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Background: The recent crises of bushfires, floods, and the COVID-19 pandemic on the southeast coast of Australia were unprecedented in their extent and intensity. Few studies have investigated responses to cumulative disasters in First Nations communities, despite acknowledgement that these crises disproportionately impact First Nations people. This study was conducted by Aboriginal and non-Aboriginal researchers in partnership with Waminda, South Coast Women's Health and Wellbeing Aboriginal Corporation, an Aboriginal Community Controlled Health Organisation (ACCHO). It investigated the collective experiences of people affected by cumulative disasters to identify the practices that support healing, and recovery for Aboriginal communities. The study addresses a knowledge gap of how Waminda, designs, manages and delivers responses to address complex health and social issues in the context of cumulative disasters. Methods: Underpinned by practice theory this study employed Indigenous-informed, narrative inquiry. Culturally-appropriate, multiple interpretive methods were used to collect data including: observations; yarns with Aboriginal community members, yarns with Waminda practitioners, management and board members; interviews-to-the-double, visual images and documentation. The data were collated and analysed using the phases of reflexive thematic analysis. Results: The paper articulates a suite of culturally safe and place-based practices that enhance social, emotional and spiritual well-being following cumulative disasters. These practice bundles include: adopting a Country-centred conception of local communities; being community-led; viewing care as a collective, relational, sociomaterial accomplishment and having fluid boundaries. These practice bundles 'hang together' through organising practices including the Waminda Model of Care, staff wellbeing framework and emergency management plan which orient action and manage risks. The paper demonstrates the need for disaster responses to be community-led and culturally situated. ACCHOs are shown to play a crucial role, and their local responses to immediate community needs are grounded in contextual knowledge and use existing resources rather than relying on mainstream system-wide interventions. Conclusions: The paper suggests crafting responses that focus on assisting communities (re)gain their sense of belonging, hope for the future, control over their lives and their capacities to care for and to be cared for by Country, are key to both enhancing healing, health and well-being and harnessing the strengths of communities. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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]

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]

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]

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]

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.

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]

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]

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]

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]

Seismic landslide hazard assessment plays a very important guiding role during urgent earthquake relief. In August 2017, an Ms 7.0 earthquake in Jiuzhaigou County, Sichuan Province, China, triggered thousands of landslides. Based on the analysis of geological settings and coseismic landslide characteristics, the Newmark model is used to complete the seismic landslide hazard assessment. Three seismic motion parameters, namely, peak ground acceleration (PGA), traditional Arias intensity (Arias_P), and improved Arias intensity (Arias_C), are adopted. A publicly published coseismic landslide catalog is used as the validation samples. The results show that the coseismic landslides are mainly distributed in the deep gullies and steep mountainous slopes on the north and south sides of the epicenter. The seismic landslide hazard accuracy based on Arias_C is the best, followed by that based on PGA and Arias_P. The spatial distribution of seismic landslide hazards based on Arias_C shows an almost standard elliptical ring and is in good agreement with that of coseismic landslides. These results fully reflect the combined influence of the epicenter and seismogenic fault on landslide development. The middle seismic landslide hazard and over are mainly located at areas with seismic intensity of VII degree and above. The Arias intensity is very suitable for rapid seismic landslide hazard assessment in emergency situations. The study results can provide scientific and technological support for rapid earthquake relief and have reference significance for future seismic landslide hazard assessment. [ABSTRACT FROM AUTHOR]

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results. [ABSTRACT FROM AUTHOR]