Silicon monoxide (SiO)@carbon (C)@carbon microspheres (CMSs) composites with core-shell structures were prepared by the hydrothermal method combined with subsequent heat treatment, using glucose as a carbon source. scanning electronic microscopy (SEM) analysis showed that this process resulted in SiO particles that were uniformly coated by the pyrolytic carbon layer and surrounded by CMSs. With increasing quantities of glucose added, the cycle performances of SiO@C@CMSs composites were improved remarkably, with a moderate amount of glucose addition resulting in the greatest performance. Specifically, among the four composite samples, SiO@C@CMSs-2 had the best cycle performance, delivering a high initial capacity of 1024.5 mAh g -1 at 50 mA g -1 and a specific capacity of 638.6 mAh g -1 after 60 cycles at a current density of 200 mA g -1 . Even at a current density of 1.5 A g -1 , it was still able to deliver a reversible capacity of about 450 mAh g -1 . This was attributed to the formation of mesoporous carbon coating and CMSs by the hydrothermal synthesis of glucose. The mesoporous pyrolytic carbon coating of glucose increases the lithium ion diffusion rate and improves the conductivity of the material. At the same time, the volume expansion of the material during lithiation/delithiation is restrained by carbon coating and the CMSs serve as a buffer to alleviate volumetric stress.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

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Previous studies on the effectiveness of improving sustainable development have acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D activities are related to energy-saving and sustainable development. The corresponding empirical evidence is scabrous and ambiguous. In this paper, we focus on the effect of green innovation R&D activities on SO2 emission which is an important greenhouse gas affect global climate change and eco-civilization. Considering that there is heterogeneity exists in the innovation activities, the R&D activities are divided into three performers with two purposes. The empirical results based on a Chinese inter-provincial dataset of 2000-2016 suggest that the green innovation R&D activities are crucial for the reduction of the SO2 emission. However, the innovation R&D activities of different purposes and performers show statistically differentiated effects on SO2 emission. The major positive effect of green innovation R&D activities on SO2 emissions reduction is mainly from enterprises and utility-type of R&D activities. A further study based on the panel threshold also indicates that effects of green innovation R&D activities on SO2 emissions are nonlinear, depending on the technology absorptive ability.

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