• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.1
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• pp.15-20
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• 2021

Purpose This article studies the relationship between the length of a kidney measured by two scanning methods: Kidney Computed Tomography (CT) and ^99mTc-Dimercaptosuccinic acid (DMSA) renal scan. Kidney CT provides a better anatomic assessment, while ^99mTc-DMSA renal scan is superior in the kidney function test. Materials and Methods From January to December of 2019, two hundred patients who had Kidney CT and Tc^99m-DMSA renal scan were chosen for this study. SPSS17.0 was selected for statistical analysis. Results Due to the effect of the breathing and resolution of ^99mTc-DMSA renal scan, it showed the kidney's relatively longer length than the length of Kidney CT. For the same kidney, the length comparison among different brands' Gamma cameras was negligible. The length difference within the same age group did not show a noticeable discrepancy. However, there was a length difference between the radio technologists. Kidney CT and ^99mTc-DMSA renal scan indicated a strong positive correlation between the length of the left and right kidney. Conclusion It is necessary to establish a standardized measurement method for measuring kidney length using ^99mTc-DMSA renal scan. The kidney's functional changes and length changes are indications of Kidney diseases. Especially, pediatric patients tend to use ^99mTc-DMSA renal scan for assessing the kidney's shape and the function to avoid potential radiation exposure during the Kidney CT. Therefore, it is significant to provide not only the kidney's functional information but also an anatomic analysis, including the kidney's size, length, and location through the ^99mTc-DMSA renal scan.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.279-287
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• 2020

This study aims to put forth an efficient way of improving body shapes by examining the effects of deep tissue massage and illite-combined deep tissue massage on body shape changes, and identifying body shape changes when applying each method. This study targeted twenty women in their thirties, and ten separate subjects were placed in different groups. Then deep tissue massage and illite-combined deep tissue massage were performed once a week, for a total of eight weeks. Moire Topography was applied before the experiments, four weeks later and eight weeks later to compare changes in spinous process inclination, shoulders and hips. The data collected were analyzed using SPSS v. 21.0, and the study results are as follows. In relation to general characteristics of the subjects, professionals occupied the highest proportion of them, and 90% of them were married. Here, 77.8% of them had experience in giving birth, and 78.6% of them chose natural birth. In addition, 57.1% of the subjects holding a majority had two children. When measuring spinous process inclination, shoulders and hips in the illite-combined deep tissue massage group and in the deep tissue massage group before the experiments, the illite-combined deep tissue massage group showed somewhat higher values in every area than the deep tissue massage group, but no statistically significant differences were not found, which means the homogeneity existed between them. When comparing body shape changes between the two massage methods, there were significant differences(p<.05, p<.01), because the illite-combined deep tissue massage group showed a much higher decline in spinous process inclination, shoulders and hips than the deep tissue massage group. This implies illite-combined deep tissue massage was more effective in improving body shapes than deep tissue massage. Therefore, illite-combined deep tissue massage is considered to be helpful in improving body shapes, and it is anticipated that this massage method can be used in relevant fields, including the skin care industry.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.189-197
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• 2023

Purpose. This study aimed to compare and evaluate the shear bond strength between various temporary prostheses resin blocks fabricated by subtractive and additive manufacturing methods bonded to self-curing reline resin. Materials and methods. The experimental groups were divided into 4 groups according to the manufacturing methods of the resin block specimens and each specimen was fabricated by subtractive manufacturing (SM), additive manufacturing stereolithography apparatus manufacturing (AMS), additive manufacturing digital light processing manufacturing (AMD) and conventional self-curing (CON). To bond the resin block specimens and self-curing resin, the reline resin was injected and polymerized into the same location of each resin block using a silicone mold. The shear bond strength was measured using a universal testing machine, and the surface of the adhesive interface was examined by scanning electron microscopy. To compare between groups, one-way ANOVA was done followed by Tukey post hoc test (α = 0.05). Results. The shear bond strength showed higher values in the order of CON, SM, AMS, and AMD group. There were significant differences between CON and AMS groups, as well as between CON and AMD groups. but there were no significant differences between CON and SM groups (P > .05). There were significant differences between SM and AMD groups, but there were no significant differences between SM and AMS groups. The AMS group was significantly different from the AMD group (P < .001). The most frequent failure mode was mixed failures in CON and AMS groups, and adhesive failures in SM and AMD groups. Conclusion. The shear bond strength of SM group showed lower but not significant bond strength compared to the CON group. The additive manufacturing method groups (AMS and AMD) showed significantly lower bond strength than the CON group, with the AMD group the lowest. There was also a significant difference between the AMD and SM group.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.149-162
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• 2024

This study is a longitudinal study of female older adults living alone, one of the most vulnerable groups in our society, to determine how their depression changes over time and what factors affect their depression. At the same time, considering that there is a large difference in age among the same older adults, this study divided the female older adults into the young-old and the old-old to see how the predictors of depression in each group differ from each other. The main findings are as follows First, depression among female older adults living alone appears to have a declining pattern over time. In the conditional model, factors affecting the initial level of the depression trajectory among women living alone were found to be associated with lower initial depression values among those living in metropolitan areas rather than non-metropolitan areas, better subjective health, and those who did not exercise. Next, we examined the factors affecting rate of change (slope) in depression among female living alone older adults and found that the higher the age, the larger the metropolitan area, the better the subjective health, the less socializing, and the more socializing, the greater the decrease in depression level. Finally, there were some differences in the pathways affecting the initial value and slope of depression among female older adults living alone between the early and late older adults. Specifically, the higher the initial level of participation in social activities, the greater the change in depression among the late older adults, while there was no significant relationship among the early older adults. In the early older adults, better initial subjective health was associated with a larger change in depression than in the late older adults. Only in the late older adults did those who regularly exercised in the early years have higher initial depression values than those who did not. Based on the results of the above analyses, suggestions were made to reduce depression among female older adults living alone.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.365-378
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• 2020

Purpose This study was performed to determine whether the T1 relaxation time of gadoxetic acid-enhanced liver MR imaging is useful for detecting and staging liver fibrosis in patients with chronic liver disease. Materials and Methods One hundred and three patients with suspected focal liver lesion underwent MR imaging and Fibroscan. Fibroscan was chosen as the reference standard for classifying liver fibrosis. T1 relaxation times were acquired before (preT1), 20 minutes after (postT1) contrast administration, and reduction rate of T1 relaxation time (rrT1) on transverse 3D VIBE (volumetric interpolated breath-hold examination) sequence using 3T MR imaging. The optimal cut-off values for the fibrosis staging were determined with ROC analysis. Results PreT1 and postT1 increased and rrT1 decreased constantly with increasing severity of liver fibrosis according to the METAVIR score (F0-F4). There were statistically significant differences between F2 and F3 in preT1 (F2, 836.0 ± 74.7 ms; F3, 888.6 ± 77.5 ms, p < 0.05) and between F3 and F4 in postT1 (F3, 309.0 ± 80.2 ms; F4, 406.6 ± 147.7 ms, p < 0.05) and rrT1 (F3, 65.4 ± 7.7%; F4, 57.3 ± 11.4%, p < 0.05). ROC analysis revealed that combination test (preT1 + postT1) was the best test for predicting liver fibrosis. Conclusion PreT1 and postT1 increased constantly with increasing severity of liver fibrosis. T1 mapping in gadoxetic acid-enhanced liver MR imaging could be a helpful complementary sequence to determine the liver fibrosis stage.

• Microbiology and Biotechnology Letters
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• v.51 no.4
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• pp.465-473
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• 2023

In this study, we aimed to evaluate the bioconversion ability of single (Lactiplantibacillus plantarum CBT LP3, Lactococcus lactis subsp. lactis CBT SL6, Streptococcus thermophilus CBT ST3) and multi-strain probiotics to convert rutin to quercetin in roasted tartary buckwheat, and to assess their biological activities. To evaluate the bioconversion efficiency, each strain was cultured for 24 h in MRS media with 5% roasted tartary buckwheat 'Hwangguem-Miso' powder. After then, rutin and quercetin contents were determined by HPLC. Additionally, the biological activities were compared before and after bioconversion of an ingredient. Anti-oxidant effects were measured by DPPH and ABTS assays. Anti-inflammatory effects were determined by measuring NO production, and levels of iNOS, TNF-α, IL-6 and IL-4 using an LPS-induced Raw 264.7 cell model. The bioconversion rate under the combination of three species of probiotics significantly increased more than single species. Antioxidant efficacy results showed the highest activity when the combination of three species of probiotics cultured. The pro-inflammatory factors such as nitric oxide, iNOS, TNF-a, and IL-6 were significantly decreased when the three types of probiotics were combined than single strain was cultured. In addition, level in the anti-inflammatory factor IL-4 was increased. The multi-strain probiotics showed increased bioconversion efficiency, effects of anti-oxidant and anti-inflammatory compared to the single strain. These findings suggest that the fermentation of tartary buckwheat by probiotics may be a valuable candidate for developing functional foods with anti-oxidation and anti-inflammation.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.393-412
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• 2024

In selecting a disposal site for high-level radioactive waste, the hydrogeological research of the site is very important, and the hydraulic conductivity and the storage coefficient are key parameters. In this study, the hydraulic conductivity obtained by two different types of field hydraulic test equipment and methods was compared and analyzed for the deep granite rockmass in the Wonju area to understand the hydraulic characteristics of the deep granite rockmass. One was to perform the lugeon test, constant pressure injection test, and slug test at a maximum depth of 602.0 m by using the auto pressure/flow injection system, and the calculated hydraulic conductivity ranged from 1.26E-9 to 4.16E-8 m/s. In the overall depth, the maximum and minimum differences of the hydraulic conductivity were found to be about 33 times, and in the same test section, the difference by test method or analysis method was 1.13 to 8.25 times. In the other, the hydraulic conductivity calculated by performing a constant pressure injection test and a pulse test at a maximum depth of 705.1 m using the deep borehole hydraulic testing system was found to be 1.60E-10 to 2.05E-8 m/s, and the maximum and minimum differences were found to be about 130 times. In the constant pressure injection test, the difference depending on the analysis method was found to be 1.02 to 2.8 times. The hydraulic conductivity calculated by the two test equipment and methods generally showed similar ranges as E-9 and E-8 m/s, and no clear trend was observed according to depth. It was found that the granite rockmass in the Wonju area where the field hydraulic test was conducted showed low or very low rockmass permeability, and although there are differences in the range of hydraulic conductivity and the depth of application that can be measured depending on the applied test equipment and test method, it is generally believed that reliable results were presented.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.219-230
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• 2010

Digital content makes big changes to our daily lives while bringing opportunities and challenges for companies. Creative firms integrate pictures, texts, videos, audios, and data by digitalization to develop new products or services and create digital experiences to promote their brands. Most articles on digital content contribute to the basic concept or development of marketing it in literature. Actually, compared with traditional value chains for common products or services, the digital content industry seems to have more potential value. Because quite a bit of digital content is free to the consumer, price is not necessarily perceived as an indicator of the quality or value of information (Rowley 2008). It becomes evident that a current theme in digital content is the issue of "value," and research on customers' perceived value of digital content is a necessity. This article argues that experiential value has an advantage in customers' evaluations of digital content. Two different but related contributions to the understanding of "value" of digital content are made here. First, based on the comparison of digital content with products and services, the article proposes two key characteristics that make experiential strategy available for digital content: intangibility and near-zero reproduction cost. On top of that, based on the discussion of the gap between company's idealized value and customer's perceived value, this article emphasizes that digital content prices and pricing of digital content is different from products and services. As a result of intangibility, prices may not reflect customer value. Moreover, the cost of digital content in the development stage may be very high while reproduction costs shrink dramatically. Moreover, because of the value gap mentioned before, the pricing polices vary for different digital contents. For example, flat price policy is generally used for movies and music (Magiera 2001; Netherby 2002), while for continuous demand, digital content such as online games and anti-virus programs involves a more complicated matter of utility and competitive price levels. Digital content companies have to explore various kinds of strategies to overcome this gap. Rethinking marketing solutions such as advertisements, images, and word-of-mouth and their effect on customers' perceived value becomes essential. China's digital content industry is becoming more and more globalized and drawing special attention from different countries and regions that have respective competitive advantages. The 2008-2009 Annual Report on the Development of China's Digital Content Industry (CCIDConsulting 2009) indicates that, with the driven power of domestic demand and governmental policy support, the country's digital content industry maintained a fast growth of some 30 percent in 2008, obviously indicating the initial stage of industry expansion. In China, anti-virus programs and other software programs which need to be updated use a quarter-based pricing policy. Customers can download a trial version for free and use it for six months or a year. If they want to use it longer, continuous payment is needed. They examine the excellence of the digital content during this trial period and decide whether to pay for continued usage. For China’s music and movie industries, as a result of initial development, experiential strategy has not been much applied, even though firms in other countries find the trial experience and explore important strategies(such as customers listening to music for several seconds for free before downloading it). For the above reasons, anti-virus program may be a representative for digital content industry in China and an exploratory study of the advantage of experiential value in customer's perceived value of digital content is done in the anti-virus market of China. In order to enhance the reliability of the survey data, this study focused on people who were experienced users of anti-virus programs. The empirical results revealed that experiential value has a positive effect on customers' perceived value of digital content. In other words, because digital content is intangible and the reproduction costs are nearly zero, customers' evaluations are based heavily on their experience. Moreover, image and word-of-mouth do not have a positive effect on perceived value, only on experiential value. That is to say, a digital content value chain is different from that of a general product or service. Experiential value has a notable advantage and mediates the effect of image and word-of-mouth on perceived value. The results of this study help provide an understanding of why free digital content downloads exist in developing countries. Customers can perceive the value of digital content only by using and experiencing it. This is also why such governments support the development of digital content. Other developing countries whose digital content business is also in the beginning stage can make use of the suggestions here. Moreover, based on the advantage of experiential strategy, companies should make more of an effort to invest in customers' experience. As a result of the characteristics and value gap of digital content, customers perceive more value in the intangible digital content only by experiencing what they really want. Moreover, because of the near-zero reproduction costs, companies can perhaps use experiential strategy to enhance customer understanding of digital content.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.59-69
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• 2010

Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.