• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.217-225
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• 2004

This study was carried out to reduce the loss of frozen dough quality during frozen storage. Using response surface method, ascorbic acid 160.4 ppm, L-cysteine 63.1 ppm, and SSL 0.6% were found to be optimum, with xanthan gum 0.3% (formula A) and Ultra tex-3 5% (formula B) added as cryoprotectants. During frozen storage at $-20^{\circ}C$, control rapidly deteriorated after 4 weeks, while formulas A and B showed slight deterioration with immutable quality after 10 weeks.

• Clinical and Experimental Reproductive Medicine
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• v.34 no.1
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• pp.19-31
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• 2007

Objective: Pathogenesis of the endometriosis is very complex and the etiology is still unclear. Our hypothesis is that there may be some difference in gene expression patterns between eutopic endometriums with or without endometriosis. In this study, we analyzed the difference of gene expression profile with cDNA microarray. Methods: Endometrial tissues were gathered from patients with endometriosis or other benign gynecologic diseases. cDNA microarray technique was applied to screen the different gene expression profiles from early and late secretory phase endometria of those two groups. Each three mRNA samples isolated from early and late secretory phase of endometrial tissues of control were pooled and used as master controls and labeled with Cy3-dUTP. Then the differences of gene expression pattern were screened by comparing eutopic endometria with endometriosis, which were labeled with Cy5-dUTP. Fluorescent labeled probes were hybridized on a microarray of 4,800 human genes. Results: Twelve genes were consistently over-expressed in the endometrium of endometriosis such as ATP synthase H transporting F1 (ATP5B), eukaryotic translation elongation factor 1, isocitrate dehydrogenase 1 (NADP+), mitochondrial ribosomal protein L3, ATP synthase H+ transporting (ATP5C1) and TNF alpha factor. Eleven genes were consistently down-regulated in the endometriosis samples. Many extracellular matrix protein genes (decorin, lumican, EGF-containing fibulin-like extracellular matrix protein 1, fibulin 5, and matrix Gla protein) and protease/protease inhibitors (serine proteinase inhibitor, matrix metalloproteinase 2, tissue inhibitor of metalloproteinase 1), and insulin like growth factor II associated protein were included. Expression patterns of selected eight genes from the cDNA microarray were confirmed by quantitative RT-PCR or real time RT-PCR. Conclusion: The result of this analysis supports the hypothesis that the endometrium from patients with endometriosis has distinct gene expression profile from control endometrium without endometriosis.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.217-232
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• 2021

This study calculated the productivity and cost of extraction and processing of logging residues by cut-to-length (CTL) and whole-tree (WT) harvesting methods. In addition, the comparative analysis of the characteristics of wood chip fuel to examine whether it was suitable for the fuel conditions of the energy facility. In the harvesting and processing system to produce the wood chips of logging residues the system productivity and cost of the CTL harvesting system were 1.6 Gwt/SMH and 89,865 won/Gwt, respectively. The productivity and cost of the WT harvesting system were 2.9 Gwt/SMH and 72,974 won/Gwt, respectively. The WT harvesting productivity increased 1.3times while harvesting cost decreased by 18.7% compared to the CTL harvesting system. The logging residues of wood chips were not suitable for CTL wood chips based on International Organization for Standardization (ISO 17225-4:2021) and South Korea standard (NIFoS, 2020), but the quality (A2, Second class) was improved through screening operation. The WT-unscreened wood chips conformed to NIFoS standard (second class) and did not conform to ISO but were improved through screening operation (Second class). In addition to the energy facility in plant A, all wood chips except CTL-unscreened wood chips were available through drying processing. The WT-unscreened wood chips were the lowest at 99,408 won/Gwt. Plants B, C, and D had higher moisture content than plant A, so WT-unscreened wood chips without drying processing were the lowest at 57,204 won/Gwt. Therefore, the production of logging residues should improve with operation methods that improve the quality of wood chips required for applying the variable biomass and energy facility.

• Journal of International Area Studies (JIAS)
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• v.14 no.4
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• pp.79-106
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• 2011

The cultural contents industries have moved closer to the centre of the economic action in many countries and across much of the world. For this reason, the concern with the development of glocal cultural contents has also been growing. According to Goldman Sock's BRICs report, Indian economy will be the engine of global economy with China. In addition, India will be a new blue chip country for large consumer market of cultual contents. The most important point for the development of glocal cultural contents is a systematic and in-depth analysis of other culture. India is a complex and multicultural country compared with Korea which is a nation-state. Therefore, this paper is intended as an understanding about India appropriately and suggestion for a strategy to enter cultural industry in India. As the purpose of this paper is concerned, we will take a close look at 9 Indian culture codes which can be classified into three main groups: 1) political, social and cultural codes 2) economic codes 3) cultural contents codes. Firstly, political, social and cultural codes are i) consistent democracy and saving common people, ii) authoritarianism which appears an innate respect for authority of India, iii) Collective-individualism which represents collectivist and individualistic tendency, iv) life-religion, v) carpe diem. Secondly, economic culture codes are vi) 1.2billion Indian people's God which represents money and vii) practical purchase which stands for a reasonable choice of buying products. Lastly, viii) Masala movie and ix) happy ending that is the most popular theme of Masala movies are explained in the context of cultural content codes. In conclusion, 3 interesting cases , , will be examined in detail. From what has been discussed above, we suggest oversea expansion strategy based on these case studies. Eventually, what is important is to understand what Indian society is, how Indian society works and what contents Indian prefers.

• 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.