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Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Kjeldahl and combustion (Dumas) methods are the main methods used to determine total nitrogen (TKN and TCN) content in agriculture biomass. However, the results obtained using these methods differ because of differences in their underlying principles. Herein, we used these two methods to determine the total nitrogen content in 1 179 crop residues (rice straw, wheat straw, corn stover, rape stalk, cotton stalk) from China, and systematically analyzed and compared in total nitrogen content and their distributions in the collected crop residues by different types with these two methods. Seven common distributions (Normal, Lognormal, Gamma, Weibull, Exponential, Laplace, Lorentz) were used to determine the data distribution types of TKN and TCN in different crop residues. The correlation between the two methods was explored using ordinary least squares regression (OLS), orthogonal regression (Orth), and least median square regression (LMS). Finally, the research reviewed the correlation between different biomass (food, flowers, grass, soil, crop, crop residues, sewage sludge and animal manure, etc.) results of two methods for measuring nitrogen content. The results showed: The distributions of nitrogen content were non-normal distributions in different crop residues. The total TKN and TCN contents were the same distributions in rice straw, wheat straw, rape stalk and total crop residues which approximately followed Lognormal, Gamma, Lognormal and Lognormal respectively. The median method was recommended for data statistics, and results from low to high were: 1) TKN: wheat straw (5.66 ± 1.07 g/kg), rape stalk (7.10 ± 1.87 g/kg), rice straw (8.20 ± 1.42 g/kg), corn stover (8.82 ± 2.23 g/kg), cotton stalk (10.42 ± 1.45 g/kg); 2) TCN: wheat straw (6.17 ± 1.17 g/kg), rape stalk (8.50 ± 2.45 g/kg), rice straw (8.59 ± 1.45 g/kg), corn stover (10.10 ± 1.91 g/kg), cotton stalk (11.75 ± 1.48 g/kg). It was found that TKN was significantly lower than TCN in all types of crop residues (P < 0.05). TKN and TCN values were also significantly different among wheat straw, cotton stalk and other crop residues (P < 0.05). Although the fitting efficiencies of OLS, Orth and LMS were the same on the determination coefficient (R2) scale, the fitting results were different. LMS was recommended because it reduced the effect of outliers compared with three methods, observed from kernel density – scatter plots. Five types of crop residues and the total had different fitting result between TKN and TCN. The correlation between TKN and TCN for total crop residues was quantified as the LMS equation. In addition, there was a gap of the linear relationships between TKN and TCN in different types of biomass. The slope of plant biomass was generally lower than that of animal manure, whose potential reason was different forms and contents of nitrogen in different biomass (ammonium nitrogen, nitrate nitrogen, nitrite nitrogen, heterocyclic nitrogen, and nucleic acid nitrogen, etc.). The results may provide extensive and reliable data for reference from large sample size, and methods support for the scientific utilization of nitrogen in crop residues. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To investigate the linear correlation between metformin-induced increase in the number of CD8+ T cells infiltrated into the cancer and metformin-mediated elevation of tumor blood perfusion. Methods 4T1 murine breast cancer cells were orthotopically injected into the fat pad of the 4th breast of BALB/C mice. Nine days later, the mice were randomly divided into the control and metformin groups. Fifteen minutes before extraction, TRITC-conjugated lectin was injected into the tail vein of the mice. Then, the extracted tumor tissues were prepared for frozen sections, which were then stained with anti-CD8 antibody for further immunofluorescent detection. Spearman correlation analysis was performed to observe the correlation between the parameters. Results Both the infiltration of CD8+ T cells [(120.8±26.9)/mm2 vs. (67.9±22.9)/mm2] and lectin-perfused area [(18357.0±5865.0) pixels/μm2 vs. (10233.0±4434.0) pixels/μm2] were significantly higher in metformintreated group than in the untreated group. In both groups, the number of the infiltrated CD8+ T cells was linearly correlated with lectin-perfused area (control: R =0.884, P =0.007; metformin: R =0.928, P <0.001). Conclusion Increased tumor perfusion is closely correlated with metformin-induced increase in the infiltration of CD8+ T cells into the tumor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The signal denoising algorithm based on linear inverse problem will lead to inadequate denoise and serious loss of details. In order to overcome this problem,this paper proposed the signal denoising algorithm based on local linear correlation. This method was based on the local linear correlation between the original signal and the noise signal. Firstly,it constructed the signal matching model by using the same scaling factor and offset in local area. Then,it constructed the regular term with the 1-norm of the original signal. Finally,it proposed a fast iterative shrinkage-thresholding algorithm to find the solution,with the convergence speed was quadratic convergence. The experimental results show that the method is robust and fast. Specifically, the proposed method is to eliminate the noise at the same time it is better to restore the high frequency component of the signal. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A pressing problem facing to missile designer is to identify the cross section loads of missile load by measured strains data which would be conveniently obtained in the flight test. A load identification method based on the measured strain data of the surface of the missile body is introduced to determine the equivalent bending moment and the axial force of the cross section. In order to eliminate the interference of the fault points of the measured data, a defect rejection principle based on linear correlation theory is presented to improve the reliability of the identification method. A verification test is designed to verify the accuracy and reliability of the identification method. Results show that the method has the high identification accuracy and can be applied to the engineering practice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the computational efficiency of the kernel minimum squared error (KMSE) algorithm, we propose a fast KMSE by using the uncorrelated sample vectors, known as basic samples, in the feature space. And we describe the theoretical relationship between the linear correlation of sample vectors in the feature space and the determinant of the kernel matrix. The kernel functions between a sample and the basic samples were used to extract the features from the sample. The whole basic samples is only a small portion of the training set. The experiments on the intrusion detection dataset KDDCUP1999 and other benchmark datasets show that the fast KMSE is computationally efficient and can achieve high classification accuracy. [ABSTRACT FROM AUTHOR]