Sentence examples for predictions are capable from inspiring English sources

The present predictions are capable of embodying the well-known S-shaped response of filled rubbers, and also verified by the comparison with the experimental and analytical results.

It is demonstrated that the model predictions are capable of capturing the non-linear characteristics of the unsaturated compression lines and allow smooth transitions (i) from elastic deformation to elastoplastic deformation at yielding (gradual yielding) and (ii) from unsaturated condition to saturated condition at high stress level (pressurized saturation).

Accordingly, both numerical techniques deliver fast and accurate predictions and are capable of outperforming equivalent analytic methods for automotive dissipative silencers.

Although breakout prediction systems (BOPS) are capable of predicting breakouts, these systems are unable to significantly reduce the number of breakouts because they only notify the operator to examine potential symptoms of breakouts.

Moreover, partial least squares (PLS) regression has demonstrated that measurements carried out with the electronic system allow establishing prediction models that are capable to infer the methodology used to age wines.

The quantitative predictions of the model are capable of giving accurate comparisons with experimental measurements, particularly with those of Kramer and co-workers on the penetration of iodoalkanes into polystyrene.

In total, the four senescence sub-models used for evaluation contain 259 unique nodes, 126 (49%) of which were eligible for prediction (meaning that they contain four or more downstream gene expression relationships and thus are capable of prediction as a hypothesis) by RCR.

The results are shown in Figure 2. In combination, the two predictors were capable of generating predictions for about 80 95% of all bacterial proteins encoded in the selected bacterial genomes, which exhibits an impressive increase versus the previous predictive capability of PSORTb 2.0 (57 75%) (Gardy et al., 2005) and PA 2.5 (67 76%) (Lu et al., 2004).

It is clearly seen that the proposed models are capable of prediction of the surface roughness.

Calculated difference (f1) and similarity (f2) factors indicate that dynamic networks are capable of accurate predictions.

Results of regression fit and validation analysis yield high R2 values and low NRMSE values, indicating that the ROMs derived from PCE are capable of meaningful predictions (compared to conventional reservoir models) and effectively represent relationships between model parameters (inputs) and model results (outputs) of interest.