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Effective short-term wind power prediction is crucial to the optimal dispatching, system stability, and operation cost control of a power system. In order to deal with the intermittent and fluctuating characteristics of the wind power time series signals, a hybrid forecasting model is proposed, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Whale Optimization Algorithm (WOA)-Kernel Extreme Learning Machine (KELM), to predict short-term wind power. Firstly, the non-stationary wind power time series was decomposed into a series of relatively stationary components by CEEMD. Then, the components were used as the training set for the KELM prediction model, in which the initial values and thresholds were optimized by WOA. Finally, the predicted output values of each component were superimposed, to obtain the final prediction of the wind power values. The experimental results show that the proposed prediction method can reduce the complexity of the prediction with a small reconstruction error. Furthermore, performance is greater, in terms of prediction accuracy and stability, with lower computational costs than other benchmark models. [ABSTRACT FROM AUTHOR]

Background: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5–methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. Methods: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. Results: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10–5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells. Conclusion: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target. [ABSTRACT FROM AUTHOR]

• A two-stage prediction model is proposed. The residual life of bearings is obtained. • The improved model achieves short-term prediction under small sample conditions. • The high-precision RUL prediction under small sample environment is realized. Rolling-element bearing is one of the main parts of rotating equipment. In order to avoid the mechanical equipment damage caused by the sudden failure of rolling-element bearings, it is necessary to monitor the condition of bearing and predict its life. Therefore, a two-stage prediction method based on extreme learning machine is proposed to predict the remaining useful life of rolling-element bearings quickly and accurately. This method uses the relative root mean square value (RRMS) to divide the operation stage of the bearing into two stages: normal operation and degradation. Starting from the normal operation stage, according to the principle of univariate prediction, a feedback extreme learning machine model is constructed for real-time short-term prediction of bearing degradation trend. Once the predicted value shows that the bearing has entered the degradation stage, the sensitive features are selected as the input by correlation analysis, and the multi variable feedback extreme learning machine model, which takes into account the dual advantages of multivariable regression and small sample prediction, is constructed to predict the remaining useful life. The experimental results show that the proposed method has higher short-term prediction accuracy and faster operation speed in the case of limited learning sample size. [ABSTRACT FROM AUTHOR]