• Journal of Mushroom
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• v.9 no.2
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• pp.59-62
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• 2011

To develop a new cultivar of King oyster mushroom(Pleurotus eryngii), G09-21 as parental strain was selected by the method of Di-mon crossing between monokaryotic strains derived from ASI 2824(Keunneutari No.2) and dikaryotic strain ASI 2887(Aeryni 3). The Pe21-51($G09-21-10{\times}2844-11$) was shown the best cultural characteristics, selected to be a new cultivar and named as 'Song-A'. The 'Song-A' was formed incompatibility line distinctly in the confrontation growth of parental strains Keunneutari No.2, Aeryni 3 and ASI 2844. The optimum temperature for mycelial growth, fruiting body development and pH arrange were $25{\sim}30^{\circ}C$, $14{\sim}16^{\circ}C$ and pH5~8, respectively. Fruiting body production per bottle was about $94.7{\pm}29.5$ g which is almost 106% quantity compared to that of other cultuvar Keunneutari No.2. And also the stip is thick and long but the number of available stipe is few. Analysis of the genetic characteristics of the new cultivar 'Song-A' showed a different DNA profile as that of the control strains, Keunneutari No.2, Aeryni 3 and ASI 2844, when RAPD(Random Amplified Polymorphic DNA) primers URP4 and 7 were used. This new cultivar 'Song-A' of Pleurotus eryngii is characterized by a small number of primordia formation and the stip is thick and long. Therefore, we expect that this new strain will save of labor and cost by without culling work.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.187-246
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• 1996

The purpose of this study is to evaluate and standardize the four scales of Questionnaire for the Sasang Constitution ClassificationII (QSCCII). QSCCII is newly prepared by statistical item analysis and is designed to examine its diagnostic discriminability. QSCCII is administered to 1366 random informants. From the survey, we could get the data for the standardization. The criteria of standardization are based on the data from 265 informants who are examined by professionals. Collected data are analyzed by internal consistency, variation analysis(ANOVA), Duncan test and discrimination analysis of SPSS PC+ V4.0 program. The results are as follows 1) The reliability of four scales for QSCCII is relatively valid. The internal consistency of Tae-yang(太陽) scale is Cronbach's ${\alpha}=0.5708$. That of So-yang(少陽) scale is ${\alpha}=0.5708$. That of Tae-eum(太陰) scale is ${\alpha}=0.5922$. That of So-eum(少陰) scale is ${\alpha}=0.6319$. 2) There is a significant difference between each group through variation analysis of four scales. 3) The process of standardization is based on the average value and standard deviation with respect to age and sex difference of each criteria. 4) This study suggests a source of standardization of Sasang Constitution Classification by providing norms in which the differences of age, sex, and number of items are taken into deep consideration. QSCCII, therefore, can be applied to every age(the 10's to the 60's) and sex groups. 5) The recalculation of the raw-score to standard value (T-score) shows that the diagnostic discriminability (Hit-ratio : 70.08%) of QSCCII brings about 37% improvement than proportional chance criteria(33.33%). Especially, Hit-ratios of Tae-eum In(74.5%) and So-eum In(70.8%) are higher than that of So-yang In(60.0%). 6) QSCC has discriminability only to male informants. Compared with QSCC, however, QSCCII has relatively efficient discriminability both to male and female informants. 7) These results would be a demonstration of the fact that the QSCCII could be used as a tool for sasang constitution classification.

• 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.

• Journal of Life Science
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• v.17 no.3 s.83
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• pp.386-395
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• 2007

A total of 27 strains of Vibrio parahaemolyticus (18 strains isolated from Korea and 9 strains from Japan) were serotyped and examined for biochemical characteristics, antimicrobial susceptibility patterns, cytotoxicity assay, thermostable direct hemolysin (TDH) production and molecular epidemiology. Using polymerase chain reaction (PCR) method and DNA probe hybridization method, the strains were tested for toxR, tdh, trh and ORF 8 genes. The V. parahaemolyticus isolated from patients were belonged to 8 different serotypes : O3:K6, O1:K38, O3:K57, O4:K9, O4:Kl2, O4:K68, O5:Kl5 and O6:K46. Urease-positive strain possessed the trh gene, and conversely, urease-negative strains lacked the gene, indicating that urease production by V. parahemolyticus strains strongly correlates with the possession of the trh gene. Most strains showed multiple resistant to more than three antibiotics and the antibiogram could be classified into 6 group (I to VI). All of the O3:K6 strains isolated in South Korea and Japan producted TDH at high levels. The TDH titers ranged between 256 and 2.048, and the average titer was 1009. To distinguish the new and increasingly common V. parahaemolyticus strains from clinical isolates, ORF 8 is a useful genetic marker. After Southern hybridization, the HindIII restriction fragment patterns of the tdh gene were grouped one type, respectively. One type showed two bands one of which was 4.3kb and the other was 11.5kb in size. Variation between the O3:K6 serotype are minor when compared to the differences seen with the non O3:K6 strains. The migration patterns of Not I -digested of the total DNA of the O3:K6 strains were similar, and only slight variations were observed between the serotypes. By contrast, the O3:K6 strains and non O3:K6 had markedly different profiles. In conclusion, Random amplified polymorphic DNA (RAPD) profile using appropriate primers was an effective epidemiological marker.