Evaluation of Neural Network for Automated Classification of Plant Component on Histological Section

Classification of plant component on image histological sections is critical for determining non-compliance type of undeclared additiveand further action for technologist, or other responsible person. However, this task is often challenging due to the absence of professional histologists or non-compliance with the conditions of microstructural analysisand also the subjective criteria for evaluation. In this study, we propose a machine learning model that automatically classifies the plant component on images histological sections. Our model uses a convolutional neural network to identify regions of plant components, then aggregates those classifications to infer predominant and minor plant components on histological sections image. We evaluated our model on an independent set of 95 images histological sections. It achieved a kappa score of 0,525 and an agreement of 66,6% with three histologists for classifying the predominant plant component, slightly higher than the inter-histologists kappa score of 0,485

Balance of protein supplements according to the criterion of convertible protein

The main sources of vegetable protein are seeds of legumes and oilseeds, which differ as by total content as by the quality. One of the least expensive and most rapid method of assessing the quality of protein is a chemical method, based on a comparative analysis of its amino acid composition, in particular, essential amino acids (EAA), and "ideal" protein. A widespread indicator of the proximity of the protein to the ideal is the minimum period, which shows how much of it can be used by the body for plastic needs (the main exchange and ensuring of body weight gain). Obviously, the more of this (convertible) protein in the product, the better (but not more than the daily value). One of the methods of obtaining a grain product with an increased convertible protein is blending, i.e. mixing in a certain proportion of different types of protein raw materials. In this case, the content of the converted mixture may be greater than in the components, and the excess less. The article presents a methodology for calculating the proportion of convertible protein in the product, as well as a new approach to the formation of effective mixtures. On the basis of this method, the results of the calculation of such mixtures on the example of a grain product with the use of collapsed white lupine, linseed cake and ginger seeds as components are shown. In all cases, there are rational proportions of the mixture, in which its convertible protein exceeds this figure in the component. The accuracy of the calculations largely depends on the accuracy of the total protein content and EAA.The main sources of vegetable protein are seeds of legumes and oilseeds, which differ as by total content as by the quality. One of the least expensive and most rapid method of assessing the quality of protein is a chemical method, based on a comparative analysis of its amino acid composition, in particular, essential amino acids (EAA), and "ideal" protein. A widespread indicator of the proximity of the protein to the ideal is the minimum period, which shows how much of it can be used by the body for plastic needs (the main exchange and ensuring of body weight gain). Obviously, the more of this (convertible) protein in the product, the better (but not more than the daily value). One of the methods of obtaining a grain product with an increased convertible protein is blending, i.e. mixing in a certain proportion of different types of protein raw materials. In this case, the content of the converted mixture may be greater than in the components, and the excess less. The article presents a methodology for calculating the proportion of convertible protein in the product, as well as a new approach to the formation of effective mixtures. On the basis of this method, the results of the calculation of such mixtures on the example of a grain product with the use of collapsed white lupine, linseed cake and ginger seeds as components are shown. In all cases, there are rational proportions of the mixture, in which its convertible protein exceeds this figure in the component. The accuracy of the calculations largely depends on the accuracy of the total protein content and EAA

Innovation technologies in activity of russian civil servants: sociological aspect

Научная новизна заключается в исследовании ранее не изученного в социологической литературе вопроса внедрения инноваций в деятельность социально-профессиональной группы государственных гражданских служащих. Кроме этого, выявлены и эмпирически апробированы факторы, определяющие специфику внедрения инноваций.Scientific novelty consists in research earlier the question of introduction of innovations not studied in sociological literature in activity of social and professional group of the civil civil servants. Besides, the factors defining specifics of introduction of innovations are revealed and empirically approved

Methods for nonparametric statistics in scientific research. Overview. Part 2

The use of nonparametric methods in scientific research provides a number of advantages. The most important of these advantages are versatility and a wide range of such methods. There are no strong assumptions associated with nonparametric tests, which means that there is little chance of assumptions being violated, i. e. the result is reliable and valid. Nonparametric tests are widely used because they may be applied to experiments for which it is not possible to obtain quantitative indicators (descriptive studies) and to small samples. The second part of the article describes nonparametric goodness-of-fit tests, i. e. Pearson’s test, Kolmogorov test, as well as tests for homogeneity, i. e. chi-squared test and Kolmogorov-Smirnov test. Chi-squared test is based on a comparison between the empirical (experimental) frequencies of the indicator under study and the theoretical frequencies of the normal distribution. Kolmogorov-Smirnov test is based on the same principle as Pearson’s chi-squared test, but involves comparing the accumulated frequencies of the experimental and theoretical distributions. Pearson’s chi-squared test and Kolmogorov test may also be used to compare two empirical distributions for the significance of differences between them. Kolmogorov test based on the accumulation of empirical frequencies is more sensitive to differences and captures those subtle nuances that are not available in Pearson’s chi-squared test. Typical errors in the application of these tests are analyzed. Examples are given, and step-by-step application of each test is described. With nonparametric methods, researcher receives a working tool for statistical analysis of the results.The use of nonparametric methods in scientific research provides a number of advantages. The most important of these advantages are versatility and a wide range of such methods. There are no strong assumptions associated with nonparametric tests, which means that there is little chance of assumptions being violated, i. e. the result is reliable and valid. Nonparametric tests are widely used because they may be applied to experiments for which it is not possible to obtain quantitative indicators (descriptive studies) and to small samples. The second part of the article describes nonparametric goodness-of-fit tests, i. e. Pearson’s test, Kolmogorov test, as well as tests for homogeneity, i. e. chi-squared test and Kolmogorov-Smirnov test. Chi-squared test is based on a comparison between the empirical (experimental) frequencies of the indicator under study and the theoretical frequencies of the normal distribution. Kolmogorov-Smirnov test is based on the same principle as Pearson’s chi-squared test, but involves comparing the accumulated frequencies of the experimental and theoretical distributions. Pearson’s chi-squared test and Kolmogorov test may also be used to compare two empirical distributions for the significance of differences between them. Kolmogorov test based on the accumulation of empirical frequencies is more sensitive to differences and captures those subtle nuances that are not available in Pearson’s chi-squared test. Typical errors in the application of these tests are analyzed. Examples are given, and step-by-step application of each test is described. With nonparametric methods, researcher receives a working tool for statistical analysis of the results