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The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6 × 10 − 4 compared with the baseline model. [ABSTRACT FROM AUTHOR]

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents' decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance. [ABSTRACT FROM AUTHOR]

Bacterial wilt, caused by Ralstonia solanacearum, one of the most destructive phytopathogens, leads to significant annual crop yield losses. Type III effectors (T3Es) mainly contribute to the virulence of R. solanacearum, usually by targeting immune-related proteins. Here, we clarified the effect of a novel E3 ubiquitin ligase (NEL) T3E, RipAW, from R. solanacearum on pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and further explored its action mechanism. In the susceptible host Arabidopsis thaliana, we monitored the expression of PTI marker genes, flg22-induced ROS burst, and callose deposition in RipAW- and RipAWC177A-transgenic plants. Our results demonstrated that RipAW suppressed host PTI in an NEL-dependent manner. By Split-Luciferase Complementation, Bimolecular Fluorescent Complimentary, and Co-Immunoprecipitation assays, we further showed that RipAW associated with three crucial components of the immune receptor complex, namely FLS2, XLG2, and BIK1. Furthermore, RipAW elevated the ubiquitination levels of FLS2, XLG2, and BIK1, accelerating their degradation via the 26S proteasome pathway. Additionally, co-expression of FLS2, XLG2, or BIK1 with RipAW partially but significantly restored the RipAW-suppressed ROS burst, confirming the involvement of the immune receptor complex in RipAW-regulated PTI. Overall, our results indicate that RipAW impairs host PTI by disrupting the immune receptor complex. Our findings provide new insights into the virulence mechanism of R. solanacearum. [ABSTRACT FROM AUTHOR]