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The community sector performs an important function in providing emergency food relief, yet food insecurity is often a chronic issue due to poverty and social exclusion. Progressive food provision models present opportunities to improve voluntary food relief services that are dominant in many countries. Informed by research with clients indicating a preference for blended service models that go beyond food provision, two government agencies partnered with a social enterprise and academics to pilot a social supermarket model. This research article (a) briefly summarizes international evidence on social supermarkets' characteristics and offerings, (b) describes a process used to collaboratively develop a social supermarket incorporating universal access and social supports, and (c) presents a rubric defining elements of a social supermarket. Applicable to other community sector settings, the rubric emphasizes dignified access and service, plus opportunities for social support and connection, which are important elements in pathways out of food insecurity. [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]

Relief logistics is vital to disaster relief management. Herein, a risk-averse two-stage distributionally robust programming model is proposed to provide decision support for planning disaster relief logistics. It is distinct from the conventional disaster relief logistics planning problem in that (i) the facility location-inventory model and the multi-commodity network flow formulation are integrated; (ii) the probability distribution information of the supply, demand, and road link capacity is partially known, and (iii) the two-stage distributionally robust optimisation (DRO) method based on the worst-case mean-conditional value-at-risk criterion is developed. For tractability, we reformulate the proposed DRO model as equivalent mixed-integer linear programs for box and polyhedral ambiguity sets, which can be directly solved to optimality using the CPLEX software. To evaluate the validity of the proposed DRO model, we conduct numerical experiments based on a real-world case study addressing hurricane threats in the Gulf of Mexico region of the United States. Furthermore, we compare the performance of the proposed DRO model with that of the conventional two-stage stochastic programming model. Finally, we report the managerial implications and insights of using the risk-averse two-stage DRO approach for disaster relief management. [ABSTRACT FROM AUTHOR]

Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible. [ABSTRACT FROM AUTHOR]

The early identification of potential landslide hazards has always been a hot and difficult issue in the field of international landslide research. In recent years, many scholars have conducted extensive and beneficial explorations in this field, making significant contributions to the effective prevention of landslide disasters. However, until now, there are very few review documents on summarizing such valuable experience in the system, which makes it difficult to meet the ever-increasing demand of researchers in scientific documents. To address the gap, this paper systematically reviews 843 documents collected by the two data platforms of Web of Science (WOS) and Scopus from 1971 to 2023 by using the bibliometric analysis software. This paper first figures out the internal relationship between documents by analysing their spatial and temporal distribution characteristics, and then emphatically analyses the application, advantages and disadvantages of different early identification methods based on the influencing factors of landslide disaster formation and multi-source data acquisition links. And finally, this paper discusses the challenges and development trends in this field from four aspects of cooperative analysis, multi-source data, topic analysis and research trends, and puts forward some suggestions. This research can help researchers to use various early identification methods reasonably and provide summary and integration services of scientific document achievements for efficient research in this field. [ABSTRACT FROM AUTHOR]

The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels. [ABSTRACT FROM AUTHOR]

Featured Application: The shock wave generator developed in this article is easy to operate and reusable. It can generate high-pressure and high-speed shock waves. Therefore, it can be widely used in the design and development of various devices, such as shock-driven power generation devices, pipeline obstruction removal devices, geothermal well productivity enhancement devices, lightweight rock fracture devices for disaster relief, shock wave soil loosening and hole fertilization devices that can reduce soil carbon emissions, etc. Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a shock wave monitoring system. It could measure the velocity of the generated shock waves at about Mach 3–5, and the output pressure exceeded 900 kg/cm2 (more than 100 times the input pressure). Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which could generate high-pressure and high-speed blast waves and could be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then rotate the magnetic energy generator for power generation. This paper used tank pressure, output pressure, gas flow, rotation speed, voltage, and current detected by the system to conduct power generation performance analysis. When the minimum rotation speed was set to 1500 rpm and three bulbs were turned on as loads, the system could generate an average voltage of 36.64 V and an average current of 211.01 mA as output (power about 7731.41 mW). [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]

The 6th World Landslide Forum (WLF6) took place in Florence, Italy from November 14 to 17, 2023. The forum focused on landslide science for sustainable development and aimed to contribute to global commitments and agreements related to disaster risk reduction and sustainable development. The event brought together researchers, stakeholders, and organizations from around the world to collaborate on priority actions for landslide risk reduction. The forum included presentations, workshops, and exhibitions, and was attended by over 1200 participants from 61 countries. The opening plenary session discussed the importance of sharing information, supporting research, and improving landslide preparedness and response. The Florence Declaration on Landslide Risk Reduction was adopted, committing to these goals. The recognition and awards ceremony honored individuals and organizations for their contributions to landslide science. The parallel sessions program covered various themes related to landslides, including international programs, multi-hazard risks, societal aspects, cultural heritage, and ground failures triggered by earthquakes. The document also provides an overview of different chapters in a book or report on landslides, covering topics such as regional and global landslide inventories, monitoring and predicting landslides, and the effects of climate change on landslides. [Extracted from the article]

Earthquakes are hazardous natural disasters, and they may cause severe damage and losses where they occur. In addition to their devastating effects, they may trigger following disasters like tsunamis and fires. Post-earthquake fires are known as the most dangerous secondary disasters and generally cause much more damage than the damage caused by the earthquake itself. The difficulty in determining and responding to ignition sources, the lack of equipment and workforce, and obstacles like collapsed buildings that block the ways to reach fires may cause catastrophic disasters after an earthquake. In recent years, Unmanned Aerial Vehicle technologies (UAVs) have shown promising performance in post-disaster response operations. Parallel to technological improvements, they have been used for many purposes, like fire-fighting, victim location detection, base station support, and material distribution in disaster areas. To manage a possible response and improve the performance of UAVs in post-earthquake fire areas, it is crucial to be prepared in advance. This study proposes an artificial neural network-based clustering approach for unmanned aerial vehicle use in post-earthquake fire areas. After conducting a detailed literature review covering post-earthquake fires, usage of UAVs in disasters, and some aspects of Self Organizing Maps, the methodology used for clustering the neighborhoods regarding their post-earthquake fire risk similarities is introduced. A real-life application is carried out to identify and cluster the regions and provide preliminary information to the decision-makers on possible interventions. Neighborhoods of Tuzla district, one of the riskiest districts in terms of post-earthquake fires in Istanbul, are clustered with Self-Organizing Maps (SOM). In a possible post-earthquake fire disaster, the Tuzla district can be divided into three areas, and UAVs can be organized more efficiently and quickly based on this cluster information. The results of this real-life application can guide decision-makers by showing which regions have similarities for UAV response in possible post-earthquake fires and where they can be intervened together. The authorities can benefit from the findings of this study while preparing disaster plans, intervention actions, and post-disaster humanitarian activities. [ABSTRACT FROM AUTHOR]

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]

This study introduces a novel approach to landslide detection by incorporating the Spatial and Band Refinement Convolution (SBConv) module into the U-Net architecture, to extract features more efficiently. The original U-Net architecture employs convolutional layers for feature extraction, during which it may capture some redundant or less relevant features. Although this approach aids in building rich feature representations, it can also lead to an increased consumption of computational resources. To tackle this challenge, we propose the SBConv module, an efficient convolutional unit designed to reduce redundant computing and enhance representative feature learning. SBConv consists of two key components: the Spatial Refined Unit (SRU) and the Band Refined Unit (BRU). The SRU adopts a separate-and-reconstruct approach to mitigate spatial redundancy, while the BRU employs a split-transform-and-fuse strategy to decrease band redundancy. Empirical evaluation reveals that models equipped with SBConv not only show a reduction in redundant features but also achieve significant improvements in performance metrics. Notably, SBConv-embedded models demonstrate a marked increase in Recall and F1 Score, outperforming the standard U-Net model. For instance, the SBConvU-Net variant achieves a Recall of 75.74% and an F1 Score of 73.89%, while the SBConvResU-Net records a Recall of 70.98% and an F1 Score of 73.78%, compared to the standard U-Net's Recall of 60.59% and F1 Score of 70.91%, and the ResU-Net's Recall of 54.75% and F1 Score of 66.86%. These enhancements in detection accuracy underscore the efficacy of the SBConv module in refining the capabilities of U-Net architectures for landslide detection of multisource remote sensing data. This research contributes to the field of landslide detection based on remote sensing technology, providing a more effective and efficient solution. It highlights the potential of the improved U-Net architecture in environmental monitoring and also provides assistance in disaster prevention and mitigation efforts. [ABSTRACT FROM AUTHOR]

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This study aims to quantitatively identify the factors associated with companies that actively enhance the feasibility of public–private partnerships (PPP) during disasters. Based on these identified relationships, the study seeks to propose strategies to improve PPP effectiveness in proactive companies. A questionnaire survey was conducted targeting companies in Japan that have concluded disaster assistance agreements with local governments. Based on the response data, scales were constructed for the following variables: "feasibility of public–private agreements (FPPA)," "experience in implementing public–private agreements (EPPA)," "social contribution to disaster-stricken area," "implementation level of disaster risk reduction measures," "feasibility of business continuity," "organizational culture toward risk and crisis (OCRC)," and "employee autonomy (EA)." Path analysis was conducted to investigate the relationships between FPPA and other variables. The results revealed that FPPA is associated with EPPA, EA, and OCRC. In terms of strategies to improve FPPA, it was suggested to promote the internal sharing of information about EPPA from other companies. Additionally, for EA and OCRC, implementing human resource policies and management practices was discussed as potential measures. [ABSTRACT FROM AUTHOR]

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can mitigate human casualties and address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains a research gap in the development of mathematical models for such scenarios, specifically incorporating operations centers/field offices and external stakeholders as relief centers. Addressing this gap, this study examines an optimization model for both before and after disaster planning in a humanitarian supply chain and logistical framework within an organization. The affected areas are defined as regional branches, operational units, major transportation hubs, operations centers/field offices, external stakeholders, and medical facilities. A mixed-integer nonlinear model is formulated to minimize overall costs, considering factors such as penalty costs for untreated injuries and demand, delays in rescue and relief item distribution operations, and waiting costs for the injured in emergency medical vehicles and air ambulances. The model is implemented using GAMS software 47.1.0 for various test problems across different scales, with the Grasshopper Optimization Algorithm proposed for larger-scale scenarios. Numerical examples are provided to show the effectiveness and feasibility of the proposed model and to validate the metaheuristic approach. Sensitivity analysis is conducted to assess the model's performance under different conditions, and key managerial insights and implications are discussed. [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]

Unequal social media attention can lead to potentially uneven distribution of disaster-relief funds, resulting in long-term inequality among regions after disasters. This study aimed to measure inequalities in social media attention to regions during disasters and explore the role of official media in reducing such inequality. This is performed by employing social media, official media, and official aggregated statistics regarding China's rainstorm disasters. Through a set of panel-data regressions and robustness tests, three main conclusions were drawn: (1) There were inequalities among regions regarding social media attention they received during rainstorm disasters. For disasters of the same magnitude, regions with low economic outcome per capita received less attention on social media. (2) Official media can reduce inequality in social media attention during disasters. Official media statements can encourage netizens to pay attention to disaster-stricken areas, and especially the overlooked underdeveloped areas. (3) Of all the measures taken by official media, timely, accurate, and open disclosure of disaster occurrences proved to be the most potent means of leveling the playing field in terms of social media attention; contrarily, promotional or booster-type messages proved futile in this regard. These findings revealed the vulnerabilities within social media landscapes that affect disaster relief response, shedding light on the role of official guidance in mitigating inequalities in social media attention during such crises. Our study advises social media stakeholders and policymakers on formulating more equitable crisis communication strategies to bridge the gap in social media attention and foster a more balanced and just relief process. [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]

The "321" prefabricated highway steel truss bridge is widely used for highway rescue, disaster relief, and emergency traffic. This paper uses a 33 m double-row monolayer "321" prefabricated highway steel truss bridge to analyze its mechanical properties and component stability. The actual traffic flow capacity of a total weight of 53.32 tons is used in this study. The results show that the maximum internal force in the truss chord (including the stiffening chord) occurs in the middle span section when a centrally distributed load is applied. Meanwhile, the maximum internal force of truss diagonal members and truss vertical bars appears at the fulcrum section. Under the eccentrically distributed load, the maximum internal forces of truss chords (including stiffening chords) appear in the middle span section, which is closest to the vehicle load, while the maximum internal forces of truss diagonal members and truss vertical bars appear in the fulcrum section, which is closest to the vehicle load. While the maximum internal forces under the eccentrically distributed load are greater than the maximum internal forces under the centered-layout load, under the vehicle load, truss chords (including stiffening chords) are prone to buckling instability, and the buckling mode is mainly reverse out-of-plane buckling. The inclined members of the truss are prone to buckling instability, and the buckling mode is mainly the combination of out-of-plane bending and two-way out-of-plane bending. Truss vertical bars have good stability and are not easy to buckle. The main conclusions of this paper can provide references for the optimal design and operation safety of prefabricated highway steel truss bridges. [ABSTRACT FROM AUTHOR]

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters. [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]

The article focuses on the legal distinction between injunctions pending appeal and stays pending appeal, particularly in the context of emergency relief granted by the Supreme Court. It examines the statutory basis, purpose, and requirements for granting both types of relief, with a specific emphasis on the relevance of certiorari in the consideration of stays. It addresses recent criticisms of the Supreme Court's granting of emergency relief and proposed changes to the Court's approach.

Disaster preparedness and relief is a key service provided by the American system of government, but emergency management practices have produced varying outcomes. Based on the existing literature, emergency management, community planning, and a history of racial inequities are inextricably interconnected. Based on the concepts presented by social equity, community planning, emergency management, and environmental policy literature, an interdisciplinary approach was used to create an original checklist of considerations for emergency managers, community planners, and policymakers to use in a collaborative manner to proactively address potential threats to their constituents and communities. [ABSTRACT FROM AUTHOR]

Purpose: The tourism sector continues to be an engine for economic growth for communities (Jenkins, 1980), and in post-disaster scenarios, destination can be overwhelmed with challenges, particularly for the local community. However, it is essential to seek the most effective path of recovery for individuals and communities. Gaps in contemporary post-disaster literature remain, as the focus often centres on infrastructure and economic developments (Cox and Perry, 2011). This paper aims to address such gaps by offering new ideas around the value of recovery focusing more on the victims, their stories and the role of tourism. Design/methodology/approach: As a theoretical paper, the applied method is described as a convenient approach to data collection and analysis. The methodological reasoning is down to the selection and examination of sources used to establish new theoretical understandings. The study applies a pragmatic research approach to embrace the spectrum of views that are present within the constructivist and positivist. The pragmatic philosophical approach supports multi-disciplinary studies and ensures a more holistic consideration of social conditions is embraced. Findings: This paper offers new knowledge and ideas for post-disaster recovery scenarios. It presents a model that incorporates a range of (traditional, digital and immersive) methods and platforms in which stories can be shared and presented. It argues that allowing victims to share stories in different formats could support rehabilitation while also providing stronger, powerful narratives for tourists, leading to more effective tourism experiences. Originality/value: Disasters leave individuals and communities in devastation and with potentially lifelong trauma. In rehabilitation of victims is a vital ingredient to the recovery of a destination and its social fabric. This research offers new ideas around the use of digital technologies to generate stories of value that not only support victims of disasters but also offer visitors the opportunity to gain a deeper understanding of challenges and difficulties of dark places, often seen as a key part of the visitor experience and motive to places of tragedy. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. OBJECTIVE: The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. METHODS: The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. RESULTS: The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. CONCLUSION: A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable. [ABSTRACT FROM AUTHOR]

This article explores a collection of circa 400 applications for 'begging letters' conserved in the archives of the Privy Council of the Austrian Netherlands from the late 1760s to the early 1780s. Such Lettres de Quête were issued in the name of the Habsburg monarch to subjects who had lost their possessions to fire or other natural disasters, and allowed their bearers to travel around begging. By means of a qualitative reading of the materials, the article uncovers underlying administrative procedures while throwing light on the social selectivity, practices and gains associated with this little-known phenomenon. By demonstrating that such 'begging letters' were customary policy practice, as they probably were in neighbouring countries, it signals their importance as an early modern mode of disaster relief and highlights their ambiguous role as a policy paradox in the context of increasing criminalisation of begging and vagrancy. [ABSTRACT FROM AUTHOR]

This research aims to investigate how Zakat is disbursed to aid in disaster relief efforts and its role in humanitarian efforts during and after disasters, using the case study of the 2018 Lombok earthquake in Indonesia. Data for this study were collected through interviews with BAZNAS managers using a semi-structured interview method. The steps in analyzing interview data involved data coding, identifying themes or patterns, developing a narrative, and developing a theory. The main findings of the research indicate that the distribution of Zakat for disasters has sustained impacts on the environment, social aspects, and the economy of affected communities. It highlights the crucial role of Zakat in disaster mitigation for humanitarian purposes. The theoretical implications of these findings affirm the critical role of Zakat in disaster mitigation, an aspect that has not been fully explored in previous academic literature. These findings contribute to the development of Islamic social finance by highlighting the role of Zakat in disaster management and exploring its potential application in humanitarian efforts and disaster mitigation. The practical implications for the government include enhancing collaboration between BAZNAS and disaster relief agencies and improving public understanding of Zakat principles for humanitarian purposes and disaster mitigation. [ABSTRACT FROM AUTHOR]

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research. [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]

Energy is known as the lifeline of national development, and from the last decade or so, the security of human energy supply has been frequently disturbed by natural disasters due to global warming and frequent geological activities. In this paper, by combing the literature, we found that the past literature on the relationship between energy and natural disasters mainly focused on measuring energy consumption under natural disasters, but less on the integrated evaluation of human economic activities, energy security and natural disasters, and less on the evaluation of the energy governance efficiency of each provincial, municipal, and autonomous region government in preventing and managing exposure to natural disasters from the perspective of Chinese provinces. Therefore, in order to fill the gap in the literature in this field, this paper collects panel data of energy consumption efficiency stages and natural disaster treatment stages from 2013 to 2017 for 30 provinces in China (excluding Hong Kong, Macao and Taiwan as well as Tibet Autonomous Region) and uses the two-stage undesirable dynamic DDF model as a framework to study the relationship between economic, environmental pollution and natural disasters for the sample data and to analyze the phase-by-phase evaluation of energy and natural disaster efficiency and make corresponding policy recommendations. The following conclusions are drawn: (1) The overall efficiency of China's eastern coastal provinces is higher than that of the central and western provinces. (2) The first-stage efficiency of Chinese provinces is better than the second stage, and the difference in efficiency of the first stage of each province is smaller than that of the second stage. (3) In terms of the efficiency of disaster prevention and relief inputs, the efficiency values are generally lower in most regions of China. (4) Finally, the annual efficiency of natural disaster losses is not high in all regions, and the efficiency values are higher in the eastern coastal regions than in the central and western regions. Accordingly, this paper proposes that each province should formulate relevant disaster prevention and economic development strategies according to regional characteristics, while the central government should also propose locally appropriate coordinated governance policies to effectively control carbon dioxide emissions and air pollution, as well as increase disaster prevention publicity and incorporate disaster prevention education-related work into the performance assessment mechanism of local governments to promote the interactive development of the two. [ABSTRACT FROM AUTHOR]

Purpose: The coordination among the various entities such as the military, government agencies, civilians, non-governmental agencies, and other commercial enterprises is one of the most challenging aspects of managing the humanitarian supply chain. Blockchain technology (BCT) can facilitate coordination, but the cost and other hindrances have limited their application in disaster relief operations. Despite some studies, the existing literature does not provide a nuanced understanding of the application of blockchain technology to improve information alignment and coordination. Motivated by some recent examples where blockchain technology has been used to trace and mobilize resources in the form of funds and materials from the origin to the destination, the authors develop a theoretical model grounded in the contingent resource-based view. Design/methodology/approach: To empirically validate the model and test the research hypotheses, the authors gathered cross-sectional data using a structured pre-tested questionnaire. In this study, the authors gathered our responses from international non-governmental organizations from twenty-four countries. The authors performed the statistical analyses using variance-based structural equation modeling (PLS-SEM) with the help of commercial software (WarpPLS 7.0). Findings: The findings of the study offer some useful implications for theory and practice. The results obtained through statistical analyses suggest that the BCT significantly affects information alignment and coordination. However, contrary to popular beliefs the study suggests that intergroup leadership has no significant moderating effect on the paths joining BCT and information alignment/coordination. Moreover, the authors found that the control variable (interdependence) significantly affects the information alignment and coordination further, which opens the room for further investigation. Practical implications: The result of the study offers some useful guidance. Firstly, it suggests that humanitarian organizations should invest in BCT to improve information alignment and coordination which is one of the most complex tasks in front of humanitarian organizations. Secondly, intergroup leadership may not have desired influence on the effects of BCT on information alignment/coordination. However, the interdependence of the humanitarian organizations on each other may have a significant influence on the information alignment/coordination. Originality/value: The study offers some useful implications for theory. For instance, how BCT influences information alignment and coordination was not well understood in the context of humanitarian settings. Hence, this study offers a nuanced understanding of technology-enabled coordination in humanitarian settings. [ABSTRACT FROM AUTHOR]

Natural hazard-triggered technological accidents (Natechs) are a type of multihazard coupled event with complex disaster evolution trends and serious consequences, resulting in high difficulty in collaborative responses. Currently, research on Natechs primarily revolves around evolutionary mechanisms and risk assessment. However, this complex form of disaster still requires relevant studies on response processes and organizational collaboration. The stochastic Petri net simulation method aligns well with this need, offering a suitable approach for addressing these gaps. Stochastic Petri nets (SPN) were introduced to clarify the Natech response process and analyze the synergistic relationship between organizations during the Natech response. First, the natech response process was framed by the logic of "scenario-task-organization"; the SPN's components were designed considering the characteristics of Natech events, and a proposal was made on how to construct the SPN model corresponding to a collaborative Natech response. Second, the performance of the analysis model was evaluated using the isomorphic Markov chain, and a method for examining organizational collaborative relationships was proposed. Finally, the model is validated by an example, and the characteristics of organizational task execution in Natech responses are summarized according to the busy probability of places and variation in the utilization of transitions. Analysis of task coordination relationships is proposed based on the SPN's structure, and the core and auxiliary workgroups are determined according to the actual task execution requirements to achieve better identification of responsible parties. Further, ways to improve Natech response collaboration are recommended: The role of the natural disaster response department is highlighted, for example, as it provides crucial information support during rescue operations. Relying solely on government departments may prove ineffective in responding to Natechs; thus, it may be necessary to involve social forces in the response effort. Flexible adjustments to deployment should be made to ensure the full utilization of limited disaster relief resources. [ABSTRACT FROM AUTHOR]

In light of the challenges posed by climate change and rural revitalization, high-quality construction in rural areas is facing significant obstacles from rainstorm flooding. This study adopts a regional disaster system analysis framework and examines the characteristics of rural rainstorm flooding disasters, including the instability of the breeding environment, the hazard characteristics, and the vulnerability of the exposure. Using this framework, we explore the various causes of rainstorm flooding in rural China, assess the effectiveness of responses to these disasters, and examine the differences between urban and rural responses. Our analysis reveals that the increase in rainstorm flooding in rural China is a complex result of multiple factors, including increased risk, instability, and vulnerability. While rural areas have made some progress in monitoring and responding to rainstorm flooding disasters, they remain weaker than cities in terms of governance and disaster management systems. To address these challenges, we recommend increased attention to rural flood control and disaster reduction, stronger preparation for disaster prevention and reduction plans, enhanced rural disaster infrastructure defense, increased financial support for water conservancy projects, and stronger professional emergency response teams, disaster relief equipment, and materials. Additionally, we recommend strengthening disaster prevention and reduction education and publicity among rural residents to promote the ability to self-rescue and mutual rescue. The disastrous impact of climate change and resulting extreme weather events on rural areas has become increasingly evident in recent years. China's vast rural areas have been particularly affected by frequent rainstorm flooding disasters. This paper identifies the increase in rainstorms, the instability of the disaster breeding environment in rural areas, and the increased vulnerability of exposures in these areas as the main reasons behind the intensification of rural rainstorm flooding. To mitigate the impact of these disasters, attention should be paid to natural disaster risk management, normalized flood control, and disaster reduction in rural areas. This requires overall coordination of rural disaster prevention and reduction planning, emphasis on improving the defense capacity of rural areas, increasing financial support for defensive water conservancy projects, promoting the construction of professional emergency rescue teams in rural areas, scientifically distributing flood prevention and disaster relief equipment and materials, strengthening publicity and education on disaster prevention and reduction, and enhancing rural disaster prevention awareness and self-rescue and mutual rescue capabilities. [ABSTRACT FROM AUTHOR]

Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model's performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon's center position, and the predicted path closely aligned with the actual trajectory. [ABSTRACT FROM AUTHOR]

In dealing with the occurrence of earthquakes, urban communities and people within the system should primarily consider the geological background, secondary hazards caused by earthquakes, countermeasures to mitigate earthquake disasters, community connectivity, and the impact of various indicators and criteria on the earthquake-proof capacity of the community. It is important to comprehensively assess these factors to ensure that a community is well-prepared to withstand an earthquake. This study takes Shenzhen (China) as an example to conduct a preliminarily evaluation of a method of earthquake-proof capacity during an earthquake, with the results reflecting the comprehensive capacity of communities. The evaluation framework focuses more on effectively improving the disaster response capacity and resilience of urban communities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been an unprecedented challenge for humanity, causing widespread disruptions to daily life and economies worldwide. However, amidst the challenges, it has also presented an opportunity to reflect on our competencies, capabilities, and objectives. One area that has gained significant attention during this time is the humanitarian supply chain management theories, which offered significant directions to the scholars engaged in operations and supply chain management, practitioners and policymakers. To further advance the theoretical boundaries of the humanitarian supply chain and expand the scope and boundaries of the discipline, a special issue has been organised. This issue includes 24 carefully selected articles that have gone through multiple rounds of review. The articles are all centred on the humanitarian crisis resulting from COVID-19 and offer a comprehensive understanding of the challenges faced by various stakeholders. Additionally, one relevant article from a previous issue has been included, which explores the potential use of drones in disaster relief operations. The articles included in this special issue have significant implications for theory, practice, and policy, providing valuable insights and direction for further research to expand the scope of the discipline. This particular issue is a crucial contribution to humanitarian supply chain management. It provides a deeper understanding of the complex challenges faced during a crisis and the best practices to overcome them. [ABSTRACT FROM AUTHOR]

Large-scale disasters occur worldwide, with a continuing surge in the frequency and severity of disruptive events. Researchers have developed several optimization models to address the critical challenges of disaster relief supply chains (e.g., emergency material reserving and scheduling inefficiencies). However, most developed algorithms are proven to have low fault tolerance, which makes it difficult for disaster relief supply chain managers to obtain optimal solutions and meet the emergency distribution requirements within a limited time frame. Considering the uncertainty and ambiguity of disaster relief information and using Interval Type-2 Fuzzy Set (IT2TFS), this paper presents a collaborative optimization model based on an integrative emergency material supplier evaluation framework. The optimal emergency material suppliers are first selected using a multi-attribute group decision-making ranking method. Multi-objective fuzzy optimization is then run in three emergency phases: early -, mid-, and late-disaster relief stages. Focusing on a massive flash flood disaster event in Yunnan Province as a case study, a comprehensive numerical analysis tests and validates the developed model. The results revealed that the proposed optimization method can optimize emergency material planning while ensuring that reserve material safety inventory is always maintained at a reasonable level. The presented method suggests a fuzzy interval to prevent emergency materials' safety inventory shortage and minimize continuous life/property losses in disaster-affected areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted life as usual around the globe. Efforts to control the spread of the virus with lockdowns and border closures pushed millions of people into food and social insecurity. Most research on humanitarian organizations have been dominated by the uncertainty and urgency of disaster response operations. However, some humanitarian organizations also operate in long-term continuous aid programs where efficiency is the key goal. We analyzed the operations of food banks in the Feeding America network and The Salvation Army USA, and found them to be ambidextrous organizations. The ambidextrous humanitarian organizations like food banks and Salvation Army, focus on long-term continuous aid programs, specifically pertaining to the sustenance of the communities they serve, but also play a key part as first responders or as local agencies aiding in disaster relief and response. We propose a framework to analyze disaster, development, and sustenance aid supply chains, and identify future research opportunities. [ABSTRACT FROM AUTHOR]

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions. [ABSTRACT FROM AUTHOR]

Face detection in UAV imagery requires high accuracy and low execution time for real-time mission-critical operations in public safety, emergency management, disaster relief and other applications. This study presents UWS-YOLO, a new convolutional neural network (CNN)-based machine learning algorithm designed to address these demanding requirements. UWS-YOLO's key strengths lie in its exceptional speed, remarkable accuracy and ability to handle complex UAV operations. This algorithm presents a balanced and portable solution for real-time face detection in UAV applications. Evaluation and comparison with the state-of-the-art algorithms using standard and UAV-specific datasets demonstrate UWS-YOLO's superiority. It achieves 59.29% of accuracy compared with 27.43% in a state-of-the-art solution RetinaFace and 46.59% with YOLOv7. Additionally, UWS-YOLO operates at 11 milliseconds, which is 345% faster than RetinaFace and 373% than YOLOv7. [ABSTRACT FROM AUTHOR]

This study delves into the efficiency and structure of the social organization network involved in flood disaster management in China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Leveraging the global knowledge graph, we analyzed social media data and news reports to identify the pivotal roles and interactions of various social organization types in flood disaster governance. The empirical findings underscore the central role of national public social organizations, evidenced by their high frequency of news coverage and mentions. Meanwhile, International Social Organizations and Local Public Social Organizations exhibit growing influence and participation. As China shifts toward a comprehensive disaster reduction strategy, the study observes increased diversity and decentralization among these organizations, with nongovernmental organizations and volunteer groups playing vital roles in resource mobilization, information dissemination, and disaster coordination. Network analysis unveils evolving interaction patterns and co-occurrence frequencies among social organizations, reflecting the evolving roles of different organization types in disaster relief. This research aims to provide actionable insights to enhance the effectiveness of social organizations in disaster management, contributing to the establishment of a more resilient and efficient disaster management framework in the GBA. [ABSTRACT FROM AUTHOR]