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In favor of the optimization, the prediction model defines the type of sources in a way that the adaptive switching methodology can converge to an optimal solution by selecting an appropriate method.
For process design and optimization the prediction of grinding kinetics enables the comparison of the energy efficiency of grinding processes at different operating parameters and for different stabilizing additives.
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A new criterion has been presented for the optimization of the prediction filters in this context.
To the best of our knowledge, most existing studies have mainly focused on the optimization of the prediction stage.
Before concluding the article, let us now study the complexity of the proposed sparsity criteria for the optimization of the prediction filters.
Biorelevant dissolution is an indispensable tool utilized during formulation development and optimization for the prediction of in vivo bioavailability of pharmaceutical agents.
In this work we discuss the benefits of using computational intelligence methods, like Support Vector Machines (SVM) for the optimization of the prediction of compounds solubility.
The results show that although model optimization reduces the prediction uncertainties of residence time at extinction and ignition, the remaining uncertainties remain rather large.
The calculation time from the weight optimization to the prediction for one evaluation set varied from 1 day and 13 h for MUTAG to 3 days and 8 h for DHFR.
The key questions to address include the level of improvement from model optimization in the prediction uncertainties of PSR residence times at extinction and ignition and the rate coefficients of reactions that must be improved in order to reduce the prediction uncertainties.
Through the optimization of the prediction parameters, we have also depicted the structural characteristics of the SMB gene clusters.
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