Cloud_vs_Local - main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics as stat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = 512;C = 10000;W = 1.4 * pow(10,6)\n",
    "f_I = pow(10,9);f_M = pow(10,12)\n",
    "N_0 = pow(10,-16);P = 0.5 \n",
    "\n",
    "def arrival_and_departure_times(param1,param2,N):\n",
    "    a = []\n",
    "    d = []\n",
    "    s = 0\n",
    "    for i in range(N):\n",
    "        s = s + np.random.exponential(param1)\n",
    "        a.append(s)\n",
    "    for i in range(N):\n",
    "        d.append(a[i] + np.random.exponential(param2))\n",
    "    return (np.array(a),np.array(d))\n",
    "\n",
    "def local_cost():\n",
    "    return ((B*C)/f_I)\n",
    "\n",
    "def cloud_cost(n):\n",
    "    h_g_2 = np.random.exponential(0.19)\n",
    "    R = W * np.log2(1 + P*h_g_2/N_0)\n",
    "    #R = np.random.choice([R,R/5],p = [0.5,0.5])\n",
    "    return (B/R + B*C*n/f_M)\n",
    "\n",
    "def next_state(local_pos,cloud_pos,action):\n",
    "    if action == 0:\n",
    "        (new_local_pos,new_cloud_pos,reward) = (local_pos + 1,cloud_pos,-local_cost())\n",
    "    else:\n",
    "        (new_local_pos,new_cloud_pos,reward) = (local_pos,cloud_pos + 1,-cloud_cost(cloud_pos))\n",
    "    return  (new_local_pos,new_cloud_pos,reward)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Server_Q_learning(N,n,mu,lamda,gamma):\n",
    "    n = 50\n",
    "    reward_list = []\n",
    "    reward_sum_list = []\n",
    "    reward_sum_avg_list = np.array([0]*N)\n",
    "    runs = 1\n",
    "    for ind in range(runs):\n",
    "        count = 0\n",
    "        Qa = np.zeros((n,n,2))\n",
    "        freq = np.ones((n,n))\n",
    "        freq_action = np.ones((n,n,2))\n",
    "        (arrivals,departures) = arrival_and_departure_times(mu,lamda,N)\n",
    "        local_pos = 0;cloud_pos = 0\n",
    "        local_actions = np.array([False]*N)\n",
    "        cloud_actions = np.array([False]*N)\n",
    "        reward_sum_list = []\n",
    "        reward_sum = 0\n",
    "        for i in range(N):\n",
    "            local_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (local_actions)).sum()\n",
    "            cloud_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (cloud_actions)).sum()\n",
    "            eps = 1/np.sqrt(freq[local_pos,cloud_pos])\n",
    "            action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            if local_pos == n:\n",
    "                action = 1\n",
    "            if cloud_pos == n:\n",
    "                action = 0        \n",
    "            if action == 0:\n",
    "                local_actions[i] = 1\n",
    "            if action == 1:\n",
    "                cloud_actions[i] = 1\n",
    "            (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "            count = count + 1\n",
    "            alpha = 1/np.power(freq_action[local_pos,cloud_pos, action],0.8)\n",
    "            Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*np.max(Qa[new_local_pos,new_cloud_pos]) - Qa[local_pos,cloud_pos,action])\n",
    "            freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "            freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1     \n",
    "            reward_list.append(reward)\n",
    "            reward_sum = reward_sum + reward\n",
    "            reward_sum_list.append(reward_sum/count)\n",
    "        reward_sum_avg_list = reward_sum_avg_list + np.array(reward_sum_list)\n",
    "    return reward_list,np.array(reward_sum_list),reward_sum_avg_list/runs,local_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Server_DoubleQ_learning(N,n,mu,lamda,gamma):\n",
    "    n = 50\n",
    "    reward_list = []\n",
    "    reward_sum_list = []\n",
    "    reward_sum_avg_list = np.array([0]*N)\n",
    "    runs = 1\n",
    "    \n",
    "    for ind in range(runs):\n",
    "        reward_sum = 0\n",
    "        count = 0\n",
    "        Qa = np.zeros((n,n,2))\n",
    "        Qb = np.zeros((n,n,2))\n",
    "        freq = np.ones((n,n))\n",
    "        freq_action = np.ones((n,n,2))\n",
    "        (arrivals,departures) = arrival_and_departure_times(mu,lamda,N)\n",
    "        local_pos = 0;cloud_pos = 0\n",
    "        local_actions = np.array([False]*N)\n",
    "        cloud_actions = np.array([False]*N)\n",
    "        reward_list = []\n",
    "        reward_sum_list = []\n",
    "        for i in range(N):\n",
    "            local_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (local_actions)).sum()\n",
    "            cloud_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (cloud_actions)).sum()\n",
    "            eps = 1/np.sqrt(freq[local_pos,cloud_pos])\n",
    "            action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            if local_pos == n:\n",
    "                action = 1\n",
    "            if cloud_pos == n:\n",
    "                action = 0        \n",
    "            if action == 0:\n",
    "                local_actions[i] = 1\n",
    "            if action == 1:\n",
    "                cloud_actions[i] = 1\n",
    "            (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "            count = count + 1\n",
    "            alpha = 1/np.power(freq_action[local_pos,cloud_pos, action],0.8)\n",
    "            if np.random.choice([True,False]):\n",
    "                Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qb[local_pos,cloud_pos,np.argmax(Qa[new_local_pos,new_cloud_pos])] - Qa[local_pos,cloud_pos,action])\n",
    "            else:\n",
    "                Qb[local_pos,cloud_pos,action] = Qb[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qa[local_pos,cloud_pos,np.argmax(Qb[new_local_pos,new_cloud_pos])] - Qb[local_pos,cloud_pos,action])\n",
    "            freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "            freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1     \n",
    "            reward_list.append(reward)\n",
    "            reward_sum = reward_sum + reward\n",
    "            reward_sum_list.append(reward_sum/count)\n",
    "        reward_sum_avg_list = reward_sum_avg_list + np.array(reward_sum_list)\n",
    "    return reward_list,np.array(reward_sum_list),reward_sum_avg_list/runs,local_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Server_SwitchQ_learning(N,n,mu,lamda,gamma):\n",
    "    reward_list = []\n",
    "    reward_sum_list = []\n",
    "    reward_sum = 0\n",
    "    reward_sum_avg_list = np.array([0]*N)\n",
    "    count = 0\n",
    "    runs = 1\n",
    "    \n",
    "    for ind in range(runs):\n",
    "        count = 0\n",
    "        Qa = np.zeros((n,n,2))\n",
    "        Qb = np.zeros((n,n,2))\n",
    "        freq = np.ones((n,n))\n",
    "        freq_action = np.ones((n,n,2))\n",
    "        return_dict = {}\n",
    "        for i in range(n):\n",
    "            return_dict[i] = {}\n",
    "            for j in range(n):\n",
    "                return_dict[i][j] = {}\n",
    "                for k in range(2):\n",
    "                    return_dict[i][j][k] = []\n",
    "        switch_list = []\n",
    "        (arrivals,departures) = arrival_and_departure_times(mu,lamda,N)\n",
    "        local_pos = 0;cloud_pos = 0\n",
    "        local_actions = np.array([False]*N)\n",
    "        cloud_actions = np.array([False]*N)\n",
    "        reward_list = []\n",
    "        reward_sum_list = []\n",
    "        reward_sum = 0\n",
    "        action = 0\n",
    "        for i in range(N):\n",
    "            local_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (local_actions)).sum()\n",
    "            cloud_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (cloud_actions)).sum()\n",
    "            eps = 1/np.sqrt(freq[local_pos,cloud_pos])\n",
    "            #action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            if local_pos == n:\n",
    "                action = 1\n",
    "            if cloud_pos == n:\n",
    "                action = 0        \n",
    "            if action == 0:\n",
    "                local_actions[i] = 1\n",
    "            if action == 1:\n",
    "                cloud_actions[i] = 1\n",
    "            if((len(return_dict[local_pos][cloud_pos][action]) >= 2)and(len(return_dict[local_pos][cloud_pos][1-action]) >= 2)):\n",
    "                if(np.var(return_dict[local_pos][cloud_pos][1 - action]) > np.var(return_dict[local_pos][cloud_pos][action])):    \n",
    "                    (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "                    return_dict[local_pos][cloud_pos][action].append(reward)\n",
    "                    alpha = 1/np.power(freq[local_pos,cloud_pos],0.8)\n",
    "                    if np.random.choice([True,False]):\n",
    "                        Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qb[local_pos,cloud_pos,np.argmax(Qa[new_local_pos,new_cloud_pos])] - Qa[local_pos,cloud_pos,action])\n",
    "                    else:\n",
    "                        Qb[local_pos,cloud_pos,action] = Qb[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qa[local_pos,cloud_pos,np.argmax(Qb[new_local_pos,new_cloud_pos])] - Qb[local_pos,cloud_pos,action])\n",
    "                    freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "                    freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1\n",
    "                    switch_list.append(1)\n",
    "                    action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "                else:\n",
    "                    (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "                    return_dict[local_pos][cloud_pos][action].append(reward)\n",
    "                    alpha = 1/np.power(freq[local_pos,cloud_pos],0.8)\n",
    "                    Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*np.max(Qa[new_local_pos,new_cloud_pos]) - Qa[local_pos,cloud_pos,action])\n",
    "                    freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "                    freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1\n",
    "                    switch_list.append(0)\n",
    "                    action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            else:\n",
    "                if np.random.choice([True,False]):\n",
    "                    (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "                    return_dict[local_pos][cloud_pos][action].append(reward)\n",
    "                    alpha = 1/np.power(freq_action[local_pos,cloud_pos, action],0.8)\n",
    "                    Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*np.max(Qa[new_local_pos,new_cloud_pos]) - Qa[local_pos,cloud_pos,action])\n",
    "                    freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "                    freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1\n",
    "                    switch_list.append(1)\n",
    "                    action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])  \n",
    "                else:\n",
    "                    (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "                    return_dict[local_pos][cloud_pos][action].append(reward)\n",
    "                    alpha = 1/np.power(freq[local_pos,cloud_pos],0.8)\n",
    "                    if np.random.choice([True,False]):\n",
    "                        Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qb[local_pos,cloud_pos,np.argmax(Qa[new_local_pos,new_cloud_pos])] - Qa[local_pos,cloud_pos,action])\n",
    "                    else:\n",
    "                        Qb[local_pos,cloud_pos,action] = Qb[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qa[local_pos,cloud_pos,np.argmax(Qb[new_local_pos,new_cloud_pos])] - Qb[local_pos,cloud_pos,action])\n",
    "                    freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "                    freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1    \n",
    "                    switch_list.append(1)\n",
    "                    action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])          \n",
    "            count = count + 1\n",
    "            reward_list.append(reward)\n",
    "            reward_sum = reward_sum + reward\n",
    "            reward_sum_list.append(reward_sum/count)\n",
    "        reward_sum_avg_list = reward_sum_avg_list + np.array(reward_sum_list)\n",
    "    return reward_list,np.array(reward_sum_list),reward_sum_avg_list/runs,local_actions,switch_list,Qa,Qb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 2000;n = 50;mu = 1/15;lamda = 1;gamma = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "r_list_q,r_sum_list_q,r_sum_avg_list_q,local_actions_q = Server_Q_learning(N,n,mu,lamda,gamma)\n",
    "r_list_dq,r_sum_list_dq,r_sum_avg_list_dq,local_actions_dq = Server_DoubleQ_learning(N,n,mu,lamda,gamma)\n",
    "r_list,r_sum_list,r_sum_avg_list,local_actions,slist,Qa,Qb = Server_SwitchQ_learning(N,n,mu,lamda,gamma)\n",
    "plt.plot(-r_sum_list_q)\n",
    "plt.plot(-r_sum_list_dq)\n",
    "plt.plot(-r_sum_list)\n",
    "\n",
    "\n",
    "plt.title(\"f_I = 10^\" + str(int(np.log10(f_I))) )\n",
    "plt.legend([\"Q-learning\", \"Double Q-learning\",\"Switch Q-learning\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_avg_list_q = []\n",
    "for i in range(len(r_list_q) - 100):\n",
    "    r_avg_list_q.append(np.mean(r_list_q[i:i+100]))\n",
    "\n",
    "r_avg_list_dq = []\n",
    "for i in range(len(r_list_dq) - 100):\n",
    "    r_avg_list_dq.append(np.mean(r_list_dq[i:i+100]))\n",
    "\n",
    "r_avg_list = []\n",
    "for i in range(len(r_list) - 100):\n",
    "    r_avg_list.append(np.mean(r_list[i:i+100]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x204fcd42690>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bZry1a9earatXr56ZQi0N5Kv1AbSzT81G16pdESALKMEZEQRBAB5iIf77rEeJndtRCnPtFBQUwNvbm38/ffp0TJ8+3WjMnTt3kJeXh+7duxutVygUaN68Of9+6dKlWL16NRITE5Gfnw+FQmFmiWjcuLHFPilNmjThl8PDwwEAqampVsVIVFQUL0S4fVJTUwGwsTJKpRJt2rTht/v5+aFu3boWj0XYpkL3pslXGmfz3H52G9tub0OUbxTGNbOeekwQBOFKGIax21VSktSqVQsMwyAuLg79+/c32x4XF4fg4GBUrlwZsbGx/HquAKYhXFzhzp07ERERYbRNKpUCADZt2oQpU6Zg0aJFiI6Oho+PD7766iucOXPGaLyXl5fF+YrFYn6ZcxvZqgZuOJ7bh6qHu4bS/213IQXqAqP32+9sx+743QAAmUiGUY2odw5BEIQ1AgMD0b17dyxbtgyTJk0yihtJSUnB+vXrMX78eIhEItSqVcvmsRo0aACpVIrExER07tzZ4pgTJ06gffv2RnWq7t6965wP4yA1atSAWCzGuXPnULVqVQBAZmYmbt26hU6dOpXInMoyFboZQoHKWIz8c+8ffvnX/35193QIgiDKHD/88APkcjl69OiBo0ePIikpCXv27EH37t1Rp04dzJw5067j+Pj4YMqUKZg0aRLWrVuHu3fv4uLFi/j++++xbt06AEDt2rVx/vx57N27F7du3cKMGTNw7tw5V348m/MdPnw4PvzwQxw6dAjXr1/H6NGjIRAIqOtyEajQYuSlmi9hSqspFrflKnPdPBuCIIiyR+3atXHu3DnUqFEDAwcORLVq1dCrVy/UqVMHJ06cMIoVKYzPP/8cM2bMwIIFC1C/fn307NkTO3fuRPXq1QEAb7/9Nl555RW8/vrraNu2LZ4+fWqzmrerWbx4MaKjo9GnTx/ExMSgQ4cOqF+/fpE711ZkGK2jSeUlQFZWFvz8/JCZmemSAmi77u3Cx8c+Nlsf+2YshALHg7kIgiDspaCgAPHx8ahevXq5uYnNmjULixcvxv79+9GuXbuSno7byM3NRUREBBYtWoTRoytO/Spb32F7798VOmaEo1FQI4vr81X58JbYr+oJgiAINm02KioKp0+fRps2bZzSYr40cunSJdy4cQNt2rRBZmYmPvvsMwBAv379SnhmZQ8SIwA8xZ4W1+cqc0mMEARBFIGRI0eW9BTcwtdff42bN29CIpGgZcuWOHbsGIKCgkp6WmUOEiMAfCQ+Ftd/e/FbzO84382zIQiCIMoCzZs3x4ULF0p6GuWC8mk7cxCpUIqYqjFm63fc21ECsyEIgiCIigVZRnTM7zgf/uf84SHyoLRegiAIgnAjJEZ0eIg8MCt6FgCqMUIQBEEQ7oTcNIWgUCtKegoEQRAEUa4hMWKBqW2m8ss5ypwSnAlBEARBlH9IjFhgaP2h8BSx6b65CqrEShAEQRCuhMSIFbzFbH0RsowQBEGUDrp06YKJEyfaHBMVFYVvvvnGLfOxBsMw2L59e4nOgcOe31lpgMSIFbwkbAtqEiMEQRCWGTFiBBiGAcMwEIvFCA0NRffu3bF69WpoNJqSnl6x+Oeff9C5c2f4+PjA09MTrVu3xtq1a0t6Wg7zxx9/4PPPPy/paRQKiRErcJYRaphHEARhnZ49eyI5ORkJCQnYvXs3unbtivfffx99+vSBSqUq6ekVie+//x79+vVDhw4dcObMGVy5cgWDBg3CO++8gylTLDdXdTdKpdKucQEBAfDxsVzYszRBYsQKXIl4EiMEQbgdrRZQ5JbMy8HeqVKpFGFhYYiIiECLFi0wffp0/PXXX9i9e7eRJSExMRH9+vWDt7c3fH19MXDgQDx+/JjfPmLECLz88stGx544cSK6dOlitE6lUmHChAnw8/NDUFAQZsyYAVv9XjMyMvDWW28hODgYvr6+eP7553H58mWr45OSkvDBBx9g4sSJmD9/Pho0aIBatWrhgw8+wFdffYVFixbhzJkzDv2OkpKSMHDgQPj7+yMgIAD9+vVDQkICv/3cuXPo3r07goKC4Ofnh86dO+PixYtGx2AYBj/++CNeeukleHl5Yd68eZg9ezaaNWuGX3/9FVFRUfDz88OgQYOQnZ3N72fqpomKisL8+fMxatQo+Pj4oGrVqlixYoXRuU6ePIlmzZpBJpOhVatW2L59OxiGQWxsrEOf2xGozogVyDJCEESJocwD5lcumXNPfwTo3NRF5fnnn0fTpk3xxx9/4K233oJGo+GFyJEjR6BSqTB+/Hi8/vrrOHz4sEPHXrduHUaPHo2zZ8/i/PnzGDt2LKpWrYoxY8ZYHP/aa6/Bw8MDu3fvhp+fH3766Sd069YNt27dQkBAgNn4rVu3QqlUWrSAvP3225g+fTo2btyItm3b2jVfpVKJHj16IDo6GseOHYNIJMLcuXPRs2dPXLlyBRKJBNnZ2Rg+fDi+//57aLVaLFq0CC+++CJu375tZNWYPXs2Fi5ciG+++QYikQirV6/G3bt3sX37dvzzzz949uwZBg4ciIULF2LevHlW57Ro0SJ8/vnnmD59OrZu3Yp3330XnTt3Rt26dZGVlYW+ffvixRdfxIYNG3D//n23xJyQGLGCl5hiRgiCIIpKvXr1cOXKFQDAwYMHcfXqVcTHxyMyMhIA8Msvv6Bhw4Y4d+4cWrdubfdxIyMjsWTJEjAMg7p16+Lq1atYsmSJRTFy/PhxnD17FqmpqZBKpQDYxnbbt2/H1q1bMXbsWLN9bt26BT8/P4SHh5ttk0gkqFGjBm7dumX3fDdv3gyNRoNVq1aBYRgAwJo1a+Dv74/Dhw/jhRdewPPPP2+0z4oVK+Dv748jR46gT58+/PohQ4aYNSDUaDRYu3YtL1refPNNHDx40KYYefHFFzFu3DgAwMcff4wlS5bg0KFDqFu3LjZs2ACGYbBy5UrIZDI0aNAADx8+tCr2nAWJEStwlpGd93ZiVKNRJTwbgiAqFGJP1kJRUud2Alqtlr/5xsXFITIykhciANCgQQP4+/sjLi7OITHSrl07/rgAEB0djUWLFkGtVkMoFBqNvXz5MnJychAYGGi0Pj8/H3fv3i3KxwLAihIAmD9/PubP1zdT/e+//1C1alWzOdy5c8csbqOgoICfw+PHj/Hpp5/i8OHDSE1NhVqtRl5eHhITE432adWqldlcoqKijI4dHh6O1NRUm/Nv0qQJv8wwDMLCwvh9bt68iSZNmkAmk/Fj2rRpY/N4zoDEiBX8pH4AgAx5RslOhCCIigfDFNtVUtLExcWhevXqdo8XCARmsR/2BmlaIycnB+Hh4RZdQf7+/hb3qV27NjIzM/Ho0SNUrmzsKlMoFLh79y569OgBAHjnnXcwcOBAfrvpeG4OLVu2xPr16822BQcHAwCGDx+Op0+f4ttvv0W1atUglUoRHR0NhcK4AriXl/l3QiwWG71nGKbQTKai7ONqKIDVCoPrDQYApOalokBVUMKzIQiCKDv8+++/uHr1KgYMGAAAqF+/PpKSkpCUlMSP+e+//5CRkYEGDRoAYG/MycnJRsexFDBpGjx6+vRp1K5d28wqAgAtWrRASkoKRCIRatWqZfQKCgqyOPdXX30VIpEIixYtMtu2fPly5OXlYdiwYQDYTBXDY4pE5s/3LVq0wO3btxESEmI2Bz8/9qH3xIkTeO+99/Diiy+iYcOGkEqlSEtLszg/V8O5vuRyOb/u3LlzLj8viREreEu8+WW5Wm5jJEEQRMVFLpcjJSUFDx8+xMWLFzF//nz069cPffr04W/aMTExaNy4MYYOHYqLFy/i7NmzGDZsGDp37sy7Hp5//nmcP38ev/zyC27fvo1Zs2bh2rVrZudLTEzE5MmTcfPmTWzcuBHff/893n//fYtzi4mJQXR0NF5++WXs27cPCQkJOHnyJD755BOcP3/e4j5Vq1bFl19+iW+++QaffPIJbty4gbt372Lx4sX46KOPMHfuXDRq1Mju38/QoUMRFBSEfv364dixY4iPj8fhw4fx3nvv4cGDBwBYa8yvv/6KuLg4nDlzBkOHDoWHh4fd53AmQ4YMgUajwdixYxEXF4e9e/fi66+/BgAj95izITFiBREjAgP2F6/UFM9USBAEUV7Zs2cPwsPDERUVhZ49e+LQoUP47rvv8Ndff/HWCoZh8Ndff6FSpUro1KkTYmJiUKNGDWzevJk/To8ePTBjxgx89NFHaN26NbKzs3kxY8iwYcOQn5+PNm3aYPz48Xj//fctBqJy5921axc6deqEkSNHok6dOhg0aBDu37+P0NBQq59p0qRJ+OOPP3Ds2DG0atWKT+1du3Ytpk+f7tDvx9PTE0ePHkXVqlXxyiuvoH79+hg9ejQKCgrg6+sLAPj555/x7NkztGjRAm+++Sbee+89hISEOHQeZ+Hr64sdO3YgNjYWzZo1wyeffIKZM2cCgFEcibNhtLYStEsJWVlZ8PPzQ2ZmJv/Hcwetf2uNAnUB9gzYgwjvCLedlyCIikNBQQHi4+NRvXp1l17siaKTnp6Obt26wdfXF7t374anp3OCfMsK69evx8iRI5GZmWnRYmPrO2zv/ZssIzYQC9kgH4VaUchIgiAIorwSEBCAAwcOoFu3bjh16lRJT8fl/PLLLzh+/Dji4+Oxfft2fPzxxxg4cKBLXUeUTXN7P3DuZ6Dvt4CPsdlOKpQiG9kkRgiCICo4gYGBvLuivJOSkoKZM2ciJSUF4eHheO2112zWLXEGJEbWv8r+3OcNDFhltEkiYHPJSYwQBEEQFYWPPvoIH330kVvPSW4ajowks1USoU6MaEiMEARBEISrIDHCkXTabJWHiPWPZcmz3D0bgiAIgqgwVGwxkvnQ5uZa/rUAAJefWO/wSBAEQRBE8XBYjBw9ehR9+/ZF5cqVwTAMtm/fbve+J06cgEgkQrNmzRw9rWvIMhYjcpXa6H39wPoAgJ+v/cy21c5IdLi9NkEQBEEQtnFYjOTm5qJp06ZYunSpQ/tlZGRg2LBh6Natm6OndB3XtvGL+VoJ6n66B5cSn/HrOkZ05JdzzvwIfNMYOPmdW6dIEARBEOUdh8VIr169MHfuXPTv39+h/d555x0MGTIE0dHRjp7SdZxZzi96MAow0ODtXy/w66L8ouAjZrsh3jk0C+dkUmj3V4zULoIgCIJwF26JGVmzZg3u3buHWbNm2TVeLpcjKyvL6OUSfMKN3s4TrUanvH1G6yJ92ZbXb1YOw6jwUPzma9wGmiAIgnAOI0aMwMsvv1zix3CUtWvXWu0C7G4SEhLAMIzFJoOlGZeLkdu3b2Pq1Kn47bffLHY0tMSCBQvg5+fHvyIjI10zudfWQmXwKxgi+hdfi38Crm/n14V5hhnt8mVgJeQqc10zH4IgiDLEkydP8O6776Jq1aqQSqUICwtDjx49cOLEiSId79tvv8XatWv59126dMHEiROdM9lCyM/Px6xZs1CnTh1IpVIEBQXhtddew/Xr191yfmcRGRmJ5ORkh5r5lQZcKkbUajWGDBmCOXPmoE6dOnbvN23aNGRmZvIvw7bTTqVqOwwO/tN8/e/DgfR7AIAh9YeYbb744KRr5kMQBFGGGDBgAC5duoR169bh1q1b+Pvvv9GlSxc8ffq0SMfz8/MrEQuDXC5HTEwMVq9ejblz5+LWrVvYtWsXVCoV2rZti9OnzUs/uBu1Wg2NRlPoOKFQiLCwMLsf/ksLLhUj2dnZOH/+PCZMmACRSASRSITPPvsMly9fhkgkwr///mtxP6lUCl9fX6OXq3icq8UBdXPzDTmpAIDqftXNNuVlP3DZfAiCILRaLfKUeSXysrd3akZGBo4dO4YvvvgCXbt2RbVq1dCmTRtMmzYNL730EgBgypQp6NOnD7/PN998A4ZhsGfPHn5drVq1sGoVW/3a0MUyYsQIHDlyBN9++y0YhgHDMEhISAAAXL9+HX369IGvry98fHzQsWNH3L1712h+X3/9NcLDwxEYGIjx48dDqbTeff2bb77BqVOn8M8//2DgwIH8Z9m2bRvfZdfRnrJ//fUXWrRoAZlMhho1amDOnDlQqVT89sWLF6Nx48bw8vJCZGQkxo0bh5ycHH475/r5+++/0aBBA0ilUiQmJiIqKgrz58/HqFGj4OPjg6pVq2LFihX8fqZumsOHD4NhGBw8eBCtWrWCp6cn2rdvj5s3bxrNd+7cuQgJCYGPjw/eeustTJ061a2Zry6VTr6+vrh69arRumXLluHff//F1q1bUb26+Y3enSRn5iMxPQ9ysdh8o5z9UoR4hiBEIEOqpoDfpN07HWg0HBBU7DItBEG4hnxVPtpuaFsi5z4z5Aw8xYV3pfX29oa3tze2b9+Odu3aQSqVmo3p3LkzVq1aBbVaDaFQiCNHjiAoKAiHDx9Gz5498fDhQ9y9exddunQx2/fbb7/FrVu30KhRI3z22WcAgODgYDx8+BCdOnVCly5d8O+//8LX1xcnTpwwutEfOnQI4eHhOHToEO7cuYPXX38dzZo1w5gxYyx+lg0bNqB79+5o2rSp0XqBQIBJkyZh6NChuHz5st0352PHjmHYsGH47rvveKE0duxYAOBjJwUCAb777jtUr14d9+7dw7hx4/DRRx9h2bJl/HHy8vLwxRdfYNWqVQgMDERISAgAYNGiRfj8888xffp0bN26Fe+++y46d+6MunXrWp3TJ598gkWLFiE4OBjvvPMORo0axbvT1q9fj3nz5mHZsmXo0KEDNm3ahEWLFrn1Hu3w3TQnJwexsbG86oqPj0dsbCwSExMBsC6WYcOGsQcXCNCoUSOjV0hICGQyGRo1agQvLy/nfZIiMOV3tphZASTmGxV6hdomxziAVskAoKqsBEFUYEQiEdauXYt169bB398fHTp0wPTp03HlyhV+TMeOHZGdnY1Lly5Bq9Xi6NGj+OCDD3D48GEA7FN7REQEatWqZXZ8Pz8/SCQSeHp6IiwsDGFhYRAKhVi6dCn8/PywadMmtGrVCnXq1MHIkSONbsSVKlXCDz/8gHr16qFPnz7o3bs3Dh48aPWz3Lp1C/Xr17e4jVt/69Ytu383c+bMwdSpUzF8+HDUqFED3bt3x+eff46ffvqJHzNx4kR07doVUVFReP755zF37lxs2bLF6DhKpRLLli1D+/btUbduXXh6siLxxRdfxLhx41CrVi18/PHHCAoKwqFDh2zOad68eejcuTMaNGiAqVOn4uTJkygoYB+yv//+e4wePRojR45EnTp1MHPmTDRu3Njuz+sMHLaMnD9/Hl27duXfT548GQAwfPhwrF27FsnJybwwKe3EJmYAAORaC5aRpDNAw5cBjQZitQIwECxKMEBBJuDh745pEgRRwfAQeeDMkDMldm57GTBgAHr37o1jx47h9OnT2L17N7788kusWrUKI0aMgL+/P5o2bYrDhw9DIpFAIpFg7NixmDVrFnJycnDkyBF07tzZofnFxsaiY8eOEFuyaOto2LAhhEIh/z48PNzMSm9KYW4YiUTCH/v+/fsAWLG1e/dus7GXL1/GiRMnjDrdqtVqFBQUIC8vD56enjhw4AAWLFiAGzduICsrCyqVymg7d84mTZqYHd9wHcMwCAsLQ2pqqs35G+4THs5mkqampqJq1aq4efMmxo0bZzS+TZs2VkMpXIHDYqRLly42/2iGkdCWmD17NmbPnu3oaV1CnTAfXErMgNySZeSpzv8oz4LI5PMqGQbITAIqVXPDLAmCqGgwDGOXq6Q0IJPJ0L17d3Tv3h0zZszAW2+9hVmzZmHEiBEA2HvG4cOHIZVK0blzZwQEBKB+/fo4fvw4jhw5gg8++MCh83l4FC6WTIUKwzA2gz9r166NuLg4i9u49VwSxq5du/j4E2tzycnJwZw5c/DKK6+YbZPJZEhISECfPn3w7rvvYt68eQgICMDx48cxevRoKBQKXox4eHiAYZhifz7Tfbhj2hMQ6y4qdNDDoteaolGELzrXr8yvu6aJYhdU+ezP30dAZKK9lAwD3DngnkkSBEGUIRo0aIDcXH35g86dO+P48eM4ePAgHxvSpUsXbNy4Ebdu3bIYL8IhkUigVhu36WjSpAmOHTtmMyDVUQYPHowDBw7g8mXjPmQajQZLlixBq1at0KBBAwBAtWrVUKtWLdSqVQsREREWj9eiRQvcvHmTH2f4EggEuHDhAjQaDRYtWoR27dqhTp06ePTokdM+j6PUrVsX586dM1pn+t7VVGgxUiPYG//8ryNqtOkNCKW4gtr4WjWQ3Rh/FMhLB1L/M9tPyQDQqMzWEwRBVBSePn2K559/Hr/99huuXLmC+Ph4/P777/jyyy/Rr18/flynTp2QnZ2Nf/75x0iMrF+/HuHh4TbLPkRFReHMmTNISEhAWloaNBoNJkyYgKysLAwaNAjnz5/H7du38euvv5plhzjCpEmT0KZNG/Tt2xe///47EhMTce7cOQwYMAC3b9/GunXrHDrezJkz8csvv2DOnDm4fv064uLisGnTJnz66acA2AwipVKJ77//Hvfu3cOvv/6K5cuXF3JU1/G///0PP//8M9atW4fbt29j7ty5uHLlikWrjKuo0GKEp3Z3YFoS3pV+gTStQRrx2j5AfgY0Jn8PFRhAQYXPCIKouHh7e6Nt27ZYsmQJOnXqhEaNGmHGjBkYM2YMfvjhB35cpUqV0LhxYwQHB6NevXoAWIGi0WgKjReZMmUKhEIhGjRogODgYCQmJiIwMBD//vsvcnJy0LlzZ7Rs2RIrV660GUNSGDKZDAcPHsSwYcMwbdo01KxZE23atMG1a9dw7do13ipiLz169MA///yDffv2oXXr1mjXrh2WLFmCatVY137Tpk2xePFifPHFF2jUqBHWr1+PBQsWFHn+xWXo0KGYNm0apkyZghYtWiA+Ph4jRoyATCZz2xwYraPJ0yVAVlYW/Pz8kJmZ6dKaIzGLj+Beahbuyd4wWj87MADbfL359+8+y8S4yB7AgJUumwtBEBWDgoICxMfHo3r16m69+BO22b17N/r374+vv/4aEyZMKOnpuJ3u3bsjLCwMv/76a6FjbX2H7b1/k2XEAE+JEBoLvxJTy4iSAXB1C5B22z0TIwiCINxKr169sHv3bqSnpyMtLa2kp+NS8vLysHjxYly/fh03btzArFmzcODAAQwfPtxtcyhb9WJdjIeYTQUrkAZBJtd/+dQm4wo4P9oPrYBpDwApNc8jCIIob3Tt2tWolEV5hWEY7Nq1C/PmzUNBQQHq1q2Lbdu2ISYmxm1zIDFigKeEFSMH2m9An0Mv6NcH1AKUKfz7vIjmQLquwEzcP0CzwW6dJ0EQBEE4Cw8PDxw4ULIZouSmMcBTwmqz1VflRusH+9RB3Up1EeQRBADICawJiHXVYwsy3DlFgiAIgih3kBgxIMCLLX528UE2nrUYz6+vIfHH1pe2YlLLSQCAq2lXgYb92Y3KPLfPkyCI8kcZyCUgCIs447tLYsSAd7rU5JfvBhr4CWuyy14i1hqSnJsMtUjXFEqpb6BHEAThKFxKal4ePdgQZRPuu1uc9GqKGTEgwt8D3eqF4OCNVKQWGOg0H7ZCa/PQ5vwqpVgKIaCv1EoQBFEEhEIh/P39+d4inp6ebi02RRBFRavVIi8vD6mpqfD39zfqB+QoJEZMCPVjc6Qf5RmIEQlrEfEW62uNKIUSyACyjBAEUWzCwsIAoNBmZwRRGvH39+e/w0WFxIgJYb6sGHmcY9BASMKKEJFA/+tSiXTN9ZRkGSEIongwDIPw8HCEhIQ4tecKQbgasVhcLIsIB4kREyp5sj6vJyqpfqWUFSMCRgABI4BGq4GKixkhNw1BEE5CKBQ65cJOEGUNEiMmiIWseyZHIwXGHgYYASDSCxMRI4JCq4BKoAvUITcNQRAEQRQLEiMmSESsGFGotUDl5mbbRQIRFBoFlCJOjFAEPEEQBEEUB0rtNYEXIyrTIvAsXNyIiosfUZFlhCAIgiCKA4kREzg3jUKlsbidEyNKIQWwEgRBEIQzIDFiAmcZUaotV5QT62JFVEKyjBAEQRCEMyAxYoLEXssIl9r75AaQeNotcyMIgiCI8giJERP0lhHLYoSzjCQIDX51uz9y+bwIgiAIorxCYsQELmZEbsUykpKbAgBIzH0EvDCXXZn/zC1zIwiCIIjyCIkREzg3jTXLSLdq3fRvauqWMxKBcz+7emoEQRAEUS4hMWKCRMQ2qFJYESOBskAAgEqr4iuzAgB2Tnb53AiCIAiiPEJixASJrhSztQBWoYDdrtKo+J41BEEQBEEUHRIjJoh1lhFrbhoRw2bTqDVqQOqr3+BdvI6FBEEQBFFRITFigj5mRAuNxrzWCF9nRKMChCLglZXsBoP+NQRBEARB2A+JERO41F4AUGrMrSN8OXitil0RUp/9qZK7fG4EQRAEUR4hMWKC2KB+iKW4EV6MaHRiRCRjf1IlVoIgCIIoEiRGTJAYiBFLJeH5CqwaJbuC61FDlhGCIAiCKBIkRkwQCBiIBLr0XguWESHDZtOoNbquvoaWEa3lfjYEQRAEQViHxIgFuLgR+9w0XOCqFlAr3TE9giAIgihXkBixABc3YqnwGZdNk6PMYVdwlhGA4kYIgiAIogg4LEaOHj2Kvn37onLlymAYBtu3b7c5/o8//kD37t0RHBwMX19fREdHY+/evUWdr1uwxzJyNuUsMgoyjFN6KW6EIAiCIBzGYTGSm5uLpk2bYunSpXaNP3r0KLp3745du3bhwoUL6Nq1K/r27YtLly45PFl3Yas/TYvQFvzy/ez7AMMAQp0gIcuI28iRq/BX7ENkFZBrjCAIoqwjcnSHXr16oVevXnaP/+abb4zez58/H3/99Rd27NiB5s2bO3p6t8BbRiyIkQjvCFT3q474zHgouRgRkQxQywG1wp3TrNDM+us6tl18AAAQCxnM7NMAb0ZHleykCIIgiCLh9pgRjUaD7OxsBAQEWB0jl8uRlZVl9HInvGXESn8as/ReEVlG3A0nRAA2BXt/XGoJzoYgCIIoDm4XI19//TVycnIwcOBAq2MWLFgAPz8//hUZGenGGer708it9Kfhglj1YoQKn7kbL4nQ6H2eXFVCMyEIgiCKi1vFyIYNGzBnzhxs2bIFISEhVsdNmzYNmZmZ/CspKcmNsyzcMmIuRnSFz5QkRtxFVJCX0ftchbqEZkIQBEEUF4djRorKpk2b8NZbb+H3339HTEyMzbFSqRRSack1nrOV2guYNMsD9AGsFDPiNqKCvHD9kd59l6cgywhBEERZxS2WkY0bN2LkyJHYuHEjevfu7Y5TFgtbqb2ADcsIFT1zG3KlsSUkV06WEYIgiLKKw2IkJycHsbGxiI2NBQDEx8cjNjYWiYmJAFgXy7Bhw/jxGzZswLBhw7Bo0SK0bdsWKSkpSElJQWZmpnM+gQvgLCMqC71p2O06McKJj6f32J9HFrp8bgSL3EQokmWEIAii7OKwGDl//jyaN2/Op+VOnjwZzZs3x8yZMwEAycnJvDABgBUrVkClUmH8+PEIDw/nX++//76TPoLzEQt1vWnsDWCV64TVwwsunxvBUmBiGclTqKHRUG8ggiCIsojDMSNdunSB1kZDuLVr1xq9P3z4sKOnKHFEvGXEztRewu0UKNm/zbeDmuH9TbEAgHylGl5St4VBEQRBEE6CetNYQKzr2quy8qRtFsBKuB25irWMBHlLwbB/LuSSq4YgCKJMQmLEAiK+HLxtMUKWkZKDs4zIxEJ4itmaI3kUxEoQBFEmITFiAbGN3jQAiZHSABczIhML4KlzzZBlhCAIomxCYsQCXACrtZgRs2wawu1wYkQqEvLVWPOo8BlBEESZhMSIBUQCnWWkkJiR9IJ0dkXHD9wyL4Ll7pMcZBWwVhCZWABPic4yQiXhCYIgyiQkRizA9aaxVg4+LT8NALDt9jZ2RePX2J8e1pv/Ec5j+Oqz/LK3VAQvKVlGCIIgyjIkRiwg1llGrGXTXHlyxXiFkKvASuXg3cHjLLYH0MgOUfD3lJBlhCAIooxDYsQCIl3MiLUAVvMddL1pVHIXzYjg0Gq1fJbT+K61AIAsIwRBEGUcEiMWKKwcvBlcozyNEtDYKWCIImFYFZfrIcRbRiibhiAIokxCYsQCYgcsI2qNWt8oDyBXjYsxbF4o0YlGPpuG6owQBEGUSUiMWKCwbJomwU34ZYVGobeMAICaXDWuxLAQHSdGqM4IQRBE2YbEiAUKqzPyUeuP+OX7Wff1AawAoCLLiCvhLCMiAQOBrmw/ZxnZcflRic2LIAiCKDokRiwgLqQcvL/Un18evns4IBAAutojZBlxLZwY4eJFADa9FwDkVlKxCYIgiNINiRELiAopB89wndkA5KnydDtRRo074AJYOcEIAL2bVAYAZBeokEPpvQRBEGUOEiMW4N00jmTGcK6a1P9cMCOCw5JlJMhb7yY7GPfY7XMiCIIgigeJEQvwAaz2pvYCgFJnIbm03gUzIjg4y4jEwDLCMAwqebJusnyqNUIQBFHmIDFiAXtSe39+4WcAQJRvFLui+Zu6LQ4IGMJhuL+JoWUEANrXDAKgb6BHEARBlB1IjFjAnqJnXmIvAECBmi1Njsg27E9VgUvnVtHh3TRC46+uVMy+pyBWgiCIsgeJEQvYUw5eLGTdAgquyBkFsLoFSzEjACAVsem9JEYIgiDKHiRGLCAqpFEeAEh1hc70YkTG/iTLiEvRZ9MwRuulOnFCbhqCIIiyB4kRC0hEhVtGJAI2g8PQMiJngBR1vsvnV1HRaLR4+9cLAMwtIzIxaxl5lEG/f4IgiLIGiREL8JYRGzEjEl0qr0KjgFarBUQyDA8PRXfPPJxPOe+WeVY0Yh9k8Mtik5gRXTFW7P+PUnsJgiDKGiRGLGBPzIjEoAS8UqMERFJcl7Kum2MPj7l2ghUUsUD/dTV1x9QO9QYABPtIQRAEQZQtSIxYQFJIBVZAHzMCAHK1HFqD9yoNVQF1BQaFbyFgjGNGagSxYkRBAawEQRBlDhIjFhDZkdor5nrRgI0bUQmF/Pvd8bsRmxrrsvlVVBQG4nBc11pG27gYEoUNAUkQBEGUTkiMWECkC0BQ2igHzzCMURCrgtGLkSf5T/Dm7jf1wa2EU1DqrB41g73QuU6w0TZOjFBqL0EQRNmDxIgFuODIAqWGDU61gmEQq1xg/qvMVea6ZoIVFEtN8jg41xq5aQiCIMoeJEYsYFjDYufVZKvjuOqrybnJuJvzwGy7XE0F0JyJtVLwgL7OiEJtW0ASBEEQpQ9RSU+gNCIyePI+fPMJ+uha1JvCBaq+s/8dqLXmxbYKqACaU1GoWJFh0TKiEyNaLVuszrQoGkEQBFF6IcuIBQxvZJZufKZYEiIAWUacjdJK9VXA2FpCrhqCIIiyBYkRCxgKEEkxnrDzVVQN1JkcupkKwHbMCEBihCAIoqxBYsQCXDYNAIT6yYp8HLKMuIb0XPMsJZFQwFdhpfRegiCIsoXDYuTo0aPo27cvKleuDIZhsH379kL3OXz4MFq0aAGpVIpatWph7dq1RZiq+2AYBjWCvQAAPlLHwmoqqfUuGxIjzoWzeLzasorF7XytEbKMEARBlCkcFiO5ublo2rQpli5datf4+Ph49O7dG127dkVsbCwmTpyIt956C3v37nV4su6kQbgvANudey1xIPEhWijZZXLTOBeuhohUJLS4nVtPtUYIgiDKFg5n0/Tq1Qu9evWye/zy5ctRvXp1LFq0CABQv359HD9+HEuWLEGPHj0cPb3bENtRhXVqm6lYeHYhQjxCkJqfCh+RJyQAPLQaAAKyjDgZvRixrKHJMkIQBFE2cXnMyKlTpxATE2O0rkePHjh16pTVfeRyObKysoxe7kZoRxXWYA+2Cmi6PB0A4CtmXTtSDeuqodRe5yLXNceTiq2IESGVhCcIgiiLuFyMpKSkIDQ01GhdaGgosrKykJ9v2Y2xYMEC+Pn58a/IyEhXT9MMLn3UlmWEq8DK1RvxFrPN2qQ6AUNixLlwIsOam4br5PskmyxSBEEQZYlSmU0zbdo0ZGZm8q+kpCS3z0Ek4Nw01p+yud40HJwYkenESVK2++ddnnmaw2bRWHPTPNVl2Xy09bLb5kQQBEEUH5dXYA0LC8Pjx4+N1j1+/Bi+vr7w8PCwuI9UKoVUKnX11GwiEnJuGuuWkWYhzYze+0h82AVdOfLTyaddMreKiFylRmJ6HgDrYoTjWZ7SHVMiCIIgnITLLSPR0dE4ePCg0br9+/cjOjra1acuFlytkR8P37U6xlPsCaFBt15vnRipo2Bvhrw4IYpNhoHAaFLF3+ZYLhOKIAiCKBs4LEZycnIQGxuL2NhYAGzqbmxsLBITEwGwLpZhw4bx49955x3cu3cPH330EW7cuIFly5Zhy5YtmDRpknM+gYsI9dUXO+NiESwhFoj5ZW8pexOsoRMjlE3jPLgMGZlYAA+J5ZiR2X0bAIDV7QRBEETpxGExcv78eTRv3hzNmzcHAEyePBnNmzfHzJkzAQDJycm8MAGA6tWrY+fOndi/fz+aNm2KRYsWYdWqVaU6rRcAhkVH8cu2xAjXuRcAfCR+AACZzk1DYsR5cPVebPUK4hxqF+4/g9rB+jAEQRBEyeFwzEiXLl1stmi3VF21S5cuuHTpkqOnKlEkIgFEAgYqjRYFSvtSRRsENQQEYkhJjDgdfZM862KEiykBgHylGt4OVs8lCIIgSoZSmU1TWuACJeUq65YRjsZBjRFTLQYQSXkxkpKbAqWagimdAeemsdSxl6Nr3RB+OV9R+N+MIAiCKB2QGLGBTMzGHthjGanqW5VdEEp4MQIABxIPuGRuFQ3OTcOlXFuiY+0gftmWa40gCIIoXZAYsYEjlhERo3MJiKQQG4gRriAaUTw4N43ERlovw+itJvN2xrl8TgRBEIRzIKe6DaRi+xuvcdVYkZ2MMIP1JEacAydGuJTrwthzPQVardZIoBAEQRClE7KM2ICzjNgy+Y9sNBL+Un+MbTLWaH3vnFwAQJbC/X11yiNKXVl+kY0AVlOoey9BEETZgMSIDXjLiI2YkcktJ+PI60cQ5hVmtN5X9ySfKc903QQrEFxZfomNAFYA+G5wc35519Vkl86JIAiCcA4kRmyQpmu49sOhOzbHCRjzX6OvrlkeWUacA++mKcQy8lLTyvzy5C2XkZJJzQoJgiBKOyRGbPAwg+0qHJuU4fC+vBiRkxhxBpybxlZqryX+uPTAFdMhCIIgnAiJERdRScPGmaTL00t4JuUDe4qecfRuHM4vf7nnJjLyFC6bF0EQBFF8SIy4iFBdOvCZ5DM2K9YS9qFSF14OnuOHIc2N3qfq3G2Pswpw5NYTu1K1CYIgCPdBYsTZeLFVQKsq9Sm98ZnxJTWbcoPCgdRe03TerHy2Cu6AH09i+Oqz+ObAbedPkCAIgigyJEaczbDtAIAwtf7pm4JYiw+XTSO2UfTMkG719KXhZ/19HUNXncaDZ2wM0JUHGU6fH0EQBFF0SIzY4NPe9R3fKbQhENYYAFDLk41dMOzsSxQNPoDVzqJn8/o35pevP8rCiTtP+feGywRBEETJQxVYbRBdMxAAEOIjdWxHITveQ1ciPiEzAW3D2lI10GKg1NgfwAoAYX4yXJ75Ao7deQK1rq/NndQcfP8vm6Z963E26oT6uGayBEEQhEOQGLGBRHfj4zI57EbEihEZwxZNm3dmHnKUOXir8VtOnV9FQqlyvAKrn6cYfZpUNlrHiZE3Vp3B2U9inDdBgiAIosiQm8YGXFM2haNlxXV9ajwEeq337cVvnTaviohKY18FVnvhMmwIgiCIkofEiA3EvGXEwdRcLRu8Gv7MuOBWYlaiU+ZVEeH6AzliGSkMhy1eBEEQhEsgMWIDTowo1BrHaoXEHwUAvJ90Gy1CWvCre//ZG9mKbKfOsSKg0Wix8hibHi1ykmUEALILqKMyQRBEaYDEiA0kBk/hKo3jhct8tFo8X6WT0brY1NjiTqvCkWfQNblt9YBiHWtMx+r8sq1uzARBEIT7IDFiA7FI/xReVJO+v9jb6H2eKq9Yc6qIKA1idjrXCbExsnA61Qnml/NJjBAEQZQKSIzYwDCNlMvmcJQXq3TFa3Ve49/nKUmMOAonBIUCBkI764xY47laQfxyvoLECEEQRGmAxIgNDEuPLzt8x/4de37BL4o1asyMnomeUT0BALnKXKfNr6Kg4JvkFT9ehGEYRAV6AgBy5BQzQhAEURogMWIDwyJlPx29Z/+O7d7RL6vZjrFeYi8AwBfnvrC0B2EDpQNN8uyBS9ned/2xU45HEARBFA8SI65C5sf+1ImRxkH68uRbb20tiRmVWTg3jcRJYkQkYI9DBXEJgiBKByRGXIWuJDxUbHGtfrX68ZvmnJpTEjMqs3BF55xlGXmxcRgAIJfcNARBEKUCEiOuQleFFWpWjIgEVHm/qCj5jr3OMWV4Sti/RS4FsBIEQZQKSIy4ChEnRpQlO49ygLNjRrykbM+gc/HpTjkeQRAEUTxIjBRCv2aVCx9kCRM3DQAMrjcYABDlG1XMWVUsnB0zwllGUrIKHKusSxAEQbgEEiOFMPo5tmJnZT+ZYzvylhEFv6pfTTZupEBd4JS5VRT0qb3O+bq2qxFodmyCIAii5CAxUghSEWvSlxexc6+hGPEQeQAA8lX5TplbRYGrwOqsvjR+HmJ+uUBJYoQonzzIfoA7z+4gR5FT0lMhiEKhqMpC4GpSKBwWI+ZuGk6MUBVWx3B2zIhYyEDAABotIFeqAQNxQhDlgfVx67Hw7EIAgI/EB3sH7IWPxKeEZ0UQ1iHLSCFwYkTuqDnfgpuGEyNKjRKv7XgN+xL2OWWO5R1nx4wwDAOZmLV4UX8aojxyM/0mv5ytyMb9rPslOBuCKJwiXd2XLl2KqKgoyGQytG3bFmfPnrU5/ptvvkHdunXh4eGByMhITJo0CQUFZSNuQmpgGXEo2NGSm0bswS/fSL+BD458gEx5plPmWZ5xZjl4Dg+dGCE3DVEeUWgURu/nnJqDzTc2k1WWKLU4LEY2b96MyZMnY9asWbh48SKaNm2KHj16IDU11eL4DRs2YOrUqZg1axbi4uLw888/Y/PmzZg+fXqxJ+8OOMsI4GCwIydGDNw0EoHEbBj1qikcpZMDWAHwlpECsowQ5RClSUmBG+k3MPfMXMw7M6+EZkQQtnH46r548WKMGTMGI0eORIMGDbB8+XJ4enpi9erVFsefPHkSHTp0wJAhQxAVFYUXXngBgwcPLtSaUlowdA04FMQq0sWMGFhGGAv1xymYtXC4AFaxyHliRCpmj0VuGqI8wllGhjUYZrT+77t/l8R0CKJQHLq6KxQKXLhwATExMfoDCASIiYnBqVOnLO7Tvn17XLhwgRcf9+7dw65du/Diiy9aPY9cLkdWVpbRq6SQGlpGHBEjFtw0ADAwrIPRe7KMFA4XwOqsmBHA0E1DYoQofyg1rGWkbkBdzI6ebbTtl+u/mI1PL0hHRkGGG2ZGEJZx6OqelpYGtVqN0NBQo/WhoaFISUmxuM+QIUPw2Wef4bnnnoNYLEbNmjXRpUsXm26aBQsWwM/Pj39FRkY6Mk2nwjAMfxMskhhRGYiR5MuYcWojXs3K5lddeXLFGdMs17giZkRGMSNEOYZz04gFYrMsmq/Of2X0/tSjU+i6pSu6bOmCa2nX3DZHgjDE5dk0hw8fxvz587Fs2TJcvHgRf/zxB3bu3InPP//c6j7Tpk1DZmYm/0pKSnL1NG3CWUeK5qbRx4wg8QwA4NOnz/SrshPZBaoEahXXxIywx3rntwu4/Ti7kNEEUbZQ6CyyEoEE1XyrWd0OANfSrkGj1UCtVeNM8hm3zZEgDHHo6h4UFAShUIjHjx8brX/8+DHCwsIs7jNjxgy8+eabeOutt9C4cWP0798f8+fPx4IFC6DRWL65S6VS+Pr6Gr1KkiLVGhHqalcYumkU7E1PCGDsMzaLJleZC5xbBSyoAlze7IzpljtcIUY4Nw0AzPzrutOOSxClAc5NIxaKUTegLqa2mWq0PUuhd31nK/RiPCm7ZB/8iIqLQ1d3iUSCli1b4uDBg/w6jUaDgwcPIjo62uI+eXl5EAiMTyMUsjeCstIXhK81olLbP2eu6FnSOSArmV1W6ONDIlRs+/rDif8COz8AFDnA7o+cNufyBB8z4tQAVr0YUZeR7yFB2EN6QTquP2UFtljAPhS1Cm1lNGZD3AZ+OT4rnl9+mv/UDTMkCHMcvrpPnjwZK1euxLp16xAXF4d3330Xubm5GDlyJABg2LBhmDZtGj++b9+++PHHH7Fp0ybEx8dj//79mDFjBvr27cuLktIO56YZ+NMp9F92EhqNHTcvzk2TdBpYXI9dztbH1fjrrEJZyhyouJUUQGYRziLlzJgRX5m++LA/VWAlyhEXH1/kl6v7sb21avjXMGrQufLqSkw/Nh1arRaHkw7z6w8/OIz1cevdNFOC0ONwOfjXX38dT548wcyZM5GSkoJmzZphz549fFBrYmKikSXk008/BcMw+PTTT/Hw4UMEBwejb9++mDev7OS7c0/kBUoNYpMy8DAjH5EBnrZ3Eprc4JQFQKzunzy8GdqkXOY3KRgGIno6t4or3DQjO1THxrOsSbp6sJfTjksQJQ0XD1IvoB7CvFj3uVggxp/9/sSxB8fw3qH3AAA77u3Aey3eg4gRQaXlH4mw8OxCdKjcAVF+UW6fO1FxKVJvmgkTJmDChAkWtx0+fNj4BCIRZs2ahVmzZhXlVKUCrlkeh8aKcEjLkSPAUwKBgNG7aTiyk/XLr6yAx+/DAbBuGznDwJM75oHZQMxs50y8nOAKMVIn1AfjutTEssN3IaeMGqIcwcWLBHoEGq0XCUS8OOH46OhHRkKEY+31tQCA1+u+jvqB9V0zUYIwgHrT2IFprIKlQlkrjt5Fq7kHMPbX8+wKkUm1VaWuuJlnIBBcF8LA2rw1RG5YDO34EiD/GQg9rqgzAujTe+UqqjVClB+4gmeWKj7XC6iHUY1G8e8vpV4CAIR6Gpdr2HZ7G7bd3oahu4a6cKYEoYfEiB2Y3gRz5cY3r39vPMb8XTcAAAfidGXxZf7GB8lPZ3/qmuVBJINUJ0YUppVZ8zOKO+VyhSvqjAD69N5N5yiDgCg/GNYYMYVhGExqOQkNAxsarf/u+e/wdeevzY+lUZqtIwhXQGLEDrjS4Rz5CmMxMmrtefOd6vYyfr+2N/tTLGN/yrN5MSJnGKCzQepdATXPM8QV5eABwEvKeikDvaSFjCSIsgMnICRCc8sIR8+onpAKpfCV+GLlCyvRILABekT1wJz2c8zGUoYN4Q5IjNiBqWUkT2HuYzVEo9ECEi+gZjfzjZxlpNlgSAwtIw36AcE63+yNf4o95/KEK2JGAKBt9UCj4xNEeYCvMWLBMsIxotEInH/jPE4MPoF24e349U2Dm8JTZBycn5af5pqJEoQBJEbsQCY2DmAtrLkav/3lH803inVipEE/SCuxaXfyNmOAEIMgsTx6EjHEVTEjUoP6MQRRXuDcNLYsI9ao6V8TRwcdxXvN33P2tAjCJiRG7EAmth0zYkouZznxCQUavWq8kXPTAJCI2GV5g74AwwDt3mE3nF8NPLpUvEmXIxQusoxIDSrrlpUCfARRGH/d/QuAbcuILaRCKbpEduHfU2dxwh2QGLEDU8uIoZtGbaEA2rjf9EWHeEsIh0j/XipgYxX4PhFV2ujH3dhVxNmWP5QuCmDlUrY1WkBlTyE7gijlKDVKPMx5CADwlngX+Ti1K9Xml0mMEO6AxIgdCEyyXfIMAljTcxWmw/Eow+CfV2xSHE2gFzacGVXONdMLbQC00qXdGaT3KjVKPCuouOm+vBhxcgCrYcq2Q32HCKKUIlfpG3MOqjuoWMdqEtQEAJCSa7kjO0E4ExIjdmDarXf1CX0vh7Px6Wbjcw2zbSQmYkSjt6pIdYXR5IadfQNrsT+v/8mvenPXm+i0uRP/xFPRUKpcEzNiKEYc6shMEKUUw2tJgCygWMdSa9nr2Onk08U6DkHYA4kROzB9as7IU+JxVgEAIFeuFxfr32rLr+NjEKQmHYfV+rx9TowYtvPm65MY9Knhml4duH+gqB+hTKPUuCZmRChgIBKwVq9tFx449dgEURIYZtIwpvWLHKSabzX+WAThakiM2EG7GuZPGOvPJAIAPtp2BQDQt2llNK7iB4CNP+CCLtF0MND8Tf2OBpYRMzcNAFTvxP7UagCToEqNtuI9vStUGtx7wpbNd3bMCKAv7f/bmftOPzZBuBvuWiI1bUdRBJoEs26aAnVBsY9FEIVBYsQOXmlRBb+OboOf3mzJr2MA5BhYRfIVKnhJ9K1++Iwb33Cg3w/6g0l99Iu6C0ZyrkHfGqku6EyrMRIuAKBFxQuynL3jOr/sbMsIACwd0gIAoFJXvN8tUf7grKxFSes1xUMXbE8BrIQ7IDFiB0IBg461gxFZSR//IWAYI/eNVCyEUMDwacA5BSaF0Tp9CHgEANHj+VWcuDjx6IR+nEG2DfbPhPpZAv+2IlpGNugsUIB5jyBnwHVfpsJnRHnAFWKkQEWWEcL1kBhxAMN6IwxjXPyMC64s0HWA7b7kCFuJleP5T4GP44Go5/hVUb5RAABfiUFcicjAvHp6GQr2fcq/rei1MFxhGeFqjZAYIcoDTwvYgomWmuQ5ikzI1kE6m3IW/z39r9jHIwhbkBhxAMObIQOgwECMCAXG8QxylQbZcttl4+sG1AVgEsDKMIBIXxjt4sOT/DIX3V5RcUXMCPc3pdReojxwPoXtk+WMOI9Aj0B+efGFxcU+HkHYgsSIA5gGpxuJEQuR6wWFlI23GMAKGImRp1K926ai+W5NC8p5S0VWRhYdMW8ZqdhWJ6J8IBKw/yNVvKsU+1iNgxqjZ1RPAECWPKvYxyMIW5AYcYAIf70wEAgY3iXDvTdl/3+PbR7PYmovwAav6sgzqFNS0cTI0VtP+OXXWlaBv2fxTc+mcO41hZpKwhNlHy61l8uEKQ4Mw2Bg3YEAKKOGcD0kRhyAYRgMi2Zz7wuUasgNLB8j2kcBADaN1XfA/HT7Nbzz6wWkZln+R7ZY9AzQp/cCyBPqrQF5yrxizb+sYViIbFCbSJecw7CQGllHiLKOPR17HYG/RqnkhYwkiOJBYsRBuD41cpUGBbpur9UCPVE3jE3ZbVcj0Gj8nusp+M0gI8QQq26aPt8A3mEAgLh8vXUlT1WxxIhU7HqhYJihQ0GsRFmH69jrdDFieo0iCCdDYsRBZCIua0bNu2lCfWS2dsEzC/1rABtuGu9gYMgmyBlgn7cXv7oiiRG1Rot/41L5966qA2IYFEtBrERZR6Vlg+a52JHiIuM6i5MYIVwMiREHkeosI6wYUevWGf8aezcON3pvIZyE3c/WU0d4M2TWe9FoVb6y4sSMbLvwAL+e1ldFbWuhCq4zEAoYPjCZLCNEWcdVlpEcZQ7FVBEuhcSIg3B1KeQqDW8Z4Vw3HFN71TN6b61HBOemUWqU5gXNGAbyduOMVlWkANYDcXr31HO1glxSYwRg/zZc3Ag1yyPKOnzMiNA5YsRHoq8YzfXIIghXQGLEQWQGlhGu6JmpGAn2Me4LodI1ektKz0Pv747hq703ABj3jzBz1QDIFRlfUCqSm8ZbpjczS11QedUQToyQZYQo6zg7gNVLrHcTZ8oznXJMgrAEiREH4W6Me68/5t00MpObpUwsxJ6JHfn3v51OxOl7TzFvZxyuP8rC0kN3ARiXbL6fdd/MDPpQlWP0viJl04QYxOFUDfS0MbL4SKjWCFFO4Fy+zuy0y6UJW3pgIghnQWLEQaro+tOIBAyf2mtqGQGAemG++LR3ff79oBWnsed6Cv9erdFCxOif/l/d8SrWXF9jdIzzjy8Yva9IlhFOIEQFeuLjnvUKGV08qAorUV44/vA4AOcFsAL60vJyDQWxEq6DxIiDcCm8Ko0WeQpOjFj+Nb7VsYbV4+QqVGaxJD/G/mjz3HJlHrDzA6ACBJJxVqfuDUItij1nIhaxfwcFuWmIMo6QYf9XIn2cV5fHatYfQTgREiMOYliXIlvXmbcoN0uzrr4AFBrjf3bun/+NTLYUswpaKM+tAjIs1y0pT3BixMPFQgSgmBGifKDRavj+VdV8qzntuJw7mcQI4UpIjDiIYcXOzHw2WMyWGKnsp4996N88AgFe7D92joUmehqtBree3eLfcyWY/TX6m2QBwwAVIMVXnzbtejFCbhqiPKDS6K8pzowZsVqckSCcCIkRBzEsksXFgNgSIyuGteKXfWQivtkbZ1UxzKgBgK/OfcUvc//83hoNhDrXTIGAAVTlv09EvpW0aVcgFZFlhCj7cJk0gHNjRshNQ7gDEiMOwjCMkasGsB4zAgCNIvwwr38j9G8egVEdqvNiZOiq09h8LhFTWk0xGn8n4w6/zIkRqVYLmU6MPBaKAIM+EdmKbJx4eKLcPbW4001DlhGiPECWEaIsQ2KkCEhNCnDJRLZvmEPbVsOS15shKsiLr59RoNTg421XEVMtBh4ig27AjP7YXHMqqUaLXAG7PlYmNbKMzD45G+8ceAc/Xf7J4rlVGhWOPjiK5JxkBz5hycOnTdsQes5CbNC5lyDKKpxlhAEDocB5Ip4sI4Q7IDFSBEwtIzWCvayMNMdXZvzEEuQRhGXdlvHvBYwAt5/dxqLzi3Aq+RQAwFOrRSM5K0zUgJFlZN/9fQCADTc2WDzfjrs7MP7geLyw7YUy9WRTYCNt2tlQnRGiPODsUvAcXGoviRHClZAYKQKmYqR+uK/d+47sEIUqlfSWEK1WC1+pfn8BBBjw9wCsvb6WX1dDqUQdBXuhUTCWY0ZylbkWz3cu5Ry/vDR2qd3zLGn+e8RmEJGbhiDsg3PTOKsUPAe5aQh3UCQxsnTpUkRFRUEmk6Ft27Y4e/aszfEZGRkYP348wsPDIZVKUadOHezatatIEy4NmPZJceTpvUOtIOyZ2Il/P+Ovawj1DOXfqzQqaGH8hB6lVEGiixmRMwxvGclSZBmNy1Zkm52PS/UDgI1xG+2eZ0lSoFQjV1fDxUvqvEA8a0h0dUYogJUoy3BuGmcGrwJ6N41hgCxBOBuHxcjmzZsxefJkzJo1CxcvXkTTpk3Ro0cPpKamWhyvUCjQvXt3JCQkYOvWrbh58yZWrlyJiIiIYk++pDC1jDiKl0QIPw/26eXi/Qz4SvSWET+Zn9HYj1t/DOb5GZDqxIihZeTOsztGYzPkGfwyV1r+Yc5Dfp1hgFtp5mmu3hzcvKq/y88nIcsIUQ5wdl8aDrKMEO7AYQm9ePFijBkzBiNHjgQALF++HDt37sTq1asxdepUs/GrV69Geno6Tp48CbGY/SeJioqyeQ65XA65XP/Fz8rKsjHa/RjWGjF0udgLwzBYM7I1Xll2ElkFSjAMg3nPzcMnxz8xu5CEeIYAHYdCIlECNzfoLCOsGCkwcdd8eORDo86a09tOx+Unl/n3Km3ZECN5uhoslTzFLuvWawgFsBLlAe5hw9mWERIjhDtw6EqvUChw4cIFxMTE6A8gECAmJganTp2yuM/ff/+N6OhojB8/HqGhoWjUqBHmz58PtVptcTwALFiwAH5+fvwrMtJ5pY2dgaFl5NtBzYt0DC6Qlas3EuYZBgD47+l/RuNCPEMAhoHUIwCAsZsmX21c/My0xff8M/ON3nuLvYs0V3fDFYTzlLjeRQMYBrCSGCHKLq6yjFA2DeEOHBIjaWlpUKvVCA0NNVofGhqKlJQUi/vcu3cPW7duhVqtxq5duzBjxgwsWrQIc+fOtXqeadOmITMzk38lJSU5Mk2Xk5Wv951y7hZH8ZVxxc+U0Gq1Rh18DanuVx2AwQXBhmWkMHKUOTiUeMisO3BpIzmT/VzebogXASiAlSgfuCpmhBM3JEYIV+JyG7hGo0FISAhWrFiBli1b4vXXX8cnn3yC5cuXW91HKpXC19fX6FWaSM3WmytrOpDWa4iPzjKi0QLzd8Xx6XMc7cLbYc+APfCTsjEknFjZ4eOF2anHkK/KR44iBwDQuUpn/NP/H9aKYsI//f/Br71+5d+/d+g9M+tLaePY7ScAAI2bRBNZRojygKstI+SmIVyJQ2IkKCgIQqEQjx8/Nlr/+PFjhIWFWdwnPDwcderUgVCozzipX78+UlJSoFCUTaXN1cAAYNZ5114Mi3mtPBaPB+nG8Rz9a/VHhLc+yNewbPy23HvYm7AXc8+w1iUPkQeq+VZDs+BmZuep5lvNqKgaAKTkWrZilRYEut9ptcCiCT1HoQBWojzAp/aSm4YogzgkRiQSCVq2bImDBw/y6zQaDQ4ePIjo6GiL+3To0AF37tyBxqDZ261btxAeHg6JxLJrorSj0hT/id1UxJy4nWH03ltiHN9h6sa5mX6TX+5UhU0VHlh3IBoHNUbPqJ7oW6MvFnVeBADwFHka7ZunyivW3F1Nvk7stY6q5Jbz6QNYS7f7iiBs4So3Dd+1V0NihHAdDn9rJ0+ejOHDh6NVq1Zo06YNvvnmG+Tm5vLZNcOGDUNERAQWLFgAAHj33Xfxww8/4P3338f//vc/3L59G/Pnz8d7773n3E/iRmLqh+JA3GO0iQpw2jF/OfUQ3rX1773ExlYB04Z6GbmsdSrKNwp9a/YFALQNb4sNvc0rsXqIjS0j1gqklRb4vjQS1xc8A8hNQ5QPXJ3aS5YRwpU4LEZef/11PHnyBDNnzkRKSgqaNWuGPXv28EGtiYmJEAj0BpfIyEjs3bsXkyZNQpMmTRAREYH3338fH3/8sfM+hZuZ068hmlbxQ79mzquVotUY/ylMM19MxcjTPLaui6kLxhKmlpHLTy5jUL1BRZmmW8hTuK9JHqDvxExuGqIs46rUXooZIdxBkb61EyZMwIQJEyxuO3z4sNm66OhonD59uiinKpVE+Hvgf91qFz7QEbTGbhhTy4hp46v0gmcWx1lCJpIZvb+Xea8oM3Qbh2+yAaxkGSEI+3FZbxqyjBBugHrTlBa0ti0jpq6VmzmJAABPsbHVwxICRoDVPVajZWhL9lSlOLXXMDjY38M9MUUUwEqUB7iihtQojyiLkBgpRTAGgsTU4lFJajmYs5pvNbuO3TqsNd5v8T6A0h0zcu+Jfm7tawa65ZxUgZUoD3Cp/uSmIcoiJEZKkG3vtkfnOsH4sEddAICW0af3mnbebB3WGhNbTMR3wki8l57Br28Z0tLu83HWlhxlTjFm7Vq2nGcL3EX4e0AgKFratKOQm4YoD+xN2AsAEDLOdW8aZtOUZqsqUbZxT4lLwiItq1XCulFtcPJuWqFjGYbB6Majgdtn0DnzBG5JxMgWCNBebf+FhxcjitIrRtaeTADgvuqrgN4ycvpeOhQqTbEbIRJEScA13PSX+Tv1uIbB80qN0mq1aIIoDnTVLQU4VFJe4gUBgK+ePMXyx0/g8Us/oCDTrl29JKzrR6FRlEr/b55CbxmKDHC8AWFRifDXn+v8/XS3nbessS9hH36M/bHUV/CtqHDu13bh7Zx6XEPxQa4awlWQZaQUwDXNs4uq7YHzq43XJZ4G6vQodFcvkT4OJVeZWyJPOBqNFv8lZ6F2qDdy5WqsOHoP2QVsFkC+Qh+8KhK4Tyc3itC3G5AryVVjieScZHxw5AMAwM74nfin/z8lPCPCFE6MOLshpmFArFwthw98nHp8ggBIjJQKfHWWEY0iAAJJOtQFodYHN3kNeHgBOPOjfp3YPiuCUCCEh8iD72tTSeaeCqeGrDp+D/N33UC/ZpVRP9wXy4/ctTiuY50gt82JYRi0qOqPi4kZFMRqhbR8vSvxftZ9xGfG800ciZJHrVHjbib7v2RPur8jMAwDqVAKuVrOpw8ThLMhMVIK8NHFR+QljoIk4DgU6Z0QNXUnPuxRF+O71jLfocc84MZOIJNN74UDLhdvsTfyVfn44MgH2NJ3izOm7xC/nr4PAPgr9hFy5awlpE1UADrUYsVHcmY+/DzEeL1VpFvnxcWNqKgkvEVMWwjEpsaSGClFxGfG88tVfKo4/fgSgQRytZzcNITLIDFSChAIGFSp5IEHz4Igf/wyv/6rvTctixGBEBixA/i2KfteYX+vmZr+NfEk/wkSshKKN+kiwt30AeBAHFvSvku9YIzrYuFzuhFuXpRRYxnTdPAMeUbJTISwCCcSpEIp3+nbmUiEEkBJMSOE66AA1lLCmI41HNuhUhRQoyu7rLRfjHzR6QsAQL4qHxqt+2+8XhJz/etQzIyL4EvCkxixyN93/zZ6nym3L2iacA+cSAjzstw9vbhQ517C1ZAYKSV4WUlltVkVVJcdA4X9RcwM/cm/3/zd7v2chb+nufCoF1byAXEictPY5PKTy0bvyTJSujC0jLgC6txLuBoSI6UELyt9WHLk+nTXfIUam88l4my8Lv20CGKEK+0MAHPPzHV8osUkPs14rlUqeaCVE7sfFxWJE900Wq0Wpx6dwtEHR/nmZWUdzorWM6onALKMlDZcLUa4IoxkGSFcBYmRUkKAl+U025wC/c1s07lEfLztKgb+dIoVKVxfGgfcNAxjXNXUnRUVlWoNHjzLN1oX7iezMtq9iHRuGmeIkbMpZzF2/1iMPzgeu+N3F/t4pQEui6Kmf00AwM1nN7HyykpsubkFBaqCkpwaAb0YcVW6vlRAbhrCtVAAaymhdVQAZvZpgBBfKVpVC0D0woPQaoFsuT6VLsHAqpCSmY9aRbCMAMAb9d/Ab3G/AWBjR+xptucMcuXmVgJFKXGL6ANYiz+f5NxkfvlB9oNiH680wN3sIrwjAABJ2Un47tJ3ANjy4wPqDCixuRF6kUBuGqKsQpaRUoJAwGDUc9XRp0llhPnJUD2QFRo3U7Lx4Fkelh66g3Wn7vPjB688A5VQZ1W48Q+QlWzpsBb5qPVHfDOtLEWW8z5EIXDxL4YtZ0pLrwsugFXlBMuIXKXPOCjNfYDsRavV8jehduHtMKHZBAyoPQC1K9UGADzIKR+CqyzjrpgRyqYhXAVZRkop3jL2TzN5Cxs4KBEa68Yn2XIk5AhQCwDS7wE/RgMf3AREhV+MGIaBp8gTWYos5KvyCx3vLOQ6MSIRCTDlhbpYvP8WPuvXyG3nt4UzU3sL1Hq3RWnukGwvSo3eOicVSfF207cBAD9d/gm3n93GqqurUMu/FnrX6F1SU6zwuEuMUNEzwlWQZaSUYtoojks53fJ2NL/uuKg9FFWfgxYMkP8MyEm1+/gyEWtVcacY4T6DRCjAWx1r4MqsF9As0t9t57cFV35eqSm+pcbQr14eLCOGn8cwALqGvz4d/ftL37t1ToQxLo8Z0YkcsowQroLESCmlR0PzegH+nmK0qR6AAS3YCouzj+ehzq1xyBToyrrn29/kzUPElpB3Z/Ah1/dFKmYzh0TC0vP1E4t0Aay2UqntxNAyUh7EiOFnMLzZdavaDZ+0/QQAZdeUNC7PphFQNg3hWkrP3YAwYnj7KFQNMA4sraaLI+nXrLLR+scq3bg8+8WITBdv4k4xYmgZKW2IdZYRVTEtIyqNCsceHOPf5zoYXFwaufD4Ar8sYARGy92rdQfACha1Rm22L+EeKICVKOuUvrsCwSMVGf95RraPAgB0qhOMt57T9wXJgK5LZ+Jpu4/NuWn+S3dfO3gugNX0c5UGuJiR4lZg3XxzM+LS4/j35cEywj11+0v9zbb5SPQF67bd3uauKREmcA8VrhIjVIGVcDWl765A8JiUBEFTg/gKLsAVABowuiybIwsBjX03UwbswTfGbSzWHB3hbPxTAGwAa2mDc9MUN5smKTvJ6P39rPt4bcdr6LCxA8YfHF8ma3Jwc24T1sZsm0QogaeItcx9fvrzUpMdVdFwtWWE3DSEqyl9dwWChxMMHB5ifZVWwwBXH8YgCNXOAmh9a/YFoK+s6A6ydXVGHma4L2jWXjg3TXHrjHA37jfqvwGxQAylRokb6TeQpcjC0QdHjVweZQXOMsJZ00zZ2FsvaJ/Jn7llToQxFMBKlHVIjJRiTC0jhmLEx8AyMlM5XD/IzhiFpsFsx193Xlw4N80b7aq57Zz2InZSBVYueDXMKwx/vPQHhtYfarT9yIMj+P7S99iTsKdY53En3Gey9tRdw78GL5yf5j+FWqPGdxe/w/GHx902x4oOZ7GwJhiLC5/aq6HUXsI1kBgpxQR5G1/8ZRL9n8uwsd5G9OCX31p1GHmKwvuh8E86KveLkdIYwCpyUp0RzjLiIfJAlF8U2oW3M9q+8cZGrLiyAh8e+RDpBfYHHJck9sQjVPdjY5gSsxOx7/4+rLy6Eu8eeNct8yP0gtFVlhEqeka4mtJ3VyB4xnWpafTe8CYuNlhWqoEULZve+yg1DdceFl5Vla8zonZjnRGDomelDYmTuvZyN27u99sytCUaBja0OPZx7uNinctdcDcgLh3cElG+UQCAiYcmlklXVFnH5dk0uvoyJx+ddMnxCaL03RUInva1gpCwsDe+HdQMq0e0MmpyZxoomKtlb36eKMDOK49w6GYqjt1+grQcy08y3EVLpVG5LSWTy1Qpjdk0XKO8gzfsLxxnCa6IHJc67SPxwaY+m7Ci+wqzsQP/GVgm6nPYYxkx7E2z+eZmfpluXu7B1TEjQgHrIs5WZLvk+ARR+u4KhBn9mkXg+XqhRusq++ufUj/tXR95YG8UXowc607dx8g15/Dmz2fxxqozFo9p6Ft2l+nVXZaRlNwULI1disUXFmNvwl679pEZxOMUB85cbuq795X4Whz/1bmvnHJeV2LtMxnSqUontAxtabb+7f1v42n+U5fNjWDhY0aErokZaRXaCgD4nlYE4WxIjJRRmlTxx9yXG+HX0W0wLDoKaiGbXlkvgEGEgVC5kZKNfIW55cPwKdewYqgrcUbMyMlHJ/HtxW+Ro7Bev+OnKz9h+eXlWHNtDT488qFdN8NW1Srxy8VJT+VicExvCn5SP365Q+UO/PJfd/8q8rncBfeZCnMBGJaKH1BbbymZdXKWayZG8Lg6ZsRbwtYyot40hKsgMVKGeaNdNXSsHQyJSIDGNdiqrNO6VcV73WoZjbuYaJ5uKWAE/M3DXbUv+AqsxbCMvL3/bay6ugqbbm6yOub0I33xNy20SM0r3PUiFektI8VJ77VmRYjwjsDwBsMRHR6Ntxq/VeTjlwT2WEYA45iSJsFN+OUTj064ZmIEj7vqjFA2DeEqyOZWThBKdVVYFbkY2CYSzatWQo9vjkKrBZ7lWS5UJBVJoVAo3GIZScpKwm1mCWQRgIapU+zjJWQmWFyfXpBu1tL+WUHhtS+4mBEAUGk0kBRRp/MxIyY3boZhMKX1lCIds6Th64wU4gIY2WgkMuQZ6BLZBd5ib369h9B64CvhHFzetVf34KLWqqHWqPkYEoJwFmQZKS9IODGSA4ZhUCfUB93rs3Emz/IsP81wNwlH03szCjJw7MExh6ox/hD7A3KF1yH2vY4Vt6cUKRDOMNBWo7WcgmtaARUA0uWFp9AaZycVwzLCpfYWcgP+sNWHRT6Hu7G31HizkGZY12sdRjYaifoB9fn1UX5RrpxesdBqtXj71/OoN2M36s3YjaipO3HhftlIuTbE5Y3yDIojknWEcAUkRsoLEraJnmHRs0qe7NNMeo4CSrXGrNS5VFS0qopj94/FuIPj0PK3lnhu03O4/OSy0fZ9Cfuw//5+AKyl4I/bf2BX/C5+e3L+PWy5ucWhc3LH4jiQeAADdwzEovOLjMZYygyyxzIiNrCMWKs1kqXIsiqCAPbGZq9LI6ZaDL88fPfwUumLv552HX3/7IuraVcBOFZQK9I3ElNasZagq2lXkZaf5pI5FgWVWoPhq8+izbwDWHsyAXuvP0aBUoMCXVfpAT+eKuEZOo67LCMANcsjXEORxMjSpUsRFRUFmUyGtm3b4uzZs3btt2nTJjAMg5dffrkopyVsYUGM+HuxTzNLDtxC7U92o9YnuxGz+Aj+vvwI5xLS+QtXhjzD7tM8ynlk1AguU56Jn6/+DGSnACnX8CD7AT448gEmH56Mow+OotOmThYDGJ/kP3H4Ixq6X/JV+YhLj8Pa62ux6PwirLq6ClqtFgcSD/BjuF4qm25s0gelpt4ALqxlX5fWA7lscCvDMBAKuP405paRpKwkvr+MNZQaJS9WCrtxB3kEIcQzBABwMfUibmXcsv3h3cift//E0J1DMWjnICRkJfDrgz2CHTpOnUp6d9zOezudNb1ikZCWiy/23MCRW0+Qmi3HdwdvAwB8pMYe67LWY8fVqb2GWTTUn4ZwBQ6Lkc2bN2Py5MmYNWsWLl68iKZNm6JHjx5ITbUdJJiQkIApU6agY8eORZ4sYQNejOizTAI8zS9Md1Jz8N7GS3ht+SlkF7A3zsNJh+0+jaVYjUNJh9D4j+6YtbUvUhOP8evHHxzPWwr8pH7weToF8tSeAGAzG8Yaxx7oj708Zjl/M197fS2+vfgtOm3uhF//+xUA4CP24UveJ2YnsoW4tFpgbW9gx/vs669xwN5p/DFtlYT/886fAGCzxLmh5aYwMSIRSrDj5R38+9LUQG/l1ZW4knbFbH2YV5hDx2kb3pbfp7TUU5nx1zWsPBbPv+dcmGF+MlTy1LsiuD5KZQG1Rg2Vhp2vqywjDMNQszzCpTgsRhYvXowxY8Zg5MiRaNCgAZYvXw5PT0+sXr3a6j5qtRpDhw7FnDlzUKNGjWJNmLACFzMi11dfDfXV3xANU1c5MrLZpx1HagfYqtj6h483kh6eM1v/dpO3cXzQcWgKqkCrZueUq9RbcLRaLZKykgp9Go3PZG8ig+sNRoeIDgj1NK69YmjhWdtrrZFL5Un+E0CZD+Tp3AURupoYT+/yY7hmeSqN+Tw8xZ788oPsB2bbAf3TqZAR8hduW3iKPVEvoB6A0iVG8nTNFqe1mYbP2n+G5yKew4RmE/j0TnsRMAL0qdEHgLFQK0nuP2U/W69GYehcJxjd6oUgpn4opvaqh9kv6SvlZuSWPreZNQzdJq4SI4acf3ze5ecgKh4OiRGFQoELFy4gJkbv7xYIBIiJicGpU9b9rJ999hlCQkIwevRou84jl8uRlZVl9CIKgbOM/KevW9Gjof5JtmWUuRiRqdiLb5bC/t9vYTfN2Y/2ma2rG1AXAJvaq9WwF8scpd4y8uPlH/Hiny9ifdx6q8dVqBW4lHoJANAoqBEAWO3tMqbxGNSpVAca6MVInjLPQKgxQI/5ug36WAYuo8Y0tgYAvMRe/PLnpz+3eF7TUvD2wGWolBYxkpafhqcFrOuqY0RH9K/dHz/G/Ii3m75dpONxvzdD8VkS/HTkLl5YcgQPnrFi5KOe9bBuVBv8PKI1Vg1vhW71Q/FS08r8eGsZaKURwwB0V7lpAPDWF+pPQ7gCh8RIWloa1Go1QkONn0hDQ0ORkpJicZ/jx4/j559/xsqVK+0+z4IFC+Dn58e/IiMjHZlmxSSYveGDEbDuCAAeEiH2T+qE/z1fC+O61DIyQwPA40z2prs7frfdPvLCbpoqg5L13aShGFx3MDpX6QyALXrGiZHTyad5V82Pl38EAHxx7guLx1Rr1Bjw9wA+ZiRQFggAiKkaYzZ2YJ2B+F/z/wEAXq71Mr8+T5UHyHUZPFJfwFv3Hc5OAdTsRZbLqFFYECNcV1oAVuuWmJaCtweuNkeeKs/ufVzJB4c/4Jed0QGW+3x/3vnT8dLwV34HFtUHrjge7GxIXHIWFuy+gVuPc6DRAv6eYoT7mX82hmFQL8wHQBkTIzpxIGJELq2Q2r1adwBlt/DZiYcnMGbfGLy17y0cSjxU0tMhTHBpNk12djbefPNNrFy5EkFBQXbvN23aNGRmZvKvpCTzdE3ChBBdKqVWA6gKWEGSm4baoT744IW68PMQY+/ETvh2UDN+F4FYbxUwvRmeTT6Lm+k3zU5j+IRryyR84n4SvrlxDtOvHoCEEUKr1SJHrgI0+pvA1+e/NtvPUkzGlltb+EDK2pVq87Egbzd9G5+1/wxdI7sCAL7q/BVmRM/ge/jU8KuBLpFdAADbbm3TW0akPoB/NVaUqAqAONaaxImRM/fMLS7Jucn88p2MO/jl+i9mYzg3kkOWEd3YHy79YPc+ruRi6kV+2RlipKa/vtnjn7f/dGznA7OB7EfAH2OLdO4dlx/hw98vo9e3BrFGb7TAvkmdrJb/5zLQMqykw5dGuBgOV1pFAH16b1lN7V15dSVOJ5/GmeQzZkH119Ou40yy5dYZhHtwSIwEBQVBKBTi8WPjbqOPHz9GWJh5cNvdu3eRkJCAvn37QiQSQSQS4ZdffsHff/8NkUiEu3fvmu0DAFKpFL6+vkYvohAM3AiQ5wB/TwC+qglc28avDvGVoV+zCHz5KlsdU50fxW8z9Okn5yRj9L7ReHXHq0an0Gq12HabPV6/mv1w/g3LvuMmBXL4cnEXDy8Af4xF6p6vAGihVesFzJEHR7Dr3i6jfW+k3zA75/wz8/n3f7z0Bx+74CPxQf/a/bGkyxLs6r8LPar1MJsLV+/ibuZdZHFdcqU+gEAAVG7Gvt86Ckg6C++sO2jB3IJSYWyGPvnwJFZfM46J+uq8eU+ZW8/YjBhHspMqyVj3mbtK8juCM8RIu/B2GNloJABj15xd8C40xzNb1BotPvj9Mn6/YBzf06VuCEJ8rH8uPw/2hluWAlj5onRO+HvZQsSwVhfOXVPWuJdxj19+Jn+GxKxEAGww/aCdg/DWvreMxhDuxSExIpFI0LJlSxw8eJBfp9FocPDgQURHR5uNr1evHq5evYrY2Fj+9dJLL6Fr166IjY0l94szEQj0gkSRA1z6jV0+vdxs6MBWkRjQogpU2fqAPS5oEQAe5jzkl2NTYwGwF7wBOwbgXib7z8oJghntZiBQ7IPn8vRipqOvcTl6XNuK0DPzUJt5CK2BZSQtPw0fH/vYaKjhPAD7ahoIBUJE+kYadTXmeKPBG/zyrWd32AWZTtx2+kg/8Ofu2Cv5EH9IZyP6trHF5trTaxbPq1rXj3fxAPonxucjny90zhyjGo0CUDoyFLiLM4c9Qbj20CiQjfEx/dsWikzfzwcOptrmFKj4XkhGhyykIaKnlN2eVwbFCFlGrJOWn4ZncuN6Q7efsWndhm7XOxl3in0urVaLz099jqG7hmLU3lG48sQ8M40wx2E3zeTJk7Fy5UqsW7cOcXFxePfdd5Gbm4uRI9mnn2HDhmHaNDZdUiaToVGjRkYvf39/+Pj4oFGjRpBIXPvPU+GQ6quw8lh5ivGRiQAI4CEIAGDsflFr9YXD5pyaA4D9J+X+eQGgfeX2AICBkTE4dOs6fnz8BKezJDgz5AzeeW628cm82PoUgUwWGoVVtfkRVl5daRTIahijYlh4yV58Jb58N9lJt39D4+pV0ViUjHYb2uGk1PKNqUny78CJ78zm8Eb9N7D+Rf3c8u4fAe7r3Urc7zDS136Rzbm6SkNQoOGFeGd/59UF4YJYHYqLUeQBBQaB1T93B35+ATj+jV27ZxWwN0yxkEGQN/s77tEw1NYu7Fwl7NN/roXmkqUVVxc84yjLlhFDlzMXw/bNxW+g1Wox48QMftsHRz7AyiuFxzfmKfNw/el1i7F29zLvYcutLbjy5ArOpZzDdxe/s3AEwhSHxcjrr7+Or7/+GjNnzkSzZs0QGxuLPXv28EGtiYmJSE5OLuQohEvgMmrkBmKEsfwn9tYVeRKCvYAZ3ii23dK7djg3x6wTxj5WT5Eu1TXtNh/a6dVkEJsCG9ESaPM2UP8lYNwZNj4DgDfy4SGSYk77OWbz6VW9F7+88OxCftnQfeQv9bf4WQojQMYKrgyDtORcZa5R3RIz9s8AHl0CtFp+DlKhFE0CG0LEsCLm20r+aH/yQ75OC/c75H83dsA9zRoWTCspuJtam7A2qOprWzQ6ApcWfSP9hpn1xSJpt1kXo2E68INzQNIZ4OBngIUquxxarRYLdsdh5l+sNcvfU4Lzn8YgYWFv/PRmq0JP7aX7v0jLkeNJthzPckveYlUYZBmxzbW0a3zF53bh7fjvdkJWAs6lnDOrqfPdpcLFw9v738agfwZh081NOJJ0xOh3Ytol/EzKmVKTLVeaKVLo9YQJEzBhwgSL2w4fPmxz37Vr1xbllIQ9cHUgcgwym6yJERn7p2e0OjFiYEI3/OesJKuEtPw03HxmHswKQH/DCGkAdNb1WxEIgRe/1I+RshkK3siHQizkn0w4Xqr5Emr718bu+N38uvSCdIzaMwp3M/VxRW81KVq32zqV6vDl6Q258PiC7R1XdAGqtoe8PuuClIlkwJEv4alSIEsoxBZfH0BdgA1xG9AlsgtvGTFMAy4Mw6dZhVrhcr+/LeztQeMoNfz0tYUuPL5QuNB5dAngvo8elYAXvwZEUmDzm4BWDeSlA97m1WCP3HqCVcfu4dhtfWB2gdIxC4eXhBWaG84kYsMZVjhNjKmNiTHFb+7oKjgXnyNZXEWhrHbu/fT4p/x1pLJ3ZbzV+C2+OKJhNWlHiH0SCwB8PFuz4GaoG1AX77d4H5+d/gwA0DioMd9KISU3pVT3aCoNUG+a8gQnRn4foV/34CybImkCZxnhxEiuSu+mMXQZPC14iqwc87Rt/qap1Cl+kY0bGCdGmHxIRQKzm7W/1N+oqBgAdN7c2UiIAGyxs6IwpvEYrOuxFstSjEvQx6XHGYVGHoz6ABc0tY13TjyJAl0WjseV34EjC9GiwNilcir5FFLzUvlUZcOOtYVh+DRb0j0/7O2r4yh+Uj90qdLF6Bw24VKw6/UBPk4AGr8K1O/LChMAOGWQeaSSA8mXochKw9hfzhsJEQB4sVG4Q3N9rnYQH8TKceSW460L3An3O3W1ZYRLGy5rbhqu9UTXyK4Y22QsAmQB6BjBVgK3lNFXFGKfxGLzzc34+OjHuJ91HwDQPKQ5qvqwwpsTJSVBliILp5NP2xSRSo0SSrXSYm8vd0FipDwhtXIT/MPcouCjs4xoNewFLF9p7MLg2HlvJy7+zoqAUFkgJrWYiNdqv4qGgbrgV878KLLRpVbKBoz6IB8ysRBSoRQCA4uNv9Qfz0U8h1r+tawdgf+nLgpCgRAtAhuiY34+Pnz6DO11PWsAIN+bLSmPKm1wtcogfK/qz76v3gmQsCKqIJ2NpZClspk+36amYV/iQ8xI06cAzz09l8+i8ZMaBF4WgogR8b+Lkg5i5YpnueIJ21f3HbDLXM31VzKt+Mq1rU8xMKv/NgD4qRPEPzSFh8q85HyDyo5l4jWvWgmXZ72AhIW98ec4Ni7qUmIG7j8t2aJttuC+N66OGSmLlhGtVstfzz5t9ykivCMAAB0iOrjkfMcesq5fISPE+y3e5/+3uYKN7uRuxl3sv78fnTZ1wph9Y3hrkCkb4jag9W+t0eK3Fth337xopbsgMVKe8DCvssqjNL4JcGJEo2bFCFeFVaPVmJXuPqvVuR/ynmHUoaWY+e+PYC6sZTdyNxexjRsYbxnJg0jAgGEYeIn01hE/qR8qe1fG1r5brR7CEdeHRXRm/2FZ2Vje7Uc+GC+r95dA3d5A768hFgpwWNMMc+v+CbzxJ+DDxkHlP2bjD2RaLdBkEAQAwtVq9M/OQV01+y+UmJXIixFHYlsYhik1QazcE7bUlpWriDhUafapLpBWYvI37/st+7NAJzqOfAUksBd/RpGDnyRLMFu0FhKwN8sIfw90rG1/fSNTaoboxdCBONu9t0oSd8WMlEXLSL4qnw/IN7RYDq0/VP9ApaNXlD5uLaMgw+LxMuWZuP70eqGCfVGXRZAIJXgu4jkARUhrLyYZBRl4bcdrmHx4Mv/5jyQdsTj22MNjRkkLJQWJkfJEh/etb1vb2+itpy5rQK1in3a2XVsHrOmN/DR9xky3qt0AALu92ZuClyIfSL/LxomcWsoO4iwqtkz7BjEjQd7sBTO6MhuH4SnyRPOQ5gBYC4Y1HO2LYgY3T4EYjEgCH53VY/DV77CkTmtow5rwjfKuZnkAQhHQdToAoECXMizTaIDqHYGRu4FWoyAGMD+LvREkZSchLZ91EXC1Q+yFu4mUuBjhytm7wDLCuX4K7VHz9C5wcR27bGrpk/mzP3NSgdv7gUNzjTa3FdzACNE+/DdCioSFvXFi6vOoEVz0742vTMxn4OQUlN4bMF9nhGJGzOCsIgJGwFcD5pjedjq/PP+5+VjYSR84b1jkkCNPmYfef/bGoH8G8cI9yjfKbNywBsP4a2eDwAYA2CrX7gxiTc5NhlKjNErP56yTpnDzmhU9y2JVa3dBYqQ8EdoQaPK65W0PjQuUeeoC9dRq9h+0UkYScP84sveydT9EjMjM3VBXYeBGyNfl7F/exP60JUZ0dT2Gi/ajTXW2lPvXnb/G8UHHcWzQMdSupI/TMCzhbojphcRhji9mf0rY2BTOipGWn4bV11bjYc5D5MjZp4Mz8ek4eTcNaDQA6PA+CgQ6MaLVAjW7AdXaA89NAgCE5zyDRCAxivdwxE0D6FOWS9xN48LiWdzfb831NbazhlINAgob9DM5iD/7MzMJWG9QkG/UXqR1W4zrGjZrS/T3eCDXOHbEITQa4MxPwKEFqOfL3nhz5KX3Buxuy0hZKQefnJPMV1r1EnuZ1SFqEtwEH7f+GC/VfAnPV30eAkbAu4otFS5MyU0x6z79Su1X+OUh9Ybg7NCz+LD1h/w6w2ubOxsMcpbuSJ9ILO7CXvusdc7mxEiwRzCfMVUSkBgpbxheKOr0sjrMQ1f8SZ3LluvOYwSQM0BGBlvS3F/mbyYAZj41KBpUkMkWouK+vBIb6aw+bBDhU60Pf16GYeAn9TO7gFpzcTzJK2YQ4fXt7E9doKzWpKpnliILN1P0dS3upOrMqjJ/vWUkvDngqwuI9GDThX1UBVgXs5wP0AQcE047Lj+CVste5EvcMsK5aVwQe9AitAW/bK3BIRS5wOah7HJIA31nZY7gemxQa6Uo9hVQk3XdVG2HjLoDcVVTnR2X9xTYbVxMzyEenAV2fwQcWYguKWsB6CwjKjkrVAw5MAf4thmQ+9TsMO7CXXVGuKfssuKmmXh4Ih/DYS2o/I0Gb2Dec/N4NzD3ILHznnmdHUsNRcO99AHSlb0rm/3v1wuohyAP1lX4rMC46Jo1lsYuxYC/B+Bs8lm7xlsiW8EGgftKfPlrqrXK0K4KXHcUEiPlDUMT6iCTLrgq/ZM3V4lSoWR/3pBK0LZaJFYz7JfYvyDHyOwbolKB8Y0A3r+iP8/xJbzPHk1tZLpUaQ0A8IQcHhLbFTAFVlKRra23G85EOsRy07VcZS6eq61PF+Wrd/pWRr7u3DIffVdXSLwAXb2RRl4RfMlzR7j3JAf/23gJjzNZiwxXmEmtcazaqLNwpZumfeX2fLNBq6Lrob4vDlL/M98uELLf6fcvs6/3LgItR7DHVGmQoDVoSZFU9As58vTConnyJvxP+AdG3H0fmBsCLKoLZBmY8I8vBp7FA1/VANb0BjIfWjiga6EAVsv891T/HbI35owTWqYPK4B5/RAACPEM4Ze5ekamtApl69vY2yZi+eXluPXsFkbvG41zKefs2odDo9XgZvpNvtqsj8SHF1jW4mA4V1axrc/FhMRIeSNCV9jJL5K9eBteoAwqs3KiQK7Ul5pRMwwfH+KXlwEPRi8cvL1CgEnXAX+DrJZD+p4xCK5nfU46a4QHo4CHyLxkuyFctVRTDLvmOoxWq8/Q0HXr5WJGOHIUORjUOhI+Us4UzV6MUqKi8VDMrpO1NshKYhh9gKUiDy1CW2Bhx4XY1HuT3dNKyWJv/gIR+8T1+enPseTIQTSevRen7rr/SdvVPU4KzahRFD1jRanWYq+mtX4F1ziyKJjMb4RoL+rm6YRSbipweAFw8Rfg8mbj/e4fB3a8ZyT63cGpR6cAuKECaxkKYDV1Sdibbt+7BhtbZym2aU/CHrN1hrFsnAXElMIsE4aY/m5H7R1V6D6G/HDpB7y641V8duoz/tzc+Z/Jn2H2ydlG4/+68xcfH0OWEcK5tBkL9PkGGKqrLTJ8h37bH2P4Rc5dAo1lP7O/RoPgAr14iQhqyN6AGQYY9pduX90TklAKeFnPWkjO1wsJX5HtC5mlgDCgmJYRVQH4Zms6d9LEFhONhuQocyAWCtC3GWv9UKpZy8ihZH3b+7Bg4+h7Xozonix61+iNhkEmY2zAWUAYof7mt/TEaeQp1Bi88jQW77uJ648s+3ldQXIOe1Fy1U2Nz6ixVmvkpkHTRN8qDh1bodIgXhuOHyQ6wXh7b1GmyMJlngWxhc4CddZCnovrgL//B/xpoZvwnQOsxdCNcDc5DVxbwbcsWUZ+uvKT0XsukLQwuO+oJTHCZZwEewRDJpTh5VovI9QzlL82VferbvGY/rrA6xVXVpiJAVMMyyo4yslHJ7HyqnEpe3+ZPwJkAbwg2XZ7m5HgOfrgKL8c6VOyveJIjJQ3JJ5Aq5H6J8OqbYEQ3Q3yzgG2qujGIfBgdOZIjWU17K/RoPkhfUEgIzeEp4nwCDIpFGZCSp5ejFSBrnNu2h3g6NfGpethbLHgagIAsNgEzy6UBUCmQedWnZVmYN2BOD3kNLpX6w5AfxGQCHU1P3RuGq6QWduwtuaBqVyhNoWDDeB0ZFvI0PCI2ATP6ksAgRzf/XsHvb87Dq1Wi/Mp53E+5bzFXhjO4HHuY74hoKvMtdyTF1fPxAwuiwYA3thmeYwVOPGYKtL58AVih5vr8XA3oqA6uN7hW3yvehkbZa8Dr60Fmr3BFmCradIMsapBo9AHjpnWiwsXENwpopNLz1NWxMiN9Bt8TY36AfWx4+UdmNpmql37eojZ7z5nvVOqlfj46McYuGMgX8V5apupODn4JD7v8Dn8pH7Y1ncbtvTZgjAv8871gHEcHNf13BqWxIi1wFNTtt4yL43gL/WHSCDC7lf01a1zDCzkXLHLOe3nkJuGcAOvrtYvP7oE3NwJwaPzqBnsBa3KDy/4xqBTXj581PonqzCVCp4GsQtG/VZM6z+YvjdBrgYKtLpA1x/bAxd/ZVON//0cODDbaKyPxIf379byr4W3GrNPulNaTbHzwxqg0QDLnwN+0LmuRDJ94SywfmTuXFwdAC69V6nWYNutbXyfijoBFsqBc0G7RXyaycxnL+oSRSOj9ULZYwil+tiDfxP/xci9IzFy70gcSjpUpHMVRnxWPL/cKqzwHi5FgbO4WHTTqE2EWYgNt58FOPF4U9aEXaFR6tO5HYUTS2IPFNR5CYtUAzEtox8ueHcGXl4KvP4bMHQrwMUQ9VwIjNqjt0Jm3C/aeYsI990N9Ah06Xm4TIvS7qYx7JJbu1JtRPlF2f0w4yFkb8hp+Wm48+wOev3RC7vidxmVja/lX8so66RWpVqoH2jdLRjsYdy6IF+Vj4uPL2J93HqjjsEKtQJ9/uxjtr89WTgJmQk4/vC42XpOCHlLvPlr+KPcR7iWdg15yjy+2KWp27okKFJvGqKMYUksJF9B9YCWUKfdwYseHdHt8WpofSPwZ985yE67if77v4DA4MGS8xcD4OuG6I9v2x8rV2nwu7oz3hQdYFc8OKfvn3NuJdBbb4ERCUTY3Gcz4tLjEB0eDT+pH8Y0HmNWLt4ulLnAU33dFFg4BudL5i0jIq4aqgazT83mx1kMgBPrY0asEZuUgfc2XkKDcF/8+EYLo4tilk6MtPUdg2MFxjViZFU2QKsIABgN5p3RWxLWx63H81VNnsqdACcQGgc1tur7Li6cZcSim0ZukKnQ1/Eup3IVa0JXi3SBxVo1kJ0MBNZ0fKL3da45kQyRAfrvzOZzSWhZTRekKBAC7xwHMhKAcLZODhePhLRbrKBxQfE4UwpUBXzmhCNtCIoCVyiwNFhGjj04hq23tuL1uq+jfQRbKffH2B9x7vE5o6DPar7VHDouZx1IyErAyqsr8TjvsdmYGv41zNbZonNkZ7zT9B0sv7wcABuoPnzPcABsryYu9XbFlRUWf7ePc83nYErf7X0trje0yniLvZGnysPr/7DlH9qFt8O9zHsAYFSEsqQgy0hFwFKZ+L3T8HrWzzgs/QD1bq8AADBeQXil9isY3vJ9+Gq08NRasYx4BrJmcA5fgywTCxQo1ZihGoUV3u/qVmTYHF/Ntxp6RvXk3SJFEiKAWdVZS6KJExncBV2sc9Mo1cb+d1+JhYJBvGXEXIzsuZaMt389j3G/XUBieh72XE9Baraxe4Jrcx/iYd7aXiDKgdAzEUKPB3xvDQB4kP3AbKwz4Nqm51n4LM7CahXW3DRgjS4NXeQBtBzu8LHlOsuIVCIEZDp32vctzNyAhaLVAjf+0c1FhhAfGUa0jwIA5ClMqlR6BbLpxwLdZdTL4An4y5pAduE3keKg1WrR/6/++ukUt0pxIXDWgFvPbrn0PPYw+9Rs/Jv0L94+8DbyVfk4knQEyy4vM8s+MawDYg+NgvRWyjsZbCXgJkFNsOuVXehUpRPmPzff2q5WkQqlGN9sPAJlrOXqzd1v8tu4LtZP85+axblwbLyx0eFzchgWYDTtfXU6+TQfb1Tka6wTITFSETC8CXvrb3zdn7HZABFPTxmPk3gCE69CAODDzHyMaTAMkb4GwU0MA4z4R/8+ZrbN03M3CqVIZ1F5fN14QL59+fcOY3rTs1DQh/sn5ALWxCYxIwDrd+4R1cP8+HzMiPkNb+ofV7H3+mM8ytTP4Y+LD3H4ZiofuLrmRAIAwNdDbDEtUC0PQV7ScIyuNR9zO7DVRrOV2WbjnAHXqdm0OaEz4WNGTFN7b+8DnrB9fxBctO64cqVOjIiEQPM39BsyEh07kGGdnhbDAAB1w3Q9ipSFBIh6VAJq6SpYKrKBFNc2R5Or5XiQw4rTblW7ufyGYijI7Y1jcBWG7o0269tgwr/mXeRntJvhsJXP8HfIia4RjUYg0icSS7stRd+ali0Q9mCphhJXtdmW+7WwYnaG4l7ICNEzqif/vllwM365X02TIoIG1AtwzC3qCkiMVASEYqD9/4CwJsDr64EAK2ZGricIwKbwTrmDYW+dxXsGFQV5DINYPSzn13PIdW3c8yQB5ucBgMNfFPYJioapGLEQQ2D6tG4YwMqlEy+LWWZUT4BHYt1Nw7lgDPlizw2MWHMOf8Wy8SBcd9hgbwmWxSxD85DmRkG7GkUQ1Dn1UUXWHO0rs6boXGWu04NY3dWpk48ZMXXTcNYLkQfbE6gIyNWcGBEAL3wO+OvM846mCxtmUQTXBQDIxOx3okBZyO+JYdjA2yi2I2xhFsDiYngTWtR5kUvPBQB1KumFoistaPZgzSU1uN5gtAhpgZiqMehfu7/FMY7irHgKS5WZM+QZ0Gq1OJh40Op+t57dwrEHx6xuN0wZPj7oOPrVYkVHDb8aRrEt1sTqjHYzSjytF6CYkYrDCwZ9PJ4lWB5jmj7rHWx5HAAE1QJ6LGBTegW2NW2Bzspwz7sFUO9DNrtFqwGu6Oo05LqoCZmpGLFQbp3zEe+7vw/5qnw+gDVLncIXPjJyURnC/XPf2sM+3fdZwmcWCRgGGhPRUDXAE4npeZi85TImb7nMr+9YOxhRgVH4pdcvOJJ0BPPOzIMWWlQSdMPZB0CeQgUvMSvkuEaGznwKPvLAcgMtZ8P9rs3cNJxlqdErrOujCHCCl4v54a18CgctSZxgZQSA7olUJmKDngsVIxycm+j2PqDxq7bHFgMu+wmw3dfJWTAMAy+xF3KVuSUaxHo6+bTVxnOG/WachUUXbRGw1F9LrVUjX5WPlNwUm/tOPz4dxwZZFiSclSpAFgBviTc6VO6A3178zaxMgjU3nqtjjeyFLCMVkVpsOusjn8bG6wf+4thxoscBTQYWOqxA52uXSiTA858C/ZcDr6wAXv6RHZCf4dh57cU0ZqTFm2ZDDOuXbL6xGU+UbMBrhkqfXWI15Y2zjNw/wVai3cZm/shVaqgMMpFqBHnhxNTn8UlvyxH3vh76p5fOkZ2x79V92P/qflSTtQUA5MhV8BB58HN1ZgfQ9IJ0vH9IHzy7ovsKpx3bFKuWEe4pu5CsLFvwMSOcGOHipBx103BzEXmwlg4AMl2BwAKVnWLEUyeormx2/PwOMOnQJJcd2xqlIb335KOThQ9yIlysR3EZ01hf56mKt76OTqY8k49ZA4wFFZdNmCHPsFoskIsjqyRl40MYhkHT4KZmlhhrFp5iNyF1EiRGKiI95gPPTcK/9T/HK/LZ7LpWo4HINi45HRf45yExMcRxT5B3DwKz/YC/zP2+xYL75w2qA0yOsxjbYpjXv+jCIvxy/wMIJKlQaNj1HSM6Wk8LNLVOZLNPNzO362Ni7szrhX+ndEGEvwdeaBCKkR2izA7jK7NsoPTSVYPdev4B/1QKOFeMmPb84bopuwJOjJjVQ+DcXMWw9ujFiM5CwGWyHJjj2IE4ASvWC1C9ZcTOomJt39Yvu7A8vNXicS6kNIgRzkU0qtEozI6ejfeav4dBdQc5VP3YFl91+srofUAhbmh7MUz/nffcPF48vLDtBT5rZ/cru3lXU02/mhjXbBzfSPNpgXlV5t3xuzHx8EQAhTfo7BrZFb2izPuVuTrw2V5IjFREgmoBMbOh8I3CRW0dTKv5J/Di14XvV0TylKxJ18u0L42fScW/S78C51YZBxEWB87kLvawmvHDVVU0RCBLRjZYy4hhNLoZpk/yQjEy85XYfD4JAFClkgdEQv2/GMMwiKmvDyBuFOGLj3rWNRpjiEjXLdhTyv7efMTsk01uMcqmm2JYaZKLS3EVnBgxe9Lk3DTFsIwoTC0jXFEyRwUO/53R78fFjOSbZtNYI7QhEKazOhajomZppDSIEe4BopK0EgbUGYAxTcbgk3afOFT92BY9q/fE63VfR5RvFEY3Gs1/5uIiFUpRP6A+AmQBqB9YHx0iOpiN4YJc1/Rcg20vbYNYIObrx1jqjXMm+Qy/zBVwtEYlWSV82flLXB1+Fc9H6ssDlBY3DcWMVGC4ZnlPNL6Fxn0UB+4i7mkqRsKbANU6sG4Ojp0fABIfoOnrxT8xf5Oz/s/2Us2X8OW5L43WiXwvI13ANtmy6S8Wm7hvBCLkyvW+9D/Gmd/cW1arhOgagQjwkuCHIc1tFmOKaRCKVcfj+d+fl8QLyHWuZYS7sPtKfLGs2zKnHdcS7Su3x8/XfjbPpnGKm8YkZqROL7agnqOBlpw4E+sD+vg+Tva6aQC7atAUl3oB9XAj/YZR0LOr4eoNlaQY4ZrAuTJ76NN2n7rkuOt7r4dKw7pdF3RcAIVagX339wFg67hwVgoBIwDXjitQFojk3GQ8ynmEJsFNsOXmFhxKOoRGQY1wNY3N2JrRbgYG1i3cZc5h+LsrLW4aEiMVGA8Je+HOU7g2GC1XbsVNAwBt3zEWIwDb86NqW7ZNfHFQFm7+95P6YWb0TL6xFACIffTdPi2m9HKYpvQG1kS+LsjRVyZCiI95hLpMLMTGse3smLy+fxDnHuAKE+1J2OM0d8qGGxsAsFUlXR0EyaUomokRV7hpOKHoaBVWQ2uaDofdNIC+Bo0TrVim3Ehn06G7RnZ12TlM4S0jzrJeFoETD9nrRUmXLy8KYoHYyNJiKCT9pH4WH0643jbb725HkEcQPj/9OQAYVVy1VoreGobpwmQZIUoc7mZ38u5TaLXaovd/KYR8nZvGzDICALW6AQ37AwIR0GgAsHEQu/7eEaBlVPFOzN3kJLZvcmGelv+RawoHo1lIM+s7mhbUEoh5K4aHpc/qINwxHmbkY8GuODzMZKuUFquDsQGZ8ky+UVaQwPXVQq3WGbHDglUYfJ0RnUuFFzaqfODfuWzgtD0YBrDqkPGi0AHLiEkTxeISn5aLLeeTEOApwcgOUUaGTK44lzvgUkVLyjJimIZeN6Cu+QBlAXB2BZD3lM30a/M2ILJdp6Mk4TpZA9ZdwpW9WBezVqvFzvidRtteqPYCIrwjEB3u2MOJ4TWvtMSMkBipwNQL0/8j5CvV8LRkuXACebZu0BIvtgEZR/2+QNwONqj16R0gNY4tYtXwZcdPzN0ICvlni64cjXeavoM119YY3SjvPy7kBh1Yy+itVpmL8wnpAAy6IhcDw2P8dPQexAH1IAu957TAxSyFvgT7lLNbgR6uy6QB9DEjZmJErsskKEQ02oJzofAxI4YutKNfAc9Nss8NZCmAVSdwVBotlGoNXxjPJtx37t95wPXtQJ2eQPuiB2h/tfcGdl1lA6SjAj3Rqqb+u/lu03eLfFxH4Z7qSyq11/C7X1USoP/ucFz9Hdg/Q//eO9SujL+SomdUT5xLOYdcZS4G1xtscUzXql2x5dYWnHx0EjX8jGtEfdX5qyJ1NB/WcBhCPENQp1Id41YfJUjpmAVRIlQ16LuRr3C9GPGy5/jB9Vkx8t9f+nV39gMNi1DxkTORF3ITEglEGN9sPBoHNcb4g+P59VKTBnZmNHmd7bEjzwGOL8azjAzM3sG6eASC4lsvzMSbRufmSLsNPIoFKjcr1vG5rIQglRrhajUr/EIspx87A16MGHbt1aiBRxfZ5WK4abgAVoklMQKw7he7xIi5y0hmIAoLlGr7xAgnrPLT2bTvhGNsmXvTvk52kp7L1chRY+HVt5F6/h6/rXlI8yIdsyhwN65/k/5F58jObjsvh2HAtfQrO/oOPbpUqsVIFZ8q+Km75TLwHNV89P11uF4yAFumvihCBGCtIQPqDCjSvq6CsmkqMIY3zEM3n9gYWTy4mBSLbhpTmr/Bum0qtyj+ie1003AYFjdT5VaHQlXIv4dQBHT8AKjBXpQDnl1BFYYt4JZTUPwnxwBPCQK89CZmrZa9EeSnXAZWdmW7EheDPBX7+/HUtaDHMvtiWYoKJ0YUGgW23NzCrsw1+N6FNy3ysc1iRgRCoMNE/QArNRrM4MYZBLDy1hY4EDdiSfgUo+1Bvu68jCQdqfJ7Rttc5V61BBfw7KwME0fhxIiHRmPdWSn2BJrqrAynlwE5rru2uYNI30izlNxVL6zC6p6rrexRNiExQgAApvx+ufBBRcSmm8aUStVYt02fxcbri1IC3U43DYdhhLlW48FX9SwUg+OvE7Ol7UVOsIwIBAxOT+uGerreKNCwNwA5w7AVbC30xHGEVVdXAQA8Nc4tL28NwzoIu+J3sQv8zd8T8Cx6PQczNw0AdJ8DcOc0LYBnDd4yoresMAxj1M3ZLix954pR3C9fJ+ilgSY9TLSur7xqSK/q7E3RzNXmJngxotWybSg+STF/TU0EukzV75RgvZR6WaF3jd5G79uGt+XFfXmBxAjhcqym9trCtAaJvU+2hjhoGYnwjuDN0Bp5CF/GvlAMjl9TkAwA6NHIseh2q4cWCbD7/Y74ckATaLUGYgQw95fbwdP8p7ibcRf/Pf2PD14NVrunN41IIOJ7qPCuGk4kFLM3hlkFVv6kugu2ys6sGi6bxiRTQ2qhgaJNLFlGbu+zb18LcFlaAg+22qYHwqBIbw918rAiH9NhNGpI77A9VBSOZik5Ce47K9ZqAe8QVjSavoRiNhOvqi6oc+tIYM/0YlsSS5ImwU347CHDSq7lCYoZIVxOHi9GHPi6eQUB4c2A5Fj2vSLPPA6gMOyMGeHwk/phZ/+duJOehGHLHgOwM2DRIAVZrhWjZrAXJrcQAg/Os59BWLx/M4ZhEOgt4S0j+ZzVRZ4FwP4aE9efXseQnUOg0RpflD9LMy+m5Co46wgXiJiS8xBvRlbGQDmD4lxizWJGODh3i8rOJ3kLqb38ceUOiBFLRfYKaZyXI1dh/q44pGXLwTBA/+YRuJOag1uPc5CUzs6LEbC/t76VJ+PnOHYuKrXGauE8p/LwAiT3jgJBAVBkuq7MvS24LB4lw7CNP21RqTqQqOtIfnop2ycowgnu3xKgkqwSzgxhC5y50y3nTkiMVHA61g7CsdtpqOTpGh/wpcRnyNR1sHXIMgIAo/cBc3XdcjPuO95EzUE3DQBU9q6MAGkogD0A7AxYlHgBA34Gto3GVW11TGucA5+VbF8ZtBnLZnJYqQBrL74eYjC6mJGiWkbinsaZCREACLLX9eAEuPTefFU+Lj6+iP+dmo4skQjfiVAsMWIWM8LBWTjSbgFVWhV+IAsVWAG9yLFbjDTQtWtX5ACpN4AzP5qngptwMO4xNpzR3eQZOQ4n/wVVQTDUefpATYGYzYCq5h8CgM2uyZGr4O9ZzPTVrEeAd5jt4oe5aZDo3KVyUxehRqP/fxOIHH9wsMWRL4HE00D3z1BwbRsAoHdOLvD8cNv7yUzKoxcjZqc0UF5FCAe5aSo4U3vVAwCzG+6tx9no+vVhbL9U9N4aDzPy0X+ZvqmVw2JEJNXfFFZ2BdJuO7Y/56M3vSgVgsTgdyG38+aj1j3xe0COptJk/YazK4DF9YH7xWvu1cQ7E18Lf2bnxBhaRuwn29EOti5AJtSLkeF7hiNL5ZwKpXzMiNjkksZZI1KuwS4sBLACBmLEXpeWUMw+ibccAfixTdFyLq7FT5v74H5GvMVdnuawGTONInwhrnQGktDt8Ky2EtBZQ0S+l/ix7WtU4VOOsy0ESz/KyMed1Gz7xNPN3ex3dG8hHW8LMnkxonh6W+/20KiBFZ2BBVXY1/zKwHknBFde/AVYFQMcmsem+v8zCQVP2ZoqMoGkcMtIowFsdh5HEdyahPsgMVLB4WpZpGbLkZyp9wN/tPUK4tNyMXFzrEPHu3D/GYatPovfTt9Hh4X/Gp+rKIXA2hnUUHD0ApfP1vxwNDBSINAHLNpb6OrcQ/aG0VBwHwFaC09gp4tXal16ZB6aMOxTc0ERLSMnEvYDAISM/u8gKkpgcDHgLCPpBelOPS5f9MzUTVNb16/DniJdKjkQu55dNrWM6ASq3JEqrBy6DsI/VvLDDwX30eevlywOyypg59ikij/qVdF/77xrz4UsfAs8IjazcxFIUDMgBN5S1pqZIzcWI7+fT0L7hf8iZvFR9F92AndSszF01Wks2B1neX67P2J/nvkReHLL+ue4ugVS3fcln2GA3FQ2sHxpWyDlin6cVgPoYkuKxf6ZwINz+vepcSjQuSg92r4LyGy0agCAyNbA+NNAbV0VZRIjpRoSIxWcIB99RPaOy4/45Wd5CkvDC+WrvTdw9NYTfLrd/ElUUhS/duep+qZjjl5M8nSioAhdN2W8GLHv5vPZIX36oPDa7+YDittHIzsFMt2NoIAr215gwzKS+5R1D6TeAJ7cxLX/tuLMU7aPxZv1huLDVh9iTEYmDiY+BGpw5cRdbwYO8QwptLtoUeCyXMzESFAd9qc93x3D2ja6EtwcnDiVF8Wlpasse02qd6XMPDHTbNh6nYvGVyZGk6r6/0tGoILY/yL//pdev4BhGHjrGiiaipHYpAx++fqjLMQsPooTd57ipyP38CzX5P96+3ggwyD+w5Z1RKvlLSNXZFKkHvuKLSj31MBi+QqboYW8YopNjcY8+0iRjQQx66r08Aqx/1hcbRcSI6WaIomRpUuXIioqCjKZDG3btsXZs2etjl25ciU6duyISpUqoVKlSoiJibE5nnAvvjIxOtYOAgCk5+qfHtUOpHseufUEn//zHxbtu4nrDy3fIGsGexXN5ymSAE10JeKtBSFmpwD3T+lfWY+AGzsBzi1RhJRRqdj+5mjXH2XiP0UoVFrdv1O6BTN8EYtd8cizIdX9TRQMoAHYSpOLGwDHTNKg0+4Ai+oCy9qyr6VtkLDzf/zmgfsWYtjZTXjvWSYCNBqgeifdFi2gKpoItRcPkQd29t+JTb03Oa2sPWBoGTGxvnG/d8MbrjWyDdxr9YxTKR2OGTGkRleganvUVuj/v44nHTEbxrkxH6mO4u+7fwMARjUcC61GH8/FgEGDwAYAAG8Ze2M2rWmTZaPGTXKmSVZa7G/G7+/sB2I3WN5ZkYsGCv3348bV31gXCkdATTbDBWDLsReHggwAumuQjy7eyr8qLspYy5phb5VCITFSJnBYjGzevBmTJ0/GrFmzcPHiRTRt2hQ9evRAamqqxfGHDx/G4MGDcejQIZw6dQqRkZF44YUX8PBh0WMRCOfSJoq9WWfm6y80htZ70ycvQ7RaLSZsuIifj8fj+3/vINvK2D/eNW+XbTec/74gQz+xnCdsQNqDC+yNd01P/WtxfWDTEN3OjMMxI4C+BPiM7deQlmM7E+ODLWyNlq1q3U2dcwlEttUPykhky4JbElT3jgB/vG37aVKRw1tGAJ2rpiATyHoInF1pPPbRJXYOAhEg88cDkRDTQljB2S03D5EqlXFzwmiDMuVuaHnvJ/VDw6CG6Fm9p9F6w74jjqDRaFFgLWaE63dj2ozRElyAaesxvGuFP4yjqb2GeAUCo3YbrXoiT+cbvnFk6QK9z2Tp3ZHREa2hUep7loR7hfOi3lvKipHtscbX0ov3WYvgu11qYmJMbaNtM/7SWSxv7gFOWXEd/v0/y3V9FLkIUmvQRMIGkiuqd2KLi9WKASJaAgNW6oV/fjEsIyoF8K2uAJ7IA/ggDpidCUy8yqfeNwkuJF7EEF6MOBZjRbgXh8XI4sWLMWbMGIwcORINGjTA8uXL4enpidWrLfvz169fj3HjxqFZs2aoV68eVq1aBY1Gg4MHneBTJJyCvy6TJiNP/+QW6qs3Ez98Zr2mgFylsRhABwBNI/0xpG1VHP2wK/yKk63D1aC4vQ/YM5W1enxdC/giClj1vH6cSa8YAICHP1uN00G4TsMXEzPw+/kHNscmprNBmKHBwcYbGr0KdJmun/vvw4FTS9mU38fX2aDKlGvALy8BVzaxJm9ryHN4fz0AfNfqI2CIQRXT1DjWJfP0LuvLB4D6L/2/vfOOb6L+//jrkibp3nsPoIwChZaWskdlKsuBiICoKAKKX1ARByj+/IKCfp0gLtyAiz2UKatsyihQaelgtCmd6c76/P745C65JumipbR8no9HH03uPpfcJ5fcve89Xm/glQyMDTRW8hyzs6DnYSMHeEXNLwffvou9nrzR+w38VxYsPK+srxZIDUqrtcK109m2xvcsIMb4uC6vj9Cwz7z66rY8IwA2pW3Cemexd2zWnlmCeBghBEUVGoBTo0JLDcIVA1cg3jceWpXxwhvoFCg85vNHpSYeR41OjxvF9HPsGeyGZweIJdOrNDpAeRFYOxH4a6GwvKznTGCCwajVa40lsTwVhdT4BWDrQL/nml5PAuO/AB7/A5ixl37WfEi0sqjhQoWXttLfRVGm0XAw8VCpdWqhJ46/YwOq0/hmdMwYuatpUGmvWq3GqVOnsHCh8UsskUiQmJiIpKSkWrY0UlFRAY1GA3d3667z6upqVFcb7yBVKvYlak54/Q9eD6RKo8Pp7GJhvTXPiEanx4lMyxeucC8HrJvRu0m614oEsY59Qf8s8fwp4P0IoCLfuKy2vIpaML3o8KXJlvhs7xXhc+vbJQw4ZLJS4QREjqQN/26cAgrTgT1vW3/TW6n0BF6QRkXfTCs61GWiH+vP+WsxIW4MOgDUC2JJyt3RG5BIoDa5WPWrsHLB9+oIKM8DRRlA+l5aCdLMOMudcb/eHm8QAj3HoVJbCcdGdO69ZjAG7WRSUR8ZAGLxPE259Q6uSSuNScYK833gwz/1VmA1UFKpwbXCCnxy2uiFGFNahs1OjtATPfIr8xHgGIDvjmQCAOSeNOnbVmqLYSHDwHEc9FV+wrbdvYyS+WOi/XE8sxDlauPvk6/IAWjZvq1MivgwdxzLoL9TVZWGhjEBmhfTLhGvX/DFz0f64Gj/gRCk+r5/gOZqTd8BnFsPbJkrvK7MUBGl1lkw7njPiF5LL/719UreOAWsn0wf3/8R/e8aDDz0jTCE76UEiFs31AnvGWnkuYBxZ2iQZyQ/Px86nQ4+Pj6i5T4+PsjNza3XayxYsAD+/v5ITEy0Ombp0qVwcXER/oKCgqyOZdw+vMHAqzzuuqgUrS+3YIyUVmnwxJrjmPKNef6Ph4Mce+YNbBpDBGiYOqe7uKslekxu1FvOGmy8ozx7rdhqVc2Ws8Y8A5l9jex+hRPgGkTd172eMt/Y1pV2FeVJ3wPsWQJ8Fgt8N8q4XG9Z+j1frQJ6TgXsPeifosb7O3iabfNqxMP0JD/6A8A1BEg0GEdP7gDsDePrEOdqSjhdFZX2hrFXTkOZ8g0Vg3KytXBvZer1UVt5/coikZfATP0XNDFW6ngRn16ZhhO5J8zWW+OBTw/h/k8PQVlGjYHn8zR4J78Q/goaetmRTj3El3NoPoONghrStja2QjhGW9YZ5Rmz4Fw0R9Sh18GQwMobwwCEkKKXk0IwzNY90xvbXugHAFQ8zZBLRbw74cmymfi5qg8A4Hi2ChhtyD/Sa2m479gXVOMDAMAB/j0gNxgYGksVSjI74++1IboeyovGx1tfpP9rHIf8SsNnI7VtWKdZ3rjUNE0ZOaN5uKPVNMuWLcO6deuwYcMG2Npav8AsXLgQJSUlwt+1a9fu4F7ee/Dlvbxs+61ScV7D8YxCpNw0ds1dtuMyur71Nw6nGZPUTC8E3YNcm1agx76G2FmPKeZ3XAEGQavxXwDDTJLqnPzQGGYNaoeXh0cCAJKuFuCxr45aHMcbcGtn9AZX02hyNTmZekWabzxtM/DSv9TFzXPIcDG4cYrqNwBW8ziqdFXQ3f8RVg17GYv6Po5FcQ/iAzdXfO3ijOe9PfFuyTm8fuh1YXygYyA8RrwHvHge6PU08OI5oN+LdKXCCYg05HDcKXGos+uBjAOwMyTmNjZMw1+Mx/WwokbLy/WrreTDmFZtPLSG6lPUfAkbCeyDfkCpNh9z9tAcm4sFF/H49sex4sQKiy9brdUZQnh6cFKaOBpW4QQJgCqDV2H5nqP48O9UrD9Jz3HhPvSUvDDOaBwtur8LZNpQfDTuYcikxjAU79E8kl6AXENi6i2DMeLpaFKNw3HwdzGKkJ1NpzkmB7OqsPeyMdePA6jR/FKaMal591vUUwdQA/aZ/ZDxDQ91ahBC8POln/He8fewJ9sQeue9W9Y+b0tY6j474CXR08M3DwvzaRC8AFsLSdgz6keDwjSenp6QSqVQKsV3zkqlEr6+tffiWLFiBZYtW4bdu3ejW7fak48UCgUUirbVBOhuhr+D4i+sNcMSn+1Lw2f70rDthX7o4u+C/anmycpfTY3Fo1/SC7amqRU9g+KBkcvpRTJuBnUFD34dOPwxXebgSbv9AoBHBNBnDr27T9sNRDfOMwKIW8ebhq1M4S+ErvYyQGlSqfDYb+IutBFDgambaQjk8Ed0mbMh/h8QQ6Wri2pU4Zz8Fgjtb3Qzc2JPU7WuGoduHMLKsyaJiK4m3pHCZPpn4JfRVqokePhy1r3/B/SbX7sapzXU5UCVCnpHX1RodEKSpUVOfw8AsCN6ANJGGSM6PRGE6WYOtNJSXuZA8x2sJefyXicHbyBqgsUhpmXpFdoK7M7ajd+v/I6zt87i7K2zmNJ5CnwcxB7jCkPekczllLBMopUDUqBLZQcclJ2BjeMlfLLvIgA5OGk5siqoXoeziZfryX5heLx3iJnUPf/Z6vQEfZbtwd75g5Bfyhsj4nCUm0n358MXM9EdgEpv5Rzr6AUMfcuYj8VrfRiMDL6SpaS6BBfyL2DZ8WUAgPWp65H0WBIUCkcaKq1DcVZETWPE3hOIMOaDEULwdxbt7RPpZsGwrw2+rD7zIC15b6iS891C+j6aiO0SCPScBrQxRdYGnW3kcjliYmJEyad8MmpCQoLV7d5//32888472LlzJ2Jj6yHJzLij8OGUtLwyHLqSjw0G1dXOfs6ICXETElznrkuGWqvH5VxxidwvM+LRO9wD/dpRN/8D3W9P+twMiQSIfwYYtMAYk3b2A0YuAyasBoa/C3h3Em8z5A3gmf20C3AjqakYq7dQ7syHb+xkUsA0ht5hmHggxwHhA2kn2XFfAI+uFZ8ULe3n9peA1f2N+hc1tEryKvIwZy+9S+/k3gkJftZ/g9vHb4ebrZvV9QCAsIHGx9Ul1sfVxqo+wIcdMXPlZnR76y9sNtGuMcPgNrd1pN6rqkY0Q6wwyZewqvDLJ6RaC9PUo4eRVCoOVf5n/39E1TD3b7gfR24cQU6ZMWxXrtbCNuBH2Pr/ISzTgRoAqgL6XZEo8uEQ/iE4WSF8Ir8WxrnIxZ4/s547AHoEuyIh3ANyGwn0BLiYoxJyT7wczQ0NvoS/rJQe23Ii9uRVmoYiA2OASHF5M//58LkiK8+uxK1Ko76ORq+Bqlpl4hlpiDEivrASOxekFqYK34mknCScu0UNtcHBg802rxVTafqapcytBZ2GVggeWE7zd25T0flupMG9aebNm4dp06YhNjYWcXFx+Oijj1BeXo7p06cDAKZOnYqAgAAsXboUAPDee+9h0aJF+OWXXxAaGirkljg6OsLRseHJaoymx8XO6Pp9Ys1xQThqRJQvXhjaHp/suYIPd/2LtLwyfHfEePc+NSEEw7v4ok8EPcl9OTUGV2+Vo4t/HcqIrQRvJ/EJvVKjg4PJnT4hREgetJNLgejHgUMfm2lUmBE9yXzZyOXA+d+o1sWZnyBoLOjUwAnDRaqGp+K00iiENT1qOiq1lUjKMU8kj/eNR5BzPfKuOgyj+RV6Db2rtbNuvFRrdfj6YIaQo+DpqMAzA8IhK8oEALjnHICeDMHfKblYtT8dl3Jo8uCDPQOx4uFu1NVucJvbyh2B6vxGtaXnQ4sSzoLgGU9dYZoM2gnWUuIqj05iNM6iPKKEcMmZPCrRXqWrwrO7n4W9jT32PrIXDjIHFFSUQeacImw3rdPT6KQ+CtwApgZGYDFRokyXD4m8GI7t3ke54ZD39e8raInUhr3cBmuf6Y2ZP57CzpRcfH8kUwjXBLiZ94bhK40cODomNjII0SpXQSStpl6JWSjUYIxcKjQquZbUMFpVahW8GmOM1OhovFuiwbwtD2FI0BB8PORjZKuMOjGjw+r4fZlhYujsWgQ4B9yRBO0mpfyWOOflu1HAq9fqVqFtRTTYDztx4kSsWLECixYtQnR0NJKTk7Fz504hqTU7Oxs5Oca7g1WrVkGtVuOhhx6Cn5+f8LdiheU4K+POE+bpgNdG0R41Wj1BueEEf383esc6a5DR/c0nt9rKJFgyNgp92xmTJO3lNogKcGkzDZ0GdvDC+w8ZQ4q5KvGd+82SKqF60U4uBZx8gJevAOMaIf3u1QEY8jow9jNgyp/idYXp9H+PKaLFqUWpAIAYnxiMDBsJD1ux+1kmkUHCSTAwaCDqDR8S+qIv8PV9tMzSFL0O2L8MZzevxPK/UrHmcCbWHM7E8r9S8dmey8KwmdIt+EC2EkXXUwVDBAD+OH0dqw9cpWJyhpOr3JCD0BhjpNykI7TV7x1/cawZpsk5CxRfA8oMYcdaSppTKqkUu73EC2vvX4sfRv6AH0b+gPX3rxeNq9BWILc8F4QQ/Pfoe6J1kR7hCPKmx2hYsDt2T9xq8b1WJa6CtAHl6MEe1Ng6llGIgnI1OA6YHG/uaQvzpMaEA+j32NfTExtn98WkOFpebVY1Z+cqfh7YCwAwItSoD7PoiFhJdu3ltYLRoq8uw7z98/DAhgewJ6sOKYfL20RPf3KhF9m91/ZCT/RCX6Xx7cabhcPqpGai7R9PtT4BtA3Pmi8zleBvAzSqa++cOXMwZ84ci+v2798vep6ZmdmYt2DcYZ4ZEIGNZ27iosmFw8Pg6rWRShAX5o7jGYU4Y8ideGlYA+O2rRAbqQSPxAbhzY0XUK3VY9X+dKx42JgHcuGG8a7Q0ZBMCJsmyHXiJcxrYueGdg5xSCunFUw3ymg4LcCRJm76Ohjztvwd/LF1/FYQkIapVcodqWBVVQlw/TjVfuhj+K3rdcDqgYDyPOIAOOMr6DkpHvXMwPWCUlzffwAwvFWoRIlQKFGscsJhiI2oZTsuo6CsGq/znhFD4m99jBGdnuCtzSlwUNhgwYhIIUxTa+WWIbylry7DyjOf4UbZDcQ7R2Dchvl0fbeJ9H+c5b7BGr0GWdU0JGPHiSuU+M/elKKqIlzQXMDFMuPd/tyeczEqbBSQavDCaCqtlqc21Jh/dkA43B3kgpeoi78zfF3MCwTmDGmH7kGuiDz6G5ANODhRzweffG5mjLgYNU1gYyd4jp7u+jS+Ol9DaM/A+tT1SJc4wt3LA17XdmBXkUEQ8MrvGBoy1PokTKpjKjgOp3XG89D0ndNxOo96AZ3kjVAyDh1gvqyi8PZVkZubzENUgK7wqnFZaH/qJbl1+Y7pAd0pGmWMMNomXQNcBGMk0M0OziYVMj2D3XA8oxBaQ95EpO9d/kNuQqKDXHEso1BQyOThL4R923lAImlCb5BLIDDnJHBsNXDC5KRv747HwxfhlS2bIXM7hj7tneBu74BpXWgr9Q5uRiOmVF0qqryoNzVP0HkXgZvJUFVpsG33bkxSnhdWeXHFeFq6HZNK9wlGCM82XRxGS4/DlbN8B7rz0FG8rqD5BnJp/Y2RlJsl+PFoFgDgsbhg5BkSNmvtCG0I0yTvexOrPen8dnISjOQABQH1jgDmVVsAslRZ+PLcl8LzrvJZovUuChcMDhqMfdf2CcteOfCKKJdikMsreLqrwSAzqexoKg+ih6PCevKuCbYyKe7r7AOcN+SGGDxGTobQ46bkG3htlEnuVcx0Y68akwRTOxvzENDnQz/H7D2zAQAn9WWAowNgMEQAiMIsIq6doHlgJiG0lW7i8BBviAC0t1GDsZED/ecDBz8wLqtqZE5UY9BU0feuLKRVbN6dgAt/0Lnz+PcAuk8Ub3fuV7EhAgCTfwd+e8JgjNym5P5dBjNGGALvjo/CY/HB0OoJ2vs4ik6WLya2R+9wd1Rr9fB0lKNncB3JkG2Iyb1DqDFSJTZGygzVErVWjDQWz/Y0CffmGeDGSbpMKoePowN0leHQVYZD6uqDFVONCeGmxyvAyUqZa130fo6Kf+UZdB+SfwaSf4YzgJqZLh5Qob9rPlAjNeAvXSwO6LtjtPQ4gu3U8LFTQKmqxoSeARgV5YenfziJURKjPo2toXKk2lrvIRNUlca797zSKkxfQ0/oWQW1aEgYmqrd0lUBoMaIluhRKpFAodMD2YZkQAsCXavPrsaWq1uE5xK9+ZgPBn6AnZk78ePFH3Gp8JKJIcKhKnc0OoWYJBbbGI0RgIZkXj/0utDF+OdRP1ufR1PBhygMhqe7ofJGqarGW5tTkBDhgeFdfI25NoAo1MFxHGQSmaAzMihwEDztzDVtTMkuzUapulTs2biZDHyTaKgSMyaH77M3N3YAwN3WHQ91aGSuh6zGa2YcAPwaICl/O/y7AzjwPn1cnE1LpP94mnY3NiUwluok8b9jSyX2Mtumkdy/C2FdexkCNlIJuge5IibEzUxW21YmxaBIbwzv4ouYEPc2kxdSH/gE36NXC/FjUqawvNRgnDjIm8mmt3MFZpjE2nUaJER4YEAHKse966ISYQu34cFVR4TGhu/1fw/t3dpjXsy8xr1nzynArCRg9gnAuwttUubkjzzOAznEHblSo27Lkz0c4C+jlsh1YpTCP2B/H1SEXsh6qY/j2Is9kblsND58JBpehqRgR85Qxhs5Gho9vRj+fNFYdWINU7XRumT6BfrPAzwjUSIVe09UNUuXQ8z7J5l6OADLcvAyqQwPRDwgEm2TclLESz6Fpqif4Hmgg8XGSL+Aftj98G481vExfDL4k4b1XLFGlQo4uQa4uNnyev4iZkhQfrCnMRzz3ZFMPL/2jPk8nQOQmV+OKd8cwy/HskWhP3c7dzjJjEaGl44gttK8MqrP2j548q8noecvwoXp+MzVBd1C/NE1LFj4y5bR31snd3GF3Kzus+AgE1c8EUKEv1rxEPfoQUk9vztNgel7Xfkb+KgrNUTsPYB+84zCh5/2pMq3hFChw0s1jt99S+h/3hhpY2EaZowwGHUQ4Gq8q3pzUwoKy9Uor9bi/Z00gdRe0URKs9bo9ADVAOk8FlIJhxn9w4RVhACnsoqQU0IvbqPCR+HPMX8iwd96mW992Kl0xvfRv6DqhQtQzjiDuMpPkVD9GZ73+pb2vAEwwqsIklJaHceF9hO2fXv6GLzx9GPGF7tudEfzVR6OMBgjXpGC8FZmnlHa3RqmpbymhkmtOPsDYz9DkVR8uhsb6I89/F24vYdFxVo+cRIANCXda+1Nk1dh1N95p+87qFbT8JOjqTIsX55toqkik8iwMH5hw0tWASrYdnQVLfk8/hUtXz66iqqY/jqFeh9qUmG44zaEpWxlUqyd0RuzB0eA46jBparSIDPfGDpRuvXA21tScPBKPl7bcB4yifFmxcPWA/6O/oj2ioat1BYzI8ZhTW4ezjv3xfmMbARqjF6VE7knBCVVVKnwl4M9iIUbm47uHTGz+0zRspr5IpvP3kTkmzsRtnA7It/ciY1namm+2nks8OxBGnoC7lwCKyHA329YXtdzGpC4GIg1UWfOPEirkEwbOz6xnTYK7GuQ5Of7/1zcDBxoO4UgLEzDYNRBO29HrJneSwgJXL1VhhV/pwrr3R2aWaDvkR9pt19Drxp7C56Yq7fKsXhTCgor1OAAPBYfgodiAs3G1YebxZWY+RMV6lLYSNDRz1g+WKHWAYZGafjHUC0ilSNg0ifAv6MABy/Y+HZGAAD4RQM5yaIST09HBfq184RDJr1z/vhQDsplkbBzPAFwGlwvqkSQuzE8oNFpsOjIIlTrqrGkzxKhgSEAIZkaANwd6kjS9e+JYgsibm96emBo9nVa7mngwPUD2HZ1GzR6DVIKaGnuxOA38PVfDlA7WDdGlvRdgpf/eRkv9JiP+4JH4ZsqKgLoqDDxMvL9hppKDfTYamD/f43PiV6cZ1B4FfCPFm9TwzMCAAkRHkiI8MAPSVkordIi5aYKT353AltsQtBZkoW5lzvjqN7ES0SM30E3WzdIJVL8OOpH4/r4V2i44dIWaGoYGzfKbtDcj5PfokROj8maHCXCJHbA8zQ/xFXhCqlEirk95+Lj0x8DACJcxXkxey8pBeNQrdVj67kc6yq8HAf4dUO6TTgiAFy/ehHKrELEhFjvkdYklJt8Zo/8CLQfZtwfPtl90AKqhPxeGK34KssDSk3aqwTXuLHwMHwOJdnA3ndo52SXRoZl7yKYMcJg1IPBkd5Cw7GbJVU4etXoIh3YwauWLZsAjhM1zZNJze8kvzp4FQevGBsEZhZU4HBaPsZG+2NQZMOS/ky9E4s2pUBqkpy7+IEu9IKXusOYdBj9GNU76PaI+IWcfIEcmOl7LAk7j/Dr/wAACjVyEBsbwzR1Zuq/p/NOY+tVWgIb5xuHkkpjjgzfnRYAtjxPPTMnck/gYgHNd5FL5RgZOhKutq64VpGDdPcgQFeC2UXFCNJo8aq3J0qlEvwcNQyT4uYLbmI+EZNHJpEhwjkKQKag9mrKFWUpnvz+BJ4f3AUTPL7Fu7/kYbn0b6GpniiniPeMNFWflKv7jK+rqQB2vCJev3MhkPwLXVeWByTMNhqH9uYXYmdbGUqrtHj5t7PQ6Qmmce+gp5MKxdIQdAQEwUO1znichgZbqJLh9S/mnIDXukFQ2hg/g2/Pf4vJ7R+CU+ElFPvTCrAQjQYejt6AnTiJ+Kmop9A/oD/sbezNtHL478robn7Ydi4HV28Zjd6/UnJhK5OKfpvVWh0+T7qFDyVAoOo03l/zBXosWti0yec1MU2U7TzG+jgbBVW+LSqnBgzfH6rTGHMl5I73AxN/AjYZlKYr8utvjOj11FOpq6ZGTmOS3JsJZowwGPWEz3d4Ye0Z0XJT0bg7gY+z0TDpEeyKM9nFgiGSEO6BpKsFKCxXY8OZG9hw5gYylzVMJGrJVmPTMrVODxicEdP7hiIuzB3AQGD+Jcsbm8Irmtbolhqu3C08njHpEVRnK7HtFgBOg4/3XMH6E9mQSjg80ScMeTC63t899i4muFJJexsJJ+hjDO7ohQBXO5Spy/DsrmdFDdwySjIwq/ssjN80XqjW8dHqMKq8Aq8axiwrvwyN6jJibjkgyMlcHG77hO04n8UByMTJzEL0e28vvng8BlEBNJn1zU0XcK2wEq/8YdR9MO3uK2rgx/cv0jRcbdaM/DQg2yBy120icGqN+ZiyXCDN5C57jyHvgJMACvNk3PY+jrhRXClUKU3o3QELRxpzNxb+eQ5rj1+DrzwKVysPI9orGv6OtSguO/tjvsQbX1fk4rAhJLb/+n7sv74fCDDmH7no9BZLbTmOQ6S7uYxAnqoK+1Kp1yE+zJ0aI/nl2HtZiSA3ezz7I/XsHV04VChzzi9T45A6EjAcgs7aFBxMy2++mwlCqIQ7ALgE1z3ewZvq+pQpgb/fpMssdT2WSGnodu+71Bg58zPtul0fWYGUP6nOCkBbagx8pfbxdxBmjDAY9cQ0d8SUOkMETYyPsy1+m5kAVzsZzt8oQXGFBlq9Hk4KGV4eEYkJK29PKpq/EA2O9KKeEABSCYdAC6qetcKLjf39Ou2ebOdGT9D/7qDLH1qDwKh+eNjzFLb9RT0jl3JUuGTQTPy3+DyKXP8nesk/ix+DzHUc5vV5BMnVn6KDWwcM6Uj1QnLLc6HRa6CQKtDNqxtO5J7A2strodFrUK2rhhwcelVUYGBFJWreC39w6gNYopN7J/g6+ELtUwG5jQRqrR7Xiyqx66JSMEYqNdZDNxxXQ8mXT2C9fpzmd8gta43Ui3STBotDF9G78BSDYJ5rCG36t30+rcji4UM09h4Wew99/lhPnMoqgp4QyG0kiK0RxuAb8HWVzcbKUW/Cx75uAbJYhRdir5/H704OeNvTvHzaTq+nleH11P3Q6vQY9clB4Xk7b6Ny7pPfnRRuGgAgYdkecABCPRzw9tguyIMbVkofxyzdT5hpsxVfZ/6n+YyRc+uBHS/TxzUF5CzhaPBgbphpzCmy1GCTx8ETuAXg+GraiZvXA6pJygbgyKdUJ6jUKEgKZYrl8S0EM0YYjHryZL8wrD4grvt/Z2yXO26MAECvUHqRaO/jhAk9xbkhE3oE4M/akvlqQacnKDBIvC97sJvIC9NgwgcJzfCQewEI6y9WdA2KB2AUPXNz5PDWQ91QUK7Gsh2XkatOgaV7PYXPdhToI3Hk5hEcuXkE94ffjxUnV0BZQdWBAxwD8HbC2xi1YRQA4Pd/fwcARMpc8YUyS3id8aVl2OBkXQJeIVVgRBhVGw32sMeJ1xLx4a5UfJ+UhdTcUihVVfBxtoWNBTf/80PawUFhg0hfJ3ibfoamQmJX9wMdR1l9/1q5fsp4oRv4Kg25+HQ2GiMhfWl/mcS3qMaFRAaAGHJVOHNNCwMOChuhWssSvDFSUK62KPhmkU4PAFlH0N6k942dXo9KgzHkqjcYc7L6GWaF5Wrkl9H+OMM6+yAmxA1vj+mCxZvpxdW06zghtGj4an45nvuJ5qOo7X0AQ/5q10sf4Vrslwh0s0OFWofvkzJRUqFBdJArRnZtXMdvgfx/jY/7PF/3eJ8o4PJWYwjP3gPoPdv6+AEv0YRXwFyPRK8Hsg7RkM+eJeZKygBwcSOgVVMdlrsAZowwGPXEx9kWb4zuhP/bRkMUQzp6Y0pCaMvulAU+nBiNF4a2x6AV+wEAqiqNWam2NYoq1NATekd/20ZW1ASa7Z+XYswb4WPhEhshzs2Ls0mlOjwcGwSdnmDZjsuQ2mcCAJyrh6HK5iLUUloiyUnUOFtiVDddmbwSR3OOCs/bu7VHkHMQPh78Mebumyssd3aLAHAOvKbFKwVFyPPqgMNV4mZ+fg5++Pshca8UAHCxlyHCcBe+MyUXO1NykbzoPlFODc8TfUIFBWMRXpGAVE57DjWkdwuPXk/vuM//alzG90LqPI4q5mqraR4PQA3C8EENfx8r8MbIjgtU8r5eJf6xTwKxT6I7gHX5KSisKoSdjR2m/0UrW3y1hqqomp17rVBQTg0RDwc5vjTo7Jgqzn7/ZBw8HOTwdbGFnhC8+sd57L2cJyjM5vnfh+r01VBoy1Cdn4n+7+/DQzGB6BHsKlTISSUcTkV4wNX+Nn4DfNfi/vPN86ksMeBl2kxTU0l/gAExtXfODh8EDF8K/LXQ+LviSdsN/PKweNn41bQSpyQb2GZQH766D+gwvL4zalaYMcJgNICn+4djTHd/2EglcLO/e5K/auJvElK6eFOF3uEe0Or0OHu9GBFejiip1MDH2Ra2MnFZMt/4zs1eDpm0CSr/+SRJ/sJbblCNNJG8Vxh60xRWFSK1MBWR7pHo184TZ2X0zjKvSA591SgofLZDakcNksvFxtDD3ms0XPFg+weRGJKInt49AQDR3tGiXXkh7mVg+DfAElpF4kgIPo14FAe8gmFnY4cgpyAcyz2GXr69rE4nsZMPNiffxMksWh77+b40HM8Q6z0M7OBl2RDhiRgC/LuTXnRUN2nSqVZNXe03TgH9/mM92fHK38BGk5LX+5YYxbs82wPP/mP9fZuAboHGHIYRHx2E3qDvERvqjv+Oj6rTOOniScN+hBAs6bME10uvY8SOt+nKehojRQZjxM3EWO7f3hMjo3wR5ulgFnaZM6SdUD4OANMGR6Eg/DP473gCbhJqJJ/OKhKFd3R6glxV1e0ZI0I36Ho2hJXaACF9GvYefE7JhT+ACV/RfBIAyDdU+zn60N9aUBzQ/VHjdrwxYlrt08IwY4TBaCDetxO6uEPIbSToHuiCs9dLhKqDz/el43+7/xWNG93ND4FudnhleEdkF1bg4S9oQqSnYxO5bvk8gLyLgE4L/PwgfW6SmGead5B0MwkFlQW4KDMm1v13+ERsPlWNg1dCofD7E3LXk6K38LD1gJutG56KekpUceGmEKsEh7uEm91pyrpPwlCTSqW6uhv7u9rh9+f6YMLKwzidXYyckirIpBw0OoKvp8YisXM9mrjxSax5l2izvktbxOu3vEC9JwDg7Af4GfohEQKsNVxQPNrTMtGYJ+p+vyYkyN0eNhIOWj1BqtKo1XElrwz/SWxf798Gx3EY3348fbLZoMNRT2OksIIaI+4mhoK93AarHo+xOL5nsBuig2h34n7tPGkrCzUNwXSR5eBr/XJ8Vfk48kvF35dbpdXo6GvpFevBtRNA8k/0cX2NkcbgapIYm32UKuVmHAQOGvRHoh4ERiw1367rw7RL+J2Uxa8DZowwGG0U/q5OValBam6pmSECANvO0YQ2X2dbnM4uRqmhjbxpUuBtYZB6x8EPaEkiT5fxwkNbG1uMbzceG9I2oFxbjt3Zu6EBPUn6yCPxcHQ08orScPBKPqpzHhIZI+enGXvl1ITjOHwy+BO8eeRN9PHrI+SmiJA1zrB8ODYIp7OLcSlHBY2OwFYmwdBO9Syh5pNYj62yvL6yCFhrktPxzH7au+TGaQiy6QNeEt/p3kG8nBTIKaHVQPd19sHRqwUordKipFJze4Z6PY8F7xlpSBhx/bO9kZFfjnBPw/faNQTgpJBoK5EoPQOl2gMZV3Ox1OYk3tdORBGcBS9hgynLA741CX3UJ3m1sZiIDaLwKrBjgbg7tYOV/B/+ZuCv12i5910AU2BlMNoofMmxqkqLdSesNCoz8PaWi9hyluZO9GvniXfHdW2aneg51fj4pqHhmXMgEC9uie5q6woAKNeUC6qnkztNxs6J6wDQEmaeGLeRAIDefr3rfPvBwYNx6NFDeH/g+42cgGX4HJxMQ08cX2fb+rdIMC3BlDvSxEWfroCv4TMPiKF/vNLmkc9orsjXQ+hzmUOLGSKAuJS9k5+zYBTkqqpQVq1Fec3uv3XRYwr1FvV6ul7DCyyEaepCYSNFR19nyG0MlzxnP+CZfdBG0+9nEJR4o3I5Jtnsw2znQwDEibANojgbIDqakNtzavPmZHAc9X4AwOY5YkMEoL1uLOETZXysbiLNm9uEeUYYjDaKsx39eZdUanCjyLrip8JGAq2ewM/FFgGudvhkUo8GnehrJbQvDSkUXDFm9Dua363Z29BKCmW5UpAL7+zRGTaG1vIJ4R7YMKsP1Fo9ugQMwr7r/TEg0EJr+Pow7F1abjzui8ZtD+MFme8J5FlbjkhNTLvedn0YeOAjy+M2zQHO/AgUposlxUe3rAT41IRQfHXwKhwUUtzfzQ/7DPkYU74xNj8c3yMA/5sYXb8XHPsZbR5XH50MmHpGbjNny687bDrcByT/gAFSo4ct1rkYUAH7U29hakKoOK9KU0kb+3EcoLpBjaiCdJofEtqXauvwPWM82gFjPr29fawP7e4DUjZSAwgAAuNo2a6NwnoOSs9ptGUAQPO5bqfEvIlgxgiD0Ubh796vKEvx90Va9jp3aHu42MlEwmap/zeyeXeED0sUGcpqHczDGXwDtL+zjFUsjjJjqIjjOPQw6RT9QMQDjd+fPnNotYkFBdL6EhvqhiEdvXGtsAJSCYcn+4XVvRGPo8n83WvZrvuj1BhR3aRaEgD1nvCVMi3EY/HBeCzemKswtJM3zt8Q5x5sP5+DDx7uXn9101oMkfJqLU5mFUFvMPz4XBW320ku5QnpC3hG0mZ2Bq9CdP5WvGdThgXpz2Dc54ex80WD0au8CHw9lHo8/LoD6XvEr+UcCDx7wETLhX6/aCM/uuh21F7PXy+BWqczl7CPnkR77+g1ADiqfqupojk41sp2JRLqlVOXGZLLG6bS3BwwY4TBaKM4G+7eTSsJHo0Lgp+LHVzsZJj/21mMjGpshl4DUBlKZ/kupBY8I30D+iI0NRTF1cUAAF8HX8T4WE5IbBJuwxABaHO5b5+wXnVTK72epom9HEeVU63BJyeWKY3LHt/QuPdsRl5M7IDnBkXQZrOEIPrtXajW6nGjWNxnqLHM//Usdqbkmi1vkDfKGg4ewByDR+fGKeArGgobbXMcC7QzcDm3FNVaHRQ2UiD7CNUA0VSYGyIAoLoOrH+c6qoAgJ07lKoqPPDpIUFIcEx3f3wyqUeDd1Ot1eOBz2j46I/nEjDv17OQcBy+mRaLcC9Hc89GffJv5A7UEFHlWA/n3EGYMcJgtFF8nOnJmu+nMqV3CPxcqJfiwZhAxIS4NVxVtTFU5Iuf+0WbDQl3CceW8VvMlrdJbJ2BuBl1j3MOBDqMAK4dpxeOHlMsGnJ3AwobYygj1NMe/yrLMGD5PjgpbPDV1FjEh5srr9aXNEPPmXAvBzgYmkR6OSkwKLKJP4uAGOD1XOBdXziiAp6cCvnEBapKLbycpEBZPcpgC9Np+AYA7D1wLKNQMEQAYMu5m1j2YFeLzS4t8fm+NPyQlCmEAwHgwVVJwuN9qbeoMdIY5I4AlMB3o4ApG4GIRnSNbkKYMcJgtFFGRvkht6QaheXVsJVJ8XjvENH6UE+HO7tDPl2BCasB78539n1bKxIJ8Nj6lt6LBpPYyQf/KstACE2e3puad1vGiMpQmv7Joz0ECf5mQ2YH2HsCFfkIUZQhv8oF3x7OgLu9HP3S0tEJoGGahDlUo8PBi+p4BPQEPu1JvVhJn9HXsndHvsEQGdbZB6ezi5Bfpsbn+9LQPdAV93X2qTPp+fsjmSJjpibHMwowsIMn2nkbpfSTrxVjw+nr4O0XhY0E0/qECl6qK8pS/H7qOvop+qM/0umgH8cBL54XlwrfYZgxwmC0UWxlUjw3KKLugc3N9J20iduwd+/aO3tG0/HKiI54un841hzOwKd70/DLsWy8MKQ9HBTWLzeqKg32p95CqIc9ugW6AgB+Okpl94sMuiJ3rCGlozdQkY/75adxqioAq/bTC/ZqWQY6SUGF5mp6tizpdUQMhTKFlkD7u9qhSqvHgX9v4fN99PV+m5mAXqHuuFFcifPXzbeXSTnBEPnpqXi42Mmg1evBcRx+OZaFX09ex18pShxOK8Dx14cK3pbFm1Nw9lqx6LUKK9R4ewwVnHtrSwoOpxVgNYZholSG92Rf0UH/vE+TiVsIZowwGIzmJSSB/jHuGdwd5AjxoJ630iotlu64hP+rpVz8v9suYd2Ja5BwwLHXElFercUbGy8I6+VSyZ3rAaWlBsTwYILzsgD4VqXj4ZvvwbcqAwDw6t95OJd0EL4utsgqoEmvHNFjM+xgD1q1pu3/Ch7cokXKDbpNqIc9JvQMgKudDMcyCqBUVSPjVjligt0wYeVhKFW1lxHHhbkby5JBQ7CqSi32/0tl7tPyygQj7mYx3YfHewejtEqLTck38efpG/jztHm/qo26vnjRaS/8qtJpEm8LwowRBoPBYDQ593UyqtFm5lvXsrhZXIl1J64BAPQEGL/yMK4bStEdFTZ4ql8YegS71upZaVISZgPb5sO/Mg0f9sgEzv8JVKcCHKAnHJLKfZFVpsLFHJVos6ck89BHkoJnE7vhgt8EnN1FK9ZsJBz6tPNEBx8nfDKpB1767Sx+P3UdH+3+Fz8czYRSVQ0JB1G1WEFZtaBhE+HlIDJEAMDPxQ5fTInBQ6uO4GRWEcZ8dthsGnMGt4dMyuHo1QIzY6eDjyM4cEhVluJLxTQsrlpE+9Rc2gp0ut/ste4EzBhhMBgMRpPjYi/D11Nj8fQPJ1FapUFeaRUu59CyXGIybvdFpWi76yaaOGXVWvznvg64ozgZuvXeOAn8Nk1YXN37BWSETES/ywRZx6iIoJ+LLT6aGI2Umyos2Qok6btAp20PdQa9+HcNcKEhFpM+Vh19aX7HzZIq3DQo2fYIdsMfzxk1Qc5dLxYMjI8ftV59Y9pPx5Rgd3t4OsphI5Ug6dWhUOv0ovUKGwlOZxfjwVVHcLjUD1qJAjb6aiiLSlCPhgbNAjNGGAwGg9Es8OXlZ6+XoN97+6DW6q2OHRvtj6kJIdDoCBZvSkGqshST4mrvFdQshA+mYnR8SToAOHhCMeA/6Gjvjme9K5BZUI6yKi2mJIQiPtwDPUPcBO2eY1cLkXSVNoQMcrcTGSIA7ejcxd8FFWqqVMtxtH+OKd0CXbFhVh9wHFdr0q63BWPk00k9cF9nH9gYGl1KJBxsJVKzcXyn438rHNALH8OPK8Tzsp5oZtUhq3CEEFL3sJZFpVLBxcUFJSUlcHZ2bundYTAYDEY9SMsrQ+KHxk7CHAd08nUWPQcAJ1sbLJ3QDWGGCq9KtQ7HMgrQK9T9zoVnbpNNyTcwd10yJByESpYNs/qIwi9NzYUbJXh7SwokHIfYUDdIOQ7/ua9DvVsTrP4nHedMkmen9w1FbOjtafDUpL7X79ZxlBkMBoPR6ojwcoCPs0LIWfB0VGD73P51bmcnl2JQZMurgjaESEP4hTdEHu0V1KyGCABEBbjgt5lWJN/rwbMD74JqOwPMGGEwGAxGs8BxHF4Z3hHzfzsLgHpA2iodfZ2x88X+yCmpgkwiQWxo8xoibY22+81gMBgMRotjGmaZf19kC+5J89PR1xkdfVkqQWOQ1D2EwWAwGIzGUa3VCY+HdGxdoRfGnYMZIwwGg8FoNvxdjf2P7OTmVR0MBsDCNAwGg8FoRmJD3LDi4e6I9HGqezDjnoUZIwwGg8FoNjiOw0MxgS29G4y7nEaFaT7//HOEhobC1tYW8fHxOH78eK3jf/vtN3Ts2BG2trbo2rUrtm/f3qidZTAYDAaD0fZosDGyfv16zJs3D4sXL8bp06fRvXt3DB8+HHl5eRbHHzlyBJMmTcJTTz2FM2fOYNy4cRg3bhwuXLhgcTyDwWAwGIx7iwYrsMbHx6NXr1747DPaaliv1yMoKAjPP/88Xn31VbPxEydORHl5ObZu3Sos6927N6Kjo/HFF1/U6z2ZAiuDwWAwGK2P+l6/G+QZUavVOHXqFBITE40vIJEgMTERSUlJFrdJSkoSjQeA4cOHWx0PANXV1VCpVKI/BoPBYDAYbZMGGSP5+fnQ6XTw8RH39fPx8UFubq7FbXJzcxs0HgCWLl0KFxcX4S8oqAWaJTEYDAaDwbgj3JU6IwsXLkRJSYnwd+3atZbeJQaDwWAwGM1Eg0p7PT09IZVKoVQqRcuVSiV8fX0tbuPr69ug8QCgUCigUJi3RmYwGAwGg9H2aJBnRC6XIyYmBnv27BGW6fV67NmzBwkJCRa3SUhIEI0HgF27dlkdz2AwGAwG496iwaJn8+bNw7Rp0xAbG4u4uDh89NFHKC8vx/Tp0wEAU6dORUBAAJYuXQoAmDt3LgYOHIgPPvgAo0ePxrp163Dy5El8+eWXTTsTBoPBYDAYrZIGGyMTJ07ErVu3sGjRIuTm5iI6Oho7d+4UklSzs7MhkRgdLn369MEvv/yCN954A6+99hrat2+PjRs3IioqqulmwWAwGAwGo9XSYJ2RloDpjDAYDAaD0fpoFp0RBoPBYDAYjKaGGSMMBoPBYDBalFbRtZePJDElVgaDwWAwWg/8dbuujJBWYYyUlpYCAFNiZTAYDAajFVJaWgoXFxer61tFAqter8fNmzfh5OQEjuOa7HVVKhWCgoJw7dq1eyIx9l6aL5tr2+Vemu+9NFfg3prvvTJXQghKS0vh7+8vqrStSavwjEgkEgQGBjbb6zs7O7fpL0NN7qX5srm2Xe6l+d5LcwXurfneC3OtzSPCwxJYGQwGg8FgtCjMGGEwGAwGg9Gi3NPGiEKhwOLFi++Zpnz30nzZXNsu99J876W5AvfWfO+ludaHVpHAymAwGAwGo+1yT3tGGAwGg8FgtDzMGGEwGAwGg9GiMGOEwWAwGAxGi8KMEQaDwWAwGC0KM0YYDAaDwWC0KPe0MfL5558jNDQUtra2iI+Px/Hjx1t6lxrM0qVL0atXLzg5OcHb2xvjxo1DamqqaMygQYPAcZzob+bMmaIx2dnZGD16NOzt7eHt7Y2XX34ZWq32Tk6lTt566y2zeXTs2FFYX1VVhdmzZ8PDwwOOjo548MEHoVQqRa/RGuYJAKGhoWZz5TgOs2fPBtD6j+mBAwfwwAMPwN/fHxzHYePGjaL1hBAsWrQIfn5+sLOzQ2JiIq5cuSIaU1hYiMmTJ8PZ2Rmurq546qmnUFZWJhpz7tw59O/fH7a2tggKCsL777/f3FMzo7a5ajQaLFiwAF27doWDgwP8/f0xdepU3Lx5U/Qalr4Py5YtE425G+YK1H1sn3jiCbO5jBgxQjSmLRxbABZ/wxzHYfny5cKY1nRsmxVyj7Ju3Toil8vJt99+S1JSUsiMGTOIq6srUSqVLb1rDWL48OFkzZo15MKFCyQ5OZmMGjWKBAcHk7KyMmHMwIEDyYwZM0hOTo7wV1JSIqzXarUkKiqKJCYmkjNnzpDt27cTT09PsnDhwpaYklUWL15MunTpIprHrVu3hPUzZ84kQUFBZM+ePeTkyZOkd+/epE+fPsL61jJPQgjJy8sTzXPXrl0EANm3bx8hpPUf0+3bt5PXX3+d/PnnnwQA2bBhg2j9smXLiIuLC9m4cSM5e/YsGTNmDAkLCyOVlZXCmBEjRpDu3buTo0ePkoMHD5J27dqRSZMmCetLSkqIj48PmTx5Mrlw4QJZu3YtsbOzI6tXr75T0ySE1D7X4uJikpiYSNavX08uX75MkpKSSFxcHImJiRG9RkhICFmyZInoeJv+xu+WuRJS97GdNm0aGTFihGguhYWFojFt4dgSQkRzzMnJId9++y3hOI6kp6cLY1rTsW1O7lljJC4ujsyePVt4rtPpiL+/P1m6dGkL7tXtk5eXRwCQf/75R1g2cOBAMnfuXKvbbN++nUgkEpKbmyssW7VqFXF2dibV1dXNubsNYvHixaR79+4W1xUXFxOZTEZ+++03YdmlS5cIAJKUlEQIaT3ztMTcuXNJREQE0ev1hJC2c0wJIWYncb1eT3x9fcny5cuFZcXFxUShUJC1a9cSQgi5ePEiAUBOnDghjNmxYwfhOI7cuHGDEELIypUriZubm2i+CxYsIJGRkc08I+tYumDV5Pjx4wQAycrKEpaFhISQ//3vf1a3uRvnSojl+U6bNo2MHTvW6jZt+diOHTuWDBkyRLSstR7bpuaeDNOo1WqcOnUKiYmJwjKJRILExEQkJSW14J7dPiUlJQAAd3d30fKff/4Znp6eiIqKwsKFC1FRUSGsS0pKQteuXeHj4yMsGz58OFQqFVJSUu7MjteTK1euwN/fH+Hh4Zg8eTKys7MBAKdOnYJGoxEd044dOyI4OFg4pq1pnqao1Wr89NNPePLJJ0Vdq9vKMa1JRkYGcnNzRcfSxcUF8fHxomPp6uqK2NhYYUxiYiIkEgmOHTsmjBkwYADkcrkwZvjw4UhNTUVRUdEdmk3DKSkpAcdxcHV1FS1ftmwZPDw80KNHDyxfvlwUcmttc92/fz+8vb0RGRmJ5557DgUFBcK6tnpslUoltm3bhqeeespsXVs6to2lVXTtbWry8/Oh0+lEJ2oA8PHxweXLl1tor24fvV6PF198EX379kVUVJSw/LHHHkNISAj8/f1x7tw5LFiwAKmpqfjzzz8BALm5uRY/C37d3UJ8fDy+++47REZGIicnB2+//Tb69++PCxcuIDc3F3K53OwE7uPjI8yhtcyzJhs3bkRxcTGeeOIJYVlbOaaW4PfP0v6bHktvb2/RehsbG7i7u4vGhIWFmb0Gv87Nza1Z9v92qKqqwoIFCzBp0iRRJ9cXXngBPXv2hLu7O44cOYKFCxciJycHH374IYDWNdcRI0ZgwoQJCAsLQ3p6Ol577TWMHDkSSUlJkEqlbfbYfv/993BycsKECRNEy9vSsb0d7kljpK0ye/ZsXLhwAYcOHRItf+aZZ4THXbt2hZ+fH4YOHYr09HRERETc6d1sNCNHjhQed+vWDfHx8QgJCcGvv/4KOzu7Ftyz5uWbb77ByJEj4e/vLyxrK8eUYUSj0eCRRx4BIQSrVq0SrZs3b57wuFu3bpDL5Xj22WexdOnSVtfb5NFHHxUed+3aFd26dUNERAT279+PoUOHtuCeNS/ffvstJk+eDFtbW9HytnRsb4d7Mkzj6ekJqVRqVmmhVCrh6+vbQnt1e8yZMwdbt27Fvn37EBgYWOvY+Ph4AEBaWhoAwNfX1+Jnwa+7W3F1dUWHDh2QlpYGX19fqNVqFBcXi8aYHtPWOM+srCzs3r0bTz/9dK3j2soxBYz7V9vv09fXF3l5eaL1Wq0WhYWFrfJ484ZIVlYWdu3aJfKKWCI+Ph5arRaZmZkAWtdcaxIeHg5PT0/Rd7ctHVsAOHjwIFJTU+v8HQNt69g2hHvSGJHL5YiJicGePXuEZXq9Hnv27EFCQkIL7lnDIYRgzpw52LBhA/bu3WvmzrNEcnIyAMDPzw8AkJCQgPPnz4tOAPwJsXPnzs2y301BWVkZ0tPT4efnh5iYGMhkMtExTU1NRXZ2tnBMW+M816xZA29vb4wePbrWcW3lmAJAWFgYfH19RcdSpVLh2LFjomNZXFyMU6dOCWP27t0LvV4vGGYJCQk4cOAANBqNMGbXrl2IjIy8q1zbvCFy5coV7N69Gx4eHnVuk5ycDIlEIoQzWstcLXH9+nUUFBSIvrtt5djyfPPNN4iJiUH37t3rHNuWjm2DaOkM2pZi3bp1RKFQkO+++45cvHiRPPPMM8TV1VVUfdAaeO6554iLiwvZv3+/qDSsoqKCEEJIWloaWbJkCTl58iTJyMggmzZtIuHh4WTAgAHCa/BloMOGDSPJyclk586dxMvL664pA+WZP38+2b9/P8nIyCCHDx8miYmJxNPTk+Tl5RFCaGlvcHAw2bt3Lzl58iRJSEggCQkJwvatZZ48Op2OBAcHkwULFoiWt4VjWlpaSs6cOUPOnDlDAJAPP/yQnDlzRqggWbZsGXF1dSWbNm0i586dI2PHjrVY2tujRw9y7NgxcujQIdK+fXtR+WdxcTHx8fEhU6ZMIRcuXCDr1q0j9vb2d7wksra5qtVqMmbMGBIYGEiSk5NFv2G+euLIkSPkf//7H0lOTibp6enkp59+Il5eXmTq1Kl33Vzrmm9paSl56aWXSFJSEsnIyCC7d+8mPXv2JO3btydVVVXCa7SFY8tTUlJC7O3tyapVq8y2b23Htjm5Z40RQgj59NNPSXBwMJHL5SQuLo4cPXq0pXepwQCw+LdmzRpCCCHZ2dlkwIABxN3dnSgUCtKuXTvy8ssvizQpCCEkMzOTjBw5ktjZ2RFPT08yf/58otFoWmBG1pk4cSLx8/MjcrmcBAQEkIkTJ5K0tDRhfWVlJZk1axZxc3Mj9vb2ZPz48SQnJ0f0Gq1hnjx//fUXAUBSU1NFy9vCMd23b5/F7+20adMIIbS898033yQ+Pj5EoVCQoUOHmn0OBQUFZNKkScTR0ZE4OzuT6dOnk9LSUtGYs2fPkn79+hGFQkECAgLIsmXL7tQUBWqba0ZGhtXfMK8pc+rUKRIfH09cXFyIra0t6dSpE/nvf/8runjfLXMlpPb5VlRUkGHDhhEvLy8ik8lISEgImTFjhtlNYFs4tjyrV68mdnZ2pLi42Gz71nZsmxOOEEKa1fXCYDAYDAaDUQv3ZM4Ig8FgMBiMuwdmjDAYDAaDwWhRmDHCYDAYDAajRWHGCIPBYDAYjBaFGSMMBoPBYDBaFGaMMBgMBoPBaFGYMcJgMBgMBqNFYcYIg8FgMBiMFoUZIwwGg8FgMFoUZowwGAwGg8FoUZgxwmAwGAwGo0X5f+ivyDemvIUAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-np.array(r_avg_list_q))\n",
    "plt.plot(-np.array(r_avg_list_dq))\n",
    "plt.plot(-np.array(r_avg_list))\n",
    "\n",
    "plt.title(\"f_I = 10^\" + str(int(np.log10(f_I))) )\n",
    "plt.legend([\"Q-learning\", \"Double Q-learning\",\"Switch Q-learning\"], loc =\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Server_WeightedDoubleQ_learning(N,n,mu,lamda,gamma,c):\n",
    "\n",
    "    reward_list = []\n",
    "    reward_sum_list = []\n",
    "    reward_sum_avg_list = np.array([0]*N)\n",
    "    runs = 1\n",
    "    \n",
    "    for ind in range(runs):\n",
    "        reward_sum = 0\n",
    "        count = 0\n",
    "        Qa = np.zeros((n,n,2))\n",
    "        Qb = np.zeros((n,n,2))\n",
    "        freq = np.ones((n,n))\n",
    "        freq_action = np.ones((n,n,2))\n",
    "        (arrivals,departures) = arrival_and_departure_times(mu,lamda,N)\n",
    "        local_pos = 0;cloud_pos = 0\n",
    "        local_actions = np.array([False]*N)\n",
    "        cloud_actions = np.array([False]*N)\n",
    "        reward_list = []\n",
    "        reward_sum_list = []\n",
    "        for i in range(N):\n",
    "            local_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (local_actions)).sum()\n",
    "            cloud_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (cloud_actions)).sum()\n",
    "            eps = 1/np.sqrt(freq[local_pos,cloud_pos])\n",
    "            action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            if local_pos == n:\n",
    "                action = 1\n",
    "            if cloud_pos == n:\n",
    "                action = 0        \n",
    "            if action == 0:\n",
    "                local_actions[i] = 1\n",
    "            if action == 1:\n",
    "                cloud_actions[i] = 1\n",
    "            (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "            reward_sum = reward_sum + reward\n",
    "            count = count + 1\n",
    "            alpha = 1/np.power(freq_action[local_pos,cloud_pos, action],0.8)\n",
    "            if np.random.choice([True,False]):\n",
    "                action_max = np.argmax(Qa[new_local_pos,new_cloud_pos])\n",
    "                action_min = np.argmin(Qa[new_local_pos,new_cloud_pos])\n",
    "                diff = np.abs(Qb[local_pos,cloud_pos,action_max] - Qb[local_pos,cloud_pos,action_min])\n",
    "                beta = diff/(diff + c)\n",
    "                Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*(beta*Qa[local_pos,cloud_pos,action_max] + (1-beta)*Qb[local_pos,cloud_pos,action_max]) - Qa[local_pos,cloud_pos,action])\n",
    "            else:\n",
    "                action_max = np.argmax(Qb[new_local_pos,new_cloud_pos])\n",
    "                action_min = np.argmin(Qb[new_local_pos,new_cloud_pos])\n",
    "                diff = np.abs(Qa[local_pos,cloud_pos,action_max] - Qa[local_pos,cloud_pos,action_min])\n",
    "                beta = diff/(diff + c)\n",
    "                Qb[local_pos,cloud_pos,action] = Qb[local_pos,cloud_pos,action] + alpha*(reward + gamma*(beta*Qb[local_pos,cloud_pos,action_max] + (1-beta)*Qa[local_pos,cloud_pos,action_max]) - Qb[local_pos,cloud_pos,action])\n",
    "            freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "            freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1     \n",
    "            reward_list.append(reward)\n",
    "            reward_sum_list.append(reward_sum/count)\n",
    "        reward_sum_avg_list = reward_sum_avg_list + np.array(reward_sum_list)\n",
    "    return reward_list,reward_sum_list,reward_sum_avg_list/runs,local_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_list_wq,r_sum_list_wq,r_sum_avg_list_wq,local_actions_wq = Server_WeightedDoubleQ_learning(N,n,mu,lamda,gamma,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d4fdf2f8d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-r_sum_avg_list_q)\n",
    "plt.plot(-r_sum_avg_list_dq)\n",
    "plt.plot(-r_sum_avg_list)\n",
    "plt.plot(-r_sum_avg_list_wq)\n",
    "\n",
    "plt.title(\"f_I = 10^\"+str(int(np.log10(f_I))))\n",
    "plt.legend([\"Q-learning\", \"Double Q-learning\",\"Switch Q-learning\",\"Weighted Double Q-learning\"], loc =\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}