Under the background of the deepening reform of China’s electric power industry, how to make use of existing resources, keep existing employees, and identify and distinguish the influencing factors of turnover have become major problems faced by companies. At the same time, the identification of influencing factors also provides a reference for relevant enterprises to do a good job in talent reserve and recruitment. The influencing factors of employee turnover are numerous and interrelated. Identifying and distinguishing the influencing factors are the focus of academic research. Meanwhile, it provides a reference for relevant enterprises to do an excellent job in personnel reserve and recruitment. Aiming at the limitation of constructing a direct matrix based on experts’ scoring method in the traditional DEMATEL model, a model combining random forest and DEMATEL is proposed, applied to identify the influencing factors of employee turnover in electric power enterprises in Qinghai Province. Based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, the empirical analysis is conducted to analyze the influencing factors of employee turnover from two perspectives of the degree of centrality and the degree of causal. The research results show that marital status, household registration, political status, type of job, and the location of the work unit are the key influencing factors, providing a theoretical basis for the early warning of employee turnover and confirming the feasibility of the random forest‐DEMATEL method proposed by us.

The goal of this paper is to establish some weighted Simpson type inequalities for functions whose first derivatives are convex involving Reimann–Liouville integral operators. In order to obtain our results, we first prove a new integral identity as an auxiliary result. Based on this identity we establish some fractional weighted Simpson type inequalities for functions whose modulus of the first derivatives are convex. Several special cases are discussed. Error estimates for some numerical quadrature rules are furnished.

Accurate prediction of time series is complex due to nonlinear characteristics but can play a significant role in practical problem. In this paper, a novel varying‐coefficient hybrid model is proposed to accurately predict the nonlinear time series. A set of fuzzy radial basis function (FRBF) neural networks is used to approximate the varying functional coefficients of the state‐dependent autoregressive model with exogenous variables (SD‐ARX). The obtained model is called the fuzzy radial basis function network‐based autoregressive model with exogenous variables (FRBF‐ARX), which combines the advantages of the FRBF in function approximation and the SD‐ARX model in nonlinear dynamics description. Then, a structured nonlinear parameter optimization method (SNPOM) and the modified multifold cross‐validation criterion are used to estimate the parameters of the proposed varying‐coefficient FRBF‐ARX model. The performances of the FRBF‐ARX model are used to predict the PM2.5 concentration and simulated SISO nonlinear process, respectively, and the performances of the proposed model are also compared and discussed. The experimental results show that the FRBF‐ARX model has better performances of accuracy on nonlinear time series forecasting than that of other models.