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The temporal variability of exergy of short-wave and long-wave radiation and its relationships with sensible heat, water vapor (H₂O) and carbon dioxide (CO₂) fluxes on a recently clear-cut area in a mixed coniferous and small-leaved forest in the Tver region is discussed. On the basis of the analysis of radiation and exergy efficiency coefficients suggested by Yu.M. Svirezhev it was shown that during the first eight months after clearcutting the forest ecosystem functions as a "heat engine" i.e. the processes of energy dissipation dominated over processes of biomass production. To validate the findings the statistical analysis of temporary variability of meteorological parameters, as well as, daily fluxes of sensible heat, H₂O and CO₂ was provided using the trigonometrical polynomials. The statistical models that are linearly depended on an exergy of short-wave and long-wave radiation were obtained for mean daily values of CO₂ fluxes, gross primary production of regenerated vegetation and sensible heat fluxes. The analysis of these dependences is also confirmed the results obtained from processing the radiation and exergy efficiency coefficients. The splitting the time series into separate time intervals, e.g. “spring–summer” and “summer–autumn”, allowed revealing that the statistically significant relationships between atmospheric fluxes and exergy were amplified in summer months as the clear-cut area was overgrown by grassy and young woody vegetation. The analysis of linear relationships between time-series of latent heat fluxes and exergy showed their statistical insignificance. The linear relationships between latent heat fluxes and temperature were in turn statistically significant. The air temperature was a key factor improving the accuracy of the models, whereas effect of exergy was insignificant. The results indicated that at the time of active vegetation regeneration within the clear-cut area the seasonal variability of surface evaporation is mainly governed by temperature variation.

The paper proposes a mathematical model for the therapy of glioma, taking into account the blood-brain barrier, radiotherapy and antibody therapy. The parameters were estimated from experimental data and the evaluation of the effect of parameter values on the effectiveness of treatment and the prognosis of the disease were obtained. The possible variants of sequential use of radiotherapy and the effect of antibodies have been explored. The combined use of radiotherapy with intravenous administration of $mab$ $Cx43$ leads to a potentiation of the therapeutic effect in glioma.

Radiotherapy must precede chemotherapy, as radio exposure reduces the barrier function of endothelial cells. Endothelial cells of the brain vessels fit tightly to each other. Between their walls are formed so-called tight contacts, whose role in the provision of BBB is that they prevent the penetration into the brain tissue of various undesirable substances from the bloodstream. Dense contacts between endothelial cells block the intercellular passive transport.

The mathematical model consists of a continuous part and a discrete one. Experimental data on the volume of glioma show the following interesting dynamics: after cessation of radio exposure, tumor growth does not resume immediately, but there is some time interval during which glioma does not grow. Glioma cells are divided into two groups. The first group is living cells that divide as fast as possible. The second group is cells affected by radiation. As a measure of the health of the blood-brain barrier system, the ratios of the number of BBB cells at the current moment to the number of cells at rest, that is, on average healthy state, are chosen.

The continuous part of the model includes a description of the division of both types of glioma cells, the recovery of BBB cells, and the dynamics of the drug. Reducing the number of well-functioning BBB cells facilitates the penetration of the drug to brain cells, that is, enhances the action of the drug. At the same time, the rate of division of glioma cells does not increase, since it is limited not by the deficiency of nutrients available to cells, but by the internal mechanisms of the cell. The discrete part of the mathematical model includes the operator of radio interaction, which is applied to the indicator of BBB and to glial cells.

Within the framework of the mathematical model of treatment of a cancer tumor (glioma), the problem of optimal control with phase constraints is solved. The patient’s condition is described by two variables: the volume of the tumor and the condition of the BBB. The phase constraints delineate a certain area in the space of these indicators, which we call the survival area. Our task is to find such treatment strategies that minimize the time of treatment, maximize the patient’s rest time, and at the same time allow state indicators not to exceed the permitted limits. Since the task of survival is to maximize the patient’s lifespan, it is precisely such treatment strategies that return the indicators to their original position (and we see periodic trajectories on the graphs). Periodic trajectories indicate that the deadly disease is translated into a chronic one.

The paper presents an analysis of the nonlinear equation of spin wave transport by the method of characteristics. The conclusion of a new mathematical model of spin wave propagation is presented for the solution of which the characteristic is applied. The behavior analysis of the behavior of the real and imaginary parts of the wave and its amplitude is performed. The phase portraits demonstrate the dependence of the desired function on the nonlinearity coefficient. It is established that the real and imaginary parts of the wave oscillate by studying the nature of the evolution of the initial wave profile by the phase plane method. The transition of trajectories from an unstable focus to a limiting cycle, which corresponds to the oscillation of the real and imaginary parts, is shown. For the amplitude of the wave, such a transition is characterized by its amplification or attenuation (depending on the nonlinearity coefficient and the chosen initial conditions) up to a certain threshold value. It is shown that the time of the transition process from amplification (attenuation) to stabilization of the amplitude also depends on the nonlinearity parameter. It was found out that at the interval of amplification of the amplitude of the spin wave, the time of the transition process decreases, and lower amplitude values correspond to higher parameters of nonlinearity.

This work is devoted to the study of the problem of modeling and analyzing complex oscillatory modes, both regular and chaotic, in systems of interacting populations in the presence of random perturbations. As an initial conceptual deterministic model, a Volterra system of three differential equations is considered, which describes the dynamics of prey populations of two competing species and a predator. This model takes into account the following key biological factors: the natural increase in prey, their intraspecific and interspecific competition, the extinction of predators in the absence of prey, the rate of predation by predators, the growth of the predator population due to predation, and the intensity of intraspecific competition in the predator population. The growth rate of the second prey population is used as a bifurcation parameter. At a certain interval of variation of this parameter, the system demonstrates a wide variety of dynamic modes: equilibrium, oscillatory, and chaotic. An important feature of this model is multistability. In this paper, we focus on the study of the parametric zone of tristability, when a stable equilibrium and two limit cycles coexist in the system. Such birhythmicity in the presence of random perturbations generates new dynamic modes that have no analogues in the deterministic case. The aim of the paper is a detailed study of stochastic phenomena caused by random fluctuations in the growth rate of the second population of prey. As a mathematical model of such fluctuations, we consider white Gaussian noise. Using methods of direct numerical modeling of solutions of the corresponding system of stochastic differential equations, the following phenomena have been identified and described: unidirectional stochastic transitions from one cycle to another, trigger mode caused by transitions between cycles, noise-induced transitions from cycles to the equilibrium, corresponding to the extinction of the predator and the second prey population. The paper presents the results of the analysis of these phenomena using the Lyapunov exponents, and identifies the parametric conditions for transitions from order to chaos and from chaos to order. For the analytical study of such noise-induced multi-stage transitions, the technique of stochastic sensitivity functions and the method of confidence regions were applied. The paper shows how this mathematical apparatus allows predicting the intensity of noise, leading to qualitative transformations of the modes of stochastic population dynamics.

The background social strain of a society can be quantitatively estimated using various statistical indicators. Mathematical models, allowing to forecast the dynamics of social strain, are successful in describing various social processes. If the number of interacting groups is small, the dynamics of the corresponding indicators can be modelled with a system of ordinary differential equations. The increase in the number of interacting components leads to the growth of complexity, which makes the analysis of such models a challenging task. A continuous social stratification model can be considered as a result of the transition from a discrete number of interacting social groups to their continuous distribution in some finite interval. In such a model, social strain naturally spreads locally between neighbouring groups, while in reality, the social elite influences the whole society via news media, and the Internet allows non-local interaction between social groups. These factors, however, can be taken into account to some extent using the term of the model, describing negative external influence on the society. In this paper, we develop a continuous social stratification model, describing the dynamics of two societies connected through migration. We assume that people migrate from the social group of donor society with the highest strain level to poorer social layers of the acceptor society, transferring the social strain at the same time. We assume that all model parameters are constants, which is a realistic assumption for small societies only. By using the finite volume method, we construct the spatial discretization for the problem, capable of reproducing finite propagation speed of social strain. We verify the discretization by comparing the results of numerical simulations with the exact solutions of the auxiliary non-linear diffusion equation. We perform the numerical analysis of the proposed model for different values of model parameters, study the impact of migration intensity on the stability of acceptor society, and find the destabilization conditions. The results, obtained in this work, can be used in further analysis of the model in the more realistic case of inhomogeneous coefficients.

In this paper average times between elementary jumps of stock returns on the Russian market were experimentally studied. Considering the scaling of the probability density function of stock returns on different time intervals it is shown that an elementary jump in the random walks scheme for financial instrument returns is a unit price change (tick) that corresponds to a single deal on the stock market.

This article is dedicated to a comparison analysis of optimization methods, in order to perform an interval estimation of electrical energy technical losses in distribution networks of voltage 6–20 kV. The issue of interval evaluation is represented as a multi-dimensional conditional minimization/maximization problem with implicit target function. A number of numerical optimization methods of first and zero orders is observed, with the aim of determining the most suitable for the problem of interest. The desired algorithm is BOBYQA, in which the target function is replaced with its quadratic approximation in some trusted region.

In 2007 Kasman conducted a series of original computer experiments with sine-Gordon kinks moving along artificial DNA sequences. Two sequences were considered. Each consisted of two parts separated by a boundary. The left part of the first sequence contained repeating TTA triplets that encode leucines, and the right part contained repeating CGC triplets that encode arginines. In the second sequence, the left part contained repeating CTG triplets encoding leucines, and the right part contained repeating AGA triplets encoding arginines. When modeling the kink movement, an interesting effect was discovered. It turned out that the kink, moving in one of the sequences, stopped without reaching the end of the sequence, and then “bounced off” as if he had hit a wall. At the same time, the kink movement in the other sequence did not stop during the entire time of the experiment. In these computer experiments, however, a simple DNA model proposed by Salerno was used. It takes into account differences in the interactions of complementary bases within pairs, but does not take into account differences in the moments of inertia of nitrogenous bases and in the distances between the centers of mass of the bases and the sugar-phosphate chain. The question of whether the Kasman effect will continue with the use of more accurate DNA models is still open. In this paper, we investigate the Kasman effect on the basis of a more accurate DNA model that takes both of these differences into account. We obtained the energy profiles of Kasman's sequences and constructed the trajectories of the motion of kinks launched in these sequences with different initial values of the energy. The results of our investigations confirmed the existence of the Kasman effect, but only in a limited interval of initial values of the kink energy and with a certain direction of the kinks movement. In other cases, this effect did not observe. We discussed which of the studied sequences were energetically preferable for the excitation and propagation of kinks.

Our purpose is to study the spatio-temporal population wave behavior observed in the predator-prey system. It is assumed that predators move both directionally and randomly, and prey spread only diffusely. The model does not take into account demographic processes in the predator population; it’s total number is constant and is a parameter. The variables of the model are the prey and predator densities and the predator speed, which are connected by a system of three reaction – diffusion – advection equations. The system is considered on an annular range, that is the periodic conditions are set at the boundaries of the interval. We have studied the bifurcations of wave modes arising in the system when two parameters are changed — the total number of predators and their taxis acceleration coefficient.

The main research method is a numerical analysis. The spatial approximation of the problem in partial derivatives is performed by the finite difference method. Integration of the obtained system of ordinary differential equations in time is carried out by the Runge –Kutta method. The construction of the Poincare map, calculation of Lyapunov exponents, and Fourier analysis are used for a qualitative analysis of dynamic regimes.

It is shown that, population waves can arise as a result of existence of directional movement of predators. The population dynamics in the system changes qualitatively as the total predator number increases. А stationary homogeneous regime is stable at low value of parameter, then it is replaced by self-oscillations in the form of traveling waves. The waveform becomes more complicated as the bifurcation parameter increases; its complexity occurs due to an increase in the number of temporal vibrational modes. A large taxis acceleration coefficient leads to the possibility of a transition from multi-frequency to chaotic and hyperchaotic population waves. A stationary regime without preys becomes stable with a large number of predators.

This scientific work is dedicated to applying of two-layer interval weighted graphs in nonstationary time series forecasting and evaluation of market risks. The first layer of the graph, formed with the primary system training, displays potential system fluctuations at the time of system training. Interval vertexes of the second layer of the graph (the superstructure of the first layer) which display the degree of time series modeling error are connected with the first layer by edges. The proposed model has been approved by the 90-day forecast of steel billets. The average forecast error amounts 2,6 % (it’s less than the average forecast error of the autoregression models).