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Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors including aviation, aerospace, vehicles, ships, electric power, and industry. However, because of the complexity and diversity of its structure, the complex traffic network is vulnerable to be attacked and faces serious security challenges. Therefore, this paper innovatively proposes a traffic network resilience recovery method based on resilience trend forecasting. In this paper, the risk value is introduced into the analysis of network fault propagation process, and the Susceptible, Infectious, Recovered, Dead‐Risk (SIRD‐R) fault propagation model is established. The resilience model of traffic network, which encompasses real‐time resilience and overall resilience, is constructed through the integration of network resilience bearing capacity and resilience recovery capacity. Then, the resilience of complex traffic network is forecasted by using long short‐term memory network, and the resilience recovery strategy of complex traffic network based on forecasting is proposed. Finally, the effectiveness and scalability of the proposed method are demonstrated through experimental analysis conducted on a diverse range of complex traffic networks, affirming its applicability in real‐world scenarios.

In this position paper on reliable networks, we discuss new trends in the design of reliable communication systems. We focus on a wide range of research directions including protection against software failures as well as failures of communication systems equipment. In particular, we outline future research trends in software failure mitigation, reliability of wireless communications, robust optimization and network design, multilevel and multirealm network resilience, multiple criteria routing approaches in multilayer networks, resilience options of the fixed IP backbone network in the interplay with the optical layer survivability, reliability of cloud computing networks, and resiliency of software-defined networks. Described research directions are frequently enhanced with examples.

The placement and scale of virtual power plants (VPPs) in distribution networks are the only topics covered in this article that pertain to the resilience of the grid to severe weather. This problem is framed as a two-objective optimization, where the expected energy that the network would not deliver in the case of an earthquake or flood (expected energy not-supplied), and the annual planning cost of the VPP, are the two objective functions to be minimized. Noted that the expected energy not-supplied in the earthquake or flood condition is considered as the resiliency index. The constraints include the formula for VPP planning, limitations on network operation and resilience, and equations for AC power flow. Uncertainties about demand, renewable power, energy prices, and the supply of network hardware and VPP components are all taken into account in stochastic programming. The proposed technique achieves a single-objective formulation in the subsequent stage by the use of a Pareto optimization strategy based on the ε-constraint method. This article uses a solver based on a hybrid of Crow search algorithm (CSA) and sine cosine algorithm (SCA) to achieve the trustworthy optimal solution with lowest dispersion in the final response. In order to tackle the problem, the proposed system looks at how the VPP affects network resilience, scales it, and combines it with the hybrid evolutionary algorithm. In the end, with the implementation of the proposed design on the distribution network of 69 buses, the obtained numerical results confirm the ability of optimal placement and dimensions of VPPs in improving the economic status, utilization and resilience of the distribution network.

Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures.

Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein–protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.

With the development of society, infrastructures have not only expanded in size, but have also formed complex dependencies among themselves, constituting closely interconnected network systems. At present, it has become a global consensus to enhance the resilience of infrastructure networks. In order to deeply understand how the interconnections among urban infrastructure systems affect the resilience performance of cities, this study adopts a combination of theoretical discussions and Python simulation techniques to systematically analyze the resilience dynamics of urban connected infrastructure networks when they encounter external perturbations. First, based on the principle of resilience construction and the multilayer network theoretical framework, we constructed a linkage model of the urban infrastructure network. Subsequently, focusing on the functional linkage perspective, we simulate the cascading failure mechanism of the network under three different external shocks, and construct a resilience assessment model for the urban infrastructure network with the help of the curve method of infrastructure system performance assessment. Finally, an empirical study is conducted with a county in Jiangxi Province as a specific case.

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain’s core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject’s functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.

Mineral extraction in resource-based cities has caused serious damage to the original ecology, resulting in poor regional vegetation growth, reduced carbon sequestration capacity, and reduced ecosystem resilience. Especially in resource-based cities with fragile ecology, the overall anti-interference ability of the environment is relatively worse. Seeking ecological network optimization solutions that can improve vegetation growth conditions on a large scale is an effective way to enhance the resilience of regional ecosystems. This paper introduces carbon sequestration indicators and designs a differential ecological networks (ENs) optimization model (FTCC model) to achieve the goal of improving ecosystem resilience. The model identifies the patches that need to be optimized and their optimization directions based on the differences in ecological function-topology-connectivity-carbon sequestration of the patches. Finally, the resilience of the ecological network before and after optimization was compared, proving that the model is effective. The results show that the sources in the Yulin ENs form three main clusters, with connectivity between clusters relying on only a few patches. The patches in the northeastern and southwest clusters are large but their ecological functions need to be improved. After optimization, 16 new stepping stones were added, 38 new corridors were added, and the ecological function of 39 patches was enhanced. The optimized ecological network resilience was improved in terms of structure, function, and carbon sinks, and carbon sinks increased by 6364.5 tons. This study provides a reference for measures to optimize landscape space and manage ecosystem resilience enhancement in resource-based cities.

Purpose Diversity – or having a range of different options – is an important part of being resilient. Yet research has not considered how diversity in terms of the governance relationship types that exist within a supply base or across a supply network relates to resilience. By drawing on a well-established global value chain (GVC) governance framework, this paper aims to investigate how different relationship governance types influence resilience at the dyadic and supply network level. Design/methodology/approach This research draws on 27 embedded cases of buyer-supplier relationships within a network, studied through 20 interviews in 11 organizations across four tiers of the Australian Defence Force supply network, including the end customer perspective, during and after a large-scale supply chain (SC) disruption. Analysis is conducted at the individual dyad and aggregated network levels. Findings At the dyadic buyer-supplier level, a variety of different resilience strategies and practices are used across the relationship governance types. Consequently, at the network level, relationships characterized by market and relational governance created more vulnerabilities during COVID-19 than hierarchical and modular governance relationships. Originality/value The GVC framework is extended to the SC domain, providing a deeper understanding of how GVC governance types in SC relationships relate to resilience strategies at the dyadic and network levels. Given that different governance relationships draw on different resilience strategies, diversity in governance relationships helps enhance overall resilience. Meanwhile, the findings show that resilience requires relational aspects to be considered alongside economic aspects of the GVC.

The convergence of optical transport and wireless access in next- and future-generation networks imposes strict QoS demands, particularly end-to-end reliability, which conventional redundancy approaches cannot meet. The paper presents an architectural framework integrating three aspects: a risk-diverse route-computation algorithm with shared-risk link group constraints that achieve polynomial complexity and overcome memory constraints. Secondly, it presents a self-optimising signal-control bus modelled as a closed-loop queueing system that maintains 95% throughput under an offered load of 400%, thereby representing a statistically significant improvement over static configurations. Lastly, it presents an adaptive multipath communication framework formalised as a multi-objective optimisation that enables application-specific trade-offs among reliability, latency, and bandwidth. Performance evaluation demonstrates polynomial versus exponential memory scaling, control-plane resilience under signalling storms, and sub-10 ms latency at 10% packet loss. As such, the discussed aspects establish design principles for reliable, resilient, and robust converged optical-wireless networks. In addition to formal architectural modelling and algorithm design, this study independently validates the proposed framework through original simulations conducted in OMNeT++ and ns-3.