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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]

Dinner Tonight Emergency Preparedness Healthy Cooking Schools (DTEPHCS) should increase awareness of risk to health during emergencies and educate individuals on how to prepare to maintain good health through nutrition and food safety during emergencies. DTEPHCS use the Health Belief Model to encourage participants to take action in creating and maintaining emergency meal kits and other emergency preparations. The target audience for DTEPHCS are families in the Gulf Coast Region of Texas, approximately 7 million people. This target population is most likely to be affected by power outages due to hurricanes and tropical storms. Dinner Tonight Healthy Cooking Schools (DTHCS) are multi-hour events with food demonstrations and additional educational presentations or demonstrations. DTEPHCS are focused on creating a preset menu of 12 recipes that use limited, alternate, or no electricity to create. These recipes follow the Dietary Guidelines for Americans recommendations and are healthy ways to fuel a family during emergencies with power outages. Additional education presentations include; water storage and purification, food safety, and more. DTHCS are evaluated using a retrospective post-questionnaire. DTHCS participants are evaluated in areas of understanding of meal planning, the nutritional content of foods, and impact of food on health; as well as the level of confidence in meal planning, recipe modification, and food safety. Over 64% of participants increased their understanding of how meal planning can help save time and reduce stress, the calorie content of the foods they prepare, and how the foods they eat impact their health. Over 70% of participants increased their confidence in meal planning to prepare healthy meals, modifying recipes to reduce calorie content, and safely preparing fruits, vegetables, and meats from the Healthy Cooking School demonstrations. In addition, 93.0% of participants were mostly or completely satisfied with the DTHCS program. In conclusion, DTHCS effectively increases audience understanding of planning and increases confidence in preparing safe and healthy meals. None [ABSTRACT FROM AUTHOR]

1. Analyze the role of hospices and research needs relative to emergency preparedness and response. 2. Evaluate at least five (5) key hospice considerations in emergency planning and response. Hospices play an important role in emergency planning and response, but there are several challenges including lack of role definition and inconsistencies in guidance. The formulation of best practices and the pursuit of a research agenda could provide further opportunities to strengthen hospices' role in planning for and managing disasters. The underlying characteristics of hospice translate into unique considerations for emergency preparedness planning. These characteristics also convey both organizational vulnerabilities and strengths when responding to disasters. The Centers for Medicare and Medicaid Services have recently expanded their Emergency Preparedness Conditions of Participation for hospice, and the COVID-19 pandemic has highlighted the importance of end-of-life care in disaster planning and response. 1,2 However, the lack of a clearly defined role at federal, state, and local authorities remain as barriers for hospices as they pursue emergency preparedness. 3 Additionally, collaboration with other preparedness partners has been identified as a challenge. Despite this, many hospice providers have experience in disasters and an examination of the academic and professional literature reveals some important roles that hospices may play in their communities during times of disaster. 4 Collaboration with other emergency management partners can provide bi-directional benefit for hospices, healthcare systems, and the communities in which they serve. Continued research in this specialized area is needed to improve hospices' emergency preparedness efforts. Further research is needed in the clarifying the roles of hospices in disasters, best practices in preparedness and response, understanding experiences of hospice providers in disasters, methods of protecting the hospice workforce, and how hospice emergency preparedness planning can help foster resilience in vulnerable communities. Advocacy / Policy / Regulations Scientific Research [ABSTRACT FROM AUTHOR]

In 2020, an estimated 424,300 youth were arrested and nearly 128,000 youth were detained in the United States, with disproportionately higher rates for minoritized youth when compared to White youth.1 Although the number of youth in detention facilities has decreased since the late 1990s, a staggering number of children and adolescents enter the justice system each year. This remains a critical issue for child and adolescent psychiatrists. By the time youth enter juvenile detention, individual mental health needs are already exceptionally high. [ABSTRACT FROM AUTHOR]

In order to study the difference between the crushing characteristics of carbon nanotubes (CNTs) concrete and ordinary concrete, this paper conducts a test between ordinary concrete and CNTs concrete with a volume content of 0.3 % at different impact speeds through the Hopkinson test, using fragmentation and particle size distribution as evaluation indexes. Crushed fragments are analyzed from the perspective of concrete pores. Finally, the nonlinear viscoelastic constitutive model and the nonlinear elastic constitutive model of CNTs concrete are then developed and compared. Based on the results, the average pores of the CNTs concrete decreased from 42.0 nm to 38.0 nm, but the pore area increased from 5.743 m2/g to 6.305 m2/g, indicating that CNTs can fill the internal pores. Meanwhile, the average particle size of broken CNTs concrete is significantly larger than that of ordinary concrete. However, the relationship between the crushing particle size and impact velocity is approximately y=A-Bln(x+C) for both concretes. According to the study of concrete constitutive models, compared to the Loand damage model, the combination of Loand model and Mazars model is more accurate in describing concrete damage characteristics, and the average correlation coefficient increases from 0.84 to 0.92. Meanwhile, compared to the Zhu-Wang-Tang (ZWT) model, Ottosen model can better capture the dynamic mechanical properties of CNT concrete, with a correlation coefficient over 0.98. • The broken carbon fiber concrete is more complete and larger, which provides a reference for disaster prevention and reduction engineering. • The traditional damage model is modified, and the modified damage model is more consistent with the actual condition. • Compared with the viscoelastic constitutive model and the viscoplastic constitutive model, the viscoplastic constitutive model is more consistent with the actual conditions. [ABSTRACT FROM AUTHOR]

Natural hazard processes, as an inherent component of mountain environments, react sensitively to global warming. The main drivers of these changes are alterations in the amount, intensity or type of precipitation, glacier melting, or thawing of permafrost ice. The hazard responses can involve a change in hazard intensity or frequency (increasing or decreasing), a shift in their location or, a shift from one type of hazard to another. As climate change impacts vary in space and time, this variability must be considered when planning measures to protect populations and infrastructure from hazardous processes. To support this, we developed a method for assessing the climate sensitivity of small individual rock releases and larger rockfall processes. The method is based on a fuzzy logic approach and uses highly resolved climate scenario data, allowing application on a regional or even larger scale. The application in a study area of 700 km2 in the central Valais (Switzerland) shows that the impacts of climate change on natural hazard processes can vary quite substantially across small spatial scales. Generally, an increase in rockfall frequency and magnitude is simulated under future warming scenarios, especially at higher altitudes. However, at lower elevations and on south-exposed slopes, a decrease in freeze-thaw cycles leads to a decrease in material availability. This knowledge is essential in discussions of how climate change should be considered in hazard and disaster management. • Climate change impact on natural hazards varies essentially at small spatial scales. • Increase in rockfall frequency and magnitude under future warming scenarios • Decrease in rockfall frequency at lower altitudes due to less freeze-thaw cycles • Depicting high variation in altitude by using high resolution climate scenarios • Fuzzy logic is well suited to depict complex system behavior. [ABSTRACT FROM AUTHOR]

• Daily Heat Index (HI) patterns correlate with heat-related EMS calls. • East Austin has higher Heat Index values and increased heat-related EMS calls. • HI frequency and intensity are positively correlated to increased EMS service demand. • Study links urban growth and heat vulnerability; highlighting targeted heat mitigation. Despite growing concerns about heat waves due to climate change and their health impacts, there has been limited research on patterns of extreme heat during summertime and their association with heat-related Emergency Medical Services (EMS) incidents. This study examines spatiotemporal patterns of the heat index (HI) and its relationship to heat-related EMS incidents in Austin-Travis County, Texas, focusing on the summers of 2020 and 2021. Collecting 47,838 heat-related EMS incidence cases and aggregating them at the tract level (N = 290), the research employs spatiotemporal analysis, spatial autocorrelation, K-means clustering, and geographically weighted Poisson regression to identify disparities in heat-related health outcomes. Key findings indicate a significant correlation between high HI frequency and intensity and increased EMS incidents, particularly in East Austin, underscoring the area's heightened vulnerability to heat. The study also reveals that heat vulnerability and urban growth patterns are closely linked to the incidence of heat-related illnesses, and its impact varies by region. These results emphasize the critical need for targeted heat resilience strategies in urban planning and emergency response. This research merges socio-economic and environmental data to offer insights into heat-related health risks, informing targeted public health policies and urban planning for more equitable and effective interventions. [ABSTRACT FROM AUTHOR]

Seismic activity induced during the development of Enhanced Geothermal Systems (EGS) is frequent and poses significant hazards. This study aims to accurately forecast the maximum magnitude (M max) of induced earthquakes to effectively manage seismic risks. Focusing on the EGS project in Gonghe County, Qinghai Province, we evaluated and optimized various widely-applied M max forecasting models, while also endeavoring to directly forecast maximum magnitudes in the post-closure phase. Initially, advanced deep learning models (such as PhaseNet and GaMMA) were employed to process seismic data, coupled with VELEST and HypoDD methods for earthquake relocation. Subsequently, four currently widely recognized maximum magnitude (M max) forecasting models (H14, NRBE, V16, and G17) were utilized to forecast and assess M max during nine hydraulic fracturing stages, six post-closure stages exhibiting tailing effects, and the entirety of the 2019–2021 period in the Gonghe EGS project. The findings indicate significant disparities in the efficacy of different forecasting models during hydraulic fracturing stages, with no model fully aligning with the complex physical mechanisms of induced seismicity. NRBE and G17 models tend to overestimate M max forecasting, potentially escalating production costs, whereas H14 and V16 models yield results closer to actual values but are susceptible to the influence of real seismic breakthroughs. Furthermore, distinct discrepancies were observed in the M max forecasting performance of the same model between hydraulic fracturing and post-closure stages. Attempts to directly forecast M max post-closure achieved certain efficacy, likely due to the cumulative injection volume exerting a degree of control over induced seismic activity in both stages. Lastly, to overcome limitations in current M max forecasting models, a hybrid model Y24, integrating the advantages of four forecasting models, was proposed, demonstrating higher accuracy and reliability in forecasting during both hydraulic fracturing and post-closure stages. The study's findings provide crucial technical support and decision-making basis for the seismic risk management of EGS projects or shale gas development projects employing hydraulic fracturing, underscoring their significance in ensuring the safety and sustainability of new energy and resource development endeavors. • Analysis of M max forecasting models highlights their varying performance during hydraulic fracturing and post-closure stages. • Direct application of forecasting models to post-closure periods achieves certain forecasting performance. • Proposal of the Y24 hybrid model, combining the strengths of different models, for improved forecasting accuracy. [ABSTRACT FROM AUTHOR]

• Constructed a geological hazard risk assessment Indicator system for specific regions. • Compared the accuracy of different evaluation models and selected the best model. • Based on the model calculation results, derive the site selection strategy for rural settlements. It has been over 20 years since the world entered the 21st century, and the economy and science and technology of various countries have developed rapidly. However, the threat and losses of natural disasters to humanity have never diminished. Global disaster risk is increasing year by year in new forms and scales. Sichuan Province, as one of the provinces with the highest number of geological disasters in China, and its rural settlements, as a high-risk area for geological disasters, are the key and difficult points for disaster prevention in China. This article selects rural settlements in Yibin City, Sichuan Province as the research area. By analyzing historical disasters and local geographical characteristics, statistical methods are used to screen and construct a geological hazard risk assessment model for rural settlements in Yibin City. A quantitative and qualitative analysis was conducted on the key indicators of geological hazard risk in rural settlements in Yibin City. Multiple models were used to comprehensively evaluate the risk of geological hazards in rural settlements in Yibin City and a zoning study was conducted. Then, the accuracy of different models was compared, and the random forest model was selected as the computation model for disaster avoidance site selection in rural settlements in Yibin City. Thus obtaining the zoning map of geological hazard risk level in Yibin City. Finally, we conducted a site selection analysis for Rural settlements with different levels of geological hazard risk. And propose strategies for optimizing site selection. [ABSTRACT FROM AUTHOR]

• This study develops a disaster risk index (DRI) for assessing disaster risks in coastal communities. • Logit Boost ML model was found the optimal model for this research. • Gabura had the highest DRI value of 0.09, ahead of Southkhali (0.06) and Banishanta (0.04) unions. • The study underscores the importance of evolving from traditional approaches to advanced data-driven tools. Communities in disaster-prone coastal areas are more vulnerable to socioeconomic, environmental, and ecological problems. Knowing how disaster-prone coastal communities are, and, ideally, their resilience against current disaster risks, is an essential first step in reducing consequences. Therefore, this study aims to develop a disaster risk index (DRI) for assessing disaster risks in three coastal communities in southwest Bangladesh. Five machine learning (ML) models and holistic approaches were adopted to conduct the analysis. This study utilized ML-based feature selection methods and considered hazard, exposure, and vulnerability components to identify the most influential indicators for evaluating disaster risk. The researchers systematically developed six distinct feature sets using filter-based feature selection methods. Five ML models then evaluate these sets to determine the optimal set of features. Evaluation metrics were used to validate the performance of ML models, such as accuracy, precision, F-measure, recall, and the receiver operating characteristics (ROC) curve. Based on the Logit Boost ML model, the optimal feature set (set 3) had the best accuracy (0.997), precision (0.997), recall (0.997), F-measure (0.997), and ROC (0.996) compared to other feature selection sets. According to the findings, Gabura had the highest DRI value of 0.09, ahead of the Southkhali (0.06) and Banishanta (0.04) unions. The study underscores the importance of evolving from traditional approaches to advanced data-driven tools to understand disaster risks. The paper's findings can provide policymakers in coastal areas with new insight into the disaster risk differences among the inhabitants, leading to more effective disaster management practices and risk reduction strategies. [ABSTRACT FROM AUTHOR]

The ongoing war in Ukraine is associated with unprecedented radiological threats to the public in Ukraine and the neighboring countries. This calls for fundamental revision of the preparedness plans and established approaches to radiological monitoring of the populations affected by potential radiological emergencies. Dosimetric information will be needed for triage of victims and support to decision makers for prioritizing mitigation actions. Retrospective dosimetry methods strive to find a solution that would achieve this and enable fast and accurate feedback with information on individual doses to the concerned public. All known approaches to emergency dosimetry, both in biological and physical (instrumental) dosimetry have limitations, in particular – preventive cost, limited availability of samples for analysis, insufficient sensitivity (high dose threshold) and/or stability of radiation-induced markers (i.e. high fading). At the moment each of considered dose assessment methods possesses some combination of the aforesaid limitations. A suggestion to overcome these deficiencies is to use ordinary table salt (NaCl), read by optically stimulated luminescence (OSL), with well-known good dosimetric properties, and which allows anybody to prepare improvised individual dosemeters at home to be carried like a regular personal dosemeter until readout of the OSL signal is possible. This paper considers pros and cons of the use of NaCl as an OSL dosemeter in an emergency situation, with emphasis on practical aspects of its application for mass dose assessments for individuals among populations affected by radiological emergency situations. • Comparison of dosimetry methods for radiological emergencies. • Applicability criteria for emergency preparedness dosimetry. • Review of the dosimetric properties of table salt (NaCl) as emergency OSL dosemeter. • Outlook of application of NaCl for mass dosimetric screening during radiological or nuclear emergency. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI. [ABSTRACT FROM AUTHOR]

• We present an experiment investigating pedestrians' responses to knife-based attacks. • The experiment uses financial incentives and an 'aggressor' to recreate a knife-based terrorist attack. • The results show that participants were stressed and reported fear during the experiment. • Both social influences and demographics had an impact on participants' response. Terrorist attacks have become both more frequent and deadlier in recent decades. Knife attacks by terrorists in particular are both high impact and relatively frequent. Understanding how individuals move when confronted by an attacker can save lives by informing the planning of transportation hubs, entertainment venues and other spaces where large numbers of people congregate. Using pedestrian dynamics to predict responses to such attacks has the potential to further improve the survival of those involved by providing insights to emergency service responders when a terrorist situation has occurred in a crowded venue. However, given the lack of appropriate data, it is currently impossible to accurately predict pedestrian movement responses to terrorist attacks. This paper describes a practical study that developed a methodology and implemented a set of experiments examining responses to knife-based attacks by an unexpected and hostile individual. The experiments used financial incentives and an 'aggressor' to recreate a knife-based terrorist attack in an ethically viable setting. The participants of this study (n = 80) were tracked using ultra wide-band sensors to provide temporal and spatial positional data in relation to the attacker and to each other. Participants subsequently completed a questionnaire to report their psychological response during the experiment, thus allowing psychological perception and movement responses to be compared. The results show that participants were stressed and reported fear during the experiment, indicating that the experimental approach is a potentially valid proxy for a real-life attack. The analysis highlights that participants' decisions to stay or run away were strongly influenced by the actions of other nearby participants, while participants' continuous movement response (movement speed) was highly dependent on relative position to the aggressor. Participant demographics were a factor with female participants less likely to move and slower moving, while older participants were also slower moving. From these results we have been able to construct a model of how pedestrians may react when faced with a sudden attack. We discuss our findings and their potential to contribute to emergency planning and response, finally we comment on opportunities for further study. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level optimization model for resource allocation in disaster response, categorizing emergency resources into personnel and materials in consideration of the coupling relationship between them. The model addresses two levels of problems, which can be conceptualized as a Stackelberg game. The upper-level saves rescue time to determine the transportation scheduling scheme from cities that provide assistance to disaster-stricken cities. The lower-level minimizes cost to determine the optimal allocation scheme of emergency resources from cities that provide assistance to emergency resource distribution centers, which can be characterized as a Nash game among the followers. A robust optimization model with budget constraints is constructed to cope with the demand uncertainty at disaster points, disruption risk at distribution centers and transportation time uncertainty from the emergency resource distribution centers to the disaster-stricken cities. An efficient emergency resource allocation scheme under multiple uncertainties ensures rapid and effective post-disaster recovery, improves disaster supply chain sustainability and reduces environmental pollution. Since the developed model is in a bi-level nonlinear format, the Karush-Kuhn-Tucker conditions and the big-M method are applied to obtain a single-level mixed integer linear programming model. To validate the efficacy of the model, a series of numerical experiments with sensitivity analysis are conducted. Compared with the leader's perspective model and the follower's perspective model, although the computational time of the bi-level model is relatively longer, it can obtain a satisfactory point for both parties. Moreover, as the disturbance of the three uncertainties continues increasing, it can be observed that the two objectives do not necessarily have a simultaneous increasing trend in any case. In addition, under the influence of uncertainty, the costs of cities that provide similar quality assistance can remain stable. [Display omitted] • A robustness model in emergency resource coupling allocation is proposed. • Cities that provide assistance are followers; Disaster-stricken cities are leaders. • A bi-level program is developed, and converted to a mixed integer one for solution. • The characteristics of robustness under multiple uncertainties are investigated. • The outcomes provide technical support for decision-making in emergency assistance. [ABSTRACT FROM AUTHOR]

• Digital drilling tests for different types of rock mass were conducted. • The drilling test model of rock equivalent brittleness index (DP-EBI model) was established. • Comparative analysis of rockburst proneness criteria, including D T , BIM , PES , A CF , and EBI was conducted. • A method for rockburst proneness evaluation of surrounding rock while drilling was proposed. Rockburst is one of the common geological disasters in underground engineering. Rockburst proneness evaluation is the premise of rockburst disaster prevention and control. It is an effective method to evaluate rockburst proneness by brittleness index. The traditional brittleness index testing requires coring the surrounding rock in the field and transporting it to the laboratory for testing. The testing process is inefficient and disconnected from the in-situ environment. The effective in-situ and accurate test of brittleness index has become a challenge. Digital drilling testing of rock mass is a new approach to solve the above problems and future research direction. The study explore a rockburst proneness evaluation method based on the interpretation of real-time drilling parameters. The key of this method is to establish the quantitative relationship model between the brittleness index and drilling parameters. Digital drilling tests on different rock types were conducted using the rock intelligent rotary cutting testing system (RCT system). The quantitative relationship between drilling parameters and rock strength parameters was analyzed. The drilling test model of rock equivalent brittleness index (DP-EBI model) is established. The verification test results show that the difference rate between the rock brittleness index with drilling test and indoor test is 11.8%. On this basis, the testing results of five rockburst evaluation methods, including equivalent brittleness index EBI , energy impact index A CF , potential energy of elastic strain PES , brittleness index modified BIM and dynamic failure time D T were compared. The effectiveness of using drilling parameters to evaluate rockburst proneness is verified. This study can provide a theoretical and methodological basis for the in-situ detection and evaluation of rockburst proneness and grade in underground engineering fields, such as mine roadways, transportation tunnels, and hydraulic tunnels. [ABSTRACT FROM AUTHOR]

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy. • A framework for coastal storm hazards early warning over very large areas is developed. • The severity of hazards is predicted every 100 m alongshore by parametric modeling. • Site-specific storm hazard indicators at vulnerable sites are predicted by XBeach. • 7-day rolling forecasts of storm hazards are communicated via a web portal. [ABSTRACT FROM AUTHOR]