The rapid expansion of electric vehicles (EVs) charging stations underscores the urgent need for robust anomaly detection systems capable of identifying potential malfunctions while preserving data privacy. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training without requiring raw data sharing. However, applying conventional FL approaches to charging station networks presents significant challenges, including non-independent and identically distributed (non-IID) data and gradient conflicts among clients. To address these challenges, we introduce FedPareto, a novel Pareto-optimal FL framework designed to manage gradient conflicts and counter malicious attacks in charging station anomaly detection. FedPareto features a gradient conflict-aware aggregation method, which adaptively adjusts client weights based on cosine similarity between gradients, and a gradient magnitude reshaping strategy to enhance model convergence. Theoretical analysis demonstrates that FedPareto achieves a convergence rate of O($\frac{1}{T}$) and attains Pareto-optimal solutions under standard smoothness and convexity assumptions. Extensive experiments on real-world charging station datasets validate FedPareto’s effectiveness. It outperforms state-of-the-art methods, exhibits better robustness against gradient-based attacks, and ensures equitable performance distribution across clients. These results highlight FedPareto’s potential as a reliable and scalable solution for anomaly detection in EV charging station networks.

IoT devices have been widely deployed in medical industry, in the objective of improving diagnostic accuracy and increasing the efficiency of healthcare systems. However, traditional centralized learning approaches often fall short in meeting strict privacy requirements and adapting to emerging diseases in clinical environment. To address this, we propose a novel federated continual learning (CL) framework for disease diagnosis (FCL4DD), designed to enable distributed and incremental learning of new disease classes while safeguarding data privacy. To combat catastrophic forgetting in CL, FCL4DD integrates a replay strategy powered by a weakly supervised diffusion model (WSDM) to generate historical data for diagnosis model training. The WSDM leverages weak supervision into diffusion model to capture the diverse characteristics of the real data, enabling the generation of high-quality synthetic samples that maintain the data’s inherent variability. To overcome the challenges of nonindependent and identically distributed (non-IID) data in federated learning, WSDM is deployed at the central server to generate synthetic disease data that conforms to the global distribution. This synthetic data is then used to retrain client models, reducing discrepancies and enhancing performance consistency across clients. Evaluations on various datasets demonstrates that our method outperforms other state-of-the-art approaches, such as FedEWC, FedLwF, FedWeIT, TARGET, and DDDR, achieving up to a 4.85% accuracy improvement over the second-best method. Code are available athttps://github.com/hysshy/FCL4DD.

Room-temperature superconductors (esp. LK-99 in the recent) have attracted extensive academic attention in recent years, both in academic circles and among the general public. This topic has spread through a number of social media channels, a plethora of contradiction in information has emerged within social networks. There arises the question on how to analyze the consistency and controversy of such scientific knowledge in the dissemination process, and how this process impact on public cognition on the scientific knowledge. In this article, taking room-temperature superconductor as example, we first designed a large language model based factual consistency detection approach to analyze the consistency between research papers and media reports. Then the consistency between media reports and comments is analyzed, by proposing a novel quantification method for media agenda-setting capability, which evaluates the agenda-setting capability of media based on emotional and positional consistencies. The results indicate that two significant deviations occur when room-temperature superconductor knowledge is spread from specialized fields to the public through the various media. One deviation is due to the specialized nature of room-temperature superconductor knowledge, leading to discrepancies between reported content and factual information in research papers. The other deviation is caused by conflicting knowledge, resulting in disparities between media reports and public perception.

The dynamics of public opinion on social media affects people’s feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics’ evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.

Examining the perceptual differences between China and the United States can facilitate a better understanding of their opinions and perspectives, helping to promote peaceful interactions among the two nations as well as the world. This study presents a data-driven approach to measure cognitive differences, investigating these differences from topical and sentimental angles regarding the unmanned balloon event that had been shot down by U.S. warplanes. We also explore the cognitive differences between news media and the followers, and the evolution of topics over time. Our findings reveal those discussions about “balloons” on social media in China and the United States display certain differences in terms of sentiment. In addition, we assess the impact of this event on U.S.–China relationship, particularly in trade. To evaluate the analytical capabilities of the popular ChatGPT model, we use this event as a case study to demonstrate that ChatGPT-like models may have limited capabilities for such kind of specialized analysis. The dataset utilized here is made available for public usage for further investigation on public opinion dynamics for similar events.

Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analyzing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.

The high-frequency gravity forward modeling relying on the residual terrain modeling (RTM) technique is essential for gravity data processing, fine gravity field modeling, geophysical inversion, and so on. However, classical gravity forward modeling methods face challenges such as series divergence and inefficient computation. To improve the computation efficiency, a novel approach using fully connected deep neural network (FC-DNN) for RTM terrain gravity field modeling is introduced in this study. By employing mean squared error (MSE) as the loss function, the method directly learns the mapping between terrain and gravity anomaly to predict RTM terrain gravity anomaly at any elevation, significantly enhancing computational efficiency. In addition, to boost the network’s generalization capability, a novel terrain information fusion regularization method is utilized to create an Improved FC-DNN with a refined loss function. The accuracy, computational efficiency, and generalization performance of FC-DNN and Improved FC-DNN are evaluated and compared in the Wudalianchi volcanic region and the Himalayas. The findings reveal that determined RTM terrain gravity fields based on both FC-DNN and Improved FC-DNN meet the mGal-level accuracy in these regions, with a remarkable 10$000\times $increase in computational efficiency compared to the classical Newtonian integration method. The Improved FC-DNN exhibits superior generalization ability, with accuracy enhancements ranging from 7% to 21% compared with FC-DNN.