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The Indian subcontinent is highly susceptible to natural disasters. To abate the effect of the disasters, the government emanates institutional mechanisms and strategies for disaster preparedness, mitigation and response. Although there is well-established institutional framework in India for disaster management, there is no study that proposes a disaster supply chain structure under the institutional framework and integrates information and digital technologies (IDT) of industry 4.0 within it. The present study overcomes this research gap. It introduces an IDT structure within the institutional framework of India for multi-agency information sharing, coordination and decision making. It formulates a mathematical model and analyses the impact of IDT on the total response time through three scenarios. The results of scenario analysis reveal the significance of IDT on the disaster supply chains and its capability to handle information delays and IDT failure. The present study can be adopted by disaster management institutions to construct and implement better response practices at the operational, planning and strategic levels. Furthermore, the proposed model in the study can be used for vaccination planning, which includes vaccine distribution, monitoring, regulation and effective implmentation as a response against the current pandemic situation caused due to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure. [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]

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

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

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

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

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

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

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

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

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

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

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

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

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]

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]

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]

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]

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 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]

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]

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 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.)

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]

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]

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]

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]

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.)

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]

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]

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]

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]

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]

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]

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]

In the twenty-first century, providing humanitarian assistance to other nations forms an essential part of the U.S. military's mission. Designed to "relieve or reduce endemic conditions such as human suffering, disease, hunger, [and] privation," the military's humanitarian tasks include such activities as assisting refugees, delivering food and medical equipment to partner nations, and removing unexploded landmines. But among the most recognized—and best publicized—of the U.S. Armed Forces' humanitarian activities are its foreign disaster relief operations. In response to such recent catastrophes as the 2010 earthquake in Haiti, the 2011 tsunami in Japan, 2019's Cyclone Idai in Mozambique, and the 2023 earthquakes in Turkey, the Department of Defense launched extensive humanitarian operations. Taking advantage of the U.S. military's vast global footprint, military leaders dispatched ships and aircraft, service personnel, relief supplies, equipment, and other material and logistical aid to the scene of these crises and to many others like them globally. Carried out for the humanitarian goals of saving lives and reducing suffering, each of these foreign disaster aid operations also shared a critical diplomatic objective: "generating goodwill" for the United States and creating a "favorable impression" of its armed forces. Never purely altruistic, the U.S. military's responses to global disasters also function as a valuable instrument of contemporary U.S. foreign policy. [ABSTRACT FROM AUTHOR]

Nowadays the recent development of new information generation processes in the Internet of Things (IoT) is applied in various factors such as the development of modern sensors, wireless networks, big data applications, automatic monitoring systems, etc. Landslide disaster monitoring is an important and practical direction of Internet of Things technology in a disaster monitoring system, which has the advantages of low cost and mature technology. With the continuous development of IoT technology, standardized data sharing and interoperability have been put forward on the agenda. Based on the Open Geospatial Consortium (OGC) Sensor Thing Application Programming Interface (API) standard, this article analyzes landslide monitoring data sharing and interoperability from the data model, shared service content, and system construction and provides a reference for the standardization of geological disaster data sharing and interoperability regarding IoT applications. Due to the characteristics of a simple layout, strong anti-damage and self-healing ability, self-organization, etc., based on the ZigBee multi-sensor and wireless Mesh network, it is widely used in the data collection and transmission of geological disaster monitoring systems. The experimental analysis is carried out based on various parameters namely computational cost, power utilization, fitness function, danger rate, evacuation time, total travel time as well as root mean square error. Finally, the results conclude that the proposed approach attained better performances in terms of cost, power utilization, and error rate. [ABSTRACT FROM AUTHOR]

In recent years, there is a significant increase in research combining social media data for disaster warning and damage assessment. When natural disasters occur, social media data can also contain rumors, which not only reduce the accuracy of assessment but also have a very negative social impact. In this paper, a multi-feature fusion neural network with attention mechanism is proposed for rumor detection, which makes the attempt to integrate user, textual and propagation features in one united framework. Specifically, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) is applied to extract user and textual features and a Graph Convolutional Neural Network (GCN) is employed to extract the high-order propagation features. In addition, both the complementary and alignment relationships between different features are considered to achieve better fusion. It shows that our method can detect rumors effectively and perform better than previous methods on the Weibo dataset. To validate the effectiveness of our model, rumor detection is conducted in the social media data collected from Typhoon Lekima on Aug 10th–14th 2019 in China, the earthquake of magnitude 6.8 on Sep 5th–9th, 2022 in Sichuan, China, the wildfire on Aug 15th–26th, 2022 in Chongqing, China. Results show that: (1) the proposed method performs well in rumor detection in disaster; (2) rumors often appear along with hot topics; (3) rumors express much negative sentiment; (4) rumor propagation networks have tighter structure and deeper propagation depth. (5) rumors account for a relatively small percentage of social media data in disaster, which means that most social media data is credible. [ABSTRACT FROM AUTHOR]

This study focuses on the identification of collapsed buildings in satellite images after earthquakes through deep learning-based image segmentation models. The performance of four different architectures, namely U-Net, LinkNet, FPN, and PSPNet, was evaluated using various performance metrics, such as accuracy, precision, recall, F1 score, specificity, AUC, and IoU. The study used satellite images taken from the area located in the south and southeast of Türkiye covering the eleven provinces which are most affected by the Mw 7.7 Pazarcık (Kahramanmaraş) and Mw 7.6 Elbistan (Kahramanmaraş) earthquakes. The results indicated that FPN and U-Net were the best-performing models depending on the performance metric of interest. FPN achieved the highest accuracy and specificity scores, as well as the best precision score, while U-Net achieved the best recall and F1 score values, as well as the best AUC and IoU scores. The training and validation accuracy and loss curves were analyzed, and the results indicated that all four models achieved an accuracy value of over 96%. The FPN model outperformed the others in terms of accurately segmenting images while maintaining a low loss value. This study provides insights into the potential of deep learning-based image segmentation models in disaster management and can be useful for future research in this field. [ABSTRACT FROM AUTHOR]

The frequent occurrence of urban flood disasters is a major and persistent problem threatening the safety of cities in China and elsewhere in the world. As this issue is so pervasive, exploring new methods for more effective risk prevention and urban flood disaster control is now being prioritized. Taking the case of the city of Zhengzhou as an example, this paper proposes using geological, hydrogeological, ecological, and environmental conditions together with appropriate engineering designs to address the problem of urban flooding. The strategy includes integrating urban sponge–hydrogeological conditions, ecological engineering, and the construction of deep underground water storage facilities. Field investigations, data collection and analysis, in situ observations, testing, and laboratory experiments, are analyzed to explain the formation mechanism and means to mitigate flood disasters in Zhengzhou. Our results suggest that the appropriate use of geological, ecological, and hydrogeological aspects, combined with effective engineering practices, can significantly improve the city's flood control capacity. These measures can solve the problem of the "once-in-a-millennium" occurrence of torrential rain disasters such as the "720" torrential rainstorm that has affected the city of Zhengzhou. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. 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.)