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Objective: To evaluate the perceptions of the coronavirus disease 2019 (COVID-19) initiated workplace strategies implemented in radiation oncology departments across Australia. Methods: A multidisciplinary team from Princess Alexandra Hospital developed a survey to address the impact of the pandemic strategies on areas such as patient care, staff education, well-being, flexible working arrangements, and research. The survey was conducted from November 2020 to April 2021. Results: Out of 210 respondents from seven institutions, 45% reported burnout and 57% experienced work work-related stress. A significant majority of respondents were in favour of continued remote work (86%, 131/153). Radiation oncologists identified administrative or non-clinical work (92%, 34/37), telehealth clinics (32%, 12/37), or radiation therapy planning (22%, 8/37) as suitable for remote work. Additionally, 54% (21/39) of the radiation oncologists plan to use telehealth more frequently, with 67% (26/39) feeling more confident with the technology. The majority (81%, 171/210) of participants favoured continuation of hybrid in-person and virtual meetings. Virtual solutions were adopted for quality assurance activities (72%, 118/165) and 52% (60/116) indicated preference for ongoing utility of virtual platforms. However, 38% (79/210) of the respondents expressed concerns about the negative impact on junior staff training. Conclusion: These findings reveal a strong inclination towards technological advancements and remote work arrangements to enable flexible working conditions. Our study suggests the need for ongoing reforms, focusing on improving clinical service delivery efficiencies and enhancing job satisfaction among clinicians. What is known about the topic? During the coronavirus disease 2019 (COVID-19) pandemic there were significant adaptations required to the delivery of clinical care. What does this paper add? There is a strong preference for ongoing work from home arrangements, especially for administrative and non-clinical work. Most radiation oncologists feel more confident using telehealth and favour its ongoing use post-pandemic. Burnout and stress remain prevalent within the workforce. What are the implications for practitioners? There is a high demand among the radiation oncology workforce for adoption of technological advances to improve work satisfaction including remote access to enable flexible work arrangements. [ABSTRACT FROM AUTHOR]

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response. [ABSTRACT FROM AUTHOR]

The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

Frequent flooding seriously affects people's safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the "7·20" Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation. [ABSTRACT FROM AUTHOR]

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide disasters in the region is facing serious challenges. This article selects the Bailong River Basin located in this region as the research area, and the historical landslide data obtained from high-precision remote sensing image interpretation combined with field validation are used as the sample library. Using machine learning algorithms and data-driven landslide susceptibility assessment as the methods, 17 commonly used models and 17 important factors affecting the development of landslides are selected to carry out the susceptibility assessment. The results show that the BaggingClassifier model shows advantageous applicability in the region, and the landslide susceptibility distribution map of the Bailong River Basin was generated using this model. The results show that the road and population density are both high in very high and high susceptible areas, indicating that there is still a significant potential landslide risk in the basin. The quantitative evaluation of the main influencing factors emphasizes that distance to a road is the most important factor. However, due to the widespread utilization of ancient landslides by local residents for settlement and agricultural cultivation over hundreds of years, the vast majority of landslides are likely to have occurred prior to human settlement. Therefore, the importance of this factor may be overestimated, and the evaluation of the factors still needs to be dynamically examined in conjunction with the development history of the region. The five factors of NDVI, altitude, faults, average annual rainfall, and rivers have a secondary impact on landslide susceptibility. The research results have important significance for the susceptibility assessment of landslides in the complex environment of human–land interaction and for the construction of landslide disaster monitoring and early warning systems in the Bailong River Basin. [ABSTRACT FROM AUTHOR]

Estimating landslide dynamics is vital for the prevention of landslide disasters. Differential interferometric synthetic aperture radar (DInSAR) based on L-band SAR satellites is an effective tool for estimating the dynamics of forested landslides that occur in Japan. High-temporal L-band SAR observations have been planned for the future. Thus, it is necessary to further investigate the specific advantages of high-temporal L-band SAR observations for estimating landslide dynamics. In this study, we used DInSAR data with different time windows to identify active landslides in Hokkaido, Japan. This study is the first attempt to demonstrate the advantages of high-temporal L-band SAR observations for estimating active landslide dynamics. We successfully observed the dynamics of two active landslides, Kounai-1 and Kounai-2, using DInSAR over a time window of 14 days. We present the first spatial observation of the dynamics of Kounai-1 and Kounai-2. In addition, we discuss the dynamics of Kounai-1 and Kounai-2 based on interferograms, and our results suggest that both landslides are subunits of the same landslide, called the Kounai landslide. These results indicate that high-temporal L-band SAR observations can mitigate cycle slips and enable the estimation of active landslide dynamics. [ABSTRACT FROM AUTHOR]

The Wenchuan earthquake strongly disturbed the earth surface and induced sufficient loose debris in the mountain basin, which supplies sufficient solid materials for debris flow initiation. Recently, a catastrophic debris flow was induced by strong rainfall on June 26, 2023 in the Banzi basin. Inconsistent with the first few years of the Wenchuan earthquake, the hillslope landslide sediment supply for debris flow occurrence is limited after 15 years of the Wenchuan earthquake, and the materials supplied for debris flow in the later period are mainly obtained from alluvium deposited along the channel. Simultaneously, this debris flow was triggered by flash flood through strongly erosion channel materials. However, although 15 years have passed since the Wenchuan earthquake, the triggering rainfall conditions have still not returned to pre-earthquake level. This debris flow first initiated in the upper branches of the basin and then gradually transported downstream at an average discharge of 806.99 m3/s, and approximately 82.80 × 104 m3 solid materials were transported out the Banzi basin to form a debris flow fan with length of 120 m and width of 260 m in the Minjiang River, which poses a serious threat to the resettled population and destroy the reconstruction infrastructures. Importantly, there are still sufficient solid materials deposited in the basin, this could result in debris flow activity continuing for an unpredictable time. Therefore, implementation early warning, prevention, and mitigation measures in this basin are still important for debris flow disaster management in the later period of Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

Marching band (MB) artists are subject to exertional heat illnesses (EHIs) similar to other active groups like laborers and athletes. Yet, they are an understudied population with no evidence-based heat safety guidelines. Presented here is a case study of the 233rd annual Bristol, RI Independence Day Parade in 2018 that resulted in over 50 EHIs, including 25 from the Saint Anthony Village marching band (MB) from suburban Minneapolis, MN. This research aims to identify the contributing factors that led to the large number of EHIs, as well as guide ensuring the safety of MB artists in future events. A human heat balance model in conjunction with local weather data was used to simulate heat stress on MB artists. Three modeling scenarios were used to isolate the roles of clothing (band uniform vs. t-shirt and shorts), weather (July 4, 2018 vs. 30-year climatology), and metabolic rate (slow, moderate, and brisk marching pacing) on heat stress. The results identify several key factors that increased heat stress. The meteorological conditions were unusually hot, humid, and sunny for Bristol, resulting in reduced cooling from evaporation and convection, and increased radiant heating. Behavioral factors also affect heat stress. The full marching band uniforms reduced evaporative cooling by 50% and the activity levels of marching 4 km over several hours without breaks resulted in conditions that were uncompensable. Finally, it is speculated that a lack of acclimatization for participants from cooler regions may have exacerbated heat-related impacts. These findings highlight several recommendations for MB directors and race organizers, including the use of summer uniforms for anticipated hot conditions, and advance parade planning that includes providing shade/hydration before and after the parade for participants, considering cooler routes that reduce radiant heating and preparing for anticipated heat-related health impacts appropriate for anticipated hot conditions. [ABSTRACT FROM AUTHOR]

The intensities and frequencies of extreme rainstorms are increasing, which may result in severe inundation of urban metro systems. Although there is some risk assessment research on regional metro systems based on spatiotemporal data, the characteristics of specific metro stations and shortcomings in the emergency response process need more consideration. In this paper, a risk analysis model for rainstorm inundation in metro systems based on a Bayesian network and a practical case study are proposed. First, the risk factors are obtained by integrating general mechanism analysis and the case study. Second, an event evolution diagram is established to represent the comprehensive evolution process of a potential event. Third, the risk analysis model is established using a Bayesian network model considering the quantitative causal relationships between risk factors. This model is used to analyze the risk of supporting emergency management, including emergency preparation based on critical risk factor sensitivity identification, prewarning response strategy development based on risk analysis as rainstorms occur, and rescue strategy development based on risk analysis as rainstorm water flows into metro tunnels. Furthermore, this model can be flexibly improved as natural hazards and metro systems change and as new problems are exposed in practical cases. [ABSTRACT FROM AUTHOR]

Background Aim Methods Results Conclusion Implications for nursing and health policy Children are a particularly vulnerable group in natural disasters such as earthquakes, and although they represent a difficult group to research in such situations, there appears to be a lack of literature investigating children's experiences in the immediate post‐earthquake period. Experienced nurses can shed light on children's experiences.This study was conducted to examine the experiences of nurses caring for children affected by the earthquake in Kahramanmaraş, Turkey.This phenomenological study was conducted with nurses who provided care to children during the Kahramanmaraş earthquake between May and August 2023. The study sample consisted of 14 nurses selected using a purposive sampling technique. Data were collected through in‐depth interviews by using the “Introductory Information Form” and “Semi‐structured Interview Form” developed by the researchers. The interviews were subsequently analyzed using Colaizzi's method.The results revealed five themes under two main themes. Under the main theme of nurse, there are subthemes of chaos, helplessness‐incompetence, while under the main theme of child, there are subthemes of unresponsiveness, intertwined needs, empathy, and care. The study also highlights the chaotic environment in the immediate aftermath of the earthquake, characterized by a high number of pediatric casualties and psychological trauma, and the nurses' feelings of helplessness as a result of being separated from their families.This study found that nurses caring for children in the earthquake had difficulties in managing children's hospital processes and they demonstrated an empathic approach by trying to communicate with the children in their care. The nurses stated that children should be supported psychologically as well as having their basic needs such as water, food, and shelter met.Health policymakers should be aware that in the early stages of major disasters such as earthquakes, children are not only physically injured but also have basic needs such as water, shelter, and food, and are emotionally affected. It is recommended that disaster management plans be developed to address all children's needs and nurses be provided with psychological support and training to improve their knowledge and skills. [ABSTRACT FROM AUTHOR]

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the efficiency of landslide extraction and explore the spatial-tem‐ poral distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management. Methods: Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter auto‐ matic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020. Results: In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost. Conclusions: From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distri‐ bution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’ s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage. [ABSTRACT FROM AUTHOR]

Objectives: The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief. Methods: To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm. Results: (1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions. Conclusions: The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network. [ABSTRACT FROM AUTHOR]

If progress is to be made toward improving geohazard management and emergency decision-making, then lessons need to be learned from past geohazard information. A geologic hazard report provides a useful and reliable source of information about the occurrence of an event, along with detailed information about the condition or factors of the geohazard. Analyzing such reports, however, can be a challenging process because these texts are often presented in unstructured long text formats, and contain rich specialized and detailed information. Automatically text classification is commonly used to mine disaster text data in open domains (e.g., news and microblogs). But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order. These deficiencies are most obviously exposed in long text fields. Therefore, this paper uses the bidirectional encoder representations from Transformers (BERT), to model long text. Then, utilizing a softmax layer to automatically extract text features and classify geohazards without manual features. The latent Dirichlet allocation (LDA) model is used to examine the interdependencies that exist between causal variables to visualize geohazards. The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards. Moreover, it can help users visualize causes, processes, and other geohazards and assist decision-makers in emergency responses. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to reveal the spatial and temporal characteristics of drought risk in the central arid zone of Ningxia, and to provide scientific basis for agricultural production, agricultural disaster prevention and control, planting structure adjustment and agricultural irrigation guidance in the central arid zone of Ningxia. [Method] The meteorological observation data of the five meteorological stations including Shapotou, Zhongning County, Tongxin County, Yanchi County, and Haiyuan County in the middle arid zone of Ningxia were collected, and the temporal and spatial variations of drought levels and the spatial-temporal evolution characteristics of drought risk over the past 39 years (1981—2019) was analyzed and evaluated by calculating the Standardized Precipitation Index (SPI) and combining with GIS technology. [Results] The SPI12 results revealed a fluctuating trend with an intensifying tendency in decennial droughts (the slope of the trend line ranged from-0.004 to 0.003). The SPI3 results demonstrated a significant spatial heterogeneity in the spatial-temporal distribution of seasonal drought risk featured with a prominent characteristics of a low risk in the south and high risk in the north at spatial scales, and continuous drought at temporal scales. The frequencies of drought in spring and winter exceeded 45%. However, the occurrence of drought in summer and autumn was not significant, with a frequency ranging from 10.26% to 43.59%. The evaluation of the temporal and spatial evolution characteristics of drought risk revealed a fluctuating trend of decennial drought risk, initially decreased and then increased. There were remarkable differences in the spatial-temporal distribution of seasonal drought risk, with a characteristic of increasing risk from south to north spatially and higher risk in spring and winter temporally but lower risk in summer and autumn. [Conclusion] The spatial and temporal heterogeneity of seasonal-scale drought risk in the central arid zone of Ningxia is obvious, with high drought risk in Shapotou District and Zhongning County, and drought prevention and control should be strengthened in the future for the spring, fall and winter seasons. [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]

Pre-hospital emergency medical service (PHEMS) is critical for the treatment outcomes of life-threatening injuries and time-sensitive illnesses. Response time, influenced by traffic conditions and the site planning of pre-hospital emergency medical facilities (PHEMFs), is the main indicator for evaluating PHEMS. In 2020, the Beijing government released the "Special Plan for Spatial Layout of Pre-hospital Emergency Medical Facilities in Beijing (2020–2022)". This paper evaluates the functional efficiency and spatial equity of this plan within Beijing's central six districts using isochrone measures to assess the accessibility of the planned PHEMFs. The isochrone coverages of the area and population were calculated, and the temporal-spatial characteristics of isochrones were concluded. The analysis revealed that while the current planning meets several objectives, challenges in service availability and equity persist. Although 10-min isochrone coverage was high, 8-min coverage was insufficient, particularly during peak hours. This highlights gaps in service accessibility that necessitate additional emergency stations in underserved areas. The current planning approach leads to significant overlap at administrative boundaries, causing service oversupply and increased costs, which calls for a city-wide planning perspective that breaks administrative boundaries to optimize resource allocation. Traffic conditions significantly impact service coverage, with congestion reducing coverage in central areas and better coverage near traffic hubs. Future planning should strategically place stations based on traffic patterns and population distribution to enhance emergency medical service accessibility and equity in urban areas. [ABSTRACT FROM AUTHOR]

The present paper argues that the standardised treatment of disaster research and practice perpetuates the production of systematic epistemic injustices against victims of disasters. On the one hand, disaster victims are often prevented from contributing with their opinions and knowledge to the processes of disaster mitigation and disaster conceptualisation. On the other hand, disaster victims tend to lack the hermeneutical resources to make sense of their experiences intelligibly, due to the existence of significant hermeneutical gaps in the hegemonic terminology on the matter. I argue that both forms of epistemic injustice, the testimonial and the hermeneutical, are sustained by an epistemic privilege between the Global North and the Global South in matters of disasters. The second group comprises what I categorise generally as 'disaster victims'. I identify two forms of structural prejudice that operate against disaster victims: one is the 'non-expert' prejudice, and the other is the colonial prejudice. Finally, because of the intercultural nature of disaster environments, I discuss the field of 'multicultural competencies' as a useful form of unveiling and counteracting the epistemic injustices contained in both disaster theory and practice. [ABSTRACT FROM AUTHOR]

Extreme precipitation events induced by tropical cyclones have increased frequency and intensity, significantly impacting human socioeconomic activities and ecological environments. This study systematically examines the spatiotemporal characteristics of these events across Hainan Island and their influencing factors using GsMAP satellite precipitation data and tropical cyclone track data. The results indicate that while the frequency of typhoon events in Hainan decreased by 0.3 events decade−1 from 1949 to 2020, extreme precipitation events have increased significantly since 2000, especially in the eastern and central regions. Different typhoon tracks have distinct impacts on the island, with Track 1 (Northeastern track) and Track 2 (Central track) primarily affecting the western and central regions and Track 3 (Southern track) impacting the western region. The impact of typhoon precipitation on extreme events increased over time, being the greatest in the eastern region, followed by the central and western regions. Incorporating typhoon precipitation data shortened the recurrence interval of extreme precipitation in the central and eastern regions. Diurnal peaks occur in the early morning and late evening, primarily affecting coastal areas. Typhoon duration (CC_max = 0.850) and wind speed (CC_max = 0.369) positively correlated with extreme precipitation, while the pressure was negatively correlated. High sea surface temperature areas were closely associated with extreme precipitation events. The atmospheric circulation indices showed a significant negative correlation with extreme precipitation, particularly in the western and central regions. ENSO events, especially sea surface temperature changes in the Niño 1 + 2 region (−0.340 to −0.406), have significantly influenced typhoon precipitation characteristics. These findings can inform region-specific disaster prevention and mitigation strategies for Hainan Island. [ABSTRACT FROM AUTHOR]

Few studies have reported nationwide changes in dental visit patterns during the COVID-19 pandemic. This study aimed to clarify the impact of the COVID-19 pandemic on the type of dental treatment in Japan based on trends in the frequency of dental clinic visits per month and dental expenses per month or day. An interrupted time-series analysis was performed to examine changes in data from the national database before and after the first declaration of a state of emergency. Between March and April 2020, the monthly dental expenses per patient (MDEPP) increased by approximately JPY 950 (JPY) (p < 0.001) in patients aged ≤64 years and ≥65 years. Dental expenses per treatment (DEPT) increased by approximately JPY 300–400 (p < 0.001) in both age groups. After April 2020, the slope of the regression line for DEPT significantly increased in those aged ≤64 years (p < 0.001) and in those ≥65 years (p = 0.024). The number of treatments per month (NTPM) increased after the declaration of a state of emergency. However, the decreasing trend in the regression line for NTPM was stronger in those aged ≤64 years (p < 0.001). The MDEPP, DEPT, and NTPM showed an increasing trend during the first state of emergency compared to those observed in the previous year. The proportion of patients with relatively severe disease may have increased because patients with mild disease refrained from undergoing dental examinations. These results suggest changes in the type of dental treatment during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Increasing levels of urbanisation and the rapid growth of modern cities require that particular attention be paid to ensuring the safety and protection of living conditions for their inhabitants. In this context, natural and human-induced disasters pose a major threat to the safety and normal operational procedures of buildings and infrastructures. In consequence, disaster management and built assets operations demand modern tools to be effectively prepared in order to better respond to such critical events. This study explores the potential of artificial intelligence in these operational fields by developing a deep learning model that is able to provide a rapid assessment of an asset's structural condition in the case of a seismic excitation. The proposed simulation model makes an accurate prediction of the damage status of individual elements in a built asset, thus leading to operational improvements across all disaster management phases. In addition, the above development integrates the deep learning algorithm into building information modelling and then uploads the graphical information to a web dashboard. By following the framework proposed, an integrative model is designed that provides a visual and user-friendly interface that allows different stakeholders to navigate and comprehend essential information on the effects of a disaster; thus enabling quicker decision making and strengthening operational resilience in critical events. [ABSTRACT FROM AUTHOR]

What are the consequential socio‐spatial vulnerabilities of fragmentation in flood risk governance (FRG) functions in Tana River County, Kenya? To answer the question, this paper utilises social differentiation theory to establish types and consequences of such fragmentation. It applies theoretical patterns and processes of social differentiation to expose that FRG diversification could result in fragmentation of FRG functions. The study employs an interview strategy that focuses on governmental and non‐governmental FRG actors. The results reveal that fragmented FRG functions in Tana River County was not an intentional policy decision, but rather a product of a transitional vacuum between policy promulgation and implementation. Furthermore, socio‐spatial vulnerabilities differentiated by patterns and processes related to age, gender, disability, and ethnicity, for instance, are consequential outcomes of fragmented FRG functions in the Tana River floodplains. The study recommends implementation of the National Disaster Management Policy in Tana River County and institutionalisation of a specific FRG policy and legal framework. [ABSTRACT FROM AUTHOR]

Landslides triggered by extreme rainfall events often cause losses of life, property damage, and environmental alterations. While past studies have assessed landslide hazards using various indices, how to select rainfall indices in rainfall-induced landslide hazard assessment is still a challenge due to the spatiotemporal asymmetry of rainfall indices. In this study, we employed three machine-learning models, namely the Random forest (RF), Support vector machine (SVM), and logistics regression models, and developed an extreme rainfall index-based model to evaluate rainfall-induced landslide hazards. To eliminate the effect of spatiotemporal asymmetry in indices, we selected six extreme rainfall indices that are highly correlated with rainfall-induced landslides and tested 63 combinations. Over the past four decades, extreme rainfall events have become more frequent and intense. Both the number and type of rainfall indices affected the assessment results of landslides in the study area. The RF model showed a better accuracy in landslide hazard assessments than did the other two models. To better predict rainfall-induced landslide hazards, an optimal model based on three extreme rainfall indices, i.e., PSSPTOT, R25mm, and Rx5day, was proposed for the study area. With climate change, the study area may encounter more intense rainfall events and experience high levels of rainfall-induced landslide hazards. Compared to the baseline, landslide hazards in the study area are projected to increase by 9.9% and 11.9% in the 2030s (2021–2050). Areas with high- and very high- levels of landslide hazards will account for more than 50% of the study area and will be mainly distributed in the central and eastern parts of the study area. This study suggested an optimal combination of extreme precipitation indicies and provided scientific information about rainfall-induced landslide hazard management under climate change. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive analysis of the influence of tropical cyclones on precipitation variations in Indochina, examining Vietnam, Laos, and Cambodia, while exploring their connection with evolving climatic variables. Covering a span of four decades (1979–2021) and integrating daily precipitation records with climatic datasets, the research elucidates tropical cyclone's contributions to the annual precipitation across distinct regions, revealing percentages of 27%, 16%, and 6% in Vietnam, Laos, and Cambodia, respectively. Spatial distribution mapping highlights concentrated intensities in central Vietnam, central Laos, and southern Cambodia. Additionally, an upward trend in Vietnam's precipitation, as a representative measure of the entire region, is observed over the study duration, while its variability exhibits marginal correlations with inter-annual and decadal-scale climatic indices. The upward trend aligns with increased precipitable water over Indochina and open oceans, increased sea surface temperatures, reinforced atmospheric low-pressure systems, and intensified westerly wind patterns post-2000. These findings underscore the complex interplay between climate variables and Indochina's precipitation dynamics, suggesting implications for disaster management and strategies to adapt to climate change. [ABSTRACT FROM AUTHOR]

Droughts pose serious threats to the agricultural sector, especially in rainfed-dominated agricultural regions like those in Argentina's Humid Pampas. This region was recently impacted by slow-evolving and long-lasting droughts as well as by flash droughts, resulting in losses reaching thousands of millions of US dollars. Improvements of drought early warning systems are essential, particularly given the projected increase in drought frequency and severity over southern South America. The spatial and temporal relationship between precipitation deficits, soil moisture and vegetation health anomalies are crucial for better understanding and representation of the agricultural droughts and their impacts. In this context, the Combined Drought Indicator (CDI) considers the causal and time-lagged relationship of these three variables. The study's objective is twofold: (1) Analyze the time-lagged response between precipitation deficits, soil moisture and satellite fAPAR anomalies; and (2) Evaluate the CDI's capability to characterize the severity of drought events on the Humid Pampas against agricultural yield estimations and simulations, as well as agricultural emergency declarations. The correlation among the variables shows strong spatial variability. The highest Pearson correlation values (r > 0.42) are observed over parts of the Humid Pampas for time lags of 0, 10, and 20 days between the variables. Although the CDI has limitations, such as its coarse spatial resolution and monthly temporal resolution of precipitation data, it effectively tracks the progression of major drought events in the region. The CDI's performance aligns well with estimations and simulations of soybean and corn yields, as well as official declarations of agricultural emergencies. Insights from this study also provide a basis for discussing potential improvements to the CDI. This study highlights the global and regional significance of evaluating and enhancing the CDI for effective drought monitoring, emphasizing the role of collaborative efforts for future advancements in drought early warning systems. [ABSTRACT FROM AUTHOR]

The impacts of climate change are accelerating worldwide, but emergency agencies and political bodies are not always equipped to anticipate where, when, or how severe the next unnatural disaster will be. Amid megafire seasons, for example, scientists are revising models of fire behavior that were calibrated to natures that increasingly diverge from known baselines and trends. Emergency responders' trust in patterns has become an occupational hazard. At these edges of knowledge, struggles to maintain responsive capacity in disrupted ecologies are at play; a larger reckoning with runaway climate change as a relational problem space is in order. Rather than making me resort to despair about the world's "end," such labors redirect my attention toward horizons of expectation in which knowledges are still actionable, not obsolete, and where capacities for future interventions are viable and not denied. These activities, enmeshed in a rubric of horizon work, shift expert authority and promote critical realignments across political, activist, and Indigenous spheres. [ABSTRACT FROM AUTHOR]

The establishment of acceptable risk standards serves as a crucial connection between earthquake disaster risk assessment and risk management. Currently, there lacks a standardized criterion for determining the acceptable level of risk pertaining to earthquake disasters. To establish a standardized system of acceptable risk of earthquake disaster, the data of disaster-affected toll, death toll, and direct economic losses resulting from earthquakes disaster with MS ≥ 5.0 in Chinese Mainland between 1991 and 2020 are utilized. These data are employed to construct the acceptable life risk F–N curve (the accumulated probability F of annual death toll ≥ N—death toll N), the acceptable economic risk F–D curve (the accumulated probability F of annual direct economic losses ≥ D—direct economic losses), and the acceptable comprehensive risk F–L curve (the accumulated probability F with earthquake occurrence of annual disaster degree ≥ L—disaster degree). Then, the acceptable risk level of earthquake disasters with MS5.0–5.9, MS6.0–6.9, MS ≥ 7.0, and MS ≥ 5.0 are determined. Moreover, the classification and consistency of acceptable types of earthquake disaster comprehensive risk, life risk and economic risk in the past 30 years are compared. Through analysis, the acceptable life risk, acceptable economic risk, and acceptable comprehensive risk criteria of earthquake disasters with MS5.0–5.9, MS6.0–6.9, MS ≥ 7.0, and MS ≥ 5.0 in Chinese Mainland are obtained. It is found that the comprehensive risk of earthquake disasters with different magnitudes is mainly at an unacceptable level, while the main types that are consistent with life risk and economic risk are tolerable level and acceptable level, respectively. The research results can provide more comprehensive theoretical data and practical basis for the effective implementation of earthquake disaster risk management. [ABSTRACT FROM AUTHOR]

This study provided a comprehensive understanding of the source process of the 1819 M 7.7 Kachchh Indian earthquake using physics‐based dynamic rupture modeling and strong ground motion simulations. We successfully simulated the spontaneous dynamic rupture along a curved non‐planar fault using the 3‐D curved‐grid finite‐difference method (CGFDM). The estimated earthquake magnitude is around 7.6, consistent with previous estimations. Our simulations accurately replicated macroscopic rupture patterns and surface deformation, showing agreement with observed data along the Allah Bund fault (ABF) with a maximum displacement ∼5.5 m at the Earth's surface. The maximum modeled coseismic slip on the fault was approximately 7.5 m. Notably, the ABF exhibited characteristics of a weak barrier (leaky barrier) at the bending part, allowing the rupture to propagate further. Despite limitations in surface deformation calculations, the modeled values aligned with the trend of surface fault slip, with a slight deviation in the epicenter toward the east compared to earlier studies. We observed a homogeneous principal stress oriented N25°E, consistent with the present day Indian plate motion. The estimated horizontal peak ground velocities (PGVh) and the maximum value of Intensity X+ aligns well with observations. Furthermore, conducting thorough case studies on significant earthquakes and potential seismic scenarios in stable continental regions is crucial. Such studies play a vital role in validating and improving dynamic rupture models. When combined with statistical methods, this research holds great promise for advancing seismic hazard assessments, earthquake engineering, and strategies for disaster management. Plain Language Summary: This paper is centered around the simulation of the dynamic rupture of the 1819 M 7.7 Kachchh earthquake in India. We have successfully replicated the earthquake's behavior using a three‐dimensional simulation method. The study's results demonstrate the significant influence of the local tectonic setting and non‐planar fault structure on earthquake generation and rupture progression. Although slight discrepancies exist between the simulation results and actual observations, the simulations capture significant trends and reproduce macroscopic rupture patterns. The estimated magnitude of the earthquake aligns well with previous studies. The study highlights the role of fault bending and its impact on surface deformation, contributing to a better understanding of seismic hazards and providing insights for seismic hazard assessments and earthquake source characterization. This work is valuable for comprehending earthquake sources, particularly from earthquake perspectives in the SCR region and other areas. Key Points: Dynamic rupture simulations of the 1819 Kachchh M 7.7 earthquake replicate co‐seismic fault slip, enhance our knowledge of the earthquake sourceWeak barrier characteristics observed at the bending part of the fault enabled further rupturing, influenced by SH and the nucleation pointContributes to understanding earthquake hazards, enhancing seismic assessment, engineering, and disaster management strategies [ABSTRACT FROM AUTHOR]

The abnormal deformation of urban surfaces threatens the human living environment, and extreme regional weather can affect the response law of surface deformation. To explore the changes in surface time series response after extreme weather and the causes of deformation in Zhengzhou, the MTInSAR was used to obtain the surface deformation from 2020 to 2022, and the time series changes of groundwater equivalent water height were retrieved by GRACE. The results show that: (a) There are three large subsidence bowls in Zhengzhou, and the maximum subsidence rate is −40.2 mm/yr. (b) The extreme rainstorm in Zhengzhou alleviated the surface deformation quickly, lasting approximately 6 months. However, surface subsidence still occurred after the extreme rainstorm. The water storage coefficient of the elastic skeleton in the Zhengzhou area showed an increasing trend. (c) Precipitation can lead to surface uplift by influencing the change of groundwater level. There is a delay time of 0.75–1 month between groundwater level change and surface uplift response in the characteristic region. These results provide scientific data support and causal analysis for disaster prevention and reduction of abnormal deformation in Zhengzhou. Key Points: Impact of extreme rainstorm on surface in Zhengzhou from 2020 to 2022There are three subsidence bowls in Zhengzhou area, and the uneven deformation is obviousExtreme rainstorm weather can alleviate the occurrence of surface subsidence in short term [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) content is a crucial parameter for CBM exploitation and disaster prevention, with accurate gas loss calculation being essential for improving measurement precision. To investigate the physical response to gas loss and quantify the impact and interrelation of various factors, this study developed a comprehensive model incorporating the effects of gas diffusion, temperature, and pressure effects on porosity and coal deformation. By utilizing the OpenFOAM platform and C++ for numerical solution development and establishing an experimental system for gas loss measurement, the model's reliability was confirmed. The new model demonstrates an 8.56% average error, representing a significant improvement over traditional methods, with error reductions of 26.59% and 18.31% compared to the square root of time and power function methods, respectively. The hierarchy of gas loss is determined to be gas flow > gas-coal deformation coupling > gas-temperature coupling > gas-coal deformation-temperature coupling. Average diffusion contributions are identified as 41.43% from surface diffusion, 30.71% from Knudsen diffusion, and 27.86% from viscous flow. The factors affecting gas loss, in decreasing order of impact, are adsorption equilibrium pressure, temperature, porosity, moisture, and adsorption constants. These findings provide theoretical support for enhancing gas content measurement accuracy and techniques, significantly benefiting CBM extraction and mine safety. [ABSTRACT FROM AUTHOR]

After a check dam is filled with sediment, the debris flow will flow out from the spillway. Insufficient discharge capacity of the spillway can lead to debris flow scouring the dam's shoulder and foundation. Therefore, it is necessary to study the discharge capacity of the spillway. It is of great importance for further optimizing the structural design of the check dam. This article analyzes discharge characteristics of debris flow of a spillway through a series of flume experiments. According to the motion states, it can be divided into five stages: contact stage of the spillway, flushing and climbing stage, increasing and reflux stage, stable discharge stage, and final discharge stage. We analyzed the influencing factors of the discharge capacity, including the type of spillway, bulk density of debris flow, scale of debris flow, flume gradient, and effective opening width of the spillway. Furthermore, we derived a calculation formula of discharge for the spillway based on Bernoulli's principle. Finally, we considered the influence of the ratio of flow area and Froude number to the discharge coefficient and established a calculation method of discharge. Consequently, we compared the calculated results with the measured results and found them to be in good agreement. Our research results provide a quantitative calculation method for the design of spillways and energy dissipation and erosion prevention projects under check dams, which is beneficial for improving engineering value and for better serving disaster prevention and reduction efforts. [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]

Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km2) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events. [ABSTRACT FROM AUTHOR]

The promotion of community resilience is a multifaceted process that warrants further exploration. This article aims to contribute to this literature stream by assessing the role of citizen science (CS) approaches in the development of community resilience, since CS is considered a promising approach for generating new knowledge by fostering the participation of citizens (non-professional scientists) in research activities. The results show that CS approaches can contribute to community resilience at the institutional level, improving emergency and recovery planning capacity; at the infrastructural level, by contributing to land use planning and urban design; and at the social level, through the creation of community ties and social capital. This contribution is relevant for the development of public policies that are more shaped to local contexts and aligned with communities' needs and expectations. [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]

Background/Aims: Athletic trainers are often the point person when sports‐related traumatic dental injuries (TDIs) occur. The aim of this study was to assess knowledge levels of the management of TDIs among athletic trainers in the Midwest United States, as well as evaluate variables that may influence knowledge levels. Material and Methods: A survey was sent to athletic trainers licensed in Minnesota, Nebraska, and Iowa. The survey included 28 multiple‐choice and fill in the blank questions split into three sections. The three sections included background, emergency management of TDIs, and opinion questions. Results: Of the participants, 100% recalled receiving medical first aid training. However, only 71% recalled receiving formal training on emergency management of dental injuries. Although 75% were confident in managing a dental injury, over 63% of participants scored less than 70% in the knowledge score section. Majority of the participants (98.4%) reported that they believe training on the management of TDIs is important. Athletic trainers working with contact sports were more likely to have experienced managing TDIs more recently than those working in noncontact sports. Conclusions: This study shows the gaps in knowledge among athletic trainers pertaining to management of TDIs and emphasizes the importance of sports community having adequate education on emergency management of such dental injuries. [ABSTRACT FROM AUTHOR]

Introduction: The management of pain following a burn is extremely complex because of the multifactorial nature of burn pain (nociceptive and neuropathic). In the pre-hospital setting and emergency department (ED), the main goal of acute pain management is to reduce the patient's pain, allowing them to maintain function and to prevent the chronification of pain. Opioids are used as first-line treatment in management of burn pain. The aim of our study was to evaluate the efficacy and adverse effects of intravenous (IV) morphine for burn pain management in the ED and to evaluate pain management in the pre-hospital setting. Methods: In this single-center observational study, patients presenting with second- and third-degree burns were enrolled in our ED. Numerical Rating Scale (NRS) and Burn Specific Pain Anxiety Scale (BSPAS) were performed at ED admission and after 1 h. Pain medications administered before arrival in the ED were reported by the rescue team. All patients received IV acetaminophen every 8 h and IV morphine according NRS. Results: Thirty patients were included in this study. At the time of arrival to the ED, > 90% of the patients reported severe pain; 95.8% of them received IV morphine to achieve pain relief. After 1 h, > 65% of patients had NRS < 3. The total amount of IV morphine was 18.12 ± 4.26 mg in the first hour. No adverse events were recorded. The BSPAS on admission to the ED was 34.8 ± 5.6, indicating severe anxiety. After 1 h, BSPAS was 12.8 ± 4.8, indicating mild anxiety. Conclusion: IV morphine used for burn pain management in the emergency setting significantly improves patient outcomes in terms of pain. IV morphine also reduced anxiety scores at 1 h. Plain Language Summary: The management of pain following a burn is extremely complex because of the multifactorial nature of burn pain. The main goal of acute pain management is to reduce the patient's pain, allowing them to maintain function and to prevent the chronification of pain. Opioids are used as first-line treatment in management of burn pain. In this single-center observational study, patients presenting with severe burns were enrolled in our hospital. Pain intensity and anxiety level were evaluated at admission and after 1 h. We evaluated pain treatment using intravenous (IV) morphine. Thirty patients were included in this study. At the time of arrival in hospital, almost all patients reported severe pain and received IV morphine to achieve pain relief. After 1 h, > 65% of patients had no pain. No adverse events were recorded related to morphine administration. The anxiety level improved after pain treatment. Finally, IV morphine used for burn pain management in the emergency setting significantly improves patient outcomes in terms of pain and reduced agitation. [ABSTRACT FROM AUTHOR]

The human community confronts a plethora of disasters, including man-made epidemics like COVID-19, environmental problems such as water and food resource depletion, biochemical warfare, and even threats from human-created artificial intelligence. Consequently, it is appropriate to term our current community a "disaster community". This paper delves into the issue of diaspora, intrinsically linked to the fragmentation problem in the disaster community, where each subgroup tends to focus solely on its own community. This issue is scrutinized by categorizing the diaspora into larger and smaller categories, with cases from both the international and domestic levels examined respectively within each category. Among the many Daoist philosophical concepts, this paper focuses on the Daoist principle of interconnectedness. In examining Daoist thought on interconnectedness, it also confirms that the notion of interconnectedness is being increasingly emphasized in modern society and across various academic disciplines. This perspective affirms the pluralistic nature of existence, while also underscoring the fundamental interconnectedness that underpins the myriad forms and phenomena. It provides a framework for addressing the challenges faced by the disaster community, particularly in relation to issues of diaspora, by emphasizing the need for coexistence and collective responsibility within a web of mutual relations. The philosophy innovatively applied from Daoism emphasizes "recognizing diversity based on Dao, while acknowledging that each existence is interconnected". Here, "recognition of diversity based on Dao" underpins pluralism, and "interconnectedness of all existence" forms the fundamental solution to the diaspora problem. This approach could be extended as a strategic response to various disasters that the human community encounters. Therefore, this paper assesses the current state of the disaster community, the severity of the diaspora issue, and some cases both between and within nations. It also discusses the core of Daoist philosophy and its creative application to overcome these challenges. The disaster community should transition towards a sustainable and open community, and on this journey, by acknowledging our interconnectedness, we can find solutions to not only the disasters we confront but also to the diaspora problem. [ABSTRACT FROM AUTHOR]

The Buddhist "Knotted Thread" originates from the ancient Indian practice of venerating sacred threads. In Indian culture, sacred threads symbolize identity and status and possess functions such as exorcism, healing, disaster prevention, blessing, protection, and divination. Initially, Buddhism opposed the use of sacred threads by its followers. However, as Buddhism evolved, it began to adopt this and other practices. Buddhist threads could be monochromatic or multicolored, with five-colored threads (五色縷) having significant cultural connections to China. In Buddhism, the five colors typically consist of four primary colors (四本色; blue, yellow, red, and white) plus an additional color. The difference between the Buddhist and traditional Chinese five colors lies in the selection of the fifth color. The five-colored threads in Buddhism have various combinations, such as blue, yellow, red, white, and black; blue, yellow, red, white, and purple; and blue, yellow, red, white and green. With Buddhism's spread into China, to align with traditional Chinese notions of five colors, the Buddhist five-colored threads gradually standardized to blue, yellow, red, white, and black. The evolution of the Buddhist five-colored threads reflects the cultural exchange between India and China. [ABSTRACT FROM AUTHOR]

The devastating Chichi earthquake in Taiwan in 1999 highlighted the importance of disaster prevention and relief operations. In response to the challenge of extreme events and compound disasters, the National Science and Technology Center for Disaster Reduction (NCDR) contributes to operations at the Central Emergency Operation Center (CEOC) of Taiwan by providing integrated information and timely suggestions, and it delivers a common operational picture to central and local governments through a decision support system. This study was undertaken to explore the emergency response towards the reconnaissance of earthquake disasters in Taiwan to illustrate how resilient cities ensure rapid crisis response with a focus on the NCDR disaster response decision support system. This study examines the decision support system used following the 2016 ML 6.6 Meinong earthquake. The results demonstrate that the first line of relief for the emergency response commander is data-driven decision-making. [ABSTRACT FROM AUTHOR]

This study compared earthquake location estimation using grid search and manta ray foraging optimization algorithm for synthetic and real earthquakes data from Van, Turkey. Both locating methods worked well, and they achieved similar results. The horizontal coordinates (latitude and longitude) of the earthquake were obtained successfully with both methods, from the inversion of the arrival times calculated from the noisy and noise-free synthetic earthquake data. However, there was some deviation in depth parameter for the noisy data. The location parameters obtained from the inversion of the real earthquake data using grid search and manta ray foraging optimization methods were in accordance with the solutions presented in previous studies. The depth parameters for the Van earthquakes did not fully match those in the previous studies, possibly due to differences in crustal velocity models. The depth parameters obtained for both Van earthquakes using both methods performed in this study are self-consistent at around 24 km. In addition, Disaster and Emergency Management Presidency and German Research Centre seismology centres also reached depth solutions near those in this study. The grid search method has some disadvantages compared with the manta ray foraging method, as it must be applied gradually, and delays reaching a solution. The manta ray foraging method is an easy, fast way to determine the kinematic location of earthquake hypocentres. [ABSTRACT FROM AUTHOR]

This study investigates the complex interplay between climate-related hazards and land cover changes in the Dibalo-Pingit-Zabali-Malayat (DPZM) River Watershed of Aurora province, Philippines. Historical typhoon data underscore the region's vulnerability to extreme weather events, emphasizing the critical need to enhance disaster preparedness and management. Land cover analysis from 1988 to 2020 reveals substantial transformations, driven largely by agricultural expansion at the expense of forest cover. Specifically, closed forest areas decreased by 11% annually from 2010 to 2015, while open forest areas decreased by 31% from 1988 to 2003. Conversely, built-up areas, annual crops, and perennial crops increased by 3%, 9%, and 30%, respectively, reflecting growing human pressures from 1988 to 2003. These land cover changes have intensified watershed degradation, as evidenced by increasing landscape fragmentation and declining mean patch size. Such alterations heighten the watershed susceptibility to natural hazards and hinders its capacity to provide essential ecosystem services. The study underscores the urgency for scienceinformed watershed management strategies to mitigate climate risks and protect the livelihoods of communities reliant on the DPZM River Watershed. Prioritizing sustainable land use practices, forest restoration, and disaster preparedness is crucial for safeguarding the watershed's ecological integrity and resilience in the face of ongoing challenges. [ABSTRACT FROM AUTHOR]

This research highlights that the livelihood systems of individuals residing in disaster-prone regions are undergoing significant changes as a result of explosive eruptions. Volcanic eruptions do significant harm to the infrastructure and ecosystems that serve as vital resources and means of support for populations. The group is very susceptible due to their dependence on natural resources for their survival. This study employs a combined qualitative and quantitative methodology to track the progression of individuals' livelihoods and presents descriptive quantitative data on the distribution of changes in livelihood systems. The findings of this study suggest that the introduction of the horticulture commodity market led to alterations in natural resources. Consequently, a significant number of individuals who were previously engaged in growing essential crops shifted to cultivating agricultural commodities to meet the demands of the market. This research has produced a compilation of community-driven disaster management strategies that are specifically designed for the local context. This research has both theoretical and practical ramifications. In theory, this research provides a substantial addition to the understanding of livelihood systems. The research findings indicate that society has a significant level of adaptability in exploiting resources within its surroundings. This research demonstrates that individuals possess inherent adaptations to endure the ecological strain caused by volcanic eruptions. [ABSTRACT FROM AUTHOR]

The Province of West Sumatra has a high threat of tsunami disaster risk. From the description of this situation, an appropriate and relevant contingency plan is needed to be used effectively when a disaster occurs. This research explains the evaluation of the Province of West Sumatra's tsunami contingency planning policy based on effectiveness and efficiency. The approach used was descriptive qualitative with purposive sampling to determine informants. Meanwhile, the data analysis technique uses the triangulation method. The research results show that in terms of effectiveness, the West Sumatra tsunami contingency plan is doubtful about its preparedness for dealing with this disaster. This is because the necessary steps have not been reviewed to adapt to the threats and resource requirements required. In the efficiency aspect of the West Sumatra tsunami contingency plan policy, the interventions to achieve the objectives have not been implemented optimally. The significance of this research is that it provides an overview of the results of a substantive analysis of policy effectiveness and efficiency, showing the importance of contingency plans that are updated with the latest conditions, both in terms of analyzing resources and disaster impacts and policies that bind stakeholders in handling Tsunami disaster risks. Theoretically, it is to produce an evaluation of public policies related to disaster management. [ABSTRACT FROM AUTHOR]

Geomagnetic activities have a crucial impact on Earth, which can affect spacecraft and electrical power grids. Geospace scientists use a geomagnetic index, called the Kp index, to describe the overall level of geomagnetic activity. This index is an important indicator of disturbances in the Earth's magnetic field and is used by the U.S. Space Weather Prediction Center as an alert and warning service for users who may be affected by the disturbances. Another commonly used index, called the ap index, is converted from the Kp index. Early and accurate prediction of the Kp and ap indices is essential for preparedness and disaster risk management. In this paper, we present a deep learning framework, named GNet, to perform short-term forecasting of the Kp and ap indices. Specifically, GNet takes as input time series of solar wind parameters' values, provided by NASA's Space Science Data Coordinated Archive, and predicts as output the Kp and ap indices respectively at time point t + w hours for a given time point t where w ranges from 1 to 9. GNet combines transformer encoder blocks with Bayesian inference, which is capable of quantifying both aleatoric uncertainty (data uncertainty) and epistemic uncertainty (model uncertainty) in making predictions. Experimental results show that GNet outperforms closely related machine learning methods in terms of the root mean square error and R-squared score. Furthermore, GNet can provide both data and model uncertainty quantification results, which the existing methods cannot offer. To our knowledge, this is the first time that Bayesian transformers have been used for geomagnetic activity prediction. [ABSTRACT FROM AUTHOR]

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Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems and alter the relationship between living organisms and the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause enormous direct damages and inestimable indirect losses, particularly in less developed and more vulnerable regions. Monitoring the spatiotemporal evolution of floods is fundamental to reducing their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor vast areas consistently, and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a large scale. Synthetic aperture radar (SAR) sensors, in particular, have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in some cases, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or no-data areas. In particular, the method relies on simple morphological arguments to expand flooded areas into no-data regions and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service. [ABSTRACT FROM AUTHOR]

Owing to its geographical position within one of the most seismically active zones globally, Iran has experienced numerous historically impactful earthquakes. To finance a part of these losses and reconstruction expenses, earthquake insurance has been offered as a rider on fire insurance policies by Iranian insurers. This mechanism, if operated well, can substantially contribute to disaster risk management. On the other hand, if the pricing and management of catastrophe risk lack sound, risk-modeling-based practices, it might add to the problems and act to the detriment of disaster risk management. In this paper, we first compare the current earthquake insurance pricing and risk management in the Iranian insurance industry to a state-of-the-art insurance regulation in the European Union (Solvency II). Then, we examine the consequences of following each approach in terms of business profitability and viability by conducting a numerical analysis on a hypothetical portfolio of property risks in Iran. In so doing, a seismic risk model has been developed by adopting the Earthquake Model of the Middle East (EMME) and a peer-reviewed vulnerability model and by developing an exposure model for residential dwellings in Iran. The results suggest that modeled earthquake premium rates are about 5 times larger than the rates currently used in the market. Furthermore, a comparison between solvency capital calculated following the methods specified by the European Solvency II policy and the Iranian Directive 69 indicates a visible underestimation of earthquake solvency capital by the Iranian insurers. It seems that maintaining the current insurance pricing and risk management practices in Iran will probably lead to a substantial accumulation of earthquake risk for domestic firms and eventually endanger the solvency of these companies in the event of large-scale earthquake losses in the future. [ABSTRACT FROM AUTHOR]

Accurate prediction and assessment of extreme flood events are crucial for effective disaster preparedness, response, and mitigation strategies. One crucial factor influencing the intensity and magnitude of extreme flood events is precipitation. Precipitation patterns, particularly during intense weather phenomena such as hurricanes, can play a significant role in triggering widespread flooding over densely populated areas. Traditional flood prediction models typically rely on single-source precipitation data, which may not adequately capture the inherent variability and uncertainty associated with extreme events due to certain limitations in the precipitation generation framework, availability, or both spatial and temporal resolutions. Moreover, in coastal regions, the complex interaction between local precipitation, river flows, and coastal processes (i.e., storm tide) can result in compound flooding and amplify the overall impact and complexity of flooding patterns. This study presents an implementation of the global copula-embedded Bayesian model averaging (BMA) (Global Cop-BMA) framework for improving the accuracy and reliability of extreme flood modeling. The proposed framework integrates a collection of precipitation products with different spatiotemporal resolutions to account for uncertainty in forcing data for hydrodynamic modeling and generating probabilistic flood inundation maps. The methodology is evaluated with respect to Hurricane Harvey, which was a catastrophic weather event characterized by intense precipitation and compound flooding processes over the city of Houston in the state of Texas in 2017. The results show a significant improvement in predictive accuracy compared to those based on a single precipitation product (e.g., the Nash–Sutcliffe efficiency (NSE) performance of a single quantitative precipitation estimation (QPE) is in the range of 0.695 to 0.846, while the Cop-BMA yields an NSE of 0.858), demonstrating the merits of the Global Cop-BMA approach. Furthermore, this research extends its impact by generating probabilistic flood extension maps that account not only for the primary influence of precipitation as a flood driver but also for the intricate nature of compound flooding processes in coastal environments. [ABSTRACT FROM AUTHOR]

Effective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using 'risk management' scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning. [ABSTRACT FROM AUTHOR]