Background: One noteworthy concern within the realm of education is the level of engagement demonstrated by students. Among the factor that can have a crucial role in this domain is teacher support, especially emotional support which has an impact on several aspects of learners’ education. Furthermore, various studies have investigated the relationship between Emotional Intelligence (EI) and learners’ engagement. Methods: Accordingly, this study investigated the possible role of trait EI and the emotional support of teachers and how these constructs may work to associate learners’ engagement. For this objective, a total of 309 Chinese students across different colleges and universities in 5 provinces of Beijing, Shanghai, Jiangsu, Hubei, and Shaanxi were enrolled. They were 126 females and 183 males, with ages ranging from 18 to 30 years old (Mean = 24.6). Results: The results of this research through running Structural Equation Modeling (SEM) demonstrated that teachers’ emotional support and trait EI both can associate students’ learning engagement. The final measurement model shows that about 73% of changes in learners’ engagement can be associated by their trait EI and teachers’ emotional support. Conclusions: This study underscores the importance of emotional support from teachers and the trait of EI in relation to students’ engagement in learning. Both factors were shown to play a significant role in associating student engagement. Moreover, this study could potentially have wider impacts on members of academic teams.

The city of Xi’an, China houses a vast collection of valuable Tang dynasty (618 AD-907 AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of a damaged mural and restore the missing content. The contour restoration network and mural restoration networks are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in peak signal-to-noise ratio (PSNR) and a 1.43% increase in structural similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small missing parts. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.

Xi'an, China houses a vast collection of valuable Tang dynasty (618AD-907AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of the damaged mural and restore the missing content. The contour restoration network and the mural restoration network are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in Peak Signal to Noise Ratio (PSNR) and a 1.43% increase in Structural Similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small parts breaking off. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.

Ancient murals are invaluable cultural heritage artifacts that frequently suffer from complex degradation due to environmental exposure and human activity. Most existing computational restoration methods rely on supervised learning and require large collections of clean reference murals, which are often unavailable or prohibitively expensive to acquire. To address this challenge, we propose the first unsupervised mural restoration method based on residual diffusion. Our approach operates solely on degraded murals and corresponding simulated degradation noise, both of which can be obtained without ground-truth supervision. Rather than directly reconstructing the original clean image, we simulate additional degradation on already-damaged murals and train the model to invert this process through residual-aware diffusion. Furthermore, we incorporate a low-rank prior during sampling to promote global structural consistency. Extensive experiments demonstrate that our method achieves performance on par with or superior to state-of-the-art supervised techniques, establishing the viability of unsupervised learning for high-quality mural restoration.