Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries.

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

The continuous change in formation configurations offers significant potential for formation flight. However, existing methods neglect the potential of autonomous navigation and learning ability based on environmental conditions, which would limit scalability for large formations to some extent. To address this, this paper presents an integrated framework that incorporates an innovative policy for configuration learning into multi-agent flight systems. Firstly, a decoupled trajectory optimization model is proposed, consisting of prediction and learning modules that ensure local adaptivity, obstacle avoidance and dynamic feasibility of formation configuration. Secondly, we design a stage cost function representing obstacle conditions, configuration variability and minimum distance constraints between agents. Then, to enhance adaptability in constrained scenarios, we introduce a reference value generation method that adjusts trajectory learning based on local observations, penalizing errors. Finally, we propose a configuration learning strategy called the local-level action planner and global cost, which coordinates local trajectory optimization with global maintenance. This method integrates distributed model predictive control (DMPC) with modified multi-agent proximal policy optimization (MAPPO), easing the challenge of tracking trajectories from varying rigid geometric shapes. Simulation results demonstrate that the proposed approach improves success rate compared to traditional methods while maintaining formation and avoiding obstacles.

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To address the escalating vulnerability of metro systems under multiple perturbations—including extreme rainfall, equipment failures, and passenger surges—this study tackles several limitations in existing research: the predominant focus on single-layer topology, the neglect of cross-layer coupling effects between physical facilities and functional systems, the lack of dynamic global information in critical node identification, and the insufficient consideration of network clustering characteristics in cascading failure analysis. Drawing on complex systems theory, this study constructs a physical–functional bilayer coupled network model, proposes three improved Deep Q-Network algorithms for identifying cross-layer critical nodes, and introduces a cluster-augmented sandpile model to simulate the differentiated propagation of cascading failures. An empirical case study of the Zhengzhou Metro network demonstrates that the constructed bilayer network exhibits scale-free properties, that the improved DQN algorithms significantly outperform classical benchmarks—including degree centrality, betweenness centrality, closeness centrality, and the greedy algorithm—in sequential disruption efficiency, and that the safety tolerance coefficient and limit coefficient exert substantial regulatory effects on network vulnerability. The methodological framework developed herein—integrating bilayer coupled modeling, deep reinforcement learning-based critical node identification, and cluster-augmented sandpile cascading failure analysis—provides a transferable technical pathway for vulnerability assessment of multilayer coupled networks, with its applicability validated through the Zhengzhou case.