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The cracking resistance was a significant factor influencing the design load of composite beams. According to previous studies, the cracking load reached only 14–40% of the ultimate load when steel-UHPC composite beams in the hogging moment, and the risk of cracking remained. A new perfobond rib shear (PBL) connector was proposed further to improve the cracking resistance of steel-UHPC composite beams. Bending behaviors were researched by analyzing the crack distribution, failure mode, and load-deflection curve for composite beam. At failure, the steel beam flange buckled significantly, and three cracks penetrated through the UHPC board at the pure bending section. Additionally, the excellent slip resistance of the new PBL connector was validated in a push-out test. The failure mode of the push-out specimens was the fracture of the perforated reinforcement. During the push-out test, no cracks were observed on the UHPC boards. Besides conducting experiments, exploring the influence of the headed stud and new PBL connector on the composite beam by numerical analysis. The numerical results proved that the new PBL connector could release tensile stresses in the UHPC slabs and enhance the cracking properties of composite beams. [ABSTRACT FROM AUTHOR]

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With the rapid advancement of modern medical technology, microscopy imaging systems have become one of the key technologies in medical image analysis. However, manual use of microscopes presents issues such as operator dependency, inefficiency, and time consumption. To enhance the efficiency and accuracy of medical image capture and reduce the burden of subsequent quantitative analysis, this paper proposes an improved microscope salient object detection algorithm based on U2-Net, incorporating deep learning technology. The improved algorithm first enhances the network’s key information extraction capability by incorporating the Convolutional Block Attention Module (CBAM) into U2-Net. It then optimizes network complexity by constructing a Simple Pyramid Pooling Module (SPPM) and uses Ghost convolution to achieve model lightweighting. Additionally, data augmentation is applied to the slides to improve the algorithm’s robustness and generalization. The experimental results show that the size of the improved algorithm model is 72.5 MB, which represents a 56.85% reduction compared to the original U2-Net model size of 168.0 MB. Additionally, the model’s prediction accuracy has increased from 92.24 to 97.13%, providing an efficient means for subsequent image processing and analysis tasks in microscopy imaging systems.Graphical Abstract: With the rapid advancement of modern medical technology, microscopy imaging systems have become one of the key technologies in medical image analysis. However, manual use of microscopes presents issues such as operator dependency, inefficiency, and time consumption. To enhance the efficiency and accuracy of medical image capture and reduce the burden of subsequent quantitative analysis, this paper proposes an improved microscope salient object detection algorithm based on U2-Net, incorporating deep learning technology. The improved algorithm first enhances the network’s key information extraction capability by incorporating the Convolutional Block Attention Module (CBAM) into U2-Net. It then optimizes network complexity by constructing a Simple Pyramid Pooling Module (SPPM) and uses Ghost convolution to achieve model lightweighting. Additionally, data augmentation is applied to the slides to improve the algorithm’s robustness and generalization. The experimental results show that the size of the improved algorithm model is 72.5 MB, which represents a 56.85% reduction compared to the original U2-Net model size of 168.0 MB. Additionally, the model’s prediction accuracy has increased from 92.24 to 97.13%, providing an efficient means for subsequent image processing and analysis tasks in microscopy imaging systems. [ABSTRACT FROM AUTHOR]