\n",
"\n",
"# Characteristic X-Rays\n",
"\n",
"[Download](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_14-Characteristic_X_Rays.ipynb)\n",
" \n",
"\n",
"[](\n",
" https://colab.research.google.com/github/gduscher/MSE672-Introduction-to-TEM/blob/main//Spectroscopy/CH4_14-Characteristic_X_Rays.ipynb)\n",
"\n",
"part of \n",
"\n",
" **[MSE672: Introduction to Transmission Electron Microscopy](../_MSE672_Intro_TEM.ipynb)**\n",
"\n",
"**Spring 2026** \n",
"by Gerd Duscher\n",
"\n",
"Microscopy Facilities \n",
"Institute of Advanced Materials & Manufacturing \n",
"Materials Science & Engineering \n",
"The University of Tennessee, Knoxville\n",
"\n",
"Background and methods to analysis and quantification of data acquired with transmission electron microscopes.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load relevant python packages\n",
"### Check Installed Packages"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done\n"
]
}
],
"source": [
"import sys\n",
"import importlib.metadata\n",
"def test_package(package_name):\n",
" \"\"\"Test if package exists and returns version or -1\"\"\"\n",
" try:\n",
" version = importlib.metadata.version(package_name)\n",
" except importlib.metadata.PackageNotFoundError:\n",
" version = '-1'\n",
" return version\n",
"\n",
"if test_package('pyTEMlib') < '0.2026.01.1':\n",
" print('installing pyTEMlib')\n",
" !{sys.executable} -m pip install --upgrade pyTEMlib -q\n",
"\n",
"print('done')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First we import the essential libraries\n",
"All we need here should come with the annaconda or any other package"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import sys\n",
"import os\n",
"if 'google.colab' in sys.modules:\n",
" from google.colab import output\n",
" from google.colab import drive\n",
" output.enable_custom_widget_manager()\n",
" \n",
"import pyTEMlib\n",
"__notebook__ = 'CH4_14-Characteristic-X_Rays'\n",
"__notebook_version__ = '2026_01_18'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## X-Ray Families\n",
"The case is not always as simple as in the case of the carbon atom in the [Introduction](CH4_12-Introduction_X_Rays.ipynb#X-Rays)\n",
"\n",
"As the atomic number $Z$ increases the number of electrons in the respective energy states increases. With increasing complexity of the shell structure more options for relaxtions are available with different probabilities.\n",
"\n",
"For example a Na atom which has an M-shell The 1s state can be filled by an L- or an M-shell, producing two different characteristic X-rays:\n",
"\n",
"- K-L$_{2,3} ($K\\alpha$): $E_X = $E_K- E_L$ = 104 eV\n",
"- K-M$_{2,3} ($K\\beta$): $E_X = $E_K- E_M$ = 1071 eV\n",
"\n",
"For heavier atoms than sodium more options exist:\n",
"\n",
"\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## X-ray Nomenclature \n",
"\n",
"Two systems are in use for naming characteristic X-ray lines.\n",
"\n",
"The oldest one is the Siegbahn system in wich first the shell where the ionization occured is listed and then a greek letter that suggests the order of their families by their **relative intensity** ($\\alpha>\\beta>\\gamma>\\eta>\\zeta$).\n",
"\n",
"For closely related memebers of a family additionally numebrs are attched such as $L \\beta_1$.\n",
"\n",
"While most commercial software still uses the Siegbahn system the [International Union of Pure and Applied Chemistry (IUPAC)](https://old.iupac.org/publications/pac/1991/pdf/6305x0735.pdf) has officially changed the system to a protocol that first dentoes the subshell were the ionization occured and followed with the term that indicates from which subshell the relaxation originates.\n",
"\n",
"So $K\\alpha_1$ is replaced by $K-L_3$.\n",
"\n",
"\n",
"\n",
"|Siegbahn| -----IUPAC----- |Siegbahn| -----IUPAC----- |Siegbahn| -----IUPAC----- |\n",
"|--------| -------------- |--------| ------------ |--------| ------------- | \n",
"| $K\\alpha_1$| $K-L_3$ |$L\\alpha_1$| $L_3-M_5$ |$M\\alpha_1$| $M_5-N_7$|\n",
"| $K\\alpha_2$| $K-L_2$ |$L\\alpha_2$| $L_3-M_4$ |$M\\alpha_2$| $M_5-N_6$|\n",
"| $K\\beta_1$| $K-M_3$ |$L\\beta_1$ | $L_2-M_4$ |$M\\beta$ | $M_4-N_6$|\n",
"| $K\\beta_2$| $K-N_{2,3}$ |$L\\beta_2$ | $L_3-N_5$ |$M\\gamma$ | $M_3-N_5$|\n",
"| | |$L\\beta_3$ | $L_1-M_3$ |$M\\zeta$ | $M_{4,5}-N_{2,3}$|\n",
"| | |$L\\beta_4$ | $L_1-M_2$ | | $M_3-N_1$|\n",
"| | |$L\\gamma_1$| $L_2-N_4$ | | $M_2-N_1$|\n",
"| | |$L\\gamma_2$| $L_1-N_2$ | | $M_3-N_{4,5}$|\n",
"| | |$L\\gamma_3$| $L_1-N_3$ | | $M_3-O_1$|\n",
"| | |$L\\gamma_4$| $L_1-O_4$ | | $M_3-O_{4,5}$|\n",
"| | |$L\\zeta$ | $L_2-M_1$ | | $M_2-N_4$|\n",
"| | |$L\\iota$ | $L_3-M_1$ | ||"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## X-ray Weight of Lines\n",
"\n",
"Within the excitation probaility of the characteristic X-ray lines are not equal. \n",
"\n",
"For sodium (see above for energies), \n",
"the ratio of $K-L_{2,3}$ to $K-M$ is approximatively 150: 1. \n",
"\n",
"This ratios between the lines are a strong function of the atomic number $Z$.\n",
"\n",
"We load the data of the X-Ray lines from data from [NIST](https://www.nist.gov/), the line weights are taken from the Program [DTSA-II](https://www.cstl.nist.gov/div837/837.02/epq/dtsa2/index.html), which is the quasi-standard in EDS analysis.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a91175bec8147bbb2cd203319c47522",
"version_major": 2,
"version_minor": 0
},
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Qrzn1ASh2hRXv7iH5UDbuni5MfLRHpa+bM0eyWfX+3lLDVtw89Xj6uqnXsa96LfsEuhPQ0pOAVp511qXe0EkAKAFgjdTJBfT9PaTv2MoPua9jteno3LcFV93dpd67k6ojJ62QL5/egs5Fy23P9OHzf28GDdz98oBLtj4c2XqOtZ8cwL+lJ7c906fS5VnMNn56ezcpx3NxM+i5bnYcoW0u/DtlnlVb/kx5VkIivdXgrw5aWK1Fdr54ejOFuRYG3tiB7ldLou9LURSFHWuSSsaO1iWtTnM+IHQtmb1qyrdiyrNgyrdiNdvL7NO6sz+j7u3WKGZ+p5/OY/3Xhzl3wlip7bVaDaFRPrTu7E/rLgGEtvGp8Q/SrLMFbF2ZiKuHnkE3dai1cZINmcVsY9mbO0lLysPL341Jj/W87ESdw1tS+O3zQzjsCq4eemxFdnXc4WV4+LgSGOapBoRhngS28sInyAN3L5dmlUJLAkAJAGukTi6grZ/AikdI8r2TFUevR3EoXHldFL3HRtVOebXoxK50Vr2/l6BwL25+6sqSbt0BN7QnbnhEhfutnb+fI3+lcsWICPpf375KZVrMNn56Zzcpx3Jx9dAzbnYcoVE+ZKUUsPSNHZjyrARHqMFfXX5hH/jjLL9/cQg3Tz13vtCv0Xft1xaH3cG6RUc4sEGdTBE9oCU+wR6Y862YC6yY862YLnpstdhxcdOdv+lLHru6q/davRYcCg5FQXGowaXiUB87HArWIhuFuRYKjZbLziotptVqcPdywcPbhTaxQfS+NgpdDVqL6lpxt3Dingx0eu1Ffz8dLu46XM8/9vB2pUU73wbRNdsUmfItLJm7g5zUQvxbGJj0aM9yx0crisJfPyWybcVJANr1CGH45C7o9FqKCm0U5BZRaFSv4YLcIgpzLeSmm8g6m3/JbAUaDbh7u5b6wWPwVlsRPbxd8fB2UbuZvV1x93ZpVNd4eSQAlJVAGr7I/updwWIG3/QY6xYd568fE/EJ8qh0CpWGIitFTbEQEKZ2b3ToFUrKsVyObk2tMAB0OBRO7c8CoE1s1QdIu7rruXZW95IgcPlbOxl8ayc2fa+OgSqeaVjXrTWd+7Vg16+nyU4pYMeaJPpNrFpg2xxYzDZ+/ni/uvyfBgbd1IHYYXXXWmq3OijMs5wPCItKlmrz8HbFw0v9MnT3crlsN3FDp9Fq6NyvJZ37tazvqjRrHl6ujHswjiVzt5N9rpAf39nN+IfiSgXcdquD3z4/yJG/1Il0PUZH0ndc25LJZO5e6lCIwAqGVVvMNrLPFZJ1Np/MswVkny0g82wBBedniJuMFkxGC5nl716Km0F/ITAsHopxfjiGR/Hj889d3fW4uOvQu2gb9f+VpkYCwGq4OA9grQvqBB7+YMqmW7s0jCMi2Ln2FL99dhD/FoZyx7U1VMX5wIrHt7TvGcKGb4+SlpRHTmphuYOrUxONmAusuBn0tGjrW61yi4PAFfF7OHs0h18+PQCoA9bHP3hFvXTVaXVa+k9sx4p397D712S6DWld67nZGpOCnCJ+it9Nxul89C5aRkzrStu4ul3qT+eixTvAXf5dRJ3xDnDnutlxLHl9O2knjaz+cB9jZ8SquQrzrax8fw8px3LRajUMub0T0QPCqnR8V3c9oW18Sg2FAbWl3ZRvLWk5NJ2/L3mep94K86yY8ywoChQV2igqtJGTWvnyi8fZqq3LF1roPbxd1HG3PsX3rnhc1BLZWMe9N3QSAFbDzJkzmTlzZkkTcq3SaiGiHxxeCac20W/ibDLPFnBqfyZHt6Y2rgCwpAVQHSju4e1KeGd/Th3I4ui21HK7tZP2ZgAQHh1Qo0HYF4LA3Zw5kkNAmJpgtjIpaGpLZEwgYR38OHs0h7+Wn+DqydH1VpeGJPNsPj+9vZv87CI8vF0YO6N7lWdGCtFYBbT05NpZ3Vk2bxenD2Tx64ID9L42ihXv7iE3zYSru47R02MI7+K8lUa0Oi2evm6XTG1TTHEomAutmIzW80GhpcyQjKKC80Mzzr9ePNtbUcBitmMx2yHXUun69bwmkr7j21X7/ET5JABsDIoDwKTNaAY8SGS3AE7tv/zqEw2Jw6GQc05dNuniGW4deoeqAeDWVHqNaVOme+DkPrUzok23mufHcnHTce0D3Tm1P4tWnfxrfbbv5Wg0GvpPas/iV7dx6M9zdB8eQVDr5r1CSPKhLFZ9sA+LyYZfqIFrZ3XHN7jx578UoipaRPlyzfRurIjfw9FtaRzfkY7DoeAd4M7YWbFVmm3tbBqtBg8v1/OziSs3W1lxKBfNyi+eWa7eW8w2NZDMVYPJQqP6uPg1h0PBzUPGSNcGCQAbg/PjADm1GRyOkjQof19hoiEzppuw2xzoXbX4BF7oUmsbF0zCl4fJPldIRnJ+qZU78rPNZCbng0bNE+YMehddnXclXkpolA/te4VwbFsam384xnUPxNV3lZxGURRsFofaMlCgtgqYC9RVKooKi1epOH9//vm5E7k47Aot2/sy5v7Yem2hFaI+RUQHMnxKND9/sh+HQyGkjQ9jZ8RWai3lhkaj1ajrU1dxApHiUCgqtKHRybjB2iABYGPQsju4GMCcA+mH8A2OBNSkrIqiNIpBtcXj//xbeJZa/cLVQ0+bmECO70zn6NbUUgFg0vnWv9A2Pnh4N74PvcrqO74tJ3amc2p/FqcPZjm1a6c2OBwKhbkW8nPMFGQXkZ9dRH5OEQXZZvJzijAX2NRgr9Baarm8ymrfM4SrJ3dplulAhLhYh16h6F20ZCTnEzciosHlR6xtmvMz7EXtkACwMdC5QOtekLgeTm3CJ64zaNR8cqY8a6P4RZiVkg9cmAF8sQ69Q9UAcFsq/Sa0KwkQi9fdbBPTtJdH8g020G1wK/b8nsymJce46cne9bpEnMOhkJ9tJq94KbzMC4/zMs0U5FpQKpFvrJhWp8Hd0wU3TxfcDXrcDHpcPfS4eaj3rh4XXvPyc6NFW19ZIk+I86K6BxPVveH0WoimQwLAxiKivxoAJm1G1/sevPzcyM8uwphhahQBYObfZgBfLLJbIC7uOvKzijh3IpeW7f2wWe0kH8o6/37DWUqrtvQa04ZDm1PIOJ3Pka2p5ab4cTgUbBZ1NQu7VcFus2OzOrCfv9ls6r3DpuBwKCiKgsOulOS6Ux+rPxwu7na9uBu2yGSlMMdy2YSyGq0GT19XvPzd8PRzx8vfTX3s64a79/mVOTzVFCkubrpG0UothBDNiQSAjcXF4wAVBZ8gD/Kzi8hNN1U7PUpdKkkBU04LoN5VHZd3eMs5jm5NpWV7P84eycFmceDp60pQeNOfGOHh7UqP0ZFsWXqCdV8dZutPidhtjlIBXmWy/DuLVqfBO8AdnyB3vAM98AlyxyfQA+9ANS2Kh49rs1o1QAghmhoJABuL1r1BqwfjGcg5hW+wB2eP5mDMaPgTQex2BzmpZWcAX6xD71AObznHsR1pDLypQ8ns38hugc2m9Sj2qnD2rz9LXpb5shN8tDoNOhctehctOr221GOtTotGy/m1bjUl98WP9W5a3Nz1uP6tK7b43tPPDU8/NwnwhBCiCZMAsLFwNUDLODizDU5txieoL6DOrm3octNMOOwKejddhUl1W3f2x93LBVOeleRD2SX5/yJjmn73bzEXVx03PNGLzLP56EuCOt2F4K74ptdKcCaEEKJGJABsTCL7qQFg0iZ8Ww4DILcRtACWdP+2MFQ4uF+n09K+Rwj71p9h64pEjBlmtHoNrTv712VV652a+b5hzwIWQgjR+Mn6KtUQHx9PdHQ0vXv3rtuCIy6MAyzOBdgYWgD/vgZwRTpcGQrAuRNGAFp18JOF54UQQohaIAFgNcycOZMDBw6wdevWui04Qu32JeMIvgY1qCrItWCz1MGaxDVwYQ3gS0/maNnWFy//C0sRNYfZv0IIIUR9kACwMTEEQHAXANwytuJ6fimzhr4kXGVbADVaDR16hZY8j2zi+f+EEEKI+iIBYGMT2Q8AzenN+ASpEyoa8jhAu81BbvEM4MsEgACd+rVAq9MQEumNX4ihtqsnhBBCNEsSADY2xeMAkzbhG9zwxwHmpBXicCi4uutKde9WJDDMi1uf7tOk1sQVQgghGhoJABub8y2AnNuDj7+6LmRDbgEsWQO4pWel8/n5hRpk/UchhBCiFkkA2Nj4tgbfCFAc+OZsBMCYnAr5aeBw1HPlyqrs+D8hhBBC1B3JsdEYtRkAu0/hc+Iz4DmMJxLh9WtBowOvEPAKhYC2MOxfENShXquafYk1gIUQojIUqxV0OjRaabMQwlnkf1NjNOT/oMfd+LZrB4DREYqiaEGxQ14KpOyC/Uvgw2Fw8Kd6raq0AAohaqJgyxaODh7CsWFXkfHhR9hzcuq7SqWYDxwg7b9vUJSYWN9VEaJKpAWwMQqIgnH/w8vuQLtjHXaHCwUPnMbL1Qh55yA/FTa9DUkb4ZvbYdA/YdhToNXVaTXtVgc5aer4xMvlABRCNE2KouDIzUXj4YHW7fITwS6W/d13nHvuebDZAEh/4w0y3n0X3wnjCbjrLtzatq1WnRwFBZgPH8F86CBFBw9iPXMGr2FX4X/brWh0lfucVBwOshZ+Rtobb4DVStZnnxH8wCwCJk9Go5evVtHwyVXaiGl1WrwC3TGmmzBmW/HqEAY+Yeqb7YfD2qdhy7uw4b9wdidc/4maS7CO5KQVojgUXD30ePq51lm5Qoi6pVgsFCUmYk1OxpqcjOXMGazJZ9TnZ87gKChAazAQNHMmAXfdicbl0pO8FLudtNf/S9annwLgc+21eA4YQNZnn1F08CA5i74hZ9E3eA4eRMBdd+M5oH+ZSWaKxYItKwtbZia29HSKjh6l6OBBzAcOYklKAkUptX3Bps3kLl1Ki+eew6Nb10vWz5aRwdkn/0XBhg0AuISFYT17lrTX/4tx9RpavvQS7p06VvXP2OgoDgeKxYKjoABHfj72/Hwcefk4CvKx5+XhyFdfV6xWFLsN7HYUmx3FZgO7reSxUlSEYinCUVSEYi5CKSrCYbGor5vNBEyZQsCdd9T36TY5EgA2cr7BHhjTTeSmmwjrcNG6uToXGD0HWvWEZbPg+G/w4RC46XMIi6uTumVdNP6vsjOAhRBq8GI5fZqiEyewnEjEkpiI5fRpUBQ0Li5o9Hr13qX43gWNqyv64GD0oS1wadlCvW8RitbHp9b+/zksFnIWLybz/Q+wpaVdetvCQtLmziVnyRJa/OffePbtW/52BQWcefQx8n//HYCg2Q8Q9I9/oNFo8J0wnsKtW8n67DPyf/2NgvUbKFi/AbcO7XGNaostMxN7Zia2rCwcRuMl66MPDsYtugvunbugNRjI/PhjzPv3c/Kmmwi48w6CHpiNzqvs0JX8PzZy9oknsGdkoHFzI/TJJ/G7+SZyl/xA6quvYt63j8QbbiDovvsImn4fGtea/fhVFAXFZELj6lrplkXFbsdRWIgjP1+9mc1qUFVUhFJkQbEUlXruMJnUIK6gQN2v+PH554rZjGK14rBaUCxWFItFHZd5vmW2ttmzs+uknOZGoyh/+xkkKs1oNOLr60tubi4+Pj71UoeErw6zf/0Zel4TSd/x7crf6Nw++OYOyE4EvTtc+ybE3Vbrdftz+Qm2rTxJ9MAwht3RudbLE6KxMh86hHHVaoqOHMFy4gSW5GSwO2eJR43BgEtoKC6tWuE9fDg+Y65BV8PPK8ViIeeHpWS8/z62lBQAtF5euEZG4tK6NS6tW+HaurX6uFUrXFq2xLhyJWmv/7fky9xnzDWEPP44Li1alBzXevYsp2fMpOjQITRuboS9Mgefa64ptw6WU6fI+uILchd/j6OwsPyK6vXoAwLQBQbiFtUGty5qwOfepTP6oNJLTdrS00md8wrGlSvVXVu0oMW/n8J7+PCSc06b9xZZ8+cD4NahA63e+C9uHS5MtLOmpnHu+efJ//VXdZuOHWn50kt4xHSr6p+4xOlZs8j/RT0eLi5o3d3RuLuhdfdA6+6Gxt0DFOVCK1xBAUpFf49apDUY0Hp7o/XyQuflhbbk5qkGrzq9+sNFrwOdHo1Oh8ZFDzo9WjdXNG5uaNzcLzx2dVPPz81N/TETGuLU+jaE7+/6JgFgDTSEC2jnz6fYtOQYHXqHMnLaJbotTNmw5D44+rP6vPe9cM1rUIuz6la9v5cTu9IZeGMHul8dXmvlCNEY2TIzyf3xR3KXLqPo0KEy72sNBlzbtsU1Kgq3tlG4RkaCXl/S8qJYrWr3mcWqts4UmbGlpWE7l4r13Dls586VO2FC4+qK9/Cr8Z04Ec/+/Ss95g3U2bi5y5eT8e57WM+cAUAfEkLg/dPxu+EGtJdp7bLn5pL+1v/IXrQIHA40BgPBM/5BwF13YT50iNMzZmLPyEAXFET4u/F4xMZetk72vDzy1qzBUVSEPjAQfWAgusBA9AEBautnFT/j8jf8wbnnnsOanAyA19VXE3D3XaS9+hrm/fsB8L/tNkIefwytu3vZv5GikLdqFedefAl7VhZotQROnULwQw9VeWxg4Y4dJN12e5X2KcXFBZ2nZ8n4S835m9bVteSxxtUVrYcHWk/PCzeD4fxjA1qDpxqIubpeuJ1vcS5+rPXwqNJ11BA0hO/v+iYBYA00hAvo+M40Vn+wj5A2Ptz4RK9Lb+xwwPrXIGGO+vzmL6DLdbVWty+e3kxumolxD8YR3qXuxh4K0VA5LBbyExLI/WEp+Rs2lHShaVxc8LrqKgx9rsTtfNCnDwmpcdetw2TClqoGhOb9+8ldupSio8dK3tcHB+M7fhy+Eybg1r59mf0VRVG7Ck0mCtavJz3+XaynTgGgCw4i6N778Lv5pipP7jAfPMi551/AtHMnAC6REdjOpaIUFeHWqRPh772LS1hYDc68ZhwmExnvvU/m/Pmlujl1vr60fPklvK+++rLHsGVnk/rSyxh/UjMxBP/zEYLuvbdK9Tg9/X7y163D9/pJhDz6KIrZfKE712QquQcutLp5el5ofath93NT1hC+v+ubBIDVEB8fT3x8PHa7nSNHjtTrBZSRnMc3L27F3cuFaa8PqtxOKx6FrR9Br6lqd3AtsFntfDh7HYoCk18dgKdv1b4ghGhMTLt2kfXZ5+o4OBc9Gv35cXp6vdrNpdeD3UHBH39gz80t2c89NhbfCePxHTMGnZ9frddTURTM+w+Qu3Qpxp9+KtVC6BoVBTotSqEJR3GgYTKVmSyhCwgg8N578b/lZrQeHtWvi8NB7rLlpL3+OvbMTAC8hg4l7PXXyx17Vx+Kjh4l5ZlnMe3YgaF3b8Lmvlaqy7oysr74ktQXX0Tr60v7tT9XuvvdfOgQiRMmglZLu5UrcG3TphpnICoiAaAEgDXSEC4gi9nGRw+tB+DeNwfj6lGJLoZDK2HRrRDYHh7YXiv1Sj+dx7cvbcXNoGfafwfJJBDR5CiKQuGff5Hx/vsUbtlS6f30ISEXWt3aVTButw4oFgt569aRu3QZ+evWXXZAvy44iMC778b/ttvQGgxOq4fdaCRrwQK0BgMBU6Y0uK5ExeHAkpSEa2RktRJRK3Y7J8aPx3LsOIH/uJ+QBx+s1H5nHvknxpUr8RlzDa3eeKPK5YpLawjf3/VNZgE3cq7uejy8XTDlWcnNMBEc7n35nSL7g0YLmcfAePZC6hgnKpkBHCYzgEXToigK+evWkfn+B5h27VJf1OvxHT8OrwEDUOx2FKsNxaaO0bswXs+Oe3Q0nv37NYggR+Pqis+IEfiMGIEtMxPzgQNoXFzRGjzQuLur48Dc3dXxXe7utVZnnY8PwbNn18qxnUGj1eIWFVX9/XU6gh98kDMPzCZr4WcE3HEH+sDAS+5jSUrCuHo1AIFV7DYWorIkAGwCfII8MOVZMaZXMgD08IOW3dXcgIkboPvNTq9TyQogsgScaCIUh4O8n9eS8eEHFB04CKhBlN+NNxI4bWq9jlmrKX1gIF6DKjmERFSZ9/DhuMfEYN67l8wPPyT0yScvuX3mJ/PB4cBz8CDcu3Spo1qK5kYCwCbAJ8iD1EQjuRmmyu8UNfh8ALi+dgLAs7IEnGi8HBYLluPHKTpyhKKjRzEfOULRwUPY0tMBNbWK/623EDh5Mvrg4HqurWjoNBoNIQ8/xKmp08j+6msC7r67wh8M1tQ0cn/4AYCg++6ry2qKZkYCwCbAN1gdiG1Mr2IAuPEtSFynDvJ2cjfthTWAZQk40bA5TCZMe/di2rUb84EDFB09iuXkyXLz8Gl9fAi44w7877wDvb9/2YMJUQFDv34Y+vSh8M8/SX/3XcJefLHc7bIWLkSxWvHo0QNDr8tkdhCiBiQAbAJ8gs4HgFVpAYzoB1o95J6G7JPq+sJOYrXYS+oiXcCiIVEUBVtKCoU7d2LauQvTrl2YDx0qdwKE1scHt44dcO/YEbfzN/fOnZ06AUI0HxqNhuCHHiTp1tvI/WEpgVOn4da29OeuPTeXnEWLAAi8T8b+idolAWATUNwCmFuVFkBXT2jdG05tVruBnRgA5pwrBAXcvVww+EgeKlF/7PkFmPfvx7x3D6Y9ezHt2lXukmX6kBA8rrgCj9iYkmDPGXn4hLiY4Yor8Bo2jPzffyfjnbfLzO7N+vJLHIWFuHXqhNeQIfVUS9FcSADYBBS3AOZlFWG3O9DpKpmqIGqwGgCe3AA973ZafbLO5gPS+ifqlmKzUXTkiBro7d2Dec8eio4dL5PHDp0O9y5d1IAvrjuGK65A37KlBHuiTgQ/9CD5v/+OceUqAu+9t2SSh6OwkOzPPgfU1j+5HkVtkwCwCfD0dUXnosVudZCfVVTSInhZbQbBulfVFkAnjgO8MP5PAkBRuyzJyRT8sZGCjRsp2LIFR15emW30YS3xiInFIzYG95gYPGJiapTAWIiacO/UCZ+xYzGuWEH6vLcI/+B9AHIWL8aek4NLRAQ+o0bVcy1FcyABYBOg0WrwCXQn+1whxnRT5QPA1r1B7w75qZBxBII7OaU+JTOApQVQOJk9P5/CP/+kYONG8jduxJp0qtT7Wm9vPGJicI+NwSO2Ox4x3WSWrmhwgmc/gHH1avLXraNwxw48unUjc/6nAAROm1blNYOFqA65ypoI32APss8VkpthIryyO7m4Q3gfdSZw4nrnBYDSAiicyJaRQd4vv2Bcs4bCrdtKT9jQ6/GI647XwIF4DhiAe3R0g0iyLMSluEZG4nf99eR8+y1pb7yB38SJ2M6dU9dmnjihvqsnmgkJAJuIkpnAVZkIAuo4wMR16u3Kms86y003YcwwAxIAiuqzpqaS9/Na8n7+mcJt20qN43ONjMRzwAA8Bw7AcOWV6Lwk1ZBofIJm/IPcpUsxbdtO0ZGjAARMnozWVSbOibohAWAT4RNcjVQwAFFDgBfg5B/gcEA11rq82PbVJwGIiA7Aw0s+yETlWU6fJu/XX8lb8zOmnTtLveceE4PPqJF4jxiBa2RkPdVQCOdxadEC/9tuI2vBAhxGI1pfX/xudn5SfiEqIgFgE1GSCqaqAWDYFeDqDaZsSN0HLWOrXQdjhonDm88B0Pta56WVEU2TYrNh2rmTvIQE8hPWYTl+vNT7HnFxeI8ahc/IEbi0alVPtRSi9gTedy85336Lo7CQgNtvR+clvSai7kgA2ERc3AWsKErlUwjo9BDZD47+rI4DrEEAuH3VSRwOhfAu/rRo61vt44imy56TQ/6GP8hPSCB/wwYcRuOFN3U6DD164D1yJN4jhuPSokX9VVSIOqAPCKDlnDnkr1tHwNQp9V0d0cxIANhE+AS5A2Ax2zEXWKvW/Ro1+EIA2H9Wtco3Zpg4VNz6N1Za/0Rp5sNHyPzoI4yrVpVaYk3n64vnkMF4Dx2K54AB6Hzlh4NoXnxGjcRn1Mj6roZohiQAbCL0Ljo8/dwoyCnCmG6uegAIkLQR7FbQuVS5/O1rknA4FFp39qdle78q7y+aJtPu3WR88CH5v/1W8ppbhw54DR2K17CheHTvLrN2hRCiHkgA2IT4BntQkFNEbkYhoVE+ld8xNAbc/cCcA2d3QXjvKpWbl2Xm0KYUQMb+CXW93cItW8j44EMKt2xRX9Ro8B49iqB778U9Orp+KyiEEEICwKbEJ8ids0erkQpGq4WoQXDwRzUdTBUDwO2rk3DYFVp18idMWv+aLUVR1DVO3/8A85496ot6Pb7jxxE47Z4yC98LIYSoPzXL+dEETJw4EX9/f2644Yb6rkqNXZgJbK76zm3OdwMnrq/SbnlZZg5uPAvAlde2qXq5okkw7dpF0i23kjxjJuY9e9C4ueF/xx20/3kNYS+9JMGfEEI0MM2+BXD27NlMnTqVhQsX1ndVaqyyyaALjRbSThqJjAm8MFu4eBzg6T/BVgR6t0qVuaO49a+jH2Ed/Ktdd9E4WZLPkP7GGxhXrgRAYzAQcPvtBEy+G31gYD3XTgghREWafQA4bNgwEhIS6rsaTlGZZNAWs43vX9uGMcPMuIfiCO8coL4R3Ak8Q6AgDZK3QpuBly0vP9vMgU1q65/M/G1e7Pn5ZH7wAVkLP0OxWECjwff6SQTPno1LSEh9V08IIcRlNOgu4PXr13PdddcRFhaGRqNh6dKlZbZ59913iYqKwt3dnZ49e7Jhw4a6r2gDUdwFnJ9ThM1qL3ebDd8eLVmqLT0p78IbGs2FVsBKdgPvWJ2Ew6YQ1sGPVp2k9a85UGw2shd9w/GRo8j86GMUiwVD375ELfmesBdflOBPCCEaiQYdABYUFNC9e3feeeedct//5ptveOihh3jqqafYuXMngwYN4pprruHUqVMl2/Ts2ZNu3bqVuZ09e7auTqPOuHu64OKuAwXyMsuOAzy+I61kti5A9rmC0htUIQDMzy5i/8bi1r821a6zaDyKjh8nceJEzj37LPasLFyjomj93rtEfDof9y5d6rt6QgghqqBBdwFfc801XHPNNRW+/8YbbzBt2jTuueceAObNm8eaNWt47733mDNnDgDbt2+vk7o2BBqNBt9gDzJO55ObbsK/xYVlhfKzi/j9i0MABIV7kXE6n+xzhaUPUBwAJm8FSwG4Vrws0Y6f1da/lu19pfWvGTDt3s3p+6Zjz81F5+tL0AMP4H/zTWhcqp4zUgghRP1r0C2Al2KxWNi+fTsjR5bOoD5y5Eg2bdpUK2UWFRVhNBpL3RqakokgF40DVBwKvy48QFGhjeAIb666U22tyT5XiKIoF3b2bwO+4eCwwanNFZZRkFPEgQ3nW/+ujar8snOiUcrf8AdJk6dgz83FvXssbVetJOCO2yX4E0KIRqzRBoAZGRnY7XZCQ0NLvR4aGsq5c+cqfZxRo0Zx4403snLlSlq3bs3WrVsr3HbOnDn4+vqW3MLDw6td/9riez4AzL1oJvDu306TfCgbvauWEVOj8W9pQKMBi8lGodFyYedS4wArHku54+ck7DYHLdv50lpa/5q03B9/4vQ//oFiMuE5cCCR8+ejDwio72oJIYSooUYbABb7e+uToihVapFas2YN6enpFBYWkpycTO/eFSdBfvLJJ8nNzS25nT59utr1ri0XZgKrYwAzkvPYvPQ4AANu6IB/C0/0Ljq8zweKORV1A1cwDlBxKBw8P46w19g20vrXhGV99jlnH3sMbDZ8xo4l/N14tJ4VDwsQQgjReDToMYCXEhQUhE6nK9Pal5aWVqZV0Fnc3Nxwc6tcfrz6cnELoM1iZ+38AzhsCm1ig+g6KKxkO/8WBozpJrJTC0uP4WszSL1P2QWmHPDwK3V8Y6YJq9mOTq+V1r8mSlEU0t96i8z3PwDA/847CX3yCTTaRv97UQghxHmN9hPd1dWVnj17snbt2lKvr127lv79+9dTreqfT7A7oI4B3PTDcbLOFuDh7cKwOzqXaq3zDzUA5cwE9m0FwZ1BccAfb5Q5fuYZdXv/lga0ukZ7+YgKKDYb555+piT4C37oIUL/9aQEf0II0cQ06E/1/Px8du3axa5duwBITExk165dJWleHnnkET7++GPmz5/PwYMHefjhhzl16hT3339/rdYrPj6e6OjoS3YX1xevAHc0Wg12q4O9vycDcNVdXTD4uJbarniGcJkuYICrn1HvN70NZ3aUeivzTD4AQa28nFxzUd8cJhNnHn6YnO++A62WFs8/R9D906WbXwghmqAG3QW8bds2hg0bVvL8kUceAeDuu+9mwYIF3HzzzWRmZvL888+TkpJCt27dWLlyJZGRkbVar5kzZzJz5kyMRiO+vr61WlZV6XRavAPcSsYAxgxpRZuYoDLb+bUobgEsJwDsPAa6XQ/7voflD8B9CaBTZ3wWB4ABEgA2KUWJiZyZ/SBFR4+icXUl7PW5+Pxthr0QQoimo0EHgEOHDi2dpqQcM2bMYMaMGXVUo8bBJ8gDY4YZ/xYG+l/fvtxtAs63AOZlmbEW2XFx05Xe4JrX4PjvkLoP/pgHQx4DLnQBSwtg02H8+WdSnvwXjoICdEFBtJ73JoZeveq7WkIIIWpRg+4CFtUTO6w1YR38GHVvN/SuunK3cfdywd1LbdXLSS2nFdAzSA0CAda/BmmHsFrs5Kap2wa0ktmgjZ1itZL66mucmf0gjoICPHr1JGrJ9xL8CSFEMyABYDU05DGAAFHdg5n4zx4EXqaVzr+4Gzi1oPwNYm6ADqPAboHls8g+k4eigIe3S5kxhaJxsaamkTR5ClmffgpAwNSpRH76qazlK4QQzYQEgNUwc+ZMDhw4cMmk0Y3BhZnA5bQAgpoY+to3wdUbkreS+ccKAALCvGRiQCNW8NdfJF5/Pabt29F6edHq7f8R+vhjsrKHEEI0IxIANmN+l5oJXMy3FYx8HoDM3TsBCJTu30ZJURQyP/mEU1OmYs/IwK1jR6IWf4fPiBH1XTUhhBB1TALAZsz/UjOBL9ZjMkQOJNPSCuCyXcuiYUp/6y3S5r4Odju+48fT5ptFuLZpU9/VEkIIUQ8kAGzGSnIBphXicFxitrVWC+P+R6atDQCBeevqoHbCmTLee68kuXPIE/9Hy1fmoPXwqOdaCSGEqC8SAFZDQ58EUlnege7o9FrsVgd5meZLblvoEo7J4Qs4CNj+HzCm1E0lRY1lfjKf9Lf+B0DI//0fgZMnyxhOIYRo5iQArIamMglEq9XgF6q2ApVZEu5vihNA+7pl42JNhxWPwGVyNIr6l/XFl6TNnQtA8EMPEjhlcv1WSAghRIMgAWAz5xd6vhu4vFyAFylZAq5dGGj1cHglrHpcgsAGLPvbb0l98UUAAv9xP0G1vESiEEKIxkMCwGaushNBSpaAaxsG184DNPDXh/DTw+Bw1HItRVXlLlvGuWeeBSBgyhSCZ8+u3woJIYRoUCQAbOYuBICX6wK+aAm4HnfChHcBDWz/FH6cLUFgA2JctYqzT/4LFAX/224j5PHHZMyfEEKIUhr0WsCi9pXMBL5EF7DD7iArRQ0AS5aAi7tN7Qr+YTrs/BwcNhgfD9ryl54TdSPv11858+hj4HDgd+MNhP77KQn+hLiUjKOw4b/QdRJ0HFnftWm+bBYw54A5F0w56uPi+1Y9oVWPeq1eU+TUAHDHjh24uLgQExMDwLJly/j000+Jjo7m2WefxdW1aSwfFh8fT3x8PHa7vb6rUmN+51cDMeVZMedbS9YHvlhuugm71YHeVYtv0EWpQ2JvUgO+7++F3V+rQeCE90EnvyvqQ+G2bZx56GGw2/EZdx0tnn0WjVYa+YWo0Nld8MUkKMyE3Ytg5AvQb5a6CpKoGkUBS4H6tzRlQWEWmLLVmzkHzEYoMpZ/b84B6yWGIQ17SgLAWuDUb+rp06fzxBNPEBMTw4kTJ7jllluYOHEi3333HYWFhcybN8+ZxdWbmTNnMnPmTIxGI76+vvVdnRpxcdPhFeBGflYR2ecKaNner8w2xd2/AWFeaLR/+2Dsdr3aErh4Kuz9Tg0CJ30EOllWrK65de6Ce/dY9EHBhL38MhqdtMYKUaGkzfDVTWoQ4hkMBenw878h8xiMeb15f4Y5HGoQl3cO8lMvBHKmnAsB3cWPC7PU7e2Wmpft5gsevuDuBx5+4O4Lge1qflxRhlMDwCNHjhAXFwfAd999x+DBg/nqq6/YuHEjt9xyS5MJAJsa/xaeagCYWlhBAKhOAKlwCbjo8XDTZ/Dt3bD/BzUIvH4+6JtGi29jofPyJOLDD9G4uKDRSyusEBU6+gt8cwfYTBDRH25bBDu/gDVPwfYFkH0SblyoBiCNkaJcCMwKMqAoTz1Xq7n8+8IsNdDLT4W8VChIUz/Hq0PnBoYA8Ag4f++vBnHuvuDmA+4+Ze+Lgz03HxlGVIec+i2hKAqO85MBfvnlF6699loAwsPDycjIcGZRwon8Qw2cPpBV4UzgCwHgJZaA6zwWbv4Cvr0TDv4IX90IVz+tjt0QdUZrMNR3FYRwLkWBxHWABiIH1HyIyf6l8P094LBC+xHqj1dXA/SbCf5R6nsnEuCTkXD7t+DfpubnUJv2LlY/cwsz1VtBhtoaV90A7mKGQPAKVe89/M4Hc+fv//7cEKgGfC4G6UJvJJwaAPbq1YsXX3yR4cOHs27dOt577z0AEhMTCQ0NdWZRwomKZwLnVDATuFIBIECn0XDL17DoNvUD9ESC+uu6/yzoeI26pJwQonnJTYZtn6oBV69p4B9Z+X1T9sDKx+D0FvW5ZzB0nagOPWl9ZdU/U3Z8rmYtUBzqcSZ+WLqnovMYmLoKvroZMg7DR1fDrV9D+JVVK+diiqJ2lbr5OH989I7PYPkDFb/v6q0GZe4+oPcAF/fy7z381EDPKxS8Q8Grhfq3ll6cJs2pV+O8efO4/fbbWbp0KU899RTt27cHYPHixfTv39+ZRQknKp4JXF4LoMVsw5ihLhNXYRfwxToMh+nr4I95sG8xnNqk3gLaQb8Z0P029de2EKJpS9kNm96GfUtAOT9hbvO7cMXtMOhR8AuveN/CLPj9Jdg2Xw3WXAygd1fH6f31oXrzDVeDuJgboEXs5VudNr8La55UH/e4S81nWl53Y8vucO9vahB4bg8suFZNexVzQ9X/Bqf+hDX/gjPbAI0ajHmFqsGVVyh4haiPgztDx1FVazk79gv8+NCF84kacr4VLhA8g9QuWBf3qtdZNBsaRan9pRzMZjM6nQ4Xl6Y1qLZ4Ekhubi4+Pj71XZ1qK8gtYsH/bUSjgen/G4rO5cKv6nMncvn+te0YfF2Z8urAqh3YeBb+/EDNFWjOVV/zCIDe06D3veovTSFE06EoamCy6X+QuP7C61GDQaNVewUAtC5qPtFB/wTf1he2czjUtFK/Pqd2Z4KanmXki2qwdCIB9n0PB38CS96F/QLbq4Gbq6fa6uXqCW5eF56n7lWDUVBn+Y588fLBVlE+LLlXXfUIoMs46DNd7Ya+3L7ZJ+GXZ9Ux0ZXV7QY1lVZlgraU3fDpGLDkQ+wtMPF96Xatoqby/V0TTg8Ac3JyWLx4McePH+exxx4jICCAHTt2EBoaSqtWrZxZVL25OA3MkSNHGv0FpCgKHz+yAYvJxi1PX0lg2IWu3v0bzpDw5WEiogO4bnZc9QooylcHWG95F3KS1Nc0OugwUs0n2HG0dDUI0ZhZTWpgtukdSD+ovqbRQbdJasAVFqe+lrQZEuacH9MH6FzV1quBj6gzTlc+Cmd3qO8Fd4Exr6nBY3nlHV2r9jIcWQM2c+XqOezfMPjRygdLDjusfRo2v3PhtZBouPJeiL1ZDTAvZs5Vcwpuee/8jFiNGugOfVINevPPT7DIL76lqud9YKk6Zi+8L9zypdqCV5Gc0/DxcMg/p7b63b5YPj+rQQJAJweAe/bs4eqrr8bPz4+TJ09y+PBh2rZty3/+8x+SkpL47LPPnFVUg9CULqDFr24jNdHIqHu70b5nSMnr678+zN51Z4gbEcGA69vXrBCHHQ79pHbFFI/pAbXLIuYmNRhsGVuzMoRobPLOqcHT0Z/VAfUtYtQuzRYx4N2iZi07iqJ2Y+5dDGd2QECbC8cO7arOzKyqggz1mOf2wrl96n3GkQvdvK5e0HMy9Lm/4m7ekxvVQPDkBvW5zhXsVkBRx8oNfVINsiqTiqUoT211zDun/ti0FN8K1PcsBWowFncbXHFH1c8XIHU//PUR7PnmQr46N1+1O7v3PeAXCTsWwu8vQ+H5CY9Rg2HUy+rf+nJOJMA3d0FRrjrp5LbvILhj2e1MOTB/FKQfUgPRqaur928omtT3d3U5NQAcPnw4PXr04LXXXsPb25vdu3fTtm1bNm3axG233cbJkyedVVSD0JQuoF8XHODQlnP0GRdFrzFRJa//8N8dnD2aw9WTu9C5b0vnFZh+GHZ9pSZfzT934fUWMRB3B3S/xfkpGOxW9QsnaTMEtIXIfuoHt3SdiLpmzlVnbu75Vr0mlQqWUjQEQYtuF4LC4M4Q1AFcPMrfvljmcTXo2/sdZB6teDu/yAvHDmyn/h+xFqgtbJZCNdgpvuWlQuo+yEsp/1g+raHPfdDj7sr/303coAaCSRvV591vg+HPNtzhIaYc9XNr60eQdeLC694tL/xdAjuoXcxVHdOXdkjNnpBzSg3qbv6idOunrQi+uF69XrzD4J61pbvPRZU0pe/v6nJqAOjr68uOHTto165dqQAwKSmJTp06YTZXspm+kWhKF9D21SfZsvQEHa8MZcTUroDaNfzJPzdQVGjjpqd6Exzu7fyC7TY4/hvs+gIOr7qQSDSgHdzzizpouiZsRXBiHRxYBodXqLPxLuYdBhF9IbK/eh8SXTt5qIpbefZ+B8YU9YO902hoP1x+wTcXVrPayrf3O7Xb0l504b3wPupYN3vR+Za14la18gJDjTqTNrgzBHVU74M7qak4Dq9Su0XP7rywud5dDUbaD1dn5BYfP/d0NU9Eo/6AahFT+ubdsno/phQFkreC3k0dx9cYOBzq59ZfH6r/pijq33/ok9BravWTSOenw6Jb1b+HVg/X/U9tZXQ44If71GvH1VudqVyZlkVRoab0/V1dTp0F7O7ujtFoLPP64cOHCQ4OdmZRwsnKmwlckFNEUaENjVZDQItKzACuDp1eXX+z40h15t/exfDHm5B1XF1d5PbFVU+dYDXBsV/h4HL1C7HoomvSEATthqmDtM/uhLyzsH+JegO1Wyeijzq+p8u4mo2tMRvVVp6936oD4i/+Mt/7rXrT6iGiH3S6Rh0LKRnv65bDcaGFy5Kvrkeq2NXhCopdff/i53arOt7MZlZ/XNjM55Ppnn9uyVNb98q7mXIudJOCGrjF3KjOLi0v15zVBGkHSne1ph9SE/xmn1RvR1aXf14anXqdd7tBzdHpXs4XXGGW2qJXHBBmJ6kTEFwM6s3VAC6eamujq0ENcEK6Qmg0uDnxx6BGU7M0K/VBq1UzHnQYrrYEpuyBtkPUv1FNeAXD3T/C0n+oE0iWzVA/CxWHGvxp9XDzZxL8CadwagvgfffdR3p6Ot9++y0BAQHs2bMHnU7HhAkTGDx4cJNbCaQp/YLIPlfAV8/+id5Nx33zBqPRaEjal8lP7+zGv6Untz3Tp+4qk7JHHediLVQHkI96qfL7HloJP9yvjqUp5tUCulwH0ePUvITFAaWlEM5sh1Ob1dvpv9QgoJhnsNqd1WtK5btaLIVw4ne1a+/I6tKD01tfqX7hB3dSxywdWa228lwssIPaGqR3U8dF6VzO384/1rqo7+nd1VupfF7nbzpXtRVToz1/r/vb/SVypymK+mVTKgiyq6+Vel78muOix3Z1ILvVpHYjWv52Kw60tHr1PC4+r+Jz0+r/Vv5Fx1XO39uK1DJs5vP3RRceO6yX/vex29R/Y2vhhTrVJZ9Wag672JsgtFvVW8wURU2Fkn5YDQYzjqj36UfUoRThfdWAMnqCGkyIxsnhgN9fVCeUXGz8+TQ6osaa0vd3dTk1ADQajYwZM4b9+/eTl5dHWFgY586do1+/fqxcuRJPz1pqRaonTekCstsdfPjAOhwOhbvn9MfL350da5LY/MNx2vcKYdQ93eq2Qvt/gO8mq48nfqCOCbycPd/BD9PVQMGntbpEXfS4yieMtdvUFpHDq9QB3cVjejRa6DRGHezdduiFL21FUbvRTv+l3pL/UltSLs7AH9RRneAScwMERJUpkszjanfgkdXqOChnZO8X1ePqdVHwfHHQrL3wXOd6UQB+USCud1dbi928zy975Xdh+auLb14tai8husMuy2g1NTu/gB8fVD8Xhv4Lhv5ffdeoyWhK39/VVSt5AH/77Td27NiBw+GgR48eDB8+3NlFNAhN7QL66tktZJ8rZNzsOMKjA1g7fz9H/kqlz/i29LqmTd1X6NcXYMPr6tqSU1ddelm5bZ/CTw8DipoXa3x8zbLu261waAVs/fjCTEVQW+iix6kLxp/+q/wB8T6tzieovVEd01TZVh5zrjquKOOY2pJlt6pjIh029b74ud1SwZqe5292y/mWuXJa8qjkf3eNtvyWwzKtihe/dn4fV4MaTLkYzudh81Sfu3qqLZUOx/k6XnSO9vOPHbbSxyuvBfPilk+9m9pFqXdX77Uul/57a3UX1eV8PV0N6rFkpRrREKXsUdNndb5WJqw5UVP7/q6OOkkE3dQ0tTyAxVa+t4fE3RkMurkDscPCWfTCn2SeKWDMjFiiYi+Rl6q2OBzqsnJHVqmTNe5LKH924Ka34ed/q497TYMxrzv3yzztkBoI7l5UOvksqEFJy1i1lTH8/M03vOF+UDsqmG16MY2m4dZfCCGcQALAWggAf/31V3799VfS0tJw/O3LZv78+c4sqt41tQto8w/H2bEmiW5DWjHwxg58+OA6HHaFO1/qh0/gZdJO1BazUU16mnFYHRt3949qqw+oXbAJc2Ddq+rzAQ+pKSRqK3gpylPzgJ3eqo7jC+8DYVfI0nZCCNHINLXv7+pw6izg5557jueff55evXrRsmVLNNKK0Kj4t1ADmexzheSkFuKwK7i66/AOqMf1JN191MXYPxoGp/+EFf+EceeXdFrzL3V1EYCrn1aXlapNbt7qOMDe99RuOUIIcV62OZvUwlQ6+XeS71ThVE4NAN9//30WLFjAnXfe6czDijpSnAom51wBmWfU2bABYV71/6ET2A5umA9f3qiuExraTZ2ssfNz9f1r5qoJaIUQoglRFIX71t7HoaxDtPdrz62db+XattdicJFeB1FzTh31bLFY6N+/vzMPKeqQ3/kWwIJcCynH1DQqga29LrVL3Wk/HIY/pz5e/X9q8KfRqmkRJPgTQjRA+ZZ8Pj/wOc9uepbci1NTVdLBrIMcyjoEwLGcY7yw5QWGfzec17a+xmljdRN5C6FyagvgPffcw1dffcV//vMfZx620bPb7Vitl8lP1hBowL+1G6Y8K8nHM3D31RLQ2q3hrODS417IPK1OCtG4wMgX1ESsDaV+NeDq6opWZqEK0SSczT/Llwe/ZMnRJeRbz/emuAcwu8fsKh1ndaKa6HtI6yH0bdmXrw99zam8U3x+4HO+OPAFg1oP4tbOt9I/rD/aS+X3FKIcTp0E8uCDD/LZZ58RGxtLbGwsLi6ll8N54403nFVUg3C5QaSKonDu3DlycnLqvnLVVGi0YLdemLxj8HFF59KAPlgURZ2Jq3O7MBmkCdBqtURFReHqWoOVR0SVORQH5wrOcdJ4kiRjEiabCS8XL7xdvUvde7mqj7UaLRa7Rb05LBTZi7Dareq9w0o733b4ufvV92kJJ7E77Hx24DMWH1lMiCGE2OBYYoNj6R7cnSCPspkR9qTv4bMDn/FL0i/Yz6/6EugeSKY5kxaeLVhz/ZpKB2oOxcHo70eTUpDCm0PfZHjkcByKg41nNvLVoa/448wfJdt28O/A60Nep61vW+eceDMgk0CcHAAOGzas4oI0Gn777TdnFdUgXO4CSklJIScnh5CQEAwGQ/2PpauEguwizIUXWiv9WxjQ6hpQANgEORwOzp49i4uLCxEREY3iOqkPvyT9woL9C3CUuz6uSqfR4enqibeLd0nQVvzYy8ULq8NKkjGp5HbKeAqLw+K0OnroPbg35l7u6noXbrrG/QOl0FpIamEqqYWppBWmYXfY6R7SnSifKKdfozaHDZ1G16Cu/TP5Z3jqj6fYnrq93PdbebUiNiiW7iHd8XH14dvD37IrfVfJ+31b9uWu6Lvo3aI3V317FXnWPD4Z+QlXtqzcsne70nZx56o7MegNrLt5He760pPxkoxJLDq0iKXHlpJvzcfTxZOXBr7E1RFXV/ucmxMJACUPYI1c6gIqzhEYEhJCYGBgPdWw6gqNFvKz1S5VrU5LUEMZA9jE5ebmcvbsWdq3b1+m5VyUbg1xNr1WT7h3OJE+kXi7eJNvzVdvlnzyLHklj23KhVVatBotbjo3XLQuuOnccNW5YnPYSC1MBdTg4J+9/snwiOENKqipyL6MfXx7+FtSClJIK0wjrTCtpOvy7wLcA+gZ2pNeob3o1aIX7f3a16j7MTkvmXt+vgdPF0++Hvs1rrr6bQVXFIVlx5fxyl+vUGAtwKA38GCPB/HQe7A7fTe703dzPOc4SjlJ1V20LoyJGsOd0XfSKaBTyevPbX6OxUcWM67dOF4aWLmlLef8OYevDn3FtW2vZc6gORVul2nK5NF1j7ItdRsA98Xex4zuM9DJqjCXJAGgBIA1cqkLyGw2k5iYSJs2bfDwqKccetVgMdnISVPXR3X10OMXIrPN6oLJZOLkyZNERUXh7l6PaXcaqB2pO7h79d14unjyyqBX0FB+UGVz2Miz5qnBmzVPDeAsakCXZ8lDq9ES4R1BG982RPpEEukTSUvPlui1lx4OrSgKRfYiHIoDV51ruds7FAcrE1fy5vY3SStMA6BXaC/+78r/o3NA55r/EWrJlpQtPPDrA5jtZcfSerp4EmIIIcQQgt1hZ2/GXorsRaW28XXzpUdID4aGD2VC+wlVCgYzTBncteouTuepExr+0/c/3NTpppqd0N9kmbN4betrBLgHMKjVIHqG9qwwyMwyZ/H85uf59dSvAFwRcgUvDXyJcO/wUtvlWfLYl7GPPel72J2+m5SCFK6OuJpbOt9SbtdwcWueh96DhJsSLjuL1+6wM3zxcDJMGcRfHc/g1oMvub3VYeWNbW/wxcEvABjUahCvDH4FH9fmGdhUhgSATggAJ02axIIFC/Dx8WHSpEmX3HbJkiU1KarBqUwA2Ni+0O02R0kKGIOPK17+jafujVljvV7qygubX+DbI99WqQWlvhRaC5m/bz4L9i+gyF6EBg3Xd7yeWXGzCPSoem9AniWPj/Z+xA9Hf2Bk5Ej+2eufTksDsvHMRh78/UGK7EX0bdmX69pdVxLwhRpC8XQpvX67xW5hX8Y+tqVuY9u5bexK34XJZip5/5o21/DiwBcr1YpntBiZunoqh7MP46Zzo8heRAvPFqyYuMKprYCPrXuM1SdXlzw36A30bdmXQa0HMbDVQFp4tgBgffJ6nt74NJnmTPRaPTPjZjKl6xSntKQpisLYH8ZyOu80Lw98mevaXXfJ7f9K+YtpP0/Dx9WHhJsScNFVrlfgx+M/8tzm5yiyFxHhHcG8YfPo4N+hxvVviiQAdMIsYF9f35IuDl9f3xpXqDG4eCm4pkar06DRaFAUBb2rdCGI+me1W1mTtAaAsW3H1nNtLs/gYmDWFbOY1GESb2x/gzUn17D4yGJWJ67mjug7mNR+Ei29Wl72OFaHle8Of8f7u98nuygbgG+PfMuf5/7klUGv0C2oW43quT55PQ/9/hBWh5WhrYfy36H/vWzg5apzpUdoD3qE9uC+2PuwOqwczDzIH2f+4KM9H7Hq5CrSTenMGzYPX7eKvw9MNhOzfp3F4ezDBLoH8vHIj5m+djrnCs6x9NhSp7UCrk9ez+qTq9FqtIxuM5q/zv1FhimD307/xm+n1THpnfw70cqrVcnzdr7tmDNoDl0CuzilDqCOgb+u3XW8u+tdlh9fftkAcNXJVQCMiBxR6eAP4Lp219Herz0P/f4Qp/JOcfvK23lhwAuMajOqRvUXTZN0AddAU2wBBMhNN2Ex2QgI80SnlwkgdaExXy+1bd3pdcz6bRaB7oH8cuMvl+2ubWi2p27n1b9e5WDWQQA0aBjQagA3dLiBweGDcdGW/oJXFIWE0wm8sf0NThpPAhDlG8VNHW/i0/2fklaYhl6j5/7u9zMtZlq1/h6/n/qdR9Y9gs1h4+qIq5k7eG6VAo3ybD67mYcTHqbAWkA733a8O/xdwrzCymxntVuZ/fts/jjzB96u3nw66lM6BXTiq4NfMeevOYQaQlk5aWWNWwELrYVMWDaBlIIU7o6+m0d7P4pDcXAo6xAbkjew4cwG9qTvKTWW767ou5jdY3atTOBJzkvmmiXXoEHDzzf8XNLy+HdWh5Vh3w4jtyiXj0Z+RN+WfatcVrY5m8fXP86WlC0ATOk6hQd6PFDmWmvOpAXQyYmgRdPw4GP/4N4H7iwV/C1evBh3d3dee+21CvebPHkyGo2G+++/v8x7M2bMQKPRMHny5BrVbfLkyTzxxBMVvjdhwoRSr1W23n/fTzQcKxJXAHBN1DWNLvgD6Bnak6/Hfs1rg1+jd4veKCj8ceYPHkp4iJGLRzJv+7ySpL77M/cz7edpzP59NieNJwlwD+Dfff7N9+O+547oO1gybgmj2ozCpth4Z9c7TFk9pWT8XGX9kvQLjySowd/IyJHMHVLz4A+gX1g/Fo5eSIghhOO5x7l95e0czDxYahuH4uCpjU/xx5k/cNe5E391fMlkies7Xk+IIYTUwlR+OPpDjevzzq53SClIIcwzjBlxMwB18k50YDTTu0/nizFfsO7mdbwy6BUmd53M/FHzeaz3Y7U2e7u1d2t6hvZEQeGnEz9VuN2Ws1vILcol0D2Q3qG9q1WWv7s/7w1/jyndpgDw6f5Pmbx6Mmfyz1TreKJpqnEAeMUVV9CjR49K3UQjctEY+48//pjbb7+dd955h8cff/ySu4WHh7No0SJMpgvjgsxmM19//TURERE1qpLD4WDFihWMHz++UttXpd6iYSq0FpJwOgGAMVFj6rUuNaHT6rgm6hrmj5rPTxN/Ykq3KQS4B5BhyuCTfZ8w5ocx3LD8Bm756Ra2ntuKq9aVad2m8dPEn7i5880lLTe+br7MHTyXlwe+jKeLJ7vSd3Hjjzey7NgyKtOZs/rkah5d9yg2xcaYqDG8OvhVp7YKdQroxJdjvqS9X3syTBlMXj25JF+doii8/OfLrEpchV6j581hb3JFyBUl+7rp3LgnRl1n+6O9H2GxVz89z76MfXx58EsA/tPvPxWOmfR392ds27H8s9c/6d2iesFWVYxrNw5Qx+pV9O9VPF5xROSIGo0/1Gv1PNLzEd4Y+gbert7sSd/DjctvZM3JNdU+pmhaavxzWlpOmrbXXnuNp59+mq+++orrr7/+stv36NGDEydOsGTJEm6//XZAnfwTHh5O27Y1S1K6ceNGtFotffr0cXq9RcP02+nfMNlMRHhH1HjMW0MR6RPJIz0f4YG4B0hITuD7I9+z6ewmDmcfBtRxjrOvmF1u9ylcGE/WI7QH/9rwL3ak7eDfG//NuuR1DAsfhq+bL35ufvi7+ePr7ou3izcajYYVJ1bwrz/+hUNxMK7dOJ7v/3ytpApp4dmCz675jIcTHubPlD+Z9essnu73NGfzz/LN4W/QoOHlQS8zsNXAMvtO6jCJj/d+TGphKkuOLuGWzrdUuXyrw8qzm57FoTgYEzWm3HLqy8jIkbz858ucyD3BgcwDdA3qWur9InsRv51SxyJeE3WNU8ocETmCroFdeXz94+xO382j6x5lS8oWHu/9OB76xpOhQjhfjQPAZ555xhn1EA3QE088QXx8PD/99BPDhw+v9H5Tpkzh008/LQkA58+fz9SpU0lISKhRfZYvX85111132SXTqltv0fCsOKF2/45pO6ZR5NOrChedCyMiRzAicgRn8s+w6ewmogOj6RrY9fI7o+YanD9qPp/u/5T4nfGsTVrL2qS1ZbbTaXT4uvmSU5SDQ3Ewsf1Enun3TK3mifN29ea9q9/jmU3P8OOJH3lm04Xviaf6PFVhcFPcCvjyny/z0d6PmNRhUpXHAn5x4AsOZx/Gx9WHx3s3rJZ/L1cvroq4ilWJq1h2fFmZAPCPM3+Qb80n1BBKXEic08oN8wrj09Gf8t6u9/h478csPrKYXWm7eG3wazJLuBmTMYB1SFEUCi22Or9VZ57PqlWrePXVV1m2bFmVg6g777yTP/74g5MnT5KUlMTGjRu54447Sm2Tl5dH7969iYuLIyYmho8++uiyx12+fPllu39rUu/yVKeewjmyzFlsPrsZgLFRDX/2b0208mrFjR1vrHTwV0yn1XFPzD18MfYLrmt7HX1b9qVLQBdaeLYoad2xK3ayzFk4FAc3dryRZ/s/WydJgl10Lrw08CXui72v5LVZcbO4ufPNl9zv+g7qWMC0wjSWHK1a6rDTead5d9e7ADza69Fqpd2pbcXdwKsSV2G1l14jvnjt39FtRjt9bV8XrQuze8zmw5EfEuQRxLGcY9y64la+Pfxttb4jROPX+EZUN2Imq53op+t+/MWB50dhcK3aP3VsbCwZGRk8/fTT9O7dG29v75L3vvzyS6ZPn17yfNWqVQwaNKjkeVBQEGPHjmXhwoVq/quxYwkKKp0c1WAwsG7dOgwGA4WFhXTr1o1JkyZVuGrKwYMHSU5OvmxQV5N6l6eq9RTOs+bkGuyKna6BXWnj26a+q9OgdQ3sysuDXi7zepG9iBxzDjlFObhoXWjrV7drxWo0Gh644gG6B3cn35JfqW5NV50r98bcy0t/vsRHez9iYoeJlZqYoSgKL255EbPdTO8WvZnQfoITzsD5+rbsS7BHMOmmdDac2cBVEVcB6njXdcnrAOd1/1ZU/uLrFvPvjf/mjzN/8MKWF/jt9G9Mj51eakymaPqkBVCUq1WrVqxbt46UlBRGjx5NXl5eyXvjxo1j165dJbdevXqV2X/q1KksWLCAhQsXMnXq1DLv63Q6DAZ1YLbZbMZut1/yV+jy5csZMWLEZVdVqWm9a1pP4TwrT6wEGvfkj/rmpnMj1DOUTgGd6jz4u9jg1oOr1I0/qcMkQg2hVWoFXJG4gk1nN+GqdeXpvk832CEDeq2+JJ/l8uPLS15fl7wOk81EuHc40YHRtVqHQI9A4q+O59Fej6LX6Nl4ZiN3rbqLO1bewS9Jv2B3NL0ct6KsGrcAGo3GZptDp6o8XHQceL7uE3J6uFSvuyciIoJ169YxbNgwRo4cyZo1a/Dx8cHb27tUy1p5Ro8ejcWizuIbNar8c87JyWHIkCEcPXqUuXPnlmklvNiyZcu45557ar3eNa2ncI7kvGR2pe9Cg6ZWW0NEw1TcCvjiny/y8Z6PmdRh0iVbAXPMObz2l5rqaXr36Q2+xfi6dtexYP8C1iWvI8ecg5+7H6sS1eTPo9uMrpPgVavRcnfXuxncejAL9y9k+fHl7E7fzcMJDxPpE8ld0Xcxrt043PWSl7SpqnELoL+/P2lp6rqXV111FTk5OTU9ZJOl0WgwuOrr/FaTD5PWrVuTkJBAZmYmI0eOJDc3t1L76XQ6Dh48yMGDB9Hpyg9A/fz82L17N4mJiXz11VekpqaWu11aWhpbt27l2muvrbV65+bmlmod3LVrF6dOnapSPYXzrExUW/+ubHklwYbgeq6NqA8TO0xUWwFNaXx/5PsKt7M5bLy29TWyi7Jp79eeKV2n1GEtq6ejf0e6BHTB5rCx+uRq8ix5Jely6voHT5RvFM/2f5afb/iZe2PuxcfVhyRjEi9seYFR34/ivd3vkWPOqdM6ibpR4wDQy8uLzMxMABISErBarZfZQzQ2xd2qOTk5jBgxotJBvo+PT6Vah0NDQ4mNjWX9+vXlvv/jjz/Sp08fQkJCqlLtKtU7ISGBK664otTt6aefrlI9hXMoilIy+7epT/4QFStuBQT4ZO8nFNmLAHUlkV1pu/h478fc/8v9DFw0kB9P/IgGDc/0e8YpSa3rQvFycMuPL+e3U79hdVhp59uu3mblBnkEMbvHbNbesJYnrnyCVl6tyDJn8e6udxmzZAyf7vu05N9ANA01Xgru+uuvZ+PGjXTp0oV169bRv39/XF3Ln7b/22+/1aSoBqepLgVXF1JTU/Hw8MDHxwej0Ui/fv34+uuviY2NLbPtuHHjGDhwYL0kc65KPWtCrpcLDmUd4sYfb8RV60rCzQl4u1a92140DRa7hbE/jOVcwTlGRI7AaDGyJ30PJpup1Hbert5Mj53O3V3vrqeaVl2GKYPh3w3Hrthp69uWE7knmBk3k/u7l11JqT7YHDZ+SfqFj/d+XJKjMswzjNk9ZnNN1DVOn6Vc12QpOCeMAfziiy9YuHAhx48fZ926dXTt2rVk0LwQFUlOTmbatGkoioKiKMyaNavCoGrgwIHceuutdVxDVVXqKZyjePLHkPAhEvw1c8WtgC9seaFUjkM/Nz96hvakV2gverXoRQe/DnWS2saZgjyCGNhqIOuS13Ei9wSgjv9rKPRaPaOjRjOyzUh+OvETb+14i7MFZ3liwxN8fuDzOls9RdSeGrcAXmzYsGH88MMP+Pn5OeuQDZq0AApnketF5VAcjFw8ktTCVN4c+ibDIyWRd3NntVt56c+XyLfmlwR97fzaNfoWKFBTHT267lEAugR04dvrvq3nGlXMZDPxxYEv+HjvxxTaCgEYGj6Uh3s+TFvf+pthXl3SAujkPIC///57yePiuLKhTsWvifj4eOLj47HbZaq8EM60PXU7qYWpeLt4M6j1pXM0iubBRefCs/2fre9q1Iqh4UPxdvUmz5LX4Ge7e+g9uDf2XiZ2mMj7u99n8ZHFJJxOYEPyBga0GsCVLa6kV4tedPbv3OhaY5srp/+E+uyzz4iJicHDwwMPDw9iY2P5/PPPnV1MvZo5cyYHDhxg69at9V0VIZqU4skfwyOHVyr5rxCNmZvOjcd7P87Q1kOZ1GFSfVenUoI8gvh333+zZNwShrYeil2xsz55Pa9ve51bfrqFQYsG8cCvD7Bw/0IOZh6UnIINmFNbAN944w3+85//MGvWLAYMGICiKGzcuJH777+fjIwMHn74YWcWJ4RoQix2S8k4r+JEuUI0dRPaT2iwq5ZcSlu/trx99dsczjrMlpQtbD23le2p28mz5pGQnEBCcgKgTtDpEdKDHqE96BHSg66BXRvNTO2mzqljAKOionjuuee46667Sr2+cOFCnn32WRITE51VVIMgYwCFs8j1Ar+d+o0Hf3+QEI8Qfr7hZ+lGEqKRsTlsHM46zNZzW/nr3F/sSNtBgbWg1DbuOndigmPoGdqTHiE96B7cHYNL3U8clTGATm4BTElJoX///mVe79+/PykpKc4sSgjRhPyZ8ifPb34egNFRoyX4E6IR0mv1dA3qStegrkzuNhmbw8ahrENsT93OjtQd7EzbSXZRNlvPbWXrOXUIlVajpaVnS8K9wwn3DifCO4Jw73Bae7cm3Du8XoLD5sKpAWD79u359ttv+de//lXq9W+++YYOHeonuaUQouGyO+x8uPdD3tv1HgqKupJDt4a/koMQ4vL0Wj3dgrrRLagbd3e9G0VRSMxNZHuaGhDuSN3B2YKznMk/w5n8M2xJ2VLmGIHugdzV9S6mdiu7pryoGacGgM899xw333wz69evZ8CAAWg0Gv744w9+/fVXvv224U5vF0LUvUxTJk9seKLkQ39Sh0k8ceUTeOg96rlmQojaoNFoaOvXlrZ+bbmx442AmhA7yZjE6bzTF27G05zOP01uUS6Z5kw0NL1sIg2BUwPA66+/nj///JM333yTpUuXoigK0dHR/PXXX1xxxRXOLEoI0YhtO7eNx9c/TropHQ+9B//u+2/GtRtX39USQtSxII8ggjyC6Bnas8x7RouR03mnCXQPrIeaNX1ODQABevbsyRdffOHswwohmgCH4mD+vvm8vfNtHIqDtr5teWPoG7Tza1ffVRNCNDA+rj50Dexa39VospweAAohRHlO5J7gta2vsfHMRgDGtRvHU32ekkHeQghRDxr/WjrC6SZPnsyECROcdiyNRsP995dd4HzGjBloNBomT55c4zKeeOKJCt9z1rkUH6+2z6cpyTBl8PmBz7n5p5sZv3Q8G89sxE3nxvP9n+fFAS9K8CeEEPVEWgBFrQsPD2fRokW8+eabeHioA/zNZjNff/01ERERNTq2w+FgxYoVLF++3BlVrZTaPJ+moNBayK+nfmXFiRVsTtmMQ3EAoNfoGdBqALN7zKajf8d6rqUQQjRvEgCKWtejRw9OnDjBkiVLuP322wFYsmQJ4eHhtG1bs0XEN27ciFarpU+fPs6oaqXU5vk0Zkeyj/Dpvk/59dSvmGymktdjg2O5tu21jGozigD3gHqsoRBCiGK12gVsNBpZunQpBw8erM1iRCMwZcoUPv3005Ln8+fPZ+rUmud1Wr58Oddddx1abd2OZqit82mMUvJTeOqPp7hh+Q38dOInTDYTEd4RzOg+gxUTV/DlmC+5tfOtEvwJIUQD4tRvzZtuuol33nkHAJPJRK9evbjpppuIjY3l+++/d2ZRjZOigKWg7m9OWu0vLy+P3r17ExcXR0xMDB999FGl973zzjv5448/OHnyJElJSWzcuJE77rijzHYTJ07E39+fG264oVLHXb58OePHj690PS5WF+ej1+uJi4sjLi6Oe+65p1r1bKhyzDm8vvV1rv3hWpYfX46CwojIEXwx5gt+mvgT/4j7BxE+0iUuhBANkVO7gNevX89TTz0FwA8//ICiKOTk5LBw4UJefPFFrr/+emcW1/hYC+HlsLov919nwdWzxocxGAysW7cOg8FAYWEh3bp1Y9KkSQQGBvLll18yffr0km1XrVrFoEGDSp4HBQUxduxYFi5ciKIojB07lqCgoDJlzJ49m6lTp7Jw4cLL1ufgwYMkJyczfPjwBns+fn5+7Nq1q1r1a6hMNhNfHvyST/Z+Qr41H4DeLXrzcI+HiQmOqefaCSGEqAynBoC5ubkEBKjdPKtXr+b666/HYDAwduxYHnvsMWcWJeqBTqfDYFBnbZrNZux2O8r51sVx48aVGofXqlWrMvtPnTqVWbNmARAfH19uGcOGDSMhIaFS9Vm+fDkjRowomYhRVXVxPk1Fkb2IJGMSO1J38NGej0gzpQHQ0b8jD/d8mAFh6so/QgghGgenBoDh4eFs3ryZgIAAVq9ezaJFiwDIzs7G3d3dmUU1Ti4GtTWuPsp1kpycHIYMGcLRo0eZO3duSauXt7c33t7el9x39OjRWCwWAEaNGlXjuixbtqzG3aq1fT5Go5GePXvi4eHBSy+9xJAhQ2pU39qWZc7ieM5xEnMTScxN5KTxJIm5iZzNP4vChaEErbxaMTNuJmPbjkWrkWxSQgjR2Dg1AHzooYe4/fbb8fLyIjIykqFDhwJq13BMjHQNodE4pSu2LuTm5pbpugwICCAiIoLdu3eTmprKpEmTuOGGGwgNDa3UMXU6XcmEIJ1OV6P6paWlsXXrVpYuXXrZbS91Ln5+frV6PidPniQsLIx9+/YxduxY9u7di4+PT6WOXxfO5p9le+r2kttJ48kKt/V28SbKL4pr2lzDTZ1uwlXnWncVFUII4VRODQBnzJhBnz59OHXqFCNGjCiZmdm2bVteeuklZxYlallCQkKZ9ZvvvvtuFixYAEBoaCixsbGsX7+eG2+8sdLHdVbw8+OPP9KnTx9CQkIuu+3lzgVq73zCwtQxn926dSM6OpojR47Qq1evSh/fmax2KyeNJ9mVvqsk4DtXcK7UNho0tPZuTRufNkT5RtHGtw1RPup9oHugdPMKIUQToVEUJ00RBZ5//nkeffTRknFVxUwmE3PnzuXpp592VlFOcfr0ae68807S0tLQ6/X85z//qdKXv9FoxNfXl9zc3DKBgNlsJjExkaioqCbT/Z2amoqHhwc+Pj4YjUb69evH119/TWxsrFPLSUhI4J133mHx4sUVbjNu3DgGDhzI448/Xu1yavt8srOzMRgMuLm5kZyczIABA9i5c2fJONmLOfN6sTqsnDae5ljOMY7nHC+5TzImYVNspbbVa/REB0bTM7QnPUN7EhcSh6+bb43KF0KIhu5S39/NhVNbAJ977jnuv//+MgFgYWEhzz33XIMLAPV6PfPmzSMuLo60tDR69OjBmDFj8PRsHN20dS05OZlp06ahKAqKojBr1iynB3+jRo1ix44dFBQU0Lp1a3744Qd69+5dZruBAwdy66231qis2j6fgwcPMn36dLRaLRqNhrfeeqvc4K+mTDYTO1N3siVlC3+e+5Mj2UewOWzlbuvp4kmXgC70atGLnqE9iQ2KleXYhBCiGXJqC6BWqyU1NZXg4OBSr//222/cfPPNpKenO6uoWhEbG8uKFSsIDw+v1PbNrQVQ1J6qXC82h439mfv5M+VPtqRsYVfaLqwOa6ltPPQetPNtRzu/drT3a19y38KzhXTjCiGaPWkBdFILoL+/PxqNBo1GQ8eOHUt9wdjtdvLz87n//vurfNz169czd+5ctm/fTkpKCj/88AMTJkwotc27777L3LlzSUlJoWvXrsybN69UvrbK2rZtGw6Ho9LBnxC1QVEUzhWcI9OcSaZJvWWYMkqeZ5gyOJR1qCT/XrEWni3o06IPfcP6EhccR5hXmMzOFUIIUSGnBIDz5s1DURSmTp3Kc889h6/vhTFErq6utGnThn79+lX5uAUFBXTv3p0pU6aUm0T6m2++4aGHHuLdd99lwIABfPDBB1xzzTUcOHCAiAh1BYKePXtSVFRUZt+ff/65ZIB+ZmYmd911Fx9//HGV6yiEMz23+TlWnF5x2e18XH24ssWV9G3Zl75hfYnwjpCWPSGEEJXm1C7gdevW0b9/f1xcXJx1yBIajaZMC2CfPn3o0aMH7733XslrXbp0YcKECcyZM6dSxy0qKmLEiBHce++93HnnnZfd9uJg0mg0Eh4eLl3AosbMZjPHThzjkd2PkGJJIcQjhECPQPXmHkiQRxCBHup9hE8Enf07o9PWLJWOEEI0V9IF7ORJIEOGDMHhcHDkyBHS0tJwOByl3h88eLDTyrJYLGzfvp0nnnii1OsjR45k06ZNlTqGoihMnjyZq6666rLBH8CcOXN47rnnqlVfIS7HYleTSnf078j342TtbCGEELXHqQHgli1buO2220hKSuLvDYsajQa73e60sjIyMrDb7WWS9oaGhnLu3LkK9ipt48aNfPPNN8TGxpYkFP78888rTFr95JNP8sgjj5Q8L24BFMIZigPAnqE967kmQgghmjqnBoD3338/vXr1YsWKFbRs2bJOxiT9vQxFUSpd7sCBA8u0Ul6Km5sbbm5uVaqfEJUlAaAQQoi64tQA8OjRoyxevJj27ds787DlCgoKQqfTlWntS0tLq/RSXkI0FDaHrSSViwSAQgghaptT80T06dOHY8eOOfOQFXJ1daVnz56sXbu21Otr166lf//+tVp2fHw80dHR5SYoFqI6imzq5KJWXq0I8giq59oIIYRo6mrcArhnz56Sxw888AD//Oc/OXfuHDExMWVmA1d1lYX8/PxSAWViYiK7du0iICCAiIgIHnnkEe6880569epFv379+PDDDzl16lS1cg5WxcyZM5k5c2bJLCIhaqrQVghA16Cu9VwTIYQQzUGNA8C4uDg0Gk2pSR9Tp04teVz8XnUmgWzbto1hw4aVPC+egHH33XezYMECbr75ZjIzM3n++edJSUmhW7durFy5ksjIyBqelRB1y2wzA9A1UAJAIYQQta/GAWBiYqIz6lGuoUOHlplN/HczZsxgxowZtVYHIWqb3WHHbJcAUAghRN2p8RjAyMjISt+aiqY+BnDy5MllltyrybE0Gg2vvPJKqdeXLl3qlFnikydPLpML8uL3nH0e5Q0vmDFjBhqNhsmTJ1fr2CabCQCdVkeIZ0hNqimEEEJUilNnAS9fvrzc1zUaDe7u7rRv356oqChnFlkvZAxg1bi7u/Pqq68yffp0/P39nXZch8PBihUrKrzunC08PJxFixbx5ptv4uHhAagreHz99dclSw9WR4GtAAA3raQYEkIIUTecGgBOmDChzHhAKD0OcODAgSxdutSpgYBo2IYPH86xY8eYM2cOr732mtOOu3HjRrRaLX369HHaMS+lR48enDhxgiVLlnD77bcDsGTJEsLDw2nbtm21j1toVSeAuOpcnVJPIYQQ4nKcmgZm7dq19O7dm7Vr15Kbm0tubi5r167lyiuv5KeffmL9+vVkZmby6KOPOrPYRkNRFAqthXV+c+Jyz9Wi0+l4+eWXefvtt0lOTnbacZcvX851112HVuvUy/iSpkyZwqefflryfP78+aUmPVWVQ3GUdAFLACiEEKKuOLUF8MEHH+TDDz8slYfv6quvxt3dnfvuu4/9+/czb968Gn1hNmYmm4k+X9VNa9XF/rztTwwuBqcca+LEiSQkJHD11VezePHiKu0XFxfHM888wyeffFLm/by8PK666iqsVit2u53Zs2dz7733XvKYy5cv5/XXX6/yOVSnrGJ33nknTz75JCdPnkSj0bBx40YWLVpEQkJCqe30ej3dunUDoFevXnz88cflHs9kM6EoCnqN3sk/x4QQQoiKOfUr5/jx4/j4+JR53cfHhxMnTgDQoUMHMjIynFlsnWvqk0AuZfbs2Xz22WdlXv/yyy/x8vIquW3YsKHMNq+++ioLFy7kwIEDZd4zGAysW7eOXbt28eeffzJnzhwyMzMrrMfBgwdJTk5m+PDhVT6HS5V1ufMICgpi7NixLFy4kE8//ZSxY8cSFFQ2cbOfnx+7du1i165dFQZ/cKH7193FvcrnIYQQQlSXU1sAe/bsyWOPPcZnn31GcHAwAOnp6Tz++OMlwdLRo0dp3bq1M4utc9WdBOKh9+DP2/6sxZpVXK6zDBs2rExrF8C4ceNKjcVr1apVmW0GDx7MqFGj+Ne//lVmxqxOp8NgUFspzWYzdrv9kl3Xy5cvZ8SIESWTMariUmVV5jymTp3KrFmzAPXHQE0UWNUJIO46d8yYa3QsIYQQorKcGgB+8sknjB8/ntatWxMeHo5Go+HUqVO0bduWZcuWAerqHv/5z3+cWWyjodFonNYV29B4e3vj7e192e1eeeUV4uLi6NixY5n3cnJyGDJkCEePHmXu3LnltqwVW7ZsGffcc0+161tRWZU5j9GjR2OxWAAYNWpUudsYjUZ69uyJh4cHL730EkOGDCmzzcXj/wx6AznkVPt8hBBCiKpwagDYqVMnDh48yJo1azhy5AiKotC5c2dGjBhRMlDfWXnZRO3Kzc1l165dpV4rXoKvJmJiYrj99tt5++23y7zn5+fH7t27SU1NZdKkSdxwww2EhoaW2S4tLY2tW7eydOnSy5Z3qfOoTFnl0el0HDx4sORxeU6ePElYWBj79u1j7Nix7N27t8zwCLPNjENxoNPqZAKIEEKIOuXUABDUVq7Ro0czevRoZx9a1KGEhASuuOKKUq8VL8FXUy+88ALffvtthe+HhoYSGxvL+vXrufHGG8u8/+OPP9KnTx9CQi6fNPly53G5sipS3ljXi4WFhQHQrVs3oqOjOXLkCL169Sq1TfH6vwa9wSlJsYUQQojKqnEA+L///Y/77rsPd3d3/ve//11y29mzZ9e0OFEHFixY4JRAr/hYfxcZGYnZXHq8W2pqKh4eHvj4+GA0Glm/fj3/+Mc/yj3msmXLGDduXKXKLq/81NRUjEZjpcq61Hlc7OLWyOzsbAwGA25ubiQnJ3PgwIFy8wQWTwBpqsMChBBCNFw1DgDffPNNbr/9dtzd3XnzzTcr3E6j0TSZADA+Pp74+Hjsdnt9V6XOjRo1ih07dlBQUEDr1q354YcfnDIbOjk5mWnTpqEoCoqiMGvWLGJjY8vdduDAgdx66611UlZ1HDx4kOnTp6PVatFoNLz11lsEBASU2kZRlJIJIJ56T2h+l5IQQoh6pFHqO0twI1Y8Czg3N7fs+C6zmcTERKKionB3lxQfojSTzcSJnBNoNVo6B3SmqKhIrhchhKgjl/r+bi5qJfWsxWLh8OHD2Gy22ji8EI3exd2/Mv5PCCFEXXNqAFhYWMi0adMwGAx07dqVU6dOAerYv1deecWZRQnRqF08AUQIIYSoa04NAJ988kl2795NQkJCqW6s4cOH88033zizKCEarVLj/1w867k2QgghmiOnpoFZunQp33zzDX379i3VrRUdHc3x48edWZQQjZbFbsHusKPRaHDXy3g/IYQQdc+pLYDp6enl5mYrKCiQcU5CnFdgU1v/DHoDWk2tDMMVQgghLsmp3z69e/dmxYoVJc+Lg76PPvqIfv36ObOoehUfH090dLRT0p+I5kfy/wkhhKhvTu0CnjNnDqNHj+bAgQPYbDbeeust9u/fz+bNm1m3bp0zi6pXM2fOZObMmSXTyIWorIvH/8kEECGEEPXFqS2A/fv3Z+PGjRQWFtKuXTt+/vlnQkND2bx5Mz179nRmUUI0SlaHFZvDhgaNtAAKIYSoN05fCzgmJoaFCxc6+7BCNAnF3b/uLu4y/k8IIUS9ceo30O23385HH33E0aNHnXlYIZqM4gkgnnpJ/yKEEKL+ODUA9PLy4r///S+dOnUiLCyMW2+9lffff59Dhw45sxhRyyZPnsyECROcdiyNRlMmEfjSpUudMjN88uTJPPHEExW+5+zzuP/++8u8N2PGDDQaDZMnT77kMUw2E8YiIyATQIQQQtQvpwaAH3zwAYcOHeLs2bO88cYb+Pr68tZbb9G1a1datmzpzKJEI+Lu7s6rr75Kdna2U4/rcDhYsWIF48ePd+pxKxIeHs6iRYswmUwlr5nNZr7++msiIiIuua/FbuGU8RQOxYGniydeLl61XV0hhBCiQrUyCMnb2xt/f3/8/f3x8/NDr9fTokWL2ihKNALDhw+nRYsWzJkzx6nH3bhxI1qtlj59+jj1uBXp0aMHERERLFmypOS1JUuWEB4ezhVXXFHhfjaHjSRjEjaHDTe9G+He4ZIXUwghRL1yagD4f//3f/Tt25egoCD+/e9/Y7FYePLJJ0lNTWXnzp3OLEo0Ijqdjpdffpm3336b5ORkpx13+fLlXHfddWi1dTeZYsqUKXz66aclz+fPn8/UqVMr3N6hODiddxqL3YJeqyfSOxKdVlcXVRVCCCEq5NRZwHPnziU4OJhnnnmG8ePH06VLF2cevsGIj48nPj4eu91epf0URUG5qPuwrmg8PJzS4pSXl8dVV12F1WrFbrcze/Zs7r333krtO3HiROLi4njmmWf45JNPyt1Gr9fTrVs3AHr16sXHH398yWMuX76c119/vWonQc3O48477+TJJ5/k5MmTaDQaNm7cyKJFi0hISCj3XKwOK51jO/PiWy8S6ROJi86lyvUVQgghnM2pAeDOnTtZt24dCQkJ/Pe//0Wn0zFkyBCGDh3K0KFDm0xAWN1E0IrJxOEedZ8PsdOO7WgMNZ90YDAYWLduHQaDgcLCQrp168akSZMIDAzkyy+/ZPr06SXbrlq1ikGDBpXa/9VXX+Wqq67in//8Z7nH9/PzY9euXZWqy8GDB0lOTmb48OF1eh5BQUGMHTuWhQsXoigKY8eOJSgoqNxzWb1xNZmmTDQaDRE+EbLurxBCiAbDqQFg9+7d6d69O7NnzwZg9+7dzJs3j9mzZ+NwOKrcYiYaFp1Oh+F8IGk2m7Hb7SiKAsC4ceNKjcVr1apVmf0HDx7MqFGj+Ne//nXZGbOXs3z5ckaMGIGHh0eV963peUydOpVZs2YBamtweRQUMk2Z6jG8WuHpImlfhBBCNBxOTwS9c+dOEhISSEhIYMOGDRiNRuLi4hg2bJizi2p0NB4edNqxvV7KdZacnByGDBnC0aNHmTt3bknrl7e3N97e3pfd/5VXXiEuLo6OHTuWec9oNNKzZ088PDx46aWXGDJkSIXHWbZsGffcc0+9nMfo0aOxWCwAjBo1qsz7uUW5GI1Gbrr6Jrw8vXhtzmuXPBchhBCirjk1APT39yc/P5/u3bszdOhQ7r33XgYPHoyPj48zi2m0NBqNU7pi60Jubm6Z7tiAgAAiIiLYvXs3qampTJo0iRtuuIHQ0NBKHzcmJobbb7+dt99+u8x7J0+eJCwsjH379jF27Fj27t1b7rWTlpbG1q1bWbp0ab2ch06n4+DBgyWPL1ZgLeBM/hl+3vEzndt0JiMxg2uvvbbCcxFCCCHqg1MDwM8//1wCviYiISGhTGqTu+++mwULFgAQGhpKbGws69ev58Ybb6zSsV944QW+/fbbMq+HhYUB0K1bN6Kjozly5Ai9evUqs92PP/5Inz59CAkJqbfzKO8aV1BIzktGURTaRbSjhWcLWsa0vOS5CCGEEPVBoxQPfhJVVjwJJDc3t0xAYDabSUxMJCoqCnf3pjH4PzU1FQ8PD3x8fDAajfTr14+vv/6a2NjYGh87Ozsbg8GAm5sbycnJDBgwgJ07dxIQEFBm23HjxjFw4EAef/zxapVVm+dRYC3g+NnjdAjtgIe7x2XPpVhTvF6EEKKhutT3d3Ph9DGAoulKTk5m2rRpajobRWHWrFlOCZpAndU7ffp0tFotGo2Gt956q8KAaeDAgdx6663VLqs2z8PTxZP8M/lcee2VlToXIYQQoj5IC2ANNLcWQFF75HoRQoi6Iy2AtbQUnBBCCCGEaLgkAKyG+Ph4oqOj6d27d31XRQghhBCiypweAH7++ecMGDCAsLAwkpKSAJg3bx7Lli1zdlH1ZubMmRw4cICtW7fWd1WEEEIIIarMqQHge++9xyOPPMKYMWPIyckpWfnDz8+PefPmObMoIYQQQghRTU4NAN9++20++ugjnnrqqVIJcnv16sXevXudWZQQQgghhKgmpwaAiYmJZZLuAri5uVFQUODMooQQQgghRDU5NQCMiooqs+wWwKpVq4iOjnZmUUIIIYQQopqcmgj6scceY+bMmZjNZhRF4a+//uLrr79mzpw5fPzxx84sSgghhBBCVJNTA8ApU6Zgs9l4/PHHKSws5LbbbqNVq1a89dZb3HLLLc4sSgghhBBCVJPTl4K79957uffee8nIyMDhcBASEuLsIoQQQgghRA04dQzgc889x/HjxwEICgqS4K+Rmjx5MhMmTHDasTQaDffff3+Z92bMmIFGo2Hy5Mk1LuOJJ56o8D1nnUvx8Wr7fIQQQoja5tQA8Pvvv6djx4707duXd955h/T0dGceXjRS4eHhLFq0CJPJVPKa2Wzm66+/JiIiokbHdjgcrFixgvHjx9e0mpVWm+cjhBBC1AWnBoB79uxhz549XHXVVbzxxhu0atWKMWPG8NVXX1FYWOjMokQj0qNHDyIiIliyZEnJa0uWLCE8PLzctEFVsXHjRrRaLX369KlpNSutNs9HCCGEqAtOXwqua9euvPzyy5w4cYLff/+dqKgoHnroIVq0aOHsokQjMmXKFD799NOS5/Pnz2fq1Kk1Pu7y5cu57rrr0Grrdlnr2jofIYQQoi7U6remp6cnHh4euLq6YrVaa7OoRkFRFKxF9jq/KYrilPqfPn2aoUOHEh0dTWxsLN99912l973zzjv5448/OHnyJElJSWzcuJE77rij3G0LCwuJjIzk0Ucfvexxly9fXu3u37y8PHr37k1cXBwxMTF89NFHld63suej1+uJi4sjLi6Oe+65p1r1FEIIIZzN6bOAExMT+eqrr/jyyy85cuQIgwcP5tlnn+XGG290dlGNjs3i4MMH19V5ufe9NQQXN93lN7wMvV7PvHnziIuLIy0tjR49ejBmzBg8PT358ssvmT59esm2q1atYtCgQSXPg4KCGDt2LAsXLkRRFMaOHUtQUFC55bz00kuV6tI9ePAgycnJDB8+vFrnYzAYWLduHQaDgcLCQrp168akSZMIDAx02vn4+fmVmxxdCCGEqE9ODQD79evHX3/9RUxMDFOmTCnJAyiahpYtW9KyZUsAQkJCCAgIICsrC09PT8aNG1cqaCvv333q1KnMmjULgPj4+HLLOHr0KIcOHeK6665j3759l6zP8uXLGTFiBB4eHtU6H51Oh8FgANRJHHb7hdZSZ52PEEII0RA5NQAcNmwYH3/8MV27dnXmYRuc+Ph44uPjsdvtVdpP76rlvreG1FKtLl2us23btg2Hw0F4eDgA3t7eeHt7X3Kf0aNHY7FYABg1alS52zz66KPMnTuXTZs2XbYOy5Ytq3G3ak5ODkOGDOHo0aPMnTu3pBXPWedjNBrp2bMnHh4evPTSSwwZUvf//kIIIcTfOTUAfPnll515uAZr5syZzJw5E6PRiK+vb6X302g0TumKrQu5ubllui4DAgKIiIggMzOTu+66q8rL++l0Og4ePFjy+O+WLVtGx44d6dix42UDwLS0NLZu3crSpUsvW+6lzsXPz4/du3eTmprKpEmTuOGGGwgNDXXK+QCcPHmSsLAw9u3bx9ixY9m7dy8+Pj6VOr4QQghRW2ocAD7yyCO88MILeHp68sgjj1xy2zfeeKOmxYk6kpCQUCalyd13380HH3zAxIkTefLJJ+nfv3+Vj3up4GfLli0sWrSI7777jvz8fKxWKz4+Pjz99NNltv3xxx/p06dPpZKNV3QuCxYsKHkeGhpKbGws69evr9J41csFc2FhYQB069aN6Ohojhw5Qq9evSp9fCGEEKI2aJQaThEdNmwYP/zwA35+fgwbNuyS2/7+++81KarBKW4BzM3NLRMImM1mEhMTiYqKwt3dvZ5q6FyKonDbbbfRqVMnnn322Vota8GCBezbt4/XX3+93PfHjRvHwIEDefzxx6tdRmpqKh4eHvj4+GA0GunXrx9ff/01sbGx1T7mxbKzszEYDLi5uZGcnMyAAQPYuXMnAQEBZbZtiteLEEI0VJf6/m4uatwCeHFQ19QCPFHaxo0b+eabb4iNjS3pev3888+JiYmp87oMHDiQW2+9tUbHSE5OZtq0aSiKgqIozJo1y2nBH6izlKdPn45Wq0Wj0fDWW2+VG/wJIYQQda3GLYAXmzp1Km+99VaZwfMFBQU88MADzJ8/31lFNQjNrQVQ1B65XoQQou5IC6CTE0EvXLiw1PqoxUwmE5999pkzixJCCCGEENXklFnARqOxpBstLy+vVAuG3W5n5cqVlRqsL4QQQgghap9TAkA/Pz80Gg0ajYaOHTuWeV+j0fDcc885oyghhBBCCFFDTgkAf//9dxRF4aqrruL7778vNdDd1dWVyMjIknQYQgghhBCifjklACxe3SAxMZHw8HC0WuevPCGEEEIIIZzDqSuBREZGAlBYWMipU6dKlskq5swUG42FEydZiyZMrhMhhBB1yakBYHp6OlOmTGHVqlXlvl/VtXMbMxcXF0ANhj08POq5NqKhK/6xVNGSckIIIYQzOTUAfOihh8jOzmbLli0lK4Skpqby4osv8t///teZRTV4Op0OPz8/0tLSADAYDGg0mnqulWiIHA4H6enpGAwG9Hqn/pcUQgghyuXUb5vffvuNZcuW0bt3b7RaLZGRkYwYMQIfHx/mzJnD2LFjnVlcg9eiRQuAkiBQiIpotVoiIiLkR4IQQog64dQAsKCgoCTfX0BAAOnp6XTs2JGYmBh27NjhzKIaBY1GQ8uWLQkJCcFqtdZ3dUQD5urqKpOnhBBC1BmnBoCdOnXi8OHDtGnThri4OD744APatGnD+++/T8uWLZ1ZVKOi0+lkbJcQQgghGgynjwFMSUkB4JlnnmHUqFF8+eWXuLq6smDBAmcWJYQQQgghqkmj1GL+icLCQg4dOkRERARBQUG1VUy9kcWkhRBCiMZHvr+hVgcdGQwGevTo0WCDv7y8PHr37k1cXBwxMTF89NFH9V0lIYQQQohaV+Mu4EceeaTS277xxhs1Lc6pDAYD69atw2AwUFhYSLdu3Zg0aRKBgYH1XTUhhBBCiFpT4wBw586dldquIaa30Ol0GAwGAMxmM3a7XVZkEEIIIUSTV+MA8Pfff3dGPcq1fv165s6dy/bt20lJSeGHH35gwoQJpbZ59913mTt3LikpKXTt2pV58+YxaNCgSpeRk5PDkCFDOHr0KHPnzm2w3dVCCCGEEM5SK2MAjx07xpo1azCZTED11zktKCige/fuvPPOO+W+/8033/DQQw/x1FNPsXPnTgYNGsQ111zDqVOnSrbp2bMn3bp1K3M7e/YsAH5+fuzevZvExES++uorUlNTq1VXIYQQQojGwqmzgDMzM7npppv4/fff0Wg0HD16lLZt2zJt2jT8/PxqtBycRqMp0wLYp08fevTowXvvvVfyWpcuXZgwYQJz5sypchn/+Mc/uOqqq7jxxhsrtb3MIhJCCCEaH/n+dnIL4MMPP4yLiwunTp0qGVsHcPPNN7N69WpnFoXFYmH79u2MHDmy1OsjR45k06ZNlTpGamoqRqMRUC+G9evX06lTpwq3Lyoqwmg0lroJIYQQQjQ2Tk0E/fPPP7NmzRpat25d6vUOHTqQlJTkzKLIyMjAbrcTGhpa6vXQ0FDOnTtXqWMkJyczbdo0FEVBURRmzZpFbGxshdvPmTOH5557rkb1FkIIIYSob05fC/jilr9iGRkZuLm5ObOoEn+fXawoSqVnHPfs2ZNdu3ZVuqwnn3yyVNobo9FIeHh4pfcXQgghhGgInNoFPHjwYD777LOS5xqNBofDwdy5cxk2bJgziyIoKAidTlemtS8tLa1Mq6CzuLm54ePjU+omhBBCCNHYOLUFcO7cuQwdOpRt27ZhsVh4/PHH2b9/P1lZWWzcuNGZReHq6krPnj1Zu3YtEydOLHl97dq1jB8/3qllCSGEEEI0JU4NAKOjo9mzZw/vvfceOp2OgoICJk2axMyZM2nZsmWVj5efn8+xY8dKnicmJrJr1y4CAgKIiIjgkUce4c4776RXr17069ePDz/8kFOnTnH//fc787TKiI+PJz4+HrvdXqvlCCGEEELUBqelgbFarYwcOZIPPviAjh07OuOQJCQklNt1fPfdd7NgwQJATQT92muvkZKSQrdu3XjzzTcZPHiwU8q/HJlGLoQQQjQ+8v3t5DyAwcHBbNq0iQ4dOjjrkA2aXEBCCCFE4yPf306eBHLXXXfxySefOPOQQgghhBDCyZw6BtBisfDxxx+zdu1aevXqhaenZ6n333jjDWcWV29kDKAQQgghGjOndgFfKtWLRqPht99+c1ZRDYI0IQshhBCNj3x/O7kF8Pfff3fm4YQQQgghRC1w6hhAIYQQQgjR8EkAKIQQQgjRzEgAWA3x8fFER0fTu3fv+q6KEEIIIUSVOXUSSHMjg0iFEEKIxke+v6UFUAghhBCi2ZEAUAghhBCimZEAUAghhBCimZEAUAghhBCimZEAsBpkFrAQQgghGjOZBVwDMotICCGEaHzk+1taAIUQQgghmh0JAIUQQgghmhkJAIUQQgghmhl9fVdAiLpktTvYciKTKyL88XJreJd/UmYBW09m13c1hBCiwejS0puuYb71XY0mp+F9AwpRS2x2BzO+3MHaA6m0Dfbkk7t7ExXkWd/VKrFs1xkeX7yHIpujvqsihBANxkPDO0gAWAskAKyG+Ph44uPjsdvt9V0VUUmKovDEkr2sPZAKwIn0Asa/8wfv3t6TgR2C6rVuDofCG2uP8M7vxwCIbulDsLdbvdZJCCEaijaBDeeHelMiaWBqQKaRNw6KovDyyoN8tCERrQZenhjDN9tOs/NUDjqthmeui+bOvpFoNJo6r1tBkY2Hv9nFz+cD0/uH/H97dx4UxZ2/D/yZgzkAQUW5BBRUBASUiAdRowkpDBrX6JZJzKGuOcyGrLoaIRoiliWLsXTjGfNTsyZZD9ysRk1cD3TVxBjFVRFEgqggKOB4cQjCwMzn94dhvk6QQ64B+nlVTSHdPd3vfmfMPHb3p7sn5o7qA4W85WshIpIKfn9zEAhJwBfHrmLDT5kAgCV/DMSrgzyw7Z0hmPBUNxiMAgt2pyJ61wVUGFr21Ov1e6X447oTOHjxJlQKOf7+cj98FO7D8EdERM2OAZDatW2J2fh0/68AgI9H++LlYHcAgMZKgeUT+2FeuA9kMmDLqWy8+eUp3CvRt0hd/8u6i3Frfsav+cXoYqvGtneHYMJTbi2ybSIiIgZAarf2peTh4+9SAAB/HtkT7zzjZTZfJpNh+oie2Dg5GDYqBU5evYtxa39Gxs3iZq3r2//lYNKGk7hTooefix12fzAUA7p3atZtEhERPYoBkNql4xm3MTM+CUYBTBrkjshRfWpcNtTXCTvfHwr3zlpk3y3F+M9P4Ifk3Cav6X55JT7ZdQFz/52MCoNAuL8z/v3nEHTrqG3ybREREdWGAZDanaScArz7z/9BbzAi3N8Zi18KqHOARx/nDtgdMQyDPTvjfnklPth6DrPiz6HwQUWT1HQgNR/PLz+Gf568BgCYEdoba197CtYqDsQnIqKWx1HAjcBRRK1P5u0STPj8Z9wrrcCwXl3w5dRgqJWKer+/wmDE6sMZWHPkMowCcLHXYNnEfhjaq2G3iskrfICY3ammUb7dHayx+CV/DO/dtUHrIyKixuP3NwNggzx6H8BLly5J+gPUmpTqKzF+7Qmk3yxGPzd7bH1nCGwa+LSPs9n3MHt7ErLulAIA/jS0B6Je8IHGqn5h0mAU+OcvWVh28BLul1dCKZdh+ggv/OW53vVeBxERNQ8GQAbARuEHqPUQQmD2v87ju3M30MVWjf/MGAZHO02j1lmqr0Ts3jRsOZUNAOjlaIsVr/SHf7fa70ifmluI+TtTcP56IQDgKY+OiJsQiD7OHRpVDxERNQ1+fzMANgo/QK3H5pPXEL3rAhRyGba+PRiDvRyabN1H0nWI/HcybhWXQymX4d1nvOBir0FRWSUKH1SgsLQChQ8qUFT28Oev+cUwGAU6aJSIesEHrw3ygJz39iMiajX4/c0A2Cj8ALUO53MKMPGLX6A3GDEv3AfTR/Rs8m3cLdEjelcK/pOSX6/lxwS4IGasX6OPQhIRUdPj9zefBUxt3L0SPd7fchZ6gxFhfk5493f3+msqnW1UWPvaU9hzPhe7zt2AWqmAnVYJe60V7LVWsHvkZ7eOWng78XQvERG1XgyA1GYZjQKztifhRsED9HCwxrKX+zXr83xlMhnG9e+Gcf27Nds2iIiIWgLvA0ht1ur/XsaxS7egsZJj3RsDYKexsnRJREREbQIDILVJxy7dworDlwAAsS8FwNdFmtdwEBERNQQDILU5NwoeYFb8OQgBTBrkgT8OcLN0SURERG0KrwGkViX5egE+2Z0KXVEZrFUK2KiVD3+qlLBWK2GjUuBcdgHulVYgoJs9Ysb6WbpkIiKiNocBkFoFIQS++eUaYvemQW8w1rm8vdYKn7/+FJ+qQURE1AAMgA3w6KPgqPGKyyrw0Y4U7E3JAwCM6uuEP4/shbIKAx7oDSjRV6K0/Lef+ofTwvo6wb2ztYUrJyIiapt4I+hGkMqNJIUQuH7vAc5fL0Dy9UIkXy+AEMBzPo4I6+sMzy42DV53am4hIracRdadUijlMswb7YtpQ3s06+1ciIhI2qTy/V0bHgGkah7oDTh++TaSHwl890orqi13KvMu4vb9it6Otgjr64QwP2cEutnXK7wJIbAtMQcLv0+FvtKIbh21WPNaEII8OjXHLhEREdEjGACpmre+Po0TV+6YTbNSyODjbIdAN3v0c+uI8koDDl68iV+u3EGG7j4ydPex9sgVONtpENbXCQO6d4Kdxgq2GiVsVEp00Chhq1bCVqOEvtKI+d+lYHdSLgAg1McRy1/uh47WKkvsLhERkeTwFHAjtMdDyGUVBvjHHEClUeCl/q54qnsnBLp1hK9LB6iV1QdcFD6owNF0HQ6m3sTRdB1K9HVfF6mQy2AwCijkMkSO6oN3hntBLucpXyIiahnt8fv7SfEIIJm5dLMYlUaBTtZW+OyV/nWezrXXWpkej1ZWYcAvV+7g4MV8ZN0uxf3yStwvr0RxWSXul1egrOLh6F6DUcDFXoPVk4IQ3KNzS+wWERERPYIBkMyk3CgEAPh3q9+1fI/SWCnwrI8jnvVxfOz8CoMRJb8FQmd7DawUvA85ERGRJTAAkpkLN4oAPAyATc1KIUdHaxWv9SMiIrIwHoIhMxd+OwIY0AwBkIiIiFoHBkAy0VcakZ5fDADwd2UAJCIiaq8YAMnk0s1i6A1G2GmUcO+stXQ5RERE1EwYAMkkNbfhA0CIiIio7WAAJJMUXv9HREQkCQyAZJLy2wjgvgyARERE7RoDIAF4eI++tLyHAZBHAImIiNo3BsAGWLt2Lfz8/DBw4EBLl9JkLuvuQ19phK1aie6drS1dDhERETUjBsAGiIiIwMWLF3H69GlLl9Jkqu7/19fVjs/lJSIiaucYAAnA/wXA5ngCCBEREbUuDIAEALiQy+v/iIiIpIIBkGAwClzMrXoGsJ2FqyEiIqLmxgBIuHrrPh5UGGCtUsCzi62lyyEiIqJmxgBIphtA+7nYQcEBIERERO0eAyDhwo2q07+8/o+IiEgKGACJI4CJiIgkhgFQ4oxGgdRcPgOYiIhIShgAJS7zTglK9AZorOTo2dXG0uUQERFRC2AAlLiq07++LnZQKvhxICIikgJ+40uc6fo/V57+JSIikgoGQImrGgHM6/+IiIikgwFQwoQQuPDbAJC+fAIIERGRZDAASti1O6UoLquESiGHt1MHS5dDRERELYQBUMKqjv75uHSAFQeAEBERSQa/9SUshTeAJiIikiQGQAlLrXoEHEcAExERSQoDoEQJIUxHADkCmIiISFoYAAGUlpaie/fu+PDDDy1dSou5fu8BCh9UwEohg7ezraXLISIiohbEAAggNjYWgwcPtnQZLarqBtDeTh2gViosXA0RERG1JMkHwIyMDPz6668YPXq0pUtpUVUjgHn9HxERkfS06gD4448/YuzYsXB1dYVMJsOuXbuqLfP555/D09MTGo0GAwYMwE8//fRE2/jwww8RFxfXRBW3HSlVA0DcGACJiIikplUHwJKSEvTr1w9r1qx57Pzt27dj1qxZ+Pjjj3Hu3DkMHz4c4eHhyM7ONi0zYMAA+Pv7V3vl5uZi9+7d8Pb2hre3d0vtUqsghECq6RnAfAIIERGR1CgtXUBtwsPDER4eXuP8v//973jrrbfw9ttvAwBWrFiBAwcOYN26daajemfOnKnx/SdPnkR8fDy+/fZb3L9/HxUVFbCzs8OCBQseu3x5eTnKy8tNvxcVFTVkt+pkNApsPnUNrwx0b5br8/IKy3CnRA+FXAZfFwZAIiIiqWnVRwBro9frcebMGYSFhZlNDwsLw4kTJ+q1jri4OOTk5CArKwvLli3DO++8U2P4q1re3t7e9HJ3d2/UPtTk410XsGB3KmbFJ8FgFE2+/qoBIL0dbaGx4gAQIiIiqWmzAfD27dswGAxwcnIym+7k5IT8/Pxm2ea8efNQWFhoeuXk5DTLdl4MdIFKIce+C/mI3pUCIZo2BF7gE0CIiIgkrVWfAq4PmUxm9rsQotq0+pg6dWqdy6jVaqjV6ide95Ma2qsLVr7aHxFbz2JbYg4626gwd5RPo9dbXmnAd2dvYPv/HgZXXv9HREQkTW02AHbp0gUKhaLa0T6dTlftqGBbFB7ggtjxAZi3MwVrj1xBJ2sV3h7u1aB1lZRXYltiNjb+lIn8ojIAgIONCs/5tP0+ERER0ZNrswFQpVJhwIABSEhIwPjx403TExISMG7cuGbd9tq1a7F27VoYDIZm3c6kQR64V6rH0v3pWLw3DZ2sVfjjALd6v/9eiR5fncjC179koaC0AgDgZKfGO8O9MGmQB2zUbfY/PxERETVCq04A9+/fx+XLl02/Z2ZmIikpCZ07d4aHhwdmz56NN998E8HBwQgJCcH69euRnZ2N9957r1nrioiIQEREBIqKimBv37zX0f15RE/cva/HxuOZiNyRDHutFZ73q/3IXdbtEvzz5DVsS8xGqf5hSO3hYI33RvTE+Ke68ckfREREEicTTT3CoAkdPXoUzz77bLXpU6ZMwVdffQXg4Y2gly5diry8PPj7++Ozzz7DM8880yL1VQXAwsJC2Nk13/V0RqPA3H8nY8fZ61Ar5fhm2iAM9nIwWya34AH2Judhz/lcpPw2yAMA/Fzs8P6zPRHu7wKF/MmvjSQiImpvWur7uzVr1QGwtWvJD1ClwYj3Np/BoTQdOqiViJ8+BM52GvwnJQ/fn89DYtZd07IKuQxP93TAW8M8McK7a4MGxRAREbVXDIAMgI3S0h+gsgoDJn+ZiMSsu7BRKVBWaTS7T+CgHp0xtp8LwgNc0MW2+UcrExERtUUMgK38GsDWqqUGgfyexkqBjVOD8cr/O4m0vIdPIQl0s8fYQFeMCXSBa0dti9ZDREREbROPADaCpf4FUVhagQMX8zGoR2f06GLTYtslIiJqD3gEkEcA2yR7ayu8HNw8j6EjIiKi9q/NPgqOiIiIiBqGAZCIiIhIYhgAG2Dt2rXw8/PDwIEDLV0KERER0RPjIJBG4EWkREREbQ+/v3kEkIiIiEhyGACJiIiIJIYBkIiIiEhiGAAbgINAiIiIqC3jIJBG4EWkREREbQ+/v3kEkIiIiEhyGACJiIiIJIYBkIiIiEhiGACJiIiIJEZp6QLasqrxM0VFRRauhIiIiOqr6ntbyuNgGQAbobi4GADg7u5u4UqIiIjoSRUXF8Pe3t7SZVgEbwPTCEajEbm5uejQoQNkMpmly2k1ioqK4O7ujpycHMkOr28o9q7h2LuGY+8ajr1rHEv1TwiB4uJiuLq6Qi6X5tVwPALYCHK5HG5ubpYuo9Wys7Pj/xAbiL1rOPau4di7hmPvGscS/ZPqkb8q0oy9RERERBLGAEhEREQkMQyA1OTUajViYmKgVqstXUqbw941HHvXcOxdw7F3jcP+WQ4HgRARERFJDI8AEhEREUkMAyARERGRxDAAEhEREUkMAyARERGRxDAAUoOsW7cOgYGBppt3hoSEYN++fab5QggsXLgQrq6u0Gq1GDlyJFJTUy1YcesVFxcHmUyGWbNmmaaxf4+3cOFCyGQys5ezs7NpPvtWuxs3buCNN96Ag4MDrK2t0b9/f5w5c8Y0n/2rWY8ePap99mQyGSIiIgCwd7WprKxEdHQ0PD09odVq4eXlhUWLFsFoNJqWYf8sQBA1wJ49e8TevXtFenq6SE9PF/PnzxdWVlbiwoULQgghlixZIjp06CB27NghUlJSxCuvvCJcXFxEUVGRhStvXRITE0WPHj1EYGCgmDlzpmk6+/d4MTExom/fviIvL8/00ul0pvnsW83u3r0runfvLqZOnSpOnTolMjMzxaFDh8Tly5dNy7B/NdPpdGafu4SEBAFAHDlyRAjB3tVm8eLFwsHBQfzwww8iMzNTfPvtt8LW1lasWLHCtAz71/IYAKnJdOrUSWzcuFEYjUbh7OwslixZYppXVlYm7O3txRdffGHBCluX4uJi0bt3b5GQkCBGjBhhCoDsX81iYmJEv379HjuPfatdVFSUGDZsWI3z2b8nM3PmTNGzZ09hNBrZuzqMGTNGTJs2zWzahAkTxBtvvCGE4GfPUngKmBrNYDAgPj4eJSUlCAkJQWZmJvLz8xEWFmZaRq1WY8SIEThx4oQFK21dIiIiMGbMGDz//PNm09m/2mVkZMDV1RWenp549dVXcfXqVQDsW1327NmD4OBgTJw4EY6OjggKCsKGDRtM89m/+tPr9di8eTOmTZsGmUzG3tVh2LBhOHz4MC5dugQAOH/+PI4fP47Ro0cD4GfPUpSWLoDarpSUFISEhKCsrAy2trb47rvv4OfnZ/oL6+TkZLa8k5MTrl27ZolSW534+HicPXsWp0+frjYvPz8fAPv3OIMHD8Y333wDb29v3Lx5E4sXL8bTTz+N1NRU9q0OV69exbp16zB79mzMnz8fiYmJmDFjBtRqNSZPnsz+PYFdu3ahoKAAU6dOBcC/s3WJiopCYWEhfHx8oFAoYDAYEBsbi0mTJgFg/yyFAZAarE+fPkhKSkJBQQF27NiBKVOm4NixY6b5MpnMbHkhRLVpUpSTk4OZM2fi4MGD0Gg0NS7H/lUXHh5u+nNAQABCQkLQs2dPfP311xgyZAgA9q0mRqMRwcHB+Nvf/gYACAoKQmpqKtatW4fJkyeblmP/6vbll18iPDwcrq6uZtPZu8fbvn07Nm/ejK1bt6Jv375ISkrCrFmz4OrqiilTppiWY/9aFk8BU4OpVCr06tULwcHBiIuLQ79+/bBy5UrTqMyqf9VV0el01f6FJ0VnzpyBTqfDgAEDoFQqoVQqcezYMaxatQpKpdLUI/avbjY2NggICEBGRgY/d3VwcXGBn5+f2TRfX19kZ2cDAPtXT9euXcOhQ4fw9ttvm6axd7WbO3cuPvroI7z66qsICAjAm2++ib/+9a+Ii4sDwP5ZCgMgNRkhBMrLy+Hp6QlnZ2ckJCSY5un1ehw7dgxPP/20BStsHUJDQ5GSkoKkpCTTKzg4GK+//jqSkpLg5eXF/tVTeXk50tLS4OLiws9dHYYOHYr09HSzaZcuXUL37t0BgP2rp02bNsHR0RFjxowxTWPvaldaWgq53DxuKBQK021g2D8LseAAFGrD5s2bJ3788UeRmZkpkpOTxfz584VcLhcHDx4UQjwc0m9vby927twpUlJSxKRJkzikvxaPjgIWgv2ryZw5c8TRo0fF1atXxcmTJ8WLL74oOnToILKysoQQ7FttEhMThVKpFLGxsSIjI0Ns2bJFWFtbi82bN5uWYf9qZzAYhIeHh4iKiqo2j72r2ZQpU0S3bt1Mt4HZuXOn6NKli4iMjDQtw/61PAZAapBp06aJ7t27C5VKJbp27SpCQ0NN4U+Ih8P6Y2JihLOzs1Cr1eKZZ54RKSkpFqy4dft9AGT/Hq/q3mBWVlbC1dVVTJgwQaSmpprms2+1+/7774W/v79Qq9XCx8dHrF+/3mw++1e7AwcOCAAiPT292jz2rmZFRUVi5syZwsPDQ2g0GuHl5SU+/vhjUV5eblqG/Wt5MiGEsPRRSCIiIiJqObwGkIiIiEhiGACJiIiIJIYBkIiIiEhiGACJiIiIJIYBkIiIiEhiGACJiIiIJIYBkIiIiEhiGACJqN2aOnUqXnrpJUuX0SAjR47ErFmzLF0GEbVTSksXQETSkZWVBU9PT5w7dw79+/dv9u2tXLkSvNc9EVF1DIBE1G7Z29tbuoRWxWAwQCaTQS7nyR8iqeP/BYioyezfvx/Dhg1Dx44d4eDggBdffBFXrlwxzff09AQABAUFQSaTYeTIkQAAo9GIRYsWwc3NDWq1Gv3798f+/ftN78vKyoJMJsO//vUvDB8+HFqtFgMHDsSlS5dw+vRpBAcHw9bWFi+88AJu3bplet/vTwEbjUZ8+umn6NWrF9RqNTw8PBAbG1vj/owcORIzZsxAZGQkOnfuDGdnZyxcuLBaXUlJSaZpBQUFkMlkOHr0KADg6NGjkMlkOHDgAIKCgqDVavHcc89Bp9Nh37598PX1hZ2dHSZNmoTS0lKz7VdWVuKDDz4w9TM6OtrsiKZer0dkZCS6desGGxsbDB482LRdAPjqq6/QsWNH/PDDD/Dz84Narca1a9dq3F8ikg4GQCJqMiUlJZg9ezZOnz6Nw4cPQy6XY/z48TAajQCAxMREAMChQ4eQl5eHnTt3Anh4qnb58uVYtmwZkpOTMWrUKPzhD39ARkaG2fpjYmIQHR2Ns2fPQqlUYtKkSYiMjMTKlSvx008/4cqVK1iwYEGN9c2bNw+ffvopPvnkE1y8eBFbt26Fk5NTrfv09ddfw8bGBqdOncLSpUuxaNEiJCQkPHFvFi5ciDVr1uDEiRPIycnByy+/jBUrVmDr1q3Yu3cvEhISsHr16mrbViqVOHXqFFatWoXPPvsMGzduNM3/05/+hJ9//hnx8fFITk7GxIkT8cILL5j1rbS0FHFxcdi4cSNSU1Ph6Oj4xLUTUTskiIiaiU6nEwBESkqKEEKIzMxMAUCcO3fObDlXV1cRGxtrNm3gwIHi/fffN3vfxo0bTfO3bdsmAIjDhw+bpsXFxYk+ffqYfp8yZYoYN26cEEKIoqIioVarxYYNG+pd/4gRI8SwYcOq1RUVFVXj/ty7d08AEEeOHBFCCHHkyBEBQBw6dMisTgDiypUrpmnTp08Xo0aNMtu2r6+vMBqNpmlRUVHC19dXCCHE5cuXhUwmEzdu3DCrLzQ0VMybN08IIcSmTZsEAJGUlFTvfSYiaeARQCJqMleuXMFrr70GLy8v2NnZmU75Zmdn1/ieoqIi5ObmYujQoWbThw4dirS0NLNpgYGBpj9XHbkLCAgwm6bT6R67nbS0NJSXlyM0NPSJ9unRbQKAi4tLjduo73qcnJxgbW0NLy8vs2m/X++QIUMgk8lMv4eEhCAjIwMGgwFnz56FEALe3t6wtbU1vY4dO2Z22l2lUlXbByIiDgIhoiYzduxYuLu7Y8OGDXB1dYXRaIS/vz/0en2d73006ACAEKLaNCsrq2rL/35a1enm39NqtfXej5q2+fttVA2mEI9cl1dRUVHnemQyWa3rrQ+j0QiFQoEzZ85AoVCYzbO1tTX9WavVVusjERGPABJRk7hz5w7S0tIQHR2N0NBQ+Pr64t69e2bLqFQqAA9Ho1axs7ODq6srjh8/brbsiRMn4Ovr22T19e7dG1qtFocPH26ydXbt2hUAkJeXZ5r26ICQxjp58mS133v37g2FQoGgoCAYDAbodDr06tXL7OXs7NxkNRBR+8QjgETUJDp16gQHBwesX78eLi4uyM7OxkcffWS2jKOjI7RaLfbv3w83NzdoNBrY29tj7ty5iImJQc+ePdG/f39s2rQJSUlJ2LJlS5PVp9FoEBUVhcjISKhUKgwdOhS3bt1Camoq3nrrrQatU6vVYsiQIViyZAl69OiB27dvIzo6uslqzsnJwezZszF9+nScPXsWq1evxvLlywEA3t7eeP311zF58mQsX74cQUFBuH37Nv773/8iICAAo0ePbrI6iKj9YQAkoiYhl8sRHx+PGTNmwN/fH3369MGqVatMt3oBAKVSiVWrVmHRokVYsGABhg8fjqNHj2LGjBkoKirCnDlzoNPp4Ofnhz179qB3795NWuMnn3wCpVKJBQsWIDc3Fy4uLnjvvfcatc5//OMfmDZtGoKDg9GnTx8sXboUYWFhTVLv5MmT8eDBAwwaNAgKhQJ/+ctf8O6775rmb9q0CYsXL8acOXNw48YNODg4ICQkhOGPiOokE4K3ySciIiKSEl4DSERERCQxDIBEREREEsMASERERCQxDIBEREREEsMASERERCQxDIBEREREEsMASERERCQxDIBEREREEsMASERERCQxDIBEREREEsMASERERCQxDIBEREREEvP/AVzUu5gkPbzAAAAAAElFTkSuQmCC",
"text/html": [
"\n",
"
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"
\n",
" Figure\n",
"
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" \n",
"
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" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_sections = pyTEMlib.utilities.get_x_sections()\n",
"K_M5 = []\n",
"ele_K_M5 = []\n",
"L3_M1 = []\n",
"ele_M3_M1 = []\n",
"L1_N3 = []\n",
"ele_L1_N3 = []\n",
"L3_N5 =[]\n",
"ele_L3_N5 = []\n",
"L2_M4 = []\n",
"ele_L2_M4 = []\n",
"for element in range(5,82):\n",
" if 'K-M5' in x_sections[str(element)]['lines']:\n",
" K_M5.append(x_sections[str(element)]['lines']['K-M5']['weight']/x_sections[str(element)]['lines']['K-L3']['weight'])\n",
" ele_K_M5.append(element)\n",
" if 'L3-M5' in x_sections[str(element)]['lines']:\n",
" \n",
" if 'L3-M1' in x_sections[str(element)]['lines']:\n",
" L3_M1.append(x_sections[str(element)]['lines']['L3-M1']['weight']/x_sections[str(element)]['lines']['L3-M5']['weight'])\n",
" ele_M3_M1.append(element)\n",
" \n",
" if 'L1-N3' in x_sections[str(element)]['lines']:\n",
" L1_N3.append(x_sections[str(element)]['lines']['L1-N3']['weight']/x_sections[str(element)]['lines']['L3-M5']['weight'])\n",
" ele_L1_N3.append(element)\n",
" \n",
" if 'L3-N5' in x_sections[str(element)]['lines']:\n",
" L3_N5.append(x_sections[str(element)]['lines']['L3-N5']['weight']/x_sections[str(element)]['lines']['L3-M5']['weight'])\n",
" ele_L3_N5.append(element)\n",
" \n",
" if 'L2-M4' in x_sections[str(element)]['lines']:\n",
" L2_M4.append(x_sections[str(element)]['lines']['L2-M4']['weight']/x_sections[str(element)]['lines']['L3-M5']['weight'])\n",
" ele_L2_M4.append(element)\n",
" \n",
"ele = np.linspace(1,94,94)\n",
"\n",
"plt.figure()\n",
"plt.plot(ele_K_M5 ,K_M5, label = 'K-M$_3$ / K-L$_3$')\n",
"plt.plot(ele_M3_M1,L3_M1, label = 'L$_3$-M$_1$ / L$_3$-M$_5$')\n",
"plt.plot(ele_L1_N3,L1_N3, label = 'L$_1$-N$_3$ / L$_3$-M$_5$')\n",
"plt.plot(ele_L3_N5,L3_N5, label = 'L$_3$-N$_5$ / L$_3$-M$_5$')\n",
"plt.plot(ele_L2_M4,L2_M4, label = 'L$_2$-M$_4$ / L$_3$-M$_5$')\n",
"plt.ylabel('')\n",
"plt.gca().set_yscale('log')\n",
"plt.xlabel('atomic number')\n",
"plt.ylabel('relative weights of lines')\n",
"\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Characteristic X-ray Intensity\n",
"\n",
"The cross section $Q_I$ (probability of excitation expressed as area) of an isolated atom ejecting an electron bound with energy $E_c$ (keV) by an electron with energy $E$ (keV) is:\n",
"\n",
"$$Q_I \\left(\\frac{ionizations}{e^- (atoms/cm^2)} \\right) = 6.51 \\times 10^{-20} \\left[ \\frac{n_s b_s}{E E_c}\\right]\\log_e (c_s E / E_c) $$\n",
"\n",
"where:\n",
"- $n_s$: number of electrons in shell or subshell $s$\n",
"- $b_s$, $c_s$: constants for a given shell $s$\n",
"- $E$: energy of the exciting electron\n",
"- $E_𝑐$: Critical ionization energy\n",
"\n",
"For example for a K shell (Powell 1979): $n_K = 2$, $b_K = 0.35$, and $c_k = 1$\n",
"\n",
"For silion the characteristic energy or binding energy for the K shell: $E_c = 1.838$ keV\n",
"\n",
"The relationship between energy of the exciting electron and critical energy is important and is expressed as **overvoltage** $U$:\n",
"$$U =\\frac{E}{E_c}$$\n",
"\n",
"\n",
"\n",
"The overvoltage of the primary electron with energy $E_0$ is expressed as:\n",
"$$ U_0 =\\frac{E_0}{E_c}$$\n",
"\n",
"We can express the cross section with the overvoltage:\n",
"\n",
"$$Q_I = 6.51 \\times 10^{-20} \\left[ \\frac{n_s b_s}{U E_c^2}\\right]\\log_e (c_s U) $$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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",
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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"source": [
"\"\"\" \n",
"(Casnati's equation) was found to fit cross-section data to\n",
"typically better than +-10% over the range 1<=Uk<=20 and 6<=Z<=79.\"\n",
"\n",
"Note: This result is for K shell. L & M are much less well characterized.\n",
"C. Powell indicated in conversation that he believed that Casnati's\n",
"expression was the best available for L & M also.\n",
"\"\"\"\n",
"shell_occupancy={'K':2,'L1':2,'L2':2,'L3':4,'M1':2,'M1':2,'M2':2,'M3':4,'M4':4,'M5':6}\n",
"import scipy.constants as const\n",
"res = 0.0;\n",
"beamE = 30\n",
"rel = []\n",
"Z= []\n",
"shell = 'M4'\n",
"for element in range(25,56):\n",
" ee = x_sections[str(element)][shell]['onset']#getEdgeEnergy();\n",
" u = ee/beamE;\n",
" #print(u)\n",
" if(u > 1.0):\n",
" u2 = u * u;\n",
" phi = 10.57 * np.exp((-1.736 / u) + (0.317 / u2));\n",
" #psi = Math.pow(ee / PhysicalConstants.RydbergEnergy, -0.0318 + (0.3160 / u) + (-0.1135 / u2));\n",
" psi = np.power(ee / const.Rydberg, -0.0318 + (0.3160 / u) + (-0.1135 / u2));\n",
"\n",
" i = ee / const.electron_mass# PhysicalConstants.ElectronRestMass;\n",
" t = beamE / const.electron_mass#PhysicalConstants.ElectronRestMass;\n",
" f = ((2.0 + i) / (2.0 + t)) * np.sqrt((1.0 + t) / (1.0 + i)) * np.power(((i + t) * (2.0 + t) * np.sqrt(1.0 + i))/ ((t * (2.0 + t) * np.sqrt(1.0 + i)) + (i * (2.0 + i))), 1.5);\n",
" getGroundStateOccupancy = shell_occupancy[shell]\n",
" res = ((getGroundStateOccupancy * np.sqrt((const.value('Bohr radius') * const.Rydberg) / ee) * f * psi * phi * np.log(u)) / u);\n",
" \n",
" rel.append(res)\n",
" Z.append(element)\n",
" #print(res)\n",
" \n",
"plt.figure()\n",
"plt.title(f\"Casnati's cross section for beam energy {beamE:.0f} keV\")\n",
"plt.plot(Z,rel)\n",
"\n",
"plt.xlabel('atomic number Z')\n",
"plt.ylabel('Q [barns]');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even though the cross section raises fast with overvoltage $U$, \n",
"too low overvoltage leads to a small cross section, which results in poor excitation of the X-ray line.\n",
"\n",
"We can also see that the intial overvoltage $U_0$ varies with atomic number $Z$, which will affect the relative generation of X-rays from different elements.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## X-ray Production in a Thick Solid Sample\n",
"\n",
"If the sample is sufficient thick so that all electrons interact ( several micrometers) the X-ray intensity is found to follow an expression fo the form:\n",
"\n",
"$$I \\approx i_p \\left[ (E_0-E_c)/E_0\\right]^n \\approx i_p [U_0-1]^n$$\n",
"\n",
"where:\n",
"- $i_p$: beam current \n",
"- $n$: exponent $n$ depends on particular element and shell (Lifshin et al. 1980) \n",
"\n",
"The exponent $n$ is ususally between 1.5 and 2. See what changes in the plot below if you change this exponent."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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",
"text/html": [
"\n",
"
\n",
"
\n",
" Figure\n",
"
\n",
" \n",
"
\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 1.5\n",
"\n",
"U_0 = np.linspace(1,10, 2048)\n",
"i_p = 1.0 # in nA\n",
"I = i_p * (U_0-1.0)**n\n",
"\n",
"plt.figure()\n",
"plt.plot(U_0,I)\n",
"plt.axhline(y=0., color='gray', linewidth=0.5)\n",
"plt.xlabel('overvoltage $U_0$ [kV]')\n",
"plt.ylabel('$I$ [nA]');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## X-ray Production in a Thin Foil\n",
"\n",
"Now we have all the ingeedients to look at the total generation of X-rays.\n",
"\n",
"The number of photons $n_X$ produced in a thin foil of thickness $t$ is :\n",
"$$ n_x = Q_I(E) \\ \\omega N_A \\frac{1}{A} \\rho \\ t $$\n",
"\n",
"- $n_X$ [photons/e$^-$]\n",
"- $Q_I(E)$ [ ionizations/e$^-$ (atoms / cm$^2$)): ionization cross section\n",
"- $\\omega$ [X-rays/ ionization]: fluorescent yield = number of X-rays generated per ionization\n",
"- $ N_A$ [atoms per mol]: Avogadro’s number \n",
"- $A$ [g/moles]: atomic weight\n",
"- $\\rho$ [g/cm$^3$]: density of sample\n",
"- $t$ [cm]: thickness of sample\n",
"\n",
"The number of X-rays increase linearly with the number of atoms per unit area ($N_A \\frac{1}{A} \\rho \\ t $).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the graph above, we can see the importance of the overvoltage $U$ by how the intensity increase with increasing $U_0$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quiz\n",
"Why is the overvoltage increasing so much while the cross section remains rather constant above the threshold? "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Navigation\n",
"- **Back: [Bremsstrahlung](CH4_13-Bremsstrahlung.ipynb)** \n",
"- **Next: [Detector Response](CH4_15-Detector.ipynb)** \n",
"- **Chapter 4: [Spectroscopy](CH4_00-Spectroscopy.ipynb)** \n",
"- **List of Content: [Front](../_MSE672_Intro_TEM.ipynb)** "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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