Bayesian Network Model of Product Information Diffusion and Reasoning of Influence

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.

Predicting the nephrotoxicity of Chinese herbal medicines based on a Bayesian network model

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.

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

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.

Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma

BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS Patients with curatively resected advanced gallbladder adenocarcinoma(T3 and T4)were selected from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.A survival prediction model based on Bayesian network(BN)was constructed using the tree-augmented na?ve Bayes algorithm,and composite importance measures were applied to rank the influence of factors on survival.The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3.The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTS A total of 818 patients met the inclusion criteria.The median survival time was 9.0 mo.The accuracy of BN model was 69.67%,and the area under the curve value for the testing dataset was 77.72%.Adjuvant radiation,adjuvant chemotherapy(CTx),T stage,scope of regional lymph node surgery,and radiation sequence were ranked as the top five prognostic factors.A survival prediction table was established based on T stage,N stage,adjuvant radiotherapy(XRT),and CTx.The distribution of the survival time(>9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)>adjuvant radiation>adjuvant chemotherapy>surgery alone.For patients with node-positive disease,the larger benefit predicted by the model is adjuvant chemoradiotherapy.The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P<0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P<0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients.Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease.

Winning Probability Estimation Based on Improved Bradley-Terry Model and Bayesian Network for Aircraft Carrier Battle

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.

Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA)

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.

Linking Structural Equation Modeling with Bayesian Network and Its Application to Coastal Phytoplankton Dynamics in the Bohai Bay

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.

Research on Bayesian Network Based User's Interest Model

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.

Bayesian Network and Factor Analysis for Modeling Pine Wilt Disease Prevalence

A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.

常规公交风险的SEM与Bayesian Network组合评估方法研究

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。

Ontology Mapping Based on Bayesian Network

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.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network

The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesize different targets which are not predominant. In this way, various features at the same time, as well as the same feature appearing at different time complement mutually; in addition, the accuracy and reliability of electronic jamming decision making are enhanced significantly.

Application of Bayesian Network Learning Methods to Land Resource Evaluation

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.

From Deterministic to Random Models: An Analysis of the New Spanish Overtaking Traffic Law

The purpose of the research is to analyze the new Spanish law of Traffic, which no longer permits exceeding by up to 20 km/hour the generic speed limits when overtaking on conventional roads. In this research, deterministic and random models are developed to analyze the associated safety risks. The deterministic model highlights the importance of dimensional analysis and provides dimensionless abacuses to analyze the problem. Next, Bayesian networks and Bayesian models are used to build a random model from the previous one, providing a general method to convert deterministic into random models. In addition, the problems of ignoring the dimensions of the variables and parameters are discussed, a common mistake to be corrected. Some examples and multidimensional graphics illustrate the huge reduction in safety and the need to review the existing end of prohibition signs, most of which must be removed. Shortly, the research results show that the distance required for overtaking with safety increases drastically.

基于CT观察退变性腰椎滑脱症与关节突关节角及关节椎弓根角的关系

目的:基于CT观察探讨退变性腰椎滑脱(DLS)与关节突关节角和关节椎弓根角的关系。方法:回顾性收集2020年1月至2022年6月四川省骨科医院收治的169例DLS症患者纳为DLS组,另选取同期于我院体检并伴有腰腿疼痛但未腰椎滑脱的169例年龄匹配患者作为健康组;对比DLS组和健康组的临床资料,单因素以及多因素logistic回归分析影响DLS的危险因素;通过平滑曲线拟合分析关节突关节角和关节椎弓根角与DLS的曲线关系,构建贝叶斯网络模型并对其预测效能进行验证。结果:单因素分析结果显示DLS组在BMI、椎间盘退变、全身关节松弛、腰椎结构及曲度发生改变、韧带松弛、骨质疏松、脱钙、腰椎小关节突病变、合并糖尿病方面均高于健康组(P<0.05);DLS组的关节突关节角与健康组相比减小,关节突关节角不对称以及退变程度为1、2级的人数比例上升,椎弓根角显著增大(P<0.05);多因素分析结果表明BMI增加、椎间盘退变、腰椎结构及曲度发生改变、韧带松弛、骨质疏松、脱钙、全身关节松弛、腰椎小关节突病变、合并糖尿病、关节突关节角减小、关节突关节角不对称、关节突关节的退变以及椎弓根角的增加都是导致DLS发生的危险因素(OR值>1,P<0.05);平滑曲线拟合结果显示,在一定范围内,关节椎弓根角与DLS呈正相关,而关节突关节角和与DLS呈负相关;贝叶斯网络模型及预测推理显示:BMI指数增加、关节突关节角减小、关节突关节角不对称以及椎弓根角的增加与DLS直接相关,当患者关节突关节角减小、关节突关节角不对称以及椎弓根角的增加的概率降为0时,患者DLS发生率由50%降低为37.2%;经过模型验证证明贝叶斯网络预测模型具有良好的区分度、准确度和有效性。结论:基于CT观察可以对DLS有更准确的诊断,且在一定范围内关节突关节角和关节椎弓根角与DLS具有一定的相关性。

概率融合的抗侧翻智能主动悬架控制研究

为了提升高质心车辆的侧倾稳定性和平顺性,降低车辆侧翻事故造成的伤亡率,提出一种基于概率融合隶属度函数构建理论的侧翻工况预测和控制方法。首先,通过采集车辆侧翻工况数据,选取车辆状态变量,基于影响权重确定与车辆侧翻相关的关键影响因子。其次,根据时间序列对数据进行时间片段划分,设计动态贝叶斯预测网络,对下一时间片段内车辆侧翻概率进行预测。最后,根据车辆性能参数与控制器参数的映射规则,建立概率融合的Takagi-Sugeno(T-S)模糊隶属度函数,设计车辆主动悬架抗侧翻鲁棒控制器。CARSIM/Simulink联合仿真结果表明,与被动悬架、半主动悬架、多目标控制主动悬架相比,所提方法可以平稳且高效地防止车辆侧翻,提升车辆行驶的安全性。

水下人-机协同搜救作业人因可靠性分析研究

为分析水下人-机协同搜救作业人因可靠性并识别其关键影响因素,根据水下搜救训练实际和相关研究结论提取人因可靠性影响因素;建立人因可靠性影响因素体系,利用解释结构模型定性分析各影响因素间相关关系,将建立的各影响因素的解释结构模型转换为贝叶斯网络拓扑结构,分别构建潜水员作业、无人遥控潜水器(ROV)作业、人-机协同作业人因可靠性分析贝叶斯网络;通过对3类贝叶斯网络进行推理,揭示影响人因可靠性的关键因素及其依赖关系,并定量分析3种作业模式下的人因可靠性变化。结果表明:人-机协同搜救作业中“误解意图”“通信不畅”“信息不对称”“作业精度不足”“丧失救助能力”等影响因素起到关键作用;当上述因素得到较好控制时,人-机协同搜救作业的人因可靠性将会表现出优于单纯的ROV或潜水员独立施救的效能。研究结果可为水下搜救作业人因可靠性分析与提升提供参考。

多种残差补偿的贝叶斯网络下的短期交通预测

为了解决道路车流量的数据生成条件时变场景下的交通预测问题,本文建立道路交通控制与交通流预测数据之间的联系,提出一种基于多种残差补偿的贝叶斯网络的短期交通预测方法。提取城市中大规模多路口主干道车道及车辆信息构造多个平行的贝叶斯网络,使用贝叶斯关系及期望最大化算法进行短期交通预测。再通过数据自相关残差补偿、车辆换道和多路口连通性的线性残差补偿提高了预测的精度,解决了传统研究对相邻路口和换道导致的误差等因素处理能力不足的问题。仿真结果表明:使用贝叶斯网络预测交通流,并基于车辆行为的残差进行精度补偿,可以更准确地预测复杂的交通演化场景的短期交通流。