Internal control is the key to achieve high-quality development of enterprises, but internal control failure cases frequently occur at home and abroad. Therefore, it is particularly important to explore ways to improve the quality of internal control and promote high-quality development of enterprises. Taking non-financial listed enterprises in China from 2015 to 2020 as research samples, this paper adopts empirical research methods to research the impact and mechanism of employee welfare on the quality of internal control of enterprises from the perspective of all-employee governance, aiming to explore ways for enterprises to improve the quality of internal control.The results showed that employee welfare can improve the quality of internal controls significantly. Furthermore, the effect is more significant in non-employee-intensive industries and high-marketization areas. Based on the analysis of internal control elements, the research found that employee welfare affects the quality of internal control mainly through two elements, risk assessment and control activities. However, the effects of employee welfare on the three other elements are insignificant. This paper enriched the relevant research on the economic consequences of employee welfare and the factors affecting the quality of internal control, and has certain enlightenment significance for the popularization of employee welfare, especially supplementary pension, and also provides a reference path for the improvement of the quality of internal control.
Power system load forecasting is crucial for power system planning, operation, and control, which reduces operational costs and improves economic efficiency. However, the current forecasting techniques, including LSTM and ARIMA models, ignore the influence of important factors like weather conditions, public holidays, and social events on power system load, which may give rise to inaccurate prediction results. To mitigate this issue, the present work makes use of the Mann-Kendall mutation detection algorithm to detect abrupt changes in power system load caused by the factors mentioned above. A correction function is then developed to improve the prediction accuracy of a conventional prediction model like ARIMA. The experimental results validate the effectiveness of the proposed approach.
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