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    <title>DSpace Community: College of Science in Al-Kharj</title>
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      <title>Damage mechanics in ferroelectric ceramics-model and application</title>
      <link>http://hdl.handle.net/123456789/5292</link>
      <description>Title: Damage mechanics in ferroelectric ceramics-model and application&lt;br/&gt;&lt;br/&gt;Authors: Bassiouny, E.&lt;br/&gt;&lt;br/&gt;Abstract: In this work, we developed a phenomenological model based on rigorous thermodynamics for the description of damage in ferroelectric materials.  The model allows to investigate the interaction between damage on one side and electric hysteresis, residual electric polarization and electric hardenings on the other. The model is applied to the torsion of a ferroelectric circular cylinder.</description>
      <pubDate>Sat, 01 Jan 2005 00:00:00 GMT</pubDate>
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    <item>
      <title>Path integral on S2: the rosen-morse oscillator</title>
      <link>http://hdl.handle.net/123456789/4593</link>
      <description>Title: Path integral on S2: the rosen-morse oscillator&lt;br/&gt;&lt;br/&gt;Authors: Inomata, A.; Kayed, M.A.&lt;br/&gt;&lt;br/&gt;Abstract: Guided by the group chain SO(3) 3 0(2), we construct an angular path integralfor the symmetric Rosen-Morse oscillator on S2. By explicit path integration, we obtainthe normalised energy eigenfunction as well as the exact energy spectrum</description>
      <pubDate>Tue, 01 Jan 1985 00:00:00 GMT</pubDate>
    </item>
    <item>
      <title>Path integral quantization of the symmetric poschl-teller potential</title>
      <link>http://hdl.handle.net/123456789/2738</link>
      <description>Title: Path integral quantization of the symmetric poschl-teller potential&lt;br/&gt;&lt;br/&gt;Authors: Inomata, A.; Kayed, M.A.&lt;br/&gt;&lt;br/&gt;Abstract: Feynman's path integral approach to quantum mechanics [I] is expected to be an alternative to Schrodinger's.Nevertheless, a limited number of problems have been solved exactly by path integration. It is a surprizing factthat such a typical example as the hydrogen atom has been left unsolved until very recently [2-41. Because of itsgaussian (fresnellian) nature, the functional integral if represented in cartesian variables can be evaluated only forquadratic potentials [5]. Use of polar coordinates has helped to relax this limitation [6], placing the Infeld-Hullpotential V= ar2 + br-* in the list of path-integable examples. However, the change of variables in path integralsis not trikial and the result is seldom beneficial to calculations. In recent years, some useful techniques have beendevised, so that coordinate transformations are more effectively utilized to carry out path integration for theAharonov-Bohm effect [7], the hydrogen atom [2,3], the Morse oscillator [8], the Dirac-Coulomb problem [4]and the charge-monopole system [9]. Now we are generally able to evaluate a path integral if it is intrinsically reduciblein the local limit to a confluent hypergeometric equation of the Infeld-Hull type. It is interesting thatthis situation is parallel to that of the Sq2.1) dynarnical group approach [lo,) 11.</description>
      <pubDate>Mon, 11 Mar 1985 00:00:00 GMT</pubDate>
    </item>
    <item>
      <title>Modeling approach for predicting PVT data</title>
      <link>http://hdl.handle.net/123456789/2695</link>
      <description>Title: Modeling approach for predicting PVT data&lt;br/&gt;&lt;br/&gt;Authors: El-M Shokir, Eissa M.; Goda, Hussam M.; Fattah, Khaled A.; Sayyouh, Mohamed H.&lt;br/&gt;&lt;br/&gt;Abstract: Neural networks are tested successfully in so many fields as pattern recognition or intelligent classifier,prediction, and correlation development. Recently, artificial neural network has gained popularity inpetroleum applications. In this paper, two directly connected neural networks are designed, using Matlab5.3,for PVT parameters determinations. The first neural network predicts the bubble point pressure values usingthe four following variables: reservoir temperature, APIo, relative gas density, and solution gas oil ratio. Theseestimated values directly used with the same four input variables in the second network to determine oilFormation Volume Factor (FVF). A comparison study between the designed Neural Network Model and otherpublished correlations displayed excellent performance, smallest average absolute relative error, and highestcorrelation coefficient for the designed Networks among all other correlations.&lt;br/&gt;&lt;br/&gt;Description: * Petroleum Engineering Dept., King Saud University** Petroleum Engineering Dept., Curtin University of Technology, Australia*** Petroleum Engineering Dept., Cairo University</description>
      <pubDate>Thu, 01 Jan 2004 00:00:00 GMT</pubDate>
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