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Disaster Recovery (DR) providers define the main services as backup and restore, both with critical data and applications. Most clients of data and application backup, fully rely on DR's services to restore them in the disaster events. Furthermore, in the normal conditions, DR provider backs up data, sometimes with application in routine or regular basis. Thus, providers should deliver the services in sophisticated way through several Key Performance Indicators (KPIs). In this paper, development of KPIs is focused on performance and assurance, which a laboratory testing simulates backup and restore services. On the other hand, experimental data such as network and system parameters are analysed to build performance and assurance framework. Meanwhile, interdependence among each performance and assurance of DRCs was used to build a relationship model. It is acted as a part of service level agreement, that a DR providers can present services dynamically. They also should adapt the computing resources to deliver services to meet SLA (Service Level Agreement) in flexible strategy. In turn, they can negotiate SLA with the customers based on this new approach. [ABSTRACT FROM AUTHOR]

Robot teleoperation gains great success in various situations, including chemical pollution rescue, disaster relief, and long-distance manipulation. In this article, we propose a virtual reality (VR) based robot teleoperation system to achieve more efficient and natural interaction with humans in different scenes. A user-friendly VR interface is designed to help users interact with a desktop scene using their hands efficiently and intuitively. To improve user experience and reduce workload, we simulate the process in the physics engine to help build a preview of the scene after manipulation in the virtual scene before execution. W e conduct experiments with different users and compare our system with a direct control method across several teleoperation tasks. The user study demonstrates that the proposed system enables users to perform operations more instinctively with a lighter mental workload. Users can perform pick-and-place and object-stacking tasks in a considerably short time, even for beginners. Our code is available at https://github.com/lingxiaomeng/VR_Teleoperation_Gen3. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the effectiveness of methods for equipping employees with food and necessities to ensure Business Continuity Plans (BCPs) under the threat and risk of disasters. Although employee food and livelihoods are essential to implementing a BCP in the event of a disaster, more research needs to be conducted on this method. For this study, participants will be divided into two teams and given a lecture and an exercise to simulate a disaster so that they can visualize it step by step. Then a comparative study is conducted before and after the exercise, as well as a comparative study of the disparity in the amount of disaster information between the two teams. The results of this experiment show that this method motivates participants to participate and effectively selects food and daily necessities that companies should be prepared to provide. It was also found that individuals share and discuss specific information with the team, enhancing the effectiveness of the selection process. [ABSTRACT FROM AUTHOR]

As one of the costliest and most frequent natural disasters, flood is a major threat to human lives and property. Flood forecasting, simulation, and monitoring plays an important role in disaster relief and mitigation. With the support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, the VIIRS (Visible Infrared Imaging Radiometer Suite) global 375-m flood products in near real-time, daily composition, and 5-day composition have been developed and derived flood extent represented in water fractions from Suomi-NPP and NOAA-20. The products have shown good quality for the spatial distribution of floodwater. However, the moderate spatial resolution is a limiting factor when obtaining inundation detail. Moreover, no vertical information on the floodwater is included in the products. Based on water's self-leveling nature, it is feasible to construct the vertical structure of floodwater using the VIIRS 375-m water fractions and high-resolution Digital Elevation Model (DEM) such as the 30-m Shuttle Radar Topography Mission (SRTM)/DEM through a downscaling process (Li et al., 2013). With impact factors including topography, land cover, tree cover, river network and watershed, the downscaling process has been integrated into a model called the Downscaling Model. By using the 30-m SRTM/DEM, the model can downscale the VIIRS 375-m floodwater fraction products into 30-m flood extent and water depth products. These products include flood information in both horizontal and vertical directions and thus are called 3-D flood products in this study. This paper presents a comprehensive introduction to the model with the results evaluated against high-resolution satellite images from Landsat-8 Operational Land Imager (OLI), Sentinel-2, and GeoEye, aerial photos, and river gauge observations. Evaluation results indicate reliable quality and promising performance of the model and the downscaled flood products. A routine system has been set up at the Cooperative Institute for Satellite Studies (CIMSS) at University of Wisconsin-Madison to generate experimental VIIRS downscaled flood extent and water depth products over the Continental USA (CONUS). The routinely generated VIIRS 30-m floodwater depth maps are available at: https://floods.ssec.wisc.edu/?products=VIIRS-3Dflood. [ABSTRACT FROM AUTHOR]

• A periodic recompression recovery scheme (PRRS) is proposed for pressure protection. • The PRRS improves the reliability of spacecraft protection. • A cabin pressure mathematical model is established. • The variation laws of cabin pressure during leakage and protection are obtained. • The PRRS reduces gas consumption by more than 85%. Spacecraft may encounter emergency pressure relief situations during manned space exploration missions, such as when micrometeoroids break through the bulkheads. The pressure emergency is one of the main threats to the missions in low earth orbit or deep space explorations to the Moon and Mars. It is critical to develop a pressure protection scheme for emergency contingencies. In this study, in order to improve the safety and reliability of spacecraft protection, a periodic recompression recovery scheme (PRRS) is proposed for cabin pressure protection. The PRRS adopts a combined mode of cabin emergency recompression, cabin pressure maintenance, gas recovery, and spacesuit protection, which can provide astronauts with a variety of safety protection methods. A mathematical model of cabin pressure control is established by using the lumped parameter method, and the gas consumption of three types of pressure protection systems are compared. The PRRS adopts the mature technologies and could provide the reliable pressure emergency protection. Compared with the traditional continuous gas supply scheme, the PRRS can reduce gas consumption by more than 85%. In the case of limited spacecraft gas resources, the PRRS promises a longer survival time for returning astronauts. This study can provide a design idea for the overall design of manned spacecraft in the future. [ABSTRACT FROM AUTHOR]

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES. • Proposed a UIES post-disaster load recovery model, effectively coordinating local resources. • Proposed a cross-energy type node weight calculation method. • Introduced a novel node-based pipeline energy storage model. • Defined a resilience evaluation index for UIES load recovery. • Conducted comprehensive simulations of the post-disaster load recovery process for UIES. [ABSTRACT FROM AUTHOR]

• Flexible robotic arms composed of X-tensegrity inspired modules are proposed. • Each planar module comprises two sets of triangular bars linked by three strings. • Adjustable angles between modules facilitate the robotic arm's 3D expansions. • Our robotic arm can achieve various bending, V-, S-, and J-shaped deformations. • Theoretical model, simulations, and experiments confirm the arm's superior capability. Robotic arms have remarkable applications in diverse fields such as medical rehabilitation, disaster relief, and space exploration. Enhancing their rigidity, load-bearing capacity, and motion simplicity is key to broadening their usage. Utilizing the admirable flexibility and strength of tensegrity structures, made of rigid bars and elastic strings, we introduce a new type of flexible robotic arm. This arm is constructed using a sequence of two-dimensional X-tensegrity inspired modules. Each module comprises two sets of triangular bars linked by three strings, enhancing the arm's ability to deform and resist impact forces. The joints between modules are stiff, allowing for angular adjustments to create three-dimensional configurations with adjustable stiffness and curvature. Through theoretical analysis, simulations, and experiments, we have shown that this tensegrity-based robotic arm exhibits superior stability, flexibility, and scalability. [ABSTRACT FROM AUTHOR]

Transportation infrastructure facilitates the mobility of goods and humans. Following flooding, blocked road access would prevent vulnerable communities from accessing essential services and disaster relief resources. To reduce the impact of damaged transportation infrastructure on community lifelines, efficient infrastructure restoration is desired. Conventionally, damage identification is often performed via field inspections. However, due to the blocked road access and safety concerns, a limited amount of damage inspection data can be collected immediately following flooding. Aimed at providing a quick prediction of highway inundation status, this research proposes a novel approach that integrates geospatial correlation to address the data sparsity issue. At the core of this approach is a Bayesian generalized linear geostatistical model (BGLGM) that measures (1) correlations between highway inundation status and the associated geospatial variables (e.g., road elevation and flood depth), (2) spatially correlated residuals that cannot be explained by the geospatial variables, and (3) parametric uncertainties. To verify and validate the proposed approach, a case study on highway flood inundation in Harris County, Texas, following Hurricane Harvey was conducted. A sensitivity analysis of the model performance to the availability of damage inspection data was conducted. The results show that the proposed approach is capable of providing accurate highway inundation prediction using limited damage inspection data, which validates the concept of integrating geospatial correlation for more accurate highway inundation representation and prediction. In addition to supporting rapid damage inspection, the validation opens up possibilities for integrating geospatial correlation into machine learning and deep learning models to enhance model performance. The region-specific geospatial correlation also has the potential to recalibrate pre-trained models, improving their generalizability to other regions. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Isomorphic Mapping Reconfigurable Multi-Agent Reinforcement Learning (IM-RMARL) framework, which is suitable for decision-making scenarios in reconfigurable multi-agent reinforcement learning. This method holds promising applications in fields such as logistics transportation systems and disaster relief. Classical multi-agent frameworks typically assume that the number of agents is fixed and remains constant throughout the training process. However, in practical applications involving reconfigurable robots, the number of agents may vary over time or according to task requirements. Additionally, classical frameworks often assume easy access to abundant experience data for training and optimization. However, in reconfigurable robot clusters, this assumption may not hold true as not all combinations exist within a single episode. Our approach effectively addresses these challenges by integrating agent mapping mechanisms and similar type of intelligent agents' experience sharing mechanisms, which aid in handling dynamic agent counts and limited experience data. Our experimental results demonstrate the effectiveness of the proposed framework, the Utilization Rate of Transport Capacity of the IM-RMARL group reaches 0.82, and the Task Completion Rate reaches 0.92. • A framework study of reinforcement learning algorithms for reconfigurable robots. • Solves the modeling problem of agent split and reorganization of reconfigurable robot. • Verifies the practical application of the algorithmic framework in the field of reconfigurable robots through actual transportation experiments. [ABSTRACT FROM AUTHOR]

• DAC donors use significantly different criteria to determine eligibility for, versus the amount of, climate finance allocated. • The same criteria which determine eligibility for access, tend to limit the amount of climate finance allocated by DAC donors. • Different types of donors utilize significantly different criteria to determine the amount of adaptation and mitigation finance allocated. • SIDS need dedicated loss and damage funds because long-term adaptation funds are often diverted to immediate post-disaster recovery. • Low-income SIDS with weak capacities are typically ineligible for climate finance, excluded by complex processes, or receive meagre allocations. Empirical studies of the determinants of access to climate finance have coalesced around a broad set of independent variables, including recipient need, merit and adaptive capacity, and donor interest. This paper provides comprehensive and nuanced insights into the allocation of the varied sources of climate finance by providing answers to the following questions: (i) Do the eligibility criteria used by donors to determine which countries receive climate finance differ from the criteria used to determine the amount of finance allocated? (ii) Do the eligibility criteria and determinants of amounts allocated for mitigation aid differ from those for adaptation aid? (iii) Do different kinds of donors determine eligibility for financing and amount of finance allocated in different ways? (iv) Do SIDS receive more (or less) climate finance than is justified by their relative vulnerability and adaptive capacity? The results provide a better understanding of the challenges faced by SIDS in accessing climate financing, highlight an underemphasized bifurcation within this group of countries, and underscore the need for further exploration of suitable types of support and modalities of delivery. [ABSTRACT FROM AUTHOR]

The research uniquely focuses on advancing the field of fuzzy sets, specifically proposing a novel approach using Hexagonal Type-2 Fuzzy Variable (HT2FV), to handle higher degrees of uncertainty. Flooding, a recurrent and impactful phenomenon, necessitates a robust approach to effective disaster management. The study delves into the humanitarian supply chain dynamics during movement crises and flood catastrophes, introducing a novel methodology to harness the potential of HT2FV. Central to the paper is the innovative defuzzification concept of Critical Value (CV) reduction, strategically employed to eliminate fuzzy components within HT2FV, transforming it into crisp data. The research extends its novelty by proposing a comprehensive approach to distributing relief materials, employing a Multi-objective, Multi-level, Multi-modal (MOMLMM) approach, with a primary focus on boats for inundated areas. Significantly, the study aims to optimize the allocation of resources, minimize time and cost under uncertain environments, and enhance disaster relief operations. The paper is the first to use new hexagonal fuzzy numbers (HFNs) and focuses on how they can be used to sort vulnerable areas using the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of the Multi-Criteria Decision-Making (MCDM) process. A case study of the 2022 Assam flood is looked at and solved using the Weighted Sum (WS), Neutrosophic Compromise (NC), and Goal Programming (GP) systematically on the LINGO solver to see if the suggested method can be used. Further, sensitivity analysis is employed to establish optimal credibility levels to validate the proposed methods. A real-life case study lends practicality, substantiating the approach's efficacy. At the same time, comprehensive comparative analysis compares and contrasts findings from different studies to synthesize existing knowledge and identify future scope, contributing significantly to the field of fuzzy sets. [ABSTRACT FROM AUTHOR]

• Construct a collaborative recovery framework involving four parts: CPS dependency model, recovery model, recovery strategies, and post-recovery assessment. • In the collaborative recovery model, factors such as repair crew, repair time, and network reconstruction are considered, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. • Develop multiple recovery strategies tailored to the structure and functional characteristics of CPS components, and evaluate the impact of each strategy on the recovery process. • Analyze the recovery effectiveness of different recovery times for cyber and physical components and different recovery strategies under various IEEE systems. • The simulation results indicate that moderately reducing repair times for cyber nodes and smaller network sizes contribute to improving repair efficiency. Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency. [ABSTRACT FROM AUTHOR]

In the context of global warming, various regions have witnessed frequent meteorological disasters, and human lives are increasingly affected by these disasters. Snow disasters are among the most severe natural disasters affecting the development of agriculture and animal husbandry in the Dadu River Basin (DRB). Therefore, it is necessary to investigate the spatiotemporal variation patterns of snow disasters and comprehensively assess their risk. Based on meteorological data from 2000 to 2019, this study analyzed the spatial distribution and temporal variations in snow disaster frequency, duration, depth, and level in the DRB. We constructed an evaluation index system for the human well-being (HWB) of farmers and herders in the DRB and quantitatively analyzed the relationship between HWB and snow disasters. Furthermore, the key factors affecting the responses of farmers and herders to snow disasters were identified. The results show that the spatial distribution characteristics of snow disaster frequency, duration, mean snow depth, and snow disaster level in the DRB were relatively similar, with high values in the north and low values in the south. The northern high-value center is located in the Aba-Maerkang-Hongyuan region in the northeastern upper reaches. This region experiences the most frequent and severe snow disasters in the DRB. There were apparent interdecadal fluctuations in the frequency and duration of snow disasters, average snow depth, and snow disaster levels. Moreover, comprehensive risk zoning for snow disasters in the DRB was conducted using Geographic Information System technology. The results showed that high-risk areas were mainly distributed in the Aba-northwestern Hongyuan region of the northeastern DRB. The sub-high-risk areas were located at the periphery of the high-risk areas, including eastern Rangtang, northern Maerkang, and eastern Hongyuan. The medium-risk areas included central Rangtang and central Maerkang. In addition, Maerkang in the upper reaches of the DRB, Kangding in the middle reaches, and Shimian in the lower reaches had high HWB levels for farmers and herders during 2000–2019, exhibiting distinct administrative center characteristics. Regarding temporal variation, the HWB level in the entire DRB gradually increased. Five typical regions, namely Aba, Maerkang, Rangtang, Luding, and Danba, have been selected to explore the possible impacts of snow disasters on the HWB of farmers and herders since the 21st century. For every 1% increase in snow disaster risk, the HWB indices of farmers and herders decreased by 0.513, 0.485, 0.321, 0.064, and 0.267%. Snow disasters affect the HWB of farmers and herders primarily through direct or indirect impacts on agricultural productivity (per capita arable land area and per capita grassland area), overall labor capacity (rural population and the number of agricultural, forestry, animal husbandry, and fishery workers), per capita meat production at the end of the year, per capita value-added of the primary industry, per capita net income of farmers and herders, and urbanization rate. • The spatial distribution characteristics of snow disaster in DRB shows high values in the north and low values in the south. • Aba-Maerkang-Hongyuan region suffers from the most frequent and severest snow disasters. • Human well-being in DRB exhibited distinct administrative center characteristics. • The increase in the risk of snow disasters will lead to a decrease in the HWB. [ABSTRACT FROM AUTHOR]

The article presents a collaboration and communication architecture for disaster stressed areas where communication losses restrain disaster response and perplex rescue operations. In these areas, the fragile access networks such as cellular or Wi-Fi systems become congested or inaccessible, requiring a reliable emergency communication infrastructure. The proposed architecture involves Unmanned Aerial Vehicles (UAVs), equipped with wireless technologies i.e., 3G/4G/5G/Wi-Fi systems, and manifestly Software Defined Radios (SDRs), enabling self-forming, self-organizing, and self-healing capabilities. The architecture uses a Hybrid Connectivity Module (HCM) which empowers cooperative communication capacities and abridge the technologies to achieve reliable communication link between UAVs, mobile-to-UAV, and UAV-to-mobile towards Ground Control Station (GCS) in catastrophic areas. The seamless connectivity via next generation SDR waveforms and designed algorithms ensure an implementation of versatile multi-user UAVs network. This enables the formation of a backbone network assisting ground devices to communicate GCS. For cooperative communication, the architecture provides a MAC-centric cross-layer protocol which offers efficient multihop routing between UAVs. The data (i.e., text, images, or voice) is routed to devices or GCS via UAVs, which can notify rescue services reliably and help to trace the catastrophic zones. Furthermore, the protocol helps to allocate network resources by creating virtual sub-nets for simultaneous interference-free data transmissions which maximizes the network throughput. The experimental results show the data latency far better than any other existing ad hoc routing protocol (e.g., OLSR or AODV) based designs which usually add delays in the emergency response operations. [Display omitted] • Robust and rapidly deployable network to expedite disaster relief response. • SDR-assisted network enabling UAV-to-UAV, mobile-to-UAV, and UAV-to-mobile communications. • HCM supports 3G/4G/Wi-Fi and SDR platforms' coexistence, empowering cooperative communication capacities. • MAC-centric cross-layer protocol to achieve diverse communication requirements. • Guaranteed QoS includes low latency, low overhead, and maximum network throughput. [ABSTRACT FROM AUTHOR]

Earthquake is one of the most destructive natural disasters. Usually it is followed by a great number of human casualties, material losses and underlying consequences. Therefore organization of efficient medical aid in regions that suffer of earthquakes is an essential task for disaster medicine services. There exists a number of regulations that define the amount of medical aid needed considering the disaster extent. However, they are usually heuristic and based on the rare disasters cases and drills. What is more, they usually prescribe using only uniform medical teams and do not consider involving specialized medical teams that can be vital in certain scenarios. In this study we propose a novel approach to investigate possible medical response teams combinations in temporary disaster medical facilities. To achieve that, we model the workflow of a field hospital to investigate optimal amount and complement of required medical teams in earthquake conditions. Our simulation model considers incoming flow intensity and patients distribution over injuries extent and localization. Moreover, we compare universal and specialized medical teams to discover limits to applicability of different field hospital personnel configurations. For each modeling scenario we found optimal amounts of universal and specialized medical teams. Our findings can be used for emergency policy making to help to define a structure of required medical aid. [ABSTRACT FROM AUTHOR]

• Proposed a new and practical problem named Time Sensitive Sweep Coverage with Minimum UAVs (TSCM). • Proposed a concise and fast algorithm called GCS. • Experimental results show that the performance of GCS algorithm outperforms the best existing algorithms. The sweep coverage of UAVs can provide emergency communication support and conduct disaster surveys in emergency disaster relief. These rescue operations are extremely time sensitive. If the UAVs cannot arrive on time, the best rescue opportunity may be missed. In the case of limited resources, it's crucial to design a collaborative sweep coverage scheme for the fewest UAVs to achieve the 100% on-time coverage rate. In this paper, we first propose a novel problem named TSCM. Under the constraints of time sensitivity and the endurance of UAVs, the objective of the TSCM problem is to minimize the number of required UAVs to cover all POIs on time. We prove that the TSCM problem is NP-hard. Accordingly, we propose the GCS algorithm for the TSCM problem. Finally, we conduct extensive simulations and the experimental results demonstrate GCS can achieve the minimum number of required UAVs and improve the performance by 14% compared with the best existing algorithms. [ABSTRACT FROM AUTHOR]

• Disaster recovery and resettlement discourses and practices are purportedly secular. • In Cagayan de Oro, Philippines, post-disaster efforts had clear religious overtones. • Catholic trustees undertook disaster management roles for which they had no expertise. • They used the resettlement of survivors to render certain people and landscapes more governable. • Their actions emphasize the role of religious organizations in producing particular development subjects. In post-disaster situations where there is a weak government, opportunities arise for so-called non-state actors to re-imagine, re-create, and govern portions of society and landscapes. The process of resettling survivors is an improvement project in which certain actors and institutions assume the functions and authority of a shadow state. This paper studies the activities of Catholic trustees in disaster relief and resettlement in Northern Mindanao, Philippines after Tropical Storm Sendong, and asks how and to what ends they engage with purportedly secular humanitarian efforts. It is based on fieldwork in Cagayan de Oro, which used an ethnographic approach and qualitative methods. The intentions motivating trustees' interventions, and the selection of activities, reveal clear Christian undertones. This unsurprising finding suggests Catholic trustees "saw like a church"; there was a demonstrated intent to govern, in the Foucauldian sense of governmental rationality, guided by the exercise of old and new pastoral power, largely mirroring a Christian social mission. Through their actions, trustees (re)asserted the historical hegemonic position of the Catholic Church in Philippine society. This framing of disaster management challenges mainstream secular discourses and practices of development and disaster management and their treatment of faith-based organizations as just another development actor. [ABSTRACT FROM AUTHOR]

Drones or unmanned aerial vehicles (UAVs) are increasingly deployed for disaster relief, from aiding in drug or supply delivery to search and rescue victims. For a large-scale mission, the more drones are used, the more difficult they are to control and manage in a timely manner. Automated control is one of the central techniques used in knowledge-based drone management systems to enhance the quality and efficiency of the disaster relief mission. Although many drone management systems have been developed, automatic control of these systems remains a difficult problem. Many approaches have "human in the loop" that trades the degree of autonomy with the efficiency and perhaps accuracy. Our goal is not to fully automate the system but to reduce user interactions where intelligible (e.g., selecting appropriate drones/actions based on prioritized needs) by means of significant advances in control mechanisms. This paper presents a knowledge-based framework for intelligent drone management by explicitly representing control knowledge for use in selecting appropriate task-specific actions. The paper describes how control decisions are derived using domain knowledge relevant to drones and disaster relief. By employing a blackboard control architecture, our framework provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during the drone mission activities. The paper illustrates the use of the framework on specific scenarios. [ABSTRACT FROM AUTHOR]

Smartification of public services is being promoted, but smart public services are at significant risk of cyberattacks. One of the most common cyberattacks is a denial-of-service (DoS) attack on unknown vulnerabilities. Recovery from DoS requires responses that may incur several hours or days of downtime. However, it is essential to enhance the resilience of smart public services by minimizing downtime. This study assumes a smart disaster prevention service using MQTT brokers and aims to enhance resilience against cyberattacks by applying immunity-based attack detection, as proposed by the authors to the MQTT brokers. Our previous work has shown that immunity-based attack detection could detect attacks on some vulnerabilities of the Mosquitto broker with high detection accuracy, but it could not correctly detect attacks on use-after-free vulnerabilities. In this paper, we propose a method for detecting cyberattacks, and demonstrate the effectiveness of the method through performance evaluation. [ABSTRACT FROM AUTHOR]

We exploit the random variation in health patterns across European regions that resulted from the Chernobyl nuclear disaster to provide new quasi-experimental evidence on the causal effect of health patterns on social capital. Our instrumental variable estimations show that the radioactive fallout is positively associated with an increase in hospital discharges after treatment for neoplasms almost thirty years later, the magnitude of the effect being in line with other studies investigating the health effects of low-dose radiation exposure. An increased incidence of neoplasms in the local area of residence induced by the radioactive fallout, and the resulting perception of higher risks of contracting potentially deadly diseases among the healthy population, generates a sizeable impoverishment of social capital and a reduction in social interactions, altruism and happiness. Our findings suggest that health care and prevention policies may have additional returns in terms of a significant increase in communities' social capital. [ABSTRACT FROM AUTHOR]

In September 2017, Hurricanes Irma and Maria devastated the Commonwealth of Puerto Rico. Following these hurricanes, comprehensive and timely data collection was, and continues to be, required to assess both the severity of damage across Puerto Rico and to inform recovery and mitigation strategies. In this manuscript, we present how remote sensing data was incorporated into this assessment and planning process, focusing on the applications for Puerto Rico's natural resources in the months following the hurricanes. We first describe how different types of satellite and airborne remote sensing data, along with existing and newly developed data processing methodologies, were applied to the damage assessment and recovery planning process for three natural resource applications: terrestrial forests, landslides, and coastal systems. We show that while remote sensing data offered a critical first assessment of the damage caused by the hurricanes, it was not always easily integrated into the recovery planning process and the variable timelines required by decisionmakers. Remote sensing data remains a powerful, if sometimes underutilized, tool in immediate and long-term disaster recovery efforts, and we conclude by suggesting future areas for improvement to facilitate the integration into natural disaster planning, assessment, and response. • Hurricanes Maria and Irma severely damaged the Commonwealth of Puerto Rico. • The damage assessment and recovery process required timely assessments. • Remote sensing data provided critical information for natural resources recovery. [ABSTRACT FROM AUTHOR]

• Determining the urban critical infrastructures in the event of an earthquake. • Designing Location-Routing-Inventory decisions in humanitarian relief chain. • Simulation of interaction of urban infrastructure in earthquake conditions. • Using a cooperative game to examine the manner of cooperation of disaster relief supply chain players. • Application of the model to a real-world disaster relief logistic case study. This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators' vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance. [ABSTRACT FROM AUTHOR]

Background/purpose: Taiwan set up disaster medical assistance teams (DMATs) after the Chi-Chi earthquake, but these teams lack unified standards.Methods: This study was divided into two phases. Phase I was a Delphi study conducted in 2019 with 26 experts who were invited to establish Taiwan's DMAT standards by modifying the World Health Organization Emergency Medical Team (WHO EMT) type I fixed standards. Phase II was a cross-sectional study conducted in 2020. A questionnaire was used to evaluate the disaster preparedness of DMATs by standards set in phase I.Results: In phase I, Taiwan's DMAT standards were established after three rounds of Delphi consensus, with a response rate of 88.5%. The major departures from the WHO EMT standards were the exclusion of obstetric care, mental health, rehabilitation, and laboratory and blood transfusion standards and the addition of an ultrasound standard. During phase II, a total of 32 teams were invited, and the response rate was 96.9%. The overall standard achievement rate was 56.9%, and the three lowest achievement rates corresponded to sanitation (22.6%), medical malpractice insurance (25.8%), and pharmacy and drug supply (25.8%). The national DMATs, official DMATs, DMATs funded by government, and DMATs with ≥10 years of experience had significantly higher achievement rates for partial or overall standards.Conclusion: Using localized standards to evaluate the disaster preparedness of each team, DMATs were found to have many shortcomings mainly due to the lack of unified government announcement standards, legal protection, and adequate financial support. [ABSTRACT FROM AUTHOR]

Wheel-legged robots are a type of mobile robot that combines the advantages of wheeled robots, such as fast and stable movement and high efficiency, with the adaptability of legged robots to complex and unstructured environments. Therefore, wheel-legged robots have great potential for application in fields such as deep space exploration, disaster relief, and wilderness exploration. This paper categorizes and summarizes the structural forms and driving modes of wheel-legged robots, dividing them into three categories: wheel-legged hybrid robots, wheel-legged separation robots, and wheel-legged transformation robots based on their structural characteristics. Finally, this paper summarizes the structure and driving aspects of wheel-legged robots and provides an outlook on their development in these two areas. The research results presented in this paper help researchers understand the development process of wheel-legged robots and serve as a valuable reference for future research. [ABSTRACT FROM AUTHOR]

Railway transportation is an important channel for emergency rescue and disaster relief. Traditionally, the method of evaluating the damage degree of bridges under different seismic intensities from the perspective of structural damage cannot be directly used to judge the traffic capacity of trains on bridges after earthquakes. It is impossible to guarantee the running safety and affect the transportation of materials after the earthquake. Because the damage state of track structure directly affects the running safety of trains, this study combines the relationship between the damage of track structure and the deformation of bridge structure after earthquake, and puts forward the transverse relative displacement of girder end (TRDGE) as the damage index of train running function of railway bridge after earthquake. Then, the OpenSees-TRBF (Train-Railway-Bridge-Foundation Coupled System Dynamic Analysis Platform) co-simulation method is used to carry out the train-track-bridge coupling dynamic calculation under different seismic damage states, and the running safety analysis database under different TRDGEs is established. Then, the Particle Swarm Optimization (PSO) algorithm is used to classify the bridge damage index of traffic capacity through different target safety speeds. Based on the probabilistic seismic demand model, the exceedance probability curve of traffic capacity damage under different seismic intensities is established. Finally, based on the curve, the running safety risk domain of trains on the bridge under different seismic intensities is obtained with a confidence level of 95 %. This study proposes a post-earthquake damage assessment method for railway bridges based on the assurance of running safety probabilities, evaluating the damage degree of the bridge with the residual traffic capacity of the bridge after the earthquake. This study can be applied to assess earthquake damage on existing railway bridges and provide references for the safe speed of trains on the bridge after an earthquake. • A seismic damage assessment method for bridge based on running safety is proposed. • The seismic damage probability curve of bridge based on running safety is established. • The post-earthquake vehicle speed threshold is established. [ABSTRACT FROM AUTHOR]

[Display omitted] • The performance of the classifier will be affected by the mapping unit. • The classification performance of RF, LR, ANN, and SVM classifiers under different mapping units is demonstrated. • The SU-RF model has the highest accuracy in the susceptibility mapping of slope geological disasters. • Susceptibility assessment and dominant factor analysis of slope geological disasters in Fuyuan County. Slope geological disaster is a type of natural disaster that is prevalent in the plateau mountainous area. Its occurrence frequently brings substantial harm to the local human population and living environment. The aim of this research is to investigate the suitability of various mapping units for different classifiers in the process of creating susceptibility zoning maps for slope geological disasters and to evaluate the effectiveness of each model. Concurrently, the susceptibility map produced by the high-accuracy model will assist relevant authorities in conducting disaster management tasks and also serve as a reference for modeling the susceptibility assessment of environmental geological disasters in other mountainous regions. Fuyuan County of China was considered as the research area, based on two different mapping units and four classifiers. Nine factors leading to disasters, such as elevation and topographic relief, were chosen as the evaluation indexes. With the help of the ArcGIS platform, the zoning map of geo-logical disaster susceptibility is drawn. Ultimately, the accuracy of the evaluation results was verified by the receiver operating characteristic curve and the confusion matrix. The findings indicate that all approaches are capable of producing susceptibility maps for geological disasters. However, the outcomes from the four classifiers that utilize slope units are more precise and logical than those that employ grid units. Lithology and the water system emerge as the most significant factors causing disasters in the study area, while the influence of fault zones is found to be minimal. The integration of the slope unit with the random forest classifier achieves the highest accuracy in predictions, maximizing the capabilities of both, and presents a promising application in the susceptibility mapping of slope geological disasters. [ABSTRACT FROM AUTHOR]

This paper addresses facility location, inventory pre-positioning and allocation of emergency supplies in disaster relief logistics by taking into account both primary and secondary disasters. To characterize the uncertainty associated with post-disaster demand and resource allocation cost, this paper constructs the statistical-distance-based ambiguity sets of possible probability distributions with the Wasserstein metric, which is utilized to measure their distances from the empirical distribution. Armed with the Wasserstein ambiguity set, this paper develops a hybrid risk-averse three-stage distributionally robust chance-constrained (TS-DRCC) model for the considered problem, which measures the risk from both quantitative and qualitative aspects. When the Wasserstein metric uses the l 1 -norm, this paper reformulates the proposed TS-DRCC model as a mixed-integer linear program (MILP) based on the strong duality theory, which can be efficiently solved via CPLEX, thereby enabling decision-makers to use it. Theoretically, this paper also proves that the proposed TS-DRCC model converges to stochastic programming (SP) model as the size of historical data approaches infinity. Finally, this paper conducts a computational study of hurricane threat in the US to indicate the superiority of our proposed TS-DRCC model in terms of demand satisfaction and out-of-sample performance compared to the model considering only primary disasters and the conventional SP model, respectively. Some key managerial insights are summarized as rules of thumb to effectively guide the integrated pre- and post-disaster relief actions in the disaster relief logistics planning practice. • A location-inventory-allocation problem with secondary disaster considerations in DRL is introduced. • A hybrid risk-averse TS-DRCC model for the LIAP under demand and cost ambiguities is formulated. • The Wasserstein ambiguity sets are constructed to address the distributional ambiguities. • Extensive numerical experiments are carried out on a realistic case study. [ABSTRACT FROM AUTHOR]

More frequent extreme weather events due to climate change impose significant challenges on the post-disaster restoration of distribution systems with significant penetration of renewable generations. To address this challenge, this paper investigates a restoration scheme for distribution networks integrated with renewable generations, and transportable energy storage systems moving along a transportation network, such as railway or road network, are used to support the network restoration after the fault event. To achieve this, for given fault conditions, topological reconfiguration is first facilitated via the soft open point (SOP) technology, and local renewable generators and loads are rapidly regrouped to form microgrids for post-disaster restoration. Then, assisted with transportable energy storage, the resources within the microgrids can be dispatched more efficiently, facilitating a more efficient post-disaster recovery process. To enable a cost-effective restoration of the distribution network, an economic model is formulated with the aim of minimizing the economic losses during the post-disaster restoration process. Case studies considering a significant number of failure scenarios show that the proposed transportable energy storage-assisted restoration scheme can effectively minimize costs during the post-disaster period considering various load types across different areas. This scheme not only achieves cost-effective post-disaster restoration but also ensures compliance with the constraints of the electrical networks, achieving up to 93.3% recovery cost reduction and 74.25% increase in critical load restoration. • A post-disaster restoration scheme considering TESS, SOP and WTG is established. • Diverse failure scenarios are designed to validate the proposed scheme. • Restoration of loads with various priorities and types is comprehensively considered. [ABSTRACT FROM AUTHOR]

Post-hurricane accessibility to critical facilities is crucial for accelerating the recovery process, which can often be disrupted by hurricane-induced flooding. The State of Florida is especially vulnerable to flooding accompanied by hurricanes, which leads to significant roadway closures and accessibility challenges that require special attention. As such, this research aims to analyze changes in accessibility to critical facilities due to Hurricane Irma-induced flooding. Accordingly, this research aims to compare the average accessibility changes to critical facilities for vulnerable populations, including older people, disabled people, and older people with a disability with the general population. To accomplish these goals, flood mapping was done by analyzing Sentinel-1 images before and after Hurricane Irma (2017) using Google Earth Engine. The accessibility change from block groups to critical facilities was analyzed using a cumulative opportunities approach in GIS, considering flooded areas as the barriers, which were later aggregated to the county level. Finally, employing the population-weighted average accessibility method, a comparison of accessibility change of vulnerable and general populations was conducted. The result shows that 1193.80 square km of areas in 43 counties of Florida, mostly in the southern portions, were affected by flooding after Hurricane Irma. Later, the accessibility changes to critical facilities considering 15-min and 30-min travel time thresholds are presented and visualized. The county-wise change in accessibility of vulnerable and general populations was also computed. The knowledge acquired from this research can improve the development of efficient and equitable post-disaster recovery plans. [ABSTRACT FROM AUTHOR]

Frequent occurrence of natural disasters has become one of the most serious challenges to human society. As rural communities are the forefront of disasters, enhancing the resilience of rural community is crucial to mitigate the impact of natural disasters and seek rapid recovery. This paper provides an analytical framework for changing trajectories of the resilience of rural communities after disasters. Taking the disaster area affected by the catastrophic debris flow in China as an example, we analyze the changing trajectories of rural community resilience after disaster in three villages with different damage levels from 2010 to 2020 based on the data from key person interviews, the disaster situation and development reports, village work records and farmers' questionnaires. We found that the changes of community resilience after disaster show obvious stages due to the difference in the intensity of effects of government intervention and community self-organization ability during disaster relief reconstruction period and self-recovery period. The resilience of communities with different disaster losses showed an increasing trend. However, during the disaster relief and reconstruction period, the resilience of the severely affected community was improved faster, followed by the most severely affected community, while the resilience of generally affected community was improved more slowly. During the self-recovery period, the resilience of the affected general community was improved faster than that of the other two communities. The evolution process of community resilience reflects the increasing role of self-organizational capacity in post-disaster community development, aligning with the overall global trend in rural community governance. In addition, there is a significant sequence in the improvement speed of different dimensions of community resilience at different stages after disasters. During the disaster relief and reconstruction period, the resilience of community management was improved faster and the resilience of environment was slower. During the self-recovery period, the economic resilience of community was improved faster and the social resilience was improved slower. Finally, some suggestions were put forward to strengthen natural disasters prevention and community resilience in disaster-prone areas. [Display omitted] • Provide an analytical framework for the changing trajectories of the resilience of rural communities after disaster. • Community self-organization ability play an important role in improving community resilience. • There are significant differences in resilience of communities with different disaster losses. • There is an obvious sequence in the improvement speed of different dimensions of community resilience after disaster. [ABSTRACT FROM AUTHOR]

• A comprehensive scoping review of drone applications in disaster management. • Drone is a great tool in mapping, search and rescue, transportation and training. • Lack of data on the use of drones in disaster victim identification. • Focus more on drone assistance to humans in victim identification to prevent delay in DVI. The use of drones has rapidly evolved over the past decade involving a variety of fields ranging from agriculture, commercial and becoming increasingly used in disaster management or humanitarian aid. Unfortunately, the evidence of its use in mass disasters is still unclear and scarce. This article aims to evaluate the current drone feasibility projects and to discuss a number of challenges related to the deployment of drones in mass disasters in the hopes of empowering and inspiring possible future work. This research follows Arksey and O'Malley framework and updated by Joanna Briggs Institute Framework for Scoping Reviews methodology to summarise the results of 52 research papers over the past ten years, from 2009 to 2020, outlining the research trend of drone application in disaster. A literature search was performed in Medline, CINAHL, Scopus, individual journals, grey literature and google search with assessment based on their content and significance. Potential application of drones in disaster are broad. Based on articles identified, drone application in disasters are classified into four categories; (1) mapping or disaster management which has shown the highest contribution, (2) search and rescue, (3) transportation and (4) training. Although there is a significant increase in the number of publications on use of drone in disaster within the last five years, there is however limited discussion to address post-disaster healthcare situation especially with regards to disaster victim identification. It is evident that drone applications need to be further explored; to focus more on drone assistance to humans especially in victim identification. It is envisaged that with sufficient development, the application of drones appear s to be promising and will improve their effectiveness especially in disaster management. [ABSTRACT FROM AUTHOR]

• In water disaster, remote controlled vehicles which work for disaster recovery have to run in water environment. • Since underwater ground is likely to be soft, the vehicle has a risk of stuck. • We have to make a method for judging whether to run or not the vehicle in underwater ground. • We must quantitatively clarify the relationship between the trafficability and the strength, bearing capacity, etc. of underwater ground. In recent years, water disasters have increased in Japan. In water disaster, remote controlled vehicles which work for disaster recovery must run in water environment. Since underwater ground is likely to be soft, the vehicle has a risk of stuck. If a vehicle gets stuck at disaster sites, rescue work is difficult because it is not easily to access to that area. We must make a method for judging whether to run or not. For this purpose, we must quantitatively clarify the relationship between the trafficability and the strength, bearing capacity, etc. of underwater ground. We measured the cone index of underwater ground. From results, we confirmed that fragile layer was formed on the surface layer in underwater ground. We measured drawbar pull of a tracked carrier in test field. As a result, maximum drawbar pull of underwater ground was lower than on the ground. After slip occurs, drawbar pull of underwater ground was smaller than ground significantly. [ABSTRACT FROM AUTHOR]

Recently, microreactor designs have been receiving significant attention in the nuclear industry due to their potential advantages in certain applications. These nuclear reactor designs have been considered to provide reliable and sustainable power for on-site installation and operation. Microreactors may be utilized to provide heat and power to hydrogen production, remote communities, and industrial facilities such as military installations, disaster relief zones, and are being considered for underwater and deep space operation as well. However, these designs and concepts remain largely untested and unproven in the commercial industry. Further research and development are still required to prove microreactor designs are safe and reliable for commercial use. Different cooling technologies have been taken into consideration for microreactor concepts since the 1960s, mainly for federal space reactor projects such as LEGOLRCS, HOMER, and KRUSTY. This work provides thermal hydraulics and analysis for the Idaho National Laboratory's MAGNET (Micro-reactor Agile Non-nuclear Experimental Testbed) facility. The MAGNET facility is currently being developed to duplicate a microreactor design using heat pipe cooling technology. Our main goal is to examine the response of the test facility under steady-state and transient operation conditions. We constructed our own estimated model of the MAGNET geometry using a software coupling method made up of MOOSE/SAM software systems. The steady state results of this work have been published in a former article. The new results mainly focus on the transient states. By communicating with the Idaho National Laboratories, we upgraded the geometry of MAGNET heat pipes. This not only verifies the design of the facility under such conditions but also benchmarks the modeling capability of the MOOSE/SAM code system that can be potentially used to model other microreactor concepts in the future. • Heat pipe micro reactor could be energy source of hydrogen production. • MAGNET is currently the experimental heat pipe micro reactor facility. • Multi-physical platform MOOSE/SAM are used to conduct the calculation. • Transient cases include different heat pipe failure numbers and times. • The results explore the safety and reliability edges of the heat pipe micro reactor. [ABSTRACT FROM AUTHOR]

Natural disasters cause significant disruption in road networks, rendering many crucial links unusable. We investigate how to identify a sequence for repairing these links that minimizes total travel time over the repair horizon, given that at each repair stage road traffic distributes according to the principle of user equilibrium. We derive an analogue of Bellman's optimality principle, allowing us to solve the problem using methods of dynamic programming. We specifically develop a bidirectional search heuristic with customized pruning and branching strategies that exploit specific properties of traffic assignment. Our experiments show that our method is scalable and performs well even on networks involving thousands of links. • Bridge repair sequencing problem complexity is reduced from factorial to exponential • We design a bidirectional search heuristic using bounds based on traffic assignment. • These bounds help create specialized branching and pruning rules for the heuristic. • A beam search is further used to reduce the number of solutions which are explored. • Our heuristic search outperforms other methods and is computationally tractable. [ABSTRACT FROM AUTHOR]

For the characteristics of high frequency and strong suddenness of dynamic disasters in deep coal mines, the traditional detection and evaluation techniques applied to shallow coal mine failed to accurately judge the risk degree of disasters. Therefore, it is of great significance to use advanced detection technologies and appropriate evaluation methods to improve the accuracy and efficiency of risk assessment in the process of coal mining. The present paper applies the rapid detection and multi-source dynamic detection technologies used in the field of mining with the purpose of improving the reliability of detection technologies for typical dynamic disaster. In this study, the data fusion technology was used to analyze data obtained from laboratory experiments, engineering survey, detection and historical data, so as to form the final dynamic and static indicators. Then, the new combined evaluation models with time series of coal and gas outburst as well as rock burst were established respectively to carry out the comprehensive risk evaluation using the least-squares method and the time-varying weight method. After the comprehensive analysis on the results of the above two evaluation models, the risk areas of the typical dynamic disasters were judged and classified. Finally, the evaluation models were coded to build an early-warning software platform that could achieve automatic evaluation and the actual 3D visualization of coal mining areas. The early-warning software platform was applied to risk assessment of dynamic disasters in Gengcun Coal Mine in Yima City, Henan Province, China. The results of the 6-month experiment showed that the risk assessment accuracy and reliability of the proposed evaluation models was 100% and 90% respectively, which indicates that the newly developed approach is reliable and can be recommended for applying in more coal mines to improve the process safety risk control. [ABSTRACT FROM AUTHOR]

• Environmental justice analysis shows inequitable hurricane recovery in Puerto Rico. • Central rural communities and Black communities were last to have power restored. • EJ index construction requires contextual environmental and demographic variables. • Disaster recovery policy must incorporate justice and equity. This paper uses an environmental justice framework to explore whether existing vulnerabilities in Puerto Rico are associated with the rate of electricity restoration after Hurricane María. Based on the literature discussing the relationship between vulnerability and environmental justice, we expected that the areas identified as vulnerable to environmental injustice would recover more slowly than less vulnerable areas. We use regression analysis to analyze how well three vulnerability indices based on environmental justice variables predict electricity restoration. We also map the resulting data to spatially situate recovery patterns. This analysis produces mixed evidence of our predictions. In addition to environmental justice factors, other factors, such as terrain and proximity to electric transmission lines, also affected recovery rates, complicating the narrative of recovery. These findings suggest that policymakers seeking to mitigate vulnerability to electricity outages in the wake of natural disasters should incorporate environmental justice analysis in their recovery prioritization decisions, and that this analysis should be contextually specific to the recovery area. Our analysis also includes the construction of environmental justice indices, which have the potential to be a useful advocacy tool for communities seeking to uncover the priorities of stakeholders engaged in recovery. [ABSTRACT FROM AUTHOR]

Manoeuvrable, responsive satellite constellations that respond to real time events could provide data on demand for time-critical tasks, such as disaster monitoring and relief efforts. The authors demonstrate the feasibility of such a system by expanding on a fully analytical method for designing responsive spacecraft manoeuvres using low thrust propulsion. These manoeuvres are perceived as a graph that enables efficient exploration and optimised selection of favourable routes that achieve mission goals while highlighting resilience and redundancy in the mission's execution. A case study is presented that considers four satellites required to provide flyovers of two targets, with an associated graph of 10,839 possible manoeuvres. Investigation of the graph highlights that a good, but not minimum time, solution can allow the system to perform well, while also providing greater resilience to changes in mission priorities and errors in execution. This analytical approach enables operators to trade-off between a loss of time by using only one satellite versus the disruption of moving multiple satellites for a potentially faster response. The impact of varying mission capabilities, such as using fewer satellites, smaller swath width or less propellant, can be evaluated by reducing the graph without recalculating manoeuvre options. [ABSTRACT FROM AUTHOR]

In large-scale disasters, evacuation using public transport, such as buses, is always essential, especially with limitations on the time window and resources available. Unfortunately, a Bus Evacuation Problem (BEP) is NP-hard in nature. This means it is impossible to find an optimal evacuation plan within a reasonable time window. Therefore, it is efficacious and requisite to use an approximation algorithm such that a sub-optimal plan can be derived quickly in large-scale disasters. In the literature, an approximation algorithm for BEP has been presented. However, there is not only a lack of rigorous proof of the approximation ratio, but also the computation time of the approximation algorithm is influenced by the number of evacuees. In this work, firstly the approximation ratio is derived as 2 n b + 7 , where n b is the number of buses, which is fundamentally more precise than the existing work. Further, the approximation ratio can be reduced to n b + 6 through a new proof provided in this work. More importantly, a new approximation algorithm is proposed in this work, with an aggregated network flow model that can be quickly solved as a linear programming problem in polynomial time. The approximation ratio of the new algorithm is proven to be n b + 1 when there is a pile of buses, and the computation time is insensitive to the number of evacuees. Finally, 10 sets of random cases and a real-life disaster, the flooding in Xingguo, China in 2019 are studied to illustrate the efficiency and practical applicability of the proposed approximation algorithm. [ABSTRACT FROM AUTHOR]

• Assess national Food Supply Chain (FSC) resilience by combining production-to-supply ratios and shipping connectivity. • Analyse the UK's FSC resilience, concerning the impacts from global challenges. • Comarative analysis of four countries to appreciate diverse FSC dynamics. • Uncover strengths and vulnerabilities of FSCs of developed and developing countries. • Offered strategic insights for policymakers in managing food security and disruptions. With the development of globalisation, countries such as the United Kingdom (UK) heavily rely on shipping for food imports and supplies. With the unpredictable impacts of black swan events, such as the Coronavirus disease (COVID-19) outbreak and the armed conflict between Ukraine and Russia, which are traditional food export countries, global freight supply chains could dramatically change, which significantly affects the global food supply chain (FSC) resilience, requiring new solutions to be found. Therefore, it is vital to assess a country's food importing system by sea to ensure its FSC resilience. However, it is challenging to analyse different food supply resilience levels on a national scale given the high uncertainties in the key relevant elements (e.g. food supplies and transportation) influencing FSC resilience. This paper aims to develop a new method to assess FSC resilience at a country level by pioneering the combination of the two most relevant attributes, its production-to-supply ratio and shipping transport connectivity, used to present food supply security separately in the current literature. Within this context, food production and import distance affecting the connectivity of various food supplies are selected to estimate FSC resilience in this study. The findings, including a new index framework to assess national food resilience, significantly contribute to a country's food security and the rational development of countermeasures and policies when necessary. To demonstrate the significance of the findings, the resilience of the UK FSC is first evaluated in a real case study, followed by a comparative study with Canada, Australia, Democratic Republic of the Congo (DRC) to identify the strengths and weaknesses of the FSCs of different countries. The findings of the studies can be used to monitor FSC resilience of the countries and provide rational policies for enhancing FSC resilience. [ABSTRACT FROM AUTHOR]

An increasing number of record-breaking extreme precipitation events worldwide has had an incalculable socioeconomic impact, challenging the ability of cities to recover and reconstruct after disasters. The key to the effectiveness of post-disaster recovery and reconstruction lies in the rational investment and allocation of reconstruction funds. However, few studies have examined the effectiveness of reconstruction fund planning in improving urban recovery and reconstruction from a post-disaster indirect economic loss assessment perspective. This study uses an improved input-output model to evaluate the degree of change in indirect economic losses under multidimensional reconstruction fund planning (funds' amount, the time efficiency of investment, and funds allocation) to measure the effectiveness. We exemplify this through a case study: a record-breaking extreme urban flood and its reconstruction planning occurred in Henan Province, China, in July 2021. The results indicate that (1) under the premise of ensuring sufficient financial input, improving the input time efficiency is a decision with a higher benefit-cost ratio, which can improve the effectiveness by 140%. (2) If funds are guaranteed, improving time efficiency is better than adding extra funds, and when both reach 80%, additional funds may not further improve the effectiveness. (3) The funds need to meet at least the recovery production needs (about 45% of direct economic losses); otherwise, the funds cannot be used to improve fortifications. The study results will provide theoretical and practical support for urban decision-makers to develop efficient and rational recovery and reconstruction programs. • Multidimensional planning for post-disaster recovery and reconstruction funds should consider the amount, time efficiency, and allocation of funds. • The amount of funds and their time efficiency have a limit to their improvement. • The funds cannot be used to improve fortification before covering production recovery needs. [ABSTRACT FROM AUTHOR]

Nowadays, factors such as global climate change, environmental damage, and the impact of human activities have led to an increase in natural disasters, and the frequency of natural disaster problems is increasing, which poses a great threat to the safety of people's lives and property. The increased frequency of natural disasters has increased the need to focus on the ability to prevent and respond to disasters. The increased frequency of natural disasters has increased the need to focus on the capacity for disaster prevention and mitigation. All parties need to take appropriate measures to reduce losses and provide assistance after a disaster occurs. Among them, volunteers are a newly emerged important force for rescue, but volunteers have characteristics that normal rescue organizations do not have, such as voluntary and fragmented nature, therefore, volunteer allocation becomes an important issue today. This study tries to construct a volunteer assignment method, which takes into account several factors in the assignment, such as volunteers' own willingness to the disaster site, the competency of volunteers' various abilities, the demand of the disaster site for the task and the time satisfaction of the disaster victims, etc. L-T2FNs are introduced in the assignment process to enhance the degree of certainty; Prospect theory's value function to consider the disaster victims' psychology; This study proposes an algorithm to reduce evidence fusion conflicts. This algorithm uses a differential evolutionary algorithm based on the Dempster–Shafer theory to train the lowest conflict index (determined by K and evidence distance) for BPA fusion. Subsequently, the assignment is demonstrated by using the 7.8 magnitude earthquake in Turkey in 2023 as an arithmetic example to provide a solution to the problem of how to allocate volunteers to the appropriate disaster sites. • Propose effective methods for conflict evidence fusion based on differential evolution. • Consider volunteers' preferences, competencies and victims' satisfaction in volunteer assignments. • Use linguistic type-2 fuzzy sets and evidence theory to reduce uncertainty in decision-making. [ABSTRACT FROM AUTHOR]

In a post-disaster response operation of a severe cyclone, like Biparjoy, the transportation of humanitarian aid (HA) is the foremost activity in the aftermath of the disaster. Here, due to the ubiquity of uncertainty in such operations, we have first devised a time-sequential complex Fermatean hesitant fuzzy set (TS-CFHFS) with all-dimensional aspects such as its structure, fundamental operations, score and accuracy function. For the transportation of HA, we have developed a multi objective multi-item green 4-dimensional transportation system under the TS-CFHF configuration. In our proposed framework, the HA from both private and public sectors is transported to distribution centers (DCs). The aid was then distributed to the disaster relief shelters (DRS) established in the affected areas. We have also incorporated several significant factors that enhance its realistic portrayal, including vehicle speed, disturbance rate, and safety while transporting HAs. The first and foremost objective is to minimize transportation time (TT), for which we have incorporated the objective vehicle speed, while other objectives are transportation cost (TC), carbon emission (CE) and job opportunities (JO) in our proposed model. To obtain the optimal solution, three approaches namely fuzzy programming (FP), global criterion method (GCM) and weighted sum technique (WST) have been utilized. The proposed model has been complemented with two specific case studies based on the nature of the safety constraints by using a numerical computation along with a comparative analysis of the results obtained by three distinct approaches that may be useful to the decision-maker to make an empirical decision that would affect emergency response actions. • TS-CFHFS is proposed to tackle uncertainty and imprecision more efficiently. • The fundamental set operations, a score and an accuracy functions are developed. • A multi-objective multi-item green 4-dimensional TP under TS-CFHFS is devised. • The model is examined for post disaster relief operations in Biparjoy cyclone. • Three techniques FP, GCM, and WST are used to obtain the optimal solution. [ABSTRACT FROM AUTHOR]

[Display omitted] • An assessment system for landslide hazard has been developed, along with proposed methods to enhance the accuracy of landslide hazard assessment. • Analyzing the Distribution Pattern of Landslides in Dengfeng. • Assessment of landslide hazard risk and analysis of dominant factors in Dengfeng. Landslides pose significant threats to human lives and property. The usefulness of mapping susceptibility in predicting these events, by providing early warning and implementing preventive measures, cannot be overstated. This study relies on the well-established grid management mechanism in Dengfeng and utilizes historical data collected from field surveys. Combining multi-source heterogeneous data obtained by aerospace technology, and using random forest model and feature selection, a landslide sensitivity assessment system based on space-ground collaboration was constructed. The study categorized areas according to their susceptibility to landslides using probabilistic forecasts. Specifically, regions were classified into four susceptibility categories: very low (landslide probabilities below 0.26), low (0.26 to 0.38), moderate (0.38 to 0.50), and high (above 0.50). This resulted in a comprehensive landslide susceptibility map that was rigorously analyzed and assessed. The findings indicate that: (1) high, moderate, low, and very low susceptibility areas for landslides in Dengfeng are 127.36, 527.49, 385.67, and 184.03 km2, respectively, accounting for 10.40 %, 43.08 %, 31.49 %, and 15.03 % of the total area. (2) The spatial distribution of landslides in Dengfeng exhibits a "multi-core clustering-radiate distribution" pattern, from the peripheral areas to the center. (3) The spatial distribution of high, moderate, low, and very low susceptibility areas for landslides is primarily influenced by factors such as distance from faults, elevation, and distance from rivers. The study has constructed a landslide sensitivity assessment system based on space-ground collaboration that can provide replicable research methods, provide scientific support for disaster reduction and prevention work, and reduce casualties and property losses caused by landslides. [ABSTRACT FROM AUTHOR]

Simulated terrains can provide rich information for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. Quickly generating an accurate simulated terrain for target areas is of great importance. However, for existing data-driven terrain generation methods, it is difficult to balance modeling accuracy and the amount of data required. To overcome this gap, this study proposes a deep learning method that integrates global information and pattern features of the local terrain (IGPN) to realize terrain generation. In our proposed IGPN method, we first apply both terrain patterns (ridge line and drainage line) and elevation points as input data; thus, the terrain features (i.e., channel and peak) and elevation of the target terrain can be provided. The local information extraction module (LIEM) and global information extraction module (GIEM) are then applied to generate local and global terrain features, respectively. Thus, global and local terrain features can be supplemented, and a more accurate terrain can be generated. Experiments show that the IGPN method performs state-of-the-art terrain-generation tasks. Specifically, compared with existing terrain generation methods (IETA, SRResNet, Bicubic, Kriging, FEN, and ERFFN), the terrain generated by IGPN is closer to the real terrain and can retain more local terrain features. • Integrate terrain pattern (line) data to greatly reduce the required elevation points. • Integrate interpolation theory to supplement global information. • Combined with deep learning method, the terrain is intelligently generated. [ABSTRACT FROM AUTHOR]

Climate change and violent conflict present two of the most substantial barriers to reaching the Sustainable Development Goals by 2030. Many of the countries bearing the greatest impacts of a changing climate also grapple with protracted armed conflict. Despite this, we still have limited knowledge about how political and environmental insecurities affect trust in the institutions that are responsible for disaster management, security and conflict resolution. In this paper we explore whether being exposed to violent armed conflict is associated with low levels of political trust, and whether this can be mitigated by state action. Relying on a household survey covering some 2000 rural households in Pakistan, we investigate how state disaster response shaped trust in conflict-affected contexts in the years following the severe flooding of 2010. Our study suggest that negative implications of violence exposure on political trust can be offset by disaster relief efforts by the state, but only in instances when the government is not involved in the violent activities. Our findings contribute to our understanding of the joint impact of conflict and disasters on political trust, and importantly shows that in some instances, government interventions in a different political domain can reduce negative consequences of armed conflict on trust. Understanding the interplay of compound risks in contexts where multiple risks are occurring simultaneously is crucial because without adequate, coordinated government action, it will be impossible to protect the lives, health and livelihoods of affected people in Pakistan and beyond. • Households exposed to protracted violence in Pakistan show lower levels of political trust than those not exposed to violence. • Negative effects of violence on political trust in Pakistan can be offset by disaster relief when the government is not perpetrating the violence. • Well-distributed social protection schemes can not only restore livelihoods and assets today, but also raise political trust for the future. [ABSTRACT FROM AUTHOR]

Pāua (abalone) are a treasured natural resource that supports a recreational fishery worth $2 million annually to the local economy of the Kaikōura district in New Zealand. From 2016, the fishery was closed for 5 years in response to widespread mortality caused by co-seismic uplift in the 7.8 M w Kaikōura earthquake. The fishery re-opened in 2021 for three months with a recreational fishing allocation of 5 tonnes. We constructed scenario models informed by fishing pressure observations and show that this catch target was severely exceeded (by a factor of ∼9). We then evaluated alternative management settings involving daily bag limit (DBL) adjustments and temporal controls on the open season. Only the most drastic modelled scenarios could achieve the 5 t allocation with similar levels of daily fishing effort showing the need for a greater focus on fishing effort to achieve sustainable management. Severely reduced DBLs (1–2 pāua) were required in all scenarios suggesting a need for significant departure from this management focus since very low DBL settings force poor cost-return ratios on fishers. Instead, temporal controls that shift opening times away from high visitation periods provide a means of maintaining modest DBLs outside of longer-term protected areas. These seasonal timing strategies can also support a greater proportion of the harvest accruing to local fishers thereby influencing benefit-sharing aspects of the recreational fishery. We discuss insights for equitable solutions to potential influxes of fishing effort and highlight the opportune aspects of disaster recovery contexts for conceiving and implementing new policy directions. [ABSTRACT FROM AUTHOR]

• We develop a methodology for the integrated location and network restoration model. • This model is a two-stage stochastic program with integer recourse. • We propose an exact algorithm based on the integer L-shaped method. • This algorithm accommodates the binary decision variables in the second stage. • The solution time increases only linearly as the number of scenarios increases. Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases with several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases. [ABSTRACT FROM AUTHOR]

As subway fires can easily cause numerous casualties, this paper proposes the idea of active disaster relief, which involves ventilation, exhaust, smoke blocking and exhaust, and other facilities coordinating centralised control to assist passenger evacuation and firefighting. The ventilation and smoke exhaust method used in multilayer crossing complex subways is analysed, and a typical interchange transfer station is selected to build a numerical model. The combined control of fire smoke is analysed based on six ventilation modes, and fire dynamics simulator software is used to perform several numerical simulations. According to the simulation results, ventilation and smoke control is used in the cases of fire on the second and third floor in the underground, the airflow speed of the stairway reached 2 m/s, which can completely restrain the smoke on the fire floor. According to the characteristics of different fire sources and smoke control scenarios, a remote monitoring model of multielement information fusion, such as ventilation path, fan characteristics, smoke exhaust channel, and smoke blocking facilities, is established, and a multielement disaster information-fusion-based collaborative centralised emergency rescue platform for subway fire disaster relief is developed. Real-time disaster analysis is performed, and intelligent control of airflow and smoke exhaust in the complex form of subway-controlled smoke turbulence field is developed to facilitate safe escape and efficient firefighting and rescue of people during emergencies. [ABSTRACT FROM AUTHOR]

• A new multi-period dynamic pre-positioning of emergency supplies problem is developed for disaster mitigation. • A distributionally robust optimization approach is proposed to deal with demand uncertainty. • The computationally tractable safe approximations of ambiguous chance constraints are reformulated. • A real-world numerical study is performed to illustrate our analytical results. The pre-positioning problem is an important part of emergency supply. Pre-positioning decisions must be made before disasters occur under high uncertainty and only limited distribution information. This study proposes a distributionally robust optimization model for the multi-period dynamic pre-positioning of emergency supplies with a static pre-disaster phase and a dynamic post-disaster phase. In the post-disaster phase, the uncertain demands are time varying and have partial distribution information that belongs to a given family of distributions. The family of distributions is described by a given perturbation set based on historical information. Therefore, the proposed model forms a semi-infinite programming problem with ambiguous chance constraints, which typically would be computationally intractable. We refine the bounded perturbation sets (box, box-ball and box-polyhedral) and develop computationally tractable safe approximations of the chance constraints. Finally, a realistic application to the Circum-Bohai Sea Region of China is presented to illustrate the effectiveness of the robust optimization model. [ABSTRACT FROM AUTHOR]

• Included demolition operation in the two-stage disaster waste management system. • Developed MIP models which capture the key features of disaster waste clean-up. • Proposed three approaches to minimise total clean-up cost and time. • Provided thorough analysis of a case study for 2009 Black Saturday bushfires. • Optimised results can help decision-maker developing the waste clean-up plan. Disaster waste clean-up after large disasters is one of the core activities at the recovery stage of disaster management, which aims to restoring the normal functioning of the disaster affected area. In this paper we considered a waste clean-up system consists of (i) demolition operation, (ii) collection of waste from customer nodes to temporary disaster waste management sites (TDWMSs), (iii) processing at TDWMSs, and (iv) transportation of the waste to final disposal sites in the recovery of disasters. A multi-objective mixed integer programming model is developed to minimise the total clean-up cost and time. Three different approaches are developed to solve the problem, which are tested with artificial instances and a real case study. Results of artificial instances indicate that the models developed can be used to obtain close to optimal solutions within an acceptable computing time. Results of the case study can facilitate the decision-makers to develop the waste clean-up with minimised total cost and clean-up time by selecting the right location of TDWMSs and setting up the proper waste clean-up schedule. [ABSTRACT FROM AUTHOR]