In order to assess the quality of dietary intake among the elderly, a survey was conducted during Jucy-August, 1999, of 230 subjects who were 65 years or older and who were living in Jeonju City. Results of the analysis of the data are as follows : Regarding Dietery Variety Score (DVS), the average number of food items consumed per person was significantly higher for males (19.6) than for females (17.7). The intake of plant food was higher than animal food for both sexes the proportion of plant versus animal foods consumed by fresh weight was 85 : 15 for males and 89 : 11 for females. Diet Diversity Score (DDS) is determined by how many from five food groups (cereal, meat, dairy, vegetable and fruit) consumed per day while Korean Diet Diversity Score (KDDS) is determined by how many from five different food groups (cereal, meat, vegetable, dairy and oil) consumed per day. The subjects'average DDS and KDDS were 4.0 and 3.5 for males, and 3.7 and 3.2 for females, respectively. Overall, the distribution of DDS was lower than that of KDDS. The average Meal Balance Score (MBS : Apply the KDDS at breakfast, lunch and dinner) was 9.1 for malts and 8.1 for females. Average daily caloric intake for males and females was 1,740 kcal and 1,433 kcal, which was 84.0% and 80.9% of the RDA, respectively. Average daily protein intake for males and females, at 67 g and 49 g (100.7% and 88.3% of the RDA), respectively, was satisfactory. However, intakes of calcium and vitamin A were below 75% of the RDA (calcium : 62.7% for males and 55.3% for females ; vitamin A : 60.7% for males and 53.9% far females). The average proportional contribution of protein/fat/carbohydrate (PFC) to total calorie intake was 15.8 : 15.7 : 68.5 for males and 13.8 : 13.2 : 73.0 for females. Distribution of energy for each meal (breakfast : lunch : afternoon snack : dinner : night snack) was 29.2 : 32.4 : 5.0 : 31.2 : 2.2 among males and 30.5 : 33.5 : 4.5 : 28.6 : 2.91 among females. The Index of Nutritional Quality (INQ) was above 1 for protein, phosphorus, iron, vitamin B
In order to verify the risk of onset of the illnesses based on gender, age, and monthly income 1,739 subjects from Hongcheon county, Gangwon province were selected. Questionnaire on demographic sociology, health condition, existence of illnesses(cancer, hypertension, stroke, diabetes, arthritis, cardiac disorders), and usage of public health services was surveyed from October 1, 2006 to October 20, 2006. Following conclusions were reached on the basis of the questionnaire : - For demographic sociological peculiarities, gender, age, occupation, and education level were evenly distributed. Most were under normal marriage(67.38%), health insurance(86.39%), 494(36.0%) individuals with less than monthly income of 1 million won, 494(36.0%) individuals with monthly income between 1 and 2 million won, 219(16.0%) with monthly income between 2 and 3 million won, and 164(12.0%) individuals with more than 3 million won, thus showing relatively low income. - For health status, 1,199(70.28%) individuals are non-smokers, 209(45.63%) individuals smoke
In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.
To obtain information on the inheritance of the quantitative characters related with the vegetative and reproductive growth of rice, the