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

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.

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

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

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]

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

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 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]

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]

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]

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]

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 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]

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]

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]

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

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

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

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 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]

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]

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]

When governments are structurally unable to provide social services, or when disaster strikes, relief organisations tend to step in. This is also the case in South and East Asia. Such organisations may be faith-based, leading to the emergence of what Asians colloquially call “rice Christians”: persons who (allegedly) convert to a religion in order to access services, or out of loyalty to the aid-supplying organisation. Such converts may continue to practice their traditional religions and beliefs. This raises the question whether rights to religion or belief are still available to “rice Christians” when governments, e.g., seek to redevelop a sacred site that formed part of their original belief system. The present article addresses that question, drawing upon the concept of multiple religious belonging (‘mrb’) and a 2017 decision of the African Court on Human and Peoples’ Rights in which the court accepted that individuals may adhere to multiple religions, especially where conversion happened within a missionary context and the now-claimed belief is Indigenous or traditional. It argues in favour of recognition of mrb within the context of freedom of religion, which would mean that also “rice Christians” can continue to rely on original belief systems to protect their practices and places. [ABSTRACT FROM AUTHOR]

Purpose: This article argues that transnational multilingual entrepreneurs, particularly immigrant Asian/American small business owners, negotiate their access to disaster recovery-related resources by tactically sharing their own user cases through translocal business networks and developing local ethnocultural collaborative entrepreneurships. Method: This study was based on a 9-month user experience study across 14 entrepreneurial sites in two cities located in U.S.-Mexico border regions. Results: User narratives from this study demonstrate that transnational multilingual small business workers tactically adopted nuanced collaboration tactics in navigating resource-constrained environments in post-pandemic workplace settings. Conclusion: The study findings suggest that the binary notion of use and non-use of multilingual resources and the arrangement of multilingual content in federal disaster relief programs should be reconsidered to better situate human-centered design for transnational multilingual users in workplaces in under-resourced disaster-specific bureaucratic writing contexts. [ABSTRACT FROM AUTHOR]

The article is devoted to exploring the possibilities of using online communication channels and modelling the relationship between offline and online channels in social work with traumatized people particularly those affected by the Chernobyl disaster. The distribution of age and number of Internet users for different categories of victims of the Chernobyl disaster has been calculated. It is shown that the percentage of Internet users, including the elderly ones, in foreign countries is higher than in Ukraine, which creates prerequisites for increasing their involvement in online communication. It is established that in the majority of scientific and educational information sources there is no grouping of the victims of the Chernobyl disaster into a separate category of the object of social work, which leads to the necessity of enriching the curricula of social workers’ professional training. [ABSTRACT FROM AUTHOR]

Earthquakes in the past have caused significant damage, injuries, and deaths. The remaining resources do not equal the amount needed. For this reason, effective and efficient disaster management methods are needed. Learning from recent research that describes how they optimize the coordination function of hospital systems to overcome the imbalance between need and care capacity, we add the bed occupancy ratio (BOR) of healthcare facilities to the modeling. This paper aims to provide information about the distribution mechanism for injured victims by minimizing the total time to arrive at the healthcare facility. This victim evacuation modeling uses the integer linear programming method with two scenarios. Simulations were carried out on 164 injured victims, estimated by residents who live in areas that cross the Great Sumatran fault. The results show that the second scenario, which involves distributing it to the nearest healthcare facility, is 3.6 hours shorter compared to taking it to the general hospital in the first scenario. [ABSTRACT FROM AUTHOR]

Natural disasters lead to massive human and financial losses yearly; thus, disaster planning is of critical importance. One of the most crucial measures for disaster planning is developing an efficient disaster relief supply chain (DRSC) network. Thus, many researchers have focused on this field while overlooking some crucial actual conditions as a result of the complexity of the problem. Consequently, this study develops a DRSC network considering the perishability of relief commodities (RCs), the gradual injection of the limited pre-disaster budgets, pre-disaster lateral transportation, and the time value of money. In this respect, a novel multi-period multi-commodity mixed-integer non-linear programming model is presented, which optimizes pre-disaster warehouse location and inventory management and the post-disaster re-procurement and distribution of RCs in each period. Utilizing a new service utility index, the proposed model strives to minimize deprivation cost while maximizing demand coverage and fair service. To provide the required RCs in the pre-disaster phase, a bidirectional quantity flexibility contract (BQFC) is proposed, which is integrated with multi-sourcing and allows for two-part buybacks, installment and delayed payments, and quantity-based discounts on its terms. The applicability and performance of the model are validated via a real case study in Mashhad, Iran. Various sensitivity analyses are provided to highlight the desirable performance of the model and achieve helpful managerial insights. [ABSTRACT FROM AUTHOR]

The scientific evidence based on experiences with past disasters points to the possibility of the occurrence of future mental health issues among those who were affected by the recent Turkey–Syria earthquake. However, post-disaster care information on factors that could give rise to mental health issues among those affected have yet to be provided. In March 2011, Tohoku University compiled and published a booklet with post-disaster healthcare information based on the experiences with the Great East Japan Earthquake. This study aimed to promote the introduction and use of this booklet for post-disaster care in Turkey and Syria by presenting the results of a satisfaction survey conducted with relevant Japanese organizations about the booklet. A total of 505 Japanese organizations participated in the satisfaction survey of, and evaluated, the booklet. The results indicated the need to consider the ease of understanding for the general public when providing information on post-disaster care through booklets. We hope that this study leads to the appropriate provision of easy-to-understand, post-disaster healthcare information to the victims of the Turkey–Syria earthquake and future disasters. [ABSTRACT FROM AUTHOR]

Natural Disaster is a harsh truth from which nobody can escape and Pakistan has been generally powerless against natural disasters by its novel geo-climatic conditions. Floods, cyclones, tsunamis, droughts, earthquakes, and landslides have been an intermittent peculiarity. Among these floods, earthquakes, and droughts risk are very high. These disasters undermine a huge number of lives and cause enormous scale financial, framework, horticulture, and productivity losses that genuinely block Pakistan's overall development. To limit the harm brought about by disasters, different endeavors have been taken by the government, society, NGOs, and worldwide networks. Disaster management possesses a significant spot in this country's policy framework. The research paper covered the Disaster profile of Pakistan and the Disaster management system in Pakistan in subtleties and concluded with Suggestions. [ABSTRACT FROM AUTHOR]

In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%. [ABSTRACT FROM AUTHOR]

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

After the natural disasters in the Shaanxi-Gansu-Ningxia border region, the Communist Party of China actively responded and took various relief measures to resettle the victims. Mainly through food and fund supply, prioritizing victims in recruitment, transferring victims to safe place, and providing additional compensation, the production and livelihood of disaster victims were secured. By reviewing the disaster relief of the Communist Party of China, some meaningful experiences and inspirations can be concluded. On the basis of material supplies, psychological and spiritual care should not be neglected; Food and fund supply should be distributed fairly and made fully use of; A joint disaster relief mechanism with other departments should be established and all social forces need be mobilized to participate in the resettlement of disaster victims; Marxist perspectives and methods are to be adopted to guide the resettlement work of disaster victims. [ABSTRACT FROM AUTHOR]

With scientists warning that climate change will increase the intensity of hurricanes, the Robert T. Stafford Disaster Relief and Emergency Assistance Act (Stafford Act) will be used by the federal government with greater frequency. But the Stafford Act is not without its flaws. After Hurricane Katrina, a Federal Emergency Management Agency (FEMA) officer declared that the Stafford Act is like “bringing a donkey to the Kentucky Derby.”1 In § 5150 of the Stafford Act, Congress directs that “to the extent feasible and practicable,” federal funds should be given to contractors and businesses local to the area affected.2 However, it is unclear what qualifies as “feasible and practicable.” To best prepare for the likely increase in the Stafford Act’s use, Congress should clarify the ambiguity of the term “feasible and practicable” found in the Stafford Act’s § 5150’s preference for local contractors. As of now, the preference for use of local contractors is easily ignored by those responsible for contracting after a disaster. Ignoring local contractors, and instead using larger, out of state companies, contributed to the federal mismanagement after both Hurricanes Katrina and Maria. Failure to clarify this preference will result in more failed federal responses to disasters. This Note will discuss the benefits of the preference for local contractors and recommend two possible definitions of “feasible and practicable” that will ensure that more locals are being used as a part of the federal government’s response to emergencies and major disasters. Finally, the Note will recommend two amendments to the Stafford Act’s reporting requirements of FEMA that will ensure greater accountability and incentivize the use of local contractors. [ABSTRACT FROM AUTHOR]

Objective: To map and classify the socio-environmental vulnerability of regions in the municipality of Blumenau (SC) to socio-environmental disasters (floods and landslides) through the construction of a socio-environmental vulnerability index (SEVI) for the period 2000-2020. Method: The SEVI map was developed from a multicriteria analysis with the aid of a geographic information system (GIS). For the SEVI to operate, it was necessary to prepare the indices of environmental susceptibility (ES) and social vulnerability (SV), which were then crossreferenced in an impact matrix. Relevance: To develop a methodology for assessing the socio-environmental vulnerability to disasters, which considers issues related both to the local natural environment and to the ability of social groups to resist/respond to disasters. Results: The results have indicated an increase in informal settlements in permanent preservation areas, environmental degradation, social inequality and consequently a population exposed to risk. In short, Blumenau presents a pattern of high susceptibility and an exacerbated increase in social vulnerability, which thereby configures a highly vulnerable scenario for socioenvironmental disasters. Contributions: Advances made in evaluation research and in formulating integrated management proposals for risk and resilience in the municipalities of Vale do Itajaí/SC/Brazil. In this context, it is essential to understand the scenarios with the greatest impact of socioenvironmental disasters, as a tool for urban planning and disaster risk management in the municipality of Blumenau (SC). [ABSTRACT FROM AUTHOR]

Estimating disaster relief supplies is crucial for governments coordinating and executing disaster relief operations. Rapid and accurate estimation of disaster relief supplies can assist the government to optimize the allocation of resources and better organize relief efforts. Traditional approaches for estimating disaster supplies are based on census data and regional risk assessments. However, these methods are often static and lack timely updates, which can result in significant disparities between the availability and demand of relief supplies. Social media, network maps, and other sources of big data contain a large amount of real-time disaster-related information that can promptly reflect the occurrence of a disaster and the relief requirements of the affected residents in a given region. Based on this information, this study presents a model to estimate the demand for disaster relief supplies using social media data. This study employs a deep learning approach to extract real-time disaster information from social media big data and integrates it with a spatial information diffusion model to estimate the population in need of relief in the affected regions. Additionally, this study estimates the demand for emergency materials based on the population in need of relief. These findings indicate that social media data can capture information on the demand for relief materials in disaster-affected regions. Moreover, integrating social media big data with traditional static data can effectively improve the accuracy and timeliness of estimating the demand for disaster relief supplies. [ABSTRACT FROM AUTHOR]

Dr. Rebecca Pender Baum interviews Dr. Cathy Malchiodi, a leading international expert, writer, and educator in the fields of art therapy, expressive arts therapy, as well as, trauma and arts in healthcare. Dr. Malchiodi has over 30 years of experience and education in trauma intervention and disaster relief. This interview will highlight her expressive arts background and the use of this modality in working with families. [ABSTRACT FROM AUTHOR]

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