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The Indian subcontinent is highly susceptible to natural disasters. To abate the effect of the disasters, the government emanates institutional mechanisms and strategies for disaster preparedness, mitigation and response. Although there is well-established institutional framework in India for disaster management, there is no study that proposes a disaster supply chain structure under the institutional framework and integrates information and digital technologies (IDT) of industry 4.0 within it. The present study overcomes this research gap. It introduces an IDT structure within the institutional framework of India for multi-agency information sharing, coordination and decision making. It formulates a mathematical model and analyses the impact of IDT on the total response time through three scenarios. The results of scenario analysis reveal the significance of IDT on the disaster supply chains and its capability to handle information delays and IDT failure. The present study can be adopted by disaster management institutions to construct and implement better response practices at the operational, planning and strategic levels. Furthermore, the proposed model in the study can be used for vaccination planning, which includes vaccine distribution, monitoring, regulation and effective implmentation as a response against the current pandemic situation caused due to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure. [ABSTRACT FROM AUTHOR]

Disaster operations management (DOM) seeks to mitigate the harmful impact of natural disasters on individuals, society, infrastructure, economic activities, and the environment. Due to the increasing number of people affected worldwide, and the increase in weather-related disasters, DOM has become increasingly important. In this survey, we focus on the post-disaster stage of DOM that involves response operations. We review studies that propose optimization models to supporting the following four relief logistics operations: (i) relief items distribution, (ii) location of relief facilities and temporary shelters, (iii) integrated relief items distribution and shelter location, and (iv) transportation of affected population. Optimization models from 127 articles published between 2013 and 2022, focusing on relief logistics operations during natural disasters, are categorized by disaster type and thoroughly analyzed. Each model provides a case study illustrating its application in addressing key relief logistics operations. We also analyse the extent to which these studies address the critical assumptions and methodological gaps identified by Galindo and Batta (Eur J Oper Res 230:201–211, 2013), Caunhye et al. (Socio-econ Plan Sci 46:4–13, 2012), and Kovacs and Moshtari (Eur J Oper Res 276:395–408, 2019) and the neglected research directions noted by the authors of other relevant review papers. Based on our findings, we provide avenues for potential future research. Our analysis shows a slow increase in the total number of papers published until 2018–2019 and a sharp decrease afterwards, the latter most likely as a consequence of the COVID-19 pandemic. More than half of the papers in our selection concern earthquakes while less than ten papers deal with wildfires, cyclones, or tsunamis. The majority of the stochastic optimization models consider uncertainty in the demand and supply of relief items, while some other crucial sources of uncertainty such as funding availability and donations of relief items (e.g., blood products) remain understudied. Furthermore, most of the papers in our selection fail to incorporate key characteristics of disaster relief operations such as its dynamic nature and information updates during the response phase. Finally, a large number of studies use exact commercial software to solve their models, which may not be computationally efficient or practical for large-scale problems, specifically under uncertainty. [ABSTRACT FROM AUTHOR]

We present a novel online optimization approach to tackle the ambulance routing problem on a road network, specifically designed to handle uncertainties in travel times, triage levels, required treatment times of victims, and potential changes in victim conditions in post-disaster scenarios. We assume that this information can be learned incrementally online while the ambulances get to the scene. We analyze this problem using the competitive ratio criterion and demonstrate that, when faced with a worst-case instance of this problem, neither deterministic nor randomized online solutions can attain a finite competitive ratio. Subsequently, we present a variety of innovative online heuristics to address this problem which can operate with very low computational running times. We assess the effectiveness of our online solutions by comparing them with each other and with offline solutions derived from complete information. Our analysis involves examining instances from existing literature as well as newly generated large-sized instances. One of our algorithms demonstrates superior performance when compared to the others, achieving experimental competitive ratios that closely approach the optimal ratio of one. [ABSTRACT FROM AUTHOR]

The present study seeks to systematically describe how humanitarian aid organizations use visuals in their natural disaster-related social media messages and to analyze their effects on social media engagement. Using Rodriguez and Dimitrova's (2011) four levels of visual framing, we performed a content analysis of 810 tweets from 38 aid organizations. The results showed that, overall, the organizations' visuals had an emphasis on victims and on disaster relief efforts. The most effective types of visual framing, however, were not those the aid organizations most commonly used. We discuss the theoretical and practical implications. [ABSTRACT FROM AUTHOR]

The Fundão dam collapse in 2015 resulted in the largest environmental disaster in Brazil's history with the release of tons of ore tailings mainly in aquatic environments. Amphibians are susceptible to contamination and they typically develop in aquatic and terrestrial habitats, making them useful indicators of environmental impact. Nonetheless, the effects of mining on amphibians are much overlooked compared with other waterborne organisms. We examined the impact of the disaster on the generalist treefrog Boana albopunctata (Spix, 1824) by measuring fluctuating asymmetry (FA) and liver mass in specimens collected in southeastern Brazil, before the disaster (2014) and at two time points after (2018 and 2022). Specimens collected in 2022 had lower FA levels in their tympanic area, which indicates the selection of individuals with more symmetrical eardrums across generations. We found liver hypertrophy after the accident, indicating the resilience of this biotransformative organ after heavy metal exposure but with likely physiological damage. This study is the first to demonstrate the impact of the Fundão dam rupture on an amphibian population living in the affected area. Our findings highlight the diverse impacts of mining on organisms and suggest that studies with amphibians can contribute in comprehending the magnitude of this and similar disasters. [ABSTRACT FROM AUTHOR]

Heavy‐duty hexapod robots have impressive stability, high load‐bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming‐based Contact Force Distribution (DQP‐based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods. [ABSTRACT FROM AUTHOR]

According to the written records and scores of grain harvest in the official historical documents of the Qing Dynasty in China, the spatial-temporal distribution and the impact caused by the 1759 AD drought in Northwest China were reconstructed, and the response of Chinese society to the drought at that time was summarized. In the spring and summer of 1759, vast areas of northern China suffered from drought, of which that experienced in the northwest region was the most serious. Starting from 27 April 1759, droughts covered Zhili, Shanxi, Shaanxi, and Gansu provinces, and the droughts in Gansu and northern Shaanxi provinces lasted until 23 July 1759. This severe drought caused the worst crop failure in Northwest China during the period 1730–1900. By implementing a series of disaster relief measures, the Qing government managed to ease the adverse societal impact of the drought in the summer of 1760. Among the relief measures, tax reduction and exemption in disaster-stricken areas, grain storage in the northwest region, and bumper agricultural harvests in Henan, Shandong, Jiangsu, and Anhui provinces in 1759 were the main reasons for the rapid recovery from the drought impacts. With better climatic conditions in the 18th century, China had higher agricultural harvest levels in the 18th century than in the 19th century. Favorable financial conditions increased drought relief efforts, which was the background for the greater societal adaptability to the climate disaster of 1759. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) offer a decentralized communication solution ideal for infrastructure-less environments like disaster relief zones. However, their inherent lack of central control and dynamic topology make them vulnerable to attacks. This paper examines the impact of various attacks on mobile nodes within two network types: randomly and uniformly distributed stationary networks. Four types of attacks are investigated: delay, dropping, sinkhole (alone), and a combined black hole attack (dropping + sinkhole). The effects of these attacks are compared using the packet delivery ratio, throughput, and end-to-end delay. The evaluation results show that all single attacks negatively impacted network performance, with the random network experiencing the most significant degradation. Interestingly, the combined black hole attack, while more disruptive than any single attack, affected the uniformly distributed network more severely than the random network. [ABSTRACT FROM AUTHOR]

The devastating Chichi earthquake in Taiwan in 1999 highlighted the importance of disaster prevention and relief operations. In response to the challenge of extreme events and compound disasters, the National Science and Technology Center for Disaster Reduction (NCDR) contributes to operations at the Central Emergency Operation Center (CEOC) of Taiwan by providing integrated information and timely suggestions, and it delivers a common operational picture to central and local governments through a decision support system. This study was undertaken to explore the emergency response towards the reconnaissance of earthquake disasters in Taiwan to illustrate how resilient cities ensure rapid crisis response with a focus on the NCDR disaster response decision support system. This study examines the decision support system used following the 2016 ML 6.6 Meinong earthquake. The results demonstrate that the first line of relief for the emergency response commander is data-driven decision-making. [ABSTRACT FROM AUTHOR]

Background: Embankment dams were built south of the Laizhou bay in China for controlling storm surge disasters, but they are not enough to replace coastal forests in protecting the land. This study was designed to evaluate the effects of embankment dams on natural forests dominated by Tamarix austromongolica and test whether the dam-shrub system is a preferable updated defense. Methods: Coastal forests on two typical flats, one before and one behind embankment dams, were investigated using quadrats and lines. Land bareness, vegetation composition and species co-occurrence were assessed; structures of T. austromongolica populations were evaluated; and spatial patterns of the populations were analyzed using Ripley's K and K1,2 functions. Results: In the area before embankment dams, 84.8% of T. austromongolica were juveniles (basal diameter ≤ 3 cm), and 15.2% were adults (basal diameter > 3 cm); behind the dams, 52.9% were juveniles, and 47.1 were adults. In the area before the dams, the land bareness was 13.7%, four species occurred, and they all were ready to co-occur with T. austromongolica; behind the dams, the land bareness was 0%, and 16 species occurred whereas they somewhat resisted co-occurrence with T. austromongolica. In the area before the dams, the T. austromongolica population was aggregated in heterogeneous patches, and the juveniles tended to co-occur with the adults; behind the dams, they were over-dispersed as nearly uniform distributions, while the juveniles could recruit and were primarily independent of the adults. These results indicate that the T. austromongolica species did not suffer from the unnatural dams, but benefited somehow in population expansion and development. Overall, the T. austromongolica species can adapt to artificial embankment dams to create a synthetic defense against storm surges. [ABSTRACT FROM AUTHOR]

The Indo-Pacific Region, a hotbed for geographical and hydro-climate disasters such as cyclones, tsunamis, floods, and typhoons, presents both direct and indirect threats to human and national security. This research seeks to bolster both forms of security by enhancing the management of these natural disasters within the region. Employing a qualitative research method, this case study is both explanatory and analytical. Data, collected through primary and secondary resources, is presented in both quantitative and qualitative forms. The research is framed from the perspective of the human security approach. The Sri Lanka Navy and Coast Guard, as the agencies responsible, strive to mitigate the effects of these disasters and provide humanitarian assistance within their jurisdictions. Their participation in humanitarian assistance and disaster relief (HADR) exercises with regional and international actors underscores their commitment to human and national security. [ABSTRACT FROM AUTHOR]

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The micro crawling robot exhibits significant potential applications in various fields such as fault detection, disaster relief, and environmental monitoring. This article introduces a high‐performance millimeter scale crawling robot driven by a miniature electromagnetic linear actuator (MELA) with a body length of 5–6 mm and a mass of 80 mg. In this article, the working principle of the micro robot is analyzed and validated, and the influence of current, frequency, and angle α between the direction of actuator's force and the crawling surface on the crawling speed are analyzed through experiments. Results show that the optimal α ranges from 50° to 55°, and a specific current and frequency are identified to achieve maximum crawling speed for robot with particular α. When α is 50°, with a current of 300 mA and a frequency of 200 Hz, the robot reached the maximum speed of 20.2 Body Length s−1(BL s−1). The proposed robot can crawl at 12 BL s−1 with a load of 110 mg, and support a maximum load of 400 mg. Additionally, the robot demonstrated diverse capabilities such as climbing on a 10° slope with a load of 110 mg, jumping on a 1 mm obstacle, and crawling on surfaces of various materials. [ABSTRACT FROM AUTHOR]

Disaster management in Pakistan has been merely focusing on managing medical, food, and rehabilitation resources, but not so much on the humanitarian ethics during the relief efforts. The National Disaster Management Framework in Pakistan focuses on documenting the ethical approaches but fails to practically incorporate them when administering relief and recovery services in Pakistan. There is not so much knowledge, awareness, and practical implications about standards of conduct, which could guide the disaster management team and social service providers about actions and behaviors conforming to morals and professional values throughout the disaster recovery phase. This study is one of the first to explore the ethical issues faced by disaster survivors after relief providers respond to their needs in the posthumanitarian fields of Pakistan. It aims to identify the gaps that exist in ethical practices required in disaster management, particularly in the relief and recovery stages of humanitarian crises in Pakistan. This study gathered data from flood victims in the Pakistani cities of Sambhrial, Hafizabad, and Rajanpur. The data collection activities were funded by the Humanitarian Innovation Initiative, Brown University, and conducted in collaboration with the Pakistan Red Crescent Society (PRCS). Our results indicate that relief workers are trained technically but not on the humanitarian ethics in terms of handling humanitarian populations on equitable and rights-based disaster management grounds. [ABSTRACT FROM AUTHOR]

Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective. [ABSTRACT FROM AUTHOR]

Supply chain planning during disasters can be challenging due to uncertainty in demand and travel time, leading to limited stocks and delivery delays. While previous studies have focused on network planning for disaster relief supply chains under uncertainty, they have not fully integrated all network components while considering various potential factors. This integration is crucial for successful humanitarian relief operations. To address this issue, we propose a comprehensive model using a two-stage mixed-integer stochastic linear programming. The model incorporates facility location, pre-positioning, direct allocation, and multi-depot vehicle routing under demand and travel time uncertainties while examining multi-echelon, multi-commodity, response deadlines, and deprivation costs. We also create an improved random forest algorithm to enhance the accuracy of demand and travel time forecasts. To obtain accurate information for effective decision-making, we develop a data-driven, exact algorithm by combining an improved random forest algorithm and Benders decomposition. Computational experiments show that our proposed algorithm outperforms the L-shaped method in finding a better solution with less running time. We provide a real case to validate our model and algorithms. Our model and solution scheme can help improve efficiency and timeliness while minimizing deficiencies in disaster relief efforts. [ABSTRACT FROM AUTHOR]

With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience. [ABSTRACT FROM AUTHOR]

Summary: Mobile Ad Hoc Network (MANETs) are indeed autonomous, fast‐deployable wireless networks that are ideal for communications in areas with limited radio infrastructure, outdoor events, military operations, and disaster relief. But the primary problem is with routing because of the nodes' mobility. This paper proposes a cluster‐based routing under energy prediction via deep learning techniques. Here, proposes a new model termed as Concatenation of Convolutional with Max‐Avg Pooling layer in Deep Convolutional Neural Network (CCMAP‐DCNN) for energy prediction, in which additional layers are inserted into the extant DCNN structure to ensure the effectiveness of energy prediction. As a result, clusters are created by grouping the nodes together. Constraints like energy, trust, distance, and latency are taken into account while choosing the cluster leader. For cluster head selection and optimal routing, proposes a new hybrid Namib Beetle Upgraded Jellyfish Search Optimization (NBUJSO) algorithm that utilizes the NBO strategy to the JSO algorithm making the selection process more optimal. The best route is then chosen by the optimum routing method, which takes into account variables like network quality and mobility to send data packets from source to destination as efficiently as possible. Finally, the data aggregation process is followed for eliminating the redundant transmission. [ABSTRACT FROM AUTHOR]

Motivation Purpose Approach and methods Findings Policy implications Qatar's humanitarian role in Afghanistan, a less‐explored example of a non‐traditional actor, is crucial in understanding international responses to geopolitical crises. Examining Qatar's involvement provides insights into the complex dynamics of humanitarian assistance, diplomatic efforts, and post‐crisis challenges.The study examines Qatar's multifaceted role in Afghanistan since 2021, highlighting its impact as a non‐traditional actor in complex humanitarian operations and diplomacy.The research combines a desk review of online sources, academic literature, reports, non‐governmental organization documents, and media articles with first‐hand observations and informal stakeholder interviews. It also analyses international aid statistics from the United Nations Office for the Coordination of Humanitarian Affairs' Financial Tracking Service and the Organisation for Economic Co‐operation and Development's Development Assistance Committee.Qatar emerged as a key humanitarian player in Afghanistan, adopting a whole‐of‐society approach. Its efforts encompassed emergency relief, evacuations, educational support, and humanitarian diplomacy, effectively bridging the Taliban with international actors after the takeover.This research highlights the impactful role of non‐traditional donors, as seen with Qatar in Afghanistan, emphasizing their potential in complex humanitarian scenarios. The study advocates recognizing and leveraging the unique capabilities of smaller states and non‐traditional actors in humanitarian responses, promoting capacity building to harness their institutional agility and societal resources effectively. [ABSTRACT FROM AUTHOR]

The alarming rise in occurrences of disasters, along with the positive development of corporate social responsibility (CSR), has led to the growing need for and involvement of businesses in disaster relief. However, this involvement differs greatly across organisations, and the fragmented research that exists has not offered an understanding of these differences and how they affect disaster relief. This study provides a comprehensive model of companies' involvement in disaster relief by integrating two disaster relief frameworks (activities and timing) into two CSR frameworks (motivation and employee engagement). The result is the MATE Model of four inextricably linked dimensions (motivation, activities, timing, and employee engagement), examined in a qualitative study with 57 interviewees across 34 organisations in Australia. The resulting Corporate Involvement in Disasters Model details the four MATE dimensions and categorises three approaches to corporate involvement in disaster relief: reactive, relational, and comprehensive. This model details a roadmap for effective business involvement in disaster relief. [ABSTRACT FROM AUTHOR]

This paper investigates the role of Islamic faith‐based organisations (FBOs) in Indonesia and examines the way in which their disaster recovery aid can be successful or less successful depending on social capital formation in communities affected by a disaster. The paper argues that Islamic FBOs play a prominent role in disaster‐affected communities by building new social capital or strengthening existing social capital. Failure to do so may affect a community's recovery and its long‐term resilience. Applying a framework that considers three types of social capital—bonding, bridging, and linking—from a comparative perspective, the paper discusses two cases of disaster recovery: one following the earthquake that struck Aceh in 2013; and the other after the Mount Kelud volcanic eruptions in East Java in 2014. In both instances, the findings highlight the importance of the village facilitator, cultural sensitivity, and understanding of local indigenous and religious practices for successful disaster recovery. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]

Emergency material security is the key to post-disaster emergency relief in complex environment engineering construction projects. Aiming at the emergency logistics center site path planning problem, the uncertain parameters of congestion time and maximum rescue time are described using trapezoidal fuzzy numbers, and the emergency logistics site path model of the two-phase engineering construction project is established. A quantum particle swarm optimization model is designed to avoid premature population convergence and maintain population diversity. The particle swarm algorithm uses the quantum spin gate's rotation angle update to represent the particle velocity update. Taking the engineering construction project in the western mountainous area as an example, the validity and applicability of the model are verified through model comparison and sensitivity analysis. The results of the example show that in the complex environment, the quantum particle swarm algorithm takes 29.35 s in convergence time, and the particle swarm algorithm takes 40.12 s, which is 36.69% higher than the efficiency of PSO. In total cost, the quantum particle swarm algorithm has a total cost of 46,632.40 RMB, and the particle swarm algorithm has a total cost of 48,319.51 RMB, which is 3.62% lower than the cost of PSO. The quantum particle swarm algorithm's total distance is 550.57 km, and the particle swarm algorithm's total distance is 579.35 km, 5.23% lower than the total distance of PSO. The model established in this study can scientifically select the location of emergency facilities and formulate the emergency rescue path, reduce the response time of emergency rescue and storage costs, and provide decision support for the scheduling of emergency supplies for engineering construction projects in complex and dangerous areas. [ABSTRACT FROM AUTHOR]

Introduction Many countries around the world employ defense capabilities in support of global health engagement (GHE) through bilateral and multilateral organizations. Despite this, there does not appear to be a strategic approach and implementation plan for U.S. DoD GHE in support of and through multilateral organizations. The purpose of this research is to identify which security multilateral organizations are engaged in GHE, as well as how and why. These findings could inform an interoperable approach for doing so going forward. Methods A systematic review was conducted to develop a list of multilateral security organizations and agreements which engage in GHE, or could potentially play a role in GHE. Results Of the 3,488 agreements and organizations identified, 15 met the inclusion criteria. Among them, 87% (13/15) of the multilateral organizations are regional and 13% (2/15) are international, all established between 1948 and 2020. The 15 organizations cover all DoD Geographical Combatant Commands. Among them, 20% (3/15) are a legally binding alliance, 73% (11/15) have a treaty, and 7% (1/15) have a diplomatic partnership. Twenty percent (3/15) have an explicit intent to improve health in either their mission statement or as part of their goals, priorities, and/or objectives. Eighty percent (12/15) engage in at least two GHE domains outlined in DoD Policy, 67% in three (10/15), and 47% in all four (7/15). The most common domain is humanitarian assistance and foreign disaster response at 100% (15/15) and least common is Nuclear, Chemical, and Biological Defense Programs at 53% (8/15). Conclusions Although there is high demand for GHE, resourcing to enable implementation has not been prioritized. Therefore, multilateral organizations continue to support what is funded (e.g. disaster response) versus prioritizing capacity building or modifying authorities and appropriations to match demand. It is also worth noting most organizations included in this review support the European theater aligning to historical defense priorities, versus emerging threats in the Indo-Pacific region. Identifying a forum within these multilateral institutions to convene GHE policy makers and practitioners is a logical next step. The forums could guide and direct priorities, devise solutions, and implement best practices. Near term efforts could include GHE financing, governance, assurance, and technical assistance within and across multilateral institutions. Recent efforts highlight growth in both interest and action to support the variety of GHE activities regionally and internationally. As the United States seeks to reinforce multilateral institutions and uphold the international and rules-based order, employing GHE through multilateral cooperation could buttress efforts. Now is a perfect time given the sustained interest in global health, amplified value of allies and partners, and renewed emphasis placed on multilateral cooperation for the DoD to design a multilateral GHE strategy and seek Congressional support to resource it accordingly. [ABSTRACT FROM AUTHOR]

Humanitarian practitioners, specifically emergency aid and relief workers, disaster responders, social workers, and crisis clinicians, are at elevated risk of experiencing trauma as a result of their professions. Approaches for responding to the silent pandemic of reactive traumatic stress, including secondary traumatic stress and vicarious traumatization among humanitarian practitioners continue to be inadequate. The current model of self‐care, emphasizing internal regulation and processes to promote positive health, wellbeing, and the ability to continue to support others, is insufficient to best equip humanitarian practitioners with protective factors to stave off reactive traumatic responses to their work. We therefore propose our theory to extend the current self‐care model to include the external focus of compassion resilience and conclude with recommendations. [ABSTRACT FROM AUTHOR]

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes. [ABSTRACT FROM AUTHOR]

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem's convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Internet of things have been widely used in military and civilian fields such as rescue, disaster relief, urban planning. Positioning service is the core technology for UAVs to perform various tasks. However, the UAV may be attacked by external conditions, resulting in its inability to obtain self-location information during mission. For the positioning problem of UAV signal interference, this paper proposes a cooperative positioning of UAV based on optimization algorithm. In order to solve the difficulty of UAV positioning, we propose the following solutions. Firstly, we construct different numbers of beacon nodes by using the flight information of UAVs in different cycles. Secondly, the unknown number of the positioning to be solved of the UAV is reduced to improve the accuracy and speed of the subsequent optimization algorithm. Thirdly, A multi-objective optimization model is established of the UAV motion parameters under inequality constraints. And we utilize a penalty function to convert the optimization model into a minimal value solution problem under no constraints. Finally, the positioning results of each UAV are obtained by the optimization algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, unmanned aerial vehicles (UAVs) have achieved rapid development due to their flexible flight modes and broad application prospects, which have played an important role in scenarios such as aerial photography, unmanned mapping, agricultural plant protection, and power inspection. At the same time, as the frequency of global forest fires is increasing year by year, traditional monitoring and search and rescue methods have little effect. People have begun to consider the introduction of UAVs for forest monitoring and disaster relief. However, in practical applications, when a single UAV performs tasks, there are problems of limited energy, poor robustness, and easy failures that affect the execution efficiency of global tasks. Therefore, it is necessary to obtain the deployment plan of the UAVs offline in advance, and then fine-tune the position of the UAVs online. This paper focuses on the solution of UAV deployment model offline. In complex forest scenarios, due to terrain and signal coverage issues, we need to optimize this when deploying UAVs. At the same time, our goal is to find the location of deployable UAVs in the forest area to maximize the global coverage area of UAVs. Therefore, we divide the forest areas where UAVs need to be deployed and perform a grid modeling on them. On the basis of grid modeling, an Adaptive Multiple Pruning Search Method (AMPSM) is proposed to solve the global UAV deployment and maximum coverage area. In our experiments, we conducted a comparative analysis with the geometric solution to solve the maximum coverage area, which proved the feasibility of the model and the method. The results show that in the complex forest environment, our research can meet the requirements to a great extent, which can be extended to more application scenarios. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a new paradigm for intelligent mobile devices to collect and share various types of sensing data in the urban environment. The recent rapid development of unmanned aerial vehicle (UAV) technology facilitates the realization of crowdsensing because UAVs have high-efficiency mobility in the urban environment and have been used in various areas of aerial photography, agriculture, plant protection, express transportation, disaster relief, and so on. However, for UAVs, one of the key issues for the archival of efficient crowdsensing is task allocation, which must balance the task quality and cost. This paper first proposes a mathematical model for task allocation for UAVs in crowdsensing. Then, for the effectiveness of data sensing, three algorithms are proposed to allocate tasks for the purpose of minimizing the incentive cost while ensuring the quality of sensing data. The proposed algorithms include the minimum cost first (MCF) algorithm, which assigns a high priority to UAVs with the lowest cost, the maximum ratio first (MRF) algorithm, which assigns a high priority to UAVs with a high ratio of sensing quality and sensing cost, and a genetic algorithm-based one (GA-TA), which comprehensively considers the factors such as UAV sensing quality, sensing cost, and execution ability. Experimental results show that, compared with MCF and MRF, GA-TA achieves the lowest total sensing cost, average task cost, and total moving distance of UAVs, and the highest average contribution of UAVs. Considering all factors, GA-TA is the best task allocation algorithm for UAVs in crowdsensing on average. [ABSTRACT FROM AUTHOR]

Objective: Disasters in developing countries result in higher human and financial losses compared to global standards, with the death rate being 12 times higher than that of developed countries. Many experts attribute the failures in disaster management to the lack of a system for documenting and analyzing disaster management functions and not leveraging the experiences and lessons learned. This study employed a qualitative data collection approach, utilizing semi-structured interviews with managers, deputies, members of operational teams, and individuals affected by the disaster in the area. This research aims to explore the challenges, strengths, and lessons learned from the response to the Khoy earthquake in Iran. Results: After conducting 40 interviews and achieving data saturation, we extracted experiences and lessons learned to investigate the performance of responsible organizations in the 2022 Khoy earthquake. The obtained data were categorized into 8 categories and 39 sub-categories. These categories encompassed warning and calling forces, disaster assessment, disaster commanding, emergency housing, supply and distribution of items, organization, and guidance of public participation and charities, psychological support, logistics operations, monitoring, evaluation, documentation, information dissemination, and media management. Planners and operational managers can use the findings to review and revise their action and prevention plans. [ABSTRACT FROM AUTHOR]

This paper presents a proposed information and communication technology-based system that uses a crowdsourcing model to collect and provide accurate and up-to-date information about flooded areas. The system aims to assist relief organizations to act more efficiently following a flood disaster. The system collects data related to four informational requirements: people and animals, facilities for living, medical facilities, and shelters and roads. The proposed system includes a malicious user detection algorithm to prevent inaccurate information and keep the data current. The paper also introduces an information aggregation algorithm and a user reputation score algorithm to identify high-scoring users. The three proposed algorithms are assessed using simulation, which shows that they can accurately identify malicious users and rank non-malicious users. By providing up-to-date information from flooded areas, the system can help relief organizations respond more effectively to a flood disaster. [ABSTRACT FROM AUTHOR]

Several major industrial disasters happen each year around the world. They usually involve limited accessibility, poor ground conditions, and toxic wastes. As a consequence, this reduces the efficiency of humanitarian operations. In such a context, flying drones may be a viable alternative: faster, no dependency on ground conditions, and larger areas scanned. They are also better suited for following the population and the crisis dynamic. For such a purpose, various issues have to be addressed such as defining and optimizing the drone's routes, their energy consumption, choosing the relay points for recharging equipment, among others. In this study, several additional features from existing works are considered: first, a probability of identifying individuals is defined. Thus, each node can be scanned several times in order to improve the observation. In addition, the nodes are prioritized according to a given heatmap. The probabilistic drone routing problem (PDRP) consists of finding a route, that is, a sequence of trips, for each drone such that the sum of the expected number of identified individuals on all routes is maximized. Constraints on energy consumption, collision avoidance and drone‐base assignment are considered. We propose a heuristic and metaheuristics based on the adaptive large neighborhood search for the PDRP. The methods are tested on theoretical instances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in order to analyze the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The western region of Sichuan Province, located in the Hengduan Mountains, is one of China's most seismically active zones. Due to limited land resources, many villages in western Sichuan Province are situated in mountainous and valley areas, facing harsh climatic conditions, lagging economic development, and insufficient infrastructure. After experiencing seismic disasters, these villages often encounter challenges, such as cut-off mountain roads, difficulties in delivering relief supplies, and sharply a significant drop in nighttime temperatures due to the high-altitude climate. Consequently, in the case of the economic underdevelopment and limited resources, how to repurpose existing buildings for disaster relief has become a crucial issue for disaster prevention and mitigation in these villages. This paper takes Ganbao Tibetan Village, located in the alpine gorge regions of western Sichuan Province, as a case study. It evaluates the interior space utilization status of different types of existing public buildings during disasters, calculates the effective utilization rate of interior space disaster reduction conversion based on the spatial layout model, and extracts the typical interior space dimensions that are conducive to disaster relief and conversion of public buildings based on the interior space utilization characteristics of disaster relief. This study provides a reference for the design of public service buildings considering the requirements of disaster resilience transformation. [ABSTRACT FROM AUTHOR]

Disaster management is a complex problem demanding sophisticated modeling approaches. We propose utilizing a hybrid method involving inverse optimization to parameterize the cost functions for a road network's traffic equilibrium problem and employing a modified version of a two-stage stochastic model to make protection decisions using the information gained from inverse optimization. Inverse optimization allows users to take observations of solutions of optimization and/or equilibrium problems and estimate the parameter values of the functions defining them. In the case of multi-stage stochastic programs for disaster relief, using inverse optimization to parameterize the cost functions can prevent users from making incorrect protection decisions. We demonstrate the framework using two types of cost functions for the traffic equilibrium problem and two different networks. We showcase the value of inverse optimization by demonstrating that, in most of the experiments, different decisions are made when the stochastic network protection problem is parameterized by inverse optimization versus when it is parameterized using a uniform cost assumption. We also demonstrate that similar decisions are made when the stochastic network protection problem is parameterized by inverse optimization versus when it is parameterized by the original/"true" cost parameters. [ABSTRACT FROM AUTHOR]

Incorporating cloud-based algorithms for disaster recovery (DR), it explores data replication, failover, virtual machine (VM) migration, and consistency algorithms. These algorithms play a pivotal role in safeguarding data and system continuity during unforeseen disruptions. Data replication ensures redundancy, failover algorithms swiftly transition to backup resources, VM migration facilitates resource optimization, and consistency algorithms maintain data integrity. Leveraging cloud technology enhances the effectiveness of these algorithms, providing robust DR solutions critical for business continuity in today's digital landscape. The recent growth in popularity of internet services on a massive scale has also raised the demand for stable underpinnings. Despite the fact that DR for big data is frequently overlooked in security research, the majority of existing approaches use a narrow, endpoint-centric approach. The significance of DR strategies has grown as cloud storage has become the norm for more data. But traditional cloud-centric DR techniques may be expensive, thus less expensive alternatives are being sought. There is persistent concern in the information technology (IT) community about whether or not cloud service providers (CPs) can guarantee data and service continuity in the event of a disaster. [ABSTRACT FROM AUTHOR]

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively. [ABSTRACT FROM AUTHOR]