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Predicting the nephrotoxicity of Chinese herbal medicines based on a Bayesian network model 认领 引用
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作者 Li-Juan Tan Liang Chen +2 位作者 Jia-Hui Huang Ze-Hai Fang Hong-Jie Liu 《TMR Pharmacology Research》 2022年第1期22-29,共8页
Objective:Based on a Bayesian network model(BNM),we constructed and evaluated a predictive model of Chinese herbal medicines(CHMs)nephrotoxicity,explored its influencing factors,and provided a reference for the preven... Objective:Based on a Bayesian network model(BNM),we constructed and evaluated a predictive model of Chinese herbal medicines(CHMs)nephrotoxicity,explored its influencing factors,and provided a reference for the prevention and control of nephrotoxicity.Methods:We searched for CHMs with nephrotoxicity through academic journals and academic works,screened non-nephrotoxic CHMs,and then tested the correlation between nephrotoxic and non-nephrotoxic CHMs and their four properties,five flavours,and channel tropism.The screened variables were used to construct the Bayesian network model(BNM),predict important factors affecting the nephrotoxicity of Chinese herbal medicines(CHMs),draw the receiver operating characteristic(ROC)curve of the model,and calculate the area under the curve(AUC)to evaluate the forecasting effect of the model.Results:Medicinal property theory(four properties and five flavours)are important factors affecting the nephrotoxicity of CHMs.Nephrotoxic and non-nephrotoxic CHMs are related to their four propertiesand five flavours(P<0.05).BNM showed that sweetness and flatness wereimportant protective factors for nephrotoxicity of CHMs;the prediction accuracy was 77.92%,the AUC result of the model ROC curve was 0.661(95%CI:0.620-0.701),and the best sensitivity(0.736)and specificity(0.571)were obtained at 0.65.Discussion:Modern mathematical statistics and modeling methods have certain reference significance and application value for the prediction of CHMs nephrotoxicity and toxicology research. 展开更多
关键词 Chinese herbal medicines four properties five flavours channel tropism prediction of nephrotoxicity Bayesian network model
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Bayesian Network Model of Product Information Diffusion and Reasoning of Influence 认领 引用
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作者 Xuehua Sun Shaojie Hou +2 位作者 Ning Cai Wenxiu Ma Surui Zhao 《Journal of Data Analysis and Information Processing》 2020年第4期267-281,共15页
Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of inform... Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior. 展开更多
关键词 Product Information Diffusion Bayesian Network Model Influence Reasoning Consumer Behaviors Clique Tree
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Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake 认领 引用 被引量：6
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作者 XU Min ZENG Guang-ming +3 位作者 XU Xin-yi HUANG Guo-he SUN Wei JIANG Xiao-yun 《Journal of Environmental Sciences》 SCIE EI CAS 2005年第6期946-952,共7页
Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of t... Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake. 展开更多
关键词 Dongting Lake chlorophyll-a Bayesian regularized BP neural network model sum of square weights
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Linking Structural Equation Modeling with Bayesian Network and Its Application to Coastal Phytoplankton Dynamics in the Bohai Bay 认领 引用
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作者 XU Xiao-fu SUN Jian +2 位作者 NIE Hong-tao YUAN De-kui TAO Jian-hua 《China Ocean Engineering》 SCIE EI CSCD 2016年第5期733-748,共16页
Bayesian networks （BN） have many advantages over other methods in ecological modeling, and have become an increasingly popular modeling tool. However, BN are flawed in regard to building models based on inadequate e... Bayesian networks （BN） have many advantages over other methods in ecological modeling, and have become an increasingly popular modeling tool. However, BN are flawed in regard to building models based on inadequate existing knowledge. To overcome this limitation, we propose a new method that links BN with structural equation modeling （SEM）. In this method, SEM is used to improve the model structure for BN. This method was used to simulate coastal phytoplankton dynamics in the Bohai Bay. We demonstrate that this hybrid approach minimizes the need for expert elicitation, generates more reasonable structures for BN models, and increases the BN model＇s accuracy and reliability. These results suggest that the inclusion of SEM for testing and verifying the theoretical structure during the initial construction stage improves the effectiveness of BN models, especially for complex eco-environment systems. The results also demonstrate that in the Bohai Bay, while phytoplankton biomass has the greatest influence on phytoplankton dynamics, the impact of nutrients on phytoplankton dynamics is larger than the influence of the physical environment in summer. Furthermore, although the Redfield ratio indicates that phosphorus should be the primary nutrient limiting factor, our results show that silicate plays the most important role in regulating phytoplankton dynamics in the Bohai Bay. 展开更多
关键词 structural equation modeling Bayesian networks ecological modeling Bohai Bay phytoplankton dynamics
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Research on Bayesian Network Based User's Interest Model 认领 引用
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作者 ZHANG Weifeng XU Baowen +1 位作者 CUI Zifeng XU Lei 《Wuhan University Journal of Natural Sciences》 CAS 2007年第5期809-813,共5页
It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing ... It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability. 展开更多
关键词 Bayesian network interest model feature selection
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Hierarchy Bayesian model based services awareness of high-speed optical access networks 认领 引用
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作者 白晖峰 《Optoelectronics Letters》 EI 2018年第2期114-118,共5页
As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve t... As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay. 展开更多
关键词 As Simulation OLT Hierarchy Bayesian model based services awareness of high-speed optical access networks
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Winning Probability Estimation Based on Improved Bradley-Terry Model and Bayesian Network for Aircraft Carrier Battle 认领 引用 被引量：1
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作者 Yuhui Wang Wei Wang Qingxian Wu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2017年第2期39-44,共6页
To provide a decision-making aid for aircraft carrier battle,the winning probability estimation based on Bradley-Terry model and Bayesian network is presented. Firstly,the armed forces units of aircraft carrier are cl... To provide a decision-making aid for aircraft carrier battle,the winning probability estimation based on Bradley-Terry model and Bayesian network is presented. Firstly,the armed forces units of aircraft carrier are classified into three types,which are aircraft,ship and submarine. Then,the attack ability value and defense ability value for each type of armed forces are estimated by using BP neural network,whose training results of sample data are consistent with the estimation results. Next,compared the assessment values through an improved Bradley-Terry model and constructed a Bayesian network to do the global assessment,the winning probabilities of both combat sides are obtained. Finally,the winning probability estimation for a navy battle is given to illustrate the validity of the proposed scheme. 展开更多
关键词 aircraft carrier battle BP neural network Bradley-Terry model Bayesian networks
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Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA) 认领 引用
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作者 游威振 钟小品 《Journal of Donghua University(English Edition)》 EI CAS 2015年第6期914-918,共5页
A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In con... A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples. 展开更多
关键词 Bayesian network(BN)model dual genetic algorithm(DGA) system reliability historical data
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Ontology Mapping Based on Bayesian Network 认领 引用 被引量：2
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作者 张凌宇 陶佰睿 《Journal of Donghua University(English Edition)》 EI CAS 2015年第4期681-687,共7页
Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models o... Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly. 展开更多
关键词 component ontology mapping multi-strategy Bayesian network model
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常规公交风险的SEM与Bayesian Network组合评估方法研究 认领 引用 被引量：4
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作者 宗芳 于萍 +1 位作者 吴挺 陈相茹 《交通信息与安全》 CSCD 北大核心 2018年第4期22-28,共7页
常规公交系统具有载客量大、班次多、线路固定等特点,存在多种安全风险隐患。为综合评估常规公交风险,对国内外554条事故数据分析整理,构建了常规公交风险指标体系。建立了常规公交风险评估的结构方程模型,得到常规公交风险因素对事故... 常规公交系统具有载客量大、班次多、线路固定等特点,存在多种安全风险隐患。为综合评估常规公交风险,对国内外554条事故数据分析整理,构建了常规公交风险指标体系。建立了常规公交风险评估的结构方程模型,得到常规公交风险因素对事故的单向拓扑结构。在结构学习的基础上,利用信息熵理论研究风险因素对预测结果可信度的影响权重,从而进行变量筛选。以失火事故为例利用贝叶斯网络模型进行了城市常规公交风险评估参数学习。研究结果表明,失火事故的主要风险因素为油气泄漏、车内外温度均较高等。在风险因素组合作用下失火事故发生概率范围为0.002 1至0.842 9。所建模型预测精度高,验证了方法的科学性和准确性,可用于进行定量化的常规公交风险评估。 展开更多
关键词 风险评估 常规公交 结构方程模型 贝叶斯网络模型 信息熵
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Application of Bayesian Network Learning Methods to Land Resource Evaluation 认领 引用
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作者 HUANG Jiejun HE Xiaorong WAN Youchua 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第4期1041-1045,共5页
Bayesian network has a powerful ability/or reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective w... Bayesian network has a powerful ability/or reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to deal with prediction, classification and clustering. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to learn a Bayesian net- work structure from given data, and then constructed a Bayesian network model for land resource evaluation with expert knowledge and the dataset. The experimental results based on the test dataset are that evaluation accuracy is 87.5%, and Kappa index is 0. 826. All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation. 展开更多
关键词 Bayesian networks data mining land resource evaluation models
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长江流域江苏段碳储量的多情景模拟和空间格局优化 认领 引用 被引量：5
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作者 彭卓越 李梦婷 +3 位作者 梁煜彬 刘亚明 方红远 殷峻暹 《环境科学》 EI CAS CSCD 北大核心 2026年第2期892-902,共11页
优化碳储量的空间格局对提高区域生态系统碳汇容量和维持区域碳平衡具有重要意义.以长江流域江苏段为例,基于土地利用/覆被变化、社会经济和气候环境数据,结合InVEST和PLUS模型预测研究区2030年自然发展、耕地保护和生态保护这3种不同... 优化碳储量的空间格局对提高区域生态系统碳汇容量和维持区域碳平衡具有重要意义.以长江流域江苏段为例,基于土地利用/覆被变化、社会经济和气候环境数据,结合InVEST和PLUS模型预测研究区2030年自然发展、耕地保护和生态保护这3种不同情景下的生态系统碳储量和空间分布格局,并借助具有决策优化能力的贝叶斯网络模型对研究区碳储量格局进行了分区优化.结果表明:①2000~2020年研究区碳储量呈下降趋势,共减少了4797.63×104t,主要原因是耕地、林地向建设用地转换.②2030年研究区生态保护情景下的碳储量为38528.91×104t,呈上升趋势,其余2种情景下的碳储量均呈现下降趋势.③通过贝叶斯网络模型,筛选出关键变量关键状态子集,将研究区划分为生态保护区、耕地保护区、水源涵养区和经济建设区这4类优化分区.研究结果可为流域土地利用可持续发展及推进流域实现“双碳”目标提供参考. 展开更多
关键词 碳储量 PLUS模型 InVEST模型 多情景模拟 贝叶斯网络模型 空间格局优化
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Predicting nitrogen surplus in agricultural lands of China using a hybrid machine learning approach with smaller datasets and fewer features 认领 引用
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作者 Hao Wang Gaofei Yin +8 位作者 Hongda Wen Feng Wang Ziwei Yang Xueying Sun Xulin Zhang Huiqing Jiao Mengyu Zhai Wenchao Li Hongbin Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期743-753,共11页
Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen... Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen use efficiency.However,traditional NS estimation methods often require extensive data,which are difficult to obtain in data-scarce regions.In this study,a BN-ML NS prediction model was developed by coupling Bayesian Networks(BN)and Machine Learning(ML)using long-term monitoring data(including climate,soil,and crops)from 2000 to 2019 in China.The key findings are as follows:Nitrogen fertilizer application rate(NR)is the dominant factor influencing NS across the seven regions;however,due to differences in climate,cropping patterns,and soil types,the impact of NR exhibits spatial heterogeneity;The BN-ML model demonstrates strong predictive performance,with R2values exceeding 0.9;compared to traditional NS prediction models,the BN-ML model maintains high accuracy using only half the number of features(e.g.,NR,Yield)and fewer than 120 data points;when validated at the small watershed scale,the model achieved over 80%prediction accuracy.By enabling reliable NS prediction with limited data,the proposed model supports more targeted and sustainable nitrogen management.It can assist national and regional authorities in identifying high-risk areas,optimizing fertilizer use,and formulating region-specific agricultural strategies. 展开更多
关键词 Bayesian network model Extreme gradient boosting Nitrogen management
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基于多情景土地利用/覆盖变化的天山北部经济区碳汇服务空间格局优化 认领 引用 被引量：2
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作者 杨华存 何浩 +1 位作者 杨红霞 韩东爽 《环境科学》 EI CAS CSCD 北大核心 2026年第5期3072-3085,共14页
在全球气候变化与“双碳”目标的背景下,优化碳汇服务空间格局是实现区域碳中和与生态经济协同发展的关键.以天山北部经济区为案例,集成PLUS模型与InVEST模型,模拟自然发展(ND)、城镇发展(UD)、耕地保护(CP)及生态保护(EP)这4种情景下2... 在全球气候变化与“双碳”目标的背景下,优化碳汇服务空间格局是实现区域碳中和与生态经济协同发展的关键.以天山北部经济区为案例,集成PLUS模型与InVEST模型,模拟自然发展(ND)、城镇发展(UD)、耕地保护(CP)及生态保护(EP)这4种情景下2035年土地利用/覆盖变化(LUCC)对碳汇服务的影响,并融合贝叶斯网络解析驱动机制,提出空间优化分区方案.结果表明:①LUCC对碳汇服务具有显著调控作用,碳储量空间分布呈“西南条带状、东北点状”特征,高值区集中于森林与草地覆盖的南部,低值区分布于北部建设用地和未利用地,呈现零星破碎化特征.②多情景模拟显示,2035年自然发展情景与城镇发展情景使碳储量较2020年分别下降0.74%和1.56%,而耕地保护情景与生态保护情景分别增加0.34%和0.02%,表明耕地保护与生态约束政策可有效减缓碳损失.③基于地理探测器与贝叶斯网络的空间优化将研究区划分为生态涵养区、生态缓冲区、耕地优化区与建设优化区,其中西北部需优先实施集约开发与碳配额管理,南部生态核心区需要严格限制开发与推进植被修复.基于碳汇服务现状开展空间格局优化,为干旱区国土空间规划与碳中和路径提供科学工具,助力“双碳”目标下生态保护红线划定与生态经济协同发展. 展开更多
关键词 碳汇服务 土地利用变化 PLUS-InVEST模型 贝叶斯网络 空间格局优化
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Analysis method for evaluating uncertainty in the machining process of thin-walled parts 认领 引用
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作者 Xiaoyue Li Zhaoze Sun +2 位作者 Hao Qi Yue Guo Shuowei Bai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期136-147,共12页
The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of ... The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of machining process is constructed.The influence mechanism of input variables on machining distortion un-certainty is clarified.Initially,the uncertain variables are collected and the inference model of machining dis-tortion is constructed based on root-branch-leaf Bayesian network structure.The weight of the influence of each input variable on machining distortion uncertainty and maximum influence path are obtained.Next,an inverse Bayesian network is established,with the uncertainty inference results used as prior information to carry out inverse inference of machining distortion uncertainty.The influence possibility of the related factors of the main influence variables on the uncertainty of machining distortion is obtained.Aviation aluminum alloy T-shaped part was taken as an example,and the influence mechanism of initial residual stress,surface residual stress,cutting force and their related factors on the machining distortion uncertainty was investigated.The results indicated that the influential weights of initial residual stress,surface residual stress and cutting force on the machining distortion uncertainty was 0.33(maximum),0.23 and 0.04 respectively.The factors related to initial residual stress,surface residual stress,and cutting force had influence weights of 0.099(maximum),0.009,and 0.001 on the machining distortion uncertainty.The probabilities of the effects were 0.920,0.075 and 0.005,respectively.Finally,the paper compares the proposed model with MC-GBRT and BiLSTM-UP,showing average improvements of 46.7%and 63.1%in stability and 35.7%and 20.6%in computational speed,respectively.This paper proposes a Bayesian network-based machining distortion uncertainty inference model that effectively reveals the mapping mechanism between initial residual stress,surface residual stress,cutting force,and the machining distortion uncertainty in thin-walled components. 展开更多
关键词 Thin-walled components Machining distortion Uncertainty Bayesian network inference model Inverse Bayesian inference
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融合模糊DEMATEL-ISM-BN的城市燃气管网安全运行影响因素分析 认领 引用 被引量：1
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作者 汪宙峰 何宸锐 +2 位作者 邓斯尹 谢凯宇 刘威 《安全与环境工程》 CAS CSCD 北大核心 2026年第2期142-153,166,共12页
随着我国城市化进程加速推进,城市燃气管网规模持续扩张,系统复杂性显著提升,燃气管网安全运行面临严峻挑战。为探究城市燃气管网安全运行影响因素间的相互作用关系及影响机理,结合文献调研与典型事故案例,构建了包含18项指标的城市燃... 随着我国城市化进程加速推进,城市燃气管网规模持续扩张,系统复杂性显著提升,燃气管网安全运行面临严峻挑战。为探究城市燃气管网安全运行影响因素间的相互作用关系及影响机理,结合文献调研与典型事故案例,构建了包含18项指标的城市燃气管道安全运行影响因素指标体系,提出了融合模糊决策实验室分析法(decision making trial and evaluation laboratory, DEMATEL)、解释结构模型(interpretive structural modeling, ISM)和贝叶斯网络(Bayesian network, BN)的综合评估模型。该模型基于模糊DEMATEL构建影响因素间的因果图,量化因素间的关联;基于ISM模型实现风险系统的层级解构,揭示风险传导路径;基于GeNie软件平台建立BN模型,实现风险概率的评估与溯源。结果表明:在所构建的指标体系中,防腐层检测周期、阴极保护、地面沉降及服役年限等因素的风险敏感性最高;BN逆向推理揭示最大致因链为“阴极保护失效→检测周期不当→管网运行风险发生”,凸显腐蚀防控关键作用;敏感性分析表明接口质量、设计人员水平等微小扰动可引发显著的风险变化。通过典型事故案例验证发现,该模型具有良好的可靠性与工程适用性,可为燃气管网安全运行风险防控提供理论支撑。 展开更多
关键词 燃气管网 模糊DEMATEL-ISM-BN 解释结构模型(ISM) 贝叶斯网络
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地铁深基坑施工坍塌风险耦合研究 认领 引用 被引量：1
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作者 方俊 黄金艳 +1 位作者 徐小琴 王景昌 《安全与环境学报》 CAS CSCD 北大核心 2026年第2期483-495,共13页
为实现地铁深基坑施工坍塌事故多因素耦合致灾机制解析与精准风险管控策略制定,提出了一种基于N-K模型和贝叶斯网络(Bayesian Network,BN)的定量耦合风险评估方法。通过对113份地铁深基坑施工坍塌事故报告的分析,识别出5类主要风险因素... 为实现地铁深基坑施工坍塌事故多因素耦合致灾机制解析与精准风险管控策略制定,提出了一种基于N-K模型和贝叶斯网络(Bayesian Network,BN)的定量耦合风险评估方法。通过对113份地铁深基坑施工坍塌事故报告的分析,识别出5类主要风险因素(人、物、管、环和技)。通过N-K模型解构多风险耦合效应,揭示风险耦合演化规律,基于N-K模型计算结果确定贝叶斯网络模型结构及参数,利用贝叶斯网络敏感性分析评估风险因素对显著风险耦合情境的影响,逆向溯源关键风险因素。结果表明,地铁深基坑施工坍塌风险随耦合因素种类的增加而变大,其中人-物-管-环-技风险耦合值最大、发生概率最高。风险因素c4(施工现场安全监管和隐患排查不到位)、d1(地质水文条件恶劣)、b4(材料、构件质量或强度不合格)、a1(安全风险意识差)和a5(违规违章施工)在高风险耦合情境中表现出高敏感性,对地铁深基坑施工坍塌风险耦合起着关键作用。 展开更多
关键词 安全工程 地铁深基坑 施工坍塌 风险耦合 N-K模型 贝叶斯网络
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基于贝叶斯网络的甲状腺癌非计划再次手术预测模型构建研究 认领 引用
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作者 袁波英 王锦毓 +5 位作者 褚嘉栋 鄢团军 江舜杰 郑伟慧 麻俊豪 林凯 《中国医院管理》 北大核心 2026年第6期67-72,共6页
目的构建临床预测模型探讨甲状腺癌根治术后非计划再次手术(unplanned reoperation,URO)的危险因素,辅助发现高风险患者。方法回顾性收集近3年某国家肿瘤区域医疗中心416例甲状腺癌根治术患者的相关指标,采用逐步后退logistic回归分析... 目的构建临床预测模型探讨甲状腺癌根治术后非计划再次手术(unplanned reoperation,URO)的危险因素,辅助发现高风险患者。方法回顾性收集近3年某国家肿瘤区域医疗中心416例甲状腺癌根治术患者的相关指标,采用逐步后退logistic回归分析确定相关影响因素,基于最大最小爬山算法结合专家知识构建贝叶斯网络模型,采用十折交叉验证评估性能,通过绘制受试者工作特征曲线评价预测效果。结果舒张压>90 mmHg、术中出血量>10 mL、术后24 h引流量>75 mL和正高级职称可能是URO发生的独立预测因子。贝叶斯网络模型显示:淋巴结切除方式、是否淋巴结继发、收缩压、术中出血量、术后24 h引流量、手术医师职称、T分期与是否发生URO直接关联,而甲状腺切除方式通过术中出血量与是否发生URO间接关联,模型准确度为95.19%。结论贝叶斯网络模型可以有效识别并预测甲状腺癌根治术后URO发生的高风险患者,辅助临床决策。 展开更多
关键词 甲状腺癌 非计划再次手术 贝叶斯网络模型
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基于贝叶斯网络探讨社区老年人轻度认知障碍的影响因素研究 认领 引用
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作者 陈蓄 杨纪元 +4 位作者 孙贇 马绍骏 肖世富 汪海娅 曹明节 《老年医学与保健》 CAS 2026年第3期399-406,共8页
目的分析社区老年人轻度认知障碍(MCI)的影响因素及其网络关系。方法利用上海老年脑健康队列研究中徐汇区社区居民的基线数据,纳入其人口学因素、生活习惯、闲暇及社交活动、病史体检资料、实验室检查、认知功能、心理状况、日常生活活... 目的分析社区老年人轻度认知障碍(MCI)的影响因素及其网络关系。方法利用上海老年脑健康队列研究中徐汇区社区居民的基线数据,纳入其人口学因素、生活习惯、闲暇及社交活动、病史体检资料、实验室检查、认知功能、心理状况、日常生活活动能力评定方面等资料。采用随机森林模型对1043名参与者进行初筛,使用爬山算法建立贝叶斯网络(BN),并根据极大似然估计进行参数估算。结果经随机森林模型筛选后,将甘油三酯-总胆固醇-体重指数(TCBI)、慢性病数量、体重指数、载脂蛋白B、载脂蛋白A1、文化程度、空腹血糖、低密度脂蛋白胆固醇、脂蛋白a(Lpa)、肌酐、饮食习惯、是否经常使用电脑手机上网12个变量纳入BN模型。以筛选出的12个变量作为网络节点,构建了一个含有13个节点和20条有向边的社区老年人MCI相关因素BN模型。TCBI、Lpa、慢性病数量和是否经常使用电脑手机上网与MCI直接相关。结论BN模型揭示了社区老年人MCI与代谢指标、生活方式及慢性病负担之间的复杂关联模式及局部路径结构。 展开更多
关键词 贝叶斯网络模型 社区老年人 轻度认知功能障碍 影响因素 随机森林
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长三角新能源汽车产业空间结构演化与动态联结 认领 引用
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作者 曹继 王雪微 +2 位作者 孙雯 曹卫东 雍睿 《经济地理》 CSSCI CSCD 北大核心 2026年第5期152-162,共11页
文章从网络外部性驱动的“流—层级”视角出发,聚焦长三角地区城市间产业要素流动如何重塑空间层级结构。首先,通过构建综合评价体系和熵权TOPSIS法量化2017—2023年长三角各城市的新能源汽车产业发展水平。随后,结合汽车零部件供应、... 文章从网络外部性驱动的“流—层级”视角出发,聚焦长三角地区城市间产业要素流动如何重塑空间层级结构。首先,通过构建综合评价体系和熵权TOPSIS法量化2017—2023年长三角各城市的新能源汽车产业发展水平。随后,结合汽车零部件供应、城市经济贸易联系和绿色专利合作三类网络,构建贝叶斯动态联结模型分析了城市在网络中的地位与联系强度对层级演化的影响。结果表明:①长三角核心城市对次核心具有显著的层级带动与功能外溢效应,“核心—外围”的梯度结构更加清晰,但外围城市受吸纳能力与路径依赖约束,层级提升相对滞后。②长三角跨省通道与省域内多核心联系同步强化,生产链、经贸链与创新链的要素流动由单向扩散转向多向耦合,网络枢纽间互动增强,带动资源在多极之间的再配置与再集聚。③长三角新能源汽车产业空间格局由单极核演化为多中心协同,核心互动更为紧密,同时动态联结模型揭示了优势资源扩散与结构性分化并存的机制:一方面通过配套协作与技术溢出提升部分次级节点,另一方面部分城市因嵌入度与吸收能力不足而出现层级固化的负向外部性。总体上,产业要素流动既推动了核心城市层级跃升,也在一定程度上抑制了外围城市的固化趋势,形成“核心引领—外围滞后”的空间层级演化特征。研究为优化长三角新能源汽车产业网络结构、提升区域协同发展水平提供了理论依据与政策参考。 展开更多
关键词 流—层级 新能源汽车 产业发展水平 空间结构 创新链 贝叶斯网络模型 长三角
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