Interdependent Networks Research Articles

SLE: a cognitive step forward-a synthesis of rethinking theories, causality, and ignored DNA structures.

Systemic lupus erythematosus (SLE) is classified by instinctual classification criteria. A valid proclamation is that these formally accepted SLE classification criteria legitimate the syndrome as being difficult to explain and therefore enigmatic. SLE involves scientific problems linked to etiological factors and criteria. Our insufficient understanding of the clinical condition uniformly denoted SLE depends on the still open question of whether SLE is, according to classification criteria, a well-defined one disease entity or represents a variety of overlapping indistinct syndromes. Without rational hypotheses, these problems harm clear definition(s) of the syndrome. Why SLE is not anchored in logic, consequent, downstream interdependent and interactive inflammatory networks may rely on ignored predictive causality principles. Authoritative classification criteria do not reflect consequent causality criteria and do not unify characterization principles such as diagnostic criteria. We need now to reconcile legendary scientific achievements to concretize the delimitation of what SLE really is. Not all classified SLE syndromes are "genuine SLE"; many are theoretically "SLE-like non-SLE" syndromes. In this study, progressive theories imply imperative challenges to reconsider the fundamental impact of "the causality principle". This may offer us logic classification and diagnostic criteria aimed at identifying concise SLE syndromes as research objects. Can a systems science approach solve this problem?

Robustness analysis of interdependent network accounting for failure probability and coupling patterns.

The robustness of interdependent networks against perturbations is an important problem for network design and operation. This paper focuses on establishing a cascading failure dynamics model and analyzing the robustness for interdependent networks, in which the states of the nodes follow certain failure probability and various connectivity patterns. First, to describe the removal mechanism of an overloaded node, the failure probability associated with the load distribution of components was proposed. Then, we present the node capacity cost and the average capacity cost of the network to investigate the propagation of cascading failures. Finally, to discuss the impact of the configuration parameters on robustness, some numerical examples are conducted, where the robustness was analyzed based on the proposed method and different interdependence types. Our results show that, the larger the overload parameter, the more robust the network is, but this also increases the network cost. Furthermore, we find that allocating more protection resources to the nodes with higher degree can enhance the robustness of the interdependent network. The robustness of multiple-to-multiple interdependent networks outperforms that of one-to-one interdependent networks under the same coupling pattern. In addition, our results unveil that the impact of coupling strategies on the robustness of multiple-to-multiple interdependent networks is smaller than that of one-to-one interdependent networks.

Enhancing the robustness of interdependent networks by positively correlating a portion of nodes

Cascading failures caused by interdependencies make modern coupled systems extremely fragile to failures. In existing network robustness enhancing methods, maximizing interlayer degree-degree correlations has been proven to be an effective way to improve the robustness of interdependent networks under random failures. Here, we propose a portion of nodes positively correlated strategy (PNC) to improve network robustness by positively correlating a portion of nodes, in which the nodes that are positively correlated are selected in descending order of degree, starting at a cutoff value. Based on percolation theory, we verify the effectiveness of PNC on different networks. And find that, when the nodes with the highest degree are preferentially correlated, i.e. the cutoff value takes the maximum degree, this strategy achieves a state-of-the-art optimization effect. In particular, for interdependent scale-free networks with power-law exponent γ that satisfies 2<γ<3 , we theoretically demonstrate that the highest degree preferentially correlated mode can maximize network robustness by changing the coupling state of the near 0 proportion of nodes. For γ = 3, such a mode can make the network turn into a second-order phase transition at the collapse point. Finally, we discuss the relationship between the robustness of optimized networks and common links in real-world networks.

Cascading failures in bipartite networks with directional support links.

We study the cascading failures in a system of two interdependent networks whose internetwork supply links are directional. We will show that, by utilizing generating function formalism, the cascading process can be modeled by a set of recursive relations. Most importantly, the functions involved in these relations are solely dependent upon the choice of the degree distribution of ingoing links. Simulation results in the limit of very large networks based on different choices of degree distributions for outgoing links, e.g., Kronecker delta, Poisson and Pareto, are indeed identical and are in excellent agreement with the theory. However, for Pareto distribution with the shape parameter 1<α<2, the convergence is slow. In general, directional networks can be more vulnerable or less vulnerable than their bidirectional counterparts. For three special settings of interdependent networks, we analytically compare their vulnerability. For practical applications it is important to predict if a system responds to the size of the initial attack continuously or if there is catastrophic collapse of the system if the attack exceeds a specific transition size. We analytically show that systems with lower average degrees are more resilient against this abrupt transition. We also establish an equivalence of this transition with the liquid-gas transition in statistical mechanics. In the last section, we derive the set of recursive relation to describe the cascading process where the initial attack is not restricted to a single network.

The Lofar Global Citizenship Radio Array - “Gloray” (Ska-Low-North)

Abstract This talk describes a proposed transformation of LOFAR, the world’s largest, most sensitive high-resolution radio telescope that operates at low frequencies (&lt;300 MHz), into a multidisciplinary intercontinental facility that will be a unique engine for capacity building and development throughout Europe, North Africa, the Middle East and West Asia. As an astronomical array, GLORAY would be the “Hubble Space Telescope” of low-frequency radio astronomy, having a resolution more than 50 times better than any other presently planned low-frequency facility, including SKA-LOW. The mutually interdependent network of LOFAR antenna stations that presently stretch from Ireland to Latvia would be extended to cover North Africa, the Middle East and West Asia and converted into “innovation hubs”. These would form an intercontinental network for pure research, applied research, capacity building, education and science diplomacy within and between the participating countries.

Microscopic Intervention Yields Abrupt Transition in Interdependent Ferromagnetic Networks.

The study of interdependent networks has recently experienced a boost with the development of experimentally testable materials that physically realize their novel critical behaviors, calling for systematic studies that go beyond the percolation paradigm. Here we study the critical kinetics and phase transitions of a model of interdependent spatial ferromagnetic networks where dependency couplings between networks are realized by a thermal interaction having a tunable spatial range. We show how the critical phenomena and the phase diagram of this realistic model are highly affected by the range of thermal dissipation and how the latter influences the microscopic kinetics of the model. Furthermore, we show the existence of a new phase where localized microscopic interventions by heating or magnetic fields yield a macroscopic phase transition. Our results unveil rich phenomena and realistic protocols for controlling the macroscopic phases of interdependent materials by means of microscopic interventions.

Vulnerability Analysis of UAV Swarm Network with Emergency Tasks

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

Percolation transitions in partially edge-coupled interdependent networks with different group size distributions

In many systems, from brain neural networks to epidemic transmission networks, pairwise interactions are insufficient to express complex relationships. Nodes sometimes cooperate and form groups to increase their robustness to risks, and each such group can be considered a “supernode”. Furthermore, previous studies of cascading failures in interdependent networks have typically concentrated on node coupling connections; however, in many realistic scenarios, interactions occur between the edges connecting nodes rather than between the nodes themselves. Networks of this type are called edge-coupled interdependent networks. To better reflect complex networks in the real world, in this paper, we construct a theoretical model of a two-layer partially edge-coupled interdependent network with groups, where all nodes in the same group are functionally dependent on each other. We identify several types of phase transitions, namely, discontinuous, hybrid and continuous, which are determined by the strength of the dependency and the distribution of the supernodes. We first apply our developed mathematical framework to ErdsRnyi and scale-free partially edge-coupled interdependent networks with equally sized groups to analytically and numerically calculate the phase transition thresholds and the critical dependency strengths that distinguish different types of transitions. We then investigate the influence of the group size distribution on cascading failures by presenting examples of two different heterogeneous group size distributions. Our theoretical predictions and numerical findings are in close agreement, demonstrating that decreasing the dependency strength and increasing group size heterogeneity can increase the robustness of interdependent networks. Our results have significant implications for the design and optimization of network security and fill a knowledge gap in the robustness of partially edge-coupled interdependent networks with different group size distributions.

A multi‐agent reinforcement learning model for maintenance optimization of interdependent highway pavement networks

AbstractPavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&amp;R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO) framework and a multi‐agent reinforcement learning algorithm. The established model was demonstrated on a highway pavement network in the real‐world, compared to a previously built two‐stage bottom‐up (TSBU) model. The results showed that, compared to TSBU, SNO produced a 3.0% reduction in total costs and an average pavement performance improvement of up to 17.5%. It prefers concentrated M&amp;R schedules and tends to take more frequent preventive maintenance to reduce costly rehabilitation. The results of this research are anticipated to provide practitioners with quantitative estimates of the possible impact of ignoring segment interdependencies in M&amp;R planning.

Percolation in interdependent networks with groups and heterogeneous weak interdependency strength under targeted attack

Pairwise dependencies in interdependent networks exist not only between ordinary nodes but also between groups of nodes, where nodes cooperate and form groups to increase their robustness to risks and each group can be considered a ‘supernode’. The interdependencies between supernodes are universal but not always complete and homogeneous. In this paper, we study the robustness of interdependent networks with groups and heterogeneous weak interdependency strength under targeted attack, where the interdependency strength could vary between different supernodes. We identify several types of percolation transitions, namely discontinuous, continuous, and hybrid, which are determined by the structure of the supernodes, the strengths of the interdependencies, and their distributions. We first apply this mathematical framework to the Erdös-Rényi and Scale-free interdependent networks with groups and homogeneous weak interdependency strength to calculate analytically and numerically the values of the phase transition thresholds and the critical interdependency strengths that distinguish different types of transitions. We then present examples of three distributions with different heterogeneous interdependency strengths. Our theoretical and numerical predictions are in close agreement, demonstrating that weaker interdependence strengths and higher levels of interdependence heterogeneity can enhance the robustness of interdependent networks under targeted attack.

Resilience of interdependent supply chain networks design and protection under the ripple effect

Firms painstakingly assemble vast supply networks of cyber and physical interdependence. Wars, pandemics, and cyber-attacks have disrupted and rippled recently through these networks. The ripple effect in interdependent networks can be caused by functional and structural failures simultaneously, known as mixed ripple effects, which have been studied less in the past. To help managers enhance network resilience against mixed ripple effects, we propose a design and protection strategy from a network structure perspective. This paper is the first to propose a growth-maturity-decline (GMD) model for designing an interdependent network structure. Then, a protection strategy based on node collaboration and redundancy is proposed. Considering that a single resilience metric makes it difficult to distinguish the performance of different strategies, we develop a three-dimensional quantitative framework to evaluate network resilience by conducting case studies on synthetic and real-life supply chain networks. The simulation results indicate that (i) the GMD model enhances network resilience at different phases; (ii) the threshold of the upper capacity limit parameter for cyber-supply networks is 2.7; and (iii) the more uniform the role distribution of firms, the more resilient the network is. This study can provide more informed management decision support for improving network resilience.

Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network

As critical lifeline systems, transportation network (TN) and electric power network (EPN) are highly susceptible to natural hazards, such as earthquakes during their service life. At the same time, restoration of damaged TN and EPN is essential to support the post-earthquake reconstruction and emergency rescue in affected areas. Restoration strategies were traditionally developed for TN or EPN separately. However, neglecting the potential interconnection between these two networks in the recovery phase may lead to detrimental consequences, as in real-world scenarios, the obtained strategy may be less efficient or even unfeasible given that recovery of one system is usually dependent on the others for service provision. Accordingly, this paper presents a resilience-based framework for post-earthquake restoration of interdependent transportation-electric power networks. In this framework, restoration independencies and functionality dependencies are introduced to represent the interaction between TN and EPN. Then, a bi-level optimization model with the objective of maximizing seismic resilience is established to characterize the network recovery problem. Furthermore, a solution algorithm that incorporates a genetic algorithm and a chromosome validity test operator is designed to obtain the near-optimal solution. Finally, the proposed framework is illustrated through two numerical examples.

Interdependent Expansion Planning for Resilient Electricity and Natural Gas Networks

This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems.

Interdependent network restoration games with incomplete information and bounded rationality.

Communities face the challenge of finding restoration strategies in the aftermath of disasters. In particular, independent and self-interested utility managers devise such strategies for infrastructure through a heuristic decentralized process. This paper takes a game-theoretic approach to model the decentralized and strategic restoration decision making with application to interdependent infrastructure. Particularly, we model the decision process using simultaneous games to investigate decision makers' conflicting preferences. We employ Bayesian games to incorporate the realistic assumptions of poor interagent communication, resulting in incomplete information. Also, we account for behavioral biases such as bounded rationality, cooperative behavior or lack thereof, and equality-driven resource allocations. We test our models using ideal, synthetic interdependent networks, and the realistic infrastructure of Shelby County, TN. Results show that cooperation leads to the best-performing decisions even if others are not cooperative. The necessity of cooperation is even higher when there is a dominant player whose service is vital to other players. Our sensitivity results highlight the significant influence of resource availability and allocation on the performance of restoration plans. Our approach enhances the practicality of decision models for community resilience, and unravels novel policy strategies such as cooperationincentives.

Cascadic failure and preferential decay via pruning mediated percolation on interdependent networks: implications for schizophrenia

IntroductionDuring adolescence the brain is dynamically changing. Destabilization and acceleration of the normal adolescent synaptic pruning process is likely a contributing factor to the neuropathology of schizophrenia. Details on whether normal pruning effects weaker synapses more or uniformly all synapses with different strengths, needs to be further evaluated. Widespread impairment in structural connectivity in schizophrenic patients involving several cortical and subcortical areas, has been previously described. In this computational study, we investigated a stochastic percolation process in interdependent networks, motivated by pathological synaptic pruning. We examined preferential decay in the connectivity decremental process, as well as differential pruning in interconnected subnetworks. Finally, the speed of the percolation process, as well as the potential for pharmacological interventions of percolation in random networks was explored. Statistical structural properties of decaying networks pinpointed several network attributes which the disintegration and phase transitions qualitatively depended on.ObjectivesThe following objectives were explored: 1.) Apart from a random percolation process, we investigated preferential decay of the connections. We introduced different percolation rules for various connection types. 2.) Based on previous experimental results, we assumed that different interconnected neural subpopulations prune differently, therefore we explored differential pruning process in the subnetworks. 3.) The speed of the percolation was studied and the pharmacological synaptic connectivity change was also analyzed.MethodsWe considered two inter-connected randomly connected networks, where the connections were removed during the percolation process. Simulations were partially performed using Octave on a Lenovo Thinkpad running the Linux operating system and partially performed on a supercomputer at UPPMAX (NAISS Small Compute 2023 Dnr: NAISS 2023/22-102).ResultsWe found that the coupled network system shows rich percolation behaviors with phase transitions for various coupling strength and coupling patterns. The phase transitions of both layers are altered qualitatively between discontinuous, mixed and continuous. Recursively developing percolation in interdependent networks can cause complete fragmentation of these networks, resulting in cascadic failure which might be related to schizophrenia symptoms.Conclusions This computational study analyzes the pruning-mediated percolation in interdependent neural networks. Consequences of the pathological overpruning were related to the attributes of the interdependent network properties. Implications for schizophrenia development and predictions for compensatorial iatrogenic percolation was also pinpointed and discussed.Disclosure of InterestNone Declared

Adora2A downregulation promotes caffeine neuroprotective effect against LPS-induced neuroinflammation in the hippocampus

Caffeine has been extensively studied in the context of CNS pathologies as many researchers have shown that consuming it reduces pro-inflammatory biomarkers, potentially delaying the progression of neurodegenerative pathologies. Several lines of evidence suggest that adenosine receptors, especially A1 and A2A receptors, are the main targets of its neuroprotective action. We found that caffeine pretreatment 15 min before LPS administration reduced the expression of Il1b in the hippocampus and striatum. The harmful modulation of caffeine-induced inflammatory response involved the downregulation of the expression of A2A receptors, especially in the hippocampus. Caffeine treatment alone promoted the downregulation of the adenosinergic receptor Adora2A; however, this promotion effect was reversed by LPS. Although administering caffeine increased the expression of the enzymes DNA methyltransferases 1 and 3A and decreased the expression of the demethylase enzyme Tet1, this effect was reversed by LPS in the hippocampus of mice that were administered Caffeine + LPS, relative to the basal condition; no significant differences were observed in the methylation status of the promoter regions of adenosine receptors. Finally, the bioinformatics analysis of the expanded network demonstrated the following results: the Adora2B gene connects the extended networks of the adenosine receptors Adora1 and Adora2A; the Mapk3 and Esr1 genes connect the extended Adora1 network; the Mapk4 and Arrb2 genes connect the extended Adora2A network with the extended network of the proinflammatory cytokine Il1β. These results indicated that the anti-inflammatory effects of acute caffeine administration in the hippocampus may be mediated by a complex network of interdependencies between the Adora2B and Adora2A genes.

Is society caught up in a Death Spiral? Modeling societal demise and its reversal.

Just like an army of ants caught in an ant mill, individuals, groups and even whole societies are sometimes caught up in a Death Spiral, a vicious cycle of self-reinforcing dysfunctional behavior characterized by continuous flawed decision making, myopic single-minded focus on one (set of) solution(s), denial, distrust, micromanagement, dogmatic thinking and learned helplessness. We propose the term Death Spiral Effect to describe this difficult-to-break downward spiral of societal decline. Specifically, in the current theory-building review we aim to: (a) more clearly define and describe the Death Spiral Effect; (b) model the downward spiral of societal decline as well as an upward spiral; (c) describe how and why individuals, groups and even society at large might be caught up in a Death Spiral; and (d) offer a positive way forward in terms of evidence-based solutions to escape the Death Spiral Effect. Management theory hints on the occurrence of this phenomenon and offers turn-around leadership as solution. On a societal level strengthening of democracy may be important. Prior research indicates that historically, two key factors trigger this type of societal decline: rising inequalities creating an upper layer of elites and a lower layer of masses; and dwindling (access to) resources. Historical key markers of societal decline are a steep increase in inequalities, government overreach, over-integration (interdependencies in networks) and a rapidly decreasing trust in institutions and resulting collapse of legitimacy. Important issues that we aim to shed light on are the behavioral underpinnings of decline, as well as the question if and how societal decline can be reversed. We explore the extension of these theories from the company/organization level to the society level, and make use of insights from both micro-, meso-, and macro-level theories (e.g., Complex Adaptive Systems and collapsology, the study of the risks of collapse of industrial civilization) to explain this process of societal demise. Our review furthermore draws on theories such as Social Safety Theory, Conservation of Resources Theory, and management theories that describe the decline and fall of groups, companies and societies, as well as offer ways to reverse this trend.

Critical behavior of cascading failures in overloaded networks.

While network abrupt breakdowns due to overloads and cascading failures have been studied extensively, the critical exponents and the universality class of such phase transitions have not been discussed. Here, we study breakdowns triggered by failures of links and overloads in networks with a spatial characteristic link length ζ. Our results indicate that this abrupt transition has features and critical exponents similar to those of interdependent networks, suggesting that both systems are in the same universality class. For weakly embedded systems (i.e., ζ of the order of the system size L) we observe a mixed-order transition, where the order parameter collapses following a long critical plateau. On the other hand, strongly embedded systems (i.e., ζ≪L) exhibit a pure first-order transition, involving nucleation and the growth of damage. The system's critical behavior in both limits is similar to that observed in interdependent networks.

Spirits and Bodies as an Interdependent Network of Relations and Approach to the Study of Religion in Brazil

In the ever-evolving religious landscape of Brazil, researchers who study contemporary phenomena of religions face a complex and often contradictory reality. The practical application of theoretical categories and concepts in this domain is an endeavor that continues to challenge researchers. Brazil’s religious landscape is characterized by an impressive diversity of beliefs, practices, and convictions that often merge and overlap across different boundaries. This complex reality poses a serious challenge to religious studies, which strives to classify, analyze, and compare phenomena in historical and contemporary dimensions. The problems begin with the basic categories, which are often blurred in their applicability and meaning. Therefore, the focus of this article is to argue that the rapid change and impermanence of categories used in religious studies are due to the deep-rooted contradictions in Brazilian religious reality, which (e)merge, transform, and respond to social, cultural, and political changes in a constant state of flux. These changes cause defined categories that may have made sense at certain times. This article argues, however, that a phenomenological perspective is needed to conceptualize the centrality of spiritual entities to comparative religion. The omnipresence of spirits pervades the entire religious field of Pentecostalism, traditional Christianity, indigenous religions and Shamanism, as well as African-derived religions, which will be exemplified in terms of their references and striking parallels when it comes to the concept of spirits while manifested in human bodies and practices.

Leveraging Blockchain Interoperability for Interdependent Networks

With the rapid emergence of technologies, coupling interdependent consumer electronics networks have become imperative to enable information exchange, holistic data analysis, and effective decision-making techniques. While interdependent networks can discover crucial information through intuitive understanding and efficient data-driven algorithms, it is crucial to leverage secure and effective data exchange among the underlying networks of consumer electronics. Blockchain comes into play when it requires ensuring trustless communication and data exchange amongst multiple interdependent networks. Blockchain interoperability is evolving and gaining widespread popularity in industry and research due to its ability to exchange data among multiple blockchains. This paper presents Divide-LeapChain, a next-generation efficient and effective interoperable blockchain framework for interdependent networks of heterogeneous consumer electronics devices. The Divide-LeapChain addresses the critical issues of intra- and inter-blockchain communication, including spatial and temporal uncertainties in data traversal, exchange, validation, and cross-chain swaps. It promises significant drops-off in verification steps without weakening blockchain integrity, which extends the possibilities to apply blockchain applications on heterogeneous embedded consumer Internet-of-Things (IoT) devices.