Prognostication of the asymmetry of population incomes in Ukrainian regions

The article presents the results of the medium-term forecast of income asymmetry of the population of Ukraine. Of the set of special research methods, statistical analysis was used. The initial information for the forecast was the official statistical data, namely: the income of the population of Ukraine in the dynamics by the type of economic activity and in the regional section over the past ten years. Construction of the forecast of the income and the definition of asymmetry was carried out based on the generalization of homogeneous phenomenons (million UAH and ten years). The average values were used to compare the obtained results, summarizing the characteristics of mass, qualitatively homogeneous socio-economic phenomenons, which scientifically predicted the main prospects. The results show a steady trend in the asymmetry of income asymmetry in Ukraine (both by type of economic activity and region), which will remain until 2024. In addition, it is found that Luhansk and Donetsk regions and the indicator (property income received) require additional research by surveying to establish the key risk factors.

The approximation of an isolated epidemic process wave using a combination of exponents

The most commonly used methods for the medium- and long-term forecasting of epidemic processes are based on the classical SIR (susceptible – infected – recovered) model and its numerous modifications. In this approach, the dynamics of the epidemic is approximated using the solutions of differential or discrete equations. The forecasting methods based on the approximation of data by functions of a given class are usually focused on obtaining a short-term forecast. They are not used for the long-term forecasts of epidemic processes due to their insufficient efficiency for forecasting nonstationary processes. In this paper, we formulated a hypothesis that the primary waves of the COVID-19 pandemic, which took place in a number of European countries, including the Republic of Belarus, in the spring-summer of 2020 are isolated and therefore can be regarded as processes close to stationary. On the basis of this hypothesis, a method of approximating isolated epidemic process waves by means of generalized logistic functions with an increased number of exponents was proposed. The developed approach was applied to predict the number of infected people in the Republic of Belarus for the period until August 2020 based on data from the beginning of the epidemic until June 12, 2020.

Long-Term GDP Forecasts and the Prospects for Growth

The growth of GDP is considered as a natural-growth process amenable to description by the logistic-growth equation. The S-shaped logistic pattern provides good descriptions and forecasts for both nominal and real GDP per capita in the US over the last 80 years. This enables the calculation of a long-term forecast for inflation, which is to enter a declining trend not so far in the future. The two logistics are well advanced, more so for nominal GDP. The assumption for logistic growth works even better for Japan whose nominal GDP per capita has already completed tracing out an entire logistic trajectory. The economic woes of industrialized countries could be attributed to the saturation of growth there, as if a niche in nature had been filled to capacity. In contrast, GDP growth in China and India is in the very early stages of logistic growth still indistinguishable from exponential patterns. The ceiling of these logistics can be anywhere between 7 and 15 times today’s levels.

New opportunities for identification of precursors of sea earthquakes

In many publications, as well as in media statements, prominent foreign and Bulgarian seismologists admit that seismology still does not have reliable methods and technical means for the identification of earthquake precursors in marine conditions (short-term forecast). Several facts, circumstances, and considerations are presented, motivating the need to immediately start experimental research in this area. A Bulgarian patent of a device for capturing underwater gas sources is offered for transmitting characteristics of the underwater gas source to a receiving device on land, indicating an impending earthquake. A strategy for experimentation and application of the device through the implementation of a new European project, uniting the forces and capabilities of the South European countries, is being proposed.

Long-term forecast of heavy metals content in wheat grain under changing climate conditions

Using the system approach, we have developed a simulation model for the long-term forecast of the content of toxic chemical elements in grain crop yield. The study was carried out by the example of wheat cultivated in Altai Krai — one of the main grain-producing regions of Russia. Wheat crops were sampled in 10 municipal districts of Altai Krai, which characterize seven different edaphic-climatic zones. The average long-term values of mean monthly air temperature and monthly precipitation for each sampling area were identified using GIS and data of the Interactive Agricultural Ecological Atlas of Russia and Neighboring Countries. A total of 19 chemical elements were considered, i.e. Pb, As, Cd, Hg, Na, Mn, Zn, Cu, Fe, Co, etc. It is shown that content of Pb, Na, Mn and Cu in wheat depend on climatic characteristics of the cultivation area. Regression dependences of element content on the average long-term air temperature and precipitation were established. Based on normalization and spatial generalization of air temperature and precipitation providing the uniform dynamics of their relative monthly values (in percent) throughout the study area, a forecast of their changes was made for 2030. A procedure for grain sampling, GIS technologies for processing meteorological and cartographic data, methods for predicting regional climate changes and establishment of quantitative relationships of chemical elements content in grain with climatic characteristics – all together make up the integral predictive simulation model for toxic substance content in grain crop yield. The model was used for estimation of Pb, Na, Mn, Cu changes in wheat by 2030. The lead (Pb) content in wheat crop delivered to elevators from certain municipal districts will exceed the maximum allowable concentration for breadgrain after 2030. Unlike Pb, Na, Mn, Cu, the content of other metals in wheat grain weakly correlate with long-term changes in air temperature and precipitation; therefore, it can hardly change significantly.

Spatialized Epidemiological Forecasting applied to Covid-19 Pandemic at Departmental Scale in France

In this paper, we present a spatialized extension of a SIR model that accounts for undetected infections and recoveries as well as the load on hospital services. The spatialized compartmental model we introduce is governed by a set of partial differential equations (PDEs) defined on a spatial domain with complex boundary. We propose to solve the set of PDEs defining our model by using a meshless numerical method based on a finite difference scheme in which the spatial operators are approximated by using radial basis functions. Such an approach is reputed as flexible for solving problems on complex domains. Then we calibrate our model on the French department of Isère during the first period of lockdown, using daily reports of hospital occupancy in France. Our methodology allows to simulate the spread of Covid-19 pandemic at a departmental level, and for each compartment. However, the simulation cost prevents from online short-term forecast. Therefore, we propose to rely on reduced order modeling tools to compute short-term forecasts of infection number. The strategy consists in learning a time-dependent reduced order model with few compartments from a collection of evaluations of our spatialized detailed model, varying initial conditions and parameter values. A set of reduced bases is learnt in an offline phase while the projection on each reduced basis and the selection of the best projection is performed online, allowing short-term forecast of the global number of infected individuals in the department.

Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption

The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.

Algorithm and Results of Short-Term Forecast of Changes in the COVID-19 Spread Coefcient in the Constituent Entities of the Russian Federation

There was a decrease in the number of COVID-19 cases across many entities of the Russian Federation towards the end of summer season-2020. However, the disease remains a relevant threat to the public health and economy and the possibility of a second epidemic wave is not excluded. Rate of infection transmission (Rt) is one of the most important indicators to justify the transition to next stage of removing/introducing restrictive measures on COVID-19.Objective of the work was to describe the algorithm of analysis and short-term forecast of coronavirus spread rate, to assess correspondence between theoretically expected and actual values of this indicator.Materials and methods. Procedure for making a short-term extrapolation forecast of Rt in 10 RF constituent entities, depending on the presence or absence of indicator trends with calculation of a 95 % confdence interval of possible changes in its value is provided.Results and discussion. It is proposed to carry out Rt forecast based on averaged values over a week, combining regression analysis and expert assessment of time series dynamics nature for prompt transition from trend forecasting to extrapolation of stationary observation sequences and vice versa. It has been demonstrated that predicted Rt values are not statistically different from actual values. When making managerial decisions on COVID-19 prevention, special attention should be paid to cases when actual value of Rt exceeds the upper limit of confdence interval. Six (20.0 %) such cases were detected in surveyed entities on calendar weeks 33–35. Three of them were registered in Trans-Baikal Territory, where upward trend was reported during that period of time. The cause of this phenomenon should be analyzed. The put forward algorithm of analysis and forecasting of the Rt value changes, which was tested in 10 entities of Russia, provides a reliable basis for making management decisions on removing/introducing restrictive measures for COVID-19 prevention.

Methodological approaches to quantifying money laundering

Subject. The article investigates the anti-money laundering issue and measuring money laundering. In Russia, this issue has not been explored rigorously enough: the widespread opinion in the works of domestic economists about the Russian specifics of laundering of proceeds derived from criminal activity impeded the adaptation of Western models. Objectives. We consider the specifics of Russian conditions for money laundering, identify methodological approaches to assessing the quantitative parameters of the banking sector's demand for cash, which correlates with the scale of money laundering. Methods. We apply the systems approach, formal logic, comparative analysis, correlation and regression analysis, and other methods. Results. We present the results of the analysis of existing methods for assessing the volume of money laundering in foreign countries, demonstrate reasons for their non-applicability in Russian conditions. The paper describes a multi-factor model of dependence of cash inflows to commercial banks on three groups of factors. Based on the retrospective information, it estimates the lower limit of money laundering in Russia and provides a short-term forecast. Conclusions. The use of anti-laundering tools should be focused on the prevention of threats to national security. Therefore, it is advisable to anticipate the dynamics of money-laundering in the country, restraining and preventing its growth. The findings may serve as a basis for further research in the field of testing the hypotheses on measuring money laundering.

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.