bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the readout noise suppression method for digital correlated double sampling
Authors: Duan Wei , Song Qian , Wei Ming-Zhi , Zhao Zhao-Wang , Wang Wei , Zhang Yu-Heng , Feng Zhi-Wei
Published: February 2021

As the areas of CCD detectors and CCD mosaics have become larger and larger, the number of readout channels in astronomical cameras has increased accordingly to keep the image readout time within an acceptable range. For the large area cameras or the mosaic cameras, the analog Correlated Double Sampling (aCDS) circuit used in traditional astronomical cameras for suppressing readout noise is difficult to integrate into the camera controllers within the constraints of the space and energy consumption. Recently, digital CDS (dCDS) technology has been developed to solve this problem, which also offers novel analysis and noise suppression methods. In this study, a mathematical model is presented to conveniently analyze the frequency characteristic of a dCDS circuit, which is then simulated by a numerical method for investigating the noise suppression capability with different sampling weights. Importantly, using this model, the extreme point with lowest readout noise can be predicted for a certain dCDS model; and for a specific CCD readout frequency, readout noise can be suppressed by selecting the proper dCDS model. A testing system is then constructed for validating the efficiency of the proposed method.
Infall in massive clumps harboring bright infrared sources
Authors: Yue Ying-Hua , Qin Sheng-Li , Liu Tie , Tang Meng-Yao , Wu Yuefang , Wang Ke , Zhang Chao
Published: February 2021

Thirty massive clumps associated with bright infrared sources were observed to detect the infall signatures and characterize infall properties in the envelope of the massive clumps by APEX telescope in CO(4–3) and C17O(3–2) lines. Eighteen objects exhibit a “blue profile” in the CO(4–3) line with virial parameters less than 2, suggesting that global collapse is taking place in these massive clumps. The CO(4–3) lines were fitted via the two-layer model in order to obtain infall velocities and mass infall rates. Derived mass infall rates range from 10−3 to 10−1
M
⊙ yr−1. A positive relationship between clump mass and infall rate appears to indicate that gravity plays a dominant role in the collapsing process. Higher luminosity clumps have a larger mass infall rate, implying that those clumps with higher mass infall rates possess a higher star formation rate.
Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning
Authors: Zhu Ke-Rui , Kang Shi-Ju , Zheng Yong-Gang
Published: February 2021

In the fourth Fermi Large Area Telescope source catalog (4FGL), 5064 γ-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazars at low Galactic latitudes and 190 other sources. We employ two different supervised machine learning classifiers, combined with the direct observation parameters given by the 4FGL fits table, to search for sources potentially classified as AGNs and pulsars in the 1336 unassociated sources. In order to reduce the error caused by the large difference in the sizes of samples, we divide the classification process into two separate steps in order to identify the AGNs and the pulsars. First, we select the identified AGNs from all of the samples, and then select the identified pulsars from the remaining cases. Using the 4FGL sources associated or identified as AGNs, pulsars and other sources with the features selected through the K-S test and the random forest (RF) feature importance measurement, we trained, optimized and tested our classifier models. Then, the models are applied to classify the 1336 unassociated sources. According to the calculation results of the two classifiers, we report the sensitivity, specificity, accuracy in each step and the class of unassociated sources given by each classifier. The accuracy obtained in the first step is approximately 95%; in the second step, the obtained overall accuracy is approximately 80%. Combining the results of the two classifiers, we predict that there are 583 AGN-type candidates, 115 pulsar-type candidates, 154 other types of γ-ray candidates and 484 of uncertain types.
GeneticKNN: a weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars
Authors: Han Bo , Qiao Li-Na , Chen Jing-Lin , Zhang Xian-Da , Zhang Yan-Xia , Zhao Yong-Heng
Published: February 2021

We combine K-nearest neighbors (KNN) with a genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by a GA. This approach has two improvements compared to KNN: one is the feature weighted by GA; the other is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors, i.e. p × z
median + (1 – p) × z
mean. Based on the SDSS and SDSS-WISE quasar samples, we explore the performance of GeneticKNN for photometric redshift estimation, comparing with the other six traditional machine learning methods, i.e. the least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), multi-layer perceptrons (MLP), XGBoost, KNN and random forest. KNN and random forest show their superiority. Considering the easy implementation of KNN, we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars. Finally the performance of GeneticKNN is better than that of LASSO, SVR, MLP, XGBoost, KNN and random forest for all cases. Moreover the accuracy is better with the additional WISE magnitudes for the same method.
A hybrid Fast Multipole Method for cosmological N-body simulations
Authors: Wang Qiao
Published: February 2021

We investigate a hybrid numerical algorithm aimed at large-scale cosmological N-body simulation for on-going and future high precision sky surveys. It makes use of a truncated Fast Multiple Method (FMM) for short-range gravity, incorporating a Particle Mesh (PM) method for long-range potential, which is applied to deal with extremely large particle number. In this work, we present a specific strategy to modify a conventional FMM by a Gaussian shaped factor and provide quantitative expressions for the interaction kernels between multipole expansions. Moreover, a proper Multipole Acceptance Criterion for the hybrid method is introduced to solve potential precision loss induced by the truncation. Such procedures reduce the amount of computation compared to an original FMM and decouple the global communication. A simplified version of code is introduced to verify the hybrid algorithm, accuracy and parallel implementation.
Spatially-resolved stellar population properties of the M51–NGC 5195 system from multi-wavelength photometric data
Authors: Wei Peng , Zou Hu , Lin Lin , Zhou Xu , Liu Xiang , Kong Xu , Ma Lu , Ma Shu-Guo
Published: February 2021

Using multi-band photometric images of M51 and its companion NGC 5195 from ultraviolet to optical and infrared, we investigate spatially resolved stellar population properties of this interacting system with stellar population synthesis models. The observed infrared excess (IRX) is used to constrain dust extinction. Stellar mass is also inferred from the model fitting. By fitting observed spectral energy distributions (SEDs) with synthetical ones, we derive two-dimensional distributions of stellar age, metallicity, dust extinction and stellar mass. In M51, two grand-designed spiral arms extending from the bulge show young age, rich metallicity and abundant dust. The inter-arm regions are filled with older, metal-poorer and less dusty stellar populations. Except for the spiral arm extending from M51 into NGC 5195, the stellar population properties of NGC 5195 are quite featureless. NGC 5195 is much older than M51, and its core is very dusty with AV
up to 1.67 mag and dense in stellar mass surface density. The close encounters might drive the dust in the spiral arm of M51 into the center of NGC 5195.
An explanation for 13 consecutive day activities of Mrk 421
Authors: Zheng Yong-Gang , Yang Chu-Yuan , Kang Shi-Ju , Bai Jin-Ming
Published: February 2021

It is surprising to find an instance of migration in the peak positions of synchrotron spectral energy distribution components during the activity epochs of Markarian 421 (Mrk 421), accompanying an orphan flare at the X-ray and GeV-TeV γ-ray bands. A geometric interpretation and standard shock or stochastic acceleration models of blazar emission have difficulty reproducing these observed behaviors. The present paper introduces a linear acceleration by integrating the reconnection electric field into the particle transport model for the observed behaviors of Mrk 421. We note that strong evidence for evolution in characteristic of multi-wavelength spectral energy distribution including shifting the peak frequency, accompanying an orphan flare at the X-ray and GeV-TeV γ-ray bands provides an important electrostatic acceleration diagnostic in a blazar jet. Assuming suitable model parameters, we apply the results of the simulation to the 13-day flaring event in March 2010 of Mrk 421, concentrating on the evolution of multi-wavelength spectral energy distribution characteristic by shifting the peak frequency. It is clear that the ratio of the electric field and magnetic field strength plays an important role in temporal evolution of the peak frequency of synchrotron spectral energy distribution component. We suggest it is reasonable that the electrostatic acceleration is responsible for the evolution of multi-wavelength spectral energy distribution characteristic by shifting the peak frequency. Based on the model results, we assert that the peak frequency of the synchrotron spectral energy distribution component may signify a temporary characteristic of blazars, rather than a permanent one.
Electrolyte-gated transistors for neuromorphic applications
Authors: Huang Heyi , Ge Chen , Liu Zhuohui , Zhong Hai , Guo Erjia , He Meng , Wang Can , Yang Guozhen , Jin Kuijuan
Published: February 2021

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck. To enhance computing performance, neuromorphic computing systems that can simulate the function of the human brain are being developed. Artificial synapses are essential electronic devices for neuromorphic architectures, which have the ability to perform signal processing and storage between neighboring artificial neurons. In recent years, electrolyte-gated transistors (EGTs) have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications. Among the various electronic devices, EGT-based artificial synapses offer the benefits of good stability, ultra-high linearity and repeated cyclic symmetry, and can be constructed from a variety of materials. They also spatially separate “read” and “write” operations. In this article, we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications. We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses. Then, we review different types of channels and electrolyte materials for EGT-based artificial synapses. Finally, we review the potential applications in biological functions.
Multiply accumulate operations in memristor crossbar arrays for analog computing
Authors: Chen Jia , Li Jiancong , Li Yi , Miao Xiangshui
Published: February 2021

Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann in-memory computing architectures. By mapping analog numerical matrices into memristor crossbar arrays, efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law. In this brief review, we present the recent progress in two niche applications: neural network accelerators and numerical computing units, mainly focusing on the advances in hardware demonstrations. The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections. The acceleration of multiple layer perceptrons, convolutional neural networks, generative adversarial networks, and long short-term memory neural networks are described. The latter one is hard computing because the solving of numerical problems requires high-precision devices. Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted. Besides, other nonvolatile devices with the capability of analog computing are also briefly introduced. Finally, we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.
Neuromorphic vision sensors: Principle, progress and perspectives
Authors: Liao Fuyou , Zhou Feichi , Chai Yang
Published: February 2021

Conventional frame-based image sensors suffer greatly from high energy consumption and latency. Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing. In this review article, we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors, and introduce the structure and functions of biological retina. In the main section, we will overview recent developments in neuromorphic vision sensors, including the silicon retina based on conventional Si CMOS digital technologies, and the neuromorphic vision sensors with the implementation of emerging devices. Finally, we will provide a brief outline of the prospects and outlook for the development of this field.
Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors
Authors: Gong Chenrong , Chen Lin , Liu Weihua , Zhang Guohe
Published: February 2021

Multi-terminal electric-double-layer transistors have recently attracted extensive interest in terms of mimicking synaptic and neural functions. In this work, an Ion-Gel gated graphene synaptic transistor was proposed to mimic the essential synaptic behaviors by exploiting the bipolar property of graphene and the ionic conductivity of Ion-Gel. The Ion-Gel dielectrics were deposited onto the graphene film by the spin coating process. We consider the top gate and graphene channel as a presynaptic and postsynaptic terminal, respectively. Basic synaptic functions were successfully mimicked, including the excitatory postsynaptic current (EPSC), the effect of spike amplitude and duration on EPSC, and paired-pulse facilitation (PPF). This work may facilitate the application of graphene synaptic transistors in flexible electronics.
Self-consistent renormalization group approach to continuous phase transitions in alloys: application to ordering in β-brass
Authors: Tokar V I
Published: February 2021

A self-consistent (SC) renormalization group approach of the effective medium kind has been developed and applied to the solution of the Ising model (IM). A renormalization group equation in the local potential approximation (LPA) derived previously for spatially homogeneous systems has been extended to the lattice case and supplemented with a self-consistency condition on the pair correlation function. To validate the approach it has been applied to the simple cubic IM and good agreement of the spontaneous magnetization calculated with the use of the SC–LPA equation with the available exact Monte Carlo simulations data has been established. Next the approach has been applied to the bcc IM corresponding to β-brass. With the use of the effective pair interaction parameters from available ab initio calculations the critical temperature, the correlation length and the long range order parameter in the vicinity of the critical point have been calculated in excellent agreement with experimental data. Qualitative and quantitative arguments have been given in support of the suggestion that the experimentally observed decrease of the effective critical exponent of the order parameter in comparison with the universal value is enhanced by the positive value of the second neighbour pair interaction found in the ab initio calculations.
Assessing natural resources using knowledge-based information processing tools
Authors: Nosov V V , Tindova M G , Ramazanov I A , Poletaeva L P , Avdotin V P
Published: February 2021

To date, other than the land market, the Russian Federation still does not have an established natural resources market due to restrictions on transactions in the natural resources segment; therefore, the market value of natural resources cannot be calculated using classical evaluation methods, and only cadastral appraisal can be conducted. We propose to assess the resources as a whole, using the evaluation of nature reserves, assessment of the resource’s ecological functions and the environmental damage. However, the existing cadastral appraisal methods take no account of resources functions and consider them separately. Moreover, the information on natural systems used for the assessment of resources is incomplete and inaccurate. Such uncertainty makes it difficult to model using classical statistical tools. Therefore, there is a need to use information processing tools based on data mining. We propose to use the fuzzy modelling techniques in natural resources assessment, as they will allow for the evaluation of nature reserves and environmental monitoring at the same time. This paper contains fuzzy models for a land plot evaluation that include linguistic variables - factors that affect the land plot value, and fuzzy rule bases that define relationships between linguistic variables. The results show that the cadastral value of land plots is 2.5 times lower than the one calculated using the fuzzy model, with regard to the ecological factor. The error between the market value of those land plots and the one calculated using the fuzzy model is 11%. Therefore, the use of proposed tools allows for the calculation of the market value of resources based on their cadastral value with an account of various factors using the comparative approach, and also for adjustments to the market value in order to increase the efficiency of natural resources use.
Mutual recognition mechanism of legally significant e-documents and data in cross-border document flow
Authors: Kustov V N , Silanteva E S
Published: February 2021

In this article we will observe mutual recognition mechanism that can provide transparent and reliable environment for data and documents in electronic form exchange across borders. Implementation of the Mutual recognition mechanism concept will help to facilitate interaction and ensure trust between parties taking part in cross-border legally significant e-documents exchange.

Mutual recognition mechanism is a set of legal, organizational, semantic and technical conditions for reciprocal recognition of the validity of data and documents in electronic form exchanged across borders among two or more countries. Mutual recognition mechanism allows public authorities, physical and legal entities to interact in the framework of relations arising during generation, sending, transmission, reception, storage and use of legally significant data and documents in electronic form exchange across borders. Cross-border exchange of legally significant data and documents in electronic form between countries can be exercised by the trust ensured by the Trust services providers. Trust in paperless transboundary interaction is a set of objective signs that are used in electronic interaction. For example, electronic signatures are recognized as electronic signatures and their signed electronic documents are validated as documents signed by subjects of legal relations (owners of electronic signatures), the identity of which is confirmed. In this article we will observe Transboundary trust environment (TTE). TTE is a set of normative and organizational-technical conditions which implementation provides the mode of trust in electronic interaction with the participation of a limited or unlimited number of members. TTE as an integral structure operates based on a set of trusted services and may include a trusted third party. Trusted Third Party (TTP) is the organization responsible for the relevant trust that should be provided to complex information technologies and organizational-legal measures to ensure legally significant electronic interaction. Also, the article describes conceptual model of the mutual recognition mechanism.
Multi-level hybrid recommender decision support system with verbal output
Authors: Novikova S V , Tunakova Y A , Shagidullin A R , Kremleva E S
Published: February 2021

The article describes decision-making methods based on intelligent learning algorithms, for the construction of which verbal elements are used. Such algorithms and methods operate in calculations with strictly quantitative data, however, taking into account the human way of perceiving information in verbal form. A person does not directly participate in the process of constructing a model, that is, its structure does not depend on expert or other human opinions, however, qualitative verbal information (for example, elements of regulatory acts, documents, orders, etc.) is embedded in the algorithm in encoded form.

Unlike the classical methods of fuzzy logic and fuzzy inference, the proposed models consist of several interconnected classical and author’s models, using author’s numerical methods. This increases their accuracy and adequacy. The great advantage is the automatic way to build these models, only on the basis of sets of initial quantitative and qualitative data, which will allow you to quickly and efficiently create recommender systems for solving a wide range of decision-making problems.

The system is built in three stages:

First, the fuzzy logical inference Takagi – Sugeno – Kang system (TSK) is automatically built on the sounding of the available quantitative and qualitative data. The advantage of such a system is that it can be retrained (adjust its parameters).

Then, after the system is built according to the available data, at the second stage its accuracy is increased using a fuzzy neural network. The drawback of the constructed system is that it does not give out a verbal answer.

At the third stage, based on the adjusted parameters of the TSK system, a Mamdani type system is constructed using a special algorithm. As a result, it can produce a verbal answer from a recommender system, and at the same time gets all the advantages of a neural network refinement.

Computational experiments are presented.
A fuzzy model for selecting travel services to produce personalized offers to Internet users
Authors: Shpolianskaya I , Dolzhenko A , Stepanov N , Potapov L
Published: February 2021

With the spread of information technologies in the field of tourism and the emergence of a specific sector of e-tourism, an increasingly urgent problem is not the traditional search for relevant information on the Internet, but the search for personally oriented, personalized information adapted to the individual needs of specific users. The main issue in e-tourism is to determine the most suitable tourist resources from the many available for use by a particular user. Recommender systems in e-tourism provide the mechanism of satisfying customized users’ requirements by supplying them with adequate information about travel resources and services. The implementation of tourist goals can be carried out by a variety of different combinations of travel services. The optimal selection of services is a complex multi-alternative task due to the lack of data about the quality of services and the complexity of the description of user preferences. The paper considers the possibility of using fuzzy logic to the development of the e-tourism support system. Based on user profile data, web services that provide information about travel services, and ratings of services by other users, the system evaluates various alternatives of service’s composition and selects the recommended objects (hotels, flights, restaurants, car rental, museums, etc.). The service-oriented paradigm has been proposed to model the composition of tourism services, and fuzzy logic is used to evaluate the quality and compliance of tourism services with user requests and preferences. The proposed fuzzy logic-based approach analyzes the quality of travel services for personal recommendations to the tourist. The proposed model can provide decision support to users to select the most appropriate travel services and to plan a trip.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.