VM-UNet++ research on crack image segmentation based on improved VM-UNet.

Cracks are common defects in physical structures, and if not detected and addressed in a timely manner, they can pose a severe threat to the overall safety of the structure. In recent years, with advancements in deep learning, particularly the widespread use of Convolutional Neural Networks (CNNs) and Transformers, significant breakthroughs have been made in the field of crack detection. However, CNNs still face limitations in capturing global information due to their local receptive fields when processing images.

[Observation on moving patterns of Oncomelania snails in rivers and canals].

Objective: To understand the moving patterns of Oncomelania snails, intermediate host of S. japonicum, in the water bodies.

Methods: Based on the biological features of the snails, methods and techniques in relation to hydraulics and silt engineering were adopted to investigate the active scrawl ability and passive movement of the snails.