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In order to establish principles for implementing requirements contained in legal acts regarding methods of preventing serious industrial accidents, a study of the procedures in place at NITROERG SA, a company manufacturing explosives, was conducted. The analysis also included regulations concerning actions to be taken in the event of an accident, aimed at minimizing negative impacts on people and the environment. Legislative solutions imply the necessity for business entities to submit reports to emergency services, while also carrying out a range of activities to counteract threats associated with serious accidents. This article presents how these recommendations are implemented by the company and the methods of obtaining data to improve the undertaken projects. However, incidents in recent years indicate the need for further verification of processes to eliminate risks. [ABSTRACT FROM AUTHOR]

Purpose: The impact of extreme heat on prisons and carceral facilities is becoming increasingly visible, yet remains overlooked by scholars, practitioners and policymakers. Prisons are a unique type of infrastructure designed to severely limit and control the movement of hundreds and even thousands of individuals as a form of punishment. This leads to many significant challenges to mitigating the risk of heat-related illness in prisons and other carceral spaces that have remained overlooked across many disciplines including emergency management, disasters, corrections and public health. Design/methodology/approach: For this study, we analyzed 192 surveys from incarcerated persons in state prisons throughout Texas to understand how incarceration and the punitive prison environment create challenges to managing extreme heat in prisons. Findings: We found that characteristics of modern incarceration, including communal distribution of resources, crowded conditions and a lack of agency for incarcerated people, create barriers to accessing resources during periods of extreme heat. Furthermore, the punitive nature of the prison environment as manifested in the relationship between staff and incarcerated persons and certain prison policies also create barriers to incarcerated persons accessing resources to reduce their risk of heat-related illness and death. Social implications: These issues are particularly relevant to the health and safety of incarcerated persons during periods of extreme temperatures but also speak broadly to the implications of incarceration, disaster risk, and the advancement of human rights for incarcerated people. Originality/value: This article addresses a gap in the literature by including the perspectives of persons incarcerated in Texas prisons experiencing extreme heat and implicates the characteristics of incarceration and punishment in the production of disaster risk. [ABSTRACT FROM AUTHOR]

The effectiveness of infrastructure resilience relies on the seamless extraction of information, timely acquisition of critical knowledge, and heightened situational awareness. The ongoing utilization of digital citizen communication through social media with response organizations during disasters remains a valuable avenue for disseminating information, ensuring the effective utilization of public resources in emergency response to crisis events. Public agencies can use this information to examine community sentiments and discussions to assess, determine, and prioritize critical areas in need of assistance. However, there are limitations on harnessing precise geolocation information from social media, as well as a lack of mitigating bias of machine learning models used during such events. These limitations can restrict emergency management personnel's ability to locate and promptly delineate actionable insights. Here, we propose a semisupervised machine learning model that utilizes approaches such as transfer learning, topic modeling (i.e., Latent Dirichlet Allocation), and natural language processing to augment data from historical and current social media posts (i.e., Twitter) with community-driven application alerts (i.e., Waze) to achieve further evidence on the location and context of emergency events. The model is designed to also mitigate machine learning bias using the Wells–Du Bois protocol. A framework was developed for this process and is illustrated through a case study on Hurricane Ian and three previous hurricanes that occurred in Florida. This fusion provides increased situational awareness and may enhance the speed of emergency response. This study establishes a foundation for equitable, real-time crisis event detection, expanding organizations' response capacity in allocating resources and reducing harmful effects of disaster, particularly within public infrastructure systems. [ABSTRACT FROM AUTHOR]

Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study, based on the Harmonic Models constructed from Long Time-series Surface Water (LTSW) dynamics, an SIA extraction approach (SHM) was introduced to enhance their accuracy and robustness, thereby improving flood mapping. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Sentinel-1/2 remote sensing data were utilized to extract LTSW. Harmonic analysis was applied to the LTSW dataset, using the amplitude terms in the harmonic model to characterise the frequency of variation between land and water for the surface units, thus extracting the SIAs. The results reveal that the harmonic model parameters are capable of portraying SIA. In comparison to the commonly used WIF thresholding method for SIA extraction, the SHM approach demonstrates superior accuracy and robustness. Leveraging the SIA extracted through SHM, a higher level of accuracy in FIA extraction is achieved. Overall, the SHM offers notable advantages, including high accuracy, automation, and robustness. It offers reliable reference water extents for flood mapping, especially in areas with active and complex hydrological dynamics. SHM can play a crucial role in emergency response to flood disasters, providing essential technical support for natural disaster management and related departments. [ABSTRACT FROM AUTHOR]

: The aim of the work is to review and analyze the performance of the University Multiprofile Hospital for Active Treatment and Emergency Medicine "Pirogov" before, during and after the pandemic of COVID-19. The medical institution has medical-statistical indicators significantly higher than the national average for this type of hospitals. After the declaration of the pandemic, the state of emergency and the reporting of the first cases of coronavirus patients in the hospital, the indicators decreased sharply and in spite of the so-called "waves" of morbidity and periods with decreasing hospitalizations. In summary, the hospital is far from the point of recovery in terms of patient flow and business as usual. In 2022, the number of patients tends to recover to its normal values. Operative activity at the facility has been consistently high over the past years and is 84% overall for all surgical facilities compared to 77% for the same pediot last year in 2021. For 2020, the operative activity is 64.21%, which can be explained by the epidemiological situation with COVID-19. In 2022, there are structures that report an operative activity of more than 99%. All trauma clinics have more than 95% operated patients. Over the years, the majority of surgical treatment is performed on an emergency basis - 86%. [ABSTRACT FROM AUTHOR]

The use of social media in crisis informatics has become increasingly popular across a range of disciplines. Leveraging natural language processing (NLP) techniques enables the analysis of textual data in novel ways and facilitates the use of social media data in disaster management. Analyzing text from social media with NLP can enhance situational awareness, accelerate information dissemination, and monitor affected communities, which is crucial for government agencies and emergency decision makers. However, a comprehensive literature review of NLP and social media data in disaster management is lacking, which presents an obstacle to rapid development in this field. To address this gap, this project surveys 324 related articles published between 2011 and 2022 and investigates the current trends and state-of-the-art NLP applications for using social media data in managing natural disasters. The bibliometric analysis reveals that existing literature focused more on earthquakes, floods, and hurricanes, concentrating on during-event periods, with Twitter (now referred to as X) as the most cited information source. Moreover, our systematic analysis identifies common NLP methodologies and identifies five major applications in current research. Finally, this study provides important insights and potential directions for future research in social media, NLP, and disaster management. [ABSTRACT FROM AUTHOR]

Our built environment is often subjected to multiple (natural) hazards' concurrent and/or sequential impacts. Modeling multihazard interactions (i.e., possible interrelationships between hazard events with their associated frequencies and severities and resulting impacts on a specific location or region of interest) and simulating multihazard scenarios (or event sets; i.e., realizations of possible multihazard interactions) to design and assess civil infrastructure is crucial. This paper presents a probabilistic framework for simulating multihazard scenarios, aiding in civil infrastructure design and risk assessment. Specifically, the proposed approach leverages temporal hypergraphs for characterizing the hazard interactions. Temporal hypergraphs are a higher dimensional generalization of a graph that can capture multi-way connections (i.e., not just pairwise) that can change over time. The temporal attributes of the hypergraphs are defined using a series of occurrence models and simulation-based approaches to generate the arrival times and features (e.g., event characteristics and local intensities) of all considered hazards over a defined space-time interval. A sliding window procedure is also proposed to support decision-makers and other end users in identifying critical multihazard scenario windows that can significantly impact the resilience of the built environment. The framework's applicability is demonstrated using an illustrative example of a hypothetical region susceptible to earthquake-induced ground shaking, liquefaction, tsunami, and riverine flooding. The proposed framework can help decision-makers design and test efficient disaster risk mitigation and management policies. Furthermore, the proposed multihazard scenario modeling framework can be combined with vulnerability/impact assessment methodologies to quantify the multihazard risk of exposed infrastructure and communities. [ABSTRACT FROM AUTHOR]

Hazard mitigation planning requires disparate agencies to negotiate through the planning and implementation processes. These agencies must strategize, develop, and implement plans for addressing scenarios of natural and human-caused hazards. Competing perspectives exist in systems engineering at multiple levels of governance. This paper introduces a framework to understand and coordinate when one or more agencies have outlier perspectives of the priority ordering of initiatives in regional hazard mitigation. Each agency perspective is represented as a scenario that disrupts system order. The paper presents a mathematical framework with 40 initiatives, six ordering criteria, and eight scenarios. The method coordinates evidence and experience that are essential to negotiation of requirements among emergency managers in hazard mitigation planning in the postpandemic era. Practical Applications: Unprecedented combinations of natural and human-caused stressors are challenging to plan for since the perspectives of participating agencies can differ significantly. The agencies that collaborate on emergency management include transportation, law enforcement, environment, prisons, parks and recreation, forestry, energy, and others. This paper facilitates negotiations among these various agencies in the hazard mitigation planning process by introducing a framework to understand and coordinate when one or more agencies have outlier perspectives of the priorities for investments in hazard mitigation. The application of the framework identifies where negotiation among the agencies is most needed in determining hazard mitigation priorities. This approach, grounded in principles of systems engineering and risk analysis, reduces cost overrun and schedule delay in the life cycle of emergency management. [ABSTRACT FROM AUTHOR]

During the fast-paced climate of the COVID-19 pandemic and as the official responses suffered from major inconsistencies and dysfunctions, volunteers and civil society activists initiated a range of strategies to monitor, gather, and disseminate risk information, going beyond the traditional top-down and expert-led interpretation of and approaches to risk mitigation. The current paper draws on growing scholarly recognition that official or government-led responses to disasters can often cause further risks, harms, and inequalities in communities (the "second disaster"), sparking community-based action to monitor and tackle such risks and harms. In so doing, it seeks to bridge the conversation between two distinct yet interrelated fields of community-based early warning systems and community-based monitoring of public goods. Drawing on an exploratory scoping review of peer-reviewed and gray literature, the paper examines the functioning of community-based monitoring of official responses to COVID-19 globally. Our analysis distinguishes four functions served by community-based monitoring: (1) tackling misinformation to enable the public to take protective action; (2) improving access to health services through service monitoring; (3) exposing and challenging misuse and abuse of authority; and (4) spearheading inquiries and probes to hold governments to account. Possibilities and limitations of such collective action are discussed in light of what we know from disaster risk reduction (DRR) scholarship. The paper concludes by recasting the focus on risk, taking it beyond the conventional realm of disaster preparedness and mitigation to cover early response and recovery, while drawing the DRR community's attention to the risk of violation of rights in the name of disaster response. It underscores the role of community-based monitoring in the wake of emergencies as an evidence-driven early warning system, raising the possibility of developing a more democratic and inclusive understanding of risk and protecting and promoting the rights of those who face the disproportionate burden of disasters. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) outbreak has caused tremendous turmoil worldwide and is yet to be contained. Therefore, mitigating the number of people infected by COVID-19 remains a fundamental policy goal for several countries. This study aims to analyse the dynamic relationship between mobility and the rate of change in the number of new infections (NNIs) in Japan. Another goal is to evaluate the effects of various policies, such as mobility control and vaccination, as well as the impact of climate factors on the NNIs. The analysis reveals a strong positive relationship between the growth rate of the NNIs and mobility. Our results also indicate that the government-imposed measure of regulating mobility shows a weaker effect on preventing the spread of infection, as the emergency declaration is repeatedly invoked. By contrast, the recent increase in the infection rate seems to reduce the spread of infection by inducing voluntary restraint, and this phenomenon has become even stronger in the recent period. Regarding the effect of vaccination, the results demonstrate little effect on attenuating the spread of infection. However, it has significantly weakened the mobility – spread relationship, suggesting that it may help in the implementation of economic revitalization policies. [ABSTRACT FROM AUTHOR]

• Using remote sensing data, the statistical studies related to earthquake precursors increased significantly. • Most of them emphasized the existence of abnormal behaviors in precursors. • An attempt has been made to address the advances and challenges in the way of earthquake prediction. Due to the considerable loss of life and damages caused by powerful Earthquakes, along with the strengthening of structures and disaster management systems, many researches are being conducted associated with the possibility of creating Earthquake warning systems. However, no reliable scientific report on successful Earthquake prediction has been published so far. Although there are a couple of published scientific papers indicating the detection of unusual changes in physical and chemical parameters obtained from ground stations, but due to the limitations of in-situ measurement in terms of number, location, time and cost, there was no noticeable progress in Earthquake warning. After the advent of remote sensing satellites, the number of statistical studies related to Earthquake precursors increased significantly, and most of them emphasized the existence of abnormal behaviors in physical and chemical precursors around the time (about 1–30 days before) and locations of powerful Earthquakes. In this study, an attempt has been made to address the advances and challenges in the way of Earthquake prediction using satellite data and provide a perspective of the future of these researches. It should be noted that the increase in the number of satellites designed and launched specially for Earthquake studies, the variety of available Earthquake precursors (multi-precursor analysis), the development of classic and intelligent anomaly detection and predictor algorithms (multi-method analysis), the provision of intelligent systems for the integration of various precursors (fusion and decision systems), the creation of cloud storing and processing data services (Google Earth Engine, Giovanni, etc.) and the development of intelligent user interfaces for public, have made researchers more hopeful for the appearance of low-uncertainty Earthquake warning systems in the near future. [ABSTRACT FROM AUTHOR]

In December 2021, an EF-4 tornado swept through several Midwest states in the United States, with Kentucky being the worst hit. Among the impacted towns was Mayfield, KY, where the historic buildings in downtown suffered significant damage. In response to this disaster, the authors conducted a reconnaissance mission to digitally document the affected historic structures. This involved capturing a series of three-dimensional (3D) point clouds, providing detailed spatial data about the impacted buildings. The resulting data set includes both the original raw data and processed information, now accessible via the DesignSafe open-access repository. This paper outlines the data collection process for the impacted buildings, and the steps undertaken to process it. The final product of this endeavor are the point clouds generated for the historic building typology, which included 10 historic buildings and 2 comparable religious buildings. These point clouds serve as invaluable resources for further analysis, aiding in understanding a disaster's impact, and guiding restoration endeavors. [ABSTRACT FROM AUTHOR]

On December 18, 2023, at 23:59, a magnitude MS 6.2 earthquake struck Jishishan County, Gansu Province. The earthquake triggered a severe liquefaction landslide-mudflow-blockage disaster chain event near Zhongchuan Town in the neighboring Haidong City, Qinghai Province (referred to as the "disaster chain event"). This event caused extensive damage to infrastructure, including residential buildings, power transmission towers, and roads, leading to significant loss of life and property. Based on extensive geological surveys conducted at the disaster site, this study employs a combination of methods, including satellite remote sensing image interpretation and three-dimensional real-world models from unmanned aerial vehicles (UAVs). The primary objective is to elucidate the fundamental developmental characteristics and evolution process of the disaster chain event. Moreover, this study preliminarily explores the formation mechanism of this disaster by integrating the topography, lithology, hydrogeological conditions, and seismic triggering factors. The results suggest that the disaster chain event was controlled by unfavorable local hydrogeological conditions and was triggered by the intense shaking of the Jishishan earthquake, resulting in the occurrence of loess liquefaction landslides. The mobilized mudflow material flowed remotely along the low-friction frozen channel. During the movement stage along the channel, the landslide-mudflow chain transitioned into a blockage failure event due to the obstruction of the downstream earth dam, thereby exacerbating the destructive capability and disaster scope of this event. Further in-depth analysis of the formation conditions and mechanisms of the disaster chain event carries significant implications for guiding disaster reduction and prevention in potential hazard points with similar geological conditions in the northwest Loess Plateau region of China. [ABSTRACT FROM AUTHOR]

As the risk of extreme heat in urban centres becomes more frequent and intense, a comparative case study of New York City, New York's and New Orleans, Louisiana's policy responses to dangerous heat highlights past approaches to protecting vulnerable populations, where improvements can be made to best suit local needs, and the areas where solutions may still be implemented. Two cities at the forefront of the climate crisis in the United States, their policy approaches are guided by their histories, their existing adaptations and behaviours, and the harsh reality the changing climate has wrought. New York City has prioritised cooling centres as a safety measure, as the city is regularly seeing hundreds of heat-related deaths per year, often in unairconditioned homes. New Orleans has prioritised the installation of back-up energy generation following mass blackouts caused by Hurricane Ida in 2021 and deaths of at least 10 residents in the resulting excessive heat exposure. In New York, census tracts with the highest percentage of Black, elderly, and foreign-born populations were most likely to have reduced access to a cooling centre. In New Orleans, tracts with the highest percentage of Black, elderly, unemployed, and impoverished populations were most likely to have reduced access to a cooling centre. Comparing both city's codes, regulations, and policies, reviewing their emergency management communication strategies on social media, and performing GIS analysis on cooling centre locations against vulnerability criteria paints a clear picture of where heat policy stands in both cities and where it could potentially expand. [ABSTRACT FROM AUTHOR]

The frequency and impact of major geological disasters are increasing worldwide, highlighting the growing importance of prompt and efficient damage analyses and response strategy development. Satellite imagery plays a crucial role in determining the extent and impact of disasters on the Earth's surface, offering significant advantages such as extensive real-time data collection and enabling time-series analysis. This facilitates the immediate detection and response to surface changes due to earthquakes, landslides, and volcanic activity. Remote sensing data are used both domestically and internationally to develop and operate real-time monitoring systems for disaster and hazard responses. In particular, South Korea operates over seven low Earth and geostationary orbit satellites. Their use has expanded to include geological disaster surveillance and oceanic and atmospheric monitoring. This study focuses on the analysis of significant geological disasters in South Korea and investigates how satellite data can contribute to disaster response and prevention. Specifically, regions in South Korea prone to three major geological hazards—earthquakes, landslides, and volcanic activities—were selected for analysis based on disaster occurrences or potential occurrences. The data collection status of low Earth orbit satellites operated by South Korea under consistent conditions from 2015 to 2023 was examined. For optical imagery, the analysis of cloud cover during the study period was conducted to assess response possibilities during disaster events. Additionally, response strategies for Synthetic Aperture Radar (SAR) were explored to complement optical imagery. Furthermore, an analysis of satellite data collection trends over time provided fundamental insights for detecting and analyzing destructive geological disasters in the future. This paper is expected to assist South Korea in effectively responding to and managing such disasters, ultimately aiding in the development of comprehensive disaster response strategies. [ABSTRACT FROM AUTHOR]

The request for quickly available forecasts of intense weather and marine events impacting coastal areas is gradually increasing. High-performance computing (HPC) and artificial intelligence techniques are crucial in this application. Risk mitigation and coastal management must design scientific workflow appropriately and maintain them continuously updated and operational. Climate change accelerating increase trend of the past decades impacted on sea-level rise, together with broader factors such as geostatic effects and subsidence, reducing the effectiveness of coastal defenses. Due to this, the support tools, such as Early Warning Systems, have become increasingly more valuable because they can process data promptly and provide valuable indications for mitigation proposals. We developed the Shoreline Alert Model (SAM), an operational Python tool that produces simulation scenarios, 'what-if' assumptions, and coastal flooding forecasts to fill this gap in our study area. SAM aims to provide decision-makers, scientists, and engineers with new tools to help forecast significant weather-marine events and support related management or emergency responses. SAM aims to fill the gap between the wind-driven wave models, which produce simulations and forecasts of waves of significant height, period, and direction in deep or mid-water, and the run-up local models, which exstimulate marine ingression in the event of intense weather phenomena. It employs a parallelization scheme that allows users to run it on heterogeneous parallel architectures. It produced results approximately 24 times faster than the baseline when using shared memory with distributed memory, processing roughly 20,000 coastal cross-shore profiles along the coastline of the Campania region (Italy). Increasing the performance of this model and, at the same time, honoring the need for relatively modest HPC resources will enable the local manager and policymakers to enforce fast and effective responses to intense weather phenomena. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to investigate on the evolution of rainstorm events and the sources of water vapor transport in Hefei under the background of urbanization, and to provide scientific basis for local rainstorm forecast and formulation of adaptive disaster prevention and reduction policies. [Methods] Trend analysis, Mann-Kendall non-stationarity test, GIS spatial interpolation and other methods were used to explore the spatiotemporal evolution of rainstorm events in Hefei, on this basis, the characteristics of rainstorm water vapor transport were analyzed based on the backward trajectory HYSPLIT model of Lagrange method. [Results (1) The urban heat island effect showed an upward trend (0.266°C/10a), impervious water surface continued to increase by 4.99%, the large-scale interannual variability of urban- suburban rainstorms had a feature of asymmetry, with an increase in the amount and number of days of urban rainstorms and a decrease in the intensity of rainstorms at a rate of 1.7 mm/(10 ad); and an increase in the intensity of suburban rainstorms at a rate of 1.1 mm/(10 ad); and a decrease in the amount and number of days of rainstorms. The spatial distribution of the large value area of rainstorm intensity extends outward from urban areas. (2) Urbanization had increased the number and extremity of rainstorm events in downwind urban areas, and the urbanization effect was more pronounced in the rapid development phase than in the slow development phase. The contribution of urbanization effects to the rainfall, number of days and intensity of rainstorms was 41.2%, 50.1%. (3) The water vapor transport from the Indian Ocean. the Bay of Bengal to the South China Sea and the Western Pacific accounted for 44%, 64% and 54% of the total water vapor, respectively, and the water vapor mass from the southwest was the main source of water vapor during the rainstorm process because of its low initial height and large specific humidity. [Conclusion] The asymmetry of rainstorm events in the suburbs of Hefei is more significant in the rapid development stage of urbanization. In the future, we should strengthen the research on the mechanism of rainstorm time change under the background of urbanization. [ABSTRACT FROM AUTHOR]

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered areas in water maps. However, these methods are not tailored for and validated in cloudy and rainy flooding scenarios with rapid water extent changes and limited clear-sky observations, leaving room for further improvements. This study investigated and developed a novel reconstruction method for time series flood extent mapping, supporting spatially seamless monitoring of flood extents. The proposed method first identified surface water from time series images using a fine-tuned large foundation model. Then, the cloud-covered areas in the water maps were reconstructed, adhering to the introduced submaximal stability assumption, on the basis of the prior water occurrence data in the Global Surface Water dataset. The reconstructed time series water maps were refined through spatiotemporal Markov random field modeling for the final delineation of flooding areas. The effectiveness of the proposed method was evaluated with Harmonized Landsat and Sentinel-2 datasets under varying cloud cover conditions, enabling seamless flood mapping at 2–3-day frequency and 30 m resolution. Experiments at four global sites confirmed the superiority of the proposed method. It achieved higher reconstruction accuracy with average F1-scores of 0.931 during floods and 0.903 before/after floods, outperforming the typical gap-filling method with average F1-scores of 0.871 and 0.772, respectively. Additionally, the maximum flood extent maps and flood duration maps, which were composed on the basis of the reconstructed water maps, were more accurate than those using the original cloud-contaminated water maps. The benefits of synthetic aperture radar images (e.g., Sentinel-1) for enhancing flood mapping under cloud cover conditions were also discussed. The method proposed in this paper provided an effective way for flood monitoring in cloudy and rainy scenarios, supporting emergency response and disaster management. The code and datasets used in this study have been made available online (https://github.com/dr-lizhiwei/SeamlessFloodMapper). [ABSTRACT FROM AUTHOR]

As the receiving terminal of liquefied natural gas (LNG), the efficient emergency response of the floating storage and regasification unit (FSRU) is crucial to ensure the safety of LNG transportation at sea. However, few existing literature study the risk issues of FSRUs during emergency operations. In order to improve the emergency response capability of FSRU, this study proposes an innovative assessment method to identify hazards, quantify and rank the risks associated with emergency response and disposal operations of FSRU accidents. Firstly, a comprehensive index hierarchy system applicable to human, equipment, environment, and management aspects of emergency response and disposal operations of FSRU accident is established through an extensive literature review, analysis of accident reports, and expert judgments. Secondly, based on the concept of Intuitionistic Fuzzy Numbers, the Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) operator is used to enhance the conventional FMEA approach. This method considers the varying levels of expert confidence and integrates subjective and objective weights of risk influential factors (RIFs), and the efficacy is validated through sensitivity analysis. Finally, a comprehensive evaluation model employing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation algorithms is used to aggregate the risk values of RIFs. The findings of this study offer decision-makers insights into risks during emergency operation, provide valuable guiding strategies for FSRU accident management, and improve the capability for emergencies at sea. [ABSTRACT FROM AUTHOR]

Beginning in 2025, water utilities in the United States are required to conduct a risk and resilience assessment (RRA) and update their emergency response plan (ERP) every five years to comply with the America's Water Infrastructure Act of 2018 (AWIA). The RRA process focuses on critical assets and identifies risks from specific events or scenarios, while the ERP outlines response actions for hazardous events. Utilities can benefit from coordinating their RRA and ERP efforts, as well as their local hazard mitigation plan (LHMP), to increase efficiency and save time and money. Combining these planning efforts allows utilities to avoid duplicating work and provides a more robust emergency planning program. [Extracted from the article]

Decisions on humanitarian responses to natural disasters are subject to considerable epistemic uncertainty. This paper advocates for postponing the decision point of pre-positioning relief supplies as close to landfall as possible and searching social media right post-landfall so that the demands can be estimated more accurately. We use a realistic hurricane preparedness case to demonstrate the effectiveness of our models and parametric estimation using social media data. The optimal timing to deploy relief supplies before hurricane landfall is noted to be 12 h in advance, which reduces the total cost by 13% more than if relief supplies are deployed 18+ hours in advance. Meanwhile, utilizing social media information can reduce the total cost as well as all kinds of specific costs being considered, excluding the point of dispensing (POD) sites setup cost, by approximately 15%. As the attitude toward risk goes from optimistic, to neutral, and to pessimistic, the number of PODs increases from 3 to 7, and to 8. A similar pattern can be noted in the total costs incurred by these decision-makers. Further, as the aversion to risk increases, locations tend to be chosen farther from landfall with these farther locations serving the less severe patients. [ABSTRACT FROM AUTHOR]

School nurses are critical components of school-based interdisciplinary teams supporting student behavioral health. The purpose of the present study is to understand the extent to which school nurses in New Mexico across grade levels manage emergencies related to behavioral health, violence, and trauma. The study also seeks to identify which topics the school nurses received continuing education (CE) for, and which topics they consider to be important to their practice. Analyses of 2019 statewide survey data show a considerable number of school nurses in New Mexico provide management for behavioral health, violence, and trauma-related emergencies, and nurses in secondary schools are significantly more likely than nurses in primary schools to report managing these emergencies and to have received CE on behavioral health topics. Our findings reaffirm the essential role of school nurses in addressing emergent student health issues and underscore the need for practice- and competency-based behavioral health CE opportunities. [ABSTRACT FROM AUTHOR]

This enquiry investigated fire-outbreak prevention mechanisms in Tanzania's Boarding Secondary Schools (BSSs). Specifically, the enquiry drew on the claim that fire-outbreaks which take place in BSSs are avertable. Based on the claim, two lines of enquiry guided the study: What mechanisms have BSSs adopted to prevent fire-outbreaks; and how adequate are the adopted mechanisms in preventing fire-outbreaks in BSSs? In that regard, the qualitative approach was deemed necessary for the enquiry. The enquiry employed purposive sampling procedures to select 95 informants from four BSSs in five selected regions. Data was sought through interviews, observations, and documentary reviews. Findings revealed various mechanisms that BSSs deploy to prevent fire-outbreaks, inter alia fire emergency exits, fire-resistant furniture, fire-resistant roofs, reliable electrical wiring systems, security guard, and strict school bylaws. However, the mechanisms were largely inadequate, hence failing to prevent effectively fire outbreaks. Limited school financial resources and lack of fire safety awareness among school members caused the inadequacy. [ABSTRACT FROM AUTHOR]

What is arguably the most important bilateral relationship in the world teeters on the brink of catastrophe as the Taiwan issue threatens to precipitate a war between the two nuclear-armed superpowers of China and the United States. Beijing demonstrated its determination to pursue unification with Taiwan during August 2022 by shooting ballistic missiles over the island. Meanwhile, the Ukraine War has increased Washington's resolve to confront perceived Chinese aggression. There are many dimensions to the unfolding crisis over Taiwan's future status—and the wisest US policy is one of realism and restraint in order to avert a disaster of global proportions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced organizations, including higher education institutions, to rapidly adjust their operations. In the face of the pandemic, most higher education institutions shut down their campuses and transitioned to emergency remote teaching mode. This study examines digital resilience in higher education institutions through the conceptual lens of disaster response management, by assessing the role played by the centralized governance of information technology (IT) investments. We posit that centralized IT helps organizations maintain customer satisfaction with services during a crisis (e.g., student satisfaction with classes during COVID-19) by facilitating the organization-wide transition to an emergency operational mode and supporting its service operations. Consolidating data on IT investment, governance, and course evaluations from 463 U.S. higher education institutions from 2017-2020, we show that centralized IT helped organizations adapt better to the pandemic in terms of maintaining student satisfaction. Moreover, we found that centralized IT investments geared toward facilitating organizational coordination and providing instructional and technical support played a pivotal role in enabling ERT and improving student ratings during the crisis. These results are corroborated by interviews with CIOs of U.S. higher education institutions. Additional analyses also suggest that the effectiveness of centralized IT governance is contingent upon organizational size, dissimilarity of local units, and the strategic role of the CIO. We also discuss theoretical extensions toward digital resilience as well as practical implications. [ABSTRACT FROM AUTHOR]

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

Purpose: This study aimed to clarify the impact of the coronavirus disease 2019 (COVID-19) pandemic on individual dentalvisit behaviour and examine the difference between elderly and other individuals regarding the impact on dental visits. Materials and Methods: An interrupted time-series analysis was performed to examine the change in data from the national database before and after the first declaration of a state of emergency. Results: The number of patients visiting a dental clinic (NPVDC), number of dental treatment days (NDTD) and dental expenses (DE) during the first declaration of a state of emergency decreased by 22.1%, 17.9%, and 12.5% in the group under 64 years of age and 26.1%, 26.3%, and 20.1% in the group over 65 years of age, respectively, compared with those in the same month of the previous year. Between March and June 2020, the monthly NPVDC and NDTD were significantly reduced (p < 0.001, p = 0.013) in those over 65 years of age. The DE did not change statistically significantly in either the under 64 group or the over 65 group. There was no statistically significant change in the slope of the regression line in the NPVDC, NDTD, and DE before and after the first state-of-emergency declaration. Conclusion: The first state of emergency greatly reduced the NPVDC, NDTD, and DE compared to those in the previous year. In people aged over 65 years, it might still be unresolved 2 years after the postponement of dental treatment owing to the first declaration of a state of emergency. [ABSTRACT FROM AUTHOR]

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

Objective Once the metro flood incident occurs during operation, mild cases will cause train delay and even train suspension, while severe cases can result in property loss and worse, personal injury. Therefore, it is necessary to analyze the urban metro flood incidents and informatize the emergency management. Method Based on statistics, the typical metro flood incidents during operation in recent years are classified, and the causes are analyzed from both objective and subjective aspects. The problems in flood management and emergency response are summarized. The intelligent requirements for emergency management information platform are discussed, suggestions and measures on emergency management informatization are put forward. Result & Conclusion The problems in management and emergency response of metro flood incidents during operation include insufficient attention to flood in external operations that arise in daily management and emergency response, unclear shutdown standards, poor guidance of emergency plans, insufficient reserves of emergency materials and unsmooth linkage mechanism, as well as untimely information communication. The measures to informatize emergency management include establishing an emergency management information platform, reasonably commanding and dispatching emergency resources, solving coordinative issues with relevant departments in emergency accidents, establishing an accident case library to provide on-site decision-making assistance, real-time on-site videoing, emergency drilling, handheld terminals and etc. Emergency management information measures can effectively reduce the probability and the impact of flood incidents.

Abstract Autonomous vehicles navigating urban roads require technology that combines low latency with high computing power. The limited resources of the vehicle itself compel it to offload task requirements to edge server (ES) for processing assistance. However, as the number of vehicles continues to increase, how edge servers reasonably allocate limited resources to autonomous vehicles becomes critical to the success of urban intelligent transportation services. This paper establishes an urban road scenario with multiple autonomous vehicles and an edge computing server and considers two main driving behaviour transition resource requests, namely car‐following behaviour requests and lane‐changing behaviour requests. Simultaneously, acknowledging that vehicles may encounter unforeseen traffic hazards when switching driving behaviours, a safety redundancy setting strategy is employed to allocate additional resources to the vehicle to ensure safety and model the vehicle resource allocation problem in the autonomous driving system. Double‐deep Q‐network (DDQN) is then used to solve this model and maximize the total system utility by comprehensively considering resource costs, system revenue, and autonomous vehicle safety. Finally, results from the simulation experiment indicate that the proposed dynamic resource allocation scheme, based on deep reinforcement learning for autonomous vehicles under edge computing, not only greatly improves the system's benefits and reduces processing delays compared to traditional greedy algorithms and value iteration, but also effectively ensures security.

Abstract Background As the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses’ disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses’ disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses. Methods A qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination. Results The results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation. Conclusions To ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses.

Policy Point-Counterpoint: Addressing Social Disparities and Resource Allocation in Rural and Urban Communities: Challenges and Solutions Social and health disparities are common problems in the United States. Rural and urban [...]

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively. [ABSTRACT FROM AUTHOR]

An earthquake early-warning system (EEWS) is essential for preserving human life. In terms of disaster management and EQ risk reduction, quickly determining the earthquake’s (EQ’s) magnitude and position is important. To prevent an EQ catastrophe, these parameters can be transmitted over an IoT network. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN), a novel technique for real-time earthquake parameter observation, is introduced in this paper to enhance earthquake early warning (EEW) systems. The proposed framework uses various sophisticated data processing methods to analyze publically accessible earthquake data from sources like the Japan Meteorological Agency. The Morphological Filtering Extended Empirical Wavelet Transformation (MFEEWT) method is first used to pre-process seismic waveform input, significantly lowering noise levels in the data. After that, from the pre-processed waveforms the relevant features are extracted via the Fast Discrete Curvelet Transform with Wrapping (FDCT-WRP) and a Stacked Autoencoder (SAE). The EGNNN, an innovative neural network model created for earthquake parameter prediction, specifically location and magnitude, uses these extracted features as input. The Osprey optimization algorithm (OOA) is employed to enhance the EGNNN model. A crucial component of EEW systems, dependable and quick alert delivery is ensured by the proposed system’s integration with an Internet of Things (IoT) framework. The EGNNN swiftly evaluates earthquake parameters and sends this vital data to a centralized IoT system, enabling pertinent entities to act promptly. Performance evaluation is conducted on the Python platform, comparing the proposed technique with existing methods. The proposed method EGNNN-EPO-IoT achieves a higher warning time of 23.34%, 3.45%, and 7.86%; 12.23%, 32.21%, and 22.09% higher accuracy compared to the other existing techniques such as 3S-AE-CNN-EPO-IoT (3 sec-Autoencoder-Convolutional neural network-Earthquake parameter observation-IoT), XGB-EPO-IoT (Extreme Gradient Boosting-EPO-IoT), and SVM-EPO-IoT (Support Vector Machine-EPO-IoT). [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has resulted in substantial morbidity and mortality globally. The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) covers 99.9% of health insurance claim receipts by general practitioners. The purpose of this study is to investigate the nationwide number of inpatient orthopedic surgeries in Japan during the effect of state of emergency (SoE) due to COVID-19. Method: The NDB has been publicly available since 2014. We retrospectively reviewed the NDB from April 2019 to March 2022. We gathered the monthly number of all inpatient orthopedic surgeries. We also classified orthopedic surgeries into the following 11 categories by using K-codes, Japanese original surgery classification: fracture, arthroplasty, spine, arthroscopy, hardware removal, hand, infection/amputation, ligament/tendon, tumor, joint, and others. By using the average number from April to December 2019 as the reference period, we investigated the increase or decrease orthopedic surgeries during the pandemic period. Results: The NDB showed that the average number of total inpatient orthopedic surgeries during the reference period was 115,343 per month. In May 2020, monthly inpatient orthopedic surgeries decreased by 29.6% to 81,169 surgeries, accounting for 70.3% of the reference period. The second SoE in 2021 saw no change, while the third and fourth SoEs showed slight decreases compared to the reference period. Hardware removal and tumor surgeries in May 2020 decreased to 45.3% and 45.5%, respectively, while fracture surgeries had relatively small decreases. Conclusion: According to NDB, approximately 1.3 million orthopedic inpatient surgeries were performed or claimed in a year in Japan. In May 2020, the first SoE period of the COVID-19 pandemic, the number of inpatient orthopedic surgeries in Japan decreased by 30%. Meanwhile, the decrease was relatively small during the SoE periods in 2021. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to investigate the specific aspects of psychological resilience among ARFF professionals. Utilizing a qualitative approach and collecting data from 11 ARFF professionals using semi-structured interviews, this study delves into an exploration of factors that contribute to psychological resilience among Aircraft Rescue and Firefighting (ARFF) personnel stationed at State Airports, a group operating under emergency circumstances within the aviation industry. The investigation reveals three overarching themes: micro-level personal factors, meso-level organizational factors, and macro-level environmental/societal factors. Key findings reveal that micro-level personal factors, including personality traits and individual coping mechanisms, significantly influence ARFF professionals' psychological resilience. Encompassing leadership quality and team dynamics, meso-level organizational factors have emerged as crucial contributors to resilience. Finally, macro-level environmental/societal factors, such as social support networks and the broader emergency preparedness context, have been identified as integral elements shaping psychological resilience. This qualitative approach, designed to capture the nuanced experiences and perceptions of ARFF professionals through a multi-level exploration of psychological resilience, extends its implications to the realms of aviation and emergency response. Micro-level considerations illuminate the significance of human resources-related decisions, such as recruitment and training, in fortifying ARFF professionals' resilience. Meso-level considerations provide evidence for the impactful role of leadership and effective safety measures in promoting resilience. Macro-level considerations underscore the importance of environmental design and spillover effects, such as family support, in shaping resilience. An understanding of the factors that contribute to psychological resilience may be directly transferable to the creation of support mechanisms and targeted interventions and may also be directly transferable to assist emergency response teams in the aviation sector. [ABSTRACT FROM AUTHOR]

AbstractThis qualitative study explores the experiences of older adult earthquake survivors in Türkiye, focusing on the intersectional challenges they faced after the earthquake of 6 February 2023. Older adults often have multiple vulnerabilities, such as age-related physical limitations, socioeconomic status, health conditions, and social isolation, which exacerbate their risks and needs during disasters. Using semi-structured interviews with 22 participants from different affected regions, this qualitative descriptive study sheds light on their problems with mobility, access to basic needs, and psychological distress. Using an intersectional framework, it explores how factors such as independence in transport, housing conditions, and family structure influence their experiences. The research emphasizes the importance of tailoring emergency response systems to address these unique challenges, including housing, healthcare, and psychosocial support. The findings show that older adults face both immediate physical and psychological challenges as well as long-term adaptation difficulties, emphasizing the need for comprehensive, inclusive disaster preparedness and response strategies. This study contributes to the literature by detailing the intersectional experiences of older earthquake survivors and advocating for policies and practices that ensure equitable support and resource allocation and ultimately promote more resilient communities. [ABSTRACT FROM AUTHOR]