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.

Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development.

The visual quality assessment of rural landscapes is vital for quantifying ecological ser-vice functions and cultural heritage, yet traditional ecological indicators fail to capture emotional and cognitive experiences. Therefore, this study introduces a meth-od for assessing rural landscape visual quality that integrates a multi-modal emotion classification model to enhance the quantitative drive towards sustainability. The experiment selected four types of landscapes from three representative villages in Dalian, China, collecting physiological data (EOG, EEG) and subjective evaluations (beauty assessment and SAM scale) from participants. Binary, ternary, and five-element classification models were constructed. The results indicate that the bi-nary and ternary classification models yielded the highest accuracy in emotion valence and arousal, while the five-element model demonstrated the lowest performance. Additionally, ensemble learning models outperformed single classifiers in binary and ternary tasks, with an average accuracy improvement of 7.59%. Moreover, the collaborative fusion of subjective and objective data enhanced the accuracy of the ternary classification by 7.7% compared to existing research, confirming the efficacy of multisource features. The findings suggest that the framework based on multi-source affective computing can serve as a quantitative tool for assessing the emotional quality of rural landscapes and promoting the sustainable development of rural areas.

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.

Intelligent lighting control systems are essential for regulating office illumination. Both illuminance levels and uniformity are important factors influencing the comfort of the office lighting environment. Thus, designing automatic control systems to regulate lighting is essential. This study addresses the issue of natural glare by proposing a method that uses a genetic algorithm (GA) to optimize a backpropagation (BP) neural network model. The model predicts the angle of window slats, with the Sun altitude and azimuth angles as inputs, and the slat angle as the output. For artificial lighting control, a linear function is proposed to manage the relationship between work plane illuminance, natural light intensity, occupancy rates, adjacent luminaire illuminance, and the dimming factor (K). The optimal K value for each luminaire is determined using the least squares method in MATLAB. The intelligent lighting system transmits dimming factors via a ZigBee tree network structure to achieve target illuminance levels. The system’s effectiveness is validated through simulations in DIAlux software, demonstrating that the workplace illuminance in occupied areas reaches 500 lx, while, in unoccupied areas, it reaches 300 lx, with an illuminance uniformity greater than 0.7. This addresses the issue of low illuminance uniformity during daytime. Additionally, the lighting power densities (LPDs) of 1.53 W/m² and 3.8 W/m² are well below the specified threshold of 6 W/m², indicating significant energy savings while maintaining compliance with office lighting standards.