• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.69-82
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• 2021

This study targeted 4,572 middle-aged women to examine the relationship between nutritional status and dietary inflammatory index according to blood glucose level using data from the Korean National Health and Nutrition Examination Survey (KNHANES). Data were analyzed using complex survey chi-square, General Linear Model and logisitc regression in SPSS Win 25.0 program. Women with high blood glucose (normal blood sugar→diabetes) had high rates of obesity and blood TG/HDL-cholesterol ratio. On the other hand, the Mean Adequacy Ratio (10 nutrients) and the intake of anti-inflammatory foods: beans, seeds, mushrooms, and fruits, were lower in the diabetic category. When we analysed the association between blood glucose and the Dietary Inflammatory Index, the risk of pre-diabetes and diabetes was significantly higher in the most pro-inflammatory diet category (Q5) compared to the most anti-inflammatory diet category (Q1). These findings suggest that nutritional education emphasizing the intake of various foods should be effectively conducted effectively in order to improve blood glucose among middle-aged women.

• The Korean Journal of Nuclear Medicine
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• v.30 no.3
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• pp.315-324
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• 1996

The purpose of this study was to compare the specific binding ratio method with model-based methods in estimating the transporter parameter $k_3/k_4$ in normal controls and Parkinson's patients with [I-123]IPT SPECT and to evaluate the usefulness of [I-123]IPT SPECT. $6.5{\pm}1.1$ mCi ($239.0{\pm}40.3$ MBq) of [$^{123}I$]IPT was intravenouly injected as a bolus into six normal controls(age:$45{\pm}13$) and seventeen patients(age:$55{\pm}8$) with Pakinson's disease(PD). The transporter parameter $k_3/k_4$ was derived using the Ichise's graphical method($R_v$) and Lassen's area ratio method($R_A$) for the dynamic IPT SPECT data without blood samples. Then, the relationships between the transporter parameter $R-v,\;R_A$ and the ratio of (BG-OCC)/OCC at 115 minutes were evaluated by linear regression analysis. $R_vs$ by Ichise's graphical method for NC and PD were $2.08{\pm}0.29$ and $0.78{\pm}0.31$, respectively. $R_As$ by Lassen's area ratio method for NC and PD were $1.48{\pm}0.16$ and $0.65{\pm}0.24$, respectively. The correlation coefficients between (BG-OCC)/OCC and $R_v$, (BG-OCC)/OCC and $R_A$, and $R_v$ and $R_A$ were 0.93, 0.90, 0.99 and their corresponding slopes were 0.54, 0.34, and 0.65, respectively. The $R_v$ and $R_A$ of NC were significantly higher than the ones of PD. That is, the $k_3/k_4$ of NC was clearly separated from the one of PD. $k_3/k_4$ showed a good correlation with the ratio of (BG-OCC)/OCC. The results indicate that the noninvasive simplified quantitative methods may be useful to measure the transporter parameter $k_3/k_4$ and the specific binding ratio method can be used for quantitative studies of dopamine transporter with [I-123]IPT SPECT in humans brains.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Tuberculosis and Respiratory Diseases
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• v.54 no.2
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• pp.210-218
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• 2003

Background : Sex specific cross sectional reference values for the lung function indices usually employ a linear model with a term for age and height. The purpose of this study was to determine the effects of the body mass index (BMI), the fat percentage of the body mass and the fat-free mass index (FFMI) on the forced expiratory volume curve. Methods : Between January 2000 and December 2001, a total of 300 subjects, 150 men and 150 women (mean age : $45{\pm}13$ years), with a normal lung function were enrolled in the study sample. This study measured the $FEV_1$, FVC and $FEF_{25-75%}$ from the forced expiratory volume curve by a spirometer and the body composition by a bioelectrical impedance method in all subjects. Multiple regression analysis was used in order to examine the effects of the body composition on the parameters derived from the forced expiratory volume curve. Results : After adjusting for age, the BMI and Fat percentage improved the descriptions of the FVC (p<0.05, $r^2=0.491$) and $FEV_1$ (p<0.05, $r^2=0.654$) in women. In contrast, the FFMI contributed significantly to the FVC (p<0.05, $r^2=0.432$) and $FEV_1$ (p<0.05, $r^2=0.567$) in men. The $FEF_{25-75%}$ correlated with the fat percentage in women (p<0.05, $r^2=0.337$). Conclusion : These results suggest that the BMI, the fat percentage and the FFMI are significant determinants of the forced expiratory volume curve. The plmonary function test, when considering the BMI, the fat percentage and the FFMI, might be useful in clinical applications.