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https://www.selleckchem.com/products/ly364947.html Wind energy is important to the transformation and development of global energy, because it is clean and renewable. However, the productivity of wind power is low due to its volatility, randomness, and uncertainty. Therefore, a new hybrid prediction model based on combined Elman-radial basis function (RBF) and Lorenz disturbance is proposed, which can promote the productivity of wind power by better predicting wind speed, firstly, applying the variational mode decomposition (VMD) algorithm to original nonstationary wind speed data to obtain several relatively stationary intrinsic mode functions (IMF), so as to fully exploit its potential characteristics. Meanwhile, the sample entropy is introduced to determine the decomposition number K. Afterwards, different IMF components with different characteristics are used for training and prediction Elman neural network with sensitivity to historical state data is used for wind speed trend components; RBF with strong nonlinear mapping capability is adopted for other srmulate wind farm control strategies, enhance the self-regulation of wind farm, and further promote global energy innovation.Lower happiness caused by environmental pollution has attracted widespread attention, but existing studies have ignored the impact of environmental governance on happiness, and hardly a research has discussed whether environmental regulations will affect happiness. To make up for the above shortfall, based on the micro data come from Chinese General Social Survey (CGSS) in 2015 and the macro data of 28 provinces in China from 2013 to 2015, this study distinguishes three types of environmental regulations which are economical environmental regulation (EER), legal environmental regulation (LER) and supervised environmental regulation (SER), and the econometrical analysis of the linear relationship or potential nonlinear relationship between them and happiness is carried by ordinary least squ

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