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Cyber-physical power system (CPPS) has significantly improved the operational efficiency of power systems. However, cross-space cascading failures may occur due to the coupling characteristics, which poses a great threat to the safety and reliability of CPPS, and there is an acute need to reduce the probability of these failures. Towards this end, this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services. On this basis, a joint improved risk-balanced service function chain routing strategy (SFC-RS) is proposed, which is modeled as a robust optimization problem and solved by column-and-constraint generation (C-CG) algorithm. Compared with the traditional shortest-path routing algorithm, the superiority of SFC-RS is verified in the IEEE 30-bus system. The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network, enhances information transmission accessibility, and effectively limits communication disruption from becoming the cause of cross-space cascading failures. [ABSTRACT FROM AUTHOR]

Aviation networks exhibit multi-layer characteristics and heterogeneity of nodes and edges. Identifying key nodes in a multi-layer heterogeneous aviation network (MHAN) and elucidating its cascading failure process are of great practical significance for enhancing the ability to resist failure and strengthening network resilience. Therefore, this paper first establishes the basic model of MHAN and then designs an intra-layer node importance evaluation method based on the improved TOPSIS-grey correlation analysis (ITG) method and an inter-layer influence weight quantification method based on the gravity model. By integrating the information transmission characteristics between network nodes, a key node identification method in MHAN through aggregating multi-source information is proposed. Finally, based on the actual overload operation of aviation networks, the initial load adjustment coefficient, capacity limit, and overload coefficient are introduced based on the traditional capacity–load model, a cascading failure model of MHAN considering overload condition and failure probability is constructed, and a node influence index based on this model is defined to verify the accuracy of the key node identification results. The instance analysis conducted on the aviation network in western China demonstrates that the intra-layer node importance evaluation method based on ITG yields results with better distinguishability and higher accuracy. The key nodes are predominantly hub en-route nodes and sector nodes. In the cascading failure model, increasing the overload coefficient and capacity limit within a specific range while reducing the initial load adjustment coefficient helps reduce the network failure scale. The key nodes identified by the node influence index are consistent with those identified by the method proposed in this paper, validating the accuracy and effectiveness of the key node identification method in MHAN through aggregating multi-source information herein. [ABSTRACT FROM AUTHOR]

There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths. [ABSTRACT FROM AUTHOR]

Storage area networks (SANs) are a widely used and dependable solution for data storage. Nevertheless, the occurrence of cascading failures caused by overloading has emerged as a significant risk to the reliability of SANs, impeding the delivery of the desired quality of service to users. This paper makes contributions by proposing both static and dynamic load-triggered redistribution strategies to alleviate the cascading failure risk during the mission time. Two types of node selection rules, respectively based on the load level and node reliability, are studied and compared. Based on the SAN component reliability evaluation using the accelerated failure-time model under the power law, the SAN reliability is evaluated using binary decision diagrams. A detailed case study of a mesh SAN is conducted to compare the performance of different cascading failure mitigation schemes using criteria of SAN reliability improvement ratio and resulting SAN reliability after the mitigation. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, public transport networks are an important part of urban transport systems, and their robustness is critical for city operation. The objective of this study is to analyze the topological properties and robustness of an urban public transport network (UPTN) with a view to enhancing the sustainability of urbanization. In order to present the topological structure of the UPTN, the L-Space complex network modeling method is used to construct a model. Topological characteristics of the network are calculated. Based on single evaluation indices of station significance, a comprehensive evaluation index is proposed as the basis for selecting critical stations. The UPTN cascading failure model is established. Using the proportion of the maximum connected subgraph as the evaluation index, the robustness of the UPTN is analyzed using different station significance indices and deliberate attack strategies. The public transport network of Xuzhou city is selected for instance analysis. The results show that the UPTN in Xuzhou city has small-world effects and scale-free characteristics. Although the network has poor connectivity, it is a convenient means to travel for residents with many independent communities. The network's dynamic robustness is demonstrably inferior to its static robustness due to the prevalence of cascading failure phenomena. Specifically, the failure of important stations has a wider impact on the network performance. Improving their load capacity and distributing the routes via them will help bolster the network resistance against contingencies. This study provides a scientific basis and strategic recommendations for urban planners and public transport managers to achieve a more sustainable public transport system. [ABSTRACT FROM AUTHOR]

The deep integration of power grid and information network enhances the controllability of the power system while reducing its ability to resist external interference. Firstly, an interdependent model of cascading failures in the cyber-physical power system (CPPS) was established based on the proposed load redistribution scheme using node load and capacity probabilities. This model was used to investigate the robustness and controllability changes in the CPPS under different coupling strategies. Secondly, the concept of surviving load percentage was utilized to quantify the power grid's ability to withstand cascading failures. The effects of different coupling strategies and capacity parameters on the robustness and controllability of CPPS were analyzed. The assessment method for power node importance was proposed based on network controllability theory and validated in the IEEE39 system. The research results indicate that the coupling of high-betweenness nodes in the power grid and high-betweenness nodes in the information network leads to a stronger resistance to interference in the power information physical network, and the capacity parameters of power grid nodes have a greater impact on the system compared to the capacity parameters of information network nodes. [ABSTRACT FROM AUTHOR]

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method's effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced. [ABSTRACT FROM AUTHOR]

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making. [ABSTRACT FROM AUTHOR]

Guided by the intelligent construction of coal mines and integrating the concept of the Cyber-Physical System (CPS), a novel approach to a coal mine information-physical fusion system is proposed. This concept emerges from the advancement of intelligent mines and the growing need for enhanced safety in coal mine operations. From a system safety perspective, this approach aims to effectively evaluate security issues by better integrating the informational and physical layers of intelligent mines. Currently, research in areas such as power grids and water networks has widely adopted concepts of cascade failures and robustness. It is suggested that the CPS framework be adapted to the mining sector. Considering the critical role of coal mines in China's energy security and their vulnerability to cyber attacks, a mine-specific CPS system is designed. This system incorporates a model of mine information-physical fusion, including networks for mine operations, management, and control, tailored to the unique conditions of mine shafts and underground settings. Utilizing Markov process theory, the paper delves into the theoretical study of the cascade failure process under deliberate attack strategies. This analysis is essential to effectively assess the safety issues within the information-physical system, providing vital support for fault prediction and safety analysis. The model of cascading failures in the mine information-physical system is developed, and based on an analysis of resilience and robustness, the study identifies key factors influencing the security of mine information-physical systems. Critical factors affecting the reliability of these systems include the number of system nodes, node coupling, edge count, and the interconnection methods of the dual-layer dependency network. The paper concludes by discussing the challenges in analyzing the security of information-physical systems and suggesting directions for future research.

When studying an unfamiliar system, we first look for the symmetry that the system has so that we can make many predictions about the possible properties of the system. The symmetry in transport network security needs to maintain a stable state and maintain a constant state of transport network security. With the development of China–Europe freight trains, the transport between Asia and Europe has gradually formed the Belt and Road (B&R) land–sea transport network. In order to analyze the cascading failure mechanism of the B&R land–sea transport network, a network cascading failure model is constructed. Then, the quantitative analysis of the connectivity indicators of the land–sea transport network is conducted from the node attack strategy, and it is compared with the Maritime Silk Road (MSR) shipping network. Finally, the robustness of the land–sea transport network under emergencies is analyzed. From the results of deliberate attacks, the attack threshold of the B&R land–sea transport network is the same as that of the MSR shipping network, and the maximum number of attacks is slightly less than that of the MSR shipping network. The Russia–Ukraine conflict has a minimal impact on the robustness of cascading failure in the land–sea transport network. The Red Sea crisis may have a significant impact on the robustness of cascading failure in the land–sea transport network. The research results can provide suggestions for improving the robustness of the B&R land–sea transport network. [ABSTRACT FROM AUTHOR]

A decentralized control algorithm for routing flow in complex networks is proposed. The aim is to fairly distribute the supply/demand among the nodes while maintaining maximum flow in the network and satisfying the flow limits of the nodes and links. The algorithm is also used for flow re-routing after a failure in (or attack on) a network to avoid a cascading failure while maintaining the maximum flow in the network. The proposed algorithm is based only on the information about the closest neighbours of each node. Consensus rules are used to achieve fairness between the nodes in the network. The performance of the proposed algorithm is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

A well-developed manufacturer requires a stable supply chain network that is robust to crises. In this study, a simulation system was developed to build a specialised supply chain model and analyse the critical nodes and risks. It makes three major theoretical contributions to literature. First, a novel supply chain model and cascading failure model are developed with adjustable power-law degree distributions for companies to identify suitable models. Second, a novel approach to the critical-node detection problem, considering cascading failure, is proposed. Finally, based on the proposed algorithm, supply chain vulnerability is analysed under two types of attacks. [ABSTRACT FROM AUTHOR]

The network infrastructures, such as the power grids and Internet, are expanding in size due to the increasing needs of our society. This brings about the problem of expanding networks with a guarantee of robustness against network disturbances that may cause catastrophic consequences. In this paper, we study the optimal network expansion in terms of network robustness against cascading failures. Specifically, we consider the network expansion as a Markovian decision process and further propose a reinforcement learning based network expansion method. Simulation results in model networks and real-world networks demonstrate that our expansion method can greatly improve network robustness. Our work provides some insights for the optimal expansion of network infrastructures. [ABSTRACT FROM AUTHOR]

The reliability of circuit systems is primarily affected by cascading failures due to their complex structural and functional coupling. Causes of cascading failure during circuit operation include the continuous degradation process of components and external random shocks. Circuit systems can exhibit asymmetric structural changes and functional loss during cascading failure propagation due to the coupling of degradation and shock and their uncertainty effects. To tackle this issue, this paper abstracts the circuit into an impedance network and constructs a component failure behavior model that considers the correlation between degradation and shock. The interactions between soft and hard failure processes among different components are discussed. Two types of cascading failure propagation processes are described: slow propagation associated with continuous degradation and damage shock, and fast propagation due to fatal shock. Based on this, a cascading failure simulation algorithm is developed. This article presents a case study to demonstrate the proposed models and to analyze the reliability of a typical circuit system. [ABSTRACT FROM AUTHOR]

To investigate the spread of cascading failure on dual-layer networks, we propose a dual-layer network model based on eigenvector centrality and define network interactions through different connection strategies. Following the local priority reallocation rule of load and the nonlinear relationship between load and capacity, we conduct theoretical analysis of the proposed model and simulate it on the BA scale-free network. The result simulation shows that under different connection strategies, when the controllable coefficient α = 0. 6 of the initial load, the dual-layer network demonstrates better robustness regardless of attacking a node with a large initial load or a small initial load. And when α < 0. 6 , the robustness of the network for attacking a node with low initial load is enhanced as the node coupling rate of the dual-layer network increases. For α ≥ 0. 6 , the variation of network robustness does not vary essentially with the node coupling rate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The cascading propagation and evolution of metro operation failures can significantly impact the safety of metro operation. To overcome this challenge, this study pre-processes a massive amount of metro operation log data through noise reduction. Moreover, a professional terminology dictionary is constructed along with a custom stop-word dictionary to segment the preprocessed data. Subsequently, the AFP-tree algorithm is employed to mine the segmented log data and identify key hazards. A weighted urban rail transit network is established, considering the effective path time cost, and the shortest travel OD path. To simulate the dynamic evolution of the failure chain propagation, a model based on disaster propagation theory is constructed. Taking the Shanghai Metro line as a case, multiple simulation scenarios are established with 25 key hazards as triggering points, and the number of cascade failure stations affected under different scenarios is outputted. The results indicate that the fault stations caused by the large passenger flow are the largest. Meanwhile, the number of stations affected by the door clamp is the smallest. The scale of fault stations reaches a maximum value in 16–20 min. Through case analysis, a positive correlation is found when the self-recovery factor is between 14 and 18, and the number of fault stations shows a significant increasing trend. The research results can provide decision-making support and theoretical guidance for rail transit operation safety management enterprises.

In order to accurately assess the risk of the hybrid AC/DC network under fault, a risk assessment method for the hybrid AC/DC system based on the transient energy function is proposed. First, based on the energy transfer relationship of the hybrid AC/DC power system, the transient energy function model of the hybrid AC/DC system is established. Based on the operating data of the power grid, the energy function is used as an efficiency variable, and the efficiency variable is integrated into the prior risk probability calculation of nodes in the network, and a Bayesian network-based risk assessment model of hybrid AC/DC system is established. Considering the dynamic update model of network cascading failures, the clique tree propagation algorithm is used to dynamically calculate the posterior risk probability of the node to realize the dynamic assessment of the network risk. Finally, the improved IEEE-39 node hybrid AC/DC system is used as an example for analysis. The results show that the proposed model can not only effectively evaluate the overall safety of the network, but also has feasibility in predicting faults, which can provide a theoretical basis for the stability control of the hybrid AC/DC system.

The early detection of cascading failure plays an important role in the safe and stable operation of the power system with high penetration of renewable energy. This paper proposes a fault propagation dynamic model based on the epidemic model, and further puts forward a method to detect the development of cascading failures. Through the simulation of the IEEE 39-bus and 118-bus systems, this model is proven to be valid and capable of providing practical technical support for the prevention of cascading failures in power systems with high penetration of renewable energy. This paper also provides an analysis method for the choice of different protection and control measures at each stage of cascading failure, which has critical significance and follow-up value.

In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

The cascading propagation and evolution of metro operation failures can significantly impact the safety of metro operation. To overcome this challenge, this study pre-processes a massive amount of metro operation log data through noise reduction. Moreover, a professional terminology dictionary is constructed along with a custom stop-word dictionary to segment the preprocessed data. Subsequently, the AFP-tree algorithm is employed to mine the segmented log data and identify key hazards. A weighted urban rail transit network is established, considering the effective path time cost, and the shortest travel OD path. To simulate the dynamic evolution of the failure chain propagation, a model based on disaster propagation theory is constructed. Taking the Shanghai Metro line as a case, multiple simulation scenarios are established with 25 key hazards as triggering points, and the number of cascade failure stations affected under different scenarios is outputted. The results indicate that the fault stations caused by the large passenger flow are the largest. Meanwhile, the number of stations affected by the door clamp is the smallest. The scale of fault stations reaches a maximum value in 16–20 min. Through case analysis, a positive correlation is found when the self-recovery factor is between 14 and 18, and the number of fault stations shows a significant increasing trend. The research results can provide decision-making support and theoretical guidance for rail transit operation safety management enterprises. [ABSTRACT FROM AUTHOR]

In the context of the high integration of information technology into power systems, an optimization strategy considering energy storage configuration for blocking control was proposed to mitigate the risk of cascading failures in cyber-physical power systems. Firstly, based on the observability and controllability of the power system, the multi-level interaction mechanism of information and physics was constructed, and the evolution process of cascading failure dominated by overload was clarified according to the interaction among systems. At the same time, the influence of energy storage connected to the power system on cascading failure blocking control was considered based on the existing regulatory means. The blocking control was described as a multi-stage optimal power flow problem, obtaining a cascading failure control scheme that balances safety and economy. The comparison of different control strategies on the IEEE-118 node system demonstrated the effectiveness of the proposed control optimization strategy and analyzed the impact of energy storage scale on blocking control. [ABSTRACT FROM AUTHOR]

Construction collapse accidents are one of the most serious accidents. A variety of causal factors interact with each other, leading to the occurrence of accidents. Some causal factors may cause other causal factors to happen one after another, eventually leading to the occurrence of accidents. The propagation process of causal factors that can lead to construction collapse accident must be clarified. Therefore, this study aims to analyze the propagation path of construction collapse causal factors based on network theory and cascading failure model. First, this study collected and screened the cases of construction collapse accidents, and conducted a statistical analysis of their basic information. Second, a construction collapse accident causation (CCAC) framework was established to identify causal factors in accident cases. Then, a CCAC network model was established. Based on multiple measurement criteria in complex network theory, the causal factors were ranked. After that, through the cascading failure analysis of the causal factors, the critical causal factor-accident propagation path was obtained. It is found that construction scheme and organization is at the beginning and is the source of other causal factors. Finally, five cases were analyzed to verify the rationality of the results. This paper provides a solution to study the propagation path of causal factors and find out the characteristics of the CCAC mechanism through a large number of accident cases, which has theoretical and practical significance for the prevention of construction collapse accidents. [ABSTRACT FROM AUTHOR]

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase. [ABSTRACT FROM AUTHOR]

Urban water networks are important infrastructures for cities. However, urban water networks are vulnerable to natural disasters, causing interruptions in water. A timely analysis of the reliability of urban water networks to natural disasters can reduce the impact of natural disasters. In this paper, from the perspective of network reliability, the reliability analysis method of urban water networks under disaster is proposed. First, a reliability model is established with the flow rate of nodes in the water network as the index. Second, the user's demand is considered, as well as the impact of water pressure on water use. Therefore, a node failure model considering node water pressure and flow rate is established. The performance degradation of the urban water network is analyzed by analyzing the cascading failure process of the network. Third, the recovery process of the urban water network is analyzed, and the changes in the reliability of the urban water network before and after the disaster are analyzed to assess the ability of the urban water network to resist the disaster. Finally, an urban water network consisting of 28 nodes, 42 edges and 4 reservoirs is used to verify the effectiveness of the proposed method.

Abstract We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences.

There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths.

Aviation networks exhibit multi-layer characteristics and heterogeneity of nodes and edges. Identifying key nodes in a multi-layer heterogeneous aviation network (MHAN) and elucidating its cascading failure process are of great practical significance for enhancing the ability to resist failure and strengthening network resilience. Therefore, this paper first establishes the basic model of MHAN and then designs an intra-layer node importance evaluation method based on the improved TOPSIS-grey correlation analysis (ITG) method and an inter-layer influence weight quantification method based on the gravity model. By integrating the information transmission characteristics between network nodes, a key node identification method in MHAN through aggregating multi-source information is proposed. Finally, based on the actual overload operation of aviation networks, the initial load adjustment coefficient, capacity limit, and overload coefficient are introduced based on the traditional capacity–load model, a cascading failure model of MHAN considering overload condition and failure probability is constructed, and a node influence index based on this model is defined to verify the accuracy of the key node identification results. The instance analysis conducted on the aviation network in western China demonstrates that the intra-layer node importance evaluation method based on ITG yields results with better distinguishability and higher accuracy. The key nodes are predominantly hub en-route nodes and sector nodes. In the cascading failure model, increasing the overload coefficient and capacity limit within a specific range while reducing the initial load adjustment coefficient helps reduce the network failure scale. The key nodes identified by the node influence index are consistent with those identified by the method proposed in this paper, validating the accuracy and effectiveness of the key node identification method in MHAN through aggregating multi-source information herein.

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies. [ABSTRACT FROM AUTHOR]

The failure of one node may cause the cascade failure, resulting in the failure of the chemical whole system. Robustness analysis of network is an effective means to prevent cascade failure. When analyzing robustness, predecessors only considered single chemical complex network, such as chemical material network and control system network. However, there is coupling between different networks. Therefore, this paper considers the coupling between networks, and a robustness analysis model of asymmetric chemical coupling network based on asymmetric dependent network model is proposed. In this paper, seven coupling dependency and two connection modes are considered, and the influence of coupling dependence and node connection modes on the robustness of network is explored. The results of the case show that the model is feasible and can well analyze the network robustness of chemical process under two types of network coupling conditions, which provides a theoretical basis for avoiding cascade failure propagation. [ABSTRACT FROM AUTHOR]

The material network in the chemical process has the characteristics of a complex network. Once the fault occurs, it will spread and lead to cascading failures, causing the chemical production process to not proceed normally. Previous studies of cascading failures have been limited by the size and characteristics of the network, which prevented the definition of the fault propagation capacity of the edge based on the actual situation. Studies on the effects of network topology and certain risk factors on the amount of fault propagation still need to be completed. Because of the above shortcomings, this study constructs a chemical material network cascading failure invulnerability analysis model that considers the failure propagation ability. Through the study of the chemical material network topology structure and the analysis of various flammable and explosive chemicals, the paper defines the fault propagation coefficient, which affects the load propagation quantity on the connecting edge. Then, based on the load‐capacity nonlinear cascading failure model, the chemical material network's invulnerability was analyzed by adjusting the model parameters. The effectiveness of the model is verified through case analysis. Compared to the existing model, the model in this paper effectively improves the invulnerability of the material network and can reduce the phenomenon of large‐scale failure of the material network due to the failure propagation capability is not considered. [ABSTRACT FROM AUTHOR]

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines' flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines' steady states of cascade trajectories to infer a specific line's state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line's failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines' states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences. [ABSTRACT FROM AUTHOR]

Cascading failure is a phenomenon that minor disturbances trigger a chain reaction failure of the system, causing the disaster to quickly spread in the system and causing part or all of the system to collapse. Traditional cascading models that based on the assumption that the propagation time of cascading failures can be ignored are inadequate for modeling and analyzing all the practical cascading failure systems. It is necessary to develop a general methodology for the cascading failure systems with cascading failure propagation speed and different load effects. This paper derives new models which extend the load effect mode and consider the propagation speed of cascading failure. In the models, the time of the cascading failure propagation is considered. Three load effect modes are proposed that consider the number of remaining working components, the influence range of the failed parts, and the distance between the failed parts and the unfailed components. Additionally, the reliability of the systems with different types of cascading failures is analyzed, and formulas computing reliability indexes are derived. Numerical examples are provided to demonstrate the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

The access of a high proportion of renewable energies has deepened the randomness and complexity of cascading failures (CFs) in power systems. In this regard, a real‐time risk assessment method for CFs in power systems with high proportion of new energy is proposed. First, combined with historical statistical data and relevant national standards, a CF simulation model that considers the off‐grid protection action of renewable energy units in the event of a power grid fault is proposed. The model is based on the continuous steady‐state power flow model, which simulates the spread of CFs via continuous power flow calculations. Second, via introducing the concept of a fault graph chain, the electrical and topological characteristics of the continuous dynamic of the power system in the process of CFs can be described. Then, through continuous CF simulation and replay buffer, a data‐driven method is used to calculate the CF risk index corresponding to the fault graph. Finally, a cascaded graph neural network is employed to fit the nonlinear mapping relationship between fault graphs and CF risk indicators. The simulation results in the IEEE 39‐bus system show that the proposed method can accurately and real‐time evaluate the risk of CFs. [ABSTRACT FROM AUTHOR]

In cyber–physical power systems (CPPSs), system collapse can occur as a result of a failure in a particular component. In this paper, an approach is presented to build the load-capacity model of CPPSs using the concept of electrical betweenness and information entropy, which takes into account real-time node loads and the allocation of power and information flows within CPPSs. By introducing an innovative load redistribution strategy and comparing it with conventional load distribution strategies, the superior effectiveness of the proposed strategy in minimizing system failures and averting system collapses has been demonstrated. The controllability of the system after cascading failures under different coupling strategies and capacity parameters is investigated through the analysis of different information network topologies and network parameters. It was observed that CPPSs constructed using small-world networks, which couple high-degree nodes from the information network to high-betweenness nodes from the power grid, exhibit improved resilience. Furthermore, increasing the capacity parameter of the power network yields more favorable results compared to increasing the capacity parameter of the information network. In addition, our research results are validated using the IEEE 39-node system and the Chinese 132-node system. [ABSTRACT FROM AUTHOR]

Failure of major drainage systems is almost always accompanied by waterlogging. Understanding the operation and failure mechanism of major drainage systems is an important prerequisite for understanding and managing waterlogging. The natural and artificial environments in mountainous cities are more complex, and the major drainage system, as an open system for excessive surface runoff discharge and storage, is more prone to local overload and cascading failure. Existing theories and methods are difficult to explain and analyze the cascading failure characteristics and principles of major drainage systems in mountainous cities. Based on the complex network technology and the principle of runoff balance, the cascade failure model of major drainage system was constructed, and the characteristics and laws of cascade failure under six kinds of terrain, six kinds of short duration rainstorm and two kinds of surface environments were quantitatively analyzed. Research has found that the cascading failure of major drainage systems and waterlogging in mountainous cities should be the same phenomenon from different perspectives, and the simulated matching degree between the two can reach 79.5%. Meanwhile, 456 potential waterlogging locations were discovered in the study area, which is much larger than the actual 73 locations. When urban construction and rainfall conditions change, these unstable potential waterlogging locations are easily apparent. Among them, terrain is the main factor restricting the cascading failure of major drainage systems. Low lying valley areas often quickly accumulate a large amount of overflow, while flat areas are prone to overflow spreading to the surrounding areas; Slope areas are not prone to overflow, but are prone to local flooding. The change of short duration rainstorm and the difference of surface environmental openness will further interfere with the cascade failure of the major drainage system. The longer the return period of the short duration rainstorm or the increase of the peak value of the rainfall pattern, the greater the discharge and storage pressure of the major drainage system per unit time, resulting in intensified waterlogging. The open and unobstructed surface environment will lead to the small-scale spread of waterlogging, but this will significantly reduce the overall degree of waterlogging in the late rainstorm. The study revealed the spatiotemporal principles and basic laws of cascading failure of major drainage systems in mountainous cities. The conclusion is helpful for mountainous cities to formulate urban construction management strategies and climate change adaptation strategies, and to cope with current and future potential waterlogging disasters.