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Recently, vlog is gaining attention as a marketing tool for marketers. In China, the commercial influence of global vloggers has been increasing, attracting the attention of scholars and practitioners. What factors should global influencers and global companies consider when utilizing 'vlog marketing' in China? To answer this, this study conducted a 2 (Vlogger’s persuasive intention: low/high) × 2 (Brand familiarity: low/high) betweensubject experimental design. The results showed that the persuasive intention of vloggers did not affect Chinese consumers' perception of the vlogger's expertise. However, when the persuasive intention was high, it had a more negative impact on consumers' perceptions of the vlogger's credibility, likability, brand attitude, and purchase intentions, compared to when the persuasive intention was low. Second, no interaction effect between the vlogger's persuasive intention and brand familiarity was found. Interestingly, it was observed that consumers rated the vlogger’s credibility and likability more positively when brand familiarity was low than when it was high. The study is different from previous studies because it investigated the effect of global vloggers using persuasion knowledge and brand familiarity.

We propose a specific sound source detection and display system that can distinguish types of sound sources and indicate their directional information using a Deep Neural Network (DNN)-based Artificial Intelligence (AI) learning model in an urban environment where sound sources are introduced from various directions. Our proposed system acquires sound source information through seven microphones and utilizes a commercially available module that outputs the results via radial LEDs (Light Emitting Diodes). We developed an AI learning model, derived through the DNN training process, which is mounted on the interface and expansion slot of the basic module, enabling the system to classify the characteristics of different sound sources and display them using LED elements. Through our experiments, we trained the DNN learning model 1,000 times, achieving a recognition accuracy of 93.90% and a test accuracy of 89.41%. We attempted to intelligently classify various sound source types and their input directions in urban environments. We expect our work to serve as a foundational study for extracting and displaying AI-based sound source characteristic data.

In this paper, we propose a novel on-device AI design strategy for dynamically adjusting subtitle positioning in IPTV content that must be stored within a secure environment to prevent unauthorized distribution. Unlike previous studies that rely on external servers or unsecured environments for video analysis, our approach embeds the AI model directly into the secure zone of the chipset, ensuring privacy and real-time performance. We uniquely utilize on-device hardware resources to enable AI-based frame-by-frame analysis without external transmission. To achieve real-time efficiency, we implement a lightweight and device-optimized AI model by defining a Region of Interest (ROI) for input videos, applying model pruning, and utilizing 8-bit quantization techniques. Additionally, we enhance text recognition performance through data augmentation during training, addressing common challenges such as subtitle overlapping with on-screen graphics or embedded text. We demonstrate that our on-device strategy outperforms conventional models, improving recognition accuracy to 99.7% and processing speed to 60 fps. Through this work, we contribute a practical solution that ensures enhanced subtitle visibility for IPTV viewers using set-top boxes while maintaining content security in a non-trainable trusted execution environment.

We empirically analyzes the key factors influencing Tobin’s Q, a widely recognized indicator of firm value, for publicly listed broadcasting media companies in South Korea. Given the intensifying competition in the broadcasting media industry driven by the rapidly evolving digital environment and the proliferation of overthe- top (OTT) video services, We incorporates variables that reflect both firm-specific and macroeconomic characteristics such as lagged Tobin’s Q, and intangible assets. Financial data from 13 broadcasting media companies were extracted from NICE credit information services, and the Arellano-Bond dynamic panel model was employed for analysis. Our results indicate that the impact of past Tobin’s Q on current firm value varies depending on the informational structure. We find that sales revenue exhibited a negative effect. Operating profit margin, however, showed a positive effect. This suggests that firm size, as measured by sales revenue, may not be positively associated with firm value. In contrast, profitability remains a key driver of market valuation. Intangible assets positively influenced firm value in the full model. Notably, lagged intangible assets showed consistent positive effects across most model specifications. Through this study, we contribute by highlighting several critical strategies. These strategies can increase the market valuation of broadcasting media firms. They include enhancing profitability. Strategically utilizing intangible assets is also important. Managing capital costs is another critical strategy. Our findings provide practical insights for corporate managers and investors regarding strategy formulation and firm valuation.

This study aims to explore the underlying motivations, satisfaction levels, and purchasing behaviors of consumers who use YouTube as a source of beauty product information and a medium for purchase. As digital platforms increasingly influence consumer decision-making, YouTube has emerged as a pivotal channel for beauty commerce, offering dynamic content through influencers, brand collaborations, and interactive features. To achieve this, a quantitative survey was conducted among South Korean women who had previously purchased beauty products through YouTube. The study employed multiple regression analysis to examine how demographic characteristics, usage motivations (such as entertainment, information-seeking, and social interaction), and user satisfaction affect actual purchasing behavior and future usage intentions. The results reveal that younger consumers report higher satisfaction with YouTube’s ease of access, interactivity, and perceived informational quality, which in turn leads to greater purchasing activity. Conversely, older consumers—while purchasing less frequently—show stronger intentions to continue using YouTube as a beauty shopping platform in the future. Additionally, factors such as influencer credibility, content engagement, and digital trust emerged as significant predictors of both satisfaction and purchasing behavior. These findings suggest strategic implications for marketers and content creators aiming to optimize beauty-related content on YouTube. Building digital trust, enhancing content interactivity, and leveraging authentic influencer partnerships are key to fostering consumer engagement and long-term loyalty within the beauty commerce ecosystem.

This paper presents a comparative review and analysis of the strategic and technical measures for supply chain security and risk assessment of 5G networks in healthcare environments. Unlike previous studies that focused on technical components such as encryption and authentication protocols of 5G networks, this study focuses on encryption and authentication and network slice security in consideration of the characteristics of healthcare environments. This paper focuses on solutions to supply chain risk issues that may arise in medical 5G networks. This research rigorously examines the inherent vulnerabilities of medical 5G infrastructure and pays attention to supply chain resilience and risk management capabilities based on the medical slice. The paper identifies the differences, limitations, and design considerations of 5G slicing security. Previous research has been limited to the analysis of individual components of security technology. The results of this study are derived from the implementation and authentication of encryption in medical 5G networks, structural vulnerabilities in network slice architecture, supply chain integrity assurance issues, and the evolving technical risk environment.

This study analyzes how artificial intelligence (AI) and data visualization technologies are integrated into artistic creation to enable aesthetic immersion, focusing specifically on Refik Anadol’s Machine Hallucinations series particularly the Space and Mars projects. Based on vast scientific datasets, Anadol reconstructs representations of space and Mars into surreal and abstract video installations, offering audiences immersive and sensory-driven experiences. Through case analysis and a visual cultural approach to the structure and expression of the works, this paper investigates how Anadol expands the boundaries of human perception and artistically materializes the concept of machinic imagination. The analysis reveals that Machine Hallucinations not only reinterprets scientific data into aesthetic forms through AI but also presents new possibilities for creative collaboration between humans and machines, thereby dissolving the boundary between art and technology. These findings contribute to a renewed understanding of the role of data and algorithms in artistic practice and offer a philosophical reflection on digital aesthetics and the evolving human–machine relationship. Ultimately, this study explores the vision and potential of AI-based art and provides meaningful implications for future interdisciplinary research at the convergence of art, science, humanity, and technology.

We study a method for removing interference and noise to estimate target range information accurately. The transmitted signal is mixed with interference and nois signal before being received. To accurately extract the desired information signal at the receiver, it is essential to eliminate these unwanted signals. This study proposes an algorithm that first obatins range information using the first FFT and then extracts the target range by calculating Doppler information with second FFT. This approach enhances target estimation accuracy. All FFT operations improve the signal-to-noise ratio through coherent intergration. The singal processing after the second FFT is similar to that of a pulse Dopppler radar. However, FFT distortion may occur due to the quantization effect caused by the saturation of the input signal during digital conversion, potentially degrading FMCW performance. To mitigatge this issue, real-time Augomatic Gaing Control is applied. The target range estimation of the proposed and existing methods is analyzed through simulation. The proposed method accurately estimates target range information, while the existing method exhibits estimation errors. The results show that the proposed method improves estimation accuracy by approximately 10% compared to the existing method.

Currently, the volume of multimedia contents and services being transmitted over the Internet is rapidly increasing. The surge in multimedia traffic load coupled with the increase of mobile multimedia devices especially in highly urbanized cities can greatly affect the quality of service and experience in multimedia services. In highly urbanized cities, mobile terminals tend to roam over heterogeneous wireless networks which are comprised of different radio access networks (RATs) where cooperation among them is necessary to provide seamless handovers for a robust multimedia service delivery. In addition, most wireless systems were deployed using centralized mode wherein significant issues in terms of latency, signaling overhead, packet loss were present. This paper deals with the integration of both host-based and network-based optimizations to provide seamless handovers to mobile multimedia devices as they roam across the heterogeneous wireless networks. It aims to propose a decentralized mobility handover scheme that integrates the advantages of both the features of Hierarchical Mobile IPv6 (HMIPv6) and Proxy Mobile Internet Protocol version 6 (PMIPv6), leveraging their advantages to reduce the handover latency of mobile multimedia devices, thus, providing a better quality of service and experience. The proposed scheme alleviates the performance of the standard MIPv6, HMIPv6, and PMIPv6 handover management protocols, addressing high latency, signaling overhead, higher packet loss rates, and most of the issues present in centralized systems.

We investigate the impact of online culinary education programs on learners' self-efficacy and educational satisfaction, focusing on a comparative analysis with offline culinary education. While prior studies have explored online learning in general education or theoretical disciplines, few have examined its effectiveness in hands-on, skill-based fields such as culinary arts. To address this gap, we apply a big data-driven approach that integrates text mining, sentiment analysis, and network analysis—methods rarely used in existing culinary education research. We collected data from diverse online sources, including blogs, news articles, academic papers, and community forums, to uncover patterns in learners’ perceptions and experiences. Our findings reveal that online culinary education offers flexibility and accessibility, which enhance self-directed learning. However, challenges such as limited hands-on practice and reduced interaction with instructors negatively affect learners' self-efficacy and satisfaction. We contribute to the field by identifying the key factors—content quality, technological infrastructure, and interaction design—that significantly influence online learners’ outcomes. Furthermore, we propose pedagogical strategies, such as the integration of virtual simulations and blended learning models, to bridge the gap between online and offline education. Through this study, we offer a new analytical perspective and practical implications for enhancing the quality of online culinary education and advancing digital learning in skill-based disciplines.

In the era of digital transformation, FoodTech is becoming an important industry that combines cuttingedge technology in the production, distribution, and consumption of food. In particular, wellness-oriented food technology emphasizing health and sustainability is attracting more and more attention. This study intends to analyze public awareness of the public awareness of Wellness FoodTech. Therefore, this study conducted big data analysis using text mining to confirm the public's perception of Wellness FoodTech. To this end, keywords related to Wellness FoodTech were collected from Naver, Daum, and Google portal sites through Textom. This study collected data by designating the collection period from March 2023 to March 2025. Based on this, visualization was performed through network analysis and Concor analysis between each keyword using UCINET 6 and NetDraw. Data from February 2021 to February 2023 were also analyzed to see if there was a change in key words. The results of the study confirmed changes in consumers' perceptions, and it was found that the importance of Wellness FoodTech in responding to health and dietary trends in modern society was more emphasized. With the continued emphasis on wellness values, FoodTech is expected to become a key factor in shaping future food consumption patterns. We suggest that wellness food tech is an important trend that reflects the needs of the times.

To curb the spread of misinformation and online harassment, the Chinese government implemented a new regulation in 2022 requiring social media platforms to display users' IP location information. We applied the third-person effect theory to examine the impact of the IP location feature on users' privacy concerns, behavioral change intentions, and their attitudes toward location disclosure and media literacy intervention policies. Through our survey of 499 Chinese social media users, we discovered that most perceive the feature's influence on others to be greater than on themselves. Moreover, we found that participants generally believed that location disclosure would have a more substantial impact on others' social media behavior than on their own, further reinforcing the widespread presence of the third-person effect in digital environments. However, our analysis revealed no significant positive correlation between the third-person effect and increased support for policy enforcement or media literacy education. We validated the applicability of the third-person effect theory in the context of Chinese social media, offering fresh insights into the psychosocial impact of such regulations by highlighting users' diverse reactions to privacy policies. Our findings provide critical empirical support for policymakers and social media platforms in formulating privacy protection measures, underscoring the importance of considering users' psychological expectations and cognitive biases in the policymaking process.

Nowadays, the production technology of game maps has long been mature, but the Tilling Effect of environmental materials is still one of the most significant problems in the development of large-scale open world maps, especially in real-time rendering of games and interactive media. When textures are repeatedly applied to large areas of terrain, the Tilling problem will occur, resulting in obvious repetitive pattern effects, which reduces the visual immersion, realism and visual fidelity of the environment. This problem is more serious in real-time rendering, and developers need to find effective solutions to eliminate the repetitive texture effect of environmental materials. By comparing traditional solutions, it is found that the Texture Variation function solution and the map random perturbation fusion solution significantly improve the visual quality of the generated environment by reducing the Tilling Effect, while maintaining high performance and scalability, and have simpler operability and controllability, making it suitable for terrain texture production in large environments. This research promotes the development of procedural textures and large-scale environmental texture generation. By solving the common problem of Tilling Effect, we provide a solution that can improve the visual quality of environmental textures in large scene environments and improve the running speed. It provides reference significance for the future scalable texture rendering and procedural texture generation.

In this paper, we understand immersive content through news data analysis based on immersive technology related to immersive content. News data were collected and analyzed using the Big Kinds database from January 1, 2023, to February 28, 2025. The primary keyword for analysis was "immersive content," and it included related terms such as "immersive media" and "immersive convergence." This research holds academic significance as it systematically visualizes industry trends and clarifies relationships between key terms in the immersive content industry through big data and network analysis methodologies. From a technological perspective, the frequent appearance of keywords such as VR, AR, Metavers, and AI suggests that technology convergence-based content development is a crucial driving force for the growth of immersive content. Future studies can utilize these findings as critical reference materials to predict technological advancements, explore potential areas of application, and develop strategic approaches. Through this, additional strategic insights for immersive content development can be provided.

We analyze the character design of the animation Catch! Teenieping from both semiotic and posthuman perspectives. Our study investigates how abstract concepts such as emotions, occupations, and desserts are visually embodied and anthropomorphized in character design for children’s media. We focused on how this visual system facilitates emotional identification and encourages expressive learning in young viewers. Through case analysis of characters like Hachuping and Owping, we explore the symbolic strategies that translate intangible values into relatable character traits. Our findings highlight the potential of emotional character design to support children’s emotional recognition and social interaction beyond passive media consumption. By integrating semiotic decoding and posthuman theory, we propose a new framework for understanding the educational and affective roles of character-driven content. This study contributes an original perspective to the field of character design by linking visual abstraction with emotional learning, and offers insights for future directions in content planning for emotionally resonant children’s media.

This paper uniquely bridges the technical and socio-cultural dimensions of ChatGPT integration in university-level English language teaching (ELT). Unlike prior research that often isolates technical efficacy (e.g., error correction) or ethical concerns, we innovatively combine quantitative outcomes with qualitative insights into equity, ethics, and human-AI dynamics. Employing a quasi-experimental design (n=50 undergraduates) and in-depth interviews (4 instructors, 15 students), we evaluated ChatGPT’s impact on writing proficiency, engagement, and critical thinking. Our quantitative results demonstrate significant improvements for ChatGPT users in grammatical accuracy (34%) and task achievement (29%), alongside a notable 42% increase in writing time—a metric seldom explored previously. However, our qualitative findings reveal critical, underexplored limitations: risks to learner autonomy, erosion of teacher-student rapport, and exacerbated equity gaps. Distinct from studies narrowly focused on AI's technical or ethical facets, we frame these issues within sociocultural theory and blended learning. We identified cultural biases in AI feedback and new academic integrity challenges, issues significantly underexplored, highlighting the necessity and originality of our study. Based on these findings, we propose a balanced pedagogical model integrating AI with structured human mentorship—a novel contribution to addressing current AI-ELT paradigm gaps. This model enhances critical thinking and inclusivity, offering actionable institutional strategies for ethical technology integration in ELT, prioritizing the humanistic core of language education and advancing the discourse beyond earlier transactional approaches.

Previous studies have generally regarded athletes as a homogeneous group and primarily focused on the general effects of strength training. We conducted this study to examine the differential effects of an eight-week lower-body strength training program on body composition and jump performance in collegiate female basketball and volleyball players. Participants performed strength training three times per week at an intensity of 12–15 RM. As a result, basketball players showed significant increases in muscle mass, reductions in body fat percentage, and improvements in vertical jump height, indicating enhanced lower limb strength and neuromuscular adaptation. In contrast, volleyball players, who already exhibited high baseline jump ability, demonstrated no significant changes, suggesting the presence of a performance ceiling effect. Our findings suggest that traditional strength training is effective for basketball players, whereas volleyball players may benefit more from plyometric-based training. This study highlights the need for sport-specific and individualized training strategies for female athletes.

This study evaluates 5G supply chain security approaches across EU and US regulatory landscapes. We move beyond traditional security research by examining how encryption systems, authentication mechanisms, and network virtualization technologies interrelate within comprehensive security frameworks. Our analysis uniquely integrates operational security perspectives with strategic policy considerations, providing insight into the complex relationship between supply chain vulnerabilities and broader risk governance approaches. This research addresses growing concerns about supply chain integrity in telecommunications infrastructure while filling significant knowledge gaps left by compartmentalized analyses of isolated technical components. By systematically comparing European and American regulatory approaches, we develop evaluation criteria for assessing encryption implementations, authentication protocols, virtualization security, and supply chain integrity measures. This creates a structured assessment methodology applicable across different implementation contexts. The findings establish a foundation for enhanced risk management that balances technical security requirements with broader strategic considerations. These insights help organizations develop more resilient security architectures for critical infrastructure deployments, ultimately supporting secure service delivery across essential 5G applications.

Food tourism has gained great traction and popularity in recent years. Against this backdrop, we aimed to understand the formation process of visit intentions among Chinese social media users who subscribe to TikTok's Korean traditional food channel. We adopt the stimulus-organism-response (S-O-R) model to achieve this research objective. Data were collected from 312 unmarried female TikTok users born after 00s in China. Analytical tools such as SPSS 26.0 and Amos 24.0 were employed for data analysis. We identified the characteristics of TikTok and the quality of food tourism information and examines their impact on the users’ travel intention of visiting Korea by observing differences in audience perception of the destination image during cognitive information processing. In addition, we also confirmed the moderating effect of subjective knowledge in the process of TikTok characteristics and food tourism information quality influencing the perceived city image. Finally, recommendations are provided on how to better integrate food tourism with short videos, thereby creating high-quality food tourism content that helps boost the tourism market in both China and Korea.

We aim to empirically examine the effects of self-directed learning on class attitude among university students, focusing on the mediating effects of social networking service (SNS)-related variables, including social networking service (SNS) addiction tendency, emotional expression through social networking service (SNS), social networking service (SNS) utilization ability, and grit. We conducted a survey with 257 university students and analyzed the collected data using the Statistical Package for the Social Sciences (SPSS) Process macro. The results of our study are as follows. First, self-directed learning demonstrated significant correlations with SNS addiction tendency, emotional expression through social networking service (SNS), social networking service (SNS) utilization ability, grit, and class attitude. Second, the social networking service (SNS)-related variables exhibited a dual mediating effect in the relationship between self-directed learning and class attitude. Third, we identified significant mediation pathways from self-directed learning to class attitude through these variables. We contribute to the literature by revealing the complex mechanisms by which SNS-related factors and grit influence students' academic behaviors. Based on these findings, we propose educational strategies to strengthen self-directed learning and provide directions for future research that emphasize both digital competency and psychological perseverance in higher education contexts.

This study, we explore young people's attitudes toward marriage in the context of rapidly changing social, economic, and cultural environments. As factors such as career aspirations, education, financial independence, and personal freedom increasingly influence relationship choices, traditional views on marriage are evolving. The research utilizes the Marital Attitude Scale (MAS), a 23-item tool scored on a four-point Likert scale, to measure students' perspectives on marriage. Data were collected from 225 university students, yielding an average MAS score of 36.23 out of 69, indicating a moderately positive attitude toward marriage. Our findings reveal that attitudes vary across age groups, with younger students showing greater caution and older students demonstrating a stronger preference for loyalty in relationships. Our study highlights the importance of understanding the personal, social, and economic factors that shape attitudes toward marriage. These insights can inform future research, premarital education programs, and policies aimed at supporting stable and healthy family structures.

With the development of artificial intelligence technology, face recognition systems based on deep neural networks are widely used in security monitoring, identity authentication, and human-computer interaction. However, recent studies have shown that face recognition systems are not fully prepared for deploymentlevel adversarial attacks, and adversarial samples can undermine the integrity and availability of face recognition systems by poisoning datasets. We demonstrate how attackers can undermine the reliability of face recognition systems by injecting crafted adversarial images into test data. In addition, the article will introduce strategies to defend against such attacks by mitigating performance degradation through defensive distillation methods. By conducting an empirical evaluation of face recognition systems with and without defense mechanisms, we show the impact on face recognition performance to ensure the integrity of the article.

The aim of this study is to explore the key antecedent condition combinations that influence the impact of AI technology on the deep and sustainable development of tourism and cultural resources through qualitative comparative analysis (QCA) methods. We set strict analysis criteria and consider geographical differences to analyze the impact of different combinations of conditions on the results. In terms of research methodology, we use group analysis techniques to identify three different patterns of antecedent condition combinations. These patterns reveal that factors such as industrial structure, government policies, market demand and enterprise R&D investment are driving the indepth development of tourism and cultural resources by AI technology. The results of the study show that the value added rate of the secondary industry, local financial expenditures in specific areas, the consumption index, and the input of R&D personnel in industrial enterprises are the key factors influencing the in-depth development of tourism and cultural resources by AI technology. These factors show variability in different regions, but have relative stability in general. The significance of the study is to provide theoretical support and empirical evidence for the innovation of AI technology on the in-depth development of tourism and cultural resources, which will help policy makers to formulate more targeted policy measures and promote the sustainable development of AI technology on the in-depth development of tourism and cultural resources.

The GraphRag technique has recently been studied meaningfully as a technique for securing high performance in search and inference without hallucination for domain-specific knowledge. GraphRAG builds a graph of the document's core concepts and the relationships between them, resulting in a connected knowledge network. To validate the practicality of GraphRAG, we implemented an EPUB reader using GraphRAG in our previous research and evaluated its retrieval performance, achieving 90% accuracy in both factual retrieval tasks and complex inferential queries, demonstrating superior performance. In this paper, we describe details of the implementation of the EPUB reader capable of retrieval and inference using the GraphRAG technique. Specifically, we detail the processes of building a graph database from EPUB files, conducting retrieval, and visualizing the constructed graphs. Furthermore, we explain how the EPUB reader was designed to run on resource-constrained devices such as the Raspberry Pi 5 single-board computer. We expected that this system serves as a representative implementation example for applications leveraging GraphRAG technology.

This study aimed to analyze changes in lower extremity muscle activation across the left and right sides and swing phases during a driver swing under two different stance conditions. The study participants included eight fully qualified members of the Korea Ladies Professional Golf Association (KLPGA). Electromyographic (EMG) activity of the gastrocnemius, tibialis anterior, and vastus lateralis was measured bilaterally while performing driver swings under feet straight stance and lead foot open stance. The results indicated that muscle activation tended to increase progressively toward the latter stages of the swing across all stance. The lead leg vastus lateralis exhibited higher activation across all phases, while the lead foot gastrocnemius showed greater activation during the backswing-to-downswing transition. However, in the follow-through phase, the trail foot gastrocnemius demonstrated dominant activation. In conclusion, muscle activity remains elevated during the latter stages of the swing following impact, demonstrating that the strength of the lead leg vastus lateralis plays a pivotal role in maintaining body stability throughout the swing.

The necessity of data science education that experiences the problem-solving process by combining information discovered from data with varied expertise is necessary to develop digital talents with the knowledge and capabilities required by the 21st-century society. This study examines a creative teachinglearning strategy that connects data science to ICT main programs and suggests an efficient data science education plan within the industrial framework of intelligent information technology. An extended data science education system that makes use of convergent problem-solving skills in the ICT curriculum was described in this research along with the implications of data-based science education. We proposed a strategy to implement and activate the design of data science education policies based on statistics and mathematics in order to enhance the effective insight of data. And also, we suggested the continuous data education management system was diagnosed, and new approaches, to improve the management of academic accomplishment in data-based science. With out research results, we would certainly boost national scientific and technology competitiveness as well as academic excellence in data science.

In order to illustrate the significance of mathematics education in fostering creative fusion talents in science, engineering, and technology, our research presents mathematical thinking and attitude to foster creativity, mathematical modeling and effects to foster fusion, and synergy effects through mathematics education to foster creative fusion. We sets the direction for future mathematics education in order to foster creative fusion talents. Second, to demonstrate the direction of mathematics education for fostering creative fusion talents, we presented the development of mathematical-based innovation technology, discovery of new industries that applied mathematics, improvement of existing industries using mathematics, and improvement of other convergence fields based on mathematics, and human resources related to mathematics. Lastly, strategies and models for enhancing teaching-learning through creative fusion of mathematics education were offered step by step to build and cultivate major capabilities in order to demonstrate the problem-solving and creative fusion thinking process. In presenting the creative convergence teaching-learning model, we presented a method of linking how the mathematics field is applied to the ICT field for academic growth and development, different from previous studies, and through convergence between other subjects. By of our results, we hope to find ways to strengthen the areas where our mathematics education is lacking and make up for them.

The supply chain has been the business’ most talked-about topic in recent years. Global supply chain unpredictability and worldwide political instability, including trade war, persist despite each nation’s proactive reaction. Because of its capacity to restore and preserve the continuity of product, information, and financial flows in the case of supply chain interruptions, supply chain resilience is a topic of great interest to both academia and industry. We must, however, prepare and react from an economic and security perspective that goes beyond corporate-level responses due to supply chain shocks and structural changes brought on by competitive supply chain strategic assetization flows in each nation, and advanced strategic technology and industries. Therefore, we performed to investigate how to use smart digitization to improve agility and resilience in an unpredictable supply chain environment and react to a newly created order. So, we present an advanced supply chain model that can track and evaluate global supply chain trends and changes, and resarched on Supply Chain Management, Digital Transformation, and Artificial Intellingence Transformation for a critical business application cases. The research results presented will contribute to promoting mutual profits beyond the simple purpose of managing the supply chain of companies. As the research results, we presented will go beyond the simple purpose of a company's supply chain management and contribute to enhancing mutual profits. If existing supply chain management simply relied on supply records, the proposed supply chain management will contribute to expanding the scope of supply chain strategies within companies and supply chain sharing between companies.

We purposed to develop a floating communication module equipped with LoRa and GPS technologies to enhance underwater drone operations by enabling real-time location tracking and long-range wireless communication. While previous studies focused on underwater communication systems, few have quantitatively analyzed how enclosure materials affect signal performance in real-world marine environments. To address this, we designed a sealed communication system and experimentally evaluated its signal sensitivity and positioning accuracy under open, PLA-sealed, and acrylic-sealed conditions. Through static positioning tests and LoRa signal strength measurements, we found that the acrylic enclosure maintained reliable performance, with only a 21.1% increase in GPS error and a 16.7% reduction in communication range compared to the open state. In contrast, the PLA case showed significant signal degradation. These results demonstrate that case materials directly impact the communication quality of underwater systems and confirm the acrylic case as a practical option for marine deployment. We contribute to the development of modular and efficient AUV communication systems by experimentally verifying enclosure-dependent signal performance and proposing a deployable design suitable for real ocean environments.

This study investigates the impact of moderate-intensity aerobic exercise, similar to the physical activity experienced by golfers during a round, on brainwave activity. Professional golfers participated in treadmill exercise at a moderate intensity (8.5 km/h for 6 minutes), and electroencephalography (EEG) changes were observed during the pre- and post-exercise. The results revealed that aerobic exercise led to a significant increase in alpha wave activity in the frontal regions, with a tendency for a decrease in theta wave activity. However, no significant changes were observed in beta wave activity in both the frontal and parietal regions. These findings suggest that moderate-intensity physical activity, akin to a golf round, influences specific brainwave patterns, which may contribute to understanding the neural mechanisms underlying cognitive performance during athletic tasks such as golf. Further research is needed to explore the relationship between exercise-induced neural changes and performance outcomes in sports settings.