An augmented reality head-up display (AR-HUD) is a promising technology in assisted driving. It provides additional information in the driving environment. However, considering the registration pro...

Confined spaces, characterized by limited natural ventilation, the absence of windows, and restricted access to natural light, present distinct challenges. While most studies focus on lighting's effect on sleep, confined spaces are now more often used for short‐term work‐rest cycles, especially in office settings. This study explores how lighting conditions affect alertness, cognitive performance, and physiological metrics such as heart rate variability (HRV) during work‐rest cycles in confined spaces. Participants performed 2‐back tasks, psychomotor vigilance tasks (PVT), and completed subjective scales under six lighting conditions, combining two levels of illuminance (300, 500 lx) and three color temperatures (2800, 5000, 6500 K). Results show higher subjective alertness during work with 300 lx and 5000 K. However, lighting conditions did not significantly affect subjective alertness during rest. Objective alertness was better at 300 lx, with 2800 outperforming 5000 K. Working memory accuracy was higher at 5000 compared with 6500 K, and reaction times were faster under 300 lx. Physiological data remained consistent across lighting conditions. These findings can inform future lighting design and management in confined spaces to improve comfort and efficiency.

Human records can assist us in understanding real descriptions and expected ideals of music. The present work examined how have music emotions been described in millions of Google books. In general, positive adjectives were more regularly used to describe music than negative adjectives, demonstrating a positivity bias in music. The emotional depiction of music has shifted over time, including a decrease in the frequency of emotional adjectives used in English books over the past two centuries, and a sudden surge in the usage of positive adjectives in simplified Chinese books during China’s Cultural Revolution. Negative adjectives were substantially less employed to describe music in simplified Chinese books than in English books, reflecting cultural differences. Finally, a comparison of different corpora showed that emotion-related adjectives were more frequently used to describe music in fictional literature.

Musical depth, which encompasses the intellectual and emotional complexity of music, is a robust dimension that influences music preference. However, there remains a dearth of research exploring the relationship between lyrics and musical depth. This study addressed this gap by analyzing linguistic inquiry and word count‐based lyric features extracted from a comprehensive dataset of 2372 Chinese songs. Correlation analysis and machine learning techniques revealed compelling connections between musical depth and various lyric features, such as the usage frequency of emotion words, time words, and insight words. To further investigate these relationships, prediction models for musical depth were constructed using a combination of audio and lyric features as inputs. The results demonstrated that the random forest regressions (RFR) that integrated both audio and lyric features yielded superior prediction performance compared to those relying solely on lyric inputs. Notably, when assessing the feature importance to interpret the RFR models, it became evident that audio features played a decisive role in predicting musical depth. This finding highlights the paramount significance of melody over lyrics in effectively conveying the intricacies of musical depth.