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The Exner equation in conjunction phenomenological sediment transport models is widely used for mathematical modeling non-cohesive river bed. This approach allows to obtain an accurate solution without any difficulty if one models evolution of simple shape bed. However if one models evolution of complex shape bed with unstable soil the numerical instability occurs in some cases. It is difficult to detach this numerical instability from the natural physical instability of bed.

This paper analyses the causes of numerical instability occurring while modeling evolution of complex shape bed by using the Exner equation and phenomenological sediment rate models. The paper shows that two kinds of indeterminateness may occur while solving numerically the Exner equation closed by phenomenological model of sediment transport. The first indeterminateness occurs in the bed area where sediment transport is transit and bed is not changed. The second indeterminateness occurs at the extreme point of bed profile when the sediment rate varies and the bed remains the same. Authors performed the closure of the Exner equation by the analytical sediment transport model, which allowed to transform the Exner equation to parabolic type equation. Analysis of the obtained equation showed that it’s numerical solving does not lead to occurring of the indeterminateness mentioned above. Parabolic form of the transformed Exner equation allows to apply the effective and stable implicit central difference scheme for this equation solving.

The model problem of bed evolution in presence of periodic distribution of the bed shear stress is carried out. The authors used the explicit central difference scheme with and without filtration method application and implicit central difference scheme for numerical solution of the problem. It is shown that the explicit central difference scheme is unstable in the area of the bed profile extremum. Using the filtration method resulted to increased dissipation of the solution. The solution obtained by using the implicit central difference scheme corresponds to the distribution law of bed shear stress and is stable throughout the calculation area.

Kuramoto model of non-linearly coupled oscillators provides a simple but effective approach to the study of the synchronization phenomenon in complex systems. In the present article we consider a particular Kuramoto model with three non-identical oscillators associated with a multi-cell radial profile of the solar meridional circulation. The top and the bottom oscillators are coupled through the middle one. The main difference of the present Kuramoto model from the previous ones consists in the non-identical coupling: coupling coefficients which tie the middle oscillator with the top and the bottom ones are different. We investigate how the value of the coupling asymmetry of the middle oscillator influences the synchronization. In the present model the synchronization conditions appear to be different the classical Kuramoto model allowing the synchronization to be reached with weaker coupling. We perform a reconstruction of coupling coefficients from the phase difference between the top and the bottom oscillators, assuming that the synchronization is reached and the natural frequencies are known. The absolute cumulative coupling is uniquely determined by the phase difference between the top and the bottom oscillators and the coupling asymmetry of the middle oscillator. In general case, higher values of the coupling asymmetry of the middle oscillator correspond to lower cumulative coupling. A unique coupling reconstruction with unknown coupling asymmetry is possible in general case only for the weak cumulative coupling. Deviations from the general case are discussed. We perform a model simulation with natural frequencies estimated from the velocities of the solar meridional flow. Heliseismological observations of the deep flow may be attributed either to the middle cell or to the deep one. We discuss the difference between these two cases in terms of the coupling reconstruction.

We propose a three-component discrete-time model of the phytoplankton-zooplankton community, in which toxic and non-toxic species of phytoplankton compete for resources. The use of the Holling functional response of type II allows us to describe an interaction between zooplankton and phytoplankton. With the Ricker competition model, we describe the restriction of phytoplankton biomass growth by the availability of external resources (mineral nutrition, oxygen, light, etc.). Many phytoplankton species, including diatom algae, are known not to release toxins if they are not damaged. Zooplankton pressure on phytoplankton decreases in the presence of toxic substances. For example, Copepods are selective in their food choices and avoid consuming toxin-producing phytoplankton. Therefore, in our model, zooplankton (predator) consumes only non-toxic phytoplankton species being prey, and toxic species phytoplankton only competes with non-toxic for resources.

We study analytically and numerically the proposed model. Dynamic mode maps allow us to investigate stability domains of fixed points, bifurcations, and the evolution of the community. Stability loss of fixed points is shown to occur only through a cascade of period-doubling bifurcations. The Neimark – Sacker scenario leading to the appearance of quasiperiodic oscillations is found to realize as well. Changes in intrapopulation parameters of phytoplankton or zooplankton can lead to abrupt transitions from regular to quasi-periodic dynamics (according to the Neimark – Sacker scenario) and further to cycles with a short period or even stationary dynamics. In the multistability areas, an initial condition variation with the unchanged values of all model parameters can shift the current dynamic mode or/and community composition.

The proposed discrete-time model of community is quite simple and reveals dynamics of interacting species that coincide with features of experimental dynamics. In particular, the system shows behavior like in prey-predator models without evolution: the predator fluctuations lag behind those of prey by about a quarter of the period. Considering the phytoplankton genetic heterogeneity, in the simplest case of two genetically different forms: toxic and non-toxic ones, allows the model to demonstrate both long-period antiphase oscillations of predator and prey and cryptic cycles. During the cryptic cycle, the prey density remains almost constant with fluctuating predators, which corresponds to the influence of rapid evolution masking the trophic interaction.

Bistability is a fundamental property of nonlinear systems and is found in many applied and theoretical studies of biological systems (populations and communities). In the simplest case it is expressed in the coexistence of diametrically opposed alternative stable equilibrium states of the system, and which of them will be achieved depends on the initial conditions. Bistability in simple models can lead to quad-stability as models become more complex, for example, when adding genetic, age and spatial structure. This occurs in different models from completely different subject area and leads to very interesting, often counterintuitive conclusions. In this article, we review such situations. The paper deals with bifurcations leading to bi- and quad-stability in mathematical models of the following biological objects. The first one is the system of two populations coupled by migration and under the action of natural selection, in which all genetic diversity is associated with a single diallelic locus with a significant difference in fitness for homo- and heterozygotes. The second is the system of two limited populations described by the Bazykin model or the Ricker model and coupled by migration. The third is a population with two age stages and density-dependent regulation of birth rate which is determined either only by population density, or additionally depends on the genetic structure of adjacent generations. We found that all these models have similar scenarios for the birth of equilibrium states that correspond to the formation of spatiotemporal inhomogeneity or to the differentiation by phenotypes of individuals from different age stages. Such inhomogeneity is a consequence of local bistability and appears as a result of a combination of pitchfork bifurcation (period doubling) and saddle-node bifurcation.

We developed a model of honeybee colony foraging based on reaction – diffusion equations. Employed bees transmit information about their food sources using dance, and job seekers in the hive can choose any dance they like and thus join the exploitation of the corresponding source. We consider two strategies of dance selection: a targeted one, when bees analyze information on the dance floor and choose the most energetic and longest dance corresponding to the most profitable source, and a simple random choice of the first dance they encounter. Modelling showed that the greatest profit (food influx into the hive) is provided by the random choice of dance, as paradoxical as it may seem at first glance. Optimization of profit by each agent for itself (targeted choice of dances) is rather a disadvantage for the colony, and “non-optimality” in dance choice can be the result of useful evolutionary adaptation.

The paper presents a hybrid artificial neural network model based on an architecture that incorporates a convolutional image encoder (CNN) and an attention module (Attention-based Multiple Instance Learning, Attention MIL). This module aggregates informative features from a sequence of frames capturing the flame spraying process. Additional technological parameters—air pressure, propane pressure, and standoff distance — are integrated into the model via a tabular channel, enabling it to account for the relationship between visual data and numerical process regime characteristics. The software implementation was developed using the Streamlit platform and the PyTorch library. It features an interactive interface for model training and result visualization, analysis of attention weights across frames, and a prediction mode for output characteristics: surface roughness ($R_a$) and the mass of the deposited coating ($m$). Experimental studies were conducted on data from real-world technological processes, and a comparative analysis of the accuracy of various model configurations was performed. The results demonstrate that the hybrid neural network, which combines visual and tabular features, achieves higher prediction accuracy compared to models using only a single modality. Furthermore, when comparing different implementations of the hybrid network, it was established that using the attention mechanism to process the series of flame spray images provides a significant increase in accuracy over a simple averaging of features without attention. The application includes an attention visualization module that creates a montage of the most significant frames and displays their attention weights, allowing users to identify which frames had the greatest influence on the prediction. The model’s capability for export to the ONNX format for integration into process control systems is also demonstrated. The proposed approach showcases the effectiveness of fusing visual and tabular information for manufacturing process monitoring tasks. The model can serve as a foundation for developing a decision support system or an automated quality control system for coatings produced by flame spraying. The limitations of the implemented model and prospects for its further development are also considered.

This paper analyzes some issues in developing numerical methods for solving problems with a Boltzmann-type linear kinetic transport equation. Existing applications of this type of equation are listed. The focus is on the problem of radiative transfer in a flat layer, which are important for experimental research practice. Key definitions and traditional limitations applied to radiative transfer problems are presented. Some features of formulating radiative transfer problems for flat layers of irregular heterogeneous composite materials that are partially transparent to electromagnetic radiation are considered. The main approaches to the numerical and numerical-analytical solution of the linear kinetic transport equation are outlined.

Some variants of the simplest grid numerical methods for solving of nonstationary kinetic problems of transport a flat layer of a medium with strong attenuation are considered. Problems with one- and two-step variants of these iterative methods are analyzed, for some of them the causes of instability and convergence absence in some of them are investigated and established. It is shown that in the explicit conservative one-step method for a layer of a homogeneous absorbing, but neither radiating nor scattering, medium, unstable modes always exist in the spectrum of harmonic solutions. These modes arise in the region of radiation propagating almost parallel to the layer boundaries, and their instability increases with increasing attenuation effects and is caused by the presence of a small coefficient before the spatial derivative in the transport equation. To limit the undesirable influence of this component, various variants of splitting the equation into two and three fractional steps are considered.

It is shown that the most preferable options are those with explicitly organized fractional steps, for which a proof of their stability and convergence, that based on the Lax’s equivalence theorem is presented. It is demonstrated that the correct building of the fractional step sequence in explicit schemes for numerical solving of the nonstationary linear kinetic transport problems can provide additional stabilization, with the scattering integral plays an important role in stabilizing them. So, when solving kinetic transport problems in media with high scattering albedo, the explicit grid method of settling with splitting the iterations into three fractional steps, that were based on physical processes proved to be the simplest and most effective. The method is implemented as Matlab code, which performs quality control during the generation of the numerical solution process. The most significant modeling results are presented, confirming that the three-step method imposes relatively moderate requirements on resources and numerical integration accuracy, and ensures conditional convergence of iterations. Its mathematical correctness is confirmed by the behavior of the equation residuals and direct control of the convergence of numerical solutions. Its physical correctness is confirmed by ensuring, for ergodic systems, the property of convergence to an invariant steady state independent of the initial conditions. Some discovered and possible limitations of the method are listed.

The work will be useful to specialists in the field of mathematical modeling, numerical methods, kinetic theory, combined heat and mass transfer, dealing with issues of interpretation of experimental data, graduate students and senior students specializing in the indicated areas.

Theory of anomalous diffusion, which describe a vast number of transport processes with power law mean squared displacement, is actively advancing in recent years. Diffusion of liquids in porous media, carrier transport in amorphous semiconductors and molecular transport in viscous environments are widely known examples of anomalous deceleration of transport processes compared to the standard model.

Direct Monte Carlo simulation is a convenient tool for studying such processes. An efficient stochastic simulation algorithm is developed in the present paper. It is based on simple renewal process with interarrival times that have power law asymptotics. Analytical derivations show a deep connection between this class of random process and equations with fractional derivatives. The algorithm is further generalized by coupling it with chemical reaction simulation. It makes stochastic approach especially useful, because the exact form of integrodifferential evolution equations for reaction — subdiffusion systems is still a matter of debates.

Proposed algorithm relies on non-markovian random processes, hence one should carefully account for qualitatively new effects. The main question is how molecules leave the system during chemical reactions. An exact scheme which tracks all possible molecule combinations for every reaction channel is computationally infeasible because of the huge number of such combinations. It necessitates application of some simple heuristic procedures. Choosing one of these heuristics greatly affects obtained results, as illustrated by a series of numerical experiments.

In this paper, we develop a new first-order method for composite nonconvex minimization problems with simple constraints and inexact oracle. The objective function is given as a sum of «hard», possibly nonconvex part, and «simple» convex part. Informally speaking, oracle inexactness means that, for the «hard» part, at any point we can approximately calculate the value of the function and construct a quadratic function, which approximately bounds this function from above. We give several examples of such inexactness: smooth nonconvex functions with inexact H¨older-continuous gradient, functions given by the auxiliary uniformly concave maximization problem, which can be solved only approximately. For the introduced class of problems, we propose a gradient-type method, which allows one to use a different proximal setup to adapt to the geometry of the feasible set, adaptively chooses controlled oracle error, allows for inexact proximal mapping. We provide a convergence rate for our method in terms of the norm of generalized gradient mapping and show that, in the case of an inexact Hölder-continuous gradient, our method is universal with respect to Hölder parameters of the problem. Finally, in a particular case, we show that the small value of the norm of generalized gradient mapping at a point means that a necessary condition of local minimum approximately holds at that point.

Financial mathematics is one of the most natural applications for the statistical analysis of time series. Financial time series reflect simultaneous activity of a large number of different economic agents. Consequently, one expects that methods of statistical physics and the theory of random processes can be applied to them.

In this paper, we provide a statistical analysis of time series of the FOREX currency market. Of particular interest is the comparison of the time series behavior depending on the way time is measured: physical time versus trading time measured in the number of elementary price changes (ticks). The experimentally observed statistics of the time series under consideration (euro–dollar for the first half of 2007 and for 2009 and British pound – dollar for 2007) radically differs depending on the choice of the method of time measurement. When measuring time in ticks, the distribution of price increments can be well described by the normal distribution already on a scale of the order of ten ticks. At the same time, when price increments are measured in real physical time, the distribution of increments continues to differ radically from the normal up to scales of the order of minutes and even hours.

To explain this phenomenon, we investigate the statistical properties of elementary increments in price and time. In particular, we show that the distribution of time between ticks for all three time series has a long (1-2 orders of magnitude) power-law tails with exponential cutoff at large times. We obtained approximate expressions for the distributions of waiting times for all three cases. Other statistical characteristics of the time series (the distribution of elementary price changes, pair correlation functions for price increments and for waiting times) demonstrate fairly simple behavior. Thus, it is the anomalously wide distribution of the waiting times that plays the most important role in the deviation of the distribution of increments from the normal. As a result, we discuss the possibility of applying a continuous time random walk (CTRW) model to describe the FOREX time series.