Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, B_t] -ABBP (back propagation based on adaptive strategy with parameters k and B_t) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the [k, B_t] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

Objective. This study focuses on the identification of risk factors, classification of stroke level, and evaluation of the importance and interactions of various patient characteristics using cohort data from the Second Hospital of Lanzhou University. Methodology. Risk factors are identified by evaluation of the relationships between factors and response, as well as by ranking the importance of characteristics. Then, after discarding negligible factors, some well‐known multicategorical classification algorithms are used to predict the level of stroke. In addition, using the Shapley additive explanation method (SHAP), factors with positive and negative effects are identified, and some important interactions for classifying the level of stroke are proposed. A waterfall plot for a specific patient is presented and used to determine the risk degree of that patient. Results and Conclusion. The results show that (1) the most important risk factors for stroke are hypertension, history of transient ischemia, and history of stroke; age and gender have a negligible impact. (2) The XGBoost model shows the best performance in predicting stroke risk; it also gives a ranking of risk factors based on their impact. (3) A combination of SHAP and XGBoost can be used to identify positive and negative factors and their interactions in stroke prediction, thereby providing helpful guidance for diagnosis.

By using the idea of principal component analysis, we propose an approach to applying the classical skewness and kurtosis statistics for detecting univariate normality to testing high-dimensional normality. High-dimensional sample data are projected to the principal component directions on which the classical skewness and kurtosis statistics can be constructed. The theory of spherical distributions is employed to derive the null distributions of the combined statistics constructed from the principal component directions. A Monte Carlo study is carried out to demonstrate the performance of the statistics on controlling type I error rates and a simple power comparison with some existing statistics. The effectiveness of the proposed statistics is illustrated by two real-data examples.