Regional determinants of the environment of educational emigration of the population of the Carpathian region

The problem aspects of the environment of educational emigration in the oblasts of the Carpathian region are determined and the directions for their regulation are substantiated. The problem aspects of the environment of educational emigration are outlined across the main determinants (defining factors): educational migration capacity; academic environment quality; educational migration motivation; cooperation between universities and industries in the research; institutional conditions in education. The paper suggests the calculation of the Regional Index of Forming the Educational Migration Environment (RIFEME) to evaluate and analyze the environment of educational migration that pushes the young students abroad. It is calculated across 28 parameters by the data for 2017-2018. The selected parameters characterize various conditions of forming the educational migration environment: educational, educational–scientific, informational, socio-economic, political, socio-demographic, innovative, institutional. An aggregate estimation of determinants (integral index) shows the place of the oblasts of the Carpathian region by the condition of forming the educational migration environment. Systemic RIFEME calculation (calculations are performed for 2017-2018) forms the information base for monitoring the environment of the population’s migration activity and condition of educational services market based on academic mobility of students, teachers, and researchers and their cross-border mobility, for detecting the problems of the educational emigration environment, and for determining the ways to regulate it. Main directions to regulate the environment of educational migration in social, scientific-technological, economic, educational, and informational public policies are substantiated. The suggested tools and measures of regulation of the educational migration environment aim to reduce the paces of educational emigration and prevent possible losses of resources capacity and human capital of domestic higher educational institutions and scientific schools.

Aggregate estimation of investments in power plant units using a parametric power function

In the course of designing new generating equipment for power plants and their thermal circuits, in the absence of information about their cost, analog indicators and/or expert assessments are used in the design practice. This approach allows us to compare various options if they can be brought to a comparable form and when the same type of equipment is used. When it is necessary to compare options that differ not only in the specified capacity, but also in the equipment configuration, a more accurate assessment of investment is required. The article proposes a method for estimating capital investment in power plants using a power parametric function. Capital investment is assessed for each unit of the power plant and its engineering system. A special feature of the approach is that the higher the cost of the unit is, the higher its thermodynamic characteristics, power, time of load use, etc. These factors are taken into account by the exponent in the power function. In addition, the correction coefficients take into account the configuration of the equipment, its climatic design, and configuration features. The combination of factors that are taken into account in the power function makes it possible to obtain an estimate of the cost of equipment in different versions. The uniformity of the problem statement makes it possible to apply the approach both to design tasks and to scientific and applied tasks of comparing the existing, newly developed and promising technologies. This paper presents the updating and development of the method developed in previous years at the department of thermal power plants of NSTU. Equations for determining investment in the main units and technical systems of power plants are presented. Estimates of investment in power plants currently under construction in Russia are made. It is shown that investment in power plants in Russia is 20-50% lower than in the USA and Europe, and 20-30% higher than in China.

The Main Directions of Improving the Environmental and Economic Efficiency of Regional Production

The paper provides theoretical grounding and the directions for improving the economic efficiency of the regional feedstuff production. The study develops a conceptual model of distributing management functions of feedstuff production between the regional authorities of state sectoral and economic management and the district level. The authors suggest the methodology for calculating potential capacity of intraregional feedstuff market, considering the demands of all categories of agricultural manufacturers. The study examines possibilities for implementing the strategy of diversified growth and development of the intraregional feedstuff market, including the terms of interaction between the large and micro-business inside the industry and cooperation of micro-business feedstuff manufacturers with agricultural production. The authors have developed the methodology for assessing the efficiency of feedstuff production, based on the aggregate estimation of the management organization, conditions for the development of feedstuff production and changes in the feedstuff production and livestock breeding industry.

INTERVAL CALIBRATION MODEL OF MULTISENSOR SYSTEM

Problem of multisensor system calibration is of great importance in a number of applications. Most often the problem is solving by means of statistical methods using data of calibration controlled experiment. However, in many cases uncertainty and inaccuracy of experimental data more reasonably to express not in terms of random errors but in terms of known bounded absolute errors. For this case based on the introduced definition of “interval readings” interval calibration model is suggested. Within interval paradigm all calibration subproblems are reasonably solved including sensor sensitivity test, most accurate sensors subset selection and aggregate estimation of measurable variable uncertainty interval. There are given a numerical examples.