期刊：
DIGITAL HEALTH,2023年9:20552076231208559 ISSN：2055-2076
通讯作者：
Cao, GH
作者机构：
[Gong, Hongcun; Deng, Sanhong; Wang, Hao] Nanjing Univ, Sch Informat Management, Nanjing, Peoples R China.;[Gong, Hongcun; Deng, Sanhong; Wang, Hao] Nanjing Univ, Int Joint Informat Lab, Nanjing, Peoples R China.;[Cao, Gaohui] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Cao, Gaohui; Cao, GH] Cent China Normal Univ, Sch Informat Management, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
通讯机构：
[Cao, GH ] C;Cent China Normal Univ, Sch Informat Management, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
关键词：
ABC theory of emotion;Health anxiety;health information-seeking behavior;rural population
摘要：
OBJECTIVE: The aim of the current study was to explore the relationship between online and offline health information-seeking behaviors, as antecedents and consequences, and health anxiety and related belief factors in rural residents. METHODS: Based on the ABC theory of emotions (ABC model), this study developed two theoretical models of the association between health anxiety and health information-seeking behavior: Placing health information-seeking behavior (both online and offline) as an outcome and antecedent, respectively, and setting three belief factors of the perceived health threat, intolerance of uncertainty, and catastrophic misinterpretations. We collected 730 self-reported data points from 20 June to 5 July 2022 for rural residents in China and empirically tested the research model and hypotheses using partial least squares-based structural equation modeling. RESULTS: The perceived health threat and intolerance of uncertainty are significant motivators of health anxiety, and health anxiety has a direct beneficial effect on both online and offline health information-seeking behaviors. Health anxiety is influenced either directly or indirectly by catastrophic misinterpretations resulting from online health information-seeking, while offline health information-seeking behavior does not contribute as strongly to health anxiety directly but mainly reinforces it through the mediating influence of catastrophic misinterpretations. CONCLUSIONS: Rural residents' health anxiety promotes their online and offline health information behaviors. And both their online and offline health information-seeking behaviors may promote health anxiety directly and through catastrophic misinterpretations. Comparing the two, online health information-seeking behaviors primarily exacerbate health anxiety through direct effects, whereas offline health information-seeking behaviors primarily affect health anxiety through catastrophic misinterpretations. We provide suggested guidelines for alleviating health anxiety and regulating health information behaviors among rural residents.
期刊：
Transportmetrica A: Transport Science,2023年19(2):Article: 1980131 ISSN：2324-9935
通讯作者：
Chen, Anthony
作者机构：
[Wang, Guangchao; Tong, Kebo] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Chen, Anthony] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China.;[Qi, Hang] Hubei Univ Econ, Inst Adv Studies Finance & Econ, Wuhan 430000, Peoples R China.;[Xu, Xiangdong] Tongji Univ, Key Lab Rd & Traff Engn, Shanghai 201804, Peoples R China.;[Ma, Shoufeng] Tianjin Univ, Coll Management & Econ, Tianjin 300072, Peoples R China.
通讯机构：
[Chen, Anthony] H;Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China.
关键词：
Stochastic user equilibrium;least perceived travel cost;Weibit model;location parameter;relative variability
摘要：
This study investigates the impacts of the least perceived travel cost on the stochastic user equilibrium (SUE) problem. The Weibit SUE models are considered since they have a location parameter that naturally capture the least perceived travel cost. Considering a positive location parameter enhances the behavioral reality by attaching a positive lower-bound to the perceived travel cost distributions. It reduces the perception variances route-specifically and causes route-specific coefficients of variation (CVs). The CVs reduce proportionally slower for shorter routes, thus contributing to resolving the scale insensitivity issue in the Weibit SUE models. In the meantime, the route-specific CVs cause better discrimination between short and long routes in terms of relative variability; more travelers shift to the shortest route between each origin-destination pair. Numerical results confirm the analytical results regarding the effects of the least perceived travel costs and demonstrate the efficiency and robustness of the proposed solution algorithm.
期刊：
Information Processing & Management,2023年60(4):103350 ISSN：0306-4573
通讯作者：
Li, DTC;Shi, FB
作者机构：
[Zheng, Chao; Wang, Jian; Li, Duantengchuan; Wang, Jingxiong; Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Zhang, Qi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Shi, Fobo; Shi, FB] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Cai, Yuefeng] ZTE Corp, Wuhan 430223, Peoples R China.;[Wang, Xiaoguang; Zhang, Zhen] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Li, DTC ] W;[Shi, FB ] C;Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要：
Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively.
