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The effectiveness of our force-field is further supported by the sequence predictions for surface residues of the SH3 domain.
While some of the turbulence models do provide reasonable predictions for the surface pressure, the predictions for surface heat flux are generally poor, and often in error by a factor of four or more.
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The comparison of computational and available experimental results shows that the proposed model reliably simulates the surface fatigue crack growth under contact loading and can be used for computational predictions of surface pitting for various contacting mechanical elements.
Our simulation results show that the new boundary conditions give better predictions for the surface pressures, compared with published experimental and DSMC data.
The maximum prediction error for surface hardness is less than 1.6%.
Although previous studies have used Landsat to create prediction models for surface temperature, and shown strong correlations between surface temperature and surface imperviousness (Yuan and Bauer 2007), few studies have simultaneously explored the relationship among SI, ground-based temperature measures, and LST calculated from thermal imagery.
Model results for metatarsal pads were considerably higher than measured values but predictions for uniform surfaces were generally within 16% of measured values.
In this study, the comparisons on predictions of surface finish for various work materials with the change of electrode polarity based upon six different neural-networks models and a neuro-fuzzy network model have been illustrated.
It constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature on this subject.
ANFIS with LOO-CV approach is an effective approach for prediction of surface roughness in end milling process.
This paper introduces a new FPM method for prediction of surface subsidence due to inclined coal seam mining.
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