Ruiguo Yu^1,2, Zhiqiang Liu^1,2, Jianrong Wang^1,3, Mankun Zhao^1,2, Jie Gao^1,3, Mei Yu^1,3,*

Computer Systems Science and Engineering, Vol.33, No.4, pp. 267-274, 2018, DOI:10.32604/csse.2018.33.267

Abstract The spatio-temporal feature with historical wind power information and spatial information can effectively improve the accuracy of wind power prediction, but the role of the spatio-temporal feature has not yet been fully discovered. This paper investigates the variance of the spatio-temporal feature. Based on this, a hybrid machine learning method for wind power prediction is designed. First, the training set is divided into several groups according to the variance of the input pattern, and then each group is used to train one or more predictors respectively. Multiple machine learning methods, such as the support vector machine regression and the decision… More >

Sánchez J¹, J Sáenz Mata¹, J Flores³, E Jurado², E Estrada Castillón², O Aguirre², G Muro¹

Phyton-International Journal of Experimental Botany, Vol.87, pp. 252-259, 2018, DOI:10.32604/phyton.2018.87.252

Abstract The chemical and physical properties of the substrate produce positive or negative effects on the germination of seeds, and different pre-germination treatments are applied to obtain better results. However, the use of soil as a substrate where the species grows is usually not evaluated or used as a treatment. The objective of the present study was to evaluate the effect of the native substrate [two types of substrate where the woody species grow (clay and sand)] on the germination of Acacia farnesiana, Larrea tridentata and Prosopis laevigata. In addition, two groups were compared in seeds (grouped and not grouped) of… More >