SAFE EDUCATIONAL ENVIRONMENT IN THE SYSTEM OFEDUCATION OF A. S. MAKARENKO

В статье выполнен анализ процесса формирования безопасной образовательной среды в системе воспитания А.С. Макаренко. Проблема безопасной образовательной среды становится востребованной в связи с происходящими изменениями в системе образования. Указывается важность внедрения программы безопасной образовательной среды в школеThe article analyzes the formation of a safe educational environment in the system of education of A.S. Makarenko. The problem of safe educational environment is becoming popular due to the ongoing changes in the education system. The importance of introduction of the program of safe educational environment at school is specifie

Training algorithms for artificial neural network in predicting of the content of chemical elements in the upper soil layer

Models based on Artificial Neural Networks (ANN) in recent years are increasingly being used in environmental studies. Among the many types of ANN, the network type Multilayer Perceptron (MLP) has become most widespread. Such networks are universal, simple, and suitable for most tasks. The main problem when modelling using MLP is the choice of the learning algorithm. In this paper, we compared several learning algorithms: Levenberg-Marquart (LM), LM with Bayes regularization (BR), gradient descent (GD), and GD with the speed parameter setting (GDA). The data for modelling were taken from the results of the soil screening of an urbanized area. The spatial distribution of the chemical element Chromium (Cr) in the surface layer of the soil was simulated. The structure of the MLP network was chosen using computer simulations based on minimization of the root mean squared error (RMSE). The model using the LM training algorithm showed the best accuracy. © 2018 Author(s)

Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area

The study is based on the data obtained as a result of soil screening in the city of Noyabrsk, Russia. A comparison of two types of neural networks most commonly used in this type of research was carried out: multi-layer perceptron (MLP), generalized regression neural network (GRNN), and a combined MLP and ordinary kriging approach (MLPRK) for predicting the spatial distribution of the chemical element Chromium (Cr) in the surface layer of the urbanized territory. The model structures were developed using computer modeling, based on minimizing of a root mean squared error (RMSE). As input parameters, the spatial coordinates were used, and the concentration of Cr - as the output. The hybrid MLPRK approach showed the best prognostic accuracy. © 2018 Author(s)

High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging

The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK. © 2017 Author(s)

Prediction the dynamic of changes in the concentrations of main greenhouse gases by an artificial neural network type NARX

The paper considered the use of one of the most accurate artificial neural networks for predicting time series - a nonlinear autoregressive neural network with external input (NARX) for predicting the dynamics of changes in the concentrations of the main greenhouse gases. The data were obtained in the course of monitoring the dynamics of changes in the main greenhouse gases on the Arctic island Belyy, Russia. The data of the surface concentration of methane, carbon dioxide, carbon monoxide and water vapor were used. A time interval of 168 hours was chosen for the study during the summer period (July-August 2016). The NARX model accurately predicted concentration changes for all greenhouse gases. © 2020 American Institute of Physics Inc.. All rights reserved

The forecast of the methane concentration changes for the different time periods on the arctic island bely

The paper predicts the changes in the concentration of one of the main greenhouse gases - methane (CH4). The forecast was made for three different time periods, each of which had its own characteristics of the dynamics of changes in the concentration of CH4. Data for the study were collected while monitoring the content of the main greenhouse gases in the surface layer of atmospheric air in the Russian Arctic (Bely Island, Yamalo-Nenets Autonomous Okrug). We compared the results of the models prediction based on the two types of artificial neural networks: Elman and nonlinear autoregressive neural network with external input (NARX). NARX showed a high prediction accuracy for all studied time intervals. © 2020 American Institute of Physics Inc.. All rights reserved

Двухшаговый комбинированный алгоритм повышения точности прогнозирования концентрации метана в атмосферном воздухе на основе нейронной сети NARX и последующего прогнозирования невязок

Climate change in the Arctic is great and can have a significant inverse effect on the global climate, which determines the global significance of climate change in the Arctic. To date, many issues regarding the mechanisms responsible for the rapid melting of Arctic ice and permafrost degradation have not been resolved. It is not known when and what consequences these changes will lead to. Assessing the relationship between global warming and greenhouse gas emissions is an important environmental challenge. Among the main greenhouse gases, the evolution and climate-forming role of the carbon dioxide have been studied. The data on the methane subcycle of the carbon cycle is much less. In the paper, the authors propose a two-step combined algorithm (NARXR) to improve the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of the residuals. Two commonly used models based on artificial neural networks (ANN) for predicting time series are compared to determine the most appropriate base model. Nonlinear autoregressive neural network with external input (NARX) and Elman’s neural network are used. For the forecast, the authors use data on the methane concentration (CH4) in the atmospheric surface layer on the Arctic Island of Bely (Russia). Data is selected for a time interval of 192 hours, because it is characterized by significant daily fluctuations in the concentration of CH4. Values corresponding to the first 168 hours of the interval are used to train the ANN, and then concentrations are predicted for the next 24 hours. The proposed approach shows more accurate forecast results. © Subbotina I. E., Buevich A. G., Sergeev A. P., Shichkin A. V., Baglaeva E. M., Remezova M. S., 2020.The authors are grateful to the Department of Science and Innovation of the Yamal-Nenets Autonomous District and to the NP Russian Center for the Development of the Arctic, city of Salekhard, for technical and logistical support of scientific expeditions to the Island of Bely. The authors also thank the reviewers for constructive criticism and useful recommendations that have improved the quality of article materials

Microwave-assisted palladium-catalyzed C-C coupling versus nucleophilic aromatic substitution of hydrogen (SN H) in 5-bromopyrimidine by action of bithiophene and its analogues

5-Bromopyrimidine reacts with 2,2′-bithiophene, [2,2′:5′, 2″]terthiophene and 2-phenylthiophene in the presence of a palladium catalyst to give 5-(het)aryl substituted pyrimidines due to the palladium-catalyzed aryl-aryl C-C coupling. However 5-bromo-4-(het)aryl- pyrimidines have been prepared from the same starting materials through the SN H-reaction catalyzed by a Lewis acid. Conditions for both types of reactions were optimized. All components of the reaction mixtures, including by-products, have been elucidated by gas-liquid chromatography/mass- spectrometry. Evidence for the structure of 4- and 5-bithiophenyl-substituted pyrimidines has first been obtained by means of X-ray crystallography analysis. Molecular orbital calculations (TDDFT), as well as the redox and optical measurements for all new compounds have also been performed. © 2013 Elsevier Ltd. All rights reserved

Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model

An algorithm based on predicting the residuals of the nonlinear autoregressive neural network model with external input (NARX), which can improve the prediction accuracy, was proposed. Data of the concentration of one of the main greenhouse gases methane (CH4) on the Arctic Island of Belyy, Russia, were used for prediction. A time interval, which was characterized by high daily fluctuations in the CH4 concentration was selected. The forecast accuracy was determined by the mean absolute error (MAE), root mean squared error (RMSE) and root mean squared relative error (RMSRE) errors. The use of the algorithm allowed to increase the forecast accuracy from 11% for RMSE to 20% for RMSRE. © 2020 American Institute of Physics Inc.. All rights reserved