Search

Accurate damage assessment after an earthquake is crucial for effective emergency response. Using ground motion information enables rapid building damage assessment when detailed damage data are unavailable. While uncertainty in earthquake parameters plays a significant role in the accuracy of rapid estimations, it is usually treated as a constant parameter rather than as a dynamic parameter that considers the amount of ground motion data collected that evolve over time. This work investigates the impact of incorporating evolving ground motion uncertainty in ground motion estimations from US Geological Survey's (USGS) ShakeMap on post-disaster damage assessments from two methodologies: the revised Thiel–Zsutty (TZR) model and Federal Emergency Management Agency's (FEMA) Hazus. Using data from the 2020 Indios earthquake in Puerto Rico and the 2014 Napa earthquake, we find that changes in uncertainty in estimates of peak ground acceleration reach 65% between early and late versions of the ShakeMap. We propose a process to integrate this evolution with the two damage assessment methodologies through a Monte Carlo simulation-based approach, demonstrating that it is critical to introduce dynamic ground motion uncertainty in the damage assessment process to avoid propagating unreliable measures. Both methodologies show that resulting damage estimates can be characterized by narrower distributions, indicative of reduced uncertainty and increased precision in damage estimates. For the TZR model, an improved estimate of post-disaster loss is achieved with narrower bounds in distributions of expected high scenario loss. For Hazus, the results show potential changes in the most probable damage state with an average change of 13% in the most probable damage state. The described methodology also demonstrates how uncertainty in the resulting damage state distributions can be reduced compared with the use of the current Hazus methodology. [ABSTRACT FROM AUTHOR]

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% accuracy in verbal abuse detection. Additionally, a psychological disaster-recovery support module was included, which demonstrated a 30% improvement in users' stress reduction rates during simulated disaster scenarios. This study demonstrates that digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues. Additionally, these approaches provide effective support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies. [ABSTRACT FROM AUTHOR]

As weather disasters intensify under the impacts of climate change, an important public policy question is whether the kinds of organizations most likely to provide disaster relief are themselves planning for climate change. A statewide Indiana survey of the leaders of a wide variety of community "social safety net" organizations (N = 467) provides rich descriptive data to understand the realities of local disaster planning as it relates to climate change. Our findings support federal disaster policy in showing first that most of these charities have been or expect to be involved in local disaster relief, either as a primary or secondary mission focus. Charities identifying as primarily disaster responders are much more likely to belong to local emergency planning networks and slightly more likely to plan for emergencies. However, most charity leaders do not acknowledge climate change's human origins and have not discussed climate change at the board level, with this position strongly related to their understanding of climate change's potential impact on the communities they serve. And most have not taken some risk mitigation steps. We conclude that the Federal Emergency Management Authority's national disaster response policy, which relies on the readiness of nonprofits to fill in service roles, does not account for the potential unpreparedness of nonprofits themselves. Policymakers should expand their "whole-community" (three-sector) approach to reinforce local network opportunities as forums for exchanging experience and knowledge, including an explicit linkage between the communication of the science of climate change and emergency planning. [ABSTRACT FROM AUTHOR]

This study investigates the dynamics of collaborative characteristics, interpersonal reactivity, and disaster situation responses in non-face-to-face settings, a response mechanism increasingly relevant in the wake of prolonged pandemics. By examining a group of 123 university students engaged in a seven-week non-face-to-face collaborative project, the research identifies relational patterns between collaborative traits such as regulation and efficacy, interpersonal empathy, and responses to disaster situations. The research methodology employs a sophisticated analytical framework comprising factor and canonical correlation analyses to identify how empathy and collaborative efficacy significantly related with disaster response in online collaborations. The mediation and moderation models analyzed confirm mutual mediation effects of collaborative regulation and interpersonal reactivity on situational empathy without significant moderation effects. This suggests there were direct causal relationships of collaborative regulation, collaborative efficacy, interpersonal reactivity on situational empathy. The findings underscore the pivotal role of empathy in collaboration during disasters offering a nuanced understanding of the social and psychological underpinnings that enable collective responses to crises in environments lacking physical interaction and illuminating the critical role of collaborative and interpersonal skills in such settings. [ABSTRACT FROM AUTHOR]

Aim Background Methods Results Discussion Conclusion Implications for nursing and health policy We evaluated Saudi Arabian nurses’ willingness to engage in nuclear and radiological disaster response.In the face of rising nuclear and radiological threats, it is essential that nurses are fully prepared to handle the health consequences of such emergencies. As frontline responders, nurses play a critical role in managing high‐stakes situations, making their readiness a key factor in ensuring public safety during these crises.A cross‐sectional survey was conducted among 612 registered nurses in Saudi Arabia. The survey used a structured questionnaire based on the theory of planned behavior, identifying behavioral intentions as the primary outcome and attitudes toward these behaviors, subjective norms, and perceived behavioral control as influencing factors. The data were analyzed using descriptive and inferential statistics.Most respondents lacked specialized training and reported strong behavioral intentions. Attitude and perceived behavioral control were key drivers of these behavioral intentions, with less impact from subjective norms. Behavioral intentions were also shaped by education, the perceived likelihood of nuclear and radiological emergencies in their region, peer influence, perceived self‐efficacy, and family concerns. Younger and married nurses showed significantly stronger behavioral intentions.Although most nurses lacked specialized training, their strong engagement intentions and positive responses to active learning and training indicate a desire to close this gap through further education. However, inadequate institutional support, trouble communicating with family, and concerns about personal radiation exposure emerged as potential barriers.Targeted educational initiatives and support systems are needed to enhance nurses’ confidence, competence, and willingness to engage in radiological and nuclear emergency scenarios.Policymakers should prioritize the development of specialized training programs and support mechanisms for nurses, ensuring they are adequately prepared to respond effectively to increasing radiological and nuclear threats. [ABSTRACT FROM AUTHOR]

Rapid urbanization and development have led to a significant number of Malaysians living in flood-prone areas, exacerbating the impact of natural disasters such as floods. This study aims to identify the factors influencing community resilience both before and after flood events in Temerloh, Pahang. Data were collected via questionnaires distributed to 350 flood victims. Key findings reveal that long-term residents exhibit higher resilience due to local knowledge and preparedness strategies. The study highlights the importance of integrating local experiences into flood management policies to enhance community resilience. The results reveal that large flood-prone areas experience frequent flooding, contributing to considerable community distress. This distress is somewhat alleviated by the support provided by government agencies and NGOs, which includes relief efforts, emergency services, and flood defense construction. Additionally, community involvement in flood management was found to have a statistically significant impact (p < 0.05). The study highlights the need for enhanced flood management strategies that address psychological aspects to reduce negative impacts on the community. [ABSTRACT FROM AUTHOR]

In this paper, we developed a model to assess the government's disaster response during the COVID-19 pandemic, exploring variations across jurisdictions dominated by different political identities in the United States. The model defines an individual's payoff as a positive function of his/her income and the government's disaster response. The individual is more prone to wear a mask if the government is more responsive to the disaster during the pandemic, and there can be a lower income loss for the individual during the pandemic when he/she has higher compliance to the government's order of face mask. Utilizing this model, we derive the government's disaster response to be positively correlated with the impact of COVID-19 deaths on masking behavior and negatively correlated with the change of the death toll relative to income. This allows us to evaluate the unobserved disaster response of the government explicitly using regression results. We compare the government's disaster response across jurisdictions dominated by different political identities in the United States. The results highlight a more resilient disaster response in Democratic states, translating into superior health system performance compared to their Republican counterparts. These findings emphasize the crucial role of policies designed to strengthen disaster response, especially in addressing collective action problems such as those posed by the COVID-19 pandemic and climate crises. [ABSTRACT FROM AUTHOR]

Disaster-based tweets during an emergency consist of a variety of information on people who have been hurt or killed, people who are lost or discovered, infrastructure and utilities destroyed; this information can assist governmental and humanitarian organizations in prioritizing their aid and rescue efforts. It is crucial to build a model that can categorize these tweets into distinct types due to their massive volume so as to better organize rescue and relief effort and save lives. In this study, Twitter data of 2013 Queensland flood and 2015 Nepal earthquake has been classified as disaster or non-disaster by employing three classes of models. The first model is performed using the lexical feature based on Term Frequency-Inverse Document Frequency (TF-IDF). The classification was performed using five classification algorithms such as DT, LR, SVM, RF, while Ensemble Voting was used to produce the outcome of the models. The second model uses shallow classifiers in conjunction with several features, including lexical (TF-IDF), hashtag, POS, and GloVe embedding. The third set of the model utilized deep learning algorithms including LSTM, LSTM, and GRU, using BERT (Bidirectional Encoder Representations from Transformers) for constructing semantic word embedding to learn the context. The key performance evaluation metrics such as accuracy, F1 score, recall, and precision were employed to measure and compare the three sets of models for disaster response classification on two publicly available Twitter datasets. By performing a comprehensive empirical evaluation of the tweet classification technique across different disaster kinds, the predictive performance shows that the best accuracy was achieved with DT algorithm which attained the highest performance accuracy followed by Bi-LSTM models for disaster response classification by attaining the best accuracy of 96.46% and 96.40% on the Queensland flood dataset; DT algorithm also attained 78.3% accuracy on the Nepal earthquake dataset based on the majority-voting ensemble respectively. Thus, this research contributes by investigating the integration of deep and shallow learning models effectively in a tweet classification system designed for disaster response. Examining the ways that these two methods work seamlessly offers insights into how to best utilize their complimentary advantages to increase the robustness and accuracy of locating suitable data in disaster crisis. [ABSTRACT FROM AUTHOR]

Abstract In Wuerzburg, Germany, a terrorist attack and a killing rampage occurred five years apart (2016 and 2021). Following a structured evaluation of the rescue mission in 2016, a bundle of quality indicators and ten “lessons learned” were defined. Aim of the presented study was to compare the two rescue missions and to critically review the lessons learned from 2016 for their implementation and feasibility. An interdisciplinary and inter-professional group of experts analyzed the data using predefined quality indicators. All lessons defined in 2016 were critically reviewed and qualified as either lessons learned or lessons identified. While seven out of ten lessons were successfully implemented after 2016 (lessons learned), three lessons didn´t work and were recategorized as lessons identified (communication, zoning and the mutual exchange of different tactical approaches). Our results demonstrate that the conclusions drawn in 2016 have helped to improve the performance of the rescue forces in 2021. In addition, the identified lessons are now the basis for further improving emergency and disaster preparedness. It is important to understand, that the process of preparedness improvement is not completed with the definition of lessons identified. These must first be integrated into response plans and then trained intensively. A lesson identified only becomes a lesson learned once it has been successfully applied.

Rapid urbanization and development have led to a significant number of Malaysians living in flood-prone areas, exacerbating the impact of natural disasters such as floods. This study aims to identify the factors influencing community resilience both before and after flood events in Temerloh, Pahang. Data were collected via questionnaires distributed to 350 flood victims. Key findings reveal that long-term residents exhibit higher resilience due to local knowledge and preparedness strategies. The study highlights the importance of integrating local experiences into flood management policies to enhance community resilience. The results reveal that large flood-prone areas experience frequent flooding, contributing to considerable community distress. This distress is somewhat alleviated by the support provided by government agencies and NGOs, which includes relief efforts, emergency services, and flood defense construction. Additionally, community involvement in flood management was found to have a statistically significant impact (p < 0.05). The study highlights the need for enhanced flood management strategies that address psychological aspects to reduce negative impacts on the community.

Approximate policy iteration (API) is a class of reinforcement learning (RL) algorithms that seek to solve the long-run discounted reward Markov decision process (MDP), via the policy iteration paradigm, without learning the transition model in the underlying Bellman equation. Unfortunately, these algorithms suffer from a defect known as chattering in which the solution (policy) delivered in each iteration of the algorithm oscillates between improved and worsened policies, leading to sub-optimal behavior. Two causes for this that have been traced to the crucial policy improvement step are: (i) the inaccuracies in the policy improvement function and (ii) the exploration/exploitation tradeoff integral to this step, which generates variability in performance. Both of these defects are amplified by simulation noise. Deep RL belongs to a newer class of algorithms in which the resolution of the learning process is refined via mechanisms such as experience replay and/or deep neural networks for improved performance. In this paper, a new deep learning approach is developed for API which employs a more accurate policy improvement function, via an enhanced resolution Bellman equation, thereby reducing chattering and eliminating the need for exploration in the policy improvement step. Versions of the new algorithm for both the long-run discounted MDP and semi-MDP are presented. Convergence properties of the new algorithm are studied mathematically, and a post-earthquake disaster response case study is employed to demonstrate numerically the algorithm's efficacy. [ABSTRACT FROM AUTHOR]

Effective coordination, networking, and voluntary civil engagement are crucial for ensuring effective disaster responses. It should be noted, however, that these recommendations are primarily derived from the experiences of Western pluralistic countries. Thus, there has been insufficient consideration given to the applicability of these recommendations to non-pluralistic countries and the ways in which non-Western authoritarian countries navigate the difficulties of coordinating and establishing networks with civil sector organizations. This study broadens the scope of research by examining non-Western authoritarian countries through an analysis of how China responded to the floods of 2020. The results of the study indicate that the Chinese flood response system was primarily government-driven, centralized, and hierarchical. According to the pluralistic model, this arrangement encounters difficulties in integrating resources and information from partners in different sectors and jurisdictions. Consequently, this poses additional challenges to the Chinese disaster response systems. Nevertheless, the study findings reveal that the Chinese system proficiently resolved problems. This was accomplished by flattening the hierarchical structure via innovative interventions and remedying the lack of voluntary civic sector engagement through mass mobilization. Consequently, it would be fallacious to hold that the pluralist model can be effortlessly transplanted to non-Western authoritarian nations without regard for their distinct political, cultural, economic, and social contexts. Points for practitioners: To improve disaster response, practitioners should consider adapting hierarchical networks and embracing flexibility. This can be achieved by reducing bureaucratic layers, which enhances effectiveness and encourages innovative interventions for streamlined decision making. It is important to emphasize provincial–local support and mass mobilization, as seen in China, which underscores the need for engaging civil sectors in non-pluralistic contexts. Context-specific approaches are crucial and should consider political, cultural, and social factors. Transforming hierarchical structures into modular networks may improve response. [ABSTRACT FROM AUTHOR]

Emergency nurses are pivotal in disaster preparedness and first aid response, being the first healthcare providers on the scene during emergencies. Their ability to assess situations swiftly, triage patients effectively, and collaborate with other professionals ensures timely and efficient care. They also contribute significantly to disaster preparedness by participating in drills and training, and by assisting in developing and implementing emergency response plans. Natural catastrophes harmed about 2 billion people worldwide between 2008 and 2018. According to the World Health Organization, countries and governments must always have catastrophe preparations and emergency health experts on hand. This review article aimed to examine the current level of disaster preparedness among nurses, to analyze some of the factors influencing nurses’ emergency preparedness and to define the roles of emergency nurses in both rapid first aid deployment and long-term disaster impact reduction. The data for this review paper are from published studies. Electronic databases of MEDLINE, PubMed, and Google Scholar were searched using the keywords: disaster preparedness, first aid response, emergency nurses, roles of nurses, and disaster response, and literature were retrieved and screened for eligibility. The date limit considered in the literature search was from 2018 to 2024 to capture only the variables identified in the study. The review revealed four issues which are (1) the Haddon Matrix Model, (2) current level of disaster preparedness among nurses, (3) roles of emergency nurses in disaster management, and (4) analyzing emergency preparedness and first aid impact of nurses in disasters. In conclusion, addressing these issues through targeted training, resource allocation, and policy implementation can enhance the overall preparedness of nurses in responding to disasters and improving outcomes for those affected. Ensuring that nurses are well-prepared, adequately trained, and equipped with the necessary resources, healthcare systems can better mitigate the impact of disasters and provide timely and effective care to those in need. This not only benefits the individuals directly affected by disasters but also strengthens the resilience of communities and healthcare systems as a whole.

At the end of September 2021, Tropical Storm Dianmu caused catastrophic floods in Thailand, placing thousands of lives, properties, infrastructure, and other things, in danger. These circumstances provide an opportunity to learn how to respond to tropical storm situations in the future. Our investigation reveals that Thailand has access to a wealth of data, including geospatial, radar, satellite, and sensor information. Thus, these data could be utilized for an urgent alert system and communication channel to mitigate and reduce the potential for large-scale destruction by tropical storms and floods in the future. [ABSTRACT FROM AUTHOR]

With the intensification and rising frequency of disasters in recent years, implementing disaster preparedness measures has become increasingly important. However, local governments must be prepared for disasters, with their limited resources of personnel and budget, to make the redressal plans relevant for facing problems such as population decline. This study aims to enhance resilience in the field of disaster management by facilitating identification of problems in the disaster management plan of local governments, and promotion of mutual understanding among those involved. For this purpose, a tool has been developed to evaluate local disaster management measures from the viewpoints of both local governments and residents, and to promote smooth communication and coordination among the stakeholders. A questionnaire survey was conducted using an index for self-assessment by local government officials, which was formulated based on the literature of the United Nations Office for Disaster Risk Reduction. Based on the survey results, the elements of resilience were classified into three groups depicting the abilities to collaborate and organize, adapt to the environment, and manage for the next generation. The evaluation was conducted in four local governments. In two regions, the residents' awareness and preparedness for a disaster were surveyed. The survey results revealed that the questionnaire should be improved; nevertheless, the authors were able to identify the problems of local governments and the fields that the residents do not know sufficiently about. The results indicate the possibility to apply this method in other regions in the future. [ABSTRACT FROM AUTHOR]

Background: The expanding use of Uncrewed Aircraft System (UAS) technology in disaster response shows its immense potential to enhance emergency management. However, there is limited documentation on the challenges and data management procedures related to UAS operation. Aims: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Methods: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Key results: UAS technology plays a significant role in search and rescue, reconnaissance, mapping, and damage assessment, alongside notable challenges such as extreme flying conditions, data processing difficulties, and airspace authorisation complexities. Conclusions: The study concludes with the need for updated infrastructure standards, streamlined policies, and better coordination between technological advancements and political processes, emphasising the necessity for reform to enhance disaster response capabilities. Implications: The findings of this study inform future guidelines for the effective and safe use of UAS in disaster situations, advocating for a bridge between state-of-the-art UAS research and its practical application in emergency response. We document how Uncrewed Aircraft System (UAS) technology was used in the 2020 Labour Day wildfires in Oregon, USA. Information from a literature review, social media, and interviews with disaster responders were compiled. Qalitative analyses on the interview data synthesised typical UAS applications and highlighted challenges. [ABSTRACT FROM AUTHOR]

Objective Setting Participants Design Results Conclusion To explore the experiences of clinician and management stakeholders involved in a rural/metropolitan collaborative mental health disaster response to the 2019–2020 Black Summer bushfires in the Snowy Valleys region of southern New South Wales (NSW), Australia.A mental health and drug health service in the Snowy Valleys region of rural NSW in collaboration with a mental health service from metropolitan Sydney, NSW.Mental health clinicians and managers from a rural health district (n = 6) and a metropolitan health district (n = 8) involved in a collaborative disaster response to the 2019–2020 Black Summer bushfire disaster in the Snowy Valleys region of southern NSW, Australia.An interpretive qualitative study design using semi‐structured individual interviews, with transcripts analysed using Reflexive Thematic Analysis.Thematic findings on participant experiences are presented under three organising constructs of before (stepping up and jumping right in), during (finding a rhythm of working together), and after (profound personal and professional impacts) the mental health disaster response.Participant experiences had shared and distinct components before, during and after the mental health disaster response, culminating in profound personal and professional impacts. Findings highlight positive aspects and challenges for clinicians participating in a rural/metropolitan collaborative mental health disaster response. The findings of this study contribute new knowledge about experiences of mental health clinicians participating in a disaster response after bushfires, from dual perspectives of members of a bushfire‐affected community and those responding from outside a bushfire‐affected community, which may inform ongoing planning of responses to disaster in Australia. [ABSTRACT FROM AUTHOR]

Background Aim Methods Findings Conclusions Implications for nursing and health policy Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses' pivotal roles and experiences in handling such crises have been disregarded.This qualitative study analyzed nurses' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster‐response efforts.This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively  sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research.The analysis exposed five main themes for pre‐tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri‐task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post‐tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases.Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake‐affected areas.Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]

Breakthroughs in international biomedical science circa 1900 meant that plague could be contained through strict quarantine regulations. These measures were successfully deployed with help from local governments during outbreaks of pneumonic plague in Manchuria (1910–11), Shanxi (1918), and elsewhere in North China. This containment shows the effectiveness of uniting international knowledge and local cooperation in disaster response. Yet, in later outbreaks in similar locations, control measures identical to those instituted a decade earlier were rejected, and plague spread largely unchecked. Historical case studies of the control and spread of infectious disease in North China reveal the complexities of the relationship between global knowledge and its broader, local integration, variation in what constitutes effective 'local' cooperation in adopting international knowledge, and the paramount importance of the locality to the landscape of disaster response. History can reveal critical issues in localisation of disaster response still salient today. [ABSTRACT FROM AUTHOR]

Debris generated and left in the wake of a disaster is one of a community's greatest obstacles in the ability to restore function. The objectives of this paper are (1) to provide systematic procedures to develop and visualize the vulnerability index (VI) based on three dimensions, and (2) to prioritize debris removal by integrating all dimensions into a geographic information system (GIS). The proposed methodology for vulnerability heatmapping consists of three dimensions: (1) physical, (2) infrastructure, and (3) environmental. An unmanned aerial vehicle (UAV) is used to quantify the debris three-dimensional (3D) volumetric information as part of the 11 metrics selected and evaluated to establish the vulnerability of a location. The proposed process incorporates all systems into a process to allow decision makers to quickly determine scope, complexity, potential impact, and recovery needs. Therefore, the outcome of this research will ensure maximum and timely utilization of limited resources to reach a greater number of people who might be facing life-threatening situations, enhancing the community's resilience. [ABSTRACT FROM AUTHOR]

Radiological disasters have been recognized as a prominent societal catastrophe worldwide since the Fukushima nuclear accident in 2011. In order to cope with such disasters, efforts have been made to improve disaster response plans and systems based on the lessons learned from the Fukushima accident. Particularly, as demonstrated by the Fukushima case, radiological disasters require the assumption of large-scale evacuation of residents. Thus, it is essential for prompt and accurate decision-making to take place at the local government's Emergency Management Center (EMC), enabling smooth execution of the missions assigned to personnel at the field. This is crucial for protecting the lives of residents and ensuring their safety. However, the existing systems for effective response to radiological disasters are relatively inadequate compared to other types of disasters. To overcome these limitations, this study aims to develop Standard Operating Procedures (SOPs) that are optimized for radiological disaster response by standardizing the Field Measure Motion Manual at nuclear safety for local governments. Based on these SOPs, The Resident Protection Measures Support System (RPMS) will be proposed, which enables the timely dissemination of specific missions and information to personnel, while providing real-time information on the situation at the field to the EMC, supporting decision-making and facilitating effective situation management. [ABSTRACT FROM AUTHOR]

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camerabased victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm--Stacked Convolutional Neural Network--Bi--Long Short Term Memory--Gated Recurrent Unit (MOASConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purposebuilt robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between corporate purpose and disaster response. To that aim, we use a unique dataset generated by Just Capital's COVID‐19 response tracker that characterizes the early responses of 928 companies included in the Russell 1000 Index generated by the COVID‐19 pandemic. We marry this data to the Business Roundtable Statement on the Purpose of a Corporation (BRT‐CPS) signatories list, which includes 210 firms that declared "a fundamental commitment to all stakeholders" as their corporate purpose. We estimate the differential impact of BRT‐CPS signees on the rest. After controlling our results with financial indicators and environmental, social, and governance (ESG) data, we convey that BRT‐CPS signees were more likely to develop strategies that benefited their surrounding communities and other stakeholders. Moreover, corporations with high ESG were less likely to have an early response that directly benefited stakeholders other than shareholders, except for layoffs and unpaid leave. Finally, we discuss the outcomes considering the symbolic management theory, corporate purpose literature, and its relation to stakeholder management. [ABSTRACT FROM AUTHOR]

This paper analyzed the quality of the xBD image-training dataset for identifying building damage across a variety of natural hazards using deep learning convolutional neural networks. Specifically, we evaluated the pros and cons of combining training datasets across multiple natural hazards and provided recommendations on using the provided training dataset to optimize classification accuracy for building damage detection. The xBD dataset was rebalanced, using random over-sampling and under-sampling methods. Random over-sampling randomly duplicates the minority class, while random under-sampling randomly cuts-off the majority class. With the balanced dataset, we used the xBD baseline architecture as a starting point in the classification and find that it overfit to the no damage class; therefore, we improved the base classification algorithm by modifying the top layers of ResNet50. We found that not all classes (destroyed, major damage, minor damage, and no damage) were uniformly identifiable across natural hazards; therefore, we retrained the weights from ImageNet, adding five new convolution, batch normalization, and max pooling layers on top of ResNet50. One dropout layer, with a rate of 0.5 was also added in-between the fully connected layers to reduce overfitting and improve performance. We also evaluate the identifiability of the four damage classes in the xbd dataset. Because classification performance was significantly higher for the "no damage" class as compared to "minor", "major", and "destroyed" classes, we evaluated merging classes. We kept the "no damage" class and created a second merged class ("damaged") representing "minor damage," "major damage," and "destroyed." We used the same architecture for the multiclass classification and the binary classification but without the ImageNet weights. Based on this work, we recommend that users be aware of performance differences across natural hazards and across damage classes. Earthquake building damage is extremely limited in the training data and, as a result, application of the trained algorithm on earthquake data cannot be evaluated given the xBD dataset. Building damage due to volcano and tsunami are also poorly represented in the training data, and do not have sufficient data for model validation (especially within all damage classes). Wind hazards are well-represented and therefore application of the algorithm trained using either the wind-only data or the multi-hazard dataset is reliable. The multi-class algorithm trained with wind hazard specific data slightly outperforms a multihazard trained multiclass model (F1 score 0.70 vs. 0.67). Both models have similar performance across all four classes (F1 > 0.5). For flood, fire, and tsunami hazards, we recommend using the binary damage classes as identifiability is low for at least two of the classes in each hazard. For flood building damage, binary classification performance resulted in a significantly higher F1 score when trained with the flood specific dataset versus the multihazard data (0.72 vs. 0.54). On the other hand, for fire building damage, classification performance is slightly higher when the model is trained on multi-hazard data, rather than trained using a fire specific dataset (F1 score 0.46 vs. 0.42). [ABSTRACT FROM AUTHOR]

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the "Zhengzhou flood." First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion's importance in short video comments, which can enhance disaster response efforts in the future. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to enhancing urban resilience and emergency management practices in the face of heatwave events, often exacerbated by the urban heat island effect. Recognizing the limitations of traditional government-led responses in shelter allocation—characterized by high costs, logistical challenges, and a lack of adaptability—this paper introduces an innovative framework that integrates community-based volunteer efforts with advanced simulation and stochastic modelling techniques. The primary objective is to optimize the allocation of resources and volunteers to improve the efficiency and cost-effectiveness of heatwave response mechanisms. Utilizing Data Envelopment Analysis (DEA), the research identifies and enhances resource and volunteer distribution strategies, paving the way for more effective emergency services. A case study conducted in Blacktown City, Western Sydney, serves to demonstrate the practical application and implications of these strategies, underscoring the necessity for a paradigm shift towards more adaptable, volunteer-focused, and resource-efficient approaches in mitigating the impact of climate-change-induced heat waves on vulnerable urban populations. The anticipated outcomes include measurable improvements in response times, cost reductions, and enhanced volunteer mobilization efficiency.

We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

Abstract Background The presence of nurses during disasters can lead to many consequences. Understanding the nurses’ experiences of these consequences can provide valuable insights. Therefore, this study was conducted to explore the experiences of Iranian nurses regarding the consequences they faced when being present during disasters. Methods This qualitative descriptive study employed a content analysis approach to examine the topic at hand. 20 nurses working in the emergency units of Kerman hospitals were selected through purposive sampling. In-depth semi-structured interviews were conducted to collect the data. The data were analyzed using qualitative content analysis proposed by Graneheim and Lundman. MAXQDA 10 was used to manage data. Results After analysis of the interviews, two main categories were identified: overlapping of job frustrations and acquiring experience in difficult conditions. The main category of acquiring experience in difficult conditions comprised the following subcategories: improved quality of care, experience and dedication in fulfilling the role, reduced stress, occupational motivation and enthusiasm, increased self-confidence, and improved social status. On the other hand, the category of overlapping of job frustrations also consisted of the subcategories of physical strength deterioration, psychological and emotional distress, discontinued from supports, feelings of hopelessness, increased exposure to violence and aggression, and occurrence of family problems. Conclusion Being present during disasters and obtaining the necessary competencies can have positive consequences that serve as turning points in the personal and professional growth of nurses. Additionally, favorable outcomes can help nurses cope with adverse circumstances. Managers can utilize the findings of this study to develop strategies aimed at reducing negative outcomes and enhancing positive ones among nurses during disasters.

Background The danger of earthquakes poses a serious threat to people worldwide. One of the most significant challenges is preparing communities to cope effectively with this disaster. Therefore, understanding earthquake hazards is critically important for preparedness, mitigation, and an effective response to this threat. This report aims to observe and conduct a preliminary assessment of the degree to which community preparedness for earthquake disasters has been implemented. Methods Empirical data were obtained from survey respondents and interviews. The respondents were members of a community affected by the Cianjur earthquake, which occurred on November 21, 2022. The data were analysed using the mean range approach, based on Likert scales. Additionally, the Spearman correlation method was employed to indicate the relationship between community awareness and infrastructure readiness. Result Based on empirical evidence and preliminary analysis, it is evident that the preparedness of the community to respond effectively to earthquake catastrophes is inadequate. An apparent lack of readiness is observed in the inadequate construction of housing that fails to meet disaster standards, and the absence of disaster response facilities is notable. Conclusions Based on our initial assumption, it appears that knowledge related to disaster resilience in this area has not been adequately disseminated or socialized. However, this premise requires further investigation.

This research explores the implementation of Disaster Preparedness Areas in Indonesia, with a focus on formulating the role of critical communication elements in strengthening disaster response. The Disaster Preparedness Area involves community participation in disaster management efforts, and this research identified twelve critical communication elements. The qualitative approach was expressed with qualitative software by describing secondary data located in the Pangandaran Regency. This study bridged data limitations, important analyses, and the context of regional differences in Indonesia. The results emphasize the need for development, implementation, and education in a comprehensive communication strategy and active participation of the community. The findings are presented in the form of a radar curve with critical communication element scores on five scales. Some limitations emerged in this study, but it was able to provide valuable insights into the implementation of Disaster Preparedness Areas by prioritizing the importance of critical communication in disaster management. Recommendations can help stakeholders develop more effective communication strategies in dealing with disasters in Indonesia.This research investigates the implementation of Disaster Preparedness Areas in Indonesia, focusing on the role of critical communication elements in enhancing disaster response. Community participation is integral to the Disaster Preparedness Area, and this study identifies twelve crucial communication elements. Employing a qualitative approach, qualitative software is utilized to analyze secondary data from Pangandaran Regency, addressing data limitations and considering regional differences in Indonesia. The results underscore the necessity for the development, implementation, and education of a comprehensive communication strategy, along with the active involvement of the community. Findings are visually represented through a radar curve, presenting scores for critical communication elements across five scales. While certain limitations are acknowledged, the study provides valuable insights into Disaster Preparedness Area implementation, emphasizing the significance of critical communication in effective disaster management. Recommendations are outlined to assist stakeholders in devising more impactful communication strategies for disaster management in Indonesia.

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

ABSTRACTDuring disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often suffer from the contradiction between the accuracy and efficiency of building damage extraction. This paper proposed a simple and effective framework to rapid recognize collapsed building objects using pre-disaster building distribution maps and post-disaster quasi-panchromatic remote sensing images. The proposed method is validated using several historical disasters in the xBD dataset and tested using three cases of earthquakes in terms of both effectiveness and efficiency. In addition, we have verified that the texture information of optical remote sensing images can be used as the main basis to judge whether a building is collapsed or not, so the panchromatic images are sufficient to enable the deep learning model to correctly recognize collapsed buildings. The experimental results indicate that using quasi-panchromatic images can alleviate the influence of style variations and diverse roof colors present in multi-spectral images on the model’s generalization performance, resulting in an average overall accuracy improvement of 2.4%. Additionally, the reduced data volume leads to an improvement in inference efficiency.

Introduction: We sought to describe the range of emergency medicine (EM) resident physicians’ perceptions and experiences of working and training during the initial coronavirus 2019 (COVID-19) pandemic surge at two, large-volume, urban training hospitals in Brooklyn, New York. Methods: A total of 25 EM resident physicians who worked at either of two large emergency departments (ED) from March 15–April 11, 2020 participated in semi-structured interviews conducted in July and August 2020. Interviews were conducted by the authors who were also emergency medicine resident physicians working in the ED during this time. We asked open-ended questions to residents about their experiences and emotions at work and outside of work, including their relationship with co-workers, patients, and their community. The interviews were audio-recorded and transcribed. We then conducted a thematic analysis to identify, classify, and define themes from interview transcripts. Iterative commonalities and differences between interview response themes were grouped to create a broadly applicable narrative of the residents’ perceptions and experiences of working and training during this initial wave of a novel pandemic. Interviewees also responded to a demographics survey. Results: Study participants described four major aspects of their perceptions and experiences of working and training during the stated time, including emotional challenges such as anxiety and feeling underappreciated; protective thoughts, including camaraderie, and sense of duty; workplace challenges such as limited knowledge surrounding COVID-19 and a higher volume of acute patients; and adaptive strategies including increased communication with ED administrators. Conclusion: Emergency medicine residents have a unique perspective and were key frontline hospital responders during a prolonged disaster and mass triage event within a local health system. Considering the chronic case and mortality fluctuations and new variants of COVID-19, as well as the anticipation of future infectious disease pandemics, we believe it is important for key decision-makers in resident education, hospital administration, and all levels of public health management to inform themselves about residents’ emotional and workplace challenges when establishing hospital and residency program disaster protocols.

Objectives: To investigate the factors associated with stress, resilience, coping styles, and emergency competencies when nurses are faced with a public health emergency. Design: This study used a cross‐sectional design. Sample: Study data came from a survey of 646 nurses who were from a tertiary hospital in Southern China in March–June 2022. Methods: Participants responded to self‐report questionnaires through a web‐based survey. Stress, resilience, emergency competencies, and response to public emergencies were assessed using the Perceived Stress Scale, Connor–Davidson Resilience Scale, the core competencies of nurses in public health emergencies, and a simplified coping style questionnaire. Results: A total of 646 nurses participated in this study. Slightly over half of the participants were ≤30 years old, and almost all were female. Resilience, positive coping, and negative coping were positively correlated with emergency competencies. Multiple linear regression analysis demonstrated that resilience, working years, and participation in the treatment of infectious diseases were significant predictors of emergency competencies. Conclusion: The findings suggest that nurses require additional training in emergency management and clinical practice to enhance their emergency competencies. More interventions and social support should be provided to improve nurses' resilience and positive coping strategies when they encounter public health emergencies. [ABSTRACT FROM AUTHOR]