Re: Sudoku for the holidays (with correct link)
Michel Gosse <[email protected]> Thu, 23 Jul 2026 11:52:21 +0200
| Newsgroups | gmane.comp.mathematics.maxima.general |
|---|---|
| Message-ID | <CACAug0cKcM=XyYO0OhW7MvhRtdmzZpMfkpjdEVHaz7-PW5kuew@mail.gmail.com> |
--===============4960039812303390768== Content-Type: multipart/related; boundary="0000000000005f210306574437fa" --0000000000005f210306574437fa Content-Type: multipart/alternative; boundary="0000000000005f210106574437f9" --0000000000005f210106574437f9 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Excellent question, which is now fundamental. What does Maxima contribute compared with other programming languages, or even compared with artificial intelligence? What benefits can we draw from using Maxima, which remains the reference open-source software for symbolic computation? An open question. For the Sudoku program, the *draw* package shows its power by making it possible to generate a standard grid using line segments of varying thickness and to place text objects in the graphic. Since the algorithms for Sudoku are already well known, it is difficult to improve them with Maxima-specific packages designed for particular uses. In a previous discussion, Maxima=E2=80=99s superiority in statistical proce= ssing was highlighted by showing that, unlike conventional statistical software, Maxima can mix numerical values and variables, thereby opening up a broad range of possibilities on this topic. It is this kind of idea that seems to me the most fruitful way to answer this interesting question. Kind regards Michel Le mer. 22 juil. 2026 =C3=A0 19:49, Henry Baker <[email protected]> a = =C3=A9crit : > Naive question: > > > Is there anything clever that Maxima can provide for Sudoku that most > other languages cannot ? > > > E.g., can matrix multiplication be useful ? > > Can tensor manipulations be useful ? > > > Can graphical notations be useful ? > > > Just curious... > > > > -----Original Message----- > From: Michel Gosse <[email protected]> > Sent: Jul 22, 2026 9:42 AM > To: <[email protected]> > Subject: [Maxima-discuss] Sudoku for the holidays (with correct link) > > > The download link was wrong, i resend the mail with the correct link... > > Hello everyone, > > For the holidays, a bit of fun with a wxMaxima program that generates > Sudoku puzzles. Written with the help of Claude, it lets you have some fu= n > with our favorite software. Of course, programming Sudoku algorithms with > Maxima isn't optimal compared to other programming languages, and this > shows in the execution time (on my machine, it takes 3 to 5 minutes to > generate a Sudoku, depending on the complexity of the randomly generated > grid). > > *To download the wxMaxima file (in zip format) :* > > https://maxima-french-doc.fr/wp-content/uploads/2026/07/sudoku-eng.zip > > and your first Maxima Sudoku =F0=9F=98=89 > > [image: sudoku-grille.png] > > > [image: sudoku-sol.png] > Have fun > Michel > --0000000000005f210106574437f9 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable <div dir=3D"ltr"><p class=3D"gmail-my-2 gmail-[&+p]:mt-4 gmail-[&_s= trong:has(+br)]:inline-block gmail-[&_strong:has(+br)]:align-top">Excel= lent question, which is now fundamental. What does Maxima contribute compar= ed with other programming languages, or even compared with artificial intel= ligence? What benefits can we draw from using Maxima, which remains the ref= erence open-source software for symbolic computation? An open question.</p>= <p class=3D"gmail-my-2 gmail-[&+p]:mt-4 gmail-[&_strong:has(+br)]:i= nline-block gmail-[&_strong:has(+br)]:align-top">For the Sudoku program= , the <strong>draw</strong> package shows its power by making it possible t= o generate a standard grid using line segments of varying thickness and to = place text objects in the graphic. Since the algorithms for Sudoku are alre= ady well known, it is difficult to improve them with Maxima-specific packag= es designed for particular uses.</p><p class=3D"gmail-my-2 gmail-[&+p]:= mt-4 gmail-[&_strong:has(+br)]:inline-block gmail-[&_strong:has(+br= )]:align-top">In a previous discussion, Maxima=E2=80=99s superiority in sta= tistical processing was highlighted by showing that, unlike conventional st= atistical software, Maxima can mix numerical values and variables, thereby = opening up a broad range of possibilities on this topic. It is this kind of= idea that seems to me the most fruitful way to answer this interesting que= stion.</p><p class=3D"gmail-my-2 gmail-[&+p]:mt-4 gmail-[&_strong:h= as(+br)]:inline-block gmail-[&_strong:has(+br)]:align-top"><br></p><p c= lass=3D"gmail-my-2 gmail-[&+p]:mt-4 gmail-[&_strong:has(+br)]:inlin= e-block gmail-[&_strong:has(+br)]:align-top">Kind regards</p><p class= =3D"gmail-my-2 gmail-[&+p]:mt-4 gmail-[&_strong:has(+br)]:inline-bl= ock gmail-[&_strong:has(+br)]:align-top"><br></p><p class=3D"gmail-my-2= gmail-[&+p]:mt-4 gmail-[&_strong:has(+br)]:inline-block gmail-[&am= p;_strong:has(+br)]:align-top">Michel</p></div><br><div class=3D"gmail_quot= e gmail_quote_container"><div dir=3D"ltr" class=3D"gmail_attr">Le=C2=A0mer.= 22 juil. 2026 =C3=A0=C2=A019:49, Henry Baker <<a href=3D"mailto:hbaker1= @pipeline.com">[email protected]</a>> a =C3=A9crit=C2=A0:<br></div><b= lockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;border-le= ft:1px solid rgb(204,204,204);padding-left:1ex"><div style=3D"color:rgb(0,0= ,0);font-family:"Courier New",Courier,monospace;font-size:10pt"><= p style=3D"line-height:1">Naive question:</p><p style=3D"line-height:1"><br= ></p><p style=3D"line-height:1">Is there anything clever that Maxima can pr= ovide for Sudoku that most other languages=C2=A0cannot ?</p><p style=3D"lin= e-height:1"><br></p><p style=3D"line-height:1">E.g., can matrix multiplicat= ion be useful ?</p><p style=3D"line-height:1">Can tensor manipulations be u= seful ?</p><p style=3D"line-height:1"><br></p><p style=3D"line-height:1">Ca= n graphical notations be useful ?</p><p style=3D"line-height:1"><br></p><p = style=3D"line-height:1">Just curious...</p><p style=3D"line-height:1"><br><= /p><p style=3D"line-height:1"></p><br></div><div style=3D"border-left:1px s= olid rgb(170,170,170);box-sizing:border-box;padding:10px 0px 10px 15px;marg= in:0px"><p style=3D"line-height:1">-----Original Message-----<br>From: Mich= el Gosse <<a href=3D"mailto:[email protected]" target=3D"_blank">mgosse3= @gmail.com</a>><br>Sent: Jul 22, 2026 9:42 AM<br>To: <u></u> <<a href= =3D"mailto:[email protected]" target=3D"_blank">maxima-d= [email protected]</a>><br>Subject: [Maxima-discuss] Sudoku fo= r the holidays (with correct link)<u></u></p><p style=3D"margin:0px;padding= :0px;line-height:1.5"><br></p><div dir=3D"ltr"><div class=3D"gmail_quote"><= div dir=3D"ltr"><p style=3D"line-height:1">The download link was wrong, i r= esend the mail with the correct link...</p><p style=3D"line-height:1">Hello= everyone,</p><p style=3D"line-height:1">For the holidays, a bit of fun wit= h a wxMaxima program that generates Sudoku puzzles. Written with the help o= f Claude, it lets you have some fun with our favorite software. Of course, = programming Sudoku algorithms with Maxima isn't optimal compared to oth= er programming languages, and this shows in the execution time (on my machi= ne, it takes 3 to 5 minutes to generate a Sudoku, depending on the complexi= ty of the randomly generated grid).</p><p style=3D"line-height:1"><strong>T= o download=C2=A0the wxMaxima file (in zip format) :</strong></p><p style=3D= "line-height:1"><a href=3D"https://maxima-french-doc.fr/wp-content/uploads/= 2026/07/sudoku-eng.zip" target=3D"_blank">https://maxima-french-doc.fr/wp-c= ontent/uploads/2026/07/sudoku-eng.zip</a></p><p style=3D"line-height:1"><sp= an style=3D"background-color:transparent">and your first Maxima Sudoku=C2= =A0</span>=F0=9F=98=89<br></p><p style=3D"line-height:1"><img src=3D"cid:ii= _19f8e5d7218cb971f161" alt=3D"sudoku-grille.png" width=3D"450" height=3D"35= 0" style=3D"background-color: transparent;"></p><p style=3D"line-height:1">= <br></p><img src=3D"cid:ii_19f8e5d7218cb971f162" alt=3D"sudoku-sol.png" wid= th=3D"450" height=3D"350"><div>Have fun</div><div>Michel<br></div></div> </div></div> </div></blockquote></div> --0000000000005f210106574437f9-- --0000000000005f210306574437fa Content-Type: image/png; name="image.png" Content-Disposition: inline; filename="image.png" Content-Transfer-Encoding: base64 Content-ID: <ii_19f8e5d7218cb971f161> X-Attachment-Id: ii_19f8e5d7218cb971f161 iVBORw0KGgoAAAANSUhEUgAAAcIAAAFeCAIAAACZ3ogqAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE QVR4nO3dd1wUR/8H8Nm7o3eQJggixYKAhYgI9ogae43dFDWaEI0agzVnjSKWGFtiYm8xNqzYSwSx oKJiQQUVEQ6E0Ovd7fz+yBNEfzxKmL3n7rjP+y9dXn53nNv97MzscstRSgkAANSUSN0NAADQbohR AAAmiFEAACaIUQAAJohRAAAmiFEAACaIUQAAJohRAAAmiFEAACaIUQAAJohRAAAmiFEAACaIUQAA JohRAAAmiFEAACaIUQAAJohRAAAmiFEAACYSdTeg5qysrHJzc9XdCgD4r3TkHUWc9v4/OU6LGw9Q 6+nOGYpJPQAAE8QoAAATxCgAABPEKAAAE8QoAAATxCgAABPEKAAAE8QoAAATxCgAABPEKAAAE8Qo AAATLf5qkn/l8uXLly5dUncrALRbUFBQcHCwuluhcXQlRk+dOjVv3jx1twJAu0mlUsTo/6cTk/ro 6GiJRGJhYaHuhgBot5iYmPDw8OjoaHU3RMNQrVX9xkulUnV3M0DtIZVKhT1DtZ2uTOorqGhxJzw8 PCwsTPCyKi0eHR0dExPz95/RLRXQLVWq3C3wNnXneM1Vv/GVR6PVvJCqrjGaUxzdUiV0S5Vq0C1a HS//ik6sjQIAqA5iFACACWIUAIAJYhQAgAliFACAic498ASgGvKUS/v/LGneP6ShsWA1+dzEc0ei Yh9mlJs4eLXu1b9jAxNOsOIgGIxGAQRQ9nDHokUbtp54WCLYi9nLkg/MHBP6w9Yz918VvEo4vWXh F2N/OJ+pE+991zYYjQIwK76zeenux2VULFxJ+cPtC9fHSdpN3zo9xEmP8DlXV02afXTt1g9bTwsw Em43IASMRgHY0IK4DREHC51czASccBdf2XfkuWHrz0K7OOkRQojIqtWoLz7u4GOQl8ULtxcQBkaj ACxo7uV1y46T7vM/F62UvhSqquL+9VsFek2DAywropmzCRozJ0ioHYCQEKMANUezz69aec6w79Jx /mSjgGVzUl/kc3VcnJSPz2w9EvMwi7eq5+XXrlvnpjY4YzUQPhSAmuJlJ1asjrUYuPwzP2NyW8DC tCC/gBB5wobQgw/yrOq71aFPjkaf2H/g3FcRC/o30BdwTyAExChAzShTDy1bf9NuyI+jvA0JUQhZ msrLFZTPfJDW6ovVvwxubMYRRdbVtWFzDm1YdThg5UAn3NLQLPg8AGpCnrx36W8PXEZ8N7yhgeDF OT19CcdJvId8M7jx3zeuJHVafT66nXn5/T9j8cyTxsFoFKAG+LTL5x+USpyurvkujhBCCC16mUuV JfukUy4YeQ9fMMbfkKE6Z2FhwRFLdw/b1zf/OeMGHnXF57Mys5TEAeetRsHHAVADnKFj4xbNzZQV G6iII4QQkVgsFotZn3ziLFxdrbiHeXnllU5RWphfSEWmZqaYQmoaxChADXD2nb8J71xpg+L26uFT Djv1/z6ijw3786OSxm1aWR86f+x0ets+jmJCCKGFccfOvaS2A5o5I0Y1DWIUQAMZtBgyvPnFn9ZM nvVqdJ+WdvLkC7u3Hsu0CJo+sCnOWY2DjwRAE4mc+syal780YveuiKs7KeEkll7dv50W2sUO302i eRCjAEIQe/Sfu7K9gZOFYDHHWTUftXjnoKyUlMxiiZWzq6MZzlYNhQ8GQAiciVNjXyfhyxrVcW1Y R/CyICisVgMAMEGMAgAwQYwCADBBjAIAMEGMAgAwQYwCALChWqv6jZdKperuZoDaQyqVCnuGajud G41W8wj4t4gqjxgVFa98dUG3VEC3VAljkXfQuRgFABAWYhQAgAliFACACWIUAIAJYhQAgAliFACA CWIUAIAJvm/0v+Mz4yJPpbv37OlniW8c10K04PIv8/cmVvUCeXH9PjMmdhDgnUnlmbdOHTl740lm ETGxdW/WsUc3f0fh37cMGg4x+t8oXkSGz1tz2350mx6IUe2kLPlLJpO9HaNleZm5vLijAC97L0/a O33q+vhCU6eGHrbkSfSei1GRp0aHR4z2NmYvDloEMVo1efLepRtvFVKRvbpbAjXFWXaeub3zm9to 7qXF4xY96fxJSB3WSyPNPv3rlnh500/WLB7ZyIQjtODepu+m7ty9ISrkxwGOuPDqEqyNVqXs4Y7w bSm2rjbonlqFFlz7bd1F4z6TRzc1ZC5Wcjfubolpm8GDGplwhBDCmTUZ1LuZXnliQmI5c3HQKsiJ /6/4zuale7Jbh4Z2tEb31CZl93etP027TBgpQIgSqjDz7NitV5D766VQTmKgzxGxWMxeHbQKJvVv oQVxGyIOFLWfM7FjnaN71d0aEA6fErk+Mrt56KhWpkLMuDnzlsO+bVlpgyLnzp7IG3KLgGAffQHq gxZBjL6B5sauW35cGTL/q2Ar0XN1twaEQ/Ojt/2RaNfzpxDBX/SuTN47e2Hk44zMv8qtW4//YXJ7 3JHUNZi1VkKzL/y04qykx5TxAeY4E2oVPuXo7kslzQb0a6QneG2Rcd3GzZo3b+ZlJ351M/KPC8/K BN8FaDaMRivwshMrfoox7Rsx1l+QWR9ojrL4g4ceGwbO6WSrgk+WcwgaNTGIEFKeemTu1ytXL/Xy WzvUBQMUHYIY/QfNizt3PU/PNmHzrMl/bynNeKnk6YmIKfEmbgI9rQ3qQAuvHj+fbdm2a4CA10da lv9XocLQwtqk4hzSdw7p2erXK+fvPCga4mKGo0V34JpZQWLr2aJFI0dj8X+IxBwhhONEYrFYhHNC e9HC6+evFpq3ausnwA36CuXXfho1aETEpeLKG8USiZgQhVwp4I5A82E0+g/OLGDckoBKG/hn28eP 3cp3nRoxugGuNlqs7E7sjSKjlq18BP0tTf0GXm7ic3dj4vI6tLP4z1W2+G5sfL7I0csDQ1HdghiF Wk7x6OadIrGXn7eRoGW5uh8ObPfHgvMrZhhnDe/c0Fz+6uHFP3YeTzcPDOvTEE+O6hbEKNRu/Iv4 O9nErp2nlcAjRM66/ZQf8iTLNketmX2EEkI4PZsmH307eYLwj1SBhkOM/lcix67TlvtR+7qY0Wsz zrr9xOXNjOt6CX+ocyZefWZs6BEqe5GWXUxM7erVszXGwaKLEKP/nYGdp6+duhsBjDgLVx9fVe5A Yubg1tBBlXsATYeLJwAAE8QoAAATxCgAABPEKAAAE8QoAAATxCgAABuqtarfeKlUqu5uBqg9pFKp sGeottO50Wg1j4B/i6jyiFFR8cpXF3RLBXRLlTAWeQedi1EAAGEhRgEAmCBGAQCYIEYBAJggRgEA mCBGAQCYIEYBAJjg+0ZBA9CiZ9FHj0UnpBbq27r7d+0f4m0pzHs4lI/3LVgfk0crb+OMW42ZN7SJ 5h/6tPh5zNGjl+68yKWmTk2Ce/Ru7y7ES56o7NSKZVGpVb12T+I9fMEYfyHf/KcbNP9YgtpOmX5m ybSIs+niOm4N6pTd333+2NGLE39c2MeF/eCkxU9vxsbfN6hjbfI6ljnTvDLmyirHZ1wI/3bx6TSx Tf0GDlzyiSvnjkaFzFoZ1t6WNUmpoihLJpO9FaN8ac6rPIl1P1r1P4J3QYyCevGpB5YsP1fU7MsN 0gHuxhwtTNg0bdrO3zaca78gxJI1MfjMlxlKPf/QTYtCTAVp7f8KzTr54/LTMseeC5ZObGMnIcqs qz/PnHtg1YagFjPbMg5JRc79Fu/o99b+ZEdnjFtfPGxkG2Ff/KcjsDYKaqVIiDyYwDcZNrG/uzFH COFMmw4dN6KTv2lRhgAve1fK0jOJjZOjtk1TacbFE3FFlp3Gjgu0kxBCiLhOwNjQHvb50cei84Qf L9Ks02s33q43fPLA+hhX1QR6DdRJ+TTuRqbIvXeg4+sLunHz4bObC1KdZqfLSjkHJ0eRvCBTll1m aOtoa6INh7zy5fNUpbh+k0Ymrwee+g19GuoduHknUdE9QE/IndGC2I0br1n1/3GAmzb0jSZCv4E6 laemyHijdi5mLy7tPnQxIa3M1MnDp03XLi0dDASorsxIy+BFJtHLPtlwLbWYUk5i5tZ2xKRJg3wt NPwdyGKJhCOlJSWUkH9aSstLy3hSLEvPpYR5fbSS8ge7NpxVdvh+SCMhulw3YVIPakSL8wuUhHv2 +zfj5277MymnUHb75I4V08ZN3XKvmH3uSktk6blU/vxJ8QdjpYuXzJ06qq2N7MLP02ftTlII0HgV Ers19jRUPrlw/llFQ3nZ+TN35JSUFJUIOaunGSc2H5Z5DRrRRoiHAHQVRqOgTuVyOaX5j5PrDYvY 8WkLGzGh+QlbZoZt37lsV+CvYxoyHp4KO/8BHzf16j6wg4sBIYQEtOvoZzl+WuSeXZd7z25nqrm5 wVm0/bjXzmt/bJ8tVYzu1cyOT4s7vH3/7XIxRygRMkXL7+3fG68fPOcjZwyoGKDzQJ309fQ4Inbp G/pJCxsxIYRw5k2Hj+3mwL+IvpTEeo+Js2oxYNwXw/+ToYQQwpn49eziJi5MiH8iwA0sVTL0GbN4 zsDG8rjt4TOmTJm94khe0PRvu1tzxNDQULD4p/kx+0+k23bq1RpDUSYYjYIacSaW5hJO4uZR+eaG npuHi5hPyMzmCRHmIfzKe7S1q8PR9EIB1gxUTOLY9stVweMKZM9f5kts67vY8BfmhlMDH3vmx8D+ QbMuHo8tdhrQxVtfoIq6CqNRUCd9FzcnsTIvN79yqhUWFFLOxMyEMS5o5tU/ftsYGZ9buTbNy82j Iisba80+8mlpTnp6ek4pkZg5ujdq6GpjwCke339ULnFv4iFU5tFX0efvKOq2aeuBwRQjzT6YoLYT uQS2diYJJ6KSyv/ZVJ54/GSi0tyvuSfj2c2ZlDyI3LnulyNP5RXbypNOn3vM234Q0EDwca6g+Of7 Z4wcOXnrg3+6heZcijyXYejXOcheoMEozb1+5b7CukUrpCgz9CColdij36i2UQu3TpuW/8nAwHqi tCv7thx4auAzYXhrY9baJoGD+rrF7to++3v5qL4t64pyHsfs23EkyTwobIifhk9jxZ7devscXLP/ h4Wmn/dpYpx9/9y+3y+WeI0e01WoFCWlCbcelBu2bMZ6Hw8Qo6BunHWHqQtzuCWbDq76fj8lnMjU td2EbycPdBXg0DRo8umSBXo/rv5j59Ir2ykhnJ6Nd6+wqRNC7DT+jorIue/s+YXLlu/aOP8SJZzI xCX488XfDG0o2LOdike37haJXbzcte03vDQRYhTUjTNp1H/O5h6hqSnpBcS8bn1nS+F+SUds+8Ho RduG5b1MScuVG9rWc7Ez0ezZ/Gsim5ajFm8flJ2W+qrU0N7F2cpA2Ox37jlrRQcLVzzpJADEKGgE zsCqnqeViorrWTi5WzipqLhKiY1s6nnaqKKyxKaBr0oK6yJcigAAmCBGAQCYIEYBAJggRgEAmOjc LaaYmJjw8HBVVFZRWdUVj4mJqfxndMvf0C1Vqtwt8Daqtarf+NmzZ6u7mwFqD6lUKuwZqu10YlIv FmvLs4IAoH10IkYBAFRH59ZGg4KCgoODBS8bHh4eFhYmeFmVFo+Ojq5Y8EK3VEC3VKlyt8Db1L2q UHPVb7xUKq34/1ZzWUd1jdGc4uiWKqFbqlSDbtHqePlXMKkHAGCCGAUAYIIYBQBgghgFAGCCGAUA YIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCC GAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgQ7VW 9RsvlUrV3c0AtYdUKhX2DNV2OjcareYR8G8RVR4xKipe+eqCbqmAbqkSxiLvoHMxCgAgLMQoAAAT xCgAABPEKAAAE8QoAAATxCgAABPEKAAAE4m6GwC6Tvl434L1MXm08jbOuNWYeUObCHx08plxkafS 3Xv29LPkhKpZ9vJ6VNTF208zi6ipQ6Og3v06eZixF6eyUyuWRaUqq/iRxHv4gjH+hsy7qNhV3vP7 mUYennYGgpXUPYhRUC9a/PRmbPx9gzrWJuKKjZxpXpnQO1K8iAyft+a2/eg2PQSKUZoXt37q9/uS 5eb1GrqZl9w5feVC1OGT45YsGOTBmElUUZQlk8neilG+NOdVnsS6H636H9WIInHPnCm3um9cO9QZ M9MaQ4yCevGZLzOUev6hmxaFmKpwN/LkvUs33iqkInvBSpbFb1m2P9mg5YSf5g30MOaIIvPy6rC5 RzauOhy4ahBbKImc+y3e0e/NbVR2dMa49cXDRrYxYin9hpIn+389mso7CVZQR+EKBOqllKVnEhsn R+GmqVUoe7gjfFuKrauNgMe74nHs1VecW5/x/T2MOUIIkdi1+WJsR4vyhzHXsoQcMBJCCKFZp9du vF1v+OSB9QUY+tCCW7sWzwgdMXj8LzcLBG+r7kGMglrR7HRZKefg5CiSF2S+ePbiVZFC8H0U39m8 dE9269DQjtYCHu/yVxl/UWOPhi6v1yKIgZ2DtYgW5OXzwu2HEEJoQezGjdes+ocOcBNm/qhUKEVm dZsEdgxoIMBSrs7DpB7USpmRlsGLTKKXfbLhWmoxpZzEzK3tiEmTBvlaCHN604K4DREHitrPmdix ztG9gpT8m2Gbab8fUOqb6lXaV/b9+2m8wQcu9sIOT8of7NpwVtnh+yGNBLoPxFl+MDLsA0KIMmnL uOs7hSmqwxCjoE60RJaeS+VZT4r7jpV+5UhePbi4f++Fn6dnKlb/OMyd/eikubHrlh9Xhsz/KthK 9FyABr/GGZhaVk41Wvxk//LNN+QOvXoHCjrCoxknNh+WeY1a0AYDRw2FGAW1Utj5D/i4qVf3gR1c DAghJKBdRz/L8dMi9+y63Ht2O1O23KDZF35acVbS44fxAeYcEXiiXZki69a+NSu3/pmq7/PZ7C9a GgtZu/ze/r3x+sFzPsKtdI2FGAV14qxaDBjX4o0tJn49u7gd2ZoQ/0TZrhnL8cnLTqz4Kca0b8RY f8Y4fhea//DwumW/nkqW2/kPm//NyOC6gj6ASfNj9p9It+3ybWsMRTUXYhQ0DWdrV4ej6YXFbLeQ aV7cuet5erYJm2dN/ntLacZLJU9PREyJN3HrM2NiBxvmYCpLPvLD7NWXsuu0Gjk/dGiws+CPG9Cs i8dji50GdPHWF7o0CAcxCmpEM6/uPZyg7z+gT7PXT8TTvNw8KrKyYb2tLrH1bNGCvr7xT8UcIYTj RGKxWCTA0I7mRK+YsSpa6T9+1exBjVQy4KWvos/fUdTt39YDJ6omw6cDasSZlDyI3BlzQ9/3p5EN /nPLuzzp9LnHvG2vgAbid//j99U2Cxi3JKDSBv7Z9vFjt/Jdp0aMbiDAMqPyyYGNZ7Pr9l8hHdxI uAfi30Bzr1+5r7Du1gopqtnw8YA6mQQO6usWu2v77O/lo/q2rCvKeRyzb8eRJPOgsCF+mj2N5V9e j0vlzfzNXl05f/6Nn3CWnoHNnYVYIS1NuPWg3LBls4Y4TTUbPh9QK4Mmny5ZoPfj6j92Lr2ynRLC 6dl49wqbOiHETsPvqPAZaTIlzb2yeeGVt34i8Q3d0czZnr39ike37haJXbzcVfobXsAOMQpqJrb9 YPSibcPyXqak5coNbeu52Jmwzeb/G5Fj12nL/ah9XUEeHBJ5Dpy3sktVd8E4UydrYa4Bzj1nrehg 4arCJ51ETt3DVrTgHGzxMBULxChoBD0LJ3cLFX9FhoGdp6+dUMVElvV9LYUqVjWJTQNfG9XugjO0 9/IV7ttadBUuQgAATBCjAABMEKMAAEwQowAATBCjAABMEKMAAGyo1qp+46VSqbq7GaD2kEqlwp6h 2k7nRqPVPAL+LaLKI0ZFxStfXdAtFdAtVcJY5B10LkYBAISFGAUAYIIYBQBgghgFAGCCGAUAYIIY BQBgghgFAGCC7xuF6qFFz6KPHotOSC3Ut3X379o/xNtSNd+uLDBlzoOzR05eTUzLVxrauPq0/ahH G1djDf9mfUIIIeXxW+dsiy9/44uhRbadJk7v5YrBj4ZBjEI1KNPPLJkWcTZdXMetQZ2y+7vPHzt6 ceKPC/u4aPjxo0w/MW/SsuhsAzsPr7p6mdcio08dOt5n3sqJAZaanqQ069H1m/FPTG2tDF+npogr kLO9dhpUQcNPA9AEfOqBJcvPFTX7coN0gLsxRwsTNk2btvO3DefaLwjR6DgqurJlQ3Sea7/wZV/5 W4kIKU3eO+eb9UfW7//I/3NPDR9LK2VpMt680/Qd3wXoqbst8B6YHsD7KBIiDybwTYZN7O9uzBFC ONOmQ8eN6ORvWpShVHfb3knx6PqNXIlvvxEtrf4+zg0b9OkXaMq/vHc/R9PHdLRQlp4vcnBy1PC0 B0IwGoX3Uj6Nu5Epcu8d6Pj6mmvcfPjs5mpsU/VQQ9fAbj0aNzF/PWIW6RvoE04s1vhw4jPSMng9 Nyc7UpYnk+UqTO3q2hhh0KOhEKPwHuWpKTLeqJ2L2YtLuw9dTEgrM3Xy8GnTtUtLByFexa5Keo37 TW1c6e98weND+6JzjHxGttTotQhCCFFkpGXyEsMTM4ctic8oo4TTs27U5dPJX/Xw1Ir7YzoGMQrv RovzC5SEe/b7N+PvyUxc3BwkSSevnD64/8SoxUtHe2vHOU0zTy6euSM+IyOryMR3xIJZvR01vdn8 qzSZnBYmPdX7+Ov5Ptblsrtn9uyPWjHtL279Dz00vvU6BzEK71Eul1Oa/zi53rCIHZ+2sBETmp+w ZWbY9p3LdgX+OqahVhxB+raefs24Vyn3b9y9F7X7RIDXUG9Tzc4icb2ggUM7NOvTP8BeQgghgR3b ei0cN//C9n23Q75uhptOmgWrLfAe+np6HBG79A39pIWNmBBCOPOmw8d2c+BfRF9K0ux7TP/gLFsM +nrqjPmrNm+RdpLc3LRkx325utv0biLHoOFfjBv8nwwlhBDOOrhXW1uSfefOC16dLYMqIEbh3TgT S3MJJ3HzcKs07tRz83AR81mZ2Rp9RpcXZGdnF5RX2iKxDerdzoHK7t7N0OiWV0lUx76OiC8qLNa+ ptd2iFF4D30XNyexMi83v/IzQoUFhZQzMTPR5Jmx4uGWLwcP+f74q8oN58QSCUcUSs0eRvMvLm77 bVPUw5I3Nubn5vEi6zrWGv+Ygc5BjMJ7iFwCWzuThBNRSRXjuvLE4ycTleZ+zT01eWVU7NLQw1D5 KOZK5usclT+7ej2NN/fwcNDoI58zyInfu2PtpjOyipEnLYo/9We6qH4rf3tNvnbpJk0+DUAziD36 jWobtXDrtGn5nwwMrCdKu7Jvy4GnBj4Thrc2Vnfb3oWzCB7Q1ela5M8zfigc9ZFvHf6vpKsHdxx8 ot/0i0EtNfthLc6288chf8w+tn76ovyR3X1slJn3z+3edfqVY49Jfd0wGNU4iFF4L866w9SFOdyS TQdXfb+fEk5k6tpuwreTB7pq+NHDGbf4YtF0PmJ91K/zzlBCCCc2b9BhgvTrgfU1vOWEMwuYuHS2 8Y8/H9646BylhOMM7FsOmTv1swBzjEU1j6YfTqAROJNG/eds7hGampJeQMzr1ne21JJHbgxcOn+z uuO4Vy9evipUGto4uziYasshr+fcfsKydp/9lZoiy+dNHFxdbAyRoBpKW44pUD/OwKqep5W6W1ED ImNbV09bdbeiRjgD63qe1upuBbyHRi+0AwBoPsQoAAATxCgAABPEKAAAE527xRQTExMeHq6Kyioq q7riMTExlf+MbvkbuqVKlbsF3ka1VvUbP2nSJHV3M0DtIZVKhT1DtZ1OTOotLS3V3QQAqLV0IkYB AFRH59ZGg4KCgoODBS8bHh4eFhYmeFmVFo+Ojq5Y8EK3VEC3VKlyt8Db1L2qUHPVb7xUKq34/1Zz WUd1jdGc4uiWKqFbqlSDbtHqePlXMKkHAGCCGAUAYIIYBQBgghgFAGCCGAUAYKJzDzwBACGEkPLs xOvX7r7IpaZOTVoFNLEzwLdC1xRiFED3lCUfDZeuvZBaRkQcoTzRc2wbumhWLzd9dTdMO2FSD6Br SuJ/la66kO/+8fzNh06cOLRl4bBGJZd+WrgjUa7ulmkpxCiAbqF5l/ZFvdRr9umscW1dzfT0zFyC Pp/3dXuTZ0cOXC9Rd+O0E2IUQLconyU+LhV7tGntULEYylkGtPHWy4+PS1Sos2VaCzEKoGPk5QpK RKI3zn2xnj5Hc16kFlJ1tUqbIUYBdIvY2dVZrEy+GZ/zOjKL79y4X05pUSFitCYQowC6hbPv2CPA rDD216V7bmUUKxVFL6/vWrD8xCueEIUck/qawANPADqGs+kyeeaT7EX7N0wZukHEEUpNG/Ub3O7S 7zH6hnh4tCYQowA6h7MJ+HLtzl53byc8Sc2X2HsHtvF8svwUFVtbWyBGawAxCqCTRGb1/ILr+f39 F/7piaelIqcGbgbqbZSWwtoogI5R3Ns04eNhs4/KKm4nyR+dvfCUugQFuYrV2TCthRgF0DESD39v SeblzT/+ce8vubI4417UyiX7nlu2H9XPAylaI5jUA+gaA59PZnz6bN6WX0IHbhBxlKcSu1Zj509p b4WF0ZpBjALoHM606fCILe1uXY1Pyiw1dGwc0NobX/DEADEKoJM4k3otOtVroe5m1ApYGwUAYIIY BQBgghgFAGCCGAUAYIIYBQBgghgFAGBDtVb1Gy+VStXdzQC1h1QqFfYM1XY6Nxqt5hHwbxFVHjEq Kl756oJuqYBuqRLGIu+gczEKACAsxCgAABPEKAAAE8QoAAATxCgAABPEKAAAE8QoAAATfN/oG2jG tQMnHxTSyts4A49Og4OcdfaCU/Lo9P7YNGUVPxHZ+fft5m0m6Nf9lmU+fpRr7ullbyhkVQBVQoy+ oexB1G9bLpS+GaNm3bwG6m6M0pLEU9u2xMmr+JGkqfGH3bzNBNxX4Y1fpsLqHKkAAAwBSURBVE4/ YjDilw2jG+hqh4P2QYxWxmemyRRi33HrZnQyrxhjcSIjCx3uJs7qw1k7WpW9cWUh8qTdM6UX633U zkHAoSjNjV237PBLBWkgXE2A/wEdzocq8BlpMmrR1svNwR4d8w/OyNLO6I0tiud7Fp7O/yB0Ujd7 4VKUZl9cvfJMkbERVyxYTYD/CcycKqF56bIizsHZEa+ZfQeacWLdzqcNR33Z1U7AEM04uXJVjNXg Cb2d0PmgbTDoqkSZkSbjxbaZ51dMu3Aj+VWZoUOj1t2HjezlbYmrzT9oXvRvW27V6ftTLwFXi5Wp h5etu+k4YtXwRleuCVYV4H8E+VBJuSwtiy+7HbnvkWnT9iEdm9sV3Dq4avLXKy/n0vf/Y90gf7Rv y0Vl8KhBDfUEq6l4tj/i1/v1R00b4omX/II2wmj0NVqgMHZv6N9w8OQvO9aVEEJoSdK+OZPXH1+3 K8T/Sx99dbdP/WjOxZ2HUpx6fxdsIVjelT3aFb452WvsukFuEsILVRXgfwij0dc4+5Dpa9dHTPw7 QwkhnJF731EhDkR2JTa5qucmdQ3//Nje2HKfPr28BLv6ltzbGr4rzXfct33rYU0UtBVGo+8mcXV3 EfN3s/7iCdH181x+/3hUkn7L7zoIdmuJ5l3cceCpgWfjrNPbtxJCCM25/RfP0/jDW4mNg/DP9gOo BGK0Ai1IuZuYqe/i28iu0vy9vKyccqamxjidyxPOXJQZNBsdINyEnvBKnhMVPTix80HFFp4Scvvo zrsSb4Gf7QdQFcToa+W3t81a+Shg5rZ5H1r+JynoXzeuP1Iaf+DjrutDUaJ4EB2bJWky0t9cuBTl rHqER/WotIF/tn382K38iA34LSbQIjhWK3DWQd1bmxfG/PzDrpuyUp4vy3lyfr10fazcrc/gNjo/ uVQ+vxWfLXJr5mOp6z0B8DaMRl/jrDtNkb4oWLRz49RhG8UijldSYuL20XfzP2lsoO62qRvNvh2f wpt3a+iECy/AWxCjlXEWzT9ZvqP7/es3HqbmyA1t3ZsFtHCz0Pn5PCGEltu3Hvlp15YNVdsZnKVf 71GjqK81hrygRRCjb+MM7b3bfuSt7mZoGJFz0JBRQSrfDWfp23Okr8p3AyAoTNEAAJggRgEAmCBG AQCYIEYBAJggRgEAmCBGAQDYUK1V/cZLpVJ1dzNA7SGVSoU9Q7Wdzo1Gq3kE/FtElUeMiopXvrqg WyqgW6qEscg76FyMAgAICzEKAMAEMQoAwAQxCgDABDEKAMAEMQoAwAQxCgDABN83Woso85LjrsY/ zVYY2Xv5t/ZzEvQ9fMqC53fjHySnFxnauXr5+HnY6AlYHKrEFzy/dTX+SWYRMbF19wtoXt9c0G/N pqUZCVeuPcwoN3HwbB7g62goZHGdghitJRTpF1bNWXYsuZhwHKE8Edu0+HTe/KHegkQp/ypmzawl kU8KCeEIoZQYOHectDCsu6v++/8t1AzNu/nbzLm/PyigRCwivJIQE49+MxeHtrER5OJIC+7umj9/ y40sJeEIoUTfMXj8gll93XX+dTk1gkl9raBI2j1/ybE0+4/Cft5//OTRncvGfSC+vXHBzzeKBShO s06uWBL51LLz1PV7o06fjNwo7eOSdf7HxXuf8QJUhyoVX/91ye+JRoETVu2JOn06as+qLwIMkw5G rL2YRwWoTjNPhX+/KcGie9ja3UdPHtuxZJhXQfS68D1JSgGK6yDEaG1QdvNAZKLSffCMb7p6WRlI jB1bDpnzXQ+7rNP7Luawn3VFt6JvFRm0Hj2pRyMbA7GeeYMO48d0tpInXb6ShhxVkbI7F2Ky9P0/ mTrQ19aA4wxsfQdP/LiJOO967D05e3XFvX3bYos8h8+a2LWJvbGeUd2AT78Z6mOWff3KM+RoTSBG awE+LfFxvsgpINDt9RqNkW+bFmZld6/fLWOtTilPKRFJJK/X5cT6+hKO8DxSVEVoYX6ZqWNDHw/z ihk8Z+NgJyHy0jL2Tlc8iomViRp9+GH9iuNF3GDYqv37Vw93x/sbawJro7UAVcjllIhEb1wTOT09 CSl/+SKDJ65MF0vOtGWn1pZXY3dvutpkXICdnjLn7q5tZ7IMmwwMcsZVWDU4m5A520Mqb6G5cZcT yiUeTTyZ16Np4bPkDGLl72FTmh5/+VpiFm9Vz7Opn3ddE7yPtYYQo7WAyMHFWZ+/dis+nff8J9nk STdu51DeIL+QZ51zcHU6h0UULAxbM2PIYRMLQ3lBQblx05GL5w9ki2eoDppxefvvMSlpiTdvvTRp FzqlL/uli+Zm51DOKO/PuZ8di82QE0IIJSIrv5Fz54/yNUOU1gBOhFqAMw3s2dFWcW/H0o2XXxQq lCWZdw+FL9r3XEmIXCHAYpciLeaPyFt/GTq37BAS0qVTKzfTosRTe84llbKXhvfgi14+uHP79v2U XN7ASF9ZrmBf66ZlZeVUmXLh6DPPMRHbD508Gbl54TBvenvr/DWXC4S4gaV7MBqtDTiTVhPmfCqT bt09a/RuEUcoNXANGdxVsuu0wkCfdXjBp+xbvPxMgf+UX77v6axPCCGKT/9cNmne2nmbPDZ+6YNn nlRK3GDQ4o2DCC1+dmbN3IjVM8otNn/fyZLtM5WIxYSInfvNnD3IW58QQlyDPp+ece+zNRePRE9o 052xui7CaLR24Eybjli2fdvqH6Z/PXZs6Ixlv639tpVZKeWs6lgxfsR82uWLiXL7D4d0c/4nMSW2 wcN6enFpl/5MVDC3HKqDM67/4YQRAYZ5sRdusk4CRMamJhxn7vdBw9eXQM6+Rct6Ynnq8zTcqq8B jEZrD87Y0TvQ0TuQEEIIzb6RlEUsW7mxPq1NCwuKqMjCyqJyHHMWVhYcfVlQiDmgSpRdWfXl2nv+ 36yd0LLil8U4I2sbY8IXFRZTYsTyoXI2zo5G3HOF4o17/jxPCSeRSDAUrQGMRmsDPmXftKFDvtn5 pGIowb88d/ae0iYwuAnrhVLk4OKsp0y5m1D5CdSSB3efKMROrs54PkYlJNYmyvTkq3FPKj0kysse Pcoj5nXrMt8F0vP29zUsvBlz+/UvZyhfXIlNURp7NsQnWhOI0dpA5NTCzzznzu8/bY7LKFOWZT++ 8POirQl6zYd/3IL5l/s4i6A+ne1Kr/wyb8PF5Dw5VRSmXtu5YOXpv8xa9fkQTzyphti9cxdPScqh lWvPJhcoCS3LfXx2zaKdD0n9rt19mVejOfO2A7s5ZkdFzN8Zl16skOclnV29aGcidevdP8BYiObr HEzqawVxg0FhXz6as37XtKG7RRyllLNoOlQ6q7eTADHHmbb6csFX+dINe+Z+vkck4ijlidim+ah5 33W1xQxQRcRug6dPSp696vDCMYcXi8VUqaDE2LXLt3NHNxHinp5hszGzx6TM3rhx2vCNIo5Qnhi6 dvl27ujGuGFYI4jRWsKgQZ/5m1rdvXIrMb1Qz9azZWAzZ8G+4Ikz9uw/f2v7x3HX7z7LLpVY1mvi /0ETe0NkqCrpuXafsylgSNz1hJSsYmJq7+77QXM3C8Gm3EaNhizdFhwfe+NRRrHEytW3dStPK4RB TaHnahEDR5/2jj4qKq5n4xnYzTNQRdWhKnrWnoFdVdbnnLFz887OzVVUXadgbQsAgAliFACACWIU AIAJYhQAgInO3WKKiYkJDw9XRWUVlVVd8ZiYmMp/Rrf8Dd1SpcrdAm+jWqv6jf/ss8/U3c0AtYdU KhX2DNV2OjGpHzBggI+Pqh4EAtA1HIdnht+k7hyvuX/b+NGjR6u7swG0m1Qq5Xm+pKREFWeo9tKh tdERI0bY29vjQgpQY8HBwRzHGRrilfZv4CjV1u864zgtbjxArac7Z6hOrI0CAKgOYhQAgAliFACA CWIUAIAJYhQAgAliFACACWIUAIAJYhQAgAliFACACWIUAIAJYhQAgAliFACAiXZ/wxO+rgkA1E5X voIFAEBFMKkHAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCC GAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGCCGAUAYIIYBQBgghgFAGDyfy5IuNiz3+n/AAAA AElFTkSuQmCC --0000000000005f210306574437fa Content-Type: image/png; name="image.png" Content-Disposition: inline; filename="image.png" Content-Transfer-Encoding: base64 Content-ID: <ii_19f8e5d7218cb971f162> X-Attachment-Id: ii_19f8e5d7218cb971f162 iVBORw0KGgoAAAANSUhEUgAAAcIAAAFeCAIAAACZ3ogqAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE QVR4nOzdeUCNWf8A8PPcpW7d9rrt3fZFqUhKylIUY8uSNYp3rCOEsYUJWVoo2zuN7JOyhRBliyiJ kEQZsrTebqLlVrrd+5zfHxluxu99Z5zTDG/n8xc3vs/Tec7zfc52n0NBCAFBEATxpRj/9AkQBEF8 20gaJQiCQELSKEEQBBKSRgmCIJCQNEoQBIGEpFGCIAgkJI0SBEEgIWmUIAgCCUmjBEEQSEgaJQiC QELSKEEQBBKSRgmCIJCQNEoQBIGEpFGCIAgkJI0SBEEgIWmUIAgCCUmjBEEQSEgaJQiCQML6p0/g y6mrq9fW1v7TZ0EQxP+rk+xRRH27vydFfcMnTxD/8zrPHUo69QRBEEhIGiUIgkBC0ihBEAQSkkYJ giCQkDRKEASBhKRRgiAIJCSNEgRBICFplCAIAglJowRBEEhIGiUIgkBC0ihBEASSb/jVJH/JzZs3 b9y48U+fBUF829zd3T08PP7ps/jqdJY0evHixbVr1/7TZ0EQ37bQ0FCSRv+oU3TqMzMzWSyWqqrq P30iBPFty8rKioiIyMzM/KdP5CsDv1l//uRDQ0P/6WImiP8doaGheO/Qb11n6dR/0EGDOxEREcuW LcMetkODZ2ZmZmVltf2ZFMsHpFg+S7ZYiE/903n8y/35k5dtjf7JB2nHnczXE5wUy2eRYvmsLyiW bzq9/CWdYmyUIAii45A0ShAEgYSkUYIgCCQkjRIEQSAhaZQgCAJJp1vw9GfBxvInpQy+tZ4ihTFq S2Vu6pn0ey9qJAo6Vr0G+w60VWeiR4UNN3etO/5E8pkfMU18V8zvr4n8O0jfFl45eyHnSUW9lKNp bN9nyNDexvhKRiLMv3j+yr1ngnqJAs/CZciYQXY4yqVNi+DuxbSbj55XNnK0ja17en+H88xltJbc OHG9uftoH2tFbDHp2ifpZ1Ozi6rEXF2rXsNHe5px8Zy6OO/g6l/zxO12P2bwvOYvH25MWlZ/HUmj nwcrzm0KPm235eAcO1xFBGtzY5eGJj2TqvEtDOVLriZknE+5HhyzdpgR8hGkzW8EAsGnabSlTlhL Mz3RNwqXVqatXbA5s0Ze28JKny28nZx58fR537Ux813VMNzUzUWHli/d97BRUdfakid5ee3I9bQz 5/3Dwqc5KiFHh6L8fUtCEp60qhqZ85VbMu9cPpN0esiqmMUe6E+W9lqKDm3YEP/cdsF33taYsnTL 85OhS2Nv18rrmJmovnh483JKanbI1hBPbfTw8PVvd+7lPVPiqXM+Zk0G1dDaKXaVx4+k0c8Rl1/Y fbywVc4OX0goyt4VnlSs3G9p5PLBfHkA6wviQ1cejN2e6hI5HPHGoNQGhMQP+OR4tTc2zdzwbMBU Hy3Uu67x1oG4zDrjURGb5zqrMwB49/z46uDYs7Enhjh/b4naaJQWH9188KHEZlLMpu8dVClA1z7Y tzIkMTHmcO89M2wQqyf94uSOI09YLkE714w24QDQKrgSsWBj6o54r57BTvKIZy6rKX9/5OGnLRBb ExqA1qL49bG5rL7LDy73MWAD+m3OtgWrUv59cGCvJa4KqMGlggoBreK1/NBSVzaOk+3sSAu+ndai 5MhVC6aOnxaZ8ZrGGRiKctKuv+H0DAgaxJcHAABKpevk4HEW4rzU9HKsR2o7XMPtPT9nKPouDOzK QY0l+e3O3VqWw6jJPdTbagvHzHeUmxJd/ujxW+TGC12ek/OK5vnMCHBQpQAAgKHmGPjDMANYln3z hRQxOKwvLHgl5boMH2rSVgps3X6+fXXAm8cFFRjLHDbkxkWdEhnwlTG2cJtuJZ19xen1ryBvAzYA ADDUXQJmje9vL1+HoWJCkaCynqFroIcx7XdqJI22A6USoKhj1bO/h40G1qKhBa/KWhj6XWzUP95q DANbW3W6OP9RI+6uVMvjxNhL0HvOFPQkCgDkGLsNHuppqyJz5nLycoBiMtHvQrq6qlrKNLU2l2kU sXi6Wgy6vq4BtVgoeSUlNhC/eSP6EElc+7YRUlxlTEOMAABYe/PnzefBd4u+d1LAl0Ylj+/cb2B3 9ZAZNqE03aevDp3vY4ReMemqiiqarWegDVrqBK9eldU043+SdyqkU9+OnJ3fUjsAAKxOXpz9WxW+ wBSLxaRgc3MzLfPoosXvxFBaVSGkgTLGdgFdkhybXNM9KMAFfXARAMDuMmpxF9nwDU9PJ2W+VbCf 0gN9aJTlMHPPyWlsrkyOhqKiRy+lLD4fva2k4OLnZ51zaP/6OM7UgVYq715lJfyc0Ww4ZIInD1PG gzVXt8Wkc0ZGznQGe/GEBAAA+LastJ7S4htIn14+eDar6DWtbmTl2HfwgK6aOO5YSVWFkGZx0kIm hedVtUBAsTVsvKctnDvUskMm3/73kTT692Do21irwNTM9EdT7RzbmoiwNvvSnXoIFZubcLZGYX3m r8eeaA/b7oNhKqJ9ZOGFTSGH8qqqXjdyHSaHrRyhh+EAbK6amuzfxWWXtsZeq1P1GOGpix5ewXZq 2KKS2RuPbAw+AgAAgFJ2Coqch2VqDABAC9Kid2Sr+m35l6MieIAlZBvYUN8AQGtBXNCpwjp1E1Mt +CwlM+3EyfS5UWGjzeQQo9PVFYJWKCp+wR4/b529hljw8PLRE6nRS95QsRuH4rionQ5Jo38TeafR frbpu0+vW8Gc6udmyKouSEs8nFXPogCkcWZRuiTl8I3mbnNH2eCfO5DjWTp2o6pLHt99+Cj1cJqr 1UQ7LO3d9+i6wpRdMXvSnkktxqwJ9tLAMCFdm7N9SdR1sfmg6aPczZSbS+6cPXo+dtkm5egQb/S2 rrTs9ObYe9oTtgbYcQD43HKzLwZbxRJICwsrXGbt2DWuizIFJK9z/r1s9em4bWdcY/wMEPv1TCN3 v4n9u/mOdtVhAQCAm2cfq/Uz112LT3rgM68bmXT6y0ga/buwzMavWyeOjDmSFLPqOKQoDt97XohH 8oYjYgWMY2oteadOP+W4rfbC1WuVQak5jZ3nBACQVGdEzV+3L/yQ477ZdlhuOtj0/NKe6Ngzj0Wq diOXL5zubYajd9mS9+u2M2UG47ftmGkrDwAAbh5evfQXzdq1c5e7S6iXKtIhWp8fj9xTyJ+83d8a 55x/G4otx6Iolt2E4HFd2iauWFou3wf2vbom43q2cIwfWkOdoefuP8u93fE0PIb34V1Lyc8vpbuZ kQmTv4qk0b8PQ8M5MDxhcpOwpOQN1OCbaMvlxeyTMkx1eLjqLRTlnL9ao9ZnkCvGVqK4oaZBLKes qfyhL8niuY/oq3v55MOHVbSdIfLJSyrTo0Oi0sq49qNWzA8caI7r3KXlDx4IKb7/AJk8x+J7eXXZ s/3h/adSL2eUyk9X3Lxa+I5lkLNzaS4AAADYWF4Lpc1JoYuuKdj5h013Rpndo1RVVSmgZm4h8zSk FM0s9JlXXwtfS4Eu9vuWoaWjxaDfippoMu/815ES+7uI66oqK2uaaKaitqmNjZm2IoMuLyyqYxjY WONaKANFd67miFRc+jhimKD/naTowA/jJvx0vlp26IFislgUkEhRlyQBAJry40LC02osJkXExcz1 xpZDAQBASkNAMZhUu5AMikEBmkadmqY4el2cultqyTF/x2hbsMVkMplM1F+CUjU2Vqea6urEMh9C Ub0IMpSUlRBvWro049c9+1KLmtt9WF9bRzM0tDTIGqgvQFqjfxNYc2lDYGzFwPUHl7pxAQAANuUl pxYDswBPc1w1tyU/+26jQg8Xe5y9TCbf2oIjzcu6JfQdrvM+PbS+zLlTQav4WOiiPoZhZereMyUq A9eEfe+Ead7nA6aRrbWy9HJmRvEEM8v3TWlakHmjSCJvb2eBWOiUzoDgCNmvPEge7PBfdMZg9E9R vhi+IcXq0ttF4/TVc5cq+/i2jeJCUe659HLIG9MNtf1Pyb/NO37ot9943cOHv7+AsDHv4vVKhomP sw6ZYfoCJI3+TShdT1+3hA0Xo9doTB/nrNHwNCs58azQcGT4aFNcPQLJb/fyG5lWjnbIX3KRRal6 jBlkcDv5lxUbRQFDHLToN8U5pw6deibXddbYHqj5Gtbdv10klrPRaL5/7Wr7w3KNe/Q0Q2unc5zH T7K/EZuw7Mf6yX4e5krNpbkpCSfuSUzH++OYwOpQ8k4T/LtnbN+5cGV1oG8P7dbn1w4fPCdUdV/u 1xX1nqV4A8b7HFt1Lnb5hvop39lrSoWP0w8nXqrWG7pgpClpjH4Jkkb/LpSm148balmR+xLDlyYA iuLoOk9cu3iqE7ZeLF2al18DtPtaquPNEJSi06wNy+mo2NTday9DAADFVDHrPyd0np8Jcu2hqyqq pPBd/tFN+Z/8hGk8OXavGeJyWpaJX1gEtX1rfMrO0GQIAMVQNOw1dcNCf3uMox4dhGHgu3JtfWTU 4cSonAQIKJaa1Xc/LgnyxrCMjVJ2nR+5SnHrL2f2bkiHEFCUvE6PCWsW/8tV5St/uHytSBr9LEq9 T9AWMzEP68OZUrYds3r/sHmVpQIRW8vQSEsR68g0pdFv/pZuivpW+K+pPH9A8A7PmdWl5dUiKUfT kK+rhOcgDP3BS6J7Sj6z4ovi6KBPXgFAqdj5rdo9ckFlSdmbdyw1AxMD1Q5az8O0GL0mpp+8Adr8 vyxKvXvApoSxr0tKhE0sdUNjPWV8V5Zt2G/O5r7/elNWIqinubrGfE0OyaBfjqTRz2NpmjlodkRg Sl5N30Ltv/+7L4isamzv0BGB32Mo8owteXhjUsqGdg6GeGN+BktZz6yLXsceg+IadHEwwB9WQcvY Wgt72LbY8hpGlhodE7tzITP1BEEQSEgaJQiCQELSKEEQBBKSRgmCIJCQNEoQBIGEpFGCIAg08Jv1 508+NDT0ny5mgvjfERoaivcO/dZ1utbon6wBfxXoyBrTQcFlny6kWD4gxfJZpC3yH3S6NEoQBIEX SaMEQRBISBolCIJAQtIoQRAEEpJGCYIgkJA0ShAEgYSkUYIgCCTkfaP/P1qYm3yx0nzYMEd8uwTB xpeZKecyC8pEcjxz50GjfezU8LwZWvo0KSw2q67dC5ApRZfpayfa4rzGsLH8SSmDb62HYwPkD1oq c1PPpN97USNR0LHqNdh3oK06hlKBDTd3rTv+5HMbyDNNfFfM749hzySx8P7Fs1fuPhM2Ai7PvJvn 0MHOevh2wmopv5OamvHghbARKunauI8Y5WWBa/tDAJtKstMu3i56JXzH1TO1dRs8COeZdzIkjf5/ JKXJEWt3PtAJ7D0UVxqVVl4OXxJ1pZKpZWqm1fL48NVzKRnzt6735aNfBdj04l523mN5LQ3uxwRE KdW1IEduf5iKc5uCT9ttOTjHDlfNgbW5sUtDk55J1fgWhvIlVxMyzqdcD45ZO8wI+QjS5jcCgeDT NNpSJ6ylmZ6feeP+XyUuPr58cWyeSMnA2oIHnmUezUhNvhgYERVop4geHNblxi7+Kel5q4qRtalK c/6lW9dSz1yYGR421gI928E3N2MWrUspARomFgackvSci6eSzk/YEDWjG7Y03amQNPp5rc+PR+69 L4IMHWwh6bKT4VvSG7v9EBc6xlyRgqKCfUuWJOyJS+8X5oOcp2lheZWU7Ry0b4OPEpaz/Rxx+YXd xwtb5ezwhYSi7F3hScXK/ZZGLh/MlwewviA+dOXB2O2pLpHDEbcdotQGhMQPaP8ZrL2xaeaGZwOm +mihFjmsubT7QF5r16k7N02x4VIANjzat3RxwuG4VJ+tY/RQo7fkHdh84rl8jznb1/pZKFJAIry5 Y9mas3u3nXHbNhZ1exXxg4Qd50pVvVZuWzZAlw3Au5cnQ4N3Ht+R5BU3DXXL1E6JjI1+TkvRoYhf S3jGmhiLR1KQfKqAtp00f7S5IgUAoJS6Tpw52ctZqbEKw27vUkGlEGga6HXMVm2tRcmRqxZMHT8t MuM16gbv7UBRTtr1N5yeAUGD+PIAAECpdJ0cPM5CnJeaXo71SG2Ha7i95+cMRd+FgV3RC6r5Ye7D ZqXe48bacCkAAKCUbceO6MYWPyl4Iv5v//e/kjzNzqmmTH1nj7ZoGz5hafeeNcNTVVyUdfs1akOa rnz06DXQ6efbT7dtYyqOydARPbnS0keFtRga6Z0QSaN/1JS/P/JoTa+gIE8NfMUjfZF7V8gwd3fT +xhTsbv/qtDlo6wx9OlrKgXvKF0DPUZrg7D0ZWl14+dGBL88vFQCFHWsevb3sMFYJAAAWvCqrIWh 38VGZjdThoGtrTpdnP+oEfct3fI4MfYS9J4zBUMSBVCibOk5eLi7+ccuNsWSl6MAk4mhPddaXfUG KlpY82ViyWvrajBgQ1096vOF4ipxGVD05u2HIR8oelMrBmwlJQXSp/8SpFP/CdiQGxd1srHf6vme WinH8cUVl5UIaIW+fOXSG4dPZxRUtCgZWNj3HuTdQxfHuL60qqKKZnAzN0+Nu13WBCHFUjbtM3nB grEOWDaqlLPzW2oHAIDVyYuzf6vCEPE9isViUrC5uZmWeaLT4ndiKK2qENIAcYPlduiS5Njkmu5B AS5Y9rSmVHpM+rGHzAeSt/lHk++2qrp62MshR+f0XnLkpFROSWYbU1jz+HEFLd+Tr4P6JKM0+4// 7uTqcz9vMAH+7sac+t+uHNh/j2XlP9YVw6BuZ0TSaDuwNvvnLeelPuvmeqgzXuEM3FTfIAXUyyPB sx8JuHxTXVbxhVuXTp1IC9gUGWiHOu0NmwWVtbD19bOmkTNC5+qB6sKME8ev/bJcKNmxdZL5V3yN Gfo21iowNTP90VQ7x7YmIqzNvnSnHkLF5iacrVFYn/nrsSfaw7b7YNjovT3p8+Or1ic/rRK+EWv0 mr1xYT8MM5KUvJKa7PMVNj07sWX/3Vbd4SPc0GeBKFWXoPWzSufu3BuavRcAAABDq39I2DQcU2Od 0ld8i/39YM217dFXWEM3znZVoQDeoTlxayuE9U+fG02KOjTNSZMJYH3BgZBl8QmbE912T0ft10u0 nceM72r1nV//tiFG176ejmqzlyQfTbw5YlVfLK2vjiHvNNrPNn336XUrmFP93AxZ1QVpiYez6lkU gDTOLEqXpBy+0dxt7igb/NvUMxT1u3TrrlT14uHdwnvJx645Lx9uinPpkOT1/aSdMQevl8nZ/2vV rB4YUp1UcDl8aWw+2953zlBnI4WG4sxTxy5HLd3GjV7QS/3rrSxfL5JGP6AFadHbs5RGRs1w7oC8 I8dmU4DJHxk01UmTCQAAlEpX/xmDLy06nXmjeJq1NVLvlVJ3GjPTqd0nXMdh3qZnDxbkPZP27fYV X2WW2fh168SRMUeSYlYdhxTF4XvPC/FI3nBErIBxnK4l79Tppxy31V68DkgSlK57wHx3AIC47Oya eTE7Iq0c/z2Rj2UMGdYXnfl58+6Lz1u1nSetC57ioY8hP8OGzN0702ttZ+/aMq5trZ1bH8/uGj8E H4s52OdgcI+Omab8n/YV32B/M1iXm36njs0r2L9yYdsn76rKpTRMi1qUxzVFXa1NcdVUWBTL1MJU psTZphZ8Jl0grKEBwL7MhOJpa1GwUoS1a9wRGBrOgeEJk5uEJSVvoAbfRFsuL2aflGGqw8M1mwVF Oeev1qj1GeSK8fkIW+rfiCQcVQ3uhysqZ+gzzGX3rav5hY0T+Ohd75bnZzeu2nGjRstlyrqgiR6G uNKb9Nn9/HpmF09PmZW5HJuBffjHEx48KJX2sCRLnv4qMlP/AYtn6eRko6fIfI/BpAAAFMVgMpkM 5HtCjm9qwJTW1dbLZjVRgwhSXGUu6tCoMOfYnr3Jee1Wq8C62jrIUNfEO7WOnbiuqrKypolmKmqb 2tiYaSsy6PLCojqGgY01rpXgUHTnao5IxaWPI852lvj29oCxk6NuNMl+yGSxmABIWtGXsMG3mdEr tmWKnWZvi9s0DV8OBQAAmqYBRVHtKgZFMRkUoGn8q8w6A9Ia/R2l7Doz3FXmA/pl/OwZB+lBi6MC zdBTEYPv1ssw/mhaavGQAIu2mVzxk/MXnkhVPLtbIl4FittcmJyQdVfOYfsUs/djf+LiS+lPad5w V7Ovum0Bay5tCIytGLj+4FI3LgAAwKa85NRiYBbgaY7rxFvys+82KvRwscf6XUc5MytTZvrDrNy6 /n1/Xw7R9DA7r56hZ4X+lU3ps5N7r9Toj44OHWejgHyu7THNba05KQ8yMiqHjdZ/X8itLzKzXkmV PLvwv+rq8rUiafTvwrQYFdAndf3BJUvqp/q5GTEqbiUdOPlC3n6Ofy/kSQOu29iRptmJ8at+ag0Y 2UOf8fZpVtKhs8Uq7ssmOKIvvulIlK6nr1vChovRazSmj3PWaHialZx4Vmg4Mny0Ka5WtOS3e/mN TCtHO7zpiNIf6Nf3WNjV6BWKr/0HWKu0VhdlHEs4X6nitswXbaQbAECX38kto5WdlatvXb3a/rBq lm7dDZEeCJRaf//RSfcSf1m0onqyr4uRfH1x9smEM0/lus4a74o7Z3cOJI3+bSiN/ovXv6XC953a 9tMJCCiGknHfOT8u9DPGcA3kbaeFh7G37jiWEHkrHgJAsTXthi9bPAf/6h7cKE2vHzfUsiL3JYYv TQAUxdF1nrh28VQnbKOYdGlefg3Q7muJewqa0ui3aGMda/P+1J2rzkIAAMXWtB3y40IchU5XVQik sPbW/vW3PvkJyyHoUDdDHbQjcOymRayXi9lx9Fj0nSMQAIqlYt7vh+D5Y8xIPvgipNj+Xwy9QUu2 OEIdfWyDixTXZvTq/UODykoqG4CKvomhGr7VN0xez8ANv06qKy+pqG3l8Iz42lz83TNKvU/QFjMx zxRnaErZdszq/cPmVZYKRGwtQyMtRayjuZRGv/lbuinqW+Gv6hTXyndF3NAgQWlFTRNQ0jYy4mE6 d4al39oY789NDlJKBhoYngdMTeeA9Qcn1pW/LK+VcDQM+XrKJBV8OVJ2/z95bUsHbexRKXl1I0t1 7GHbsFUNzFUNOig4AIClaeag2RGBKXk1fQu1Domsamzv0BGBf8dS1jW11sUbk6Fm4tAhpdEeW9XA siOrS6fxdU/jEgRBfPVIGiUIgkBC0ihBEAQSkkYJgiCQkDRKEASBhKRRgiAINPCb9edPPjQ09J8u ZoL43xEaGor3Dv3WdbrW6J+sAX8V6Mga00HBZZ8upFg+IMXyWaQt8h90ujRKEASBF0mjBEEQSEga JQiCQELSKEEQBBKSRgmCIJCQNEoQBIGEpFGCIAgk5H2jn5C+Lbxy9kLOk4p6KUfT2L7PkKG9jRWx vTddIsy/eP7KvWeCeokCz8JlyJhBdurYXoHcIrh7Me3mo+eVjRxtY+ue3t/hPPOPaGFu8sVK82HD HNWwRYeNLzNTzmUWlInkeObOg0b72KnhKRbp06Sw2Ky6di9AphRdpq+daIu16sO6V4+FChaW2jj3 e4JNr7JSUm7kl9ZCJQNbj6Ej+pnj2OYPCi5Gb04t+9y2eyw7/7DpzmSH5b+KpFFZ0sq0tQs2Z9bI a1tY6bOFt5MzL54+77s2Zr4rjozRXHRo+dJ9DxsVda0teZKX145cTztz3j8sfJoj+o4ZUJS/b0lI wpNWVSNzvnJL5p3LZ5JOD1kVs9gDZVvoz5CUJkes3flAJ7D3UFxpVFp5OXxJ1JVKppapmVbL48NX z6VkzN+63pePXjlh04t72XmP5bU0ZPYCoJTqWpAjtyd5cnT1ovvf7f33RENc/Tu66lrEj5suVTA1 Tcx0qedpt9JTUn1Wxizrx0MtdShpfC0QCD5Jo/S7t9V1LI1RX/t+3F8lkkZlNN46EJdZZzwqYvNc Z3UGAO+eH18dHHs29sQQ5++RN++WFh/dfPChxGZSzKbvHVQpQNc+2LcyJDEx5nDvPTNsEK8D/eLk jiNPWC5BO9eMNuEA0Cq4ErFgY+qOeK+ewU4YG0itz49H7r0vggwdbCHpspPhW9Ibu/0QFzrGXJGC ooJ9S5Yk7IlL7xfmg5ynaWF5lZTtHLRvg48SlrP9rOZnJ3anlNE4XyMPX1/YuuWSQG9YWOT83tos IH2d80vImpPb4tydQvogNkkZhqM2HRr1yfEEKStmxjZNmtKb7Gn3BcjY6EeS3+7crWU5jJrcQ72t WDhmvqPclOjyR4/fIj+j6fKcnFc0z2dGgEPbdrwMNcfAH4YZwLLsmy9QtzWH9YUFr6Rcl+FDTdo6 ZGzdfr59dcCbxwUVGDcebyk6FPFrCc9YE2OtkRQknyqgbSfNH22uSAEAKKWuE2dO9nJWaqxC3+wd SAWVQqBpoNcx3VTYcD9x04qgyeNm77rXgLUVB6sy0nIb1bxmzHTTZgEAAFPLdUbQUJ36zHOZdfjb i/D1pX/vfWDkv9DPhLSrvgRJox9BjrHb4KGetiofH/YMOXk5QDGZ6AN1dHVVtZRpam0us4sdi6er xaDr65BvQUpeSYkNxG/eiD5EEte+bYQUV5mLrU/flL8/8mhNr6AgTw18tUb6IveukGHu7qb3MaZi d/9VoctHWWPo09dUCt5RugZ6jNYGYenL0upGCXJMWVKJlKGsb+vm6WqGY9BSJnD5qzIp08TWRuby yVnbW7NbCvOf4P0dAIAN2Xv33lYfHTTGlCTRL0PK7SN2l1GLu8j8nW54ejop862C/ZQe6KOALIeZ e05OY3NlcjQUFT16KWXx+XrIWVrBxc/POufQ/vVxnKkDrVTevcpK+Dmj2XDIBE/kkbQ2sCE3Lupk Y7/V8z21Uo5jCQkAAEBcViKgFfrylUtvHD6dUVDRomRgYd97kHcPXRxDEdKqiiqawc3cPDXudlkT hBRL2bTP5AULxr7vESCi1HpOWdYTACAtPjDzTgKGiB8wWSwKvGtuhgD8fqZQ/K6FBk2CyloIMF1V AAAA4sLEuCvS/j9NsME5Pda5kDT6GVB4YVPIobyqqteNXIfJYStH6GGotcxbL7UAACAASURBVGyu Wru9HsVll7bGXqtT9RjhqYseXsF2atiiktkbj2wMPgIAAIBSdgqKnIdlagwAWJv985bzUp91cz3U Ga9wRPw9cFN9gxRQL48Ez34k4PJNdVnFF25dOnUiLWBTZKAd6joD2CyorIWtr581jZwROlcPVBdm nDh+7ZflQsmOrZPMv+aqzzTtYslJvn3t6ku/Ke/3jqcFVy/nt0LQ3CibW5HBqrT9ZwRWAWG98ban O5evuS79c+R4lo7dqOqSx3cfPko9nOZqNdEOfTL9I7quMGVXzJ60Z1KLMWuCvTDsOw5rc7Yvibou Nh80fZS7mXJzyZ2zR8/HLtukHB3ijdzWhTXXtkdfYQ3dONtVhQIYx1oBAOLWVgjrnz43mhR1aJqT JhPA+oIDIcviEzYnuu2ejtqvl2g7jxnf1eo7v/58eQAAcO3r6ag2e0ny0cSbI1b1xXlJMaNU+4wf nnD7WPyqUEng8G7adEXumfgTD8RMCkCAc2xU/OjE8Tw5j9VDsK0w6JRIGv0MSs1p7DwnAICkOiNq /rp94Ycc9822Y//X//cnwKbnl/ZEx555LFK1G7l84XRvMxxLO1vyft12psxg/LYdM23lAQDAzcOr l/6iWbt27nJ3CfVC6sHSgrTo7VlKI6NmOHdA3pFjsynA5I8MmuqkyQQAAEqlq/+MwZcWnc68UTzN 2hrpEUCpO42Z6dTuE67jMG/TswcL8p5J+3b7mis/x376ptV0+PbT8RE3f4UUQ9l65PIfJbER5zkc Dr6maH3WibRKnvePvUhTFMnXXJP+buKGmgaxnLKmstzvn7B47iP66l4++fBhFW2H/ryWVKZHh0Sl lXHtR62YHzjQHFdakpY/eCCk+P4DrD+ObrH4Xl5d9mx/eP+p1MsZ4SrDutz0O3VsXsH+lQvbPnlX VS6lYVrUojyuqe+K+f1RFqZSXDUVFsUytZCd3GCbWvCZdIGwhgYA23cTPhyRp61FwUpR01e/PpKl 1+eHbR4zGwSvyutZPBO+Jn1tTQSUt9fB9q0H+DrjfHaTwRhvO7n//o+J/4A05T+QFB34YdyEn85X y95gFJPFooBEimHxTVN+XEh4Wo3FpIi4mLne2HIoAABIaQgoBpNqF5JBMShA06idcBbP0snJRk+R +R6DSQEAKIrBZDIZyL+DHN/UgCmtq62XLXRRgwjHIgMozDm2Z29yXq1sbFhXWwcZ6poYVxt0BPju bWVl5dt3gKWsZ25jbawpT0mePv5NzDK3tcCV82B15tV8iX7vPhakMYWIFOAHTL61BUeal3VL6Dtc 5/0d3Poy504FreJjoYt618HK1L1nSlQGrgn73gnflyjbMI1srZWllzMziieYWb6/yWhB5o0iiby9 nQVag45Sdp0Z7irzAf0yfvaMg/SgxVGBZuipiMF362UYfzQttXhIwPv8IH5y/sITqYpnd0vEyklx mwuTE7Luyjlsn2L2fkhGXHwp/SnNG+5qhr2dixX96sSKuUfA2O1xs2zlAAAAvr2RnF7FcZzgroOp 9sDaO7ceSzQGu5AsioyU4AeUqseYQQa3k39ZsVEUMMRBi35TnHPq0Klncl1nje2BuhYE1t2/XSSW s9Fovn/tavvDco179ERcdshxHj/J/kZswrIf6yf7eZgrNZfmpiScuCcxHe+PYwKrIzEtRgX0SV1/ cMmS+ql+bkaMiltJB06+kLef499LETU2123sSNPsxPhVP7UGjOyhz3j7NCvp0NliFfdlExy/8m4s 03LwCPtTO09sXK/0va+tYs3j9KQjGc1WgdMH4cqi4F3B/UIxp0c3DOtzOz1ShB9Rik6zNiyno2JT d6+9DAEAFFPFrP+c0HkYvttBV1VUSeG7/KOb8j/5CdN4cuxeM2W0xhHLxC8sgtq+NT5lZ2gyBIBi KBr2mrphob/9V/+aCUqj/+L1b6nwfae2/XQCAoqhZNx3zo8L/YwxVE1522nhYeytO44lRN6KhwBQ bE274csWz/HR/rqfLQAAhuHIVetEm7ck7l13AwKKweV7fL8peKI1trWdkt/uP2xk8q3Mv/oa8g0g abQdef6A4B2eM6tLy6tFUo6mIV9XCU8JMfQHL4nuKfnMvAbF0cGx2IRSsfNbtXvkgsqSsjfvWGoG JgaqWJYW/AFDb9CSLY5QRx/b4CLFtRm9ev/QoLKSygagom9iqIbvzJm8noEbfp1UV15SUdvK4Rnx tbkd0JtnGHy3LNqJ0uXhHHBlaPYI2BQ/tqairPodR4dvqC6PN/cbDlsZ3V/VmKx0woCk0T9iKPKM LXl4Y1LKhnYOhnhjfgZLWc+si17HHkNe29JBG3tUSl7dyFIde9g2bFUDc1Wcbw75BMXRsXLA97YW GUwFTSNLzY6IzNI0c+iQwJ0ReRQRBEEgIWmUIAgCCUmjBEEQSEgaJQiCQNLpppiysrIiIiI6InIH he244FlZWbJ/JsXShhTLZ8kWC/Ep+M368ye/atWqf7qYCeJ/R2hoKN479FvXKTr1ON5eTxAE8Xmd Io0SBEF0nE43Nuru7u7h4YE9bERExLJly7CH7dDgmZmZHwa8SLF8QIrls2SLhfjUPz2q8OX+/MmH hoZ++H3/5LBOx53M1xOcFMtnkWL5rC8olm86vfwlpFNPEASBhKRRgiAIJCSNEgRBICFplCAIAglJ owRBEEg63YKn/6qlMjf1TPq9FzUSBR2rXoN9B9qqY1q8L32aFBabVdfu1c2Uosv0tRNtMV8GWpib fLHSfNgwR2zbPkmE+RfPX7n3TFAvUeBZuAwZM8gOQ7nAhpu71h1/IvnMj5gmqNuOfnKoxvInpQy+ tR6OHa1l0LVP0s+mZhdVibm6Vr2Gj/Y0Q92K7wPYVJKddvF20SvhO66eqa3b4EHOetjefi8W3r94 9srdZ8JGwOWZd/McOhhj8M6GpFFZsDY3dmlo0jOpGt/CUL7kakLG+ZTrwTFrhxlhKCfY9OJedt5j eS0NmRewU0p1Leih25OUJkes3flAJ7D3UExptLno0PKl+x42KupaW/IkL68duZ525rx/WPg0R9T9 TaXNbwQCwadptKVOWEszPXHugQwrzm0KPm235eAcO4x1vuX5ydClsbdr5XXMTFRfPLx5OSU1O2Rr iCeGTUrgm5sxi9allAANEwsDTkl6zsVTSecnbIia0Q3DnvLi4uPLF8fmiZQMrC144Fnm0YzU5IuB EVGBdsgbYHVKJI1+BEXZu8KTipX7LY1cPpgvD2B9QXzoyoOx21NdIoej3xi0sLxKynYO2rfBRwnH 6f4/Wp8fj9x7XwQZ2N7GLi0+uvngQ4nNpJhN3zuoUoCufbBvZUhiYszh3ntm2CDVIEptQEj8gPaf wdobm2ZueDZgqo8WtnajuPzC7uOFrXJ2uAICAABoLYpfH5vL6rv84HIfAzag3+ZsW7Aq5d8HB/Za 4qqAGFv8IGHHuVJVr5Xblg3QZQPw7uXJ0OCdx3ckecVNQ9zsFcCaS7sP5LV2nbpz0xQbLgVgw6N9 SxcnHI5L9dk6Ru+r36bqK0TGRj+Aopy06284PQOCBvHlAQCAUuk6OXichTgvNb0cdbd3AIBUUCkE mgZ6HbqFWEvRoYhfS3jGmvguLF2ek/OK5vnMCHBQpQAAgKHmGPjDMANYln3zhRTbYd6DDbf3/Jyh 6LswsCuOgmotSo5ctWDq+GmRGa8xXMN2mm4lnX3F6fWvIG8DNgAAMNRdAmaN728vX4d+KLry0aPX QKefbz/dto2pOCZDR/TkSksfFdYiN9KbH+Y+bFbqPW6sTdv4A6VsO3ZEN7b4ScETMWrszomk0Q9o wauyFoZ+Fxv1j89jhoGtrTpdnP+oEbnuwppKwTtK10CP0dogLH1ZWt34uRFBNE35+yOP1vQKCvLU wJhGq6uqpUxTa3OZjeZYPF0tBl1f14Cz3w0AAC2PE2MvQe85U7AkUQCgVAIUdax69vewwVgkAAAA JI/v3G9gd/Vw/ThwQmm6T18dOt/HCPlQFFeJy4CiN28/DPlA0ZtaMWArKSmgNhehRNnSc/Bwd/OP Q6EUS16OIq/w+WKkU/8BxWIxKdjc3EzLPF1o8TsxlFZVCGmAuAeytKqiimZwMzdPjbtd1gQhxVI2 7TN5wYKx79t4yGBDblzUycZ+q+d7aqUcxxISAAAAy2HmnpPT2FyVj6cJRUWPXkpZfL4e3vuOLkmO Ta7pHhTggjrm+js5O7+ldgAAWJ28OPu3KjxBAQAAwLdlpfWUFt9A+vTywbNZRa9pdSMrx76DB3TV xHBTUZr9x393cvW5nzeYAH93Y079b1cO7L/HsvIf64o8ekmp9Jj0Yw+ZDyRv848m321VdfWwl0MN 3jmRNPoBQ9/GWgWmZqY/mmrn2NYWgrXZl+7UQ6jY3ITa7ILNgspa2Pr6WdPIGaFz9UB1YcaJ49d+ WS6U7Ng6yRz9MsDa7J+3nJf6rJvroc54hRxOFpurpib7d3HZpa2x1+pUPUZ46uIcSIP1mb8ee6I9 bPs3sI08ALChvgGA1oK4oFOFdeomplrwWUpm2omT6XOjwkabIacjStUlaP2s0rk794Zm7wUAAMDQ 6h8SNg3rHJD0+fFV65OfVgnfiDV6zd64sB+2hR2dDEmjH8k7jfazTd99et0K5lQ/N0NWdUFa4uGs ehYFII3eeZVoO48Z39XqO7/+bSOvrn09HdVmL0k+mnhzxKq+iK0vWHNte/QV1tCNs11VKIB7EPAj uq4wZVfMnrRnUosxa4K9NHDedXRJyuEbzd3mjrLBt019B4KtYgmkhYUVLrN27BrXRZkCktc5/162 +nTctjOuMX4GiP16qeBy+NLYfLa975yhzkYKDcWZp45djlq6jRu9oJc6rmJnKOp36dZdqerFw7uF 95KPXXNePtyULHr6AiSNymCZjV+3ThwZcyQpZtVxSFEcvve8EI/kDUfECsgDUpS605iZTu0+4ToO 8zY9e7Ag75m0bzeUC0EL0qK3ZymNjJrhjKsz/Eew6fmlPdGxZx6LVO1GLl843dsM7wLMlrxTp59y 3FZ78b6NFhHFlmNRFMtuQvC4Lm1LkFhaLt8H9r26JuN6tnCMH1JDHTZk7t6ZXms7e9eWcXwWAAC4 9fHsrvFD8LGYg30OBvfANE1J6boHzHcHAIjLzq6ZF7Mj0srx3xP5ZL7kLyNptB2GhnNgeMLkJmFJ yRuowTfRlsuL2SdlmOrwOqJuUTxtLQpWihBHDGBdbvqdOjavYP/KhW2fvKsql9IwLWpRHtcUyyJ2 SWV6dEhUWhnXftSK+YEDzbFnayjKOX+1Rq3PINeOexDgRamqqlJAzdxCJu1TimYW+syrr4WvpUAX 5daSPrufX8/s4ukps2CZYzOwD/94woMHpdIelihj0rCl/o1IwlHV4H4ILmfoM8xl962r+YWNE/gY 1qV2NuTJI0NcV1VZWdNEMxW1TW1szLQVGXR5YVEdw8DGGrVqQWHOsT17k/ParVaBdbV1kKGuiTqH zOJZOjnZ6Cky32MwKQAARTGYTCYDwz3RlB8XEp5WYzEpIi5mrjf+HAoAFN25miNScenj2KHLwXCi VI2N1ammujrZNUJQVC+CDCVlJdT7iqZpQFFUuzAUxWRQgKZRx2zEt7cHjJ0cdaNJ9kMmi8UEQNKK fQVbp0Baox/BmksbAmMrBq4/uNSNCwAAsCkvObUYmAV4mqPOSFPc5sLkhKy7cg7bp5i9H/sTF19K f0rzhruaoUWnlF1nhrvKfEC/jJ894yA9aHFUoBn6cxJWpu49U6IycE3Y904dNQXRkp99t1Ghh4v9 NzQyx+rS20Xj9NVzlyr7+LatWICi3HPp5ZA3ppshYrEzzW2tOSkPMjIqh43Wf187Wl9kZr2SKnl2 4SNWRjkzK1Nm+sOs3Lr+fX9fJdL0MDuvnqFnZUGaol+CpNGPKF1PX7eEDRej12hMH+es0fA0Kznx rNBwZPhoU/RkxHUbO9I0OzF+1U+tASN76DPePs1KOnS2WMV92QTHr3qVCay7f7tILGej0Xz/2tV2 P6G4xj16muG47yS/3ctvZFo52qF+9+dvJe80wb97xvadC1dWB/r20G59fu3wwXNCVfflfl1RbytK rb//6KR7ib8sWlE92dfFSL6+OPtkwpmncl1njUf+ghSlP9Cv77Gwq9ErFF/7D7BWaa0uyjiWcL5S xW2ZrzVZOfolSBqVQWl6/bihlhW5LzF8aQKgKI6u88S1i6c6YenFyttOCw9jb91xLCHyVjwEgGJr 2g1ftnjO1766h66qqJLCd/lHN+V/8hOm8eTYvWaIy2kBAIAuzcuvAdp9LbFNQf89GAa+K9fWR0Yd TozKSYCAYqlZfffjkiBvHFeUYzctYr1czI6jx6LvHIEAUCwV834/BM8fY4Z+y1Ia/RZtrGNt3p+6 c9VZCACg2Jq2Q35c+NXXxa8WSaPtUMq2Y1bvHzavslQgYmsZGmkpYhw8ZvJ6Bm74dVJdeUlFbSuH Z8TX5nbMs5+hN2jJFkeoo4/j5Bn6g5dE95R8ZhaM4uigdl7fB9LoN39LN0V9q46rjZR6n6AtZmKe Kd4Sp9S7B2xKGPu6pETYxFI3NNZTxvcrMDWdA9YfnFhX/rK8VsLRMORjDE5xrXxXxA0NEpRW1DQB JW0jIx7Omt7pkDT6R5S8mr6F2n//d1+GrWpgrmrQUdHbyGtbOmhjikUpG9o5GGIK9v8cQtXY3qFD jwAAS9PMQbNjQlMKWsbWWh0TG7BVDSw7qrqwlHVNrXU7JnbnQh5BBEEQSEgaJQiCQELSKEEQBBKS RgmCIJCQNEoQBIGEpFGCIAg08Jv1508+NDT0ny5mgvjfERoaivcO/dZ1utbon6wBfxXoyBrTQcFl ny6kWD4gxfJZpC3yH3S6NEoQBIEXSaMEQRBISBolCIJAQtIoQRAEEpJGCYIgkJA0ShAEgYSkUYIg CCTkfaN/ABtfZqacyywoE8nxzJ0HjfaxU8P2rt8Wwd2LaTcfPa9s5GgbW/f0/q63MbZtiqVvC6+c vZDzpKJeytE0tu8zZCjG4KClMjf1TPq9FzUSBR2rXoN9B9qqYyoV6dOksNisunbvhaYUXaavnWiL uXbSwtzki5Xmw4Y54ttTqqX8TmpqxoMXwkaopGvjPmKUF47tjKDgYvTm1LLP7S/HsvMPm+6Mb+c/ WPfqsVDBwlL7G9oG66tD0mh70srL4UuirlQytUzNtFoeH756LiVj/tb1vnz0goKi/H1LQhKetKoa mfOVWzLvXD6TdHrIqpjFHsj7HwMgrUxbu2BzZo28toWVPlt4Oznz4unzvmtj5rtiyBiwNjd2aWjS M6ka38JQvuRqQsb5lOvBMWuHGWGoPrDpxb3svMfyWhoyewFQSnUt6KHbk5QmR6zd+UAnsPdQTGkU 1uXGLv4p6XmripG1qUpz/qVb11LPXJgZHjbWAjEnQUnja4FA8Ekapd+9ra5jaYxC24+7PcmTo6sX 3f9u778n4tnJoHMiaVQWXXYyfEt6Y7cf4kLHmCtSUFSwb8mShD1x6f3CfFDvPPrFyR1HnrBcgnau GW3CAaBVcCViwcbUHfFePYOdUFsCjbcOxGXWGY+K2DzXWZ0BwLvnx1cHx56NPTHE+XukPc0BAFCU vSs8qVi539LI5YP58gDWF8SHrjwYuz3VJXI4+uY9tLC8Ssp2Dtq3wUcJNdZ/0Pr8eOTe+yLI0MEW siXvwOYTz+V7zNm+1s9CkQIS4c0dy9ac3bvtjNu2sWhJiWE4atOhUe0/g4KUFTNjmyZN6Y1v47/m Zyd2p5TRHbwZw/8+8gSSISlIPlVA206aP9pckQIAUEpdJ86c7OWs1FiFvH03rC8seCXlugwfatLW IWPr9vPtqwPePC6oQN14HEh+u3O3luUwanIP9bYLyjHzHeWmRJc/evwWtekCRTlp199wegYEDeLL AwAApdJ1cvA4C3Feano58pkDIBVUCoGmgV6HblDfUnQo4tcSnrEmxvoueZqdU02Z+s4ebdE2dsLS 7j1rhqequCjr9mucDUYAAADw9aV/731g5L/QzwRHF6DhfuKmFUGTx83eda8B+7l2PiSNfiR9kXtX yDB3d9P7WCqK3f1XhS4fZY1cdSl5JSU2EL95I/pQa8W1bxshxVXmIrfoIMfYbfBQT1uVj5EYcvJy gGIykQcwacGrshaGfhcbmW07GQa2tup0cf6jRuRbENZUCt5RugZ6jNYGYenL0upGCWrIP2jK3x95 tKZXUJCnBsb63lpd9QYqWljLbhsvr62rwYANdfUYni+yYEP23r231UcHjTHF03+USqQMZX1bN09X LDtkd3akU/+RuKxEQCv05SuX3jh8OqOgokXJwMK+9yDvHro4Rt8VXPz8rHMO7V8fx5k60Erl3aus hJ8zmg2HTPDkIddjdpdRi7vI/J1ueHo6KfOtgv2UHsijgBSLxaRgc3MzLfPQpcXvxFBaVSGkAeIG y9Kqiiqawc3cPDXudlkThBRL2bTP5AULxjqo4rm9YUNuXNTJxn6r53tqpRzHErINp/eSIyelckps mWPVPH5cQcv35OvgbZ6ICxPjrkj7/zTBBtM8EKXWc8qyngAAafGBmXcS8ATtxEga/QA21TdIAfXy SPDsRwIu31SXVXzh1qVTJ9ICNkUG2qHPeSvYTg1bVDJ745GNwUcAAABQyk5BkfNwzAF9AIUXNoUc yquqet3IdZgctnKEHnJwhr6NtQpMzUx/NNXOsa3nDWuzL92ph1CxuQl5yKBZUFkLW18/axo5I3Su HqguzDhx/Novy4WSHVsnmWOY16vN/nnLeanPurke6oxXyOFkUfJKarJZDTY9O7Fl/91W3eEj3LC2 8GBV2v4zAquAsN6k4fiVImn0I3FrK4T1T58bTYo6NM1JkwlgfcGBkGXxCZsT3XZPR+3Xw9qc7Uui rovNB00f5W6m3Fxy5+zR87HLNilHh3jrYVtRJcezdOxGVZc8vvvwUerhNFeriXZKiPeevNNoP9v0 3afXrWBO9XMzZFUXpCUezqpnUQDS6MNqEm3nMeO7Wn3n179t5NW1r6ej2uwlyUcTb45Y1Rfx3GHN te3RV1hDN852VaEA5o62LMnr+0k7Yw5eL5Oz/9eqWT0UccYWPzpxPE/OY/UQMpX+1SJp9CM5NpsC TP7IoKlOmkwAAKBUuvrPGHxp0enMG8XTrK2Rcl1L3q/bzpQZjN+2Y6atPAAAuHl49dJfNGvXzl3u LqFemHqwlJrT2HlOAABJdUbU/HX7wg857pttx/6v/+8/YpmNX7dOHBlzJClm1XFIURy+97wQj+QN R8QKCshDBupOY2Y6tfuE6zjM2/TswYK8Z9K+3VDqJy1Ii96epTQyaoYz6qPkP4D1RWd+3rz74vNW bedJ64KneOhjXYAJ67NOpFXyvH/sRZqiXy+SRj+guGoqLIplaiE7is82teAz6QJhDQ0AShqVlj94 IKT4/gOsP95kLL6XV5c92x/efyr1cka6EOKGmgaxnLKmstyH2Dz3EX11L598+LCKtkNuxjA0nAPD EyY3CUtK3kANvom2XF7MPinDVIfXEQ0kiqetRcFKEeKIAazLTb9Tx+YV7F+5sO2Td1XlUhqmRS3K 45r6rpjfH329bsvzsxtX7bhRo+UyZV3QRA9D7MsN4OuM89lNBmO87eT++z8m/imkn/CRHN/UgCmt q62XvX1FDSIss+lSGgKKwaTaxWFQDArQNGpvU1J04IdxE346Xy174hSTxaKARIq8VAuI66oqK2ua aKaitqmNjZm2IoMuLyyqYxjYWKO2kKAw59ievcl5tbJnDutq6yBDXRN1Wp3Fs3RystFTZL7HYFIA AIpiMJlMBoamHXybGb1iW6bYafa2uE3TOiCHAgCrM6/mS/R797Eg7Z2vGbk6HzH4br0M44+mpRYP CbBoe/iLn5y/8ESq4tndErGgmEa21srSy5kZxRPMLN83LGhB5o0iiby9nQXi0CiTb23BkeZl3RL6 Dtd5nx9aX+bcqaBVfCx0UZ+UsObShsDYioHrDy514wIAAGzKS04tBmYBnuaoY7oUt7kwOSHrrpzD 9ilm78cexMWX0p/SvOGuZmjRKWXXmeGuMh/QL+NnzzhID1ocFWiGofkgfXZy75Ua/dHRoeNs8C2I bwfW3rn1WKIx2IVk0a8buTwymBajAvqkrj+4ZEn9VD83I0bFraQDJ1/I28/x74U8acBxHj/J/kZs wrIf6yf7eZgrNZfmpiScuCcxHe/vpYHYNKJUPcYMMrid/MuKjaKAIQ5a9JvinFOHTj2T6zprbA/k kTpK19PXLWHDxeg1GtPHOWs0PM1KTjwrNBwZPtoUPRlx3caONM1OjF/1U2vAyB76jLdPs5IOnS1W cV82wfHr7sbS5Xdyy2hlZ+XqW1evtvsJpWbp1t0Qxwjpu4L7hWJOj27oy5aJDkWujyxKo//i9W+p 8H2ntv10AgKKoWTcd86PC/2MMRQTy8QvLILavjU+ZWdoMgSAYiga9pq6YaG/PXpnkFJ0mrVhOR0V m7p77WUIAKCYKmb954TOw/KVF0rT68cNtazIfYnhSxMARXF0nSeuXTzVCcu8jbzttPAw9tYdxxIi b8VDACi2pt3wZYvn+KB/zbRj0VUVAimsvbV//a1PfsJyCDrUzVAH/fwlv91/2MjkW5l36De8CHQk jbZHcW1Gr94/NKispLIBqOibGKohTnPLxlax81u1e+SCypKyN+9YagYmBqr4gsvzBwTv8JxZXVpe LZJyNA35ukr4ri2lbDtm9f5h8ypLBSK2lqGRliLGMXUmr2fghl8n1ZWXVNS2cnhGfG0utvVf7TD0 Bi3Z4gh19LGcPMPSb22M9+dmwSglA9T+xXuGw1ZG91c17sCVTgyD75ZFO1G6HTJX2HmQNPoZlLy6 kaV6BwVnKeuZddHrmNgMRZ6xJa9jYgNKXk3fQq2DggO2qoG5age/DhH1FAAAIABJREFUIkNe29JB G1cwhpqJQ4eVRhuWppmDZsceguLoWDnge1tLZ0UeQgRBEEhIGiUIgkBC0ihBEAQSkkYJgiCQkDRK EASBhKRRgiAINPCb9edPPjQ09J8uZoL43xEaGor3Dv3WdbrW6J+sAX8V6Mga00HBZZ8upFg+IMXy WaQt8h90ujRKEASBF0mjBEEQSEgaJQiCQELSKEEQBBKSRgmCIJCQNEoQBIGEpFGCIAgk5H2jn5II 8y+ev3LvmaBeosCzcBkyZpCdOvprhGHDzV3rjj+RfOZHTBM8u1R+OFRj+ZNSBt9aTxHr++Nh48vM lHOZBWUiOZ6586DRPnZq2N6u3CK4ezHt5qPnlY0cbWPrnt7f9TbGdvLSt4VXzl7IeVJRL+VoGtv3 GTIUY3AAm15lpaTcyC+thUoGth5DR/Qzx7URsjjv4Opf88TtXgzN4HnNXz7cGHPjp7Xkxonrzd1H +1gj75XTSZE02k5z0aHlS/c9bFTUtbbkSV5eO3I97cx5/7DwaY6oO2ZIm98IBIJP02hLnbCWZnqi 7STcHqw4tyn4tN2Wg3Ps8F1caeXl8CVRVyqZWqZmWi2PD189l5Ixf+t6Xz76IaAof9+SkIQnrapG 5nzllsw7l88knR6yKmaxB4Yni7Qybe2CzZk18toWVvps4e3kzIunz/uujZnvqoYh2dFV1yJ+3HSp gqlpYqZLPU+7lZ6S6rMyZlk/Ho59R1//dude3jMlnjrnY9ZkUA2tOCsLAAC0FB3asCH+ue2C77yt 8T55Ow+SRmVIi49uPvhQYjMpZtP3DqoUoGsf7FsZkpgYc7j3nhk2SEVFqQ0IiR/Q/jNYe2PTzA3P Bkz10cJWe8XlF3YfL2yVs8MVEAAA6LKT4VvSG7v9EBc6xlyRgqKCfUuWJOyJS+8X5oOajugXJ3cc ecJyCdq5ZrQJB4BWwZWIBRtTd8R79Qx2Qt0WrvHWgbjMOuNREZvnOqszAHj3/Pjq4NizsSeGOH9v idqWhq8vbN1ySaA3LCxyfm9tFpC+zvklZM3JbXHuTiF90JukUkGFgFbxWn5oqSu+rWb+qCl/f+Th py2wY/Zt6SzI2OhHdHlOziua5zMjwEGVAgAAhppj4A/DDGBZ9s0X6Nu9fwI23N7zc4ai78LArjh2 LGstSo5ctWDq+GmRGa9R973/hKQg+VQBbTtp/mhzRQoAQCl1nThzspezUmMVcqnA+sKCV1Kuy/Ch Jm2lwNbt59tXB7x5XFCB/GtIfrtzt5blMGpyD/W2es4x8x3lpkSXP3r8FrlNB6sy0nIb1bxmzHTT ZgEAAFPLdUbQUJ36zHOZdegtRigSVNYzdA30OjK/wYbcuKhTIgM+roGIzoqk0Y/o6qpqKdPU2lzm 6c/i6Wox6Pq6BuxdqceJsZeg95wpWJIoAFAqAYo6Vj37e9ho4L2o0he5d4UMc3c3vY9xFbv7rwpd Pgp9419KXkmJDcRv3og+FLC49m0jpLjKXORbG3KM3QYP9bRV+RiJIScvBygmEz05SctflUmZJrY2 MucpZ21vzW4pzP/sGPhfQ1dVVNFsPQNt0FInePWqrKYZ88MRAFh78+fN58F3i753UiBpFAnp1H/E cpi55+Q0NlfmroOiokcvpSw+H3OjgC5Jjk2u6R4U4IJll2IAgJyd31I7AACsTl6c/VsVnqAAAADE ZSUCWqEvX7n0xuHTGQUVLUoGFva9B3n30MWxFbuCi5+fdc6h/evjOFMHWqm8e5WV8HNGs+GQCZ7o A4zsLqMWd5H5O93w9HRS5lsF+yk9MAyNMlksCrxrboYA/B4Mit+10KBJUFkLAeLpS6oqhDSLkxYy KTyvqgUCiq1h4z1t4dyhlpjGL2HN1W0x6ZyRkTOdwV4sETsxkkZlsLlq7fZ6FJdd2hp7rU7VY4Sn Ls7HNazP/PXYE+1h27/6zdgBALCpvkEKqJdHgmc/EnD5prqs4gu3Lp06kRawKTLQDv2eVrCdGrao ZPbGIxuDjwAAAKCUnYIi52GZA/odFF7YFHIor6rqdSPXYXLYyhF6GLKoaRdLTvLta1df+k0xa7uN aMHVy/mtEDQ3yubWL0JXVwhaoaj4BXv8vHX2GmLBw8tHT6RGL3lDxW4ciuHsaUFa9I5sVb8t/3JU BA+Qw3V2JI1+Hl1XmLIrZk/aM6nFmDXBXpj2HX8fuyTl8I3mbnNH2XTk3AE24tZWCOufPjeaFHVo mpMmE8D6ggMhy+ITNie67Z6O2q+HtTnbl0RdF5sPmj7K3Uy5ueTO2aPnY5dtUo4O8cbXBZDjWTp2 o6pLHt99+Cj1cJqr1UQ71G4Apdpn/PCE28fiV4VKAod306Yrcs/En3ggZlIAAvQhIKaRu9/E/t18 R7vqsAAAwM2zj9X6meuuxSc98JnXDbHiSMtOb469pz1ha4AdBwD0EYhOj6TRP4BNzy/tiY4981ik ajdy+cLp3mZ4l4G05J06/ZTjttoLx7KYv4Ecm00BJn9k0FQnTSYAAFAqXf1nDL606HTmjeJp1tZI ua4l79dtZ8oMxm/bMdNWHgAA3Dy8eukvmrVr5y53l1AvVTxFRKk5jZ3nBACQVGdEzV+3L/yQ477Z dqgPMY799E2r6fDtp+Mjbv4KKYay9cjlP0piI85zOBzU82boufvPcpf9hNLwGN6Hdy0lP7+U7maG Mvzd+vx45J5C/uTt/tY4hmUIkkY/JalMjw6JSivj2o9aMT9woDmuocsPoCjn/NUatT6DXLGH7hgU V02FRbFMLUxl6grb1ILPpAuENTQAKGlUWv7ggZDi+w+QuaFZfC+vLnu2P7z/VOrljFQ/xQ01DWI5 ZU1luQ+xee4j+upePvnwYRVtZ4g8FcfS6/PDNo+ZDYJX5fUsnglfk762JgLK2+vgHJH4gKGlo8Wg 34qaaKSpYbri5tXCdyyDnJ1LcwEAAMDG8loobU4KXXRNwc4/bLoznknPzoTM1Mtqyo8LCU+rsZgU ERcz1xt/DgUAiu5czRGpuPRx/Gbqqhzf1IAprautl+2pihpEWGbTpTQEFINJtYvDoBgUoGnUqWlJ 0YEfxk346Xy17IlTTBaLAhIp+gI2+O5tZWXl23eApaxnbmNtrClPSZ4+/k3MMre1kPvv//0/oksz ft2zL/X/2rvPgCiutm/gZ3YXll6lNykLCCzSpAhGREXsJbZYsOXVoN5iNFbUVZQIii3WqAgWUBMs 0SR2IyqCIAIiIggovUgRWMrC7pz3QyIlD/fzJM7ZO3Bz/T4lu8m1w9mZ/5w5c3bO6+YuL9Z/qKNZ Gv00mI11UHJ6A5wcef1k2R+xfp/ex2az2ezecW7vaaA32gGX3Yi4VqgyYuv2RU5S6U4ghEQvElIa 5Z1d+b3ncopl7OFuePbizRt5Y/z/yIfW7F9vZUtUhjnyGO4/bCMbK2XJ3cdxeTPNeH9kD13++NFr MZdva8FwaJRtbGUhJ0mLT6ycOF7nj++z7d3T5FJaxddCl3EHgi64tGHZBTTtu+NLbGQRQgjXPrp6 v0Ju4ExPHaZ7D8WtTfvxXE6OlmPo+D+2FDem3X5Yxurv68KwOqUzfGVY51+CiNMPzl51zWDKlt0T yf0kuW+BGG2H61KTXrfKWms0pz74rcs7lKKJ8yAzElOUxTnPXzSyLQfayjOv9R/DtpjsP+TGjtNr 1tTPn+phxCpNjI26/JbLD5jtzvg32HIuM2bxHx2NXvdN/ZypXuZKzUXPfo6+9FxsOmM24/t6lKrX 56MMkq4e2/Ct0H+MfT+6Ju/plXNXcmXtlkxzZn4aY/P8JvCvHLr07Q6lRRNtFKpf3Y+9ENdsOe/L UYxTFFFaw2f4/rDpl6PrQ+rnjuZrSipf3T8fc+e93tjASabwg6MeB2K0HV1RWiHBLS8u7nzxp3fY JnOORpgpM9996aK0F9VI+zOeeq8661Ma3qt31FKhp64c2HIJI4qlZPJZwDdfTzUhsPdw+k/dHkZ9 t//sz4cEVzFCFEvB0H1+yNez+cxHPSgFpyUh6+ndR2+c2HYXI4QotoqZd4DgX1P7k9jvWYaTNgUL w/fERAQ/wohiKRp7Ldq58gsi920oZbcVuzYp7D92LSLkPsaIorg6zjO3rl7optKrdp0+AmK0HUvf b83eQeJu5qpQcjrMb0cghBClMXTFHgcFfUvpNTulPmT5HrNWLbJ9FkrResrmyLHLiwvLGpCKfn9D NXJztSgV26mbTkwKLCssrmnhqBn0N1AlV5xrPHzlwWGL3xeVvBdK5DQNjXWVCLY9S9PZf+fZadWl xe9b5HSMDdW5BDNOxnBoQPhnC2uKC8vraUVdE2NNxvf/u8e2mLJ131CuAaFpEX0RxGg7StnQ1t5Q uh+hasK3l+onIMTRNLPXlEpliqtuxFOXSmmEOMp6ZgP0pFObpaBlwtOSTm2E2PKaRjzptDhCFFfD iKchpeIfP0PRYIC9gXQ/478c3KkHAABGIEYBAIARiFEAAGAEYhQAABjpc7eY4uPjw8LCpFFZSmWl Vzw+Pr7zP0Oz/A6apVudmwX8Ge61/vrGBwYG/tPNDMB/D4FAQPYI7e36xEW9WtfHiAIAAEF9IkYB AEB6+tzYqKenp5eXF/GyYWFh69atI15WqsUfP37cPuAFzdIOmqVbnZsF/Nk/Parw6f76xgsEgva/ 9y8O60hvY3pOcWiWbkGzdOsTmqVXx8vfAhf1AADACMQoAAAwAjEKAACMQIwCAAAjEKMAAMBIn5vw 9H+TNBRkpGXllzXKaZtY8gdaaPaK1eQRQogWFr9ITsstrxfLa1k4eTgbk1qTD1ckXb6VJezyRGuK a+Ez3ZPM46xxU1lG0rPs0nqJnKYJ33UQT4PsftlWnZ2U9LJYKKtlPtDN0ZjxQnwIIYQ/vPjlemp1 d+vusY2GzPAxI7rb4KbS7LwWrQFmmuRaprU6Ozkpo+gDVjKwcXWz0Sb5zOk+BmK0C/p9/KGg0Ku5 QoQohDBGXMNhgTvWjTZhutRjF/jD7U1zzxiGRgXYEmt/XJt0fNO3P2TVYcSiEKYRW4M/ZXXQksHa BIJOlHXjZNSDlq4xquxnOZVEjDa9viDYFJFSLUFsFkVLaMQ1GrHq27W+hmTapiX3p9CtRx+WtCIK IYzYmo7+W7bOsWe8shb+kH7tTNQbcTdvyQ7Wm0w0RnF13J7A7Q+NVlzYQ2jVOVH+z2GCww+KRX/s LTJ6Q5aHBI03Jbqf9x0Qo53gqlt7Q6++VRu+evdXI3gqooL4yNCwn/bvNBtwZHZ/csMfotxrV1Ka MckH7eP3t8J3XHwtO2hR+NefO/WTlD6/8t3uyB9DdhtE7Jqgy/TAoytLy8Vs+8VHNvh0rAREseRV Cew94tcxoREpEv7c0G9mDNLj1OfcObR9/939+2ztwycy3nCEGxIObjoYz/nsX3sXjrDTasm6sif4 RFRopF3kCkeGSyaxDD8PjfZt67rmTFPaibXhLweNdWO81l8ndMWtfQd+q6LZRqQqNqedEBx4UG81 I3jNLHd9VJZ0cU94zHc7zlkeW2jVa669ehIYG+2kMfVxaiPXfV7gWGtNLltGxcz7qy+Hq7flPUks ZbpmOkIISSrTb8ZG7g8KWH06W9TNkk+fDr9/fCtZqOj51YZZzrpyFEfRwHX2xuXeas1ptx6UMN90 uqK0HKuaWJrq6nTQ1lIhsHab5E1cXDGymrFqnpu+AouSVbMaEzjPjdvyMiG1nnkT0YXXz9yu0p24 dt1kR0NlGa6W/YxV8901mp8nZHXXi/x7OAoa2jpdqFbdvviINXLFVx4ElzWSFF8PP5IiUSJ3yY3r HsXeKJFxWBC0eIiJsoyMsrHnom3/Gqr47vrl5GZSH9K3QIx2wJjGGLE4nI7V4NiyshwK0TSJFEWt 2bfP/nDjyRuhrCKH7DCUpLykTMKxdHHsOHwpFVv7/mxJRVkl423HdWXljZSuoZ4UVvYV1zWztI35 lh3LxlPy2joqFC1qETEuTpclxudiQ++Rtu2BT2mPC/nxStRSB/KXYeL8S4eufvBYvMiD4OKd4rex u0+8tpi/eBi5ZJa8y37TwrYY7N7R26fU3AbbytSnPctmfnrpi+CivgOl5OzjrvY04fyppzaL3bRl JLUZMWfuVsnZEBkCREh+yJroIQgh/P7q6lkHKwhU/Iit77NsM1/DtvPK7uLSonKapaquxvjwk1SU ltNsrcrf9q55kJL/XiSna+0+etbc8bZqzFuF677ytHvnF3DT64TUGkrPx5rpMvUIiQvyCiXcQTwT 8ftX8U8zS0VKBua2A/nGKlI4H+CKm8cuFFj6Bw0jM3iJEEJIlBOz6/Q724Cjk4wuXSZWFbW1ijFi sbp8e2wZWQrXFhULsQPzPabPgRjthOo3fN3uhh3rDm2YeU1RVa6toaFVwW7uzuCpJj280071sxni 3eUVUf7lyFvlbPOFQ00YZ0ZreWkVLSq9Gts0xHuoL3r/OunJlQPJSblbD64aTOyQw3WpsWfuvynP TUvJpxwXbpltzXjXxMKaWhGSF6XuWxJ2p6Dl91tMSIk3eWPIco9+ZLOi6dm5s6nKfrvGk5m6gBBC qPnl6bCYcseVW8fps18Qq4oQ29DEkC3Jf55WO9n347mq6UXKq1aMG4VCjCBG/7Yeng//YeLS+B+u ptbIGTp7+/qO9HE1VWrMvn3xfl7LP71hfwduzL+9f+XK79Mp/oLV05ivV48bxArmVi6Tgg4fFgQu WRK4ae/JgwFOciW/Hol52Upig38nqshJT0vPeFvdypGTo1vbCJRsbRFhXJ3wc4rW9B1Rl2/cuhYd vthNIe/KzvBfK8mOTZf+eu5Onc2UqQ5yxEoKn5/cFVvnsfzrUTqEY43SGTbWTVmYcGLXxdSKJom4 sSQ5Zvuem+9phMRtcFH/KaA32oEujN25526Dy6rvt4wzlEUIIfGCh+GB2w5vO2URsZTfG+aCtBTF nd53ODatSt5i9Jr1y/zMCNwFonR81x/27fyCvPkkf98rqVcTE/KX8K3JXCFT2n5BJ/0QEpU+idge cmrTdoUTe6boMwsQNoeNEKUxfJVg3mAlCiGk5zxj3ZLX83fE/3y/ZPRMYh3H1ozLlzNlPYJG6ZHr myceDb/WMnTzCh/C3WaEEKI0R369Mbc65NLxVV8cZ1EIYyXrydM/e3QhXlYOJo9+CojRdnTpk7js Np3JM/0MPyYmR8tr1jjLexGPHmYv5vN7eFuJCm7uFRy4U8y1GhW45ctxdupSGAH8iGNibsymM6pq aITIfgxXf/CihT731918mPh+8hRtJgc1paCoyKZkB7g6dPwMgVJ1dOFxHma/K5IgUjHamPzLvUo1 r0B3xnNRP6JLbpy9XaHu4JF/7cxbhBCiK7IaMV2e+MPpD8rGBOb2U5puSw9Hj89If5lbXM/RsfUY zMvdcxuzNTQIzjHoQ3p4NPwnYWFDI2apqqt2ProoVXVVCpc0CIleBJJHl98KWb07HjvM371xtqMm wWjDDYUZ2ZWyxvbW2p36462iVkwpKSkwPOjEmZErQ+OMFx1c492RQRwNDVUWahQ2YoQY1efqG/Sj kiViSedCNE0jxJGRIdZxbEi+m1Cv6jXUQZ5QRYSQRILZVE3qT+dSP34KTSPUlHTpXLKMhz6Zuf0s ZaOBXkYDf/8X+u3Nty0sAzNTApcvfRCMjbZj6RobykgKM17WdorM5qyMXDHbwMRQil075nD9oxPH 4hsHzA/91p9ohiKEUGv6maC1aw89/NDRLLgmJTlHomDDN2f4WWxNFXZ5YVJSZqf5irguJ7uMltUz YHw5y7FwdlAXv4x/2rHp+H3ik2yxrIUV8zHjPzSnPnomVHT0sCcYQCyT2Udv3u3k5t7JWhTbYcXF O3dvbB+pyLC8OPNUwIxZm34ub2+Wtpx7D95iY09P5jck+ySI0XaUqufE4dotid9vOx6XX9eGxcLi pOjt++7UKLtOHEHwBqwUND9/kFjHdRw1rF9TbRcf6pslDGtTGp6j3VWE8ce+jXle3kLTotrc344K jia0mU6cPpjpZSylM2Skk2LN3SN7f876IEa4raHgSeSOE0kirWFj3Zg/EYDrNGWihejxIcGJRwUN YrGw6ElEyMmkFu2Rn3szn071O3H284wmGWtHO2I3l6SPY+Fiy6l8Ern/h8yaNklTReaNfaGxBWpD /SdbQIp+Erio70ApuS7dvqxecPzi1kUXWSwKYxqxNR39t60dpdWjR4zoisLiVtycsHfulL1d32Gb zDkasYjH6OCgNHxWCYoaQqIjVs+KYLMoWoKRoumYtcHzBzDvgFHaY1avfRMUdn3Psvv72WwsEdOI qzc4QLDUlcRzVThmMzeuyN948MKWBRf++PG47uCAbQFEiiOEkORt2osaSt/PkuCUe+nj8udvWPBu W9T3y6ceZ1GYxhxt1/8XvGqoem/6I3oSiNHOKAXelODTQ988S854V93CUTOycRlkoyNHeudStPbz nyccoE3s1C9n6Tt3vlc3w7eUmr0G8340peo4f8+50a+SU14X17bJaZk7uDmZqhLaepb2kK+PDpyc lpT+tlIoketnwndx4ZF7qpaMydgtpwZlJj7NKmlAKka2rq422gQvvyUsY5+58/W8pHwxzNZ1mzZP TcVantCuSCnZzd4d9Vnq07S8yhY5vQFu7rbwgCcGIEb/BxlNnocfz0N6H0ApWPvOtSZYkKXnPs3f /f/+75ig5HRsh4yxlU5xjkp/lxH9XaRTHFFcbbuh4+2kUlvWfPgcc6lU7oLScZ3q70q4pqKRk4+R E9mifVSPHvIDAICeD2IUAAAYgRgFAABGIEYBAIARiFEAAGAEYhQAAJjBvdZf33iBQPBPNzMA/z0E AgHZI7S363O90b+4B/xdSJp7jJSKdz67QLO0g2bpFvRF/hd9LkYBAIAsiFEAAGAEYhQAABiBGAUA AEYgRgEAgBGIUQAAYARiFAAAGIHnjXaBK5Iu38rqun4dxbXwme5JZhUR3FSWkfQsu7ReIqdpwncd xNMg+wW0VWcnJb0sFspqmQ90czRWJPEk3uacO5cSSrtbi4Sl7TLJz5bYepgIIYRElW9yPqjwLHXI Lcohqct/9jTtbbVYXsfSxX2gAdN1+LoS1+SlvcguqGxR1DMdMNDeRKXXrMMhrnublvzibWUjVtK1 HjTYXq8XrYPSw0CMdiHKunEy6kFL1xhV9rOcSiJGm15fEGyKSKmWIDaLoiU04hqNWPXtWl9DMl9C S+5PoVuPPixpRRRCGLE1Hf23bJ1jzzTlcHP27TNRz9q6eYtjpzDCz1aZWf0unyVM+X71+uvcOd8f n2dG5LwlLntwYHP4L/lNiKIQphFb02nBtuAvbMlEaVvRrV1B++8VtSCKQggjpGg+fl3IiiHk1jVA CCFc+uOKRfcHnzj8BbkVwSRlv+3bFP5rfhP6Y7UcGd1Bc9ZtnOMACyx/CojRzujK0nIx237xkQ0+ HWvrUCx5VQLNJH4dExqRIuHPDf1mxiA9Tn3OnUPb99/dv8/WPnyiLuN9FzckHNx0MJ7z2b/2Lhxh p9WSdWVP8Imo0Ei7yBWOzNbMoNRHBJ1zFXVdoaQt7/xGQZzRmM+Yb3kH/CHhSPi1EjEyI1VRnHc+ OPSXUoMx69Yt8jbj1qRfO7grImL7MctTq1wUGFenCy6F7rv33niiYP0CTxNuw5t7R0P2XQ/bw7MK HadNrF1wQ2rs9Syx4mBSBRFCdMGPIbt+LdQYGrhr+VgbleZ3CTF79/wQuf2g8amgoRCkfx+MjXZG V5SWY1UTS1NdnQ7aWioEFu+RvImLK0ZWM1bNc9NXYFGyalZjAue5cVteJqTWd7OI0t/d8MLrZ25X 6U5cu26yo6GyDFfLfsaq+e4azc8TssRMa1Pyato6XWi2xJ+/Uz9ocaCfDrlDDlfHHdx3t1GB1HJD CCHR88tXsyXm0zesHGWpzuUo6DnP3Lx2rHbVndi4WuZtjiuS4nNa1Uf8vwBvMxUZNlfDevQKfzdu U1r88wbm1RESFSf/cvHk7rUBm64WMV3etSvJ2/t3Xrdq+a1cO8lWU4aSUTH9bMnmRU5ytY9vJtSR 2PQ+B2K0E1xXVt5I6RrqSWF4S1zXzNI25lvqtrc4Ja+to0LRohYR4+J0WWJ8Ljb0HmnbHviU9riQ H69ELXUgfr2BK24eiX5r5b90FLkuF8IVt/YdiFefHjDBgFjj06XZb+pZBm4eph1tIG8/2ElZlJGc wbzRMU3TiGJzOB3fKEdWlvX768zhuvTr0ZduJxe0yslzyHYQJaUl5TTX1sVevv0lStPWTo8lriyr IrHtfQ5c1HciqSgtp9lalb/tXfMgJf+9SE7X2n30rLnjbdWYn2247itPd1l1Dje9TkitofR8rJkv mS4uyCuUcAfxTMTvX8U/zSwVKRmY2w7kG0vhdgeue3wyKrXfpO/GkxuoQ5Lia+FHnuvNOTDbOjGJ WFUsbmvDiMXqsp2UjAwHtZYUVdDIhNkfwNLz8LE9e+Te2Rif/rP56uy2yqeRMQktqm7DB5G4Lqa0 xwbHjEUISfKiFi+JZl6wU2mLCWu3+OgPlO30mqi0uJpmmWmoQsfqE0CMdtJaXlpFi0qvxjYN8R7q i96/Tnpy5UByUu7Wg6sGq5HqEOC61Ngz99+U56al5FOOC7fMtmb8HWBhTa0IyYtS9y0Ju1PQ8vst JqTEm7wxZLlHP6I9mbac2Kg4idfGaVbElkBG4neXdp941d//0EwetziRWFnE0jU2lKWTUtPKaN7H zG/LS0mvxTS3XkgzvhRjGU8J3tmweWNU4LQYZWWZlrpGsYbbsl0bRmr18NFFGT37oXqdX8CNL86d fVwv7+BDeHfpK+Dc0wE3iBXMrVwmBR0+LAhcsiRw096TBwOc5Ep+PRLzspXcx4gqctLT0jPeVrdy 5OTo1u5ugf9drS0ijKsTfk7Rmr4j6vKNW9eiwxe7KeRd2RlMvvMjAAATwUlEQVT+ayXJsS5cGxf9 U6HB2Ble5O5DiHJiwiLzLRetmWZK+JxOKXmMG6Ylzjy3K+JJkVAsaa7M+CksJLZAglCbmMRooyjv 3oWbWc3Kpq4+I319vZ0M5GrSr8XGlzIejv5Poj9kXvl22foLeUoeS1f4ERyn6UugN9qB0vFdf9i3 8wvy5pP8fa+kXk1MyF/CtyZzhUxp+wWd9ENIVPokYnvIqU3bFU7smaLPbO9lc9gIURrDVwnmDVai EEJ6zjPWLXk9f0f8z/dLRs8kdflNF/zyY0Irf9l4S2K7TXPm6bCYUvtlRycZkR+AoBRdAzYvKBec Ph807zyLQhhzTXynj+LE3BFzZRnnRVtm1PYjiawRWyO+GdKPjRBCoqLrwSv2h+8w4R2e1b8XdFCw MPfmib3Hf3ktVLGbKlj/5Wf6EAefBtrtf8cxMTdm0xlVNTRCZI9zrv7gRQt97q+7+TDx/eQpjHoB lIKiIpuSHeDqoNQxTUvV0YXHeZj9rkiCCMVo26tfb+TJOq/1JtZlwXVx5y6/5fIGVN05exohhHBt eg1N47Rrp5GmLoG5/ZSS3Zzws8NfpWfkFNRgTUs3T/vaKP9oSqufOtM2Eec8fFiMLBfN8uz3ccfg Go3+YuT5xCtx8UUz+zMceZU2LHx9KXTb8SdVqvaT1y/3H2lB9lcUfQzEaDvcUJiRXSlrbG+t3Wns vVXUiiklJabTtcWZkStD44wXHVzj3bG/cjQ0VFmoUdiIEWJUn6tv0I9KloglnQvRNI0QR0aG1OHR +vJuXDnXYZ4bwYmFtISmWI1ZN6Oz2l+hMULpP0dncGwJze2nFPRsPfRsPRBCCOHqlLwqpOZqqsn4 j2hsaMSUkbpK50IsVXVVFt3Q0Mh85FWq2vJjt6w9mqHktfzgmsnWkKBM9eTv+j+tNf1M0Nq1hx5+ 6BhOxDUpyTkSBRu+OcOuKFtThV1emJSU2dzxGq7LyS6jZfUMGA/rcyycHdTFL+Ofdmw6fp/4JFss a2FlSqgTLc56nFDFsfFwUSF30FHqY8Nu3O3k9skFFmyW2bzjt+/ePDBNj+En0YWxa76YuTI6t30g lC65fy9TounhZcO4/8A2MDFki3NfZDZ1vIbrXr0sksgYmuj36F+E4opfj0SmIdcV4VumQIaSADHa jtLwHO2uIow/9m3M8/IWmhbV5v52VHA0oc104vTBTHc2SmfISCfFmrtH9v6c9UGMcFtDwZPIHSeS RFrDxropMd6TuU5TJlqIHh8SnHhU0CAWC4ueRIScTGrRHvm5N/PpVAghhCQFqWnVLFMHPrEpC9LH MnAaqFL74sJ3kc8qRBJR9ZsHx0JOv5RxnD3DifkPKij9ERPcVWru7N9+7mmxUEy31uU/PLntWEKz ls9EgnfgpABXJcS9aFEfPNpNvqG2iw8NIph9/yngor4DpeGzSlDUEBIdsXpWBJtF0RKMFE3HrA2e P4DAUac9ZvXaN0Fh1/csu7+fzcYSMY24eoMDBEtdmacoQhyzmRtX5G88eGHLggssCmEayegODtgW QKQ4QghXp6cV0ip+Vga96cTLNpu2bmnO5qMxa744z6IwxpSq3ReCoAlE/ghKc+Q328q3bz97ar3/ KRZFYZpGXD2vZVuXu/fsHp6kpLBEgmtubZ1x60/vcOyXn9v/OcGfpvUVEKOdUaqO8/ecG/0qOeV1 cW2bnJa5g5uTqSqhCzSW9pCvjw6cnJaU/rZSKJHrZ8J3ceFpEpt+KWMydsupQZmJT7NKGpCKka2r q402gR+x/gG36rjPXTDK2Uq6V6uU2sAJ/v7YnlAXGiGu2cTgU64ZianZZUIZLZ6zh4MhuQc8UaoD /cOj/TKTkl8VfRDLaZrwBw0i+I3+gaXhMNGfzeaTG0xRs58wT1XcTb+TpWOjROpD+hSI0T+j5HRs h4yxlU5xjkp/lxH9XaRTHFFcbbuh4+2kUZpl6DnT31Malbug1OzHzbUnXJSrxx+qxydctKO6tu2Q sVLaXRBCCFHqDhP8HQgW5PT3nt2fYD0AY6MAAMAMxCgAADACMQoAAIxAjAIAACMQowAAwAjEKAAA MIN7rb++8QKB4J9uZgD+ewgEArJHaG/X53qjf3EP+LuQNPcYKRXvfHaBZmkHzdIt6Iv8L/pcjAIA AFkQowAAwAjEKAAAMAIxCgAAjECMAgAAIxCjAADACMQoAAAwAs8b7dCcc+dSQml3C5iztAmsUvkn oso3OR9UeJY6csRKSurynz1Ne1stltexdHEfaEDuAcUIIUlDQUZaVn5Zo5y2iSV/oAXxxxNLDS0s fpGcllteL5bXsnDycDYmtCIAQpLChxcevO36AGRKxW7MJGfGy2shhBCiGwpSn6blVjYiRS3zgW6O /VWIPjUbt1S8TEx6XdGqqMtzdLPXI7cn9jUQo+1wc/btM1HP2rp5i2NHaJXK9s8Spny/ev117pzv j88zI3JFIC57cGBz+C/5TYiiEKYRW9NpwbbgL2yJRCn9Pv5QUOjVXCFCFEIYI67hsMAd60abyP7f /+9fhz/c3jT3jGFoVIAtsd0S1yYd3/TtD1l1GP2+tgpbgz9lddCSwdoEWh3XPL8aFZkq7vIiy3Dm oAnO/RjnHa57fnLj1gtZDRixWYiWIKRoMXnjzuWDma9pihBCuCEjJjg4KqVKgiiEMJLV8/pqe9Ak c3ILJvQlEKPtKPURQedc/7SmV1ve+Y2COKMxn+kS7NnhDwlHwq+ViJEZqYrivPPBob+UGoxZt26R txm3Jv3awV0REduPWZ5a5aLAtDiuurU39OpbteGrd381gqciKoiPDA37af9OswFHZvcnNyokyr12 JaUZGxIriBB+fyt8x8XXsoMWhX/9uVM/SenzK9/tjvwxZLdBxK4JzL9RSUVpOVbwXP39vwZ1dM0p jqIGgT5jU/KJ0AvZ8h4BOwIn8Puhqoyre4OPX9l9mB+12Zv5gnm48nbYllMvNceu2zZ7CE/lw/Po 0B0xR8Iu8o/6M10Dt0+CsdEOlLyatk4Xmi3x5+/UD1oc6EdwmS9cHXdw391GBXlyJUXPL1/NlphP 37BylKU6l6Og5zxz89qx2lV3YuNqmS/12Jj6OLWR6z4vcKy1Jpcto2Lm/dWXw9Xb8p4kltIENl5S mX4zNnJ/UMDq09lkF6bE7x/fShYqen61YZazrhzFUTRwnb1xubdac9qtByUENl1UXlaDdMyt9Drv M9qaSgT6JqIXD+KrZF3mr55qr8WlKK6W/fQVM2zYdckJmd1dLv1N4szYMwmNvNlBK0bZ6CjIyOu7 LVj5BV+5OjnxXXdjWuD/AjH67+GKm0ei31r5Lx2lTTBEK27tOxCvPj1gggGx0z5dmv2mnmXg5mHa cQDL2w92UhZlJGeImFbHmMYYsTicju1ly8pyKETTJFIUtWbfPvvDjSdvhLKKHLJrUkrKS8okHEsX x47uG6Via9+fLakoq2S+7XRlabmErWegS77/hoX1IiU9K75Fxzp2lKauNge1tYiYb7g4Jz6hnGU9 YkT/9v2FbTbrwKVLB2dDX/STwEX9v4PrHp+MSu036bvxhuTONZLia+FHnuvNOTDbOjGJWFUsbmvD iMXqsp2UjAwHtZYUVdDIhNEfQCk5+7irPU04f+qpzWI3bRlJbUbMmbtVcjZTPYm0jPyQNdFDEEL4 /dXVsw5WEKj4EVvfZ9lmvoZt53VGxaVF5TRLVV2NeWJLKkoraEXtd5e+XfnwRUGtRNnAzmvC7Fkj LZjfwqI0fTef9e38Cv7w7MnLVo6FDY/xeDQWvsuvQOouFpotZWlPkrKraHUjnt1AW31FWFn5E0GM /httObFRcRKvjdOsyN2RFr+7tPvEq/7+h2byuMWJxMoilq6xoSydlJpWRvM+JltbXkp6Laa59UKa 6TUH1W/4ut0NO9Yd2jDzmqKqXFtDQ6uC3dydwVOZxbP0Uf1shnh3eUWUfznyVjnbfOFQE+b3gGpL y5vp2oTYX+28Pxtm1Vqe+TT+QujTlMKwfYv4xCZJ4IonZy/EF5ZmP08tUfxs+apJzE9d+EN1Labk 6x5uXfhLQkUbQghhxFIfOHdrsL892ekofUUPPxD+Kbg2LvqnQoOxM7yYD+d/JMqJCYvMt1y0Zpop 4ZMXpeQxbpiWOPPcrognRUKxpLky46ewkNgCCUJtYgKDXeLS+B+uptbIGTp7+/qO9HE1VWrMvn3x fl4L89L/Obgx//b+lSu/T6f4C1ZPM2V+8UoLaWXeAI9ZO47u37h8ScAqwcGT4bOs8JsfDl8pJDLa 8funNJZkvUhPf1X4gebKy0pau1te/m/CIlErlhQ++Pkd78vdZ3+6detq5I5Ztjj9dPChJw1EB6f7 DOiNdocu+OXHhFb+svGWxNqnOfN0WEyp/bKjk4zIDz9Riq4BmxeUC06fD5p3nkUhjLkmvtNHcWLu iLmyTM8DdGHszj13G1xWfb9lnKEsQgiJFzwMD9x2eNspi4ilfKJznqSkpSju9L7DsWlV8haj16xf 5mdGYloP22zq9iNTO71AKdvPmuV5XRCX8LR8lok+mfMv22zazohpCDe9u3to6+6DG1pVI7f4MByR 4LDZCLENJ2/cNM1WFiGETDwXra/IXHgo7vrjgMGjCYx39DUQo91oe/XrjTxZ57XexG4t4bq4c5ff cnkDqu6cPY0QQrg2vYamcdq100hTl8DcfkrJbk742eGv0jNyCmqwpqWbp31tlH80pdVPneEFB136 JC67TWfyTD/Dj4nJ0fKaNc7yXsSjh9mL+fwevguJCm7uFRy4U8y1GhW45ctxdurSvIki199cn/1b TVWNBOkTbRdKof+IgDkPH3yb8OB5i4+PPJNaLAUlRYpSGTjIquMUSOk4ORuxXxQXlEqQWg//Rnsg aLH/qfXl3bhyrsM8N3IX9IiW0BSrMetmdFb7KzRGKP3n6AyOLaG5/ZSCnq2Hnq0HQgghXJ2SV4XU XE2ZztbGwoZGzFJVV+0cx5SquiqFSxqEPfwakC6/FbJ6dzx2mL9742xHTZIJimvzU/NqVcwcLTQ6 Wga3trRilqKSAsNzlyjxwNLDmS4rDwc4tw/NU/IamgqIbhQ2YcRoshylaagnTxWIxV2GHmgaI4rD ITxZoo+AsdH/QZz1OKGKY+PhokJuj6LUx4bduNvJ7ZMLLNgss3nHb9+9eWCaHsNPogtj13wxc2V0 bvtAKF1y/16mRNPDy4bpiZKla2woIynMeNl5BmpzVkaumG1gYtij58fg+kcnjsU3Dpgf+q0/2QxF COG6+GMb1gZFpjR3vEaXPUspwuq2dkzvA3E0FCVl+U+f5XaaJEqX5+TUIRV9fcZ3gWRsXezlhM/j 05vaX5IUJSYUShR4Vj37G+2pIEb/TFKQmlbNMnXg96IhIpaB00CV2hcXvot8ViGSiKrfPDgWcvql jOPsGU6MRwEpVc+Jw7VbEr/fdjwuv64Ni4XFSdHb992pUXadOILgXDApaH7+ILGO6zhqWL+m2i4+ 1DczvvPGMvbxs+W+v7lv10+Z1a2Ybq569euerWdeydhNmzKQ6YAx23z4SB6n8Kd9h+/lN0gQFn14 c+9QSPRr1H/UaHvGo9GUypCpfnrVN3YHRz8raxK31eXdOxgSnY1NJ0xxY/ybtz4JLur/BFenpxXS Kn5WBj06If6EbTZt3dKczUdj1nxxnkVhjClVuy8EQRNI/BGUkuvS7cvqBccvbl10kcWiMKYRW9PR f9vaUVo9+kxDVxQWt+LmhL1zp+zt+g7bZM7RiEU8Zh0vlsGkjUGlgt1XDyyPO/D7z95ZqjbTt2z5 3Jh5j45tOn19YP6mA9d2fHltJ5uNJWKMFExGfrN1ng2Je3pyDl9u+rJwU0TEmtkRvz9rQM5k5Ddb 5w3oDTcMeyCI0T/BrTrucxeMcraS7sUNpTZwgr8/ttcgFURcs4nBp1wzElOzy4QyWjxnDwdDYnMX KQXelODTQ988S854V93CUTOycRlkoyNHOkMVrf385wkHaBNreTlL37nzvboZvqXU7DUInGBY2p7L DztMepGc+qa0nlbUs3R2czAkNYddxmT05lNuM58lvyysakJKOub2gxxNVYm1jbz1zF1nvNISUnIq mjjqJvburjx1CINPBS33JyxDz5n+nlL/GErNftxce8JFuXr8oXp8wkU/ktHkefjxPKRUHSFEKVj7 zrUmWJCl5z7N351gwW4/RNHQwdvQQTrFZTR4HqOk1uaUgqHjcENHKVXvU3rTlSsAAPRAEKMAAMAI xCgAADACMQoAAIz0uVtM8fHxYWFh0qgspbLSKx4fH9/5n6FZfgfN0q3OzQL+DPdaf33jFy5c+E83 MwD/PQQCAdkjtLfrExf1n3/+OZ8vrYlAAPQ1FNWjf3fxD/inc/zT/d2Nnzdv3j/d2AD0bgKBgKbp 5uZmaRyhvVcfGhudM2eOjo4OnEgB+GReXl4URcnJwZL2XVAY9/Bnnf1bFNWLNx6A/3p95wjtE2Oj AAAgPRCjAADACMQoAAAwAjEKAACMQIwCAAAjEKMAAMAIxCgAADACMQoAAIxAjAIAACMQowAAwAjE KAAAMAIxCgAAjPTuJzzB45oAAP+4vvIIFgAAkBK4qAcAAEYgRgEAgBGIUQAAYARiFAAAGIEYBQAA RiBGAQCAEYhRAABgBGIUAAAYgRgFAABGIEYBAIARiFEAAGAEYhQAABiBGAUAAEYgRgEAgBGIUQAA YARiFAAAGIEYBQAARiBGAQCAEYhRAABg5P8Dl+7f/021sm8AAAAASUVORK5CYII= --0000000000005f210306574437fa-- --===============4960039812303390768== Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: inline --===============4960039812303390768== Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: inline _______________________________________________ Maxima-discuss mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/maxima-discuss --===============4960039812303390768==--