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This briefing provides a short introduction to a supplement of the Ciria Manual on Scour at Bridges and Other Hydraulic Structures published in 2021. The supplement captures additional knowledge since the scour manual was published in 2015. This note is intended for designers and asset managers of structures in the water environment that may be subject to scour.

Supply chain resilience assessment and strengthening has become a strategic topic in supply chain management. Extant literature on the subject of supply chain resilience assessment mainly focuses on evaluating the operational performance (e.g., inventory, capacity, lead time) while overlooking aspects of the financial performance. This article presents an event-based Bayesian approach as an effective tool for modeling the causal relationships among variables at different time intervals to evaluate resilience across operational and financial criteria. The model focuses on two key elements of resilience—reliability (the ability of the system to sustain an original state) and recoverability (the ability of a system to recover following a disruption), which allows us to investigate how different operational and financial factors affect the network reliability, recoverability, and resilience. This article contributes to the literature in three ways—first, we incorporate a financial perspective to the quantitative study of supply chain resilience; second, we evaluate both the financial and operational aspects from a longitudinal perspective; third, we show that the financial performance of individual supply chain entities has a strong influence on the overall supply chain resilience. From a practical standpoint, the article presents a model that can help managers identify the weakest nodes in their supply chain, considering operational and financial aspects over time, allowing them to design and operate more resilient supply networks.

The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.

Brazil has endured multiple political, economic, and environmental crises—and now the COVID-19 pandemic—which have drawn social inequalities into razor sharp relief. This contribution analyzes the resilience of rural families facing these crises in southern Bahia. These families have benefited from various redistributive policies over the years, including redistributive land reforms (RLRs), conditional cash transfers (CCTs), and recent emergency aid (EA) payments related to the pandemic. Each (re)distributive approach involves different notions of distributive justice informed by competing background theories of “the good,” which hold implications for concepts of resilience. Drawing on long-term research with RLR communities in Bahia, this article considers the gains achieved by different redistributive programs. Families who acquired land through RLR projects appear more resilient, especially in the face of crisis.

The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

Supply chain disruptions recurrently challenge end-to-end operations owing to the ambiguous understanding of the role of governance in impacting supply network resilience. This paper scrutinises the relevant literature to understand the plethora of interpretations in the domain of supply chain governance and resilience while further provides a new perspective on the representation of the interplay between governance and resilience in supply chains. In this regard, the Systems Thinking lens is adopted to pull together the typologies and constructs of supply chain governance and resilience from the literature. Methodologically, System Dynamics modelling principles are leveraged to capture the underpinning structural interdependencies in a causal loop diagram (CLD). The study reveals that endogenous and exogenous supply chain governance processes and mechanisms support the intrinsic and extrinsic resilience in networks. Overall, this research contributes to the supply chain risk management domain by synthesising the interplay between governance and resilience, identifying pertinent typologies and through articulating research propositions that can inform decision-making at policy and managerial levels.

© Copyright 2022 The Author(s). In this article, with the increased disruptions faced by businesses and the occurrence of natural disasters in the world, supply chain resilience remains a major challenge especially for small- and medium-sized enterprises (SMEs). Despite the relevance of SMEs to the economy, there is limited scholarly work on resilience practices in SMEs and a limited understanding of how SMEs can achieve resilience. To understand the role of supply chain resilience in SMEs, we undertake a systematic literature review (SLR), which results in the identification and analysis of 101 journal articles, published between 2006 and 2019, on SME supply chain resilience. Our analysis into SME supply chain resilience highlights four focal areas: 1) the role of collaboration and culture; 2) the role of SMEs’ capabilities; 3) the role of Information Systems; and (4) the role of cost and financing. Our SLR investigation identifies future research directions and focal areas tailored to SMEs to help them to assess and develop their supply chain resilience.

Engineering managers work with system designers, system operators, and stakeholders who value the system's performance and seek to deliver stakeholder value. Engineering resilient systems is an emerging research field since all systems face future uncertainties including new missions, new requirements, unplanned environments, and unplanned events that cause disruptions during operation. The ability of a system to provide stakeholder value depends on the system design and the ability of the system operator to react to the disruption(s). A resilient design offers options to respond to new missions, environments, and disruptions through system automation or operator actions. The engineering resilience literature uses overlapping definitions. It does not adequately distinguish between resilient design principles and the means they provide for operators to respond to mission disruptions and the ability to modify the system for new missions. This article uses the engineered systems resilience literature to provide a clearer lexicon. It then proposes a framework, the principles-means-ends diagram, for engineering managers to communicate with stakeholders, operators, and designers about the benefits of engineered systems’ resilience, the opportunities for designers to use resilience design principles to create more resilient designs, and the roles of the operators in achieving resilience during operations. We propose a principles-means-ends diagram for resilient engineered systems, which provides a holistic view for designers, operators, and stakeholders to visualize how their actions connect to others and encourages communication between them to create a more resilient engineered system.

Software-defined wireless sensor networks (SDWSNs) can be functionally affected by malicious sensor nodes that perform arbitrary actions, e.g., message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in SDWSNs. In this paper, we design a hierarchical trust management scheme for SDWSNs (namely TSW) to detect potential threats inside SDWSNs while promoting node cooperation and supporting decision making in the forwarding process. TSW evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the SDWSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Furthermore, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communications and trust management threats such as black-hole, selective forwarding, denial of service, bad mouthing, and ON-OFF attacks.

As typical applications of cyber-physical systems (CPSs), connected and automated vehicles (CAVs) are able to measure the surroundings and share local information with the other vehicles by using multi-modal sensors and wireless networks. CAVs are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this paper, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical CPSs. To minimize the performance degradation resulting from the usage of estimation for control, we provide an H∞ controller for CACC-equipped CAVs. The designed controller is capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

Hardware Malware Detectors (HMDs) have recently been proposed to make systems more malware-resistant. HMDs use hardware features to detect malware as a computational anomaly. Several aspects of the detector construction have been explored, leading to detectors with high accuracy. In this paper, we explore whether malware developers can modify malware to avoid HMDs detection. We show that existing HMDs can be effectively reverse-engineered and subsequently evaded. Next, we explore whether retraining using evasive malware would help and show that retraining is limited. To address these limitations, we propose a new type of Resilient HMDs (RHMDs) that stochastically switch between different detectors. These detectors can be shown to be provably more difficult to reverse engineer based on recent results in probably approximately correct (PAC) learnability theory. We show that indeed such detectors are resilient to both reverse engineering and evasion, and that the resilience increases with the number and diversity of the individual detectors. Furthermore, we show that an optimal switching strategy between the RHMDs base detectors not only reduces misclassification on evasive malware but also maintains high classification accuracy on non-evasive malware. Our results demonstrate that these HMDs offer effective defense against evasive malware at low additional complexity

The parallel connection of DC-DC converters requires the development of an appropriate control strategy that regulates load voltage and shares current amongst participating converters. This paper proposes a resilient and robust cooperative distributed control approach that simultaneously ensures voltage regulation and balanced current sharing in parallel DC-DC converters in the presence of false data injection attacks on control input channels. Based on analytical tools from network control and Lyapunov stability theory, concise stability certificates are derived. The proposed cooperative distributed control strategy guarantees resilience against unknown bounded attacks on the actuators of DC-DC converters and the robustness to uncertainties in load parameters and the physical parameters of converters. Furthermore, the control design for each converter does not require any knowledge about the number of participating converters. The detailed simulation and experimental results verify the satisfactory performance of the proposed method in voltage regulation and balanced current sharing in parallel converters, as well as resilience to bounded false data injection attacks.

The phenology of Arctic plants is an important determinant of the pattern of carbon uptake and may be highly sensitive to continued rapid climate change. Eriophorum vaginatum L. (Cyperaceae) has a disproportionate influence over ecosystem processes in moist acidic tundra, but it is unclear whether its growth and phenology will remain competitive in the future. We investigated whether northern tundra ecotypes of E. vaginatum could extend their growing season in response to direct warming and transplanting into southern ecosystems. At the same time, we examined whether southern ecotypes could adjust their growth patterns in order to thrive further north, should they disperse quickly enough. Detailed phenology measurements across three reciprocal transplant gardens over a 2-year period showed that some northern ecotypes were capable of growing for longer when conditions were favourable, but their biomass and growing season length was still shorter than those of the southern ecotype. Southern ecotypes retained large leaf length when transplanted north and mirrored the growing season length better than the others, mainly owing to immediate green-up after snowmelt. All ecotypes retained the same senescence timing, regardless of environment, indicating a strong genetic control. Eriophorum vaginatum may remain competitive in a warming world if southern ecotypes can migrate north.

Resilience and flexibility are the means by which a network stands for an efficient service provider platform. Cognitive packet network (CPN) is such a paradigm that solely depends on the behavioral and intelligence capacity of the packets that carry valuable information within the network. Unlike conventional networks, CPN relies on the decision-making ability of the packets. In CPN, different types of packets take part in finding the optimal route from the source to destination. Further, packets dynamically adapt environmental changes by using recurrent neural network-based learning algorithms. Mapping cognitive features along with enrichment of quality of service is the main task of CPN. Due to continuous updates in the packet information vulnerable elements are found to be less prone to attack the CPN, thus resulting in a robust networking solution. In this paper we firstly present a novel taxonomic structure of the CPN. Then, we discuss the stringent routing techniques normally used in CPN. We also investigate various adaptive aware features in CPN. Next, we present intuitive emergency service and sensor network design prospects of the CPN. The article also discusses various means of cognitive aware mitigation aligned with the packets in CPN. Finally, we deliver how CPN is secure and available security aspects.

This article is concerned with the secure state estimation problem for cyber-physical systems under intermittent denial-of-service (DoS) attacks. Based on a switching scheme and the cascade observer technique, a novel resilient state observer with a switched compensation mechanism is designed. Moreover, a quantitative relationship between the resilience against DoS attacks and the design parameters is revealed. Compared with the existing results, where only the boundedness of the estimation error is guaranteed under DoS attacks, the exponential convergence of the estimation error is achieved by employing the proposed observer scheme, such that the estimation performance is improved. More specifically, in the disturbance-free case, it is proven that the state estimation error converges exponentially to 0 despite the existence of DoS attacks. Finally, simulation results are provided to illustrate the effectiveness and merits of the proposed methods.

Vision systems applied to drones, automatic vehicles, and robots have become an increasingly popular sensing method. However, vision sensors that make up these systems are vulnerable to malicious input attacks, which can lead to serious consequences. Prior work on attacking cameras of automatic vehicles shows that lasers can cause failure of camera-based functionalities, but it lacks analysis of the results and does not conduct experiments in the actual scenarios.In this paper, a laser-based attack on cameras and binocular vision sensors of drones is presented. First, we propose a threat model that describes how an adversary attacks the drone then perform feasibility analysis of the attack from theory and practice. Next, we design multi-variable experiments in the lab to systematically study the effectiveness of the attack, and further analyze how each variable affects the results. To get intuitive and fine-grained results, multidimensional image similarity is used to measure the effects. In particular, experiments in the actual scenarios are carried out, and results show that the attack can make obstacle avoidance, target recognition and tracking completely failed. Finally, lightweight countermeasures based on hardware and software are proposed to improve sensor resilience against the attack.

Previous work has demonstrated that structural forms of empowerment tend to enhance individual and team resilience. However, there is hardly any knowledge about how structural empowerment affects organizational resilience. Moreover, a widespread (though largely untested) assumption is that, in adverse times, power and authority need to be centralized at the top to enhance organizational resilience. This paper explores the effects of empowerment on organizational resilience in an in-depth case study of a Dutch home care organization, in which employees are structurally empowered. The findings from this case study suggest that structural empowerment positively affects organizational resilience, but that this effect is contingent upon a climate of psychological safety as well as top management’s sustained commitment to structural empowerment. We move beyond the extant conceptualization of psychological safety by demonstrating its inter-level nature in the context of structural empowerment, which operates across organizational levels when employees also engage in discussions on tactical and strategic issues. Overall, this study provides an in-depth understanding of how organizations can enhance their resilience by empowering their members, thus also challenging the common wisdom about centralizing power in adverse times.

Federated learning facilitates the collaborative training of a global model among distributed clients without sharing their training data. Secure aggregation, a new security primitive for federated learning, aims to protect the confidentiality of both local models and training data. Existing secure aggregation solutions, however, fail to defend against Byzantine failures which are common in distributed computing systems. It is challenging to realize efficient and secure aggregation schemes while mitigating Byzantine faults simultaneously. In this work, we propose a new secure and efficient aggregation framework, SEAR, for Byzantine-robust federated learning. Relying on a trusted execution environment, Intel SGX, SEAR protects the privacy of clients' models while enabling Byzantine resilience. Considering current Intel SGX's architecture (i.e., limited trusted memory), we propose two data storage modes for implementing aggregation algorithms efficiently in SGX. Choosing appropriate storage mode to implement aggregation algorithms brings significant efficiency improvements. To balance the efficiency and performance of aggregation, we propose a sampling-based method to detect the Byzantine failures efficiently without degrading the performance of the global model. We implement and evaluate SEAR in a LAN environment, and the experiment results show that SEAR is computationally efficient and robust to Byzantine adversaries.

Engaging in well-being behaviors may promote resilience, which can protect against burnout. This descriptive, correlational analysis utilized baseline data from health care workers enrolled in the Web-based Implementation of the Science for Enhancing Resilience longitudinal study ( N = 2,383). The study aimed to describe the association of (a) types of well-being behaviors (regular exercise, yoga, meditation, spent time with a close friend, vacation) and (b) total number of well-being behaviors with resilience (emotional thriving and emotional recovery), covarying for sociodemographic and professional characteristics. General linear model findings indicated that each well-being behavior was significantly associated with greater emotional thriving, while only exercise and spending time with friends were significantly related to greater emotional recovery. Emotional thriving and emotional recovery were also significantly higher among health care workers reporting more well-being behaviors. Engaging in well-being behaviors may be one part of the solution toward increasing resilience in health care workers that warrants further investigation.

Real-world infrastructures modeled as complex networks have been popular topics in recent decades. As these infrastructures develop, becoming critical to support millions of people, their resilience in the face of disasters and attacks becomes an increasing concern to the engineers and stakeholders. Using a graph theory and complex network approach, we conducted an empirical study on the resilience of five metro, or subway systems selected globally. A multi-phase resilience framework was implemented from the resilience cycle concept, including preparedness, robustness, recovery, and adaptation. It also incorporated the historical flow data into the resilience analysis to account for user behavior impacts. The paper demonstrates a systematic comparison of five metro networks and explores trends in each resilience phase. Generally, diverse performances were observed among the five metro networks, with the Singapore metro showing the best resilience. It is robust to both random disruption and targeted attacks. Analysis suggests its advantage may come from both its moderate heterogeneous topology and the flow pattern, exhibiting the smallest average travel distance. Besides, the result highlights the significance of commuters' relocation behavior between neighboring stations in the recovery stage. For the adaptation from a long-term perspective, we developed a screening tool to investigate the impact of the network expansion on the existing part of network by mapping its relationship with the characteristics of the newly connected nodes. In general, the observation of the networks' different behaviors in flow-weighted analysis symbolizes that flow is necessary for consideration in the resilience design of real-world transportation systems.

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Background and Aims Integrated IBD care is effective but not routinely implemented. Validated methods that simultaneously address mind and body targets such as resilience may improve access and outcomes. We describe the development and implementation of the GRITTTM Method and its impact on resilience, health care resource utilization (HCRU) and opioid use in IBD. Methods Consecutive patients from an academic IBD center were evaluated for low resilience based on provider referral. Low resilience patients were invited to participate in the GRITTTM program. Primary outcome was % reduction in HCU. Secondary outcomes were change in resilience, corticosteroid and opioid use. Patients were allocated into 2 groups for analysis: GRITT Participants (GP) and Non-Participants (NP). Clinical data and HCU in the year prior to enrollment were collected at baseline and 12 months. One-way repeated measures MANCOVA evaluated group X time interactions for the primary outcome. Effect size was calculated for changes in resilience over time. Results Of 456 screened IBD patients 394 were eligible; 184 GP and 210 NP. GP had greater reduction in HCU than NP: 71% reduction in ED visits; 94% reduction in unplanned hospitalizations. There was a 49% reduction in opioid use and 73% reduction in corticosteroid use in GP. Resilience increased by 27.3 points (59%), yielding a large effect size (d=2.4). Conclusion Mind-body care that focuses on building resilience in the context of IBD care may be a novel approach to reduce unplanned health care utilization and opioid use but large, multi-center, randomized, controlled trials are needed.

In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.

Most microgrid protection schemes found in published literature suffer from a lack of generality in that they work well for the assumed topology, including type and placement of sources. Other generic protection schemes tend to be too complicated, too expensive, or both. To overcome these draw-backs, a topology-agnostic, scalable, and cost-aware protection based on fundamental principles that work in the presence of high penetration of inverter-based resources (IBRs) is developed and tested in this paper. The protection system also implements stable automatic reconfiguration of the healthy sections of the system after clearance of fault, thus increasing resilience by self-healing. To achieve this ambitious goal, stable inverter models are developed that operate in unbalanced networks in grid-connected and islanded modes, even with 100% IBRs, share power without conflicting controls, and can ride through faults while limiting fault currents. The scheme is tested for primary and backup protection and reconfiguration on the IEEE 123-node feeder in grid-connected and islanded modes with 15 IBRs connected to the system.

Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs must execute correctly in the presence of hardware faults. Full duplication provides the needed assurance but incurs a prohibitive 100% overhead. Algorithmic techniques are known to offer low-cost solutions, but the practical feasibility and performance of such techniques have never been studied for CNN deployment platforms (e.g., TensorFlow or TensorRT on GPUs). In this paper, we focus on algorithmically verifying Convolutions, which are the most resource-demanding operations in CNNs. We use checksums to verify convolutions, adding a small amount of redundancy, far less than full-duplication. We first identify the challenges that arise in employing Algorithm-Based Error Detection (ABED) for Convolutions in optimized inference platforms that fuse multiple network layers and use reduced-precision operations, and demonstrate how to overcome them. We propose and evaluate variations of ABED techniques that offer implementation complexity, runtime overhead, and coverage trade-offs. Results show that ABED can detect all transient hardware errors that might otherwise corrupt output and does so while incurring low runtime overheads (6-23%), offering at least 1.6X throughput to workloads compared to full duplication.

Natural disasters, such as extreme weather events (EWEs), can cause significant damage to power systems. In fact, it is expected that the intensity and frequency of EWEs will increase the next years due to climate change, making power system resilience enhancement necessary. This paper proposes a transmission resilience planning solution by determining the lines to be placed underground in order to minimize load shedding in the most cost-efficient way taking into account historical EWEs (HEWEs). The problem is formulated as a stochastic robust optimization problem and is decomposed into a master problem determining the lines to be placed underground and a number of subproblems, where each subproblem corresponds to a HEWE and provides the worst damage scenario that leads to the maximization of load shedding. The problem is solved using a decomposition-based algorithm. The proposed method is illustrated on a modified IEEE Reliability Test System and IEEE 118-bus System and it is evaluated by applying a Sequential Monte Carlo simulation.

The integration of phasor measurement units (PMUs and phasor data concentrators (PDCs in smart grids may be exploited by attackers to initiate new and sophisticated false data injection (FDI attacks. Existing FDI attack mitigation approaches are generally less effective against sophisticated FDI attacks, such as collusive false data injection (CFDI attacks launched by compromised PDCs (and PMUs as we demonstrate in this paper. Thus, we propose a secure and resilience-enhanced scheme (SeCDM to detect and mitigate such cyber threats in smart grids. Specifically, we design a decentralized homomorphic computation paradigm along with a hierarchical secret sharing algorithm to facilitate secure ciphertext calculation of state estimation residuals. Following this, a centralized FDI detector is implemented to detect FDI attacks. Findings from the security analysis demonstrate our approach achieves enhanced conventional FDI and CFDI attack resilience, and findings from our performance evaluations on the standard IEEE 14-, 24-, and 39-bus power systems also show that the communication overheads and computational complexity are reasonably low.

In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES.

The increase of extreme weather events with large adverse impact has caused enormous loss to power systems. Coordination of various emergency measures can effectively reduce the overall loss over the time-horizon of these events. However, coordination of various dispatch stages against extreme weather conditions has not been thoroughly addressed. We first propose a methodology to coordinate these dispatch stages based on a hierarchical defender-attacker model (HDAM), in which various dispatch stages are modelled as defenders, whereas the extreme weather event is modelled as the attacker. Second, frequency security constraints, load shedding, and transmission line switching are modelled with the piecewise linear AC power flow in the HDAM. An adaptive approximation of cosine function in power flow equation is applied to reduce the computational complexity. Third, the HDAM is recast as a two-stage robust optimization model and solved by the nested column-and-constraint generation algorithm. Thus, the robust resilience trapezoid and coordination strategy during extreme weather events are obtained. By inference-based sensitivity analysis of the discrete line outage scenarios, the critical lines are identified for pre-event hardening. Case studies using IEEE benchmark systems validate that coordination of multiple dispatch stages is effective to enhance power system resilience by reducing the overall loss.

Poverty reduction is a common challenge facing the whole world. China has taken a series of anti-poverty measures and great achievements have been made. However, the question of how to lift and keep rural households above poverty line remains a worthy subject of academic attention. Building on surveyed results in Lankao County in Henan province of China, this study proposes a theoretical framework for explaining the paths to achieving the livelihood resilience of rural households after shaking off poverty. We analyze the generative mechanisms of livelihood resilience of rural households. Results show that the livelihood resilience of the households who have shaken off poverty is closely related to their capacity to utilize accessible resources, learn new knowledge and exploit external resources. Households having modern agricultural productive systems or living in specialized villages have a relatively high level of resilience than those do not. Three types of capacities are identified as the Triggers, Pushers and Optimizers, respectively, acting on the generative process of livelihood resilience for households. They interact among one another to promote households' livelihood resilience. Finally, we propose that China should take additional measures to enhance rural households’ livelihood resilience to facilitate a sustainable development. More importantly, such experience is of great value for improving livelihood resilience of rural households in China and other developing countries.

Catastrophic weather has significantly battered the U.S. Gulf Coast in recent years and exposed critical deficiencies in the resilience across communities and organizations. These deficiencies compel the devising of strategies to identify critical infrastructure components that require more attention with regard to building resilience. This article presents a holistic approach to assessing urban resilience by studying the coresilience of infrastructure networks. For this purpose, Tallahassee, Florida is used as a case study with a focus on both power and roadway networks and includes real-life disaster data from three extreme weather events that recently hit the study area. This article contributes to the coresilience concept through: 1) ­developing a geographical information system-based information-gathering approach to obtain an integrated infrastructure network and feed the causality models, 2) developing novel coresilience metrics to spatially identify and evaluate the high-risk locations, and 3) presenting a comprehensive case study and application of the developed approaches by using real-life data from three major storms that hit the study area.

This environmental epidemiology article examines the understudied issue of Latino risk in the context of climate change. The largest ethnic minority group in the U.S., Latino socioeconomic characteristics put them disproportionately in harm’s way for the adverse health impacts of climate change, including being subject to diverse forms of discrimination in gaining access to government resources for prevention and recovery from climate impacts. The specific ways climate change is experienced in Latino communities varies by physical location, employment and residence patterns, and community resilience. Failure to address the threat of climate change, and to identify the specific needs of those who are most vulnerable, including not including them directly in preparedness planning, enhances the likelihood that official responses to climate change will perpetuate historic patterns of denial of Latino concerns about the environment, and existing patterns of inequality, and widen prevailing differences in climate-driven adversity.

NAND flash memory has been widely adopted in emerging storage systems. To ensure data security, the support of data sanitization in NAND flash memory-based storage systems is widely employed. Although some existing studies made efforts in employing encryption-based, erasure-based and scrubbing-based secure deletion approaches to achieve the security requirement, they suffer from the risk of being deciphered, the severe performance, and the scrubbing disturbance problems. Meanwhile, 3D NAND flash technology which stacks flash cells in vertical direction is gaining traction in the modern systems. This made the problems more severe because of the increased number of scrubbing disturbance directions in 3D NAND flash memory. To address the above issue, this work proposes ADS, an approximate-data-aware data sanitization scheme with the assistance of the error-resilient data of modern applications, which guarantees the highest degree of security for security-sensitive data sanitization (i.e., storage systems do not keep any old version of secure data once secure data are updated). ADS classifies request data into approx-secure, precise-secure, approx-unsecure, and precise-unsecure data by considering two factors including data error resilience and data privacy. Then, a novel data allocation strategy is proposed to selectively interleave secure data and approximate data within the flash blocks, which creates the scrubbing-friendly data patterns to minimize the overhead of secure deletion. Our experimental results show that, ADS reduces the average secure deletion latency by 58.93% over the state-of-the-art.

The speed and robustness of fault section location directly affect the resilience of the distribution network when the latter is exposed to extreme events. This article is proposing a fast and robust fault section location method for distribution system considering the outage reports from smart meters in low-voltage network and notifications from remote fault indicators (RFIs) in medium-voltage network. To make section location faster, a proposed location model moves underreporting variables and misreporting variables to offline recognition. To make section location more robust, the location model proposes new constraints of expected RFI states to remove the limitation of multiple faults on different paths to all sources. Simulation results on a utility feeder with distributed generations demonstrate that the proposed method has faster location and better robustness, which is helpful to the resilience enhancement of the distribution network.

Power systems have been transformed into cyber-physical systems that integrate electric grids with advanced information technology and operational technology. To ensure reliable and resilient operations of such systems, it is important to understand the system vulnerability and quantify system resilience. This article provides an overview of existing work on vulnerability assessment and resilience quantification related to cyber-physical power systems, and it identifies research gaps and opportunities to enhance resilience. Specifically, we first review the definition and architecture of cyber-physical power systems, and then, summarize existing approaches to assess vulnerability and resilience. Later, we identify several research gaps that have not been well addressed yet and point out possible future work to fill the gap. Although this article focuses on cyber-physical power systems, the research can also benefit stakeholders of other critical infrastructures.

The development of software-defined industrial networks (SDIN) promotes the programmability and customizability of the industrial networks and is suitable to cope with the challenges brought by new manufacturing modes. For building more scalable and reliable SDIN, a distributed control plane with multi-controller collaboration becomes a promising option. However, as the brain of SDIN, the security of the distributed control plane is rarely considered. In addition to suffering direct attacks, each controller is also subjected to attacks propagated by other controllers because of information sharing or management domain takeover, resulting in the spread of attacks in a wider range than a single controller. Therefore, in this paper, we study attacks against SDIN with distributed control plane, demonstrate their propagation across multiple controllers, and analyze their impacts. To the best of our knowledge, we are the first to study the security of SDIN with distributed control plane. In addition, since the existing defense mechanisms are not specifically designed for distributed SDIN and cannot defend it perfectly, we propose an attack mitigation scheme based on deep reinforcement learning (DRL) to adaptively prevent the spread of attacks. Specifically, the novelty of our scheme lies in its ability of learning from the environment and flexibly adjusting the switch takeover decisions to isolate the attack source, so as to tolerate attacks and enhance the resilience of SDIN.

The growing impact of weather-related power outages on economy and society in the last decades underlines the rising need for power system resilience. Power system resilience can be boosted through adoption of probabilistic approaches and preventive actions building on smart grid capabilities. Decisions on the best-performing preventive action, however, are nontrivial and must consider the expected impact of an upcoming event, weather forecasts, fault probabilities, and their corresponding uncertainties. This article presents a three-stage decision-making methodology that is based on assessing weighted preevent and postevent performance loss and considers spatial uncertainty of fault probabilities, modeled by probability distributions. The methodology is demonstrated using preventive actions, such as additional network constraints and islanding, aiming to mitigate cascading failures in transmission networks. Their performance loss is compared to the traditional N-1 criterion. Simultaneous faults of up to three lines are considered as initiating cause in the IEEE 30-bus network and the 489-bus German transmission network to verify potential and scalability of the methodology. Results show that the decision-making methodology effectively identifies the best-performing action to reduce the risk of cascading failures for any level of uncertainty.

Connected transportation systems in smart cities can be regarded as Cyber-Physical-Social Systems (CPSSs) or Socio-Technical Systems (STSs) due to the presence of connectivity and complex interaction between human and technological infrastructure. Safety and security are essential requirements for such transportation CPSS. Specifically, resilience against cyber-attacks is crucial to address the vulnerabilities of Information and Communication Technologies (ICT) within the infrastructure. In this work, we explore a cyber-attack detection paradigm that leverages the socio-technical nature of such transportation CPSS. Essentially, we exploit the redundancies between physical signals (received from vehicle and infrastructure sensors) and social signals (received from consumers' mobile devices and social media) to detect cyber-attack occurrences. The proposed scheme is developed combining system and control theoretic tools and natural language processing techniques. A case study of a vehicular platoon is considered on which extensive simulation studies have been performed. Such simulation results under different types of cyber-attacks illustrate the promising nature of the proposed approach.

By combining independent systems (e.g., financial systems, power infrastructure, transportation networks), systems of systems (SoSs) can provide additional services. The ability to withstand faults, minimize service disruption, and efficiently recover (resilience) is a key performance characteristic. Current resilience metrics, however, require improvement. Each SoSs is approached with different assumptions about fault magnitude and recovery strategy. Thus, engineers currently have difficulty answering the basic question: Between alternative designs A and B, which is more resilient? SoS resilience metrics should reflect that the system interfaces are the most potent leverage point, have a standardized fault and recovery strategy, incorporate SoS dynamics, and apply to both natural and artificial systems of systems. In response, we develop the system of systems resilience metric (SoSRM). SoSRM methodology uses the definition of SoS to identify appropriate fault locations and graph theory to define fault and recovery duration. SoSRM is applied to two case studies to demonstrate application. First, an intertidal oyster reef ecosystem's system dynamics model is presented to demonstrate SoSRM methodology. Next, a military SoS demonstrates the application of SoSRM to a stochastic multi-agent system. Validation simulations show that SoSRM reflects expected fault impact and that methodological choices for fault duration do not drive SoSRM results. This article presents a new design tool capable of comparing the resilience between two SoS architectures.

Improving the resilience of complex engineered and engineering systems (CES) includes planning for complex resilience situations, in which there may be multiple threats, interactions, and disruptions. One challenge in the modeling of CES is the identification of how interactions in a complex situation occur and their combined influence on CES resilience. This article presents a resilience holon that can be used to analyze complex resilience situations. It is made up of 24 elements (defining types of resilience, threats, interactions, and disruptions), which have varying importance to specific situations. Holons can be linked together hierarchically or in a network. An application of the resilience holon to a documented real-world resilience situation, widespread flooding in a city, illustrates its use. Pathways taken by threats and disruptions, as the flood effects cascaded through the city, are shown as connections between holons. The resilience holon could be used to decompose diverse resilience situations involving CES, to identify where critical vulnerability points are and how the whole resilience situation could be improved. The visual nature of the resilience holon could be used in an interactive way, allowing stakeholders to better understand the full resilience picture of CES that they use or operate.

Housing constitutes a basic need for all living beings. Unfortunately, natural hazards, including floods, pose a severe threat to housing infrastructure systems. In turn, this paper develops a framework to quantify the resilience of housing infrastructure systems against flood hazards. The parameters for this resilience are based on the literature and knowledge from experts. This paper gauges the significance of each resilience parameter by using the analytic hierarchy process (AHP) and the fuzzy AHP. The evaluated values are then compared to observe the effectiveness of fuzzy AHP over AHP. The evaluated importance of each parameter will help stakeholders focus on the most important parameters and, in turn, boost the flood resilience of infrastructure. This paper then implements the developed framework in a study area to quantify local flood resilience. This resilience value will help stakeholders in the considered area to understand the resilience of local housing infrastructure.

Institutions are often regarded either as rules or as equilibria sustained by self-interested agents. I ask how these two theories can be combined. According to Philip Pettit’s Virtual Control Theory, they explain different things: rules explain why regularities persist; self-interest why they are resilient. Thus, his theory reconciles the two theories by adjusting their domains of application. However, the available evidence suggests that rules and self-interest often combine as sources of motivation. Because of this, it is better to integrate the theories rather than to reconcile them. Inspired by Cristina Bicchieri’s theory of social norms, I incorporate the notion of rule-following into a game-theoretic account of institutions. According to the resulting Rules-and-Equilibrium Theory, institutions are rule- or norm-governed social practices. The theory does not only explain their persistence and resilience, but also their fragility, which provides another reason for preferring the proposed integration to Pettit’s reconciliation.