• Journal of Nutrition and Health
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• v.48 no.6
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• pp.476-487
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• 2015

Purpose: This study was conducted to examine the concentration of nutrients in transitional breast milk from Korean lactating mothers and to evaluate daily intakes of their infants based on the Dietary Reference Intakes for Koreans 2010 (KDRIs 2010). Methods: Breast milk samples were collected at 5~15 days postpartum from 100 healthy lactating Korean mothers. Macro- and micro-nutrients, and immunoglobulin (Igs) concentrations in breast milk were analyzed. Results: The mean energy, protein, fat, and carbohydrate concentrations in breast milk were $59.99{\pm}8.01kcal/dL$, $1.47{\pm}0.27g/dL$, $2.88{\pm}0.89g/dL$, and $6.72{\pm}0.22g/dL$. The mean linoleic acid (LA), a-linolenic acid (ALA), arachidonic acid (AA), and docosahexaenoic acid (DHA) concentrations were $181.44{\pm}96.41mg/dL$, $28.15{\pm}8.89mg/dL$, $5.67{\pm}1.86mg/dL$, and $5.74{\pm}2.57mg/dL$. The mean vitamin A, vitamin D, vitamin E, vitamin $B_1$, vitamin $B_2$, vitamin $B_{12}$, and folate concentrations were $2.75{\pm}1.75{\mu}g/dL$, $2.31{\pm}1.12ng/dL$, $0.74{\pm}1.54mg/dL$, $3.02{\pm}1.84mg/dL$, $7.51{\pm}20.96{\mu}g/dL$, $61.78{\pm}26.78{\mu}g/dL$, $63.71{\pm}27.19ng/dL$, and $0.52{\pm}0.26{\mu}g/dL$. The mean concentrations of calcium, iron, potassium, sodium, zinc, and copper were $20.71{\pm}3.34mg/dL$, $0.59{\pm}0.86mg/dL$, $66.71{\pm}10.35mg/dL$, $27.72{\pm}10.16mg/dL$, $0.44{\pm}0.41mg/dL$, and $70.48{\pm}30.41{\mu}g/dL$. The mean IgA and total IgE concentrations were $61.85{\pm}31.97mg/dL$ and $235.00{\pm}93.00IU/dL$. The estimated daily intakes of infants for protein, vitamin D, vitamin E, vitamin $B_2$, vitamin $B_{12}$, iron, potassium, sodium, zinc, and copper were sufficient compared to KDRIs 2010 adjusted by transitory milk intakes. The estimated infants' intakes of energy, fat, carbohydrate, vitamin A, vitamin C, vitamin $B_1$, folate, and calcium did not meet KDRIs 2010 adjusted by transitory milk intakes. Conclusion: In general most estimated nutrient intakes of Korean breast-fed infants in transitory breast milk were sufficient, however some nutrient intakes were not sufficient based on KDRIs 2010. These results warrant conduct of future studies for investigation of important dietary factors associated with nutrients in breast milk to improve the quality of breast milk, which may contribute to understanding nutrition in early life and promoting growth and development of breast-fed infants.

• 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.

• Journal of agricultural medicine and community health
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• v.30 no.1
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• pp.1-14
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• 2005

Objectives: This study attempts to analyze the length of hospital stay and expenses of frequent disease admitted in a Vaterans Hospital. Methods: Data was collected from January 1, 2001 to December 31, 2003 from the Claim records of 9,640 patients in a Vaterans Hospital. Results: The results were as follows: 1. In age & sex distribution, there was male 70.9%, female 29.1%, and 35.8% of them is 70 age group. Frequency by insurance program was Health insurance 78.1%, Medical aid 14.2%, no insurance 4.1%, others 3.6%. Distribution of each department was internal medicine 28.3%, orthopedic surgery 21.3%, surgery 16.6%, neurosurgey 7.1%, pediatrics 5.9%. Also, in the veterans group, male to female patient ratio was 99.3% male to 0.7% female, them over 70 years old was 51.6%, and them which live in daejeon was 43.5%. 2. In frequency of disease, there was gastroenteritis 4.8%, pneumonia 3.8%, cartaract 3.7%, cerebral infarct 3.2%, hyperplasia of prostate 3.0%. In frequency of korean standard classification of diseases, there was injury and poisoning and certain other consequences of external causes 17.1%, diseases of digestive system 16.1%, diseases of musculoskeletal system and connective tissue 13.9%, diseases of respiratory system 9.4%, diseases of genitourinary system 8.6%. Also, in veterans group, frequency of them was diseases of musculoskeletal system and connective tissue 19.4%, diseases of digestive system 16.8%, injury and poisoning and certain other consequences of external causes 15.7%, diseases of genitourinary system 9.7%, diseases of circuatory system 8.2%. 3. Average length of hospital stay was 29.0 days for total patients, 51.8 days for the veterans group, 15.7 days for the non-veterans one. Average total expenses was 3,669,579 won, the veterans group 7,263,877 won, the non-veterans one 1,560,333 won. The ratio of insurer to insuree was 55.2 : 44.8, the ratio of amount paid by patient in the veterans group 61.7%, in the non-veterans one 33.0%. 4. In items of medical expenses, fee for hospital accommodation was 34.7%, fee for medication 13.2%(injection 7.8%, drug 5.4%), fee for service 48.6%(physical therapy 26.3%, operation 9.7%, laboratory examination 5.2%, radiological examination 3.1%, etc), others 3.4%. In them for the veterans group, fee for physical therapy was 35.3%, fee for hospital accommodation 35.2%, fee for injection 6.2%, fee for operation 5.9%, for the non-veterans one, fee for hospital accommodation 35.7%, fee for operation 16.4%, fee for injection 11.4%, fee for laboratory examination 8.3%. 5. In the comparison of the frequency by Korean standard classification of diseases and distance between the hospital and home, the region under 21.5Km was more frequent in symptoms, signs an abnormal clinical and laboratory findings 56.0%, injury and poisoning and certain other consequences of external causes 55.6%, diseases of the eye and adnexa 52.9%, the one over 21.5Km was more frequent in neoplasms 57.4%, diseases of musculoskeletal system and connective tissue 55.9%, diseases of genitourinary system 53.5%.