Supersaturated total dissolved gases (TDGs) generated by dams can cause gas bubble trauma and mortality in fish in downstream waters. In this study, we evaluated air entrainment and generation of supersaturated TDG at two submerged low-level outlets (LLOs) at Hugh L. Keenleyside Dam, British Columbia, Canada. Specifically, we determined how air entrainment (less than 1% volume fraction) through the gate well, discharge level through the outlets, and geometry of the stilling basins at the south and north ends of the dam influenced supersaturated TDG generation. A mathematical formulation was developed, incorporating physical processes including air entrainment, bubble breakup, and gas transfer. Numerical modeling was also adopted to validate turbulence and flowfield downstream of the submerged low-level outlets. Despite being 8-m shallower, significantly higher TDG levels were measured in the stilling basin of the south LLO (about 120%) compared to that of the north LLO ($\le 110\%$ ). Results show that turbulence in the stilling basin can produce smaller bubbles and increase the mass transfer coefficient across bubbles, which will substantially enhance gas transfer and TDG generation. Higher TDG generated in the shallower LLO was therefore attributed to the water depth generating stronger turbulence flow, with more efficient gas transfer. This study improves TDG prediction and helps inform the development of operational alternatives during periods of high TDG generation to mitigate impacts on the aquatic environment.

Defocus deblurring is a challenging task in the fields of computer vision and image processing. The irregularity of defocus blur kernels, coupled with the limitations of computational resources, poses significant difficulties for defocused image restoration. Additionally, the varying degrees of blur across different regions of the image impose higher demands on feature capture. Insufficient fine-grained feature extraction can result in artifacts and the loss of details, while inadequate coarse-grained feature extraction can cause image distortion and unnatural transitions. To address these challenges, we propose a defocus image deblurring method based on a hybrid CNN–Mamba architecture. This approach employs a data-driven, network-based self-learning strategy for blur processing, eliminating the need for traditional blur kernel estimation. Furthermore, by designing parallel feature extraction modules, the method leverages the local feature extraction capabilities of CNNs to capture image details, effectively restoring texture and edge information. The Mamba module models long-range dependencies, ensuring global consistency in the image. On the real defocus blur dual-pixel image dataset DPDD, the proposed CMDDNet achieves a PSNR of 28.37 in the Indoor dataset, surpassing Uformer-B (28.23) while significantly reducing the parameter count to only 9.74 M, which is 80.9% less than Uformer-B (50.88 M). Although the PSNR on the Outdoor and Combined datasets is slightly lower, CMDDNet maintains competitive performance with a significantly reduced model size, demonstrating its efficiency and effectiveness in defocus deblurring. These results indicate that CMDDNet offers a promising trade-off between performance and computational efficiency, making it well suited for lightweight applications.