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Calcium is a major nutrient regulating metabolism in a plant. Deficiency of calcium results in a growth decline of plant tissues. Ca may be lost from forest soils due to acidic atmospheric deposition and tree harvesting. Plant-available calcium compounds are in the soil cation exchange complex and soil waters. Model of soil calcium dynamics linking it with the model of soil organic matter dynamics ROMUL in forest ecosystems is developed. ROMUL describes the mineralization and humification of the fraction of fresh litter which is further transformed into complex of partially humified substance (CHS) and then to stable humus (H) in dependence on temperature, soil moisture and chemical composition of the fraction (nitrogen, lignin and ash contents, pH). Rates of decomposition and humification being coefficients in the system of ordinary differential equations are evaluated using laboratory experiments and verified on a set of field experiments. Model of soil calcium dynamics describes calcium flows between pools of soil organic matter. Outputs are plant nutrition, leaching, synthesis of secondary minerals. The model describes transformation and mineralization of forest floor in detail. Experimental data for calibration model was used from spruсe forest of Bulgaria.

We consider “predator – prey” model taking into account the competition of prey, predator for different from the prey resources, and their interaction described by the second type Holling trophic function. An analysis of the attractors is carried out depending on the coefficient of competition of predators. In the deterministic case, this model demonstrates the complex behavior associated with the local (Andronov –Hopf and saddlenode) and global (birth of a cycle from a separatrix loop) bifurcations. An important feature of this model is the disappearance of a stable cycle due to a saddle-node bifurcation. As a result of the presence of competition in both populations, parametric zones of mono- and bistability are observed. In parametric zones of bistability the system has either coexisting two equilibria or a cycle and equilibrium. Here, we investigate the geometrical arrangement of attractors and separatrices, which is the boundary of basins of attraction. Such a study is an important component in understanding of stochastic phenomena. In this model, the combination of the nonlinearity and random perturbations leads to the appearance of new phenomena with no analogues in the deterministic case, such as noise-induced transitions through the separatrix, stochastic excitability, and generation of mixed-mode oscillations. For the parametric study of these phenomena, we use the stochastic sensitivity function technique and the confidence domain method. In the bistability zones, we study the deformations of the equilibrium or oscillation regimes under stochastic perturbation. The geometric criterion for the occurrence of such qualitative changes is the intersection of confidence domains and the separatrix of the deterministic model. In the zone of monostability, we evolve the phenomena of explosive change in the size of population as well as extinction of one or both populations with minor changes in external conditions. With the help of the confidence domains method, we solve the problem of estimating the proximity of a stochastic population to dangerous boundaries, upon reaching which the coexistence of populations is destroyed and their extinction is observed.

We consider an approach based on linear algebra methods to analyze the rate of electron transport through the cytochrome $b_6 f$ complex. In the proposed approach, the dependence of the quasi-stationary electron flux through the complex on the degree of reduction of pools of mobile electron carriers is considered a response function characterizing this process. We have developed software in the Python programming language that allows us to construct the master equation for the complex according to the scheme of elementary reactions and calculate quasi-stationary electron transport rates through the complex and the dynamics of their changes during the transition process. The calculations are performed in multithreaded mode, which makes it possible to efficiently use the resources of modern computing systems and to obtain data on the functioning of the complex in a wide range of parameters in a relatively short time. The proposed approach can be easily adapted for the analysis of electron transport in other components of the photosynthetic and respiratory electron-transport chain, as well as other processes in multienzyme complexes containing several reaction centers. Cryo-electron microscopy and redox titration data were used to parameterize the model of cytochrome $b_6 f$ complex. We obtained dependences of the quasi-stationary rate of plastocyanin reduction and plastoquinone oxidation on the degree of reduction of pools of mobile electron carriers and analyzed the dynamics of rate changes in response to changes in the redox state of the plastoquinone pool. The modeling results are in good agreement with the available experimental data.

The problem of harvest optimization remains a central challenge in mathematical biology. The concept of Maximum Sustainable Yield (MSY), widely used in optimal exploitation theory, proposes maintaining target populations at levels ensuring maximum reproduction, theoretically balancing economic benefits with resource conservation. While MSYbased management promotes population stability and system resilience, it faces significant limitations due to complex intrapopulation structures and nonlinear dynamics in exploited species. Of particular concern are the evolutionary consequences of harvesting, as artificial selection may drive changes divergent from natural selection pressures. Empirical evidence confirms that selective harvesting alters behavioral traits, reduces offspring quality, and modifies population gene pools. In contrast, the genetic impacts of non-selective harvesting remain poorly understood and require further investigation.

This study examines how non-selective harvesting with constant removal rates affects evolution in genetically heterogeneous populations. We model genetic diversity controlled by a single diallelic locus, where different genotypes dominate at high/low densities: r-strategists (high fecundity) versus K-strategists (resource-limited resilience). The classical ecological and genetic model with discrete time is considered. The model assumes that the fitness of each genotype linearly depends on the population size. By including the harvesting withdrawal coefficient, the model allows for linking the problem of optimizing harvest with the that of predicting genotype selection.

Analytical results demonstrate that under MSY harvesting the equilibrium genetic composition remains unchanged while population size halves. The type of genetic equilibrium may shift, as optimal harvest rates differ between equilibria. Natural K-strategist dominance may reverse toward r-strategists, whose high reproduction compensates for harvest losses. Critical harvesting thresholds triggering strategy shifts were identified.

These findings explain why exploited populations show slow recovery after harvesting cessation: exploitation reinforces adaptations beneficial under removal pressure but maladaptive in natural conditions. For instance, captive arctic foxes select for high-productivity genotypes, whereas wild populations favor lower-fecundity/higher-survival phenotypes. This underscores the necessity of incorporating genetic dynamics into sustainable harvesting management strategies, as MSY policies may inadvertently alter evolutionary trajectories through density-dependent selection processes. Recovery periods must account for genetic adaptation timescales in management frameworks.

The collective behavior of crowd leaving a room is modeled. The model is based on molecular dynamics approach with a mixture of socio-psychological and physical forces. The new algorithm for complicatedly branched space is proposed. It suggests that each individual develops its own plan of escape, which is stochastically transformed during the evolution. The algorithm includes also the separation of original space into rooms with possible exits selected by individuals according to their probability distribution. The model is calibrated on the base of empirical data provided by fire case in the nightclub “Lame Horse” (Perm, 2009). The algorithm is realized as an end-user Java software. It is assumed that this tool could help to test the buildings for their safety for humans.

The article deals with the modeling of the number of employed population by branches of the economy at the national and regional levels. The lack of targeted distribution of workers in a market economy requires the study of systemic processes in the labor market that lead to different dynamics of the number of employed in the sectors of the economy. In this case, personal strategies for choosing labor activity by economic agents become important. The presence of different strategies leads to the emergence of strata in the labor market with a dynamically changing number of employees, unevenly distributed among the sectors of the economy. As a result, non-linear fluctuations in the number of employed population can be observed, the toolkit of agentbased modeling is relevant for the study of the fluctuations. In the article, we examined in-phase and anti-phase fluctuations in the number of employees by economic activity on the example of the Jewish Autonomous Region in Russia. The fluctuations found in the time series of statistical data for 2008–2016. We show that such fluctuations appear by age groups of workers. In view of this, we put forward a hypothesis that the agent in the labor market chooses a place of work by a strategy, related with his age group. It directly affects the distribution of the number of employed for different cohorts and the total number of employed in the sectors of the economy. The agent determines the strategy taking into account the socio-economic characteristics of the branches of the economy (different levels of wages, working conditions, prestige of the profession). We construct a basic agentoriented model of a three-branch economy to test the hypothesis. The model takes into account various strategies of economic agents, including the choice of the highest wages, the highest prestige of the profession and the best working conditions by the agent. As a result of numerical experiments, we show that the availability of various industry selection strategies and the age preferences of employers within the industry lead to periodic and complex dynamics of the number of different-aged employees. Age preferences may be a consequence, for example, the requirements of employer for the existence of work experience and education. Also, significant changes in the age structure of the employed population may result from migration.

Now days the main research activity in the field of nanotechnology is aimed at the creation, study and application of new materials and new structures. Recently, much attention has been attracted by the possibility of controlling magnetic properties using a superconducting current, as well as the influence of magnetic dynamics on the current–voltage characteristics of hybrid superconductor/ferromagnet (S/F) nanostructures. In particular, such structures include the S/F/S Josephson junction or molecular nanomagnets coupled to the Josephson junctions. Theoretical studies of the dynamics of such structures need processes of a large number of coupled nonlinear equations. Numerical modeling of hybrid superconductor/magnet nanostructures implies the calculation of both magnetic dynamics and the dynamics of the superconducting phase, which strongly increases their complexity and scale, so it is advisable to use heterogeneous computing systems.

In the course of studying the physical properties of these objects, it becomes necessary to numerically solve complex systems of nonlinear differential equations, which requires significant time and computational resources.

The currently existing micromagnetic algorithms and frameworks are based on the finite difference or finite element method and are extremely useful for modeling the dynamics of magnetization on a wide time scale. However, the functionality of existing packages does not allow to fully implement the desired computation scheme.

The aim of the research is to develop a unified environment for modeling hybrid superconductor/magnet nanostructures, providing access to solvers and developed algorithms, and based on a heterogeneous computing paradigm that allows research of superconducting elements in nanoscale structures with magnets and hybrid quantum materials. In this paper, we investigate resonant phenomena in the nanomagnet system associated with the Josephson junction. Such a system has rich resonant physics. To study the possibility of magnetic reversal depending on the model parameters, it is necessary to solve numerically the Cauchy problem for a system of nonlinear equations. For numerical simulation of hybrid superconductor/magnet nanostructures, a computing environment based on the heterogeneous HybriLIT computing platform is implemented. During the calculations, all the calculation times obtained were averaged over three launches. The results obtained here are of great practical importance and provide the necessary information for evaluating the physical parameters in superconductor/magnet hybrid nanostructures.

This work deals with numerical modeling of motion of the multibody systems consisting of rigid bodies with arbitrary masses and inertial properties. We consider both planar and spatial systems which may contain kinematic loops.

The numerical modeling is fully automatic and its computational algorithm contains three principal steps. On step one a graph of the considered mechanical system is formed from the userinput data. This graph represents the hierarchical structure of the mechanical system. On step two the differential-algebraic equations of motion of the system are derived using the so-called Joint Coordinate Method. This method allows to minimize the redundancy and lower the number of the equations of motion and thus optimize the calculations. On step three the equations of motion are integrated numerically and the resulting laws of motion are presented via user interface or files.

The aforementioned algorithm is implemented in the software complex that contains a computer algebra system, a graph library, a mechanical solver, a library of numerical methods and a user interface.

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.

Numerical simulation of moving sportsman external flow is presented. The unique method is developed for obtaining integral aerodynamic characteristics, which were the function of the flow regime (i.e. angle of attack, flow speed) and body position. Individual anthropometric characteristics and moving boundaries of sportsman (or sports equipment) during the race are taken into consideration.

Numerical simulation is realized using FlowVision CFD. The software is based on the finite volume method, high-performance numerical methods and reliable mathematical models of physical processes. A Cartesian computational grid is used by FlowVision, the grid generation is a completely automated process. Local grid adaptation is used for solving high-pressure gradient and object complex shape. Flow simulation process performed by solutions systems of equations describing movement of fluid and/or gas in the computational domain, including: mass, moment and energy conservation equations; state equations; turbulence model equations. FlowVision permits flow simulation near moving bodies by means of computational domain transformation according to the athlete shape changes in the motion. Ski jumper aerodynamic characteristics are studied during all phases: take-off performance in motion, in-run and flight. Projected investigation defined simulation method, which includes: inverted statement of sportsman external flow development (velocity of the motion is equal to air flow velocity, object is immobile); changes boundary of the body technology defining; multiple calculations with the national team member data projecting. The research results are identification of the main factors affected to jumping performance: aerodynamic forces, rotating moments etc. Developed method was tested with active sportsmen. Ski jumpers used this method during preparations for Sochi Olympic Games 2014. A comparison of the predicted characteristics and experimental data shows a good agreement. Method versatility is underlined by performing swimmer and skater flow simulation. Designed technology is applicable for sorts of natural and technical objects.