Objectives:It is known that children with attention-deficit hyperactivity disorder (ADHD) experience significant difficulty in recognizing facial emotion, which involves processing of emotional facial expressions rather than speech, compared to children without ADHD. This objective of this study is to investigate the differences in facial emotion recognition between children with ADHD and normal children used as control. Methods:The children for our study were recruited from the Suwon Project, a cohort comprising a non-random convenience sample of 117 nine-year-old ethnic Koreans. The parents of the study participants completed study questionnaires such as the Korean version of Child Behavior Checklist, ADHD Rating Scale, Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. Facial Expression Recognition Test of the Emotion Recognition Test was used for the evaluation of facial emotion recognition and ADHD Rating Scale was used for the assessment of ADHD. Results:ADHD children (N=10) were found to have impaired recognition when it comes to Emotional Differentiation and Contextual Understanding compared with normal controls (N=24). We found no statistically significant difference in the recognition of positive facial emotions (happy and surprise) and negative facial emotions (anger, sadness, disgust and fear) between the children with ADHD and normal children. Conclusion:The results of our study suggested that facial emotion recognition may be closely associated with ADHD, after controlling for covariates, although more research is needed.
The rapid spread of COVID-19 worldwide makes an uncertain impact on the development of digital finance in China. In this background, the measurement of digital financial risk and analysis of influence factor become the focus of the financial field. Therefore, this article builds the indicator system of digital financial risk and uses the Lagrange multiplier method to obtain the optimal comprehensive weight of AHP and entropy weight. Then, this article measures the digital financial risk indexes of China's major regions with high-level economic development from 2013 to 2020. Furthermore, the maximum likelihood estimates of the unknown parameters of skew-normal panel data model are obtained based on the EM algorithm, and the comparative study of the normal and skew-normal panel data models is conducted under AIC and BIC. Finally, based on the result of the model, the influence factors of digital financial risk of China's economically developed regions under COVID-19 are analyzed to provide data support for the prevention and governance of digital financial risk.
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