Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. Security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes.

Monitoring and forecasting aerial animal migration benefit biological conservation, aviation safety, and agricultural production. Due to the lack of large-scale observation data and quantitative knowledge of aerial animal migration mechanisms, it is difficult to build a numerical simulation system for migration prediction. However, the extensive deployment of weather radars makes it possible to obtain large-scale aerial migration information. Meanwhile, artificial intelligence technologies provide new insights into the modeling of complex system. In this article, we develop a deep-learning model to predict aerial migration from the perspective of spatio-temporal evolution. Specifically, an undirected graph is applied to describe the geographic structure of the weather radar network, and then graph convolution and gated recurrent unit (GRU) are combined to extract spatio-temporal features of migration information. In addition, a multi-head self-attention mechanism is applied to enhance long-term dependence. Experiments are conducted to validate the effectiveness of the proposed model on the data from the Chinese weather radar network. The results show that our model can achieve state-of-the-art performance among the competing methods. Moreover, improvements from graph convolution and multi-head self-attention are also analyzed. In future applications, more weather radar data will be collected to enrich the dataset and build an aerial migration monitoring and prediction system.

Power grid expansion, load demand increase, and renewable energy integration all present severe challenges to the grid operations. As transmission networks become increasingly stressed, research on voltage stability analysis (VSA) has attracted widespread attentions. Voltage instability usually occurs within a short period, which makes it difficult to be detected. To assess the stability margin, P-V curve method has been widely used for years. However, this approach is only effective when the load power factor or reactive power load does not change. To overcome this weakness, P-Q curve method can be applied to analyze the voltage stability problems. In this paper, the performance of both P-V and P-Q methods is tested on the IEEE 14 bus system. Simulation results show that P-Q curve method is more intuitive and convenient than P-V curve method on assessing the load margins.