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data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAAAXNSR0IArs4c6QAAIABJREFUeF7t3VGoJcd95/Gqnpc5Z2R7WQwrsGIIGCyxhg0kJCQbIj0Ij9GD5loQC4SDFwmBRoTxGEzELjgM2FmQI5yRCBqB8IBBWEgBaWZIFumih1nww9pPDtjIApMHxQYZ/LDe9dw7L7dr+Xeqev+3bnVX9z2nb93T52sw2NOnq7o/XXN+/a/uM2UN/0EAAQQQQACBExewJ94jHSKAAAIIIICAIYAZBAgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjAAEEEEAAgQICBHABdLpEAAEEEECAAGYMIIAAAgggUECAAC6ATpcIIIAAAggQwIwBBBBAAAEECggQwAXQ6RIBBBBAAAECmDGAAAIIIIBAAQECuAA6XSKAAAIIIEAAMwYQQAABBBAoIEAAF0CnSwQQQAABBAhgxgACCCCAAAIFBAjgAuh0iQACCCCAAAHMGEAAAQQQQKCAAAFcAJ0uEUAAAQQQIIAZAwgggAACCBQQIIALoNMlAggggAACBDBjYLTAhQsXzltr3wk7Oue+cPPmzXdHN7TiDo8++uh/sNbettbeb4z5tbX24bfffvufU82Gzxpjbt+8efOifObChQvXjDE3jnPs0l5VVdfrun7y1q1bv1rxVIrsfuHChUvW2hfj66dc/2Vvb+9LZ8+evUc5h2Pt9S5yQnSKwIYJEMAbdsFKH+4Xv/jF/+Sce7mu68dKB48EiHjcvHnzpZyLD8y3jDH/O4TmNgdwuI7i4Zx7Sd+EiIu19hNiure39/Tu7u4d7TuHm4/ceGE7AichQACfhPKM+ugLYB2In//8588tl8tXrbXPy+k75/67MebfOef+vbX2ljHmr3KVs2/jTWPMI76Nr+qwHROgITSMMf/gnPu4tBP2r6rqI+fccyFswnk4596QKtdfvj+01l51zn3LOfdVte0nci5xBe5D7Jn4uH3V+Snn3KO+cv8fUmVKyIWK1O/zM+fcQ0NucvzxXhz6+eAqwWuM2dGzAH5245Jz7m+stX+ZCuAxNz4zGvqcCgJrFyCA1046/wZDuDjnXgnTuX5Kt61IEwH8njFGAulJ59yHxpj3c9WrD8j3JSyj0PisTJ1q6fhY4qugqrb/aq29uL+///XFYvGChE9fAMvUqzFGpqofsNZ+2hhz3Tn3sHPuStjmw1ym5S9JmC4Wi6fCuenjliozDstwE+Cc+/Fxp7THBrD//ANy7fRNTJglsNY+628C2puS4En1O/+/35zhyQkQwCdnPbueVBA3z4AzFfBzdV1/raqqv5OquK7rB/sCWAd4eK4bV17HqYBl+tla+7gx5oNQ/eUqYL/PFQlrfxF3JIB1YIZgOjg4+MszZ878fajaw0UP1X5X9aiqfam0O59lrzqI4hkMbahvePznjgSwDu9Vj4X9Edh2AQJ420fAiucfpiw7Kj89BX1qAvjMmTP3OueekKnoXAUcQtZa2xvAKrAuL5fLq3KTkXohLDd9O3UQ62nu6NJ/zzn3R35aXG9qX7bSFXLXy24rDid2R2CrBAjgrbrc6z9Z/4V+XgVwmNqUN2y/IdVcmM4cUwH7KW2Z+m2moNVbzJfDC0PHrYDlueqFCxf+m7X2P8tzUF8BNy+W+XB+zzn3zfCcd0gFHE3rylR84xCL5wI4fH7MuY2dgtbH1NVPqgKm+l3/3x9a3G4BAni7r//os0/8BKl9WSj6WdDr0rh6CWtUBSz7Ru3Ji1wrv4Sl3oBufkqlpoblzd9nnHM/k5fEnHO/zAVw9NOc9mWqxMtjrVFXAPvAk+fkn/QXpW0vd5FOIoCpfnNXge0IjBcggMebsQcCCCCAAAIrCxDAKxPSAAIIIIAAAuMFCODxZuyBAAIIIIDAygIE8MqENIAAAggggMB4AQJ4vBl7IIAAAgggsLIAAbwyIQ0ggAACCCAwXoAAHm/GHggggAACCKwsQACvTEgDCCCAAAIIjBcggMebsQcCCCCAAAIrCxDAKxPSAAIIIIAAAuMFCODxZuyBAAIIIIDAygIE8MqENIAAAggggMB4AQJ4vBl7GGP8KkjNakfbsDSdXsbPOfdKaqUjGRhqIQYjK0Tt7u7eGboCUtfAGrMy0mkanPHCHcaYdoGJeFnEPtNwTvGCFWFxjkQ/skvT12Kx+FNZdEO5DF7k4jRZcizzFCCA53ldJz0rCZnFYvGCtfaHsqauLBc4aYeFG/crAV0PKyn1HY4P4Fedc79bVdUzcnOyzQFsjNlZZVnGYB1ubGT5SFmOsu+aSCCHfvX/LjyM6B6BIwIEMINitICvRJ6w1n7fOffc3t7e02fPnr2nqqq3rLXPhopYB09iib4v6C9SY8xPjDF/JQcTlgjsqnjkM4mqp104XipGWVrQt3VoCcOeKlPWL37Rbz9UJaXWxu1qJwSwMWZX1gS+cePGc9pBV7M6RKy1V6RNf9zfds49aq39F1/FvVBV1S+cc5dluUK9LKM2kqUUnXMP+fWOZbnFrxhjPmGMeURvyy3z2HVuoS/n3JfDmsx9g6cv/I5zU+LtjAS6v/6XwixDFNSvyjKY/uanDePRA50dEJhYgACeGHiOzfsvzw/29/d/sFwu9ZedLEQvX5AvhSBSX4TXjDE3VOg2YX1wcPCRrKtrjLkm+3WFnW5P9tFh77+Y35f9U6Efqqae8JWwar/Mw8Lzzrkr0Zq/0kQb9Kn2VAX898aYv/BtPB5c+gLYWvtpY8x1Y8wrEnJyTFJ1SzjLNgkbfaPjHdrKXIeSn3p9LTwiiG4C2us0ZnweJ4D19G+4sfI3UPqGp9dUH2OYuu6aso5DP7F+dXPjN+a8+SwCUwkQwFPJzrTdRLC2X+Y6PM+dO/cZXR13BVkcIpotrtRC+HUF8P7+/neXy+WbUvHpdvQXf+qyxNVYfBNwnApYbjzqur7XGPPZ0Ke/QYhvRJoA9RXwDf/ZHQnuqqoObQvBEULcV8z6+ab8Ufvss2f6V2443hny3HVdw9gbvmGtfTx+Z8CH5tVQvffcKElonw+zAuGmRJ6zyz7xNHXcTt8xrOs8aQeBMQIE8BgtPmviaWFP0k7Z7uzsPC9T03VdP2iM+SD3vC7zLE/Cqply1MF/586dn0dB2/Qvn9UV+dDLNVUA++pegvQXdV3/33UEsHbwAd/1jDU79Rqqw5MI4vjGLb7RCjcbMn2eum5+nLSPOFJh2zUtHdrrO4ahY4XPIbBOAQJ4nZpb0FYcVnGAypdgVVV/UNf1fVLFhS9UPU089Ms3mlqWqq2ZUpX9Q3Udqp/QZpg+7npLuaMCjqeg2+CXzx+3ApZKb2dn579Iceac+18qgMN0ufTzkFR+Qytgfywv13X9mBxb/NxdOWQDeOy5jZ2C1tZ9Va6fVm4q23A9/bVvbGQMdbyE1RvI8bUeWmlvwV9jTvGUCBDAp+RCbMJhdE3xxc81/TPd2zoEE9PJTdXqn2km3zCOqu3XxUimdhMVsGzSVXA7Da1fPuoz1i9u6Z/LjA2puMpSofVN9Yz7PXmZyhjzbWPM54ZMQYeXyuJn0IlnnM1LZ5kXoNqX1PzNzKDnomMDOPrpVvuCmPTZ5x22h5uTcBOXeCmvPe6u6rfvGDbh7xzHOG8BAnje13eWZxeHSzw9OcuT5qQQQGB2AgTw7C7p/E8oUU23P12a/9lzhgggMBcBAnguV5LzQAABBBDYKAECeKMuFweLAAIIIDAXAQJ4LleS80AAAQQQ2CgBAnijLhcHiwACCCAwFwECeC5XkvNAAAEEENgoAQJ4oy4XB4sAAgggMBcBAnguV5LzQAABBBDYKAECeKMuFweLAAIIIDAXAQJ4LleS80AAAQQQ2CgBAnijLhcHu60C4V//MsZcZj3bbR0FnPfcBAjguV3Ric+n45+BbBYAOE7XUXvtsoZj2upbeGBMO6U/27fq0kkFsFrc4ci10ItulLaifwTmIEAAz+EqFjiH3NqrYw9pzJJ/cdvbEMBjPVf9fM8SgTeowFfVZX8E/k2AAGYkHEsgFcDypV1V1cecc5dlub14oXe9NNyQJf/02sNdy/z5Zf1kMYZXwvKHeqk751xbncfVu97WhdB3Tnp5PL3sYVi20RjzSGg3HF9i+cAv7O/v/2C5XLZLKKp9wtKCl6y1L8qfO+f0EnyXjDEPhPNOrM3cLjs41kF713V9r7X2HW0Uzvfu3bu/XS6Xr1prf+qc+5Y/xva45f/L7Eh8/WIjfV7HGpDshMAGChDAG3jRTsMhdwWwMeaiLKIux6gXi89VzKkKuCuADw4OPorbNsbsSBAl9nnTOfeSVG1621BDv8+Rc/LH0K5jrM9vsVg8FYLRn9fLdV0/Fta1DX3HgZmbBYingBOBe35dDqklHlNT0GqN6A/FXx+TtfbxrgCO15DW13PoteFzCGy6AAG86Vew0PH3BHCy4sk9PxwTwFKRhaCR0w9T0Pv7+1/vqCSbqjFUn3Fl3kfYdROQqgpDVd8XwIlF5duF6scGsD/3a8aYZlo4GPdU1FkHXZkmZjDavoJZXNlqy74bKGvtbWvt/erzv7bWPvz222//c6EhTbcInLgAAXzi5PPo8BQHsEyHPt/3RT4miPsCWN8ERMFzXk/ZhulVVS2+K9Oyq1bA0qcP7Sfquv6OtfaK3ITIn/tp4bU5xGG/agBXVdXOHszjbwRngcB4AQJ4vBl7+KrTWntpb2/vS7u7u3f8F7Q8k+yqgOU55nn9eQ3ZUwE3zzj98+NvSJUk+znnmmld+d9STRljbqsp6Pa5aNfFylWbYb+h0+BRAB+pFGW7CmA9Jd5Mb8v0dN90dV8ALhaLF6y1P3TOfTy8je6Pe7RD39vWvsJ+X7/xPqACPnL95OYo1RZ/sRDYNgECeNuu+Irn2/czpL6XpkKAWGuf8YfQ/Mzl3Llzn3HOvRdepvLh2rxQFfX1ug/bpqoLL1rJy0BS8Trn/kj2Sbzc007x6pezfD/tC01dLH3nlHihKrx8dKgC1n1F+3zbGPO5uq6fDM+HUy+QxcdtjDk0XatvTkLlf1yHvgBOvXSmXsI6Um33Xb/EODrWT9BWHM7sjkBRAQK4KD+dz00gVREOrUbnZsH5IIBAvwABzAhBYM0C0c+tpHWquzUb0xwCcxAggOdwFTkHBBBAAIGNEyCAN+6SccAIIIAAAnMQIIDncBU5BwQQQACBjRMggDfuknHACCCAAAJzECCA53AVOQcEEEAAgY0TIIA37pJxwAgggAACcxAggOdwFTkHBBBAAIGNEyCAN+6SccAIIIAAAnMQIIDncBU5BwQQQACBjRMggDfuks3zgP2/HtW5WMNpPWu/FOLVsKDCaT1OjgsBBE6fAAF8+q7JqT6ivsUYVjlwAvjf1jX2yxgm/+nK1EINXeZ60QfnXLNIxCrXh30RQGD9AgTw+k23osXUesBbceIncJKpm5GhyyfGh6dXczqBQ6cLBBAYIUAAj8Dio/9fIBXAiSXwmmUF+9yiivpQ5SfhUVXVx5xzl2W5Qudc216iL70U4FeMMZ8wxjwiyxWG6eHEPs1yhPGf68UTus5J/7nuQ86177iHzCB0rKh03hizE3tGVXF7rsE8FcDxMegKOVoukUUk+EuPwIQCBPCEuHNuOhXA/nnokZAY4pCq8Hx4NAvWSxtVVb1lrX22b0F3f1yvWWsf9p+7JPvKFKxfBP6GhK6EkG5PH6MOrdw5+XauR2v6Sp/Z4+6aRdDHJseVWC/5SND64D8S0qkA7qqK42vAMopDRi6fQeD4AgTw8e22es9UeKgF23+0t7f3pd3d3TtDkXoCuAlPXRUeHBx8VFXVodBTFV+yUkxVnnph+3jR+1AV5s6pJ4CPHHd84xAbRlX1odmDrhuBeOlDPUsQqvFwA6KN5Flz6rPW2hf1NYs/M/R68jkEEMgLEMB5Iz6REOh7BpwLrRToSQRwJrTbN5lTFWLXOR0jgC+pkPt1qNRzgywVwP6Y3rDWPu7DfVAF3BXEPC/OXQW2I7BeAQJ4vZ5b01ruJaxUMPXhjAngO3fu/Hy5XL7pnHs3fru3b8rYT0G/n9rHWntJqvazZ8/eY629bYy5Fn8udU5jAtgf96vW2uclMGOPUKUbYy7LNLne3hPAL9d1/djdu3d/600+1M+Jc6Gq3c+dO/cZ51zT3q1bt361NYOZE0WgkAABXAh+U7vte4koMR2a/fmLqiw/GUzCtKcOj/jFpK4XifoCOHHszUtG0q+EV3hpy1p7yzn3S//cWFes8jy2OaeOKe2mvcVi8VSY9o2POzbS0+BjA1j6UFPnUk1frev6PgngRD9y7M1LZ4np9ubPfXvx+bbbNnXMctwInFYBAvi0XhmOa3YCHdXyNWPMkap8difPCSGAwBEBAphBgcAJCiSqz+xPtU7w8OgKAQROUIAAPkFsukIAAQQQQCAIEMCMBQQQQAABBAoIEMAF0OkSAQQQQAABApgxgAACCCCAQAEBArgAOl0igAACCCBAADMGEEAAAQQQKCBAABdAp0sEEEAAAQQIYMYAAggggAACBQQI4ALodIkAAggggAABzBg4FQL+3y4+P3YZw1Nx8BwEAgggcAwBAvgYaNu8S8eC983i86usoHPaA1gvbjBkjdzw+fiz0SIOh5Yj9LbvyPga0sc2j0POHYE5CBDAc7iKJ3gOfrWhK8aYK/v7+z9YLpd/bYz5nbquv7ZKAJ/gKYzuauzSimHZQ9/RA2F5QL8ykiyj+JKsPtS3FGDX0omjD54dEEDg1AoQwKf20pzOA/OV3aecc7+pquqfnHN/Zoz5Y1nj1ldu7xljmqUF46X7whq7YQlC59yXnXM/lvV3rbX3G2Oa5fx2d3fvhNDzCn8oS+05574lbTrn3qiq6npd109K6IclCJ1zV+TPU/vEa/vGutHyfe1xyOdSaxUPuTp+OcU2gOMg10sV1nX9oLQpxxmCWv4/U/JDpPkMApspQABv5nUrdtQ+VD4wxvx+VVUyfv7ROfeEtfb7epH51Pq9ITSttVJB39CLzschp9bGleX6HrDWftoYc90593AI2lQAS5hL0Mf76EXqE+F73lp7KYRdCE7pR90chN0OTRv3XYiOAH7LWvusWOnp/LqunxGTcENSVdVrzrn/uLe397TckBS74HSMAAKTCRDAk9HOs2EVwCaElkxDSwAfHBx8FAVW8hln6vlmKoDjwPaiO30B3LVPJoAvheozVfGuqwKWtvVzXmPM6/JnMnsgAWyt/aEx5mkJaD+D8BwBPM+/R5wVAs3ffRgQGCMQAlhXr/555Q1jzI4PsotxBazC5yvGmH+9cePGc7rfbQlgfc56Stpa+7i19hvW2odVddxW5WOuEZ9FAIHNECCAN+M6nZqjDGHbE8Dvy3PM+G1pH7Av13X9mJ+Cbj4XTuwYAdxM5Yaq2xhzW1fGYZo7VM0jp6BlCluex15MVcRDL0Y8BR3vp1+00ucvn1sul/Ky1ru5Z9dDj4XPIYDA6RMggE/fNTnVR9QXwFVVfeScCy9htdOrKiQvS3Dr57tVVf1PtU9z7jJFnQtT9dKUTHNfrev6vtw+fbByXtZaeQ4r/1npJazoha5wTl+Qc49+zvRVHbBjf+p0qgcKB4cAAlkBAjhLxAcQQAABBBBYvwABvH5TWkQAAQQQQCArQABnifgAAggggAAC6xcggNdvSosIIIAAAghkBQjgLBEfQAABBBBAYP0CBPD6TWkRAQQQQACBrAABnCXiAwgggAACCKxfgABevyktIoAAAgggkBUggLNEfAABBBBAAIH1CxDA6zelRQQQQAABBLICBHCWiA8ggAACCCCwfgECeP2mtIhAr4D6t7Cbfxt7CNdx9hnSLp9BAIFyAgRwOfuN7Dle5civ+HPROffQrVu3ftV1UqlFHKYA0OvtOuf6FjtoFkeY4hhybXaFad+6wycVwHpRCufcz8J1Df1ba+83xhxa57nvfPV+oT35fLRutPxRts14kYvUutLxsfhlMd80xjwi25xz7XWPtmX7l/2P6xC5Dhp7fjy855z7tf771XcMYR9jzCfjRUV6/m5esta+6LcPcsiNcbYPEyCAhznxKS8gAWeMuSL/3d/f/8FyufxrY8zv1HX9tdMQwOFC+RsDWVKwXfJQbZPlBm+UCuCuwdQXwCcxAP3NS7sGcTDc39//rl8e8SUxG3qcfr3jZtlIWeO46xz0usiZMXRJ2hizRKNe8jHuJ7EcZLNc5oBj+ECtbHV+b2/vS7u7u3f6As4Y84AsbznUJFwL59zfWGv/NhxXuGlwzh25FmfPnr2nqqrWO7ccphyvXiZUztv//d7pW77zJMbitvRBAG/LlV7Tefoq5FPOud9UVfVPzrk/M8b8sbX2eV9hhOUIpdpoK1BdAfs24sXn3/GHeGgpwL6KOiwfqCu1dQewHGtVVR9zzl2WqkJXXVG13VaLiarrlfCFFi052FRC8efDOQS/1D7ymfgLNhUwyujQbEDK1X8Zv2GtffzOnTs/D6HrnPtxVVXX67p+Ur6k/fG+Kte8L1j7boJ0/+v+XGg7dZxhHMbnFK6lrpBzf2WG3IjEx6Cq2m8OuZGIAzK+xrr9uq7vNca04enP9aG+2al4ZkXflOTOn+2rCxDAqxtuVQv+y/IDY8zvV1Ul4+cfnXNPWGu/r7+M4y+e8MXnsa6GL4X4S2zIXXsMnrprX1cFrKfY7969+9sQSn7t47Zi0tXjYrH4U/1FmBogqSn53Bd6vE8icGV2ovkC1ucfV019A1ZNb37SWvuwXNO4aosfQ/TdJFVV9YvUzUvYZ2j1G244xkyVdlSLMqX7ZX/9ntvb23t6sVg8Za0Vu9845743dGZkyFjV52et/T1jzFVr7S3n3C9XCGBd5Z631r4m16qu6wfFKcxYGGN+Yoz5XLhx6rpO+gZwzA3IVn3xTXSyBPBEsHNtVgWwPMdrpitlGloC+ODg4KPo2V77PCk8A4ur1Y7F69uKsefLXT+3kmr70D5rDuB22jO0a4yRm5C22tBftGfOnLnXOSczAT/qmp5cRwD7UGqn00Ob/tFA+9xTVdS9zx51NeQDqgkrP90qX/RhluJ1abOvAlZf6kYM5PN6GjuaqWimZ8f8nfE3Xe2NXNe+8TPRELIhgK21P63r+j5/0zL40cTQ/sO4MMb8gzHmz8VCAl+O9zgB7K958lpIAPvZmj+RKeq4yk8ZqZuUd51zMvtx2xhzbcixjblefDYtQAAzMkYJhADWVYK7OZvVAAAMX0lEQVSqbnf8F8vFVAXsq6G/kCnOUC0PnX7UB6mnSqWdE6iAdQDLl/T7fQEcniGqL/8jQbyuAPZ9PFHX9XestfJc/us+7LJTxImZBHnG2oZh/Ex4bNUan2N8rYc+D00N0DGVc9hfj8lws2iMuS3hO2aWYOgMgPQb34jIs+Ix07zxFHRsEVfYcpMUqtjcvirM2+f+Q/YZ9YXBh3sFCGAGyCiBVHBEAfy+3D0n3pZuqgtfeTTPGSU8j/MXXu+jpoU/1FXUFBWwDgxBc87pKWipyI+8kNMVFD0B3PkiUGof+YJfLBYvWGt/6Jz7eKhchkyPJgJYKqv4Jawj1enQANHHkAq44xyjrpxT3n2DOe4vei9BpqCHVtTt+B3yl0ePxfjmMbd/7u/HgJfM5Oaxc3Yh9RKWHgO542P7agIE8Gp+W7d3XwCHaUv/E4hD05Txl52+U0/8vCT7Mw31sw6Z5r6qphEPTU37oGza0z8F8Rcu+5OLvmOLXo5qX8JK7HPoZbTwYlTqGKKfqzT75Y47fqktqrzCz2/a48uE1DV1fO0LcdG5Zl/oUkHZtqdfyjtO9Rs5DH1Zr52ujR9TjP1JUc/Lcr3jte+nUF3XIpo6bz4Wjr/vWugXA4/zM6TUC41b9yV3gidMAJ8gNl1tnsBxpsg37yw5YgQQKCFAAJdQp8+NESCAN+ZScaAIbJwAAbxxl4wDRgABBBCYgwABPIeryDkggAACCGycAAG8cZeMA0YAAQQQmIMAATyHq8g5IIAAAghsnAABvHGXjANGAAEEEJiDAAE8h6vIOSCAAAIIbJwAAbxxl4wDRgABBBCYgwABPIeryDkggAACCGycAAG8cZeMA0YAAQQQmIMAATyHq8g5IIAAAghsnAABvHGXrPwB63/wfeg/3h79A/LZxRbKn+VqR6D/of9gJC1G6yXLH2UXhOhbNCBe77Zr/eGhZxNdp+y6zNLucY9BrRF9aHGHrmOIFjXIusmxRXZH1o0e6sLnEJhCgACeQnXGbSbW4k0uw9dFkFpNaW5cQ1f6GbKmbbyMn/d/bm9v7+mzZ8/eU1XVW9baZ/26yIfW8x3r6sOqWVNY2vaLs1/Waz/HbcbnOmSJQX1OxhhZQ7pZwlLajtcgjlbRatZils/mlukLx6mX64vXqB7rw+cRWLcAAbxu0Zm3Fy9OoL/U6rp+sKqqjznnLsuShPHyb/4LtlkXOHypx2vLdi0CH7PqqktXmDqQfH8SSrIm6ktdy8JJW3VdP2Ot/bQx5hFd1Sf2yVaFQxdwGPK5OKQj73slwMJ6r97yIefcQ7du3frVqkNxyM2SXK9VjiFx/dvrFQJZ2nfOXamq6npd10/KuYVZmLD4fM8NX3uDeO7cuc/oNZxX9WF/BFYVIIBXFdyy/fvWAzbGfNYYc1ECQFjiMEwFsK7odnd37wz50o9DSYf2YrF4KgrcV621z/sKsQ1/XbnJ551z7znnvhzWDQ6VVhwwQy63nENVVb/ouxEZUv1KX4kKU9a3fc1a+7Dc8Mhn9vf3v7tcLt80xvzEGPO5EFJDjrXrM0Or+HATcdxj6AjgB+SmQlXKH1ZV9Ypzrqn85RpbayX4f+Oc+15fha6q6neGro+7ihv7IjBGgAAeo8VnZYrwUAWrQ9UHsK422/ALdPH+uqI7ODj4yFrbTIFKGPdUNe0i6+ozzQLtvsppvqj1/w5Tqtba+9U+zXNEH8DNPv4moK3CVKX9oyHPV1XFbOTz0raEo3PuJR0UQ6ZqlZk+39flz+WmQs04/Im075z7sa4Su/zixd7jKlIF37tharjnWlzysx6jjiEaD+0UdOI579W6ru8LAWyt/an8fwnoITdr/gbqqtwUWmsfDzeI65gh4OsAgVUFCOBVBbds/9wUdFf12RXAoULx4f2B3//dPtZcVbqzs/O8tfb7vkL8QIKvr+JMVOGHpkHlWMYEcRwMsdnQ6jJloM/DWvt71tp3QoAOfS7aZ6urzjC1nbsWqxxDXAHHfQU759wb/pn07ag6PnRjo/ePn5+rm8U28Lfsry+ne8oECOBTdkFO++HEX/L+jdXzUu31Tf/2BXB4+cda+3/quv5OrjrJBZgEdFVVfyCVkjw7DO11fdkPCWA5/qHTxrq67QiBY78spc8hMRUvsxMyA3HxuOPItz+4jVWPoS+AE2OtnX3RlW3feEm8hHVkNuK4VuyHwKoCBPCqglu4v/6ZiH6upiu9+I3T8JMTxXXoZyRjv/j1T6GkTedc+1OW8NMTY0xTLYU+45+khGPXU9XxFHR0rof6yVSG16y1z6SOLfVsPNOWvEj0YtxWmD2QCtTv30zD903fD6lmo89k24yuRfbzoRINPr6/Zjz4xxC35VFB/BO3vp9jdZ1X4mdIh37ytIV/fTnlUyRAAJ+ii7Gth8LPQ7b1ynPeCGy3AAG83de/6NnrF25yPycpeqB0jgACCEwgQABPgEqTCCCAAAII5AQI4JwQ2xFAAAEEEJhAgACeAJUmEUAAAQQQyAkQwDkhtiOAAAIIIDCBAAE8ASpNIoAAAgggkBMggHNCbEcAAQQQQGACAQJ4AlSaRAABBBBAICdAAOeE2I4AAggggMAEAgTwBKg0iQACCCCAQE6AAM4JsR0BBBBAAIEJBAjgCVBpEgEEEEAAgZwAAZwTYjsCCCCAAAITCBDAE6DSJAIIIIAAAjkBAjgnxHYEEEAAAQQmECCAJ0ClSQQQQAABBHICBHBOiO0IIIAAAghMIEAAT4BKkwgggAACCOQECOCcENsRQAABBBCYQIAAngCVJhFAAAEEEMgJEMA5IbYjgAACCCAwgQABPAEqTSKAAAIIIJATIIBzQmxHAAEEEEBgAgECeAJUmkQAAQQQQCAnQADnhNiOAAIIIIDABAIE8ASoNIkAAggggEBOgADOCbEdAQQQQACBCQQI4AlQaRIBBBBAAIGcAAGcE2I7AggggAACEwgQwBOg0iQCCCCAAAI5AQI4J8R2BBBAAAEEJhAggCdApUkEEEAAAQRyAgRwTojtCCCAAAIITCBAAE+ASpMIIIAAAgjkBAjgnBDbEUAAAQQQmECAAJ4AlSYRQAABBBDICRDAOSG2I4AAAgggMIEAATwBKk0igAACCCCQEyCAc0JsRwABBBBAYAIBAngCVJpEAAEEEEAgJ0AA54TYjgACCCCAwAQCBPAEqDSJAAIIIIBAToAAzgmxHQEEEEAAgQkECOAJUGkSAQQQQACBnAABnBNiOwIIIIAAAhMIEMAToNIkAggggAACOQECOCfEdgQQQAABBCYQIIAnQKVJBBBAAAEEcgIEcE6I7QgggAACCEwgQABPgEqTCCCAAAII5AQI4JwQ2xFAAAEEEJhAgACeAJUmEUAAAQQQyAkQwDkhtiOAAAIIIDCBAAE8ASpNIoAAAgggkBMggHNCbEcAAQQQQGACAQJ4AlSaRAABBBBAICdAAOeE2I4AAggggMAEAgTwBKg0iQACCCCAQE6AAM4JsR0BBBBAAIEJBAjgCVBpEgEEEEAAgZwAAZwTYjsCCCCAAAITCBDAE6DSJAIIIIAAAjkBAjgnxHYEEEAAAQQmECCAJ0ClSQQQQAABBHICBHBOiO0IIIAAAghMIEAAT4BKkwgggAACCOQECOCcENsRQAABBBCYQIAAngCVJhFAAAEEEMgJEMA5IbYjgAACCCAwgQABPAEqTSKAAAIIIJATIIBzQmxHAAEEEEBgAgECeAJUmkQAAQQQQCAnQADnhNiOAAIIIIDABAIE8ASoNIkAAggggEBOgADOCbEdAQQQQACBCQQI4AlQaRIBBBBAAIGcAAGcE2I7AggggAACEwgQwBOg0iQCCCCAAAI5AQI4J8R2BBBAAAEEJhAggCdApUkEEEAAAQRyAgRwTojtCCCAAAIITCBAAE+ASpMIIIAAAgjkBAjgnBDbEUAAAQQQmECAAJ4AlSYRQAABBBDICRDAOSG2I4AAAgggMIEAATwBKk0igAACCCCQEyCAc0JsRwABBBBAYAIBAngCVJpEAAEEEEAgJ0AA54TYjgACCCCAwAQCBPAEqDSJAAIIIIBAToAAzgmxHQEEEEAAgQkECOAJUGkSAQQQQACBnAABnBNiOwIIIIAAAhMIEMAToNIkAggggAACOQECOCfEdgQQQAABBCYQIIAnQKVJBBBAAAEEcgIEcE6I7QgggAACCEwgQABPgEqTCCCAAAII5AQI4JwQ2xFAAAEEEJhA4P8B54wUWPDKi9IAAAAASUVORK5CYII=

add code here to report the sum of all the numbers in the list.

add code to report the average of all the numbers in the list.

add code to report true if the list contains at least one negative number, and false if all numbers are non-negative.

false

add code to report true if each value in the list is greater than or equal to the one before it..

true

add code to report the largest number in the list..

add code to report a new LIST that is the same as the argument, except all negative numbers have been replaced by their absolute value. .

add code to report a new LIST that contains only the even numbers from the argument list..

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere resulti2

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere resulti1

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra 1 1 11 1

pt:a primeira palavra de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _

pt:a última palavra de _ 1 1

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ 121

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

67,90,-2,0,190,84478919089,67,5,90,2,0,190,890,2,0,190,8