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Kobrakov K. I. Kurnetsov D. N. Ruchkina A. G. Sharpar N. M. 《Theoretical Foundations of Chemical Engineering》2020,54(5):1052-1056
Theoretical Foundations of Chemical Engineering - New research results, as well as those published earlier by the authors, on the synthesis of azo compounds based on nitroanilines obtained by the... 相似文献
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Nurullah Bulut Bahar Atmaca Gülsün Akdemir Evrendilek Sibel Uzuner 《Journal of Food Safety》2020,40(6):e12855
Seed processing technologies are essential for seed safety and functionality through protection of physicochemical quality, pathogen inactivation, aflatoxin detoxification and alleviation of mutagenicity. Design of a pilot-scale unit of pulsed electric fields (PEF) to treat sesame seeds with respect to quality parameters, Aspergillus parasiticus inactivation and aflatoxin reduction as well as alleviation of aflatoxin mutagenicity were prompted in this study. PEF energy ranged from 0.97 to 17.28 J achieved maximum reductions of peroxide value and acidity number of 67.4 and 85.7%, respectively, and did not change color L*, a*, b* and hue values. A 60% reduction of A. parasiticus counts occurred at the maximum PEF energy. Aflatoxins G1, G2, B1, and B2 contents decreased by 94.7, 92.7, 86.9, and 98.7%, respectively. Except for the samples treated by 2.16 J with 100 μg/plate and by 6.80 J with 10 μg/plate, PEF treatment provided elimination of aflatoxin mutagenity. It is concluded that PEF treatment can be used to treat sesame seeds with preservation of physicochemical properties, inactivation of A. parasiticus and decomposition of aflatoxins with reduced mutagenicity. 相似文献
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Theoretical Foundations of Chemical Engineering - The extraction of nitric and oxalic acids by neutral compounds TBP, DBBP, and TBPO have been studied. The optimal conditions of their separation... 相似文献
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Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning. 相似文献
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Journal of Communications Technology and Electronics - Abstract—The two-dimensional diffraction problem of a plane polarized TM-wave on two identical solid silver nanocylinders is considered.... 相似文献
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Metallurgical and Materials Transactions B - The process of obtaining titanium tetrachloride (TiCl4) from the quartz-leucoxene concentrate (QLC) from the Yaregskoe oil-titanium field was studied.... 相似文献