WIMS QCM Pronote et XML

Accueil Forums Programmation WIMS Programmation d’exercices OEF WIMS QCM Pronote et XML

Mots-clés : ,

Vous lisez 3 fils de discussion
  • Auteur
    Messages
    • #5764
      Merci, ca m’a aidé
      Up
      0
      Down
      Pas très utile.
      markey
      Participant

      Bonjour,

      j’ai un collègue qui, avec le confinement, s’est lancé dans la réalisation de QCM avec Pronote. Après avoir vu WIMS, il a été convaincu par WIMS, mais il souhaiterait récupérer ses QCM pour les implanter dans WIMS ensuite.
      Ces QCM sont téléchargeables et sont au format XML (voir ci après pour un exemple).
      Et ma question est la suivante: y a-t-il moyen d’exploiter ces fichiers au format XML pour en faire des exercices wims OEF?
      ——————————————————————–
      Exemple de fichier : (Désolé pour la longueur!)

      <?xml version= »1.0″ encoding= »UTF-8″?>
      <quiz>
      <!–Titre du QCM–>
      <question type= »category »>
      <category>
      <text>
      <![CDATA[<infos><name>S3-Données</name><answernumbering>123</answernumbering><niveau>2NDE</niveau><matiere>SC.NUMERIQ.TECHNOL.</matiere></infos>]]>
      </text>
      </category>
      </question>
      <question type= »numerical »>
      <name>
      <text>
      <![CDATA[En quelle année les cartes perforées ont-elles inventées ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape une année)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[1930]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Le disque dur a été inventé en :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[1946]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[1958]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[1956]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[En 1970 E. L. Codd invente le modèle]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[relationnel]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[préférentiel]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[concurrentiel]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Le premier tableur apparu en 1979 se nomme]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>0</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[Visicalc]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »numerical »>
      <name>
      <text>
      <![CDATA[Le président Obama lance le projet Open Government Initiative en]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape une année)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[2009]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Le numéro de téléphone d’un contact est une donnée.]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[Vrai]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[Faux]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Une donnée est :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[une valeur décrivant un objet, une personne, un événement digne d’intérêt pour celui qui choisit de la conserver]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[forcément personnelle]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[un élément précis et unique qui permet d’identifier clairement la personne à laquelle elle se rapporte]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Coche les propositions qui sont des données :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>false</single>
      <answer fraction= »20″ format= »plain_text »>
      <text>
      <![CDATA[positions GPS]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »20″ format= »plain_text »>
      <text>
      <![CDATA[historique de navigation]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »20″ format= »plain_text »>
      <text>
      <![CDATA[formulaires remplis]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »20″ format= »plain_text »>
      <text>
      <![CDATA[recherches web]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »20″ format= »plain_text »>
      <text>
      <![CDATA[consultation de musique et videos en streaming]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »cloze »>
      <name>
      <text>
      <![CDATA[Complète les blancs]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      Les deux types de caractères qui permettent de coder l’information en langage binaire sont le {:SHORTANSWER:%100%0#} (absence de signal) et le {:SHORTANSWER:%100%1#} (signal transmis)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Assemblés par groupe de 8 les bits forment des :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un mot)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[octets]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »matching »>
      <name>
      <text>
      <![CDATA[Classe ces unités de mesure binaire dans l’ordre croissant.]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <subquestion>
      <text>
      <![CDATA[1]]>
      </text>
      <answer>
      <text>
      <![CDATA[Ko]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[2]]>
      </text>
      <answer>
      <text>
      <![CDATA[Mo]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[3]]>
      </text>
      <answer>
      <text>
      <![CDATA[Go]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[4]]>
      </text>
      <answer>
      <text>
      <![CDATA[To]]>
      </text>
      </answer>
      </subquestion>
      <shuffleanswers>true</shuffleanswers>
      </question>
      <question type= »matching »>
      <name>
      <text>
      <![CDATA[Classe chaque support de stockage de données en fonction de son type :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <subquestion>
      <text>
      <![CDATA[disque dur]]>
      </text>
      <answer>
      <text>
      <![CDATA[interne]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[SSD]]>
      </text>
      <answer>
      <text>
      <![CDATA[interne]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[clé USB]]>
      </text>
      <answer>
      <text>
      <![CDATA[externe]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[Disque portable]]>
      </text>
      <answer>
      <text>
      <![CDATA[externe]]>
      </text>
      </answer>
      </subquestion>
      <shuffleanswers>true</shuffleanswers>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Quels sont les avantages d’un cloud ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>false</single>
      <answer fraction= »33″ format= »plain_text »>
      <text>
      <![CDATA[sauvegarde sécurisée]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »33″ format= »plain_text »>
      <text>
      <![CDATA[synchronisation]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[confidentialité des données personnelles]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »33″ format= »plain_text »>
      <text>
      <![CDATA[accès universel]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Quel terme anglais désigne un espace de stockage en ligne accessible par internet ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un mot)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>0</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[cloud]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Sur l’image suivante, comment appelle-t-on l’élément A ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un seul mot)

      ]]>
      </text>
      </questiontext>
      <image_base64>
      <text>
      <![CDATA[iVBORw0KGgoAAAANSUhEUgAAAUAAAABmCAYAAAC6Ekg1AAAAAXNSR0IArs4c6QAAAARnQU1BAACx
      jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAE6nSURBVHja7J0HdFTXtbC/0aj33ntBqKAGqAuB
      ANF7Nzau4F4Tx4lT30vivMRxim2MG8Zg00EU0UQHASoIJCSQUO+9d03Vf2ew/TuxYwsMY8uZvRYL
      rZm595x77rnf2fvsffcWdXV1DfM9SV9fH6ampow2GRoawsDAAJFIdM/bGhwcxMjISKPXJ5FI0NfX
      18j1qWRgYABjY+M7OlYqlaKrq4uOjs6onpuabk+hUDA8PKweO03Jd7nPdyr9/f2YmJj8x+9Fw6pR
      +J6kvb0dGxubUQdA1WRVDaomANHT04O5ubnGJ41qomoKgN3d3VhYWNzxYqSnp4dYLB7Vc1PT7clk
      MjUAVQudpuS73Oc7lc7OTqysrLQA1AJQC0AtAH+cAFRBrqam5lufH2dnZ+zs7LQA1AJQC0AtAH88
      ANyzZw9xcXHqefBNkpKSwrp167QA1AJQC0AtAH8cAFTtAR88eJAFCxZ8KwA/+OAD1q5dqwWgFoBa
      AGoBOPoBWFpayqlTp9TjtnDhwjsDoNAprRf4Dh46Q0NDjbT1fXmBVV5uTcl38Q6qHmQV/LRe4NuT
      0ewFVvU9LS1N3ffk5GQOHDjAypUrvwCgXC7nT3/6E6+++uq/LIxvv/02a9as0WqAWg1QqwFqNUAZ
      SqVSowvd3dAAa2trOXr0qNrkdXBwUH+m2gP8sgl87do1CgoKCAkJUf/TmsBaAGoBqAXgqDeBT58+
      TUdHB4sXL/4Xjf/fAajS/mxtbSksLOTvf/+7FoBaAGoBqAXg6AWg6ritW7eqzV1fX9+vfP/vADxz
      5gxTpkzhxIkTTJ8+XQtALQC1ANQCcPQBUNU/lTmbnZ3Ngw8++B/N9X8H4H8SLQC1ANQCUAvAUQFA
      1X1VgW3MmDFERkZ+42+1ANQCUAtALQB/NAAsLy9Xh7csW7bsG19juysA1CZDuLOHTpsM4e6JNhmC
      Ngzmc1GFt6hEtd830vm3b98+tdPj266lqKiIRx55RKsBajVArQao1QB/WBpga2srO3bsYNGiRbi6
      ut7WeVXXoQrp+Tr5cjIE1Xz+94VSC0AtALUA1ALwe7vPKnBlZmZSVlbG6tWr7/p91GaD0QJQC0At
      AH+QAFTN7b179xIVFUVgYOA9aVMLQC0AtQDUAvAHB8Dr16+TlZWlfoXtmxKWagGoBaAWgFoA/mgA
      qJrL27Ztw93dnYSEhHvephaAWgBqAagF4A8CgCUlJRw/flwd3vL5e7zfOwC/LQymsEXOoOKbQwzk
      imGC7UWY6I88FEH1svKq1Q/g5u4x6gDY1dUpPLCWGgFER3sbVja2iDR4fd3dXZiZW6CjIQC2tbVg
      a2t/h7Duw8DA8K6HczQ21OPk7KKxMdd0e6pQJ5UDQpMhVlXlpRQW3tDYwvr5Yv6daoKcLe7Cw+ab
      Uz919MtxtdLHwXzkq0lubi4fbd3Niz//zagDYPqZk8QmTEasgRiq44cPMn32PI1Omoz0s0yIikVP
      Q9pBasou5i1efkfH3sjPw1VYRC0sre5qnza99zYPP/6MxsZc0+3V19UgFSDo5eOnsTZXzJnC5awM
      jT6r39kEHs0ALLp8Ej3ncHxd7q5pMSIAKgY5cfQC04Sbfv1qKePGB9x9AMo62bvtIHrGhgTEzcDP
      2fLWSltwESu/OCzuMGXhNwHw8tk0QiclU5d/DY+wMKqvpWMfkEB7bQkePmNurbrdLcjEFliaGlB7
      LR/r0BC+aZt7/7Z3cQ+cQkSY/1e+a63Io1nHA0+jPvqMbHE0v6Wx1FVW4erlqQagkbIHqX0oY52/
      3oweaK+kacgSb5cvPQiKAQ5u3wtG+niFTmKcr9MdAamuIJPLJdWCKWnB9Dkz0NcR3VUAdjfcpLzH
      gmAnEY0DRng43brGgd5ejM3MvvTLYfLS07hZXoetkyu9zS2IjQ3wjkgk2NvxWwGYfSaN8MnJ1Aj3
      1TM0DPWGglJK/xCYGH9pHgifpe3Zg0Qsxtp5LPExof9dAOwWzD/VIZZW1ncEQJlgLkvkSrLzCtl+
      6AwzFq5Sf+bvaIyl8XfXqAbaG8i+ehlzl1DaS9Kx8AgnMjxY8wCUtvOHn/6TVa8+Q8aB80xJ9OJ6
      dS9xE324fOUGeqZWdHX2MnvWNL7pmflGAPZXsONAMytXBPPxux/hIwDIKyyQrGPpTF8+m5xTGYht
      HAj1suBSdjEJ0yeRf/E8ciML+to6SZ43Dz2x6LYAuPuN32GTcB/yG+lELp7FqQNnmblqIRePpzMx
      1JPs640kTQ5m+57LrLl/Fhc+3oLP6gUUpJwiYv5cbI2+es6N6/9KQMwifMz7yCosJ3rSLOwtb4Gu
      8PhBsoX5Nc3bnlYLL1rzL2HkEoSLsRgjvSH2HTyJOb1ILf1Ys3IG6ecyMbT3xGa4k+KGDqbNTCbz
      QAqu8bMx76+moGaAqUmxiKStvPvWGZY/EE3q0TwSwm3ILW9n7sxJnD58jNrGetY9+5NvXyw+3YLV
      zFl0XUnHOXwS19JPYT82Ggd5HTebpCTPmILOdwBgTfZpjhTVsywxiOvtFhh23EDfyY9zH77H6j/+
      gcITJ/CKn4q77a194tTNnzJ3xTQ2by7gobWT2PzeBzz45DPfCsBdwn21n7QayXXhPs6dIsyBfAK8
      Ddl/oZ+HFgSSU9jM1ORExIpePnjzAEsenImekQkVN3JxdbIW7oAl17PPYes3EUtpDWX1fThb6mDg
      HkWQp82PA4Cq12de++3P8fT2FSbW3C/2LL4OgErhtCqwNfdIaeqWMihTIlcOY6IvxkoAXV3FTQ6m
      pvL8T165qxfcfOMcNyROVGdd4+Enl31/JrAAwE3vp+PorqStXoaeoYwILxPqdE2QmgZQf3IrEQGW
      2E16AlezOwfgL15+n6AIT0ImTSX9YBbBtk1Ui3Qx0HWkt88cB90G0JUTNSWaq5V99HYOIeooZMI4
      D1ocZhPprndbADy0eQuOPvY0Ftaio9NGG/pYmwttmY5Dp3gvPTIR0Q+8TOGR9ax89GdqAA4K87+y
      s19Qutx58ok5X/vwtze2ExVqh3fsAo5eKOaxJUmfAfAIEh9X6nPLcAoPpznnEleEhzHExxFbcwl5
      xZUUVMvwMu9k6SOPkVs8jE7bTRSSTkKDAmgUK8i9Woa0VxdT4cFUCHNwzk//iLdBO2/8cTfJC8K5
      cL6QSRNdOXPsJBYhE5k8Yx4nt38gAOm5EQDwIyTBoUhuZGEXOZfay6cpKGzH1tuN+xdMxcDGBr3v
      BMCzNJvb0FlWio6BPv3D7ohbcgRNQoeQMFf2Z5Ug6THh5Z89+C8AfPW5vxIU5Ydf9Gyigty+FYCp
      mz/B0duOppt1YCAnZuZkMi/mozTwp+/mXnqlIuIe+TnB1nLefG0TU+bHYW7vhrgtj80nK/nJukWk
      pR6loLgPL4chYsbZ0+AgaJRnD3Hf2kd/PAD8xYtPYmVtywOPPo6rm8cXADTWF9EnUSIVtDsV/FSa
      haGeGDszPezN9L+iadwrE7j5xiXqjNyEByebB55Y8v0C8MMsFs+15fUPc4nykNHZryQsMYAuo/E0
      nPmEcT7GmE1ci7vFd9QA74sR2usQ2ssgLlTKjToJAcG+5F2TYSG9ibmlkvoOARIC1Jrre6GrkIgx
      jlRbziDGU/+2AZi8NJn/+clfWboymuoWBSETgriW14m9biXtQyZMWjiboxs3skrQPFQANI1wp/h6
      C74h8YwPdv7KOTd/8A6OzgGYDF6lSmqFvWc0QV5WuDg5qQFonJhM/oZfYjw+STB5q5B0dePv5oqb
      qyk5GeeY//SvsBruZevuVFz8pzJUfRWxUkaAvxudwuQvzryBm18oA1UZgulmx7QVCzGRtfH7VzYw
      IXEsnTJzukoyMNQZwjlmJu11DfR3Ngga4MsjAOD71FjaIxWOGRvoydXqZvpqOnCyEhYhc2uSVzyA
      md53A2Cv93haD/+dQb/p1GdeQc/cCnMB5oFTZpFzuRCPMeOJj/b/qgb4+PQR7wGqADhzyTR+9/Lf
      mTcnkNp2Cc5jg7l2uYpxrnI6paYkLlqIhWiAv/zyb4QkhGJk5URv1RWMTKyQ6+pT0dZNf0O3YKbr
      ETXWmlaPhZQf/YTlAit+NAD8n1dfIjI6HmtbOzUEPgegrakubtYGI/Ycap0g99AJorqj38FncidO
      kFNCP6fOmX+Lyy1lpFcNMzPySxvsqmn2H64hNWUn8xav4PKR9/FLWoel4cgv7rs5QYY/O53oK59v
      em/9yJ0S33BtP1onyB1c8w8SgO3t7d8IwOxaGf4u/19FURUcUV287pdSz3T0yzATVk9bk5HHYqnC
      YP78x18yd2YCo02KBLNrjJ8H4rucgeTrJO96CaHBYzQaBlNcVo2Plyu6txFbp9L6VZAWqfFxC1Ij
      7XNOrmCShwcK51AK59C5rWutrW/G2toCE6O7W6TqTHoOUxImaGzMNd1eR2cPMuFZdrCz1libb763
      i0sZlzX6rH5bBprv1Qu8b+c7/O8vnxh1ADx+KpOkxIno6orveVv7Us+wcO5kjYbBnD5/WTCpwtDX
      19NIe9t3p7Fq2Yw7OvbqtZt4ujtjbXV3g8X/+c52nn9qlcbG/B9Cey9osL3q2kYkEiljfDUXhxs7
      fS2XMq+OXhP4vxGAWWlHOFXcw8P3z+LTD7Zi6h6Mj72+GoDS9jp+ty2fPz8/g5+9+Da//tuLnN5z
      isbKQuY9+ySFB08Sv2omB996D99Fa9AtzOBASROGAwqmTgtDYeVOlJfltwNwWhDP/mQbvsH2zFy8
      CMPqTA63O/D0ZCue+dkuPNxNCZgST1NeA5U3r2HnbYGbfwxLpo29pwCsKbrKxh2Z3Pfkco5tO4TY
      yo11i4PYn9VCac4lfCclsSLGi/XvpfL0U4s4duQE5dcqkLv78dSyOD58bTuLf/cwp0cIwJbya2xI
      KWD1g4uxk1ax65KS8f66Iwegsp9//GU7UgtrZk1wYO/xmzz+9FK2bdyJbcAEHGX1HDuVx4pXfkLW
      gQMsmRrA1qP5SPTNSfAyYtfBbCKig1EoXHjikchRA0ClXMKmv+5k9ivLSfm/LepwomVLYpDfQwAO
      9LXz7tunWLUijO37zlIpc+TKyVQtAEcbAA9s2oVXsDM5FUN09Q6REBdBZ0ONGoAZJ04xJJh7nuNj
      OLrlGH7R4+gqKUck60PX1Qv9xnoSVk4i5VgxIuUQ4wxlXBoQhruthZjwICQO3iT4Wn87AIUH8ZmX
      9xGXHMii2bFs3ZiK2FDJ6qXRfLCjgftm27DtRD2yjgF0dPV57tkpbNm0nzUPL7qnADz80T4mLo7g
      YqaE+EA9Nh++xrx4L7q6JQy7e5G3N5uocEtO3Gjn5Udj2HG8ksbiVpwMa4letJQz208w8+VVnBsh
      AC/tS6FQbsXyBZPIOJJCZZcvkeFGIwegAIL2wWEO7kzBFCNiJvuRJcxvMycv6rNyefjJmbz37h7W
      PbGSNwUgTY9w4HThAPExEQQFOLNv40GW3J/AW+/f4IVn40cNAAc72tn1yWmmPjuXo+t3ITNzYGZS
      KHLpvQNgfXUNhw/n8thTC8g5eQzLsCk8tOqZ0QfArIoe9R7fN4lMOcxkf0vMDXV/dAAc7Ong3Pls
      uiWCCeroT3dhJh7enkyND+LXP38HtzEOdOp5YiWRMTHEkMz8bhwMFUQImk/qgeuEBxtzoVZKQ0kt
      ixPG4y08vPpFVzh1cxDv8OARA/CdrXU8vXYCvY2V/OrdE7jp9+ATk0j2mXLMdBt58NkH2b05+wsA
      fvTBfh5Ze28BePTjfYTPDScjV86i6b5sF4BobmNCkJc9DWbCorHzFO1D3bS3d7N08RTcJ4xHv7mC
      k8eymbduNVc/PUjQI/NHDMDW+kaMzJV8/OZB2vWNaOs24aGl4/DyGLkJfOCTXbgkzKTuzCkiYn25
      Wt2LkbUb9Tn5rJjjzp5yKx6c7Ko2gecnReMyxplP//ExiY89zpXdoxOAKjm39Rg+SyLY/X4RbkYN
      BE+fjI783prAOz5KZfkj89i8cRsPPnof8aPRBL5XMloAWJiZwfmSNlYsmcapw2lITdywFTS5ME9L
      SkROxAvm5omDR+nTcWTRTH/eeu8EYX4OxE2L4N2/7cLax5mVi5IYaixlY2op+hYidIaUJMWNZcDY
      niBns28HYHIoh8+ognT9uJGVgcO4SGyNRBw+dBixSTBJUWZs2S+YviaW1FeWYOlqjJvPeBIiXO8p
      AJsqb7ArtYCFK6dy5NAFPHzHYK0nZ3y4F+98cISxiXEkh7hx5kQODg5G+I/zZ//mVHqsbHl4QQK5
      x7PwSI4ibYQAzDuXzrWGAabMSMTdpJ8j6V042slGrAEOtpTzfxszCIsYQ6CzmJMZ1dy3ega7tx7C
      KTiUCPMh+lxC8bcSqQG4YmYEhy4J2ru+GWuWTyHzeCZxU4JJPVbNgnlBowqABWeu4DQljDObd9Mj
      thMsmEBk93gP8Jxwfyclh3HswBVmLYj9Ye4B9vf3fy8AVJW7e3/9n3h0zQJGm2RfucH4sADE4nvv
      BT5/MZdJceEavb6reTcZF+yLnobqRZw8k8W0KVF3dGxJWQ1ODjaYmd3dnHK7951k2aJpGhtzTbfX
      3NKOVCrHzdVBY20+89O/CgC8otG53NvbK8yN/6xkiBQKxfcCwLy8PGHlfYtfv/LYqAPgyTPZTBbM
      WU14gQ8eOc+8WQka9QKfS79CTFSIxrzAu1JOsHzx9DubR/kleLg7YWVpdlf7tP79XTy9brnGxlzT
      7dXWNTE0JMXP111jbSbNfVrjGmBXVxeWlpZaE/hORS4ZorGtB2dne3SG5QxIhrl46QpJkyJobmpH
      JoyenaO9+hW/ru4+LM2NqKttQS4cq0rTZGVjzmBnB90yEU6WRupzqWLkHJzsUQz0Y2Ruhlj0LSbw
      nATq61qFc6qOs6O7tY0huRIbW1t0lDJMTI0Z6BtAqZRjaGqGbGAQfeGznrY2BuS6uDha3hMTeFho
      r6GxCycXW9qbW9A1NsfKVJ++QTlDPV2YWNvQ3S70VabE3skBkUwimF2DyMTGWJvp0VDfjqOrPbtG
      aAL3COPY0SvBXDBpdOXC9eqZUlFecVthMB3C2Cn1jbEy0aWptQ9nJ2s6W1vRN7NCTymhrUcqjJeV
      2gR+7onl1Ne3YGBugZ2F8T2dZxoLgxlW0tMno7OrQzNhMEJ79XXNGFpaM2/xM9o9wNEGwCN79mFq
      YUqPdRAdmSngMwNHvXaSorx47R/ZrFw6htRzjTx/fwBP/XQbr7/1LB0lN9i4p5rFiTZ0WtpRmFdJ
      tLceVVIXjBuLEIeGkODnxPrfv03MmkdI8Lf5ZgBODeAP/7zKioUepJ6tR9Lew9IVEzh6+hoOejrq
      jeYDH+6jq6MG2xnLMMjJwT3RnZN5A0RYSGixHsvC8U53HYCXTxyjvl+OpWcgkrZuKksrWTU/jCPX
      24UHrAeFwpBFCZ7s+uQQq37yMJf2HaK4eAgXT0OC3MyoFmAmsgtDUn9jRADsbGsl+/hxJGNjqcsS
      TClBAY+KCsZrpAAcambD9usMDzTg42LJgLB6eYaNIyO7FIHl2Cq60ReLCV48n6Of7GbSWCs6LXwp
      uZzHg08sxehHAMDqG1l8fFLGQ4t9NAJASWsVb59v44GpQSxc9pwWgKMNgCo5s/8gthOn4KPXxCHh
      /ll+BsCnnt9KRJQDXYbexJm3YxXowaVCEetmOvLmR6Usn2xCidiO6vR0GvplLLlvMUMFuYgjJ2DX
      Ws7FZl0qCkp4ce2cbwXgume3Mz7Slj5jT1quXCM0xpthUxt0Bc3rcwAqGEbsZIGovo92KwMWLZ2J
      9XAPf/r9UX7x2xV3HYCpH6YQvWIi6ekDxPqL2HSkkBmRDugqdRly9+LqjgyeeC6Bj3ZfY/V0Vw6W
      6VJ/8jh98g6cbfxZ8FwcRzdVIDbtGlkgtKCBb9iwn8fXTOSTI32YS8pwHeePz214gWV9HazfdgEX
      HQWxSWO4lN+OpZcvdYJW72Qn5UrFIOtevI9tH+zk/oUT2bYvg2FLD55dPemevo2jEQAOSzmScpzS
      ZhsWzvPUCAArc7LYermK+Ig4Xv3V/2gBONoAeOXkaVqtfZgZ4YGsvYJ9lxVfAPCdLZU8u24cr//t
      MHJFD/6B3pRUN/PK2um8vekWAPN7RYKZbMyMQAM+OFRDgiNqAOZt/QQdRy9qb5aw9Mm1eJqLvxGA
      qjCYJx/y442/H8fAyIZlSeYcrhRj2lrBwofms/mfe7A3NWDyqsn87qn3WPVEHC3GfsRadHPgmoJH
      FwTddQCe2b0fuyB3qpsNGevvRv65dMTGekQFuXCpXZea3BqWTVRy3Twe65YC/KIi6Kjs4PLZUwKw
      THEI9+LaDT3EkuoRAVDS08rWSw08Ms2dD7bmoy9rJmhiCN4jjgMcZP07B7n/yeWcF/ru4etA+7Ap
      zX1iOisqMdOVE+RiBuMncX7nHmLGOOMWE0vR4X24zVqOn9noBmBLaT5bTxdyvXCAX7w0C6Xs3gOw
      s6kVsbUh297PZsuBHVoAjjYAnjlyiIo2BXHTpzHGWkFVK5QVF5IUP459e07To9Bh6qwoRDJ9PJzN
      qS+rwdzDjprSXryddenWMaHxehZXaxU8sHwK/Y3NgtlnRUNRM4EhbigFjeRmp5hAN4v/DEDh/Du3
      n6B/WI+5S6bQVtVJUJAzly9fZ5yfPdtSM4hLno5RVwtO/p6UXS3EMyKQ4kuXKOvSZ/HsCdyOv3qk
      AJQOdLL3wDUWr0rk7KFj2I0Zj6WOHG9fe9KEsQmYmqDuk76rB/1NDTi5OZN7MZ0BS18BLmbs2p7J
      gjXTODDCPUDpYA/NPWLcHEy4kn4B7MciGmob8R7gQHs92w7nYGJpw5w4P46er2DJoiguHDqDU0Qk
      ltIWzt1oZ+nsSN7asJ1n1i4mJeUY5p5BzIjy/XHsASqGuFHcg6mZQiMaoGKwl09TLjBz0TQWLXjq
      hwfAb6sJcq8kPz+fv7/+W5YunMpok4IbZQSN9UZHA2EwOVdvJQrQZDaEwqIKdbIHTXi5VXIpK5/Y
      qJA7OraqugE7WytMTO7uDt2xE5eYOT1WY2Ou6fba2ruQyeQ4OdpqrM0/vv6xAMAcjT6r37kmiHYP
      8KuiTYZwd0WbDEGbDEFrAv9ApamyjHN5DcyeHcXFtAt4RIyntvimYAIHs3fPORR6uoTFRFJ7NYd2
      qQJrB3e8rQ3wC/ZRK239LfWUDhgT5qLL1h3nQM+AybOiaSlvV2fF7hVG3y84FHPlIGOCvL+i6H0O
      wBtXLnOtppcZyQnYGIk4k1VCUmwQmafPUdE+iKObL446HeRWd2Bu60q4YH6fza8STF99Zi+cjKWe
      zj0A4DA3cwQACeZ4yqEM5DqGrJ4fyc3qPhpu5uM5MQqz/gZOX29l+dx4aiqq6Gysp8/cjdgxluw/
      kMW0JVM5tv/EiAA4KJjT+9JySUhOxMVYQn6ZBKW84zYAqOTCifNILZyY4G3O8YsVzJsbTdbp81j5
      hdBXXUhJbSdxM2dweFcKjz84k0NHMzFz98FO1s618hYsbW1wc/IStHKXUQXA8mulOIT6UZWTRbvI
      AU97A40AsL2+hrSL19Gzcubvf1mvBeBoA2Bnazst1SWU9YJUbE9nSSGuXnZqJ8ibH5Xw5MNBvLv9
      CnpDch5dl4RYrMuBLYdY/OgCdWGZfR/voFxhxk9XhvDh/k4emm/Lm1uuoyNVCvjQ4YknEti6+xwm
      cjkrH5n7HwAYxV//sp+nnpzBhbwuJrgO8Pan2dz/woNk7TrEwvtncXDXYfQkMGPtYk68u4thSwvG
      LUjEVaSDkeHtVXcbKQBbyov445/T+O37L2DWW8+GXaUsT3Qkt7YbrBwou1iErmKAJSsSMbMx5siu
      s1Q26hHg0MWwSB//KQFkZsjQVdaNCIA30k8iHhODv4MJF4/tJ7fRnbgI45EDcKCVMzclNOadxUDX
      hMBxrtQOiOmTm9JaXMrj6+bw6YfbWPTwKj58bwcT3Qwwi5jOQMlNgqdM5PAoTYbQ11DJL3+Xwsvv
      PMqB93KwM+wgfHoCw1LNaIBVBdlcl7jx2i9+owXgqDOBlTKO7jqC5fgJlKRnU9LYxaToAKYKAHzl
      N0eYNsOT3OudyDvbmZgYhK2rO1WXrqgBqOxv5+lffIq9QTfLnlzNu2+kYWsn5YFHl3NkTz6tjXXE
      TwvgWmEDHuZG3wDAydSUFHPqYi66TuMYzDuBvpsjdUMuOHSV4BoaSFFeEXZiBd2mRvToOZFgNEC1
      tQ22BkZMTwhH9zaqld2OCXz0w/1EPbaQ64fS8JqaTM3Fs9iYm9Pj4sHVrWcpl7TjbS5oydET6bf3
      YCjzDAVllThaeDH/NsNgrpw4Q41MhrGeGaq39EqrDW8vG4wgldeucLZeF/OGCqL/LQxmzeoINqY1
      sW7ZeLUJ/PRj8zlzOoMLVxr56SsPcHTzKE+GsCiMA9vq8DaqxTMuGrFcMwDc8+lu5t23jCkztCbw
      qANgzvmL1LT14R0STHlBCe0SEZ52hmoN8Oe/Pcq0ZC+q20W0FJUSNTkIAzMLKi5cxDE0GFlXG37x
      SfhbyHj/4/3omoUza7ySvVc7UDZLaW2qZ1JyIBX1/VjKh/4zAKdH8H9vpRIW6k5tSyfDBi48Ljyk
      n3y4l86uLvzDgykrqcdRV8QMAbwf/eMj7MysUXo7YyecMSxqIvam4nsKwJPb97Ns1XzBrD3FtBh/
      3tmZjYWzO7aSejo7ejHw9mbVzChO7TtHUVMHyePtOHe1Ec+YqXSX5Y0IgNczsinvELRLpTFd7ZVk
      Fuvy2IqwEWeDkXTU8Mxv97L6/mREbVXcrO0lYXYch/dnYGJrzxzfYSrt45jsqa8G4AQPS2owYaCp
      kdmrVpC+fZQD8L4kUv6yBbmxOYsWJtzzZAi3RMr761NY9/RK7R7gaASganhUlcR0xTrqmig6OmJO
      nFY5QVTpy2/hSiwWo1Qq1GUSVHUSdATjVqkumSD6ImGCUqm89Z3wT6FQqr8bHlaqj1F5k0Wq33+N
      lva5Bshn/bh1PpG6HMOwclidRl59Dp1bqeRV5/j8vErl8Bf9ux0fyu0AUNUHdZuf//9ZanyFXBgr
      dUr9z/qtI1J/rhonobeoqgnI5Uq1I2mkThD1vVAov3A+qa4vr6B45BqgcLxcoVTfNlU5gy/fV9Fn
      48dnaf0/d4LI5be+E3/pGlXt6uiIRhUAP78v6nkoXGFtfZPGnCCfj9cPEoBtbW3fCwBVNUH+7w+v
      Mis5ltEmxSXV6pfI7/ZD8LXjdKOMcUG+Gr2+0vJavD1dNJLtRiWq7DMRYXeWvbquvkVdE8TYyODu
      akwXrpIYH6GxMdd0e52dver6PnZ2Vhpr850PUzReE2RwcBAjIyOtBng3RRsGc3dFGwajDYPRmsA/
      eBmm4moG1wdcMZQ0kBTjy6u/3IGbrxU2fqHUXUrHyMkaO1dfhquv0So2QaIwItisE+uEhehcL6Db
      REH2zUbMdAxIihvDkLkr47+pKPDnAJw2jpdf3YnHWFvGBodybHMKr7z1KtUXDlPUYYmeVDDtdEx5
      YFUY6XtP0TvcR0H7EMYYsWbtPCxuU1MdKQBvCtd8MKOC6csX49hfyoliY+5LsiH18q2aIH6JSeiV
      55FbI+GFn60i+9RZKgrKkbmO4YklMXz6t93Mevk+To4QgO1VN3h3Xz4r71+Ag6KevRlyxnmLbsME
      HmTD33YgsbBlhqomSFoRjz+5mO0f78IucCJOg7VcLOzkiZfu49MPdhJgLqKgX4Q+pqxcFsoHm04i
      MjbmhceWYqSnMwoBOMyOj7fQInJmQVKwZpIh9LTy+lspjF8wn9+/qPUCj1oAKuUyMtJPUS/x+eJd
      4Bd+tofYBBeasacj9yoBE31x8hlDW26B2gu8bcOnmIgN6HJxxqerlw4zqBkYxs7ImFgvC2qN3W+v
      JshkLwIjQji08RCT75tP87UTdPQ7IJUoBQCa8/zTsRz5cB89DNJpYY2FkTkr58Ryu5b6iDXAYQWH
      tu3BImIaxh1XySmzY+p4MS2dg4jcvMjbl4WLo5TqHhH33z+Jo8eLaaroxd2omoi58ziy+QSLf71m
      xCnxMw/so0BixeolkzmfuouqTj8iI27DCywdoKFfh5Mpe4SlwYToBD9yyrsxsfegLjsPDxcoaZGx
      ePV8dmzahbsAzA57Z3Xh7yQfPd47WsXcWTH4eztgOhoBKGlhw8eVuBs34JMQfc9T4qu3i9IzkQSN
      If2jbLam7dUCcDSbwF+XDOG5J0J5/Y97MbF24KmnbyX1/PAvG5Ab6zLoFo9/600Cksex84N0/Cd6
      Ejo7EW9dHZqK8inVc73tmiDIB/jn2+ewtJVhbaJHU6v4KwDsFYlJfHQ+jhowgQdayjh0DZbHGvH+
      nlY8bVrwc7OnycKZvBRh8iNjvKs+HQ4OBIRFQGU+p84WsOzJB8ndens1QerKKjF30Gf7+2l06xrS
      3CVonAvG3lZNkLTd+zAal0hHxjnGxwkArOjG2NaD+pxriISFI8TLEvHERM7t2IOvviEJjy1CdeaW
      umZMXazZv2ErY+YtY4KbyegE4KYK3E0a8ZkkAFADyRCKL2QyFCAA8OPLbDu2RwvA0QxAeU8D54sE
      2PTVCSbwGP7yxmE8fKyw8B1L7cUsLFxtMLa0xVwyROLceA5u342Rvh3xSxLZ/bet+ET6UFTfJZjA
      Isb6uJB+VZiMXu4smhr+zQCcHsJrrx/FSzCBHVyd6WqQo9OVS9Cs2ZRcqUMmG+bmjWK8x7liqxQ0
      E/0hqgaH1fnrkmYl42iqc08AmHPyDNdbJEyekYinSR8Hz3Zga9rHxPE+vLnhMGMnx6JXc52iOhmR
      cb5EhgWwZ+MBugTt9PFlSeQcvojXnDiOjxCAV06doaB5iEnTJwntDQjtdeLuoryNmiCl/P7dS0yM
      CsDffpiTGTWsfmAmO7ak4hgcgqOsidySbu5bt4hP3t9JmIMJlUp9VGHkMZHBpB6/hImRGUuWJ2Op
      Lx59ABRk55ZP6TFwJjk2QCMmsLS3hb9tOM74edP57XO/+uEBcHBw8HurCfLRe3/mqceWMdrkUnY+
      keOD0BXfeyfI6XOXmTJpIhr0gahrnoSH+KOnp5maIEdPXGLWHSYCKCqpwtnRDgvzu6uRbd11lNXL
      Z2lszDXdXmNTGxKpqqCUk8bafPipP2i8JkhPTw/m5v95cRTJ5fKRA7A3B/oLvv13ZhPBJPgbf6Kq
      CbJ98z/4+UsPjToAnknPISE2XCMAPJJ2gVnJ8RoFYHpGHlETgtHXEABTDp5m8fykOzo2/0Yp7q6O
      WFrc3WR9721K4fGHF2tszDXdXl19sxqAPl6uGmtzzrKXRnlNkJatYDhGFW37LbZGEdjf/6Mzgft7
      e0HX8FZNkMTx9PYMoAorNTM3Q188TEdnH0YmpujrCEOqq4tIqUCBDnriO6fX52EwKidMV9+QuvhP
      X3evuhazrp4Bhvo6GBjoC9/LkasChYd1MNITCZNbQX//AKqba2hkLJhuIw9puZ09QOmQFD1DfQZ6
      +xjW1cfUUA+JTIFsUFWXxASRTEqf0BcrQUOTCg+cUi5FrnPrd6o3EcRC33eONBBaIaeju/+z8YYh
      qZLCm2W3FQbT/1k/jYVx6+6TCOA0EfreK9xWY8TDcnoH5VgJn6nCYJ54eAF9g6qa2DpYWZvS29nD
      sJ4+lqZ3Pzm+5mqCDDMkUdDc2qq5QGhhbip1xEyasW6U7wH+FwNQ0tfO+k/O4evtgaGuTO0E+c2f
      zrJgng+XrvfjZ1KHvmsoZUWlRHrbYxIzEavqm5Tqf7uj49sBGMvmdw7gGeRIWbsu9XkVzFwQhqml
      LQUCrFQp8WtyrlDY18fRjE7+8VQoO49UUFzWw+xZfjg4u+PpbH7XAdjTXMPLL3zKax89wqebBdNG
      0cXDi2JJvdZGa1Mz+sbW6HbVYcQgE5avpvD4QUqKerHztGDJ3Ch+/vQH/Hbnb0bsBKnOv0R2kxFT
      44JpL74otGPO5NtJhjDUzD8+ykZH2U2QpxUNbYOEJ0Zy+nSeAGITdcYXiXSYmNVLOLxlN546Sswm
      hGKCPk5GwtiWSTHurmfakvk4meiNSgDW3cxhY9qQxmqCKIb6+PP/vsu0557ghQde/C8CoFk09F1T
      LTlfXn6++KuqqpLsi2n/Vg7xS781jwex8Q8GgB0lV/nbkTICfL2wM1KoAfjsT3YRHedEk9IO/WEl
      Lz48hfzjh7jUaEi3sh+j9hZCFi5h8ncFYKwnfzo1wKvLg9Re4J++9AHjon1wDwym+WrBlwAoZaBf
      gYerMaWFnVy4WEJUrBvhkyYR4nr3ASgTtL/jnx4h5rGFWAx0s/7TsySEO6Ij00Hi8VlNkJ/MY+fH
      O4meFkl25TB1Z88ho4NVTz/Ljf3HCLkNL/DJT3ZQLjYjOSmW0swLVHS53nYyBIWqn5+cxknQIP+9
      JoijnZSCWgkPP7NSXRPEXTlAs7UDRqbWLEtQpeG/gNjMjnWrk9DXFY1CAMo4tvcYN5ttWaShmiBK
      mZzyvDy63cby3I8NgDv2pNE/MMTYMZ7ERYd+jQb4n0+dl5tHyijSAHtqSznbqEvr9Su4eriqs8Go
      a4I8Po43/nYMS2MJK9fex9mUA+ib2eA2OfLuaYDTQ/jDP8/xynNTOXbqOlWVEp579tae2Y6PUv8F
      gCZOPnRkp9Kj64lUbsvaB0Jvu83bTobw0Ax2CBrqyidWcOnoScYHOJPTa0hlVjljrPoxnxCPQUsF
      3pER9Df0kHnyODMfXkXGx7cXBtNQ3YiFlYyPN5xCbmVIabWYh5eFjDwMRjHEhvX7WP74Si7u2Y+3
      v7B4yQzpHDKgvbQcUz0F49zMUYbFc37Xv4bBVFwrYMjZG93KXFrswoj3Mh11AGwpyePTsyUUFQ7w
      yoszNFITRK3sXLlCm4v/jw+Ar/9zCy8/v+a/ZA9wmPRjx9FxDaa/uVadEHXbJ0fpVYqZs2wWrqZy
      tm46RkBCIj7C3yJHRwx6O+jSMcXF0vA77wE2VpSw70Ipa5ZN48T+NBr7pZjbu+BtNEBedSfjIyfi
      YGOAnokltspObrbJuXI2kyEdEV4BQcyOD7wnAKy6Xo6ZvS7b9mViZmtLTKAf/mOdObD1KEHTJ5Gd
      doxOiQ4ToqOIHOdO1ulT9Fv7MzXMlZobFdgEeXNwhADsqKtg14ky1jw0HWN5P/mlA8hlI0+IOtBa
      w8a9GUI/7Vk4aQwHTpVx38p4Tu9NwyUyFmtJEyfz21i9OJ63N2xnxkRvTuXVqI9dsmoJ2Uf20mvs
      waq5kdztt6Q1VxNkkNzrXVgLa7Km9gB7mlsYNLVi0cJRXhPEoGsPhlYhXwDwnxt2MGNqtDrTRIC/
      1//nX/tVpFbfXOVeWxNkZKKtCfItANbWBLkj0dYEuQsaoGoAm1racbCz/ldt4UfqBf5ctMkQ7q5o
      kyFokyGMShP4P8qPCIA9rQ0cPl/EjNmJWNBPVbuI8s/KYqYeSGdQGIuoxDjcTCQcOJyNX/gEPEwU
      1PR24+fti6FIRn5JFeaGZjR1DBAb7qouSxgUaD9yAM6Koqisn8CxdgzLpRw/mo7I2o3pkS4UV0sY
      62XM9aI24b7oYmvcw+mcCqInT8LbzvieAnCgs5lDp64xbc5USjIvobB2JS7YieKafppK8vGYGIWn
      tTElN6sZM9aDmqo6eloa6DF1I8JJzN7j+cxakERa6shqggz1tLE/7Spx0ybRWJBDp64tdsLiPnIA
      DpN19iIyS2fCPc04cbGCObMncuXsBSzHhNJZlk95Qw+xydNI3ZnCU4/MJfXwRax8xjIlzHNUA7Cu
      uBqrMe4UnL+AzMwddzt9jQBwWDbIgUOZJMyIZ94PsSymFoDfLJlnTjE2ZgqWhiLSdn5Km0U0dl9+
      F3htMG9sysJC1sSKtas5vmMPFraOiGzFtOt4Msmkhqw6OYOdEtLSinnzraV89HGZoF3EjByAX3oX
      ODP1IOaR0zGuvczVLn3am4xYu9SVf757mWGxPkP91fz8lQcEkyqPmckT7ikAs85dxDfAmQOnSnH3
      8achP53pUyK5Ut2J0syGqpxKHp7lzasfX+St3y0nZdsJyur1CXTuIyAuFgfjXralSzFT1o8IgEWX
      zmAQEI+3lQ47Us6wcvG029MAB1s4njdAy410jA1M8RvrRIvCiPYBPTrKKnli3Ry2fbSDuQ+uYJOq
      JoirPjaxc2m5eoXxybEYj1IA9jVW84tf7eSV959HVtdP+tGjRM9OAqkm3gW+SLu7N1cPFbFt33Yt
      AEcbAM/uP0RZh6BlhfiiGOynud/ui2QIz/10NzGTXKltFmEoPIAvPTSFwtNHOV9rQuKCUA5vO42X
      qZSpgpZx7HQVfX16mFoP0dxgescA3L5pD4sfXoqevImP3jnDsHXglwBozPKZ9uw6cRVbNx/unxd1
      TwE4rJCxbVMK0UsWoN9QwokKGf4C1GzMzNQ1Qa5sS8cl2J7epm5mJbiTPezMgACxm1UCGH/6EmWn
      UmmzDGOo+eaIAJh15Dg3Ovvw9w8g5+w1TGxtCA93w+s2TOC6ogLSSoawbK37ak2QByaw8XAN61ZE
      qU3gpx6Zw96Uk5Q1Knjx+WWY6I5OAKrkVkr8mZg21/HR9gzmL4lHqQEA9rXWsO3wNapqujibfn6U
      A7D9oAC3Cr51R94kAKySfxQAzL2YTXN3HzKRHjWlhVQMOjFjgr0agL/+42nmzPGhoFqGu0E9hnZj
      KS2tZLynLaYxE2k9tJt0mT+vzLZmz0kBgDJbYj3q2ZZjxh9eiL8tAP7ytfPMXxyClbKVa81G6HXV
      4JYwiay9xxgrmMZtRm405tYLRl4zE2LHUVzZxGMrZ9xTAB58/33anUOY5G/J3987zYMPJlNbUcm0
      KD82pl5H19gIi6FOzmbcYJagrS2YEcmFo5ncqG5gSrQ/rUN9DMmc6WotHREAC7Ov0Njfj66+HR3d
      nQx0DeAf6Ir3CMNgpN31vPS/+4R+zqKvuojadgnhieM5lXYNfVMz5o7VocwmhiQvAzUAw13M6bQQ
      AF5dyeSly3E1GeUAXBpP9vE8Brt6iUoM0wgA+1sbOXm1lHaJFR++9aY2G8xoA6BCLle/2qVOuT6s
      RK4UAHE2W10TRPVq1/CwoP0J36kcwgP9Q+gZCH+rCnyo6nAoFciHdVCljlPXohAWDpXfRCofRl9P
      PHIAzk1kcFAiwE2EsbEhksEh0NHF0EAXuUyGROiUiZE+MplS6Mcwg0MyDAwNhbZE9xSA0qEhZArV
      K3mqfsjVtTNU7w+rnENDg5+NhY7oVl0N0a3aJFKJcB1C31Wvsg0MyjA2MWTHCJ0gSoWCQYlcGAMD
      JELbOrr6XL9RMmITeFi4HwODUnU/VWOneiXMWBg31XiK9Q3QUb3YqCNW509UO0GeXCn8XtWOHob6
      9/a96HsNQHWNFuG+qO6ZSKxHY1OLZpwgAl76hblrZGRIfLK2JsgXoqoJ8qff/2JU1gS5WVLNGB93
      4VnRTE2Q4EBfjSZDUNUE8fJwEUA2SmqCCPBTLQx3VWP6L6gJIhMWd3sN1gTZsHGftibIaNMAv060
      YTB3V7RhMNowGK0J/AOV7LNnSc8uxjs6hpayUspbpEyb6KVOiPqH11Jw8rDAbux4Qs2aSM1owtbJ
      hVg3BZm9Lsz0lFOnZ4frQDn7Kgx5dEbAHQMwN/0sl0tbMbb14YEFERz9dAduMxajW3qVw0UNmOro
      kyj0acjEkTA3c44dzmLmnGiq8q6yO7sccz0DZi9Kxm0Eb6WMFICVeZkcPHeTmHmL8RPXc6JIlyUJ
      1pwslOClrKbbIYAJHoZs3niehx5LJv38RRqKq2mUm7Ew0Ymdx8tY/MBCck6fGxEAO+tK+OhgPovn
      xqoXoar6buHvGHxGnBFawqb1uxkyt2N6hD37Tt7k0ccWsPvTvdgFjseyv46LWSXMWvcoF/ft5765
      Eew4mofM2IqXHkge/QAc6uWDHSeoqGzhwYfmo6OQ3TMADg9L2PL+KVbeH82bG/biOi6et/76Ny0A
      R58GqGTzu5tZvvZh+sqzuNDihImqKFKUFy//KpWkqe6UdxujN9TD04/N5sLBVOqklvTqS5jmaUud
      sRvVZ84jNdLlgfvnoXcnAJwaxD+2F/Hio4k0VNRh7WzGJ7uz0TMyINhwiNxhfRwNjQh1MqRSz5VE
      f2u2bDzGmkdnUXj2PGfbZbiZGTM5MRIzA/FdA2B1ZS2WxkMcyZPhNFxCcbMn0yaIGbZ2IfvkSZzG
      T8G2v5ITwsLw0pMJbD9UxmBjBXJDExwZJHxRJAf21GJj2T8yL3DqfkpFjiyZFY2s8Tp7csWEud9G
      USThHpV3i8k4sh/DYWN1luqr1b0YWbtRn5PPI0/M5MP3dvDIutW8tWE7kwOtudpqwMQAb3yC3TEa
      7QBE9TpgBZvOdDE3xumeaoA383I5cqaK5ImOXB9UEj9hIsuXP60F4GgDoKy7kfdOtPLM0hC2b9rN
      woeXka4ygQUArt9cwbNPhPKGoAkaOtvy7KMzyTl2lJvdpgQn+HDlyAU8VUWM9p3Fy3QAp8TVLIt2
      uH0ATgngH3sqWB5vxfr3jjFzij/ptVKaSitZGh+Oy6x4vPXFtBZf56bI+d8AmIEyPJxAM4MR1zEe
      KQD725t5d/tFnn5mMYb99eqaIF42LUyenUx3+XUKZA5MCbRj66YjRIbYIXf3Zv+HGfhZdpK4ei6n
      T19E2m+Frn7fiABYVVSChbMx6RkDiAcqSFg4g7LbKYwuyJn9B1H4xNB7+QIT4v+1KNKqhT5szzfi
      kRmeahN4xbwpWDma8sb/fMiaX7wwqr3An8vFE6fwTUhkSAP5AFWJOnzcDBjyChEUg3OkHj2pBeBo
      A2BHjaDZyJ2I8TZm3+4zLFo27dYeYIwff/w/4Qb722DiHSiYgHVk3RxEbGhMnKcFuuMCqTuSSrlC
      n4RpyfhY67F33wmWLEq+fQAKJvClE8ep6lLQ3DaIpakhD6+ZQ3/NDdanlGLuZKAufTnW24kzORV4
      +3jQXVyIvr0tTpbm3GjpxFoA5ISEeMY4mt41AO5c/w691t5Ex0YSbC9n76l27Ex7mTQ5kq6ackrl
      1kz0tuLIgQs4C/QYFx7Cjg+30iIxZV6cG6nZZUxbuIDr6ekjAmD28VPcaJMQPzWRqquXmTZrMrm3
      sQeoqgny67fOExc/Trgfck5lVrNaGMdtHx/EMTiYWGsp3W4TGGerowbgwsQAjlypRilo2I+tmYWB
      aPQD8MKJ00RNTaKh/t7vAaYdOC/MhbGCtXIGQ7dw3v3r6z88AEokku+tJsiWD//KC0/fx2iT9Eu5
      xESGaMQJooLt9KQojTpBMrLz1QkYNFUTJPXoeebNmnRHx94oqsDV2R4Li7ubnurjT1N56P55Ghtz
      TbdX39iKVACgl6eLxtpc8dCvNF4TpLu7W5gbFj9MAH6y8Q1efGY0AjDvMwDe+zCRNAGAyUnRGg2D
      ycgqEAAYgJ6+ZgB48Mh55s/+YQFwkwCkhzUIpE2fCO09oEEANrSqa4J4ezprrM1la375wyuKpDWB
      v1nkcjkKhRJdPT11ULRYV5dTZ7LUgdBK5bAqzlMNCtX2mkQiE6CoJ/w9jBKR+jPV8A6r46JFwnnU
      8dHIVcHDYh1kqhoZw8Pqc6uCdD9vR/ylvbovaoIoPz+HUh0QrDpOR0es/q0qLdfwsBKpVI462FpP
      rG5TV/37YeGfQt0PHbEueiOA9ohfhRPOKZXJ1b8TMfzZ9QltCuOiVNwaKx3RrT6LhT4qhYsYVv0T
      6aivXyl8LhL+H2kg9JfbU8hk6qDl/Oslt7UHqArYFgZOPW4ymUI4161gcpEwNqprUAWsq4K5VSbw
      M48vE+7/rQB2fQM95FIpw0Kb+vdA87/XJvDnY62uzyFcT0PjvQ+EVrWpipWVCPNST5jXP8hAaC0A
      v1luXsvj2KHTjEmeS3tFFR09EOBt/pkX+BBTp3lS0WZIgHk97TpOtLV2EuNlwLFmcx4NN+FKUxcH
      zzXx3s9i+HB/J/PClbx2qIK/vzSDX/1iKzFT/KloGsKwvQbncT6UlQ3w/DNzv0i4+S/vAj/oxz/W
      Z9DSUEOs8JmVvSu1edc/ywidxcHSHrytjAkda8Uv/5bBlj/PZf2HuZSX15E00x9HN18mBDndNQCe
      S91PXbccY5+JhJrUcDDXlHUzrUm50kpDRTkWrr5M94f1Hxfxxp/vI2XnfkoKOzD3sGP1whh+/fyH
      /PyTV0ecETrz4EGKOiQ4jQ3kxtlriIz0mTQ5GO8Re4Fbef2d0+joyZk4xoaC0namLpjM4YPpGFo7
      4ShpprlLyqy1qzi4eRceSJF6e2OMISHeBqRkNaI/2MmS+5fgYDx6aoJIe9r4xYsbefGddWz951EB
      RhLmLJqJSH7vAFhTms/fN+Twm5eSSM0spmfQim1bPtACcLQBEKWU9ev38eSzK2gsLmTrhXrCPM3U
      AHzhZ3uIjXehTmGDnqBZvPjIFPLTDpEhwM/e0wAPA7E6c3NX8yA+QTZcL9DB1qAGV0cTjD3Hse3t
      gyQmB9LYJayQXV2sfGQup/buJWr+Esz0/hWAz7y8j7h4R4qr9RhsqiIszhc3/wB1+MYtAGayPbcJ
      D2sTkqI92J9Shl+MMzczmigqKid2ig9+4eOZ6Gt31wCoksKMc9QZuENtAZVd7sQGy1BIhIfus5og
      a56YRsquM6xYFMz+PAl1FzMR6bSzeN0T3Nh/nNBHR54S//rFE5zNayVsRiK5+4+htHEhPsLztpIh
      qIr0bNichoOgBcZO/beaIPZSCutlrHly+a2aIMODdNg7Y2RixfxIBz7YeRkrRxc1vPXEo6cmiGxw
      iHMpZxl730ycZYO8u/EA02clqlOr3SsADg4McWDHCfXcLL50juMtNuxc/08tAEcbAPubytlRLObB
      SDt6lLoc2Xkaew+rL9JhPft4CG+8cRgLUyWr167g3L6DiIyt8ZgUyul3PsY9cjy6pu6Ias9S2mZP
      VWkxPn7WdCrNMJEaMydSh+whV3QqSwQAzmLzmztZ9ex9X3gcv04DVL0D+3U1Qdpd/RnvaIqiu5Kd
      5yQ46+RzpcJcMDfNef7pkb9yOOJA6NwrnGvUIcmhm705DVTU6ZE83pLxAU5cGzSh7FIpzz6VrA6D
      CRhji0dkONLmXi6lHSf5oZVkbr69miDHNu8lcEYIGRdq6ZOaY9Rfid/EcSMPhFZIeW/9HhatFdre
      ewC/IBdq+3XpVZjQVlyKia6CEA8L5KFxpP9bTZDKgkIULl4oSnJocQgnYZTVBFEnQ1g5lRPv7mba
      muUouzQTBjNn8STEpmI2C/P2k0O7tAAcbQDsbWuiQ8cKN9NhPtmWyrj4JNqqS0mKC2LTxlQGRHrM
      XjYbd1MJn3yUpq4J4meuQMfFGVl1BV26RujpW+Cq30NaVgMTEiNwESZE3qUc+vVsiZvowakzl9Hp
      ayO/rovpCxYS6Gz8r3uAMydy5XoPE0JsyMqpo6LwKi1DcswdXPHS7+VabTcTIsPIycxFtfEYEz8B
      YwNr/OzlXCrppTznMv0CUO09/Vg1a8JdA+CJfXu40SgjdtpUIr2MuFrUg5FYythAF3Z/dIjQudPx
      dzCjIK8MMzNd3L09uJiWRp+1P7MivajIK8EubAyHRgjA/rZ6Pt6dzapHFnDl2BFEjoFYG0pHvAfY
      31LFO9svCFqcE4sm+ZFyvIQHH5jMsR2HcYuOx0bSyHFBw3xo5RTWb9jO1DA3Tl6rVec+WrZ6GZdS
      d6lrgqxZGMfdfg38XgOw+no5Rs5GbPnkNGbW1iQlhN9TDVAlN4T76+ttywfbzzB/5RxWLn12dNcE
      uZuirQkyMtHWBPlm0dYEuTPR1gTRaoB3LNpkCHdXtMkQtMkQtCbwD1jqK+pw8Hah4FImZl7Bggl6
      g6T4ENKOXqRXISIuKR4XEzn7Uy8xdkIkgR7mnD92mm5jR+ZNCqahrJjzpd2sTA6htGoAPy8zikvb
      hPMW0zIgxdbegb6WZoaEtjz9g4gOdvkqAOVDXCntZHyAPSWl7YwZ40BrdRMSXSlKoR2b4T565DIu
      pF9DIRwXER3NGBfzewpAuaRf0FzymT41kH1HstAzsWDR9DCqGoZwsYQupSHD7TVcrupjXlI4DXVN
      9LU30m3kyngfc44eukzSgnj2jxCAkr4OUtOuEJ2UgKvZMGW1Q/T0NN8WAHOFeyizcCHUw5QTlyqY
      lRzBtQsZWPqF0FKcR1XrANGTEzmwM4WFk8eRUdQgHGXEgiWxXDp8CpmNOzOjx446ALZUN2Lq4UTl
      5cso7HyxEA9pBoAKCUdS0wlMSOC+lc9oATjaANhQXshrr6Xx278sJe1yB/111Xh52f//miCPBfP6
      R1lYy+tY/NiDHN++i7FhXuRLXYkzbmJAz4j0gn7ujzMn/WYXlRUinl7zVWcGg02sVzk6HpvwtRpg
      YeYlDuQ28tKjs3j//Sz1cee3p9FuOkx5nyFL/MwoUhdGH0Oiv41GNMCze/Zj7e9KXb8TM0INeHvL
      FVbP8KJwyJyr+3cSNHc5fdVVuA43opwwm7ozx9XFzAPcBjEx0Md2nDvXS40RD1aOCIDFWecQ+8Xg
      a61PzulDpFc7kxhhfBvJEFo4nNlNe0kGpiZmePnY0yM2p64LuquqeWrtXHZt2sn0B5ax5f2d/+IE
      qco6yxXjEPyHyrENDMPRZPSEwQy01PPKK5/wyruPsOOdAjxtOwmdFMewBgqjVxcVILV2ZcemYxw9
      dVoLwNGoAR78MIWJ0wO41mpG69VcHHzs1AB86vmtjI91pr3P5P+1d/ZRUV9nHv8MM8z7wDDDO6KO
      gIiggggIgqipCFrE10O0Bm3Sxt3uprunp7s9m92ek9PTk6ZN0j1tNlsTk9S6vr9AxACKBo2iRgEB
      AUEBRRTQ8OIwwDDAAPubyWZP2jSRRBjD7jz/zZlz587v3t/9/J7nd5/7fJG7a/jJtiV2TZDO4CW0
      n82n+oGF7yYYaHMPYVWElx1yP3zhAAvivLjX4Ubv3XpmxUzHMCeSlHDlVwAwmd//6k28AnRoIhK5
      fbHxcwCUoTXo6W18iNhdxLlLtzH4akhZnYZBL59QAB4XxmVhZgznz5vxFzXhmZhCe+k55iYm0d90
      3V4MYbFBzpt/Ok3m8pmUCJ/7zp2mvuUO/vpQMn68iII/3kKsNo4JgBeP5VPeYWJOeLhdIe5Wq5bY
      KMXX8gDvN9SRW/kQ/cP7X9AE2ZoVyzvHGnl+c4I9BA4c6eOuSodc48n29bEc2nWM2vZR/vZHG/BW
      SyYNAG32qSbIUs7uyKbhkzY2bNmEdNQxIfDVoiJadaG8/E8vOQE4WQGYmJnIvsNl9rLuQVM+TYT+
      xasXWL82hOLKLgKkbfjMjKa6opr4yGmUd1qZKu5jVOdH2aWbxM9z517bIO1Gj//1AHtM3WRsmI9U
      oWaWn+hLAbhsng+HK0VsXhrA2zuOYOpTsmbDAlpK67HoFMxLTaLw5V8TkJxMV5+UqOlavHz98NOr
      JxSApYUFNJmseM+O535VMRszM3g/9zRrVy+no76aawN6Lu7Zz+JNq7B0drIkOYYrRRVU1TcSN1tP
      Q+cAUr9I+ltqxqYKV1aJadhC400jn3Q2ca1ZwY+eiR2zJoi15wE/e/l9nn12FW3XyzH2jxIUHcGF
      4pv2UzLp4a7UaeNYHiS3A9DgMopnUgy2URxob+cWcjz62vGOSiLSXzkJAbiMW6cruX6riZSViVgd
      EAI3Xb3Iux/3sG11NM98/1+cAJyMABywCAtVLqPXaEKiVAkeTwnLkqMxGXuxIkLr4Y5UPEpnRzcK
      tQalXCJ8Z2QICXqtGou5D5NlBG8PFf0DIyhkYvr7h4TfNTM4MopY4oreXfnpd3LJFwCYvmIRCOGy
      7RSbxdxPv/B/rEI7pVJpP1ImkbnaBW5GRGDqFqArtFOqVKgV0gkF4OiIla6HZjz0bgxZBpHJXYVr
      sumRSO1H4ayjInpNJruOim0nzqZb0tfTw4hEjlruQldnLx6e7hwc4zvAYSFk6+q2oPd0w2V0xD5e
      tTcaxuwBjlgH6TD2CQ8xCVqNHKNpAJ1OTY+xG6lSLczWsO1cI5L/0QTZvm01JvOgva2HzgNz90OG
      XaTo3Me/LtZEA9A2P2JhXszCfIikSjocUA7Lvnb6zXT3CetHJmfluh87NUE+M5smyCu/fJGVKYuY
      bFZX3/SpJojLxKfBXKupF0K+EEdmwXCzsZkZ0wMEuDomDaasopboyLBv1PZe6wMBfu4oFeOtCVJG
      cmK0w8b8rNDfEgf21yU8zG3n3L09dQ7r8813jjo1QSajB/iX5kyDGV9zpsE402CcIfC31Foaajl5
      pZn09CROZH+ARG9ArxhmWUIov3+jAK8ADZ4h85itbqew5AGevv5E+ysQhwSh6mjhltGCSO5FVLCO
      3H3ZdLvIUbn7sCZtPoUHc5izcjXGimp8kyIxV99AFBzElM+FwTYALl8wjdPNMtbE+ZKd/aEAxET+
      cLCYv3smhYO7jrAyaz3Xz1zB3NdJk1mI4kQqvps2i4NHLiISQs+s9Hj2HT7FoEjCxvUrkXff5vC1
      AbalRjwWADvuNXAo9yoZ31vBhzlFKLynsm7pDM5WPaSpqpQZ8QlI7tRS1dLHlq1rqLtawr0bzQz6
      Gng6JYoTewqJ27KCE2ME4L3acvI+aiR57Xe4VlCEVevPLIP2awBwkEO7PsCi0bN4jp6Ccw1syVqN
      +c51blq80FubOVfVwQ+2pPLmjgPETvOgtmcEV5SkrwoXQvXztncLfH9jCtJJdBb4c4Ew2buP0yMT
      rj9hJkMTCsBBjuwtJi01lL3Zl/GeHcVvXnrZCcDJBsAPjx5hxG828wNdOFp8n6eSI2msrbNvgrz4
      i0LS0gxcuzuK60C3PY3i3LHjPBjWErEiDo87dZTd78VFM420mAB2vv4eQTFzUeu9iQxU8NahMjwC
      /QjsbCNoUyrdZy/iErOAMLX0LzzABH772kG2Zy1mb2k/qX5GzlxtJSwtlfrswyjDYxHdukH/gAnt
      nHCUSjcMLu18UD8iLPRpqFUWjp66TVpyGBo3HeVF5+gVj7J+gwDDv1Imf6wAPPGnHOatjORyxTBL
      5qnZ+/7HpC0K5l5XH+KA6VQeKyEpfT6WhlIkc5O4caGS1rtWgjT3CF+azJ4dhTz/2vYxnwUuO3WK
      lmEX3PQBNFbcQKxWMSdsCoaxngXuN1LbJab81HGkIwpiE4JpHPTkk5ozSLwXMnT3OnMNHgzNieXs
      gSNMF1kRh4ehUqgJkZnIqbGwJHI6ATOE/mSSyQdAay873z6JRKMgKTlqQoXRq0ouCw+YFrZuTkCp
      VfL2W4UczTvpBOBkA2BdRQ0qlYiy6ru0q6bS31BBaKiBpwQAvvHHegF6c/ndq8dR+HvxwnMpXD1Z
      wPVuNVGpAgCFBVXZ3g9COxsA972Ty7qsNPu7w8rCPD5qG+Fm5W0yF4YRnCkA8NwFZLExhKqkXwiB
      zxScYHDUhTmLk9n/+h/wmjWVxjYZs91HmRXuQ8nHd9AqXXhq62pUQrjc392Dq8aVd1/bTdKmTAw+
      Cs4cOopraAznc08Q6CXGPXY9zyT5f2MA5r+XQ/Ta+Vy8MkDG8mD2vJODr+ARBwlj0a6bQnn2Fdas
      nMH75Z+QMlOFedo8rNcvcfZyPd/7++eo3Pf1iiEU7DrAVGGsqhu6aW8BhbWFuYuivoYqHBTn5dHj
      F42l4hILkkJoGPQj3ush+VUuDDTXMj9ES19oLOcPHyFIKiVO8BDdEGHp6RWAK+G//n0Pi7K2Mtdf
      MfkAONTBq78rxVfeTmz6dxANTXwxhI1ZKex9N4fFm9ayeb0zEXrSAfBMbj5NJliWEk9R/iksUh+C
      fGQsWxjMS7/MZVaEF66BoYSKmylrtDIilrI5bTb/uV8ICYUwaU3KfHbnXiN0tgFzfQ1ibx+kSgXG
      toc894M1dNWVcL7FhYb6BmRiCdufXfdn4dVnAOxtbeDf9lfxq2fjyavqYWPyTPJzTtI77MqGjDh+
      /uJuYiI8MCq1yAQARkcYOH62HJVMwarUBRw8egadRomvhwx9ZDJxggd6YP9xMjdnfGGDZczVYKqv
      cDivhtVPL6cg/2MCp07FQzZM0sIQfvMfeQTHR1MjgHtWdAR6L0+WJ8/jwI4c2oUw8h+2ruLikSJC
      NizjwzECsL70PPnFt4lZtUIIgQuwKr2Jj5kx5nJY/Q9u8rPfnmHpsiimu1koutxM1vNP49bfwkc3
      XXA11VDWYOKFv9nIjrcOEKYR0yJ3Qya0XTA3hNyiEjQKBesyV+GlmIQe4OgQO9/YTa+LjoyMhROe
      BpMnzG+AfogP6vqJjw7j5//6mhOAnwfgnvde55//cSuTzWw7donxkQ7ZJc0vvEBaSoIAKcdtghRf
      qiBuQbi9iq8jzAb5telLv1moVdNA4BQftO6acf1PO3fl8MNtax025jt3ZQv9rXNYf7bdc1sF8yDD
      FIf1mZ75U66UVn67ANjZ2flEANja2sorr7yCTCabdAC0pQ9IJI7RyxgaGnIYiJ7E9T3uNdrK/dte
      KYz3LvnAwIBD701H92eTJ7CZI1K5PjPbIQLbmnekmc1me77st84DtJkAX/R6PZPNent77Ym9jkhN
      eZSoy0SYrYSQ7aZxVOrNo5S7vsosFosdnuJx9sYdfW86uj/bQ8e29KVSqcP6fJx5/j8XAjsB6ASg
      E4BOADoB6ASgE4BOADoB6ASgE4BOADoB6ASgE4BOADoB6ASgE4COAeCwTTn7CVlXVxc6nY7JZo4E
      YE9PDxqNxqHX5wSg4+9NR/f3/wWARqMRrVb75QAUFvMTA+CT8G7Gwx5VYWI87VHb+BNhNqjYUjIc
      BcBHCdd8lQ0ODtpTdsY7ncPR96aj+7OlOtkA6MgUq8eZ58dxVtTqL6+L+d96fQN1bMqwZgAAAABJ
      RU5ErkJggg==]]>
      </text>
      </image_base64>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[table]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Sur l’image suivante, comment appelle-t-on l’élément B ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un seul mot)

      ]]>
      </text>
      </questiontext>
      <image_base64>
      <text>
      <![CDATA[iVBORw0KGgoAAAANSUhEUgAAAUAAAABmCAYAAAC6Ekg1AAAAAXNSR0IArs4c6QAAAARnQU1BAACx
      jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAE6nSURBVHja7J0HdFTXtbC/0aj33ntBqKAGqAuB
      ANF7Nzau4F4Tx4lT30vivMRxim2MG8Zg00EU0UQHASoIJCSQUO+9d03Vf2ew/TuxYwsMY8uZvRYL
      rZm595x77rnf2fvsffcWdXV1DfM9SV9fH6ampow2GRoawsDAAJFIdM/bGhwcxMjISKPXJ5FI0NfX
      18j1qWRgYABjY+M7OlYqlaKrq4uOjs6onpuabk+hUDA8PKweO03Jd7nPdyr9/f2YmJj8x+9Fw6pR
      +J6kvb0dGxubUQdA1WRVDaomANHT04O5ubnGJ41qomoKgN3d3VhYWNzxYqSnp4dYLB7Vc1PT7clk
      MjUAVQudpuS73Oc7lc7OTqysrLQA1AJQC0AtAH+cAFRBrqam5lufH2dnZ+zs7LQA1AJQC0AtAH88
      ANyzZw9xcXHqefBNkpKSwrp167QA1AJQC0AtAH8cAFTtAR88eJAFCxZ8KwA/+OAD1q5dqwWgFoBa
      AGoBOPoBWFpayqlTp9TjtnDhwjsDoNAprRf4Dh46Q0NDjbT1fXmBVV5uTcl38Q6qHmQV/LRe4NuT
      0ewFVvU9LS1N3ffk5GQOHDjAypUrvwCgXC7nT3/6E6+++uq/LIxvv/02a9as0WqAWg1QqwFqNUAZ
      SqVSowvd3dAAa2trOXr0qNrkdXBwUH+m2gP8sgl87do1CgoKCAkJUf/TmsBaAGoBqAXgqDeBT58+
      TUdHB4sXL/4Xjf/fAajS/mxtbSksLOTvf/+7FoBaAGoBqAXg6AWg6ritW7eqzV1fX9+vfP/vADxz
      5gxTpkzhxIkTTJ8+XQtALQC1ANQCcPQBUNU/lTmbnZ3Ngw8++B/N9X8H4H8SLQC1ANQCUAvAUQFA
      1X1VgW3MmDFERkZ+42+1ANQCUAtALQB/NAAsLy9Xh7csW7bsG19juysA1CZDuLOHTpsM4e6JNhmC
      Ngzmc1GFt6hEtd830vm3b98+tdPj266lqKiIRx55RKsBajVArQao1QB/WBpga2srO3bsYNGiRbi6
      ut7WeVXXoQrp+Tr5cjIE1Xz+94VSC0AtALUA1ALwe7vPKnBlZmZSVlbG6tWr7/p91GaD0QJQC0At
      AH+QAFTN7b179xIVFUVgYOA9aVMLQC0AtQDUAvAHB8Dr16+TlZWlfoXtmxKWagGoBaAWgFoA/mgA
      qJrL27Ztw93dnYSEhHvephaAWgBqAagF4A8CgCUlJRw/flwd3vL5e7zfOwC/LQymsEXOoOKbQwzk
      imGC7UWY6I88FEH1svKq1Q/g5u4x6gDY1dUpPLCWGgFER3sbVja2iDR4fd3dXZiZW6CjIQC2tbVg
      a2t/h7Duw8DA8K6HczQ21OPk7KKxMdd0e6pQJ5UDQpMhVlXlpRQW3tDYwvr5Yv6daoKcLe7Cw+ab
      Uz919MtxtdLHwXzkq0lubi4fbd3Niz//zagDYPqZk8QmTEasgRiq44cPMn32PI1Omoz0s0yIikVP
      Q9pBasou5i1efkfH3sjPw1VYRC0sre5qnza99zYPP/6MxsZc0+3V19UgFSDo5eOnsTZXzJnC5awM
      jT6r39kEHs0ALLp8Ej3ncHxd7q5pMSIAKgY5cfQC04Sbfv1qKePGB9x9AMo62bvtIHrGhgTEzcDP
      2fLWSltwESu/OCzuMGXhNwHw8tk0QiclU5d/DY+wMKqvpWMfkEB7bQkePmNurbrdLcjEFliaGlB7
      LR/r0BC+aZt7/7Z3cQ+cQkSY/1e+a63Io1nHA0+jPvqMbHE0v6Wx1FVW4erlqQagkbIHqX0oY52/
      3oweaK+kacgSb5cvPQiKAQ5u3wtG+niFTmKcr9MdAamuIJPLJdWCKWnB9Dkz0NcR3VUAdjfcpLzH
      gmAnEY0DRng43brGgd5ejM3MvvTLYfLS07hZXoetkyu9zS2IjQ3wjkgk2NvxWwGYfSaN8MnJ1Aj3
      1TM0DPWGglJK/xCYGH9pHgifpe3Zg0Qsxtp5LPExof9dAOwWzD/VIZZW1ncEQJlgLkvkSrLzCtl+
      6AwzFq5Sf+bvaIyl8XfXqAbaG8i+ehlzl1DaS9Kx8AgnMjxY8wCUtvOHn/6TVa8+Q8aB80xJ9OJ6
      dS9xE324fOUGeqZWdHX2MnvWNL7pmflGAPZXsONAMytXBPPxux/hIwDIKyyQrGPpTF8+m5xTGYht
      HAj1suBSdjEJ0yeRf/E8ciML+to6SZ43Dz2x6LYAuPuN32GTcB/yG+lELp7FqQNnmblqIRePpzMx
      1JPs640kTQ5m+57LrLl/Fhc+3oLP6gUUpJwiYv5cbI2+es6N6/9KQMwifMz7yCosJ3rSLOwtb4Gu
      8PhBsoX5Nc3bnlYLL1rzL2HkEoSLsRgjvSH2HTyJOb1ILf1Ys3IG6ecyMbT3xGa4k+KGDqbNTCbz
      QAqu8bMx76+moGaAqUmxiKStvPvWGZY/EE3q0TwSwm3ILW9n7sxJnD58jNrGetY9+5NvXyw+3YLV
      zFl0XUnHOXwS19JPYT82Ggd5HTebpCTPmILOdwBgTfZpjhTVsywxiOvtFhh23EDfyY9zH77H6j/+
      gcITJ/CKn4q77a194tTNnzJ3xTQ2by7gobWT2PzeBzz45DPfCsBdwn21n7QayXXhPs6dIsyBfAK8
      Ddl/oZ+HFgSSU9jM1ORExIpePnjzAEsenImekQkVN3JxdbIW7oAl17PPYes3EUtpDWX1fThb6mDg
      HkWQp82PA4Cq12de++3P8fT2FSbW3C/2LL4OgErhtCqwNfdIaeqWMihTIlcOY6IvxkoAXV3FTQ6m
      pvL8T165qxfcfOMcNyROVGdd4+Enl31/JrAAwE3vp+PorqStXoaeoYwILxPqdE2QmgZQf3IrEQGW
      2E16AlezOwfgL15+n6AIT0ImTSX9YBbBtk1Ui3Qx0HWkt88cB90G0JUTNSWaq5V99HYOIeooZMI4
      D1ocZhPprndbADy0eQuOPvY0Ftaio9NGG/pYmwttmY5Dp3gvPTIR0Q+8TOGR9ax89GdqAA4K87+y
      s19Qutx58ok5X/vwtze2ExVqh3fsAo5eKOaxJUmfAfAIEh9X6nPLcAoPpznnEleEhzHExxFbcwl5
      xZUUVMvwMu9k6SOPkVs8jE7bTRSSTkKDAmgUK8i9Woa0VxdT4cFUCHNwzk//iLdBO2/8cTfJC8K5
      cL6QSRNdOXPsJBYhE5k8Yx4nt38gAOm5EQDwIyTBoUhuZGEXOZfay6cpKGzH1tuN+xdMxcDGBr3v
      BMCzNJvb0FlWio6BPv3D7ohbcgRNQoeQMFf2Z5Ug6THh5Z89+C8AfPW5vxIU5Ydf9Gyigty+FYCp
      mz/B0duOppt1YCAnZuZkMi/mozTwp+/mXnqlIuIe+TnB1nLefG0TU+bHYW7vhrgtj80nK/nJukWk
      pR6loLgPL4chYsbZ0+AgaJRnD3Hf2kd/PAD8xYtPYmVtywOPPo6rm8cXADTWF9EnUSIVtDsV/FSa
      haGeGDszPezN9L+iadwrE7j5xiXqjNyEByebB55Y8v0C8MMsFs+15fUPc4nykNHZryQsMYAuo/E0
      nPmEcT7GmE1ci7vFd9QA74sR2usQ2ssgLlTKjToJAcG+5F2TYSG9ibmlkvoOARIC1Jrre6GrkIgx
      jlRbziDGU/+2AZi8NJn/+clfWboymuoWBSETgriW14m9biXtQyZMWjiboxs3skrQPFQANI1wp/h6
      C74h8YwPdv7KOTd/8A6OzgGYDF6lSmqFvWc0QV5WuDg5qQFonJhM/oZfYjw+STB5q5B0dePv5oqb
      qyk5GeeY//SvsBruZevuVFz8pzJUfRWxUkaAvxudwuQvzryBm18oA1UZgulmx7QVCzGRtfH7VzYw
      IXEsnTJzukoyMNQZwjlmJu11DfR3Ngga4MsjAOD71FjaIxWOGRvoydXqZvpqOnCyEhYhc2uSVzyA
      md53A2Cv93haD/+dQb/p1GdeQc/cCnMB5oFTZpFzuRCPMeOJj/b/qgb4+PQR7wGqADhzyTR+9/Lf
      mTcnkNp2Cc5jg7l2uYpxrnI6paYkLlqIhWiAv/zyb4QkhGJk5URv1RWMTKyQ6+pT0dZNf0O3YKbr
      ETXWmlaPhZQf/YTlAit+NAD8n1dfIjI6HmtbOzUEPgegrakubtYGI/Ycap0g99AJorqj38FncidO
      kFNCP6fOmX+Lyy1lpFcNMzPySxvsqmn2H64hNWUn8xav4PKR9/FLWoel4cgv7rs5QYY/O53oK59v
      em/9yJ0S33BtP1onyB1c8w8SgO3t7d8IwOxaGf4u/19FURUcUV287pdSz3T0yzATVk9bk5HHYqnC
      YP78x18yd2YCo02KBLNrjJ8H4rucgeTrJO96CaHBYzQaBlNcVo2Plyu6txFbp9L6VZAWqfFxC1Ij
      7XNOrmCShwcK51AK59C5rWutrW/G2toCE6O7W6TqTHoOUxImaGzMNd1eR2cPMuFZdrCz1libb763
      i0sZlzX6rH5bBprv1Qu8b+c7/O8vnxh1ADx+KpOkxIno6orveVv7Us+wcO5kjYbBnD5/WTCpwtDX
      19NIe9t3p7Fq2Yw7OvbqtZt4ujtjbXV3g8X/+c52nn9qlcbG/B9Cey9osL3q2kYkEiljfDUXhxs7
      fS2XMq+OXhP4vxGAWWlHOFXcw8P3z+LTD7Zi6h6Mj72+GoDS9jp+ty2fPz8/g5+9+Da//tuLnN5z
      isbKQuY9+ySFB08Sv2omB996D99Fa9AtzOBASROGAwqmTgtDYeVOlJfltwNwWhDP/mQbvsH2zFy8
      CMPqTA63O/D0ZCue+dkuPNxNCZgST1NeA5U3r2HnbYGbfwxLpo29pwCsKbrKxh2Z3Pfkco5tO4TY
      yo11i4PYn9VCac4lfCclsSLGi/XvpfL0U4s4duQE5dcqkLv78dSyOD58bTuLf/cwp0cIwJbya2xI
      KWD1g4uxk1ax65KS8f66Iwegsp9//GU7UgtrZk1wYO/xmzz+9FK2bdyJbcAEHGX1HDuVx4pXfkLW
      gQMsmRrA1qP5SPTNSfAyYtfBbCKig1EoXHjikchRA0ClXMKmv+5k9ivLSfm/LepwomVLYpDfQwAO
      9LXz7tunWLUijO37zlIpc+TKyVQtAEcbAA9s2oVXsDM5FUN09Q6REBdBZ0ONGoAZJ04xJJh7nuNj
      OLrlGH7R4+gqKUck60PX1Qv9xnoSVk4i5VgxIuUQ4wxlXBoQhruthZjwICQO3iT4Wn87AIUH8ZmX
      9xGXHMii2bFs3ZiK2FDJ6qXRfLCjgftm27DtRD2yjgF0dPV57tkpbNm0nzUPL7qnADz80T4mLo7g
      YqaE+EA9Nh++xrx4L7q6JQy7e5G3N5uocEtO3Gjn5Udj2HG8ksbiVpwMa4letJQz208w8+VVnBsh
      AC/tS6FQbsXyBZPIOJJCZZcvkeFGIwegAIL2wWEO7kzBFCNiJvuRJcxvMycv6rNyefjJmbz37h7W
      PbGSNwUgTY9w4HThAPExEQQFOLNv40GW3J/AW+/f4IVn40cNAAc72tn1yWmmPjuXo+t3ITNzYGZS
      KHLpvQNgfXUNhw/n8thTC8g5eQzLsCk8tOqZ0QfArIoe9R7fN4lMOcxkf0vMDXV/dAAc7Ong3Pls
      uiWCCeroT3dhJh7enkyND+LXP38HtzEOdOp5YiWRMTHEkMz8bhwMFUQImk/qgeuEBxtzoVZKQ0kt
      ixPG4y08vPpFVzh1cxDv8OARA/CdrXU8vXYCvY2V/OrdE7jp9+ATk0j2mXLMdBt58NkH2b05+wsA
      fvTBfh5Ze28BePTjfYTPDScjV86i6b5sF4BobmNCkJc9DWbCorHzFO1D3bS3d7N08RTcJ4xHv7mC
      k8eymbduNVc/PUjQI/NHDMDW+kaMzJV8/OZB2vWNaOs24aGl4/DyGLkJfOCTXbgkzKTuzCkiYn25
      Wt2LkbUb9Tn5rJjjzp5yKx6c7Ko2gecnReMyxplP//ExiY89zpXdoxOAKjm39Rg+SyLY/X4RbkYN
      BE+fjI783prAOz5KZfkj89i8cRsPPnof8aPRBL5XMloAWJiZwfmSNlYsmcapw2lITdywFTS5ME9L
      SkROxAvm5omDR+nTcWTRTH/eeu8EYX4OxE2L4N2/7cLax5mVi5IYaixlY2op+hYidIaUJMWNZcDY
      niBns28HYHIoh8+ognT9uJGVgcO4SGyNRBw+dBixSTBJUWZs2S+YviaW1FeWYOlqjJvPeBIiXO8p
      AJsqb7ArtYCFK6dy5NAFPHzHYK0nZ3y4F+98cISxiXEkh7hx5kQODg5G+I/zZ//mVHqsbHl4QQK5
      x7PwSI4ibYQAzDuXzrWGAabMSMTdpJ8j6V042slGrAEOtpTzfxszCIsYQ6CzmJMZ1dy3ega7tx7C
      KTiUCPMh+lxC8bcSqQG4YmYEhy4J2ru+GWuWTyHzeCZxU4JJPVbNgnlBowqABWeu4DQljDObd9Mj
      thMsmEBk93gP8Jxwfyclh3HswBVmLYj9Ye4B9vf3fy8AVJW7e3/9n3h0zQJGm2RfucH4sADE4nvv
      BT5/MZdJceEavb6reTcZF+yLnobqRZw8k8W0KVF3dGxJWQ1ODjaYmd3dnHK7951k2aJpGhtzTbfX
      3NKOVCrHzdVBY20+89O/CgC8otG53NvbK8yN/6xkiBQKxfcCwLy8PGHlfYtfv/LYqAPgyTPZTBbM
      WU14gQ8eOc+8WQka9QKfS79CTFSIxrzAu1JOsHzx9DubR/kleLg7YWVpdlf7tP79XTy9brnGxlzT
      7dXWNTE0JMXP111jbSbNfVrjGmBXVxeWlpZaE/hORS4ZorGtB2dne3SG5QxIhrl46QpJkyJobmpH
      JoyenaO9+hW/ru4+LM2NqKttQS4cq0rTZGVjzmBnB90yEU6WRupzqWLkHJzsUQz0Y2Ruhlj0LSbw
      nATq61qFc6qOs6O7tY0huRIbW1t0lDJMTI0Z6BtAqZRjaGqGbGAQfeGznrY2BuS6uDha3hMTeFho
      r6GxCycXW9qbW9A1NsfKVJ++QTlDPV2YWNvQ3S70VabE3skBkUwimF2DyMTGWJvp0VDfjqOrPbtG
      aAL3COPY0SvBXDBpdOXC9eqZUlFecVthMB3C2Cn1jbEy0aWptQ9nJ2s6W1vRN7NCTymhrUcqjJeV
      2gR+7onl1Ne3YGBugZ2F8T2dZxoLgxlW0tMno7OrQzNhMEJ79XXNGFpaM2/xM9o9wNEGwCN79mFq
      YUqPdRAdmSngMwNHvXaSorx47R/ZrFw6htRzjTx/fwBP/XQbr7/1LB0lN9i4p5rFiTZ0WtpRmFdJ
      tLceVVIXjBuLEIeGkODnxPrfv03MmkdI8Lf5ZgBODeAP/7zKioUepJ6tR9Lew9IVEzh6+hoOejrq
      jeYDH+6jq6MG2xnLMMjJwT3RnZN5A0RYSGixHsvC8U53HYCXTxyjvl+OpWcgkrZuKksrWTU/jCPX
      24UHrAeFwpBFCZ7s+uQQq37yMJf2HaK4eAgXT0OC3MyoFmAmsgtDUn9jRADsbGsl+/hxJGNjqcsS
      TClBAY+KCsZrpAAcambD9usMDzTg42LJgLB6eYaNIyO7FIHl2Cq60ReLCV48n6Of7GbSWCs6LXwp
      uZzHg08sxehHAMDqG1l8fFLGQ4t9NAJASWsVb59v44GpQSxc9pwWgKMNgCo5s/8gthOn4KPXxCHh
      /ll+BsCnnt9KRJQDXYbexJm3YxXowaVCEetmOvLmR6Usn2xCidiO6vR0GvplLLlvMUMFuYgjJ2DX
      Ws7FZl0qCkp4ce2cbwXgume3Mz7Slj5jT1quXCM0xpthUxt0Bc3rcwAqGEbsZIGovo92KwMWLZ2J
      9XAPf/r9UX7x2xV3HYCpH6YQvWIi6ekDxPqL2HSkkBmRDugqdRly9+LqjgyeeC6Bj3ZfY/V0Vw6W
      6VJ/8jh98g6cbfxZ8FwcRzdVIDbtGlkgtKCBb9iwn8fXTOSTI32YS8pwHeePz214gWV9HazfdgEX
      HQWxSWO4lN+OpZcvdYJW72Qn5UrFIOtevI9tH+zk/oUT2bYvg2FLD55dPemevo2jEQAOSzmScpzS
      ZhsWzvPUCAArc7LYermK+Ig4Xv3V/2gBONoAeOXkaVqtfZgZ4YGsvYJ9lxVfAPCdLZU8u24cr//t
      MHJFD/6B3pRUN/PK2um8vekWAPN7RYKZbMyMQAM+OFRDgiNqAOZt/QQdRy9qb5aw9Mm1eJqLvxGA
      qjCYJx/y442/H8fAyIZlSeYcrhRj2lrBwofms/mfe7A3NWDyqsn87qn3WPVEHC3GfsRadHPgmoJH
      FwTddQCe2b0fuyB3qpsNGevvRv65dMTGekQFuXCpXZea3BqWTVRy3Twe65YC/KIi6Kjs4PLZUwKw
      THEI9+LaDT3EkuoRAVDS08rWSw08Ms2dD7bmoy9rJmhiCN4jjgMcZP07B7n/yeWcF/ru4etA+7Ap
      zX1iOisqMdOVE+RiBuMncX7nHmLGOOMWE0vR4X24zVqOn9noBmBLaT5bTxdyvXCAX7w0C6Xs3gOw
      s6kVsbUh297PZsuBHVoAjjYAnjlyiIo2BXHTpzHGWkFVK5QVF5IUP459e07To9Bh6qwoRDJ9PJzN
      qS+rwdzDjprSXryddenWMaHxehZXaxU8sHwK/Y3NgtlnRUNRM4EhbigFjeRmp5hAN4v/DEDh/Du3
      n6B/WI+5S6bQVtVJUJAzly9fZ5yfPdtSM4hLno5RVwtO/p6UXS3EMyKQ4kuXKOvSZ/HsCdyOv3qk
      AJQOdLL3wDUWr0rk7KFj2I0Zj6WOHG9fe9KEsQmYmqDuk76rB/1NDTi5OZN7MZ0BS18BLmbs2p7J
      gjXTODDCPUDpYA/NPWLcHEy4kn4B7MciGmob8R7gQHs92w7nYGJpw5w4P46er2DJoiguHDqDU0Qk
      ltIWzt1oZ+nsSN7asJ1n1i4mJeUY5p5BzIjy/XHsASqGuFHcg6mZQiMaoGKwl09TLjBz0TQWLXjq
      hwfAb6sJcq8kPz+fv7/+W5YunMpok4IbZQSN9UZHA2EwOVdvJQrQZDaEwqIKdbIHTXi5VXIpK5/Y
      qJA7OraqugE7WytMTO7uDt2xE5eYOT1WY2Ou6fba2ruQyeQ4OdpqrM0/vv6xAMAcjT6r37kmiHYP
      8KuiTYZwd0WbDEGbDEFrAv9ApamyjHN5DcyeHcXFtAt4RIyntvimYAIHs3fPORR6uoTFRFJ7NYd2
      qQJrB3e8rQ3wC/ZRK239LfWUDhgT5qLL1h3nQM+AybOiaSlvV2fF7hVG3y84FHPlIGOCvL+i6H0O
      wBtXLnOtppcZyQnYGIk4k1VCUmwQmafPUdE+iKObL446HeRWd2Bu60q4YH6fza8STF99Zi+cjKWe
      zj0A4DA3cwQACeZ4yqEM5DqGrJ4fyc3qPhpu5uM5MQqz/gZOX29l+dx4aiqq6Gysp8/cjdgxluw/
      kMW0JVM5tv/EiAA4KJjT+9JySUhOxMVYQn6ZBKW84zYAqOTCifNILZyY4G3O8YsVzJsbTdbp81j5
      hdBXXUhJbSdxM2dweFcKjz84k0NHMzFz98FO1s618hYsbW1wc/IStHKXUQXA8mulOIT6UZWTRbvI
      AU97A40AsL2+hrSL19Gzcubvf1mvBeBoA2Bnazst1SWU9YJUbE9nSSGuXnZqJ8ibH5Xw5MNBvLv9
      CnpDch5dl4RYrMuBLYdY/OgCdWGZfR/voFxhxk9XhvDh/k4emm/Lm1uuoyNVCvjQ4YknEti6+xwm
      cjkrH5n7HwAYxV//sp+nnpzBhbwuJrgO8Pan2dz/woNk7TrEwvtncXDXYfQkMGPtYk68u4thSwvG
      LUjEVaSDkeHtVXcbKQBbyov445/T+O37L2DWW8+GXaUsT3Qkt7YbrBwou1iErmKAJSsSMbMx5siu
      s1Q26hHg0MWwSB//KQFkZsjQVdaNCIA30k8iHhODv4MJF4/tJ7fRnbgI45EDcKCVMzclNOadxUDX
      hMBxrtQOiOmTm9JaXMrj6+bw6YfbWPTwKj58bwcT3Qwwi5jOQMlNgqdM5PAoTYbQ11DJL3+Xwsvv
      PMqB93KwM+wgfHoCw1LNaIBVBdlcl7jx2i9+owXgqDOBlTKO7jqC5fgJlKRnU9LYxaToAKYKAHzl
      N0eYNsOT3OudyDvbmZgYhK2rO1WXrqgBqOxv5+lffIq9QTfLnlzNu2+kYWsn5YFHl3NkTz6tjXXE
      TwvgWmEDHuZG3wDAydSUFHPqYi66TuMYzDuBvpsjdUMuOHSV4BoaSFFeEXZiBd2mRvToOZFgNEC1
      tQ22BkZMTwhH9zaqld2OCXz0w/1EPbaQ64fS8JqaTM3Fs9iYm9Pj4sHVrWcpl7TjbS5oydET6bf3
      YCjzDAVllThaeDH/NsNgrpw4Q41MhrGeGaq39EqrDW8vG4wgldeucLZeF/OGCqL/LQxmzeoINqY1
      sW7ZeLUJ/PRj8zlzOoMLVxr56SsPcHTzKE+GsCiMA9vq8DaqxTMuGrFcMwDc8+lu5t23jCkztCbw
      qANgzvmL1LT14R0STHlBCe0SEZ52hmoN8Oe/Pcq0ZC+q20W0FJUSNTkIAzMLKi5cxDE0GFlXG37x
      SfhbyHj/4/3omoUza7ySvVc7UDZLaW2qZ1JyIBX1/VjKh/4zAKdH8H9vpRIW6k5tSyfDBi48Ljyk
      n3y4l86uLvzDgykrqcdRV8QMAbwf/eMj7MysUXo7YyecMSxqIvam4nsKwJPb97Ns1XzBrD3FtBh/
      3tmZjYWzO7aSejo7ejHw9mbVzChO7TtHUVMHyePtOHe1Ec+YqXSX5Y0IgNczsinvELRLpTFd7ZVk
      Fuvy2IqwEWeDkXTU8Mxv97L6/mREbVXcrO0lYXYch/dnYGJrzxzfYSrt45jsqa8G4AQPS2owYaCp
      kdmrVpC+fZQD8L4kUv6yBbmxOYsWJtzzZAi3RMr761NY9/RK7R7gaASganhUlcR0xTrqmig6OmJO
      nFY5QVTpy2/hSiwWo1Qq1GUSVHUSdATjVqkumSD6ImGCUqm89Z3wT6FQqr8bHlaqj1F5k0Wq33+N
      lva5Bshn/bh1PpG6HMOwclidRl59Dp1bqeRV5/j8vErl8Bf9ux0fyu0AUNUHdZuf//9ZanyFXBgr
      dUr9z/qtI1J/rhonobeoqgnI5Uq1I2mkThD1vVAov3A+qa4vr6B45BqgcLxcoVTfNlU5gy/fV9Fn
      48dnaf0/d4LI5be+E3/pGlXt6uiIRhUAP78v6nkoXGFtfZPGnCCfj9cPEoBtbW3fCwBVNUH+7w+v
      Mis5ltEmxSXV6pfI7/ZD8LXjdKOMcUG+Gr2+0vJavD1dNJLtRiWq7DMRYXeWvbquvkVdE8TYyODu
      akwXrpIYH6GxMdd0e52dver6PnZ2Vhpr850PUzReE2RwcBAjIyOtBng3RRsGc3dFGwajDYPRmsA/
      eBmm4moG1wdcMZQ0kBTjy6u/3IGbrxU2fqHUXUrHyMkaO1dfhquv0So2QaIwItisE+uEhehcL6Db
      REH2zUbMdAxIihvDkLkr47+pKPDnAJw2jpdf3YnHWFvGBodybHMKr7z1KtUXDlPUYYmeVDDtdEx5
      YFUY6XtP0TvcR0H7EMYYsWbtPCxuU1MdKQBvCtd8MKOC6csX49hfyoliY+5LsiH18q2aIH6JSeiV
      55FbI+GFn60i+9RZKgrKkbmO4YklMXz6t93Mevk+To4QgO1VN3h3Xz4r71+Ag6KevRlyxnmLbsME
      HmTD33YgsbBlhqomSFoRjz+5mO0f78IucCJOg7VcLOzkiZfu49MPdhJgLqKgX4Q+pqxcFsoHm04i
      MjbmhceWYqSnMwoBOMyOj7fQInJmQVKwZpIh9LTy+lspjF8wn9+/qPUCj1oAKuUyMtJPUS/x+eJd
      4Bd+tofYBBeasacj9yoBE31x8hlDW26B2gu8bcOnmIgN6HJxxqerlw4zqBkYxs7ImFgvC2qN3W+v
      JshkLwIjQji08RCT75tP87UTdPQ7IJUoBQCa8/zTsRz5cB89DNJpYY2FkTkr58Ryu5b6iDXAYQWH
      tu3BImIaxh1XySmzY+p4MS2dg4jcvMjbl4WLo5TqHhH33z+Jo8eLaaroxd2omoi58ziy+QSLf71m
      xCnxMw/so0BixeolkzmfuouqTj8iI27DCywdoKFfh5Mpe4SlwYToBD9yyrsxsfegLjsPDxcoaZGx
      ePV8dmzahbsAzA57Z3Xh7yQfPd47WsXcWTH4eztgOhoBKGlhw8eVuBs34JMQfc9T4qu3i9IzkQSN
      If2jbLam7dUCcDSbwF+XDOG5J0J5/Y97MbF24KmnbyX1/PAvG5Ab6zLoFo9/600Cksex84N0/Cd6
      Ejo7EW9dHZqK8inVc73tmiDIB/jn2+ewtJVhbaJHU6v4KwDsFYlJfHQ+jhowgQdayjh0DZbHGvH+
      nlY8bVrwc7OnycKZvBRh8iNjvKs+HQ4OBIRFQGU+p84WsOzJB8ndens1QerKKjF30Gf7+2l06xrS
      3CVonAvG3lZNkLTd+zAal0hHxjnGxwkArOjG2NaD+pxriISFI8TLEvHERM7t2IOvviEJjy1CdeaW
      umZMXazZv2ErY+YtY4KbyegE4KYK3E0a8ZkkAFADyRCKL2QyFCAA8OPLbDu2RwvA0QxAeU8D54sE
      2PTVCSbwGP7yxmE8fKyw8B1L7cUsLFxtMLa0xVwyROLceA5u342Rvh3xSxLZ/bet+ET6UFTfJZjA
      Isb6uJB+VZiMXu4smhr+zQCcHsJrrx/FSzCBHVyd6WqQo9OVS9Cs2ZRcqUMmG+bmjWK8x7liqxQ0
      E/0hqgaH1fnrkmYl42iqc08AmHPyDNdbJEyekYinSR8Hz3Zga9rHxPE+vLnhMGMnx6JXc52iOhmR
      cb5EhgWwZ+MBugTt9PFlSeQcvojXnDiOjxCAV06doaB5iEnTJwntDQjtdeLuoryNmiCl/P7dS0yM
      CsDffpiTGTWsfmAmO7ak4hgcgqOsidySbu5bt4hP3t9JmIMJlUp9VGHkMZHBpB6/hImRGUuWJ2Op
      Lx59ABRk55ZP6TFwJjk2QCMmsLS3hb9tOM74edP57XO/+uEBcHBw8HurCfLRe3/mqceWMdrkUnY+
      keOD0BXfeyfI6XOXmTJpIhr0gahrnoSH+KOnp5maIEdPXGLWHSYCKCqpwtnRDgvzu6uRbd11lNXL
      Z2lszDXdXmNTGxKpqqCUk8bafPipP2i8JkhPTw/m5v95cRTJ5fKRA7A3B/oLvv13ZhPBJPgbf6Kq
      CbJ98z/4+UsPjToAnknPISE2XCMAPJJ2gVnJ8RoFYHpGHlETgtHXEABTDp5m8fykOzo2/0Yp7q6O
      WFrc3WR9721K4fGHF2tszDXdXl19sxqAPl6uGmtzzrKXRnlNkJatYDhGFW37LbZGEdjf/6Mzgft7
      e0HX8FZNkMTx9PYMoAorNTM3Q188TEdnH0YmpujrCEOqq4tIqUCBDnriO6fX52EwKidMV9+QuvhP
      X3evuhazrp4Bhvo6GBjoC9/LkasChYd1MNITCZNbQX//AKqba2hkLJhuIw9puZ09QOmQFD1DfQZ6
      +xjW1cfUUA+JTIFsUFWXxASRTEqf0BcrQUOTCg+cUi5FrnPrd6o3EcRC33eONBBaIaeju/+z8YYh
      qZLCm2W3FQbT/1k/jYVx6+6TCOA0EfreK9xWY8TDcnoH5VgJn6nCYJ54eAF9g6qa2DpYWZvS29nD
      sJ4+lqZ3Pzm+5mqCDDMkUdDc2qq5QGhhbip1xEyasW6U7wH+FwNQ0tfO+k/O4evtgaGuTO0E+c2f
      zrJgng+XrvfjZ1KHvmsoZUWlRHrbYxIzEavqm5Tqf7uj49sBGMvmdw7gGeRIWbsu9XkVzFwQhqml
      LQUCrFQp8WtyrlDY18fRjE7+8VQoO49UUFzWw+xZfjg4u+PpbH7XAdjTXMPLL3zKax89wqebBdNG
      0cXDi2JJvdZGa1Mz+sbW6HbVYcQgE5avpvD4QUqKerHztGDJ3Ch+/vQH/Hbnb0bsBKnOv0R2kxFT
      44JpL74otGPO5NtJhjDUzD8+ykZH2U2QpxUNbYOEJ0Zy+nSeAGITdcYXiXSYmNVLOLxlN546Sswm
      hGKCPk5GwtiWSTHurmfakvk4meiNSgDW3cxhY9qQxmqCKIb6+PP/vsu0557ghQde/C8CoFk09F1T
      LTlfXn6++KuqqpLsi2n/Vg7xS781jwex8Q8GgB0lV/nbkTICfL2wM1KoAfjsT3YRHedEk9IO/WEl
      Lz48hfzjh7jUaEi3sh+j9hZCFi5h8ncFYKwnfzo1wKvLg9Re4J++9AHjon1wDwym+WrBlwAoZaBf
      gYerMaWFnVy4WEJUrBvhkyYR4nr3ASgTtL/jnx4h5rGFWAx0s/7TsySEO6Ij00Hi8VlNkJ/MY+fH
      O4meFkl25TB1Z88ho4NVTz/Ljf3HCLkNL/DJT3ZQLjYjOSmW0swLVHS53nYyBIWqn5+cxknQIP+9
      JoijnZSCWgkPP7NSXRPEXTlAs7UDRqbWLEtQpeG/gNjMjnWrk9DXFY1CAMo4tvcYN5ttWaShmiBK
      mZzyvDy63cby3I8NgDv2pNE/MMTYMZ7ERYd+jQb4n0+dl5tHyijSAHtqSznbqEvr9Su4eriqs8Go
      a4I8Po43/nYMS2MJK9fex9mUA+ib2eA2OfLuaYDTQ/jDP8/xynNTOXbqOlWVEp579tae2Y6PUv8F
      gCZOPnRkp9Kj64lUbsvaB0Jvu83bTobw0Ax2CBrqyidWcOnoScYHOJPTa0hlVjljrPoxnxCPQUsF
      3pER9Df0kHnyODMfXkXGx7cXBtNQ3YiFlYyPN5xCbmVIabWYh5eFjDwMRjHEhvX7WP74Si7u2Y+3
      v7B4yQzpHDKgvbQcUz0F49zMUYbFc37Xv4bBVFwrYMjZG93KXFrswoj3Mh11AGwpyePTsyUUFQ7w
      yoszNFITRK3sXLlCm4v/jw+Ar/9zCy8/v+a/ZA9wmPRjx9FxDaa/uVadEHXbJ0fpVYqZs2wWrqZy
      tm46RkBCIj7C3yJHRwx6O+jSMcXF0vA77wE2VpSw70Ipa5ZN48T+NBr7pZjbu+BtNEBedSfjIyfi
      YGOAnokltspObrbJuXI2kyEdEV4BQcyOD7wnAKy6Xo6ZvS7b9mViZmtLTKAf/mOdObD1KEHTJ5Gd
      doxOiQ4ToqOIHOdO1ulT9Fv7MzXMlZobFdgEeXNwhADsqKtg14ky1jw0HWN5P/mlA8hlI0+IOtBa
      w8a9GUI/7Vk4aQwHTpVx38p4Tu9NwyUyFmtJEyfz21i9OJ63N2xnxkRvTuXVqI9dsmoJ2Uf20mvs
      waq5kdztt6Q1VxNkkNzrXVgLa7Km9gB7mlsYNLVi0cJRXhPEoGsPhlYhXwDwnxt2MGNqtDrTRIC/
      1//nX/tVpFbfXOVeWxNkZKKtCfItANbWBLkj0dYEuQsaoGoAm1racbCz/ldt4UfqBf5ctMkQ7q5o
      kyFokyGMShP4P8qPCIA9rQ0cPl/EjNmJWNBPVbuI8s/KYqYeSGdQGIuoxDjcTCQcOJyNX/gEPEwU
      1PR24+fti6FIRn5JFeaGZjR1DBAb7qouSxgUaD9yAM6Koqisn8CxdgzLpRw/mo7I2o3pkS4UV0sY
      62XM9aI24b7oYmvcw+mcCqInT8LbzvieAnCgs5lDp64xbc5USjIvobB2JS7YieKafppK8vGYGIWn
      tTElN6sZM9aDmqo6eloa6DF1I8JJzN7j+cxakERa6shqggz1tLE/7Spx0ybRWJBDp64tdsLiPnIA
      DpN19iIyS2fCPc04cbGCObMncuXsBSzHhNJZlk95Qw+xydNI3ZnCU4/MJfXwRax8xjIlzHNUA7Cu
      uBqrMe4UnL+AzMwddzt9jQBwWDbIgUOZJMyIZ94PsSymFoDfLJlnTjE2ZgqWhiLSdn5Km0U0dl9+
      F3htMG9sysJC1sSKtas5vmMPFraOiGzFtOt4Msmkhqw6OYOdEtLSinnzraV89HGZoF3EjByAX3oX
      ODP1IOaR0zGuvczVLn3am4xYu9SVf757mWGxPkP91fz8lQcEkyqPmckT7ikAs85dxDfAmQOnSnH3
      8achP53pUyK5Ut2J0syGqpxKHp7lzasfX+St3y0nZdsJyur1CXTuIyAuFgfjXralSzFT1o8IgEWX
      zmAQEI+3lQ47Us6wcvG029MAB1s4njdAy410jA1M8RvrRIvCiPYBPTrKKnli3Ry2fbSDuQ+uYJOq
      JoirPjaxc2m5eoXxybEYj1IA9jVW84tf7eSV959HVtdP+tGjRM9OAqkm3gW+SLu7N1cPFbFt33Yt
      AEcbAM/uP0RZh6BlhfiiGOynud/ui2QIz/10NzGTXKltFmEoPIAvPTSFwtNHOV9rQuKCUA5vO42X
      qZSpgpZx7HQVfX16mFoP0dxgescA3L5pD4sfXoqevImP3jnDsHXglwBozPKZ9uw6cRVbNx/unxd1
      TwE4rJCxbVMK0UsWoN9QwokKGf4C1GzMzNQ1Qa5sS8cl2J7epm5mJbiTPezMgACxm1UCGH/6EmWn
      UmmzDGOo+eaIAJh15Dg3Ovvw9w8g5+w1TGxtCA93w+s2TOC6ogLSSoawbK37ak2QByaw8XAN61ZE
      qU3gpx6Zw96Uk5Q1Knjx+WWY6I5OAKrkVkr8mZg21/HR9gzmL4lHqQEA9rXWsO3wNapqujibfn6U
      A7D9oAC3Cr51R94kAKySfxQAzL2YTXN3HzKRHjWlhVQMOjFjgr0agL/+42nmzPGhoFqGu0E9hnZj
      KS2tZLynLaYxE2k9tJt0mT+vzLZmz0kBgDJbYj3q2ZZjxh9eiL8tAP7ytfPMXxyClbKVa81G6HXV
      4JYwiay9xxgrmMZtRm405tYLRl4zE2LHUVzZxGMrZ9xTAB58/33anUOY5G/J3987zYMPJlNbUcm0
      KD82pl5H19gIi6FOzmbcYJagrS2YEcmFo5ncqG5gSrQ/rUN9DMmc6WotHREAC7Ov0Njfj66+HR3d
      nQx0DeAf6Ir3CMNgpN31vPS/+4R+zqKvuojadgnhieM5lXYNfVMz5o7VocwmhiQvAzUAw13M6bQQ
      AF5dyeSly3E1GeUAXBpP9vE8Brt6iUoM0wgA+1sbOXm1lHaJFR++9aY2G8xoA6BCLle/2qVOuT6s
      RK4UAHE2W10TRPVq1/CwoP0J36kcwgP9Q+gZCH+rCnyo6nAoFciHdVCljlPXohAWDpXfRCofRl9P
      PHIAzk1kcFAiwE2EsbEhksEh0NHF0EAXuUyGROiUiZE+MplS6Mcwg0MyDAwNhbZE9xSA0qEhZArV
      K3mqfsjVtTNU7w+rnENDg5+NhY7oVl0N0a3aJFKJcB1C31Wvsg0MyjA2MWTHCJ0gSoWCQYlcGAMD
      JELbOrr6XL9RMmITeFi4HwODUnU/VWOneiXMWBg31XiK9Q3QUb3YqCNW509UO0GeXCn8XtWOHob6
      9/a96HsNQHWNFuG+qO6ZSKxHY1OLZpwgAl76hblrZGRIfLK2JsgXoqoJ8qff/2JU1gS5WVLNGB93
      4VnRTE2Q4EBfjSZDUNUE8fJwEUA2SmqCCPBTLQx3VWP6L6gJIhMWd3sN1gTZsHGftibIaNMAv060
      YTB3V7RhMNowGK0J/AOV7LNnSc8uxjs6hpayUspbpEyb6KVOiPqH11Jw8rDAbux4Qs2aSM1owtbJ
      hVg3BZm9Lsz0lFOnZ4frQDn7Kgx5dEbAHQMwN/0sl0tbMbb14YEFERz9dAduMxajW3qVw0UNmOro
      kyj0acjEkTA3c44dzmLmnGiq8q6yO7sccz0DZi9Kxm0Eb6WMFICVeZkcPHeTmHmL8RPXc6JIlyUJ
      1pwslOClrKbbIYAJHoZs3niehx5LJv38RRqKq2mUm7Ew0Ymdx8tY/MBCck6fGxEAO+tK+OhgPovn
      xqoXoar6buHvGHxGnBFawqb1uxkyt2N6hD37Tt7k0ccWsPvTvdgFjseyv46LWSXMWvcoF/ft5765
      Eew4mofM2IqXHkge/QAc6uWDHSeoqGzhwYfmo6OQ3TMADg9L2PL+KVbeH82bG/biOi6et/76Ny0A
      R58GqGTzu5tZvvZh+sqzuNDihImqKFKUFy//KpWkqe6UdxujN9TD04/N5sLBVOqklvTqS5jmaUud
      sRvVZ84jNdLlgfvnoXcnAJwaxD+2F/Hio4k0VNRh7WzGJ7uz0TMyINhwiNxhfRwNjQh1MqRSz5VE
      f2u2bDzGmkdnUXj2PGfbZbiZGTM5MRIzA/FdA2B1ZS2WxkMcyZPhNFxCcbMn0yaIGbZ2IfvkSZzG
      T8G2v5ITwsLw0pMJbD9UxmBjBXJDExwZJHxRJAf21GJj2T8yL3DqfkpFjiyZFY2s8Tp7csWEud9G
      USThHpV3i8k4sh/DYWN1luqr1b0YWbtRn5PPI0/M5MP3dvDIutW8tWE7kwOtudpqwMQAb3yC3TEa
      7QBE9TpgBZvOdDE3xumeaoA383I5cqaK5ImOXB9UEj9hIsuXP60F4GgDoKy7kfdOtPLM0hC2b9rN
      woeXka4ygQUArt9cwbNPhPKGoAkaOtvy7KMzyTl2lJvdpgQn+HDlyAU8VUWM9p3Fy3QAp8TVLIt2
      uH0ATgngH3sqWB5vxfr3jjFzij/ptVKaSitZGh+Oy6x4vPXFtBZf56bI+d8AmIEyPJxAM4MR1zEe
      KQD725t5d/tFnn5mMYb99eqaIF42LUyenUx3+XUKZA5MCbRj66YjRIbYIXf3Zv+HGfhZdpK4ei6n
      T19E2m+Frn7fiABYVVSChbMx6RkDiAcqSFg4g7LbKYwuyJn9B1H4xNB7+QIT4v+1KNKqhT5szzfi
      kRmeahN4xbwpWDma8sb/fMiaX7wwqr3An8vFE6fwTUhkSAP5AFWJOnzcDBjyChEUg3OkHj2pBeBo
      A2BHjaDZyJ2I8TZm3+4zLFo27dYeYIwff/w/4Qb722DiHSiYgHVk3RxEbGhMnKcFuuMCqTuSSrlC
      n4RpyfhY67F33wmWLEq+fQAKJvClE8ep6lLQ3DaIpakhD6+ZQ3/NDdanlGLuZKAufTnW24kzORV4
      +3jQXVyIvr0tTpbm3GjpxFoA5ISEeMY4mt41AO5c/w691t5Ex0YSbC9n76l27Ex7mTQ5kq6ackrl
      1kz0tuLIgQs4C/QYFx7Cjg+30iIxZV6cG6nZZUxbuIDr6ekjAmD28VPcaJMQPzWRqquXmTZrMrm3
      sQeoqgny67fOExc/Trgfck5lVrNaGMdtHx/EMTiYWGsp3W4TGGerowbgwsQAjlypRilo2I+tmYWB
      aPQD8MKJ00RNTaKh/t7vAaYdOC/MhbGCtXIGQ7dw3v3r6z88AEokku+tJsiWD//KC0/fx2iT9Eu5
      xESGaMQJooLt9KQojTpBMrLz1QkYNFUTJPXoeebNmnRHx94oqsDV2R4Li7ubnurjT1N56P55Ghtz
      TbdX39iKVACgl6eLxtpc8dCvNF4TpLu7W5gbFj9MAH6y8Q1efGY0AjDvMwDe+zCRNAGAyUnRGg2D
      ycgqEAAYgJ6+ZgB48Mh55s/+YQFwkwCkhzUIpE2fCO09oEEANrSqa4J4ezprrM1la375wyuKpDWB
      v1nkcjkKhRJdPT11ULRYV5dTZ7LUgdBK5bAqzlMNCtX2mkQiE6CoJ/w9jBKR+jPV8A6r46JFwnnU
      8dHIVcHDYh1kqhoZw8Pqc6uCdD9vR/ylvbovaoIoPz+HUh0QrDpOR0es/q0qLdfwsBKpVI462FpP
      rG5TV/37YeGfQt0PHbEueiOA9ohfhRPOKZXJ1b8TMfzZ9QltCuOiVNwaKx3RrT6LhT4qhYsYVv0T
      6aivXyl8LhL+H2kg9JfbU8hk6qDl/Oslt7UHqArYFgZOPW4ymUI4161gcpEwNqprUAWsq4K5VSbw
      M48vE+7/rQB2fQM95FIpw0Kb+vdA87/XJvDnY62uzyFcT0PjvQ+EVrWpipWVCPNST5jXP8hAaC0A
      v1luXsvj2KHTjEmeS3tFFR09EOBt/pkX+BBTp3lS0WZIgHk97TpOtLV2EuNlwLFmcx4NN+FKUxcH
      zzXx3s9i+HB/J/PClbx2qIK/vzSDX/1iKzFT/KloGsKwvQbncT6UlQ3w/DNzv0i4+S/vAj/oxz/W
      Z9DSUEOs8JmVvSu1edc/ywidxcHSHrytjAkda8Uv/5bBlj/PZf2HuZSX15E00x9HN18mBDndNQCe
      S91PXbccY5+JhJrUcDDXlHUzrUm50kpDRTkWrr5M94f1Hxfxxp/vI2XnfkoKOzD3sGP1whh+/fyH
      /PyTV0ecETrz4EGKOiQ4jQ3kxtlriIz0mTQ5GO8Re4Fbef2d0+joyZk4xoaC0namLpjM4YPpGFo7
      4ShpprlLyqy1qzi4eRceSJF6e2OMISHeBqRkNaI/2MmS+5fgYDx6aoJIe9r4xYsbefGddWz951EB
      RhLmLJqJSH7vAFhTms/fN+Twm5eSSM0spmfQim1bPtACcLQBEKWU9ev38eSzK2gsLmTrhXrCPM3U
      AHzhZ3uIjXehTmGDnqBZvPjIFPLTDpEhwM/e0wAPA7E6c3NX8yA+QTZcL9DB1qAGV0cTjD3Hse3t
      gyQmB9LYJayQXV2sfGQup/buJWr+Esz0/hWAz7y8j7h4R4qr9RhsqiIszhc3/wB1+MYtAGayPbcJ
      D2sTkqI92J9Shl+MMzczmigqKid2ig9+4eOZ6Gt31wCoksKMc9QZuENtAZVd7sQGy1BIhIfus5og
      a56YRsquM6xYFMz+PAl1FzMR6bSzeN0T3Nh/nNBHR54S//rFE5zNayVsRiK5+4+htHEhPsLztpIh
      qIr0bNichoOgBcZO/beaIPZSCutlrHly+a2aIMODdNg7Y2RixfxIBz7YeRkrRxc1vPXEo6cmiGxw
      iHMpZxl730ycZYO8u/EA02clqlOr3SsADg4McWDHCfXcLL50juMtNuxc/08tAEcbAPubytlRLObB
      SDt6lLoc2Xkaew+rL9JhPft4CG+8cRgLUyWr167g3L6DiIyt8ZgUyul3PsY9cjy6pu6Ias9S2mZP
      VWkxPn7WdCrNMJEaMydSh+whV3QqSwQAzmLzmztZ9ex9X3gcv04DVL0D+3U1Qdpd/RnvaIqiu5Kd
      5yQ46+RzpcJcMDfNef7pkb9yOOJA6NwrnGvUIcmhm705DVTU6ZE83pLxAU5cGzSh7FIpzz6VrA6D
      CRhji0dkONLmXi6lHSf5oZVkbr69miDHNu8lcEYIGRdq6ZOaY9Rfid/EcSMPhFZIeW/9HhatFdre
      ewC/IBdq+3XpVZjQVlyKia6CEA8L5KFxpP9bTZDKgkIULl4oSnJocQgnYZTVBFEnQ1g5lRPv7mba
      muUouzQTBjNn8STEpmI2C/P2k0O7tAAcbQDsbWuiQ8cKN9NhPtmWyrj4JNqqS0mKC2LTxlQGRHrM
      XjYbd1MJn3yUpq4J4meuQMfFGVl1BV26RujpW+Cq30NaVgMTEiNwESZE3qUc+vVsiZvowakzl9Hp
      ayO/rovpCxYS6Gz8r3uAMydy5XoPE0JsyMqpo6LwKi1DcswdXPHS7+VabTcTIsPIycxFtfEYEz8B
      YwNr/OzlXCrppTznMv0CUO09/Vg1a8JdA+CJfXu40SgjdtpUIr2MuFrUg5FYythAF3Z/dIjQudPx
      dzCjIK8MMzNd3L09uJiWRp+1P7MivajIK8EubAyHRgjA/rZ6Pt6dzapHFnDl2BFEjoFYG0pHvAfY
      31LFO9svCFqcE4sm+ZFyvIQHH5jMsR2HcYuOx0bSyHFBw3xo5RTWb9jO1DA3Tl6rVec+WrZ6GZdS
      d6lrgqxZGMfdfg38XgOw+no5Rs5GbPnkNGbW1iQlhN9TDVAlN4T76+ttywfbzzB/5RxWLn12dNcE
      uZuirQkyMtHWBPlm0dYEuTPR1gTRaoB3LNpkCHdXtMkQtMkQtCbwD1jqK+pw8Hah4FImZl7Bggl6
      g6T4ENKOXqRXISIuKR4XEzn7Uy8xdkIkgR7mnD92mm5jR+ZNCqahrJjzpd2sTA6htGoAPy8zikvb
      hPMW0zIgxdbegb6WZoaEtjz9g4gOdvkqAOVDXCntZHyAPSWl7YwZ40BrdRMSXSlKoR2b4T565DIu
      pF9DIRwXER3NGBfzewpAuaRf0FzymT41kH1HstAzsWDR9DCqGoZwsYQupSHD7TVcrupjXlI4DXVN
      9LU30m3kyngfc44eukzSgnj2jxCAkr4OUtOuEJ2UgKvZMGW1Q/T0NN8WAHOFeyizcCHUw5QTlyqY
      lRzBtQsZWPqF0FKcR1XrANGTEzmwM4WFk8eRUdQgHGXEgiWxXDp8CpmNOzOjx446ALZUN2Lq4UTl
      5cso7HyxEA9pBoAKCUdS0wlMSOC+lc9oATjaANhQXshrr6Xx278sJe1yB/111Xh52f//miCPBfP6
      R1lYy+tY/NiDHN++i7FhXuRLXYkzbmJAz4j0gn7ujzMn/WYXlRUinl7zVWcGg02sVzk6HpvwtRpg
      YeYlDuQ28tKjs3j//Sz1cee3p9FuOkx5nyFL/MwoUhdGH0Oiv41GNMCze/Zj7e9KXb8TM0INeHvL
      FVbP8KJwyJyr+3cSNHc5fdVVuA43opwwm7ozx9XFzAPcBjEx0Md2nDvXS40RD1aOCIDFWecQ+8Xg
      a61PzulDpFc7kxhhfBvJEFo4nNlNe0kGpiZmePnY0yM2p64LuquqeWrtXHZt2sn0B5ax5f2d/+IE
      qco6yxXjEPyHyrENDMPRZPSEwQy01PPKK5/wyruPsOOdAjxtOwmdFMewBgqjVxcVILV2ZcemYxw9
      dVoLwNGoAR78MIWJ0wO41mpG69VcHHzs1AB86vmtjI91pr3P5P+1d/ZRUV9nHv8MM8z7wDDDO6KO
      gIiggggIgqipCFrE10O0Bm3Sxt3uprunp7s9m92ek9PTk6ZN0j1tNlsTk9S6vr9AxACKBo2iRgEB
      AUEBRRTQ8OIwwDDAAPubyWZP2jSRRBjD7jz/zZlz587v3t/9/J7nd5/7fJG7a/jJtiV2TZDO4CW0
      n82n+oGF7yYYaHMPYVWElx1yP3zhAAvivLjX4Ubv3XpmxUzHMCeSlHDlVwAwmd//6k28AnRoIhK5
      fbHxcwCUoTXo6W18iNhdxLlLtzH4akhZnYZBL59QAB4XxmVhZgznz5vxFzXhmZhCe+k55iYm0d90
      3V4MYbFBzpt/Ok3m8pmUCJ/7zp2mvuUO/vpQMn68iII/3kKsNo4JgBeP5VPeYWJOeLhdIe5Wq5bY
      KMXX8gDvN9SRW/kQ/cP7X9AE2ZoVyzvHGnl+c4I9BA4c6eOuSodc48n29bEc2nWM2vZR/vZHG/BW
      SyYNAG32qSbIUs7uyKbhkzY2bNmEdNQxIfDVoiJadaG8/E8vOQE4WQGYmJnIvsNl9rLuQVM+TYT+
      xasXWL82hOLKLgKkbfjMjKa6opr4yGmUd1qZKu5jVOdH2aWbxM9z517bIO1Gj//1AHtM3WRsmI9U
      oWaWn+hLAbhsng+HK0VsXhrA2zuOYOpTsmbDAlpK67HoFMxLTaLw5V8TkJxMV5+UqOlavHz98NOr
      JxSApYUFNJmseM+O535VMRszM3g/9zRrVy+no76aawN6Lu7Zz+JNq7B0drIkOYYrRRVU1TcSN1tP
      Q+cAUr9I+ltqxqYKV1aJadhC400jn3Q2ca1ZwY+eiR2zJoi15wE/e/l9nn12FW3XyzH2jxIUHcGF
      4pv2UzLp4a7UaeNYHiS3A9DgMopnUgy2URxob+cWcjz62vGOSiLSXzkJAbiMW6cruX6riZSViVgd
      EAI3Xb3Iux/3sG11NM98/1+cAJyMABywCAtVLqPXaEKiVAkeTwnLkqMxGXuxIkLr4Y5UPEpnRzcK
      tQalXCJ8Z2QICXqtGou5D5NlBG8PFf0DIyhkYvr7h4TfNTM4MopY4oreXfnpd3LJFwCYvmIRCOGy
      7RSbxdxPv/B/rEI7pVJpP1ImkbnaBW5GRGDqFqArtFOqVKgV0gkF4OiIla6HZjz0bgxZBpHJXYVr
      sumRSO1H4ayjInpNJruOim0nzqZb0tfTw4hEjlruQldnLx6e7hwc4zvAYSFk6+q2oPd0w2V0xD5e
      tTcaxuwBjlgH6TD2CQ8xCVqNHKNpAJ1OTY+xG6lSLczWsO1cI5L/0QTZvm01JvOgva2HzgNz90OG
      XaTo3Me/LtZEA9A2P2JhXszCfIikSjocUA7Lvnb6zXT3CetHJmfluh87NUE+M5smyCu/fJGVKYuY
      bFZX3/SpJojLxKfBXKupF0K+EEdmwXCzsZkZ0wMEuDomDaasopboyLBv1PZe6wMBfu4oFeOtCVJG
      cmK0w8b8rNDfEgf21yU8zG3n3L09dQ7r8813jjo1QSajB/iX5kyDGV9zpsE402CcIfC31Foaajl5
      pZn09CROZH+ARG9ArxhmWUIov3+jAK8ADZ4h85itbqew5AGevv5E+ysQhwSh6mjhltGCSO5FVLCO
      3H3ZdLvIUbn7sCZtPoUHc5izcjXGimp8kyIxV99AFBzElM+FwTYALl8wjdPNMtbE+ZKd/aEAxET+
      cLCYv3smhYO7jrAyaz3Xz1zB3NdJk1mI4kQqvps2i4NHLiISQs+s9Hj2HT7FoEjCxvUrkXff5vC1
      AbalRjwWADvuNXAo9yoZ31vBhzlFKLynsm7pDM5WPaSpqpQZ8QlI7tRS1dLHlq1rqLtawr0bzQz6
      Gng6JYoTewqJ27KCE2ME4L3acvI+aiR57Xe4VlCEVevPLIP2awBwkEO7PsCi0bN4jp6Ccw1syVqN
      +c51blq80FubOVfVwQ+2pPLmjgPETvOgtmcEV5SkrwoXQvXztncLfH9jCtJJdBb4c4Ew2buP0yMT
      rj9hJkMTCsBBjuwtJi01lL3Zl/GeHcVvXnrZCcDJBsAPjx5hxG828wNdOFp8n6eSI2msrbNvgrz4
      i0LS0gxcuzuK60C3PY3i3LHjPBjWErEiDo87dZTd78VFM420mAB2vv4eQTFzUeu9iQxU8NahMjwC
      /QjsbCNoUyrdZy/iErOAMLX0LzzABH772kG2Zy1mb2k/qX5GzlxtJSwtlfrswyjDYxHdukH/gAnt
      nHCUSjcMLu18UD8iLPRpqFUWjp66TVpyGBo3HeVF5+gVj7J+gwDDv1Imf6wAPPGnHOatjORyxTBL
      5qnZ+/7HpC0K5l5XH+KA6VQeKyEpfT6WhlIkc5O4caGS1rtWgjT3CF+azJ4dhTz/2vYxnwUuO3WK
      lmEX3PQBNFbcQKxWMSdsCoaxngXuN1LbJab81HGkIwpiE4JpHPTkk5ozSLwXMnT3OnMNHgzNieXs
      gSNMF1kRh4ehUqgJkZnIqbGwJHI6ATOE/mSSyQdAay873z6JRKMgKTlqQoXRq0ouCw+YFrZuTkCp
      VfL2W4UczTvpBOBkA2BdRQ0qlYiy6ru0q6bS31BBaKiBpwQAvvHHegF6c/ndq8dR+HvxwnMpXD1Z
      wPVuNVGpAgCFBVXZ3g9COxsA972Ty7qsNPu7w8rCPD5qG+Fm5W0yF4YRnCkA8NwFZLExhKqkXwiB
      zxScYHDUhTmLk9n/+h/wmjWVxjYZs91HmRXuQ8nHd9AqXXhq62pUQrjc392Dq8aVd1/bTdKmTAw+
      Cs4cOopraAznc08Q6CXGPXY9zyT5f2MA5r+XQ/Ta+Vy8MkDG8mD2vJODr+ARBwlj0a6bQnn2Fdas
      nMH75Z+QMlOFedo8rNcvcfZyPd/7++eo3Pf1iiEU7DrAVGGsqhu6aW8BhbWFuYuivoYqHBTn5dHj
      F42l4hILkkJoGPQj3ush+VUuDDTXMj9ES19oLOcPHyFIKiVO8BDdEGHp6RWAK+G//n0Pi7K2Mtdf
      MfkAONTBq78rxVfeTmz6dxANTXwxhI1ZKex9N4fFm9ayeb0zEXrSAfBMbj5NJliWEk9R/iksUh+C
      fGQsWxjMS7/MZVaEF66BoYSKmylrtDIilrI5bTb/uV8ICYUwaU3KfHbnXiN0tgFzfQ1ibx+kSgXG
      toc894M1dNWVcL7FhYb6BmRiCdufXfdn4dVnAOxtbeDf9lfxq2fjyavqYWPyTPJzTtI77MqGjDh+
      /uJuYiI8MCq1yAQARkcYOH62HJVMwarUBRw8egadRomvhwx9ZDJxggd6YP9xMjdnfGGDZczVYKqv
      cDivhtVPL6cg/2MCp07FQzZM0sIQfvMfeQTHR1MjgHtWdAR6L0+WJ8/jwI4c2oUw8h+2ruLikSJC
      NizjwzECsL70PPnFt4lZtUIIgQuwKr2Jj5kx5nJY/Q9u8rPfnmHpsiimu1koutxM1vNP49bfwkc3
      XXA11VDWYOKFv9nIjrcOEKYR0yJ3Qya0XTA3hNyiEjQKBesyV+GlmIQe4OgQO9/YTa+LjoyMhROe
      BpMnzG+AfogP6vqJjw7j5//6mhOAnwfgnvde55//cSuTzWw7donxkQ7ZJc0vvEBaSoIAKcdtghRf
      qiBuQbi9iq8jzAb5telLv1moVdNA4BQftO6acf1PO3fl8MNtax025jt3ZQv9rXNYf7bdc1sF8yDD
      FIf1mZ75U66UVn67ANjZ2flEANja2sorr7yCTCabdAC0pQ9IJI7RyxgaGnIYiJ7E9T3uNdrK/dte
      KYz3LvnAwIBD701H92eTJ7CZI1K5PjPbIQLbmnekmc1me77st84DtJkAX/R6PZPNent77Ym9jkhN
      eZSoy0SYrYSQ7aZxVOrNo5S7vsosFosdnuJx9sYdfW86uj/bQ8e29KVSqcP6fJx5/j8XAjsB6ASg
      E4BOADoB6ASgE4BOADoB6ASgE4BOADoB6ASgE4BOADoB6ASgE4COAeCwTTn7CVlXVxc6nY7JZo4E
      YE9PDxqNxqHX5wSg4+9NR/f3/wWARqMRrVb75QAUFvMTA+CT8G7Gwx5VYWI87VHb+BNhNqjYUjIc
      BcBHCdd8lQ0ODtpTdsY7ncPR96aj+7OlOtkA6MgUq8eZ58dxVtTqL6+L+d96fQN1bMqwZgAAAABJ
      RU5ErkJggg==]]>
      </text>
      </image_base64>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[descripteurs]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[descripteur]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Sur l’image suivante, comment appelle-t-on l’élément C ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un seul mot)

      ]]>
      </text>
      </questiontext>
      <image_base64>
      <text>
      <![CDATA[iVBORw0KGgoAAAANSUhEUgAAAUAAAABmCAYAAAC6Ekg1AAAAAXNSR0IArs4c6QAAAARnQU1BAACx
      jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAFC3SURBVHja7J0HeBTX1b9fadV7710IoQJCAtSF
      QIDovRsbd9wd24njxEm+5Psncb7EsZPYxrhhjE3vIJroIEBICCQESKj33ru26j+7GMcVr4S0WMn+
      nocHPbs7c+feufPOOXPPnKPT2trax31SZ2cnZmZm96v5Aau3txdDQ0N0dHSGvK2enh6MjY012j+x
      WIyBgYFG+qdUd3c3JiYmA9pWIpGgp6eHrq7uoB6TpuemptuTy+X09fWpxk5TupfzPFB1dXVhamr6
      g9/r9ClH4T6pqakJW1vb+9X8gKWcrMpB1QQg2tvbsbCw0Gj/lJNGOVE1BcC2tjYsLS0HtK3yZqSv
      r49IJBrUY9L03NR0e1KpVAVA5Y1OU7qX8zxQtbS0YG1t/YPfawE4AGkBOLjSAlALwHuREnLl5eXf
      +93Xrx8XFxfs7e2/8b0WgAOQFoCDKy0AtQC8F+3atYuYmBjVPLib9uzZw5o1a77xmRaAA5AWgIMr
      LQC1AByolM+ADxw4wPz5838UgB9//DFPPvnkNz7TAnAA0gJwcKUFoBaAA1FBQQEnT55UjduCBQsG
      BkDhoLSrwP2U8qIzMjLSSFv3axVYucqtKd3L6qDyQlbCT7sK3D8N51Vg5bEnJyerjj0xMZH9+/ez
      YsWKrwAok8n4y1/+wuuvv/6NG+N7773H6tWrv7EvrQU4AGktwMGV1gK8PxagQqHQ6I1uMCzAiooK
      jhw5onJ5HR0dVZ8pnwF+3QW+du0a169fZ8yYMap/d6R1gQdJWgAOrrQA1LrA6ujUqVM0NzezaNGi
      b1j83wag0vqzs7MjJyeHf/zjH1/9TgvAQZIWgIMrLQC1APyx7TZv3qxyd0eMGPGd778NwNOnTzN5
      8mSOHz/OtGnTvvqdFoCDJC0AB1daAGoB+H1SHp/SnU1PT+fhhx/+QXf92wD8IWkBOEjSAnBwpQWg
      FoDflvK8KsE2cuRIwsPD7/pbLQA1LC0AB1daAGoB+HUVFRWpwluWLl1619fY7uieAKhNhtB/aZMh
      DK60yRC0YTB3pAxvUUr5vE/d+bd3717VoseP9SU3N5fHHnvsG59pLcABSGsBDq60FqDWAmxoaGDb
      tm0sXLgQNze3fu1X2Q9lSM/36evJEJTz+ds3Si0AByAtAAdXWgD+9wJQCa5Lly5RWFjIqlWrBv08
      arPBDIG0ABxcaQH43wlA5dzevXs3ERERBAYGDkmbWgAOgbQAHFxpAfjfB8AbN26QlpameoXtbglL
      71VaAA6BtAAcXGkB+N8DQOVc3rJlCx4eHsTFxQ15m1oADoG0ABxcaQH43wHA/Px8jh07pgpvufMe
      71DrRwH4Y2EwOfUyeuR3DzGQyfsIdtDB1ED9UATly8orVz2Eu4enRgZiMNXa2iJcsFYaAURzUyPW
      tnZoBkW31dbWirmFJboaAmBjYz12dg4D2rarqxNDQ6NBD+eoqa7C2cVVI/2/H+0pQ52UCxCaDLEq
      LSogJ+emxm6sSt1zTZAzea142t499VNzlww3awMcLdS/m2RmZvLp5p28/Kv/0dhgDJZSTp8gOm4S
      Ig3EUB07dIBps+ZqdNKkppxhfEQ0+hqyDpL27GDuomUD2vZmdhZuwk3U0urHA2b7ow0fvsejTz2v
      kf7fj/aqKsuRCBD09vXTWJvLZ0/mclqqxtpT6p5d4OEMwNzLJ9B3CWWE6+C6FmoBUN7D8SPnmSqc
      9BtXCxg9LmBAbd0VgNIWdm85gL6JEQEx0/FzsVJ9XHr9AtZ+MVgOMGXh3QB4+UwyIRMTqcy+hufY
      sZRdS8EhII6minw8fUeqftPVVo9UZImVmSEV17KxCRnD3R5z79vyAR6Bkwkb6/+d7xqKs6jT9cTL
      uJNOYzucLG5bLJUlpbh5e6kAaKxoR+IQwiiX73eju5tKqO21wsf1axeCvJsDW3eDsQHeIRMZPcL5
      G9uoC6TK65e4nF8muJKWTJs9HQPdgd2ofqi9tupbFLVbEuysQ023MZ7Ot/vY3dGBibn5137ZR1ZK
      MreKKrFzdqOjrh6RiSE+YfEE+zh9Z7/fBmD66WRCJyVSLpxXr5CxqB4oKCR09YKpydfmgfBZ8q5d
      iEUibFxGERsVonYf/yMA2Ca4f8pNrKxtvvqsPwCUCu6yWKYgPSuHrQdPM33BStVn/k4mWJncu0XV
      3VRN+tXLWLiG0JSfgqVnKOGhwYM6qGoBUNLEn37xL1a+/jyp+88xOd6bG2UdxEzw5fKVm+ibWdPa
      0sGsmVO52zVzVwB2FbNtfx0rlgfz2Qef4isAyHtsIGlHU5i2bBYZJ1MR2ToS4m3JxfQ84qZNJPvC
      OWTGlnQ2tpA4dy76ou/u924A3PnWH7CNewDZzRTCF83k5P4zzFi5gAvHUpgQ4kX6jRoSJgWzdddl
      Vj84k/OffY7vqvlc33OSsHlzsDP+7j7Xr/07AVEL8bXoJC2niMiJM3Gwug26nGMHSBfm11QfBxos
      vWnIvoixaxCuJiKM9XvZe+AEFnQgsfJj9YrppJy9hJGDF7Z9LeRVNzN1RiKX9u/BLXYWFl1lXC/v
      ZkpCNDqSBj549zTLHook6UgWcaG2ZBY1MWfGRE4dOkpFTRVrXvj5j86F1E2fYz1jJq1XUnAJnci1
      lJM4jIrEUVbJrVoJidMno86DoR8CYHn6KQ7nVrE0PogbTZYYNd/EwNmPs598yKo//4mc48fxjp2C
      h93t58RJGzcxZ/lUNm68ziNPTmTjhx/z8DPf3e+3AbhDOK8OE1chviGcxzmThTmQTYCPEfvOd/HI
      /EAycuqYkhiPSN7Bx+/sZ/HDM9A3NqX4ZiZuzjbCGbDiRvpZ7PwmYCUpp7CqExcrXQw9Igjyum2A
      DHsAKl+feeP3v8LLZ4QwseZ89czi+wCoEHarBFtdu4TaNgk9UgUyRR+mBiKsBdBVFt/iQFISP/v5
      a4Pa4bqbZ7kpdqYs7RqPPrN0SAZVXQBu+CgFJw8FjVVS9I2khHmbUqlnisQsgKoTmwkLsMJ+4tO4
      mf/wbn4MgL9+9SOCwrwYM3EKKQfSCLarpUxHD0M9Jzo6LXDUqwY9GRGTI7la0klHSy86zTmMH+1J
      veMswj2++/7k3QB4cOPnOPk6UJNTga5uI40YYGMhtGU2Gt283bRLdYh86FVyDq9lxeO/VAGwR5j/
      JS1dgtHlwTNPz/7OPpUXf1NNExEh9vhEz+fI+TyeWJyg+i7n2GHEvm5UZRbiHBpKXcZFrggX4xhf
      J+wsxGTllXC9TIq3RQtLHnuCzLw+dBtvIRe3EBIUQI1ITubVQiQdepgJF6ZcmIOzf/FnfAybeOvP
      O0mcH8r5czlMnODG6aMnsBwzgUnT53Ji68cCkF780bmQuulTxMEhiG+mYR8+h4rLp7ie04SdjzsP
      zp+Coa0t+j+6l7sB8Ax1Fra0FBaga2hAV58HovoMwZLQZcxYN/al5SNuN+XVXz6s+v0dAL7+4t8J
      ivDDL3IWEUHu39nvtwGYtPELnHzsqb1VCYYyomZM4tKFbBSG/nTe2k2HRIeYx35FsI2Md97YwOR5
      MVg4uCNqzGLjiRJ+vmYhyUlHuJ7XibdjL1GjHah2FCzKMwd54MnHVW38RwDw1y8/g7WNHQ89/hRu
      7rcXMJQANDHQoVOsQCJYd0r4KS0LI30R9ub6OJgbfMfSGCoXuO7mRSqN3YULJ52Hnl48JIOqNgA/
      SWPRHDve/CSTCE8pLV0KxsYH0Go8jurTXzDa1wTzCU/icZcFULUswAeihPaahfZSiQmRcLNSTEDw
      CLKuSbGU3MLCSkFVswAJAWp1VR3QmkPYSCfKrKYT5fVdyP0YABOXJPK/P/87S1ZEUlYvZ8z4IK5l
      teCgV0JTrykTF8ziyPr1rBQsDyUAzcI8yLtRz4gxsYwLdvnOPjd+/D5OLgGY9lylVGKNg1ckQd7W
      uDo7qwBoEp9I9rrfYDIuQXB5SxG3tuHv7oa7mxkZqWeZ99xvse7rYPPOJFz9p9BbdhWRQkqAvzst
      wuTPu3QTd78QuktTBdfNnqnLF2AqbeSPr61jfPwoWqQWtOanYqTbi0vUDJoqq+lqqRYswFd/dC6k
      bvqIcisHJMI2owK9uFpWR2d5M87Wwk3IwobE5Q9hrgYB7wbADp9xNBz6Bz1+06i6dAV9C2ssBJgH
      Tp5JxuUcPEeOIzby9uODb1iAT037wfa+D4AzFk/lD6/+g7mzA6loEuMyKphrl0sZ7SajRWJG/MIF
      WOp087ffvM2YuBCMrZ3pKL2Csak1Mj0Dihvb6KpuE9x0fSJG2dDguYCiI1+wTGCFUv8RAPzf118h
      PDIWGzt7FQSUUgLQzkwPdxtDtVcOtYsg6mlAiyDKM3oPayYDWQQ5KRznlNnzVH931ReSUtrHjPCv
      PWBXTrMf6EPSnu3MXbScy4c/wi9hDVZ3fXb5zc7d2yJI35e70/nO5xs+XKv+osRd+qauht0iyAD6
      /JMEYFNT010BmF4hxd/13yaKsuCIsvN6X0s909wlxVy4e9qZqh+LpQyD+euff8OcGUMfDDnYyhXc
      rpF+nogGOQPJ9ynrRj4hwSM1GgaTV1iGr7cbev2IrVNa/UpIK4+z70tIqXvMGZmCSx4aKOxDIexD
      t199raiqw8bGElPjwS1SdTolg8lx4wd1nz+l9ppb2pEK17Kjvc2970xNvfPhDi6mXtZYe0r9WAaa
      +7oKvHf7+/y/3zyt0QEZDB07eYmE+Ano6Q1u8O33aW/SaRbMmaTRMJhT5y4LLtVYDAzUeXp179q6
      M5mVS6cPaNur127h5eGCjfXgBov/6/2t/OzZlRrpv1L/FNp7SYPtlVXUIBZLGDlCc3G40dOe5OKl
      qxprT6mfdBjMcABgWvJhTua18+iDM9n08WbMPILxdTBQAVDSVMkftmTz159N55cvv8fv3n6ZU7tO
      UlOSw9wXniHnwAliV87gwLsfMmLhavRyUtmfX4tRt5wpU8cit/Ygwtvqru2rADg1iBd+voURwQ7M
      WLQQo7JLHGpy5LlJ1jz/yx14epgRMDmW2qxqSm5dw97HEnf/KBZPHTWgPqsLwPLcq6zfdokHnlnG
      0S0HEVm7s2ZREPvS6inIuMiIiQksj/Jm7YdJPPfsQo4ePk7RtWJkHn48uzSGT97YyqI/PMopNQFY
      X3SNdXuus+rhRdhLStlxUcE4fz31Aajo4p9/24rE0oaZ4x3ZfewWTz23hC3rt2MXMB4naRVHT2ax
      /LWfk7Z/P4unBLD5SDZiAwvivI3ZcSCdsMhg5HJXnn4s/Mfb64eGEoAKmZgNf9/OrNeWsef/PleF
      Ey1dHIVsCAHY3dnEB++dZOXysWzde4YSqRNXTiRpAXhHwwWA+zfswDvYhYziXlo7eomLCaOlulwF
      wNTjJ+kV3D2vcVEc+fwofpGjac0vQkfaiZ6bNwY1VcStmMieo3noKHoZbSTlYrcw3I31RIUGIXb0
      IW7E3V0QFQCFC/H5V/cSkxjIwlnRbF6fhMhIwaolkXy8rZoHZtmy5XgV0uZudPUMePGFyXy+YR+r
      H104oD6rC8BDn+5lwqIwLlwSExuoz8ZD15gb601rm5g+D2+ydqcTEWrF8ZtNvPp4FNuOlVCT14Cz
      UQWRC5dweutxZry6krNqAvDi3j3kyKxZNn8iqYf3UNI6gvBQY/UBKICgqaePA9v3YIYxUZP8SBPm
      t7mzN1VpmTz6zAw+/GAXa55ewTsCkKaFOXIqp5vYqDCCAlzYu/4Aix+M492PbvLSC7GDOs+GEoA9
      zU3s+OIUU16Yw5G1O5CaOzIjIQSZZOgAWFVWzqFDmTzx7HwyThzFauxkHln5/PADYFpxu+oZ390k
      VfQxyd8KCyP1FwWGCwB72ps5ey6dNrHggjr505ZzCU8fL6bEBvG7X72P+0hHWvS9sBZLmTDGiEvZ
      bTgayQkTLJ+k/TcIDTbhfIWE6vwKFsWNw0e4eA1yr3DyVg8+ocFqA/D9zZU89+R4OmpK+O0Hx3E3
      aMc3Kp7000WY69Xw8AsPs3Nj+lcA/PTjfTz25NAC8MhnewmdE0pqpoyF00awVQCiha0pQd4OVJsL
      N43tJ2nqbaOpqY0liybjMX4cBnXFnDiaztw1q7i66QBBj81TG4ANVTUYWyj47J0DNBkY09hmyiNL
      RuPtqb4LvP+LHbjGzaDy9EnCokdwtawDYxt3qjKyWT7bg11F1jw8yU3lAs9LiMR1pAub/vkZ8U88
      xZWdwxOASp3dfBTfxWHs/CgXd+NqgqdNQlc2tC7wtk+TWPbYXDau38LDjz9A7HB0gYdKwwWAOZdS
      OZffyPLFUzl5KBmJqTt2giU31suKfB1nYgV38/iBI3TqOrFwhj/vfnicsX6OxEwN44O3d2Dj68KK
      hQn01hSwPqkAA0sddHsVJMSMotvEgSAX87u2rwJgYgiHTiuDdP24mZaK4+hw7Ix1OHTwECLTYBIi
      zPl8n+D6mlpRVZKPlZsJ7r7jiAvrX2bdO1IXgLUlN9mRdJ0FK6Zw+OB5PEeMxEZfxrhQb97/+DCj
      4mNIHOPO6eMZODoa4z/an30bk2i3tuPR+XFkHkvDMzGCZDUBmHU2hWvV3UyeHo+HaReHU1pxspeq
      bQH21Bfxf+tTGRs2kkAXESdSy3hg1XR2bj6Ic3AIYRa9dLqG4G+towLg8hlhHLwoWO8G5qxeNplL
      xy4RMzmYpKNlzJ8bNKjzbKgBeP30FZwnj+X0xp20i+wFDyYQ6RA/AzwrnN+JiWM5uv8KM+dH/zSf
      AXZ1dd0XACrL3X209i88vnr+/Wj+npR+5SbjxgYgEg39KvC5C5lMjAnVaP+uZt1idPAI9DVUL+LE
      6TSmTo4Y0Lb5heU4O9pibj64OeV27j3B0oVTNdL/+9FeXX0TEokMdzfNZGVR6vlf/F0A4BWNtadU
      R0eHMDd+2MjQkcvl9wWAWVlZwp33XX732hP3o/l70onT6UwS3FlNrAIfOHyOuTPjNLoKfDblClER
      YzS2Crxjz3GWLZo2oG2zsvPx9HDG2sp8QNv/kNZ+tIPn1gwsQcNwaK+ispbeXgl+Izw01mbCnOc0
      bgG2trZiZfXDC41aF/hHJBP3UtPYjouLA7p9MrrFfVy4eIWEiWHU1TYhFUbP3slB9Ypfa1snVhbG
      VFbUIxO2VaZpsra1oKelmTapDs5Wxqp9KWPkHJ0dkHd3YWxhjugubFO5wLPjqKpsEPap3M6etoZG
      emUKbO3s0FVIMTUzobuzG4VChpGZOdLuHgyEz9obG+mW6eHqZKVud1VS1wXuE9qrrmnF2dWOprp6
      9EwssDYzoLNHRm97K6Y2trQ1CccqVeDg7IiOVCy4XT1IRSbYmOtTXdWEk5sDO9R0gduFcWzuEGMh
      uDR6MqG/+mYUFxX3KwymWRg7hYEJ1qZ61DZ04uJsQ0tDAwbm1ugrxDS2S4Txsla5wC8+vYyqqnoM
      LSyxtxxY1Tp1pbEwmD4F7Z1SWlqbNRMGI7RXVVmHkZUNcxcNw0WQodJwAeDhXXsxszSj3SaI5kt7
      wHc6TvpNJER488Y/01mxZCRJZ2v42YMBPPuLLbz57gs0599k/a4yFsXb0mJlT05WCZE++pRKXDGp
      yUUUMoY4P2fW/vE9olY/Rpz/D2erubMI8qd/XWX5Ak+SzlQhbmpnyfLxHDl1DUd9XdWD5v2f7KW1
      uRy76UsxzMjAI96DE1ndhFmKqbcZxYJxzmr3WV0AXj5+lKouGVZegYgb2ygpKGHlvLEcvtEkXGDt
      yOVGLIzzYscXB1n580e5uPcgeXm9uHoZEeRuTpkAMx37sYirbqoFwJbGBtKPHUM8KprKNMGVEgzw
      iIhgvNUFYG8d67beoK+7Gl9XK7qFu5fX2NGkphcgsBw7eRsGIhHBi+Zx5IudTBxlTYvlCPIvZ/Hw
      00sYysx5mgJg2c00Pjsh5ZFFvhoBoLihlPfONfLQlCAWLH1RC8A7Gi4AVOr0vgPYTZiMr34tB4Xz
      Z/UlAJ/92WbCIhxpNfIhxqIJ60BPLubosGaGE+98WsCySabki+wpS0mhukvK4gcW0Xs9E1H4eOwb
      irhQp0fx9XxefnL2D7Z9B4BrXtjKuHA7Ok28qL9yjZAoH/rMbNETLK87AJTTh8jZEp2qTpqsDVm4
      ZAY2fe385Y9H+PXvl6vdX3UBmPTJHiKXTyAlpZtofx02HM5hergjego9ej28ubotladfjOPTnddY
      Nc2NA4V6VJ04RqesGRdbf+a/GMORDcWIzFrVC4QWLPB16/bx1OoJfHG4EwtxIW6j/fHtxyqwtLOZ
      tVvO46orJzphJBezm7DyHkGlYNU720u4UtzDmpcfYMvH23lwwQS27E2lz8qTF1ZNHNK3cTQCwD4J
      h/cco6DOlgVzvTQCwJKMNDZfLiU2LIbXf/u/WgDe0XAB4JUTp2iw8WVGmCfSpmL2XpZ/BcD3Py/h
      hTWjefPtQ8jk7fgH+pBfVsdrT07jvQ23AZjdoSO4ySZMDzTk44PlxDmhAmDW5i/QdfKm4lY+S555
      Ei+L73+e+PUwmGce8eOtfxzD0NiWpQkWHCoRYdZQzIJH5rHxX7twMDNk0spJ/OHZD1n5dAz1Jn5E
      W7ax/5qcx+erv2qpLgBP79yHfZAHZXVGjPJ3J/tsCiITfSKCXLnYpEd5ZjlLJyi4YRGLTf11/CLC
      aC5p5vKZkwKwzHAM9ebaTX1E4jK1AChub2DzxWoem+rBx5uzMZDWETRhDD5qxwH2sPb9Azz4zDLO
      CcfuOcKRpj4z6jpFtBSXYK4nI8jVHMZN5Nz2XUSNdME9KprcQ3txn7kMv8F9zPgNaQKA9QXZbD6V
      w42cbn79ykwU0qEHYEttAyIbI7Z8lM7n+7dpAXhHwwWApw8fpLhRTsy0qYy0kVPaAIV5OSTEjmbv
      rlO0y3WZMjMCHakBni4WVBWWY+FpT3lBBz4uerTpmlJzI42rFXIeWjaZrpo6we2zpjq3jsAx7igE
      i+RWi4hA9+9PCaMCoLD/7VuP09Wnz5zFk2ksbSEoyIXLl28w2s+BLUmpxCROw7i1Hmd/Lwqv5uAV
      FkjexYsUthqwaNZ4tXLS3ZG6AJR0t7B7/zUWrYznzMGj2I8ch5WuDJ8RDiQLYxMwJU51TAZunnTV
      VuPs7kLmhRS6rUYIcDFnx9ZLzF89lf1qPgOU9LRT1y7C3dGUKynnwWEUOr2Naj8D7G6qYsuhDEyt
      bJkd48eRc8UsXhjB+YOncQ4Lx0pSz9mbTSyZFc6767by/JOL2LPnKBZeQUyPGDGk80xjzwDlvdzM
      a8fMXK4RC1De08GmPeeZsXAqC+c/+9MD4I/VBBkqZWdn8483f8+SBVPuR/P3pOs3Cwka5YOuBsJg
      Mq7eThSgyWwIObnFqmQPmljlVupiWjbREWMGtG1pWTX2dtaYmg7uE7qjxy8yY1q0Rvp/P9prbGpF
      KpXh7GSnsTb//OZnAgAzNNaeUvdcE2SoNFwswO+TNhnC4EqbDEGbDGGopHWB71G1JYWczapm1qwI
      LiSfxzNsHBV5twQXOJjdu84i19djbFQ4FVczaJLIsXH0wMfGEL9gX5XR1lVfRUG3CWNd9di87Szo
      GzJpZiT1RU2qrNgdwuj7BYdgoehhZJDPdwy9OwC8eeUy18o7mJ4Yh62xDqfT8kmIDuLSqbMUN/Xg
      5D4CJ91mMsuasbBzI1Rwv89klwqurwGzFkzCSl99a1V9APZxK0MAkOCO7zmYikzXiFXzwrlV1kn1
      rWy8JkRg3lXNqRsNLJsTS3lxKS01VXRauBM90op9+9OYungKR/cdVwuAPYI7vTc5k7jEeFxNxGQX
      ilHImvsBQAXnj59DYunMeB8Ljl0oZu6cSNJOncPabwydZTnkV7QQM2M6h3bs4amHZ3DwyCXMPXyx
      lzZxrageKztb3J29Bat8cCu4DTUAi64V4BjiR2lGGk06jng5GGoEgE1V5SRfuIG+tQv/+NtaLQDv
      aLgAsKWhifqyfAo7QCJyoCU/Bzdve9UiyDuf5vPMo0F8sPUK+r0yHl+TgEikx/7PD7Lo8fmqwjJ7
      P9tGkdycX6wYwyf7Wnhknh3vfH4DXYlCwIcuTz8dx+adZzGVyVjx2JwfAGAEf//bPp59Zjrns1oZ
      79bNe5vSefClh0nbcZAFD87kwI5D6Ith+pOLOP7BDvqsLBk9Px43HV2MjfpX3U1dANYX5fLnvybz
      +49ewryjinU7ClgW70RmRRtYO1J4IRc9eTeLl8djbmvC4R1nKKnRJ8CxlT4dA/wnB3ApVYqeolIt
      AN5MOYFoZBT+jqZcOLqPzBoPYsJM1AdgdwOnb4mpyTqDoZ4pgaPdqOgW0SkzoyGvgKfWzGbTJ1tY
      +OhKPvlwGxPcDTEPm0Z3/i2CJ0/g0DBNhtBZXcJv/rCHV99/nP0fZmBv1EzotDj6hjAZwtdVej2d
      G2J33vj1/2gBeEfDBYAopBzZcRircePJT0knv6aViZEBTBEA+Nr/HGbqdC8yb7Qga2liQnwQdm4e
      lF68ogKgoquJ5369CQfDNpY+s4oP3krGzl7CQ48v4/CubBpqKomdGsC1nGo8LYzvAsBJlOfncfJC
      JnrOo+nJOo6BuxOVva44tubjFhJIblYu9iI5bWbGtOs7E2fcTZmNLXaGxkyLC0WvH9XK+uMCH/lk
      HxFPLODGwWS8pyRSfuEMthYWtLt6cnXzGYrETfhYCFZy5AS6HDzpvXSa64UlOFl6M6+fYTBXjp+m
      XCrFRN8c5Vt6BWVG/csGI6jk2hXOVOlhUV1M5LfCYFavCmN9ci1rlo5TucDPPTGP06dSOX+lhl+8
      9hBHNg5PACqlSoawcCz7t1TiY1yBV0wkoiFOhnBHuzbtZO4DS5k8XesCf6XhAsCMcxcob+zEZ0ww
      RdfzaRLr4GVvpLIAf/X7I0xN9KasSYf63AIiJgVhaG5J8fkLOIUEI21txC82AX9LKR99tg8981Bm
      jlOw+2ozijoJDbVVTEwMpLiqCytZ7w8DcFoY//duEmNDPKiob6HP0JWnhIv0i09209Lain9oMIX5
      VTjp6TBdAO+n//wUe3MbFD4u2At7HBsxAQcz9Z9XDgSAJ7buY+nKeYJbe5KpUf68vz0dSxcP7MRV
      tDR3YOjjw8oZEZzce5bc2mYSx9lz9moNXlFTaCvMUguAN1LTKWoWrEuFCa1NJVzK0+OJ5WPVzgYj
      bi7n+d/vZtWDieg0lnKrooO4WTEc2peKqZ0Ds0f0UeIQwyQvAxUAx3taUY4p3bU1zFq5nJStwxyA
      DySw52+fIzOxYOGCuCFPhnBbEj5au4c1z63QPgP8uoYLAJXDo6wkpifSVdVE0dUVcfyUchFEmb78
      Nq5EIhEKhVxVJkFZJ0FXcG4VqpIJOl8lTFAoFLe/E/7J5QrVd319CtU2ytVkHeXvv8dKu2MB8uVx
      3N6fjqocQ5+iT5VGXrUP3dup5JX7uLNfhaLvq+PrzxpKfwCoPAZVm3f+/zI1vlwmjJUqpf6Xx62r
      o/pcOU7C0aKsJiCTKVQLSeougqjOhVzx1eKTsn9Z1/PUtwCF7WVyheq0KcsZfP286nw5fnyZ1v/O
      IohMdvs70df6qGxXd4D1f39IQw3AO+dFNQ+FHlZU1WpsEeTOeP0kAdjY2HhfAKisCfJ/f3qdmYma
      W/ofLOXll6leIh/si+D7pAy5GR00tDFo31ZBUQU+Xq4ayXajlDL7TNjYgWWvrqyqV9UEMTE2HNRj
      Onv+KvGxYRrp//1or6WlQ1Xfx95+IMWkBqb3P9mj8ZogPT09GBv/cIiU1gIcgLRhMIMrbRiMNgxm
      qKR1gQdNfRRfTeVGtxtG4moSokbw+m+24T7CGlu/ECovpmDsbIO92wj6yq7RIDJFLDcm2LwFm7gF
      6N64TpupnPRbNZjrGpIQM5JeCzfG3a0oMHdqgozm1de34znKjlHBIRzduIfX3n2dsvOHyG22Ql8i
      uHa6Zjy0ciwpu0/S0dfJ9aZeTDBm9ZNzseynpaouAG8JfT6QWsy0ZYtw6irgeJ4JDyTYknT5dk0Q
      v/gE9IuyyCwX89IvV5J+8gzF14uQuo3k6cVRbHp7JzNffYATagKwqfQmH+zNZsWD83GUV7E7VcZo
      H51+uMA9rHt7G2JLO6Yra4Ik5/LUM4vY+tkO7AMn4NxTwYWcFp5+5QE2fbydAAsdrnfpYIAZK5aG
      8PGGE+iYmPDSE0sw7kdYkTrSDAD72PbZ59TruDA/IVgzyRDaG3jz3T2Mmz+PP76sXQX+SsMNgAqZ
      lNSUk1SJfb96F/ilX+4iOs6VOhxozrxKwIQROPuOpDHzumoVeMu6TZiKDGl1dcG3tYNmcyjv7sPe
      2IRob0sqTDz6VxNkkjeBYWM4uP4gkx6YR9214zR3OSIRKwQAWvCz56I5/Mle2umhxdIGS2MLVsyO
      pr+eutoWYJ+cg1t2YRk2FZPmq2QU2jNlnIj6lh503L3J2puGq5OEsnYdHnxwIkeO5VFb3IGHcRlh
      c+ZyeONxFv1utdop8S/t38t1sTWrFk/iXNIOSlv8CA/rxyqwpJvqLl1O7Nkl3BpMiYzzI6OoDVMH
      TyrTs/B0hfx6KYtWzWPbhh14CMBsdnBRFf5O8NXnwyOlzJkZhb+PI2bDEYDietZ9VoKHSTW+cZFD
      nhJfqbyUS4iDRpLyaTqbk3drAXhHww2ASn1fMoQXnw7hzT/vxtTGkWefu53U85O/rUNmokePeyz+
      DbcISBzN9o9T8J/gRciseHz0dKnNzaZA363fNUGQdfOv985iZSfFxlSf2gbRdwDYoSMi/vF5OA2w
      n/1xgbvrCzl4DZZFG/PRrga8bOvxc3eg1tKFrD3C5EfKODcDmh0dCRgbBiXZnDxznaXPPEzm5v7V
      BKksLMHC0YCtHyXTpmdEXatgcc4f1a+aIMk792I8Op7m1LOMixEAWNyGiZ0nVRnX0BFuHGO8rRBN
      iOfstl2MMDAi7omFKPdcX1mHmasN+9ZtZuTcpYx3H9wM1BoD4IZiPExr8J0oAFADyRDyzl+iN0AA
      4GeX2XJ0lxaAdzQcAShrr+ZcrgCbzkrBBR7J3946hKevNZYjRlFxIQ1LN1tMrOywEPcSPyeWA1t3
      YmxgT+zieHa+vRnfcF9yq1oFF1iHUb6upFwVJqO3Bwun/HDK+9thMGN4480jeAsusKObC63VMnRb
      MwmaOYv8K5VIpX3cupmHz2g37BSCZWLQS2lPnyp/XcLMRJzM+metqAvAjBOnuVEvZtL0eLxMOzlw
      phk7s04mjPPlnXWHGDUpGv3yG+RWSgmPGUH42AB2rd9Pq2CdPrU0gYxDF/CeHcMxNQF45eRprtf1
      MnHaRKG9bqG9FjxcFf2oCVLAHz+4yISIAPwd+jiRWs6qh2aw7fMknILH4CStJTO/jQfWLOSLj7Yz
      1tGUEoUByjDyqPBgko5dxNTYnMXLErEyGNznv5p6Brj98020G7qQGB2gERdY0lHP2+uOMW7uNH7/
      4m9/egDs6em5bzVBPv3wrzz7xNL70fw96WJ6NuHjgtATDf0iyKmzl5k8cUK/wljuVcqaJ6Fj/NHX
      10xNkCPHLzJzgIkAcvNLcXGyx9JicC2yzTuOsGrZTI30/360V1PbiFiiLCilfqLce9Wjz/5J4zVB
      2tvbsbD44ZujjkwmUx+AHRnQdf3Hf2c+AUyD7/oTZU2QrRv/ya9eeUSjAzIYOp2SQVx0qEYAeDj5
      PDMTYzUKwJTULCLGB2OgIQDuOXCKRfMSBrRt9s0CPNycsLIc3GR9H27Yw1OPLtJI/+9He5VVdSoA
      +noPrHLgQDR76SvDvCZI/WYwGqmMtr3773pyweHBu/5kOLrAXR0doGd0uyZI/Dg62rtRhpWaW5hj
      IOqjuaUTY1MzDHSFIdXTQ0chR44u+qKB0+tOGIxyEaa1s1dV/KezrUNVi1lP3xAjA10MDQ2E72XI
      lIHCfboY6+sIk1tOV1c3ypNrZGwiuG7qh7T05xmgpFeCvpEB3R2d9OkZYGakj1gqR9qjrEtiio5U
      QqdwLNaChSYRLjiFTIJM9/bvlG8iiIRj365uILRcRnNb15fjDb0SBTm3CvsVBtP15XGaCOPW1ikW
      wGkqHHuHcFpNEPXJ6OiRYS18pgyDefrR+XT2KGti62JtY0ZHSzt9+gZYmQ1+cnzN1QTpo1csp66h
      QXOB0MLcVOiKmDh9zU/PBdYCUD2JO5tY+8VZRvh4YqQnVS2C/M9fzjB/ri8Xb3ThZ1qJgVsIhbkF
      hPs4YBo1AeuyWxQY/PhCx910G4DRbHx/P15BThQ26VGVVcyM+WMxs7LjugArZUr88owr5HR2ciS1
      hX8+G8L2w8XkFbYza6Yfji4eeLmoHyenLgDb68p59aVNvPHpY2zaKLg28lYeXRhN0rVGGmrrMDCx
      Qa+1EmN6GL9sFTnHDpCf24G9lyWL50Twq+c+5vfb/0ftRZCy7Iuk1xozJSaYprwLQjsWTOpPMoTe
      Ov75aTq6ijaCvKypbuwhND6cU6eyBBCbqjK+iCV9RK1azKHPd+Klq8B8fAimGOBsLIxtoQSTtiqm
      Lp6Hs+ngxkhqCoCVtzJYn9yrsZog8t5O/vr/PmDqi0/z0kMv/xcB0DwSOq8BX9/9v/8uLS0h/ULy
      t8ohfu23FrEgGtpKXP1Rc/5V3j5cSMAIb+yN5SoAvvDzHUTGOFOrsMegT8HLj04m+9hBLtYY0abo
      wripnjELFjPpXgEY7cVfTnbz+rIg1SrwL175mNGRvngEBlN39frXACihu0uOp5sJBTktnL+QT0S0
      O6ETJzLGbfABKBWsv2ObDhP1xAIsu9tYu+kMcaFO6Ep1EXt+WRPk53PZ/tl2IqeGk17SR+WZs0hp
      ZuVzL3Bz31HG9GMV+MQX2ygSmZOYEE3BpfMUt7r1OxmCXHmcX5zCWbAgv10TxMlewvUKMY8+v0JV
      E8RD0U2djSPGZjYsjVOm4T+PyNyeNasSMNAbXq/CfXnGOLr7KLfq7FiooZogCqmMoqws2txH8eJ/
      GgC37Uqmq7uXUSO9iIkM+ffvvrIAf3jXWZlZ7BlGFmB7RQFnavRouHEFN083VTYYVU2Qp0bz1ttH
      sTIRs+LJBzizZz8G5ra4TwofPAtw2hj+9K+zvPbiFI6evEFpiZgXX7j9zGzbp0nfAKCpsy/N6Um0
      63khkdnx5EMh/W6z38kQHpnONsFCXfH0ci4eOcG4ABcyOowoSStipHUXFuNjMawvxic8jK7qdi6d
      OMaMR1eS+ln/wmCqy2qwtJby2bqTyKyNKCgT8ejSMeqHwch7Wbd2L8ueWsGFXfvw8RduXlIjWnoN
      aSoowkxfzmh3CxRjYzm345thMMXXrtPr4oNeSSb19mOJ9TYb+GT6HmmkJkh+FpvO5JOb081rL0/X
      SE0QpUqvXKHR1f8/D4Bv/utzXv3Z6u/+7j/QBVbCPOXoMXTdgumqq1AlRN3yxRE6FCJmL52Jm5mM
      zRuOEhAXj6/wt46TE4YdzbTqmuFqZTTgVu88A6wpzmfv+QJWL53K8X3J1HRJsHBwxce4m6yyFsaF
      T8DR1hB9UyvsFC3capRx5cwlenV18A4IYlZsoNpt9geApTeKMHfQY8veS5jb2REV6If/KBf2bz5C
      0LSJpCcfpUWsy/jICMJHe5B26iRdNv5MGetG+c1ibIN8OKAmAJsri9lxvJDVj0zDRNZFdkE3Mqn6
      CVG7G8pZvztVOE4HFkwcyf6ThTywIpZTu5NxDY/GRlzLiexGVi2K5b11W5k+wYeTWeWqbRevXEz6
      4d10mHiyck54v2qsqCPN1QTpIfNGKzbCPVlTzwDb6+rpMbNm4YJhXhPEsHUXRtZjvgLgv9ZtY/qU
      SFWmiQB/769+19N0FYn13avca2uCqCdtTZC7S1sTZGDS1gS5rXuyAJUDWFvfhKO9zTethf9IC/Df
      0iZDGFxpkyFokyEMlbSrwPeo9oZqDp3LZfqseCzporRJh6Ivy2Im7U+hRxiLiPgY3E3F7D+Ujl/o
      eDxN5ZR3tOHnMwIjHSnZ+aVYGJlT29xNdKibqixhUKCDWu3fKYuZW9hF4Ch7+mQSjh1JQcfGnWnh
      ruSViRnlbcKN3EbhvOhhZ9LOqYxiIidNxMd+YItI6gKwu6WOgyevMXX2FPIvXURu40ZMsDN55V3U
      5mfjOSECLxsT8m+VMXKUJ+WllbTXV9Nu5k6Ys4jdx7KZOT+B5CT1aoL0tjeyL/kqMVMnUnM9gxY9
      O+yFm7v6AOwj7cwFpFYuhHqZc/xCMbNnTeDKmfNYjQyhpTCboup2ohOnkrR9D88+NoekQxew9h3F
      5LFegz21vqGhBmBlXhnWIz24fu48UnMPPOwNNALAPmkP+w9eIm56LHN/imUxtQC8uy6dPsmoqMlY
      GemQvH0TjZaR2H/9XeAng3lrQxqW0lqWP7mKY9t2YWnnhI6diCZdLyaalpNWKaOnRUxych7vvLuE
      Tz8rFKyLKLXa//a7wJeSDmARPg2TistcbTWgqdaYJ5e48a8PLtMnMqC3q4xfvfaQ4FJlMSNx/ID6
      rC4A085eYESAC/tPFuDh6091dgrTJodzpawFhbktpRklPDrTh9c/u8C7f1jGni3HKawyINClk4CY
      aBxNOtiSIsFcUaUWAHMvnsYwIBYfa1227TnNikVT+2cB9tRzLKub+pspmBia4TfKmXq5MU3d+jQX
      lvD0mtls+XQbcx5ezgZlTRA3A2yj51B/9QrjEqMZypiEIa0JUlPGr3+7ndc++hnSyi5SjhwhclYC
      aKAmSN75CzR5+HD1YC5b9m7VAvCOhgsAz+w7SGGzYGWNGYG8p4u6LvuvkiG8+IudRE10o6JOByPh
      AnzlkcnknDrCuQpT4ueHcGjLKbzNJEwRrIyjp0rp7NTHzKaXumqzAQNw64ZdLHp0CfqyWj59/zR9
      NoFfA6AJy2Y4sOP4VezcfXlwbsSA+qwuAPvkUrZs2EPk4vkYVOdzvFiKvwA1W3NzVU2QK1tScA12
      oKO2jZlxHqT3udAtQOxWqQDGX7xC4ckkGq3G0lt3Sy0Aph0+xs2WTvz9A8g4cw1TO1tCQ93x7ocL
      XJl7neT8XqwaKr9bE+Sh8aw/VM6a5REqF/jZx2aze88JCmvkvPyzpZgO4YsxmkmJPwOzuko+3ZrK
      vMWxKDQAwM6GcrYcukZpeStnUs4NcwA2HRDgVsyPPpE3DQDrxLv+ZLgAMPNCOnVtnUh19CkvyKG4
      x5np4x1UAPzdn08xe7Yv18ukeBhWYWQ/ioKCEsZ52WEWNYGGgztJkfrz2iwbdp0QACi1I9qzii0Z
      5vzpJfVqStwB4G/eOMe8RWOwVjRwrc4Y/dZy3OMmkrb7KKME17jR2J2azCrByatjfPRo8kpqeWLF
      wJ6rqQvAAx99RJPLGCb6W/GPD0/x8MOJVBSXMDXCj/VJN9AzMcayt4UzqTeZKVhr86eHc/7IJW6W
      VTM50p+G3k56pS60NhSoBcCc9CvUdHWhZ2BPc1sL3a3d+Ae64aNmGIykrYpX/t9e4Thn0lmWS0WT
      mND4cZxMvoaBmTlzRulSaBtFgrehCoChrha0WAoALyth0pJluA3u68bfkEYAuCSW9GNZ9LR2EBE/
      ViMA7Gqo4cTVAprE1nzy7jvDHICDqOECQLlMpnq1S5VyvU+BTCEA4ky6qiaI8tWuvj7B+hO+Uy4I
      d3f1om8o/K0s8KGsw6GQI+vTRZk6TlWLQrhxKNdNJLI+DPTVW0C5vQgST0+PWICbDiYmRoh7ekFX
      DyNDPWRSKWLhoEyNDZBKFcJx9NHTK8XQyEhoa2ALJ+oCUNLbi1SufCVPeRwyVe0M5fvDysWh3p4v
      x0JX53ZdDZ3btUkkYqEfwrErX2Xr7pFiYmrENjUXQRRyOT1imTAGhoiFtnX1DLhxM19tF7hPOB/d
      PRLVcSrHTvlKmIkwbsrxFBkYoqt8sVFXpMqfqFoEeWaF8HtlO/oYGQzte9FDDUBVjRbhvCjPmY5I
      n5raes0sggh46RLmrrGxEbGJP8FFkPtZE+Qvf/z1sKwJciu/jJG+HsK1opmaIMGBIzSaDEFZE8Tb
      01UA2TCpCSLAT3ljGEz9N9QEkQo3dwcN1gRZt36vtibIHQ0XC/D7pA2DGVxpw2C0YTBDJa0LfI9K
      P3OGlPQ8fCKjqC8soKhewtQJ3qqEqH96Yw/OnpbYjxpHiHktSam12Dm7Eu0u51KHKzO8ZFTq2+PW
      XcTeYiMenx7Q7/bvADAz5QyXCxowsfPloflhHNm0Dffpi9AruMqh3GrMdA2IF46p19SJse4WHD2U
      xozZkZRmXWVnehEW+obMWpiIuxpvpagLwJKsSxw4e4uouYvwE1VxPFePxXE2nMgR460oo80xgPGe
      Rmxcf45Hnkgk5dwFqvPKqJGZsyDeme3HCln00AIyTp1VC4Atlfl8eiCbRXOiVTeh0qo24e8ofNXO
      CC1mw9qd9FrYMy3Mgb0nbvH4E/PZuWk39oHjsOqq5EJaPjPXPM6Fvft4YE4Y245kITWx5pWHEtXY
      /8ClEQD2dvDxtuMUl9Tz8CPz0JVLhwyAfX1iPv/oJCsejOSddbtxGx3Lu39/WwvAOxouAAQFGz/Y
      yLInH6WzKI3z9c6YKosiRXjz6m+TSJjiQVGbCfq97Tz3xCzOH0iiUmJFh4GYqV52VJq4U3b6HBJj
      PR56cC79taluL4IE8c+tubz8eDzVxZXYuJjzxc509I0NCTbqJbPPACcjY0KcjSjRdyPe34bP1x9l
      9eMzyTlzjjNNUtzNTZgUH4654Y9breoCsKykAiuTXg5nSXHuyyevzoup40X02biSfuIEzuMmY9dV
      wnHhxvDKM3FsPVhIT00xMiNTnOghdGE4+3dVYGvVpd4qcNI+CnScWDwzEmnNDXZlihjr0Y+iSMI5
      KmoTkXp4H0Z9Jqos1VfLOjC2cacqI5vHnp7BJx9u47E1q3h33VYmBdpwtcGQCQE++AZ7MPhJsP4t
      TVmA3Q3FbDjdypwo5yG1AG9lZXL4dCmJE5y40aMgdvwEli17TgvAOxouAJS21fDh8QaeXzKGrRt2
      suDRpaQoXWABgGs3FvPC0yG8JViCRi52vPD4DDKOHuFWmxnBcb5cOXweL2URo71n8Dbrxjl+FUsj
      HfvVvgqAkwP4565ilsVas/bDo8yY7E9KhYTaghKWxIbiOjMWHwMRDXk3uKXj8i0ApqIIDSXQ3FDt
      OsbqArCrqY4Ptl7guecXYdRVpaoJ4m1bz6RZibQV3eC61JHJgfZs3nCY8DH2yDx82PdJKn5WLcSv
      msOpUxeQdFmjZ9CpFgBLc/OxdDEhJbUbUXcxcQumU9ifwuiCTu87gNw3io7L5xkf+82iSCsX+LI1
      25jHpnupXODlcydj7WTGW//7Cat//dKwXgW+owvHTzIiLp5eDeQDVCbq8HU3pNd7jGAYnCXpyAkt
      AO9ouACwuVywbGTORPmYsHfnaRYunXr7GWCUH3/+P+EE+9ti6hMouICVpN3qQWRkQoyXJXqjA6k8
      nESR3IC4qYn42uize+9xFi/snyt1xwW+ePwYpa1y6hp7sDIz4tHVs+kqv8naPQVYOBuqSl+O8nHm
      dEYxPr6etOXlYOBgh7OVBTfrW7ARADk+LpaRTj+exURdAG5f+z4dNj5ERocT7CBj98km7M06mDgp
      nNbyIgpkNkzwsebw/vO4CPQYHTqGbZ9spl5sxtwYd5LSC5m6YD43UlLUAmD6sZPcbBQTOyWe0quX
      mTpzEpn9eAaorAnyu3fPERM7WjgfMk5eKmOVMI5bPjuAU3Aw0TYS2tzHM9pOVwXABfEBHL5ShkKw
      sJ9YPRPDIXwMqykAnj9+iogpCVRXDf0zwOT954S5MErwVk5j5B7KB39/86cHQLFYfN9qgnz+yd95
      6bkH7kfz96SUi5lEhY/RyCKIErbTEiI0ugiSmp6tSsCgqZogSUfOMXfmxAFtezO3GDcXBywtBzc9
      1Webknjkwbka6f/9aK+qpgGJAEBvL1eNtbn8kd9qvCZIW1ubMDd+uPb2fQXgF+vf4uXnhyMAs74E
      4NCHiSQLAExMiNRoGExq2nUBgAHoD3Hs2x0dOHyOebN+WgDcIADpUQ0CacMXQnsPaRCA1Q2qmiA+
      Xi4aa3Pp6t/89IoiaV3gu0smkyGXK9DT11cFRYv09Dh5Ok0VCK1Q9CnjPFWgUD5eE4ulAhT1hb/7
      UKCj+kw5vH2quGgdYT+q+GhkyuBhkS5SZY2Mvj7VvpVBunfaEX3tWd1XNUEUd/ahUAUEK7fT1RWp
      fqtMy9XXp0AikaEKttYXqdrUU/2+T/gnVx2HrkgPfTWgrfarcMI+JVKZ6nc69H3ZP6FNYVwU8ttj
      patz+5hFwjEqhE70Kf/p6Kr6rxA+1xH+VzcQ+uvtyaVSVdBy9g31A6FV51PYXhg41bhJpXJhX7eD
      yXWEsVH2QRmwrgzmVrrAzz+1VDj/twPYDQz1kUkk9AltGgyB5T/ULvCdsVbV5xD6U10z9IHQyjaV
      sbJiYV7qC/P6JxkIrQXg3XXrWhZHD55iZOIcmopLaW6HAB+LL1eBDzJlqhfFjUYEWFTRpOtMY0ML
      Ud6GHK2z4PFQU67UtnLgbC0f/jKKT/a1MDdUwRsHi/nHK9P57a83EzXZn+LaXoyaynEZ7UthYTc/
      e37OVwk3v/Eu8MN+/HNtKvXV5UQLn1k7uFGRdePLjNBpHChox8fahJBR1vzm7VQ+/+sc1n6SSVFR
      JQkz/HFyH8H4oB8vg6guAM8m7aOyTYaJ7wRCTMs5kGnGmhk27LnSQHVxEZZuI5jmD2s/y+Wtvz7A
      nu37yM9pxsLTnlULovjdzz7hV1+8rnZG6EsHDpDbLMZ5VCA3z1xDx9iAiZOC8VF7FbiBN98/ha6+
      jAkjbble0MSU+ZM4dCAFIxtnnMR11LVKmPnkSg5s3IEnEiQ+PphgxBgfQ/ak1WDQ08LiBxfjaDJ8
      aoJI2hv59cvrefn9NWz+1xEBRmJmL5yBjmzoAFhekM0/1mXwP68kkHQpj/Yea7Z8/rEWgHc0XACI
      QsLatXt55oXl1OTlsPl8FWO9zFUAfOmXu4iOdaVSbou+YFm8/NhkspMPkirAz8HLEE9DkSpzc2td
      D75Btty4roudYTluTqaYeI1my3sHiE8MpKZVuEO2trLisTmc3L2biHmLMf/y+roDwOdf3UtMrBN5
      Zfr01JYyNmYE7v4BqvCN2wC8xNbMWjxtTEmI9GTfnkL8oly4lVpLbm4R0ZN98Qsdx4QR9j/a5f4E
      QueknqXS0AMqrlPS6kF0sBS5WLjovqwJsvrpqezZcZrlC4PZlyWm8sIldHSbWLTmaW7uO0bI4+qn
      xL9x4ThnshoYOz2ezH1HUdi6Ehvm1a9kCMoiPes2JuMoWIHRU75VE8RBQk6VlNXPLLtdE6Svh2YH
      F4xNrZkX7sjH2y9j7eSqgve9VPr7Pg0lAKU9vZzdc4ZRD8zARdrDB+v3M21mvCq12lABsKe7l/3b
      jqvmZt7Fsxyrt2X72n9pAXhHwwWAXbVFbMsT8XC4Pe0KPQ5vP4WDp/VX6bBeeGoMb711CEszBaue
      XM7ZvQfQMbHBc2IIp97/DI/wceiZeaBTcYaCRgdKC/Lw9bOhRWGOqcSE2eG6pPe6oVuSLwBwJhvf
      2c7KFx74asXx+yxA5Tuw31cTpMnNn3FOZsjbSth+VoyLbjZXii0Ed9OCnz2n/iuHagdCZ17hbI0u
      CY5t7M6oprhSn8RxVowLcOZajymFFwt44dlEVRhMwEg7PMNDkdR1cDH5GImPrODSxv7VBDm6cTeB
      08eQer6CTokFxl0l+E0YrX4gtFzCh2t3sfBJoe3d+/ELcqWiS48OuSmNeQWY6skZ42mJLCSGlG/V
      BCm5noPc1Rt5fgb1jqHEDbOaIKpkCCumcPyDnUxdvQxFq2bCYGYvmojITMRGYd5+cXCHFoB3NFwA
      2NFYS7OuNe5mfXyxJYnRsQk0lhWQEBPEhvVJdOvoM2vpLDzMxHzxabKqJoifhRxdVxekZcW06hmj
      b2CJm0E7yWnVjI8Pw1WYEFkXM+jStyNmgicnT19Gt7OR7MpWps1fQKDLvzPPqQA4YwJXbrQzfowt
      aRmVFOdcpb5XhoWjG94GHVyraGN8+FgyLmWifPAYFTseE0Mb/BxkXMzvoCjjMl0CUB28/Fg588dz
      BKoLwON7d3GzRkr01CmEextzNbcdY5GEUYGu7Pz0ICFzpuHvaM71rELMzfXw8PHkQnIynTb+zAz3
      pjgrH/uxIzmoJgC7Gqv4bGc6Kx+bz5Wjh9FxCsTGSKL2M8Cu+lLe33pesOKcWTjRjz3H8nn4oUkc
      3XYI98hYbMU1HBMszEdWTGbtuq1MGevOiWsVqtxHS1ct5WLSDlVNkNULYhjs18CHGoBlN4owdjHm
      8y9OYW5jQ0Jc6JBagErdFM7vCB87Pt56mnkrZrNiyQs/PQD2pybIYEpbE0Q9aWuC3F3amiADk7Ym
      yG1pLcABSJsMYXClTYagTYYwVNK6wIOgquJKHH1cuX7xEubewYILepOE2DEkH7lAh1yHmIRYXE1l
      7Eu6yKjx4QR6WnDu6CnaTJyYOzGY6sI8zhW0sSJxDAWl3fh5m5NX0CjsN4/6bgl2Do501tfRK7Tl
      5R9EZPC/g1O/AqCslysFLYwLcCC/oImRIx1pKKtFrCdBIbRj29dJu0zK+ZRryIXtwiIjGek6MCio
      C0CZuEuwXLKZNiWQvYfT0De1ZOG0sZRW9+JqBa0KI/qayrlc2snchFCqK2vpbKqhzdiNcb4WHDl4
      mYT5sexTE4DizmaSkq8QmRCHm3kfhRW9tLfX9QuAmcI5lFq6EuJpxvGLxcxMDOPa+VSs/MZQn5dF
      aUM3kZPi2b99DwsmjSY1t1rYypj5i6O5eOgkUlsPZkQOLHXX3TTUAKwvq8HM05mSy5eR24/AUtSr
      GQDKxRxOSiEwLo4HVjyvBeAdDRcAVhfl8MYbyfz+b0tIvtxMV2UZ3t4O/64J8kQwb36aho2skkVP
      PMyxrTsYNdabbIkbMSa1dOsbk3K9iwdjLEi51UpJsQ7Prf7uYgY9taxVLnQ88c1ndHcAmHPpIvsz
      a3jl8Zl89FGaartzW5NpMuujqNOIxX7m5KoKo48k3t/2nvqsLgDP7NqHjb8blV3OTA8x5L3Pr7Bq
      ujc5vRZc3bedoDnL6Cwrxa2vBsX4WVSePqYqZh7g3oOpoQF2oz24UWCCqKdELQDmpZ1F5BfFCBsD
      Mk4dJKXs/7d35lFR3uce/wwDM8wGA8OO2wiIiAqIgKCImoqiQVwP0brVNLHNvWl7eu69npt7e05O
      T0+a2yS9p03TmsQk1rqAC0QMoKho3BUQEBUVUFxwY3EYYBj2+77jtW2aJiLCSzmd73+cOS/P/N7l
      M8/z/p7Fj4RJ6mdohvCQ7DON1F87jVajwxjghVnuwh0TNFbf5LVXXmTnZ+nMXrWMLR+lf2UTpPrs
      UYrUEwm2VuExLhwfzdBJg7E8rGHDhj+xYeM60n5fxiiPR4RNn0qPBIPRb5aX0e4+jLTP9pN7ON8O
      wCcaKgAUlbUpg6jZIZTW6qg9X4x3gKcNgK/9eBuRcX7UN2twdtXx07UzbDNB6gNnUHs0h4sPrLwY
      Z+SeaxDzx3vaIPfK62lMjvHkTp0LzbcrGBs1CuOEcBJD1d8CwAR++8sP8PR3Rzd+GjdOVf0VAJXo
      jQaaqx4hd5Vx7PQNjD46EhckYTT0rUlobwG4TzgvU1KjOH7cgp+sGo9pidQWHmPitHhaqy/bmiFM
      NzrzwR8PkTp7DAXC3y3HDlFRcxM/QzApP5pK7mfXkWtNvQLgqb05FNeZmRAaapsQd/2unugI1TN5
      gPcrr5BV+gjDo/tfmwmyZnU0m/ZW8eqKOFsIPLy7hdsad5x1HqxfEs3OzXspr+3hh68txUvbv1Uy
      0swEmcnRjRlUPrzH0pXLUfRIEwKfz8/nrnswb/37m3YAPtFQA+C01Gls31Vka+seMOxxIvTP3znJ
      kkVBnChtwF9xD+8xkVwsuUhs+EiK6zsZIW+hx92XotPXiA1z5c69dmpNbn/2AJvMjaQsnYRCpWWs
      r+wbATgrzJtdpTJWzPTno427MbeoWbh0MjWFFVjdVYTNjSfvrf/BPyGBhhYFEaP0ePr44mvoW6pG
      bwFYmJdLtbkTr3Gx3C87wbLUFD7POsSiBbOpq7jIhTYDp7buYPry+Vjr65mREMW5/BLKKqqIGWeg
      sr4NhW84rTWXejcVrqgUc5eVqmsmHtZXc+GWitdWRfd6Jkhn0wM2vPU569bN597lYkytPQREjufk
      iWu2KpnkUCeu6GOYHeBsA6DRoQeP+CjEs9hWW8t1nHFrqcUrIp5wv/6dEScNAGdx/VApl69Xkzhv
      Gp0ShMDV50/xyZkm1i6IZNX3/tMOwCcaSgBsswoPqrOSZpMZR7VG8HgKmJUQidnUTCcy9G6uKOQ9
      1Nc1otLqUDs7Cp+Z6MARg16L1dKC2dqNl5uG1rZuVEo5ra0dwv+10N7dg9zRCYOr+vFnzl/1LEQA
      Js+ZCkK4LFaxWS2ttArfp1M4Tq1W20rKHJVOtgE33TIwNwrQFY5TazRoVYo+rbfXpXDdnTQ8suBm
      cKHD2o7S2UlYkziPRGErhevskdFsNtvmqIg7ceLckpamJrodndE6O9BQ34ybhyvpvXwH2CWEbA2N
      VgweLjj0dNvOV/nVyl57gN2d7dSZWoQfMUf0OmdM5jbc3bU0mRpRqLXC1eoS6xpx/P+ZIOvXLsBs
      abcd6+buhqXxEV0OCtxd+78v1kADULw+cuG6WITrIVOoqZOgHZaotlYLjS3C86N0Zt7iH/3jAXAw
      Z4K8/Ys3mJc4dTDMP5euVFQ/ngniMPBpMBcuVQghX5CUWTBcq7rF6FH+AlylSYMpKiknMvzZu2WL
      unP3gQA/V9Sq/p4JUkTCtEhJ1i/qqGBvhoT2GoQfc7HO3cvDXTKbH2zaY58J8kRDyQP8W9nTYPpX
      9jQYexrMQMkeAj+nairLOXDuFsnJ8ezP+AJHgxGDqotZccH89v1cPP11eASFMU5bS17BAzx8/Ij0
      UyEPCkBTV8N1kxWZsycRge5kbc+g0cEZjas3C5MmkZeeyYR5CzCVXMQnPhzLxavIAgMY9ldhsAjA
      2ZNHcuiWkoUxPmRkHBaAOI0/pJ/gX1Ylkr55N/NWL+HykXNYWuqptghRnEzDi0ljSd99CpkQeq5O
      jmX7roO0yxxZtmQezo032HWhjbVzx//dNfcWgHV3KtmZdZ6U787hcGY+Kq8RLJ45mqNlj6guK2R0
      bByON8spq2lh5ZqFXDlfwJ2rt2j3MfJSYgT7t+YRs3IO+3sJwDvlxWR/WUXCou9wITefTr0fY436
      ZwBgOzs3f4FVZ2D6BAO5xypZuXoBlpuXuWb1xNB5i2NldXx/5Vw+2JhG9Eg3ypu6cUJN8vxQIVQ/
      Lr5b4HvLElEMoVrgv6iDjC37aFIK648bQ8eAArCd3dtOkDQ3mG0ZZ/EaF8Gv3nzLDsAnGioAPLxn
      N92+45g03IE9J+7zQkI4VeVXbJsgb/w8j6QkIxdu9+DU1mhLozi2dx8PuvSMnxOD280rFN1vxkE3
      kqQofz5+71MCoiaiNXgRPlzFhzuLcBvuy/D6ewQsn0vj0VM4RE0mRPuXd3ePPcA4fv1uOutXT2db
      YStzfU0cOX+XkKS5VGTsQh0ajez6VVrbzOgnhKJWu2B0qOWLim7hQR+JVmNlz8EbJCWEoHNxpzj/
      GM3yHpYsFWD4d9rk9xaA+/+YSdi8cM6WdDEjTMu2z8+QNDWQOw0tyP1HUbq3gPjkSVgrC3GcGM/V
      k6Xcvd1JgO4OoTMT2Loxj1ffXd/rWuCigwep6XLAxeBPVclV5FoNE0KGYextLXCrifIGOcUH96Ho
      VhEdF0hVuwcPLx3B0WsKHbcvM9HoRseEaI6m7WaUrBN5aAgalZYgpZnMS1ZmhI/Cf7RgTzm0doFt
      6mzm448O4KhTEZ8QMaCD0csKzgo/MDWsWRGHWq/mow/z2JN9wA7AJxoqALxScgmNRkbRxdvUakbQ
      WllCcLCRFwQAvv9ZhQC9ifzmnX2o/Dx5/eVEzh/I5XKjloi5AgCFB6q0thWE40QAbt+UxeLVSbZ3
      h6V52Xx5r5trpTdInRJCYKoAwGMnUUZHEaz5WwDO4Ejuftp7HJgwPYEd7/0Bz7EjqLqnZJxrD2ND
      vSk4cxO92oEX1ixAI4TLrY1NOOmc+OTdLcQvT8XoreLIzj04BUdxPGs/wz3luEYvYVX81xti9haA
      OZ9mErloEqfOtZEyO5CtmzLxETziAOFc1LoPozjjHAvnjebz4ockjtFgGRlG5+XTHD1bwXf/9WVK
      tz9bM4TczWmMEM7VxcpGamtA1VnDxKkRzzAVDk5kZ9PkG4m15DST44OobPcl1vMROWUOtN0qZ1KQ
      npbgaI7v2k2AQkGM4CG6IMPa1CwA15E//e9Wpq5ew0S//i29kwSAHXW885tCfJxriU7+DrIBzgMU
      myEsW53Itk8ymb58ESuW2BOh/6yhAsAjWTlUm2FWYiz5OQexKrwJ8FYya0ogb/4ii7HjPXEaHkyw
      /BZFVZ10yxWsSBrH73cIIaEQJi1MnMSWrAsEjzNiqbiE3MsbhVqF6d4jXv7+QhquFHC8xoHKikqU
      ckfWr1v8lfDqCQCb71by3zvK+OW6WLLLmliWMIaczAM0dzmxNCWGn72xhajxbpjUepQCACPHG9l3
      tBiNUsX8uZNJ33MEd50aHzclhvAEYgQPNG3HPlJXpHxtg6XX3WAunmNX9iUWvDSb3JwzDB8xAjdl
      F/FTgvjV77IJjI3kkgDusZHjMXh6MDshjLSNmdQKYeSP18zn1O58gpbO4nAvAVhReJycEzeImj9H
      CIFz6VR7ERs1utftsFofXGPDr48wc1YEo1ys5J+9xepXX8KltYYvrzngZL5EUaWZ13+wjI0fphGi
      k1Pj7IJSOHbyxCCy8gvQqVQsTp2Pp2oIeoA9HXz8/haaHdxJSZky4Gkw2cL19Td08MWVVmIjQ/jZ
      f71rB+ATiQDc+ul7/MdP1gyG+eeSuGM3LTZckl3SnLyTJCXGCZCSbhPkxOkSYiaH2rr4SiER8ouS
      Z/bpWLExxfBh3uhddf36nT7enMkraxdJsv7H9jIEe4slsyfunosdzAOMwySzmZz6b5wrLJXMnqin
      ArC+vn5QAHj37l3efvttlErlYJh/LonpA46O0szL6OjokAxEg7G+512j2O5ffKXQ37vkbW1tkt6b
      UtsTxxOIkiKV64nEIgLxmZdSFovFli/7TRo0D1CUAF8MhuerWx0MNTc32xJ7pUhNedpQl4GQ2EJI
      vGmkSr152uSub5PVarXBU97P3rjU96bU9sQfHfHRVyj6lizfFz3Pde6r/mFDYFF2AD5ddgB+u+wA
      7JvsAHwsOwD7IDsA+1d2ANoBOFCyA3AAZAdg/8oOQDsAB0p2AA6A7ADsX9kBaAfgQOmpAOwSJ2cP
      khoaGnB3l64Yu78kJQCbmprQ6fo3xeNpsgNQ+ntTanv/LAA0mUzo9fpv/FwmPMyDBsDB8G76Q0/r
      MNGfeto2/kBIhIqYkiEVAJ82uObb1N7ebkvZ6e90DqnvTantialOIgClTLF6nuvcV4nOilb7zX0x
      /w96fQN1jYOj6QAAAABJRU5ErkJggg==]]>
      </text>
      </image_base64>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[collection]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[données]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[contenu]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[valeurs]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Quelles données sont structurées?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>false</single>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[La liste de mes titres de chansons préférées faites sur une feuille]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[La liste de mes titres les plus écoutés sur Spotify]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[Mes numéros de téléphone dans mon smartphone]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[Une photocopie de la semaine de mon agenda avec les devoirs donnés]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[L’ensemble de la photothèque sur mon smartphone]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[Le tableau sur le brouillon d’une analyse de document]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[CSV signifie]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[Comma Separated Values]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[Coma Sanctionned Values]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[Canva Separated Values]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Un fichier CSV est appelé aussi fichier plat.]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[Vrai]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[Faux]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »cloze »>
      <name>
      <text>
      <![CDATA[Complète les blancs.]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      Une {:SHORTANSWER:%100%base#} de données regroupe plusieurs collections de données reliées entre elles. Pour établir des liens, il faut disposer d’un {:SHORTANSWER:%100%descripteur#} commun.

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Qu’est-ce qu’une métadonnée ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[une donnée à propos d’une autre donnée]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[la donnée à l’origine d’une collection de données]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[la première donnée d’une collection]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Sous Windows, comment accéder aux métadonnées ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>true</single>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[clic droit > Propriétés > Détails]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[menu Données > Informations Métadonnées]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »0″ format= »plain_text »>
      <text>
      <![CDATA[clic droit > Informations > Détails]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Comment nomme-t-on la surabondance de données et leur utilisation actuelle ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape deux mots anglais)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>0</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[big data]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »shortanswer »>
      <name>
      <text>
      <![CDATA[Comment s’appelle la suite d’instructions informatiques qui permet de traiter automatiquement un grand nombre de données ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[

      (tape un seul mot)

      ]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <answer fraction= »100″ format= »plain_text »>
      <text>
      <![CDATA[algorithme]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »multichoice »>
      <name>
      <text>
      <![CDATA[Dans quels domaines la surabondance de données est-elle un enjeu ?]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <single>false</single>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[santé]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[économie]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[politique]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      <answer fraction= »25″ format= »plain_text »>
      <text>
      <![CDATA[environnement]]>
      </text>
      <feedback>
      <text>
      <![CDATA[]]>
      </text>
      </feedback>
      </answer>
      </question>
      <question type= »matching »>
      <name>
      <text>
      <![CDATA[Associe chaque impact environnemental des data centers à son origine :]]>
      </text>
      </name>
      <questiontext format= »html »>
      <text>
      <![CDATA[]]>
      </text>
      </questiontext>
      <externallink></externallink>
      <usecase>1</usecase>
      <defaultgrade>1</defaultgrade>
      <editeur>0</editeur>
      <subquestion>
      <text>
      <![CDATA[électricité]]>
      </text>
      <answer>
      <text>
      <![CDATA[fonctionnement des serveurs]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[eau]]>
      </text>
      <answer>
      <text>
      <![CDATA[refroidissement]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[métaux rares]]>
      </text>
      <answer>
      <text>
      <![CDATA[fabrication]]>
      </text>
      </answer>
      </subquestion>
      <subquestion>
      <text>
      <![CDATA[réchauffement climatique]]>
      </text>
      <answer>
      <text>
      <![CDATA[rejet de chaleur]]>
      </text>
      </answer>
      </subquestion>
      <shuffleanswers>true</shuffleanswers>
      </question>
      </quiz>

    • #5765
      Merci, ca m’a aidé
      Up
      0
      Down
      Pas très utile.
      byache
      Participant

      Bonjour Benoît, Ce serait possible de créer un programme par exemple en python pour transformer un QCM pronote en oef, mais il faut être motivé… Il y en a probablement pour quelques heures… A part ça, je ne sais pas si tu vu mais il y a un outil pour convertir la liste des élèves de pronote vers le format reconnu par wims.
      Paul

    • #5766
      Merci, ca m’a aidé
      Up
      0
      Down
      Pas très utile.
      bernadette
      Maître des clés

      Il suffirait de le transformer en la syntaxe compliquée

      :question1
      numéro des bonnes réponses
      choix1
      choix2
      choix3
      :question2
      numéro des bonnes réponses
      choix1 de la question2
      choix2 de la question2
      choix3 de la question2
      choix4 de la question2
      etc
      
      

      etc etc

      Ensuite, un modèle préparé du type qcm à la suite le gère !

      Bernadette

    • #5771
      Merci, ca m’a aidé
      Up
      0
      Down
      Pas très utile.
      markey
      Participant

      Bonjour Bernadette,
      Effectivement j’ai vu l’outil pronote, c’est ce qui m’a donné l’idée de poser la question sur le XML…..Je vais essayer de voir avec Georges pour créer un « convertisseur » car la base de QCM pronote « partagés librement » est impressionnante: regarde ici: https://www.index-education.com/fr/qcm-liste.php

      A bientôt
      Benoît

Vous lisez 3 fils de discussion
  • Vous devez être connecté pour répondre à ce sujet.