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。

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Comparison of Statistical Models for Regional Crop Trial Analysis

Based on the review and comparison of main statistical analysis models for estimating variety-environment cell means in regional crop trials, a new statistical model, LR-PCA composite model was proposed, and the predictive precision of these models were compared by cross validation of an example data. Results showed that the order of model precision was LR-PCA model > AMMI model > PCA model > Treatment Means (TM) model > Linear Regression (LR) model > Additive Main Effects ANOVA model. The precision gain factor of LR-PCA model was 1.55, increasing by 8.4% compared with AMMI.

Quality Management Model for Phase I Clinical Drug Trials:A Structural Equation Model

This study aimed to construct a quality management model for phase I clinical drug trials.A cross-sectional survey was conducted and data were collected from 604 respondents at 69 institutions in China engaged in phase I clinical drug trials.Exploratory and confirmatory factor analyses were used to develop the survey tool.Structural equation modeling was used to construct a quality management model for phase I clinical drug trials.The results showed that the final survey tool had good reliability and validity(Cronbach’sα=0.938,root mean square error of approximation=0.074,comparative fit index=0.962,and Tucker—Lewis index=0.955).The model included five dimensions:government regulation,industry management,medical institution management,research team management,and contract research organization(CRO)management.In total,22 measurement items were obtained.The structural equation model indicated government regulation,industry management,medical institution management,and CRO management significantly affected the quality of phase I clinical drug trials(β=0.195,β=0.331,β=0.279,andβ=−0.267,respectively;P<0.05).Research team management had no effect on the quality of trials(β=0.041,P=0.610).In conclusion,the model is valuable for identifying factors influencing phase I clinical drug trials and guiding quality management practices.

An efficient approach for shadow detection based on Gaussian mixture model

An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.

An ultrasound-guided percutaneous electrical nerve stimulation regimen devised using finite element modeling promotes functional recovery after median nerve transection

Percutaneous electrical nerve stimulation of an injured nerve can promote and accelerate peripheral nerve regeneration and improve function.When performing acupuncture and moxibustion,locating the injured nerve using ultrasound before percutaneous nerve stimulation can help prevent further injury to an already injured nerve.However,stimulation parameters have not been standardized.In this study,we constructed a multi-layer human forearm model using finite element modeling.Taking current density and activated function as optimization indicators,the optimal percutaneous nerve stimulation parameters were established.The optimal parameters were parallel placement located 3 cm apart with the injury site at the midpoint between the needles.To validate the efficacy of this regimen,we performed a randomized controlled trial in 23 patients with median nerve transection who underwent neurorrhaphy.Patients who received conventional rehabilitation combined with percutaneous electrical nerve stimulation experienced greater improvement in sensory function,motor function,and grip strength than those who received conventional rehabilitation combined with transcutaneous electrical nerve stimulation.These findings suggest that the percutaneous electrical nerve stimulation regimen established in this study can improve global median nerve function in patients with median nerve transection.

Randomized controlled trials on Roy adaptation model nursing in patients with acute myocardial infarction in china: a systematic review

Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searched the Cnki,Wanfang and Vipdatabases,to get randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction.The search period was from inception to October 2020.According to the Cochrane risk bias assessment tool,the quality of the studies included was appraised.Results:A total of 55 studies were retrieved,and 11 were eventually included in the study.Among the studies included,the first study was published in 2008.The overall quality of the 11 studies included was relatively low.Conclusions:The overall quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction was not high,which would hinder the evidence transformation as well as clinical practice.

Frequentist Model Averaging and Applications to Bernoulli Trials

In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.

Regression Model to Analyze Differential Response to Treatment in Randomized Controlled Clinical Trial

The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating the effect of a certain intervention. However, in clinical trials, even after randomization, there are situations where the patients differ substantially with respect to the baseline value of the outcome variable. Many a times the response to interventions depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Although variation in outcome associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments be constant across all baseline levels. To overcome this difficulty, Weigel and Narvaez [7] proposed a regression model for two treatment groups to analyze individual response to treatments in randomized controlled clinical trials. The authors reviewed the model suggested by Weigel and Narvaez and extended further for three or more treatment groups. The utility of the model was demonstrated with real life data from a randomized controlled clinical trial of bronchial asthma.

An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.

砖木结构古建筑火灾风险影响因子评估研究

提出模糊DEMATEL-ISM的方法,在建立古建筑火灾评价指标体系的基础上,对归属于4大类风险因素的25个潜在风险因子进行关联性分析,得出因子的因果属性及因子间的逻辑层次结构关系,从而对古建筑火灾风险进行评估。利用模糊DEMATEL-ISM法对华阴市西岳庙灏灵殿进行实例分析,结果表明:疏散与扑救条件、电器设备隐患的原因度较大,需要对其予以控制;耐火等级、火灾荷载的结果度大,为事故发生的主要原因;疏散与扑救条件、易燃易爆危险品、电器设备隐患、安全责任制、灭火应急预案和演练为古建筑火灾事故的关键风险因子,可为古建筑火灾风险管控工作提供相应参考。

基于BP-DEMATEL的山西省冬小麦水足迹影响因素识别

针对山西省冬小麦水足迹历史演变及关键影响因素识别问题,分析了1993—2021年山西省冬小麦水足迹历史演变规律,利用BP神经网络-决策实验室(BP-DEMATEL)模型对冬小麦水足迹演变的关键影响因素进行识别,将其分为驱动型因素及特征型因素,并揭示了影响因素之间的作用机制。结果表明:1993—2021年山西省冬小麦水足迹及单位水足迹均呈下降趋势;2021年山西省冬小麦总水足迹为27亿m^(3)(蓝水占比57%),单位水足迹为1 122 m^(3)/t,与前期高点相比分别下降38%(较1994年)和21%(较1993年);气温、相对湿度和灌溉面积为冬小麦水足迹演变的关键驱动型因素,化肥施用量和农业机械总动力为关键特征型因素。

基于组合赋权云模型的塔台管制系统运行安全评估

随着我国民航业的迅猛发展,空中交通流量不断攀升,机场塔台管制系统的运行安全面临严峻考验,因此,正确识别和评估风险成为有效提高塔台管制系统运行安全水平的重要前提。为精准且有效地完成对塔台管制系统运行的安全评估,构建了基于模糊决策实验室分析法(Decision-Making Trial and Evaluation Laboratory,DEMATEL)网络层次分析法(Analytic Network Process,ANP)熵权法云模型的塔台管制系统运行安全评估模型。首先,通过流程图法并结合人机环管的思想建立塔台管制系统运行安全评估指标体系,并采用模糊DEMATEL法确定评估指标间的相互影响关系,绘制评估指标之间的网络结构图;然后,通过ANP法确定各评估指标的主观权重,采用熵权法确定各评估指标的客观权重,并通过博弈论方法计算评估指标的综合权重;最后,将权重结果与评语层云模型结合使用对各级安全评估指标进行安全评估,通过MATLAB软件完成算例的仿真分析。结果表明:该塔台管制系统运行安全等级在一般和良好之间,仍需采取有效措施对某些风险因素进行控制;同时验证了该模型在塔台管制系统运行安全评估中具有可行性和准确性,对塔台管制系统安全评估具有指导意义。

基于模糊DEMATEL-超效率DEA的可再生能源发电技术综合效益评估方法

“双碳”目标下,各类可再生能源发电技术发展迅速,综合权衡不同可再生能源发电方案的综合效益对可再生能源的优化设计具有重要意义。综合考虑经济效益、环境效益、能源效益和社会效益4个层面,提出了一种基于模糊决策试验和评价实验(decision making trial and evaluation laboratory,DEMATEL)与超效率数据包络分析(data envelopment analysis,DEA)模型的可再生能源发电技术综合效益评估方法。该方法分为投入-产出指标体系构建和综合评估2个阶段。首先,利用三角直觉模糊数处理模糊评价信息,将其与DEMATEL相结合量化各指标之间相互影响关系,基于指标间逻辑分析结果建立投入-产出评估指标体系。然后,基于超效率DEA模型对各可再生能源发电方案进行评估排序,结合投入冗余和产出不足分析结果给出各方案的针对性改善建议,以期为进一步选择和确定可再生能源产业发展战略提供参考。最后以某省10类可再生能源发电单元为研究对象,基于所提研究方法进行综合评估和分析,并与多准则妥协解排序法和熵权法进行对比分析,验证了所提方法的有效性。

上市公司影子银行化对金融稳定的影响与机制研究

影子银行因其规模的日益扩大及其带来的巨大影响而受到政府与学术界的广泛关注。本文基于上市公司财务数据和金融稳定相关数据,运用CRITIC指数构建法和误差修正模型,对上市公司影子银行化对金融稳定的影响与机制进行了理论分析与实证检验。研究发现:上市公司影子银行化提高了经济金融化程度,进而增加金融脆弱性而影响金融稳定。金融稳定更多受到东部、中部地区上市公司影子银行化的影响。相较于规模较小与盈利能力较弱上市公司,规模较大上市公司与盈利能力较强上市公司影子银行化对金融稳定造成的影响更大。研究结果有助于深化影子银行发展对金融稳定影响的理解,对强化上市公司金融化行为监管,维护金融稳定,实现金融高质量发展具有重要的政策意义。

基于D-I-M协同模型的企业数字化转型财务管理风险识别研究

数字化正在重塑经济发展的轨道,成为赋能经济增长新动力。精准识别财务管理风险中的关键指标及其相互关系,对企业更好地完成数字化转型尤为关键。融合企业人力、运营、财务、环境4个基本维度,从链式传导视角构建转型背景下企业财务管理风险指标体系。通过决策试验与评价实验室(DEMATL)法,分析各影响因素之间中心度和原因度关系,并利用解释结构模型(ISM),将矩阵进一步分析得到影响因素的层级划分,归纳影响因素之间的内在指向关系,最后运用交叉影响矩阵相乘(MICMAC)法计算各因素的驱动—依赖关系以判断其属性类别。提出通过D-I-M协同分析的方法,识别财务管理风险路径,为企业进行多链融合数字化转型、规避财务管理风险提供参考。

结合STPA和DEMATEL-ISM的民机起落架收放系统风险研究

为从系统整体角度完成对起落架收放系统的风险辨识和影响分析,将系统理论过程分析(Systematic Theory Process Analysis,STPA)与决策实验室分析-解释结构模型(Decision Making Trial and Evaluation Laboratory Interpretive Structural Modeling,DEMATEL-ISM)相结合来开展分析。首先,定义事故和系统级危险,以民机进近阶段放下起落架为例,运用STPA完成对风险因素的系统化辨识;其次,基于最大平均熵减(Maximum Mean De-entropy,MMDE)算法帮助DEMATEL-ISM模型确定阈值,完成对风险因素影响的重要性分析并识别可能引发系统级危险的风险传递路径,据此挖掘关键致因场景,以给出风险预防建议。结果显示:线路性能退化或失效、位置作动控制组件(Position Action Control Unit,PACU)核心处理器故障为关键原因因素,收放作动筒作动异常、机组成员操作不当、起落架指示灯显示异常、起落架液压选择阀作动异常、PACU信息接收有误为关键结果因素,这些因素均涉及多条可能引发系统级危险的风险传递路径,应予以重点控制。

基于DEMATEL-AISM的矿山救护指挥员能力素质研究

为探究矿山救护指挥员能力素质要素及相互间的关系,促进矿山应急救援水平的进一步提升,通过文献研究和界值法分析,从政治素质、身体素质、业务素质、心理素质和管理素质5个方面筛选能力素质的核心要素,建立矿山救护指挥员能力素质指标体系,采用决策实验室算法和对抗解释结构模型联用的方法,构建因果关系和对抗多层拓扑结构,分析了矿山救护指挥员能力素质的多要素重要程度和耦合关系。结果表明,应急救援理论和知识、心理承受能力、纪律意识、对党忠诚是有效提高矿山救护指挥员能力素质的重要因素,避免队伍自身伤亡能力、临危不乱、应急救援决策能力和灾害事故研判能力是关键要素,避免队伍自身伤亡能力为帕累托最优集。

曳引电梯部件故障相关性分析

针对电梯部件之间的故障影响,提出将决策试验与评价实验室法和解释结构模型结合建立电梯部件故障层次结构模型,并改进直接影响矩阵的确定方式。首先从多方面获取电梯部件故障关联评价,其次使用熵权法确定评价的权重并计算直接影响矩阵,最后使用建立的电梯部件故障相关模型得到电梯部件故障因果关系和故障传递规则。结果表明:电动机、制动器、限速器为强原因部件,曳引钢丝绳、轿厢以及导向系统的部件为强结果部件;电梯部件故障关联规则体现为同层级或跨层级传递。通过分析部件故障机理,所提方法得到的部件故障传递规则与电梯系统实际运行情况相符。

数字出版物的最优信息披露策略研究:以电子书为例

考虑电子书厂商信息披露策略,即提供免费试读样章供消费者在线试读.基于消费者购买决策取决于免费试读和口碑效应两个因素的基本假设,构建了电子书信息披露策略的数学模型并进行求解,通过更新函数衡量信息披露对消费者的认知改变,分析厂商收益的最优化问题.结果表明,最优信息披露策略取决于消费者初始认知和实际质量之间的相对大小关系.最优的信息披露量和价格受到初始认知、实际质量和口碑效应影响,两个决策变量之间的交互影响关系也在文中讨论.研究结果为厂商的信息披露策略以及定价策略提供理论性的指导和借鉴.