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Science created technology and offered us a new era on the surface through internet networks that connected the world like a village. Gradually, we arrived at the 5G era, which started its journey from 1G in the 1980s. At this present stage, 5G networks are following network optimization strategies including edge computing, adaptive numerology, and Network Function Virtualization (NFV) to acquire and maintain ultra-low latency communication (URLLC), certainly focusing on 1 ms latency, which was around 20 ms for 4G networks. This paper analyzes the danger of the latency fluctuation of 5G networks in real-time applications in terms of driving autonomous vehicles and determines the reasons behind the sudden increment of latency under 5G connectivity. To address these concerns, the 5G octa-cell network architecture is introduced to consistently provide 1 ms latency of connectivity for autonomous vehicles. A comparative analysis between the existing hexa-cell 5G networks and the proposed integration of octa-cell 5G network architecture is presented, and the results have shown that octa-cell networks provide a consistent 1 ms latency which is required for autonomous cars to travel safely.

This study investigates the prioritized business model factors of the Mnet Asian Music Awards (MAMA) using the Business Model Canvas (BMC) framework and Interpretive Structural Modeling (ISM). MAMA, hosted by CJ ENM’s Mnet, is one of the most prominent annual music events in Asia, and it plays a critical role in promoting K-pop globally while fostering cultural exchange. However, MAMA faces several challenges, including high logistics costs, complex international partnerships, and audience engagement across different regions. This study utilizes in-depth interviews with seven industry experts to analyze the critical components of MAMA’s business model. The BMC framework was used to identify the most important factors, while ISM, a methodology for analyzing and prioritizing complex interrelationships between factors, helped to prioritize them based on their influence and interconnectedness. The study found that Key Resources such as brand awareness and artist performances were the most critical factors, followed by Value Propositions and Key Partnerships while Revenue Streams and Cost Structures were lower-priority factors. The study concludes by discussing the implications for event organizers and suggesting areas for future research, such as the impact of digital transformation on international music awards and strategies to enhance audience engagement.

With the rapid growth of social media advertising, this study explores how ad location and users’ brandfollowing status influence perceptions of ad and brand attitudes. Experimental findings reveal that ad location had a significant effect on consumers’ attitudes toward the ad. Specifically, native ads embedded within social media newsfeeds elicited more favorable responses compared to banner ads positioned in the right column. Additionally, brand-following status strongly influences brand attitudes, with ads from followed Facebook Pages leading to more positive brand evaluations than ads from non-followed pages. These findings highlight the strategic advantage of native ad placements and the importance of cultivating brand followers to enhance advertising effectiveness.

In this paper, we propose a method for designing precoding vectors in integrated sensing and communication systems. When designing an optimization problem to maximize the signal-to-noise ratio (SNR) of a radar signal, existing methods use the signal-to-interference plus noise ratio (SINR) of communication users as a constraint, so precoding vectors for all users must be designed simultaneously, which requires a lot of calculations and takes a lot of time to find the optimal solution. On the other hand, the proposed method can solve the problems of existing methods by using the signal-to-leakage plus noise ratio (SLNR) of communication users as a constraint and designing each user's precoding vector independently. The performance of the proposed technique was analyzed through simulation, and it was shown that the SNR value of the radar signal is not significantly affected by the SLNR value of the communication user.

This study explores the impact of grade level and cultural contexts on AI perception among Korean and Chinese university students, focusing on the field of design education. The design field was chosen as it represents an intersection of technological innovation and practical application, making it an ideal context for analyzing human-AI interaction. A survey of 250 students (125 from each country) assessed AI knowledge, AI perception, AI-human role collaboration, and AI learning experiences. The results showed that Korean students scored higher in AI knowledge and perception, while Chinese students excelled in AI learning experiences. Academic progression was found to have a significant influence on AI perception, revealing common patterns that transcend national contexts. However, the interaction effects between nationality and academic progression were not statistically significant, suggesting that academic development impacts students in both countries similarly. This study not only examines differences between nations but also uncovers universal trends across academic levels, emphasizing the importance of designing AI education strategies that account for cultural differences while fostering universal competencies through practical experience and knowledge accumulation.

The more precisely an AI system collects and analyzes user information, the more effectively it can tailor future recommendations for each user. However, gathering comprehensive information for individual users remains a significant challenge because they may have concerns about privacy or find the process bothersome. To encourage users to willingly provide diverse and meaningful personal information, we applied two widely discussed concepts in psychology and economics to conversational agents: Self-Disclosure and Utility Theory. Our study revealed that while both conversational strategies influenced user experience, the Utility Theory strategy, when combined with questions targeting opinions and emotions, enhanced users’ willingness to disclose personal information and improved their overall disclosure experience. These results highlight the importance of tailoring conversational strategies to information types to encourage self-disclosure effectively. Based on these findings, we propose design considerations for efficiently gathering user information through conversation.

This paper presents a novel hybrid data drift detection framework, the Ensemble Modeling-based Hybrid Drift Score (EM-HDS), that integrates Principal Component Analysis (PCA), Variational Autoencoders (VAEs), and ensemble modeling to effectively detect both global structural shifts and localized non-linear variations in feature space. PCA identifies global changes by monitoring variance alignment and principal component transformations, while VAEs enhance sensitivity to localized anomalies through probabilistic modeling of reconstruction errors. The EM-HDS framework integrates complementary techniques using a Random Forest ensemble model, effectively capturing complex, non-linear relationships between PCA and VAE metrics. Experimental evaluations on synthetic datasets with simulated drift and real-world COCO image features demonstrate the robustness and adaptability of the proposed method. EM-HDS delivers superior drift detection performance, significantly improving detection accuracy, particularly in scenarios involving simultaneous global and local drifts, surpassing standalone PCA and VAE approaches. Although the framework requires careful tuning of hyperparameters to adapt to specific datasets, its ability to dynamically adjust to diverse drift patterns makes it a practical and effective solution for real-time monitoring and adaptation in dynamic environments. This research establishes a strong foundation for enhancing the reliability of machine learning models in complex, real-time applications for future work.

We designed an explainable AI system to predict diabetes by integrating ensemble learning models and interpretability tools. Traditional diagnostic models often lack transparency, making them less suitable for clinical applications where interpretability is essential. This study aims to design an explainable artificial intelligence (AI) system for diabetes prediction that balances predictive accuracy and interpretability. To achieve this, we developed an ensemble model combining Random Forest, XGBoost, and Logistic Regression within a Voting Classifier framework. The Synthetic Minority Oversampling Technique (SMOTE) was employed to address class imbalance in the dataset, ensuring reliable predictions across both majority and minority classes. For interpretability, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were integrated into the system to provide global and local explanations of model predictions. Experimental results demonstrated the ensemble model's high performance, achieving a recall score of 0.846 and an AUC-ROC score of 0.874, which are crucial metrics in minimizing false negatives in medical diagnoses. Key features such as BMI, glucose level, and age were identified as significant contributors to diabetes risk. The integration of explainability tools ensures that healthcare professionals can understand both overarching patterns and patient-specific predictions, fostering trust in clinical decisionmaking. This approach bridges the gap between complex machine learning models and practical medical applications, offering a robust and transparent tool for improving patient outcomes.

Artificial intelligence (AI) systems are increasingly becoming integral components of several industries. However, they are vulnerable to adversarial attacks. Through adversarial attacks, the behavior of AI models can be manipulated leading to severe consequences that require effective approaches. In this paper we explore how the guidelines outlined in International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) can be applied to enhance the security and resilience of AI systems against adversarial attacks. We propose a novel framework that can effectively detect and mitigate such attacks thus ensuring robust AI system deployment. Our proposed framework consists of three major sections of Risk Management, System Architecture and Design as well as Continuous Monitoring and Improvement thus aligning with ISO/IEC standards. Furthermore, our research addresses the gap between theoretical understanding and practical implementations, provides detailed strategies and real-world case studies making it a robust framework for mitigating adversarial threats and enhancing AI security.

Generative AI has rapidly advanced, yet most models are predominantly trained in English, raising questions about their performance in non-English languages. This study explores whether AI-generated essays in Korean are distinguishable from human-written texts and how they compare in perceived quality. We conducted an experiment with 116 South Korean participants, evaluating essays generated by OpenAI's ChatGPT and NAVER's HyperClova X against a human-authored university essay. Participants rated each essay on a 9-point scale to assess whether they believed it was AI- or human-written and provided quality scores out of 100. The results revealed that distinguishing AI-generated texts from human-authored ones was challenging. ChatGPT (5.44) and HyperClova X (5.50) received AI judgment scores near 5, indicating uncertainty in classification, while the human-authored text scored 4.17, showing a slight inclination toward human recognition. Correct identification rates were only slightly above chance (59.6% for ChatGPT, 54.8% for HyperClova X, and 60.6% for the human essay). Notably, ChatGPT's essay received the highest quality score (85.38), surpassing the human-authored text (82.87). Despite identifying AI authorship, participants still rated AI-generated writing highly for structure, coherence, and vocabulary. These findings suggest that AI can not only mimic human writing but also outperform it in perceived quality. However, AI-generated texts still exhibit identifiable patterns, such as grammatical precision and emotional detachment. We highlight the evolving role of AI in text generation and emphasize the need for further refinement in non-English language models. Recently, ChatGPT and other generative AIs have been improving rapidly in performance. However, most generative AI models are trained in English and tend to perform well when asked and answered questions in English. Therefore, we aim to investigate whether generative AI can perform well in Korean, a language of non-English-speaking countries, and whether people can distinguish between generative AI-authored content and human-authored content.

Smart home and artificial intelligence (AI) technologies are developing rapidly, and various AI-based smart home systems can improve the quality of living for people. In this research, focusing on the AI-based smart home services for convenience, we explore the individual factors influencing the individuals’ attitude toward AI-based smart home services. Specifically, we first examine whether prior AI experience and the perceived benefits of AI affect the attitude toward the AI-based smart home services (RQ1). Second, we examine whether the prior AI experience affects the attitude toward the AI-based smart home services via the perceived benefits of AI (RQ2). Third, we examine whether the mediation effect of the perceived benefits of AI on the association between prior AI experience and attitude toward the AI-based smart home services is moderated by digital self-efficacy (RQ3). To answer the research questions, we conducted some statistical analyses (i.e., hierarchical multiple regression analysis, mediation analysis, and moderated mediation analysis) using the Koreans who were aware of the AI-based smart home services. We find that (1) only the perceived benefits of AI are positively associated with the attitude toward AI-based smart home services; (2) the prior AI experience impacts the attitude toward AI-based smart home services via the perceived benefits of AI; and (3) the indirect effect of prior AI experience on attitude toward AI-based smart home services, via the perceived benefits of AI, significantly increases as digital self-efficacy increases from low to high level. Our findings provide important implications to enhance the attitude toward AI-based smart home services.

Heat dissipation testing for automobile LED lamp design is a crucial step to ensuring optimal lighting performance and extending product lifespan. We propose a machine learning-based real-time interactive system for assessing heat dissipation in LED lamp designs. Unlike traditional methods that require expertise in computational fluid dynamics (CFD), our system allows designers to directly evaluate whether their designs meet thermal requirements without specialized CFD knowledge. We designed an interactive system that enables real-time adjustments of design parameters, such as the number of LED diodes or the size of the heat dissipation plate, providing immediate feedback and optimization. It significantly reduces the production cycle by streamlining the design validation process, thereby enhancing manufacturing efficiency. By enabling rapid iteration and adaptation to market trends, the system improves business competitiveness in the automotive manufacturing and parts production industry. We conducted experimental validation that confirms that our method provides accurate thermal dissipation assessments at a fraction of the computational cost of conventional approaches. These findings highlight the potential of machine learning-driven design tools in accelerating innovation in the automotive manufacturing sector.

This study developed a real-time object detection and tracking system for an autonomous grenade launcher using frame differencing and adaptive thresholding. The system was designed to efficiently track moving objects in dynamic environments, focusing on human movement recognition. To assess its effectiveness, experiments were conducted by varying threshold values and analyzing their impact on detection accuracy. The results confirmed that a threshold of 30 optimally detected human movements while minimizing noise. Object detection experiments included analyzing detection results, cumulative motion visualizations, and object separation after background removal. The system achieved Precision 66.7%, Recall 88.9%, and F1 Score 76.2%, demonstrating reliable performance under general conditions. A comparison with a standard performance classification table further validated its accuracy. These findings suggest that the proposed method can be optimized for real-world applications requiring precise and robust object tracking.

Tactile sensing is fundamental to human interaction with the physical world, and its importance has grown significantly with advancements in robotics and artificial intelligence. To ensure precise and repeatable gesture execution, we integrated the sensor with a UR3 robotic manipulator. Additionally, validation tests were conducted in real-world environments, including outdoor rough surfaces, industrial products with diverse materials such as glass and wood. The linear gesture enabled fundamental assessment of tensile response, the S-shaped gesture examined the sensor’s capability to capture complex nonlinear deformations, and the circular gesture evaluated its sensitivity to continuous rotational movement. Experimental results demonstrated that each gesture produced unique deformation patterns, offering valuable insights into the sensor’s response characteristics. These findings contribute to the development of standardized methodologies for tactile sensing and provide a foundation for improving robotic tactile perception in diverse applications.

The purpose of this study is to analyze the purchase journey for consumers who purchase sportswear brands with omnichannel that can show cross-over shopping behavior such as webrooming, showrooming, and morooming. As a research method, first, through four sections (touch points) of McKinsey's consumer purchase decision-making journey and prior interviews, the contact points of the sportswear brand consumer's purchase journey were identified and confirmed through expert meetings. The travel map was constructed by classifying the experience factors that occur at the contact points for the confirmed purchase journey into expected value and CLUE factors according to their characteristics. The subjects of the study first conducted an in-depth interview with three consumers of sportswear brands, and then interviewed 20 consumers who had purchased sportswear brands with omnichannel established over the past year through a semi-structured questionnaire based on the performance of the first year's assignments and in-depth interviews. The consumer decision-making journey map was finally prepared, including the contact points, expected value, and CLUE factors according to the shopping journey. As a result of the study, the consumer purchase journey was divided into four primary stages: consideration, evaluation, purchase, and post-purchase experience. These stages were subsequently delineated into nine more granular steps, which were as follows: needs assessment, consideration of alternatives, active consideration, collection of specific information, decision on purchase method, purchase, confirmation and evaluation, sharing, and communication. In the purchase journey map, expected value, CLUE, and physical evidence for the purchase journey stage and touch point were presented, respectively, and expected value, CLUE factors, and physical evidence were presented.

The sportswear brand industry has more active multi-channels than other industries, and in recent years, it is more customer-oriented and is building omnichannels that value interconnectivity between channels. Therefore, this study intends to develop an experience scale according to the decision-making journey by applying qualitative and quantitative research methods to the four customer contacts suggested by McKinsey for consumers who use the omni-channel of sportswear brands that are more intensely developed after the COVID-19 pandemic. Eight steps were carried out to achieve the research topic's purpose, following a combination of the scale development methods by Churchill and the procedures outlined by Netmeyer, Bearden, and Sharma. In the first step, related prior studies were explored. In the second step, preliminary factors were extracted. In the third step, interviews with customers were conducted using a semi-structured questionnaire. Steps 4 and 5 involved conducting a preliminary survey and a main survey, respectively. With this data, exploratory factor analysis was performed in step 6. Step 7 involved confirmatory factor analysis. Finally, in step 8, predictive validity was verified through regression analysis between the developed scale and performance variables. Through this procedure, four factors were extracted: convenience, personalization, accessibility, and interaction. Accordingly, a total of 16 measurement items were derived. It is judged that this research on the scale development process can provide basic data on the consumer's purchase decision process. Additionally, the developed scale can be used for further research on more diverse shopping experiences.

Recently, an increasing number of Korean-Americans (KA) are living in China because of their studies or their parents' work. Immigration abroad can negatively affect their mental health as they face many practical challenges. When unstable mental health conditions are combined with rapid sexual changes in adolescence, a variety of sexually problematic behaviors can occur. The purpose of this study is to identify the differences in sexual knowledge, sexual attitudes, and sexual behavior of KAs living in China and Korea. The subjects were 59 KAs in Korea and 59 KAs attending international schools in China. Sexual knowledge (20 questions), sexual attitude (25 questions), and sexual behavior (25 questions) were used as research tools. To test the difference between the two groups, ANCOVA was performed using non-homogeneous variables as covariates. As a result of the study, there was no difference in the total score of sexual knowledge, sexual attitude, and sexual behavior between Korean and Chinese KAs (F=1.078, p=0.301). However, knowledge about pregnancy and childbirth was significantly higher in Korean KA than in Chinese KA (F=3.961, p=0.049). This is a result reflecting the fact that Korean adolescents are repeatedly learning about 'pregnancy and childbirth' in various subjects. Since adolescence is a time when preliminary parental education is necessary, it is necessary to provide knowledge and information about it to KAs living in China. Another difference was that Chinese KAs scored significantly higher on pornographic sexual behavior than Korean KAs (F=6.626, p=0.011). This is thought to be because KAs residing in China mainly obtain sexual knowledge through the Internet and can easily access pornography through the Internet. Based on the research results, it is proposed to provide media education to KAs living in China so that they can correctly judge and select sexual content they encounter on the Internet.

This study focuses on the influences of urban art expressions in modern 3D animation, analyzing <Teenage Mutant Ninja Turtles: Mutant Mayhem> as a case study. Examining the film’s aesthetic choices, environment design, characters, and animation. And reveals the contribution of non-realistic animation and stylized animation to the expression of urban art by blending graffiti, street art motifs, and doodle drawings. In this paper, we highlight the contribution of TMNT: Mutant Mayhem's unique visual narrative and NPR's ability to convey innovative visual concepts of urban representation in an innovative visual narrative in an animated film.

Music plays a crucial role in expressing and inducing emotions. In broadcasting, background music is a vital element that shapes the atmosphere and narrative of programs, enriching viewer experiences. In this paper, we present the various roles of background music in broadcasting. We discuss its contributions to eliciting emotional responses, enhancing narratives, setting moods, establishing time and place, capturing viewer attention, and facilitating scene transitions. With specific examples from various genres, we show how such musical choices contribute to the overall success of programs. Furthermore, we present factors to consider in the selection and production of background music, aiming to help broadcasters utilize it more effectively. By enhancing the quality of broadcast content and providing deeper emotional impact to viewers, this exploration of strategic use and importance of musical selection in broadcasting underscores its cultural and societal impacts. We expect the multifaceted roles of background music in eliciting emotional and cognitive responses, reinforcing thematic content, and maximizing communication effectiveness in broadcasting.

In this paper, we examine a state of a performer’s neutrality with a focus on rethinking and articulating key terms, concepts, and practical assumptions relevant to the thinking and approach of theatre practitioners and directors. To begin with, we examine the central value of a performer’s neutrality by addressing the key training sources with principles including the performer’s suppleness, openness, and softness. Particularly, this research engages in addressing common problematic issues among those inexperienced or studentperformers specifically whose difficulty through acting and training is defined as an effect of his/her inner conflict due to maintaining his/her familiarity or superfluous traits. In this manner, we argue that the performer’s body is far from interconnected unity by means of the opposite of essential energy on stage. To study on the issues and answer the questions addressed the thesis, we explore a performer’s creative state as an ideal state from those practitioners’/directors’ concepts and approaches including Lao-tzu’s concept of ‘nothing but everything’ centered on the notion of becoming true suppleness/softness. We then move on discussing the performer’s optimal bodily experiences (flow or flow experience) in articulating Mihaly Csikszentmihalyi’s concept of flow, and both Phillip Zarrilli and Eugenio Barba’s central concept of acing/ performer training with their practical thoughts. As a consequence of this, the research findings underline the importance of the performer’s own creative experience in order to lead his/her body to the precise structure for each training/discipline session. We call it the performer’s own authentic journey towards developing and creating new possibilities. In other words, as the performer acknowledges and allows his/her body through an ongoing process in a sense of ease as well as sustaining his/her substance, the performer’s body also becomes a whole unity where the dialectical terms or elements are transcended and/or bodymind dualism/dichotomy disappeared. Here, we argue that a state of the performer’s neutrality/flexibility connotes his/her body is inspired by the performer’s own creative choice simultaneously the body is also fully grounded in a series of every tiny moment on stage. That is, the performer’s body works in a state of his/her own creativity as well as inspiration, namely, the deepest dimension of acting and the highest level of performance.

The rapid advancement of generative AI technologies is transforming the field of package design, enabling the quick creation of diverse design options. This study aims to develop a systematic prompt engineering methodology for the effective utilization of generative AI in package design. The research focuses on key design elements, including form, color, typography, and material, with the goal of deriving optimized prompt patterns. By applying the derived prompt patterns to real-world package design projects, the study evaluates their effectiveness and identifies areas for improvement. The research methodology also involves a literature review and case studies to establish a theoretical foundation and explore effective prompt patterns. The study presents a framework for translating traditional packaging design principles into AI-readable prompts, demonstrating the potential of prompt engineering as a bridge between human creativity and AI capabilities. This research advances the integration of generative AI into the design field, broadening its applicability and enhancing overall efficiency. The study acknowledges its limitations and highlights the need for future research to refine prompts and expand the framework to address the evolving needs of the packaging industry.

With the growing reliance on deep learning-based image compression techniques, ensuring the security of compressed image data has become increasingly important. Traditional encryption methods operate directly on raw image pixels, often resulting in high computational costs and inefficiencies in real-time applications. In this paper, we propose a lightweight encryption method for securing compressed images within an autoencoder-based framework. Instead of encrypting the image itself, our approach focuses on shuffling the latent tensor using a randomly generated mixing order, which is then encrypted as the key. This method significantly reduces the size of encrypted data while maintaining strong security. Our experiments, conducted on the CIFAR100 dataset, demonstrate that even a few random mixing operations make the latent tensor and the decoded image unreadable, preventing unauthorized reconstruction of the original image. Moreover, the proposed method achieves substantial computational efficiency compared to conventional encryption methods such as ChaCha20, making it particularly suitable for time-sensitive applications, including real-time drone image transmission and surveillance.

This study examines Namyang Dairy’s crisis management strategies following its 2013 scandal, analyzing its internal and external business environment. Using crisis management frameworks, it identifies the company's reactive approach and ineffective communication. Despite challenges, Namyang Dairy's strengths in premium product manufacturing, distribution, and branding, including its 2014 Baekmidang expansion, offer recovery potential. To achieve sustainable growth, proactive crisis management, CSR initiatives, and brand rehabilitation are essential. With this study, I contribute to the understanding of crisis management strategies in consumer-facing industries by examining Namyang Dairy's Case. This case highlights the importance of risk management and adaptive strategies in consumer-sensitive industries.

This study aims to develop a digital literacy-based model for university English teaching and learning to enhance learners' competencies, including communication, critical thinking and collaboration. The study designed a teaching-learning model that focuses on communication-oriented integrated English education. The model was designed to fit the concept of digital literacy. The teaching-learning model of this study presented the types of activities and the roles of learners and teachers according to the approach, method, and seven procedures. This study contributse to the effective integration of technology in digital educational environments and fosters research on its application in English education in the AI era.

Despite advances in Natural Language Processing (NLP) and Large Language Models (LLMs), developing a CBT chatbot that provides emotional support and fosters continuous engagement remains a challenging task. To address this, we proposed SupportlyChat, which is a fine-tuned LLaMA with a custom dataset and provides emotion analysis visualization. We curated the custom dataset by integrating mental health conversation dataset and HappyDB dataset which collects people’s happy moments. We conducted a user study with 10 participants using conditions including standard chatbot responses, emotion analysis reports with standard responses, and emotion analysis reports with empathetic responses. The findings indicate that participants found empathetic responses helpful in shifting towards positive thinking with our proposed method. Additionally, they preferred emotional visualization for its ability to enhance professionalism and recognize inherent emotions. Based on these findings, we suggest utilizing empathetic responses in psychological counseling to emotionally support clients and using emotion analysis reports to promote continuous engagement.

This study investigates the application of Polyvagal Theory (PVT) in improving stress management and resilience in individuals and communities, specifically focusing on its relevance to modern Korean society. Developed by Stephen W. Porges, PVT emphasizes the ventral vagal pathway (VVP) as a core physiological mechanism for promoting safety and emotional regulation. This research addresses pressing psychological and social issues in Korea, including high suicide rates, low social trust, and diminished life satisfaction, through the lens of PVT. Integrating insights from attachment theory, Lazarus' stress theory, and Maslow's hierarchy of needs, the study provides a multidimensional framework for understanding the relationship between safety, behavior, and stress responses. Practical recommendations such as mindfulness-based interventions, family-oriented stress management programs, and community-focused resilience initiatives are proposed to foster individual and collective well-being. Highlighting the interplay between physiological and psychological safety, this study offers actionable strategies to advance social cohesion and sustainable development in Korea while identifying areas for future research and application.

We investigated the pyrolysis of Acetal Resin (POM), Polyethylene (PE), and Polypropylene (PP) at 260.5°C and 290°C to assess the release of toxic byproducts as plastic waste production and disposal continue to rise. Using Gas Chromatography techniques, including Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Flame Ionization Detection (GC-FID), we analyzed the emitted compounds. We calculated concentration ratios, revealing that in the GC-MS analysis, POM had the highest mean ratio of 3.3785, followed by PE at 0.4543 and PP at 2.4851. In the FID analysis, POM had the highest mean ratio of 1.3782, with PE at 0.8099 and PP at 1.5527. Our results demonstrate that temperature significantly influences the types and concentrations of released substances, with certain hazardous compounds showing a marked increase at higher temperatures. This study provides valuable insights into optimizing pyrolysis conditions and minimizing environmental risks associated with plastic waste processing. Future research will further enhance pyrolysis methodologies and contribute to the development of regulatory guidelines for safer applications.

The shape of the plasma jet by the dielectric potential barrier discharge method varies depending on the applied flow rate and the size of the electric field, and this change is represented by the difference in the spectral distribution due to the difference in density of the DBD plasma jet. The difference between the generation and intensity of active species through the analysis of the spectrum of the generated plasma jet is an important factor in utilizing the device. In this paper, a prototype of an atmospheric pressure volume DBD type plasma jet generator injected with Ar gas was designed and manufactured according to the proposed design method. In order to generate the optimized shape of the plasma jet, the optimum plasma jet shape was generated while sequentially increasing the applied power, and in order to analyze the characteristics of the generated plasma jet, the characteristics of the plasma jet using a spectrometer were analyzed. The optimization design technology of the plasma jet generator according to bio-application was verified by confirming the method of establishing an optimal plasma jet shape in the plasma jet generator through the design method of the proposed system and the results of active species on EOS.