This study aims to identify the associations between teacher mental health and student mental health. Cross-sectional data were collected from 127,877 students aged 9–20 years and 2,759 teachers across 31 provinces in China. The mental health of students and teachers were assessed by well-being (life satisfaction and positive mental health), and psychological distress (depression and anxiety). Controlling for demographic variables, multilevel regression analyses suggest that higher teacher positive mental health was linked to higher student positive mental health and lower student depression; higher teacher depression were correlated with higher student depression; and teacher life satisfaction and anxiety were not correlated with any indicators of student mental health. The study highlights the significant association between teacher mental health and student mental health.
Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.