Pai Chai University

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.

Non-standard service-oriented microenterprises face significant challenges in digital transformation due to heterogeneous tasks and the absence of standardized workflows. This study proposes a lightweight, embedded platform construction methodology-the Task-Service-HCI (TSH) method-which integrates Task-Technology Fit (TTF), Service Process Customization (SPC), and Human-Computer Interaction (HCI) into a closed-loop mechanism for task recognition, workflow configuration, and interactive feedback. Based on this method, a Reinforcement Learning-driven Task-Technology Fit Optimization (RL-TTFO) system architecture is designed to enable semantic task parsing and adaptive workflow generation.A prototype platform was deployed in the professional organizing industry, collecting behavioral logs and survey data from 300 users. Structural equation modeling results show that task-technology fit has a significant positive effect on user satisfaction (β = 0.62, p < 0.001), with configuration participation playing a mediating role in perceived control (p < 0.01). Regression analysis further indicates that the RL-TTFO mechanism increases usage frequency by 35.4% and improves perceived system intelligence by 27.1% compared with static workflow configurations (p < 0.001). These findings demonstrate that the proposed approach provides a sustainable, low-barrier digital transformation solution for resource-constrained microenterprises operating in highly customized service environments.

This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4-0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition-modeling-reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer-LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R2 compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification.

Conventional wearable monitoring devices often suffer from insufficient data accuracy and low posture recognition rates, making them inadequate for the demands of professional sports health monitoring. To address these issues, this study proposes a wearable monitoring system for running gait analysis based on the Diffusion Transformer (DiT). The system aims to achieve high-precision running posture recognition and real-time motion monitoring through multi-sensor data fusion and advanced deep learning architecture. First, a wearable system was developed using a nine-axis Micro-Electro-Mechanical System (MEMS) inertial sensor and an UltraWide Band (UWB) positioning module. Data quality was enhanced through sensor calibration, noise compensation, and an adaptive filtering algorithm. Then, a DiT-LSTM running posture recognition model was constructed by integrating the DiT with a Long Short-Term Memory (LSTM) network to perform posture recognition within the wearable system. Experimental results show that on the Human3.6M dataset, the DiT-LSTM model achieved an accuracy of 97.54%, a precision of 97.61%, a recall of 97.73%, an F1-score of 97.58%, and an Area Under the Curve (AUC) of 98.61%. On the HumanEva dataset, the model attained an accuracy of 96.39%, a precision of 96.47%, a recall of 96.8%, an F1-score of 96.92%, and an AUC of 97.9%, all outperforming other algorithms. The complexity assessment showed that DiT-LSTM reached 14.7 GFLOPs on the Human3.6M dataset, with a per-epoch training time of 62.3 seconds, while its per-sample inference latency was only 9.4 ms, meeting real-time monitoring requirements. In the UWB-based position drift correction experiment, Sample 1 achieved mean errors of 0.637 m, 0.581 m, and 0.349 m on the X/Y/Z axes, with corresponding RMSE values of 0.041 m, 0.023 m, and 0.025 m, demonstrating high positioning accuracy and stability. By combining multimodal sensors with the DiT-LSTM model, the study offers reliable technical support for running gait analysis, injury prevention, and personalized training guidance.

Although Adaptive Traffic Signal Control (ATSC) can alleviate congestion issues to some extent in traditional signal control systems, it still faces challenges in dealing with complex and dynamic traffic environments, such as difficulties in agent coordination, high computational complexity, and unstable optimization results. To address these challenges, this paper proposes a multi-agent deep reinforcement learning algorithm based on SENet, called SE-A3C. The SE-A3C algorithm enhances the feature extraction capability and adaptability of the neural network by introducing the Squeeze-and-Excitation (SE) module from SENet. This allows the model to focus more precisely on high-information features and capture interdependencies between different channels, thereby improving the model’s discriminative ability and decision-making performance. Additionally, the algorithm incorporates Nash equilibrium concepts to maintain a relative balance among agents during coordinated control, avoiding suboptimal competition between agents and significantly improving system stability and efficiency. Experimental results show that, compared to traditional A3C, DQN, and Ape-X algorithms, the SE-A3C algorithm significantly improves the efficiency of traffic signal control and the overall throughput of traffic flow in complex traffic scenarios.

BackgroundGiven the high prevalence of mental health problems among university students in South Korea and Japan, understanding and fostering their inner strengths is important. This study assessed resilience and its correlates among undergraduate students from both countries.MethodsA cross-sectional survey was conducted in Wonju City (South Korea) and Bunkyo City (Japan) using an online questionnaire. The survey included validated instruments: the 14-item Resilience Scale and the 20-item Purpose in Life (PIL) Scale. A total of 598 valid responses were included in the analysis. Descriptive statistics were used to assess levels of resilience and spiritual health, while quantile regression was used to examine associations between resilience and spiritual health, as well as socio-demographic and lifestyle factors.ResultsLow resilience was observed in 57.1% of South Korean students (median age 21; 71.5% female) and 67.3% of Japanese students (median age 19; 58% female). Purpose in life (PIL) was positively associated with resilience across all quantiles in both groups (Korea β = 0.53–0.62; Japan β = 0.56–0.65). Among Korean respondents, club affiliation, age, and female gender were linked to higher resilience at specific quantiles. Among Japanese respondents, financial satisfaction was inversely associated with resilience at the 25th and 50th quantiles (β = − 2.52 to − 4.76), while academic satisfaction was positively associated at the 25th quantile (β = 4.19).ConclusionGiven the high prevalence of low resilience, targeted strategies to enhance resilience are needed. The findings highlight purpose in life as a key positive correlate, alongside sociodemographic and financial factors with variable effects across resilience levels.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-24738-1.

NASICON-type Li1+XFeXTi2-X(PO4)3 (x = 0.1, 0.3, 0.4) solid electrolytes for all-solid-state Li-ion batteries were synthesized using a sol–gel method. This study investigated the impact of substituting Fe3+ (0.645 Å), a trivalent cation, for Ti4+ (0.605 Å) on ionic conductivity. Li1+XFeXTi2-X(PO4)3 samples, subjected to various sintering temperatures, were characterized using TG-DTA, XRD with Rietveld refinement, XPS, FE-SEM, and AC impedance to evaluate composition, crystal structure, fracture-surface morphology, densification, and ionic conductivity. XRD analysis confirmed the formation of single-crystalline NASICON-type Li1+XFeXTi2-X(PO4)3 at all sintering temperatures. However, impurities in the secondary phase emerged owing to the high sintering temperature above 1000 °C and increased Fe content. Sintered density increased with the densification of Li1+XFeXTi2-X(PO4)3, as evidenced by FE-SEM observations of sharper edges of larger quasi-cubic grains at elevated sintering temperatures. At 1000 °C, with Fe content exceeding 0.4, grain coarsening resulted in additional grain boundaries and internal cracks, thereby reducing the sintered density. Li1.3Fe0.3Ti1.7(PO4)3 sintered at 900 °C exhibited the highest density among the other conditions and achieved the maximum total ionic conductivity of 1.51 × 10−4 S/cm at room temperature, with the lowest activation energy for Li-ion transport at 0.37 eV. In contrast, Li1.4Fe0.4Ti1.6(PO4)3 sintered at 1000 °C demonstrated reduced ionic conductivity owing to increased complex impedance associated with secondary phases and grain crack formation.

Abstract This study explores coaching as a pedagogical framework to enhance translation learners’ competence development through a problem-solving lens. Grounded in three core assumptions — translation essentially as a complex, iterative process of resolving ill-structured problems; translation competence as an ill-structured problem-solving competence with translation thinking abilities as its core; and the necessity of cultivating learners’ translation thinking and facilitating their translation learning processes to promote translation competence development and acquisition — the research proposes an interconnection model integrating constructivism, complexity theory, and metacognitive self-regulation. To operationalize this framework, two sub-models are introduced: (1) the Translation Problem-Solving Coaching (TPSC) model, which guides learners through structured cycles to develop adaptive thinking; and (2) the Student-Centred Learning Process Coaching (SCLPC) model, which supports learners in aligning their learning processes with goals. Case-based validation illustrates how coaching scaffolds cognitive and metacognitive growth. Implications for translation pedagogy are discussed.