Recent advances in 3D data-driven digital twin research have revealed limitations in existing tree reconstruction methods, which rely solely on either scanning or procedural generation. To address this issue, this study proposes a hybrid pipeline that integrates data-driven reconstruction and procedural generation using Gaussian Splatting(GS) data. The proposed method converts multi-view GS outputs into dense point clouds and extracts a stable skeletal structure through color-density-based graph analysis. Fine branches and leaves are procedurally generated using a space colonization algorithm that incorporates botanical principles, achieving a natural and structurally coherent form. Quantitative evaluations using Chamfer distance and Intersection-over-Union metrics demonstrate high geometric similarity and volumetric consistency with the original GS data. The proposed GS-based hybrid framework ensures both visual realism and biological plausibility, enabling efficient and reliable digital twin tree modeling.

As the web game market grows, ensuring service stability through load testing has become increasingly important. Web games comprise a variety of functions with distinct internal logic, ranging from simple data retrieval to complex transaction processing. Therefore, a comparative performance analysis of load-testing tools that accounts for these functional characteristics is crucial for achieving reliable and efficient service operation. This paper evaluates the performance of four widely used load-testing tools—JMeter, k6, Gatling, and Locust—under representative web-game workloads. To emulate realistic database read and write patterns, we implement the core server logic of the web game Pokerogue in a cloud environment rather than simply issuing HTTP requests. We classified workload patterns into write-intensive, read-intensive, and mixed types using distributed tracing, and measured request-generation capability and system resource consumption across five key game functions. Our experiments show that each tool demonstrates distinct strengths depending on the characteristics of individual web-game functions. Specifically, k6 demonstrated high request-generation performance in write-intensive scenarios, while JMeter showed strong performance in read-oriented tasks; Gatling exhibited efficient memory usage in mixed workloads, whereas Locust proved suitable for resource-constrained environments. These results indicate that the selection of a load-testing tool should be informed not only by its request-generation performance but also by the workload characteristics of the target game function. By systematically analyzing function-specific workload patterns together with the performance and resource-usage behavior of each tool, this study aims to provide empirical evidence that can be usefully applied in practical load-testing workflows for web-game services.

This study longitudinally examined how adolescents’ game use over time affects self-control, health status and health management, academic achievement, and family and peer relationships. To this end, we analyzed five-wave panel data from a national game user panel survey and sequentially applied latent growth models (LGMs), parallel-process LGMs, and time-varying covariate models. First, unconditional LGMs were estimated for eight variables—game use, self-control, health status, health management, academic achievement, parent–child communication, academic stress, and peer stress. The results showed that game use increased slightly but significantly over time, whereas self-control, health management behaviors, and academic achievement exhibited overall declining trajectories. Parent–child communication started at a relatively high level and showed no significant mean change, while academic and peer stress remained at relatively low levels with minimal mean-level fluctuation across waves. Because the mean of the linear slope factor was not significant for parent relationship, academic stress, or peer relationship, these variables were treated as comparatively stable contexts and excluded from subsequent models focusing on change dynamics. Next, we estimated conditional LGMs in which game use was included as a time-varying covariate in the growth models for self-control, health management, and academic achievement. Across all waves, higher game use was consistently associated with lower concurrent self-control, and with poorer health management and lower school grades at most time points. For health management, the negative association with game use was pronounced at the first three waves but its magnitude attenuated over time and became statistically nonsignificant at later waves. By contrast, academic achievement displayed a robust pattern of “increased game use → lower concurrent grades” at all five waves, indicating a stable negative association between frequent game use and short-term academic performance in adolescence. Finally, to investigate the long-term interrelations among game use, self-control, health management, and academic achievement, we estimated parallel-process LGMs. The results indicated that higher initial game use was associated with lower initial levels of self-control, health management, and academic achievement. Moreover, adolescents whose game use increased more rapidly over time showed steeper declines in self-control, greater deterioration in health management, and sharper drops in academic performance. A specific indirect pathway from initial game use to the slope of health management via the linear slope of self-control was statistically significant, suggesting that heavy game use can undermine health management behaviors partly by eroding self-regulation. In contrast, a mediation model specifying the slope of academic achievement as the final outcome did not converge, limiting interpretation of the game use–self-control–achievement pathway. Taken together, the findings demonstrate that the quantitative aspect of game use—how long adolescents play—has meaningful longitudinal implications for the developmental trajectories of self-control, health management, and academic achievement. At the same time, parent–child communication and academic and peer stress displayed relatively stable mean levels, implying that in this sample family and peer relationships functioned more as background contexts than as domains that deteriorate rapidly in tandem with game use. Rather than framing game use solely as a pathological disorder, the results underscore the importance of early monitoring of initial game-use levels and growth rates, as well as preventive interventions aimed at strengthening self-control and digital literacy, to protect adolescents’ health, learning, and family and peer relationships.

This study developed a patient-specific cardiac motion-based virtual reality cardiovascular intervention simulator for training purposes. Personalized 3D cardiac models were generated from medical images using AI-based nnU-Net segmentation, and ECG-synchronized motion was integrated to reproduce physiological cardiac cycles through P-QRS-T waveform analysis. In particular, this study went beyond simple simulation by designing and implementing serious game elements including difficulty adjustment by user skill level, real-time haptic feedback, and a quantitative scoring system to maximize educational effectiveness. Real-time stent insertion was implemented at performance exceeding 60fps through the Extended Position-Based Dynamics (XPBD) algorithm. Experimental results showed that the segmentation model achieved high accuracy with an average Dice Similarity Coefficient (DSC) above 0.90, and the dynamic model demonstrated biomechanical behavior similar to clinical data, showing 12.3% coronary artery diameter change and 3.2mm positional displacement during the cardiac cycle.

The purpose of this study was to develop and validate a measurement tool for assessing university students’ social and emotional competence in the context of a changing higher education environment. To achieve this goal, preliminary items were constructed based on a review of previous studies and expert consultation, and both a pilot test and a main survey were conducted with university students across the country. Exploratory factor analysis revealed that social and emotional competence consisted of five factors, that is to say, self-understanding, self-regulation, relationship management, social adaptation, and emotional adaptation. Confirmatory factor analysis further verified the model’s goodness of fit and construct validity. The results indicated that university students’ social and emotional competence reflects not only developmental characteristics but also the requirements of social and emotional adaptation within university life. Accordingly, the developed scale can serve as an empirical basis for objectively diagnosing students’ levels of social and emotional competence and can provide foundational data for establishing various student support systems, including coaching, career guidance, learning support, and extracurricular programs. Furthermore, by comprehensively understanding social and emotional competence, this study contributes to the design of educational intervention models that promote the enhancement and development of such competencies among university students.

The game industry has continually embraced technological advancements to enhance visual realism and narrative immersion. Recently, the emergence of generative artificial intelligence has transformed the overall approach to game production, improving efficiency and expanding creative possibilities across various domains such as asset creation, character design, dialogue generation, and sound design. This study examines how artificial intelligence influences both the development process and expressive methods of game production, focusing on representative cases that employ generative AI in distinct ways. By analyzing four commercial game examples, the study identifies the roles and implementation strategies of AI in each case. The results indicate that generative artificial intelligence is utilized in multiple aspects of production, including automated asset generation, narrative variation, and pipeline optimization. Through this analysis, the study demonstrates that generative artificial intelligence functions as a key technological factor driving structural changes in the creative processes of the game industry and serves as a foundational reference for future research on AI-based game production.

In digital games, typography serves not only as a vehicle for conveying information but also as a crucial visual element that shapes the game’s identity and emotional atmosphere. However, prior research has predominantly focused on graphics, backgrounds, and character design, with systematic analyses of typographic expression remaining limited. This study concentrates on the emotional functions of typography in games by analyzing 25 PC games across five representative genres: role-playing (RPG), shooting (FPS/TPS), strategy (RTS/TBS), MOBA (AOS), and horror. The titles of these games were assessed using a seven-point scale based on typographic variables—weight, form, spacing, slant, baseline, and visual effects—and subsequently translated into emotional dimensions: robustness, stability, dynamism, traditionality, and fantasy. Based on this framework, genre-specific emotional typologies were identified. The results indicate that RPGs emphasize grandeur and mythic symbolism; FPS/TPS games highlight robustness and dynamism; strategy games exhibit order and stability; MOBAs convey competitive dynamism; and horror games strongly employ fantasy and anxiety. By classifying genre-specific emotional types of typography, this study expands the scope of game graphic design research to include textual expression. Practically, it provides design guidelines that help align typographic choices with genre-specific emotional characteristics. Nonetheless, the study is limited to PC games and a single-researcher evaluation, suggesting the need for future research to incorporate diverse platforms and user-based assessments.

Webtoons have evolved beyond simple digital content to become core assets in the content industry as original IPs, with high potential for cross-media and cross-platform expansion. In recent years, transmedia strategies based on story universes and emotional immersion design have positioned webtoon IPs at the center of content convergence. This study analyzes the transmedia adaptation of the webtoon 『Hell is Other People』by Kim Yongki, focusing on how the narrative structure changes and media convergence strategies emerge when the webtoon is adapted into a television drama.The findings reveal that the original webtoon constructs a narrative centered on psychological anxiety, fear of others, and relational distrust within the closed space of a Seoul guesthouse, offering readers a deeply immersive emotional experience. In contrast, the drama adaptation transforms this into a sensory-based emotional experience, employing audiovisual stimuli and expanding the story from a first-person psychological narrative to a multi-perspective story universe. By integrating the prequel webtoon and implied narrative elements, the drama forms a more structured plot and presents possibilities for future extensions such as side stories and franchise developments. This study highlights how webtoon IPs can evolve from single emotional narratives into expansive universe-based content, and demonstrates the strategic importance of differentiated emotional immersion designs across platforms for the future of the content industry.