• Journal of Preventive Medicine and Public Health
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• v.25 no.3 s.39
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• pp.247-268
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• 1992

This study was conducted to evaluate the effect of VDT work on eyes and vision among workers in a TV manufacturing plant. The study subjects consisted of 264 screen workers and 74 non-screen workers who were less than 40 years old male and had no history of opthalmic diseases such as corneal opacities, trauma, keratitis, etc and whose visual acuity on pre-employment health examination by Han's test chart was 1.0 or above. The screen workers were divided into two groups by actual time for screen work in a day : Group I, 60 workers, lesser than 4 hours a day and group II, 204 workers, more than 4 hours a day. From July to October 1992 a questionnaire was administered to all the study subjects for the general charateristics and subjective eye symptoms after which the opthalmologic tests such as visual acuity, spherical equivalent, lacrimal function, ocular pressure, slit lamp test, fundoscopy were conducted by one opthalmologist. The proportion of workers whose present visual acuity was decreased more than 0.15 in comparison with that on the pre-employment health examination by Han's test chart was 20.6% in Group II. 15.0% in Group I and 14.9% in non-screen workers. However, the differences in proportion were not statistically significant. The proportion of workers with decreased visual acuity was not associated with the age, working duration, use of magnifying glass and type of shift work (independent variables) in all of the three groups. However, screen workers working under poor illumination had a higher proportion of persons with decreased visual acuity than those working under adequate illumination (P<0.05) . The proportion of workers whose near vision was decreased was 27.5% in Group II, 18.3% in Group I, and 28.4% in non-screen workers and these differences in proportion were not statistically significant. Changes of near vision were not associated with 4 independent variables in all of the three groups. Six out of seven subjective eye symptoms except tearing were more common in Group I than in non-screen workers and more common in Group II than in Group I (P<0.01). Mean of the total scores for seven subjective symptoms of each worker(2 points for always, 1 point for sometimes, 0 point for never) was not significantly different between workers with decreased visual acuity and workers with no vision change. However, mean of the total scores for Group II was higher than those for the Group I and non-screen workers (P<0.01). Total eye symptom scores were significantly correlated with the grade of screen work, use of magnifying glass, and type of shift work. There was no independent variable which was correlated with the difference in visual acuity between the pre-employment health examination and the present state, the difference between far and near visions, lacrimal function, ocular pressure, and spherical equivalent. Multiple linear regression analysis for the subjective eye symptom scores revealed a positive linear relationship with actual time for screen work and shift work(P<0.01). In this study it was not observed that the VDT work decreased visual acuity but it induces subjective eye symptoms such as eye fatigue, blurred vision, ocular discomfort, etc. Maintenance of adequate illumination in the work place and control of excessive VDT work are recommended to prevent such eye symptoms.

• Journal of Preventive Medicine and Public Health
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• v.4 no.1
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• pp.77-87
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• 1971

A study of the knowledge, attitude and practices about the maternal and child health of 305 married women residing in apartments at the Yonsei Community Health area was conducted during the period from November to December 1970 using designed questionnaire with well trained interviewers. The results and findings obtained from the study are summarized as follows: A. Pregnancy and Birth Questions were asked about their last child. 1. 16.4% of the women were pregnant. 2. Among 281 women who had experienced delivery, 48.0% were assisted by doctor or midwisves for their last delivery, while the rest of women delivered their last baby at home without any professional's assistance. The higher the level of education or the greater exposure to mass communication, the more the deliveries were assisted by doctors or midwives. Those women who were born and raised in cities had more deliveries assisted by doctors and midwives than those who were not. 3. Kinds of delivery sheets used. Among 141 cases of home delivery 68% used cement bag paper or vinyl sheets. Three% used nothing and remained used unsterile materials. 4. Among 141 cases of home delivery, 70.2% used scissors. The rest of them used other methods. 5. 47.3% of the women had a rest for one month or more after birth. The higher the level of education, the longer the period of rest was observed. 6. 52.4% of the women fed the colostrum to their babies. This was not related to the mother's education. 7 About half(42.9%) of the women had poor knowledge about a proper diet for the pre and post natal period. B. Child Health 1. Knowledge and practice regarding to the immunization for their children: Most of the women (93.2%) could name at least one kind of immunization. 20.3% could name 6 kinds of immunization. Mothers education level did not influence their ability to name immunizations. 85.2% of children had been immunized at least once. 2. Morbidity of last born children: 48.1% of their last born children were found to have been sick during the last year. Less than half(41.5%) of the sick children were seen by doctor. 3. Counselling at well baby clinic: Most of the women(76.5%) had no counselling for their children. Registration rate at the well baby clinic at the Severance Hospital was 13.2%. 45.9% wanted to visit to the well baby clinic at the Severance Hospital. 4. Weaning Period: 44.6% said that the beginning of the weaning for their last born children was from 6 months to twelve months of age. The most important reason of weaning was the health of both mothers and children. 5. Knowledge and Practice regarding birth and death Registration: 64.6% of the women could name correctly the Ku-office as the place for the registration. Only 29.2% registered the birth of their last born children within 14 days. C. Knowledge, Attitude and Practice regarding to family planning Most: of the women accepted the idea of family planning. 97.7% could name at least one contraceptive method. 35.4% were found to be current users of contraceptive methods. The ideal number of children was 3.1 in average.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• 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.

• Radiation Oncology Journal
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• v.16 no.1
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• pp.43-49
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• 1998

Purpose : Carcinomas arising in the gall bladder(GB) or extrahepatic biliary ducts are uncommon and generally have a poor prognosis. The overall 5-year survival rates are less than $10\%$. Early experiences with the external radiation therapy demonstrated a good palliation with occasional long-term survival. The present report describes our experience over the past decade with irradiation of primary carcinomas of the gallbladder and extrahepatic biliary duct. Materials and Methods : From Feb. 1984 to Nov. 1995, thirty-three patients with carcinoma of the GB and extrahepatic biliary duct were treated with external beam radiotherapy with curative intent at our institution. All patients were treated with 4-MV linear accelerator and radiation dose ranged from 31.44Gy to 54.87Gy(median 44.25Gy), and three Patients received additional intraluminal brachytherapy(range, 25Gy to 30Gy). Twenty-seven Patients received postoperative radiation. Among 27 patients, Sixteen patients underwent radical operation with curative aim and the rest of the patients either had bypass surgery or biopsy alone. In seventeen patients, adjuvant chemotherapy was used and eleven patients were treated with 5-FU, mitomycin and leucovorin. Results : Median follow up period was 8.5 months(range 2-97 months). The overall 2-year and 5-year survival rates in all patients were $29.9\%$ and $13.3\%$ respectively. In patients with GB and extrahepatic biliary duct carcinomas, the 2-year survival rates were $34.5\%$ and $27.8\%$ respectively. Patients who underwent radical operation showed better 2-year survival rates than those who underwent palliative operation($43.8\%\;vs.\;20.7\%$), albeit statistically insignificant(p>0.05). The 2-year survival rates in Stage I and II were higher than in Stage III and IV with statistical significance(p<0.05). Patients with good performance status in the beginning showed significantly better survival rates than those with worse status(p<0.05). The 2-year survival rates in combined chemotherapy group and radiation group were $40.5\%$ and $22.0\%$ respectively. There was no statistical differences in two groups (p>0.05). Conclusion : The survival of patients with relatively lower stage and/or initial good performance was significantly superior to that of others. We found an statistically insignificant trend toward better survival in patients with radical operation and/or chemotherapy, More radical treatment strategies, such as total resection with intensive radiation and/or chemotherapy may offer a better chance for cure in selective patients with carcinoma of gall bladder and extrahepatic biliary ducts.

• Journal of Preventive Medicine and Public Health
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• v.29 no.4 s.55
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• pp.747-764
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• 1996

This study intended to obtain an useful information for health management of lead exposed workers and determine biological monitoring interval in early period of exposure by measuring the lead exposure indices and work duration in all male workers (n=433 persons) exposed less than 1 year in 6 storage battery industries and in 49 males who are not exposed to lead as control. The examined variables were blood lead concentration (PBB), Zinc-protoporphyrin concentration (ZPP), Hemoglobin (HB) and personal history; also measured lead concentration in air (PBA) in the workplace. According to the geometric mean of lead concentration in the air, the factories were grouped into three categories: A; When it is below $0.05mg/m^3$, B; When it is between 0.05 and $0.10mg/m^3$, and C; When it is above $0.10mg/m^3$. The results obtained were as follows: 1. The means of blood lead concentration (PBB), ZPP concentration and hemoglobin(HB) in all male workers exposed to lead less than 1 year in storage battery industries were $29.5{\pm}12.4{\mu}g/100ml,\;52.9{\pm}30.0{\mu}g/100ml\;and\;15.2{\pm}1.1\;gm/100ml$. 2. The means of blood lead concentration (PBB), ZPP concentration and hemoglobin(HB) in control group were $5.8{\pm}1.6{\mu}g/100ml,\;30.8{\pm}12.7{\mu}g/100ml\;and\;15.7{\pm}1.6{\mu}g/100ml$, being much lower than that of study group exposed to lead. 3. The means of blood lead concentration and ZPP concentration among group A were $21.9{\pm}7.6{\mu}g/100,\;41.4{\pm}12.6{\mu}g/100ml$ ; those of group B were $29.8{\pm}11.6{\mu}g/100,\;52.6{\pm}27.9{\mu}g/100ml$ ; those of group C were $37.2{\pm}13.5{\mu}g/100,\;66.3{\pm}40.7{\mu}g/100ml$. Significant differences were found among three factory group(P<0.01) that was classified by the geometric mean of lead concentration in the air, group A being the lowest. 4. The mean of blood lead concentration of workers who have different work duration (month) was as follows ; When the work duration was $1\sim2$ month, it was $24.1{\pm}12.4{\mu}g/100ml$, ; When the work duration was $3\sim4$ month, it was $29.2{\pm}13.4{\mu}g/100ml$ ; and it was $28.9\sim34.5{\mu}g/100ml$ for the workers who had longer work duration than other. Significant differences were found among work duration group(P<0.05). 5. The mean of ZPP concentration of workers who have different work duration (month) was as follows ; When the work duration was $1\sim2$ month, it was $40.6{\pm}18.0{\mu}g/100ml$, ; When the work duration was $3\sim4$ month, it was $53.4{\pm}38.4{\mu}g/100ml$ ; and it was $51.5\sim60.4{\mu}g/100ml$ for the workers who had longer work duration than other. Significant differences were found among work duration group(P<0.05). 6. Among total workers(433 person), 18.2% had PBB concentration higher than $40{\mu}g/100ml$ and 7.1% had ZPP concentration higher than $100{\mu}g/100ml$ ; In workers of factory group A, those were 0.9% and 0.0% ; In workers of factory group B, those were 17.1% and 6.9% ; In workers of factory group C, those were 39.4% and 15.4%. 7. The proportions of total workers(433 person) with blood lead concentration lower than $25{\mu}g/100ml$ and ZPP concentration lower than $50{\mu}g/100ml$ were 39.7% and 61.9%, respectively ; In workers of factory group A, those were 65.5% and 82.3% : In workers of factory group B, those were 36.1% and 60.2% ; In workers of factory group C, those were 19.2% and 43.3%. 8. Blood lead concentration (r=0.177, P<0.01), ZPP concentration (r=0.135, P<0.01), log ZPP (r=0.170, P<0.01) and hemoglobin (r=0.096, P<0.05) showed statistically significant correlation with work duration (month). ZPP concentration (r=0.612, P<0.01) and log ZPP (r=0.614, P<0.01) showed statistically significant correlation with blood lead concentration 9. The slopes of simple linear regression between work duration(month, independent variable) and blood lead concentration (dependent variable) in workplace with low air concentration of lead was less steeper than that of poor working condition with high geometric mean air concentration of lead. The study result indicates that new employees should be provided with biological monitoring including blood lead concentration test and education about personal hygiene and work place management within $3\sim4$ month.

• Journal of Nutrition and Health
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• v.50 no.3
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• pp.270-283
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• 2017

Purpose: This study was conducted to examine coffee consumption behaviors, dietary habits, and nutrient intakes by coffee intake amount among university students. Methods: Questionnaires were distributed to 300 university students randomly selected in Gongju. Dietary survey was administered during two weekdays by the food record method. Results: Subjects were divided into three groups: NCG (non-coffee group), LCG (low coffee group, 1~2 cups/d), and HCG (high coffee group, 3 cups/d) by coffee intake amount and subjects' distribution. Coffee intake frequency was significantly greater in the HCG compared to the LCG (p < 0.001). The HCG was more likely to intake dripped coffee with or without milk and/or sugar than the LCG (p < 0.05). More than 80% of coffee drinkers chose their favorite coffee or accompanying snacks regardless of energy content. More than 75% of coffee takers did not eat accompanying snacks instead of meals, and the HCG ate them more frequently than LCG (p < 0.05). Breakfast skipping rate was high while vegetable and fruit intakes were very low in most subjects. Subjects who drank carbonated drinks, sweet beverages, or alcohol were significantly greater in number in the LCG and HCG than in the NCG (p < 0.01). Energy intakes from coffee were $0.88{\pm}5.62kcal/d$ and $7.07{\pm}16.93kcal/d$ for the LCG and HCG. For total subjects, daily mean dietary energy intake was low at less than 72% of estimated energy requirement. Levels of vitamin C and calcium were lower than the estimated average requirements while that of vitamin D was low (24~34% of adequate intake). There was no difference in nutrient intakes by coffee intake amount, except protein, vitamin A, and niacin. Conclusion: Coffee intake amount did not affect dietary nutrient intakes. Dietary habits were poor,and most nutrient intakes were lower than recommend levels. High intakes of coffee seemed to be related with high consumption of sweet beverages and alcohol. Therefore, it is necessary to improve nutritional intakes and encourage proper water intake habits, including coffee intake, for improved nutritional status of subjects.

• Journal of the Korean Society of Food Science and Nutrition
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• v.12 no.4
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• pp.342-349
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• 1983

This study was undertaken to ascertain food and nutritional evaluating data on the processing of fermented sea cucumber (Stichopus japonicus) entrails. In the experiment, the crude proteolytic enzyme from the entrails tissue of raw and fermented sea cucumber during the days of ripening was extracted. The optimal activity condition for the crude enzyme and the compositional changes of amino acid of the protein and free amino acid in the raw and fermented sample were also investigated. 1. Less than three kinds of proteolytic enzymes that each enzyme has optimal activity condition at pH 3.1 $50^{\circ}C$(A-enzyme), pH 5.7 $50^{\circ}C$(B-enzyme) and pH 7.7 $45^{\circ}C$(C-enzyme), respectively were believed to be exist in the entrails tissue of sea cucumber. 2. A-enzyme and C-enzyme were strongly inhibited with the increase of the salt concentration, and B-enzyme was activated at the 1% salt concentration and was inhibited above the 5% salt concentration. 3. The result of the effect of several salt ions on the proteolytic activity showed that A-enzyme was slightly inhibited in the presence of all salt ions added, B-enzyme was activated in the presence of the all salt ions except $Cu^{2+}$ and C-enzyme was activated in the presence of $Ca^{2+}$ and $Mn^{2+}$, and inhibited by $Cu^{2+}$, $Co^{2+}$ and $Mg^{2+}$. 4. When the effects of the ripening days on the proteolytic activity of the crude enzymes were analysed, the activity of the A-enzyme was slightly weakened with the lapse of the fermentation days, whereas the B-enzyme was not influenced by the fermentation days. 5. In the analysis of amino acid composition of the protein of the samples, the 8 days fermented sea cucumber entrails showed the diminution of all kinds of amino acid. Apparently diminished amino acids were arginine, alanine, glutamic acid, glycine, serine, valine, threonine and lysine etc., and methionine, histidine and isoleucine were slightly decreased. 6. In the analysis of free amino acid composition of the 8 days fermented sample, glutamic acid, aspartic acid, leucine and lysine were rich, while histidine, methionine, proline and tyrosine were poor. The most of free amino acids were increased during the fermentation procedure and especially in lysine, histidine, threonine, glutamic acid, methionine, valine and leucine.