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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]

The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational costs and energy usage. Conventionally, data from UAVs are processed using cloud computing, where they are sent to the cloud for analysis. However, this method might not meet the real-time needs of disaster relief scenarios. In contrast, edge computing provides real-time processing at the site but still struggles with computational and energy efficiency issues. To overcome these obstacles and enhance resource utilization, this paper presents a convolutional neural network (CNN) model with an early exit mechanism designed for fire detection in UAVs. This model is implemented using TSMC 40 nm CMOS technology, which aids in hardware acceleration. Notably, the neural network has a modest parameter count of 11.2 k. In the hardware computation part, the CNN circuit completes fire detection in approximately 230,000 cycles. Power-gating techniques are also used to turn off inactive memory, contributing to reduced power consumption. The experimental results show that this neural network reaches a maximum accuracy of 81.49% in the hardware implementation stage. After automatic layout and routing, the CNN hardware accelerator can operate at 300 MHz, consuming 117 mW of power. [ABSTRACT FROM AUTHOR]

Background/Aim. Resuscitation and surgical medical aid are the most important medical procedures in disaster relief operations. The limited time frame available for lifesaving activities in such circumstances demands that resuscitation and surgical medical teams be established and trained for timely reaction and disaster medical support prior to disaster occurrence. The aim of the study was to analyze the awareness among the hospital medical staff in the Plovdiv Region of the hospital response plan in case of disaster, which concerns surgical and resuscitation team composition and tasks for disaster medical support. Methods. An anonymous survey, consisting of 55 questions about hospital staff awareness of the medical activities described in the hospital disaster medical support plan, was conducted between July and September 2019 among 295 hospital medical professionals in the Plovdiv Region, Bulgaria. Descriptive statistics and Pearson's χ2 test were used in the statistical analysis of results. Results. The survey showed that the majority of medical staff demonstrated complete ignorance regarding the presence, number, and tasks of the specialized medical teams planned for hospital disaster response. Employees of multidisciplinary hospitals for active treatment were more familiar with the aforementioned plans than their colleagues from the university multidisciplinary hospitals. Conclusion. Insufficient awareness about human resources foreseen for disaster medical support negatively impacts the hospital surge capacity, leading to inadequate medical assistance in such cases. Hospital tactical disaster preparedness must be ameliorated by conducting a more rigorous training program among medical staff related to the planned hospital activities in case of disastrous events, especially for those working in emergency departments, intensive care units, and surgical departments. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios. [ABSTRACT FROM AUTHOR]

The U.S. federal government manages many domestic and global operations, including environmental disasters. With the need to both mitigate and adapt to climate change, legislative and executive branches have spurred research efforts as the impacts of the Anthropocene accelerate around the country. The Army Corps of Engineers' overlapping interest in security and providing technological answers to mitigate weather disasters has led to recent research and development, including facilitating the federal mandate to convert military fleets to electric vehicles by 2027 while also building a hydrogen fuel cell emergency operations vehicle. The emergency vehicle, H2Rescue, has recently been tested in the field, and further refinements in the technology are leading towards a transition out of development and into production. However, the engineered solution must also attend to the social dimensions of disaster relief. This paper examines past environmental disasters in one location, the Navajo Nation, to describe how the vehicle could provide a combination of technological and societal future research possibilities for environmental anthropology. [ABSTRACT FROM AUTHOR]

Despite many discussions on the impacts of natural disasters on fertility outcomes, their implications for fertility preferences as a mechanism remain unexplored. This study proposes that natural disasters could promote fertility preferences by reinforcing traditional values. The empirical analysis relies on a post-tsunami longitudinal survey of individuals in the provinces of Aceh and North Sumatra in Indonesia frankenberg2020study. Out of the 28,376 survey respondents, our sample considers around 10,700 individuals aged 15 or more, whose responses are available for the variables used in the estimation. The results from individual-level cross-sectional regressions suggest that the ideal number of children increased with the extent of tsunami damage in that region. The increased fertility preference was more pronounced for people with greater religiosity and those who strengthened their family ties after the tsunami, supporting that the shift in fertility preference was intertwined with the rise of traditional values. For policymakers, our findings raise the need to consider the potentially lasting implications of shifts in cultural attitudes toward fertility beyond immediate post-disaster relief efforts. To address the limitations of our cross-sectional evidence, future research will need to investigate the dynamic relationship between fertility preferences and natural disasters using alternative data. [ABSTRACT FROM AUTHOR]

Coastal Bengal is one of the most vulnerable territories in India due to its periodic exposure to severe cyclonic storms. In this context, it is noteworthy that during the second half of the nineteenth century, cyclonic appearances in Bengal became more frequent. Official records reveal that devastating cyclones ravaged this province in 1850, 1851, 1864, 1867, 1869, 1874, 1876, 1885, and 1897. However, in 1874, the intensity of the storm and the height of the storm wave were extremely high. Never before had such a severe cyclone, which became known as the Midnapore and Burdwan Cyclone, struck this province. The present article tells the story of this cyclone in the Bengal Province from a historical perspective. The discussion deals mainly with two subjects. First, it briefly analyses the formation and progress of the 1874 cyclone and its landfall in Bengal. The article shows how the violence of the associated wind force and high storm waves affected the province. In this context, the article examines both the physical and human impacts of the cyclone and addresses the crises that resulted in society. The discussion that I then take up looks into the issue of whether this catastrophic destruction helped generate new meteorological research in terms of developing cyclonic theories. Second, the discussion seeks to address several issues regarding what help the survivors received from the state and how the colonial administration handled the crisis. To understand these issues, not only the question of the efficiency of the government's relief operations towards the disaster but also the attitude of the colonial state towards the recovery must be considered. All these are relevant questions in revisiting the cyclone of 1874. [ABSTRACT FROM AUTHOR]

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

Disaster preparedness by corporate organizations is receiving international attention and is being implemented. However, the efforts taken by many local governments for facilitating corporate disaster preparedness are insufficient. Planning of corporate disaster prevention policies by local governments involves the following questions: What are the experiences of local governments in promoting corporate disaster prevention? How do businesses engage in natural disaster preparedness? This study compiled the corporate disaster prevention and relief initiatives of the California county governments' disaster prevention and relief programs and examined the corporate disaster prevention-related actions of each county government in terms of disaster prevention and relief planning. This study explored the development, nature, and operation of corporate disaster preparedness programs by the California county government and demonstrated the direction and practices of local governments and corporations for promoting disaster preparedness and the construction of response systems. [ABSTRACT FROM AUTHOR]

Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system's performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention. [ABSTRACT FROM AUTHOR]

This study explores a methodology for mapping the 3D spatial information of evacuation routes, aiming to acquire lightweight data to enhance the flexibility of evacuation route development. Utilizing 3D-LiDAR (Light Detection and Ranging) technology, we integrated an Inertial Measurement Unit (IMU) with an existing algorithm to compensate for motion distortion, thereby increasing the mapping accuracy of interior spaces characterized by a paucity of structural features. Given the extensive volume of point cloud data generated by LiDAR, which is impractical for direct application in evacuation route mapping, we categorized the data into local and global maps. This paper presents a strategy to minimize the data volume necessary for generating optimized evacuation route maps, employing the Octomap data format for efficient global map storage. This approach not only addresses the challenges of handling large datasets but also contributes to the development of more accurate and user-friendly evacuation planning tools. [ABSTRACT FROM AUTHOR]

In emergency management, the transportation scheduling of emergency supplies and relief personnel can be regarded as the multi-objective shortest path problem with mixed time window (MOSPPMTW), which has high requirements for timeliness and effectiveness, but the current solution algorithms cannot simultaneously take into account the solution accuracy and computational speed, which is very unfavorable for emergency path decision-making. In this paper, we establish MOSPPMTW matching emergency rescue scenarios, which simultaneously enables the supplies and rescuers to arrive at the emergency scene as soon as possible in the shortest time and at the smallest cost. To solve the complete Pareto optimal surface, we present a ripple spreading algorithm (RSA), which determines the complete Pareto frontier by performing a ripple relay race to obtain the set of Pareto optimal path solutions. The proposed RSA algorithm does not require an initial solution and iterative iterations and only needs to be run once to obtain the solution set. Furthermore, we prove the optimality and time complexity of RSA and conduct multiple sets of example simulation experiments. Compared with other algorithms, RSA performs better in terms of computational speed and solution quality. The advantage is especially more obvious in the computation of large-scale problems. It is applicable to various emergency disaster relief scenarios and can meet the requirements of fast response and timeliness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

With the increased social-emotional needs of students since the COVID-19 pandemic, schools offer an opportunity to support student wellness and address needs. This article describes one district's efforts to develop a systematic, equitable, and collaborative continuum of supports. The district used Elementary and Secondary School Emergency Relief funding from the U.S. Department of Education to establish new initiatives and support for students experiencing mental health struggles. These initiatives and supports include hiring a school social worker, a multi-tiered systems of support (MTSS) framework, implementing universal screening tools for early identification and intervention, using data-driven decision-making, and incorporating progress monitoring efforts to document the impact of services and supports that have been provided to their students. As new initiatives with time limited funding often fail to make a lasting impact, using sustainability strategies is critical. This article describes the district's systematic, equitable, and collaborative approaches with sustainability considerations. We also describe barriers and next steps to assist other districts in planning for change. We conclude with a discussion of implications for practice and research. [ABSTRACT FROM AUTHOR]

After decades of presuming that climate adaptation is a private good benefitting only those receiving resources to reduce individual climate risks, respondents in a survey experiment among the climate-vulnerable in Bangladesh chose less-particularistic adaptation projects than "electoral connection" disaster relief theories predict and more "short-sighted" projects than international diplomats anticipate. This article reports on the experiment, which asked a representative national sample of Bangladeshis whether they favor spending funds on short-term particularistic solutions (disaster relief stockpiles), medium-term inclusionary and non-excludable solutions (ocean embankments), or long-term, public goods solutions (the development of flood-resistant rice seeds). More respondents chose "middle ground" embankment spending, and a statistically significant change in respondent propensities was tied to their lived experience with climate vulnerability rather than electoral incentives. The logic of their choices contradicts existing explanations, implying that a reconsideration of vulnerable community preferences, and how to address them, may be needed. [ABSTRACT FROM AUTHOR]

Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognized strengths that can be utilized pre-, peri-, and post-disaster. One of these strengths is older adults' unique social capital that stems from long-standing connections with other members of their respective communities. Using data from in-depth, semistructured interviews with farmers in British Columbia 3–11 months after the 2021 floods, this research explored the experiences of older adult farmers' recovery. The farmers discussed how they leveraged their social capital to aid in their recovery efforts from the flood event. By using their bonding social capital, older adult farmers transformed their existing, deep-rooted connections into post-disaster assistance. This, in turn, generated the idea of the therapeutic community, helping community members cope in the aftermath. This research indicated the need to further examine how older adults in disaster settings can be viewed as assets with community knowledge and skills as opposed to solely as a vulnerable population. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates digitalization in the shipping industry by analyzing over 500 industry presentations from an eight-year span to discern key trends and nascent signals. Employing optical character recognition, advanced natural language processing techniques, and similarity metrics, the research enhances topic interpretability. Through Theil–Sen regressions and diffusion metrics, it identifies trends and emerging signals, noting a rise in interest in smart ports and supply chain management, signaling a shift toward more intelligent technology integration. However, attention to supply chain management shows a decline. The research tracks a shift from broad technology themes to specific areas like cybersecurity and blockchain, reflecting a narrative pivot to tackle particular digital challenges and opportunities. The study detects weak signals, including terms like "subsea" and "drone", suggesting forthcoming industry innovations and shifts, notably toward ESG considerations. An additional machine learning analysis corroborates findings on key topics like energy efficiency and crew welfare, also spotlighting virtual disaster recovery and ERP projects as emerging areas of interest. This work aids in comprehending the fluid digitalization landscape in shipping, highlighting the sector's ongoing evolution, and underscoring the need for further inquiry into autonomous shipping and related domains. [ABSTRACT FROM AUTHOR]

Climate Change will increase the frequency and intensity of disasters necessitating United States Department of Defense (DoD) intervention. The US Congress and DoD have produced reports and demanded action for climate change-related security concerns, but have largely left foreign disaster relief capacity unaddressed. The DoD does not have dedicated force structure for humanitarian assistance and disaster relief (HA/DR), potentially compromising mission readiness and operational effectiveness as well as jeopardizing U.S. foreign interests. Proposed mitigations for the status quo include bolstering international organizations, expanding the United States Agency for International Development (USAID) capacity to respond to disasters, and creating dedicated DoD HA/DR force structure. Adopting one or more of the mitigation strategies bolsters the DoD’s ability to respond to crises, secure national interests, and prevent malign states from subverting the international system. [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.

The article discusses the book "Disasters, Migration, Refugee Policy, and Other Fast or Slow Catastrophes" by Maria Cristina Garcia, which examines the impact of climate change on migration and refugee policies in Central America and the Caribbean. The book focuses on specific cases, such as the volcanic eruptions in Montserrat and the effects of Hurricane Mitch in Nicaragua and Honduras. It argues that U.S. foreign policy, including Cold War policies and neoliberal economic reforms, has exacerbated the impacts of climate-related disasters and contributed to the displacement of people. The book also highlights the failures of U.S. policy in responding to disasters in Puerto Rico and the U.S. Virgin Islands. The author calls for a reevaluation of refugee policies to include provisions for climate-displaced persons and stronger international action to address climate change. [Extracted from the article]

The book "Catastrophes, confrontations, and constraints: how disasters shape the dynamics of armed conflict" by Tobias Ide explores the interplay between natural disasters and armed conflicts. The author examines how disasters can impact conflict intensity and duration, proposing three causal mechanisms: impact on motivations, impact on the strategic context, and impact on communication. The book uses qualitative comparative analysis to identify complex patterns and provides insights into the diverse impacts of disasters on armed conflict dynamics. It also highlights the potential for disasters to act as catalysts for peace and environmental stewardship, suggesting opportunities for integrating peacebuilding and conservation efforts. Overall, the book contributes to the understanding of conflict dynamics and the interaction between environmental disasters and socio-political factors. [Extracted from the article]

While market-based solutions are increasingly being proposed to address major societal and development issues, they are also often considered antithetical to issues such as climate change, poverty alleviation, and disaster response. In particular, the interorganizational systems involved in such market solutions give rise to multiple contradictory tensions, known as paradoxes. We, therefore, adopt a paradox lens to explain the dynamics through which different actors within these systems navigate the contradictions that are generated. Drawing on a global qualitative study of multi-country risk pools that provide rapid capital in the immediate aftermath of a disaster, we advance paradox theory by showing how organizational actors' interactions (a) maintain equilibrium by generating mutually reinforcing balance among paradoxes, while (b) the clustering of poles from different paradoxes generates disequilibrium, and (c) the reknotting of poles from different paradoxes restores equilibrium. As our process framework shows, these dynamics form an iterative cycle between equilibrium and disequilibrium that is essential in enhancing the promise of market-based solutions to address development issues, which in our study refers to increasing the rapid availability of capital to respond to disasters such as hurricanes, earthquakes, and droughts. [ABSTRACT FROM AUTHOR]

Humanitarian logistics research has recently received tremendous interest from researchers and practitioners due to its importance in assisting relief operations. While there is an increasing trend for mathematical models related to preparedness and response phases for disaster operations management, recovery-phase models are not as emphasised as other phases due to scarce data and model complication from NP-hard nature of the models. One particular approach that can provide a sufficiently good solution for the NP-hard problems is the metaheuristic approach. In this research, we explore the bi-criteria integrated response and recovery model for making strategic post-disaster decisions in the relief distribution and short-term network restoration. Next, with a focus on considering conflicting objectives between fairness and cost of this problem, we propose a hybrid approach with its evolutionary component based on the non-dominated sorting genetic algorithm-II (NSGA-II) called HNSGA-II. The proposed HNSGA-II is compared against the exact method using the approximate Pareto-front analysis. The proposed algorithm is verified using a case study from a risk assessment tool called Hazus to illustrate how to cope with the aftermath of an earthquake. Finally, results are evaluated using a Hypervolume-based technique and computation time to illustrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Frequent natural disasters challenge relief network efficiency. This paper introduces a stochastic relief network with limited path capacity, develops an equilibrium model based on cumulative prospect theory, and formulates it as a stochastic variational inequality problem to enhance emergency response and resource allocation efficiency. Using the NCP function, Lagrange function, and random variables, the model dynamically monitors disasters, enabling rational resource allocation for quick decision-making. Compared to traditional methods, our model significantly improves resource scheduling and reduces disaster response costs. Through a random network example, we validate the model's effectiveness in aiding intelligent decision-making for relief plans and resource allocation optimization.

Multi-view stereo plays an important role in 3D reconstruction but suffers from low reconstruction efficiency and has difficulties reconstructing areas with low or repeated textures. To address this, we propose MVP-Stereo, a novel multi-view parallel patchmatch stereo method. MVP-Stereo employs two key techniques. First, MVP-Stereo utilizes multi-view dilated ZNCC to handle low texture and repeated texture by dynamically adjusting the matching window size based on image variance and using a portion of pixels to calculate matching costs without increasing computational complexity. Second, MVP-Stereo leverages multi-scale parallel patchmatch to reconstruct the depth map for each image in a highly efficient manner, which is implemented by CUDA with random initialization, multi-scale parallel spatial propagation, random refinement, and the coarse-to-fine strategy. Experiments on the Strecha dataset, the ETH3D benchmark, and the UAV dataset demonstrate that MVP-Stereo can achieve competitive reconstruction quality compared to state-of-the-art methods with the highest reconstruction efficiency. For example, MVP-Stereo outperforms COLMAP in reconstruction quality by around 30 % of reconstruction time, and achieves around 90 % of the quality of ACMMP and SD-MVS in only around 20 % of the time. In summary, MVP-Stereo can efficiently reconstruct high-quality point clouds and meet the requirements of several photogrammetric applications, such as emergency relief, infrastructure inspection, and environmental monitoring. [ABSTRACT FROM AUTHOR]

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities' energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts. [ABSTRACT FROM AUTHOR]

Low latency and high bandwidth heralded with 5G networks will allow transmission of large amounts of Mission-Critical data over a short time period. 5G hence unlocks several capabilities for novel Public Protection and Disaster Relief (PPDR) applications, developed to support first responders in making faster and more accurate decisions during times of crisis. As various research initiatives are giving shape to the Network Application ecosystem as an interaction layer between vertical applications and the network control plane, in this article we explore how this concept can unlock finer network service management capabilities that can be leveraged by PPDR solution developers. In particular, we elaborate on the role of Network Applications as means for developers to assure prioritization of specific emergency flows of data, such as high-definition video transmission from PPDR field users to remote operators. To demonstrate this potential in future PPDR-over-5G services, we delve into the transfer of network-intensive PPDR solutions to the Network Application model. We then explore novelties in Network Application experimentation platforms, aiming to streamline development and deployment of such integrated systems across existing 5G infrastructures, by providing the reliability and multi-cluster environments they require. [ABSTRACT FROM AUTHOR]

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

Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work. [ABSTRACT FROM AUTHOR]

This study addresses the crucial challenge of efficient resource allocation during emergencies, aiming to minimize loss of life and property. We analyzed a case study for the 2015 floods in Tamil Nadu, India, where local emergency centers in Chennai and surrounding cities distributed relief materials. Additionally, employing a Bi-objective Solid Transportation Problem (BOSTP) model, minimizes both cost and time of delivery. Further, the traditional methods like Row Maximum, Least Cost, and Vogel's Approximation are compared to BOSTP, which shows that Column Maximum yields an optimal result based on cost and time constraints. However, introduced a novel algorithm for BOSTP using trapezoidal neutrosophic numbers(TNN) to account for inherent uncertainties in logistics, specifically shipment cost and time. Lastly, performance analysis of the BOSTP model and sensitivity analysis under various scenarios demonstrate its effectiveness in enhancing rapid humanitarian assistance during emergencies. This case study contributes to developing efficient bi-objective models for future disaster response efforts. [ABSTRACT FROM AUTHOR]

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches. [ABSTRACT FROM AUTHOR]

The LRAD, the brand name of Genasys' industry-leading acoustic hailing devices (AHDs) product range, has been discussed in the context of the weaponisation of sound. This article contributes a critical science and technology studies perspective by approaching AHDs as security sound technologies: as products within a security and disaster audio industry, that partake in, and are symptomatic of, a broader logic of securitisation. The goal here is neither decrying AHDs as invested in injurious sound nor making distinctions between purportedly violent and benign AHD features and applications, but to inquire into "the concept of an AHD" and, in turn, to understand the logics and workingsthat underlay AHDs' framing and adoption as 21st-century security sound solutions across the military-civilian, government-corporate continuum. While there are important differences between AHD models, all AHDs produce narrow and directional acoustic beams and broadcast high-intensity, far-reaching "unmistakable" voice commands and piercing deterrent tones. What makes AHDs "sound" and otherwise desirable as "workhorses" for a broad range of security and safety applications? The article examines different capability gaps these devices avowedly "bridge" and define. In particular, it discusses AHDs' humanitarian violence, disaster communications and relief, crowd control, and perimeter security applications and functions within US contexts. [ABSTRACT FROM AUTHOR]