The Aesthetics Scale is one of the most commonly used aesthetics evaluation scale in HCI. In this study, a Chinese version of the Aesthetics Scale (C-AS) was translated and validated using the forward–backward translation method. The C-AS passed the content validity and face validity tests. We then recruited 310 Chinese users and tested the psychological properties of the C-AS using the China Railway Customer Service Center ticketing platform as the test material. The experimental results showed that the C-AS has high reliability, validity, and sensitivity. Following rigorous testing, the results of this work may provide Chinese UX practitioners with a standardized aesthetics scale that researchers and practitioners can use with confidence.

Serious games hold significant potential for application in the domain of driving training, however, the actual acceptance of these serious games by novice users remains to be validated. This study explores novice drivers’ acceptance of serious games for driving training by integrating flow theory (perceived enjoyment [PE], perceived control [PC], concentration [CON]) with the TAM. Data from 335 Chinese novice drivers participating in a simulation test were analyzed via covariance-based structural equation modeling (CB-SEM). Key findings: (i) PE, PC, CON, and perceived ease of use (PEOU) significantly affect perceived usefulness (PU); PE and CON also influence PEOU. (ii) PE, CON, PEOU, and PU directly predict behavioral intention (BI), while PC shows no significant impact on BI or PEOU. (iii) Female participants scored higher than males in PU, PE, CON, and BI. Our findings provide valuable practical insights for the design and development of driving serious games.

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis ($\texttt{sisPCA}$), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), $\texttt{sisPCA}$ incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate $\texttt{sisPCA}$'s connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis.