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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.

Acute respiratory infections are a major public health concern because they are the leading cause of illness and death in many countries. Therefore, there is great interest in developing models and methods capable of modeling the spread of these infections within communities, with the aim of controlling outbreaks and preventing their spread. Agent-based models (ABM) are one of the most important tools in epidemiological research for modeling epidemic dynamics in realistic populations, but they face significant challenges in terms of computational complexity in their operation and calibration of epidemiological data, as parameter estimation typically requires repeated simulations across large parameter spaces to determine plausible values for key epidemiological parameters. This paper addresses the problem of alleviating computational constraints in the inverse problem of calibrating an ABM model for simulating the spread of respiratory infections in Saint Petersburg. The paper proposes the application of machine learning surrogate to link epidemic trajectories to underlying epidemiological parameters, enabling them to quickly infer parameter estimates from observed epidemic data. This is done by formulating the task of calibrating ABMs against epidemiological data as a supervised learning problem, where sequences extracted from epidemiological trajectories are associated with underlying epidemiological parameters. The research was based on evaluating the performance of attention-based sequence modeling, probabilistic deep learning, and distributional regression for inferring parameter estimates from truncated sequences of epidemic trajectories. Experimental evaluations have demonstrated the effectiveness of this approach and its practical and straightforward application. The results also indicated the superiority of attention-based sequence modeling, as it showed more consistent performance across metrics and horizons in accurate parameter estimation and credible uncertainty quantification. Distributional regression modeling also showed good performance with specific strengths in point accuracy while probabilistic deep learning performed poorly, especially at longer input horizons.

A spatially distributed mathematical model is proposed for the evolution of five myocardial tissue states — healthy myocardium, inflammatory infiltrate, necrosis, replacement fibrosis, and interstitial fibrosis — in the form of a multicomponent parabolic reaction – diffusion system. At each spatial point the tissue state is described by a vector of Dirichlet concentration parameters, which allows one to encode both the expected tissue composition and the uncertainty of classification associated with transition zones of damage. For a truncated system that coincides with the original one inside an invariant region, local Lipschitz continuity and quasi-positivity of the extended reaction operator are established. Global existence of a weak solution, non-negativity, an explicit a priori upper bound, and a quantitative exponential lower bound are proved. These results imply invariance of the admissible region and hence existence of a global weak solution to the original model. Uniqueness is established in a strengthened class of solutions characterized by additional gradient regularity. For an auxiliary reversible transition matrix, a formal entropy balance is derived for smooth positive solutions; for the clinically motivated irreversible transition matrix, a modal spectral stability condition is formulated for the linearized problem. On the numerical side, a positivity condition is derived for the reaction substep of a splitting scheme relative to an adaptive upper trajectory bound, and numerical experiments are conducted for a reproducible calibrated computational variant. In the one-dimensional scenario, by day $60$ the replacement-fibrosis fraction at the lesion center increases from $0.091$ to $0.855$, while the total concentration parameter $\alpha_0$ rises from $11.0$ to $15.9$; in the two-dimensional scenario, on day $14$ the mean value of $\overline p_4^{}$ is $0.431$ in the lesion core versus $0.097$ in the healthy region. These results confirm the mathematical well-posedness of the proposed model and its applicability for the quantitative description of post-infarction scar formation and heterogeneous transition zones.

In the article, a quasi-periodic two-component dynamical model with possibility of defining the cardio-cycle morphology, that provides the model with an ability of generating a temporal and a spectral cardiosignal characteristics, including heart rate variability is described. A technique for determining the cardio-cycle morphology to provide realistic cardio-signal form is defined. A method for defining cardio-signal dynamical system by the way of determining a three-dimensional state space and equations which describe a trajectory of point’s motion in this space is presented. A technique for solving equations of motion in the three-dimensional state space of dynamical cardio-signal system using the fourth-order Runge–Kutta method is presented. Based on this model, algorithm and software package are developed. Using software package, a cardio-signal synthesis experiment is conducted and the relationship of cardio-signal diagnostic features is analyzed.

We consider the optimal motion control problem for a mobile device with an external rigid shell moving along a prescribed trajectory in a viscous fluid. The mobile robot under consideration possesses the property of self-locomotion. Self-locomotion is implemented due to back-and-forth motion of an internal material point. The optimal motion control is based on the Sugeno fuzzy inference system. An approach based on constructing decision trees using the genetic algorithm for structural and parametric synthesis has been proposed to obtain the base of fuzzy rules.

Numerical modeling of shear-induced platelet activation in haemodialysis arteriovenous fistulas was carried out in this work. The goal was to investigate the mechanisms of threshold shear-induced platelet activation in fistulas. For shear-induced platelet activation to take place, shear stress accumulated by platelets along corresponding trajectories in blood flow had to exceed a definite threshold value. The threshold value of cumulative shear stress was supposed to depend on the multimer size of von Willebrand factor macromolecules acting as hydrodynamic sensors for platelets. The effect of arteriovenous fistulas parameters, such as the anastomotic angle, blood flow rate, and the multimer size of von Willebrand factor macromolecules, on platelet activation risk was studied. Parametric diagrams have been constructed that make it possible to distinguish the areas of parameters corresponding to the presence or absence of shear-induced platelet activation. Scaling relations that approximate critical curves on parametric diagrams were obtained. Analysis showed that threshold fistula flow rate is higher for obtuse anastomotic angle than for sharp ones. This means that a fistula with obtuse angle can be used in wider flow rate range without risk of platelet activation. In addition, a study of different anastomosis configurations of arteriovenous fistulas showed that the configuration “end of vein to end of artery” is among the safest. For all the investigated anastomosis configurations, the critical curves on the parametric diagrams were monotonically decreasing functions of von Willebrand factor multimer size. It was shown that fistula flow rate should have a significant impact on the probability of thrombus formation initiation, while the direction of flow through the distal artery did not affect platelet activation. The obtained results allowed to determine the safest fistula configurations with respect to thrombus formation triggering. The authors believe that the results of the work may be of interest to doctors performing surgical operations for creation of arteriovenous fistulas for haemodialysis. In the final section of the work, possible clinical applications of the obtained results by means of mathematical modeling are discussed.

Based on the Holstein Hamiltonian, the dynamics of the charge introduced into the molecular chain of sites was modeled at different temperatures. In the calculation, the temperature of the chain is set by the initial data ¡ª random Gaussian distributions of velocities and site displacements. Various options for the initial charge density distribution are considered. Long-term calculations show that the system moves to fluctuations near a new equilibrium state. For the same initial velocities and displacements, the average kinetic energy, and, accordingly, the temperature of the T chain, varies depending on the initial distribution of the charge density: it decreases when a polaron is introduced into the chain, or increases if at the initial moment the electronic part of the energy is maximum. A comparison is made with the results obtained previously in the model with a Langevin thermostat. In both cases, the existence of a polaron is determined by the thermal energy of the entire chain.

According to the simulation results, the transition from the polaron mode to the delocalized state occurs in the same range of thermal energy values of a chain of $N$ sites ~ $NT$ for both thermostat options, with an additional adjustment: for the Hamiltonian system the temperature does not correspond to the initially set one, but is determined after long-term calculations from the average kinetic energy of the chain.

In the polaron region, the use of different methods for simulating temperature leads to a number of significant differences in the dynamics of the system. In the region of the delocalized state of charge, for high temperatures, the results averaged over a set of trajectories in a system with a random force and the results averaged over time for a Hamiltonian system are close, which does not contradict the ergodic hypothesis. From a practical point of view, for large temperatures T ≈ 300 K, when simulating charge transfer in homogeneous chains, any of these options for setting the thermostat can be used.

A simple statistical model is developed for the dynamics of the mean global annual temperature. The model combines the logarithmic effect of carbon dioxide concentration increase and the input by climatic cycles. Model parameters are determined from data of instrumental observations for 1850–2010. The model confirms the presence of climatic cycles with the period of 10.5 and 68.8 years in the global temperature dynamics. The trajectories of the global temperature changes for the XXI century are obtained under the scenarios of carbon dioxide concentration changes from the 5^th IPCC Assessment Report. The comparison revealed that the global temperature trajectories from the Report are 0.9–1.8 °C above those obtained in the model.

Distributed saddle point problems (SPPs) have numerous applications in optimization, matrix games and machine learning. For example, the training of generated adversarial networks is represented as a min-max optimization problem, and training regularized linear models can be reformulated as an SPP as well. This paper studies distributed nonsmooth SPPs with Lipschitz-continuous objective functions. The objective function is represented as a sum of several components that are distributed between groups of computational nodes. The nodes, or agents, exchange information through some communication network that may be centralized or decentralized. A centralized network has a universal information aggregator (a server, or master node) that directly communicates to each of the agents and therefore can coordinate the optimization process. In a decentralized network, all the nodes are equal, the server node is not present, and each agent only communicates to its immediate neighbors.

We assume that each of the nodes locally holds its objective and can compute its value at given points, i. e. has access to zero-order oracle. Zero-order information is used when the gradient of the function is costly, not possible to compute or when the function is not differentiable. For example, in reinforcement learning one needs to generate a trajectory to evaluate the current policy. This policy evaluation process can be interpreted as the computation of the function value. We propose an approach that uses a smoothing technique, i. e., applies a first-order method to the smoothed version of the initial function. It can be shown that the stochastic gradient of the smoothed function can be viewed as a random two-point gradient approximation of the initial function. Smoothing approaches have been studied for distributed zero-order minimization, and our paper generalizes the smoothing technique on SPPs.

The paper proposes a set of fairly simple analysis algorithms that can be used to analyze a wide range of protein-protein interactions. In this work, we jointly use the methods of Brownian and molecular dynamics to describe the process of formation of a complex of plastocyanin and cytochrome f proteins in higher plants. In the diffusion-collision complex, two clusters of structures were revealed, the transition between which is possible with the preservation of the position of the center of mass of the molecules and is accompanied only by a rotation of plastocyanin by 134 degrees. The first and second clusters of structures of collisional complexes differ in that in the first cluster with a positively charged region near the small domain of cytochrome f, only the “lower” plastocyanin region contacts, while in the second cluster, both negatively charged regions. The “upper” negatively charged region of plastocyanin in the first cluster is in contact with the amino acid residue of lysine K122. When the final complex is formed, the plastocyanin molecule rotates by 69 degrees around an axis passing through both areas of electrostatic contact. With this rotation, water is displaced from the regions located near the cofactors of the molecules and formed by hydrophobic amino acid residues. This leads to the appearance of hydrophobic contacts, a decrease in the distance between the cofactors to a distance of less than 1.5 nm, and further stabilization of the complex in a position suitable for electron transfer. Characteristics such as contact matrices, rotation axes during the transition between states, and graphs of changes in the number of contacts during the modeling process make it possible to determine the key amino acid residues involved in the formation of the complex and to reveal the physicochemical mechanisms underlying this process.