A supply driven dynamic input-output inoperability model (SDIIM), which extends the classical dynamic inoperability input-output model (DIIM), is described in this paper. The classical DIIM was developed to help understand the infrastructure interdependencies of deliberate external attacks or unfortunate natural disasters, such as terrorist attacks or earthquakes and flooding. However, classical DIIM is a demand-driven model. More supply driven sectors exist than demand-driven sectors in interdependent infrastructure systems, but value-added perturbation is the predominant feature in supply driven sectors for an interrupt event. Moreover, compared to classical DIIM, the value added by the proposed SDIIM can be controlled more easily than the final demand of a disruptive event because the final demand perturbation may be affected by the psychological effect on consumers. A supply driven input-output inoperability model (IIM) has also been proposed to complement the classical IIM. However, the supply driven...

Urban rail transit has received much attention in the last two decades, and a significant number of cities have established urban rail transit networks (URTNs). Although URTNs have brought enormous convenience to the daily life of citizens, system failures still frequently occur, therefore the vulnerability of URTNs must be a concern. In this paper, we propose a novel measurement called the node strength parameter to assess the importance of nodes and present a redundant recovery scheme to imitate the system recovery of URTNs subjected to failures. Employing three malicious attacks and taking the Nanjing subway network as the case study, we investigated the network vulnerability under scenarios of different simulated attacks. The results illustrate that passenger in-flow shows the negligible impact on the vulnerability of the node, while out-flow plays a considerable role in the largest strength node-based attack. Further, we find that vulnerability will decrease as passenger out-flow increases, and the vulnerability characteristics are the same with the increase in the construction cost of URTNs. Considering different attack scenarios, the results indicate that the highest betweenness node-based attack will cause the most damage to the system, and increasing the construction cost can improve the robustness of URTNs.

The capability of cyber-physical power system (CPPS) to recover from cascading failures caused by extreme events and restore prefailure functionality is a critical focus in resilience research. In contrast to the strongly coupled systems studied by most researchers, this article examines weakly coupled CPPS, exploring result-oriented recovery approaches to enhance system resilience. Various repair methods are compared in terms of the resilience of weakly connected CPPS across different coupling modes and probabilities of failover. Utilizing the Q-learning algorithm, an optimized sequence for network restoration is obtained to minimize the negative influence of failures on network functionality while reducing power loss. The proposed method's effectiveness and generalizability have been comprehensively verified through simulation experiments by establishing weakly coupled CPPS for the IEEE 39, IEEE 118, and IEEE 300 networks and their corresponding scale-free networks. Its rationality was verified through two recovery mechanisms: single-node recovery and multinode recovery. By comparing the proposed method with heuristic recovery methods and optimization-based recovery methods, we found that it can significantly accelerate network recovery, and improve network resilience, achieving better resilience centrality. These findings provide valuable insights for decision making in CPPS recovery work.