Method for designing the optimal sealing depth in methane drainage boreholes to realize efficient drainage

The purpose of underground methane drainage technology is to prevent methane disasters and enable the efficient use of coal mine methane(CMM),and the sealing depth is a key factor that affects the performance of underground methane drainage.In this work,the layouts of in-seam and crossing boreholes are considered to analyze the stress distribution and failure characteristics of roadway surrounding rock through a numerical simulation and field stress investigation to determine a reasonable sealing depth.The results show that the depths of the plastic and elastic zones in two experimental coal mines are 16 and 20 m respectively.Borehole sealing minimizes the air leakage through the fractures around the roadway when the sealing material covers the failure and plastic zones,and the field test results for CMM drainage at different sealing depths indicate that the CMM drainage efficiency increases with increasing sealing depth but does not change once the sealing depth exceeds the plastic zone.Moreover,sealing in the high-permeability roadway surrounding rock does not have a strong influence on the borehole sealing performance.Considering these findings,a new CMM drainage system for key sealing in the low-permeability zone was developed that is effective for improving the CMM drainage efficiency and prolonging the high-concentration CMM drainage period.The proposed approach offers a valuable quantitative analysis method for selecting the optimum sealing parameters for underground methane drainage,thereby improving considerably the drainage and utilization rates of CMM.

Rare-earth ions coordination enhanced ratiometric fluorescent sensing platform for quantitative visual analysis of antibiotic residues in real samples

Levofloxacin(LVFX)as a representative drug of quinolone antibiotics is widely used in clinical,and its residues enriched in water bodies and sideline products seriously damage human health.It is imperative to develop a real-time/on-site sensing method for monitoring residual antibiotics.Here,we report a portable sensing platform by utilizing a composite fluorescent nanoprobe constructed by the cerium ions(Ce^(3+))coordination functionalized Cd Te quantum dots(QDs)for the visual and quantitative detection of LVFX residues.This fluorescent probe provides a distinct color variation from red to green,which shows a good linear relationship to LVFX residues concentrations in the range of 0-6.0μmol/L with a sensitive limit of detection(LOD)of 16.3 nmol/L.The smartphone platform with Color Analyzer App installed,which could accomplish quantified detection of LVFX in water,milk,and raw pork with a LOD of 27.9nmol/L.The facile sensing method we proposed realizes rapid visualization of antibiotics residual in the environment and provides a practical application pathway in food safety and human health.

温度场下岛-桥结构屈曲薄膜的动力学分析

基于岛-桥结构的柔性电子器件已被用于健康监测和皮肤电子等领域.但是柔性电子器件在工作中极易受工作温度变化等激励产生振动,进而影响器件的灵敏度与可靠性.因此论文研究在温度场作用下岛-桥结构屈曲薄膜的动力学问题.首先,基于Euler-Bernoulli梁理论,建立温度场作用下岛-桥结构屈曲薄膜的动力学控制方程.其次,通过引入新变量,将原动力学方程引入Hamilton体系中,得到相应的Hamilton正则方程.随后,采用辛Runge-Kutta方法求解该Hamilton正则方程,并与经典Runge-Kutta方法对比,数值结果显示了辛算法在求解非线性动力学方程时高精度、高数值稳定性的优势,进一步讨论了温度变化量、预应变、阻尼系数等对屈曲薄膜动力学响应的影响.论文研究为柔性电子器件动力学设计提供了理论参考.

CasNet:A Cascade Coarse-to-Fine Network for Semantic Segmentation

Semantic segmentation is a fundamental topic in computer vision. Since it is required to make dense predictions for an entire image, a network can hardly achieve good performance on various kinds of scenes. In this paper, we propose a cascade coarse-to-fine network called CasNet, which focuses on regions that are difficult to make pixel-level labels. The CasNet comprises three branches. The first branch is designed to produce coarse predictions for easy-to-label pixel regions. The second one learns to distinguish the relatively difficult-to-label pixels from the entire image. Finally, the last branch generates final predictions by combining both the coarse and the fine prediction results through a weighting coefficient that is estimated by the second branch. Three branches focus on their own objectives and collaboratively learn to predict from coarse-to-fine predictions. To evaluate the performance of the proposed network, we conduct experiments on two public datasets: SIFT Flow and Stanford Background. We show that these three branches can be trained in an end-to-end manner, and the experimental results demonstrate that the proposed CasNet outperforms existing state-of-the-art models, and it achieves prediction accuracy of 91.6% and 89.7% on SIFT Flow and Standford Background, respectively.

Plasmonic Cu_(27)S_(24)nanocages for novel solar photothermal nanoink and nanofilm

Copper sulfide(Cu_(x)S)as a plasmonic solar photothermal semiconductor material that expands the light collection range by altering localized surface plasmon resonance(LSPR)to the near-to mid-infrared(IR)spectral region.The versatile synthesis strategies of Cu_(x)S nanostructure offer its variability of morphology and provide additional freedom in tuning the optical property.Particularly,nanocage(or nanoshell)has hybridized plasmon resonances as a result of super-positioned nanosphere and nanocavity,which extends its receiving range of solar spectrum and increases light-to-heat conversion rate.Here,we offer novel“nanoink”and“nanofilm”developed from colloidal Cu_(27)S_(24)nanocages with excellent solar photothermal response.Via combining experimental measurement and theoretical calculation,we estimated the optical properties of covellite Cu_(27)S_(24).And based on obtained dielectric functions,we then calculated its solar photothermal performance,which was further validated by our experimental measurement.The simulation results showed that hollow Cu_(27)S_(24)nanocages have excellent solar photothermal performance,and exhibit much higher solar photothermal conversion efficiency than solid Cu_(27)S_(24)nanospheres.