No, surfdata is completely different.  For example, here I have 5 rows of points with 6 points in each row.

> X := [1.25, 2.15, 2.80, 3.60, 5.10, 5.70, 
        2.80, 3.75, 5.35, 5.90, 6.75, 7.60, 
        5.40, 6.05, 7.45, 8.20, 9.40, 10.25, 
        6.90, 8., 9.20, 10.10, 10.95, 12.15, 
        9.30, 9.55, 10.80, 12.15, 13.20, 13.85]; 
  Y := [1.85, 3.65, 6.20, 8.25, 10.25, 12.45, 
        1.10, 3.45, 4.90, 6.65, 9.35, 11.35, 
        -.35, 1.75, 3.75, 6., 8., 9.60, 
       -1.10, 1.25, 2.60, 4.95, 6.95, 8.85, 
       -2.30, .10, 2.15, 4.45, 6., 8.25];
  Z := [0.17, 0.52, 0.80, 0.85, 0.59, 0.13,
        0.22, 0.72, 0.90, 0.91, 0.51, 0.20,
       -0.09, 0.39, 0.49, 0.43, 0.04, -0.11,
       -0.21, 0.13, 0.03, -0.15, -0.51, -0.62,
       -0.02, 0.00, -0.28, -0.68, -0.92, -0.91];
  plots[surfdata]([seq([seq([X[i+6*j],Y[i+6*j],Z[i+6*j]],i=1..6)],j=0..4)],axes=box,labels=[x,y,z]);

No, surfdata is completely different.  For example, here I have 5 rows of points with 6 points in each row.

> X := [1.25, 2.15, 2.80, 3.60, 5.10, 5.70, 
        2.80, 3.75, 5.35, 5.90, 6.75, 7.60, 
        5.40, 6.05, 7.45, 8.20, 9.40, 10.25, 
        6.90, 8., 9.20, 10.10, 10.95, 12.15, 
        9.30, 9.55, 10.80, 12.15, 13.20, 13.85]; 
  Y := [1.85, 3.65, 6.20, 8.25, 10.25, 12.45, 
        1.10, 3.45, 4.90, 6.65, 9.35, 11.35, 
        -.35, 1.75, 3.75, 6., 8., 9.60, 
       -1.10, 1.25, 2.60, 4.95, 6.95, 8.85, 
       -2.30, .10, 2.15, 4.45, 6., 8.25];
  Z := [0.17, 0.52, 0.80, 0.85, 0.59, 0.13,
        0.22, 0.72, 0.90, 0.91, 0.51, 0.20,
       -0.09, 0.39, 0.49, 0.43, 0.04, -0.11,
       -0.21, 0.13, 0.03, -0.15, -0.51, -0.62,
       -0.02, 0.00, -0.28, -0.68, -0.92, -0.91];
  plots[surfdata]([seq([seq([X[i+6*j],Y[i+6*j],Z[i+6*j]],i=1..6)],j=0..4)],axes=box,labels=[x,y,z]);

DuncanA's suggestion sounds good, but it may be even slower than gepo's.  What Randprime does is take random polynomials and check them for primeness; what Nextprime does is take polynomials one-by-one in lexicographic order and check for primeness.  It doesn't even have the constraint that the constant coefficient is nonzero. 

DuncanA's suggestion sounds good, but it may be even slower than gepo's.  What Randprime does is take random polynomials and check them for primeness; what Nextprime does is take polynomials one-by-one in lexicographic order and check for primeness.  It doesn't even have the constraint that the constant coefficient is nonzero. 

Plotting should work in Maple 13 under Linux.  The error message sounds suspicious.  There is actually a type `has_unit`  (at least, there is a procedure `type/has_unit`), so I don't know why your error message would say there isn't one.  And I don't know what this has to do with plot.  There may be something wrong with your installation: you might contact Technical Support.  Or perhaps there are some initialization files that are redefining things in strange ways.  Look for a file called .mapleinit in your home directory, and one called init in Maple's lib directory (e.g. /usr/local/maple/lib).

Plotting should work in Maple 13 under Linux.  The error message sounds suspicious.  There is actually a type `has_unit`  (at least, there is a procedure `type/has_unit`), so I don't know why your error message would say there isn't one.  And I don't know what this has to do with plot.  There may be something wrong with your installation: you might contact Technical Support.  Or perhaps there are some initialization files that are redefining things in strange ways.  Look for a file called .mapleinit in your home directory, and one called init in Maple's lib directory (e.g. /usr/local/maple/lib).

Please start a new topic. But the answer to your question is, assuming you meant (-1/a*ln(x+1))^(1/n), I wouldn't expect there to be a closed-form antiderivative (except in the cases where 1/n is an integer).  You can get numerical values for definite integrals for given a and n, though, or series in powers of x.

Please start a new topic. But the answer to your question is, assuming you meant (-1/a*ln(x+1))^(1/n), I wouldn't expect there to be a closed-form antiderivative (except in the cases where 1/n is an integer).  You can get numerical values for definite integrals for given a and n, though, or series in powers of x.

I would hesitate to use the word "understands" here.  I think it's basically looking for key words.  There are also lots of examples that it gets wrong, sometimes amusingly so.  For example:

What is the length of the curve x^2 + y^2 = 1

Input interpretation: Dead Man's Curve runtime x^2 + y^2 = 1

Result: x^2 (91 minutes)+y^2 = 1

I would hesitate to use the word "understands" here.  I think it's basically looking for key words.  There are also lots of examples that it gets wrong, sometimes amusingly so.  For example:

What is the length of the curve x^2 + y^2 = 1

Input interpretation: Dead Man's Curve runtime x^2 + y^2 = 1

Result: x^2 (91 minutes)+y^2 = 1

Of course it will vary a lot, but e.g. the following do produce correct answers:

Find the derivative of x/(2+x) at x=3

Find the integral of x/(2+x) from x = 0 to 3

Find the Maclaurin series of x/(2+x)

What is the volume of a cone of height 3 and radius 2

What is the probability of a full house in poker

What is the maximum of sin(x) for x from 0 to pi

Of course it will vary a lot, but e.g. the following do produce correct answers:

Find the derivative of x/(2+x) at x=3

Find the integral of x/(2+x) from x = 0 to 3

Find the Maclaurin series of x/(2+x)

What is the volume of a cone of height 3 and radius 2

What is the probability of a full house in poker

What is the maximum of sin(x) for x from 0 to pi

Using Alec's suggestion:

> M := Matrix([[0, 2.986666666, -17.60000000, 24, 0, 0, 0, 0], 
      [-1.*lambda, -1.934222224, 26.88000000, -105.6000000, 120, 0, 0, 0], 
      [.9333333334*lambda, .3034074074-1.*lambda, -11.60533334, 107.5200000, -352.0000000, 360, 0, 0], 
   [-.1777777778*lambda, .9333333334*lambda, 1.517037037-1.*lambda, -38.68444447, 298.6666667, -880.0000000, 840, 0], 
   [0, -.1777777778*lambda, .9333333334*lambda, 4.551111111-1.*lambda, -96.71111120, 672.0000000, -1848.000000, 1680], 
       [1, -1.836019358*10^(-26), 0, 0, 0, 0, 0, 0], 
       [0, 2, 6, 12, 20, 30, 42, 56], 
       [0, 0, 6, 24, 60, 120, 210, 336]]);
  A:= map(coeff,M,lambda,0);
  C:= map(coeff,M,lambda);
  LinearAlgebra:-Eigenvalues(A,C);
                     [     Float(infinity) + 0. I     ]
                     [                                ]
                     [     Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [                       14       ]
                     [-3.97978495755474000 10   + 0. I]
                     [                                ]
                     [  -740.004741143996966 + 0. I   ]
                     [                                ]
                     [  -30.7991222949092000 + 0. I   ]

Of course, the infinite entries don't correspond to actual solutions of det(A - lambda*C = 0).  Actually that sixth entry is rather suspicious, too.
Trying it with higher values of Digits shows the sixth entry increasing in magnitude, so it's likely an effect of roundoff.  Trying it with exact rationals:
 

> Ar:= convert(A, rational);
  Cr:= convert(C, rational);
  Er:= LinearAlgebra:-Eigenvalues(Ar, Cr);

      [[  204246701150522400973508695782965504   
Er := [[- ------------------------------------ + 
      [[   529957647646980854980724055135675     

                  8                  
  ---------------------------------- 
  2649788238234904274903620275678375 

                                                                         (1/2)
  13795158869385597517200922922630545749628777758719592799334409698885221     

  ]  [  204246701150522400973508695782965504   
  ], [- ------------------------------------ - 
  ]  [   529957647646980854980724055135675     

                  8                  
  ---------------------------------- 
  2649788238234904274903620275678375 

                                                                         (1/2)
  13795158869385597517200922922630545749628777758719592799334409698885221     

  ]]
  ]]
  ]]
> evalf[14](Er);
                             [ -30.79912229923]
                             [                ]
                             [-740.00474120715]

Using Alec's suggestion:

> M := Matrix([[0, 2.986666666, -17.60000000, 24, 0, 0, 0, 0], 
      [-1.*lambda, -1.934222224, 26.88000000, -105.6000000, 120, 0, 0, 0], 
      [.9333333334*lambda, .3034074074-1.*lambda, -11.60533334, 107.5200000, -352.0000000, 360, 0, 0], 
   [-.1777777778*lambda, .9333333334*lambda, 1.517037037-1.*lambda, -38.68444447, 298.6666667, -880.0000000, 840, 0], 
   [0, -.1777777778*lambda, .9333333334*lambda, 4.551111111-1.*lambda, -96.71111120, 672.0000000, -1848.000000, 1680], 
       [1, -1.836019358*10^(-26), 0, 0, 0, 0, 0, 0], 
       [0, 2, 6, 12, 20, 30, 42, 56], 
       [0, 0, 6, 24, 60, 120, 210, 336]]);
  A:= map(coeff,M,lambda,0);
  C:= map(coeff,M,lambda);
  LinearAlgebra:-Eigenvalues(A,C);
                     [     Float(infinity) + 0. I     ]
                     [                                ]
                     [     Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [    -Float(infinity) + 0. I     ]
                     [                                ]
                     [                       14       ]
                     [-3.97978495755474000 10   + 0. I]
                     [                                ]
                     [  -740.004741143996966 + 0. I   ]
                     [                                ]
                     [  -30.7991222949092000 + 0. I   ]

Of course, the infinite entries don't correspond to actual solutions of det(A - lambda*C = 0).  Actually that sixth entry is rather suspicious, too.
Trying it with higher values of Digits shows the sixth entry increasing in magnitude, so it's likely an effect of roundoff.  Trying it with exact rationals:
 

> Ar:= convert(A, rational);
  Cr:= convert(C, rational);
  Er:= LinearAlgebra:-Eigenvalues(Ar, Cr);

      [[  204246701150522400973508695782965504   
Er := [[- ------------------------------------ + 
      [[   529957647646980854980724055135675     

                  8                  
  ---------------------------------- 
  2649788238234904274903620275678375 

                                                                         (1/2)
  13795158869385597517200922922630545749628777758719592799334409698885221     

  ]  [  204246701150522400973508695782965504   
  ], [- ------------------------------------ - 
  ]  [   529957647646980854980724055135675     

                  8                  
  ---------------------------------- 
  2649788238234904274903620275678375 

                                                                         (1/2)
  13795158869385597517200922922630545749628777758719592799334409698885221     

  ]]
  ]]
  ]]
> evalf[14](Er);
                             [ -30.79912229923]
                             [                ]
                             [-740.00474120715]

Really, it does return undefined.  For example:

> evalf(Int((-1.*sin(.5000000000*x)/(6.283185308+x)+sin(.5000000000*x)/(6.283185308-1.*x)
  +2.*sin(.5000000000*x)/x)*exp(-.5000000000e-2*x^2)*exp(-.1250000000e-2*x^2)*cos(1.918520382*x),
     x = Float(-infinity) .. Float(infinity)));

                      Float(undefined)

On the other hand:

> evalf(Int((-1.*sin(.5000000000*x)/(6.283185308+x)+sin(.5000000000*x)/(6.283185308-1.*x)
  +2.*sin(.5000000000*x)/x)*exp(-.5000000000e-2*x^2)*exp(-.1250000000e-2*x^2)*cos(1.918520382*x),
     x = -infinity .. infinity));

.18884e-8

Well, at least sometimes... this seems to be somewhat non-deterministic.