{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", " **Chapter 4: [Spectroscopy](CH4_00-Spectroscopy.ipynb)** \n", "\n", "
\n", "\n", "\n", "\n", "# Introduction to Core-Loss Spectroscopy\n", "Working with X-Sections\n", "\n", "[Download](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_07-Introduction_Core_Loss.ipynb)\n", " \n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", " https://colab.research.google.com/github/gduscher/MSE672-Introduction-to-TEM/blob/main/Spectroscopy/CH4_07-Introduction_Core_Loss.ipynb)\n", "\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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Core --Loss Spectroscopy\n", "\n", "As we can see in figure below the energies of the core shells are well defined and can be viewed as delta functions, unlike in the low loss region where the broad valence bands are the initial state. In both cases, however, we excite electrons into the conduction band.\n", "\n", "\"core-loss\"\n", "*Excitation from a core-shell state up into the conduction band above the Fermi level.*\n", "\n", "If we look at the transition between two states $< \\Phi_f | H | \\Phi_i >$ the transition should be quite sharp. In the case of the low-loss spectrum, we have many initial (the valence) states and many final (the conduction) states. The spectrum will be a convolution of these states. \n", "\n", "The features of the core--loss edges are, therefore, much sharper than any details in the low--loss region. Because only the final states contribute to the features. These sharp features enable a wide variety of analysis to determine the chemical compositions and chemical bonding, probing the local conduction band of the sample.\n", "\n", "\n", "\n", "### Chemical Composition\n", "\n", "In this chapter we use the area under the ionization edge to determine the chemical composition of a (small) sample volume. \n", "The equation used to determine the number of atoms per unit volume $N$ (also called areal density) is:\n", "\\begin{equation}\n", "I_{edge}(\\beta, \\Delta E) = N I_{0}(\\beta) \\sigma_{edge}(\\beta, \\Delta E)\n", "\\end{equation}\n", "\n", "$I_0$ is the number of electrons hitting the sample, and so directly comparable to the beam current.\n", "\n", "The equation can be approximated assuming that the spectrum has not been corrected for single scattering:\n", "\\begin{equation} \n", "I_{edge}(\\beta, \\Delta E) = N I_{low-loss}(\\beta,\\Delta E) \\sigma_{edge}(\\beta, \\Delta E)\n", "\\end{equation}\n", "where $\\beta$ is the collection angle and $\\sigma_{edge}$ is the **partial** cross--section (for energy window $\\Delta E$) for the core--loss excitation.\n", "\n", "\n", "> \n", "> It is this cross-section $ \\sigma_{edge}$ that we want to explorein this notebook.\n", ">\n", "We will do the chemical composition in the [next notebook](CH4_08-Chemical_Composition.ipynb)\n", "\n", "\n", "\n", "## Load important packages\n", "\n", "### Check Installed Packages\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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') < '0.2026.1.0':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install --upgrade pyTEMlib -q\n", "\n", "print('done')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import all relevant libraries\n", "\n", "Please note that the EELS_tools package from pyTEMlib is essential." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyTEM version: 0.2026.1.0\n" ] } ], "source": [ "%matplotlib ipympl\n", "import sys\n", "\n", "import matplotlib\n", "import matplotlib.pylab as plt\n", "import numpy as np\n", "\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 the configuration files of pyTEMlib (we need access to the data folder)\n", "import pyTEMlib\n", "\n", "# For archiving reasons it is a good idea to print the version numbers out at this point\n", "print('pyTEM version: ',pyTEMlib.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Cross-Sections\n", "\n", "\n", "The form factors are from:\n", "X-Ray Form Factor, Attenuation, and Scattering Tables\n", "NIST Standard Reference Database 66\n", "\n", " DOI: https://dx.doi.org/10.18434/T4HS32\n", "\n", "Detailed Tabulation of Atomic Form Factors, Photoelectric Absorption and Scattering Cross Section, and Mass Attenuation Coefficients for Z = 1-92 from E = 1-10 eV to E = 0.4-1.0 MeV\n", "C.T. Chantler,1 K. Olsen, R.A. Dragoset, J. Chang, A.R. Kishore, S.A. Kotochigova, and D.S. Zucker\n", "NIST, Physical Measurement Laboratory\n", "\n", "The cross sections are part of the pyTEMlib package and are stored as a pickled dictionary in the package data directory.\n", "\n", "Below are the lines for accessing the cross sections with eels_tools of pyTEMlib." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "all_cross_sections = pyTEMlib.eels_tools.get_x_sections()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Cross Sections\n", "\n", "Please add your favourite element ot the list of atomic numbers.\n", "\n", "With the code cell above we made the whole database of cross secitons available for this notebook." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8151fc22bab8474db5ceff763a46aeb7", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -----Input ------------ #\n", "atomic_numbers = [58, 28]\n", "# ----------------------- #\n", "fig, ax = plt.subplots()\n", "for Z in atomic_numbers:\n", " ax.plot(all_cross_sections[str(Z)]['ene'], all_cross_sections[str(Z)]['dat'], label = all_cross_sections[str(Z)]['name'])\n", "\n", "ax.set_xlim(0,1500)\n", "ax.set_ylim(0,2.5e17)\n", "ax.set_xlabel('energy_loss (eV)')\n", "ax.set_ylabel('cross section (atoms/nm$^2$)')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List All Edges of an Element " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ce-O3: 19.8 eV \n", "Ce-O2: 19.8 eV \n", "Ce-O1: 37.8 eV \n", "Ce-N6: 85.9 eV \n", "Ce-N5: 110.0 eV \n", "Ce-N4: 110.0 eV \n", "Ce-N3: 207.2 eV \n", "Ce-N2: 223.3 eV \n", "Ce-N1: 289.6 eV \n", "Ce-M5: 883.3 eV \n", "Ce-M4: 901.3 eV \n", "Ce-M3: 1185.4 eV \n", "Ce-M2: 1272.8 eV \n", "Ce-M1: 1434.6 eV \n", "Ce-L3: 5723.4 eV \n", "Ce-L2: 6164.2 eV \n", "Ce-L1: 6548.8 eV \n", "Ce-K1: 40443.0 eV \n" ] } ], "source": [ "element = str(58)\n", "for key in all_cross_sections[element]:\n", " if isinstance(all_cross_sections[element][key], dict):\n", " if 'onset' in all_cross_sections[element][key]:\n", " print(f\"{all_cross_sections[element]['name']}-{key}: {all_cross_sections[element][key]['onset']:8.1f} eV \")\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or ordered" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All edges\n", " Ce-K1: 40443.0 eV \n", " Ce-L1: 6548.8 eV \n", " Ce-L2: 6164.2 eV \n", " Ce-L3: 5723.4 eV \n", " Ce-M1: 1434.6 eV \n", " Ce-M2: 1272.8 eV \n", " Ce-M3: 1185.4 eV \n", " Ce-M4: 901.3 eV \n", " Ce-M5: 883.3 eV \n", " Ce-N1: 289.6 eV \n", " Ce-N2: 223.3 eV \n", " Ce-N3: 207.2 eV \n", " Ce-N4: 110.0 eV \n", " Ce-N5: 110.0 eV \n", " Ce-N6: 85.9 eV \n", " Ce-O1: 37.8 eV \n", " Ce-O2: 19.8 eV \n", " Ce-O3: 19.8 eV \n", "Major edges\n", " Ce-K1: 40443.0 eV \n", " Ce-L3: 5723.4 eV \n", " Ce-M5: 883.3 eV \n", " Ce-N5: 110.0 eV \n" ] } ], "source": [ "major_edges = ['K1', 'L3', 'M5', 'N5']\n", "all_edges = ['K1','L1','L2','L3','M1','M2','M3','M4','M5','N1', 'N2','N3','N4','N5','N6','N7','O1','O2','O3','O4','O5','O6','O7', 'P1', 'P2', 'P3']\n", "first_close_edges = ['K1', 'L3', 'M5', 'M3', 'N5', 'N3']\n", "\n", "element = str(58)\n", "\n", "def list_all_edges(Z):\n", " element = str(Z)\n", " print('All edges')\n", " for key in all_edges:\n", " if key in all_cross_sections[element]:\n", " if 'onset' in all_cross_sections[element][key]:\n", " print(f\" {all_cross_sections[element]['name']}-{key}: {all_cross_sections[element][key]['onset']:8.1f} eV \")\n", "\n", "def list_major_edges(Z):\n", " element = str(Z)\n", " print('Major edges')\n", " for key in major_edges:\n", " if key in all_cross_sections[element]:\n", " if 'onset' in all_cross_sections[element][key]:\n", " print(f\" {all_cross_sections[element]['name']}-{key}: {all_cross_sections[element][key]['onset']:8.1f} eV \") \n", "## Here with the function of the EELS_tools package \n", "list_all_edges(element)\n", "list_major_edges(element)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting all edges of an element in view\n", "\n", "Now, let's do it graphically" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e45f67cac5cf439c8d3897e5f1ad49c2", "version_major": 2, "version_minor": 0 }, "image/png": 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", "text/html": [ "\n", "
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\n", " Figure\n", "
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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "major_edges = ['K1', 'L3', 'M5', 'N5']\n", "all_edges = ['K1','L1','L2','L3','M1','M2','M3','M4','M5','N1', 'N2','N3','N4','N5','N6','N7','O1','O2','O3','O4','O5','O6','O7', 'P1', 'P2', 'P3']\n", "first_close_edges = ['K1', 'L3', 'M5', 'M3', 'N5', 'N3']\n", "\n", "def get_Z(Z):\n", " \"\"\"\n", " returns the atomic number independent of input as a string or number\n", " \n", " input:\n", " Z: atomic number of chemical symbol (0 if not valid)\n", " \"\"\"\n", " all_cross_sections = pyTEMlib.eels_tools.get_x_sections()\n", " \n", " Z_out = 0\n", " if str(Z).isdigit(): \n", " Z_out = Z\n", " elif isinstance(Z, str):\n", " for key in all_cross_sections:\n", " if all_cross_sections[key]['name'].lower() == Z.lower(): ## Well one really should know how to write elemental \n", " Z_out = int(key)\n", " return Z_out\n", "\n", "\n", "class ElementalEdges(object):\n", " def __init__(self, ax, Z):\n", " self.ax = ax\n", " self.labels = None\n", " self.lines = None\n", " \n", " self.Z = get_Z(Z)\n", " self.color = 'black'\n", " self.Xsections = pyTEMlib.eels_tools.get_x_sections()\n", " self.cid = ax.figure.canvas.mpl_connect('draw_event', self.onresize)\n", " \n", " #self.update()\n", " def set_edge(self,Z):\n", " self.Z = get_Z(Z)\n", " \n", " \n", " self.update()\n", " def onresize(self, event):\n", " self.update()\n", " \n", " def update(self):\n", " \n", " if self.labels != None:\n", " for label in self.labels:\n", " label.remove()\n", " if self.lines != None:\n", " for line in self.lines:\n", " line.remove()\n", " if self.Z>0:\n", " self.labels = [] ; self.lines =[] \n", " x_min, x_max = self.ax.get_xlim()\n", " y_min, y_max = self.ax.get_ylim()\n", " x_bounds = ax.get_xlim()\n", " element = str(self.Z)\n", " Xsections = self.Xsections\n", " for key in all_edges:\n", " if key in Xsections[element]:\n", " if 'onset' in Xsections[element][key]:\n", " x = Xsections[element][key]['onset']\n", " if x > x_min and x < x_max:\n", " if key in first_close_edges:\n", " label2 = self.ax.text(x, y_max,f\"{Xsections[element]['name']}-{key}\",\n", " verticalalignment='top', rotation = 0, color = self.color)\n", " else:\n", " label2 = self.ax.text(x, y_max,f\"\\n{Xsections[element]['name']}-{key}\",\n", " verticalalignment='top', color = self.color)\n", " line2 = self.ax.axvline(x,ymin = 0,ymax = 1,color=self.color)\n", "\n", " self.labels.append(label2)\n", "\n", " self.lines.append(line2)\n", " \n", " \n", " def disconnect(self):\n", " if self.labels != None:\n", " for label in self.labels:\n", " label.remove()\n", " if self.lines != None:\n", " for line in self.lines:\n", " line.remove()\n", " self.labels = None\n", " self.lines = None\n", " self.ax.figure.canvas.mpl_disconnect(self.cid)\n", " def reconnect(self): \n", " self.cid = ax.figure.canvas.mpl_connect('draw_event', self.onresize)\n", " ax.figure.canvas.draw_idle()\n", " \n", "fig, ax_Xsec = plt.subplots() \n", "for Z in atomic_numbers:\n", " ax_Xsec.plot(all_cross_sections[str(Z)]['ene'], all_cross_sections[str(Z)]['dat'], label = all_cross_sections[str(Z)]['name'])\n", "ax_Xsec.set_xlim(100,1450)\n", "ax_Xsec.set_ylim(0,1e17)\n", "plt.legend(); \n", "Z = 58\n", "edges = ElementalEdges(ax_Xsec, 'Ce')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make the lines disappear" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "edges.disconnect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and reappear in the plot above" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "edges.set_edge(Z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's set another edge" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "edges.set_edge(28)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find Edges Listed in Xsection Dictionary\n", "\n", "please note that the two functions below are as ususal available in the EELS_tools of pyTEMlib" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Major Edges within 7.0 eV of 284.0\n", "\n", " C -K1: 283.8 eV \n", " Ru-M5: 279.4 eV \n", "\n", "All Edges within 7.0 eV of 284.0\n", "\n", " C -K1: 283.8 eV \n", " Kr-M1: 288.3 eV \n", " Sr-M2: 279.8 eV \n", " Ru-M5: 279.4 eV \n", " Ru-M4: 283.6 eV \n", " Ce-N1: 289.6 eV \n", " Eu-N2: 283.9 eV \n", " Gd-N2: 288.5 eV \n", " Tb-N3: 285.0 eV \n", " Os-N4: 289.4 eV \n" ] } ], "source": [ "# --- Input ----\n", "edge_onset = 284\n", "maximal_chemical_shift = 7\n", "# -------------\n", "print(f'Major Edges within {maximal_chemical_shift:.1f} eV of {edge_onset:.1f}')\n", "print(pyTEMlib.eels_tools.find_all_edges(edge_onset, maximal_chemical_shift, major_edges_only=True))\n", "print(f'\\nAll Edges within {maximal_chemical_shift:.1f} eV of {edge_onset:.1f}')\n", "print(pyTEMlib.eels_tools.find_all_edges(edge_onset, maximal_chemical_shift))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3a7178ae9d7d40a5a032ef6af7f9ec5a", "version_major": 2, "version_minor": 0 }, "image/png": 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2y4W5ZYBp+QH5NVT0825ORT4bJT8XFf3MWmPLli2YOHEiVq9ejV69esHHxwcTJkxAYmIigOr7vlo6zpUpc2WU990oXY7qPv419XuTlZWFvn374ujRo1i0aBEOHjyIY8eOYdu2bQAq/vtMNY99AOsYpVKJqKgo/Pjjj7h161a1jZD19/c36WBsyf/93/9hy5YtGDZsGLZv344hQ4ZYtV1UVBS+/fZbREdHQ6fTYcCAAXB3d0dISAj27t2Lffv2oW/fvlaHypqm72ezfPlyiyNYLf1Rrk6+vr44evQohBBGP8pJSUnQaDQm/exK/3DXlPT0dHz//feYP38+XnvtNcPy/Pz8cv/Q6v9oJSQkmDx2+/ZtKBQKk1oFa1/Xxo0bMWDAAKxcudJoeWZmplXbWyqrPmiUZG6ZJdaW39fX1+zAq4o8l61Z+5l1cnICAJPBBebClp+fH5YtW4Zly5bhxo0b2LlzJ1577TUkJSVh9+7d1fZ9tXScSwYtJycnpKenm6x37969cvdfnpLfjdK/77dv3y6zX211qKnfm/379+P27ds4ePCg0UwJtpwHkcrGGsA6aO7cuRBC4Nlnn0VBQYHJ44WFhfjuu+8qtM9hw4bhwIEDVo2WdXJywrZt2zBy5Eg89NBD+Pbbb616jkGDBuHOnTtYtmwZevbsCXd3dwByMNy+fTuOHTtmVfNvddVulKdPnz7w8vLCuXPncN9995m96GtEarKsUVFRyMrKwo4dO4yW60ehWtOxvSZIkgQhhElgX716NbRabZnbRkZGolGjRvjyyy8hhDAsz87OxtatWw0jgytbrtJlOn36tMnoYmtFRkYiODgYmzZtMirr9evX8dtvv1Vqn2Xp378//vzzT5w7d85o+ebNmyu9z8rWgleWtZ/ZwMBAODk54fTp00brlfeb0qRJE8yaNQuDBw/GiRMnAFTP9xWAxeNccvBQeHg4Ll26ZBRck5OTTT4PZb3vlh574IEHAMj/yJR07NgxnD9/vsa/7zX1e6MPiqW/m//9738rtT+qeawBrIN69eqFlStXYsaMGejatSuef/55tG3bFoWFhTh58iQ+/fRTtGvXDqNGjbJ6n2+99RZ+/PFH9OvXD/PmzUP79u2RlpaG3bt3Y/bs2WjVqpXR+iqVCps2bcK0adMwduxYfPbZZxb7KOk98MADkCQJe/bsMUyBAcjBcOLEiYbb5Wnfvj02b96MLVu2oGnTpnByckL79u2tfq3WcnNzw/LlyzFx4kSkpKRg7NixCAgIwN27d3Hq1CncvXvXpJapJso6YcIEfPLJJ5g4cSLi4uLQvn17/PLLL3j33XcxfPjwKveZrCwPDw/069cPH3zwAfz8/BAeHo7o6GisWbOm3IltFQoF3n//fYwbNw4jR47E3/72N+Tn5+ODDz5AWloa3nvvvUqXa+TIkXj77bcxf/589O/fHxcvXsRbb72FiIgIaDSaCu9PoVDg7bffxrRp0/DII4/g2WefRVpaGhYsWFChJmBrvfTSS1i7di2GDRuGt956C4GBgfjyyy9x4cIFQ3kqqlmzZnB2dsYXX3yB1q1bw83NDSEhIQgJCanu4gOw/jMrSRLGjx9vmIi4Y8eO+P333/Hll18a7S89PR0DBw7E008/jVatWsHd3R3Hjh3D7t278eijjwKonu8rINd06Y9zeno65s+fDycnJ8ydO9ewzjPPPIP//ve/GD9+PJ599lkkJyfj/fffN5nQ293dHWFhYfj2228RFRUFHx8fw3dF/zvw0UcfYeLEiVCpVIiMjERkZCSee+45LF++HAqFAsOGDTOMAg4NDcU//vGPKh2b8tTU703v3r3h7e2N6dOnY/78+VCpVPjiiy9w6tSpan4FVG1sNfqEyhcbGysmTpwomjRpIhwdHYWrq6vo3LmzePPNN0VSUpJhPWtGAQshxM2bN8WUKVNEUFCQUKlUIiQkRDz++OPizp07QgjzI111Op148cUXhUKhEP/73//KLXPnzp0FAPHrr78alsXHxwsAwtfX12j0nRDmR4PFxcWJIUOGCHd3dwHAMOrO0kjcskYallR6FLBedHS0GDFihPDx8REqlUo0atRIjBgxwuh5LD23pbJaYm5UnhBCJCcni+nTp4vg4GDh4OAgwsLCxNy5c0VeXp7RegDEzJkzy3yOksLCwsSIESNMlpvbj/59/OCDDwzLbt26JcaMGSO8vb2Fu7u7GDp0qPjzzz9NRrbq35+DBw8a7XPHjh2iR48ewsnJSbi6uoqoqCijz4YQxZ+Bu3fvWvWa8vPzxZw5c0SjRo2Ek5OT6NKli9ixY4fJCE1zr6fk6y/9/Vi9erVo0aKFcHR0FC1bthRr16412acllt5n/WMl3yshhPjzzz/FoEGDhJOTk/Dx8RFTp04VGzZsMBktaenzYq5cmzZtEq1atRIqlcriyFQ9/Xfh2LFjRsstHYuJEycKV1dXo2XWfmbT09PFtGnTRGBgoHB1dRWjRo0ScXFxRmXMy8sT06dPFx06dBAeHh7C2dlZREZGivnz5xuNVBfCuu+rOfrP6Oeffy5efPFF4e/vL9Rqtejbt684fvy4yfobNmwQrVu3Fk5OTqJNmzZiy5YtZt/3ffv2ic6dOwu1Wi0AGB3ruXPnipCQEKFQKExGyS9ZskS0bNlSqFQq4efnJ8aPHy9u3rxptO+KHH9zavv35rfffhO9evUSLi4uwt/fX0ybNk2cOHHC5PeZo4DrBkmIEnXhRESV8O2332L06NE4c+aMVeeuJVPPPfccNm3ahOTkZKuaMomIqoJNwERUafn5+Th8+DA+/vhj+Pv7o3nz5rYuUr3w1ltvISQkBE2bNkVWVha+//57rF69Gm+88QbDHxHVCgZAIqq0hIQEDB8+HJGRkfjiiy8Moz6pbCqVCh988AFu3boFjUaDFi1aYOnSpfj73/9u66IRkZ1gEzARERGRneE0MERERER2hgGQ6qS4uDhIkoT169fX6PMMGDDAaP6v6vbuu++azLcFAAcPHoQkSTh48GCNPXdNWbBgQa1NRl1bzL2mFStWmP386Y/dN998U+5+J02ahPDw8GoqpUySJLMXa6fWKSwsxMKFCxEeHg61Wo1WrVph+fLl1Va+jz76CJIkYffu3RbX+d///gdJkgxniaiqLVu2oG3btnB2doYkSYiNja2W/RI1aLYdhExkXl5enoiJiTGa7qYmnD17Vpw9e7bG9u/q6moyBYgQ8tQYMTExIj09vcaeu6aYm8Khvrt586aIiYkxWta2bVuz01JYmhLInCtXrogTJ05UVzGFEPLUHGPHjhUxMTFGl/j4eKu2nzZtmlCr1eL9998XBw4cEK+99pqQJEm888471VK+e/fuCbVaLR577DGL6/Tq1Uv4+/uLgoKCKj9fUlKSUKlUYtSoUeLgwYMiJibGZOoYIjLFQSBUJ6nVaoune6pObdq0qfHnMMfDw6NWXh9Zp3HjxtV22sWSmjVrVu37BOQzbFTm83P27FmsWbMG77zzDv75z38CkGvBk5OTsWjRIkyfPt3kPMAV5evri4cffhg7duxAcnKyyblsL1y4gJiYGLz88stQqVRVei4AuHTpEgoLCzF+/HijU5DZSk5OTqXPcmMtIQTy8vLg7Oxco89DDRubgKlOstQE/MsvvyAqKgru7u5wcXFB7969sWvXLqN11q9fD0mScODAATz//PPw8/ODr68vHn30Udy+fdto3dJNwJMmTbLYxLZgwQIAQF5eHl5++WV06tQJnp6e8PHxQa9evUxObyVJErKzs7FhwwbDPvTPZakJeOfOnYbTpLm7u2Pw4MEmpzjTN1eePXsWTz31FDw9PREYGIgpU6aYPX+pOfv27UNUVBQ8PDzg4uKCPn364OeffzZZb9euXejUqRPUajUiIiLw4Ycfmt1fWloapk6dCh8fH7i5uWHEiBG4evWq0fumd/nyZTz99NMICAiAWq1G69at8cknnxito9PpsGjRIkRGRsLZ2RleXl7o0KEDPvroI4uvSQiBwMBAzJw507BMq9XC29sbCoXC6Py7S5cuhYODg+E8paWbgMPDw3H27FlER0cbjl3pptzCwkK8/vrrCAkJgYeHBwYNGmRyqkVzTcCSJGHWrFn4/PPP0bp1a7i4uKBjx474/vvvLb626rJjxw4IITB58mSj5ZMnT0Zubm6ZzbZ61hy/qVOnoqCgwOSMHwCwbt06AMCUKVPKfa7yvg+TJk3C/fffDwB44oknjL5jlsTHx+O5555DaGgoHB0dERISgrFjxxo+H/rfj7i4OKPtzH1nBwwYgHbt2uHQoUPo3bs3XFxcDK9r//79GDBgAHx9feHs7IwmTZpgzJgxyMnJMWyfkpKCGTNmoFGjRnB0dETTpk3x+uuvm5w7Wf+ZWbVqFVq3bg21Wo0NGzaU+/4RlcnGNZBEZpk7u8fBgweFSqUSXbt2FVu2bBE7duwQQ4YMEZIkic2bNxvW05/loGnTpuKFF14QP/30k1i9erXw9vYWAwcONHqe0rPPX7lyxaRpbfz48QKA2LJlixBCiLS0NDFp0iTx+eefi/3794vdu3eLOXPmCIVCITZs2GDYV0xMjHB2dhbDhw837Evf3KxvRtSfGUAIIb744gsBQAwZMkTs2LFDbNmyRXTt2lU4OjqKw4cPG9bTN8FGRkaKN998U+zdu1csXbpUqNVqMXny5HLf288//1xIkiRGjx4ttm3bJr777jsxcuRIoVQqxb59+wzr7du3TyiVSnH//feLbdu2ia+//lp069ZNNGnSxKgJWKvVivvvv184OTmJ9957T+zZs0csXLhQtGjRwuSMFGfPnhWenp6iffv24rPPPhN79uwRL7/8slAoFGLBggWG9RYvXiyUSqWYP3+++Pnnn8Xu3bvFsmXLjNYx58knnxQtW7Y03D9y5IgAIJydncUXX3xhWD5s2DDRvXt3k/dU78SJE6Jp06aic+fOhmOnb8rVH7vw8HAxbtw4sWvXLrFp0ybRpEkT0aJFC6HRaAz7MXfGBv223bt3F1999ZX44YcfxIABA4SDg4P466+/ynx9+u29vb2Fk5OTcHR0FF26dBFr164tdzv9++Pv72+yPCsrSwAQc+fOLXN7a4+fVqsVYWFholOnTkbbazQaERwcLHr27FluWa35Ply5ckV88sknAoB49913jb5j5ty6dUsEBwcLPz8/sXTpUrFv3z6xZcsWMWXKFHH+/HkhhOUzBpn7zvbv31/4+PiI0NBQsXz5cnHgwAERHR0trl27JpycnMTgwYPFjh07xMGDB8UXX3whnnnmGZGamiqEECI3N1d06NBBuLq6ig8//FDs2bNH/Otf/xIODg5i+PDhRs8NQDRq1Eh06NBBfPnll2L//v3izz//LPc9JCoLAyDVSeYCYM+ePUVAQIDIzMw0LNNoNKJdu3aicePGhtPM6X/AZ8yYYbTP999/XwAQCQkJhmXlnX7oq6++EpIkiXnz5llcR6PRiMLCQjF16lTRuXNno8cs9QEs/cdEq9WKkJAQ0b59e6HVag3rZWZmioCAANG7d2/DMn1Yef/99432OWPGDOHk5GRyur2SsrOzhY+Pjxg1apTRcq1WKzp27GgUinr06CFCQkJEbm6uYVlGRobw8fExCku7du0SAMTKlSuN9rl48WKTAPjggw+Kxo0bm/R9nDVrlnBychIpKSlCCCFGjhxpEh6ssXr1agFA3LhxQwghxKJFi0SrVq3EQw89ZAjHBQUFwtXV1eiYmuvXWF4fwNJ/pL/66isBwKgvoaUAGBgYKDIyMgzLEhMThUKhEIsXLy73NT799NPiiy++EIcOHRLffPONGDZsmAAg3njjjXK3HTx4sIiMjDT7mKOjo3juuefK3N7a4ydE8Xtasg/kd999JwCUe1rJinwfKtInc8qUKUKlUolz585ZXKeiARCA+Pnnn43W/eabbwQAERsba/F5Vq1aJQCIr776ymj5kiVLBACxZ88ewzIAwtPT0+j9JaoqNgFTvZCdnY2jR49i7NixcHNzMyxXKpV45plncOvWLZPmt4ceesjofocOHQAA169ft+o5o6Oj8cwzz2D8+PF45513jB77+uuv0adPH7i5ucHBwQEqlQpr1qzB+fPnK/PycPHiRdy+fRvPPPMMFIrir6WbmxvGjBmDI0eOGDUdAeZfX15eHpKSkiw+z2+//YaUlBRMnDgRGo3GcNHpdBg6dCiOHTuG7OxsZGdn49ixY3j00UeNJnd2d3fHqFGjjPYZHR0NAHj88ceNlj/11FNG9/Py8vDzzz/jkUcegYuLi9HzDx8+HHl5eThy5AgAoHv37jh16hRmzJiBn376CRkZGeW9hQBgOJH9vn37AAB79+7F4MGDMWjQIOzduxcAEBMTg+zs7Eqf9F6vKp+vgQMHwt3d3XA/MDAQAQEBVm37xRdf4Omnn0bfvn0xZswY/PDDDxg5ciTee+893L17t9ztyxrBXdZjFTl+gNysrFAosHbtWsOydevWwdXVFU888USZZazM98EaP/74IwYOHIjWrVtXeFtLvL298cADDxgt69SpExwdHfHcc89hw4YNuHr1qsl2+/fvh6urK8aOHWu0fNKkSQBg0iXjgQcegLe3d7WVm4gBkOqF1NRUCCEQHBxs8lhISAgAIDk52Wh56c7narUaAJCbm1vu8509exajR49G3759sWbNGqPHtm3bhscffxyNGjXCxo0bERMTg2PHjmHKlCnIy8ur0OvS05fd0uvT6XRITU01Wl6Z16fv5zR27FioVCqjy5IlSyCEQEpKClJTU6HT6RAUFGSyj9LLkpOT4eDgYDJ4IDAw0GQ9jUaD5cuXmzz38OHDAQD37t0DAMydOxcffvghjhw5gmHDhsHX1xdRUVE4fvy4xdcGAGFhYWjWrBn27duHnJwcxMTEGAKg/p+Effv2wdnZGb179y5zX+Wpyuer9Lb67a3Z1pzx48dDo9GU+/74+vqafE8A+R+sgoKCMgeAVOT4AfKxiIqKwpdffon8/Hzcu3cP33//PR577DGj8GvpuYCKfR+scffu3Wof7GOujPrPYEBAAGbOnIlmzZqhWbNmRn1Yk5OTERQUZBK6AwIC4ODgYHKczD0PUVVwFDDVC/qO/AkJCSaP6Qd2+Pn5Vctz3bp1C0OHDkWTJk2wdetWk5GKGzduREREBLZs2WL0412643ZF6AOBpdenUCiq5b9//Xu0fPlyi6NIAwMDUVhYCEmSkJiYaPJ46WW+vr7QaDRISUkxChCl1/P29jbU2JYcqFFSREQEAMDBwQGzZ8/G7NmzkZaWhn379mHevHl48MEHcfPmzTJHWUZFReHbb79FdHQ0dDodBgwYAHd3d4SEhGDv3r3Yt28f+vbtawhsDYEoOqFTydoyc9q3b4/NmzcjMTHRKMifOXMGANCuXTuL21bk+OlNnToVe/fuxbfffovbt2+joKAAU6dOLff11NT3wd/fH7du3SpzHX2Nd+nvc8lwW5KlWtO+ffuib9++0Gq1OH78OJYvX46XXnoJgYGBePLJJ+Hr64ujR49CCGG0j6SkJGg0GpPfs4Y29ybZHmsAqV5wdXVFjx49sG3bNqNaEp1Oh40bN6Jx48Zo2bJllZ8nPT0dw4YNgyRJ+OGHH+Dh4WGyjiRJcHR0NPpBTkxMNBkFDFhfqxMZGYlGjRrhyy+/NPwxB+Sama1btxpGQlZVnz594OXlhXPnzuG+++4ze3F0dISrqyu6d++Obdu2GdVqZmZm4rvvvjPap37qjS1bthgt37x5s9F9FxcXDBw4ECdPnkSHDh3MPre5mjEvLy+MHTsWM2fOREpKisnozNIGDRqEO3fuYNmyZejZs6ehtikqKgrbt2/HsWPHrGr+rUqNXG37/PPPoVKp0LVr1zLXe/jhhyFJkskI0vXr18PZ2RlDhw61uG1ljt/o0aPh6+uLtWvXYt26dWjZsqVh1G5Zaur7MGzYMBw4cMCku0hJ+lHbp0+fNlq+c+fOCj8fIHdT6dGjh2Gk9IkTJwDIn8esrCyTieI/++wzw+NENYk1gFRvLF68GIMHD8bAgQMxZ84cODo6YsWKFfjzzz+xadOmavkP+emnn8a5c+fw6aef4ubNm7h586bhMf1ccSNHjsS2bdswY8YMjB07Fjdv3sTbb7+N4OBgXL582Wh/7du3x8GDB/Hdd98hODgY7u7uiIyMNHlehUKB999/H+PGjcPIkSPxt7/9Dfn5+fjggw+QlpZm9VkeyuPm5obly5dj4sSJSElJwdixYxEQEIC7d+/i1KlTuHv3LlauXAkAePvttzF06FAMHjwYL7/8MrRaLZYsWQJXV1ekpKQY9jl06FD06dMHL7/8MjIyMtC1a1fExMQY/pCVrJX66KOPcP/996Nv3754/vnnER4ejszMTFy5cgXfffcd9u/fDwAYNWoU2rVrh/vuuw/+/v64fv06li1bhrCwMLRo0aLM1/jAAw9AkiTs2bMHCxcuNCwfNGgQJk6caLhdHn1t2ZYtW9C0aVM4OTmhffv2Vr7TNeODDz7AuXPnEBUVhcaNGyMpKQlr1qzBnj17sGDBAqNao88++wxTpkzB2rVrMWHCBABA27ZtMXXqVMyfPx9KpRLdunXDnj178Omnn2LRokXlzgFo7fHTU6vVGDduHJYvXw4hhNWf45r6Prz11lv48ccf0a9fP8ybNw/t27dHWloadu/ejdmzZ6NVq1bo1q0bIiMjMWfOHGg0Gnh7e2P79u345ZdfrH6eVatWYf/+/RgxYgSaNGmCvLw8Q19I/WdvwoQJ+OSTTzBx4kTExcWhffv2+OWXX/Duu+9i+PDhVe6jSlQuGw5AIbLI3ChgIYQ4fPiweOCBB4Srq6twdnYWPXv2FN99953ROvpRfMeOHTNabmkUX8mRnmFhYQKA2UvJ0azvvfeeCA8PF2q1WrRu3Vr873//MzuSNDY2VvTp00e4uLgIAIbnMlcWIYTYsWOH6NGjh3BychKurq4iKipK/Prrr0br6J/n7t27Zl936dGL5kRHR4sRI0YIHx8foVKpRKNGjcSIESNMRlLu3LlTdOjQQTg6OoomTZqI9957z+zrTElJEZMnTxZeXl7CxcVFDB482DAFy0cffWS07rVr18SUKVNEo0aNhEqlEv7+/qJ3795i0aJFhnX+/e9/i969ews/Pz/Dc0+dOlXExcWV+9qEEKJz584CgNF7Fx8fLwAIX19fk5HS5l5TXFycGDJkiHB3dxcADKN5LY06NfeZtTQKeObMmSZlDgsLMztivKSdO3eK+++/X/j7+wsHBwfh7u4u+vbtKzZt2mSyrv7zUPo7VFBQIObPny+aNGkiHB0dRcuWLcV//vOfMp+3JGuOX0mnTp0SAIRSqRS3b9+2+nmEsO77UJFRwELIZ32ZMmWKCAoKEiqVSoSEhIjHH39c3Llzx7DOpUuXxJAhQ4SHh4fw9/cXL7zwgmG0e+nfj7Zt25o8R0xMjHjkkUdEWFiYUKvVwtfXV/Tv31/s3LnTaL3k5GQxffp0ERwcLBwcHERYWJiYO3euyMvLM1rP0meGqCokIUrUrxMRVZMvv/wS48aNw6+//lrlARdERFS9GACJqMo2bdqE+Ph4tG/fHgqFAkeOHMEHH3yAzp07G6aJISKiuoN9AImoytzd3bF582YsWrQI2dnZCA4OxqRJk7Bo0SJbF42IiMxgDSARERGRneE0MERERER2hgGQiIiIyM4wABIRERHZGQZAIiIiIjvDAEhERERkZxgAq8mhQ4cwatQohISEQJIkk/M7licvLw+TJk1C+/bt4eDggNGjR5usM2nSJEiSZHJp27Zt9bwIIiIisgsMgNUkOzsbHTt2xMcff1yp7bVaLZydnfHiiy9aPAfkRx99hISEBMPl5s2b8PHxwWOPPVaVohMREZGd4UTQ1WTYsGEYNmyYxccLCgrwxhtv4IsvvkBaWhratWuHJUuWYMCAAQAAV1dXrFy5EgDw66+/Ii0tzWQfnp6e8PT0NNzfsWMHUlNTMXny5Gp9LURERNSwMQDWksmTJyMuLg6bN29GSEgItm/fjqFDh+LMmTNo0aJFpfa5Zs0aDBo0CGFhYdVcWiIiImrIGABrwV9//YVNmzbh1q1bCAkJAQDMmTMHu3fvxrp16/Duu+9WeJ8JCQn48ccf8eWXX1Z3cYmIiKiBYwCsBSdOnIAQAi1btjRanp+fD19f30rtc/369fDy8jI7WISIiIioLAyAtUCn00GpVOKPP/6AUqk0eszNza3C+xNCYO3atXjmmWfg6OhYXcUkIiIiO8EAWAs6d+4MrVaLpKQk9O3bt8r7i46OxpUrVzB16tRqKB0RERHZGwbAapKVlYUrV64Y7l+7dg2xsbHw8fFBy5YtMW7cOEyYMAH//ve/0blzZ9y7dw/79+9H+/btMXz4cADAuXPnUFBQgJSUFGRmZiI2NhYA0KlTJ6PnWrNmDXr06IF27drV1ssjIiKiBkQSQghbF6IhOHjwIAYOHGiyfOLEiVi/fj0KCwuxaNEifPbZZ4iPj4evry969eqFhQsXon379gCA8PBwXL9+3WQfJQ9Reno6goOD8dFHH+HZZ5+tuRdEREREDVaDCICLFy/Gtm3bcOHCBTg7O6N3795YsmQJIiMjLW5jKbCdP38erVq1qsniEhEREdlUgzgTSHR0NGbOnIkjR45g79690Gg0GDJkCLKzs8vd9uLFi0Zn16jsnHxERERE9UWDqAEs7e7duwgICEB0dDT69etndh19DWBqaiq8vLxqt4BERERENtQgagBLS09PBwD4+PiUu27nzp0RHByMqKgoHDhwoKaLRkRERGRzDa4GUAiBhx9+GKmpqTh8+LDF9S5evIhDhw6ha9euyM/Px+eff45Vq1bh4MGDFmsN8/PzkZ+fb7iv0+mQkpICX19fSJJU7a+FiIiIqp8QApmZmQgJCYFC0SDrwsrV4ALgzJkzsWvXLvzyyy9o3LhxhbYdNWoUJEnCzp07zT6+YMECLFy4sDqKSURERDZ28+bNCmeFhqJBBcAXXngBO3bswKFDhxAREVHh7d955x1s3LgR58+fN/t46RrA9PR0NGnSBDdv3oSHh4fRuh0X7oFWJ3Dg5f7w93CSF373HTB+PNCtG7BvHwBg79lE/OOrU+jSxAufTe1R4TITERFRxWRkZCA0NBRpaWnw9PS0dXFsokFMBC2EwAsvvIDt27fj4MGDlQp/AHDy5EkEBwdbfFytVkOtVpss9/DwMAmACrULhE7A3cMDHvoAGBgoX+fmAkXr+/vmQ6F2QZ6kNtkHERER1Rx77r7VIALgzJkz8eWXX+Lbb7+Fu7s7EhMTAQCenp5wdnYGAMydOxfx8fH47LPPAADLli1DeHg42rZti4KCAmzcuBFbt27F1q1ba66g+oCXkWFY5OYkH4KsfE3NPS8RERFRCQ0iAK5cuRIAMGDAAKPl69atw6RJkwAACQkJuHHjhuGxgoICzJkzB/Hx8XB2dkbbtm2xa9cuw2nZaoS7u3ydmVm8SC0fgmwGQCIiIqolDSIAWtONcf369Ub3X3nlFbzyyis1VCIL9AEwIwMQApAkuKqLawCFEHZdHU1ERES1wz7HPtuKvglYq5X7AaK4CbhQK5Cv0dmqZERERGRHGABrk5sboK/hK+oH6OpYXAnLfoBERERUGxgAa5NCYdwMDECpkODqqAQAZOUxABIREVHNYwCsbfr5hopOVwcA7k4qAEAmAyARERHVAgbA2mZmKhj3on6AmXmFtigRERER2RkGwNpmtgZQDoAZrAEkIiKiWsAAWNvM1gDKTcAZrAEkIiKiWsAAWNv0NYBmm4BZA0hEREQ1jwGwtulrAM0OAqlCDWAhaw+JiIjIOgyAtc1ME7CHcxVrAM+eBVxcgNdeq2rpiIiIyA4wANY2M4NAPKpaA/j664BGAyxZUtXSERERkR1gAKxtZU4DU8kaQEfHqpaKiIiI7AgDYG0rYxqYSgdAtbqqpSIiIiI7wgBY28zVAKqrOA0MAyARERFVAANgbTMzDYyHcxVPBccASERERBXAAFjbzE4DU8VTwbEPIBEREVUAA2BtK2Mi6EqfCo41gERERFQBDIC1rWQfQJ0OQPFE0AUaHfI12orvkwGQiIiIKoABsLbpA6AQQHY2AMBN7WB4uFL9ANkETERERBXAAFjbnJ0Bh6LAV9QPUKmQ4F4UAjNyK9EPsGQAFKKqJSQiIqIGjgGwtklS9U8GXbIJWFPJfoRERERkNxgAbcHsQJAqTAVTMgAWFFSlZERERGQHGABtobqngikZAPPzq1IyIiIisgMMgLZQxmTQlTobiFJZfJsBkIiIiMrBAGgLZmoAPfRzAeZWsQ8fAyARERGVgwHQFszUAHoW1QCmV2YUcEkMgERERFQOBkBbMDMKuEoBsOTULwyAREREVA4GQFvQ1wCWbAJmDSARERHVEgZAW6juGsCSGACJiIioHAyAtmBuEEh1BUDOA0hERETlYAC0hTIGgVTqVHAl+wDm5VWlZERERGQHGABtwUwNoGdV5gEsiQGQiIiIysEAaAtmBoGU7AMoStboVRQDIBEREZWDAdAWvLzkazMBsFArkFuorfy+c3OrUDAiIiKyBwyAtqAPgGlphkUujko4KCQAlRgIUrLGkAGQiIiIysEAaAv6AJiXZ2iylSSpeqaCYRMwERERlYMB0Bbc3QFJru0zOxVMThUCIGsAiYiIqBwMgLagUBQPBCnRDFwtcwGyBpCIiIjKwQBoK2b6ARZPBaOp2L7YB5CIiIgqgAHQVsoIgFWqAWQAJCIionIwANqK2QDoAIBNwERERFSzGABtpawmYNYAEhERUQ1iALSV6mwC5rmAiYiIqAIYAG2FfQCJiIjIRhgAbcVsAHQEAKTmFFR+vwyAREREVA4GQFsxEwC9XeQawLSqTATNJmAiIiIqBwOgrZgJgD6ulawB5DyAREREVAEMgLZiJgB6ucgBMD23EFqdMN3GGqwBJCIionIwANqK2QAoNwELUYWBIKwBJCIionIwANqKmQCoUirgrpYng670QBAGQCIiIioHA6CtmAmAAOBd1A8wrSIBkPMAEhERUQUwANqKPgDm5RmFNv1I4NTsKjQBi0r2HyQiIiK7wABoK+7ugCTJt9PTDYv1A0FSKtsELARQWIVpZIiIiKjBYwC0FYUC8PSUb5udC5CTQRMREVHNYAC0pTKmgkmtyGTQpZt8GQCJiIioDAyANURY0w+vjMmgq1QDyIEgREREVAYGwJomlfFYGaeDS8lmEzARERHVDAZAW6quJuDSWANIREREZWAAtCWzg0CqOA8gwBpAIiIiKhMDoC2ZawJ2LZoHsCo1gAyAREREVAYGQFsy2wewqAk4u8C6gSTmsAmYiIiIysAAaEtlBECNTiArX1O5/bIGkIiIiMrAAGhLZgKgs6MSagf5sKRVthmYNYBERERUhgYRABcvXoxu3brB3d0dAQEBGD16NC5evFjudtHR0ejatSucnJzQtGlTrFq1qhZKW4KZAAiUaAa2diAIB4EQERFRBTSIABgdHY2ZM2fiyJEj2Lt3LzQaDYYMGYLs7GyL21y7dg3Dhw9H3759cfLkScybNw8vvvgitm7dWnsFtxQAiyaDrvRcgAyAREREVAYHWxegOuzevdvo/rp16xAQEIA//vgD/fr1M7vNqlWr0KRJEyxbtgwA0Lp1axw/fhwffvghxowZU9NFllmsAdSfD5hNwERERFT9GkQNYGnp6ekAAB8fH4vrxMTEYMiQIUbLHnzwQRw/fhyFhVWYgqUi9AEwNdVocYWbgEtjDSARERGVoUHUAJYkhMDs2bNx//33o127dhbXS0xMRGBgoNGywMBAaDQa3Lt3D8HBwSbb5OfnIz8/33A/IyOjaoX19dXvWA5tzs4AAC+XCs4FWLoPIGsAiYiIqAwNrgZw1qxZOH36NDZt2lTuupJkfKJe/bx7pZfrLV68GJ6enoZLaGho1Qrr5gY4FGXw5GTDYh/XSpwNpCTWABIREVEZGlQAfOGFF7Bz504cOHAAjRs3LnPdoKAgJCYmGi1LSkqCg4MDfPU1c6XMnTsX6enphsvNmzerVmBJAvTN1CkphsX68wFzEAgRERHVhAYRAIUQmDVrFrZt24b9+/cjIiKi3G169eqFvXv3Gi3bs2cP7rvvPqhUKrPbqNVqeHh4GF2qzEwA1A8C+f50An69cq/i+2QTMBEREZWhQQTAmTNnYuPGjfjyyy/h7u6OxMREJCYmIrdETdjcuXMxYcIEw/3p06fj+vXrmD17Ns6fP4+1a9dizZo1mDNnTu0WXl/bWKIJWD8IBADGrT5a/j44DyARERFVQIMIgCtXrkR6ejoGDBiA4OBgw2XLli2GdRISEnDjxg3D/YiICPzwww84ePAgOnXqhLfffhv/+c9/am8KGD1zNYCujhZWthJrAImIiKgMDWIUsChdA2bG+vXrTZb1798fJ06cqIESVUAZTcCVxhpAIiIiKkODqAGs18w0AXu5GNcA6nTlB1wjDIBERERUBgZAWzNTA+jhZFwxm1zeaGB9DaiLi3zNJmAiIiIqAwOgrZkJgKXnIUzKtDLQFU0kzRpAIiIiKgsDoK2ZCYClJWXkW3zMiJOTfM0aQCIiIioDA6CtmekDWNqdDNYAEhERUfVhALQ1a2oAM8upAdT3AWQAJCIiIiswANqahQAY7utiuF3hGkA2ARMREVEZGABtTd8EnJcH5OQYFm+c1gPuRaOB71jbB7BkALRibkQiIiKyTwyAtubmBjgUTftSohawsbcLlj7eCUAFRgHrB4EAQL6VoZGIiIjsDgOgrUmSxWbgQA81ACtGAZfuAwiwHyARERFZxABYF1gYCRzoIdfo3c3Kh9aas4GoVICi6JAyABIREZEFDIB1gYUaQF9XR0gSoNUJJGdb0aQrSRwIQkREROViAKwLLARAB6UCfm5WNgPrcSoYIiIiKgcDYF1QxlyA+n6AZU4FU3LEL88GQkREROVgAKwLyjgbSJCHXKN3O92KQFeyCZg1gERERGQBA2BdUEYNYIiXXKOXkGZloNPXADIAEhERkQUMgHVBGQEw2FOu0UuwpgYQ4CAQIiIiKhcDYF1QRhOwvgbwdlk1gOb6ALIGkIiIiCxgAKwLqqsGkNPAEBERkRUYAOuCMgOgXKOXmJ4HnTWTQXMQCBEREZWDAbAu0AfA5GTj5lwAQZ5OkCSgQKtDcnZB+ftiEzARERGVgwGwLtD3AczPNwluKqUC/kWTQSekWwh1JUMjm4CJiIioHAyAdYGbG+DgIN821wzsVTQXYFo5oU6SWANIRERE5WIArAskqey5AIv6AVqsASyJNYBERERUDgbAuqKMqWAqNBKYg0CIiIioHAyAdYUVZwOxOBcg5wEkIiKiCmAArCv0NYD37pk8ZHUNIOcBJCIiIiswANYVfn7ytbkAWJHzAbMGkIiIiMrBAFhX+PvL13fvmjwUUlQDeCczHxqtruz9sA8gERERlYMBsK7QB0AzNYD+7mqolBK0OoE7mfmm25bsA+jqKl/n5NRAIYmIiKghYACsK/RNwGZqAJUKCSFFcwHeSikj2EmSPKcgAGRmVncJiYiIqIFgAKwrymgCBoDG3kUBMLWcpl13d/k6K6u6SkZEREQNDANgXVFGEzAANPZyAWBFAGQNIBEREZWDAbCuKNkEXLJPX5HiGkAzTcAl12cNIBEREZWDAbCu0NcA5uWZHcDR2MeKJuCSfQCzsswGSSIiIiIGwLrC1bV4Dj8z/QAbexc1AaeVM7pXXwOo03EqGCIiIjKLAbCukKQyRwLrm4AT0vLKngvQxaX4NvsBEhERkRkMgHVJGQNBAtydoFJK0JibC7BkU69CYdwMTERERFQKA2BdUsZUMFbPBQhwJDARERGViQGwLimjCRiwYi5ASZKvORKYiIiIysAAWJdwLkAiIiKqBQyAdYnVZwOxciQwawCJiIjIDAbAuqS8JmBLcwGWnu+PNYBERERUBgbAGlKpKZgDAuTrpCSzD+vnArxpqQaQfQCJiIjICgyANUyCZP3KgYHy9Z07Zh8OLQqACel5KCxrLkDWABIREVEZGADrEn0AtFADGOCuhtpBAa1O4HZaGQNBWANIREREZWAArEv0TcDZ2fKlFIVCQqiPXAt4o+RcgOwDSERERBXAAFiXuLkBzvJAD0u1gGFFAfB6spl+gOwDSERERFZgAKxLJKm4FtBCP8AmvmZqAEtjDSARERGVgQGwrilnIEgTfROwuRpAPdYAEhERURkYAOuacgaChBXVAF5nH0AiIiKqJAbAuqa8JmAfVwDAzZQciNLBj30AiYiIyAoMgHVNOU3Ajb2dIUlAVr4GKdkF5vfBGkAiIiIqAwNgXVPO2UCcVEoEeTgBKGMgCGsAiYiIqAwMgHVNOTWAAEznAmQfQCIiIqoABsC6ppxBIEAZcwGW7gOYnw8UFlZ3CYmIiKieYwCsa8oZBAIUjwS22ASsrwEE2AxMREREJhgA6xp9DWBKisXau9Dy5gJ0dARUKvk2AyARERGVwgBY1/j4AEqlfNviXIDyVDDXU4rOF1y6DyBQ3AzMfoBERERUCgNgXaNQFDcDJyaaXSW8qAn4TkY+cgo0xQ/o+wACxc3ArAEkIiKiUhgA66LgYPk6IcHsw14ujvB2kZt44+6VMxUMawCJiIioFAbAuqicAAgA4X5yM/C1e9nmV2ANIBEREVnAAFgX6QOghSZgAIgo6gcYl5zNPoBERERUIQ0mAB46dAijRo1CSEgIJEnCjh07ylz/4MGDkCTJ5HLhwoXaKXBZrKgBjDBXA8g+gERERGQFB1sXoLpkZ2ejY8eOmDx5MsaMGWP1dhcvXoSHh4fhvr+/f00Ur2KqowmYNYBERERkQYMJgMOGDcOwYcMqvF1AQAC8vLyqv0BVUYEawLh72eaPImsAiYiIyIIG0wRcWZ07d0ZwcDCioqJw4MCBMtfNz89HRkaG0aVGVKAGMDm7AHklp4LRYw0gERERWWC3ATA4OBiffvoptm7dim3btiEyMhJRUVE4dOiQxW0WL14MT09PwyU0NLSmCidfJyaaH+ABwE3tAH93NQAgNadAXsg+gERERGSFBtMEXFGRkZGIjIw03O/Vqxdu3ryJDz/8EP369TO7zdy5czF79mzD/YyMjJoJgUFB8nVBgXxKOF9fs6tF+LribmY+UnMKEVz6QdYAEhERkQV2WwNoTs+ePXH58mWLj6vVanh4eBhdaoSjY3HoK7MZWD4jSGp2gemDrAEkIiIiCxgASzh58iSCg03q0mxDXwtoRT/A1Ox80wdZA0hEREQWNJgm4KysLFy5csVw/9q1a4iNjYWPjw+aNGmCuXPnIj4+Hp999hkAYNmyZQgPD0fbtm1RUFCAjRs3YuvWrdi6dautXoKx4GDg7NkyA2DTogCYllMoL2AfQCIiIrJCgwmAx48fx8CBAw339X31Jk6ciPXr1yMhIQE3btwwPF5QUIA5c+YgPj4ezs7OaNu2LXbt2oXhw4fXetnNqsBIYLNNwKwBJCIiIgsaTAAcMGAAhIURswCwfv16o/uvvPIKXnnllRouVRVYEwB9XSFJQJ5GZ/ogawCJiIjIAvYBrKtCQuTrMgKgk0qJRl7OkMBzARMREZH1GADrqkaN5Ov4+DJXax7gVnzHUh/AMmpGiYiIyP4wANZV+gB461aZqzXzdzP/gL4GUAggJ6caC0ZERET1HQNgXdW4sXwdHw/ozPTxK2IxALq4FN9mP0AiIiIqgQGwrgoKAhQKQKMB7t61uFozf1dI5pp4FYriZmD2AyQiIqISGADrKpUKCAyUb5fRDFyyD6DJYGCOBCYiIiIzGADrMn0zcBkB0MfVEU4qJQAgI6/Q+EGOBCYiIiIzGADrspL9AC2QJAk+ro4AzARA1gASERGRGQyAdZmVI4F9XVQAgIzcUgHQ01O+Tkur5oIRERFRfcYAWJdZ0QQMwFADmJ6nKfWAj3ydmlrdJSMiIqJ6jAGwLrNyMmhDACxdA6gPgMnJ1V0yIiIiqscYAOsyK2sAvYsCYGaeBlpdiSlh9AEwJaUmSkdERET1FANgXVYyAJZxOjcPtQMAQKsTuJ2WW/yAr698zQBIREREJTAA1mX6JuCcHCA93eJqSkXxOYCvJJUY8csmYCIiIjKDAbAuc3YuDnHlNAMDgJAkXLpTYs4/NgETERGRGQyAdZ2+GfjmTatWv1gyALIJmIiIiMxgAKzrQkPl6xs3rFqdNYBERERUHgbAui4sTL6+ft3yOiUGiFxJyioeCVwyAJYxiISIiIjsCwNgXWdNACyiUEjIK9ThZkqOvEAfALVaICOjhgpIRERE9Q0DYF2nD4BWNAF7OsunhDM0Azs7yxeAzcBERERkwABY11WgBtDLRZ4Q2mw/QE4FQ0REREUYAOs6fQCMjwcKC82vU9S/z6uoBvDiHTNzAbIGkIiIiIowANZ1gYGAoyOg05V7TmCvolPCXeZUMERERFQGBsC6TqEAmjSRb5fTDOzpLAfAv+5moVCrkxeyCZiIiIhKYQCsD6zsB+imVsLVUYlCrcD15Gx5IZuAiYiIqBQGwPqgvABY1AdQkiS0CHQHAFxMLOoHyCZgIiIiKoUBsD6wdiSwJKFloBuAEqeEYw0gERERlcIAWB9UYCqYlkU1gJcSSwVA9gEkIiKiIgyA9UEFAmCrIA8AwIXEojN/sAaQiIiISmEArA/0o4Bv3JCngymtxHl+WwfLNYDXU3KQla9hH0AiIiIywQBYQ0pksqoLDZWng8nPB+7csbyeJMHXTY1ADzWEAC4mZrAJmIiIiEwwANYwSaqGnahUxbWAf/1V7uptguVm4HMJmcZNwOZqD4mIiMjuMADWF82ayddWBMDW+gB4u0QNoE4HZGaWsRURERHZCwbA+kIfAK9eNX2sVHuzPgCeT8gAnJ3lC8B+gERERASAAbD+aNpUvi6rBrCovblNSPFIYK1OsB8gERERGWEArC8q0AQc7usKJ5UCeYU6xCVncyoYIiIiMsIAWF+U1QRcilIhGeYDPJ+QwalgiIiIyAgDYH2hbwJOSjIdzGFmzhmzA0HYBExERERgAKw/PD2La/Is1QKWmHNG3w/wfEIGm4CJiIjICANgfVKBZuA2RWcEOccmYCIiIiqFAbA+sWYkcJHIIA9IEnAnIx85bnJtIJuAiYiICGAArF8sjQQ20wfQTe2AMB8XAEC8xHkAiYiIqBgDYH1SXhNwqfPOtW3kCQD4SzjJCxgAiYiICAyA9Yu+CfjKFatW71AUAM/nOcgLGACJiIgIDID1S4sW8nVcHJCfX+7q7RvLATA2u6hmkH0AiYiICAyA9UtwMODmBuh0xv0AzfQBBIB2RTWAFwpU8oKUFHlbIiIismsMgPWJJAGRkfLtixfNP16Ch5MKEX6uSHOSp4SBTgdkZNRwIYmIiKiuYwCsb1q1kq/NBUAz2jfyRL5KjQJnV3lBUlINFYyIiIjqCwbA+qasGkAzOhT1A0zxLJoM+vbtmigVERER1SMMgPWNuQBooQ8gINcAAkC8s7e8gAGQiIjI7jEA1jf6AHjhgmnwK9UHEJDnApQk4Kazl7yAAZCIiMjuMQDWN/qpYFJTgXv3yl3dTe2AZv5uuOPGJmAiIiKSMQDWNy4uQJMm8m1r+wE28kSSm498hwGQiIjI7jEA1kel+wGW0QcQkCeEvsMASEREREUYAOsjS1PBmOkDCMgjgfUBUDAAEhER2T0GwPqoglPBtAn2xD0PPwBFAbCcGkMiIiJq2BgA66OSI4Gt4OyohHczud+gIjcXSE+vqZIRERFRPcAAWB+1bi1f//UXkJdnVY1e2+bBSFcXnQ2EzcBERER2jQGwPgoJAby8AK3WuBnYQh9AAOga5o1Ed04FQ0RERAyA9ZMkAW3byrfPnrVqky5NvA1zARbcuFVTJSMiIqJ6gAGwvmrXTr62MgCG+jgj3VseCJJ08WpNlYqIiIjqAQbA+kpfA/jnn1b1AZQkCcpGjQAAaVeu12TJiIiIqI5jAKyvzDUBl9EHEADcm8ojgQtusgmYiIjInjWYAHjo0CGMGjUKISEhkCQJO3bsKHeb6OhodO3aFU5OTmjatClWrVpV8wWtLvom4KtXgZwcqzYJbt0UAOCQmAjBuQCJiIjsVoMJgNnZ2ejYsSM+/vhjq9a/du0ahg8fjr59++LkyZOYN28eXnzxRWzdurWGS1pNAgIAPz+5+ff8eas2CW3XHADgk34Pt1Jza7J0REREVIc52LoA1WXYsGEYNmyY1euvWrUKTZo0wbJlywAArVu3xvHjx/Hhhx9izJgxNVTKatauHXDwoNUDQdRNQgEAAVkp+PF6CkJ9XGqwcERERFRXNZgawIqKiYnBkCFDjJY9+OCDOH78OAoLC81uk5+fj4yMDKOLTen7ASYlWbd+UBAAwFGnwYU/r9VQoYiIiKius9sAmJiYiMDAQKNlgYGB0Gg0uHfvntltFi9eDE9PT8MlNDS0Nopqmb4foF45g0Dg6Ih8b3kuwFtn/6qhQhEREVFdZ7cBEJCnRilJPzCi9HK9uXPnIj093XC5efNmjZexTPoawApQNAoBAGRdu4GcAk11l4iIiIjqAbsNgEFBQUhMTDRalpSUBAcHB/j6+prdRq1Ww8PDw+hiU5UIgKrQxgAAv8xk/HE9tbpLRERERPWA3QbAXr16Ye/evUbL9uzZg/vuuw8qlcpGpaogHx8gOLhi24TINYCBWSk4ejWlBgpFREREdV2DCYBZWVmIjY1FbGwsAHmal9jYWNy4cQOA3Hw7YcIEw/rTp0/H9evXMXv2bJw/fx5r167FmjVrMGfOHFsUv/JK1gKW1wcQMAqAR64m11ChiIiIqC5rMAHw+PHj6Ny5Mzp37gwAmD17Njp37ow333wTAJCQkGAIgwAQERGBH374AQcPHkSnTp3w9ttv4z//+U/9mQJGr/RAkPKUCICnbqWxHyAREZEdajDzAA4YMKDMs1usX7/eZFn//v1x4sSJGixVLahoP8CiANg4NxWFWoET19Nwfwu/GigYERER1VUNpgbQblWyBjAkRx4AcvQam4GJiIjsDQNgfdemTfHtCvQB9EhPhkKnZT9AIiIiO8QAWN95eABNmli/fmAg4OAAhVaLwKwUxN5MQ26BtubKR0RERHUOA2BDUJF+gEolEBEBAOhcmCz3A7zB+QCJiIjsCQNgQ9Crl3wdEGDd+s2bAwD6KdIBAEfZDExERGRXGswoYLs2Zw7QsyfQv7916xcFwI558jmPj3BCaCIiIrvCGsCGwNkZGDwYcHS0bv2iANgkJR4A2A+QiIjIzjAA2qOiAOhyIw7Bnk4o0Op4XmAiIiI7wgBoj4oCoHTlCvo08wUAHLp815YlIiIiolrEAGiPwsMBhQLIycFgH/nsKYcuMQASERHZCwZAe+ToKIdAAL11qZAk4EJiJu5k5Nm2XERERFQrGADtVVEzsHv8dXRo7AUAiGYtIBERkV1gALRXRQEQly+jf0t/AGwGJiIishcMgPZKHwCvXEH/ln4AgMOX70GrEzYsFBEREdUGBkB7VSIAdmzsBXcnB6TnFuL0rTSbFouIiIhqHgOgvSoRAB0UEvq2kGsBD126Z8NCERERUW1gALRXERGAJAGZmcDdu+jXQu4HGH0pycYFIyIioprGAGivnJyA0FD59pUr6Fc0ECT2ZhrScwptWDAiIiKqaQyA9qzESOAQL2e0CHCDTgC/XGEzMBERUUPGAFjDJFsXoCwtWsjXV64AgKEW8OBFNgMTERE1ZAyA9qzEQBAAiGoVAADYfyGJ08EQERE1YAyA9qxUAOwW4QNPZxWSswtwPC7FhgUjIiKimsQAaM9K9AGEEFApFYhqLdcC7jl3x4YFIyIioprEAGjPmjaVr9PTgRS5xm9ImyAAwE9nEyEEm4GJiIgaIgZAe+biAjRqJN++dAkA0L+lP5xUCtxKzcX5hEwbFo6IiIhqCgOgvWvbVr4+dQoA4OyoNEwK/dPZRFuVioiIiGoQA6C969xZvj550rBoSFu5GZj9AImIiBomBkB7ZyYARrUKgFIh4XxCBm6m5NioYERERFRTGADtnT4AnjkDaDQAAG9XR3QP9wHAZmAiIqKGiAHQ3jVvDri5AXl5wIULhsUPtg0EAOw5y2ZgIiKihoYB0N4pFEDHjvLtEs3Ag4v6AR67noJ7Wfm2KBkRERHVEAZAMtsPsJGXMzo09oQQwI9/shmYiIioIWEAJLMBEAAe6hgCANhxMr62S0REREQ1iAGQigNgbCxQ4uwfD3UMgUIC/rieihvJHA1MRETUUDAAkjwZtEoFpKUBcXGGxQEeTujT3A8A8G0sawGJiIgaCgZAAhwdi88IUqoZ+OFO8qnitsfG89zAREREDQQDIMks9AN8sG0gnFQKXL2bjTPx6TYoGBEREVU3BkCSdekiX5cKgO5OKgxuI08Js+Pk7douFREREdUABkCSWagBBIDRneTRwDtP3YZGq6vNUhEREVENYAAkWceOgCQBt28DSUlGD/Vr6Q9vFxXuZeXj17+SbVRAIiIiqi4MgCRzcwNatJBvl6oFVCkVGFU0J+C3nBOQiIio3mMApGJlNAPrRwPvPpuInAJNbZaKiIiIqhkDIBXTDwT5/XfTh5p4IdzXBTkFWuyM5WAQIiKi+owBkIr17StfR0cDOuPBHpIk4ekeTQAAG49e55yARERE9RgDIBW77z7A1RVISQHOnDF5eGzXUDg6KPBnfAZO3eKcgERERPUVAyAVU6mKawEPHDB52MfVESPaBwMANh65XpslIyIiomrEAEjGBg6Ur80EQAAY31NuBv7u1G2k5RTUVqmIiIioGjEAkjF9AIyOBrRak4e7NPFGqyB35Gt0+OaPW7VcOCIiIqoODIBkrHNnwMMDSE83Ox2MJEkY3zMMAPDl0RscDEJERFQPMQCSMQcHoH9/+baFZuDRnRvB1VGJq/ey8RvPDEJERFTvMACSqXL6AbqpHfBIF3liaA4GISIiqn8YAMmUPgAePgwUFppdZVwPuRl4z7k7uJORV1slIyIiomrAAEimOnQAfHyArCzgjz/MrtI62APdwr2h1Qms/fVaLReQiIiIqoIBkEwpFOX2AwSA5/o1AwB8ceQG0nPN1xQSERFR3cMASOY98IB8vX+/xVWiWgWgZaAbsvI1+DwmrnbKRURERFXGAEjm6fsB/vorkJ9vdhWFQsLzA+RawLW/xiG3wHTeQCIiIqp7GADJvDZtgIAAIDcXiImxuNqoDiFo7O2MlOwCbDl2oxYLSERERJXFAEjmSRIwbJh8e+tWi6s5KBX4W7+mAID/Hb6GQq2uNkpHREREVcAASJY99ph8vXUroLMc7B67LxR+bo6IT8vFztjbtVQ4IiIiqiwGQLJs8GDA0xNISJD7AlrgpFJiyv0RAICV0X9Bp+Pp4YiIiOoyBkCyzNERePhh+fbXX5e56vieYXB3csCVpCzsPX+nFgpHRERElcUASGV7/HH5+ptvymwG9nBSYUIv+ewgH+27zFpAIiKiOowBkMpmZTMwAEy9vyncnRxwLiEDO2Lja6mAREREVFENKgCuWLECERERcHJyQteuXXH48GGL6x48eBCSJJlcLly4UIslrgcq0Azs4+qIGQOaAwA+/Oki8go5LyAREVFd1GAC4JYtW/DSSy/h9ddfx8mTJ9G3b18MGzYMN26UPTfdxYsXkZCQYLi0aNGilkpcj1g5GhgAJvcJR4inE26n52H9b3E1XzYiIiKqsAYTAJcuXYqpU6di2rRpaN26NZYtW4bQ0FCsXLmyzO0CAgIQFBRkuCiVyloqcT0yeDDg4QHcvg389luZqzqplHh5SCQA4JMDV5CaXVAbJSQiIqIKaBABsKCgAH/88QeGDBlitHzIkCH4rZzA0rlzZwQHByMqKgoHDhwoc938/HxkZGQYXeyCWm11MzAAjO7cCK2DPZCZp8Hy/VdquHBERERUUQ0iAN67dw9arRaBgYFGywMDA5GYmGh2m+DgYHz66afYunUrtm3bhsjISERFReHQoUMWn2fx4sXw9PQ0XEJDQ6v1ddRp+mbgckYDA4BSIWHe8FYAgM+PxOF6cnZNl46IiIgqoEEEQD1JkozuCyFMlulFRkbi2WefRZcuXdCrVy+sWLECI0aMwIcffmhx/3PnzkV6errhcvPmzWotf502ZIg8Gvj2baCcmlIA6NvCH31b+KFQK/D+TxdroYBERERkrQYRAP38/KBUKk1q+5KSkkxqBcvSs2dPXL582eLjarUaHh4eRhe7oVYDTz0l3x40CFi+vNxN5g5rDUkCdp1OwIkbqTVcQCIiIrJWgwiAjo6O6Nq1K/bu3Wu0fO/evejdu7fV+zl58iSCg4Oru3gNx7RpxbdffBHIySlz9TYhHhjTpTEAYN62MyjUlt10TERERLWjQQRAAJg9ezZWr16NtWvX4vz58/jHP/6BGzduYPr06QDk5tsJEyYY1l+2bBl27NiBy5cv4+zZs5g7dy62bt2KWbNm2eol1H1dusgXvc8/L3eT14a1gpeLChcSM/Hpoas1WDgiIiKyVoMJgE888QSWLVuGt956C506dcKhQ4fwww8/ICxMPj1ZQkKC0ZyABQUFmDNnDjp06IC+ffvil19+wa5du/Doo4/a6iXUfZIE7N5d3BT8738D2rIne/ZzU+PNkW0AAB/9fBlX72bVdCmJiIioHJIQgidtraSMjAx4enoiPT3dpD9g+Gu7AAB/vDEIvm5qWxSv5mRlAWFhQEoK8NVXxSOELRBCYMLa33H48j10j/DB5md7QqEwPziHiIioppX199teNJgaQKpFbm7ACy/It997DyjnfwhJkvDuI+3hrFLi92sp2HzMjkZPExER1UEMgFQ5s2YBLi7AiRPArl3lrh7q44I5D8pnCFn8w3ncycir6RISERGRBQyAVDl+fnIIBIBXXgE0mnI3mdQ7HB1DvZCZr8G/dvwJ9j4gIiKyDQZAqry5cwFfX+D8eWD16nJXVyokLBnTHg4KCXvO3cH3pxNqoZBERLUoPb3cbjFEdQEDIFWelxcwf758+803ASvOjdwqyAMzBjQDIM8NyNPEEVGD8eqr8u+ijw/Qpw/w3HPARx8B+/YBCQkVC4ZJScCnnwLffgtcv85QSdXOwdYFaIjsqmlz+nTg44+BS5fkASHvvlvuJi9EtcCvfyXjj+upmPnlCXwzvTecVMpaKCwRUQ36+Wf5Oi0N+O03+VKStzfQti3Qrh3Qvn3xxcvLeL1Ll+TTb16/XrzMywvo2BHo1Kn40rq1fJYmokrgNDBVYGkYuRACEXN/AACc+Ndg+Lg62qqItePbb4HRowEnJ+DiRaBJk3I3SUjPxfCPDiM1pxDP9AzD26Pb1Xw5iah2FRQAycny5d694tul7+tvp6YCKpU800B1XFxdAWUt/nMZFgbcuAFs2CC/jrNniy9//QXoLJwNKTQU6NBBDoPh4cC//gXcvSvvz9tb3r6w0HQ7lQpo0wbo3Ln40rEjYKfTmlQEp4FhAKwSBsAiQgADBwLR0cC4ccDGjVZtdvBiEiatOwYAWP5UZ4zqGFKTpSSi6pCZKY/+T0iwHOT0l8xMW5dW/sdUHwi9veV/UPWXsLDi24GBgKKKvaJcXIDcXDnsNW1q/FheHnDhAnDuHHDmTPGlxAkKjHTpIk+87+8vB+nz54HYWPly6pR8nWrhHOvNmhmHwk6dAJ7m1AgDIANglTAAlvDHH8B998m3f/8d6NbNqs0++OkCPjnwF9zUDvjuhfsR4edag4Ukogq7cUNuyvz1V/ly6pTlmixzFAq5T5yvr3zx8zN/29dXXk+jkSebr+qlnLMUmXB0lGviLAXE0FA54FmSnS2HTEDuD+3ubt3zpqUBf/4JnD5dHApbtQKWLi27Jk8I+dicPClfYmPl65sW5lkNDDQOhZ07yyG1qqG3nmIAZACsEgbAUiZMkM8P3K2b/IdCpSp3E41Wh3Grj+LotRS0DvbA9hnsD0hkMxqNHET0Ye/XX4Fbt0zXa9JEDg/WhDovr9oPGUIA+fmmofDePTkgXb8uhyf9JT7eulDr7y+HwubNgRYtgJYti6/T0uT3RK2WawElG53t6N694jCov7540fzrc3eXm4xLhsI2beQw3MAxADIAVgkDYCnx8XLn5rQ0uQ/LW29ZtdmdjDwM/+gwkrML8MR9oXhvTHtItvrxJLInGRnAkSPFYe/oUTkolaRUysGgd295ZGufPkCjRrYpb00pLARu3y4OhKUD4vXrpu9Lae7ucpN348aWa+FsJTtbrlnU1xaePCnfz883XVelkgeqlO5XaG2NZj3BAMgAWCUMgGZ89RXwxBPyf/yHD8t/NKzwy+V7eGbtUQgBvDI0EjMGNK/hghLZGX2TYcnavTNnTGuGPD2BXr2Kw1737vJgCnsmhPyP7Y0bwLVrwOXL8uXSJfn69u3idQcPBvbssVlRrabRyH0SS4bC2Fj5dZrTvLlxINT3K6yn/6wzADIAVgkDoAUTJwKffQZERMg/KFZ+udb/eg0LvjsHAPj3Yx0xpmvjGiwkkR3IygK+/loeTPDrr3ItfWkREcVhr08fuQmwNkfONgRZWcCVK3JA7NkTCAiwdYkqRwi5trN0KDTXDQCQm/s7dSoOhB07yv0Xrej+Y2sMgAyAVcIAaEFGhvxDEBcHTJoErFtn9aaLfzyP/0ZfhYNCwppJ3dC/pX+NFZOoQRICOHZMPjvPpk3GTZcODnINjj7s9e4NhHD0PZXj7l3jfoWnTsm1h+b6FTo6yk3IpYNh6bkObYwBkAGwShgAy/DLL0D//vIPxNdfA2PHWrWZTicw+6tY7Ii9DRdHJbY81wvtG3vWcGGJGoCUFHkKptWr5aZdvebNgfHj5e9j9+5lj2QlslZurjw/YclpaU6dsjz1T5MmpqEwIsJmo5AZABkAq4QBsBxvvAG8844899bp03LnaCsUaHSYsv4YfrlyD35ujtj2fB808eUfLSITOp08/+bq1cDWrcWd+p2c5H+6pk0D+vWrt/20qJ7R6eSWn1OnjENhXJz59d3d5QmwSwbDdu0AZ+caLyoDIANglTAAlqOwUG5iOn5c/nIfOmR1f8DMvEI88d8jOJeQgXBfF2x9vjd83XjKIyIA8qCDDRuANWvkSYf1OnYEnn0WePpp+R8vorogLU2uBChZW3j2rPlRyAoFEBlpXFPYqRMQFFStRWIAZACsEgZAK1y7Jo8ovHNHHh23a5fVHYSTMvLwyIrfEJ+Wi9bBHvh8anf4MQSSvdJogB9/lGv7du0qnujY3V0OfNOmAV27sraP6ofCQnl+wpI1hbGxcn9DcwIC5FPl6c+j3K6dPGCpktPTMAAyAFYJA6CVjh8HBgyQ56KaOFEeFGLlH6krSVl48tMjuJeVj6b+rtg4tQdCvGq+eYCozvjrL2DtWvl7k5BQvLxPHzn0PfYYp2mhhkEIIDHRtF/hpUuWJ+oOD5fDYMlgGBkpT8hdBgZABsAqYQCsgB9+AB56SK61ePNNYOFCqze9di8b41cfRXxaLhp5OeOLaT0QzlPGUUOWlwds3y7X9u3fX7zcz0/+J2rqVKB1a9uVj6g25eTIp8srfSn5D1FJSqV8dpbSwbBpU8MURwyADIBVwgBYQf/7H/Dcc8W3p02zetP4tFyMX30U1+5lw99djY1TeyAyqGHNTE+EnBxgxQpgyRL5lF6AXFs+ZIj8fXnoIbs4TReRVZKT5b6EZ84YB0NLk1k7OcnNxu3aIaN5c3i++SYDoK0LUV8xAFbCv/4FLFok/xf21VfAo49avendzHw8s+YoLiRmwstFhQ2Tu6NjqFfNlZWotuTlAZ9+CixeLDeBAUBoKDBlCjB5snz+WSIqnxDyIKk//zQOhufOyVPXFMkA4AkwANq6EPUVA2AlCCH/QduwQR7t9emncnOWldJzCjFx3e+IvZkGV0clVk/shl7NfGuwwEQ1qKBA7tu3aFHx2RbCw+VuEs88I0/cTERVp9XKgxKLAmHGiRPw3L7drgOgbWZgJPslSXK/pilT5E6906YB770nB0MreLqosHFaD/Rq6ovsAi0mrD2KjUeug//HUL2i0cjBLzISmD5dDn+NGgGrVskjIydPZvgjqk5KpTwp+ujR8hy169fbukQ2xwBItc/BQQ6Br70m3587F3j5ZcujvEpxUztg3eRuGN4+CIVagTd2/IlXt55GXqG2BgtNVA20WuDLL+V+SFOmyBPkBgYCH30kn0v2b39jHz8iqhUMgGQbkiT3d1q6VL7/f/8nj24sLLRqcyeVEp883QWvDm0FhQR8dfwWnvhvDG6n5Za/MVFt0+nkM3V06ACMGwdcviyP6P3gA+DqVeDFF+UO6kREtYQBkGzrH/8APv9crhXcuBEYMcLyRKClSJKE5wc0w4Yp3eHlosKpW+kYtfwXxPyVXMOFJrKSEMB338kTNI8dK3dE9/KST5F49SowZw7PzUtENsEASLY3fjzw7bfy+R/37pVP+3PwoNWb923hj+9m3Y82wR5Izi7A+DVHsfrwVfYLJNsRAtizB+jZU566JTZWPmPBm2/KHdHnzav0GQyIiKoDAyDVDcOHA0ePAq1ayUP4o6LkyaK11vXrC/WRzxf8SOdG0OoEFu06j2kbjuNORl4NF5yolIMHgX79gAcfBH7/Xa7he+01OfgtXCjXABIR2RgDINUd7dvLp42bNEnuM7VggXz+4Nu3rdrc2VGJpY93xIJRbeCoVODnC0kYvDQa3/xxi7WBVPOOHwcGDQIGDgR++UXu0/ePf8hNvYsXA76croiI6g4GQKpbXF3l6TE++0y+feCA3CT87bdWbS5JEib1icD3L96Pjo09kZGnwZyvT2Hy+mNISOcAEaoBf/0FPPkk0K0b8PPPgEoFzJwpL1+6VB7lS0RUxzAAUt30zDPAH38AHTvKg0JGjwZGjZJrU6zQMtAdW5/vjVeHtoKjUoGDF+9iyNJD+OrYTdYGUvVISgJeeEHutrBlizyyfcIEeYTvxx8DISG2LiERkUUMgFR3RUYCR44Ar74qjxL+/nugbVvgrbfkU2eVw0GpwPMDmmHXi/ejU6gXMvM1eGXraUxcdww3U3Jq4QVQg5SVJX8GmzWTg55GAwwbJg/02LCBp20jonqBAZDqNicn+Uwhp08DDzwgB7/584F27YAff7RqFy2KagPnDW8FRwcFDl26i6h/R+OdXeeQnmPdvINEKCwEVq6UzyYwf74cBO+7D9i/H/jhB3mOPyKieoIBkOqH1q2BffuAzZvlprW//pJHDo8cKXe+L4dSIeG5fs3ww4t9cX9zPxRodfjf4Wvo98EBrD58FfkankWELBAC+OYbufZ5xgzgzh259m/LFnmU78CBti4hEVGFMQBS/SFJwBNPABcuyKeOUyqBXbvkzvcjRsh/jMvRPMANn0/tjvWTuyEy0B3puYVYtOs8Bi2NxnenbrN/IBk7dAjo1Qt47DG5b5+/v9zse+4c8Pjj8meSiKgeYgCk+sfdHfjwQ/mP8DPPAAqF3ATXowcwdCgQE1Pm5pIkYUBkAH74e18sGdMeAe5q3EzJxQubTmL0it9w9CrPJGL3/vxTHnTUv788P6WrqzyJ819/ySN8eb5eIqrnGACp/mrZUp4u5uJFee5ApRL46Segd29gyBBg9255PkELlAoJT3RrgoP/HIDZg1vCxVGJUzfT8MSnRzBm5W/46WwidDrWCNqVmzeBKVPk0efffy9/pp5/HrhyRZ7EmWfvIKIGggGQ6r/mzeW5Ay9dAqZOlUcM790rj8xs3lweRHLnjsXNXRwd8GJUCxz85wCM69EEjkoF/rieir99/gcG/V80Nv1+A3mF7CPYoP31FzB7tvxPxbp18j8OY8bItcwrVgBBQbYuIRFRtZIEOz1VWkZGBjw9PZGeng4PDw/DciEEIub+AAA48a/B8HFlc1GtiosDli2Tp+RIS5OXqVTAI48A06cDAwaU2XcrKSMP636Lw8Yj15GZpwEA+LmpMblPOMb3CIOni6qmXwHVBp1OHkn+ySdybbH+p7BfP2DJEvk8vkTUIFn6+21PGACrgAGwjsvNBb76Cli1Sp5PUC8yUu47+Nhjco2PBVn5Gmz+/QbW/nINt9PleQddHJV4/L5QPN2jCVoGsjmwXkpOBtaulad0uXatePnQocDf/y6fw5eDO4gaNAZABsAqYQCsR2Jjgf/+F9i4UZ6/Ta9TJ3k052OPyc3FZhRqdfj+9G38N/oqLiRmFm8a6oUnuoViZIdguDuxVrDOO35cru3bvLl4InEvL7nP3/PPWzz+RNTwMAAyAFYJA2A9lJkp1wp+/bU8r6C2RN++Ll3kMDhmjNkwIITA4cv3sPHIdey/kARN0QARZ5USw9sH44luoegW7g2JtUd1R16efKw/+UQezavXubM8mveppwAXF9uVj4hsggGQAbBKGADruXv3gB075EC4f79xGGzeXG4KfPBBeaJfNzejTe9m5mPbiVvYcvwmrt7NNiyP8HPFY/c1xpgujRHo4VRLL4SMFBbKx3PLFmD7duN+oI8/Lge/nj3ZzEtkxxgAGQCrhAGwAbl7Vw4LX30FREfL53fVU6mAPn2KA2HHjvLcg5CP9Ykbqdhy7Ca+P52AnAI5REoS0CPCB6M6hmBYu2B+BmrLpUvy3H2JicXLQkOBv/0NmDYNCAy0XdmIqM5gAGQArBIGwAYqIwM4cECeU/Cnn4CrV40f9/eXA6H+0qULoFYjO1+DXacT8NXxmzh+PdWwulIh4f7mfhjZIRhD2gbB05n9BWvMggXyfH2+vvJZY554Qj5GSqWtS0ZEdQgDIANglTAA2okrV4rD4P79QHa28eNqtXw6On0g7N0btxTO2HU6Ad+dvo0/4zMMqzoqFegf6Y9RHUMwqHUAXBwdavnFNHD33w/8+ivwv//JNX5ERGYwADIAVgkDoB0qKJBHk/76a/Hl3j3T9Zo3lwcadOmChIhW+F4ZhC038nElqXgEsqODAj0ifNCvhT/6tfRHy0A3DiCpiowMwMdH7st57RoQHm7rEhFRHcUAyABYJQyABCGAy5eNA+GFC+ZXDQlBdtsOOOPfFLscAhHt3Ai3PAMgJLk/YZCHE/q19EO/lv64v7kfvFz4uamQ77+Xz9/brJlca0tEZAEDIMD2J6KqkCR5MumWLYHJk+VlycnAyZPAiRPy9cmTwKVLkG7fhtvt2+gFoFfR5hq1E+IDmuC0RzAueTfGld9D8ZFvKF72DUHrMH/0b+mPPs390KGxJ5xU7MdmVnq6PJp7/Xr5/uDBtiwNEVG9wABIVN18fYFBg+SLXlYWcOpUcSA8cQI4fx4O+XkIu3kJYbhktAuNpMB172Bc8Q3FCZ9G+NY7GIrmzeDXoTWadWmNLs38EeLlXMsvrI569ll5rj+9oUNtVxYionqCAZCoNri5FQ8S0dP3VTt/Hjh3Tr4uujhkZqJZSjyapcSb7KpQocQtzwAc8Q1BQVgE1JEt4N+xNULvaw9V03DA09O+5rj75Rf5evx4YMgQuRmYiIjKxABIZCtKpTxYpHlz49AiBBAfbwiD4tIl5F26As3lK3C+dQMqTSEiUhMQkZoAXPkD+Nl4t3lOLsgNDAGahMK5WQScIsLkufBCQ4EmTeRr5wZSe3j3LpCQIAfeFSsAd56fmYjIGgyARHWNJAGNG8uXwYMhATDENZ0OiI9H3oVLiD9xFqlnLkB75S+43opDSEoifHIz4JSXA6frV4DrV4DDB8w+hfD1hdSoERAUBAQHyxdzt0udAaXOOXVKvm7WjOGPiKgCGACJ6hOFAggNhVNoKJoNjjIsFkLgRkoOjl9NRPyfV5B24Qryrl6HOvE2gjPuIiTzHkIy7iI48x7cCnIhJSfLg1VOny77+dzcisNgQIA8Cbali5+ffNaU2hQbK1937Fi7z0tEVM8xABI1AJIkIczXFWG+zYBuzQA8CADIytfgYmImzidkYG9iBs7fzkB8XAI8k+8gMDMZAdmp8M9ORUBWCvyzUhGQnYLAnDQEZKXCuSBXHrxy5Yr106p4eRmHQh8fwNu7+OLlZf6+Wl25F66vAWQAJCKqEAZAogbMTe2ArmHe6BrmbVim0wnEp+Xir7tZiLuXjbjkHBy4l424e9m4lZoDXdHMoK75OUYB0TcnHf65GQjVZiGoIAt+uRnwyk6Da0Yq1OmpkIQA0tLky+XLFSuos3P5IdHc/ZMn5e07darye0VEZE8YAInsjEIhIdTHBaE+LkCk8WMFGh1upuYg7l42rhVd4pKzcfJeDhLScw3h0GSfOi0887Lgk5OBMF02miEHTbTZCNTkwEeTA6+8bLjnZsIlOxPqrHQ4ZGZAkZoqz+EnBJCbK19u367ci2INIBFRhTAAEpGBo4MCzfzd0MzfdPBHoVaHOxl5SEjPw+20XMSn5eJ2Wi5up+nvq/GXiyf+ArDfiufycHJAgKsKTZSFCFUUIAR5CNLmwl+TA+/8bLjnZcE1JxMu2RlwzMiAlJ4GpKYaX7RaoGdPeWQzERFZjQGQiKyiUirQ2NsFjb1dLK6TkVeIhLTigJiUmY+7+ktWPu4V3S7Q6pCRp0FGngZy70IFAJeii2/xDp3ki8IP8HRWwdvFEV4uKvi4OsLLWYUARSFcfbzgc+wmvF1U8HR2hIezAzycVHB3coC7kwpKhR3NiUhEZCUGQCKqNh5OKngEqRAZZHlKFiEEMnI1uJuVZxIQ9bfTcgqRmlOAtJxCZOVroBNAak4hUnMKzewxqcwyuToq4e6kgoezHAjdnYwDon65h5MD3NQOcFU7wNXRAS5qJVwdHeCqVsLF0YFBkogaFAZAIqpVkiTB00UFTxcVmgeUP3dfgUaHtJyCogBYYLidkl18u+TjmXkaZOYVIq9QBwDILtAiu0CLxIyqldtJpSgKhA5wcVQart3UDnApERRdHJVwVinhpL9WKeCsKl7m5KCEc9Fj8jIFHJUKSPZ09hYisjkGwBogtFp0TLgOn5xsFMb4QwzsCkmptHWxiOolRwcFAjycEODhVKHtCjQ6ZOYVFgVCORRm5BUio+h+Rm6hYXlmngYZeYXIztfIgTFfY7itLRr5kleoQ15hAZKzC6r9NUoSigNhUWh0UimhdlBA7aCEo4NCvm1YpihaVnRfVWo9/WMqBdRKeV2VUgEHpQRHpfFtB6UCKqUEVdFy1nQS2QdJCGFhXF/9s2LFCnzwwQdISEhA27ZtsWzZMvTt29fi+tHR0Zg9ezbOnj2LkJAQvPLKK5g+fbrVz5eRkQFPT0+kp6fDw8MDAJD1fTTuvf4RtLfvGtZThvjD752/w21k/8q/OCKqdUII5Gt0yCkKhTkFWmTla5BToEF2vn6ZHBRz8jXIytcit1CLvEItcguKb+cVyrfl+zrkFWiRU1gcLusShQQ4KBVF4bAoGCokqPQhUiHB0UG+1ofGkgFSpZSKQqXpcqPgWWKfhscU8nKlUoJSkuCgkKBQlLqWJDgoS9xWKKBQwOhaKRXvQ6mQLwoJrGUlA3N/v+1Ng6kB3LJlC1566SWsWLECffr0wX//+18MGzYM586dQ5MmTUzWv3btGoYPH45nn30WGzduxK+//ooZM2bA398fY8aMqVQZsr6Pxp0pbwClftO1CXfl5WsXMQQS1SOSJBXVyCnh4+pY7fsv1OqKQ2KBzhAScwu0yNdoka/RoUCjQ75GJ98v1JVYpjVaXqDVFT1uZjuNDhqtQKFWh0KtfLtAq4NGJ0xCqE7ItacFGl21v15bKxkkzYVEo0uJ5Q7mHlcooJQgXxsCqLyuJAEKSd6HQiF/jhQSoJSkottF9xWS4TGFJJfNcFtC0f2Sy4qXS/r9Fz0mFe1Pf1shGQdf+T6Mn18q8fzmnkth5nlR/HokyM8llSiDBPkx6JeVXl8Bi/vQb2t4jIG9RjWYGsAePXqgS5cuWLlypWFZ69atMXr0aCxevNhk/VdffRU7d+7E+fPnDcumT5+OU6dOISYmxqrnLPkfhLurK653ecyo5s+IBChDAhD2x1dsDiaiOkOrk4OhRidQqNGhUKdDoVZAUxQWCzQCGp2uKDwKowBpFCZLBMzi9XQoKLGvwqLn0OiKti+6XXI7jU5Apyt5rYNOABqdDlqtgFbIoVWrK7WukK8bxl800pODYfmB0WS9omtAH4iN19fmZ+PogodYA1jfFRQU4I8//sBrr71mtHzIkCH47bffzG4TExODIUOGGC178MEHsWbNGhQWFkJl5pym+fn5yM/PN9xPT08HIAfBgiOnLYc/ABCANj4JyT/HwLFnB2tfGhFRrVEWXZwUkGfmMfwM6hfUfTqdcUjUCiEHxxIhUacT0BSto9OVWleng1aHEveLL6X3rSmxTCcENEXPIyCg1QE6IQdSIeT9CSGgE/JyLeTHdDp5mSjah06/jq7EbSE/jxDF+9SWWFcIfRlQ9Hz61wvDcxo9P4z3Z/Q8QkBnKHuJ/Re9TgEA+nIARfsoavgSJcpY9FhdpcvPASC/NnvVIALgvXv3oNVqERgYaLQ8MDAQiYmJZrdJTEw0u75Go8G9e/cQHBxsss3ixYuxcOFCk+WhoaEY6eiHZR6tyi3r5FGP4PuCe+WuR0RERDUrOTkZnp6eti6GTTSIAKhXur+AEKLMPgTm1je3XG/u3LmYPXu24X5aWhrCwsJw48YNqz9AX1i1FlVURkYGQkNDcfPmTbutzq8reCzqFh6PuoPHou5IT09HkyZN4OPjY+ui2EyDCIB+fn5QKpUmtX1JSUkmtXx6QUFBZtd3cHCAr6+v2W3UajXUarXJck9PT36Z6wgPDw8eizqCx6Ju4fGoO3gs6g6Fon50bagJDeKVOzo6omvXrti7d6/R8r1796J3795mt+nVq5fJ+nv27MF9991ntv8fERERUUPRIAIgAMyePRurV6/G2rVrcf78efzjH//AjRs3DPP6zZ07FxMmTDCsP336dFy/fh2zZ8/G+fPnsXbtWqxZswZz5syx1UsgIiIiqhUNogkYAJ544gkkJyfjrbfeQkJCAtq1a4cffvgBYWFhAICEhATcuHHDsH5ERAR++OEH/OMf/8Ann3yCkJAQ/Oc//6nQHIBqtRrz58832yxMtYvHou7gsahbeDzqDh6LuoPHogHNA0hERERE1mkwTcBEREREZB0GQCIiIiI7wwBIREREZGcYAImIiIjsDANgJa1YsQIRERFwcnJC165dcfjwYVsXqcE5dOgQRo0ahZCQEEiShB07dhg9LoTAggULEBISAmdnZwwYMABnz541Wic/Px8vvPAC/Pz84Orqioceegi3bt2qxVfRMCxevBjdunWDu7s7AgICMHr0aFy8eNFoHR6P2rFy5Up06NDBMJlwr1698OOPPxoe53GwncWLF0OSJLz00kuGZTwetWfBggWQJMnoEhQUZHicx8IYA2AlbNmyBS+99BJef/11nDx5En379sWwYcOMppmhqsvOzkbHjh3x8ccfm338/fffx9KlS/Hxxx/j2LFjCAoKwuDBg5GZmWlY56WXXsL27duxefNm/PLLL8jKysLIkSOh1Wpr62U0CNHR0Zg5cyaOHDmCvXv3QqPRYMiQIcjOzjasw+NROxo3boz33nsPx48fx/Hjx/HAAw/g4YcfNvwh43GwjWPHjuHTTz9Fhw4djJbzeNSutm3bIiEhwXA5c+aM4TEei1IEVVj37t3F9OnTjZa1atVKvPbaazYqUcMHQGzfvt1wX6fTiaCgIPHee+8ZluXl5QlPT0+xatUqIYQQaWlpQqVSic2bNxvWiY+PFwqFQuzevbvWyt4QJSUlCQAiOjpaCMHjYWve3t5i9erVPA42kpmZKVq0aCH27t0r+vfvL/7+978LIfi9qG3z588XHTt2NPsYj4Up1gBWUEFBAf744w8MGTLEaPmQIUPw22+/2ahU9ufatWtITEw0Og5qtRr9+/c3HIc//vgDhYWFRuuEhISgXbt2PFZVlJ6eDgCGE6nzeNiGVqvF5s2bkZ2djV69evE42MjMmTMxYsQIDBo0yGg5j0ftu3z5MkJCQhAREYEnn3wSV69eBcBjYU6DORNIbbl37x60Wi0CAwONlgcGBiIxMdFGpbI/+vfa3HG4fv26YR1HR0d4e3ubrMNjVXlCCMyePRv3338/2rVrB4DHo7adOXMGvXr1Ql5eHtzc3LB9+3a0adPG8EeKx6H2bN68GSdOnMCxY8dMHuP3onb16NEDn332GVq2bIk7d+5g0aJF6N27N86ePctjYQYDYCVJkmR0XwhhsoxqXmWOA49V1cyaNQunT5/GL7/8YvIYj0ftiIyMRGxsLNLS0rB161ZMnDgR0dHRhsd5HGrHzZs38fe//x179uyBk5OTxfV4PGrHsGHDDLfbt2+PXr16oVmzZtiwYQN69uwJgMeiJDYBV5Cfnx+USqXJfwNJSUkm/1lQzdGP7CrrOAQFBaGgoACpqakW16GKeeGFF7Bz504cOHAAjRs3Nizn8ahdjo6OaN68Oe677z4sXrwYHTt2xEcffcTjUMv++OMPJCUloWvXrnBwcICDgwOio6Pxn//8Bw4ODob3k8fDNlxdXdG+fXtcvnyZ3w0zGAAryNHREV27dsXevXuNlu/duxe9e/e2UansT0REBIKCgoyOQ0FBAaKjow3HoWvXrlCpVEbrJCQk4M8//+SxqiAhBGbNmoVt27Zh//79iIiIMHqcx8O2hBDIz8/ncahlUVFROHPmDGJjYw2X++67D+PGjUNsbCyaNm3K42FD+fn5OH/+PIKDg/ndMMcWI0/qu82bNwuVSiXWrFkjzp07J1566SXh6uoq4uLibF20BiUzM1OcPHlSnDx5UgAQS5cuFSdPnhTXr18XQgjx3nvvCU9PT7Ft2zZx5swZ8dRTT4ng4GCRkZFh2Mf06dNF48aNxb59+8SJEyfEAw88IDp27Cg0Go2tXla99PzzzwtPT09x8OBBkZCQYLjk5OQY1uHxqB1z584Vhw4dEteuXROnT58W8+bNEwqFQuzZs0cIweNgayVHAQvB41GbXn75ZXHw4EFx9epVceTIETFy5Ejh7u5u+NvMY2GMAbCSPvnkExEWFiYcHR1Fly5dDNNhUPU5cOCAAGBymThxohBCHtY/f/58ERQUJNRqtejXr584c+aM0T5yc3PFrFmzhI+Pj3B2dhYjR44UN27csMGrqd/MHQcAYt26dYZ1eDxqx5QpUwy/Pf7+/iIqKsoQ/oTgcbC10gGQx6P2PPHEEyI4OFioVCoREhIiHn30UXH27FnD4zwWxiQhhLBN3SMRERER2QL7ABIRERHZGQZAIiIiIjvDAEhERERkZxgAiYiIiOwMAyARERGRnWEAJCIiIrIzDIBEREREdoYBkIiIiMjOMAASERER2RkGQCIiIiI7wwBIREREZGcYAImIiIjsDAMgERERkZ1hACQiIiKyMwyARERERHaGAZCIiIjIzjAAEhEREdkZBkAiIiIiO8MASERERGRnGACJiIiI7AwDIBEREZGdYQAkIiIisjMMgERERER2hgGQiIiIyM4wABIRERHZGQZAIiIiIjvDAEhERERkZxgAiYiIiOwMAyARERGRnWEAJCIiIrIzDIBEREREdub/AY815pHrBtZiAAAAAElFTkSuQmCC", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class EdgesAtCursor(object):\n", " def __init__(self, ax, energy, data, maximal_chemical_shift=5):\n", " self.ax = ax\n", " self.maximal_chemical_shift = maximal_chemical_shift\n", " self.energy = energy\n", " self.label = None\n", " self.line = None\n", " self.marker, = ax.plot(energy[0], data[0], marker=\"o\", color=\"crimson\", zorder=3)\n", " \n", " self.cursor = matplotlib.widgets.Cursor(ax, useblit=True, color='blue', linewidth=2, horizOn=False, alpha=.3)\n", " self.cid = ax.figure.canvas.mpl_connect('button_press_event', self.edges_on_click)\n", " #self.mouse_cid = ax.figure.canvas.mpl_connect('motion_notify_event', self.mouse_move)\n", " \n", "\n", " def edges_on_click(self, event):\n", " if not event.inaxes:\n", " return\n", " x= event.xdata\n", " if self.label is not None:\n", " self.label.remove()\n", " if self.line is not None:\n", " self.line.remove()\n", " if event.button == 1:\n", " self.label = plt.text(x, plt.gca().get_ylim()[1], pyTEMlib.eels_tools.find_all_edges(x, self.maximal_chemical_shift, True),\n", " verticalalignment='top')\n", " else:\n", " self.label = plt.text(x, plt.gca().get_ylim()[1], pyTEMlib.eels_tools.find_all_edges(x, self.maximal_chemical_shift),\n", " verticalalignment='top')\n", " self.line = plt.axvline(x=x, color='gray')\n", "\n", " \n", "fig, ax = plt.subplots()\n", "plt.title(f'Click with left for major and right mouse button for all \\n ionization edges within {maximal_chemical_shift:.1f} eV of cursor')\n", "\n", "cursor = EdgesAtCursor(ax, all_cross_sections['16']['ene'], all_cross_sections['16']['dat'], 5)\n", "ax.plot(all_cross_sections['16']['ene'], all_cross_sections['16']['dat']*2, label = 'S')\n", "ax.plot(all_cross_sections['42']['ene'], all_cross_sections['42']['dat'], 'r', label = 'Mo')\n", "ax.set_xlim(0,500)\n", "ax.set_ylim(0,2.5e17);\n", "#plt.connect('button_press_event', edges_on_click)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find Edges Depending on Cursor Postion" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e7afe1a6e7e149f59b77fd8ceb945a8f", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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The angle dependence is given by the experimental set-up and can be calculated by the convolution of collection and convergence angle.\n", "\n", "Here we use the method of [Pierre Trebbia, Ultramicroscopy **24** (1988) pp.399-408](https://doi.org/10.1016/0304-3991(88)90130-1)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def effective_collection_angle(ene, alpha, beta, beam_kv):\n", " \"\"\" effective collection angle for convergent beam setup\n", " \n", " \n", " \n", " Original abstract of function y = effbeta(ene, alpha, beta, beamkV)\n", " # \n", " # This program computes etha(alpha,beta), that is the collection\n", " # efficiency associated to the following geometry :\n", " #\n", " # alpha = half angle of illumination (0 -> pi/2)\n", " # beta = half angle of collection (0 -> pi/2)\n", " # (pi/2 = 1570.795 mrad)\n", " #\n", " # A constant angular distribution of incident electrons is assumed\n", " # for any incident angle (-alpha,alpha). These electrons impige the\n", " # target and a single energy loss event occurs, with a characteristic\n", " # angle theta-e (relativistic). The angular distribution of the\n", " # electrons after the target is analytically derived.\n", " # This program integrates this distribution from theta=0 up to\n", " # theta=beta with an adjustable angular step.\n", " # This program also computes beta* which is the theoretical\n", " # collection angle which would give the same value of etha(alpha,beta)\n", " # with a parallel incident beam.\n", " #\n", " # subroutines and function subprograms required\n", " # ---------------------------------------------\n", " # none\n", " #\n", " # comments\n", " # --------\n", " #\n", " # The following parameters are asked as input :\n", " # accelerating voltage (kV), energy loss range (eV) for the study,\n", " # energy loss step (eV) in this range, alpha (mrad), beta (mrad).\n", " # The program returns for each energy loss step :\n", " # alpha (mrad), beta (mrad), theta-e (relativistic) (mrad),\n", " # energy loss (eV), etha (#), beta * (mrad)\n", " #\n", " # author :\n", " # --------\n", " # Pierre TREBBIA\n", " # US 41 : \"Microscopie Electronique Analytique Quantitative\"\n", " # Laboratoire de Physique des Solides, Bat. 510\n", " # Universite Paris-Sud, F91405 ORSAY Cedex\n", " # Phone : (33-1) 69 41 53 68\n", " #\n", " # \n", " \"\"\"\n", " \n", " \n", " z1 = beam_kv*1000. ; # eV\n", " z2 = ene[0];\n", " z3 = ene[-1]\n", " z4 = 100.0\n", " z5 = alpha*0.001 # rad\n", " z6 = beta*0.001 # rad\n", " z7 = 500 # number of integration steps to be modified at will\n", "\n", " # main loop on energy loss\n", " \n", " for zx in range(int(z2),int(z3),int(z4)): #! zx = current energy loss\n", " eta=0.0;\n", " x0=float(zx)*(z1+511060.)/(z1*(z1+1022120.)); # x0 = relativistic theta-e\n", " x1 = np.pi/(2.*x0);\n", " x2=x0*x0+z5*z5;\n", " x3=z5/x0*z5/x0;\n", " x4=0.1*np.sqrt(x2);\n", " dtheta=(z6-x4)/z7;\n", " #\n", " # calculation of the analytical expression\n", " #\n", " for zi in range(1, int(z7)):\n", " theta=x4+dtheta*float(zi);\n", " x5=theta*theta;\n", " x6=4.*x5*x0*x0;\n", " x7=x2-x5;\n", " x8=np.sqrt(x7*x7+x6);\n", " x9=(x8+x7)/(2.*x0*x0);\n", " x10=2.*theta*dtheta*np.log(x9);\n", " eta=eta+x10;\n", " \n", " \n", " \n", " eta=eta+x2/100.*np.log(1.+x3) ; # addition of the central contribution\n", " x4=z5*z5*np.log(1.+x1*x1); # normalisation\n", " eta=eta/x4;\n", " #\n", " # correction by geometrical factor (beta/alpha)**2\n", " #\n", " if (z6\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": [ "# ----Input ----------\n", "effective_collection_angle = 20\n", "# --------------------\n", "\n", "S_Xsection = pyTEMlib.eels_tools.xsec_xrpa(energy_scale, 200, 16, effective_collection_angle )/1e10 \n", "Mo_Xsection = pyTEMlib.eels_tools.xsec_xrpa(energy_scale, 200, 42, effective_collection_angle, shift=0)/1e10 # xsec is in barns = 10^28 m2 = 10^10 nm2\n", "\n", "fig, ax1 = plt.subplots()\n", "\n", "ax1.plot(energy_scale, S_Xsection, label='S X-section' )\n", "ax1.plot(energy_scale, Mo_Xsection, label='Mo X-section' )\n", "ax1.set_xlabel('energy_loss [eV]')\n", "ax1.set_ylabel('probability [atoms/nm$^{2}$]')\n", "\n", "\n", "plt.legend();\n", "fig.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "The cross section is key to determine the chemical composition of an EELS spectrum. \n", "These cross sections are dependent on:\n", "- acceleration voltage\n", "- effective collection angle\n", "- element\n", "\n", "So these experimental parameters have to be provided for a calculations of cross sections.\n", "\n", "We will use these cross sections in the [chemical compostions notebook](CH4_08-Chemical_Composition.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Navigation\n", "- **Up Chapter 4: [Imaging](CH4_00-Spectroscopy.ipynb)** \n", "- **Back: [Dielectric Function](CH4_03-Drude.ipynb)** \n", "- **Next: [Chemical Composition](CH4_08-Chemical_Composition.ipynb)** \n", "- **List of Content: [Front](../_MSE672_Intro_TEM.ipynb)** \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" }, "toc": { "base_numbering": "7", "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "384px" }, "toc_section_display": true, "toc_window_display": true }, "vscode": { "interpreter": { "hash": "838e0debddb5b6f29d3d8c39ba50ae8c51920a564d3bac000e89375a158a81de" } } }, "nbformat": 4, "nbformat_minor": 4 }