Social Influence Analysis： Models, Methods, and Evaluation

Social influence analysis （SIA） is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and eval- uation aspects related to SIA. We divide SIA models into two types： microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.

Influencing factor analysis of interception probability and classification-regression neural network based estimation

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.

Analysis on the Influencing Factors of Low-carbon Economy and Its Mitigation Countermeasures in Sichuan Province

[Objective]The study aimed to analyze the influencing factors of low-carbon economy and its mitigation countermeasures in Sichuan Province.[Method]Taking Sichuan Province as an example,an extended STIRPAT model was established firstly,then the impacts of population,economy and technology on carbon emissions from 2000 to 2009 were analyzed econometrically by using the principal component analysis method.Finally,some corresponding countermeasures to reduce carbon dioxide emissions were put forward.[Result]At present,population scale had the greatest influence on carbon emissions in Sichuan Province,then energy consumption per industrial added value and the proportion of industrial added value to GDP.In addition,the influence of population scale on carbon emissions was still greater than that of population structure,and technical factor also has certain explanatory power on carbon emissions.Some countermeasures,like controlling population growth,advocating low-carbon life style and consumption model,paying more attention to the strategic adjustment of industrial structure to gradually reduce the proportion of high-carbon industries,encouraging energy consumption and emissions reduction plus scientific and technological innovation in a new energy technology filed,could be adopted to reduce carbon dioxide emissions,so as to adjust to the development of low-carbon economy in Sichuan Province.[Conclusion]The research could provide references for the establishment of policies for reducing carbon emissions.

Transient pressure analysis of polymer flooding fractured wells with oil-water two-phase flow

The oil-water two-phase flow pressure-transient analysis model for polymer flooding fractured well is established by considering the comprehensive effects of polymer shear thinning,shear thickening,convection,diffusion,adsorption retention,inaccessible pore volume and effective permeability reduction.The finite volume difference and Newton iteration methods are applied to solve the model,and the effects of fracture conductivity coefficient,injected polymer mass concentration,initial polymer mass concentration and water saturation on the well-test type curves of polymer flooding fractured wells are discussed.The results show that with the increase of fracture conductivity coefficient,the pressure conduction becomes faster and the pressure drop becomes smaller,so the pressure curve of transitional flow goes downward,the duration of bilinear flow becomes shorter,and the linear flow appears earlier and lasts longer.As the injected polymer mass concentration increases,the effective water phase viscosity increases,and the pressure loss increases,so the pressure and pressure derivative curves go upward,and the bilinear flow segment becomes shorter.As the initial polymer mass concentration increases,the effective water phase viscosity increases,so the pressure curve after the wellbore storage segment moves upward as a whole.As the water saturation increases,the relative permeability of water increases,the relative permeability of oil decreases,the total oil-water two-phase mobility becomes larger,and the pressure loss is reduced,so the pressure curve after the wellbore storage segment moves downward as a whole.The reliability and practicability of this new model are verified by the comparison of the results from simplified model and commercial well test software,and the actual well test data.

Development and validation of a predictive model for acute-onchronic liver failure after transjugular intrahepatic portosystemic shunt

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a cause of acute-onchronic liver failure(ACLF).AIM To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model.METHODS In total,379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort,and 123 patients from Nanfang Hospital were included in the external validation cohort.Univariate and multivariate logistic regression analyses were performed to identify independent predictors.The prediction model was established based on the Akaike information criterion.Internal and external validation were conducted to assess the performance of the model.RESULTS Age and total bilirubin(TBil)were independent risk factors for the incidence of ACLF within 1 year after TIPS.We developed a prediction model comprising age,TBil,and serum sodium,which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort.CONCLUSION Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis.Our model showed satisfying predictive value.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

网络消费行为影响要素的回溯性研究——基于Meta-Analysis的分析策略

基于网络经济的快速发展,不同学者对于网络消费行为的影响要素,形成了丰富的研究积累。由于采用不同的理论模型、前提假设与实证基础,因而存在着研究结果间的结论差异。以过往研究文献为数据来源与分析对象,对于行为影响要素的再度发掘与论证,成为此项研究的主旨。本研究采用知识图谱(CiteSpace)、自然语言分析(NLPIR)及元分析(Meta-Analysis)方法,对于2015—2020年期间的相关文献为分析样本,共计全样本文献数量223篇,可用于元分析的文献61篇。研究发现,在影响网络消费行为的要素中,感知有用性、信任、服务质量、交互信息、社交因素具有正向性功能;而感知风险、交易成本则具有负向性功能。同时,针对于研究结果,提出了有关研究维度与类型、情境性切换等方面的研究思考与设想。

A Micro Perspective of Research Dynamics Through“Citations of Citations”Topic Analysis

Purpose:Research dynamics have long been a research interest.It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject.A micro perspective of research dynamics,however,concerning a single researcher or a highly cited paper in terms of their citations and“citations of citations”(forward chaining)remains unexplored.Design/methodology/approach:In this paper,we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance,and topic innovation in each generation of forward chaining.Findings:For highly cited work,scientific influence exists in indirect citations.Topic modeling can reveal how long this influence exists in forward chaining,as well as its influence.Research limitations:This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations.Paraphrasing or semantically similar concept may be neglected in this research.Practical implications:This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining.This can serve as an inspiration on how to adequately evaluate research influence.Originality:The main contributions of this paper are the following three aspects.First,besides research dynamics of topic inheritance and topic innovation,we model topic disappearance by using a cross-collection topic model.Second,we explore the length and character of the research impact through“citations of citations”content analysis.Finally,we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton’s publications and the topic dynamics of forward chaining.

Investigation of contemporary college students’mental health status and construction of a risk prediction model

BACKGROUND Due to academic pressure,social relations,and the change of adapting to independent life,college students are under high levels of pressure.Therefore,it is very important to study the mental health problems of college students.Developing a predictive model that can detect early warning signals of college students’mental health risks can help support early intervention and improve overall well-being.AIM To investigate college students’present psychological well-being,identify the contributing factors to its decline,and construct a predictive nomogram model.METHODS We analyzed the psychological health status of 40874 university students in selected universities in Hubei Province,China from March 1 to 15,2022,using online questionnaires and random sampling.Factors influencing their mental health were also analyzed using the logistic regression approach,and R4.2.3 software was employed to develop a nomogram model for risk prediction.RESULTS We randomly selected 918 valid data and found that 11.3%of college students had psychological problems.The results of the general data survey showed that the mental health problems of doctoral students were more prominent than those of junior college students,and the mental health of students from rural areas was more likely to be abnormal than that of urban students.In addition,students who had experienced significant life events and divorced parents were more likely to have an abnormal status.The abnormal group exhibited significantly higher Patient Health Questionnaire-9(PHQ-9)and Generalized Anxiety Disorder-7 scores than the healthy group,with these differences being statistically significant(P<0.05).The nomogram prediction model drawn by multivariate analysis includ-ed six predictors:The place of origin,whether they were single children,whether there were significant life events,parents’marital status,regular exercise,intimate friends,and the PHQ-9 score.The training set demonstrated an area under the receiver operating characteristic(ROC)curve(AUC)of 0.972[95%confidence interval(CI):0.947-0.997],a specificity of 0.888 and a sensitivity of 0.972.Similarly,the validation set had a ROC AUC of 0.979(95%CI:0.955-1.000),with a specificity of 0.942 and a sensitivity of 0.939.The H-L deviation test result was χ^(2)=32.476,P=0.000007,suggesting that the model calibration was good.CONCLUSION In this study,nearly 11.3%of contemporary college students had psychological problems,the risk factors include students from rural areas,divorced parents,non-single children,infrequent exercise,and significant life events.

考虑多源误差影响的谐波减速器传动误差建模与分析

为解决现有谐波减速器传动误差建模方法的不足,提出一种考虑装配误差、齿面误差的综合误差建模方法。该方法首先建立装配误差和齿面误差数学模型,并进行误差影响参数分析。其次,测量实验样机装配偏心量和歪斜角度,基于模型得到对应偏心误差、歪斜误差模型理论值,齿面加工误差幅值按一般加工工况大小代入模型,得到对应加工误差模型理论值;然后,分别进行500 h、1500 h、3000 h时长的磨损实验,磨损后进行齿面形貌扫描和数据分析,得到对应的齿面形貌参数,基于模型得到对应的磨损误差模型理论值。最后,综合考虑多源误差影响,设置3组同型号样机,进行传动误差实验。实验结果表明:考虑装配偏心、歪斜和齿面加工等影响因素,传动误差的实验测量值与模型理论值误差在1.7~3.3%之间;综合考虑装配偏心、歪斜、齿面加工、齿面磨损等影响因素,在磨损500 h、1500 h、3000 h后传动误差的实验测量值与模型理论值误差分别在-3.45~2.07%、-5.88~-2.94%、1.33~5.33%之间。

面向网络公害社群的社交网络特性分析方法

随着网络的普及和网民群体的壮大,网络水军、网络公害社群等互联网公害组织严重威胁我国网络环境的安全。针对这类组织远离国内监管、隐蔽性和伪装性强的特点,提出了面向网络公害社群的社交网络特性分析方法。在用户特性层面,通过中心性测量对组内成员的影响力分布进行计算,找出影响力最大的核心用户,并分析核心成员的匿名性特点。在复杂网络特性层面,通过小世界效应和无标度特性的分析,研究了该社群内部的集群状况和资源分布状况。在时序特性层面,对核心成员的发文内容进行预处理后,利用BERTopic进行动态主题建模,得到社群主题随时间的变化关系。大量的实验性能分析验证了本文方法的有效性,研究成果可用于分析公害社群相关特征,追踪犯罪活动轨迹,为公害组织的侦查和打击工作以及自动化检测系统的设计工作提供理论参考。

糖尿病专科护士核心能力影响因素的结构方程分析

目的通过相关性分析和结构方程模型阐明影响糖尿病专科护士核心能力的影响因素,以期为糖尿病专科护士的管理和培养提供理论依据。方法选取广东省护理学会2016年—2021年培养的糖尿病专科护士为研究对象。采用自制的核心能力相关因素问卷(包括糖尿病护理及教育相关行为、糖尿病护理科研相关行为、继续教育相关行为、工作环境相关支持共4个维度)、糖尿病专科护士核心能力问卷对研究对象进行调查。共计发放问卷213份,回收有效问卷213份,有效回收率100%。采用相关性分析和结构方程模型对糖尿病专科护士核心能力的影响因素进行分析。结果广东省糖尿病专科护士核心能力总体处于中等偏上水平,得分较低者为科研能力20.02±6.86分,得分最高者为健康教育能力70.25±9.45分。糖尿病护理及教育相关行为、糖尿病护理科研相关行为、继续教育相关行为、工作环境相关支持与糖尿病专科护士核心能力之间具有相关性(r=0.156~0.764,P<0.05);结构方程模型显示,护理及教育相关行为对核心能力的影响最大(0.25),其次为科研相关行为(0.23),继续教育相关行为(0.13),工作环境相关支持对核心能力的影响最小(0.07)。科研相关行为、继续教育相关行为、工作环境相关支持可通过护理及教育相关行为的中介作用间接影响糖尿病专科护士核心能力。结论护理管理者应该重视糖尿病专科护士护理及教育相关行为,促进其科研相关行为,鼓励继续教育相关行为并提供工作环境相关支持来提高糖尿病专科护士核心能力。

基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型

根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。

全生命周期视角下汽车产业碳排放影响因素的研究——以吉林省为例

为实现中国的“双碳”目标,降低汽车产业碳排放成为一项重要举措。研究影响汽车产业碳排放的主要因素对制定低碳发展策略具有重要意义。为分析2012—2021年吉林省汽车产业总体碳排放及其驱动因素的变化,采用向量自回归检验(VAR)模型探究汽车产业的碳排放影响因素。研究发现,有4个影响吉林省汽车产业碳排放产生的主要因素,按影响程度由大到小依次为居民消费水平、家用汽车保有量、交通运输业碳排放、汽车制造碳排放。基于分析结果,提出相应建议促进吉林省汽车产业实现碳减排:基于清洁智能生产,进行节能技术升级;给予购车补贴,建设充电设施;建设公共交通,优化线路布局;回收废旧部件,形成循环经济等。

基于Meta-SEM的建筑工人安全行为影响机制研究

为梳理建筑工人安全行为的前因变量并分析其影响机制,基于从众动机和计划行为理论,构建了建筑工人安全行为影响机制理论模型,运用元分析-结构方程模型(Meta-Structural Equation Modeling,Meta-SEM)法对国内外64项实证研究中的128个独立样本进行系统分析,以明确各前因变量与建筑工人安全行为的相互作用关系并探讨企业规模的调节作用。结果表明:在个体层面,安全意识、安全态度、安全动机与建筑工人安全行为的相关系数分别为0.663、0.513、0.509;在组织管理层面,安全氛围与建筑工人安全行为的相关系数为0.678;安全意识、安全态度、安全动机是安全氛围与建筑工人安全行为之间的中介变量;企业规模对安全意识、安全动机、安全氛围与建筑工人安全行为的关系间存在显著调节作用,其中,中小型企业的影响更显著。研究结论可为后续建筑工人安全行为研究提供一定的理论基础,也为建筑业管理者提供了管理依据。

基于社会生态学模型的孕妇身体活动影响因素网络分析

目的:基于社会生态学模型,通过网络分析技术构建孕妇身体活动影响因素的网络结构,同时挖掘核心因素,为开发孕妇身体活动促进方案提供借鉴。方法:采用便利抽样法,于2022年8月至2023年1月选取广州市某三级甲等医院产科门诊行常规产检的602名孕妇为调查对象。采用一般资料调查表、孕期身体活动问卷和孕妇身体活动影响因素问卷进行调查。采用网络分析法构建孕妇身体活动影响因素的网络结构。结果:孕妇身体活动的总当量为(106.68±92.80)MET·h/周。59.4%的孕妇未达到指南推荐的身体活动标准。网络分析结果显示,孕妇身体活动影响因素中的个体、人际和环境因素分别形成3个集群,但彼此紧密联系,各因素之间互相影响。其中“我对妊娠期健康的重视程度”具有最大的强度、紧密度和中介度。结论:半数以上的孕妇身体活动不足,孕妇对妊娠期健康的重视程度是网络中最核心的因素。临床医护人员应从孕妇个体、人际和环境因素多角度出发,开展以提高孕妇对妊娠期健康重视程度为核心的身体活动促进方案。

老年人驾驶暴露行为影响因素交互作用分析

年龄增长带来的身体机能衰退迫使老年人调整驾驶行为,自我调节作为补偿老年人健康状况和降低驾驶风险的策略,能够有效延长驾驶时间和降低驾驶风险。为探究老年人个人生理心理因素和社会环境因素对自我调节中驾驶暴露行为的影响,设计问卷并在昆明市开展调查,回收老年人出行调查问卷后对问卷数据进行分析处理,采用Cronbachα信度系数、KMO测度值和验证性因子检验问卷信度效度。统计分析发现,不同年龄段和就业情况的老年驾驶人在驾驶暴露行为上存在显著差异,病史和药物服用会影响老年人驾驶暴露行为,老年人对短距离的频繁出行更有信心。通过构建中介效应分析和结构方程模型探究老年驾驶人驾驶暴露行为影响因素,结果表明:年龄与驾驶暴露行为(r=-0.157^(**),p<0.05)呈显著负相关,驾驶能力与驾驶暴露行为(r=0.182^(**),p<0.05)呈显著正相关;驾驶能力在社会支持和驾驶暴露行为之间起完全中介作用。

糖尿病患者发生医院内感染的影响因素风险预警模型构建与预防

目的探讨糖尿病患者发生医院内感染的影响因素、预警模型构建及预防措施。方法收集2020年1月至2023年1月该院内科收治的1059例糖尿病住院患者的临床资料,分析发生医院内感染的分布特点和危险因素并构建预警模型,总结护理预防措施。结果1059例患者中发生医院内感染63例,发生率为5.95%,其中下呼吸道感染占55.56%(35/63),胃肠道感染占17.46%(11/63),泌尿道感染占12.70%(8/63),口腔感染占6.35%(4/63),皮肤感染占3.17%(2/63),足部感染占3.17%(2/63),其他占1.59%(1/63)。住院时间长、使用抗菌药物种类多、有侵入性操作、空腹血糖值高均是医院内感染的独立影响因素,差异均有统计学意义(P<0.05)。医院内感染影响因素风险预警模型为Logit(P)=-4.122-0.782×住院时间+2.598×空腹血糖+5.510×使用抗菌药物种类-2.240×侵入性操作。结论糖尿病患者医院内感染发生率较高,其危险因素预警模型预测拟合度较好,预测价值较高。住院期间积极采取有针对性的护理措施可有效预防医院内感染。

基于回归模型的碳排放影响因素研究

本文分析了两个经济体的碳排放结构和经济发展趋势,发现新兴经济体在经济发展的同时碳排放不断增加,而发达经济体在经济发展的同时碳排放却在减少;构造了STIRPAT模型,研究了碳排放的相关影响因素,发现人均GDP、私家车保有量和人口数量是影响碳排放量的三个关键因素,为减少碳排放提供了参考和借鉴。

基于STIRPAT模型的山东省居住建筑运行碳排放影响因素研究

为有效实施建筑领域节能减排,实现双碳目标,以2005-2021年山东省居住建筑运行阶段碳排放数据为研究对象,利用STIRPAT模型对人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗、能源消耗强度和能源结构等影响碳排放的因素进行岭回归分析。结果表明:人口数量、人均GDP、人均消费支出、人均可支配收入、城镇化水平、居住建筑面积、单位建筑面积能耗和能源消耗强度等的增加会促使碳排放量的增加,但能源结构的增加会抑制碳排放量的增加,其中人口数量是影响碳排放的关键因素。据此提出山东省居住建筑节能减排的建议和对策。