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We consider multi‐period blood supply chain network design in disaster situations that involve blood donor groups, permanent and temporary blood collection facilities, blood banks, and hospitals. We use a discrete scenario set to model the uncertain blood supply and demand, and the unforeseeable disruptions in permanent blood collection facilities, blood banks, and road links arising from a disaster, where instead of complete failure, disrupted permanent blood collection facilities and blood blanks may only lose part of their capacities. To design a reliable blood supply network to mitigate the possible disruptions, we present a two‐stage adaptive robust model that integrates the location, inventory, and allocation decisions incorporating a blood sharing strategy, where blood can be delivered from a disrupted/non‐disrupted blood bank to disrupted blood banks to enhance the flexibility of the relief network. For this novel problem, we devise an exact algorithm that integrates column‐and‐constraint generation and Benders decomposition and introduce several non‐trivial acceleration techniques to speed up the solution generation process. We conduct extensive numerical studies on random data sets to evaluate the algorithmic performance. We also conduct a case study in Tehran to demonstrate its real‐life applicability and examine the impacts of key model parameters on the solutions. The numerical results verify the benefits of our model over typical benchmarks, that is, deterministic and stochastic models, and the superiority of our solution algorithm over the CPLEX solver and two well‐known solution approaches, that is, column‐and‐constraint generation and Benders decomposition. Finally, based on the numerical results, we derive managerial insights from the analytical findings. [ABSTRACT FROM AUTHOR]

Motivation: 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. Purpose: 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. Approach and methods: 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. Findings: 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. Policy implications: 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]

In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters. [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequency, intensity, and duration of heatwave events have markedly increased, bearing profound implications for both natural ecosystems and human societies. To effectively cope with this challenge, it is imperative to accurately identify and comprehensively assess the risks posed by heatwaves. This study undertakes a systematic approach and robust methodology to assess heatwave risks by leveraging a diverse array of data sources—encompassing remote sensing, statistical analyses. The methodology integrates the risk triangle theory alongside established risk assessment frameworks laid out by the Intergovernmental Panel on Climate Change (IPCC). Employing a three‐dimensional evaluation framework encompassing hazard, exposure, and vulnerability, we unravel spatial–temporal patterns, high‐risk zones, and dominant dimensions of heatwave risks contributing to potential disasters. Results indicated that during 1999–2008, roughly 27% of the study areas were affected by high and above risk levels of heatwaves, and the areas with high and very high hazard, exposure, and vulnerability accounted for approximately 19.5%, 10%, and 32.5%, respectively. During 2009–2018, the proportion of areas with high and very high risk, hazard, and exposure increased to about 31%, 26%, and 14%, respectively, while the percentage of areas with high and very high vulnerability decreased to about 24.43%. Notably, Xinjiang and the western part of Northwestern China are characterized by hazard‐dominant conditions, while Southern China's risk profile has shifted from 1999–2008 to 2009–2018 from high hazard and vulnerability conditions to a more complex interaction involving hazard, exposure, and vulnerability. Moreover, Northern China and the northern segment of Southwestern China exhibit simultaneous high‐risk rankings across hazard, exposure, and vulnerability dimensions, forming a comprehensive high‐risk zone. These findings characterize heatwave risk patterns and offer critical insights for risk management decisions, guiding effective disaster prevention and relief measures to ensure socio‐economic stability and public health. [ABSTRACT FROM AUTHOR]

Summary: High‐throughput satellites play an important role in emergency disaster relief, maritime, and other fields. A new generation of high‐throughput satellites with large deployable antennas and broadband beamforming networks, namely, very high‐throughput satellites (VHTS), is developing towards hundreds, even thousands of extremely narrow beams with Tbps capacity, which puts forward higher requirements for satellite pointing and system construction costs. In order to solve the problem that those traditional beam pointing measurement and calibration algorithms are difficult to apply or the performance is limited, this paper builds a service beam pointing measurement and calibration architecture. A user terminal‐assisted beam pointing measurement algorithm based on the Gauss‐Newton method is proposed for the general case, which can effectively reduce the construction cost of onboard and ground pointing measurement system, and improve the measurement accuracies of three axes of the satellite. Simulation results demonstrate the excellent performance under the ideal scenario. To achieve the future engineering application under the non‐ideal scenario, the terminal positioning error can be first neglected, then the pattern processing error and the terminal signal measurement error must be reduced by decreasing the pattern sampling interval, increasing the number of participant terminals, and other means. By comparing with a traditional beam pointing measurement algorithm, the proposed algorithm can achieve much lower beam pointing error than the baseline. [ABSTRACT FROM AUTHOR]

Earthquake interaction across multiple time scales can reveal complex stress evolution and rupture patterns. Here, we investigate the role of static stress change in the 2023 Mw 7.8 and 7.6 earthquake doublet along the East Anatolian Fault (EAF), using simulations of 19 historical earthquakes (M ≥ 6.1) and the 2023 earthquake doublet from 1822 to 2023. Focusing on six cascading sub‐events during the 2023 Kahramanmaraş earthquake doublet, we reveal how one sub‐event's stress alteration can impact the emergence and rupture of subsequent sub‐events. Our analysis unveils that the 2023 Mw 7.8 earthquake was delayed due to stress shadow effects from historical events, while the 2023 Mw 7.6 earthquake was accelerated as a result of stress increases from historical events and ultimately triggered by the 2023 Mw 7.8 earthquake. This study underscores the importance of grasping earthquake preparation, rupture initiation, propagation, and termination in the context of intricate fault systems worldwide. Based on these results, we draw attention to increased seismic hazards in the Elazig‐Bingol seismic gap of the EAF and the northern section of the Dead Sea Fault (DSF), necessitating increased monitoring and preparedness efforts. Plain Language Summary: On 6 February 2023, a doublet of earthquakes (Mw 7.8 and 7.6) struck the borders between southeastern Turkey and northern Syria. Aiming to discover the influence of past earthquakes on the initiation, propagation, and termination of the doublet, as well as the prospective seismic hazards of the seismic gaps in the targeted region, we demonstrated spatiotemporal variations in stress along the EAF before, during, and after 2023 using the earthquake data during the past two centuries. We revealed how the stress changes induced by one sub‐event can influence the occurrence and behavior of subsequent sub‐events, and consequently on how the rupture process evolves over time and space. Our research is crucial for gaining insights into underlying mechanisms governing earthquake preparation, rupturing and propagation in earthquake sequences, and migration within the EAF. We also suggest special attention should be paid to the raised seismic hazards between the Palu and Ilica cities in the Elazig‐Bingol seismic gap of the EAF and the northern section of the DSF due to stress loading. This might be helpful in the development of effective strategies for disaster prevention and relief in the regions of southeastern Turkey, northern Syria, and Lebanon. Key Points: The 2023 Mw 7.8 earthquake was delayed due to stress shadow caused by historical earthquakes between 1822 and 2023The 2023 Mw 7.6 earthquake was promoted caused by historical earthquakes and finally triggered by the 2023 Mw 7.8 earthquakeSpecial attention should be paid to the raised seismic hazards in the seismic gap of the northeastern EAF and the northern DSF [ABSTRACT FROM AUTHOR]

Beginning in 2025, water utilities in the United States are required to conduct a risk and resilience assessment (RRA) and update their emergency response plan (ERP) every five years to comply with the America's Water Infrastructure Act of 2018 (AWIA). The RRA process focuses on critical assets and identifies risks from specific events or scenarios, while the ERP outlines response actions for hazardous events. Utilities can benefit from coordinating their RRA and ERP efforts, as well as their local hazard mitigation plan (LHMP), to increase efficiency and save time and money. Combining these planning efforts allows utilities to avoid duplicating work and provides a more robust emergency planning program. [Extracted from the article]

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]

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]

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]

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]

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]

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]

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]

Summary: Wireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion, physical damage, or hardware malfunction. The failure of some nodes, like cut vertex nodes, can partition the network into multiple network segments. Most of the solutions for network partition recovery proposed in the literature depend on the assumption that the network is obstacle‐free. However, an obstacle‐free environment is not possible in real‐life situations. In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth, UAVs can be employed to strengthen the connections in wireless networks by coordinating with ground nodes since they can render services in rough areas where ground nodes cannot provide services. Thus, our research is based on using UAVs as relay nodes to reconnect the disjoint partitions. This paper proposes two algorithms: Drone assisted partition recovery algorithm (DAPRA) and drone assisted detection and partition recovery algorithm (DADPRA). In both algorithms, partitions are detected by the sink node. In DAPRA sink node determines the failed cut‐vertex node and sends UAV to the location of the failed cut‐vertex node. In DADPRA algorithm, UAV identifies the failed cut‐vertex node and reconnects the disjoint network segments. DAPRA and DADPRA are analyzed according to the state‐of‐the‐art parameters, that is, recovery and detection time, UAV's travel distance, and the total messages transmitted. The proposed algorithms are compared with similar Distributed Partition Detection and Recovery using UAV (DPDRU) approach. The simulation results show the proposed algorithms detect network partitioning in less time as compared to DPDRU approach. [ABSTRACT FROM AUTHOR]

This article explores the use of social media and crowdsourcing technologies in crisis and disaster management. It discusses how these technologies are increasingly being used to gather and share information during crises, and how they are addressed in national and international policy agendas. The article also examines the challenges and uses of social media in crisis management, emphasizing the need for inclusive frameworks, policies, and guidelines. It concludes by presenting seven papers that critically examine the role of social media and crowdsourcing in bridging the gap between public and private actors in crisis management. The text highlights the importance of understanding the perspectives and needs of different stakeholders and calls for further research in diverse regions. [Extracted from the article]

Premium subsidies can be used to address low demand for natural hazard insurance when it is partly caused by governmental disaster relief payments. We analyze how the introduction of ex ante premium subsidies affects the frost insurance demand of German winegrowers after the government changed insurance regimes to avoid ex post disaster relief payments. We find that the implementation of a premium subsidy in an immature market with low levels of participation, presumably caused by strong anticipation of disaster relief, is effective in increasing overall frost insurance demand. Receiving disaster relief payments 3 years before the introduction of the subsidy seems to make farmers more responsive toward the premium subsidy. [ABSTRACT FROM AUTHOR]

I examine the design of optimal insurance contracts considering the possibility of government disaster relief payments. This work focuses on the impact of (risky and ambiguous) government disaster relief on the shape of optimal private insurance contracts. I demonstrate that the optimal insurance contract is a straight deductible contract in the case of a fixed probability of government relief. This result is robust to ambiguity in the probability of relief payments, even for ambiguity‐averse decision makers. If government disaster relief becomes more likely for larger losses, then the optimal insurance contract features coinsurance above a deductible. I also extend this analysis to more general stochastic dominance relationships between disaster relief and loss magnitude. [ABSTRACT FROM AUTHOR]

Disasters affect hundreds of millions of people every year and the response of governments is crucial in alleviating the suffering of those affected. Despite the importance of contracting in response to disasters, research on this topic is conspicuous by its absence. This paper begins to address this gap by investigating the choice of procurement contract type by US federal agencies during disaster management operations. The research relies on 47,560 contracts issued by the US federal government in response to 14 major disasters between 2005 and 2016. We build on agency theory to investigate the choice of the contract type made by federal agencies at the different stages of a relief operation. This research provides empirical evidence of the key factors underpinning the choice of contract in the context of disaster management, namely the amount of spend per contract and the type of acquisition (product or service), and reveals the moderating role of the stage of the relief operation. [ABSTRACT FROM AUTHOR]

Sweetpotato (Ipomoea batatas L.) was known as a minor root crop in Ghana. Starting in 2010, the International Potato Center (CIP) and multiple partners implemented orange‐fleshed sweetpotato (OFSP) projects to reposition this crop in Ghana. CIP‐led projects from 2010 to 2021 were identified as Research for Development Initiative projects. These projects consisted of (i) new variety development and commercial seed systems (Sweetpotato Action for Security and Health in Africa: 2009–2019); (ii) OFSP products for business development, breeding‐seed system linkages, strategic awareness for demand creation, sensitization, capacity building, advocacy, and strengthen public‐private partnerships (Jumpstarting OFSP: 2014–2017); (iii) research on shelf‐life and appropriate storage practices (two U.S. Agency for International Development's Office of U.S. Foreign Disaster Assistance: 2013–2015 and 2017–2018); and (iv) innovative on‐farm variety testing using the ClimMob‐CitizenScience method (Triadic Comparison of Technologies– roots, tubers, and bananas scaling: 2020–2021). In November 2020, CIP withdrew from Ghana, and the scaling partner, the Reputed Agriculture 4 Development Foundation, led this project up to the end. Strengthened partnerships have driven the expansion of OFSP into value chains. Key partners driving changes were private sector producers and processors, small‐holder farmers, Council for Scientific and Industrial Research institutes, universities, Women in Agricultural Development, Government Extension, Ghana Health Service, and Ghana School Feeding Program. AGRA OFSP for scaling in Ghana (2021–2022) revealed OFSP notably contributed to food security, health, and wealth. It fits in the Food System and contributes to Food Justice in Ghana. Future scaling will depend on consistent policies and efforts to respond to challenges and opportunities along the value chain for sustainability. Impact pathways of theories of change and scaling helped us understand the scaling progress of OFSP in Ghana. Core Ideas: A series of International Potato Center (CIP)‐led projects in Ghana from 2010 to 2021 led to significant scaling of orange‐fleshed sweetpotato (OFSP).Independent, joint program partners were essential for accelerating the process of scaling.Using theories of change and scaling approaches was useful for guiding and tracking progress.Policy support for OFSP must continue to ensure continued scaling in the livelihood system.OFSP can increasingly contribute to Food Justice in Ghana. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating natural disasters worldwide. Such disasters are often accompanied by strong precipitation and other weather factors, making it more difficult to identify affected areas. Moreover, synthetic aperture radar (SAR) technology can capture images in a 24‐hr window and penetrate clouds and fog. Change detection (CD) technology based on SAR images is generally utilized to locate disaster‐stricken areas by analyzing the differences between pre‐ and post‐disaster images. However, this method faces two main challenges: the presence of speckle noise, which reduces the difference detection accuracy, and the lack of a suitable SAR data set for flood disaster CD. Therefore, this study proposes a novel two‐stage approach for locating flood disaster areas, known as the denoising‐change detection approach (D‐CDA). The first stage comprises a nine‐layer denoising network with an encoder‐decoder structure known as the SAR denoising network (SDNet). It utilizes a multiresidual block and a parallel convolutional block attention module to extract features during the encoding process to suppress the noise component. In the second stage, a novel convolution neural network is proposed to detect the changes between bitemporal SAR images, namely, the coordinate attention fused network, which combines the siamese network and UNet++ as the backbone, and fuses coordinate attention modules to enhance the change features. Moreover, a CD data set (Zhengzhou flood data set) was constructed using Sentinel‐1 SAR images based on the 2021 flood disaster in Zhengzhou, China. Simulations verify the effectiveness of the proposed method. The experimental results indicate that D‐CDA achieves favorable detection performance in locating flood disaster areas. Key Points: We designed a deep learning network for synthetic aperture radar (SAR) image denoisingWe combined SAR image denoising and change detection (CD) techniques for flood disaster localizationWe constructed a data set for flood CD using SAR images and a data set for SAR image denoising [ABSTRACT FROM AUTHOR]

In coal mines, the effective extraction of relief gas and the prevention of dynamic disasters depend on accurate predictions of the spatial distribution of mining stress in the overlying rock. However, at present, the prediction of stress relief and concentration zones based on operational experience or empirical modeling differs significantly from actual measurements in the field. The fundamental problem is the difficulty in modeling the transfer mechanism of the mining overburden load under the influence of key strata. In this study, a mechanical model of the spatial distribution of the mining stress field based on the structure of overburden key strata was established. The model demonstrates that the key strata are the main bearing bodies of the overlying rock, both before and after the fracture, and the overburden rock load can be transferred to the coal and rock mass around the stope through the key strata in the fracture and bending subsidence zones, thus forming a mining stress field. Equations for calculating the plane distributions of mining stress under the key strata at different horizons and abutment pressures on the stope were established, and a spatial distribution prediction method for the mining stress field based on the overburden key strata structure was proposed. The study area was the Gaojiapu Coal Mine, where it was found that the key stratum of the 400.45 m thick Luohe Formation sandstone bore most of the overlying rock load and transferred the load to the coal and rock mass around the stope. This load distribution is the root cause of a high concentration and wide influence range of mining stresses, leading to serious deformation of the main roadway and frequent dynamic disasters. The mining stress concentration was maximized at a mining length of 1458 m × 1458 m. [ABSTRACT FROM AUTHOR]

Making reference to recent scholarly discussions on neoliberalism and disaster recovery, in this paper I discuss how the implemented neoliberal doctrines of governance have reinforced the existing asymmetrical power relationships between the state, international agencies and citizens. This process constitutes a major barrier to achieving sustainable recovery after the 2015 Nepal earthquake. In particular, three issues, namely the risk of disempowering local communities' capacity, lack of commitment to long‐term recovery, and commodification of the recovery process, have resulted that the recovery policy basically favours the interests of international donors, and NGOs, not those of local communities. To achieve sustainable recovery, post disaster interventions must be socially inclusive. Local affected communities should be meaningfully engaged, and that social learning and sustainability transformation should be enabled as these are key processes at the core of community resilience and of any community resilience‐building strategy. [ABSTRACT FROM AUTHOR]

After major adversity, some people rely on their religious faith and networks for comfort, support, and material goods and services. Consistent with this behavior are findings that adversity has a positive causal effect on the importance of religion in people's lives. Using a large high‐frequency US dataset, we estimate the causal effects of natural disasters on stated religious importance and attendance at religious services. Effects are identified by comparing changes in outcomes over time within counties affected by a natural disaster with changes over time in other counties from the same state. We find that most estimates are near‐zero in magnitude; for the full sample, for subgroups defined by religious affiliation, demographics, and income, and for different disaster types. However, significant negative effects are found immediately postdisaster, suggesting a short‐term crowding‐out effect in which recovery activities limit time for worship. This explanation is supported by a finding that people are less "well rested" in the first weeks postdisaster. [ABSTRACT FROM AUTHOR]

Researchers across disciplines have long sought to collect 'perishable data' in the context of disasters. Yet, this data type is neither consistently defined nor discussed in specific detail in the literature. To address this gap, this paper defines perishable data and provides guidance on ways to improve both how it is collected and shared. Here, perishable data is conceptualised as highly transient data that may degrade in quality, be irrevocably altered, or be permanently lost if not gathered soon after it is generated. Perishable data may include ephemeral information that must be collected to characterise pre‐existing hazardous conditions, near‐miss events, actual disasters, and longer‐term recovery processes. This data may need to be gathered at multiple points in time across varying geographic scales to accurately characterise exposure, susceptibility to harm, or coping capacity. The paper considers ethical and logistical challenges and discusses opportunities to advance equitable perishable data collection and dissemination. [ABSTRACT FROM AUTHOR]

Firms responded to the COVID‐19 pandemic by participating in an unprecedented level of corporate social responsibility (CSR). The authors build on the existing CSR and crisis response and disaster relief literatures by examining nine CSR types employed during the pandemic (donating in‐kind, donating money, creating content, free products, free services, communication of operational changes, employee support, financial support, and shifting production). Then, they test the impact of starting versus stopping each CSR type on consumer perceptions of authenticity, attitude, and purchase intentions in two experiments, replicated in 2020, 2021, and 2022. Consumers distinguish between CSR types and rate employee and financial support most favorably and creating content and communication of operational changes least favorably. Starting (vs. stopping or no) CSR increases authenticity and subsequent attitude and purchase intentions. Interestingly, stopping CSR is worse than no CSR in 2020, but no CSR is worse than stopping CSR in 2022. This indicates that consumers discredit firms for stopping CSR during the acute phase of the pandemic but discredit firms for not engaging in any CSR after the acute phase ends. Finally, consumers rate small (vs. large) firms more favorably for engaging in some CSR types. Taken together, these results help inform how firms should engage in CSR activities in response to crises and disasters. [ABSTRACT FROM AUTHOR]

The article titled "Disasters in the Philippines: Before and After Haiyan" explores the multidimensionality of disaster in the Philippines, with a focus on the impact of Super-typhoon Haiyan in 2013. The book incorporates perspectives from various disciplines and includes direct accounts of disaster-affected individuals, particularly low-income women and children. It analyzes the challenges of disaster management, vulnerability, and adaptation in the Philippines, and provides insights into the long-term realities of disaster recovery. The book is divided into two parts, with the second part focusing on the experiences of people in the Visayas region directly affected by Typhoon Haiyan. It also highlights the importance of understanding the diverse ways in which disaster affects individuals and their societies in different contexts. The book offers valuable insights into marginalized experiences of disaster, particularly the roles of women in survival and recovery. [Extracted from the article]

Xinzhuang Coal Mine is a typical kilometer-deep rockburst mine in the Ningzheng mining area of China Huaneng Group, which is still in the capital construction period. At present, there are a few studies on the rockburst mechanism and prevention of this mine. In order to explore the occurrence mechanism and main influencing factors of rockburst under the complex geological conditions of large buried depth, thick topsoil, and hard overburden in Xinzhuang Coal Mine, this paper, based on the analysis of the basic geological data of the mine, deeply explores the comprehensive disaster factors of Xinzhuang Coal Mine; then, uses the analytic hierarchy process to carry out quantitative analysis of each disaster factor, and finally, it obtains the impact type and occurrence mechanism of the mine. Through research, it is found that the main disaster factors of Xinzhuang Coal Mine are mining depth, coal seam thickness, coal seam thickness change, tectonic stress field, hard overburden, roadway layout, bottom coal reservation, and tunneling activities. The key to the process of the rockburst disaster in Xinzhuang Coal Mine is the superimposed effect of static and dynamic loads on the roadway surrounding the rock system. The potential rockburst type during excavation and mining is the "high static and dynamic load disturbance" type. [ABSTRACT FROM AUTHOR]

The article discusses the shift in the international humanitarian response to the Syrian refugee crisis from short-term emergency relief to a development and resilience-based approach. This shift was prompted by the protracted nature of the conflict and the need for long-term solutions. While the integration of humanitarianism with development has its benefits, it also raises concerns about the dilution of refugee rights and the entanglement of aid organizations with government agendas. The author argues for an indeterminative critique of resilience, acknowledging both its neoliberal echoes and the need for structural solutions to displacement. [Extracted from the article]

Disaster corruption is a vexing problem, damaging state legitimacy and exacerbating human suffering. Mexico has a history of both major disasters and persistently high levels of corruption. A magnitude 7.1 earthquake in 2017 provided an opportunity to study change over time in expectations and tolerance of corruption in disaster relief. Twenty years earlier, Mexico City residents expected, on average, essentially three out of 10 hypothetical trucks loaded with humanitarian assistance to be lost to corruption but expressed near zero tolerance of such conduct. By 2018–19, Mexico City residents expected more than one‐half of all relief, six out of 10 trucks, to be stolen, and could tolerate three out of 10 trucks being pilfered. Similar results were found at the national level. Hence, Mexicans appear to be giving up on the state. Addressing corruption in disaster risk reduction and humanitarian relief specifically might provide a template for improving public trust across other state institutions. [ABSTRACT FROM AUTHOR]

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree‐based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six‐degree‐of‐freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of state‐of‐the‐art 3D path planning algorithms. [ABSTRACT FROM AUTHOR]

Blockchain technology has been suggested as a groundbreaking solution for disaster prevention and relief. However, there is a dearth of both practical applications and systematic academic research into how those existing blockchain solutions actually help to prevent disasters and/or alleviate their negative impacts. The purpose of this paper was to extend previous research by investigating existing blockchain solutions using a rigorous multiple case study approach. Based on stakeholder theory, a structured framework is developed that examines the roles of key stakeholders in disaster situations, illustrates organizations' motivations to deploy blockchain technology, identifies various use cases, lists the relevant blockchain properties, and highlights contingency factors for successful implementations. The findings demonstrate that blockchain applications can improve both disaster prevention as well as disaster relief efforts by streamlining information flows and augmenting the capabilities of core stakeholders. Furthermore, numerous propositions can be derived from the framework that can be used as starting points for further in‐depth investigations. This study familiarizes practitioners with blockchain's potential for disaster management and provides academics with a theory‐based foundation for future research. [ABSTRACT FROM AUTHOR]

Weather‐related disasters during the COVID‐19 pandemic exacerbated the vulnerabilities of individuals and households, and concurrent disasters are becoming more of the norm as we face more extreme weather patterns. This study seeks to extend administrative burden scholarship to better understand the administrative burdens (real and perceived) that a citizen experiences when applying for disaster assistance while facing concurrent disasters. Using logistic regression analysis and Generalized Structural Equation Modeling, we analyze primary survey data from households in five Texas counties that were impacted by the 2021 Winter Storm Uri. Our findings indicate that learning, compliance, and psychological costs, disaster‐related damages, and infrastructure losses, coping with concurrent disasters during the pandemic, and social vulnerability factors, such as age and the lack of insurance increase administrative burdens and difficulties for disaster aid applicants. Practical implications include recommendations to more effectively address concurrent disasters and reduce associated administrative burdens and inequities in disaster assistance programs. [ABSTRACT FROM AUTHOR]

We analyze the effects of early‐life shocks in the Philippines and find that in utero exposure to severe typhoons is associated with adverse outcomes. We exploit variations in typhoon exposure and sharp increases in short‐term disaster relief efforts in the 1960s. Before the increase in disaster relief efforts, in utero exposure to severe typhoons was associated with higher mortality (a 9% reduction in cohort size); survivors exhibited similar levels of human capital as the unaffected. After the increase in disaster relief, the mortality effects were mitigated; however, survivors exhibited lower human capital in the long term. We offer suggestive evidence that the observed changes in adverse long‐term effects are due to the relief efforts' effectiveness in increasing survival probability. [ABSTRACT FROM AUTHOR]

Disaster survivors are often criticised for being dependent on humanitarian (and development) assistance. This dependency is perceived pejoratively by civil servants and other elites, including non‐governmental organisation staff. Officials offered up such narratives in relation to the disaster response and recovery programmes following the Nepal earthquake of 2015. Using a Bourdieusian framework, and undertaking qualitative inquiry in four earthquake‐affected districts of Nepal, this paper contrasts the official narratives of dependency syndrome with people's perspectives and lived experiences. The findings problematise official discourse. Aid was frequently insufficient, poorly targeted, or non‐existent. Moreover, the Bourdieusian framing highlights the agency of survivors, as their habitus predisposed them to help others. It broadens the notion of assistance and dependence, suggesting that social and cultural (as well as economic) capital are vital resources for recovery. Lastly, it shows that dependencies are not necessarily bad. Greater attention to these non‐economic capitals and 'good dependencies' could expedite recovery from future disasters. [ABSTRACT FROM AUTHOR]

The Australian bushfires in 2019–20 triggered massive amounts of charitable giving from the community. This study applied agenda‐setting theory to examine if and how disaster news coverage influenced public donations in response to the crisis. A survey of 949 Australians found that people perceived news coverage of the event to be a strong influence on the amount they donated to bushfire appeals, over and above past giving levels. Furthermore, media coverage was more influential in participants' charity selection than both peer influence and direct communications from the charities. Next, a textual analysis of international news coverage of the event (N = 30,239 unique articles) was conducted. Compared to a control corpus of text, news coverage of the disaster used words related to 'money' and 'support' at disproportionately high frequencies. Together, the studies suggest that the media plays an agenda‐setting role in determining how and to what extent people give to disaster appeals. [ABSTRACT FROM AUTHOR]

Any future outer space exploration and exploitation should more fully consider disaster and health risks as part of aiming for sustainability. The advent of the so‐called "New Space" race, age, or era characterized by democratization, commercialization, militarization, and overlapping outer space activities such as tourism presents challenges for disaster‐related and health‐related risks in and for outer space. Such challenges have been extensively researched for earth, but less so for space. This article presents an overview of key aspects for addressing disaster and health risks in outer space within a wider sustainability framing. After an introduction providing background and scope, this article's next section considers some key health and disaster risks within sustainable outer space and offers insights from earth. The following two sections apply this knowledge by focusing on how analogue missions and international legal and voluntary regimes can each be used to reduce risks and potentially make outer space healthier and safer. The findings advocate that there is a wealth of knowledge and experience about mitigating risks to health and disaster risk reduction on earth that can inform spaceflight and exploration. The examples explored include the physical, legal, and regulatory aspects of the "New Space" industry, which highlights the relevance of equating examples on earth. The article concludes that expectations must be managed regarding scenarios for which response, rescue, and recovery are precluded, prompting a necessary focus on prevention and risk reduction. In doing so, earth‐based scenarios and aspects of the so‐called "Old Space" offer useful insights and should be examined further for "New Space." Integr Environ Assess Manag 2023;19:994–1001. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: More should be done to draw on the wealth of knowledge and experience that exists on earth for addressing health risks and disaster risk reduction and response (DRR/R) for sustainable outer space exploration.As the space industry grows, there is a danger that pursuing the opportunities of outer space may be counterproductive to humanity's recent efforts to transition to more sustainable living.Embracing sustainability and risk reduction can create a better and safer future on earth and in outer space. [ABSTRACT FROM AUTHOR]

The article discusses the history of flood risk management in Australia and the recent increase in disastrous flood events. It highlights the persistent failures of flood risk management and the worsening flood risk in the country. The article also emphasizes the need to consider the changing socio-governmental context in which flood management operates and the importance of addressing the barriers to effective flood risk management. The author suggests that flood risk researchers should reflect on the utility of their outputs and the emerging management challenges in order to contribute positively to flood risk management. [Extracted from the article]

This study aims to facilitate a more reliable automated postdisaster assessment of damaged buildings based on the use of multiple view imagery. Toward this, a Multi‐View Convolutional Neural Network (MV‐CNN) architecture is proposed, which combines the information from different views of a damaged building, resulting in 3‐D aggregation of the 2‐D damage features from each view. This spatial 3‐D context damage information will result in more accurate and reliable damage quantification in the affected buildings. For validation, the presented model is trained and tested on a real‐world visual data set of expert‐labeled buildings following Hurricane Harvey. The developed model demonstrates an accuracy of 65% in predicting the exact damage states of buildings, and around 81% considering ±1 class deviation from ground‐truth, based on a five‐level damage scale. Value of information (VOI) analysis reveals that the hybrid models, which consider at least one aerial and ground view, perform better. [ABSTRACT FROM AUTHOR]

After disasters, such as in the aftermath of a major earthquake, the road network can be blocked by debris from collapsed buildings, impacting accessibility to the affected population. In addition, people move and assemble in various points of the city. In this context, road network accessibility becomes an important issue for logistics operations responsible for the relief and the distribution of supplies to the affected population. We propose metaheuristics for the multi-period Work-troops Scheduling Problem (WSP), extending a contribution from the literature. The WSP is an NP-hard problem. This study brings several contributions to the WSP such as a dedicated local search and two metaheuristics: a Greedy Randomized Adaptive Search procedure and an Iterated Local Search. Results are performed on theoretical instances, and on a realistic instance of Port-au-Prince city in Haïti after the 2010 earthquake, where the problem has 16,660 vertices and 19,866 routes representing the urban network, and more than 500 blocked roads. The method developed improved the results from the literature and the results indicate its robustness. Moreover, the best-known results for the case of the Haïti instances are presented in this study. [ABSTRACT FROM AUTHOR]

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The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues. [ABSTRACT FROM AUTHOR]

Behind humanitarian assistance and disaster relief (HADR) efforts are governance networks of actors working together to facilitate, coordinate, and deliver joint aid and response initiatives. Militaries are key stakeholders in these networks because of their unique capabilities, particularly in Southeast Asia. This paper explores the role of military governance networks in shaping HADR affairs. It presents a framework for examining military‐to‐military relational structures in humanitarian and disaster contexts using network theory and social network analysis. It also assesses the nature of military ties and their influence on two capability areas essential to humanitarian activities in the region: (i) the distribution of military assets and equipment used in HADR; and (ii) the coordination of HADR exercises and training. This paper seeks to provide insights into how governance network features and properties affect the capacity of ASEAN (Association of Southeast Asian Nations) militaries to use available resources efficiently and to achieve shared objectives in regional disaster response. [ABSTRACT FROM AUTHOR]

The year 2020 provided a rare opportunity to examine how US households experience economic hardship when natural disasters occur during a large‐scale public health crisis, such as the COVID‐19 pandemic. Using a nationally representative sample of adults, this study examines the effect of natural disasters on household economic hardships during a pandemic, measured by food insecurity, mortgage or rent delinquency, and unemployment. The study estimated individual fixed‐effect models after controlling for time trends and time‐variant covariates. We find that while a disaster incident in the community during a pandemic is not associated with household economic hardships in aggregate, its effects are experienced differently by people according to the community‐level severity of COVID‐19 and the individual‐level factors characterized as vulnerability and resilience. Our findings have implications for targeted disaster relief and for building resilience among residents to adverse events. [ABSTRACT FROM AUTHOR]

Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well‐known Duckworth–Lewis–Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two‐round questionnaire survey among 300 purchase managers. The means and variances of the participants' estimates of probabilities of meeting the revised targets within the scheduled time for various model‐generated supply scenarios were used to find the participants' risk preferences. In the second‐round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches—one, at the group level by estimating the within‐ and the between‐group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk–return trade‐off distributions. [ABSTRACT FROM AUTHOR]