This issue of the Korean Journal of Aerospace and Environmental Medicine (Vol.36, No. 1) presents five articles addressing emerging topics in aerospace medicine, aeromedical evaluation, and space medicine policy. An original article proposes a neuro-adaptive cockpit communication support system integrating artificial intelligence and neurophysiological monitoring to reduce pilot cognitive workload and enhance situational awareness. Another original study examines the prevalence and aeromedical implications of cardiac conduction abnormalities among Korean Air Force pilots, providing evidence for risk-based surveillance strategies in aeromedical screening. A review article discusses the clinical value of the video head impulse test for quantitative assessment of vestibular function in pilots exposed to high-acceleration flight environments. Another review revisits the historical development and current relevance of the “1% rule,” a widely used framework for aeromedical risk assessment in aviation safety. Finally, a brief communication summarizes a panel discussion on the future development of space medicine in Korea, emphasizing interdisciplinary collaboration and national research governance.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study investigates the relationship between self-efficacy and teammember exchange (TMX) among aviation service students, and examines how these factors influence academic achievement and collaborative behavior. Self-efficacy, based on Bandura’s Social Cognitive Theory, is defined as an individual’s belief in their ability to overcome challenges, while TMX reflects the quality of social exchanges among team members. Methods: A convenience sample of undergraduate students from an aviation service department was recruited, yielding 65 valid responses. Self-efficacy was measured using the New General Self-Efficacy Scale along with additional validated items, and TMX was assessed with a TMX-10 scale, both utilizing a 5-point Likert scale. Data analysis included descriptive statistics, K-means clustering to identify behavioral segments, ANOVA for group comparisons, and regression analysis to explore the relationship between self-efficacy and TMX. Results: The analysis revealed four distinct behavioral clusters: confident collaborator, team player, reserved individual, and solo achiever. Results indicated that higher selfefficacy is associated with enhanced TMX and academic performance. Moreover, significant differences in self-efficacy and TMX scores were observed across the clusters, and regression analysis confirmed a positive relationship between selfefficacy and the quality of team interactions. Conclusion These findings highlight the importance of fostering both self-efficacy and effective team exchanges to optimize collaborative learning environments in aviation service education. Tailored educational interventions based on behavioral clustering can further enhance academic outcomes and prepare students for professional challenges.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study investigates the relationship between self-efficacy and teammember exchange (TMX) among aviation service students, and examines how these factors influence academic achievement and collaborative behavior. Self-efficacy, based on Bandura’s Social Cognitive Theory, is defined as an individual’s belief in their ability to overcome challenges, while TMX reflects the quality of social exchanges among team members. Methods: A convenience sample of undergraduate students from an aviation service department was recruited, yielding 65 valid responses. Self-efficacy was measured using the New General Self-Efficacy Scale along with additional validated items, and TMX was assessed with a TMX-10 scale, both utilizing a 5-point Likert scale. Data analysis included descriptive statistics, K-means clustering to identify behavioral segments, ANOVA for group comparisons, and regression analysis to explore the relationship between self-efficacy and TMX. Results: The analysis revealed four distinct behavioral clusters: confident collaborator, team player, reserved individual, and solo achiever. Results indicated that higher selfefficacy is associated with enhanced TMX and academic performance. Moreover, significant differences in self-efficacy and TMX scores were observed across the clusters, and regression analysis confirmed a positive relationship between selfefficacy and the quality of team interactions. Conclusion These findings highlight the importance of fostering both self-efficacy and effective team exchanges to optimize collaborative learning environments in aviation service education. Tailored educational interventions based on behavioral clustering can further enhance academic outcomes and prepare students for professional challenges.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study investigates the relationship between self-efficacy and teammember exchange (TMX) among aviation service students, and examines how these factors influence academic achievement and collaborative behavior. Self-efficacy, based on Bandura’s Social Cognitive Theory, is defined as an individual’s belief in their ability to overcome challenges, while TMX reflects the quality of social exchanges among team members. Methods: A convenience sample of undergraduate students from an aviation service department was recruited, yielding 65 valid responses. Self-efficacy was measured using the New General Self-Efficacy Scale along with additional validated items, and TMX was assessed with a TMX-10 scale, both utilizing a 5-point Likert scale. Data analysis included descriptive statistics, K-means clustering to identify behavioral segments, ANOVA for group comparisons, and regression analysis to explore the relationship between self-efficacy and TMX. Results: The analysis revealed four distinct behavioral clusters: confident collaborator, team player, reserved individual, and solo achiever. Results indicated that higher selfefficacy is associated with enhanced TMX and academic performance. Moreover, significant differences in self-efficacy and TMX scores were observed across the clusters, and regression analysis confirmed a positive relationship between selfefficacy and the quality of team interactions. Conclusion These findings highlight the importance of fostering both self-efficacy and effective team exchanges to optimize collaborative learning environments in aviation service education. Tailored educational interventions based on behavioral clustering can further enhance academic outcomes and prepare students for professional challenges.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study investigates the relationship between self-efficacy and teammember exchange (TMX) among aviation service students, and examines how these factors influence academic achievement and collaborative behavior. Self-efficacy, based on Bandura’s Social Cognitive Theory, is defined as an individual’s belief in their ability to overcome challenges, while TMX reflects the quality of social exchanges among team members. Methods: A convenience sample of undergraduate students from an aviation service department was recruited, yielding 65 valid responses. Self-efficacy was measured using the New General Self-Efficacy Scale along with additional validated items, and TMX was assessed with a TMX-10 scale, both utilizing a 5-point Likert scale. Data analysis included descriptive statistics, K-means clustering to identify behavioral segments, ANOVA for group comparisons, and regression analysis to explore the relationship between self-efficacy and TMX. Results: The analysis revealed four distinct behavioral clusters: confident collaborator, team player, reserved individual, and solo achiever. Results indicated that higher selfefficacy is associated with enhanced TMX and academic performance. Moreover, significant differences in self-efficacy and TMX scores were observed across the clusters, and regression analysis confirmed a positive relationship between selfefficacy and the quality of team interactions. Conclusion These findings highlight the importance of fostering both self-efficacy and effective team exchanges to optimize collaborative learning environments in aviation service education. Tailored educational interventions based on behavioral clustering can further enhance academic outcomes and prepare students for professional challenges.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.