Dynamic Properties and Fractal Characteristics of 3D Printed Cement Mortar in SHPB Test.

Comparing with the traditional construction process, 3D printing technology used in construction offers many advantages due to the elimination of formwork. Currently, 3D printing technology used in the construction field is widely studied, however, limited studies are available on the dynamic properties of 3D printed materials. In this study, the effects of sand to binder ratios and printing directions on the fractal characteristics, dynamic compressive strength, and energy dissipation density of 3D printed cement mortar (3DPCM) are explored. The experiment results indicate that the printing direction has a more significant influence on the fractal dimension compared with the sand to binder ratio (S/B). The increasing S/B first causes an increase and then results in a decline in the dynamic compressive strength and energy dissipation of different printing directions. The anisotropic coefficient of 3DPCM first is decreased by 20.67%, then is increased by 10.56% as the S/B increases from 0.8 to 1.4, showing that the anisotropy is first mitigated, then increased. For the same case of S/B, the dynamic compressive strength and energy dissipation are strongly dependent on the printing direction, which are the largest printing in the Y-direction and the smallest printing in the X-direction. Moreover, the fractal dimension has certain relationships with the dynamic compressive strength and energy dissipation density. When the fractal dimension changes from 2.0 to 2.4, it shows a quadratic relationship with the dynamic compressive strength and a logarithmic relationship with the energy dissipation density in different printing directions. Finally, the printing mortar with an S/B = 1.1 is proved to have the best dynamic properties, and is selected for the 3D printing of the designed field barrack model.

Divergent accumulation of microbial necromass and plant lignin components in grassland soils.

The means through which microbes and plants contribute to soil organic carbon (SOC) accumulation remain elusive due to challenges in disentangling the complex components of SOC. Here we use amino sugars and lignin phenols as tracers for microbial necromass and plant lignin components, respectively, and investigate their distribution in the surface soils across Mongolian grasslands in comparison with published data for other grassland soils of the world. While lignin phenols decrease, amino sugars increase with SOC contents in all examined grassland soils, providing continental-scale evidence for the key role of microbial necromass in SOC accumulation. Moreover, in contrast to clay's control on amino sugar accumulation in fine-textured soils, aridity plays a central role in amino sugar accrual and lignin decomposition in the coarse-textured Mongolian soils. Hence, aridity shifts may have differential impacts on microbial-mediated SOC accumulation in grassland soils of varied textures.

A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images.

This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.