A Joint Scheduling Scheme for WiFi Access TSN.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

Improving Graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for Graph Anomaly Detection.

The era of Industry 4.0 is gradually transforming our society into a data-driven one, which can help us uncover valuable information from accumulated data, thereby improving the level of social governance. The detection of anomalies, is crucial for maintaining societal trust and fairness, yet it poses significant challenges due to the ubiquity of anomalies and the difficulty in identifying them accurately. This paper aims to enhance the performance of the current Graph Convolutional Network (GCN)-based Graph Anomaly Detection (GAD) algorithm on datasets with extremely low proportions of anomalous labels. This goal is achieved through modifying the GCN network structure and conducting feature extraction, thus fully utilizing three types of information in the graph: node label information, node feature information, and edge information. Firstly, we theoretically demonstrate the relationship between label propagation and feature convolution, indicating that the Label Propagation Algorithm (LPA) can serve as a regularization penalty term for GCN, aiding in training and enabling learnable edge weights, providing a basis for incorporating node label information into GCN networks. Secondly, we introduce a method to aggregate node and edge features, thereby incorporating edge information into GCN networks. Finally, we design different GCN trainable weights for node features and co-embedding features. This design allows different features to be projected into different spaces, greatly enhancing model expressiveness. Experimental results on the DGraph dataset demonstrate superior AUC performance compared to baseline models, highlighting the feasibility and efficacy of the proposed approach in addressing GAD tasks in the scene with extremely low proportions of anomalous data.

Glutathione-depletion reinforced enzyme catalytic activity for photothermal assisted bacterial killing by hollow mesoporous CuO.

The emergence and prevalence of drug-resistant bacteria caused by the overuse of antibiotics pose new challenges to the treatment of bacterial infections. In this work, hollow mesoporous CuO nanozymes (HM-CuO nanozymes) as excellent antibacterial agents were prepared by a template method. The synthesized HM-CuO nanozymes exhibit peroxidase-like catalytic activity, which can efficiently catalyze H2O2 to generate toxic reactive oxygen species (ROS), causing fatal damage to bacteria. Moreover, the hyperthermia of HM-CuO produced by photothermal therapy (PTT) not only effectively kills bacteria but also enhances the catalytic activity of nanozymes and produces more ROS. Moreover, the HM-CuO nanozymes have a glutathione (GSH)-depleting function to effectively consume GSH in bacteria and generate Cu(I) with higher catalytic effect, which can significantly improve the sterilization effect and produce a 100% inhibitory rate against E. coli and S. aureus. Overall, the HM-CuO nanozymes with strong peroxidase-like catalytic activity, excellent photothermal performance and GSH consumption ability offer a promising synergistic strategy for clinical bacterial infection.