• Journal of the Korean Society of Radiology
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• v.12 no.7
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• pp.909-917
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• 2018

3D printing technology is expected to be an innovative technology of the manufacturing industry during the 4th industrial revolution, and it is being used in various fields including biotechnology and medical field. In this study, we verified the printing materials through Monte Carlo simulation to evaluate the radiation shielding ability of the raw material using this 3D printing technology. In this paper, the printing materials were selected from the raw materials available in a general-purpose FDM-based 3D printer. Simulation of the ICRU phantom and the shielding system was carried out to evaluate the shielding effect by evaluating the particle fluence according to the type and energy of radiation. As a result, the shielding effect tended to decrease gradually with increasing energy in the case of photon beam, and the shielding effect of TPU, PLA, PVA, Nylon and ABS gradually decreased in order of materials. In the case of the neutron beam, the neutron intensity increases at a low thickness of 5 ~ 10 mm. However, the effective shielding effect is shown above a certain thickness. The shielding effect of printing material is gradually increased in the order of Nylon, PVA, ABS, PLA and TPU Respectively.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.317-340
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• 2023

In a business context, ESG is defined as the use of environmental, social, and governance factors to assess a firm's progress in terms of sustainability. Social media has enabled the public to actively share firms' good and/or bad deeds, increasing public interest in ESG management. Therefore, this study aimed to investigate the association of firm performances with the respective sentiments towards each of environmental, social, and governance activities, as well as comprehensive ESG sentiments, which encompass all environmental, social, and governance sentiments. This study used panel regression models to examine the relationship between social media ESG sentiment and the Return on Assets (ROA) and Return on Equity (ROE) of 143 companies listed on the KOSPI 200. We collected data from 2018 to 2021, including sentiment data from a variety of social media channels, such as online communities, Instagram, blogs, Twitter, and other news. The results indicated that firm performance is significantly related to respective ESG and comprehensive ESG sentiments. This study has several implications. By using data from various social media channels, it presents an unbiased view of public ESG sentiment, rather than relying on ESG ratings, which may be influenced by rating agencies. Furthermore, the findings can be used to help firms determine the direction of their ESG management. Therefore, this study provides theoretical and practical insights for researchers and firms interested in ESG management.

• Information Systems Review
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• v.21 no.1
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• pp.91-111
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• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• Information Systems Review
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• v.19 no.2
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• pp.159-183
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• 2017

Despite the rapid increase in the use of mobile Internet, only a few studies examined digital divide issues in the mobile internet environment. Prior studies on digital divide mainly focused on the usage environments of consumers. The present study analyzes mobile digital divide by considering user environments and the cognitive values of users. Therefore, we estimated the cognitive value of users in mobile Internet environments through contingent valuation method (CVM). We also analyzed the digital divide factors that cause this value difference. We found that domestic users perceive mobile Internet value up to 51,723 Korean Won per month. We analyzed the value differences of digital divide factors, such as "accessibility," "competency," and "usability." Based on this analysis, we found that a difference of 31,878 Korean Won was generated due to accessibility. Competitiveness resulted in a difference of 7,736 Korean Won, whereas that of usability resulted in 6,463 Korean Won. Accessibility is the most important digital divide factor that maximizes the difference of cognitive value of users in a domestic mobile Internet environment.

• Information Systems Review
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• v.25 no.1
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• pp.233-255
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• 2023

The development of information technology(IT) has brought so-called "online experience" to satisfy our daily needs. The market for online experiences grew more during the COVID-19 pandemic. Therefore, this study attempted to analyze how the features of online experience services affect customer satisfaction by crawling structured and unstructured data from the online experience web site newly launched by Airbnb after COVID-19. As a result of the analysis, it was found that the structured data generated by service users on a C2C online sharing platform had a positive effect on the satisfaction of other users. In addition, unstructured text data such as experience introductions and host introductions generated by service providers turned out to have different subjectivity scores depending on the purpose of its text. It was confirmed that the subjective host introduction and the objective experience introduction affect customer satisfaction positively. The results of this study are to provide various implications to stakeholders of the online sharing economy platform and researchers interested in online experience knowledge management.

• Journal of Cardiovascular Imaging
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• v.31 no.3
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• pp.125-132
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• 2023

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.53-59
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• 2024

The novel "The Grapes of Wrath" shows the painful reality of Americans during the Great Depression through the migration journey of the Tom Jod's family, who had no choice but to move from Oklahoma to California and their hardships in California. This presents empathy for their wrath and offers hope for a brotherly solution. This article presents institutional solutions against each novel situation from the perspective of the Constitution, labor law, and competition law. From a constitutional perspective, the poverty of Oklahoma's smallholders is not a result of choice, but is caused by the natural environment and capital concentration, so it is suggested that the government have to intervene to guarantee a minimum standard of living to realize equal rights. From a labor law perspective, worker supply projects are unconstitutional because they constitute intermediate exploitation of labor, and immigrants like the Joad family have the right to form labor unions. From a competition law perspective, it was shown that the large landowners' setting of fruit prices constitutes predatory pricing, and the farmers' attempts to pay similarly low wages constitute collusion. Through this, the attempt was made to recognize that the law is a means to resolve the public wrath that may currently exist, and to show that the story in the novel can bring empathy and understanding to minorities. It is hoped that reading novels can be a way to help interpret the law and sympathize with others as an indicator of a just society.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• The Korean Journal of Mycology
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• v.25 no.1 s.80
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• pp.46-67
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• 1997

From 1985 to 1993, we collected 20 isolates throughout Kangwon and obtained 6 isolates from other sources. A. mellea formed rhizomorph actively, and some of A. osroyae were poor in the formation of rhizomorph and some without formation of rhizomorph. A. tabescens was active in the growth of aerial mycelium and poor in the development of rhizomorph. In A. gallica, the mycelium development among the isolates were variable greatly, and especially in isolate A8(KNU-250), the mycelial development was similar to that of A. osroyae, but A8(KNU-250) showed the feature of A. gallica to change medium into brown color. In PCR-RFLP analysis of the IGS region in rDNA, the homology between each isolate in the A. mellea and A. ostoyae showed 100% homology. A. tabescens showed $0.919{\sim}0.974$ homology, and A. gallica showed $0.619{\sim}1.000$ homology. A19 and A12 showed 100% homology as the same group, but compared with other subgroups they showed less than 10% homology as $0.051{\sim}0.108$ value. In RAPD analyses, the isolates of A. mellea showed high homology among themselves as $0.983{\sim}1.000$, and A. ostoyae also showed high similarity. The homology between isolates of A. tabescens showed $0.594{\sim}0.953$ value because A. gallica showed $0.280{\sim}0.733$ value, and the variations between isolates were greater than other species. Especially, A19 and A22 were identified as new novel group which were remoted from other groups, and the homology between these two isolates showed 0.921 value, and the genetic similarity between these groups and other 4 groups showed less than 7% as $0.012{\sim}0.069$ value. Of 5 species identified in this study, 4 species were identified as A. mellea, A. ostoyae, A. tabescens, and A. gallica that were already reported ones and 1 species was suggested as a new specie in Korea.