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We first check whether the model obtained by constraint-based modeling method can reproduce the experimental data used for parameter estimation, which can give us the information about quality of "in-sample fit" (how good is the model fitting the data used for parameter estimation).
The modeling method can be further divided into determinable modeling and stochastic modeling and is used to nicely reflect internal changes of geological body.
Our modeling method can be generalized and extended to the modeling of other bio-processes.
In this case, stochastic modeling method can effectively display and evaluate 3D geological model's uncertainty.
The novel MPDNN model has improved the consistency for long MM probes, and this modeling method can potentially be used for the prediction of oligonucleotide microarray hybridizations.
From this second study, we elicit ideas for increasing the extent to which the object-oriented modeling method can support the design of a training and assessment system.
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Qualitative scenario modeling methods can evaluate opportunities and risks of their products in a specific market.
Details on the QEU modeling methods can be found in the memo High-resolution QEU maps for ACIS-I and S1,2,3 chips.
The integration of advanced characterization, mechanical testing, and modeling methods can rationalize heretofore unexplained aspects of these structures.
Without experimental data and tuned adjustable parameters, none of recommended modeling methods can be used confidently for estimating solubility of gases in ionic liquids.
Detailed modeling methods can also be found from the previous study of Choi et al. (2012).
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