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This study examines public discourse and engagement with artificial intelligence (AI) on Korean social media following ChatGPT's release. Using corpus analysis to focus on Korean-language interactions, this research identifies key themes and public perceptions that have developed around AI. The findings show a diverse range of sentiments—from enthusiasm to skepticism—that highlight ChatGPT’s influence on AIrelated conversations. Terms like "ChatGPT" and "AI ethics" reflect both curiosity and concerns about AI’s role in society. A deeper analysis explores topics such as anthropomorphism, ethics, AI capabilities, and societal impact. This study also reveals a mix of attitudes: while many appreciate AI’s potential, concerns remain around job displacement, privacy, and control. Metaphors portraying AI as both a helpful tool and a potential risk illustrate these complex views. This research emphasizes the importance of Korean public perspectives in guiding ethical AI development, ensuring technology aligns with societal values. As AI continues to evolve, further research will be crucial to understanding how public engagement and perceptions shift within the Korean context.

This study proposes a method for developing an integrated service system for implementing a pet plant management service that combines reality and virtuality using digital twin technology. Data on the plant's temperature, humidity, light intensity, and soil moisture were collected through a client system based on Raspberry Pi. In addition, environmental controls such as lighting and watering were implemented based on this data. The software was implemented as a web application, enhancing the efficiency of development and maintenance. Data synchronization with the server was achieved using the HTTP REST method, and real-time bidirectional data transfer issues were resolved through SSH tunneling. From the user's perspective, generative AI was used to create virtual avatars based on photos of plants taken by the users, enhancing emotional interaction with the users through personified avatars. However, the creation and personification of avatars using existing generative AI services have limitations in terms of consistency and user-customized interactions. Therefore, further research is needed to address this issue by utilizing user data for more sophisticated personification and interaction.

Drawing on theories from personality psychology and motivation, this study examined the acceptance of generative Artificial Intelligence (AI) technology. It analyzed the impact of demographic characteristics, usage level of generative AI, user personality traits, and motivation on trust, satisfaction, and intention to use (including willingness to pay) for the service. An online survey was conducted targeting users with experience in generative AI (N = 400). The results indicated that functional motivation directly influenced trust, satisfaction, and intention to use when the AI service was both free and paid. Hedonic motivation affected trust, satisfaction, and intention to use when the AI service was free and significantly influenced intention to use when it was paid, only when mediated by trust. Frequency of use directly impacted both intentions. Among personality factors, agreeableness, conscientiousness, and extraversion impacted the intention to use AI, both paid and free, when mediated by trust. Satisfaction directly influenced the intention to use when the AI service was free but did not exhibit direct or indirect effects on the intention to use for paid AI services. Conclusion: These findings present valuable insights for researchers and practitioners, aiding in understanding individual acceptance of generative AI technology based on personal characteristics and suggesting practical implications for marketing strategies, such as the development of generative AI features and personalized messages.

Semi-supervised learning (SSL) has shown great promise in utilizing both labeled and unlabeled data, especially using consistency regularization To enhance the model's learning efficiency, we proposed a dualbranch co-training approach. After dividing the training data into two subsets, each branch utilizes a teacherstudent model pair, where the teacher's weights are updated via an exponential moving average (EMA). The framework combines supervised loss and unsupervised loss to optimize the model. Upon label expansion (e.g pseudo labeling), an additional otherside view is introduced, promoting agreement between the branches' predictions on shared data. This loss mitigates errors arising from incorrect pseudo-labels and enhances the overall robustness of the training process. By dynamically adjusting pseudo-label inclusion based on confidence thresholds, our methodology reduces the impact of noisy data and prevents overfitting. As a result, we could demonstrate the effectiveness of the proposed method in leveraging unlabeled data while maintaining high performance.

The international community is increasingly strengthening carbon emission regulations to achieve the 2050 carbon neutrality goal. In this context, telecommunication companies are also expected to face inevitable economic costs due to carbon emissions trading. While telecommunication companies contribute to indirect carbon reductions by providing ICT solutions that lower emissions in various sectors, current carbon regulations do not account for these contributions. We study quantitatively measures the contributions of telecommunication companies to carbon reduction in South Korea and proposes a framework to calculate their net carbon emissions. The quantification of avoided emissions revealed that annual avoided emissions enabled by ICT solutions across various industries amount to approximately 23.8 million MtCO2e, which is about 2.8 times higher than the combined emissions of South Korea’s three major telecommunication companies. We aim to re-evaluate the contributions of telecommunication companies to carbon reduction and suggest a desirable direction for improving future carbon-related regulations.

Objectives: This study aimed to identify evidence from a systematic review for managing urinary incontinence and promoting continence using conservative behavioral approaches among older adults with cognitive impairment or dementia. Methods: Design is the literature review with narrative synthesis. Data sources are electronic searches of published clinical interventions in Korean and English from 1990 to 2023 using KoreaMed. The review method is the PRISMA statement, which was followed, and the method for systematic review was established. Results: We found three trials focused on one on prompted voiding and two pelvic floor muscle training combined with or without other muscle strengthening strategies for older adults in care homes and patients with cognitive impairment. Reductions in the number of incontinence episodes and urgency were reported in each trial. Conclusions: We found that each trial has the advantages for urinary incontinence with cognitive impairment in older persons. In this systematic review, there are only three conservative behavioral intervention studies for urinary incontinence management in Korea, so there is a limitation in concluding the intervention effects.

The purpose of this study is to investigate the structural relationship between e-sports and the user's selfimage, e-sports attitude, continuous use intention, and Continuous use Intention. Therefore, the subject of this study was a total of 300 university students who used the games of e-sports events in Korea. Among the collected data, 288 copies, excluding 12 copies of data that are difficult to use, were used by adopting the final analysis data. Data processing was performed using SPSS 27 and AMOS 21. Confirmatory factor analysis was performed to verify the centralized validity and discriminant validity of the measurement items, and the hypothesis was verified through structural equation model analysis. We obtained the following results through hypothesis testing. First, among the image congruence factors, the real self-image and the ideal self-image were found to have a positive effect on the e-sports attitude. Second, it was found that the e-sports attitude had a positive effect on the continuous use intention. Third, it was found that the e-sports attitude had a positive effect on Continuous use Intention. Fourth, Continuous use intent was found to have a significant effect on the Continuous use Intention. Through this result, it was found that the self-image of e-sports users plays an important role in forming the e-sports attitude, and ultimately affects the Continuous Use Intention and the Referral Intention.

In this study, we analyze the service discovery protocol problem in a smart home environment and propose a deep learning-based prediction model. Currently, the service discovery protocols used in smart homes are divided into IP-based and non-IP-based, and there are limitations in service discovery owing to the interoperability issues between them. Although smart gateways support protocol conversion between heterogeneous networks, there is a limitation that smooth service discovery between smart home devices is difficult due to the lack of advertising and broadcasting functions for service discovery. To solve this problem, we propose a deep learning-based prediction model utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The experimental results show that the proposed model can accurately predict future service demands by learning various service request patterns and enables faster and more efficient service discovery than existing protocols. The proposed model has been shown to improve the interoperability between devices in a smart home environment that changes in real-time and significantly reduces the service discovery time.

This research focuses on the design of AI-driven virtual teachers to promote the development of personalized and intelligent education. In the post-pandemic era, the demand for online education has surged, but traditional virtual teacher products have limitations in responding to personalized needs and real-time knowledge updates. To address this, this paper proposes an innovative virtual teacher information model that combines technologies such as natural language processing, computer vision, and knowledge graphs. This model supports adaptive teaching, provides personalized learning experiences, and interacts with students through automatic knowledge updates. Through comparative analysis and technical analysis of virtual teachers, this paper demonstrates the advantages of AI-driven virtual teachers in aspects such as interactivity, knowledge base, emotional computing, and cost-effectiveness. Research results show that virtual teachers not only achieve significant results in knowledge updates, personalized teaching, and emotional interaction but also provide a viable path for educational technology innovation. Future research and technological development can further enhance the potential of virtual teachers in educational equity and learning efficiency.

This paper analyzes the development status of mobile identity cards and self-sovereign identity (DID) technology both domestically and internationally, and examines case studies from major countries that have implemented these technologies. It reviews the adoption status of mobile identity cards in South Korea, including use cases in both the public and private sectors, and compares these with digital identity policies and applications in key countries worldwide, including the EU, the United States, Australia, and Asia. The paper also explores the multifaceted impact of digital identity and DID technology on the reliability, universality, convenience, and security of identification services that are based on alternatives to the national resident registration number. Consequently, this study proposes strategies for the stable implementation of digital identity and DID technology for authentication services.

In contemporary concept design workflows, character design often relies on static visual forms, such as model sheets and orthographic views. These methods frequently make it challenging for team members, other than the original designer, to fully grasp the emotional and dynamic aspects of a character. Consequently, significant time is required in subsequent design stages to align the character’s feel and features across the team. To address this issue, this study introduces an improved workflow—Technical Concept Design—which leverages rapidly advancing generative AI technologies to provide AI-driven generative support for designers. Based on the typology of design knowledge and the concept of evolutionary creativity, this research provides project examples demonstrating the potential of generative AI in character design. We delves into the application of AI tools in technical concept design and proposes potential research opportunities. The study aims to optimize design workflows, enhance efficiency, and elevate creative outcomes. The results indicate that technical concept design has broad application prospects in fields such as video games, films and animation . Furthermore, it significantly improves human-computer interaction and team collaboration, offering both theoretical insights and practical guidance for the future of the design discipline.

This study investigates the impact of AI chatbot interactions on elementary English learners’ willingness to communicate (WTC), confidence, and anxiety. Conducted with 23 fifth-grade EFL students in South Korea, the research utilized Replika, a text-based chatbot, over a 14-week intervention. Results showed significant improvements in learners' confidence and reductions in anxiety, highlighting the chatbot's potential to create an affectively supportive and engaging environment. However, there was no statistical significance in WTC. Qualitative data indicated that the gamified features motivated learners, while challenges included difficulties in understanding complex chatbot responses. Pedagogical implications emphasize the importance of providing modified and comprehensible input, teacher scaffolding, and multimodal interaction formats to enhance learning outcomes. The findings suggest that while AI chatbots can positively influence affective factors, further research is needed to explore their impact on oral communication and linguistic proficiency.

This study reviews the recent literature of which topic related to the application of machine translation (MT) tools in English education. 26 studies published between 2012 and 2023 are selected and analyzed based on the following criteria: publication years, target learners, language skills, effect variables, educational utilization, types of translation tools, and research methodologies. The key findings reveal that MT tools, such as Google Translate and Naver Papago, are frequently used for reading and writing. While the majority MT studies have involved tertiary-level learners' use and perceptions in MT, little attention has been paid to those of young learners. As for publication years, the result indicates three phases such as early adoption, expansion, and refinement, showing increased integration of AI-based tools into the more recent studies. The study also highlights recent trends regarding the cognitive and affective benefits of MT tools, alongside challenges such as over-reliance and cultural and textual inaccuracies. Implications for optimizing MT tools in English education will be discussed.

We purposed to propose a method to analyze the movement during whole-body exercise using a 2D camera. Specifically, we filmed the participants performing a specific whole body movement in front of the 2D camera in a normal state and a Pseudo-Elderly state that imitated the posture of older adults. We used the posture analysis algorithm YOLO to obtain two-dimensional spatial coordinate information for 16 parts of the body and perform a movement analysis for the two states. Consequently, we were unable to identify any significant differences between young people and the Pseudo-Elderly in terms of the total amount of differences in the coordinates of the 𝑥-axis (lateral movement) and 𝑦-axis (front-back movement) for the 16 parts of the body. However, we could identify significant differences between young people and the Pseudo-Elderly by analyzing each part of the body, such as the head and ankle. Since we suppose that the method proposed in this study can analyze the movement state of whole body movements using a 2D camera that is usually used in daily life, we hope to apply it to a system for monitoring and improving the movement of older adults at home and a system for early detection of diseases with movement disorders in the future.

This study introduces a novel ontology matching model designed to address ontology heterogeneity by leveraging both textual and structural information within ontologies, alongside external data. The model employs a word embedding approach to refine word vectors for enhanced discrimination between semantically similar and associative descriptions. Additionally, it adopts BERT for generating dynamic word vectors, enabling the nuanced distinction of polysemous terms. Our model calculates structural similarity by transforming ontologies into graph structures and applying the SimRank algorithm to calculate the entities' structural similarity within these graphs. The matching process employs a stable matching algorithm to secure stable one-to-one correspondences, while one-to-many matches are determined through similarity thresholds and comparative analysis

In this paper, we propose a highly secure technique for hiding confidential data in the lower bits of image pixels using multiple encryption methods. In the proposed technique, in order to preserve the characteristics of the boundary where the image outline exists, 1 bit of confidential data encrypted multiple times is hidden in the LSB (Least Significant Bit) of the pixel existing on the boundary. In the pixels existing in the flat surface where the pixel value of the image has little change, 2 bits of confidential data encrypted multiple times are variably hidden in the lower 2 bits including the LSB. When the encrypted confidential data is hidden by applying the proposed technique, the image quality of the stego image is up to 49.69 dB, and the amount of confidential data hidden is increased by up to 92.2% compared to the existing LSB method. If there are no encryption key values, the encrypted confidential data hidden in the stego image cannot be extracted. Even if the confidential data encrypted multiple times is extracted from the stego image, the encrypted confidential data cannot be decrypted without the encryption key values, so the security of the confidential data hidden in the stego image is maintained very strongly. The proposed technique can be effectively used to hide copyrightrelated confidential information in general commercial images such as webtoons, where reversible confidential data hiding techniques do not need to be applied.

The smart order and kiosk industry has rapidly advanced since the COVID-19 pandemic. This study aims to analyze user experiences and perceptions of smart orders and kiosks by leveraging big data and surveys. Through this analysis, the study identifies positive and negative aspects, user inconveniences, and improvement strategies, ultimately contributing to future research and kiosk development. Data collection was conducted using TEXTOM, covering a three-year period from January 2021 to January 2024, when contactless services became increasingly prevalent due to the pandemic. We set the keywords "smart order" and "kiosk" for data gathering. Analysis was performed using ConCor, revealing eight distinct clusters, and we identified "Digital Self-Service Innovation" as the core keyword. This research is significant in presenting challenges and improvement strategies for kiosk development and smart order services.

The Korean meal kit industry is also rapidly developing in line with this global trend. Korean meal kits are starting to attract attention not only from domestic consumers but also from overseas, securing competitiveness in the global market. In particular, the possibility of exporting Korean meal kits is gradually expanding as interest in Korean food increases due to the Korean Wave. Overseas consumers are giving positive reviews on the unique taste and healthy ingredients of Korean food, which are further strengthened by easy access through meal kits. In conclusion, as shown by big data analysis, Korean food is more than just food and has a strong attraction to consumers around the world. The global meal kit industry is paying attention to this trend, and we expect that through the Korean meal kit, we will be able to achieve industrial growth along with the globalization of Korean food.

Two-sided markets, often referred to as platforms, are becoming increasingly prevalent across industries such as digital technology, financial services, and retail. These platforms create value by facilitating interactions between distinct user groups, leveraging cross-side network effects, strategic pricing, and user experience optimization. We explores these mechanisms through qualitative and quantitative analyses of successful platforms like Uber and Airbnb. Our Findings reveal that strong network effects drive growth, strategic pricing attracts and retains users, and seamless user experiences foster trust and engagement. We highlights the importance of balancing these elements to sustain competitive advantage in the platform economy.

The entire process of modeling university buildings in 3D and visualizing holograms based on the data is covered in this study through photogrammetry technology using drones. After taking images of the school buildings from various angles using the DJI Mini 4 Pro drone, the high-resolution 3D model was constructed using Agisoft Metashape software. Afterward, the Unreal engine was used to extract multi-view images from various angles and output them in a three-dimensional form through the hologram printer. This study comprehensively covers all the steps from drone-based image collection, photogrammetry, 3D modeling, visualization, and hologram printing, and discusses in depth the impact of this technology on architecture and design. In particular, the innovative visualization technique combining drones and holograms has the potential to be utilized as an important tool not only in architectural visualization but also in education and training, design review, and industrial applications. Furthermore, this study suggests the expandability of this technology and explores directions in which it can be applied in various industrial fields.

The virus that spreads through the air continues to perplex civilization. Distancing is one way we are attempting to stop transmission, but it is still insufficient to control many populated places, including big shopping malls. The purpose of the study was to operate the quarantine support system in such densely populated locations in order to stop the virus from spreading. An IoT-based service that uses the smartphone’s MAC address to measure crowd density is designed and implemented in this study as a quarantine support system. It uses LEDs to visually remind users of the crowd density, or it uses a ventilation fan to change the direction and flow of air. By running the quarantine support system in densely populated regions, this technology can stop the virus from spreading. It can also avoid crowd-crowded accidents beforehand by precisely estimating the level of risk that crowd density poses.

In this study we explore the integration of Large Language Models (LLMs), specifically GPT-4o-mini, with traditional stock market technical indicators—MACD, RSI, and Bollinger Bands—to enhance stock market prediction and decision-making frameworks. By combining quantitative analysis with qualitative insights generated by LLMs, this research demonstrates how artificial intelligence can improve the interpretability and predictive accuracy of technical trading strategies. Historical data for Tesla and Palantir, spanning six months, was analyzed with indicators calculated to identify market trends and anomalies. LLM integration provided contextual narratives that complemented technical signals, enhancing interpretability for investors. The performance evaluation revealed significant improvements in risk-adjusted returns, alpha generation, and prediction accuracy when LLM insights were incorporated into trading strategies. Key contributions include a novel methodology for structuring indicator outputs for LLM analysis, scalability across diverse stocks, and the potential for democratizing access to advanced financial analytics. Challenges such as computational complexity, data sensitivity, and the dynamic nature of financial markets are discussed, alongside opportunities for real-time adaptive models and expanded indicator integration. This research highlights the transformative potential of LLM-augmented technical analysis and offers a foundation for future innovations in AI-driven financial decision-making.

Today's leadership research is not limited to the positive aspect. There is a need to verify the negative influence of leadership and manage the phenomenon of decreased organizational performance, such as decreased innovative behavior, that may occur as a result. A representative study on the negative influence of leadership is toxic leadership. Toxic leadership involves the following behaviors: 1) egocentrism, 2) negative mood, 3) unappreciation, 4) instability and uncertainty, and 5) autocratic management behavior. Toxic leadership has a negative impact on organizational performance. This study explains how the sub-dimensions of toxic leadership reduce organizational performance-related factors such as innovative behavior. This paper proves that toxic leadership has a negative impact on innovative behavior by influencing the behaviors of members within the organization such as promotive voice and prohibitive voice. As a result, it was proven that toxic leadership has a negative impact on innovative behavior by reducing promotive voice and influencing prohibitive voice. The role of leadership in an organization is important. If leaders do not exert negative influences such as toxic leadership, the participation and voice of members will increase, and innovative behavior will also increase. The significance of this study is that it explains the types of leadership that leaders should avoid and seeks ways to improve organizational performance. Leaders should avoid negative leadership such as toxic leadership and create a more positive voice for members in the organization. They should recognize that such efforts can lead to improved organizational performance such as innovative behavior.

Middle-aged women are at a stage where immune function tends to decline, making it essential to develop programs that strengthen the immune system and maintain health. While forest environments have become an effective exercise venue for modern health management, studies on the effects of exercise in forest environments on improving immune function have shown inconsistent results. Therefore, this study analyzed the effects of forest walking exercise on immune function in middle-aged women. Seven middle-aged women participated in a 10-week forest walking exercise program, conducted twice a week, followed by blood analysis. The exercise intensity was progressively increased based on the Borg Scale of Perceived Exertion. Blood analysis was performed using indicators to evaluate immune cell activity, and the results were as follows: A significant improvement was observed in NK cell activity, while no significant changes were found in T-cells, B-cells, neutrophils, lymphocytes, monocytes, eosinophils, or basophils. These findings suggest that exercise in forest environments has a greater impact on innate immune cells, such as NK cells. It is also believed that physical activity conducted in a forest environment may have contributed to the enhancement of NK cell activity. Future studies should focus on long-term research and program development considering various aspects of the immune system.

In service-oriented industries such as fitness centers, the role of employees is crucial. Consequently, there is a growing interest in whether employees develop competencies through professional experience. As a result, employee experience management has garnered increasing attention. Employee experience management recognizes that managing employee experiences is as essential as managing customer experiences in creating differentiation. The key lies in providing positive employee experiences to enhance engagement and foster passion, enabling employees to deliver extraordinary customer experiences. This study focuses on employee in fitness centers, aiming to explore their professional experiences shaped by the employee life cycle, organizational processes, and interactions with colleagues and customers. Through narrative exploration, the study seeks to develop a reliable employee experience scale. The objective of ‘Research Subject 1’ was to gain an in-depth understanding of individual experiences of fitness center employees through narrative exploration. As a result, in-depth interviews were conducted with nine fitness center instructors, and the employee life cycle was divided into six stages. Positive and negative experiences at each stage were identified. Additionally, from the perspective of employee experience management, experiences were categorized into cultural experience, physical experience, and technological experience, and a content analysis was conducted. Based on the findings from these in-depth interviews, expert panel discussions were held, resulting in the development of 24 measurement items across three dimensions. The objective of ‘Research Subject 2’ was to explore the segments, structures, and attributes of professional experiences identified through the narrative exploration process. Following this, the study aimed to propose a validated and reliable scale for measuring professional employee experience. The study referenced domestic and international scale development procedures. First, exploratory factor analysis(EFA) and reliability analysis were conducted based on the findings of Research Topic 1 to refine the scale. Next, confirmatory factor analysis(CFA) and reliability analysis were performed. Finally, predictive validity was verified through multiple regression analysis, using organizational trust as the dependent variable. Through these procedures, a scale for employee experience was developed, comprising 18 items across three dimensions.

Service companies such as fitness centers are industries where the capabilities of employees are very important. This is because they respond to customers at the point of contact with customers who use the fitness center. Therefore, in order to strengthen the capabilities of employees are very important. This is because they respond to customers at, employees are attracting attention as a human resource(HR) management method in which occupational experience management is important in the workplace. Thus, this study aims to empirically analyze a structural equation model(SEM) that sets employee experience in fitness centers as exogenous variables and job satisfaction, organizational commitment, organizational trust, and customer orientation as endogenous variables.The sample included fitness center staff, 250 people were selected by convenience and snowball sampling, and 226 people were used for the final analysis. Data were subjected to frequency and reliability analysis(Cronbach α) using SPSS 28. SEM analysis at AMOS 21 followed a two-step approach: confirmatory factor analysis(CFA) and path analysis to verify model fit and hypothesis. In the case of Hypothesis 1 (employee experience and job satisfaction), technical experience had no significant effect on job satisfaction, whereas organizational culture experience and organizational support experience had a positive effect, leading to partial acceptance. Hypothesis 2, 3, and 4 (the influence of job satisfaction on organizational commitment, organizational trust, and customer orientation) all showed positive effects and were fully supported. In the case of Hypothesis 5 (employee experience and organizational commitment), the organizational support experience had a positive effect on organizational commitment, leading to partial acceptance. Hypothesis 6 (employee experience and organizational trust) showed that organizational culture experience and technical experience positively influence organizational trust, leading to partial acceptance. Finally, Hypothesis 7 (employee experience and customer orientation) found that only organizational support experience positively influenced customer orientation, leading to partial acceptance.

This study examines what changes are made in investors' decision-making when the P2P lending platform provides additional credit scoring information along the way. It is found that the newly adopted external credit scoring information mitigates investors' risk avoidance tendency by reinforcing, not replacing, the internal credit scoring information and consequently by diminishing the relative importance of terms of loan in investors’ funding decision-making. This provides an empirical evidence for how the risk-averseness of investors to fully take on the credit risk of a borrower is mitigated when a credit scoring system provided by a credible external body is newly introduced. The results of this study can also provide realistic instructions to participants in P2P lending markets by identifying the relative importance of factors beyond merely a discussion of their statistical significance.

Technological innovation activities of a company drive product innovation, which enhances the company's competitiveness and facilitates sustainable management. This study analyzed the impact of technological innovation activities on ESG performance. The results of the study are as follows. First, technological innovation activities were found to have a significant positive (+) impact on a company's ESG performance. Second, among the technological innovation activities, R&D expenditures treated as costs were shown to have a significant positive (+) influence on ESG performance. This result can be attributed to the much higher proportion of R&D investments treated as costs compared to those treated as intangible assets. Third, it was found that companies with a higher foreign ownership ratio and larger firm size showed a significant positive (+) impact of technological innovation activities on ESG performance. This suggests that larger companies are more efficient in managing technological innovation activities. In conclusion, IT companies actively engaged in technological innovation can secure competitiveness, improve business performance, and ultimately enhance their ESG outcomes. Therefore, it is evident that continuous technological innovation activities are necessary to maintain the competitiveness of the IT industry. Furthermore, this study is expected to contribute to IT companies maintaining their competitiveness and achieving sustainable management through technological innovation activities.

We are designed to investigate gender-specific differences in muscle activation around the knee joint during squat jumps and single-leg squat jumps. The vastus medialis (VM), vastus lateralis (VL), biceps femoris, and semitendinosus muscle activities of 11 males and 9 females were measured using electromyography (EMG). Results revealed that males had significantly higher VM:VL and hamstring-to-quadriceps (H:Q) ratios than females across all conditions. Females exhibited reduced VM activation, as indicated by lower VM:VL ratios, and a greater reliance on quadriceps activation, as shown by lower H:Q ratios, particularly during single-leg squats. Anatomical factors, such as an increased Q-angle in females, contribute to muscle imbalances, compromising knee joint stability and increasing the risk of injuries like anterior cruciate ligament (ACL) tears. These findings highlight the need for targeted training, including hamstring strengthening and VM activation, for female athletes to improve knee stability and reduce injury risks. Future research should focus on long-term muscle activation patterns across diverse sports scenarios.

We are designed to analyze the effects of forest walking and aquatic recovery on electroencephalography (EEG) and heart rate variability (HRV) to determine the physiological and psychological impacts of aquatic recovery. Ten adults aged 20 years and older participated in the study, which was conducted under two conditions: forest walking alone and forest walking followed by aquatic recovery. EEG and HRV were measured before and after each condition. The results showed that forest walking significantly increased Delta and Theta waves, indicating a state of relaxation, and these effects were further enhanced by aquatic recovery. However, increases in Beta, SMR, and Gamma waves during aquatic recovery suggested heightened arousal and stress due to increased exercise intensity and duration. HRV analysis revealed that forest walking positively influenced parasympathetic activation and autonomic balance, whereas aquatic recovery acted as an additional physical load, limiting its recovery potential. This study suggests that static recovery or lowintensity dynamic recovery may be more effective in maximizing relaxation effects after forest walking. Future research should consider various recovery intensities and environments to develop and validate optimal recovery strategies.