The first geostationary sensors produced dramatic images of storms on short time scales, permitting their evolution to be monitored as never before. Prediction of weather now benefits from numerical weather prediction models, which require temperature and humidity inputs from soundings. Significant weather is often located in cloudy areas where infrared (IR) soundings are degraded or fail, and ...

MOTIVATION Proteins function through interactions with other proteins and biomolecules. Protein-protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of it...

the motivation for this work is to propose a first thorough review of dough rheological models used in numerical applications. although many models have been developed to describe dough rheological characteristics, few of them are employed by researchers in numerical applications. this article reviews them in detail and attempts to provide new insight into the use of dough rheological models.

In computational physics and engineering, numerical models are developed to predict the behavior of a system whose response cannot be measured experimentally. A key aspect of science-based predictive modeling is the assessment of prediction credibility. Credibility, which is demonstrated through the activities of Verification and Validation, quantifies the extent to which simulation results can...

We compare structural different methods of the artificial intelligence for wind power prediction modeling and build additionally ensembles of the models. As input variables for these prediction methods weather data of a numerical weather prediction model are used. The performance of the presented methods is compared to the predictions of the neural network based model.

Acknowledgments

We present the linear analysis of recent time filters used in numerical weather prediction. We focus on the accuracy and the stability of the leapfrog scheme combined with the Robert–Asselin–Williams filter, the higher-order Robert–Asselin type time filter, the compositetendency Robert–Asselin–Williams filter and a more discriminating filter.

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smo...