVMG+ffsQGxuLlStXio5TYXfu3EFc3OgyfrnpHF3jwZQF9e7dGzNnzsQrr7yCs2fPio5jlBs3bqBXr17w9/fHkUuXkBoaaphWpYifAFwub2OSBPTpY79zWzK7oNVqcfToUZw4cQLTpk0THccoubm5+Oc//wlPT0+sXj0XTz31CySppN/kld80rhmzlpiYGAQEBKBNmzZ2cxrSgQMH0LBhQzz/fEf4+iZwz5jN69ChA5YsWYI33ngDx48fFx3HKLdv38Zzzz2HatWqISnpW4SE3C3prSOcpmtClwx2UB07dqRKlSpRRkaG6Cilun79OvXo0YMkSaK6detSbGys4YGSl7E27ubmRpSYKPbAmNOIiYkhSZLoyy+/FB2lVHq9nubMmUNubm6k1Wpp+vTplJOTwzVjdiMtLY3c3d2pZ8+eoqOU6dtvv6UGDRqQJEkUHh5Oly9f5p4xu9K7d29yd3en1NRU0VFKlZycTH379iVZlqlmzZq0detWIuK3jjyYUkBOTg75+flR8+bNRUcp5vfff6du3brlD6KMT9g3AAAgAElEQVS+/vrr4k+Kiqp4K9zcDF/HmBVNmDCBXFxc6MqVK6KjFKLX62nWrFmk0+lIq9XSm2++STk5OYWewzVj9iIhIYFkWabFixeLjlLMvn37qH79+iRJEnXp0qXYzwLuGbMXer2eatWqRU8//TTp9XrRcQpJTk6mPn365A+itm3bVuw5ztw1Hkwp5MqVK+Ti4kITJ04UHYWIDIOorl27kiRJVK9ePdq5c2fZX5DXCkkquwmS5DhtYHYpKCiIqlatWmywIoJer6eZM2eSTqcjnU5Hb731Vpm5uGbMXqxatYokSaKDBw+KjkJERLt27aKAgACSJImeffZZunr1aqnP5Z4xe3Hz5k1ydXWlYcOGiY5CRIY8vXv3JkmSqHbt2uWeCeKsXePBlIK+/PJLkiSJNm/eLCzD1atX6dlnnyVJkqh+/fq0a9cu4784MZFo0CAirZZIpyvcBJ3OcP+gQfb/+SyzaxkZGVSpUiUKDw8XliEnJ4feeuut/EHUzJkzjf7NIteM2YtBgwaRVqullJQUYRl27txJ9erVI0mSqGvXrmUOogrinjF7sW/fPpIkidatWycsw/Xr16lnz575g6gdO3YY/bXO2DWJiEj0dVuO7O9//zs+/PBDnD17Fo0aNbLafq9evYoxY8bg4MGDCAgIQFRUFHr27GnaxlJSgOhow/LUqamGxQACAw1Tr9j1FYPMUSQlJaFVq1b4xz/+gUWLyp3A32IeP36MWbNmYfXq1ZAkCX//+9+xYMECyHLF5/bhmjFbl5ubi6eeegq5ubm4fPmySf/OTfXNN9/gtddew7Vr19CtWzds3LgRtWrVqvB2uGfMHsybNw8LFizAiRMn0KJFC6vt98aNGxg7diz279+P2rVrY/Xq1ejfv79J23KqrokezTmDkJAQ8vX1pczMTMX3deXKFQoPDydJkqhBgwa0b98+xffJmC1Yv349SZJUsU9fTZSTk0PTp08nrVZLbm5uNGfOHJs7x50xJaSkpJBOp6OBAwdaZX+xsbFUp04dkiSJevToQdevX7fKfhkTrUuXLuTp6Unp6emK7+v333+n7t27l309PSsVT41uBdZYF+e3335DeHg4AgICcOPGDezfvx+XLl1C9+7dFdsnY7Ykb12cgQMHKrYuzuPHjzFt2jR4eHggKioK//jHP5Cenm7yp1GM2Zu8dXH++9//KrouTmxsLOrUqYOBAweiSZMm+OOPP7B37174+5e72iFjDmHfvn1wd3dH+/btFdvHH3/8gW7duqFOnTr45Zdf8M033+DKlSvo16+fYvt0RHyan5X8/PPPaNq0KaZMmYIPPvgAAHD7tuEj0KQkIC0N8PIyrCQ9dqzxH4H++uuvGDNmDL7//ns89dRTWLt2LZ599lnlDoQxG9e4cWM8ePAA165dyx/gmNu1x48f480338RHH30EtVqNN998E3PnzuUBFHNa7777LmbPno0jR44gNDQUgGVe07766itMnToVf/zxB3r16oWNGzeievXqyh0IYzbs2rVreOqppzB69GisX78egGV6du3aNYwdOxZxcXGoV68e1qxZg969eyt3II5O8CdjTiVvXZylS/9HERGGi/C02pIvzouIMMzbX5pLly5RWFgYSZJETz/9NMXFxVntOBizZQXXxUlIILO6lp2dTVOmTCFXV1dyd3en+fPn8+l8jD2Rty7OgQP3zX5N27ZtG9WsWZNkWaY+ffrQzZs3rXcgjNmw2NhYkiSJ5s/fZXbPrly5Ql26dMmflGzPnj3WOxAHxoMpK+vQIYaAByRJuSZNG3nx4kVq3749SZJEjRo1splpahmzJQkJCQRMJBeXbJOmaM3OzqbXXnuNNBoNeXh40MKFC3kQxVgRer2evL1nkCQ9NPk1bevWrfmDqL59+1JycrKYg2HMhnXvvt2s9458Pb2yeDBlRaUvaDaZAA0BIEBNwOpiC5pduHCB2rVrR5IkUePGjenw4cOiD4cxmxUVReTi8qjCXVu9OociIyPzB1GLFy/mQRRjpYiKItLp9Ca9pn3xxRfk7+9PsixTv379eBDFWCnMee94+fJl6tSpE0mSRE899RR9++23og/HIfE1U1aSmAh07gw8fFj0kXcBzAGwFsAYAKee3B+c/wxZzkRubic0aZKB9evXIywsTPnAjNkpc7oGZMDNrQ/mzu2Nt956i6+JYqwU5vXsISSpM/r398eGDRtQpUoVpeMyZpfMe++YhdzcjmjYMI2vp1cYD6asZNAgYMcOw+8MCvME8DyA6DK+Wo/w8Hv47rvKSsVjzGGY0zVJysXAgRK+/FJSLiBjDsC817Rc9O2bg2++cVUqHmMOwdz3jh07/olDhxxtUSfbw792tYLbt4Hdu0sqwyMADwDcAuACQAUgCMCfRZ6nwvHjlZGSonhUxuyauV0jkrF7t8RdY6wM5r+myThwwJV7xlgZLPHeMTHRj3tmBTyYsoLo6NIeOfvkv4cBJAI4D+AKgOeKPVOSytoOYwzgrjFmDdwzxpTHPbMfPJiygqQkICurpEd8nvx3GIAWABoBeAXAyWLPzMwEzpxRKiFjjoG7xpjyuGeMKY97Zj94MGUFaWmlPRIAw8ezxl2fkZpqoUCMOSjuGmPK454xpjzumf3gwZQVeHmV9Wh7AJ8BOAfgNwD/AtC6xGf6+JR4N2PsCe4aY8rjnjGmPO6Z/eDBlBUEBQFabWmP7oHhtwzNADwFoB6Ar4s9S6cDAgMVCsiYg+CuMaY87hljyuOe2Q+eGt0Kbt8G6tYt7dxX42i1wLVrgB/PcMlYqbhrjCmPe8aY8rhn9oM/mbKCqlWB3r0Ns6qYQpKAPn24DIyVh7vGmPK4Z4wpj3tmP/iTKSspfRXr8rm5AQcPAsHB5T+XMWfHXWNMedwzxpTHPbMP/MmUlYSEAO+/b/jHXRFuboav4zIwZhzuGmPK454xpjzumX1QzZs3b57oEM4iJATw9QXi4oDHj8t+riT9VYZJk6yTjzFHwV1jTHncM8aUxz2zfXyaH2OMMcYYY4yZgE/zY4wxxhhjjDET8GCKMcYYY4wxxkzAgynGGGOMMcYYMwEPphhjjDHGGGPMBDyYYowxxhhjjDET8GCKMcYYY4wxxkzAgynGGGOMMcYYMwEPphhjjDHGGGPMBDyYYowxxhhjjDET8GCKMcYYY4wxxkzAgynGGGOMMcYYMwEPphhjjDHGGGPMBDyYYowxxhhjjDET8GCKMcYYY4wxxkzgFIOppk2b4rvvvhMdgzGHw91iTHncM8aUxz1jpnKKwdS5c+fQuXNn0TFK9N1336FWrVqiYxRz/fp1DBgwAL6+vqhVqxbWrl0rOhKzQdytivvPf/6D9u3bw83NrcS/u1OnTqF169Zwc3ND69atcerUKeuHZDaFe1Zx5fXs5ZdfRqNGjSDLMqKjo62ej9ke7lnFldWzixcvYsCAAfDz84Ovry969uyJn3/+WUxQhTnFYIpV3IgRIxAQEIDk5GTs3LkTs2bNQlxcnOhYjNk9X19fvP7665gxY0axxx49eoQBAwZgxIgRSE1NxejRozFgwAA8evRIQFLG7FdZPQOA5s2bIyoqCq1atbJyMsYcR1k9u3fvHvr374+ff/4ZycnJaNOmDQYMGCAgpRWQE6hbty7t37+fRo8eTbNnz86/Py4ujmrWrFnoecuWLaPAwEByc3Ojv/3tb3Tr1i3q1asXeXh4UNeuXenPP/8kIqLffvuNANC6deuoRo0aVL16dXr//fdLzbBz505q0qQJeXh4kL+/P7333nv04MED0mq1JEkSubu7k7u7O12/fp30ej0tWbKE6tevT76+vjRkyBC6e/euSfs1RXp6OgGg27dv5983YcIEGjFihEX3w+wfd8t069evp/Dw8EL37d27l/z9/Sk3Nzf/vtq1a9Pu3bsVy8FsH/fMdCX1rKCwsDD65JNPFNs/sx/cM9OV1zMiort37xIAunPnjmI5ROFPporYvn079u/fj4sXL+Lrr79G7969sXjxYty5cwe5ublYtWpVoefHxcXhl19+wb59+/Duu+/i22+/LXG748aNw7p165Ceno6zZ8/i2Wefhbu7O3bv3g1/f388ePAADx48gL+/P1atWoUdO3bg4MGDuHHjBnx8fPDqq6+atN93330X3t7epd5KQkSF/pv357Nnzxr998hYUdyt8p07dw5BQUGQJCn/vqCgIJw7d86k7THnwz1jTHncs4o7dOgQqlevjsqVK1tke7aEB1NFTJ48GdWqVUPNmjXRsWNHhIaGomXLlnB1dUVERAROnjxZ6Plvv/023N3dERgYiLFjx+Lzzz8vcbsuLi44f/487t+/Dx8fnzJPLVi3bh0WLVqEWrVqwdXVFfPmzcO2bdvw+PHjCu93xowZuHfvXqm3knh6eiIsLAwLFixAVlYWfvzxR2zfvh0PHz4s76+PsVJxt8r34MEDeHl5FbrPy8sL6enpJm2POR/uGWPK455VzB9//IFXX30VH3zwgdnbskU8mCqiWrVq+X/W6XTF/v/BgweFnl+7du38P9etWxc3btwocbvbt2/Hrl27ULduXYSHh+Po0aOlZrh69SoiIiLyfyPQpEkTqFQqJCcnV3i/pvrss8/w22+/oXbt2pg0aRKGDx9ukxc/MvvB3Sqfh4cH7t+/X+i++/fvw9PT02oZmH3jnjGmPO6Z8VJSUtCjRw9ERkZi6NChVt+/NTjVYMrd3b3Qpyu3bt0ye5u///57/p+vXbsGf3//Ep8XEhKC2NhY3L59GwMHDsQLL7wAAIVO58lTu3Zt7N69u9BvBbKyslCzZs0K73fx4sXw8PAo9VaaunXr4ptvvkFKSgqOHz+Ou3fvok2bNmX/ZTCnxd0yvltladq0KZKSkgqdYpuUlISmTZuatD3mWLhnlukZY2XhnlmuZ6mpqejRowf69++P2bNnm7wdW+dUg6kWLVpg165d+PPPP3Hr1i2sXLnS7G0uWLAADx8+xLlz5/DJJ5/gxRdfLPacR48e4bPPPkNaWhpcXFxQqVIlqFQqAIbfbty9exdpaWn5z584cSJmz56Nq1evAjCM6mNjYyu8XwCYNWtW/jm2Jd1K89NPPyE9PR2PHj1CTEwM9u3bh2nTplX474c5B+6W8d3S6/XIysrC48ePkZubi6ysLOTk5AAAOnfuDJVKhVWrViE7OxsffvghAODZZ5+twN8cc1TcM8v0LO+YsrKyQETIyclBVlYWcnNzjf+LYw6Le2aZnt2/fx89e/ZEWFgY3n333Yr9hdkZpxpMjRw5Es2bN0e9evXQo0ePUv9RVUR4eDieeuopdO3aFdOnT0ePHj1KfN6mTZtQr149VKpUCWvXrkVMTAwAoHHjxhg6dCjq168Pb29v3LhxA1OnTkX//v3Ro0cPeHp6om3btjh+/LhJ+zXV3r17Ub9+ffj4+GDt2rXYs2cP/Pz8LLoP5ji4W8bbtGkTdDodJk2ahMOHD0On02HChAkAAI1Ggx07duDf//43vL29sXHjRuzYsQMajcaiGZh94p4Zr6yeAUCPHj2g0+lw5MgRvPzyy9DpdDh06JBFMzD7xD0zXlk9++qrr5CYmIhPPvmk0Kdc165ds2gGWyBRwfNJHFSdOnUQExODTp06WWybV65cQUBAAHJycqBWqy22XVvdL2Ml4W4xpjzuGWPK454xUzn8J1MpKSlISUlBvXr1REdhzKFwtxhTHveMMeVxz5g5HHowlZiYiIYNG2Ly5MmoU6eO6DiMOQzuFmPK454xpjzuGTOXU5zmxxhjjDHGGGOW5tCfTDHGGGOMMcaYUngwxRhjjDHGGGMm4MGUlX30EeDuDsgyIEml32TZ8LyPPhKdmDH7xF1jTHncM8aUxz2zbap58+bNEx3CWXz0ETB9OlBgYe0y5eQAcXGAry8QEqJsNsYcCXeNMeVxzxhTHvfM9vEEFFaSmAh07ly0DPcBBAO4DEAPQAdgBoB/FvpaNzfg4EEgONg6WRmzZ9w1xpTHPWNMedwz+8Cn+VnJkiVAZmbRe7MA+AP4DkAOgOkA3gYQX+hZmZmGr2eMlY+7xpjyuGeMKY97Zh/4kykruH0bqFsXyMoy5tk6AK8BeK/QvVotcO0a4OenQEDGHAR3jTHlcc8YUx73zH7wJ1NWEB1t7DPPwvAbhy7FHpGkimyHMefEXWNMedwzxpTHPbMfPJiygqQkY36z8BBAJwCNAfQp9mhmJnDmjOWzMeZIuGuMKY97xpjyuGf2gwdTVpCWVt4zHsNQBDWAE6U+KzXVcpkYc0TcNcaUxz1jTHncM/uhFh3AGXh5lfVoLoAmANIB/ArArdRn+vhYNBZjDoe7xpjyuGeMKY97Zj/4kykrCAoyXARYsmYAbgL4CYBvqdvQ6YDAQMtnY8yRGLpW2pw63DXGLIFf0xhTHvfMfvBgygqCg88iOzu7hEe+h6EIGQBqAJCe3CKLPZMIGDNGwZCM2bmHDx/ixx+nIKvEk8y5a4xZiqvr59wzxhTWvv1Ffu9oJ3gwpaBTp06hZcuW6NYtCJUqfQ9JKvob8zAAVMItqtCzJAno04entmSsJA8fPsTo0aPh5eWFfftiEBR0A5JU9FncNcbMtWHDBvj5+eGNN0aiZs0kfk1jTAHnzp1DSEgIOnVqDA+Pw9wzO8CDKQX8+OOPaNGiBVq1agVJkvDjjz9i//5nodMVe4dnFJ0OmDnTwiEZs3MPHjzAqFGj4OXlhW+++Qb/93//hz///BMbNjSATmfaNrlrjBX3r3/9C1WqVMHEiRPRvXt3/Pnnn/jqqzb8msaYBZ09exbBwcEIDAxEdnY2jh8/jgMHunHP7AAPpizohx9+QFBQEIKDg6FWq3Hq1Kn8gVVICPD++4Bb6dcIlkinI7z/PhAcrExmxuzNgwcPMGLECHh7e2PXrl348MMPcffuXURGGk5xMLVrbm7grjFWwLp161ClShVERkaiV69euHfvHjZv3oxKlSpxzxizkKSkJAQHByMoKAiPHj3C8ePHkZSUhJCQEDPeO+Zyz6yJmNkSEhIoMDCQJEmi1q1b05kzZ0p9blQUkZsbkSQRGc5mLfkmSbkkSRnk5zeX9Hq9FY+GMduUlpZGQ4cOJZVKRZUrV6Z169aV+XxjuwboCXhAc+Zct9KRMGbb1qxZQ76+vqRWq2nkyJGUnp5e6nMr8poGPKDu3bdb8UgYs10nT56kli1bkiRJ1Lx5czpx4kSpz63Ye8eH5O09k987WhEPpsxw7NgxatasGUmSRMHBwWUOogpKTCQaNIhIqyXS6QoXQacz3D9oENHevXfJ1dWVXnzxRYWPhDHblZaWRi+99BKpVCqqUqUKrV+/3uivNbZrQUF/Iy8vL8rIyFDwSBizbatXryYfHx9Sq9U0atSoMgdRBRnbs3nzviFJkig2NlbhI2HMdp08eZJatGhBkiRRixYtyhxEFWRsz/73v/vk7u5Offr0UfhIWB6JiEqbR5iV4ujRo5gwYQLOnz+PkJAQbNy4EU2bNq3wdlJSgOhow+rUqamGtQACAw0zr+RdMHjgwAF0794dH374Yf5pTIw5g/v37+Pll1/Gtm3b4OvriyVLlmDcuHEmbau8rj169Ag1a9ZE7dq18eOPP1r0OBizdatXr8bbb7+N9PR0jBgxAmvWrIFbRc8rgnGvaePHj8e///1vXLp0CXXq1LHocTBmy3788Uf87W9/Q1JSElq0aIGNGzeiRYsWFd6OMT07fvw42rdvj0WLFmHGjBkWPQ5WAtGjOXsSHx9PTZo0IUmSKDQ0lM6fP2+V/c6fP59kWTb6txeM2bPU1FQaPHgwqVQqqlq1Km3cuNEq+718+TKp1WqKjIy0yv4YE23FihXk7e1NarWaxo4da7VPZps1a0bVqlWjnJwcq+yPMZESExMLXQpy+vRpq+x3xYoVJMsyHT582Cr7c2Y8mDJCfHw8NW7cmCRJorZt29KFCxesnqFr167k4eFh9GkXjNmb1NRUev7550mWZapatSpFR0dbPcPWrVtJkiTasmWL1ffNmLWsWLGCvLy8SK1W07hx46x+emt6ejp5enpSly5drLpfxqypItfTK2XgwIGk0+no7t27Vt+3M3HY0/xu3zZ8DJqUBKSlAV5ehtWkx441fs79+Ph4TJgwAT///DPatWuH6OhoNGzYUNHcpXn8+DFq164NX19fnDt3TkgGxoqyRM/+/PNPTJgwATt27EDVqlXx3nvvYcSIEYrmLsuUKVPw0Ucf4cKFC2jQoIGwHIwVZG7XcnNzsWLFCixYsAAPHz7EmDFjsGrVKmi1WsWzl+TUqVNo3bo15s6di3nz5gnJwFhRlnhNO378OMaPH49z586hdevW+OSTT9CsWTNFc5cmNzcX9evXh0qlwi+//AJZ5km8FSF6NGdpCQlEERGGC/G02pIv0IuIMDyvNHFxcfT000+TJEkUFhZGFy9etN4BlOH3338njUZDY8aMER2FOTlL9Ozu3bs0cOBAkmWZqlevTjExMdY7gHK0bt2aKleuTNnZ2aKjMCdnbtf0ej0tW7aMKlWqRC4uLvTKK69QZmamdQ+iFGvXriVJkmjfvn2iozAnZ4nXtCNHjlDTpk1JkiRq06YNnT171noHUIaUlBTSarX0/PPPi47isBxqMGX81JGG50VFFf76uLg4atiwIUmSRB06dKBLly6JOZAyfP311yRJktWuI2GsKHN7lpKSQgMGDCBZlqlGjRq0efNmMQdShszMTPL29qZ27dqJjsKcmDld0+v1tHTpUvL09CQXFxeaOHGizQyiCnrppZfI1dWVbt68KToKc1LmvqaJup6+IuLi4kiSJFq1apXoKA7JYQZTeWX46x/+YALcCAABDUosRl4pDhw4kD+I6tSpE12+fFn04ZTprbfeIpVKZTO/9WDOo3jPyu9aXs9SUlKof//+JMsy+fv72/x1SefPnyeVSkXTp08XHYU5IVNf09as0dPixYvJ09OTNBoNRUZG2vQnrHq9nho2bEg1a9bkdXGY1Znz3tEWrqeviIULF5Isy5RQ1sdrzCQOcc1UYiLQuTPw8GHBe98CoALwXwDZAC6V+LWS9BBE4QgPd8cnn3yCgIAApeNaRIcOHXD27FncuHHDpClsGauoknsGGNM1lSoLubmd4O9/AytXrsTgwYOVjmsRMTExGDVqFHbs2IH+/fuLjsOchDmvaUAG1OpumDgxGMuXL4dGo1E2rAXcu3cPtWrVQqdOnbBr1y7RcZiTMO+9YyaIOqF9e43Q6+krqlevXoiPj8eNGzdQqVIl0XEchkMMpgYNAnbsMPzOoLgOAG6h9BeeXPTqlYndu90Vy6cEXheHWVvZPQPK7poeoaE3cexYLcXyKYXXxWHWZu5r2sCBhK++UimWTwm8Lg6zNvN6pkePHpnYu9dDsXxKyM3NRZ06deDp6YmffvpJdByHYffTety+DezeXdYbvPLI+O47d6SkWDKV8jQaDRISEnDmzBm8+uqrouMwB2d+z1Q4fbqW3fUMADZs2IBGjRqhTZs2ePz4seg4zMFZ4jVtzx6V3XUtNDQUy5cvx+zZs3Ho0CHRcZiDs8Rr2qFDHnbXM1mWkZCQgN9++03orLmOxu4HU9HR5m9DkiyzHWsLCAjA559/jo8++ghffPGF6DjMgTlzzwDg6NGjePjwIXr06CE6CnNwzty1119/Hc899xx69eqFO3fuiI7DHJgz98zf3x+xsbHYvHkzNmzYIDqOQ7D7wVRSEpCVZd42MjOBM2csk8faBg8ejNdeew0jRozAL7/8IjoOc1DO3jMPDw8cOnQIBw8exD//+U/RcZgDc/auffnll6hatSratGmD3Nxc0XGYg3L2nvXs2RMzZ87ExIkTkZSUJDqO3bP7wVRammW2k5pqme2IsGrVKjRv3hzt2rXDo0ePRMdhDoh7BrRo0QIfffQRFi5ciP3794uOwxyUs3dNlmX88MMPuHnzJoYMGSI6DnNQzt4zAFi0aBHCwsLQsWNHPCw+sxSrALsfTHl5lfZIFoB7APQAcp/8ufRfQ/j4WDqZdcXHxyM3Nxfh4eGiozAHVHrPgIp0zd579vLLL+Oll17Cc889h1u3bomOwxwQv6YBVapUwe7du/HVV19h9erVouMwB8Q9Mzhw4AC0Wi3atWsnOopds/vBVFAQoNGUdFF4LwA+AI4B+O3Jn3uVuA2dDggMVCyiVWi1Wnz//fdITEzEm2++KToOczBBQYBWW9opN8Z1zRF6BhimS69Tpw6Cg4P5NCRmcYGBuVCrc0p4xLle0zp37owFCxbg9ddfR2Jioug4zMEY3jvqS3jEuXqmVquRkJCAn376CS+//LLoOHbLrqdG3759OyZPXoCbN48C0Jm8Ha0WuHYN8POzXDZReF0cZmlXr17F8OF/x/fffwbumQGvi8Ms7fHjx5g1axZWrdqC7OyLALQmb8uRutazZ098//33vC4Os5hdu3bhlVfm4o8/4sGvaQaxsbGIiIjApk2bMHz4cNFx7I5dfjK1bds21KpVCy+88AJataqF3r0lSJJp25IkoE8fxygDAIwYMQJ/+9vfMHjwYFy7dk10HGbHrl69ii5duiAgIAC3biWhffv7/8/emcdFdZ7t/37ODMMMOy64I4q7QmJEXMB9V6oOao27qSYRK8YYk2peiWu0En2lLkSjtVitPxKplTdijNUYI3GB2IRorbXGSAyxSA2hhIyIh+v3xzgEcICBmTPPLM/385kPycxhnus4XOece87z3Jfw2WMCAgLo9OnT9OGHH9Jvf/tb3nIETsyjR4/o1VdfJV9fX9qxYwe9+uocmjjRU3jtMR988AH5+/tTZGQkbykCJ+fYsWMUEhJCMTEx1LVrYxo9moTPHjNhwgRasmQJzZkzh/75z3/yluN8wIl499130bJlS0iShJiYGOTn5wMAsrIALy/AmBhQv4eXF5CdzXnHFCAsLAxBQUEoKxMoVv4AACAASURBVCvjLUXgZNy6dQsDBw4EYwwdOnTAqVOnAAifmWPr1q2QJAlnz57lLUXgZJSVlWHp0qXQarXw8vJCQkICZFkGILxWnby8PGg0GsyYMYO3FIETkp6ejuDgYDDGMGrUKOTl5QEQPjNHZGQkAgMDYTAYeEtxKpyimEpNTUWLFi0gSRImTJiAgoKCJ7ZJTq6/KXS6ciQnc9ghO1BcXAxfX18MHjyYtxSBk3Dz5k0MGDAAjDF07NgRp0+ffmKbhvhMoylzWZ8BwMSJE6HT6cwelwSC6pSVlWHJkiXw9PSEt7c3Vq9eXVFEVaYhXvPygst67cSJE2CMYc+ePbylCJyEI0eOoE2bNmCMYfTo0bh79+4T24hrx6oYDAY0atQIvXv35i3FqXDoYurQoUNo3rw5JEnCxIkT67xYMZmCsdqNwFg5GCtBUNAbZk9irkJOTg4kSUJCQgJvKQIH5ubNm4iOjgZjDJ06dcKZM2dq3d5ynwFqdSkYi8O1a9fsszMckGUZbdu2Rbt27Vz6eCKwjtLSUixevLiiiFqzZk2dfy/1OacR/YiRI/9ip73hw+uvvw6VSoWcnBzeUgQOzJEjR9C6dWswxjBmzBizRVRl6nvtGBi4wqWP9Tdu3IBarcaSJUt4S3EaHLKYOnjwYEURpdfrcf/+fYt/NzsbiI0FtFpAp6v+bYLx+dhY4IMPCuDp6Ynp06cruCf82b17NxhjOHnyJG8pAgfjxo0b6N+/Pxhj6Ny5c72mqlnqs+xsoG/fvi4/baCgoABarRaxsbG8pQgcjNLSUsTHx0Oj0cDHxwfr1q2r14WYpV5LSEgHYwwZGRkK7g1/Bg4cCD8/PxQXF/OWInAw0tLS0KpVK0iShHHjxlUsBbEES3126tQP0Ol0GD9+vIJ7wp9Dhw6BMYa0tDTeUpwChyqmDhw4gGbNmkGSJEyaNKleRVR17t0DEhOBWbOAmBjjz8RE4/MmTp48CcYYdu/ebQP1jsu0adPg6elZ57czAvfg+vXr6NevHxhj6NKlC86dO9fg97LEZ6WlpWjcuDEiIiJsoN5xOXPmDBhj2LZtG28pAgegtLQUCxcurCiiNmzYYNW32ZZ4bc6cOdBoNLhz544N9sAxKSsrQ1BQEMLCwnhLETgINa2nbwiW+CwzMxOSJGHLli02UO+4LFiwAGq1Grdu3eItxeFxiGIqJSUFQUFBkCQJkydPRmFhod3GTkhIcPlpA7Iso1OnTmjVqpVL35oW1M7169fRt29fMMbQtWtXZGZm2m1s07SBxYsX221MHmzYsAGSJCErK4u3FAEnSktLERcXB41GA19fX2zcuNGux92uXbuiRYsWLt186Pbt2/Dw8MDzzz/PW4qAI5XX048fP96u61Y3b94MSZJw/vx5u43Jg6eeegpNmzZ16eOJLah3MZWfD2zaBMyYYazaZ8ww/n/lqt1S9u3bh6ZNm0KlUmHKlCl2LaIqM3jwYJefNlBYWAhvb2+MHj2atxSBhdjKa9euXUNkZCQYY+jWrRu3g39qaqpbTBsYNWoUvL29UVRUxFuKwAJs5TODwYAFCxbAw8MDvr6+2LRpE5cvr4qKiuDj44MRI0bYfWx7cvToUTDGcPDgQd5SBBZiK68dOnSoooiyZD29UowbNw5eXl5WzaJydEpKSuDv748BAwbwluLQWFxMZWUBer1x3qhWa34+qV5v3K4u9u7dW1FETZ06lftFR1lZGZo1a+by0wYuXrwISZKwceNG3lIEtWArr129ehW9e/cGYwzdu3d3iG/Q4uLiXH7agCzLaNWqFTp37sxbiqAWbOUzg8GAF198ER4eHvDz80NiYiL3GQCXL1+GJElYs2YNVx1K8/LLL0OlUuH69eu8pQhqwVZes2Y9vRLIsow2bdqgQ4cO3D2vJFeuXIFKpcKKFSt4S3FYLCqm6tO9q7bWrHv27EGTJk2gUqkwbdo07kVUZXJzc+Hh4YH58+fzlqIoSUlJIhfHgbGF165evYqIiAgwxtCjRw9cvHjR/jtSCz179kTTpk1RWlrKW4pi5OXlwdPTU+TiOCi28JnBYMD8+fPh4eEBf39/bNmyxaEuqHbu3AnGmNmIA1dC5OI4NrbwWuX19LGxsdyLqMrk5+dDq9Vi6tSpvKUoyt69e92iwU1DqbOYskXWxa5du9C4cWOoVCpMnz7doYqoyqSnG7shHThwgLcURRG5OI6JtV7LyclBr169wBhDWFiYw67bMU0biI6O5i1FUUQujmNirc8MBgPmzZsHtVoNf39/bN26le8O1cKUKVOg1WqtWpDv6JhycVy9wY0zYq3X9u/fz209fX04ffo0GGNIdtXwqcfMnj3b5RvcNJRai6ma06FvgqgFiAhEKhAtNGuI3/zmMBo1agSVSoUZM2Y4xZqkZcuWQaVSuXwuTkhICEJCQhzqm1R3xhqvabUyOneeAcYYwsPDke0EsexXr16FSqXC8uXLeUtRlJUrV7p8gxtnwrpzWjl+8Ys1UKvVCAgIQFJSEu/dqRNZlhEaGorg4GCXPta7S4MbZ8K81+r2GZEx6D0gYDj39fT1Yc2aNZAkCZcvX+YtRVG6dOni8g1uGkKtxZReX9Ot2WAQtQHRXRDtfGyMo9W2eQTG/ozZs2c7RRFVGXfJxdHpdNDr9bylCGC91/z8/up0B/F9+/a5xbSBQYMGuXyDG2fBWp95ePyf07W+v3//vsjFEdgd816zxGdGr7VqddFhZzHVxLBhw+Dj4+PSx3p3aXBTX2otpqovFDQ+8h8b4ESl59qBqM8T23p6ljeoyx9vTLk4vXr14i1FUc6ePQvGmFN8w+rqWOs1rbZhHTV5I3JxBPbEXX1mysXZvHkzbymKInJxHIP8fHNes9xnzuq1srIytGjRAt26deMtRVGys7MhSRLWrVvHW4rDIFG9Of3456hKz3Ujoq+f2FKSGKWk1H8E3mg0Grpw4QLl5OTQ4sWLectRjIEDB9Kbb75JS5cupezsbN5yBE9gudcYI6f0WkpKCoWGhlJkZCQ9evSItxxFUKvVlJWVRdevX6cXXniBtxzBE7i+z6KioigxMZFee+01unDhAm85ivH2229T9+7dqU+fPvTw4UPectwW8x6x3GdEzuk107H+5s2b9Nxzz/GWoxgRERH0u9/9jt544w366KOPeMtxCGotph48MPfsfTO/1oiISp/Y0mAgunKlodL40rFjRzp48CDt2LGD0tLSeMtRjBUrVtCIESNoyJAh9N///pe3HLfFnb128eJFKi4upjFjxvCWohht27alw4cP0969e+lPf/oTbzluizv77JVXXqGxY8fS8OHD6fvvv+ctRzHOnz9PDx8+pGHDhvGW4rZ8+aU5r1nuMyLn9Vrr1q3pL3/5C+3fv5/279/PW45iLFq0iGJjY2ncuHF079493nK404A7U42JqLzac4VE5Gl268LC+o/gKEydOpXi4uJo2rRp9PXX5r89cQWOHz9OAQEBFBkZyVuKoAru4TU/Pz86e/YsffTRR7R27VrechRjwoQJtGTJEpozZw7985//5C1HUIF7+IyIKD09nZo0aUJ9+vSh8vLq++waeHl5UWZmJl24cIFWrFjBW45bUlRk7tn6+YzIeb02duxY+s1vfkPz5s2jv//977zlKMZ7771HLVu2pN69e7vs8cRSGlBMmb7t+Wul564RUTuzWwcG1n8ER2Lnzp0UFhbm0tMGJEmirKwsun37Ns2cOZO3HEEF7uO1Z555hrZv306rV6926WkD//u//0sRERHUr18/emD+NonA7riPzyRJouzsbPr2229p+vTpvOUoRo8ePWj37t20adMmOn78OG85boe/v7ln6+czIuf22saNG6lv374UFRVFP/30E285imA6nhQUFJBer+cthyu1FlNarblng4ioDRHNJ6J7RPQ2Ed0ioie/AdLpiMLCrNbInczMTHr48CENHTqUtxTFaNmyJaWnp9OhQ4fonXfeqfLavXtEiYlEM2cS/eIXxp+JiUQFBZzEuiDCa0QLFy6kyZMnu/y0gU8++YQYYzRgwIAqzwufKY/wGVFQUBBlZGTQe++9R8nJybzlKMa8efNo1qxZpNfr6dtvv614XvhMecLDzXnNcp8RuYbXPvroI9JoNBQdHc1bimI0atSI/vrXv9KxY8doy5YtVV5zK6/V1p3CfOcjU1ZA88edWWrOCnDGbiw14S65OAkJCZAkCTk5OcjKMrY31Wqf/FvQ6YzP6fXGPAmBdQivGTHl4rRp08ZtcnGEz+yH8NnPuFsuzvnzZcJndsJ8Nz/LfeZKXrt16xbUajUWLlzIW4qibNq0CZIk4fz58255TmtgzlTdD8aA2Fh77YZ9SElJcZtcHE/PJdDpyuv8/BmrmlguaBjCaz9z//59eHl5ISYmhrcURTl06BCIFkCjKRM+sxPCZ1UZPny4W+TiaDSLIUkG4TM7Irz2M2lpaWCMITU1lbcURRk7diw8POLd8tqx1mKq5rT4uh9eXkB2tr12w37MnTvX5XNxdux4BKKSen/ermIKHgivVeX8+fMun4uTnAyo1Q+Ez+yI8FlV3CEXJzkZ0GofCZ/ZGeG1qsTHx0OtVuPmzZu8pSjGzp0yGHPPa8daiynAuJP1NYRW+8gl/nFqolu3bmjRogXKysp4S7E5NR8A/WG8NW96eLjFAdCeNMRrrnIgMsfmzZshSRIyMzN5S7E5wmf8aJjPyl3WZ3fu3IFGo8GcOXN4S7E5wmd8Eee0qvTq1QuNGzdGaWkpbyk2x929VmcxBfxsCEtu20mSARrNYhQVFSmtnRvFxcXw8fHBsGHDeEuxOTXfmvcH0ew6P39XujXPA8u9JoPoRzz3nAtNOjZDTEwMdDod7t+/z1uKTRE+44vlPisHUQk6dtzCW7KiZGRkgDGGffv28ZZiU4TP+GOp14iM57TExP/ylqwYBoMBgYGB6Nu3L28pNsfdvWZRMQUYq8bYWOPCMZ2u6j+EaUFZbCxw6ZKMFi1aoEuXLkrq5s7ly5chSRLWrFnDW4rNqHnRqGWGIHKdRaM8sdRr06ZtgVqtxo0bN3hLVgxZlhEcHIzQ0FCXaUghfOYYWOqzQ4duQKVSYeXKlbwlK8ry5cuhUqlw9epV3lJsgvCZ42CJ1/T6crRo8QuEhIS4zLHeHNevX4dKpcKyZct4S7EZwmv1KKZM3LsHJCYCs2YBMTHGn4mJVf8RTNMGZs+ebUutDsfOnTvBGMOpU6d4S7EJmzbVZQj2+OELoq1mt9PpjH8PAuuxxGsRERFo1KiRS04bMJGfnw+tVospU6bwlmIThM8cC0t8tnv3bjDGcOLECX5C7UBUVBT8/f1RUlLCW4rVCJ85HnV5raCgADqdDnq9nq9QhTlw4AAYY0hPT+ctxSYIrzWgmLKU48ePgzGGvXv3KjWEQzB16lRotVrk5+fzlmI1M2bU9s3BXhDlgagIRPNhnPt62uy2s2bx3hP3wTRtoE+fPrylKMrp06fBGMPOnTt5S7Ea4TPnZPr06fD09EReXh5vKYpRWlqKpk2bomfPnrylWI3wmXNy9uxZMMaQlJTEW4qizJ8/Hx4eHsjNzeUtxWqE14BaQ3utYcyYMbR8+XJ68cUX6erVq0oNw51Dhw5R69atKSIigsrLy3nLsYqiotpenUdELYnIj4j2EFFjIjIf+FhYaGtlgprQarV04cIF+uyzz+iVV17hLUcxhg4dSmvXrqX4+Hj67LPPeMuxCuEz5+TAgQMUEhJCkZGRTn+srwmNRkOXLl2iK1eu0MKFC3nLsQrhM+dk4MCBtGHDBlq6dCllZ2fzlqMYe/bsoS5dulBkZCQ9evSItxyrEF4jUqyYIiLasGED9e/fn6Kjo+mnn35ScihuSJJE2dnZdP/+fRo/fjxvOVbh71+frRkRwewrgYG2UCOwlM6dO9P+/ftp69atlJ6ezluOYqxcuZKGDh1KQ4YMof/+97+85TQY4TPnRJIkysrKoh9++IHGjRvHW45itGvXjlJTU2nXrl307rvv8pbTYITPnJfly5fTyJEjaciQIfTDDz/wlqMY58+fJ4PBQCNGjOAtxSqE1xQupoiIPvroI9JoNNS/f3+lh+JGQEAAnTp1ij744AN66623eMtpMOHhRFqtuT/yXCJ6k4h+IKIHRLSQiP5DRPOf2FKnIwoLU1KlwBwzZsyg+fPn05QpUyg3N5e3HMX44IMPyNfXl/r27ctbSoMx+szcK8Jnjo6fnx+dOXOGTp48SRs3buQtRzEmTZpEixYtopkzZ9K//vUv3nIahPCZc5ORkUEBAQHUp08fl70T7OPjQ+fOnaNz587RG2+8wVtOgxHXjkSKrZmqzK1bt6BWq7FgwQJ7DMcNZ87FkWUZy5f/L4gMZuayXgOR1+O5rgQibxBtNDvn1dk7sjg7YWFhCAoKcskMNBPOnotz9Oh5MGYurFf4zFlISkoCYwxnz57lLUVRnDkX5/r17yFJpcJnTkxeXh48PT0xffp03lIUxdTg5uTJk7yl1BtZlrFq1U63v3a0SzEFAEeOHAFjDIcOHbLXkFxwtlwcWZaxZs0aeHt7Q6PRoH37Lx5nq9TextLcwxWyApydkpIS+Pn5YdCgQbylKIoz5uKcOXMGnTp1AmMMjRp9LHzm5Oj1emi1WhQUFPCWohjOmItz//59TJw4EZIkwdMz43Emn/CZs3LixAkwxrB7927eUhTF1ODm7t27vKVYhCzLSExMhJ+fHzw8PNC27WW3PqfZrZgCgCVLlrhNLk779u0dOitBlmWsXr0a3t7e8PT0xOLFi1FaWlpLinXdD1dIsXYFcnJy3CoX58qVK7yl1MqZM2fQsWNHMMYQHR2NmzdvCp+5ALIso127di6fi3Pt2jWoVCosXbqUt5RaKSgowIQJEyBJElq0aIFDhw4Jn7kICQkJUKlUyMnJ4S1FMWRZRqdOndCqVSuHPp7IsoyNGzfC19cXHh4eWLBgAQwGg9t7za7FFAD07t0bjRo1gsFgsPfQdsOUizN58mTeUp7AeEt2Fby8vODp6YmXX375iSlhpsTy+pohOZnTTgmeYM+ePW6RixMdHe2wuTinTp1Chw4dwBjDgAEDcPPmzSqvC585P+6Wi3P06FHeUp6goKAA48ePryiiUlNTq7zeEJ/pdOXCZw7G4MGD4efnh+LiYt5SFKOwsBDe3t4YPXo0bylPIMsyNmzYAF9fX2g0GsTFxT0x/dedz2l2L6ZM0wYiIyPtPbRdMeXibN++nbcUAEYjrFy5El5eXtBqtVi6dGmt62pMpmCsLjPIIPoRb775vR33RmAJM2bMgEajcYtcnKeffpq3lApOnTqF0NBQMMYwaNAg3Lp1q8ZtLfWZcfrEj4iNdb459a7OuXPnIEkStm7dyluKophycW7fvs1bCgDjl5bjxo2DJElo2bIl3n333Rq3rZ/PStC8+Wo77onAEsrKytCsWTOEhYXxlqIoWVlZkCQJGzdu5C0FgPHacf369fDx8YFGo8GiRYtqXUNZ32vH1av/bce9UQ67F1MAcP36dahUKrz88ss8hrcb69atgyRJyOZ4/1KWZbz++uvQ6XTQarVYtmyZxc0JsrON81i1WmM6ddVv7ozPx8YCISGT0aJFC5dueuCsdO7c2eGnDViLozS4OXnyJNq3bw/GGAYPHmzxRaelPnvppQOQJAnnz59XeE8E9WXjxo2QJAkXL17kLUVRHKHBTX5+PsaOHQtJktCqVSscPnzYot+z1GfHjv3bqRvcuDK5ubnw8PDA/PnzeUtRlKSkJEiSxLXBTfX19PHx8RY3orHUa126zHTaBjfV4VJMAcDBgwfBGMORI0d4SbALw4cPh4+PD4qKiuw6rizLWLFiRUUR9dprrzX4BHjvHpCYaEynjokx/kxM/LnzSlFREXx8fDB8+HAb7oHAFhQVFcHb2xujRo3iLUVR0tLSwBh7YoqPPTh+/DjatWsHxhiGDBnS4G/u6/IZAIwbNw5eXl5O0+DGnRg9ejS8vb1RWFjIW4piFBcXw9fXF4MHD7b72Hfv3sWYMWPAGEPr1q2RlpbWoPexxGfvv/++0zW4cReOHj0KxhgOHDjAW4qi6PV66HQ6uze4qWk9fUOoy2sGgwEBAQFO1eCmJrgVUwDw4osvOtS0ASUoKytDixYt0LVrV7uN99prr0Gn00Gn02H58uV2+Rbx8uXLkCQJa9asUXwsQf0wTRvYsGEDbymKsnjxYrs2uMnIyEBISAgYYxg2bBhyc3MVH1OWZbRp0wYdOnRw6buNzogsy2jVqhU6derk0p9NTk4OJElCQkKCXcbLy8vDqFGjwBhDmzZt7PYF7GuvvQaVSoWrV6/aZTyB5SxbtgwqlQrXrl3jLUUxZFlGSEiI3RrcmNbTm4ooc+vplcDU4GbZsmWKj6UkXIspAAgPD+c+bUBpTLk4s2fPVmyMsrIyvPrqq9BqtdDpdFixYoXdT+g7d+4EYwynT5+267iCutm2bZtb5OJERESgUaNGik4byMjIQNu2bcEYw/Dhw3Hnzh3FxjKHqcHN1KlT7TquoG7u3r3rVrk4Sja4ycvLw8iRI8EYQ3BwMJfmF1FRUQ7b4Mbd6du3LwIDA126mZk9GtzIsoyEhISKpmR1radXgv3794MxhvT0dLuOa0u4F1Pukotz/PhxMMawd+/eiufy84FNm4AZM4y3QGfMMP5/fYLLysrKsGzZsooiauXKlVy/FZ0yZQq0Wi3y8/O5aRCYJzY21m1ycfr06VPleVt47f33368ookaMGGH3IqoypgY3O3fu5KZBYJ6TJ0+6VS5O5QY3tvDZnTt3MGLECDDG0LZtW64XWKWlpWjSpAl69uzJTYPAPKWlpWjcuDEiIiJ4S1EUU4ObpKSkiuds4TPTenpTU7L6rKdXgnnz5sHDw8MuMzyUgHsxBfyci/P666/zlqIoK1asgEqlwv/7fzeh1xsX4Wm15hfn6fVAVlbN71VWVoaXX34Znp6e8PLyQkJCgkNMLZFlGaGhoWjTpo1D6BH8jCkXp23bti792Zga3CxduhRZWbDaa0ePHkWbNm3AGMOoUaMcpjvimjVrIEkSLl++zFuKoBoJCQmQJAmff/45bymKUTkX5+JF2Wqf3blzB8OGDQNjDCEhIcjIyLDfztSCqcFNXFwcbymCaty4cQNqtRrx8fG8pSiKqcFNSsrfrfaZLdfT25oePXqgWbNmDqOnPjhEMQX8nItz/Phx3lIUJTT0LRCV1JkUzZj5/vtlZWVYsmQJPD094e3tjdWrVzvchfH9+/eh0+kQExPDW4qgGqZpAxMnTuQtRVEOHDgAogXw9CyzoB2yea8dOXKkoogaPXq0QybTDxs2DD4+Pi6dveKsDB48GL6+vi792RQWFkKjWQxJMjTYZ7m5uRg6dCgYY2jXrp1DXgMcPnyYW4MbQe2kpqaCMWZxV0dnpVu3bVZdO8qyjOXLl9t9PX194NngxlocppgCXD8Xx9h/vyYjnAQRgSjEbKBZaWkpFi9eXFFErVmzxuGKqMpkZmZCkiRs3ryZtxRBNdwhFyc5GVCpHljss8peS0tLQ+vWrcEYw5gxYxyyiDJRVlaG5s2bo1u3brylCKrhDrk4ycmAVvuoQee027dvY8iQIWCMoX379g4fMB4fH2/XBjcCy4mLi4Nara4108+Zseba0RHW09eHzz//3K4NbmyFQxVTANClSxeXzMXJyqorGboRiPzMXuSp1Q+gVveFj48P1q1b5zT/Nps3bxa5OA6KK+fi1O61mn1m/FavBIz1xrhx45xm3Z+pwc3cuXN5SxFUw5Vzcaw5p0mSAUQRCA0NxcmTzhNE3atXL5fJxXE1evbsiaZNm7rcZ2PdtWMpNJr+DrGevj7s2rULjDGnOjY4XDHlqrk4en1tidDxIGoNokE1XOQ9Qvfu/3AaI1QmJiZG5OI4KGPGjHHJXJyavVaXzwAiGePGOV93qIyMDJGL46Ckp6e7ZC6Otee06Gjn+LKiMqYGN66Qi+NqlJSUwN/fH9HR0byl2BRrfdaly1WnvHacNm0aPD09HXpmSGUcrpgCfs7FWb9+PW8pNiE//8nFgj8/7oDIA0QXa73I02rr16nFUZBlGcHBwQgNDXVKQ7sysiyjdevW6Nixo8t8NjV7zTKfObPXli9fLnJxHBRXy8Vx53Oaq+TiuCJXr16FSqXC8uXLeUuxCe7sM1mW0bFjR6eZqSaRA9K7d29KSkqihIQE+vjjj3nLsZqUlNpejSGiYUTUp9b3YKyu93FMJEmi7OxsysvLo2effZa3HEElTJ/NN998QzNnzuQtxybU7BHLfEbkvF7buHEj9e3bl6Kiouinn37iLUdQibfeeot69+5NUVFR9ODBA95yrMadz2ldu3allJQU2rJlC6Wnp/OWI6hE9+7dac+ePbRp0yY6fvw4bzlW484+kySJsrKy6IcffqCYmBjecurEIYspIqL4+HjS6/U0ZswY+s9//sNbjlV8+SWR+fPnu0R0nYgO1/keBgPRlSs2FmYngoKCKCMjg9LS0ig5OZm3HEElmjdvTu+//z6lpqbSO++8w1uO1Zj3muU+I3Jur3300Ufk4eFB0dHRvKUIqnH27FmSJMklPht3P6fNnDmTfvWrX9GUKVMoNzeXtxxBJZ577jmaPXs26fV6+vbbb3nLsQp391lAQACdPn2aPvzwQ/rtb3/LW06tOGwxRUR0+PBhatmyJUVERFB5eTlvOQ2mqKimV1KJqJSI/IlIRURnieg2EXmZ3bqw0Pba7MXQoUNp9erVFB8fT3/72994yxFUYsSIEfTGG29QXFwcffHFF7zlWIV5r9XPZ0TO6zWNRkNZWVl05coV+vWvf81bjqASGo2GLl26RDk5ObR48WLecqxCnNOI9u7dS126dKHIyEh69OgRbzmCSqSkpFCHDh2c/rMRPiPq06cPbdmyhf7nf/6HPvnkE95yNhoB1gAAIABJREFUasShiynTNKR79+7RpEmTeMtpMP7+Nb2yh4hyiOjzx49eRNSKiC6b3TowUAFxduSNN96gIUOG0KBBg+jHH3/kLUdQidWrV9OgQYNo4MCBTv3ZmPda/XxG5Nxea9euHaWmptLbb79N7777Lm85gkqEhobSn/70J9qxYwelpaXxltNgxDnNyPnz58lgMNCIESN4SxFU49KlS1RcXExjxozhLaXBCJ8ZWbJkCY0fP55Gjx7tsDPVHLqYIiJq1KgRnTx5kv7v//6PkpKSeMtpEOHhRFotzLzShIjCKz18iMiDiLo+saVORxQWpqRK+3DixAny9fWlPn3qXrsisC8nT54kb29v6t+/P28pDSY8nMjDo/o3kZb7jMg1vDZp0iRatGgRzZw5k/71r3/xliOoxC9/+UuKi4ujadOm0VdffcVbToMIDyfy9DQ3W8S9zmk+Pj6UmZlJn3zyCb3xxhu85Qgq4ePjQ2fPnqWPPvqI1q5dy1tOgxDXjj/z5z//mYKCgigyMtIxZ6rx7oBhKZs2bXLKXJySkhL88peLQPRTjd3DLHk4a0cWc5hycebMmcNbiqAaplycefPm8ZZSb/bu3YvGjbsKr1VC5OI4Lj179kSTJk2c7rO5evUqnn56pPBZJXbv3u10uTjuws6dO8EYw6lTp3hLqRcGgwEzZy4VPqtEQUEBtFotYmNjeUt5AqcppgBjLo6Xl5dT5OIUFxdj9uzZUKvVaNSoEZ566qtasgJqfzAGOODfjlWIXBzHxZSLs3//ft5SLGL37t1o3LgxVCoVpk2bhpiYh8JrjxG5OI6LKRcnKiqKtxSLuHr1KiIiIsAYQ48ePTBo0H3hs0qYcnHy8vJ4SxFUY8qUKdBqtU4RxG4wGDB//nx4eHjA398fPXrcAGPlwmePOXPmDBhjSEpK4i2lCk5VTDlDLk5xcTFmzJgBlUqFRo0aITk5GYAlKdY1P7y8gOxszjumACIXx3F59dVXHT4XZ9euXRVF1PTp01FUVARAeK06IhfHcXGGXJycnBz06tULjDGEhYUhKysLgPBZdWRZRqdOnZwmF8edkGUZHTp0QJs2bRz2szEYDJg3bx7UajUCAgKwdetWAMJn5tiwYQMkSao4FjkCTlVMAcDdu3fh6emJadOm8ZZSheLiYkyfPh0qlQqNGzfGrl27ntgmObn+pvD0LMPjeswliYqKgr+/P0pKSnhLEVSjX79+CAwMhMFg4C2lCsnJyWjUqBFUKhVmzZqF4uJiM9vU32teXnBZrx04cACMMaSnp/OWIqhGSkoKGGPIyMjgLaUKOTk5eOaZZ8AYQ3h4OLLNXJU1xGc6XbnL+qywsBDe3t4YPXo0bymCahQWFsLLywsxMTG8pVShpKQEc+fOrSiizN1xaYjPNBrXvnYcNWoUvL29K75E5Y3TFVMAcOrUKTDGzBYs9qaoqAjTpk2DSqVCkyZNsHv37lq3N5mirukRjAEq1QOoVL926WkDpaWlaNKkCXr27MlbiqAapaWlaNy4MXr16sVbCgDj3PfAwECo1WrMnj3bbBFVGcu9Vg6iHzF5snPNqa8vpqkjubm5vKUIqjF37lxoNBrcuXOHtxR8/vnn6NmzJxhjeOqpp3D58uVat6+fz0rQsuVaO+0JHy5evAhJkrBx40beUgTVOH/+PCRJwubNm3lLQUlJCebMmQO1Wo3AwEBs27at1u3rc+2oVj8AYwtx69YtO+2N/ZFlGa1atULnzp15SwHgpMUUAKxatQqSJOHzzz/nMn5RURGmTp0KlUqFpk2bYu/evRb/bna2cR6rVgvodNW/tTM+Hxtr3K5z584uP23g1q1bUKvVWLBgAW8pgmrcvHkTarUaixYt4qZh27ZtFUXUnDlz6nUX01Kv/frXf3DKBjf1JSwsDEFBQSgrK+MtRVCNbt26oXnz5tw+m8uXL+Opp54CYww9e/as17nVUp+9//5deHh4YP78+QruCX+SkpIgSRLOnj3LW4qgGps3b4YkScjMzOQyfvX19Dt37rT4d+tz7dizZ080bdrU6Rrc1Ie8vDx4enpixowZvKU4bzEFAEOGDIGvr2+d31DbksLCQkyZMqWiiLKmgcK9e0BiIjBrFhATY/yZmFi180pRURG8vb0xatQoG6h3XNLS0sAYQ2pqKm8pgmq8++67YIzh8OHDdh03KSkJAQEBUKvVeO6556yaCmqJ10aPHg1vb2+naHDTUIqLi+Hr64vBgwfzliKoRnFxMXx8fDBs2DC7jpudnY3w8HAwxvDMM88gJyenwe9lic+OHj0KxhgOHDhgA/WOy8SJE6HT6VBQUMBbiqAaMTEx0Ol0uH//vt3GrGk9fUOwxGemBjfR0dE2UO+4nDhxAowx7Nmzh6sOpy6mysrK0Lx5c/To0UPxsQoLCzF58mRIkoSgoCCkpKQoPqaJrKwsSJKEDRs22G1MHsTHx0OtVuPGjRu8pQiqsXDhQqjVaty8eVPxsbZu3Qp/f3+o1WrMmzfPbuvpTNMGOnXq5NJ3gnNyciBJEhISEnhLEVTj8uXLkCQJa9asUXys7OxshIWFgTGGXr16WVVE1ZelS5c6fIMba5FlGSEhIQgJCXHp44kzIssygoODERoaqvhnY8l6eqVwhgY3tmDlypVQqVR2PYZVx6mLKcCYi6PRaJ7IxcnPBzZtAmbMMFbuM2YY/7++/fbv37+P2NhYSJKEZs2acWsXvW3bNjDGcObMGS7j2wuRi+O4PPPMM0/k4tjKZ7IsY8uWLfD396+YBsSj8YWpwc306dPtPrY9Ebk4jktycrLZXBxbee3ixYvo0aMHGGOIiIjAlStXbKjecvr27euQDW5sSUFBAXQ6HfR6PW8pgmrk5+dDq9ViypQp1Z63jc/qu55eKfbt2+eQDW5szaBBg+Dn52fXmWqVcfpiCgDef//9ilycrCxArzfOHdVqzc8p1euN7SZr4/79+9Dr9ZAkCc2bN3eIKQmxsbHQarUuPW1A5OI4LgaDAQEBAYiKirKZz2RZRmJiIvz8/ODh4YEXX3yR+8XVyZMnwRjjdvKzF9OnTxe5OA7K1KlTK3JxbOW1ixcvonv37mCMoXfv3twjKRytwY1SnD171iFzcQTA6dOnwRjDzp07beYza9bTK4UjNbhRirKyMgQFBSEsLIzL+C5RTAHGXBzG4qDVyhZ1O6mpDXJBQQEmTJgASZLQokULHDp0yP47UwOyLKNdu3Zo27atS08bMOXiLF26lLcUQTWuXr0KxuKgVpda5TNZlrFp0yb4+vrCw8MDCxYs4F5EVSYhIYFrgxt7IHJxHBdTLk5AwAp4eZVb5bXz58+jW7duYIwhMjLSoabW3bhxA2q1GvHx8bylKIoj5uIIjKxbtw5EC6DVPrLKZ7ZcT68E3bp1Q4sWLVy6+dDt27fh4eGB559/3u5ju0wxlZwMSNJPDc6VKSgowPjx4yuKKEdthGCaNjBx4kTeUhTFlItz9OhR3lIElUhONuZXNNRnsixj48aN8PX1hUajQVxcnMNO6Rw8eLDdG9zYG5GL47hs2VICoh8b7LXMzEx06dIFjDH07dsX169f57tDNZCamsqlwY29cbRcHIER47WjocE+47mevj6YGtwMHz6ctxRFMTW4OXjwoF3HdYliquaE6BAQSSAiEHmAaPYT2+h05YiOXgJJktCyZUu8++67vHenTs6dOwdJkioSsl0VUy7O7du3eUsRwDqfeXmVIy7u9/Dx8YFGo8GiRYsctogyUVZWhmbNmnGbNmAvRC6O42GN1zw9H6Ft20lgjKFfv34OW0RVJi4uzm4NbnjhaLk4AuuvHYcMeZX7evr6YM8GNzwxNbix57HPJYopvb6mILOjICp6/N8Zj81xoNo2j6DVZjjdt2IbN24UuTgCu2Ktzxg7gvj4eIcvoiqTm5srcnEEdsdarzVqdMbpuqL27NnziQY3roYj5eIIrPeZp+cxh1hPXx927txptsGNq9GnTx+7Nrhx+mIqP//JxYLmH8cfG2LJE69ptfXv1OIIjBkzRuTiCOyCbXxW7pQ+S09Pd4tcHL1eL3JxHAB3PaeZcnGioqJ4S1EUUy6Oqze4cXTc1WcAMGXKlIoGN65KaWkpGjVqhIiICLuMJ5GTk5JS1xY9iIgR0Vgi8iSi3zyxBWOWvI/jcezYMQoMDKTIyEgqLy/nLUcRfHx8KDMzkz755BN64403eMtxW2zjM+aUPhs/fjy98sorNHfuXPrHP/7BW45ipKWlUbNmzah3794uezxxBtz1nObl5UWffvopXbx4kVasWMFbjmKMGjWKVq5cSXFxcfTll1/yluO2uKvPiIhSU1OpdevWFBER4bLHeo1GQxcvXqQvvviCXnrpJcXHc/pi6ssviR48qG2Lq0RUSkQ7iag/Efk9sYXBQHTliiLyFEWSJMrOzqZvvvmGZs6cyVuOYoSHh9Pbb79N69evpw8//JC3HLfEnX1GRPTWW29R7969KSoqih7U/g/htJiOJ/n5+TR58mTectwWd/Za9+7dac+ePbRp0yY6fvw4bzmKsXbtWhowYAANGDCAfvzxR95y3BJ39pnpWH///n2aMGECbzmK0bFjR/rjH/9I27dvpz//+c+KjuX0xVRRkSVbaYhoIRF9R0SzzG5RWGg7TfakefPm9P7771Nqaiq98847vOUoxgsvvEDTpk2jCRMm0Hfffcdbjtvh7j4jIjp79ixJkkTR0dG8pShGkyZN6MSJE5Senk6/+93veMtxS9zda8899xzNnj2b9Ho9ffvtt7zlKMapU6dIq9VS//79eUtxS9zdZwEBAXTq1Ck6fvw4bdmyhbccxZg2bRq9+OKL9Oyzz9LXX3+t2DhOX0z5+9dna5mIvjL7SmCgLdTwYcSIERXTBr744gvechTjwIED1LZtW5ee1uioCJ8Zpw1cuHCBcnJyaPHixbzlKMbAgQPpzTffpKVLl1J2djZvOW6H8BpRSkoKhYaGUmRkJD169Ii3HEVQq9WUnZ1N169fpxdeeIG3HLdD+IyoX79+lJiYSK+99hp9+umnvOUoxttvv009evSgPn36KHY8cfpiKjycSKs198rfiWgxEf2biB4S0ZtEdIOM81+rotMRhYUpKNIOrF27lgYNGkQDBw502WkDkiTRpUuX6IcffqBx48ZVee3ePaLERKKZM4l+8Qvjz8REooICTmJdDOEzIx07dqSDBw/Sjh07KC0tjbccxVi+fDmNHDmShgwZQv/9738rnhc+Ux7hNSMXL16k4uJiGj16NG8pihEcHEyHDx+mvXv30sGDB6u8JrymLMJnRl555RUaO3YsjRgxgr7//nvechTj008/pYcPH9LQoUOrPG8zn9mlzYWC1NyR5RqI/EFEjx+eIJpltluLs3ZkqU5ZWRmaN2/uNrk4GzZsQFaWsb2pVvvk34FOZ3xOrzfmSQgajvBZVRYuXOg2uTidOnXCxYuy8JmdEF77GXfLxbl27Zo4p9kJ4bOfkWUZwcHBCA0NhSzLvOUoxpUrV6BSqbB8+XKb+8zpiymgtqyAuh+MAbGxvPfAdphycebNm8dbiqIkJSWBaAE8PR/V+dkzVjWxXNAwhM+q4i65OCrVr6FSPRA+syPCaz/jTrk4Ot3L8PIqF16zE8JnP5Ofnw+tVospU6bwlqIo+/bte3ztWGZTn7lEMVVzinXdDy8vIDub9x7YFlMujjMkcjeU5GRApTLU+7MWJ5+GI3xWFXfIxUlOBjw9Hwmf2Rnhtaq4Qy7O9u1lICoRXrMjwmdVOX36NBhj2LlzJ28pimG8dnxgc5+5RDEFGHe0vqZw5QPRsmXLKqYNuBo1HwDPgagpiBiMIXthIDI88Zm72gHQnjTMZ+Uu67OrV69WTBtwNYTP+CLOaT8jyzJCQ0PRpk0bl5yGJLzGD+Gzqqxbtw6SJCHbBf+olPSZyxRTwM+mqPvWnQyiH/HSS//gLVlR+vXrh8DAQBgMBt5SbErNt+abgigURIUgyoFxrnNstc/etW7N88Byn5WD6Ef07Lmbt2RFSUlJAWMMGRkZvKXYFOEz/ljqNaJHICpBUtID3pIV4/79+/Dy8sK4ceN4S7E5wmt8qe+144svfsFbsqIMHz4cPj4+KCoq4i3FpijpM5cqpgBj5Rgba1w8ptNV/QczLSqLjQVGjnwdnp6euHv3Lm/JilFaWorGjRujV69evKXYjJoXjQJEGhCtrvT/ESDq8sR2Wi3vvXB+LPXZrl2fgTGG7du385asKHPnzoVGo8GdO3d4S7EJtvKZKyzO5o0lXps4UUZAwHA8/fTTvOUqyvnz5yFJEhITE3lLsRninOYYWHpOmzhxPTw8PHD79m3ekhWjrKwMLVq0QNeuXXlLsRlK+4wBgPVNBx2PggKilBRjOnVhoTELICyMaO5coqZNicrLy6lLly704MEDun37NkmS03eJN8tXX31FXbp0obi4ONq2bRtvOVaTmEi0alVNyeXTiejS48dtIoomopeIaFOVrXQ6op9+Ulanu1CXz4iI1q9fT6tWraJLly5RREQET7mK0r17dyosLKRvvvmG1Go1bzlWYSufrVlD9Oqrymp1F+ry2tdff02dOnWi+fPn09tvv81brmJs2bKFXnvtNfrkk08oKiqKtxyrEec0x8KSc1p4eDjl5+dTXl6e0x/ra+Lbb7+l0NBQmjZtGqWkpPCWYzWK+8xeVaEjUlhYCC8vL4wdO5a3FEV59913wRjD4cOHeUuxmhkzarsN/z6IdCAytTQNBZFsdluBfRkxYgS8vb1dbtpAZYqLi+Hj44Nhw4bxlmI1tvLZrFm898S9OHLkCBhjOHToEG8pihITEwOdTof79+/zlmI14pzmfJSUlMDPzw+DBg3iLUVRMjIywBjDvn37eEuxGqV95pq3YywkICCATp06RSdOnKDExETechTjl7/8JcXFxdG0adPoq6/Mp3g7C0VFNb3yiIgmElEUERWRMWSvhIj62keYoFZOnDhBfn5+1KdPH95SFMPHx4fOnj1LZ86cobVr1/KWYxW28llhoQLiBDWi1+vppZdeotmzZ9O//vUv3nIUIz09nZo2bUqRkZFUXl7OW45ViHOa8+Hl5UXnzp2jzMxMSkhI4C1HMcaOHUu/+c1v6Pnnn6e///3vvOVYhdI+c+tiioioX79+9NZbb9GKFSsoMzOTtxzF2LlzJ4WFhVHfvn3p4cOHvOU0GH//ml75iohkIvo9EfkRUUcimktEX9pFl6B2JEmizz77jG7dukVz5szhLUcxnnnmGdq+fTutXr2aTp8+zVtOg7GVzwIDba9NUDtbt26lnj17Ov2xvjYkSaLs7GzKy8ujZ599lrccqxDnNOckPDycdu3aRW+++SZ9+OGHvOUoxsaNG6lfv34UFRVFPznxXFKlfeb2xRQR0dKlS+kXv/gFjRw5kr7//nvechQjMzOTHj16REOHDuUtpcGEhxNpteZe6UxEaiJ6gYgeEFEuEe0nouZPbKnTKShQUCMtW7ako0eP0oEDB+j3v/89bzmKsXDhQvrlL39JMTExdO/ePd5yGoStfBYWpqBIQY188sknBIAGDhzIW4piBAUFUUZGBqWlpdGOHTt4y2kw4pzmvMyfP5+mT59OEyZMoO+++463HMU4ffo0aTQap16jqLjP7DZh0cGRZRlt27ZF+/btXTLHwoSz5+J89tk3YKymwLVUEPnDmBXAQNQKRFdF5yMH4/XXX4dKpcKVK1d4S1EMWZbRoUMHp83Fycsrg0r10GqfiW5+/Lh+/TpUKhWWLl3KW4qiOHsuzoEDH6J6po04pzkXnTt3RqtWrZzyWG8pt27dglqtxoIFC3hLaRA5OXchSaWK+UwUU5UoKCiAVqvF5MmTeUtRFGfMxbl9+zYGDRoExhi8vT98nPdQ24JC8w+RyeEYDBgwAP7+/igpKeEtRTFMDW5iYmJ4S7GYsrIyLF26FFqtFpJ0tMZFuMJnzsHBgwfBGMPRo0d5S1EUZ8zFSU9PR3BwMBhjCArKfJzLJ7zmjBQVFcHb2xujR4/mLUVR0tLSwBhDamoqbykWc+fOHQwfPhyMMeh0Hyh27SiKqWqcOXNG5OI4ELdu3cKAAQPAGEPHjh1x+vTpWlKs636ItHjHoKysDE2bNsVTTz3FW4qimHJxNm/ezFtKrZSVleHll1+Gp6cnvL29sWrVKly8KAufuQDPP/+8yMVxII4ePYo2bdqAMYbRo0cjLy9PnNNcgKysLEiShA0bNvCWoijx8fFQq9W4ceMGbym1kpubi2HDhoExhpCQEGRkZCjqM1FMmcHZpw1YSrdu3dC8eXOUlZXxlvIEN2/eRHR0dJUiqjKmxPL6miE5mdMOCZ7A2acNWMrmzZshSRIyMzN5S3mCsrIyLFmypKKIWr16dZWpKsJnrkFYWBiCgoIc8lhvK+7cuQONRoPZs2fzlmKWI0eOoHXr1mCMYcyYMbh7926V14XXnJ9t27aBMYazZ8/ylqIoERERaNSoEUpLS3lLeYLc3FwMHToUjDG0b98ex48fr/K6Uj4TxVQNjBw50i1ycXx9fR0qF+fGjRuIiooCYwydO3eu9aBkMgVjdd+eFScdx8RdcnHGjx/vULk4paWliI+Pryii1q1bV+N8f+Ez58fdcnH27t3LW0oFaWlpaNWqFSRJwtixY58ooiojvOb8xMbGQqvVoqCggLcUxTAYDAgMDESfPn14S6ng9u3bGDx4cEURdeLEiRq3VcJnopiqAVmW0bJlS3Tp0oW3FEW5fPkyJEnCmjVruOq4fv06+vXrB8YYunTpgnPnzln0e9nZxnmsWi2g01U1gk5nfD42VkyDcGSWLFniFNMGrEGWZQQHByM0NJTrIuXS0lIsWrQIGo0GPj4+WL9+vUV6hM+cn5ycHKhUKqxcuZK3FEVZsWKFQzS4OXz4cEURFRMTg/z8fIt+T3jNuZFlGe3atUNISIhLN6RwlAY3ldfTh4aG4uTJkxb9nq19JoqpWsjLy4NGo8GsWbN4S1GU5ORkMMZw6tQpu499/fp19O3bt95FVHXu3QMSE4FZs4CYGOPPxETRTcxZ6N27Nxo1agSDwcBbimLk5+dDq9ViypQpdh+7tLQUCxcuhEajga+vLzZs2NCgE73wmXOzZ88eMMZq/dbWFYiOjubW4CY1NRUtWrSAJEkYP358g+9QCK85LwUFBdDpdNDr9bylKMqBAwe4Nbi5desWBg4cWLEUpKHXr7bymSim6uD48eMON21ACaZOnQqtVmvxt2fWcu3aNfTp0weMMXTr1s0h15MI7Idp2kBkZCRvKYpy+vRpMMawc+dOu4xnMBiwYMGCiiJq06ZNLv1tqaBuZsyYAY1Gg7y8PN5SFKO0tBRNmzbF008/bbcxDx06VFFETZgwwaWneQnq5ty5c5AkCVu3buUtRVHmz59v1wY3da2n54UopixA5OLYjqtXr6J3795gjKF79+44f/68YmMJnAvTtIGXX36ZtxRFsUeDG4PBgBdffBEeHh7w8/NDYmKiKKIEFYhcHNtx8OBBNG/eHJIkQa/XO8y6SAF/Nm3aBEmScPHiRd5SFMUeDW4qr6fv1KkTzpw5o9hYDUEUUxYyYMAA+Pn5uUUuzrhx46o8n58PbNoEzJhhvA06Y4bx/+tzG/Tq1auIiIgAYww9evRw+YOLoGGYcnGOHDnCW4qimMvFsYXPDAZDRStsf39/bN682aUvmAUNw5SLM2rUKN5SFMWUi1O5wY0tfAYYpzg1a9YMkiQhNjZWFFECs4wZMwbe3t4oLCzkLUUxiouL4efnh8GDB1d53hZeu3HjBvr371+xFMRROyWKYspC3C0XJzExEVlZgF5vXIin1ZpfoKfXA1lZNb/flStX0KtXLzDGEBYWhqzaNhYIgIo7Ku6Si2MLnxkMBsybN6+iiHL1qSUC63GXXJzFixdDrVYjLS3Xap8BwP79+xEUFARJkjBp0iSXvkgWWI8sy2jdujU6derk0l9sVW5wY4tzmq3W09sLUUzVg9u3b8PDw8Plc3G2bNkCogXw9HzU4NaROTk5eOaZZ8AYQ3h4uMtndglsS3h4uFvk4qhUv4ZK9aDBPispKcFzzz0HtVqNgIAAUUQJ6oW75OIEB28AUQkYK29wK+SUlJSKImrKlCmiiBJYzN27d+Hp6Ynp06fzlqIou3fvtvrasfJ6+q5duzrNenpRTNUTd8jFSU4GVCpDpT98MvMIMxtq9vnnn6Nnz55gjOGpp57C5cuXee+OwAlxh1yc5GTA0/NRg3xWUlKCuXPnQq1WIzAwENu2beO9OwInxdVzcYyZMuUN8hkA7N27F02bNoVKpcLUqVNFESVoECdPngRjDLt37+YtRTGM144PGuS1a9euITIy0mnX04tiqgGYcnGuX7/OW4rNycqqKx06/7Ehtpn5puEnEEXg6aefFkWUwGpM0wZef/113lJsjjU+U6keQKXqg8DAQGzfvp33rgicHFMuTtu2bV1uGpI1PtNoHsLffxhUKhWeffbZKusbBYKGsGrVKkiShM8//5y3FJtjjdckyXjt6Mzr6RkAkKDeREZG0ldffUV5eXmk1Wp5y7EZsbFER48+/k7BLM8TUQoRlRKRVO01mYYOLabTpwMUVChwJ/bu3UsvvPACZWRk0JgxY3jLsRnW+uypp27TF1+EKqhQ4E785z//oeDgYBo1ahT95S9/4S3HZljrs9atL9Pf/96F/Pz8FFQpcCeGDh1Kn332GX333Xfk4+PDW47NsNZrgwcX0ZkzjRRUqCyimGogDx48oJYtW1LHjh3p0qVLvOXYhHv3iNq2JXrwoLatAonoKSL62OyrWi3RN98QNW1qe30C92TmzJl0+PBh+vrrr6lly5a85ViN8JnAEcnMzKRBgwbRli1baMmSJbzlWI3wmcARefToEbVp04aaNm1KX375JW85NkF47cnyUGAhWq2WLly4QH/7299o6dJ+Fss9AAAM70lEQVSlvOXYhJSUura4QEQ/ENHaGrdgzJL3EQgs5+DBg9S+fXuKjIyk8vJy3nKsRvhM4IhER0fTm2++Sa+88opLfEEofCZwRNRqNV26dImuX79Ozz//PG85NkF4TRRTVtG5c2f64x//SElJSS4xNeLLL+v6ZiGBiPyJaGCNWxgMRFeu2FiYwO25dOkS/fDDDzR27FjeUqxG+EzgqCxfvpxGjRpFw4YNox9++IG3HKsQPhM4KsHBwZSWlka///3v6eDBg7zlWI3wmiimrGbatGn0wgsv0NSpUyk3N5e3HKsoKqpri0+ISF/n+xQW2kKNQPAzfn5+dObMGfrrX/9Kb775Jm85ViF8JnBkjh07RoGBgU5/J1j4TODIjB8/npYtW0Zz586lf/zjH7zlWIXwmiimbMKuXbuoa9eu1Lt3b3r06BFvOQ3G37+2V3cTURkR1X0hGxhoI0ECQSV69+5NSUlJlJCQQB9//DFvOQ1G+EzgyEiSRNnZ2fTNN9/QzJkzectpMMJnAkcnMTGRIiMjKSoqih7UfmvHoRFeE8WUzbhw4QKVlpbSsGHDeEtpMOHhRBqNXMOrSUTUjohqbwCg0xGFhdlamUBgJD4+nvR6PY0ZM4b+85//8JbTIMLDibTamvr+CJ8J+NO8eXN6//33KTU1ld555x3echqE8JnAGfj4449JkiSKjo7mLaXBGK8da7qL7R5eE8WUjfDy8qJz587Rp59+Sv/zP//DW069+eSTT+idd/rTw4cPa9jiH0R0q873AYjmzrWlMoGgKocPH6YWLVpQRESE001D+v777+ns2edq+RZS+EzgGIwYMYJWrlxJcXFx9MUXX/CWUy/Ky8vpp5+S6cGD0hq2ED4TOAYajYYuXbpEOTk5tHjxYt5y6s2lS5foD38YRA8furfXRDFlQ8LDw2nXrl20ceNG+uCDD3jLsYiPP/6YOnfuTIMHD6bmzSUaMUImxhr2XowRjR3rvK0tBc6BJEmUlZVF9+7do0mTJvGWYxHff/896fV6atq0Kf3tbyeoV68C4TOBw7N27VoaNGgQDRw4kH788UfecuqkvLyc3nrrLQoMDKQNG5ZQSMg/iLGGpb8InwnsRWhoKP3pT3+iHTt2UFpaGm85FnHhwgXq0aMH9evXj/z8HtCwYWXufU7jmxnsmsyaNQsajQZ5eXm8pdTImTNn0LFjRzDGMGDAANy8eROAJSnWNT+8vIDsbM47JnAbzp07B0mSsHXrVt5SaqSgoAATJkyAJElo0aIFDh06BED4TOA8lJWVoXnz5ggLC+MtpUZkWcbGjRvh6+sLDw8PxMXFobS0VPhM4FQsXLgQarW64nrMEcnMzETXrl3BGEOfPn1w7do1AOKcJoophejSpQtatmwJWZZ5S6nCqVOn0KFDBzDGMHDgQNy6deuJbZKT628KLy/j7wkE9mTTpk2QJAkXL17kLaUKBQUFGD9+PCRJQsuWLZGamvrENsJnAmchNzcXHh4emDdvHm8pVZBlGRs2bICvry80Gg0WLlyI0tLSKtsInwmciWeeeQZNmjR54u+YN5mZmejSpQsYY+jbty+uX7/+xDbu7DVRTClEUVERvL29MXLkSN5SABiLqNDQUDDGMGjQILNFVGVMpmCsdiMw5jpmEDgnY8aMgZeXFwoLC3lLQX5+PsaNGwdJktCqVSscPny41u2FzwTOQnp6Ohhj2L9/P28pkGUZ69evh4+PDzQaDRYtWlTrxafwmcBZMBgMCAgIQFRUFG8pAIwzQExFVP/+/XHjxo1at3dXr4liSkGysrIgSRLWr1/PTcPJkyfRvn17MMYwePBg3L592+Lfzc4GYmMBrRbQ6aoaQaczPh8b6/y3ZwXOjSzLaN26NTp27MjtTnB+fj7Gjh1rcRFVGeEzgbOwbNkyqFSqiqk99kaWZaxbtw7e3t7w9PREfHy8xd/gC58JnIWrV69CpVJh+fLl3DScPXsWnTp1AmMMUVFRdRZRlXFHrzEA4L1uy5XZvn07vfTSS/TRRx/R4MGD7Tbuhx9+SAsXLqSvv/6ahgwZQn/4wx8oODi4Qe9VUECUkmJMpy4sNGYBhIUZO6849YJBgcvw73//m0JCQig2NpYOHTpk13F/9atf0YcffkitWrWi3/3ud6TX1x1OaA7hM4Ez0K9fP/rnP/9J3333HWm1WruMWV5eTuvWraO33nqLHj16RHFxcfTWW2+RWq2u93sJnwmcgf3799Nzzz1Hx44do7Fjx9pt3I8//pheeOEFunnzJkVFRVFKSgqFhoY26L3cymu8qzl3YNKkSdBqtcjPz1d8rIyMDISEhIAxhmHDhiE3N1fxMQUCR+DkyZNgjGHXrl2Kj5WXl4dRo0aBMYY2bdrg6NGjio8pEDgCpaWlaNy4MXr16qX4WLIsY9WqVRV3ol5++WWUlZUpPq5A4AjMnTsXGo0Gd+7cUXys06dPVzQlq2k9vaBmRDFlB2RZRvv27dG2bVvFpiFlZGSgbdu2YIxh+PDhdjGfQOBorFq1CpIk4fPPP1fk/fPy8jBy5EgwxhAcHIz09HRFxhEIHJmbN29CrVZj0aJFiry/LMtISEiAl5cXtFotli5dKooogVvSvXt3NG/eXLG///qupxeYR0zzsxPff/89tW7dmkaOHElHjx4lIqJ794y3QL/8kqioiMjf35gk/dxzlt8CPXbsGC1atIi++eYbGjFiBP3hD3+gli1rT5oWCFyZoUOH0meffUbfffcd+fj4EJH1Xvv222/pV7/6FZ06dYqCg4Npx44dFBMTo+yOCAQOzHvvvUfPPvssvffeezR58mQist5n5eXllJCQQElJSVReXk7x8fG0YcOGBk3nEwhcgR9//JFatmxJkZGRdOrUKSKyzbXjX//6V1qwYAF9/fXXNGjQIEpJSaG2bdsqtyOuDu9qzp0w5eK89NJB6PXGRXharfnFeXq9sW9/TRw9ehRt2rQBYwyjRo1y6EwrgcCemHJxevTogawsWOW1O3fuYNiwYWCMISQkBBkZGfbdGYHAgTHl4hw5cscqn8myjBUrVkCn00Gr1eK1114Td6IEgsdcvnwZkiThhRf2WH3teOLEiYqmZEOHDhVLQWyEKKbszMSJJ0D0Ixgrb1DbyCNHjlQUUaNHj8bdu3f57IhA4MDk5uZCkhZCpXrQoBatubm5GDp0KBhjaNeuHY4fP85vZwQCB6Z16zdBVNKgc5osy1i+fDl0Oh10Oh2WL18uiiiBwAzPPvvx42tHuUHXjsePHxfr6RVEFFN2pOZAs3gQaUBEIFKDaPsTgWZpaWlo3bo1GGMYO3asKKIEglpITgY8Pcvq7bX16/+DIUOGgDGG9u3b48SJE7x3RSBwWJKTAZ3OXBFVu8927HiEV199FVqtFjqdDq+//rrDBdwLBI6CNdeOYj29fRBrpuxEdjbR4MFEP/1U/ZXfEtFKItpFRHOJ6IvHz0dUbMHYT0Q0mMaODaJ9+/ZRUFCQ4noFAmfFGq8RlVCrVjPoD3/4NY0YMUJpqQKB02Ktzzw9R9Grrw6hNWvWkCRJSssVCJwSa68dgUE0YkQg7du3j1q3bq28YDdFFFN2IjaW6OhR43cGVfEloklElFLLb5fTuHEP6dgx+2R6CATOjDVeY6yc9HqJ/vxn5fQJBK6Atec0vZ7RkSPs/7d3P6lNhGEAxp9pEZJsSgrttgqCGxsEm403SMELeAHpLXKAnqCrbjyBkLgpobh0IbTqVulKUrAUhLZSaBcJCOav07yfzvT5bQKZL+Fb5CG8mWEStT2pFO7aWat1RadTjdqehvw5KIF+H7rdcTH8An4C34EHwDLQAH78sW6Jg4MKp6fhW5UK7a6t3dws0elga9IUi/hO63YzO5OmWERnvV7VzhJwmEpgf3/SkU/Dx/fAB+AL8A14ObIyy6a9jySwNSkFO5Pi2VlxOEwlcHQEl5fjjtSHj6+AZ8AT4DXwcWTlxQUcH0ftUCoHW5Pi2ZkUz86Kw2EqgfPzSUceMTg9O99142dnC9qQVFK2JsWzMymenRWHw1QCKyvTjr4A3gCfga/AHvB87Mp6fezTkoZsTYpnZ1I8OysOh6kEGg2oTLwR3zsGvzI8BR4DD4G3I6uqVdjcDNqgVBK2JsWzMymenRWHt0ZPoN+HjY1J177Op1KBkxNYW1vcvqSysTUpnp1J8eysODwzlcD6OrRag7uq5JFlsL1tDNIstibFszMpnp0Vh2emEpn8L9az1WpweAhbW7PXSvedrUnx7EyKZ2fF4JmpRJpN2N0dfLj/Rq02eJ0xSPOxNSmenUnx7KwYltvtdvtfb+K+aDZhdRV6Pbi+nr42y37HsLOTZn9SWdiaFM/OpHh29v/zMj9JkiRJysHL/CRJkiQpB4cpSZIkScrBYUqSJEmScnCYkiRJkqQcHKYkSZIkKYdbHzoq+lJo9bIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulation with local clocks method\n",
    "# In this variant, the matrix Lambda is not precomputed\n",
    "\n",
    "# for each step of the simulation\n",
    "for i in range(1,n_steps):\n",
    "    # consider the current configuration\n",
    "    x = np.unravel_index(states[i-1],shape=n_states)\n",
    "    x = np.array(x)\n",
    "    # define a list of admissible destination configurations, \n",
    "    # and the rates of the corresponding transitions\n",
    "    destinations = list()\n",
    "    transition_rates = list()\n",
    "    # admissible destinations differ from the current configuration\n",
    "    # for the state of a single agent\n",
    "    for agent in range(n_agents):\n",
    "        # construct an admissble destination and append it to the list\n",
    "        y = np.array(x)\n",
    "        y[agent] = 1-y[agent]\n",
    "        y_id = np.ravel_multi_index(tuple(y), dims = n_states)\n",
    "        destinations.append(y_id)\n",
    "        # compute the transition rate from x to y\n",
    "        rate = 0\n",
    "        for other_agent in range(n_agents):\n",
    "            rate += beta*W[agent,other_agent]*phi[x[other_agent], x[agent], y[agent]]\n",
    "        transition_rates.append(rate)\n",
    "    # given all transition rates from x to admissible y\n",
    "    # compute w_x, the clock rate of configuration x\n",
    "    # Notice: transition_rates is the x-th row of Lambda\n",
    "    transition_rates = np.array(transition_rates)\n",
    "    w_x = np.sum(transition_rates)\n",
    "    # if w_x = 0, then the process stays in x\n",
    "    if w_x == 0:\n",
    "        states[i] = states[i-1]\n",
    "    # else, transition probabilities are computed as transition_rates/w_x\n",
    "    # and the next config is chosen accordingly.\n",
    "    # Notice: this is the x-th row of P\n",
    "    else:\n",
    "        probabilities = transition_rates / w_x\n",
    "        states[i] = np.random.choice(destinations, p=probabilities)\n",
    "        \n",
    "# Plot the infection spread\n",
    "pos = nx.spectral_layout(G)\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "for t in range(0,n_steps):\n",
    "    plt.subplot(3,4,t+1)\n",
    "    x_id = states[t]\n",
    "    x = np.array(np.unravel_index(x_id, shape = n_states))\n",
    "    nx.draw_spectral(G,\n",
    "        with_labels=True,\n",
    "        nodelist=np.argwhere(x==0).T[0].tolist(),\n",
    "        node_color = 'r')\n",
    "    nx.draw_spectral(G,\n",
    "        with_labels=True,\n",
    "        nodelist=np.argwhere(x==1).T[0].tolist(),\n",
    "        node_color = 'b')\n",
    "    plt.title('jump step = {0}'.format(t+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Simulate other epidemic models, like the SIS and SIR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO DO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random graphs as a model for a citation network\n",
    "The real world network in this problem is a citation network with 10000 papers. It can be loaded with the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import collections \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "citation = sio.loadmat('citation1.mat', variable_names=['citation'])\n",
    "citation = citation['citation']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a directed network where each node represents a paper and a link from node i to j means that paper i cited paper j. The variable `citation` is an array with 3 columns. Each row contains the tail and head node of a link and the weight associated to such link in the adjacency matrix of the graph (in this case, the weight is either 1 or 0). The graph is constructed from the `citation` variable as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.DiGraph()\n",
    "for i,j,w in citation:\n",
    "    if w != 0:\n",
    "        G.add_edge(i,j)\n",
    "\n",
    "n_nodes_cit = nx.classes.function.number_of_nodes(G)\n",
    "n_links_cit = nx.classes.function.number_of_edges(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we analyze how to approximate the citation graph with different random graph models. We then compare the obtained approximations to see which one is the best for this example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Erdos-Renyi-model\n",
    "To approximate the citation network we construct an Erdos-Renyi-model with as many nodes as the real network, that is n. Remember that the expected number of links in such model is $\\binom{n}{2}p$ where $p$ is the probability of there being a link between any two distinct nodes i and j. We chose p such that the expected number of links is the same as the number of links in the actual citation network. By construction, this random graph will not have any self loop.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construction of the Erdos-Renyi model\n",
    "\n",
    "# chose p so that the expected number of links is the same as n_links_cit\n",
    "# factor 2 at denominator is because G is directed\n",
    "p = n_links_cit/(sp.special.binom(n_nodes_cit,2) * 2) \n",
    "# add links between couple of different nodes with probability p\n",
    "WER = np.random.choice([0,1], size=(n_nodes_cit, n_nodes_cit), p= [1-p,p])\n",
    "# GER has no self loops\n",
    "for n in range(n_nodes_cit):\n",
    "    WER[n,n] = 0\n",
    "    \n",
    "GER = nx.from_numpy_array(WER, create_using=nx.DiGraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The configuration model \n",
    "Here we construct a configuration model to approximate the citation network. To do this, we proceed as follows: given the in and out-degree of the real network, we pick uniformly at random one out-link and one in-link and connect them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construction of the Configuration model\n",
    "\n",
    "WCM = np.zeros((n_nodes_cit, n_nodes_cit))\n",
    "residual_out_degree = np.array(list(degree for node, degree in G.out_degree()))\n",
    "residual_in_degree = np.array(list(degree for node, degree in G.in_degree()))\n",
    "\n",
    "# for each node\n",
    "for i in range(n_nodes_cit):\n",
    "    # while there are unconnected out \"half links\"\n",
    "    while residual_out_degree[i] > 0:\n",
    "        available_targets = np.arange(n_nodes_cit)\n",
    "        # a target is available if its residual in degree is positive\n",
    "        available_targets= available_targets[np.argwhere(residual_in_degree > 0)].flatten()\n",
    "        # randomly connect the link to the targets\n",
    "        target = np.random.choice(available_targets)\n",
    "        WCM[i,target] = 1\n",
    "        # decrease out/in degree of the connected nodes\n",
    "        residual_in_degree[target] -=1\n",
    "        residual_out_degree[i] -=1\n",
    "\n",
    "\n",
    "GCM = nx.from_numpy_array(WCM, create_using=nx.DiGraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preferential attachment model\n",
    "The preferential attachment is a model for growing random graphs which was explicitly introduced to model citation networks. We now explore this model to see if it is capable of approximating the real citation network.\n",
    "\n",
    "We generate a random graph by the preferential attachment model, so that the final number of nodes n is the same as in the real citation network and so that the degree distrubution is similar. To do this, we start with directed graph $G_0$ containing 2  nodes connected to each other.\n",
    "At timestep $t$ $(t\\geq1)$, we add a new node to the graph and connect it to $c$ other nodes already present in $G_{t}$, the resulting graph is called $G_{t+1}$. The value of $c$ is chosen randomly with probabilities according to the out-degree distribution of the real citation network. For each of the $c$ new connections, the destination node is chosen with probability proportional to the in-degree distribution of $G_{t}$ plus a constant $a$ , so that the probability that node $i$ is chosen is given by\n",
    "$$\n",
    "p_i(t)=\\frac{w_i^-(t) + a}{\\sum_j (w_j^-(t)+a)}.\n",
    "$$\n",
    "When $a \\neq 0$, each node has an intrinsic probability of being selected as a neighbor from new nodes, regardless of its in-degree.\n",
    "The process stops when the total number of nodes $n$ is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preferential attachment\n",
    "GPA = nx.DiGraph()\n",
    "# GPA is initialized to G0, containing two nodes connected to each other\n",
    "GPA.add_edges_from([(0,1),(1,0)])\n",
    "\n",
    "# compute out degree distribution of real citation graph\n",
    "\n",
    "# degree sequence contains the out degree of nodes ordered from larger\n",
    "# to smaller\n",
    "degree_sequence = sorted([d for n, d in G.out_degree()], reverse=True) \n",
    "# count for each out-degree value, how many nodes have that out-degree\n",
    "# degreeCount is a list of tuples (degree value, number of nodes)\n",
    "degreeCount = collections.Counter(degree_sequence)\n",
    "# zip() returns an iterator of tuples where the first item in each passed tuple \n",
    "# is paired together, the second item in each passed tuple are paired together\n",
    "# and so on.\n",
    "# In this way we obtain deg, which is the tuple of degree values, and\n",
    "# cnt which is the tuple of the counts.\n",
    "deg, cnt = zip(*degreeCount.items())\n",
    "deg_distr = np.array(cnt)/sum(cnt)\n",
    "\n",
    "# set a, the intrinsic probability of a node to be selected as a neighbor \n",
    "# from new nodes\n",
    "a = 1 \n",
    "\n",
    "# add the remaining nodes one at a time\n",
    "for node in range(2,n_nodes_cit):\n",
    "    # compute degree of new node according to deg_distr (degree distribution\n",
    "    # of the real citation graph).\n",
    "    # the min guarantees that the new node is not assigned a degree\n",
    "    # larger than the current size of GPA\n",
    "    degree = min(np.random.choice(deg,p=deg_distr), len(GPA)) \n",
    "    # choose `degree` neighbors for node to connect to according to their \n",
    "    # in-degree in the current approximation GPA.\n",
    "    # compute updated in degree sequence\n",
    "    in_deg_PA = [d for n, d in GPA.in_degree()] \n",
    "    # add a so that also node with 0 in-degree have non-zero probability\n",
    "    # of being chosen\n",
    "    in_deg_PA = np.array(in_deg_PA)+a \n",
    "    # normalize to obtain a probability distribution\n",
    "    in_deg_PA = in_deg_PA/sum(in_deg_PA)\n",
    "    # replace=False guarantees no neighbor is chosen twice\n",
    "    neighbors = np.random.choice(np.arange(len(GPA)), p=in_deg_PA, size=degree, replace=False)\n",
    "    # add the new node (new node is added even if its out-degree is zero)\n",
    "    GPA.add_node(node)\n",
    "    # add the new links \n",
    "    for neigh in neighbors:\n",
    "        GPA.add_edge(node,neigh) \n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing models\n",
    "We use the following measures to investigate how the Erdos-Renyi-model, Configuration Model and Preferntial Attachment model are able to approximate the real citation network.\n",
    "1. Diameter \n",
    "2. Average distance between nodes\n",
    "3. Degree distribution\n",
    "\n",
    "**Remark:** since the network is not strongly connected, with the usual computation the Diameter becomes infinity. To get a more meaningful result, one can get this diameter as the maximum diameter of each connected component. A similar argument applies to the average distance between nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diameter: 10\n",
      "Diameter ER: 27\n",
      "Diameter CM: 28\n",
      "Diameter PA: 9\n"
     ]
    }
   ],
   "source": [
    "# Diameter\n",
    "length_dict = dict(nx.all_pairs_shortest_path_length(G))\n",
    "lengths = [lenght for d in length_dict.values() for lenght in d.values()]\n",
    "diameter = max(lengths)\n",
    "print(\"Diameter:\", diameter)\n",
    "\n",
    "length_dict_ER = dict(nx.all_pairs_shortest_path_length(GER))\n",
    "lengths_ER = [lenght for d in length_dict_ER.values() for lenght in d.values()]\n",
    "diameter_ER = max(lengths_ER)\n",
    "print(\"Diameter ER:\", diameter_ER)\n",
    "\n",
    "length_dict_CM = dict(nx.all_pairs_shortest_path_length(GCM))\n",
    "lengths_CM = [lenght for d in length_dict_CM.values() for lenght in d.values()]\n",
    "diameter_CM = max(lengths_CM)\n",
    "print(\"Diameter CM:\", diameter_CM)\n",
    "\n",
    "length_dict_PA = dict(nx.all_pairs_shortest_path_length(GPA))\n",
    "lengths_PA = [lenght for d in length_dict_PA.values() for lenght in d.values()]\n",
    "diameter_PA = max(lengths_PA)\n",
    "print(\"Diameter PA:\", diameter_PA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average distance: 2.6242914979757086\n",
      "Average distance ER: 11.01228146878949\n",
      "Average distance CM: 10.06914742662541\n",
      "Average distance PA: 2.0900760066842756\n"
     ]
    }
   ],
   "source": [
    "# Average distance\n",
    "\n",
    "ad = np.average(lengths)\n",
    "print(\"Average distance:\",ad)\n",
    "ad_ER = np.average(lengths_ER)\n",
    "print(\"Average distance ER:\",ad_ER)\n",
    "ad_CM = np.average(lengths_CM)\n",
    "print(\"Average distance CM:\",ad_CM)\n",
    "ad_PA = np.average(lengths_PA)\n",
    "print(\"Average distance PA:\",ad_PA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# out-degree\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "degree_sequence = sorted([d for n, d in G.out_degree()], reverse=True) \n",
    "degreeCount = collections.Counter(degree_sequence)\n",
    "deg, cnt = zip(*degreeCount.items())\n",
    "\n",
    "degree_sequence_ER = sorted([d for n, d in GER.out_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_ER = collections.Counter(degree_sequence_ER)\n",
    "deg_ER, cnt_ER = zip(*degreeCount_ER.items())\n",
    "\n",
    "degree_sequence_CM = sorted([d for n, d in GCM.out_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_CM = collections.Counter(degree_sequence_CM)\n",
    "deg_CM, cnt_CM = zip(*degreeCount_CM.items())\n",
    "\n",
    "degree_sequence_PA = sorted([d for n, d in GPA.out_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_PA = collections.Counter(degree_sequence_PA)\n",
    "deg_PA, cnt_PA = zip(*degreeCount_PA.items())\n",
    "\n",
    "plt.figure(figsize=[20, 10])\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "plt.bar(deg, cnt, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Out Degree\")\n",
    "ax.set_xticks([d + 0.4 for d in deg])\n",
    "ax.set_xticklabels(deg);\n",
    "\n",
    "ax = plt.subplot(2,2,2)\n",
    "plt.bar(deg_ER, cnt_ER, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Out Degree ER\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_ER])\n",
    "ax.set_xticklabels(deg_ER);\n",
    "\n",
    "ax = plt.subplot(2,2,3)\n",
    "plt.bar(deg_CM, cnt_CM, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Out Degree CM\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_CM])\n",
    "ax.set_xticklabels(deg_CM);\n",
    "\n",
    "ax = plt.subplot(2,2,4)\n",
    "plt.bar(deg_PA, cnt_PA, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Out Degree PA\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_PA])\n",
    "ax.set_xticklabels(deg_PA);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remark**: Degrees of the real network and of the CM approximation coincide, since the CM model is such that the resulting graph has a prescribed degree distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in-degree\n",
    "\n",
    "degree_sequence = sorted([d for n, d in G.in_degree()], reverse=True)  # degree sequence\n",
    "degreeCount = collections.Counter(degree_sequence)\n",
    "deg, cnt = zip(*degreeCount.items())\n",
    "\n",
    "degree_sequence_ER = sorted([d for n, d in GER.in_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_ER = collections.Counter(degree_sequence_ER)\n",
    "deg_ER, cnt_ER = zip(*degreeCount_ER.items())\n",
    "\n",
    "degree_sequence_CM = sorted([d for n, d in GCM.in_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_CM = collections.Counter(degree_sequence_CM)\n",
    "deg_CM, cnt_CM = zip(*degreeCount_CM.items())\n",
    "\n",
    "degree_sequence_PA = sorted([d for n, d in GPA.in_degree()], reverse=True)  # degree sequence\n",
    "degreeCount_PA = collections.Counter(degree_sequence_PA)\n",
    "deg_PA, cnt_PA = zip(*degreeCount_PA.items())\n",
    "\n",
    "plt.figure(figsize=[20, 10])\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "plt.bar(deg, cnt, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree\")\n",
    "ax.set_xticks([d + 0.4 for d in deg])\n",
    "ax.set_xticklabels(deg);\n",
    "\n",
    "ax = plt.subplot(2,2,2)\n",
    "plt.bar(deg_ER, cnt_ER, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree ER\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_ER])\n",
    "ax.set_xticklabels(deg_ER);\n",
    "\n",
    "ax = plt.subplot(2,2,3)\n",
    "plt.bar(deg_CM, cnt_CM, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree CM\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_CM])\n",
    "ax.set_xticklabels(deg_CM);\n",
    "\n",
    "ax = plt.subplot(2,2,4)\n",
    "plt.bar(deg_PA, cnt_PA, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree PA\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_PA])\n",
    "ax.set_xticklabels(deg_PA);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remaek**: The in-degree distribution of the PA approximation presents a few nodes with very high in-degree (over 100) and large portion of nodes (the majority) having degree 0. If we exclude both extreme values, the rest of the PA degree distribution is an accurate approximation of the real one, as the following picture illustrates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 750.75)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20, 10])\n",
    "\n",
    "# in degree of real network\n",
    "ax = plt.subplot(1,2,1)\n",
    "plt.bar(deg, cnt, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree\")\n",
    "ax.set_xticks([d + 0.4 for d in deg])\n",
    "ax.set_xticklabels(deg);\n",
    "\n",
    "# we extract the x and y ranges from the real network diagram\n",
    "# in order to represent only the non-extremal portion of the PA histogram\n",
    "xleft, xright = plt.xlim()  # return the current xlim\n",
    "yleft, yright = plt.ylim()  # return the current ylim\n",
    "  \n",
    "ax = plt.subplot(1,2,2)\n",
    "plt.bar(deg_PA, cnt_PA, width=0.80, color=\"b\")\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"In Degree PA\")\n",
    "ax.set_xticks([d + 0.4 for d in deg_PA])\n",
    "ax.set_xticklabels(deg_PA);\n",
    "plt.xlim(xleft, xright)   # set the xlim \n",
    "plt.ylim(yleft, yright)   # set the ylim "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Try to tune the a parameter in the PA model (the intrinsic probability of a node to be selected as a neighbor from new nodes, regardless of its in-degree) to obtain a better approximation of the real in-degree distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO DO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The results give an experimental verification of how the preferential attachment model is best suited to approximate citation networks. Indeed, it reaches the best approximation results with respect to all the analyzed measures (for the degree distributions the previous remarks hold)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}