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The proposed kinetic model successfully predicted the inulin hydrolysis in wider ranges of experimental conditions.
The parameters of the ANN model are adjusted by the back propagation learning algorithm using wide ranges of experimental datasets.
These findings will be useful in kinetic studies to prevent incursion in certain ranges of experimental conditions that could lead to erroneous interpretation of the obtained kinetic data.
Modelling of such complex systems is essentially phenomenological; thus, the estimated kinetic parameters lack mechanistic meaning and are functions of the ranges of experimental conditions used.
Current study presents genetic programming (GP) as a powerful tool in order to develop a novel correlation for estimation of IFT in hydrocarbon/water systems under wide ranges of experimental conditions.
The Equations of Motion (EoM) for the proposed model are then obtained and the simulation results are shown and compared with idealised ranges of experimental results suggested in the literature.
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As for parameters, we identify settings that yield good performance across a range of experimental conditions.
Here we review the range of experimental coating technologies currently being explored as the basis for future practical AF coatings.
The predicted results are in the range of experimental results.
Furthermore, these methods are compatible with a broad range of experimental designs, including survey vignettes and conjoint designs.
In summary, we hypothesize that BOLD fractal scaling provides a consistent, robust correlate of cognitive effort across a range of experimental manipulations.
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