This study selected water-cement ratio 0.5 and slump 16 cm for ACI mix design, and used nano- clay to replace 0.1%, 0.3% and 0.5% of cement by weight to make cylinder and angle column specimens. The effects of differe...This study selected water-cement ratio 0.5 and slump 16 cm for ACI mix design, and used nano- clay to replace 0.1%, 0.3% and 0.5% of cement by weight to make cylinder and angle column specimens. The effects of different high temperatures on the compressive strength and heat transfer coefficient were tested, and the ignition loss of nano-clay cement paste was measured. The results showed that an appropriate replacement of nano-clay for 0.3% - 0.5% of cement can enhance the strength and heat transfer coefficient of concrete, especially 0.3% replacement. The 0.1% nano- clay replacement for cement reduces the strength and heat transfer coefficient of concrete. When the ambient temperature exceeds 300?C, the nano-clay concrete strength begins to decline, and the heat transfer coefficient decreases greatly, the effect of hot environment on the strength trend of nano-clay concrete is similar to that on normal concrete. When the nano-clay replaces cement by 0% - 0.5%, the ignition loss is approximately in exponential and logarithmic relationships to the compressive strength respectively when the ignition loss is smaller than and greater than 6%.展开更多
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by ...Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.展开更多
文摘This study selected water-cement ratio 0.5 and slump 16 cm for ACI mix design, and used nano- clay to replace 0.1%, 0.3% and 0.5% of cement by weight to make cylinder and angle column specimens. The effects of different high temperatures on the compressive strength and heat transfer coefficient were tested, and the ignition loss of nano-clay cement paste was measured. The results showed that an appropriate replacement of nano-clay for 0.3% - 0.5% of cement can enhance the strength and heat transfer coefficient of concrete, especially 0.3% replacement. The 0.1% nano- clay replacement for cement reduces the strength and heat transfer coefficient of concrete. When the ambient temperature exceeds 300?C, the nano-clay concrete strength begins to decline, and the heat transfer coefficient decreases greatly, the effect of hot environment on the strength trend of nano-clay concrete is similar to that on normal concrete. When the nano-clay replaces cement by 0% - 0.5%, the ignition loss is approximately in exponential and logarithmic relationships to the compressive strength respectively when the ignition loss is smaller than and greater than 6%.
文摘Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
