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Yu-Jia Lin,Hsiao-Ting Chang,Ming-Hwai Lin,Ru-Yih Chen,Ping-Jen Chen,Wen-Yuan Lin,Jyh-Gang Hsieh,Ying-Wei Wang,Chung-Chieh Hu,Yi-Sheng Liou,Tai-Yuan Chiu,Chun-Yi Tu,Yi-Jen Wang,Bo-Ren Cheng,Tzeng-Ji Ch 한국한의학연구원 2021 Integrative Medicine Research Vol.10 No.2
Background: Medical staff may have difficulties in using conventional medicine to manage symptoms among terminally ill patients, including adverse effects of the treatment. Traditional Chinese medicine (TCM) is regarded as a complementary or alternative medicine, and has been increasingly used in the field of palliative medicine in recent years. This study aimed to investigate the experiences of and attitudes toward using TCM among palliative care professionals, and to provide preliminary information about its use in palliative care. Methods: This was a cross-sectional survey study conducted in eight inpatient hospice wards in Taiwan between December 2014 and February 2016. The questionnaire was self-administered, and was analyzed with descriptive statistics including Pearson’s Chi-square test and Fisher’s exact test. Results: A total of 251 palliative care professionals responded to the questionnaire, of whom 89.7% and 88.9% believed that the use of TCM could improve the physical symptoms and quality of life in terminally ill patients, respectively. Overall, 59.8%, of respondents suggested that TCM had rare side effects, and 58.2% were worried that TCM could affect the liver and kidney function of patients. In total, 89.7% and 88.0% of professionals agreed there were no suitable clinical practice guidelines and educational programs, respectively, for TCM use in palliative care. Conclusions: Most of the respondents agreed there was insufficient knowledge, skills-training, and continuing education on the use of TCM in terminally ill patients in Taiwan. These results show that to address patient safety considerations, guidelines about use of TCM in palliative care should be established.
Jia-Lin Juang,Hsieh-Lung Hsu 국제구조공학회 2008 Steel and Composite Structures, An International J Vol.8 No.6
This paper presents an effective hysteretic model for the prediction and evaluation of steel reinforced concrete member seismic performance. This model adopts the load-deformation relationship acquired from monotonic load tests and incorporates the double-pivot behavior of composite members subjected to cyclic loads. Deterioration in member stiffness was accounted in the analytical model. The composite member performance assessment control parameters were calibrated from the test results. Comparisons between the cyclic load test results and analytical model validated the proposed method’s effectiveness.
Huan Chen,Jyh-Yih Hsu,Jia-You Hsieh,Hsin-Yao Hsu,Chia-Hao Chang,Yu-Ju Lin 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.12
The predictive maintenance of wind turbines has become a critical issue with the rapid development of wind power generation. The early detection of abnormal operation conditions can prevent failure status, which takes a long time to recover. Energy waste can also be reduced while maintenance efficiency can be improved by using a supervisory control and data acquisition (SCADA) system to monitor the operation status of wind turbines. Massive data are generated from different sensors during wind turbine operation, and SCADA can be used to gather reports about hundreds of possible abnormal conditions. The popular maintenance methods have been mostly designed on the basis of statistical analysis and data mining. However, such schemes need not only big data but also sophisticated processing techniques. This study addresses the aforementioned challenges by proposing a deep learning model with comprehensive data preprocessing and hyperparameter tuning on batch size to achieve abnormal early detection. The necessary data preprocessing is initially conducted besides the conventional data cleaning and normalization steps, and time-series data windowing and label settings are also performed. Then, the imbalanced classes in the records are addressed by adopting an augmentation scheme called the synthetic minority oversampling technique. Principal component analysis is also used to enhance the training. Finally, the proposed deep learning method with fine-tuning is compared with three machine learning models for early anomaly event detection. Experimental results show that the proposed scheme can identify potential faults 72 hours before they occur, and the precision rate exceeds 90 %.