• Food Science of Animal Resources
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• v.29 no.1
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• pp.40-46
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• 2009

The aim of this study was to determine the relationship between sensory properties and Warner-Bratzler shear (WBS) for branded Hanwoo beef. Eight subprimal cuts purchased from the branded Hanwoo beef of 3 quality grades ($1^{++}$, $1^+$, 1) at 13 stores were determined the tenderness using WBS and evaluated the sensory properties (tenderness, flavor, juiciness, overall acceptance) by trained sensory panels. The results of sensory evaluation were analyzed by four WBS value classes (<3.46 kg, 3.46-4.09 kg, 4.09-4.72 kg, >4.72 kg). The results from the sensory evaluation (tenderness, flavor, juiciness, overall acceptance) for subprimal cuts of WBS force value less than 3.46 kg had high scores, whereas WBS force value more than 4.72 kg had low scores (p<0.05). Correlation coefficient of WBS measurements with sensory ratings was -0.67 (tenderness), -0.53 (flavor), -0.49 (juiciness), and -0.57 (overall acceptance). From these results, consumers can distinguish sensory taste of branded Hanwoo beef using WBS categories and beef industry can apply index of taste for brand Hanwoo beef by WBS categories.

• Journal of Life Science
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• v.27 no.11
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• pp.1315-1323
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• 2017

Turmeric is a rhizomatous herbaceous perennial plant (Curcuma longa (CL)) of the ginger family, Zingiberaceae. A yellow-pigmented fraction isolated from the rhizomes of CL contains curcuminoids belonging to the dicinnamoyl methane group. Curcumin is an important active ingredient responsible for the biological activity of CL. However, CL is not usually used as a food source due to its bitter taste. The present study was designed to determine the effect of the CL fermented by Rhizopus oryzae (FCL) on pro-inflammatory factors such as nuclear factor ${\kappa}B$ ($NF-{\kappa}B$), tumor necrosis factor alpha ($TNF-{\alpha}$), interleukin-6 (IL-6), nitric oxide (NO), prostaglandin $E_2$ ($PGE_2$), inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) in lipopolysaccharide (LPS)-induced RAW 264.7 cell line. The cell viability was determined by MTT assay. To evaluate the anti-inflammatory effect of FCL 80% EtOH extracts, IL-6 and $TNF-{\alpha}$ were measured by ELISA kit. Also, the amount of $NO/PGE_2/NF-{\kappa}B$ was measured using the $NO/PGE_2/NF-{\kappa}B$ detection kit and the iNOS/COX-2 expression was measured by Western blotting. The results showed that the FCL reduced NO, $PGE_2$, iNOS, COX-2, $NF-{\kappa}B$, IL-6 and $TNF-{\alpha}$ production without cytotoxicity. These results suggest that FCL extracts may be a developed the functional food related to anti-inflammation due to the significant effects on inflammatory factors.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.1
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• pp.1-8
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• 2019

This study was conducted to predict the energy requirements for maintenance and growth of female Korean black goats during their growth and pregnancy phases. Fifty female goats ($18.7{\pm}0.27kg$) in their growth phase with an average age of 5 months were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in metabolic energy (ME) [2.32 (G1), 2.49 (G2), 2.74 (G3), 2.99 (G4), and 3.24 (G5) Mcal/kg] until they were 9-month-old. After natural breeding, 50 female goats ($30.7{\pm}0.59kg$) were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in ME [2.32 (P1), 2.43 (P2), 2.55 (P3), 2.66 (P4), and 2.78 (P5) Mcal/kg]. The average feed intake ranged between 1.5 and 2.0% of the body weight (BW), and there was no significant difference between the treatment groups with goats in growth or pregnancy phases. Average daily gain (ADG) in diet demand during the growth phase increased with an increasing ME density and ranged from 46 to 69 g/d (p<0.01). Feed conversion ratio (FCR) improved with the ME density during the growth phase (p<0.01). The intercept of the regression equation between ME intake and ADG indicated that energy requirement for maintenance of goats during growth and pregnancy phases was $103.53kcal/BW^{0.75}$ and $102.7kcal/BW^{0.75}$, respectively. These results may serve as a basis for the establishment of goat feeding standards in Korea. Further studies are required to assess the nutrient requirement of goats using various methods for improving accuracy.

• Journal of agricultural medicine and community health
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• v.46 no.1
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• pp.12-22
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• 2021

Backgrounds: Proper distribution and supply of physicians are factors that affect national health care systems. This study investigated the payment distribution levels and the determinants that influence the salary levels of hospital hired physicians. Methods: We analyzed 4,014 job advertisements posted on an internet invitation information site about physician recruitment from May 2016 to May 2019. We used univariate analysis to determine the relationship between average monthly salary and the other related variables. Multiple regression analysis was used to determine the predictors of physician salary level. Results: The average monthly salary for the service physician was 15.4 million won, highest for orthopedic surgeons with 22.24 million won, and lowest for diagnostic laboratory physician with 11.4 million won. The factors significantly associated with average monthly salary were; non-major specialty, housing provision, no severance pay, and incentives(p<0.05). Non-major specialty, incentives, and the regions were predictors of the average standardized monthly salary(p<0.05). Conclusion: Factors associated with average monthly salary as revealed by this study were; medical specialty, hospital regional location, housing provision, payment of retirement allowance, and payment of other incentives respectively. However, this study was a cross-sectional study, and further studies will be required.

• Food Science of Animal Resources
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• v.28 no.3
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• pp.283-288
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• 2008

Eight subprimal cuts purchased from the branded Hanwoo beef of 3 quality grades ($1^{++}$, $1^+$, 1) at 13 stores were evaluated the tenderness using Warner-Bratzler shear (WBS). The beef frequency ratio (%) depending on postmortem aging periods were investigated. The 37.5 (quality grade $1^{++}$), 45.8 (quality grade $1^+$), and 36.4% (quality grade $1^{++}$) of branded beef were aged for < 7 d, 26.6 (quality grade $1^{++}$), 47.2 (quality grade $1^+$) and 36.4% (quality grade 1) were aged for $7{\sim}13$ d, and 31.3 (quality grade $1^+$), 4.2 (quality grade $1^+$) and 25.8% (quality grade $1^+$) were aged for 14 to 20 d. The temperature of cold room in stores was ranged average 2.27 to $2.42^{\circ}C$. WBS values for ansimsal (tenderloin), witdngsimsal (ribeye), cheggtsal (shortloin), doganisal (knuckle) and moongchisatae (hind shank) from branded Hanwoo beef of quality grade $1^{++}$ were tender than those from branded Hanwoo beef of quality grade 1 (p<0.05). WBS values for ansimsal (tenderloin) were 2.56 (quality grade $1^{++}$), 2.76 (quality grade $1^+$) and 3.10 kg (quality grade 1), respectively, and those for doganisal (knuckle, quality grade $1^{++}$), hongdukesal (eye of round, quality grade $1^+$) and bosupsal (top sirloin, quality grade 1) were 4.76, 4.96 and 5.66kg, respectively (p<0.05). The frequency ratio (%) of WBS < 3.9 kg in the all subprimal cuts from branded Hanwoo beef of quality grade $1^{++}$ were 100 [ansimsal (tenderloin) and cheggtsal (shortloin)], 87.5 [witdngsimsal (ribeye)] and 62.5% [bosupsal (top sirloin)], whereas that of WBS > 4.6 kg were 50.0% [hongdukesal (eye of round) and doganisal (knuckle)]. The frequency ratio of WBS < 3.9 kg in the an subprimal cuts of quality grade $1^+$ were 100 [ansimsal (tenderloin) and witdngsimsal (ribeye)] and 44.4% [cheggtsal (shortloin) and gurisal (chuck tender)], whereas that of WBS > 4.6 kg were 66.7 [hongdukesal (eye of round)], 55.6 [doganisal (knuckle)] and 44.4% [bosupsal (top sirloin)]. The frequency ratio (%) of WBS < 3.9 kg in the all subprimal cuts of quality grade 1 were 88.9 [ansimsal (tenderloin)], 62.5 [cheggtsal (shortloin)] and 44.4% [witdngsimsal (ribeye)], whereas that of WBS > 4.6 kg were 100.0 [doganisal (knuckle)] 62.7 [hongdukesal (eye of round)], 62.5 [gurisal (chuck tender)] and 55.6% [moongchisatae (hind shank)]. From these results, subprimal cuts from branded Hanwoo beef were marketed with short aging periods and high frequency ratio (%) of WBS > 4.6 kg.

• 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.