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Abstract\nHIGHLIGHTSCFRP laminates are commonly used in aircraft structures due to their high structural efficiency. The phenomenon-based failures of CFRP laminates have poor predictability for the complex failure process with buckling and matrix fractures. This work carried out seven shear tests of CFRP laminates. It also attempts to reveal its failure characteristic points by directly modeling the measured strain data based on the relative deformation distribution catastrophe. The failure characteristic points are revealed by constructing the stressing state modes/parameters and applying the clustering analysis criterion, whose accuracy can be verified by comparing the shear failure characteristic points of nondestructive/impacted CFRP laminates.Conduct 7 nondestructive/impacted CFRP laminates shear tests.Construct the modes and characteristic parameters characterizing the shear-stressing state of CFRP laminates.Applying clustering analysis criterion to reveal the characteristic loads of CFRP laminates.Verify the accuracy and robustness of the characteristic loads. [ABSTRACT FROM AUTHOR]

As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propose a modified Swin-UNet network model. Firstly, the Swin Transformer Blocks are improved using a residual post-normalization and scaling cosine attention mechanism, which makes the training process of the model more stable and improves the accuracy. Secondly, we use the log-space continuous position biasing method instead of the bicubic interpolation position biasing method. This method solves the problem of performance loss caused by the large difference between the resolution of the pretraining image and the resolution of the spine image. Finally, we introduce a segmentation smooth module (SSM) at the decoder stage. The SSM effectively reduces redundancy, and enhances the segmentation edge processing to improve the model’s segmentation accuracy. To validate the proposed method, we conducted experiments on a real dataset provided by hospitals. The average segmentation accuracy is no less than 95%. The experimental results demonstrate the superiority of the proposed method over the original model and other models of the same type in segmenting the spinous processes of the vertebrae and the posterior arch of the spine. [ABSTRACT FROM AUTHOR]