• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1229-1238
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• 2005

The objective of the present study was to investigate the efficacy of physical water treatment (PWT) technologies using different catalytic materials and an electronic anti-fouling device in the mitigation of mineral fouling in a once-through flow system with mini-channel heat exchanger. Effects of flow velocity and water hardness on the effectiveness of PWT technologies were experimentally studied. The artificial water hardness varied from 5.0 to 10 mo1/m$^{3}$ as CaCO$_{3}$. For 10 mo1/m$^{3}$ solution, fouling resistance reduced by 13-40$\%$ depending on flow velocity and types of PWT devices. On the other hand, fouling resistance reduced by 21-29$\%$ depending on the PWT devices for 5 mo11m3 solutions. The PWT device using alloy of Cu and Zn as catalyst (CM2) was slightly more effective than the others. SEM photographs of scale produced from the 10 mol/m$^{3}$ solution at 1.0 m/s indicated that calcium carbonate scales without PWT devices were needle-shaped aragonite, which is sticky, dense and difficult to remove. Scales with the PWT devices showed a cluster of spherical or elliptic shape crystals. Both the heat transfer test results and SEM photographs strongly support the efficacy of PWT technologies using catalytic materials and an electronic anti-fouling device in the mitigation of mineral fouling.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.930-937
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• 2017

Objective: The I pig is a long nurtured longstanding breed in Vietnam, and contains excellent indigenous genetic resources. However, after 1970s, I pig breeds have become a small population because of decreasing farming areas and increasing pressure from foreign breeds with a high growth rate. Thus, there is now the risk of the disappearance of the I pigs breed. The aim of this study was to focus on classifying and identifying the I pig genetic origin and supplying molecular makers for conservation activities. Methods: This study sequenced the complete mitochondrial genome and used the sequencing result to analyze the phylogenetic relationship of I pig with Asian and European domestic pigs and wild boars. The full sequence was annotated and predicted the secondary tRNA. Results: The total length of I pig mitochondrial genome (accession number KX094894) was 16,731 base pairs, comprised two rRNA (12S and 16S), 22 tRNA and 13 mRNA genes. The annotation structures were not different from other pig breeds. Some component indexes as AT content, GC, and AT skew were counted, in which AT content (60.09%) was smaller than other pigs. We built the phylogenetic trees from full sequence and D loop sequence using Bayesian method. The result showed that I pig, Banna mini, wild boar (WB) Vietnam and WB Hainan or WB Korea, WB Japan were a cluster. They were a group within the Asian clade distinct from Chinese pigs and other Asian breeds in both phylogenetic trees (0.0004 and 0.0057, respectively). Conclusion: These results were similar to previous phylogenic study in Vietnamese pig and showed the genetic distinctness of I pig with other Asian domestic pigs.

• Journal of Preventive Medicine and Public Health
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• v.35 no.4
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• pp.313-321
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• 2002

Objectives : To identify the risk factors of dementia among the elderly in a large city. Methods : A cross-sectional study was conducted in July 2001, with potential participants selected by stratified two stage cluster sampling of the elderly population of Keumgog dong, Busan. A total of 452 elderly people aged 65 years and over, underwent a two phase diagnostic procedure. Mini-mental State Examination-Korean (MMSE-K) and Samsung Dementia Questionnaire were used for the 1st stage, and the Clinical Dementia Rating Scale (CDR), the Bartel ADL, and IADL Index, the Korean Geriatric Depression Scale (KGDS), the Modified Hatchinski Ischemic Scale (MHIS), and other laboratory tests were used for the 2nd stage. Results : Of the 446 participants finally chosen, 45 were confirmed with dementia, and 363 as normal, with the rests not confirmed with dementia or as normal, were excluded from the analysis. According to the logistic regression analysis, the risk of dementia was significantly higher In: people aged 80 and above (OR=4.36, 95% CI=1.97-9.62), illiterate (OR=3.58, 95% CI=1.71-7.46), who had a history of strokes (OR=6.35, 95% CI=2.71-14.87), or who had 3 history of hyperlipidemia (OR=4.74, 95% CI=1.65-13.61), compared to their counterparts. Conclusions : These results suggest that efforts to prevent strokes and hyperlipidemia can significantly decrease the risk of dementia.

• Molecules and Cells
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• v.44 no.5
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• pp.310-317
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• 2021

Cluster of differentiation 1 (CD1) is a family of cell-surface glycoproteins that present lipid antigens to T cells. Humans have five CD1 isoforms. CD1a is distinguished by the small volume of its antigen-binding groove and its stunted A' pocket, its high and exclusive expression on Langerhans cells, and its localization in the early endosomal and recycling intracellular trafficking compartments. Its ligands originate from self or foreign sources. There are three modes by which the T-cell receptors of CD1a-restricted T cells interact with the CD1a:lipid complex: they bind to both the CD1a surface and the antigen or to only CD1a itself, which activates the T cell, or they are unable to bind because of bulky motifs protruding from the antigen-binding groove, which might inhibit autoreactive T-cell activation. Recently, several studies have shown that by producing T_H2 or T_H17 cytokines, CD1a-restricted T cells contribute to inflammatory skin disorders, including atopic dermatitis, psoriasis, allergic contact dermatitis, and wasp/bee venom allergy. They may also participate in other diseases, including pulmonary disorders and cancer, because CD1a-expressing dendritic cells are also located in non-skin tissues. In this mini-review, we discuss the current knowledge regarding the biology of CD1a-reactive T cells and their potential roles in disease.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.