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The results obtained using the model used in the present study are in excellent agreement with many experimental studies, much better than the agreement with the earlier models reported in the literature.
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The model used was the LIFEPRED numerical model, using the natural diffusion coefficients.
The model used is obtained using dominant system variables that represent active and passive dynamical elements.
The result of K2P can be explained by the fact that the distance method uses exactly the model used to generate the data.
We weighted the model used the stabilized weights by using the 'scwgt stabw' statement in the procedure.
The alignment is used to build the model using a local installation of Modeller (Sali and Blundell, 1993).
We constructed the RIFs used in the model using generalized oscillator model (GOM) as expanded to crystal size dependence.
A support vector machine learning algorithm was used to train the model using the training set.
We have used LPSolve and programmed the model using AMPL language.
The DECERNS software tool was used to evaluate the model using the multi-attribute value theory method (Figure 1).
Harrell's c statistic was calculated for both the model using stage alone and the comprehensive model using additional covariates.
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