• Journal of Applied Biological Chemistry
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• v.66
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• pp.213-220
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• 2023

The most common skin disease, acne, often occurs in adolescence, but it is also detected/observed in adults due to air pollution and drug abuse. One of the causative agents of acne, Cutibacterium acnes (C. acnes) plays a role in the development of skin acne by inducing inflammatory mediators. Torreya nucifera (TN) is an evergreen tree of the family Taxaceae, having well reported antioxidant, anti-proliferative, liver protection, and nerve protection properties. Improvement of these bioactive properties of natural products is one of the purposes of natural product chemistry and pharmaceuticals. We believe biorenovation could be one improvement strategy that utilizes microbial metabolism to produce unique derivatives having enhanced bioactivity. Therefore, in this study, the C. acnes-induced RAW264.7 inflammation model was used to evaluate the anti-inflammatory activity of the biorenovated Torreya nucifera product (TNB). The results showed improved viability of TNB-treated cells compared to TN-treated cells in the concentration range of 50, 100, and 200 ㎍/mL. At non-toxic concentrations, TNB inhibited the production of nitric oxide and prostaglandin E2 by suppression of inducible nitric oxide synthase and cyclooxygenase-2 protein expression. TNB also attenuated the expression of interleukin-1β, interleukin-6, interleukin-8, and tumor necrosis factor-α induced by C. acnes. Furthermore, TNB inhibited the nuclear factor-κB signaling pathway, a transcription factor known to regulate inflammatory mediators. Based on these results, this study suggests the potential of using TNB as natural material for the treatment of acnes and thus, supporting our postulation of biorenovation as an bioactivity improvement strategy.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.1-32
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• 2024

This study aims to explore the utilization of kiosks, a case of self-service technology in library services, by applying task-technology fit theory to reveal the factors that affect the satisfaction and continued use of library kiosk services and to conduct a review of library non-face-to-face services. We organized the kiosk characteristic factors through a literature review and established a research model mediated by related theories. We collected 229 valid questionnaire data from users with experience using library kiosks and analyzed them using SPSS 26.0 and SmartPLS 4.0 programs. The analysis results confirmed that the fit of library services and self-service technology was significantly influenced by the usefulness and enjoyment of kiosk technology characteristics and the kiosk-friendly environment of the usage environment attributes. In addition, we found the fit between library services and self-service technology to significantly affect library kiosk usage satisfaction and intention to continue using the kiosk, so this study proposed a plan for library kiosk services utilizing the significant factors. In addition, to effectively use the kiosks as a non-face-to-face library service, we suggest operating them in line to provide library information materials, install them in various locations within the library to increase accessibility, and provide education on how to use them for learning and to raise positive awareness of the kiosks for the digitally disadvantaged.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.169-178
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• 2024

While stable mid- to large-scale underground hydrogen storage infrastructures are needed to meet the rapidly increasing demand for hydrogen energy, evaluating the safety of explosion vibrations in adjacent buildings is becoming important because of gas explosions in underground hydrogen storage facilities. In this study, a numerical analysis of vibration safety effects on nearby building structures was performed assuming a hydrogen gas explosion disaster scenario in a low-depth underground hydrogen storage facility. A parametric study using a meta-model was conducted to predict changes in ground dynamic behavior for each combination of ground properties and to analyze sensitivity to geotechnical influencing factors. Directly above the hydrogen storage facility, the unit weight of the ground had the greatest influence on the change in ground vibration due to the explosion, whereas, farther away from the facility, the sensitivity of dynamic properties was found to be high. In addition, in evaluating the vibration stability of ground building structures based on the predicted ground vibration data and blasting vibration tolerance criteria, in the case of large reinforced concrete building structures, the ground vibration safety was guaranteed with a separation distance of about 10-30 m.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.49-59
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• 2024

In this paper, we examine the impact of XAI explanations on user satisfaction and cognitive load according to the risk levels defined in the EU AI Act. XAI aims to make the internal processes of complex AI models understandable to humans and is widely used in both academia and industry. The importance and value of XAI are continuously rising; however, there has been little research determining the necessary level of explanation according to AI system risk levels. To address this gap, we designed an experiment with 120 participants, divided into 8 groups, each exposed to one of four levels of explainability(XAI) within low-risk and high-risk financial AI systems. A quantitative approach was used to measure cognitive load, user satisfaction, mental effort, and the clarity of the material design across the different AI system interfaces. The results indicate that the amount of information in explanations significantly affects cognitive load and user satisfaction, depending on the risk level. However, the impact of the level of explanation on user satisfaction was mediated by the material design, which determined how easily the information was understood. This research provides practical, regulatory, and academic contributions by offering guidelines for determining the necessary level of explanation based on AI system risk levels.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.215-229
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• 2024

The purpose of this study was to explore various factors that affect the organizational commitment of health teachers' behavioral characteristics and explain the causal relationship between each factor. As an exploratory study, the subjects were about 500 people working as health teachers. A survey was conducted, and 190 responses were collected. Descriptive statistics were analyzed using SPSS, and the measurement model and hypotheses were tested using structural equation modeling. As a result of the analysis, the behavioral characteristics of health teachers were found to have a positive effect on their self-efficacy, and the behavioral characteristics and self-efficacy were found to have a positive effect on emotional commitment. In addition, health teachers' self-efficacy has a positive effect on emotional commitment, which means that rather than their behavioral characteristics having a direct effect on emotional commitment, their behavioral characteristics indirectly affect emotional commitment through a mediator called self-efficacy. It shows that it has an impact. In particular, it was confirmed that the school nurse's level of awareness of the importance of the task affected self-efficacy and emotional commitment. The implication of this study is that it explained the phenomenon of transfer of their behavioral characteristics to organizational commitment by empirically revealing that the competency, or behavioral characteristics, of health teachers are a factor affecting organizational commitment through self-efficacy. The goal is to empirically demonstrate that awareness of task importance, a job characteristic, affects self-efficacy and organizational commitment. The results of the study are expected to be able to suggest directions for capacity building and operation of health teachers.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.10-19
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• 2024

In this study, FEM analysis was performed with the goal of optimal design of corrugated CFRP panels reinforcing H-shaped beams with slender plate webs. The buckling reinforcement performance of corrugated CFRP panels according to various specifications was evaluated, and in particular, a new reinforcement method was proposed by analyzing the effect of the ratio of vertical reinforcement according to the net height of the abdomen of the H-type beam on the location of the first elastic buckling mode. To minimize the amount of CFRP used, the attachment angle was set to 45 degrees. Furthermore, parameter analysis was performed according to changes in the specifications of the corrugated CFRP panel, and the buckling reinforcement performance of the corrugated CFRP panel was evaluated through the ductility factor. In addition, we attempted to use the material efficiently by simultaneously considering the maximum load and ductility factor along with the volume of the corrugated CFRP panels. It was confirmed that the model with two or three-layer CFRP laminate have a high ductility factor and efficient use of materials, and that the buckling reinforcement performance is predominantly affected by the length and height of the corrugated CFRP panel rather than the width.

• The Journal of Small Business Innovation
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• v.19 no.4
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• pp.19-39
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• 2016

With increasing adoption of smart products and complexity, companies have shifted their strategies from stand alone and competitive strategies to business ecosystem oriented and cooperative strategies. The win-win growth of business refers to corporate efforts undertaken by companies to pursue the healthiness of business between conglomerates and partnering companies such as suppliers for mutual prosperity and a long-term corporate soundness based on their business ecosystem and cooperative strategies. This study is designed to validate a theoretical proposition that the win-win growth strategy of Samsung Electronics and cooperative efforts among companies can create a healthy business ecosystem, based on results of case studies and surveys. In this study, a level of global market access of small and mid-sized companies is adopted as the key achievement index. The foreign market entry is considered as one of vulnerabilities in the ecosystem of small and mid-sized enterprises (SMEs). For SMEs, the global market access based on the research and development (R&D) has become the critical component in the process of transforming them into global small giants. The results of case studies and surveys are analyzed mainly based on a model of a virtuous cycle of Creativity, Opportunity, Productivity, and Proactivity (the COPP model) that features the characteristics of the healthiness of a business ecosystem. In the COPP model, a virtuous circle of profits made by the first three factors and Proactivity, which is the manifestation of entrepreneurship that proactively invests and reacts to the changing business environment of the future, enhances the healthiness of a given business ecosystem. With the application of the COPP model, this study finds major achievements of the win-win growth of Samsung Electronics as follows. First, Opportunity plays a role as a parameter in the relations of Creativity, Productivity, and creating profits. Namely, as companies export more (with more Opportunity), they are more likely to link their R&D efforts to Productivity and profitability. However, companies that do not export tend to fail to link their R&D investment to profitability. Second, this study finds that companies with huge investment on R&D for the future, which is the result of Proactivity, tend to hold a large number of patents (Creativity). And companies with significant numbers of patents tend to be large exporters as well (Opportunity), and companies with a large amount of exports tend to record high profitability (Productivity and profitability), and thus forms the virtuous cycle of the COPP model. In addition, to access global markets for sustainable growth, SMEs need to build and strengthen their competitiveness. This study concludes that companies with a high level of proactivity to invest for the future can create a virtuous circle of Creativity, Opportunity, Productivity, and Proactivity, thereby providing a strategic implication that SMEs should invest time and resources in forming such a virtuous cycle which is a sure way for the SMEs to grow into global small giants.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Cartoon and Animation Studies
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• s.33
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• pp.99-126
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• 2013

This study is meant to be seeing how fun of animation works by reviewing it theoretically and coordinating it to suggest the structure which integrates fun of animation and validates the proposed fun model. After reviewing fun theoretically, the fun of animation could be able to coordinate that fun of animation is consist of perceptive fun, cognitive fun, and psychological fun. Perceptive fun is induced by visual, auditory and other sensory information and it is directly affected the image, sound, and movement. Cognitive fun can be obtained by reasoning and interpretation to mobilize their knowledge with sensuously perceived stimulation and it is directly affected the story. Psychological fun occurs when the audience see the animation. The psychological fun is the psychological emotional state when the audience watches animation by relieving psychological congestion. It consists of fun of unfamiliarity or identification. By suggesting research model and validating it how the perceptive fun, cognitive fun, and psychological fun affects each other, perceptive fun enhances cognitive fun and psychological fun. Although cognitive fun enhances psychological fun, cognitive fun enhances psychological fun twice than perceptive fun. Also when perceptive fun affects psychological fun, cognitive fun shows the indirect effect as a parameter. In conclusion, perceptive fun affects psychological fun directly and be enhanced through cognitive fun. Fun of animation can be experienced when perceptive fun caused by accepting sensory information of animation instantly, cognitive fun caused by interpretation and understanding sensory information of animation, and psychological fun caused by relieving psychological identity through recognition fuses and acts as one. An animation emphasized a certain element is difficult to be loved by the audience. In this reason, an harmonical combination among the elements of story, image, sound and movement are important to combinate harmoniously for a successful animation to make the audiences fun by arising funny emotions.