Emotion detection is a fundamental component in the field of Affective Computing. Proper recognition of emotions can be useful in improving the interaction between humans and machines, for instance, with regard to designing effective user interfaces. This study aims to understand the relationship between emotion and pupil dilation. The Tobii Pro X3-120 eye tracker was used to collect pupillary responses from 30 participants exposed to content designed to evoke specific emotions. Six different video scenarios were selected and presented to participants, whose pupillary responses were measured while watching the material. In total, 16 data features (8 features per eye) were extracted from the pupillary response distribution during content exposure. Through logistical regression, a maximum of 76% classification accuracy was obtained through the measurement of pupillary response in predicting emotions classified as fear, anger, or surprise. Further research is required to precisely calculate pupil size variations in relation to emotionally evocative input in affective computing applications.

In this study, we employ the novel method of quantile mediation analysis to explore dynamic relationships among hydropower energy consumption, economic growth, and carbon dioxide emissions in Taiwan during the period between 1990 and 2020. The empirical results show that hydropower energy consumption only has a direct effect on reducing carbon dioxide emissions at a 0.2 distribution of carbon dioxide emissions. Moreover, results indicate that economic growth does not have a mediating effect between hydropower energy consumption and carbon dioxide emissions at any distribution of carbon dioxide emissions. Lastly, evidence produced by this study supports the existence of an environmental Kuznets curve in the context of Taiwan.