• Journal of Korean Medicine Rehabilitation
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• v.19 no.4
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• pp.95-113
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• 2009

Objectives : Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) is a hair care product which is composed of ten plant extracts used in oriental medicine. This study was carried out to investigate the effects of Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) on hair regrowth and cytokine changes in a shaving model of C57BL/6 mice. Materials and Methods : Five-week-old mice were acclimated for 1 week at a temperature between $21-23^{\circ}C$, 40-60% relative humidity, and 12h of a light/dark cycle before beginning of the experiment. There were three experimental groups including 50% ethanol (EtOH, control), a positive control of 3% Minoxidil, and 30% Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) in 50% ethanol in 18 female mice. The test compounds were topically treated once a day over 12 days. The hair regrowth was photographically and histologically determined during the experimental period of 12 days. Revelation of EGF, $TGF-{\beta}1$ and IL-6 in hair follicle were also determined using immunohistochemistry. In addition to that, IL-6, $TNF-{\alpha}$, and $IL-1{\beta}$ in skin tissue were determined using enzyme-linked immunosorbent assay(ELISA). Results : Hair regrowth in 3% Minoxidil and Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) groups was promoted earlier and faster than the control group. Concentrations of hairs and thick-hair ratio in 3% Minoxidil and Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) groups were promoted than the control group. EGF was moderately positive in hair follicle of 3% Minoxidil and Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) groups, but negative in the control group. $TGF-{\beta}1$ was not significantly difference between the groups. IL-6 in hair follicle of Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) group was negative, but weakly positive in 3% Minoxidil and control group. IL-6 and $IL-1{\beta}$ in skin tissue were significantly decreased in Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) group, but there was not significantly decreased in 3% Minoxidil and control group. $TNF-{\alpha}$ in skin tissue was significantly decreased in 3% Minoxidil and Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) groups. Conclusions : These results suggest that Saengbal-eum-II($Sh{\bar{e}}ngf{\grave{a}}-y{\breve{i}}n-ll$) has hair growth promoting activity and it can be used for treatment of alopecia. And these effects relate to EGF revelation of hair follicle and a decrease IL-6, $TNF-{\alpha}$, and $IL-1{\beta}$ in skin tissue.

• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.497-507
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• 1997

This study was conducted to evaluate the association of various lifestyle with blood pressure. The data were obtained from the individuals who got routine health examination in Department of Occupational Medicine, Yeungnam University Hospital from June to September, 1996. Among these people, we selected 130 cases of hypertensives (97 males, 33 females) and 150 normotensives(70 males, 80 females) and study was conducted. The authors collected the information of the risk factors related to hypertension such as age, family history of hypertension, fasting blood sugar, serum total cholesterol, alcohol consumption(g/week), smoking history, relative amount of salt intake (low, moderate, high), the frequency' of weekly meat consumption, BMI, daily coffee consumption(cups/day) and the frequency of regular exercise(frequency/week) through questionnaire and laboratory test. By simple analysis, BMI was significantly associated with hypertension in male(p<0.05), and the frequency of weekly meat consumption was significantly associated with hypertension in female(p<0.05). Using logistic regression model, elevated odds ratio was noted for fasting blood sugar, serum total cholesterol, family history of hypertension, alcohol consumption, salt intake and BMI, and reduced odds ratio was noted for coffee consumption and exercise in male but fasting blood sugar(odds ratio=1.022, 95% CI=1.000-1.044), family history in both of parents(odds ratio=3.301, 95% CI=1.864-4.738), salt intake(odds ratio=1.690, 95% CI=1.082-2.298) and BMI(odds ratio=1.204, 95% CI=1.065-1.343) were statistically significant(p<0.05). In female, elevated odds ratio was noted in serum total choles terol, family history of hypertension, BMI and meat consumption. Of all these variables, the family history of hypertension in either of parents(odds ratio=4.981, 95% CI=3.650-6.312), family history in both of parents(odds ratio=16.864, 95% CI=14.577-19.151), BMI(odds ratio=1.167, 95% CI=1.016-1.318) and meat consumption(odds ratio=2.045, 95% CI=1.133-2.963) showed statistically significant association with hypertension in female(p<0.05).

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Distribution Research
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• v.17 no.1
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• pp.65-86
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• 2012

The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.