Interdependent Networks Research Articles

Modeling Resilience of Metro-Based Urban Underground Logistics System Based on Multi-Layer Interdependent Network

The metro-based underground logistics system (M-ULS) has been identified as an effective solution to urban problems resulting from the expansion of urban freight traffic. However, there is a paucity of current research that examines the resilience of a M-ULS in the context of unexpected events during operations. Therefore, this paper presents a methodology for assessing the resilience of the M-ULS. The method considers the propagation paths of various failures in a multi-layered, interdependent network that includes topology, functionality, facilities, and information, as well as network performance indicators based on network freight flow and logistics timeliness. The effectiveness of the method is demonstrated using the case of the Nanjing Metro. The results show that the type of disruption, the duration, and the direction of train travel all have a significant impact on the resilience of the M-ULS. The method proposed in this paper provides a scientific basis for the assessment and optimization of M-ULS resilience and also offers new insights into the use of urban rail transit to promote the sustainable development of urban logistics.

Model of Policy Network in Advocacy for the Formulation of Qanun Draft on Protection and Fulfillment of the Rights of People with Disabilities

This study aims to analyze the interaction and interdependence of policy networks, identify inhibiting and supporting factors, and formulate an effective policy network model in the formulation of the Qanun for the protection and fulfillment of the rights of persons with disabilities in North Aceh Regency. A qualitative approach was used in this study, with data collection methods including in-depth interviews, focus group discussions (FGD), observations, and document studies.The results of the study indicate that the interaction and interdependence among actors in the policy network in North Aceh Regency are still not optimal. The involved actors often work separately without adequate coordination, and differences in interests and priorities hinder the achievement of common goals. The main inhibiting factors include a lack of resources, differences in interests among actors, and low public awareness of the rights of persons with disabilities. On the other hand, supporting factors include the commitment of the local government, support from NGOs, and initiatives from the community of persons with disabilities.An effective policy network model includes enhancing coordination among actors, establishing regular communication forums, strengthening the capacity of involved actors, and active participation from the community of persons with disabilities. By implementing this model, it is expected that the formulation of the Qanun for the protection and fulfillment of the rights of persons with disabilities in North Aceh Regency can proceed more effectively and inclusively.

Impact of firm’s supply chain network position on digital transformation: evidence from China

Purpose Given the complexity and increasing interdependence of supply chain networks, a broader perspective beyond a simple binary relationship is needed to analyse the impact of supply chain networks on firms. Based on social network theory, this study aims to explore the relationship between firms’ network position in the supply chain network and digital transformation, as well as the moderating effects of structural holes and network partners’ digitalisation. Design/methodology/approach This study analyses a sample of Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2011–2020 using regression analysis to test hypothesised relationships. Findings A firm’s centrality in the supply chain network is positively related to its digital transformation, and that the firm’s structural holes and the level of network partners’ digitalisation can both strengthen the positive influence of network centrality on digital transformation. Moreover, the heterogeneity analysis reveals that the positive effect of firms’ network centrality on digital transformation is more pronounced in non-SOEs and upstream firms. Practical implications In the pursuit of digital transformation, firms should recognise the significance of their position in the supply chain network and enhance their capability for information and resource acquisition by proactively strengthening their business cooperation with other network members. Moreover, firms should emphasise the value of open networks and their partners in supply chain networks. Originality/value This study applies social network theory to investigate the role of a firm’s external supply chain network in its digital transformation, clarifying the context and boundaries in which a firm’s position in the supply chain network influences digital transformation. The results extend the research and theoretical perspective on digital transformation and the consequences of supply chain networks.

MODELING AND MANAGING RESILIENCE AND RISK FOR INTERDEPENDENT NETWORKS

Catastrophic events have the potential to cause major disruptions to interdependent networks such as supply chains, critical infrastructures and other networks that are vital for the functioning of our society. Addressing the resilience and risk of compromised networks is challenging due to a variety of factors. First, these networks are becoming increasingly interdependent, such that network recovery is contingent upon recovery in connected networks. Additionally, interdependent networks may have multiple functions, system users, owners, and stakeholders. There is a need for data-informed decision-making models that address resilience and risk for these interdependent infrastructure networks. We address these issues by: 1) Modeling interdependencies among infrastructure networks using a multi-layer interdependent network framework, 2) Quantifying the relationship between network resilience and the inter-network connection structure, and 3) Supporting management and strategic decision-making for investment in inter-network connection structures, supporting risk mitigation needs and strategies. Our paper combines network optimization and network science methods to produce a unified framework to analyze resilience. We apply the methods of this paper to the interdependent infrastructure network of Puerto Rico following the 2017 Hurricane Maria. This paper supports decision-making for data-informed resilience management for interdependent infrastructure networks.

Modeling and upgrade of disaster-resilient interdependent networks using machine learning

Recent global emergencies emphasize the critical role of reliable communication networks. As dependence on critical infrastructures grows, the focus shifts from isolated failures to designing networks capable of withstanding disasters, taking into account their interdependence with infrastructures like the power grid. This paper investigates the problem of the disaster resilient upgrade of interdependent networks, focusing on enhancing network resilience during emergencies and ensuring a service-level agreement. We analyze how the interdependency between the networks affects the disaster resilience and propose heuristic methods for network operators to improve resilience against disasters. Furthermore, to address the challenge of hidden interdependencies, we present a novel approach using graph neural networks for predicting interdependency between networks based on historical data of failures. Using simulations with real networks and earthquake data, we demonstrate that limiting the number of interdependent edges per node significantly affects resilience. We show that if sufficient data is available graph neural networks can learn the connection between failures and interdependencies, and capable of predicting interdependencies. Additionally, we show that selecting appropriate upgrade methods can reduce network upgrade costs by up to 20%.

Enhancing the phytoextraction capacity of chromium-contaminated soil by co-addition of garbage enzymes and microelectrolytic iron-carbon fillers

Most improved strategies for phytoextraction do not achieve a synergistic enhancement of chromium (Cr) accumulation capacity and biomass. This study investigated the impacts of co-addition of garbage enzyme (GE) and microelectrolytic iron-carbon filler (MF) on soil physicochemical properties, as well as form and uptake of Cr during aging and phytoextraction process. The response of rhizosphere microbial community to co-addition and its role in enhancing the remediation performance of ryegrass was further analyzed. Co-addition of GE and MF during the 12-day aging process resulted in an increase of nutrients, a shift from an oxidising to a reducing soil environment, a decrease of Cr(VI) content, and an enhancement of soil microbial community diversity and richness, creating a suitable environment for subsequent phytoextraction. During the 40-day phytoextraction process, co-addition played a crucial role in facilitating the establishment of a complex, efficient and interdependent ecological network among soil microorganisms and contributed to the evolution of microbial community composition and functional pathways. An increase in the relative abundance of Trichococcus, Azospirillum and g_norank_f_JG30-KF-CM45 elevated soil nutrient levels, while a decrease in the relative abundance of TM7a and Brucella reduced pathogen harbouring. Meanwhile, co-addition increased the relative abundance of Bacillus, Arthrobacter and Exiguobacterium, attenuated Cr phytotoxicity and improved soil biochemical activity. These markedly diminished oxidative damage and improved ryegrass growth by reducing malondialdehyde accumulation. In addition, regular additions of GE and the increase in relative abundance of norank_fnorank_o_Microtrichales led to rhizosphere acidification, which inhibited short-term Cr immobilization and contributed to a notable increase in phytoextraction efficiency. This study presents a strategy to enhance phytoremediation efficiency and soil quality during phytoextraction of Cr-contaminated soils.

A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events

A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events

Demographics of co-ageing complex systems: from infected worms to chess games.

Ageing, as defined in terms of the slope of the probability of death versus time (hazard curve), is a generic phenomenon observed in nearly all complex systems. Theoretical models of ageing predict hazard curves that monotonically increase in time, in discrepancy with the peculiar ups and downs observed empirically. Here we introduce the concept of co-ageing, where the demographic trajectories of multiple cohorts couple together, and show that co-ageing dynamics can account for the anomalous hazard curves exhibited by some species. In our model, multiple interdependency networks inflict damage on one other proportional to their number of functional nodes. We then fit our model predictions to three datasets describing (i) co-ageing worm-pathogen populations and (ii) competing tree species. Lastly, we collect data on the mortality statistics of (iii) chess games to demonstrate that co-ageing dynamics is not exclusive to biological systems.

A Sea of Households: Ordering Violence and Mobility in the 
Inter-Imperial Caribbean

Abstract Historians have paid more attention to the inner life of households than to their legal and political significance in early European overseas empires. This article analyses the legal role of households in the seventeenth century Caribbean, with an emphasis on Jamaica and Suriname. It argues that households were key to organising maritime violence and composing regional order. Imperial agents in the Caribbean—soldiers, sojourners, servants, and officials—drew selectively from European political and legal discourses about dominium to define households as essential to the constitution of colonial communities and governance. In imperial and colonial legal imagination, households were necessary for the constitution of political communities and their presence fortified arguments for interpolitical violence, especially maritime raiding. Affirming the rights of household heads to hold and command captives, imperial policies to foment household formation and regulate conflicts within households underpinned a regional regime of raiding, captive taking, and enslavement. The regional regime centred on legalities of violence. Demand for coerced labour in early plantation households fuelled a 
circum-Caribbean economy of captive taking and plunder, while settlers invoked the defence of households to authorise privateering and local warfare. As the primary legal framework for absorbing and commanding coerced labour, households became the object of inter-imperial competition and a vehicle for constructing enslavement as an institution. Contests over the rights of settlers to relocate to competing colonies at times pitted expansive understandings of the dominium of household heads—the exercise of private power over household subordinates—against the public authorities they nominally sustained. Such conflicts worked to reinforce the centrality of households to the expansion of plantation slavery. The politics of households made them an unstable underpinning for colonial governance and a site of resistance to the emerging plantocracy. Officials in Jamaica struggled to manage the volatile relation between raiding and planter household formation. In Suriname, Indigenous and African captives struggled to escape and subvert forms of slavery and coercion imposed under cover of household dominium. Examining the significance of households in colonial thought illuminates important and often overlooked continuities in the legal politics of nascent Caribbean colonies and the rise of a regional slave regime. Viewed from the colonial household, legal change across the Caribbean did not follow discrete stages of conquest, privateering, and plantation slavery. Instead, it evolved in relation to shifting accommodations between public and private claims to authority and legitimate violence. Authorising warfare and converting captives into property, households formed a legal fulcrum for balancing interdependent networks of raiding, slaving, and planting in emergent slave societies. This constellation of private rights and public authority, organised around captive taking ventures and slave-holding households, spanned the seventeenth-century Caribbean and produced an inter-imperial legal regime in which the rights of slave owners came to occupy the very centre of visions of regional order.

Does Prosocial Automation Increase Driver’s Well-being?

As societies transition to hybrid mobility systems, interactions between automated vehicles (AVs) and human users in public spaces become more complex, highlighting the critical role of prosocial behaviors. These behaviors are essential for the seamless operation of interdependent transportation networks, helping to address integration challenges of AVs with human-operated vehicles and enhancing well-being by creating more efficient, less stressful, and inclusive environments. This study explores the impact of receiving prosocial behaviors on the cognition, riding performance, and well-being of micromobility users in simulated urban traffic scenarios. Using a mixed-method design, the research contrasts two types of social interactions—prosocial and asocial—across three temporal conditions: relaxed, neutral, and urgent. The Structural Equation Modeling underscored the intricate relationships between prosocial behaviors, satisfaction, cognition, riding performance, and well-being. Incorporating social factors into driving systems could enhance safety and improve urban mobility solutions.

Optimal resilience-based restoration plan for disrupted interdependent infrastructure networks under uncertainty

The functioning of vital services in a society depends heavily on the proper and continuous operation of critical infrastructure networks, including water, transportation, power, natural gas, and telecommunication. Such networks are essential for maintaining the economy, security, and overall quality of life. However, since these critical networks are interdependent; hence, they are susceptible to various types of disruptions caused by natural disasters, failures, or malicious activities, leading to varying degrees of performance impacts that could directly affect people's daily lives. Thus, a disruption to one network can quickly spread to others, resulting in significant impacts on daily life. Therefore, restoring such disrupted networks in a timely manner is crucial to ensure a prompt recovery and minimize the impact on daily life. In this work, we consider the problem of restoring a set of interdependent infrastructure networks following a disruptive event. We study two essential factors that can accelerate the recovery process: (i) the restoration facilities location, which are considered as dispatching points for restoration crews, and (ii) the routes of the restoration crews within the interdependent networks. Accordingly, we formulate two optimization models using mixed integer programming. The first model, restoration facility location model, finds the optimal location for restoration facilities within interdependent infrastructure networks considering uncertainty in disruptions. The second model, restoration crew routing model, determine the optimal routes for restoration crews within the interdependent infrastructure networks considering: (i) travelled distance, (ii) restoration time required for disrupted network components, and (iii) different importance of network components. The two optimization models are solved through generated interdependent power and water networks to demonstrate their effectiveness.

Third party interventions promote cooperation on the interdependent networks: A perspective based on prospect theory.

Third-party intervention is a beneficial means to alleviate conflicts and promote cooperation among disputants. The decision-making of disputants is closely related to the intensity of the impact of third-party intervention on their profits. Actually, disputants often decide whether to adopt cooperative strategies based on their own perceived rather than actual gains or losses brought about by third-party intervention. We, therefore, introduce prospect theory to explore the formation and maintenance of cooperation in a system composed of third parties and disputants, which, respectively, constitute two sub-networks of the interdependent networks. Both interveners and disputants participate in a prisoner's dilemma game, and the third-party intervener will pay a certain price to impose certain punishments on the defectors of the disputed layer. The simulation results show that the introduction of third-party intervention based on the prospect theory alleviates the conflicts in the dispute layer and promotes cooperation among disputants, which indicates that third parties such as governments or organizations should appropriately consider the risk attitudes of disputants when mediating their conflicts. The level of cooperation at the dispute layer is inversely proportional to the intervention cost and directly proportional to the intervention intensity. Our research may shed some new light on the study of the evolution of cooperation under third-party intervention.

Robustness of multilayer interdependent higher-order network

In real-world complex systems, most networks are interconnected with other networks through interlayer dependencies, forming multilayer interdependent networks. In each system, the interactions between nodes are not limited to pairwise but also exist in a higher-order interaction composed of three or more individuals, thus inducing a multilayer interdependent higher-order network (MIHN). First, we build four types of artificial MIHN models (i.e., chain-like, tree-like, star-like and ring-like), in which the higher-order interactions are described by the simplicial complexes, and the interlayer dependency is built via a one-to-one matching dependency link. Then, we propose a cascading failure model on MIHN and suggest a corresponding percolation-based theory to study the robustness of MIHN by investigating the giant connected components (GCC) and percolation threshold. We find that the density of the simplicial complexes and the number of layers of the network affect its penetration behavior. When the density of simplicial complexes exceeds a certain threshold, the network has a double transition, and the increase in network layers significantly enhances the vulnerability of MIHN. By comparing the simulation results of MIHNs with four types, we observe that under the same density of simplicial complexes, the size of the GCC is independent of the topological structures of MIHN after removing a certain number of nodes. Although the cascading failure process of MIHNs with different structures is different, the final results tend to be the same. We further analyze in detail the cascading failure process of MIHN with different structures and elucidate the factors influencing the speed of cascading failures. Among these four types of MIHNs, the chain-like MIHN has the slowest cascading failure rate and more stable robustness compared to the other three structures, followed by the tree-like MIHN and star-like MIHN. The ring-like MIHN has the fastest cascading failure rate and weakest robustness due to its ring structure. Finally, we give the time required for the MIHN with different structures to reach the stable state during the cascading failure process and find that the closer to the percolation threshold, the more time the network requires to reach the stable state.

MrgprA3 neurons drive cutaneous immunity against helminths through selective control of myeloid-derived IL-33.

Skin uses interdependent cellular networks for barrier integrity and host immunity, but most underlying mechanisms remain obscure. Herein, we demonstrate that the human parasitic helminth Schistosoma mansoni inhibited pruritus evoked by itch-sensing afferents bearing the Mas-related G-protein-coupled receptor A3 (MrgprA3) in mice. MrgprA3 neurons controlled interleukin (IL)-17+ γδ T cell expansion, epidermal hyperplasia and host resistance against S. mansoni through shaping cytokine expression in cutaneous antigen-presenting cells. MrgprA3 neuron activation downregulated IL-33 but induced IL-1β and tumor necrosis factor in macrophages and type 2 conventional dendritic cells partially through the neuropeptide calcitonin gene-related peptide. Macrophages exposed to MrgprA3-derived secretions or bearing cell-intrinsic IL-33 deletion showed increased chromatin accessibility at multiple inflammatory cytokine loci, promoting IL-17/IL-23-dependent changes to the epidermis and anti-helminth resistance. This study reveals a previously unrecognized intercellular communication mechanism wherein itch-inducing MrgprA3 neurons initiate host immunity against skin-invasive parasites by directing cytokine expression patterns in myeloid antigen-presenting cell subsets.

A systemic approach for assessing infrastructure component importance in hazard-prone communities

Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.

Developing resilience pathways for interdependent infrastructure networks: A simulation-based approach with consideration to risk preferences of decision-makers

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

Revealing the crosstalk between LOX+ fibroblast and M2 macrophage in gastric cancer by single-cell sequencing

Background/AimsGastric cancer (GC) ranks among the prevalent types of cancer, and its progression is influenced by the tumor microenvironment (TME). A comprehensive comprehension of the TME associated with GC has the potential to unveil therapeutic targets of significance.MethodsThe complexity and heterogeneity of TME interactions were revealed through our investigation using an integrated analysis of single-cell and bulk-tissue sequencing data.ResultsWe constructed a single-cell transcriptomic atlas of 150,913 cells isolated from GC patients. Our analysis revealed the intricate nature and heterogeneity of the GC TME and the metabolic properties of major cell types. Furthermore, two cell subtypes, LOX+ Fibroblasts and M2 Macrophages, were enriched in tumor tissue and related to the outcome of GC patients. In addition, LOX+ Fibroblasts were significantly associated with M2 macrophages. immunofluorescence double labeling indicated LOX+ Fibroblasts and M2 Macrophages were tightly localized in GC tissue. The two cell subpopulations strongly interacted in a hypoxic microenvironment, yielding an immunosuppressive phenotype. Our findings further suggest that LOX+ Fibroblasts may act as a trigger for inducing the differentiation of monocytes into M2 Macrophages via the IL6-IL6R signaling pathway.ConclusionsOur study revealed the intricate and interdependent communication network between the fibroblast and macrophage subpopulations, which could offer valuable insights for targeted manipulation of the tumor microenvironment.

General theory for extended-range percolation on simple and multiplex networks.

Extended-range percolation is a robust percolation process that has relevance for quantum communication problems. In extended-range percolation nodes can be trusted or untrusted. Untrusted facilitator nodes are untrusted nodes that can still allow communication between trusted nodes if they lie on a path of distance at most R between two trusted nodes. In extended-range percolation the extended-range giant component (ERGC) includes trusted nodes connected by paths of trusted and untrusted facilitator nodes. Here, based on a message-passing algorithm, we develop a general theory of extended-range percolation, valid for arbitrary values of R as long as the networks are locally treelike. This general framework allows us to investigate the properties of extended-range percolation on interdependent multiplex networks. While the extended-range nature makes multiplex networks more robust, interdependency makes them more fragile. From the interplay between these two effects a rich phase diagram emerges including discontinuous phase transitions and reentrant phases. The theoretical predictions are in excellent agreement with extensive Monte Carlo simulations. The proposed exactly solvable model constitutes a fundamental reference for the study of models defined through properties of extended-range paths.

The recovery strategy of interdependent networks under targeted attacks

To effectively mitigate failures in interdependent systems during targeted-attack scenarios, a common approach is to pre-store repair resources. The question arises: what constitutes an appropriate quantity of these pre-stored repair resources? The paper introduces a novel recovery strategy aimed at providing guidance for this issue. Current recovery strategies frequently emphasize the dynamic interplay between cascading failures and recovery processes, indicating that interventions during the recovery phase are permissible. In this context, the recovery strategy focus on recovering a predetermined number of failed nodes that are adjacent to the largest connected component of each individual network, along with their dependent nodes, at each recovery stage. Simulation results demonstrate that this strategy significantly enhances the capacity to prevent system breakdowns for interdependent networks subjected to targeted attacks. Therefore, by determining the necessary recovery steps to prevent system failures and the appropriate repair resources required for each step, this novel strategy can serve as a valuable reference for the pre-storage of repair resources. Significantly, the strategy can be effectively applied to interdependent networks associated with critical infrastructure, such as power grids and communication networks.

The impact of conditional coupling based on environment comparison between interdependent networks on the evolution of cooperation

The coupling between interdependent networks is a crucial factor affecting the evolution of cooperation. Actually, not all nodes in different sub-networks of the interdependent network can establish a one-to-one corresponding coupling relationship, and whether the connection between corresponding nodes can be built is often related to the local environment and global environment where the individual is located. Therefore, we explore how the conditional coupling between interdependent networks based on the comparisons of local and global environments affects the spread of cooperative behavior. We consider two types of conditional coupling modes: static coupling and dynamic coupling. The classic prisoner’s dilemma is chosen as the fundamental game model. The numerical simulation results on the interdependent network constructed by square lattices show that conditional coupling based on environmental comparison favors the propagation of cooperation, regardless of the type of conditional coupling. And the promotion level of cooperation is proportional to the coupling strength. Moreover, we further perform the simulation experiments on the interdependent networks composed of ER (Erdös–Rényi) random networks. Our explorations may be helpful for understanding the survival and maintenance of cooperation in the interconnected and interrelated systems.