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The paper considers the application of swarm intelligence technology to build agent-based simulation models. As an example, a minimal model is constructed illustrating the influence of information influences on the rules of behavior of agents in the simplest model of competition between two populations, whose agents perform the simplest task of transferring a resource from a mobile source to their territory. The algorithm for the movement of agents in the model space is implemented on the basis of the classical particle swarm algorithm. Agents have a life cycle, that is, the processes of birth and death are taken into account. The model takes into account information processes that determine the target functions of the behavior of newly appeared agents. These processes (training and poaching) are determined by information influences from populations. Under certain conditions, a third population arises in the agent system. Agents of such a population informatively influence agents of other populations in a certain radius around themselves, changing.

As a result of the conducted simulation experiments, it was shown that the following final states are realized in the system: displacement of a new population by others, coexistence of a new population and other populations and the absence of such a population. It has been shown that with an increase in the radius of influence of agents, the population with changed rules of behavior displaces all others. It is also shown that in the case of a hard-to-access resource, the strategy of luring agents of a competing population is more profitable.

Simulation model of corruption in hierarchical systems which takes into account individual strategies of elements and collective behavior of large groups is proposed. Evolution of various characteristics like level of corruption or ratio of corrupted elements and their dependence on external parameters are discussed. The effectiveness of various anticorruptional strategies is examined by means of numeric analysis.

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.

In our research, we focus on the problem of classification for silent speech recognition to develop a brain– computer interface (BCI) based on electroencephalographic (EEG) data, which will be capable of assisting people with mental and physical disabilities and expanding human capabilities in everyday life. Our previous research has shown that the silent pronouncing of some words results in almost identical distributions of electroencephalographic signal data. Such a phenomenon has a suppressive impact on the quality of neural network model behavior. This paper proposes a data processing technique that distinguishes between statistically remote and inseparable classes in the dataset. Applying the proposed approach helps us reach the goal of maximizing the semantic load of the dictionary used in BCI.

Furthermore, we propose the existence of a statistical predictive criterion for the accuracy of binary classification of the words in a dictionary. Such a criterion aims to estimate the lower and the upper bounds of classifiers’ behavior only by measuring quantitative statistical properties of the data (in particular, using the Kolmogorov – Smirnov method). We show that higher levels of classification accuracy can be achieved by means of applying the proposed predictive criterion, making it possible to form an optimized dictionary in terms of semantic load for the EEG-based BCIs. Furthermore, using such a dictionary as a training dataset for classification problems grants the statistical remoteness of the classes by taking into account the semantic and phonetic properties of the corresponding words and improves the classification behavior of silent speech recognition models.

Industrial robots have made it possible for robotics to become a worldwide discipline both in economy and in science. However, their capabilities are limited, especially regarding contact tasks where it is required to regulate or at least limit contact forces. At one point, it was noticed that elasticity in the joint transmission, which was treated as a drawback previously, is actually helpful in this regard. This observation led to the introduction of elastic joint robots that are well-suited to contact tasks and cooperative behavior in particular, so they become more and more widespread nowadays. Many researchers try to implement such devices not with trivial series elastic actuators (SEA) but with more sophisticated variable stiffness actuators (VSA) that can regulate their own mechanical stiffness. All elastic actuators demonstrate shock robustness and safe interaction with external objects to some extent, but when stiffness may be varied, it provides additional benefits, e. g., in terms of energy efficiency and task adaptability. Here, we present a novel variable stiffness actuator with a magnetic coupler as an elastic element. Magnetic transmission is contactless and thus advantageous in terms of robustness to misalignment. In addition, the friction model of the transmission becomes less complex. It also has milder stiffness characteristic than typical mechanical nonlinear springs, moreover, the stiffness curve has a maximum after which it descends. Therefore, when this maximum torque is achieved, the coupler slips, and a new pair of poles defines the equilibrium position. As a result, the risk of damage is smaller for this design solution. The design of the joint is thoroughly described, along with its mathematical model. Finally, the control system is also proposed, and simulation tests confirm the design ideas.

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 dynamics of various gaseous substances is of great importance in the vital activity of phytoplankton. The dynamics of oxygen and carbon dioxide are the most indicative for aquatic plant communities. These dynamics are important for the global ratio of oxygen and carbon dioxide in the Earth’s atmosphere. The goal of the work is to use the mathematical modeling to study the role of oxygen and carbon dioxide in the life of aquatic plant organisms, in particular, the phytoplankton. The series of mathematical models of the dynamics of oxygen and carbon dioxide in the phytoplankton body are proposed. The series of models are built according to the increasing degree of complexity and the number of modeled processes. At first, the simplest model of only gas dynamics is considered, then there is a transition to models with the interaction and mutual influence of gases on the formation and dynamics of energy-intensive substances and on growth processes in the plant organism. Photosynthesis and respiration are considered as the basis of the models. The models study the properties of solutions: equilibrium solutions and their stability, dynamic properties of solutions. Various types of equilibrium stability, possible complex non-linear dynamics have been identified. These properties allow better orientation when choosing a model to describe processes with a known set of data and formulated modeling goals. An example of comparing an experiment with its model description is given. The next goal of modeling — to link gas dynamics for oxygen and carbon dioxide with metabolic processes in plant organisms. In the future, model designs will be applied to the analysis of ecosystem behavior when the habitat changes, including the content of gaseous substances.

A model of the behavior of the active neuron, which was the development of the model described in Shamis A.L. [Shamis, 2006], is designed. Proposed topology is locally connected matrix of the active neural network and the structure integration of information from different sources. An example of the script behavior robot controlled by this neural network is described. The results of experiments with the software implementation of a neural network are presented.

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.

Investigation of the influence of external fields on living systems is one of the most interesting and rapidly developing areas of modern biophysics. However, the mechanisms of such an impact are still not entirely clear. One approach to the study of this issue is associated with modeling the interaction of external fields with internal mobility of biological objects. In this paper, this approach is used to study the effect of external fields on the motion of local conformational distortions — kinks, in the DNA molecule. Realizing and taking into account that on the whole this task is closely connected with the problem of the mechanisms of regulation of vital processes of cells and cellular systems, we set the problem — to investigate the physical mechanisms regulating the motion of kinks and also to answer the question whether permanent and periodic fields can play the role of regulators of this movement. The paper considers the most general case, when constant and periodic fields are switching on and off asynchronously. Three variants of asynchronous switching on/off are studied in detail. In the first variant, the time intervals (or diapasons) of the actions of the constant and periodic fields do not overlap, in the second — overlap, and in the third — the intervals are putting in each other. The calculations were performed for the sequence of plasmid pTTQ18. The kink motion was modeled by the McLaughlin–Scott equation, and the coefficients of the equation were calculated in a quasi-homogeneous approximation. Numerical experiments showed that constant and periodic fields exert a significant influence on the character of the kink motion and regulate it. So the switching on of a constant field leads to a rapid increase of the kink velocity and to the establishment of a stationary velocity of motion, and the switching on of a periodic field leads to the steady oscillations of the kink with the frequency of the external periodic field. It is shown that the behavior of the kink depends on the mutual arrangement of the diapasons of the action of the external fields. As it turned out, events occurring in one of the two diapasons can affect the events in the other diapason, even when the diapasons are sufficiently far apart. It is shown that the overlapping of the diapasons of action of the constant and periodic fields leads to a significant increase in the path traversed by the kink to a complete stop. Maximal growth of the path is observed when one diapason is putting in each other. In conclusion, the question of how the obtained model results could be related to the most important task of biology — the problem of the mechanisms of regulation of the processes of vital activity of cells and cellular systems is discussed.