• Asian-Australasian Journal of Animal Sciences
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• v.18 no.2
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• pp.151-157
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• 2005

Sheep production is affected by genetic and non-genetic factors. A knowledge of these factors is essential for efficient management and for the accurate estimation of breeding values. The objective of this study was to establish the non-genetic factors which affect birth weight and weaning weight in Dorper, Mutton Merino and indigenous Sabi sheep breeds. A total of 2,625 birth and weaning weight records from Grasslands Research Station collected from 1991 through 1993, were used. The records were collected from indigenous Sabi (939), Dorper (807) and Mutton Merino (898) sheep. A mixed classification model containing the fixed effects of year, birth status and sex was used for identification of non-genetic factors. Sire within breed was included as a random effect. Two factor interactions and three factor interactions were important in indigenous Sabi, Mutton Merino and Dorper sheep. The mean birth weights were 4.37${\pm}$0.04 kg, 4.62${\pm}$0.04 kg and 3.29${\pm}$0.04 kg for Mutton Merino, Dorper and Sabi sheep, respectively. Sire had significant effects (p<0.05) on birth weight in Mutton Merino and indigenous Sabi sheep. Year of lambing had significant effects (p<0.05) on birth weight in indigenous Sabi, Mutton Merino and Dorper sheep. The effect of birth status was non significant in Dorper and Mutton Merino sheep while effect of birth status was significant on birth weight in indigenous Sabi sheep. In Indigenous Sabi sheep lambs born as singles (3.30${\pm}$0.05 kg) were 0.23 kg heavier than twins (3.07${\pm}$0.05 kg), in Mutton Merino lambs born as singles (3.99${\pm}$0.08 kg) were 0.07 kg heavier than twins (3.92${\pm}$0.08 kg) and in Dorper lambs born as singles (4.41${\pm}$0.04 kg) were 0.02 kg heavier than twins (4.39${\pm}$0.04 kg). On average males were heavier than females (p<0.05) weighing (3.32${\pm}$0.04 kg vs. 3.05${\pm}$0.07 kg) in indigenous Sabi, 4.73${\pm}$0.03 kg vs. 4.08${\pm}$0.05 in Dorper and 4.26${\pm}$0.07 kg vs. 3.66${\pm}$0.09 kg in Mutton Merino sheep. Two way factor interactions of sire*year, year*sex and sex*birth status had significant effects (p<0.05) on birth weight in indigenous Sabi, Mutton Merino and Dorper sheep while the effect of year*birth status was non significant on birth weight in Indigenous Sabi sheep. The three way factor interaction of year*sex*birth status had a significant effect (p<0.01) on birth weight in indigenous Sabi and Mutton Merino. Tupping weight fitted as a covariate had significant effects (p<0.001) on birth weight in indigenous Sabi, Mutton Merino and Dorper sheep. The mean weaning weights were 17.94${\pm}$0.31 kg, 18.19${\pm}$0.28 kg and 14.39${\pm}$0.28 kg for Mutton Merino, Dorper and Indigenous Sabi sheep, respectively. Effects of sire and sire*year were non significant on weaning weight in Dorper and Mutton Merino while year, sex and sex*year interaction had significant effects (p<0.001) on weaning weight. On average males were heavier than females (p<0.001) at weaning. The respective weaning weights were 18.05${\pm}$0.46 kg, 18.68${\pm}$0.19 kg, 14.14${\pm}$0.15 kg for males and 16.64${\pm}$0.60 kg, 16.41${\pm}$0.31 kg, 12.64${\pm}$0.32 kg for females in Mutton Merino, Dorper and Indigenous Sabi sheep. Lambs born as singles were significantly heavier at weaning than twins, 0.05 kg, 0.06 kg and 0.78 kg for Mutton Merino, Dorper and Indigenous Sabi sheep, respectively. Effect of tupping weight was highly significant on weaning weight. The three way factor interaction year*sex*birth status had a significant effect (p<0.01) on weaning weight. Correction for environmental effects is necessary to increase accuracy of direct selection for birth weight and weaning weight.

• Journal of dental hygiene science
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• v.12 no.4
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• pp.413-421
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• 2012

The main purpose of this study is to present practical data for the development of the health enhancing programs for the dental hygiene students. This data is based on the investigation of the psychosomatic self-reported symptoms of the dental hygiene students. Out of the random selection of the current dental hygiene students in Gyeonggi-do and Gangwon-do districts. We conducted a survey and analyzed the collected data from 432 respondents. The results are as follows: 1. The mental appeals (19.37) were higher then the physical appeals (17.53) and of the items in psychosomatic symptoms, the point of mental instability the highest (21.85); whereas, that of mouth and anal scored the lowest (14.59). 2. In terms of the religion, statistical significance was shown among physical appeals (p<.01), mental appeals (p<.05), multiple subjective symptom (p<.01), digestive organs (p<.01), aggressiveness (p<.01), nervousness (p<.01), and eye and skin (p<.05), mental instability (p<.05). 3. Concerning the living conditions, Statistical significance was found on the items such as physical appeals (p<.05), mental appeals (p<.01), depression (p<.001), irregular and life (p<.001), multiple subjective symptom (p<.01), lie scale (p<.01) and mouth and anal (p<.05), digestive organs (p<.05). 4. As for regular health check-ups, statistical significance was shown in the following items such as mental appeals (p<.05), depression (p<.01), multiple subjective symptom (p<.05), mental instability (p<.05).

• The Korean Journal of Mycology
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• v.27 no.6 s.93
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• pp.394-398
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• 1999

Taxonomic and genetic analysis of Phytophthora species belonging to six different morphological groups (GI, GII, GIII, GIV, GV, GVI) was conducted using RAPD method. Amplified fragments ranged $0.3{\sim}3.2$ kb in their molecular weights. Among total of 145 bands, there were 109 polymorphic bands. Seven isolates of P. infestans showed high similarities of $0.92{\sim}0.99$, and P. infestans isolate 3 from potato showed similarities of $0.93{\sim}0.95$ compared with other P. infestans. Among isolates of P. capsici, similarities of $0.77{\sim}0.86$ were observed and they were grouped in 80% level. P. cinnamomi and P. cryptogea isolates which belonging to group GVI showed very similar RAPD fingerprinting pattern. Primers OPA-04, OPA-17, OPA-18, OPA-19, and OPB-12 showed high level of differences among the tested isolates in major bands and molecular weights. The similarity between the isolates was 0.67. P. megasperma and P. sojae in group GV showed similarity of 0.65. These two isolates showed big differences in single major band in reactions with primers OPA-08, OPA-17, and OPA-19. Phytophthora-specific and P. infestans-specific molecular markers were also selected with one of the random primers tested. In reaction with primer OPA-20, all the genus Phytophthora showed common band at 600 bp, and all the P. infestans isolates showed specific band at 680 bp. These markers can be useful for identification of Phytophthora speices or P. infestans. As a result, P. infestans isolated from tomato and/or potato can easily be differentiated from other Phytophthora species with this primer.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.509-516
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• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Journal of Life Science
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• v.20 no.3
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• pp.365-373
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• 2010

To monitor newly emerged influenza virus variants and to investigate the prevalence pattern, our laboratory performed isolation of the viruses from surveillance sentinel hospitals. In the present study, we analysed influenza A/H1N1, A/H3N2, B viruses isolated in Busan during the 2006/07 and 2007/08 seasons by sequence analysis of the hemagglutinin (HA1 subunit) and neuraminidase (NA) genes. The isolates studied here were selected by the stratified random sample method from a total of 277 isolates, in which 15 were A/H1N1, 16 were A/H3N2 and 29 were B. Based on the phylogenetic tree, the HA1 gene showed that A/H1N1 isolates had a 96.7% to 97.7% homology with the A/Brisbane/59/2007, A/H3N2 isolates had a 98.4% to 99.7% homology with the A/Brisbane/10/2007, and B isolates had a 96.5% to 99.7% homology with the B/Florida/4/2006(Yamagata lineage), which are all the vaccine strains for the Northern Hemisphere in 2008~2009 season. In the case of the NA gene, A/H1N1 isolates had 97.8% to 98.5% homologies, A/H3N2 isolates had 98.9% to 99.4% homologies, and B isolates had 98.9% to 100% homologies with each vaccine strain in the 2008~2009 season, respectively. Characterization of the hemagglutinin gene revealed that amino acids at the receptor-binding site and N-linked glycosylation site were highly conserved. These results provide useful information for the control of influenza viruses in Busan and for a better understanding of vaccine strain selection.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.6
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• pp.453-458
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• 2001

The Rural Development Administration (RDA) of Korea now operates a system called Rice Variety Selection Tests (RVST), which are now being implemented in eight Agricultural Research and Extension Services located in eight province RVST's objective is to provide accurate yield estimates and to select well-adapted varieties to each province. Systematic evaluation of entries included in RVST is a highly important task to select the best-adapted varieties to specific location and to observe the performance of entries across a wide range of test sites within a region. The rice yield data in RVST for ordinary transplanting in Kangwon province during 1997-2000 were analyzed. The experiments were carried out in three replications of a random complete block design with eleven entries across five locations. Additive Main effects and Multiplicative Interaction (AMMI) model was employed to examine the interaction between genotype and environment (G$\times$E) in the biplot form. It was found that genotype variability was as high as 66％, followed by G$\times$E interaction variability, 21％, and variability by environment, 13％. G$\times$E interaction was partitioned into two significant (P＜0.05) principal components. Pattern analysis was used for interpretation on G$\times$E interaction and adaptibility. Major determinants among the meteorological factors on G$\times$E matrix were canopy minimum temperature, minimum relative humidity, sunshine hours, precipitation and mean cloud amount. Odaebyeo, Obongbyeo and Jinbubyeo were relatively stable varieties in all the regions. Furthermore, the most adapted varieties in each region, in terms of productivity, were evaluated.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

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

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