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

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness. [ABSTRACT FROM AUTHOR]

To address the issue of poor accuracy in typhoon wind data, this paper presents a methodology for the calibration of typhoon wind data while conducting an analysis and evaluation of the associated uncertainties. The approach commences with the introduction of two wind field data calculation models. The first model facilitates the correction of buoy attitude, thereby transforming inaccurate buoy wind speed data into real-speed data. In parallel, the second model enables the conversion of buoy-observed true velocity into neutral stable stratified wind parameters, leveraging satellite-derived data for precise calculations. Subsequently, the paper undertakes the task of spatio-temporal alignment between buoy data and satellite observations. Ultimately, a comprehensive comparative analysis is conducted by juxtaposing the ERA5 database with data collected from a moored buoy equipped with the R.M. YOUNG wind monitor. A new simulation method for satellite wind speed data inversion is proposed, and the experimental results demonstrate the effectiveness of the proposed calibration method in enhancing the accuracy of typhoon wind field data. In particular, the maximum wind speeds recorded were 20.15 m s−1 and 13.22 m s−1 during Typhoon "Jangmi (202005)" and "Maysak (202009)," respectively. Furthermore, the mean square errors (MSE) for our method were measured at 0.31 m s−1 and 0.28 m s−1, outperforming the satellite-derived indicators. The expanded uncertainty of measurement results for the two typhoons was calculated at 0.39 m s−1 and 0.34 m s−1, closely aligning with the MSE values. These computational models present a valuable means of enhancing the precision and reducing uncertainty in satellite-derived data. The findings presented in this paper hold great promise for applications in typhoon forecasting, investigations of air-sea interactions, and disaster prevention and mitigation efforts. [ABSTRACT FROM AUTHOR]

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. [ABSTRACT FROM AUTHOR]

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is "first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions". This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall. [ABSTRACT FROM AUTHOR]

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region. [ABSTRACT FROM AUTHOR]

Rapid and accurate structural damage assessment after an earthquake is important for e0cient emergency management. The widespread application of surveillance cameras provides a new possibility for improving the e0ciency of assessment. However, it is still challenging to directly assess the structural seismic damage based on videos captured by indoor surveillance cameras during earthquakes. In this study, we elaborate on the concept of estimating the structural natural frequency based on the relative pixel displacement of inter-stories. Furthermore, we propose a strategy for post-earthquake structural damage assessment that integrates the computer vision and time-frequency analysis. *is approach aims to navigate the di0culties inherent in earthquake damage assessment and improve emergency responses. The relative pixel displacement between the camera and the 5xed features on the 6oor is extracted from videos by using the Harris corner detection and Kanade--Lucas--Tomasi algorithms. The structural natural frequency is estimated using the synchroextracting transform-enhanced empirical wavelet transform. The natural frequency shift-related seismic damage index is de5ned and calculated for damage assessment. A shake table experiment of a smallscale steel model is conducted to verify the accuracy and feasibility of the approach, and the practicality of the proposed approach is further veri5ed by utilizing the data from a full-scale reinforced concrete benchmark model experiment.Theresults demonstrate that the approach can accurately and e0ciently evaluate the structural damage after an earthquake based on the video captured by surveillance cameras during the earthquake. The error of the acquired damage index is less than 0.1. We will apply more advanced algorithms in the future to alleviate this problem. [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]

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]

"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]

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]

Accurate and in-depth rainfall studies are crucial for understanding and assessing precipitation events' patterns, intensities, and impacts, enabling effective planning and management of water resources, agriculture, and disaster preparedness. Despite many rainfall studies in Bangladesh at the national and regional scales, study on the spatiotemporal rainfall variability is still rare at the local scale. The current study aims to apply Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) techniques to assess the long-term annual and seasonal rainfall trends and variability over the southeast region of Bangladesh. Monthly rainfall data from ten Bangladesh Meteorological Department climate stations between 1981 and 2022 was used for the analysis on annual and four seasonal scales. The precipitation concentration index results showed significant variations in annual rainfall across the study area, whereas seasonal PCIs were consistent with moderate rainfall. According to standardized rainfall anomaly findings, each station experienced at least one severe to extremely severe drought episode during the 42-year study period. Homogeneity tests revealed significant breakpoints in some rainfall datasets, while 78% were declared homogeneous. MK, MMK, and ITA techniques revealed similar increasing and decreasing trend patterns throughout the study area. Annual rainfall showed an upward trend in the coastal part and a downward trend in the northern part of the study area, with monsoon rainfall exhibiting a similar trend pattern. The ITA technique outperformed the MK and MMK techniques in detecting trends, identifying significant increasing and decreasing trends in 76% (38 out of 50) of the observations, while the MK and MMK techniques detected trends in only 8% and 44% of the total observations, respectively. The outcome of the current study is expected to be helpful for the sustainable planning and management of water resources in the southeast region of Bangladesh. [ABSTRACT FROM AUTHOR]

Integrating coastal vegetation into sea dikes is a nature-based approach aimed at combining disaster prevention with ecological sustainability in coastal areas. This study investigates the impact of vegetation on the wave run-up on dikes through numerical analysis. The numerical model used in the study solves the Reynolds-averaged Navier–Stokes equations by adding a vegetation resistance force to account for momentum loss. A stabilized k–ω shear stress transport model considering the vegetation effect was adopted for turbulence closure. A series of numerical simulations was carried out on the wave run-up (Ru) on dikes, focusing on the effects of different vegetation heights, densities, zone lengths, and dike slopes under various wave conditions. The results indicate that vegetation can significantly decrease Ru and may cause the wave to change from breaking to nonbreaking on dikes. The Ru behaviors depend on whether waves break and can be well characterized by the Iribarren number and dimensionless wave momentum flux parameter under breaking and nonbreaking conditions, respectively. Finally, the multivariate non-linear regression (MNLR) and artificial neural network (ANN) methods were adopted to explore a prediction model for evaluating Ru. Comparisons showed that the prediction performance of the ANN model is superior to that of the MNLR model. The ANN model has the potential as a promising predictive tool for obtaining wave run-up on dikes with vegetated foreshores under breaking and nonbreaking conditions. [ABSTRACT FROM AUTHOR]

Accurate estimation of the wind stress under extreme conditions is crucial for modeling storm surges and storm waves, which is important to the development of a warning system for coastal disaster prevention. The problem, however, is highly challenging owing to the presence of complex ocean surface processes under the action of unusually strong wind. In this study, the existing atmospheric wave boundary layer model is significantly enhanced by including various effects of wave breaking. Both the effect of wave breaking on the dissipation of energy and its effect on the transfer of momentum within the atmospheric boundary layer are carefully formulated. The wind stress coefficients obtained with the enhanced model are shown to be in good agreement with the measurements in not only deep but also shallow waters. The enhanced atmospheric wave boundary layer model is coupled with ocean wave as well as circulation models to simulate typhoon-induced storm surges and storm waves in the Pearl River delta region. The computational results show that the coupled model with improved evaluation of the wind stress is substantially advantageous when compared with existing approaches. [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]

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]

In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans. [ABSTRACT FROM AUTHOR]

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

Motivation: India has seen severe economic and social impacts from COVID-19, with the most vulnerable people having suffered most. Mumbai's response has been multi-faceted, drawing on numerous actors such as local and state government, but also businesses and citizens. Disaster management is increasingly necessary and Mumbai's policy response during wave two of COVID-19 provides relevant lessons. Purpose: We explain Mumbai's policy response to wave two of COVID-19, termed the Mumbai model, applying a systems-thinking approach. Methods and approach: We adopt a systems-thinking approach, applying a framework developed in Duvendack and Sonne (2021) to analyse a case study based on secondary data and information on the policy response to COVID-19 in Mumbai. Findings: The Mumbai model emerged as a pragmatic policy response. Key actors changed between waves one and two, from a broad set of actors to a narrower set focused on the unfolding healthcare crisis. This resulted in a reconfiguration of networks and partnerships. Decentralized decision-making further changed the structure of the networks to a hub-and-spoke model. Communication and feedback loops shifted from primarily top-down to two-way information flows to support decision-making and resource allocation driven by data. This was enabled by clear leadership and political support. Policy implications: To tackle a crisis of this magnitude, having diverse actors collaborating effectively through networks that enable knowledge and information flows is crucial. Governments can use the systems approach for crisis management in large cities which require swift action and efficient flow of information to assess a situation and to prioritize response efforts. Further research is required to better understand how governments can build capacity and policy structures to enable systemic approaches to crisis management in varied contexts. [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]

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]

Natural disasters present a significant and growing threat to the well-being of children. Every year 175 million children globally are expected to be affected by natural disasters, including floods, cyclones, droughts, heatwaves, severe storms, and earthquakes. In emergencies, children of all age groups, especially those under 5, are the most affected part of the community, with child mortality rates 2-70 times higher than average. Clean water, sanitation and hygiene measures, vaccination to prevent infectious diseases, providing psychological support to vulnerable children in an age-appropriate approach, and paying particular attention to children with special needs are extremely important. Healthcare personnel and families should have adequate information and preparation to do what is necessary before, during, and after emergencies to minimize the negative effects on children. In this review, we aim to discuss the effects of emergencies on children and the prevention methods. [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]

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

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]

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]

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]

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]

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]

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]

On April 29, 2020, China entered a normalization stage of prevention and control. By December 2021, more than 40 outbreaks had occurred in China, which reflected the shortcomings of the pandemic prevention and control measures in China at that time. As the capital city of China, Beijing faces more pressure in epidemic prevention and control. We used the COVID-19 cluster containment evaluation indicators to determine the effects of prevention and control measures on four COVID-19 outbreaks in Beijing. After considering the specificity and operability of evaluation indicators and the availability of evaluation data, the evaluation system in our study consisted of six dimensions: epidemic prevention and control effect, discovery and detection ability, precision prevention and control capability, public protection effect, medical treatment and nosocomial infection prevention and control ability, and information release and public opinion response ability. The composite scores of the prevention and control effects of the Xinfadi, Shunyi, Daxing, and Ejina Banner-associated COVID-19 outbreaks in Beijing were 62, 82, 87, and 76, respectively. In the six dimensions, the epidemic prevention and control effect, discovery and detection ability, precision prevention and control capability, and public protection effect scores for the Xinfadi outbreak were lower than those for the Shunyi, Daxing and Ejina Banner-associated outbreaks. The medical treatment and nosocomial infection prevention and control ability scores for the outbreak associated with Ejina Banner were lower than those for the Xinfadi, Shunyi, and Daxing outbreaks. In managing cluster outbreaks, Beijing was able to detect index cases early enough to reduce the scale and duration of the outbreak and consistently release official information to reduce public panic, standardize the management of centralized quarantine sites to prevent cross-infection, adhere to the "dynamic COVID -zero" strategy to accurately prevent and control outbreaks, reduce the societal influence of the pandemic, and coordinate the epidemic prevention and control and socio-economic development. [ABSTRACT FROM AUTHOR]

Rice is a staple grain crop extensively cultivated in Fujian Province, China. This study examined the impact of high-temperature stress on rice yield and its components, focusing on four representative rice varieties, including early and middle rice grown in Fujian Province. Results indicate significant yield losses, with the most severe reduction of 60.8% observed during the flowering stage of early rice and over 40% during the meiosis and flowering stages of middle rice. High-temperature stress primarily affects early rice yield more at the flowering stage than at the grain-filling stage, whereas in middle rice, it is more severe at the meiosis stage than at the flowering stage. Leveraging historical climatic data spanning the past 20 years, a simulation model for high-temperature stress on rice yield was developed to assess disaster-induced yield loss rates, aiming to enhance prevention and disaster damage assessment for rice under high-temperature stress. Application of the model to four rice planting sites in Fujian Province revealed contrasting temporal changes between loss rates and meteorological yield, with middle rice experiencing more severe damage than early rice. The model's effectiveness is validated by the strong correspondence between yield loss rate and meteorological yield across different regions, highlighting its robust simulation capabilities. [ABSTRACT FROM AUTHOR]

Purpose: This paper argues that extractivist logic creates the environmental conditions that produce "natural" hazards and also the human conditions that produce vulnerability, which combined create disasters. Disaster Risk Creation is then built into the current global socio-economic system, as an integral component not accidental by-product. Design/methodology/approach: As part of the movement to liberate disasters as discipline, practice and field of enquiry, this paper does not talk disasters per se, but rather its focus is on "extractivism" as a fundamental explanator for the anthropogenic disaster landscape that now confronts us. Findings: Applying a gender lens to extractivism as it relates to disaster, further highlights that Disaster Risk Management rather than alleviating, creates the problems it seeks to solve, suggesting the need to liberate gender from Disaster Risk Management, and the need to liberate us all from the notion of managing disasters. Since to 'manage' disaster risk is to accept uncritically the structures and systems that create that risk, then if we truly want to address disasters, our focus needs to be on the extractive practices, not the disastrous outcomes. Originality/value: The fundamental argument is that through privileging the notion of "disaster" we create it, bring it into existence, as something that exists in and of itself, apart from wider socio-economic structures and systems of extraction and exploitation, rather than recognising it for what it is, an outcome/end product of those wider structures and systems. Our focus on disaster is then misplaced, and perhaps what disaster studies needs to be liberated from, is itself. [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]

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]

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]

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

A typhoon disaster is a common meteorological disaster that seriously impacts natural ecology, social economy, and even human sustainable development. It is crucial to access the typhoon disaster information, and the corresponding disaster prevention and reduction strategies. However, traditional question and answering (Q&A) methods exhibit shortcomings like low information retrieval efficiency and poor interactivity. This makes it difficult to satisfy users' demands for obtaining accurate information. Consequently, this work proposes a typhoon disaster knowledge Q&A approach based on LLM (T5). This method integrates two technical paradigms of domain fine-tuning and retrieval-augmented generation (RAG) to optimize user interaction experience and improve the precision of disaster information retrieval. The process specifically includes the following steps. First, this study selects information about typhoon disasters from open-source databases, such as Baidu Encyclopedia and Wikipedia. Utilizing techniques such as slicing and masked language modeling, we generate a training set and 2204 Q&A pairs specifically focused on typhoon disaster knowledge. Second, we continuously pretrain the T5 model using the training set. This process involves encoding typhoon knowledge as parameters in the neural network's weights and fine-tuning the pretrained model with Q&A pairs to adapt the T5 model for downstream Q&A tasks. Third, when responding to user queries, we retrieve passages from external knowledge bases semantically similar to the queries to enhance the prompts. This action further improves the response quality of the fine-tuned model. Finally, we evaluate the constructed typhoon agent (Typhoon-T5) using different similarity-matching approaches. Furthermore, the method proposed in this work lays the foundation for the cross-integration of large language models with disaster information. It is expected to promote the further development of GeoAI. [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]

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]

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions. [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: 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]

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]