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The mathematical and experimental modeling requires very attentive treatment.
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The model requires very little data as basis and can therefore be used in the initial stages of reactor design.
The proposed FE model is simple compared to the existing numerical models, requires very low computational effort and may be potentially used for the design and optimization of multifunctional nanocomposites.
While the conventional methods calculating multiaxial fatigue life with critical plane model require very long-time, this method outputs instantaneously result for a given set of input after the ANN model has been trained.
These closed-loop models, or system models, require very careful tuning.
Assessment of individual exposure variability and validation of exposure models require very intensive data collection that is feasible only in an even smaller number of subjects.
Complex constructive solid modeling usually requires very fast and expensive special purpose hardware, which is not widely available.
DEA is a non-parametric mathematical modeling technique which requires very few assumptions and can be used in performance evaluation problems with complex relations between multiple inputs and multiple outputs.
However, the development of models for mixtures requires very substantial effort regarding the storage and representation of mixtures, calculation of descriptors, and implementation of correct validation protocols.
The automation is applied to a special design problem of Model-based Predictive Controls that may contain the heuristic modeling uncertainty in case of custom modeling, and that also requires very frequent system analyses during development.
Alternatively, microgeographic, seasonal and female quality constraints may be simultaneously reduced if one can analyze the changes of mate attractiveness within individual females (see [58]), e.g. in random regression animal models [84], but this requires very large datasets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com