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- 저자 : Po-Kun Tsai (검색결과 2 건)
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Mohsen Saffari,Kun-Chia Chang,Jung-Sheng Chen,Marc N. Potenza,Cheng-Fang Yen,Ching-Wen Chang,Po-Ching Huang,Hsin-Chi Tsai,Chung-Ying Lin 대한신경정신의학회 2023 PSYCHIATRY INVESTIGATION Vol.20 No.11
Objective Problematic use of social media (PUSM) may affect sleep quality and self-stigma in people with schizophrenia and consequently reduce their quality of life (QoL). This longitudinal study investigated if sleep quality and self-stigma mediated relationships between PUSM and QoL.Methods One-hundred-and-ninety-three outpatients with schizophrenia were recruited from a psychiatric center in Taiwan from April 2019 to August 2021 and participated in a longitudinal study at intervals of three months between measurements. QoL was assessed using the World Health Organization Quality of Life Questionnaire Brief Version; sleep quality using the Pittsburgh Sleep Quality Index; self-stigma using the Self-Stigma Scale-Short; and PUSM using the Bergen Social Media Addiction Scale. Via SPSS 20.0, general estimating equation models assessed temporal associations between variables. Via R software, mediating effects of self-stigma and sleep quality were examined through Monte Carlo simulations with 20,000 repetitions.Results Mean scores of physical, psychological, social and environmental QoL ranged from 11.86 to 13.02. Mean scores of sleep quality and self-stigma were 9.1±4.5 and 2.2±0.8, respectively. Sleep quality and self-stigma were directly related to QoL (p<0.001) and mediated indirect relationships between PUSM and all components of QoL with a range of 95% confidence intervals spanning from -0.0591 to -0.0107 for physical QoL; -0.0564 to -0.0095 for psychological QoL; -0.0292 to -0.0035 for social QoL; and -0.0357 to -0.0052 for environmental QoL.Conclusion Sleep quality and self-stigma mediated relationships between PUSM and QoL in people with schizophrenia. Developing interventions targeting PUSM, sleep, and self-stigma may help improve QoL in people with schizophrenia.
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Min-Yuan Cheng,Minh-Tu Cao,Po-Kun Tsai 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.1
Failure of ground anchor is a major cause of landslides and severe natural hazards, especially in the highly developed mountainous areas such as New Taipei City. Accurately estimating load on ground anchors is thus essential for evaluating the stability status of slope to prevent landslide from happening. This study first employed correlation analyses to identify possible influential factors of load on ground anchors. Second, various artificial intelligence models were used to map the relationship of the found influencing factors with the current load on ground anchors. The results indicated that the symbiotic organisms search-optimized least squares support vector regression (SOS-LSSVR) model had the optimal accuracy by earning the smallest value of mean absolute percentage error (9.10%) and the most outstanding value of correlation coefficient (R = 0.988). The study applied the established inference model for the real case of estimating load on un-monitoring ground anchors. The analyzed results strongly advised administrators to conduct site surveying and patrolling more frequently to take early proper actions. In summary, the obtained results have demonstrated SOS-LSSVR as an effective alternative for the conventional subjective evaluation methods, which is able to rapidly provide accurate values of load on un-monitoring ground anchors.
KCI
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