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RISS 인기검색어
- 검색키워드
- 저자 : Junqi Lu (검색결과 3 건)
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Zhi Hao,Junqi Shen,Xiang Sheng,Zong Shen,Le Yang,Xuefeng Lu,Zhu Luo,Qiang Zheng 한국섬유공학회 2020 Fibers and polymers Vol.21 No.2
This paper details a new method for improving the interfacial bonding between PA66 short fiber (PSF) and natural rubber (NR) by reaction of the methacrylic acid (MAA)-grafting-modified PSF with rubber during vulcanization. Carboncarbon double bonds introduced to the SF surface by MAA grafting were opened, and a vulcanization reaction occurred between the modified PSF and rubber in the presence of sulfur. The chemical reactions were verified by FTIR and XPS. The processing rheological behaviors of the compounded composites were investigated by a rubber processing analyzer (RPA). The morphology of modified PSF was characterized by AFM and SEM. The improved interfacial bonding was confirmed by DMA, which enhanced deformational stress at definite elongation of the NR/PSF composites. The volume concentration of the MAA solution for grafting on the SF surface had a great influence on the interfacial bonding and mechanical properties of the composites; when the volume concentration was 30 %, the modified PSF-reinforced NR/CB had the best interfacial bonding and mechanical properties.
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Jingjuan Hu,Haihua Luo,Jieyan Wang,Wenli Tang,Junqi Lu,Shan Wu,Zhi Xiong,Guizhi Yang,Zhenguo Chen,Tian Lan,Hongwei Zhou,Jing Nie,Yong Jiang,Peng Chen 생화학분자생물학회 2017 Experimental and molecular medicine Vol.49 No.-
Chronic high-salt diet-associated renal injury is a key risk factor for the development of hypertension. However, the mechanism by which salt triggers kidney damage is poorly understood. Our study investigated how high salt (HS) intake triggers early renal injury by considering the ‘gut-kidney axis’. We fed mice 2% NaCl in drinking water continuously for 8 weeks to induce early renal injury. We found that the ‘quantitative’ and ‘qualitative’ levels of the intestinal microflora were significantly altered after chronic HS feeding, which indicated the occurrence of enteric dysbiosis. In addition, intestinal immunological gene expression was impaired in mice with HS intake. Gut permeability elevation and enteric bacterial translocation into the kidney were detected after chronic HS feeding. Gut bacteria depletion by non-absorbable antibiotic administration restored HS loadinginduced gut leakiness, renal injury and systolic blood pressure elevation. The fecal microbiota from mice fed chronic HS could independently cause gut leakiness and renal injury. Our current work provides a novel insight into the mechanism of HS-induced renal injury by investigating the role of the intestine with enteric bacteria and gut permeability and clearly illustrates that chronic HS loading elicited renal injury and dysfunction that was dependent on the intestine.
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Yi-Qing Ni,Su-Mei Wang,Gao-Feng Jiang,Yang Lu,Guobin Lin,Hong-Liang Pan,Junqi Xu,Shuo Hao 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.4
Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-loosenesscaused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFSCNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.
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