• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.217-225
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• 2024

To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.

• Information Systems Review
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• v.21 no.3
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• pp.111-129
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• 2019

Recently, artificial intelligence (AI)-enabled products and services such as smartphones, smart speakers, chatbots are being released due to advances in AI technology. Thus researchers making effort to reveal that consumers' intention to adopt AI-enabled products. Yet, little is known about the intended adoption of AI-enabled products. Because most of studies has been not consideredthe perceived utility value of consumers for each attribute by classified based on the characteristics of AI-enabled products. Therefore, the purpose of this study is to investigate the difference in importance between attributes that affect the intention to adopt of AI-enabled products. For this, first, identified and classified the attributes of AI-enabled products based on IS Success Model of DeLone and McLean. Second, measured the utility value of each attribute on the adoption of AI-enabled products through conjoint analysis. And we employed construal level theory to see whether there are differences in the relative importance of AI-enabled products attributes depending on the temporal distance. Third, we segmented the market based on the utility value of each respondent through cluster analysis and tried to understand the characteristics and needs of consumers in each segment market. We expect to provide theoretical implications for conceptually structured attributes and factors of AI-enabled products and practical implications for how development efforts of AI-enabled products are needed to reach consumers need for each segment.

• Information Systems Review
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• v.19 no.2
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• pp.115-136
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• 2017

Online communities have become substantial aspects of people's daily lives. However, only a few communities succeed and attract the majority of users, whereas the vast majority struggle for survival. When various communities coexist, important factors should be identified and examined to maintain attraction and achieve success. The concept of coexistence as been extensively explored in organizational ecology literature. However, given the similarities and differences between online communities and traditional organizations, the direct application of organizational theories to online contexts should be cautiously explored. In this study, we follow the roadmap proposed by Davis et al. (2007) in conducting agent-based modeling and simulation study to develop a novel theory based on the previous literature. In the case of two coexisting communities, we find that community size and participation costs can significantly affect the development of a community. A large community can attract a high number of active members who frequently log in. By contrast, low participation costs can encourage the reading and posting behaviors of members. We also observe the important influence of the distribution of interests on the topic trends of communities. A community composed of a population that focuses on only one topic can quickly converge on the topic regardless of whether the initial topic is broad or focused. This simulation model provides theoretical implications to literature and practical guidance to operators of online communities.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of Life Science
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• v.34 no.2
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• pp.94-104
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• 2024

β-Lapachone is a natural quinone compound originally obtained from the bark of the lapacho tree (Tabebuia vellanedae), which has been used in traditional medicine in several South and Central American countries for treating various diseases. Although β-lapachone has been reported to have potent anticancer activity in many types of cancer cells, its effect on the proliferation of hepatocellular carcinoma (HCC) cells is still unclear. Therefore, in this study, we investigated the effect of β-lapachone on the proliferation of human HCC Hep3B cells. According to our results, the decrease in cell viability of Hep3B cells caused by β-lapachone was closely related to the induction of apoptosis, which was confirmed through changes in nuclear morphology and flow cytometry. In addition, in Hep3B cells treated with β-lapachone, the expression of Bcl-2, an anti-apoptotic factor, was decreased, while the expression of Bax, an apoptosis inducer, was increased, and the activity of the caspase cascade was also increased. However, in the presence of a pan-caspase inhibitor, β-lapachone-induced apoptosis was weakened, indicating that the induction of apoptosis by β-lapachone was caspase-dependent. Moreover, β-lapachone treatment activated extracellular-regulated kinase (ERK) signaling while inhibiting activation of the phosphoinositide 3 kinase (PI3K)/Akt pathway. Furthermore, the effect of the ERK inhibitor on suppressing the induction of apoptosis by β-lapachone was minimal, and the PI3K inhibitor significantly increased β-lapachone-induced apoptosis. The findings from this study will contribute to a better understanding of the anticancer activity of β-lapachone in HCC cells.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.192-202
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• 2024

Ocean reanalysis data are extensively used in ocean circulation and climate research by integrating observational data with numerical models. This approach overcomes the spatial and temporal limitations of observational data and provides high-resolution gridded information that considers the physical interactions between ocean variables. In this study, I extended the previously produced 12-year (2011-2022) Northwest Pacific regional ocean reanalysis data to create a long-term reanalysis dataset (K-ORA22E) with a horizontal resolution of 1/24° spanning 30 years (1993-2022). These data were analyzed to diagnose long-term ocean climate change in the Korean marginal seas. Analysis of the K-ORA22E data revealed that the axis of the Kuroshio extension has shifted northward by approximately 6 km per year over the past 30 years, with a significant increase in sea surface temperature north of the Kuroshio axis. Among the waters surrounding the Korean Peninsula, the East Sea exhibited the most significant temperature increase. In the East Sea, the temperature increase was more pronounced in the middle layer than in the surface layer, with the East Korea Warm Current showing a rate two to three times higher than the global average. In the central Yellow Sea, where the Yellow Sea Bottom Cold Water appears, temperatures increased over the long-term, but decreased along the west and south coasts of the Korean Peninsula. These spatial differences in long-term temperature changes appear to be closely related to the heat transport pathways of warm water from the Kuroshio Current. High-resolution regional ocean reanalysis data, such as the K-ORA22E produced in this study, are essential foundational data for understanding long-term variability in the Korean marginal seas and analyzing the impacts of climate change.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.43-54
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• 2024

This study employs text mining techniques to compare user responses across various Over-The-Top (OTT) service platforms. The primary objective of the research is to understand user satisfaction with OTT service platforms and contribute to the formulation of more effective review strategies. The key questions addressed in this study involve identifying prominent topics and keywords in user reviews of different OTT services and comprehending platform-specific user reactions. TF-IDF is utilized to extract significant words from positive and negative reviews, while BERTopic, an advanced topic modeling technique, is employed for a more nuanced and comprehensive analysis of intricate user reviews. The results from TF-IDF analysis reveal that positive app reviews exhibit a high frequency of content-related words, whereas negative reviews display a high frequency of words associated with potential issues during app usage. Through the utilization of BERTopic, we were able to extract keywords related to content diversity, app performance components, payment, and compatibility, by associating them with content attributes. This enabled us to verify that the distinguishing attributes of the platforms vary among themselves. The findings of this study offer significant insights into user behavior and preferences, which OTT service providers can leverage to improve user experience and satisfaction. We also anticipate that researchers exploring deep learning models will find our study results valuable for conducting analyses on user review text data.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.339-348
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• 2024

Debris flow is a typical type of mountainous sediment disaster that can cause widespread damage to both lives and property, making it essential to understand its behavioral characteristics for effective prevention. In this study, pre- and post-event Light Detection And Ranging(LiDAR) data from the Dosan-ri area in Bonghyeon-myeon, Yeongju-si, Gyeongsangbuk-do, Republic of Korea where debris flows occurred in 2023, were used to calculate the actual affected area and terrain change volume caused by the debris flow. These calculated values were then compared with those derived from the numeric simulation model, Morpho2DH, based on field surveys and laboratory investigation data. Additionally, the model's applicability was assessed by conducting cross-sectional elevation analyses based on the extent of the affected area and comparisons of the results. The findings indicate that the debris flow affected area and terrain change volume estimated by the Morpho2DH model were approximately 152% and 178% higher, respectively, compared to the LiDAR-based results. Pearson correlation analysis of the cross-sectional elevation changes showed a positive correlation, with Pearson Correlation Coefficients(PCC) of at least 0.65

• The Journal of Engineering Geology
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• v.34 no.3
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• pp.429-445
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• 2024

The Journal of Engineering Geology (JEG), a leading academic journal in the field of engineering geology in South Korea, was first published in 1991 and has since been publishing academic papers and research findings. While several literature reviews have been undertaken on specific research areas in recent decades, comprehensive reviews focusing on JEG have been relatively limited. To address this gap, this study applied the latent Dirichlet allocation (LDA) model to analyze the main research topics and trends, and undertook network analysis to identify relationships between topics over different periods. Results for the LDA indicate seven key research topics categorized into three trends: Classic, Emerging and Stable topics. Classic topics include 'Geophysics' and 'Structural geology', which were major subjects in the early years, with their focus shifting to other areas over time. Emerging topics such as 'Hydrogeology' and 'Geohazards' have gained prominence in recent years. Stable topics including 'Geotechnical structures', 'Geomechanics', and 'Environmental geology' have maintained consistent research interest. Network analysis revealed that Structural geology was the central topic prior to 2008, while Geotechnical structures became the focal point of research after 2008, with a shift in research focus. The results of this study contribute to our understanding of research trends and the development of JEG, providing insights for the setting of future research directions.

• Studies on Life and Culture
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• v.52
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• pp.57-84
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• 2019

For married immigrant women, social participation is a process of meeting and interacting with community members on a continuous or regular basis. Community participation of married immigrant women has an important role not only in adapting to the new environment but also in improving their quality of life in terms of their self-esteem and life satisfaction. It also forms an important social network of the community and strengthens social capital. However, their social participation should be based not on the level of 'adaptation' or 'settlement', but on mutual awareness that respects and accepts the social and cultural background of migrant women and their native countries, that is interculturalism. Unlike multiculturalism which emphasizes cultural differences, interculturalism emphasizes mutual understanding and mutual interaction among various cultures based on universality. This study examines various theories and policy models of the multiculturalism, and introduces the recently discussed interculturalism. Then, this study applies the issue of the migrant women's social participation by conducting and analyzing a qualitative research. This study took deep interviews with leaders of three selected social economic organizations and conducted Focus Group Interviews with major participants of those groups.