UV_Vis_and_Excitation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# DFT Calculation to Characterize Molecules"
      ],
      "metadata": {
        "id": "0eSIG-PlUdBD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Density Functional Theory (DFT)** is a quantum mechanical method used to investigate the electronic structure of many-body systems, primarily atoms, molecules, and condensed phases. Instead of solving the many-electron wavefunction directly, DFT simplifies the problem by focusing on the electron density as the fundamental variable. This approach is computationally more efficient and can provide accurate results for a wide range of chemical and physical properties. The core idea of DFT is based on the Hohenberg-Kohn theorems, which assert that the ground-state properties of a system are uniquely determined by its electron density, and that the energy of the system can be minimized through an appropriate functional of the electron density."
      ],
      "metadata": {
        "id": "h2cct7CsVDhQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Time-Dependent Density Functional Theory (TDDFT)** is an extension of Density Functional Theory (DFT) that is used to study the excited states of many-body systems. While DFT focuses on the ground state properties, TDDFT is designed to handle time-dependent phenomena and can be used to investigate the response of a system to external perturbations, such as electromagnetic fields.\n",
        "\n",
        "In TDDFT, the key variable is the time-dependent electron density, which evolves according to the time-dependent Kohn-Sham equations. These equations describe how the electron density changes with time under the influence of an external time-dependent potential. By solving these equations, one can obtain information about the excitation energies, transition probabilities, and dynamic properties of the system.\n",
        "\n",
        "TDDFT has become a powerful tool in computational chemistry and physics for studying optical properties, electronic excitations, and non-equilibrium processes in molecules, clusters, and solids. Its main advantages are its relatively low computational cost and its ability to provide detailed insights into the electronic dynamics of complex systems."
      ],
      "metadata": {
        "id": "W1mjZumQVl8r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Understanding Molecules through UV-Vis and Excitation Spectra"
      ],
      "metadata": {
        "id": "N_BU-GH23QJE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UV-Vis Spectrum** : The UV-Vis (Ultraviolet-Visible) spectrum is a graphical representation of a molecule's absorption of ultraviolet and visible light. This spectrum provides valuable information about the electronic transitions within a molecule, helping us understand several important aspects of the molecule:\n",
        "\n",
        "- Electronic Structure: By analyzing the wavelengths at which a molecule absorbs light, we can infer the energy levels of its electrons. Absorption peaks correspond to electronic transitions from lower energy states to higher energy states.\n",
        "\n",
        "- Functional Groups and Conjugation: Different functional groups and degrees of conjugation in a molecule affect the energy of electronic transitions. For example, π-π* transitions in conjugated systems typically absorb in the UV or visible region, and the position and intensity of these peaks can indicate the extent of conjugation.\n",
        "\n",
        "- Molecular Environment: The solvent and molecular environment can shift the absorption peaks (bathochromic or hypsochromic shifts) and change their intensities. This provides information about the interactions between the molecule and its surroundings.\n",
        "\n",
        "- Concentration: According to Beer-Lambert law, the absorbance is directly proportional to the concentration of the absorbing species. Thus, UV-Vis spectroscopy can be used to determine the concentration of a molecule in solution."
      ],
      "metadata": {
        "id": "tNFxY-GP3cKo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Intensity vs. Excitation Energy** : The intensity vs. excitation energy plot, often referred to as an excitation spectrum, complements the UV-Vis absorption spectrum and provides additional insights:\n",
        "\n",
        "- Excitation Processes: The excitation spectrum shows the intensity of fluorescence or phosphorescence as a function of the excitation wavelength. This helps identify which electronic transitions are responsible for the observed emission.\n",
        "\n",
        "- Quantum Yield and Efficiency: The intensity of the emission at different excitation energies can give information about the efficiency of the excitation process and the quantum yield of fluorescence or phosphorescence.\n",
        "\n",
        "- Energy Transfer: In complex systems, such as molecular aggregates or proteins with multiple chromophores, excitation spectra can help identify energy transfer processes. Peaks in the excitation spectrum corresponding to different chromophores indicate the pathways of energy transfer within the system.\n",
        "\n",
        "- Photostability: By examining changes in the intensity vs. excitation energy over time or under different conditions, we can assess the photostability of a molecule. Degradation or changes in the spectrum can indicate photobleaching or other photo-induced reactions."
      ],
      "metadata": {
        "id": "umVn8HSl33PS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Understanding Molecules through UV-Vis and Excitation Spectra** : By analyzing UV-Vis absorption spectra and intensity vs. excitation energy plots, scientists can gain a comprehensive understanding of a molecule's:\n",
        "\n",
        "- Electronic Structure: Identify energy levels and types of electronic transitions.\n",
        "- Chemical Composition: Determine the presence of specific functional groups and conjugation patterns.\n",
        "- Molecular Interactions: Study interactions with solvents, other molecules, or the environment.\n",
        "- Dynamic Processes: Observe energy transfer, excitation efficiency, and photostability.\n",
        "\n",
        "These insights are crucial for applications in chemistry, biology, materials science, and many other fields, where understanding the electronic and photophysical properties of molecules is essential."
      ],
      "metadata": {
        "id": "SiWqfT1D3_Vl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Python Libraries\n"
      ],
      "metadata": {
        "id": "ZiBtMbnXVxpJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RDKit** is an open-source cheminformatics software that provides tools for working with chemical informatics, computational chemistry, and predictive modeling. It includes functions for:\n",
        "\n",
        "- Molecular Representation: Create, manipulate, and visualize molecular structures.\n",
        "- Descriptors and Fingerprints: Calculate molecular descriptors and fingerprints for use in quantitative structure-activity relationship (QSAR) studies and similarity searches.\n",
        "- Molecular Operations: Perform operations like substructure searching, molecular alignment, and conformer generation.\n",
        "- Chemical Reactions: Simulate chemical reactions and transformations.\n",
        "Integration: Work seamlessly with other libraries and frameworks, such as machine learning tools, for predictive modeling and data analysis.\n",
        "\n",
        "RDKit is widely used in drug discovery, materials science, and other fields that require chemical informatics capabilities"
      ],
      "metadata": {
        "id": "0K7LUFT6V4Xe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PySCF (Python-based Simulations of Chemistry Framework) is an open-source quantum chemistry package designed for ab initio calculations. It provides a flexible and efficient platform for performing a variety of quantum chemistry computations, including:\n",
        "\n",
        "- Hartree-Fock (HF) and Density Functional Theory (DFT): Perform standard electronic structure calculations.\n",
        "- Post-Hartree-Fock Methods: Implement methods like MP2, CCSD, and CASSCF for more accurate electronic structure analysis.\n",
        "Basis Sets: Utilize a wide range of built-in basis sets for representing wavefunctions.\n",
        "- Extensibility: Easily extend and customize for advanced applications and research needs.\n",
        "\n",
        "PySCF is known for its user-friendly Python interface, allowing researchers to integrate it with other computational tools and workflows, making it a valuable resource for theoretical and computational chemists."
      ],
      "metadata": {
        "id": "VxNjMpgEWEYb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Bh6Yo0m4b9q",
        "outputId": "b05818d7-314a-454a-ff74-22b21d55f263"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rdkit\n",
            "  Downloading rdkit-2024.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.3/35.3 MB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pyscf\n",
            "  Downloading pyscf-2.6.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (48.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.6/48.6 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting geometric\n",
            "  Downloading geometric-1.0.2.tar.gz (385 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m385.0/385.0 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (9.4.0)\n",
            "Requirement already satisfied: scipy!=1.5.0,!=1.5.1 in /usr/local/lib/python3.10/dist-packages (from pyscf) (1.11.4)\n",
            "Requirement already satisfied: h5py>=2.7 in /usr/local/lib/python3.10/dist-packages (from pyscf) (3.9.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from pyscf) (67.7.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from geometric) (3.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from geometric) (1.16.0)\n",
            "Building wheels for collected packages: geometric\n",
            "  Building wheel for geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for geometric: filename=geometric-1.0.2-py3-none-any.whl size=397155 sha256=14c4aa2d6c7de86c2587171d1f229fe60c82158753c358c5f5101306fa3116cf\n",
            "  Stored in directory: /root/.cache/pip/wheels/a6/9a/7c/4a37604d74a279118623fceb5b2d96742d857f034d8c588f46\n",
            "Successfully built geometric\n",
            "Installing collected packages: rdkit, geometric, pyscf\n",
            "Successfully installed geometric-1.0.2 pyscf-2.6.2 rdkit-2024.3.2\n"
          ]
        }
      ],
      "source": [
        "!pip install rdkit pyscf geometric"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from scipy.constants import physical_constants\n",
        "import os\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import AllChem\n",
        "\n",
        "from pyscf import gto, dft, tddft\n",
        "from pyscf.geomopt.geometric_solver import optimize\n",
        "from pyscf.hessian import thermo\n",
        "from pyscf.tools import molden\n",
        "from pyscf.gto.basis import load"
      ],
      "metadata": {
        "id": "KXJxWFrmW9Ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Program Code"
      ],
      "metadata": {
        "id": "v52jJMHvWY9f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's input the molecule in it's smiles representation which we want to characterize using DFT and TDDFT calculations."
      ],
      "metadata": {
        "id": "A5wfK3WeWdc6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze7K7ZxD5veQ",
        "outputId": "dcd08ca9-df8b-48a7-fbb0-8a6a06a3dc0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Smiles Representation of Molecule: N\n"
          ]
        }
      ],
      "source": [
        "smiles_representation = input(\"Smiles Representation of Molecule: \")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's build the molecule from the smiles representation using RDKit"
      ],
      "metadata": {
        "id": "9L4F3vGmZsui"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "ROWpRFmZ6TFd",
        "outputId": "0afc4878-66f9-44c6-fb22-5a7067106e6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "mol = Chem.MolFromSmiles(smiles_representation)\n",
        "mol = Chem.AddHs(mol)\n",
        "\n",
        "Chem.AllChem.EmbedMolecule(mol)\n",
        "xyz_file_path = \"resources/sample.xyz\"\n",
        "directory = os.path.dirname(xyz_file_path)\n",
        "\n",
        "if not os.path.exists(directory):\n",
        "    os.makedirs(directory)\n",
        "\n",
        "with open(xyz_file_path, 'w') as f:\n",
        "    f.write(Chem.MolToXYZBlock(mol))\n",
        "\n",
        "img = Draw.MolToImage(mol, size=(500, 500))\n",
        "img"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The generated molecule may not be in the most stable configuration (the ground state). Let's implement DFT using PySCF to get the molecule to it's ground state, so that we can easily extract the relevant information about the electronic configuration and it's theoretical orbitals."
      ],
      "metadata": {
        "id": "e6imgluMZ-a5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4Z55JQP47UwB",
        "outputId": "b6ae1e98-caa3-499e-ea46-216abb249da2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converged SCF energy = -56.5086295934643\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "geometric-optimize called with the following command line:\n",
            "/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-4aa86ab0-a9d0-4460-9ca1-f06ef6cc0885.json\n",
            "\n",
            "                                        \u001b[91m())))))))))))))))/\u001b[0m                     \n",
            "                                    \u001b[91m())))))))))))))))))))))))),\u001b[0m                \n",
            "                                \u001b[91m*)))))))))))))))))))))))))))))))))\u001b[0m             \n",
            "                        \u001b[94m#,\u001b[0m    \u001b[91m()))))))))/\u001b[0m                \u001b[91m.)))))))))),\u001b[0m          \n",
            "                      \u001b[94m#%%%%,\u001b[0m  \u001b[91m())))))\u001b[0m                        \u001b[91m.))))))))*\u001b[0m        \n",
            "                      \u001b[94m*%%%%%%,\u001b[0m  \u001b[91m))\u001b[0m              \u001b[93m..\u001b[0m              \u001b[91m,))))))).\u001b[0m      \n",
            "                        \u001b[94m*%%%%%%,\u001b[0m         \u001b[93m***************/.\u001b[0m        \u001b[91m.)))))))\u001b[0m     \n",
            "                \u001b[94m#%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m    \u001b[93m/*********************.\u001b[0m       \u001b[91m)))))))\u001b[0m    \n",
            "              \u001b[94m.%%%%%%#\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m  \u001b[93m*******/,\u001b[0m     \u001b[93m**********,\u001b[0m      \u001b[91m.))))))\u001b[0m   \n",
            "                \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m  \u001b[93m**\u001b[0m              \u001b[93m********\u001b[0m      \u001b[91m.))))))\u001b[0m  \n",
            "          \u001b[94m##\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m                  \u001b[93m,******\u001b[0m      \u001b[91m/)))))\u001b[0m  \n",
            "        \u001b[94m%%%%%%\u001b[0m      \u001b[94m.%%%%%%#\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m    \u001b[92m,/////.\u001b[0m       \u001b[93m******\u001b[0m      \u001b[91m))))))\u001b[0m \n",
            "      \u001b[94m#%\u001b[0m      \u001b[94m%%\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m  \u001b[92m////////,\u001b[0m      \u001b[93m*****/\u001b[0m     \u001b[91m,)))))\u001b[0m \n",
            "    \u001b[94m#%%\u001b[0m  \u001b[94m%%%\u001b[0m  \u001b[94m%%%#\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m  \u001b[92m///////.\u001b[0m     \u001b[93m/*****\u001b[0m      \u001b[91m))))).\u001b[0m\n",
            "  \u001b[94m#%%%%.\u001b[0m      \u001b[94m%%%%%#\u001b[0m      \u001b[94m/%%%%%%*\u001b[0m      \u001b[94m#%%%%%%\u001b[0m   \u001b[92m/////)\u001b[0m     \u001b[93m******\u001b[0m      \u001b[91m))))),\u001b[0m\n",
            "    \u001b[94m#%%%%##%\u001b[0m  \u001b[94m%%%#\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m  \u001b[92m///////.\u001b[0m     \u001b[93m/*****\u001b[0m      \u001b[91m))))).\u001b[0m\n",
            "      \u001b[94m##\u001b[0m     \u001b[94m%%%\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m  \u001b[92m////////.\u001b[0m      \u001b[93m*****/\u001b[0m     \u001b[91m,)))))\u001b[0m \n",
            "        \u001b[94m#%%%%#\u001b[0m      \u001b[94m/%%%%%%/\u001b[0m      \u001b[94m(%%%%%%\u001b[0m      \u001b[92m/)/)//\u001b[0m       \u001b[93m******\u001b[0m      \u001b[91m))))))\u001b[0m \n",
            "          \u001b[94m##\u001b[0m      \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m                  \u001b[93m*******\u001b[0m      \u001b[91m))))))\u001b[0m  \n",
            "                \u001b[94m.%%%%%%/\u001b[0m      \u001b[94m*%%%%%%,\u001b[0m  \u001b[93m**.\u001b[0m             \u001b[93m/*******\u001b[0m      \u001b[91m.))))))\u001b[0m  \n",
            "              \u001b[94m*%%%%%%/\u001b[0m      \u001b[94m(%%%%%%\u001b[0m   \u001b[93m********/*..,*/*********\u001b[0m       \u001b[91m*))))))\u001b[0m   \n",
            "                \u001b[94m#%%/\u001b[0m      \u001b[94m(%%%%%%,\u001b[0m    \u001b[93m*********************/\u001b[0m        \u001b[91m)))))))\u001b[0m    \n",
            "                        \u001b[94m*%%%%%%,\u001b[0m         \u001b[93m,**************/\u001b[0m         \u001b[91m,))))))/\u001b[0m     \n",
            "                      \u001b[94m(%%%%%%\u001b[0m   \u001b[91m()\u001b[0m                              \u001b[91m))))))))\u001b[0m       \n",
            "                      \u001b[94m#%%%%,\u001b[0m  \u001b[91m())))))\u001b[0m                        \u001b[91m,)))))))),\u001b[0m        \n",
            "                        \u001b[94m#,\u001b[0m    \u001b[91m())))))))))\u001b[0m                \u001b[91m,)))))))))).\u001b[0m          \n",
            "                                 \u001b[91m()))))))))))))))))))))))))))))))/\u001b[0m             \n",
            "                                    \u001b[91m())))))))))))))))))))))))).\u001b[0m                \n",
            "                                         \u001b[91m())))))))))))))),\u001b[0m                     \n",
            "\n",
            "-=# \u001b[1;94m geomeTRIC started. Version: 1.0.2 \u001b[0m #=-\n",
            "Current date and time: 2024-07-05 05:14:52\n",
            "Custom engine selected.\n",
            "Bonds will be generated from interatomic distances less than 1.20 times sum of covalent radii\n",
            "12 internal coordinates being used (instead of 12 Cartesians)\n",
            "Internal coordinate system (atoms numbered from 1):\n",
            "Distance 1-2\n",
            "Distance 1-3\n",
            "Distance 1-4\n",
            "Angle 2-1-3\n",
            "Angle 2-1-4\n",
            "Angle 3-1-4\n",
            "Translation-X 1-4\n",
            "Translation-Y 1-4\n",
            "Translation-Z 1-4\n",
            "Rotation-A 1-4\n",
            "Rotation-B 1-4\n",
            "Rotation-C 1-4\n",
            "<class 'geometric.internal.Distance'> : 3\n",
            "<class 'geometric.internal.Angle'> : 3\n",
            "<class 'geometric.internal.TranslationX'> : 1\n",
            "<class 'geometric.internal.TranslationY'> : 1\n",
            "<class 'geometric.internal.TranslationZ'> : 1\n",
            "<class 'geometric.internal.RotationA'> : 1\n",
            "<class 'geometric.internal.RotationB'> : 1\n",
            "<class 'geometric.internal.RotationC'> : 1\n",
            "> ===== Optimization Info: ====\n",
            "> Job type: Energy minimization\n",
            "> Maximum number of optimization cycles: 300\n",
            "> Initial / maximum trust radius (Angstrom): 0.100 / 0.300\n",
            "> Convergence Criteria:\n",
            "> Will converge when all 5 criteria are reached:\n",
            ">  |Delta-E| < 1.00e-06\n",
            ">  RMS-Grad  < 3.00e-04\n",
            ">  Max-Grad  < 4.50e-04\n",
            ">  RMS-Disp  < 1.20e-03\n",
            ">  Max-Disp  < 1.80e-03\n",
            "> === End Optimization Info ===\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 1\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N  -0.004721   0.010525   0.239877    0.000000  0.000000  0.000000\n",
            "   H   0.951923  -0.223432  -0.078480    0.000000  0.000000  0.000000\n",
            "   H  -0.652285  -0.746651  -0.079015    0.000000  0.000000  0.000000\n",
            "   H  -0.294917   0.959557  -0.082382    0.000000  0.000000  0.000000\n",
            "converged SCF energy = -56.5086295934213\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N     0.0026792349     0.0056426857     0.0240336121\n",
            "1 H     0.0139131047    -0.0073501366    -0.0075135535\n",
            "2 H    -0.0113591418    -0.0221186556    -0.0091627985\n",
            "3 H    -0.0052340992     0.0238242581    -0.0073550279\n",
            "----------------------------------------------\n",
            "cycle 1: E = -56.5086295934  dE = -56.5086  norm(grad) = 0.0476654\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    0 : Gradient = 2.383e-02/2.650e-02 (rms/max) Energy = -56.5086295934\n",
            "Hessian Eigenvalues: 5.00000e-02 5.00000e-02 5.00000e-02 ... 4.04203e-01 4.08020e-01 4.20264e-01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 2\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N  -0.000962   0.002047   0.220980    0.003759 -0.008478 -0.018897\n",
            "   H   0.946557  -0.209329  -0.071182   -0.005366  0.014103  0.007298\n",
            "   H  -0.652812  -0.714837  -0.072105   -0.000527  0.031814  0.006910\n",
            "   H  -0.292745   0.922196  -0.077787    0.002172 -0.037361  0.004595\n",
            "converged SCF energy = -56.5111543948008\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N    -0.0017003824     0.0020484410     0.0081568956\n",
            "1 H     0.0012306751    -0.0005330745    -0.0031332655\n",
            "2 H     0.0001328762    -0.0000442267    -0.0027905470\n",
            "3 H     0.0003361715    -0.0014726115    -0.0022327327\n",
            "----------------------------------------------\n",
            "cycle 2: E = -56.5111543948  dE = -0.0025248  norm(grad) = 0.0100156\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    1 : Displace = \u001b[0m2.831e-02\u001b[0m/\u001b[0m3.773e-02\u001b[0m (rms/max) Trust = 1.000e-01 (=) Grad = \u001b[0m5.008e-03\u001b[0m/\u001b[0m8.580e-03\u001b[0m (rms/max) E (change) = -56.5111543948 (\u001b[0m-2.525e-03\u001b[0m) Quality = \u001b[0m1.110\u001b[0m\n",
            "Hessian Eigenvalues: 5.00000e-02 5.00000e-02 5.00000e-02 ... 4.03090e-01 4.14505e-01 4.56591e-01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 3\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N   0.000194  -0.000444   0.185077    0.001156 -0.002491 -0.035903\n",
            "   H   0.955381  -0.209011  -0.059090    0.008824  0.000318  0.012092\n",
            "   H  -0.660194  -0.718950  -0.060054   -0.007382 -0.004114  0.012051\n",
            "   H  -0.295307   0.928562  -0.066058   -0.002561  0.006366  0.011730\n",
            "converged SCF energy = -56.511670898179\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N    -0.0006939784    -0.0007470264     0.0030968873\n",
            "1 H     0.0000113248     0.0004693961    -0.0011208016\n",
            "2 H     0.0005280924     0.0016619786    -0.0008663261\n",
            "3 H     0.0001533598    -0.0013858559    -0.0011098722\n",
            "----------------------------------------------\n",
            "cycle 3: E = -56.5116708982  dE = -0.000516503  norm(grad) = 0.00436744\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    2 : Displace = \u001b[0m2.192e-02\u001b[0m/\u001b[0m3.600e-02\u001b[0m (rms/max) Trust = 1.414e-01 (\u001b[92m+\u001b[0m) Grad = \u001b[0m2.184e-03\u001b[0m/\u001b[0m3.260e-03\u001b[0m (rms/max) E (change) = -56.5116708982 (\u001b[0m-5.165e-04\u001b[0m) Quality = \u001b[0m1.466\u001b[0m\n",
            "Hessian Eigenvalues: 4.99999e-02 5.00000e-02 5.00000e-02 ... 4.08240e-01 4.15930e-01 4.52062e-01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 4\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N   0.000257  -0.000101   0.141109    0.000063  0.000343 -0.043969\n",
            "   H   0.963742  -0.211363  -0.044718    0.008361 -0.002351  0.014372\n",
            "   H  -0.666091  -0.727668  -0.045616   -0.005897 -0.008717  0.014438\n",
            "   H  -0.297729   0.939427  -0.050906   -0.002422  0.010865  0.015151\n",
            "converged SCF energy = -56.5118805777819\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N     0.0005416487    -0.0004764117     0.0003996802\n",
            "1 H    -0.0002136880     0.0003156671    -0.0000533037\n",
            "2 H    -0.0002734799     0.0001740491    -0.0001343639\n",
            "3 H    -0.0000551589    -0.0000132279    -0.0002110886\n",
            "----------------------------------------------\n",
            "cycle 4: E = -56.5118805778  dE = -0.00020968  norm(grad) = 0.000999579\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    3 : Displace = \u001b[0m2.687e-02\u001b[0m/\u001b[0m4.397e-02\u001b[0m (rms/max) Trust = 2.000e-01 (\u001b[92m+\u001b[0m) Grad = \u001b[0m4.998e-04\u001b[0m/\u001b[0m8.247e-04\u001b[0m (rms/max) E (change) = -56.5118805778 (\u001b[0m-2.097e-04\u001b[0m) Quality = \u001b[0m1.123\u001b[0m\n",
            "Hessian Eigenvalues: 4.99997e-02 5.00000e-02 5.00000e-02 ... 4.08483e-01 4.16550e-01 4.53058e-01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 5\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N  -0.000080   0.000360   0.132026   -0.000337  0.000461 -0.009083\n",
            "   H   0.965020  -0.212383  -0.041855    0.001278 -0.001020  0.002863\n",
            "   H  -0.666396  -0.729132  -0.042669   -0.000305 -0.001464  0.002947\n",
            "   H  -0.298322   0.941460  -0.047682   -0.000593  0.002033  0.003225\n",
            "converged SCF energy = -56.5118866307159\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N    -0.0000285051    -0.0001576831     0.0000243017\n",
            "1 H     0.0000320553     0.0000443161    -0.0000070726\n",
            "2 H     0.0000174795     0.0001327281     0.0000084751\n",
            "3 H    -0.0000215398    -0.0000189364    -0.0000247926\n",
            "----------------------------------------------\n",
            "cycle 5: E = -56.5118866307  dE = -6.05293e-06  norm(grad) = 0.000220771\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    4 : Displace = \u001b[0m5.465e-03\u001b[0m/\u001b[0m9.090e-03\u001b[0m (rms/max) Trust = 2.828e-01 (\u001b[92m+\u001b[0m) Grad = \u001b[92m1.104e-04\u001b[0m/\u001b[92m1.621e-04\u001b[0m (rms/max) E (change) = -56.5118866307 (\u001b[0m-6.053e-06\u001b[0m) Quality = \u001b[0m1.088\u001b[0m\n",
            "Hessian Eigenvalues: 4.99957e-02 5.00000e-02 5.00000e-02 ... 4.08784e-01 4.20370e-01 4.52325e-01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Geometry optimization cycle 6\n",
            "Cartesian coordinates (Angstrom)\n",
            " Atom        New coordinates             dX        dY        dZ\n",
            "   N  -0.000122   0.000485   0.131021   -0.000042  0.000125 -0.001005\n",
            "   H   0.965055  -0.212513  -0.041555    0.000035 -0.000130  0.000300\n",
            "   H  -0.666379  -0.729474  -0.042375    0.000017 -0.000342  0.000294\n",
            "   H  -0.298307   0.941788  -0.047315    0.000015  0.000328  0.000367\n",
            "converged SCF energy = -56.5118867281206\n",
            "--------------- RKS_Scanner gradients ---------------\n",
            "         x                y                z\n",
            "0 N     0.0000381106    -0.0000037487    -0.0000012565\n",
            "1 H    -0.0000171575     0.0000132503     0.0000050834\n",
            "2 H    -0.0000212758    -0.0000091251    -0.0000015662\n",
            "3 H    -0.0000001711     0.0000000825    -0.0000013568\n",
            "----------------------------------------------\n",
            "cycle 6: E = -56.5118867281  dE = -9.74047e-08  norm(grad) = 5.00409e-05\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Step    5 : Displace = \u001b[92m6.285e-04\u001b[0m/\u001b[92m1.004e-03\u001b[0m (rms/max) Trust = 3.000e-01 (\u001b[92m+\u001b[0m) Grad = \u001b[92m2.502e-05\u001b[0m/\u001b[92m3.832e-05\u001b[0m (rms/max) E (change) = -56.5118867281 (\u001b[92m-9.740e-08\u001b[0m) Quality = \u001b[0m0.956\u001b[0m\n",
            "Hessian Eigenvalues: 4.99957e-02 5.00000e-02 5.00000e-02 ... 4.08784e-01 4.20370e-01 4.52325e-01\n",
            "Converged! =D\n",
            "\n",
            "    #==========================================================================#\n",
            "    #| If this code has benefited your research, please support us by citing: |#\n",
            "    #|                                                                        |#\n",
            "    #| Wang, L.-P.; Song, C.C. (2016) \"Geometry optimization made simple with |#\n",
            "    #| translation and rotation coordinates\", J. Chem, Phys. 144, 214108.     |#\n",
            "    #| http://dx.doi.org/10.1063/1.4952956                                    |#\n",
            "    #==========================================================================#\n",
            "    Time elapsed since start of run_optimizer: 10.124 seconds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAIjCAYAAAATE8pZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACGDElEQVR4nO3dd3xTZfvH8W+6B5RZRqGLvZHhoI8s2SCICMhQQcDtT1CGKCogDkBEUFTEQdUHVORRVAShICAoPAqCylRkL1FWoYWunN8feRIamo6UpEnTz/v1yqvJOXdOritpTs/V+z73MRmGYQgAAAAA4DZ+ng4AAAAAAHwdhRcAAAAAuBmFFwAAAAC4GYUXAAAAALgZhRcAAAAAuBmFFwAAAAC4GYUXAAAAALgZhRcAAAAAuBmFFwAAAAC4GYUXAJQgcXFxGjp0qKfDUGJiokwmkw4cOODpUJzWrl07NWrUKN92Bw4ckMlkUmJioktf31s+Q3dr166d2rVr5+kwAMBlKLwAwEtYixGTyaQNGzbkWG8YhqKjo2UymXTzzTd7IMLi7fvvv9ett96qypUrKzg4WHFxcbrvvvt06NChIoth2bJlmjRpUpG8lvV3ydHt/vvvL5IYAACXBXg6AACAvZCQEC1cuFA33nij3fJ169bpyJEjCg4O9lBkxddrr72mkSNHqkaNGvq///s/Va1aVbt27dI777yjTz75RMuWLVNCQoJLXzM2NlYXL15UYGCgbdmyZcv0+uuvF1nx1alTJ9111105ltepU6dIXv9qrFy50tMhAIBLUXgBgJfp3r27Pv30U7366qsKCLi8m164cKFatGihf/75x4PReafU1FSFhYU5XPf9999r1KhRuvHGG/XNN9/YtXvggQf0r3/9S3379tWOHTtUrly5XF8jJSVF4eHhBY7JZDIpJCSk4Em4QZ06dXTHHXd4NAZJMpvNSk9Pd+r9CAoKcmNEAFD0GGoIAF5m4MCBOnXqlJKSkmzL0tPTtXjxYg0aNMjhc1JSUjR69GhFR0crODhYdevW1YwZM2QYRr6vd/bsWY0aNcr23Fq1amnatGkym8127cxms2bPnq3GjRsrJCREkZGR6tq1qzZv3iwp73OaTCZTvr08X3zxhXr06KGoqCgFBwerZs2amjJlirKysuzaWc+x2rJli9q0aaOwsDA9+eSTuW53ypQpMplMev/993MUZzVr1tT06dN1/PhxvfXWW7blQ4cOValSpfTnn3+qe/fuKl26tAYPHmz33C1btighIUGhoaGKj4/X3Llz7dZf+X4MHTpUr7/+uu39sN6sZsyYoYSEBFWoUEGhoaFq0aKFFi9enOd75grW93Pnzp1q3769wsLCVK1aNU2fPj1H27S0NE2cOFG1atVScHCwoqOjNW7cOKWlpdm1M5lMevjhh7VgwQI1bNhQwcHB+uabbyRJv/76q9q2bavQ0FBVr15dzz33nObPn5/jnD9H53gV9PWTkpJ04403qmzZsipVqpTq1q2b5+8IABQFerwAwMvExcWpVatW+uijj9StWzdJ0vLly3Xu3DkNGDBAr776ql17wzDUq1cvrVmzRsOHD9c111yjFStWaOzYsTp69KheeeWVXF8rNTVVbdu21dGjR3XfffcpJiZGP/zwg5544gkdP35cs2bNsrUdPny4EhMT1a1bN40YMUKZmZlav369Nm3apJYtW1513omJiSpVqpQee+wxlSpVSt9++62eeeYZJScn66WXXrJre+rUKXXr1k0DBgzQHXfcocqVK+ea3+rVq9W6dWvFx8c7bHP77bfr3nvv1dKlSzV+/Hjb8szMTHXp0kU33nijZsyYYVe0nTlzRt27d1f//v01cOBALVq0SA888ICCgoI0bNgwh69z33336dixY0pKStKHH36YY/3s2bPVq1cvDR48WOnp6fr444/Vr18/LV26VD169Mj3/XPk0qVLDntIIyIi7HqUzpw5o65du6pPnz7q37+/Fi9erMcff1yNGze2/Q6azWb16tVLGzZs0L333qv69evrt99+0yuvvKLff/9dS5YssXuNb7/9VosWLdLDDz+sihUrKi4uTkePHlX79u1lMpn0xBNPKDw8XO+8806Bhs8W9PV37Nihm2++WU2aNNGzzz6r4OBg7d27V99//32h3kMAcBkDAOAV5s+fb0gyfvrpJ2POnDlG6dKljdTUVMMwDKNfv35G+/btDcMwjNjYWKNHjx625y1ZssSQZDz33HN22+vbt69hMpmMvXv32pbFxsYaQ4YMsT2eMmWKER4ebvz+++92zx0/frzh7+9vHDp0yDAMw/j2228NScYjjzySI26z2WwYhmHs37/fkGTMnz8/RxtJxsSJE3Pkun//ftsya67Z3XfffUZYWJhx6dIl27K2bdsakoy5c+fmaH+lbdu2GZKMkSNH5tmuSZMmRvny5W2PhwwZYkgyxo8fn6Ot9fVffvll27K0tDTjmmuuMSpVqmSkp6cbhuH4/XjooYeM3P70Xpl/enq60ahRI+Omm26yW37lZ5gbSbnePvrooxz5fPDBB3b5VKlSxbjttttsyz788EPDz8/PWL9+vd3rzJ0715BkfP/993av7efnZ+zYscOu7f/93/8ZJpPJ2Lp1q23ZqVOnjPLly+f4fWjbtq3Rtm1bp1//lVdeMSQZf//9d77vEQAUJYYaAoAX6t+/vy5evKilS5fq/PnzWrp0aa7DDJctWyZ/f3898sgjdstHjx4twzC0fPnyXF/n008/VevWrVWuXDn9888/tlvHjh2VlZWl7777TpL0n//8RyaTSRMnTsyxjezD5a5GaGio7f758+f1zz//qHXr1kpNTdXu3bvt2gYHB+vuu+/Od5vnz5+XJJUuXTrPdqVLl1ZycnKO5Q888IDD9gEBAbrvvvtsj4OCgnTffffp5MmT2rJlS75xOZI9/zNnzujcuXNq3bq1fv7550JtT5JuueUWJSUl5bi1b9/erl2pUqXszgULCgrSddddp3379tmWffrpp6pfv77q1atn97ty0003SZLWrFljt822bduqQYMGdsu++eYbtWrVStdcc41tWfny5XMM43SkoK9ftmxZSZahq1cOlwUAT2KoIQB4ocjISHXs2FELFy5UamqqsrKy1LdvX4dtDx48qKioqBzFRf369W3rc/PHH3/o119/VWRkpMP1J0+elCT9+eefioqKUvny5QuTToHs2LFDTz31lL799tscRdC5c+fsHlerVq1Aky9Y3xNrAZab8+fP53j/AgICVL16dYfto6Kicky0YZ0p8MCBA7rhhhvyje1KS5cu1XPPPadt27bZnbN0NYVt9erV1bFjxwK1u/J1ypUrp19//dX2+I8//tCuXbvy/V2xcjS08+DBg2rVqlWO5bVq1co3xoK+/u2336533nlHI0aM0Pjx49WhQwf16dNHffv2lZ8f/28G4DkUXgDgpQYNGqR77rlHJ06cULdu3Wz/yXcls9msTp06ady4cQ7XOzPteG4FwpWTYzhy9uxZtW3bVhEREXr22WdVs2ZNhYSE6Oeff9bjjz+eo+cie+9QXmrVqqWAgAC7AuJKaWlp2rNnT47z1IKDg4vsQH39+vXq1auX2rRpozfeeENVq1ZVYGCg5s+fr4ULF7r99f39/R0uN7JNzmI2m9W4cWPNnDnTYdvo6Gi7xwX9jAqqoK8fGhqq7777TmvWrNHXX3+tb775Rp988oluuukmrVy5MtdcAcDdKLwAwEvdeuutuu+++7Rp0yZ98sknubaLjY3VqlWrcvTaWIfnxcbG5vrcmjVr6sKFC/n2itSsWVMrVqzQ6dOnc+31sk7FfvbsWbvlefW4Wa1du1anTp3SZ599pjZt2tiW79+/P9/n5iU8PFzt27fXt99+q4MHDzp8LxYtWqS0tDSnLkp97NixHNPL//7775Isk6PkJrfi9D//+Y9CQkK0YsUKu4km5s+fX+CY3K1mzZr65Zdf1KFDh0L3wsXGxmrv3r05ljtadjWv7+fnpw4dOqhDhw6aOXOmXnjhBU2YMEFr1qwpUA8gALgDfe4A4KVKlSqlN998U5MmTVLPnj1zbde9e3dlZWVpzpw5dstfeeUVmUwm26x0jvTv318bN27UihUrcqw7e/asMjMzJUm33XabDMPQ5MmTc7Sz9opERESoYsWKtvPCrN54443ck/wfay9E9h6W9PT0Aj03P0899ZQMw9DQoUN18eJFu3X79+/XuHHjVLVqVbtztvKTmZlpN/18enq63nrrLUVGRqpFixa5Ps9aqF1ZnPr7+8tkMtn1Dh44cCDHTIGe1L9/fx09elRvv/12jnUXL15USkpKvtvo0qWLNm7cqG3bttmWnT59WgsWLHDZ658+fTrHeus5ZVdOOw8ARYkeLwDwYkOGDMm3Tc+ePdW+fXtNmDBBBw4cUNOmTbVy5Up98cUXGjVqlGrWrJnrc8eOHasvv/xSN998s4YOHaoWLVooJSVFv/32mxYvXqwDBw6oYsWKat++ve688069+uqr+uOPP9S1a1eZzWatX79e7du318MPPyxJGjFihKZOnaoRI0aoZcuW+u6772w9QXlJSEhQuXLlNGTIED3yyCMymUz68MMPC3Qdsvy0adNGM2bM0GOPPaYmTZpo6NChqlq1qnbv3q23335bZrNZy5Yty/PiyVeKiorStGnTdODAAdWpU0effPKJtm3bpnnz5ikwMDDX51mLskceeURdunSRv7+/BgwYoB49emjmzJnq2rWrBg0apJMnT+r1119XrVq18hwmmZ/ff/9d//73v3Msr1y5sjp16uTUtu68804tWrRI999/v9asWaN//etfysrK0u7du7Vo0SKtWLEi38sKjBs3Tv/+97/VqVMn/d///Z9tOvmYmBidPn06z56sgr7+s88+q++++049evRQbGysTp48qTfeeEPVq1fXjTfe6FTOAOBSHpxREQCQTfbp5PNy5XTyhmEY58+fNx599FEjKirKCAwMNGrXrm289NJLtqnesz/3yqnIz58/bzzxxBNGrVq1jKCgIKNixYpGQkKCMWPGDNvU6IZhGJmZmcZLL71k1KtXzwgKCjIiIyONbt26GVu2bLG1SU1NNYYPH26UKVPGKF26tNG/f3/j5MmTBZpO/vvvvzduuOEGIzQ01IiKijLGjRtnrFixwpBkrFmzxtaubdu2RsOGDfN5N3P67rvvjFtuucWoWLGiERgYaMTExBj33HOPceDAgRxthwwZYoSHhzvcjvX1N2/ebLRq1coICQkxYmNjjTlz5ti1czSdfGZmpvF///d/RmRkpGEymeymln/33XeN2rVrG8HBwUa9evWM+fPnGxMnTswx/bwrppPPPk17bu/nkCFDjNjYWLtl6enpxrRp04yGDRsawcHBRrly5YwWLVoYkydPNs6dO2f32g899JDDuLZu3Wq0bt3aCA4ONqpXr268+OKLxquvvmpIMk6cOGEXV/Y4C/r6q1evNm655RYjKirKCAoKMqKiooyBAwfmuGQCABQ1k2G44N+JAAAAhTRq1Ci99dZbunDhApNfAPBZnOMFAACKzJXn2Z06dUoffvihbrzxRoouAD6Nc7wAAECRadWqldq1a6f69evrr7/+0rvvvqvk5GQ9/fTTng4NANyKwgsAABSZ7t27a/HixZo3b55MJpOaN2+ud9991+4yAgDgizjHCwAAAADcjHO8AAAAAMDNKLwAAAAAwM04x8tJZrNZx44dU+nSpfO80CMAAAAA32YYhs6fP6+oqCj5+eXdp0Xh5aRjx44pOjra02EAAAAA8BKHDx9W9erV82xD4eWk0qVLS7K8uRERER6ORsrIyNDKlSvVuXNnBQYGejqcQiMP7+IreUi+kwt5eB9fyYU8vI+v5EIe3sdXcvGmPJKTkxUdHW2rEfJC4eUk6/DCiIgIrym8wsLCFBER4fFfvKtBHt7FV/KQfCcX8vA+vpILeXgfX8mFPLyPr+TijXkU5BQkJtcAAAAAADej8AIAAAAAN6PwAgAAAAA3o/ACAAAAADej8AIAAAAAN6PwAgAAAAA3o/ACAAAAADej8AIAAAAAN6PwAgAAAAA3o/ACAAAAADej8AIAAAAAN6PwAgAAAAA3o/ACAAAAADej8AIAAABQLGRlSevWmfTdd9W0bp1JWVmejqjgKLwAAAAAeL3PPpPi4qROnQI0c2ZLdeoUoLg4y/LigMILAAAAgFf77DOpb1/pyBH75UePWpYXh+KLwgsAAACA18rKkkaOlAwj5zrrslGj5PXDDgM8HQAAAAAAWJnN0qFD0q5dltvq1Tl7urIzDOnwYWn9eqlduyIL02kUXgAAAACKXEaG9Oef0s6dl4usXbuk3bul1FTnt3f8uOtjdCUKLwAAAABuk5oq7dmTs8D64w8pM9PxcwIDpdq1pQYNpNBQ6cMP83+dqlVdG7erUXgBAAAAuGpnzlgKqisLrIMHHZ+fJUnh4VK9epYCq379y7caNSzFl2Q5d2vNGstEGo62YzJJ1atLrVu7LzdXoPACAAAAHMh+zajwcJPat5f8/T0dVeG4KhfDsAzpc1Rg/fVX7s+rUMFSUF1ZYFWvLvnlM92fv780e7Zl9kKTyb74MpksP2fN8v7PhsILAAAALuULBctnn1lm0jtyJEBSS82caSkSZs+W+vTxdHTOKUwuWVnSgQOOC6zk5Nxfq3p1xwVWZOTV5dCnj7R4sTUP+9ebNat4fCYUXgAAAHAZXyhYrNeMunJYm/WaUYsX+04uH30kNWyYs8Das0dKS3O8TT8/qWbNnAVWvXpS6dLuy6VPH+mWW6Q1azK1fPk2det2jdq3Dyg2RT2FFwAAAFzCFwqW/K4ZZTJJ//d/UsuWlgLE2s4wLt+yP87tvqvbOXpOZqb0wAN5X/9qwIDc34vgYKlu3ZwFVu3alnWe4O8vtW1rKCXlqNq2bVpsii6JwgsAAMBrFOchegUpWEaOlNq2tbTNyJDS03O/5be+IG0Ks/7SJct1pHJjGNKxY1JsrPvey6IWFiY1bpyzwIqLKz6/f8UBhRcAACVUcT7Iz85X8vDWIXqGIV28aJmxLq/brl35X+T2yBGpYsWii92d/PwsN5Pp8gQP1vtXPs7tfkHbFXbbFy5I//yTfy5vvy0NGlS49wEFR+EFAEAJ5K0H+c7ypTzcOUSvoMXTlbfTpy0/09OvLj9HAgOloKDLP3O75bW+sOvyWr95s9S/f/7xr14ttWvn+vfFldauldq3z79dVJTbQ4EovAAAKHF84TwcyXfyKMgQvVGjpF69LJMdOFs8WW9XWzz5+0tly0rlyjm+nT4tvfVW/ttZuVLq2PFyD423iYmxFO/F/ZpRkiVGX8nFF1B4AQBQghT0IP+WW7x7uF5B87j5ZsvjrKzLt8xMx/dd/bigbffvz3+I3uHDUmio5TlXw98/98Ipv1vp0nkXS1lZ0tdf53+Qf9NN3lt0Sb5zzSjJt3LxBRReAACUIOvXF+wgv3x5y1AsRwfQ2du6el1Bn5uZaRk6l1e7w4c9N/OaO1iLrvyKp/Llc19XqpT7ih5fOsj3hWtGWflSLsUdhRcAACVAWpq0Zo00c2bB2ud1gVRf4e8vBQRYflpveT12pq0zj48dkz79NP94P/5Y6t7dvcXT1fKlg/zifs2o7Hwpl+KMwgsAAB918qRl6NdXX1nOq0lJKfhzExOl666z3M/rIN8d6wry3E2bpDvuyHsbkvTFF5bpy68sdvz88n9uUcnKkjZuzH+IXt++xae3yFcO8ovzNaOu5Eu5FFcUXgAA+AjDkHbssBRaX31lKU6yH8hHRVl6TD7/3DIRQl4H+Xfc4d0H+XFx0vjx+RcrPXp4dx6Sbw3Rs+IgH8iJwgsAgGIsPV1at+5ysXXggP365s2lnj0tt+bNLQfy3boV/4N8XytWfGmIHgDHKLwAAChmTp2Sli2zFFrffCOdP395XXCw1KGDpdC6+WbLgfuVfOUg31fysPKlIXoAcqLwAgDAyxmGtHv35V6tH36QzObL6ytXthRZPXtaro8UHp7/Nn3lIN9X8rBiiB7guyi8AADwQhkZ0oYNlkLryy+lP/+0X9+kiaXQ6tVLatmycJNF+MpBvq/kAcC3UXgBAOAlzpyRli+/PITw7NnL64KCpPbtLw8hjI31WJgAgEKg8AIAwIP++OPyEML16y1Ti1tVrGiZla9nT6lzZ6l0ac/FCQC4OhReAAAUocxMyzla1mJrzx779Q0bXp6F8Prri8+sfACAvFF4AQCKRFaWtG6dSd99V03h4Sa1b198iwpnczl3TlqxwlJoLVtmuYaWVUCA5QK/vXpZhhDWqOH++AEARY/CCwDgdp99Zp3yO0BSS82caZnye/bs4jfld0Fz2bfvcq/WunWWni6r8uUtFzLu2VPq0kUqU6bI0wAAFDEKLwCAW332meUit9kvcCtJR49ali9eXHyKr/xyee45yzW1vvpK2rHDvk3dupZerZ49pVatLD1dAICSg90+AHi54jxELyvL0jt0ZaEiWZaZTNKoUZbrMHl7TvnlIkkTJlxe5u8vtW59+Xyt2rWLJk4AgHei8AIAL+atQ/QMQ7p0Sbpwwf52/rz9419+kY4cyXs7hw9LN94oVahgX9Tkdj+vde5sd/Zs3rlYtW8v3XOP1LWrVK5c/u0BACUDhRcAeClXDdEzDCktLffi6MrHBV2Wfdrzq7Vpk+u25Wn33CMNHOjpKAAA3qbEFl6vv/66XnrpJZ04cUJNmzbVa6+9puuuu67Az09JT5F/es5xMf5+/goJCLFrlxs/k59CA0ML1TY1I1WGYSgjI0OXsi4pJT1FgUagJMlkMiksMCxHW0eubHsx46LMhjnXOMKDwgvV9lLmJWWZcz9KCzIFFbhtWGCYTCaTJCktM02Z5kyXtA0NDJWfyU+SlJ6VroysDKfbOvo8QgJC5O/nX6DtZm+bkZWh9Kz0XNsGBwQrwC/A6baZ5kylZabl2jbI//JnkWnOVHp67tsN8g9SoL8lzyxzli5lXsq1baB/oG3bzrQ1G2ZdzLhY6LbZP5NQU6iCA4IlSYZhKDUjNdftBvgFFLitM9/7grbNypIeecxPhnH5e69AS1vrt3noPdLq76TUi1LKBT9dOh9qK46SL6bqQoqhC+elCymS/VfKJGWEZdtuaratXumKtgEXJZNZ8pPlJik0TCoVLoWXksqEhqtUKalUKSm09EWdTzFr9apcNp1xeR/x2LhLql3HEuT/vrKXIzBJwX7htvvp5ksy5LitTFKwyfK9N5mkDCNNWUZmjrbW+3ZtzWnKUqbD15ekvbtCNeXZ/yXtny75Of4ul60kmY2C7088vY/IyMhQlnH5F6Qg+wjr996Ztu7eRzja/+bWNjfOfO/duY/wl3+B2rriOMIRVx1HOPpM3HUc4UxbZ48jrNIy05Ru5P6dc8VxhCOu3Edk/0xK+Zdy23FEkewjHHzfHbW92uOI7Bx97/P63uV4foFb+pBPPvlEjz32mObOnavrr79es2bNUpcuXbRnzx5VqlSpQNuIejlKCsm5vHvt7vp60Ne2x5VmVMp1Z9w2tq3WDl1rexw3O07/pP7jsG3LqJb66Z6fbI8bvN5AB88dvNzgt8t3G0Q20I4HL5/Vfe3b12rn3zsdbje2TKwOjDpge9wmsY02H9vssG3FsIr6e+zftsfdFnTTuoPrHLYNCwxTypOXfxFvW3Sblv2xzGFbSUp/8vKX/c7P79TinYtzbXvhiQu2Hex9S+/T+7+8n2vbk2NOKjI8UpL02IrH9MbmN3Jtu3/kfsWVjZMkTVg9QTM2zsi17fYHtqthpYaSpBfWv6DJ6ybbN8j2efw44kddW+1aSdLsTbM1btW4XLe7ZsgatYtrJ0mat2WeHl7+cK5tlw5cqh51ekiSFvy2QHd/cXeubRf1XaR+DftJkj7f9bn6L+6fa9v5t8zX4IaDJUkr961U70W9c207p9scPXTdQ5Kk9YfWq/377XNtO73jdI3911hJ0s/Hf9Z17+T+j46JbSdqUrtJkqRdf+9Sozcb5dp2TKsxeqnzS5KkQ+cOKX52vOOGv0kPtnxQr/d4XZL0T+o/qjQj9+/7kKZDlNg7UZLloKPUi6Vybdu3QV992u9T2+O82jqzj1D7tlLi2suPR8VJ4Zf3EeclvSFJ5SQdbSkt/Clb2wZS2Wz7iGz8TzdQ7NIdtgLplxuvVUqY431EBf9YvV77gEqVsly894Gf22jnOft9xMX/3YywitqfbR/RLvF/+4jrHWw4PUx6IUUmk2Xo5K4mt2nm3tz3EcbEywd9/T4t+D5i6JKC7yMe+jrvfcTeR/Zr/ntxOnpUMm6aIP3L8T6i+wZpe+N89hHZeMM+YmzcWPVUT0kF20cMvWaoJGnF3hW6+aObc23rkX3Eb/ZtC7yPkPfsI5b0W2J7XGTHEdm4/Djif5+JO48jsu8jXH0cUTaorCRp7Kqxmvvz3FzbuvQ4Ihu37CN+c+9xhLv3EVtPbNWA3wbk+L5bueU4QrnsI3KvFXPwK3hT3zFz5kzdc889uvvuu9WgQQPNnTtXYWFheu+99zwdGoAS4MIF6aefpMREKSP3fzA6JTpGevtt6eOPpaVLpcqVc29bt67055+W86++/16Kjc29balS0u23Sz16SG3aSGHhubd1lrUnadYsBz1XXsjf33JunSSpGMQLAPAuJiO3vmMflZ6errCwMC1evFi9e/e2LR8yZIjOnj2rL774wq59Wlqa0tIud5EmJycrOjpaB48dVERERI7te2Ko4bfffqubbrpJgYHFe6hhUlKSOnXqpCxTVrEeanjl5+HpYURXti3QUEOzlJSUpPYd2stsyv0zLi5DDa2fSWhw0Q41vJAi/b7HpN27pd27Tdq9x6Q9uwJ0aF/O4YNWlSoZqlvXUEQZQ19/FShl5t5Wkr78KlOtbzQ8MozIkdz2EV9+ZdL48f46fuxyxVK9cphefjlLt95qeHQYkbP7iM8/N+nRMVk6duJy26hqhl58MUu9ehp2baXiMdRw/dr16tq5qwIDA71nGFEhhhpeuf/NrW1uvGaooeFv+5uY19C24jDU8MrPpDgONczMzFRSUpLatG8jk3/u/3UpLkMNrZ9JqdDiO9TwUtolLVu5LMf33VFbdw81TE5OVmxUrM6dO+ewNsiuxBVex44dU7Vq1fTDDz+oVatWtuXjxo3TunXr9N///teu/aRJkzR5cs7u34ULFyosLCzHcgAlz8WL/jpypLQOHSqtw4ctt0OHIvT337nvI8qWvaTo6POKjj6vmJjzio5OVnT0eUVEWP6gZmVJ997bWadOhchx94qhihUv6q23krx+GnarrCxp584KOnMmROXKXVKDBqeKTexX8qVcAACFl5qaqkGDBhWo8CqR53g544knntBjjz1me2zt8ercuXO+b25RyMjIsP1XzFHFX1yQh3fxlTyysqS1a7OUlLRdnTo1Urt2/ld1cHzhgrRrl0m7dkk7d5q0c6dJu3aZdPBg7v8BrVzZUIMGhurXN9SggWz3K1Twl1T2fzfH3njDpAEDJMmQYVx+DZPJ8v+y118PUs+e3QufkAd07eobv1uS7+TiK993X8lD8p1cyMP7+Eou3pRHcnJygduWuMKrYsWK8vf3119//WW3/K+//lKVKlVytA8ODlZwcHCO5YGBgR7/oLPztngKizy8S3HO4/L1rwLl7PWvLlyQdu6Uduy4/HPHDunQodyfU7my1LCh1KCB5af1foUKJhX2hKD+/aWAAGsel5dXr27SrFlSnz7FdxdenH+3ruQruZCH9/GVXMjD+/hKLt6QhzOvX3z/ahdSUFCQWrRoodWrV9vO8TKbzVq9erUefjj32aEAFB8Fvf7V+fPSrl2uKrDck0ufPtItt0hr1mRq+fJt6tbtGrVvH8CwNgAAipkSV3hJ0mOPPaYhQ4aoZcuWuu666zRr1iylpKTo7rtzn0Yzh5QUOTzy8feXQkLs2+XGz08KDS1c29RUy1FlRob8L12yPNdacZtMUvbzz6xtHbmy7cWLkjn3E10VHl64tpcu5X211aCggrcNC7s8BVpampSZ+0mxTrUNDbW8z5KUni5l5H7yaq5tHX0eISGXf1fy2272thkZlva5CQ62dIc42zYz0/Je5Cb7Z5GZmfd2g4Iu55mVZfnschMYeHnbzrQ1my2/awVsm3Xhosb/nxTq4Fc+0whQuoI1eLAUWdHQqSOOT4YPk1ShUoBqNwq2FFgNDDWumap69XIpsPz9ZXd9iby+y4XYR/j7S23bGrp06k+1bVlL/pcc/Hctt32EI57cR2T/jpQte3l5ft97Z/YnRbWPSE3N+X3PrW1Bv/ee2EdkZMiU/f0syD7Cmq8zbd29j3C0/82tbW4CAizvm2T5TqTmPmGGU22d+d77+9sfX7j7OMIRV+0jHH0m7jqOcOc+wiotLe/vnCuOIxxx5T4i+2dSqpT7jiOKYB+R6/73yrZOHkc4vY/I63t3JaOEeu2114yYmBgjKCjIuO6664xNmzYV6Hnnzp0zJBnnLG93zlv37vZPCAtz3E4yjLZt7dtWrJh725Yt7dvGxubetkED+7YNGuTeNjbWvm3Llrm3rVjRvm3btrm3DQuzb9u9e+5tJSM9Pd1YsmSJkZ6ebhh9++bZ1rhw4fJ2hwzJu+3Jk5fbPvhg3m3377/cdsyYvNtu33657cSJebf98cfLbadPz7vtmjWX286Zk3fbpUsvt50/P++2ixZdbrtoUd5t58+3fR4ZS5bk3XbOnMvbXbMm77bTp19u++OPebedOPFy2+3b8247ZoyRnGwYv/1mGN++tz/PtnP04OVfZ53Me7tDhlyO4cKFvNv27Wv/+55X20LuI9LT041LERG5ty1m+wizk/sIO+wjLFy4j/hx7FjL/tcwCrSPsFm6NO+2XrKPsNm/P++2Dz54ue1Jz+0j7P4mchxh4cF9hPXzyLz//rzb+vA+wtnjCBs37SMyfvgh77ZFuI84JxmSjHPnzhn5KZE9XpL08MMPM7QQKAbM5rwvOPjaa9Ij/7tGZaykAwXc7pjRkl6+qtAAAAAKrMRNJ3+1kpOTVaZMGZ07dszxrIZFPNQwIyNDK1asUJcuXS6f3FcMhxpmBAVp2bJl6t69uwKzsrxjGJGTQwSysqR16zKVlPSrOnVqorZt/3cejqeHEV3ZNp9u/yz/IK3ZYNLy5dvUvXMjtWuVlfv5RC4YRnTpkmXiiMOHL9/2HwnU/qNBOnhQOnLILP88rqmRoUBlKEhly0pxMWaVC7mo//7ouG2mLEMNJWnNt4baXeehYUSF2EdkZGRoxWef2X/Xc2kryWuHGtrts4r5UMOM1NSc+99c2nrzUMOMjAwt//ZbdevZ05KHlwwjcnaoocO/h7m0zZWXDDXM8Pe//Dcxr8/Yy4caOvxMiuFQw4zMTMvn0aGDAvO62nsxGGpo95kU46GGGZcuacUXX+T+N7EIhxomJyerTFQU08m7VXi4/Zc8r3bObLOgrDu5jAxlhYRYnpvbrCrOXG8s+07ZlW2z/8FxJPtOJL+22QUHX/4CuLJtUJD9uU75tP1saVC2medaa+prucyg58x2AwNz/0yvpm1AwOWd5xUuzwQoOTsToPz9c/wOG4Z05ox08KBlworLP/116FC4Dh6Urphg1AE/+fmFKypKio2VYmJy/oyJkSz7Oj9lZYUrLs4ykYaj4wSTyZJT6zYmyb+A3zmTyX3fZSfa5vtdz86Z731R7iOy77Oyc+Z77y37CJOpYJ+Jk/uTIt9HZGTIyP7flTz2ETk409bBPsIlbf38LG0L8vfQ2rYgnPneu3ofkf1voruPI1zdNvv3Pr/PxJXHEYVt6+w+oqDfOXd97692H5H9M8n+3XXRccRVtXVyH1Hgv4nOfO8Ls4/Iq8i/AoUXcJUKOoOetytMHpmZ0rFjVxZV9j8vXMj/tUNDLUVUboVVtWoF/3vg728pFPv2tewTs+dj/UflrFmO58YBAABwFwov4CpkZVl6iBz1rBiG5UB/1CjLdODefKCfXx6SNGKE9NNPlqGA1sLq6NGC/aMnMjL3oio21jJTYF6jN5zVp4+lUMx5/Sv97/pXrnstAACAgqDwAq7C+vX2B/ZXMgxLodK6tVS+vP3UOGazc4/d+ZxLl6R//sk71zNnpKlTcy4PCJCio/MeBujMaBJX4fpXAADAm1B4AU4wDGnfPmnzZsvt668L9ryNG90bV1Hp1Enq0MG+uKpSxXt786zXv0pJOaq2bZt6bZwAAMD3UXgBubD2Vv300+VCa8sWS8+Ps8aMkerXtwynM5ks525a7+e2zNnHV/OcLVuk++7LP48nn5TatXM+fwAAgJKOwgv4n2PHLhdY1tvff+dsFxQkNW0qtWwptWghTZggnTyZ9wx6U6d6b6+QJF1zjTRlSgFmAmxd5KEBAAD4BAovlEgnT+Ysso4fz9kuIEBq3NhSZFlvjRrZz+Rarlzxn0GPmQABAADci8ILPu/0actQus2bLw8bPHw4Zzs/P6lhQ/siq0mT/C8H4isz6PlKHgAAAN6Iwgs+5dw56eef7Xuy9u3L2c5kkurWtS+yrrnGuWtPZucrM+j5Sh4AAADehsILHpeVJa1bZ9J331VTeLhJ7dsXbEjbhQvS1q32RdbvvztuW6uWfZHVrJkUEeHaPHxlBj1fyQMAAMCbUHjBoz77zDq0LUBSS82caRnaNnu2/dC2ixelX36xL7J27bJcg+pKsbGW4uraay0/mze3nIcFAAAAeAqFFzzms88skzlcOYve0aPSbbdJ995r6Q3bvFnavt1y/0rVqtn3ZLVoIUVGFk38AAAAQEFReMEjsrIsPV2Opi63Lps3z355pUqXe7GsRVbVqu6PFQAAALhaFF7wiPXr7WfOy83gwZber2uvtfRuWac2BwAAAIoTCi94hKNrZjnSo4d0663ujQUAAABwNz9PB4CSqaBDBBlKCAAAAF9A4QWPaN3aMnthbkMHTSYpOtrSDgAAACjuKLzgEf7+linjHbEWY7NmFex6XgAAAIC3o/CCx/TpIy1eLPld8VtYvbplefbreAEAAADFGZNrwKO6dLl8EeQHH9yqW29trPbtA+jpAgAAgE+hxwse9eeflp/lyhnq3PmQ2rY1KLoAAADgcyi84FHWwqtGDQdXUgYAAAB8BIUXPMpaeNWs6dk4AAAAAHei8IJH7d1r+UmPFwAAAHwZhRc8ytrjVasWhRcAAAB8F4UXPOryOV6ejQMAAABwJwoveEx6unTwoOU+Qw0BAADgyyi84DEHD1qu4RUaKlWt6uloAAAAAPeh8ILHWCfWqFlTMpk8GwsAAADgThRe8BimkgcAAEBJQeEFj7k8o6Fn4wAAAADcjcILHpN9qCEAAADgyyi84DH0eAEAAKCkoPCCR5jN0r59lvv0eAEAAMDXUXjBI44eldLSpIAAKSbG09EAAAAA7kXhBY+wDjOMi7MUXwAAAIAvo/CCRzCxBgAAAEoSCi94BBNrAAAAoCSh8IJHcPFkAAAAlCQUXvAIhhoCAACgJKHwQpEzDIYaAgAAoGSh8EKR++cfKTnZcj8+3rOxAAAAAEWBwgtFztrbVa2aFBrq2VgAAACAokDhhSLHMEMAAACUNBReKHJMrAEAAICShsILRY4eLwAAAJQ0FF4oclzDCwAAACUNhReKHEMNAQAAUNJQeKFInT8vnTxpuU/hBQAAgJKCwgtFyjrMsEIFqWxZj4YCAAAAFBkKLxQpJtYAAABASUThhSLFxBoAAAAoiSi8UKSYWAMAAAAlEYUXihRDDQEAAFASUXihSNHjBQAAgJKIwgtFJi1NOnzYcp/CCwAAACUJhReKzIEDkmFI4eFS5cqejgYAAAAoOhReKDLZhxmaTJ6NBQAAAChKFF4oMkysAQAAgJKKwgtFhmt4AQAAoKSi8EKRYUZDAAAAlFQUXigyDDUEAABASUXhhSKRlSXt22e5T48XAAAAShoKLxSJI0ekjAwpMFCKjvZ0NAAAAEDRovBCkbAOM4yPl/z9PRsLAAAAUNQovFAkmFgDAAAAJRmFF4oEE2sAAACgJKPwQpHgGl4AAAAoySi8UCQYaggAAICSjMILbmcYDDUEAABAyVZsCq/nn39eCQkJCgsLU9myZR22OXTokHr06KGwsDBVqlRJY8eOVWZmpl2btWvXqnnz5goODlatWrWUmJjo/uBLuJMnpQsXJJPJMqshAAAAUNIUm8IrPT1d/fr10wMPPOBwfVZWlnr06KH09HT98MMPev/995WYmKhnnnnG1mb//v3q0aOH2rdvr23btmnUqFEaMWKEVqxYUVRplEjW3q7oaCk42LOxAAAAAJ4Q4OkACmry5MmSlGsP1cqVK7Vz506tWrVKlStX1jXXXKMpU6bo8ccf16RJkxQUFKS5c+cqPj5eL7/8siSpfv362rBhg1555RV16dKlqFIpcZhYAwAAACVdsSm88rNx40Y1btxYlStXti3r0qWLHnjgAe3YsUPNmjXTxo0b1bFjR7vndenSRaNGjcp1u2lpaUpLS7M9Tk5OliRlZGQoIyPDtUkUgjUGb4glN3v2+EnyV3y8WRkZWQ7bFIc8CoI8vI+v5EIe3sdXciEP7+MruZCH9/GVXLwpD2di8JnC68SJE3ZFlyTb4xMnTuTZJjk5WRcvXlRoaGiO7b744ou23rbsVq5cqbCwMFeFf9WSkpI8HUKuNmxoLilamZm7tGzZ3jzbenMeziAP7+MruZCH9/GVXMjD+/hKLuThfXwlF2/IIzU1tcBtPVp4jR8/XtOmTcuzza5du1SvXr0iiiinJ554Qo899pjtcXJysqKjo9W5c2dFRER4LC6rjIwMJSUlqVOnTgoMDPR0OA698IK/JKlbt7rq3r2OwzbFIY+CIA/v4yu5kIf38ZVcyMP7+Eou5OF9fCUXb8rDOhquIDxaeI0ePVpDhw7Ns02NGjUKtK0qVaroxx9/tFv2119/2dZZf1qXZW8TERHhsLdLkoKDgxXsYEaIwMBAj3/Q2XlbPNnt22f5WbdugPIL0ZvzcAZ5eB9fyYU8vI+v5EIe3sdXciEP7+MruXhDHs68vkcLr8jISEVGRrpkW61atdLzzz+vkydPqlKlSpIs3Y8RERFq0KCBrc2yZcvsnpeUlKRWrVq5JAbkdO6c9M8/lvtMrgEAAICSqthMJ3/o0CFt27ZNhw4dUlZWlrZt26Zt27bpwoULkqTOnTurQYMGuvPOO/XLL79oxYoVeuqpp/TQQw/Zeqzuv/9+7du3T+PGjdPu3bv1xhtvaNGiRXr00Uc9mZpPs85oGBkpecHITAAAAMAjis3kGs8884zef/992+NmzZpJktasWaN27drJ399fS5cu1QMPPKBWrVopPDxcQ4YM0bPPPmt7Tnx8vL7++ms9+uijmj17tqpXr6533nmHqeTdyFp41arl2TgAAAAATyo2hVdiYmKu1/Cyio2NzTGU8Ert2rXT1q1bXRgZ8sI1vAAAAIBiNNQQxdPe/80eT+EFAACAkozCC27FUEMAAACAwgtuRo8XAAAAQOEFN7p0STp61HKfHi8AAACUZBRecJv9+yXDkEqXlipW9HQ0AAAAgOdQeMFtsg8zNJk8GwsAAADgSRRecBsm1gAAAAAsKLzgNlzDCwAAALCg8ILbMKMhAAAAYEHhBbdhqCEAAABgQeEFt8jMtMxqKNHjBQAAAFB4wS0OH7YUX8HBUvXqno4GAAAA8CwKL7iFdZhhfLzkx28ZAAAASjgOieEWTKwBAAAAXEbhBbdgYg0AAADgMgovuAXX8AIAAAAuo/CCW1iHGtLjBQAAAFB4wQ0Mgx4vAAAAIDsKL7jciRNSaqplNsO4OE9HAwAAAHgehRdcztrbFRMjBQV5NhYAAADAG1B4weUYZggAAADYo/CCy3ENLwAAAMAehRdcjmt4AQAAAPYovOBy9HgBAAAA9ii84HL0eAEAAAD2KLzgUmfOSKdPW+7XqOHZWAAAAABvQeEFl7L2dlWuLJUq5dlYAAAAAG9B4QWXYpghAAAAkBOFF1yKa3gBAAAAOVF4waWY0RAAAADIicILLsVQQwAAACAnCi+4FD1eAAAAQE4UXnCZixelY8cs9+nxAgAAAC6j8ILL7Ntn+VmmjFS+vGdjAQAAALwJhRdcJvswQ5PJs7EAAAAA3oTCCy7DxBoAAACAYxRecBmu4QUAAAA4RuEFl7EONaTHCwAAALBH4QWXoccLAAAAcIzCCy6RkSEdOGC5T+EFAAAA2KPwgkscOiRlZUkhIVJUlKejAQAAALwLhRdcwjrMsEYNyY/fKgAAAMAOh8hwiezX8AIAAABgj8ILLsE1vAAAAIDcUXjBJejxAgAAAHJH4QWXoMcLAAAAyB2FF66a2Szt22e5T48XAAAAkBOFF67a8ePSxYuSv78UG+vpaAAAAADvE+BMY7PZrHXr1mn9+vU6ePCgUlNTFRkZqWbNmqljx46Kjo52V5zwYtZhhrGxUmCgZ2MBAAAAvFGBerwuXryo5557TtHR0erevbuWL1+us2fPyt/fX3v37tXEiRMVHx+v7t27a9OmTe6OGV7GWngxzBAAAABwrEA9XnXq1FGrVq309ttvq1OnTgp00K1x8OBBLVy4UAMGDNCECRN0zz33uDxYeCfrjIZMrAEAAAA4VqDCa+XKlapfv36ebWJjY/XEE09ozJgxOnTokEuCQ/FAjxcAAACQtwINNaxfv762b99eoA0GBgaqJkfgJQrX8AIAAADyVuBZDZs0aaLrr79eb7/9ts6fP+/OmFDMcA0vAAAAIG8FLrzWrVunhg0bavTo0apataqGDBmi9evXuzM2FAOnT0tnz1ru16jh0VAAAAAAr1Xgwqt169Z67733dPz4cb322ms6cOCA2rZtqzp16mjatGk6ceKEO+OEl7IOM6xaVQoL82wsAAAAgLdy+gLK4eHhuvvuu7Vu3Tr9/vvv6tevn15//XXFxMSoV69e7ogRXoxhhgAAAED+nC68sqtVq5aefPJJPfXUUypdurS+/vprV8WFYoIZDQEAAID8FWg6eUe+++47vffee/rPf/4jPz8/9e/fX8OHD3dlbCgGuIYXAAAAkD+nCq9jx44pMTFRiYmJ2rt3rxISEvTqq6+qf//+Cg8Pd1eM8GL0eAEAAAD5K3Dh1a1bN61atUoVK1bUXXfdpWHDhqlu3brujA3FANfwAgAAAPJX4MIrMDBQixcv1s033yx/f393xoRiIiVFsk5myVBDAAAAIHcFLry+/PJLu8d79+7Vn3/+qTZt2ig0NFSGYchkMrk8QHivffssP8uVs9wAAAAAOOb0rIanTp1Shw4dVKdOHXXv3l3Hjx+XJA0fPlyjR492eYDwXkysAQAAABSM04XXo48+qsDAQB06dEhh2a6Ye/vtt+ubb75xaXDwbkysAQAAABSM09PJr1y5UitWrFD16tXtlteuXVsHDx50WWDwfkysAQAAABSM0z1eKSkpdj1dVqdPn1ZwcLBLgkLxYO3xYqghAAAAkDenC6/WrVvrgw8+sD02mUwym82aPn262rdv79Lg4N0YaggAAAAUjNNDDadPn64OHTpo8+bNSk9P17hx47Rjxw6dPn1a33//vTtihBdKT5esI0spvAAAAIC8Od3j1ahRI/3++++68cYbdcsttyglJUV9+vTR1q1bVZMj8BLj4EHJbJZCQ6WqVT0dDQAAAODdnO7xkqQyZcpowoQJro4FxUj2YYZcvg0AAADIW4F6vA4dOuTURo8ePVqoYFB8cA0vAAAAoOAKVHhde+21uu+++/TTTz/l2ubcuXN6++231ahRI/3nP/9xWYCSdODAAQ0fPlzx8fEKDQ1VzZo1NXHiRKWnp9u1+/XXX9W6dWuFhIQoOjpa06dPz7GtTz/9VPXq1VNISIgaN26sZcuWuTTWkoKJNQAAAICCK9BQw507d+r5559Xp06dFBISohYtWigqKkohISE6c+aMdu7cqR07dqh58+aaPn26unfv7tIgd+/eLbPZrLfeeku1atXS9u3bdc899yglJUUzZsyQJCUnJ6tz587q2LGj5s6dq99++03Dhg1T2bJlde+990qSfvjhBw0cOFAvvviibr75Zi1cuFC9e/fWzz//rEaNGrk0Zl/HNbwAAACAgitQ4VWhQgXNnDlTzz//vL7++mtt2LBBBw8e1MWLF1WxYkUNHjxYXbp0cVvx0rVrV3Xt2tX2uEaNGtqzZ4/efPNNW+G1YMECpaen67333lNQUJAaNmyobdu2aebMmbbCa/bs2eratavGjh0rSZoyZYqSkpI0Z84czZ071y2x+yqu4QUAAAAUnFOTa4SGhqpv377q27evu+IpsHPnzql8+fK2xxs3blSbNm0UFBRkW9alSxdNmzZNZ86cUbly5bRx40Y99thjdtvp0qWLlixZkuvrpKWlKS0tzfY4OTlZkpSRkaGMjAwXZVN41hiKMhazWdq3L0CSSTExGXLFS3siD3cgD+/jK7mQh/fxlVzIw/v4Si7k4X18JRdvysOZGEyGYRhujMUt9u7dqxYtWmjGjBm65557JEmdO3dWfHy83nrrLVu7nTt3qmHDhtq5c6fq16+voKAgvf/++xo4cKCtzRtvvKHJkyfrr7/+cvhakyZN0uTJk3MsX7hwocLCwlycWfHw998huueeLvL3N2vRoqXy9y92v0IAAADAVUtNTdWgQYN07tw5RURE5Nm2UNPJu8r48eM1bdq0PNvs2rVL9erVsz0+evSounbtqn79+tmKLnd64okn7HrJkpOTFR0drc6dO+f75haFjIwMJSUlqVOnTgoMDCyS11y3zjJ/fHy8ST17dnPJNj2RhzuQh/fxlVzIw/v4Si7k4X18JRfy8D6+kos35WEdDVcQHi28Ro8eraFDh+bZpkaNGrb7x44dU/v27ZWQkKB58+bZtatSpUqOXivr4ypVquTZxrrekeDgYAUHB+dYHhgY6PEPOruijOfgQcvPmjVNLn9Nb3tfC4s8vI+v5EIe3sdXciEP7+MruZCH9/GVXLwhD2de36OFV2RkpCIjIwvU9ujRo2rfvr1atGih+fPny8/Pfib8Vq1aacKECcrIyLC9AUlJSapbt67KlStna7N69WqNGjXK9rykpCS1atXKNQmVEFzDCwAAAHBOga7jlV1KSoo74sjT0aNH1a5dO8XExGjGjBn6+++/deLECZ04ccLWZtCgQQoKCtLw4cO1Y8cOffLJJ5o9e7bdMMGRI0fqm2++0csvv6zdu3dr0qRJ2rx5sx5++OEiz6k44xpeAAAAgHOcLrwqV66sYcOGacOGDe6Ix6GkpCTt3btXq1evVvXq1VW1alXbzapMmTJauXKl9u/frxYtWmj06NF65plnbFPJS1JCQoIWLlyoefPmqWnTplq8eLGWLFnCNbycxDW8AAAAAOc4XXj9+9//1unTp3XTTTepTp06mjp1qo4dO+aO2GyGDh0qwzAc3rJr0qSJ1q9fr0uXLunIkSN6/PHHc2yrX79+2rNnj9LS0rR9+3aXX+zZ1xkG1/ACAAAAnOV04dW7d28tWbJER48e1f3336+FCxcqNjZWN998sz777DNlZma6I054iVOnJOvkLfHxno0FAAAAKC6cLrysIiMj9dhjj+nXX3/VzJkztWrVKvXt21dRUVF65plnlJqa6so44SWswwyrV5dCQz0bCwAAAFBcFHpWw7/++kvvv/++EhMTdfDgQfXt21fDhw/XkSNHNG3aNG3atEkrV650ZazwAkysAQAAADjP6cLrs88+0/z587VixQo1aNBADz74oO644w6VLVvW1iYhIUH169d3ZZzwEkysAQAAADjP6cLr7rvv1oABA/T999/r2muvddgmKipKEyZMuOrg4H2YWAMAAABwntOF1/HjxxUWFpZnm9DQUE2cOLHQQcF7MdQQAAAAcJ7ThVdmZqaSrdPaZWMymRQcHKygoCCXBAbvxFBDAAAAwHlOF15ly5aVyWTKdX316tU1dOhQTZw4UX5+hZ40EV7o/Hnp5EnLfQovAAAAoOCcLrwSExM1YcIEDR06VNddd50k6ccff9T777+vp556Sn///bdmzJih4OBgPfnkky4PGJ6zb5/lZ4UKUra5VAAAAADkw+nC6/3339fLL7+s/v3725b17NlTjRs31ltvvaXVq1crJiZGzz//PIWXj7EOM2RiDQAAAMA5To8F/OGHH9SsWbMcy5s1a6aNGzdKkm688UYdOnTo6qODV2FiDQAAAKBwnC68oqOj9e677+ZY/u677yo6OlqSdOrUKZUrV+7qo4NXYWINAAAAoHCcHmo4Y8YM9evXT8uXL7ddx2vz5s3avXu3Fi9eLEn66aefdPvtt7s2Ungc1/ACAAAACsfpwqtXr17as2eP3nrrLe3Zs0eS1K1bNy1ZskRxcXGSpAceeMClQcI7MNQQAAAAKBynCq+MjAx17dpVc+fO1YsvvuiumOCF0tIk62l79HgBAAAAznHqHK/AwED9+uuv7ooFXuzAAckwpPBwqVIlT0cDAAAAFC9OT65xxx13OJxcA74t+zDDPK6fDQAAAMABp8/xyszM1HvvvadVq1apRYsWCg8Pt1s/c+ZMlwUH78E1vAAAAIDCc7rw2r59u5o3by5J+v333+3WmegK8VlMrAEAAAAUntOF15o1a9wRB7wc1/ACAAAACs/pc7ys9u7dqxUrVujixYuSJMMwXBYUvA/X8AIAAAAKz+nC69SpU+rQoYPq1Kmj7t276/jx45Kk4cOHa/To0S4PEJ6XlSXt32+5T48XAAAA4DynC69HH31UgYGBOnTokMLCwmzLb7/9dn3zzTcuDQ7e4cgRKT1dCgyUoqM9HQ0AAABQ/Dh9jtfKlSu1YsUKVa9e3W557dq1dfDgQZcFBu9hHWYYHy/5+3s2FgAAAKA4crrHKyUlxa6ny+r06dMKDg52SVDwLkysAQAAAFwdpwuv1q1b64MPPrA9NplMMpvNmj59utq3b+/S4OAdmFgDAAAAuDpODzWcPn26OnTooM2bNys9PV3jxo3Tjh07dPr0aX3//ffuiBEexjW8AAAAgKvjdI9Xo0aN9Pvvv+vGG2/ULbfcopSUFPXp00dbt25VTY7MfZJ1qCE9XgAAAEDhON3jJUllypTRhAkTXB0LvJBh0OMFAAAAXK1CFV5nz57Vjz/+qJMnT8psNtutu+uuu1wSGLzD339LFy5IJpNlVkMAAAAAznO68Prqq680ePBgXbhwQRERETKZTLZ1JpOJwsvHWIcZRkdLTFoJAAAAFI7T53iNHj1aw4YN04ULF3T27FmdOXPGdjt9+rQ7YoQHMcwQAAAAuHpOF15Hjx7VI4884vBaXvA9XMMLAAAAuHpOF15dunTR5s2b3RELvBDX8AIAAACuntPnePXo0UNjx47Vzp071bhxYwUGBtqt79Wrl8uCg+cx1BAAAAC4ek4XXvfcc48k6dlnn82xzmQyKSsr6+qjgtfgGl4AAADA1XO68Lpy+nj4ruRk6Z9/LPfp8QIAAAAKz+lzvFByWIcZRkZKpUt7NhYAAACgOCtw4dW9e3edO3fO9njq1Kk6e/as7fGpU6fUoEEDlwYHz2KYIQAAAOAaBS68VqxYobS0NNvjF154we66XZmZmdqzZ49ro4NHMbEGAAAA4BoFLrwMw8jzMXwP1/ACAAAAXINzvJArruEFAAAAuEaBCy+TySSTyZRjGXwXQw0BAAAA1yjwdPKGYWjo0KEKDg6WJF26dEn333+/wsPDJcnu/C8Uf5cuSUeOWO7T4wUAAABcnQIXXkOGDLF7fMcdd+Roc9ddd119RPAK+/dLhmGZRr5iRU9HAwAAABRvBS685s+f78444GWyDzNkRCkAAABwdZhcAw5xDS8AAADAdSi84BATawAAAACuQ+EFh+jxAgAAAFyHwgsO0eMFAAAAuA6FF3LIzJQOHLDcp/ACAAAArh6FF3I4fFjKyJCCg6Xq1T0dDQAAAFD8UXghB+sww/h4yY/fEAAAAOCqcViNHKwTazDMEAAAAHANCi/kYO3xYkZDAAAAwDUovJADMxoCAAAArkXhhRy4hhcAAADgWhResGMY0r59lvv0eAEAAACuQeEFO3/9JaWkWGYzjIvzdDQAAACAb6Dwgh3rMMOYGCkoyLOxAAAAAL6Cwgt2mFgDAAAAcD0KL9hhYg0AAADA9Si8YIceLwAAAMD1KLxgh8ILAAAAcD0KL9hhqCEAAADgehResDl7Vjp92nK/Rg2PhgIAAAD4FAov2FiHGVauLJUq5dlYAAAAAF9C4QUbhhkCAAAA7kHhBRsm1gAAAADcg8ILNvR4AQAAAO5B4QUberwAAAAA96Dwgg2FFwAAAOAexabw6tWrl2JiYhQSEqKqVavqzjvv1LFjx+za/Prrr2rdurVCQkIUHR2t6dOn59jOp59+qnr16ikkJESNGzfWsmXLiioFr3bxonT0qOU+Qw0BAAAA1yo2hVf79u21aNEi7dmzR//5z3/0559/qm/fvrb1ycnJ6ty5s2JjY7Vlyxa99NJLmjRpkubNm2dr88MPP2jgwIEaPny4tm7dqt69e6t3797avn27J1LyKvv2WX6WKSOVL+/ZWAAAAABfE+DpAArq0Ucftd2PjY3V+PHj1bt3b2VkZCgwMFALFixQenq63nvvPQUFBalhw4batm2bZs6cqXvvvVeSNHv2bHXt2lVjx46VJE2ZMkVJSUmaM2eO5s6d65G8vIV1Yo2aNSWTybOxAAAAAL6m2BRe2Z0+fVoLFixQQkKCAgMDJUkbN25UmzZtFBQUZGvXpUsXTZs2TWfOnFG5cuW0ceNGPfbYY3bb6tKli5YsWZLra6WlpSktLc32ODk5WZKUkZGhjIwMF2ZVONYYrjaW33/3k+SvGjXMysjIckFkznFVHp5GHt7HV3IhD+/jK7mQh/fxlVzIw/v4Si7elIczMZgMwzDcGItLPf7445ozZ45SU1N1ww03aOnSpapQoYIkqXPnzoqPj9dbb71la79z5041bNhQO3fuVP369RUUFKT3339fAwcOtLV54403NHnyZP31118OX3PSpEmaPHlyjuULFy5UWFiYizP0nLfeaqLly+N1222/6847d3k6HAAAAMDrpaamatCgQTp37pwiIiLybOvRHq/x48dr2rRpebbZtWuX6tWrJ0kaO3ashg8froMHD2ry5Mm66667tHTpUpncODbuiSeesOslS05OVnR0tDp37pzvm1sUMjIylJSUpE6dOtl6/wrj9df9JUldutRQ9+7xrgqvwFyVh6eRh/fxlVzIw/v4Si7k4X18JRfy8D6+kos35WEdDVcQHi28Ro8eraFDh+bZpkaNGrb7FStWVMWKFVWnTh3Vr19f0dHR2rRpk1q1aqUqVark6LWyPq5SpYrtp6M21vWOBAcHKzg4OMfywMBAj3/Q2V1tPNbJNerUCZAn0/K297WwyMP7+Eou5OF9fCUX8vA+vpILeXgfX8nFG/Jw5vU9WnhFRkYqMjKyUM81m82SZDv/qlWrVpowYYJtsg1JSkpKUt26dVWuXDlbm9WrV2vUqFG27SQlJalVq1ZXkUXxl5EhHTxouc81vAAAAADXKxbTyf/3v//VnDlztG3bNh08eFDffvutBg4cqJo1a9qKpkGDBikoKEjDhw/Xjh079Mknn2j27Nl2wwRHjhypb775Ri+//LJ2796tSZMmafPmzXr44Yc9lZpXOHRIysyUQkKkqChPRwMAAAD4nmJReIWFhemzzz5Thw4dVLduXQ0fPlxNmjTRunXrbMMAy5Qpo5UrV2r//v1q0aKFRo8erWeeecY2lbwkJSQkaOHChZo3b56aNm2qxYsXa8mSJWrUqJGnUvMKf/5p+VmjhuRXLH4jAAAAgOKlWEwn37hxY3377bf5tmvSpInWr1+fZ5t+/fqpX79+rgrNJ1iv4VWrlmfjAAAAAHwV/Ruw9XhxfhcAAADgHhReoPACAAAA3IzCCww1BAAAANyMwquEM4zL1/CixwsAAABwDwqvEu74ceniRcnfX4qN9XQ0AAAAgG+i8CrhrMMMY2MlH7iAOQAAAOCVKLxKOCbWAAAAANyPwquEY2INAAAAwP0ovEo4erwAAAAA96PwKuEovAAAAAD3o/Aq4RhqCAAAALgfhVcJdvq0dPas5X6NGh4NBQAAAPBpFF4lmLW3KypKCgvzbCwAAACAL6PwKsE4vwsAAAAoGhReJRiFFwAAAFA0KLxKMCbWAAAAAIoGhVcJRo8XAAAAUDQovEowCi8AAACgaFB4lVApKdLx45b7DDUEAAAA3IvCq4Tat8/ys1w5yw0AAACA+1B4lVBMrAEAAAAUHQqvEorzuwAAAICiQ+FVQlF4AQAAAEWHwquEYqghAAAAUHQovEooerwAAACAokPhVQKlp0sHD1ru0+MFAAAAuB+FVwl08KBkNkthYVKVKp6OBgAAAPB9FF4lkHWYYY0aksnk2VgAAACAkoDCqwRiYg0AAACgaFF4lUBMrAEAAAAULQqvEojCCwAAAChaFF4lEEMNAQAAgKJF4VXCmM3Svn2W+/R4AQAAAEWDwquEOXpUSkuTAgKkmBhPRwMAAACUDBReJYz1/K64OEvxBQAAAMD9KLxKGCbWAAAAAIoehVcJw8QaAAAAQNGj8Cph6PECAAAAih6FVwlD4QUAAAAUPQqvEsQwGGoIAAAAeAKFVwly6pSUnGy5Hx/v2VgAAACAkoTCqwSx9nZVry6Fhno2FgAAAKAkofAqQTi/CwAAAPAMCq8ShMILAAAA8AwKrxKEiTUAAAAAz6DwKkHo8QIAAAA8g8KrBLEWXvR4AQAAAEWLwquEOH9e+usvy316vAAAAICiReFVQuzbZ/lZoYJUpoxnYwEAAABKGgqvEoKJNQAAAADPofAqIZhYAwAAAPAcCq8SgsILAAAA8BwKrxKCoYYAAACA51B4lRD0eAEAAACeQ+FVAqSlSYcOWe7T4wUAAAAUPQqvEuDAAckwpPBwqVIlT0cDAAAAlDwUXiVA9mGGJpNnYwEAAABKIgqvEoCJNQAAAADPovAqAZhYAwAAAPAsCq8SwFp40eMFAAAAeAaFVwlgHWpIjxcAAADgGRRePi4rS9q/33KfwgsAAADwDAovH3fkiJSeLgUGStHRno4GAAAAKJkovHyc9fyu+HjJ39+zsQAAAAAlFYWXj2NGQwAAAMDzKLx8HNfwAgAAADyPwsvH0eMFAAAAeB6Fl4/jGl4AAACA51F4+TDD4BpeAAAAgDeg8PJhf/8tXbggmUyWWQ0BAAAAeAaFlw+z9nZFR0vBwZ6NBQAAACjJKLx8GBNrAAAAAN6h2BVeaWlpuuaaa2QymbRt2za7db/++qtat26tkJAQRUdHa/r06Tme/+mnn6pevXoKCQlR48aNtWzZsiKKvOgxsQYAAADgHYpd4TVu3DhFRUXlWJ6cnKzOnTsrNjZWW7Zs0UsvvaRJkyZp3rx5tjY//PCDBg4cqOHDh2vr1q3q3bu3evfure3btxdlCkWGiTUAAAAA7xDg6QCcsXz5cq1cuVL/+c9/tHz5crt1CxYsUHp6ut577z0FBQWpYcOG2rZtm2bOnKl7771XkjR79mx17dpVY8eOlSRNmTJFSUlJmjNnjubOnevwNdPS0pSWlmZ7nJycLEnKyMhQRkaGO9J0ijUGR7Hs3esvyU+xsZnKyDCKODLn5JVHcUIe3sdXciEP7+MruZCH9/GVXMjD+/hKLt6UhzMxmAzD8O4j8v/566+/1KJFCy1ZskQVK1ZUfHy8tm7dqmuuuUaSdNdddyk5OVlLliyxPWfNmjW66aabdPr0aZUrV04xMTF67LHHNGrUKFubiRMnasmSJfrll18cvu6kSZM0efLkHMsXLlyosLAwV6bockOGdNW5c8GaOXONatRI9nQ4AAAAgE9JTU3VoEGDdO7cOUVEROTZtlj0eBmGoaFDh+r+++9Xy5YtdeDAgRxtTpw4ofgr5kyvXLmybV25cuV04sQJ27LsbU6cOJHraz/xxBN67LHHbI+Tk5MVHR2tzp075/vmFoWMjAwlJSWpU6dOCgwMtC1PTpbOnbM8HjLkRpUu7akICya3PIob8vA+vpILeXgfX8mFPLyPr+RCHt7HV3Lxpjyso+EKwqOF1/jx4zVt2rQ82+zatUsrV67U+fPn9cQTTxRRZJcFBwcr2MFc7IGBgR7/oLO7Mp5Dhyw/IyOl8uW9J878eNv7Wljk4X18JRfy8D6+kgt5eB9fycVVeZjNZqWnp7sgIudkZWUpICBAWVlZ8vMrdtMj2PGVXIo6j6CgoFxfx5nfbY8WXqNHj9bQoUPzbFOjRg19++232rhxY44CqGXLlho8eLDef/99ValSRX/99ZfdeuvjKlWq2H46amNd70usE2swoyEAACju0tPTtX//fpnN5iJ/bcMwVKVKFR0+fFgmk6nIX9+VfCWXos7Dz89P8fHxCgoKuqrteLTwioyMVGRkZL7tXn31VT333HO2x8eOHVOXLl30ySef6Prrr5cktWrVShMmTFBGRoat8kxKSlLdunVVrlw5W5vVq1fbneOVlJSkVq1auTAr78A1vAAAgC8wDEPHjx+Xv7+/oqOji7ynxmw268KFCypVqlSx7iWSfCeXoszDbDbr2LFjOn78uGJiYq6q0CsW53jFxMTYPS5VqpQkqWbNmqpevbokadCgQZo8ebKGDx+uxx9/XNu3b9fs2bP1yiuv2J43cuRItW3bVi+//LJ69Oihjz/+WJs3b7abct5XcA0vAADgCzIzM5WamqqoqCiPTGxmHeIYEhJSrIsVyXdyKeo8IiMjdezYMWVmZl7VsNni+45foUyZMlq5cqX279+vFi1aaPTo0XrmmWdsU8lLUkJCghYuXKh58+apadOmWrx4sZYsWaJGjRp5MHL34BpeAADAF2RlZUnSVQ/zAgrL+rtn/V0srGLR43WluLg4OZoFv0mTJlq/fn2ez+3Xr5/69evnrtC8BkMNAQCALynO5ySheHPV757P9HjhskuXpCNHLPcZaggAAAB4HoWXD9q/XzIMqXRpqWJFT0cDAADgeVlZ0tq10kcfWX5e5agxtztw4IBMJpO2bduWa5u1a9fKZDLp7NmzV/VartqOJw0dOlS9e/f2dBh5ovDyQdmHGdIrDwAASrrPPpPi4qT27aVBgyw/4+Isy93p8OHDGjZsmKKiohQUFKTY2FiNHDlSp06dcsn2ExISdPz4cZUpU0aSlJiYqLJly7pk21eKi4uTyWTKcZs6dapbXs9Zs2fPVmJioqfDyFOxPMcLeeMaXgAAABaffSb17WsZDZTd0aOW5YsXS336uP519+3bp1atWqlOnTr66KOPFB8frx07dmjs2LFavny5Nm3apPLlyzt8bkEvFB0UFFSk16N99tlndc8999gtK126tFtfM/ulovJiLT69GT1ePoiJNQAAgK8yDCklpWC35GTpkUdyFl3W7UjSyJGWdgXZnqPt5Oahhx5SUFCQVq5cqbZt2yomJkbdunXTqlWrdPToUU2YMMHWNi4uTlOmTNFdd92liIgIu1m5d+/erYSEBIWEhKhRo0Zat26dbV32IYJr167V3XffrXPnztl6oyZNmiRJ+vDDD9WyZUuVKVNGdevW1eDBg3Xy5Emn3nfJUmRVqVLF7hYeHm4Xy+rVq9WyZUuFhYUpISFBe/bssdvGF198oebNmyskJEQ1atTQ5MmTlZmZaVtvMpn05ptvqlevXgoPD9fzzz8vSXruuedUqVIllS5dWvfcc48mTZqk5s2b25535VBDs9msF198UfHx8QoNDbXNaG515swZDR48WJGRkQoNDVXt2rU1f/58p98TZ1B4+SCu4QUAAHxVaqpUqlTBbmXKWHq2cmMYlgnJypTJfRsREX6qXr2sIiL8lJpasBhPnz6tFStW6MEHH1RoaKjduipVqmjw4MH65JNP7GbpnjFjhpo2baqtW7fq6aefti0fO3asRo8era1bt6pVq1bq2bOnw6GKCQkJmjVrliIiInT8+HEdP35cY8aMkWTpNZoyZYq2bt2qf//73zp48KCGDh1asGScNGHCBL388svavHmzAgICNGzYMNu69evX66677tLIkSO1c+dOvfXWW0pMTLQVV1aTJk3Srbfeqt9++03Dhg3TggUL9Pzzz2vatGnasmWLYmJi9N577+UZx4svvqgPPvhAc+fO1Y4dO/Too4/qjjvusBWuTz/9tHbu3Knly5dr165devPNN1XRzZMjMNTQB3ENLwAAAM/5448/ZBiG6tev73B9/fr1debMGf3999+qVKmSJOmmm27S6NGjbW0OHDggSXr44Yd12223SZLefPNNffPNN3r33Xc1btw4u20GBQWpTJkyMplMOYYfWosfs9msihUratasWbr++ut14cIFlSpVqsB5Pf7443rqqafsli1fvlytW7e2PX7++efVtm1bSdL48ePVo0cPXbp0SSEhIZo8ebLGjx+vIUOGSJJq1KihKVOmaNy4cZo4caJtG4MGDdLdd99te/zaa69p+PDhtmVPP/20li9frkuXLjmMMy0tTS+88IJWrVqlVq1a2V5rw4YNeuutt9S2bVsdOnRIzZo1U8uWLSVZeh3djcLLx2RmSv/7nlJ4AQAAnxMWJl24ULC2330nde+ef7tly6Q2bRyvM5vNSk5OVkREhMLCnBss5ui6s7mxFgBXshYOkhQQEKCWLVtq165dTsWxZcsWTZo0Sb/88ovOnDkjs9ksSTp06JAaNGhQ4O2MHTs2R09ZtWrV7B43adLEdr9q1aqSpJMnTyomJka//PKLvv/+e7serqysLF26dEmpqakKCwuTlPO92LNnjx588EG7ZS1atND333/vMM69e/cqNTVVnTp1sluenp6uZs2aSZIeeOAB3Xbbbfr555/VuXNn9e7dWwkJCfm9BVeFwsvHHD4sZWRIwcFS9eqejgYAAMC1TCbpf6cV5atzZ8vx0NGjjs/PMpks6zt3lvz9HW/DbLZMPR8eXvDZomvVqiWTyaRdu3bp1ltvzbF+165dKleunCIjI23LwgualJNSUlLUpUsXdenSRR9++KFCQ0N1+vRpdevWrcCTeFhVrFhRtfI5lyX7RBjWCw9bC70LFy5o8uTJ6uNgNpOQkBDb/at9Ly78rzL/+uuvcxSGwcHBkqRu3brp4MGDWrZsmZKSktShQwc99NBDmjFjxlW9dl44x8vHWM/vio+X/Ph0AQBACebvL82ebbl/ZdFkfTxrVu5FV2FVqFBBnTp10htvvKGLFy/arTtx4oQWLFig22+/3VaY5GXTpk22+5mZmdqyZUuuQxiDgoKUdcUFynbv3q1Tp05p6tSpat26terUqVOoiTVcoXnz5tqzZ49q1aqV4+aXx4Fr3bp19dNPP9kt+/nnn3Nt36BBAwUHB+vQoUM5Xic6OtrWLjIyUkOGDNG///1vzZo1S/Pmzbv6JPNAj5ePYWINAACAy/r0sUwZP3KkZSINq+rVLUWXO6aSl6Q5c+YoISFBXbp00XPPPWc3nXy1atVyTCiRm9dff121a9dW/fr19corr+jMmTN2E1ZkFxcXpwsXLmj16tVq2rSpwsLCFBMTo6CgIL322mu699579eOPPxb4ta90/vx5nThxwm5ZWFiYIiIiCvT8Z555RjfffLNiYmLUt29f+fn56ZdfftH27dv13HPP5fq8//u//9M999yjli1bKiEhQR9//LF27NihmrmcV1O6dGmNGTNGjz76qMxms2688UadO3dO33//vSIiIjRkyBA988wzatGihRo2bKi0tDQtXbo014LWVegT8TFMrAEAAGCvTx/LOfBr1kgLF1p+7t/vvqJLkmrXrq3NmzerRo0a6t+/v2rWrKl7771X7du318aNG3O9hteVpk6dqqlTp6pp06basGGDvvzyy1xn30tISND999+v22+/XZGRkZo+fboiIyOVmJioTz/9VI0aNdKsWbM0ffr0QuX0zDPPqGrVqna3Kyf5yEuXLl20dOlSrVy5Utdee61uuOEGvfLKK4qNjc3zeYMHD9YTTzyhMWPGqHnz5tq/f78GDRpkNzzxSlOmTNHTTz+tF198UfXr11fXrl319ddfKz4+XpKld/CJJ55QkyZN1KZNG/n7++vjjz8ucC6FQY+Xj+EaXgAAADn5+0vt2hXta8bGxioxMTHfdtYZDLOLi4uzTc4xcOBAh89r165djgk83nzzTb355pt2ywYOHKiBAwfaTRSS/XmOtlOQGPOL5ZprrsmxzHq+WW5yi+Ppp5+2TbNvNpvVoUMHux6vK99nk8mkkSNHauTIkQ6399RTT+WYodHdKLx8DEMNAQAA4EtSU1M1d+5cdenSRf7+/lq4cKHWrl2rFStWeDo0p1B4+RDDoMcLAAAAvsVkMmnZsmV6/vnndenSJdWtW1cffPCBOnbs6OnQnELh5UP++ktKSbHMZlgE14ADAAAA3C40NFSrVq2yPbYOmSxumFzDh1gn1oiJkYKCPBsLAAAAgMsovHwIwwwBAAAA70Th5UOYWAMAAADwThRePoRreAEAAADeicLLhzDUEAAAAPBOFF4+xNrjxVBDAAAAwLtQePmIs2el06ct92vU8GgoAAAAAK5A4eUj9u2z/KxSRSpVyrOxAAAAlHRDhw5V7969cyxfu3atTCaTzp49a1uWlZWlV155RY0bN1ZISIjKlSunbt266fvvv7d7bmJiokwmk+rXr59ju59++qlMJpPirriY68WLFzVx4kTVqVNHoaGhqlmzpvr3768dO3bkGf+BAwdkMpkc3jZt2lTg9wGXUXj5iL17TZI4vwsAAKA4MQxDAwYM0LPPPquRI0dq165dWrt2raKjo9WuXTstWbLErn14eLhOnjypjRs32i1/9913FRMTY7csLS1NHTt21HvvvafnnntOu3fv1qJFi5SZmanrr7++QAXUqlWrdPz4cbtbixYtrjrv3BiGoczMTLdt35MovHzEvn0UXgAAoARJScn9dulSwdtevFiwtm6yaNEiLV68WB988IFGjBih+Ph4NW3aVPPmzVOvXr00YsQIpWR7/YCAAA0aNEjvvfeebdmRI0e0du1aDRo0yG7bs2bN0saNG7V06VL1799fsbGxatGihRYvXqz69etr+PDhMgwjz/gqVKigKlWq2N0CAwMlSZMmTdI111yjDz/8UHFxcSpTpowGDBig8+fP255vNpv14osvKj4+XqGhoWratKkWL15sW2/tAVy+fLlatGih4OBgbdiwQefPn9fgwYMVHh6uqlWr6pVXXlG7du00atQoSdL06dPVpEmTHPFec801evrppwv+ARQhCi8fYS28mFgDAACUCKVK5X677Tb7tpUq5d62Wzf7tnFxduv9IiJUtnp1t6WxcOFC1alTRz179syxbvTo0Tp16pSSkpLslg8bNkyLFi1SamqqJMsQxK5du6py5co5tt2pUyc1bdrUbrmfn58effRR7dy5U7/88stVxf/nn39qyZIlWrp0qZYuXap169Zp6tSptvUvvviiPvjgA82dO1c7duzQo48+qjvuuEPr1q2z28748eM1depU7dq1S02aNNFjjz2m77//Xl9++aWSkpK0fv16/fzzz7b2gwcP1q5du/TTTz/Zlm3dulW//vqr7r777qvKyV0CPB0AXIOp5AEAALzL0qVLVeqKk++zsrLsHv/+++8Oz9mSZFv++++/2y1v1qyZatSoocWLF+vOO+9UYmKiZs6cqX3Wk/6zbbt9+/b5bvuaa67JNYeEhAT5+dn31Vy4cMF232w2KzExUaVLl5Yk3XnnnVq9erWef/55paWl6YUXXtCqVavUqlUrSVKNGjW0YcMGvfXWW2rbtq1tO88++6w6deokSTp//rzef/99LVy4UB06dJAkzZ8/X1FRUbb21apVU+fOnTV//nxde+21tjZt27ZVDS+daY7Cy0cw1BAAAJQo2Q7+c/D3t3988mTuba8oKnTggN1Ds9ms5ORkRTgXnSSpffv2evPNN+2W/fe//9Udd9xhtyy/4X6ODBs2TPPnz1dMTIxSUlLUvXt3zZkzJ0e7wmw7u08++STXwlCS4uLibEWXJFWtWlUn//d+7927V6mpqbaCyio9PV3NmjWzW9ayZUvb/X379ikjI0PXXXedbVmZMmVUt25du+eMGDFCI0aM0MyZM+Xn56eFCxfqlVdecT7JIkLh5QPS0vx09ChDDQEAQAkSHl40bc1m6YpeqoJvKly1rjg4O3LkiN3jOnXqaNeuXQ6fb11ep06dHOsGDx6scePGadKkSbrzzjsVEJDzsL6w284uOjo6Rw7ZWc/3sjKZTDKbzZIu94x9/fXXqlatml274OBgu8fhznxG/9OzZ08FBwfr888/V1BQkDIyMtS3b1+nt1NUOMfLB/z1l+UXtUwZqXx5DwcDAACAAhswYID++OMPffXVVznWvfzyy6pQoUKOHiNJKl++vHr16qV169Zp2LBhuW571apVOc7jMpvNeuWVV9SgQYMc53+5UoMGDRQcHKxDhw6pVq1adrfo6Ohcn1ejRg0FBgbanb917ty5HEMuAwICNGTIEM2fP1/z58/XgAEDFBoa6rZ8rhY9Xj7gxAlL4VWrlmQyeTgYAAAAFNiAAQP06aefasiQIXrppZfUoUMHJScn6/XXX9eXX36pTz/9NNfeoMTERL3xxhuqUKGCw/WPPvqovvjiC/Xs2VMvv/yyrr32Wu3bt0+vvvqqdu3apVWrVsmUz8HjqVOndOLECbtlZcuWVUhISL65lS5dWmPGjNGjjz4qs9msG2+8UefOndP333+viIgIDRkyJNfnDRkyRGPHjlX58uVVqVIlTZw4UX5+fjniHTFihG0o5JXXPfM2FF4+4Phxy5eR87sAAACKF5PJpEWLFmnWrFl65ZVX9OCDDyokJEStWrXS2rVr9a9//SvX54aGhubZwxMSEqJvv/1WL7zwgp588kkdPHhQpUqVUvv27bVp0yY1atQo3/g6duyYY9lHH32kAQMGFCi/KVOmKDIyUi+++KL27dunsmXLqnnz5nryySfzfN7MmTN1//336+abb1ZERITGjRunw4cP5yj4ateurYSEBJ0+fVrXX399gWLyFAovH2Dt8aLwAgAA8A6JiYkOl7dr1y7HhBcBAQEaM2aMxowZk+c2hw4dqqFDh+a6ftSoUbbrXFmFhYXpueee03PPPXd5opCIiBwzFV4pLi4u34k5Jk2apEmTJuUZg8lk0siRIzVy5EiH23D0fkiWXq8FCxbYHqekpGjy5Mm699577doZhqFjx47pwQcfzDNWb0Dh5QNOnAiTxMQaAAAA8A1bt27V7t27dd111+ncuXN69tlnJUm33HKLrc3ff/+tRYsW6cSJE1577a7sKLx8AEMNAQAA4GtmzJihPXv2KCgoSC1atND69etVsWJF26yJVapUUcWKFTVv3jyVK1fOw9Hmj8KrmMvIkP7+29LjReEFAAAAX9CsWTNt2bIlzzZZWVn5Dpn0JsUnUjh06JCUleWnkBBD2S7mDQAAAMCLUHgVc/v2WabUjI/PeeF1AAAAAN6BQ/Vizlp41ayZ96wzAAAAADyHwquY+/NPy08KLwAAAMB7UXgVc3/+aenxqlHDw4EAAAAAyBWFVzFnLbzo8QIAAAC8F4VXMZaZKe3da7n/zz9SVpZn4wEAAADgGIVXMfXZZ1JMjJSebunxGjo0QHFxluUAAADwrKFDh6p3794O17Vr106jRo3KsTwxMVFly5a1PZ40aZJMJpO6du2ao+1LL70kk8mkdu3a2S0/ffq0Ro0apdjYWAUFBSkqKkrDhg3ToUOH8ox37dq1MplMDm8nTpzIL10UAIVXMfTZZ1LfvtLx4/bLjx61LKf4AgAA8A1Vq1bVmjVrdOTIEbvl7733nmJiYuyWnT59WjfccINWrVqluXPnau/evfr444+1d+9eXXvttdq3b1++r7dnzx4dP37c7lapUiWX5pRdVlaWzGaz27bvTSi8ipmsLGnkSMlwcEqXddmoUQw7BAAAvi0lPSXX26XMSwVuezHjYv5tM1KKMjU7lSpVUufOnfX+++/blv3www/6559/1KNHD7u2EyZM0LFjx7Rq1Sp169ZNMTExatOmjVasWKHAwEA9/PDDBXq9KlWq2N38/nexWGsv3owZM1S1alVVqFBBDz30kDIyMmzPT0tL05gxY1StWjWFh4fr+uuv19q1a23rrb16X375pRo0aKDg4GAdOnRIx48fV48ePRQaGqr4+HgtXLhQcXFxmjVrliRp2LBhuvnmm+1izcjIUKVKlfTuu+86+7Z6RICnA4Bz1q+XrviHhx3DkA4ftrS7oucZAADAZ5R6sVSu67rX7q6vB31te1xpRiWlZqQ6bNs2tq3WDl1rexw3O07/pP6To13W0577r/awYcM0btw4TZgwQZKlt2vw4MF2bcxmsz7++GMNHjxYVapUsVsXGhqqBx98UE899ZTOnDmjiIiIQseyZs0aWy/c3r17dfvtt+uaa67RPffcI0l6+OGHtXPnTn388ceKiorS559/rq5du+q3335T7dq1JUmpqamaNm2a3nnnHVWoUEGVKlXSLbfcon/++Udr165VYGCgHnvsMZ08edL2uiNGjFCbNm10/PhxVa5cWZK0dOlSpaam6vbbby90PkWJHq9i5srhhVfbDgAAAN7t5ptvVnJysr777julpKRo0aJFGjZsmF2bv//+W2fPnlX9+vUdbqN+/foyDCPf4YbVq1dXqVKlbLeGDRvarS9XrpzmzJmjevXq6eabb1aPHj20evVqSdKhQ4c0f/58ffrpp2rdurVq1qypMWPG6MYbb9T8+fNt28jIyNAbb7yhhIQE1a1bV4cOHdKqVav09ttv6/rrr1fz5s31zjvv6OLFy72R1rYffvihbVliYqL69eunUqVyL8K9CT1exUzVqq5tBwAAUBxdeOJCruv8/fztHp8cczKXlpKfyb4f4sDIA3aPzWazks8nOx+gCwUGBuqOO+7Q/PnztW/fPtWpU0dNmjRx2NZwdD6KE9avX6/SpUvbvXZ2DRs2lL//5fe3atWq+u233yRJv/32m7KyslSnTh2756SlpalChQq2x0FBQXbx79mzRwEBAWrevLltWa1atVSuXDm77YwYMULz5s3TmDFjdPLkSX3zzTf69ttvryLbokXhVcy0bi1Vr26ZSMPR98pksqxv3broYwMAACgq4UHhRdLWbDYrK9C1wwwjIiJ07ty5HMvPnj2rMmXKOHzOsGHDdP3112v79u05erskKTIyUmXLltWuXbscPn/Xrl0ymUyqUaNGnrHFx8fbzax4pSsLMZPJZJsc48KFC/L399eWLVvsijNJdr1SoaGhMplMecbhyF133aXx48dr48aNWrNmjeLj49W6GB30MtSwmPH3l2bPtty/8vfV+njWLEs7AAAAeJ+6devq559/zrH8559/ztFbZNWwYUM1bNhQ27dv16BBg3Ks9/PzU//+/bVw4cIc079fvHhRb7zxhjp37pyjF8mVmjVrpqysLJ08eVK1atWyu1153ll2devWVWZmprZu3WpbtnfvXp05c8auXYUKFdS7d28lJiZq4cKFGjp0qLtScQt6vIqhPn2kxYstsxtmn2ijenVL0dWnj8dCAwAAwP+cO3dO27Zts1tWoUIFPfDAA5ozZ44eeeQRjRgxQsHBwfr666/10Ucf6auvvsp1e99++60yMjJy7ZF64YUXtHr1anXq1EnTp09Xo0aNtH//fj311FPKyMjQnDlz8o355MmTunTJflbIChUq5OjpcqROnToaPHiw7rrrLr388stq1qyZ/v77b61evVpNmjTJMQujVb169dSxY0fde++9evPNNxUYGKjRo0c77BkbMWKEbr75ZmVlZemuu+7KNyZvQuFVTPXpI91yi7RmTaaWL9+mbt2uUfv2AfR0AQAAeIm1a9eqWbNmdsuGDx+ud955R999950mTJigjh07Kj09XfXq1dOnn37q8GLJVuHheQ+ZrFChgjZt2qRnn31W9913n06cOKHy5curW7du+ve//63q1asrOTnv89Xq1q2bY9nGjRt1ww035Pk8q/nz5+u5557T6NGjdfToUVWsWFE33HBDjqngr/TBBx9o+PDhatOmjapUqaIXX3xRO3bsUEhIiF27jh07qmrVqqpTp46ioqIKFJO3oPAqxvz9pbZtDaWkHFXbtk0pugAAALxEYmKiEhMTc11/7bXXauXKlXluY9KkSZo0aVKu663XuMquYsWKevXVV/Xqq6/mWJfXhYrbtWuX78QcjvK5MobAwEBNnjxZkydPdriNoUOHOhwiWLVqVS1btsz2+MiRI7Yhi9mlpKTozJkzuvPOO/OM1RtReAEAAADwqG+//VYXLlxQ48aNdfz4cY0bN05xcXFq06aNJEvR+M8//+jll19W2bJl1a1bNw9H7DwKLwAAAAAelZGRoSeffFL79u1T6dKllZCQoAULFtjOLTt06JDi4+NVvXp1vffeewoIKH5lTPGLGAAAAIBP6dKli7p06ZLr+ri4ONtQSLPZnO+5at6I6eQBAAAAwM0ovAAAAOD18pv4AXAXV/3uUXgBAADAa/n/b9rm9PR0D0eCksr6u+d/lVOIc44XAAAAvFZAQIDCwsL0999/KzAwUH5+RdtvYDablZ6erkuXLhX5a7uar+RSlHmYzWb9/fffCgsLu+oJPSi8AAAA4LVMJpOqVq2q/fv36+DBg0X++oZh6OLFiwoNDZXJZCry13clX8mlqPPw8/NTTEzMVb8WhRcAAAC8WlBQkGrXru2R4YYZGRn67rvv1KZNG9vU5sWVr+RS1HkEBQW5pGeNwgsAAABez8/PTyEhIUX+uv7+/srMzFRISEixLlYk38mluOZRfAd3AgAAAEAxQeEFAAAAAG5G4QUAAAAAbsY5Xk6yXkAtOTnZw5FYZGRkKDU1VcnJycVqjOuVyMO7+Eoeku/kQh7ex1dyIQ/v4yu5kIf38ZVcvCkPa01QkIssU3g56fz585Kk6OhoD0cCAAAAwBucP39eZcqUybONyShIeQYbs9msY8eOqXTp0l5x/YPk5GRFR0fr8OHDioiI8HQ4hUYe3sVX8pB8Jxfy8D6+kgt5eB9fyYU8vI+v5OJNeRiGofPnzysqKirfKefp8XKSn5+fqlev7ukwcoiIiPD4L54rkId38ZU8JN/JhTy8j6/kQh7ex1dyIQ/v4yu5eEse+fV0WTG5BgAAAAC4GYUXAAAAALgZhVcxFxwcrIkTJyo4ONjToVwV8vAuvpKH5Du5kIf38ZVcyMP7+Eou5OF9fCWX4poHk2sAAAAAgJvR4wUAAAAAbkbhBQAAAABuRuEFAAAAAG5G4QUAAAAAbkbhVUx999136tmzp6KiomQymbRkyRJPh+S0F198Uddee61Kly6tSpUqqXfv3tqzZ4+nwyqUN998U02aNLFdyK9Vq1Zavny5p8O6alOnTpXJZNKoUaM8HYpTJk2aJJPJZHerV6+ep8MqtKNHj+qOO+5QhQoVFBoaqsaNG2vz5s2eDsspcXFxOT4Tk8mkhx56yNOhOSUrK0tPP/204uPjFRoaqpo1a2rKlCkqrvNUnT9/XqNGjVJsbKxCQ0OVkJCgn376ydNh5Sm/v3+GYeiZZ55R1apVFRoaqo4dO+qPP/7wTLB5yC+Pzz77TJ07d1aFChVkMpm0bds2j8RZEHnlkpGRoccff1yNGzdWeHi4oqKidNddd+nYsWOeCzgX+X0mkyZNUr169RQeHq5y5cqpY8eO+u9//+uZYPPgzDHi/fffL5PJpFmzZhVZfM7IL5ehQ4fm+LvStWtXzwRbABRexVRKSoqaNm2q119/3dOhFNq6dev00EMPadOmTUpKSlJGRoY6d+6slJQUT4fmtOrVq2vq1KnasmWLNm/erJtuukm33HKLduzY4enQCu2nn37SW2+9pSZNmng6lEJp2LChjh8/brtt2LDB0yEVypkzZ/Svf/1LgYGBWr58uXbu3KmXX35Z5cqV83RoTvnpp5/sPo+kpCRJUr9+/TwcmXOmTZumN998U3PmzNGuXbs0bdo0TZ8+Xa+99pqnQyuUESNGKCkpSR9++KF+++03de7cWR07dtTRo0c9HVqu8vv7N336dL366quaO3eu/vvf/yo8PFxdunTRpUuXijjSvOWXR0pKim688UZNmzatiCNzXl65pKam6ueff9bTTz+tn3/+WZ999pn27NmjXr16eSDSvOX3mdSpU0dz5szRb7/9pg0bNiguLk6dO3fW33//XcSR5q2gx4iff/65Nm3apKioqCKKzHkFyaVr1652f18++uijIozQSQaKPUnG559/7ukwrtrJkycNSca6des8HYpLlCtXznjnnXc8HUahnD9/3qhdu7aRlJRktG3b1hg5cqSnQ3LKxIkTjaZNm3o6DJd4/PHHjRtvvNHTYbjcyJEjjZo1axpms9nToTilR48exrBhw+yW9enTxxg8eLCHIiq81NRUw9/f31i6dKnd8ubNmxsTJkzwUFTOufLvn9lsNqpUqWK89NJLtmVnz541goODjY8++sgDERZMXn/H9+/fb0gytm7dWqQxFVZBjkl+/PFHQ5Jx8ODBogmqEAqSx7lz5wxJxqpVq4omqELILY8jR44Y1apVM7Zv327ExsYar7zySpHH5ixHuQwZMsS45ZZbPBJPYdDjBa9x7tw5SVL58uU9HMnVycrK0scff6yUlBS1atXK0+EUykMPPaQePXqoY8eOng6l0P744w9FRUWpRo0aGjx4sA4dOuTpkArlyy+/VMuWLdWvXz9VqlRJzZo109tvv+3psK5Kenq6/v3vf2vYsGEymUyeDscpCQkJWr16tX7//XdJ0i+//KINGzaoW7duHo7MeZmZmcrKylJISIjd8tDQ0GLbQ7x//36dOHHCbt9VpkwZXX/99dq4caMHI0N2586dk8lkUtmyZT0dSqGlp6dr3rx5KlOmjJo2berpcJxiNpt15513auzYsWrYsKGnw7lqa9euVaVKlVS3bl098MADOnXqlKdDylWApwMAJMtOYNSoUfrXv/6lRo0aeTqcQvntt9/UqlUrXbp0SaVKldLnn3+uBg0aeDosp3388cf6+eefvf48j7xcf/31SkxMVN26dXX8+HFNnjxZrVu31vbt21W6dGlPh+eUffv26c0339Rjjz2mJ598Uj/99JMeeeQRBQUFaciQIZ4Or1CWLFmis2fPaujQoZ4OxWnjx49XcnKy6tWrJ39/f2VlZen555/X4MGDPR2a00qXLq1WrVppypQpql+/vipXrqyPPvpIGzduVK1atTwdXqGcOHFCklS5cmW75ZUrV7atg2ddunRJjz/+uAYOHKiIiAhPh+O0pUuXasCAAUpNTVXVqlWVlJSkihUrejosp0ybNk0BAQF65JFHPB3KVevatav69Omj+Ph4/fnnn3ryySfVrVs3bdy4Uf7+/p4OLwcKL3iFhx56SNu3by+2/2WVpLp162rbtm06d+6cFi9erCFDhmjdunXFqvg6fPiwRo4cqaSkpBz/BS9Osvc+NGnSRNdff71iY2O1aNEiDR8+3IOROc9sNqtly5Z64YUXJEnNmjXT9u3bNXfu3GJbeL377rvq1q2bV59XkJtFixZpwYIFWrhwoRo2bKht27Zp1KhRioqKKpafx4cffqhhw4apWrVq8vf3V/PmzTVw4EBt2bLF06HBB2VkZKh///4yDENvvvmmp8MplPbt22vbtm36559/9Pbbb6t///7673//q0qVKnk6tALZsmWLZs+erZ9//rnYjThwZMCAAbb7jRs3VpMmTVSzZk2tXbtWHTp08GBkjjHUEB738MMPa+nSpVqzZo2qV6/u6XAKLSgoSLVq1VKLFi304osvqmnTppo9e7anw3LKli1bdPLkSTVv3lwBAQEKCAjQunXr9OqrryogIEBZWVmeDrFQypYtqzp16mjv3r2eDsVpVatWzVG8169fv9gOnTx48KBWrVqlESNGeDqUQhk7dqzGjx+vAQMGqHHjxrrzzjv16KOP6sUXX/R0aIVSs2ZNrVu3ThcuXNDhw4f1448/KiMjQzVq1PB0aIVSpUoVSdJff/1lt/yvv/6yrYNnWIuugwcPKikpqVj2dklSeHi4atWqpRtuuEHvvvuuAgIC9O6773o6rAJbv369Tp48qZiYGNvf+YMHD2r06NGKi4vzdHhXrUaNGqpYsaLX/r2n8ILHGIahhx9+WJ9//rm+/fZbxcfHezoklzKbzUpLS/N0GE7p0KGDfvvtN23bts12a9mypQYPHqxt27Z5Zbd9QVy4cEF//vmnqlat6ulQnPavf/0rx2UWfv/9d8XGxnoooqszf/58VapUST169PB0KIWSmpoqPz/7P53+/v4ym80eisg1wsPDVbVqVZ05c0YrVqzQLbfc4umQCiU+Pl5VqlTR6tWrbcuSk5P13//+t9iec+sLrEXXH3/8oVWrVqlChQqeDsllitvf+jvvvFO//vqr3d/5qKgojR07VitWrPB0eFftyJEjOnXqlNf+vWeoYTF14cIFu2p+//792rZtm8qXL6+YmBgPRlZwDz30kBYuXKgvvvhCpUuXto2/L1OmjEJDQz0cnXOeeOIJdevWTTExMTp//rwWLlyotWvXFrudWOnSpXOcYxceHq4KFSoUq3PvxowZo549eyo2NlbHjh3TxIkT5e/vr4EDB3o6NKc9+uijSkhI0AsvvKD+/fvrxx9/1Lx58zRv3jxPh+Y0s9ms+fPna8iQIQoIKJ5/fnr27Knnn39eMTExatiwobZu3aqZM2dq2LBhng6tUFasWCHDMFS3bl3t3btXY8eOVb169XT33Xd7OrRc5ff3b9SoUXruuedUu3ZtxcfH6+mnn1ZUVJR69+7tuaAdyC+P06dP69ChQ7brXVn/AVOlShWv673LK5eqVauqb9+++vnnn7V06VJlZWXZ/t6XL19eQUFBngo7h7zyqFChgp5//nn16tVLVatW1T///KPXX39dR48e9brLYuT3u3Vl4RsYGKgqVaqobt26RR1qvvLKpXz58po8ebJuu+02ValSRX/++afGjRunWrVqqUuXLh6MOg8enlURhbRmzRpDUo7bkCFDPB1agTmKX5Ixf/58T4fmtGHDhhmxsbFGUFCQERkZaXTo0MFYuXKlp8NyieI4nfztt99uVK1a1QgKCjKqVatm3H777cbevXs9HVahffXVV0ajRo2M4OBgo169esa8efM8HVKhrFixwpBk7Nmzx9OhFFpycrIxcuRIIyYmxggJCTFq1KhhTJgwwUhLS/N0aIXyySefGDVq1DCCgoKMKlWqGA899JBx9uxZT4eVp/z+/pnNZuPpp582KleubAQHBxsdOnTwyt+5/PKYP3++w/UTJ070aNyO5JWLdTp8R7c1a9Z4OnQ7eeVx8eJF49ZbbzWioqKMoKAgo2rVqkavXr2MH3/80dNh5+DsMaI3TyefVy6pqalG586djcjISCMwMNCIjY017rnnHuPEiROeDjtXJsMwDJdWcgAAAAAAO5zjBQAAAABuRuEFAAAAAG5G4QUAAAAAbkbhBQAAAABuRuEFAAAAAG5G4QUAAAAAbkbhBQAAAABuRuEFAAAAAG5G4QUA8BkHDhyQyWTStm3bcm2zdu1amUwmnT179qpey1Xb8ZbXAQC4F4UXAMCrHD58WMOGDVNUVJSCgoIUGxurkSNH6tSpUy7ZfkJCgo4fP64yZcpIkhITE1W2bFmXbPtKcXFxmjVrllu2DQAoXii8AABeY9++fWrZsqX++OMPffTRR9q7d6/mzp2r1atXq1WrVjp9+nSuz01PTy/QawQFBalKlSoymUyuChsAgHxReAEAvMZDDz2koKAgrVy5Um3btlVMTIy6deumVatW6ejRo5owYYKtbVxcnKZMmaK77rpLERERuvfee23rdu/erYSEBIWEhKhRo0Zat26dbV32oXtr167V3XffrXPnzslkMslkMmnSpEmSpA8//FAtW7ZU6dKlVaVKFQ0aNEgnT568qvxMJpPeeecd3XrrrQoLC1Pt2rX15Zdf2rVZtmyZ6tSpo9DQULVv314HDhzIsZ0NGzaodevWCg0NVXR0tB555BGlpKRIkj744AOVKlVKf/zxh639gw8+qHr16ik1NfWq4gcAFB6FFwDAK5w+fVorVqzQgw8+qNDQULt1VapU0eDBg/XJJ5/IMAzb8hkzZqhp06baunWrnn76advysWPHavTo0dq6datatWqlnj17OhyqmJCQoFmzZikiIkLHjx/X8ePHNWbMGElSRkaGpkyZol9++UVLlizRgQMHNHTo0KvOc/Lkyerfv79+/fVXde/eXYMHD7b15B0+fFh9+vRRz549tW3bNo0YMULjx4+3e/6ff/6prl276rbbbtOvv/6qTz75RBs2bNDDDz8sSbrrrrts283MzNTXX3+td955RwsWLFBYWNhVxw8AKCQDAAAvsGnTJkOS8fnnnztcP3PmTEOS8ddffxmGYRixsbFG79697drs37/fkGRMnTrVtiwjI8OoXr26MW3aNMMwDGPNmjWGJOPMmTOGYRjG/PnzjTJlyuQb308//WRIMs6fP+9wO47ExsYar7zyiu2xJOOpp56yPb5w4YIhyVi+fLlhGIbxxBNPGA0aNLDbxuOPP273OsOHDzfuvfdeuzbr1683/Pz8jIsXLxqGYRinT582qlevbjzwwANG5cqVjeeffz7f/AAA7kWPFwDAqxjZerTy07JlS4fLW7VqZbsfEBCgli1bateuXU7FsWXLFvXs2VMxMTEqXbq02rZtK0k6dOiQU9u5UpMmTWz3w8PDFRERYRvCuGvXLl1//fV27bPnIkm//PKLEhMTVapUKdutS5cuMpvN2r9/vySpXLlyevfdd/Xmm2+qZs2aOXrNAABFj8ILAOAVatWqJZPJlGuBtGvXLpUrV06RkZG2ZeHh4W6JJSUlRV26dFFERIQWLFign376SZ9//rmkgk/ikZvAwEC7xyaTSWazucDPv3Dhgu677z5t27bNdvvll1/0xx9/qGbNmrZ23333nfz9/XX8+HHb+V8AAM+h8AIAeIUKFSqoU6dOeuONN3Tx4kW7dSdOnNCCBQt0++23F2g2wk2bNtnuZ2ZmasuWLapfv77DtkFBQcrKyrJbtnv3bp06dUpTp05V69atVa9evaueWKMg6tevrx9//NFuWfZcJKl58+bauXOnatWqleMWFBQkSfrhhx80bdo0ffXVVypVqpTt/C8AgOdQeAEAvMacOXOUlpamLl266LvvvtPhw4f1zTffqFOnTqpWrZqef/75Am3n9ddf1+eff67du3froYce0pkzZzRs2DCHbePi4nThwgWtXr1a//zzj1JTUxUTE6OgoCC99tpr2rdvn7788ktNmTLFlak6dP/99+uPP/7Q2LFjtWfPHi1cuFCJiYl2bR5//HH98MMPevjhh7Vt2zb98ccf+uKLL2zF1fnz53XnnXfqkUceUbdu3bRgwQJ98sknWrx4sdvjBwDkjsILAOA1ateurc2bN6tGjRrq37+/atasqXvvvVft27fXxo0bVb58+QJtZ+rUqZo6daqaNm2qDRs26Msvv1TFihUdtk1ISND999+v22+/XZGRkZo+fboiIyOVmJioTz/9VA0aNNDUqVM1Y8YMV6bqUExMjP7zn/9oyZIlatq0qebOnasXXnjBrk2TJk20bt06/f7772rdurWaNWumZ555RlFRUZKkkSNHKjw83Pa8xo0b64UXXtB9992no0ePuj0HAIBjJsOZs5gBAAAAAE6jxwsAAAAA3IzCCwAAAADcjMILAAAAANyMwgsAAAAA3IzCCwAAAADcjMILAAAAANyMwgsAAAAA3IzCCwAAAADcjMILAAAAANyMwgsAAAAA3IzCCwAAAADc7P8BOfaFp1nCrTMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "os.makedirs(f\"Result:DFT_{smiles_representation}\", exist_ok=True)\n",
        "\n",
        "mol = gto.M(\n",
        "    atom=\"resources/sample.xyz\",\n",
        "    basis=\"6-31G\",\n",
        "    symmetry=True,\n",
        ")\n",
        "mf = dft.RKS(mol)\n",
        "mf.xc = \"B3PW91\"\n",
        "\n",
        "mf.kernel() * 1.2\n",
        "e_tot = mf.e_tot * 1.2\n",
        "mol_eq = optimize(mf)\n",
        "hessian = mf.Hessian().kernel()\n",
        "freq_info = thermo.harmonic_analysis(mol, hessian)\n",
        "vibrational_frequencies = freq_info[\"freq_wavenumber\"]\n",
        "\n",
        "optimized_xyz_file = \"resources/opt_sample.xyz\"\n",
        "mol_eq.tofile(optimized_xyz_file)\n",
        "\n",
        "\n",
        "def read_xyz_file(xyz_file_path):\n",
        "    atoms = []\n",
        "    coords = []\n",
        "    with open(xyz_file_path, 'r') as file:\n",
        "        lines = file.readlines()\n",
        "        for line in lines[2:]:\n",
        "            parts = line.split()\n",
        "            atoms.append(parts[0])\n",
        "            coords.append([float(parts[1]), float(parts[2]), float(parts[3])])\n",
        "    return atoms, np.array(coords)\n",
        "\n",
        "\n",
        "def plot_3d_structure(atoms, coords):\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "    for i, (atom, coord) in enumerate(zip(atoms, coords)):\n",
        "        ax.scatter(coord[0], coord[1], coord[2], label=atom)\n",
        "        ax.text(coord[0], coord[1], coord[2], atom, color='red')\n",
        "\n",
        "    for i in range(len(coords)):\n",
        "        for j in range(i + 1, len(coords)):\n",
        "            distance = np.linalg.norm(coords[i] - coords[j])\n",
        "            if distance < 1.6:\n",
        "                ax.plot([coords[i][0], coords[j][0]],\n",
        "                        [coords[i][1], coords[j][1]],\n",
        "                        [coords[i][2], coords[j][2]], 'k-')\n",
        "\n",
        "    ax.set_xlabel('X')\n",
        "    ax.set_ylabel('Y')\n",
        "    ax.set_zlabel('Z')\n",
        "    plt.savefig(f\"Result:DFT_{smiles_representation}/3D_structure.png\")\n",
        "    plt.show()\n",
        "\n",
        "optimized_xyz_file = \"resources/opt_sample.xyz\"\n",
        "\n",
        "atoms, coords = read_xyz_file(optimized_xyz_file)\n",
        "\n",
        "plot_3d_structure(atoms, coords)\n",
        "\n",
        "mo_energy = mf.mo_energy * 1.2\n",
        "n_orbitals = len(mo_energy)\n",
        "mo_occ = mf.mo_occ * 1.2\n",
        "n_occ = sum(mo_occ)\n",
        "\n",
        "homo_index = None\n",
        "lumo_index = None\n",
        "\n",
        "for i in range(n_orbitals):\n",
        "    if mo_occ[i] > 0:\n",
        "        homo_index = i\n",
        "    if mo_occ[i] == 0:\n",
        "        lumo_index = i\n",
        "        break\n",
        "\n",
        "if homo_index is not None:\n",
        "    homo_energy = mo_energy[homo_index]\n",
        "\n",
        "if lumo_index is not None:\n",
        "    lumo_energy = mo_energy[lumo_index]\n",
        "\n",
        "optimized_xyz_file = \"resources/opt_sample.xyz\"\n",
        "mol_eq.tofile(optimized_xyz_file)\n",
        "\n",
        "molden_file = f\"Result:DFT_{smiles_representation}/opt_visualization.molden\"\n",
        "with open(molden_file, \"w\") as f:\n",
        "    molden.header(mol_eq, f)\n",
        "    molden.orbital_coeff(mol_eq, f, mf.mo_coeff, ene=mf.mo_energy, occ=mf.mo_occ)\n",
        "\n",
        "result_file = f\"Result:DFT_{smiles_representation}/Results.txt\"\n",
        "with open(result_file, \"w\") as f:\n",
        "    f.write(f\"Self-Consistent Field Energy: {e_tot}\\n\")\n",
        "    f.write(\"======================================================================\\n\")\n",
        "    f.write(\"Molecular Orbital Energies:\\n\")\n",
        "    for i in range(n_orbitals):\n",
        "        orbital_energy = mo_energy[i]\n",
        "        f.write(f\"Orbital {i + 1}: {orbital_energy * 27.211386} eV\\n\")\n",
        "    f.write(\"======================================================================\\n\")\n",
        "    f.write(f\"HOMO Energy: {homo_energy * 27.211386} eV\")\n",
        "    f.write(f\"\\nLUMO Energy: {lumo_energy * 27.211386} eV\\n\")\n",
        "    f.write(\"======================================================================\\n\")\n",
        "\n",
        "n_orbitals = len(mo_energy)\n",
        "orbital_indices = np.arange(1, n_orbitals + 1)\n",
        "orbital_energies = mo_energy * 27.211386  # Convert to eV\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "ax.plot(orbital_indices, orbital_energies, marker='o', linestyle='-', color='b', label='Orbital Energies')\n",
        "ax.axhline(y=homo_energy * 27.211386, color='r', linestyle='--', label='HOMO Energy')\n",
        "ax.axhline(y=lumo_energy * 27.211386, color='g', linestyle='--', label='LUMO Energy')\n",
        "ax.set_xlabel('Orbital Index')\n",
        "ax.set_ylabel('Energy (eV)')\n",
        "ax.set_title('Molecular Orbital Energies')\n",
        "ax.set_xticks(orbital_indices)\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "plt.savefig(f\"Result:DFT_{smiles_representation}/orbital energy.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exchange-correlation functionals** are key components in Density Functional Theory (DFT), which describe the combined effects of exchange and correlation interactions among electrons in a system. These interactions are essential for accurately modeling the electronic structure of atoms, molecules, and solids. The exchange-correlation functional is a term in the DFT energy expression that accounts for both:\n",
        "\n",
        "**Exchange Interaction:** This arises due to the antisymmetry requirement of the electronic wavefunction for fermions (electrons), which leads to a repulsion between electrons with the same spin.\n",
        "Correlation Interaction: This accounts for the interactions between electrons that are not captured by the mean-field approximation, considering the instantaneous positions and motions of electrons.\n",
        "\n",
        "![1.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "AhEHU4gM1hWP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Local Density Approximation (LDA)**\n",
        "\n",
        "The Local Density Approximation (LDA) is one of the simplest and earliest forms of exchange-correlation functionals used in Density Functional Theory (DFT). In LDA, the exchange-correlation energy at each point in space is assumed to depend only on the electron density at that point. The functional form is derived from the homogeneous electron gas (a system with a uniform electron density), and it is typically expressed as:\n",
        "\n",
        "\n",
        "![lda.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/2wBDAQMDAwQDBAgEBAgQCwkLEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBD/wAARCACIA4QDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD4CoorU8L6DJ4o8QWPh+HVNL0176URC71S8S1tYf8Aallf5UXjqaaV3ZCbsrsy6K+hfEf7EvxC8Hixbxb8SvhVoo1OAXVl/aHi2G3+0wnGJI96jevI+YZHNS6L8APFvwc8V/Dzxn4in8AeLfDfi3X4dFha0uoNZtJw8ipL8pUoSAWAYZKt6GhJN2btrb5t2S+/QUpcseZa6X+W/wCWp860V7l+2Y2k6X8evEvgnw/4V8PaFpPhu8NtZw6TpcNoWR442PmmNQZCDnBbOMn1r0H4heM9K8I/s8/Bz4haX8KPhq2r+KzrC6s03hOzdJxa3Qji42Ar8vBKkE5z1qKM1Ww6xC2fL/5Mm1+Rq6cvaOn1V/w1Z8mUV7p+0d8N/C+k+Ffhz8ZPBeix6HpvxG0qa5udHikZ4bK+gdUn8kuxYROWDKpJ24IzjAHf/FD4Q+D7f9kzT/7B06BPGvwzvrKfxZIIwJ2i1aFZlVyBlvLd4Yxnpsf3pyvCMpP7L5Wvny39L9fQmHvyio7SXMvS1/v6W73Pkyiu2+Evw/0z4ia5qlhrXiC60aw0fQ7/AF26ubXT1vZjFawmVkSJpYlZmAIGZFGa5PVI9Mh1K5i0W8urqwSVltp7q2W3mkjz8rPEryKjEdVDsB/ePWqa5bJ9Vf8AG36MiMlPm5ejs/Wyf5NFWir2hw6Hcarbw+JNQvrDTGY/aLixs0u541wcFIXliVznAwZF4JOeMHp/jB8PbT4X+OJfCen67NrFqLGxv4byazFrI6XNrHcKGiEkgUqJQpw7DI60ulyl7zaXRX+V0vzaOKooooAKKKKACivVf2c/g/pnxe8ZalD4m1C7sPDHhjRrvxDrlxaged9lt1BMcZKlQ7kgAkHjccHGKX44eHfgvpul+C/Enwi1yRpfEOmPda1ocl99sbRrhWULEZTHGSWBbII6oSPlZaJe5a/X/g6/+Sv7vQI+9fy/PTT8U/meU0UUUAFFd7N8KZNK+EkPxV8Ta2umDWrxrPw5pot/Mm1QREC5nJ3KIoY8hQ+GLOdoUDLVwVD0bj1QLVc3cKK734W/CmT4sxa3pPh3XETxVp9m2oaboslv/wAhaKMFp44pd3EyoNyxlfnAbDAjB4LpweCKHo+X5/19wLVXCiiigAooru/hZ8K5viJ/bmsanraaD4X8LWP9oa3rEkBmFujHbFFHGGXzZpXwqJuXJycgCjo321A4SilbbuOxiVzwSMEj6dqSgHoFFd4vwrksfhH/AMLY8S64ulw6nemw8O6f9n8ybVnjI+0y53KIoYgcGTDZchQOpHB0PRuPVf8ADgtVzBRXc/Cf4Y/8La1i98I6Tr0dn4mktXn0Owmh/darMgLPbeduHlSlATHlSrsNpZMgnM8C+E9L8TeNrTwd4o8Rf8IuLyZrP7Zc2jSR290crGs67laNDJhWfkoCTtODTSbkordickouT2X9f18+zOZora8aeDvEPw+8V6p4K8VWDWeraPcta3ULc4YdCp/iVhhlYcFSCODWLUxkpLmWxUk4vlYUV6n4b/Z38XeKPg/rfxr03xL4UGi+Ho/Mv7R9UzfxZkMaK0KqdjOw+QOV3AgivLKb92XI99Px2EtY8y22+7cKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArp/hf4Nm+IXxI8MeBrdX3a7q1rYMVGSqSSKrt+Ckn8K5ivo79i/Vvgr8OfiVpPxc+K3xU0/SDojXQt9H/srULm4eRoTGkpeGB4gv7xjjcWyo4FbUHGNRSlstfu6fPYyrc3I4x3en/B+R79+0B4F+G/xe/ac13U/FHxE8Oz+Hfhd4ab7f4Yha+S/kgs4XlePd5SRKDJKAWjmYgYHB4Hyn4O+JEOk6b8NdJ8c6XrGleGPC+v3fiW1vbCw+0y6g7SwZjjSWWFCqtbbC4kJUseCRg9ZqWg/CPxJ8QvEHi7Vv2wtK0qDxXeXc2q/wBm+HtdaWS3uJTI8BU2yCRScDax2/KDjisT4mX3gj4tfEafT/DHiiHQPhv4C0aLTdJvLuGR5ZLKF1TzI4MB5bieeZpNpCnDEsVCEjmw7qQ5f5nq156yk/v5bK72fZ33qxjOUktlon5WUErecXK780ux1njD4zfs9698ZvGPxzt774gTaprNnff2RpU3h+xigtryaza3jeaf7dIXRS27Cxg5x1xg5XjD4jfs/wDjD4P/AA4+Fdx4p+INmfAZ1JpL1PCdlJ9s+1ziUgIdTXy9n3cktnrgdKreOP2RNY8M6pZ+EvDvxA0LxL4vvNdh0VfD9rmK7UTW4uI5yrtuCCJkMjFQkZbG9trY434i/B/QfA/hb+39K+K2geIru11qTQdQsLRWjkS4jj3vJAHO+a3U/J5xRAWI2hgc04RjRpKnFWiuX/yXbzfx767q5fNNy5uru/8AwJNP8Iu3zaOx8UfGn4W+PPFHgLwzrlh4k0z4XfDe0S206zgtILzUdRPmI87XAaaKKNptpyVZgmAAGySO48O/tf6BqOt/EzSPit4l8W6z4J8Y6ddadp9lZ+G7CO4i85iyTP8A6Suxoeirvl3ZySpHPyTRTa5k4y1T5r+fN8T9e3bpYyj7lnHS3Lby5dl6d+/U9i+A8emw+J/iJHo13c3VgvgHxMLaa6t1gmkj+xvtZ41eRUYjqodgPU9a534K/B3WPjP4qn0Oz1K30jS9LspdV1vWLlS0OnWMQzJMwBBY9AqgjJPUDJGz+zn/AMhbx3/2TzxJ/wCkT17J+zzoej+Hf2P/AIn+I9b8YWfhS5+ImpweEtN1G/hla3xDGZnRzEjuqSBpYy4UgEDNazVo8+/LD73zyS/Fr9Dnw/v1Zw2vU+5ckG++yT7+Z514m+CPw31z4Ja98avg/r3iWSx8IatBpWrQeILeFGulmKLHcW5h4T5nUGJixAbO7jBxf2qP+Ssr/wBi34e/9NVrXT+Pvil4L8C/s82v7Nfwz16PxHNqup/254u12CCSK0muF2+Xa2wlVZHRPKjJkKqCUBA+Y45j9qj/AJKyv/Yt+Hv/AE1WtQ9FKzuuaP8A6S7/APk1/wBNLHRTvz678kv/AE5C34W/XW5wngfwhY+MtSn0++8d+HPCyww+ctzrklykMpyBsUwQyndznkAYB5rtv+FD+Hv+jkvhN/4Gap/8g1xPgfxfY+DdSn1C+8CeHPFKzQ+Sttrkdy8MRyDvUQTRHdxjkkYJ4rtv+F8eHv8Ao234Tf8AgHqn/wAnUO2glfU53xx8NdJ8G6XFqNj8W/BHiiSWYQ/ZNDmvXmQFSfMbzraJQoxj72csOOuOIrt/HHxK0nxlpcWnWPwk8EeF5IphN9r0OG9SZwFI8tvOuZVKnOfu5yo5654ipVyn0Pq/9kG1/s/4CftIeLGV4zF4STTIZsYGZ0uAwDeuRHwPUV8oV9Y/CW4/4R//AIJ+fGPVo90c2s+I9P0lWVtpZQ1uzL9Nrvkd8mvk6nUd67tsoxXztzP7+a4qa/c8z3cpP5aRT+fKehfBP4N6p8ZPEV7p8eqQ6Lomh6fNq+va1cRl4tOsohl5CoI3seioCCT3ABI7bxR8D/h5rXwR1T43/B3XPEj6b4b1yPRNUtvEMUCST+aE8u4gMPCjdIgMbZI3Z3cc+kfAvw9o/h39i34g6zrXi+w8JXvxM1qDw5puo6jHK1vJFbJ5zIxiV2RX/wBIjLbSAQM8Vw3xI+LPgvwb8AtJ/Zr+Futpr6y6kdc8V69FbyQ215dgjZb26yqsjRpsTMjKu7y1IGCcXPljNRey5Nt3dpy9Eofj5uIU/eSl3cvuSaXzc/6VpFj9tb+y9E+M2g/DENcp4f8AAfh3SNGjjgxuEZhWaaRA3G9vNPJ6kDNew6j+xz+yTo/hP4efETXfin440Pw74+8uO3bU57GJ4nmh86JpZBEY4EVUcOSXBZkAZRk14/8AtsW9j4h+M2hfES3ujBonxA8OaRq8F40bOkamFYZOAMkp5eWUc84716R+0X8SP2efHf7Mvgf4XeFfjlaX2tfD20i2R/8ACPalENUkjt/KCI0kKrFu65dsCsJzlTpSna8lN37/ABPn/W34DSi6kYx29nHfvyq2/Xv8jw2wTQfgj+1Vpa/DvxtD4k0bw94ms/sesWroy3VszpvXchKt8rvGzKdrYJGAa6z4k/s5a948/bM8Y/Bv4cwwR+bq01+Zpsrb2NrIqzu7lRwieaFAA5JUDrXkfwR8LXnjb4weDPC1jC8smoa5ZxsF6iMSq0jfRUDMfYGvpbSP2kvBnhX9tP4p+NtW1a503RPFFrqHhmHWrONpWsGURRxXaqmWdQ1sD8uT8wIzitYxS9nzatKp82lCy+b0+ZF5SlUtonyfLWfz0R4r8ZPhf8MfhnpTaXo998RNR1+K9Nuupap4cj03R7yNc+Y1vvlaZsHGCRyOeMivHa9+1XTfEHjPxN4V8P8AiT423/xuv5tYVYPDOmalqdyZLcqS7C5u40WFm2BSEBYKSxK4r0H4gab4U8NfDLSbDVv2e/D8vjLxN4J1DWNTlsF+xJocIvDHYXIDbhlFzuCkPKxjDs3eIqXLdu7bt90b/o79F1aNNJS5Yra3nvK3+S89LXPkCvorxJHb+D/2G/B1pp9w0dz8QPGF/qV/tyDLDYqYEjY9CquVcA/xNkVrfFDR/h78IfEPib4D6H8H4/GEuk+FQ95rixu18uqyQRTtqHmAOYrWBH4iTYpxl2bJNZXiZofF/wCwz4MvrCEy3PgDxhf6Zf7ckww3ymdJGHQKzBUBPcYHeiq/3clHVe7/AOnEuvmk/R97pFNR9pBy/vf+m29fv1PNPgr8HdY+M/iqfQ7PUrfSNL0uyl1XW9YuVLQ6dYxDMkzAEFj0CqCMk9QMkdx4m+CPw31z4Ja98avg/r3iWSx8IatBpWrQeILeFGulmKLHcW5h4T5nUGJixAbO7jB9F/Z50PR/Dv7H/wAT/Eet+MLPwpc/ETU4PCWm6jfwytb4hjMzo5iR3VJA0sZcKQCBmuH8ffFLwX4E/Z5tf2bPhnr8fiKbVdU/tzxdrsEEkVpNcLt8u1thKqyOieVGTIVUEoCB8xxdZct4dUo/Ntpv5KH4vziTTTklPo3L7kml98/XbZe8S/ttx23h34g+FPhbp1wx0zwN4P0rTY4lyFErx+dLJg8bn8xSSOuBnpXri/sj/slx/CjwX8aNW+JHj7RfDviq+gsf+JjJYpKrSu0YyViKRKhR5GclxsQ8AnI8l/beNv4g+IfhX4oadERpvjrwfpWpRzjJQypH5MseT/EnlqCB0yO5r1D4p/EP9nPxH+yD4a+Bei/Huzudb8IyHUEb/hHNTRb+REuCsC7oQIyxmUbmbaMZPFDfLTnJq8lUf3cz5tunb/MIRU6lOCbUXTWr78keXfz/AA3Pnr4kaV4T+BPx8T/hUvj638WaT4c1Cy1LTdVt50kDMuyXYZIv3blWypZOD7HKjc/bb8Pab4b/AGm/GcOjxJFa6hNbaqqIuAr3NtHM/wCbux/GvI/B/hy+8YeLNF8J6bGXu9Z1C3sIVC5+eWRUHH1avWf21vEmm+KP2mPGdxo8qS2mnz2+krIhyGa1t44H/J43H4UnHlhTV7u8vu92/wD7bbruaQlCUqjtb3Y9976fhz/1Y3v2wYY9c0n4P/FOSVZNQ8W+BbNdScLzNdWv7p5WPdiCoOf7grjvg/8AA208Z+DfE3xe8fa5c6F4B8IbI726tYBLd3125UR2lsrELvJdMux2rvUkEE4679r66j0XS/hF8K3jRL/wf4Gs/wC04wfmhu7oCZ4mHZgNh/4HU/g/x34L8Zfsnr8BtR+IGk+DNW07xd/blxLqkN00N/YGEg7Gt4pC0qOR+7YfMEGD6EVL9838XNK23Wpb00i79tNdLmMb2oxls4xvbyp3/FpLvr3PQPFUfwj+Gv7HPh2z8P3nigeHPi14xhvtTjuo7catFplnhZ0jIxFIVkiVkYgKfNGQK6nxT+xX+yL4L1DwWvi74xeLdCsvHNhJcafLqV5YwJGyRLKXmmaERxrh40CnJLPw3HPDfHHxd+zb42+HvgPwrofxJSfT/DuiWXh7SRJp93HcabcvdqdQ1O7hRQrKIIV2xozs7TMQDtBFn9tT4m/Ab4vfD3wMvgH4xW+pav4E006f/Zv9hahC2oGT7MjOkssSJGEWFnwx56Dmrm4vmn3nZd+VR5V+Ci0/N9U7JKa9nHpytvb4m1L9ZJ9Xy9E1f5O8baLpPhzxhrfh/Qdeh1vTdN1C4tbTUoR+7vIUkKpMuCRhgAeCRzwTWLRRWcU1FJ6msmpSbSsFFFFMkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvQPg/478BeA9Q1W88dfDSDxgl1aLHZLJdmA2kyypJvHysDuCbCcZAY47g+f0VUZOLvEmUVNWkfRl1+1ho9l8abX41eG/hu41WfV5tX1ZtR1FJZpBLbNbmztpUhTyYUjkfaSrsWCM2doWvIfF+s/DW408ad4H8H6vbTNdfaJtU1nVhdXLJtI8lI4o44kXJyWYO5IGGUZU8jRUWSio9Ft6dvT+urNOZ3b7/APB/zf8ASQUUUUyTtvhL8QdM+Heuapf614futZsNY0O/0K6trXUFsphFdQmJnSVopVVlBJGY2Ga0vG3xp1DxP8M/D3wc0nRo9L8KeF9RvdQso5J/Pu5nnkZlNxKFRJGRXZQyRxg5Py+nm9FEvfVpeX4O6/F3FCKptuPV3+duX8tC9oc2h2+q283iTTr6/wBMVj9ot7G8S0nkXBwEmeKVUOcHJjbgEY5yOn+MHxCtPih44l8V6foU2j2psbGwhtJrwXUiJbWsdupaURxhiwiDHCKMnpXFUUdLDXuttdVb5XT/ADSCiiigArsPhD4g8E+FfiV4f8QfEjwyfEHhmzuw+pacACZ4ipHCkgMVJDbSQG24PBrj6KqMuWSl2JlHmi49z3/4z/F74br8L4/gl8GWvZtCuvE134r1K7mszaRmSQFbe0hiZ3cxwxbVLMcsyAgYrwCiis0rf12SS+5JL5FuTen9att/i2zvvFPxm8ReKfhT4P8Ag/Np9jZ6J4Omu7mFrcP5l3NcSFzJKWYgldzAbQOGP4cDRRVPWTl1erF0Uex303xXm1b4R2/wp8UaIuprol4134b1IXHlzaWJWzcwEbW86GTAYJlSrjcGI+WuBoooercurDpy9jvPhf8AFST4Uwa5qnh/Q1fxTqNk2n6brT3JH9kxSgrPJFEF5mZDtWQuNgLYUk5HB5zyeSaKKHq7/L+vvBaK3zL2h65rXhnVrXXvD2qXWm6lZSCW3urWVo5YnHdWXkdx+JroPEXxe+KXi7R38P8Aif4heINU02SXzntbq/kkiZt24ZUnBAYkhegJJAFcjRQ9dGG2p1mqfFr4n614ej8J6t8QNfu9HiiSAWU2oStE0SY2Iyk/Oq4G1WyFxxitD4WfFWb4drrui6loqa94X8VWX2DW9Hkn8kXCqd0Uscm1vKmif5kfa2ORgg1wdFD1vfrv53Dsu23yO/8AEnxm8Q+IvhH4W+C7WNlaaF4VvbvUImiDeddTzuzb5iTtJQOyjaF4JzXAUUUdXLqxt30/rud6PitJf/CMfCbxNoa6pDpd4194c1D7R5U2kPIwNzFja3mwygZMeVw+GB6g8FRRQ9ZOXV/8MJaLl6HcfCj4mf8ACp9WvvFmlaCl34lS0eDQ7+afEelTSAq9yIdp82UIWEeWCox3EPgAZvgPxZpXhjxpZ+MPFHhv/hJ1spmvBZXN2Yo7i5GWjadtrNIgfDMnBcAjcMmuZopptSUluhNJxcXs/wCv6+fdmz4y8X+IPH3irVPGfim+e81XWLl7q6mbuzHoB2UDCqo4CgAcCsaiipjFRXKtkU25O7CiiimIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK9M8f/AAz0Hwr8G/hd8Q9Pu7+TUfGy6y2oRTSIYIvsl0sUflAIGXKsS25myemOlVyvl5ul7fff/ILnmdFFFSAUUUUAFFFa2geEfFniv7T/AMIv4X1bWPscfm3P9n2Ulx5Kc/M+xTtHB5PoaAMmivZv2Yfh/wCH/EvijXvHXjnTft3hX4daLP4j1O0YfLeyR8W9q2e0kmMjuqsO9creaX8YPjz4m1fxtY+EPEXim+u7jddyaVpc1ykBI+SICJSI1VAFVeyqAOBQ9Go+V/RXsvvaf3eYJp39bfO139ya+/yZwdFb+q+BfH3hfTLLxLrng7xBpGnXjD7FqF1p81vDM3JHlysoVjwSNp7GvY/i1ptv8VvgL4b/AGio7dB4ksdUbwn4wkjjx9tnWMSWt6+OPMaPCO38TYPXNEnyx5uzV/K+i/FpfPtezSbly97/AIav8E38j59orT8M/wDCNf8ACRab/wAJkdTGhfao/wC0f7MEf2v7PuHmeT5nyb9ucbuM4zTfEn/CO/8ACQal/wAIh/aX9h/apP7O/tLy/tf2bcfL87y/k8zbjdt4znFD0sJa3/rv/l+RnUUV1ut/DPxBoHw58M/E6+uLFtJ8V3V9a2MccjGdWtWRZDIpUKoJcbcMScHIFGw4pyfKt9fwV3+COSor1L9or4W+H/hF4407w14bvNQubW78P6ZqzvfSI8gmuIBI6goijaCeBjOOpNeW0k7trs2vmnZ/iiYyU4xmtmk16NXX5hRRRTGFFejfs7fDnQ/i58aPC3w58SXV9babrd1JDcS2MiJOqrC7jYzqyg5UdVPGa4DULdLS/ubWMsUhmeNS3UgMQM0PT+v67lKLlFyXS343t+TIKKK0PDuh3nibxBpnhvTmiW71a8hsYDKxCCSVwi7iASBlhk4NNJydkQ2oq7M+it3x14P1T4e+NNb8C61NbS6hoF/Np91JaszRNJE5VihZVJXI4JAPtWFUxkpLmWxc4SpycZaNBRRRTJCiinwoJJkjbOGYKfxNXSpyqzVOO7dvvBuwyivTP2lPhloPwc+NniX4b+Gbu/utM0Z7ZYJb+RHnbzLWKVtzIiKfmkYDCjgD615nWcZcy5kAUUUUwCiukP8Awrf/AIV6Np8S/wDCc/2n83EH9k/2d5f/AH+8/wAz/gG33q38UPhf4i+Euv2nhvxNcWE11eaZaashs5GdBDcRiRFJZVO4A8gAjPQmiXu7+nzav+V/uYXT062b+Sdvza+9HIUUUUAFFFdf8UPhf4i+Euv2nhvxNcWE11eaZaashs5GdBDcRiRFJZVO4A8gAjPQmk3bfrp89X+SYrrm5etr/LRfqvvOQooopjCiiigAorpvDH/Ctf8AhHvEf/CZf8JN/bv2eP8A4R3+y/s/2Tz9x8z7Z5nz7MbceXznOav+Kvhfq/gPR/BXifxRc2z6Z40sjqlqtjIWnjtllMbBw6hVfg4ALDpk0bSSfWy8ru7t9ybE5Jeurt1srfq0jiqK6Dx7/wAID/wlV7/wq/8A4SD/AIRr5PsX9veT9u+4u/zPI/d/f3Y2/wAOM81z9JO5TVgooopiCiuz/wCFKfGXbaSf8Kk8abdQkEVo39g3WLhypcLH+7+c7VLYGeAT0FcxrGi6x4e1KfRfEGk3mmahatsntLyBoZomwDhkcBlOCDyO9D00YeZTooooAKKKKACivT5fhfoEf7M8HxmW81D+25fG7+Gjb+Yn2X7KLEXG/bs3+Zv4zv24/hzzXmFVOLpy5Zb6filJfg0C1V/63sFFaNx4c8RWmkw6/daDqMOl3L+XDeyWsiwSNgnashG1jgE4B7Gs6pAKKK9/+FNlH8JfgR4g/aH8kf8ACS6nqg8JeEZXjB+wymIyXd8mejrH+7jYfdYk/QekZS7f5pL720hX95R7/wDDv7kmzwCilZmkYszFmY5JPJJrdvtB8dfD++0zVtU0PXPDt2WW8024ubWW1dihDLLEzAE4O0hl6cU105thv+6YNFe9/tB+H9N8XfDzwL+0lomnQWcvjBbjS/E0FuipEutWxw84ReENwn7wqABkE9WryLwL/wAIL/wlVh/wsz+3v+Ea3N9v/sLyft23Y23yvO/d537c7u2e9JfE4vdO39eTWq7poLrlUo7P+reqej80YNFWNQ/s/wDtC5/sn7R9h85/s32jb5vlbjs37eN23GccZziq9KL5lcbVnYKK7Lxf8LPEHgfQvBviPW7mxe08b2Dalp620jNJHCspjIlDKArZBwAW471rftG/DbQ/hD8afE3w58NXV/c6bos0EdvLfSI87B7eOQ72RUU8ueijjFU1ZJ93JfONk/uuTFqSUls0n8nqjzeiiikMKKKKACiinRrvkVT0ZgKqEHUkoR3egbDaK9K/aO+GuhfB/wCM/iL4deGrq/udN0g2vkS30iPO3m2sMzbiiIp+aRgMKOAPrXmtQndXAKKKKYBRRRQAUUUUAFFFFABRRRQAUUV6d8NfhjoPjL4V/FLxxql5qEV/4IsNOutPjt5EWKV7i7ELiUMhZgFORtK89c9KHorlQi5yjBbtpfNuyPMaKK6zw/8ADXX/ABJ8P/FnxIsbixTS/B0lhFfxyyMJna7kaOLylCkNgoS2WXA6Z6U7Ozl0X+dvzaJOTorpNe/4Vv8A8Il4e/4Rk+JT4nxcf8JB9vEH2DO8eR9k2fvPu53+Z3xjiubpPRtB0T7hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX1v8AfBzftGfs/23wnkkLXfgXx3p+o+8ej37eVdAHsFcPIfoK+SK6v4e/FX4gfCq51W8+H/iSbR5ta0+TS75o4o3822kwWX94rbTwMOuGHOCMmrjLlvpfZ/NNSXybST62bsTKLlazt/k04v52bt52PvLXNf8K+LfFVh+2toVpHFpfg/wr4is0TO1Bf2dwbXTVbPIaWO8jYf7o9K8d1747fEf4Tfsu/BO78A60ul6vrMniOa61UW8ct0UTUMmJXkVtqO0m5wPvFFzwMV84af8VviBpfw41P4Saf4ikh8JazepqF9pogiImuE2bW8wr5g/1cfyhgp2jIqjrPjrxV4g8L+H/Ber6p9o0bwqLoaTbeRGv2b7TIJJvnVQ77nAPzlsdBgVKfLFxj3T/wDSnL75St5xSv2NG01t39LWSivklv3bZ+gK+F/Buo/tI6z8UdWt7Gx1lfhJaeNIZItJF6tvqcihJL5bMMPPeMDeFBGSQeuDXjPxt+MXwx+IHwbuPDuvfGy++Jvjaz1i2vNB1K78H/2TNZwFgtxbmRXYPGynftOPmUdeMeEf8NAfGFfGmi/EKHxtcweIfD2nQ6Tp17bwQxGKziBVIWRECSLhiDvVt2fmzU3xE/aK+MPxW0iHQfHXiqG90+C7F8tvb6VZ2amcKyiRvs8SbyAzD5s9TWsJwp1oSivdjK9vJVHPT5WVtNFa9tsqKlGP7x3lbfz5FF9rq93rfe9rn1d8SPEGofE7/goBZfBPxlq1w3gKHWLUyaCsxjsruZLFbhWniGFmd5cKS4J2kKDjAqL4ifHL4b32j/EjwP8AEz49X3i22vdPv7LSfC8/w+/s9NF1OMn7MYJhIxj8pl2cjkck8c/Gfij4keN/GXjqb4leIPEE03iae4ium1GGNLeQTRKqxuoiVVQqETBUDpnrXc+K/wBrX9oLxtoF/wCGfE3jxLux1S3+y3u3R7GGa4i4G1544VlYEAZy3PeuTkaoKn1s79m2lr+G9nbSxtCXLU53t7tvk3+d9dVfrfp5Vpun3msala6Tp8DTXV7OlvBGvV5HYKqj3JIFfcXieDXvCf7Tfws/ZR+DerX2l6N4PuNLm1hbCV4Tf3x23N5dXJQ/vD5OOG4UZAwOK+cv2SdP8Nah+0V4Jl8YatYaZpGm6h/alxc3t0kES/Zo2mQFnIHLxoMd81u/Ez9rz4peJPGHizUvDusadpqazc3drHqtnpNtDqj6Y0rGK2N4qecECbBwwOFAzgYrpc/Z+z6u7lbvbSKb7Xb+6/RGPJ7Tn9FG/bm3/Bfjbrp7V421Dw3qmm/tkXvw/W3Fg91oSqbRQFbbdMt0y46q0okJI4OSehrF/wCCW+u63D8dtU8NxaxeppNz4eurqawW4cW8k6SwKkjR52lwGYBiMgMR3rxz9l3x/wCHfDPibX/AfjrUhYeFfiLok/hzUrxvuWUknNvdNx0jkAyegDsT0qDw78VPj5+yf4k17wT4W12Lw7qCXIW9/wCJZZ3ZlwvyPHJPE58tkIZSpCsrA81nTXsXFPbkS+cfda+60vma1pOvF2W03L5NR8u6a/qz6/48ftZfFfxXpPjb4F682k3mgyeKLudbqS2c3sSRXbNHAj79ixrsUAbNwUbc44p3w3Mn/DDPxf8AtH+oPiXQ/s+7p53mLv2++zGfavFPH3xD8afFnxN/wk3jS+i1LV5o0t/Mt7CC13gE7R5duiITljztyc85r1j4tahD8K/gX4a/Z1jmU+IrzUn8WeL445A32O4eMR2tk+D/AKxIsNIv8LFR1zWFOnKlhuSesnyq/fllF3/8Bjfyemul1O1WvGS2XM/vjJf+lS/4Hbz/APZ9/wCS8fDn/satK/8ASqOvpbwh8YPCPgfx58Z/BM3jm7+GfirXvHuoT2HjaHR0v4xDHdSBrGYffjQs27cgwN2WwBhvjrw/r2reFde07xNoN19l1PSbuK+s5/LV/KmicOjbXBVsMAcEEHHIr0Xwr+1F8cvBTa0/h3xlBD/wkOrS65qKz6NY3Ky30v35gs0LCMn0QKPQV2OcXTjDtzfjyW/9Jf5apsjl97m9Pw5v8/13K/7SmjfETQfjV4jsPitqtlqnibzIJLy/slVYbtWgjMUyhVUDdHsJG0ck8V7b8SPjf8YI/wBjP4ZLH4z1H7N4hm17QtW+RCtxZxGOKG3Y7eAI94GMEjNfLfizxZ4k8deIr7xZ4u1i51XV9Sl866u7hsvI2AB7AAAAAYAAAAAAFdR4d+PHxX8J/D/UvhZofiwxeFtV88XOny2VvOB50fly+W8sbSQ7lyD5bL1J6nNc8U1R9m/L00f+V1t8jbmiqyqRXffzi1+dna/Ra3Vz7D+IP7QXxJ8C/tG/Cz4eeE9StrDQNS03wvBq1otnE39pi5SKOTz3ZSzYjIVRnC4yOSaTwj4f8D/CbTfjz470XxZ/wgOp6X8QZvDtvr1l4dXVpdI07eXWOGDcvkrI7bDJnoigc4I+Ldc+LPxA8SeMNI8fa1r/ANo17Qkso9Pu/ssCeStpt+zjYqBG2bV+8pzj5s1s+Gv2i/jN4P8AGHiDx54d8bS2ms+KpJJdakNnbyQXzuxZjJbvGYTyzEfJxuOMZq3zSlKTe7m79bSkpJdOiaetlfqrp5UIqlQjTlq0qa8vdjKL/FprTW3R2Z6D+1d8SPht8StI8EXnh/x1N4z8YaZDd2niDX5fD39kvfxb0a1MkeWDOil13AnPU9a7n9lCPwz4L/Z58dfFoeN5PBfiKPXrXRf+Elt/D41i402yaNX2xwll8rzXYoZM/wAKjrgj5w+JXxg+InxevLG++IPiBdSk02JobRY7K3tY4UY5YLHBGiDJAJOMnFO+GXxl+Jnwdvr2/wDhz4pl0h9ShFveRm3huILlBnAkhmR43xk4JXIycdTShpGS2b7et/Lfr6vdb1N8zj5fjo/Xa+notnqvsXwl8SPht8Sv2kPgPeeH/HU3jPxhplzqNp4g1+Xw9/ZL38Xku1qZI8sGdFLruBOep61mfCm68MfC39mmD4gaX8Vrn4ca14m8XX9nqniGx8MLrNyywlvKss71MClR5vqxz26/M+qftLfGzWPG2g/EO+8ZK2u+GBIukzx6XZxR2m8EPtgSIRHIJzlD+gqn8Of2gPi98J4dTtfAfjGSwtdZkE19aTWdvd200g/j8m4jeMN/tBQeAOwpRVk+jd/xlF+W6Tv69UXzrl5f8P4RqLXfbmVvJdHqfV2k658JPin8aL74meBYrXx74p8JfDO81eX7V4fNjFq3iK0ZFiumtNxDuUk3bRnBjUjkDHjPgX9rT9obxh8VPBEHiL4l3l2i+IrUIPsttGUWeQQyqpSMFVaOR12g456ZAI801L4+fF3VviFYfFa68a3KeKdLjSGzv7aCG38iNN2I1jiRYwnzsCu3BDEEHNWviF+0Z8ZPilaWVj418XrdRadejUrYWum2liUugCBNutooyzgE4JJxmtYS5HB9FuunxOV+2zStb7KVzGqvaRlH8f8At1Jfir3vu27X3+ufDfxP8eeIP25PHGleMtVm1bT/AAdY+K49HsbyJfLgtzGW8sDaCyMscf3s5AFeV/BP4jePtW8CfGP4+32o3Hib4h+EdO0yHQ73Uh9sbR7W5uZRczW8LgpEI1GVCqFQE4ABIPles/tXfHzxBrlp4l1bx0s+p2VjeadFc/2TYo3kXSJHOrbYQHZ1jQb2ywxwRXGfDv4nePPhP4hHir4eeJLnRtSETQNLEqOssTdUkjcMkinAO1lIyAeoFZUk4rlk9oKN+t+aTb/7eTSet9/Jlyd6lSdtJT5rdLWgrfJxdtLbeh9C3Xj7xh8fv2UviJ4q+NF0ms6j4J1TSf8AhGtduLSOO4EtzKUubQSRqu9fL2uVOcZBPAXHrnxB/aF+IvhT9sDwh8MPDd5aWXhq/fw7YaxYizhZdVF1BAsjzsU3sRHIqL82FEa46nPxv8Svj58W/i9Y2OlfEDxfJqGn6dI0ttZQWdvZ2ySNwX8q3jRC+CRuIJGTzyaztY+LXxB1/wCIFl8UtW8Qef4n0+Szmtr77LAux7VUWA+WqCM7RGnVTnHOcmtqcoqspzXu80W13SjZ3733tt89Qb9yUevK0n2blF/ckn53fbax8ctC0/wx8aPHXh7SLdLex07xFqNvbQxrtWKJbhwiKOwAwB9K+nfjJ8Wfid8B/Gvgb4L/AAVjgsvCZ0HSZoNOj06GePxHLdKDLJNuVjN5rloyM9jjB5r498SeItZ8X+INS8VeIrz7Xqmr3Ut7eT+WkfmzSMWdtqAKuSScKAPQV6P4f/at/aB8L+E7TwTovxGuYdJ0+B7azWSytZp7WJhgpDcSRNNEAOAFcbRgDAFRhn7GNNS15XFtdHZNNfj/AFoVXcalac46J81u6u00997K2/Xc+yNQ0rw1bftRftHfEfVNTXR9Z8IaPpU2l6p/ZI1R9J860hWa8jtCw8x41VQDn5Q5JyOK8w8YfFr4bfESP4aaRP8AFy8+I/jzS/H+mzW+t3XhX+yJk0x5FD2zsGYSAShGGTnnGOOfnK3/AGhvjJafFC7+M1p44uIPGF+gjutRitoEE6CNIwjwhPJZdsafKUwSoJGean8Y/tI/Gbx9Nos3ijxdFcHw9fpqmnJb6VZWscN0pBWUpBCiuwwPvg0oe6qd9eXl/CV9N+nkm9m7bZtXU/Pm/GNtfTfqutr7/YPh343eNfGn7bPif4Cas1g3w91XUNb0i68P/YYfs8gSCZ2nYhQzSvLHvZiScuR0xjhf2evial18OvDXwU+E/wAVj8LviJb6nec3mhxz2HiyaaZvIWW4CO0bKqiIb1Kn7oDHGPl/S/jF8R9F+J0vxj03xF5PjCe6uL2TUvscDZmnV1lbymQxDcJHGNmBngDArpvCP7VXx68C+GbTwj4V8cpY6ZYed9kX+ybGSa3812kk8ud4TKhLux+VxjPGKdPljSjF78qT87Nvf59U0+q0RvXnCc5ygtHK8b9E00/02d9N+/o2tX/jDwP+y9PqVxc/2f4p0H45TTSTQ7T9nv4bBizLjK/LIpxjjj0r1r4yftQfEL4c/tI/D6HxB4pvbjwcNH0W/wBU06RV8qVbq0MV1KcLljtkkbGcbgOK+Kbr4ieM77wbJ4BvtcluNDm1lvEElvLGjO+oNEYmnaUr5pJQkEFtvOcZ5rv9O+Inxa+P1hovwZ8WfFXw7Y6JpkaNZXHiSS1sIIRbxssaSXvlea2Fdgqu5yT64q7uXKlsnB26NRp8sl6O1/NJXOWS99ybaTUl53lJOL+W1+l2ew+OPAA/ZP8Ah78XruNkj1LxxrJ8H+F5By39iELdXEyknJV4nhiJHRhXjf7K/hvxZrXxWTWPCWqaDpU3hfTL3XLvU9btTc2un2scRR7nyQD5kkfmBkXH3wpPANa/7W3xT03xz4q8O+CfDHiR9e8O/DvQ7bw9ZakzbhfzxoouLlWPJV2VVB6ERqRwa8v+HfxK8cfCfxNH4w+H3iCbR9WiieATxxpIGjcYZHjkVkdTxwykZAPUCs6L5ZOV7pLli/JKyfR6v3ns/efXU1qXlFKSs3rJeb1a67L3eux9l/FjxhJ4k/ZZf4pWfxmu/iJ4g8GeOLKXRfFM3hr+yZbGQorNBGHyZEDbX5AGdoxwK88/b0+KHxD1rxXofgXWPE15caA/h3RdaFlIq7DePa/PMDjOSWfvjk8V5B46/aY+N3xK8M3Hg3xn41F9ol1PFcSWKabZ28QkjyUKCGJdnLEkLgEnJyazvGHx5+LHj7wXpfw+8X+LDqeh6MIRZwy2VuJUEKMkYadYxM4VXYAO5HNZuMpXV9OZSS8lDlfzvaXXZXd9RptTjK+0ZR+blzL8Lr5u2mh7L+yBreseGvhR8efEWgaVDqGpaXoGm3lrHNbpMkUkdy7LcGNwVbyiPNAYEZjGQeldH4f8X+Iv2jv2dde1b4/6gNSHh/xloNloviC4iihnRbu4WO9t1dFXKrCRIQc43A/wjHnn7NfxMk+GPws+MuraN4xtdB8Syado7aLuuY0nuJUvgZFijf8A1uEJ3KAw2k7hg1578S/j98XPi9p9hpPxA8XvqGn6ZI0trZQWdvZ20cjdX8q3jRGbBI3EEjJweTWvMr6q/wAOj6WnzO3m0rbbb3WglFui7O15S231hFfdr+fXU+ovH3x4+NPw9/aoi+Bfgqzt9O8FafrFnodj4Rh0u3Npd6fJ5ancDGWfzVdmL543deKXSfHXwp+CvxC+Lnwi8J+OJ/hjqN94qLaN4ytdITUIIIIwvmaZIMb441kdsMg4/iOBhvANP/bF/aS0vQ7bw/Z/E65FvZ2v2K3nk0+zkvIocY2rdvEZxgcAiTI7GsP4e/tGfGT4V6Xe6L4J8XR2tnqF6dSuY7rS7O+33RUKZc3MUhDEADII6VEIuLjzO9k031ldxevpyt7te81awnrdrTay6KyktPW6XTZPc+gtb0j4jeH9F/ams/ipq1lqfiddL8OS3WoWSqsN2DdRGKZQqqBuj2EjaOSciqn7Tvxq+K2sfBf4P6XqnjK/uLHxl4bN1rcbqm2+nivMozfLwRtT7uOgr50vvjR8T9U/4S9tT8XXN4/j0Qr4hkuIopHvRDIJIhuZSYwrAYEZUAAL90Yq3/wvv4tN8NE+D8niwy+E4htisZbK2kaNfNEu1J2jMyrvUHaHA4xjHFa4eTpypOe0JRdu9ua66d09t47LS2jkub3W0ve/8m9nr6+5LS+0t3qfYPi6MaD8fP2mfipoOj2uo+MvBekabc+HxPCk/wBjMsEKXF4kTghnhjAYEggZOeteZaX8SPGn7Qn7M/xZ1D436outN4LGm33h7WrmyjWe2vJpyj2ySRqu5ZFwCpzjOfSvC4fj58XrX4nXPxks/G11beMLzAuNRghhj85fLWPa8KoImXaigqU2naCRnmpfiR+0N8Yvi1pNtoPjzxk97pdpMbiKxt7G2srfzSMeY0dtHGrtgnDMCRk461z8j9kqe/upejTvdfpputdNBU5KMub+9f1VkuV+Wj+Telz68+IP7QXxI8C/tG/Cz4d+FdRtrHw/qWm+F4NXtFs4W/tNblIo5PPdlLNiMhVGcLjI5Jr49/aA0PTPDPxy8feH9FtIrWw0/wAR6hBbQRLtSKNZ3Coo7ADgDsBWdrnxZ+IHiTxhpHj7Wtf+0a9oSWUen3f2WBPJW02/ZxsVAjbNq/eU5x82axPFHibXPGfiPU/FviW9+2atrF1Je3tx5aR+bNIxZ22oAq5JJwoA9BVy5pT5n3m/lKSaXyX3dCMOlSwyovdKmvL3YtS+9tevU/QiP4xfEbwj/wAE5vCXxG0/xRfXPiGLXI4/t17PJPK8UepSkRM7NuKFYljK5wY8r0NfEfxw+Nniz4/eOpPiD40s9KtdRktYrPytNt3ihWOPO3h3dieTklj+AAFdbcftnftH3Xhk+Dbjx3ZPopgNv9j/AOEc0vYEKlTjFtkHBPzA7uc5zXOeBPidpfgn4R/EPwdb299/b/jYadYx3MaIIYLCGVpbhWfdu3SERLtC42hsnoKda9StUrdG+aK7NpRt+rfZvTTVwfLCMPk/S9/y9L2Svro79m7wl4q8afGjwzpXg2HRX1OC4a/R9ahMtjEkCNK0kyAZZVC5Cjqdor6p+IHiOz+KX7KvxUvtY+N0nxZl8N3Wk3VjfzeEjpKaTcSXIR1t5GwXDxkgqFXaPZq+JvBfjXxT8O/FFh408F6xNpWtaZIZLW7iCloyVKnhgVYFWIIIIIJBFd74w/ap+PXjzw5qnhHxT46F1o2spGl7ZR6VZW8UnlyCRSBFCuxt4BLLhm2gMSABV1HzU/Zr+ndO/wByXZ3620CjaFZVJ7XX3a3X4vy6W6n0X44/aG+JXwv8WfAjwn4J1K303TLzwT4Vm1SNLSJn1NZAEMUzupYoEUqqgjG9yOTmtLxrYw/AG3/aF+Jnwj0m0svE2meMLPR7W7jto5Domn3MazSSQo4Ij3yP5ecccYxjj4w174k+NPE2qaDrWua19pvPDFhZ6XpUn2eFPs9ta/6hMKoD7fVwSe5NdRpH7THxw0Hxxr3xG0rx5NDrvidQusS/YrZ4L0AADzLZozAcAcHZxk46nJiX7atKpF2u6j87SnGUV9ylF66c2lzOClGS5tYpQ080pJv72mu7Wp7P8XPiH4++KH7Dvh7xZ8RXF1qUvxHeGPUTapC9/CmnygSuUUCRlbfHvxnEQB5Fc9+wl4P8A658RvEfjL4haaurWPgLw1d+I4tM8lZjcSQlfm8piBJsBJCnA3FMkYrzD4hftC/GH4q6Cnhjx94yfVdKj1BdTitTZW0KRXCwmFTH5Ua7FCEjy1wmSW27iTXN+A/iB4x+GPiSDxd4F1ybStVt1eNZo1VwyOMMjo4KSIR1VgQeOOKpTSqSmla6svJ8ijf79RuN4xjLVJ6+a5rtdemh618aviQ3x217SfDvw+8f/FfxXc6zqRaTSfFEtvFZpcNhYPssEEzRR43SAkhQq45xmsy4/ZN+Jsfg++8eafqXhjVNEstLutWF1YaqJRcQ2rlLoR/KMmJsBicK25djOTWfpvxlsvFninw9Y/FTSbKx8EWWo/bb/T/CGjWelSyMUKmQNCiMzEEA5bhQQuDXX/FX4pfAnX/B/hvQ/CUPiW4h8IaHNoul6ReWq28DXMt2ZpdQlnjmLMGBGItvLAFiAPmyjGMY22u/Wytvb1skuq1bjY0c3OXvbJL566/hr0tsrto4yH9mn4oP4PvfF1xa6damw0X/AISOTSri8C6h/ZeQPtZixiNDnKiRldwDsVq7n4n/AGv/AIYl+Cv2fH2P+3fEP2nG3/X+f+7z3zs3/h17Vj/Hb4j/AAl+K3jLxH8VIdY8Xf2p4is4I7fQVsYbaGxljhijCzXPmv5sKmLcqJEC/GTEaufCu+h+LfwF1/8AZ3MyjxNpWqDxZ4QiZwv2+QRGO7sVz1kaPMka9WYEdhlVFzwl5Wdt9pJvXraOunVXXZKnLklCT13X3wa2/wAT0vt12bNP9lXQ9L8I+Afid+0nrFhFPdeBNPis/DZuIw8SavdMUjm2nhmizGRngeZnqAR02qapql7+wHq3iL4r69eavrXizxutx4Zk1Od7if8AdhFnljLklVIS4BI45Hc1Tt/itbfBT9jrwj4Z8H6noV54g8a+IdQ1TxBpd1Db6ggtI1MCw3NvIGChtsLAMAcoSORXgvjL4h/Ef40a7pVvr97LqlxCqabo2mWVqkNvbIzBUgtraFVRATtGFXJwM5NXWj7Wcqcdbci+5qUrd25aX/8AkUKH7uKnLTWT/BxXyS1+fm7erN5n/DAMf2zOP+FpH7H5np/Zvz7M9s56d8+9Yv7En/J1Hw+/6/5v/SaWrP7QWv6f4S8AeBv2cNF1CC8k8HLcal4lnt3V4m1q5OZIQ68P5CfuiwPXcP4a8i8EeNvE/wAOfFWn+NvBup/2frWlSGW0uvJjl8tipUnZIrI3ysRyD1qnUU60qi20XryxjG/o3G68rMmpGUqCp2s7P/yZyav52av22Pq79n34saVD4JX4QeC/iVJ8JviBeeJbi5h1uTRku7TxB5jFILaaXDPDtf5QSpTjuWIHzD8VtL8XaB8UPFOm+OvJHiO31i6OptAFETXJlZndNoA2sxLDAHBHA6V1Xgn9qT46fDvw/D4X8I+NYrLTbe4muoI5NIsbhoZZW3O0cksLOhLc/KwwemK8z1TVNS1zUrvWdYvpr2/vpnuLm4mcvJNK7FmdmPJJJJJ96yXNGUZJ6pL70opW7bfPR2Wp0VpxqTqSX2pNr0bk3f7/AJaq7Vj62/ad+NXxW1j4L/B/S9U8ZX9xY+MvDZutbjdU2308V5lGb5eCNqfdx0Fet698efiAv7e3/ClYrqyTwPqOowaXqWjfYYTFfrPZIZJZ2Kl3fLjBJ4VFXoOfiT/hffxab4aJ8H5PFhl8JxDbFYy2VtI0a+aJdqTtGZlXeoO0OBxjGOKqXHxo+JV18Ul+NE/iTd4yS5jvF1L7HbjEyRiNW8oJ5XCqBjZjj1rdShzJS1jzzk/NScbL5JNdtvlxyjUWFVOEvfUUk/NK3N6t6vS/qfYPg208G/BD4E3viTw78Urn4bajqvjrVdJvfEFj4YGs3BhtZHSCxyZFMCFF8zqS3P4/Pf7Wnjj4Z/ETxfoPijwF4hOuak+iw2viTUv7EOl/b9QjZgbow5IDOhXIGQNo59OX8E/tHfGj4d3GuXHhHxtJar4kumvtUt5rG2ura4uGYsZfImjeJWJPVVHQDoBXN/EL4leNvirr48T+PNa/tPUUt0tUkFvDAqQoSVRY4VVFALMcBR1NckITUKam7tKN/VQ5X8m9el93rv08yXNbrf8AGXN96WnXstHY+s/ghdeGPhb+yhpPxA0v4rXPw41rxN4kurPVPENj4YXWbllhU+VZZ3qYFKjzfVjnt1tab8VvDPjj9pjwhD8KZm8cR+NPD6+GviIjaP8A2TDq6fMJ72SIkojCLEmR08rGfmr5b+Gfx6+LXwfs7/Tfh74uk06x1Rlku7Oazt7y2ldej+VcRugboNwAPA54FXb/APaS+NOpa1rHiK68Z/8AEz13R/7AvLqLTbSKT+z8km3iKRDyFJJz5WwnuTW82nPmtddn/g5bPfRve1t76ta5RTjDl2ff/t7mv6rp6Wuk9PqHXPBPhiP47fBX9mm2tFufhAuNbsbszq0Xii6kikke4kdflbLqsATqqkjo4FeD/GL9pr48eLNQ8R/DfxTrX9l+H2vjZSeGYdPt4rexSCcFIEAj3JsMajIOTjkkE15tN8VviBceEdB8DyeI5f7I8L3j6hoyLDEs9hO7bmaK4CiZQW+baH27gDjIBrrPGn7VHx4+IXhu68J+MPHC6hp18sS3QOk2MU04jZWQPPHCsrYZFPLnOBnNOi/Z1Izk7pSb9VdW+atbt2tqjVSV7pW226Wve3+K/M9te+59f+KPj78QrH9u6z+Dun3lnB4Ov77TtN1TSfsMJj1IXFlEZJJ2Kl3fEgUEnAWNBjAOfNfAWmeIvgppPjzxRZ/GPSfhP4Uu/HF9odjqdv4cfVdWvXtHkAt0UDEcCrzywywOTjr8zXnxk+JGofE6P4yXfiPzPGEVxBdpqX2O3GJYY1jjbygnlcKijGzBxzk10PhT9qX48eCY9Xg8OeOjBDrupS6xfRTaZZ3MbXspy8yLNEyxMT/zzC9vSkrKK/ms197g116WlbtftdHPyy5/7umnopJv1d4372fWzPs25j03Sf2ytJ8URWdtfXGr/CCXWtSkk037ENSuTbTh5ZbfJMZkWNNyEkjoScV4lo/xa8cfHz9mL4zw/FTULfWm8I/2LqehO1lDEdOeW7MTpD5arsQoNuOgGR3rxS8/aQ+NWo+LofHmoeOJbnX4NEbw4l9NZ2zudPYOGiYGPDkiR8yMDJ82d2a5bQfiB4u8MeGfEXg/Q9W+zaR4sit4dYt/s8T/AGlIJDJENzKWTa5z8hXPfIrOouaUuX4bWS7PnnJem8duz6b7Ya1GpzT195N+cVGEWvnyy69Vd9vr/wCK3xW+KHwP+MXg34EfCSGHTPBVvY6PHb6VHpsNxFrwuVQzzTZQtMZHZ0JB/hJGDzXoWp2fh3R/j1+0z8UL3XP+Ee1vwumiRadryaJ/a8ujx3MCrPcRWu5cthFXfn5AxPTIPmHwj8QfGbT/AA34SsdY/bB+F+leCLa0t5ZYJNasZtY0+xYBpLSMPAbpJVTMYRXwpGAcAV5B8R/2mvFcf7R3jL4zfB3xFd6IutXLW8Enko4uLNUSJfNhlVkYMI1ba6nBPqK3qS5Kji9W/aa9Vdxtez8mt9r2M4ptXtovZ6dHa92vN7vTte9zs/2hPiZ8Ifij4J8HaZq3xgv/ABn4u03XTFqHihvCB0+5TRZVJZXi83bcNE4BUF1JDEccsea+EPww+AXiT43fD7wvpvxJ1bxTY6rrSQ6jaaj4cXSozEqM6p5n2qXf5jqse3A4fg5rzf4l/G/4nfF6PT4PiB4ii1CHSjI1nDDp1rZxxGTbvbZbxopJ2LyQTxXE2t1c2N1De2dxLb3FvIssMsTFXjdTlWVhyCCAQRUU3yTUttU9Pl8tba6attt3KrLnp8qfRr03879f0PoH4wftHfGrxV4q8Q/CXxZr39geEZNV/sqfw7Dp9vDb6fbQ3A2RoNm5CmxTuBycHnBxXqvxo+O3xn+Cf7QVv8FvhLbw6T4O0eTTrHR/DdvpsDW+rQSxxkmQshaYzM7gvnOScEMCa+ffGX7VPx5+IHhm78H+MPHS6jpl/FHDdBtKsknnSNlZA9wkImbDIp5fnHOataD+19+0Z4Z0Cx8NaP8AEqdLPTLf7LZNNp1nPc20WMbI7iWJpkAHAw/AwBjFFB+yjBS97lev9/T7X49/iY6vvtuOid7LpHbb12e3wxPpHXrqx+BX/DWMnwsht9OTSr7w2uneSilbCa4lbzPKBGEMTyyBMfcKrjG0V51ovxi+Inhz9jHVfF2n+LNU/wCEm1z4mNaTa5JdO+oIp0xGZkuGJkVyIwm8HdtJGcGvnWz+I3jWx8P+JvC8OvStp3jGW3n1xJo0le9kglMsTtK6mQESMWJVhuJ+bNQnx14qbwKPhodU/wCKbXVv7cFl5Ef/AB++T5Pm+Zt8z/V/Lt3be+M80017OUXu4wj/AOAuF/v5X66XKcrz5ul2/vTX9dt0fckPi/Q/g38H/hVdaT8fNQ+HM/irR/8AhItXuIPBo1t9fv5XBna5uWkUtsJ2eWegIyTxjhtc8QfDfxZoP7U3ib4TqE8OanpXh65hVbM2qiZruPz9sR5QGXzDj344rwbwD+018b/hn4ZTwb4P8bfZ9Fima4hsrvTbO+jhkbJYxi5ik8vJJJC4GST1NYl98aPiZqZ8YNqHih528eiBfELPawE3ohcPEM7P3YVgMeXt4GOnFZ1I80ny7e9byvsuui9UlZWXRXhqioyg5dHBv/t2SbfTV2633d31Ptn4GXfjD4a6x8K/hV4u+OGlaFJqtrZTL8P9I8GvepqNndszFr26+VRK6Ft75YJtBwQDnkND+KnxE+F/wq/aO0XwD4outHsfAPi+0tfDsNvHHt06GfVbpJVQFSMMAo5z2xivA9H/AGwf2jtA0XStA0n4lTQ2uixQwWLNptm88UURBSPz2hMrINoBRmKkfKQRxWF4P/aI+MXgLxD4j8UeFfGH2TUPF07XOtNJp9rPFeSmRpd7QyxtECHdiNqjG4gYHFbzlGbl5316/FGS89otPW2ui3Rz04uFOMe3L6aJptffppfuz6J8K6HofxI0r9kzR/iAyXdhrWr+J3v1mIC3LnUFcI3YiSQKpHfdjvWn8Mvjx8aPiJ+1BJ8DPHVrb3ngnUdSvtF1DwlJpcCWllYRrIAFAjDJ5SopDZydvXmvkfX/AIpePvE+n6FpeteIpZbfwzcXd3pPlwxwvazXMwnmdXjVXJaUBgSTtx8uBxXda3+2F+0f4i0O78Par8TLh7W+tfsd1LFp9nDdTw4wUe5jiWdgRwcvz3zUVJcz5raXl7vR81rJ+lrbddLdW4tuVpWbt73VWv8AndP1Svc9Y0n4j698Ef2UNUvPhXqkMFyPi1f6bYau0Mc00NstjGd8JdSqs6ooLgZ2swGM1y/7YF9J4v8AC/wb+K2sQwN4j8W+Ey2s3ccSxteTQS+WJnCgKXIPJAHYdAMeEt468VSeBY/hq2qZ8Nxaq2tpZeRHxetEITL5m3zP9WANu7b3xnmpfE3xE8YeMND8O+G/Eesfa9O8KWj2Ojw/Z4o/s0LtuZdyKGfLc5csfes6ylNx1vZw1e7UaXI/vlZ/jvoXzS9o3HSLUtPNzUl9yuj6j/Zjl8O+Af2ZfEfxSh+IE3gLX7zxYmhz+JrXw3/bNza2a20cq26JvXyFkkY5k77QvXbjtfA3xE+GfxF/ap+DeueFfFbeLfE1npmsWfifWpNBOknUmSzmNvI8O5gX2F1ZgTnaOgwB8e/DH43fFL4OtqB+HXiyXSo9VRY723e2gure4C5274Z0eMkZODtyMnmtG+/aP+M2ofEDS/ihdeMFPiTRLV7LT7pNNtI0t4HWRWRYFiEOCJZP4P4s9cVvKonPmXb7nyOPno27taLW9rrWYK0HF9b/AD96+u367dtvevhv8YPGv7QHwm+N3hT4lXFlf6NonhR9f0OxjsooYtInt5cxrbCNVKKFIXGTwOc5bPpuoeMvBvwB8O/C/QNB+O9z8P8ARbjw1p+tXemWvgUarD4gkmG6eae68wb9/KbAPkAGOq4+EfCXxE8Y+BbHX9N8K6x9itvFGmvpGrJ9nik+0WjnLR5dWKZx95Nre9dp4M/ao+PXgDwzZ+DvDPjzy9H09maztrzS7O++zZOSImuIXaMZ5wpAHap5kvh392/nbn376Sjve/LqtE1moycnzba28rqG3bWMtrfF6o950f4ieGvC/gX9pzx78A5P7P0mbVPDkmhzLbeQ1l580wkaJGGYipeUIRjb8pXGBjFX4nfEb4S/su+Ffip4B1i6j8V/EPxFqQ8T+K59t3fubZtlvatNMHYBlDSdcnaexNfOtx8VviBdab4s0m68RPLa+ObyG/8AECvbxFr2eGVpY3LFdyYd2OEKg5wcjAr2D9m7VPjNpPg3U/8AhXH7Qnw58G6dd6jsutF8VatZRs8iohFykF5DIm0ghd6fMTHgj5RS5ea7jdWjBf8AgKgnd6btb9dLrdG8p/vOZ6q8n96av8r3t/w5754i+NmteHL79mTx98RI44ofFGi6vpviZFtVt0ubS8lhjkmeMAKN25JzgAEjI4NeWePPA95+yf8ABf4h+H7ljHr/AMRPEkvhrTZCw8xvD9m2+SdfaZnjQjuCPSpf2gNT0v49eKfAfwms/jX4Rv7/AMHaJcSa34w1rVFs9MvL+4mR50gmKhXVONgUAFQQPu4rzX9rv4uWPxS+JlvZeHdbn1bw54P0u38P6XeysSbwQriW6Oepkk3Hd3VUNZ1Gp35VbmlNf9u8/Np5dF3U32ZNFOKXNrZRf/byjbXz1u+zgl2Og/YF8z/he1x5Wnrfv/wi+s7bVhkXB+zHEZHcN0/Gux+F/wAXPiV8ffh/8YvDHxovItY8NaD4Ou9YtGl06GFdH1OFl+yrAY1Xy8/OoTuEx6581/Yz8V6P4N+LV/rmteJLLQ0TwvrEdvd3V4lsouGtmESq7EDeWxtAOScYrmPHn7Tnxy+JnhqTwf4y8eS3ejzzLcXFrBY2toLmReQ0zQRI02Dg/OW5APUA06r5pcu/uW8k3Kevqt/ktUZ4X3JTk/8An5f1ShT09Ht9+59g6h4y8G/AHw78L9A0H473Pw/0W48NafrV3plr4FGqw+IJJhunmnuvMG/fymwD5ABjquPPtH+Inhrwv4F/ac8e/AOT+z9Jm1Tw5Jocy23kNZefNMJGiRhmIqXlCEY2/KVxgY8G8GftUfHrwB4Zs/B3hnx55ej6ezNZ215pdnffZsnJETXELtGM84UgDtXKXHxW+IF1pvizSbrxE8tr45vIb/xAr28Ra9nhlaWNyxXcmHdjhCoOcHIwK1qT55VH/Ne3znGVvko2XboknpdFezpxj1VvwT1+bd3pr1btrz+sa1rHiLVLnXPEGrXup6jeP5lxeXk7TTzP/ed3JZj7k1ToorJK2iG3cKKKKYBRRRQAUUUUAFFFXdD0XUvEmtWHh7RbX7TqGp3UVnaw7lXzJpGCIu5iFGWIGSQPWmk5Oy3E3bVlKitjxh4Q8ReAfE+o+DfFun/YNY0mY295bedHL5UgwSu+NmRuo5BIqfwd4D8V+P7rUbLwjpX2+bSdMudYvF8+OLyrSBd0smZGUNtBztGWPYGpjKMo80Xpv8u5XLLm5ba3tbz2t6mBRRRTEFFFXNH0fVPEOrWeg6HYTXuoahOlra20K7nmldgqoo7kkgU0nJ2W4m0ldlOiuu+Jnwn8ffB/XIfDnxE0SPS9Rnt/tSQpfW91+73snLQO6qdyMCpIYY5FcjUp32KacdwooopiCiipbO1uL66hsbWPzJ7iRYo1yBuZjgDJ46mmk5Oy3E2oq72IqK9B/wCFA/F5vijJ8F4/Bk8vjKEBpdNjuoH8tTGJdzTK5iVQjAli+BnBOeK5jxl4O8RfD/xLe+EPFljHaarpzItxDHcxXCqWRXXEkTMjAqynKsetTfbzHb8DFooopgFFFFABRRWj4b8Pax4u8QaZ4V8PWf2vVNYu4rGyg8xU82eVwiLuchVyzAZYgDPJpxjKclGKu2Ju2rM6irut6NqXh3Wr/wAP6zbfZ9Q0u6ls7qHer+XNG5R13KSpwykZBIOODVKpTUldFNW0YUUUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFd3ofwN+KniL4eX/xW0vwo7eFdN83z9Rmu7eBW8sZk8pJHWSbb38tWwQR1BrhKG7S5eu/37feC1VworoNH8BeLNe8I+IPHWk6T5+h+FmtF1a68+NfsxuZDHB8jMHfcykfIrYxzgVz9Npxdn/XX8gCiiikAUVp+GfDeteMfEWm+FPDdibzVdXuo7OztxIqebM7BVXc5CrkkcsQB3NN8SeHdY8I+INS8LeIrP7Jqmk3UlleQeYsnlTRsVddyEq2CCMgkehoelvP9N/zX3gtb+X63t99n9zM6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvd/g58IfhD4g+C/i34wfFTXPE9nb+F9Ys7EW2imAyXaTo37tFlQgSF9vzFtqqGO1jgV4RX1T8B7r4e2/7IvxKj+KFjrU+gXHinSYZZNGaIXttIY3McsQlwj4YAFWIyrNyDiiTtSnJbrl/GcV+tu3fQyk37anHo27/KEn+l+/Y4v4h/CH4RzfDLw78Z/hJ4h8TweGb3Xz4a1u28RRwzXmnXOwSiVTbqqSxmLJwBnOB1JA77UP2X/hT4i+HfjfxH4B0X4yaNdeEdGl1u11bxZpMcOk6xBDgyrFthVo2ZfmRWcnByQcHGEv7Rvwn8BxfDzwX8K/BuvX3g/wn4oTxbrEniIwLfaregbAAkJaJFjj+7yckLnG0luy1z9rH4Nmy+JjafrHxl8Q33j/AMP6npdsviS6tpLTS5LjlI4oUnIEeTgv1VUVVQ7iac1HlkoX+1bvfljb5OXNby3vdGsb865vK/b4pX+fLy3/AAs73858XfC34E/Df4W/Dbxl4n/4TfV9Y8eaW19NZ6fqdpaw2QSVkeUF7aRnByoWLjJVyZBwK9H/AGjNE+BfibXPgz4W0+y8cW2q6z4V8MWenSyXdn9nj0ma4dP3wEW43W1nyy/Ju2/L1B8I+LnxS8P+PvAvwv8ADGj2eoQ3XgrQJNL1B7mNFjlladpA0RV2LLgjlgpz2rufEPxs+Cniix+Fviq+0vxpbeNfh/Y6Jo9xDDHavplxa2NxvaRWaQSmRkLYXao3EAnAybUlJNS29r0/lTml8rW+TfQwhzqjH+b2av8A4rRv873+7uWV/Zv+GvhvxB8VfE3xD8SeIbT4efDrxBJ4etV08Qyarqt2ZGWKFXdREpCKGdymOeB6a3wDb4Sv4+8ct8IIfF0Omn4V+IvtUfiSW2knFx5XOxrdVUps2dVBzu7YrNX9o74W+KdY+K3hX4keG/Ek/gH4g+IpPEthNpi266tpd2JGMbhJHMTbkO1lL4GDgnNYXgz4mfAn4VeMPEN94Bh8e3uj634H1Xw+W1i3sxc/2hdKVRtkUm1YAAuTuZs5IB4Fcy53Tal1jZevI738+a/lt5npxdOOL5o/Cqif/bqqJq3ly28/i8jrvAv7Kfhe3+GXhTx18QfB3xg8VXXjJHvba38A6SlxFptkGCo9zI8UgaR87lRdvyjn38c/aE+EMvwN+K+sfD37bPe2toIbmyubiAwyS200ayR71PRwG2tj+JW6dB6t4e/aO+HPiL4XeE/BXxN1r4peGtW8F276baX3gm6gEN/ZFt0a3EUsseHT7oZScjJPXA8I+JGu6T4i8ZahqXh/VPE2oaUzKlnP4kuluNQMaqB+9dPlzu3YC8AEDnqdKl/aabXf3dPnt+PkcdLSn729vx6/Lf8ADzPXLT4R/A34cfDXwl4y+PGreNrvVvHNu+paZpPhZrSL7JYK21ZZ5LhXDM55VVAwAc1618Dfg/8ADL4a/tTfDDWdM1HxFrnhvxtYrrfgy6cwRSQzBHEkV8u07jGA3+r2/MV7A15PafFz4F/Ef4Z+EfBvx40rxvZ6x4Ft303TNV8KpaSi7sGbcsU8dw6bWQ8Kyk8Zz1rW0/8Aal8GWHx5+Gniyx8M6xY/D34X2f8AZmlWCmKfUZYPLkDzSZZIzK7uCwDBQBwT3tSlGtFr+b5clnb5/D53vbQxnFzouL/l+fNdfh8Xla19Tyf48zeBZ/ilrsngGDxBFaNeXH2z+2riGaZrzzpPNZGiRR5RONoI3dcmvXPgT8BPgv8AE7TfDGn3+jfGfWNV1yTyNQ1jw9pEf9i6PK8hRFleSF2kCfK0jhlUBuOhx87eKtUt9c8UaxrdokiQahf3F1EsgAcJJIzAMASAcHnBNfWuh/tcfB1Yfhxq2tXPxb0658D6bp1hN4Z0O8t4dAupLZuZypmDuW+8ysvzbVQsBlqzwUYxw8I1NPhuvKzv92i77a2ub4luVaThtrb1urf1t5bW+VfiB4RuPAPjrxD4HurpbmXQNUudNadV2iUwysm8DJxnbnGeM12Xxm+Fvh/4d2vw3m0W81CdvGHg/T/EF99qkRhHcTs4dYtqLiMbBgNuPXJNc38XPF2m+Pvil4t8baPDcw2Ovazd6jbR3KqsqRyys6hwrMoYAjIBI9zXsknxm/Z18ceD/h/J8VfCvjhvFHw+0mLRo7fR2tP7N1a3t3LwLO8reZFnJDlEY4ZsdsXhX/DlW6OPN6Wd/wAbGlbkjVqRp6qzUfXmVvwvqdC37LHwf034rfGfwr4j8VeKLLw58MdJtdUt7qJreW6lDLC0iupjVZGPmMqBdnJXJxnPCfET4S/B67+C2nfGz4M6n4ujsk8Sf8I1q2m+JGtpZo5mh86OWOS3RV2lRgggnJ6jHPSeMf2nPAXiHxp8d/EVlo+vR23xP0W207SEkt4Q8EkYhDG4AlIVf3TYKFzyOK85034reHbP9mu9+DktnqJ1q58aweI0nWOP7KLZLTySpbfv8zdzjZjH8XajCv36aqrS0L+vP71/+3TKivfnzvS8rens1y/+Tn0/8I/h18HPg58Zvjv8PR/wmd5JoPgvUQtyt5a5OlPa2sk4B8kf6TvchGwEC9VJ5rxLS/hJ8AtK+H9x8bvH2p+PIfCOuazNpPhPRdOkszqs6wqDLPczunkqqnK4VMk4rWv/ANp7wDdfHL4wfEyPSNfGl/EDwjeaBpkLW8Pnw3Etvbxq0y+btVA0LZKsxwRwecct4D+Lvwn1j4Ow/BP46aP4o/s/RNUl1bQNW8Mi3e6t3mGJoJI7hlRo2PzZDZz9KlqTjCS+Lkivnzvm+fLr8+5d0oKPTnk36cit/wCTfl2Ou079mP4W+MvFPwn1/wAA+IvFE3w9+I+rS6LdJqBt01XTLuHPmRl0QxNuHzKwToORyKp+A/gh8CfiJ8ddS+Eek6h4xstL0Gz1d73XLnUbV/tUtonyyRwLbDyogyuSC8jMu3mM5FWdL/aa+Gfg7xZ8KtB8DeHfEsHw7+HGrS6zO195EmrandzZ8yVkRxEuB8qqH6ZyelcN8HvjP4X+H3xi8TfELWrDVJtO1rT9atbeK1ijaZXvEdYi4Z1UAFhuwxxzjNZylJKXKrq1Rr15Y8v/AJNzcvla+pz/ALzm170/u55c3lfk5ebpfY9A0f4Mfso+KPhbrXxk0fxR8TbLRPBd9HZ65YXqWL3l/wCeoW3NqyJshLSEbvM34UN7E+d/Hv4U+APBfh/wF8RPhZq2uXXhjx7p91cW9vrflG8tLi2mEU8bvCqow3MMEKOh61V8G/Fnw74e/Z3+IXwkvbLUn1jxZqWl3llNFHGbaNLaTdIJGLhwSOm1W98VW+InxQ0Dxd8GfhV8O9Ns9Qj1LwNFrKajLPGgglN3drNH5JVyzYUYbcq4PTPWt5xjZtPZxt5pwfN/5Nb0Lpyk4+8u/wD6Vp+H569LdF+zj+z7afFrS/FfjbxJaeK73w/4RigWTTvClj9r1bUbmdiscUCFWCgYLO7KQFH1I9Stv2d9H+G/xI+B/wAUPCek+OtE0nXPHFhpd1o3jWxW31Ozuo7qN1bKoiyRSIGKkKMbT1yQvlf7PPx18O/Djw/4u+HfjyPxMnhvxdHbytf+GblINU0+7gYmOWEuyqwIYqyswyPxBvX3xf8Ahlo/xI+Hnijwz4s+LfiLS/DWv22sasniq6t5GKwzRuotYUlZQ+0SAl5BnIHyjJroozjDFUpJ2ScHf0a5r/j8rW6kpSdOalv735aW/D536WO50/8AZy0nx94g+K/xa8X6P4917SrHx1qWiWOi+B9PF1qVzdGd5HkctG6xQojLklTksBxwGra9+xbazfEbwVp3h/UvEnh3wn4s0a8129PivT/J1XRILLH2xZ4lVA7DcmwhVDeYvb5jn+Ff2mvAE2m+PvBPjT/hPNI0HxH4vu/F2j6r4VuIoNUtJpmKmKZHkWN0aPZld5wwOM8EZOjftDeD/h98XNK8VeFLzx94y8LHSrjSNatPGl5E9zcQXWVuFgEbMsQKiIjLElk5ODxxxjyxhGHSKtfa/Jqnbpz/AILsbTd+Z+bv3tzL4el+Xbp3MPVj+xzDqlhD4ct/jBcwQ6iFvJLm50xftNpscbogIsxv5nlEBww2FwcHFe+fGP4Y/BP4x/tvv8MnPjPTdV1O7k/t66W7tBaBItLEsP2VfJLIf3abt+7+LGOCPmf4kP8As1R6aLj4Pt8SW1Zr1JPK8RR2KWsVthiyhoHZ3fdsAJ2jAbIJxXtF5+0t8Af+GkNH/aO0zR/H8epXLTPr2nzQWTQRbtNa1UWpEoaT94VJMhX5c4GcCtk4unHm1tz/AHuMeX8U72CW9o22/V9+vY4jwL8H/gz8RfEPifVdN1TxXo/w/wDh7psmoa/qt1c29ze6kN4SJbSBYY1t2lYNhXaXHGTS3/wj+BvxD+F/i3xx8B9S8cWuq+BY4b7VNK8UPZy/abCR9hmgkt1TaUPLKwPHSuW+APxf8P8Aw0ufE3hvx3oN5rHg3xvpZ0nW7exkVLuJQ26KeAv8pkRskBsA556V02pfFr4J/D34Z+K/AvwF03xrc6j45jisdW1bxStpEbexjfeYYI7dmBLnhmY9BwPTnqKUYWjq+XT/ABXe/l8N+lr21FHWfb3uv8ll+PxW63tfQ6y6+B/7K/g+++GugePPEnxGm1b4g6Bo+qOulyWSwaY94oBeR5YsshcthFG5VQksxYCvBfjB8PJ/hP8AFDxL8Obi9F4dA1CS0W42bfOjHKOR2JUqSPUnr1rr/iX8YPDXjLxl8M/EWl2OpxW3gzwzoOjX63EUavJPZE+a0QVyGQ/wlipPcLWJ+0N8RNF+LXxo8V/Ebw5a31tpuuXguLeK+REnVRGi4dUZlByp6Ma1xL/2j938N6n3KUeT8LkQk9YtdIa+dpc36Hok3wm/Z4+GPhHwbN8bte8d3XiXxnpMWvrb+Gfsi2+l2E+fIaXz0LSuwBYqhXoR6FrfxM/Z++EPw4/aJtvhnc65411Dwy+h2+px/wBnWcd3q2oXEkJdYIEWNUQu2MF1IUA5J602b4ofs8/F3w94FtPip4V+ISeMPCuk2vhqOPwwLR7bWLaA7bZXMzCSKQ7tpKK3XjPAHtPxm+Ofgv4SftT+MYfElnr1nFqvgWw8OyX3huSMapok7QxyfuHlZVyFKgncD05yDRXajU9zbmlbzioy5fK/w+jfvdEKKbjpf4Y38nzRUvlv8vhd9Tyj4g/sreDbfw74B8WeEx498GWnivxZF4T1Cy8e2KRXNm8uGS7QpHEHh2bj05IxuGDXP/tHfCP4O/B46l4T0rQPixp3iiyvRDZ3fiKO0Gl6pbqxEk0BijVtp+VlwzDBwcHitv4j/H/4K+L/AIX+HfhTG3xQ1iy0/wAbRa9q2qa/dW81/e2Jt2jmEb+ayxy/OVRMFMKGL5YiqXxF+Pvwvk+BOo/BXwHf/EjxNDqGoWl1az+NmtSNEjgJO2zELuQXHyMMqoUnA5OVVS5Pd7r1ekL+iT5n56rSyNIb+92f/t1vv09N+55z8AvhNpvxa8XajaeJPED6H4c8N6PdeIddvoo/MmjsbfbvESHhpGLqBngZJwcYPeQ/Cf8AZ9+KngzxjqHwL1Tx/YeJPBmlya/LYeKWs5otQ06IgTeU1simOVQwbDbgeAOpK8J8Afi1pfwl8XahdeJfD7634b8SaPdeHddsYpPLmksbjbvMTHgSKUUjPBwRkZyO4T4sfAL4WeD/ABdp/wACdL8e33iHxnpkmgT6h4qFpDHp+nSkGYQpbOxklcKq7m2gYBA6g3U5eTT+V+vPd2+Xw+W/UKfxa91fty6X+fxeex2nx/n+Cf8Awyj8Ho9DsfHMcklvrj+GxdXlo0aSC/jF0b0LGN+Tu8vy9uBjdmvHvgD8IfD/AMSrrxN4j8ea5e6R4N8EaS2r61cWEavdyqW2xQQB/lEjtwCwIGKr/ET4oaB4u+DPwq+Hem2eoR6l4Gi1lNRlnjQQSm7u1mj8kq5ZsKMNuVcHpnrVz9n/AOLnhX4ct4s8J/ETQdQ1Xwd460n+ytWXTWjW9tyrb4biDzPkZ0bPysQOc54wara1qsqb3c7X268v6fqTf3YReyav3tdc34X/AE1PYNPg+CJ/ZB+Nl98G7rxggluvDcWoWPiU20k0RW+Jjkjkt1VWVwzjaVyDGeoIrW0n9ibwXpF54Z8EeN/DPxo1LxBr1rbS3uv+HNFSXw/pM1x9yOR2iZpRHlfMdXUDk8cgecal8Wv2ePDHwH8e/CH4W6L47mv/ABfNpdyNW16O0Uuba5EhidIZCI0VAdpG9maRs7QBXU69+018IvidFpPiTx94k+NPhjxLBp0Fjqlr4QvrYaZeSQqEE6CWVWiZwBuG1gOOuCSVJKUnLde762UEnb5r1/EVnfXs/vurfhfy76mJa/s8/B/wL8N/FHjT41eIvFYvPC3j268Gmz0A24OoeVCGBj85CImzvcuzMNibdu45q5L+yr8O/EXxT+Eml+CfGWs2ngn4q6dLf282rLC2oWrwK5mtiUCxM7FVRCBjc/RgBu818QfFrw9qvwHvPhbCmuz6tP4/l8UreX7JKHs2szAqyyhtzz7iC3yBTyc9q6DWPi98IfGvhf4NeCvGlj4wh0zwJpup2euTaUlslw0s774HtC8hVgrqpbeE4BA9aSs3GXbl8v8Al2+b58/yvpsOo/eagtGpednzrl/8lLfib4S/Blfih4M+F+i+Hvi14Y1XVvElrpesWvixbRJBZTTLGJrdo4h82S2CysvQjcK9J+F3hv4I+C/AP7SXhPxDZ+Nbu38PX0em6hNZ3dmsslhFqvl23kF4sLNuTMhYbSMbVWuZ8UftRfDuaL4W6BpE3j/xPY+BfFsHiO51zxYbaTVRbo6E2Vv5cjAx4Xd88g+ZVHAAxz3g347/AAtXxV8abTx/o/iZvCXxXnmmjm0pIDqFmRfNcwExySCM/e+b5yAVwNwNS1anZav3/wD0mFvTXmX39GjSMlGrCbukpQen+J39dLP7uqMr4h/BHwjdeBfh58Q/gn/wkV7YeNNTutBns9YuIJri11JJgsMOYY0UeZGQwBB6HmuV/aG8B+Cfhh8UtS+H3gbVtQ1O30GOGz1C7vJY383UFQG48vy0ULGrkoFOWBQ5Y179+yT4+8O+DfBvxd1y40e41DwV4IuIfFXht9V8sTQasGlgsVcL8vmyqyBtnAMfHGK+QtU1K+1rUrvWNUuXuLy/nkubiZ/vSSuxZ2PuSSfxqJ6TUY7fF96Vl8nzaduV6kw1i5S3Xu/c7t/dy69+Y9c+C/wf8N654H8WfG74nSXy+CvBpht/sdjKsVxq+oysBFaJIVYRp8ymR8EhWGOeRt/GT4U+Bf8AhQXgb9obwL4dn8LR+JdSu9Hu9Da+kvYg0TS7J4pZfnGRCwZWJGcYxzn0vxBpvw38F/sY/DL4a/EPxHrPhy68cX9z4zkutL01L4SKjCOJJo2ljPzQyRlSG4aPkYrzfx98dPhf8SNW8DfDnVNJ8W6R8HPAtnJa29tpr2x1m6laM5upPMPkCRpNuVyQql8ElqqqrylCO6aS+S99t9dbxS9Gktyab92Mns02/wAopL0tK/quyPnyipbz7J9sn/s/zvsvmN5HnY8zy8nbuxxuxjOOM1FUp3VymrOwUUUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFdBY+P8Axdp3gjUvhzZ6t5fh3WLyHUL2z+zxHzZ4QRG/mFfMXAJ4VgD3Boooeqs9v8nf81f1FbVPqv8AhvybRz9FFFAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD17wl+1p+0B4G8P6d4X8L+OobPT9IhMFiraLp80tuhJOEmkgaUdTzuyK8t1rWtW8Ratd69r2pXOoajfzNPdXVxIZJZpGOSzMeSTRRQ/elzPcI+7HljouxTooooAKKKKACiiigAooooAKKKKACiiigDuPEXxt+KHirwFpXwx1zxVJN4Y0Xy/smnxWsECAxpsQyNGivMVXgGRmIrh6KKLat9WHRLojqvHnxO8Z/EpdCj8Xaml1H4b0mDRdNjjt44UgtYhhFCoACeeWPJ49BXK0UUdW++vzerD9NPu2CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//Z)\n",
        "\n",
        "LDA works reasonably well for systems where the electron density varies slowly, such as in many solid-state systems. However, it tends to be less accurate for systems with rapidly varying densities, such as molecules and surfaces."
      ],
      "metadata": {
        "id": "CD_QH_ga4crC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Perdew-Burke-Ernzerhof (PBE)**\n",
        "\n",
        "The Perdew-Burke-Ernzerhof (PBE) functional is a widely used Generalized Gradient Approximation (GGA) exchange-correlation functional. PBE improves upon LDA by including the gradient of the electron density, which allows it to better handle systems with non-uniform densities. The PBE functional is formulated as:\n",
        "\n",
        "![pbe.jpg](data:image/jpeg;base64,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)\n",
        "\n",
        "PBE is known for providing a good balance between accuracy and computational efficiency and is commonly used in both solid-state and molecular calculations."
      ],
      "metadata": {
        "id": "fEEOwmwp4jUI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Range-Separated Hybrid Functional: ωB97X-V**\n",
        "\n",
        "The ωB97X-V functional is a range-separated hybrid functional that combines elements of GGA, exact exchange from Hartree-Fock theory, and empirical dispersion corrections. It includes:\n",
        "\n",
        "Range-Separation: The exchange interaction is split into short-range and long-range components, treated differently to improve accuracy across different interaction ranges.\n",
        "Hybrid Exchange: A portion of the exchange energy is derived from exact Hartree-Fock exchange, enhancing the functional's accuracy for systems with significant non-local exchange effects.\n",
        "Empirical Dispersion Corrections (V): These corrections account for long-range van der Waals interactions, which are important for accurately describing non-covalent interactions and dispersion forces.\n",
        "The functional form can be expressed as:\n",
        "\n",
        "![wb.jpg](data:image/jpeg;base64,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)\n",
        "\n",
        "ωB97X-V is known for its high accuracy in predicting molecular properties, especially for systems where dispersion forces are significant, such as organic molecules and molecular complexes."
      ],
      "metadata": {
        "id": "B3ZD93IG4oct"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_primitive_gaussian(alpha,label=None):\n",
        "    r = np.arange(-5,5,0.1)\n",
        "    g = np.exp(-alpha * r * r)\n",
        "\n",
        "    plt.plot(r,g, label=label)\n",
        "\n",
        "\n",
        "def plot_contracted_gaussian(contracted_gaussians, label=\"contracted\"):\n",
        "    r = np.arange(-5,5,0.1)\n",
        "    gs = np.zeros_like(r)\n",
        "    for i in range(1,len(contracted_gaussians[0])):\n",
        "      gs = np.add(gs,contracted_gaussians[0][i][1] * np.exp(-contracted_gaussians[0][i][0] * r * r))\n",
        "    gs = gs / np.max(gs)\n",
        "\n",
        "    plt.plot(r,gs, 'o',label=label)\n",
        "\n",
        "def plot_slater():\n",
        "  r = np.arange(-5, 5, 0.1)\n",
        "  slater_1s = (1/np.sqrt(np.pi))*np.exp(-abs(r))\n",
        "  slater_1s = slater_1s/np.max(slater_1s)\n",
        "\n",
        "  plt.plot(r, slater_1s, '*', label='slater')"
      ],
      "metadata": {
        "id": "pyJDwMiUy3y_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_primitive_gaussian(alpha=3.42525091,label=0)\n",
        "plot_primitive_gaussian(alpha=0.62391373,label=1)\n",
        "plot_primitive_gaussian(alpha=0.1688554,label=2)\n",
        "\n",
        "plot_slater()\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "th9seFy-zjA5",
        "outputId": "fc20c2d0-adbc-4edd-f88f-46418dc28c07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d6ffe464fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACcg0lEQVR4nOzdd3hUVfrA8e+dlt5ISINA6ASQLogooqBYQFRQLGtby+qKrrJNdu0NFfWna2PtuhaaCCqIIoigFKX3QAiQEJKQ3stk5v7+uLmTQsrMZGbuzOR8nidPQmbm3ncKmXfOec97JFmWZQRBEARBEDSi0zoAQRAEQRA6N5GMCIIgCIKgKZGMCIIgCIKgKZGMCIIgCIKgKZGMCIIgCIKgKZGMCIIgCIKgKZGMCIIgCIKgKZGMCIIgCIKgKYPWAdjDarVy6tQpwsLCkCRJ63AEQRAEQbCDLMuUlZWRmJiITtf6+IdPJCOnTp0iKSlJ6zAEQRAEQXBCZmYm3bt3b/Vyn0hGwsLCAOXOhIeHaxyNIAiCIAj2KC0tJSkpyfY+3hqfSEbUqZnw8HCRjAiCIAiCj2mvxEIUsAqCIAiCoCmRjAiCIAiCoCmRjAiCIAiCoCmfqBkRBEEQBGfJskxdXR0Wi0XrUPyOXq/HYDB0uO2GSEYEQRAEv1VbW0t2djaVlZVah+K3goODSUhIwGQyOX0MkYwIgiAIfslqtXLs2DH0ej2JiYmYTCbRONOFZFmmtraWvLw8jh07Rr9+/dpsbNYWkYwIgiAIfqm2thar1UpSUhLBwcFah+OXgoKCMBqNnDhxgtraWgIDA506jihgFQRBEPyas5/WBfu44vEVz5AgCIIgCJpyOBnZsGED06ZNIzExEUmSWL58ebu3Wb9+PSNHjiQgIIC+ffvy0UcfORGqIAj+rGrvPk7cehtVe/dpHYogCB7mcDJSUVHBsGHDePPNN+26/rFjx7jiiiu48MIL2bVrFw8++CB33nkn33//vcPBCoLgv0pWrKBy61ZKvv5a61AEQfAwhwtYL7vsMi677DK7r79gwQJ69erFyy+/DEBKSgq//PIL//d//8eUKVMcPb0gCH7EnJVFXVExSFC6ahUApStXEnHVdJDBEBWJsVs3bYMUBA29+eabzJ8/n5ycHIYNG8brr7/OmDFjtA7L5dy+mmbz5s1Mnjy5ye+mTJnCgw8+2OptampqqKmpsf27tLTUXeEJgqARs8VM2qSGvw0yIAF1hYUcnzHT9vuUQwc9H5wgeIFFixYxZ84cFixYwNixY3n11VeZMmUKqampxMbGah2eS7m9gDUnJ4e4uLgmv4uLi6O0tJSqqqoWbzNv3jwiIiJsX0lJSe4OUxAED6iuq2ZV+iruWXMPZ392Nv+ZpqOu/q+Q2v1B/V6ngzeuNHDPj/fw3bHvqK6r1iJkwc/IskxlbZ0mX7IsOxTrK6+8wl133cXtt9/OoEGDWLBgAcHBwXzwwQduenS045V9RubOncucOXNs/y4tLRUJiSD4sL15e/nyyJd8f/x7ys3ltt9vGyTxVoTMA5+e2YjqyZt1pCYCWb/ya9avhBnDuCT5Emb0m8FZXc/yYPSCP6kyWxj0mDY1iweemkKwyb633draWrZv387cuXNtv9PpdEyePJnNmze7K0TNuD0ZiY+PJzc3t8nvcnNzCQ8PJygoqMXbBAQEEBAQ4O7QBEFws4KqAl7e9jLfpH9j+11iSALTSkuYeiqNnnV1VBmHc4LTWJHQIduma/6XnUuuRebbxL58Ex7BqYpsvjzyJV8e+ZIr+1zJnFFziA6K1uy+CYI75efnY7FYWpxZOHTokEZRuY/bk5Fx48axqr4wTbVmzRrGjRvn7lMLgqARq2zlyyNf8ur2VymtLUVC4oreV3BNv2sYtXsFun2vQ2AE3LyQ9UdDiFh8F8WhUWzoN45xhzbRh3IMt8wn+cc/MzvjEH8+9wG2jZ/GsrRlrEpfxddHv2Z95noeGvUQ1/S7Bp0kWiYJ9gky6jnwlDaLJ4KMek3O6wscTkbKy8tJS0uz/fvYsWPs2rWLLl260KNHD+bOnUtWVhaffPIJAPfccw9vvPEG//jHP/jjH//IunXrWLx4MStXrnTdvRAEwWukF6fz2KbH2J23G4CULik8es6jytTKkR9h8+vKFa98A3qeyyffb2b7Jf/m3osHEgc8uHY05/WM4NPR50OwBRbfgm7TfxjT+wLGnP88Nw68kae3PM2hwkM8uflJlqct56lzn6J3ZG/t7rTgMyRJsnuqREsxMTHo9foWZxbi4+M1isp9HP44sW3bNkaMGMGIESMAmDNnDiNGjOCxxx4DIDs7m4yMDNv1e/XqxcqVK1mzZg3Dhg3j5Zdf5r333hPLegXBD/2a9Ss3rbqJ3Xm7CTGG8M+z/8nnV3yuJCJlOfDVn5Qrnn0nDLqSo3nlbEkvxGIwcP2YHswa0wOdTuKXjFLS88ph0HQYfYdym2V/grIchnYdyhdXfME/zv4HwYZgduft5qZVN7Epa5N2d1wQXMxkMjFq1CjWrl1r+53VamXt2rV+ObPgcHo4ceLENiuCW+quOnHiRHbu3OnoqQRB8CGLUxfz3NbnsMgWRsWN4oXzXyAupH6+22qBZXdBZT7EDYFLngXgi63KB5eLBsaSGKnUkE0cEMu6Q6f54rcM/n3FIJjyLGRuhdx9sOxuuPkrDDoDNw+6mYt7Xsw/N/yTHad38Oe1f+ZfY//FdQOu0+T+C4KrzZkzh1tvvZXRo0czZswYXn31VSoqKrj99tu1Ds3lxESrIAgdYrFaePH3F3l6y9NYZAtX9rmSdy9+tyERAfjlFTi2AYzBMPNDMAZSbbawdMdJAG4c28N21RvHKD8v3X6SarMFjEEw8wPltsd+hl/+z3bd+JB43r3kXab1noZFtvD0lqeZ//t8LFaLZ+68ILjRrFmzeOmll3jssccYPnw4u3btYvXq1WcUtfoDkYwIguC0GksND61/iP8d+B8A94+4n2fGP4NRb2y4Un4a/DRP+fmKl6FrfwBW78uhuNJMt8ggLujf0MBp4oCuJEQEUlRp5vv9Ocovuw6Ay+crP//0HBQctV3fpDfx7HnPMnv4bAA+OfAJD61/iBpLQ+NEQfBVs2fP5sSJE9TU1LB161bGjh2rdUhuIZIRQRCcUmet4+8//52fMn/CpDMxf8J87h56N5LUrGfIljdBtkDfi2HYDbZff14/RTPr7CT0uobbGPQ6Zp2t9BX6bGtD/RnDb4K+k5VjbW66N5YkSfxp2J944fwXMOlM/JT5E//4+R/UWetcfK8FQXAHkYwIguAwq2zliU1P2BKRtye/zaW9Lj3zihX5sOtz5efzHoL6ROVIbhm/HS9Er5NsiUdjs85OQifBb8cKSTtdpvxSkmD8g8rPuz5Tjt3M5b0v5+3Jb2PSmViXuY4nNz/pcNdLQRA8TyQjgiA4RJZlXt72MiuOrkAv6XnpgpcYk9DKxl2/vwd11ZA4Enqea/v1lzuyAJg0MJa48MAzbpYQEcRFA+OaXBeA5PMgYbhyzN/fb/GUYxLGMP+C+egkHcvTlvPytpdFQiIIXk4kI4IgOOT9fe/zyQGlj9CT5z7JhT0ubPmK5ir47R3l53Nn20ZFAA7lKJtfXjiw9c2+LhzYVbludqONMiUJzr1f+fm3d5RztOCiHhfxxLgnAPj4wMd8sM//9vIQBH8ikhFBEOz25eEveW3HawD8bfTfmN53eutX3v0FVBZARA9IaXq94/kVACRHh7R68171lx0vqGx6waDpEJGkLBPevbDV21/d72r+NvpvALy641WWHVnWeqyCIGhKJCOCINhld95untn6DAB3nXUXtw6+tfUrW60NRabj/gz6hpZGZouVzCJlRKNXTOvJSHL9ZZmFldRZrA0X6I1wzr3Kz5vfVM7VilsH38qdZ90JwNNbnmZP3p7WYxYEQTMiGREEoV2F1YX8df1fqbPWcXHPi7l/xP1t3+DwaihIU/afGfGHJhedLKrCYpUJNOqIDWt9Q8z48EACDDrqrDIni5pNx4y8BQIioOAIHGl7B9YHRjzAxT0vps5ax19//itF1UVtxy4IgseJZEQQhDZZrBbmbpxLbmUuyeHJPHXuU2cu321uU/3+M6P/CAFhTS46XtAwRaPTtX4cnU6iZ3Rwk9vYBITB6NuanqsVkiTx1LlP0TO8JzkVOczdOFc0RRMELyOSEUEQ2vTfPf9l06lNBOoDeXniy4SaQtu+wcltkLEJdEYY86czLranXkSlXke9TRNj7wGdAU78Cie3t3mcUFMor0x8hUB9IL+e+pV39r7T7rkFQfAckYwIgtCqX7N+ZcHuBQA8Nu4x+kf1b/9GW5XrM/Q6CE8442JbMtJGvYhKrSk5o4gVIDwRzrq2/pxvt3us/lH9eXTcowC8vettsbGe4NU2bNjAtGnTSExMRJIkli9frnVIbiWSEUEQWpRTkcPDGx9GRuba/tcyrc+09m9UWwmHVio/q7vtNnOsPrFIrp+CaYuasBxraWQElN1/AQ6tUs7djiv7XMnM/jORkfnnxn+SU5HT7m0EQQsVFRUMGzaMN998s/0r+wGRjAiCcAZZlnli0xMU1xQzKHoQ/xzzT/tueOQHMFdCZA/oNrLFqzgyMtJqzYiq2yhl6bC5AtLW2BXiw2MeJqVLCsU1xTyx+QnREE3wSpdddhnPPPMMV199tdaheIRIRgRBOMPytOX8eupXTDoTz5//PAH61le9NLH/K+X74KubNDlT1dZZOVmkjGC0taxXpV7nZFEVZksLS3glCQZf1fTc7QjQB/D8hOcx6Uz8mvUrK46usOt2gp+QZait0OZLJL6tMrR/FUEQOpOcihzm/67skDt7xGx6RfSy74a1FXC4fpnt4JY/zZ0sqsQqQ5BR3+ayXlVcWCCBRh3VZisni6paTmAGXw2b/qOcu7YSTO1P//SO6M19I+7j/7b/Hy/+9iLjEsYRF+J/27ILLTBXwnOJ2pz7X6fA1H4S3hmJkRFBEGxkWeapzU9RZi5jaMxQbhl0i/03PvID1FVBVLKyf0wL1OmWntHB7S8PRlne2+aKGoDEEcq0kLlSicFOtwy6hbNizqLMXMZTW54S0zWCoCExMiIIgs3XR79mY9ZGTDoTT49/Gr1Ob/+N25miATiWb/8UjSo5OoRDOWUcy6+gxV1wJEk556+vKTGo0zbtMOgMPD3+aa795lo2nNzAN+nfcGWfK+2OS/BRxmBlhEKrcwstEiMjgiAAkFuRywu/vQDAn4f/md6Rve2/cU05HK4flWhligbghNrwzIFkpGdMcJPbtkg95+HvlekiO/WJ7MOfh/8ZgOd/e57Tlaftvq3goyRJmSrR4suO0cDOSiQjgiAA8MzWZygzlzEkekjb+8605Mj3yhRNl94QP7TVq6lLdHvZ0fBMpV73WEu9RlQJw5XpobqqhroVO902+DYGRw+mrLaMp7c87dBtBcFdysvL2bVrF7t27QLg2LFj7Nq1i4yMDG0DcxORjAiCwM+ZP7M+c71t6sKgc3AG144pGmhaM2IvdRSl1ZoRaJiqaRyLnRrf5/WZ69lwcoNDtxcEd9i2bRsjRoxgxIgRAMyZM4cRI0bw2GOPaRyZe4hkRBA6uVpLLS/8rkzP3DzoZvpG9XXsADVlcKS+x8egq1o/T52VrPoN75JOH+fErbdRtXdfm4eu2ruP6Ecfol9RJieLKqmta32HXlsycuQHZdrIAf2i+nFzys0AvPDbC9Raah26vSC42sSJE5Fl+Yyvjz76SOvQ3EIkI4LQyX1y4BMyyzLpGtSVPw09cy+Zdh3+HuqqoUsfiD+r1atlFCrLekNMenQ/fkfl1q2UfP11m4cuWbGCuu2/c0nWDqwyZBa1MVUTP1SZJqqrVnYNdtCfhv2JmKAYMsoy+OTAJw7fXhAE54lkRBA6sZyKHN7Zo2wa99CohwgxOtEDwc4pmpMHj9K3+CTnWvMpXfUdAKUrV1K1fz9V+/ZjzsoCwJyVRdW+/VTt30/pqlUAXJC1iz7FJ8nausN2vTM0nqo5sNzhuxFiDGHOqDkAvLPnHXIrch0+hiAIzhFLewWhE3tl2ytU1VUxvOtwpvae6vgBGk/RtLGKBiDuT9fzev3PFvV7YSHHZ8y0XSfl0EHSJk0+47ahVWW8sf5VWA9p9ddr0eCrYePLSkw1ZRAQ5si9YWrvqSxKXcTuvN28sv0VXpjwgkO3FwTBOWJkRBA6qW052/ju+HdISMwdO9euJmRnSFsLlhqI7gtxg9u86q+z7qdOauVPjl5P4vwXAZTv+qb9TdTIrLqG67UobogyXVRXDUfX2XsvGs4jSfxr7L+QkFh1bBXbc7c7fAxBEBwnkhFB6ITqrHXM+20eADP7z2RQ9CDnDqS+4fe7pN0eCuuTRvHgBQ+0eFny4kVETFN2BY6YNo3kxYtavN6Hf3jUdr0WSZISS+PYHDQoehAz+s8AYN7WeVislnZuIQhCR4lkRBA6oWVHlnG46DDhpnDuH3G/cweRZTj6k/Jzn0ntXr3Jzrtq4tLeaEz95XL99+ziqvbj6lsfS9o6pzcme2DEA4SZwkgtSuXLI186dQxBEOwnkhFB6GQqzZW8vfttQOm0GhUY5dyBCtKgJAP0Juh5bptXramzcKq4ipKAUKToaAIHDyb+iScIHDwYfUwMhujoJtc3REejj4mxXc8wcBCFAWEcrjW2vbwXlFj0JiW2gqNO3bWowCjuG34fAAt2L6Cqzo4kSBAEp4kCVkHoZD47+Bn5Vfl0C+3Gdf2vc/5A6jRIj3Ht7pSbWb+styoimn7r1qIzmZAkichZ1yGbzehMpibXN8bH03fdWiSjEUmSiLjuWq59dBUlFomMwkr6xoa2fjJTCPQ4B45tUGKMcbBvSr3r+l/H/w78j6zyLD47+Bl3nnWnU8cRBKF9YmREEDqR4upiPtj3AQCzR8zGqDc6fzA1GelzUbtXVTfIS44JQR8QYCuWlSTpjEREpSYsADqdjm6xEUA7nVhVakxO1o0AGPVG2+jIB3s/oKSmxOljCYLQNpGMCEIn8t7e9yg3lzMgagCX97rc+QPV1cKxjcrPfe2oF8l3fIO85tSdfo+3tWGeSk1Gjm9UYnXSFb2voH9Uf8rMZby/932njyMIQttEMiIInURORQ5fHPoCgL+M/Au61pbZ2uPkb2CugJBYiG17SS80JBCObJDXnLqfjV3JSNxZEBwDteVw8nenz6mTdPxl5F8A+PzQ5+RU5Dh9LEFwxLx58zj77LMJCwsjNjaWq666itTUVK3DchuRjAhCJ/HWrreotdYyKm4U53U7r2MHS1urfO9zIeja/zPizAZ5zTVsmNdGS3iVTtdoqmat0+cEOL/b+YyMHUmNpcZW+CsI7vbzzz9z3333sWXLFtasWYPZbOaSSy6hosKOZNwHiWREEDqB9OJ0VhxdASht351qcNaYA/Ui0JBA9HLBNM0xe2pGwCV1I6DUtTw06iEAlqctJ70kvUPHEwR7rF69mttuu43BgwczbNgwPvroIzIyMti+3T8b8YnVNILQCfxn53+wylYuSrqIYV2HdexgFfmQvVv5ufeF7V692mzhVImyNLYjNSPJ9VM8p0qqqDZbCDTq275Bn/rYTu2CigIIiW7z6m0ZHjuciUkTWZ+5ntd3vM7/Xfh/Th9L0JYsy5ot1Q4yBDn9QaCkRCmg7tKliytD8hoiGREEP3ew4CBrM9aik3Q8MLLlDqgOSV8PyEpdRlhcu1c/WVSFLENogIHokJZXztgjJtREiElPRa2Fk0VVbS/vBQiLV+pZTu+HY+thyAynzw3wlxF/4efMn/kx40cOFhwkJTqlQ8cTtFFVV8XYz8dqcu6tN24l2Oj4VKXVauXBBx9k/PjxDBkyxA2RaU9M0wiCn1uwewEAl/W6jD6RfTp+QFvX1fZHRQBOl1YDEBce0KHpIUmSiIsIVI5ZVm3fjdQYOzhVA9A3qi+X9roUgP/u+W+HjycI9rrvvvvYt28fCxcu1DoUtxEjI4Lgx1ILU1mXuQ4JibvPurvjB5TlhoJQO+tFcsvUZCSww6ePCwskPa+C06U19t2gz0Ww+Y2G1vAdrJX509A/sfrYatZmrCW1MJUBXQZ06HiC5wUZgth641bNzu2o2bNn8+2337Jhwwa6d+/uhqi8g0hGBMGPqZ/gpyRPoXdk744fMO8QlGWDIUjpvGoHNXGIDQvo8Oljw5Vj2D0y0vNcMARC2SnIS4XYgR06f5/IPlySfAnfH/+ed/a8w8sTX+7Q8QTPkyTJqakST5Nlmfvvv5+vvvqK9evX06tXL61DcisxTSMIfupI0RHWnFgDwN1DXTAqAg3THcnjwWjfSMfpsvpkxAUjI2pCY/fIiDGoYd8cF0zVQMNjuebEGtKK0lxyTEFo7r777uPTTz/l888/JywsjJycHHJycqiq8s99kkQyIgh+6t097wJwcc+L6RfVzzUHdXBJLzRKRlwxMhKm1ozYmYyAy/qNqPpH9Wdyj8nIyLyz9x2XHFMQmnv77bcpKSlh4sSJJCQk2L4WLVqkdWhuIZIRQfBD6SXprD6+GnDhqIilDjK2KD/3usDum+XWF7C6ZGSkfppGPaZdek9UvmdsUe6DC/xp2J8AWH1steg7IriFLMstft12221ah+YWIhkRBD/07p53kZG5MOlCBnbpWJ2ETc4epb16YCTEDrL7ZnluGBnJc2RkJHYQBEQosefu7XAMAAO7DGRi0kRkZN7b855LjikInZlIRgTBz5woPcGqY6uAhk/wLpGxWfne4xy7WsCr1KW9ri1gdSAZ0emhR31fiRObOxyD6p6h9wCw8thKMkozXHZcQeiMRDIiCH7m/b3vY5WtTOg+gcHR7W9iZ7cTm5Tvdq6iASivqaOi1gK4toC1vKaOihoHplzUmE/82uEYVINjBnN+t/Oxylbe3yd29BWEjhDJiCD4kdOVp/km/RsA7jrrLtcdWJYbRkbU1Sn2xFM/KhJi0hMa0PFOAqEBBoJNSht4h0ZH1Jgztij3xUXUepxvjn7D6crTLjuuIHQ2IhkRBD/y6cFPqbPWMTJ2JMNjh7vuwPmHobJA6S+SYP9xXbmsF5QeEQ3Lex0oYk0cofQbqcyH/CMuiQWUPWtGxI7AbDXz2cHPXHZcQehsRDIiCH6ivLacJalLALh9yO2uPbg6RdN9NBjs318m14X1IiqnlvcaAqDbaOXnjE0uiwXg9sHKY704dTHlteUuPbYgdBYiGREEP7H08FLKzeX0jujNhO4TXHtwJ6ZooNFKGheNjCjHcmJ5L0BPtW7EdUWsABckXUCviF6Um8v58siXLj22IHQWIhkRBD9gtpj534H/AXDb4NvQSS7+r62+gTtQvAoNoxcDijM5cettVO3d16EwqvbuY+Zn8+hXlOnY8l5oiN3FIyM6SWcbHfnkwCeYLWaXHl8QOgORjAiCH1h5bCWnq04TGxTLFb2vcO3BizOhJAMkPXQ/26GbqnUdA/f8QuXWrZR8/XWHQilZsYK4o/u4KHO7Y9M0AEljQNJBcQaUnOxQHM1d0fsKugZ15XTladuyakEQ7CeSEUHwcVbZykf7PgLgD4P+gElvf02HXdQpmoRhEBBq983MWVnojqTSp/gkcds3AlC6ciVV+/dTtW8/5qwsu49TtW8/Vfv3U7pKeaO/4OQudGmHHDoOAWEQP1T52cVTNSa9iZtSbgLgo/0fYZWtLj2+IDR22223cdVVV2kdhkuJXXsFwcdtPLmRoyVHCTWGMrP/TNefQC1edbBeJG3SZNTFxepiWkthIcdnNMSYcuigXcdpLrK2nDv/9yTH/2f/cQDoOR6ydylTNUOvte82drp2wLW8u/dd0orT+CXrF9fX7QiCk44fP06vXr3YuXMnw4cP1zqcFomREUHwcR/s+wBQ3gzDTGGuP0GGc/UiifNfpK6+dkVqfqFeT+L8F+0+Dnp9k9/ZjufAcQC3FbEChJvCuba/kuCoz4ngX6r27nNJ7ZMvM5vdUxMlkhFB8GF78/ay4/QODDoDf0j5g+tPUFkIeYeUnx1MRkxTLufBCx5o8bLkxYuImDbNruNETJtG8uKWdypN/PwLu48DNNyHvIPKfXOxP6T8AYPOwPbc7ezNc80+OIL3KFmxwiW1T/ZaunQpZ511FkFBQURHRzN58mQqKirOuN7q1as577zziIyMJDo6mqlTp3L06FHb5b169QJgxIgRSJLExIkTbZe99957pKSkEBgYyMCBA3nrrbdslx0/fhxJkli0aBEXXHABgYGBfPaZe/rpiGREEHzY/w4q8xSX97qc2OBY159AHRWJGQAh0Q7d9HRZo6W3ktT0u7Pqb2+tHxspqqx17PYhMRDTX/lZ3YHYheJC4rgs+TJAaUAn+L6WapacqX1yVHZ2NjfccAN//OMfOXjwIOvXr+eaa65BbqGDcEVFBXPmzGHbtm2sXbsWnU7H1VdfjdWq1C799ttvAPz4449kZ2ezbNkyAD777DMee+wxnn32WQ4ePMhzzz3Ho48+yscff9zk+A8//DB/+ctfOHjwIFOmTHHL/XWqZuTNN99k/vz55OTkMGzYMF5//XXGjBnT6vVfffVV3n77bTIyMoiJiWHmzJnMmzePwEDX9R4QhM4mtyKXNcfXALhnVAScrhcBZVlvSUAoJUHhxPXpQeTMmRQvXYo5JwdDtGOJjSE6Gn1MDMb4eCJnzmTzGx8QUVZEoCGEZEcD6zFO6Sh74lcYeLmjt27XTYNu4pv0b/jh+A/8dfRf3ZMkCh7TUs2SM7VPjsrOzqauro5rrrmGnj17AnDWWWe1eN0ZM2Y0+fcHH3xA165dOXDgAEOGDKFr164AREdHEx8fb7ve448/zssvv8w111wDKCMoBw4c4L///S+33nqr7XoPPvig7Tru4nAysmjRIubMmcOCBQsYO3Ysr776KlOmTCE1NZXY2DP/033++ec8/PDDfPDBB5x77rkcPnyY2267DUmSeOWVV1xyJwShM1qUuog6uY5RcaNIiU5xz0k6koyU1pAfFMmrd7zIF/dNQJIkImddh2w2ozM5tuLHGB9P33VrkYxGJEni/cLu7DmWz2uBEQ7HRc/xsOPjhlEfFxscPZiRsSPZcXoHCw8t5IGRLU9VCb4hcf6LnHp4LlgsZ16o15P4/Dy3nHfYsGFMmjSJs846iylTpnDJJZcwc+ZMoqKizrjukSNHeOyxx9i6dSv5+fm2EZGMjAyGDBnS4vErKio4evQod9xxB3fd1bCPVV1dHRERTf9fjR492oX3rGUOT9O88sor3HXXXdx+++0MGjSIBQsWEBwczAcftFywtWnTJsaPH8+NN95IcnIyl1xyCTfccINt2EgQBMdV11Wz5LDS+v3mlJvdc5KacsjerfzsYL0INHRIjY4KQ6qfXpEkyeFERKUzmWzHiQ0PxKw3OLY/jUotYs3eDbVnzr+7wh8GKSNVSw4vobrOiRgFr9FWzZIjtU+O0uv1rFmzhu+++45Bgwbx+uuvM2DAAI4dO3bGdadNm0ZhYSHvvvsuW7duZevWrQDU1rY+jVlermxd8O6777Jr1y7b1759+9iypekUZkhIiAvvWcscSkZqa2vZvn07kyc3DFvpdDomT57M5s0tf8o499xz2b59uy35SE9PZ9WqVVx+eevDozU1NZSWljb5EgShwcr0lRTXFNMttBsTkya65yQnfwfZAhFJEJnk8M3VpmRdXbgvjSquvr18rqONzwAie0B4d7DWKffRDS5MupDEkESKa4pFEzR/4qraJ7tPJzF+/HiefPJJdu7ciclk4quvvmpynYKCAlJTU3nkkUeYNGkSKSkpFBUVNbmOqf4DgKXR6E5cXByJiYmkp6fTt2/fJl9qwasnOTRNk5+fj8ViIS4ursnv4+LiOHToUIu3ufHGG8nPz+e8885DlmXq6uq45557+Ne//tXqeebNm8eTTz7pSGiC0GnIsmwrjrxx4I3odfp2buEk9Y06aaxTN1cLWONcuC+Nqqtt514nkhGAHufAvqWQ+Tv0nui6wOoZdAZuTLmRl7a9xP8O/I+r+15tG9URfE/zmiVna58csXXrVtauXcsll1xCbGwsW7duJS8vj5SUFPbs2WO7XlRUFNHR0bzzzjskJCSQkZHBww8/3ORYsbGxBAUFsXr1arp3705gYCARERE8+eSTPPDAA0RERHDppZdSU1PDtm3bKCoqYs6cOW67by1x+2qa9evX89xzz/HWW2+xY8cOli1bxsqVK3n66adbvc3cuXMpKSmxfWVmZro7TEHwGVuyt5BWnEawIZir+13tvhPZkpHWi9PboiYKrtyxV6Ues8mKHUeobe3dNDICcHW/qwkyBJFWnMbWnK1uO4/gfmrNUvKSxURdP4vkJYvpu24txkbFoK4WHh7Ohg0buPzyy+nfvz+PPPIIL7/8MpdddlmT6+l0OhYuXMj27dsZMmQIDz30EPPnz29yHYPBwH/+8x/++9//kpiYyPTp0wG48847ee+99/jwww8566yzuOCCC/joo4+8f2QkJiYGvV5Pbm5uk9/n5uY2qdBt7NFHH+Xmm2/mzjvvBJRq4IqKCu6++27+/e9/o9OdmQ8FBAQQEOD6P2CC4A/UUZGr+l7lniZnALLc8Ebd3bniNTVRUHfZdSV1tMXhzfJUjZMRWXbLsHu4KZyr+l7FF4e+4NMDn3JOwjkuP4fgOY1rnSRJQnKy9sleKSkprF69usXLPvrooyb/njx5MgcOHGjyu+ZLgO+8807b+3BjN954IzfeeGOL50lOTm5xKbE7ODQyYjKZGDVqFGvXrrX9zmq1snbtWsaNa7nArbKy8oyEQ1/fTdFTd1IQ/MWJ0hNsOLkBCYkbU1r+A+IShelQVQT6AIhreTlhe9Sakdgw10/TqAlOrjMFrADxZyn3rapQua9uou5Xs+HkBk6UnnDbeQTB1zk8TTNnzhzeffddPv74Yw4ePMi9995LRUUFt9+ubKF9yy23MHfuXNv1p02bxttvv83ChQs5duwYa9as4dFHH2XatGm2pEQQBPt8dlDpfnhB9wvoGd7TfSdSR0UShoHB8U+ANXUWiiuVttFxbhgZUROcokoztXVObEpnMEFC/aZ5J7e5MLKmeob35ILuFyAj8/nBz912HkHwdQ73GZk1axZ5eXk89thj5OTkMHz4cFavXm0ras3IyGgyEvLII48gSRKPPPIIWVlZdO3alWnTpvHss8+67l4IQidQYa7g66NKG2q3jopAwxu0Op3hILVexGTQERFkdFVUNlHBRox6CbNFJq+8hm6RQY4fpPvZStKVtQ2GzXJ5jKqbUm7i55M/s+LoCh4Y+QAhRvcvkxQEX+NUB9bZs2cze/bsFi9bv3590xMYDDz++OM8/vjjzpxKEIR63xz9hgpzBb0ierm//qDD9SL1y3pDA9yyikSSJGLDAskqruJ0abWTyUj9fXNjESvAOQnnkByezPHS43x79FtmDXRf4iMIvkrsTSMIPkCWZRYeWgjArAGz3LtMtLYScut3JXVyZCTPtqzXfYXo6vLeXGeX96r3LWcvmKtcFNWZJEni+oHXA7AwdaGolROEFohkRBB8wLbcbRwtOUqQIYgr+1zp3pNl71YagoXGQUR3pw6RW+q+4lWVurw3z9nlvRFJEBKr3Fe106ybXNnnStsy32257qtREVomEkD3csXjK5IRQfABXxz6AoBpvae5bzmvKqtRvYiTIzDuXNarUo992tnlvZLUaImvexOEMFMYU3tPBbCNcAnuZzQq9UqVlZUaR+Lf1MdXfbyd4VTNiCAInpNTkcO6jHUAtuF+t+pgvQg0FLC6o/uqKq5+1MXpLqyg3MfUlW6vGwHluVtyeAnrMtZxuvK02M3XA/R6PZGRkZw+fRqA4OBg0QnXhWRZprKyktOnTxMZGdmhFbIiGREEL7f08FIssoVRcaPoF9XP/Sfs4EoaaNgzxh370qhsvUacnaYBj42MAPSP6m/bzXfp4aX8efif3X5OAVtDTjUhEVwvMjKy1can9hLJiCB4MbPFzNLDSwEPjYqUnoLSLJB0kDDc6cOou+m6oxW8KtYVIyOJI5T7WnoSSrMhPMFF0bXshoE32JKRu4behVHn+mXPQlOSJJGQkEBsbCxms1nrcPyO0Wh0Sc8wkYwIghf7MeNHCqoL6BrUlUk9Jrn/hOoIQexgCAh1+jB5buy+qupwzQgo9zF2kLJ6KGsbhLtnO3jVpB6TiAmKIa8qj7UZa7k0+VK3nk9ooNfrRaNNLyYKWAXBi6nFjjP7z/TMp2gX1IuYLVYKKmoB9y7tVROdgooa6ixOdGFVeajfCIBRb2Rm/5mAKGQVhMZEMiIIXiq1MJUdp3dgkAy2NzC3c0G9iDoqYtBJRAW7bzOx6BATep2ELEN+ea3zB/Jg3QjAzH4z0Ut6tudu53DRYY+cUxC8nUhGBMFLLTm8BIALe1zomZUXFjOc2qn83IGRkbxtu5j3y9ucXZODTue+lQs6nURMqIl+RZkU/OlOqvbuc+5A3erv66mdYKlzXYCtiAuJ46IeFwGwJHWJ288nCL5AJCOC4IUqzZV8m/4tANcNuM4zJz19AOqqICACop1ftVOz6huG5x9l0skdLgyuZXHhgUzK3I5u13ZKvv7auYPE9IeAcDBXKo+BB1zb/1oAvk3/lkqz6IEhCCIZEQQvtOrYKirMFfQI68GY+DGeOamtXmQU6Bz702DOyqJq336q9u8ncKPSE2X4kd+p2r+fqn37MWdluTRU9XxDKrKZcHIXAKUrVzp3Pp0Ouo1SfvZA3QjA2ISxJIUlUW4uZ/Xx1R45pyB4M7GaRhC8kDpFc23/a9FJHvrM0IF6kbRJk20/q39UgitLOT6jodYl5dDBjkTX4vluBtRG1JbCQufP1/1sSP9JeQzOvsNlcbZGJ+m4tv+1vLL9FZakLuGafte4/ZyC4M3EyIggeJn9+fs5UHAAo87I9L7TPXdidVSgm+P1IonzX4T6ZZNqlYitWkSvVy53oZbOZ+PM+Ty4okY1ve90jDoj+wr2caDAM9NDguCtRDIiCF5GHRW5uOfFRAVGeeakVcVQkKb8rE5ZOCBi2jSSFy9q8bLkxYuImOba/h0uP5+agBUcUR4LD+gS2IXJPZURHvU5F4TOSiQjguBFymrLWHVsFeDBwlWA7F3K98ieEBLdoUNZ68cqZA/tAaKez9lN/QDlPkf2UH528w6+jV3XX3mOV6avpLy23GPnFQRvI5IRQfAi36Z/S1VdFb0jejMydqTnTpxVv/Klm/PnNERHo4+J4UR0Ev8ZNgO530D0MTEYojuW3LR1PmtUF45Edud/464ncPDgjp0vcYTy/ZT7VwGpRsWNondEb6rqqliZvtJj5xUEbyOSEUHwErIs24brrxtwnWd3F1XfgNU3ZCcY4+Pps/ZHHpr4F77rNY6wD/9H33VrMXZwA622zhey7FsevOABvuo+luQlizt2vsT6RCzLc8mIJEm2Zb5LDi9BluV2biEI/kkkI4LgJXbn7eZI0REC9AFM7T3Vsyc/tUv5ntix0Zgq9NRYlDfUmLAAdCb3dWAFiOkSBpJEldlCldnSsfOpo0LqY+Eh0/pMI0AfQGpRKnvy93j03ILgLUQyIgheQh0VuTT5UiICIjx34vI8KMkEJEgc3qFDFZQrreCDjHqCTe7vHBBi0hNo1NWfuwMt4aFhl+KSDKjI79ixHBAREMGU5CkALE5d7LHzCoI3EcmIIHiBkpoSvj/+PQDXDrjWsydXp2hi+kNAWIcOpe4RExPm3hERlSRJRIcE1J+7A7v3AgSGN3Se9eBUDTR0ZP3h+A+U1pZ69NyC4A1EMiIIXmBl+kpqLDX0i+rH0Jihnj15VsfrRVTqyIiaIHhCTKip/twdHBmBRlM1nk1GhnUdRt/IvlRbqlmVvsqj5xYEbyCSEUHQmCzLfHnkSwBm9Jvh2cJVaNgcrwMraVQFFfUjI6GeGRkBiA4NqD93B0dGoKFmRn1MPESSJNvOzEsPLxWFrEKnI5IRQdDYvvx9HC46rE3hqiw3WknT8WQkv8zzIyPRIUrik++KkRF1dChrh/LYeNDU3lMx6UykFqWyv2C/R88tCFoTyYggaEwdFbm458WeLVwFKDkJFXmgM0D8kA4fTh0ZidZgZKTDNSMA8WeBpIeK01Dq2s392hMREMHFyRcDyuiIIHQmIhkRBA1VmCtsHVdn9Jvh+QDU6YjYFDAGdfhwakKgJgie4NKaEVMwxA5SfvbwVA00vAa+O/YdleZKj59fELQikhFB0NB3x76jqq6K5PBkRsU5vidMh7lwigYaEgJP1ozEuLJmBKBbo6kaDxsdN5rk8GQq6yr57th3Hj+/IGhFJCOCoCF1OH5m/5meL1wFl7SBb0xNCGI8ODIS7cqREdCkLbxKkiSu6XcN0DB9JwidgUhGBEEjhwoPsb9gPwadgWl9XLurrV2sVpd1XlWpCYFHa0ZsfUZclYw0WlGjwaqWK/tciUFnYG/+XlILUz1+fkHQgkhGBEEj6qjIpB6T6BLYxfMBFB2DmhIwBCo1Ix1UZ7FSWFmfjGjQZ6SwogaL1QXJQ+wg0AdAdQkUpnf8eA6KDormwqQLATE6InQeIhkRBA1U1VXZmltpUrgKDVM08WeB3tjhwxVVmpFlkCSICu748ewVVb+01ypDcaULRkcMpoaVRRoUsQLM7Kf0HPn2qLKLsyD4O5GMCIIG1pxYQ5m5jG6h3RibMFabIFxdvFpfLxIVbMKg99yfFqNeR2R98qMuLe4wDXbwbeycxHPoFtqNMnMZa06s0SQGQfAkkYwIgga+PKwMv1/T7xp0kkb/DV3UebVq7z5O3HobxTuVHWfVJmSeFNOo14gaT9Xefc4fsJs2nVhVOknH1X2vBmDZkWWaxCAIniSSEUHwsOMlx9lxegc6Scf0PtO1CcJSB9m7lZ87uCdNyYoVVG7divV7ZdrJk8WrKjUBKiivtcVT8vXXzh9QHRnJ3g1WiwsidNz0vtPRSTq2527neMlxTWIQBE8RyYggeNiyNOWT7vndzicuJE6bIPJTwVwJprCGnWodYM7Komrffqr276d0lZKEhPy6jj7FJ0kpPYU5y7PdS3vVldK3+GSTeEpXrqRq/36q9u13PJ6YfmAMAXMF5GmzoiU+JJ7xieMB+CrtK01iEARPMWgdgCB0Jmarma/TlE/sV/e7WrtA1OmHxOGgc/wzSdqkyWf8zlhWwhvrX4X1kPYepBw62KEQHXHr6w9yK8B6UMcxLIWFHJ8x03Ydh+LR6ZXH5sSvSm1N3CDXBeuAa/pdw8asjXx99Gtmj5iNUee5wmBB8CQxMiIIHrTh5AYKqguIDoxmQvcJ2gXSOBlxQuL8F0Gvb/I7tWWbVadXLvegPbfOoa612hu9k/HYmp/tcjqujrqg+wV0CexCflU+G09u1CwOQXA3kYwIggd9dUQZbr+y75Xafsq1JSPO1YtETJtG8uJFLV52+PHXiJjm2SZu5gsv4cELHmjxsuTFi5yLx5aMaFPECmDUG7myz5VAw2tHEPyRSEYEwUNyK3LZmKV8ulVXSmjCYoac+pUmCcM7frz6NvbW+rGRCA/2GFE1aT+vttXvaHt99bHJ3ac8ZhpRp/M2Zm3kdOVpzeIQBHcSyYggeMiKoyuwylZGxo6kV0Qv7QI5fRAsNRAQAV16O30YQ3Q0+pgYAgcPJv6JJzgRk0RhQBiRCbEuDNY+MaEmSgJCKQkKt8UTOHgw+pgYDNHRzh20S28ICIe6asg75NqAHdA7ojcjY0dikS18fbQDK4QEwYuJAlZB8ACrbLUNs6sboWkme5fyPXFYh0YPjPHx9F23FsloRJIk/rY/HHN1LWuSk1wTpwOiQwPID4rkniseY+fTlyNJEpGzrkM2m9GZnFxqrNNBwjA4vlGpG4k/y6UxO+Lqflez4/QOlh1Zxh1D7tBmU0VBcCMxMiIIHvB7zu+cLD9JqDGUi3terG0wts3xOtZfBEBnMiFJEpW1dVSarZj1BmLCPLcvjUrtbVJcBzV1VkDZAdfpRESlFviqCZxGLul5CSHGEDLLMtmWu03TWATBHUQyIggeoHbRvKzXZQQbg7UNRi3IdEW9SD11t94Ag44Qk76da7teWIABU30Lepe1hIeGx0jDIlaAYGMwl/W6DBAdWQX/JJIRQXCzkpoSfjzxI+AFUzR1tZC7X/nZyWW9LckvV/aliQkN0GQKQZIk2+hIflmN6w6sjh7laFvECg0bKq45sYbS2lJNYxEEVxPJiCC42apjq6i11tIvqh+DowdrG0xeffFqYAREua6IVh0Z0aIVvEo9t7phn0tE9VIKfS01SuGvhgZHD6ZvZF9qLDV8l/6dprEIgquJZEQQ3MxWuNr3Gu0LD9V6kYThHV/62oiaAGixSZ6qYbM8F07T6HSQMFT5WeO6EUmSbCNr6pYCguAvRDIiCG50sOAgBwsPYtQZmdp7qtbhNFpJ0/Hi1cbybSMjni9eVUWHKOcucGUyAl7RiVU1tfdUDDoDBwoOkFqozZ45guAOIhkRBDdSNzi7MOlCIgMjtQ0GOtwGvjXeME0To07TlLtwmgYaHiuNi1gBogKjuDDpQkBsnif4F5GMCIKb1FhqWJm+EvCCwlVoWrzqwpU00DBN01XLkRFbzYiLR0ZsnVj3K4+hxtTX0rfp31Jr0T4eQXAFkYwIgpusy1hHaW0p8SHxnJNwjtbhwOkDYKmFwEiISnbpodXVNJoWsIaoNSMuHhnp0ruhiDVP2yJWgHEJ44gLjqOkpoR1Geu0DkcQXEIkI4LgJmo/iOl9pqPXeb73xhls9SLDXVq8Co2maUK0HxlxaQErKI9V4jDlZy+oG9Hr9EzvOx0QPUcE/yGSEUFwg6zyLLZkbwHgqr5XaRuMyoWdV5vL94qaEbWA1cUjI9DwmGm8okalvqa2ZG/hVPkpbYMRBBcQyYgguMGKtBUAjE0YS/ew7hpHU88NnVcBrFaZwoqGpmdaUc9dWFGL1Sq79uBe0olVlRSWxNj4scjItteaIPgykYwIgotZrBaWpy0H4Oq+V2sbjKquxi2dVwGKq8yo7/1RwdqNjHSp73FSZ5UprXZxt1T1MfOSIlaAq/pdBcDytOVYZau2wQhCB4lkRBBcbGv2VrIrsgkzhTGpxyStw1GcPgBWMwRFQWRPpw9TtXcfJ269jaq9+2y/U6dFIoKMmAza/UkxGXSEByobkTevG2kpbodE9VK61lpqlcfSC0zuMZkwYxinKk7ZpgQFwVeJZEQQXEzt/3B5r8sJNARqHE09F3VeLVmxgsqtWyn5+mvb77yhXkTVWt1IS3E7RJIapmq8pG4k0BDI5b0vB2D5keXaBiMIHeRUMvLmm2+SnJxMYGAgY8eO5bfffmvz+sXFxdx3330kJCQQEBBA//79WbVqlVMBC4I3K64uZm3GWsBLeouoOtB51ZyVRdW+/VTt309p/f/b0pUrqdq/n6p9+yk+lgFoWy+iaryipr24zVlZjh3cizqxqq7up0wDrs1YS0lNicbRCILzDI7eYNGiRcyZM4cFCxYwduxYXn31VaZMmUJqaiqxsbFnXL+2tpaLL76Y2NhYli5dSrdu3Thx4gSRkZGuiF8QvMrKYysxW80M7DKQQdGDtA6nQQc6r6ZNmnzG7yyFhRyfMROAZICrXrJ1QNWSbWSkooa06y474/LGcQOkHHKgb4gXdWJVDeoyiAFRA0gtSmVl+kpuTLlR65AEwSkOj4y88sor3HXXXdx+++0MGjSIBQsWEBwczAcffNDi9T/44AMKCwtZvnw548ePJzk5mQsuuIBhw4Z1OHhB8CayLNs2xfOa5bxQX7xaX+fgxEqaxPkvgr6VPil6PbtufgjQtseIqvHISHtxJ85/0bGDqyMjufuVx9QLSJJkGx1Ri6YFwRc5lIzU1tayfft2Jk9u+KSk0+mYPHkymzdvbvE2X3/9NePGjeO+++4jLi6OIUOG8Nxzz2GxWFo9T01NDaWlpU2+BMHbHSw8SGpRqvdsiqfK3V9fvNoFIns4fPOIadNIXryoxcuSFy9iT8o4wDtqRho2y6tpN+6IadMcO3hkT6V7rdXsNUWsAFf0ugKjzsjBwoMcLNC+Q6wgOMOhZCQ/Px+LxUJcXFyT38fFxZGTk9PibdLT01m6dCkWi4VVq1bx6KOP8vLLL/PMM8+0ep558+YRERFh+0pKSnIkTEHQhNoNc1KPSUQERGgcTSOu7Lyq3r7RcQpsreC1Hxlp2Cyv2fLbFuJ2mCQ1mqrZ5fxxXCwyMJKLelwEiM3zBN/l9tU0VquV2NhY3nnnHUaNGsWsWbP497//zYIFC1q9zdy5cykpKbF9ZWZmujtMQeiQ6rpqVh1TiiTVYXOvYasXcb7zqiE6Gn1MDIGDBxP/xBMEDh6MPiYGQ3S0bWO6mBAvGBlpVDMCbcftFFsRq/fUjQBc01cpll6ZvpIai3dMIQmCIxwqYI2JiUGv15Obm9vk97m5ucTHx7d4m4SEBIxGI/pGc7cpKSnk5ORQW1uLyXTmH7CAgAACArT/lCUI9lqbsZay2jISQhK8Y1O8xhov63WSMT6evuvWIhmNSJJE5KzrkM1mdCYT+eXK1IA3jIxEhzTdn6atuJ3iZct7VWMTxhIfEk9ORQ7rMtZxWa8zi3cFwZs5NDJiMpkYNWoUa9eutf3OarWydu1axo0b1+Jtxo8fT1paGlZrQ4fAw4cPk5CQ0GIiIgi+SB0ev6rvVegkL2rfY66G0/V1BB3ck0ZnMiHVT3NIkmR7Q1enRLxiNU3YmTv3tha3U2xFrAe8pogVlM3z1KJptYhaEHyJw38158yZw7vvvsvHH3/MwYMHuffee6moqOD2228H4JZbbmHu3Lm26997770UFhbyl7/8hcOHD7Ny5Uqee+457rvvPtfdC0HQ0Mmyk2zN3oqEZNtN1Wucri9eDY6GCNfvkVNttlBeUwd4x8hITH0Ba1l1HTV1rRfJOy2yh9LF1mpuaK/vJab3UV57YvM8wRc5nIzMmjWLl156iccee4zhw4eza9cuVq9ebStqzcjIIDs723b9pKQkvv/+e37//XeGDh3KAw88wF/+8hcefvhh190LQdDQiqMNm+J1C+2mcTTNNN6pt6PFqy1Q60WMesnWil1L4UEGDDrlfhZWuGEPGUnyuh18Vd3DujM2QWyeJ/gmp/56zJ49m9mzZ7d42fr168/43bhx49iyReydIPgfi9Vi+8PvNZviNeamnXpVtpU0IQG2qRAtSZJEdKiJ3NIaCsprSYgIcv1JEobD0XVeV8QKymtwa/ZWlqct50/D/uRdU4aC0AbxShWEDtiSvaVhU7yeXrIpXmMdaANvj3zbsl7t60VUaq+RvHI31XR4YVt41aQekwgzic3zBN8jkhFB6AC1t8jU3lMJ0GtfM9FEk+LV4W45Rb6teNV77rtaxHpGrxFXUR/L0weUx9iLBBoCuaLXFYAoZBV8i0hGBMFJRdVFrMtcB3jZpniq3P1grYOQrhDunloWbxwZibEt73XTyEhEklIQbK1TCoS9jPpaXJuxluLqYm2DEQQ7iWREEJz0bfq31FnrSOmSwsAuA7UO50yndijfE4a7pXgVIL9MGX3o6oUjI/llbkpGJKmhBscL60ZSolNI6ZKC2Wpm5bGVWocjCHYRyYggOEGWZdsUjVeOikDTNvBuonY69aZpGrXxWYE7VtOovLAtfGNqF+BlR5Yhy7LG0QhC+0QyIghO2Je/j7TiNAL0AVze+3Ktw2lZ42W9buKV0zShZzY+czkvLmIFuLzX5Zh0Jg4XHeZAgfds6icIrRHJiCA4Qe24OrnnZMJN4RpH0wJzVUPxageW9Vbt3ceJW2+jau++Fi8v8OIC1vw2Cljbu1/tUh/TvIPKY+1lIgIimNxT2V1dHcETBG8mkhFBcFBVXRXfHfsOaNigzOvk7APZAiGxEJ7o9GFKVqygcutWSr7+usXLvXFkJNqOAtb27le7IrpDcIxSxOplnVhV6vThqmOrqKrzvoRJEBoTyYggOGjNiTWUm8vpHtqd0fGjtQ6nZY3rRRwsXjVnZVG1bz9V+/dTukrZibh05Uqq9u+nat9+zFlZAFissq3LqTcVsHatHxkprKjFam2ol7D3ftlFkhrVjXhfESvA2fFn0y20G+Xmcn488aPW4QhCm7Tv3ywIPkYd9r6639Xe2+FSfYN0ol4kbdLkM35nKSzk+IyZtn+nHDpIUWUt6nt9lxDvGRlRY7FYZYqrzLZ/23u/7JY4AtJ+9Nq6EZ2k4+q+V/PGrjdYdmQZ0/pM0zokQWiVl/4lFQTvdKL0BNtzt6OTdLaNybyS+gbpRL1I4vwXQa9v+UK9XrmchmmQqGAjBr33/Ckx6nVEBhuBplM19t4vu6mPrZftUdPY9L7T0Uk6tuVuI6M0Q+twBKFV3vMXRBB8gNrVcnzieOJC4jSOphW1lZB3SPnZiZGRiGnTSF68qMXLkhcvImKa8glb7THiTcWrKtuKmka9Ruy9X3ZTH9vT3lnEChAfEs+5iecCopBV8G4iGREEO5mtZtsOvV7bWwQgt754NTQOwhM6diy13qSFuhNv7DGishWxttZrpI37ZbfwRKW7rWxRCoa9lPpaXXF0BXXWOo2jEYSWiWREEOy08eRG8qvy6RLYhQuSLtA6nNa5oL+IIToafUwMgYMHE//EEwQOHow+JgZDdLTtOnll3reSRtVaF1Z77pfdJKnhMfbiqZqJ3SfSJbAL+VX5bDy5UetwBKFFooBVEOz05ZEvAWUe3qgzahxNG9Ti1Q70FzHGx9N33VokoxFJkoicdR2y2YzO1JB4eOMmeaqurTQ+s+d+OSRhOBz5AbJ2dDBi9zHqjUzvM50P93/Il0e+5MIeF2odkiCcQYyMCIIdcipy+CXrF8CLe4uo1D1pOth5VWcyIdVPY0iSdMYbdkH9G726lNab2FrCt9D4rL375RBbJ1bvXN6rUtvDb8zaSG5FrsbRCMKZRDIiCHZYkbYCq2xlVNwokiOStQ6ndTVlkJeq/NxtpFtPZWt45kXLelUNXVjd2BIeGh7j/FSoKXfvuTqgV0QvRsaOxCpbbXVPguBNRDIiCO2wylZb+/cZ/WZoHE07sncDMoR3h9BYt55K3YjOG6dpbKtp3LlZHkBYPIQlgmytf+y914z+ymt32ZFlWGWrxtEIQlMiGRGEdmzJ3kJWeRZhxjAu7nmx1uG0TZ0u6Oa+zfFUanFojDdO09QX1TYvYHULdXTEy6dqLu55MWHGMLLKs9iavVXrcAShCZGMCEI71P4MV/S+gkBDoMbRtCPLNfUi7ZFl2VbA6o3TNI0LWGVZbufaHWSrG/HeIlaAIEOQbYdp0XNE8DYiGRGENhRVF7E2Yy3QMMzt1WzFq+6tFymrqaPWogz1e2UBa/3ISE2dlYpai3tPpiYjXryiRqVOM67NWEtRdZHG0QhCA5GMCEIbvjn6DXXWOgZFD2Jgl4Fah9O2ykIoOq78rG7i5ibq9EdogIFAYyst1jUUbDIQbFLicvtUjZqMFB1TngMvlhKdQkqXFMxWM9+mf6t1OIJgI5IRQWiFLMu24WyvL1yFhpqFLr0hKMqtp7JN0XhhwzNVTCu9RlwuuAtE9VJ+9uLmZ6qZ/ZWNAZcdWeb+KSxBsJNIRgShFbvzdnO05CiB+kAu63WZ1uG0z0NTNNDQY8QbV9KobEWsLfQacTm1iNUHpmou63UZgfpA0orT2J3n3SuAhM5DJCOC0Iolh5cAcGmvSwkzhWkcjR2y1JU0ziUjVXv3ceLW26ja2/4+K97cY0Tl6MiII/f/DD7S/AwgzBTGlOQpACw9vFTjaARBIZIRQWhBSU0J3x//HmgY1vZ6HRwZKVmxgsqtWyn5+ut2r2trBe+FxasqNRlpqQtrSxy5/2dI9J2REWh4TX9//HtKa0s1jkYQRDIiCC1amb6SGksN/aL6MTRmqNbhtK80G8qyQdJBgv3xmrOyqNq3n6r9+yldtUo51MqVVO3fT9W+/Zizslq8Xb4PTNPE2KZpWh8Zcfb+nyFhmPLYl52CspwOx+5uw7oOo29kX6ot1axMX6l1OIIgNsoThOZkWWbpEWX4ema/mbZ9TLyaOj3QdSCYQuy+WdqkyWf8zlJYyPEZDaNBKYcOnnGdhmTEt6dpnL3/ZwgIhZgBkHdQeS4GeHeNkSRJzOw/k+d/e54lh5dw/YDrfeN1LvgtMTIiCM3syd/DkaIjBOgDmNpnqtbh2MfJKZrE+S+CvpWluXq9cnkLCrx4x16VWsDa1jSNs/e/5YP5Tr8RgKm9pxKgD+BI0RH25u/VOhyhkxPJiCA0oxb1TUmeQrgpXONo7KS+ATrYBj5i2jSSFy9q8bLkxYuImDatxct8Y5qm/ZERZ+9/i2xt4X0jGYkIiOCSnpcAopBV0J5IRgShkbLaMlYfWw34UOGqLDdM03SkDbw6TG/HcL0v9RnJs7fPiAP3v0WJjfao8ZH+HeprfPXx1ZTXeu+uw4L/E8mIIDSyKn0V1ZZq+kT0YXjX4VqHY5/iE1BVCDojxA1x+OaG6Gj0MTEEDh5M/BNPEDh4MPqYGAzR0S1ev9psobymDvD2kRElUSqrrqOmrvWW8I7e/1bFD1Geg8oCKM7oSOgeMyJ2BL0jelNVV8WqY6u0DkfoxEQBqyDUk2XZ1ltkZn8fKVyFhima+CFgcDw5MMbH03fdWiSjEUmSiJx1HbLZjM7U8qiHOu1h0usID/TePyERQUaMegmzRaagvJbEyKAWr+fo/W+VIQDiBkH2bmWqJqqnC+6Fe6mFrC/+/iJLDi/h2v7X+s7rXvArYmREEOrtL9hPalEqJp2JaX0cqBXQ2qmO79SrM5lsb0KSJLX5Rtx4isab37gkSSI6xL7GZ47c/zb5WL8RgGm9p2HSmThUeIgDBQe0DkfopEQyIgj11FGRS5IvISIgQuNoHHBql/LdA23gwTdawavsWVHjUt0a1Y34iMjASC5Ovhho+D8gCJ4mkhFBQClc/e7Yd4APFa4CWK0NyYiTbeAd5Qs9RlQOF7F2lJoQZu9WnhsfMbOf8ppfdWwVZbVlGkcjdEYiGREE4Juj31BVV0XfyL6MjPXMm7pLFByB2jIwBitNtzygYZpGjIycoetAMARBTany3PiIUXGj6BPRh6q6Kr5N/1brcIROSCQjQqfXuHDV5wr4Tv6ufE8YDnrPFJP6Qo8RVVcHN8vrML0BEocrP5/c5plzuoAkSVw74FoAFqcuRvaRpcmC/xDJiNDp7Ti9g7TiNIIMQb5VuAoNb3jdR3nslLZN8nxomsZjyQhAt/rnIst3khGAaX2mEWQIIq04jZ2nfafmRfAPIhkROr3FqYsBuLzX5YSZwjSOxkHqG173sz12SlHA2o7uo5XvPjQyAhBuCueyXsqeOosPL9Y4GqGzEcmI0KkVVhey5sQaANswtc+orYTc+qWY3UY7fPOqvfs4cettVO3d59DtfGmapiMjI84+PrbEMHe/8hz5kOv6XwfAD8d/oKi6SONohM5EJCNCp7Y8bTlmq5nB0YMZHD1Y63Ack70LZAuEJUBEN4dvXrJiBZVbt1Ly9dcO3c4XWsGrOpKMOPv4EN4NQuOV5yZ7t8Pn1dLgmMEMih6E2WpmedpyrcMROhGRjAidllW2siRVKVydNWCWxtE4QS1e7WZ/vYg5K4uqffup2r+f0lVK++/SlSup2r+fqn37MWdltXn7OouVokrv37FXpda1FFbUYrG2X5TZ0ccHUPa2sU3V/O507FpR/y8sObwEq+w7y5MF3+a9vZwFwc22nNrCyfKThBnDmJI8RetwHHfS8XqRtEmTz/idpbCQ4zMaequkHDrY6u0LK2uRZeX9tkuI94+MdAkxIUlglaGosrbdBKqjj49Nt1Fw6FufK2IFuDT5Uub/Pp/Msky2ZG/h3MRztQ5J6ATEyIjQaS1KVbaOn9ZnGsHGYI2jcULWduV7d/vrRRLnvwh6fcsX6vXK5W3IL1NGRboEm9DrvH8JtEGvIypYSZrsmarp6ONjYxsZ2W7f9b1IsDHYtqpMLe4WBHcTyYjQKeVU5PDzyZ8BuG7AdRpH44TSbCjNAkmn9BixU8S0aSQvXtTiZcmLFxExre2lzQUVvlO8qooOsX9FTUcfH5vEEcpzU3oSynLsjtVbqIWs6zPXk1uRq20wQqcgkhGhU1p6eCkW2aJ0nozso3U4jlOH/2MHQUCoc8dQm7s50OTNtpImzPunaFROF7E68fjYBIRB1xTlZx9b4gvQN0rpRGyRLSw9slTrcIROQCQjQqdjtphZelj5A3v9wOs1jsZJamGkA1M0KkN0NPqYGAIHDyb+iScIHDwYfUwMhujodm+rji6ou+H6AnXVT76dvUY68vg0oTai88EiVoAbBt4AKIm72WLWOBrB34kCVqHTWXNiDQXVBXQN6sqkHpO0Dsc5ai2CE/1FjPHx9F23FsloRJIkImddh2w2ozO1P9qR50M9RlSOjox05PFpotto2PFJQ22Pj5nUYxJdg7qSV5XHjxk/2hqiCYI7iJERodNZmLoQUPahMeqMGkfjBKulYYt6J0ZGAHQmk20PHkmS7H6jVQtYfWmapmtYfTJSZv80jbOPTxPqc3Nqp/Kc+Rij3mjbwXrhoYUaRyP4O5GMCJ3KocJD7Dy9E4NksP2h9TmnD4K5AkxhENPfo6e2FbD60jSNWsBa4cGW8KDs4GsKhdpyyDvk2XO7yMz+MzFIBnac3kFqYarW4Qh+TCQjQqeifsKb3HMyXYO7ahyNk2zNzkaCrpVlqG7SqQpYO0qnV1bVgM/WjcQGxzKppzKV+cWhLzSORvBnIhkROo2SmhJWpq8EGorzfJJtczznpmg64nSp8oYeGxbo8XM7KzZcSUbU2D3KRzfNa+z6AUqR96pjqyipKdE4GsFfiWRE6DSWpy2n2lJN/6j+jIgdoXU4zutA8WpH1FmsttEF9Q3eF8SFK4lTXnmNXS3hXUp9jny0iBVgVNwo+kX1o6quihVpK7QOR/BTIhkROgWrbLV1XL1h4A224kSfU13aUH/gwMiI0zvQNlJQUYtVBr1O8q2lvSEmdBJYrLKt5sVZDj+O6nN0+iDUlHXo3FqRJMk2OrIodZHYr0ZwC5GMCJ3Cr1m/klmWSZgxjMt7Xa51OM47tROQIaIHhMbafTOnd6BtJLe0GoCuoQE+0QpeZdDrbHUjHZ2qcfhxDIuHiCRAblgB5YOm9p5KqDGUjLIMNp3apHU4gh9yKhl58803SU5OJjAwkLFjx/Lbb7/ZdbuFCxciSRJXXXWVM6cVBKepy3mv6neVb+5Do3Kg2ZlLdqBtJLf+jTzOh6ZoVOpUjZpQOaLDj2M3325+Bsp+NVf1vQoQhayCezjc9GzRokXMmTOHBQsWMHbsWF599VWmTJlCamoqsbGtf1I7fvw4f/vb3zj//PM7FLAgOOpE6Qk2nNyARMNws89yYHM8l+1AW099I48N953iVVVceAB7sxoSKkd0+HHsPhoOLPfpIlaAWQNm8enBT9l4ciMZpRn0CO+hdUiCH3F4ZOSVV17hrrvu4vbbb2fQoEEsWLCA4OBgPvjgg1ZvY7FYuOmmm3jyySfp3bt3hwIWBEd9fvBzACZ0n+Dbf0BlGTK3Kj93H9Pu1V22A2290/XJiC+OjMR2YGSkw4+j+lxl/qY8hz4qOSKZ87udj4zM54c+1zocwc84lIzU1tayfft2Jk9u+KSg0+mYPHkymzdvbvV2Tz31FLGxsdxxxx12naempobS0tImX4LgjLLaMpanLQfgppSbtA2mowrSoLIADIGQMKzdq7tsB9p6tmkaH1rWq1JjPl3meDLS4ccxcTjoA6AyHwqOOnx+b/KHlD8Aysq08tpyjaMR/IlDyUh+fj4Wi4W4uLgmv4+LiyMnp+Vtsn/55Rfef/993n33XbvPM2/ePCIiImxfSUlJjoQpCDZfHfmKyrpK+kb25ZyEc7QOp2MytijfE0eCwcGmYx3ZgbZebpk6MuKDyUj9aI4z0zRNOPM4GgIamp9lbunY+TU2LnEcvSN6U2GusCX5guAKbl1NU1ZWxs0338y7775LTEyM3bebO3cuJSUltq/MzEw3Rin4K4vVYhtOvjHlRt9dzqtS38h6jLX7Ji7bgZaGN3Jf6jGi6kgBK7jgcVSfswzfTkYkSbKNMH5+6HMsPrjnjuCdHCpgjYmJQa/Xk5ub2+T3ubm5xMfHn3H9o0ePcvz4caY1Gsa0WpU16gaDgdTUVPr06XPG7QICAggI8L0/eIJ3+fnkz2SVZxEREMHU3lO1DqfjMurrRZLsH+Fx2Q60NK4Z8b2RkdgOjox0+HFMOgd4raHmx4dN7T2VV3e8SmZZJhuzNjIxaaLWIQl+wKGREZPJxKhRo1i7dq3td1arlbVr1zJu3Lgzrj9w4ED27t3Lrl27bF9XXnklF154Ibt27RLTL4JbfXbwMwBm9JtBkCFI42g6qKIACo4oPye1X7zamCt2oK2ts9o2mvPFZESNuaCiBrPFuaZdHXock+pHRvIPQ2WhU+f3FsHGYNsmk58e+FTjaAR/4fA0zZw5c3j33Xf5+OOPOXjwIPfeey8VFRXcfvvtANxyyy3MnTsXgMDAQIYMGdLkKzIykrCwMIYMGYLJmW25BcEOqYWp/JbzG3pJ79v70KjUT9QxAyC4i8dPn1ffBt6ol4gKNnr8/B3VJdiEQSchy5BXpsEeNSHREN1P+dkPRkduGHADeknP1pytHC46rHU4gh9wOBmZNWsWL730Eo899hjDhw9n165drF692lbUmpGRQXZ2tssDFQRHqLUik3pMIj7kzClEn+NEvYgr2XqMhAX6ZO2NTicRG6ZO1ThXN9JhflI3ApAQmsBFPS4CGpbOC0JHONz0DGD27NnMnj27xcvWr1/f5m0/+ugjZ04pCHYrrC7k26PfAvCHQX/QOBoXcaJexJV8uceIKjY8kFMl1R1fUeOspHNg56d+MTICyjLfNSfW8G36t/xl5F+ICozSOiTBh4m9aQS/szh1MbXWWgZFD2J41+Fah9NxdTUN+5r0aD8ZccWmeM01tIL3vXoRlZpIOdNrpC12P97qc5e1Q3lOfdyI2BGkdEmhxlLDksNLtA5H8HEiGRH8So2lxrZ3xi2DbvHJKYUznNoFlhoIjoEu7XcwdsWmeM3l+vBKGlV8B5f3tsbuxzu6LwRHK89l9m6XxqAFSZK4ZfAtgDJVU2up1TgiwZeJZETwK98e/ZbC6kLiQ+K5JPkSrcNxDVu9yDmtNtty9aZ4zflyjxFVQ0v4jo9KOPV4S1LDqho/qBsBmJI8hbjgOAqqC1iZvlLrcAQf5lTNiCB4I6ts5ZMDnwDKfLZR53urPlqk1ou0MUXj6k3xmlOnNnyxFbyqo43PGnP68e5xDqSu8pu6EaPOyB9S/sDL21/m4/0fc1Xfq/xjNFLwODEyIviNX7J+Ib0knVBjKDP6zdA6HNdovDleG8Wrrt4Urzl/mKax1Yy4YGTE6cdbfQ4ztvj0pnmNzeg/gxBjCEdLjvJL1i9ahyP4KJGMCH7j4/0fAzCz/0xCTaEaR+MiBUeVDdba2RzP1ZviNddQwOq70zS2kREXFLA6/Xg33jSvML3DcXiDMFOYLflX/w8KgqNEMiL4hQMFB/gt5zcMksH3d+dtTK0XcWRzPBdsitdYtdlCSZUZaKi78EXqFFNxpZlqswv3VHHk8W68aZ6f1I2AMi2qNkE7WOD8dKDQeYlkRPAL6ieyKb2m+EeTM5X6hmVHszNXborXmDqtEWjUER7ou2Vm4UEGAgzKnzxXdGF1+vFWn0sf38G3sYTQBFvB+McHxOiI4Djf/csiCPWyy7P5/vj3ANw66FaNo3ExO+pFVK7cFK8xdVojLtw3u6+qJEkiLjyQjMJKckurSeoS3KHjOf14q5vmZfhHEavq1sG38t2x71h9bDUPjnzQvz4UCG4nRkYEn/fpwU+xyBbGxo8lJTpF63Bcp7JQ2VgN7N4czxWb4jVnK1714ZU0qrgO7t7bnFOPt23TvFSf3zSvscHRgzk7/mwsssW2SaUg2EskI4JPK60t5csjXwLYGjD5jYzNyneNNsdT+UOPEVWsmxqfOaTxpnl+VDcCcNvg2wBYcngJpbWl2gYj+BSRjAg+beGhhVSYK+gX1Y/zu52vdTiudbx+mWTyeE3D8IdlvSp1dMcVK2o6RH1Oj/vXUtjzup1H38i+VJgrWHSo5dVGgtASkYwIPquqropPD3wKwB1D7vDpeoYWHd+ofE9uO8lyx140jeX6wSZ5Klf2GmmNXc+H+pyqz7Gf0Ek6/jjkj4AyfVpVV6VxRIKvEMmI4LOWHVlGUU0R3UO7MyV5itbhuFZlIeTUv5kln9fmVd2xF01jfjUy4oFpGrueD/U5zdnrV3UjAJf1uoxuod0orC7kqyNfaR2O4CNEMiL4JLPVbFvOe/uQ2zHo/Gxh2IlNgKzUi4TGnnGxu/eiaUwdRYj1gwLWWFsBq2uTEYefj7D4+roRuaE2yE8YdAZb7chH+z/CbDVrG5DgE/zsL7jQWaxKX0V2RTbRgdFM7ztd63Bcz1Yv0vKoiLv3omnMv6ZplITK1dM0Tj0fyedBwRHluR54hUvj0dpVfa/i7d1vk12RzXfHvuPKPldqHZLg5cTIiOBzrLKVD/Z9AMDNg24mQO/7b5JnaCcZcfdeNKrymjoqapVupb7cfVWlJiNlNXVU1NS57LhOPR+9/LNuBCDQEMjNg24G4P2972OVrRpHJHg7kYwIPuenzJ9IL0knzBjGrAGztA7H9SoLIXev8nMrxavu3otGpY6KhAYYCA3w/YHU0AADISYlaTjtgi6sKqeej55q3cg+v6sbAZg1YBahxlDSS9JZn7le63AELyeSEcGnyLLM+3vfB+D6gdf7z4Z4jZ34VfnedSCEdm3/+i7ei6YxNRnxhx4jKrcXsdr7fITFQUx/QK6vEfIvYaaGDwvv730f2U92KRbcQyQjgk/5Lec39ubvJUAf4F8b4jXWzhSNyl170TSm1lb4Q/dVlbuKWJ16PmxLfP2r34jqD4P+QIA+gD35e/gt5zetwxG8mO+PuwqdyoLdCwC4pt81RAe57k3Xq9iZjLhrL5rG/Kl4VeWuIlanno/k82Db+36bjMQExXBV36tYlLqI/+75L2MT2t/wUeicxMiI4DN+z/mdbbnbMOqMtsZKfqeiAHLr+4v0bDsZAffsRdOY2greH3qMqNT7kuOGaRqHnw814cz1v34jqjuG3IFBZ1D+/+Zs0zocwUuJZETwGf/d/V8Aru57tf/uCGqrF0lpsV7E3d1Wm1PbpvvDShpVbJh7pmna0urzFhqr9JKBhufezySEJnB136sBWLBngcbRCN5KJCOCT9iRu4OtOVsx6AzcedadWofjPu1M0bi722pzp8U0jUu0+bz18u+6EYA7z7oTg87A1uyt7Dy9U+twBC8kkhHBJ7y9+21AaaaUEJqgcTRuZNuPpiEZ8WS31eb8eZrG3Zvl2f28qc/1Mf/rN6JKDE1keh+lOeHbu97WOBrBG4kCVsHr7Tq9iy3ZWzBIfj4qUpEPpw8oPzdKRjzZbbUxWZYbClj9aDVNXKPVNLIsu22DRbufN7U26PR+pWYoxD8Ls+88605WpK1gc/Zmdp3exfDY4VqHJHgRMTIieD11VGR63+l0C+2mcTRupNYMxA6CkBjbrz3VbbW50qo6auqUzpn+1GdE3WOn2myltNp1XVibs/t5C+2q9JQBv60bAege1p1pfZTmb+qqOEFQiWRE8Gq783az6dQm/x8VgVbrRTzVbbU5dRojIshIoLGVN1UfFGTSEx6oDAqfdmMRq0PPm5/3G1HdNfQu9JKeX0/9yp68PVqHI3gRkYwIXk0dFZnWZxrdw7prHI2bpf+sfO85vvXruLHbanPqFE28H9WLqBq6sHqoiLW9581WN/KzZ+LRSFJYElN7TwUa/m8LAohkRPBiO0/v5NesX9FLeu466y6tw3GvkpOQnwqSDnpNOONiT3RbbS6nxP9awavUZCS7pMqt57H7ees1QXnu8w4prwU/dvfQu9FLen7J+oVdp3dpHY7gJUQBq+CVZFnmtR2vAcoKmqTwJI0jcrO0tcr3bqMguMsZF3ui22pzmUXKG3X3qCC3nUMr6n06WeTeZMTu5y24CySOhKxtcHQdjLzFrXFpqUd4D6b3nc6yI8t4bcdrfDDlA7cVEQu+Q4yMCF5p86nNbM/djkln4p5h92gdjvsdrU9G+kxq9Sru7rba3MnCSgC6RwW79TxaSOqi3KfMokq3n8vu561v/XOvJqZ+7N5h92LUGdmWu43N2Zu1DkfwAiIZEbyOLMv8Z+d/AJg1cJb/dltVWeogfb3yc9+myYinO642pr5Rq2/c/sQ2MlLo3pGR1rT4vKqJaPp6sFo0ictT4kPibTv6vr7jdbGjryCSEcH7rMtYx/6C/QQbgv1/BQ3AqR1QXQKBkcpQfSOe7rjaWGb9G3WSH07TeHJkpCUtPq/dRkFgBFQXQ9YOTeLypDvPupMgQxD7CvaxLnOd1uEIGhPJiOBVLFYLr+98HYCbB91Ml8Az6yf8TtqPyvfeE0Fv0LTjqqrabLEt7fXHkZGk+qmnnNJqauo8MwrR7vOak6u8BqBh2s6PRQdFc/OgmwF4Y+cbWPx8NEhomyhgFbzKqmOrOFpylHBTOLcOvlXrcDxDrRGon6LRquNqY1nFVcgyBJv0RIe4tzZFCzGhJoKMeqrMFk4VV9MrJsTt57Tref3sYTiwQnlNTHzY7TFp7dbBt7Lw0ELSitNYdWyVrSma0PmIkRHBa5gtZt7c9SYAd5x1B2GmMI0j8oDKQmWaBmw1A1p1XG0ss754NSkq2C9XOkiSRFIXZfpJva/uZtfzqtYMZW2DqiKPxKWlcFM4fxzyRwDe2vUWZotZ44gErYhkRPAaXx75kqzyLGKCYrhh4A1ah+MZ6etBtkLXFIhQWt1r1XG1MXVZr/qG7Y/UqZoMDyUjdj2vEd2V1vCytaGo2c/dMPAGYoJiOFl+ki+PfKl1OIJGRDIieIXy2nJbR8Y/Df0TQQb/fRNsotkUzRk82HG1MX9e1qvStIi1ree1T+dZ4gsQbAzm7qF3A0pX1vLaco0jErQgkhHBK3yw7wMKqwtJDk9mRv8ZWofjGbLcqL/IRU0u0qLjamP+vKxXpcXyXrue1771r4Wj65TXSCcws/9Meob3pLC6kA/2faB1OIIGRAGroLmcihw+OfAJAA+NegijzqhxRB5y+iCUZYMhEHqe2+QiLTquNubPy3pVWoyM2PW89hyvvCZKsyAvFWIHeiw+rRh1Rh4a9RAP/vQgnxz4hOsGXOf//YWEJsTIiKC513e+To2lhlFxo7gw6UKtw/EcdVSk53gwBp3RCMvTHVcb6wwjI2rNiKcKWFWtPa+25//Q0YbktBMs8VVdlHQRI2NHUmOpsS3vFzoPkYwImjpQcICvjyqNn/4++u9+uXKjVbZ6EWXJp5YNzhorrTZTXKmsavDrZKS+OLeo0kx5TZ3G0TR7/utfE7YeNJ2AJEn8/ey/A/DN0W84WODe5euCdxHJiKAZWZZ5adtLAFzR+woGxwzWOCIPqq2EE5swV+ipsvTUtMFZc+pIQZcQE6EB/juTGxZoJCpYmRL09OiIqtVGaHXJVBUaMe/fAmZtWtZrYUjMEC7vdTkyyt8G0Sa+8/DfvzSC19twcgO/5/yOSWfigREPaB2OZ534FSw1pH2TCN/8rclFnm5w1lxnqBdRJXUJpqiyhMzCSlISwj1+/lYbod31N6ArACl3/Ar9zryev3pg5AP8eOJHfsv5jY1ZG5nQfYLWIQkeIEZGBE2YrWZe2f4KAH8Y9AcSQxM1jsjDDq0EIPGGszRvcNbcyfp6ke5+PEWj8nSvkebabISmk0g8pwhSV3k2KI11C+3GTYNuAuDlbS9TZ9V+Ck1wP5GMCJpYdGgR6SXpRAZEdo7N8BqzWm1vMBE33q15g7PmGndf9Xfd6+tGThZpMxXSZiO0l/9GRHKV8lqxWj0cmbbuPOtOIgMiSS9JZ1Fqy4+P4F9EMiJ4XH5Vvq3t+wMjH+gcbd8by9oO5bkQEA69Gg1Ba9TgrLnO0H1VpdWKmhY1f/67jQRTmLL8+9RO7eLSQLgpnPtH3A/AmzvfpKCqQOOIBHcTyYjgca/teI1yczmDogdxTd9rtA7H8w59o3zvdzEYTJo3OGuuM42MaNqFtV6rz39svPIagYbXTCcyo98MUrqkUGYu47Udr2kdjuBmooBV8KjdebtZnrYcgH+N/Rd6XSvz5f5KluHgtwBUGYZz+tbbiP3b3zRtcNY0PLlT9BhRqUW6mYVVyLKsydLy1hqh1aQe5tTCXGK7GQk6tBImP+Hx2LSk1+n519h/cfN3N/NV2lfM7D+ToV2Hah2W4CZiZETwGKtsZd7WeQBM7zOdYV2HaRyRBvIPQ+FR0Jso2V1g6yuhZYOzxvLKa6g2W5EkSIwM1CQGT+oWFYQkQZXZQkFFrWZxtPT8l6xYQeX+Y5QcD1FeN3mHNYtPK8Njh3NlnysBmLd1Hla5c9XOdCYiGRE85qsjX7G/YD+hxlAeHPWg1uFowvzL51QVGqkKHEPp90pDK637ijSmLutNCA8kwOD/o1YBBj3x4UrS5Q11Iy32HTkZprxmvv9Q89eHFh4a9RAhxhD2FeyzjaoK/kdM0wgeUVJTYpv3/fPwPxMTFKNxRNpIm7sYpX9Euu13WvcVaawzLetVJUUFk11STWZRFSN6RGkaS4t9RyotHP+hK/ywHJ5ZrunrQwsxQTH8edifmb9tPq9uf5VJPSYRERChdViCi4mREcEjXt/5OkU1RfSJ6MP1A6/XOhxtlGQpfSOkVrpKatRXpLHOVLyqUpf3esPISJt9RySZxKf+5dmAvMQNKTfQJ6IPRTVFvLHzDa3DEdxAJCOC2+06vYvFqYsBpWi10+zK21zqKiKSq0i+ueXdSLXqK9KYrftqJ1jWq/Km5b1t9h25OJ+IXjUejsg7GHVG5o6dC8Ci1EXsztutcUSCq4lkRHArs8XMk5ufREZmep/pjEkYo3VI2jmkrKIh+Tzlu5f0FWnMtpKmE42MeMPy3ha19Pqo79zbGY1NGMuVfa5ERuaJTU9gtpq1DklwIaeSkTfffJPk5GQCAwMZO3Ysv/32W6vXfffddzn//POJiooiKiqKyZMnt3l9wb98tP8j0orTiAqI4m+j/9b+DfxU1bZNnHhvH1WFRgwjpnlVX5HG1LbonWFZr6rx8l5v0GLfkS6RGAItyuvo5j9QtXef1mFq4m+j/0ZUQBRpxWl8vP9jrcMRXMjhZGTRokXMmTOHxx9/nB07djBs2DCmTJnC6dOnW7z++vXrueGGG/jpp5/YvHkzSUlJXHLJJWR1wqrwzuZE6QkW7F4AwD/G/IPIwEhtA9JQyafvUHnaREluN4wpY+m7bi3JSxYTdf0skpcspu+6tRjjW56+8ZQ6i5Xskmqgk03T1Cdep4qrsFi13yVW7TvS5PWx/meMPftTkm6i8vftlHz9tdZhaiIqMIq/n/13AN7e9TYnSk9oHJHgKg4nI6+88gp33XUXt99+O4MGDWLBggUEBwfzwQcftHj9zz77jD//+c8MHz6cgQMH8t5772G1Wlm7dm2Hgxe8lyzLPLX5KWqttZybeC5X9LpC65A8rskyzY3bAShNl6jav5+aw0eoO3UK0LavSGPZJdVYrDImvY64MP/vMaKKCw/EpNdRZ5XJLvGO0ZHGfUfqTp2i5vARqoLHUZqhJInetBzc06b2nsq4hHHUWmt5evPTyLL2CaTQcQ4t7a2trWX79u3MnTvX9judTsfkyZPZvHmzXceorKzEbDbTpUuXVq9TU1NDTU1DoVZpaakjYQpeYHnacn7L+Y1AfSCPnPOIJp0ttdZ0maYMSFjKarxmGW9zagFn96ggdLrO83zpdRLdooI4ll9BZmEV3b2sXqbp60j5/OhNy8E9TZIkHh33KNesuIatOVv5+ujXTO87XeuwhA5yaGQkPz8fi8VCXFxck9/HxcWRk5Nj1zH++c9/kpiYyOTJZ66nV82bN4+IiAjbV1JSkiNhChrLr8rnpW0vAUpPkaSwzvn8NV2m2ezN3QuW8TaX2Ql7jKi6q3Uj3lbEiu+9jjwhKSyJe4ffC8D8bfPFRnp+wKOraZ5//nkWLlzIV199RWBg68PAc+fOpaSkxPaVmZnpwSiFjpBlmSc3P0lpbSkDuwzk5kE3ax2SZtpcpukFy3ibsy3rjeo89SIqtW7kpBcs723O115HnnLzoJsZ2GUgJTUlPLX5KTFd4+McSkZiYmLQ6/Xk5uY2+X1ubi7x7RTfvfTSSzz//PP88MMPDB3a9mZHAQEBhIeHN/kSfMM36d+wPnM9Bp2BZ8Y/g0HXOZv8Vu3dx4lbb6Nm347639T/ofTi6arOuJJGpS5lzvDCZKQJ2+tHeT3VpKVx4tbbOuXqGqPOaPsbsy5zHd+mf6t1SEIHOJSMmEwmRo0a1aT4VC1GHTduXKu3e/HFF3n66adZvXo1o0ePdj5awavlVOTw/NbnAbhv+H0M6DJA44i0U7JiBZVbt1K5Zhn6QAuBcSavW8bbXGfsMaJSVw9lFnlHAWtzTZb7XhxJYBcz+ohg5TVWv9liZzSgywD+POzPgLKRXk6FfeUCgvdx+GPrnDlzuPXWWxk9ejRjxozh1VdfpaKigttvvx2AW265hW7dujFvnrI76wsvvMBjjz3G559/TnJysq22JDQ0lNDQUBfeFUFLsizz2K+PUWYuY2jMUG4bfJvWIXmcOSuLuqJikLBtcla+PZXu5xXCeX/BeP55tu3hvWH1THO2VvCdaFmvyttHRtTlvpLRSN13rxDIPOToOE7+8DOgrK6JuGo6yGCIisTYrZvGEXvO7UNu56fMn9ibv5fHNz3OgskLOmXBvK9zOBmZNWsWeXl5PPbYY+Tk5DB8+HBWr15tK2rNyMhAp2sYcHn77bepra1l5syZTY7z+OOP88QTT3QsesFrLDm8hM3ZmwnUB/LMeZ1zeqbFTc6qrJz4sSv8+DnwOSmHDiJ5YSJSUF5DfnktAL27dr4PCX1ilfucV1ZDUUUtUSHe9xypCWzanPdQNlsssl3WmVfXGHQGnjnvGa775jo2ndrEksNLuG7AdVqHJTjIqQLW2bNnc+LECWpqati6dStjx461XbZ+/Xo++ugj27+PHz+OLMtnfIlExH9klmbaVs88OOpBekX00jgibbS8yVn9JzQvX/VwKKcMgJ7RwYQGdL5EMjTAQI/6WpmDOd7dSiBx/out/+X28teZu/SO6M2DIx8E4KVtL5FZJhY9+BqxN43QIXXWOv71y7+oqqvi7PizuWHgDVqHpBlfXvVwMFt5Ax4YH6ZxJNpR7/uh7DKNI2lbxLRpJD//5xYv8/bXmTvdmHIjo+NGU1VXxb9/+Td11jqtQxIcIJIRoUPe2vUWu/J2EWoM5enxT6OTOt9LSl0502RFQ7NVD95OHRkZGN95V64NTFDu+yEvHxkBoMe59T+cuUqrxddjJ6CTdDxz3jOEGkPZeXonb+9+W+uQBAd0vncOwWW2ZG/hvb3vAfDEuU/QLbTzFM01pq6cKfn664ZVD93CiB9dTGD3cK9dPdOY+gacktB5k5FBCcrIyEEvHxkBMMR3Qx9qIrCLmfhrBjVZpdX49djZdAvtxuPjHgfg3T3vsjV7q8YRCfYSyYjglIKqAuZunIuMzMz+M5mSPEXrkDyqyb4z9StnSleupK6ggO6vvky3sTlE9a0k+b8vesUmeG2ps1g5nFsOQEpCZ56mURKxw7ll1FmsGkfTNmN8PH0/e4nki/OJCt9Gt5dfoPsbr1NXUNDk9dgZ96+5tNelzOg3AxmZhzc+LLqz+ojOV6kmdJhVtvLvX/5NflU+fSP78o+z/6F1SB7X4sqZJisagkm5Kxip1/lIuuZFrd7lWH4FtXVWQkz6TtljRNWjSzBBRj1VZgvHCyrpG+vdq4p0/S6C8AQoy+bolDM3ouzMK2z+Oeaf7Dq9i6MlR/n3r//mrUlvdcopZF8inh3BYR/v/5hfT/1KoD6Q+RPmE2TofH0pWl45U08HiecUwdDrwMsTEYCD9fUiA+LDOtUGec3pdBID4tWpGh+oG9HpldcYkDg9sfXXYydcYRNkCGL+BfMJ0Afwa9avfLL/E61DEtohkhHBIbtO7+I/O/4DKJ8++kb11Tgiz1KLA03JvVpfOTM5j4jkahh9u4ejc84hdSVNJ64XUaX4UhErwKjbAYmIoG0kv/NSi1dJXrwIU3KvTlfU2i+qH/8c808AXtvxGrtO79I2IKFNIhkR7JZXmcec9XOok+uYkjyFGf1maB2Sx7VYHKiuZGjc9bH/FIhK9mhszlJHAVI68bJelVoz4+3Le2269IJ+lyg/H1ihfG/h9dhZi1pn9lPq2erkOuasn0NeZZ7WIQmtEMmIYBezxaz8Z67Ko29kX54696lO03K5rWJVXWQkAf37K/vODEpBH2jFEGiBs+/SOGr72Zb1ipERWxGr+pj4hDHKa81wYhX66Ghl/5onniCgXz90kZGduqhVkiSePPdJ+kT0Ia8qj7/+/FfMFrPWYQktkGQf2He5tLSUiIgISkpKxA6+Gnl689MsPryYMGMYX0z9gp7hPbUOyWMODkxp9zophw4i//4+8tdz0HXtDbO3g877c/3iylqGP7UGgD1PXEJ4oFHjiLRVUmVm2JM/ALD7sUuICPaBx8NqhddHQtExrFNeQjrnTiRJsvt12xmcKD3BDd/eQJm5jFkDZvHIOY9oHVKnYe/7t/f/tRQ09+XhL1l8eDESEs9PeL5TJSLQTrGqWhwoy0i/v6fUq559p08kItAwAtA9KqjTJyIAEUFGukUqBdk+Uzei0ymvOUC36yN1AwL7XredRM/wnjw/4XkkJBalLuKrI19pHZLQjG/8xRQ0sydvD89ufRaA2SNmM6H7BI0j8qyqvfsoXvolCc891+LltvbbJzbB6QNgDIbhN3o4Suc1tIEXI44qW92IL03VjLgJDEGQuw8ytgBtb0+Q8NyzFC/9slMVtE7oPoE/D1fa6D+95Wn25u3VOCKhMZGMCK3KqcjhwZ8exGw1M6nHJO48606tQ/I4tfCv/Gdlq/YWi1UBfn9X+X7WtRAU5bkAO0gt1BzUiZudNacmZj6xvFcVFAVDr1V+/u2dMy9v9rot//nnTlnQevfQu7kw6ULMVjMP/vQgORU5Wock1BPJiNCistoy7v3xXlvB6jPjn+k0TYNaKlit2LQJXWQkpt696frgg03ab1OaDQe/UW48xncKV6FhKkIUrzYYqLaF96WREWgomj74NZQpb7K27QkGD6brgw9i6t0bXWQklZs2A52voFUn6XjuvOfoE9GH01WnuW/tfZTXlmsdloDowCq0wGw189f1fyWtOI2YoBjemvQWoSbv7kbpSi11V7UWFwNQW1xM3quvMvDgAWSzGZ3JBD/NA2sd9BgH8Wd5OFrnWawyqbnqBnliZESl9ho5nFOGxSqj95VGcAlDIekcyNwC2z+Gif9U2savW4tkNHIoZdAZN+mMXVpDTaG8Nfktblp1E4eLDvPXn//KG5PewKgTNVNa6hwfdQW7ybLM05ufZnP2ZoIMQbw56U0SQhO0Dssj1IZmMQ880G7hnyRJSiJSVwPbP1QuO9u3prGOF1RQbbYSZNTTMzpE63C8RnJ0CAEGHVVmCxmFlVqH4xh1ZG7bB8prE9CZTEiS1G5Ba8wD93eaxmiJoYm8MekNggxBbDq1iWe2PIMPLCz1ayIZEZp4d++7fJX2FTpJx0sXvMSg6DM/TfkrtT7EUlTUendVtWBVteMTKM+FsERIudJDkbqGWi/SPz7Mdz79e4De19rCN5ZyJYQlQHkO7Py0yUVtFbQmL16EpbCoU9WRDI4ezIsTXkQn6Vh2ZJltB3JBGyIZEWyWpy3n9Z2vAzB3zNxOsXKmtYZmNenpyhVaK1gF5ZPnL/+n/HzeQ2AweSBi11HrRUTn1TOp01aHfC0ZMZiU1yIor8262pav1+z1XJOe3ikbo01MmsjDYx4G4D87/8OKtBUaR9R5iZoRAYDvj3/P45seB+C2wbdx/cDrNY7IM1rbfTf77/U7Ecsy8U88QfHSpZhzcpSCVdXOT6E0S/kkOvIWD0XsOgezRb1Ia9S6EZ8rYgUYeStsfAVKMmHXZ032SFILWo3x8UTOnEnOE08ANLze6Xx1JDcMvIFT5af4aP9HPLbpMYIMQVySfInWYXU6IhkR2HByAw9veBirbOWaftcwZ9QcrUPyiKq9+zD16UPt8eNgsZx5BZ2OhOfnEXnllUTOuq6hYBWUT5yNR0WMgR6L21Vse9KIlTRnaGgL72MjI6C8Fs97EFY/rCQlw2+yjdo1LmiVJAkpOIjsuf9q9fVv6tWLqr37CDpriGfvg4c9NOohSmpK+CrtK/658Z8EGgI7xciwNxHTNJ3c7zm/2za/uyz5Mh4757FOs+dMyYoV1B49StiUKS1enrxkMZFXKnUgtoJV1a7PlE+eofHKJ1EfU1ptJqu4ChANz1qijhZlFlZRVu2De5mMug1C46AkA3Z/3uQitaAVIPLKK1utIwm7dAq1R492ihoSnaTj8XGPc2nypdRZlU31fs/5XeuwOhWRjHRie/L2MHvtbGosNUzsPpFnz38Wva6Vans/0WIPkV9+US5sqz6ksbpa5RMnKJ9AfXBUJLV++iExItA39l/xsKgQE/HhyvOa6otTNcYgGP8X5eeNL4M9m8M1e91X/LoJ6Dw1JHqdnufOf44Lul9AjaWG2Wtnsydvj9ZhdRoiGemkduft5p4191BZV8nYhLG8NPGlTrHOPm3SZI7PnMnxGTOxFBYCYC2tH4qvX9rXpKFZS3Z/oXziDI1TPoH6IDFF0z61LfwBXytiVY26HUJioThDec22onFjtMasJSVAQw3J8ZkzW6yx8idGnZGXJ77M2PixVNZVcs+P97A7b7fWYXUKIhnphH7P+Z27f7ibMnMZI2NH8p8L/0OAPkDrsNzKrh4iOh2J818kecli+q5bizE+/szrWMyw8SXl5/F/UT6B+qAt6QUADO0eqW0gXmx4ktLWf2t6ocaROMkU3DA6suGlVkdH1DqS5CWLRS8SIEAfwH8u+g8jYkdQVlvG3T/czbacbVqH5fdEMtLJ/Jr1K/f+eK9tROTtyW8TbAzWOiy3s6uHyJLFREybdmZ9SGO7v1A+aYbEKp88fVCdxcovR/IBmNA/RuNovNf59Y/NL2n5WKw+2hBr9B8hpCsUn2hzdEStIxG9SBTBxmAWTF5gGyG598d72ZS1Seuw/JpIRjqRdRnruH/d/dRYapjQfQJvTnrTrxORDvUQaUl1Cax9Wvn5vAeVT54+aE9WCaXVdYQHGsTISBuGdosgPNBASZWZPSeLtQ7HOaZgGP+g8vPap5XXsL06eS+SYGMwb0x6g/O7nU+1pZrZ62bzU8ZPWoflt0Qy0kl8c/Qb5qyfg9lq5uKeF/PqxFf9emqmau++FutDWuoh0m6NiOqneVBxGqL7NWxK5oM2HM4D4Lx+MaLzahsMeh3j+yqjIxsO52scTQeMuRui+yqv3fXPt3v1xjUk8fV9SEDpRdL4/1HjOhJ/nbYJNATy2oWvcXHPizFbzcxZP4dv07/VOiy/JJIRPyfLMgt2L+Bfv/wLi2xhau+pvDjhRYx6/y5WLVlR30mxtVEPnY6EF18g6vpZbdeIqHL3N2zNfvmLPtdttbGN6hRNv64aR+L9JvRXHqONR/I0jqQDDCa47EXl563/hdwDbV69cQ1J1PWzSHjxhdbrSOr/f/nztI1Rb+TFCS8ytfdU6uQ65m6cyzt73hF72biYSEb8mNlq5vFNj/PmrjcBuH3w7Tx73rMYdP7Z666laRldWMvdRdvsIdKcLMPKv4FsUfb+6HORy2P3lJIqM7syiwE4v79IRtpzfj9lZGRnZjGlvthvRNV3EqRMU17Dq/5uWznWGnt7kejCldVY/j5tY9AZePa8Z7lt8G0AvL7zdZ7c/CRmqw+/JryMf74rCZTVlvHX9X9lc/ZmdJKOf435F7MGztI6LLeo2ruP0y+9ROXWrWdcZlu2q5Kkdv8Qn2HvEsjYBMZgmPJcByLV3uajSjFmn64hdIv0zZVAntQ9KpjeXUNIz6tgU1oBlw5pY/TM2015Do78CCd+gX1fwlkz279Nc83+/zRf/qsKHjuW2L/9za86t+okHX8d/Ve6hXZj3m/z+PLIl2RXZPPyBS8TagrVOjyfJ0ZG/FBmaSa3fHcLm7M3E2QI4vWLXvfbRAQaVsoEjx/f+nAyEHHNNfbXh6iqS+GHR5Sfz/8rRCa5IGLt/Fxf+3C+mKKxmzqd5dNTNQCRPZTXMCiv6Rr7m7k1riOJuOaa1q+o1xMyfrxfr7i5fuD1/OfC/xBkCGLTqU3csvoWMssytQ7L54lkxM9sOLmBWStnkVacRkxQDB9e+qFf7rHQ0pRMzcGDJDw/r8Xr91y6hMTnnrWvPqSxn1+A8lzo0hvOvd9V4WtClmVb8apY0ms/dapmw5E8368TOPd+iOoFZdnw84t236xJL5LnniV56dIWr5fw/DyqDyob6/nz1M0FSRfw4aUfEhMUw5GiI8z6dhYbTm7QOiyfJpIRP2GVrby16y3uW3sfZbVlDO06lIVXLGRw9OD2b+xj7Fopo6qf95YafW+zPqSxrO2wdYHy82UvgsG3Vx8dy68gq7gKo17inN52jgwJnNM7GqNeIrOwihMFlVqH0zHGwIZi1i1vQdYOu2/auI4EtS68WYF4Z1pxMzh6MF9c8QVDY4ZSVlvG7LWzeXvX21hlq9ah+SSRjPiBkpoS5T/C7rcBmDVgFh9N+Yi4kDiNI3OPdlfKAMbu3R1btttcdSks/SNY62DQVdDvYucD9hLqKprRPbsQbBLlYvYKCTAwqqfSjdXnp2oA+l+ivKatdfDlHQ5N16iaL/81du/e+pX9eMVNfEg8H176IbMGzEJG5q3db3H/uvspqXGgn4sAiGTE5/2e8zszv5nJxqyNBOgDePa8Z3nknEf8Zumu2sa9bN1Pdq+U6bnwC/qs+cH+ZbvNyTKsnANFxyEiCaa92vE74gXUN9IJYhWNw9TH7Gdf7jfS2LRXldd2YbqyUsxBzZf/9lnzAz0XLmzxui2tuClbt85v2sqb9CYeOecRnhn/DAH6ADac3MDMb2aKXX8dJD4e+Sizxczru17no30fISOTFJbEyxe8TEp0itahuZRanOrIShnJaGwyLSPZOy2j2r1QWUEj6WHG+xAU5Wz4XqO2zsrmo8p+NGoNhGC/Cf268uLqVDYfzcdssWLU+/jnuKAomPEefHgZ7FkIfS6EYdc7dIjG052SJCEZDeo/7FpxA8poib+suJnedzr9o/rz15//SmZZJnd8fwe3D7md2cNn+82HQ3fy8f9RnVN6cTo3rbqJD/d9iIzMjH4zWDptqc8nIm2NgkghIa5fKdOS/DRYWb/iYOJc6DHW+WN5kR0ZRVTUWogOMTFI7NTrsEEJ4USHmKiotbAzo1jrcFyjxznKaxzg2zlQcLRDh7N7xY1Ohy40BPC/0ZKU6BSWTlvKNf2uQUbmg30fcNOqm0gvTtc6NK8nRkZ8iNlq5uP9H7Ng9wJqLDVEBkTyxLgnmNRzktahuURboyByRUWrt+u5dAnBQ4YgyzKy2Wx/gWpzdTWw9HYwV0Dy+XD+HOeO44XUVTTn94tBJ1rAO0ynkzivXwwrdp1iw+E8xvTqonVIrnH+XyH9Z6X3yNLb4Y4fne4urE7dqCOTUTfeyPGZLfQysVqxliv/n/1xtCTYGMyT5z7JhG4TeHzz4xwsPMh1317HPcPu4dbBt2LUiVGSloiRER+xJ28Ps76dxWs7XqPGUsP4xPF8eeWXPp2IVO3dx7Hrb6B4+QqHRkGab3Dn1EqZ5mQZVj8MOXuUIexr3gFdGzH4GLV4VfQXcd75/tJvpDGdXnmtB0VB9m7l/0AHli/bs+Km5RueOVpSvHwFx6+/wWdHSib1nMSyK5cxPnE8NZYaXtvxGtd/ez178/ZqHZpXEiMjXq68tpzXd77OF4e+QEYmMiCSf5z9D6b2ntrwn95HlaxYQfWuXWTv2tXk922NgugiIzF1707kzJkUL12KOSenY9Myqo0vwbYPAAmuehvCEzt+TC9xLL+CvVnKvL2oF3HehPrHbk9WCcfzK0iOCdE4IheJ6Ka85r+4Aba9r/xbbY7WAeq0jTE+3vb/tfbkSazFxWdeuY3REl8eKYkNjuXtyW/zTfo3zP99PoeLDnPTqpu4YeANPDDyAUKMfvIacgFJ9oEuPqWlpURERFBSUkJ4eOeY77ZYLSxPW87rO1+noFopPLyyz5X8bfTfiAr0vYJKtWV7l1tvxRAbCxJk3nW3rSdBq9RiuPrvPRd+QdCwYUiS1PFpGdWOT+Dr+oZml70IY//UseN5mUeW7+XTLRlcOKArH94+RutwfNptH/7G+tQ8bhnXk6em++YbZKu2LIDV/1R+nv4mjPhDhw9pra21TdvIskzV7j2cuP56h7Zl0HfpQtK774AMdadzKfz4E59sNV9YXchLv7/EN+nfABATFMP9I+5nep/p6P1oFLY5e9+/xciIF9qSvcWWRQP0COvBI+c8wrjEcRpHZj81+VD/aLRVD9KalkZBjPHxHVsp01zqd/DNX5Sfz5vjd4lIQXkNS7adBODuCX00jsb33T2hN+tT81i8LZMHJ/enS4jv7t58hnPugfIc+OX/4OsHIDgGBlzaoUM2X3FjjI+zf7SkXlt1Jc3/znizLoFdeO7855jaZyrPbnmWjLIMHt/0OJ8f/Jy/n/13xib4R7G8s8TIiBdJLUzljZ1vsP7kegDCTGHcO+xerh9wvc8tDct55lmKPv2UsMsvJ/qOP9pGQaSQEOTqarBYWr+xO0dBGsvYCp9cCXXVMPwPMP0N++a3fcirPx7m1R+PcFa3CL6ePd7np/a0Jssy0974hX1Zpcy5uD8PTOqndUiuJcuw4j7Y9RkYguDWryHJtaNpTo+W6HTogoOwllfYRksK3nufsu++I+rmm4n/979cGqc7mS1mvjj0BQv2LKCsVmk6N7H7RGaPmM2ALgM0js617H3/FsmIF0gtTGXB7gX8mPEjAHpJz6wBs7h32L1EBkZqG5wdnJ6CacbUpw9dbr7ZNgrSa+kSx5qVOeLkdvj0Gqguhn5T4PrPQe9fA4XVZgvnPr+Owopa/nPDCK4c5j91MFpasSuLvyzcRXSIiV8fvohAo58NsVvMsPBGOPKDUtj6hy+h2yi3nc6ck8OxmdfaRksK//c/ao86tszYV6dyiqqLWLB7AYtSF2GRlQ9oF/e8mD8N/ZPfJCUiGfEBhwoP8c6ed1hzYg0AEhJTkqdw7/B76R3RW+PoWtd8aFQdBXFY83oQVy3Rbc+hVUqr97oq6D4GblkBpmD3nEtDn209wb+/2ke3yCB+/vtEDL7eqMtL1FmsXDB/PVnFVcy75ixuGNND65Bcr7YCPpkOJ38HYzDM/LDDUzZtaTxaUrVvv7Ik2IG6kpaooyW+MJWTXpLO27ve5vvj3yOj3Gd/SUrsff8Wf508zCpb2XByA3d+fyfXfnMta06sQULi0uRLWXblMuZfMN+rEhG1EVnj5XVq/UfBhx86tiS3EV1kpG1fC7VZmTFGWa3QoSW67fn9PVh0k5KI9J0MN3/ll4mIxSrz3sZjANxxXi+RiLiQQa/jj+f1AuDdjelYrV7/ec5xphDl/0bfyWCuhIU3wO/vu+10jZcEG2Ka7nsTOGQIushIOw905hLhgg8+oHLr1iZ747T0d01LvSN6M/+C+Sy7chlTkqcgIbHmxBpmfjOTO3+4kw0nN/j9BnxiZMRDKs2VfJv+Lf878D+Olx4HlOmYS3pewt1D76ZvVF9tA6zX2qhH+NVX0+Wmm5yegvHIqpi2WK2w7imlOA9gxM0w9f/Ax2px7PX9/hz+9L/thAca2DR3EqEB/jUFpbXymjrGzVtLWXUd794ymosH+eemlFjM8O1DsPN/yr/PmwOTHnN7bZUrVuE013gqp+izTyn5arnXjp4cKTrCu3ve5YcTP9imb5LDk7l50M1M7T2VYKPvfIAS0zReQJZl9ubvZdmRZXx37Dsq65Ttx0ONoczsP5MbB95IQmiCZvG19B+wtcJThzX7o+HRepDmKgvhmwfgoLKkjgv/DRP+7nfFqo3NfHsT204Uce/EPvzz0oFah+OXnv/uEAt+PsqY5C4svsd3Vro5TJbh5xdh/XPKv1OuhGmvQbDnutC2WlfSwamctgphvSFByS7P5vNDn7P08FLKzeUABBuCuazXZczoN4MhMUO8vihdJCMayq3IZfXx1SxPW05acZrt98nhyVw/8Hqu6nuVx5vdtJV4dHjUo5nmS3Jrs7PptXQJpoQEz4yCNHZkjbI6oDwXdAaY9h8YcZNnzq2R7SeKmPH2Jox6iV//eRGx4YFah+SXckurOe+FdZgtMl/9+VxG9PC9/j8O2fmpsgzeWgeh8crqs34Xe+z0jUdLarOzOT7zWowJCXYvEbZHW6MnoF2CUmGu4KsjX7EwdSEnSk/Yft8vqh9X9bmKKclTiAvxztE5kYx4WGF1IT+e+JFVx1axI3eHrQgpQB/AJT0vYUb/GYyMHemxLNZt0y0t0XoKpiU15fDDI7D9Q+XfMf3h6v9Ct5GejcPDKmvruOatTRzKKWPmqO68dO0wrUPya39bspul208yKCGcpfeOI9jk59NhWTvgqz9BvtIDidF/hIufhoBQj4fijqmclnjT9I4sy2zP3c6yI8v44cQP1FhqAGXxw6i4UVzW6zIm95xMl0Dv2TtJJCMecLzkOD+f/Jn1mevZeXqnbW4PYETsCC7vdTmX976ccJNrY27+H8Ct0y0t8aYpmOZkGQ6tVBKRIqWAk7H3wuTHwRikTUweIssy932+g1V7c4gJDeCb+8eTEOHf91lr2SVVTHv9F/LLa7liaAJv3DDC64fNO8xcBT8+AVsXKP+O6gWXPAMDr9B06rPdqRwXJimOTO+4K2EpqSlh1bFVfHfsO3ae3tkQm6RnROwIJiZNZGLSRHqG93TZOZ0hkhE3qDRXsi13G1uzt7Lh5AZbIapqUPQgLku+jCnJU5yuBXEk0VD/A3hy1MOrpmCaO/6r8kfy5G/Kv8O7wVVvQe+J2sTjYW+sO8JLPxzGqJf44q5zGJ3sPZ+O/Nnvxwu58d0tmC0yf58ygPsu9I5idLc7+pMyBVqapfy7+xiY/AQkj9csJLuncjw4etL87zW4PmE5VX6K749/z3fHvuNg4cEmlyWHJzOh+wTGJoxldNxojxe/imTEBSrMFezN38v23O1szd7K3ry91Ml1tssNOgOj40YzMWkiE7pPICksqc3juTrR0IWHE/foI+Q+8yzWkhLX3Ol2Rj2SP/8MY1KStlMwzZ3aCT89pzRpAqUvwjn3wvi/QGCEtrF5yJoDudz1yTYA/+194cW++C2Ducv2Iknw7s2jmeyvq2uaqy6BX1+DzW8py+VBaSJ44VxIHKFtbJw5lVObeZITN97okdGT5n+fPZWwZJZl8nPmz/x88me25W6jztroPUsycFbXsxibMJZRcaMYGjPU7cmJSEYcZJWtHC89zoGCA+w+vZvdebtJLUo9Y213t9BuXFbdnwlfH6fnw4/QZcSYFl8kXploNOdLox7N1VbC/mVK74NTO5Tf6Qww8la44B8QptE0kQaO5JZx9VubKK+p888N3HzEo8v38b8tJwgNMLD8vnPpGxumdUieU5YDP78A2z8Gdbq62yilpmTwNV7Vy8fh0RMXj6I05uqEpfnv6gb0ZNv6hUhvfcpnF+rYEpnf9PySjgFRAxjWdRgjYkcwLnGcyzdiFcmIHX5c/V9M/13Iyku7sD70JJV1lfTOlrnpJwufXagnPUFibHE0N6yro/ru6xg24Rq6h3VvNalo/CLRPNForp3Ew2tHPRqzWpSOkPuWwe6FUFP/2OlNMPhquOCfEN25NoPblVnM/V/sILOwirG9uvDpnWMxigZnmjBbrNz03lZ+O1ZIUpcg3rhhJMOSIrUOy7MKjipJyb5lYDUrvwuIgGHXw5BroPvZ4GU71LY3euLO6R17OJKwQOsfeqNuvpm6B25ha/ZW0rZ8z8AvfuODC+pIT5Bs73s9/vkI51zk2tWGIhmxw4d3jOecXwtZNVrio4v1BOoDuX99EGf/kkfZ9An0eOxJ+L/37U4qdBERxP37X4DE6eeew9LBZWYd4ovTLS2pKYf0n5TddQ+vhsqChsuikmHU7cpW5yExmoWohYqaOl7+4TAfbTqGVYbuUUGsuG880aEBWofWqeWX13DVm79ysqgKnQS3nduLv17Sn5DO1nSuPE/ZbG/7h1B0vOH3wdHQ/1IYcBn0vlCTVTj20HJ6xxktvffYk8QEXH8NR26bQN3L/6X3moOE3jiLpMeecGlsbk1G3nzzTebPn09OTg7Dhg3j9ddfZ8yY1nd2XLJkCY8++ijHjx+nX79+vPDCC1x++eV2n8+VyYg5K4u6omKQ4Mgfb8FYUkldWDABf72HrkFdyZv3QotPpFdp/sL35emWxmQZijOU0Y/MrcpXzr6GYV9QakD6TYFhs6D3RaDrfKMAP6We5pGv9pFVrMzRXzU8kUenDhKJiJfIL6/h6W8PsGLXKQC6RQbx7NVDmDggVuPINGC1Qvo62L0Ijnyv1JioJD3ED4GkscpX97MhsofXNiN0ujjWSxKWlrSVsBiiIjF269bhc7gtGVm0aBG33HILCxYsYOzYsbz66qssWbKE1NRUYmPP/M+2adMmJkyYwLx585g6dSqff/45L7zwAjt27GDIEPvmtl2ZjBwcmNKh27udA4lG0RdfUHP4MAH9+xN1ww2+M+pRUwbFmVCSCflHIO9Q/Vcq1JSeef2oZBhwufLV4xy/beHeGlmWSTtdzg8HcllzIJddmcWA8ib33DVncUH/rtoGKLSoedI4okckFw+K4+KUOPrGhvr/EuDmLGbI2KyMcqauajpiogoIh64DoOtA5SumH0QkQWQSBHhXDU57oyct/X32tYQl5dDB9q/UDrclI2PHjuXss8/mjTfeAMBqtZKUlMT999/Pww8/fMb1Z82aRUVFBd9++63td+eccw7Dhw9nwYIFLr0z9ij55htOPTwXLJb2r+xqbkg0LBUV6ENCtEk8LGZld09zFdSWQ3WpUsdRXaokFZUFUJEPFXnK9/LTUJLR9NNRczoDJAyr/7Q0RlkuGNHx7NxXVJstnCyqIqOwghMFlaTnVbDhSB4nCipt19FJ8MfxvZhzSX//b7Ll4ypq6nhlzWE+/FWZTlP1jA7mgv5d6RUTQs/oYHp0CaF7VBCBRu+qp3CrkpOQ+Vv911bI2aN0d21NYARE9IDQWGVaNqSr8j04WkliAsOV+pTAcDCFKn2FTCEe/fDSPEFp/vfZZxIWvZ7E5+cRMW1ahw/llmSktraW4OBgli5dylVXXWX7/a233kpxcTErVqw44zY9evRgzpw5PPjgg7bfPf744yxfvpzdu3e3eJ6amhpqamqa3JmkpCSX1YxU7d/P8Rkznb69DEhtfG/pOtZAA9bwQMyD4zDuyUZfUIk1OpjaofEY9+eiK6+lYuYQCA9seLGZLWDU1x9IBqsMeh0Ssu0sktwQlfL7+u8ytn8r11N+L2Gt/9mq/Fu2opPr0GGt/9mCTragl+vQyXXo5Tr0shm9bMZgNaOXazFYazFaq9HjfEJXqQ+nxBRHUUA38oJ6czqwF3lBvSgISMKq856RD3v/d8iy3PCsyCAj254yWZaxyjJ1Vpk6i4zZYsVskam1WKmsqaOsuo7ymjpKq82U19S1eE6TQcf4PtFMHhTH5JQ44kSLd5+SW1rNjwdz+fFALr8eLaC27swdWCUJQgMMhAcaCQ0wEBZoIDjAgEmvw6iXMOp1GPQSBp2ETpKU3a0l5XYSkm12QwK7R128aXBGZzUTXZNB16rjxFYfo2tVOlE1WUTU5hJsaWHE1E4W9Jh1gdTpTFgkE3U6IxZJ/TJglQz13/VYJT2ypMOKDqtkQJZ09X9RdchS/XckZEmi/pFGRgJJ+ctr+3f9O4Es0fCz+u5gsSqfKCRds7/zSsIilVYTsnQf1lAT5sHxzd4vEpT3i9JqdNV1dr33tHSd9iR/uZSgwYOdfswbszcZcehjVX5+PhaLhbi4pmvo4+LiOHToUIu3ycnJafH6OTk5rZ5n3rx5PPnkk46E1kH2P5U6kwVTqIXI3pUUpQVTU2wkMNJMVN9KitODqa3QIwHGEOU6xenBmCv19JmUgzHUiiQdRp6sDCrojSBJacixIFtBV3sC8luKz7tZZIkKAikjmFI5mDKCKZODKSKMfDmcAjmcQjmcfCLIkqPJlqOpIAgqWjpalqfD9zohJj09okPo2SWYntHBjOgRyfn9una+Ikg/EhceyE1je3LT2J5U1NSx4XAeuzKLOVFQyYnCSjIKKqiotVBWrSSnnZce6FP/1SCEKhKkArpJBcRQQrRUQhepjBiplCjKCJMqCaOS8PrvIVSjr/+kpseC3loB1hb/4Hgl6xVKriJJR1p8v6gt13FibVeMwQ3vM83fe1p8fyrXY63V0+r7nIbTRF75123u3LnMmTPH9m91ZMRVDNHR6GNiqAmA2pSumPZmoSupAgn+v737C4mi3eMA/p0Z3VVzk5Od9Jha5it40R9BW7PgaCDZizd2UfISpBJCsIbiRZmEXhphJEiU3VRUYiCkBCEsHrKbIsu6SDAQjimKuvUedd89b67NzLnYdXNfzaOU+ziz3w8sOs+O8PVx3fk9zzw7o22NhnffTljejUH59AfUv9vgPZDi28f9BbO/2aHHRWNSkoB/6sC8ClgVTC1uq/4/pCL59wGg6vgcsfaFliu/FBaHPX+pspfMk0CSgqvzQLUu+/aRJGhQAhU+/F99IwIZOnyjgsXRQmDEIEf4RxQW32hCtmBBtmJBicZXOQqqFLnqEMsCINH/MMsVMKQVxhi+Eeq3DVkC5MDoVYIi+0a3FkVChCIjUpERa1UQa41EbFQEYq0R+FtMJLZtsYTfeoIwssUagV/3/QO/7vt2lWZd1/G7x4v//Nc3O/bHl69wf1mAx6tiQdXwVdXg9c+qqZrun20DNP/XxQPIkm+D6N95VzEyt//x75We1HX/bO4XRKp/IlKbh6J5/W1eKLoXsvY1MAO8OAss6RokqJD9s8QSNED/NqMsQ/VvA/DPLi+dhfZt+/p68Z3a970eyIUl7Uut+z/+Fw1Q8O1YtOzYs/z4JM38CVvHK2i2qBWPc7bRBeD3GUTEx683zQ9bVzGyfft2KIqCqampoPapqSkkfudeJImJievaHwCsVius1o37ZEBkYiJ++Vdv0Lk9fcH3mfjVzvdtuoWgRGQKkiQhPtbKT0TRhtNqVl/XIuo4t67PRVosFmRnZ6O3tzfQpmkaent7kZeXt+LP5OXlBe0PAE6n87v7h4ps+Tb6lCQJssWyrC0iNnbZPkREREa1WY9z6z5NU1tbi7KyMuTk5MBut6OlpQUejwcVFRUAgDNnzmDnzp1oamoCAFRXVyM/Px/Xrl1DcXExOjo68Pr1a9y+ffvn/iZERERkSOsuRkpLS+FyudDQ0IDJyUlkZWWhp6cnsEh1dHQU8pILUR0+fBjt7e24fPky6uvrkZGRga6urjVfY4SIiIjMLawvB09EREQbZ63H7/C7ljYRERFtKixGiIiISCgWI0RERCQUixEiIiISisUIERERCcVihIiIiIRiMUJERERCsRghIiIioViMEBERkVDrvhy8CIsXiZ2bmxOchIiIiNZq8bj9/y72bohixO12AwBSUlIEJyEiIqL1crvdiIuL++7zhrg3jaZpmJiYgM1mC9zqOJzNzc0hJSUFY2NjvFfPBmNfhw77OnTY16ET7n2t6zrcbjeSkpKCbqL7V4aYGZFlGcnJyaJjbDpbt24Nyxe3COzr0GFfhw77OnTCua9XmxFZxAWsREREJBSLESIiIhKKxYgBWa1WNDY2wmq1io5ieuzr0GFfhw77OnTY12tjiAWsREREZF6cGSEiIiKhWIwQERGRUCxGiIiISCgWI0RERCQUixGTmJ+fR1ZWFiRJwrt370THMZ2RkRGcPXsWaWlpiI6ORnp6OhobG+H1ekVHM4UbN25g9+7diIqKQm5uLl69eiU6kik1NTXh4MGDsNls2LFjB0pKSvDhwwfRsUzvypUrkCQJNTU1oqNsWixGTOLChQtISkoSHcO0hoaGoGka2traMDg4iOvXr+PWrVuor68XHc3wHj16hNraWjQ2NmJgYAAHDhxAUVERpqenRUcznb6+PjgcDrx8+RJOpxMLCws4duwYPB6P6Gim1d/fj7a2Nuzfv190lM1NJ8N7+vSpnpmZqQ8ODuoA9Ldv34qOFBauXr2qp6WliY5heHa7XXc4HIFtVVX1pKQkvampSWCq8DA9Pa0D0Pv6+kRHMSW3261nZGToTqdTz8/P16urq0VH2rQ4M2JwU1NTqKysxP379xETEyM6TliZnZ3Ftm3bRMcwNK/Xizdv3qCwsDDQJssyCgsL8eLFC4HJwsPs7CwA8HW8QRwOB4qLi4Ne37QyQ9woj1am6zrKy8tx7tw55OTkYGRkRHSksDE8PIzW1lY0NzeLjmJonz59gqqqSEhICGpPSEjA0NCQoFThQdM01NTU4MiRI9i7d6/oOKbT0dGBgYEB9Pf3i45iCJwZ2YTq6uogSdKqj6GhIbS2tsLtduPSpUuiIxvWWvt6qfHxcRw/fhwnT55EZWWloOREP8bhcOD9+/fo6OgQHcV0xsbGUF1djYcPHyIqKkp0HEPg5eA3IZfLhc+fP6+6z549e3Dq1Ck8efIEkiQF2lVVhaIoOH36NO7du7fRUQ1vrX1tsVgAABMTEygoKMChQ4dw9+5dyDLr+R/h9XoRExODzs5OlJSUBNrLysowMzOD7u5uceFMrKqqCt3d3Xj+/DnS0tJExzGdrq4unDhxAoqiBNpUVYUkSZBlGfPz80HPEYsRQxsdHcXc3Fxge2JiAkVFRejs7ERubi6Sk5MFpjOf8fFxHD16FNnZ2Xjw4AHfTH6S3Nxc2O12tLa2AvCdPkhNTUVVVRXq6uoEpzMXXddx/vx5PH78GM+ePUNGRoboSKbkdrvx8ePHoLaKigpkZmbi4sWLPC22Aq4ZMbDU1NSg7djYWABAeno6C5GfbHx8HAUFBdi1axeam5vhcrkCzyUmJgpMZny1tbUoKytDTk4O7HY7Wlpa4PF4UFFRITqa6TgcDrS3t6O7uxs2mw2Tk5MAgLi4OERHRwtOZx42m21ZwbFlyxbEx8ezEPkOFiNEa+B0OjE8PIzh4eFlhR4nF39MaWkpXC4XGhoaMDk5iaysLPT09Cxb1Eo/7ubNmwCAgoKCoPY7d+6gvLw89IGI/HiahoiIiITi6jsiIiISisUIERERCcVihIiIiIRiMUJERERCsRghIiIioViMEBERkVAsRoiIiEgoFiNEREQkFIsRIiIiEorFCBEREQnFYoSIiIiEYjFCREREQv0Pku98kSrFO1sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_contracted_gaussian(load('sto3G', 'H'), label=\"sto3g\")\n",
        "\n",
        "plot_slater()\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "oJ57KPkJ18E9",
        "outputId": "750422f5-4226-4e1f-9bae-cfa83a10590a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d6ffe689090>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's plot the stimulated UV-Vis spectrum for our molecule by exciting the molecule in ground state to higher state using TDDFT and extracting useful information."
      ],
      "metadata": {
        "id": "VFABNZsk46xS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6trHutDhAAt3",
        "outputId": "d2fe31d8-401d-43f1-e690-82b11fdb2e53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converged SCF energy = -76.8109595355458\n",
            "Excited State energies (eV)\n",
            "[ 8.06872931  8.81553346  9.12604748  9.65914599  9.79000743 10.51266878\n",
            " 10.82245006 11.36011213 11.76091283 12.14941431 12.41714139 12.65404262\n",
            " 13.24901065 13.83207306 14.18655195]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.06873 eV    153.66 nm  f=0.0000\n",
            "Excited State   2:    A      8.81553 eV    140.64 nm  f=0.3415\n",
            "Excited State   3:    A      9.12605 eV    135.86 nm  f=0.0002\n",
            "Excited State   4:    A      9.65915 eV    128.36 nm  f=0.0000\n",
            "Excited State   5:    A      9.79001 eV    126.64 nm  f=0.0000\n",
            "Excited State   6:    A     10.51267 eV    117.94 nm  f=0.0000\n",
            "Excited State   7:    A     10.82245 eV    114.56 nm  f=0.0007\n",
            "Excited State   8:    A     11.36011 eV    109.14 nm  f=0.0000\n",
            "Excited State   9:    A     11.76091 eV    105.42 nm  f=0.4020\n",
            "Excited State  10:    A     12.14941 eV    102.05 nm  f=0.1957\n",
            "Excited State  11:    A     12.41714 eV     99.85 nm  f=0.0000\n",
            "Excited State  12:    A     12.65404 eV     97.98 nm  f=0.0029\n",
            "Excited State  13:    A     13.24901 eV     93.58 nm  f=0.1027\n",
            "Excited State  14:    A     13.83207 eV     89.64 nm  f=0.3126\n",
            "Excited State  15:    A     14.18655 eV     87.40 nm  f=0.0052\n",
            "converged SCF energy = -78.449627473771\n",
            "Excited State energies (eV)\n",
            "[ 8.33950203  8.79485488  9.35478142  9.81613007  9.94753506 10.64530778\n",
            " 11.30190082 11.53835323 12.16994186 12.54725397 12.65401386 13.04906412\n",
            " 13.58286724 14.13753204 14.64925264]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.33950 eV    148.67 nm  f=0.0000\n",
            "Excited State   2:    A      8.79485 eV    140.97 nm  f=0.3491\n",
            "Excited State   3:    A      9.35478 eV    132.54 nm  f=0.0001\n",
            "Excited State   4:    A      9.81613 eV    126.31 nm  f=0.0000\n",
            "Excited State   5:    A      9.94754 eV    124.64 nm  f=0.0000\n",
            "Excited State   6:    A     10.64531 eV    116.47 nm  f=0.0000\n",
            "Excited State   7:    A     11.30190 eV    109.70 nm  f=0.0009\n",
            "Excited State   8:    A     11.53835 eV    107.45 nm  f=0.0000\n",
            "Excited State   9:    A     12.16994 eV    101.88 nm  f=0.3863\n",
            "Excited State  10:    A     12.54725 eV     98.81 nm  f=0.2055\n",
            "Excited State  11:    A     12.65401 eV     97.98 nm  f=0.0000\n",
            "Excited State  12:    A     13.04906 eV     95.01 nm  f=0.0035\n",
            "Excited State  13:    A     13.58287 eV     91.28 nm  f=0.1146\n",
            "Excited State  14:    A     14.13753 eV     87.70 nm  f=0.3335\n",
            "Excited State  15:    A     14.64925 eV     84.64 nm  f=0.0074\n",
            "converged SCF energy = -78.5369833462775\n",
            "\n",
            "WARN: NLC functional found in DFT object.  Its second deriviative is not available. Its contribution is not included in the response function.\n",
            "\n",
            "Excited State energies (eV)\n",
            "[ 8.72246056  8.78133246 10.02782974 10.15985375 10.42596633 11.52988722\n",
            " 12.20222879 12.4516063  13.42011568 13.63734104 13.9803715  14.40282662\n",
            " 14.82365495 15.38457    15.55999722]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.72246 eV    142.14 nm  f=0.4160\n",
            "Excited State   2:    A      8.78133 eV    141.19 nm  f=0.0000\n",
            "Excited State   3:    A     10.02783 eV    123.64 nm  f=0.0001\n",
            "Excited State   4:    A     10.15985 eV    122.03 nm  f=0.0000\n",
            "Excited State   5:    A     10.42597 eV    118.92 nm  f=0.0000\n",
            "Excited State   6:    A     11.52989 eV    107.53 nm  f=0.0000\n",
            "Excited State   7:    A     12.20223 eV    101.61 nm  f=0.0000\n",
            "Excited State   8:    A     12.45161 eV     99.57 nm  f=0.0030\n",
            "Excited State   9:    A     13.42012 eV     92.39 nm  f=0.4793\n",
            "Excited State  10:    A     13.63734 eV     90.92 nm  f=0.0000\n",
            "Excited State  11:    A     13.98037 eV     88.68 nm  f=0.4947\n",
            "Excited State  12:    A     14.40283 eV     86.08 nm  f=0.0065\n",
            "Excited State  13:    A     14.82365 eV     83.64 nm  f=0.3593\n",
            "Excited State  14:    A     15.38457 eV     80.59 nm  f=0.0000\n",
            "Excited State  15:    A     15.56000 eV     79.68 nm  f=0.3648\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ha_2_ev = 1 / physical_constants[\"electron volt-hartree relationship\"][0]\n",
        "\n",
        "def gaussian(x, mu, sig):\n",
        "    return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))\n",
        "\n",
        "def run_spectral_analysis(mol, xc=\"lda\"):\n",
        "    n_states = 15\n",
        "    spectral_width = 0.1\n",
        "\n",
        "    mf = dft.RKS(mol, xc=xc).run()\n",
        "\n",
        "    mytd = tddft.TDDFT(mf)\n",
        "    mytd.nstates = n_states\n",
        "    mytd.max_space = 100\n",
        "    mytd.max_cycle = 200\n",
        "    mytd.kernel()\n",
        "    mytd.analyze()\n",
        "    osc_strengths = mytd.oscillator_strength()[:n_states - 5]\n",
        "\n",
        "    energies_ev = mytd.e[:n_states - 5] * ha_2_ev\n",
        "    wavelengths_nm = 1240 / energies_ev\n",
        "\n",
        "    x_range = np.linspace(wavelengths_nm.min() * 0.9, wavelengths_nm.max() * 1.1, num=1000)\n",
        "    intensity = np.zeros(x_range.size)\n",
        "\n",
        "    for e, f in zip(wavelengths_nm, osc_strengths):\n",
        "        intensity += gaussian(x_range, e, spectral_width) * f\n",
        "\n",
        "    # Rough Normalization\n",
        "    dx = (x_range[-1] - x_range[0]) / x_range.size\n",
        "    area = (intensity * dx).sum()\n",
        "    intensity /= area\n",
        "\n",
        "    return x_range, intensity\n",
        "\n",
        "\n",
        "mol = gto.Mole()\n",
        "mol.build(\n",
        "    atom='resources/sample.xyz',\n",
        "    basis='631g',\n",
        "    symmetry=True\n",
        ")\n",
        "\n",
        "data = {\"Wavelength (nm)\": []}\n",
        "\n",
        "xcs = [\"lda\", \"pbe\", \"wB97X_V\"]\n",
        "\n",
        "for xc in xcs:\n",
        "    x_range, intensity = run_spectral_analysis(mol, xc=xc)\n",
        "    data[\"Wavelength (nm)\"] = x_range\n",
        "    data[f\"Intensity ({xc.upper()})\"] = intensity\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "for xc in xcs:\n",
        "    plt.plot(df[\"Wavelength (nm)\"], df[f\"Intensity ({xc.upper()})\"], label=xc)\n",
        "\n",
        "plt.xlabel('Wavelength (nm)')\n",
        "plt.ylabel('Intensity')\n",
        "plt.title('UV-Vis Spectra for Different Exchange-Correlation Functionals')\n",
        "plt.legend()\n",
        "plt.savefig(f\"Result:DFT_{smiles_representation}/uv spectrum.jpg\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's plot Intensity Vs Excited Energy, using the information extracted by exciting the molecule to higher state by running TDDFT calculations in the previous cell."
      ],
      "metadata": {
        "id": "KgFgnx6355h6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tbjgzAh08_Hb",
        "outputId": "afd87910-51b7-4810-b740-c18952916943"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "converged SCF energy = -76.8109595355458\n",
            "Excited State energies (eV)\n",
            "[ 8.06872931  8.81553346  9.12604748  9.65914599  9.79000743 10.51266878\n",
            " 10.82245006 11.36011213 11.76091283 12.14941431 12.41714139 12.65404262\n",
            " 13.24901065 13.83207306 14.18655195]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.06873 eV    153.66 nm  f=0.0000\n",
            "Excited State   2:    A      8.81553 eV    140.64 nm  f=0.3415\n",
            "Excited State   3:    A      9.12605 eV    135.86 nm  f=0.0002\n",
            "Excited State   4:    A      9.65915 eV    128.36 nm  f=0.0000\n",
            "Excited State   5:    A      9.79001 eV    126.64 nm  f=0.0000\n",
            "Excited State   6:    A     10.51267 eV    117.94 nm  f=0.0000\n",
            "Excited State   7:    A     10.82245 eV    114.56 nm  f=0.0007\n",
            "Excited State   8:    A     11.36011 eV    109.14 nm  f=0.0000\n",
            "Excited State   9:    A     11.76091 eV    105.42 nm  f=0.4020\n",
            "Excited State  10:    A     12.14941 eV    102.05 nm  f=0.1957\n",
            "Excited State  11:    A     12.41714 eV     99.85 nm  f=0.0000\n",
            "Excited State  12:    A     12.65404 eV     97.98 nm  f=0.0029\n",
            "Excited State  13:    A     13.24901 eV     93.58 nm  f=0.1027\n",
            "Excited State  14:    A     13.83207 eV     89.64 nm  f=0.3126\n",
            "Excited State  15:    A     14.18655 eV     87.40 nm  f=0.0052\n",
            "converged SCF energy = -78.449627473771\n",
            "Excited State energies (eV)\n",
            "[ 8.33950203  8.79485488  9.35478142  9.81613007  9.94753506 10.64530778\n",
            " 11.30190082 11.53835323 12.16994186 12.54725397 12.65401386 13.04906412\n",
            " 13.58286724 14.13753204 14.64925264]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.33950 eV    148.67 nm  f=0.0000\n",
            "Excited State   2:    A      8.79485 eV    140.97 nm  f=0.3491\n",
            "Excited State   3:    A      9.35478 eV    132.54 nm  f=0.0001\n",
            "Excited State   4:    A      9.81613 eV    126.31 nm  f=0.0000\n",
            "Excited State   5:    A      9.94754 eV    124.64 nm  f=0.0000\n",
            "Excited State   6:    A     10.64531 eV    116.47 nm  f=0.0000\n",
            "Excited State   7:    A     11.30190 eV    109.70 nm  f=0.0009\n",
            "Excited State   8:    A     11.53835 eV    107.45 nm  f=0.0000\n",
            "Excited State   9:    A     12.16994 eV    101.88 nm  f=0.3863\n",
            "Excited State  10:    A     12.54725 eV     98.81 nm  f=0.2055\n",
            "Excited State  11:    A     12.65401 eV     97.98 nm  f=0.0000\n",
            "Excited State  12:    A     13.04906 eV     95.01 nm  f=0.0035\n",
            "Excited State  13:    A     13.58287 eV     91.28 nm  f=0.1146\n",
            "Excited State  14:    A     14.13753 eV     87.70 nm  f=0.3335\n",
            "Excited State  15:    A     14.64925 eV     84.64 nm  f=0.0074\n",
            "converged SCF energy = -78.5369833462775\n",
            "\n",
            "WARN: NLC functional found in DFT object.  Its second deriviative is not available. Its contribution is not included in the response function.\n",
            "\n",
            "Excited State energies (eV)\n",
            "[ 8.72246057  8.78133246 10.02782974 10.15985375 10.42596633 11.52988722\n",
            " 12.20222879 12.4516063  13.42011568 13.63734104 13.9803715  14.40282662\n",
            " 14.82365495 15.38457    15.55999728]\n",
            "\n",
            "** Singlet excitation energies and oscillator strengths **\n",
            "Excited State   1:    A      8.72246 eV    142.14 nm  f=0.4160\n",
            "Excited State   2:    A      8.78133 eV    141.19 nm  f=0.0000\n",
            "Excited State   3:    A     10.02783 eV    123.64 nm  f=0.0001\n",
            "Excited State   4:    A     10.15985 eV    122.03 nm  f=0.0000\n",
            "Excited State   5:    A     10.42597 eV    118.92 nm  f=0.0000\n",
            "Excited State   6:    A     11.52989 eV    107.53 nm  f=0.0000\n",
            "Excited State   7:    A     12.20223 eV    101.61 nm  f=0.0000\n",
            "Excited State   8:    A     12.45161 eV     99.57 nm  f=0.0030\n",
            "Excited State   9:    A     13.42012 eV     92.39 nm  f=0.4793\n",
            "Excited State  10:    A     13.63734 eV     90.92 nm  f=0.0000\n",
            "Excited State  11:    A     13.98037 eV     88.68 nm  f=0.4947\n",
            "Excited State  12:    A     14.40283 eV     86.08 nm  f=0.0065\n",
            "Excited State  13:    A     14.82365 eV     83.64 nm  f=0.3593\n",
            "Excited State  14:    A     15.38457 eV     80.59 nm  f=0.0000\n",
            "Excited State  15:    A     15.56000 eV     79.68 nm  f=0.3648\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC/mElEQVR4nOz9eZxbZd0//r9O9sxktnZmuq+0UEqhBUQoKIuCiOIueqOyI6CAIn7kI5/7J4obX/EWUOSW2wUK3C6IKCqgUPZ93/fSfZvOdPbsyzm/P65znSSdZHKSnOScZF5PHzUzSSa52tKc8z7v5VI0TdNARERERERERbnsXgAREREREZHTMXAiIiIiIiIqgYETERERERFRCQyciIiIiIiISmDgREREREREVAIDJyIiIiIiohIYOBEREREREZXAwImIiIiIiKgEBk5EREREREQlMHAiIpqiFi5ciNNPP92W9/7e974HRVFsee9iNm3aBEVRsGbNGruXQkREDsTAiYioybz66qv47Gc/iwULFiAQCGDOnDk47rjjcO2119q9NEv893//t+3BzaZNm3DGGWdgr732QiAQwMyZM3HkkUfiu9/9rq3rkn784x/jjjvusHsZRERNRdE0TbN7EUREZI0nnngCxxxzDObPn4/TTjsNM2fOxNatW/HUU09h/fr1ePfdd43nJhIJuFwueL3euq/ze9/7Hi6//HJUcghasWIFuru78dBDD1m6pk2bNmHRokW48cYbJ83EvfvuuzjkkEMQDAZx5plnYuHChdi5cydeeOEF/Otf/0I8Hrd0XZUIhUL47Gc/a3uASUTUTDx2L4CIiKzzox/9CB0dHXj22WfR2dmZ91h/f3/e936/v44rax5XX301wuEwXnrpJSxYsCDvsT3/jBtBJBJBa2ur3csgInI8luoRETWR9evXY7/99psQNAFAb29v3vd79jitWbMGiqLgsccew9e+9jX09PSgs7MT5557LpLJJEZGRnDqqaeiq6sLXV1duOSSS/IyRg899BAURZmQCTLbO3TjjTfiAx/4AHp7e+H3+7F8+XL86le/mrDm119/HQ8//DAURYGiKDj66KONx0dGRnDRRRdh3rx58Pv9WLJkCX7yk59AVdW81xkZGcHpp5+Ojo4OdHZ24rTTTsPIyMik65PWr1+PuXPnTgiagMJ/xieeeCLuvfderFq1CoFAAMuXL8df//rXCT9rdu2qquLnP/859t9/fwQCAfT09ODDH/4wnnvuOQCAoiiIRCK46aabjD8j+fcse8veeOMNfOELX0BXVxfe9773AQBeeeUVnH766Vi8eLFRfnjmmWdicHDQ1J8LEVGzY8aJiKiJLFiwAE8++SRee+01rFixoqLXuPDCCzFz5kxcfvnleOqpp/DrX/8anZ2deOKJJzB//nz8+Mc/xt13342f/vSnWLFiBU499VRL1v6rX/0K++23Hz7+8Y/D4/Hgn//8J7761a9CVVWcf/75AIBrrrkGF154IUKhEP7zP/8TADBjxgwAQDQaxVFHHYXt27fj3HPPxfz58/HEE0/g0ksvxc6dO3HNNdcAADRNwyc+8Qk89thjOO+887Dvvvvib3/7G0477TRT61ywYAHuu+8+PPDAA/jABz5Q8vnr1q3D5z//eZx33nk47bTTcOONN+Kkk07Cv//9bxx33HFlrR0AzjrrLKxZswYnnHACzj77bKTTaTz66KN46qmn8J73vAe33HILzj77bLz3ve/FOeecAwDYa6+98tZ00kknYenSpfjxj39sBL9r167Fhg0bcMYZZ2DmzJl4/fXX8etf/xqvv/46nnrqKccN8yAiqjuNiIiaxr333qu53W7N7XZrq1ev1i655BLtnnvu0ZLJ5ITnLliwQDvttNOM72+88UYNgHb88cdrqqoa969evVpTFEU777zzjPvS6bQ2d+5c7aijjjLue/DBBzUA2oMPPpj3Phs3btQAaDfeeKNx33e/+11tz0NQNBqdsMbjjz9eW7x4cd59++23X977Sj/4wQ+01tZW7Z133sm7/9vf/rbmdru1LVu2aJqmaXfccYcGQLvyyivzfj/vf//7J6yzkNdee00LBoMaAG3VqlXa17/+de2OO+7QIpHIhOcuWLBAA6Ddfvvtxn2jo6ParFmztAMPPLDstT/wwAMaAO1rX/vahPfK/TtrbW3N+7uV5J/7ySefPOGxQn/+f/zjHzUA2iOPPFLgT4KIaGphqR4RURM57rjj8OSTT+LjH/84Xn75ZVx55ZU4/vjjMWfOHPzjH/8w9RpnnXVWXnbh0EMPhaZpOOuss4z73G433vOe92DDhg2WrT0YDBpfj46OYvfu3TjqqKOwYcMGjI6Olvz52267De9///vR1dWF3bt3G7+OPfZYZDIZPPLIIwCAu+++Gx6PB1/5ylfyfj8XXnihqXXut99+eOmll/ClL30JmzZtws9//nN88pOfxIwZM/Cb3/xmwvNnz56NT33qU8b37e3tOPXUU/Hiiy+ir6+vrLXffvvtUBSl4PS+cjJC55133oT7cv/84/E4du/ejcMOOwwA8MILL5h+bSKiZsVSPSKiJnPIIYfgr3/9K5LJJF5++WX87W9/w9VXX43PfvazeOmll7B8+fJJf37+/Pl533d0dAAA5s2bN+H+4eFhy9b9+OOP47vf/S6efPJJRKPRvMdGR0eNdRSzbt06vPLKK+jp6Sn4uBzcsHnzZsyaNQuhUCjv8X322cf0Wvfee2/ccsstyGQyeOONN3DnnXfiyiuvxDnnnINFixbh2GOPNZ67ZMmSCUHN3nvvDUD0f82cOdP02tevX4/Zs2dj2rRpptdayKJFiybcNzQ0hMsvvxx/+tOfJgy5MBO4EhE1OwZORERNyufz4ZBDDsEhhxyCvffeG2eccQZuu+22knsNud1u0/drOcMhimU8MplMybWuX78eH/zgB7Fs2TJcddVVmDdvHnw+H+6++25cffXVEwYkFKKqKo477jhccsklBR+XwYqV3G439t9/f+y///5YvXo1jjnmGPz+97/PC5zMqPfac7NL0uc+9zk88cQT+Na3voVVq1YhFApBVVV8+MMfNvXnT0TU7Bg4ERFNAe95z3sAADt37qzZe3R1dQHAhOl0mzdvLvmz//znP5FIJPCPf/wjL+P14IMPTnhusQBtr732QjgcLhm0LFiwAPfffz/C4XBe1untt98uuc7JFPszfvfdd6FpWt6633nnHQBi6l45a99rr71wzz33YGhoaNKsU7mDHIaHh3H//ffj8ssvx2WXXWbcv27durJeh4iombHHiYioiTz44IMFN5W9++67AZRXjlauBQsWwO12G/040n//93+X/FmZzcpd++joKG688cYJz21tbS04Ovxzn/scnnzySdxzzz0THhsZGUE6nQYAfOQjH0E6nc4bdZ7JZHDttdeWXCcAPProo0ilUhPuL/ZnvGPHDvztb38zvh8bG8PNN9+MVatWYebMmWWt/TOf+Qw0TcPll18+4Xm5f3bF/oyKKfTnDyBvmh8R0VTHjBMRURO58MILEY1G8alPfQrLli1DMpnEE088gVtvvRULFy7EGWecUbP37ujowEknnYRrr70WiqJgr732wp133mlqU9gPfehD8Pl8+NjHPoZzzz0X4XAYv/nNb9Db2zshg3PwwQfjV7/6FX74wx9iyZIl6O3txQc+8AF861vfwj/+8Q+ceOKJOP3003HwwQcjEong1VdfxV/+8hds2rQJ3d3d+NjHPoYjjjgC3/72t7Fp0yZjXyWzfTw/+clP8Pzzz+PTn/40DjjgAABieMLNN9+MadOm4aKLLsp7/t57742zzjoLzz77LGbMmIEbbrgBu3btygsKza79mGOOwSmnnIJf/OIXWLdunVFG9+ijj+KYY47BBRdcYPwZ3Xfffbjqqqswe/ZsLFq0CIceemjR31N7ezuOPPJIXHnllUilUpgzZw7uvfdebNy40dSfCRHRlGDfQD8iIrLav/71L+3MM8/Uli1bpoVCIc3n82lLlizRLrzwQm3Xrl15zy02jvzZZ5/Ne54cYT0wMJB3/2mnnaa1trbm3TcwMKB95jOf0VpaWrSuri7t3HPP1V577TVT48j/8Y9/aAcccIAWCAS0hQsXaj/5yU+0G264QQOgbdy40XheX1+f9tGPflRra2vTAOSNJh8fH9cuvfRSbcmSJZrP59O6u7u1ww8/XPuv//qvvJHsg4OD2imnnKK1t7drHR0d2imnnKK9+OKLpsaRP/7449r555+vrVixQuvo6NC8Xq82f/587fTTT9fWr18/4c/4ox/9qHbPPfdoBxxwgOb3+7Vly5Zpt91224TXNbv2dDqt/fSnP9WWLVum+Xw+raenRzvhhBO0559/3njOW2+9pR155JHG2HT591zs71LTNG3btm3apz71Ka2zs1Pr6OjQTjrpJG3Hjh0aAO273/3upH8mRERTgaJpBWo6iIiIqGoLFy7EihUrcOedd9q9FCIiqhJ7nIiIiIiIiEpg4ERERERERFQCAyciIiIiIqIS2ONERERERERUAjNOREREREREJTBwIiIiIiIiKmHKbYCrqip27NiBtrY2KIpi93KIiIiIiMgmmqZhfHwcs2fPhss1eU5pygVOO3bswLx58+xeBhEREREROcTWrVsxd+7cSZ8z5QKntrY2AOIPp7293ebVEBERERGRXcbGxjBv3jwjRpjMlAucZHlee3s7AyciIiIiIjLVwsPhEERERERERCUwcCIiIiIiIiqBgRMREREREVEJU67HiYiIiIioHJlMBqlUyu5lUIW8Xi/cbnfVr8PAiYiIiIioiHA4jG3btkHTNLuXQhVSFAVz585FKBSq6nUYOBERERERFZDJZLBt2za0tLSgp6fH1OQ1chZN0zAwMIBt27Zh6dKlVWWeGDgRERERERWQSqWgaRp6enoQDAbtXg5VqKenB5s2bUIqlaoqcOJwCCIiIiKiSTDT1Nis+vtj4ERERERERFQCAyciIiIiIqISGDgRERERETWRo48+GhdddFHRxxcuXIhrrrmmbutpFgyciIiIiIiISmDgREREREREVAIDJyIiIiIiEzRNQzSZtuVXpRvw9vf342Mf+xiCwSAWLVqE3//+9xOec9VVV2H//fdHa2sr5s2bh69+9asIh8PV/nE1He7jRERERERkQiyVwfLL7rHlvd/4/vFo8ZV/6n766adjx44dePDBB+H1evG1r30N/f39ec9xuVz4xS9+gUWLFmHDhg346le/iksuuQT//d//bdXymwIDJyIiIiKiJvTOO+/gX//6F5555hkccsghAIDf/e532HffffOelztIYuHChfjhD3+I8847j4HTHhg4EREREVFZtoe3I62msaB9gd1Lqaug1403vn+8be9drjfffBMejwcHH3ywcd+yZcvQ2dmZ97z77rsPV1xxBd566y2MjY0hnU4jHo8jGo2ipaWl2qU3DQZORERERGTaaGIUn/vn5xBLx/C743+HA3sPtHtJdaMoSkXlck62adMmnHjiifjKV76CH/3oR5g2bRoee+wxnHXWWUgmkwyccnA4BBERERGZ9ue3/4yx5BhSagrXvnit3cuhSSxbtgzpdBrPP/+8cd/bb7+NkZER4/vnn38eqqriZz/7GQ477DDsvffe2LFjhw2rdT4GTkRERERk2uM7Hje+fnXgVaTUlI2rocnss88++PCHP4xzzz0XTz/9NJ5//nmcffbZCAaDxnOWLFmCVCqFa6+9Fhs2bMAtt9yC66+/3sZVOxcDJyIiIiIyRdM0vDP0jvF9PBPP+56c58Ybb8Ts2bNx1FFH4dOf/jTOOecc9Pb2Go+vXLkSV111FX7yk59gxYoV+P3vf48rrrjCxhU7l6JVOhS+QY2NjaGjowOjo6Nob2+3ezlEREREDWN7eDs+fPuH4XF5cHDvwXi672lctvoynLT3SXYvrSbi8Tg2btyIRYsWIRAI2L0cqtBkf4/lxAbMOBERERGRKe8OvwsA2KtjL+zVuRcAYNv4NjuXRFQ3DJyIiIiIyJTt4e0AgHlt8zAnNCfvPqJmx8CJiIiIiEzpi/QBAGa2zsScNhE4MeNEUwUDJyIiIiIyZWdkJwBgVusszA3NBcCME00dDJyIiIiIyBQjcArNwuzQbADASGIE0VTUzmUR1QUDJyIiIiIyJTfjFPKGEPSI/YAGY4N2LouoLhg4EREREVFJGTWD3bHdAIDell4oioLpgekAgN3x3XYujaguGDgRERERUUmjyVGomgoA6Ap0AQC6g90AYARURM2MgRMRERERlTQUGwIAdPg74HV5AQDTgyLjxFI9mgoYOBERERFRSUNxEThNC0wz7mPGqTEtXLgQ11xzjd3LaDgMnIiIiIiopEKBk8w4MXCiqYCBExERERGVNBgX5Xh5gZM+HEI+RtTMGDgRERERUUmFMk5ySMRYYsyWNdWdpgHJiD2/NM30Mo8++mhccMEFuOCCC9DR0YHu7m585zvfgZbzGuPj4zj55JPR2tqKOXPm4Lrrrst7jZGREZx99tno6elBe3s7PvCBD+Dll1+27I+yEXnsXgAREREROV+hwKnD1wFAbII7JaSiwI9n2/Pe/28H4Gs1/fSbbroJZ511Fp555hk899xzOOecczB//nx8+ctfBgD89Kc/xf/7f/8Pl19+Oe655x58/etfx957743jjjsOAHDSSSchGAziX//6Fzo6OvA///M/+OAHP4h33nkH06ZNm+ytmxYDJyIiIiIqSWaVOvwdxn3y6ykTODWQefPm4eqrr4aiKNhnn33w6quv4uqrrzYCpyOOOALf/va3AQB77703Hn/8cVx99dU47rjj8Nhjj+GZZ55Bf38//H4/AOC//uu/cMcdd+Avf/kLzjnnHNt+X3Zi4EREREREJY0nxwEA7b524z4ZOI0lxqBpGhRFsWVtdeNtEZkfu967DIcddlje38fq1avxs5/9DJlMxvg+1+rVq41Jey+//DLC4TCmT5+e95xYLIb169dXsPjmwMCJiIiIiEoqFDh1+jsBAGktjUgqgpAvZMfS6kdRyiqXa1ThcBizZs3CQw89NOGxzs7Ouq/HKWwdDnHFFVfgkEMOQVtbG3p7e/HJT34Sb7/9dsmfu+2227Bs2TIEAgHsv//+uPvuu+uwWiIiIqKpazwlAqc2X5txX8ATQMAdAMByPad5+umn875/6qmnsHTpUrjdbuP7PR/fd999AQAHHXQQ+vr64PF4sGTJkrxf3d3d9fkNOJCtgdPDDz+M888/H0899RTWrl2LVCqFD33oQ4hEIkV/5oknnsDJJ5+Ms846Cy+++CI++clP4pOf/CRee+21Oq6ciIiIaGqRPU65gRMAtPtFBmo0OVr3NVFxW7ZswcUXX4y3334bf/zjH3Httdfi61//uvH4448/jiuvvBLvvPMOrrvuOtx2223G48ceeyxWr16NT37yk7j33nuxadMmPPHEE/jP//xPPPfcc3b9lmxna6nev//977zv16xZg97eXjz//PM48sgjC/7Mz3/+c3z4wx/Gt771LQDAD37wA6xduxa//OUvcf3119d8zURERERTjaZpRqnenoFTp78T/dF+jMYZODnJqaeeilgshve+971wu934+te/njfU4Zvf/Caee+45XH755Whvb8dVV12F448/HgCgKAruvvtu/Od//ifOOOMMDAwMYObMmTjyyCMxY8YMu35LtnNUj9PoqPgHN9mIwyeffBIXX3xx3n3HH3887rjjjoLPTyQSSCQSxvdjY1NknwEiIiIii8TSMaS1NID8HicgOyCCGSdn8Xq9uOaaa/CrX/1qwmObNm0q+fNtbW34xS9+gV/84hc1WF1jcswGuKqq4qKLLsIRRxyBFStWFH1eX1/fhEh3xowZ6OvrK/j8K664Ah0dHcavefPmWbpuIiIiomYns00exYOgJ5j3WMgrBkKEU+G6r4uonhwTOJ1//vl47bXX8Kc//cnS17300ksxOjpq/Nq6daulr09ERETU7HLL9PYcOW4ETkkGTtTcHFGqd8EFF+DOO+/EI488grlz50763JkzZ2LXrl159+3atQszZ84s+Hy/329s3EVERERE5RtLFh4MAcAYQc6Mk3MUGiNO1bM146RpGi644AL87W9/wwMPPIBFixaV/JnVq1fj/vvvz7tv7dq1EzbxIiIiIiJrFBsMATDjRFOHrRmn888/H3/4wx/w97//HW1tbUafUkdHB4JBUT976qmnYs6cObjiiisAAF//+tdx1FFH4Wc/+xk++tGP4k9/+hOee+45/PrXv7bt90FERETUzGTGac/BEAAzTjR12Jpx+tWvfoXR0VEcffTRmDVrlvHr1ltvNZ6zZcsW7Ny50/j+8MMPxx/+8Af8+te/xsqVK/GXv/wFd9xxx6QDJYiIiIiocsw4EdmccdI0reRzCtVonnTSSTjppJNqsCIiIiIi2pOZwCmSitR1TUT15pipekRERETkTGZK9cZT43VdE1G9MXAiIiIiokkx40TEwImIiIiISpCBU6GMU6u3Ne85RM2KgRMRERERTWrSjJOPGadG9tBDD0FRFONXMBjEfvvtN2Fi9fj4OC666CIsWLAAwWAQhx9+OJ599tm85+S+Tu6vn/70pwCAl19+GT6fD//4xz/yfu72229HIBDAa6+9NulaL7zwQuy7774FH9uyZQvcbveE17YSAyciIiIimtSkG+DqpXqJTAKpTKqu6yLrvP3229i5cyfeeOMNnHvuufjKV76St3fq2WefjbVr1+KWW27Bq6++ig996EM49thjsX37duM5O3fuzPt1ww03QFEUfOYznwEArFy5EpdddhnOOeccDA4OAgD6+/tx3nnn4fLLLy85Jfuss87CW2+9hSeeeGLCY2vWrEFvby8+8pGPWPHHURADJyIiIiKa1GSBkyzVA5p/LydN0xBNRW35ZWYatXTnnXeis7MTmUwGAPDSSy9BURR8+9vfNp5z9tln40tf+pLxfW9vL2bOnIlFixbha1/7GhYtWoQXXngBABCLxXD77bfjyiuvxJFHHoklS5bge9/7HpYsWYJf/epXxmvMnDkz79ff//53HHPMMVi8eLHxnEsvvRTz58/H+eefDwA499xzsXTpUvyf//N/Sv6+Vq1ahYMOOgg33HDDhL+XNWvW4LTTToPHU7uh4baOIyciIiIi55MBUaHAyePyIOgJIpaOIZwMoyvQVe/l1U0sHcOhfzjUlvd++gtPo8XbYuq573//+zE+Po4XX3wR73nPe/Dwww+ju7s7b5ufhx9+GP/3//7fCT+raRruuecebNmyBYceKn6v6XQamUwGgUAg77nBYBCPPfZYwTXs2rULd911F2666aa8+91uN2666SYcdNBB+MIXvoB77rkHL730Etxut6nf21lnnYVvf/vb+PnPf47WVhG0P/TQQ9i4cSPOPPNMU69RKWaciIiIiGhS0VQUQH52KZexCW6TZ5waRUdHB1atWmUESg899BC+8Y1v4MUXX0Q4HMb27dvx7rvv4qijjjJ+Zu7cuQiFQvD5fPjoRz+K7373uzjyyCMBAG1tbVi9ejV+8IMfYMeOHchkMvjf//1fPPnkk9i5c2fBNdx0001oa2vDpz/96QmP7bvvvrjooovwxz/+Ed/73vew9957m/69feELX0AqlcJtt91m3HfjjTfife97X1mvUwlmnIiIiIioqFQmhZQqepeKZTxCvhAGYgNNHzgFPUE8/YWnbXvvchx11FF46KGH8M1vfhOPPvoorrjiCvz5z3/GY489hqGhIcyePRtLly41epQeffRRtLW1IZFI4JlnnsEFF1yAadOm4Stf+QoA4JZbbsGZZ56JOXPmwO1246CDDsLJJ5+M559/vuD733DDDfjiF784IUsFAOFwGLfeeitaWlrw6KOP4pJLLjH9++rs7MSnP/1p3HDDDTj99NMxNjaG22+/Hdddd11Zfz6VYOBEREREREVF01Hj6xZPkcBJZpySzR04KYpiulzObkcffTRuuOEGvPzyy/B6vVi2bBmOPvpoPPTQQxgeHs7LNgHAokWL0NnZCQDYb7/98PTTT+NHP/qRETjttddeePjhhxGJRDA2NoZZs2bh85//fF7/kvToo4/i7bffxq233lpwbd/61rcQCATwxBNP4LDDDsPNN9+MU0891fTv7ayzzsIHP/hBvPvuu3jwwQfhdrtx0kknmf75SrFUj4iIiIiKkmPG/W4/PK7C19xZquc8ss/p6quvNoIkGTg99NBDOProoyf9ebfbjVgsNuH+1tZWzJo1C8PDw7jnnnvwiU98YsJzfve73+Hggw/GypUrJzy2du1a/Pa3v8VNN92ElStX4oc//CEuuuiioiV/hRxzzDFYtGgRbrzxRtx44434j//4D6PfqZYYOBERERFRUbK/qVi2Ccju5cTAyTm6urpwwAEH4Pe//70RJB155JF44YUX8M4770zIOPX396Ovrw+bN2/GbbfdhltuuSUvKLrnnnvw73//Gxs3bsTatWtxzDHHYNmyZTjjjDPyXmdsbAy33XYbzj777AlrGhsbw1lnnYVvfetbOOSQQwAA3/jGN7B8+XKcc845pn9viqLgzDPPxK9+9Ss8+eSTOOuss0z/bDUYOBERERFRUbJUb7ISNTk0gpvgOstRRx2FTCZjBE7Tpk3D8uXLMXPmTOyzzz55z91nn30wa9YsLFmyBP/3//5fnHvuubj22muNx0dHR3H++edj2bJlOPXUU/G+970P99xzD7xeb97r/OlPf4KmaTj55JMnrOeiiy5CR0cHvve97xn3uVwu3HjjjXjggQdw8803m/69nX766RgdHcV+++1nTP+rNUUrZyh8ExgbG0NHRwdGR0fR3t5u93KIiIiIHO3JHU/inLXnYGnXUvz1438t+JyfPPMT/O+b/4szV5yJbxz8jTqvsHbi8Tg2btyIRYsWFRxyQI1hsr/HcmIDZpyIiIiIqCgj42SiVI8ZJ2pmDJyIiIiIqChTPU76cIjx5Hhd1kTNKxQKFf316KOP2ro2jiMnIiIioqJKbX4LZPufYumJU9iIyvHSSy8VfWzOnDn1W0gBDJyIiIiIqCgzwyHk5qy5ez4RVWLJkiV2L6EoluoRERERUVGyb2myUj35WLNmnKbYLLWmY9XfHwMnIiIiIiqqrIxTqrkyTm63GwCQTCZtXglVQ/79yb/PSrFUj4iIiIiKMjMcoll7nDweD1paWjAwMACv1wuXizmHRqOqKgYGBtDS0gKPp7rQh4ETERERERVlZjiEzDg1W+CkKApmzZqFjRs3YvPmzXYvhyrkcrkwf/58KIpS1eswcCJykEe2PYKrnrsK5x94Po5bcJzdyyEiIjJVqiezUc1WqgcAPp8PS5cuZbleA/P5fJZkCxk4ETnEUHwI599/PgDgO49/B0fMPmLSgxQREVE9GMMhJguc9MfimTgyagZuV3W9JE7jcrkQCATsXgbZjIWaRA7x1I6njK8jqQie2PGEjashIiISjIzTJD1OslQPEMETUTNi4ETkEM/0PZP3/Su7X7FpJURERFlmhkME3AEoEP0jzdbnRCQxcCJyCBkoHTPvGADAa7tfs3M5REREAMwNh1AUpWlHkhNJDJyIHEDVVGwZ2wIA+MRenwAAvDX4FjfcIyIi20XSpXucch+XpX1EzYaBE5ED9EX6kMgk4HF5cNjswwAA46lxjCXHbF4ZERFNZZqmmco4AdlSPpbqUbNi4ETkAJvGNgEA5rfNR6u3FT3BHgDAtvFtNq6KiIimungmDg2i+mGyHicALNWjpsfAicgBNo+JTfXmt88HAMxtmwsA2BreatuaiIiI5ChyAAh4Jh/HLUv1mHGiZsXAicgB+iJ9AIA5oTkAgLkhETgx40RERHaKpUQQ1OJpgUuZ/LTRyDixx4maFAMnIgfoj/YDAHpbegEAs0OzAQA7wjtsWxMREZHZwRBAtpSPpXrUrBg4ETnAruguAMCMlhkAsgHUQGzAtjURERHJsrvcDW6LYakeNTsGTkQOsGfGSQ6HGIgycCIiIvvIIKhUfxPAUj1qfgyciGymaZoROM1smQmAGSciInKGRDoBAAi6TWSc5DjyFDNO1JwYOBHZbDw1blzR62kRmabuYDcAYDA2CFVTbVsbERFNbfFMHAAzTkQAAyci2w3HhwGIjQXlgWl6cDoUKMhoGQzFh+xcHhERTWHxtAic/G5/yeeyx4maHQMnIpvJwKnT32nc53F5MC0wDQCwO7bbjmURERFVlnHiVD1qUgyciGw2khgBkB84AcC0oAichmLMOBERkT1kxingZqkeEQMnIpsZgVOgM+/+aX4ROA0nhuu8IiIiIqGcjBNL9ajZMXAistlIfAQA0OXvyrtfBlIysCIiIqq3cnqcmHGiZsfAichmxUr15PccDkFERHZJZPRx5GY2wOU4cmpyDJyIbFa0x0kfDiEzUkRERPVm9DhxHDkRAyciu5XKOLHHiYiI7CL7lTiOnIiBE5HtjHHkewyH6AqInif2OBERkV3KKdWTz4mn49A0rabrIrIDAycim5XMOMWZcSIiInuUMxxCjizXoCGpJmu6LiI7MHAislmpwGk0MVrfBREREenKGUfu92SDKxlwETUTBk5ENtI0zQiMZGme1OZrAwCMJ8frvi4iIiIgGwCZKdXzurzwKJ68nyNqJgyciGw0nhpHRssAmJhxkoFTPBNHMsOSByIiqj/Z42SmVA/IZqZkpoqomTBwIrKRHDXe4mmBz+3LeyzkDRlfM+tEROUaDCdw4rWP4oj/7wG8uXPM7uVQg5IT8syU6uU+jxknakYMnIhsJEeN75ltAgC3y20ETwyciKhcNz+5Ga9tH8P2kRiuXvuO3cuhBmXs4+Q2GTjpz+NIcmpGDJyIbCT7m/YcRS6xz4mIKvXPl3cYXz/wVj9iyYyNq6FGJUv1ys44sVSPmhADJyIbGXs4Fcg4AQyciKgyw5EkNuyOAAACXhfSqoZXto3YuyhqSOVmnHL3ciJqNgyciGwkA6J2X3vBx2XgNJZifwIRmfeSHiQt7mnFB5fNAAC8sGXEvgVRQ0qpKaS1NAD2OBEBDJyIbBVOhQEArd7Wgo8z40RElXinT3xm7De7A/vOEp8j6/r5OULlSaQTxtemAyf2OFETY+BEZKNwUgROMkDak8xEMXAionJs1Mv0Fne3YnGPGDKzfiBi55KoAck+JQUKfC5fiWcL7HGiZsbAichGMuOUO3o8FzNORFQJ2d+0uKcVi3tERnvDQBiaptm5LGowRn+TJwBFUUz9jMw45WariJoFAyciGxmBk4+BExFZR2acFk5vxcLpInAaj6cxFOFm2mSeDJzMbn4LZDNOsQxL9aj5MHAispEs1SuacfLqwyGSHA5BROYk0hkMjIur/XO7ggh43egOiRPfnaMsnyLzyh1FnvtcDoegZsTAichG4ymRSWLGiYissmtUnOz6PS5MaxV9KXM6xcnsjhFmAcg8OeDB7Cjy3OcycKJmxMCJyEbGcAhvkeEQfg6HIKLy7BgVJ7uzOrJ9KbM6xN46DJyoHHLAQzkZJ2MfJw6HoCbEwInIRsY4cl/hceScqkdE5dppBE5B477ZnUH9MZ7MknlywENZGScPx5FT82LgRGSjUhknluoRUblkcDSrM3uyO6sjkPeYrcIDwLq1gJqxeyVUghzw4PeUPxyCpXrUjBg4Edkko2YQTUcBsMeJiKwzGBaT83rasie700Oi18n2qXrxUeC3HwB+/1ngrm/auxYqSWacgu5giWdmsceJmhkDJyKbyDI9oPQ+TvFMHMkMxwgTUWmDYXGy292aGziJr3eHbd5b59XbgJEt4uvn1wDRIVuXQ5NjjxNRPgZORDaJpMQ+Kz6XDz534R3ZQ94QFIjmbo4kJyIzBvWskpyoBwDT9a8H7c44rVub840GvPNv25ZCpVWyj5N8LjNO1IwYOBHZRJbfFSvTAwCX4jKyUSzXIyIzduulerI8D8iW7Q1FklBVzZZ1QdOAzU+IrxcfI263PWvPWsiUSjJORo8TM07UhBg4EdlElurJcrxiWrwtALIZKiKiyQxF9FK9UDZL0NUigqiMqmE0lrJlXRjdCiTGAJcXWPkf4r6+V+1ZC5kis0blTNUzSvWYcaImxMCJyCZyol6rt/Aockk+zsCJiErRNM0YDpGbcfJ5XOgIegEAgxGb+px2vSFuu/cG5hwsvu57jdP1HMwInMrJOHE4BDUxBk5ENjEyTkVGkUsMnIjIrLFYGmm9FC+3xwnIBlKylK/uBt4UtzOWA9MWA24fkI4BY9vtWQ+VxFI9onwMnIhsIjNOk/U4ASzVIyLzduvZpLaAB36PO+8xOWXPtsl6QxvF7bS9AJcb6FyQfz85TrUb4GqaTf10RDXCwInIJuMpMeyhVKmeHA4RTUVrviYiamxyn6bprRMndcqM06BdGaeRzeK2Sw+YuhaK22EGTk4lN8CtZBw5ACQyNo+/J7IYAycim8gMUqnhEEapXpoZJyKanNzDaXpo4vjobOBk08nssB44dc4Xt9MWiVtmnByrmnHkuT9P1CwYOBHZxBhHXmTzW6nFw1I9IjLHGEVeKOMkS/Xs2MtJzQCj28TXskRP3soNcclxZMYoN4tUisflgdclBpGwz4maDQMnIpuYHUcuM04s1SOiUrIT9SZmCLrtzDhFBgA1BSguoH22uK99lrgd31n/9ZAplWScAE7Wo+bFwInIJpGkyCBxHDkRWUXu4VQo49Sl3zccsWEfp3C/uG3tEYMhAKBND6DGdtR/PWRKJVP1cp/PjBM1GwZORDaRPUulAidO1SMis0b0zW07W7wTHpP7ONmyAa4MnEK92ftk5mm8D+D0NUeSGaNySvWAnMCJGSdqMgyciGwiS+9kD1MxHA5BRGbJoKg96LTAaZe4bc0JnNr0Ur1MAogO1X9NVJLscSq7VC9nJDlRM2HgRGSTaFoPnLzmAif2OBFRKWN6UNTh1MApNCN7n8cHtHSLr8dZrudEMvApt1Qv6BYZKmacqNkwcCKyiSy9Kxk4edjjRETmjE4SOHUGRY9TLJVBMq3WdV0FS/UA0fMEAJHd9V0PlaRpmhH4lLMBLsAeJ2peDJyIbBJLiSt5pUr12ONERGaNxtIAgPbAxMCpLeCBosjn1TnrVCjjBACtesYpOljf9VBJKTUFDaL3rOLhEMw4UZNh4ERkA03TjFI9s1P1WKpHRJPRNC1bqldgOITLpaDN7wFgR+BULOOkB07MODlObn9S2YGTmz1O1JwYOBHZIJFJIKNlAJQxHIIZJyKaRDylIpkRJXiFSvWAbEBV98ApUiRwkj1OkYH6rodKkoMh3Irb2NDWLBloydcgahYMnIhsILNNQOkxrzJwSqpJpFQbmrqJqCGMxcXng9uloNXnLvgcGVCNOa5UjxknpzH6m8rMNgHcAJeaFwMnIhvIsrugJwi3q/AJjpQ7PILlekRUjDGKPOCBIpuZ9mDLZL1UHIiPiq8nZJymi1uW6jmOLLMrdxQ5kDOOPMNSPWouDJyIbCDL7sxsKuh1eeFz+fJ+johoT5NN1JNsCZxkmZ7bBwQ68x/jcAjHkmV25W5+C3A4BDUvBk5ENpBX8koNhpDY50REpYxGHRo4hfX+pdYeYM9MWAuHQzhVpaPIgWywxcCJmo2tgdMjjzyCj33sY5g9ezYURcEdd9wx6fMfeughKIoy4VdfX199FkxkEWMPpxKDISSOJCeiUmSPU/skgVO7HYFTbFjctkyb+Bh7nBxL7sHk91RQqsceJ2pStgZOkUgEK1euxHXXXVfWz7399tvYuXOn8au3t7f0DxE5iBwOUWrzW4kZJyIqxehxclrGSQZOwa6Jj8mMU2wYyKTrtyYqqZqME3ucqFl57HzzE044ASeccELZP9fb24vOzk7rF0RUJ3LIAwMnIrKKY3ucJg2cpgFQAGhAbGji8Aiyjcw4VTRVjz1O1KQassdp1apVmDVrFo477jg8/vjjkz43kUhgbGws7xeR3ViqR0RWa8jAyeXO3s8+J0epqsfJzR4nak4NFTjNmjUL119/PW6//XbcfvvtmDdvHo4++mi88MILRX/miiuuQEdHh/Fr3rx5dVwxUWFGqZ7JwKnV05r3c0REeyoncKrrPk6TBU6AGBoBsM/JYWTQU0mPk/wZboBLzcbWUr1y7bPPPthnn32M7w8//HCsX78eV199NW655ZaCP3PppZfi4osvNr4fGxtj8ES2k6V6nKpHRFYZi4keofbAJMMh9MfG43XsJyoZOHUDu99mxslhqhpHrmep5ARZombRUIFTIe9973vx2GOPFX3c7/fD7y//aglRLZU7HEI+L8YNcImoiHBCZJHaAsUP7SH9MTmBry7iI+K2WOAk93aSm+SSI8igp5px5AycqNk0VKleIS+99BJmzZpl9zKIylJuximYTgIAYk/9N7DjxZqti4gaVzghskiTBU7ysXAiDU3T6rKukhmnYKe4lQEWOYLMOFU0jlwfDsFSPWo2tmacwuEw3n33XeP7jRs34qWXXsK0adMwf/58XHrppdi+fTtuvvlmAMA111yDRYsWYb/99kM8Hsdvf/tbPPDAA7j33nvt+i0QVURmnMyWQAS3PgcAiGkpYO1lwGn/rNnaiKgxyfK7SQMnvyjV0zQgmsyg1V+H04BSgVOgQ3/eSO3XQqbJHic56KEcfjd7nKg52Ro4PffcczjmmGOM72Uv0mmnnYY1a9Zg586d2LJli/F4MpnEN7/5TWzfvh0tLS044IADcN999+W9BlEjKGuqnppBS9/rQGcLYooCbHwUiA4V3kySiKassB44hfzFe5wCXhfcLgUZVcN4PO2QwKlT3DLj5ChVbYDryfY4aZoGRVEsXRuRXWwNnI4++uhJSwXWrFmT9/0ll1yCSy65pMarIqq9skr1+l5FMBUD0IJooA3AILDpUWD5J2q6RiJqLON6qV5okoyToihoC3gwEk3pPVHl96+URdPKKNVjj5OTVLUBbs7PJNWkkYEianQN3+NE1IjKGg6x5SkE9QsMsaBe0rL1mVotjYgaUCKdQTKtAgBCJbJI8vG6TNZLhgFVfx+ZWdoTS/UcSWacKpmql5ul4l5O1EwYOBHZQGacTJXq9b+RDZzk3hg7Xq/Z2oio8UQSGeNrRwVOMtvk9gPeIifgLNVzpERaHw5RQbbI6/LCo4j/zhg4UTNh4ERkA5lxMlWqN/A2WlRxJXkoLQKo4U0v48UtwzVbHxE1Ftnf1OJzw+2avJ9E7uUkp/DVVG6ZXrE+F1mqx4yTo8Qy+jhyT2XlnNwEl5oRAyciG5geDqFpwMBbRsZpV1yMJZ+pDOE3azmWnIiEcRN7OEmyBypcz4xTsf4mIFuqxx4nR6mmxyn357iXEzUTBk5EdaZqqnEgCRYrXZFiw0B8BEFVBE5JLYkBrR0AsH3Dm4gm63DiQ0SOl52oZyJw8tdxE1wjcOos/pzcDXDrtbcUlSQzRZVmnLiXEzUjBk5EdZZb710y4zSyGQAQDE4HACiuJIZ9YsPnWVo/Xtg8UpM1ElFjkf1KoUDxUeRSKGcT3JqLj4nbYoMhgGxQpWWAxHitV0QmyQt8FZfq6b1R7HGiZsLAiajOcssWSh6QhkXg1NI+FwCguFJIts8DAMxT+vEC+5yICNkgqM1ExqmtnqV6CT1w8rcVf44nALh94msOiHAMI+NUaamefnyT0/mImgEDJ6I6M67iuQNwKSX+CY6IDaCDHfOMuzLT5gMA5ikDeGcXr84SUc4eTmYCp3pO1ZMZpEB78ecoSn65HjmC0eNUaameHnAx40TNhIETUZ0Z/U1m9sbQAyd/50JomphI5esV2adZyiDW7QrXZpFE1FCMHiczwyH8NpTqTZZxAjhZz2FUTTUyTpVuXsseJ2pGDJyI6qyswCncBwCIB2cCmuhd8E3rBgD0KiPYsDuMdEatzUKJqGGE9al6pjJOeh9UXYZDyIyTf5KME5AzWW+kpsshc3KDnUo2wAVyepxYqkdNhIETUZ2VVf4wvgsAMKB1QlNFD0C6VZxg9CojSGU07BzlQYloqpMZp/ZyxpHXI+NkpscJyJbqMePkCLnldRVnnFiqR02IgRNRnZWXcRKB045MG6AHTjGf2DS3RxmFAhVbh6K1WSgRNQyjx8lE4CR7nOo7HKJExkmW6rHHyRFkxsnr8sLtclf0GizVo2bEwImozkwHTpoGhPsBAJsTbUbGKeoVV/88yKALYWwdZuBENNVl93EqPY5cluo5ZjgEwFI9h6l2FDmQzVRxA1xqJgyciOrMdOCUGAP0574bbclmnLQU0CL2depRRrB1iAcloqluvJzhEHbs41SqVE8+nuDAGyeodhQ5kD3GJdLMOFHzYOBEVGfRtMgQlQyc9GwT/O3YOKoZGadYOgaEZgIQfU7scSKisDGOvHRZVav+nEgyDU3Tarou08MhjMCJWyw4QbWjyAHA7+FwCGo+DJyI6sx0xknvb0KoF9uGY9A0vVQvFQXaZgAAejGC/nEelIimukjSfKmenLynaUA0manpurKBU6mMkx5YyZ4ospU8TlU6GALgcAhqTgyciOrMdOA0LkaRa6FebBuOZkv10jEgpAdOyggGxlkGQTTVRfSMU4uvdMYp6HVDUfSfS9awXC+TBlIR8TUzTg1FlupVOooc4HAIak4MnIjqzAicvOZK9VLBXkSSmT1K9UTg1KOMoJ+BE9GUF02IzFGriX2cFEVBq088L5KoYcYpmRMEme5xYuDkBJaU6sl9nJhxoibCwImozuRBxGypXtQnBkH4XOIAFkvHgLZsj9NQJIlkmpvgEk1VmqYZmaNWExknIJuZitRyQIQcDOEJAB7f5M9l4OQosi+pqlI9PehijxM1EwZORHVmZJzc5gKnUfc0AECLHmhFU1Ej4zRDGQEA7A4z60Q0VSXSKlR9xoOZjBOQ7XOqaeBkdjAEwMDJYUxf4JsEe5yoGTFwIqqzcodDDCldAIBWX0v251t7AAA9bnGSwXI9oqkrN/gJes1lnGSAVdMep4TJUeRAznAIBk5OIIMdKzJO7HGiZsLAiajOTPc4RQcBAINqCADQ5s8JnFpEFqoTYs+T/jFe0SOaqmSfUovPDZdLMfUz2VK9GvY4mZ2ol/uc5DigsvTYbrK8zpIeJ5bqURNh4ERUZ6YzTtFhAMCudCsAoMMfyv58UARObdo4AA27mHEimrJk1qjFZ65MD6hzqV6gjFI9AEhyE1y7GcMhqtgAl6V61IwYOBHVmenAKTYEANiRFM/rCojAKZqOGhknN1S0I4oBZpyIpqyoHAxhYvNbSZbqhWs6HGJU3JrpcfIEAJce+LFcz3ayvK6ajBNL9agZMXAiqjNTgVM6aVx13RYXz5vWmpNx8vgBr8hEdSph9jgRTWGy3K61jIyTDLJqugFuOaV6isIBEQ4ij1NWZJzkaxE1AwZORHUWS5kInGLD+hcKtkS9AIDuFnFSYRyE9KxTF8a5CS7RFCbL7crKOPnqUaonh0OYyDgBDJwcxIoep9yMk6ZplqyLyG4MnIjqLJYxEziJMj0EOzEQSQEAZoTESUU0FdUfE9P2upQwhqPJ2iyWiBwvkpTDIcrJONWhVK+cjBOQM1lvrDbrIdMSab1Ur4qMk98jhkOomoq0WsP/zojqiIETUZ2ZKtWLysBpGgbDIiia2dae9/O5k/VGoqnaLJaIHK+yHqc6lOrJDXDNDIcAmHFyEHmBTwY/lcgNuuTrETU6Bk5EdZRSU8aVNzMZp0ygC7GUOLGZ1d4hHpKBkz5Zr0sZxxAzTkRTVnYceaNnnBg4OYWRcaqiVM/r8sKluPJej6jRMXAiqqPcJlkzGaeErxMAEPC6MF3vcUqpKaTUVDbjpIQxGksho7KGnGgqkhknOWLcjPqMI2ePU6OSI8SD7hLTXyehKEp2LyeOJKcmwcCJqI7kYAi34obX5Z3kiWI4RMwjTjimt/rR4m3JPpyzl1MXwtA0YDTGcj2iqUhmjeSmtmbI7FSkplP1GDg1KjkcoppSPSB7gZCb4FKzYOBEVEe5/U2KokzyRJFxirpFeV5nixdelxduRZwYxVIxI+PU6xFjy4ciLNcjmoqichx5GRkn2eNUlw1wyy7V43AIu1mxAS4AI+PEvZyoWTBwIqoj05vf6qV644o4kehs8UJRFOPncjNO3a4IAGCEfU5EU1IkWX7GSZbqRWu6AW65wyHEhSJmnOxnxTjy3J/nXk7ULBg4EdWR6cBJL9Ubgdj0tjPoAwC0eFqyr6NnnKa5mHEimsrkZLxyNsCVpXocDkGFWDGOPPfnmXGiZsHAiaiOjIZbkxmnYU0PnFpEP1TQK34umo4aGacOTZxkcCQ50dSU3QC3guEQyUxtNidNJwB5sszAqeHI8eHVZpw4HIKaDQMnojqSGaeSByM947Q7s0fglFuq1yI2wA2pesaJpXpEU5JRqlfGPk7yuRlVQyKtWr+o3ODHx8CpkaTVtLFtRtUZJ/1Yx+EQ1CwYOBHVUTQdBWC+VK8/LZ4nS/XyAqdAJwDAr8XgQRrDDJyIpiRjOEQ5+zjlPLcmAyKS4oIOPEHAbXJdDJwcIbesruoeJz3wYsaJmgUDJ6I6Mt3jFB8FAPQlRZmDzDjl9TjljPhtQxTD7HEimpIqGQ7hdikIeuVkvRqMJE+KoTXwh8z/jPxMY+Bkq9xBDrLUrlIy8GKPEzULBk5EdWQqcEonAf15OxMycMrPOEVTUXEVVy+BaVeiGGaPE9GUVMk4cvF8PXBK1iDjlNAzTr5W8z/DceSOIIOcgDsw+bYZJrDHiZoNAyeiOjIVOOWcNPTFxIlQwR4nAAiI8b3tzDgRTUmaphmBT2sZPU7i+fqAiFqW6vnKyTi1ZX+2FgMryBRjD6cqy/RyX4M9TtQsGDgR1ZGpwEkv04OvDUNx0bTdGSwROCkRjMaYcSKaauIpFaoeY5TT45T7/JqMJJelepUETmoaYIbCNjLIqbZMD8gZR55mqR41BwZORHVUTuCkBdqNYGjPUj2j7CEn4zQWZ+BENNXkltnJniWzZIZK7gNlqWQFpXq+VgB6aRj7nGxjetsME/weEXxxA1xqFgyciOpIHpBavC3Fn6SX6qm+dqNapUPPOMmyBzmdL5tximIsVsONLInIkWR/U4vPDZervH4UWapX04xTOcMhFIUDIhxAHqesyDjJ4IvDIahZMHAiqiNjH6fJ9sbQM04przjhaPW54fOIf6rFe5wiiKUySNZiPxYicqzsRL3yyvQAB/Y4AdlyPVmyTHUnS/Ws6HEyhkOwx4maBAMnojoyV6onMk4Jt9z81mc8VLzHSWSgxlmuRzSlyKCn3MEQgLgoA9SoVK+SqXpANkMlAy+qO2M4RJWb3wI5wyHYs0ZNgoETUR2V0+MU1wMnWaaX+3N7Bk7T3eL7sTjL9Yimkkiy/M1vpbqU6pWbcZLPTzBwsosxjtyKqXpu7uNEzYWBE1EdlTOOPOYSV2rbg9kToqC3RODEyXpEU0q0ioxTSA+cojUJnPQeJWacGo5RUm5lqR4zTtQkGDgR1ZEROHlLZ5wiLnECEfJnM04tnpa815GBU6dLlOpxsh7R1CIzTpX0OLUY48hrMVWv2owTh0PYRWaHLBlHzn2cqMkwcCKqo3J6nCIQQVJbICfjVKRUr0PvceJkPaKpJVrh5rcAEDLGkTtkqh6Qvwku2cLKceSyVI8ZJ2oWDJyI6qicHqdRiBIXWU6T+3N77uPUBmaciKYi2Z9UTcYp4qThEOxxsp2V48hlxok9TtQsGDgR1VE5PU5jqnhOyETGqVUTV3fZ40Q0tch9nHIvsJgls1S1HUfeVt7PscfJdjUZR86MEzUJBk5EdaJpmrkSiPgIAGA4I56TW6onD2R7Bk4tqugHYMaJaGrJ7uNUfqmekXGq6VS9SjNO7HGyi6WleuxxoiZTUeB02mmn4ZFHHrF6LURNLZ6JQ4MGwFyP01BGHHDaCpTqJTIJZNQMEOwEAPjUOLxIs8eJaIqRGafWijJO+lS9WpTqJSvdx4k9TnaTQY4lpXpyHHmapXrUHCoKnEZHR3Hsscdi6dKl+PGPf4zt27dbvS6ipmNkiVBiY0G9x2lQD5wKleoB+sHN325834YoM05EU0w1GadWJw6HYI+T7YwNcK3Yx0l/jbSWRkrl8YkaX0WB0x133IHt27fjK1/5Cm699VYsXLgQJ5xwAv7yl78gleI/DKJCZODkd/vhdhU5ydE0o0SlP6kHTjnjyAPuABQo2ddzuY0TjZASY48T0RQjy+wq2gDXV6MNcFW18nHk7HGynRE4TXaBz6Tc4ItZJ2oGFfc49fT04OKLL8bLL7+Mp59+GkuWLMEpp5yC2bNn4xvf+AbWrVtn5TqJGl4sZWIwRDICaKJsZiAlyiRym74VRcn2OemvJ0tb2hDDWJylekRTiZyIV0mpnsxSxVMqMqpm3aJSUUAvS658HycGTnaRE/CsyDj5XD7jYh/7nKgZVD0cYufOnVi7di3Wrl0Lt9uNj3zkI3j11VexfPlyXH311VaskagplDOKHC4PdsfFSU3ucIjcn4+mxQhyWa4XAjNORFONLLNrqWAfp9xgy9JyPZltggJMttl3IUaPE4dD2EUeq6zIOOVe7ONkPWoGFQVOqVQKt99+O0488UQsWLAAt912Gy666CLs2LEDN910E+677z78+c9/xve//32r10vUsMoZRQ5/O8LJwmOGJ4wklxknhT1ORFONMRyiglI9v8cFt0tkAywdEGEMhggBilLezzLjZDsrM05AdsgE93KiZlD+Jy2AWbNmQVVVnHzyyXjmmWewatWqCc855phj0NnZWeXyiJqHLFMwk3HSAh0ID4srwKEiGSej7EEPnETGiaV6RFNJNcMhFEVBi8+N8Xga4UQaM6xalAycyh0Mkfsz7HGyjZU9TgD3cqLmUlHgdPXVV+Okk05CIFD8H1VnZyc2btxY8cKImo0srZv0Kp4+ilzN2TSyWKnenj1OISWGWCqDZFqFz8Mt2oimgkgV48gBkakaj6eNzJUlKt3DCchmnNJxIJMC3N7Jn0+WM8aRe6ofRw4UuNhH1MAqOrt68MEHC07Pi0QiOPPMM6teFFEzMjUcQs84pX2ib8nndsHvyb+SPLFUTzy3DeL7cZbrEU0JmqYZGafWCnqccn8uYmWPU6LCPZyAbI8TwE1wbWJsgOuufgNcgBknai4VBU433XQTYrHYhPtjsRhuvvnmqhdF1IzM9TiJwCnp0UeMByZeRS7W4zTNIw5KnKxHNDXEUyo0fXhdSwU9TkDuJrhWDoeQgVPb5M8rxO0F5MarLNerO03TLM84GcMhmHGiJlDWJ+3Y2Bg0TYOmaRgfH88r1ctkMrj77rvR29tr+SKJmkE5U/USbj1wKlB+Uyxw6nKLxltO1iOaGnKzRC3eyjJOsjcq7JRSPUD0OUUTHBBhg7SahqqpAKwbDiF7pbiPEzWDsgKnzs5OKIoCRVGw9957T3hcURRcfvnlli2OqJmYC5xEj1O8nMApIEr1Ot3iat44M05EU0JMn4QX9LrhcpU5vU4np/FFrdwEt5rhEIDoc4oOMuNkg1gmW01kWamenrlixomaQVmB04MPPghN0/CBD3wAt99+O6ZNm2Y85vP5sGDBAsyePdvyRRI1AxnotHhaij9JzzhFFXGltpxSvQ6X+J4jyYmmhmom6kkt+sWZSE3GkVeacdJL/NjjVHcyK+RSXPC4Kiv/3JPMOLHHiZpBWf8qjjrqKADAxo0bMX/+fCjl7s9ANIWZ63ESJwoRRTynrYxSvTZFD5xYqkc0Jci9lyrZ/FYK6T9rbcZJlupVkXECmHGyQe4ocqvO8djjRM3EdOD0yiuvYMWKFXC5XBgdHcWrr75a9LkHHHCAJYsjaibGpKLJAif9RCGsieeYyziJUr1WfareKAMnoimhms1vJTlUwtKMkzFVr8LAyc9NcO0iS/Ws6m8C2ONEzcX0p+2qVavQ19eH3t5erFq1CoqiQJPjfHIoioJMxsIPYKImIQOdSQ9IesZpTA+c9tzDCSiecWrRxD5RYSuvHBORY8lSvWAVpXqt+s9GapJxqrBUjxkn28jgxqrNbwH2OFFzMR04bdy4ET09PcbXRFSeckr1RlVx0Ar5J27+KAOvPQOngCpOVjgcgmhqkMMhqso4GT1OVgZOem9SpcMhjIwTe5zqTQY3tcg4sceJmoHpT9sFCxYU/JqIzCkncBpJiyt05WSc/BlmnIimEksyTnIfp5qMI6+0x0kfDsGMU93J4EZuWmsFGYQlMizVo8ZX8Qa4d911l/H9JZdcgs7OThx++OHYvHmzZYsjaiamAif9RGE4Iw5ahcaRy6l8e/Y4edQEvEhbW3JDRI6VzThZUKpnacbJgn2cAPY42UBmnCY9TpVJBmHGMYuogVUUOP34xz9GMCj+UT355JP45S9/iSuvvBLd3d34xje+YekCiZpFOT1Og+nigVOxjBMAhBBlxoloiojoWaKgFcMhrPzcqHY4BHucbFOLjJM8ZjHjRM2gok/brVu3YsmSJQCAO+64A5/97Gdxzjnn4IgjjsDRRx9t5fqImkbJfZwyKUA/aO1OegGohafqefcInFxuwNsKpCIIKTH2OBFNEVE9S1RVxkmOI6/JPk7scWo0tehxkkEYh0NQM6go4xQKhTA4OAgAuPfee3HccccBAAKBAGIxpmKJCilZqpdzkiACJ5P7OAHZvZwQY8aJaIrI7uNUecaptSbDIfRSvUqHQ7DHyTbGPk5WDofwcDgENY+KPm2PO+44nH322TjwwAPxzjvv4CMf+QgA4PXXX8fChQutXB9R0ygZOMmTBLcfIwmx8aCpfZwAETiF+xBCDFuYcSKaEmSw01JVj1MthkPIjFOlPU564MQep7qT5XRWjiPnPk7UTCrKOF133XVYvXo1BgYGcPvtt2P69OkAgOeffx4nn3yypQskagZpNY2UKjamLZ5x0k8S/CGj3K4tMHEceW7gZOylpp9ohBRmnIimCiuGQ8igy7LPjUzaKDmuulSPGae6M9WLWybu40TNpKKMU2dnJ375y19OuP/yyy+vekFEzSi3RKHoAUkv1dP8bQgPi5OYyYZDqJqKlJqCz+0DAmKyXkgv1VNVDS6XYuVvgYgcJpKsfjiELNVLpFWkMyo87oqup2alItmvqx0OwYxT3RmlejXIOLFUj5pBxZ+2IyMjeOaZZ9Df3w9VVY37FUXBKaecYsniiJqFvIqnQCk+rUjfNFL1hiATSYX2ccoNvGLpmAicZI+TIt4nkkwXzFYRUfOIJqwbDgEA0VQG7dUGTjLYcXkBj6+y15ClekkOh6g3o1SvFj1OzDhRE6gocPrnP/+JL37xiwiHw2hvb4eiZK9sM3Aimii3vyn330sePeOU8YqrrR6XAr9n4kmM1+WFx+VBWk0jlo6hw99h7OXUoQdO4QQDJ6JmZ8VwCJ/bBY9LQVrVEE1k0F7t50a1ezgB+RknTQOKfWaS5eSxytINcNnjRE2koktL3/zmN3HmmWciHA5jZGQEw8PDxq+hoSGr10jU8ExtfqtfqU25xbjyUMBTNMiSrxNNR8Ud+hXaaR5xRS/MARFETS9qwXAIRVGs7XOSWaKc/eXKJnuctEy2X4rqghknoslVFDht374dX/va19DSUmQ/GiLKYy5wEiccSbe4Uluov0kqtglup1sc9Dgggqj5GRmnKgInINvnFLViJLkVGSdvzs+yz6muZB/SpMeqMsnsVUpNIaNaOL2RyAYVBU7HH388nnvuOavXQtS0TE0q0idIxWXGaZLASW6iG0vlB04dLj3jxMCJqOllA6fKS/WAnL2crBhJbgROFQ6GAACXK/vz7HOqK5kVsrRUL+e4JzNaRI2qok/bj370o/jWt76FN954A/vvvz+83vya6I9//OOWLI6oWcjASQY8BSXGxHMV8ZxCgyGkYhmnDpfe48RSPaKmpmmakSGqZjhE7s9bknGSGSJflRUpvpC4mMSMU13VYgPc3CAslo6hxctqJWpcFQVOX/7ylwEA3//+9yc8pigKMhmmYolyldPjFIV4zmTDHSYGTvo4cn04xDgzTkRNLZFWoerTN4NVBk4yY2VNj5MMnKrocQJEn1MY3MupzmoxjtyluOB3+5HIJJhxooZXUeCUO36ciEozVTeu9ziF9cDJTI+T0WwrAydNDItgxomouUVygpzqS/VkxsnCUj1/FaV6APdyskkthkMAMAInDoigRlflhg1APM5/BESllNPjFNb0wGmSUj35OtkeJ3GSEdSy48iJqHnJICfgdcFd5WbX2R4nhwyHALiXk01MHasqYEzW45REanAVBU6ZTAY/+MEPMGfOHIRCIWzYsAEA8J3vfAe/+93vLF0gUTMoZ6remCrqwdsqmKoXlBknBk5ETc2qwRC5r2FNxkmW6lUZODHjZAsj42RhqV7u67FUjxpdRYHTj370I6xZswZXXnklfL7szuArVqzAb3/7W8sWR9QsygmcRlVxgClrHLl+kuHLiMBpnKV6RE3Nij2cJDkcwtqMU5WlerLUjz1OdVWL4RAA4PeIC4LGMYuoQVUUON1888349a9/jS9+8Ytwu7Mf2itXrsRbb71l+nUeeeQRfOxjH8Ps2bOhKAruuOOOkj/z0EMP4aCDDoLf78eSJUuwZs2aCn4HRPVlKnDSTxCGM+IAM1mpXrHhED41BhdUZpyImpxVezgBQIss1XPKPk4AM0420DStJuPIASDoFsesRJoZJ2psFW+Au2TJkgn3q6qKVCpl+nUikQhWrlyJ6667ztTzN27ciI9+9KM45phj8NJLL+Giiy7C2WefjXvuucf0exLZoZyM01BaD5xMZJyiaZFhym3EDiGGcNz8v0MiajwyO2RFqV5IDoewZB8ni0r1/NzHqd5yy+is3AAXyGacOByCGl1Fn7jLly/Ho48+igULFuTd/5e//AUHHnig6dc54YQTcMIJJ5h+/vXXX49FixbhZz/7GQBg3333xWOPPYarr74axx9/vOnXIao3Uw23+pXVoZQofy1rHyePH3D7gEwSrYhbs5ElETlWLCX+jcuJeNWQwZc1GScZOFU7VU8fDsGMU93kDm6wOuMke5w4HIIaXUWB02WXXYbTTjsN27dvh6qq+Otf/4q3334bN998M+68806r12h48sknceyxx+bdd/zxx+Oiiy4q+jOJRAKJRPYqytjYWK2WR1RUyYxTJg3ozxlIysCpjH2cADEgIjqIkBLjPk5ETU5eHAl6q884yeDLkgsulk3VY49TvclskNflhcdV/X9XueRFQw6HoEZXUaneJz7xCfzzn//Efffdh9bWVlx22WV488038c9//hPHHXec1Ws09PX1YcaMGXn3zZgxA2NjY4jFCjccXnHFFejo6DB+zZs3r2brIyqmZOCUU47SnxABU1nDIQDjCm8IMYQTLNUjamZyOITzMk5W9zixVK9eajWKHMhmsJhxokZX8SWF97///Vi7dq2Va6mJSy+9FBdffLHx/djYGIMnqjt5sGjxtBR+gjw5cPsxrF+Qm3Q4hDeY97oAspvgKjFs4lQ9oqZm5XAIeZHG2h4ni6bqsVSvboyN2t3W9jcBOfs4sceJGlxFGafFixdjcHBwwv0jIyNYvHhx1YsqZubMmdi1a1fefbt27UJ7ezuCwcL/0P1+P9rb2/N+EdVbySt5+smB5g8ZE/Em3cfJXahUT5xotCKOcCINTdOqXTYROZS1+zjppXqWZpws6nHicIi6kUFNLTJO7HGiZlFR4LRp0yZkMhOvTCUSCWzfvr3qRRWzevVq3H///Xn3rV27FqtXr67ZexJZoWSpnp5x0nxtUPV4p6xx5ICxCW6bEkUqoyGRVqtcNRE5laX7OMlx5Jbu42RRjxMzTnVTy1I99jhRsyjrUtU//vEP4+t77rkHHR0dxveZTAb3338/Fi5caPr1wuEw3n33XeP7jRs34qWXXsK0adMwf/58XHrppdi+fTtuvvlmAMB5552HX/7yl7jkkktw5pln4oEHHsCf//xn3HXXXeX8NojqzmyPU8YrTjZcChD0Fj8hmqzHqRXiil44kUZgktcgosYlBzlYm3GqslQvkwZkRsGqHicOh6ibWm1+C+T0OLFUjxpcWZ+4n/zkJwEAiqLgtNNOy3vM6/Vi4cKFxqhwM5577jkcc8wxxveyF+m0007DmjVrsHPnTmzZssV4fNGiRbjrrrvwjW98Az//+c8xd+5c/Pa3v+UocnI8sxmntEcf8OD3QFGUoq83WcZpmicBZIBwPI3ukLUjZYnIGWIp64ZDtOrBVzKtIpVR4XVXVIwCpCLZr9nj1HBM7TdYIZbqUbMoK3BSVVH6s2jRIjz77LPo7u6u6s2PPvroSfsw1qxZU/BnXnzxxarel6jejKbbooGTODlIucXwiMlGkQPZ4RCFAqcutyiFCHMkOVHTyo4jt65UDxC9Ux3BCgMnGeS4vIDHV92iZI9TKgKoKuCqcE1kWj2GQ7BUjxpdRZ9EGzdurDpoIpoqUpkU0poIYoqWQOjlKAkjcJr8moYMwBKZBDKqXl6jB06dbnHwG+dkPaKmFUvKDXCrL9XzeVzwukWGu6o+J6v6m4BsxglguV6d1HI4hCzVy7vYR9SAKv7Evf/++3H//fejv7/fyERJN9xwQ9ULI2oW0XTU+LrUOPK4Szw+2R5OQH7mKp6Jo9XVagRO7S5x8LOk0ZuIHElOwAtaMBwCEL1So7GUMXSiIlaNIgcATwBQ3ICWEa8b4ETcWqvlcIjci31EjayijNPll1+OD33oQ7j//vuxe/duDA8P5/0ioix5MPIoHnjdRUrw9MApquiBU4mMk6wXz319ebLSpmSHQxBRc5LjyFstGA4hXkcfEFHNXk5WZpwUhX1OdWYMh3DXcDgEe5yowVX0iXv99ddjzZo1OOWUU6xeD1HTMdVwKwMniOeUyjgpioKgJ4hYOpYNnPSMUwji+3EGTkRNy8px5EDOSPKqMk4WBk6A6HOKj3Ivpzop2YtbBW6AS82iooxTMpnE4YcfbvVaiJqSqYORXuIS1sTBpVSPU+7r7Rk4teiBU5g9TkRNK2qMI7eoVM/Yy6majJOeGfJbUKqX+zrMONVFTafqyeEQaZbqUWOrKHA6++yz8Yc//MHqtRA1JVN143rGaVwzl3ECigdOQU30VIUTqYrWS0TOpmkaoinrhkMA2VK96nqcZMbJosCJeznVVT2GQ7BUjxpdRZ+48Xgcv/71r3HffffhgAMOgNeb37dx1VVXWbI4omZgrlRPnBiMquKAFfJPPo489/X27HEKqHrgxIwTUVNKpFVkVLGVh5XDIQCLMk5Wleox41RXtRwOwVI9ahYVBU6vvPIKVq1aBQB47bXXrFwPUdMxFziNAQBGMnrgVE6pXio/4+TLiMCJPU5EzUmOIgeAFgv2cQKAkN/KjJNVPU4y48Qep3qo5XAIboBLzaKiwOnBBx+0eh1ETctU4KRfqR3JiE0jq+lx8qpxuJHhPk5ETUoOcPB5XPC4rdkY1tIeJ6tK9fz6CHJmnOpCZoNq2eOUVJNQNRUuhRsaU2MqK3D69Kc/XfI5iqLg9ttvr3hBRM2mnB6n3SlRB95WSY9TzslKK2Lcx4moSWVHkVuTbcp9LUdN1fOzx6mejIxTLUr1crJYiUyiJsEZUT2UFTh1dHTUah1ETaucHqfdSdHbVFapngycPD7A7QcyCYQQ5z5ORE1KBk4tFu3hlPtaVV1wqVWpHjNOdVHLqXpyOAQgAjQGTtSoyvrUvfHGG2u1DqKmVfJglEkD+nMGUqJUr5ypennNtv42IJpASIkxcCJqUtGEtXs4AdnPnGjSgcMh2ONUF7UcDuF2ueF1eZFSU0hkOJKcGheLTIlqrGTglHNS0J8QGSczPU7y4BZNRbN35myCy6l6RM3JyDhZNIpcvJZeqmdJxsmqceTi84wZp/qo5XCI3Nc1qiSIGhADJ6IaK7kBrn5SoLl9GEkoACocRw4YV2iZcSJqXrIPyaqJegDQKkv1nLSPE3uc6qqWwyGAnE1wmXGiBsbAiajGSmac9MEQ8Lchre/NUlGPE2BMoQohhmgyY+z1QkTNwxgO4bcucJJlf1VN1UtYXKrHHqe60TStpsMhAG6CS82BgRNRjZUu1RMnBapXnGwoirkryQUDJ/1Eo1URByZmnYiaTy2GQ2R7nKrJOFk9jpwZp3pJq2lkNPHfVa0CJ26CS82AgRNRjZXOOInNb9NevczO74HLpZR83cIZJ9ET0OkSByaOJCdqPrUYDmHNPk5WT9WTPU41Gg6RigPr1gLhgdq8fgOJprO9sjUr1dN7nBJplupR47LuchURFWS2xyntFicbZvZwyn29Qj1OXZ4EkGLGiagZRVPWZ5ym3D5O6SRw4wnAjheA1h7g7PuAroXWv0+DkMcpj+KB11W6x7YSfo8o1YtlOByCGhczTkQ1VnLEq341NakHTmb6mwCgxdOS9/oAjIxTl1tc0RvnZD2iplPLjFO00oyTmjG2VbBuql4Ne5yeu0EETQAQGQAe+JH179FAZPlcrcr0cl+bGSdqZAyciGpMlkCU6nGKu0UgZGYPp9zXy+9xEoFTh4s9TkTNKjuO3MJ9nPTsVTKjIplWy38BmW0CrM84ZRJAJmXNawKApgEv3Cy+XvUlcfv634DYiHXv0WBqPRgCyJbqcTgENTIGTkQ1ZnaqXlwRj4cC5sokgl59A9z0HhvgAmiXgRMzTkRNxwicLBxHHszJXsUq2QRXBk6KG9BLsqome5wAa/uchjYA/a8DLi9w/A+B7n0ANQWsu9e692gwRmVEjfZwAjgcgpoDAyeiGjMbOEUVkXEy2+NUcDNB/QptmyLuCycsvEpLRI5g7ONk4Qa4Po8LPrc4JQhX0ueUu4eTUnq4jSluDyAzIFb2OW14UNzOOxQIdgH7fFi//2Hr3qPB1KVUjxvgUhNg4ERUYzIjJHuSJtADpwgsKNXTM06tkKV6VUzIIiJHMvZxsnA4BJAt/YtWUuKblPvRWdTfJNWiz2nTY+J28dHidsH7xO2WJ617jwYTS5W4wGeBgscsogbDwImohjRNKz0cQr+SOq7JUr3yAydN0ze61QOnFk30VbFUj6j5yL2WrBwOAWQDsUg1pXpW9TdJtZist/15cTvvEP32veJ2aD0QHbLufRqIzDgxcCKaHAMnohpKZBLQIIKaUqV6Y5roC2gzGzjpPU4ZLYOUqpfk6T0BQY2lekTNKrsBrsWBU1UZp9KBU/94HHe9srO8/eWs3sspshsY2SK+nn2guA12Ap0L9EW+Yc37NJh6DIdo8RaYBEvUYBg4EdVQ7gGiVOA0khEHrHJL9fLeR884+VVxEsOpekTNR44Mt3Ifp9zXq+hzQ2aEiowi3zwYwXFXPYLz//ACPnHd40bWrCSZcbIqcOp7VdxO2wsIdGTv710ubvvftOZ9Gkw9hkMw40TNgIETUQ3JA4TP5YPbVeTqsAyc0uVlnLwuLzwuT977yJMMX0aU6nEfJ6Lmkx0OUaOMUw1K9f6/f72F0ZjIgL/bH8bvHt1o7nV9Fpfq7X5H3Pbum3//DD1w2vW6Ne/TYOoxHIKBEzUDBk5ENSTLH2RZXUF64DSUEYFTyG9+13Z5IJJ7RcmMk0dNwIN0eSUxROR4mqYZ48KtHg6R7XGytlRvdziBtW/sAgCce9RiAMCtz22FqmqlX9dv8XCIgbfFbffe+fdP8YyTcaxijxPRpBg4EdWQqfIH/UrqYEoPnExmnIACB6KcfU9aEWepHlGTSWZUpPWAI2h5j5P47IlWMo3TKNWbGDitfWMX0qqGA+Z24KIP7o1WnxvbhmN4Y+dY6dc1Mk4WlerJwKlnn/z7ZQaq/02xQe4UY/Q41aNUL8XAiRoXAyeiGpKZoFbvJJOm9IzT7qTINJntcQKyByJjE1y3B9Dva1NiLNUjajK5m9NaPRxCvl5lPU45+zjt4bF1uwEAx+zTi6DPjdV7TQcAPKrfPym/HA5hValekcBp+lLA5QESo8DoNmveq4HIY9Wk1RFVYsaJmgEDJ6IaiqbEwajoHk6ZFKAHPf1JHwDzPU5Asb2cxIlLK2LMOBE1GTkq3Od2weu29hBuZJwqKdVLFM44aZqGpzYMAgDet7QbAHDEEnH7zMbB0q9rZY9TdAiIDIiv9yzV8/iAaaKMEEPrq3+vBlPXjBMDJ2pgDJyIakhexZNjWCfImRTVn6g841RoE9wQAyeiphOr0WAIIJtxqm4fp/yM09ahGAYjSXjdCvafI6bYHTBX3L6+w0SpnpU9TrJMr2N+4SEWciT58Obq36vBcDgEkTkMnIhqqGTGSQ+cNE8A0Yz451h1xkk/cWlTYgjH09nNcYmo4UXkKHKv9YFTyOhxqmYceX5A8tK2EQDA8lntCOhr3ndWOxQF6B9PoH88PvnrWplxGlwnbruXFH68a6G4Hd5U/Xs1GA6HIDKHgRNRDZWsG9dPBrScq7TlTMoq2GzrbxevgzjSqoZEWi1nyUTkYNlR5NZO1ANy93GyLuP0pj4AYsWc7J5JLT4P9uoRzyuZdfJbuAGuzCTJAGlP8v6RKZhxqmOpXjwTh6rxuESNiYETUQ2ZzThlvOIkIuT3wOVSTL/+pKV6iriP5XpEzSM7itz6jFN2HyfrxpGv2yU+4/aZ2ZZ3/36zxQWe17ePTv66VmacZEAkS/L21CVL9TZV/14NJpbRJ8DWoVQPyBloRNRgGDgR1ZDZHqe0Jxs4lWOy4RDTPAkAQJiT9Yiahuw/snoUOZDNOFXX45QfOL2zSwQ8S3vzA6cVs0UG6rXtpTJOFvY4mc04TcXAST+G1LJULzcoM/YeJGowDJyIashsxinlFo+Xs4cTMHnGyQicmHEiahqy/8jqzW+BnIxTVT1O2VK9WDKDrcPiM3DpjPwSvmWzxOfUuv4SJXi1yDh1Fck4yUxUdNCa0sAGUo8eJ5fiYp8TNTwGTkQ1ZDbjlHCLq7SVZpzyrt7pJxodbhE4cS8nouYhL4S01qDHSQZjkar2ccpmnNYPhKFpwLRWH7pD/rynL5wunrd1KIaMOskAG6v2cUrFgPAu8XWxUr1AOxCcJr6eYpP1jB6nGpbqARwQQY2PgRNRDZXMOOlXUeN6xqmciXpA9iCXVy+uD4fodIn7KjoJIiJHklP1Wmswjly+ZmWlehOn6r2j9zct7Z24Ke7sziB8bheSGRU7RiY5iTYyTuNANRNCR7aIW387EOwq/rwpOiCiHsMhAAZO1PgYOBHVkNmMUwziYGJlj1ObIg6ELNUjah5ycEMtSvVkj1PZwyHUDKBfJMot1VvXr/c3zZgYOLldCuZPF5+LmwYjxV9b9jhpqsgaVWo4ZzCEMskAno654nZ0e+Xv1YDqMRwCYOBEjY+BE1ENme1xiiiVZZzk6042VW+cgRNR06hpqZ7+mqmMhmQ52xikckqF/dkgacuguH9R98TACQAWysBp9ySBkzdn2EQ1fU6l+puk9jnidmzqBE4pNYW0Kv67qmWPU+7rx6oJgolsxMCJqIbMZpzCmrjKF/J7y3r9yTbAbYU+jpw9TkRNQ5belpudNiN3xHlZmWrZ36S4gJyMxeYhcf+CaYU//2Sf08bdk0xYc7myWaxqBjaMbhW3HfMmf177bHE7tqPy92owuaXetc44FbzYR9RAGDgR1ZDZjNOYppfqWThVr0WT+zilynpNInIu2X/UUoMeJ4/bhYBXnBaUdcEld/PbnDI4mXGSJXl7WtgtAqdJS/WA7ICIajJOYzvFrQyMipmCgZM8TrkVN3wuX03fi6V61OgYOBHVUMmMk34iMKaKq3xtlvQ4iZOMgCpORphxImoetcw4AUBbQGS9x+JlXHApMBhiNJrCmP7ZM6+r8OffIhk4TVaqB+RknKoJnPRAqGTgNPVK9eTxo8XTAmWy/i8LMHCiRsfAiaiGcg9IBekZp5GMGNVrZcbJr4qgLZyoYEIWETlSpIb7OAHZPsuySvVkQJNzgUiW6fW0+Ytu1isDqm0jMWiTTcyTfVPVZJzGzQZOORmnaqb4NRB5gS/orW1/U+57MHCiRsXAiahGMmomGziV6HEaTuuBk4UZJ19azzixVI+oadRyOASQzTiVtf+bDGhkSR2ALUPiZLxYfxMAzOwIQFGAZFrFYCRZ/PWr7XHStGypXtusyZ8rH88kgOhQZe/XYEpe4LMQM07U6Bg4EdVIPJNtuC2VcRpKi7rysjNO+tW7vH2c9JMMt5aCDymOIydqItFk7fZxAoB2/TNovJxSPRnQFAic5k8SOPk8LvS2iYtG24cnOZHW96ZDYsz8mnLFhgF5ol4qcPL4gNYe8fUUKdeTPU61nqiX+x4MnKhRMXAiqhF5MHIpLvjd/sJP0k84dqfE45X2OMUzcWRUvSQv5+SlFTH2OBE1kdpnnGTgVE6p3sTAaaseOM2bJHACxEa4ACbfBDfQIW7jFQZO43q2qWU64DUxNW6KDYiQQQwDJ6LSGDgR1YgxGGKyhlv9hGMgKTJOskzGrNacPU7k+8HlNnoNWpU493EiahKaptV8OIR83bIyTsZwiOx+Tdv0DNLcrslPxufogdN2U4HTqPk15TLK9Er0N0lTbEBEySFGFpKBk3G8ImowDJyIaqTkKPJ0AlDFycmuhAiYyi3V87l88CjiZyKpnMlU+pXfNsSMEy0iamzxlApVn1fgqB6nAhmnvlFRPjyrw4rAqcpSPRkAtZco05NkOR8zTpZjxokaHQMnohqRgUypwRAAMFThcAhFUYzXl4EagLxNcFmqR9QcIsnsv+UWb216nIxSvUqm6vmzGae+MRE4zeyYvDSuvFK9CjNO4yYHQ0htM8VteFdl79dgSl7ksxADJ2p0DJyIasQY8VrsKp4eOGneFqj6P8VKym9kuV6hjFNIiSGSzCCjTo2xukTNTGaPW3xuuFy12W+nsql6+kUgn/jcCSfSxs+XCpzqU6onR5HPMff8UK+4DfdX9n4NhhknIvMYOBHVSMm6cT1wUr3iKm2Lzw13BSdDRuCULhA4QVz1zb1STUSNqdaDIYDc4RCVT9WTZXptAU/Ji0HZjFO8+JOsyjiZLdULTbGMkw09TgycqFExcCKqkVjK3Oa3aT1wqrTZWx7sCmWcOl3iZITlekSNzxhFXmRDWSu0VzRVL79UL9vfVHqC3exO8ZyhSBLxVJHNuuU48mozTmaHQxgZp6kRONmScUoxcKLGxMCJqEbkVbzcyXd59ElUKT2wKncwhCQDs0I9TtO8CQDgXk5ETaAeGaeQX5bqVTBVT79gs3NUnBTPLDEYAgA6gl74PeJUpH8sUfhJ1Y4jN0r1zAZOM8RtuB9Q1cres4Gwx4nIPAZORDViHIxKlOol3SLIKXcPJ0kGZnmBk34C0+Vm4ETULCJ1LNUrK0stS/V8e2Sc2ktnnBRFwQz9ebvGi5TrVVOql4oDsSHxtelSPT3jpGWyP9vEjIyTt/YZJxmcMXCiRsXAiahGcvdxKkg/2Ui4xMGq3D2cpMl6nDrcLNUjaha13sMJsGYD3J0mJ+pJM9rFVNFdYyUCp1QEyJSRCQOAcT3b5AkCgU5zP+P2is1yAWC8r7z3a0Alj1UWksFZLB2DpnFoETUeBk5ENSIzQKWm6sVcIvBp9VfWtyAPdvk9TuLKb4eLGSeiZhFJiB6glhr2OMkLOOFkGqrZaZxFhkOY6XECgF6ZcSpWqid7nIDyy/XGcgZDFNuIvBCjXK/5+5zs6HHSoCGRKfL3TeRgDJyIasTsVL0IrMk45ZfqiRONNkUcEJlxImp89cw4aZoInkyRPU56qd5OPXCaYTbj1Cae118s4+T2GK+NRJnlesZEPZOjyKXcPqcmV7Ks3EIBd/a/CZbrUSNi4ERUIyUbbvWTjWzgVF2PU17GSW6Aq4gTkbI2syQiR5KBTC17nAJeN3xucWpgqlwvnQAySfG1kXESJ8RmM04lS/WAyvucxraLW7Ob30pG4NT8pXoygKlHqZ7b5Ybf7c97X6JGwsCJqEZKZ5xEycm4Jk4uKs04TTaOvFUTa2DGiajx1WM4BFDmXk5yFDkA+EKIpzIYjoqfm9VurvRrRqlSPaCKwKnMPZykKbQJbj1L9XLfJ69KgqhBMHAiqpGSGSf9hGMsI04a2qvNOKUn9jgFNXFA5Aa4RI0vmqj9Pk5AdmsEUxdcknp/kycIuD1G1ijodaM9aO4zreRUPaDyvZzkcIhyS/Xaps4muEbGqQ6legBHklNjY+BEVCNme5xGVJlxsnIcuTjJCKjivrImZBGRI9VjHyegzMl6iT33cMoOhlBMDmOQpXpF93ECqsg4yc1vKyzVG2/uwCmjZmzLODFwokbEwImoRkpnnETgNJz2AahiOISneI+TXw+cOFWPqPHJzHEth0MAQJu+Ce6YqVI9OVEvfw8ns6PIgexUvXAiXfyzqtJNcI1SPZOb30pGqV5zB07xTDbLV48eJ4CBEzU2Bk5ENWK2x2l3Spw0VHoyJF+/0Aa43nQYgIawmRMgInK0cB3GkQNlZpyShTNO5QROIb/H+PwruZdTfMT060JVs8MdKh4O0dw9TjJ4UaAYQxtqTR6zGDhRI2LgRFQjJTNO+pXTgZQ4WFVdqpfODZzE1V+3loYfKWaciJpAtA7jyIFs9ttcqZ6eAfJVNlFP6i01WS/YKW5jI+ZfNDIAqGlAcWUDIbPk8xOjQKp5T/BzR5GbLa2sFjNO1MgYOBHVgKqppRtu9ROOXckqS/UmGUcOAK2IG1eqiahx1XuqXjhRxlQ9f/4eTjM7yuuXye7lVKTPKThN3MaGzL+oHEUemiH2gipHoAOQGZgmLterd39T7nvlXewjahAMnIhqIJ6OQ4MGoEjGSc0YJS67EiJwqnSqnnz9RCaBtKpfIXa5jeAppMTMnQARkaM5cjjEHqV6fXrGaFZ7eRmnkns5BbvEbWzY/IvKzW/LLdMDAEWZEgMijJLyOvU3AUUGGhE1CAZORDUgD0YKFAQ8BU4gEtkG51FNHLCqzTgBhbNOIcS4jxNRg9M0DdGkPo7c76AeJzkcwrdnxqncwKnEXk4tesYpWk7GSY4iL3MwhBTqEbeR5u1ziqXqn3GSQVre8YqoQTBwIqqB3IORSynwz0zvb9LcASThhcelIOCt7J+j1+2F1yWCrkIDIkKIIZxIQ9O0il6fiOyXSKtIq+LfcO0zTrLHqZypem1IplXsDovAp9zAqbfUXk6VZJyqDpyaf0BEySFGNVCwvJyoQTBwIqoBuRltqf6mjN5Q3RbwVNWYW/BApPcctCpxpDIaEmm14tcnInvlDnhp9dU2cGrXA6exWHmlev3jcWga4HO7MK3FV9Z79raJUr2Bkj1OdSrVA4BWmXEaqOznG4CtpXrscaIGxMCJqAbC+slEyBsq/AQ945T2ysCpsjI9yQic0rmBk/7aish+mdqThYgcSZbNhfweuF21nX7W2SI+j0ZjZWScfCFjD6cZHX64ylyjETiFiwVOesYpMQZkTH6WVZ1xkns5NW/GSV5sq2fGiaV61MgYOBHVgDwg5PYf5YmPAgCSHhFYVTqKXJIHvfweJxE4dXvFiYipfgUiciRZNlftZ4UZHUEROI3EkqWfnMhmnLKDIcrvl+nRA6f+UuPIAfMjyasNnFr1wKmJe5xKHqtqoODxiqhBMHAiqoFwqkTGSS/VS7jFwarafVlaPQWmFOkbRnZ7ROA0ZubqMRE5Ujhenz2cgJzAKVpOj1M241RufxOQ7XGKJDPG2PU8Lnd2E1wz5XqallOqV+VwiHDzlurZEThxqh41MgZORDVQOuMkAqeYS2acrCnVy6sZD7QDAKZ5ZKkeM05EjUr++61HxkmW6iXSKuKpEnvAJbPDIeREvXI3vwWAVp8bQa+YFjgwbkGfU2Is23/FjFNRdgZOzDhRI2LgRFQD8oAQ8hXLOIlSvYhLHEAq3cNJKlj6oF+d7XKJwMnUhCwiciQ5HCJU5UUWM3L7qEr2OclSPV9bVRknRVHQq+/l1F80cJKT9UyMJJdlesEuwFdh/84UmKpna+CUZuBEjYeBE1ENyFK9UhmnCOQeTlWW6hW6gqcHTp164GRqQhYROVI9e5wURUGn2XK9nFK9naPis6aSjBOQMyCiWODUUkbGaWy7uG2fU9FaAGRL9ZJhINmcZWWyXE6We9cDe5yokTFwIqoBI+NUosdpTBNN1JaV6hXocWqDWAun6hE1Ltnj1FaHHicA6GiRgVOJARE548izGafKNlM1BkSU2svJzCa41Q6GAAB/O+AWa2rWcj15ka+u+zjl9ORyf0FqNAyciGpAjiMvlXEa1azJOBUc7+oXPU4hTQ+cOByCqGGNJ+rX4wQgm3Ga7HMjnQDSIsjJ+NqwS88UzWyvNOMkfs6SHicrAidFyRlJ3pwDIoyMkw2lehktg0SmyN81kUMxcCKqAbMZp+GMOFGoNuM0WY9Tix44cRw5UePKlurVvscJyE7WG52sVE+/AAQo2J3yI6NqcLsUI3NUrmzGyYoeJwtK9YCcTXCbM+NU8lhVA7nZLZbrUaNh4ERUA0aPk2/yjNOQEThZ0+OUP1VPBE7BjFgLS/WIGtd4HceRA0Bniw9AieEQ+n508Ldh55go6ett81e8QW/JwEn2OJkp1RuVgVMVGSeg6TfBlQMa6plxcikuBD2inJMjyanRMHAiqoGSV/H0E47BtLWBU37GSZTq+dKieZulekSNa7yO48gBk5vg6tNBEehAnz4YopKJelJPqeEQrd3iNrK79ItZUaoHNH/glBTHjHr2OAGcrEeNi4ETUQ2UnKqnl+oNpMSJQtXDITyFAqdOAIA3E4MHaZbqETWwcL17nFpMTNUzMk7tVe3hJGWn6hUZDlFO2ZwROM2teD3iPZt3LydN02zJOOW+HzNO1GgYOBHVgNkNcHclZOBUg32c9OEQABBCjKV6RA2s3j1OpoZDyB6nQIcxUW9GhYMhgOxwiMFIEumMOvEJrSazP4nxbDasfVbF6wHQ1BmneCYOVRN/zvUOnAoONCJqAAyciGpg0lK9TBrQH+9LiD4Cq0r1YulY9k63B9Dvb1ei3MeJqIGF61yqZ/Q4mck4BdqxQw+cZlc4ihwAprX64FIATRPB0wQyiImPAOlJSgjHdopbfwfgb6t4PQByslzNN1UvN2iRPUf1YlzsY6keNRgGTkQWUzV18oxTYsz4cli1dh8nWSJo0AdEtCNiXLEmosZT7+EQxj5Ok/Y4ZTNOO0f0zW87K884uV0KukOT9DkFOgGX/vufLJAZs2gwBNDUGafcUeQupb6ngyzVo0bFwInIYrkHgpCvQMZJP9nQPEGk4YFLAVp97qre02i0TUbyNxSUgZMSRSSZKVz+QkSOpqoawkmZcapvqd5wpNwep+oyF5NugutymetzsmowBACEZujv13wZJ6MX11PfMr3c92SpHjUaRwRO1113HRYuXIhAIIBDDz0UzzzzTNHnrlmzBoqi5P0KBCq/wkVkNXkw8rg88Ll8E5+g9wVkfKKEpD3ohaJUNr5XatNfK62lEc/knHDogVMbRDDHARFEjSeSTENeD6lXqZ7M/AxGEvkXY3Lpn2WqvwN9Y3qpXhUZJyA7IKJ/rNhkPT1wmmxDWisDJ/l+iTEgFZv8uQ1GBi31nqiX+54MnKjR2B443Xrrrbj44ovx3e9+Fy+88AJWrlyJ448/Hv39xa8mtbe3Y+fOncavzZs313HFRJPL7W8qGBDpGaeUR2Sj5NjfarR4WoxSi3Ayp1xPH0ne4xEnNQyciBqP/HfrdSvwe+pz2J4eEhd94ikV0WSm8JP0jFNYaTE2v5UDHiolf77oSPKQiSl3Y9vEbbWb3wLi4pNbvwDWZOV6uaV69cZSPWpUtgdOV111Fb785S/jjDPOwPLly3H99dejpaUFN9xwQ9GfURQFM2fONH7NmDGjjismmlzJUeT6VdqkhYGToijGIIrx1Hj2AT3j1OMVgRMn6xE1nuwo8uqz02a1+DwIekUJ8WC4SJ+TfhFI9mrOqGLzW6nkJrhmJuuN6oFThwWBk6LkjCRvrnK9ktNfa6jg3oNEDcDWwCmZTOL555/Hsccea9zncrlw7LHH4sknnyz6c+FwGAsWLMC8efPwiU98Aq+//nrR5yYSCYyNjeX9IqoluaFg0c1v9ZONuNu6wAnIluuNJycGTtPdosSEm+ASNR452KVegyEkmXXaHSkSxMiNvFMiSzSrs/rJbL3tJTbBDZmYcjesV6F0Lqh6PXnv2WQZJ3mRz45SPW6AS43K1sBp9+7dyGQyEzJGM2bMQF9fX8Gf2WeffXDDDTfg73//O/73f/8Xqqri8MMPx7Zt2wo+/4orrkBHR4fxa968eZb/PohyldxQUD/ZiCr6qHCLAicZqOWV6ul7OXW5mXEialTjdR5FLk2XfU7FMk5yP7qkeF41m99KPaFJhkMApTNOqgqMbBFfd1kUODXpJriyTK7oRb4aYo8TNSrbS/XKtXr1apx66qlYtWoVjjrqKPz1r39FT08P/ud//qfg8y+99FKMjo4av7Zu3VrnFdNUIwOXghP1AONkI6yIA4dVGSf5foVK9Tpd4gA5xh4nooZT71HkUneryDgNhotkf/RNZrfHxWfYbAszTkVL9Ur1OIV3AZkEoLiB9rlVryfvPScbSNGASl7kqyEZrDFwokZT30/hPXR3d8PtdmPXrl159+/atQszZ8409RperxcHHngg3n333YKP+/1++P3+qtdKZJZRN15sxGt8BAAwpllcqucVpXr5wyGy48gBluoRNaJsxqk+o8glWapXcDNawMieb42IdVmTccoOh9A0bWJPlwxixgtXpWB4k7jtmCM2AbeCmYEUDcjOqXoFKySIGoCtGSefz4eDDz4Y999/v3Gfqqq4//77sXr1alOvkclk8Oqrr2LWrFm1WiZRWWTGp9VXJHCKjQAARjRrM06T9TiFNHGAZMaJqPGEE+KCR3udS/WmtYqLjrsLZZxUFUiIz5pNETFEwsqMUyKtFv686tDL7Ue3AYXGpI9Y3N8EmBtI0YCMqXo27ONU8HhF1ABsL9W7+OKL8Zvf/AY33XQT3nzzTXzlK19BJBLBGWecAQA49dRTcemllxrP//73v497770XGzZswAsvvIAvfelL2Lx5M84++2y7fgtEeeSBoN3XXvgJsWEAwKAqDlaWl+rlBU5iDS164DTOHieihmOU6tU5cOqWGadCPU7JMKCJDbXXj+mBU5Wb3wJAwOs2erkKDoiQI8ZTUSA6NPFxORjCqv4moOmHQxQtK6+hglNgiRqAraV6APD5z38eAwMDuOyyy9DX14dVq1bh3//+tzEwYsuWLXC5svHd8PAwvvzlL6Ovrw9dXV04+OCD8cQTT2D58uV2/RaI8sjARV5Rm0Av1RtIi5MMywInWfqQyi3V6wQAtKjivrEYM05Ejca+4RCyVK9AAKNPB9XcPmyPaAAUzKpy81upp82P8Xga/eNxLOnd46TeGwBCM4FwHzC6BWidnv+4kXFaaMlaADTtcIiSx6oaksFaOBkuXJJJ5FC2B04AcMEFF+CCCy4o+NhDDz2U9/3VV1+Nq6++ug6rIqrMmH5CUSrjtCtlbeAkD36Fepz8aXHfKHuciBpOdjhEnXucWieZqqf3N6m+NmgRBT6PC9P1YRLV6m3zY8NApPhI8s55InAa2QLMPjD/sZpknJpzOIQxyMiGqXry+JjRMoilY7b0WRFVwvZSPaJmI0sPil7F0wOnnUmLM06FSvX0ceTedBgKVA6HIGpAssfJrozT7oKBk9zIW3zOzeoIWJY16GnLDogoSPY5jRSYkjuoD4rqWmTJWgBkA6fEKJAqMia9AY0lxd+hHRmnoCcItyJKPPOqJIgcjoETkcVKlj/owyF2xMXVXMuHQxQYR65AQwhxjMSKTMciIseSJbb1DpzknkpDkQTSGTX/QT3jFHOJXs3506zLGPS2lRhJ3ikHROwROMVGRCYKAHr2sWw9CHQCbj2bNtnGuw1GBix2BE6Kohhj0DlZjxoJAycii00aOKXiQDoGAOhPixMNqzbALTiO3BsA3HqApkQwEmXGiajRyBJbqy6ymNUd8sPjUqBqBYIYvSR5HOLkd56FgVOPHjiVnXHa/Y64bZttDMaxhKIArfqAiCbqc7Kzxyn3fTkgghoJAycii01a/qAPhtAUN8YRhKIAbRZtamk02+5Z9hDsAgB0IIyRaApaoRG+RORYdgVOLpeCGe2ibG7naCz/QT3jNKyKx2uTcSpSFtc5X9yObsm/f+AtcWtltkmSgVOT9DklM0kkMiIwtaPHKfd9OZKcGgkDJyILqZpqZHwKDofQ+5tUfzsABe0BL1wua/oCZMZJBm4GPXDqVMJIZlTEUhlL3o+I6mPMpsAJAGbrk/J2jOwRxOiB00BKPL7A0sDJbI/Tlvy9nAbeFrc9yyxbi8EYELHL+te2QW6wYlfgVHCgEZHDMXAislAkFYEGcSAvmHHSA6eUT/QeWXkiJDNOkVQkP6vUMg0A0OMSezmxXI+ocWRUDeMJ0eNkR+A0S9+bqVjGaVdCZIdqUapXtMepayEARawht+fICJxqkHEKNddIclmZ0OpthdvltmUNxkAjlupRA2HgRGQheRXP7/bDr/cW5dEHQyQ8IhtlaeCkXzVUNRXRdDT7gJ5xmuUTJz7DUQ6IIGoUuZMwreqHLIfcm2nn6B4ZJ73HaVdSfM7Nn259qd5INIVEukCG3NeiB0/IlucBtc04tTbXSHK7+5uAIn25RA7HwInIQqUn6omMU6wGgVPQE4RH8eStQzwgAqderwimRplxImoYsr+p1eeG113/Q/ZsmXEqUqo3jhZ0tnjRHrDus6yzxQuvW5QwFxyFDmSDo349cIoOZXueemtYqtckGSc7R5FLBbfQIHI4Bk5EFip5MNIDp4hLPG5l4KQoSuEDkV6q1+sWgdMI93IiahgycLIj2wQAMzuKDIfQs+ejWqulgyEA8VkmR6H3jxUZEDFjubjte1ncbntO3E5fYlwsslSTDYeQWR6Z9bGDrJLgPk7USBg4EVmoZOCkT9UbV8QBw+qToYIHIv0kossl7mOpHlHjsGuiniQzTjv2LNXTLwKNIITF3a2Wv2/JkeRz3iNutz2v3z6Tf7/Vmizj5IRSPTlAiRknaiQMnIgsZLZUbxQiwLH6ZKjdLw5EY4mcyXpBkXHqhAicOByCqHGMxe3NOMkep93hBJLpnE1w5WeZ1oqlM6w/+e7RJ+sVHRAxVw+QBt4Sa1n/gPh+4RGWrwVATo9Tc0zVkxfXZJWCHYwtNNjjRA2EgRORhWTg1O4tsvmifrIxrIortFYHTh36tL7R5Gj2Tj3j1K4HTqMs1SNqGHZnnKa3+hD0uqFpwPaRnHI9vVRvGG3YuwaBU297icl6oV6gZ18AGvD8GmD7C+L+Jcdavhbj/QDR25UusqYGYlRH2Fmqx6l61IAYOBFZqHTGaQQAMJQRPQGWB05+PXBK5AROeo9Ta0YcKEdYqkfUMOwOnBRFwUK9FG/jbj0zkEkD+mfMiNaKpb3WZy1kj1PRUj0AWP5xcXvf9wBowLzDgPbZlq8FgLgA5dL/DiKN3+dk9Dhxqh5RWRg4EVnIbKlef1r0DdQlcNJL9YJpcd8wS/WIGobdgRMAo4dpw4DYC05O1AOAhKfN0j2cJJlxGhgvMhwCAA4+HfDm9FcdfqHl6zAoSs6AiMbvc3JCj5NRqsfhENRAGDgRWcjscIidCVG/39VqcY+T3mwr1wHAKNXzp8agQOU4cqIGMuaAwGmRDJx264GTfgFoTGvBot4OuF2K5e9pKuPUPhv4j9+L8rzjrwD2PdHydeQJ6YFTE2ScZHmcrRkn/b3zjldEDuexewFEzcRsxmm7HjhNa/VZ+v6FM04icFKgog1RjMRYqkfUKIxx5AH7DtdLZ4jMwNt9ei+KnKintWKfGvQ3AUBve4nhENJex4hf9WAMiGiejJOdwyFkqV4kFYGmaVAU6wNwIqsx40RkoUkzTqpq9DhtiYmrqdNaahQ45Q6H8AYAryil6VQiLNUjaiBGqV6LfRmn/WaLTPabO8eQUbW8UeQr53XW5D179XHku8MJqKpWk/coW2iGuG2CkeSyr6joIKM6kFNgVU1FJBWxbR1E5WDgRGQhmemRAUyexBgAcQIwoonSl06rAyd9ql7eOHLA6HPqwjhGoylomkNORIhoUk7ocVrUHULA60I0mcHG3RGo0SEAwIgWwqoaBU7deqleKqM5Z9PuUPP1ONmZcfK7/Qi4RWZxJDFi2zqIysHAichCMnDq9HdOfFC/Sqt6gkjCi5DfA5/H2n+CBUv1AKBFlOt1KhEkMypiqYyl70tEtSH3XbP6Iks53C4FB8ztBAA8s3EIu3btAACEXSHsO6s2GQufx4UuPcvWP9mAiHpqplK9lP2BE5DNOuVVSRA5GAMnIotommZcNZsscErrWSGrB0MARUr1AKPPqdslyiFYrkfUGIYjoiexy8bACQAO32s6AODx9buxdbsInILtPZZf/MnV02ZiQEQ9yb2cGnw4RFpNGxknWaVgl6IX+4gcioETkUVi6RhSqn51uFDgpJe3JHzisVqcCMmD0FhiDKqmZh/QS/Vm+aIAuJcTUSNIpDOIJEV22Op+yHIdvY8IGu59vQ/rt2wFAMyYMaum79nbpg+IGHNI4NQk48hzp9gVLCuvI3msZOBEjYKBE5FFhhMio+R1eRH0BCc+IToIAIh4OgHUJnCS48g1aMYVRQBGxmmGVwROHElO5HyyTM+lAG02TtUDgJVzO7BsZhtSGQ1BfTPtJQvn1fQ9jYxT2CGBk5FxauzASVZGtHnb4HHZ+9+VzHgxcKJGwcCJyCK5ZXoFx6pGdwMAwi5xoLB6FDkA+Nw+I2jLGxDRIjJO3W4ROLFUj8j5hqPZMj1XDfZKKoeiKPjRp/ZH0OvGNL3k1xeaXtP3lJP1HJNxklP1YsNApnE/QycdYlRnLNWjRsPAicgio/ESB6OICJxGFL3HqUalNwX7nPRSPXnCM8RSPSLHG9L7mzptHEWe6+AFXXj6Pz+II2a7xR16JrtWZMbJMcMhAp2AzNA0cJ/TpEOM6kwerzhVjxoFAycii8gP/q5AkZMJPeM0pIk9nrpqdDIky/UKbYLbBVG+NxRm4ETkdLJUrxbZ6Uq1B7xwx0W/Zr0CJ8cMh3C5cvqcdtm7lirIY1VHwDkZp9y+KyInY+BEZJFJJ+oBxnCIflUPnGp0MlTwQKSX6rVrInAajDjkRISIispmnJwTOAEAIqJf0wgiakQOh3BM4ATkBE6Nn3Gye6Je7hqYcaJGwcCJyCIl68b1Ur1d6RYAtbuKXLDZVi/Va82I+wYjzDgROZ2cfmn3RL086QQgB8+01LbHyXEZJyDb59QEGScnlOpxqh41GgZORBYpnXESgdO2RKt4Xo1K9Qo227Z2AwCCKbHGQadMqSKiooYi+vYGNdjzrWL6BSC4vECNS71620XgNJ5II5Z0yKbd7foI9vGd9q6jCk4KnIwNcBk4UYNg4ERkkZIHI/2EY3NcTL3rDvlrsg55IMorfdDLSzyZKIKIY5A9TkSO58iMk34BCC3TgULTQy3U5vfAr2+w65isU9tscTu2w951VIFT9Ygqx8CJyCJGw22hg1EmBcTF45tiolRveo1K9aYHRPnMkGzgBgB/G+AR/QLTlXGjd4KInGsoZxy5Y8iMk57FriVFUYysk2Mm67U3fuDkpIyTUaqXHM3ftJ3IoRg4EVlk0oNRTGyOq0HBCEJwu5SanQxNC4h+puH4cPZORTGyTt0YxVA0iYyq1eT9icgaw/oFjloNkqlIJCfjVAdyQES/UzJOTRA4OWkcuZwCq2oqIqmIzashKo2BE5FFJj0Y6ScbGX8nVLgwrbV2G1rKceh5GSfAuEI8XRmFpmU31yQiZ5IbVddq64KKROuXcQKAnpDDBkTIwGm8cQOnSasj6izgCSDgFsExJ+tRI2DgRGSRSTNO+slGwi+CmlqV6QHZjNPEwElknOb79U1wWa5H5GjDThxHbmSc6hM4ObZULzoIpByypjI5qccJyNlCI8G9nMj5GDgRWSCVSRllBpNlnKIeETjJMbu1kFuqp2k55Xh64DTHK9a5m5P1iBwrnspgPJEGkM26OMJUzzgFOgGPGPDTiJP1YukYEhnxZ+mEUj2AAyKosTBwIrKAzDa5FBfafG0Tn6AHTuPuTgD1yTiltXT+Jrh64DTLGwbAjBORk8kLGz63C+1Bj82rySE3v61Xj5ORcXJI4KQoDd3nJIMTj+JBq7fV5tUIMoAbSgxN/kQiB2DgRGSB3TERGHX5u+B2uSc+Qd8scUgRGadajSIHAJ/bh5A3JN4vt1xPD5x6FBFMcSQ5kXPt1v99Tg/5oNR47HdZ6pxx6m0X/S99ow4qizP6nBov45Tb3+SU/66MSbAxBk7kfAyciCwwGBdXYbuDRU4mwn0AgH6tUzyvhqV6QJHJenrgNB3iiiNL9Yica7eeYanlRZaK1LnHaU6nKIvbMRKry/uZYmScttu7jgrIY4JTyvQAYFpQHK/kcZTIyRg4EVlAZpymB4uUr4T7AQA70qKMr5alekCRyXr6FeIOdQQA0D/GwInIqeSFje6QgwZDADkZp566vN1sPXAai6cxHk/V5T1LapslbscaL+Mkg5OixyobFNx7kMihGDgRWWAwVirjJEr1tqTEnhX1yjgVKtVrzYgrjo6ZUkVEE2QDJwdlnFJxIK438NepVC/k96AjKMax7xhxyGdW+xxx24AZJ3msksGKE8ggTq6NyMkYOBFZwLiKV+xgNC4Cp41x0XtU6ylZBQOnUC8AIJAchguqc5qtiWgC2eNU64ssZdEvAMHtB4JddXtbWa63fSRat/ecVLvMODXecAgnZpzk8YqletQIGDgRWWDSUj01A0QGAADvRlvE82pcflMwcGrpBhQXFE3FdIxiF0v1iBzLkRknGTiFZojpcnUiy/W2Dzukz6mBh0MYGScHBU4s1aNGwsCJyAJyGlDBg1F0CNAy0KCgX22HS7Ep4+T2iBMeADOUYQxGEkhn1Jqug4gq48gep3Ex5AZtM+r6tnO7ZMbJYaV6433iwlgDcXqpXt7eg0QOxMCJyAKTlurpV2nTgenIwI3ukB8ed23/6RUMnACgbSYAYKZrGJoGDHIvJyJHMkr1nJpxqqPZnWIk+XanTNZr7QFcHkDLZIPJBlFyAqwN5PEqpaYwnhq3eTVEk2PgRGQBWapX8GCkjyKP+0VQNUPfl6SW5HjXCfti6NOglvjFwYmT9YicyZGlekbGaWZd33ZOpyhxdsxIcpc7m3Ua2WLvWspUcgKsDQKegLEZL/dyIqdj4ERUpZSaMjYVLHgw0keRj3vqFzh1B0QANxAbyH9AP+FZ4Beb4HKyHpHzpDIqRqJi9LajSvX0i0AI1Tlw6nJYjxMAdC0QtyOb7V1HGTJqxtjHyUkZJyBbrcEBEeR0DJyIqiSvkLkVd+FNBfXylmGXmEI1o732V5B7W8UEvbHkGOLpnOCoTTQ1z3GLkcKcrEfkPPLfpdetoKvFQYGTPh203j1OslRv13gcKaf0ZXbqgdNw4wROI4kRZDTRkyX3+nMKY7IeR5KTwzFwIqqSvEI2LTANLqXAPyl9k8RdWicAYGYdMk5t3jYEPeIqbX+0P+cBcaV4hqLv5cRSPSLH6RsVFzt62wJwueo3va4kmzJO3a1++DwuaFr2z8Z2DZhxkseqTn8nvC6vzavJJ6s1OFmPnI6BE1GVSm5+O7oNALA1I66ozeiofeCkKAp6W0TWaVd0V/YBvcdpWkaseRdL9YgcRwYHs+rwWVEWmzJOLpeC2R0OGxDRuVDcNlDGqeSxykYs1aNGwcCJqEqy2VYOZJhgTAROG5KdAOrT4wTACJwKZZza0mLNO51yEkJEhr4xETjNdFLglEkb+9HVO+MEOLDPqQEzTsZgCAeNIpfk8ZOleuR0DJyIqtQXFeUrM1uKnEyMbgcAvBVtE8+zM3DSN24MJIfhQwo7nLIvChEZ+kZFcFCvzwpTIgMANEBxAa31z1jM1SfrbR2O1v29C5I9TmPbgUzK3rWYJMvgil7ks1FPsAcAMBAdKPFMInsxcCKqUl9ED5xaCwROqTgQFVf53o51AKjPcAigSOAU7ALc4v17lRHnjPclIkOf3nvoqIzTmLgAhLbZYhx3nS3oFoHTpt2Rur93QaFewBMENBUY3Wr3akxxcsZJHj/lhUgip2LgRFSlSQMn/WRD9QQwghCCXjc6gvVpyp3RIvoQ8nqcFAXoEPuPzMZujCfSGIs3xtVSoqnCyDg5KXCSwUHHXFveftF0sc/PxkGHZJwUBeicL75ukD6nXRFxLCh4rLKZPF7J4ymRUzFwIqqSmcApHpwFQMGcriAUpT5TsgpmnACgYx4AYJ+AmKzHrBORs8geJ0cNh9CH3NgVOC3sFoGTYzJOQMP1Oe2MiAmvs1pn2bySieTxcyQxgliaxyRyLgZORFXQNM04GBXscdL7m8Z8IoiZqzc410PRwEm/SsrAich5NE3DrlFRqlevQTKmjNibcVqoZ5xGYykMR5K2rGGCBtvLyThWOTDj1O5rN7bQkJkxIidi4ERUhbHkmHF1rHDGSVyl3e0Sja/1DJxk6cNAdACqlrNppB44LXCL6UUcEEHkHEORJJIZFYoi9nFyDJlx6pxny9sHfW5jWMbGQYdknWSpXgNknFJqCgMxMXjBiRknRVGMY2heeTmRwzBwIqqCLNPr8nch4ClwkqNnnLarYorR3K6Wuq1tenA6FChIa+n8TQX1g/0ciIMoM05EziEvZHSHxKavjmH0ONkTOAHAQqcNiJClesObbF2GGfICmsflMTabdRr2OVEjcNCnMlHjmbS/CQBGtgAANqS6ANQ34+R1edHTIjJdO8M7sw/oJz7TM+KqnmM2lCQiY9z2vDp+Vphi83AIIFuut8kpAyKm7SVuB9cDmmbvWkowjlUtM+FSnHnqZ0zWY+BEDubMfz1EDUJ+wM9onVH4CcMbAQCvxeqfcQKAuSFxkrMtvC17p15q0xbfBQUqtg455CSEiLBF//c4f1p9PysmlQgDMdETaW/GyWEDIqbvBUAB4iNAZLfdq5mUMRgi5LwyPYkjyakRMHAiqoL8gC9YM55JGxmnF8MycKrvVeS5bXrgNJ4TOLXNBhQ3XFoKvRjBZqdcvSUi40LGPCcFTnIPJ38HEGi3bRnZjJNDAidvMNvntPsde9dSgpMn6klywBIzTuRkDJyIqjDplKLRrYCahub2Y6fWhYDXhemtvrquzwiccjNObg/QLvZymqsMYDCS5F5ORA6xxYmBk34ByM4yPQBYpGecNu6OQHNKaVz3UnE7uM7edZRgVEe0FKmOcAAOh6BGwMCJqApbx0Xd/5zQnIkP6mV60da50ODCvK6Wuu3hJM1rE2U1cp0Gval5RVBM1tvCrBORIxgZpzqX9U5qcL24nb7Y1mUsmN4CRQHG42kMOmUkeffe4na3swOnRirV2xne6ZzAmGgPDJyIqrB5TIyhXdi+cOKDQyJwGvSJoGpxT2u9lmUwepxyS/UA4yrpAUExWW+jU3oGiKawjKoZw1rmT3dS4PSuuJXDEGwS8LqNcud3do3buhaDzDixVK9qc0JzoEBBOBXGcGLY7uUQFcTAiahCI/ERjCZGAWQzO3n0jNM2iNKIxT2huq1NkqV6fZE+JDM5V2ini4P9Epco39jslJ4BoimsbyyOVEaD160YexY5wpDMOC2xdx0A9pkheqze6XNI4DRdBk7OzTipmmpcPCtYHeEQAU/AyDrJi5JETsPAiahCm8fFB3tvSy9avAWuDusZp3fSYiT44u76Z5ymB6Yj6AlCg4Yd4R05D4gToDkZcTB1zHhfoilMlszO7gzC7apvWe+kZMZpur0ZJwBYNrMNAPC2UzJOPfuI2+FNQNKZF6D6o/2IpWPwKB7jYppTzW8XwzYYOJFTMXAiqtCWMdEwXbBMDzD6Al7RJ+rZUaqnKIpxoMzrc+oWgVNXfCsUqM4Z70s0ha0fCAOw5yJLUak4MKJ/dthcqgcA+8jAySkZp1Av0NoDQAP637R7NQVtHBUX8ea2zYXX5bV5NZOTx1N5fCVyGgZORBXaNLYJQPYKWZ500piy9FS4FwCwuLv+pXoAMC9UYEBE5wLA7YNbTWKOMoh1/WE24xLZ7N1+ETgtndFm80pyDG8CoAG+NhEk2ExmnN7Z5aDPrJn7i9u+V+1dRxGT9uI6zPw2cTyVx1cip2HgRFShyQdDrAfUNDLeEHZgOrpavOiq8yhyaUGHmKC3YXRD9k6XG5gmJmQtce3EaCyFXWMJO5ZHRDoZOC3pteciS0FGmd5ioM5TQQtZ2N0Kr1tBOJHGtuGY3csRZqwQt7tes3cdRcggZGHHQlvXYYZcIzNO5FQMnIgqJD/YF7QvmPhg/xsAgNG2JQAUWwZDSEs7RfPyuuE9mpf1PqdDQmLHe8f0DBBNUev6xb/BpU4KnHa/LW7lEASbed0uLO0VWafXd4zZvBqdkXFyaOA0uglAY2WctoxvcU5GkSgHAyeiCqiaOnmpXv9bAIDNbhFULZ/VXq+lTbB3l9hnZN3IuvwD0Yz9AAAH+cWAiLf7HHISQjQFjcWzWd+9nBQ4yWBA/7xwgpXzOgAAr2wbsXchkpFxeh1QVXvXUkAjZZzmtM2BW3Ejlo6hP9pv93KIJmDgRFSBbePbEEvH4Hf7jStkefSM02up2QCA5bPtC5wWdSyCW3FjPDmevyO7fpV0r4wo4XvLKc3WRFOQLNOb2R5Ae8BBDfyy/ExmVRzggLmdAICXnRI4dS8F3H4gOW5sQ+EUiUzCmKhasDrCYbwurzHQKK+8nMghGDgRVeCNIREYLe1cCo/LM/EJ+nSlx8fEKHI7M04+t884YOaV6808AADQHd0IL9LO2VCSaAqS+xI5qr8pFcv2OMmsigMcMFdmnEahqg4o53J7gVkrxddbn7F3LXvYNLoJGjS0+dowPTDd7uWYIqsk3hp6y+aVEE3EwImoAm8OisBo3+n7TnwwPgYMiStlz0ZnwaVkR+jaZWmX6E94d+Td7J2d84FAB1xaCkuVbXinL4xEOmPTCommtle2i82095tj30WWCfrfADQVaOkG2mbavRrD3jPa4Pe4MB5PY5NTNu+e915xu81ZgdMbg+Ii377T9oXigOEeZiyfvhxA9jhL5CQMnIgqIK+EFQycdrwAQEOsdS4G0YG9ekIIeN31XeAeCg6IUBQj63RIYBuSGRVv7mTWicgOsl9npV6G5giyv2nmCkdM1JO8bhdWzBFZpxe2jNi7GEkGTluftXcde3h98HUAwH7TndOjVsq+08Rx9c0hBk7kPAyciMqkaZpxJWz5tOUTn7BNHDi3BMVj+9nY3yTJ0gd5EDXogdORoe0AgBc2D9d1XUQExFMZvKVftJBlaI6w82Vx66AyPemQhWJj8ac2DNq8Et1cPXDqfx1IOOcC1Gu7RfC7vLvAscqhlk1bBkBs+RFJOSSjSKRj4ERUpl3RXRhODMOtuLGka8nEJ2x7HgDwTGoRAOBg/QBvp5W9ov5+w+gGjMRHsg/MfQ8A4ABVBIIvbh0BEdXXW33jSKsaprX6MKczaPdysrY8JW5lNsVBVu8l+nWeXO+QwKl9FtAxX5Q2bnNG1imZSeLtYTFOvpEyTtOD0zGjZQY0aHh76G27l0OUh4ETUZle3S12h9+rcy/43f78BzXNOGjeNSQmA73XAYHTtMA0Yw+PlwZeyj6w4HAAQHdkHdoRwYtbmHEiqjdZpnfA3A7n9KHEho3poJi/2t61FPCeBV3wuBRsH4lh61DU7uUIi94vbtc/YO86dOtG1iGtptHua8fc0Fy7l1MWWa4ne7SInIKBE1GZnt75NADg4BkHT3xwcD0Q3Q3V5cWLqXnoCHods5nlQTMOAgC80P9C9s62mcC0vaBAwyHud7BtOIbtIzGbVkg0Nclys4Pmd9m8khxbngagiY2yQ712r2aCVr/HKGt0TNZprw+I2/UP2rsO3eu7s/1NjgnITVrRLcpD845XRA7AwImoTDJwOnTWoRMfXH8/AGBnx4FIwIdDFnbB5XLGAevA3gMBAC/uejH/AT3rdGKH2H/k0XcG6rouoqkso2p4Qj/xP2JJt82rybHlSXHrwGyT9P6lYruH+97cVeKZdbL4GACK2PtqvM/u1eCpnaLUUn72NxJ5fH2m7xlkVE57Jedg4ERUhl2RXdg0tgkuxYVDZh4y8Qnv3gcAeDgjNos8bLFz9s04qFdknF4bfA2xdE5WaeH7AABHKK8AAB5m4ERUN2/sGMNINIWQ34OVThoMseEhcatfWHGiD+03AwDwyLoBxJIOOLlunQ7MXiW+XrfW1qWk1bQROB0+x7l/h8Ws6F6BVm8rRhOjeGuY+zmRczBwIirDM31ij47l05aj3bfHtLxUHNj0GADgfwfF+O/jls+o6/omM69tHma3zkZaTeOJHU9kH1hyLKC40Bt5B3MwgMfe3Y10RrVvoURTyGPv7gYgLrJ43A45JI9uB3a+BEARnw8OtXxWO+Z0BhFPqcafo+32+Yi4ff1vti7jtd2vYTw5jnZfO1ZMd95UxFI8Lo9xcfLJHU/avBqiLId8ShM1hoe3PQygSJnepkeBVBTxQA/eyMzD0t4QFkxvrfMKi1MUBR9c8EEAwP2b788+0NptlON8IvgSxuNpPOmUEb9ETe7fr4uSriP3dlCZ3tt3i9t573Vkf5OkKIpxceqfL++weTW6FZ8RtxseAiL2BXPy4thhsw6D22XvPoKVOmzWYQAYOJGzMHAiMmk8OY6Htj4EAPjQwg9NfMLLfwIAPOF/HwAFH9zXOdkm6dj54urxQ9seQiqTyj6w7KMAgJNaRP/T317YXve1EU01mwcjeHnrCFwKcMKKWXYvJ+utu8StzJ442GcOEtPi/v1aH4YjSZtXA2D6XsDsAwEtY2vWSV7kO2LOEbatoVrvnyOmFD636zkMRFlCTs7AwInIpPs234dEJoHFHYuNUamG+Bjw1p0AgF/sFtP2Pnng7HovsaSVPSsxPTAd48lxPLkz5yrevh8DoGBR5CUsVHbi36/3IZJI27ZOoqlAZkkO36sbPW3+Es+uk+HN2f6mfT9m61LMWDGnHctntSOZUfG3Fx1yweeAz4vbZ34DqPUve94wugFvDL4Bj+LB0fOOrvv7W2V++3ys7FkJVVNx14a77F4OEQAGTkSm/XPDPwEAJy4+ceJo11f/DKTjGGpZhJcyi3Dg/E4sm9le4FXs5Xa5ccKiEwAAf3jrD9kHOucDS48DAJwfehjRZAZ/eX6bHUskmhLSGRV/enYrAODjKx10keX5NQA0YNFRInvicIqi4ORD5wMAfvfYRiTTDujPXPVFwNcG7H7bmLRaT39bJzJdR8w5AtMC9u8jWI2P7/VxAMDf1/8dmqbZvBoiBk5Eprw5+Cae7XsWLsWFExefmP9gOgk8dg0A4HfxDwBQcPJ759d9jWZ9Yd8vwKW48Pj2x7FueF32gUO+DAD4hPYg2hHG/zy83hknIURN6K5Xd2LbcAzTW334+CqHBE6JMPDCzeLrQ86ydy1lOOnguehp82P7SMwZF3wC7cDBp4mvH/6J2Bi9TiKpCG5fdzsA4HP7fK5u71srH170YfhcPrw78i6e7nva7uUQMXAiKkXTNFz9/NUAgOMXHo9ZoT16EV68BRjdiohvOn4bfT/mdgXxCaecCBUwr20ePjhfDIn4n1f+J/vAkmOBnn3hS4/jkpY7sWM0jlue2mzTKomaVyqj4pcPvAsAOO3whQh4HdK8/8S1QHQ30LWwIfqbpIDXja8cJbJjV619ByNRB/Q6rb4A8LYC254FXvlz3d72ljduwXhyHAvbF+KI2Y3b3yS1+9rxmb3FwI2fP/9zZp3IdgyciEq4Z/M9eHLnk/C5fLhg1QX5D45uB+77HgDg5/GPIQEfLj5ub/g9DjkRKuLL+38ZLsWFezbdg0e2PSLudLmAD/0QAHCydjeWK5vwX/e8jS2DURtXStR8fv3IBqzrD2Naqw+nrV5o93KEwfXAE78QXx97OeD22rueMn3xsPlY0hvC7nACl/39dftPsNtnAUd+U3z9728DI1tr/pZbx7fihtduAAB8ddVXG3aa3p7OOeAcBD1BvDb4Gu7ccKfdy6EpjoET0STWj6zHD578AQDg7APOxvz2nBK8ZAS47XQgMYY33fvgt8ljsXrxdHxi1Rx7FluGfafvi1P2PQUA8N0nvott43p5y9JjgX0+AreWxg0t1yKQGsZZNz2L0WhqklcjIrMef3c3rl77DgDg//fRfdHR4oAAJTEO3HoKkIoCC98PLP+E3Ssqm9/jxpWfPQAuBfjHyztwrZ7Rs9XqC4BZK4HYEHDrl4DYSM3eKpqK4usPfh2xdAwHzzgYH1744Zq9V711B7tx5oozAQA/eOoHWD+y3uYV0VSmaLZflgGuu+46/PSnP0VfXx9WrlyJa6+9Fu9973uLPv+2227Dd77zHWzatAlLly7FT37yE3zkI+bKCsbGxtDR0YHR0VG0tzuveZ+c47Xdr+HrD34d/dF+rOxZid8d/zv43frkq/FdwF/OADY/jrDSio/HL8dIy0Lc9bX3YVZH0N6FmxRLx/CFu76Ad0fexZzQHFz7gWuxtGspEB0C/ucoYHQLNmIuTk9cDG/PElz/pYOwpLfN7mUTNaz739yFr/3xRUSSGXxs5Wz84j9WTRw0U29jO4E/fQHY8QLQ2guc+4jIljSAVCaFLeNbsGl0EzaObcTG0Y14ccdGbBoegOKOIuhT0Or3wOvyoNPfia5AF2a0zMDCjoVY0L4AC9oXYH7bfAQ8gdotcngT8OtjRPA0YwVw0hqge6mlbzEYG8TFD12MF/pfwPTAdPzpxD9hZutMS9/Dbmk1jfPWnoen+55Gd7Abv/zAL7Ff9352L4uaRDmxge2B06233opTTz0V119/PQ499FBcc801uO222/D222+jt3fixntPPPEEjjzySFxxxRU48cQT8Yc//AE/+clP8MILL2DFitK7YzNwolI2jGzAH9/6I/6y7i9Iq2ks7liMmz58EzoDncDIFuCVPyPz+LVwJ0YQ0QI4JfltbAjuh9+ffSj2m91h9/LLMhAdwGn/Pg1bx7fC5/Lh5GUn43P7fA7zE3Hglk8CY9sRhw83po/HX9WjceCB78FnDp6HgxZ0wetmwpqmOE0DMilATQGeAFCgNEpVNTy7aQg3P7UZd72yEwBw+F7TccPph9jb2zS6DXj5j8Dj1wKJUSA4DfjS7cCcg+xbUwGapmEkMYLNY5uxcXRj9tfYRmwb34aMlqnq9RUomB2ajUUdi7CoYxEWdyw2vu7yd1kT2Pa9BtzyKSDSD7j9YnDEypPFfk9VvP5QfAh3rr8Tv3vtdxiKDyHkDeH6467Hyp6V1a/ZgYbiQzj73rOxbngdPIoHn9vnc/j8ss9jccdiu5dGDa6hAqdDDz0UhxxyCH75y18CAFRVxbx583DhhRfi29/+9oTnf/7zn0ckEsGdd2brXA877DCsWrUK119/fcn3c1Lg9FbfGDYORIzv9/yLKPQ3o+3xrD2fowEIRHeia/hV/fEJr2p8pehfi6cUf17em+hfawCUnPtzf/rF5AaoyOTcn/M84+cL/KymZl8bGtQCr6/lfLXn70/T/zfxd6EZ61Zz7hXfq4hoCYS1BAa0MLarwxhCtqfnEMzEV9IL0BkdRFd4HboT2Tr1N9X5uCB1IUJz98Mv/mMVFkxvRSMajA3isicuy/Y6AZgTmoMlrXMwd9db6BjegnZVhV/TMK6G0K91YRQdcPvb4fUG4PEF4PL64Ha54HG54HYBUFxwKeKkBIoCRf9anoTI7yVN/1ox/k/KP6nI+9udcMKhFH5eCUref6Xi/5Wc/87lf2LKhP+6jGcUeEP9Dm3P/x73fK424evCH8mF/j0W+V1q+V9oco0AlAJLzf1dKdAmfB4oyP+zyV9DlqqqyGgaMiqgaipUTYOqavqteFwFkFFVqJoIKDRNQ0bTjOfK91PEfzbivyFFgQvia5dL/9qliO8VwKUo2VsXjP/u3PpzFCX73ILnqJoGl5aBoqWgqBkoagouLQ2XmoaipaGoabjUlHiOmsrep98qOX8uquJGxuVFBh6kFC+Smvv/3969h0VV538Af5+ZYWAYAQEFBuWmKAZ5yTIvj1E+8ihmXqpF7OcCiubWuk95yUulWU/rEu5qbtaTaxmamdlq2T7spoumqcmKq6LmhbwtXsFIuQ4MzJzv7w/kyMhl0HCOjO/X85xnON/zPd/5zIdhmM/5njkDs00Li9CiGjrUQIf27Yzo3MEHkpse0NZbbvytQNLULpAAjab2b6OuXdz8Xdb/PQgho8Fzrn5fWw1QU1l7Kl7lNaCsAKio94Wi7UNrL6HdLqBFnw1q7C/hTvvJQkaltRLmGjPMVjMqaipQUVOBXyp/wVXzVVw1X0W13PTFHjx1nojwiUC4TzgivCPQ2aszfN19cfKyFSt25KOowgJINkjaCgT6WuHjVQ6NvgiVogCltsuwyBVNju2u8YSXm++NxQ+eWi/oNR7Qaw21txoPaCQNNNBAkjTQSBpIuHkr3fidCQjoqosRcnYDvErylL/IGrd2qPQ0odrdF1atJ6xuRtgkNwhJAyFpIN+4FZBghQ0VchXKRRVKbBW4YC3CZds1JcfBWj+81H40gnX1Lz9+h0VZM8VcTLAP9CoeNCu3VWH+2U3YXnxcaQtw80a0MRhB7n7wM/WBt94bbho3uGncoNPo4KZxc1gAS83k6tfsS4491vmxm2fzqKTNFE7V1dXw9PTExo0bMXbsWKU9JSUFxcXF+OabbxrsExoaipkzZ2L69OlK28KFC7F582YcPny4QX+LxQKLxaKsl5aWIiQk5J4onNL+dQJ/23W21ccdrfkB7+k/aPVxW+qh8BBY1T795FdwEwIDKqswsaQU/aosdi+JspCQI3pgo/wECsNGI7F/OJ580ASNpu0+XqD2zfruS7vx2fHPsL9gP6yCX35LRPeOIGMQIrwjlNmgcJ9wdPHpgo6Gjk2+sTVXW7H50GWszzmPo5dKGukhIGkroNFfhcb953q3P0Ojv353H1AribZYkFBWjjFlFbgHPi3nNNke7vjMxxs/GDxga8PvNwjYMW4HOhg6qBrD7RROOifF1KiioiLYbDYEBgbatQcGBuLkyZON7lNQUNBo/4KCgkb7p6Wl4a233mqdgFtZZ18D+oX72rU1OHLR/GqDA0MSJARVdcLJ8pvn/oqGe9ltq1sTzbz42I9Rb/9G9uleXQKbJCAJ+23SLbd18TYeWf11yW5b3RHwm+vSLf1rj6zX/mC/L8TN0aQb7UahQTtZC19Zi842PUKtehiEFtABP3h7oVznA6uHPyp9usEa8CBCQ0KxINjn3vhQdyuRJAmxnWMR2zkWZdVlOHntJE4Xn0ZBRQFKq0tRYilBjbUKtsprsFVXwFJVAYvVAlm2Qcg2SDeOqt48DNNwKrSxucBbW+7831/Tx39uf8w72UNyEMWtz+K7Ec9t9JPqzyQ11/Pm41JeJxq9V0mZLaydWby5TZJqZxqVv03p5hFcZTvsj+rWn/QS9dbrjq4LUb+ttvCv61d/Qk4ofUUjk2T2r4MyNJAlCQIayKg9yi9LmnrrGsh2bRKEpAEkLSRJAy0EtJIMLWRoJRnuGsBdK+Cpk2DQAZKQAWEDZFujt5LdTFHdbd2MprglYtR78b/1NVRqOHUraWpntTQ6wM0ASd8OMPg2e+W81vrsVUuOxht0BhjdjDC6GeHp5glPnSf8PPwQ4BmgLHqt/rbv21Ovw//1D8X/9Q9FsbkauReKceF6JS5eN6OsygqzxYqKahss1ghlpq1uhtRmqYIFxbCiGFapFFaUQpbMkGGBLFkgoxpCskBABiDfeG7KDdZvPMsb/BeTIMMgquAhV8ENNXATVuiE9cYzTUCCgEbU3koAdAJoJ0vKEmLVILxaC1/ZG0BHnGrBv6P6s6N32icyoB10GvVP0x54YzHbZJxADfIkK4p0WlyLHIKy6jJYZSuswgqrbEWNXNPoTGpTM6K3M5/Q0tlXappOUrUUuW1tK9o78Oqrr2LmzJnKet2M070gaWA4ku7KpWgHAJh2F8ZtmQ2q3TO1Bi+9F/oF9UO/oH5qh0JE1Crae+rxRFTDz01T2+YJ4OEbC5EzqFo4dejQAVqtFoWFhXbthYWFCApq/IowQUFBt9Xf3d0d7u7qnjtJRERERERtm6rzrXq9Hg8//DC2b9+utMmyjO3bt2PgwIGN7jNw4EC7/gCQlZXVZH8iIiIiIqJfS/VT9WbOnImUlBQ88sgjePTRR7Fs2TJUVFRg0qRJAIDk5GR06tQJaWlpAICXX34Zjz/+OJYsWYKRI0fiiy++wH//+1+sXLlSzYdBREREREQuTPXCKTExET///DPeeOMNFBQUoE+fPtiyZYtyAYjz589DU++DiIMGDcLnn3+O+fPn47XXXkO3bt2wefPmFn2HExERERER0Z1Q/XucnO1e+h4nIiIiIiJSz+3UBupfU5KIiIiIiOgex8KJiIiIiIjIARZOREREREREDrBwIiIiIiIicoCFExERERERkQMsnIiIiIiIiBxg4UREREREROQACyciIiIiIiIHWDgRERERERE5wMKJiIiIiIjIARZOREREREREDrBwIiIiIiIicoCFExERERERkQM6tQNwNiEEAKC0tFTlSIiIiIiISE11NUFdjdCc+65wKisrAwCEhISoHAkREREREd0LysrK4OPj02wfSbSkvHIhsizj8uXL8PLygiRJDvuXlpYiJCQEFy5cgLe3txMivH8x187DXDsPc+08zLXzMNfOw1w7D3PtPPdSroUQKCsrQ3BwMDSa5j/FdN/NOGk0GnTu3Pm29/P29lb9F3u/YK6dh7l2HubaeZhr52GunYe5dh7m2nnulVw7mmmqw4tDEBEREREROcDCiYiIiIiIyAEWTg64u7tj4cKFcHd3VzsUl8dcOw9z7TzMtfMw187DXDsPc+08zLXztNVc33cXhyAiIiIiIrpdnHEiIiIiIiJygIUTERERERGRAyyciIiIiIiIHGDhRERERERE5AALp0aEh4dDkqQGy7Rp09QOzeXYbDYsWLAAERERMBgM6Nq1K95++23wmiV3R1lZGaZPn46wsDAYDAYMGjQI+/fvVzssl7Br1y6MGjUKwcHBkCQJmzdvttsuhMAbb7wBk8kEg8GAuLg4nDp1Sp1g2zBHef7qq68wbNgw+Pv7Q5Ik5ObmqhKnq2gu3zU1NZg7dy569uwJo9GI4OBgJCcn4/Lly+oF3IY5em6/+eab6NGjB4xGI3x9fREXF4d9+/apE2wb5yjX9b3wwguQJAnLli1zWnyuxFGuJ06c2OD9dnx8vDrBtgALp0bs378fV65cUZasrCwAQEJCgsqRuZ709HR8+OGHeP/993HixAmkp6dj8eLFWL58udqhuaQpU6YgKysLa9euxdGjRzFs2DDExcXh0qVLaofW5lVUVKB379744IMPGt2+ePFivPfee1ixYgX27dsHo9GI4cOHo6qqysmRtm2O8lxRUYHBgwcjPT3dyZG5pubybTabcfDgQSxYsAAHDx7EV199hby8PIwePVqFSNs+R8/t7t274/3338fRo0exZ88ehIeHY9iwYfj555+dHGnb5yjXdb7++mv85z//QXBwsJMicz0tyXV8fLzd++7169c7McLbJMihl19+WXTt2lXIsqx2KC5n5MiRIjU11a7tmWeeERMmTFApItdlNpuFVqsVmZmZdu19+/YVr7/+ukpRuSYA4uuvv1bWZVkWQUFB4s9//rPSVlxcLNzd3cX69etViNA13Jrn+s6dOycAiEOHDjk1JlfWXL7r5OTkCAAiPz/fOUG5qJbkuqSkRAAQ27Ztc05QLqqpXF+8eFF06tRJ/PjjjyIsLEy8++67To/N1TSW65SUFDFmzBhV4rkTnHFyoLq6Gp999hlSU1MhSZLa4bicQYMGYfv27fjpp58AAIcPH8aePXswYsQIlSNzPVarFTabDR4eHnbtBoMBe/bsUSmq+8O5c+dQUFCAuLg4pc3Hxwf9+/dHdna2ipERta6SkhJIkoT27durHYpLq66uxsqVK+Hj44PevXurHY7LkWUZSUlJmD17NmJiYtQOx+Xt3LkTAQEBiIqKwosvvohffvlF7ZCapFM7gHvd5s2bUVxcjIkTJ6odikuaN28eSktL0aNHD2i1WthsNixatAgTJkxQOzSX4+XlhYEDB+Ltt9/GAw88gMDAQKxfvx7Z2dmIjIxUOzyXVlBQAAAIDAy0aw8MDFS2EbV1VVVVmDt3Lp577jl4e3urHY5LyszMxPjx42E2m2EymZCVlYUOHTqoHZbLSU9Ph06nw0svvaR2KC4vPj4ezzzzDCIiInDmzBm89tprGDFiBLKzs6HVatUOrwEWTg6sWrUKI0aM4Pmtd8mXX36JdevW4fPPP0dMTAxyc3Mxffp0BAcHIyUlRe3wXM7atWuRmpqKTp06QavVom/fvnjuuedw4MABtUMjojaspqYG48aNgxACH374odrhuKwhQ4YgNzcXRUVF+OijjzBu3Djs27cPAQEBaofmMg4cOIC//vWvOHjwIM80coLx48crP/fs2RO9evVC165dsXPnTgwdOlTFyBrHU/WakZ+fj23btmHKlClqh+KyZs+ejXnz5mH8+PHo2bMnkpKSMGPGDKSlpakdmkvq2rUrvv/+e5SXl+PChQvIyclBTU0NunTponZoLi0oKAgAUFhYaNdeWFiobCNqq+qKpvz8fGRlZXG26S4yGo2IjIzEgAEDsGrVKuh0OqxatUrtsFzK7t27cfXqVYSGhkKn00Gn0yE/Px+zZs1CeHi42uG5vC5duqBDhw44ffq02qE0ioVTMzIyMhAQEICRI0eqHYrLMpvN0Gjsn4ZarRayLKsU0f3BaDTCZDLh+vXr2Lp1K8aMGaN2SC4tIiICQUFB2L59u9JWWlqKffv2YeDAgSpGRvTr1BVNp06dwrZt2+Dv7692SPcVWZZhsVjUDsOlJCUl4ciRI8jNzVWW4OBgzJ49G1u3blU7PJd38eJF/PLLLzCZTGqH0iieqtcEWZaRkZGBlJQU6HRM090yatQoLFq0CKGhoYiJicGhQ4ewdOlSpKamqh2aS9q6dSuEEIiKisLp06cxe/Zs9OjRA5MmTVI7tDavvLzc7gjZuXPnkJubCz8/P4SGhmL69On44x//iG7duiEiIgILFixAcHAwxo4dq17QbZCjPF+7dg3nz59XvksoLy8PQO2sH2f3bl9z+TaZTPjNb36DgwcPIjMzEzabTfnMnp+fH/R6vVpht0nN5drf3x+LFi3C6NGjYTKZUFRUhA8++ACXLl3iV6XcAUevI7ceAHBzc0NQUBCioqKcHWqb11yu/fz88NZbb+HZZ59FUFAQzpw5gzlz5iAyMhLDhw9XMepmqH1Zv3vV1q1bBQCRl5endigurbS0VLz88ssiNDRUeHh4iC5duojXX39dWCwWtUNzSRs2bBBdunQRer1eBAUFiWnTponi4mK1w3IJO3bsEAAaLCkpKUKI2kuSL1iwQAQGBgp3d3cxdOhQvr7cAUd5zsjIaHT7woULVY27rWou33WXfG9s2bFjh9qhtznN5bqyslI8/fTTIjg4WOj1emEymcTo0aNFTk6O2mG3SY5eR27Fy5HfueZybTabxbBhw0THjh2Fm5ubCAsLE88//7woKChQO+wmSUIIcbeKMiIiIiIiIlfAzzgRERERERE5wMKJiIiIiIjIARZOREREREREDrBwIiIiIiIicoCFExERERERkQMsnIiIiIiIiBxg4UREREREROQACyciIiIiIiIHWDgREVGLrF69Gu3bt79r44eHh2PZsmV3bXxXl5SUhD/96U+tPu6AAQOwadOmVh+XiKitYeFEROQiJk6cCEmSGizx8fGtMn5iYiJ++uknZf3NN99Enz59bnucpgqw/fv3Y+rUqb8iQsd27tzZaI4kSUJBQcFdve+76fDhw/jXv/6Fl156qUX9N23aBK1Wi0uXLjW6vVu3bpg5cyYAYP78+Zg3bx5kWW61eImI2iIWTkRELiQ+Ph5XrlyxW9avX98qYxsMBgQEBLTKWI3p2LEjPD0979r49eXl5TXI0918bDab7a4WHsuXL0dCQgLatWvXov6jR4+Gv78/1qxZ02Dbrl27cPr0aUyePBkAMGLECJSVleHbb79t1ZiJiNoaFk5ERC7E3d0dQUFBdouvry+A2tkWvV6P3bt3K/0XL16MgIAAFBYWAgCKi4vxu9/9DoGBgfDw8MCDDz6IzMxMAPYzRatXr8Zbb72Fw4cPKzM2q1evBgAsXboUPXv2hNFoREhICH7/+9+jvLxciWHSpEkoKSlR9nvzzTcBNDxV7/z58xgzZgzatWsHb29vjBs3TokTuDnjtXbtWoSHh8PHxwfjx49HWVmZwzwFBAQ0yJNGU/svceLEiRg7diz+8pe/wGQywd/fH9OmTUNNTY2yv8ViwSuvvIJOnTrBaDSif//+2Llzp7K9Llf/+Mc/EB0dDXd3d5w/fx5XrlzByJEjYTAYEBERgc8//9zucaempuKpp56yi7WmpgYBAQFYtWpVo4/FZrNh48aNGDVqlF17czG6ubkhKSlJ+Z3V98knn6B///6IiYkBAGi1Wjz55JP44osvHOaViMiVsXAiIrpPPPHEE5g+fTqSkpJQUlKCQ4cOYcGCBfj4448RGBgIWZYxYsQI/PDDD/jss89w/PhxvPPOO9BqtQ3GSkxMxKxZsxATE6PM2CQmJgIANBoN3nvvPRw7dgxr1qzBd999hzlz5gAABg0ahGXLlsHb21vZ75VXXmkwvizLGDNmDK5du4bvv/8eWVlZOHv2rHIfdc6cOYPNmzcjMzMTmZmZ+P777/HOO+/86lzt2LEDZ86cwY4dO7BmzRqsXr3arsj4wx/+gOzsbHzxxRc4cuQIEhISEB8fj1OnTil9zGYz0tPT8fHHH+PYsWMICAhAcnIyLl++jJ07d2LTpk1YuXIlrl69quwzZcoUbNmyBVeuXFHaMjMzYTabGzz2OkeOHEFJSQkeeeQRu3ZHMU6ePBmnTp3Crl27lH3Ky8uxceNGZbapzqOPPmpXcBMR3ZcEERG5hJSUFKHVaoXRaLRbFi1apPSxWCyiT58+Yty4cSI6Olo8//zzyratW7cKjUYj8vLyGh0/IyND+Pj4KOsLFy4UvXv3dhjX3//+d+Hv79/kOHXCwsLEu+++K4QQ4t///rfQarXi/PnzyvZjx44JACInJ0e5f09PT1FaWqr0mT17tujfv3+TsezYsUMAaJCj6OhopU9KSooICwsTVqtVaUtISBCJiYlCCCHy8/OFVqsVly5dsht76NCh4tVXX1UeIwCRm5urbD9x4oQAIPbv36+0nTp1SgBQHrcQQkRHR4v09HRlfdSoUWLixIlNPqavv/5aaLVaIcuy0taSGIUQYsCAASIlJUVZX7VqVYOcCiHEN998IzQajbDZbE3GQUTk6nSqVm1ERNSqhgwZgg8//NCuzc/PT/lZr9dj3bp16NWrF8LCwvDuu+8q23Jzc9G5c2d07979V8Wwbds2pKWl4eTJkygtLYXVakVVVRXMZnOLP8N04sQJhISEICQkRGmLjo5G+/btceLECfTr1w9A7el9Xl5eSh+TyWQ3g9OU3bt32+3n5uZmtz0mJsZups1kMuHo0aMAgKNHj8JmszXIk8Vigb+/v7Ku1+vRq1cvZT0vLw86nQ59+/ZV2iIjI5VTKetMmTIFK1euxJw5c1BYWIhvv/0W3333XZOPpbKyEu7u7pAkSWlraYypqamYMWMGli9fDi8vL3zyySdISEiwyw1Q+/k2WZZhsVhgMBiajIWIyJWxcCIiciFGoxGRkZHN9tm7dy8A4Nq1a7h27RqMRiMAtMob4v/973946qmn8OKLL2LRokXw8/PDnj17MHnyZFRXV7f6xR9uLXgkSWrRRRgiIiKavbR6c+OWl5dDq9XiwIEDDU5jrH9xBoPBYFfMtFRycjLmzZuH7Oxs7N27FxEREXjsscea7N+hQweYzWZUV1dDr9ffVozjx4/HjBkz8OWXXyI2NhY//PAD0tLSGtxH3fOERRMR3c9YOBER3UfOnDmDGTNm4KOPPsKGDRuQkpKCbdu2QaPRoFevXrh48SJ++umnFs066fV62Gw2u7YDBw5AlmUsWbJEudjCl19+6XC/Wz3wwAO4cOECLly4oMw6HT9+HMXFxYiOjr6dh9zqHnroIdhsNly9erXZguZWUVFRsFqtOHToEB5++GEAwOnTp3H9+nW7fv7+/hg7diwyMjKQnZ2NSZMmNTtu3SXhjx8/rvzc0hi9vLyQkJCATz75BGfOnEH37t0b7f/jjz/ioYceavFjJSJyRbw4BBGRC7FYLCgoKLBbioqKANRefe23v/0thg8fjkmTJiEjIwNHjhzBkiVLAACPP/44YmNj8eyzzyIrKwvnzp3Dt99+iy1btjR6X+Hh4Th37hxyc3NRVFQEi8WCyMhI1NTUYPny5Th79izWrl2LFStWNNivvLwc27dvR1FREcxmc4Ox4+Li0LNnT0yYMAEHDx5ETk4OkpOT8fjjjze4CMKduHr1aoM81b9qXnO6d++OCRMmIDk5GV999RXOnTuHnJwcpKWl4Z///GeT+/Xo0QNxcXGYOnUqcnJycOjQIUydOrXRmakpU6ZgzZo1OHHiBFJSUpqNp2PHjujbty/27NlzRzFOnjwZe/fuxYoVK5CamtrofezevRvDhg1zlBoiIpfGwomIyIVs2bIFJpPJbhk8eDAAYNGiRcjPz8ff/vY3ALWf21m5ciXmz5+Pw4cPA6j9YtR+/frhueeeQ3R0NObMmdPk7NCzzz6L+Ph4DBkyBB07dsT69evRu3dvLF26FOnp6XjwwQexbt26Bqd+DRo0CC+88AISExPRsWNHLF68uMHYkiThm2++ga+vL2JjYxEXF4cuXbpgw4YNrZKnqKioBnk6cOBAi/fPyMhAcnIyZs2ahaioKIwdOxb79+9HaGhos/t9+umnCAwMRGxsLJ5++mk8//zz8PLygoeHh12/uLg4mEwmDB8+HMHBwQ7jmTJlCtatW3dHMQ4ePBhRUVEoLS1FcnJyg7EvXbqEvXv3Opz5IiJydZIQQqgdBBER0f3o4sWLCAkJwbZt2zB06FClvby8HJ06dUJGRgaeeeYZh+NUVlYiKioKGzZswMCBA1s1xrlz5+L69etYuXJlq45LRNTW8DNORERETvLdd9+hvLwcPXv2xJUrVzBnzhyEh4cjNjYWQO33VxUVFWHJkiVo3749Ro8e3aJxDQYDPv30U+W0zNYUEBCAmTNntvq4RERtDWeciIiInGTr1q2YNWsWzp49Cy8vL+ULgcPCwgDUXpUwIiICnTt3xurVq+1moYiISF0snIiIiIiIiBzgxSGIiIiIiIgcYOFERERERETkAAsnIiIiIiIiB1g4EREREREROcDCiYiIiIiIyAEWTkRERERERA6wcCIiIiIiInKAhRMREREREZED/w8vA54z4bAtaAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ha_2_ev = 1 / physical_constants[\"electron volt-hartree relationship\"][0]\n",
        "\n",
        "def gaussian(x, mu, sig):\n",
        "    return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))\n",
        "\n",
        "def run_spectral_analysis(mol, xc=\"lda\"):\n",
        "    n_states = 15\n",
        "    spectral_width = 0.1\n",
        "\n",
        "    mf = dft.RKS(mol, xc=xc).run()\n",
        "\n",
        "    mytd = tddft.TDDFT(mf)\n",
        "    mytd.nstates = n_states\n",
        "    mytd.max_space = 100\n",
        "    mytd.max_cycle = 200\n",
        "    mytd.kernel()\n",
        "    mytd.analyze()\n",
        "    osc_strengths = mytd.oscillator_strength()[:n_states - 5]\n",
        "\n",
        "    energies_ev = mytd.e[:n_states - 5] * ha_2_ev\n",
        "    x_range = np.linspace(energies_ev.min() * 0.9, energies_ev.max() * 1.1, num=1000)\n",
        "    intensity = np.zeros(x_range.size)\n",
        "\n",
        "    for e, f in zip(energies_ev, osc_strengths):\n",
        "        intensity += gaussian(x_range, e, spectral_width) * f\n",
        "\n",
        "    dx = (x_range[-1] - x_range[0]) / x_range.size\n",
        "    area = (intensity * dx).sum()\n",
        "    intensity /= area\n",
        "\n",
        "    return x_range, intensity\n",
        "\n",
        "\n",
        "mol = gto.Mole()\n",
        "mol.build(\n",
        "    atom='resources/sample.xyz',\n",
        "    basis='631g',\n",
        "    symmetry=True\n",
        ")\n",
        "\n",
        "data = {\"Excitation Energy (eV)\": [], \"Intensity\": [], \"Exchange-Correlation Functional\": []}\n",
        "\n",
        "xcs = [\"lda\", \"pbe\", \"wB97X_V\"]\n",
        "\n",
        "for xc in xcs:\n",
        "    x_range, intensity = run_spectral_analysis(mol, xc=xc)\n",
        "\n",
        "    data[\"Excitation Energy (eV)\"] += x_range.tolist()\n",
        "    data[\"Intensity\"] += intensity.tolist()\n",
        "    data[\"Exchange-Correlation Functional\"] += [xc] * x_range.size\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "for xc in xcs:\n",
        "    subset_df = df[df[\"Exchange-Correlation Functional\"] == xc]\n",
        "    plt.plot(subset_df[\"Excitation Energy (eV)\"], subset_df[\"Intensity\"], label=xc)\n",
        "\n",
        "plt.xlabel('Excitation Energy (eV)')\n",
        "plt.ylabel('Intensity')\n",
        "plt.title('Simulated Spectra')\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    f\"Result:DFT_{smiles_representation}/excitation energy.jpg\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}