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As the Duwamish Valley community in Seattle, Washington, U.S.A. and other environmental justice communities nationally contend with growing risks from climate change, there have been calls for a more community-centered approach to understanding impacts and priorities to inform resilience planning. To engage community members and identify climate justice and resilience priorities, a partnership of community leaders, government-based practitioners, and academics co-produced a survey instrument and collected data from the community using the Seattle Assessment for Public Health Emergency Response (SASPER), an approach adapted from the Centers for Disease Control and Prevention's Community Assessment for Public Health Emergency Response (CASPER). In addition, we conducted a process and outcome project evaluation using quantitative survey data collected from volunteers and qualitative semi-structured interviews with project team members. In October and November 2022, teams of volunteers from partner organizations collected 162 surveys from households in the Duwamish Valley. Poor air quality, extreme heat, and wildfires were among the highest reported hazards of concern. Most Duwamish Valley households agreed or strongly agreed that their neighborhood has a strong sense of community (64%) and that they have people nearby to call when they need help (69%). Forty-seven percent of households indicated willingness to get involved with resilience planning, and 62% of households said that they would use a Resilience Hub during an emergency. Survey volunteers evaluated their participation positively, with over 85% agreeing or strongly agreeing that they learned new skills, were prepared for the survey, and would participate in future assessments. The evaluation interviews underscored that while the SASPER may have demonstrated feasibility in a pre-disaster phase, CASPER may not meet all community/partner needs in the immediate disaster response phase because of its lack of focus on equity and logistical requirements. Future research should focus on identifying less resource intensive data collection approaches that maintain the rigor and reputation of CASPER while enabling a focus on equity. [ABSTRACT FROM AUTHOR]

Background: The challenges of evidence-informed decision-making in a public health emergency have never been so notable as during the COVID-19 pandemic. Questions about the decision-making process, including what forms of evidence were used, and how evidence informed—or did not inform—policy have been debated. Methods: We examined decision-makers' observations on evidence-use in early COVID-19 policy-making in British Columbia (BC), Canada through a qualitative case study. From July 2021- January 2022, we conducted 18 semi-structured key informant interviews with BC elected officials, provincial and regional-level health officials, and civil society actors involved in the public health response. The questions focused on: (1) the use of evidence in policy-making; (2) the interface between researchers and policy-makers; and (3) key challenges perceived by respondents as barriers to applying evidence to COVID-19 policy decisions. Data were analyzed thematically, using a constant comparative method. Framework analysis was also employed to generate analytic insights across stakeholder perspectives. Results: Overall, while many actors' impressions were that BC's early COVID-19 policy response was evidence-informed, an overarching theme was a lack of clarity and uncertainty as to what evidence was used and how it flowed into decision-making processes. Perspectives diverged on the relationship between 'government' and public health expertise, and whether or not public health actors had an independent voice in articulating evidence to inform pandemic governance. Respondents perceived a lack of coordination and continuity across data sources, and a lack of explicit guidelines on evidence-use in the decision-making process, which resulted in a sense of fragmentation. The tension between the processes involved in research and the need for rapid decision-making was perceived as a barrier to using evidence to inform policy. Conclusions: Areas to be considered in planning for future emergencies include: information flow between policy-makers and researchers, coordination of data collection and use, and transparency as to how decisions are made—all of which reflect a need to improve communication. Based on our findings, clear mechanisms and processes for channeling varied forms of evidence into decision-making need to be identified, and doing so will strengthen preparedness for future public health crises. [ABSTRACT FROM AUTHOR]

Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques. [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]

This study examined the relationship between risk perception and evacuation behavior, and the sociospatial contexts that affect evacuation. It is argued that risk perception and contextual conditions have a joint effect on evacuation. Data were collected from 3,756 evacuees in sixteen cities and towns affected by the 2011 Tohoku earthquake off the Pacific coast. Using geographic information systems (GIS), data from questionnaires on risk perception and activity diary surveys on behavioral response were combined with geospatial data. The main finding was that high risk perception leads to evacuation behavior, but that the difference between immediate and delayed evacuation depends on the sociospatial context. Delayed evacuation, compared with immediate evacuation, tends to occur in the following contexts: (1) the physical context (e.g., being inland at the time of the disaster impact); (2) the household context (e.g., the need to confirm the safety of household members); and (3) the social context (e.g., the need for assistance with mobility). This study extends the literature on disaster science and cognitive-behavioral geography by considering the relationship between perception and behavior in urban spaces in emergencies. The findings are relevant to the development of disaster preparedness planning to minimize human losses. [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]

This study examined the drought preparedness of drought-prone communities in the Sudano-Sahelian zone of Cameroon. The study employed a mixed-method approach, using qualitative and quantitative data collection methods, including interviews, household surveys, focus group discussions, and field observations. The data were then analysed using SPSS for quantitative data and content analysis for qualitative data. The results reveal that the respondents primarily rely on their personal and community abilities to prepare for droughts. A significant proportion (χ2 = 11.676, P = 0.020) of the respondents depend on community leaders for drought information, while others rely on family and friends. They also use indigenous knowledge and construct boreholes/wells to prepare for droughts. However, limited government support, inadequate extension/scientific support services, and insufficient formal training limited drought preparedness. Age, household size, and income significantly influenced perceived preparedness for drought. Based on the findings, the study concludes that informal/locally-driven strategies are important in drought preparedness and should be strengthened. The study recommends the establishment of drought management committees at the grassroots level and the effective use of indigenous knowledge in combination with scientific knowledge for drought planning and adaptation. Given the geographical variation in climatic stressors, a focus on a specific event such as drought has enhanced the understanding of drought disaster preparedness at the community level for effective planning and policy interventions. This study contributes to the literature in the disaster preparedness discipline with a lens on community drought preparedness. [ABSTRACT FROM AUTHOR]

Introduction: Hyponatraemia is the most common electrolyte disorder in inpatients resulting mainly from an imbalance in water homeostasis. Intravascular fluid status assessment is pivotal but is often challenging given multimorbidity, polypharmacy and diuretics use. We evaluated the utility of point‐of‐care ultrasound (POCUS) as an adjunct tool to standard practice for fluid assessment in severe hyponatraemia patients. Methods: Patients presenting with severe hyponatremia (Serum Sodium [Na] < 120 mmol/L; Normal range: 135−145 mol/L), managed by standard care were included. Hyponatraemia biochemistry work‐up and POCUS examination were undertaken. Both clinician and POCUS independently assigned one of the three fluid status groups of hypovolaemia, hypervolaemia or euvolaemia. The final diagnosis of three fluid status groups at admission was made at the time of discharge by retrospective case review. Clinician's (standard of care) and POCUS fluid assessments were compared to that of the final diagnosis at the time of discharge. Results: n = 19 patients were included. Median Na on admission was 113 mmol/L (109‐116), improved to 129 ± 3 mmol/L on discharge. POCUS showed the higher degree of agreement with the final diagnosis (84%; n = 16/19), followed by the clinician (63%; n = 12/19). A trend towards higher accuracy of POCUS compared to clinician assessment of fluid status was noted (84% vs. 63%, p = 0.1611). Biochemistry was unreliable in 58% (n = 11/19) likely due to renal failure, polypharmacy or diuretic use. Inappropriate emergency fluid management was undertaken in 37% (n = 7/19) of cases based on initial clinician assessment. Thirst symptom correlated to hypovolaemia in 80% (4/5) cases. Conclusion: As subjective clinical and biochemistry assessments of fluid status are often unreliable due to co‐morbidities and concurrent use of medications, POCUS can be a rapid objective diagnostic tool to assess fluid status in patients with severe hyponatraemia, to guide accurate emergency fluid management. [ABSTRACT FROM AUTHOR]

Now a days, assessment of flood risk is a significant concern worldwide, particularly in regions experiencing rapid urbanization and climate change. This study utilizes the analytic hierarchy process and Geographic Information Systems to assess flood risk under changing climate in the Lower Tapi river basin (LTRB) in India. The objective is to develop flood hazard and flood vulnerability maps and subsequently to create a flood risk map by integrating them in LTRB in a Base and Advance scenario, i.e., under changing climate. The change point study to identify Advance scenario has been employed to distinguish the year from which sudden change in the data emerge out. The flood risk contributing factors are included in this study are Elevation, Distance from river, Average annual rainfall, Drainage density, Normalized Difference Vegetation Index, Slope, Clay percentage, Topographic Wetness Index, Population density, Average Crop production, Land use Land cover, and Road River intersection. The developed flood risk map has been separated into five zones of the level of risk, namely very high, high, moderate, low, and very low-risk zones. The flood risk maps developed in Base scenario and in Advance scenario are compared to investigate the impact of changing climate in Lower Tapi river basin. It is revealed that the high and very high-risk zone area proliferated, and very low and moderate risk area condensed in the Advance scenario. The developed flood risk maps in Base and Advance scenario can aid in effective disaster management and planning for the region under changing climate. [ABSTRACT FROM AUTHOR]

Debris flow has always been a serious problem in mountainous areas. Accurate debris flow susceptibility (DFS) assessment and interpretable prediction results play an important role in the prevention and control of debris flow disasters. Some commonly used machine learning algorithms based on Boosting ensemble techniques were widely used in the study of geohazard susceptibility due to its excellent predictive ability. However, the Categorical Boosting (CatBoost) and Natural Gradient Boosting (NGBoost) have not yet been applied in the field of DFS assessment, and few geohazard studies systematically compare and research these boosting-based algorithms. Meanwhile, previous researches have mostly focused on comparing the predictive ability of algorithms, identifying the susceptibility zones of the entire study area, and ranking the importance of the indicators, but little thorough analysis of the relationship between the indicators and debris flow susceptibility on different types of construction land. The aims of this study were to explore the optimal boosting-based DFS model, and the distribution characteristics and change rules of DFS in the study area, so as to provide decision supports for debris flow disaster prevention and reduction. This was the first time that six boosting-based machine learning algorithms have been compared in the study of DFS assessment. After determining the optimal model, the change rules of indicators in the entire study area and two types of construction lands under different DFS levels were studied respectively. An eXplainable Artifcial Intelligence (XAI) method called SHapley Additive exPlantations (SHAP), combined with zonal statistics function in geographic information system (GIS) were adopted to explore how each indicator affects the occurrence of debris flows. The results showed that the CatBoost performed best and provided the most reasonable DFS result among six boosting-based models. We found that debris flows were more likely to occur along rivers and construction lands at low altitude. Rural areas faced more stronger pressure from rainfall and were featured by worse disaster-breeding environment than urban areas. This research enriches the application of machine learning in DFS assessment, explores the changing trends of indicators between different DFS levels, and provides suggestions for better debris flow disaster prevention and mitigation management. [ABSTRACT FROM AUTHOR]

This paper examines the form, function and impact of previous Olympic security arrangements and their intersection with planning practice. Drawing from prior and ongoing empirical research investigating the security practices at summer Olympic Games, the paper argues that wider shifts towards 'total' security models comprising continually reproduced security motifs can be observed that are increasingly standardized, mobile, globalized and planned-in. For most Olympic organizers, preparations now necessarily include attempts to equate spectacle with safety and to 'design-out' terrorism by relying on highly militarized tactics and expensive and detailed contingency planning. Such securitizing practices have intensified in form and scale since 9/11, with such intensification set to continue at the XXXIII Olympiad in Paris, where a vast security infrastructure is being embedded into the large-scale and long-term master-plans for the central city. This represents a high point in spatial planning practice through embracing principles of security-by-design where Games-time security infrastructure, whilst providing effective protection, becomes a less visible but permanent, physical legacy that can also contribute to local programmes of regeneration, climate resilience and crime prevention. The paper concludes by reflecting upon what the continual evolution of security infrastructure means for the balancing of planned-in security and spectacle at future Olympiads. [ABSTRACT FROM AUTHOR]

The vector information of mountainous roads is great significance, including regional economics, emergency response, disaster management, among others. Nevertheless, there are irregular deformations in existing vector data, coupled with incomplete extraction results for mountainous roadways. These issues pose challenges in establishing a matching model between road extraction results and existing vector data, significantly reducing the efficiency of automated updates for the current vector data. Targeting roads as a distinct artificial feature and leveraging their topological connectivity, we propose a vector data updating method based on matching point pair grouping. Firstly, we employ the SDUNet model to extract mountainous road and utilize the Superglue model to match road binary images with vector binary images, resulting in sets of initial matching point pairs. Subsequently, we propose a road line extraction model that groups matching point pairs, eliminates misaligned pairs based on road topological connectivity, and establishes a mapping relationship between road lines and vector lines. Ultimately, through road line optimization and vector line optimization methods, we accomplish the automatic updating of vector data for mountainous roadways. Through a comprehensive analysis of over 2600 vector data, our method demonstrates substantial advantages in terms of both F1 and IoU compared to alternative methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the effects of a disaster education intervention on children's risk perception and preparedness. It also sought to advance longitudinal studies, during an 18‐month time period, of risk perception and preparedness by using a quasi‐experimental methodology in child‐centred disaster research. This study used a quasi‐experimental longitudinal research design to measure the effects of disaster education on children. These effects were examined on children's risk perception and preparedness in the Van and Kocaeli provinces of Türkiye during the 18‐month period, with a sample of 720 school children. Experimental and control groups were randomly allocated, controlling for age, school grade and school enrolment. The disaster education intervention was designed to improve the children's awareness of natural hazards and their knowledge of disaster risk reduction using discussion, visual materials and interactive teaching of emergency management. The results indicate that the disaster education intervention had a positive effect on children's risk perception and perceived importance of preparedness in both study locations. It also helped children to understand the risks and hazards in their living environments. More importantly, this study showed that disaster education enhanced the protective measures taken by children for disasters. This is the first study using the quasi‐experimental longitudinal research design to measure the effects of disaster education on children's risk perception and the importance of preparedness. The findings are of relevance for organisations such as government departments and non‐government organisations when designing or improving disaster education programmes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic presented wide-ranging leadership challenges to public health leaders and public health organizations. In its wake, as the necessity of reconstructing public health and modernizing the Centers for Disease Control and Prevention (CDC) is considered, we reviewed reports from the Commonwealth Fund and the CDC and other leadership-focused literature to identify common themes for a new generation of public health leaders. We posit that this new generation must have the ability to communicate (build and maintain trust and accountability); forge, facilitate, and promote partnerships; connect public health and health care systems; build information systems that provide accessible, actionable data; engage in systems and strategic thinking and action; center equity and inclusivity and understand structural racism as a fundamental driver and creator of health inequities; and achieve and maintain resilience and self-care. For each of the 7 abilities, we offer a description, assess what COVID-19 taught us about the necessity of the ability for public health leaders, and offer suggestions for developing (or honing) one's skill set, mindset, and tool set in this regard. (Am J Public Health. 2024;114(6):626–632. https://doi.org/10.2105/AJPH.2024.307633) [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]

COVID-19 has affected the various aspects of social service delivery worldwide. Explicitly, pediatric service was affected mainly due to the travel restrictions, and children were at heightened risk due to these circumstances. Therefore, this study aims to analyze the Pediatric Clinical Practice in a Chinese children's hospital to describe and acknowledge the impact of COVID-19 on the pediatric healthcare system. For this purpose, the hospital's outpatient registration database was the primary information source, which comprises demographic data, including age, gender, city of residence, and hospital departments visited. Data on patients admitted from January 25, 2019, to April 16, 2019, were collected and used as a baseline. The results revealed that the number of pediatric visits to the case study hospital decreased during the COVID-19 pandemic, accounting for 40% of that in 2019, especially during the early stage of the pandemic. The proportion of pediatric patients from outside Chongqing decreased by approximately 14% in the early stages and increased by 24% in the second and third stages, corresponding to an overall decrease of 8.5%. Accordingly, the hospital responds to COVID-19 by providing valuable information and a guiding framework to the rest of the world on how to respond to a crisis. [ABSTRACT FROM AUTHOR]

Some scholars argue that the public is generally myopic in their attitudes about disaster preparedness spending, because they prefer to spend money on disaster response rather than preparedness, despite the greater cost effectiveness of the later. Given voters' general lack of policy information, it is possible that limited support for preparedness comes from lack of information about its efficacy. In this paper, we build on these studies by examining how people respond to new information about the effectiveness of policy initiatives in the context of public health and the COVID-19 pandemic. Through two online survey experiments with over 3400 respondents, we demonstrate that information can lead people to update attitudes about preparedness, illustrating the potential for information campaigns to increase support for preparedness policies. Our results suggest that information about the efficacy of preparedness can increase support for these policies, and the information effect exists even for individuals whose prior beliefs were that public health programs were ineffective. These results suggest that information can make people more supportive of preparedness spending, which could provide electoral incentives for its provision. We conclude by providing some directions for future research to enhance our understanding of public opinion and preparedness spending. [ABSTRACT FROM AUTHOR]

The sliding process and characteristics of earthquake-induced landslides are complex, and many factors affect landslide movement. The damage caused by landslides is often closely related to their movement characteristics. Therefore, establishing an efficient and quick analysis model for earthquake-induced landslide movement is very important in considering multiple control factors in the sliding process. Based on the typical characteristics of earthquake-induced landslide movement, considering the influences of earthquake action, friction characteristics of sliding surfaces, topographic relief, and morphological changes in landslide masses, a widely applicable quick analysis model for earthquake-induced landslide movement (ELQA) is constructed based on energy conservation. Compared with the existing landslide movement models, this model can consider multiple control factors and significantly improve the applicability of the model. Through comparisons with theoretical analysis, model tests, and actual landslide (Kolka glacial detrital flow), the movement distance of the landslide front edge obtained by the ELQA model matches well with the comparison results, indicating that the ELQA model can reproduce earthquake-induced landslide movement more accurately and quickly. Based on the ELQA model, the influences of control parameters (terrain sampling point density, time step, calculated point number) and calculation parameters (internal friction angle, slip mass volume, terrain concavity, and convexity characteristics) on the calculation results are studied. The results show that control parameters such as terrain sampling point density, time step, and calculated point number have limited influence on the movement characteristics; as long as appropriate values are selected, the error rate can be controlled within 5%. The calculation parameters have a significant influence on the movement characteristics. For example, the influence of the internal friction angle on the coverage area reaches up to 83.75%. The slip mass volume has an insignificant influence on the maximum movement speed, but it on the coverage area reaches approximately 20%. The influence of the terrain concavity and convexity characteristics on the results is less than 10%. The ELQA model is suitable for in-situ estimation and can provide effective suggestions for disaster prevention and reduction in earthquake-prone areas. [ABSTRACT FROM AUTHOR]

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows. [ABSTRACT FROM AUTHOR]

With the launch of very high resolution (VHR) satellites such as the Cartosat-2S series, Cartosat-3, WorldView-series, Pleiades-Neo series in the recent past, high resolution digital surface models (DEMs) with ~1 m grid interval can be generated using high resolution stereo images using the approach of digital photogrammetry. These high resolution DEMs are very handy for applications in city modelling, change detection, disaster management, infrastructure planning and so on. In this study, high resolution DEM is generated at a grid interval of 1 m using Cartosat-2S across-track images (0.65 m resolution) and WorldView-3 in-track stereo images (0.31 m resolution) over a small region over Mumbai. Both the generated DEMs are compared with each other in qualitative and quantitative ways and their relative accuracies are evaluated towards accurate city modelling. [ABSTRACT FROM AUTHOR]

In terms of natural disasters, lightning is India's most devastating threat, causing over 2500 casualties per year, according to the latest annual report of the National Crime Record Bureau. When compared to numbers reported from other parts of the world, this one is significantly higher. Bihar ranks as one of the most vulnerable Indian states in terms of lightning-related casualties and injuries. Lightning-related casualties and injuries in lightning hotspots in Bihar, India, are analysed and presented for the first time using the casualty and injury data received from the field offices of the Disaster Management Department, Government of Bihar. Also, the spatial and temporal patterns and causes of lightning deaths in the state of Bihar have been linked to the intra-annual cloud-to-ground lightning strike frequency distribution. During 2017–2022, on average, there were 271 human casualties and 57.2 lightning injuries every year because of lightning in the small state of Bihar. The casualty rate per million per year was 2.65 during the period under study, which is higher than India's average (2.55). Lightning-related damages peaked from May to September, with June and July having the most (58.8% of total casualties and 59.43% of total injuries). Most of these casualties and injuries (about 76.8%) caused by lightning occurred from 1230 to 1830 IST. Several hotspots, mainly in the southern and eastern parts of the state, have been identified. Most of the casualties occurred in rural settings. Men between the age groups of 11–15 and 41–45 living in rural areas were particularly vulnerable. The authors contend that lightning mitigation actions and education campaigns regarding the risks associated with lightning should be undertaken with urgent priority to reduce the lightning casualty rate in the state of Bihar, India. [ABSTRACT FROM AUTHOR]

Climate change brings intense hurricanes and storm surges to the US Atlantic coast. These disruptive meteorological events, combined with sea level rise (SLR), inundate coastal areas and adversely impact infrastructure and environmental assets. Thus, storm surge projection and associated risk quantification are needed in coastal adaptation planning and emergency management. However, the projections can have large uncertainties depending on the planning time horizon. Excessive uncertainties arise from inadequately quantified ocean-climatic processes that control hurricane formation, storm track, and SLR in time of climate change. For this challenge, we propose an objective-based analytical-statistical approach using the National Oceanic and Atmospheric Administration's (NOAA)'s Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model in scenario analysis of the storm surge impacts. In this approach, synthetic hurricanes (wind profile and track direction) are simulated to yield the likely range of the maximum envelope of water (MEOW), the maximum of the maximum (MOM), local wind speed, and directions. The surge height and time progression at a location are analyzed using a validated SLOSH model for a given adaptation or planning objective with a set of uncertainty tolerance. We further illustrate the approach in three case studies at Mattapoisett (MA), Bridgeport (CT), and Lower Chesapeake Bay along the US Atlantic coast. Simulated MOMs as the worst-case surge scenarios defined the long-term climate risk to the shoreside wastewater plants in Bridgeport and environmental assets in the Lower Chesapeake Bay. The wind-surge probability envelopes in simulated MEOWs provide location-specific estimates of the storm surge probability for local adaptation analysis at four locations in Lower Chesapeake Bay and at Mattapoisett of the southeastern Massachusetts coast. Using the constraints of local bathymetry and topography, the wind-surge probability curves and time progression also provide quantitative probability estimates for emergency response planning, as illustrated in the Mattapoisett case study. [ABSTRACT FROM AUTHOR]

Introduction. The COVID-19 pandemic has had a significant impact on healthcare professionals globally, with nurses facing diverse challenges at the forefront. Despite their resilience, nurses are experiencing emotional burdens, which have contributed to a growing intention to abandon the profession. Understanding these factors is crucial for addressing the global nursing shortage. Methods. A qualitative descriptive approach was utilized for this study. Nurses who were actively working during the last waves of the pandemic in Catalonia, Spain, were intentionally recruited through social media and personal contacts, and data were collected through online semistructured interviews until data saturation was reached. Data were analyzed using Braun and Clarke's thematic analysis method. Results. Fourteen nurses, with an average of 22.8 years of work experience, were interviewed. Thematic analysis revealed three main themes: (1) the impact of COVID-19 on health, (2) factors influencing the decision to stay, and (3) recommendations to improve crisis management. Conclusion. Nurses faced significant emotional impacts but demonstrated dedication and resilience. Their decision to persevere was influenced by factors such as responsibility, guilt, and economic stability. Urgent measures are necessary to provide tailored mental health support and recognize emotional challenges in crisis preparedness. [ABSTRACT FROM AUTHOR]

Background: Neutrophils are granulocytes with essential antimicrobial effector functions and short lifespans. During infection or sterile inflammation, emergency granulopoiesis leads to release of immature neutrophils from the bone marrow, serving to boost circulating neutrophil counts. Steady state and emergency granulopoiesis are incompletely understood, partly due to a lack of genetically amenable models of neutrophil development. Methods: We optimised a method for ex vivo production of human neutrophils from CD34+ haematopoietic progenitors. Using flow cytometry, we phenotypically compared cultured neutrophils with native neutrophils from donors experiencing emergency granulopoiesis, and steady state neutrophils from non-challenged donors. We carry out functional and proteomic characterisation of cultured neutrophils and establish genome editing of progenitors. Results: We obtain high yields of ex vivo cultured neutrophils, which phenotypically resemble immature neutrophils released into the circulation during emergency granulopoiesis. Cultured neutrophils have similar rates of ROS production and bacterial killing but altered degranulation, cytokine release and antifungal activity compared to mature neutrophils isolated from peripheral blood. These differences are likely due to incomplete synthesis of granule proteins, as demonstrated by proteomic analysis. Conclusion: Ex vivo cultured neutrophils are genetically tractable via genome editing of precursors and provide a powerful model system for investigating the properties and behaviour of immature neutrophils. [ABSTRACT FROM AUTHOR]

Background: Following the COVID-19 pandemic declaration, the World Health Organization recommended suspending neglected tropical diseases (NTD) control activities as part of sweeping strategies to minimise COVID-19 transmission. Understanding how NTD programs were impacted and resumed operations will inform contingency planning for future emergencies. This is the first study that documents how South-East Asian and Pacific NTD programs addressed challenges experienced during the COVID-19 pandemic. Methodology/Principal findings: Data was collected through semi-structured interviews with 11 NTD Program Coordinators and related personnel from Fiji, Papua New Guinea, The Philippines, Timor-Leste, and Vanuatu. Constructivist grounded theory methods were drawn on to generate an explanation of factors that enabled or hindered NTD program operations during the COVID-19 pandemic. The COVID-19 pandemic disrupted NTD programs in all countries. Some programs implemented novel strategies by partnering with services deemed essential or used new communications technology to continue (albeit scaled-back) NTD activities. Strong relationships to initiate cross-program integration, sufficient resources to implement adapted activities, and dedicated administrative systems were key enabling factors for recommencement. As the COVID-19 pandemic continued, exacerbating health resources scarcity, programs faced funding shortages and participants needed to find efficiencies through greater integration and activity prioritisation within their NTD units. Emphasising community-led approaches to restore trust and engagement was critical after widespread social anxiety and disconnection. Conclusions/Significance: Sustaining effective NTD programs during a global emergency goes beyond managing immediate activity disruptions and requires attention to how NTD programs can be better ensconced within wider health programs, administrative, and social systems. This study underscores the importance of pre-emergency planning that reinforces NTD control programs as a critical service at all health systems levels, accompanied by governance arrangements that increase NTD staff control over their operations and strategies to maintain strong community relationships. Ensuring NTD units are supported via appropriate funding, personnel, and bureaucratic resources is also required. Author summary: Disruptions to health services during the COVID-19 pandemic may delay progress in neglected tropical diseases (NTDs) control by several years. Current studies of how NTD programs responded to the challenges arising from the COVID-19 pandemic are largely from African settings and focus on earlier periods during the pandemic when policy advice was most stringent. In contrast, this study documents how NTD staff and other related personnel in five South-East Asian and Pacific countries adapted to changing situations throughout the pandemic. We found a difference in the strategies NTD programs initially used to manage the immediate challenges of strict new public health measures compared to those used later in the pandemic's evolution when seeking to recommence usual programs. Initially, some NTD programs devised new ways of offering modified services, primarily though cross-program integration with other health services that were allowed to continue operating. When operations re-commenced, NTD programs faced financial challenges that meant human and resource efficiencies in these units needed to be found. The cumulative impact of pandemic-related restrictions and misinformation eroded community trust in health programs more broadly and required NTD staff to rebuild local relationships. The lessons learned in these settings may help NTD programs in these and other regions prepare for prolonged future emergencies. [ABSTRACT FROM AUTHOR]

Objective: Given the frequency of disasters worldwide, there is growing demand for efficient and effective emergency responses. One challenge is to design suitable retrospective charts to enable knowledge to be gained from disasters. This study provides comprehensive understanding of published retrospective chart review templates for designing and updating retrospective research. Methods: We conducted a systematic review and text analysis of peer-reviewed articles and grey literature on retrospective chart review templates for reporting, analysing, and evaluating emergency responses. The search was performed on PubMed, Cochrane, and Web of Science and pre-identified government and non-government organizational and professional association websites to find papers published before July 1, 2022. Items and categories were grouped and organised using visual text analysis. The study is registered in PROSPERO (374,928). Results: Four index groups, 12 guidelines, and 14 report formats (or data collection templates) from 21 peer-reviewed articles and 9 grey literature papers were eligible. Retrospective tools were generally designed based on group consensus. One guideline and one report format were designed for the entire health system, 23 studies focused on emergency systems, while the others focused on hospitals. Five papers focused specific incident types, including chemical, biological, radiological, nuclear, mass burning, and mass paediatric casualties. Ten papers stated the location where the tools were used. The text analysis included 123 categories and 1210 specific items; large heterogeneity was observed. Conclusion: Existing retrospective chart review templates for emergency response are heterogeneous, varying in type, hierarchy, and theoretical basis. The design of comprehensive, standard, and practicable retrospective charts requires an emergency response paradigm, baseline for outcomes, robust information acquisition, and among-region cooperation. [ABSTRACT FROM AUTHOR]

Background: Rainfall-induced floods represented 70% of the disasters in Japan from 1985 to 2018 and caused various health problems. To improve preparedness and preventive measures, more information is needed on the health problems caused by heavy rain. However, it has proven challenging to collect health data surrounding disasters due to various inhibiting factors such as environmental hazards and logistical constraints. In response to the Kumamoto Heavy Rain 2020, Emergency Medical Teams (EMTs) used J-SPEED (Japan-Surveillance in Post Extreme Emergencies and Disasters) as a daily reporting tool, collecting patient data and sending it to an EMTCC (EMT Coordination Cell) during the response. We performed a descriptive epidemiological analysis using J-SPEED data to better understand the health problems arising from the Kumamoto Heavy Rain 2020 in Japan. Methods: During the Kumamoto Heavy Rain 2020 from July 5 to July 31, 2020, 79 EMTs used the J-SPEED form to submit daily reports to the EMTCC on the number and types of health problems they treated. We analyzed the 207 daily reports, categorizing the data by age, gender, and time period. Results: Among the 816 reported consultations, women accounted for 51% and men accounted for 49%. The majority of patients were elderly (62.1%), followed by adults (32.8%), and children (5%). The most common health issues included treatment interruption (12.4%), hypertension (12.0%), wounds (10.8%), minor trauma (9.6%), and disaster-related stress symptoms (7.4%). Consultations followed six phases during the disaster response, with the highest occurrence during the hyperacute and acute phases. Directly disaster-related events comprised 13.9% of consultations, indirectly related events comprised 52.0%, and unrelated events comprised 34.0%. As the response phases progressed, the proportions of directly and indirectly related events decreased while that of unrelated events increased. Conclusion: By harnessing data captured by J-SPEED, this research demonstrates the feasibility of collecting, quantifying, and analyzing data using a uniform format. Comparison of the present findings with those of two previous analyses of J-SPEED data from other disaster scenarios that varied in time, location, and/or disaster type showcases the potential to use analysis of past experiences to advancing knowledge on disaster medicine and disaster public health. [ABSTRACT FROM AUTHOR]

Understanding the impact of various climate forcing agents, such as aerosols, on extreme precipitation is socially and scientifically vital. While anthropogenic absorbing aerosols influence Earth's energy balance and atmospheric convection, their role in extreme events remains unclear. This paper uses convective‐resolving radiative‐convective‐equilibrium simulations, with fixed solar radiation, to investigate the influence of absorbing aerosols on extreme precipitation comprehensively. Our findings reveal an underappreciated mechanism through which absorbing aerosols can, under certain conditions, strongly intensify extreme precipitation. Notably, we demonstrate that a mechanism previously reported for much warmer (hothouse) climates, where intense rainfall alternates with multi‐day dry spells, can operate under current realistic conditions due to absorbing aerosol influence. This mechanism operates when an aerosol perturbation shifts the lower tropospheric radiative heating rate to positive values, generating a strong inhibition layer. Our work highlights an additional potential effect of absorbing aerosols, with implications for climate change mitigation and disaster risk management. Plain Language Summary: Aerosols, particles suspended in the atmosphere, can interact with the incoming solar radiation by scattering or absorbing it. Aerosol species that absorb solar radiation generate local warming of the atmosphere. This local warming changes the vertical profile of temperature and by that affects cloud and precipitation development. In this paper we use idealized computer simulations to investigate the effect of absorbing aerosols on precipitation, and specifically on extreme precipitation events in the tropics. We demonstrate that under certain conditions, absorbing aerosols can strongly enhance extreme precipitation even despite reducing the mean. We show that this trend can be explained by a mechanism previously reported for much warmer climate conditions than currently found on Earth, involving heating by radiation of the lower part of the troposphere. These results have implications for climate change mitigation and disaster risk management. Key Points: The effect of absorbing aerosol on extreme precipitation is examined in idealized convective‐resolving radiative‐convective‐equilibrium simulationsAerosol perturbation that shifts the lower tropospheric radiative heating rate to positive values strongly enhances extreme precipitationThis trend is explained by a mechanism reported before for hothouse climate conditions involving a shift into an "episodic deluge" regime [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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