The traditional security risk monitoring technology cannot adapt to cyber-physical power systems (CPPS) concerning evaluation criteria, real-time monitoring, and technical reliability. The aim of this paper is to propose and implement a log analysis architecture for CPPS to detect the log anomalies, which introduces the distributed streaming processing mechanism. The processing mechanism can train the network protocol feature database precisely over the big data platform, which improves the efficiency of the network in terms of log anomaly detection. Moreover, we propose an ensemble prediction algorithm based on time series (EPABT) considering the characteristics of the statistical log analysis to predict abnormal features during the network traffic analysis. We then present a new asymmetric error cost (AEC) evaluation criterion to meet the characteristics of CPPS. The experimental results demonstrate that the EPABT provides an efficient tool for detecting the accuracy and reliability of abnormal situation prediction as compared with the several state-of-the-art algorithms. Meanwhile, the AEC can effectively evaluate the differences in the cost between the high and low prediction results. To the best of our knowledge, these two algorithms provide strong support for the practical application of power industrial network security risk monitoring. INDEX TERMS Ensemble learning, security risk monitoring, big data, log analysis, evaluation criteria.

This paper considers an adaptive event‐triggered robust H∞ control for the Takagi–Sugeno (T‐S) fuzzy under the networked Markov jump systems (NMJSs) with time‐varying delay. First, a new adaptive event‐triggered scheme is developed to guarantee the T‐S fuzzy NMJSs, and as a result, communication energy consumption reduced while device efficiency is maintained. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and the fuzzy controller. One of the main objectives of this article is to construct the fuzzy state‐feedback controller (mode‐dependent) in a closed‐loop form for stochastic stability for all admissible parameter uncertainties with an H∞ performance index. Different from the conventional triggering mechanism, in this paper, the parameters of the triggering function are based on a new adaptive law that is obtained online rather than a predefined constant. To achieve the less conservative control design, a new type of stochastic Lyapunov–Krasovskii functional is designed by decomposing method, in which the delay interval transforms into various equidistant subintervals in terms of linear matrix inequalities. An example of a truck‐trailer application is used to demonstrate the effectively of the proposed algorithms.

Global health, as well as worldwide development regimes, was seriously threatened by the COVID-19 pandemic and Delta variant outbreaks. In addition to pledging to adapt to and mitigate climate change, experts, economists, and policymakers expressed their determination to do so. Green growth and sustainable development have become the focus of policymakers and governments. The progress toward green economic efficiency (GEE), which will benefit the economy, society, and environment, continues. In terms of green growth and development, implementing environmental regulations and policies has been one of the most challenging aspects of the process. China, the world's second-largest economy, has begun its journey to GEE. Nonetheless, the green economy faces many challenges. The objective of the study is to use AHP analysis to analyze environmental regulation and GEE in China. Accordingly, the study identified three alternative approaches to achieve GEE by analyzing four criteria and ten sub-criteria in the context of environmental regulations in China. The analytical hierarchy process (AHP) has been used to rank criteria, sub-criteria, and alternative approaches. According to the model, China's best path to GEE is through resource efficiency and green purchasing strategies. This article offers an insightful assessment of sustainable development in the Chinese economy.

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Summary This paper is focused on reliable controller design for a composite‐driven scheme of networked control systems via Takagi‐Sugeno fuzzy model with probabilistic actuator fault under time‐varying delay. The proposed scheme is distinguished from the other schemes as mentioned in this paper. Aims of this article are to solve the control problem by considering the H∞, dissipative, and L2−L∞ constraints in a unified way. Firstly, to improve the efficient utilization of bandwidth, the adaptive composite‐driven scheme is introduced. In such a scenario, the channel transmission mechanism can be adjusted between adaptive event‐triggered generator scheme and time‐driven scheme. In this study, the threshold is dependent on a new adaptive law, which can be obtained online rather than a predefined constant. With a constant threshold, it is difficult to get the variation of the system. Secondly, a novel fuzzy Lyapunov‐Krasovskii functional is constructed to design the fuzzy controller, and delay‐dependent conditions for stability and performance analysis of the control system are obtained. Then, LMI‐based conditions for the existence of the desired fuzzy controller are presented. Finally, an inverted pendulum that is controlled through the channel is provided to illustrate the effectiveness of the proposed method.