During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

This paper is to discuss the state, trend, and frontiers of development of cyber-physical-social systems (CPSSs) in China. The demand for developing CPSS is discussed in detail, followed by the Artificial societies, Computational experiments, Parallel execution (ACP) approach for CPSS and knowledge automation. The development of ACP based on CPSS in transportation, energy, information, Internet of Things, and Internet of Minds (IoM) is discussed to demonstrate the cutting-edge applications in CPSS. Finally, the blockchainized IoM technology and the concepts of parallel society are described. This paper will contribute to the transition from the current social construct to a futuristic intelligent society.

Artificial intelligence (AI) technology has become an important trend to support the analysis and control of complex and time-varying power systems. Although deep reinforcement learning (DRL) has been utilized in the power system field, most of these DRL models are regarded as black boxes, which are difficult to explain and cannot be used on occasions when human operators need to participate. Using the explainable AI (XAI) technology to explain why power system models make certain decisions is as important as the accuracy of the decisions themselves because it ensures trust and transparency in the model decision-making process. The interpretability issue in DRL models in power system emergency control is discussed in this article. The proposed interpretable method is a backpropagation deep explainer based on Shapley additive explanations (SHAPs), which is named the Deep-SHAP method. The Deep-SHAP method is adopted to provide a reasonable interpretable model for a DRL-based emergency control application. For the DRL model, the importance of input features has been quantified to obtain contributions for the outcome of the model. Further, feature classification of the inputs and probabilistic analysis of the outputs in the XAI model is added to interpretability results for better clarity.

In this paper, we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation (HM-KA) as the technical mechanism of hybrid aug

The ever-increasing penetration of Electric Vehicles (EVs) has emerged as an evident challenge within the Power Distribution System (PDS). Existing Distribution System State Estimation (DSSE) models are constrained by the low-observability and often neglect the spatio-temporal correlations inherent in the PDS. To address this, our study proposes a robust learning architecture named meta physics-informed graph TimesNet that can effectively learn the evolving topology of the PDS, as well as capture the spatio-temporal correlations of the PDS state, even under the condition of limited data availability. The model leverages a combination of a graph convolutional network and TimesNet as the foundational modules for constructing an encoder-decoder framework. Furthermore, to capture the spatio-temporal heterogeneity of the PDS state, our study inserts a topology learning module driven by a meta physics-informed graph bank into the encoder-decoder framework. Subsequently, power flow calculations are performed on the predicted power to reduce error accumulation. The IEEE 8500-node test feeder and UTD19 are utilized as the datasets for the PDS and urban transportation system, respectively. By using 42 electric vehicle charging stations as coupling points, a novel large-scale power-transportation coupled system dataset is constructed through multi-source information fusion. The performance of our proposed model on this dataset has shown to surpass existing DSSE methods in terms of accuracy (MAPE, etc.) and effectiveness, particularly considering the increasing prevalence of EVs. Codes are available at https://github.com/lishijie15/MPGTN-for-DSSE .

Motor-bearing fault diagnosis is critical for industrial equipment reliability, yet traditional data-driven methods require extensive labeled data, which are often scarce in real-world applications. To address this challenge, we propose a Transformer transfer learning network (TTLN) for accurate fault diagnosis under cross-condition scenarios, particularly when target domain data are limited. First, we develop a Transformer-based fault diagnosis model that captures long-range dependencies in sequential data through self-attention, achieving high accuracy under single operating conditions. Second, we introduce the TTLN framework, which integrates domain adaptation to align marginal and conditional distributions, enabling robust cross-condition fault diagnosis with minimal target domain samples. Finally, we validated the proposed method on the CWRU and PU datasets, demonstrating the TTLN’s superior performance in data-scarce scenarios. For example, the TTLN achieved over 95% accuracy with only 100 target samples, outperforming traditional methods and fine-tuning-based approaches. The results underscore the TTLN’s effectiveness in cross-condition fault diagnosis, offering a practical solution for industrial applications with limited labeled data.