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The crumb rubber (CR) asphalt has some defects of high viscosity and poor storage stability, which brings great challenge to the high-quality construction of the CR asphalt pavement. To improve the interaction between the CR and base binder, the Eucommia ulmoides gum (EUG) with double-bond structure similar to trans-polyoctenamer rubber (TOR) was used to modify the CR asphalt. However, the original EUG double bond is basically inactive at room temperature and cannot form the effect of TOR. Open double bonds of EUG with asphalt and rubber powder form a network structure similar to TOR-modified rubber asphalt by high-temperature mixing with EUG in a torque rheometer. The effects of modified CR on rubber asphalt were analyzed by macro- and micro-experiments such as rotational viscosity tests, segregation tests, FTIR tests, and PG tests. It was found that the high-temperature mixing process works in both physical and chemical ways to mix the CR and EUG into an inseparable substance. The modified CR has higher chemical activity after desulfurization and degradation, which allows it to form a more effective chemical connection with asphalt. EUG can build a stable spatial crosslinking structure in CR asphalt due to the sulfurization reaction, which significantly improves the construction workability and system stability of the CR asphalt. [ABSTRACT FROM AUTHOR]

Medical images contain complex information, and the automated analysis of medical images can greatly assist doctors in clinical decision making. Therefore, the automatic segmentation of medical images has become a hot research topic in recent years. In this study, a novel architecture called a contoured convolutional transformer (CCTrans) network is proposed to solve the segmentation problem. A dual convolutional transformer block and a contoured detection module are designed, which integrate local and global contexts to establish reliable relational connections. Multi-scale features are effectively utilized to enhance semantic feature understanding. The dice similarity coefficient (DSC) is employed to evaluate experimental performance. Two public datasets with two different modalities are chosen as the experimental datasets. Our proposed method achieved an average DSC of 83.97% on a synapse dataset (abdominal multi-organ CT) and 92.15% on an ACDC dataset (cardiac MRI). Especially for the segmentation of small and complex organs, our proposed model achieves better segmentation results than other advanced approaches. Our experiments demonstrate the effectiveness and robustness of the novel method and its potential for real-world applications. The proposed CCTrans network offers a universal solution with which to achieve precise medical image segmentation. [ABSTRACT FROM AUTHOR]