• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.

• The Korean Journal of Pharmacology
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• v.32 no.3
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• pp.323-334
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• 1996

Male Mongolian gerbils $(60{\sim}80g)$ were given DL-difluoromethylornithine (DFMO; 250mg/kg, ip) and methylglyoxal bis(guanylhydrazone) (MGBG; 50 mg/k, ip), respectively, 1 h prior to transient (7 min) occlusion of bilateral common carotid arteries (OBC7) and a daily dose of one of them for 6 days after recirculation, and the polyamine contents, activities of ornithine and S-adenosylmethionine decarboxylases (ODC and SAM-DC), and light microscopic findings of the hippocampus were evaluated. The hippocampal putrescine (PT) levels of the control gerbils treated with saline (STGr), markedly increased after OBC7, showing a peak level at 24 h after recirculation. The peak PT level was reduced in DFMO treated gerbils (DTCr) and in MGBG treated gerbils (MTGr). And 7 days after recirculation, the PT level of DTGr was decreased to about 75% of the PT level in the sham operated group (nonTGr) and to about 55% of the STGr level, respectively. The hippocampal spermidine (SD) level of STGr tended to decline, showing the lowest value at 8 h after recirculation. But the spermidine (SD) level of DTGr was somewhat higher at 8 h after OBC7 than those of STGr and MTGr The hippocampal spermine (SM) levels of all the experimental groups were little changed for 7 days after OBC. OBC7 markedly increased the hippocampal ODC activity. reaching a maximum (about 3 times higher than preischemic level) at 8 h and rapidly recovered to the control value by 24 h in STGr gerbils, and the OBC7-induced increase of ODC activity was significantly attenuated by DFMO or MGBG treatment. Whereas OBC7 induced a rapid decrease of the hippocampal SAMDC activity follwed by gradual recovery to the preischemic level, and the decrease of the SAMDC activity was slightly attenuated by DFMO or MGBG treatment. 7 Days after OBC7 the histological finding of the hippocampal complex stained with cresyl violet showed an extensive delayed neuronal damage in the CA1 region and to a lesser extent, in the dentate gyrus, sparing the CA3 region. And the neuronal death was aggevated by DFMO but significantly attenuated by MGBG. The immunochemical reactivity of hippocampus to anti-GFAP antibody was significantly increased in the CA1 region and to a lesser extent, in the dentate gyrus 7 days after OBC7, but was little changed in the CA3. And the increase of the anti-GFAP immunoreactivity was moderately enhanced by DFMO and significantly suppressed by MGBG. These results suggest that the polyamine metabolism may play a modulatory role in the ischemic brain damage.

• Journal of the Korean Society of Food Culture
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• v.14 no.4
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• pp.371-383
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• 1999

This report gave analysis of food demand both in Korea and Japan through introducing the concept of cohort analysis to the conventional demand model. This research was done to clarify the factors which determine food demand of the household. The traits of the new model for demand analysis are to consider and quantify those effects on food demand not only of economic factors such as expenditure and price but also of non-economic factors such as the age and birth cohort of the householder. The results of the analysis can be summarized as follows: 1) The comparison of the item-wise elasticities of food demand demonstrates that the expenditure elasticity is higher in Korea than in Japan and that the expenditure elasticity is -0.1 for cereal and more than 1 for eating-out in both countries. In respect to price elasticity, the absolute values of all the items except alcohol and cooked food are higher in the Korea than in Japan, and especially the price elasticities of beverages, dairy products and fruit are predominantly higher in Japan. In this way, both expenditure and price elasticities of a large number of items are higher in Korea than in Japan, which may be explained from the fact that the level of expenditure is higher in Japan than in Korea. 2) In both of Korea and Japan, as the householder grows older, the expenditure for each item increases and the composition of expenditure changes in such a way that these moves may be regarded as due to the age effect. However, there are both similarities and differences in the details of such moves between Korea and Japan. Those two countries have this trait in common that the young age groups of the householder spend more on dairy products and middle age groups spend more on cake than other age groups. In the Korea, however, there can be seen a certain trend that higher age groups spend more on a large number of items, reflecting the fact that there are more two-generation families in higher age groups. Japan differs from Korea in that expenditure in Japan is diversified, depending upon the age group. For example, in Japan, middle age groups spend more on cake, cereal, high-caloric food like meat and eating-out while older age groups spend more for Japanese-style food like fish/shellfish and vegetable/seaweed, and cooked food. 3) The effect of the birth cohort effect was also demonstrated. The birth cohort effect was introduced under the supposition that the food circumstances under which the householder was born and brought up would determine the current expenditure. Thus, the following was made clear: older generations in both countries placed more emphasis upon stable food in their composition of food consumption; the share of livestock products, oil/fats and externalized food was higher in the food composition of younger generation; differences in food composition among generations were extremely large in Korea while they were relatively small in Japan; and Westernization and externalization of diet made rapid increases simultaneously with generation changes in Korea while they made any gradual increases in Japan during the same time period. 4) The four major factors which impact the long-term change of food demand of the household are expenditure, price, the age of the householder, and the birth cohort of the householder. Investigations were made as to which factor had the largest impact. As a result, it was found that the price effect was the smallest in both countries, and that the relative importance of the factor-by-factor effects differed among the two countries: in Korea the expenditure effect was greater than the effects of age and birth cohort while in Japan the effects of non-economic factors such as the age and birth cohort of householder were greater than those of economic factors such as expenditures.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Oral Medicine and Pain
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• v.32 no.4
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• pp.441-447
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• 2007

Internal derangement of the temporomandibular joint(TMJ) is defined as an abnormal relationship of the articular disc to the condyle. Mandibular manipulation is one of the conservative treatments to be considered first to manage the patients with anterior disc displacement without reduction. Mandibular manipulation is used to increase articular mobility and to restore the displaced disc into an anatomically normal position. While Farrar's technique has been popularly used, Minagi et al., Mongini and Suarez introduced the manipulation technique conducted by the patients themselves. But there is no study on the efficacy of self-manipulation technique, comparing with conventional one. The aim of this study was to investigate the efficacy of the conventional and self-manipulation technique, which was modified to complement the previously described technique by Minagi et al., in the treatment of patients with anterior disc displacement without reduction. TMD patients, who visited Department of Oral Medicine of Seoul National University Dental Hospital from December, 2002 to November, 2004 and were diagnosed as anterior disc displacement without reduction by TMJ magnetic resonance imaging (MRI) were enrolled. Conservative treatments including physical therapy, exercise, behavioral therapy, stabilization splint therapy, and manipulation therapy were done to every single patient until the symptomsimproved enough to discharge the patient. The charts were reviewed retrospectively according to the type of manipulation. In the results, patients whose maximum mouth opening was more than 40 mm was higher in the self-manipulation group(69.9%) than in the conventional manipulation group(42.9%). But difference between two groups was not significant. According to the fact that we decided to discharge the patients whentheir mouth opening increased to more than 40 mm and subjective symptoms such as pain and discomfort were improved as well, treatment period of discharged patients was significantly shorter in the self-manipulation group($29.2{\pm}12.3$ weeks) than in the conventional manipulation group ($61.0{\pm}38.0$ weeks) (p<0.01). In conclusion, in the treatment of TMD patients with anterior disc displacement without reduction, the self-manipulation technique which is performed by patients themselves is an effective treatment modality for increasing the range of mouth opening and shortening the total treatment period.

• Korean Journal of Organic Agriculture
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• v.20 no.4
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• pp.619-630
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• 2012

Miscellaneous grain crops has been appeared as a well-being food and the demand of them are increasing recently. It is urgent to study especially about the functional materials of foxtail millet, common millet and sorghum. This experiment was conducted to evaluation and comparison several functional materials of fatty acids, anthocyanin content, total phenol content and DPPH assay of rice, and foxtail millet, common millet and sorghum produced organically so that these results would provide as a basic information for developing functional products by using miscellaneous grain crops. Total content of fatty acids was in order of foxtail millet (0.649%) and common millet (0.33%), sorghum (0.172%) and rice (0.111%) respectively. The content of unsaturated fatty acid was also in order of foxtail millet (0.511%) and common millet (0.269%), sorghum (0.122%) and rice (0.069%) respectively. Although there was no detection of anthocyanin content in rice, foxtail millet and common millet, sorghum showed high content of anthocyanin content. Sorghum of Mongdangsusu showed the highest anthocyanin content (137.5mg/g). In the total phenol content of rice, foxtail millet, common millet and sorghum, rice of Chucheongbyeo had high content ($13.70{\mu}g/g$) whereas Daeanbyeo was the lowest content ($10.07{\mu}g/g$). Foxtail millet of Hinchajo, common millet of Byeorukgijang and sorghum of Chalsusu showed the highest total phenol content of $25.8{\mu}g/g$, $69.4{\mu}g/g$ and $682.2{\mu}g/g$ respectively. In the average of total phenol content among rice, foxtail millet, common millet and sorghum, foxtail millet, common millet and sorghum showed $12.25{\mu}g/g$, $16.95{\mu}g/g$, $51.01{\mu}g/g$ and $447.4{\mu}g/g$ of total phenol content respectively. The average of total phenol content of sorghum was $26.3{\mu}g/g$. It is 36.3 and 26.3 times higher compared with rice and foxtail millet respectively. In the antioxidant activity of seeds by DPPH(1,1-diphenyl-2-picrylhydrazyl) assay for rice, foxtail millet, common millet and sorghum, rice of Chucheongbyeo, foxtail millet of Ganghywajo and common millet of Geumeungijang showed the highest antioxidant activity with 3.6%, 4.78% and 13.4% respectively. Antioxidant activity of sorghum ranged from 88.47 to 90.11%. The average of antioxidant activity among four crops, the highest antioxidant activity was obtained from sorghum (89.50%) and the next was common millet (6.13%), foxtail millet (2.43%), and rice (1.60%) in their order of antioxidant activity. The average of antioxidant activity of sorghum showed 55.9, 37.0 and 15times higher compared with rice, common millet and foxtail millet respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.9
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• pp.1244-1255
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• 2011

The purpose of this study was to compare the perception of meals and nutrition knowledge among high school students in Seoul. A survey was carried out on 548 male/female students in general and vocational high schools. The general high school students showed higher frequency of breakfast than the vocational high school students (p<0.001). As for the reasons for eating alone, the general high school students showed high frequency of 'busy', whereas the vocational high school students revealed high frequency of 'irregular meal times' (p<0.001). Concerning the habit of eating alone, 'irregular meal times (25.0%)', 'unbalanced diet (22.4%)', and 'instant food (16.6%)' were observed in that order (p<0.01). The percentage of high school students who regarded family meals as meals eaten with every member of their family was 70.6% (p<0.05). The percentage of general high school students who ate family meals was 61.8% and that of vocational high school students was 50.0% (p<0.01). When agreement with attitudes, environment, and participation in family meals was evaluated using a Likert scale (strongly agree 5 points, strongly disagree 1 point), the general high school students showed a higher level of agreement than the vocational high school students, and the results showed a significantly higher level of agreement as the frequency of family meals increased. Likewise, the groups who scored a higher level of nutrition knowledge had positive attitudes, environment, and participation in family meals with significant differences.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Korean Journal of Poultry Science
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• v.16 no.2
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• pp.105-127
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• 1989

Although poultry industry in Japan has been much developed in recent years, it still needs to be developed , compared with developed countries. Since the poultry market in Korea is expected to be opened in the near future it is necessary to maximize the Productivity to reduce the production costs and to develop the scientific, technologies and management organization systems for the improvement of the quality in poultry production. Followings ale the summary of poultry industry in Japan. 1. Poultry industry in Japan is almost specized and commercialized and its management system is ： integrated, cooperative and developed to industrialized intensive style. Therefore, they have competitive power in the international poultry markets. 2. Average egg weight is 48-50g per day (Max. 54g) and feed requirement is 2. 1-2. 3. 3. The management organization system is specialized and farmers in small scale form complex and farmers in large scale are integrated.