International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The article explains the elements that have the largest share in intellectual capital and explains the possibility of quantitative assessment of the specific weight of these elements in intellectual capital. Here on the example of the Republic of Azerbaijan on the basis of statistical data the value of works and services on intellectual capital in Azerbaijan is analyzed, as well as the dynamics of added value created at the expense of intellectual capital. In the article, the impact of added value created by intellectual capital on economic growth in Azerbaijan has been evaluated in the software package Eviews-12.

The purpose of the research is to present a solution to production planning optimization problems, in cases of short-term customer orders and variable production load. The main reasons for the emergence of the task are the introduction of SMEs to unregulated electricity market and the increasing price of electricity due to the imposition of additional components in the formation of the price.The lack of traditional market and the small number of regular customers of SMEs make it difficult to forecast electricity consumption, and therefor impractical to request electricity for long-term periods of time. A possible solution related to the implementation of operational management systems, which, through the introduction of standard models, will enable the implementation of optimization algorithms for production planning.

In this paper we consider the task of estimating the volume of recreational fish catch yield in a water body. We design mathematical models for the tasks varying in complexity and generality and demonstrate how they can be solved using Bayesian approach. A Markov Chain Monte Carlo (MCMC)-based Bayesian approach is widely used as a solution for stochastic models. To solve the simplest case of a proposed model we have used the PyMC library. The proposed models were later used to estimate the recreational fishing capacity of the water bodies in Qazaqstan.

An important task of analyzing the behavior of individuals in society, in a large crowd of people is the development of mathematical models of behavior. The complexity lies in the heterogeneous nature of individuals, in the diversity of their behavioral characteristics, interests, and features of social behavior. Here the possibility of mathematical modeling of crowd behavior is considered and a mathematical model is proposed.

The article develops a simulation model, structural and functional schemes of an intellectualized information-analytical system for monitoring air pollution (for high concentrations of carbon dioxide and sulfur, as well as dust particles with a dispersion of 2-5 microns) within the industrial area of the city. A feature of such a system is the ability to determine, dynamically monitor in real t ime the spatial map of the distribution of the concentration of the above harmful impurities and its forecasting, which would quickly determine areas of the city where the concentration of such impurities exceeds the allowable norm. The main advantage of the developed information -analytical system is the ability to conduct intelligent monitoring of air quality, which will allow for highly accurate and objective forecasting of changes in distribution and redistribution in space over time of harmful impurities. Tests of the developed information -analytical system allowed to investigate its work under different conditions and modes of measuring experiment, as well as to virtually determine the rational parameters of such a monitoring system. A satisfactory discrepancy of the results obtained with the help of experimental measurements wa s found in 7-13% in comparison with the data obtained by simulation, which confirms the correctness and adequacy of the compiled model.

This study proposes a mixed integer linear programming (MILP) approach to the design and management of the biodiesel / diesel (SC) supply chain. The approach was applied to two scenarios with different locations of the fuel blending centers. For both scenarios, optimization problems were formulated and solved while satisfying environmental and economic criteria. The latter are defined in terms of minimum total costs and minimum greenhouse gas emissions generated during the performance of all activities included in the supply chain. The approach was applied to a real case from Bulgaria, where its 27 administrative regions were considered. An analysis was carried out in terms of uncertainties regarding the obtained optimal routes for transportation of raw materials and products, the optimal quantities of feedstocks for cultivation and optimal quantities of biodiesel produced, which is transported to the search areas under both scenarios.

The economic benefits have been also addressed, evaluating the energy production and distribution throughout the year and cost of electricity generation for small PV plants up to 2MW. Renewable energy sources, including solar, wind, hydropower, biofuels and other renewable sources that may be developed in the future are the main focus of the energy transition ensuring a safe step on the path to an intensity system. low energy, with minimal impact of greenhouse gases (GHG) and with a minimal cost for end users. Over the last two years PV systems have attracted a large amount of monetary and investment globally, especially in EU countries. The responsible ministry (MEI) and its subordinate institutions have drafted and approved the “National Strategy of Energy 2018-2030 “, consisting of 6 possible scenarios of energy transition towards a sustainable, reliable and diversified energy system. The national strategy seeks to meet its objectives based on Renewable Energy Sources (RES) and Energy Efficiency (EE). Hence, the economic aspects and identification of the most influenced parameters is identified and highlighted.

Sustainability is a complex issue and framework for companies, with the common goal of meeting economics, environmental awareness and social justice. The focus should be on achieving medium- and long-term goals, and the environmental and social impacts of corporate operations should be examined. The Sustainability Balanced Scorecard, which takes into account ESG aspects, is one of the strategic controlling tools that can be adapted to this framework. The Sustainability Balanced Scorecard also includes a sustainability dimension to embed environmental, social or ethical considerations, including sustainability objectives and performance indicators. The paper attempts to identify the most usable, optimized Sustainability Balance Scorecard model for companies that will be part of sustainability reports in the near future. I have done research on Environmental Management Accounting / Green Controlling, the paper presents a conceptual model, using an interpretive and synthesizing approach by recombination of work in the field.

In this paper, we analyse the relationship between the primary surplus/deficit and debt for the group of eleven Central and Eastern European EU countries. Temporary spending and temporary output are also added to the regression equation as explanatory variables. We use annual data for the period between 1997 and 2020, obtained from Eurostat. The estimated panel regression model passes the employed specification and diagnostic tests. Our analysis reveals there is a significant positive response of the primary surplus to an increase in debt, providing empirical support for the sustainability of fiscal policy in the observed panel of countries during the observed period.

We examine a different approach to complete decarbonization of the Russian economy, in a world where climate policy is increasingly requiring radical reduction of emissions wherever possible. We propose an energy system that can supply solar, and wind generated electricity to fulfill all demand and which accounts for intermittency problems. This is instead of a more usual approach of planning for expensive carbon capture and storage, and a massive increase in energy efficiency and therefore a drastic reduction in energy use per unit Gross Domestic Product (GDP). Coupled with this massive increase in alternative energy, we also propose using excess electricity to generate green hydrogen. Hydrogen is a known technology that can function as storage for future electricity needs or for potential fuel use. Importantly, green hydrogen can be used as a re-placement export for Russia’s current fossil fuel exports and will likely provide higher revenues. The analysis was carried out using the highly detailed modeling framework, the High-Resolution Renewable Energy System for Russia (HIRES-RUS) representative energy system. The modeling showed that there are a number of feasible combinations of wind and solar power generation coupled with green hydrogen production to achieve 100% decarbonization of the Russian economy.

This paper examines the applicability of the neural networks in developing predictive models. A predictive model based on artificial neural networks has been proposed and training has been simulated by applying the Long Short-Term Memory Neural Network module and the time series method. Python programming language to simulate the neural network was used. The model uses the stochastic gradient descent and optimizes the mean square error. Business indicators for forecasting the results of the activity and the risk of bankruptcy of a company are forecasted and a comparison of the obtained forecast values with the actual ones is performed in order to assess the accuracy of the forecast of the developed model. As a result, it can be noted that business indicators can be successfully predicted through the Long Short-Term Memory Neural Network and the forecasted values are close to the actual ones.

The problem of study of the atmospheric state with the accumulation of polluting gases in the atmosphere as a result of anthropogenic activity is very actually. One of the major polluting gases at large ratios is carbon dioxide. The recent study aims to obtain a quantitative expression of the variation of the atmospheric temperature as the result of the variations of carbon dioxide con centrations. It is known that the state of the atmospheric gas is described approximately by the model of the ideal gas. As the altitude increases, the gas pressure and the temperature in the troposphere decrease. The adiabatic constant of atmospheric air can be found from the adiabatic equation which is based on the ideal gas model. The natural logarithm of the pressure depending on the natural logarithm of the temperature allows the determination of this adiabatic constant. The obtained result coincides within the limits of the real ones, fact that denotes the correctness of the application of the ideal gas model. The known data of the pressure as a function of height allow to determine the fictive molar mass of the atmospheric gas from the dependence of the natural logarithm of pressure as a function of the a ltitudes and the result of the fictive molar mass coincides within the limits of the real ones. NASA observatories periodically record from the 1980s until now the permanent increase of the average temperature of the atmosphere with a speed of the order of (0,02 oC/year) and of the concomitant increasing of the concentration of carbon dioxide in the atmosphere. This fact currently concludes that the increase of global average temperature is influenced by the increase of the concentration of carbon dioxide. These concomitant increases allow to elaborate one empirical expression of the variation of the global average temperature depending on the variation of the carbon dioxide concentration. The obtaining of this empirical expression is based on the equation of state of the ideal gas and also on the adiaba tic equation. The graphical representation of the temperature variation as a function of the concentration variation allows the recalculation of the adia batic constant of the atmospheric air which value is within the limits of the real ones and denotes the validity and correctness of the suggested method. The recalculated values of the temperature variation according to the empirical formula must coincide with the real ones recorded. The coincidence is confirmed by the functional dependence ΔTe = f (ΔT). In order to ascertain the average speed of accumulation of carbon dioxide in the atmosphere, the graph of the dependence ΔC = f (time) is represented which proportionality coefficient is the accumulation speed of order (3,833 mg / m3/ year).