Wind power prediction based on wind speed forecast using hidden Markov model

This study examines a new approach for short-term wind speed and power forecasting based on the mixture of Gaussian hidden Markov models (MoG-HMMs). The proposed approach focuses on the characteristics of wind speed and power in the consecutive hours of previous days. The proposed method is carried out in two steps. In the first step, for the hourly prediction of wind speed, several wind speed features are employed in MoG-HMM, and in the second step, the results obtained from the first step along with their characteristics and wind power features are used to predict wind power estimation. To increase the prediction accuracy, the data used in each step are classified, and then for each class, one HMM with its specific parameters is used. The performance of the proposed approach is examined using real NREL data. The results show that the proposed method is more precise than other examined methods.     

Tension and structure in crustacean fast and slow muscle fibres

Résumé Les fibres de l'abdomen du homard pourvues de longues striations ont développé une plus grande tension par unité de surface, en coupe transversale, que les fibres munies de striations courtes.     

Cellular and molecular pathogenesis of type 1A diabetes

Type 1A diabetes is an organ-specific autoimmune disease resulting from destruction of insulin-producing pancreatic beta-cells. The main susceptibility genes code for polymorphic HLA molecules and in particular alleles of class II MHC genes (DR, DQ and DP). Polymorphisms of individual genes outside the MHC also contribute to diabetes risk but recent evidence suggests that there are additional non-HLA genes determining susceptibility linked to the MHC. It is now possible using genetic and autoantibody assays to predict the development of type 1A diabetes in the majority of individuals, and trials of diabetes prevention are underway.