查看更多>>摘要：Slender structures often lead to vibration discomfort for occupants when exposed to wind forces.This study proposes an innovative method for assessing comfort against wind-induced vibrations for slender structures that combines field monitoring,numerical simulations,codal provisions,and Chang's comfort chart.The method utilizes ambient vibration tests(AVT)and operational modal analysis(OMA)to create a reliable finite element(FE)model for the structure.It involves analyzing the time history and calculating the peak acceleration values at various points within the structure using synthetic ambient wind forces derived from superposing waves.The comfort assessment compares peak acceleration values estimated from time history analysis against those pro-vided in Chang's chart for different comfort levels.The effectiveness of the proposed method is demonstrated through a case study on a tall,slender reinforced concrete(RC)staircase structure,confirming its suitability for practical applications.
查看更多>>摘要：Rapid and accurate acquisition and analysis of information is crucial for emergency management,but traditional methods have limitations such as incomplete information acquisition and slow processing speed.The natural language oriented spatial scene reconstruction method provides a new solution for emergency management,but existing generative models have limited understanding of spatial relationships and lack high-quality training samples.To address these issues,this paper proposes a novel spatial scene reconstruction framework.Specifically,the BERT based spatial information knowledge graph extraction method is used to encode the input text,label and classify the encoded text,identify spatial objects and relationships in the text,and accurately extract spatial information.Additionally,a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition,and based on the obtained biases,a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph.Finally,use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints.In addition,a high-quality training sample set of"text-scene-knowledge graph"was constructed.
查看更多>>摘要：The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change.This study employs cellular automata principles to analyze forest fire behavior,taking into account meteorological elements,combustible material types,and terrain slopes.The Wang Zhengfei model is utilized to compute fire spread speed,and a multifactor coupled forest fire model is developed.Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min,which is consistent with the experimental results.Using the forest fire in Anning city,Yunnan Province,as a case study with a mean burned area of 2281 ha,the burned area,rate of change in burned area,and burning area demonstrated an increasing trend,with fluctuating states in the rate of change of the burning area.Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed,vegetation type,and maximum slope reveals that under wind influence,the fire site adopts an elliptical shape with the downwind direction as the major axis.Quantitatively,when the wind speed increases from 2 m/s to 10 m/s,the burned area expands by a factor of 1.37.The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types,and the burned area increases by a factor of 1.92 when the maximum slope increases from 5°to 25°.
查看更多>>摘要：Stringent fire prevention requirements are imperative in expansive environments.Fire detection in diverse large-scale settings typically relies on sensor-based or AI-driven target detection methods.Traditional fire detectors often suffer from false alarms and missed detections,failing to meet the fire safety requirements of large-scale structures.Many existing target detection algorithms are characterized by substantial model sizes.Some detection terminals in large structures face challenges deploying these models due to constrained computational resources.To address this issue,we propose a lightweight model,YOLOv8-EMSC,derived from YOLOv8n.The incorporation of C2f_EMSC,replacing the C2f module,significantly reduces the model parameters in the enhanced YOLOv8-EMSC model compared to YOLOv8n,thereby enhancing model inference speed.Extensive testing and validation using a custom-built large-scale infrared fire dataset demonstrates a 9.6%reduction in parameters compared to the baseline model for YOLOv8-EMSC,achieving an average precision of 95.6%,surpassing both the baseline and mainstream models and significantly enhancing fire detection accuracy in expansive environments.
Pavel V.YemelinSergey S.KudryavtsevNatalya K.Yemelina
432-448页
查看更多>>摘要：This study focuses on developing an industrial and occupational safety management system for enterprises that contain chemically hazardous sites.The methodology,based on an expert approach,enabled the authors to design the structure of the risk management system at such enterprises.It also facilitated the identification of clusters and their descriptors,along with their roles in evaluating the state of the safety management system.The proposed methodology features a flexible and universal structure,making it applicable for assessing industrial and occupational safety across different enterprises,taking into account the specific technological aspects of production processes.In this case study,the authors examined the accident rates,injury hazards,and health risks associated with chemically hazardous sites in enterprises located in the Republic of Kazakhstan.The findings of this study provide a methodological approach that industrial enterprises can use to evaluate the effectiveness of their safety management systems.This allows for the development of measures aimed at preventing chemical accidents and reducing their impacts.
查看更多>>摘要：Istanbul is one of Turkey's most important financial and industrial centers,and it is located in a region with a high potential for seismicity.Due to its historical architecture and high level of urbanization,the city has a large population and is particularly vulnerable due to the building stock that will be affected by earthquakes.In the event of a possible earthquake in Istanbul,it is crucial that the hospital staff have high levels of disaster resil-ience/resilience.This is particularly important given the seismically isolated and earthquake-resistant structure of Istanbul Kartal Dr.Lütfi Kırdar City Hospital and its capacity to serve those injured by the earthquake.This study examines the resilience levels of hospital staff at Kartal Dr.Lütfi Kırdar City Hospital in the face of earthquake disasters and the various factors that affect these resilience levels.The data for this study were collected using a 13-question personal information form and the'Individual Disaster Resilience Assessment(IDRA)'scale developed by DiTirro(2018).Descriptive statistics,Pearson Chi-square tests,Independent Samples T-tests,and One-Way ANOVA were used to analyze the data.The research found that the hospital staff's IDRA scores averaged 3.27.It was concluded that the mean resilience score of the participants was above the medium level.The research findings show that receiving disaster training or being prepared for disasters in advance significantly influences individual resistance/resilience.In this context,it is essential to determine the earth-quake resistance levels of all healthcare workers in Istanbul,especially those at the city hospital where the study was conducted.Necessary training should be provided,and simulation-based disaster drills should be planned and integrated into in-service training programs.Additionally,projects should be developed to ensure that healthcare workers can reach their hospitals safely during disaster situations.
Moustafa AbdelwanisHamdan Khalaf AlarafatiMaram Muhanad Saleh TammamMecit Can Emre Simsekler...
460-469页
查看更多>>摘要：This study conducts an in-depth review and Bowtie analysis of automation bias in AI-driven Clinical Decision Support Systems(CDSSs)within healthcare settings.Automation bias,the tendency of human operators to over-rely on automated systems,poses a critical challenge in implementing AI-driven technologies.To address this challenge,Bowtie analysis is employed to examine the causes and consequences of automation bias affected by over-reliance on AI-driven systems in healthcare.Furthermore,this study proposes preventive measures to address automation bias during the design phase of AI model development for CDSSs,along with effective mitigation strategies post-deployment.The findings highlight the imperative role of a systems approach,integrating technological advancements,regulatory frameworks,and collaborative endeavors between AI developers and healthcare practitioners to diminish automation bias in AI-driven CDSSs.We further identify future research directions,proposing quantitative evaluations of the mitigation and preventative measures.
查看更多>>摘要：With the development of urbanization,underground commercial buildings(UCB)are facing severe challenges in fire safety management due to their unique structure and environmental characteristics.This study constructed a fire casualty risk assessment model that combines fuzzy fault tree analysis(FFTA)and Bayesian network(BN),aiming to quantitatively analyze the dynamic risk of casualties caused by fires in UCB.Fault tree analysis(FTA)is used to comprehensively identify the key risk factors leading to fire casualties in UCB,involving 55 basic events,and the occurrence probability of basic events was calculated via a fuzzy set.The FTA model was transformed into a BN structure via conversion rules and was optimized.The optimized BN model can dynamically analyze the specific fire evolution process and quantify the impacts of different emergency response measures on fire control,evacuation,and casualties.Innovatively,from the post-incident(a historical case study)and pre-incident(two potentially different fire scenarios)perspectives,various emergency plans were scientifically evaluated,providing reasonable suggestions and decision support for emergency management.The results indicate that the model can effectively guide the formulation of fire prevention and control strategies and emergency response work of UCB and provide an innovative tool for improving the safety of UCB and reducing fire accidents and casualties.
查看更多>>摘要：Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network.However,when dealing with different financial fraud scenarios,existing methods face challenges,resulting in difficulty in effectively ensuring financial security.In fraud sce-narios,transaction data are generated in real time,in which a strong temporal relationship between multiple fraudulent transactions is observed.Traditional dynamic graph models struggle to effectively balance the tem-poral features of nodes and spatial structural features,failing to handle different types of nodes in the graph network.In this study,to extract the temporal and structural information,we proposed a dynamic heterogeneous transaction graph embedding(DyHDGE)network based on a dynamic heterogeneous transaction graph,considering both temporal and structural information while incorporating heterogeneous data.To separately extract temporal relationships between transactions and spatial structural relationships between nodes,we used a heterogeneous temporal graph representation learning module and a temporal graph structure information extraction module.Additionally,we designed two loss functions to optimize node feature representations.Extensive experiments demonstrated that the proposed DyHDGE significantly outperformed previous state-of-the-art methods on two simulated datasets of financial fraud scenarios.This capability contributes to enhancing security in financial consumption scenarios.
查看更多>>摘要：In light of escalating urbanization trends and climate change impacts worldwide,the susceptibility of urban power grids to natural disasters has become an overarching global concern.Prior research has predominantly concentrated on singular calamities while often disregarding cumulative repercussions from multiple concurrent events affecting power grid resilience.This investigation presents an exhaustive framework for assessing grid vulnerabilities by quantifying diverse impacts from potential natural disaster scenarios and delineating adaptive pathways for evaluating inadvertent occurrences.The framework amalgamates an extensive array of metrics—including probability assessments,system state evaluations,trigger threshold analyses,responsiveness mea-surements,and adaptability adjustments—within a dynamic scenario-oriented model.The inquiry progresses through distinct stages:formulating an all-encompassing methodology for assessing vulnerabilities;assessing varied impacts stemming from different environmental perils;mapping out post-disaster evolutions;and executing a case analysis focusing on an urban power grid.Concentrating specifically on rainfall,snowfall,and freezing incidents,the case analysis uses locale-specific data to appraise grid susceptibilities while employing multi-criteria decision analysis(MCDA)to facilitate decision-making.During this deliberative process,optimal strategies are derived,and mitigative actions are recommended with the aim of diminishing power-grid vulnerabilities.This investigation underscores intricate risk dynamics within urban power grids while presenting a feasible framework for sustainable planning and effective emergency responses in confronting natural hazards.
万方数据