• Journal of the Korean Society of Food Science and Nutrition
• /
• v.34 no.5
• /
• pp.659-666
• /
• 2005

The purpose of this study was to compare the intake status of calcium, phosphorus, iron, and zinc of Korean adults residing in different regions. Subjects were recruited and divided into three groups according to the districts where they lived, which included rural (n=137), coastal (n=100), and urban district (n=117). Subjects were interviewed using a general questionnaire and 24-hour recall method for dietary intake. The average age of the subjects were 58.1 years for rural district, 57.7 years for coastal district, and 48.6 years for urban district. There was no significance in total food intake by regions. The food intakes from cereals, mushrooms, vegetables of rural district, that from fishes of coastal district, and those from sugars, milks, oils of urban area were the highest among three districts. The calcium, phosphorus, iron, and zinc intakes were $60.1\%,\;123.9\%,\;95.2\%,\;and\;73.1\%$ of RDAs, respectively. The calcium intakes as percentage of RDA in rural and coastal district were significantly (p<0.01) lower than that in urban district. A larger number of subjects from coastal or urban district ate under $75\%$ of zinc RDA compared to those from rural village. Major sources of dietary calcium in total subjects were anchovy, kimchi, milk, soybean curd, rice, ice cream, sea mustard, yogurt, loach, and welsh onion. Rice supplied $15.5\%$ for phosphorus, $22.1\%$ for iron, and $35.9\%$ for zinc of total intake. Except for rice, major sources of dietary zinc were pork, beef, small red bean, dog meat, chicken, jacopever, soybean curd, glutinous millet, and kimchi. In conclusion, the food and mineral intakes of adults differed according to the regions in which they resided. The food and nutrient intakes of coastal district were not satisfactory, and calcium and zinc intakes of three regions did not meet RDAs. Therefore, it is required unique and discriminatory nutritional education with each region for increasing intakes of calcium and zinc.

• Korean Journal of Poultry Science
• /
• v.17 no.3
• /
• pp.141-156
• /
• 1990

The experiments were conducted to investigate the possibility of improving the effectiveness of the existing method to estimate the edible meat weight in the live broiler chicken. A total of 360 birds, five male and female chicks from each line were sacrificed at Trial 1 (body weight 900-1, 000g), Trial 2 (body weight 1.200-1, 400g), Trial 3(body weight 1, 600-1, 700), and Trial 4(body weight 2, 000g) in order to measure the body weight, edible meat weight of breast, thigh and drumsticks, and various components of body weight. Each line was reared at the Poultry Breeding Farm, Seoul National University from the second of july, 1987 to the thirteenth of September, 1987. The results obtained from this study were summarized as follows ： 1. The average body weights of each line( H. T, M, A) were $2150.5\pm$34.9, $2133.0\pm$26.2, $1960.0\pm$23.1, and $2319.3\pm$27.9, respectively. at 7 weeks of age. The feed to body weight eain ratio for each line chicks was 2.55, 2.13, 2.08, and 2.03, respectively, for 0 to 7 weeks of age. The viability of each line was 99.7. 99.7, 100.0, and 100.0%, respectively, for 0 to 7 weeks of age.01 was noticed that A Line chicks grow significantly heavier than did T, H, M line chic ks from 0 to 7 weeks of age. The regression coefficients of growth curves from each line chicks were bA＝1.015, bH＝0.265, bM＝0.950 and bT＝0.242, respectively. 2. Among the body weight components, the feather. abdominal fat, breast, and thigh and drumsticks increased in their weight percentage as the birds grew older, while neck. head, giblets and inedible viscera decreased. No difference wat apparent in shank, wings and hack. 3. The weight percentages of breast in edible part for each line thicks were 19.2, 19.0, 19.9 and 19.0% at Trial 4, respectively. The weight percentages of thigh and drumsticks in edible part for each line chicks were 23.1, 23.3, 22.8, and 23.0% at Trial 4. respective1y. 4. The values for the percentage meat yield from breast were 77.2. 78.9 73.5 and 74.8% at Trial 4 in H, T, M and A Line chicks. respectively. For thigh and drumstick, the values of 80.3, 78.4. 79.7 and 80.2% were obtained. These data indicate that the percentage meat yield increase as the birds grow older. 5. The correlation coefficients between body weight and blood. head, shanks. breast. thigh-drumstick were high. The degree if correlation between abdominal fat(%) and percentage of edible meat were extremely low at all times, but those between abdominal fat (%) and inedible viscera were significantly high.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.137-154
• /
• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.1-25
• /
• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.3 no.1
• /
• pp.30-40
• /
• 2000

Purpose: During the first year of life, cow's milk protein is the major offender causing food allergy. Cow's milk allergy (CMA) affects 2~7% of infants, of which approximately one-half show predominantly gastrointestinal symptoms. We studied the clinical types of cow's milk allergy with predominantly gastrointestinal symptoms (CMA-GI) of childhood. Methods: The retrospective study was performed on 30 (male 22, female 8) patients who had diagnosed as CMA-GI during 2 years and 3 months from March 1995 to June 1997. Results: 1) Children with CMA-GI presented in the three types of clinical manifestation on the basis of time to reaction to milk ingestion: Quick (Q) onset (5 cases), Slow (S) onset (20 cases), Quick & Slow (Q&S) (5 cases). 2) Age on admission of the three groups was significantly different (p<0.05): (Q onset: $81.4{\pm}67.1$ days, S onset: $31.9{\pm}12.7$ days, Q&S: $366.0{\pm}65.0$ days). Although the body weight at birth was 10~95 percentile in all patients, body weight on admission was different: (Q onset: 10~50 percentile, S onset: below 10 percentile, Q&S: 10~25 percentile). S onset group was significantly different compared with other groups (p<0.05) and 90% of this one was failure to thrive below 3 percentile. 3) Peripheral leukocyte counts were as followings: (Q onset: $5,700{\sim}12,300/mm^3$, S onset: $10,000{\sim}33,400/mm^3$, Q&S: $5,200{\sim}14,900/mm^3$). Slow onset group was significantly different compared with other groups (p<0.05). Serum albumin levels on admission were as followings: (Q onset: $4.2{\pm}0.4\;g/dl$, S onset: $3.0{\pm}0.3\;g/dl$, Q&S: $4.0{\pm}0.3\;g/dl$). S onset group was significantly different compared with other groups (p<0.05) and 85% of this one was below 3.5 g/dl. 4) Although morphometrical analysis on small intestinal mucosa did not show enteropathy in Q onset and Q&S groups, all cases of S onset revealed enteropathy: 45% of this one showed subtotal villous atrophy, 55 % showed partial villous atrophy. 5) Allergic reaction test to other foods was not performed in S onset group because of ethical problem and high risk in general condition. In Q onset group, allergic reaction to one or two other foods: soy formula, weaning formula and eggs. Q&S goup revealed allergic reactions to several foods or to most of all foods except protein hydrolysate formula: eggs, potatos, some kinds of sea food, apples, carrots, beef and chicken. 6) Serum IgE level, peripheral eosinophil counts, milk RAST, soy RAST, skin test were not significantly different among groups. Conclusion: CMA-GI may present in three clinical ways on the basis of time to reaction to milk ingestion, typical clinical findings and morphologic changes in the small bowel mucosal biopsy specimens. This clinical subdivision might be helpful in diagnostic and therapeutic approaches in CMA-GI. Early suspicion is mandatory especially in S onset type because of high risks with malnutrition and enteropathy.