Journal articles on the topic 'Network resilience'
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Wang, Liang, Xiaolong Xue, and Xun Zhou. "A New Approach for Measuring the Resilience of Transport Infrastructure Networks." Complexity 2020 (August 17, 2020): 1–16. http://dx.doi.org/10.1155/2020/7952309.
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Resilience is an important property of transport infrastructure networks. Resilient transport infrastructure networks can retain the performance in case of disturbances and quickly recover to the original performance level after the disturbances. This study proposes a new approach that can measure the resilience of transport infrastructure networks. The new approach gives a unified conceptual framework for measuring the resilience of transport infrastructure networks. A network simulation-based method is used to analyze the influential factors of transport infrastructure network resilience. A new measuring method is developed based on network diversity characteristic to quantitatively measure the system resilience and node resilience of transport infrastructure networks. China railway and air transport networks are selected as a case study to applicability of new approach. This new approach provides strong supports for academic and industrial fields to measure, analyze, enhance, and optimize the resilience of transport infrastructure networks.
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Shi, Jialu, Xuan Wang, Chengxin Wang, Haimeng Liu, Yi Miao, and Fuyi Ci. "Evaluation and Influencing Factors of Network Resilience in Guangdong-Hong Kong-Macao Greater Bay Area: A Structural Perspective." Sustainability 14, no. 13 (June 30, 2022): 8005. http://dx.doi.org/10.3390/su14138005.
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Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA’s economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis; (2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems; (3) network resilience presented an obvious core–edge structure, and the spatial correlation and spillover effect between blocks were substantial; and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries.
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Meng, Yangyang, Xiaofei Zhao, Jianzhong Liu, and Qingjie Qi. "Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network." Sustainability 15, no. 12 (June 8, 2023): 9309. http://dx.doi.org/10.3390/su15129309.
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With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.
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Liu, Huifang, Xiaoyi Shi, Pengwei Yuan, and Xiaoqing Dong. "Study on the Evolution of Multiple Network Resilience of Urban Agglomerations in the Yellow River Basin." Sustainability 14, no. 18 (September 6, 2022): 11174. http://dx.doi.org/10.3390/su141811174.
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To promote the healthy development of urban agglomerations in the Yellow River Basin, we construct a multi-city network-resilience evolution model based on social network theory, combined with QAP regression analysis and analyze the evolution of multiple-city network resilience in the Yellow River Basin in 2014 and 2021 by screening and drawing on indicators in social networks. The results show that (i) only the financial linkage network and the information exchange network are resilient networks, and the magnitude of the evolution of resilience level in the Yellow River basin is finance > information > innovation > transportation. (ii) Except for the increase in the hierarchy of transportation networks, other networks show the trend of flattening. (iii) The matching of the information exchange network shows a shift between heterogeneity and homogeneity, and the transmission and aggregation of the network fluctuate. Based on the study’s findings, a path to improve the resilience level of the Yellow River Basin urban agglomeration by consolidating the status of core cities, optimizing the structure of multiple city networks, and optimizing the flow of factors is proposed.
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Mendoza, Nancy, and Christine Fruhauf. "GRANDPARENT CAREGIVERS: THE RELATION BETWEEN SOCIAL NETWORKS AND RESILIENCE." Innovation in Aging 3, Supplement_1 (November 2019): S678—S679. http://dx.doi.org/10.1093/geroni/igz038.2507.
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Abstract Grandparents raising grandchildren experience multiple challenges as they take on the unexpected role of caring for their grandchildren, which usually occurs under stressful and stigmatizing conditions. Many of the challenges grandparents experience are well documented in the research. Less attention is given to understanding how a grandparent caregiver’s social network changes when s/he becomes a caregiver and how her/his social network influences resilience. Thus, the purpose of this study was to use social network analysis (SNA) to examine the relation between social networks and resilience in grandparents raising their grandchildren. This was done by conducting face-to-face interviews with twenty grandparents raising grandchildren after they completed a survey measuring social support, social isolation, and resilience. The interview protocol included questions related to participants’ social network, social support, and services. Prior to the interviews, using data from the surveys participants were identified as representing one of four resilience quadrants: resilient, maladaptive, competent, and vulnerable. Qualitative analysis of grandparent’s social networks across groups indicated resilient grandparent caregivers’ networks were structured in a way that provided more opportunities for the inflow of new information and resources. Whereas the proportion of professionals in maladaptive grandparent caregivers’ networks tended to be less than for other networks. This could suggest that for grandparent caregivers, having professionals in one’s network can be beneficial. Findings from the current study provide opportunities for future research such as identifying ways to help grandparent caregivers structure their social networks to promote resilience.
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Liu, Xueming, Daqing Li, Manqing Ma, Boleslaw K. Szymanski, H. Eugene Stanley, and Jianxi Gao. "Network resilience." Physics Reports 971 (August 2022): 1–108. http://dx.doi.org/10.1016/j.physrep.2022.04.002.
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Colbourn, Charles J. "Network Resilience." SIAM Journal on Algebraic Discrete Methods 8, no. 3 (July 1987): 404–9. http://dx.doi.org/10.1137/0608033.
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Podobnik, B., D. Horvatic, T. Lipic, M. Perc, J. M. Buldú, and H. E. Stanley. "The cost of attack in competing networks." Journal of The Royal Society Interface 12, no. 112 (November 2015): 20150770. http://dx.doi.org/10.1098/rsif.2015.0770.
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Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator–prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor's nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.
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Guo, Jiuxia, Yang Li, Zongxin Yang, and Xinping Zhu. "Quantitative method for resilience assessment framework of airport network during COVID-19." PLOS ONE 16, no. 12 (December 3, 2021): e0260940. http://dx.doi.org/10.1371/journal.pone.0260940.
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The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.
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Huang, Lei, Haifeng Huang, and Ying Wang. "Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network." Applied Sciences 13, no. 15 (July 31, 2023): 8835. http://dx.doi.org/10.3390/app13158835.
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With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.
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Cainey, Jill M. "Resilience and reliability for electricity networks." Proceedings of the Royal Society of Victoria 131, no. 1 (2019): 44. http://dx.doi.org/10.1071/rs19005.
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Electricity networks in Australia operate in a highly regulated framework. This framework monitors network investment to ensure positive benefits for customers and includes incentivised performance standards that cover reliability. In the current standards, major event days are excluded from the statistics for outages, because they are deemed to be outside the control of the network operators. Outages on major event days are typically the result of severe weather and tend to be prolonged and have a significant negative impact on customers, but current regulations do not cover such events. The ability of any system to be ready for and recover from a major event is described as resilience, but resilience is not an incentivised activity for electricity networks and the impact of climate change means that major event days are increasing in number, leading to higher costs for customers. Without a regulatory focus on resilience, a network may meet or exceed reliability standards, while still not being resilient in major events. Investing in reliability does not always deliver resilience, but investing in resilience is demonstrated to deliver significant improvements in both resilience and reliability, resulting in beneficial performance outcomes for customers using cost-effective and efficient network investment approaches.
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Bruce, Ann, Cornell Jackson, and Chrysa Lamprinopoulou. "Social networks and farming resilience." Outlook on Agriculture 50, no. 2 (January 12, 2021): 196–205. http://dx.doi.org/10.1177/0030727020984812.
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The resilience of food systems, including agricultural systems, has become a high profile issue in the face multiple disease, environmental and social challenges. Much of agriculture takes place in remote locations where social networks, or connections between individual actors, have been implicated in increasing resilience. We examine a case study of Orkney, Scotland, a remote rural location, using interviews and Social Network Analysis. This case study provides evidence indicative of resilient patterns of social networks, emphasising the importance of schools, transport links and livestock markets in creating and maintaining these networks. These domains are rarely included in agricultural policy, highlighting the need for wider framing of questions. Our research suggests Social Network Analysis is a fruitful avenue for investigating resilience of agricultural systems that can identify hitherto hidden elements.
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Alenazi, Mohammed J. F. "ENRN: A System for Evaluating Network Resilience against Natural Disasters." Mathematics 11, no. 20 (October 11, 2023): 4250. http://dx.doi.org/10.3390/math11204250.
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The frequency and severity of natural disasters is surging, posing an urgent need for robust communication network infrastructure that is capable of withstanding these events. In this paper, we present a groundbreaking graph-theoretic system designed to evaluate and enhance network resilience in the face of natural disasters. Our solution harnesses the power of topological robustness metrics, integrating real-time weather data, geographic information, detailed network topology data, advanced resilience algorithms, and continuous network monitoring. The proposed scheme considers four major real-world U.S.-based network providers and evaluates their physical topologies against two major hurricanes. Our novel framework quantifies the important characteristics of network infrastructure; for instance, AT&T is identified to have fared better against Hurricane Ivan (57.98 points) than Hurricane Katrina (39.17 points). We not only provide current insights into network infrastructure resilience, but also uncover valuable findings that shed light on the performance of backbone U.S. networks during hurricanes. Furthermore, our findings provide actionable insights to enrich the overall survivability and functionality of communication networks, mitigating the adverse impacts of natural disasters on communication systems and critical services in terms of improving network resiliency via adding additional nodes and link or rewiring.
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Kou, Gang, Xiangrui Chao, Yi Peng, and Fan Wang. "NETWORK RESILIENCE IN THE FINANCIAL SECTORS: ADVANCES, KEY ELEMENTS, APPLICATIONS, AND CHALLENGES FOR FINANCIAL STABILITY REGULATION." Technological and Economic Development of Economy 28, no. 2 (March 28, 2022): 531–58. http://dx.doi.org/10.3846/tede.2022.16500.
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Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of financial networks, we review the main advances in the literature on network resilience and financial networks. Further, we review the key elements and applications of financial network resilience processing in financial regulation, including financial network information, network resilience measures, financial regulatory technologies, and regulatory applications. Finally, we discuss ongoing challenges and future research directions from the perspective of resilience-based financial systemic risk regulation.
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Mao, Xinhua, Xin Lou, Changwei Yuan, and Jibiao Zhou. "Resilience-Based Restoration Model for Supply Chain Networks." Mathematics 8, no. 2 (January 23, 2020): 163. http://dx.doi.org/10.3390/math8020163.
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An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.
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Veitch, P., and D. Johnson. "ATM network resilience." IEEE Network 11, no. 5 (1997): 26–33. http://dx.doi.org/10.1109/65.620519.
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Zebrowski, Chris. "Acting local, thinking global: Globalizing resilience through 100 Resilient Cities." New Perspectives 28, no. 1 (March 2020): 71–88. http://dx.doi.org/10.1177/2336825x20906315.
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This article investigates the globalization of resilience by examining a particular and prominent vehicle for the dissemination of resilience ideas: the Rockefeller Foundation’s 100 Resilient Cities (100RC) initiative. As a philanthropic initiative organized through a network of international cities, 100RC demonstrates how the spread of resilience thinking has been facilitated by exploiting changes in the structures and processes of global governance afforded by neoliberal globalization. The analysis focuses on explicating100RC’s animating logic of governance, which is committed to the cultivation of network connectivity. Rather than directly fostering resilience, connectivity is established as a condition under which resilience solutions can be immanently surfaced from the interactions of a diverse selection of stakeholders brought together through these networks. The article situates this governmental logic within broader changes associated with neoliberal globalization, namely the emergence of multi-scalar governance networks, the rise of philanthrocapitalism and the inception of platform capitalism. The conclusion discusses the implications of this analysis for further study of the relation between connectivity, danger, knowledge and value contained within resilience discourses.
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Yan, Xian. "Key Factors Influencing Network Resilience in Dynamical Networks." Frontiers in Computing and Intelligent Systems 3, no. 3 (May 17, 2023): 99–101. http://dx.doi.org/10.54097/fcis.v3i3.8577.
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There has been much recent research focusing on the resilience of networks, providing theoretical insights into the effective response of real-world systems systems to disasters. However, few studies have analyzed the factors that affect the resilience of networks. And the network operation process varies greatly so that the dynamic behavior of the network is a factor that has to be considered. To bridge these gaps, we analyze the factors affecting dynamic network resilience in terms of network dynamics. There are two main influencing factors: differentiation of failure probability, differentiation of impact. We build a generic resilience model for the network and validate these influencing factors by simulating them in different networks. By summarizing these factors, we point out constructive strategies. These strategies can help dynamic networks enhance network resilience, which is an important criterion for reducing network failures in real-world systems.
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Li, Yue, and Jun Peng. "Evaluation of Expressive Arts Therapy on the Resilience of University Students in COVID-19: A Network Analysis Approach." International Journal of Environmental Research and Public Health 19, no. 13 (June 23, 2022): 7658. http://dx.doi.org/10.3390/ijerph19137658.
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As an alternative to traditional verbal counselling, expressive arts therapy has been shown to be an effective method of mental health care, particularly when dealing with stressful public interactions, such as those associated with COVID-19. However, few studies have been conducted to determine the efficacy of expressive arts therapy on the resilience of psychologically exposed university students during COVID-19. Furthermore, since network analysis appears to be a popular approach in psychological research, it has not been used in recent intervention studies for resilience. As a result, the current study utilized a network analysis approach to determine the efficacy of expressive arts therapy on the resilience of university students during the COVID-19 pandemic. A total of 263 students in a comprehensive university in China were selected for the therapy group between March and November 2021. In a pre-post design, students’ resilience was assessed using the Resiliency Scale for University Students (RSUS). The extended Bayesian information criteria (EBIC) and graphical LASSO were used to estimate and define paired resilience networks, and the strength, betweenness, and closeness indices were utilized to determine the centrality of the six facets of resilience. Additionally, we verified the stability and accuracy. It was discovered that significant differences appeared between the paired networks before and after expressive arts therapy. Facets of self-efficacy, self-acceptance and problem-solving in resilience were notably improved after the therapy, with the variable of emotional stability sustained at the mean level. Meanwhile, the network analysis has highlighted the central variable of self-efficacy in the pre-intervention and support from friends in the post-intervention. The connectivity among the components of problem solving, support from friends, and support from family was enhanced, with support from friends playing the role of hub nod in the following network. By utilizing a network analytic approach, expressive arts therapy can be more targeted in intervening in resilience mechanisms. As a proxy for efficacious problem-solving, intervention should be calibrated to the cultivation of social support networks, especially in the support from friends.
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Sanduleac, Mihai, Alexandru Sandulescu, Cristina Efremov, Constantin Ionescu, Ioan Catalin Damian, and Alexandru Mandis. "Aspects of Design in Low Voltage Resilient Grids—Focus on Battery Sizing and U Level Control with P Regulation in Microgrids of Energy Communities." Energies 16, no. 4 (February 15, 2023): 1932. http://dx.doi.org/10.3390/en16041932.
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Energy communities and their resiliency are both relatively new subjects of interest that need deeper analysis. The concepts are emerging in the current trend of energy decarbonization, combined with unpredictable external factors, such as extreme weather, or nearby conflicts, such as wars. To be resilient against electrical network supply outages or heavy blackouts, energy communities need appropriate design of their electrical microgrids to maintain an acceptable level of activity in both normal and critical situations. The paper deals with aspects of the design of the electrical network used by energy communities, seen as microgrids designed to offer an acceptable level of safe operation and energy resilience. While electrical network resilience covers both the public network (main grid) and local microgrid of an energy community, the paper focuses on the safe operation and resilience related to the local microgrid as a distinct goal from the one of preserving the main grid’s functionality. The first section considers definitions of energy resilience and some of the existing preoccupations on the subject. A second section of the paper presents different aspects of the design of a microgrid, with the purpose of increasing its safety in operation and energy community resilience. The section addresses electrical network architectures, multiple roles of storage resources related to prosumers and to islanded microgrids with high-RES penetration, and other aspects such as the impact of electrification of heating through, e.g., heat pumps. The next sections present selected use cases, which develop some of these design aspects by using typical real data for the analysis and assessing solutions to address resilient microgrid challenges. The selected use cases consider simplified approaches for real-time and short-term storage needs and operational use in microgrids, maintaining voltage levels in a high-RES scenario by using battery P control, and optimization of storage resources to cope with the needs. Conclusions are given in a final section, which also presents future work for a presumed continuation with other use cases related to storage means, safe operation, and resilient design of energy communities microgrids.
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Rha, Jin Sung. "Trends of Research on Supply Chain Resilience: A Systematic Review Using Network Analysis." Sustainability 12, no. 11 (May 26, 2020): 4343. http://dx.doi.org/10.3390/su12114343.
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Researchers have defined resilient supply chain management in various ways and have analyzed and explained it using many managerial theories. Thus, identifying trends in existing studies could serve as a foundation for future supply chain resilience studies. However, despite the accumulation of a wide body of literature on resilient supply chains, few studies have analyzed the research trends systematically. Therefore, the present study aimed to synthesize and summarize research trends in the supply chain resilience domain using network analysis. The Scopus database and Google Scholar were used to search for research articles on supply chain resilience. We conducted an analysis by visually representing coauthorship, cocitation, PageRank, and keyword networks for 825 research articles and 1725 authors. This study identified the main topics, key articles, and major author groups of supply chain resilience research. The findings are expected to help expand the scope of research to a wide range of subfields in supply chain resilience research in the future.
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Al Adem, Samar, Daniel Schepis, and Sharon Purchase. "Orchestrating network resilience within humanitarian aid networks." Industrial Marketing Management 107 (November 2022): 190–203. http://dx.doi.org/10.1016/j.indmarman.2022.09.016.
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Nagaishi, Ellie, and Kazuhiro Takemoto. "Network resilience of mutualistic ecosystems and environmental changes: an empirical study." Royal Society Open Science 5, no. 9 (September 2018): 180706. http://dx.doi.org/10.1098/rsos.180706.
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It is theorized that a mutualistic ecosystem's resilience against perturbations (e.g. species extinction) is determined by a single macroscopic parameter (network resilience), calculable from the network. Given that such perturbations occur owing to environmental changes (e.g. climate change and human impact), it has been predicted that mutualistic ecosystems that exist despite extensive environmental changes exhibit higher network resilience; however, such a prediction has not been confirmed using real-world data. Thus, in this study, the effects of climate change velocity and human activities on mutualistic network resilience were investigated. A global dataset of plant–animal mutualistic networks was used, and spatial analysis was performed to examine the effects. Moreover, the potential confounding effects of network size, current climate and altitude were statistically controlled. It was demonstrated that mutualistic network resilience was globally influenced by warming velocity and human impact, in addition to current climate. Specifically, pollination network resilience increased in response to human impact, and seed-dispersal network resilience increased with warming velocity. The effect of environmental changes on network resilience for plants was remarkable. The results confirmed the prediction obtained based on the theory and imply that real-world mutualistic networks have a structure that increases ecosystem resilience against environmental changes. These findings will enhance the understanding of ecosystem resilience.
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Wang, Chuanshi. "Network resilience: impact on small-world network." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 1337–43. http://dx.doi.org/10.54254/2755-2721/6/20230748.
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Lots of complex systems in the real world have network structures, and a number of these structures have small-world property. This kind of structures are called small-world networks. Examples include the world's air transportation system, electric power systems, and human functional brain network, and small-world property is one of the key reasons why these systems function efficiently. However, for complex systems, in addition to their efficiency, resilience or robustness is also one of the concerns, as these systems need to ensure that they do not completely collapse on their own in case of failure of a small number of their components. The purpose of this paper is to try to find and explain the factors that affect the robustness of small-world network by comparing different classes of small-world networks and analysing differences between them and possible causes of these differences, in order to get an idea to optimize the robustness of small-world networks while preserving their small-world property.
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Garshasbi, Maryam, Golam Kabir, and Subhrajit Dutta. "Stormwater Infrastructure Resilience Assessment against Seismic Hazard Using Bayesian Belief Network." International Journal of Environmental Research and Public Health 20, no. 16 (August 17, 2023): 6593. http://dx.doi.org/10.3390/ijerph20166593.
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Resilient stormwater infrastructure is one of the fundamental components of resilient and sustainable cities. For this, the resilience assessment of stormwater infrastructure against earthquake hazards is crucial for municipal authorities. The objective of this study is to develop a resilience assessment framework for stormwater pipe infrastructure against seismic hazards. A Bayesian belief network (BBN)-based stormwater infrastructure resilience model is constructed based on the published literature and expert knowledge. The developed framework is implemented in the city of Regina, Canada, to assess the city’s stormwater pipe infrastructure resilience. The outcome of the model indicates that proposed BBN-based stormwater infrastructure resilience model can effectively quantify uncertainties and handle the nonlinear relationships between several reliability and recovery factors. The model is also capable of identifying the most sensitive and vulnerable stormwater pipes within the network.
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Aloui, Aymen, Nadia Hamani, and Laurent Delahoche. "Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty." Sustainability 13, no. 24 (December 20, 2021): 14053. http://dx.doi.org/10.3390/su132414053.
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To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.
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PAU, Valentin, and Dorina Luminița COPACI. "VIRTUALIZATION AS A MEANS TO ENSURE THE RESILIENCE OF PEER-TO-PEER COMMUNICATION SYSTEMS." ANNALS OF THE ACADEMY OF ROMANIAN SCIENTISTS Series on ENGINEERING SCIENCES 14, no. 2 (2022): 97–107. http://dx.doi.org/10.56082/annalsarscieng.2022.2.97.
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Resilience to failures and deliberate attacks is becoming an essential requirement in most point-to-point communication networks today. The present paper presents a survey of strategies to ensure resilience in peer-to-peer communication networks by means of operating systems virtualization. Virtual machines can ensure the resiliency of the peer-to-peer communication network both by their very use and by providing service isolation. For hands-on research, OpenVZ as a Linux implementation of OS-level virtualization has been used.
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Bragatto, Tommaso, Massimo Cresta, Fabrizio Cortesi, Fabio Gatta, Alberto Geri, Marco Maccioni, and Marco Paulucci. "Assessment and Possible Solution to Increase Resilience: Flooding Threats in Terni Distribution Grid." Energies 12, no. 4 (February 23, 2019): 744. http://dx.doi.org/10.3390/en12040744.
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In recent years, because of increasing frequency and magnitude of extreme weather events, the main stakeholders of electric power systems are emphasizing issues about resilience. Whenever networks are designed and development plans are drawn, this new feature must be assessed and implemented. In this paper, a procedure to evaluate the resilience of a distribution network against flooding threats is presented. Starting from a detailed analysis about the resilience of each asset of the grid, the procedure implements the exploration of the network in order to evaluate the impact of interruptions (e.g., in terms of number of disconnected users) produced by the specific threat; then, it calculates the resilience indices of the whole system. The procedure is applied with respect to the flooding threats, on a real distribution network in the center of Italy (i.e., the distribution network of Terni). Referring to this case study, the proposed method suggests countermeasures able to reduce the impact of flooding events and evaluates their benefits. Results indicate that, at the present time, the network is adequately resilient with respect to flooding events, as demonstrated by the index values. However, the remedial actions identified by the procedure are also able to improve the resilience of the network and, in addition, they are in agreement with the development plan already established by the distribution system operator (DSO).
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Gautam, Mukesh, Timothy McJunkin, and Ryan Hruska. "A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization." Sustainability 16, no. 6 (March 15, 2024): 2449. http://dx.doi.org/10.3390/su16062449.
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This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.
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Gao, Lin, Mingzhen Wang, Anshuang Liu, and Huafeng Gong. "Comprehensive Evaluation of Urban Road Network Resilience Facing Earthquakes." Mathematical Problems in Engineering 2021 (April 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/6659114.
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The road network’s transport capacity and traffic function will be directly reduced if urban roads are damaged by earthquakes. To effectively improve the resistance and recovery ability of urban road networks facing earthquake disasters, the establishment of an aseismic resilience evaluation method for the urban road network is the research goal. This paper’s novelty introduces the concept of engineering resilience into the aseismic performance evaluation of urban road networks. It reveals the internal influence principle of nodes and independent pathways on the aseismic resilience of the network. This paper’s significant contribution is to establish a reasonable and comprehensive urban road network aseismic resilience evaluation method. This method can realize the calculation of the aseismic resilience for the existing network, reconstruction network, and new network and propose the optimization, transformation, and layout for the network. The MATLAB program for the whole process calculation of aseismic resilience is developed. The overall network’s aseismic resilience is obtained by the sum of the product of the node importance and the average number of the reliable independent pathways.
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Sun, Ruijiao, Yisheng Liu, and Jianghu Zhao. "Innovation Network Reconfiguration Makes Infrastructure Megaprojects More Resilient." Computational Intelligence and Neuroscience 2022 (September 15, 2022): 1–16. http://dx.doi.org/10.1155/2022/1727030.
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Innovation management of infrastructure megaprojects is a challenging task. There are many risks in the process of innovation in engineering technology, such as shortage of funds, policy fluctuations, and difficulties in the transformation of achievements. Meanwhile, innovation organizations involve multiple participants, which makes cooperation complicated. Therefore, resilient innovation is proposed and considered as a tool that can optimize innovation management. The resilience of innovation depends largely on partnerships at the organizational level, which is rarely explored in current studies. This research aims to examine the relationship between organizational resilience and innovation network characteristics. Based on a survey of 164 participants in infrastructure innovation projects, the structural equation model (SEM) is used to explore the factors that influence organizational resilience. The findings show that there is a positive correlation between network characteristics and organizational resilience. Furthermore, the strength of network connections has a direct impact on the preventive and resistance ability of resilience. Network heterogeneity has an impact on the dual ability of resilience. Finally, a case study of the Qinghai-Tibet Railway innovation network shows that based on the above influence paths, we can find a strategy to reconstruct the network to improve resilience.
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Chen, Mingyu, and Huapu Lu. "Analysis of Transportation Network Vulnerability and Resilience within an Urban Agglomeration: Case Study of the Greater Bay Area, China." Sustainability 12, no. 18 (September 9, 2020): 7410. http://dx.doi.org/10.3390/su12187410.
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Recently, urban agglomerations have become the main platform of China’s economic development. As one of those, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has an important strategic position in national blueprints. Its amazing achievement is inseparable from reliable and resilient transportation networks. With the aim of improving the sustainability of the GBA, this paper presents a novel view of vulnerability and resilience of integrated transportation networks within an urban agglomeration. According to complex network theory, the integrated transportation network model of the GBA was established. Various scenarios were considered to improve the overall level of defensive ability, including random failures, targeted attacks and natural hazards. Vulnerability and resilience assessment models were developed to investigate the influences on the whole network. Finally, a simulation analysis was conducted on the GBA to examine the variations in network performance when faced with different attack scenarios. The results indicate that the transportation network of the GBA is more vulnerable and has less resilience to targeted attacks, while natural hazards had little influence on the performance, to a certain extent. Moreover, the betweenness recovery strategy seemed to be the best choice for every attack scenario.
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Xue, Zhaojie, Yunliang Fang, Wenxiang Peng, and Xiangsheng Chen. "A Hybrid Heuristic Algorithm for Maximizing the Resilience of Underground Logistics Network Planning." Applied Sciences 13, no. 23 (November 22, 2023): 12588. http://dx.doi.org/10.3390/app132312588.
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In recent times, there has been a sharp increase in the congestion of ground transportation, the scarcity of land resources, and various disasters. Hence, there is an urgent need to find an effective and sustainable approach to transportation. The construction of an underground logistics network, where transportation activities occur beneath the surface of the ground, is anticipated to emerge as a future trend. This study aims to formulate a resilient-maximizing plan for the underground logistics network, ensuring optimal meeting of transportation demands in the aftermath of ground disasters. Accordingly, a two-stage linear programming model is established to determine the layout plan for the most resilient underground logistics network. The first phase of the model is designed to generate viable layouts for the underground logistics network, while the second phase is dedicated to evaluating the resilience of the proposed layout plan. During the evaluation of network resilience, Monte Carlo simulations are used to simulate disaster scenarios. Given the inherent complexity of the model, the traditional solver cannot efficiently solve the problem. Thus, a new hybrid heuristic algorithm is designed to obtain solutions that maximize network resilience. The results show the effectiveness of the designed algorithm and the significant improvement in network resilience achieved by numerical experiments. Moreover, sensitivity analyses are conducted to reveal the relationships between resilience and budget, as well as resilience and the capacity of underground pipelines. It has a significant impact on sustainability when making decisions regarding network planning.
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Pagani, Alessio, Guillem Mosquera, Aseel Alturki, Samuel Johnson, Stephen Jarvis, Alan Wilson, Weisi Guo, and Liz Varga. "Resilience or robustness: identifying topological vulnerabilities in rail networks." Royal Society Open Science 6, no. 2 (February 2019): 181301. http://dx.doi.org/10.1098/rsos.181301.
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Many critical infrastructure systems have network structures and are under stress. Despite their national importance, the complexity of large-scale transport networks means that we do not fully understand their vulnerabilities to cascade failures. The research conducted through this paper examines the interdependent rail networks in Greater London and surrounding commuter area. We focus on the morning commuter hours, where the system is under the most demand stress. There is increasing evidence that the topological shape of the network plays an important role in dynamic cascades. Here, we examine whether the different topological measures of resilience (stability) or robustness (failure) are more appropriate for understanding poor railway performance. The results show that resilience, not robustness, has a strong correlation with the consumer experience statistics. Our results are a way of describing the complexity of cascade dynamics on networks without the involvement of detailed agent-based models, showing that cascade effects are more responsible for poor performance than failures. The network science analysis hints at pathways towards making the network structure more resilient by reducing feedback loops.
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Pratelli, Antonio, Pietro Leandri, Rosaria Aiello, and Reginald Roy Souleyrette. "Intersection Redesign for Network Resilience and Safety." Promet - Traffic&Transportation 33, no. 2 (April 6, 2021): 297–308. http://dx.doi.org/10.7307/ptt.v33i2.3554.
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This paper describes a procedure for improving the resilience of roadway networks. A methodology is outlined that develops a time-dependent and performance-based resilience index. This methodology was applied to an Italian road, with the aim of optimizing intersections that are critical due to inadequate baseline capacity. The methodology uses a calibrated microscopic traffic model (using Aimsun™) whereby average delay at intersection approaches are estimated by an analytical model. From the simulation, average speed over time is obtained for each approach. These values in turn are used as inputs for calculating each intersection’s resilience index. The procedure allows the identification of less resilient intersections, and provides design solutions for each of them. Lastly, a safety assessment is tested for one of the intersections.
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Savaliya, Abhishek, Rutvij H. Jhaveri, Qin Xin, Saad Alqithami, Sagar Ramani, and Tariq Ahamed Ahanger. "Securing industrial communication with software-defined networking." Mathematical Biosciences and Engineering 18, no. 6 (2021): 8298–313. http://dx.doi.org/10.3934/mbe.2021411.
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<abstract> <p>Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91–99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.</p> </abstract>
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Лисенко, Сергій Миколайович. "МЕТОД ЗАБЕЗПЕЧЕННЯ РЕЗИЛЬЄНТНОСТІ КОМП’ЮТЕРНИХ СИСТЕМ В УМОВАХ КІБЕРЗАГРОЗ НА ОСНОВІ САМОАДАПТИВНОСТІ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (December 25, 2019): 4–16. http://dx.doi.org/10.32620/reks.2019.4.01.
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The dynamic expansion of cyber threats poses an urgent need for the development of new methods, methods, and systems for their detection. The subject of the study is the process of ensuring the resilience of computer systems in the presence of cyber threats. The goal is to develop a self-adaptive method for computer systems resilience in the presence of cyberattacks. Results. The article presents a self-adaptive system to ensure the resilience of corporate networks in the presence of botnets’ cyberattacks. Resilience is provided by adaptive network reconfiguration. It is carried out using security scenarios selected based on a cluster analysis of the collected network features inherent cyberattacks. To select the necessary security scenarios, the proposed method uses fuzzy semi-supervised c-means clustering. To detect host-type cyberattacks, information about the hosts’ network activity and reports of host antiviruses are collected. To detect the network type attacks, the monitoring of network activity is carried out, which may indicate the appearance of a cyberattack. According to gathered in the network information concerning possible attacks performed by botnet the measures for the resilient functioning of the network are assumed. To choose the needed scenario for network reconfiguration, the clustering is performed. The result of the clustering is the scenario with the list of the requirement for the reconfiguration of the network parameters, which will assure the network’s resilience in the situation of the botnet’s attacks. As the mean of the security scenario choice, the semi-supervised fuzzy c-means clustering was used. The clustering is performed based on labeled training data. The objects of the clustering are the feature vectors, obtained from a payload of the inbound and outbound traffic and reports of the antiviral tool about possible hosts’ infection. The result of clustering is a degree of membership of the feature vectors to one of the clusters. The membership of feature vector to cluster gives an answer to question what scenario of the network reconfiguration is to be applied in the situation of the botnet’s attack. The system contains the clusters that indicate the normal behavior of the network. The purpose of the method is to select security scenarios following cyberattacks carried out by botnets to mitigate the consequences of attacks and ensure a network functioning resilience. Conclusions. The self-adaptive method for computer systems resilience in the presence of cyberattacks has been developed. Based on the proposed method, a self-adaptive attack detection, and mitigation system has been developed. It demonstrates the ability to ensure the resilient functioning of the network in the presence of botnet cyberattacks at 70 %.
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Bachmann, Ivana, Patricio Reyes, Javier Bustos, and Alonso Silva. "Multidimensional Network Resilience Analysis." IEEE Latin America Transactions 14, no. 6 (June 2016): 2912–14. http://dx.doi.org/10.1109/tla.2016.7555274.
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Mustafa, Albara M., and Abbas Barabadi. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks." Energies 14, no. 15 (July 22, 2021): 4439. http://dx.doi.org/10.3390/en14154439.
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Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.
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El-Taie, Mustafa, and Aaras Y. Kraidi. "Crafting Resilient Consensus Mechanisms for The Web3.0 Network Through Edge Intelligence." International Journal of Wireless and Ad Hoc Communication 8, no. 1 (2024): 21–32. http://dx.doi.org/10.54216/ijwac.080103.
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The era of independent, secure, and scalable networks and applications that Web3.0 promised has arrived. The resilience and reliability of the network are directly tied to the architecture of the consensus mechanisms used in this context. In the paper "Crafting resilient consensus mechanisms for the Web3.0 network through edge intelligence," the authors describe a novel approach to strengthening consensus protocols by leveraging edge computing and artificial intelligence. The primary purpose of this project is to improve Web 3.0 security by implementing consensus methods based on edge intelligence. The goal of this attempt is to reduce the inefficiencies, scalability challenges, and environmental concerns associated with more conventional approaches such as proof-of-work and proof-of-stake. The proposed method combines real-time network research with local transaction verification. This eventually leads to more scalable, secure, and effective consensus procedures, which increases the resilience and greatly decreases the cost of Web3.0 networks.The proposed method recognizes the inefficiencies, lack of scalability, and environmental unfriendliness of standard consensus procedures like the Proof of Work (PoW) and Proof of Stake (PoS) consensus processes. This approach makes use of edge intelligence in real time to assess the state of the network and make appropriate adjustments in response. What emerges is a consensus process that is greener, more scalable, and more successful overall. In addition, we provide the local transaction verification (LTV) technique, which allows edge nodes to validate transactions locally, therefore reducing latency and maximizing transaction efficiency. Our findings demonstrate how edge intelligence might improve Web3.0 consensus processes. Extensive simulations and tests show that the suggested approaches outperform conventional consensus mechanisms in terms of efficiency, security, and scalability. Cost reductions for Web3.0 network operators are also emphasized to emphasize the value of our strategy. Consensus procedures for Web3.0 networks that include edge intelligence provide a viable path toward attaining the required resilience, efficiency, and scalability. This study lays the way for a new age of distributed systems, guaranteeing the resiliency and flexibility essential to the success of Web3.0
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Rivera, Fernando I., Naim Kapucu, and Christopher Hawkins. "Rural Community Disaster Resiliency: Self-Organizing Collective Action among Farmworkers in Central Florida." International Journal of Mass Emergencies & Disasters 33, no. 2 (August 2015): 213–27. http://dx.doi.org/10.1177/028072701503300204.
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In this article we examined how voluntary and self-organizing efforts contributed to disaster resiliency in a rural community in Central Florida. We analyzed data from a focus group with farmworkers in Central Florida to investigate how self-organizing collective action can help develop more resilient communities in socially vulnerable populations. We identified three major themes within our coding scheme: past disaster experiences, self-organizing collective action, and challenges to self-organizing collective action and resilience. The results indicated that past disaster experiences provided an opportunity for these farmworkers to mobilize their social capital and network partnerships to self-organize and develop disaster resilience. The findings indicated that self-organizing collective action could be effective in creating disaster resilience, even in socially vulnerable populations. Nonetheless, the results also indicated certain challenges to self-organizing collective action and resilience such as: language barriers, an anti-immigrant sentiment, poor relations with law enforcement, and lack of work. These challenges are constant reminders that the goal of creating truly disaster resilient communities cannot be reached if these conditions are not lessen or eradicated.
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Imteaj, Ahmed, Vahid Akbari, and Mohammad Hadi Amini. "A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems." Sensors 23, no. 3 (January 20, 2023): 1200. http://dx.doi.org/10.3390/s23031200.
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The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.
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Su, Yi-Zhu, and Wei-Chang Yeh. "Binary-Addition Tree Algorithm-Based Resilience Assessment for Binary-State Network Problems." Electronics 9, no. 8 (July 27, 2020): 1207. http://dx.doi.org/10.3390/electronics9081207.
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Applications in real life are composed of different kinds of network systems; these networks may be interfered by uncontrollable or unpredictable disruptive events involving natural disasters, human errors, evil-intentioned attacks, or other disturbances. Any of these disruptive events will cause networks to malfunction and possibly result in large economic losses. As a result, it is important to assess network resilience which is a measure to describe how a network system recovers its performance and functionality to a satisfactory level from a disruptive event. Inspired by the measures of reliability evaluation used in binary-state networks, this paper proposes a binary-addition tree algorithm-based resilience assessment for binary-state networks and applies it on a wildfire network with wireless sensors. Considering the stochastic nature of disruptive events, the proposed binary-addition tree algorithm-based resilience assessment comprehensively enumerates all the possible disruptive events and all the corresponding recovery strategies, and then calculate the network resilience. Furthermore, recovery cost limit is concerned in this paper for decision makers who choose the recovery strategies with their recovery cost limit and resilience requirement.
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Qi, Qingjie, Yangyang Meng, Xiaofei Zhao, and Jianzhong Liu. "Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro." Sustainability 14, no. 18 (September 15, 2022): 11555. http://dx.doi.org/10.3390/su141811555.
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An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system’s complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network’s robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node’s failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network’s robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study’s findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.
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Malek, Arif Fikri, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Jasrul Jamani Jamian, Li Wang, and Munir Azam Muhammad. "Power Distribution System Outage Management Using Improved Resilience Metrics for Smart Grid Applications." Energies 16, no. 9 (May 8, 2023): 3953. http://dx.doi.org/10.3390/en16093953.
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Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.
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Guo, Jiuxia, Zongxin Yang, Qingwei Zhong, Xiaoqian Sun, and Yinhai Wang. "A novel resilience analysis methodology for airport networks system from the perspective of different epidemic prevention and control policy responses." PLOS ONE 18, no. 2 (February 27, 2023): e0281950. http://dx.doi.org/10.1371/journal.pone.0281950.
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As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks’ post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience.
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PATIL, GOPAL R., and B. K. BHAVATHRATHAN. "EFFECT OF TRAFFIC DEMAND VARIATION ON ROAD NETWORK RESILIENCE." Advances in Complex Systems 19, no. 01n02 (February 2016): 1650003. http://dx.doi.org/10.1142/s021952591650003x.
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Certain capacity degradation levels increase travel times on road networks, while traffic demand remains met. Resilience of a road network is higher, if it can take-in higher levels of degradation without leaving any part of the demand unmet. It is important for planners to quantify this, and it can be obtained as the output of an optimization problem. The resultant measure of resilience is demand-specific. To generalize the resilience measure, its sensitivity to change in demand should be studied. We observe that irrespective of the difference in network size or network topology, resilience decreases with increase in demand. We perform computational experiments on different network topologies to investigate the relationship between network resilience and traffic demand. Based on this, we introduce the area under the demand-resilience curve as a generalized index of resilience (GIR). We compare the GIR with traditional network indicators and find that it is in certain ways, better.
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Raicu, Serban, Eugen Rosca, and Dorinela Costescu. "Resilience of Urban Technical Networks." Entropy 21, no. 9 (September 12, 2019): 886. http://dx.doi.org/10.3390/e21090886.
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The need to overcome the insulated treatment of urban technical infrastructures according to the nature of the transferred flows is argued. The operation of urban technical networks is affected by endogenous and exogenous random events with consequences for users. By identifying these operational risks and the difficulties of estimating the impact on the performance of the urban technical networks, the authors chose to study the risk management through a concise expression—in relation to the engineering resilience and its connections with vulnerability. Further, the research is confined to the case of urban traffic networks for which the resilience is expressed by the capabilities of these networks of resistance and risk absorption (both motivated by the redundancy in design and execution). The dynamics of the network, in correlation with the resistance and absorption capacities, is introduced by three states for which the signal graph is built. In a stationary regime, the probability of each state is computed. These probabilities allow the calculation of the entropy of the network, relevant for assessing the preservation of the network functionality.
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Kumar, Shriram Ashok, Maliha Tasnim, Zohvin Singh Basnyat, Faezeh Karimi, and Kaveh Khalilpour. "Resilience Analysis of Australian Electricity and Gas Transmission Networks." Sustainability 14, no. 6 (March 10, 2022): 3273. http://dx.doi.org/10.3390/su14063273.
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Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.
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Rodriguez, Rusber, German Alfonso Osma Pinto, Javier Enrique Solano Martínez, Robin Roche Robin Roche, and Daniel Hissel. "A Framework for the Resilience of LV Electrical Networks with Photovoltaic Power Injection." Tecnura 25, no. 70 (October 1, 2021): 71–89. http://dx.doi.org/10.14483/22487638.18629.
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Context: Electrical distribution networks have undergone several changes in the last decade. Some changes include incorporating distributed energy sources, such as solar photovoltaic (PV) generation systems. It could modify the performance of the electrical network and leads to new challenges such as evaluating the impacts of the PV integration, the response to electrical and climatic disturbances, and the planning and restructuring of networks. Electrical network behavior versus PV integration could be evaluated by quantifying the variation in operation and including network resilience.
Objective: Propose a reference framework to evaluate the resilience of LV electrical networks with PV power injection.
Methodology: This paper addresses the framework for evaluating the performance of a low voltage (LV) electrical network in the face of the integration of PVs. It collects research related to evaluating the resilience of electrical networks on severe climate changes, natural disasters, and typical maneuvers. Then, it proposes a guideline to evaluate the performance of LV electrical networks with the integration of PV generation sources and includes resilience. For this, the determination of resilience evaluation indices is proposed. The indices are obtained from a normalized transformation of the measurable electrical parameters of the networks. The parameters are those that present the most affected by PV integration or are significant in the performance of the networks. Finally, it presents the evaluation of a proposed resilience index for a university building LV network as a case study.
Results: The resilience assessment proposal is applied to a case study. When evaluating the resilience of the voltage at the common coupling point of the PV, an index of 0.84 is obtained, equivalent to 59.8 hours of overvoltage.
Conclusions: It is possible to improve the resilience of the BT network through management strategies. In the case study, a 29% reduction in overvoltage hours was obtained by applying a curtailment strategy to the PV system.
Financing: ECOS-Nord, Minciencias and Universidad Industrial de Santander.