A Bifactor Analysis Approach to Construct Validity and Reliability of the Affective Exercise Experience Questionnaire among Chinese College Students

Affective exercise experience as an emerging theoretical concept has great potential to provide a more nuanced understanding of individual factors that influence exercise behavior.However,concerning the Affective Exercise Experiences(AFFEXX)questionnaire,it has not been examined yet whether the structural score of the AFFEXX is a useful index to predict physical activity(refers to any bodily movement produced by skeletal muscles that requires energy expenditure).Furthermore,there is currently a gap in knowledge regarding the psychological mechanisms that can explain the relationship between affective exercise experiences and the level of physical activity(PA).In order to adress these gaps in the literature,we conducted two studies among Chinese collge students that aimed(i)to investigated whether the total score of the three AFFEXX-C constructs(antecedent appraisals,core affective exercise experiences,and attraction-antipathy towards exercise)is a relaible indicator that can be utilized in research and pratical settings and(ii)to evalute the specific psychological mechanisms that can explain the relationship between affective exercise experience and PA.In Study 1,we recruited 801 voluntary Chinese college students for bifactor and correlational analyses.In Study 2,875 Chinese college students were enrolled to verify our findings from Study 1 and to explore the aforementioned mechanism.Results from the bifactor analyses supported our hypothesis that the total scores of the three AFFEXX-C constructs can be used among Chinese college students to establish a link with PA.Additionally,our results suggested that core affective exercise experiences and attraction-antipathy mediated the relationship between antecedent appraisals and the level of moderate-to-vigorous intensity PA.Therefore,measuring affective exercise experiences using the AFFEXX-C,specifically the total scores of each individual construct may be a useful approach to predict future PA levels.

Models for Assessment and Computational Analysis of Hardness of the Heat Affected Zone in Water Cooled Aluminum Weldment

Models have been derived for assessment and computational analysis of the hardness of the heat affected zone (HAZ) in aluminum weldment. The general model;γ = 1.2714[(αβ/α + β)] was found to predict the HAZ hardness of aluminum weldment cooled in water as a function of the HAZ hardness of both mild steel and cast iron welded and cooled under the same conditions. The maximum deviations of the model-predicted HAZ hardness values γ, α and β from the corresponding experimental values γexp, αexp and βexp were less than 0.02% respectively.

Establishment and evaluation of a mouse model of affective disorder combined with atherosclerosis

Objective:To establish a mouse model of affective disorder complicated with atherosclerosis(AS)by high fat feeding and chronic mild unpredictable stimulation(CUMS),and to provide an animal model for the later study of the prevention and treatment of affective disorder complicated with atherosclerosis by traditional Chinese medicine.Methods:10 C57BL/6J mice were used as blank group,and 20 ApoE-/-mice were randomly divided into AS group and AS+CUMS group.After one week of adaptive feeding,except for the blank group,the other two groups were fed with high fat diet.Meanwhile,the AS+CUMS group was given chronic unpredictable mild stress.The model was evaluated after 16 weeks of modeling.During the experiment,the body weight,food intake,excitability,hair color and other general morphology of mice in each group were observed and recorded.Behavioral indexes(Sucrose preference tests and Open Field test)were detected in each group.The levels of 5-hydroxytryptamine(5-HT)and hypothalamic-pituitary-adrenal(HPA)axis hormones,including adrenocorticotropin(ACTH)and corticosterone(CORT),were detected by ELISA.Serum lipid levels,including total cholesterol(TC),triglyceride(TG),low density lipoprotein cholesterol(LDL-C)and high density lipoprotein cholesterol(HDL-C),were detected by ELISA.HE staining was used to observe the pathological condition of aorta.Results:Compared with blank group,the excitability and food intake of AS+CUMS group were significantly decreased.There were no significant differences in sugar water consumption and activity capacity of mice in AS group,while sugar water consumption and activity capacity of mice in AS+CUMS group were significantly decreased(P<0.01).The serum 5-HT levels in AS group and AS+CUMS group were decreased,and the levels of ACTH and CORT in AS+CUMS group were significantly decreased(P<0.01),and the levels of ACTH and CORT in AS+CUMS group were significantly increased(P<0.01).The serum TC,TG and LDL-C levels of mice in AS group and AS+CUMS group were significantly increased(P<0.01),while the HDL-C level of mice in both groups was significantly decreased(P<0.01).HE staining results showed that the area and degree of plaques in the active vascular lumen of AS group and AS+CUMS group were larger and heavier.Conclusion:High fat feeding combined with CUMS was successful in establishing a mouse model of emotional disorder combined with atherosclerosis.

An Affective-Motivational Interface for a Pedagogical Agent

This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedagogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent learning system. This work focuses on the design of the inference engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is based on an affective-motivational model. This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.

Factors Affecting Quality of Equipment Maintenance Based on Structural Equation Modeling

Factors affecting equipment maintenance quality usually include personnel,equipment and facilities,raw materials and spare parts, craft, environment and quality management. These six factors affecting maintenance quality are not all the same degree. In order to find out which factor can influence equipment maintenance quality greatly and achieve sophisticated management,characteristic quantities of six factors are presented. A structural equation modeling( SEM) is designed using the six factors as latent variables and their characteristic quantities as the observed variable.According to the basic data obtained from the questionnaire survey,calculate the standardized regression weight by parameter estimation of the SEM. Then the key factors affecting the quality of the equipment maintenance are determined with the help of the standardized regression weight.

Exploring the Impact of Factors Affecting the Lifespan of HIVs/AIDS Patient’s Survival: An Investigation Using Advanced Statistical Techniques

This study investigates the impact of various factors on the lifespan and diagnostic time of HIV/AIDS patients using advanced statistical techniques. The Power Chris-Jerry (PCJ) distribution is applied to model CD4 counts of patients, and the goodness-of-fit test confirms a strong fit with a p-value of 0.6196. The PCJ distribution is found to be the best fit based on information criteria (AIC and BIC) with the smallest negative log-likelihood, AIC, and BIC values. The study uses datasets from St. Luke hospital Uyo, Nigeria, containing HIV/AIDS diagnosis date, age, CD4 count, gender, and opportunistic infection dates. Multiple linear regression is employed to analyze the relationship between these variables and HIV/AIDS diagnostic time. The results indicate that age, CD4 count, and opportunistic infection significantly impact the diagnostic time, while gender shows a nonsignificant relationship. The F-test confirms the model's overall significance, indicating the factors are good predictors of HIV/AIDS diagnostic time. The R-squared value of approximately 72% suggests that administering antiretroviral therapy (ART) can improve diagnostic time by suppressing the virus and protecting the immune system. Cox proportional hazard modeling is used to examine the effects of predictor variables on patient survival time. Age and CD4 count are not significant factors in the hazard of HIV/AIDS diagnostic time, while opportunistic infection is a significant predictor with a decreasing effect on the hazard rate. Gender shows a strong but nonsignificant relationship with decreased risk of death. To address the violation of the assumption of proportional hazard, the study employs an assumption-free alternative, Aalen’s model. In the Aalen model, all predictor variables except age and gender are statistically significant in relation to HIV/AIDS diagnostic time. The findings provide valuable insights into the factors influencing diagnostic time and survival of HIV/AIDS patients, which can inform interventions aimed at reducing transmission and improving early diagnosis and treatment. The Power Chris-Jerry distribution proves to be a suitable fit for modeling CD4 counts, while multiple linear regression and survival analysis techniques provide insights into the relationships between predictor variables and diagnostic time. These results contribute to the understanding of HIV/AIDS patient outcomes and can guide public health interventions to enhance early detection, treatment, and care.

Exploring the Impact of Factors Affecting the Lifespan of HIVs/AIDS Patient’s Survival: An Investigation Using Advanced Statistical Techniques

This study investigates the impact of various factors on the lifespan and diagnostic time of HIV/AIDS patients using advanced statistical techniques. The Power Chris-Jerry (PCJ) distribution is applied to model CD4 counts of patients, and the goodness-of-fit test confirms a strong fit with a p-value of 0.6196. The PCJ distribution is found to be the best fit based on information criteria (AIC and BIC) with the smallest negative log-likelihood, AIC, and BIC values. The study uses datasets from St. Luke hospital Uyo, Nigeria, containing HIV/AIDS diagnosis date, age, CD4 count, gender, and opportunistic infection dates. Multiple linear regression is employed to analyze the relationship between these variables and HIV/AIDS diagnostic time. The results indicate that age, CD4 count, and opportunistic infection significantly impact the diagnostic time, while gender shows a nonsignificant relationship. The F-test confirms the model's overall significance, indicating the factors are good predictors of HIV/AIDS diagnostic time. The R-squared value of approximately 72% suggests that administering antiretroviral therapy (ART) can improve diagnostic time by suppressing the virus and protecting the immune system. Cox proportional hazard modeling is used to examine the effects of predictor variables on patient survival time. Age and CD4 count are not significant factors in the hazard of HIV/AIDS diagnostic time, while opportunistic infection is a significant predictor with a decreasing effect on the hazard rate. Gender shows a strong but nonsignificant relationship with decreased risk of death. To address the violation of the assumption of proportional hazard, the study employs an assumption-free alternative, Aalen’s model. In the Aalen model, all predictor variables except age and gender are statistically significant in relation to HIV/AIDS diagnostic time. The findings provide valuable insights into the factors influencing diagnostic time and survival of HIV/AIDS patients, which can inform interventions aimed at reducing transmission and improving early diagnosis and treatment. The Power Chris-Jerry distribution proves to be a suitable fit for modeling CD4 counts, while multiple linear regression and survival analysis techniques provide insights into the relationships between predictor variables and diagnostic time. These results contribute to the understanding of HIV/AIDS patient outcomes and can guide public health interventions to enhance early detection, treatment, and care.

融合中式元素的三维室内场景情绪化渲染

情绪具有主观性,利用计算机技术自动生成一个既满足真实性又与目标情绪匹配的虚拟室内场景是一项充满挑战的任务。目前缺乏对室内场景情感表达进行识别和评估的技术方法,且在充分考虑情感诉求的前提下,如何提升场景渲染结果真实性也是室内场景设计需要考虑的重要因素。针对上述问题,提出了一种融合中式元素的虚拟室内场景情绪化渲染算法。首先,利用深度学习算法从一个包含25 000张图片的家庭室内场景数据集中提取不同情绪的特征,训练情绪分类器,用于识别和评估渲染过程中虚拟室内场景的情绪表达。其次,为了保证渲染结果真实性,设计了一种场景物体纹理颜色真实性度量算法。然后,研究了根据目标情绪自动渲染虚拟室内场景的优化算法、融合中式元素的风格迁移算法,对场景中的物体进行细粒度的中式风格化处理,提升渲染结果的空间内涵、文化底蕴以及情感表达,增强视觉感染力。最后,在四个不同的室内场景中对该算法进行了实验,并通过对实验结果和用户调研数据的统计分析验证了该算法的正确性和有效性。

整合类内差异与类间关联的隐喻感情预测

隐喻感情预测有助于改进社交媒体内容的用户体验,同时在心理健康监测和虚拟心理治疗方面也具有潜在价值。此外,它还可以更精确地识别目标受众的感情需求,优化广告策略,提升商业效益。为了进一步提升情绪识别与情感检测的有效性,提出了一种整合类内差异与类间关联的多模式隐喻感情识别架构。首先引出三种单模式模型,包括视觉语义模型,文本语义模型和音频语义模型,从文本、视觉和音频三种不同的数据源中分别提取每个模式的个性化差异特征;随后引出一种深度层次多模式模型,通过中间层融合的方式对多模式之间的关联性进行学习,更好地利用双模式与三模式之间提供的互补信息;最后,基于决策层融合的方式将上述四种模型进行融合,在一种端到端的框架中,实现多模式隐喻感情的预测。在开源的数据集中进行的大量消融实验与对比实验证明了方法的有效性。

AI语音助手用户虚拟在线体验影响因素

在以ChatGPT为代表的ICT技术快速发展和社会变革环境下,以AI语音助手为例的智能应用对用户体验和行为的影响成为重要研究问题。文章基于认知情感意图(Cognition-Affect-Co⁃nation,CAC)框架,从AI语音助手技术特征出发分析感知拟人性、感知关系性、感知娱乐性对于用户认知评价的感知服务质量和感知使用成本,以及在认知评价作用下的情感体验对用户采纳意愿的影响。利用结构方法和fsQCA混合研究方法,通过对328份受访者的问卷调查进行实证检验。研究发现:AI语音助手的感知拟人性增加用户的感知使用成本,感知关系性和感知娱乐性有利于感知服务质量;情感体验在用户认知评价与用户行为意图结果之间起中介作用;根据fsQCA数据结果总结并诠释5种AI语音助手用户在线虚拟体验组态路径,验证并补充结构方程研究发现。文章为AI语音助手用户虚拟在线体验影响因素研究提供了新的理论研究视角,深化了客户旅程理论在用户虚拟在线体验中理论应用,为各界利用AI语音助手增强用户体验提供参考。

基于STIRPAT模型的京津冀地区碳排放影响因素分析

采用STIRPAT模型,分析了影响京津冀地区碳排放的主要因素.首先,利用IPCC方法对京津冀地区碳排放量进行测算.其次,在现有研究的基础上,筛选出与碳排放相关的影响因素,运用主成分分析法、岭回归法和线性回归法对京津冀地区碳排放量影响因素进行了实证分析.最后,在京津冀不同区域分别构建了改进的STIRPAT模型,探究关键因素的影响程度,并对碳排放影响因素进行了深入分析.实证研究表明,2000—2019年京津冀地区碳排放结构与变化趋势明显不同.同时,各区域关键影响因素各不相同,同一因素在不同区域产生的效果也有所不同.

新能源汽车政策下居民出行意向多群组分析

为研究新能源汽车政策对不同群体居民出行意向影响存在的差异性问题,提出新能源汽车政策下居民出行意向多群组模型.以计划行为理论为基础,整合居民对新能源汽车政策的态度、响应程度以及低碳理念,构建新能源汽车政策下居民出行意向模型,并对模型进行检验及修正,划分多群组,对修正后的模型进行多群组分析,研究新能源汽车政策对不同出行方式偏好群体影响机理的差异性.研究结果表明:居民对新能源汽车政策的态度、响应以及低碳理念对出行态度、主观规范、知觉行为控制以及行为意向的影响在不同出行方式偏好下存在显著差异;居民对新能源汽车政策的态度越积极、低碳理念越强,则选择新能源汽车或公共交通出行的意向越强;居民对新能源汽车政策的响应越积极,则选择新能源汽车出行的意向越强;改善居民对新能源汽车政策的态度、响应,可促进新能源汽车的使用;通过提升居民的低碳理念,可提高居民采用公共交通或新能源汽车出行的意向.研究可为通过交通政策调整居民出行意向以及制定交通政策提供参考.

基于情感认知差异的产品造型设计研究

通过设计师与用户两个角色的情感认知差异,探讨造型设计要素与情感感知之间的关联,引起设计师对用户情感需求的重视。研究设计师与用户对产品情感认知的发展现状,采用语义差异法与PAD情感空间量表、回顾式口语分析的方法,以智能加湿器为实验样本,研究设计师与用户对产品造型的情感认知差异。对比两者情感感知评价的数据,结合口语分析,得出设计师与用户在情感三维度上对产品造型认知存在的偏差。结论该方法可为情感设计提供新的探讨思路。

Reaction视频中用户弹幕信息交互行为的情感反应生成机理研究

深入挖掘Reaction视频中弹幕信息交互行为的情感反应机理有助于理解用户弹幕创作背后的情感生成原因及情感变化过程。本文基于情感反应模型,利用定向内容分析法对哔哩哔哩网站中11个热门视频的弹幕信息资源、视频内容以及reactor反应情况展开编码研究,构建了Reaction视频中用户弹幕信息交互行为的情感反应生成机理模型。研究发现,Reaction视频弹幕信息交互行为中的情感反应生成机理总体上遵循“信息刺激-情感反应”的路径,信息刺激有时会独立唤醒情绪或特定情感态度,有时也会通过唤醒特定情感态度进而影响情绪或内化情感态度的生成。该模型有助于提升情感反应理论在计算机协助交流中的情境化探索,也将为社交媒体中用户与信息交互提供优化建议。

基于Kano模型的西南地区农贸市场更新设计研究

目的旨在从情感化设计的角度出发,结合Kano模型的相关方法,根据用户的具体需求,对昆明篆新农贸市场的更新设计策略进行研究。方法通过分析情感化设计的三个层次,寻找用户痛点,设计并发放Kano问卷,分析统计各项需求的属性类别,并通过计算Better-Worse四象限散点图,对各功能进行优先级排序,最后得出昆明篆新农贸市场的更新设计重点。结果用户认为优化功能分区、摊位标准化设计、无障碍设计是市场必须要达到的要求;对导向设计、公共座椅、规划交通流线和停车位、预约停车位(小程序)具有强烈需求;对商品寄送到家(小程序)共享推车或共享菜篮、线上外卖点单(小程序)、特色摊位、餐饮区域、共享菜地、共享摊位,以及非遗文化展示售卖空间有较大期待。结论根据Kano模型的分析结果,按照必备属性、期望属性、魅力属性的顺序对功能进行设计,达到了集市空间的更新发展目标,有助于提升我国西南地区农贸市场活力、带动当地经济、保护和传承乡村文化,为我国西南地区农贸市场的更新发展提供新的研究视角。

基于改进SW模型的黄土高原畦灌冬小麦蒸散特性研究

冬小麦是黄土高原地区重要的粮食作物,以畦灌为主的地面灌溉是该地区普遍采用的灌溉方法。明确畦灌条件下不同灌水量对冬小麦蒸散耗水规律的影响可以为冬小麦合理的灌溉管理提供参考。利用改进的Shuttleworth-Wallace模型(SW模型)计算2016-2019年间黄土高原地区畦灌冬小麦的蒸散量,对蒸散发及其组分的影响因素进行分析。结果表明:改进的SW模型模拟畦灌冬小麦蒸散具有较高的精度,R2为0.88~0.90,RMSE为0.39~0.42 mm/d,MAE为0.29~0.30 mm/d,RRMSE为0.08~0.09,NSE为0.88~0.90,d为0.97。冬小麦生育期内E/ET呈先下降后升高的趋势。随着灌水量的减少,日蒸腾量有明显的下降,高水处理M1、低水处理M2、雨养处理M3分别为1.19~1.24 mm/d、0.93~1.05 mm/d和0.67~0.69 mm/d;E/ET表现出升高趋势,M1、M2、M3处理分别为28%~35%、34%~39%和41%~47%。随干旱程度增加,控制E/ET变化的主要因素从叶面积指数(LAI)转变为LAI和土壤含水量,高水处理冬小麦的蒸散发ET主要受净辐射控制,雨养处理冬小麦ET则受净辐射、土壤含水量和叶面积指数的综合影响。

高校辅导员情感劳动在“一站式”学生社区中的应用

为提升高校思政工作的系统化、精细化,众多高校开展了“一站式”学生社区综合管理模式建设。辅导员作为高校“一站式”学生社区综合管理模式建设中的核心力量,通过情感劳动(Affective labor),在开展学生思政教育工作过程中发挥了重要作用。辅导员情感劳动的内容包括情绪及情感感知、自我情绪管理和调节、情感联结、情感教育等。遵循自我情感和人际情感两种工作机制,辅导员在“一站式”学生社区通过空间前置、时间延伸及多元互动等途经,从深度挖掘情感资源、合理把握情感表达、积极拓展情感劳动方式、有效减少情感失调等方面着手,不断提高辅导员情感劳动的价值,助力“一站式”学生社区综合管理模式建设走深走实。

基于健康信念模式的前臂骨折康复训练器情感化设计研究

文章旨在分析前臂骨折康复训练器的情感化设计需求,总结出基于健康信念模式的前臂骨折康复训练器情感化设计思路。首先,文章分析了国内外前臂骨折康复训练器现存的设计问题,通过调研总结前臂骨折患者的需求特征,将健康信念模式与情感化设计相结合,确定康复训练器设计的切入点,聚焦于产品的功能实用性、外观设计,以及交互方式等,以期为前臂骨折康复训练器的情感化设计提供参考。

社交媒体使用与情绪研究:理论基础与测量指标构建

情绪即社会信息。智能传播时代,媒介技术的发展使得情绪更易被感知和传播,情绪因素对传播效果的影响也日趋重要。依据动机中介信息处理有限容量模型,结合情绪的认知和生理心理研究基础,可探索受众在接触社交媒体信息时的认知加工过程。通过评述主观和客观层面的情绪测量方法与参考指标,如情绪量表,以及脑电、功能性磁共振、眼动、皮肤电和心电图等,研究显示:情绪作为预测因素,无论在个体层面还是群体层面,都影响着受众社交媒体使用的程度;当下被动社交媒体使用理论假设的修正,也呼吁更精确、更短时间间隔、认知和生理的指标来进行情绪测量,将瞬时效果、中期效果和长期效果结合起来。基于此,研究提出社交媒体使用与情绪研究的测量指标构建,包括社交媒体信息类型、动机系统、认知加工过程、情绪的测量方法与具体维度,同时考虑到受众个体特异性和媒介介质特异性,以期为后续结构性情绪测量研究提供一定的参考。