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It is observed that the data can be satisfactorily fitted using Tòth model with excellent fitting accuracy around 2.2% within the experimental range.
Our study reveals that highly parameterized geostatistical models perform the best among the models compared, while the zonation model with excellent knowledge of stratigraphy also yields comparable results.
The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness.
Simple constitutive equations have been selected based on this interpretation, which lead to a model with excellent predictive capabilities for the uniaxial and equibiaxial mechanical response of human amnion.
The procedure results in a constrained polynomial model with excellent descriptive and interpolating features in comparison with an extended Ratkowsky-type model and classical polynomial model, by combining specific properties of both model types.
Following structural models for lamellar growth that account for epitaxial γ branching from α surfaces, the experimental data were modeled with a parallel two-stage kinetic model with excellent fits up to ∼70% transformations.
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The GFA and LSSVM models were performed the rigorous internal and external validation, further verifying these models with excellent robustness and predictive ability.
The D-optimal design yielded quadratic models with excellent fit and predictive power for the responses water flux through the membrane, and the enrichment factor towards water.
An improved structure mechanical modeling with excellent accuracy is developed for single-cell thin-walled closed-section composite beams based on previous work.
On the other hand, it is easy to construct complex models with excellent or even perfect fit, which are mechanistically meaningless and do not generalize (i.e., "over-fitting").
We report un-weighted multilevel binary logistic regression models with excellent SRH as the reference category.
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