To address the problem of difficult performance assessment of train control on-board system after recovery from failures, we have proposed a resilience assessment methodology that uses reliability as an indicator of system resilience. Since the system failures are time-dependent, we adopted the Discrete Time Bayesian Network method to obtain the system\'s reliability before and after failure. Subsequently, we used an exponential recovery model to quantify the system\'s performance curve during the recovery phase, and finally utilized the resilient triangle area method to quantify its resilience size. Analyzing the CTCS3-300T train control on-board system, we found that the resilience of the system with cold standby redundancy design and hot standby redundancy design were 89.44 % and 87.34 %, respectively, indicating a slight decrease in system performance after recovery from failures compared to pre-failure levels. At that time, it was necessary to adjust operational plans based on actual conditions to avoid greater impact on the railway network. This paper realizes performance resilience of train control on-board system after failure recovery, which can be applied to similar systems and provide theoretical references for realizing intelligent maintenance of the high-speed train.

Resilience assessment for urban drainage systems is a fundamental aspect of building resilient cities. Recently, some scholars have proposed the Global Resilience Analysis (GRA) method, which assesses resilience based on the functional performance of different system failure scenarios. Compared to traditional system dynamics methods, the GRA method considers the impact of internal structural failure on resilience but requires a large amount of computation. This research proposed an improved GRA method to enhance computational efficiency and practicality by reducing the number of system scenario simulations. Firstly, a hydrodynamic model of the drainage network of Haidian Island has been constructed using the Storm Water Management Model (SWMM) and Python. Secondly, the GRA method was improved using cluster analysis and convergence analysis to reduce the simulation scenarios. Thirdly, a resilience assessment index was established through system function functions, and two types of resilience enhancement measures, centralized and distributed, were proposed. The results show: (i) resilience assessment increases the computational efficiency by 25% compared to the traditional GRA method; (ii) the resilience index of the existing drainage network within Haidian Island is less than the design value (0.7) in all failure scenarios, indicating a lower level of recovery capability; (iii) compared to the centralized strategy, which is only effective when the system failure level is less than 9%, the distributed strategy enhances the resilience of the urban drainage system at a higher failure level (77%).

The traditional UAV swarm assessment indicator lacks the whole process description of the performance change after the system is attacked. To meet the realistic demand of increasing resilience requirements for UAV swarm systems, in this paper, we study the modeling and resilience assessment methods of UAV swarm self-organized networks. First, based on complex network theory, a double layer coupled UAV swarm network model considering the communication layer and the structure layer is constructed. Then, three network topological indicators, namely, the average node degree, the average clustering factor, and the average network efficiency, are used to characterize the UAV swarm resilience indicators. Finally, the UAV swarm resilience assessment method, considering dynamic evolution, is designed to realize the resilience assessment of the UAV swarm under different strategies in multiple scenarios. The simulation experiments show that the UAV swarm resilience assessment, considering dynamic reconfiguration, has a strong correlation with the network structure design.

A robust and resilient nitrogen (N) flow system can effectively ensure consistent food production and consumption activities while preserving environmental quality. In this study, we constructed an indicator system to evaluate N flow system resilience including food production and consumption, at the county scale on the Qinghai-Tibet Plateau (QTP) from 1998 to 2018. The subsystem coupling coordination degree (CCD) and the effect of N losses on N flow system resilience were subsequently explored. The results indicated that despite the overall N flow system resilience remaining low and exhibiting spatiotemporal disparities from 1998 to 2018, over 90 % of the counties experienced improvements. High resilience areas (>0.15) were mainly concentrated in some counties in Sichuan Province, where N losses were positively correlated with system resilience. The level of resilience depended on agricultural and livestock development, and the CCD of subsystems was also high (>0.5) in this region, with the most balanced environmental and socioeconomic development. The low system resilience areas were concentrated in the eastern part of the QTP, where human activities caused substantial disturbances. The fragmentation of the agro-pastoral system coupled with the low system resilience of the food production and driving pressure subsystems led to low CCD between subsystems. In contrast, the western regions, characterized by a stable food production system, high food self-sufficiency, and weak dependence on external systems, showed a higher degree of system resilience and resistance. Our findings provide a reference for N resource management and policy formulation for food production and consumption in the agricultural and pastoral areas of the QTP.

BACKGROUND: The coronavirus pandemic has had a profound impact on organization and delivery of care. The challenges faced by healthcare organizations in dealing with the pandemic have intensified interest in the concept of resilience. While effort has gone into conceptualising resilience, there has been relatively little work on how to evaluate organizational resilience. This paper reports on an extensive review of approaches to resilience measurement and assessment in empirical healthcare studies, and examines their usefulness for researchers, policymakers and healthcare managers.
METHODS: Various databases (MEDLINE, EMBASE, PsycINFO, CINAHL (EBSCO host), Cochrane CENTRAL (Wiley), CDSR, Science Citation Index, and Social Science Citation Index) were searched from January 2000 to September 2021. We included quantitative, qualitative and modelling studies that focused on measuring or qualitatively assessing organizational resilience in a healthcare context. All studies were screened based on titles, abstracts and full text. For each approach, information on the format of measurement or assessment, method of data collection and analysis, and other relevant information were extracted. We classified the approaches to organizational resilience into five thematic areas of contrast: (1) type of shock; (2) stage of resilience; (3) included characteristics or indicators; (4) nature of output; and (5) purpose. The approaches were summarised narratively within these thematic areas.
RESULTS: Thirty-five studies met the inclusion criteria. We identified a lack of consensus on how to evaluate organizational resilience in healthcare, what should be measured or assessed and when, and using what resilience characteristic and indicators. The measurement and assessment approaches varied in scope, format, content and purpose. Approaches varied in terms of whether they were prospective (resilience pre-shock) or retrospective (during or post-shock), and the extent to which they addressed a pre-defined and shock-specific set of characteristics and indicators.
CONCLUSIONS: A range of approaches with differing characteristics and indicators has been developed to evaluate organizational resilience in healthcare, and may be of value to researchers, policymakers and healthcare managers. The choice of an approach to use in practice should be determined by the type of shock, the purpose of the evaluation, the intended use of results, and the availability of data and resources.

Cyber-physical systems (CPSs) are monitored and controlled by a computing and communicating core. This cyber layer enables better management of the controlled subsystem, but it also introduces threats to the security and protection of CPSs, as demonstrated by recent cyberattacks. The resulting governance and policy emphasis on cybersecurity is reflected in the academia by a vast body of literature. In this article, we systematize existing knowledge on CPS analysis. Specifically, we focus on the quantitative assessment of CPSs before and after the occurrence of a disruption. Through the systematic analysis of the models and methods adopted in the literature, we develop a CPS resilience assessment framework consisting of three steps, namely, (1) CPS description, (2) disruption scenario identification, and (3) resilience strategy selection. For each step of the framework, we suggest established methods for CPS analysis and suggest four criteria for method selection. The framework proposes a standardized workflow to assess the resilience of CPSs before and after the occurrence of a disruption. The application of the proposed framework is exemplified with reference to a power substation and associated communication network.The case study shows that the proposed framework supports resilience decision making by quantifying the effects of the implementation of resilience strategies.

Interdependent infrastructure systems are vulnerable to the cascading effect of failures resulting from random failures and natural disasters. The data provided in this work is the processed data used for the proposed resilience assessment framework for interdependent water and transportation networks dealing with both types of failure [1]. The case study is the interconnected networks of water and transportation in Tampa, Florida. The data for the random failure is obtained from the developed algorithmic framework and the land use and social vulnerability data provided by the U.S. Census datasets. We then used a subset of this produced data to construct predictive models for the network resilience to random failures. As for the natural disaster scenario, we focused on hurricane Irma in 2017 as it directly affected the focused region in Florida. We used the specific guidelines and the raw flooding data for this hurricane, provided by FEMA, to estimate the standing water for each geographical area (polygons) and the associated network components. We labeled the areas as failed and undamaged based on the estimated water levels. Finally, we used this data for developing a geospatial Geographical Weighted Regression (GWR) model to predict the resilience in each polygon. We present the final dataset for water and transportation networks to facilitate reusability for any future resilience study in the selected urban area.

The quality of the environment as well as public health is convincingly coupled with the functioning of a power subsector. The power subsector plays a pivotal role in the sense that it emerges as the key cross-sectional element for the society\'s functioning (production, services, healthcare, education and others). A modern society consists of infrastructure systems that are primarily dependent on continuous electricity supplies. Each and every element of the electric power infrastructure is unique, and thus, its malfunction can disrupt the functioning of an important part of the electric power infrastructure. In conjunction with ensuring the functioning of electric power infrastructure, our attention must be drawn to the resilience issue. As far as the resilience of electric power infrastructure is concerned, it can resist weather-related events ensuring there are no disruptions in continuous electricity supplies. First, in the introductory part, the article presents the legal framework in the Slovak Republic. Second, it describes the current state of the electric power infrastructure of Slovakia. Third, it handles the state of the level of security risk assessment. Later on, in the literature review, besides turning to the issue of resilience assessment, the authors focused on the area of resilience of power engineering. Furthermore, the article scrutinizes resilience assessment in Slovakia, and it briefly examines approaches towards natural threats. In addition, the article demonstrates several approaches towards flood resilience. Having used different methods, the primary concern is to devise a framework for resilience assessment. Therefore, the included case study examines aspects of the proposed framework for resilience assessment. In conclusion, our aim was, in most respects, to outline an innovative methodological framework for increasing the resilience of electricity infrastructure.

Natural disasters and human-made disasters are threatening urban areas globally. The resilience capacity of the urban system plays an important role in disaster risk response and recovery. Strengthening urban disaster resilience is also fundamental to ensuring sustainable development. Various practices and research for enhancing urban disaster resilience have been carried out worldwide but are yet to be reviewed. Accordingly, this paper gives a scientometric review of urban disaster resilience research by using CiteSpace. The time span (January 2001-January 2021) was selected and divided into three phases based on the number of publications. In addition, according to keyword statistics and clustering results, the collected articles are grouped into four hotspot topics: disaster risk reduction, specific disaster resilience research, resilience assessment, and combination research. The results show that most of the existing research is in the first two categories, and articles in the second and fourth categories both show a high growth rate and could be further research directions. The review indicates that urban disaster resilience is essential for a city\'s sustainable development. Moreover, the findings provide scholars a full picture of the existing urban disaster resilience research which can help them identify promising research directions. The findings can also help urban government officials and policymakers review current urban disaster management strategies and make further improvements.

Climate change is causing the decline of coral reef ecosystems globally. Recent research highlights the importance of reducing CO2 emissions in combination with implementing local management actions to support reef health and recovery, particularly actions that protect sites which are more resilient to extreme events. Resilience assessments quantify the ecological, social, and environmental context of reefs through the lens of resilience, i.e., the capacity of a system to absorb or withstand stressors such that the system maintains its structure and functions and has the capacity to adapt to future disturbances and changes. Resilience assessments are an important tool to help marine managers and decision makers anticipate changes, identify areas with high survival prospects, and prioritize management actions to support resilience. While being widely implemented, however, there has not yet been an evaluation of whether resilience assessments have informed coral reef management. Here, we assess the primary and gray literature and input from coral reef managers to map where resilience assessments have been conducted. We explore if and how they have been used to inform management actions and provide recommendations for improving the likelihood that resilience assessments will result in management actions and positive conservation outcomes. These recommendations are applicable to other ecosystems in which resilience assessments are applied and will become increasingly important as climate impacts intensify and reduce the window of opportunity for protecting natural ecosystems.