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Changes in abundance for emerging populations can develop according to several dynamic scenarios. After rapid biological invasions, the time factor for the development of a reaction from the biotic environment will become important. There are two classic experiments known in history with different endings of the confrontation of biological species. In Gause’s experiments with ciliates, the infused predator, after brief oscillations, completely destroyed its resource, so its $r$-parameter became excessive for new conditions. Its own reproductive activity was not regulated by additional factors and, as a result, became critical for the invader. In the experiments of the entomologist Uchida with parasitic wasps and their prey beetles, all species coexisted. In a situation where a population with a high reproductive potential is regulated by several natural enemies, interesting dynamic effects can occur that have been observed in phytophages in an evergreen forest in Australia. The competing parasitic hymenoptera create a delayed regulation system for rapidly multiplying psyllid pests, where a rapid increase in the psyllid population is allowed until the pest reaches its maximum number. A short maximum is followed by a rapid decline in numbers, but minimization does not become critical for the population. The paper proposes a phenomenological model based on a differential equation with a delay, which describes a scenario of adaptive regulation for a population with a high reproductive potential with an active, but with a delayed reaction with a threshold regulation of exposure. It is shown that the complication of the regulation function of biotic resistance in the model leads to the stabilization of the dynamics after the passage of the minimum number by the rapidly breeding species. For a flexible system, transitional regimes of growth and crisis lead to the search for a new equilibrium in the evolutionary confrontation.

This paper demonstrates the advantages of using artificial intelligence algorithms for the design of experiment theory, which makes possible to improve the accuracy of parameter identification for an elastostatic robot model. Design of experiment for a robot consists of the optimal configuration-external force pairs for the identification algorithms and can be described by several main stages. At the first stage, an elastostatic model of the robot is created, taking into account all possible mechanical compliances. The second stage selects the objective function, which can be represented by both classical optimality criteria and criteria defined by the desired application of the robot. At the third stage the optimal measurement configurations are found using numerical optimization. The fourth stage measures the position of the robot body in the obtained configurations under the influence of an external force. At the last, fifth stage, the elastostatic parameters of the manipulator are identified based on the measured data.

The objective function required to finding the optimal configurations for industrial robot calibration is constrained by mechanical limits both on the part of the possible angles of rotation of the robot’s joints and on the part of the possible applied forces. The solution of this multidimensional and constrained problem is not simple, therefore it is proposed to use approaches based on artificial intelligence. To find the minimum of the objective function, the following methods, also sometimes called heuristics, were used: genetic algorithms, particle swarm optimization, simulated annealing algorithm, etc. The obtained results were analyzed in terms of the time required to obtain the configurations, the optimal value, as well as the final accuracy after applying the calibration. The comparison showed the advantages of the considered optimization techniques based on artificial intelligence over the classical methods of finding the optimal value. The results of this work allow us to reduce the time spent on calibration and increase the positioning accuracy of the robot’s end-effector after calibration for contact operations with high loads, such as machining and incremental forming.

This paper focuses on the problem of modeling and analyzing dynamic regimes, both regular and chaotic, in systems of coupled populations in the presence of random disturbances. The discrete Ricker model is used as the initial deterministic population model. The paper examines the dynamics of two populations coupled by migration. Migration is proportional to the difference between the densities of two populations with a coupling coefficient responsible for the strength of the migration flow. Isolated population subsystems, modeled by the Ricker map, exhibit various dynamic modes, including equilibrium, periodic, and chaotic ones. In this study, the coupling coefficient is treated as a bifurcation parameter and the parameters of natural population growth rate remain fixed. Under these conditions, one subsystem is in the equilibrium mode, while the other exhibits chaotic behavior. The coupling of two populations through migration creates new dynamic regimes, which were not observed in the isolated model. This article aims to analyze the dynamics of corporate systems with variations in the flow intensity between population subsystems. The article presents a bifurcation analysis of the attractors in a deterministic model of two coupled populations, identifies zones of monostability and bistability, and gives examples of regular and chaotic attractors. The main focus of the work is in comparing the stability of dynamic regimes against random disturbances in the migration intensity. Noise-induced transitions from a periodic attractor to a chaotic attractor are identified and described using direct numerical simulation methods. The Lyapunov exponents are used to analyze stochastic phenomena. It has been shown that in this model, there is a region of change in the bifurcation parameter in which, even with an increase in the intensity of random perturbations, there is no transition from order to chaos. For the analytical study of noise-induced transitions, the stochastic sensitivity function technique and the confidence domain method are used. The paper demonstrates how this mathematical tool can be employed to predict the critical noise intensity that causes a periodic regime to transform into a chaotic one.

When modeling turbulent flows in practical applications, it is often necessary to carry out a series of calculations of bodies of similar topology. For example, bodies that differ in the shape of the fairing. The use of convolutional neural networks allows to reduce the number of calculations in a series, restoring some of them based on calculations already performed. The paper proposes a method that allows to apply a convolutional neural network regardless of the method of constructing a computational mesh. To do this, the flow field is reinterpolated to a uniform mesh along with the body itself. The geometry of the body is set using the signed distance function and masking. The restoration of the flow field based on part of the calculations for similar geometries is carried out using a neural network of the UNet type with a spatial attention mechanism. The resolution of the nearwall region, which is a critical condition for turbulent modeling, is based on the equations obtained in the nearwall domain decomposition method.

A demonstration of the method is given for the case of a flow around a rounded plate by a turbulent air flow with different rounding at fixed parameters of the incoming flow with the Reynolds number $Re = 10^5$ and the Mach number $M = 0.15$. Since flows with such parameters of the incoming flow can be considered incompressible, only the velocity components are studied directly. The flow fields, velocity and friction profiles obtained by the surrogate model and numerically are compared. The analysis is carried out both on the plate and on the rounding. The simulation results confirm the prospects of the proposed approach. In particular, it was shown that even if the model is used at the maximum permissible limits of its applicability, friction can be obtained with an accuracy of up to 90%. The work also analyzes the constructed architecture of the neural network. The obtained surrogate model is compared with alternative models based on a variational autoencoder or the principal component analysis using radial basis functions. Based on this comparison, the advantages of the proposed method are demonstrated.

We consider a spatiotemporal model of a tritrophic system describing the interaction between prey, predator, and superpredator in an environment with nonuniform resource distribution. The model incorporates superpredator omnivory (Intraguild Predation, IGP), diffusion, and directed migration (taxis), the latter modeled using a logarithmic function of resource availability and prey density. The primary focus is on analyzing the multistability of the system and the role of cosymmetry in the formation of continuous families of steady-state solutions. Using a numerical-analytical approach, we study both spatially homogeneous and inhomogeneous steady-state solutions. It is established that under additional relations between the parameters governing local predator interactions and diffusion coefficients, the system exhibits cosymmetry, leading to the emergence of a family of stable steady-state solutions proportional to the resource function. We demonstrate that the cosymmetry is independent of the resource function in the case of a heterogeneous environment. The stability of stationary distributions is investigated using spectral methods. Violation of the cosymmetry conditions results in the breakdown of the solution family and the emergence of isolated equilibria, as well as prolonged transient dynamics reflecting the system’s “memory” of the vanished states. Depending on initial conditions and parameters, the system exhibits transitions to single-predator regimes (survival of either the predator or superpredator) or predator coexistence. Numerical experiments based on the method of lines, which involves finite difference discretization in space and Runge –Kutta integration in time, confirm the system’s multistability and illustrate the disappearance of solution families when cosymmetry is broken.

A mathematical model describing the dynamics of HIV-1 infection in a single lymph node during the initial period of infection development is presented. Within the framework of the model, the infection of an individual is set by a nonnegative finite function describing the rate of entry of the initial viral particles into the lymph node. The equations of the model are derived with consideration of two factors: 1) the interaction of viral particles with naive CD4+ T lymphocytes in various phases of the cell cycle; 2) contact interaction between multiplying naive CD4+ T lymphocytes and infected CD4+ T lymphocytes producing viral particles. The specific feature of intercellular contact interactions is the formation of complexes consisting of pairs of these cells. The duration of the complexes’ existence is determined by the distribution functions over finite time intervals. The model is presented as a high-dimensional system of nonlinear delay differential equations, including two equations with distributed delay, and is supplemented with non-negative initial data. In the absence of HIV-1 infection, the model is reduced to four delay differential equations describing the number of naive CD4+ T-lymphocytes in different phases of the cell cycle. The global solvability of the model (the existence and uniqueness of the solution on the semi-axis) is determined, and the non-negativity of the solution components is established. To carry out computational experiments with the model, an algorithm for numerically solving the used system of differential equations are developed based on the semi-implicit Euler scheme for the case of uniform distribution of durations of the complexes existence. The results of computational experiments aimed at approximation the numerical solution of the model to describing the kinetics of HIV-1 infection spread in its acute phase, including the eclipse phase, are presented. The variable used as the observable is the variable describing the number of viral particles per milliliter of blood on days 10–12 after the onset of acute infection. The dynamics of the observable variable is numerically studied depending on the variation of the model parameters reflecting the patterns of complex formation and the formation of cells producing viral particles. The possibility of attenuation of HIV-1 infection in the lymph node at certain values of some of the model parameters is shown.

Comparative analysis of two models of porous medium (Dacry and Brinkman) on an example of mathematical simulation of transient natural convection in a porous vertical cylindrical cavity with heat-conducting shell of finite thickness in conditions of convective cooling from an environment has been carried out. The boundary-value problem of mathematical physics formulated in dimensionless variables such as stream function, vorticity and temperature has been solved by implicit finite difference method. The presented verification results validate used numerical approach and also confirm that the solution is not dependent on the mesh size. Features of the conjugate heat transfer problems with considered models of porous medium have been determined.

Article is devoted to the creation of a mathematical control of the bank investment process. The whole process of building optimal control may be divided into two components: in the first place, there is the identification of the functions describing the liquid capital movement in the bank and, in the second place, the use of these functions in the scheme of dynamic programming. Before this problem was discussed in the article "Optimal control of the bank investment as a factor of economic stability" in the 4^th issue for 2012. In the present article considers this modification of the solution, in particular, we use ℜ(φ) as a function of reinvestment, where φ is inflow of liquid capital realized at the previous step of control.

Formation of adequate behavioral patterns in condition of the unknown environment carried out through exploratory behavior. At the same time the rapid formation of an acceptable pattern is more preferable than a long elaboration perfect pattern through repeat play learning situation. In extreme situations, phenomenon of imprinting is observed — instant imprinting of behavior pattern, which ensure the survival of individuals. In this paper we propose a hypothesis and imprint model when trained on a single successful pattern of virtual robot's neural network demonstrates the effective functioning. Realism of the model is estimated by checking the stability of playback behavior pattern to perturbations situation imprint run.

We consider a time-delayed quadratic discrete model of population dynamics under the influence of random perturbations. Analysis of stochastic attractors of the model is performed using the methods of direct numerical simulation and the stochastic sensitivity function technique. A deformation of the probability distribution of random states around the stable equilibria and cycles is studied parametrically. The phenomenon of noise-induced transitions in the zone of discrete cycles is demonstrated.