- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.22 No.1
Abstract
This study examines the relationship between children's screen time and ADHD symptoms, focusing on how this relationship may vary based on child-related factors (age and gender) and parent-related factors (parental aggravation, household income, and social support). Utilizing data from the National Survey of Children's Health (NSCH 2022; n = 25,104 U.S. children, ages 6–17), we found that increased screen time is linked to greater severity of ADHD symptoms. This relationship is particularly pronounced among younger children (ages 6–11), those whose caregivers report lower levels of parental aggravation, and children from higher-income households. In contrast, child gender and parental social support do not significantly affect this relationship. These findings highlight the need for targeted parental mediation to alleviate the negative impacts of screen time on vulnerable groups. Limitations of this study include issues related to causality, reliance on caregiver-reported measures, and limited data on the types and content of screen time. Overall, the findings provide valuable insights for parents, educators, and policymakers.
Abstract
Visitor research has long been a key focus in museum studies. The paradigm shift brought about by new museology emphasizes a more comprehensive exploration of visitors, particularly regarding their identity characteristics. This approach prioritizes personal traits over visit-related characteristics. This study employs a questionnaire analysis method to differentiate between visitors' personal characteristics and their visiting traits from the perspective of identity. It considers "visiting frequency" as a regulatory condition and examines how visiting motivation influences the visitor experience within this context. The findings indicate that the characteristics of museum visits significantly impact the overall visitor experience. This supports the research of Crompton and Kasser (2009), which highlights the importance of distinguishing between identity-related visitor characteristics. By segmenting these characteristics, museums can better understand visitor needs and create a diverse visiting environment that caters to their desires for leisure, entertainment, and edutainment.
Abstract
This paper introduces a method for improving the accuracy of human posture recognition using non-wearable sensors. We tackle the challenges associated with motion recognition when using a low-precision depth camera. In our study, we define pose recognition as a classification task within a multi-dimensional space. We propose a spatial modeling approach that utilizes a lazy learning system, enabling robust posture recognition despite low-quality motion data or significant variations in user actions. The input motion data is transformed into informative feature vectors, normalized to the user's initial pose. Experimental results show high performance and accuracy, even in the presence of unstable or erroneous motion input.
Abstract
This exploratory mixed-methods study examined the experiences of first-year engineering students in integrating problem-based learning (PBL) and practical learning. It also explored how these experiences relate to students' self-efficacy, peer evaluation, and early academic adjustment. The study involved 16 first-year engineering students enrolled in two project-based courses. Quantitative analyses assessed the reliability and construct validity of self-, peer-, and team-evaluation instruments, while qualitative data from 32 semi-structured interviews were analyzed using thematic analysis. The qualitative findings revealed several recurring themes, including authentic engagement in problem exploration, collaborative idea development, peer interaction, and emerging academic confidence. Quantitative results showed a relatively strong alignment between self-assessment and peer-assessment scores, whereas the relationship between individual self-perceptions and team-level evaluations was more variable. Given the small sample size and single-institution context, these findings should be interpreted with caution. Nevertheless, the study offers valuable insights into how integrated PBL and practical learning environments may enhance collaborative engagement and knowledge application among first-year university students. The implications for instructional design and multi-source evaluation practices in project-based learning contexts are discussed.
Abstract
This study presents an AI-augmented self-interview method that utilizes a large language model (LLM) as both the interviewer and an initial analytic assistant. This approach addresses long-standing challenges in autoethnographic self-study, particularly issues of subjectivity and weak audit trails. The primary contribution is a standardized and reproducible workflow that outlines interviewer prompts, turn-taking rules, audit-trail artifacts, and a human adjudication stage. This structure helps restore organization and reflective distance in self-research. In a proof-of-concept case, the workflow generated stable, quote-anchored themes and an explicit codebook with traceable interpretive moves. While we do not claim that this method is superior to human-led interviews, we provide evidence for procedural objectivity—defined by transparency and traceability—as well as reliability indicators such as short-interval test-retest stability and alignment between LLM-generated codes and human adjudication. Additionally, we propose a pre-registered design for a controlled comparison between human and LLM interviews, and we provide prompts and templates for others to reuse. Overall, this work positions LLMs as methodological supports rather than replacements, clarifying what is innovative, what is currently achievable, and what requires further validation.
Abstract
NFT (Non-Fungible Token) art, fueled by blockchain technology and its connection to virtual currencies, gained significant attention beyond the art world. However, after peaking in 2021, interest and trading activity declined and eventually stagnated. This study examines the research landscape and market dynamics of the NFT art system from a systemic perspective, proposing future research directions. We utilize the Technology Adoption Life Cycle (TALC) and the concept of the Chasm, compiling abstracts of 153 Scopus-indexed articles up to October 2024, along with market data from various platforms. Using VOSviewer, we categorize the literature by research type while analyzing transaction data.Our findings reveal that prior research on the NFT art market predominantly focuses on the pre-chasm stage of the TALC, highlighting the innovative nature of digital art as a medium of exchange and the decentralization it promotes. Based on these results, we recommend that future studies investigate the reasons behind the stagnation of the NFT art market, particularly by addressing the challenges of valuing intangible assets from the perspective of market stakeholders. Furthermore, additional research should explore system-level strategies that can enhance broader adoption and support the sustainable growth of NFT art.
Abstract
End-to-end (E2E) testing of web user interfaces is a crucial but resource-intensive endeavor, often complicated by intricate user workflows, fragile scripts, and significant maintenance costs. Traditional UI testing frameworks, such as Playwright, offer precise control but demand substantial manual effort and are sensitive to changes in the user interface. To address these challenges, this paper introduces an approach that utilizes Large Language Models (LLMs) for the automated generation and execution of E2E tests. We present two complementary paradigms: (1) a JSON-based domain-specific language (DSL) that facilitates declarative test specification and deterministic execution through the Playwright Model Context Protocol (MCP), and (2) an agent-based testing framework in which an LLM dynamically plans and executes actions based on high-level objectives, progress state, and evolving UI snapshots. As a case study, we employ the Vendure e-commerce framework, which offers a representative testing environment featuring functionalities like product search, shopping cart management, payment workflows, and administrative tasks. The JSON-based DSL approach is evaluated for its effectiveness in enhancing test script readability, reusability, and maintainability. In parallel, the agent-based model showcases adaptability and self-healing capabilities. Experimental results indicate that LLM-driven test automation alleviates the burden of manual script creation, improves test coverage across user and administrative scenarios, and provides resilience against changes in application interfaces. By integrating structured JSON-based methods with adaptive agent-based reasoning, this work lays the groundwork for more robust, flexible, and scalable AI-driven test automation frameworks.
Abstract
The growing demand for efficient and reliable propulsion systems in small and medium-sized vessels has revealed the limitations of conventional shaft-based systems, which often suffer from low transmission efficiency, high maintenance costs, and excessive noise and vibration. Even advanced pod-type propulsors frequently fail to meet the specific efficiency needs of small underwater vehicles operating in confined spaces. The hubless rim-driven thruster (RDT) presents a novel propulsion concept by integrating the ducted propeller and electric motor into a single, compact unit, eliminating the traditional bulky shaft and sealing components. This design enhances hydrodynamic efficiency and offers superior resistance to entanglement, making it an ideal solution for challenging marine environments. This study explores the parametric design and optimization of hubless RDT blades to enhance propulsive performance in shallow-water and small-scale underwater applications. Using STAR-CCM+ simulations, we systematically analyze the impact of paddle blade pitch angle and chord length on hydrodynamic performance. The resulting comprehensive dataset is utilized to train a machine learning surrogate model, facilitating rapid performance prediction and optimization. By employing an improved adaptive Sparrow Search Algorithm, we identify the optimal geometric parameters, achieving a 3–7% increase in propulsion efficiency while maintaining structural reliability under optimal operating conditions. The findings of this research provide a robust, data-driven methodology for designing the next generation of highly efficient and reliable propulsion systems for small underwater vehicles, contributing significantly to the field of marine engineering.
Abstract
This study proposes a ‘Smart Apple Guardian (SAG)’ architecture for apple diseases, leveraging Internet of Things (IoT) sensors and artificial intelligence (AI) technologies. Specifically, the proposed architecture employs a deep learning model—incorporating CNNs, RNNs, and LSTMs—to target anthracnose and Marssonina blotch and predict disease outbreaks using environmental data (e.g., temperature, humidity, precipitation, and wind speed) collected through weather and soil IoT sensors. The training dataset was constructed based on disease occurrence history data from 2013 to 2024 from the Rural Development Administration (RDA). A key contribution of this study is the proposal of an Optimal Input Data Window for AI that integrates the existing disease-specific incubation periods (11 days for anthracnose and 21 days for Marssonina blotch) into a 15-day model. Experimental results show that the training dataset with the proposed 15-day incubation period showed higher prediction accuracy than the datasets with existing disease-specific incubation periods. Specifically, the RNN model achieved the best performance in Anthracnose prediction, with an accuracy of 0.9727, a specificity of 0.9455, and a sensitivity of 1.0000. In contrast, the LSTM model achieved superior performance in Marssonina blotch prediction, with an accuracy of 0.9200, a specificity of 0.8982, and a sensitivity of 0.9418. In conclusion, this study demonstrates that applying a unified incubation period can improve both the predictive accuracy and data efficiency of AI models, providing a robust framework for precision agriculture.
Abstract
Fluorescence In Situ Hybridization (FISH) is a widely used cytogenetic imaging technique that enables precise visualization of chromosomal abnormalities across a range of clinical contexts. This paper presents a comprehensive visual taxonomy of FISH probe types—deletion/duplication probes (Del/Dup), break-apart probes (BAP), dual-color fusion probes (DC/DF), and centromeric enumeration probes (CEP)—and contextualizes their diagnostic applications across hematologic malignancies, solid tumors, and genetic disorders. By systematically organizing representative FISH imaging patterns and linking them to disease-specific interpretations, this work offers a content-centric reference framework for clinicians, educators, and researchers. The structured atlas not only enhances understanding of probe logic and signal interpretation but also supports the development of digital content systems and clinical decision support tools that integrate cytogenetic imaging with diagnostic workflows.
Abstract
This study investigates the user experience and feasibility of the music therapy-based multi-modal mobile game app, Everyone's Harmony. The app consists of two main components: the Listening Therapy section, which serves as the starting page, and the Playing Therapy section, featuring a melody-based structured rhythm game with an improvisation mode. In Listening Therapy, users can select animal characters that produce unique sounds and act as interactive controls. In Playing Therapy, a squid character takes on a therapist-like role, offering encouragement and guidance throughout the gameplay. The app's reward system enables participants to earn "Courage Points" for improvisational performance, which can be used to unlock characters, and "Hearts" through Listening Therapy, necessary for accessing the rhythm game. This pilot, single-group exploratory study assessed the feasibility and initial indications of a multi-modal music-based mobile intervention for depression and anxiety. Participants who completed eight 30-minute sessions over eight weeks showed exploratory changes in mean self-reported depression-anxiety scores, measured using the Korean Depression-Anxiety Scale. Since no control condition was included, these findings should be viewed as preliminary and are intended to guide the design of future controlled trials.
Abstract
This study investigates how the international shipping sector can fairly and effectively reduce greenhouse gas (GHG) emissions in Areas Beyond National Jurisdiction (ABNJ) by integrating the principle of Common But Differentiated Responsibilities (CBDR) into International Maritime Organization (IMO) regulations. Current IMO instruments, such as MARPOL Annex VI, employ uniform obligations via the No More Favourable Treatment (NMFT) principle; however, this approach insufficiently addresses disparities in technological and economic capacity between developed and developing states and complicates enforcement for ships registered under Flags of Convenience (FOC). By comparatively analyzing major international environmental law instruments—including the UNFCCC and the Paris Agreement—this paper diagnoses gaps and conflicts between the NMFT paradigm and the CBDR principle within the IMO context. Legal and institutional reform proposals are presented to embed differentiated responsibilities, technical and financial support mechanisms, and improved enforcement for GHG emission reduction from ships operating in ABNJ. Integrating the CBDR principle into IMO regulatory frameworks is argued as essential to enhance regulatory legitimacy and to achieve both equity and effective decarbonization of the global shipping sector in compliance with 2050 climate neutrality goals.