Sentence examples for this combination of modelling from inspiring English sources

This combination of modelling variables within and between time periods has the potential to increase our understanding of variation while expanding the scope and value of the study.

A strength of this analysis is the combination of modeling corroborated by empirical studies to provide a baseline estimate of predicted premature mortality associated with secondary organic particulate formation.

The present work proposes a combination of modelling techniques, addressing these challenges.

The model with B = 0.09 year was obtained as the optimum, and this combination of the model parameters is also acceptable with respect to the best-fitted viscoelastic model according to a Chi-squared test.

This combination of culture models could represent parts of the human body in a microphysiological system.

Significant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux.

The PARATUCK‐2 model (55) can then be viewed as a Tucker‐2 model X = G × 1 A ( 1 ) × 2 A ( 2 ) with the core tensor G ∈ C R 1 × R 2 × I 3 given by the Hadamard product of C ∈ C R 1 × R 2 and F ∈ C R 1 × R 2 × I 3 along their common modes {r1,r2} G = C ⊙ { r 1, r 2 } F This combination of a Tucker‐2 model for with a PARAFAC model for gave rise to the name PARATUCK‐2.

It was shown that the statistical narrow band model (SNB) combined with the discrete ordinate method (DOM) reproduced the experimental results with highest accuracy, and this combination of the radiation models were adopted in the subsequent parametric studies in Part II.

We determined empirically that this combination of transformations maximizes model performance, while providing a simple procedure for converting predicted PAH tissue concentrations from modeled units into original wet weight concentration units using a 4th power transformation.

For this model to be applied in clinical decisions making, further validations are imperative in order to assure that this combination of cytokines would successfully model the outcome in other patients populations.

This combination of layer-based modeling and exact model reduction results in the fewest ODEs without the introduction of an additional approximation error as introduced by layer-based modeling.