Food security and import substitution: major strategic objectives of contemporary agricultural policy

Rural areas and the agricultural sector are currently experiencing a radically new social and economic situation which barely fits the existing national agricultural policy as a long-term instrument for stimulating the agri-food market and government’s support of the agrarian sector and, primarily, agriculture that underlies it. In the age of globalization of national agri-food markets, food supply security based on import substitution can be ensured in a macro-economic environment that favors the development of a competitive agricultural industry. The main factor preventing the industry from developing is the unequal inter-industrial exchange biased against the rural economy. The article proposes the author’s simple tried and tested schema for evaluating how inter-industry pricing relationships and governmental financial support (in the form of subsidies) to agricultural organizations affect their margins. The evaluation leads to the following conclusions: – Given its real contribution to the national economy, the agro-industry is self-sufficient for its own development, i.e. the state objectively has the potential to increase its expenses on the elimination of the negative consequences from an unequal inter-industry exchange; – Federal expenditures on such state support must secure a margin for agricultural goods producers that enables stimuli for workers’ efficiency and an affordable credit system for the technical and technological upgrade of the facilities required for the production of competitive goods; – The issues of competitive growth of agricultural products require solutions primarily on a federal scale. An essential factor of competitive growth of individual types of national food and agricultural raw materials should involve science proven EEU agricultural treaties. The article discusses the major priorities in developing a common agricultural policy in the new integration institution

Прогнозування частоти неперіодичних сигналів на основі згорткових нейронних мереж

The problem on creation of mathematical support for construction of forecast models based on convolutional neural networks is solved in the work. A method is proposed for using convolutional neural networks to predict the frequency of non-periodic signals. To determine the frequency of the signal, it was divided into parts, after which a fast Fourier transform was used for each part. The spectrograms obtained after the transform are used as inputs to the learning of the neural network. The output value depends on the presence or absence of a frequency that is above the critical value on the predicted interval. The first layer of the neural network uses a three-dimensional convolution, and on the next layers - a onedimensional convolution. Between the convolutional layers, there are subsampling layers used to accelerate learning and prevent retraining. The neural network contains two output neurons which determine the presence of a frequency that exceeds the critical value. The practical task of predicting the frequency of vibration of aircraft engines during their tests is solved. The construction of different neural network models, their training and testing on the data that were collected from vibration sensors during the testing of the aircraft engine has been performed. To increase the amount of data, augmentation is used. To do this, several copies of the signal with changed frequencies are added. The models constructed differ in the amount of data used and in the forecasting time. Comparison of the test results of all the models has been performed. The maximum forecasting time that can be achieved with the proposed method is determined. This time is enough for the pilot to react and change the flight mode or to land the helicopter.Вирішено завдання створення математичного забезпечення для побудови прогнозних моделей на основі згорткових нейронних мереж. Запропоновано метод використання згорткових нейронних мереж для прогнозування частоти неперіодичних сигналів. Вирішено практичне завдання прогнозування частоти вібрацій авіаційних двигунів при проведені їхніх випробувань. Виконано побудову нейромережевих моделей, їхнє навчання та тестування на даних, які було зібрано з датчиків вібрацій при проведені випробувань авіадвигуна. Порівняно результати тестування всіх побудованих моделей

METHODS OF LARGE-SCALE SIGNALS TRANSFORMATION FOR DIAGNOSIS IN NEURAL NETWORK MODELS

Context. The problem of dimensionality reduction of diagnosis signals for their use in neural network models is considered. The object of the study was the process of transformation of diagnosis input signals for their subsequent use in the synthesis of predictivemodels.Objective. The goal of the work is the creation of the methods for the conversion of diagnosis signals as a result of the application of which new signals will be obtained, which in turn will be used in the construction of neural network predictive models and will significantly reduce the synthesis time of the model by reducing their dimension and the allocation of the necessary components that characterize the state of the individual elements of the object of diagnosis.Method. The methods of reducing the dimension of the input signals of diagnosis and isolation of their components, which characterize the state of the individual elements of the object of diagnosis on the basis of expert knowledge about the process of diagnosisare proposed. The developed methods are based on the methods of digital signal processing. Based on the expert knowledge of the object and the process of diagnosis, the necessary signal conversion procedures and their parameters are selected. In accordance withthe requirements for the desired accuracy and detail of the forecast, the optimal degree of averaging of the signal is selected, which directly affects the speed of constructing the predictive model. The proposed methods can be used in the transformation of diagnosissignals of various diagnostic processes where there is a need to build neural network predictive models based on high-dimensional signals. The developed methods were investigated for the conversion of diagnostic signals obtained on a complex object of technicaldiagnostics, namely, on the transmission of the helicopter. On the basis of the received signals, a neural network model was synthesized, the training of which requires much less computational resources, while the prediction accuracy remains optimal.Results. The developed methods are implemented programmatically and investigated in solving the problem of predicting the future state of the helicopter transmission during the diagnosis process.Conclusions. The experiments have confirmed the effectiveness of the developed methods and allow us to recommend them for use in practice in solving diagnostic problems. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in practice for solving the problems of diagnosis and automatic classification on the features. The prospects for further research may include the search for the best parameters of the developed methods, optimization of their software implementations, as well as experimental study of the proposed methods on a large set of practical problems of diagnosing complex objects of different nature by their diagnostic signals

Obtaining of hydroxylated fullerenes Y@C82OX(OH)Y, Y2@C82OX(OH)Y, Y2C2@C82OX(OH)Y and electrophysical characteristic of composite film based thereon

The article presents, for the first time, the results of the research on composite film obtained from hydroxylated endohedral metallofullerenes (EMF) Y@C82, Y2@C82, with Y2C2@C82 and highest fullerenes as dopant. The composite film has been established to have electric conductivity and to be a ferroelectric with the value of residual polarization of ~0.75 mkC/cm2. The impedance spectroscopy of this sample allowed us to determine dispersion of dielectric permittivity and conductivity in the range of frequencies of 0.5Hz–100MHz. It is stated that the value of the high-frequency dielectric permittivity of films is e' = 2.8. However, with reduction in the electric field frequency, real and imaginary parts of e increase to values ~10^4–10^5. Such increase in dielectric permittivity is connected with increase in polarizing caused by accumulation of mobile electric charges (electrons of ions, protons) on boundaries of the structural defects of a film, which are divided by thin dielectric interlayers. The film is solid electrolyte with the ionic conductivity of ~5*10^(-7) S/cm