- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.21 No.2
Abstract
Strategic or critical material issues have recently drawn significant attention. While all materials are important to a certain degree, identifying critical ones remains essential due to time and resource constraints. This paper first provides a comprehensive diagnosis of material criticality at the product level, focusing on the following dimensions: Imbalance between demand and supply, Importance of the material to the object (e.g., product), Supply accessibility (including materials that cannot or should not be accessed), Dynamic factors or abrupt changes. Based on this diagnosis, the paper proposes a methodology to alert and evaluate critical materials in products, serving as a decision-making support tool for industries. A unique feature of this methodology is its product-level focus. Additionally, it enables personalization across different contexts by integrating dynamic factors into indicators and scenarios. A basic scenario example is included to illustrate how to derive calculation criteria from the diagnostic framework.
Abstract
In industrial settings, multivariate time series generated from various monitoring metrics are abundant. Anomaly detection in these time series is crucial for applications such as fault diagnosis and root cause analysis. Recent advancements in unsupervised methods, particularly autoencoder (AE)-based reconstruction architectures, have made significant progress in this area. These systems learn the normal data distributions and produce substantial reconstruction errors when encountering anomalies. While AEs are effective at reconstructing subtle abnormal patterns due to their strong generalization capabilities, this can also result in a high false negative rate. Furthermore, AE-based models often fail to account for inter-variable dependencies across different time scales. In this paper, we propose an enhanced anomaly detection framework that builds upon the Multiscale Wavelet Graph Autoencoder (MEGA) by replacing the Graph Convolutional Network (GCN) with Simplified Graph Convolution (SGC) to improve model performance. The key idea is to utilize the spectral methods of SGC to process the multivariate time series data, integrating Discrete Wavelet Transform (DWT) into the AE. We conducted experiments on three public multivariate time series anomaly detection datasets, and the results demonstrate that the improved model using SGC outperforms existing methods.
Abstract
In recent years, deep learning has gained significant traction in the fields of skincare and dermatology, with research primarily focused on two areas: cosmetic skin assessments and clinical diagnoses of skin diseases. Approximately two-thirds of the literature is dedicated to medical conditions such as melanoma and seborrheic keratosis, while the remaining third addresses cosmetic issues like UV damage, oiliness, pore size, and wrinkles. Although both domains utilize a similar methodological framework—training deep learning models on skin images for classification—they remain largely separate. Recent publications from 2024 and 2025 indicate a growing interest in integrating deep learning across these two fields. AI-driven skincare applications have shown promise in delivering rapid, personalized assessments, although challenges like biases in skin tone analysis continue to exist. In contrast, medical applications have made strides with deep learning models for skin cancer detection, achieving high accuracy and proving their potential as decision-support tools for dermatologists. The convergence of these domains presents a significant opportunity for hybrid models that could link cosmetic and medical applications, particularly for early skin cancer detection. By utilizing the extensive data and feature extraction techniques prevalent in skincare analysis, deep learning models could improve predictive accuracy and aid in the early identification of malignant conditions. This integration could facilitate proactive skin health monitoring by incorporating dermatological risk assessments into routine skincare evaluations. However, realizing this potential necessitates addressing challenges related to data diversity, model generalizability, and ethical considerations in AI-driven diagnostics. Bridging deep learning applications in skincare and dermatology not only offers the promise of improved early detection of serious skin conditions but also paves the way for more accessible, AI-powered dermatological care. Continued interdisciplinary collaboration is essential to develop these hybrid models and fully harness their potential in enhancing skin health diagnostics.
Abstract
Public digital platforms are increasingly embedding gamification mechanics to increase civic engagement, often without adequate consideration of their efficacy or suitability. This paper critically explored how commercial-style gamification could influence user participation in a civic context through an in-depth study of Metaverse Seoul, a public metaverse platform launched by the Seoul Metropolitan Government. Based on qualitative feedback from 43 users of Seoul Digital Innovation Governance Group, findings indicated that participants desired more commercial gamification features to promote daily engagement. However, this revealed a mismatch between user expectations shaped by commercial platforms and civic-oriented principles of public service delivery. This study highlights the need to align gamification strategies with intrinsic civic motivations and to establish appropriate success metrics for public platforms. It emphasizes the importance of understanding citizen motivations and expectations when designing technology-mediated civic participation platforms. This research concludes that commercial gamification approaches are incompatible with civic missions. Thus, reconceptualization is needed for public contexts.
Abstract
Screenwriting manuals are comprehensive guides designed to instruct and aid individuals in the art of screenwriting. A total of 60 screenwriting manual books were systematically analyzed with topic modeling methods to identify a topographical overview of the field along with similarity analysis using Word2Vec. The analysis employed TF-IDF, LDA, and Word2Vec techniques to identify key themes and semantic structures. All 60 screenwriting manual books contained similar contents focusing on four categories - story, film, audience, and industry. The core theme running through these categories was identified as 'character-centered story'. Clustering analysis revealed limited differentiation in content among manuals despite their proliferation in the market. Dual characteristics of screenwriting, such as in-between art and craft, writing and filmmaking, and autonomous and disciplinary, were detected through semantic analysis. This study makes methodological contributions to screenwriting research by demonstrating how computational text analysis could complement traditional qualitative approaches. Findings of this study can inform the development of new screenwriting manuals that address gaps in current offerings and serve as a framework for evaluating screenwriting pedagogy. This paper can help us design a new screenwriting manual with a difference and make a methodological contribution to the field of screenwriting research.
Abstract
As South Korea faces a rising incidence of solitary deaths, particularly among older adults, attention has turned to artificial intelligence (AI) as a potential tool for early detection, emotional support, and care outreach. This paper examined the emergence and effectiveness of three representative AI care platforms—Naver’s CLOVA Carecall, KT’s AI Care Speaker, and SK Telecom’s NUGU Carecall—in mitigating risks associated with social isolation and unattended death. Through an analysis of their operational mechanisms, reported outcomes, and integration with local welfare systems, this study highlights the promise of AI in supplementing overstretched human care services. However, it also identifies significant challenges, including technological limitations, ethical concerns, privacy risks, and disparities in digital access. Drawing on these findings, this paper proposed a hybrid AI–human care model emphasizing integration, community engagement, and policy reform. It concludes by calling for a long-term, ethically grounded strategy that aligns technological innovation with South Korea’s demographic realities and cultural values, aiming not only to prevent solitary death, but also to restore dignity and connection in an aging society.
Abstract
This research tackles a major challenge faced by indie game developers: the high financial and technical barriers to producing quality graphics and animations, primarily due to dependence on expensive commercial software like 3D Max or Cinema 4D. Blender 3D, a free and open-source platform, offers a promising solution to these challenges. However, there is a lack of scholarly research on how effectively Blender 3D can address these issues and improve creative and technical workflows in indie game development. This study aims to fill that gap by analyzing real-world case studies and practical applications of Blender 3D, evaluating its impact on the creative processes and technical efficiency of indie game studios. Additionally, the research investigates the integration and interoperability of Blender 3D with popular game engines like Unity and Unreal Engine, specifically examining how this integration streamlines the development pipeline from concept to final product, ultimately helping to level the competitive playing field in the ever-evolving indie gaming industry.
Abstract
Social media live-streaming is increasingly being utilized as a platform for shopping, with sports enterprises dedicating significant time, money, and resources to this endeavor. However, effectively leveraging social media live-streaming shopping characteristics to attract and motivate consumers to purchase sports products remains challenging for these enterprises. On that account, this research aimed to investigate key elements of social media live-streaming shopping characteristics that couled influence purchase intention. Employing the extended technology acceptance model (ETAM) as a theoretical model, this research explored how interactivity, entertainment, visualization, and professionalization of social media live-streaming shopping characteristics could impact attitudes, trust, and purchase intention. Data were gathered via a questionnaire survey involving 675 participants using the convenience sampling method. The primary findings of the structural equation model (SEM) largely affirmed the validity of the current model and underscored significant impacts of interactivity, entertainment, professionalization, visualization, attitudes, trust, and purchase intention. This study found that compared with other social media live-streaming characteristics (interactivity, visualization, and entertainment), professionalization had the greatest impact on attitude and trust. Results showed that attitudes largely affected consumers' purchasing intention. This study offers valuable management insights to platforms, sports enterprises, and streamers.
Abstract
This paper recontextualized AI rationality through the lens of bounded rationality and media studies, arguing that AI’s influence could extend well beyond algorithmic computations to reshape societal norms and communication processes. We examined how the transition from a purely utilitarian model of rationality to one constrained by data availability, computational limitations, and potential biases could impact both ethical considerations and practical outcomes. By framing AI as a mediator of human experience, our analysis revealed how inherent design choices could inadvertently perpetuate cultural or systemic biases. Drawing on philosophical perspectives and media theory, this study suggests that a more nuanced, context-aware model of bounded rationality can better guide AI systems toward responsible and inclusive decision-making. This paper concludes with recommendations for interdisciplinary collaborations—spanning ethics, engineering, and policy—to ensure that AI’s development and deployment are aligned with human values, cultural diversity, and practical realities of real-world constraints.
Abstract
Objective: Discharge against medical advice (DAMA) in elderly patients with suicidal behavior presents significant clinical and ethical challenges, particularly during public health crisis such as the COVID-19 pandemic. This study investigated clinical characteristics and risk factors associated with DAMA among elderly individuals (≥ 65years) presenting to emergency departments (EDs) in South Korea during the social distancing period. Methods: A retrospective observational study was conducted using data from the National Emergency Department Information System (NEDIS) between January 1, 2019 and December 31, 2020. Results: A total of 4,351 and 3,931 cases were reported in 2019 and 2020, respectively. DAMA rate increased slightly from 14.6% to 14.9% (p < .001) while ED discharge rates decreased from 15.0% to 14.3%. Intensive care units (ICU) admissions increased from 69.0% to 70.3% (p < .001) and post-hospitalization DAMA increased from 10.9% to 12.7% (p = .007). Mortality after admission increased from 10.2% to 11.3% (p = .007). DAMA was significantly associated with older age (OR: 1.39, p < .001) and the use of highly lethal methods such as hanging (OR: 5.54, p < .001) and poisoning (OR: 5.39, p < .001). Conclusions: These findings underscore the need for ethical frameworks and multidisciplinary interventions to support decision-making and prevent recurrent suicidal behavior among elderly patients.
Abstract
This study was conducted to analyze differences in pathways of suicide risk and the influence of related risk factors among adolescents based on their interpersonal sensitivity. To achieve this, a survey was conducted using data from 496 high school students in the metropolitan area, including their levels of suicide ideation and urges, suicide planning, suicide attempts, and their experiences of related risk factors. Path analysis and multigroup analysis were performed to analyze collected data. Results of the analysis showed that the group with high interpersonal sensitivity had significantly larger coefficients for pathways leading to suicide ideation and urges, suicide planning, and suicide attempts than the group with low interpersonal sensitivity.
Abstract
This study aimed to identify latent clusters underlying suicide phenomena in South Korea from 2011 to 2020, a period marked by the country's highest suicide rates. To achieve this, 12,570 news articles were collected from BIG KINDS, a news article database of the Korea Press Foundation, and analyzed using big data techniques. Text mining was applied to article titles using Textom, followed by CONCOR analysis in UCINET6. Results were visualized using NetDraw. Through frequency analysis, 7,542 keywords were extracted. Of them, 86 high-frequency keywords were selected for network analysis. The CONCOR analysis revealed seven key thematic clusters: school, public officials, military, family, anomie, suicide attempts, and suicide locations. This study contributes to a deeper understanding of interconnected socio-cultural factors influencing suicide dynamics in South Korea. By examining a large, diverse dataset over a ten-year period, this research offers new insights into the evolution of suicide-related discourse and the role of media in shaping public attitudes. Findings of this study provide valuable implications for suicide prevention strategies, policy-making, and future research on the role of media in shaping societal perceptions of suicide.
Abstract
This study aimed to enhance children's safety by developing an internationally standardized children's accidental ingestion safety design that could effectively prevent accidental ingestion of small objects. Although most toys and stationery are now labelled with a children's accidental ingestion safety sign, the number of childhood accidental ingestion incidents continue to increase. This paradox reveals that conventional warning symbols fail to effectively communicate hazard information to young children due to inadequate alignment with their cognitive and perceptual capabilities. Through a systematic analysis of existing international safety standards, this research identified four fundamental deficiencies in current warning symbols: visual ambiguity and cross-national inconsistency, inadequate child user identification, insufficient depiction of prohibited objects, and absence of consequence visualization of swallowing. There are factors that can collectively compromise children’s risk perception. Employing a mixed-methods approach, this study conducted a comprehensive market audit of 100 toy packages across Korea and China, complemented by direct preference testing with 28 children aged 2–7 years. Findings were synthesized into an evidence-based shape–color–image design framework fully compliant with ISO 7010 and ISO 3864 standards. The proposed design added hair to clarify the image of a child and added a pained expression and tears to express pain more strongly. In addition, the unclear object to be put in the mouth was clarified by representing it as a block. This study contributes both theoretical insights into child-centered risk communication and a practical, validated symbol template. The implementation of this ISO-compliant design by regulatory bodies and manufacturers has the potential to yield measurable reductions of accidental-ingestion incidents globally.
Abstract
Manufacturers and service providers need new innovative tools to effectively leverage the value of Voice of Customer (VoC). These unconventional and unstructured data require specialized methods for analysis and interpretation. The current methodology primarily involves extracting keywords related to customer needs from VoC, followed by proposing corresponding management and marketing strategies. However, diverse interpretations of these keywords can result in irrelevant noise due to variations in employees' professional and working backgrounds, which could hinder decision makers' understanding of customer needs. To address this issue, this paper focused on managers and designers across functional departments as research subjects, aiming to identify relevant factors that could influence noise generation in VoC analysis work and suggest appropriate strategies for mitigating such noise.
Abstract
“Reverse run” in K-pop refers to the sudden rise in popularity of a fading song, which is often triggered by memetic videos on YouTube. In this study, we argue that this cultural diffusion is possible without resources of giant agencies or fanbases because individuals are loosely and flexibly connected through digital networks. To test this claim, we collected 1,308 YouTube videos and 611,809 comments on them. We then analyzed user engagement with these videos. For the reverse run case, there was an extensive user engagement with memetic videos that covered, mimicked, or remixed idol groups’ music videos or media-produced stage footage, which drove virality. This pattern differed from those of other immediate chart-toppers, which often resulted from big agency promotions or strong fan support. This study indicates that memetic videos play a crucial role in the bottom-up cultural diffusion of reverse run by creating a fandom-like phenomenon where individuals could connect through personalized expression.
Abstract
The rapidly growing webtoon industry is facing increasing demands for production methods that are efficient, scalable, and economically viable. While traditional 2D webtoon creation allows for unique artistic expression, its labor-intensive nature poses significant challenges in maintaining visual consistency across large projects, meeting tight production schedules, and effectively managing budgets. This research investigates the potential of Blender 3D, a free and powerful 3D modeling and rendering software, to transform character modeling in webtoon production. Our goal is to thoroughly assess its ability to enhance both the efficiency of the creative process and the visual quality of the final product. We employ a hands-on approach, detailing each stage of character development using Blender 3D, which includes modeling intricate details, applying advanced textures, creating adaptable rigging systems for animation, and achieving high-quality rendering outputs. The empirical results indicate that Blender 3D significantly accelerates production workflows, maintains a high standard of visual quality, and offers substantial savings in both time and costs. As a result, integrating Blender 3D into webtoon production pipelines has the potential to empower individual creators and small to medium-sized studios, enabling them to reach visually competitive standards that were once primarily attainable by larger, more financially robust production companies.