Delay of Surgery for Spinal Metastasis due to the COVID-19 Outbreak Affected Patient Outcomes

Chia-Jung Hsieh,Chun-Yu Wu,Yen-Heng Lin,Yu-Cheng Huang,Wen-Chi Yang,Tom Wei-Wu Chen,Wei-Li Ma,Wei-Hsin Lin,Feng-Ming Hsu,Furen Xiao,Shih-Hung Yang,Dar-Ming Lai,Chang-Mu Chen,Shin-Yi Chao,Fon-Yih Tsuan 대한척추신경외과학회 2023 Neurospine Vol.20 No.4

Objective: The present study is to analyze the effects of the coronavirus disease 2019 (COVID 2019) outbreak and the subsequent lockdown on the outcomes of spinal metastasis patients. Methods: The study was a retrospective analysis of data from a prospective cohort study. All patients underwent surgical intervention for spinal metastases between January 2019 and December 2021 and had at least 3 months of postoperative follow-up. The primary outcome was overall mortality during the 4 different stages (pre-COVID-19 era, COVID-19 pandemic except in Taiwan, national lockdown, lifting of the lockdown). The secondary outcomes were the oncological severity scores, medical/surgical accessibility, and patient functional outcome during the 4 periods as well as survival/mortality. Results: A total of 233 patients were included. The overall mortality rate was 41.20%. During the Taiwan lockdown, more patients received palliative surgery than other surgical methods, and no total en bloc spondylectomy was performed. The time from surgeon visit to operation was approximately doubled after the COVID-19 outbreak in Taiwan (75.97, 86.63, 168.79, and 166.91 hours in the 4 periods, respectively). The estimated survival probability was highest after the national lockdown was lifted and lowest during the lockdown. In the multivariate analysis, increased risk of mortality was observed with delay of surgery, with emergency surgery having a higher risk with delays above 33 hours, urgent surgery (below 59 and above 111 hours), and elective surgery (above 332 hours). Conclusion: The COVID-19 pandemic and related policies have altered daily clinical practice and negatively impacted the survival of patients with spinal metastases.

SEASONAL VARIABILITY OF CARBONATE SYSTEM CHARACTERISTICS IN A CORAL REEF ECOSYSTEM, SOUTHERN TAIWAN

Pei-Jie Meng,Ming-Yih Leu,Cheng-Hao Tang,Chung-Chi Chen,Kwee-Siong Tew,Chia-Ming Chang 한국수산과학회 양식분과 2015 한국수산과학회 양식분과 학술대회 Vol.2015 No.5

Research on the Structure and Application of Fuzzy Environmental Impact Assessment Model

Tien, Shiaw-Wen,Hsneh, Chia-Hsiang,Chung, Yi-Chan,Tsai, Chih-Hung,Yu, Yih-Huei The Korean Society for Quality Management 2004 The Asian Journal on Quality Vol.5 No.2

Any business activities may have impact on environment to a certain extent. Enterprises must find appropriate approaches to measure the impact on these environmental aspects, which can be used as the basis to direct enterprises' efforts to improve the environmental impact. The method used to evaluate significant factors in life cycle assessment standards is the one most commonly used by enterprises in general to measure environmental impact. By this method, the decisive factors of each environmental aspect are given scores according to the preset scoring standard of the organization. The scores are added up for each aspect and ranked to assess major environmental aspects. The drawback of this assessment method, that is, it ignores the degree to which each of these factors affects the environment, results in poor credibility. Therefore, this study attempts to solve some qualitative problems by applying to fuzzy theory, in particular, by identifying appropriate fuzzy numbers through fuzzy sets and membership function. Moreover, the study seeks to obtain a crisp value in the process of defuzzifization in order to make up for the shortfall of the original method in dealing with relative weight of decisive factors and thus increase its applicability and credibility. The department of light production of an electronics company is used as an example in this study to measure environmental aspects by employing both the traditional significant factor method and the fuzzy environmental impact assessment model proposed in this study. Based on verification and comparison of results, the model proposed in this study is more feasible as it reduces partiality in decision-making by taking the relative weights of decisive factors into consideration.

Development of a Microclimate Model for the Environmental Control Strategy Decision in a Desktop Growth Chamber

( Shao-xuan Luo ),( Chia-chyi Wu ),( Ming-yih Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

The plant factory was a rapid-developed agricultural technology in East Asia in the past decade. It provides a well-controlled environment for crop growth to guarantee better yield or quality. However, its high energy consumption always leads to criticism. A good environmental control strategies would achieve better plant growth environment or efficient energy utilization. Unfortunately, the control strategy was usually based on the previous growth experience in the field. It is necessary to develop a fast and low-cost, easy-replicated approach to verify the performance of the control strategies. The objective of this study was to develop a microclimate model to simulate the environment conditions when using different environmental control strategies. This model was based on temperature-humidity equilibrium and plant evapotranspiration equations. The ambient environmental conditions, parameters of control strategy, plant evapotranspiration rate were input into this model and the outputs were the calculated temperature and humidity. A desktop grow chamber was used to verify the feasibility of the microclimate model. Ten plants of hydroponically grown lettuce were grown in this chamber. The results shows that the simulation results of this model fit the real cultivation results. This model provided an easy and low cost way to screen the feasible control strategies by simulated the environment change in a plant factory.

Development of Intelligent Supplemental Lighting in Greenhouse

( Chin-chung Yang ),( Chia-chyi Wu ),( Ming-yih Chang ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

Greenhouses supply better growth environment for crops and improve their yields or quality. The canopy covering on the greenhouse always restrict the sunlight entering. The supplemental lighting is the most popular method to alleviate the light shortage in greenhouse. However, the presetting scheduled light cannot be adjusted according to the real-time weather condition. It will cause the lighting useless or insufficient. This study was to develop an intelligent supplemental interlighting system. The light intensity on the plants measured a photoflux sensor, was transmitted to the computer to accumulate the light integral. Since noon, the computer recalculated the difference between the accumulating light integral (ALI) and the target daily light integral (DLI), and modified the time to switch on/off the supplemental lighting on every 5-miniute interval. A field experiment with three treatments was conducted: (1) Intelligent supplemental lighting (Intelligent, target DLI 10 mol/m2/day), and (2) scheduled supplemental lighting (scheduled, 15:00 - 22:00), and (3) no light supplement as check. In each treatment were fifteen plants of lettuce grown in a small plastic greenhouse, respectively. The results show that the yields of intelligent and scheduled treatments were significantly more than that of check treatment. The lettuce fresh weight in intelligent and scheduled treatment was 60.0% and 48.4% higher than that in check treatment, respectively. In the 45-day treatment, the lighting hours in scheduled treatment was 132 hours, and only 41.2 hours in intelligent treatment. The lighting hours was only one third of that in scheduled treatment. It proved the intelligent supplemental lighting promised a better yield and a better electric energy use efficiency.

Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection

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 %.

Research on the Structure and Application of Fuzzy Environmental Impact Assessment Model

( Shiaw Wen Tien ),( Chia Hsiang Hsieh ),( Yi Chan Chung ),( Chih Hung Tsai ),( Yih Huei Yu ) 한국품질경영학회 2004 The Asian Journal on Quality Vol.5 No.2

Antenatal Depression in East Asia: A Review of the Literature

Mei-Chun Hsiao,Mei-Chun Hsiao,Chia-Yih Liu 대한신경정신의학회 2012 PSYCHIATRY INVESTIGATION Vol.9 No.2

This current study’s goal is to summarize the literature regarding Antenatal Depression (AD) in the East Asian countries of Taiwan, China (including Hong Kong and Macau), Japan, and Korea. The main search utilized a Pub med Chinese Electronic Periodical Service (CEPS) literature review using keywords ‘AD’, and ‘Prenatal Depression’ with searches for ‘Japan’, ‘Korea’, ‘Taiwan’, ‘Hong Kong’ and Macau’. The rates of AD in East Asia appear to be relatively close to those in the Western literature, although certain studies showed slightly decreased rates. Many of the risk factors for AD were the same in the Eastern and Western literature. These risk factors included demographic factors such as younger age, smoking, low education and income, and unemployment. Other risk factors were physical symptoms such as menstrual pains and nausea. Finally, psychological factors such as a poor response to the pregnancy, poor spousal support, and poor family support were associated with AD. With regard to treatment, there were no studies examining the administration of psychotropic medications for AD. The literature from East Asia both confirmed many Western findings and made unique contributions to the literature on AD. The treatment of AD in East Asia appears to be an entity which, despite its morbidity, has not been adequately studied.

Turbid Density Current Venting through Reservoir Outlets

Fong-Zuo Lee,Jihn-Sung Lai,Yih-Chi Tan,Chia-Chi Sung 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.2

The planning and design of an outlet operation by releasing a turbid density current from a reservoir requires accurate prediction ofoutflow concentration for sluicing sediment through outlet structures. This study investigates outflow concentration and ventingefficiency through reservoir outlets in a reservoir sluicing operation related to turbid density current. A 3D numerical model isemployed to simulate a venting operation for a turbid density current in typhoon-induced flood events. A simple and efficientformula derived from theoretical analysis with experimental data is proposed for estimating outflow concentration and ventingefficiency. By adopting the proposed formula to avoid time-consuming calculation using the 3D numerical model, the estimatedoutflow concentration and venting efficiency through reservoir outlets have shown good agreement with the measured and simulatedresults in typhoon flood events. This demonstrates that the formula provides an efficient approach for engineering practice in realtimereservoir venting operations.

Do Somatic Symptoms Predict the Severity of Depression? A Validation Study of the Korean Version of the Depression and Somatic Symptoms Scale

전상원,윤서영,고영훈,조숙행,김용구,한창수,윤호경,Chia-Yih Liu 대한의학회 2016 Journal of Korean medical science Vol.31 No.12

This study aimed at exploring the psychometric characteristics of the Korean Version of the Depression and Somatic Symptoms Scale (DSSS) in a clinical sample, and investigating the impact of somatic symptoms on the severity of depression. Participants were 203 consecutive outpatients with current major depressive disorders (MDD) or lifetime diagnosis of MDD. The DSSS was compared with the Montgomery-Åsberg Depression Rating Scale (MADRS) and the 17-items Hamilton Depression Rating Scale (HAMD). The DSSS showed a two-factor structure that accounted for 56.8% of the variance, as well as excellent internal consistency (Cronbach’s alpha = 0.95), concurrent validity (r = 0.44–0.82), and temporal stability (intraclass correlation coefficient = 0.79). The DSSS had a high ability to identify patients in non-remission (area under receiver operating characteristic [ROC] curve = 0.887). Maximal discrimination between remission and non-full remission was obtained at a cut-off score of 22 (sensitivity = 82.1%, specificity = 81.4%). The number of somatic symptoms (the range of somatic symptoms) and the scores on the somatic subscale (SS, the severity of somatic symptoms) in non-remission patients were greater than those in remission patients. The number of somatic symptoms (slope = 0.148) and the SS score (slope = 0.472) were confirmed as excellent predictors of the depression severity as indicated by the MADRS scores. The findings indicate that the DSSS is a useful tool for simultaneously, rapidly, and accurately measuring depression and somatic symptoms in clinical practice settings and in consultation fields.