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  1.  25
    Influence of Awe on Green Consumption: The Mediating Effect of Psychological Ownership.Liying Wang, Guangling Zhang, Pengfei Shi, Xingming Lu & Fengsen Song - 2019 - Frontiers in Psychology 10:479702.
    The increasing demand for environmental protection has given rise to burgeoning research on green consumption. The present research adds to this expanding literature by investigating a novel predictor of consumer green consumption: awe. As a self-transcendent emotion, awe arises when people encounter perceptually vast stimuli that overwhelm their existing knowledge and mental structures, and, meanwhile, this also elicits a need for accommodation. This research proposes and demonstrates that, compared with happiness and a neutral affective state, experience of awe promotes green (...)
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  2. Psychometric Properties of the Chinese Revision of the Pitt Wellness Scale for People in the University Environment.Xiangru Yan, Ye Gao, Hui Zhang, Chunguang Liang, Haitao Yu, Liying Wang, Sisi Li, Yanhui Li & Huijuan Tong - 2022 - Frontiers in Psychology 13.
    BackgroundThe number of students enrolled in higher education in China accounts for more than one-fifth of the world, and universities, as a community of faculty, staff and scholars, currently do not have a scale that specifically assesses the well-being of the population in the environment of Chinese universities. However, the University of Pittsburgh has developed a comprehensive well-being scale, referred to as the Pitt Wellness Scale, specifically to measure people’s well-being in a university environment.AimsInvestigate the psychometric properties of the Pitt (...)
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    A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data.Guokai Zhang, Haoping Xiao, Jingwen Jiang, Qinyuan Liu, Yimo Liu & Liying Wang - 2020 - Complexity 2020:1-10.
    The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will (...)
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