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We present a methodology for the assessment of possible pre-failure bridge deformations, based on Synthetic Aperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009–2018. Historical satellite datasets include Envisat data spanning 2003–2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.

Over the last decade, the protection of urban infrastructures has become a focus in German security policies. These point not solely to the multiple external infrastructural threats (e.g. natural disasters, terrorist and cyber-attacks), but also to the endogenous risks of cascading failures across geographical and functional borders that arise from interlocking and often mutually dependent infrastructures. As geographical nodes in infrastructurally mediated flows, cities are considered to be particularly vulnerable to infrastructure breakdowns. Their capability to prevent and to prepare for infrastructural failures, and thus to manage infrastructural interdependencies, is seen as a major prerequisite for resilient societies. However, as our article demonstrates, the institutional capacity of the local authorities and utility companies for risk mitigation and preparedness is limited. Drawing on qualitative research in selected German cities, we argue that the governance of critical infrastructures involves considerable challenges: it overarches different, often fragmented, policy domains and territories and institutionally unbundled utility (sub-) domains. Moreover, risk mitigation and preparedness are usually not based on experience from past events, but on destructive scenarios. They involve considerable uncertainty and contestations among local decision-makers. Interviews with local experts indicate that effective governance of critical infrastructures requires more regulatory efforts by national policies. At the same time, they point to the need for identifying and assessing place-based vulnerabilities, for defining locally differentiated mitigation and preparedness strategies and for the training of local utility companies as well as crisis management. 在过去十年中，保护城市基础设施已成为德国安全政策的重点。这不仅指向多种外部基础设施威胁（例如自然灾害，恐怖主义和网络攻击），而且指向跨越地理和功能边界的级联故障的内生风险，这些风险源于互锁且通常相互依赖的基础设施。作为由基础设施调节的流量的地理节点，城市被认为特别容易受到基础设施故障的影响。城市有能力预防基础设施故障并为之做好准备，从而管理基础设施的相互依赖性，被视为社会复原性的一个主要先决条件。但是，正如我们的文章所表明的那样，地方当局和公用事业公司在消减风险和为风险做准备方面的制度能力有限。借鉴选定的德国城市的定性研究，我们认为关键基础设施的治理涉及相当大的挑战：它包含不同的、通常是支离破碎的政策领域和地理范围，以及制度上非捆绑的公用事业（子）领域。此外，风险缓解和准备通常不是基于过去事件的经验，而是基于破坏性情景。它们涉及相当大的不确定性和当地决策者之间的争议。对当地专家的访谈表明，关键基础设施的有效治理需要在全国性监管政策方面做出更多的努力。与此同时，还需要确定和评估基于地方的脆弱性，确定差异化的本地风险消减和准备策略，并培训当地公用事业公司以及危机管理人才。

PurposeCritical infrastructure (CI) plays an essential role in reading, reacting and responding while dealing with natural disasters. This study address food supply chain resilience by proposing an FSC resilience model that explains the food product and transport flow via production, processing, distribution and retailing in circumstances of (CI) collapses post a natural disaster.Design/methodology/approachA combination of qualitative methods was conducted to obtain a comprehensive overview of the food and beverage sector in Puerto Rico. The full dataset comprised of seven focus groups for a total of 52 participants and 12 in-depth interviews.FindingsFSC resilience is seen in this study through the managerial actions taken by members of the Chain: innovating, transforming, adapting, and flexibilising business models and operations.Originality/valueThis study is the first to address FSC resilience from the perspective of net food importer economy in the context of natural disasters and prolonged Critical infrastructure (CI) breakdown, and the first one in proposing an FSC resilience model that explains the food product and transport flow via production, processing, distribution and retailing in circumstances of CI collapses post a natural disaster.

This paper highlights the crucial role of wireless sensor networks (WSNs) in the surveillance and administration of critical infrastructures (CIs), contributing to their reliability, security, and operational efficiency. It starts by detailing the international significance and structural aspects of these infrastructures, mentions the market tension in recent years in the gradual development of wireless networks for industrial applications, and proceeds to categorize WSNs and examine the protocols and standards of WSNs in demanding environments like critical infrastructures, drawing on the recent literature. This review concentrates on the protocols and standards utilized in WSNs for critical infrastructures, and it concludes by identifying a notable gap in the literature concerning quality standards for equipment used in such infrastructures.

The on-going digitalization of our critical infrastructures is progressing fast. There is also a growing trend of serious and disrupting cyber-attacks. The digital services are often fragile, and with many weaknesses and vulnerabilities. This makes exploiting and attacking the services a little too easy. If the services verifies all inputs, many security threats will be avoided. Similarly, if one diligently tests the services with malformed inputs, one will uncover many security and software quality problems. In this paper we investigate “input validation” and “fuzz testing” as a means to improve security. The aim is not exhaustive coverage, but to provide indications of usefulness and to serve as a call for action.

Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, we outline future directions to guide the integration of advanced AI methodologies into protecting critical infrastructures. Our paper provides a strategic roadmap for researchers and practitioners committed to fortifying national infrastructures against emerging cyber threats through this synthesis of current challenges, benchmarking strategies, and innovative AI applications.

Purpose Critical infrastructures (CIs) for essential services such as water supply and electricity delivery are notoriously vulnerable to disruptions. While extant literature offers important insights into the resilience of CIs following large-scale disasters, our understanding of CI resilience to the more typical disruptions that affect CIs on a day-to-day basis remains limited. The present study investigates how the interorganizational (supply) network that uses and manages the CI can mitigate the adverse consequences of day-to-day disruptions. Design/methodology/approach Longitudinal archival data on 277 day-to-day disruptions within the Dutch national railway CI were collected and analyzed using generalized estimating equations. Findings The empirical results largely support the study’s predictions that day-to-day disruptions have greater adverse effects if they co-occur or are relatively unprecedented. The findings further show that the involved interorganizational network can enhance CI resilience to these disruptions, in particular, by increasing the overall level of cross-boundary information exchange between organizations inside the network. Practical implications This study helps managers to make well-informed choices regarding the target and intensity of their cross-boundary information-exchange efforts when dealing with day-to-day disruptions affecting their CI. The findings illustrate the importance of targeting cross-boundary information exchange at the complete interorganizational network responsible for the CI and to increase the intensity of such efforts when CI disruptions co-occur and/or are unprecedented. Originality/value This study contributes to our academic understanding of how network-level processes (i.e. cross-boundary information exchange) can be managed to ensure interorganizational (supply) networks’ resilience to day-to-day disruptions in a CI context. Subsequent research may draw from the conceptual framework advanced in the present study for examining additional supply network-level processes that can influence the effectiveness of entire supply networks. As such, the present research may assist scholars to move beyond a simple dyadic context and toward examining complete supply networks

Critical infrastructure systems (CISs) have a fundamental role in delivering commodities that are essential to various functions in urban systems. The resilience of CISs concerns the robustness of system performance against extreme events, the ineffectiveness of disturbance propagation, and the efficiency of post-disaster system performance restoration. The resilience of CISs is significantly impacted by the interconnectivity among CISs and the interactions among different systems. Although this impact has been recognized by numerous studies, it has rarely been comparatively assessed using different metrics that reflect the different perspectives of various stakeholders. Moreover, the existing literature on the impact of interdependencies in the context of CIS disaster risk reduction has primarily focused on the resistance stage rather than the entire life cycle of disaster events. To address these gaps, this study assesses this impact at different stages of the life cycle of disturbance events, analyzes the effect of interdependencies on determining the total resilience of CISs, and discusses the implications of the results in the context of resilience enhancement of CISs in practice. To achieve this objective, this study models interconnected CISs using four different network-based approaches, simulates the disturbance propagation process and system restoration process of CISs in three different scenarios, and measures the resilience of disturbed CISs with three different resilience metrics. A case study of three CISs in a middle-sized city in Eastern China was conducted. The CISs included an electric power system, a telecommunication system, and a water supply system. The results revealed that the vulnerability of CISs to extreme events would be significantly underestimated if interdependencies of the CISs were not considered, which would cause a misleading estimation of the total resilience of the CISs. The findings also suggested the importance of considering the interdependencies of CISs in the sequencing of restoration tasks to optimize the efficiency of post-disaster restoration tasks.

In recent years, the world has seen great achievements in the development of the infrastructure needed for information and communication technologies, and widespread and growing access to high-speed Internet. Today, governments, businesses, civil society and individuals across the planet are increasingly connected to this vast network. However, the significant growth of the internet has also led to new cybersecurity challenges for the global community. The more interconnected our societies become, the more vulnerable our critical infrastructures are to cyber threats. More and more critical infrastructures are falling victim to DDoS or malware attacks, and the trend is not decreasing. It is therefore urgent to find defence methods against these attacks In this article, we present the state of the art in attack methods against critical infrastructures, as well as the defence methods implemented against these attacks.

Cyber intrusions into critical infrastructure inflict economic and physical damage. Extensive research is needed to identify and mitigate intrusions in power grid infrastructure. The modern solution is to use a data science time-series approach to identify the intrusion based on the electric grid data collected from the sensors. This paper addresses the new vision of the data science time-series modelling approach to integrate it with the existing power system security system. In this paper, the Advanced Autoregressive Moving Average (A AR.MA) model is designed to detect the possible intrusion of the given data set. An attack forecast is a model to predict possible cyber intrusions using real-time data input from sensors. By investigating the statistical properties of the sensors' data set. intrusion detection is possible with a high accuracy of about 90%. Using AAR.MA, the operators have the benefit of an effective alert system to adjust their configuration and other resource allocation to tackle intrusions with low impact. MATLAB software is used to monitor the TREE 9-bus and IEEE 33-bus test systems against possible cyber-attacks using the proposed AARMA model.