Twelve healthy male students were recruited to investigate the physiological response to different noise exposure and mental workload (MW) conditions, while performing multi-attribute task battery (MATB) tasks. The experiments were conducted under three noise exposure conditions, with different sound pressure levels and sharpness. After adaptation to each noise condition, the participants were required to perform the resting test and the MATB task tests with low, medium, and high MW. The electroencephalogram (EEG), electrocardiogram (ECG), and eye movement data were obtained, during the periods when participants were in the resting and task taking state. The results showed that subjects’ physiological responses at rest were unaffected by noise exposure conditions. However, during the execution of MATB tasks, the elevated sound pressure level and increased sharpness were significantly correlated with increased mean pupil diameter and heart rate variability (HRV). These responses suggested that the human body defends itself through physiological regulation when noise causes adverse effects. If the negative effects of noise were more severe, this could damage the body’s health and result in a significant drop in task performance. The elevated mental demands led to increased stress on the subjects, which was reflected in a considerable increase in theta relative power. Either high or low MW was related with reduced saccade amplitude and a decrease in weighted task performance, indicating an inverted U-shaped relationship between workload level and work performance.

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

Fifteen participants were exposed in an enclosed environmental chamber to investigate the effects of elevated carbon dioxide (CO₂) concentration on their cognitive abilities. Three CO₂ conditions (1500, 3500, and 5000 ppm) were achieved by constant air supply and additional ultrapure CO₂. All participants received the same exposure under each condition, during which they performed six cognitive tests evaluating human perception, attention, short-term working memory, risky decision-making, and executive ability. Generalized additive mixed effects model (GAMM) results showed no statistically significant differences in performance on the reaction time (RT) tests, the speed perception test, and the 2-back test. This suggests that elevated CO₂ concentrations below 5000 ppm did not affect participants’ perception and short-term working memory. However, a significant increase in response time was observed in the visual search (VS) test, the balloon simulation risk test (BART), and the Stroop test at 5000 ppm compared to lower exposure concentrations. The slower responses reflected the detrimental effects of elevated CO₂ concentrations on visual attention, risky decision-making, and executive ability. The findings suggest that the control level of CO₂ concentrations should be tighter in enclosed workplaces where rapid response and operational safety are required.

According to a previously built situation-awareness (SA) model based on attention allocation, with the ACT-R (Adaptive Control of Thought, Rational) theory for analyzing pilot cognitive processes for situation elements, an SA mathematical model was improved to predict pilot SA during exposure to different display interfaces and missions. An indicator-display monitoring task was performed under different experimental conditions for SA model verification, while the SA global assessment technique (SAGAT), performance measures, 10-dimensional SA rating technique (10-D SART), and eye movement measures were adopted to comprehensively assess the operator's SA. The experimental results revealed that theoretical prediction values calculated using the improved SA model were strongly correlated with the operation performance, and thus confirming the model validation. The SAGAT was shown to be a more effective method than SART in this research, and the overall SAGAT accuracy rate, as well as the accuracy response time, are effective indices for SA measurement. Eye-movement indices, such as the fixation/saccade ratio, which corresponds to the mode of information perception and extraction, was examined to be sensitive to operator's SA changes. The Advances of the improved SA model have been achieved in predicting and indicating SA by using human behaviors, including operation performance, SAGAT response behaviors, and visual behaviors. Thereby, it provides a new auxiliary tool for quantitative characterization of pilot's SA during cockpit interface design optimization and ergonomic evaluation.

Individuals exposed to elevated noise levels experience heightened emotional intensity, leading to increased cognitive disruption and a higher likelihood of accidents. This study seeks to investigate the impact of noise exposure on human cognitive performance, and the moderating role of emotion. Twelve healthy male college-age students underwent exposure to three noise conditions, each characterized by different sound pressure levels and sharpness. Each condition included an initial acoustic/thermal adaption period lasting approximately 40 min, followed by intermittent questionnaire tests and a battery of computerized cognitive tests. Statistical analysis revealed that reducing noise levels proved advantageous, enhancing perceived sound quality, positive emotions, and auditory perception abilities, while concurrently reducing false alerts and accelerating execution speed. Many of these effects were found to be counteracted by elevated sharpness. Correlation analyses and partial least squares structural equation modeling (PLS-SEM) results suggested that human emotions mediate the relationship between noise exposure and cognitive performance. The potential underlying mechanism suggests that negative feelings towards noise contribute to poor emotional states, subsequently influencing cognitive processes and impairing executive function. The outcomes of this study provide valuable insights into the mechanism of noise exposure and its effects on human cognition and subjective perceptions.

With the continuous application of high-power electronic equipment in aircraft, highly efficient heat transfer technology has been emphasized for airborne applications. In this paper, a thermal management system based on an antifreeze liquid cooling loop and a vapor compression refrigeration loop is presented for high-power airborne equipment in a helicopter. The simulation models of the thermal management system are built in order to study its control strategy for the changing flight conditions. The antifreeze-refrigerant evaporator and air-refrigerant condenser are specially validated with the experimental data. A dual feedforward proportion integration differentiation and expert control algorithm are adopted in the inlet temperature of the cold plate and sub-cooling control of the refrigerant by regulating the compressor speed and the fan speed, respectively. A preheating strategy for antifreeze is set up to decrease its flow resistance in cold day conditions. The control strategy for the thermal management system is finally built based on the above control methods. In this paper, two extreme conditions are discussed, including cold and hot days. Both the simulation results show that the superheated, sub-cooling and antifreeze inlet temperature of the cold plate can be controlled at 3 to8 °C, −10 to −3 °C and 18 to22 °C, respectively. Under the same changing flight envelope, the coefficient of performance of the vapor compression refrigeration loop is relatively stable on the cold day, which is about 6, while it has a range of 2.58–4.9 on the hot day.