The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant e...The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.展开更多
In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statist...In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.展开更多
On December 18,2023,a magnitude 6.2 earthquake struck Jishishan County,Gansu Province,triggering a liquefaction-induced flow slide along the loess-mudstone contact zone and causing significant casualties and property ...On December 18,2023,a magnitude 6.2 earthquake struck Jishishan County,Gansu Province,triggering a liquefaction-induced flow slide along the loess-mudstone contact zone and causing significant casualties and property losses.The event featured low-slope,large-scale,runout distance sliding and exhibited a clear cascading disaster chain.Its characteristics closely resemble the catastrophic mudflow at the nearby Lajia Ruins approximately 4,000 years ago.Using high-resolution oblique photogrammetry,cone penetration testing,surface wave analysis,and horizontal-to-vertical spectral ratio methods,this study examines the stratigraphy,groundwater conditions,and geomechanical properties of the affected zone.Results indicate that saturated loess overlying impermeable mudstone formed a high-moisture mass vulnerable to seismic disturbance.Seismic resonance triggered the liquefaction of weakly structured loess,which slide along the contact interface and evolved into a runout distance mudflow.Underground water and terrain modification created a composite weak zone of saturated loess and softened mudstone,which intensified the disaster chain-from earthquake to liquefaction,flow slide,and mudflow.This study contributes to the understanding of deep-seated liquefaction-flow slide disasters,thereby advancing more effective risk mitigation strategies in the Loess Plateau and comparable loess-covered seismic regions.展开更多
Water-sand gushing(WSG)disasters in confinedaquifers pose significantchallenges to the utilization of deep underground spaces in soft soil areas.Since few studies have considered the impact of confined aquifer thickne...Water-sand gushing(WSG)disasters in confinedaquifers pose significantchallenges to the utilization of deep underground spaces in soft soil areas.Since few studies have considered the impact of confined aquifer thickness and confinedwater pressure on WSG disasters,a novel visual model test system was developed to investigate the influencingcharacteristics and mechanisms of the two aforementioned factors.The test results showed that the WSG process in clay aquiclude-confinedaquifer composite strata exhibits two prominent stages.First,the sand loss zone expands vertically in an ellipsoid shape.Then,it expands horizontally once the ellipsoid reaches the boundary of the clay layer.The sand loss continues until the overlying clay sinks to the bottom to clog the gushing crack,creating a large sinkhole at the surface.Increasing the confinedaquifer thickness can increase the vertical expansion of the ellipsoid and delay the clay-clogging effects,thereby considerably increasing the severity of sand loss,stratum deformation,and surface settlement.An increase in the confinedwater pressure markedly increases the hydraulic gradient along the seepage path,which contributes to increasing the gushing rates of water and sand.As a result,substantial sand loss occurs before the clay clogs the gushing crack,inducing more cracks and deeper sinkholes at the surface.All the aforementioned results provide insights into the effects of confinedaquifer on WSG disasters in clay aquiclude-confinedaquifer composite strata.展开更多
As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Fo...As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Forum’s“Global Risks Report 2023”[2]highlights natural disasters as a major threat to sustainable development,especially for densely populated cities.展开更多
Background: Disaster preparedness is a critical aspect of nursing education, enhancing students’ ability to respond effectively in emergencies. However, the extent to which nursing curricula influence disaster prepar...Background: Disaster preparedness is a critical aspect of nursing education, enhancing students’ ability to respond effectively in emergencies. However, the extent to which nursing curricula influence disaster preparedness awareness remains underexplored. Our study found that 39% of students reported improved awareness after three years, highlighting the need for targeted curriculum enhancements. Purpose: To evaluate changes in disaster preparedness awareness among nursing students over three years of education and identify gaps in current curricula impacting this awareness. Results: Findings indicate that while 39% of students showed improved awareness, significant gaps remain, suggesting the need for a dedicated course on emergency preparedness. Conclusions: This study emphasizes the importance of integrating comprehensive disaster preparedness education within nursing curricula to address these gaps and foster resilience in future healthcare professionals.展开更多
At first glance(一瞥),10-year-old B.Kenit from the coastal town of Visakhapatnam in India looks like any other school-going child,but there is more than meets the eye.Inspired by a tsunami drill conducted in his schoo...At first glance(一瞥),10-year-old B.Kenit from the coastal town of Visakhapatnam in India looks like any other school-going child,but there is more than meets the eye.Inspired by a tsunami drill conducted in his school when he was eight,the third grader be-came a Disaster Risk Reduction(DRR)advocate,educating his fel-low students and community members on early warning,evacua-tion,and search and rescue.展开更多
Snow and freezing disasters are recurrent weather and climate phenomena that affect the world annually.These events exert a significant influence on numerous aspects of life,including transportation,power supply,and d...Snow and freezing disasters are recurrent weather and climate phenomena that affect the world annually.These events exert a significant influence on numerous aspects of life,including transportation,power supply,and daily activities,and result in considerable economic losses.This study aims to provide a comprehensive analysis of the regions affected by these disasters,the preventive and responsive measures employed,recent advancements in key materials,and the challenges encountered.By doing so,we can gain a deeper understanding of the vital role,significant advantages,and untapped potential of key materials for effectively preventing and responding to snow and freezing disasters.Furthermore,promoting research and utilization of these materials not only contributes to the development of the safety and emergency equipment industry but also strengthens the supply of advanced and suitable safety and emergency equipment.展开更多
The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
Based on the site investigation of a lightning stroke accident in a coal mine in Weiyuan County during a strong thunderstorm process on the night of August 10,2024,combined with the investigation data of the accident ...Based on the site investigation of a lightning stroke accident in a coal mine in Weiyuan County during a strong thunderstorm process on the night of August 10,2024,combined with the investigation data of the accident site,the causes of the lightning stroke accident were analyzed,and the corresponding rectification suggestions were put forward.展开更多
The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer cons...The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer considerable potential for automating this task,their practical application in scientific domains is nonetheless constrained by high application programming interface(API)costs and computational resources required for fine-tuning.To address these limitations,a cognition-enhanced instruction framework(CEIF)is proposed,wherein a high-performance teacher model(such as DeepSeek-R1)provides dynamic feedback,prompt refinement,and training data optimization to guide the learning process of low-parameter models.Experimental results demonstrate that the low-parameter models(6B-9B)optimized via the CEIF achieve approximately 85%accuracy in battery literature extraction tasks,approaching the performance of GPT-4 while requiring only a single NVIDIA RTX 3090 GPU.Furthermore,the emergence of an"Aha moment"characterized by rapid performance improvement during specialized learning is observed,offering novel theoretical insights for the design and optimization of domainspecific models.展开更多
The increasing frequency and severity of natural disasters,exacerbated by global warming,necessitate novel solutions to strengthen the resilience of Critical Infrastructure Systems(CISs).Recent research reveals the si...The increasing frequency and severity of natural disasters,exacerbated by global warming,necessitate novel solutions to strengthen the resilience of Critical Infrastructure Systems(CISs).Recent research reveals the sig-nificant potential of natural language processing(NLP)to analyze unstructured human language during disasters,thereby facilitating the uncovering of disruptions and providing situational awareness supporting various aspects of resilience regarding CISs.Despite this potential,few studies have systematically mapped the global research on NLP applications with respect to supporting various aspects of resilience of CISs.This paper contributes to the body of knowledge by presenting a review of current knowledge using the scientometric review technique.Using 231 bibliographic records from the Scopus and Web of Science core collections,we identify five key research areas where researchers have used NLP to support the resilience of CISs during natural disasters,including sentiment analysis,crisis informatics,data and knowledge visualization,disaster impacts,and content analysis.Furthermore,we map the utility of NLP in the identified research focus with respect to four aspects of resilience(i.e.,preparedness,absorption,recovery,and adaptability)and present various common techniques used and potential future research directions.This review highlights that NLP has the potential to become a supplementary data source to support the resilience of CISs.The results of this study serve as an introductory-level guide designed to help scholars and practitioners unlock the potential of NLP for strengthening the resilience of CISs against natural disasters.展开更多
The occurrence characteristics and impacts of agricultural meteorological disasters during the main growth period of potatoes in Ulanqab City were analyzed.According to the development needs of modern potato industry,...The occurrence characteristics and impacts of agricultural meteorological disasters during the main growth period of potatoes in Ulanqab City were analyzed.According to the development needs of modern potato industry,some countermeasures for meteorological services in the disaster prevention and mitigation of potatoes were proposed,such as strengthening intelligent and digital meteorological services,and building a full-chain meteorological service for the entire growth period of potatoes.The aim is to reduce the impact of disasters and increase the yield and quality of potatoes through intelligent and digital meteorological services.展开更多
Disaster risk reduction,an essential function of protected areas(PAs),has been generally overlooked in PA design.Using primates as a model,we designed a disaster risk index(DRI)to measure the disaster sensitivity of p...Disaster risk reduction,an essential function of protected areas(PAs),has been generally overlooked in PA design.Using primates as a model,we designed a disaster risk index(DRI)to measure the disaster sensitivity of primate species.High-conservation-need(HCN)areas were identified by both their richness and number of threatened primate species.We also constructed high-disaster-risk(HDR)areas and climate-sensitive(CS)areas based on a disaster risk assessment and temperature change under climate change.We overlaid HCN and HDR areas to obtain HDR-HCN areas.We defined species conservation targets as the percent of each species’range that should be effectively conserved using“Zonation”.Landslides had the highest DRI(1.43±0.88),but have been overlooked in previous studies.PA coverage in HDR-HCN(30%)areas was similar to that in HCN areas(28%),indicating that current PA design fails to account for disaster risk reduction.About 50%of the HDR-HCN areas overlapped with CS areas.Presently,43%of primate species meet their conservation targets.Fifty-seven of primate species would meet their conservation targets and 67%of primates could benefit from PA expansion if HDR-HCN areas are fully incorporated into PAs.Increasing PA coverage in HDR-HCN areas is essential to achieving both primate conservation and disaster risk reduction.The study calls for integrating disaster risk reduction into PA design guidelines,particularly in regions like the western Amazon,and recommends flexible conservation approaches in other areas.展开更多
To mitigate ecological degradation and improve human well-being,the Chinese Government has implemented the largest disaster resettlement program from 2011 to 2020.Ankang Prefecture,as one of the key regions in Shaanxi...To mitigate ecological degradation and improve human well-being,the Chinese Government has implemented the largest disaster resettlement program from 2011 to 2020.Ankang Prefecture,as one of the key regions in Shaanxi Province where this largescale resettlement program was performed,has provided a model for observing and evaluating the impact of the resettlement project,both within Shaanxi Province and across other regions of China.As a place where a number of protection and development policies converge,the economic and social development of Ankang is confronted with multiple constraints.Measuring livelihood resilience and further evaluating its impact in this region is key to the delivery and output of disaster resettlement programs to improve human well-being.We attempted to empirically examine the significance and impact of livelihood resilience in the context of disaster resettlement.This study expanded the social–ecological system resilience theory to examine rural household livelihood systems.We used the spatial vector method and 657 field research data collected in July 2021 from Ankang Prefecture to measure the livelihood resilience of rural households and elucidate both general and specific aspects.The sustainable household well-being(SHWB)of rural households was measured in five dimensions concerning the Millennium Ecosystem Assessment(MA)report.In econometrics,we used coarsened exact matching(CEM)to stratify the sample and reduce the computational bias.We then applied group regression to test the effect of livelihood resilience on SHWB empirically.The findings indicate that:(1)livelihood resilience is significantly and positively related to SHWB,and it is conducive to the level of well-being;(2)disaster resettlement has a negative effect on SHWB;(3)energy and medical facilities in resettlement infrastructure and services play active roles in SHWB.These results have policy implications for strengthening livelihood resilience and improving human well-being and important implications for livelihood development in rural areas across China and other developing nations.展开更多
This study addresses the limitations of traditional disaster medicine course assessments,including single evaluation formats,delayed feedback mechanisms,and gaps in competency mapping,by developing a diversified asses...This study addresses the limitations of traditional disaster medicine course assessments,including single evaluation formats,delayed feedback mechanisms,and gaps in competency mapping,by developing a diversified assessment system leveraging the Rain Classroom platform.The system incorporates six interconnected evaluation components across the learning cycle:pre-class preparation,pre-class tests,case discussions,skills assessment,post-class tests,and post-class feedback,collectively forming a three-dimensional“cognitive-skill-attitude”assessment framework.In the assessment design,the weighting of practical skill evaluation is elevated to 40%to prioritize the development of students’disaster response competencies.Additionally,an innovative multi-subject evaluation model(“self-peer-teacher”)is implemented within disaster scenario simulations,utilizing standardized scoring rubrics.This methodology not only enables comprehensive performance evaluation but also fosters critical teamwork and reflective practice.Implementation outcomes demonstrated that the system effectively evaluates learning progress through multi-modal assessments,enhances disaster rescue knowledge and skill proficiency,and successfully achieves predefined pedagogical objectives.展开更多
Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree c...Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.展开更多
Background Efficient disaster victim detection(DVD)in urban areas after natural disasters is crucial for minimizing losses.However,conventional search and rescue(SAR)methods often experience delays,which can hinder th...Background Efficient disaster victim detection(DVD)in urban areas after natural disasters is crucial for minimizing losses.However,conventional search and rescue(SAR)methods often experience delays,which can hinder the timely detection of victims.SAR teams face various challenges,including limited access to debris and collapsed structures,safety risks due to unstable conditions,and disrupted communication networks.Methods In this paper,we present DeepSafe,a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset.DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories.Subsequently,Detectron2 is used to precisely locate and outline the victims.Results Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection.The model effectively identified and located victims under the challenging conditions presented in the dataset.Conclusion DeepSafe offers a practical tool for real-time disaster management and SAR operations,significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.展开更多
Landslides represent a growing global challenge,particularly in mountainous and rapidly urbanising regions where environmental degradation and socio-economic vulnerabilities converge.This study investigates the interr...Landslides represent a growing global challenge,particularly in mountainous and rapidly urbanising regions where environmental degradation and socio-economic vulnerabilities converge.This study investigates the interrelationships between Integrated Landslide Disaster Risk Management(ILDRiM)and the United Nations Sustainable Development Goals(SDGs),advancing a systemsbased understanding of landslide risk as a socially constructed and development-driven phenomenon.Drawing on a narrative literature review and a Design Structure Matrix(DSM),the research identifies eight critical drivers of landslide disaster risk:deforestation,climate change,urbanisation,infrastructure development,community vulnerability,exposure to landslides,ineffective governance,and lack of scientific integration into policymaking.The DSM framework systematically maps these drivers against the 17 SDGs to evaluate the nature and strength of their interactions.The analysis reveals significant synergies,where addressing specific drivers supports multiple SDGs,and trade-offs,where risk reduction efforts may inadvertently hinder other development objectives if not implemented inclusively and strategically.Findings underscore the transformative potential of embedding ILDRiM within national and local development frameworks.Prioritising governance reform,scientific innovation,and resilient infrastructure(SDGs 16,17,and 9)is particularly effective for advancing landslide risk reduction while supporting broader sustainability outcomes.The study also highlights the need for anticipatory,cross-sectoral,and community-driven approaches to risk governance.This research offers actionable insights for policymakers,practitioners,and researchers seeking to align disaster risk management with sustainable development planning.It proposes a novel methodology for assessing systemic interlinkages between disaster risk drivers and the SDGs.It calls for further research to refine data integration,address context-specific risks,and strengthen the evidence base for risk-informed development.By operationalising ILDRiM through the SDG framework,this study supports creating more resilient,equitable,and sustainable communities in landslide-prone regions.展开更多
Interdependencies between critical infrastructures and the economy amplify the effects of damage caused by disasters.The growing interest in impacts beyond physical damage and community resilience has spurred a surge ...Interdependencies between critical infrastructures and the economy amplify the effects of damage caused by disasters.The growing interest in impacts beyond physical damage and community resilience has spurred a surge in literature on economic modeling methodologies for estimating indirect economic impacts of disasters and the recovery of economic activity over time.In this review,we present a framework for categorizing modeling approaches that assess indirect economic impacts across natural hazards and anthropogenic disasters such as cyber attacks.We first conduct a comparative analysis of macroeconomic models,focusing on the approaches capturing sectoral interdependencies.These include the Leontief Input-Output(I/O)model,the Inoperability Input-Output Model(IIM),the Dynamic Inoperability Input-Output Model(DIIM),the Adaptive Regional Input-Output(ARIO)model,and the Computable General Equilibrium(CGE)model and its extensions.We evaluate their applicability to disaster scenarios based on input data availability,the compatibility of model assumptions,and output capabilities.We also reveal the functional relationships of input data and output metrics across economic modeling approaches for inter-sectoral impacts.Furthermore,we examine how the damage mechanisms posed by different types of disasters translate into model inputs and impact modeling processes.This synthesis provides guidance for researchers and practitioners in selecting and configuring models based on specific disaster scenarios.It also identifies the gaps in the literature,including the need for a deeper understanding of model performance reliability,key drivers of economic outcomes in different disaster contexts,and the disparities in modeling approach applications across various hazard types.展开更多
文摘The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.
基金supported by the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202307)the Basic Research Fund of CAMS(No.2023Z016)+1 种基金the National Natural Scientific Foundation of China(No.42275037)the Jiangsu Collaborative Innovation Center for Climate Change。
文摘In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.
基金National Natural Science Foundation of China Grant Nos.52378543,52378544 and 52408525the Natural Science Foundation of Heilongjiang Grant No.LH2024E119。
文摘On December 18,2023,a magnitude 6.2 earthquake struck Jishishan County,Gansu Province,triggering a liquefaction-induced flow slide along the loess-mudstone contact zone and causing significant casualties and property losses.The event featured low-slope,large-scale,runout distance sliding and exhibited a clear cascading disaster chain.Its characteristics closely resemble the catastrophic mudflow at the nearby Lajia Ruins approximately 4,000 years ago.Using high-resolution oblique photogrammetry,cone penetration testing,surface wave analysis,and horizontal-to-vertical spectral ratio methods,this study examines the stratigraphy,groundwater conditions,and geomechanical properties of the affected zone.Results indicate that saturated loess overlying impermeable mudstone formed a high-moisture mass vulnerable to seismic disturbance.Seismic resonance triggered the liquefaction of weakly structured loess,which slide along the contact interface and evolved into a runout distance mudflow.Underground water and terrain modification created a composite weak zone of saturated loess and softened mudstone,which intensified the disaster chain-from earthquake to liquefaction,flow slide,and mudflow.This study contributes to the understanding of deep-seated liquefaction-flow slide disasters,thereby advancing more effective risk mitigation strategies in the Loess Plateau and comparable loess-covered seismic regions.
基金financedby the National Natural Science Foundation of China(Grant No.52090083)the Shanghai Rising-Star Program(Grant No.23QB1404800).
文摘Water-sand gushing(WSG)disasters in confinedaquifers pose significantchallenges to the utilization of deep underground spaces in soft soil areas.Since few studies have considered the impact of confined aquifer thickness and confinedwater pressure on WSG disasters,a novel visual model test system was developed to investigate the influencingcharacteristics and mechanisms of the two aforementioned factors.The test results showed that the WSG process in clay aquiclude-confinedaquifer composite strata exhibits two prominent stages.First,the sand loss zone expands vertically in an ellipsoid shape.Then,it expands horizontally once the ellipsoid reaches the boundary of the clay layer.The sand loss continues until the overlying clay sinks to the bottom to clog the gushing crack,creating a large sinkhole at the surface.Increasing the confinedaquifer thickness can increase the vertical expansion of the ellipsoid and delay the clay-clogging effects,thereby considerably increasing the severity of sand loss,stratum deformation,and surface settlement.An increase in the confinedwater pressure markedly increases the hydraulic gradient along the seepage path,which contributes to increasing the gushing rates of water and sand.As a result,substantial sand loss occurs before the clay clogs the gushing crack,inducing more cracks and deeper sinkholes at the surface.All the aforementioned results provide insights into the effects of confinedaquifer on WSG disasters in clay aquiclude-confinedaquifer composite strata.
基金the Ministry of Science and Technology National Key Research and Development Program(2023YFC3805000)the National Natural Science Foundation of China(52025083 and 52208501)the Shanghai Science and Technology Innovation Action Plan(22dz1201400).
文摘As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Forum’s“Global Risks Report 2023”[2]highlights natural disasters as a major threat to sustainable development,especially for densely populated cities.
文摘Background: Disaster preparedness is a critical aspect of nursing education, enhancing students’ ability to respond effectively in emergencies. However, the extent to which nursing curricula influence disaster preparedness awareness remains underexplored. Our study found that 39% of students reported improved awareness after three years, highlighting the need for targeted curriculum enhancements. Purpose: To evaluate changes in disaster preparedness awareness among nursing students over three years of education and identify gaps in current curricula impacting this awareness. Results: Findings indicate that while 39% of students showed improved awareness, significant gaps remain, suggesting the need for a dedicated course on emergency preparedness. Conclusions: This study emphasizes the importance of integrating comprehensive disaster preparedness education within nursing curricula to address these gaps and foster resilience in future healthcare professionals.
文摘At first glance(一瞥),10-year-old B.Kenit from the coastal town of Visakhapatnam in India looks like any other school-going child,but there is more than meets the eye.Inspired by a tsunami drill conducted in his school when he was eight,the third grader be-came a Disaster Risk Reduction(DRR)advocate,educating his fel-low students and community members on early warning,evacua-tion,and search and rescue.
基金supported by the National Natural Science Foundation of China(Nos.52273220 and 22205243)。
文摘Snow and freezing disasters are recurrent weather and climate phenomena that affect the world annually.These events exert a significant influence on numerous aspects of life,including transportation,power supply,and daily activities,and result in considerable economic losses.This study aims to provide a comprehensive analysis of the regions affected by these disasters,the preventive and responsive measures employed,recent advancements in key materials,and the challenges encountered.By doing so,we can gain a deeper understanding of the vital role,significant advantages,and untapped potential of key materials for effectively preventing and responding to snow and freezing disasters.Furthermore,promoting research and utilization of these materials not only contributes to the development of the safety and emergency equipment industry but also strengthens the supply of advanced and suitable safety and emergency equipment.
文摘The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
文摘Based on the site investigation of a lightning stroke accident in a coal mine in Weiyuan County during a strong thunderstorm process on the night of August 10,2024,combined with the investigation data of the accident site,the causes of the lightning stroke accident were analyzed,and the corresponding rectification suggestions were put forward.
基金supported by the National Natural Science Foundation of China(NSFC)under grant numbers of 52277222,52406256,and 52177217the Shanghai Science and Technology Development Fund under grant number 22ZR14445000the Artificial Intelligence for Research Paradigm Reform Enabling Discipline Leapfrog Program Project Funding Grant。
文摘The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer considerable potential for automating this task,their practical application in scientific domains is nonetheless constrained by high application programming interface(API)costs and computational resources required for fine-tuning.To address these limitations,a cognition-enhanced instruction framework(CEIF)is proposed,wherein a high-performance teacher model(such as DeepSeek-R1)provides dynamic feedback,prompt refinement,and training data optimization to guide the learning process of low-parameter models.Experimental results demonstrate that the low-parameter models(6B-9B)optimized via the CEIF achieve approximately 85%accuracy in battery literature extraction tasks,approaching the performance of GPT-4 while requiring only a single NVIDIA RTX 3090 GPU.Furthermore,the emergence of an"Aha moment"characterized by rapid performance improvement during specialized learning is observed,offering novel theoretical insights for the design and optimization of domainspecific models.
基金financial support from the National Science Foundation(NSF)EPSCoR R.I.I.Track-2 Program,awarded under the NSF grant number 2119691.
文摘The increasing frequency and severity of natural disasters,exacerbated by global warming,necessitate novel solutions to strengthen the resilience of Critical Infrastructure Systems(CISs).Recent research reveals the sig-nificant potential of natural language processing(NLP)to analyze unstructured human language during disasters,thereby facilitating the uncovering of disruptions and providing situational awareness supporting various aspects of resilience regarding CISs.Despite this potential,few studies have systematically mapped the global research on NLP applications with respect to supporting various aspects of resilience of CISs.This paper contributes to the body of knowledge by presenting a review of current knowledge using the scientometric review technique.Using 231 bibliographic records from the Scopus and Web of Science core collections,we identify five key research areas where researchers have used NLP to support the resilience of CISs during natural disasters,including sentiment analysis,crisis informatics,data and knowledge visualization,disaster impacts,and content analysis.Furthermore,we map the utility of NLP in the identified research focus with respect to four aspects of resilience(i.e.,preparedness,absorption,recovery,and adaptability)and present various common techniques used and potential future research directions.This review highlights that NLP has the potential to become a supplementary data source to support the resilience of CISs.The results of this study serve as an introductory-level guide designed to help scholars and practitioners unlock the potential of NLP for strengthening the resilience of CISs against natural disasters.
文摘The occurrence characteristics and impacts of agricultural meteorological disasters during the main growth period of potatoes in Ulanqab City were analyzed.According to the development needs of modern potato industry,some countermeasures for meteorological services in the disaster prevention and mitigation of potatoes were proposed,such as strengthening intelligent and digital meteorological services,and building a full-chain meteorological service for the entire growth period of potatoes.The aim is to reduce the impact of disasters and increase the yield and quality of potatoes through intelligent and digital meteorological services.
基金supported by the Ministry of Science and Technology of China(Grant No.2022YFF1301500)the National Natural Science Foun-dation of China(Grants No.32000352,32171485,and 32371741)+1 种基金the Natural Science Foundation of Guangdong Province(Grant No.2021A1515010968)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(Grant No.23lgzy002).
文摘Disaster risk reduction,an essential function of protected areas(PAs),has been generally overlooked in PA design.Using primates as a model,we designed a disaster risk index(DRI)to measure the disaster sensitivity of primate species.High-conservation-need(HCN)areas were identified by both their richness and number of threatened primate species.We also constructed high-disaster-risk(HDR)areas and climate-sensitive(CS)areas based on a disaster risk assessment and temperature change under climate change.We overlaid HCN and HDR areas to obtain HDR-HCN areas.We defined species conservation targets as the percent of each species’range that should be effectively conserved using“Zonation”.Landslides had the highest DRI(1.43±0.88),but have been overlooked in previous studies.PA coverage in HDR-HCN(30%)areas was similar to that in HCN areas(28%),indicating that current PA design fails to account for disaster risk reduction.About 50%of the HDR-HCN areas overlapped with CS areas.Presently,43%of primate species meet their conservation targets.Fifty-seven of primate species would meet their conservation targets and 67%of primates could benefit from PA expansion if HDR-HCN areas are fully incorporated into PAs.Increasing PA coverage in HDR-HCN areas is essential to achieving both primate conservation and disaster risk reduction.The study calls for integrating disaster risk reduction into PA design guidelines,particularly in regions like the western Amazon,and recommends flexible conservation approaches in other areas.
基金jointly supported by the National Natural Science Foundation of China(Grant No.71803149 and No.72271142)the Ministry of Education Humanities and Social Science Research Youth Fund Project(Grant No.22YJCZH110 and No.22XJC630007)+4 种基金the China Postdoctoral Science Foundation(Grant No.2022M721904)the Natural Science Foundation of Shaanxi Province(Grant No.2023JCYB607 and No.2024JC-YBQN-0758)the Social Science Foundation of Shaanxi Province(Grant No.2023R290)the Innovation Capability Support Program of Shaanxi(Program No.2025KG-YBXM-113)the Scientific Research Program Funded by The research institute of new urbanization and human settlement in Shaanxi Province of XAUAT(Grant No.2023SCZH14)。
文摘To mitigate ecological degradation and improve human well-being,the Chinese Government has implemented the largest disaster resettlement program from 2011 to 2020.Ankang Prefecture,as one of the key regions in Shaanxi Province where this largescale resettlement program was performed,has provided a model for observing and evaluating the impact of the resettlement project,both within Shaanxi Province and across other regions of China.As a place where a number of protection and development policies converge,the economic and social development of Ankang is confronted with multiple constraints.Measuring livelihood resilience and further evaluating its impact in this region is key to the delivery and output of disaster resettlement programs to improve human well-being.We attempted to empirically examine the significance and impact of livelihood resilience in the context of disaster resettlement.This study expanded the social–ecological system resilience theory to examine rural household livelihood systems.We used the spatial vector method and 657 field research data collected in July 2021 from Ankang Prefecture to measure the livelihood resilience of rural households and elucidate both general and specific aspects.The sustainable household well-being(SHWB)of rural households was measured in five dimensions concerning the Millennium Ecosystem Assessment(MA)report.In econometrics,we used coarsened exact matching(CEM)to stratify the sample and reduce the computational bias.We then applied group regression to test the effect of livelihood resilience on SHWB empirically.The findings indicate that:(1)livelihood resilience is significantly and positively related to SHWB,and it is conducive to the level of well-being;(2)disaster resettlement has a negative effect on SHWB;(3)energy and medical facilities in resettlement infrastructure and services play active roles in SHWB.These results have policy implications for strengthening livelihood resilience and improving human well-being and important implications for livelihood development in rural areas across China and other developing nations.
基金supported by the Anesthesiology Department Teaching Development Foundation of Naval Medical University under grant(2024MZQN02 and 2024MZQN03).
文摘This study addresses the limitations of traditional disaster medicine course assessments,including single evaluation formats,delayed feedback mechanisms,and gaps in competency mapping,by developing a diversified assessment system leveraging the Rain Classroom platform.The system incorporates six interconnected evaluation components across the learning cycle:pre-class preparation,pre-class tests,case discussions,skills assessment,post-class tests,and post-class feedback,collectively forming a three-dimensional“cognitive-skill-attitude”assessment framework.In the assessment design,the weighting of practical skill evaluation is elevated to 40%to prioritize the development of students’disaster response competencies.Additionally,an innovative multi-subject evaluation model(“self-peer-teacher”)is implemented within disaster scenario simulations,utilizing standardized scoring rubrics.This methodology not only enables comprehensive performance evaluation but also fosters critical teamwork and reflective practice.Implementation outcomes demonstrated that the system effectively evaluates learning progress through multi-modal assessments,enhances disaster rescue knowledge and skill proficiency,and successfully achieves predefined pedagogical objectives.
文摘Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.
基金Supported by European Union’s Horizon 2020 Research and Innovation Program(739578)the Government of the Republic of Cyprus through the Deputy Ministry of Research,Innovation,and Digital Policy.
文摘Background Efficient disaster victim detection(DVD)in urban areas after natural disasters is crucial for minimizing losses.However,conventional search and rescue(SAR)methods often experience delays,which can hinder the timely detection of victims.SAR teams face various challenges,including limited access to debris and collapsed structures,safety risks due to unstable conditions,and disrupted communication networks.Methods In this paper,we present DeepSafe,a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset.DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories.Subsequently,Detectron2 is used to precisely locate and outline the victims.Results Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection.The model effectively identified and located victims under the challenging conditions presented in the dataset.Conclusion DeepSafe offers a practical tool for real-time disaster management and SAR operations,significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.
基金DGAPA-UNAM for providing financial support to conduct landslide risk research through Project PAPIIT IN300823。
文摘Landslides represent a growing global challenge,particularly in mountainous and rapidly urbanising regions where environmental degradation and socio-economic vulnerabilities converge.This study investigates the interrelationships between Integrated Landslide Disaster Risk Management(ILDRiM)and the United Nations Sustainable Development Goals(SDGs),advancing a systemsbased understanding of landslide risk as a socially constructed and development-driven phenomenon.Drawing on a narrative literature review and a Design Structure Matrix(DSM),the research identifies eight critical drivers of landslide disaster risk:deforestation,climate change,urbanisation,infrastructure development,community vulnerability,exposure to landslides,ineffective governance,and lack of scientific integration into policymaking.The DSM framework systematically maps these drivers against the 17 SDGs to evaluate the nature and strength of their interactions.The analysis reveals significant synergies,where addressing specific drivers supports multiple SDGs,and trade-offs,where risk reduction efforts may inadvertently hinder other development objectives if not implemented inclusively and strategically.Findings underscore the transformative potential of embedding ILDRiM within national and local development frameworks.Prioritising governance reform,scientific innovation,and resilient infrastructure(SDGs 16,17,and 9)is particularly effective for advancing landslide risk reduction while supporting broader sustainability outcomes.The study also highlights the need for anticipatory,cross-sectoral,and community-driven approaches to risk governance.This research offers actionable insights for policymakers,practitioners,and researchers seeking to align disaster risk management with sustainable development planning.It proposes a novel methodology for assessing systemic interlinkages between disaster risk drivers and the SDGs.It calls for further research to refine data integration,address context-specific risks,and strengthen the evidence base for risk-informed development.By operationalising ILDRiM through the SDG framework,this study supports creating more resilient,equitable,and sustainable communities in landslide-prone regions.
基金supported by the Stanford Graduate Fellowship,the Center for Urban Science and Progress at New York Universitythe National Science Foundation under award number CMMI-2053014.The views and opinions expressed in this paper are those of the authors alone.
文摘Interdependencies between critical infrastructures and the economy amplify the effects of damage caused by disasters.The growing interest in impacts beyond physical damage and community resilience has spurred a surge in literature on economic modeling methodologies for estimating indirect economic impacts of disasters and the recovery of economic activity over time.In this review,we present a framework for categorizing modeling approaches that assess indirect economic impacts across natural hazards and anthropogenic disasters such as cyber attacks.We first conduct a comparative analysis of macroeconomic models,focusing on the approaches capturing sectoral interdependencies.These include the Leontief Input-Output(I/O)model,the Inoperability Input-Output Model(IIM),the Dynamic Inoperability Input-Output Model(DIIM),the Adaptive Regional Input-Output(ARIO)model,and the Computable General Equilibrium(CGE)model and its extensions.We evaluate their applicability to disaster scenarios based on input data availability,the compatibility of model assumptions,and output capabilities.We also reveal the functional relationships of input data and output metrics across economic modeling approaches for inter-sectoral impacts.Furthermore,we examine how the damage mechanisms posed by different types of disasters translate into model inputs and impact modeling processes.This synthesis provides guidance for researchers and practitioners in selecting and configuring models based on specific disaster scenarios.It also identifies the gaps in the literature,including the need for a deeper understanding of model performance reliability,key drivers of economic outcomes in different disaster contexts,and the disparities in modeling approach applications across various hazard types.
