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- 저자 : Boyan Li (검색결과 4 건)
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Ji Chunxiang,Li Yingyue,Xiao Qingchen,Li Zishan,Wang Boyan,Geng Xiaowan,Lin Keqing,Zhang Qing,Jin Yuan,Zhai Yuqian,Li Xiaoyu,Chen Jin 한국미생물·생명공학회 2023 Journal of microbiology and biotechnology Vol.33 No.8
Arbuscular mycorrhizal fungi (AMF) are widespread soil endophytic fungi, forming mutualistic relationships with the vast majority of land plants. Biochar (BC) has been reported to improve soil fertility and promote plant growth. However, limited studies are available concerning the combined effects of AMF and BC on soil community structure and plant growth. In this work, a pot experiment was designed to investigate the effects of AMF and BC on the rhizosphere microbial community of Allium fistulosum L. Using Illumina high-throughput sequencing, we showed that inoculation of AMF and BC had a significant impact on soil microbial community composition, diversity, and versatility. Increases were observed in both plant growth (the plant height by 8.6%, shoot fresh weight by 12.1%) and root morphological traits (average diameter by 20.5%). The phylogenetic tree also showed differences in the fungal community composition in A. fistulosum. In addition, Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed that 16 biomarkers were detected in the control (CK) and AMF treatment, while only 3 were detected in the AMF + BC treatment. Molecular ecological network analysis showed that the AMF + BC treatment group had a more complex network of fungal communities, as evidenced by higher average connectivity. The functional composition spectrum showed significant differences in the functional distribution of soil microbial communities among different fungal genera. The structural equation model (SEM) confirmed that AMF could improve the microbial multifunctionality by regulating the rhizosphere fungal diversity and soil properties. Our findings provide new information on the effects of AMF and biochar on plants and soil microbial communities.
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Comparative Study on Anti-interference of Nondestructive Testing System with Annular Array Probes
Ru Bai,Zhiwei Li,Tengda Yang,Boyan Li,Aiyu Dou,Jiakun Tu,Jiaqi Li,Chuanjia Kou,Zhenghong Qian 한국자기학회 2024 Journal of Magnetics Vol.29 No.1
A pulsed eddy current nondestructive testing (PECT) system with annular array probes is designed for inner defect detection of metal pipelines. Tunneling magnetoresistance (TMR) array probes are applied to detect the magnetic signal generated by the inner surface defect of the metal pipelines. Compared to single probe structure, the annular array probes have advantages of high sensitivity, large scanning area and internal realtime detection. However, since many probes are densely arranged in the array, signal interference may occur between adjacent probes. Therefore, two methods of anti-interference, magnetic shielding and time-sharing excitation, are proposed in this work. By modeling and simulation, the working principles of the two antiinterference modes are analyzed and compared respectively. The experimental results show that both methods can reduce the interference between adjacent TMR probes. Compared with the time-sharing excitation method, the magnetic shielding method exhibits better performance with stronger differential signal peak and smaller error, and proves to be a more effective method for the defect detection of the metal pipelines.
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Li Ruichen,Xu Boyan,Qi Yunliang,Xu Weiyang 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.1
The feasibility of the calculation model and calculation methods is verified by engine bench test and visual rapid compression machine (RCM) test as reported in this paper. Using AVL software FIRE, the processes of low speed preignition (LSPI) and super knock triggered by the oil particles have been studied by numerical analysis of downsized turbocharged direct injection (DI) engines with different proportions of ethanol-gasoline blended fuel and different operating conditions (1200 r∙min1, 1600 r∙min1). The results show that the E10 and E20 fuel engines produce super knock successively; with the increase of the ethanol proportion, even if the LSPI phenomenon (1200 r∙min1) still occurs and leads to the subsequent knock process, the pressure rise amplitude is obviously reduced, and no super knock phenomenon occurs at the time when the ethanol proportion reaches 30 %; there is no LSPI phenomenon in the engine after the blending rate is above 50 %. The overall conclusion is that there must be LSPI before the super knock in ethanol-gasoline blended fuel downsized turbocharged DI engines, but the LSPI not always lead to super knock. With the increase of ethanol proportion, even if the LSPI occurs in the engine cylinder, it only causes regular knock.
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Nolinear Multi-component Spectroscopy Analysis Based on Evolutionary Construction Optimazation
Boyan Cai,Hui Cao,Yanbin Zhang,Lixin Jia,Gangquan Si,Zhongjian Li 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Spectroscopy has been widely used to evaluate product quality or to predict components. To deal with the nonlinearity of spectral data, artificial neural networks (ANN) are widely used. One weakness of ANN is we have no accurate analytical method to design a optimal network structure. A multivariate component prediction method based on optimized neural network combined with evolutionary algorithm (EA) for spectral analysis is proposed in the paper. For the proposed method, ANNs are combined with nonlinear adaptive evolutionary programming algorithm (NAEP) to evolve ANNs architecture including the number of hidden nodes and the number of hidden layers. And the root-meansquares error of cross-validation (RMSECV) is the fitness function of NAEP. In order to present the effectiveness of this method, back propagation neural network (BP) and ANN with genetic algorithm (ANN-GA) methods were also used for component predicting models. An application research has been demonstrated with spectral data which is recorded in an experiment of meat content determination. Results indicate that our method has the ability to design the best ANN structure to predict more accurate and robust as a practical spectral analysis tool.
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