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Next, the utility of the Transition Interface Sampling was examined as the method was recently shown to be effective for predicting the rate of dislocation-precipitate interactions.
The method is found to be effective for predicting the spatial variation in the response when both the direct and reverberant fields participate strongly, a case not well predicted by statistical energy analysis, or by the conductivity method.
The standard design procedure of comparing Ct will not be effective for predicting the response of the organism to varying dose, C, and contact time, t, and a plot of ozone produced oxidant residual against organism inactivation for various contact times is proposed for design purposes.
While homology-based methods can be effective for predicting the functions of remote homologs, these methods perform poorly as the evolutionary distance between homologous proteins increases.
Akin to the prediction of single chain protein structure, homology modeling and threading techniques (Bienkowska and Lathrop, 2005; Bowie et al., 1991; Dunbrack, 2006; Jones et al., 1992; Pieper et al., 2006; Xu et al., 2003) are likely to be effective for predicting PPIs (Lu et al., 2002; Singh et al., 2006).
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The response surface methodology was effective for predicting and optimizing the batch flotation process of FCMC.
It was demonstrated that the obtained model was effective for predicting lipid productivity of the isolated microalga.
To clarify whether intraoral ultrasonography (I-US) is effective for predicting metastasis of tongue cancer to the cervical lymph nodes.
The results in this figure show that the proposed model is effective for predicting the dip-position.
Upon validation in the ORBIT-AF population, C-statistics showed that the GARFIELD-AF risk tool was effective for predicting 1-year all-cause mortality using the full and simplified model for all-cause mortality: C-statistics 0.75 (0.73 to 0.77) and 0.75 (0.73 to 0.77), respectively, and for predicting for any stroke or systemic embolism over 1 year, C-statistics 0.68 (0.62 to 0.74).
With the results, we also demonstrated that using protein-protein network is effective for predicting protein phenotypes.
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