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- 저자 : ( Chuan Mu Chen ) (검색결과 4 건)
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Efficiency and Risk in Commercial Banks – Hybrid DEA Estimation
Mu-Jen Chen,Yung-Ho Chiu,Chyanlong Jan,Yu-Chuan Chen,Hsiang-Hsi Liu 연세대학교 동서문제연구원 2015 Global economic review Vol.44 No.3
The hybrid DEA model can solve the difference between radial inputs and non-radial inputs and evaluate efficiency. This is a pioneering study that uses the hybrid DEA model, evaluating the proportionate inputs with a radial measure and the non-proportionate inputs with a non-radial measure, in order to examine the impact of non-performing loans (NPLs) on the efficiency of Taiwan’s banking sector from 2006 to 2010. In summary, this research demonstrates the following: (1) Only nine banks remained in the top efficiency list during these years: China Development Industrial Bank, Mega International Commercial Bank, Chinatrust Commercial Bank, Cathay United Bank, Bank of Kaohsiung, Industrial Bank of Taiwan, Taiwan Cooperative Bank, Land Bank, and Bank of Taiwan. (2) Risk is an important factor that should be taken into consideration when evaluating banking efficiency. (3) From the hybrid DEA model, we find that most of the inefficient banks have an inefficiency factor caused primarily by too many NPLs (risk). (4) The efficiency of Taiwan’s large-scale banks is significantly better than the small-scale bank. By looking at the inefficiency index, the largescale bank’s inefficiency is caused by NPLs. For the small-scale bank, both radial variables and non-radial variables have equal importance in improving its efficiency.
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Ratiometric Fluorescent Polymeric Thermometer for Thermogenesis Investigation in Living Cells
Qiao, Juan,Hwang, Yoon-Ho,Chen, Chuan-Fang,Qi, Li,Dong, Ping,Mu, Xiao-Yu,Kim, Dong-Pyo American Chemical Society 2015 ANALYTICAL CHEMISTRY - Vol.87 No.20
<P>Intracellular temperature has a fundamental effect on cellular events. Herein, a novel fluorescent polymer ratiometric nanothermometer has been developed based on transferrin protein-stabilized gold nanoclusters as the targeting and fluorescent ratiometric unit and the thermosensitve polymer as the temperature sensing unit. The resultant nanothermometer could feature a high and spontaneous uptake into the HeLa cells and the ratiometric temperature sensing over the physiological temperature range. Moreover, the precise temperature sensing for intracellular heat generation in HeLa cells following calcium ions stress has been achieved. This practical intracellular thermometry could eliminate the interference of the intracellular surrounding environment in cancer cells without a microinjection procedure, which is user-friendly. The prepared new nanothermometer can provide tools for unveiling the intrinsic relationship between the intracellular temperature and ion channel function.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancham/2015/ancham.2015.87.issue-20/acs.analchem.5b02791/production/images/medium/ac-2015-02791t_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ac5b02791'>ACS Electronic Supporting Info</A></P>
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Yi-Guang Wang(Yi-Guang Wang), Hsin-An Chang(Hsin-An Chang), Mu-Hong Chen(Mu-Hong Chen), Nian-Sheng Tzeng(Nian-Sheng Tzeng), Jin Narumoto(Jin Narumoto), Chih-Sung Liang(Chih-Sung Liang), Ta-Chuan Yeh( 대한신경정신의학회 2025 PSYCHIATRY INVESTIGATION Vol.22 No.9
Objective This study develops an eXtreme Gradient Boosting (XGBoost) regression model to identify key predictors of mortality and 5-year survival in dementia patients, highlighting the role of comorbidities. The findings highlight key risk factors that may facilitate targeted adjustments in clinical care and resource allocation for high-risk patients. Methods We used Taiwan’s National Health Insurance dataset to develop and validate an XGBoost model predicting 5-year survival in dementia patients aged 65 years or older. The cohort (n=6,556) was split into 80% for training, 10% for validation, and 10% for testing. A total of 24 variables, including comorbidities and demographic factors, were selected as predictors. Hyperparameters were tuned to optimize performance, with a learning rate of 0.1, 1,000 estimators, and a maximum depth of 10. Regularization techniques were applied to prevent overfitting. Results The XGBoost model achieved 81.86% accuracy in predicting 5-year survival, with a receiver operating characteristic area under the curve of 0.81 and a log loss of 0.61. Of the 37 initial features, 24 were included, and the top 10 predictors were nasogastric tube insertion, chronic kidney disease, cancer, lung disease, urinary tract infection, fracture, peripheral vascular disease, antidepressant use, hypertension, and upper gastrointestinal issues. Conclusion The XGBoost model effectively predicts 5-year survival in dementia patients, identifying key predictors that can guide targeted care, preventive strategies, and healthcare resource planning.
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