Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu) scripts. Beginners often start with the regular script, advancing to more intricate styles like cursive. Each style, marked by unique historical calligraphy contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification is pivotal. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphy styles from the Tang dynasty (690–907 A.D.), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5–96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in the categorization of both font and artistic styles. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as Regular (Kai Shu), Running (Xing Shu), Official (Li Shu), and Cursive (Cao Shu) scripts. Beginners often start with Regular script, advancing to more intricate styles like Cursive. Each style, marked by unique historical calligraphers' contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification are pivotal. This study introduces an innovative Convolutional Neural Network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphers' styles from the Tang dynasty (690-907 A.D), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5-96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in both font and artistic style categorization. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.

1. Optimal and reliable traction is a central requirement for heavy-haul freight operations, where locomotives routinely operate near adhesion limits under demanding train loads [1]. The wheel-rail...

Rail surface degradation in heavy haul freight operations is governed by frictional energy dissipated at the wheel-rail contact, characterised by the creep energy dissipation parameter Tγ, and the material-specific wear law mapping contact energy density to volumetric wear rate. For the AS60 head-hardened rail and Class B wheel material pairing considered in this study, this relationship is represented using an experimentally established piecewise wear law developed from a twin-disc testing. This paper presents a physics-informed quantum neural network (PIQNN) for virtual sensing of the creep energy dissipation parameter Tγ and the corresponding wear ate from simulation-derived contact and vehicle-response parameters. The proposed architecture couples a variational quantum encoder with a classical physics block that embeds the AS60 wear law as a differentiable forward-pass module, structurally constraining the wear rate prediction to be physically admissible. The model is trained and evaluated using datasets derived from 972 multibody simulations performed in GENSYS, spanning operating speed, track irregularity, and suspension friction conditions. The strongly entangled PIQNN achieves a test Tγ R² of 0.807 against 0.280 for a parameter-matched classical multilayer perceptron baseline, despite additional dimensionality reduction imposed by the qubit register. A shallower circuit configuration exhibits barren plateau failure, confirming entanglement structure as a critical trainability determinant. The results demonstrate the feasibility of using a physics-informed quantum architecture as a wear-focused virtual sensor for estimating wheel-rail contact energy and wear rate under simulated heavy haul operating conditions.