Interdependent Networks Research Articles

Diarrheal disease and gut microbiome.

Diarrheal disease remains a great public health problem in many countries. Enteric infections caused by several viral, bacterial and parasitic species not only affect the host, but also alter the gut microbiome. The host physiology dictates the intestinal milieu and decides the composition and richness of gut microbiota, which forms a homeostatic ecosystem with numerous functions and provide protection against invading pathogens. During diarrheal infection, patients are affected by gut microbial dysbiosis, which benefits the pathogenic and pro-inflammatory bacteria by enhancing their colonization and proliferation. Gut microbes are associated with several pathophysiological mechanisms, including distorted motility, intestinal barrier dysfunction, malabsorption, immunity disorder, systemic inflammation and changes in the gut-organ axis. Several abiotic factors and childhood malnutrition have negative influences on the gut microbiota, including antibiotics that lead to antibiotic-associated diarrhea and persistent infection. DNA sequencing and bioinformatic analyses enhanced our perception of the gut microbiota, network of metabolic interdependence and their role in health and disease. However, the precise functions of microbiota in gut homeostasis are not well defined. In this chapter, we recapitulate the impact of gut microbiota on diarrheal pathogens, their importance in the immune system and how reshaping the gut microbiota can help during the recovery phase. Additionally, we discuss about impediments and influences beyond diarrhea, particularly on the nutritional status of children.

Resilience Optimization of Wide-Area Control in Smart Distribution Grids

Large-scale power distribution networks rely on the wide-area control (WAC) function to conduct daily grid operations. Grid control is even more critical during extreme events as the WAC function is required to orchestrate the response to contingencies and enhance the power system resilience through failure localization, isolation, and service restoration. Both power and telecommunication domains are involved in control applications, giving rise to multiple cyber-physical interdependencies. This paper proposes a resilience-based optimization of the distribution service restoration (DSR) by coordinating strategies of crew dispatch and manual/remote switches operation. The telecommunication service and underlying infrastructure are identified as main enablers of the co-optimization as all considered resilience strategies communicate with the control center that collects crisis management information. Therefore, the availability of telecom points in terms of power supply is examined in this work. Failure propagation in the coupled power-telecom network is highlighted, and differences in failure propagation between overhead and underground power lines are explored. The proposed approach is formulated as a mixed-integer linear programming (MILP) model, evaluated under a multi-feeder interdependent power-telecom test network. Results show that combined scheduling of resilience strategies as well as prioritization of power supply to telecom points-of-interest, yield an enhanced recovery strategy.

Enhancing the Robustness of Interdependent Power Grids and Communication Networks by Maximally Disjoint Dual Paths

Enhancing the Robustness of Interdependent Power Grids and Communication Networks by Maximally Disjoint Dual Paths

Optimal Backup Power Deployment for Communication Network With Interdependent Power Network

Smart grid has a power network with an interdependent communication network. In such a system, a node failure can lead to another failure in its dependent node in the other network. These inter-network failures can occur recursively in a cascading process, resulting in a complete system collapse. Such cascading failure process can be interrupted by providing backup power to communication nodes, so that they can continue operation when the power nodes they depend on have failed. It is costly to install a backup power unit at each communication node. We have used the two-stage percolation theory to determine the system robustness as a function of backup power deployment density and backup power unit capacity. Then, we propose a novel scheme to determine the optimal backup power deployment density and unit capacity, which can minimize the backup power deployment cost without compromising a desired system robustness. Through extensive simulations, we have found that when the probability of initial node failure is below its critical value, the deployment cost may decrease linearly with a smaller network size and may increase exponentially with a higher robustness requirement.

The role of urban spatial structure in energy resilience: An integrated assessment framework using a hybrid factor analysis and analytic network process model

This study investigates the relationship between urban spatial structure and energy resilience at the local level. Sub-district no.7 of District no.1 of Tehran was selected as the study area. First, to evaluate the energy resilience of 250 blocks of the study area, 28 indicators in four components of urban geometry and morphology, land use, public transportation, and passive design were adopted. Subsequently, using a hybrid factor analysis and analytic network process (F'ANP) model, flexibility, interdependence, coordination capacity, equity, efficiency, self-organization, independence, and diversity were determined as the eight underlying energy resilience principles related to the spatial structure of the study area. The composite index results reveal that principles and indicators related to transportation have a more significant role in energy resilience than operational energy-related ones. Also, increasing compactness without simultaneous attention to the mass-space ratio and adequate provision of green spaces does not necessarily lead to increased energy resilience and may even detract from resilience. To illustrate the practical implications of the study findings, we recommend action plans that can contribute to enhancing the energy resilience of the study area. The proposed globally applicable framework is expected to give urban planners a better understanding ofurban form and spatial structure implicationsfor energy resilience. It can also inform planning and decision making for achieving urban climate change adaptation and mitigation targets.

Cooperative Evolution and Symmetry Breaking in Interdependent Networks Based on Alliance Mechanisms

Cooperative Evolution and Symmetry Breaking in Interdependent Networks Based on Alliance Mechanisms

Bayesian Estimation of Multivariate Panel Probits with Higher-Order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong

Bayesian Estimation of Multivariate Panel Probits with Higher-Order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong

Percolation on interdependent networks with different group size distributions under targeted attack

Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.

The Indonesian Electronic Information and Transactions Within Indonesia’s Broader Legal Regime: Urgency for Amendment?

Cyberspace is the interdependent network of information technology infrastructures such as the internet, telecommunications networks, and computer systems. Meanwhile, Indonesia’s Law Number 11 of 2008 and its amendment through Indonesian Law Number 19 of 2016 governing cyberspace have been viewed to contradict and infringe other areas of law, such as protection of press or freedom of expression. Hence, this study seeks to identify the controversies and problems regarding the law deemed urgent for amendment. Further, this study creates recommendations so the government may amend electronic information policy more fairly and efficiently. This study uses a judicial normative and comparative approach. This research tries to analyze the existing regulations and the implementation and compare Indonesia’s cyberspace regulation with other States’. This study finds that Articles 27(3) and 28(2) of the law criminalize defamation and hate speech in an overly broad manner and that Article 40(2)(b) allows the government to exercise problematic censorship. As a result, they have infringed the freedom of the press and general freedom of expression in practice. In response to this, this study compares similar provisions from other States and recommends amendment the articles to become narrower and more clearly defined.

Cognitive Load Balancing Approach for 6G MEC Serving IoT Mashups

The sixth generation (6G) of communication networks represents more of a revolution than an evolution of the previous generations, providing new directions and innovative approaches to face the network challenges of the future. A crucial aspect is to make the best use of available resources for the support of an entirely new generation of services. From this viewpoint, the Web of Things (WoT), which enables Things to become Web Things to chain, use and re-use in IoT mashups, allows interoperability among IoT platforms. At the same time, Multi-access Edge Computing (MEC) brings computing and data storage to the edge of the network, which creates the so-called distributed and collective edge intelligence. Such intelligence is created in order to deal with the huge amount of data to be collected, analyzed and processed, from real word contexts, such as smart cities, which are evolving into dynamic and networked systems of people and things. To better exploit this architecture, it is crucial to break monolithic applications into modular microservices, which can be executed independently. Here, we propose an approach based on complex network theory and two weighted and interdependent multiplex networks to address the Microservices-compliant Load Balancing (McLB) problem in MEC infrastructure. Our findings show that the multiplex network representation represents an extra dimension of analysis, allowing to capture the complexity in WoT mashup organization and its impact on the organizational aspect of MEC servers. The impact of this extracted knowledge on the cognitive organization of MEC is quantified, through the use of heuristics that are engineered to guarantee load balancing and, consequently, QoS.

Infection with a hypervirulent strain of Helicobacter pylori primes gastric cells toward intestinal transdifferentiation

BackgroundIntestinal metaplasia, gastric-to-intestinal transdifferentiation, occurs as a result of the misexpression of certain regulatory factors, leading to genetic reprogramming. Here, we have evaluated the H. pylori-induced expression patterns of these candidate genes. MethodsThe expression levels of 1) tissue-specific transcription factors (RUNX3, KLF5, SOX2, SALL4, CDX1 and CDX2), 2) stemness factors (TNFRSF19, LGR5, VIL1) and 3) tissue-specific mucins (MUC5AC, MUC2) were evaluated by quantitative real-time PCR in gastric primary cells (GPCs), in parallel with two gastric cancer (MKN45 and AGS) cell lines, up to 96h following H. pylori infection. ResultsFollowing H. pylori infection of GPCs, RUNX3 declined at 24h post infection (−6.2 ± 0.3) and remained downregulated for up to 96h. Subsequently, overexpression of self-renewal and pluripotency transcription factors, KLF5 (3.6 ± 0.2), SOX2 (7.6 ± 0.5) and SALL4 (4.3 ± 0.2) occurred. The expression of TNFRSF19 and LGR5, demonstrated opposing trends, with an early rise of the former (4.5 ± 0.3) at 8h, and a simultaneous fall of the latter (-1.8 ± 0.5). This trend was reversed at 96h, with the decline in TNFRSF19 (−5.5 ± 0.2), and escalation of LGR5 (2.6 ± 0.2) and VIL1 (1.8 ± 0.3). Ultimately, CDX1 and CDX2 were upregulated by 1.9 and 4.7-fold, respectively. The above scenario was, variably observed in MKN45 and AGS cells. ConclusionOur data suggests an interdependent gene regulatory network, induced by H. pylori infection. This interaction begins with the downregulation of RUNX3, upregulation of self-renewal and pluripotency transcription factors, KLF5, SOX2 and SALL4, leading to the downregulation of TNFRSF19, upregulation of LGR5 and aberrant expression of intestine-specific transcription factors, potentially facilitating the process of gastric-to-intestinal transdifferentiation.

Evaluation of Multi-Objective Optimization Techniques for Resilience Enhancement of Electric Vehicles

The pervasiveness of electric vehicles (EVs) has increased recently, which results in the interdependence of power and transport networks. Power outages may adversely impact the transportation sector, and the available energy may not be sufficient to meet the needs of all EVs during such events. In addition, EVs will be used for diverse purposes in the future, ranging from personal usage to emergency response. Therefore, the allocation of energy to different EVs may have different degrees of societal-, community-, and individual-level benefits. To capture these diverse aspects, the energy allocation problem to EVs during outages is modeled as a multiobjective optimization (MOO) problem in this study. Three indices are formulated to quantify the value of different EVs for societies, communities, and individuals during outages, and, correspondingly, three objective functions are formulated. The formulated MOO problem is solved using the five most widely used MOO solution methods, and their performance is evaluated. These methods include the weighted-sum method, lexicographic method, normal boundary intersection method, min–max method, and nondominated sorting genetic algorithm II. To compare the performance of these methods, two indices are proposed in this study, which include the demand fulfillment index and total demand fulfillment index. The former is for analyzing the demand fulfillment ratio of different priority EVs, while the latter is for the demand fulfillment analysis of the whole EV fleet requiring a recharge. In addition, the computational complexity, variance, and additional constraints required by each method are also analyzed. The simulation results have shown that the lexicographic method has the best performance when the relative priorities are known, while the min–max method is the most suitable method if the priorities are not known.

Gene network modeling via TopNet reveals functional dependencies between diverse tumor-critical mediator genes.

SUMMARYMalignant cell transformation and the underlying reprogramming of gene expression require the cooperation of multiple oncogenic mutations. This cooperation is reflected in the synergistic regulation of non-mutant downstream genes, so-called cooperation response genes (CRGs). CRGs affect diverse hallmark features of cancer cells and are not known to be functionally connected. However, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency >50%, as indicated by genetic perturbations. Here, we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the malignant state. Our network modeling methodology, TopNet, takes the approach of incorporating uncertainty in the underlying gene perturbation data and can identify non-linear gene interactions. In the dense space of gene connectivity, TopNet reveals a sparse topological gene network architecture, effectively pinpointing functionally relevant gene interactions. Thus, among diverse potential applications, TopNet has utility for identification of non-mutant targets for cancer intervention.

XDeathDB: a visualization platform for cell death molecular interactions

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway’s commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/.

The Legal Regime of Basic Multiplicities in Bankruptcy Proceedings

The paper substantiates the position that the entrepreneurial market is no longer a collection of individual participants one way or another interacting with each other through voluntary communication "clothed" in a legal form, but a potentially interdependent network, the presence of which is found in the conditions of a property crisis of one of its elements. In this vein, the bankruptcy procedure is an arena for the collision of multidirectional interests of multiplicities discovered in this process, the most important of which are the unions of the meeting of creditors. They engage persons included, together with the debtor, in a corporate group, qualified by the author as multiplicities of simple partnerships. These partnerships have a specific purpose, they arise at the moment of capital pooling (initiated by a corporate group) or objective bankruptcy (initiated by independent creditors), they are endowed with a certain amount of powers within the framework of the bankruptcy procedure with due regard to the specifics of their status and they cease to exist at the moment of an actual achievement of the set goal, which is not always connected with the termination of the trial. A feature of the studied varieties of a simple partnership is the predominant involuntary association of its participants when they are forced to interact with each other due to the insolvency of their counterparty. The uniqueness of this type of a partnership is manifested, among other things, in the form of a contribution to such a partnership, since, entering into civil law relations at the time of the objective solvency of the future debtor, his counterparties do not realize that their reciprocal contribution under the obligation is nothing more than "contribution" to the property (potentially bankruptcy) assets of the future partnership that arises at the time of actual bankruptcy of the person with whom they enter into a legal relationship. In addition, the author demonstartes the need to clarify the legal nature of this type of partnership, of which the debtor and related persons are members, in order to prevent the latter from participating in the bankruptcy process along with the debtor's independent creditors.

Times series forecasting for urban building energy consumption based on graph convolutional network

The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction.

Network ties, institutional roles and advocacy tactics:Exploring explanations for perceptions of influence in climate change policy networks

The extent to which a policy actor is perceived as being influential by others can shape their role in a policy process. The interest group literature has examined how the use of advocacy tactics, such as lobbying or media campaigns, contributes to an actor’s perceived influence. The policy networks literature, in turn, has found that network ties and occupying certain institutional roles can explain why actors are perceived as influential. When investigating what explains perceptions of influence, interest groups scholars have not accounted for network interdependencies and network scholars have so far not examined the advocacy tactics used by interest groups. This paper addresses the gap at the intersection of these two literatures by investigating the relationship between network ties, institutional roles, advocacy tactics and the presence of influence attribution ties in climate change policy networks. Exponential random graph models are applied to network data collected from the organisations participating in the national climate change policymaking processes in six EU countries that vary by the extent to which they are majoritarian or consensual democracies: Czechia, Finland, Germany, Ireland, Portugal, and Sweden. The results show that network ties and institutional roles are better predictors of influence attribution ties than advocacy tactics and that there is no pattern in the relationship between advocacy tactics and influence attribution ties across different institutional contexts. These findings suggest that because influence is primarily associated with structural factors (network ties and institutional roles) that more established policy actors are likely to have more influence, which may inhibit the need for a significant step change in climate policies.

Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

Seismic resilience assessment of urban interdependent lifeline networks

With increasing interdependence of various lifeline networks, natural disasters, especially seismic disasters damage will further degrade overall network performance. Hence, based on the concept of risk, reliability and vulnerability, some scholars assessed the resilience of multi-layer lifeline networks. However few studies considered bidirectional interdependence of lifelines, which would immensely exacerbates the consequences of seismic disaster. And the existing research of resilience assessment generally assumed that lifeline networks can recover to their initial performance rather than limited recovery which is more practical. Therefore, this study presents a framework and method for assessing the resilience of lifeline networks. And a performance response function for interdependent lifelines is proposed based on its functional characteristics. Then, we use an IEEE 30-bus and Belgium 20-node gas interdependent network as examples to verify the effectiveness of the framework. The results showed that, for each network, the recovery resource and budget had various effects on the resilience. And our framework can provide a quick reference for optimal decision-making in various scenarios under seismic disasters.

Joint optimization of structure and protection of interdependent infrastructure networks

Nowadays, critical infrastructures are essential for the normal operation of economics and social life. Because of the increasing number of terrorist attacks and other accidents, research on strengthening infrastructure protection has become essential. Meanwhile, the operation of many types of infrastructures mainly relies on the configuration of networks. The interdependent infrastructure network (IIN), which is a network comprising multiple infrastructure networks with different functions under supporting relationships, is highly influenced by the interdependency structure of these networks and the protection strategy. To better understand the relation between the interdependency structure and protection in two phases on infrastructure resilience and analyze their influence on IIN performance, this study investigates the IIN in a community and proposes a joint optimization method for the two phases based on an incomplete-information game under the situation of intentional attacks. Employing the maximal-flow index (MFI) as the performance metric, a multi-objective planning approach is proposed for the joint optimization of interdependency structure and protection. Furthermore, an improved probabilistic solution discovery algorithm (PSDA) is developed to solve the planning model, and the joint optimization is analyzed using extensive experimentation, including three real-world infrastructure examples.