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In the conditions of the development of modern economy, human capital is one of the main factors of economic growth. The formation of human capital begins with the birth of a person and continues throughout life, so the value of human capital is inseparable from its carriers, which in turn makes it difficult to account for this factor. This has led to the fact that currently there are no generally accepted methods of calculating the value of human capital. There are only a few approaches to the measurement of human capital: the cost approach (by income or investment) and the index approach, of which the most well-known approach developed under the auspices of the UN.

This paper presents the assigned task in conjunction with the task of demographic dynamics solved in the time-age plane, which allows to more fully take into account the temporary changes in the demographic structure on the dynamics of human capital.

The task of demographic dynamics is posed within the framework of the Mac-Kendrick – von Foerster model on the basis of the equation of age structure dynamics. The form of distribution functions for births, deaths and migration of the population is determined on the basis of the available statistical information. The numerical solution of the problem is given. The analysis and forecast of demographic indicators are presented. The economic and mathematical model of human capital dynamics is formulated on the basis of the demographic dynamics problem. The problem of modeling the human capital dynamics considers three components of capital: educational, health and cultural (spiritual). Description of the evolution of human capital components uses an equation of the transfer equation type. Investments in human capital components are determined on the basis of budget expenditures and private expenditures, taking into account the characteristic time life cycle of demographic elements. A one-dimensional kinetic equation is used to predict the dynamics of the total human capital. The method of calculating the dynamics of this factor is given as a time function. The calculated data on the human capital dynamics are presented for the Russian Federation. As studies have shown, the value of human capital increased rapidly until 2008, in the future there was a period of stabilization, but after 2014 there is a negative dynamics of this value.

Theoretical and experimental investigations of water vapor interaction with porous materials are carried out both at the macro level and at the micro level. At the macro level, the influence of the arrangement structure of individual pores on the processes of water vapor interaction with porous material as a continuous medium is studied. At the micro level, it is very interesting to investigate the dependence of the characteristics of the water vapor interaction with porous media on the geometry and dimensions of the individual pore.

In this paper, a study was carried out by means of mathematical modelling of the processes of water vapor interaction with suffering pore of the cylindrical type. The calculations were performed using a model of a hybrid type combining a molecular-dynamic and a macro-diffusion approach for describing water vapor interaction with an individual pore. The processes of evolution to the state of thermodynamic equilibrium of macroscopic characteristics of the system such as temperature, density, and pressure, depending on external conditions with respect to pore, were explored. The dependence of the evolution parameters on the distribution of the diffusion coefficient in the pore, obtained as a result of molecular dynamics modelling, is examined. The relevance of these studies is due to the fact that all methods and programs used for the modelling of the moisture and heat conductivity are based on the use of transport equations in a porous material as a continuous medium with known values of the transport coefficients, which are usually obtained experimentally.

The article describes the modes with the exacerbation of social and biological history. The analysis of the possible causes of the sharp acceleration of biological and social processes in certain historical periods is carried out. Using mathematical modeling shows that hyperbolic trends in social and biological evolution may be the result of transitional processes in periods of expansion of ecological niches. Accelerating biological speciation due to the fact that its earlier life change inhabitancy, making it more diverse, saturating the organic, thus creating favourable conditions for the emergence of new species. In the social history of the expansion of ecological niches associated with technological revolutions, of which the most important were: Neolithic revolution — the transition from appropriating economy to producing economy (10 thousand years ago), “urban revolution” — a shift from the Neolithic epoch to the bronze epoch (5 thousand years ago), the “axial age” — transition to the development of iron tools (2.5 thousand years ago), the industrial revolution — the transition from manual labor to machine production (200 years ago). All of these technological revolutions have been accompanied by dramatic population growth, changes in social and political spheres. So, observed in the last century, hyperbolic nature of some demographic, economic growth and other indicators of world dynamics is a consequence of the transition process, which began as a result of the industrial revolution and to prepare for the transition of the society to a new stage of its development. Singularity point of hyperbolic trend shows the end of the initial phase of the process and marks the transition to the final stage. The mathematical model describing the demographic and economic changes in the era of change is proposed. It is shown that a direct analogue of the contemporary situation in this sense is the “axial age” (since 8 century BC to the beginning of our era). The existence of this analogy allows you to see into the future by studying the past.

Despite numerous achievements of modern science the problem of lightning initiation in an electrodeless thundercloud, the maximum electric field strength inside which is approximately an order of magnitude lower than the dielectric strength of air, remains unsolved. Although there is no doubt that discharge activity begins with the appearance of positive streamers, which can develop under approximately half the threshold electric field as compared to negative ones, it remains unexplored how cold weakly conducting streamer systems unite in a joint hot well-conducting leader channel capable of self-propagation due to effective polarization in a relatively small external field. In this study, we present a self-organizing transport model which is applied to the case of electric discharge tree formation in a thundercloud. So, the model is aimed at numerical simulation of the initial stage of lightning discharge development. Among the innovative features of the model are the absence of grid spacing, high spatiotemporal resolution, and consideration of temporal evolution of electrical parameters of transport channels. The model takes into account the widely known asymmetry between threshold fields needed for positive and negative streamers development. In our model, the resulting well-conducting leader channel forms due to collective effect of combining the currents of tens of thousands of interacting streamer channels each of which initially has negligible conductivity and temperature that does not differ from the ambient one. The model bipolar tree is a directed graph (it has both positive and negative parts). It has morphological and electrodynamic characteristics which are intermediate between laboratory long spark and developed lightning. The model has universal character which allows to use it in other tasks related to the study of transport (in the broad sense of the word) networks.

The numerical methods used to simulate the evolution of hydrodynamic systems require the considerable use of computational resources thus limiting the number of possible simulations. The data-driven simulation technique is one promising approach to the development of heuristic models, which may speed up the study of such models. In this approach, machine learning methods are used to tune the weights of an artificial neural network that predicts the state of a physical system at a given point in time based on initial conditions. This article describes an original neural network architecture and a novel multi-stage training procedure which create a heuristic model of a two-phase flow in a heterogeneous porous medium. The neural network-based model predicts the states of the grid cells at an arbitrary timestep (within the known constraints), taking in only the initial conditions: the properties of the heterogeneous permeability of the medium and the location of sources and sinks. The proposed model requires orders of magnitude less processor time in comparison with the classical numerical method, which served as a criterion for evaluating the effectiveness of the trained model. The proposed architecture includes a number of subnets trained in various combinations on several datasets. The techniques of adversarial training and weight transfer are utilized.

A model, describing the influence of external factors on temporal evolution of phytoplankton distribution in a horizontally-homogenous water layer, is presented. This model is based upon the reactiondiffusion equation and takes into account the main factors of influence: mineral nutrients, insolation and temperature. The mineral nutrients and insolation act oppositely on spatial phytoplankton distribution. The results of numerical modeling are presented and the prospect of applying this model to reconstruction of phytoplankton distribution from sea-surface satellite data is discussed. The model was used to estimate the chlorophyll content of the Peter the Great Bay (Sea of Japan).

In this paper we propose a mathematical model of cancer tumour occurrence in a quasi twodimensional epithelial tissue. Basic model of the epithelium growth describes the appearance of intensive movement and growth of tissue when it is damaged. The model includes the effects of division of cells and intercalation. It is assumed that the movement of cells is caused by the wave of mitogen-activated protein kinase (MAPK), which in turn activated by the chemo-mechanical signal propagating along tissue due to its local damage. In this paper it is assumed that cancer cells arise from local failure of spatial synchronization of circadian rhythms. The study of the evolutionary dynamics of the model could determine the chemo-physical properties of a tumour, and spatial relationship between the occurrence of cancer cells and development of the entire tissue parameters coordinating its evolution through the exchange of chemical and mechanical signals.

The article contains results of modeling of bi-modal microstructure evolution of VT6 alloy during isothermal forging. The model of recrystallization based on dislocation approach to nucleation and growth of nuclear was made to calculate recrystallization processes of the secondary alpha phase. The globularization process of lamellar alpha phase was calculated with assumption of diffusion-control migration of beta-phase boundary due to grain boundary diffusion of vanadium atom. The theoretical results were compared with experimental one.

In the paper the model for a one-dimensional non-equilibrium riverbed process is proposed. The model takes into account the suspended and bed-load sediment transport. The bed-load transport is determined by using the original formula. This formula was derived from the thin bottom layer motion equation. The formula doesn’t contain new phenomenological parameters and takes into account the influence of bed slope, granulometric and physical mechanical parameters on the bed-load transport. A number of the model test problems are solved for the verification of the proposed mathematical model. The comparison of the calculation results with the established experimental data and the results of other authors is made. It was shown, that the obtained results have a good agreement with the experimental data in spite of the relative simplicity of the proposed mathematical model.

The study is based on a three-dimensional hydrodynamic global climate coupled model, including ocean model with real depths and continents configuration, sea ice evolution model and energy and moisture balance atmosphere model. Aerosol concentration from the year 2010 to 2100 is calculated as a controlling parameter to stabilize mean year surface air temperature. It is shown that by this way it is impossible to achieve the space and seasonal uniform approximation to the existing climate, although it is possible significantly reduce the greenhouse warming effect. Climate will be colder at 0.1–0.2 degrees in the low and mid-latitudes and at high latitudes it will be warmer at 0.2–1.2 degrees. The Pareto frontier is investigated and visualized for two parameters — atmospheric temperature mean square deviation for the winter and summer seasons. The Pareto optimal amount of sulfur emissions would be between 23.5 and 26.5 TgS/year.