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The paper investigates the dynamics of a finite-dimensional model describing the interaction of three populations: prey $x(t)$, its consuming predator $y(t)$, and a superpredator $z(t)$ that feeds on both species. Mathematically, the problem is formulated as a system of nonlinear first-order differential equations with the following right-hand side: $[x(1-x)-(y+z)g;\,\eta_1^{}yg-d_1^{}f-\mu_1^{}y;\,\eta_2^{}zg+d_2^{}f-\mu_2^{}z]$, where $\eta_j^{}$, $d_j^{}$, $\mu_j^{}$ ($j=1,\,2$) are positive coefficients. The considered model belongs to the class of cosymmetric dynamical systems under the Lotka\,--\,Volterra functional response $g=x$, $f=yz$, and two parameter constraints: $\mu_2^{}=d_2^{}\left(1+\frac{\mu_1^{}}{d_1^{}}\right)$, $\eta_2^{}=d_2^{}\left(1+\frac{\eta_1^{}}{d_1^{}}\right)$. In this case, a family of equilibria is being of a straight line in phase space. We have analyzed the stability of the equilibria from the family and isolated equilibria. Maps of stationary solutions and limit cycles have been constructed. The breakdown of the family is studied by violating the cosymmetry conditions and using the Holling model $g(x)=\frac x{1+b_1^{}x}$ and the Beddington–DeAngelis model $f(y,\,z)=\frac{yz}{1+b_2^{}y+b_3^{}z}$. To achieve this, the apparatus of Yudovich's theory of cosymmetry is applied, including the computation of cosymmetric defects and selective functions. Through numerical experimentation, invasive scenarios have been analyzed, encompassing the introduction of a superpredator into the predator-prey system, the elimination of the predator, or the superpredator.

One of the principles of forming a competitive market environment is to create conditions for economic agents to implement Nash – Cournot optimal strategies. With the standard approach to determining Nash – Cournot optimal market strategies, economic agents must have complete information about the indicators and dynamic characteristics of all market participants. Which is not true.

In this regard, to find Nash – Cournot optimal solutions in dynamic models, it is necessary to have a coordinator who has complete information about the participants. However, in the case of a large number of game participants, even if the coordinator has the necessary information, computational difficulties arise associated with the need to solve a large number of coupled equations (in the case of linear dynamic games — Riccati matrix equations).

In this regard, there is a need to decompose the general problem of determining optimal strategies for market participants into private (local) problems. Approaches based on the iterative decomposition of coupled matrix Riccati equations and the solution of local Riccati equations were studied for linear dynamic games with a quadratic criterion. This article considers a simpler approach to the iterative determination of the Nash – Cournot equilibrium in an oligopoly, by decomposition using operational calculus (operator method).

The proposed approach is based on the following procedure. A virtual coordinator, which has information about the parameters of the inverse demand function, forms prices for the prospective period. Oligopolists, given fixed price dynamics, determine their strategies in accordance with a slightly modified optimality criterion. The optimal volumes of production of the oligopolists are sent to the coordinator, who, based on the iterative algorithm, adjusts the price dynamics at the previous step.

The proposed procedure is illustrated by the example of a static and dynamic model of rational behavior of oligopoly participants who maximize the net present value (NPV). Using the methods of operational calculus (and in particular, the inverse Z-transformation), conditions are found under which the iterative procedure leads to equilibrium levels of price and production volumes in the case of linear dynamic games with both quadratic and nonlinear (concave) optimization criteria.

The approach considered is used in relation to examples of duopoly, triopoly, duopoly on the market with a differentiated product, duopoly with interacting oligopolists with a linear inverse demand function. Comparison of the results of calculating the dynamics of price and production volumes of oligopolists for the considered examples based on coupled equations of the matrix Riccati equations in Matlab (in the table — Riccati), as well as in accordance with the proposed iterative method in the widely available Excel system shows their practical identity.

In addition, the application of the proposed iterative procedure is illustrated by the example of a duopoly with a nonlinear demand function.

Numerous contemporary studies confirm that neurons, astrocytes and blood vessels function as a unified dynamic system. Consequently, the concept of the integrated neurogliovascular unit (NGVU), encompassing these components, has emerged and gained significant traction in recent years. According to this framework, normal brain function relies on a broad complex of interactions between NGVU elements, while the disruption of these links may underlie various neuropathologies. Understanding the processes within a single NGVU, as well as the organization of connections between multiple units, is a prerequisite for successful diagnosis and therapy of neurological disorders.

In this work, we developed an NGVU model that, for the first time, integrates a detailed description of synaptically coupled excitatory and inhibitory neuronal networks (accounting for the E/I balance), extracellular environment dynamics (potassium, glutamate, GABA), and norepinephrine-modulated astrocytic activity, with subsequent regulation of local blood flow.

A key conceptual feature of the model is the integration of multiscale processes — ranging from ion dynamics at the level of individual Hodgkin – Huxley neurons to substance diffusion across a network of 100 NGVUs — into a single system of coupled nonlinear differential equations. This approach enabled the investigation of the ensemble’s collective dynamics and the identification of novel functional regimes.

Numerical experiments established that extracellular potassium dynamics and positive feedback play a decisive role in the formation of stable spatial excitation structures. It is shown that under local stimulation, activity remains confined due to potassium diffusion outflow; however, supercritical excitation initiates self-sustaining autowave regimes. The stabilization of these regimes leads to the formation of spatial patterns morphologically similar to Turing structures. These patterns, characterized by alternating zones of high and low activity, are independent of specific initial conditions but sensitive to parameter variations. This suggests that the system operates in a dynamic instability (chaos) regime, which is consistent with the concept of self-organized criticality of the brain under physiological conditions. The model successfully reproduces experimentally observed phenomena, including bursting and sensitivity to extracellular potassium. The results provide new perspectives for analyzing the pathophysiological mechanisms of brain function.

We comsider a model of the dynamics a political system of several parties, accompanied and controlled by the growth of social tension. A system of nonlinear ordinary differential equations is proposed with respect to fractions and an additional scalar variable characterizing the magnitude of tension in society the change of each party is proportional to the current value multiplied by a coefficient that consists of an influx of novice, a flow from competing parties, and a loss due to the growth of social tension. The change in tension is made up of party contributions and own relaxation. The number of parties is fixed, there are no mechanisms in the model for combining existing or the birth of new parties.

To study of possible scenarios of the dynamic processes of the model we derive an approach based on the selection of conditions under which this problem belongs to the class of cosymmetric systems. For the case of two parties, it is shown that in the system under consideration may have two families of equilibria, as well as a family of limit cycles. The existence of cosymmetry for a system of differential equations is ensured by the presence of additional constraints on the parameters, and in this case, the emergence of continuous families of stationary and nonstationary solutions is possible. To analyze the scenarios of cosymmetry breaking, an approach based on the selective function is applied. In the case of one political party, there is no multistability, one stable solution corresponds to each set of parameters. For the case of two parties, it is shown that in the system under consideration may have two families of equilibria, as well as a family of limit cycles. The results of numerical experiments demonstrating the destruction of the families and the implementation of various scenarios leading to the stabilization of the political system with the coexistence of both parties or to the disappearance of one of the parties, when part of the population ceases to support one of the parties and becomes indifferent are presented.

This model can be used to predict the inter-party struggle during the election campaign. In this case necessary to take into account the dependence of the coefficients of the system on time.

The paper addresses the problem of modeling the formation and propagation of panic states in social groups with relatively stable structures of interpersonal interactions. Panic is interpreted as a nonlinear process of emotional contagion arising from the interaction between individual psychological characteristics and collective effects within a social environment. In contrast to models focused on the spatial dynamics of moving crowds, the proposed approach concentrates on quasi-stationary interaction networks that reflect informational and emotional contacts among individuals.

The developed discrete network dynamical system integrates individual temperament parameters (sanguine, choleric, phlegmatic, melancholic), the structure of social connections, and nonlinear mechanisms of collective behavior. The individual dynamics of panic are described using an S-shaped growth function, which ensures boundedness of the emotional arousal level and captures the stages of its formation and saturation. Social influence is modeled on a graph of interpersonal interactions (an Erdos –Renyi random network) through local contacts between individuals.

Additionally, the model incorporates the effects of collective contagion and avalanche-like amplification driven by the average panic level in the group, as well as a baseline stress factor depending on group size. Numerical simulation is implemented in a discrete iterative form, allowing for the analysis of both individual and group panic trajectories. A quantitative indicator of the panic propagation rate is introduced, defined by the time required for the group to reach a state close to full panic.

A comparative analysis of heterogeneous and homogeneous groups is conducted, demonstrating that group heterogeneity significantly accelerates panic propagation due to inter-temperament interactions: highly excitable individuals act as initiators of emotional contagion, while more stable individuals partially dampen its dynamics. The evaluation of the model quality using the coefficient of determination shows a high degree of consistency within the simulation data.

The practical significance of the work lies in the potential application of the model for analyzing the resilience of social groups to panic states, assessing risks at mass events, and developing intelligent systems for monitoring collective behavior. Future research directions include extending the model to account for directed and dynamic networks, as well as its calibration based on empirical data.

Many properties of ordinary differential equations systems solutions are determined by the properties of the equations in variations. An ODE system, which includes both the original nonlinear system and the equations in variations, will be called an extended system further. When studying the properties of the Cauchy problem for the systems of ordinary differential equations, the transition to extended systems allows one to study many subtle properties of solutions. For example, the transition to the extended system allows one to increase the order of approximation for numerical methods, gives the approaches to constructing a sensitivity function without using numerical differentiation procedures, allows to use methods of increased convergence order for the inverse problem solution. Authors used the Broyden method belonging to the class of quasi-Newtonian methods. The Rosenbroke method with complex coefficients was used to solve the stiff systems of the ordinary differential equations. In our case, it is equivalent to the second order approximation method for the extended system.

As an example of the proposed approach, several related mathematical models of the blood coagulation process were considered. Based on the analysis of the numerical calculations results, the conclusion was drawn that it is necessary to include a description of the factor XI positive feedback loop in the model equations system. Estimates of some reaction constants based on the numerical inverse problem solution were given.

Effect of factor V release on platelet activation was considered. The modification of the mathematical model allowed to achieve quantitative correspondence in the dynamics of the thrombin production with experimental data for an artificial system. Based on the sensitivity analysis, the hypothesis tested that there is no influence of the lipid membrane composition (the number of sites for various factors of the clotting system, except for thrombin sites) on the dynamics of the process.

We propose a family of Gauss –Newton methods for solving optimization problems and systems of nonlinear equations based on the ideas of using the upper estimate of the norm of the residual of the system of nonlinear equations and quadratic regularization. The paper presents a development of the «Three Squares Method» scheme with the addition of a momentum term to the update rule of the sought parameters in the problem to be solved. The resulting scheme has several remarkable properties. First, the paper algorithmically describes a whole parametric family of methods that minimize functionals of a special kind: compositions of the residual of a nonlinear equation and an unimodal functional. Such a functional, entirely consistent with the «gray box» paradigm in the problem description, combines a large number of solvable problems related to applications in machine learning, with the regression problems. Secondly, the obtained family of methods is described as a generalization of several forms of the Levenberg –Marquardt algorithm, allowing implementation in non-Euclidean spaces as well. The algorithm describing the parametric family of Gauss –Newton methods uses an iterative procedure that performs an inexact parametrized proximal mapping and shift using a momentum term. The paper contains a detailed analysis of the efficiency of the proposed family of Gauss – Newton methods; the derived estimates take into account the number of external iterations of the algorithm for solving the main problem, the accuracy and computational complexity of the local model representation and oracle computation. Sublinear and linear convergence conditions based on the Polak – Lojasiewicz inequality are derived for the family of methods. In both observed convergence regimes, the Lipschitz property of the residual of the nonlinear system of equations is locally assumed. In addition to the theoretical analysis of the scheme, the paper studies the issues of its practical implementation. In particular, in the experiments conducted for the suboptimal step, the schemes of effective calculation of the approximation of the best step are given, which makes it possible to improve the convergence of the method in practice in comparison with the original «Three Square Method». The proposed scheme combines several existing and frequently used in practice modifications of the Gauss –Newton method, in addition, the paper proposes a monotone momentum modification of the family of developed methods, which does not slow down the search for a solution in the worst case and demonstrates in practice an improvement in the convergence of the method.