This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights.

This study uniquely integrates physiological and subjective data to predict comfort. To optimize lighting conditions in art galleries, this study investigates the interactive effects of painting tones (cool, medium, warm) and illuminance levels (50, 150, 300 lx) on visual comfort. Using decorative paintings as experimental stimuli, 30 participants were exposed to nine distinct lighting scenarios. Subjective questionnaires and eye-tracking data were collected to establish five predictive models. An additional cohort of 10 participants served as an external validation set. Results indicate that the interaction between tone and illuminance exerts a significant influence on comfort. The optimal combinations identified were cool tone + 50 lx, warm tone + 150 lx, and medium tone + 300 lx. Among the models, XGBoost demonstrated superior predictive performance (R² = 0.928 in the test set; R² = 0.884 in external validation). SHAP analysis revealed that the coefficient of variation in pupil diameter was the most critical predictor, followed by fixation count and related features. Both global and individual feature contributions to comfort were elucidated, offering a robust scientific foundation for the precise regulation of lighting environments in art galleries.

This study aims to improve the accuracy and efficiency of apple defect detection under complex lighting conditions. A novel approach is proposed that integrates filtered imaging with color space reconstruction, utilizing YOLOv12 as the detection framework. “Red Fuji” apples were selected, and an imaging platform featuring adjustable illumination and RGB filters was established. Following pre-experimental optimization of imaging conditions, a dataset comprising 1600 images was constructed. Conversions to RGB, HSI, and LAB color spaces were performed, and YOLOv12 served as the baseline model for ablation experiments. Detection performance was assessed using Precision, Recall, mAP, and FPS metrics. Results indicate that the green filter under 4500 K illumination combined with RGB color space conversion yields optimal performance, achieving an mAP50–95 of 83.1% and a processing speed of 15.15 FPS. This study highlights the impact of filter–color space combinations on detection outcomes, offering an effective solution for apple defect identification and serving as a reference for industrial inspection applications.