Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Effect of a comprehensive geriatric assessment nursing intervention model on older patients with diabetes and hypertension

BACKGROUND The Comprehensive Geriatric Assessment(CGA)was introduced late in China and is primarily used for investigating and evaluating health problems in older adults in outpatient and community settings.However,there are few reports on its application in hospitalized patients,especially older patients with diabetes and hypertension.AIM To explore the nursing effect of CGA in hospitalized older patients with diabetes and hypertension.METHODS We performed a retrospective single-center analysis of patients with comorbid diabetes mellitus and hypertension who were hospitalized and treated in the Jiangyin Hospital of Traditional Chinese Medicine between September 2020 and June 2022.Among the 80 patients included,40 received CGA nursing interventions(study group),while the remaining 40 received routine nursing care(control group).The study group's comprehensive approach included creating personalized CGA profiles,multidisciplinary assessments,and targeted inter-ventions in areas,such as nutrition,medication adherence,exercise,and mental health.However,the control group received standard nursing care,including general and medical history collection,fall prevention measures,and regular patient monitoring.After 6 months of nursing care implementation,we evaluated the effectiveness of the interventions,including assessments of blood glucose levels fasting blood glucose,2-h postprandial blood glucose,and glycated hemoglobin,type A1c(HbA1c);blood pressure indicators such as diastolic blood pressure(DBP)and systolic blood pressure(SBP);quality of life as measured by the 36-item Short Form Survey(SF-36)questionnaire;and treatment adherence.RESULTS After 6 months,the nursing outcomes indicated that patients who underwent CGA nursing interventions experienced a significant decrease in blood glucose indicators,such as fasting blood glucose,2-h postprandial blood glucose,and HbA1c,as well as blood pressure indicators,including DBP and SBP,compared with the control group(P<0.05).Quality of life assessments,including physical health,emotion,physical function,overall health,and mental health,showed marked improvements compared to the control group(P<0.05).In the study group,38 patients adhered to the clinical treatment requirements,whereas only 32 in the control group adhered to the clinical treatment requirements.The probability of treatment adherence among patients receiving CGA nursing interventions was higher than that among patients receiving standard care(95%vs 80%,P<0.05).CONCLUSION The CGA nursing intervention significantly improved glycemic control,blood pressure management,and quality of life in hospitalized older patients with diabetes and hypertension,compared to routine care.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

A novel refined dynamic model of high-speed maglev train-bridge coupled system for random vibration and running safety assessment

Running safety assessment and tracking irregularity parametric sensitivity analysis of high-speed maglev train-bridge system are of great concern,especially need perfect refinement models in which all properties can be well characterized based on various stochastic excitations.A three-dimensional refined spatial random vibration analysis model of high-speed maglev train-bridge coupled system is established in this paper,in which multi-source uncertainty excitation can be considered simultaneously,and the probability density evolution method(PDEM)is adopted to reveal the system-specific uncertainty dynamic characteristic.The motion equation of the maglev vehicle model is composed of multi-rigid bodies with a total 210-degrees of freedom for each vehicle,and a refined electromagnetic force-air gap model is used to account for the interaction and coupling effect between the moving train and track beam bridges,which are directly established by using finite element method.The model is proven to be applicable by comparing with Monte Carlo simulation.By applying the proposed stochastic framework to the high maglev line,the random dynamic responses of maglev vehicles running on the bridges are studied for running safety and stability assessment.Moreover,the effects of track irregularity wavelength range under different amplitude and running speeds on the coupled system are investigated.The results show that the augmentation of train speed will move backward the sensitive wavelength interval,and track irregularity amplitude influences the response remarkably in the sensitive interval.

Research on the Assessment System of Computational Mechanics Courses Based on the TOPSIS Entropy Weight Model

This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qualitative evaluation methods.The system not only pays attention to students’practical operation and theoretical knowledge mastery but also puts special emphasis on the cultivation of students’innovative abilities.In order to realize a comprehensive and objective evaluation,the assessment and evaluation method of the entropy weight model combining TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)multi-attribute decision analysis and entropy weight theory is adopted,and its validity and practicability are verified through example analysis.This method can not only comprehensively and objectively evaluate students’learning outcomes,but also provide a scientific decision-making basis for curriculum teaching reform.The implementation of this diversified course evaluation system can better reflect the comprehensive ability of students and promote the continuous improvement of teaching quality.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

Risk Assessment of Multi‑Hazards in Hangzhou:A Socioeconomic and Risk Mapping Approach Using the CatBoost‑SHAP Model

As the global push for sustainable urban development progresses,this study,set against the backdrop of Hangzhou City,one of China’s megacities,addressed the confict between urban expansion and the occurrence of urban geological hazards.Focusing on the predominant geological hazards troubling Hangzhou-urban road collapse,land subsidence,and karst collapse-we introduced a Categorical Boosting-SHapley Additive exPlanations(CatBoost-SHAP)model.This model not only demonstrates strong performance in predicting the selected typical urban hazards,with area under the curve(AUC)values reaching 0.92,0.92,and 0.94,respectively,but also,through the incorporation of the explainable model SHAP,visually presents the prediction process,the interrelations between evaluation factors,and the weight of each factor.Additionally,the study undertook a multi-hazard evaluation,producing a susceptibility zoning map for multiple hazards,while performing tailored analysis by integrating economic and population density factors of Hangzhou.This research enables urban decision makers to transcend the“black box”limitations of machine learning,facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area.This approach holds the potential to ofer valuable insights for the sustainable development of cities worldwide.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Assessment of public perspectives and barriers towards dengue preventive practices using the Health Belief Model in Puducherry,India:A cross-sectional study

Objective:To assess the perspectives and barriers towards dengue preventive practices among the residents of Puducherry,India.Methods:A cross-sectional survey was conducted in 300 households in Puducherry,using a population-proportionate(7:3)distribution from urban and rural areas by grid sampling.One adult interview per household was conducted and the participants were selected using a KISH grid.A semi-structured questionnaire based on the Health Belief Model(HBM)with additional questions on knowledge assessment was used.Knowledge was assessed based on the correctness of answers and the HBM scores were calculated on a 5-point Likert scale.Participants were categorized based on the median score under each domain.Logistic regression was used for adjusted analysis and models were built to predict the performances in each domain.Results:Four percent of the participants lacked basic knowledge regarding dengue transmission.While 208(69.3%)participants did not consider themselves at risk of contracting dengue within the next year,majority perceived dengue as a disease with low severity.Around 49.3%(148)were skeptical about the benefit of time and money spent on dengue prevention.Inadequate government efforts were stated as the major barrier(47.0%)and frequent reminders(142,47.3%)as the major cue to action.Age above 50 years(aOR 1.78,95%CI 1.04-3.06,P=0.037)and rural locality(aOR 2.68,95%CI 1.52-4.71,P=0.001)were found to be significantly associated with poor knowledge scores.Urban participants had a significantly higher chance to perceive low susceptibility as compared to the rural counterparts(aOR 1.74,95%CI 1.05-2.9,P=0.03).Participants with less than a high school education had low perceived benefits(aOR 2.46,95%CI 1.52-3.96,P<0.001)and low self-efficacy scores(aOR 2.66,95%CI 1.61-4.39,P<0.001).Conclusions:This study identifies key gaps in dengue prevention,including low perceived susceptibility,mild disease perception,limited knowledge of breeding sites,and overreliance on government efforts.Tailoring interventions to community needs,stratified to factors influencing the community perspectives can significantly improve dengue prevention efforts.

Assessment of the three representative empirical models for zenith tropospheric delay(ZTD)using the CMONOC data

The precise correction of atmospheric zenith tropospheric delay(ZTD)is significant for the Global Navigation Satellite System(GNSS)performance regarding positioning accuracy and convergence time.In the past decades,many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications.But there is no comprehensive evaluation of these models for the whole China region,which features complicated topography and climate.In this study,we completely assess the typical empirical models,the IGGtropSH model(gridded,non-meteorology),the SHAtropE model(scattered,non-meteorology),and the GPT3 model(gridded,meteorology)using the Crustal Movement Observation Network of China(CMONOC)network.In general,the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63,37.13/2.20,and 38.27/1.34 mm for the GPT3,SHAtropE and IGGtropSH model,respectively.However,the models had a distinct performance regarding geographical distribution,elevation,seasonal variations,and daily variation.In the southeastern region of China,RMSE values are around 50 mm,which are much higher than that in the western region,approximately 20 mm.The SHAtropE model exhibits better performance for areas with large variations in elevation.The GPT3 model and the IGGtropSH model are more stable across different months,and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

The Construction of a Learning-Oriented Speaking Assessment Model in College English Based on the Online Diagnostic Assessment

Since language is a tool for communication,proficiency in English communication is a fundamental necessity for talent in the 21st century.However,surveys reveal that most college students at private colleges possess inadequate oral English skills,and what some have learned is“mute English.”Therefore,developing their English-speaking skills is another challenge faced by students attending private schools.Online diagnostic assessment methods are growing globally with the use of technology.UDig diagnostic assessment system is one of the online English diagnostic assessment platforms currently being widely used in China.Therefore,the present work is conducted to investigate and conduct an oral English learning-oriented assessment model in college English using the online diagnostic assessment.With the research result,it is hoped that the study could provide useful information for improving UDig system and make a better use of it in college oral English learning and teaching.

Modelling the monthly hydrological balance using Soil and Water Assessment Tool(SWAT)model:A case study of the Wadi Mina upstream watershed

Modelling the hydrological balance in semi-arid zones is essential for effective water resource management,encompassing both surface water and groundwater.This study aims to model the monthly hydrological water cycle in the Wadi Mina upstream watershed(northwest Algeria)by applying the Soil and Water Assessment Tool(SWAT)hydrological model.SWAT modelling integrates spatial data such as the Digital Elevation Model(DEM),land use,soil types and various meteorological parameters including precipitation,maximum and minimum temperatures,relative humidity,solar radiation and wind speed.The SWAT model was calibrated and validated using data from January 2012 to December 2014,with a calibra-tion period from January 2012 to August 2013 and a validation period from September 2013 to December 2014.Sensitivity and parameter calibration were conducted using the SWAT-SA program,and model performance evaluation relied on comparing the observed discharge at the outlet of the basin with model-simulated discharge,assessed through statistical coefficients including Nash-Sutcliffe Efficiency(NSE),coefficient of determination(R2)and Percent Bias(PBAIS).Calibration results indicated favourable objec-tive function values(NSE=0.79,R2=0.93,PBAIS=-8.53%),although a slight decrease was observed during validation(NSE=0.69,R2=0.86,and PBAIS=-11.41%).The application of the SWAT model to the Wadi Mina upstream watershed highlighted its utility in simulating the spatial distribution of different components of the hydrological balance in this basin.The SWAT model revealed that approximately 71%of the precipitation in the basin evaporates,while only 29%contributes to surface runoff or infiltration into the soil.

Assessment of sediment connectivity using modelling and field-based approaches in the Slavíč River catchment(MoravskoslezskéBeskydy Mts,Czech Republic)

Mountain streams act as conveyors of sediments within the river continuum,where the physical transport of sediments between river reaches through the catchment or between individual parts(e.g.,between hillslopes and channels)of the catchment is assumed.This study focused on sediment connectivity analysis in the SlavíčRiver catchment in the MoravskoslezskéBeskydy Mts in the eastern part of the Czech Republic.The connectivity index and connectivity index target modelling were combined with an analysis of anthropogenic interventions.Additionally,field mapping,grain size of bed sediments and stream power analysis were used to obtain information about connectivity in the catchment.Based on the analysis and obtained results,terrain topography is the current main driving factor affecting the connectivity of sediment movement in the SlavíčRiver catchment.However,the modelling provided valuable information about high sediment connectivity despite different recent land use conditions(highly forested area of the catchment)than those in historical times from the 16th to 19th centuries when the SlavíčRiver catchment was highly deforested and sediment connectivity was probably higher.The analysis of anthropogenic interventions,field mapping,grain size of bed sediments and stream power analysis revealed more deceleration of sediment movement through the catchment,decreased sediment connectivity with bed erosion,and gradual river channel process transformation in some reaches.Field mapping has identified various natural formations and human-induced changes impacting the longitudinal and lateral connectivity in the SlavíčRiver.For instance,embankments along 48%of the river's length,both on the right and left banks,significantly hinder lateral sediment supply to the channel.Stream power index analysis indicates increased energy levels in the flowing water in the river's upper reaches(up to 404.8 W m^(-2)).This high energy is also observed in certain downstream sections(up to 337.6 W m^(-2)),where it is influenced by human activities.These conditions lead to intensified erosion processes,playing a crucial role in sediment connectivity.Similar observations were described in recent studies that pointed out the long-term human interventions on many streams draining European mountains,where a decrease in sediment connectivity in these streams is linked with sediment deficits and the transformation of processes forming channels.

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。