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Nickel is an important key resource and plays an increasingly important role in new energy technologies. The stability of its worldwide supply chain is crucial for addressing country risks and unforeseen events. To explore the impact of country risks on the stability of the global nickel supply chain, this study conducts a complex network analysis and panel regression analysis on nickel ore, ferro-nickel, and the stainless-steel trade data from 2000 to 2022. The study reveals the mechanisms by which economic and political risks affect the trade structure of nickel commodities and identifies the drivers of global nickel supply chain patterns. The study finds that an increase in economic risk promotes diversification of supply sources, while political risk hurts export scale and resource control ability. Industrial structure adjustments and R&D investments have a significant impact on trade structure, especially on the stainless-steel trade. China is a major consumer and importer, and occupies a central position in the global nickel trade network, while Indonesia and the Philippines' policy changes have a significant impact on the market. This study provides an empirical basis for the risk management of the global nickel supply chain and a scientific basis for policy formulation and strategic planning. [ABSTRACT FROM AUTHOR]

The command and control (C2) network is a complete organizational system that connects operational units at all levels based on command relationships. Its purpose is to ensure that the C2 system can fully perform its command functions, achieving high precision and efficiency in decision‐making. As warfare models evolve rapidly from network‐centric warfare to multi‐domain operations, traditional C2 networks, which utilize a tree structure for connectivity, exhibit only a single hierarchical relationship, making it challenging for different operational units at the same level to interconnect. Furthermore, with the diversification of warfare, the three types of nodes in traditional C2 network models are insufficient to encompass all operational units. In response, this paper proposes a method for edge weighting in C2 networks based on a combination of node attributes and network attributes described by complex network theory. The node attributes mainly include node information transmission capacity, task coordination ability between nodes, node distance, and response time. The network attributes are primarily represented by hierarchy and betweenness centrality. Additionally, the traditional C2 network model's three types of nodes are expanded to five types of nodes. Based on the edge weighting method, internal command edges, inter‐network collaborative edges, and cross‐level command edges are generated within the C2 network. Simulation results demonstrate that the constructed C2 network model's characteristic parameters are superior to those of traditional C2 networks and collaborative C2 networks. This improvement enhances command and coordination abilities, aligns more closely with real‐world scenarios, and effectively improves network command and control efficiency. [ABSTRACT FROM AUTHOR]

The permeability of fractured rock mass is directly related to engineering safety and resource exploitation efficiency. However, due to the fracture network’s complexity and the fracture surface’s irregularity, accurate prediction of the permeability of fractured rock mass has always been a difficult problem in the field of geotechnical engineering. Complex network theory provides a powerful tool for studying the spatial distribution and connection characteristics of fracture networks, while fractal theory can effectively describe the roughness and self-similarity of fracture surfaces. Based on the above two theories, this paper analyzes the permeability of fractured rock mass. The permeability model of the rough fractured rock is derived by considering the roughness of the fracture. It has been discovered that the permeability of fractured rock is influenced by structural parameters, including the self-similarity index, roughness and porosity. Specifically, permeability is directly proportional to the surface density of the fractures but inversely proportional to the roughness of the fracture surfaces. The model is compared with the existing numerical simulation results to verify its correctness and effectiveness. [ABSTRACT FROM AUTHOR]

Numerous studies indicate that unsafe behaviors of construction workers (UBCW) are the direct causes of most construction safety incidents. To unveil the interconnections among unsafe behaviors and the underlying mechanisms of different accident types, this study extracts the unsafe behavior chains from accidents. Based on the theory of complex networks, we constructs both the whole network of UBCW and four subnetworks corresponding to different accident types by using unsafe behavior chains. The findings reveal that the degree distributions of all networks conform to truncated power-law distributions, highlighting the critical role of hub nodes in accident occurrences. Moreover, the whole network exhibits small-world characteristics, underscoring the need to address the propagation and clustering effects of unsafe behaviors, reassess risk management approaches, and devise targeted control strategies and measures. By analyzing the topological structures of the networks, this paper ranks the importance of different behaviors across various metrics and proposes tailored accident prevention recommendations through comparative analysis of different accident types. This research holds significant theoretical and practical implications for refining accident causality models and enhancing safety management practices at construction sites. However, the study has limitations, notably future studies should focus on the absence of specific construction accident databases and the unaddressed link between accident severity and unsafe behaviors. [ABSTRACT FROM AUTHOR]

With the increasing connectivity across regions of the modern society, innovation networks have become a prominent paradigm in the field of innovation geography. Considering the ongoing regional integration of the Yangtze River Delta and current challenges posed by uneven development in innovation capabilities, examining the impact of inter-urban innovation networks on the enhancement of urban innovation capacity holds substantial research significance. However, current scholarly research seldom explores the evolution of dual-layer networks. Furthermore, studies on the effects of innovation networks tend to focus only on their direct impacts on network entities, with limited analyses of innovation proximity and spillover effects. Therefore, this study constructed a double-layer innovation network for the Yangtze River Delta for the period 2010 to 2019 based on patent collaboration and transfer data. It characterizes the basic evolutionary features of this double-layer network, analyzes the structural development differences between the collaboration and transfer subnetworks, investigates the dynamics of their coupling evolution, and explores the spatial spillover effects of the dual-layer network structure on urban innovation capacity. The findings reveal that: (1) Internal connectivity within the double-layer innovation network of the Yangtze River Delta has strengthened, enhancing accessibility and efficiency while reducing internal centripetal tendencies, thus achieving increasingly coordinated intercity innovation linkages; (2) The initial distribution of the transfer and collaboration networks was concentrated along the southeastern coastal areas, but subsequent polarization eased, with a more marked decrease in hierarchical structure in the transfer network, which also exhibited a higher level of development compared to the collaboration network; (3) The overall coupling coordination of the two sub-networks has risen, although with increased dispersion, leading to pronounced polarization but without severe effects; (4) Regression results from spatial econometric models indicate that node strength and importance within the double-layer network positively influence urban innovation capacity, with both showing significant negative spatial spillover effects. Urban economic vitality and consumer demand are positively correlated with local innovation capacity; the enhancement of economic vitality in neighboring cities also promotes local innovation capacity. However, the optimization of industrial structures in adjacent cities negatively impacts local innovation capacity. These findings suggest that the Yangtze River Delta should continue to implement integration policies, with core cities taking the lead and less-developed cities actively integrating into the innovation network to promote the region's overall enhancement of innovation capacity. Moreover, cities should not only draw on advanced external expertise but also increase their own technological outputs, balancing both "bringing in" and "going out." This approach not only strengthens the coupling between the two subnetworks, but also enables overall optimization of the innovation network. This study provides novel and profound insights into the construction of intercity innovation networks in the Yangtze River Delta and offers valuable references for improving and coordinating urban innovation capacity across the region.

Introduction In international trade network, countries are classified into different societies. These societies are formed based on commercial relations. Countries that are in the same society have close trade relations, while countries that are in different societies have much weaker trade relations, which shows that classification phenomenon has a meaningful effect on the field of international trade of resources. These societies also significantly promote free trade and improve commercial security and create favorable business conditions. For countries that rely heavily on foreign resources, establishing trade zones or joining a trade zone for their long-term development is of paramount importance. The division of societies in the trade network is based on geographical location or gross domestic product, not regional trade agreements. Some researchers have investigated the evolutionary characteristics of societies and analyzed the sustainability of international trade. In addition, some researchers argue that societies increase their commercial power by stabilizing the flow of resources in international trade, and their international position improves through cooperation with other countries. Several studies have provided a lot of knowledge about the society and the structural features of the international trade network, but few studies have dealt with the formation of trade areas and what promotes the formation of a trade area. The formation of trade communities/regions can be a strategy to reduce restrictions and increase trade interactions and its security through close communication between countries. Therefore, the current study can become more necessary in the situation where the current international business cooperation has become more and more important. Therefore, two main questions arise that need to be answered: 1) How are business areas formed, and 2) What factors influence the formation of business areas? Based on the studies, resource dependence theory and complex network theory can explain these questions well. Resource dependence theory states that if a firm is highly dependent on the target market, then it will be constrained by the actors in that market. Complex network theory is also a useful tool for systematically analyzing interactions between countries, especially when there are many countries involved and strong links between them exists. The purpose in fact was to investigate and recognize the influence of the resource dependence theory and the complex network theory in the formation of international trade areas in line with the integration of global economy. Methodology The data were extracted from the 22 countries active in international trade and based on the availability of the data of the official COMTRADE database of the United Nations in the period of 2011-2021. They account for the entire international trade. In order to analyze the data and estimate the models, negative binomial regression was used because when there are countable and discrete data as the response variable, simple linear regression is not a suitable fitting method. So, Poisson regression was applied, which is considered a method in "generalized linear models" where the probability function for the "response variable" is considered to be "Poisson distribution" and suitable for count data. Findings The trade partner factor has a positive effect on the formation of international trade communities, that is, when a country cooperates with a large number of trade partners or has a superior position in the international trade network, it is more likely that other countries will form the same pattern. Therefore, when a country considers itself dependent on the resources of other countries, the possibility of forming a similar society will increase. Finally, network position plays a positive role in moderating the relationship between resource dependence and international trade areas. Discussion and Conclusion Based on resource dependence and complex network theory, and analyzing the decisive factors affecting the accession process of a country to the same trade zones, the dependence of the country's imports on the external environment is an integral factor in joining a country to the same trade zones. In fact, in choosing partners for the formation of commercial zones, countries attach great importance to the ability to provide resources of commercial partners. On the other hand, position of the network plays a positive role in the formation of a business community. Countries with a higher network position can not only access resources or reach the target market faster, but also have more control over the flow of resources. Therefore, it is more likely that countries will establish closer trade relations with countries that have a higher network position in order to increase their economic power. The more central a country's network position is, the easier it is to choose to join larger trade areas with other countries. Also, according to the resource dependence theory, the more central a country's network position is, the more likely it is to join the same trade areas as other countries. Network and resource considerations both simultaneously play a role in the strategic choice of national trade. In the case of countries that have the same resource abundance, a country can choose to form the same society as countries that have a more central network position. Finally, due to the development of export markets for domestic products, countries are easily affected by the network position. This will enable them to choose other countries that are in a more central network position to form the same society, rather than trying to trade among them. Based on the economic freedom factor and the diversity of the importing country, countries that can strengthen resource trade, can reduce or manage their dependence on other countries. In addition, they can balance the inventory and production of global resources.

The white paper on China's Arctic Policy, which proposed the joint construction of the Polar Silk Road (PSR), was officially published in January 2018. As a short and economically feasible sea route, the PSR will inevitably affect the carrier's market choice behaviour, thereby affecting the foreland network structure and foreland pattern of China's coastal container ports (CCCP). Grasping the evolution trend of CCCP foreland under the PSR will help predict the development trend of the port and shipping market in advance and enable measures to be taken to adapt to the changing market environment. This paper constructs the port foreland network evolution (PFNE) model and presents a complex network delineation method of port foreland to simulate the effects of PSR on CCCP foreland evolution in different scenarios. Results show that the PSR's addition to the CCCP foreland network will improve shipping connectivity, increase the connection between long-distance ports, reshape the clustering groups, promote the orderliness of the network and help the development of small and medium-sized ports. China's global maritime transport pattern will change, which is mainly reflected in the enhanced shipping links between CCCP and Asia, Europe and Africa, while the importance of the Americas for CCCP weaken. PSR has a more obvious role in promoting the establishment of maritime links between China's northern ports and the world ports. In the discussion, we propose the development policy of CCCP under the PSR. [ABSTRACT FROM AUTHOR]

During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems. [ABSTRACT FROM AUTHOR]

AbstractThe rise in natural disasters and climate-induced events, such as wildfires, hurricanes, and flooding, has significantly affected urban life. These events can disrupt daily activity and flows of individuals and goods on road and transit networks. To enhance urban resilience against disasters, it’s crucial to study and understand road network vulnerability, utilizing data-driven insights to inform planning and preparedness efforts. The aim of this paper is to develop a data-driven exploratory approach to assess vulnerability in road networks in response to a disruption. To accomplish this, we compare the centrality of road segments before, during, and after disaster, considering the network topological structure and movement activity as it is observed through large tracking data of cellphone traces on the network. The novelty of our approach lies in inferring the impact from movement data, instead of manually removing links from the network. The results obtained from this study suggest that incorporating movement data into the assessment of network functionality provides a more realistic estimation of the road network vulnerability in response to a disruption, compared to solely using network topology. [ABSTRACT FROM AUTHOR]

The analysis of complex system reliability is an area of growing interest, particularly given the diverse and intricate nature of the subsystems and components these systems encompass. Tackling the reliability of such multifaceted systems presents challenges, including component wear, multiple failure modes, the cascading effects of these failures, and the associated uncertainties, which require careful consideration. While traditional studies have examined these elements, the dynamic interplay of information between subsystems and the overarching system has only recently begun to draw focus. A notably understudied aspect is the reliability analysis of complex real-time systems that must adapt to evolving operational conditions. This paper proposes a novel methodology for assessing the reliability of complex real-time systems. This method integrates complex network theory, thus capturing the intricate operational characteristics of these systems, with adjustments to several key complex network parameters to define the nuances of communication within the network framework. To showcase the efficacy and adaptability of our approach, we present case studies on railway and maritime systems. For the railway system, our analysis spans various operational scenarios: from single train operations to simultaneous operations across multiple or different radio block center regions, accounting for node and edge failures. In maritime systems, the case studies employing the VHF data exchange system under operational scenarios are subject to network reliability analysis, successfully pinpointing critical vulnerabilities and modules of high importance. The findings of our research are promising, demonstrating that the proposed method not only accurately evaluates the overall reliability of complex systems but also identifies the pivotal weak points—be it modules or links—warranting attention for system enhancement. [ABSTRACT FROM AUTHOR]

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential "vulnerability" in the development of the global logistics network. The three-step response strategy of "Prewarning–Response–Postrepair" is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs. [ABSTRACT FROM AUTHOR]

To maximize the utilization of limited energy and achieve flexible and efficient grid connection for spacecraft power supply systems' a composite grid topology optimization model for power transmission and signal communication is proposed based on deep reinforcement learning (DRL). Various interpretable component models are employed based on knowledge distillation principles to analyze the optimization mechanism. Firstly' the transformation law of the control domain of the spacecraft bus voltage regulation in the on-orbit operation stage is analyzed' and the composite network topology model of power transmission and signal communication is established by combining the node propagation parameters. Secondly asynchronous advantage actor-critic (A3C) is utilized to adaptively optimize potential operational reliability risks in routing distribution and topology of the electrical signal transmission network. Finally various interpretable components are used to perform knowledge destination on the trained DRL model forming an interpretable quantitative analysis method. The proposed method theoretically predicts optimal grid-connected processes of space power supply under random shadow effects, providing theoretical support and reference for designing space power supply controllers under higher task requirements and complex environments. [ABSTRACT FROM AUTHOR]

Rail transit is an important means of ensuring the sustainable development of urban transportation, but disturbance events caused by natural disasters, human factors, and other influences can lead to disruptions in rail transit operations. To cope with the impact of disturbance events on urban rail transit networks, and to explore the changes in rail transit network performance and recovery strategies under the influence of disturbance events from a resilience perspective, this paper overviews the existing research on resilience assessment and recovery strategies for urban rail transit networks. Firstly, the characteristics of the urban rail transit network and the model construction method are analyzed. Secondly, on the basis of combing the connotation development of system resilience, urban rail transit network resilience is defined, while the existing resilience metrics and assessment indexes are classified and summarized. Finally, the failure scenarios and recovery strategies of urban rail transportation network are deeply studied and discussed. The research results show that urban rail transit network resilience has been widely concerned by scholars, and certain results have been achieved in three aspects of resilience connotation, resilience assessment and recovery strategy. Nevertheless, further research is needed on these aspects. We propose future research directions that involve exploring modeling methods aligned with actual network topologies, developing unified indexes for resilience assessment and focusing on resilience assessment and recovery strategies under uncertain disturbance events. The research results can provide an important reference for the resilient operation and sustainable construction of urban rail transit. [ABSTRACT FROM AUTHOR]

Nickel is an important key resource and plays an increasingly important role in new energy technologies. The stability of its worldwide supply chain is crucial for addressing country risks and unforeseen events. To explore the impact of country risks on the stability of the global nickel supply chain, this study conducts a complex network analysis and panel regression analysis on nickel ore, ferro-nickel, and the stainless-steel trade data from 2000 to 2022. The study reveals the mechanisms by which economic and political risks affect the trade structure of nickel commodities and identifies the drivers of global nickel supply chain patterns. The study finds that an increase in economic risk promotes diversification of supply sources, while political risk hurts export scale and resource control ability. Industrial structure adjustments and R&D investments have a significant impact on trade structure, especially on the stainless-steel trade. China is a major consumer and importer, and occupies a central position in the global nickel trade network, while Indonesia and the Philippines’ policy changes have a significant impact on the market. This study provides an empirical basis for the risk management of the global nickel supply chain and a scientific basis for policy formulation and strategic planning.

During the reclamation process of coal mining subsidence areas, mechanical compaction hinders the rapid recovery of reclaimed mine soil (RMS) functions to their pre-mining levels. To optimize reclaimed farmland management, which can promote the recovery of compacted soils, this study aims to explore the key factors and underlying mechanisms affecting RMS recovery from a systemic perspective using complex network theory (CNT). Soil samples from reclaimed farmland at different recovery stages (0, 2, 6, 12, 16, and 22 years) and adjacent non-subsided cultivated soils (NCS) were collected at a depth of 0 ∼ 20 cm in the eastern plains mining region of China. Twenty-four soil indicators were measured, and their evolution over RMS recovery was analyzed. CNT was employed to systematically analyze the complex network relationships among these indicators, identifying key factors and underlying mechanisms affecting RMS recovery. The results indicated that compaction led to soil macroaggregate (MA) destruction, mineralization losses of organic carbon and nitrogen, reduced microbial activity, degraded soil fertility, and increased complexity and disorder in the relationships among soil indicators. Re-cultivation had a positive effect on the recovery of RMS. After 22 years of cultivation, significant improvements in soil structure were observed, with MA increasing by 30.95 % (P

Standards play significant roles in the development of technology and economics, while the cooperation between drafters directly determines the quality of standard systems. The cooperation prediction is a significant while challenging problem for seeking new cooperation chances between drafting units due to their differences in experience and professional ability. In this study, an integrated artificial intelligence method is proposed for cooperation prediction using the link prediction method, text analysis, and network modeling. Specifically, we develop a multi-layer standard network formed by standard citation relationships and cooperation relationships between drafters. Then, a set of novel metrics is designed for predicting the cooperation between drafters considering the knowledge, experience, and professional capability. These metrics are further integrated into a neural network to improve the prediction accuracy. The priorities of our method in terms of prediction accuracy are verified with realistic data of Chinese environmental health standards. The prediction results provide strong support for the selection of drafters and further optimize the structure of standard systems. [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]

Following the Belt and Road, the Air Silk Road has also been proposed. The coordinated development of multiple transportation modes, including air, land, and water, will create a strong transportation force in node cities. However, the current insufficient supply of cargo in various regions and the lack of integration among different transportation modes result in low transportation efficiency, which in turn affects the further advancement of the Belt and Road. To investigate these issues and attempt to find a solution, we selected 44 candidate cities from the prefecture-level cities in China as nodes based on relevant government policies, and constructed an integrated transportation network. For each node city, we first calculated the values of six classical indicators and then used the CRITIC to assign weights to each indicator. Subsequently, we employed the TOPSIS method combined with Grey Relational Analysis (GRA) to compute the comprehensive score for each node city. Based on the spatial layout and government policies under the BRI, eight cities, including Wuhan, Chongqing, Tianjin, Shanghai, Guangzhou, Lianyungang, Hefei, and Dalian, were finally recommended as the consolidation centers of the integrated transportation network. It is hoped that the results of this analysis can provide some insights for the government to outline and build the consolidation centers of the integrated transportation network composed of railway, air, highway, and water routes, which in turn can offer insights for elevating the Belt and Road Initiative (BRI) to a new level. [ABSTRACT FROM AUTHOR]

With the improvement of social production efficiency and the enhancement of the supply chain system, the traditional linear supply chain model is gradually evolving into a more complex and dynamic industrial chain network. This article uses complex network theory combined with the basic attributes of the industrial chain and supply chain to conduct a comprehensive and in-depth analysis of the integrated circuit industry chain. Firstly, a cooperative network model of the integrated circuit industry chain in Shaanxi Province is established based on the supply chain relationships of enterprises. Secondly, the study analyzes the basic characteristics of the collaborative network model. Thirdly, this study explores the efficiency, resilience, and innovation capacity of industrial chains using a novel set of indexes: the industry chain efficiency index (ICEI), the industry chain resilience index (ICRI), and the industry chain innovation capability index (ICICI). By employing principal component analysis (PCA), the study provides a comprehensive evaluation of industrial chain performance. The findings reveal that the ICEI highlights the critical importance of average path length and network density, showing that shorter paths and higher density are associated with greater efficiency. The ICRI emphasizes the roles of average degree and standard deviation, indicating that higher connectivity and lower variability contribute to resilience. The ICICI identifies the clustering coefficient and network density as key factors, suggesting that tight-knit networks foster innovation. These results offer significant insights into the dynamics of industrial chain collaboration and provide practical recommendations for enhancing supply chain management. Finally, the effectiveness of the proposed method is demonstrated through a case study. The results of the case study indicate the following: (1) Key Enterprises' Identification: The analysis identified key enterprises like Samsung Semiconductor and HT-tech with the highest betweenness centrality, highlighting their crucial intermediary roles within the network; (2) Efficiency and Innovation Assessment: Compared with foreign-owned and other immigrant businesses, local businesses generally perform below average in terms of efficiency and resilience, indicating that there is room for improvement in technology adoption and innovation capabilities. [ABSTRACT FROM AUTHOR]

This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive traffic dominance evaluation system. The results show the following: (1) Both static and dynamic traffic dominance display core-periphery structures centered around Huangpu District. (2) Dynamically, distinct variations in RWC emerge across different times and transport modes; during the early hours (0:00–6:00), shared bicycles show unique spatial distributions, the subway network experiences a notable decrease in RWC yet maintains its spatial pattern, and taxis exhibit intermediate characteristics. Conversely, the RWC for all modes generally increases during morning (6:00–12:00) and evening (18:00–24:00) peaks, with a pronounced decrease in subway RWC in the latter period. (3) The integration of dynamic evaluations significantly modifies conventional static results, emphasizing the impact of population movements on traffic dominance. This comprehensive analysis provides crucial insights into the strategic management and development of urban traffic infrastructure in Shanghai. [ABSTRACT FROM AUTHOR]

To solve the problem of urban freight demand and build an efficient, practical, intelligent, green, and sustainable new logistics system, this paper considers the application of the subway network to urban freight transportation and studies the location problem of subway transit stations in the urban freight network. According to the differences between different subway stations, the nodal degree, medial centrality, proximity centrality, and regional accessibility are proposed as the evaluation indexes, and the improved fuzzy analytic hierarchy method and entropy weight method are used to calculate the index weight. The TOPSIS evaluation method is used to evaluate the importance of each subway station, and the importance evaluation model of subway stations is constructed. Combined with the distribution location and transportation demand of urban express delivery outlets, a two-tier planning model for the location of subway transfer stations was constructed with total cost and customer satisfaction as the objective functions, and the case studies were carried out by taking Jiangning District, Lishui District, and Gaochun District of Nanjing as the research objects. The results show that Hohai University Focheng West Road, Zhengfang Middle Road, Qunli, and Gaochun can be transformed into subway transfer stations and used as transshipment centers of the urban cargo transportation network. Compared with the original ground transportation network, 52.87% of the ground transportation distance in the optimized transportation network is replaced by subway transportation, and the total cost of logistics transportation is reduced by 8.73%, which verifies the feasibility of subway for urban cargo transportation, reduces logistics transportation costs, and relieves the pressure of ground transportation, which is of great significance for the sustainable development of urban logistics. [ABSTRACT FROM AUTHOR]

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN's vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study's insights can guide policy interventions to further strengthen the network against future shocks and promote global food security. [ABSTRACT FROM AUTHOR]

Ecological networks are important channels that describe ecological systems and thus facilitate interactions between communities and ecosystems. Building a reasonable and effective ecological network is imperative for maintaining a healthy ecosystem. Taking Baiyangdian Basin as an example, an ecological network was constructed through “source–resistance surface–corridor”. Based on complex network theory, an ecological network was abstracted into a topological structure. The characteristics of the ecological network were analyzed from robustness and redundancy indices. Simulation of nodes was conducted to evaluate the plasticity of the constructed ecological network. The results indicate that: (1) A total of 145 ecological source areas were identified in Baiyangdian Basin, with a total area of 6479.24 km2. In total, 338 ecological corridors with a total length of 3939.87 km were identified. Further, 104 ecological pinch points were identified. (2) The complex network in the research area had scale-free characteristics, with a topological structure of 8 communities, multiple closed loops, and good connectivity. (3) The distribution of nodes in the network was uneven, with node 52 being the most important node in the network. The analyzed characteristics of the ecological network in Baiyangdian Basin is imperative for optimizing the structure of the ecological network.

Currently, with the gradual advancement of urbanization and industrialization, ecological patches are severely fragmented and corridors are cut off, leading to the construction of regional ecological networks that can connect fragmented patches and corridors with regional characteristics. However, there is limited research on the relationship between ecological networks and Carbon Utilization Efficiency (CUE) based on complex network theory. This paper takes the ecologically fragile Southeast Tibet as the research area and investigates the variation patterns of CUE through the establishment of ecological spatial networks. The Graphab Model is employed to extract the ecological space network of Southeast Tibet. The Graphab Model integrates the complex network with the ecological space network to compute the topological index of ecological nodes and the overall ecological network. Later, MOD17A2HGF GPP and MOD17A2HGF PSNnet data from 2013 to 2021 are used as the primary data for calculating the CUE of ecological nodes. The connection between topological index and ecological nodes is analysed. The results indicate that, with interannual variations, ecological nodes dropped by 15, and the number of ecological galleries decreased by 7. However, the average values of various topological indices showed an increasing trend, suggesting that the ecological remediation items in Southeast Tibet have made some achievements. Nevertheless, the analysis of CUE in Southeast Tibet reveals a downward trend in the carbon fixation rate of vegetation in recent years. Through correlation analysis, it was found that the correlation between various topological indices and CUE was the lowest and insignificant in 2013, while from 2015 to 2021, all topological indices were significantly positively correlated with CUE. Through Principal Component Analysis (PCA), It is recommended to reduce and shorten redundant ecological corridors by aggregating small ecological patches and increasing stepping stone patches, and increase ecological galleries to enhance the carbon fixation ability of plants. This research lays a scientific foundation for addressing ecological issues, landscape patterns, and developing ecological networks in Southeast Tibet.

Previous research on urban rail transit (URT) evolution mainly focused on network topology, neglecting ridership attributes. This study extracts ridership and network topology indicators from Chinese URT data. Employing a self-organizing mapping neural network model, it divides China’s URT development into four stages. The initial stage and the development stage form the framework of URT network. The network diameter reaches the maximum in the networked operation stage. In the mature stage, URT network densification occurs alongside a significant increase in resident ridership. It is also found that each network indicator has a significant nonlinear relationship with ridership attributes. These findings are of guiding significance for urban planners to accurately understanding URT’s future development and rational network planning and construction. [ABSTRACT FROM AUTHOR]

The current approach to selecting sites for abandoned spoil areas primarily relies on qualitative methods, often overlooking the impact of policy factors on decision-making. Traditional single-site selection strategies may not be flexible enough to accommodate evolving external policy demands. Addressing this challenge is crucial for ensuring the site selection for abandoned spoil areas is both scientifically sound and policy-compliant. This research integrates various analytical methods, including principal component analysis, complex network theory, the CRITIC method, and the ordered weighted averaging method, to thoroughly evaluate the factors influencing site selection. Utilizing geographic information system (GIS) technology, the study simulates different policy scenarios, such as construction cost, social and ecological concerns, natural security, spatial accessibility, and a comprehensive balance approach. It specifically analyzes the suitability of the spoil site of a segment of the Chongqing ZW Railway under these policy conditions. Based on the actual policy situation in the local area, six potential suitable sites were screened with the help of field investigation. This study can offer a methodological framework and theoretical guidance for optimally locating mountain railway engineering waste disposal sites. In addition, the methodology presented in this study can be adapted to the development and change in policy scenarios. [ABSTRACT FROM AUTHOR]

Tourism demand forecasting is proven essential to optimize management, increase revenues, and control risks. Spatial effects have been widely considered in forecasting models and achieved good results. However, existing studies paid little attention to the dynamicity and hierarchy of spatial correlations among tourist attractions within tourism destination complex networks. This study addresses this paucity by proposing a novel deep learning model, namely, multi-hierarchy dynamic spatiotemporal network (MHDSTN). Innovatively, dynamic and hierarchical spatiotemporal features are extracted for forecasting improvement by integrating transformer, gated graph convolution, and long short-term memory. The effectiveness, robustness, and interpretability of the model are demonstrated through an empirical case in Beijing, China. Results indicate that incorporating dynamic and hierarchical spatial effects remarkably improves forecasting accuracy. [ABSTRACT FROM AUTHOR]

Variability of sea surface temperature (SST), characterized by various spatiotemporal scales, is a proxy of climate change. A network analysis combined with empirical mode decomposition is newly presented for examining scale-dependent spatial patterns of SST variability. Our approach is applied to SST anomaly variability in the East/Japan Sea (EJS), consisting of satellite-derived daily datasets of 0.25° × 0.25° resolution from 1981 to 2023. Through the spatial distribution of instantaneous energy in intrinsic modes and features of intrinsic-mode networks, scale-dependent spatiotemporal features are found. The season-specific spatial pattern of energy density is observed only for weekly to semiannual modes, while a persistent high-energy distribution in the tongue-shaped region from East Korea Bay (EKB) to the Sub-Polar Front (SPF) is observed only for annual-to-decadal modes. The seasonality is apparent in the time evolution of energy only for weekly-to-annual modes, with a peak in summer and an increasing trend since the 2010s. Hubs of intrinsic-mode networks are observed in the whole southern area (some northern part) of EJS during the summer (winter), only for monthly to semiannual modes. Regional communities are observed only for weekly to seasonal modes, while there is an inter-basin community with annual-to-biennial modes, incorporating two pathways of East Sea Intermediate Water (ESIW). [ABSTRACT FROM AUTHOR]

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north–south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network. [ABSTRACT FROM AUTHOR]

Interactions between internal tides and mesoscale eddies are an important topic. However, examining modulations of internal tides inside a mesoscale eddy based on observations is difficult due to limited observation duration and inaccurate positioning within the eddy. In order to overcome these two practical limitations, we use the active navigation capability of underwater gliders to conduct measurements inside the targeted eddy and utilize the wavelet approach to investigate modulations of internal tides with diurnal and semidiurnal periods inside the eddy. Based on the wavelet's frequency–time locality, we construct scale-specific networks via wavelet coherence (WC) from multivariate timeseries with a small sample size. The modulation of internal tides is then examined in terms of temporal evolutionary characteristics of the WC network's topological structure. Our findings are as follows: (1) the studied eddy is vertically separated into two layers, the upper (<400 m) and lower (>400 m) layers, indicating that the eddy is surface intensified; (2) the eddy is also horizontally divided into two domains, the inner and outer centers, where the modulation of internal tides seems to actively occur in the inner center; and (3) diurnal internal tides are more strongly modulated compared to semidiurnal ones, indicating the influence of spatial scales on the strength of interactions between internal tides and eddies. [ABSTRACT FROM AUTHOR]

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer’s disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

In order to explore the internal relation of mechanical and electrical accidents in coal mine from the perspective of human factors, a method of mechanical and electrical accidents analysis is proposed, which integrates human factors analysis and classification system and complex network. Firstly, an improved human factors analysis and classification system is used to identify the cause of the accident and the cause chain, and the cause network is constructed. Secondly, the key causes are identified after the standardization of each index, and the disaster rate and vulnerability are introduced to identify the key cause chain. Finally, the evolution path of unsafe behavior is constructed. The results show that the network has the characteristics of small world, and the chain effect between the accident causes is obvious. The key cause of the accident is insufficient organizational safety supervision and mechanical and electrical management disorder, the key cause chain is poor individual safety awareness caused by insufficient safety education, and organizational factors are the source of the evolution path of unsafe behavior of miners.

In the domain of gas pipeline operations, significant safety hazards prevail. This study delves into the triggering mechanisms behind gas pipeline leakage incidents, pinpoints key risk factors contributing to these occurrences, and proposes effective control measures. By classifying risk coupling forms associated with various accidents, the N-K model formula is utilized to assess the coupling degree of primary risk factors. The resultant coupling relationship is integrated into the University of California at Irvine NETwork (UCINET), forming a complex network model for quantitative analysis. Secondary risk factors' coupling degrees are measured, and the network's characteristic parameters are scrutinized. Noteworthy risk factors within the network include improper maintenance (A3), unclear or missing signage (C4), equipment aging and wear (B10), unauthorized occupation of ground space (C5), failure to implement rules and regulations (D3), and failure of electrical protection (B5). By controlling the above risk factors, disrupting the coupling process of risk factors, and blocking the transmission pathways of risk factors, the objective is to prevent leakage incidents and ensure the safe operation of gas pipelines.

Abstract A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one‐sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

The analysis of complex system reliability is an area of growing interest, particularly given the diverse and intricate nature of the subsystems and components these systems encompass. Tackling the reliability of such multifaceted systems presents challenges, including component wear, multiple failure modes, the cascading effects of these failures, and the associated uncertainties, which require careful consideration. While traditional studies have examined these elements, the dynamic interplay of information between subsystems and the overarching system has only recently begun to draw focus. A notably understudied aspect is the reliability analysis of complex real-time systems that must adapt to evolving operational conditions. This paper proposes a novel methodology for assessing the reliability of complex real-time systems. This method integrates complex network theory, thus capturing the intricate operational characteristics of these systems, with adjustments to several key complex network parameters to define the nuances of communication within the network framework. To showcase the efficacy and adaptability of our approach, we present case studies on railway and maritime systems. For the railway system, our analysis spans various operational scenarios: from single train operations to simultaneous operations across multiple or different radio block center regions, accounting for node and edge failures. In maritime systems, the case studies employing the VHF data exchange system under operational scenarios are subject to network reliability analysis, successfully pinpointing critical vulnerabilities and modules of high importance. The findings of our research are promising, demonstrating that the proposed method not only accurately evaluates the overall reliability of complex systems but also identifies the pivotal weak points—be it modules or links—warranting attention for system enhancement.

In order to enhance the power supply reliability of distribution network under wind disasters and provide assistance for distribution network operators to formulate post disaster repair strategies, this paper proposes an optimization method for key nodes and lines evaluation and distribution network repair strategy under wind disaster. Firstly, based on the complex network theory, we considered the status of nodes and lines in the topology and working state. The multi-dimensional evaluation indexes are proposed including improved node degree, node cohesion degree, node intermediate number, weighted line intermediate number, line transfer power flow entropy and line failure rate, etc. Then, the entropy weight method is used to objectively weight the six indicators, and TOPSIS algorithm is used to rank the comprehensive importance of nodes and lines in the distribution network. On this basis, the dynamic programming theory is used to optimize the emergency repair strategy of the fault distribution network after the wind disaster. And the problem of restoring as many key nodes and lines as possible in a short time is proposed. Finally, the method is applied to the IEEE 39 bus system network to verify the feasibility.

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Building a scientific and reasonable ecological network is the key for optimizing the pattern of territorial development and protection, and is of great significance for ensuring regional ecological security and promoting the virtuous cycle of ecosystems. In previous studies, nodal attack method (destruction of ecological source area) was often used in the "robustness" evaluation of ecological networks. Actually, the ecological corridor is more fragile than the source area, and thus the nodal attack method is not reasonable. In this study, taking Jiuquan City as the research area, based on the circuit model to construct the ecological network, we carried out the topology optimization of ecological network by using three strategies (random edge increase, node degree and priority edge increase with low node intermedium number) in complex network theory. We compared and analyzed the "robustness" of ecological network before and after optimization by constructing edge attack strategy, and selected the best network optimization strategy. The results showed that 65 ecological source areas were identified in Jiuquan City, with a total area of 20275.15 km², and that grassland accounted for 89.5% of the source area. We identified 179 ecological corridors with a total length of 6387.16 km, 158 ecological barrier points with a total area of 1385.5 km². The unused land accounted for 92.2% of the total barrier points area. We identified 63 ecological pinch points, mainly concentrated in the source edge and corridor intersection. Among them, the spatial distribution of 11 barrier points and pinch points was consistent, which was the key area to be repaired in ecological network optimization. The three optimization strategies had significantly improved the stability of ecological network in Jiuquan City. The relative size of the maximum connected subgraph and the edge connected rate of the ecological network of the optimization strategy of adding edges according to degree were all the most stable under random attack mode and deliberate attack mode, which was the best optimization scheme for ecological network in Jiuquan City. [ABSTRACT FROM AUTHOR]

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer’s disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders. [ABSTRACT FROM AUTHOR]