optimiz-rs/examples/notebooks/03_optimal_control_tutorial.ipynb

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   "id": "118782fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-16T15:57:28.465029Z",
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     "shell.execute_reply": "2026-02-16T15:57:30.665798Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Libraries loaded successfully\n",
      "\n",
      "📚 This tutorial covers:\n",
      "  1. Regime Switching Systems\n",
      "  2. Jump Diffusion Processes\n",
      "  3. Combined MRSJD Models\n",
      "  4. Numerical Methods (Finite Differences, Upwind Schemes)\n",
      "  5. Practical Parameter Selection\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style\n",
    "sns.set_style('whitegrid')\n",
    "plt.rcParams['figure.figsize'] = (14, 6)\n",
    "plt.rcParams['font.size'] = 11\n",
    "\n",
    "print(\"✅ Libraries loaded successfully\")\n",
    "print(\"\\n📚 This tutorial covers:\")\n",
    "print(\"  1. Regime Switching Systems\")\n",
    "print(\"  2. Jump Diffusion Processes\")\n",
    "print(\"  3. Combined MRSJD Models\")\n",
    "print(\"  4. Numerical Methods (Finite Differences, Upwind Schemes)\")\n",
    "print(\"  5. Practical Parameter Selection\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfec9d8",
   "metadata": {},
   "source": [
    "## 2. Mathematical Background <a id=\"math\"></a>\n",
    "\n",
    "### Stochastic Differential Equations (SDEs)\n",
    "\n",
    "A general SDE has the form:\n",
    "\n",
    "$$\n",
    "dX_t = \\mu(X_t)dt + \\sigma(X_t)dW_t\n",
    "$$\n",
    "\n",
    "where:\n",
    "- $\\mu(X_t)$ = **drift** (deterministic trend)\n",
    "- $\\sigma(X_t)$ = **diffusion** (volatility)\n",
    "- $dW_t$ = **Wiener process** increment: $dW_t \\sim \\mathcal{N}(0, dt)$\n",
    "\n",
    "### Key Properties\n",
    "\n",
    "**Itô's Lemma** (chain rule for SDEs):\n",
    "\n",
    "For $Y_t = f(X_t)$:\n",
    "\n",
    "$$\n",
    "dY_t = f'(X_t)dX_t + \\frac{1}{2}f''(X_t)\\sigma^2(X_t)dt\n",
    "$$\n",
    "\n",
    "**Feynman-Kac Formula** (connects PDEs to expectations):\n",
    "\n",
    "$$\n",
    "V(x,t) = \\mathbb{E}_x\\left[ \\int_t^T e^{-\\rho(s-t)} L(X_s)ds + e^{-\\rho(T-t)}\\Phi(X_T) \\right]\n",
    "$$\n",
    "\n",
    "satisfies the PDE:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial V}{\\partial t} + \\mu(x)\\frac{\\partial V}{\\partial x} + \\frac{1}{2}\\sigma^2(x)\\frac{\\partial^2 V}{\\partial x^2} - \\rho V + L(x) = 0\n",
    "$$\n",
    "\n",
    "### Example: Ornstein-Uhlenbeck Process\n",
    "\n",
    "Mean-reverting process:\n",
    "\n",
    "$$\n",
    "dX_t = \\theta(\\mu - X_t)dt + \\sigma dW_t\n",
    "$$\n",
    "\n",
    "- $\\theta$ = speed of mean reversion\n",
    "- $\\mu$ = long-term mean\n",
    "- $\\sigma$ = volatility\n",
    "\n",
    "**Half-life**: $t_{1/2} = \\frac{\\ln 2}{\\theta}$"
   ]
  },
  {
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   "execution_count": 2,
   "id": "b25e1746",
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    "execution": {
     "iopub.execute_input": "2026-02-16T15:57:30.670266Z",
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     "iopub.status.idle": "2026-02-16T15:57:31.331171Z",
     "shell.execute_reply": "2026-02-16T15:57:31.330002Z"
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   "outputs": [
    {
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X06VLF3777TdKlCih7Pfo0aMcPHiQDz/8EFtbW5YsWcLIkSOVOnOffPIJN2/eZP369VhYWDB37lyTY69Rowa7d+/m9u3blClTJk3nSwghRN4WGhqabKknW1tbbG1tAbh79y7du3fH0tKSAQMGYGFhwdq1a/njjz9ysrmAIcBXv3592rRpw9GjR5k/fz4PHz5Uvkg1mjBhAt26daNQoUIUL1480/u9desWo0ePplevXnTq1IkVK1YwceJE9uzZw7Nnzxg8eLDSjxszZgy//vprpvZnrKWbMCBqFBsby8CBAwkICKBPnz64u7vz77//smTJEp48ecKsWbNo2rQpgYGBbNiwgQEDBij9C6Pk7lcT+vXXX3FwcKBv377ExsayatUqevTowaZNm9LVZ0itPUbpuT8BWLx4Mebm5vTq1Qtzc3NWrFjBqFGj2LFjxytVk1gIYSCBWpEhISEhaDSaNNWdNdb/DAgIMJn+wQcfKME9QMmatbe3Z8OGDcpgNG5ubvj6+rJ//366du3KiRMnePr0Kd9//70S+OnSpQsDBw5UsqiqVKmCl5cX+/fvp2HDhsofyKlTp+Lg4MC2bdtwcnICDN8Ajxgxgnnz5tG2bVtcXV2TPZYyZcrw008/Ka8tLCxYuHAhp06dytaaka1atTLpXEVERLBly5ZkAwZnzpxh48aN+Pr6mgSfW7ZsSZcuXViyZInJt+CBgYHs27ePIkWKAFC/fn0aN27Mjh07TAK1MTExjBs3jn79+gGGa9irVy9+/PFHevToQb58+Zg3bx5hYWFs375dqQnas2dPZsyYwapVq+jcuTPlypVj06ZNXL58malTpyqDBBhHVv/tt99M3hsAjx8/pl+/flhbW7N69epkA4wJqdVqpYOt0+l4/Pgxmzdv5vDhwzRv3twkkBMdHc2kSZPo0qWLMm3evHncunWL2bNnKwG5Hj164OnpyZw5c1i/fj0ffvght27dYu3atbRp04bZs2ejUqkAKF68OOPHj+fgwYPUrVuXqVOn0rBhQxYvXqzso1evXrRt25Zp06axZcsWgoKCOHr0KD179uTTTz9VlrOzs+PIkSOEhobi6OjI9u3b8fT0ZObMmcoyb7/9NrNmzeLmzZvUqFEjyXOi0Wj477//eP/995M9bx4eHrzzzjscOHDAJFC7b98+GjVqlChAqtPpmDx5MmXLljX5/Pbq1YtevXoxbdo0GjVqhJWVFcuWLePtt9/m559/VjKbe/bsSbdu3Th27Bh6vV45f5A1n7v47wMwBAsvXrzIrFmzsLOzY+jQocq8tP6eaN68Odu3b0etVivLHT16lJCQEKWkxI4dOzh27BgLFy40yV6uXbs2w4YNY8OGDSYZ//ny5WP+/PnKoGFgCHafPXuW1q1bp/jFWFhYGJ999hmDBw8GDLWYjbWGIyMjsbW1ZdWqVdy7d49FixbRpEkTwPC5++CDD0yymZPTsWNHgoOD+f777zl37pxS5sDR0ZEmTZowfPhwZVA2Z2dnGjRowOHDhwkPD8fe3h6AQ4cOERERoXyetm3bhr29PYsXL1au+3vvvceIESO4du0aZcqUoV27dnz//ffky5dPObcPHjxg4cKF9O7dW/kCCAx/D1q1asWcOXNYsGCBMv3p06f89ttvSmDW0tKSKVOmoFar2bp1q/JevHbtGseOHUt07MbRvc+cOSOBWiGEeEMk7I/G9/HHH/PJJ58A8P333xMZGcn69euVvxdt27alZcuWOdLO+Dp16qT8XezZsycjR45k8+bN9O3b1yQo16hRI7744gvltb+/f6b2GxgYyHfffUerVq0Aw5gHQ4YM4fr16/zxxx/Y2dkB8OjRI37//XfCwsIyVffd2dkZINn+y5UrV7hx4wZjx45lwIABgKGPoNfr8ff3R6/XU65cOSpXrsyGDRuoVatWosHEkrtfTch4/1K4cGEAGjduTPv27fn++++ZP39+mo8ptfYY/fzzz2m6PzGKjo5m7969ODo6Aob7hT59+rBr1y4J1ArxCpLSByJD9Ho9gEkwITnJLZNURh4YHrmOP2L4O++8Axj++ANKgG7JkiUcPXoUjUaDSqVi2bJliTKg4nvx4gWnT5+mXr16xMbGEhQURFBQEC9evKBZs2ZER0dz/PjxFI8lYWfLmKlrbFt2adOmjclr4wBPCYPfRsZHjRs1aqQcZ1BQEIULF8bT05M///zTZPnKlSsrQVowBOmcnJwSlUmws7OjZ8+eymsLCwt69uyJRqPh77//RqfTsX//fqpUqYKdnZ3Jvo2Pyv/1118A/Pnnnzg4ONCxY0eTfYwZM4YdO3aYZIM+f/6cvn37EhwczOrVq03ampoOHTrg4+ODj48PtWvXpnPnzmzcuJH27dsnyiyAxO/LAwcO4OHhkegafPjhhzg4OCh1XP/66y/0ej19+vQxCTK2adOGTZs2Ub9+ff7++2/CwsJo3ry5ybnRaDTUr1+fy5cv8/TpUxwcHHBwcGD37t1s2bJFeYR+1KhRbNmyRelkFSpUiDt37rBw4UKlc12/fn127dqVbJAWDAErjUaTaHCthJo0acJ///2nvA8CAgI4f/68SXak0dWrV7l//z5NmjRRsk+CgoIICwujSZMmBAYGcunSJcAQmFu1apUSGAMICgrCwcGBmJgYpRyFUVZ87uK/D3x8fGjUqBGfffYZHh4ebNiwQckkSc/viXbt2hETE2OSLbN7925cXFyUjvXevXtxcHCgatWqJte8SpUqODs7J/osVqtWLU2/V5NjvDkyevvtt4mNjVVuYvbv30/JkiWVIC2AlZWVcgOTFv379+fw4cNMmzaN5s2b4+LiQmhoKFu3bqVNmzb8888/yrJt27YlKiqKgwcPKtN+//13ChYsqAzOUqhQIcLDw5k2bZrySJ+xZEJKXyYcOHAArVZLkyZNTM6tlZUVNWrU4MiRIyaPehYqVMhklOZSpUoBhvd5/PdisWLFCA4ORqPRmOzP+KVOVjyuKYQQIm/44osvWLFiRZL/2rdvDxi+rD58+DDvvfeeEqQFw9+dhP3cnBD/C3ZACdgZ++BGyd2LZZS5ublJ/8L4d7ZOnTpKkBZQvtDN7P2T8W98/H53fAULFsTMzIxffvmFffv2ERERAcA333zDmjVrkl0vvrSeo/fff18J0oKhH+Pj48PRo0fRarVp2kZ6pPX+xKhu3brK/QMkvr8WQrxaJKNWZIirqyuWlpY8f/481WWNwURjZq1RcqN3J8xoNQZtjfVnq1SpQv/+/VmxYgUDBw7Ezs6OWrVq0aJFC95///1kH4V+8OABer2eDRs2sGHDhiSXefToUYrHkj9/fpPXxkdKjG0LDg5OFGRydnbGyspKyYRL7o+1sbORVMZcwnNlrKmZcF9GxrqiydUfjR8Ih8TnHAzHZjwuo6JFi5o8RgMoAa6HDx/y4sULQkNDOXr0KD4+Pknu23iOHz58SLFixRJdrwIFCiQ63i1btmBmZoZOp+Pq1aupBhjjmz17trI9lUqFo6MjpUuXNukwxpfwGvv7+1OjRo1EnTkrKyuKFSum1Ao2/m/slMZfzhhYN16X+NkLCT169IiCBQsydepUJkyYgK+vL5MmTaJSpUo0adKEjh074uLiAhjqkg4ZMoQFCxawYMEC3nrrLRo0aEDnzp0TtSM+Y83S+B22pDRr1oz58+dz8OBBunXrxv79+3FwcOC9995LtKzx2L7//nvlMfukjs3b2xtLS0uuX7/Ozp07uXPnDvfv3zf50sH4RZBRZj53Rsb3QWxsLP/88w8rVqygUqVKzJkzx+R3U3p+T1SvXp2iRYuyZ88eOnfurDzS36ZNG2Xf9+/fJywsLNnPQ8Ja0wmPNb2SO1fG3zv37t2jevXqidZLb4aoi4sLXbp0oUuXLuh0Ov777z+WLVvG/v37mTx5Mrt27QIMXxy4uLgoJSJCQkI4evQovXv3VoKjw4cP57///mPt2rWsXbsWDw8PGjRoQMeOHU1KkyRkfM/FzxhJKCgoSLm+Cc+NMSCe3PSE70Nj1k9yj8AKIYR4/bz77rvKF4vJefHiBREREUoAMr633noru5qWJDs7u0RPnRn76gkzZpO7F8soR0dHk75Xan9nE95npJfxS+jknoYsWLAgX3zxBXPmzGHEiBFYWVlRvXp1mjZtSvv27ZWyFSlJ6zlKqjRVsWLFOH78OEFBQWl6CjU90np/YpTwHCXsSwshXi0SqBUZolKpqFq1KhcuXCA6OjrFx3HPnDmDvb095cqVM5keP4MpLdPj++KLL+jVqxf79+/n6NGjHD9+nEOHDrF582ZWrVqV5DekxkBFt27dlJIJCSXVwYovtW9eP/nkE06fPm0ybfXq1dSsWVN5PCf+AELxhYSEAC8f44kvLeckPp1Oh7W1daJan8lJ6/ZTOn5zc3PlHDdq1Mjkce74jEETrVabKOibHDc3N3744QeGDx/O1KlT8fHxSfOjUt7e3ukK7CbMZtTr9YkCNkY6nU4JehuPPaVzZOwMTZ48OdlAqrGj16pVK+rWrcvBgwc5cuQIf//9NzNnzmT58uVs2bIFd3d3PD092bdvH3///Td//fUXx44d4+eff2bVqlUsWbIk2bIAxjYmd1xGnp6elCxZkv379yuB2gYNGiR53YzHNmzYsCQDgfDyZmX69OmsXr2asmXLUqVKFWUwtDVr1iQ5OFdmPndG8d8H9erVo2rVqgwZMoQ+ffrw22+/KaUL0vN7QqVS0bp1a5YtW0ZQUBAnTpwgIiJCeTTfuL2iRYsybdq0JLeV8HdnZrJpIfXPcmxsbJLXLy2fxVu3brFlyxZatmypfPlg3GeVKlVYuHAhffr04dSpUwQHB+Pi4oKVlRXNmzdny5YthISE8McffxATE2NS17dgwYJs3bqVs2fP8ueff3Ls2DHWrl3LunXrmD59erIDVhrfc/Pnz0/2S4f4v0+T+xIvLRk1kL4nSYQQQrx5kupXJTVgaVJyImCW8O9geu8vjJJLOsns39n0unLlCvAyOzQpffv2pXXr1hw4cICjR49y8uRJjh8/ztq1a9m0aVOqwdqsukdKSUYybtN6f2KU0WsthMgdEqgVGdahQwdOnjzJ+vXrkx2E68KFC5w5c4aOHTumuaOSmqCgIK5fv061atXo27cvffv2JTw8HF9fX/bt28fFixepWLFiovWKFi2q/JwwI/DBgwdcv349Td+spuSLL75QHlM3MgaojUGqGzduJJklZhzFPivqBBUtWpRjx47x1ltvJcpkPnTokJKRmV5PnjxBq9WadDju3r0LGL6td3V1xdbWFo1Gk+gcBwUFcebMGeXx4aJFi3LhwgV0Op1J5+Hq1av8/PPPDBw4UAm+tG/fnooVK+Lr68vo0aOZM2dOkoONZQcPDw/u3r2bqG6qRqPB399fCVgZ319+fn4m1zcmJobPP/+c5s2bK8s4OzsnOj/nz58nLCwMGxsbIiMjuXbtGkWKFKF9+/a0b98enU7HihUrmDVrFjt27KB///7cuHEDMzMz6tevr9QSPnv2LH379mXVqlXJBmqN3+qnpSZps2bNWLFiBf7+/pw5cybZbFnjsdnY2CQ6tuvXr/P48WNsbW15+PAhq1evpmXLlnz33Xcm5zQtGfpJSelzl5x69eoxZMgQFi9ezMSJE5X6Yen9PdGuXTuWLFnCwYMHOXz4MB4eHlStWlWZ7+Hhwblz56hevXqiTvPu3bspWbJkuo41s0qUKMG9e/cSTTdmp6YkODiYZcuWodfrTQK18Xl6enL69GmTAHTbtm3ZsGEDhw8f5uDBg3h6eirlKwBu375NREQENWrUoEaNGnzxxRfcunWLnj17snz58mQDtcZr5e7uTpUqVUzmnThxAkhbADqtjJ+XrM5AEkIIkbe5urri5OSk9InjS/j31djnTVheJ6seQY+IiODFixcmJcTi99XTw9jfT9jWhKXRcsuuXbtQqVQ0atQoyfmhoaFcu3YNLy8vpR6/RqNh9uzZrF69mr/++ivLaggnzGAFw3l3cnJSroWZmVmic6nRaAgNDU33/tJ6fyKEyJvkqxWRYe3ataNatWrMmzePw4cPJ5rv5+fHqFGjcHFxMRkQKbMOHDhA3759TfZpb2+vBDiNnQpjR8j4baO7uzsVKlRg586dJjUG9Xo9U6dOZfjw4coj4RlVvnx53nvvPZN/xowuY32mlStXEh4ebrKeVqtl5cqViWo7ZZRxBPsffvjBZPr58+cZNmwYq1atytB21Wo1e/fuVV5rNBpWrVqFvb09Pj4+WFhYKLVYz58/b7Lu/PnzGTFiBLdu3QKgQYMGqNVqdu7cabLc+vXr2bVrV5KPMbVq1Yo6derw66+/KoMYZbfGjRvz8OHDRO1cs2YN4eHhNGzYEDAcDxjaH98ff/zB3r170el01K5dGxsbG5YtW2bSUQsODmbEiBH4+vpibm7O06dP+eCDD/j555+VZczMzJROl7m5OSqViiFDhjBu3DiTb+LfffddLC0tU/zmPH/+/FhZWfH48eNUj79p06bExMQwZcoUrKysqFu3bpLLlS9fHnd3d9auXatkh4PhPfLFF18wYsQIYmNjlXmlS5c26VhevnxZyYqNX1c0LVL63KVk+PDhlCtXjn379rFnzx4g/b8nypQpw7vvvsvu3bs5evSoSaYoGLLLIyIiWLlypcn03bt3M3r0aH7//fdU22m8llmRbdOsWTNu3rxpkoGs1WpZt25dqutWqVKF4sWL8+uvv3LhwoVE858/f87+/fupXbu2STC7atWqeHh4sHfvXk6ePJmontqECRMYNmyYUjsODO8PJycnk/exsfyJkfHGbMmSJSbTHzx4wEcffcTcuXOzNIvHWPIifjBfCCGEUKlUNG3alNOnT5v8fQwLC2PTpk0myxq/LL969aoyTa/XK/2QrLBx40aTbS9btgwzM7NkA5rJcXFxwcLCwqStQJr6Ltntr7/+YteuXbRq1SrZJyIvXLhAr1692LJlizLNyspKycBN7p4xI/bt22eSNHDhwgVOnz5N48aNlb5IgQIFuH79ukm/fc+ePYn6vWlpT1rvT4QQeZNk1IoMU6lULFiwgGHDhjFkyBAaNWpE7dq1sbS05NKlS2zfvh1HR0cWL16cpXV5WrVqxdKlSxk3bhw9evSgWLFi3Llzh3Xr1lG1alUlm9EY6FuxYgWNGjWicePGTJo0iT59+tC5c2d69uyJm5sbBw4c4NixY3Tv3h1PT88sa2dCrq6ujB8/nsmTJ9OhQwfat29PwYIFef78OXv37uXy5ct89tlnWTKaeP369WnWrBnr16/n8ePH1KtXj+fPn7N27VqcnJwYOXJkhrbr5OTExIkTuXbtGgULFmT79u1cuXKFKVOmKKUIPv/8c06dOkXfvn3p3r07JUuW5OTJk+zevZsGDRoogb5u3bqxdetWfH19OX/+PF5eXvzzzz/s2LGDQYMGUbBgwSRHn/3yyy9p3bo1kyZNYuvWrVmaMZeUwYMHs3//fsaNG8c///yDl5cXFy5cYNu2bVSoUIEePXoAhkEDevbsybp16wgMDKRevXo8fvxYeV+2aNECCwsLPvvsM+Vx7vbt22Nubs6vv/5KQEAA8+bNw8LCgpIlS9K8eXOls1W5cmWCg4NZu3Yt+fLlo3Xr1qhUKgYPHsyUKVPo168fzZs3R6/Xs337dqKioujVq1eyx2RpaUn16tUTBdOTUrFiRQoXLszhw4dp2bJlsmVOLC0tmTx5MiNHjqRDhw507doVR0dHtm3bxtWrV/n888/Jly8f9vb2FC1alOXLl6PVavHw8ODGjRts2rRJ6ZiGh4djb2+f/ouVTpaWlsyYMYOuXbsybdo0fHx8cHFxSffviXbt2ikD0yUM1Hbp0oUdO3YwZ84c5UkAPz8/1q1bR9GiRdM0iJfxd9miRYuoXbt2svVu06J///7KZ6xXr14ULlyYvXv3cvHiRSD1R/fmzZunfLabN2+Ot7c31tbW3Llzh23btmFmZpYo212lUtGmTRsWL16s/Bzf4MGDGTZsGL169aJdu3ZYWVlx4MAB7t+/b1IywtXVlZs3b7Ju3Tpq1qyJp6cn/fr1Y8WKFfTs2ZOWLVsSFRXF2rVr0Wq1jBs3LsPnKSnGz0tymepCCCFeP3///TdPnjxJdn7NmjUpVKgQo0eP5tixY/Tr14/evXvj4uLCb7/9luiJn5o1a+Lu7s7SpUuJjY2lcOHC7Nmzh6dPn2ZJe62srFiyZAlPnjzhrbfe4uDBgxw7doyhQ4emO6PW1taWJk2asHfvXsaMGUONGjU4f/48f/75Z4afzkuvBw8esH37dsAQuFSr1fz777/s27cPT09PJk+enOy6tWrVolKlSsydOxd/f3+8vLx4/Pgxa9eupUSJEsrTaMZ+1oYNG1Cr1Yn6KWkRGxtL9+7d6d69Oy9evGDlypW4uroyatQoZZk2bdqwbNkyhg4dStOmTbl9+zabN282GYQsre1J6/2JECJvkkCtyBRXV1dWr17Nzp072bx5MwsWLCA6OppixYoxaNAgunfvnunBcRJycHBg1apVzJ8/nx07dvDs2TPc3Nzo0aMHw4cPV5Z7//33+eOPP9ixYwfnzp2jcePGVKpUiQ0bNrBgwQLWrl1LdHQ0xYsXZ8KECfTs2TNL25mULl26UKJECdasWcOvv/5KUFAQLi4ulCtXjp9++kkZKT4rfPfddyxfvpxt27bxzTff4OLiQq1atRg5cmSSBe/TolixYnz00UfMmzePBw8eUKpUKb777juTkeaLFSvGxo0bmT9/Ptu3byc0NJQiRYrwySefMHDgQCUYZ2VlpVzHffv2sXnzZooXL87kyZPp3r17sm0oXrw4H330Ef/73/9YunQpH3/8cYaOJa2cnJz49ddfmT9/PgcOHGDz5s0UKVKEjz76iCFDhpgEiidNmkSJEiXYsGED3377LW5ubnTr1o2PP/5YqdvVp08fChcuzLJly1iwYAGWlpaULVsWX19fpcMIMHPmTEqXLs2ePXv4/fffsbW1xcfHh5EjRypffPTs2RMbGxvWrVvHvHnz0Ol0lC9fnp9++ok6deqkeFz169dnxowZBAQEJCqPkVCTJk1Ys2YNzZs3T3G5pk2bsnLlShYvXszSpUvR6/WULl2aWbNmKXVbrays+Omnn/j2229Zv349Wq2WIkWKMHToUMqUKcPw4cP5+++/lZGUs9u7775L//79Wbp0Kd988w0zZ85M9++J1q1bM2vWLN5+++0kB5NbsWIFixcvZs+ePezdu5cCBQrQunVrPvnkkzT9fuzevTsnT55k5cqVXL16NVOBWgcHB9auXcvMmTPZsGEDWq2WunXrMnnyZMaPH5/qFx8VKlRg9+7dLF++nOPHj/PXX38RExND4cKFadu2LUOGDEkyG75du3YsXryYqlWrUqRIEZN5jRo1YtGiRfz8888sWrSI6OhoPD09mTNnjsnNySeffMKXX37JN998w/Dhw3nrrbcYN24cpUuXZv369cyZMwc7OzvKly/Pxx9/TOXKlTN8npJy9uxZypYtm6j9QgghXl+pjfewaNEiChUqhJubGxs2bGDWrFmsWbMGc3NzWrduja2trckTUhYWFvz888/MnDmTVatWYW1tTbNmzZgwYUKGAoQJ2dnZsWjRImbMmMFvv/1G0aJFmTx5cobvc77++mvs7Ow4dOgQ+/fvp2rVqqxZs4ahQ4dmuq1pcfbsWc6ePau8trOzo1SpUnzyySd8+OGHyQ4ODIYvmH/88Ud++OEH/vzzTzZs2ICzszNNmzZl5MiRSvKBj48PLVu25M8//+Ts2bM0a9Ys3e0cPHgwjx8/Zv78+cTExFC7dm2++OILk4HdRo4ciVarZdeuXZw6dYp3332XpUuXMn/+fJMs27S0Jz33J0KIvEelz0yOvxBCiDwpKCiIRo0aMXLkSPr165fbzRE5JCgoCGdn50QDWxhLMaxcuTJTgeDk+Pn50axZM6ZNm0aXLl2yfPvZLTg4mDp16vDFF18kO1CiEEIIkdCCBQtYuHAhu3fvzpKn5oQQQrz+pEatEEK8gVxdXenUqZNJ3S7x+pszZw7VqlUjLCzMZPqePXuwsLBIceTkzFi/fj12dnZZNmhHTtu1axf29vZ07tw5t5sihBBCCCGEeI1J6QMhhHhDDRkyhB07dnDw4EFlADrxemvTpg1btmyhd+/edOjQAUtLS44ePcrBgwf5+OOP0zQIW3p8+eWX+Pv7c+zYMQYOHKjUss5LNBoNy5Yt4+OPPzYZJE0IIYQQQgghsppk1AohxBvK3d2dsWPH8r///Q+dTpfbzRE5wMfHh2XLluHo6MiiRYuYNWsWT548YcaMGXzyySdZvr8XL17wzz//0LZtW0aMGJHl288JGzZswM3NLUfqmAshhBBCCCHebFKjVgghhBBCCCGEEEIIIXKZZNQKIYQQQgghhBBCCCFELsvVQG1wcDCTJ0+mXr16eHt70717d86ePavMv3v3LoMHD6ZKlSrUrl2bKVOmEBkZmYstFkIIIYQQQgghhBBCiKyXq4OJffrppwQGBjJv3jzy58/PmjVrGDBgAFu3biVfvnz06tWLSpUqsXHjRgICAvjiiy/Q6XR89dVXadq+TqcjNjYWMzMzVCpV9h6MEEIIIYRIN71ej06nw8LCAjMzedgrNdK/FUIIIYR4tWWmf5trNWr9/Pxo1qwZv/zyC1WrVgUMB9KsWTNat26NmZkZmzZt4o8//sDa2hqAjRs3sn79ejZv3pymjqlGo+HixYvZehxCCCGEECLzKlSogJWVVW4345Un/VshhBBCiLwhI/3bXMuozZcvH0uXLqVChQrKNJVKhUqlQq1Wc+nSJZo2baoEaQG6dOlCly5d0rwPY9S6QoUKmJubZ13jU6DX61Gr1Tg5OUmWQx4k1y/vk2uY98k1zPvkGuZtOX39tFotFy9elGzaNMqN/m188vlOnZyj1Mk5Sp2co9TJOUqdnKPUyTlKnZyj1CU8R5np3+ZaoNbJyYn69eubTNu3bx9+fn6MHz+enTt30rhxY7755hv27duHpaUlTZs2ZeTIkSbB25QY30BmZmY51vnX6/WoVCp5HC2PkuuX98k1zPvkGuZ9cg3ztpy+fsaHu+S9kjbG82Rubp5rgVozMzPMzc3lmiVDzlHq5BylTs5R6uQcpU7OUerkHKVOzlHqkjtHGTlfuVqjNr5z587h6+tLs2bNaNCgAWFhYfz000+8//77LFy4kEePHjF16lQCAwOZPXt2uratVqtzNFAbEREByA1HXiTXL++Ta5j3yTXM++Qa5m05ff10Ol2270MIIYQQQoi84JUI1B44cIDPP/8cb29v5syZA4CFhQWlSpVSBg4rX748Wq2WUaNGMW7cOPLnz5/m7Ts5OeVo6QMAZ2dnuTnNg+T65X1yDfM+uYZ5n1zDvC2nr59Wq832fQghhBBCCJEX5Hqgdu3atUyfPp0WLVowc+ZMpchuoUKF8PT0NFnW+Prhw4fpCtQaa9/mFOP+5OY0b5Lrl/fJNcz75BrmfXIN87acvH7yHhFCCCGEEMIgV0dt+OWXX5g6dSo9e/Zk3rx5JiOhVa9enQsXLihZHQA3btzA3NwcDw+P3GiuEEIIIYQQQgghhBBCZItcy6i9e/cuM2bMoGnTpgwZMoRnz54p82xsbBgwYAAdO3bkyy+/pF+/fvj7+zNz5kzatWuHq6trlrdHq9USExOT6e3o9Xo0Gg1RUVGSIZIHZeb6WVpa5sqgHkIIIYQQScmq/m180tdNnZyj1L0K50j67kIIIV5FuRao3bdvHzExMezfv5/9+/ebzOvQoQPffvstq1evZtasWbRr1w5HR0fatm3L6NGjs7Qder2eJ0+eEBwcnGXb1Ol0PH/+PMu2J3JWZq6fi4sLhQoVkk65EEIIIXJNdvRv45O+burkHKXuVThH0ncXQgjxqsm1QO3QoUMZOnRoistUrFiRtWvXZms7jJ1Yd3d37OzsMv1HWq/Xo9VqMTc3lz/4eVBGr59xhOyAgAAAChcunF1NFEIIIYRIUVb3b+OTvm7q5BylLrfPkfTdhRBCvKpyfTCx3KTVapVObHoGJ0tJbnc6ROZk5vrZ2toCEBAQgLu7uzxKJYQQQogclx392/ikr5s6OUepexXOkfTdhRBCvIpydTCx3Gas2WVnZ5fLLRGvC+N7KavrwQkhhBBCpIX0b4VIO+m7CyGEeNW80YFaI/mmW2QVeS8JIYQQ4lUgfRIhUiefEyGEEK8aCdQKIYQQQgghhBBCCCFELpNA7Wtg3LhxeHl5pfjvdREREcG6detyuxlCCCGEECIbpbV/27t3b8aNG5fLrc2YRo0a0ahRI8LCwhLNGzduHL179zaZFhsbS+fOnbl06RIAwcHBTJ48mXr16uHt7U337t05e/asyTonTpygY8eOVKpUiRYtWrBr165U27Vu3ToaN25MxYoV6dGjB1euXEnXcUVFRTF37lwaNWpElSpV6NixIwcPHkxxnRcvXvDZZ59RvXp1atSowddff01kZKTJMnv27KFVq1ZUrFiR9u3bc+LECWXesWPHaNOmDR988AG9evXi+fPnPH36lJYtWyZ5foUQQohXlQRqXwMTJkzg2LFjyj+A8ePHJ5r2Oli+fDnLli3L7WYIIYQQQohs9Kb0bx8+fMisWbPStOyyZct46623KF++PACffvop//77L/PmzWPz5s28/fbbDBgwgDt37gBw+/ZthgwZQt26ddmyZQtdunRh7NixJgHOhLZu3cqsWbMYOXIkW7ZswcPDg379+hEUFJTmY5o2bRo7d+7kyy+/ZNu2bTRp0oSPP/6YU6dOJbvOiBEj8PPzY+XKlXz//fccPnyYr776Spl/8uRJxowZwwcffMDWrVvx8fFh8ODB3L59G4CzZ88yfvx4fv31VxwcHNixYwcFCxakVatWzJw5M81tF0IIIXKbBGpfA46Ojri5uSn/kpv2OtDr9bndBCGEEEIIkc3elP5tsWLF2LBhA3///XeKy4WGhrJ06VIGDBgAgJ+fH8ePH+err76iWrVqlCpVikmTJuHu7s7OnTsBWLVqFV5eXowePZoyZcowYMAAWrRowc8//5zsfn788Ud69epF27Zteeutt5gxYwa2trZs3LgxTccTGRnJtm3b+PTTT6lfvz4lSpRg2LBh1KhRg82bNye5zr///svp06eZOXMm7777Lj4+PkyZMoXt27fz9OlTAH766SeaNGlCnz59KFOmDF988QXvvvsuq1atAmDUqFH4+Phw7do1zp07x1tvvQUYMq63b9+On59fmtovhBBC5DYJ1L4B/vzzTzp27EjFihVp2rQp//vf/9BoNMp8Ly8vNmzYQI8ePahQoQItW7bk3LlzbNiwgQYNGuDt7c2oUaOIiooCYMuWLdSrV4/ffvuNOnXqUKVKFYYPH650pAA0Gg2zZ8+mbt26VKlSha5du5pkPmzZsoWmTZsybdo0qlatyrBhwwA4cOAAXbp0oXLlylSoUIGOHTty9OhRABYsWMDChQt5+PAhXl5e+Pv7J/lYWPxp/v7+eHl5sWTJEmrXrk3jxo0JCwsjNDSUSZMmUatWLapWrUqfPn24ePFi9lwAIYR4BYRGxhCpieXo1cdsO32XQHVk6isJIUQeEB4ejq+vL9WqVaNq1aqMGzeOiIgIZf7t27cZNGgQVapUoU6dOnz22WcEBgYq87VaLStXrqR58+ZUqFCB5s2bs379emX+qVOneOedd1i6dCk1a9akY8eODB8+nD59+pi0486dO3h5eXHz5k0ePnxIuXLl2LJlS4ptb9u2LT4+PkyYMCHFR/Q3bNhAoUKF8PT0BCBfvnwsXbqUChUqKMuoVCpUKhVqtRowZJn6+PiYbKdWrVr8888/SSY/PH/+nHv37pmsY2FhQbVq1Thz5kyKxxG/DT/++CP16tUzmW5mZqa0K6GzZ8/i5uZGmTJllGk1atRApVLxzz//oNPpOHfuXKJjqVmzpkm7Dh06RPfu3enRowd169YFwMXFBR8fH1auXJmm9gshhBC5zSK3G/Aq0uv1RMdoM7yuVqvF3Fyf4VFErS3Ns2wE0iNHjjBq1Ch8fX157733uH//PlOnTuXu3bt8//33ynLfffcdM2bMoGTJkowbN46hQ4dSvnx5li5dyt27d/nss8/YuHGjEgANCgpi1apV/O9//8PKyoqvvvqKgQMHsnXrViwsLPD19eX27dvMmTOHggUL8ueffzJ06FAWLlxIgwYNALh//z4BAQFs27aNqKgoLl26xCeffMIXX3yhBFTnzp3L2LFjOXz4MP379yciIoLdu3ezadMmXF1d03wetm7dyqpVq4iMjMTe3p7u3btjY2PDkiVLcHBwYPv27XTv3p0NGza8VjV9hRDi7lM1//k9Z8Wh60TF+9v29/WnzOpdKxdbJoQQWeOPP/5g6NChbNmyhZs3bzJ69GgKFy7MyJEjefr0KT169KBNmzaMGzeOyMhIFixYQLdu3fj999+xs7Pj22+/Zfv27UyaNIkKFSpw5MgRpk+fTnR0NH379gUMwdzDhw+zYcMGIiMjefToEcOHD+fx48cULlwYgG3btlGhQgU8PT3RaDQcPXoUJyenFNuuUqmYPn06bdq0YebMmUydOjXJ5Q4cOED9+vWV105OTiavAfbt24efnx/jx48H4MmTJxQqVMhkGXd3dyIjI3nx4kWivvSTJ08AlOOJv861a9dSPA4jGxsb6tSpYzLtwoULnDx5kokTJya5ztOnTxPt08rKChcXFx4/foxarSYiIiLJYzG2ec+ePYwdO5avvvqKTp06mSzXoEEDfvzxR7788ss0HYMQ4vUVGBiY6EsjvV5PaGgojo6OJnEYJyen1+bpDZG3SKA2Ab1ez6crT3DF/0WuteHdYvmY+6FPlgRrf/zxR7p27coHH3wAQPHixfn666/58MMP8ff3x8PDA4BOnTrRqFEjANq1a8eUKVOYPHkyJUuWpGzZsvz888/cvHlT2W5MTAwzZ85UamTNnj2bVq1aceLECYoXL87vv//Otm3bePvttwHo168f165dY9myZUqgFmDYsGEUK1YMgKtXrzJp0iR69OihzO/Tpw+DBg3i+fPnFC5cGDs7O8zNzdP9C7NHjx7KI1AnTpzg/PnznDx5EhcXF8BQ4+vcuXOsXr2a6dOnp2vbQgjxqgqJ0DB06dEk51158IIYrQ5Lc3m4Rog3wsaNMHkyhIZmelPm6V3B0RGmToXOnTO976RUrFiR0aNHA4a+bu3atZUBt9avX0+hQoVMgoT/+9//qFWrFnv37qVZs2asX7+ecePG0aZNGwBKliyJv78/S5cu5cMPP1TW69+/PyVLlgTA09OTAgUKsGPHDoYMGYJOp2P79u0MHjwYQOmvpqU/X7RoUb744gsmT55M8+bNEwU6dTodFy9eVPrzSTl37hy+vr40a9ZM6WtHRUVhZWVlspzxdfyn64yMg3clXMfa2pro6OhUjyMpd+7cYfjw4VSsWJGuXbsmuUxkZGSifcbfr/GpvuTapdPpWLp0KUWLFmXbtm1s27aN1q1b061bN8BwrZ48eWISVBdCvHkCAwPp028gwaERieYZku1M/7q5ONqxesXPEqwVOU4CtUnIomTWV8KVK1e4cOECmzZtUqYZH3W6ffu2EqgtUaKEMt/W1hYwdHSNbGxsTDp09vb2SpAWoEyZMjg7O3Pjxg3lsa34AVcwBHcTZhUYO7sAb7/9Ns7OzixdupQ7d+7g5+enfHuv1WYsw9ko/vFdvnwZvV5Pw4YNTZbRaDQZ7oQKIcSr6NjVx8nOi9HqOHL5EY0reuRgi4QQuWb2bEhjVmRKMtxNnj072wK18fuTAM7Ozjx8+BAw9IVv3rxJlSpVTJaJjo7m9u3b3Llzh5iYGKpWrWoyv0aNGqxatYrnz58nuR8LCwvatm3L9u3bGTJkCCdPniQoKIjWrVtn6Bi6devGvn37mDhxIr///rvJvODgYGJjY8mfP3+S6x44cIDPP/8cb29v5syZo0y3trZOFJA1vjb29+OzsbExWcYoOjo6yeVTc+7cOYYNG0ahQoX48ccfsbS0THK5hPcZ8fdrZ2eHtbV1iu0yMzNj69atybbDmDkcGBgogVoh3mBqtZrg0Ag863fCKX9Bk3mxMTFYxPsdpX7+lJuHN6NWqyVQK3KcBGoTUKlUzP3QJwtKH2S8fEFWlj7Q6XQMHDiQDh06JJoX/xeOhUXit4KZWfJZVkl1tIzHbQwEr1u3Dnt7+xS3aewQApw+fZoBAwbQoEEDqlatSps2bYiMjGT48OHJtiMpsbGxiabF349Op8PBwSHJmmHJdSCFECIv8g8KT3H+qr9uSKBWiDfF2LEwaVKmM2rjVzZNc2/V0RHGjMnUflOSMAsqPp1OR61atZJ87N3R0ZEHDx4kux6Y9pGNAUOjTp06sWzZMi5dusSOHTto3Lgxzs7OGR78dtq0abRp04ZvvvnGZLrxvsDYpvjWrl3L9OnTadGiBTNnzjTJOi1cuDABAQEmywcEBGBnZ4ejo2OibRmDmAEBASb1YgMCAihYsGCi5VPyxx9/8Pnnn1OpUiV++OGHJPdnVKhQIQ4cOGAyTaPREBwcjLu7Oy4uLtjZ2SV5LGlpl/G8pXRvI4R4czjlL4hrQdP+b0xMjMQCxCtDArVJUKlU2Fhl7NQYArWqTAVqs5Knpyd37941ySg9deoUq1ev5quvvsLOzi5D2w0ODubBgwdK2YKbN28SFhbGO++8Q758+QDDt9bvvPOOss53332HmZkZI0eOTHKby5cvp2bNmixYsECZtmbNGuBlFnDCc2ppaZlo4AU/Pz+TwGxCZcuWJSwsjJiYGKUcAsDEiRPx8vKie/fuqR6/EELkBY+CEj/aBdCpVik2n7zL05BI/J+H4ZHfIYdbJoTIcZ07Z01Ga7ykhLzwGJqnpye7d++mcOHCShAzODiYL774gn79+lG+fHksLS35559/lJJd8HKAK2dn52S3XaZMGapUqcKePXs4ePAgc+fOzVRbixQpwrhx45g4cSLFihVTAqf58uXD0tKSoKAgk+V/+eUXpk6dSu/evZkwYUKifnK1atU4ffq0ybSTJ0/i7e2dZNAyf/78lCpVilOnTikDd8XGxnL27NlET8ql5NChQ4wePZrGjRszZ86cJMsaxFe9enXmzJmDn5+fcs9ibHfVqlVRqVR4e3tz+vRpunTpoqx36tQpqlWrlmp7jFnR7u7uaT4GIYQQIrfI14qvuUGDBrFv3z4WLlzI3bt3OXHiBL6+voSGhmY6hX/MmDFcunSJ8+fPM3bsWKpUqUL16tXx9PSkYcOGfPnllxw6dIgHDx7w008/sWTJEpNyCgkVLlyY69evc/bsWfz9/dm8ebMy4JnxUSc7OztCQkK4e/cuMTExVK5cmWvXrrFjxw4ePHjAokWLuHHjRortrlu3Lm+//TajR4/m5MmT+Pn58c0337BlyxaTwK0QQuR1j5LJqC1XNJ/y84AfDqOJzVx5GSGEeFX16NGD0NBQPv/8c65du8a1a9cYPXo0Fy9epGzZsjg4ONCtWzfmz5/P77//jp+fH+vWreOXX36hf//+qSZedOrUibVr12JjY0Pt2rWV6VqtlsDAQKW+alp16dKFOnXqJMr0rVixIpcvX1Ze3717lxkzZtC0aVOGDBnCs2fPCAwMJDAwkNC4rOnevXtz4cIF5syZw+3bt1m+fDl79+5l4MCBynaCg4MJDg5WXvfv358VK1awdetWbt26xfjx44mKiqJzvCB/YGAg4eFJ/30JCQnhiy++4N1332XChAmEhIQo7TLuJ+G5qVSpEt7e3owePVoZeGzy5Mm0b99eyZjt168fu3btYsWKFdy+fZtZs2Zx9epVkxrCybly5QpFihSRQK0QQog8QQK1r7kWLVrw3XffceDAAdq0acOYMWOoU6cOCxcuzPS227Rpw+DBgxk4cCCenp4sWbJE6cx+9913NGvWjMmTJ9OqVSu2bdvG9OnTkyzBYDRixAgqV67M0KFDad++PRs3bmTGjBnY2Nhw8eJFAJo1a4abmxtt27blypUrtG3blp49ezJt2jTatWvHo0ePUu2wmZubs3z5csqXL8+oUaNo27YtZ86cYeHChdSqJSOgCyFeDzq9nscvDBm1PmVNHw11sjV9tCsgJDLH2iWEEDmpWLFirF27lvDwcLp3706vXr2wtLRk9erVSu1SX19f+vTpw5w5c3j//fdZv349kydPpn///qluv2XLluj1etq3b29SguHJkyfUrVuX3bt3p7vN06ZNS1QqoEmTJpw6dUp5vW/fPmJiYti/fz916tQx+WccGNfT05MffviBw4cPK33r2bNnK9myAJ988gmffPKJ8rpr166MGDGC//3vf3Tq1ImHDx+yYsUK5VwB1KlTh+XLlyfZ9iNHjqBWq/nvv/+oV6+eSbuM+3n8+DF169Zlz549gOGJuYULF+Lh4cGHH37IqFGjqFevHl999ZXJPmfMmMH69evp0KEDJ0+e5McffzQp0ZCcU6dOKYMmCyGEEK86lT6jRZTyAK1Wy/nz56lcuXKStauioqK4e/cupUqVSvFR+fTIihq1r7otW7bg6+vL9evXc7spWS6z1y873lMiffR6PSEhITg7O7+2n8HXnVzDrBEQEknv+YcwN1Ox07cFNx+rGbn8OAA/DKrDsJ+OKctO71GDamWybqAEuYZ5W05fv9T6a8JUbvRv43sT+rrp8eDBA5o1a8aePXuUwcay4xwFBwfTqFEjVq1aRYUKFbJkmxm1Z88eHjx4wODBgzO8jZx6HwUGBtKoUSO2b99O6dKlE81/lfvu8rc0dXKOUifn6KXbt2/Tq/9QqnYclmqN2qCn/vyz5QfWLk/bF0KvO3kfpS7hOcpM/1YyaoUQQojX0KMXhsdSC7nYYW5mRlHXl4M72lhZYBavk/UkOOlatkIIIZL2+PFj9u3bx8SJE6lbt64SpM0uLi4u9O/fn5UrV2brflKj0+lYtWoVTZs2zdV2pNXatWtp3bp1kkFaIYQQ4lUkgVohhBDiNWQcSKyIq2HQSEdbS+q+XZiKJVwp5GLHN71qKMs+eSGBWiGESI8XL14wbtw41Go1X375ZY7sc/Dgwdy9e5cLFy7kyP6SYmZmxurVqylVqlSutSGtnj59yt69e/H19c3tpgghhBBpZpHbDRB5T8eOHenYsWNuN0MIIUQKnsZlyRZysVOmTezsrfxcuWQBBjV5m58OXCVQnb7BboQQ4k33zjvv8O+//+boPq2srNiyZUuO7jO5duQFBQsWZN++fbndDCGEECJdJKNWCCGEeA2pI2MAcLFL/oa6gKOhHl9QmARqhRBCCCGEECK3SUatEEII8Zp48CyMQHUUt56EcOjiQ8BQ8iA5+R2tAXgeGp0j7RNCCCGEEEIIkTwJ1AohhBCviYGLDyea5mibfEatq4Nk1ArxutLr9bndBCFeefI5EUII8aqR0gdCCCHEa0CrS/pmM6WMWte4jNpIjVaCtUK8JiwtDZ/5iAgZJFCI1Bg/J8bPjRBCCJHbJKNWCCGEeA0EhyddviCljFpbKwtsrcyJ1Gj53+8XmfJB9exqnhAih5ibm+Pi4kJAQAAAdnZ2qFSqLNu+Xq9Hq9Vibm6epdt9ncg5Sl1unyO9Xk9ERAQBAQG4uLhgbm6e420QQgghkiKBWiGEEOI1EBSWXKA25SyhamXcOHr1CcHhmuxolhAiFxQqVAhACdZmNZ1Oh5mZPJiXEjlHqXsVzpGLi4vyeRFCCCFeBRKoFUIIIV4Dz0OTLl3glEJGLUD7GqU4evUJoVESqBXidaFSqShcuDDu7u7ExMRk6bb1ej2hoaE4OjpKtmgy5Byl7lU4R5aWlpJJK4QQ4pUjgdrXwLhx49i6dWuKy1y/fj2HWmPqxYsXHDhwgC5dumT7vjZv3szKlSt58OAB7u7udO7cmQEDBigdsBcvXjBt2jSOHDmCSqXi/fffZ+zYsdja2ia7zatXrzJ9+nQuXbqEq6srffv2pU+fPtl+LEIIkV7JBWrtbVL+U2/MuA2NzNpgjhAi95mbm2d5IEqv1xMdHY2NjY0EIZMh5yh1co6EEEKIpEmg9jUwYcIEPvvsM+V1nTp1GD9+PK1atcrFVhnMmjULf3//bA/U7tixgy+//JJJkybh4+PDpUuXmDRpEhqNho8//hiAESNGEBkZycqVK1Gr1UyYMIGIiAhmzpyZ5DZfvHhBv379aNSoEV9//TXnz5/n66+/xt7enk6dOmXr8QghRHrdeBxi8rrBu0Xo18gLs1RugB1sDIHa8KgYdHp9qssLIYQQQgghhMgeEqh9DTg6OuLo6JhompubWy616CW9PulRyLPa+vXrad++Pd26dQOgePHi3L17l40bN/Lxxx/z77//cvr0aXbv3k2ZMmUAmDJlCgMHDuTTTz+lYMGCibb522+/YWlpyZQpU7CwsKBMmTL4+fmxdOlSCdQKIV4per2es7cDTaY52VlSyMUu1XWNGbU6PURExyqBWyGEEEIIIYQQOUsq3CcnPDz5f1FRaV82MjLjy2YRnU7HkiVLaN68OeXLl8fb25uBAwdy//59ZRkvLy/mz59Pw4YNqVOnDvfu3SMyMpIvv/ySmjVr4u3trWTujhs3Tlnv3Llz9OzZk4oVK9KgQQO+/vprwsLCgJclGU6fPo2Xl1eSbTt16hReXl74+/snO6137954eXkl+W/BggUAfP755wwYMMBk22ZmZoSEGDLMzp49i5ubmxKkBahRowYqlYp//vknybadPXuWGjVqYGHx8vuMWrVqce/ePZ49e5b6iRdCiBwSoYnlmdr0b1Ppgk5pWtfKwhxrS8Oj0VL+QAghhBBCCCFyj2TUJsfBIfl5rVrBrl0vX7u7Q0QEACoSnNT69eGvv16+LlkSkgvyVasGZ85krL0pWL16NcuWLWPmzJmULVuW+/fvM2nSJL799lt++OEHZblffvmFn376Ca1WS8mSJRkxYgRXrlzhu+++o0CBAixcuJA//viD9u3bA3Dt2jX69evHRx99xPTp03n27BmzZs2if//+bNiwgQkTJhAVFcWTJ0+UgGpGLFiwINmBMOzsDNliVatWNZkeGhrK+vXrqVu3LgBPnz6lcOHCJstYWVnh4uLC48ePk9z2kydPKFu2rMk0d3d3AB4/fkyBAgXSfzBCCJEN1BGG35HWFmZ837825+89p1kljzSv72hjSXSMlrAoCdQKIYQQQgghRG6RQO0boHjx4sycOZOGDRsCULRoUVq0aMHevXtNlmvXrh0VKlQA4MGDB+zbt4+ff/6Z9957D4DZs2dz7tw5Zflly5ZRu3Zthg4dCkDJkiWZO3cuTZo04fTp09SsWRMbGxssLS0zVYbBxcUlXcuHh4czbNgwoqOjGTt2LACRkZFYWSUe+dza2pro6OgktxMVFZVoHWtra4Bk1xFCiNygjtQA4GhnRamCTpRKYzatkaOtJc9CowgKiwKcs6GFQgghhBBCCCFSI4Ha5MQ9vp+khKPnBgQoP+r1erRaLebm5oYRTM0SVJe4dy/57SZcNos0atSI//77j++//567d+9y9+5dbt26lagua4kSJZSfr1y5AkCVKlWUadbW1lSsWNFkGT8/P5NljG7fvk3NmjWzpP0DBw5MtjzBkCFDlEAxQGBgIEOGDMHf359ly5bh4WHIKLOxsUGj0SRaPzo6WsnKTSipdYwB2uTWEUKI3KCOMPyucrZN/IVUWpQt4szdgFD2nHtAjbfcZQRuIYQQQgghhMgFEqhNjr19xpbV60GrNQRzk7rRTc92s8jSpUtZtGgRHTp0wMfHh759+3Lw4EF2xS/fgCEwaWQeF4zW6XTJblen09GmTRuTQKmRq6trutoYPyCaMDg6ffp0ohLWBY7j7Pwy8+v27dsMHDgQnU7HunXr8PT0VOYVKlSIAwcOJNpncHCwUs4goUKFChEQLwgPKK+TGnxMCCFyizFQ62iXsYHAOtYszf7//Dlx4yktpu3m627VqFVWfs/lZYcuPkQdqaF9jVK53RQhhBBCCCFEGslgYm+AH3/8keHDh/PVV1/RrVs3KleuzL1799Dr9cmu4+XlhUql4vz588o0jUbD5cuXldeenp7cunWLEiVKKP9iY2P55ptvlLqvac3Kij+Y2MOHD03mFSxY0GQf8f8ZyyI8ePCADz/8EFtbW3799VeTIC1A9erVefLkCX5+fsq006dPA4nr28Zf559//kGr1SrTTp48SalSpcifP3+ajksIIXKCOm4QMKcMZtSWdHekU63SyuvZ289nRbNELgmPjmHmtvMs3ncl0SBzQgghhBBCiFdXrgZqg4ODmTx5MvXq1cPb25vu3btz9uzZRMvp9XoGDBhA7969c6GVeV/hwoU5fvw4t27d4s6dO3z33Xf88ccfSZYCMCpWrBgtW7Zk6tSpnDhxglu3bjFhwgSePHmiBF/79+/PlStX+Prrr7l9+zb//vsvn332Gffu3aNkyZKAoURAQEAADx48SLGNs2fP5tq1a5w5c4YlS5ak+xjHjx+PRqNh3rx5WFhYEBgYqPwDqFSpEt7e3owePZoLFy5w8uRJJk+eTPv27ZXs2KioKAIDA5XAbKdOnQgLC2PChAncunWLLVu2sHLlSoYMGZLu9gkhRHYy1qh1tstYoBagZZXiys9hUbFoYrUpLC1eZVtO3lV+Nr43hBBCCCGEEK++XA3Ufvrpp/z777/MmzePzZs38/bbbzNgwADu3LljstyqVas4duxYLrUy75s1axZRUVF06tSJXr16cePGDb7++mueP3/Oo0ePkl1v6tSpVK1alU8++YRu3bphb29PlSpVsLQ0PFpbuXJlfv75Z65evUqHDh346KOPKFWqFCtXrlQG4Wrfvj2RkZG0bt2ap0+fJruvKlWqMGDAAEaMGEHbtm3TdXxPnz7l9OnThISE0K5dO+rUqWPyDwyZvQsXLsTDw4MPP/yQUaNGUa9ePb766itlO7t376Zu3bo8efIEgPz58/Pzzz9z9+5dOnTowMKFCxk7diwdOnRIV/uEECK7KaUPbDNW+gCgaH57ZvSsobx++Dw80+0SOS8kQsPaIzeV16Fx2dZCCCGEEEKIV1+u1aj18/Pj+PHj/PLLL8qj55MmTeLo0aPs3LmTkSNHAnD9+nUWLVpE5cqVc6upec7169dNXr/77rts2LAh0XIffPBBsutER0dz8uRJJk6cyLfffqtMb968uUl9Vh8fH3x8fJJtS4UKFThy5EiqbR48eDBTpkxRXo8ePTrVdYwKFiyYqP1JyZ8/P/Pnz092fseOHenQoYNJqYOKFSsmee6EEOJVYny8vYCjTSpLpqxqaTfeKuTErSdqngRHUqqgU1Y0T+SgJ8ERJq/DoiRQK4QQQgghRF6Raxm1+fLlY+nSpVSoUEGZplKpUKlUqNVqwBAs/PzzzxkxYgSlSslgGDnJysqKr7/+mi+//JLbt29z79495syZw6NHj2jRokVuN08IIUQ8gXGBWjcn20xvq5CLHZA44CfyhqDQaJPXoVL6IFN0Oh3z58+nbt26VK5cmUGDBqVYzunmzZsMHjyYmjVr4uPjw4gRI0yeXtJqtVSsWBEvLy+TfwsWLMiJwxFCCCGEEK+4XMuodXJyon79+ibT9u3bh5+fH+PHjwcMdUvd3d3p1asXvr6+Gd6XXq9PcuAs47Tk5mdWdmwzJy1ZsoTZs2fTrVs3tFot77zzDsuWLaN06dJZdmzZfQ0yIyPteZWP501hPPdy/vMuuYbpF6iOBKCAk3Wmz1uhfC8DtRndllzDnBWpieXMrUCqli5AUJjp4GHqyJh0X4ecvn6v8vvkhx9+4JdffuHbb7+lUKFCzJ49m4EDB7Jz506lzJPRixcv6NevH97e3qxZswaNRsO3337LwIED2bp1K9bW1ty7d4/o6Gi2b99uMjCpnZ1dTh+aEEIIIYR4BeVaoDahc+fO4evrS7NmzWjQoAFHjhxh586d7NixQxm8KqPUajVmZomThzUaDTqdDq1Wa/K4e2bpdLos21ZuKlu2LD/99FOi6Vl5rqpVq8bly5ezfLuZkZnrp9Vq0el0hIaGEh0dnfoKIsvp9XoiIgyZgJn93SFyh1zD9ImO0aKOq0NqTQwhISGZ2p5TXOzp4TN1hrcl1zDn6PV6Rq05x5OQKEq72+NV2LRcxbPg0HRfx5y+fq9qv0mj0bB8+XI+//xzGjRoAMB3331H3bp1+eOPP2jdurXJ8gcOHCAiIoJZs2ZhY2MoQzJ79mwaNGjAuXPn8PHx4fr16zg4OFCuXLmcPhwhhBBCCJEHvBKB2gMHDvD555/j7e3NnDlzCAoKYvz48Xz11Vcm9VAzysnJCXNz80TTo6KieP78Oebm5knOz4ys3p7IWRm9fubm5piZmeHo6KjcpImcZczMcnZ2lgBRHiXXMH2uPwoGwM7KgsJurpk+Z275DIOIabQqnJ2dM7QNuYY550lwBE9CDFm0dwLCuRNgOgjcmTsvGNqiYrquQ05fv1fli9qErl27Rnh4uEktficnJ9555x3OnDmTKFDr4+PDDz/8YPL335goYCzrdf36dcqUKZMDrRdCCCGEEHlRrgdq165dy/Tp02nRogUzZ87EysqK3bt3ExgYyPjx45UyCMbs1ypVqrBr1y6KFCmS5n0Ya98mNT2l+RkR//E9uTnNezJ7/bLjPSXSz3j+5RrkXXIN0+7QRUP9y+pvuSX59Eh6OdhYAhChic3U+ZdrmDMev4hMcrpXEReuPwrm0YsI7gaEUaZQ+gaGy8nr96q+R548eQJA4cKFTaa7u7sr8+Lz8PDAw8PDZNrSpUuxsbGhevXqANy4cYPY2FgGDBjAtWvXKFiwIB9++CHt2rVLd/tyq7yIlDZJnZyj1Mk5Sp2co9TJOUqdnKOXkjsH8csXJuyTyLkzkPdR6hKeo8ycq1wN1P7yyy9MnTqV3r17M2HCBOVD0bRpU7y9vU2WnTNnDk+ePGHOnDm4u7vnRnOFEEKIV87Nx4bH2t8rVyhLtmdnbegahEfHZMn2RPZ69CI8yekDm5RjzOqTADx+EZ6mQO2joHDm775E1/dKU9rVMkvbmRdFRhqC4Alr0VpbW6epnMSaNWtYu3YtEydOxNXVFTAMNqbT6RgxYgSFChXi8OHD+Pr6EhMTQ+fOndPVvuRKe2U3KW2SOjlHqZNzlDo5R6mTc5Q6OUcvhYaGotVqiY2JISbGtJ+b8Ome2JgYtFotoaHpLyH1OpL3UeoSnqPMlPbKtUDt3bt3mTFjBk2bNmXIkCE8e/ZMmWdjY0OJEiVMlre3t09yuhBCCPEmMw4e5eaUNeVW7I2B2qjYLNmeyF6PgpIO1JYr6kLtcoU4fu0Jz8PSVjP9+10XOX/vOf/efcaGT2pnZTPzJGMJA41GY1LOIDo6Gltb22TX0+v1fP/99yxevJiPPvqI3r17K/N+//13tFot9vb2AJQrV45Hjx6xbNmydAdqkyvtld2ktEnq5BylTs5R6uQcpU7OUerkHL3k6OiIubk5FpaWWFq+/ELaeI4sLCyUc2RhaYm5uTmOjo4ZLgX2OpH3UeoSnqPMlPbKtUDtvn37iImJYf/+/ezfv99kXocOHfj2229zqWVCCCFE3qDX6wmKC8Lld8iiQK2x9EF0LHq9numbz/FMHcXI9ytQqmD6Hp8X2e95aOIgrI2lOVYW5uR3tI5bJipN2wpUp225N4Wx5EFAQADFixdXpgcEBODl5ZXkOjExMfj6+vL777/j6+tL3759TeYnVb++bNmy7NixI93ty83SIlLaJHVyjlIn5yh1co5SJ+codXKODJI7/vjlC5Oa96afNyN5H6Uu/jnKzHnKtUDt0KFDGTp0aJqXl8CtEEIIYSosKhZNrOGxGte4oFxmGUsfxGh1XH7wgqNXDbU4hy49yrqRjSmQRZm7Iv1uP1FT0MVWqSMMEJFEiQrfjlWAl8H7oDRm1FpZ5Pxj9K+ycuXK4eDgwKlTp5RArVqt5sqVK/Tq1SvJdcaOHcv+/fuZO3cu77//vsk8tVpNkyZNGDduHB07dlSmX7x4EU9Pz+w7ECGEEEIIkWfk+mBiryyNBmIz8NinXg9aLZibQ0Yj6BYWkKAeWm5p1KgRHTp04JNPPsnR/W7ZsgVfX1+uX7+eo/sVQoi8xFj2wMHGEiuLrHkE2tbqZddg/3/+JvOeBEdIoDaXXPV/wagVf1PIxZZVnzRSpodHG/oqNd5yw83ZlvbVS1LczRF4GbwPSmNGbfz3kAwWYahN26tXL+bMmYOrqytFixZl9uzZFCpUiGbNmqHVagkKCsLR0REbGxu2bNnC7t27GTt2LDVq1CAwMFDZlqOjI05OTtSqVYvvvvuO/PnzU6JECf744w927NjBkiVLcvFIhRBCCCHEq0ICtUnRaOD0aQgLS/+6ej3odGBmlvFArYMD1KjxygRrhRBCvJqMmZKuDlmTTQtgbqbCzsqCCE0s+84/MJkXFZPxWksic45fM2Q2PwmONJkeEReo7VCzNN6lC5jMy0xGbXh0LC4ZbexrZMSIEcTGxjJx4kSioqKoXr06y5Ytw9LSEn9/fxo3bsw333xDx44d+f333wGYNWsWs2bNMtmOcZkZM2awYMECvvzyS54/f06ZMmWYP38+devWzY3DE0IIIYQQrxgJ1CYlNtYQpLWyAut03vxmNqM2Otqw79jYDAVqt2zZwsKFCzl06FD69y2EECJPMdYezaqyB0Z2NoZArR6wMFNRrIADdwNCidTIAGO5xcL8ZRBVr9crda+MGbX2Nom7dPkdDYHatNaojY4XiA8K11A0w619fZibmzNmzBjGjBmTaJ6Hh4fJkz/Lly9PdXsODg74+vri6+ubpe0UQgghhBCvBwnUpsTaGpIY9CFFWVH6QKPJ2HqpOHr0KAsXLuT69etERppm5PTp04cJEyYkuV5gYCADBw7k1KlTuLm5MWDAAHr27KnM37hxI6tXr8bPzw8zMzPeeecdfH19qVChAmAon9CzZ0/Onz/PsWPHsLKyok2bNowbNw4LC8NbcP/+/cyfP5979+5RoUIF3nvvvWw5B0II8TrJ6oHEjBxtLHkWN7CUm7Mtro423A0IJUojGbW5xcLsZZ8iPDpWqVMbHmWoUWtnlbhLZ8y0VkfGoInVploeIyxevdurD9VUKF0k0+0WQgghhBBCpJ2MGvGG2LdvH4MGDaJu3bps3bqVJUuWULBgQRo2bMixY8cYOXJksuv+9ttvVKtWjR07dtCvXz+mT5/O/v37AUOAdcqUKQwcOJA9e/awcuVKoqOjmThxosk2vv/+e6pXr86OHTsYO3Ysa9euVR4RPHfuHJ988gnNmzdnx44ddOjQgaVLl2bfyRBCiNdEdpQ+ACjobKv8nM/eGltLQ4AvKkYyanPD2iM3WXPkpvL6Rdx11+v1SumDpDJqHW0tsYzLxH2RhvIHYZEvA7XLD9/h9hN1ptothBBCCCGESB/JqH0N7Nixgy+//BKA2NhYYmJiqFLFMOJzkSJF2LVrF0uXLqVJkyZ8/PHHAJQqVYrJkyczfPhwIiIicHNzS3b7TZo0YejQocp658+fZ/ny5TRt2hQXFxemT59O27ZtAShatCidO3dmypQpJtuoU6cOffr0AaBYsWKsWbOGc+fO0b59e9auXYu3t7dJ227cuMHq1auz8CwJIcTrJ0gpfZC1GbUFXeyUn/PZWykDjEVKRm2O0+r0rDl8w2RacHg0xQo4sHT/VYxDftlZWyZaV6VS4epozdPgSJ6HRZtcV6NrD19w87Ga96sWJywqNsH6WXYYQgghhBBCiDSQQO1roFGjRlSqVAmAP/74gzVr1rBmzRoApbTAzZs3adOmjcl6NWrUUOaVKFEi2e1XrVrV5HWlSpU4fPgwANWrV+f27dssWrSIO3fu4Ofnx/Xr19HpdCbrlClTxuS1o6MjMTGGzJ0bN25Qu3Ztk/lVqlSRQK0QQqTieTZl1LrHy6h1trfGPO6xeyl9kPNCIxOXQ/p89UnGtqvEllN3lWnWFkk/JOXqEBeoTaJO7a3HIYxc/jcAYVEx6PR6k/ml3B0z03QhhBBCCCFEOkmg9jXg4OCAg4MDAPnz58fCwiLFwKuRPu6GzBgwTY6ZmenNn06nwypuoLOdO3cybtw42rRpg7e3Nx988AE3btxIlFFrlcTAaMb9q1SqRIFdS8vEmUFCCCFMBYUZgm/5szyj1rT0gSbWEKCNzETpg4CQSKJitBQv4JDp9r1JkitZMGv7fyavVcmkvxZyseOqfzCPgiISzdt/wV/5eeWfhkGx3Jxs8CrqQrUSzsluUwghhBBCCJE9pEbtG8LT05Nz586ZTDt9+jQAXl5eKa57+fJlk9f//PMPnp6eACxdupTOnTvz7bff0rNnT6pXr86DBw+Al4HY1JQrV45///3XZNqlS5fStK4QQryp9Ho9QaHZk1Fbobir8rOFuUopfZAwo/bm4xCCw1OvfRoRHcuIZccZ/tNRnryI4ILfc0YtPy41UNMgOCL1AUYbvJv8oF/F8hsC4w+eh5lM1+v1nLoZkGh5TayOiZ288fEskM6WCiGEEEIIITJLMmpfA1FRUYSGhgJQt25d6tatS2BgIADm5ua4uroyYMAAPv30UxYvXkyLFi24d+8eU6dOpXHjxpQuXTrF7e/atYty5crRoEEDDhw4wP79+1m1ahUAhQsX5ty5c1y+fBlHR0cOHTrE2rVrAdBoNFhbpx486N+/P126dGHmzJl07dqVixcvKtsQQgiRtAhNLFExhsBp/iwO1LrYWzPy/QrsOHOPJhU9OHr1MQBRmpcZtTceBfPJsuO4OlizfnQTk/WvPHjBN1vOMaTZO9R9pwj7/3vAi7iA7ocL/1SWW/HnNaZ1r5GlbX/dxM+otTBTEasz/RJ01ScNTQZ/S6hYXAaz/zPTQO3Z24E8fpE4y7ZXPc/MNFcIIYQQ4rUQo9Hg5+eXpmWdnJxSHPdHiPSQQG1KolPPEkpErwetFszNMzYKRwb2uXv3bnx9fZOcV7RoUQ4dOkSrVq2wtbVl0aJFLF26lAIFCtC5c2eGDBmS6vYHDBjAn3/+ybx58yhatChz586lZs2aAEyaNInJkyfTq1cvrKysKFeuHLNmzWL06NFcvHiRatWqpbr9t99+m59++onZs2ezdu1aPD09GTp0KHPmzEnfiRBCiDeIMZvWztoCG6us/3Peyrs4rbyLAyQ5mNiJG08N7Uji0fyJ688QoYll2uZ/2fdOEe4EhCa5j+gYqXmbGmPGcv13CuPbsQpzd15g/3+GkgXDW7xLoSQGCIvPI789AP5B4SbTD118CEDjCkU5GPezo60l71ctnqXtF0IIIYTIayLDQrh79w5jJnyFlVXqCREujnasXvGzBGtFlpBAbVIsLMDBAcLCQJP6I4cm9HrQ6cDMLOPDJTs4GNqQRh07dqRjx46pLtewYUMaNmyYrqYcOnQoxfnFihVjxYoViaa3atUqxW0YBzsz8vHxYcuWLSbTBg0alJ6mCiFEnqfV6XkeGmUymBfA/WdhqHiZHQkvA6RZXfYgKTaW5kDyNWqjY7RYxy0Dhmxfo+ZTdyW73YjojNe8fVMEhxv6IS721qhUKj5rUxG/wFCiNFqaVy6W6vrGesOhkTFEamKxtbJAq9Nz9rbhyZuWVYopgVrPws6Ym5mluXSREEIIIcTrSBMVid7MgjJ1OuLukfL4P+rnT7l5eDNqtVoCtSJLSKA2KVZWUKMGxGbgBjKzGbVgCNImMfiWEEKI19vcHf9x8OJD5nzoo9SJ1cRqGbT4MACbxzTDwcYw2KJxILGcCNTa2xi6C+FRsej0eu4HhpkEWZ+HRlHE1ZC5GZVClmzdtwsrZRTAMMCYSFlolGHATydbw3VXqVTM718bnR7MzVLvZ9hbW2JvbUF4dCyBIZEUd3PkeWgU6sgYLMxUvFMsn7KsZDgLIYQQQrzk6OqGa0GP3G6GeMNIoDY5VlYZC5ZmRaBWCCHEGyc4PFrJbDx29bESqFVHxCjLnLkVQMPyRQF4rgwkZpPtbXO2s4pri4Ztp+6yZP9Vk/nxA7WRKWTJ1nvHNFCrjowhLCpGCT6LxMLjArX28c6RSqXCPB1dDHdnW+4GhBKgjqK4myPquAHKnOysMDczo131kmw/c49+DVMeXFQIIYQQQgiRvcxyuwFCCCGEgOuPgpWf7axffo8aHv0yUPvv3WfcfxaGOlKjZNTmd8z+jFpnO8M+giM0iYK0AIHqKOXnSE3ygdoanu5YJMgC/e/e8yxq5espLC5Qm5lgtltcKQ1jBrM60rBNx7gs3Y+av8OGT5tQoUT+zDRVCCGEEEIIkUmSUSuEEEK8Al7EG5QrNPJlcDY8XobqvvP+7P/Pn7c98il1bHMio9bJzhDQS+7ReHXky3ruCUsf9K7nybPQKBxtrbCxNGfFxw05dTMAv8BQdp7149ydQGqXK5R9jc/jwqIM198YVM0IdyfDeyRQCdTGZdTaGjKlVSoVLvbZH/AXQgghhBBCpEwCtUIIIcQr4EX4y2DnRb8g/rr8CGc7K2K1OpPldHq4/OCFEjTNiRq1dlYWWJqbEZOgLUbGYCKYZtT2qPMWPet5oopXCsjd2ZY21Uqw99/7gNSpTU1YEqUP0ssY1A9QG851aOTL0gdCCCGEEEKIV4cEakFGNxZZRt5LQoiMip9Rey8wlG+2/AsYgp1JufVEDUB+x+zPqFWpVDjZWSp1cROKX57BmFFbPL8dfRqUNQnSxpcvLsAcFJb0NoWBMVDrmJnSB06mpQ9CIkwHKBNCCCGEEEK8Gt7oGrWWloYblIiIiFxuiXhdGN9LxveWEEKkVXIBy1+O3UpxvZzIqIWXdWrjq/9OYeDlgFcAURpDoNbG0jzF7RlLNkigNnl6vT5LatQaM2oD1VH8euwWaw7fAF6WPhBCCCGEEEK8Gt7ojFpzc3NcXFwICAgAwM7OLtnMn7TS6/VotVrMzc0zvS2R8zJ6/fR6PREREQQEBODi4oK5ecoBCiGESCg4PP0BS3MzFQVdbLOhNYm52L8M6vVr6EXlUvm5+TiEw1ceEx6v9EFUXOmD1AO1LzNq1x65SbfaZbA0f6O/P04kKkaLVmd4UsPBJuNdNmOg9vGLCFb8eV2Z7pJDQX4hhBBCiNdZjEaDn59fqstpNBqsrNL2RbmTkxNubm6ZbZrIg97oQC1AoUKGAUyMwdqsoNPpMDOTm828KjPXz8XFRXlPCSFEWkVEx3L7qTrFZQq62GKmUuHqYM3lBy8AKOXuiJVFznwxVCBeiYXyxV0pVzQfj4IMTxGExSt9EBlX+sDaMuXfo87x6qOuOXwDOytzOtYqnZVNzvOMg8pZmpthnUrgOyX5Ha1xdbBOlL3cuELRTLVPCCGEEOJNFxkWwt27dxgz4SusrJL/EjxGo8H/gR/FSpbCwjz1UJyLox2rV/wswdo30BsfqFWpVBQuXBh3d3diYmJSXyEVer2e0NBQHB0dJaM2D8rM9bO0tJRMWiFEhpy88ZSI6FgKudiS39EGVwdromK0nLkVqCxjaW7GsmENuPtUzdClRwEoW8Qlx9pYwOlloDafvaETah+X5WnMqNXr9aw4dA1IPaPWwtwMJ1tL1HHByIMXH0qgNoEXcVnWLvZWmepTmJuZMbGzN5+uPKFM+2VUY5NguRBCCCGESD9NVCR6MwvK1OmIu0eJZJd7ePMSd/yWU8qnXYrLAaifP+Xm4c2o1WoJ1L6B3vhArZG5uXmWBNn0ej3R0dHY2NhIoDYPkusnhMgNj4LCAahSqgCjWlcEYPnBayaB2npx9WCLuzlS/53CvAiPpkPNUjnWxviDWbk4GAJ8xrqpxsHELt4PIiwuaGthnvrv0N71y7LxxB0CQiLxfx6OXq+X373xGAeYc7HPfImCd4u58o5HPq74v4jbpgRphRBCCCGyiqOrG64FPZKdH/LsSZqWE0ICtUIIIUQu0uv1XI4Lnrk5vaw3Gx2rVX4e16Ey73kZyqqYm6kY38k7ZxsJmMerH2tnZeg+2FsbArWPgiK48SiYjX/fVpYJVKdec7dt9ZI0r1yMtt/uJSpGS2hUjAxwFY+xbnG+LAqqxg+em0uJJiGEEEIIIV450ksXQgghckBQWBR/X3+CXq83mX7o4kPO3XkGvBz0CTCpSdqwfNFM1SjNClVKFQAM7TJmvcYvhzBjy7+cjpcBHB4dS1pYW5orj+AHhkRlVXOzhSZWm+j6ZacX4RogazJqARmsTQghhBBCiFec9NiFEEKIHDDxlzN8/ds//PGfv8n05YeuKz+7xQt8dqpVmneL5WPk+xVyrI0pKV7AgUUD67Dy4wbKNAcbSz5sUBaAxy8iTJbvXSftZRmMxx2ojsx8Q7PJxftBtPlmLxuO30594Szw8Hk420/fA7IuUFuxRP4s2Y4QQgghhBAie0jpAyGEECKbaXV6bj9VA7Dv/AOaVy7GoYsPOXTpIbp4GZqFXOyUn53trJjX970cb2tK3irsnGhak4oerPrrhsm0DZ82gZi0B13dnGy59UTNpftB1CpbMNPtzAy9Xs+sbeext7Hk45blAYjR6pi+6RwAK/68TsdapbCyyN4M5/G/nFIGE8vnkDWB2k4+hsHaani6Z8n2hBBCCCGEEFlLMmqFEEKILPBMHcW203eJSOKR//uBocrPxvkr/7rOmVuBBMUNGNWicjEK5bNLtO6rzs3JhuIFHJTXfeqXVUoZpFXVMoayCjvP+hGr1WVp+9Jj2+m7tPt2L4cuPWLnWT+iYgx1glccuqYETQGuPwrJ9rY8CX4Z6K5Y3DVLtmlpbsYHdd6idEGnLNmeEEIIIYQQImtJRq0QQgiRBcasOcGjoAj8n4crmZhGV+IGCwPwfx6OXq8nPMo0oNupVtpLBbxKVCoVH9Quw6zt/wGmdWvTqpV3cRbuuUxUjJbw6Nh0B3qzyuJ9V0xeqyM02DjbsvnkXZPpAcERkEXB06RodS+D1XZWFpQpJIFVIYQQQog3SYxGg5+fX6rLOTk54ebmlgMtEjlFArVCCCFEJq09cpNHQYYarYcuPiQiOpYmFT3wLm3IFL3qH6wsG6PVERKhwc7agrCoGGW6W7yBxPKamvHKFWRk0DNzMzNsLM0NgdqomFwL1CYUGqkxGeDNKECdvYOevQjTKD//OKSuMnibEEIIIYR4/UWGhXD37h3GTPgKK6uUS2C5ONqxesXPEqx9jUigVgghhEiFXq8nLCoWR1vLJOevOfyyRmt4dCwHLz7k4MWH7Jv0PgCX/YNMlg8IiSRSY8ioLVfUhXc88mFrlXf/JDvYWNK+RknO331O9TIZ6yQ62FgqGbU5JVarw8LcUAVKH69WsFFIhCGQ7mJvRXD4y+BpQEj2Dnr2LNQQCC7gZENBl7xXDkMIIYQQQmScJioSvZkFZep0xN2jRLLLqZ8/5ebhzajVagnUvkby7l2hEEIIkQN0ej3TNp3j+LUnzOhRg6rpDEQ+U0fxKCgCMxUUdbXnwfNw7gWGEhppCAJO6OSdZNZmXvNR83eVn5MKeqbGztoCQiE8XpZxdgoIiWTIkiM0qViU4S3KJ1lbeNGeS/w8rD62VhYmgdqn2RyoDYoL1OZ3SH8ZCSGEEEII8XpwdHXDtaBHbjdD5DAZTEwIIYRIweX7QRy/9gSAq3G1Znf940fHWfv4ftdFtLqUg5IX7z8H4K1CzpR0dwRg7o4Lyvy8nEmblextDOchpzJqf/v7NhHRsew4Y6j9FRyhSbSMf1A4D4PClRIVHWsa6gg/CgrP8H61Oj1HrzzmeWjy5RNC4/bnbJd0BrcQQgghhBDi9SSBWiGEECIFt5+qlZ/VcVmw+877Ex4dy+5z9wkKS7le6eMXhtq1Jd0dqZZENq6NVfprur6OHGwMQcnw6JzJqI3V6kxeP0um7mykRktY3HVvXrkYAE9eRBClyVhA+bvfLzBt8zmWHbyW7DLGbdtIEF8IIYQQQog3igRqhRBCiBTcfRqq/BySRNblrcfqRNOMomK0vAiPBsDVwVoJ9MVnaS5/igHsrQ2B2rConMmojZ8Jff1RMGPXnExyuUB1JMYlixWwx9nOCj1w/1lYuvd5/1kY+//zB+DgxYfJLheh0QJgK0F8IYQQQggh3ii5encYHBzM5MmTqVevHt7e3nTv3p2zZ88q8zdv3kybNm2oXLkyzZo1Y+nSpWi12lxssRBCiDeNX+DLQO1flx+hidUSFfMymHjt4Ytk11VHaHgR9jJQq1KpqFKqQPY1Ng+zszZkj0bkUI3a+Bm1I5YdV35uWaUYHzYoq7y+F2C4/s52VpibmSnlK/wC0xeo1ev1LNpzSXn9ViGnZJc1DjQnZTGEEEIIIYR4s+RqoPbTTz/l33//Zd68eWzevJm3336bAQMGcOfOHXbs2MGXX35Jr1692LFjB6NGjWLJkiUsXrw4N5sshBDiDXE/MJQxq09w9WGwyfT1R28RqXn5peGvx28DUCougBff0auPOXrVUN82X9zAUOM6VM6eBudxL0sf5ExGrbGMRUIj369Aj7qevOORD4BLDwyBeGNgtYSbAwBzdvzHzweuMnPrv6nWKQZ4EhzJ+XvPlddRMcl/8RylZNRKoFYIIYQQQog3Sa7dAfj5+XH8+HF++eUXqlatCsCkSZM4evQoO3fu5OTJk7Rv355u3boBULx4ce7evcvGjRv5+OOPc6vZQggh3hAHLj7kgl9Qoum/HLulBBXjK1vEmbsBoSbTlu6/qvycz8EaABd7a8oXd+XS/cTbfpM521kBEBSXgZzdXiSzH5VKBbwsO2C8Tm8VcgaghNvLgPzGE3cAeL9qCcoXd01xf8aB6CzMVMTq9CkGal9m1ErpAyGEEEIIId4kuZZRmy9fPpYuXUqFChWUaSqVCpVKhVqt5vPPP2fAgAEm65iZmRESEpLTTRVCCPEGCk/mEfwirnZJDiTlWdgZa8vkA2sucYFIIMXl3lTuzraAoSZsTjDWDo6vTMGX5Qhs4q5RdFxA9a3ChkBt8QIOidZ7FprygHIA/959BkCV0obSFykNRhYpg4kJIYQQQgjxRsq1QK2TkxP169fHyurljeu+ffvw8/Ojbt26VK1alVKlSinzQkNDWb9+PXXr1s2N5gohhHjDhCZ4NL7Bu0UAw2PpsXGPuv8yqrEy38HGkoJxwcaEani6UzS/vfLap2xBAOytJRBn5O5sKA0REJI9gVp1pIbrj4IBw0BiwUkEaqd8UF35OWGQ1Fj64N1irkr2r9HT4IgU9/1MHcWBC4bBwxpXKApApEaLXp90yQRjaQ07CdQKIYQQQgjxRnll7gDOnTuHr68vzZo1o0GDBibzwsPDGTZsGNHR0YwdOzbd29br9cneDGU1fXg4+PujV6sh7vFJkXfo9XpQq+X65WFyDfO+V+Uaql8YyhiYqWBK41KUyGfNX5cfmTya7xwUQNcKblx4Ek4tu1g26xM/zv5pbQ+aeboa/jbETWvppsK+XjHedbdH/+BBThxOjsrINSwQYQiMPw+NItbvPuZmWXvtP9l0jSdhGua0LENRJysSlpXt8E4B8ocEoo97cMc6+mXA2N7SjEJhz9GHB2EGfNu0BB9tv6nM9/N7yia1Gkdrc8O1TuCOvxqdXk8JF2uq2xqOU6vTo/G7j5V54u/MI0PDAbBRB6N/kDP9l/iU62dtDTY2ObM/IYQQQgghxKsRqD1w4ACff/453t7ezJkzx2ReYGAgQ4YMwd/fn2XLluHh4ZHu7avVaszMciZ5WHX/PtqTJwm3tVXq3Im8Q6/XE6vREG5lJdcvj5JrmPe9Ktcw+LkjYMF491Devn0OrR7McEGHoU3WKj1RR4/QDejmANq/HxIW4gQYHpl3MtPxrk0s7z28SPijxNuvAfAAwnPoeHJSRq6hlR4scCFWr+LBn0dxs8ja4N2TMMPgYEf+vkx9Bw3gZDLf9el9wg+/DL5aPLcFDEHK0ubRRBw5oszLpwcnM2fUOkPf4uDtYA4SDECthxcxT3DI99VWgD3umlC0fx8DDG0JOnwMR/PExxkebHjvcfUS4X45M7hafMbrpzY3h8KFs31/Op0u2/chhBBCCCFEXpDrgdq1a9cyffp0WrRowcyZM01KIdy+fZuBAwei0+lYt24dnp6eGdqHk5MT5uY5Uw9Q7+hIhJ0ddl5eEiTKg/R6PfqwMOwdHOT65VFyDfO+V+Uahj4OBHS4lymBvbNh8LD6mhD+fGSoR2praY59uXIm6wzLH82ks8GMKu9EMw8bVPBGvg8zeg1dHgfyLFpHTLHSyjnPCtFaPdwNAGBHiA2exd3hodpkGe9yHtg7vdyn480wUBvC6JU9XLD3NP2ieHEpLbF6GH7sOWGxL4OtsaU9sTRX4RemxcvZApVKxYtrofA8gqJuLji9XQyr+0/R6EBV+i3sbRP3T6KfPgONlnylimOfzyrR/Oym1+vh+nXsnZxQOTtn+/602uQHVhNCCCGEEOJNkquB2l9++YWpU6fSu3dvJkyYYHIz9+DBAz788EOcnJxYtmwZhTOR0WEcpCxHxO0nR/cpspTx2sn1y7vkGuZ9r8I1VGsMWX7OVmZKO3p7OiiBWiuzxEHYGu427G5RMMsf28+LMnINHSzNeBatIyxWn6XXPjDKNBA496IhSFvAxoxnUYbrXMrJ0mSf1vHSYivkT5wZXMDW0IWqWdCagw9fDiYWrNHz7RU1F4I0fFHJmUZFbXkaadhHITtzVCoVthYqNBo9UdqkjzMiLvBrZ2GWa58B45cMObF/+V0thBBCCCGEQa4Fau/evcuMGTNo2rQpQ4YM4dmzZ8o8Gxsbxo8fj0ajYd68eVhYWBAYGKjMd3Nzy40mCyGEeEOEx+iIi9PibPWydE4Ru5fZj7YWSZfUkSBtxjlaGc5daEzWPgr/NDLpjE2fgjZ4F7DC1doM8wTBwicRL9d5N4WsVjcb04zYF9E6LgRpANjhF0GDIjZcCza8Lmpv6HbZmJsRgpYorZ6oWB0b7oTTqIgtxRws0Gj1hMS9+Vytc+ZpICGEEEIIIcSrIdcCtfv27SMmJob9+/ezf/9+k3m1a9fm9OnTALRr1y7RutevX8+RNgohhHgz+YcbgnSu1mYmAVmVSkUfTwf+fhrFuMouudS615eDpeFch8VkbX3a51FJB2qrFbCiVsGkB8tqUcyO3Q8iaVPCDosUgu8FkgjUGj2O0HIhSENglA57CxWV8xsCvrZx2bqRsXpm/RfC8afRHHsSxU/13HgW11ZrM3C2kqC/EEIIIYQQb5JcC9QOHTqUoUOH5tbuhRBCiGTdDzMM4ORhn/jPZE9PB3p6OuR0k94IjnGB2lBN1mbUhiYT+H07hUxZLxdLNjd1x9Yi5WCpm41pZvWL6JdB4WCNji/PBgNQr7ANVnEBWuM2I2L1HH8aDcD9MMN6AXHZv2625lISQAghhBBCiDdMrg8mJoQQQrxqHoQbArXFHeTR85zkYGkITMYfnCsrhCVTSsHRMuVAqDHDNyWezpbYmquI1BrabKx5axQVN71BkZeZu/Zx+02Y6XvoYSTROsPyBZMYZEwIIYQQQgjxekv9DkQIIYR4wzyLy2osZCffZ+YkY0btyadRqSyZPsmVUjDLgozV/DbmrGvkRp+4LOs7oTFJLlck3nvJPq6cxqnAaJNlZv4XwuobYQC4S6BWCCGEEEKIN44EaoUQQogE1HEZmE6pZFyKrGUM1D4I13IjOOmAZ3otvqJm5/0IAAZ4ZU/JCntLM97NZwnAtWTaHf+9ZB9X+uBsoCbRckFxNW6LJVF2QwghhBBCCPF6k0CtEEIIEc+Wu+GciQugOVrJn8mc5On8Mjh5L8wQ8DwdEM3qG6Ho9ekvhxAcrWXbvQjltXM2Xs8icYHVpKosqABr83iB2jSUVCjuIIFaIYQQQggh3jRyByqEEELEuRcaw5KrocprpzQE1ETWKediRZ1C1oCh1uvD8FgmnX3Bulvh/BeUOPs0Pr1ez/Lroey6/zIwq05Q8sAxG69nARsz4seB49c31oPJwGB2qQxQBlBM6iMLIYQQQgjxxpE7UCGEECLO6BNBJq+zM7AnklYiLpP0RkgM/Q8/U6ZHa1POqD33TMOG2+HMv6RWsm/VGtP01rQMDpZRZiqVSR3alAYDs48XqO1Yyo7+CUoymKnATWrUCiGEEEII8caRO1AhhBAC0On1RMSaBgOdrKRGbU4rYGMIUJ54ajrQVlQqgdoL8TJuL78wlE1QxyQM1KqYUMUFSzMYX8U5K5prooj9y+BqaUfLZJezs3jZ/SruYEHn0va0LWGnTHOxMsM8CwY6E0IIIYQQQuQtEqgVQgghgBBN4uKi2ZmBKZLmZpN0JmlUbMqB2viDeH12MojwGF2ijNqi9hbUK2zDtmYFqV/YNvONTaBovAHASjtZJFviIF65Wiq5WmGuUjH4bcck5wshhBBCCCHeHHIHKoQQQgABkYkDtZZmEjHLaZXyW5m8LhRXAiAyhYxajVbPlRemNWyfRelMgu+u1mbKgF4W2XRd33IyBGrtLVRUKWDNJG9D9u7H7zqZLJff5mX3yzgIWfz3WmwSA5IJIYQQQgghXn8ypLAQQggBBEZpAfB0ssDVxpwyTvInMjdYmauYV8uVT08a6gWXc7HkSaSWyBQyam+qY0iYEK2O0REabzCxhbXzZ0t746tb2AZHSzPKOFnibGWGdwFrtjYrmCjgX9HVik8rOPGWc9LlEWJ0KWcPCyGEEEIIIV5PchcqhBBCAIGRhkBtITsLJnq75G5j3nDv5LOkt6cD+azNuB8WC5BioPZFdOIU1BfROh7Erdvfy4H8yZRUyErmKhVV3axNpiWVla1SqWhezC7xdEAPlHSU7pkQQggh3myBgYGo1eo0LavRaLCyskpxGT8/P2K1sVnRNCGyldwJCCGEEEBAXEatm61UBcptKpWKXp4OAKy6EQqkXPogPCZxoPafwGhOBRgGJKvpbp1o/qvo+/fys/FOGAPKOaa+sBBCCCHEayowMJA+/QYSHBqR6rIxGg3+D/woVrIUFubJh7iiIiN4+Pgx3pqYZJcR4lUggVohhBACCIyrUeueA5mXIu1s4urKppRRG7/EgdFe/0gA6hSypqRj0iUGXjVeLpZM9M6X280QQgghhMhVarWa4NAIPOt3wil/wRSXfXjzEnf8llPKpx3uHiVSXM5vy3JiYyVQK15tEqgVQgghgGdKRq0Eal8ltsZArTb5EbbC4jJq3ytozYmn0cQP27ZKosSAEGml0+lYuHAhGzduJDQ0lOrVqzN58mSKFSuW5PI3b95k9uzZ/Pfff5iZmVG9enXGjRtHkSJFlGXWrVvH8uXLCQwMpHz58kycOJF33nknpw5JCCGEyDOc8hfEtaBHisuEPHsCgKOrW4rLGpcT4lUnz3cKIYR44wVGarkabPh23U0yal8pthaGrkpKGbVhcRm1JR0t+Lqai8m8/DbS1REZ98MPP/DLL78wdepUfv31V3Q6HQMHDkSj0SRa9sWLF/Tr1w8bGxvWrFnDTz/9RFBQEAMHDiQ62lCGY+vWrcyaNYuRI0eyZcsWPDw86NevH0FBQTl9aEIIIYQQ4hUkdy9CCCHeeFvvhSs/F7aTQO2rxNbCkFEblUKN2tC4jFoHCzNquFnjYf/yGuazluspMkaj0bB8+XJGjBhBgwYNKFeuHN999x1Pnjzhjz/+SLT8gQMHiIiIYNasWZQtW5by5csze/Zsbt++zblz5wD48ccf6dWrF23btuWtt95ixowZ2NrasnHjxpw+PCGEEEII8QqSQK0QQog33pMIQ9mDOoWscbKSP42vEoe4QG1oEgOGGYXHxgVqLVWoVCryWb+8ho6WquxtoHhtXbt2jfDwcHx8fJRpTk5OvPPOO5w5cybR8j4+Pvzwww/Y2Ngo08zMDO9FtVrN8+fPuXfvnsn2LCwsqFatWpLbE0IIIYQQbx6pUSuEEOKNFxBpCNQ2LWqbyy0RCbnEBV2Do1OqUWvItnW0NCxra/4yUGumkkCtyJgnTwy17AoXLmwy3d3dXZkXn4eHBx4eprXxli5dio2NDdWrV+fx48fJbu/atWtZ2XQhhBBCCJFHSaBWCCHEGy8wyhAElIHEXj0uVoZroo7Ro9XpMTczDbxqtHruh8UC4BpXj7a4gzmnA3O2neL1ExkZCYCVlZXJdGtra0JCQlJdf82aNaxdu5aJEyfi6urKnTt3kt2esYZteuj1evT65EuCZBfjfnNj33mFnKPUyTlKnZyj1Mk5SiAmBs6ehadP4flzw7+gIGwfPwZzc/Te3uDjAxUrgqVlbrc2RTl5TY370uv1qPLoF/zZ/TmQz1rqEp6jzJwrCdQKIYR4o2m0eoI1cYFaGUjsleNopcIM0AHBGh35E1yjs8+iCY/VU8DGjLLOhpuOHm858ChCS4MiNok3KEQaGUsYaDQak3IG0dHR2Nomn32v1+v5/vvvWbx4MR999BG9e/dOtL34UttectRqtVJaISfp9XoiIiIA8uwNbXaTc5Q6OUepk3OUOjlHgF6P+cWLWK1fj+WmTZg9e5ZoEWvjDytXGlaxs0NbpQqx1asT27AhsXXrwit2/kJDQ9FqtcTGxBATE5PisrExMYCeWK02xWVTWk6r1WZom1m1/8xsU6vVEhoamqYvkTNKPmupS3iOdLrknwZMjQRqhRBCvNFuhBg6P/YWKqln+goyV6lwtjLjhUaXZKD2QVw2baX8VkqZA3tLM76smi/H2ypeL8YSBQEBARQvXlyZHhAQgJeXV5LrxMTE4Ovry++//46vry99+/ZNcntlypQx2V7BggXT3T4nJyfMzXP+yyVjhoizs7PcrCVDzlHq5Byl7nU+R4GBgajV6lSXc3Jyws3NLdn5r/M5StXjx7BuHaxejerSpXStqoqIwOL4cSyOH4f//Q+9jw9MnQqNGmVTY9PP0dERc3NzLCwtsUwl+9fC0hJQYWFunuKyyS1nfB9ZWFgo76O0bjMr9p/Zbeq0WoKCgnB0dExxm5D6Zyo5b/RnLY0SnqOEwf/0kECtEEKIN9rhx1EA+BS0lo7HK8rFOi5Qm0Sd2sBIyYYW2aNcuXI4ODhw6tQpJVCrVqu5cuUKvXr1SnKdsWPHsn//fubOncv7779vMi9//vyUKlWKU6dOKQOKxcbGcvbsWXr06JHu9qlUqlz7nWXct/zOTJ6co9TJOUrd63iOAgMD+bD/IIJDI1Jd1sXRjtUrfk4xsPQ6nqMUaTTwzTcwfbqh1EF81tbQti1UqQL584OrK3pXV0ItLXG0tkZ16hScOAF//w1+fspqqhMnoEkTQ6B22jRDeYRclpPX07ivvPgeigwL4e7dO4yd+DVWVtapLp+Wz1Ry3rjPWgbEP0eZOU8SqBVCCPFGiozVEaXVc/WF4THkWu7ymPyrytnK8Hh3iCaJQG2U4dtqCdSKrGZlZUWvXr2YM2cOrq6uFC1alNmzZ1OoUCGaNWuGNl4Gi42NDVu2bGH37t2MHTuWGjVqEBj4slCycZn+/fszffp0SpQoQYUKFVi6dClRUVF07tw5F49UCCFyjlqtJjg0As/6nXDKn/zTBOrnT7l5eDNqtTpDQaXX0j//QP/+cOGC6fT33oMPP4QuXSBfgieK9Hp0ISHg7Aw1asAnnximP3oEhw4Zgr5XrhimHTpk2Nb77xumV6iQ/cckMkUTFYnezIIydTri7lEixWXlM5V3SKBWCCHEG+nzk0HcCY1FF1fn/S1n+ZP4qrKzMHwjHalNXJT/WVygtoBNztfqFK+/ESNGEBsby8SJE4mKiqJ69eosW7YMS0tL/P39ady4Md988w0dO3bk999/B2DWrFnMmjXLZDvGZbp27UpoaCj/+9//CA4Opnz58qxYsQJXV9fcODwhhMg1TvkL4lrQI7ebkTdERcGUKTBrFhgfpzY3h5Ej4aOP4K230r/NIkWgVy/o3h1+/RW+/BJu3zbM27ULDhyAJUsMAWDxynN0dZPP02tE7kqFEEK8cbQ6PbfUscprOwsVBW0lI/NVZWseF6iNNQ3U6vV6AiLjMmrl+olsYG5uzpgxYxgzZkyieR4eHly/fl15vXz58jRtc8CAAQwYMCDL2iiEEOI1duoU9OsHV6++nFaxIqxYAd7emd++uTn07Aldu8KqVYaA8IMHEB0NffsasnjnzoVUaqoKIbKOpJ8IIYR444TEmD5CX9rRQhmISrx6rOMCtVEJMmoDo3SoY/SYqaConQRqhRBCCPEa2bQJ6tZ9GaS1tDQEUs+cyZogbXyWljBwINy4AUOHvpy+YAE0bQoBAVm7PyFEsiRQK4QQ4o2TcFCqwnbygMmrzNYi6UDtlbj6wmUcLbCxkC6NEEIIIV4Ty5dDt24vBwyrVs2Q3TppElhZZd9+bWxg8WJYuvRlFu3hwy/3L4TIdnJXI4QQ4o3zIkGg1tlKsmlfZTZK6QPT63YzxHDzUi5fNt6wCCGEEELkpHnzYMAA0MX1e/r2hRMncnZwr0GDDAHawoUNrx88gDp1YPPmnGuDEG8oCdQKIYR4o9wLjWH8mRfK6yJ25nQqbZ+LLRKpsY3Llk2YUesfbqhPW8JBMqKFEEIIkcfp9YaM2c8+ezlt1ChYtgwscqGv4+MDZ88a/gfDoGYffAB79uR8W4R4g0igVgghxBtl9Y0w5WcPe3NWNHDD1Vrqm77KbJIZTOxhuGFAuCJSn1YIIYQQeZlOByNGwLRpL6d9/bUhu9YsF8M2RYrAn3/C/9m77/CmyvYP4N/Mtulu6aDsvQUZAsqSJSjKEl8c4EJFBZTXiSC+LsCfKIKA4hZEURQRBURk7y3I3qVA6W7TNDs5vz9ORtOZdJ2m/X6uy8sznpzcTU5Ccuc+9zNunLhutQKjRwM7dkgXE1ENx0QtERHVGoIg4HiWxbV+V0ONhNGQtwKLmEzMJghI1osVtfVZUUtERET+ShCAp58GFi50b5s/H5g5E6gOk90GBABffw2MGSOuGwzAsGHAkSPSxkVUQzFRS0REtcZ1vQ05ZjvUcuCLPnUwvDETtf6gqMnEMox2WAVAKQNiAvlxhoiIiPzUvHni5F0AoFAA334rVtdWJwoF8N13wB13iOtarbh85oy0cRHVQPxmQ0REtUaaQazAjNco0SBECUV1qFKgUhXV+iDTJD6XUQFyyPk8EhERkT/64w/gxRfd60uXAuPHSxdPSdRqcTKx224T19PSgEGDgCtXpI2LqIZhopaIiGqNbLM4e26Emok9fxJUROuDDKP4XEYHsj8tERER+aF//wXuv19sfQAAb7wBPPCAtDGVJjhYTC537CiuJyWJydr0dGnjIqpBmKglIqJaI8eRqA1XM7nnTwKVhStqM4zuilqi/LRardQhEBERlSw1Fbj7bkDnmOT2vvvEnrT+ICIC2LABaNFCXD97FnjwQXFCNCIqN0m/3WRnZ2PmzJno06cPOnfujPvvvx8HDx507d+zZw9GjRqFjh07YsiQIVi7dq2E0RIRkb9zV9QyuedPgh2JWr3V/QUgw8SKWipar169MHXqVOzYsQOCIJR+AyIioqpkMgEjRwKJieJ6167iZF1yP/p8GhcHbNwo/h8A/voLmDVL2piIaghJ3wn++9//4siRI/jwww/xyy+/oE2bNnj88cdx8eJFXLhwAU899RR69+6NVatWYcyYMXj55ZexZ88eKUMmIiI/lu1I7kWwCtOvhKrE58tkB0yO9gfO1gesqKWCFi1aBLlcjsmTJ6Nv37744IMPcOnSJanDIiIiEtscPPEEsHu3uF6vHvDbb4DGDye4bdQI+P57wDlXwBtvAFu2SBsTUQ2glOqOExMTsWvXLnz//ffo0qULAOD111/Hjh078PvvvyMjIwOtWrXC1KlTAQDNmjXDyZMn8cUXX6Bnz55ShU1ERH4s29X6gMk9f6JRyiCXAXYB0FnsCFAokGvhc0lF6927N3r37g2dTof169djzZo1+Oqrr9ChQweMHj0aQ4cORUhIiNRhEhFRbbRgAbBsmbgcFASsWQMkJEgbU3n07w+8+abYtsFuF3vu/vMPEB8vdWREfkuybzeRkZH47LPP0KFDB9c2mUwGmUwGrVaLgwcPFkrI9ujRA4cOHeJlbERE5DNBEHAp1woAiGYVpl+RyWQIVYnVGqkGsTetzpGoDVHxuaSihYSEYMyYMZg/fz4mT56M06dP4/XXX0fv3r3xzjvvQOfsC0hERFQVTpwAXnnFvb5sGdC5s3TxVJTp04HBg8XllBQxWWuzSRsTkR8rU0WtyWTC8ePHkZ6eDrlcjtjYWLRq1QqBgYFeHyMsLAx9+/b12LZhwwYkJibitddew6+//or4Ar/CxMbGwmAwICsrC1FRUd4HnJcHKIroYadQAPljzssr/hhyufiLV2lj9XrAbPbcZjS6Z3IsSCbzjKGixgKe8foy1mQquRG4L2MDA92XQpjNJb9h+zI2IMDdw8diAazWihmrUrmXSxurVrvPK6tVHF/ScZXK6jPWZit8nuanVLofC1/G2u3iOVERYxUK8TEGxHPXaPR+rMEgbnOeT/nJ5eI54WQwFH9cX8YWfH36MpbvEeKy83Vf3HNYzveIk5lmZGbnIUIhQ6dgm/s58uU9Iv/rnu8RxY/N/xyqVBXyHhFtN8NotuGVbdfxZOsQmIxmAHKxf62v7xEljeV7RNGPkcFQ8ms5ONjzuCW9PguOLelcKyOz2Yy///4bq1evxu7duxETE4NHHnkEo0aNQnJyMmbPno0pU6bgq6++qvD7JiIiKsRsBh56yP3Z5vnngdGjJQ2pwsjlwHffATffDFy7BmzdCvzvf8Dbb0sdGZFf8ilRu2/fPnz99dfYvXs3zAU+VAcGBqJnz554+OGH0aNHD58DOXz4MKZNm4bBgwejX79+MBqNUDu/VDk41wved6kSEopMrAp33gn88Yd7Q2wsZHp9kYcQ+vb17LfSuDFk6emFxskABDVpAuHHH90bx4yBLDm56OM2aQL89JN7w7hxkBXTR02oW1e8NMLpiScgO3Wq6LEREWJzb6cpUyA7fLjosYGBwI4d7g0vvwzZrl1FjgUA4cAB98rMmZBt2lT82O3b3Umbd9+FrIQJ4YS//gIiI8WVDz+E7Oefix/722/uS0QWLYLsu++KH7tiBdCsmbjy1VeQff558WO/+QZCw4Zi1fYPP0C2YEHxYz/9FHC07cAvv0D2/vvFj503D+jVS1xZtw6yt94qfuzs2cDAgeLKli2QTZtW/NiZM8XZQgFgzx7IHK1Cihz70kvibKIAcOQIZBMnFj92yhRg3Dhx5dQpyB55pPixTzwBPPmkuHLxImRjxxY/9qGHgOeeE1eSkyEbPrz4sffe6/7FOSsLMuevtEWNvesu8cMAAMFgQOiddxY/dsAAYM4c17qsd+/ix952G/DRR+4NgwZBVkyCR+jcGViyxL3h7rshy84uemybNsDSpe4NfI8QVxzvETIAoUWNLed7RDsArkfnzXxjfXyPQLt24grfI8SVIt4j8j+HFfUe8UmBsQE978LcoU8jRCWD4MN7BAwGyPr0KX4s3yMgA6AJDYUwdKg7kTt0KGTbthV9XI3GPWs1AIweDdm6dUWOBQAhf8J33Dhg/XqgmGOXxWuvvYa//voLJpMJAwYMwCeffIJevXpB5vihp2HDhnjqqafw2muvVdh9EhERleiNN8SWAADQti0we7ak4VS4mBhgxQqgXz/xx9p33xU/295xh9SREfkdrxK1N27cwPTp03HmzBnccccdWLRoEVq2bInIyEjY7XZkZGTg5MmT2LdvH55//nm0atUKs2fPRoKXvVb+/vtvvPjii+jcuTPmzp0LAAgICCiUkHWuB+Wv1ioHq8WCvJwc13p4CWNtVit0+caGCQKKqNUDANgFAQadzvWFINhuL36s3Q59vi83Grsdxc1fLdjtyPN2rCB4jA2y2Up8svNf/hdktXo9NtBqhaq0sY6qmtLG5uXlQXBUUgVYLFCXNFavh+CIo7Sxer0edsdYtdmMgFLGGh1fstUmE0qqETcYDLA5jqvyYazSZEJJZ7DRaITVOdZoLHmsyeQaqzAYUFILepPJBEsZxsoNBgSXMNZsNsPsHKvXlzjWYrHA5Bgr0+tRUodAj7F5eSWOtVqtMDrGCno9wrwcCxSdEHSyWa0w5BtbUgw2m81jbHBJ7xEFXvd8j/Cj9wiDwTXWl9c93yMq/j3C0QEBMrMBeSbv3yNgMJT4uud7hHOwAK1W66p+DSnldZ+T7zNSsMVS4ms5/1iNxVJ8DGV06tQpPPfcc7j77rsRERFR5JhWrVrhww8/rOB7JiIiKsLOncB774nLKhWwfLnnFS41Ra9ewKxZYrGNIIgVxCdOALGxUkdG5FdkghcNX++66y488cQTuOeeeyCXl9wLzmw2Y/Xq1fj666+xfv36UgP47rvv8O6772LIkCF47733XFWzd955JwYPHoznn3/eNXblypWYNWsWDh06VGocgPil6J9//kHH5s2hqKLWB0JiIvT79kHToYMrUcvLmv2n9YGgUkFnMCAkJAQyq5WXNfs6thq0PhDsdujS08XnkK0P/PI9QhAE6HS6ws9hOd4jBIsFT+zIQJrRjne6RqBDlLrYsWx9gHK/R3g8hxXU+mD4X6keQ+1yBSxKFX4cEINwlYytDypwrCAIyDt7FsFDh0Lm/NG9Elsf2MxmHD1/Hp06dSr685qPFi5ciMcff7zQD/s6nQ7z58/H9OnTy30fUnJ+vq2ox8tXgiAgJycH4eHhRf87S3yMvMDHqHQ19TG6cOECHnpsIrqMegZRcfWLHZeZchWHVi3Gd199imbOK58K8IvHSKsFOnYELl8W1+fM8exTW8mq/DGy24Hhw91XLo8dC/zwg8+H8fY8AYBLJw5i7Zf/h5GT30G9Ji3LNM5isUCVrw2it8esqPuvimN685oqjl+81iRW8DEqz+c1rypqf/nlF6/7z6rVatx3332423mpZQm+//57vP322xg3bhymT5/u8YR37doV+/fv9xi/d+9edO7c2askbX6ykBDIvHlgfJkBuLixwcGAWu2aGA2AZ9KiNNVhrC+/7vkyNqCkOrVyjFWr3V/AyztWEFzPncyX4+ZPRPjDWKXSnZCpyLEKBaApqQ6vjGNlMu/HyuWQaTTif978I+LtcStzbHV43Ven9whBgMxuL/k59PE9Itkix1W7GsoAoGV8GGTKYv4dqcj3k/yqw+u+Kt8jinsOy/EeMfWWOMz5J6fQsBCVHDK5D+8RvryfANVjbFW/7gUBssBAz88ylRivzNvXUQkuXLiAzMxMAMCiRYvQunVrhId7Xit19uxZ/PTTT36fqCUiIj/y/PPuJG3v3sCLL0oZTeWTy4EvvwTatAEyM8V2CA884G7FRUSl8upbWElJ2oyMDERHRxfaXlp7gkuXLmHWrFkYNGgQnnrqKaTn6/caGBiIcePGYeTIkZg7dy5GjhyJbdu24c8//8QXX3zhTchEREQuxzLFis/WESoEFpekpWrt9oQgtIlQ4eGtnv3hlXL+qk9AUlISJk6c6EosT5o0qchxo2vKxC1ERFT9/for8PXX4nJoqNh/XoIrIapcbKzYw3/8eHH96aeBPn2A8JKaTRKRk9eTie3fvx9vvvkmPvroI7Ro0cK1fcaMGbh8+TJmz56NTp06eX3HGzZsgMViwcaNG7Ex/4Q2AEaOHIk5c+Zg8eLFeP/99/Htt9+ifv36eP/999GzZ0+v74OIiGh7shHz/tUCAFpGeFlRStVSmJpJdipav379sHnzZtjtdgwcOBArV65EVFSUa79MJoNGoym2Zy0REVGFSktzT6YKAAsWAI0bSxZOlXvoIbEX74YNwLVrwKuvAp8UnBqWiIriVaL25MmTePLJJ9GpU6dC1bXjx4/Hp59+ikcffRQ//fSTRxK3JBMnTsTEEmaUBoA+ffqgTwkzMxMREZXm3SPZruUGwV7/PknVUJDCs3r2+Q4lTR1ItY1zEttNmzYhISGBPdSIiEg6L78MOK8aHjkSePhhaeOpajIZsGQJ0K6dOL/Pp5+K/Wr79pU6MqJqz6tvrIsXL8aAAQPwwQcfFNrXs2dP9OjRA08//TQWLlyI+fPnV3iQRERE3rLYBWSZ7Jj3bw6GN/Lsq1kvuBZcblaDyWQyNAxR4IrOhmmdwtEvwYdeqFSjTZs2DdOnT0dISAgWLlxY4tjZs2dXUVRERFQr7doFfPONuBweLlaS1sYfDxs1AmbPBqZMEdefeAI4etS3XvZEtZBXidp//vkHS5YsKXa/TCbDhAkT8N///rfCAiMiIvLVV2dy8fPFPEQHyJFqtONwutljf31W1Pq9NzpHIsNkQ8doHyaUoxrv6tWrsNvtrmUiIiJJWK3AM8+41999F4iLky4eqT3zDPDDD8CePcC5c8Bbb4nJWyIqllffWPPy8hAWVvLlhfHx8dBqtRUSFBERka++Op2LHy/mAQBSjfZC+59rH4boQFbU+rv6IUrUD2HCnTwtW7asyGUiIqIqtXAhcOyYuNy5M1BKu8caT6EAvvgCuPlmwGwG3n8fGDNGfGyIqEhezcpRv359nDlzpsQxp0+fRlxt/qWIiIgktSfVVOy+XvEBuLOhptj9RFSzXL9+HTqdDgCwd+9evPXWW/jjjz8kjoqIiGq069eBmTPFZZkMWLxYTFTWdm3bAjNmiMs2G/DUU4C9cFEFEYm8StQOGTIECxYscH3gLSgvLw8LFixA//79KzQ4IiIib+WYi//AV48tD4hqjY0bN2Lw4ME4evQorly5ggkTJmDPnj2YMWMGli9fLnV4RERUU73wApCbKy5PmAB07y5tPNXJK68A7duLywcPArz6hahYXiVqH3vsMVgsFowYMQLLli3D8ePHkZSUhGPHjuGbb77B3XffDaPRiCeffLKy4yUiIirEJgjQlpCorc9JxIhqjcWLF+Pxxx9Hz5498fvvvyMhIQFr167FrFmz8N1330kdHhER1USbNgErVojL0dHsw1qQWg3kn3h+2jSgmEJAotrOqxKjoKAgfP/99/jf//6HOXPmuCZrAACFQoEhQ4bglVdeQWRkZKUFSkREVJxcswChhP0tw1VVFgsRSevChQtYuHAh5HI5du3ahb59+0Iul6NTp064du2a1OEREVFNYzYDkya51997T0zWkqf+/YERI4DVq4HkZGDOHOCdd6SOiqja8fpa0MjISMyfPx8ZGRk4deoUcnJyEBUVhfbt2yM0NLQyYyQiIipRttlW4v4GnHyKqNYICwtDbm4ucnNzcezYMTzxxBMAgCtXriAiIkLa4IiIqOb58EPg9GlxuWdP4NFHpY2nOnv/fWDtWsBiAebOBZ54AmjUSOqoiKoVn7+5RkdHo1evXpURCxERUZkU1582SCFD37qBUMhkVRwREUmlb9++mDlzJoKDgxEaGorbbrsNu3fvxv/+9z/069dP6vCIiKgmuXHDXRUql4sTiMm96jBZOzVvDjz3nJikNZmAl18GfvxR6qiIqhWv3kEuXryIRx99FPfddx/27NlT2TERERF5TRAEnMiyAAA0SndCtq5GgV8Gx+K5DmFShUZEEnj99dfRuXNnaDQafPLJJ1Cr1Th06BA6deqEV155RerwiIioJnnzTSAvT1yeOBHo1EnScPzCjBlATIy4/NNPwM6d0sZDVM14VVE7bdo0jB49Gq1bt8bjjz+O3bt3Q6Vivz8iotpi6dlcZJjseL59GGTVrDr1r6sGfHtWnIygUYgSp7LFpG2IUsZKWqJaKDAwEK+++qrHtsmTJ0sUDRER1VhnzgCffy4uh4QAb7whbTz+IjxcrEJ+6ilx/fnngf37WYlM5OBVojYlJQWtW7dGy5YtodfrodfrER4eXtmxERFRNWAXBCw/L1YKjGikQZOw6vVD3aencl3LrSJUrkStwVbS9GJEVJMdPHgQhw8fhsVigSB4vhdMyj/hCxERUVm99hpgc8yT8PLLQGystPH4k8cfBxYtAo4dAw4dApYuBR55ROqoiKoFrxK1Tz75JF566SWEh4djzJgxTNISEdUixnwJT0vRrWAlpVHKoLeKMdbVKFzb0wwlTzBGRDXTokWL8PHHHyMsLAwhISEe+2QyGRO1RERUfrt3A6tWicvx8cB//yttPP5GoQDmzQMGDBDXp00D7r1XrEwmquW8StQ+8MAD6NevHwwGA5o1a1bZMRERUTVisLoTtXZUvyrV/H1pIwPcl0wFq3j5FFFt9MMPP2Dq1Kl4ynlJJRERUUUSBLGC1unNN4HgYOni8Vf9+wMjRgCrV4uTsv3f/wFvvSV1VESS8+pbrMFgQEJCgk9JWoPBUOagiIio+tDnS9QardUvUSuDO1EbqpLjw55RaB6mxGs3R0gXFBFJJjc3F8OGDZM6DCIiqql++w3YtUtcbt0aeOwxaePxZ++/Dygd9YPz5gFpadLGQ1QNeJWoHT16NFavXl2ox1dRLBYLVq5ciZEjR5Y7OCIikl7+itrq2Pc12yy2OGgToUKnaDXaRaqxqFcddIhSSxwZEUmhc+fOOHLkiNRhEBFRTWS1ipfpO82e7U40ku+aNwcmTBCXdTrgvfekjYeoGvDqHeWLL77A66+/jrlz5+KOO+5A37590apVK0RFRcFutyMzMxMnTpzA3r17sXbtWrRs2RKfO2c/JCIiv7A/1YSt1w14tl2YR9sAvc3dmNZQzSpqrXYBWrMY0/+6REAuk5VyCyKq6YYNG4a3334bx48fR9OmTaFWe/5oM2LECGkCIyIi//fVV8Dp0+LybbcBw4dLG09NMGMG8M03gNEoTjDGfr9Uy3mVqE1ISMCXX36JvXv34uuvv8azzz4Lq9XqMUatVqNnz5744IMPcOutt1ZKsEREVHleP5gFAIgKVGBC61DXdo/WB9WsojbHLHbNlcuAUDV70hIRMH36dADAN998U2ifTCZjopaIiMomLw944w33+v/9H8AigfKrVw945hngww/FZO077wAvvCB1VESS8alGv0ePHujRowcMBgNOnDiB9PR0yOVyxMTEoHXr1ggKCqqsOImIqIqkGGwe6x6tD6pZRW2WSaz2jVDLoeAHZSICcNpZ6URERFSR5s0TJ70CgFGjABaoVZxXXwU++0xsf/D551COGSN1RESSKVMzlaCgIHTt2rWiYyEiomqgYF2qvhr3qHUmaiMDWE1LRJ6uX7+OCxcuoFu3bsjLy0N0dLTUIRERkb/KzgY++EBcViiAWbMkDafGiYkBpk4F3n4bsFoR9fHHUkdEJBl+syUiIo/JIgsWphqs7h611a31QZZjIjEmaonIyWw2Y+rUqejfvz+eeuoppKWl4Y033sCjjz4KnU4ndXhEROSPFiwQk7UAMH480KqVpOHUSC+8AERGAgBCfvsNjfR5EgdEJA1+syUiquXMNgEnsyyudXm+RK0gCFh2zp3YyJ+0rQ5cFbXsT0tEDp988glOnz6Nb7/9FgEBAQCAcePGITExEXPnzpU4OiIi8jvZ2WLbA0CspnX0QqcKFh4OvPwyAEBmt+PJyxckDohIGvxmS0RUiwmCgCm7M/DfvZmubTY7kGex4/vzOmy5boQ5X262+rY+UEgcCRFVF2vXrsXrr7+O7t27u7Z1794d7777LjZt2iRhZERE5Jfmz/espm3WTNJwarTJk4G4OABA//RU1E9ispZqHyZqiYhqsRyzgEu5Vo9tW5ONeHxbOr49q8N7R3M89mnN1aui9oZj4rM6gfznjIhEKSkpaNiwYaHtdevWRU5OThG3ICIiKkbBatoZMyQNp8YLDvaoWL573Q8SBkMkjTJ9sz19+jSmTZuGsWPHIiUlBcuXL8e+ffsqOjYiIqpkaUZbkduzCiRkm4aKc0/e0Bc9XipXdWKSuX5wmebGJKIaqFmzZtizZ0+h7WvXrkXz5s0liIiIiPzW/PmA80e+hx8GmjaVNp7a4MknYUlIAAC0O3UYDc4ekzggoqrlc6L2+PHjuO+++3D16lUcP34cZrMZp06dwuOPP45t27ZVRoxERFRJikvUFtQ9VuzzeMNgg12oHu0PrHYByY7EcYMQJmqJSDR58mS8++67mD17Nmw2G3799VdMnToVixYtwlNPPSV1eERE5C/yV9MqlexNW1UCApD17LOu1T6rv5YwGKKq53Oidu7cuXj00UexbNkyqFQqAMA777yDBx98EB9//HGFB0hERJUnxZHo7BEbgFtiAood1zJCBYUMsNiBDGP1aH+QYrDBKgABcrY+ICK322+/HQsWLMDx48ehUCjw5ZdfIikpCfPmzcMdd9whdXhEROQvPvqI1bQSyR05EskBgQCAVkd2If7SGYkjIqo6ZaqoHTFiRKHtDz74IC5cYKNnIiJ/YbIJ+PRULgAgXqPA/7pEFDs2OkCB2CBxwi5nX1ipOfvlRgYoIJfJJI6GiKqTPn36YPny5Thy5AiOHj2Kn3/+mUlaIiLyXna2mKgFxGra116TMpraR63Gdw0auVb7sqqWahGfrxVVqVTQ6XSFticnJyMoKKhCgiIiosp3JtviWu4THwiFXIbZt0Ri2v6sQmMjA+SICVQgWW9DupftEiqbziq2YAhWMUlLRCKtVoutW7fi9OnT0Ol0CA0NRbt27dC3b18EBwdLHR4REfmLefNYTSuxP+IT8GRqCsK12Wi3fzNiki4grUEzqcMiqnQ+V9QOHDgQH330EbRarWvbhQsX8O6776Jfv34VGRsREVWiszliova2uAC0i1IDACLU7n8WGoYoXMsRarmrvUB6NWl9kOao7A1Wsu0BEQErV65E//798fLLL+OHH37A1q1b8cMPP+C///0v+vXrh19//VXqEImIyB9kZXlW07I3rSTMcgU23T7Ctd7nt2+lC4aoCvn87faVV15BXl4eevToAYPBgFGjRmHYsGFQKBR4+eWXKyNGIiKqBOe1YqK2RbjKtS08X6K2WZh7u1ohQ51AMXFbHSpqk3RWzD8u/mDIiloi+uuvvzBz5kyMGDECGzduxJEjR7B9+3YcPnwY69evx/DhwzFjxgzs2bNH6lCJiKi6W7gQcBamPfII0KSJpOHUZjtvHYy8kHAAQIfdfyEq+YrEERFVPp9bH8hkMqxYsQJ79uzByZMnYbfb0bJlS/Tu3RtyOauaiIj8RZZJrIyN17grZ6MC5BjaIAhKuQztI1XYct3o2ueuqJU+UfvzpTzXcoiSiVqi2u6bb77BY489hpdeeqnQviZNmmDGjBkICgrC119/jZ49e0oQIRER+YW8PGD+fHFZLgemTZM2nlrOHBCIPXc9gIE/fgK5YEfvNd/it6delzosokrlc6J2xIgR+Oijj9CzZ09+0CUi8mN6R4/XIIU70SmTyfB8B/FXa0EQkGqwoVWEWFkb46io3XnDhD0pRvSMC6ziiN3yhYxgFX8kJKrtzpw5gzfeeKPEMXfddRd+/PHHKoqIiIj80pdfAhkZ4vLYsexNWw3sGzwGt/2+DEF6HTrtWIetoyYgJ6au1GERVRqfv90aDAYEBkr35ZyIiLxntQuw2YUi9+mtYkVtcYlOmUyG+5qFoGN0AACgQ7TalSCd/U8OBKHo41YFhcydqbVJFwYRVRN6vR6RkZEljomKiipyQlwiIiIAgNkMzJ3rXn/1VeliIReTJgT7hvwHAKCw2dDr92USR0RUuXxO1I4fPx6TJ0/G8uXLsWPHDhw4cMDjPyIiqh7sgoBJuzLw1I70IpO1zopajZetA0JVcrzcUay2NdkE5FoEXNFZ8WeSHvYqTtrmWdwTmhms1WNyMyKSjiAIUCgUJY6Ry+WS/sBERETV3PffA0lJ4vKwYUCHDtLGQy57hvwHpkANAKDz1jUIzUyTOCKiyuNz64MPP/wQAPD2228X2ieTyXDq1KnyR0VEROWmswi4lGsFANww2FAv2PMt39dELQD0SwjCklO5yDTZcUNvwxuHspBpssNiB+5upKm44EuRaXInZ83M0xLVejKZDDIZ+1UTEVEZ2e3Ae++511lNW60YQiOwf9Bo9P59GVQWM25duxwbxj0vdVhElcLnRO2mTZsqIw4sWbIEO3fuxLJl7jL2EydOYM6cOTh+/DjCw8MxbNgwTJkyBWq1ulJiICKqSQz5egJkGO2oF+zeZxMEGG3OHrW+XVwRr1Eg02THlN0ZcN7Dn0n6Kk3UZufLzj7QLLiEkURUGwiCgNtuu63Cj2u327Fw4UKsXLkSubm56NatG2bOnIkGDRqUersnn3wSHTt2xOTJkz32DR48GImJiR7bRo4ciTlz5lR4/ERE5KXVq4HTp8Xl3r2BSvg3hcpn950PoMefP0FlMaHbplXYPuIRGEIjpA6LqML5nKitV69ehQexfPlyfPTRR+jatatrW1ZWFh577DEMGTIE77zzDq5cuYJXXnkFdrsdL7/8coXHQERU0+RvCZBisBXY507i+lJRCwDxQQqczLIg/wXEBY9fmQRBcN3f4l7RaBKmqrL7JqLqafbs2ZVy3MWLF+P777/HnDlzEB8fj/fffx8TJkzA77//XmzhgNlsxsyZM7Fjxw507NjRY59er0dSUhKWLFmCdu3aubZz/gciIgkJApD/x7Jp06SLhYqVFxGNQ7ffgx5/rYTaZMQtG3/BtlGPSx0WUYXzOVE7fvz4EvcvXbrU62OlpKTgjTfewL59+9C4cWOPfYcOHUJ2djZeeuklhISEoFGjRrj77ruxY8cOJmqJiLygz5eMTTPaitynkgNqhY+JWk3hPpC5FgEmm4AAH4/lC0EQsPCEFmq5DHqrADmABsE+/zNGRDXQyJEjK/yYZrMZX331FV588UX069cPADBv3jz07t0bf/31F4YNG1boNocPH8bMmTNhNBoRFhZWaP/58+dht9tx8803Izw8vMJjJiKiMti8GXDOt9OxIzBkiLTxULF23/UAbtn4C+SCHd03/IRdwx6UOiSiCufzZGL16tXz+C8uLg5GoxHHjh3DzTff7NOxTpw4AZVKhTVr1hSqOIiKigIA/PDDD7DZbLh69Sq2bdtWaBwRERUtf6K2YMWr3lFt62s1LSBW1BYlw1i5VbUXc63444oBqy7rAQAxQQqfk8xERN46ffo08vLy0LNnT9e2sLAwtG3bttgJdLdt24bevXtj9erVCA0NLbT/zJkzqFOnDpO0RETVSf6rMl59FWDP82orO7YejvcYCAAI0Wah07a1EkdEVPF8LkUq7tKyRYsW4caNGz4dq3///ujfv3+R+zp37oynn34a8+fPx7x582Cz2dCjRw/MnDnT15AhCEKVzfIrCAJgsUAwGvkG74cEQYBgMkFQKvn8+Sk+h256g9m1nKpzvC855OnFScaC5DKP7d6IV7pbKrzWToOvLxqRbLDjUHIe6tYPKGfUxT+HJoPVY1y9QN9jp6rB16F/EwQBgtUqfqapgs9PVfUZzVfOz7V169b12B4bG1vsZ96pU6eWeMwzZ85Ao9FgypQpOHz4MCIjIzF69GiMHz8ecrnP9RNERFReBw4Aznl4mjUD7r1X2nioVLvufgg37fkLAHDbuu/x82OvSBwRUcWqsGtGhw8fjhEjRuDtt9+ukOPpdDpcvHgRDz74IO655x4kJSVh9uzZeP311/Fe/tkYvaDVaqvuw69OB4taDeHaNX459UeCAJPJBFlAAJ8/f8Xn0CUzRwlATJzeyDNDd+2aa1+6TgEgEIGCxWO7N0ItMgDixGF19elINwYBkGHhWQN6Ix3lLnIt5jnMyBNjdro1IA+6aznlvDOqFHwd+jdBgDEgADadDsip/NeY3W4vfZAEDAYDABTqRRsQEICcMj4u586dg1arxR133IFnn30Whw4dwvvvv4+cnBw899xzPh2rKgsRirrf6ppgrw74GJWOj1Hpaupj5OvfU9JjUCGP0Zw5cH5SEV56CVAoquRHyqrir+dRSfEmN2mNC+1vQbPj+xF9IwldzhzFHxVwX4IgQFYLPreW5Xzw1/OoKhV8jMrzWFVYovbIkSNQKIq+HLYsnB9aFyxYAABo164dwsPD8cgjj+CRRx5BmzZtvD5WWFhYhcZWEiEkBDkREQgOD68VL/KaRhAEWHNy+Pz5MT6HbsKRa8CNCwCADLsSwUOGuB4T08kbwLWziIqLRsjQm3w6rkYQ0Pi7Q7DaBTS9uzd6bTiNLWfSAACy2wciJKh8k3sV9xxaTqcCV0+71oeNHVSu+6HKw9ehf3M+f2EJCZBVwQ/dNlvFtk2x2+0V8gO9c4Ivs9nsMdmXyWRCUFBQmY75+eefw2QyudoitGrVCjqdDp988gkmT57sU9xVWoiQjyAI0OvFFjR8fReNj1Hp+BiVrqY+Rrm5ubDZbLBaLLBYLMWOs1ossNlsyM3NLfbHsfI+RvLz5xH6668AAHt8PLQjRlTJD5RVyV/Po9LOk6133o9mx/cDAO7avQFvCoDVZiv1nAKEIscV/CxS0lhfjlvdjlnaa6o4/noeVaWCj1F5ChEqZDIxnU6HM2fO4IEHHihzIAUdOnTINXGDk7M/7eXLl31K1Mpksqo7mRQKyEJDxf94AvsfQYDMbufz58/4HLro4f6BymS1Y2tiLvp3qAcAyLGnAgDCwzSQFdFHsSQKAIsn9oUgAEqFHI/d0QFbzmwGAGjlaoSHhpQv8HzP4elr2Zj3xzHY7QKGdm7oGqJRK32Om6oQX4f+zfn8yeVV8vxV9H306dMHI0eOxMiRI9G0adMyH8fZ8iA1NRUNG7rff1JTU9GqVasyHVOtVheq0G3ZsiX0ej1ycnIQGRnp9bGqshAhP2eFSDh/iCkWH6PS8TEqXU19jEJDQ6FQKKBUqaBSFf/jvlKlgkKhQGhoaLF9vcv9GH3xBWSOY8ieew7hsbG+H6Oa89fzqLTzJLHTrUhu1BJ1E8+iefIV3BoVD6VCUeo5BcgKjXM+Rkql0vUYFTfWl+OWdVxlHrO011Rx/PU8qkoFH6PyFCL4nKhNSEgo9MSoVCo89NBDuOeee8ocSEFxcXE4c+aMxzbnepMmTSrsfoiIaiqD2bOn628HLrsTtXli/9qI4LL1lFXkq+KKDQ9C3UgNkrP00Obri1teZqsNb/x4EDl68Zhnr7t/+X33wVsq7H6IqGaZNGkSVq9ejc8//xwdO3bEqFGjcNdddyEkxLcfkVq3bo2QkBDs27fPlajVarU4efIkHnroIZ/jEgQBgwYNwogRIzBp0iTX9n///RcxMTE+JWmBKi5EKOa++WWteHyMSsfHqHQ18THy9W8p7e8v82OUlgZ8+624HBIC2cSJNbZdkz+eR6XGKpNh590PYcxCcQ6jKXk5+LGc9+VPj095lPVc8MfzqKrlf4zK8zj5nKidMmUK4uPjC11qZbVacfLkSdx0k2+X0BbnkUcewRNPPIGPPvoIo0aNwrVr1/Dmm2+iX79+aN26dYXcBxFRTZZnEhO1CVEaXM/U4/S1bHy87l9MvrODK/kZrlGXdAivhQWpkZyldx23ImTkmjyOl5ItXkoypmdTtK3vW0KDiGqPsWPHYuzYsbh06RJWr16NJUuWYPbs2RgwYABGjx6NW2+91avjqNVqPPTQQ5g7dy6ioqJQr149vP/++4iPj8fgwYNhs9mQmZmJ0NBQj9YIxZHJZBg0aBC+/PJLNG3aFO3bt8eePXvwxRdfYPr06eX9s4mIyBeffAI4J6WdMAGIiJA0HPLdie4DMXDFJ4hMT8ZgkwE7byQBTVpKHRZRufnc2GrAgAHIzs4utP3q1asYN25cRcQEAOjduzeWLFmC3bt3Y/jw4Zg+fTr69u2LefPmVdh9EBHVZDl5JgBAjxZxrm1/HLoi7qvgRG24RrzU5s2fDmHj0asVcsyCSd+UHHFin9By9sAlotqhSZMmmDp1KjZs2IBnnnkGmzZtwuOPP47+/fvj66+/9uqStClTpuDee+/FjBkzcP/990OhUODLL7+ESqVCcnIyevXqhXXr1nkd0wsvvIAJEybgww8/xJ133omvv/4a06dPx3333VeeP5WIiHxhMAALF4rLCgXg42SOVD3YlUrsvsvdfvOOnd7/e0xUnXlVUbt8+XJ89dVXAMTLtkaPHl2oolar1SIhIaHMgcyZM6fQtr59+6Jv375lPiYRUW104UYOdEYrMh2J2qZxYR77F/153JUEjQiumESt3uxOePxxKBHN48Pw/c7zuLdnU7RKiCjTMXP0Jo/1TJ24HhpUMTETUc129OhRrF69GuvWrYPZbMagQYMwatQoJCcn4+OPP8a///6LDz/8sMRjKBQKvPTSS3jppZcK7atfv36hNl35bd68udA2pVKJZ599Fs8++6zvfxAREVWM774TWx8AwJgxQOPGkoZDZXe43z3o89OnCDXkofs/u7EnIwXa6LjSb0hUjXmVqB01ahSysrIgCAIWLVqEIUOGIDg42GNMcHAwBg8eXClBEhGRdw5eSMP07/d7bEuI0nisrzmQiACVOAFNVEjZetQWZLa6E7VXM/Lw24HL2H4yGdtPJmPRhF5oXte3hvWAu49uQY1iyjlZGRHVaIsXL8Zvv/2GK1euoH379pg6dSqGDRvm0aNWpVJh5syZEkZJRESSsNuBDz5wr7/wgnSxULlZAoPwd5c+GLlzPZR2G3quX4END7FCmvybV4naoKAg16QHMpkMjz/+OIKCgio1MCIi8t2Za9mFthU1YZjJIiZW60YGF9pXFs/c0Q6zVh1Bao4BOqMF/17JdO37essZvPuA75N/FdXvVi4DmsX7nvQlotrju+++wz333IPRo0ejRYsWRY5p1qxZkVWyRERUw61dCzivhujbF+jaVdp4qNz+7tYPQ3b+iSAI6LJ5NbaMngBzUMV8xyGSgs89aidNmgSVSoWUlBRcv34d169fx7Vr13Dp0iWsWbOmMmIkIiIvZeqMhbZFBgegcUxokePDKqjfa5v6kVg2pT/iI8Qf8a5m5Ln2/XMpHXkmS6nHsAsC3lhxAK+vOAC7ICC7iERt/egQBDqqgYmIijJ16lT897//LZSk1ev1+OabbwAAbdu2xQMPPFDErYmIqEbLX0374ovSxUEVRhscip8cidlAQx46b/1d4oiIyseritr8du7ciVdeeQWZmZmF9gUGBuKee+6pkMCIiMh3GbmmQtuC1Aq8/3APHL6Qjvlr/4XebHXtk8lkFXr/retF4ka2wWOb1S7gfLIWHRtHF3u7PKMFfxxKxN5zqQCANG1DaItM1PLXcSIqLDMzE0bH7N0zZ85E27ZtERkZ6THm5MmT+PDDD/HII49IECEREUnu4EFg2zZxuVUr4M47pY2HKsynwWF42KADAPRc/wP23TEGgpzFHeSffE7Ufvjhh2jbti3GjRuH5557DnPnzsX169exYMECzJ49uzJiJCIiLzkn3HIK16ghk8kQFqRGv/YJ6No8Bm/8eBDHr2RixC2NK/z+W9eLwNYT113rbepH4NTVbKRpDSXcCvhkw0lsPHbVtZ6SY0SeSUwo1wkNRHqumICpG6kp8vZEVLtt374dr776KmQyGQRBwL333ltojCAInKSWiKg2K9ibVu7zBcZUTZ1RqfFvy5vQ4ewxRKYlo82BbTjZvb/UYRGVic+J2vPnz2PWrFlo3bo12rRpA41Gg3HjxkGj0eDLL7/EwIEDKyNOIiIqRa7BgjPXswEAfdrWxamrWXjzP908xoQEqvDBwz2RlK5DXETF9xof1LE+/j52FedvaNG2fiTqRQfj1NVspOYUTtQazVYoFHKoFHKPJC0ApGqN0DsTtWHuRG1CFCtqiaiwESNGoF69erDb7Xj44YexYMEChIe7+1nLZDJoNBq0bNlSwiiJiEgyly8DK1eKyzExwLhxkoZDFe+v24aiw9ljAIBb133PRC35LZ8TtQqFAqGhYq/DRo0a4ezZs+jZsyd69OiB9957r8IDJCKi0iVn6fHEJ9tc6xMHt0V0aGCx4xvUCSl2X3mEBKqw6IneMFpsUClkWL79PAAgTevZO3fj0auY98cxNIkNxcIJvRAWpILW4O5jez3L4GrR0LBOCE47JklrElt0r10iom7dxB+mli5dis6dO0Op9PljLhER1VQLFgA2cTJdTJoEBBb/OZn808nm7ZHSoBniki6g4dljqH/uX1xt0UHqsIh85vMn2BYtWmDz5s0YN24cmjZtikOHDuHhhx/GjRs3KiM+IqqhMnVGhGvUUPCSowrx1spDsNjsAIB2DSJLTNJWBeeEXzHhYhzpBVof/P3vVdjsAs7f0OJaZp4rSTthYGt88fdpbDudCpVCPDdub18PNzWKhlIhQ9v6nj0niYgAYOHChXj88ccRFBSE/fv3Y//+/cWOnTRpUhVGRkREktNqgS++EJcDA4Gnn5Y2HqocMhl23/kARi55GwDQc90PWPkcE7Xkf3xO1D755JOYMmUKVCoVhg0bho8//hhPPvkkzpw5gx49elRGjERUw1y4kYNnPt+J3m3qYsa9naUOp0a4mKJ1LT93V/X5QBIdIiZqC/bOzcq3vudsCgAgQCnHiFua4M8jSbiakefaHxakQuemdaogWiLyV6tWrcKDDz6IoKAgrFq1qthxMpmMiVoiotrmq6+A3Fxxefx4sfUB1UjHbrsDA1csQmhOJtrt24yNadeRHZMgdVhEPvE5UTtw4ECsXLkSCoUCdevWxRdffIGvv/4aAwYMwJQpUyojRiKqYX7dfxkAsONUssf2dK0RYRoV1ErO0Fke9aMrp61BWYQEqQAAOqPFY3v+Vghf/H0aAFAnLAgqhRwDO9TDN1vPuvZrAnj5MhGVbPPmzUUuExFRLWeziW0PnJ57TrpYqNLZVGocGHQv+v/8GeSCHd3//Akbxj0vdVhEPvH5muNJkyYhMDAQrVu3BgDccsst+OSTTzBz5kxERERUdHxEVANp1O7Em9kq9opKStfhwfmbMHPFQanC8lsXbuS4lt8f3wMKuUzCaDyFBBZO1OaZLK6JwvKrEyZW3zavG+6xnYlaIiIiIiqTNWuAS5fE5TvuANq2lTYeqnT7B42GRaUGAHTZ8hsC9DqJIyLyjc/ffvfu3YuAgIDKiIWIagm7ILiWb2Tp0TAmFEu3iRWURy6lSxWWXzJabHjm852u9Q4NoySMprBQR6I2z2iFXRAgl8mQnKkHAIQEKhGmUeO6Y72Oo69uqKMK14mJWiIqTevWrSGTefcj1alTpyo5GiIiqjY++si9/PzzUkVBVUgfFomjve9E182rEWjIQ+etv2PPnfdLHRaR13z+9jty5EjMnTsXzz77LBo1agS1Wl0ZcRFRDSUIgkcy9sCFNGToTNh+0t0GwWy1sf2Bl3af9pzI0dtERVUJDhT/mREA6E1WhASqsPrAZQBA07gwWG1C4URtoDtRq5TLXJOKEREVZ9asWdXu/Y+IiCR2+DCwfbu43KaNWFFLtcKeoWPRdfNqAECPP1dg3x1jpA2IyAc+J2q3bduGK1euYMOGDUXuZ5UCEZVkx6kbHhNFrd5/GTGOS96dMnNNiI/UVHVo1ZrZasOZ6zloUy8CynyJyyvp7kt53hjTRYrQSqRWKhCglMNktUNnsCAkUIVjlzMAAPfd2gxrD11xjXW2PgjTuH8AVCrkTL4QUalGjRoldQhERFTd5K+mfe45gJ8pa420+k1xrmNPtDi6B5FpyWh9cBsuhISXfkOiasDnRO3TTz9dGXEQUS2x5fg1j/XUHAOy80we2zJ0RiZqC1iw7jg2Hr2Kh/q0wLi+LQGI1cnOpPcTA9vg1tbxUoZYrJAgFUy5JuiMFpitNqTmGAAALeqGIzzYnZR1JmqD87U64OdpIvLGtGnTMH36dISEhGDatGnFjpPJZJg1a1YVRkZERJJITgZWrBCXo6KAceOkjYeq3O47H0CLo3sAAD3Xr8DaMU9JHBGRd8rU+oCIqKy0BvekUiqFHBabHWarHQBQPyoYVzPzkK41ShVetbXx6FUAwHfbzyE5S49ebeLx7ZazuJyWCwCoFxUsZXglCg5QIcORqD2XnAMBQJBagXCN2qOtQYPoEACe7RsiQ9gTnYhKd/XqVdjtdtcyERHVcosXAxbH946nngI0LAKpbS50uAWp9Zog9tolNDpzFI2Tr5R+I6JqoEwztGzbtg1ffvklLl68iB9//BGrVq1Cw4YNMXz48IqOj4hqEEEQcDlVTCwufqIX5vz6j8el+03iwnA1Mw83sg1ShVgtmSw2j/VN/17Dpn89K5PrR1ffRK1zcrAcvRmzVh0BAMRHaAq1NGhQJ6TQbaNDAgttIyIqaNmyZUUuExFRLWQwAJ9+Ki4rlcCzz0obD0lDJsOeoWMx/IvZAIA79m/GIolDIvKGzzO07Nq1C5MmTUJCQgK0Wi3sdjusViumTZuG1atXV0KIRFQTGM1W/LrvEnRGC4LUCjSoEwJNvkvcVQo5msSGAgCS8iVvCfhkw4kS93dpFlOtE7XOBOyxxAzXth4t4wAA9/dqjk6No/HW2K5F3rZ7y9jKD5CIahyTyYSff/4Zs2fPxgcffIDff/8dVqtV6rCIiKgqLF8OpDsmL77vPqBePWnjIckc6zUU+pAwAECPE4cQa+NnAar+fE7Ufvzxx3jhhRcwZ84cKBTirOxTp07F1KlT8eWXX1Z4gERUvZmtNnz0xzHsPnOjxHFv/XwYSzaKkw32al0XaqUC3Vu4k3AWmx0NY8SEXmJ6buUF7GfsgoD1R5KK3R8fEYQZoztX6wm32jeIAgBs+Ee8HDkiWI1Hbm8FAIgODcR743qge4s4j9u8N7YTHh/QGqO6N6naYInI750+fRoDBgzAu+++iwMHDmD79u2YMWMG7r77brZFICKq6QTBcxKx55+XKhKqBiwBgTjYX2zfqbTb8Jie3zOp+vM5UXvmzBn079+/0PYhQ4bgyhX2/CCqbbaeuI71R5Lw5k+HYDAX/wvloQtpruWWCeKMm/f3au4xppGj8jIpXQdBECohWv9z5lp2ifv/c1tzj8rk6uimRlGQQUzGA0D96MItDgpqHBOMMT2bQiH3+Z8pIqrl3n33XbRv3x7bt2/HqlWr8Ntvv2HLli2IjY3FO++8I3V4RERUmf7+GzjhuBrtttuAbt2kjYckt3/wvbDJxSLDR/S5UFrMEkdEVDKfvwGHhoYiNTW10Pbz588jPDy8QoIi8jer91/Cs5/vwJFL6VKHUuVSc9wTfx08n1Zof0auEdcy8zy2xYQFAUChKtDYcHG7wWyDvoSkr5MgCPjzyBVcuJHjc9z+4pe9F0vcH65RV1EkZRcXoUGftnVd684WF0REleHo0aN48cUXERrqfq+JiorCq6++ir1790oYGRERVTpW01IB2ug4nOwuFhvG2u3ofoyfBah68zlRe/fdd2PWrFk4ffo0ZDIZ8vLysH37drz99tu48847KyNGokpldVT5lVWuwYJPNpzE+RtabC4wwVNtkJzlTsLmGMRfJy+laKE3iYnWBz7ahMcWbfW4TZ2woieIClQroVGL1aGZuaZS7/vwxXTM++NfPPP5Tmj1Ne+XUZPFhn3nxB/G/nv3TUWOCXNM1FXdPTOkHZrEhqJxTCjuu7WZ1OEQUQ0WFxdXZFFBTk4OIiMjJYiIiIiqxNmzwLp14nLDhsCIEZKGQ9XH3iH/cS0P3L1BbJFBVE35nKh9/vnn0aRJE4wYMQJ6vR4jR47Ek08+iZYtW2Lq1KmVESNRpRAEAXN/O4q7Zq3HHW+vxdYT172+rdZgxuXUXFxJy0Wa1uDanmuwlHi71BwDJi7Zjt8OXC71PraduI7Xlu+r9hNrXc/Uu5b1JisOXUzDxM92YPGGE9AZi348YopJ1AJAVEgAACArz4tEbb4KZmdCsyY5npQJs9WOOqGB6NAwqsgxoUHVv6IWACKCA7D4yd749KnersppIqKKcv36ddd/48ePx4wZM7B161ZotVrodDrs27cPb7zxBp577jmpQyUiosry8cfu5WefBZTVuz0YVZ2kFh1wIaExAKBhciIanzoibUBEJfD5nUulUuGDDz7Ac889h5MnT8Jut6Nly5Zo3rx56TcmqkaSMvKw8Zh7UpEP1hxF33yXZxfncmouXvh2jysJOWloe9c+raHkqs4/DibiUmouFv95AsO7NYbZaoPFakdwoGdV5LXMPMxaJf7jsfHoVTw2oLXXf1dVS8nxTNRuPS4mvM8n5+D4lcwib5P/cv137u+Gt38+jOfuFB/HqNAAXM3MQ2auCZdTc7H/fCpGdm8ClaLw70onk7Ly3XfJSXJ/lJgmJunb1I8sdI44hWn8o6IWAOTVeMIzIvJv/fv392inIwgCJk6cWGjbtGnTMIIVVkRENU9ODvDNN+JyUBAwYYKk4VA1I5Nhwy2345nVXwMAevy5ApfbdpY4KKKileknJrvdjqSkJCQnJ0MulyM2Nrb0GxFVMzey9B7rZqsdZqut1Nv9fvCyR6Xo9pPuStzSKmoD1QrXcnaeCS8v24uMXBOWTenvmhAqz2TBgnX/usYlVpOKWrsgQAbPvrI2u4AsnbvyVWe0YP95sbL1Umou3vjxYKHjTBrazuMY3ZrH4teX74BCLm6LDBYras9cz8bsX8VkdXRIAAbcVN/jOIIg4FKq1rVuspavhUV1lO2oKo4ODfCYMKxJbCgupYozlob5SUUtEVFlWrp0qdQhEBGRlL76CtA5vjeNHw9EFX01GtVe+9vcjBFrliLBbkPrg9sQkXoN2bH1pA6LqBCfE7WpqamYMGECzp49i/DwcNhsNuh0OnTu3BlLlizxmLiBqDq7kS0majs1jsa/VzJhswu4nJqLuOCSq/6SCyR4nQkzoPREbf4E5cQlO1yX91/N0KFlQgQA4PUfDuBEvkrRq+k6CIJQaOKtqmSzC5jy5U4EqBT44OGerlhy9CbY87X3OXo5A9l5xVcVf/F0XzSoE1JouzNJCwCRjtYHq/Zdcm07m5xTKFGbpjXCYHYn1s2W0pPs/ibH8ViGa9RQKeSYcW9nGMxW6E1WfLLhJABAWUSlMRFRbXPLLbdIHQIREUnFZgMWLnSvT5kiXSxUbdkUSnylCcUMXTbkgoDuG1Ziw7jnpQ6LqBCfE7VvvfUWVCoV1q1bh6ZNmwIAzp07h1deeQWzZs3C7NmzKzxIosrgTNQ2jg1FnsmKc8k5yMozI85R0Vn87Qwe6/mTs7kGc4lJVecEW4BnD9bNx6+jZUIEbHY7zlzLBiBeJm4XBFzNzMN/Pvwb306+HUFqafospWkNOH9DrF7VGa0IdUxglVFgwq8rpVT/1o8OLvW+Qou4xL+oPr0Ft9XkitoIxznZu01d1/Zvt55FuwacFIeIqCCTyYQff/wRZ8+ehc2W7wc9sxnHjx/Hhg0bJIyOiIgq3Nq1wMWL4vKgQUDbttLGQ9XWt5pQvGzMg9pqQZctv2HLvU/AHFT6d1SiquRzKdbu3bvx5ptvupK0ANCiRQvMnDkTmzZtqtDgiCrT1Yw8AEDdSA3CHInH3FJ6zNoFAak5YqJ2VPcmhfZb7YJHlWdBecX0Uf113yUcupiGlGwDrHYBaqUca6cPde3P0ZtdiVIpGPP9Tdn5EswZuUavj9G2fqRXVcH5L/F3upyWW2jb0csZHuvetK3wN1mOitqIYM/2BhHBAfjh+QF48z/dpAiLiKhae+edd/D+++/j3Llz+O2333DlyhXs2LED69atQ//+/aUOj4iIKtqCBe5lThpJJchQKLC3060AgEBDHjrtWCdxRESF+ZyoDQ4OhsVSONmkUqmgUvnPpDZUu5mtNhxLFBN97RpEIdTR5zNHX3KiNktngsVmh1wmQ8fG0UWO+X7HuSK32wXBo6K2oKOXM3AtU0weJ0QGQy6TYeJg96/BaTmG4m5a6fInmLPzTLiYosXH6/7F2ysPFRorl4nJb6eFE3ph1oO34K2x3iUVi0rUZuSaYCrQ2mCbozdw07gwACi0vybI1otJ8cgiqrwD1UqPlhFERCTatGkTZs+ejR9//BH16tXD22+/jS1btmDAgAFFfoYlIiL/JT9xArLNm8WVFi2AoUNLvgHVen/fOsS13OPPHyGz17wrM8m/+ZyonTJlCmbOnIlTp065tl29ehXvvPMOJk2aVKHBEVWWQxfSYTDbEBkcgGbxYQjTOCtqS/4C52yXEBMWiC7NYqDMlyiLiwgCAJy8mlXodn8fu4rhc/7EthPXC+1zkstkrsv56zlaBIy4pTEaOnq6pmmrNlGblK7DpC92Yt+5FI/HJSvPjG+3nMEfh67AahegUshxd9dGrv33dGvscUl+XHgQujSNcbVLKE1wgOe4AJU4AVtKtrs3sF0QkOJoQdG5aR0A4mRwNYnVZndN1BZRSjsOIiJy02q16NxZnMm5efPmOHnyJFQqFZ566ils2bJF4uiIiKgiBXz2mXtl8mRAzvkbqGTX4hvgYtsuAIA6yVfQ/NheiSMi8uRzw8uFCxciIyMDo0aNQnBwMJRKJXJyciAIAv755x+89dZbrrH5k7lE1cHl1FzI5TKsO5wIABhwUz3IZTKEOypqtY7WB9cy85CpM+GmRp5Vs87kYFxEEFQKOSbd2R6/7ruEQR3ro3VCBF5curfIybQWrj9eaiLxh53nXUnHZo4qUZlMhl6t4/H9zvNIqeKK2k//OolzyTmYueIgXhre0bU9O8+Ew5fSXevzHr0VCrkMvx8UH9P/3NYMq/a6JwLzNkHrlL+iNkitQN3IYFxM0eJGtgENY8TJCvOMVjjnMIt2TD5W0ypqzybnwGy1IzRI5foRgIiIShcVFYWMjAwkJCSgcePGOHv2LAAgMjIS6enppdyaiIjKIi0tDVpt6a3aEhMTYbUVf5WhT9LTof7pJ3E5LAx45JGKOS7VeHuH/AdNT4pXh/ZYvwIn27GlHFUfPidqn3/++UoIg6jyGS02PLVkOwAgXCMmZnu3iQcAhGqciVoLBEHA44u3AQCax4dh1oPdXeOdFbVxEeKl/UNvboihNzcE4J7cKv8kYU4RwQEwmPWFthd0+KL4BbJlQrhrW0y4mKSr6tYH+ato808SlpFrhN0upkmXTemP2PAgCIKA/9zaDPWigxEVEohebeKxcs9FhAQqvepLm1/+RG1woArxEUG4mKJFcr6KWp1RjC1ApUCwY/Kxmtaj9t/ETADATY2iIffxMSQiqs369OmDN998E7Nnz0aXLl0wa9YsDBo0COvWrUN8fLzU4RER1ThpaWkY/+gEZOeW/n3HaNDjWnIyOpsroBXN559DZnTMmfHYY0BoaPmPSbXCmS69kRmbgKjU62hxbC9iriciu1FzqcMiAlCGRO3IkSMrIw6iSpd/ojBnL9p4R8LVNZmY3oIMnXvc+RtavLXyED54uCcAuKpa48MLVzhGOio79SYrth6/jj7t6roSbMUl2upHB7smNXNSKeRoVS/Cte6MTWesoF+evZRrdD8Op/K1cziWmOlqeVAnLBCAWPn72IDWrjGt60Vi7vgeiC3icSpNcL5EbUiAynUfzjYA649cwUd//AsACA1UQa0UL28y1bDWB86J2hpEcxZSIiJfvPzyy3j11Vexf/9+PPDAA/jxxx8xZswYKJVKvPfee1KHR0RU42i1WmTn6tGi72iERceVOPbaueNIXPUVrNZyJmotFmDxYgCAIJNBxjaM5ANBrsD+wWMw5Lv5AICeG3/G+gmvShwVkcjnRK3ZbMbKlStx9uxZmM2el3jLZDLMmjWrwoIjqkgFL40PVClclbLOHqBZeSacu5HrMe74lUzX8o0sz4ra/IIDlFAp5LDY7Jj96xGYbTYM7tgAAJCpM3qMfX98D1zLzEP/9vWQkq3Hk59ud13KP6pHE4Q5WjEAQJAjcWkweyZqrTY73vzpIOpHh+CR21vBZLEhTKNGRcgzWlx/KyAmZ52cPXjrRQWXWOnZoVHRk62VRpOvR21woBIhgc5Etfhhzpmkde4PUIo9bM01rPWB8+91/v1EROSdsLAwLHZ8eQeAzz77DKdOnUKdOnUQGxsrYWRERDVbWHQcouLqlzgmJ/1GxdzZqlWQXbsmLg8bBjRrVjHHpVrjcL97cPvKzxBgMqDzzvXYcv+zMAazKpuk53Oi9pVXXsHff/+NNm3aICCAE9yQ/zCYPRN5cRFBrsvyo0PFqs30XCM2n0wpdNsPfz+K/97dEdcdycuEqMKJWplMBk2A0lWtu/9cKgZ3bACD2VrovlsmRLj63zaMCcXMMV3w5kqxR0635p5fIp0VpnqTZ6L21LVs7D+fhv3n07Dt5HXojFYsnXx7uSee+mHneXyz5Uyp45z9dCta/tYHSoUcoYHFT/QWEqiC2jHZWE2rqM1zJGqDmaglIvKZyWTC77//jnPnzkGtVqNly5Zo2bKl1GEREVFFmT/fvTxlinRxkN8yBofinz53ofvGn8Vk7ZY12D3sQanDIvI9Ubt9+3Z8+OGHGDRoUIUGsmTJEuzcuRPLli1zbUtNTcWcOXOwfft2KBQK9OrVC9OnT0dUVFSF3jfVDsYCFZdx+S7Lr+NI1OpNVhy7kl3othv+uYoJA9u4+sTWiyr6cvR6UcGuRG1MmHj8zNzCPWsDHclFp/BgdyVs01jPX/GC1I5EbYGK2ux8vXAzHPdxMSUXnZuWP1HrjaE3NyjX/RQnSO1+bFolRCDE0fph56lk/H7Q87UfGqhCgKP1QY2rqHUk5llRS0Tkm9OnT2PChAnIy8tDkyZNYLPZsHTpUixevBiff/456tcvudqLiIiquQMHgD17AAC2Nm0g799f4oDIX+0d8h903/gzAKD7Xz9hz51jIcgVpdyKqHLJfb1BWFgYmjRpUqFBLF++HB999JHHNrPZjMceewzXr1/H0qVL8dlnn+H06dN45ZVXKvS+yf8JgoAN/yTh7PXsEscZCyQ681eeagKUHpWcRTmZlAUBgEatdLVMKOjVkZ0QEigex1kBej0rr8ix+bVMiEC7BpEY1LF+oQpKZ6LWYLJCEMQGCSnZerzz8+FCx3H2NS2P/H9bXIEeszPu7YxVLw3GqpcGo2FM5VwWIpPJ8OI9HfHI7a3wyO0tEeJohWC1C1i4/rjH2JAgFVRKZ0VtzUrUuipqSzkviYjI07vvvov27dtj+/btWLVqFX777Tds2bIFsbGxeOedd6QOj4iIymvBAtei6amnAE68S2WUkdAI5zqK89FEpiWj1aEdEkdEVIZE7cSJEzF79mwkJSWV+85TUlIwceJEzJ07F40bN/bY98cff+DatWtYuHAh2rZti44dO+LVV1/FpUuXoNPpij4g1UoHzqfhw9+PYfKXu0ocZyzQfqBgstVZVVucQxfTAIhtD2TFfBiIi9Dg8QFtAAAbj13FmevZrnYJJVEp5PjwkVvx4j0dC+1zJuqsdgEWm3h5/7dbzxZ5nPQKSNQ6Wyx8NrEPxvX1vEw0KiQAwYGqSr8cf1DH+ri/V3Mo5HJXRW1R6oQGuitqa1rrA8fzwNYHRES+OXr0KF588UWE5pv9OyoqCq+++ir27t0rYWRERFRuycnAjz8CAISoKJjHjJE4IPJ3e4f8x7Xc888fJYyESORzorZly5Y4fvw4Bg8ejDZt2hT6zxcnTpyASqXCmjVr0LGjZ4Jq586d6NGjB+rUcffB7N27N/7++2+EhIT4Gjb5mZRsPVIdbQbyO3k1C+uPXHFVlgLuBCoAj+0FGSyeFbX52w0AQHyEZ/VoQWsOJAIAEoppe+AUmi+xOHvVEVzPFCtqG8eEQi4DJg5uW+LtCwpUuysqnUnUc8k5rm3DujTE/b2aAyh/Ra3VZndNYhURHID+HRLQsI779Rahqfq+1MVd+j+6RxOM6tHUNdma3mSFzV5zkrXuycRYUUtE5Iu4uDikpqYW2p6Tk4PIyEgJIiIiogrz6aeAxTF3xRNPAJrCc4cQ+eL8TT2QFi+29Wty8hDiEs9JHBHVdj5nAKZPn47GjRvjnnvugaacb4r9+/dH/2L6yVy6dAldu3bFokWLsHr1alitVvTq1QsvvfQSwsLCfLofQRBKTOBVJOd9VdX91UQmiw0TP9sBvcmKxwe0xpieTQEAyVl6TP16NwCgflQw2jcU+5XmryLNzjNh6bazOHg+DfMevdU1SRggtg7IL0Kj9nieujSLwf7zaShNQqSmxOc3NF9iMTPX6ErU3tOtEfq3T0CgWunT+SGXAQEqBUwWG/QmK8I1ao/+tPf2aIqDjmR1utZYrnPPeVy5TEwQymUyjLylMeavE1sOhGlUVX5uhxRz6f+jt7eCUiFHcIASQWoFDGYbrqTp0CgmxO9fgxm5RldSXuPj+VIT8H3U//E59G9V/fxVxP1cv37dtTx+/HjMmDEDM2fOROfOnSGXy3HixAm88cYbeO6558p9X0REJBGTSUzUAoBCATzzjLTxUI0gyOXYPXgMhi/9EADQ488V2NtrqMRRUW3mc6L26tWrWLNmTaFWBRVNp9Nh9erV6NmzJz744APk5ORg9uzZeOaZZ7Bs2bJiLz0vilarhVzuc/FwmQiCAL1evNTdlxjJ7Ua2wZWk+nLTabSvq0FCZBA2HrnqGnPg7HU0CBd7k15J1bq2f7HxOP4+ngIA+G3feYzu5p7wKjvXs1es3G5BTo67MrVVbOHWB/WjNEjJMcBiy/cl0uZ5u0Ks7sRxRLAa6VqxMjhQboPJkAdT4ULhUgWp5DBZbEhOy0QgzMh1VFsueawbguQWqCG2dcjQ6kuOrRRX08THKDRQhVyt+LjqDe6ArcY85Jiq9ry2m4vuPZuny3UtN4kJxslrWhy9mIwIdWylvAZtdgEKeeX97em5JtgFAbFhgXj/txOu7VaTHjm28re08Cd8H/V/fA79W1U/f/YKuBqif//+HrEKgoCJEycW2jZt2jSMGDGi3PdHREQSWLECcF4xMXo00KABUI7vPkROh3sNxR0rlyDQkIebdm1A6M29pA6JajGfE7UdOnRAYmJipSdqlUolNBoNPvjgA6hUYoVieHg4xowZg3///Rc33XST18cKCwuDQlE1M/c5q0LCw8P55bSMruV6fmG7obOjTeNwaE3uvsgX0wwIDw+HIAhI15ld251JWgA4eyMP4eHhrnVB7t4HAMHBGo/9QcHuS/zb1IvAC/fchNjwIJy5loP56/7F1Qwxidn3poYIDy++qruR0p3wDdOokaMXk6oJMZEe9+eL4EAVsvUWvPbTMbx4T0c4i48S4qKhUshRJ1K8D7NNKPN9AIAtQ3wsI0ICXMdpXs8K4IK4PSKizMcuq7BiKq3y/51tGkTj5DUtrmZbXNsr8jV4PTMPk77YhUEd6+PpO3xrXeENq82OCV/sR67BgpeHd8SxJPEDZ4BSjpjo2neZLt9H/R+fQ/9W1c+fzVb+ySCXLl1aAZEQEVG1JQjA/Pnu9SlTpIuFahxzoAaHbh+O29Z9D5XFjNuP7MQKqYOiWsvnRO3w4cMxbdo03HvvvWjQoIEriepUUVUK8fHxEATB4/gtWrQAIFb1+pKolclkVfpF0Xl//HJaNjl6s8f6pdRc9JfJPHrWnryaBQFAnsnmqr4t6N8rmdCbrQgOEM8hk8X9RVCjVqJDo2iP5yhAla8XrNmKBnXESUhuahyNL5/pB63ejEydCY1j3ZOTFCUyJBCP9W+Frzafgc5ohdbx94Rr1GU+JyJDAnEtU6xumrvmqOtvUCvFHyCCHX1MDWabV/fh/BJecGyaVqzcjAkLcu3r2Dgazwxph0YxIZKc0wXvc0zPpmhVL8Jje8u6EQDE3r35X38VFe/6I0nQm6347cBlPH1H2wp/HLQGC3INYrL9/34Tn9+IYDW+f35grX0f4fuo/+Nz6N+q8vmriPu45ZZbityemZkJpVLpc9ssIiKqZnbtAo4cEZe7dAFuvVXaeKjG2XfHGPRcvwJywY6BB7dDFRIhdUhUS/mcqJ05cyYA4LPPPiu0TyaTVViitlu3bli6dCmMRiMCA8UKxbNnxZnuGzVqVCH3QdVTdl7BRK14CX5qjvvy7zyTFYmpubDZi+9rZ7MLOHo5A7e2igcAGMxiQvfB3i0w5tamCFIXf/oX7GcLiNWxYRp1EaML69kyDl9tPoOMXCPMVrFCuODkZb545o522Hz8Gn7ec9G1LVTj/hFD4/hb9Oaik9b52QUB//16NyADPnj4Vo/L+VMcyfDYcPfEajKZDMO7NS5z7BVtwsDCkxa2SBCraC/c0OJyai4iK3jOM5XC3TolJduA+MiKnbTAOXFYft1bxFZqqwUiopps6dKl+Oyzz5CRkQEAqFOnDh5//HE88sgj0gZGRERlk7+a9rnnAJkMYC98qkDZsfVwumsftD2wFZG6HNyjVKHmTFVN/sTnRO3p06crI45Cxo4di+XLl+OFF17A888/D61Wi//973/o3r072rVrVyUxkDScFbV1IzVIztLjakYeBEFwVdTGhAUiTWvEwQtpOJoofgFrlRABAQLOXhcvGW8UE4LENB0yde5Jt5wJ4IhgdYlJWsC7hGdJnAldZ5JWKZe5kqll0Sw+DM3iw3D0cgbOJYt/Y1iQO/Eb5JhwS2+yQhCEEquTjlxKx6lr2QCApHSdR4VwSrb4GMdHBBV102orIVKDJrGhuJSaixe+3YMF4zujHB0gCskxuH88+GXfRTw7pH3FHRxwVdPm5/yBgYiIfLNixQq8//77eOCBB9CtWzcIgoADBw7gww8/REhICO69916pQyQiIl9cuQL8+qu4HBcH3HeftPFQjbV3yFi0PbAVADAxT4vF/DGAJFDmGbauX7+OHTt2wGg0uqoVKlJUVBSWL18Oq9WKMWPG4Omnn0aHDh2wcOHCCr8vql6y88TkavsGUQCAlGw9EtN00JutUCnk6N+hHgDgi02nceB8GgAxqXtL81jXcvN4MUtnyJdwvZYp9phNiAou9r4f7tcSADDlzg7l+htCAj1bgljtJSdPvdWirjv7GBZUuKLWZhdgsRX/u9/1zDy8tny/a/3CDc/m+6lFVNRWB8pSKktlMhnmPNQdUSEBYrV1ur5C79/ZEgIAth6/XsLIsslfUftAr+aYProzureIrfD7ISKqDb755hu88sormDZtGgYOHIhBgwbhtddewyuvvIJvv/1W6vCIiMhXixYBzn7mTz8NBFTw5XNEDpfb3IzkRmLLzS4WM5olnZc4IqqNfC7xM5vNeOWVV7B+/XrI5XJs2LAB7733HvLy8vDxxx8jJCSk9IMUYc6cOYW2NW7cGEuWLCnT8ch/ZTmqYJvGhUKjVkJvtmLKlzsBiInKjo2j8eOuCx63qR8djJHdmyAyJAB92ybgmy1i5bfRLP6DbhcEJGeJybt6JSRqx97WDL2aR6BBfJ1y/Q1KhRxhQSpoHZWSjWLK9rooaHi3xjh/Iwdnr+egUxN3jIH5qnX1Jqurd63Tt1vPIF1rxD+XPX9U+XrLGUSFBuJmx7HSc909aquTh/q2xDdbzmCAI0lflIjgAMRHaJCpM0FnLF9FdEHp+RK1OmPpVcvesgsCLFa7q6K2S9M6ePj2VuU+LhFRbXb9+nX06dOn0PbevXvjvffekyAiIiIqK5leD3z+ubiiUgETJ0obENVsMhn2DL0foz59CwAwcNcG/NH3LomDotrG54raTz75BKdPn8a3336LAMcvWePGjUNiYiLmzp1b4QFS7ZO/T2rr+hEAAJOjhUC7BpFoUy+y0G3qRmoQEqjCsC6NEBqkcrU2cFbUpuUYYLHZoZTLSqwWlclkHpWq5TGsq7uX8mP9W1fIMRvHhuLjx3vh92lDcN+tzVzbFXIZAlVicrbg5Gq5Bgu+33Eefx296jEhGyBWiuZPejsnPvO2F29Vue/WZvjwkZ54fljJlc4hjucuz/EYHL+SiS3Hr5X7/rPytdCwCwKMFu9mKLcLAr74+xS2n0wucv+sX47gwfmbsGyb2H87NKh6Pe5ERP4oISEBx48fL7T933//RZ065fshloiIqlbo6tVAVpa48sADYusDokp0vOcgaDVioVWXE/sRlpEicURU2/icqF27di1ef/11dO/e3bWte/fuePfdd7Fp06YKDY5qpxvZYuVrfIQGr43qjIQo98RNbetHQhNQuBA8rkDyNdCVqBUTas5etdGhgVU2QdP9vZpjwsDW+Hby7ejRsmI/UBSsmAXgelzyt3uw2Ox48KO/SzyW87J7m93uSnBWVLK6oijkMrRrEFXk351fqKPlhM4k/k0vfLsHc379p1CLB1/YBcHVN9kpz8uK3f3nUrFyz0W8+8vhIvfvOJWMXIPF9eOESlnmbjREROQwduxYvPnmm/j+++9x+vRpnD59GsuXL8dbb72F0aNHSx0eERF5SSYICM/fsua556QLhmoNqzoAm7qIV+Yo7Hbc8tfPEkdEtY3PrQ9SUlLQsGHDQtvr1q2LnJyyJ0OIAMBosbkm/YqL0CA0SIXHbm+NdxyJrrYNxGracX1aYNn2c7itdTyaxoaifcMoj+MEqcWEntGRtHQmbItK8lYWtVKBMT2blT6wgmjUSmTC5FFRe/Z6tqsauTjO8fkntAqtZolabznj3vjvDfx2yF1Jezk1F83iyza7mM5ogd3RRD5IrYDBbEOeyYI6CCz1ts5WEkXJMxaeQCxNayhiJBER+WL8+PG4du0aZs2aBZvNBkEQoFQqMXbsWDz99NNSh0dE5DfS0tKg1WpLHZeYmAirrWJbjwFA96wMqC84rv7r2xe4+eYKvw+iomzq0ht37VgHNYCum3/FtlGPwxJQ+vc/oorgc9aqWbNm2LNnD8aMGeOxfe3atWjevHmFBUa1U4qjmjY4QOlKunVvGYsuTesgJjwIEcFiu437ezdHv/YJqBcVXGSvUGcbAOcl6s4q00B1yRWZ/izIkYTOy5eovZSa61oee1sz1I8Owdw1RwEAASoFTBYbrmXmITXH4EpqhwQqoZD7Z2Wns3VAqtbksb1gRawvnO0gNAFKhGvUMJj1HpN/lcSab2I3uyBAnu9cLdiGAgDG3sb3UCKi8jp8+DBefvllPPfcc7h48SIAoGnTpmWeR4GIqDZKS0vD+EcnIDu39El6jQY9riUno7PZu8/I3vrPtST3yvPPV+ixiUqSExKOX4OC8R9DHjQ6LW7a+ScODRghdVhUS/icqJ08eTKmTp2K8+fPw2az4ddff8WlS5ewYcMGzJs3rzJipFrksiOxWD/a/WVKrVRg1oPdPcYp5HKPMQUFFWh94EzUatRVV1Fb1epFBeNccg5OXs1ytVq4cEP8BTwyOABjezVHkFqJhCgNftx1AcNvaYzXlu8HADyycAv+b1wPAP7dJzU0sOjn93pW6R8wi+NM8oZr1Ah2JMMT03Ro1yCqpJvh/1b/g03/uqt6DWYrggPclcqpBapnXxt1s2tSNyIiKrvJkyfjiy++QLt27XDTTTdJHQ4RkV/SarXIztWjRd/RCIsuuY3btXPHkbjqK1itFZeojb+RhB5ZjomQmzQB7r67wo5N5I1Pg8PwH0MeAKDHnytwqP9woAImlCYqjVdlc23atEFGhvgmefvtt2PBggU4fvw4FAoFvvzySyQlJWHevHm44447KjVYqvkupoiJxaZxoeU6TmAxrQ8Ca3CitnuLWADA5n+vIc/RozUpXQcAeHJQG1fyul2DKLw1thta1HW3ArDZBVflaHg1m0jMF8Ulma9l5rmW955Nwed/n4LNLnh1zJw89+PirIidv/ZfXEnLLfY2NrvgkaQFAIPJcwKyG9liorZVQgSmj+6MPm3rehUPERGVLCoqCrm5xb9HExGR98Ki4xAVV7/E/0IiK77YoN+2P9wrU6YAipp7ZSRVT0dVATjXqCUAIO7qRTQ9fkDiiKi28CprJQieCY0+ffqgT58+lRIQ1W7OS/WbxoWV6zi1saK2R8s4RAYHIE1rxOZ/r+Puro1wPUtMUCZEBRcaX/CxcD721W0iMV+EFZNkTsyXVH3jx4MAgIZ1QnBHpwYlHi87z4Rl28+5jr3/XKpr30+7L+LF4R2LvF1RbQ30JguQr6/tRUe1c+emdZikJSKqQH369MFTTz2Fvn37olGjRggICPDYP2nSJIkiIyIibwTlZuOWg9sAAPbgYMgfe0ziiKi22njrELRIPAsAuHXdD7jY4RaJI6LaoOZmrcgvpWnFyZfqRmrKdRznZGIGi7Oitub3qNUEKHFb6zj8cegKMnONMJqtyMgVe7UmRBV+PJUKz4L6LcfFCtDGseVLkkspokCitnl8GM7f0CIj1wSt3uyRyHVWb5fkh53nXeNaJ0RAZ7Dg5NUsAMCxKxnF3u5qhq7QNr3Zs6L2XLI4+WL+ymYiIiq/DRs2IDo6GsePH8fx48c99slkMiZqiYiqua6bVkNtEa9q044Zg4gw//1+Qv7tSNsuyKpTF5HpyWj5zy7UuXYZ6fUaSx0W1XBeJ2rXr1/v1SQMI0aMKE88VMs5L793ThpWVoEqR0WtybP1QU2uqAWAMMel/7lGCy44EowhgSrX9pIkZYjVt12a+m+f1ILnzZS7OmDWL4dxI9uAy2m5aN/Q3VdWZ7Rg49GrsNjsuLNzwyKPd/SymIy9u2sjPNC7OW5vn4Bf91/CmgOJSMk2IF1rRJ2wwrN/Xs3IK7TN+WMBILZGuOyo8m0Wzw+eREQVafPmzVKHQEREZSS3WtH9r5UAADuAnPHjESFpRFSb2RUK7LvjPgxZPh+A2Kv2j8dflTgqqum8zlq98847pY6RyWRM1FKZCYLgmripvJffO/us5hosMFtttaKiFgBCHY9brsGCH3ddAAC0qR/h0zFa1fNtfHUSHuyZkA5SKRAXocENR1I1z+hOlt7INmDumqMAgPYNo9CwjucPUXlGi2tyuwd7t4BMJkNCVDCeHdIeB86nITlLjxvZ+iITtRm5xkLbdpxKdk0Wlmsww2YXIAMQU8TtiYjIdzdu3MDGjRsREBCAvn37Ii6u5MlviIio+mm3bxPCstIAADuiY1C/YdEFFURV5VD/4bj9l88RYNSj0/a12HTfRBhCI6QOi2owrxO1u3btQnR0dGXGQrWc0WKDxWYHUP4JrSKC1QhQKWCy2JCWY3RV1gbV8IraEEeiVme04FKqWFE7pmczn47hz4+RSiFHSKAKOqM4mVqgWuk6l7LyTNh7NsU19viVTNfyvrMphRK16blGCBCT35EhnpW60aGBSM7SY+PRq4gKCSjUAzjbMQFZfmsPXcGTg9oiUKVApk5sSRGmUUMh92pORyIiKsHBgwcxYcIEGI3iD2UajQYLFixAr169JI6MiIi8Jgjouf4H1+qP9RriBQnDIQIAkyYEh/vdg55/roDabEK3Tb9i+4hHpQ6LajCvMgQyx0znRJXJWU2rVsoRoCpf5atMJkNceBAAIClDB4PF0fogwH+TkN4ICRQTtQcvpLn609aPLjyRmNOSp/pg0tD2VRJbVQkJdD/HQWolIhxVtp9tPOWqoC3o1LXsQtucyV7nY5pftCNx++c/SXh00dZC+7PyTEXeT1K6zmN/VEj5WnwQEZFo/vz56NmzJ7Zv345du3ahd+/emDNnTrmPa7fbsWDBAvTu3RudOnXCE088gaSkJK9uN2HCBHz88ceF9q1fvx533nknbrrpJowYMQJ79uwpd5xERDVBg3P/ov6FkwCApHpNsD9Ig8TERFy4cKHY/y5duoQLFy4gLS1N4uipJts75D+wO/Jit2xYCYXVInFEVJN5lbUSBKGy4yBy9acN06gr5MeB+EgNrqTr8MaPB13bAsuZAK7uQgskFRVyWYn9fhvHhqJxbCgWrj9e7Bh/E5ivIjhQrUCEpvRkaEq2vtA2Z5uE4CKS+9Ghnu0KjBabx7mV7UjEvn5vZxxNzMCaA4kAgMupuWhRNxxZjora8vZiJiIi0cmTJ/Hjjz8iNjYWAPDaa6+hX79+0Ol0Xs2xUJzFixfj+++/x5w5cxAfH4/3338fEyZMwO+//w61uuirf8xmM2bOnIkdO3agY8eOHvv27t2Ll156CS+//DJuu+02/Pzzz3jyySexevVqNGvm2xUwREQ1Tc9137uWN3bvj0u7NuCl6f+DWl38Z2abzQaFQoGIUA2Wfv0FYmJiqiJUqmWy4urhdNe+aHtgK8Ky09Fuz9841nuo1GFRDeVVonbkyJEICGBCgSqXuz9t+doeOMVHBBXaFhehqZBjV1cFqz+jQgKgkJee9H7zP13xf6v/wX/vvqmyQqsy9aOCXb1lVQp5ob61RbmepYcgCB4/EOSZiq+ojQr1fD9MStehRd1wAMCNbD3O3xDbTsSGB+HZIe1hNNvw19GrOJqYgUEd67sStayoJSKqGHq9HhEREa71uLg4qFQq5OTklDlRazab8dVXX+HFF19Ev379AADz5s1D79698ddff2HYsGGFbnP48GHMnDkTRqMRYUXMUv75559j4MCBGD9+PADglVdewZEjR/Dtt9/irbfeKlOcREQ1QUTqNbTdvxUAkBsRjR0tb4KwZxOa9RqF2PqNir2d1WKBXpuJc9t+gVarZaKWKs2eO+9H2wNbAQC3rv8Bx3oNAXj1OVUCr1ofzJ49u1zVCETe2OPoH1ovqmKSqXVCC0/S1KCENgA1QXCBpGKklxWbPVrG4ZeXBqNXm7qVEVaVKjgZWoCy+CpqZ19avcmKXKPn5Su6kipqQzzPLWc/YABYtfeSa9lZMTvgpnoAgI1Hr+KxRVuRlKFz7K+YHyWIiGq7gj+2AYBCoYDdbi/zMU+fPo28vDz07NnTtS0sLAxt27bFgQMHirzNtm3b0Lt3b6xevRqhoaEe++x2Ow4fPuxxPADo3r17sccjIqoteq7/EXJBfM/eN3gMrErxe01oVAyi4uoX+19kXH2ERXPySKp8ia064VqT1gCAhEun0ej0EYkjopqqZjfsJL+y6/QNAMCdnYv/xdQXUQWSaVEhAYUSmTVNnbBAdGwcjaOXMwAAHRpFeX3bmtKL+q7ODfH30SR0aCROfhgb7q6sbtcgEjey9a7+vUFqJSKDA5CVZ0JqtsGjmlvvqKgt6pwJKzDZXVJ6nmv5epZ7uU6YeA52alwHCVEaXM/U41pmHq5limMa1OEPYERE1dWNG+Lnkrp1PX/EjI2Nde0raOrUqcUeT6vVQq/XIz4+3uvjlUQQBEnakznvl63RisfHqHR8jEon9WNUlfcbmJeLzlvXAADM6gAcGDgKuHK+1Ns5Y8z/f55TnqQ+j8qqKuPNf/6U+J1YJsOeOx/AvYtmAgBuXfc9Ett0rooQK1RZzgd/PY+qUsHHqDyPFRO1VCEyco1IztKjfUPvE4P5Gc1WZOeJrQ9aJoRXSEwF+4jWhqSYXCbD/43rgX8upyMl24CBjkrO2kQToMT/3d8J4eHieXRToyhMubM9msWHo2VCOARBwJ3vrgcAyOVAaJAKWXkm6EzFVNQWlagN8tx21VEhCwAWm1gJ8PLwjpDn+4e+Tmggrmd69sJtElv4slgiIiqbr776CkFB7h/nrFYrli5d6vr3wGnSpEleHc9gMABAoV60AQEByMnJ8Tk+o9FY7PFMpqInoSyJVquFXO7VxXEVShAE6PXiv2c15UfeisbHqHR8jEon9WOUm5sLm80Gq8UCi6XkiZOsFgsAAVabrUxje278BQFG8W893OtOaAODvT6mzWaDzWqFzWZDbm5umd6fazKpz6Oyqozzr6RxNpvNq7H/dOmDQZF1EJ6VjlaHdiDs6qUKuf/K+JuKGlvW14m/nkdVqeBjVJ6rupiopQrx5KfboTNaMP+x29C6wKXn3kjNEb8MaQKURfYELYvoAn1E60XV7LYH+XVqXEfqEKoNmUyGu7o0yr/BtSiXyVytDfSOxKyTztEKIaSI1gcF+yhfzXBX0eoM4u0KVt0W/EFNBnEyNyIiKr+EhASsX7/eY1tMTAw2bdrksU0mk3mdqA0MFH/wNZvNrmUAMJlMHglhbznnezCbzR7by3q8sLAwKBRVP0mqs0IkPDycX9aKwceodHyMSif1YxQaGgqFQgGlSgWVquTvZ0qVCoAMSoXC57FyqxW3bvwZAGCXybBv2ANQqVReHdP5GCmUSigUCoSGhhb6ca62k/o8KqvKOP+KG+d8jJRKpesxKvaYKhX23/EfDFqxCHJBQK+/f8HRbv3Kdf+V8TcVN7asrxN/PY+qUsHHqGDy3xdM1FK5pWuNrqTW0csZZUrUpjgStXHhvn9RKU7B1gcVeWyqGRRyGQLV4ttgnskzUas3FV9RG6rx3HY9Mw82ux0KuRy5jkRtaIGq25gwz/MxTKNGoKrqv2ATEdVEmzdvrvBjOlsepKamomHDhq7tqampaNWqlc/Hi4iIgEajQWpqqsf21NRUxMX53l9RJpNJ9mXJed/8slY8Pkal42NUOikfo6q6z/Z7/0Z4pvi+eKZzb2TU9b4NnjPG/P/n+VSYP77WqjLWgudRaQ4OGIG+v34JtcmIm7f+Dk37WyozvApX1nPBH8+jqpb/MSrP41T110tRjXP0crprOc9Ycrl9cZyJ2tgKTKaGBCrRt634JStco8bQzg1LuQXVNnUjNa6KWV2BczfXIFY8FVXhrVF7/sZltQu4kW1w3M6RqA30rKh9tH9rj/WC7ROIiKh6ad26NUJCQrBv3z7XNq1Wi5MnT6Jbt24+H08mk6Fz587Yv3+/x/Z9+/aha9eu5Y6XiMjvCAJuXfe9a3X3XQ9IGAyRdwwh4fin910AgACTAf0P75A4IqppmKilcruWr+/mj7svwFaGXhzO/rRRIQGljPSeTCbDa6M7488Zd+L75wcgvMCl6FR7vT22G25rFYfH+rd2VcwWrKh1npMRwYXPm6J+HUtK18Fqs0NvFo9TsKI2NjwIU+5s71oPDeL5SERUnanVajz00EOYO3cuNm3ahNOnT2Pq1KmIj4/H4MGDYbPZkJaW5uo9641HH30Ua9euxddff40LFy7g//7v/3Dq1Ck8/PDDlfiXEBFVT41PHUbCpdMAgGtNWiOx9c0SR0Tknd133g+74zvh4ANboeYkW1SB2PqAyu1GtucESav2XsKYW5v5dIw8x0RORV1mXl4ymQxKBcvzye2WFrG4pUUsALh61BasBs/Ri4labxP8VzPykJTunlSsyEnI8h2LFbVERNXflClTYLVaMWPGDBiNRnTr1g1ffvklVCoVrl69igEDBmD27NkYNWqUV8fr1asXZs2ahcWLF2PevHlo3rw5Pv30UzRr5tvnJiKimuDWtfmraR/0mEuCqDrLrNsQp7r1Q7v9WxCpy8FohRLe/2wrHYvZjMTERK/GhoWFISYmppIjoqIwUUvlVjBRu+7IFZ8Ttc6JnIKLmLiJqDK5K2rdiVpBEEpN1N7fqzk2/JOEW5rH4s9/kvD536c89ivkhT9oRgS7K8ZZUUtEVP0pFAq89NJLeOmllwrtq1+/Ps6cOVPsbYvrmztixAiMGDGiokIkIvJLcWnX0dpxyXhOVCxOdB8gcUREvtl914Not38LAGBSnhZzq3lVrUGXg0uXLuKl6f+DWl36lcwRoRos/foLJmslwKwYlVuKozfnW2O7YuaKg7ieqYfVZodSUXpnDavNji83ncaf/yQBqJyKWqKSOH8ccPaWBcSJxCw2sYVHeHDR/4g9cnsrPNyvJXaevuE6f52evqNtkbdpWCfEtcwqbyIiIiKqrQbv+tO1vHfoWNiVTE2Qf0lqeRMSW3VEozNH0cZqQfuzx5DV1PfJRquK2WiAIFeiWa9RiK1f8qR92owUnNv2C7RaLRO1EuC7IZWL2WpDRq5Y5N+ybgTkMsAuAFqDGVEhgSXe1mqz4+Vle3EiKcu1jRW1VNWck4XtPpOCNK0BMWFByHZU0waqFAhUKYq9rUwmQ9PYsELbB3dsUOT4/NW5mTpTecImIiIiIvJLUXYbbnVU05oCNTjYf4S0ARGV0a67HkSjM0cBAEN2rMUPQ8ZIHFHpQqNiEBVXX+owqAScTIzKJTXHAAFiQisiWO26nNs5EVNJNv17zSNJCwDBAayoparVsXG0a9l5PrraHhQxkVhB8ZGaQhOOabz4wSF/dS0RERERUW3xRJ4Waqt4Nduh2++BScPPxeSfznTpjeQoce6TNhdPoq5jcjyi8mCilsrlhqPtQVxEEGQymSth5U2i9q+jVwttCw5kRS1VrejQQPRwTCymN4m9knMc52+EpvTePQq5DO+P64EJA1sDAAZ0qFfi+MVP9MZ/bm2G+3s1L0/YRERERER+J8BswhN5uQAAm1yBPXc+IHFERGUnyBVYn6+/8m1/fCdhNFRTMCtG5XI9Mw8AEB+hASBOlpSYpkOOvvTLuq9l5BXaxtYHJIWCE4plO85fbypqAaBhTCgaxoTirs6NEKguvlUCADSLD0Oz+MLtEoiIiIiIaro+R3cjShDngvj31sHIqRMvcURE5bPzpu64e8OPiLHb0W7vJmwc+yxyYupKHRb5MVbUUpml5hjw6/5LANyXcUdovKuotQuC+/LyfH072fqApOBsVaA3elbU5j83vT2OXMZJwoiIiIiICpLbrBi6d5Nrfefd4ySMhqhiWFRqfKERC3EUdht6rv9B4ojI3zFRS2X2w87zuJ6pR0igCiO7NwEgVtQCcE0wVhydwQK7IAAAwoLcydnQICZqqeo5K7n1ZkeiVu9sfeBbopaIiIiIiIrWbu/fiMnJBAAca9kRqQ3ZCoxqhi+DQ2FWirmMLpt/Q6BOK3FE5M94nTmVWUqO2J92XN8WiA4NBAA0cFTWXk7LLfG22Y5EWHCA0tXnFhUYMKsAAFiySURBVHBfgk5UlTSOSu48R0Vtdp5vrQ+IiIiIiKgEgoBevy9zrf7Z5y4JgyGqWJlyBXZ16YPb921CgMmAbpt+xY7hD0sdVq2QlpYGrda7xHhYWBhiYmIqOaLyY6KWyizXkWx19qcF4Oq9eeB8Gg6cT0W35rFF3tZVsRgcgFE9muDjdcfx8vCOlRwxUdGck9g5e9S6K2pLn0yMiIiIiIhK1vzYXtRNPAcAOKRS40yTNih5Cl4i//JXr6Hou38z5IKAHn+uwJ6hY2FV8/tkZUpLS8P4RycgO1fv1fiIUA2Wfv1FtU/WMlFLZaY1iMmssHyXhzeJDXUtrz+SVGyi1lWxqFHjrs4N0bdtAtsekGScrQ/yTJ6tD3ztUUtERERERIXlr6ZdEBwOJed1oBomNToeJ2+5He33bUZodgY6bfsDBweNljqsGk2r1SI7V48WfUcjLDqu5LEZKTi37Rdotdpqn6hlj1ryidlqw7YT16E1mKE1iNWH+XvMBqmV6NQ4GoC7OrEoWr07ySuTyZikJUk5J7HTOxK1uUbx3OV5SURERERUPgkXTqLpiYMAgOSoWKwN1JRyCyL/tOMed7uDXn98B7nNKmE0tUdYdByi4uqX+F9pidzqhIla8sk3W85g1qojeP2HA66kVliQZ9XhsK6NAAAWq73Y4zgnbXJWMhJJSRPg2frA2auW5ycRERERUfnkr6Zd32Mg7KympRoquWkbnO/QHQAQlXoN7fZukjgi8kfVJlG7ZMkSjBs3rtj9M2bMQP/+/aswIirK38euAQBOX8sGAMhQeAKwILWY3DKYbcUex+jYF6RWVHyQRD5yVoWnZBuw4Z8k6BwVtZzcjoiIiIio7KJuJKHt/i0AgNzwKOy8qbvEERFVrvyTiPVe8y0gCBJGQ/6oWiRqly9fjo8++qjY/X///TdWrlxZdQFRsRRyz18/Q4JUhbY5k68Gc/Fl/s59zqQukZQigsUm7za7gA9/P+bazkQtEREREVHZ3bp2OeSCeKXl3iFjYVHy8zXVbJfadkFS8/YAgPgr59HyyC6JIyJ/I2miNiUlBRMnTsTcuXPRuHHjIsekpqbi9ddfxy233FK1wREAwGix4avNp3ExRYs3VhxAps7ksb9g2wMA0LgqaktK1IoVtYFM1FI1EBKkgrzAJVgKuQwBymrxWxYRERERkd8JyUrHzdv+AACYAjU4MHCUxBERVQGZrHBVLZEPJM1CnDhxAiqVCmvWrEHHjh0L7RcEAa+++iqGDx/ORK1Evt58Gj/uuoCnP9uBvedSC+0PK2KyJVfrA1PxiVqjq6KWrQ9IenKZDMGBnj8aBAcoIWP/LCIiIiKiMrlt7XKoLOIk0gcGjoIxJEziiIiqxpnOvZFarwkAoNGZo2h4+h9pAyK/Imk5Y//+/UvsO/vNN98gLS0Nn376KZYsWVLm+xEEAUIV9QVx3ldV3V9lO5GUWeL+0CBVob810JF8NVntsNpsUMg9fw+4kaXH5uPXxbEqRbV6rGra81cblfU5zDVYPNaDAwuf21Q1+Dr0f3wO/VtVP388T4iIah6NNhtd/14FALCo1Nh91wMSR0RUdQS5HDvuGY/Rn7wJAOjz27fYNuxBiaMif1Ftrzs/ffo0Fi5ciOXLl0OtLnx5vS+0Wi3k8qopHhYEAXq9HgBqRjVeKV+eApVATk6OxzaLze5aTk3PgibA8zR748d/3Ie3mgvdXko17vmrhSrqOQxQyqrVuVmb8HXo//gc+reqfv7sdnvpg4iIyK/0+HMFAkwGAMCh24dDF1GnUu/PYjYjMTHRq7Fms9mrHIO34wAgLCwMMTExXo2l2uHfW+9A/5WfITI9GS3/2YWG3fpKHZJP8r+mBEFAbm4uQkNDC3025Llf8aplotZkMuHFF1/E008/jdatW5f7eGFhYVAoquYSe2dVSHh4eI34cpqqNZW4Pzo8GOHh4R7bBEGAQi6DzS5AGahBeFiQx/7E9DzXclREaKHbS6mmPX+1UVmfw85N6+DwxXTXukwmr1bnZm3C16H/43Po36r6+bPZbJV+H0REVHUC9Dp03/ATAMCmUGDX3eMq9f4MuhxcunQRL03/H9TqgBLHWsxmXE1KRIPGTaBUFJ8O8XacU0SoBku//oIJK3KxK5XYNexBDPtmLgBg2K6/8InEMXmrqNeUzWYrMq/Gc7/iVctE7dGjR3Hu3DksXLgQixYtAgBYLBZYrVbcfPPN+Pzzz9G1a1evjyeTyar0i6Lz/vz9y6ldEJCjN5c4JlwTUOjvlMlkCFIroTNaYLTYPfbvOZPiMVajrn59QGvK81ebleU5fHl4J+w/n4qfdl/A1Yw83NykDs8BCfF16P/4HPq3qnz+eI4QEdUs3f9aiSC9DgDwT+87kVMnvlLvz2w0QJAr0azXKMTWb1Ti2GvnjuNi4ldo0nN4iWO9HQcA2owUnNv2C7RaLZNV5OFIv3vQb9WXCNFmofupQ2hcJ0HqkLxS1GvKarFAqfKco4jnfuWolonam266CX/99ZfHtmXLluGvv/7CsmXLEBcXJ1FktYvJ4q5wWTalPy6n5uL1FQc8xtjsRbdG0ASIidoz17Kx5sBlPNSnBS7c0OJ/Px30GBeorpanINVCkSEBuKNTA/Rtl4BrGXloEhcqdUhERERERH5FZTSg57ofAAB2mRw77nm4yu47NCoGUXH1SxyTk37Dq7HejiMqiSUgEHuHjsXAHz+BXBDwvC4H26QOygf5z3+LxQKVqvBk8lTxqmWWLDAwEI0aef5qFR4eDqVSWWg7VZ78ido6YYEwmq2FxoQGFn0KhWvUSM0xYO6aowCAdK0Re86mFBqnVlZN72AibwWqFGgWzxlpiYiIiIh81XXzrwjOzQYAHO85EJl1G0obEJHE9g+6F7f9vgxBeh3uN+hwPCsNaNJS6rCoGmOWjIplNIuJ2gCVAnKZDJEhga5900bdjLu7NsIdNxf9D29ksGfT9aKStAAQxIpaIiIiIiIiv6e0mHHbH8td69tHPCphNETVgzE4FHuHjAUAqADcuXWNtAFRtVdtsmRz5swpcf/kyZMxefLkKoqmdrDZBZy8moUW8WGuFgTZeSbMXnUEd3VphIZ1QgCIFYYAEBqkwkN9WsBis6NfuwT0a1d8f5XIkOKbuP/ntmYI16ihlMtQN1JTgX8RERERERERSeG2wzsQlpUGADjZtS9SGzSTOCKi6mHP0LHo8cd3CDIb0evwdhxKv1HpvZvJf7Githb74+BlvPjtHsxedcS1bem2s/jncgbe/eUwjI7WB85ELQCM69sSj/VvXeqxQ4PcFbVDOjVwLWsClHisf2uM7tEUw29pUhF/BhEREREREUlIKQgYuv0P1zqraYncjCFh+KtbPwCA0mZD79++lTYgqtaYqK3FfjuQCADYey7VtS0nz+xaTtMaAIitD3xlsdpdy+0bRrmWY8ICixpOREREREREfupeQx5iHNW0527qgevN2kocEVH18mf3/siVyQAAnbeuQVhG0e0hiZiorcU0AYU7X2gN7kTtr/suAfCsqPVWcL5JxurkS87GhQf5fCwiIiIiIiKqnuR2G17UZbvWt49kNS1RQTpNCD7XiJNWK60W9FqzVOKIqLqqNj1qqeoF50vU2uwCFHIZrqTrXNtOJGUBAALVvidqR3ZvgpNJWejfoR6i8/WrbeDoe0tERERERET+77Zj+9HUZgUAXGzXFYmtb5Y4opohLS0NWq3Wq7FhYWGIiYmp5IiovBaHhOEZixGBZhO6bPkNO4Y/gtwoPm/kiYnaWkytdBdUZ+QaEahSIDtf6wOnslTUhgWp8d64HgCAPKPFtT0+gpOHERERERER1QRyqxUjd6xzrW++90kJo6k50tLSMP7RCcjO1Xs1PiJUg6Vff8FkbTWXKVdgS49BGLr9D6gsZvT6fSnWP/yC1GFRNcNEbS2WZ7K6llNzDK7lAJUCJsdEYs718sjfYqEOe9QSERERERHVCDdv+wMxORkAgOMtOuBK607SBlRDaLVaZOfq0aLvaIRFx5U8NiMF57b9Aq1Wy0StH9jQaygG7PsbapMRXTetxo57HoYuso7UYVE1wkStnxIEAQIAuaMZdVnkGtyVrpk6E3SOyte29SNx5FK6a59aWb5ErUwmw7i+LXExRYvuLWLLdSwiIiIiIiKSnsJiRt9fv3Strx44WsJoaqaw6DhExdWXOgyqQLkh4TgwcDRuW7scKosJvf5Yhj/HTZU6LKpGOJmYn3pr5SE8tmgrjPkqX32lM+ZP1BpdVbX1o4M9xp29nl3m+3B6qE8LzBzTBQo5TzkiIiIiIiJ/13nLGkQ4Zq7fEBCESw2aSxwRkX/YOewhWFTiXD5d/16FkOz0Um5BtQmzZn7IZhew+0wKkrP0OH4ls8zHyV9R+8mGk8jINQIAQoNUHuO6NWcVLBEREREREYmUZhP6rv7atT4nNEK6YIj8TF5ENA4MGgUAUJtN6PPr16XcgmoTJmr9UK7BPeFXWTsfmCw2WGx2j21/Hb0KAAgNUntsf7BPi7LdCREREREREdU4XTavRlhWGgDgUMubcNRRHUhE3tlx93iYAoIAAF03/YqItOsSR0TVBRO1figj1+Ra1uebEMwX+dseFBQa6K6obRoXhpBAVbFjiYiIiIiIqPZQmYzo89s3rvVVfe6SLhgiP5UXEY29Q8cCAJQ2K/r98oXEEVF1wUStH8rKcydqS0q4liR/24OCQgJVeH5YB0SFBODFe24q0/GJiIiIiIio5un29y8Izc4AAJy4pT+uxDeQOCIi/7Rr2EMwBIcCADptX4c61y5JHBFVB0zU+qFMndG1XFLCtSTOBG9ClAYLJ/Ty2BcapMLQmxvih6kD0Sw+vOyBEhERERERUY2hMhrQa81SAIBdJsOWe5+QOCIi/2UMDsXOu8cDAOSCHf1XLpE4IqoOmKj1Q5n5Wh9sOnYNZqsNu07fgNFi8/oYzkRtSKAKdUIDPfax1QEREREREREV1HP9DwjRZgEATvQYiNQGzSSOiMi/7b3jPuSGRwEA2u/bjLoXT0kcEUmNiVo/dD0rz7V8OS0Xd8/+E2+tPIRf93lfJu+sxA0NVCFMo0b+OclCg5ioJSIiIiIiIjeNNgu9fl8GALDJFdgymtW0ROVlCQzCtpGPudYH/vSphNFQdcBErR9KSs8rcvvGo1e9PkZuvopahVwGudydqmWiloiIiIiIiPLr++vXCDSI30UP334P0us1ljYgohriUP8RyKpTFwDQ4ugetEo8J3FEJCUmav2MIAi4kq4rcl+LusX3kxUEAfvOpeDQhTQAgM5RURviSMra7IJrrFqpqKhwiYiIiIiIyM9FpF5Dt40/AwDMAYGspiWqQDaV2qPf85itawBBKOEWVJMxUetndEarq7/sgsdvw5pXh2DS0PYAAKPZWuzt/jh0BTNXHMRr3+/HlbRcjx61RERERERERMUZ8NMSKG3i9809Q++HLrKOxBER1SzHeg1BWkJjAECrpAsYaDJIGxBJholaP6M1mAEAgSoFWiVEIEClcLUq0JeQqL2YonUtP/HpdqRk6wG42xy0TBCrcfu1S6iUuImIiIiIiMj/xF86g467/gQA5IWEY+fd4ySOiKjmsSuU2HTfU67113OzILPbJYyIpMJErZ9xVsLm7yMbHKAEAOhNxSdqM3ONHuv7zqWKx3FU1L5+bxc8PqA1ptzZvkLjJSIiIiIiIv81aMUi1/L2kY/BpAmRMBqimutUt9txrUlrAEAHqwW3HtkpcUQkBSZq/Yyrt2y+lgUaR6I2r6RErc7kse7sduI8Tmx4EO67tRmC2QqBiIiIiIiIALQ8ewwtju0FAGTVqYv9g0ZLHBFRzSXI5djw0HOu9ZEbV0JlZAuE2oaJWj+TW0RFrUbtRUWtI1GbEKXx2B4SxMQsEREREREReZIJAkb8vsy1vum+ibCp1BJGRFTzXW7bBYda3gQAiNRm4ba1yyWOiKoaE7V+JreEitocvRlvrzwEs9XmcRu7ICArT0zUNokJ9dgXygpaIiIiIiIiKqB/WgoaJl0AACQ3aoF/b7tD4oiIaocVA0bC4li+7fdlCMlKlzQeqlpM1PoZZ49az0Ste3nn6RvY8E+Sx22ydCbY7ALkMqBBHc9+QiFM1BIREREREVF+JhMmXr7gWt14/yQIcqYPiKrCjeg4fK0Ri+wCTAYMWPmpxBFRVeI7rZ9xJWrztz4IUHiMuZqR57F+4YYWAFA/OgRRoYEe+9j6gIiIiIiIiPKL+OYb1Hf0xrzYrivO39RD4oiIapf/C42APlBsXXnz1t8Rl3hW4oioqjBR62e0ejMAz5YFCrkcSrnMte5sj+B0/kYOAKBZfBjC8iVm5TKZq78tEREREREREZKTEbl4MQDALpNj/bjnAZms5NsQUYXKlCvwR797AAByQcCQ7xYAglDKragmYKLWzySl6wAA8RGek4KplO6nMk3rOSvgpdRcAECzuDBEBAe4tteN1EDGf3CJiIiIiIjI6bXXIM8Tr9Lc1XMQUhq1lDggotppU8/ByIqpCwBodnw/WvyzW+KIqCowUetHbHYBFx1J1+bxYR77VAr3U5mlM3nsu5GlBwDUiwpG2/qRru0dGkZVVqhERERERETkb/bvB775BgCgVSrxx533SxsPUS1mVanx1/2TXOt3LF8Auc0qYURUFZio9SM3svQwWWwIUMpRL9pzUjBlvkSt1tH64NDFNPx24DJuZIuJ2rgIDQJUCswY3Rk3NYrCg31aVF3wREREREREVH3Z7cCUKa7VLxo1RV5IWAk3IKLKdqLHQFxp0QEAEHvtErpsWi1tQFTpmKj1Ixk6IwAgJjwICrlny4L8rQ9yDRYIgoDXlu/H4j9PuBK38ZFBAIDebevi/fE9ERseVEWRExERERERUbX2/ffAvn0AAHOzZlhVt77EARERZDJseOg51+qAnz6FRpslYUBU2Zio9SPOicTCgtSF9k0e2t61bBcE5DjGOoUGqRAcoCp4MyIiIiIiIqrtdDrglVdcq+nTp8MmZ7qAqDpIankT/uk9FACgydNi0IpFEkdElUkpdQDkPWdlbFhQ4YRrt+ax+OmFQXho/iaYrXZcTsv12D/wJv4aSkREREREREWYMwe4fl1cvvtuGPr0Ab75XtqYSFJpaWnQarVejTWbzVCrCxeUlXVcYmIirOzF6uGvByaj9cHtCDTkocuWNTh0+3BckjooqhRM1PoJi82OTzecAACEaop+YwvXqBEWpEZ6rhHHr7hL4UODVHhqUJsqiZOIiIiIiIj8yMWLwNy54rJKBXzwgbTxkOTS0tIw/tEJyM7VlzrWYjbjalIiGjRuAqWi+BSTt+MAwGjQ41pyMjqbLT7HXlPpIupg030Tcde34utz2Ff/h50PTJY4KqoMTNT6ibWHEmGy2gEUXVHrlJ4r9rFdtu2sa9snT/aGTCYr7iZERERERERUGwkCMHUqYDKJ688/D7RoAVy4IGlYJC2tVovsXD1a9B2NsOi4EsdeO3ccFxO/QpOewxFbv1G5xznHJq76ClYrE7X5HRg0Gp23rkHdxHNIuHwG/Q/vwB9SB0UVjolaP3H2eo5ruaRes3ERQUjJNrjWe7epi5gwThpGRERE5Cu73Y6FCxdi5cqVyM3NRbdu3TBz5kw0aNCgyPFZWVl45513sH37dshkMtx11114+eWXERTk/iw2ePBgJCYmetxu5MiRmDNnTqX+LURERVq9GlizRlyOjwdmzJA0HKpewqLjEBVXchvFnPQbAIDQqJgSx3o7Lv9Y8mRXKLH20Zcx4X9PAADGbF2DWWF1JI6KKlq16Q6+ZMkSjBs3zmPb5s2bMXr0aNx8883o378/3nvvPRiNRokilJbNLriWC04Ult9Lwzt5rLeqF15ZIRERERHVaIsXL8b333+Pt99+GytWrIDdbseECRNgNhf9WWzKlClITEzEN998g/nz52Pbtm343//+59qv1+uRlJSEJUuWYOfOna7/pk+fXkV/ERFRPlotMDnfpdPz5wNhYdLFQ0SlutKqIw73HQYACDYa8EZuVim3IH9TLRK1y5cvx0cffeSx7eDBg5g0aRIGDRqEX3/9FW+88QbWrVuHN998U5ogJXYtM8+13LZBZLHjOjSMQu82dV3rQzo1rNS4iIiIiGois9mMr776ClOmTEG/fv3QunVrzJs3Dzdu3MBff/1VaPyRI0ewf/9+vPfee2jXrh169uyJt956C7/99htSUlIAAOfPn4fdbsfNN9+MmJgY13+hoaFV/ecREYnVs9euictDhwJjxkgbDxF5ZeP9k2DQhAAAHjTo0CzxbCm3IH8iaaI2JSUFEydOxNy5c9G4cWOPfStWrED37t0xceJENG7cGH379sXUqVPx+++/F1vFUJPdyBabeN/fq7lHIrYoY25til6t4zH/sdsQWkI/WyIiIiIq2unTp5GXl4eePXu6toWFhaFt27Y4cOBAofEHDx5ETEwMmjVr5tp2yy23QCaT4dChQwCAM2fOoE6dOggP5xVPRCSxAweAhQvF5aAgYNEigPOaEPmFvPAobPrP0671h9Z8A7nNKmFEVJEk7VF74sQJqFQqrFmzBosWLcI15695AB577DHI5Z55ZLlcDovFAp1Oh6ioqKoOVxKCIODN/2/vvsOjqPY/jr/TQyABgoFwpZfQQ0IREJCiICCIgPhT6RBFQZEiTbqFconSAtINKEi5RASFq6JeRaUjglJFiPRQU0jf7O+PNStLQgommU3yeT3PPuycOTvz3XMmy+x3z5zZcIDoOMsk2t2bVMbJMeP/QGv8qwSTejbMi/BERERECqTLly3z45Uta/sDeenSpa3r7nTlypU0dV1dXSlRogSXLl0CLIlaDw8Phg0bxsGDBylZsiQ9evSgb9++ac57M2M2mzGbzZlXzGGp+zVi3/mF2ihzaqPM5WobJSfDiy/i8Ne2zVOnQqVKlhuL3bF/e5cao73EmpP9dT/bSW//93sc2Uub5oU7j6P8dBP2fY91p+72dVS6fI4Kl/6k2baP+bFLn8xfeB8yayMjz0nu5zW5Eevdf2v/ZB+GJmrbtm1L27Zt011Xu3Ztm+WkpCRCQ0OpW7duoUnSAsTEJ7Pr5BXrskbIioiIiOS+uDjLzVldXV1tyt3c3IiMjEy3/t11U+sn/HU39VOnThEVFcXjjz/O0KFDOXDgALNnzyYyMpLXXnstW/FFRUVlO7mbE8xmM7Gxliu98tMX2rykNsqc2ihzudlGbgsXUuTQIQBMdeoQPWAA3PW5Fh0djclkIjkpiaSkpAy3l5yUBJhJNplyrG5W65lMJkzJyYbtP7WuyWQiOjo63f8f7kd22/9e+7/f4yg3+j83jpOc2r/JZDJ0//dbd3m7p3nzwzk4Am03LuHX+g9zrWz601/+0/3f3Uap9XL62M+OnPo7yQl3/62lpKTc97YMTdRmVXJyMmPGjOHUqVOsWbMm26/Py+x+Tv/yGREZm+4+JHfo1/38T32Y/6kP8z/1Yf6W1/1nr8eJu7s7YJmrNvU5QEJCAkWKFEm3fnrTcyUkJODh4QHAsmXLSEhIsM5JW6NGDWJiYnj//fd59dVXs5V49fLywsnJKVvvKSek9lfx4sWVYLsHtVHm1EaZy7U2Cg+H6dMt+3BwwHHZMoo/kPau8Z6enjg5OeHs4oKLS8aDhZxdXAAHnJ2ccqxuVuqltpGTs7Mh+7+zrpOTE56enjk2tU122/9e+7/f4yg3+j83jpOc2H9qGzk7O1vbyKhjOrt1z1SszuKiXgy5HYVLUiI9PpjFB5MWY07nfOKf7D+9Nkqtl9PHfnbk1N9JTrj7by29xHZW2X2iNiYmhuHDh7N3715CQkLw9/fP9jbycsRBTv/yefbSDZtlI36lKEz0637+pz7M/9SH+Z/6MH/L6/77JyMOclPqNAYRERFUqPD36JSIiAhq1KiRpr6vry87duywKUtMTOTWrVuULl0asIzOvXvUrZ+fH7GxsURGRlKy5L1vGHs3BwcHw/6+Uvetv+97UxtlTm2UuRxvI7MZXn0VUj/jX34Z7piH++5927vUGO0l1qz21dWrV4mKisqwzp9//klyNuYcTUpM5M8//0yzf7PZTHR0NJ6enjg4OODl5YWPj0+m27OXNs0L9nYcZdc7niV42s2d0jciqHT8EI13bGJv+5y9MWBGbXSvYy89iYmJ6V59lJ6sHKv302e5+f/OnZ/Z/2Qfdp2ojYiI4IUXXuDChQusWLGCxo0b39d28nLEQU7/8hmTdNNmWTefyF36dT//Ux/mf+rD/E99mL/ldf/9kxEHualmzZoUK1aMPXv2WBO1UVFRHD16lN69e6ep37hxY4KDgwkPD6dixYoA7N27F4CGDRtiNptp164dTz31FK+88or1dUeOHMHHxydbSVoRkfuyYQN89pnledmy1pG1kneuXr1K3wFB3IpOe+XsneLjYrlw6RINEjO+nBsgLiaSM2f+YPSEqbi6uqVZbzKZrPmQEp4erP5geZaStZI/xDk4EtotiDErLH/P7daGcDKwObd8/pX7+87k2LtTUmIi58+FU75SZZydMk9FFuZj1W4TtZGRkfTr14+YmBjWrFmT7siFrMrrX2pz6pdPU0oKa3b+bl0e9aS/vvDmAf26n/+pD/M/9WH+pz7M3/Ky/+z1GHF1daV3794EBwfj7e3Ngw8+yOzZs/H19aV9+/aYTCZu3LiBp6cn7u7u1K9fnwYNGjBixAimTp1KbGwskydP5qmnnqJMmTIAtGvXjhUrVlClShXq1q3Lrl27WL58ORMmTDD43YpIgXfpEgwZ8vfy/PmgQUB5LioqilvRsVRv1QOvUmXuWe/CqV8JD1tJcnLmidrE+DjMjs5UbdGd0uUqplmfnJSEs4sLUdevcOq7TURFRRXK5FdBdqJqbfY92o3GX3+CW0IcTy6bwerx8yGXz7EyO/budOHUr/wRvpLKzbpmWrewH6t2m6idMWMG586dY/ny5Xh7e3P16lXrOm9vb0Pm5Mpr567dJjLWMtfZjF5NaFAl7dxBIiIiIpI7hg0bRnJyMhMnTiQ+Pp7GjRuzYsUKXFxcOH/+PI8++igzZsyge/fuODg4EBISwrRp0+jXrx9ubm506NCB8ePHW7c3atQoihUrxnvvvcfly5cpV64cEyZM4JlnnjHwXYpIgWc2Q1AQ3PhrWr1nnoGnnzY2pkLOq1QZvMuUu+f6yGuXs71NT2+fdLeZlJSU6fydkv99+fyr+P38I8VvRFDtyB4Cv/uMn1t3yZN93+vYu1PqMZ2VuoWdXSZqTSYT27ZtIykpiX79+qVZ//XXX1OuXMHv2NQk7b+8PZSkFREREcljTk5OjB49mtGjR6dZV65cOU6cOGFTVqpUKebPn3/P7Tk7OzN06FCGDh2a47GKiNzTypWwbZvlua8vLFpkbDwikuMSPIqxJWg8ff49AoAOH83ld/+mRHsXvhGp+Z3dJGpnzpxpfe7k5MThw4cNjMY+pCZqSxbNeK4PERERERERkTTOnoXhw/9eXrYMSpUyKhoRyUWnAptzqEVHAn7YTpHb0XRZMZO1rwfn+hQIkrMcjQ5A7i01UVvCI2t3xRMREREREREBICUF+veHmBjL8sCB0LmzoSGJSO7a3ncEMV7eANQ8uJOG32w2NiDJNiVq7VhqotZLiVoRERERERHJjgUL4LvvLM8rVoQ5c4yNR0RyXZxnCT594Q3rcsfV7/HAhbPGBSTZpkStnTKbzWzZdxaA4krUioiIiIiISFYdPw7jxv29/MEH4OVlXDwikmdONHqEvY91B8A1MYGnQybhnJxkcFSSVXYzR63YOnbhlnVErWcRJWpFREREREQkCxIToW9fiI+3LL/2GrRpY2xMIpKnvug9nErHfqb0hTP86+wJev5vC58aHVQ2JCUmEh4enmGd8PBwkk3JeRRR3lGi1s5ExSaydX84ri5/D3auVla/fIqIiIiIiEgWjB8P+/ZZnvv5wfTpxsYjInkuyc2dja++xeCJA3BOTqLT7q9p7V3G6LCyJC4mkjNn/mD0hKm4urrds158XCwXLl2iQWLBGi2sRK2dGbbyRy7djLUuVy9bnPoVdVdOERERERERycSnn8J771meu7rC2rXg4WFsTCJiiCsV/fjquaF0/HAuAAtvXWPm7Whjg8qCxPg4zI7OVG3RndLlKt6z3oVTvxIetpLkAjatgxK1diQ+MdkmSQtQp3xJHBwcDIpIRERERERE8oWzZ6F//7+X330XGjY0KhoRsQO7OzxL9V92Ue3wHsqmmOgftoxP6jSAfJBn8vT2wbtMuXuuj7x2OQ+jyTu6mZgduR6TkKasRNF7D/MWERERERERITERnn0Wbt2yLPfoAUOHGhqSiBjP7OhI2MtTiPIoBkDgsYM89NV/DI5KMqJErR2Jjks7XLtEUd1ITERERERERDIwfjzs2WN5XqUKrFiRL0bMiUjuiynxAMu69LEud1g9h/InDxsYkWREiVo7EhOfTqLWQyNqRURERERE5B7unpd2wwYoXtzYmETErhyqXo+QopYb1Tubkvm/OeModvOawVFJepSotSPRcYlpyh701sTvIiIiIiIikg7NSysiWTTNsyTHK9cCwOvWNf5v3nicCtiNuAoCJWrtREKSiZmfHLIpK1eqKOUfKGZMQCIiIiIiImK/bt+G7t01L62IZInJwYHFz71CpHdpACqe+IXHP5pncFRyNyVq7cRXh8+nKWtU1QcHzSskIiIiIiIid0pJgX794OefLcual1ZEsiC6WHE+Hvlvkp1dAGj6xQbq79xmcFRyJ2ejAyjMTl+OxMvDFR+vIly4fjvN+pa1yhoQlYiIiIiIiNi1adNg0ybLc09P2LIly/PSXr16laioqAzrhIeHk2xK/qdRiogduli1Np8NHMNTS98B4MllM7hSriqXK9cwODIBJWoNczYimiHLfqCUpxtrhz/GqUuRNuvHdw+kbgVvg6ITERERERERu7R+Pbz5puW5gwOsWwd16mTppVevXqXvgCBuRcdmWC8+LpYLly7RIFHzV4oURAfbdOXB00dp/PUnuCQl8NycMSx5O5RYr5JGh1boKVFrkB1/TXVwPTqBSzdjOfLnDeu6WX2aEFDpAaNCExEREREREXu0b5/tzcNmz4ZOnbL88qioKG5Fx1K9VQ+8SpW5Z70Lp34lPGwlybrRkEiBta3fKHzDT1H+918pefUSvYJHETphkdFhFXqao9YgR8/ftD7vH/Kt9fnmsY8rSSsiIiIiIiK2LlyArl0hPt6yPGAAjBx5X5vyKlUG7zLl7vkoVlLfSUUKOpOLK+tGzCSqpA8A5U/9So+QSTikpBgcWeGmRK0Bzl2L4bdzN9OUB1Z+gCKuGuQsIiIiIiIid4iNhaeegkuXLMstWsD77+vmYSLyj0R7l+bDsXOIL1IUgNr7v6P3V/8Bs9ngyAovJWoNcOxC2iQtWOalFREREREREbFKTISnn4b9+y3LlSpBWBi4uRkalogUDFcq+rF++ExMTk4AtN/3P4bezviGg5J7lKg1wKUbaSdub1q9NMU9XA2IRkREREREROySyQT9+sH27ZZlT0/YsgV8fIyNS0QKlNP+Tfj0hQnW5beib9Lo8G4DIyq8lKjNQ7EJySQmm7h4M22itmJpTwMiEhEREREREbtkNsOrr8K6dZZld3fYuhXq1TM2LhEpkA616sw3T79oXQ7auJiKx382MKLCSYnaPHL8wi16z/ua/iHfcuCPq2nWV/JRolZEREREREQs3KdPx2HxYsuCkxNs3AitWhkblIgUaP/rPoj/BTwMgIspmednj+Jfp48aHFXhojtX5ZFNu//gdkIytxOSrWW+JYpw+VYcAJU1olZEREREREQA3nsP9+Dgv5dXrYLOnY2LRzKUlJhIeHh4pvXCw8NJNiVnWi+35Jc4xUAODoR2fI7kYz/zWEIcRWJj6DfjVZaPncfV6nWNjq5QUKI2j0TFJaYpq1Pem8u3LuDo4EC5B4oZEJWIiIiIiIjYlQ8+wOH11/9enj8fevUyLh7JUFxMJGfO/MHoCVNxdc34Bm/xcbFcuHSJBolJeRTd3/JLnGI8k5MT/Uv48HURD2qcPU6R29EMmjWcDyYt4kpFP6PDK/CUqM0jCYmmNGUvtqtF85q+1PhXCVycNAuFiIiIiIhIobZyJQQFWRfNU6fi8OqrBgYkmUmMj8Ps6EzVFt0pXa5ihnUvnPqV8LCVJCfnfQI0v8Qp9iHW0ZF5/V5n7LoFVDzxCx63o+j/9lA+mPQ+ERWqGR1egaZEbR6JT0qbqC1R1I3mNX0NiEZERERERETsSkiI5eZhf0l46SVcJ00yMCDJDk9vH7zLlMuwTuS1y3kUzb3llzjFeAlu7nw4di59ZwyjwqkjFI2JpP87Q/lg4iKulq9qdHgFloZx5pG4RM3vIiIiIiIiIumYNcsmSWt+7TXipk8HBwcDgxKRwi6xSFE+HDePc1VqA1As6iYD3h6Kz/k/DI6s4FKiNo+kN6JWRERERERECjGzGSZPhnHj/i6bOBHee09JWhGxCwkexVg5+j0uVK4JQLGoGwya9iLVlKzNFUrU5pH4v+ao1Vy0IiIiIiIigtkMr78Ob731d9n06ZZlJWlFxI7EF/Vk1RshXPwrWesRE8W4j+bxeHyswZEVPMoa5oEUs9k6onba/zWiTIkijO5a3+CoRERERERExBCJifDCC5aRs6nmzoXx4w0LSUQkI/HFvPhg4iJO120MgFtyEh/djKDlvm8NjqxgUaI2DxwOv259Xqd8SVa/2pbH/DOevFtEREREREQKoBs3oEMHWLHCsuzgAEuXwmuvGRuXiEgmEjyK8dHYuRx+uD0ATkD/T1bQKmyF5SoB+ceUqM1l+36PYOyHe6zLri5OBkYjIiIiIiIihjl5Epo2hW//GoHm5gZr11pG14qI5AMmZxc2DX2T7U3aWsse3biEzitn4WhKNjCygkGJ2lz22YE/bZYdNdeQiIiIiIhI4fPtt5Yk7alTlmUfH/jmG3j2WWPjEhHJJrOjI2vbPc0kz5LWsod2hNF3+qt4RN00MLL8T4laERERERERkdy0YgW0bw83/0pg1KkDe/fCww8bG5eIyD+wsFhxlj7zMslOzgBUOXqAl97oy79OHzU4svxLidpcdu5ajNEhiIiIiIiIiBHi4uCllyAoCJL/uiS4Y0f46SeoVMnQ0EREcsKegOaETnqf6BKlAChx/QpBU1+gwbefGhxZ/mQ3idolS5bQp08fm7Jjx47Ru3dvAgICaNu2LatXrzYouvuTmGzi0s3b1uXmNX0NjEZERERERETyzLFj0KQJLFnyd9mwYbBlC3h5GReXiEgO+7NGfd6f/iHhNeoD4JycxFNL36HL8hk4JSUaHF3+YheJ2jVr1jB37lybsps3bzJgwAAqVKjApk2bGDp0KMHBwWzatMmYIO/DuWu3STFDMXcX1o14jHHdAowOSURERERERHKT2QyhodCoERw5YikrUgSWL4d588DZ2dDwRERyQ0zJBwiduIjd7Xtayxp//QlBU1+g7LXLBkaWvxj6P8SVK1eYMmUKe/bsodJdl31s2LABFxcX3nzzTZydnalatSrh4eEsXbqUHj16GBNwNv15LRqAij7FKFnMzeBoREREREREJFdFR8OQIfDRR3+X1a4NGzZY5qUVESnATM4ubBswmotVatFlxSxckhJ48I9jvL18Bg4enlxPSTE6RLtn6Ija3377DRcXF7Zs2UL9+vVt1u3fv5+HHnoI5zt+bWzatClnz57l2rVreR1qlplSUtj+y0X+s+sP9v1+FYCKPp4GRyUiIiIiIiK56rvvoEED2yRtUBDs26ckrYgUKodadWb5tGVcK1sBANfkJGZF3WDkB7Pwun7F4Ojsm6GJ2rZt27JgwQLKly+fZt3ly5fx9bWd07V06dIAXLp0KU/iux+fH/yT0O/PsPzr43x95AIArWqXNTgqERERERERyRW3bsGLL0Lr1vD775YyT0/4+GNYtgw8PIyMTkTEEJcq1+T9GR/ZTIVQ+/RvDB3zHP47t1umiZE07HZynPj4eFxdXW3K3Nws0wckJCRka1tmsxlzHhwAe3+PYNF/j9qUebg641/RO0/2L/9c6rGi/sq/1If5n/ow/1Mf5m953X86TkQkX/vkExg6FO4cTNSsGaxeDdWqGReXiIgdSHJzZ9uA0Xz3gC991i3iXykmisTG8PSiKfj/9CXb+o3khm/awZuFmd0mat3d3UlMtL0zXGqC1iObv0hGRUXh6Jj7g4dv3IpOU+boaNm/5A9ms5nY2FgAHBwcDI5G7of6MP9TH+Z/6sP8La/7L0VzlYlIfnTxIgwbBnfe7LpYMZg5E15+2fJFUEREAPi1Si0e9vkXH5etQLNDPwLgd+hHqvy6lx+69GFn1/4kubkbHKV9sNtEra+vLxERETZlqctlypTJ1ra8vLxwcnLKsdjuJaCaC3DCpqyomwvFixfP9X1Lzkgd1VO8eHElF/Ip9WH+pz7M/9SH+Vte95/JZMr1fYiI5JiYGAgOhtmz4a8ftQB44gl4/31IZ1o/ERGBKEcnlj/zMmfbPEmnVe9S/EYEzslJtP5kJQE7t7G9zwiONW5tdJiGs9tEbePGjVm3bh0mk8maZN29ezeVK1emVKlS2dqWg4NDnnzR8C3x90jfDgHl+frIBcY8FaAvqflM6vGifsu/1If5n/ow/1Mf5m952X86RkQkX0hOhg8+gMmT4fLlv8t9fGD+fPi//wN9nomIZOrYQ2047d+EVmEraLbtY5xNyZS4dpnn5ozl93pNCG3S1ugQDWW312P06NGDmJgYJkyYwO+//05YWBihoaEMHjzY6NDuycHBgZCg5ozqVJPhnevx2RsdqVvB2+iwRERERERE5H6YzbB9OwQEWG4YlpqkdXa2TH1w7Bg8+6yStCIi2ZDo7sFXz7/Kollr+b1eE2t5tSN7eHv5DFbdjODBy+cMjNA4dpuoLVWqFMuXL+fMmTN069aNkJAQxowZQ7du3YwOLUPVfIvzUNXsjfgVERERERERO5KSAp9+Ck2bQqdO8Ntvf6/r0QOOHoV58yCbV3uKiMjfrj1YidXj5/PxiFncesDXWt4lPpapC97g6QUTKXUx3MAI857dTH0wc+bMNGX+/v6sX7/egGhERERERESk0ElOhnXrLDcFuzM5C9CkCbz7LjRvbkxsIiIFkYMDxx5qw6mAh2n4zWaab1pGiZgoHM1m/H/6krq7dnC4+ePs6vQclyrXNDraXGc3iVoRERERERERQ0RFwYcfWhKxZ87YrvP3h4kT4emnNcWBiEguSXZ1Y0+H/yPMtzz/WjSNMaZkPGOjcTSnEPDDdgJ+2M7ZmgHs6vgcxxs9YnS4uUaJWhERERERESmc9u+HJUvg44/h9m3bdQ8/DG+8YZn6QAlaEZE8kejiysJixbn+4kS6HT9I888/wiMmCoBKxw9R6fghbvqUZXv9ZvyYYjI42pynRK2IiIiIiIgUHlFRsH49LF4MBw+mXf/445YEbcuWStCKiBgkwc2dnU/1Z3fHZ/H/YTvNtq+j9AXLFQ8lr17i+R1h9ACOrJnHiY7Pcqp+M1Kc83+aM/+/AxEREREREZGM3L4Nn39umX922zZISLBd7+kJvXvD4MFQv74xMYqISBpJbu4ceLQbB9o+RdUje2i2fR1+h34CwA1o9Ns+Gv22jxivkhx5+HEOPdKJS5Vq5Nsf2pSoFRERERERkYLn9m348kvL6NmtWyE2Nm2dRo0sydlnn4VixfI+RhERyRoHB077N+W0f1MeuHCW6hsX03Df/yidkgJAsaibNPvvOpr9dx03Sv+L441acbxhK/6s4W9w4NmjRK2IiIiIiIjkf2YzHD8O27dbHt9/D4mJaeuVLg09e8KAAdCwYd7HKSIi/8i1Byuxr93T9As/zYT2T/PoiUPUPPA9zslJAHhHXOThbR/z8LaPuV2sOEdqNyAuMcbgqLNGiVoRERERERHJn8LD4YcfLEnZL7+Es2fTr+ftDT16wP/9H7RqBQVgHkMRkcIu2cGBwzUDud7x/3CPiaLurq+os/dbKh07gJPJcqOxojGRNN37LQ0dHLh48SJUrWpw1BnT/04iIiIiIiJi/xIT4ehR2L0bdu60PM6du3f9ihWhY0d48kl47DFwccm7WEVEJE/FF/Nif7se7G/XA/eYKPwO/UTN/f+j+i+7cYuPJdHREbOjo9FhZkqJWhEREREREbEvt29bkrI//wwHDsDBg3D4cPpTGaRycYFHHrEkZzt1gpo18+3NZERE5P7FF/PicIsOHG7RAefEBDz3f8fO3duZ7etrdGiZUqJWRERERCQdKSkphISEsHHjRqKjo2ncuDGTJ0+mfPny6da/efMmb7/9Nt9//z0ODg488cQTjBkzhiJFiljrbN++nQULFnD+/HmqVKnC2LFjadasWV69JRH7kpICV67AH39Y5pY9dsySnD12zDKlQWaKFIGmTaFlS8ujWTMoWjT34xYRkXwj2dWN01Vrc+mX/xkdSpYoUSsiIiIiko5Fixaxdu1aZs6cia+vL7NnzyYoKIitW7fi6uqapv6wYcOIi4sjNDSUqKgoJkyYQGxsLLNmzQJg9+7djB49mjFjxtC8eXP+85//8OKLL7J582aq2vl8aSL3JT4eLlywPM6ft/z755/wxx94/v675Xl8fNa25eAAfn7QoIHlBmDNm1uep/O3KCIikl8pUSsiIiIicpfExERWrlzJ66+/TuvWrQGYM2cOLVu25Msvv6Rz58429X/++Wf27t3Ltm3brEnXN998k6CgIEaOHEmZMmVYtmwZjz32GH379gVg7Nix/Pzzz6xatYo333wzT9+fSLaZTBAVBTduwM2btv9euwYREX8/rlyxPG7cSHdTDoBTRvvy8oLataFWLahf35KYrV8fPD1z452JiIjYDSVqRURERETucvz4cW7fvm0zLYGXlxe1a9dm3759aRK1+/fvx8fHx2Zk7EMPPYSDgwMHDhygQ4cOHDx4kHHjxtm8rkmTJnz55Ze5+2ak4ElJgeRkSEr6+5GY+PfzhATLckLC38/j4yEuzvK48/nt2xAba/n3zkdUlO0jJiZH34K5SBGoXBmHKlWgShWoVu3v5GzZsppbVkRECiUlakVERERE7nL58mUAypYta1NeunRp67o7XblyJU1dV1dXSpQowaVLl4iKiiI2Nhbfu25ica/t2Y21a2HzZjCbrUUeSUmWmzZlxx2vz9G6d9a/83X32sa96ty5jbu3d3dZZo+UFIolJYGTk3XZ+m/qw2y2jFBNXU59bjKl/0hOtn1kt43yWtGi4OMDDz5oeZQr9/e/5cphrlyZSHd3ipcooYSsiIjIHQp0otb81wmMyWTK032mpKRgMplw0ElHvqP+y//Uh/mf+jD/Ux/mb3ndf6nnaWY7SzzFxcUBpJmL1s3NjcjIyHTrpzdvrZubGwkJCcT/NQ9nettLSEjIclyp7ZScnJz7bRYRAS+9hENKik2xE5CS/ivkLw7kQhs5OlrmY82jOVnNDg5QrJhlugFPT8t0BJ6eULIklChheaQ+f+ABS2LWxwdKlcr0hl5msxlTVBTJycn6f+IezGYzJpMpx9soJSUFdzc34iOvEu107+0mxtzCo0gREqJuEH3tQobbzI26WalnNpsxJSeTEH3TkP3nZt2c2mZqGzk5OxeY95TT+7+zjVL/1vLL+8+rNk2vjfJy/zmxzfjIq7i7uZGSkkJycnKGde/H3Z/Z/+T81sFsb2fFOSgxMZEjR44YHYaIiIiIZKJevXrpJjqN8sUXXzBs2DB++eUX3N3dreWvvfYaiYmJvP/++zb133rrLQ4fPszGjRttyps1a8bgwYPp2rUrTZs2ZenSpbRq1cq6fs2aNbz33nscOHAgS3Hp/FZEREQkf7if89sCPaLW2dmZevXq4ejoqF9qRUREROxQ6gheZ2f7Oi1NncYgIiKCChUqWMsjIiKoUaNGmvq+vr7s2LHDpiwxMZFbt25RunRpSpQogYeHBxERETZ1IiIiKFOmTJbj0vmtiIiIiH37J+e39nVGnMMcHR3tamSGiIiIiOQPNWvWpFixYuzZs8eaqI2KiuLo0aP07t07Tf3GjRsTHBxMeHg4FStWBGDv3r0ANGzYEAcHBxo0aMDevXvp2bOn9XV79uyhUaNGWY5L57ciIiIiBVeBTtSKiIiIiNwPV1dXevfuTXBwMN7e3jz44IPMnj0bX19f2rdvj8lk4saNG3h6euLu7k79+vVp0KABI0aMYOrUqcTGxjJ58mSeeuop64jZAQMG8OKLL1K7dm0eeeQRNm3axLFjx3jnnXcMfrciIiIiYg8K9By1IiIiIiL3y2Qy8d577xEWFkZ8fDyNGzdm8uTJlCtXjvPnz/Poo48yY8YMunfvDsD169eZNm0aO3fuxM3NjQ4dOjB+/Hjc3Nys29y8eTOLFi3i8uXLVKtWjdGjR9OsWTOj3qKIiIiI2BElakVEREREREREREQM5mh0ACIiIiIiIiIiIiKFnRK1IiIiIiIiIiIiIgZTolZERERERERERETEYErU5pCUlBTmz59Py5YtCQgI4IUXXuDcuXNGhyXZcOvWLSZPnswjjzxCgwYNeO6559i/f7/RYcl9OHPmDIGBgYSFhRkdimTT5s2b6dSpE/Xq1eOJJ55g+/btRock2ZCcnMy8efNo06YNgYGB9OrVi0OHDhkdlmTBkiVL6NOnj03ZsWPH6N27NwEBAbRt25bVq1cbFJ0YTcdH5tJro2+++YYePXoQGBhI27ZtmTVrFvHx8QZFaLz02uhOEydOpG3btnkYkf1Jr40iIiIYOXIkjRo1okmTJowaNYobN24YFKHx0muj3377jT59+hAYGEjr1q0JDg4mMTHRoAiNkdl32V27dtG9e3fq169Phw4d+Pzzzw2M1hiZtdGmTZvo0qULAQEBtG/fnqVLl2IymQyMOO9lNSdiNpsZNGhQhp/pBVVmbXTmzBlefPFFAgMDad68OW+++SZxcXHZ2ocStTlk0aJFrF27lrfeeot169aRkpJCUFBQofsPIj8bOXIkP//8M++99x6bNm2iVq1aDBo0iD/++MPo0CQbkpKSeP3114mNjTU6FMmmTz/9lAkTJtCrVy8+//xzOnfubP27lPzh/fffZ+PGjbz11lts3ryZypUrExQUREREhNGhSQbWrFnD3Llzbcpu3rzJgAEDqFChAps2bWLo0KEEBwezadMmY4IUw+j4yFx6bbR//35eeeUV2rVrxyeffMKUKVPYtm0b06ZNMyZIg6XXRnfasWMHGzduzLuA7FB6bZSYmMjAgQO5ePEiq1evZunSpRw/fpyxY8caE6TB7vV5NHDgQKpUqcLmzZt56623CAsLy/B4K4gy+i57+vRpBg8eTMuWLQkLC6Nnz56MGTOGXbt2GR12nsqojbZs2cKUKVPo3bs3W7ZsYfjw4SxZsoT333/f6LDzVFZzIqtWreKHH34wKEpjZdRGN2/epHfv3jg7O7Nx40Zmz57NV199xaxZs7K3E7P8YwkJCebAwEDzmjVrrGWRkZFmf39/89atWw2MTLLq7NmzZj8/P/P+/futZSkpKebHHnvMPHfuXAMjk+x69913zX379jX7+fmZN23aZHQ4kkUpKSnmNm3amGfOnGlTPnDgQPPixYsNikqy68knnzTPmDHDuhwdHW328/Mzf/HFFwZGJfdy+fJl8+DBg80BAQHmDh06mHv37m1dt3jxYnOLFi3MSUlJ1rJ3333X3L59eyNCFQPo+MhcRm00atQoc//+/W3qf/LJJ+Y6deqYExIS8jpUw2TURqmuXLlibtq0qbl3797mNm3aGBClsTJqo02bNpkDAgLMV69etZZ9//335kcffdQcHR1tRLiGyKiNvvrqK7Ofn59Ne0yfPt3cuXNnI0I1RGbfZSdNmmR++umnbV4zcuRI88CBA/M6VMNk1kbPPvusecKECTavCQkJMbdq1SqPIzVOVnMix48fNzdq1Mj8zDPPpPuZXpBl1kbz5883P/LII+b4+Hjr+g0bNpi7detmTklJyfJ+NKI2Bxw/fpzbt2/TrFkza5mXlxe1a9dm3759BkYmWVWyZEmWLl1KvXr1rGUODg44ODgQFRVlYGSSHfv27WP9+vXMnDnT6FAkm86cOcOFCxfo0qWLTfmKFSsYPHiwQVFJdpUqVYpvv/2W8+fPYzKZWL9+Pa6urtSsWdPo0CQdv/32Gy4uLmzZsoX69evbrNu/fz8PPfQQzs7O1rKmTZty9uxZrl27ltehigF0fGQuozYaOHBgmlGPjo6OJCUlERMTk5dhGiqjNgLL5bPjxo2ja9euPPTQQwZEaLyM2uiHH36gadOmPPDAA9ayli1bsmPHDooVK5bXoRomozby9vYG4OOPP8ZkMnH+/Hm+++67dI+3giqz77L79++3yVWA5TP7wIEDmM3mvA7XEJm10euvv86gQYNsXuPo6EhkZGReh2qYrOREEhISeP311xk2bBiVK1c2KlTDZNZGP/zwA+3atcPNzc26vmfPnoSFheHg4JDl/ShRmwMuX74MQNmyZW3KS5cubV0n9s3Ly4tWrVrh6upqLfviiy8IDw+nZcuWBkYmWRUVFcWYMWOYOHFimr9FsX9nzpwBIDY2lkGDBtGsWTN69uzJN998Y3Bkkh0TJkzAxcWFRx99lHr16jFnzhzmz59PhQoVjA5N0tG2bVsWLFhA+fLl06y7fPkyvr6+NmWlS5cG4NKlS3kSnxhLx0fmMmqj2rVr2/xIlZSURGhoKHXr1rUmlgqDjNoIIDQ0lKtXrzJy5Mg8jsx+ZNRGZ86coVy5cixcuJB27drRpk0bJk2aVOgGkmTURg0aNODll19m3rx51KtXj0cffZQyZcowefJkAyI1RmbfZe/1mR0XF8fNmzfzOlxDZNZGDRs2tEk8RkdH8/HHHxeqXEBWciKzZ8+mdOnS9O7d26gwDZVZG505c4bSpUszY8YMWrduTbt27fj3v/9NQkJCtvajRG0OSJ0Y+M7OAnBzc8t2h4h9OHjwIOPHj6d9+/a0bt3a6HAkC6ZOnUpgYGCaEZmSP6SOLho7diydO3dm5cqVNG/enCFDhhS6+bPys99//x1PT08WLlzI+vXr6d69O6+//jrHjh0zOjTJpvj4+HTPawCd24iOj2xKTk5mzJgxnDp1iilTphgdjt04fvw4ISEhzJ49O83xJBYxMTFs3ryZEydO8O677/Lmm29y4MABhgwZUmhGQmYmJiaGP/74g169erFx40bmzZvH2bNnmTRpktGhGebu77LpfWanLhfWe+pk9H3/9u3bDBkyhISEBMaMGWNMgHbg7jb6/vvv2bp1K9OnT8/W6NCC7O42iomJYdmyZSQkJBASEsLo0aPZunUrEydOzNZ2nTOvIplxd3cHLB9yqc/BcqJapEgRo8KS+7Rjxw5ef/11GjRoQHBwsNHhSBZs3ryZ/fv3s3XrVqNDkfvk4uICwKBBg+jWrRsAtWrV4ujRo3zwwQdpLtcS+3Pp0iVGjRpFaGgojRo1AqBevXr8/vvvLFiwgEWLFhkcoWSHu7t7mi9vqQk4Dw8PI0ISO6LjI+tiYmIYPnw4e/fuJSQkBH9/f6NDsgupl8++/PLLmh4nA87Oznh4ePDuu+9az5WKFy9Oz549OXLkiI4nLCP8IiMjmT9/PgB16tShePHi9O/fn/79+1OrVi2DI8xb6X2XdXNzS/OZnbpcGPMVGX3fv3r1KoMHD+b8+fOsWLGCcuXKGRSlse5uoxs3bvDGG28wdepUypQpY3R4diG948jZ2ZnKlSszdepUAOrWrYvJZGL48OGMGzeOUqVKZWnbGlGbA1Ivs777rtYRERE6iPOZjz76iFdffZU2bdqwePFim7lFxH5t2rSJ69ev07p1awIDAwkMDARgypQpBAUFGRydZEXqZ6Wfn59NebVq1Th//rwRIUk2/fLLLyQlJdnM2QRQv359wsPDDYpK7pevr2+65zWAzm1Ex0cWRURE0KtXLw4dOsSKFSto1aqV0SHZjV9++YVTp04REhJiPXdbsmQJFy9eJDAwkP379xsdol3w9fWlcuXK1iQtQPXq1QF0fvSXAwcOpHvuAXD27FkDIjLOvb7Lli1bNt3PbA8PDzw9PY0I1TAZfd8/ffo0zzzzDNevX2fNmjVpjqvCIr02+u6777h69SpvvPGG9TN769at7N+/n8DAQC5evGh02HnqXseRr6+v9TM6VeryhQsXsrx9jajNATVr1qRYsWLs2bPHOg9fVFQUR48eLbRzd+RHa9eu5a233qJPnz5MmDBBw/nzkeDgYOLj423K2rdvz7Bhw3jyyScNikqyo06dOhQtWpRffvnFOhoT4OTJk5rfNJ9InfvsxIkTNiN8Tp48SaVKlQyKSu5X48aNWbduHSaTCScnJwB2795N5cqVszwaQAouHR+Zi4yMpF+/fsTExLBmzRpq1KhhdEh2xd/fny+//NKm7MMPP+TLL7/kww8/VML/L40bN2b16tXEx8dbr9w8efIkABUrVjQyNLtRpkwZTpw4YVOWulyYbnaU0XfZRo0asXfvXpv6u3fvpkGDBjg6Fp6xexm10blz5+jXrx9eXl6sWLGi0N7z5F5t1K5dOxo0aGBTNzg4mMuXLxMcHGydp74wyOg4aty4MYcPH8ZsNlvLT548iZOTU7ZGZytRmwNcXV3p3bs3wcHBeHt78+CDDzJ79mx8fX1p37690eFJFpw5c4bp06fTrl07Bg8ebHPHYnd390L3S2N+c6+T+VKlSulEP59wd3cnKCiIhQsXUqZMGfz9/fn888/58ccfCQ0NNTo8yQJ/f38aNmzI2LFjmTJlCr6+vmzevJldu3bx8ccfGx2eZFOPHj1Yvnw5EyZMICgoiMOHDxMaGsq0adOMDk3sgI6PzM2YMYNz586xfPlyvL29uXr1qnWdt7e3NcFdWLm7u6dJNBYvXhxnZ2clIO/w7LPPsmbNGkaNGsXw4cOJiopi6tSpNGnShDp16hgdnl3o378/L7zwAnPnzqV79+5cuHCBadOm0bp160IzrUZm32X79OlDt27dCA4Oplu3bnz33Xf897//Zfny5QZGnbcya6M33niDxMRE3nvvPZydnW0+s318fIwIOc9l1kZ3fzYXLVo03fKCLLM2GjRoEN27d2fKlCkMGDCA8+fPM2vWLLp27ZqtG4kqUZtDhg0bRnJyMhMnTiQ+Pp7GjRuzYsUKm8tUxH598cUXJCUl8dVXX/HVV1/ZrOvWrRszZ840KDKRwmPIkCEUKVKEOXPmcOXKFapWrcqCBQto0qSJ0aFJFjg6OvL+++8zd+5cxo8fT2RkJH5+foSGhlovQZT8o1SpUixfvpx33nmHbt264ePjw5gxY6xzSEvhpuMjYyaTiW3btpGUlES/fv3SrP/6668L7byHkj3e3t6sWbOGGTNm0LNnT1xdXXnssccYN26c0aHZjZYtW7JkyRIWLlzIqlWrKFmyJO3ateO1114zOrQ8k5XvsosWLWL27NmsWrWKcuXKMXv27EJ1D4iM2qh58+bWEcddu3ZN89q7R2wXVMqJZC4rbbR69Wr+/e9/07VrVzw9PXnyyScZMWJEtvbjYNbtIkVEREREREREREQMVXgmJBERERERERERERGxU0rUioiIiIiIiIiIiBhMiVoRERERERERERERgylRKyIiIiIiIiIiImIwJWpFREREREREREREDKZErYiIiIiIiIiIiIjBlKgVERERERERERERMZgStSIiIiIiIiIiIiIGU6JWRCQHjBs3jho1amT4CAsLo0aNGobEFxoayttvv52luq+88go7duzI5YhEREREJDNZOccsSGJjY1mzZo1h+7948SIdO3bk9u3bAKxdu5ZWrVrRs2dPTp8+bVM3NjaWhQsX0rlzZwICAmjRogVDhw7lt99+s9aJi4ujU6dOXLx4MU/fh4jkXw5ms9lsdBAiIvlddHQ08fHx1uUWLVrwxhtv0KlTJ2uZp6cn0dHR+Pj45Glsf/75J71792br1q0UL1480/rh4eH06dOHLVu2UKJEidwPUERERETSlZVzzLw+t8xNISEhhIWF8c033xiy/0GDBvHEE0/QvXt3Lly4wMCBA5k1axbHjx/nv//9L6GhoQDcuHGDXr164ezszCuvvEKtWrWIjIwkNDSUHTt2sHTpUpo0aQJAWFgYn332GStXrjTkPYlI/qIRtSIiOcDT0xMfHx/rI70yd3d3Q06kFy5cyBNPPJGlJC1AxYoVCQgIYNWqVbkcmYiIiIhkJCvnmAWJkePIdu/ezfHjx+natStgGQ3r6elJjRo1qF27NjExMda606ZNIyEhgTVr1vD4449ToUIF6tWrR3BwMAEBAUydOpWUlBQAunbtyokTJ9i1a5ch70tE8hclakVE8sjdUx/UqFGD9evX8/zzz1OvXj06duzIwYMHWb9+Pa1bt6ZBgwYMHz7cZhTFwYMH6dWrF/7+/rRu3Zpp06bZnDTe7cqVK3z++ed07NjRWnb9+nWGDRtGkyZN8Pf359lnn2Xv3r02r+vUqRNr164lISEhB1tARERERHLat99+S/fu3fH396ddu3bMnTuXxMRE6/rsnnOGhYXxyCOPsGHDBlq0aEFgYCBDhw7lypUr1m0mJiYye/ZsWrZsSWBgIM888ww//PCDdX1YWBjt2rXj7bffpmHDhgwZMgSAHTt20LNnTwICAqhXrx7du3dn586dACxYsICQkBAuXLhAjRo1OH/+POPGjaNPnz427/fOsvPnz1OjRg2WLFlC8+bNefTRR4mJiSE6OppJkybRtGlTGjZsSN++fTly5EiG7bhy5Urat2+Pk5MTANWqVaNOnTo0bNiQAQMGMGLECACuXbvGV199Rd++ffHy8rLZhoODA2+++SZz587FwcEBACcnJx5//HE++OCDLPaoiBRmStSKiBhozpw5BAUF8emnn+Lp6clLL73EF198wdKlS5kxYwY7duxg48aNABw/fpwBAwbQsmVLtmzZQnBwML/99hsDBw685+iD7777Di8vL/z9/a1lU6dOJSEhgY8++oitW7dSuXJlhgwZQmxsrLVOq1atiIqK4sCBA7nbACIiIiJy377//nuGDx/OM888w2effcaUKVPYvn07o0ePtqmXnXNOsFzav2rVKubOncuqVau4dOkSQUFBJCcnAzB+/Hh+/PFHgoOD+eSTT+jYsSMvvfQS//vf/6zb+PPPP4mIiGDz5s2MGDGCX3/9lVdffZUnnniCrVu3smHDBry9vRkzZgyJiYkMHDiQgQMH4uvryw8//EDZsmWz3A6ffPKJNd6iRYvywgsvcO7cOZYsWcKGDRsICAjgueee4+jRo+m+PjY2lp9++onWrVvblE+bNo0ff/yRXbt20bx5cwCOHTuGyWSiQYMG6W6rYsWK1KhRw5qoBWjdujU//fQTcXFxWX5PIlI4KVErImKgHj160LZtW6pUqULXrl2JjIxk8uTJ+Pn58fjjj1OrVi1OnToFwIoVK2jevDkvvfQSlSpVolGjRrz77rv88ssvaUbEpjp06BDVq1e3Kfvzzz/x8vKifPnyVKxYkQkTJjB//nzr6AGAIkWKUK5cOX7++efce/MiIiIi8o8sXryYZ555hmeffZYKFSrQokULpk2bxn//+1/Onz9vrZedc06ApKQkZs2aRaNGjfD392f27NmcPHmSXbt2ER4ezmeffcaMGTNo0qQJlSpVYsCAATzxxBOsWLHCJr4hQ4ZQvnx5qlevjpOTE5MmTaJ///6UL1+eWrVq0bdvX27cuMH169cpWrQoHh4eODk54ePjY3Numpnnn3+eatWqUa9ePXbv3s2hQ4eYO3cu9evXp2rVqowcOZKAgABWr16d7uuPHj1KUlISfn5+adaVLFkSV1dX63JkZCRAlqcVA/Dz8yMpKcnmRmMiIulxNjoAEZHCrGLFitbnRYoUAaBChQrWMnd3d+ula0ePHiU8PJzAwMA02zl9+rT1hgV3unbtGqVKlbIpe+WVVxg9ejRffPEFDRs2pEWLFnTu3Bk3Nzebet7e3ly7du3+35yIiIiI5KqjR49y+PBh/vOf/1jLUq+0On36NOXKlQOyd84JULRoUerWrWtdrlq1KsWLF+fkyZPWabeef/55m1iSkpLSTAVQqVIl6/NatWpRvHhxli5dyh9//EF4eDjHjx8HwGQyZf/N3+HO9/fbb79hNptp06aNTZ3ExMR7Tut19epVgDTnzenx9vYG4NatWzb7zcprUvcjInIvStSKiBjI2Tntx7CjY/oXO6SkpNClSxdeeumlNOtST/7S29bdJ77t2rVj586d7Ny5k59++okPPviAkJAQNmzYYDP61mQy3TMWERERETFeSkoKQUFBdOvWLc26O280lp1zTgAXF5c0ZSaTCScnJ2sieM2aNRQtWjTDbbq7u1uf7927l0GDBtG6dWsaNmxIly5diIuLY+jQofeMIz2p0y/caz8pKSkUK1aMsLCwNPXuHBmbXtxZSRjXrVsXFxcXDh48SP369dOs37VrF6tWreKtt96y9kHqdnVuLSKZ0aeEiEg+Ub16dX7//XcqVqxofSQnJzNjxgwuXbqU7mt8fHy4ceOGdTkxMZEZM2Zw7tw5OnXqxNtvv82OHTtwdHS0mVMMLHOTlS5dOjffkoiIiIj8A9WrV+fMmTM254eXL1/m3//+N7dv377v7d66dYtz585Zl0+dOkVMTAy1a9e2/rB/9epVm/2GhYWlmxxNtXLlSpo0acKCBQvo378/zZs3t57DpiZ/75zXFSwJ47tvnBseHp5h7H5+fsTExJCUlGQT37Jly/j666/TfU1qQvXO8+Z78fLy4vHHH2f16tVpYktJSWHx4sWcOXOGBx54wFp+/fp1AJ1bi0imlKgVEcknBg4cyNGjR5k2bRqnT5/m559/ZtSoUZw9e9bmsrI7+fv7c+LECVJSUgDLKIIjR44wadIkDh06xPnz5wkLCyM2NtZmSoWbN29y8eJFm5uQiYiIiIh9eeGFF/jiiy8ICQnhzJkz7Nq1i/HjxxMdHW0zovZ+jB49ml9//ZVDhw4xZswYAgMDady4MdWrV6dNmzZMmTKFb775hnPnzrFs2TKWLFliM53C3cqWLcuJEyfYv38/58+fZ9OmTcybNw/AOu2Ch4cHkZGRnDlzhqSkJAICAjh+/Dhbtmzh3LlzLFy4kJMnT2YYd8uWLalVqxYjRoxg9+7dhIeHM2PGDMLCwqhatWq6r6lZsyZubm73vNnY3caOHYuTkxPPPfccX331FefOnWP//v0MGTKEQ4cOMX36dJuk89GjR3Fzc6NGjRpZ2r6IFF5K1IqI5BMBAQEsX76cY8eO0a1bN15++WUqV65MaGjoPS/jatu2LTExMTYnnXPmzKF8+fK8/PLLdOjQgXXr1hEcHEyjRo2sdfbu3YuXlxeNGzfO9fclIiIiIvenQ4cOzJkzhx07dtClSxdGjx5NixYtCAkJ+cfb7tKlCy+++CJBQUFUr16dJUuWWJOPc+bMoX379kyePJlOnTqxefNm3nnnnXSnYEg1bNgwAgICeOmll3jqqafYuHEj06dPx93dnSNHjgDQvn17fHx8ePLJJzl69ChPPvkkvXr14u2336Zr165cvHiRfv36ZRi3k5MTK1eupG7dugwfPpwnn3ySffv2ERISQrNmzdJ9jYeHBw8//DC7d+/OUtuULl2aDRs20Lx5c2bPns0TTzzB8OHDcXV1Zf369TRs2NCm/p49e3j44Yfx8PDI0vZFpPByMKdeYyAiIgXSqFGjKF68OJMnT87yawYPHkzNmjUZMWJELkYmIiIiIvYmLCyM8ePHc+LECaNDyVM//vgjI0eOZOfOnfccBHE/EhMTadmyJXPmzOHhhx/Ose2KSMGkEbUiIgXcK6+8wvbt27M05xZY7hB85MgRBgwYkMuRiYiIiIjYh+bNm+Pn58fmzZtzdLubN2/Gz89PSVoRyRIlakVECrjKlSszaNCgLF8CFxwczOTJkylRokTuBiYiIiIiYkemT5/OihUr/tGN2O4UGxvLihUrmD59eo5sT0QKPk19ICIiIiIiIiIiImIwjagVERERERERERERMZgStSIiIiIiIiIiIiIGU6JWRERERERERERExGBK1IqIiIiIiIiIiIgYTIlaEREREREREREREYMpUSsiIiIiIiIiIiJiMCVqRURERERERERERAymRK2IiIiIiIiIiIiIwZSoFRERERERERERETHY/wNZ2RHRZVxxMwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 OU Process Analysis:\n",
      "  Half-life: 1.39 seconds\n",
      "  Theoretical equilibrium std: 2.00\n",
      "  Observed equilibrium std: 2.31\n"
     ]
    }
   ],
   "source": [
    "# Simulate Ornstein-Uhlenbeck process\n",
    "def simulate_ou(theta, mu, sigma, x0, T, dt):\n",
    "    \"\"\"\n",
    "    Simulate Ornstein-Uhlenbeck process using Euler-Maruyama method\n",
    "    \n",
    "    dX_t = θ(μ - X_t)dt + σ dW_t\n",
    "    \"\"\"\n",
    "    n_steps = int(T / dt)\n",
    "    t = np.linspace(0, T, n_steps)\n",
    "    X = np.zeros(n_steps)\n",
    "    X[0] = x0\n",
    "    \n",
    "    for i in range(1, n_steps):\n",
    "        dW = np.random.normal(0, np.sqrt(dt))\n",
    "        X[i] = X[i-1] + theta * (mu - X[i-1]) * dt + sigma * dW\n",
    "    \n",
    "    return t, X\n",
    "\n",
    "# Example: Temperature control\n",
    "theta = 0.5  # Mean reversion speed\n",
    "mu = 20.0    # Target temperature (°C)\n",
    "sigma = 2.0  # Noise level\n",
    "x0 = 10.0    # Initial temperature\n",
    "T = 10.0     # Time horizon (seconds)\n",
    "dt = 0.01    # Time step\n",
    "\n",
    "t, X = simulate_ou(theta, mu, sigma, x0, T, dt)\n",
    "\n",
    "# Plot\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Trajectory\n",
    "ax1.plot(t, X, linewidth=1.5, color='steelblue', label='Temperature')\n",
    "ax1.axhline(y=mu, color='red', linestyle='--', label=f'Target μ={mu}')\n",
    "ax1.fill_between(t, mu-sigma, mu+sigma, alpha=0.2, color='red', label='±σ band')\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Temperature (°C)')\n",
    "ax1.set_title('Ornstein-Uhlenbeck Process (Mean-Reverting System)')\n",
    "ax1.legend()\n",
    "ax1.grid(alpha=0.3)\n",
    "\n",
    "# Distribution at equilibrium\n",
    "equilibrium_samples = X[len(X)//2:]  # Second half (near equilibrium)\n",
    "ax2.hist(equilibrium_samples, bins=30, density=True, alpha=0.7, color='steelblue', edgecolor='black')\n",
    "\n",
    "# Theoretical distribution: N(μ, σ²/(2θ))\n",
    "x_range = np.linspace(X.min(), X.max(), 100)\n",
    "theoretical_std = sigma / np.sqrt(2 * theta)\n",
    "from scipy.stats import norm\n",
    "ax2.plot(x_range, norm.pdf(x_range, mu, theoretical_std), \n",
    "        'r-', linewidth=2, label=f'Theory: N({mu:.1f}, {theoretical_std:.2f}²)')\n",
    "\n",
    "ax2.set_xlabel('Temperature (°C)')\n",
    "ax2.set_ylabel('Probability Density')\n",
    "ax2.set_title('Equilibrium Distribution')\n",
    "ax2.legend()\n",
    "ax2.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n📊 OU Process Analysis:\")\n",
    "print(f\"  Half-life: {np.log(2)/theta:.2f} seconds\")\n",
    "print(f\"  Theoretical equilibrium std: {theoretical_std:.2f}\")\n",
    "print(f\"  Observed equilibrium std: {equilibrium_samples.std():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78636a0b",
   "metadata": {},
   "source": [
    "## 3. Regime Switching Systems <a id=\"regime\"></a>\n",
    "\n",
    "### Motivation\n",
    "\n",
    "Many real systems exhibit **multiple operating modes** or **regimes**:\n",
    "- Weather: sunny ↔ rainy ↔ stormy\n",
    "- Manufacturing: normal ↔ maintenance ↔ failure\n",
    "- Traffic: free-flow ↔ congested ↔ gridlock\n",
    "- Economic activity: expansion ↔ recession\n",
    "\n",
    "### Continuous-Time Markov Chain\n",
    "\n",
    "The regime $i_t \\in \\{1, 2, ..., N\\}$ follows a Markov chain with **transition rate matrix** $Q$:\n",
    "\n",
    "$$\n",
    "\\mathbb{P}(i_{t+dt} = j | i_t = i) = \n",
    "\\begin{cases}\n",
    "q_{ij} dt & \\text{if } i \\neq j \\\\\n",
    "1 + q_{ii} dt & \\text{if } i = j\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "where $q_{ii} = -\\sum_{j \\neq i} q_{ij}$ (rows sum to zero).\n",
    "\n",
    "### Coupled HJB System\n",
    "\n",
    "The value function $V^i(x)$ in regime $i$ satisfies:\n",
    "\n",
    "$$\n",
    "\\rho V^i(x) = \\sup_u \\left[ \\mu^i(x,u) (V^i)'(x) + \\frac{1}{2}(\\sigma^i)^2(x,u) (V^i)''(x) + L^i(x,u) + \\sum_{j \\neq i} q_{ij}[V^j(x) - V^i(x)] \\right]\n",
    "$$\n",
    "\n",
    "Key insight: The term $\\sum_{j \\neq i} q_{ij}[V^j(x) - V^i(x)]$ represents the **expected change in value due to regime switching**.\n",
    "\n",
    "### Stationary Distribution\n",
    "\n",
    "The long-run probability of being in each regime solves:\n",
    "\n",
    "$$\n",
    "Q^T \\pi = 0, \\quad \\sum_i \\pi_i = 1\n",
    "$$\n",
    "\n",
    "### Parameter Selection Tips\n",
    "\n",
    "| Parameter | Typical Range | Effect | How to Choose |\n",
    "|-----------|--------------|--------|---------------|\n",
    "| $q_{ij}$ | 0.1 - 10.0 | Regime persistence | Higher = faster switching. Set $q_{ij} = 1/\\text{expected duration}$ |\n",
    "| $\\mu^i$ | Problem-specific | Drift in regime $i$ | Estimate from data or physics |\n",
    "| $\\sigma^i$ | $> 0$ | Volatility in regime $i$ | Measure from observations or experiments |\n",
    "\n",
    "**Example**: If regime 1 typically lasts 5 time units, set $q_{12} \\approx 0.2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79238099",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-16T15:57:31.335006Z",
     "iopub.status.busy": "2026-02-16T15:57:31.334669Z",
     "iopub.status.idle": "2026-02-16T15:57:35.959770Z",
     "shell.execute_reply": "2026-02-16T15:57:35.957422Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Regime Switching Analysis:\n",
      "  Observed frequencies: [0.3002 0.3802 0.3196]\n",
      "  Theoretical stationary: [0.41573034 0.3258427  0.25842697]\n"
     ]
    }
   ],
   "source": [
    "# Simulate regime-switching process\n",
    "def simulate_regime_switching(Q, regime_params, x0, T, dt):\n",
    "    \"\"\"\n",
    "    Simulate regime-switching stochastic process\n",
    "    \n",
    "    Args:\n",
    "        Q: Transition rate matrix (N x N)\n",
    "        regime_params: List of (mu, sigma) for each regime\n",
    "        x0: Initial state\n",
    "        T: Time horizon\n",
    "        dt: Time step\n",
    "    \"\"\"\n",
    "    n_steps = int(T / dt)\n",
    "    n_regimes = Q.shape[0]\n",
    "    \n",
    "    t = np.linspace(0, T, n_steps)\n",
    "    X = np.zeros(n_steps)\n",
    "    regimes = np.zeros(n_steps, dtype=int)\n",
    "    \n",
    "    X[0] = x0\n",
    "    regimes[0] = 0  # Start in regime 0\n",
    "    \n",
    "    for i in range(1, n_steps):\n",
    "        current_regime = regimes[i-1]\n",
    "        \n",
    "        # Check for regime transition\n",
    "        for j in range(n_regimes):\n",
    "            if j != current_regime:\n",
    "                if np.random.rand() < Q[current_regime, j] * dt:\n",
    "                    current_regime = j\n",
    "                    break\n",
    "        \n",
    "        regimes[i] = current_regime\n",
    "        \n",
    "        # Evolve state according to current regime\n",
    "        mu, sigma = regime_params[current_regime]\n",
    "        dW = np.random.normal(0, np.sqrt(dt))\n",
    "        X[i] = X[i-1] + mu * dt + sigma * dW\n",
    "    \n",
    "    return t, X, regimes\n",
    "\n",
    "# Example: 3-regime system (Slow/Normal/Fast)\n",
    "Q = np.array([\n",
    "    [-0.5,  0.3,  0.2],  # Slow regime\n",
    "    [ 0.4, -0.7,  0.3],  # Normal regime\n",
    "    [ 0.3,  0.4, -0.7]   # Fast regime\n",
    "])\n",
    "\n",
    "regime_params = [\n",
    "    (0.1, 0.2),  # Slow: low drift, low vol\n",
    "    (0.3, 0.4),  # Normal: medium drift, medium vol\n",
    "    (0.5, 0.8)   # Fast: high drift, high vol\n",
    "]\n",
    "\n",
    "t, X, regimes = simulate_regime_switching(Q, regime_params, x0=0.0, T=50.0, dt=0.01)\n",
    "\n",
    "# Plot\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
    "\n",
    "# State trajectory\n",
    "colors = ['blue', 'green', 'red']\n",
    "for i in range(len(t)-1):\n",
    "    ax1.plot(t[i:i+2], X[i:i+2], color=colors[regimes[i]], alpha=0.8, linewidth=0.8)\n",
    "\n",
    "ax1.set_ylabel('State X')\n",
    "ax1.set_title('Regime-Switching Process')\n",
    "ax1.grid(alpha=0.3)\n",
    "\n",
    "# Regime evolution\n",
    "ax2.step(t, regimes, where='post', linewidth=1.5, color='black')\n",
    "ax2.set_ylabel('Regime')\n",
    "ax2.set_yticks([0, 1, 2])\n",
    "ax2.set_yticklabels(['Slow', 'Normal', 'Fast'])\n",
    "ax2.set_title('Regime Evolution')\n",
    "ax2.grid(alpha=0.3)\n",
    "\n",
    "# Regime distribution\n",
    "regime_counts = np.bincount(regimes, minlength=3) / len(regimes)\n",
    "ax3.bar([0, 1, 2], regime_counts, color=colors, alpha=0.7, edgecolor='black')\n",
    "ax3.set_xlabel('Regime')\n",
    "ax3.set_ylabel('Frequency')\n",
    "ax3.set_xticks([0, 1, 2])\n",
    "ax3.set_xticklabels(['Slow', 'Normal', 'Fast'])\n",
    "ax3.set_title('Regime Distribution')\n",
    "ax3.grid(alpha=0.3, axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Compute stationary distribution\n",
    "from scipy.linalg import null_space\n",
    "pi_stationary = null_space(Q.T)\n",
    "pi_stationary = pi_stationary / pi_stationary.sum()\n",
    "\n",
    "print(\"\\n📊 Regime Switching Analysis:\")\n",
    "print(f\"  Observed frequencies: {regime_counts}\")\n",
    "print(f\"  Theoretical stationary: {pi_stationary.flatten()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3751412",
   "metadata": {},
   "source": [
    "## 4. Jump Diffusion Processes <a id=\"jumps\"></a>\n",
    "\n",
    "### Motivation\n",
    "\n",
    "Continuous diffusion models fail to capture **sudden, discrete events**:\n",
    "- Market crashes/rallies\n",
    "- Equipment failures\n",
    "- Policy changes\n",
    "- Natural disasters\n",
    "- Phase transitions\n",
    "\n",
    "### Lévy Processes and Compound Poisson\n",
    "\n",
    "A jump diffusion process combines:\n",
    "1. **Continuous diffusion**: $\\sigma dW_t$\n",
    "2. **Discrete jumps**: $dJ_t = \\sum_{i=1}^{N_t} Y_i$\n",
    "\n",
    "$$\n",
    "dX_t = \\mu dt + \\sigma dW_t + dJ_t\n",
    "$$\n",
    "\n",
    "where:\n",
    "- $N_t \\sim \\text{Poisson}(\\lambda t)$ = number of jumps by time $t$\n",
    "- $Y_i \\sim F$ = jump size distribution\n",
    "- $\\lambda$ = **jump intensity** (expected jumps per unit time)\n",
    "\n",
    "### HJB with Jump Integral\n",
    "\n",
    "$$\n",
    "\\rho V(x) = \\sup_u \\left[ \\mu(x,u) V'(x) + \\frac{1}{2}\\sigma^2(x,u) V''(x) + L(x,u) + \\lambda \\int [V(x+y) - V(x)] F(dy) \\right]\n",
    "$$\n",
    "\n",
    "The integral term $\\lambda \\mathbb{E}[V(x+Y) - V(x)]$ represents the **expected value change from jumps**.\n",
    "\n",
    "### Jump Size Distributions\n",
    "\n",
    "| Distribution | Density | Use Case |\n",
    "|--------------|---------|----------|\n",
    "| Normal | $\\mathcal{N}(\\mu_j, \\sigma_j^2)$ | Symmetric jumps (up/down equally likely) |\n",
    "| Exponential | $\\lambda e^{-\\lambda y}$ | One-sided jumps (failures, crashes) |\n",
    "| Laplace | $\\frac{1}{2b}e^{-|y-\\mu|/b}$ | Heavy-tailed jumps |\n",
    "| Uniform | $U(a, b)$ | Bounded jumps |\n",
    "\n",
    "### Parameter Selection\n",
    "\n",
    "| Parameter | Typical Range | Effect | How to Choose |\n",
    "|-----------|--------------|--------|---------------|\n",
    "| $\\lambda$ | 0.01 - 5.0 | Jump frequency | Count events per unit time from data |\n",
    "| $\\mu_j$ | Problem-specific | Average jump size | Measure typical event magnitude |\n",
    "| $\\sigma_j$ | $> 0$ | Jump size variability | Standard deviation of observed jumps |\n",
    "\n",
    "**Rule of thumb**: If you expect ~1 jump per 10 time units, set $\\lambda = 0.1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "733539e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-16T15:57:35.963250Z",
     "iopub.status.busy": "2026-02-16T15:57:35.962947Z",
     "iopub.status.idle": "2026-02-16T15:57:36.683892Z",
     "shell.execute_reply": "2026-02-16T15:57:36.682687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Jump Diffusion Analysis:\n",
      "  Expected jumps: 40.0\n",
      "  Observed jumps: 50\n",
      "  Average jump size: -0.536 (theoretical: -0.5)\n",
      "  Std of jumps: 0.178 (theoretical: 0.2)\n",
      "\n",
      "  Impact: Final value with jumps = -7.04 vs 17.97 without jumps\n"
     ]
    }
   ],
   "source": [
    "# Simulate jump diffusion process\n",
    "def simulate_jump_diffusion(mu, sigma, lambda_jump, jump_mean, jump_std, x0, T, dt):\n",
    "    \"\"\"\n",
    "    Simulate Merton jump diffusion model\n",
    "    \n",
    "    dX_t = μ dt + σ dW_t + dJ_t\n",
    "    \n",
    "    where J_t is compound Poisson with Normal jumps\n",
    "    \"\"\"\n",
    "    n_steps = int(T / dt)\n",
    "    t = np.linspace(0, T, n_steps)\n",
    "    X = np.zeros(n_steps)\n",
    "    jumps = np.zeros(n_steps)\n",
    "    \n",
    "    X[0] = x0\n",
    "    \n",
    "    for i in range(1, n_steps):\n",
    "        # Diffusion component\n",
    "        dW = np.random.normal(0, np.sqrt(dt))\n",
    "        dX = mu * dt + sigma * dW\n",
    "        \n",
    "        # Jump component\n",
    "        n_jumps = np.random.poisson(lambda_jump * dt)\n",
    "        if n_jumps > 0:\n",
    "            jump_sizes = np.random.normal(jump_mean, jump_std, n_jumps)\n",
    "            total_jump = jump_sizes.sum()\n",
    "            dX += total_jump\n",
    "            jumps[i] = total_jump\n",
    "        \n",
    "        X[i] = X[i-1] + dX\n",
    "    \n",
    "    return t, X, jumps\n",
    "\n",
    "# Example: System with occasional failures/shocks\n",
    "mu = 0.5        # Baseline drift\n",
    "sigma = 0.3     # Continuous volatility\n",
    "lambda_jump = 2.0  # 2 jumps per time unit (on average)\n",
    "jump_mean = -0.5   # Negative jumps (failures)\n",
    "jump_std = 0.2     # Jump size variability\n",
    "x0 = 10.0       # Initial state\n",
    "T = 20.0        # Time horizon\n",
    "dt = 0.01\n",
    "\n",
    "t, X, jumps = simulate_jump_diffusion(mu, sigma, lambda_jump, jump_mean, jump_std, x0, T, dt)\n",
    "\n",
    "# Also simulate without jumps for comparison\n",
    "t_nodiff, X_nodiff, _ = simulate_jump_diffusion(mu, sigma, 0.0, 0.0, 0.0, x0, T, dt)\n",
    "\n",
    "# Plot\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True)\n",
    "\n",
    "# Trajectories comparison\n",
    "ax1.plot(t, X, linewidth=1.5, color='red', label='With Jumps', alpha=0.8)\n",
    "ax1.plot(t_nodiff, X_nodiff, linewidth=1.5, color='blue', label='Pure Diffusion', alpha=0.6)\n",
    "\n",
    "# Mark jump times\n",
    "jump_times = t[jumps != 0]\n",
    "jump_values = X[jumps != 0]\n",
    "ax1.scatter(jump_times, jump_values, color='black', s=50, zorder=5, label='Jump Events', alpha=0.7)\n",
    "\n",
    "ax1.set_ylabel('State X')\n",
    "ax1.set_title('Jump Diffusion Process vs Pure Diffusion')\n",
    "ax1.legend(fontsize=11)\n",
    "ax1.grid(alpha=0.3)\n",
    "\n",
    "# Jump sizes over time\n",
    "ax2.stem(t, jumps, linefmt='red', markerfmt='ro', basefmt=' ', label='Jump Sizes')\n",
    "ax2.axhline(y=0, color='black', linewidth=0.8)\n",
    "ax2.set_xlabel('Time')\n",
    "ax2.set_ylabel('Jump Size')\n",
    "ax2.set_title('Jump Events')\n",
    "ax2.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Statistics\n",
    "n_observed_jumps = np.sum(jumps != 0)\n",
    "expected_jumps = lambda_jump * T\n",
    "avg_jump_size = jumps[jumps != 0].mean() if n_observed_jumps > 0 else 0\n",
    "\n",
    "print(\"\\n📊 Jump Diffusion Analysis:\")\n",
    "print(f\"  Expected jumps: {expected_jumps:.1f}\")\n",
    "print(f\"  Observed jumps: {n_observed_jumps}\")\n",
    "print(f\"  Average jump size: {avg_jump_size:.3f} (theoretical: {jump_mean})\")\n",
    "print(f\"  Std of jumps: {jumps[jumps != 0].std() if n_observed_jumps > 0 else 0:.3f} (theoretical: {jump_std})\")\n",
    "print(f\"\\n  Impact: Final value with jumps = {X[-1]:.2f} vs {X_nodiff[-1]:.2f} without jumps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca7aba3",
   "metadata": {},
   "source": [
    "## 5. Combined MRSJD Models <a id=\"mrsjd\"></a>\n",
    "\n",
    "### Why Combine Regime Switching and Jumps?\n",
    "\n",
    "Real systems often exhibit **both**:\n",
    "1. **State-dependent behavior** (regimes)\n",
    "2. **Sudden shocks** (jumps)\n",
    "\n",
    "Examples:\n",
    "- **Manufacturing**: Normal/maintenance regimes + equipment failures (jumps)\n",
    "- **Power grid**: Low/high demand regimes + blackout events (jumps)\n",
    "- **Epidemic**: Endemic/outbreak regimes + super-spreader events (jumps)\n",
    "\n",
    "### Full MRSJD Dynamics\n",
    "\n",
    "$$\n",
    "dX_t = \\mu^{i_t}(X_t)dt + \\sigma^{i_t}(X_t)dW_t + dJ_t^{i_t}\n",
    "$$\n",
    "\n",
    "where:\n",
    "- Drift $\\mu^i$ and volatility $\\sigma^i$ depend on current regime $i_t$\n",
    "- Jump intensity $\\lambda^i$ and distribution $F^i$ also regime-dependent\n",
    "- Regime switches according to $Q$\n",
    "\n",
    "### Coupled HJB with Both Effects\n",
    "\n",
    "$$\n",
    "\\boxed{\n",
    "\\rho V^i(x) = \\sup_u \\left[ \\mu^i V^i_x + \\frac{(\\sigma^i)^2}{2} V^i_{xx} + L^i(x,u) + \\lambda^i \\int [V^i(x+y) - V^i(x)] F^i(dy) + \\sum_{j \\neq i} q_{ij}[V^j(x) - V^i(x)] \\right]\n",
    "}\n",
    "$$\n",
    "\n",
    "This is the **most general** formulation combining:\n",
    "1. ✅ Diffusion: $(\\sigma^i)^2 V^i_{xx}$\n",
    "2. ✅ Jumps: $\\lambda^i \\int [V^i(x+y) - V^i(x)] F^i(dy)$\n",
    "3. ✅ Regime Switching: $\\sum_{j \\neq i} q_{ij}[V^j(x) - V^i(x)]$\n",
    "4. ✅ Optimal Control: $\\sup_u$\n",
    "\n",
    "### Numerical Solution: Finite Differences with Upwind Schemes\n",
    "\n",
    "**Grid discretization**: $x_k = x_{\\min} + k \\Delta x$, $k = 0, ..., N$\n",
    "\n",
    "**Value function approximation**: $V^i(x_k) \\approx V^i_k$\n",
    "\n",
    "**Derivatives**:\n",
    "- Forward: $V_x \\approx (V_{k+1} - V_k) / \\Delta x$\n",
    "- Backward: $V_x \\approx (V_k - V_{k-1}) / \\Delta x$\n",
    "- Central: $V_{xx} \\approx (V_{k+1} - 2V_k + V_{k-1}) / (\\Delta x)^2$\n",
    "\n",
    "**Upwind scheme** (for stability when $\\mu \\neq 0$):\n",
    "$$\n",
    "V_x \\approx \n",
    "\\begin{cases}\n",
    "(V_{k+1} - V_k) / \\Delta x & \\text{if } \\mu > 0 \\text{ (forward)} \\\\\n",
    "(V_k - V_{k-1}) / \\Delta x & \\text{if } \\mu < 0 \\text{ (backward)}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "**Why upwind?** Prevents numerical oscillations when advection dominates diffusion.\n",
    "\n",
    "### Algorithm: Value Iteration\n",
    "\n",
    "```\n",
    "1. Initialize V^i_k = 0 for all regimes i and grid points k\n",
    "2. Repeat until convergence:\n",
    "   For each regime i:\n",
    "     For each grid point k:\n",
    "       a. Compute derivatives V_x, V_xx\n",
    "       b. Compute jump integral ∫[V(x+y) - V(x)]F(dy)\n",
    "       c. Compute regime switching term Σ q_ij[V^j - V^i]\n",
    "       d. Optimize over control: u* = argmax_u RHS(u)\n",
    "       e. Update: V^i_k ← RHS(u*) / ρ\n",
    "3. Convergence check: ||V_new - V_old|| < tol\n",
    "```\n",
    "\n",
    "### Computational Complexity\n",
    "\n",
    "- **Per iteration**: $O(N \\cdot M \\cdot K)$\n",
    "  - $N$ = number of regimes\n",
    "  - $M$ = grid points\n",
    "  - $K$ = control discretization\n",
    "- **Iterations**: Typically 100-1000\n",
    "- **Total**: $O(10^5 - 10^7)$ operations\n",
    "\n",
    "**Speedup techniques**:\n",
    "- Parallel computation across regimes (Rayon)\n",
    "- Adaptive grid refinement\n",
    "- Policy iteration instead of value iteration\n",
    "- Sparse matrix operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f104f849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-16T15:57:36.687408Z",
     "iopub.status.busy": "2026-02-16T15:57:36.687121Z",
     "iopub.status.idle": "2026-02-16T15:57:41.069722Z",
     "shell.execute_reply": "2026-02-16T15:57:41.068651Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 MRSJD Analysis:\n",
      "  Total jumps: 32\n",
      "  Time in Stable regime: 18.2 (90.8%)\n",
      "  Time in Volatile regime: 11.8 (59.2%)\n",
      "  Average jump size (Stable): -0.106\n",
      "  Average jump size (Volatile): -0.290\n"
     ]
    }
   ],
   "source": [
    "# Simplified MRSJD simulation (for illustration)\n",
    "def simulate_mrsjd(Q, regime_params_list, x0, T, dt):\n",
    "    \"\"\"\n",
    "    Simulate Markov Regime Switching Jump Diffusion\n",
    "    \n",
    "    Each regime has: (mu, sigma, lambda_jump, jump_mean, jump_std)\n",
    "    \"\"\"\n",
    "    n_steps = int(T / dt)\n",
    "    n_regimes = Q.shape[0]\n",
    "    \n",
    "    t = np.linspace(0, T, n_steps)\n",
    "    X = np.zeros(n_steps)\n",
    "    regimes = np.zeros(n_steps, dtype=int)\n",
    "    jump_events = []\n",
    "    \n",
    "    X[0] = x0\n",
    "    regimes[0] = 0\n",
    "    \n",
    "    for i in range(1, n_steps):\n",
    "        current_regime = regimes[i-1]\n",
    "        mu, sigma, lam, jmu, jsig = regime_params_list[current_regime]\n",
    "        \n",
    "        # Check regime transition\n",
    "        for j in range(n_regimes):\n",
    "            if j != current_regime and np.random.rand() < Q[current_regime, j] * dt:\n",
    "                current_regime = j\n",
    "                break\n",
    "        \n",
    "        regimes[i] = current_regime\n",
    "        \n",
    "        # Diffusion\n",
    "        dW = np.random.normal(0, np.sqrt(dt))\n",
    "        dX = mu * dt + sigma * dW\n",
    "        \n",
    "        # Jumps (regime-dependent)\n",
    "        n_jumps = np.random.poisson(lam * dt)\n",
    "        if n_jumps > 0:\n",
    "            jump_size = np.sum(np.random.normal(jmu, jsig, n_jumps))\n",
    "            dX += jump_size\n",
    "            jump_events.append((t[i], jump_size, current_regime))\n",
    "        \n",
    "        X[i] = X[i-1] + dX\n",
    "    \n",
    "    return t, X, regimes, jump_events\n",
    "\n",
    "# Example: 2-regime system with regime-dependent jumps\n",
    "Q = np.array([\n",
    "    [-0.3, 0.3],\n",
    "    [0.5, -0.5]\n",
    "])\n",
    "\n",
    "# Regime 0: Stable (low vol, rare small jumps)\n",
    "# Regime 1: Volatile (high vol, frequent large jumps)\n",
    "regime_params_list = [\n",
    "    (0.2, 0.3, 0.5, -0.1, 0.05),  # Stable: mu, sigma, lambda, jump_mu, jump_sigma\n",
    "    (0.1, 0.8, 2.0, -0.3, 0.15)   # Volatile\n",
    "]\n",
    "\n",
    "t, X, regimes, jump_events = simulate_mrsjd(Q, regime_params_list, x0=5.0, T=30.0, dt=0.01)\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=True)\n",
    "\n",
    "# State trajectory\n",
    "regime_colors = ['blue', 'red']\n",
    "for i in range(len(t)-1):\n",
    "    axes[0].plot(t[i:i+2], X[i:i+2], color=regime_colors[regimes[i]], alpha=0.8, linewidth=1.0)\n",
    "\n",
    "# Mark jumps\n",
    "if jump_events:\n",
    "    jump_t = [j[0] for j in jump_events]\n",
    "    jump_idx = [np.argmin(np.abs(t - jt)) for jt in jump_t]\n",
    "    axes[0].scatter([t[i] for i in jump_idx], [X[i] for i in jump_idx], \n",
    "                   color='black', s=60, zorder=5, marker='x', label='Jumps')\n",
    "\n",
    "axes[0].set_ylabel('State X')\n",
    "axes[0].set_title('MRSJD: Combined Regime Switching + Jump Diffusion')\n",
    "axes[0].legend()\n",
    "axes[0].grid(alpha=0.3)\n",
    "\n",
    "# Regime evolution\n",
    "axes[1].step(t, regimes, where='post', linewidth=1.5, color='black')\n",
    "axes[1].fill_between(t, regimes, alpha=0.3, step='post', \n",
    "                     color=['blue' if r==0 else 'red' for r in regimes])\n",
    "axes[1].set_ylabel('Regime')\n",
    "axes[1].set_yticks([0, 1])\n",
    "axes[1].set_yticklabels(['Stable', 'Volatile'])\n",
    "axes[1].set_title('Regime Transitions')\n",
    "axes[1].grid(alpha=0.3)\n",
    "\n",
    "# Jump events by regime\n",
    "if jump_events:\n",
    "    regime_0_jumps = [j for j in jump_events if j[2] == 0]\n",
    "    regime_1_jumps = [j for j in jump_events if j[2] == 1]\n",
    "    \n",
    "    if regime_0_jumps:\n",
    "        axes[2].scatter([j[0] for j in regime_0_jumps], [j[1] for j in regime_0_jumps],\n",
    "                       color='blue', s=50, alpha=0.7, label='Stable Regime Jumps')\n",
    "    if regime_1_jumps:\n",
    "        axes[2].scatter([j[0] for j in regime_1_jumps], [j[1] for j in regime_1_jumps],\n",
    "                       color='red', s=50, alpha=0.7, label='Volatile Regime Jumps')\n",
    "\n",
    "axes[2].axhline(y=0, color='black', linewidth=0.8)\n",
    "axes[2].set_xlabel('Time')\n",
    "axes[2].set_ylabel('Jump Size')\n",
    "axes[2].set_title('Jump Events by Regime')\n",
    "axes[2].legend()\n",
    "axes[2].grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Statistics\n",
    "print(\"\\n📊 MRSJD Analysis:\")\n",
    "print(f\"  Total jumps: {len(jump_events)}\")\n",
    "regime_times = [np.sum(regimes == i) * dt for i in range(2)]\n",
    "print(f\"  Time in Stable regime: {regime_times[0]:.1f} ({regime_times[0]/T*100:.1f}%)\")\n",
    "print(f\"  Time in Volatile regime: {regime_times[1]:.1f} ({regime_times[1]/T*100:.1f}%)\")\n",
    "if jump_events:\n",
    "    avg_jump_0 = np.mean([j[1] for j in jump_events if j[2] == 0]) if len([j for j in jump_events if j[2] == 0]) > 0 else 0\n",
    "    avg_jump_1 = np.mean([j[1] for j in jump_events if j[2] == 1]) if len([j for j in jump_events if j[2] == 1]) > 0 else 0\n",
    "    print(f\"  Average jump size (Stable): {avg_jump_0:.3f}\")\n",
    "    print(f\"  Average jump size (Volatile): {avg_jump_1:.3f}\")"
   ]
  },
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    "## 6. Practical Parameter Selection Guide <a id=\"params\"></a>\n",
    "\n",
    "### How to Choose Parameters for Your Problem\n",
    "\n",
    "#### Step 1: Identify Regimes\n",
    "\n",
    "Ask: Does the system have distinct \"modes\" or \"states\"?\n",
    "\n",
    "**Examples**:\n",
    "- Manufacturing: Normal / Degraded / Failed\n",
    "- Weather: Clear / Cloudy / Storm\n",
    "- Network: Low / Medium / High traffic\n",
    "\n",
    "**Tip**: Start with 2-3 regimes. More regimes = more parameters to estimate.\n",
    "\n",
    "#### Step 2: Estimate Regime Persistence\n",
    "\n",
    "**Question**: How long does each regime typically last?\n",
    "\n",
    "**Formula**: $q_{ij} = \\frac{1}{\\text{expected duration in regime } i}$\n",
    "\n",
    "**Example**: If \"Normal\" regime lasts ~10 time units:\n",
    "- Total exit rate from Normal: $q_{01} + q_{02} = 0.1$\n",
    "- Split based on transition probabilities\n",
    "\n",
    "#### Step 3: Characterize Within-Regime Dynamics\n",
    "\n",
    "For each regime $i$:\n",
    "\n",
    "| Parameter | Method | Example |\n",
    "|-----------|--------|----------|\n",
    "| $\\mu^i$ | Sample mean of increments | $\\bar{\\Delta X} / \\Delta t$ |\n",
    "| $\\sigma^i$ | Sample std of increments | $\\text{std}(\\Delta X) / \\sqrt{\\Delta t}$ |\n",
    "| $\\lambda^i$ | Count events per time | $N_{\\text{jumps}} / T$ |\n",
    "| Jump mean | Average jump size | $\\bar{Y}$ |\n",
    "| Jump std | Std of jump sizes | $\\text{std}(Y)$ |\n",
    "\n",
    "#### Step 4: Validate with Simulations\n",
    "\n",
    "Before solving the HJB:\n",
    "1. Simulate the process with chosen parameters\n",
    "2. Check if trajectories \"look right\"\n",
    "3. Compare summary statistics to data\n",
    "4. Adjust and iterate\n",
    "\n",
    "### Common Pitfalls and Solutions\n",
    "\n",
    "| Problem | Symptom | Solution |\n",
    "|---------|---------|----------|\n",
    "| Too many regimes | Overfitting, unstable estimates | Use 2-3 regimes; combine similar ones |\n",
    "| Wrong time scale | Unrealistic dynamics | Match $q_{ij}$ to actual durations |\n",
    "| Numerical instability | Oscillations, divergence | Reduce grid spacing, use upwind scheme |\n",
    "| Slow convergence | Many iterations needed | Better initial guess, increase tolerance |\n",
    "| High dimensionality | Curse of dimensionality | Reduce state space, use approximations |\n",
    "\n",
    "### Sensitivity Analysis\n",
    "\n",
    "Always check how results change with parameters:\n",
    "1. Vary each parameter by ±20%\n",
    "2. Observe impact on optimal policy and value function\n",
    "3. Identify which parameters matter most\n",
    "4. Focus calibration efforts on sensitive parameters\n",
    "\n",
    "### When to Use vs. Not Use\n",
    "\n",
    "✅ **Good fit for HJB optimal control**:\n",
    "- Continuous state space (position, temperature, concentration)\n",
    "- Known or learnable dynamics\n",
    "- Quantifiable objectives\n",
    "- Medium-dimensional problems (1-3 state variables)\n",
    "- Offline planning acceptable\n",
    "\n",
    "❌ **Not recommended**:\n",
    "- Purely discrete decisions (use dynamic programming)\n",
    "- Unknown dynamics (use reinforcement learning)\n",
    "- High-dimensional state (>5 variables)\n",
    "- Real-time requirements (<1ms response)\n",
    "- Purely deterministic problems (use calculus of variations)\n",
    "\n",
    "### Alternative Approaches\n",
    "\n",
    "| Method | When to Use | Pros | Cons |\n",
    "|--------|-------------|------|------|\n",
    "| **LQR/LQG** | Linear dynamics, quadratic cost | Fast, analytical solution | Limited to LQ problems |\n",
    "| **MPC** | Need real-time receding horizon | Handles constraints well | Computational cost |\n",
    "| **RL (DQN, PPO)** | Unknown dynamics | Model-free, flexible | Sample inefficient |\n",
    "| **PID Control** | Simple SISO systems | Easy to tune | No optimality guarantee |\n",
    "| **Bang-Bang** | Hard constraints | Simple implementation | Non-smooth control |\n",
    "\n",
    "### Further Reading\n",
    "\n",
    "1. **Books**:\n",
    "   - Fleming & Rishel: \"Deterministic and Stochastic Optimal Control\"\n",
    "   - Øksendal & Sulem: \"Applied Stochastic Control of Jump Diffusions\"\n",
    "   - Bertsekas: \"Dynamic Programming and Optimal Control\"\n",
    "\n",
    "2. **Papers**:\n",
    "   - Guo & Hernandez-Lerma: \"Continuous-Time Markov Decision Processes\"\n",
    "   - Pham: \"Continuous-time Stochastic Control and Optimization with Financial Applications\"\n",
    "\n",
    "3. **Software**:\n",
    "   - This library (optimizr): Generic optimal control solvers\n",
    "   - PROPT: MATLAB optimal control toolbox\n",
    "   - CasADi: Nonlinear optimization and optimal control"
   ]
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   "source": [
    "## Summary and Next Steps\n",
    "\n",
    "### What We Covered\n",
    "\n",
    "1. ✅ **Optimal Control Theory**: HJB equations, value functions\n",
    "2. ✅ **Regime Switching**: Markov chains, coupled HJB systems\n",
    "3. ✅ **Jump Diffusion**: Lévy processes, compound Poisson\n",
    "4. ✅ **MRSJD Models**: Combined framework for complex systems\n",
    "5. ✅ **Numerical Methods**: Finite differences, upwind schemes, value iteration\n",
    "6. ✅ **Parameter Selection**: Practical guidance and sensitivity analysis\n",
    "\n",
    "### Key Takeaways\n",
    "\n",
    "- Optimal control finds the **best** policy, not just a good one\n",
    "- HJB equations require **solving PDEs** (computational cost)\n",
    "- Regime switching captures **state-dependent behavior**\n",
    "- Jumps model **sudden events** and tail risk\n",
    "- Start simple (2 regimes, pure diffusion) then add complexity\n",
    "\n",
    "### Exercises for Practice\n",
    "\n",
    "1. **Temperature Control**: Design an optimal heating/cooling policy to maintain room temperature near 20°C while minimizing energy cost\n",
    "\n",
    "2. **Inventory Management**: Optimize reorder policy for warehouse with regime-switching demand (normal/holiday)\n",
    "\n",
    "3. **Robot Navigation**: Find optimal path for robot avoiding obstacles with uncertain dynamics and occasional sensor failures (jumps)\n",
    "\n",
    "### Next Tutorial\n",
    "\n",
    "- **Hidden Markov Models (HMM)**: When regime is not directly observable\n",
    "- **MCMC Sampling**: Bayesian inference for parameter estimation\n",
    "- **Sparse Optimization**: High-dimensional problems with sparsity\n",
    "\n",
    "---\n",
    "\n",
    "**Questions?** Open an issue on the repository or consult the API documentation.\n",
    "\n",
    "**Happy Optimizing! 🚀**"
   ]
  }
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