• Geophysics and Geophysical Exploration
• /
• v.11 no.3
• /
• pp.250-262
• /
• 2008

In order to assess the feasibility of the dipole-dipole electric method to the investigation of metallic ore deposit, both field data simulation and inversion are carried out for several simplified ore deposit models. Our interest is in a vein-type model, because most of the ore deposits (more than 70%) exist in a vein type in Korea. Based on the fact that the width of the vein-type ore deposits ranges from tens of centimeters to 2m, we change the width and the material property of the vein, and we use 40m-electrode spacing for our test. For the vein-type model with too small width, the low resistivity zone is not detected, even though the resistivity of the vein amounts to 1/300 of that of the surrounding rock. Considering a wide electrode interval and cell size used in the inversion, it is natural that the size of the low resistivity zone is overestimated. We also perform field data simulation and inversion for a vein-type model with surrounding hydrothermal alteration zones, which is a typical structure in an epithermal ore deposits. In the model, the material properties are assumed on the basis of resistivity values directly observed in a mine originated from an epithermal ore deposits. From this simulation, we can also note that the high resistivity value of the vein does not affect the results when the width of the vein is narrow. This indicates that our main target should be surrounding hydrothermal alteration zones rather than veins in field survey. From these results, we can summarize that when the vein is placed at the deep part and the difference of resistivity values between the vein and the surrounding rock is not large enough, we cannot detect low resistivity zone and interpret the subsurface structures incorrectly using the electric method performed at the surface. Although this work is a little simple, it can be used as references for field survey design and field data Interpretation. If we perform field data simulation and inversion for a number of models and provide some references, they will be helpful in real field survey and interpretation.

• The korean journal of orthodontics
• /
• v.27 no.2
• /
• pp.349-357
• /
• 1997

Epidermal growth factor(EGF), a single chain polypeptide of 53 amino acids with a molecular weight of 6,045 Da, was first isolated from the male mouse submandibular glands. EGF stimulates cellular proliferation and differentiation in several tissues and accelerates the rate of wound healing. EGF is bound to the specific receptor(EGFR) on the cell membrane of its target cell. EGFR is a transmembrane glycoprotein with a molecular weight of 170,000 Da and is detectable on a large variety of cell types and tissues. The authors investigated the expression of EGFR in the normal and inflamed human gingival epithelium to study the role of EGFR in the inflammation of the gingival epithelium, and the expression of EGFR in the dental follicle by using in situ mRNA hybridization and immunohistochenistry. The results weree as follows : 1. The expression of EGFR mRNA in the normal gingival epithelium on in situ mRNA hybridization was mainly localized on the basal cell layer, and the spinous layer was weakly positive The granular and cornified layers were negative 2. The expression of EGFR protein in the normal gingival epithelium on inmunohistochemistry was localized on the cornified and granular layers, and the spinous layer was weakly positive. The basal cell layer was completely negative 3. The expression of EGFR mRNA in the inflamed gingival epithelium on in situ mRNA hybridization was evenly and homogeneously distributed in the whole layers of the gingival epithelium except the cornified layer. The staining intensity appeared to increase progressively from the basal cell layer to the cornified layer. 4. The expression of EGFR protein in the inflamed gingival epithelium on immunohistochemistry was evenly and homogeneously distributed in the whole layers of the gingival epithelium. The staining intensity appeared to increase progressively from the cornified layer to the basal cell layer. 5. Strong positive reaction was seen in the epithelial cell rests of Malassez, whereas only background staining was seen in other cells of the dental follicle. In conclusion, the up-regulation of EGFR in the inflamed gingival epithelium and the high amounts of EGFR in the epthelial cell rests of Malassez in the dental follicle can be regarded as responses to the possible damages to the oral environment to maintain the homeostatic conditions.

• Korean Chemical Engineering Research
• /
• v.56 no.4
• /
• pp.479-495
• /
• 2018

This study was carried out to research the multimedia fate modeling, concentration distribution and impact assessment of polycyclic aromatic hydrocarbons (PAHs) emitted from automobiles, which are known as carcinogenic and mutation chemicals. The amount of emissions of PAHs was determined based on the census data of automobiles at a target S-city and emission factors of PAHs, where multimedia fugacity modeling was conducted by the restriction of PAHs transfer between air-soil at the impervious area. PAHs' Concentrations and their distributions at several environmental media were predicted by multimedia fugacity model (level III). The residual amounts and the distributions of PAHs through mass transfer of PAHs between environment media were used to assess the health risk of PAHs at unsteady state (level IV), where the sensitivity analyses of the model parameter of each variable were conducted based on Monte Carlo simulation. The experimental result at S-city showed that Fluoranthene among PAHs substances are the highest residual concentrations (60%, 53%, 32% and 34%) at all mediums (atmospheric, water, soil, sediment), respectively, where most of the PAHs were highly accumulated in the sediment media (more than 80%). A result of PAHs concentration changes in S-city over the past 34 years identified that PAHs emissions from all environmental media increased from 1983 to 2005 and decreased until 2016, where the emission of heavy-duty vehicle including truck revealed the largest contribution to the automotive emissions of PAHs at all environment media. The PAHs concentrations in soil and water for the last 34 years showed the less value than the legal standards of PAHs, but the PAHs in air exceeded the air quality standards from 1996 to 2016. The result of this study is expected to contribute the effective management and monitoring of toxic chemicals of PAHs at various environment media of Metropolitan city.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.95-108
• /
• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The korean journal of orthodontics
• /
• v.31 no.4 s.87
• /
• pp.467-477
• /
• 2001

This investigation was designed to determine the effects of wire size, bracket width and the number of bracket on bracket-wire dynamic frictional resistance during simulating arch wire-guided tooth movement in vitro. For simulation of an arch wire-guided tooth movement, we simulated tooth, periodontal ligament and cancellous bone. Maxillary premolar and 1st molar were simulated as real sized resin teeth, the simulated resin teeth which its root was coated by polyether impression material which its elastic modulus is similar to periodontal ligament were embedded in steel housing with inlay wax which its elastic modulus is similar to cancellous bone. Stainless steel wires in four wire size (0.016, 0.018, $0.016\;{\times}\;0.022,\;0.019\;{\times}\;0.025$ inch) were examined with respect to three (stainless steel) bracket widths (2.4, 3.0, 4.3mm) and the number of medium bracket(one, two, three) included in the experimental assembly under dry condition. The wires were ligated into the brackets with elastomeric module. The results were as follows : 1. In all the brackets, frictional resistance increased with increase in wire size. But, statistically similar levels of frictional resistance were observed between 0.018 inch and $0.016\;{\times}\;0.022$ inch wires in narrow bracket and also between 0.016 inch and 0.018 inch wire in wide backet. 2. The frictional forces produced by 0.016 inch wire were statistically similar levels in all the brackets. In 0.018 inch round wire, wide bracket was associated with lower amounts of friction than both narrow and medium brackets. In $0.016\;{\times}\;0.022,\;0.019\;{\times}\;0.025$ inch rectangular wire, wide bracket produced target friction than both narrow and medium brackets. In all the wirer, narrow and medium bracket demonstrated no statistical difference in levels of frictional resistance. 3. Frictional resistance increased with increase In number of medium bracket. 0.016 inch round wire demonstrated the greatest increment in frictional resistance, followed by $0.019\;{\times}\;0.025,\;0.016\;{\times}\;0.022$ inch rectangular wire which were similar level in increment of frictional resistance, 0.018 inch wire demonstrated the least increment. The increments of frictional resistance were not constantly direct proportion to number of bracket.

• The Korean Journal of Ecology
• /
• v.24 no.3
• /
• pp.157-168
• /
• 2001

Herbicides have been used to control weeds for decades. If detoxification upon exposure to herbicides requires considerable amounts of energy, it could affect the pattern of resource allocation to growth and reproduction of crops. We examined the effects of three levels of a herbicide (Control, Low, and High) on germination, growth and reproductive characters of Glycine max treated twice, i.e., before and after seed germination. Since flowering time of G. max was separated into two groups, flowering time was also considered as a variable in this study. The rate of seed germination tended to be higher at the low level of herbicide compared to other levels. Chlorosis and shape variation of leaves were apparent after the second herbicide treatment, but completely disappeared after six weeks of treatment. The herbicide effects on growth characters were somewhat different between early and late flowering plants, but plants treated with both low and high levels of herbicide reduced their growth compared to those in the control group regardless of flowering time. Plants at the high level of herbicide exhibited the highest growth rate later in the season, suggesting that plants compensated to some extent for reduced growth. However, growth reduction among plants at the high level of herbicide was persistent until the end of growing season. Among plants flowered late in the season, plants in the control level bore a higher number of nodules per plant than those in other levels; such a pattern did not exist among plants flowered early in the season. Plants treated with low and high levels of herbicide produced a lower number of flowers than those in the control. Thus, the herbicide examined affected not only the growth and reproductive characters of non-target crops but also the development and growth of root nodules.

• Journal of Dairy Science and Biotechnology
• /
• v.34 no.4
• /
• pp.203-216
• /
• 2016

Probiotic microorganisms are thought to provide health benefits when consumed. In 2001, the World Health Organization defined probiotics as "live microorganisms which confer a health benefit on the host, when administered in adequate amounts." Three methods for screening potential probiotics have currently widely available. (1) In vitro assays of potential probiotics are preferred because of their simplicity and low cost. (2) The use of in vivo approaches for exploring various potential probiotics reflects the enormous diversity in biological models with various complex mechanisms. (3) Potential probiotics have been analyzed using several genetic and omics technologies to identify gene expression or protein production patterns under various conditions. However, there is no ideal procedure for selecting potential probiotics than testing cadidate strains on the target population. Hence, in this review, we provide an overview of the different methodologies used to identify new probiotic strains. Furthermore, we describe futre perspectives for the use of in vitro, in vivo and omics in probiotic research.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.99-122
• /
• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Management & Information Systems Review
• /
• v.38 no.2
• /
• pp.67-81
• /
• 2019

In accordance with K-IFRS 1109, financial instruments are classified to amortized cost (AC), fair value through other comprehensive income (FVOCI) and fair value through profit or loss (FVPL). And disposal gains are prohibited to be recirculated for net income when FVOCI financial instruments would be sold in the future, so-called recirculation prohibition. This research investigates whether accumulated other comprehensive income of available-for sale financial assets(AFS) under K-IFRS 1039, could affect reclassified amounts to the FVPL securities from the AFS securities. Also, this study investigates the effects of the reported income on the reclassified FVPL, because CEOs are likely to try earnings management when net income is predicted to be less than target or is low, comparing other firms. As a result of empirical analysis, first, I find that accumulated other comprehensive income of the AFS has a positive impact on the reclassified FVPL. Second, level of reporting income has no significant impact on the reclassified FVPL. Third, interaction effects are significantly positive on the firms which have more other comprehensive income and less level of reported income. Fourth, the effects of the bank and securities are more distinct than those of the manufactures. This study is the first research to investigate earnings management through AFS at the timing of the first adoption of K-IFRS 1109. Empirical results of this study provide evidence of earnings management on the reclassification of FVPL which gives meaningful implications to regulators, academic researchers and auditors.