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In recent decades, cataclysmic events, deterioration of air and water quality, and loss of biodiversity have forced us to look for ways to save nature. One of the ways to solve the problems is to ensure rational environmental management, which is possible by establishing an effective balance between consumption and compensation by creating an effective financial mechanism. The purpose of the study is to assess the efficiency of the current financial mechanism for environmental management in Ukraine and to determine the prospects for its improvement. The study uses analysis, synthesis, specification, systematization, and generalization. The graphical method was used to assess environmental taxes, and mathematical modelling was used to analyze the dependence of emissions on direct costs and capital investments in air protection and climate change. Environmental taxes in Ukraine are an ineffective instrument of the financial mechanism of environmental management. Their share in the structure of domestic GDP is lower than the share in the EU. The author suggests ways to improve them: to replace the CO2 tax with an energy tax; to cancel the tax-free limit of 500.000 tons of CO2 emissions per year; to change the structure of tax distribution; to introduce tax rebates. The correlation and regression analysis of the dependence of air pollutant emissions on current expenditures and capital investments in air protection and climate change issues showed the existence of a feedback loop. Investment support for environmental management should be provided from various sources in the following areas: national, local and international finances - primarily for the restoration of air, water and contaminated areas; own funds and international investments - for the modernization and greening of production. [ABSTRACT FROM AUTHOR]

The research is aimed at creating a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. Two components of positioning accuracy are considered: geometric and kinematic errors and elastic static deformations. The dynamic error in the partial system of motion of the robot manipulator links is analyzed. The equation of partial motions is obtained from Lagrange's differential equation of motion of the II kind. The system of differential equations for the positioning error was solved analytically by Euler's method. An example of modeling the position and orientation error of the gripper due to temperature deformations of the third link for the manipulator scheme is given. An example of the modeling of static deformations and errors of the manipulator with elastic pliability of the robot links is given. An example of dynamic error modeling in a partial system of motion of the robot links is given. The proposed method of modeling robot gripper positioning errors makes it possible to increase the positioning accuracy of the industrial robot and minimize the vibration of the gripper. Having a mathematical model of positioning errors, it is possible to compensate for the positioning error by changing the speed of movement of the gripper reference point before determining the direct kinematic task. [ABSTRACT FROM AUTHOR]

The increase in exploitation durability of two-layer cast rolls with the working layer made of high chromium cast iron allows one to significantly improve the quality of rolled metal as well as to increase the economic efficiency of the manufacturing process. However, it is severely hindered due to the massiveness of castings, the impossibility of both evaluating mechanical properties along the depth of the working layer, and providing the structural uniformity of the working surface and the decrease in stresses. In our research, aiming to enhance the exploitation durability of sheet rolls, it is recommended to achieve structural uniformity by CuMg alloying, which increases the concentration of copper up to 2.78 wt.% in certain zones and, owing to the accelerated austenite decomposition at a high temperature during the cool-down of the castings, led to the reduction in excessive strength and the level of heat stresses in the castings. We propose the regimes of cyclic heat treatments which, due to the decomposition of retained austenite and the fragmentation of structure, control the level of hardness to reduce and uniformize the level of stresses along the length of a barrel. A further improvement in the predictions of exploitation durability using XGboost method, which was performed based on the chemical composition of the working layer of high-chromium cast iron and heat treatment parameters, requires taking into account the factors characterizing exploitation conditions of specific rolling mills and the transformations of structural-phase state of the surface obtained by a non-destructive control method. As the controlled parameter, the hardness measured on the roll's surface is recommended, while the gradient change in mechanical properties along the working layer depth can be feasibly analyzed by a magnetic method of coercive force measuring. [ABSTRACT FROM AUTHOR]

The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, "Random Forest Regressor" and "Gradient Boosting Regressor", show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the "Random Forest Regressor" and "Gradient Boosting Regressor" models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the "Random Forest Regressor" model on the training data set is 7.14 times smaller than that of the "Gradient Boosting Regressor" model. At the same time, MAE is 2.67 times smaller in the "Random Forest Regressor" model than in the "Gradient Boosting Regressor" model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the "Random Forest Regressor" algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the "Random Forest Regressor" model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages. [ABSTRACT FROM AUTHOR]

To improve energy and environmental security in urban environments and in accordance with the requirements of the EU, the task of sustainable developing agriculture in urban agglomerations and monitoring it using satellite images becomes relevant. The aim of the work is the development of methods and means for determining stable islands of thermal energy to substantiate the optimal locations for plant growing practices in the metropolis. The research was conducted in Kyiv, the largest metropolis of Ukraine. Data from the Landsat 8 and 9 satellites were used because of the free data and they have better spatial resolution and an available archive of observation results. It was established that the temperature map of the city of Kyiv shows differences in temperature between different parts of the city, probably due to the presence of different sources of heat radiation. It is obvious that the standard deviation of the temperature in the plots depends on many factors, in particular, the season and the type of land use. It is necessary to find alternative solutions for the development of crop production in this area, taking into account the characteristics of thermal emissions. Based on software products from free satellite monitoring providers, EO Browser, a specialized software solution (web application) has been created for monitoring agricultural plantations in an urban environment. A collection of LANDSAT 8 satellite images was used. Areas with stable heat emissions were found, which are due to the operation of a modern shopping and entertainment center, and non-traditional crops for landscaping adapted to its design are proposed. As a result of research, strong heat emissions were recorded for some objects, for them the temperature exceeds the surrounding area by 4 °C, while the minimum standard deviation in January is 0.5 °C, the maximum in July is 2.8 °C, in April and October—1.7 °C and 1.2 °C, respectively. [ABSTRACT FROM AUTHOR]