• Korean Journal of Ichthyology
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• v.12 no.2
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• pp.129-136
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• 2000

The aucha perch, Coreoperca kawamebari was collected in Tam-jin river from February to June 1998. It was reared in the laboratory and observed the spawning behavior and early life history. Spawning season was from mid of April to the end of May in the Tam-jin river. The fertilized eggs were demersal of adhesive, transparent and spherical in shape. Egg diameter was 2.21~2.65 mm with several oil globule of 0.058~0.343 mm. Hatching occurred about 194 hours 23 minutes after fertilization at water temperature of $18{\sim}22^{\circ}C$. Newly-hatched larvae were 5.09~5.68 mm in total length(TL, mean: 5.38mm) with 10~11+18=28~29 myotomes and opened mouth and anus. Melanophores were distributed on the eye lens, on the head, around the yolk, on the dorsal part and the abdominal region of the trunk. After hatching 5 days larvae attained 6.12~6.68 mm in TL (mean: 6.47 mm), and the yolk sac was completely absorbed and transformed to postlarva stage. The larvae reached to the juvenile stage with all the fins were formed with complete set of fin rays (D. XII-12~13; A. III-8~10; P. 11~13; V. I-4~5) at the 22 days after hatching and of the larvae was 11.54 mm in total length. In 32 days after hatching, the juvenile was 13.05 mm in TL. This period was similar to adult in body form and the spot.

• Advances in pediatric surgery
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• v.9 no.1
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• pp.45-51
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• 2003

A nationwide survey on choledochal cyst was undertaken among 39 members of the Korean Association of Pediatric Surgeons. The members were required to complete a questionnaire and the case registration form for each patient during the five year period of 1997 to 2001. Three hundred and forty eight patients were registered from 32 institutions. The average number of patients per surgeon was one to two every year. The male to female ratio was 1:3.4. The age of patients on diagnosis was $49.0{\pm}44.4$ months. The geographic distribution was 34.8% in Seoul and Kyoungki-do, 33.3% in Kyoungsang-do, 17.9% in Cholla-do, and 8.5% in Choongchung-do, in order of frequency. The three common clinical presentations were abdominal pain (63.8%), vomiting (35.3%), and jaundice (29.1%). Only seven patients (2%) presented with classic triad, and 25 patients were diagnosed by antenatal ultrasonographic examination. According to the Todani Classification, 238 patients (7l.3%) were type 1, 3 (0.9%) type 11, and 93 (27.8%) type IV. At the time of the operation, three important associated conditions were choledocholithiasis in 45 patients (15.1%), liver fibrosis (Grade 1-4) in 35, and previous operative procedure for biliary diseases in 10. Associated anomalies were observed in 13 patients (3.8%). Three hundred thirty nine (98.8%) of 343 lesions were treated by Cyst excision and Roux-Y hepaticoiejunostomy. One hundred seventy-six patients had an anomalous arrangement of the pancreatobiliary ductal system (APBD): APBD was not in 92 patients, biliary duct joined to the pancreatic duct in 51, and pancreatic duct joined to the biliary duct in 26. There were 8.5% early, and 7.7% late phase operative complications. The major complications were bleeding, anastomotic leakage, and acute pancreatitis. The combination of acute abdomen and choledochal cyst may suggest spontaneous rupture. Because of the development of late intrahepatic bile duct stones, long term follow up after cyst excision and hepaticojejunostomy is required. The optimal time of surgical intervention should also be considered in the situation of routine use of antenatal ultrasonographic examination. This is the first review of the choledochal cyst in Korea and provides baseline data for future comparisons.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.141-156
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• 2008

Market structure analysis is a very useful tool to analyze the competition boundary of the brand or the company. But most of the studies in market structure analysis, the concern lies in nondurable goods such as candies, soft drink and etc. because of the their availability of the data. In the field of durable goods, the limitation of the data availability and the repurchase time period constrain the study. In the analysis of the automobile market, those of views might be more persuasive. The purpose of this study is to analyze the structure of automobile market based on some idea suggested by prior studies. Usually the buyers of the automobile tend to buy upper tier when they buy in the next time. That kind of behavior make it impossible to analyze the structure of automobile market under the level of automobile model. For that reason I tried to analyze the market structure in the brand or company level. In this study, consideration data was used for market structure analysis. The reasons why we used the consideration data are summarized as following. Firstly, as the repurchase time cycle is too long, brand switching data which is used for the market analysis of nondurable good is not avaliable. Secondly, as we mentioned, the buyers of the automobile tend to buy upper tier when they buy in the next time. We used survey data collected in the U.S.A. market in the year of 2005 through questionaire. The sample size was 8,291. The number of brand analyzed in this study was 9 among 37 which was being sold in U.S.A. market. Their market share was around 50%. The brands considered were BMW, Chevrolet, Chrysler, Dodge, Ford, Honda, Mercedes, and Toyota. �� ratio was derived from frequency of the consideration set. Actually the frequency is different from the brand switch concept. In this study to compute the �� ratio, the frequency of the consideration set was used like a frequency of brand switch for convenience. The study can be divided into 2 steps. The first step is to build hypothetical market structures. The second step is to choose the best structure based on the hypothetical market structures, Usually logit analysis is used for the choice best structure. In this study we built 3 hypothetical market structure. They are type-cost, cost-type, and unstructured. We classified the automobile into 5 types, sedan, SUV(Sport Utility Vehicle), Pickup, Mini Van, and Full-size Van. As for purchasing cost, we classified it 2 groups based on the median value. The median value was $28,800. To decide best structure among them, maximum likelihood test was used. Resulting from market structure analysis, we find that the automobile market of USA is hierarchically structured in the form of 'automobile type - purchasing cost'. That is, result showed that automobile buyers considered function or usage first and purchasing cost next. This study has some limitations in the analysis level and variable selection. First, in this study only type of the automobile and purchasing cost were as attributes considered for purchase. Considering other attributes is very needful. Because of the attributes considered, only 3 hypothetical structure could be analyzed. Second, due to the data, brand level analysis was tried. But model level analysis would be better because automobile buyers consider model not brand. To conduct model level study more cases should be obtained. That is for acquiring the better practical meaning, brand level analysis should be conducted when we consider the actual competition which occurred in the real market. Third, the variable selection for building nested logit model was very limited to some avaliable data. In spite of those limitations, the importance of this study lies in the trial of market structure analysis of durable good.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1256-1262
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• 1997

Background : Leukocyte-endothelial adhesion molecules have been implicated in the pathogenesis of inflammatory disease. ICAM-1, VCAM-1 and E-selectin are cell surface adhesion molecule on vascular endothelial cells. They are up-regulated by inflammatory cytokines and regulate the adhesion and migration of leukocytes across the endothelium. Tuberculosis, a granulomatous disorder is an infection caused by Mycobacterium tuberculosis. The clinical manifestations of tuberculosis are dependent on the cellular immune response to tubercule bacilli. Circulating adhesion molecules are probably formed by cleavage and release into the circulation of the extracellular domain of the membrane bound form. The elevated levels of circulating adhesion molecules have been reported in numerous disease state. To evaluate their role as markers of disease activity in tuberculosis, we measured a sE-selectin, sVCAM-1 and sICAM-1 levels in the serum with severities of mild, moderate and far advanced pulmonary tuberculosis. Methods : The control and test groups were divided as follows. Group I : control(n=5), Group II : patients with mild pulmonary tuberculosis(n=12), Group III : pateints with moderate pulmonary tuberculosis(n=20), Group IV : patients with far advanced pulmonary tuberculosis(n=19). Serum sICAM-1, sVCAM-1 and sE-selectin were measured by ELISA kit Results : Serum soluble adhesion molecules are elevated in patients with pulmonary tuberculosis, Circulating ICAM-1 levels were significantly elevated in patients with moderate and far advanced pulmonary tuberculosis when compared with control group. When compared with control group, serum sVCAM-1 levels showed significant elevation in patients with mild, moderate and far advanced pulmonary tuberculosis. Serum sE-selectin levels were significantly elevated in patients with far advanced pulmonary tuberculosis when compared with control group. Conclusion : These results suggest that sICAM-1, sVCAM-1, and sE-selectin may be invloved in the pathogenesis of tuberculosis. And, particularly, sICAM-1 and sVCAM-1 may be useful markers of the disease activity.

• Journal of Life Science
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• v.21 no.9
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• pp.1301-1309
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• 2011

The major function of adipocytes is to store fat in the form of triglycerides. One of the signaling pathways known to affect adipogenesis, i.e. fat formation, is the WNT/${\beta}$-catenin pathway which inhibits the expression and activity of key regulators of adipogenesis. The purpose of this research is to find genes among the WNT/${\beta}$-catenin pathway which regulate adipogenesis by using small interfering (si) RNA and to find the association of single nucleotide polymorphisms (SNPs) of the gene with serum triglyceride levels in the human population. To elucidate the effects of ${\beta}$-catenin siRNA on adipogenesis key factors, PPAR${\gamma}$ and C/EBP${\alpha}$, we performed real-time PCR and western blotting experiments for the analyses of mRNA and protein levels. It was found that the siRNA-mediated knockdown of ${\beta}$-catenin upregulates adipogenesis key factors. However, upstream regulators of the WNT/${\beta}$-catenin pathway, such as DVL2 and LRP6, had no significant effects compared to ${\beta}$-catenin. These results indicate that ${\beta}$-catenin is a candidate gene for human fat accumulation. In general, serum triglyceride level is a good indicator of fat accumulation in humans. According to statistical analyses of the association between serum triglyceride level and SNPs of ${\beta}$-catenin, -10,288 C>T SNP (rs7630377) in the promoter region was significantly associated with serum triglyceride levels (p<0.05) in 290 Korean subjects. On the other hand, serum cholesterol levels were not significantly associated with SNPs of the ${\beta}$-catenin gene. The results of this study showed that ${\beta}$-catenin is associated with fat accumulation both in vitro and in the human population.

• Journal of the Korean Society of Food Science and Nutrition
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• v.22 no.6
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• pp.651-657
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• 1993

The protective effects of dietary vatamin E and selenium on peroxidative damage and hematopoietic inhibition by lead poisoning were investigated in rats. Male Sprague-Dawley rats weighing 150$\pm$5g were divided into six groups according to dietary vitamin E and / or selenium levels, i.e. control(vitamin E, 40mg/kg diet), 0E(without vitamin E, Se), 40E(vitamin E, 40mg/kg diet ; without Se), 200E(vitamin E, 200mg/kg diet ; without Se), 200ES(vitamin E, 200mg/kg diet ; Se, 0.5ppm) and 0Es(without vitamin E ; Se, 0.5ppm) groups. All experimental groups were fed ad libitum 2000ppm lead in diet except control for 4 weeks. Hemoglobin contents and hematocrit values of lead groups were lower than control group except 200ES group and were the lowest in 0E group. Aminolevulinic acid dehydratase(ALAD) activities of blood and liver were sequentially reduced in 200ES, 200E, 0ES, 40E and 0E groups, compared to control, were as urinary aminolevulinic acid (ALA) excretions were increased in the groups which represented low ALAD activity. Heapatic superoxide dismutase(SOD) activities was lower in 0E, and higher in 40E, 200E and 200ES groups, compared with control. Glutathione peroxidase(GPX) activities of liver were reduced in 0E and 40E groups, but those of 0ES, 200E and 200ES groups were significantly increased. Especially GPX activities in 200ES and 200ES groups were not different from control group. The reduced glutathione contents in liver were lowest in 0E and 40E groups, compared with control, whereas levels of the oxidized form were opposite phenomena of that. Liver lipid peroxide values of 0E, 0ES, 40E and 200E groups were 6.4, 2.9, 2.1 and 1.3 fold higher than control, respectively, but 200ES groups was not different from control.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.11
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• pp.812-820
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• 2011

This study was conducted to characterize $CO_2$ biomineralization on several minerals (i.e., CaO, MgO, $SiO_2$) by bottle test in an aqueous solution and solidified sludge using different aerobic bacterial strains like Bacillus megaterium and Bacillus pasteurii by batch test. These bacteria promote the formation of microenvironments that facilitate the precipitation of mineral phases that were unsaturated in the bulk solution. For one type of mineral solely amended, the $CO_2$ was reduced at the highest of 4.0 mmol for MgO while it was not that much lower for CaO and $SiO_2$ showing 1.1 and 0.3 mmol $CO_2$2, respectively. For two types of minerals simultaneously amended, the $CO_2$ was reduced at the greater extent for both Ca + Mg and Mg + Si showing 2.7 and 2.3 mmol, respectively whereas it was less for Ca + Si at 1.8 mmol. For solidified sludge, the $CO_2$ reduction rate changed depending on the volume of solidified sludge placed in the medium and the input $CO_2$ concentration.. The reduction rate of $CO_2$ was increased with increasing the volume of solidified sludge. Results of XRD analysis indicate that $CaCO_3$ (Calcite) was dominantly formed among others (e.g., Aragonite, Dolomite). SEM analysis showed that the sample with Bacillus pasteurii, could more form minerals rather than control. As demonstrated in this study, $CO_2$ would be effectively sequestered in biomineralization process.

• Economic and Environmental Geology
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• v.31 no.3
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• pp.201-213
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• 1998

From the Jungwon and Munkyeong areas which are among the famous producers of the carbonate-type groundwaters in Korea, various kinds of natural waters (deep groundwater, shallow groundwater and surface water) were collected between 1996 and 1997 and were studied for hydrogeochemical and environmental isotope (${\delta}^{34}S_{so4}$, ${\delta}^{18}O$, ${\delta}D$)systematics. Two types of deep groundwaters (carbonate type and alkali type) occur together in the two areas, and each shows distinct hydrogeochemical and environmental isotope characteristics. The carbonate type waters show the hydrochemical feature of the 'calcium(-sodium)-bicarbonate(-sulfate) type', whereas the alkali type water of the 'sodium-bicarbonate type'. The former type waters are characterized by lower pH, higher Eh, and higher amounts of dissolved ions (especialJy, $Ca^{2+}$, $Na^{+}$, $Mg^{2+}$, $HCO_3{^-}$ and $SO_4{^{2-}}$). Two types of deep groundwaters are all saturated or supersaturated with respect to calcite. Two types of deep groundwaters were both derived from pre-thermonuclear (about more than 40 years old) meteoric waters (with lighter 0 and H isotope data than younger waters, i.e., shallow cold groundwaters and surface waters) which evolved through prolonged water-rock interaction. Based on the geologic setting, water chemistry, and environmental isotope data, however, each of these two different types of deep groundwaters represents distinct hydrologic and hydrogeochemical evolution at depths. The carbonate type groundwaters were formed through mixing with acidic waters that were derived from dissolution of pyrites in hydrothermal vein ores (for the Jungwon area water) or in anthracite coal beds (for the Munkyeong area water). If the deeply percolating meteoric waters did not meet pyrites during the circulation, only the alkali type groundwaters would form. This hydrologic and hydrogeochemical model may be successfully applied to the other carbonate type groundwaters in Korea.

• 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.