bluuebunny/crossref_metadata_2025_split huggingface dataset:
Methods of system composition and decomposition as a tool for efficient processing of large amounts of experimental data
Authors: Zbrishchak S G , Zvyagin L S 
Published: February 2021

Abstract
The industrial age led to the emergence of geographically distributed industrial complexes operating in conditions of limited resources and internal competition. Accordingly, the problem of optimizing the regional distribution of industrial objects arose, which was fully solved only with the development of computer technology, which made it possible to quickly and efficiently process large amounts of experimental data. Mathematical approach to solving the economic problems helped to solve some difficult problems. To solve these problems, in the second half of the last century, a whole set of mathematical methods was developed, which made it possible to finally turn Economics into an exact science. When solving large-scale national economic problems, a systematic approach and mathematical methods such as stochastic programming, discrete programming, convex programming, nonlinear and linear programming were used. The decomposition method is one of the most popular ways to solve tasks and goals of any type, which are based on their detailed analysis and division of the process into several stages. This method is most often used in such fields as Economics, mathematics, etc. There is such a method as the approximation-combinatorial method of decomposition and composition of systems. the considered approximation method offers a modification of the listed methods, which can lead to a decrease in their “sensitivity” to changes in the problem conditions, which certainly makes it possible to expand the class of problems that can be solved using the listed methods.
Synergy of factor analysis based on multiplicative index models and graph theory
Authors: Balashova E A , Chernova I I , Ksenofontova Kh Z 
Published: February 2021

Abstract
This article addresses the issues of solving the problems of factor analysis using the concepts of graph theory.
Factor analysis, a section of multivariate statistical analysis combining mathematical and statistical methods to reduce the dimension of the studied multivariate trait, was important in statistical research.
The index method of factor analysis was used in the study of the development of socio-economic systems and the adoption of appropriate policy decisions. This method provides for the use of indices - in terms of indicators, represented in the form of ratios, i.e. in dimensionless values. Thus, indices are peculiar mathematical models of the investigated processes and phenomena. Indexes and index models are the new categories used in the index method of factor analysis.
Factor analysis is used to identify internal non-measurable patterns in various fields of science. The technique for conducting factor analysis is determined by the initial task.
The central problem of the index method of factor analysis is that the end result of estimating factor increments in the mode depends on the order of the multiplier indices.
In turn, graph theory is one of the most extensive sections of discrete mathematics, widely used in solving economic and managerial problems and other fields of knowledge.
To visually display the order of the factors in the model, you can use the graphoanalytic method, which allows you to present a mathematical factor multiplicative model in the form of gras, which, in turn, allows you to identify the influence of individual factors on the effective indicator.
This article explores the problem of using graph theory in solving the problems of factor analysis, which allows for corresponding generalizations, considering all the many possible solutions, including the influence of the order of the partial indices on the factors under consideration.
Forecasting the dynamics of financial time series based on neural networks
Authors: Ivanyuk V A , Abdikeev N M , Tsvirkun A D 
Published: February 2021

Abstract
Forecasting is one of the high-demand data mining problems, but also a very difficult one. The difficulties of forecasting are associated with insufficient quality and quantity of input data, the changes in the environment where the process takes place, and the impact of subjective factors. A forecast always implies some margin of error, which depends on the forecast model used and the completeness of the input data. Methods based on neural networks are the most relevant and highly-demanded techniques today. Neural networks are great for finding accurate solutions in an environment characterized by complex or fragmented information. In the field of finance and economics, the values of time series parameters can be more accurately modelled using neural analysis methods. Artificial neural networks have more common and flexible functional forms than statistical methods. They can generalize information and provide a qualitative forecast under conditions of uncertainty and crisis. The article proposes a forecasting model based on a neural network that can predict the price of a financial asset in a well-defined time interval. Ten technical indicators are used as input signals, and the closing price of the next period is used as an output signal.
Neural networks and their application in forecasting problems
Authors: Ivanyuk V A , Pashchenko F F 
Published: February 2021

Abstract
The report describes popular machine learning methods and applications of neural networks. It reveals methods of training neural networks and offers a method of forecasting based on neural networks for modelling financial time series. Neural networks have recently gained in popularity among scholars. In particular, neural networks are widely used in the field of visualization and image recognition. But the practical significance of neural networks does not end there, they also find usage in such areas as forecasting, classification, clustering and modelling. This success can mainly be attributed to the remarkable property of models based on neural networks – they can «see» non-linear connections in contrast to many models, which for the most part have linear connections only. Currently, the use of neural networks is developing in the following directions: Stock market and macroeconomic forecasting (Neuro XL, OptimuStock, StocksNeural); Speech recognition and man-machine dialogue (Siri, Alexa, Cortana, Alice); Imitation of intellectual activity (weak AI in Siri, Alexa, Cortana, Alice); Improving low-quality and noisy information (DeepImagePrior). The advantages of using neural networks include: versatility. simplicity. Neural networks are able to model dependencies also in cases when there is a large number of variables.
Modelling a human capital of an economic system with neural networks
Authors: Ketova К V , Vavilova D D 
Published: February 2021

Abstract
In the paper, a problem of modelling size, structure and dynamics of a human capital is solved. The research object is an economic system. The subject is a practice of applying neural networks to socio-economic parameters modelling, specifically a human capital. The objective of the paper is to build an adapted neural network algorithm with the purpose of modelling the parameter being studied. Two human capital components are estimated; these are its quantitative and qualitative properties. The key element of a quantitative property (namely, the population reproduction) has a bearing on stability of a human capital development. The quantitative property is multifold: its aspects are healthcare, culture, education and science. To estimate a human capital structure, a population is being divided onto social clusters on the basis of these aspects. As a part of the study, it was found that such mathematical modelling instrument as neural networks is very suitable for conducting a cluster analysis of a given social system. Neural networks are effective means to solve poorly formalized problems; they are tolerant to frequent changes of an environment and can be used to process a vast set of contradictive or incomplete data. The data base comprises demographic data, volume of investments into qualitative human capital properties, and socio-economic development indicators of a given economic system. A gradation of demographic elements of the society based on physical condition and cultural and educational level is built, according to which a statistical data is gathered to solve the clusterization problem. A volume of investment into a human capital is defined by budgetary costs and private investments of the people. Modelling human capital investment dynamics is performed with neural networks being applied as well. The neural network model used herein is a multilayer perceptron with sigmoid logistic activation function. Neural network modelling of predicted values of investment volumes has proved its effectiveness. An estimation of a human capital for the period of 2000-2019, as well as its forecast for years 2020-2025, is exemplified by Russian economic system. Calculations showed that the indicator being studied has been demonstrating the biggest growth rates since 2013, with an ongoing growth to be expected. Evaluated results correlate with a Russian human capital index dynamic pattern, which is defined by UN specialists, qualitatively. A proposed method of a human capital prognosis and estimation can be used furthermore to compare and estimate socio-economic state of Russia’s regions.
Ensemble forecasting method
Authors: Ivanyuk V A , Tsvirkun A D 
Published: February 2021

Abstract
The purpose of this article is to analyze the time series based on aggregate forecasting methods. Forecasting time series comprises an important scientific and technical task which is relevant in various sectors of economy and production. The main objective of any forecasting process is to get the ability to assess trends in changes to a particular factor. To predict the behaviour of future processes, a qualitative analysis of data is required, which provides the basis for the forecast. It is also important to choose the appropriate forecasting method. The forecast obtained may not be always correct, so it is crucial to determine how accurately it is built. In the present paper, characteristics of time series are provided. The main mathematical methods of time series forecasting are analyzed and their classification is presented. Criteria for the accuracy and reliability of forecasting models are defined. The accuracy of the forecast is an important criterion for evaluating the forecasting method. The aggregate ensemble forecast has proven to be an effective method for enhancing the accuracy of forecasting. The article provides an example of constructing an aggregate ensemble forecast. The types of nonlinear models are described and several methods are selected for constructing the aggregate ensemble forecast. The practical application of exponential smoothing is considered, the MA (moving average) model and the AR (autoregressive) model are constructed.
The application of tools of neural networks and artificial intelligence in the recreational sphere
Authors: Dorofeeva A A , Kazak A N , Nyurenberger L B , Shamaeva N P , Selezneva I A 
Published: February 2021

Abstract
The developed neural network mathematical model designed to assess the effectiveness of inbound tourism in various regions of the country. Using the mathematical model, the distribution of the significance of factors influencing the choice of tourists when visiting certain regions is revealed, which should be taken into account when adjusting the federal and regional policies in the field of tourism and recreational activities. The use of this kind of computer program will allow experts from relevant departments and travel agencies to increase the validity of managerial decisions and strategic developments. Thus, the competitiveness of the regions is enhanced and a new approach to the development of the recreational sphere is introduced.
Probabilistic approach to calculating the measure of uncertainty in computational problems of management, economics and finance
Authors: Byvshev V A 
Published: February 2021

Abstract
An approach to calculating the measure of uncertainty in computational problems of management, Economics and Finance is proposed. The approach consists of direct and inverse problems. In the direct problem, the values of the uncertainty measure of the desired quantities are calculated from the known values of the uncertainty measure of known quantities. In the inverse problem, the values of the vagueness measure of the source data are searched for, at which the specified values of the vagueness measure of the desired values are provided. The inverse problem is Hadamard ill-posed problem, and an additional condition is involved for its solution in the form of the principle of equal influences. A definition of “soft” computing is given within the framework of the paradigms of econometrics and the theory of errors [1]. The approach proposed in this paper to calculating the uncertainty measure in a direct problem is compared with the approach of fuzzy set theory and soft computing by L. Zadeh [2]. Two examples of calculating the measure of vagueness of the desired values are discussed: in the dealer problem (training example) and in the problem of evaluating the attractiveness of a real investment project. It is concluded that when conducting financial, economic and managerial calculations in a situation of vagueness, the discussed probabilistic approach should be used, which delivers elegant and easy-to-interpret results. The paper concludes with the application of a probabilistic approach to estimating the measure of vagueness of endogenous variables in descriptive models.
Issues of organization of expertise and problems of expert assessments
Authors: Paderno P I , Burkov E A , Lavrov E A 
Published: February 2021

Abstract
In the course of preparation and carrying out of various expertises, in a number of cases methodological and organizational problems arise due to both the insufficiently high level of training of the working group members who develop the program and methodology of the expertise, and various restrictions, that could have a material impact on the credibility of the results. These restrictions generally are either financial in nature or are associated with a lack of sufficiently qualified and objective experts in the subject area. Objective: to develop a set of techniques that allow to expand the possibilities of using expert assessment methods in the preparation and carrying out of modern expertise in the presence of restrictions associated with external conditions. Methods: methods and approaches of system analysis, integration of individual expert assessments are used. Results: a comprehensive analysis of the problems faced by specialists in the organization of various expertises has been carried out. A number of techniques have been proposed that make it possible to significantly simplify the process of preparing and carrying out expertises but at the same time do not negatively affect the adequacy of the results obtained. Practical significance: the results obtained can be widely used in the course of various expertises, both in the process of selecting experts and in the process of direct obtaining and combining expert assessments. In addition, the use of obtained results in terms of different organizational and material restrictions will allow to expand the scope of possible applications of expert assessments. Prospects: the proposed approach and the set of techniques lay the methodological basis for creating a decision support system that will give specific recommendations to specialists involved in the development of program and methodology of the expertise at all stages of their activities. The introduction of such a system will greatly contribute to the simplification of various expertises, and will also increase confidence in their results.
Biochar: a low-cost adsorbent of Methylene Blue from aqueous solutions
Authors: Kujawska J , Wasag H 
Published: February 2021

Abstract
Removal of Methylene Blue (MB) from aqueous solutions by compost-biochar and plant biomass-biochar mixtures is investigated under batch conditions to determine the adsorption capacity of biocarbons under variable parameters (initial Methylene Blue and biochar concentrations, temperature, pH of the solution). The experimental data have been computed using Freundlich adsorption isotherms. The adsorption percentage onto the biocarbons is shown to correlate linearly with the adsorbent quantity and concentration, MB-biochar contact time and pH. The results from the adsorption process are well correlated with the Freundlich model: R2 > 0.989 for compost-biochar and R2 > 0.977 for biomass-biochar. Biocarbons have been shown to be an effective and low-cost material for the removal of dye and colourisation from an aqueous solution.
Management of the atmospheric pollution mitigation through investment into modernization of heat generation units
Authors: Orynycz O , Wasiak A , Tucki K , Wiszowata K 
Published: February 2021

Abstract
The aim of the work was the investigation of the impact of particulate matter PM collection installations, and the type of fuel being used, on the emission into the atmosphere of particulate matter PM and gas pollutants that come from the operation of the energy installation in the food processing plant. The plant uses electricity from the grid for both production and social purposes. Thermal energy is produced locally using coal, and high-methane natural gas fired boilers. The heat generated is mainly used for technological purposes, in the form of steam supplied for individual production processes. The work presents results of pollutant emission measurements from the power equipment used during the period of time between years 2014 to 2017. The collected data are interpreted by means regression analysis and graphically presented. Annual emissions before and after installation of dedusting equipment at the plant were also compared.
A comparative analysis of a four-stage and five-stage cold forging of a hollow element
Authors: Wójcik Ł , Winiarski G 
Published: February 2021

Abstract
The paper presents the results of a computer simulation of the process of cold forging of a hollow element with an outer flange. Computer testing was conducted in DEFORM 2D/3D using the calculation module 2D. A tube-shaped billet, made of 42CrMo4 grade steel and the inner diameter of Ø45mm, wall thickness 8 mm and length 193.5 mm was used. The analysed process was conducted in two variants. The first one was conducted in four stages, with the first two stages consisting of upsetting the flange part and the next two stages forging on cones. In the second variant, an additional stage of forging on cones was performed, aiming at elimination of the folding on the inner wall of the forging. An analysis of the technology was conducted on the basis of distributions of effective strain, values of the Cockcroft-Latham fracture criterion and progresses of the forming force. On the basis of the research conducted, it was claimed that the five-stage forging process is more favourable.
Comparison of the degree of microbiological contamination of selected closed rooms using the method of fast detection with a multi-sensor matrix
Authors: Malec A , Garbacz M , Guz Ł , Suchorab Z 
Published: February 2021

Abstract
The problem of microbiological contamination has been an important issue in the construction and environmental industry for many years. The concept of Sick Building Syndrome (SBS) means a combination of different health conditions, which can be eradicated by being in a specific interior. The basic prerequisite for an effective analysis of the issue is a detailed knowledge of its causes and consequences for people. In many cases, the reason for SBS is an increase in humidity of building partitions, but also excessive humidity in closed rooms, which becomes the cause of microbiological contamination of building objects. These factors create appropriate conditions for the development of microorganisms, adversely affecting the durability of the building, its technical condition, and above all the health of residents. This article presents a comparison of two rooms of technical nature, characterised by a different degree of microbiological load. For the analysis, a room with a high degree of microbiological contamination and a room in which high quality internal air quality is maintained were selected. The degree of a microbiological load will be determined on the basis of sensory measurements carried out with the use of microbiological, chemical, and rapid detection method using a multisensor matrix. The multi-sensor matrix forms the basis for electronic nose technology, which can also be used to assess internal air quality.
Crushing analysis of energy absorbing materials using artificial neural networks
Authors: Rogala M , Gajewski J , Głuchowski D 
Published: February 2021

Abstract
This article presents the use of artificial neural networks in data analysis. The subject of the research were energy-absorbing materials under oblique loading. The forces obtained during the analysis were used to determine the crushing indicators. The numerical analysis was performed using the FEM Abaqus software. The specimens were loaded with the same force at different angles, i.e. 15, 30, 45, 60 degrees. During the numerical analyses, the normal and shear forces were measured. The tests were carried out under both static and dynamic load. On the basis of the MLP and RBF networks, analyses were carried out to study the relationship between the foam properties and the crushing efficiency indicators.
Virtual therapy using Type 1 Diabetes Direct Simulator
Authors: Nowicki T 
Published: February 2021

Abstract
This paper introduces the Type 1 Diabetes Simulator (T1DDS) and presents its potential in virtualization of type 1 diabetes mellitus therapy. The body of a person with type 1 diabetes does not produce insulin and this hormone must be supplied from the outside constantly to avoid death. Moreover, the insulin doses must be correct in order to minimize short- and long-term health complications. The dose determination is challenging due to the high dynamics of the insulin – glucose metabolic system. For the reason computer therapy aided systems have been developed for last decades to help solve the problem. Type 1 Diabetes Direct Simulator (T1DDS) is the author’s original computer program that imitates physiology of a type 1 diabetic. The aim of the simulator is to enable virtual medical interaction with such a patient within the scope of blood glucose level change after a meal intake and insulin administration. The paper illustrates the ideas behind the direct simulator. Describes and argues its model assumptions and explains its implementation. An exemplary application in medical education is presented and discussed as well.
CFD investigation of the aerodynamic characteristics of the autogyro with a double tail stabilizer
Authors: Czyż Z , Karpiński P , Skiba K 
Published: February 2021

Abstract
Drag, lift and pitching moment were calculated for the tested aircraft sectioned into the fuselage and stabilizer. The analysis also covered the pressure distribution on the surface of this aircraft and on its symmetry plane. The values of the analysed forces and moments on individual parts of the stabilizer for the defined angles of attack were also obtained. The conducted analysis is a preliminary aerodynamic study of a new type of aircraft which is a combination of a gyroplane and a multi-rotor. The obtained results allow the assessment of the aerodynamic performance of the aircraft with a double tail stabilizer and constitute the basis for further optimisation studies.