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    <title>MaplePrimes - answers and comments on Question, Getting a weird result with integration of an absolute value</title>
    <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration</link>
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    <description>The latest answers and comments added to the Question, Getting a weird result with integration of an absolute value</description>
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      <title>MaplePrimes - answers and comments on Question, Getting a weird result with integration of an absolute value</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration</link>
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    <item>
      <title>absolute value</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#answer129696</link>
      <itunes:summary>&lt;p&gt;Why are you taking the absolute value in your assignment to ErTime? Alternatively, if you take the absolute value of the error, then why do you expect it to integrate to zero?&lt;/p&gt;
&lt;p&gt;As shown by your first plot, at first one curve lies above, and then this switches. And ErTime may be defined as one minus the other. What's your objection to defining ErTime as the simple difference -- allowing for and retaining the sign -- without taking the absolute value?&lt;/p&gt;
&lt;p&gt;The presence of the sign of ErTime at every point in time is what allows for error "one way" (red above green) to be compensated for later on by error "the other way" (green above red).&lt;/p&gt;
&lt;p&gt;If you remove the absolute value from the definition of ErTime, then all the plots should make sense. The very last plot, of the integral of ErTime from zero to K, will tend to zero as K the upper limit of initegration tends to infinity. And the slope of that last plot will change sign at the same point in time that the red and green curves cross over in the first plot -- namely at the point at which ErTime itself the becomes zero, and incidentally in agreement with the middle plot.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;Why are you taking the absolute value in your assignment to ErTime? Alternatively, if you take the absolute value of the error, then why do you expect it to integrate to zero?&lt;/p&gt;
&lt;p&gt;As shown by your first plot, at first one curve lies above, and then this switches. And ErTime may be defined as one minus the other. What's your objection to defining ErTime as the simple difference -- allowing for and retaining the sign -- without taking the absolute value?&lt;/p&gt;
&lt;p&gt;The presence of the sign of ErTime at every point in time is what allows for error "one way" (red above green) to be compensated for later on by error "the other way" (green above red).&lt;/p&gt;
&lt;p&gt;If you remove the absolute value from the definition of ErTime, then all the plots should make sense. The very last plot, of the integral of ErTime from zero to K, will tend to zero as K the upper limit of initegration tends to infinity. And the slope of that last plot will change sign at the same point in time that the red and green curves cross over in the first plot -- namely at the point at which ErTime itself the becomes zero, and incidentally in agreement with the middle plot.&lt;/p&gt;</description>
      <guid>129696</guid>
      <pubDate>Tue, 17 Jan 2012 00:44:14 Z</pubDate>
      <itunes:author>Pseudomodo</itunes:author>
      <author>Pseudomodo</author>
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    <item>
      <title>Simplification of TotEr?</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#answer129697</link>
      <itunes:summary>&lt;p&gt;I have doubt about the simplification of TotEr. Here are the arguments. Let us consider&lt;/p&gt;
&lt;p&gt;&amp;gt; TotEr := int(ErTime, t = 0 .. infinity);&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br&gt;&lt;img 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" alt=""&gt;&lt;/p&gt;
&lt;p&gt;You apply the &lt;a href="http://www.maplesoft.com/support/help/search.aspx?term=expand"&gt;?expand&lt;/a&gt; command to this output. Such usage is not described in Maple Help. It can produce a wrong result or NULL. For example,&lt;/p&gt;
&lt;p&gt;&amp;gt; expand(limit(exp(-x)/(exp(-x)+sin(x)*exp(-2*x)), x = infinity));&lt;br&gt;&lt;br&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;&lt;/p&gt;
&lt;p&gt;On the other hand,&lt;/p&gt;
&lt;p&gt;&amp;gt; eval(ErTime, [k[0] = 3, k[2] = .1, x[2](0) = 30, k[1] = .2]);&lt;br&gt;&lt;br&gt;|1.000000000 + 0.06666666667 exp(-0.2 t) x[1](0) - 1.000000000 exp(-0.2 t) + &lt;br&gt;&lt;br&gt;&amp;nbsp; 3.333333333 (-0.3 exp(0.1 t) + 0.02 x[1](0) exp(-0.1 t) - 0.3 exp(-0.1 t)&lt;br&gt;&lt;br&gt;&amp;nbsp;&amp;nbsp; + 0.30 - 0.02 x[1](0)) exp(-0.1 t)|&lt;br&gt;&amp;nbsp;and&lt;/p&gt;
&lt;p&gt;&amp;gt; TotEr := int(eval(ErTime, [k[0] = 3, k[2] = .1, x[2](0) = 30, k[1] = .2]), t = 0 .. infinity);&lt;br&gt;&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Float(infinity)&lt;br&gt;&lt;br&gt;&lt;/p&gt;
&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;I have doubt about the simplification of TotEr. Here are the arguments. Let us consider&lt;/p&gt;
&lt;p&gt;&amp;gt; TotEr := int(ErTime, t = 0 .. infinity);&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;&lt;/p&gt;
&lt;p&gt;You apply the &lt;a href="http://www.maplesoft.com/support/help/search.aspx?term=expand"&gt;?expand&lt;/a&gt; command to this output. Such usage is not described in Maple Help. It can produce a wrong result or NULL. For example,&lt;/p&gt;
&lt;p&gt;&amp;gt; expand(limit(exp(-x)/(exp(-x)+sin(x)*exp(-2*x)), x = infinity));&lt;br&gt;&lt;br&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;&lt;/p&gt;
&lt;p&gt;On the other hand,&lt;/p&gt;
&lt;p&gt;&amp;gt; eval(ErTime, [k[0] = 3, k[2] = .1, x[2](0) = 30, k[1] = .2]);&lt;br&gt;&lt;br&gt;|1.000000000 + 0.06666666667 exp(-0.2 t) x[1](0) - 1.000000000 exp(-0.2 t) + &lt;br&gt;&lt;br&gt;&amp;nbsp; 3.333333333 (-0.3 exp(0.1 t) + 0.02 x[1](0) exp(-0.1 t) - 0.3 exp(-0.1 t)&lt;br&gt;&lt;br&gt;&amp;nbsp;&amp;nbsp; + 0.30 - 0.02 x[1](0)) exp(-0.1 t)|&lt;br&gt;&amp;nbsp;and&lt;/p&gt;
&lt;p&gt;&amp;gt; TotEr := int(eval(ErTime, [k[0] = 3, k[2] = .1, x[2](0) = 30, k[1] = .2]), t = 0 .. infinity);&lt;br&gt;&lt;br&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Float(infinity)&lt;br&gt;&lt;br&gt;&lt;/p&gt;
&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;</description>
      <guid>129697</guid>
      <pubDate>Tue, 17 Jan 2012 01:04:01 Z</pubDate>
      <itunes:author>Markiyan Hirnyk</itunes:author>
      <author>Markiyan Hirnyk</author>
    </item>
    <item>
      <title>exact vs floating-point</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#answer129714</link>
      <itunes:summary>&lt;p&gt;Using 64bit Maple 15.01 on Windows 7, I get&lt;/p&gt;
&lt;pre&gt;int(simplify(eval(ErTime,
                       [k[0]=3,k[1]=2/10,k[2]=1/10,x[1](0)=0,x[2](0)=30])),
    t = 0 .. infinity);

                                          5
&lt;/pre&gt;
&lt;p&gt;It looks like the floating-point values k[1]=0.2 and k[2]=0.1 are causing the problem and producing the infinite result that Markiyan showed.&lt;/p&gt;
&lt;p&gt;I have not pinpointed the particular place that thing are going astray with the float values for the parameters.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;Using 64bit Maple 15.01 on Windows 7, I get&lt;/p&gt;
&lt;pre&gt;int(simplify(eval(ErTime,
                       [k[0]=3,k[1]=2/10,k[2]=1/10,x[1](0)=0,x[2](0)=30])),
    t = 0 .. infinity);

                                          5
&lt;/pre&gt;
&lt;p&gt;It looks like the floating-point values k[1]=0.2 and k[2]=0.1 are causing the problem and producing the infinite result that Markiyan showed.&lt;/p&gt;
&lt;p&gt;I have not pinpointed the particular place that thing are going astray with the float values for the parameters.&lt;/p&gt;</description>
      <guid>129714</guid>
      <pubDate>Tue, 17 Jan 2012 19:17:22 Z</pubDate>
      <itunes:author>pagan</itunes:author>
      <author>pagan</author>
    </item>
    <item>
      <title>Thank you for your help.
I don't expect</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#comment129707</link>
      <itunes:summary>&lt;p&gt;Thank you for your help.&lt;/p&gt;
&lt;p&gt;I don't expect the integral to be zero. Any confusion is probably due to my lack of clarity though, apologies about that.&lt;/p&gt;
&lt;p&gt;Rather, the spurious result that maple gives me (almost certainly due to my own spurious use of the expand function) suggests that the integral, even with the absolute values, gives zero error in the specific case where k_0 = k_2 x_{2}(0). The plots I showed demonstrate that this can't be true.&lt;/p&gt;
&lt;p&gt;As for why I am interested in having the absolute value of the error - I only want zero error in the case where the two curves are identical. Not where the area before and after crossing cancel out. I want this to be the case because otherwise my approximation would appear to be a good predictor of the exact result for all times when it is not.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;Thank you for your help.&lt;/p&gt;
&lt;p&gt;I don't expect the integral to be zero. Any confusion is probably due to my lack of clarity though, apologies about that.&lt;/p&gt;
&lt;p&gt;Rather, the spurious result that maple gives me (almost certainly due to my own spurious use of the expand function) suggests that the integral, even with the absolute values, gives zero error in the specific case where k_0 = k_2 x_{2}(0). The plots I showed demonstrate that this can't be true.&lt;/p&gt;
&lt;p&gt;As for why I am interested in having the absolute value of the error - I only want zero error in the case where the two curves are identical. Not where the area before and after crossing cancel out. I want this to be the case because otherwise my approximation would appear to be a good predictor of the exact result for all times when it is not.&lt;/p&gt;</description>
      <guid>129707</guid>
      <pubDate>Tue, 17 Jan 2012 16:14:12 Z</pubDate>
      <itunes:author>TommySnowman</itunes:author>
      <author>TommySnowman</author>
    </item>
    <item>
      <title>thanks</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#comment129727</link>
      <itunes:summary>&lt;p&gt;&lt;a href="http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration#comment129707"&gt;@TommySnowman&lt;/a&gt;&amp;nbsp;Thanks for clearing that up. I misunderstood which you expected.&lt;/p&gt;</itunes:summary>
      <description>&lt;p&gt;&lt;a href="http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration#comment129707"&gt;@TommySnowman&lt;/a&gt;&amp;nbsp;Thanks for clearing that up. I misunderstood which you expected.&lt;/p&gt;</description>
      <guid>129727</guid>
      <pubDate>Tue, 17 Jan 2012 23:33:02 Z</pubDate>
      <itunes:author>Pseudomodo</itunes:author>
      <author>Pseudomodo</author>
    </item>
    <item>
      <title>Hi, thanks for your help.&amp;nbsp;I agree</title>
      <link>http://www.mapleprimes.com/questions/129680-Getting-A-Weird-Result-With-Integration?ref=Feed:MaplePrimes:Getting a weird result with integration of an absolute value:Comments#comment129708</link>
      <itunes:summary>&lt;p&gt;Hi, thanks for your help.&lt;/p&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;p&gt;I agree that the issue is probably my use of the expand function, although I disagree&lt;/p&gt;
&lt;p&gt;that the TotEr should be infinite. I think Maple is making an error here - I mean look&lt;/p&gt;
&lt;p&gt;at the following:&lt;/p&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&lt;/pre&gt;
&lt;pre&gt; t = 0 .. 100);&lt;br&gt;                          4.999546030&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 1000);&lt;br&gt;                          5.000000098&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 10000);&lt;br&gt;                          5.000000998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 100000);&lt;br&gt;                          5.000009998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 10000000);&lt;br&gt;                          5.000999998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 1000000000);&lt;br&gt;                          5.099999998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 100000000000);&lt;br&gt;                          15.00000000&lt;/pre&gt;
&lt;pre&gt;&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;This seems very odd to me, and I can see no way in which the function would jump&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;back into 'life' like this. &lt;/pre&gt;</itunes:summary>
      <description>&lt;p&gt;Hi, thanks for your help.&lt;/p&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;p&gt;I agree that the issue is probably my use of the expand function, although I disagree&lt;/p&gt;
&lt;p&gt;that the TotEr should be infinite. I think Maple is making an error here - I mean look&lt;/p&gt;
&lt;p&gt;at the following:&lt;/p&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&lt;/pre&gt;
&lt;pre&gt; t = 0 .. 100);&lt;br&gt;                          4.999546030&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 1000);&lt;br&gt;                          5.000000098&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 10000);&lt;br&gt;                          5.000000998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 100000);&lt;br&gt;                          5.000009998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 10000000);&lt;br&gt;                          5.000999998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 1000000000);&lt;br&gt;                          5.099999998&lt;/pre&gt;
&lt;pre&gt;&amp;gt; int(subs([k[0] = 3, k[1] = .2, k[2] = .1, x[1](0) = 0, x[2](0) = 30], ErTime),&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;t = 0 .. 100000000000);&lt;br&gt;                          15.00000000&lt;/pre&gt;
&lt;pre&gt;&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;This seems very odd to me, and I can see no way in which the function would jump&amp;nbsp;&lt;/pre&gt;
&lt;pre&gt;back into 'life' like this. &lt;/pre&gt;</description>
      <guid>129708</guid>
      <pubDate>Tue, 17 Jan 2012 16:21:20 Z</pubDate>
      <itunes:author>TommySnowman</itunes:author>
      <author>TommySnowman</author>
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