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Yao-Chun Yang,Min-Hong Hsieh,Jui-Teng Chien,Keng-Chang Liu,Chang-Chen Yang 대한골다공증학회 2023 Osteoporosis and Sarcopenia Vol.9 No.1
Objectives: Sacral insufficiency fracture (SIF) is not an uncommon osteoporosis fracture among the elderly. Aside from traditional treatments, sacroplasty and teriparatide (TPTD) injection have been introduced. This report aims to compare the effects of sacroplasty and teriparatide on clinical outcomes of SIF. Methods: Thirty-one elderly patients with SIF were enrolled in this retrospective observational study. Four male patients were excluded. Fourteen patients who received TPTD for 6 months were classified into the TPTD group (TT), and 13 who underwent sacroplasty were classified into the sacroplasty group (SS). All patients in both groups were instructed to take calcium and vitamin D supplements daily. Their symptoms and signs, visual analog score (VAS), Oswestry disability index (ODI), and radiographic studies were retrospectively reviewed. Results: The TT group showed significantly lower VAS than SS group after 3 (P < 0.001) and 6 months of treatment (P < 0.001). The TT group also has significant lower ODI than SS group after 1 (P = 0.010), 3 (P = 0.005) and 6 months (P < 0.001) of treatment. Upon generalized estimating equations (GEE) analysis, the TT group showed significantly more reduction in both VAS and ODI compared to the SS group at 1 month (P = 0.022, P = 0.001), 3 months (P < 0.001, P < 0.001), and 6 months (P < 0.001, P < 0.001) post-treatment. Conclusions: Postmenoposal woman with SIF who received TPTD healed better than those who underwent sacroplasty after 1 month treatment.
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 %.