Sentence examples for predictions under various from inspiring English sources

The model gives satisfactory life predictions under various thermo-mechanical loading conditions.

We will use the ratio p = p x p a to make predictions under various scenarios.

In this paper, two different forms of an original multiaxial fatigue damage parameter related to the maximum fatigue damage plane are proposed for performing fatigue life prediction under various loading conditions loadings.

Tables 1 and 2 compare the performances (using several different measures) of the secondary structure prediction under various decodings of our k-mer model for the given profile, versus the original profile-only performance (classification based on maximal probability at each residue position).

Age patterns of fertility and survival were predicted under various diet scenarios using the model, revealing that the predictions of the model are in good agreement with experimental data.

In this paper, a fatigue life prediction model under various load spectra, using the strain energy density factor approach and the plastic zone size near crack tip as main parameters in calculating effective strain energy density factor, has been proposed.

It is shown that the proposed method outperforms the general Markovian-based prediction schemes under various traffic load scenarios.

Equation 3 was used to calculate expected prediction accuracy under various combinations of n and r.

Finally, the model predictions are verified under various experimental conditions and the unique selectivity of this multimodal system is explored and compared with traditional anion exchange resins.

Dimensionless analysis was done on the EIS data sets providing insightful correlations for prediction of i0 under various experimental conditions.

This paper proposes comprehensive local sensitivity analyses (LSA) and global sensitivity analyses (GSA) methodologies to evaluate continuously reinforced concrete pavement (CRCP) performance predictions with MEPDG inputs under various climatic and traffic conditions.