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This paper presents a comprehensive safety assessment of hydrogen production using Alkaline Water Electrolysis (AWE). The study utilizes various risk assessment methodologies, including Hazard Identification (HAZID), What-If analysis, Fault Tree Analysis (FTA), Event Tree Analysis (ETA), and Bow Tie analysis, to systematically identify and evaluate potential hazards associated with the AWE process. Key findings include the identification of critical hazards such as hydrogen leaks, oxygen-related risks, and maintenance challenges. The assessment emphasizes the importance of robust safety measures, including preventive and mitigative strategies, to manage these risks effectively. Consequence modeling highlights significant threat zones for thermal radiation and explosion risks, underscoring the need for comprehensive safety protocols and emergency response plans. This work contributes valuable insights into hydrogen safety, providing a framework for risk assessment and mitigation in hydrogen production facilities, crucial for the safe and sustainable development of hydrogen infrastructure in the global energy transition. • Comprehensive safety assessment of hydrogen production via Alkaline Water Electrolysis. • Identification of critical hazards including hydrogen leaks and oxygen-related risks. • Consequence modeling for unignited hydrogen releases and jet fire scenarios. • Emphasis on robust safety protocols and emergency response measures. • Provides practical insights for risk mitigation in hydrogen production facilities. [ABSTRACT FROM AUTHOR]

• The flame propagation process of R152a under different RH conditions was recorded • The R152a reaction activation energy is reduced in O 2 and O 2 /H 2 O environments. • OH is the crucial active fragment that promotes the oxidation of R152a. • The combustion mechanism of R152a in humid air was revealed. 1,1-difluoroethane (R152a) is one of the most prospective low GWP refrigerants but may trigger fires and explosions once it leaks. Understanding the flame propagation process and combustion mechanisms at different relative humidity conditions is essential to use flammable refrigerants safely. In this study, a high-speed camera has recorded the flame propagation of R152a combustion near the flammability limit. Combining with macroscopic experimental phenomena, we revealed the effect of H 2 O on the microscopic mechanism of R152a combustion by using reactive force field molecular dynamics simulations with a reliable force field. The promotion effects of H 2 O on the oxidation of R152a were revealed from an atomistic perspective. The differences of oxidation intermediates and products in different environments were analyzed for the first time. The H 2 O/O 2 environment was the most potent promoter of R152a decomposition, followed by the O 2 and the pyrolysis environment. The O 2 /H 2 O environment reduced the apparent activation energy of R152a, significantly enhanced the consumption rate of R152a, and enriched the number of species. O 2 reacts with H 2 O or H to form OH to accelerate the reaction process. The H 2 O could provide more OH active fragments for the reaction. Moreover, it promotes the formation of HF and H 2, H 2 O + F→HF + OH, H 2 O + H→H 2 + HO. This study aims to provide a guiding theory for the safe application and disaster prevention of flammable refrigerants. [ABSTRACT FROM AUTHOR]

• Improving Coastline Edge Detail: EDS module enhances edge fitting. • Bridging Semantic Gap: CSDS and AFM collaboration enhances semantic information. • Outperforms with mIoU: CSAFNet achieves remarkable 96.72% mIoU value. The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 1 1 The dataset was initially labeled as "sea-land segmentation data" by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction. [ABSTRACT FROM AUTHOR]

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people's lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes. [ABSTRACT FROM AUTHOR]

The presented evidence-based clinical practice guideline (CPG) is proposed as a National CPG where we adapted the international recommendations for the emergency management of seizures in children beyond the neonatal period to suit the health care in Egypt. The quality of evidence and the strength of recommendations are indicated. This study aimed to standardize the treatment of acute epileptic seizures and to provide an easy-to-apply acute treatment protocol that will allow immediate and appropriate seizure control. This is part of a larger program by the Egyptian Pediatric Clinical Practice Guidelines Committee (EPG) in collaboration with the staff of pediatric departments of 15 Egyptian universities and the National Research Centre. EPG was affiliated later to the Supreme Council of the Egyptian University Hospitals aiming to define the topics of, assign authors to, and assist in the adaptation of pediatric evidence-based CPGs according to a national strategic plan (http://epg.edu.eg). The committee is guided by a formal CPG adaptation methodology: the "Adapted ADAPTE." The Egyptian Childhood Seizure Group (ECSG) reviewed the results of the Appraisal of Guidelines for Research and Evaluation II assessment and decided to adapt the recommendations of three source CPGs: American Epilepsy Society, Italian League Against Epilepsy, Neurocritical Care Society, and Neurologic & Psychiatric Society of Zambia. Eight implementation tools were included. A comprehensive set of multifaceted CPG implementation strategies was provided for the clinicians, patients, nurses, and other relevant stakeholders contextualized to the national settings. Our experience with this adaptation methodology provides useful insight into its national utilization in Egypt. [ABSTRACT FROM AUTHOR]

[Display omitted] • MCTracker integrates motion correlation and mix-scale feature enhancement to track small, dense, and blurry objects effectively. • CDFE, IFMCM, and MSEB structures enhance contrast encoding, motion correlation, and multi-scale feature integration. • STFM resolves temporal-spatial fusion inconsistencies, boosting fusion expressive capability for temporal-spatial features. The video data captured by satellites, colloquially referred to as "gazing," holds significant value for analyzing object status and dynamically tracking movements. Such data and their associated applications play pivotal roles in disaster response, urban traffic monitoring, national defense, and security operations. The multi-object tracking task in satellite video surveillance is inherently more complex than single-object tracking, presenting challenges such as tracking dense small objects and limited capability in multi-object tracking in ambiguous environments. This study proposes a satellite video multi-object tracking (MCTracker) method that considers inter-frame motion correlation and multi-scale cascaded feature enhancement, achieving outstanding performance in multi-object tracking tasks. This study builds upon a differentially encoded backbone network incorporating both global and local information, proposing an Inter-frame Motion Correlation Module (IFMCM) to enhance inter-frame dynamic continuity. A Mixed-scale Enhancement Block (MSEB) is designed to address the challenge of detecting and tracking small objects under multiscale effects. Additionally, a Spatio-temporal Fusion Module (STFM) is introduced to improve the feature fusion representation capability among modules. Through experiments conducted on two open-source datasets, AIR-MOT and SAT-MTB, it has been demonstrated that the proposed MCTracker exhibits efficient synergistic consistency in detection and ReID tasks. On the AIR-MOT dataset, the object tracking accuracy, as measured by MOTA, reaches 59.4 %, achieving optimal performance. In the SAT-MTB dataset's four-class multiple object tracking task, particularly in tracking small-scale objects such as cars and ships, the proposed method demonstrates comprehensive optimal performance, yielding robust tracking results. The satellite video-based multiple object tracking approach proposed in this study holds significant reference value for further exploration of object motion states and trajectory tracking. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fusion of varied data sources are used to develop more informed flood extent maps. • SVM outscored the MLE, RF, Iso cluster, and Otsu's algorithms in all strategies. • PCA-based technique outperforms the other three approaches. • First and Second approaches performed better in VV than VH polarisation. Accurate and timely mapping of flood extent is crucial for effective disaster response and management. However, existing methods often face challenges in integrating diverse data sources and leveraging advanced techniques for precise inundation mapping. This research aimed to address this gap by proposing and evaluating four different methods for improving flood extent mapping in Sindh, Pakistan, by integrating various data sources, including precipitation, Synthetic Aperture Radar (SAR), buildings, population, and optical data. The first strategy involves categorizing post-flood SAR data immediately using non-parametric (Support Vector Machine (SVM) and Random Forest (RF)), parametric (Maximum Likelihood Estimation (MLE)), and cluster (ISO) algorithms for Vertical transmit and Vertical received (VV) and Vertical transmit and Horizontal received (VH) polarisations. The second method relies on Otsu's method for automatic thresholding on post-flood event VH and VV data. The third approach utilizes both pre- and post-flood event data by creating a stack of Pre-event VH, Pre-event VV, Post-event VH, and Post-event VV data, which is then classified using the SVM, RF, MLE, and ISO algorithms. Lastly, the fourth method employs the stack of Principal Component Analysis (PCA) bands, consisting of PCA components 1, 2, and 3, to categorize data for flooding using the SVM, RF, MLE, and ISO algorithms. The rationale behind evaluating these diverse methods was to identify the most effective approach for accurately mapping flood extent by exploiting the strengths of different algorithms and data processing techniques. The accuracy and precision of these approaches were rigorously evaluated using Landsat-9 Normalized Difference Water Index (NDWI) as a reference dataset, along with error matrix and compound value (C v) metrics. The findings show that a PCA-based technique with SVM is marginally superior (based on the overall accuracy and Kappa coefficient with a value of 92.20 % and kappa coefficient 0.821) than the other approaches tested in the study. By reducing the dimensionality of the dataset while preserving relevant information, PCA offers a computationally efficient approach to flood extent mapping. Among all the approaches, the collective performance of the algorithms was computed using the C v metric. The SVM algorithm with a C v of 1.25 demonstrated the best performance, followed by the RF algorithm with C v of 1.5, and the MLE algorithm with C v of 2.5. The worst performing algorithm among all is found to be ISO with C v of 4. It comes into view that first and second approach underperformed in VH polarisation compared to the VV polarisation and provide a more accurate representation of flooded area in VV polarisation. The flood extent with highest accuracy, together with the world's gridded population, latest Microsoft's Global ML Building Footprints, and OpenStreetMap building data are used in conjunction to estimate the number of people and buildings at risk within the study area. [ABSTRACT FROM AUTHOR]

It is the position of the Society for Nutrition Education and Behavior that for effective recovery from and resilience to disasters, it is essential that impacted individuals and communities have access to safe, nutritious, and culturally and contextually appropriate foods and beverages, and receive emergency-related food and nutrition education before, during, and after a disaster. Despite the increasing number, duration, and intensity of disasters worldwide, there is relatively limited guidance for research, policy, and practice about addressing the emergency-related food and nutrition needs of affected populations. Although nutrition emergencies tend to be understudied, emerging efforts are working to advance food and nutrition security during disaster response and recovery. To help elevate the importance of emergency-related food and nutrition education before, during, and after a disaster, Society for Nutrition Education and Behavior, which represents the unique professional interests of nutrition educators worldwide, summarizes the relevant literature and puts forth recommendations for all those who are engaged in this work in the following 4 key areas: (1) improving communication and outreach, (2) fostering community engagement and locally-driven preparedness, (3) building the evidence base and translating the evidence into action, and (4) training current professionals and the next generation of public health leaders. Altogether, before, during, and after a disaster, those who engage in this work, among other allies, can help elevate the importance of nutrition education and other strategies to promote healthy eating behaviors through research, policy, and practice. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests that nonadherence to treatment guidelines for seizures may affect patient outcomes. We examined the feasibility of conducting a larger investigation to test this hypothesis in the pediatric population. We retrospectively reviewed charts of patients aged ≤18 years who presented with seizure to the emergency departments of two Ontario hospitals in 2019 to 2021. Patients were grouped by seizure duration (<5 minutes [n = 37], ≥5 minutes [n = 41]). We examined nonadherence to guideline-recommended treatment, adverse outcomes (hospitalization, length of stay, respiratory complications), and missing values for key variables. Of 78 patients, 34 (44%) did not receive guideline-recommended treatment. Nonadherence was similar in the two groups (<5 minutes: 46%; ≥5 minutes: 41%). Common deviations included administering an antiseizure medication (ASM) for seizures of less than five minutes (46%), a delay (>10 minutes) between the first and second ASM doses (50%), and use of a benzodiazepine for the third dose (45%). Hospitalizations were common in both seizure duration groups (∼90%), whereas respiratory complications were relatively rare. Time of seizure onset was missing in 51% of charts, and none contained the time of first contact with emergency services when patients were transported by ambulance. We found evidence of substantial nonadherence to guideline-recommended treatment of pediatric seizures. Medical records do not contain sufficient information to comprehensively investigate this issue. A multicenter prospective study is the most feasible option to examine the association between nonadherence to guideline-recommended treatment and patient outcomes. [ABSTRACT FROM AUTHOR]

In an industry known for its workplace hazards, such as the management and manipulation of animals that could bite, kick or cause considerable damage simply because of their size, combined with long working hours, lifting of heavy loads and the general mental stress, it is perhaps surprising that the veterinary industry is not also known for its safety culture and structures. One would expect that where such hazards and risks have been identified, there would be many and varied levels of education on risk and hazard management, a comprehensive set of tools with which to mitigate these risks as well as discussion and debriefing of significant adverse events to ensure they do not occur again. One would also assume that there would be a strong sense of safety culture in the workplace and that personnel would expect each other to ensure that the health and safety of themselves and their colleagues was a number one priority. Yet, is this the case in the veterinary industry? A request was made by the Association of Veterinary Anaesthetists (AVA) to provide 'safety guidelines' for use in general practice, particularly pertaining to pregnancy. The AVA set up a task force to address these concerns and to determine if guidelines could be created. This article is offered as a starting point for considering safety in the veterinary industry in a broad sense, with the hope that in the future there may be development of such guidelines. It is hoped that this article also provides the stimulus for further research in this area. [ABSTRACT FROM AUTHOR]

Burn mass casualty incident (BMCI) preparedness is lacking across Canada. A focused exploration of the current policies, protocols and practices in Alberta that address the response to a BMCI was conducted. In this case study, data were gathered from documents outlining the health system response to a mass casualty incident and health care professionals directly involved. Interviews were conducted online, recorded and transcribed. Qualitative description was used to code common themes across documents and transcripts. Fifteen documents and nine participant interviews were included in this study. Overall, the current policies, protocols and practices in place were limited to all-hazards mass casualty incident planning and did not address the specialized needs of burn patients. Deficiencies included no burn-specific plan at each of the two burn centres, a lack of provincial-level recognition of the unique challenges associated with a BMCI and no established Canadian burn disaster communication plan. Suggestions of strategies for a burn plan included forward triage, patient movement, use of telemedicine, partnering skilled and non-skilled staff, and procuring additional supplies. For best patient outcomes the provincial health authority needs to provide dedicated time for burn care experts to develop BMCI response plans to better address this unique hazard. • All hazards mass casualty (MC) plans do not work for multiple burn injured patients. • Participant communication and decision-making practices deviated from the MC plan. • Participants expressed concerns about the overall ability to respond to a burn MC. • Creating burn capacity includes strategies for space, staffing, and supplies needed. [ABSTRACT FROM AUTHOR]

Flood detection from synthetic aperture radar (SAR) imagery plays an important role in crisis and disaster management. Based on pre- and post-flood SAR images, flooded areas can be extracted by detecting changes of water bodies. Existing state-of-the-art change detection methods primarily target optical image pairs. The nature of SAR images, such as scarce visual information, similar backscatter signals, and ubiquitous speckle noise, pose great challenges to identifying water bodies and mining change features, thus resulting in unsatisfactory performance. Besides, the lack of large-scale annotated datasets hinders the development of accurate flood detection methods. In this paper, we focus on the difference between SAR image pairs and present a differential attention metric-based network (DAM-Net), to achieve flood detection. By introducing feature interaction during temporal-wise feature representation, we guide the model to focus on changes of interest rather than fully understanding the scene of the image. On the other hand, we devise a class token to capture high-level semantic information about water body changes, increasing the ability to distinguish water body changes and pseudo changes caused by similar signals or speckle noise. To better train and evaluate DAM-Net, we create a large-scale flood detection dataset using Sentinel-1 SAR imagery, namely S1GFloods. This dataset consists of 5,360 image pairs, covering 46 flood events during 2015–2022, and spanning 6 continents of the world. The experimental results on this dataset demonstrate that our method outperforms several advanced change detection methods. DAM-Net achieves 97.8% overall accuracy, 96.5% F1, and 93.2% IoU on the test set. Our dataset and code are available at https://github.com/Tamer-Saleh/S1GFlood-Detection. [ABSTRACT FROM AUTHOR]

Visual geo-localization enables the UAV to know its position in GNSS-denied application scenarios, such as disaster emergency response, forest inventory management and indoor facility monitoring. However, there are still some challenges in using oblique-view imagery, especially for UAV images with large oblique viewing angles, to perform visual geo-localization. On the one hand, using geotags of references as image-level geo-localization results is insufficient for oblique-view imagery, because the UAV is often situated at a distance and not directly over the observation area. On the other hand, the obvious differences in viewpoint, viewing angle, and scale between UAV oblique-view images and geo-tagged orthographic reference images severely limit the accuracy of all three steps in pixel-level geo-localization, including image-based retrieval, matching and space resection. In this study, we proposed a coarse-to-fine visual geo-localization method to address these challenges for UAV position determination using oblique-view imagery, assisted by orthographic references. Firstly, we proposed a novel image-level geo-localization method by employing Segments Direction Statistics (SDS) feature patterns and bilateral retrieval strategy. Secondly, we matched UAV oblique-view images with geo-tagged orthographic reference images using feature points and descriptors extracted by the improved SuperPoint network, incorporating the Discrete Gaussian Pyramid Module (DGPM) and the Two-stage Matching Results Refinement Module (TMRRM). Thirdly, based on the matched points with 2D pixel coordinates and 3D ground coordinates, we adopted the triangular pyramid constrained resection method with angle similarity constraints to accurately determine the pixel-level position of UAV. We conducted experiments using a publicly available multi-view images dataset (University-1652), as well as two self-collected datasets of oblique-view images captured by UAVs. Experiments indicate that the localization error of the proposed method is less than 0.1 m for applications with the flight height about 200 m. Meanwhile, the error is about 10 m for UAVs with extremely high flight height (more than 4 km) and large oblique viewing angle (more than 60°). And the average time cost of the proposed method is about 400 ms. Therefore, experimental results demonstrated the effectiveness and efficiency of the proposed method in automatically obtaining reliable UAV positions using oblique-view imagery, as well as its robustness for various UAV oblique-view image-based applications. [ABSTRACT FROM AUTHOR]

Previous studies show that the COVID-19 pandemic affected the number of surgeries performed. However, data on the association between the COVID-19 pandemic and otolaryngologic surgeries according to subspecialties are lacking. This study was performed to evaluate the impact of the COVID-19 pandemic on various types of otolaryngologic surgeries. Methods. We retrospectively identified patients who underwent otolaryngologic surgeries from April 2018 to February 2021 using a Japanese national inpatient database. We performed interrupted time-series analyses before and after April 2020 to evaluate the number of otolaryngologic surgeries performed. The Japanese government declared its first state of emergency during the COVID-19 pandemic in April 2020. We obtained data on 348,351 otolaryngologic surgeries. Interrupted time-series analysis showed a significant decrease in the number of overall otolaryngologic surgeries in April 2020 (-3619 surgeries per month; 95% confidence interval, -5555 to -1683; p < 0.001). Removal of foreign bodies and head and neck cancer surgery were not affected by the COVID-19 pandemic. In the post-COVID-19 period, the number of otolaryngologic surgeries, except for ear and upper airway surgeries, increased significantly. The number of tracheostomies and peritonsillar abscess incisions did not significantly decrease during the COVID-19 pandemic. The COVID-19 pandemic was associated with a decrease in the overall number of otolaryngologic surgeries, but the trend differed among subspecialties. [ABSTRACT FROM AUTHOR]

Disaster preparedness is crucial for mitigating the impact of disasters and saving lives. Ensuring preparedness for disaster response and management, particularly among nurses in disaster-prone countries, is essential due to the serious threats disasters pose to affected populations. This study was conducted to appraise and synthesize the literature examining preparedness for disaster management and response among nurses in countries with the highest disaster risks. A systematic review of the literature published from 2000 onwards was conducted using five databases: SCOPUS, PubMed, MEDLINE, PsychINFO, and CINAHL. Twenty-one relevant articles were identified for this review. The findings revealed that nurses in countries at high risk of disasters had inadequate disaster knowledge and skills, leaving them ill-prepared to respond to such situations. Previous experience in disaster relief, disaster-related education and training, and higher academic qualifications were associated with increased readiness for disaster response and management. The review's findings provide insight into the current state of disaster preparedness among nurses in disaster-prone nations. The review identified evidence highlighting the value of disaster-related education and training, as well as disaster response experience, in fostering preparedness among nurses. These results can inform policymakers, hospital administrators, and nurse administrators in developing strategies for promoting disaster preparedness in nurses through evidence-based training programs and interventions. [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]

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management. [ABSTRACT FROM AUTHOR]

As large-scale disasters continue to become increasingly common worldwide, nursing homes, whose residents are more vulnerable to disaster-related health and psychosocial shocks, and their staff, are carrying progressively more responsibility for health care readiness practices. Implementation science is a research discipline that seeks to improve uptake of evidence-based practices, such as health care readiness planning, and thus has potential to improve nursing home care delivery during and after disasters. We describe the limited field of existing evidence-based strategies in the peer-reviewed literature that seek to advance health care readiness in the nursing home setting and illustrate how implementation science can better support health care readiness planning for nursing homes. We rest on 3 main themes: (1) implementation science frameworks can strengthen nursing home staff engagement around health care readiness; (2) implementation science can support tailoring of emergency preparedness plans to individual nursing homes' unique needs; and (3) implementation science can advance the integration of nursing homes into local, state, and federal health care readiness planning initiatives. Finally, research is urgently needed to both generate and disseminate implementation strategies that increase uptake of evidence-based health care readiness practices in the nursing home setting. [ABSTRACT FROM AUTHOR]

Practitioners can face significant challenges when managing the airways of infants and neonates because of their unique anatomical and physiological features. The requirement for emergency airway management in this age group is rare. Details of emergency airway techniques in paediatric practice guidelines are missing or lack consensus, and it is known that outcomes for affected children can be poor. Ideally, these children should be managed by experienced paediatric airway practitioners working in a team. However, situations can arise where practitioners, unfamiliar and inexperienced with infants, find themselves in charge. So, what happens when such a practitioner encounters this life-or-death scenario and feels ill-equipped to act? The ethical and legal issues surrounding the management of this emergency are clearly defined, but they can be unknown or misunderstood by doctors. Compounding the extreme stress of the scenario is the moral and ethical dilemma of whether to act or not. The following discussion explores these issues and examines the philosophical and psychological perspectives. [ABSTRACT FROM AUTHOR]

Hydrogen refueling stations (HRSs) play a vital role in the hydrogen energy industry and are being built worldwide. However, the potential risk of hydrogen leakage poses a significant challenge. Accurately predicting the consequences of such accidents is crucial for effective mitigation. Traditional methods, like numerical simulation, provide accurate results but lack timely predictions. To address this, we propose a hybrid surrogate model that combines Generative Adversarial Network (GAN) and Long Short-Term Memory Network (LSTM), incorporating Deep Neural Network (DNN) for predicting hydrogen leakage consequences in HRSs based on source parameters. The surrogate model was trained using training samples generated through numerical simulation. Reduced-scale experiments were conducted to verify the results of numerical simulations and assess the surrogate model's generalization ability. Results demonstrate the model's ability to predict hydrogen distribution after an accident, indicating its potential for real-time decision making in emergency response plans. • Hybrid GAN and LSTM surrogate model for accident consequence prediction in HRS. • Fused DNN enables consequence prediction based on leakage source parameters. • Training surrogate models using results from numerical simulation. • Generalizability of surrogate model was verified using reduced-scale experiments. [ABSTRACT FROM AUTHOR]

Rainfall patterns are vital in shaping ecosystems and supporting life on Earth. However, changing rainfall distribution and intensity due to environmental factors and climate change have become increasingly evident in recent years. This study aims to utilize machine learning (ML) and Artificial intelligence (AI) techniques to predict rainfall trends in the Kokrajhar region, a district in Assam, India. Accurate rainfall prediction is crucial for effective water resource management, agriculture planning, and disaster preparedness in the area. To achieve this objective, 31 years of historical rainfall data from Kokrajhar have been collected. Various machine learning algorithms, including Random Forests, MLP, ETS, and MEDIAN, have been employed. The models were trained and evaluated using the data set, with performance metrics mean absolute percentage error (MAPE). The results suggest that the ETS model (Average MAPE 424.11% after removing outliers) exhibited the best performance among the other models in the second fold. In the third fold, the MLP model outperformed the others. These findings demonstrate the effectiveness of AI and ML techniques in predicting rainfall trends in the Kokrajhar area. [ABSTRACT FROM AUTHOR]

The increasing prominence of natural disasters and the variability in climate change have underscored the significance of research in contemporary and prospective studies. This investigation zeroes in on floods, a ubiquitous natural calamity that plagues regions globally. Bihar stands out as a state exceptionally vulnerable to persistent and intense flooding, with a staggering 69.70 lakh hectares—equivalent to nearly 74 percent of its geographical expanse—enduring its detrimental consequences. The repercussions of such inundations are widespread, permeating multiple facets of life and erecting impediments to both the welfare and the developmental trajectory of local communities. Vital sectors, including infrastructure, agriculture, and livestock, bear the brunt of the damage, catalyzing the dislocation of innumerable households. In an agile response to these adversities, the state's administrative machinery has mobilized disaster response brigades, ensuring timely aid and succor to the distressed populace. To adeptly predict impending floods, this study harnesses data from 1991 to 2022, integrating pivotal metrics such as the 'Total Population affected (in lacs)', 'Mean Annual Rainfall', and 'Crop damage (in INR lakhs)'. By adopting an LSTM (Long Short-Term Memory) model, the research elucidates its findings via illustrative line plots, furnishing an accessible rendition of the flood trajectory predictions over the forthcoming five-year span. These predictive insights are instrumental for disaster management agencies, enhancing their strategic planning and readied responses to imminent flood events in Bihar. Capitalizing on these forecasts enables authorities to architect proactive interventions, ensuring reduced flood repercussions, bolstered community resilience, and an elevated state of disaster preparedness in the region. [ABSTRACT FROM AUTHOR]

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model's accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR's accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable. [ABSTRACT FROM AUTHOR]

Postpartum depression (PPD) is an illness that is difficult for the affected women themselves to recognize. Moreover, many mothers believe that mothers should not complain about the mental difficulties of taking care of their children. Therefore, in addition to self-evaluation for PPD, evaluation from others is also necessary. We aimed to develop a novel measure to screen for PPD based on a parent-rating scale that is administered to the parents of postpartum mothers. The 15-item maternity-monitoring scale by parents (MMSP) was designed and applied to the feasibility cohort (n = 61) and the emergency cohort (n = 55). The Edinburgh Postnatal Depression Scale (EPDS) (threshold score of 8/9) was used to evaluate a high risk of PPD. An egogram-based index, the over-adaptation index for depression (OAID), was performed along with the EPDS and MMSP. In the feasibility cohort, MMSP was moderately correlated with EPDS. In the emergency cohort, under the circumstance of the state of emergency declaration over the coronavirus disease 2019 in Japan, application of the MMSP was delayed, resulting in the proportion of parents who overlooked PPD symptoms in their daughters increasing from 33 % to 50 %. Our findings suggest that a novel approach of parent-rated PDD screening of postpartum women is potentially possible, and the MMSP is a potential candidate for screening. Moreover, the OAID is also helpful in identifying women with hidden PPD, along with the EPDS. The performance of the MMSP should be confirmed in the parents of patients with PPD diagnosed by psychiatrists. • The Edinburgh Postnatal Depression Scale (EPDS) has been the most used measurement of PPD, but required greater precision. The novel approach, parent-rating depression screening MMSP and over-adaptation index for depression OAID, are helpful to distinguish PPD. The MMSP, followed by a composite measure constructed using the EPDS and OAID, will broadly contribute to the screening of PPD. [ABSTRACT FROM AUTHOR]

VIIRS flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) and the International Charter Program for rescue and relief efforts. However, some water bodies, like irrigated or flooded paddy rice fields, and water in seasonal wetlands, are detected as floodwater instead of permanent, seasonal, controlled, or normal water in the VIIRS flood products. Because these kinds of floodwater don't cause any severe disasters, they should be delineated as non-hazard floods to differentiate from real hazardous floods, the latter of which generally pose a significant risk and damage to human life, property, and the environment. Hazardous floods are usually caused by intensive rainfall, rapid snowmelt, or ice jams, they appear accidentally and tend to be short-term events. Non-hazard floodwaters generally result from tides, wetlands, agricultural irrigation, and other human activities, and thus they appear regularly and often stand for a longer time. Therefore, non-hazard floodwaters are some kind of standing water and usually have much larger long-time flood frequency or probability than hazardous floods. Based on this feature, it's possible to combine long-time flood frequency calculated as the ratio of total flooding days over total clear days, including both flooding and no-flooding days, from VIIRS flood products with some additional information, such as agriculture statistics and land cover and land use map, to identify non-hazard floodwater in paddy rice fields and wetland. In this study, non-hazard floodwater bodies are classified into four types: paddy rice fields, wetlands, riparian and coastal tidal flooding, and other non-hazard floods, including possible fishing ponds. In this study, we selected several examples, such as the 2023 California flood event and some large paddy rice fields in China, to demonstrate how to identify water in paddy rice fields. The Florida flood event due to Hurricane Ian in 2022 is used to demonstrate how to separate wetlands from real hazardous floods. In addition to non-hazard floodwaters in wetlands, some coastal tidal flooding in Florida is also detected. The non-hazard floodwaters extracted from the proposed methods are compared with change detection analysis and good agreement is found. The paddy rice fields obtained from this study are compared and evaluated with the rice distribution data from the USDA. The quantitative evaluation results from the confusion matrix calculation demonstrate that the accuracy is 99.96%, recall is 83.69%, precision is 61.57%, and the F1-score is 70.95%. The high accuracy indicates that the method developed in this study for paddy rice field extraction from the VIIRS flood products is feasible. [ABSTRACT FROM AUTHOR]

In the event of a fire or explosion at a hybrid hydrogen-gasoline fueling station, the consequences can be significantly more severe compared to a traditional refueling station or hydrogen refueling station. It is imperative to establish an effective assessment model specific to the hybrid station to prevent and mitigate such accidents. This paper introduces a temporal dynamic assessment model designed to evaluate the emergency response and rescue capabilities of hybrid hydrogen-gasoline fueling stations. The model is grounded in dynamic scenario construction and utilizes information-driven theory. This innovative approach allows for evaluations of the risk evolution process at different stages of an accident. The proposed assessment model combines the Temporal Weighted Average (TOWA) and Temporal Weighted Geometric Average (TOWGA) operators to integrate assessment values across different stages including the initial disaster assessment, emergency action plans, effectiveness feedback, and emergency response adjustment. This integration yields a comprehensive dynamic assessment value for the emergency response and rescue capabilities of the hybrid hydrogen-gasoline fueling station. Based on the dynamic assessment of a previously occurring accident, the specialized emergency management department performed most effectively during the incident response stage, achieving a commendable assessment value of 3.184. Following closely was the government emergency response department, with assessment values of 2.865 and 2.277. This study offers a novel analytical perspective for emergency management in hybrid hydrogen-gasoline fueling stations by incorporating the findings of both dynamic and static assessments. • The information-driven theory is introduced to form dynamic emergency response scenario construction analysis modle. • For the first time, the emergency response and rescue capacity assessment were carried out bothly at each stage of the accident. • The TOWA-TOWGA hybrid operator modle is used to evaluate the emergency response and rescue capabilities. • The assessment results can reflect various level of emergency response and rescue capability under different accident periods. [ABSTRACT FROM AUTHOR]

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations. [ABSTRACT FROM AUTHOR]

The exponential growth of COVID-19 cases in early 2020 presented a massive challenge for healthcare systems and called for the adaptation of emergency care routines and intensive care capacities. We, therefore, analyzed a possible impact of the COVID-19 pandemic on the general structure and emergency preparedness of burn centers in German-speaking countries through a cross-sectional descriptive survey questionnaire. The survey was conducted for the first time in January 2019 by Al-Shamsi et al. before the beginning of the COVID-19 pandemic. It was performed for a second time in November 2020 during the second wave of COVID-19 infections in German-speaking countries. We noticed a pronounced increase in the preparation for a great number of patients in need of intensive care including the enlargement of overall capacity when necessary. We also showed a notable decrease in the specific preparation for burn disasters and also reduced communication with first responders and other burn centers. To what extent these alterations were caused by the impact the pandemic had on healthcare systems could not be determined in this study and should be the subject of future research. • Increase in the preparation for a great number of patients in need of intensive care during the Covid-19 pandemic. • Increase in the ability of the burn centers to enlarge overall capacity when necessary during Covid-19 pandemic. • Decrease in the specific preparation for a burn disaster during the COVID 19 pandemic. • Reduced communication with first responders and other burn centers during the Covid 19 pandemic. [ABSTRACT FROM AUTHOR]

The identification of events from social media platforms such as Twitter (now known as X) is a hot research problem. It has applications in diverse domains such as journalism, marketing, public safety, crisis management and disaster response. The process includes the identification, monitoring, and analysis of events or incidents while they are being discussed or reported on Twitter. When it comes to identifying events from tweets (i.e. feeds from Twitter), many of the currently available event detection methods mainly rely on keyword burstiness features or structural changes in the network. However, due to the intricate characteristics of tweets and the ever-changing nature of events, they frequently fail to recognise noteworthy occurrences before they become trending. Moreover, these methods face difficulties when it comes to capturing the evolving characteristics of events with limited or insufficient contextual information. In this paper, we propose a window-based tweet-processing method called EDTBERT for detecting events and tracking the evolution of events over time. Our proposed method utilizes the structural and semantic affinities that exist among words in tweets. The method starts by generating graph of tweets, where tweets are represented as nodes, and edges are the similarities between tweets. The method utilizes overlapping hashtags and named entities to capture the structural relationship between tweets. Additionally, a pre-trained sentence transformer model, specifically BERT, is employed to collect contextual knowledge and find semantically similar tweets. Next, the graph clustering technique is employed to identify optimized event clusters. Subsequently, our method generates chain of event clusters for each event to track the evolving variation of the event over time by utilising the "Maximum-Weight Bipartite Graph Matching" (MWBGM) algorithm. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches. [ABSTRACT FROM AUTHOR]

Hydrogen release and dispersion from hydrogen refuelling stations have the potential to cause explosion disaster and bring significant causalities and economic losses to the surroundings. Real-time spatial hydrogen plume concentration prediction is essential for the quick emergency response planning to dissipate such flammable vapor cloud and prevent explosion disaster. Deep learning approaches have recently been applied to real-time gas release and dispersion modeling, however, are 'over-confident' for spatial plume concentration and boundary estimation, which could not support the robust decision-makings. This study proposes a hybrid deep probability learning-based spatial hydrogen plume concentration prediction model, namely DPL_H 2 Plume by integrating deep learning and Variational Bayesian Inference. Numerical model of hydrogen release and dispersion from hydrogen refuelling station is built to construct the benchmark dataset. By using such dataset, two pre-defined parameters, namely Monte Carlo sampling number m = 300 and dropout probability p = 0.1 are determined to ensure the model's tradeoff between inference accuracy and efficiency. Comparison between our proposed model and the state-of-the-art model is also conducted. The results demonstrate that our model exhibits a competitive accuracy of R 2 = 0.97 as well as an inference time 3.32 s. In addition, our model gives the comprehensive estimations including not only spatial hydrogen plume concentration but also its uncertainty. Also, our model provides the more accurate estimation at plume boundary compared to the state-of-the-art model. Overall, our proposed model could provide reliable alternative for constructing a digital twin for emergency management of hydrogen refuelling station. • Deep probabilistic learning-based hydrogen plume prediction model is proposed. • Optimal hyperparameters of variational Bayesian inference are determined. • Model accurately predicts real-time spatial hydrogen plume concentration given scenario-related parameters. • Model predicts additional uncertainty information for comprehensive emergency decision-makings. [ABSTRACT FROM AUTHOR]

In March 2021, the 3rd wave of COVID-19 pandemic had ended and the second emergency declaration in January in Tokyo and 3 other prefectures had been lifted. The restrictions on the social activities imposed on the Japanese people and it had become established as "the new lifestyle norm" for each age group. Following our first survey, we conducted a questionnaire survey again to determine the physical changes in all age groups and compared the findings. The 1st survey was conducted after the initial emergency declaration lifted in July 2020, and the 2nd survey was 10 months after the first survey March 2021. And the analysis was conducted by simple and cross tabulation. The number of valid responses were obtained over 12,000 samples for the both surveys. One year later, "Corona locomo" decreased from the previous year in all age groups, especially school students, while it increased in "80s∼". "Corona stress" decreased sharply among children, especially elementary school students, while it increased slightly in 20s, 30s, and "80s∼". The physical condition was closely related to the proportion of exercising population and duration in each age group. "The new lifestyle norm" as a countermeasure against COVID-19 crisis had a significant influence on physical condition in each age group as "the secondary health damage". Forcusing on "With Corona era", it is considered necessary for preventing "locomo-frail", to improve the exercise lifestyles by selecting tailor-made training methods. [ABSTRACT FROM AUTHOR]

After the fire at Notre-Dame de Paris in April 2019, conservation scientists and art historians joined forces with the chief architect and the monument curators to facilitate the management of emergency operations on the stained-glass windows. Information and methodologies were provided for the swift removal of twenty-five 19th and 20th-century windows following the disaster. Restoration and lead decontamination protocols were studied in laboratories and inside the building in order to determine the duration and cost of the upcoming restorations. Thanks to the careful handling of the fire by the firefighters, the stained-glass windows of Notre-Dame were mostly preserved and very little thermal stress breakage was observed on the panels. However, the necessity to use all the teams available inside the building to deal with the generalized lead contamination caused by the roof fire considerably slowed down all the restoration operations. Given the limited time remaining before the building would be reopened, the teams decided to follow two different protocols: a decontamination protocol of the stained glass that remained in the building, and the complete off-site decontamination then restoration of the panels that had been removed. The presence of scaffolding inside the cathedral also provided the opportunity to draw up new restoration charts for the medieval stained glass in the rose windows. This new document would update the previous chart published in 1959, which was based on binocular observations from the ground. The disaster in Notre-Dame de Paris has reminded everyone that a building can quickly be destroyed by a fire, even today. One of the immediate consequences has been the revision of many cathedral security procedures in case of fire, and we can now reflect on improving the management of stained-glass windows after such a catastrophe. [ABSTRACT FROM AUTHOR]

This research paper presents an analytical approach to earthquake time-series forecasting using the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The objective of this study is to investigate the effectiveness of the SARIMAX model in earthquake forecasting by considering relevant exogenous variables, such as historical seismic activity, geological characteristics, and geodetic measurements. We start introducing the SARIMAX models, explaining its mathematical formulation and the incorporation of exogenous variables. The research methodology involves collecting earthquake time-series data from seismological databases and preprocessing the data for analysis. Various SARIMAX models are constructed and evaluated using statistical measures, such as root mean square error and mean absolute error, to assess their forecasting accuracy. Additionally, the impact of different exogenous variables on the predictive performance of the models is analyzed. The results of this research contribute to the field of earthquake prediction by demonstrating the applicability and efficacy of the SARIMAX model in capturing the temporal patterns and dynamics of seismic events. The practical reason of the results provides valuable information for decision-makers and stakeholders involved in disaster preparedness and mitigation strategies. [ABSTRACT FROM AUTHOR]

Sexual harassment in academia is endemic driven by gender-based inequalities and sustained through organizational tolerance, and its impact extends beyond the primary victim(s). Applying principles of emergency management provides a framework for institutions to balance their obligations to the primary victim(s) while also acknowledging the need to restore the well-being and culture of secondary victims. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for computing, visualizing, and analyzing critical decision boundaries for the selection of shortest paths in a given environment. Decision boundaries are defined as the points in a map from which two or more different shortest paths exist towards a destination. This paper introduces the problem of visualizing their evolution, taking into account moving obstacles, moving goals, and as well multiple goals. The proposed visualizations enable analyzing which paths should be taken and at which departure times, such that a destination can be reached by the shortest possible path when taking into account a moving target or time-varying areas to be avoided. The proposed techniques are also applied to the analysis and improvement of exit placement in a given environment, in order to improve the evacuation flow in emergency situations. [Display omitted] • This research presents a unique method for detecting decision boundaries in a given environment, based on the analysis of the generator points of the Shortest Path Map (SPM) rather than employing traditional scalar field topological methods relying on cell neighborhood information which can be affected by the representation resolution. • The proposed approach introduces tools and techniques to visualize the evolution of decision boundaries when considering dynamically-changing obstacles and targets, and to design exit placement to equalize the escape flow distribution. • This novel approach supports decision-making applications related to navigation and environment modeling in emergency evacuation planning. • By analyzing and visualizing SPM decision boundaries, the lengths of globally-optimal Euclidean shortest paths are taken into account, instead of grid-based accumulated distances used in other approaches. [ABSTRACT FROM AUTHOR]

Adequate and effective emergency preparedness for hospital surge capacity is a prerequisite to ensuring standard healthcare services for disaster victims. This study aimed to identify, review, and synthesize the preparedness activities for and the barriers to hospital surge capacity in disasters and emergencies. We systematically searched seven databases (PubMed, MEDLINE, CINAHL, Scopus, Embase, Ovid, and PsycINFO). We included all English peer-reviewed studies published in January 2016 and July 2022 on surge capacity preparedness in hospital settings. Two independent researchers screened titles and abstracts, reviewed the full texts, and conducted data extractions using CADIMA software. We assessed the rigor of the included studies using the NIH quality assessment tools for quantitative studies, the Noyes et al. guidelines for qualitative studies, and the MMAT tool for mixed methods studies and summarized findings using the narrative synthesis method. We also used PRISMA reporting guidelines. From the 2560 studies identified, we finally include 13 peer-reviewed studies: 10 quantitative, one qualitative, and two mixed methods. Five studies were done in the USA, three in Iran (n = 3), and the remaining in Australia, Pakistan, Sweden, Taiwan, and Tanzania. The study identified various ways to increase hospital surge capacity preparedness in all four domains (staff , stuff , space , and system); among them, the use of the Hospital Medical Surge Preparedness Index and the Surge Simulation Tool for surge planning was noteworthy. Moreover, nine studies (69%) recognized several barriers to hospital surge capacity preparedness. The review provides synthesized evidence of contemporary literature on strategies for and barriers to hospital surge capacity preparedness. Despite the risk of selection bias due to the omission of gray literature, the study findings could help hospital authorities, public health workers, and policymakers to develop effective plans and programs for improving hospital surge capacity preparedness with actions, such as enhancing coordination, new or adapted flows of patients, disaster planning implementation, or the development of specific tools for surge capacity. PROSPERO CRD42022360332. [ABSTRACT FROM AUTHOR]

Patient education is a professional obligation for all nurses. Public health messaging in emergency departments during disasters can help prevent further risk or illness for affected communities. In this study, Australian emergency nurse Key Informants share their perceptions and experiences of preventative messaging provided in their departments during disasters and the governance and processes in place to support this practice. The qualitative phase of a mixed methods study, where semi structured interviews were utilised, and data analysed using a six step Thematic analysis. Three themes were identified: (1) Part of the Job; (2) It's all in the delivery; and (3) Preparation is the key. These themes include concepts involving the confidence and competence of nurses providing messages, what, when and how messages are being given and how prepared both the department and staff are regarding patient education during disaster events. Nurse confidence is a key factor in the delivery of preventative messages during disasters, potentially resulting from a lack of exposure, a junior workforce and minimal training. Leaders agree that departments are not preparing or supporting messaging practice, with an absence of specific training, formal guidelines, and patient education resources; and that improvement is needed. [ABSTRACT FROM AUTHOR]

Conducting a vulnerability assessment of hydrogen stations is beneficial for a more comprehensive understanding of safety risks, timely identification of weak links in hydrogen stations to reduce accident risks. This paper proposes a vulnerability assessment method for hydrogen stations in industrial parks based on the improved combination ordered weighted averaging (COWA) operator and ordinal relationship method (G1) and cloud gravity center theory. The vulnerability assessment index system for hydrogen stations considers the vulnerability of personnel, facilities, materials, environment, and emergency response capabilities. The Pythagorean fuzzy decision-making trial and evaluation laboratory (PFDEMATEL) method is used to determine the centrality of the evaluation index, and the comprehensive weight is determined by combining the COWA operator and the G1 method. The cloud gravity center is utilized to determine the vulnerability assessment level by calculating the weighted similarity. A hydrogen station is used as an example to verify the applicability and objectivity of the method. The simulation results indicate that the proposed method has high efficiency and is suitable for vulnerability evaluations. The research findings can provide a theoretical reference and decision-making basis for improving safety management and emergency response of hydrogen stations. [Display omitted] • A vulnerability evaluation index system for hydrogen stations was built. • Pythagorean fuzzy numbers address the haziness and ambiguity of assessment. • Apply PFDEMATEL-COWA and G1 method combination assignment to obtain more objective results. • The cloud center of gravity theory is used for the first time to better evaluate the vulnerability of hydrogen stations. [ABSTRACT FROM AUTHOR]

There emerges increasing doubt regarding whether the policy composite indicators are applicable to the COVID-19 pandemic. A few early studies demonstrate that the association between some composite indicators of policy preparedness and the risk of COVID-19 is statistically insignificant, and the relation between any composite indicators and recovery process (e.g., vaccination coverage) remains unexplored. To examine the relation between composite indicators and pandemic risk (as well as the vaccination coverage) with robustness, this study applies different policy preparedness indicators by using data from multi-sources. A cross-sectional analysis was performed. Regression analysis is adopted to examine the relation between four policy preparedness indicators (i.e., [1] International Health Regulations core capacity index, [2] Global Health Security Index, [3] epidemic preparedness index, and [4] World Governance Index) and COVID-19-confirmed cases/death/vaccination coverage at different time points. The linear regression is performed, and the spatial distribution of indicators are illustrated. Countries with higher ranking in policy preparedness indexes can experience less severity of pandemic risk (e.g., confirmed cases and mortality) and faster recovery process (e.g., higher vaccination coverage). However, slight disparity in effectiveness exists across different indicators. Results show that the policy preparedness indicators have predictive value of the confirmed cases, mortality, and vaccination coverage of COVID-19 pandemic, given sufficiently long-time span is observed. [ABSTRACT FROM AUTHOR]

Stadiums are an important part of the entertainment and sporting cultures of communities around the world, but the combination of outdated infrastructure with poor safety planning, large numbers of people gathering within a confined space, and the high frequency of such events have led to a number of significant disasters in the past. This is a descriptive analysis of stadium disasters occurring between 1901 and 2021 which may provide useful insight for event safety personnel and disaster medicine specialists to better prevent and mitigate the effects of potential future stadium disasters. Data were collected using a retrospective database search of the Emergency Events Database (EM-DATS) for all stadium-related accidental disasters occurring between January 1, 1901 - and July 30th, 2022. A disaster is defined by Centre for Research on the Epidemiology of Disasters (CRED) in its glossary as 'technological accidents of an industrial nature, or involving industrial buildings'. All categories and definitions are predetermined by the EM-DATS as per their glossary. The 24th May 1964 Estadio Nacional disaster in Lima, Peru, was the worst (in terms of deaths) to date with 350 deaths. This is followed by the 1982 Luzhniki Stadium disaster in Moscow, Russia (340 deaths), the 2001 Accra Sport Stadium disaster in Ghana (123 deaths), and the 1985 Hillsborough Stadium disaster in Sheffield, England (96 deaths). Fourteen of the 40 stadium disasters occurred in Africa, 11 in Europe, 10 in the Americas, and five in Asia. A total of 40 stadium disasters were included, leading to 2025 deaths and 6640 injuries. This equated to an average of 50.6 deaths and 166.0 injuries per disaster. Given the potential risk of mass casualty events, stadiums should incorporate disaster medicine education, training, and expertise in their emergency medical plans. [ABSTRACT FROM AUTHOR]

In early 2020, at COVID-19's onset in the United States, the American Dental Association recommended postponing elective dental procedures to minimize viral spread. Subsequently, concerns arose that this could result in increased dental infections and resultant emergency department visits. This study quantifies the number and severity of dental infections at the onset of the early COVID-19 outbreak in early 2020 in the Northeast United States resulting in emergency room visits and radiographic imaging compared to 2017–2019 with an analysis of geographic population characteristics. Cross-sectional head and neck imaging performed at an East Coast hospital system was retrospectively reviewed for dental infections from March 2020 through December 2020, and compared to prior years 2017–2019. Inclusion criteria included radiology reporting of a dental infection, ranging in severity. Electronic medical records (EMR) and imaging reports were queried for patient characteristics and dental findings. There were 735 confirmed imaging reports of odontogenic infections. There was a significant increase in imaging reporting of odontogenic infections in the post-shutdown period. These were more frequently early-type infections, involved a higher proportion of male and non-white patients, and the patients were more often from disadvantaged zip codes when compared with prior years. These findings highlight the varying impact of outpatient dental office closures on different socioeconomic groups in the setting of a pandemic. Potential implications include increased morbidity and mortality for patients, as well as increased cost and resource allocations for the healthcare system. • The pandemic closures of dental clinics were associated with a significant increase in odontogenic infections by imaging. • There was a higher proportion of male and non-white patients with odontogenic infections compared with prior years. • There was also a higher proportion of patients from financially disadvantaged zip codes. [ABSTRACT FROM AUTHOR]

Limited research on food systems and food insecurity (FI) following disasters finds contextual differences in post-disaster food systems that shape dimensions of FI. Measurement limitations make it difficult to address FI and develop effective practices for disaster-affected communities. To develop, validate, and test a Disaster Food Security Framework (DFSF). Mixed-methods approach was used, including in-depth interviews to understand lived experiences during disasters; expert panel input to validate DFSF designed using responses from in-depth interviews; and quantitative testing of robustness of DFSF using the coronavirus disease 2019 pandemic as a disaster example. The in-depth interviews included participants from Vermont (n = 5), North Carolina (n = 3), and Oklahoma (n = 2) who had been living in those states during Hurricane Irene (2011), Hurricane Florence (2018), the Moore tornadoes (2013), and coronavirus disease 2019 pandemic (2020). The expert panel consisted of researchers and practitioners from different US geographical regions and food-related disciplines (n = 18). For the quantitative testing survey, data from 4 US states (New York, New Mexico, Vermont, and Maryland; n = 3,228) from the National Food Access and COVID Research Team was used. The outcomes from the in-depth interviews were dimensions of disaster FI, those from the expert panel was a content validity ratio, and those from the quantitative testing was the number of items and components to be included. Inductive and deductive reasoning were using when reporting on the in-depth interviews and expert panel results, including frequencies. The quantitative testing was conducted using multiple correspondence analysis. The in-depth interviews revealed four dimensions of FI: availability (supply and donation), accessibility (economic, physical, and social), acceptability (preference and health), and agency (infrastructure and self-efficacy). The panel of experts reported high content validity for the DFSF and its dimensions (content validity ratio >0.42), thus giving higher credibility to the DFSF. Multiple correspondence analysis performed on 25 food-related variables identified one component with 13 indicators representing three of the four dimensions: availability, acceptability, and accessibility, but not agency. [ABSTRACT FROM AUTHOR]

We compared external ventricular drains (EVDs) with percutaneous continuous cerebrospinal fluid (CSF) drainage via ventricular access devices (VADs) for the acute management of hydrocephalus in adults. This was a retrospective review of all ventricular drains inserted for a new diagnosis of hydrocephalus into noninfected CSF over four years. We compared infection rates, return to theatre, and patient outcome between EVDs and VADs. We explored the effect of duration of drainage, frequency of sampling, hydrocephalus aetiology, and catheter location on these outcomes using multivariable logistic regression modelling. We included 179 drains (76 EVDs and 103 VADs). EVDs were associated with a higher rate of unplanned return to theatre for replacement or revision (27/76, 36%, vs. 4/103, 4%, OR: 13.4 95%CI: 4.3–55.8). However, infection rates were higher in VADs (13/103, 13% vs. 5/76, 7%, OR: 2.0, 95%CI: 0.65–7.7). EVDs were 91% antibiotic impregnated whereas VADs were 98% nonimpregnated. In multivariable analysis, infection was associated with duration of drainage (median: 11 days prior to infection for infected drains vs. 7 days total for noninfected drains), but not drain type (VADs vs. EVDs OR: 1.6, 95%CI: 0.5–6). EVDs had a higher rate of unplanned revisions but a lower infection rate compared to VADs. However, in multivariable analysis choice of drain type was not associated with infection. We suggest a prospective comparison of antibiotic impregnated VADs and EVDs using similar sampling protocols to assess whether VADs or EVDs for acute hydrocephalus have a lower overall complication rate. [ABSTRACT FROM AUTHOR]

• COVID-19 vaccine hesitancy was heterogeneous both in terms of time and magnitude. • Following COVID-19 news, trust levels, risk preferences, and vaccine confidence predicted intention reversals. • Lowering the cost of following the news for the population may raise vaccine uptake. This paper investigates the prevalence and determinants of three main states of people's willingness to be vaccinated (WTBV) against COVID-19 – willing, unwilling and hesitant – and the occurrence and predictors of shifts between these states over time. Understanding the dynamics of vaccine intentions is crucial for developing targeted campaigns to increase uptake and emergency response preparedness. A panel survey consisting of 9 quarterly waves of data collected between April 2020 and January 2022. Baseline data included 24 952 adults from Germany, UK, Denmark, the Netherlands, France, Portugal, and Italy recruited from online panels to construct census-matched nationally representative samples. Self-reported COVID-19 vaccine intention was the main outcome. Multinomial logit random effects models were used to analyze the relationships of interest. All results reported as relative risk ratios (RRR). Hesitancy to get vaccinated was the most unstable vaccine intention, with on average 42% of ever hesitant respondents remaining in this state through future waves, followed by the 'unwilling' (53%) and 'willing (82%). Following COVID-19 news, trust in information from the government, GPs and the WHO, risk preferences, risk perceptions, and confidence in vaccines (or lack thereof) predicted vaccination intention reversals. Risk preferences acted both as an impediment and as a facilitator for the vaccine uptake depending on the initial vaccine intention. This study revealed the dynamic nature of COVID-19 vaccine intentions and its predictors in 7 European countries. The findings provide insights to policymakers for designing more effective communication strategies, particularly targeted at hesitant and unwilling to vaccinate population groups, to increase vaccine uptake for future public health emergencies. [ABSTRACT FROM AUTHOR]

To optimize the early care of burned patients, protocols were developed that guide pre-hospital care and the need to transfer to a specialized burn treatment unit. Burn disasters are an important public health concern in developed and developing nations. Among the early steps in disaster preparedness is the understanding of geographic locations and capacity of burn care facilities. We aimed to map and classify medical facilities that provide burn care in Brazil and to undertake a location-allocation analysis to identify which could be targeted to increase capacity. A review of burn hospitalizations was conducted using Brazilian Ministry of Health data. Capacity was defined by number of burn patients admitted each year and bed type. Spatial population data per one-square kilometer were obtained from World Pop as a raster dataset. A road network dataset using Open Street Map data was created to conduct the drive time analysis. Location/allocation analysis was conducted to identify the proportion of Brazil's population living within 2- and 6-hours' drive time of a burn care capable hospital, stratified by the level of hospital capacity. Hospitals were ranked according to number of additional people served. We found 26.471 burn admissions. Of these, 3.508(13,2 %) were ICU admissions. A total of 735(2,7 %) hospital deaths occurred under the selected burn codes. In all, 1.273 facilities admitted burn patients, and 263(20,7 %) reported ICU admissions of burn patients. Seventeen hospitals were classified as maximum capacity facilities. Additional 23 hospitals were identified as potential targets for capacity building. Most maximum capacity hospitals are clustered in the Southeast of Brazil. Currently, 40.8 % of the Brazilian population live within 2 h of a maximum capacity facility. A large part of the population lives farther than 6 h away from a maximum capacity hospital. Most of the potential targets for capacity building are located near the coast of Brazil. We mapped and classified facilities that provide public burn care in Brazil. We identified public facilities that could be targeted to increase capacity to improve access for patients in the event of a burn disaster. Mapping, planning, and coordinating response is key for optimal outcomes in Mass Casualties Incidents. Cataloging and understanding local resources is a crucial first step in disaster management. Inequality in profiles can determine specific regional needs. Specialized burn centers are rare in regions other than the southeast. Health equity should be considered when planning disaster preparedness initiatives. Location-allocation modelling may assist in universal and equitable burn care service offerings. This study proposes an initial step in the classification and mapping of available burn treatment centers and population coverage in Brazil. • Burn disasters are an important public health concern in developed and developing nations. • Mapping, planning, and coordinating response is key for optimal outcomes in Mass Casualties Incidents. • Location-allocation modelling may assist in universal and equitable burn care service offerings. • Cataloging and understanding local resources is a crucial first step in disaster management. [ABSTRACT FROM AUTHOR]

Squall lines observed over the sahelo-sudanese regions are responsible for most of the annual rainfall recorded in these regions. They originate from the development of convective processes embedded in the Mesoscale Convective Systems (MCS). The impact assessment of the West African Monsoon (WAM) on the development of squall lines in the sahelo-sudanese regions, particularly the far north region of Cameroon is studied. In this study, the Tropical Rainfall Measuring Mission data (TRMM 3A12 and 3B43) and ERA5 reanalysis data from 1998 to 2008 have been used. The technique used is based on the use of the Climate Data Operator (CDO) software and the NCAR command language (NCL). Analysis of these data shows that the seasonal distribution of winds from the saharan heat-low and the Saint Helena high is the dominant atmospheric dynamics associated with the disturbances behind the development of squall lines. This distribution makes it possible to specifically draw up the evolution of the InterTropical Discontinuity (ITD) on the African continent, and thus, highlight the contribution of the WAM on precipitation in the far north region of Cameroon. This study is performed to contribute to the prediction of hydro-meteorological phenomena to improve disaster risks management associated with floods and landslides in this region, the most vulnerable in Cameroon. [ABSTRACT FROM AUTHOR]

Considerable research has been conducted in over a decade on traffic management systems that utilize Wireless Sensor Networks (WSNs) to mitigate congestion and prioritize emergency vehicles. The traffic management is becoming a wide interesting area for both academic and industrial researchers. The real-time traffic management is a dynamic scheme and is very challenging to provide an accurate signalling time and priority for a specific vehicle. This paper introduces a novel emergency vehicle information passing system that utilizes Radio Frequency (RF) sensors. This research study presents an innovative system for transmitting emergency vehicle information, which makes use of Radio Frequency (RF) sensors. The system effectively transmits crucial data, such as vehicle ID, approaching direction, mileage driven, and destination time. By doing so, it enables other vehicles to allocate appropriate space and facilitate smoother passage for emergency vehicles. The main intention of this approach is to improve the communication speed among the nodes and to reduce the response time. The communication among the nodes is done with different frequencies to enhance the method's effectiveness. We also propose a priority-based MAC (PMAC), which guarantees a slot allocation for emergency message transmission in the network. The effectiveness of the proposed approach is assessed through simulation using NS-2. The findings emphasize the effectiveness of the RF sensor when it comes to its ability to respond quickly and showcasing the PMAC's capability to reduce end-to-end delay. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) that emerged in 2019 and spread globally in 2020 has resulted in the imposition of lockdowns or a state of emergency in many cities worldwide. In Japan, a "new lifestyle" is being advocated. We hypothesize that the new lifestyle has changed people's use of their upper limbs during the COVID-19 pandemic. Therefore, through this questionnaire study, we aimed to determine the factors associated with exacerbation of symptoms during the pandemic and to investigate the current status of patients who require hand surgery. This study was a prospective multi-center questionnaire survey. This study was conducted in Japan from December 2020 to July 2021 at university and general hospitals in nine prefectures. A questionnaire was administered to patients who visited a hospital with symptoms of nerve entrapment syndrome, osteoarthritis, or tenosynovitis. A total of 502 patients with a mean age of 63.8 years responded. The 240 patients who experienced exacerbation (exacerbated and markedly exacerbated) were compared with other patients (unchanged, improved, and markedly improved). An increase in the time spent on personal computers and smartphones was associated with exacerbation of hand symptoms. Patients who wanted to undergo surgery but were postponed due to COVID-19 accounted for 23.5% of the outpatients. The mean scores for pain, jitteriness, and anxious depression in these patients were significantly higher than those of patients who did not want surgery. Our results suggest that an increase in the time spent on personal computers and smartphones is associated with exacerbation of hand symptoms during the COVID-19 pandemic. Patients who wanted to undergo surgery but were postponed by COVID-19 experienced greater pain, jitteriness, and anxious depression. [ABSTRACT FROM AUTHOR]

Door-to-needle time (DNT) is a critical consideration in emergency management of acute ischemic stroke (AIS). Deficiencies in the widely applied standard hospital workflow process, based on international guidelines, impede rapid treatment of AIS patients. We developed an in-hospital stroke system to reduce DNT and optimize hospitals' emergency procedures. To investigate the effect of the in-hospital stroke system on the hospital workflow for AIS patients. We performed a retrospective study on AIS patients between June 2017 and December 2021. AIS cases were assigned to a pre-intervention group (before the in-hospital stroke system was established) and a post-intervention group (after the system's establishment). We compared the two groups' demographic features, clinical characteristics, treatments and outcomes, and time metrics data. We analyzed 1031 cases, comprising 474 and 557 cases in the pre-intervention and post-intervention groups, respectively. Baseline data were similar for both groups. Significantly more patients in the post-intervention group (41.11%) were treated with intravenous thrombolysis (IVT) or endovascular therapy (ET) compared with those in the pre-intervention group (8.65%) (p < 0.001). DNT was markedly improved (decreasing from 118 (80.5–137) min to 26 (21–38) min among patients in the post-intervention group treated with IVT or bridging ET. Consequently, a much higher proportion of these patients (92.64%) received IVT within 60 min compared with those in the pre-intervention group (17.39%) (p < 0.001). Consequently, their hospital stays were shorter (8 [6–11] days vs. 10 [8–12] days for the pre-intervention group; p < 0.001), and they showed improved National Institutes of Health Stroke Scale (NIHSS) scores at discharge (−2 [−5–0] vs. −1 [−2–0], p < 0.001). DNT was significantly reduced following implementation of the in-hospital stroke system, which contributed to improved patient outcomes measured by the length of hospital stay and NIHSS scores. [ABSTRACT FROM AUTHOR]

Vaccines are a daycare and school requirement for children. The Public Readiness and Emergency Preparedness (PREP) Act 3rd amendment grants pharmacists and pharmacy interns the capability to vaccinate children at ages 3-18 years according to the Centers for Disease Control and Prevention Advisory Committee on Immunization Practices schedule of recommended vaccines. Before this, only some state boards of pharmacy allowed pharmacists to administer pediatric vaccines. Despite these opportunities, immunization training programs lack the comprehensive training of pediatric vaccines and administration for pharmacists and pharmacy interns without additional financial cost. All pharmacists should be adequately trained and pharmacies be reimbursed to provide vaccines to all ages of pediatric patients. Advocacy is necessary to ensure children have reliable, affordable, and convenient access to lifesaving vaccines today and after the PREP Act expires. [ABSTRACT FROM AUTHOR]