• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.257-258
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• 2004

The dose from radionuclides released from high-level radioactive waste (HLW) glasses as they corrode must be taken into account when assessing the performance of a disposal system. In the performance assessment (PA) calculations conducted for the proposed Yucca Mountain, Nevada, disposal system, the release of radionuclides is conservatively assumed to occur at the same rate the glass matrix dissolves. A simple model was developed to calculate the glass dissolution rate of HLW glasses in these PA calculations [1]. For the PA calculations that were conducted for Site Recommendation, it was necessary to identify ranges of parameter values that bounded the dissolution rates of the wide range of HLW glass compositions that will be disposed. The values and ranges of the model parameters for the pH and temperature dependencies were extracted from the results of SPFT, static leach tests, and Soxhlet tests available in the literature. Static leach tests were conducted with a range of glass compositions to measure values for the glass composition parameter. The glass dissolution rate depends on temperature, pH, and the compositions of the glass and solution, The dissolution rate is calculated using Eq. 1: $rate{\;}={\;}k_{o}10^{(ph){\eta})}{\cdot}e^{(-Ea/RT)}{\cdot}(1-Q/K){\;}+{\;}k_{long}$ where $k_{0},\;{\eta}$ and Eaare the parameters for glass composition, pH, $\eta$ and temperature dependence, respectively, and R is the gas constant. The term (1-Q/K) is the affinity term, where Q is the ion activity product of the solution and K is the pseudo-equilibrium constant for the glass. Values of the parameters $k_{0},\;{\eta}\;and\;E_{a}$ are the parameters for glass composition, pH, and temperature dependence, respectively, and R is the gas constant. The term (1-Q/C) is the affinity term, where Q is the ion activity product of the solution and K is the pseudo-equilibrium constant for the glass. Values of the parameters $k_0$, and Ea are determined under test conditions where the value of Q is maintained near zero, so that the value of the affinity term remains near 1. The dissolution rate under conditions in which the value of the affinity term is near 1 is referred to as the forward rate. This is the highest dissolution rate that can occur at a particular pH and temperature. The value of the parameter K is determined from experiments in which the value of the ion activity product approaches the value of K. This results in a decrease in the value of the affinity term and the dissolution rate. The highly dilute solutions required to measure the forward rate and extract values for $k_0$, $\eta$, and Ea can be maintained by conducting dynamic tests in which the test solution is removed from the reaction cell and replaced with fresh solution. In the single-pass flow-through (PFT) test method, this is done by continuously pumping the test solution through the reaction cell. Alternatively, static tests can be conducted with sufficient solution volume that the solution concentrations of dissolved glass components do not increase significantly during the test. Both the SPFT and static tests can ve conducted for a wide range of pH values and temperatures. Both static and SPFt tests have short-comings. the SPFT test requires analysis of several solutions (typically 6-10) at each of several flow rates to determine the glass dissolution rate at each pH and temperature. As will be shown, the rate measured in an SPFt test depends on the solution flow rate. The solutions in static tests will eventually become concentrated enough to affect the dissolution rate. In both the SPFt and static test methods. a compromise is required between the need to minimize the effects of dissolved components on the dissolution rate and the need to attain solution concentrations that are high enough to analyze. In the paper, we compare the results of static leach tests and SPFT tests conducted with simple 5-component glass to confirm the equivalence of SPFT tests and static tests conducted with pH buffer solutions. Tests were conducted over the range pH values that are most relevant for waste glass disssolution in a disposal system. The glass and temperature used in the tests were selected to allow direct comparison with SPFT tests conducted previously. The ability to measure parameter values with more than one test method and an understanding of how the rate measured in each test is affected by various test parameters provides added confidence to the measured values. The dissolution rate of a simple 5-component glass was measured at pH values of 6.2, 8.3, and 9.6 and $70^{\circ}C$ using static tests and single-pass flow-through (SPFT) tests. Similar rates were measured with the two methods. However, the measured rates are about 10X higher than the rates measured previously for a glass having the same composition using an SPFT test method. Differences are attributed to effects of the solution flow rate on the glass dissolution reate and how the specific surface area of crushed glass is estimated. This comparison indicates the need to standardize the SPFT test procedure.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.12
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• pp.1554-1559
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• 2008

This study was performed to investigate the hypoglycemic effect of single and repeated oral administration of medicinal herbal mixture (AD) in streptozotocin (STZ) induced diabetic rats. Angelica decursiva, Lycium chinense and Adenophora triphylla var. japonica Hara were selected by oral glucose tolerance test (OGTT) and mixed for AD mixture. In an oral glucose tolerance test, the AD inhibited the increase in blood glucose levels at 1 hr and 2 hr and decreased incremental glycemic response area under the curve. In a single administration of AD1 (100 mg/kg) and AD2 (500 mg/kg), significant reductions by 5.3% and 12.3% were observed in fasting blood glucose level for 4 hours. During the 1 month of the experimental period, AD1 and AD2 was given to the STZ induced diabetic rats. At 4th week, the fasting blood glucose levels of AD1 and AD2 caused a fall of 25.5% and 37.9%, respectively. In addition, the body weights were decreased by 7.7% (AD1) and 1.7% (AD2), respectively, compared with diabetic control (DC, decreasing of 10.2%). This study suggests that AD could be potentially useful for fasting and post-prandial hyperglycemia treatment and all these effects concluded to the use of this plant extract to manage diabetes mellitus.

• 한국균학회소식:학술대회논문집
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• 2015.11a
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• pp.16-16
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• 2015

Ectomycorrhizal (ECM) mushrooms play a major role in plant growth promotion through symbiotic association with roots of forest trees. They also provide an economically important food resource to us and therefore they have been studied for their artificial cultivation for decades in Korea. We have secured bio-resources of ECM mushrooms from Korean forests and performed their physiological studies. To investigate the cultural characteristics, the fungi were cultured under different conditions (medium, temperature, pH of the medium, inorganic nitrogen source). More than 90% of total 160 strains grew on three solid media (potato dextrose agar, PDA; sabouraud dextrose agar, SDA; modified Melin-Norkrans medium, MMN). The rate of mycelial growth on malt extract agar (MEA) was lower than those of three media (PDA, SDA, MMN). None of the Tricholomataceae strains grew on MEA. Many strains of ECM mushrooms were able to grow at the temperature range of $15{\sim}25^{\circ}C$ on PDA, while they showed poor growth at $10^{\circ}C$ or $30^{\circ}C$. In particular, the growth rates of both Gomphaceae and Tricholomataceae were significantly lower at $10^{\circ}C$ than at $30^{\circ}C$. The optimal pH of many strains was pH 5.0 when they cultured in potato dextrose broth (PDB). Fifty-seven percent of tested strains grew well on medium containing ammonium source than nitrate source. Many strains of Tricholomataceae showed a notable growth on ammonium medium than nitrate medium. Twenty-three percent of strains preferred nitrate source than ammonium source for their mycelial growth. The production and activity of two enzymes (cellulase and laccase) by ECM fungi were also assayed on the enzyme screening media containing CMC or ABTS. Each strains exhibited different levels of enzymatic activities as well as enzyme production. The number of laccase-producing strains was less than that of cellulase-producing strains. We found that 77% of tested strains produced both cellulase and laccase, whereas 2% of strains did not produce any enzymes. The morphological characteristics of mycelial colony were also examined on four different solid media. Yellow was a dominant color in mycelial colony and followed by white and brown on all culture media. ECM mushrooms formed mycelial colonies with a single or multiple colors within a culture medium depending on the strains and culture media. The most common shape of mycelial colony was a circular form on all media tested. Other families except for Amanitaceae formed an irregular colony on MMN than PDA. All strains of Tricholomataceae did not form a filamentous colony on all media. The pigmentation of culture media by mycelial colonies was observed in more than 50% of strains tested on both PDA and SDA. The degree of pigmentation on PDA or SDA was higher than MMN and brown color was dominant than yellow color. The production of exudates from mycelial colony was higher on PDA than MMN. Brown exudates were mainly produced by many strains on PDA or SDA, whereas transparent exudates were mainly produced by strains on MMN. We observed the mycelial colonies with a single or multiple textures in just one culture plate. Wrinkled or uneven colony surfaces were remarkably observed in many strains on PDA or SDA, while an even colony surface was observed in many strains on MMN. Sixty percent of Tricholomaceae strains formed wrinkled surface on PDA. However, they did not form any wrinkle on MMN plate. Cottony texture was observed in mycelia colonies of many strains. Velvety texture was often observed in the mycelial colonies on SDA than PDA and accounted for 60% of Suillaceae strains on SDA.

• Proceedings of the Ginseng society Conference
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• 1984.09a
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• pp.27-36
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• 1984

It was previously reported that red ginseng extract inhibited carcinogenesis by urethan, DMBA and aflatoxin $B_1E (Cancer Detection and Prevention, 6: 515-525, 1983). In an attempt to investigate the mechanism of the anticarcinogenic effect of ginseng, we assayed natural killer (N.K) activity in mice treated with urethan and benzo(a)pyrene. In our experiment newly born Swiss Webster mice, less than 24 hrs. old, were given a single subcutaneous injection of lmg of ure-than and 40ug of benzo(a)pyrene. The mice had been administered with ginseng since weaning, and sacrificed at various intervals. Major organs were examined both, with the naked eye and microscopically. N.K. activity of spleen cells was analyzed in a 12-hour $^{51}Cr^-release$ assay against YAC-1 cells. Administration of ginseng resulted in an increase of N.K. activity by $18\%$ at 4 weeks, $20\%$ (P < 0.05) at 6, $29\%$ (P < 0.05) at 12, and $13\%$ at 24 following a single injection of urethan. At the same time, significantly lower incidences of lung adenoma were noted at 6 weeks $(50\%)$ and 12 weeks $(27\%)$ following the administration of ginseng to urethan-injected mice. This result indicates that the enhancement of N.K. activity by ginseng makes a contribution to its anticarcinogenic effect. On the hand, N.K. activity was suppressed by benzo(a)pyrene during the time span of this experiment and it almost returned to the level of controls following the adminsitration of ginseng. However, the lung adenoma induced by benzo(a)pyrene began to occur at 48 weeks in which N.K. activity had naturally declined to a very low level in all experimental mice, and administration of ginseng did not decrease the incidence. In explanation of this result, we might propose that the recovery of the N.K. activity by ginseng had little effect on the incidence of lung adenoma because of the long latent period of carcinogenesis by benzo(a)pyrene. In conclusion, these results suggest that the anticarcinogenic effect of ginseng in urethan-treated mice may be related to the augmentation of N.K. activity.

• Journal of Ginseng Research
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• v.23 no.4
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• pp.222-229
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• 1999

Histopathological study has been carried out to elucidate the protective effect of Korean red ginseng water extract (KRG-WE) on 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-induced acute toxicity in male guinea pigs. Forty male guinea pigs ($200{\pm}20g$) were divided into 4 groups: normal controls (group 1) received vehicle and saline; group 2 (single TCDD-treated) received TCDD (5 ${\mu}g/kg$, single dose) and saline; group 3 received KRG-WE (200 mg/kg, i.p.) for 2 weeks starting 1 week before TCDD-exposure; group 4 received same dose of KRG-WE for 7 days from the day of TCDD-exposure. Weights of liver, testis, kidney, spleen and lung of the TCDD-exposed guinea pigs were significantly decreased. Thymus was severely shrunken, thereby could not be distinguished from adipose tissue in group 2 animals. Focal interstitial inflammation and fibrosis were observed from the lung parenchyma of group 2 animals. Furthermore, moderate swelling of hepatocyte, diffused aggregates of hemosiderin-laden macrophages from the Prussian blue stained spleen, marked decrease in spermatogenesis, and pyknotic and degenerative changes in the renal tubules were observed from intestinal organs of group 2 animals. On the other hand, histopathological damage was moderately to markedly alleviated in groups 3 and 4, but pretreatment of KRG-WE was more effective than the simultaneous treatment. In particular, TCDD-induced testicular atrophy was significantly attenuated by KRG-WE (p<0.01). From these results, it could be suggested that Korean red ginseng might be a useful herb that prevented TCDD-induced toxicity on liver, testis, kidney and spleen.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.9
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• pp.1476-1485
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• 2004

Of solvent-extracts prepared from the 90 kinds of Korean traditional tea and rice gruel plants, cold-water extract from peels of Citrus unshiu (CUI-0) showed the most potent intestinal immune system modulating activity through Peyer’s patch whereas other extracts did not have the activity except for cold-water extracts of Laminaria japonica, Polygonatum japonicum, Poncirus trifoliata, and hot-water extracts of Gardenia jasminoides, Lycium chinense having intermediate activity. CUI-0 was further fractionated into MeOH-soluble fraction (CUI-1), MeOH insoluble and EtOH-soluble fraction (CUI-2), and crude polysaccharide fraction (CUI-3). Among these fractions, CUI-3 showed the most potent stimulating activity for the proliferation of bone marrow cells mediated by Peyer’s patch cells, and contained arabinose, galacturonic acid, galactose, glucose, glucuronic acid and rhamnose (molar ratio; 1.00:0.53:0.45:0.28:0.28:0.19) as the major sugars, and a small quantity of protein (9.4%). In treatments of CUI-3 with pronase and periodate (NaIO₄), the intestinal immune system modulating activity of CUI-3 was significantly reduced, and the activity of CUI-3 was affected by periodate oxidation particularly. The potently active carbohydrate-rich fraction, CUI-3IIb-3-2 was further purified by anion-exchange chromatography on DEAE-Sepharose FF, Sepharose CL-6B and Sephacryl S-200. CUI-3IIb-3-2 was eluted as a single peak on HPLC and its molecular weight was estimated to be 18,000 Da. CUI-3IIb-3-2 was consisted mainly of arabinose, galactose, rhamnose, galacturonic acid and glucuronic acid (molar ratio;1.00:0.54:0.28:1.45:0.63) in addition to a small amount of proteins (3.2%). In addition, CUI-3IIb-3-2 showed the activity only through Peyer’s patch cells, but this fraction did not directly stimulate proliferation of bone marrow cells. It may be concluded that intestinal immune system modulating activity of peels from C. unshiu is caused by pectic polysaccharides having a polygalacturonan moiety with neutral sugars such as arabinose and galactose.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.