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Objective: Given the frequency of disasters worldwide, there is growing demand for efficient and effective emergency responses. One challenge is to design suitable retrospective charts to enable knowledge to be gained from disasters. This study provides comprehensive understanding of published retrospective chart review templates for designing and updating retrospective research. Methods: We conducted a systematic review and text analysis of peer-reviewed articles and grey literature on retrospective chart review templates for reporting, analysing, and evaluating emergency responses. The search was performed on PubMed, Cochrane, and Web of Science and pre-identified government and non-government organizational and professional association websites to find papers published before July 1, 2022. Items and categories were grouped and organised using visual text analysis. The study is registered in PROSPERO (374,928). Results: Four index groups, 12 guidelines, and 14 report formats (or data collection templates) from 21 peer-reviewed articles and 9 grey literature papers were eligible. Retrospective tools were generally designed based on group consensus. One guideline and one report format were designed for the entire health system, 23 studies focused on emergency systems, while the others focused on hospitals. Five papers focused specific incident types, including chemical, biological, radiological, nuclear, mass burning, and mass paediatric casualties. Ten papers stated the location where the tools were used. The text analysis included 123 categories and 1210 specific items; large heterogeneity was observed. Conclusion: Existing retrospective chart review templates for emergency response are heterogeneous, varying in type, hierarchy, and theoretical basis. The design of comprehensive, standard, and practicable retrospective charts requires an emergency response paradigm, baseline for outcomes, robust information acquisition, and among-region cooperation. [ABSTRACT FROM AUTHOR]

This special issue of Research Ethics focuses on research ethics and research integrity during global crises, particularly the challenges faced during the COVID-19 pandemic. The papers in this issue discuss the changing dynamics and pressures of conducting research during crisis situations, including the need for speed in developing vaccines and treatments. Some papers highlight the importance of adhering to ethical and methodological standards, while others reflect on new ethical considerations that emerged during the pandemic. The issue aims to contribute to ongoing reflection and improvement in research ethics practice, both during crises and in general. The editorial also introduces the new co-Editors-in-Chief of Research Ethics and welcomes new Associate Editors to the journal. [Extracted from the article]

Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China's Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals' cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual's cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual's knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual's familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers' following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings. [ABSTRACT FROM AUTHOR]

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

With the development of economic globalization and increasing international trade, the maritime transportation system (MTS) is becoming more and more complex. A failure of any supply line in the MTS can seriously affect the operation of the system. Resilience describes the ability of a system to withstand or recover from a disaster and is therefore an important method of disaster management in MTS. This paper analyzes the impact of disasters on MTS, using the data of Suez Canal "Century of Congestion" as an example. In practice, the severity of a disaster is dynamic. This paper categorizes disasters into different levels, which are then modelled by the Markov chain. The concept of a repair line set is proposed and is determined with the aim to minimize the total loss and maximize the resilience increment of the line to the system. The resilience measure of MTS is defined to determine the repair line sequence in the repair line set. Finally, a maritime transportation system network from the Far East to the Mediterranean Sea is used to validate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016–2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap – OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps. [ABSTRACT FROM AUTHOR]

Active shooter events involving an armed perpetrator(s) on campus are one of the main risks facing K-12 schools. Defined as planning for and responding to emergency situations, emergency or crisis management allows for an 'acceptable' level of risk to be achieved. This paper will go through the four principles of emergency management, detailing what each stage involves and how it can reduce risk. The first of these is mitigation, which prevents crises occurring in the first place. Effective risk and threat assessment are pertinent to this stage. Secondly, there is preparedness, which enhances the capacity of an organization to respond to various incidents. This involves drafting emergency management plans and practicing these to ensure readiness to respond. The next principle is responding to a crisis, denoting the actions taken during and immediately after a crisis, should one transpire. The final facet of emergency management planning is recovery, referring to the short-to-long-term phase of restoring a community following an incident. This paper will share insights obtained from a recent event, The Briefings, held by the I Love U Guys foundation, one of the leading school safety organizations in the United States. Specifically, the paper will focus on a possible training approach to active shooter events and other emergencies, the organization's emergency management framework called the 'Standard Response Protocol'. Additionally, this paper will incorporate relevant scholarly readings in order to provide an introduction to the topic of emergency management. [ABSTRACT FROM AUTHOR]

Objectives: Hospitals must maintain their effective operations during and after disasters. Due to the current increase in disasters, hospital resilience has drawn scholarly attention. This study aimed to review studies on the changes in the definition of hospital resilience after COVID‐19, build a conceptual framework for careful measurement, and identify the main dimensions of hospital resilience emphasized during the COVID‐19 pandemic. Design: The initial phase of this study was a systematic review of articles published before the COVID‐19 pandemic to extract the hospital resilience‐related dimensions for the second phase. The second phase involved text‐mining articles published both before and after the emergence of COVID‐19. Setting: In the systematic review phase, 12 databases were searched from 2006 to January 2020, including Scopus, Web of Science, MEDLINE through PubMed, Embase, ERIC, ProQuest, the Cochrane Library, Emerald, Springer, Science Direct/ELSEVIER, Google Scholar, and SID (for Persian language papers). Then, after COVID‐19, articles published in these databases between January 2020 and May 2022 were evaluated using text mining. Result: During the systematic phase, 17 out of 1530 papers published before COVID‐19 were synthesized to collect components of hospital disaster resilience. These identified components were the inputs for the text‐mining phase. The text mining on pre‐COVID papers resulted in six clusters, with the highest weight (0.65) belonging to general resilience and disaster preparedness, while in the post‐COVID text mining phase, including 70 papers, 8 clusters have been identified, with the highest weight cluster (0.78) focusing on the mental and psychological aspects of resilience among healthcare workers. Conclusion: Following the COVID pandemic, scholarly attention has shifted to the more personal dimensions of hospital resilience, including psychological resiliency. It seems necessary for policymakers to focus more on the individual and psychological resilience of hospital staff. [ABSTRACT FROM AUTHOR]

This paper presents the results of a severe accident parametric sensitivity study performed on a model of a generic integral pressurized water reactor (iPWR). The analyzed sequence is a loss of coolant accident (LOCA)-type scenario, postulating that safety systems are not available. In this work, a MELCOR 2.2 input deck of a generic iPWR was developed based on publicly available data. The iPWR used in this work is a generic iPWR with a thermal power of about 160 MWth, characterized by a compact steam generator and a submerged containment configuration. A hypothetical scenario considered in this work was an unmitigated small break LOCA leading to a severe accident with partial core degradation, due to the postulated assumptions. In the presented paper, 16 sensitivity cases were calculated and analyzed, focusing mainly on heat transfer, decay heat, and core degradation parameters. The selected parameters and their combination caused partial core degradation and showed significant differences in investigated variables such as core degradation and hydrogen generation, as well as examined CPU time consumption. This a preparatory work performed in the framework of the Horizon Euratom SASPAM-SA project, which aims to investigate the applicability and transfer of the operating large light-water reactor knowledge and know-how to the iPWR, taking into account European licensing analysis needs for the severe accident and emergency planning zone. [ABSTRACT FROM AUTHOR]

Introduction: This paper presents a structured review of the use of crisis management, specifically examining the frameworks of surge capacity, resilience, and dynamic capabilities in healthcare organizations. Thereafter, a novel deductive method based on the framework of dynamic capabilities is developed and applied to investigate crisis management in two hospital cases during the COVID-19 pandemic. Background: The COVID-19 pandemic distinguishes itself from many other disasters due to its global spread, uncertainty, and prolonged duration. While crisis management in healthcare has often been explained using the surge capacity framework, the need for adaptability in an unfamiliar setting and different information flow makes the dynamic capabilities framework more useful. Methods: The dynamic capabilities framework's microfoundations as categories is utilized in this paper for a deductive analysis of crisis management during the COVID-19 pandemic in a multiple case study involving two Swedish public hospitals. A novel method, incorporating both dynamic and static capabilities across multiple organizational levels, is developed and explored. Results: The case study results reveal the utilization of all dynamic capabilities with an increased emphasis at lower organizational levels and a higher prevalence of static capabilities at the regional level. In Case A, lower-level managers perceived the hospital manager as brave, supporting sensing, seizing, and transformation at the department level. However, due to information gaps, sensing did not reach regional crisis management, reducing their power. In Case B, with contingency plans not initiated, the hospital faced a lack of management and formed a department manager group for patient care. Seizing was robust at the department level, but regional levels struggled with decisions on crisis versus normal management. The novel method effectively visualizes differences between organizational levels and cases, shedding light on the extent of cooperation or lack thereof within the organization. Conclusion: The researchers conclude that crisis management in a pandemic, benefits from distributed management, attributed to higher dynamic capabilities at lower organizational levels. A pandemic contingency plan should differ from a plan for accidents, supporting the development of routines for the new situation and continuous improvement. The Dynamic Capabilities framework proved successful for exploration in this context. [ABSTRACT FROM AUTHOR]

Background: Disasters can cause casualties and significant financial loss. In accordance with the Sendai Framework for Disaster Risk Reduction, areas affected by disasters must be built back better. Accurate post-disaster damage and loss assessments are critical for the success of recovery programs. This scoping review aimed to identify the components and entities of the healthcare sector's post-disaster damage and loss assessment program. Methods: An comprehensive search for relevant literature was performed using several databases, including the Web of Science, PubMed, Scopus, ProQuest, and Magiran. The search was limited to papers published between 2010 and 2022. In addition, we searched the grey literature for resources related to post-disaster damage and loss assessments. Study selection and data extraction were evaluated by a third reviewer. The main themes were determined through a consensus process and agreement among team members. Results: A total of 845 papers were identified, 41 of which were included in the review. The grey literature search yielded 1015 documents, 23 of which were associated with the study's purpose. The findings were classified into five main themes, 20 subthemes, and 876 codes. The main-themes include the following: Concepts and Definitions; Post-Disaster Damage and Loss Assessment Procedures; Healthcare sector procedures; Assessments Tools, and Methods; Intra-sectoral, Inter-sectoral, and cross-cutting issues. Conclusions: The existing corpus of literature on post-disaster damage and loss assessment programs within the healthcare sector offers only limited insights into the entities and components involved. It is of great importance that stakeholders have an extensive grasp of these pivotal concepts and principles, as they are fundamental in enabling effective responses to disasters, informed decision-making, and facilitating rehabilitation and reconstruction efforts. Consequently, there is a considerable scope for further investigation in this area. Scoping review registration number: https://osf.io/nj3fk. [ABSTRACT FROM AUTHOR]

In 2019, the Chinese government put forward the concept of social co-governance in the field of emergency management. This paper discusses whether the adjustment in the governance concept promotes public participation in emergency management practices and what the mechanisms are to achieve successful public participation in a wildfire emergency. Taking the Beibei wildfire in Chongqing in 2022 as a study case, this paper establishes the public participation mechanisms for wildfire rescue and management with a qualitative case study method. The result shows that adjustments to emergency management concepts and the pressure of wildfire governance make local governments and commanders seek help from the public; key persons and informal networks play an important role in public participation in a wildfire emergency; and constructing social memory in social networks, whether government-led or public-organized, is an important way to enhance public participation in wildfire governance. Our findings, grounded in the reality of the growing complexity of wildfires, provide useful lessons for future wildfire governance in other provinces in China, as well as valuable references for other countries or regions. [ABSTRACT FROM AUTHOR]

Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods. [ABSTRACT FROM AUTHOR]

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal Frontiers in Earth Science titled "Prevention, Mitigation, and Relief of Compound and Chained Natural Hazards." It discusses the increasing frequency of extreme natural disasters due to global climate warming and frequent earthquakes, which pose significant threats to human life and property. The editorial highlights the importance of preventing, mitigating, and relieving compound and chained natural hazards, and the role of technological advancements in addressing these hazards. The document provides an overview of nine published papers that focus on earthquakes, geological hazards, earthquake-triggered landslides, and landslide susceptibility. It concludes by emphasizing the need for continued research on comprehensive natural hazards and disaster chains, beyond earthquakes and geological disasters, such as meteorological events, floods, droughts, wildfires, and tsunamis. [Extracted from the article]

The human community confronts a plethora of disasters, including man-made epidemics like COVID-19, environmental problems such as water and food resource depletion, biochemical warfare, and even threats from human-created artificial intelligence. Consequently, it is appropriate to term our current community a "disaster community". This paper delves into the issue of diaspora, intrinsically linked to the fragmentation problem in the disaster community, where each subgroup tends to focus solely on its own community. This issue is scrutinized by categorizing the diaspora into larger and smaller categories, with cases from both the international and domestic levels examined respectively within each category. Among the many Daoist philosophical concepts, this paper focuses on the Daoist principle of interconnectedness. In examining Daoist thought on interconnectedness, it also confirms that the notion of interconnectedness is being increasingly emphasized in modern society and across various academic disciplines. This perspective affirms the pluralistic nature of existence, while also underscoring the fundamental interconnectedness that underpins the myriad forms and phenomena. It provides a framework for addressing the challenges faced by the disaster community, particularly in relation to issues of diaspora, by emphasizing the need for coexistence and collective responsibility within a web of mutual relations. The philosophy innovatively applied from Daoism emphasizes "recognizing diversity based on Dao, while acknowledging that each existence is interconnected". Here, "recognition of diversity based on Dao" underpins pluralism, and "interconnectedness of all existence" forms the fundamental solution to the diaspora problem. This approach could be extended as a strategic response to various disasters that the human community encounters. Therefore, this paper assesses the current state of the disaster community, the severity of the diaspora issue, and some cases both between and within nations. It also discusses the core of Daoist philosophy and its creative application to overcome these challenges. The disaster community should transition towards a sustainable and open community, and on this journey, by acknowledging our interconnectedness, we can find solutions to not only the disasters we confront but also to the diaspora problem. [ABSTRACT FROM AUTHOR]

The present paper argues that the standardised treatment of disaster research and practice perpetuates the production of systematic epistemic injustices against victims of disasters. On the one hand, disaster victims are often prevented from contributing with their opinions and knowledge to the processes of disaster mitigation and disaster conceptualisation. On the other hand, disaster victims tend to lack the hermeneutical resources to make sense of their experiences intelligibly, due to the existence of significant hermeneutical gaps in the hegemonic terminology on the matter. I argue that both forms of epistemic injustice, the testimonial and the hermeneutical, are sustained by an epistemic privilege between the Global North and the Global South in matters of disasters. The second group comprises what I categorise generally as 'disaster victims'. I identify two forms of structural prejudice that operate against disaster victims: one is the 'non-expert' prejudice, and the other is the colonial prejudice. Finally, because of the intercultural nature of disaster environments, I discuss the field of 'multicultural competencies' as a useful form of unveiling and counteracting the epistemic injustices contained in both disaster theory and practice. [ABSTRACT FROM AUTHOR]

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management. [ABSTRACT FROM AUTHOR]

Women, children, the aged, and the physically challenged, among others, constitute the vulnerable population in every society but are usually catered for in crisis-ridden situations by the leadership of developed nations, contrary to what obtains in Nigeria, consequent upon rural banditry attacks. This paper investigated how leadership's attitude to banditry attacks on rural communities in Benue State, Nigeria, has rendered the vulnerable miserable with the manner in which the various Internally Displaced Persons' Camps (IDPCs) have been established to give relief to the victims. It adopted a descriptive research method, extracted data using secondary sources of data collection, and used Emergency Management Theory (EMT) to explain the paper. It reveals that before the escalation of rural banditry in Benue State, women, children, and the physically challenged were carrying out their various agricultural practices and other personal businesses without any hindrance. It observed that people appeared to have been merely protected by the efforts of their various family members and their various communities. It argued further that bandits' attacks on these rural communities created an upheaval in the environment, bringing about looting, kidnapping, maiming, and loss of lives and property of serious magnitude. The deadly attacks on these various communities, their desertion, and the displacement of family members led to a humanitarian crisis of multiple dimensions. The need to take care of the vulnerable population by the government led to the setting up of Internally Displaced Persons' Camps (IDPCs). The paper concluded that these IDPCs are poorly managed and characterized by corruption and negligence, among others. It is recommended that security in rural communities be beefed up to forestall further bandits' attacks, while the IDPCs should be properly protected, funded, and better managed. [ABSTRACT FROM AUTHOR]

Managing disasters and public health emergencies poses a complex challenge, particularly in maintaining the crucial elements of surge capacity, often referred to as the 4S: staff, stuff, space, and system. While discussions surrounding the management of these emergencies typically emphasize their impact on emergency healthcare services, resources, and capabilities, it is essential to recognize the inherent limitations of these resources. Therefore, integrating nonmedical resources such as community staff, supplies, and spaces into the response chain is equally important. Among community facilities, hotels are particularly intriguing due to their organizational and structural capabilities to serve as alternative care sites for lightly injured or non-injured emergency victims. This narrative review explored the potential use of hotels as alternative care sites and the legal implications associated with such utilization. The results confirmed a high potential for using hotels as alternate care sites. However, data concerning its practical and legal implications are insufficient. This paper suggests further research to investigate the criteria for utilizing hotels in this capacity, including admission guidelines for disaster victims and relevant ethical and legal considerations. [ABSTRACT FROM AUTHOR]

Background: Every year, people in Canada are evacuated due to wildland fires to avoid death, injury, and illness from fire and smoke events. Aims: In this paper, we provide an overview of evacuations recorded in the Canadian Wildland Fire Evacuation Database between 1980 and 2021. Methods: Our analysis covers evacuations in Canada from 1980 to 2021. We provide summary statistics including number of evacuations and evacuees, evacuation duration, seasonality, evacuation causes, community types, structure losses, and fatalities. We also investigate temporal and spatial patterns. Key results: Between 1980 and 2021, there were 1393 wildland fire evacuation events with 576,747 people evacuated. During this period, there was an overall increase in frequency of evacuations, number of evacuees, and duration of events. Structure loss occurred during 194 evacuation events, with 4105 homes burned. We estimate wildland fire evacuations cost at least CAD3.7 billion (excluding structural losses), jumping to CAD4.6 billion if we include productivity losses. Indigenous peoples are disproportionately impacted in wildfire evacuations compared to the general Canadian population. Conclusions: Wildland fire evacuations continue to occur across Canada and are increasing. Implications: The findings from this study give us a better understanding of the characteristics of wildland fire evacuations, which can help guide emergency management. This paper summarises data on wildland fire evacuations in Canada between 1980 and 2021. There were 1393 wildland fire evacuation events with 576,747 people evacuated, costing approximately CAD4.6 billion CDN including productivity losses. [ABSTRACT FROM AUTHOR]

This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity. [ABSTRACT FROM AUTHOR]

In this paper, the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is adopted to monitor the Line of Sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in northeast China using Sentinel-1 Synthetic Aperture Radar (SAR) images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross wavelet transform with monthly precipitation data are also conducted, to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding during 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in vicinity of the Liushan paleochannel. The results of this paper have fully taken in account for the complications of large topographic relief, geological conditions, spatial distribution and temporal evolution characteristics of surface displacements in opencast mining area. The wide range and long time series dynamic monitoring of opencast mine are of great significance to ensure mine safety production and geological disaster prevention in the investigated mining area. [ABSTRACT FROM AUTHOR]

Sichuan-Tibet Railway spans several watersheds such as Jinsha River and Yalong River, and Potential landslides are frequent along the route, which poses serious hazards to the normal construction and operation of the railroad. The traditional time-series InSAR technology is limited by the number of images and other restrictions, and has a long solution time, making it difficult to obtain information on short-term occurrence of deformation and unable to perform wide-area potential landslides monitor quickly. In this paper, taking a geological hazard-prone area in the Jinsha River basin (or a section of the Sichuan-Tibet line) as an example, based on the Sentinel-1 satellite SAR data provided by the Copernicus program of ESA, the model corrects the interferometric superposition deformation results obtained from a small number of SAR images (less than 7), decodes the corrected rate results, and identifies a total of 13 areas where deformation obviously occurs A total of 13 typical areas with significant deformation were identified. The identified typical areas were time-series solved and their deformation was traced. The method provides a new idea for identification and monitor of Potential landslides in a wide area and further promotes the development of disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The national earthquake and national volcanic eruption prediction programs of Japan started in 1965 and 1974, respectively, based on the recommendation of the Geodesy Council. As a result, observations of seismic and volcanic activity have been enhanced and our understanding of the mechanisms of earthquakes and volcanic eruptions has made significant advances. However, these research results did not fully contribute to mitigating the damage caused by the 1995 Kobe earthquake or the 2011 Tohoku earthquake. In order to make further contributions to disaster mitigation, we recognize the importance of research to apply the scientific understanding of earthquakes and volcanic eruptions in collaboration with researchers in related research fields. The Earthquake and Volcano Hazards Observation and Research Program (2014–2018) was thus started on the basis of a recommendation of the Council for Science and Technology. In this Program, research in the area of forecasting the hazards of earthquakes and volcanic eruptions has become one of the main pillars of research. Disaster information and knowledge of the disaster process, which involve the interaction of hazards and natural and social vulnerability, have been investigated in cooperation with earth scientists, engineers, and social scientists. On the basis of our results, research on literacy for disaster resilience was selected as one of the main pillars of research in the Second Observation and Research Program of Earthquake and Volcano Hazards (2019–2023), because a better understanding of and immediate information about disasters are important for disaster mitigation. This special issue, which aims to disseminate the results of almost five years of research activities on literacy for disaster resilience in that Program, includes 15 papers and survey reports from multidisciplinary fields of study. The collection begins with Kimura and Ikeda's important paper discussing the conceptual structure of the knowledge and abilities necessary to build disaster resilience capacities by analyzing school and community-based efforts, followed by a variety of empirical studies that are loosely categorized into two groups. The first group of papers concerns the understanding of disaster processes and mechanisms themselves as a factor inducing disaster prevention and mitigation. First, Ebina and Sugawara, and Sugimori attempt to learn lessons from the pre-modern earthquake disasters based on past picture maps and scrolled records, respectively, followed by Ohkura's interesting paper that points to problems in the Aso Volcano disaster mitigation system by analyzing the 2021 phreatic eruption process. Next, utilizing information and communication technology to support effective evacuation, Inoguchi, and Shiozaki and Hashimoto develop a disaster management toolkit and the tsunami drill program, respectively. The following two papers of Takubo et al. discuss behavioral mechanisms in a disaster period based on their simulation experiments from a cognitive science perspective. The second group investigates methods of enhancing people's literacy for disaster resilience, paying special attention to the interaction between science and civil society, led by Sawada and Sato, and Muroi, both discussing the disaster risk reduction efforts of small and medium-sized enterprises and of community-based organizations, respectively, in anticipation of the Nankai Trough earthquake and tsunami. Next, Kimura and Aikawa propose an education program to enhance the disaster awareness of high school students who have never experienced disasters. Further, four papers deal with the issue of people's access to scientific knowledge focusing on the roles of university institutes, including case studies of so-called open science initiatives, by Yamori, Nakamichi, and Sakamoto and Nakamichi, and finally, a comparative study of several research centers in volcanic localities by Horii et al. These studies argue for the importance of the concept of literacy for disaster resilience to embed scientific knowledge in society and interconnect theory and practice, but the study of such a concept, especially in relation to earthquakes and volcanic eruptions, has just begun. We hope that this issue will encourage discussions from a wide range of scientists, experts in education and disaster management, and policy makers. [ABSTRACT FROM AUTHOR]

Understanding the impact of various climate forcing agents, such as aerosols, on extreme precipitation is socially and scientifically vital. While anthropogenic absorbing aerosols influence Earth's energy balance and atmospheric convection, their role in extreme events remains unclear. This paper uses convective‐resolving radiative‐convective‐equilibrium simulations, with fixed solar radiation, to investigate the influence of absorbing aerosols on extreme precipitation comprehensively. Our findings reveal an underappreciated mechanism through which absorbing aerosols can, under certain conditions, strongly intensify extreme precipitation. Notably, we demonstrate that a mechanism previously reported for much warmer (hothouse) climates, where intense rainfall alternates with multi‐day dry spells, can operate under current realistic conditions due to absorbing aerosol influence. This mechanism operates when an aerosol perturbation shifts the lower tropospheric radiative heating rate to positive values, generating a strong inhibition layer. Our work highlights an additional potential effect of absorbing aerosols, with implications for climate change mitigation and disaster risk management. Plain Language Summary: Aerosols, particles suspended in the atmosphere, can interact with the incoming solar radiation by scattering or absorbing it. Aerosol species that absorb solar radiation generate local warming of the atmosphere. This local warming changes the vertical profile of temperature and by that affects cloud and precipitation development. In this paper we use idealized computer simulations to investigate the effect of absorbing aerosols on precipitation, and specifically on extreme precipitation events in the tropics. We demonstrate that under certain conditions, absorbing aerosols can strongly enhance extreme precipitation even despite reducing the mean. We show that this trend can be explained by a mechanism previously reported for much warmer climate conditions than currently found on Earth, involving heating by radiation of the lower part of the troposphere. These results have implications for climate change mitigation and disaster risk management. Key Points: The effect of absorbing aerosol on extreme precipitation is examined in idealized convective‐resolving radiative‐convective‐equilibrium simulationsAerosol perturbation that shifts the lower tropospheric radiative heating rate to positive values strongly enhances extreme precipitationThis trend is explained by a mechanism reported before for hothouse climate conditions involving a shift into an "episodic deluge" regime [ABSTRACT FROM AUTHOR]

This paper presents a Laplace transform model for an urban tunnel ventilation system. This model allows one to witness higher performance for supervisory control and data acquisition (SCADA) in terms of monitoring and control of an urban area tunnel based on measurement systems. This proposed model illustrates the ventilation control system framework as well as the emergency response system for urban area tunnels such that smoother controllability and higher security in the operation of tunnels can be envisioned. The salient contributions of this work can be stated as a novel method for modeling tunnel ventilation systems and the implementation of an emergency response plan for a futuristic intelligent transportation system. The simulation results exhibit that the proposed model outperforms the ventilation system in the high-density traffic jams and further the efficient operation of the tunnel. Likewise, comparison results and experimental results are addressed to emphasize the validation of this method and to be helpful in proving the reliability of the results obtained in this study. These results show that the ventilation control system reaches the desired CO value either in high-traffic volume conditions or in normal traffic conditions. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts' waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing's historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of "1 h rainfall intensity water logging index" and "the waterlogging risk index in historical and cultural districts" were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. [ABSTRACT FROM AUTHOR]

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations. [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]

Disaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case. Plain Language Summary: This paper addresses the complex challenge that many regions face when dealing with multiple interconnected disasters, like floods, droughts and earthquakes. Traditional Disaster Risk Management methods often fall short, particularly in long‐term planning that considers factors such as climate change. We apply a step‐by‐step approach known as "Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR)" to assist decision‐makers in better understanding and planning for these intertwined risks over time. This paper employs a hypothetical test case where different sectors (e.g., agricultural sector) seek effective ways to prepare for and respond to flood and drought risks. The DAPP‐MR method facilitates this by breaking down the process into smaller, more manageable steps. This approach allows for an easier understanding of the direct effects of disasters and gradually incorporates more complex interactions between different risks. Our findings suggest that this step‐by‐step method can aid decision‐makers in grasping the connections between various factors and their significance in formulating both short‐ and long‐term strategies. The study also reveals that interactions between different risks can produce unexpected, nonlinear effects, adding complexity to the interpretation of policy outcomes. Key Points: A multi‐risk modeling case with interacting hazards and sectors is created to explore effects for decision‐making under deep uncertaintyDynamic Adaptive Policy Pathways for Multi‐Risk has proven effective for pathway analysis to grasp interactions and their relevance through a structured, stage‐by‐stage approachMulti‐risk interactions can alter the decision‐making outcome because of changes in performance or timings of potential pathways [ABSTRACT FROM AUTHOR]

Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship's search route in the regional search stage by updating the ship's navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks. [ABSTRACT FROM AUTHOR]

This research paper explores the integration of novel technologies in hospital emergency evacuations, particularly in Operating Rooms (ORs) and Emergency Departments (EDs). It examines the application of advanced tools like simulation modeling, Building Information Modeling (BIM), Digital Twin technology, sensor data, and Artificial Intelligence (AI) to improve evacuation strategies in the building. The study extends to in-depth case studies for assessing the practicality of existing protocols, while also highlighting the critical importance of staff training and preparedness. Additionally, it addresses the ethical and psychological impacts of emergencies on patients and healthcare staff, underscoring the need for technology to be complemented with human-centered care. The paper concludes by emphasizing the ongoing necessity for innovative research in enhancing safety and operational resilience in healthcare emergency management. [ABSTRACT FROM AUTHOR]

Background: Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. Objective: This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. Methods: A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. Results: Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. Conclusion: The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies. [ABSTRACT FROM AUTHOR]

This review paper addresses the escalating operation and maintenance costs of nuclear power plants, primarily attributed to rising labor costs and intensified competition from renewable energy sources. The paper proposes a paradigm shift towards a technology-centric approach, leveraging mobile and automated robots for physical security, aiming to replace labor-intensive methods. Focusing on the human–robot interaction principle, the review conducts a state-of-the-art analysis of dog robots' potential in infrastructure security and remote inspection within human–robot shared environments. Additionally, this paper surveys research on the capabilities of mobile robots, exploring their applications in various industries, including disaster response, exploration, surveillance, and environmental conservation. This study emphasizes the crucial role of autonomous mobility and manipulation in robots for diverse tasks, and discusses the formalization of problems, performance assessment criteria, and operational capabilities. It provides a comprehensive comparison of three prominent robotic platforms (SPOT, Ghost Robotics, and ANYmal Robotics) across various parameters, shedding light on their suitability for different applications. This review culminates in a research roadmap, delineating experiments and parameters for assessing dog robots' performance in safeguarding nuclear power plants, offering a structured approach for future research endeavors. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). [ABSTRACT FROM AUTHOR]

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town's collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT Over the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action). [ABSTRACT FROM AUTHOR]

The widespread use of mobile phones and social media platforms provides valuable information about users' behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users' opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. [ABSTRACT FROM AUTHOR]

With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management. [ABSTRACT FROM AUTHOR]

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach. [ABSTRACT FROM AUTHOR]

Geomorphic environments play a crucial role in influencing the preservation and characteristics of tsunami deposits. This paper introduces a global database of tsunami deposits, encompassing information on deposit locations, thematic data such as geomorphic environments and proxies and bibliographic details. Additionally, the database features maps incorporating environmental parameters and the precise locations of tsunami deposits. The primary utility of this database lies in assessing progress and identifying gaps in knowledge. It also involves analysing the relationship between environmental parameters and interpreting areas with varying probabilities of tsunami deposit preservation. The files are readily compatible with GIS software and can seamlessly integrate into spatial databases associated with tsunamis or other hazards. This contributes significantly to disaster risk management, enhancing preparedness and response efforts by providing a comprehensive historical dataset on tsunamis. Future applications of the database include the incorporation of modern deposits, boulders and new data from paleotsunami and historical studies. By enhancing data with thematic information, such as dating techniques and creating timelines, the database facilitates a more comprehensive understanding. The correlation between geomorphic environments and proxies aids in selecting sampling sites and identifying suitable proxies for analysis. Encouraging an open‐access approach, this database invites all interested researchers to include and modify additional information. The information compiled for this database serves multiple purposes: (1) assessing the global distribution of tsunami deposits; (2) identifying knowledge gaps in tsunami deposits; (3) guiding the selection of study areas for further research and finally; (4) enabling a meta‐analysis of the information gathered. [ABSTRACT FROM AUTHOR]

Water content and primary fractures can change the mechanical characteristics of rock, making it easy to induce geological disasters. Therefore, direct shear tests of red sandstone under the action of water-fracture were carried out in this paper. The results show that shear strength of rock samples with fractures is less than that of intact rock samples. With the increase of primary fracture dip angle, shear strength and macroscopic crushing area of the rock sample increases first and then decreases with 20° as the boundary. It shows that the primary fractures weaken the shear mechanical properties and change the macroscopic failure mode. The shear performance of water-bearing rock samples is weaker than that of intact rock samples, and the weakening degree of water-saturated on shear performance of rock samples is lower than that of unsaturated water state. The fracture surfaces of rock samples are divided into 'shortest path single through type', 'longest path single through type' and 'cross path through type'. The failured rock samples are divided into 'single through type' and 'cross through type'. The research results can provide reference for geological disaster management under relevant conditions. [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]

Older adults are at high risk of experiencing injury, exacerbations of their chronic conditions, and death when evacuations are ordered because of hurricanes or natural disasters. The Homebound seniors residing in Galveston County are at a particularly high risk of morbidity and mortality during evacuations for hurricanes. This paper described the impact of a quality improvement intervention designed and implemented by nurse practitioners during the 2022 and 2023 hurricane season. The education program aimed at increasing the hurricane preparedness of the home-bound patients. Of these patients, 190 returned pre and post surveys. Interventions showed a 43% increase in patients having an evacuation plan in the event of a hurricane, 633% increase in STEAR registration, 16% increase in patients having access to emergency supplies, and 34% increase in patients having an emergency contact list with up-to-date medication list. All improvements on hurricane preparedness items were significant (p <.0001) except for the need for assistance in case of an evacuation. Our findings suggest a need for continuous hurricane preparedness education in the community to ensure safe evacuation, improve hurricane preparation, and increase the number of seniors who registered with the State of Texas Emergency Assistance Registry. [ABSTRACT FROM AUTHOR]

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas. [ABSTRACT FROM AUTHOR]

In the context of smart campuses, effective emergency management is crucial for ensuring the safety and well-being of students, staff, and visitors. This paper presents a comprehensive support tool designed to enhance emergency management on smart campuses, integrating a low-cost people-counting system based on cameras and Raspberry Pi devices. It introduces a newly designed architecture and user interfaces that enhance the functionality and user experience of a smart campus disaster management system. Finally, a usability evaluation has been carried out to validate the brand-new user interfaces devoted to emergency management. [ABSTRACT FROM AUTHOR]