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( Jae Hoon Moon ),( Min Kyung Hyun ),( Ja Youn Lee ),( Jung Im Shim ),( Tae Hyuk Kim ),( Hoon Sung Choi ),( Hwa Young Ahn ),( Kyung Won Kim ),( Do Joon Park ),( Young Joo Park ),( Ka Hee Yi ) 대한내과학회 2018 The Korean Journal of Internal Medicine Vol.33 No.4
Background/Aims: We evaluated the prevalence and characteristics of thyroid nodules detected by thyroid ultrasound (US) at health checkups and the associated clinical parameters. Methods: A total of 72,319 subjects who underwent thyroid US at three health checkup centers in Korea from January 2004 to December 2010 were included in this study. The correlations between the presence of thyroid nodules and other clinical parameters were analyzed. Results: The prevalence of thyroid nodules and cysts was 34.2% (n = 24,757). Thyroid nodules were more prevalent in women and older age groups. Among the subjects with thyroid nodules with size information (n = 24,686), 18,833 (76.3%) had nodules measuring ≤ 1.0 cm. Women and older age groups showed higher proportion of larger nodules. Percentage of women, age, body mass index (BMI), waist circumference, body fat composition, blood pressure, and the level of fasting glucose, total cholesterol, and low density lipoprotein cholesterol were higher in the subjects with thyroid nodules compared to those without nodules. The prevalence of metabolic syndrome and overt/subclinical thyrotoxic state was higher in the subjects with thyroid nodules. In the multivariable logistic regression analysis, women, age, BMI, metabolic syndrome, and thyrotoxicosis were independently associated with the presence of thyroid nodules. Conclusions: The high prevalence of thyroid nodules in people who underwent thyroid US at a health checkup suggests that increased detection of thyroid nodules resulted in an increased prevalence in the general population. However, metabolic disturbances may also have contributed to the increase in thyroid nodule prevalence in Korea.
Application of artificial neural network in smart protected horticulture: A review
Jae Won Lim,Md Nasim Reza,Sun-Ok Chung,Ka Young Lee,Se Yoon Lee,Ki Nam Lee,Bram Lee 사단법인 한국정밀농업학회 2023 정밀농업과학기술지 Vol.5 No.1
Efficient agriculture is being emphasized due to the increase in the global population and the decrease in agricultural-related workers. Due to a desire to boost productivity and a dearth of available laborers, smart agriculture is receiving a lot of attention. In particular, artificial neural network technology with customized IoT devices is preferred for maximizing productivity and efficiency. Predicting yields and automatic control of environment are made possible by training machine learning models with different data combinations. Technologies like autonomous greenhouse climate management and disease and pest detection and prediction help farmers reduce crop losses by taking over for human limitations. Therefore, the purpose of this article was to explore applications of smart protected horticulture using artificial neural networks. These cases were studied through greenhouse monitoring and control systems, as well as greenhouse pest detection and disease prediction.