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

Risk management is an essential component of effective campus recreational sport programming. However, research on risk management policies, procedures, and practices within recreational sports is both limited and outdated. As such, this study provides an overview of current risk management policies, procedures, and practices across North American collegiate recreational sports departments. Specifically, the results emphasize current training practices for both part-time and full-time staff, practices in establishing and updating emergency action plans, and contents covered in emergency action plans. The results suggest that while the vast majority of recreational sport programs have established risk management procedures and engage in risk management training, they diverge in best practices related to emergency action plans and in terms of contents covered in such plans. Applications of these results for practitioners and institutions are emphasized, including discussions around best practices in risk management. [ABSTRACT FROM AUTHOR]

Relief supply inventories are important in disaster response operations. To address the problem of dead stocks and avoid relief supply expiration, such inventories must be renewed periodically. Different from the traditional shelf-life rotation mechanism (i.e. renewal of the entire inventory at the expiration date), which pays more attention to decreasing the quantity of dead stocks, the novel period rotation mechanism that we design in this study will gradually rotate relief supplies according to their shelf life. We propose a mathematical programming model to minimise the total cost of relief organisations and conduct a case study and analyse the sensitivity of key parameters using real-world data. Results show that our rotation mechanism can substantially reduce the financial burden on relief organisations and improve the quality of relief supplies for victims. The novelty of our study includes: (1) We introduce a new approach that can rotate multiple times, through which we consider the quality of relief supplies; (2) We incorporate recycling and remanufacturing into the rotation process; and (3) We adopt an inductive reasoning method to develop the model with demand and time uncertainties. We prove the feasibility of our model and contributions of our study to humanitarian logistics practice and research. [ABSTRACT FROM AUTHOR]

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

ABSTRACTNowadays, 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.

This essay draws on the author's experiences in the Faculty Senate at Pennsylvania State University and cochairing a committee on COVID-19 and shared governance for the American Association of University Professors. [ABSTRACT FROM AUTHOR]

Lately, there has been increased interest among researchers in studying the resilience of manufacturing supply chains. However, the Covid-19 pandemic has caused severe disruptions in global supply chains, which have led to calls for greater resilience in these supply chains. This study provides insights into the impact of the Covid-19 pandemic on supply chain resilience by conducting a multiple case study in three intertwined industries based on the dynamic capability view and the relational capability view as a theoretical underpinning. Data were collected during the pandemic in a two-stage interview process with 18 supply chain and production experts directly involved in crisis management. Internal and external documents supplemented the interviews. The results revealed seven higher-level capability groups for building resilience in intertwined supply chains during a pandemic outbreak: agility, collaboration, digital preparedness, flexible redundancy,human resource management, contingency planning, and transparency and visibility. Each capability group is supported by associated capabilities extracted from the data analysis. The findings obtained based on the results of the multiple case study are discussed, and implications for management and future research directions are presented. [ABSTRACT FROM AUTHOR]

This study investigates the impact of supply chain (SC) disaster readiness on SC resilience and robustness and the subsequent impact on firms’ financial performance in the context of the COVID-19 outbreak. Drawing on the dynamic capabilities view and organisational readiness for change theory, we provide a theoretical model and assess data gathered of 398 French firms using structural equation modelling. The findings corroborate the role of SC disaster readiness in setting the stage for resilience and robustness. In addition, the results indicate the positive influence of SC resilience on financial performance; however, the effects of SC robustness on performance are not demonstrated. Based on these results, we offer theoretical and practical implications for the extant literature and provide further avenues for research. [ABSTRACT FROM AUTHOR]

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

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

"The Urban Rehabilitation of Post-Disaster Scapes" is a book that examines the use of urbanscapes during the disaster process, focusing on the pre-disaster, disaster event, and post-disaster phases. The book includes 18 case studies of natural and man-made disasters from 1991 to 2022, covering various types of disasters such as floods, storms, earthquakes, wildfires, and sociological and technological disasters. The authors analyze the role of the urbanscape in each phase of the disaster process and discuss urban rehabilitation strategies. While the book provides a comprehensive discourse on disaster resilience and green growth approaches, it lacks case studies from the African continent. Overall, the book is recommended for researchers, students, and professionals in academia, disaster management, urban planning, and architecture. [Extracted from the article]

The damage assessment of numerous buildings after the earthquake is still a challenge by traditional methods as it requires a significant amount of time and resources for carrying out building-by-building seismic damage assessment. This study presents the machine learning (ML)-based damage assessment of buildings after an earthquake. The applicability of the machine learning model will be limited considering only structural properties or ground motion characteristics. Thus, in this study, the applicability of different machine learning techniques is explored using real-world earthquake damage datasets considering both the structural properties and ground motion characteristics. Initially, the entire dataset is used to train the ML models to classify the building's damage into five categories ranging from null to slight damage to collapse. Later, the dataset is divided into three traffic light damage tags: green, yellow, and red. The performance of the ML model to classify the building's damage into three damage tags is found better than that of classifying it into five damage categories. This research aims to be beneficial in planning and decision-making for emergency response and recovery following an earthquake. [ABSTRACT FROM AUTHOR]

Using a social diagnosis approach to COVID-19-related trauma, this research bridges the fields of sociology of medicine, disaster response, digital sociology, and digital divides. Bringing these literatures into dialogue, we problematize the digitally mediated trauma ensuing from COVID-19. We unpack two emergent media pathways or channels leading to a social diagnosis of trauma specific to sharp increases in reliance on digital media occasioned by the pandemic. The research advances the theoretical concept of the digital media trauma paradox in which trauma ensues from both oversaturation from toxic digital content and exclusion from digital resources. In either case, digital media engagements may act as a social determinant of health, particularly digital inequalities that to co-occur with other forms of disadvantage. The research closes by arguing that social diagnosis approaches are an excellent tool to understand the complexities of disaster response in the digital age. [ABSTRACT FROM AUTHOR]

Extracting building footprints from extensive very-high spatial resolution (VHSR) remote sensing data is crucial for diverse applications, including surveying, urban studies, population estimation, identification of informal settlements, and disaster management. Although convolutional neural networks (CNNs) are commonly utilized for this purpose, their effectiveness is constrained by limitations in capturing long-range relationships and contextual details due to the localized nature of convolution operations. This study introduces the masked-attention mask transformer (Mask2Former), based on the Swin Transformer, for building footprint extraction from large-scale satellite imagery. To enhance the capture of large-scale semantic information and extract multiscale features, a hierarchical vision transformer with shifted windows (Swin Transformer) serves as the backbone network. An extensive analysis compares the efficiency and generalizability of Mask2Former with four CNN models (PSPNet, DeepLabV3+, UpperNet-ConvNext, and SegNeXt) and two transformer-based models (UpperNet-Swin and SegFormer) featuring different complexities. Results reveal superior performance of transformer-based models over CNN-based counterparts, showcasing exceptional generalization across diverse testing areas with varying building structures, heights, and sizes. Specifically, Mask2Former with the Swin transformer backbone achieves a mean intersection over union between 88% and 93%, along with a mean F-score (mF-score) ranging from 91% to 96.35% across various urban landscapes. [ABSTRACT FROM AUTHOR]

Flooding is a recurring issue in India, affecting 10–15 states annually during monsoon season and coastal regions due to cyclones. Microwave satellite data from SAR sensors like RISAT-1A, launched in February 2022, offers near real-time flood mapping crucial for relief efforts. Various acquisition modes, including medium resolution ScanSAR (MRS) mode, coarse resolution ScanSAR (CRS) mode and fine resolution Strimap (FRS) mode, provide suitable resolutions for flood mapping. Case studies demonstrate the behaviour of the backscatter coefficient in flood pixels, essential for flood map preparation. Validation against optical datasets shows a high accuracy of 91% in CRS, 94% in MRS and 94% in FRS, which is acceptable for near real-time mapping. The 67 and 91 flood maps were generated in 2022 and 2023 respectively, aiding state and central disaster management. [ABSTRACT FROM AUTHOR]

Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at http://dx.doi.org/10.12157/IOCAS.20240410.002 (Gao et al., 2024). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase would inevitably bring unexpected challenges to vulnerable populations. There is a need to identify the emergent needs of underprepared, vulnerable populations under lockdowns to inform future pandemic management. In this study, the messages posted on an online mutual help-requesting platform during the 2022 Shanghai lockdown were leveraged as the near real-time agent to represent the side effects of the lockdown policy. This work explores the spatiotemporal correlation of the posts to their potential influencing factors and mines knowledge from the textual content of the posts. The results indicate that the help requests were clustered in downtown Shanghai. The access to profuse medical resources and public services may be hindered in districts with more stringent lockdown policies. The help requests' content unveils a need for medication and groceries under lockdown. It also underscores that the elderly population was affected most by the lockdown. Based on these insights, policymakers can better anticipate and address the urgent residential needs that may arise in future lockdowns. This knowledge can contribute to more effective planning and preparedness efforts to mitigate the impact on vulnerable populations and ensure their well-being during similar crisis situations. [ABSTRACT FROM AUTHOR]

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) is currently a source of angst, because of its ability to give us content that sounds uncannily like a real person, and because of concern that people will not stop at using it as a tool to generate and synthesize ideas, but instead will cede control over our words, and then our thoughts. This editorial details each article in Creative Nursing Vol. 30 Issue 2, highlighting the ways in which social media, different kinds of AI, and other tools for connectivity can be used for good: finding our purpose, uniting people over long distances, expediting knowledge implementation, managing large volumes of literature, advancing health equity, and enriching nursing education. [ABSTRACT FROM AUTHOR]

Background: Health education programs can enhance self-efficacy and disaster preparedness. However, research incorporating the Health Belief Model (HBM) in education for infectious disasters is lacking. Aims: Examine the effectiveness of an HBM-based education program on self-reported self-efficacy and self-reported preparedness for infectious disasters in rural populations. Methods: In total, 109 eligible participants were allocated to two groups: participants who received HBM-based education (n = 55) or regular education (n = 54), presented at in-person meetings, with individual follow-up conducted on WhatsApp. Self-reported self-efficacy and preparedness for infectious disasters in both groups were measured at 4 and 8 weeks. Findings: Participants showed a significant interaction between groups and in time to increase in self-reported self-efficacy and self-reported preparedness to deal with infectious disasters. After 4 and 8 weeks, the groups had statistically significant differences (p ≤.001) in all outcome measures. Conclusion: HBM-based education was associated with increased self-reported self-efficacy and self-reported infectious disaster preparedness among the rural population. Follow-up by nurses is essential to implementing continuing health belief education programs to enhance rural populations' self-efficacy and infectious disaster preparedness. [ABSTRACT FROM AUTHOR]

Objectives: We describe our co-design process aimed at supporting the reintegration of essential care partners into long-term care homes during the COVID-19 pandemic. Methods: More specifically, using a co-design process, we describe the pre-design, generative, and evaluative phases of developing a virtual infection prevention and control course for essential care partners at our partnering long-term care home. For the evaluative phase, we also provide an overview of our findings from interviews conducted with essential care partners on the expected barriers and facilitators associated with this virtual course. Results: Results from these interviews indicated that the virtual course was viewed as comprehensive, detailed, engaging, refreshing, and reliable, and that its successful implementation would require appropriate resources and support to ensure its sustainability and sustainment. Findings from this study provide guidance for the post-design phase of our co-design process. Conclusion: Our careful documentation of our co-design process also facilitates its replication for other technological interventions and in different healthcare settings. Limitations of the present study and implications for co-designing in the context of emergent public health emergencies are explored in the discussion. [ABSTRACT FROM AUTHOR]

Aim: Coronavirus disease 2019 emerged in December 2019 and spread worldwide. This study aimed to clarify the impact of the coronavirus disease 2019 pandemic on the diagnosis and treatment of hepatocellular carcinoma (HCC) in Japan. Methods: First, we collected the monthly numbers of HCC‐related general medical practices from January 2019 to December 2021 at liver disease‐specific medical institutions in Japan. Next, we collected individual clinical information from patients with newly diagnosed HCC during this period. Results: There was a decrease in the number of HCC‐related medical practices, including referrals, enhanced abdominal ultrasonography and radiofrequency ablation, in Japan's first state of emergency (SOE; April–May 2020) compared with 2019. Fewer patients were diagnosed with new HCC during the first SOE than before or after it. There was no difference in tumor diameter, number of tumors or Barcelona Clinic Liver Cancer stage between patients diagnosed before the first SOE and those diagnosed during or after the first SOE. The median waiting times for treatment of patients diagnosed during and after the first SOE were 31 and 37 days, which were significantly shorter and not longer than that of patients diagnosed before the first SOE (36 days), respectively. Conclusion: The number of HCC‐related general medical practices decreased during the first SOE. However, the coronavirus disease 2019 pandemic did not lead to HCC progression by diagnostic delays or cause HCC treatment delays in Japan. [ABSTRACT FROM AUTHOR]

Background:In December 2021, the Region 1 Disaster Health Response System, the state of Vermont, and the National Emergency Tele-Critical Care Network partnered to provide statewide access to disaster teleconsultations during COVID-19 surge conditions. In this case report, we describe how a disaster teleconsultation system was implemented in Vermont to provide access to temporary tele-critical care consultations during the Omicron COVID-19 surge. Methods:We measured the time from request of service to implementation and calculated descriptive statistics. Results:Seven of Vermont's 14 hospitals requested the service. Despite a technology solution capable of providing services within hours, mean time to service implementation was 27 days (interquartile range 20–41 days). Conclusions:Integration of disaster teleconsultation systems into state and local emergency management plans are needed to bring administrative start-up times in line with technical readiness. [ABSTRACT FROM AUTHOR]

Earthquakes are one of the most terrible and catastrophic natural disasters, responsible for widespread infrastructure and population losses. This study presented an integrated procedure to assess seismic risk considering the seismic hazard, vulnerability, coping capacity and resilience in the seismically active area in Northern, Pakistan. High-resolution satellite imageries coupled with extensive field investigations have led to the creation of building footprints along with building typological information. Twenty-eight envisaging subfactors related to seismic hazard, physical, social and economic vulnerability accompanied by response and recovery were utilized for the evaluation of detail and comprehensive seismic risk map. Overriding the constraint of limited factors and using the rich vulnerability data on a local scale, a detailed risk assessment at high resolution is performed to identify sites prone to high seismic risk. Prepared with Arc GIS grid technology and Analytical Hierarchy Process, the seismic vulnerability map revealed that 1.8 km2 (6.4%) and 0.5 km2 (1.8%) of the total area have high to very high vulnerability. The final seismic risk map shows that villages including Chella Bandi, Dhanni Mysiba, Makri, Dherian Syedian, Ranjata, Baila Noor Shah, Taami, Middle Gojra, Lower Gojra, Rasheed Abad and Upper Chattar lie in high-risk class while Pilot, Madina Market area, Shahnara and Domail villages fall in very high class taking up an area of 1.8 km2 (6.4%). Seismic risk hotspots identified in this study can help decision-makers in prioritizing the deployment of funds and other resource allocation in these areas. This knowledge informs building codes and land use planning, guaranteeing higher building standards in seismically active areas. Critical structures in designated hotspots are the main focus of infrastructure design and retrofitting efforts can be aimed at improving their seismic resilience framework. Plans for emergency responses can be created taking into account possible effects in high-risk regions to enable effective and focused crisis management. Furthermore, these results impact public awareness campaigns, insurance plans, and community involvement programs to lessen the overall damage. [ABSTRACT FROM AUTHOR]

In 1954, a devastating flood struck the entire Yangzi River basin, causing the first major natural disaster in the newly established People's Republic of China (PRC). The young regime had just embarked on its First Five-Year Plan, aimed at achieving rapid economic development and industrialization. The flood swept across several provinces, displaced populations, and submerged farmlands, posing both a new challenge and an opportunity for the regime to shape the socioeconomic landscape of the affected regions. This article uses a localized perspective to examine how the rural society of Hubei Province, the hardest-hit area, responded to the disaster. The central government's self-help doctrine delegated much power to local governments and communities. Local officials and rural cadres had considerable leeway in managing the relief campaign as they simultaneously followed the state macroeconomic policy of rural collectivization. The 1954 flood in Hubei Province serves as a case study that illustrates the complexities of disaster management in the early years of the PRC. [ABSTRACT FROM AUTHOR]

Background: Duty to care is both an integral concept in health care and a fundamental nursing obligation. But nurses' perceptions of duty to care can be moderated by their experiences in the practice setting. Research examining nurses' perceptions of their duty to care during the COVID-19 pandemic could shed light on how the pandemic is affecting the nursing workforce. Purpose: This study aimed to examine nurses' sense of duty to care during the early months of the pandemic, using the Nash Duty to Care Scale (NDCS), and to compare the high-scoring nurses with the low-scoring nurses. Methods: This quantitative study used a descriptive, cross-sectional design. It was conducted among licensed RNs enrolled at two accredited nursing programs in the Northeast region of the United States. Data were collected via a demographics questionnaire and the NDCS. A two-step cluster procedure was used to categorize participants into two groups: those with high perceived duty to care (HPDC) and those with low perceived duty to care (LPDC). Independent t tests were performed to compare NDCS results between the two groups. Results: Nearly two-thirds (61%) of the participants had total NDCS scores indicating an HPDC, while 39% had scores indicating an LPDC. Of the NDCS's four subscales, perceived obligation and perceived risk were the most important in separating participants into the low- and high-scoring groups. Conclusions: This study adds to the literature about the components that affected nurses' perceived duty to care and willingness to report to work during the early months of the pandemic. Just as nurses have a duty to care, health care organizations have an obligation to provide a safe working environment so that nurses can fulfill that duty without sacrificing personal safety. The study findings may guide health care leaders, systems, and organizations regarding how to create safer work environments that support the nurse's duty to care during disasters. This study aimed to examine nurses' sense of duty to care during the early months of the pandemic, using the Nash Duty to Care Scale, and to compare the high-scoring nurses with the low-scoring nurses. [ABSTRACT FROM AUTHOR]

This article explores the origins of Japan's security policy after the Asia-Pacific War. During the occupation, the Japanese government decided on the maintenance of US forces in the country after the Japanese Peace Treaty. To explain the rationale behind this, existing studies emphasize the Japanese government's external security calculations. Highlighting the interaction between Japanese foreign and domestic policies, this article challenges this conventional claim by arguing that internal security calculations strongly influenced the Japanese government's decision. With the creation of the Ashida memorandum in September 1947 and the expectation that the General Headquarters (GHQ) would allow for stronger police forces, Japanese Foreign Minister Ashida Hitoshi and his staff sought to restrict the stationing of US forces in Japan only to national emergencies. As opposed to such expectation, however, GHQ's police reform significantly weakened Japan's internal security capabilities, forcing Tokyo to depend on US forces for dealing with internal communist threats. [ABSTRACT FROM AUTHOR]

To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation‐based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth‐year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well‐evaluated and feasible over a longer period. [ABSTRACT FROM AUTHOR]

Public information and early warning are key components of social disaster prevention because they can complement planning, and multi-disciplinary coordination and minimize losses. Social media has increasingly become an important communication channel for users to disseminate useful public and emergency information in these incidents. Communications, including web-based services, between governments, communities, and people involved in the management of disasters are mandatory for the purposes of early warning, rescue services, and recovery. Purpose of this article is exploring how people may use or had used the social media applications during the disaster and provide the advantages of social media in disaster management all over the world but specifically for citizens of Türkiye and Iraq. To get the response of the users, a Google form based survey questionnaire was distributed to the users through the internet. Two hundred and eleven persons, from both genders responded, ages ranged from 17 years to above 45, from different educational levels, inhabiting Türkiye (n = 105, 49.8%), Iraq (n = 88, 41.7%), and other countries (n = 18, 8.5%). The study shows that depending on the culture, social media usage behaviors change for disaster situations. During the disaster, Iraqi citizens preferred to use the Facebook application, meanwhile Türkish citizens prefer to use WhatsApp application to communicate. Both, governments and private sector, are invited to establish and continuously update the infrastructure and facilities of an effective social media communication system, plans should be made to encourage all sectors of society to learn how to use social media in the event of a disaster. [ABSTRACT FROM AUTHOR]

Landslides are a severe geohazard around the world. When moving soil masses discharge into a large water body, a tsunami can be generated and exacerbate the devastating effects of the landslide as well as extend the affected area. In this study, based on on-site geological investigation and monitoring, a numerical depth-averaged, two-phase model is established for a hypothetical tsunami in the Suofengying reservoir induced by the potential Bianjiazhai landslide in China which has been previously identified as critical. The analysis of the simulation results shows that the maximum wave amplitude measured at gauge point closest to the landslide is 31 m, and the tsunami reaches the reservoir's dam about 66 s after the landslide initiation. The inundation map provides potential risk areas that could be affected if a landslide occurs with the anticipated characteristics. Under similar conditions, the research results will help guide reservoir operation and landslide-tsunami disaster prevention. Simultaneously, examining the sequence of events in the tsunami disaster chain facilitates the analysis of the fundamental physics governing the propagation of tsunamis within reservoirs. This analysis contributes to the prediction and prevention of landslide-induced tsunami disasters occurring along reservoir banks. [ABSTRACT FROM AUTHOR]

Developing real-time automatic intuitive devices based on electroencephalography (EEG) to improve hazard recognition performance (HRP) is essential in construction engineering management. However, existing studies generally developed devices based on unimodal data, failing to distinguish the role played by brain activities in different visual areas and ignoring the possible existence of other psychological mechanisms. Therefore, this study aims to use multimodal data based on brain-eye synergy to predict HRP and reveal the cognitive mechanism of hazard recognition to develop intuitive hazard recognition devices. Accordingly, we designed experiments to investigate this problem: (1) 53 construction workers completed a 120-trial hazard recognition task in a laboratory environment (N=6,360), (2) an EEG recorder collected the participants' EEG activities and an eye-tracking device recorded the data of eye movement, and (3) the power spectral density (PSD) of EEG was calculated and then imported into Matlab2018a in combination with EEGLAB for analysis. The results showed that the prediction results of hazard recognition have a time-variant effect. For fall-related hazards, EEG signals within 0–320 ms after the onset were significant in predicting HRP; for electric-related and fire-related hazards, EEG signals within 560–640 ms after the starting point were the most valuable. Also, compared with fall-related hazards, it took a significantly longer time to recognize electric-related and fire-related hazards, which consumed more attentional resources. Finally, the frontal lobe requires more attentional resources to finish cognitive activities than the occipital lobe to finish visual information processing activities, and the occipital lobe needs to process visual information first, and then transmit it to the frontal lobe for analysis and judgment. For the body of knowledge, this study, finding the time-variant effect of HRP, improves the theory of construction hazard recognition from an eye-brain synergy perspective and provides guidance for developing intuitive hazard recognition devices and improving management performance. This study combined eye-movement data and EEG data to predict the performance of hazard recognition using multimodal data. Not only did this study reveal the mechanism of hazard recognition, but it also proposed suggestions for project management and device development. Managers are suggested to implement refined management and regularly monitor the physiological signals of employees. Some construction companies started to use smart devices to measure workers' arousal, emotional valence, emotional regulation ability, fatigue, workload, or safety attention level before work every day. And it is recommended to provide safety reminders to employees based on whether there are any abnormalities in their physiological indicators. In addition to project management, the results of this study can serve the development or optimization of hazard recognition equipment. Developing devices cannot only consider EEG information, but also combine EEG information with eye-movement information. Meanwhile, developers can also consider different types of hazards and different brain regions to improve device performance. [ABSTRACT FROM AUTHOR]

The management and operation of water distribution networks (WDNs) by establishing district metered areas (DMAs), especially in existing and old WDNs, has many advantages, including water leakage control, ease of operation, and network management in emergencies. Using DMAs in existing WDNs designed and developed traditionally is a challenging issue because of the need to cover several criteria. In the present study, a method for creating DMAs is suggested to minimize the cost as well as to reduce the average pressure in high-pressure WDNs. The proposed method uses the Girvan–Newman clustering and nondominated sorting genetic algorithm (NSGAII) optimization algorithms. This method was implemented on Modena's and Wolf–Cordera Ranch's WDNs. The proposed method can generate several solutions based on performance indicators, including cost, hydraulics, and quality. The results show that by applying the proposed method in the Modena and Wolf–Cordera Ranch WDNs, the average pressure of the networks was reduced by 17% and 31%, respectively, in comparison with the primary WDNs in the base demand and peak demand conditions. This method can be used by water utilities for partitioning WDNs, especially when the pressure is high. [ABSTRACT FROM AUTHOR]

In recent decades, the Inner Mongolia Reach of the Yellow River (IMRYR) in China has experienced new characteristics in terms of ice conditions due to human activities and climate change. It is crucial to develop a comprehensive river ice model for this reach that reflects the changing physical characteristics of ice events. However, simulating river ice process in the IMRYR using numerical model is challenging due to the scarcity of observed data on river geometry, hydrology, meteorology, and river ice conditions. With latest field measurements, this study examined the spatiotemporal variations of ice conditions in the IMRYR. In the development of river ice model, this study tackled various challenges such as incomplete river geometry, insufficient tributary flow information, and the identification of ice bridge locations. By improving the one-dimensional river ice model, the study simulated the ice development process during the 2012–2013 ice flood season in the IMRYR. A comparative analysis was conducted between the simulated and observed water temperature, ice cover thickness, ice front locations, and flows, showing a good agreement. This study analyzed and determined the ice hydrodynamics, thermodynamics, and relevant river parameters, providing a deeper understanding of the intricate interactions and dynamics associated with the formation and behavior of river ice in the Yellow River. This research can provide scientific basis and methodological support for ice disaster prevention and control in the Yellow River, as well as insights and technical means for simulating comprehensive river ice processes in natural rivers and artificial channels. The Yellow River is one of the rivers in China that experiences frequent ice-related disasters, with the main occurrences happening in the Inner Mongolia Reach (IMR). In recent decades, the IMR has faced new challenges in ice evolution, including changing climatic conditions, severe channel siltation, and expanded ice-related channel storage, making the understanding of ice conditions more complex. The China Institute of Water Resources and Hydropower Research, commissioned by the Hydrographic Bureau of the Yellow River Conservancy Commission, conducted numerical simulations of the ice processes in the IMR. Based on the new characteristics of ice conditions in the Yellow River, a mathematical model was proposed to simulate the development of winter ice conditions in data-scarce regions. The one-dimensional river ice process model was enhanced to enable dynamic simulations of the ice processes in the IMR, and accurately capturing the evolution of winter water temperature, ice cover thickness, ice front progression, flow rate, and water level. This study provides valuable scientific basis for ice forecasting, ice control operations, and disaster prevention in the Yellow River. The improved model has been adopted by the Hydrographic Bureau of the Yellow River Conservancy Commission, and additional categories and sampling frequencies of hydrological and meteorological data have been tailored for the Inner Mongolia Reach considering specific data requirements for the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Debris flow is a typical natural disaster in the middle reaches of the Dadu River, Southwest China. Field physical model tests were conducted to reveal the mechanism of river blocking by debris flow in the middle reaches of the Dadu River. The dynamic processes of riHver blocking by debris flows were revealed, and based on which three typical river-blocking modes of debris flow are observed, i.e. thrust-type river blocking, mixed-flow-type river blocking and progressive river blocking. The test results showed that the material composition of debris flows plays an important role in the river-blocking mode, only the tests that adopted the mixed soil and gravel exhibited the thrust-type river blocking mode. The material composition has a controlling effect on the thrust-type river-blocking model. Mixed-flow-type river-blocking mode appears most often in the tests with an intersection angle of 60°, because the small intersection angle is conducive to the mixing of the debris flow and the water in the main channel. The debris flows composed of sand tend to block the river with mixed-flow-type river-blocking mode, accounting for 50% of the occurrences in the model tests. The high flow rate and discharge in the main channel and the low flow rate and discharge in the branch channel are the key factors controlling the progressive river-blocking mode. The test results in this study can support the debris flow disaster prevention and mitigation in this area. [ABSTRACT FROM AUTHOR]

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

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

The world is witnessing a difficult time trying to circumscribe the spread of COVID-19 pandemic. The situation has become even more challenging as several natural disasters and emergencies coincided with this pandemic and created a situation of concurrent emergency. Effectively managing concurrent emergencies are extremely challenging. Only by utilizing and applying the knowledge gathered while combating a real-life concurrent emergency, one can further be prepared to face such emergencies. Hence, this study intends to identify the critical success factors (CSFs) having a major role in effective management of concurrent emergencies and evaluate the indispensable links among the factors. Twelve CSFs are identified from Pareto analysis, for which feedback from 46 experts involved in the management of emergencies is utilized. In addition, the revised rough-decision making trial and evaluation laboratory (rough-DEMATEL) approach is suggested to analyze the indispensable links among the CSFs and further, these factors are ranked based on the average vector length. The recent case of concurrent emergency in Bangladesh- floods amid a pandemic is considered to establish the applicability of this method. The findings of this research reveal that to handle concurrent emergencies 'Incremental improvement of proactive measures', 'Resilient supply chain and logistics network', and 'Government leadership and military cooperation' are the most critical factors to concurrent emergency management (CEM). These CSFs play a major role in ensuring the effectiveness of CEM. Thus, this study can be a building block in developing an effective CEM plan for the policymakers, managers, and practitioners. [ABSTRACT FROM AUTHOR]

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

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

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]