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In some cases, information warfare results in almost whole population accepting one of two contesting points of view and rejecting the other. In other cases, however, the “majority party” gets only a small advantage over the “minority party”. The relevant question is which network characteristics of a population contribute to the minority being able to maintain some significant numbers. Given that some societies are more connected than others, in the sense that they have a higher density of social ties, this question is specified as follows: how does the density of social ties affect the chances of a minority to maintain a significant number? Does a higher density contribute to a landslide victory of majority, or to resistance of minority? To address this issue, we consider information warfare between two parties, called the Left and the Right, in the population, which is represented as a network, the nodes of which are individuals, and the connections correspond to their acquaintance and describe mutual influence. At each of the discrete points in time, each individual decides which party to support based on their attitude, i. e. predisposition to the Left or Right party and taking into account the influence of his network ties. The influence means here that each tie sends a cue with a certain probability to the individual in question in favor of the party that themselves currently support. If the tie switches their party affiliation, they begin to agitate the individual in question for their “new” party. Such processes create dynamics, i. e. the process of changing the partisanship of individuals. The duration of the warfare is exogenously set, with the final time point roughly associated with the election day. The described model is numerically implemented on a scale-free network. Numerical experiments have been carried out for various values of network density. Because of the presence of stochastic elements in the model, 200 runs were conducted for each density value, for each of which the final number of supporters of each of the parties was calculated. It is found that with higher density, the chances increase that the winner will cover almost the entire population. Conversely, low network density contributes to the chances of a minority to maintain significant numbers.

In the presented work, a hybrid optimal simulation model of an assembly shop in the AnyLogic environment has been developed, which allows you to select the parameters of production systems. To build a hybrid model of the investigative approach, discrete-event modeling and aggressive modeling are combined into a single model with an integrating interaction. Within the framework of this work, a mechanism for the development of a production system consisting of several participants-agents is described. An obvious agent corresponds to a class in which a set of agent parameters is specified. In the simulation model, three main groups of operations performed sequentially were taken into account, and the logic for working with rejected sets was determined. The product assembly process is a process that occurs in a multi-phase open-loop system of redundant service with waiting. There are also signs of a closed system — scrap flows for reprocessing. When creating a distribution system in the segment, it is mandatory to use control over the execution of requests in a FIFO queue. For the functional assessment of the production system, the simulation model includes several functional functions that describe the number of finished products, the average time of preparation of products, the number and percentage of rejects, the simulation result for the study, as well as functional variables in which the calculated utilization factors will be used. A series of modeling experiments were carried out in order to study the behavior of the agents of the system in terms of the overall performance indicators of the production system. During the experiment, it was found that the indicator of the average preparation time of the product is greatly influenced by such parameters as: the average speed of the set of products, the average time to complete operations. At a given limitation interval, we managed to select a set of parameters that managed to achieve the largest possible operation of the assembly line. This experiment implements the basic principle of agent-based modeling — decentralized agents make a personal contribution and affect the operation of the entire simulated system as a whole. As a result of the experiments, thanks to the selection of a large set of parameters, it was possible to achieve high performance indicators of the assembly shop, namely: to increase the productivity indicator by 60%; reduce the average assembly time of products by 38%.

Collective behavior can serve as a mechanism of thermoregulation and play a key role in the joint survival of a group of organisms. In higher animals, such phenomena are usually the subject of study of biology since sudden transitions to collective behavior are difficult to differentiate from the psychological and social adaptation of animals. However, in this paper, we indicate several important examples when a flock of higher animals demonstrates phase transitions similar to known phenomena in liquids and gases. This issue can also be studied experimentally within the framework of synthetic systems consisting of self-propelled robots that act according to a certain given algorithm. Generalizing both of these cases, we consider the problem of phase transitions in a dense group of interacting selfpropelled agents. Within the framework of microscopic theory, we propose a mathematical model of the phenomenon, in which agents are represented as bodies interacting with each other in accordance with an effective potential of a special type, expressing the desire of agents to move in the direction of the gradient of the joint thermal field. We show that the number of agents in the group, the group power, is the control parameter of the problem. A discrete model with individual dynamics of agents reproduces most of the phenomena observed both in natural flocks of higher animals engaged in collective thermoregulation and in synthetic complex systems. A first-order phase transition is observed, which symbolizes a change in the aggregate state in a group of agents. One observes the self-assembly of the initial weakly structured mass of agents into dense quasi-crystalline structures. We demonstrate also that, with an increase in the group power, a second-order phase transition in the form of thermal convection can occur. It manifests in a sudden liquefaction of the group and a transition to vortex motion, which ensures more efficient energy consumption in the case of a synthetic system of interacting robots and the collective survival of all individuals in the case of natural animal flocks.With an increase in the group power, secondary bifurcations occur, the vortex structure in agent medium becomes more complicated.

Classical Chanter–Thornley model of mushroom growth has been modified and implemented in AnyLogic simulation environment by means of system dynamics, discrete-event and agent-based approaches. A numerical case study of the model is presented and the problem of optimum age at harvest, providing the maximum integral yield for all fruiting “waves” is solved.

Modeling the fight against terrorist, pirate and robbery acts at sea is an urgent scientific task due to the prevalence of force acts and the insufficient number of works on this issue. The actions of pirates and terrorists are diverse. Using a base ship, they can attack ships up to 450–500 miles from the coast. Having chosen the target, they pursue it and use the weapons to board the ship. Actions to free a ship captured by pirates or terrorists include: blocking the ship, predicting where pirates might be on the ship, penetrating (from board to board, by air or from under water) and cleaning up the ship’s premises. An analysis of the special literature on the actions of pirates and terrorists showed that the act of force (and actions to neutralize it) consists of two stages: firstly, blocking the vessel, which consists in forcing it to stop, and secondly, neutralizing the team (terrorist groups, pirates), including penetration of a ship (ship) and its cleaning. The stages of the cycle are matched by indicators — the probability of blocking and the probability of neutralization. The variables of the act of force model are the number of ships (ships, boats) of the attackers and defenders, as well as the strength of the capture group of the attackers and the crew of the ship - the victim of the attack. Model parameters (indicators of naval and combat superiority) were estimated using the maximum likelihood method using an international database of incidents at sea. The values of these parameters are 7.6–8.5. Such high values of superiority parameters reflect the parties' ability to act in force acts. An analytical method for calculating excellence parameters is proposed and statistically substantiated. The following indicators are taken into account in the model: the ability of the parties to detect the enemy, the speed and maneuverability characteristics of the vessels, the height of the vessel and the characteristics of the boarding equipment, the characteristics of weapons and protective equipment, etc. Using the Becker model and the theory of discrete choice, the probability of failure of the force act is estimated. The significance of the obtained models for combating acts of force in the sea space lies in the possibility of quantitative substantiation of measures to protect the ship from pirate and terrorist attacks and deterrence measures aimed at preventing attacks (the presence of armed guards on board the ship, assistance from warships and helicopters).

Data hiding in digital images is a promising direction of cybersecurity. Digital steganography methods provide imperceptible transmission of secret data over an open communication channel. The information embedding efficiency depends on the embedding imperceptibility, capacity, and robustness. These quality criteria are mutually inverse, and the improvement of one indicator usually leads to the deterioration of the others. A balance between them can be achieved using metaheuristic optimization. Metaheuristics are a class of optimization algorithms that find an optimal, or close to an optimal solution for a variety of problems, including those that are difficult to formalize, by simulating various natural processes, for example, the evolution of species or the behavior of animals. In this study, we propose an approach to data hiding in the hybrid spatial-frequency domain of digital images based on metaheuristic optimization. Changing a block of image pixels according to some change matrix is considered as an embedding operation. We select the change matrix adaptively for each block using metaheuristic optimization algorithms. In this study, we compare the performance of three metaheuristics such as genetic algorithm, particle swarm optimization, and differential evolution to find the best change matrix. Experimental results showed that the proposed approach provides high imperceptibility of embedding, high capacity, and error-free extraction of embedded information. At the same time, storage of change matrices for each block is not required for further data extraction. This improves user experience and reduces the chance of an attacker discovering the steganographic attachment. Metaheuristics provided an increase in imperceptibility indicator, estimated by the PSNR metric, and the capacity of the previous algorithm for embedding information into the coefficients of the discrete cosine transform using the QIM method [Evsutin, Melman, Meshcheryakov, 2021] by 26.02% and 30.18%, respectively, for the genetic algorithm, 26.01% and 19.39% for particle swarm optimization, 27.30% and 28.73% for differential evolution.

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.

Algorithms of wireless sensor networks operation based on modified ZigBee/IEEE 802.15.4 protocol stack and problems of energy saving with simultaneous decrease of network latency are studied. Theoretical computations are given. Roles distribution and routers schedule assignment algorithms are described. Both results of experiments carried out with real devices and results of simulations with ns-2 (open-source network simulator) are given and analyzed.

The nonlinear restrictive (with restrictions of the inequalities type) dynamic mathematical model of the committed competition vacant market of many goods in conditions of the goods deliveries time-lag and of the linear dependency of the demand vector from the prices vector is offered. The problem of finding of prices and deliveries of goods into the market which are optimal (from seller’s profit standpoint) is formulated. It is shown the seller’s total profit maximum is expressing by the continuous piecewise smooth function of vector of volumes of deliveries with breakup of the derivative on borders of zones of the goods deficit, of the overstocking and of the dynamic balance of demand and offer of each of goods. With use of the predicate functions technique the computing algorithm of optimization of the goods deliveries into the market is built.

We propose a four-component model of a plankton community with discrete time. The model considers the competitive relationships of phytoplankton groups exhibited between each other and the trophic characteristics zooplankton displays: it considers the division of zooplankton into predatory and non-predatory components. The model explicitly represents the consumption of non-predatory zooplankton by predatory. Non-predatory zooplankton feeds on phytoplankton, which includes two competing components: toxic and non-toxic types, with the latter being suitable for zooplankton food. A model of two coupled Ricker equations, focused on describing the dynamics of a competitive community, describes the interaction of two phytoplanktons and allows implicitly taking into account the limitation of each of the competing components of biomass growth by the availability of external resources. The model describes the prey consumption by their predators using a Holling type II trophic function, considering predator saturation.

The analysis of scenarios for the transition from stationary dynamics to fluctuations in the population size of community members showed that the community loses the stability of the non-trivial equilibrium corresponding to the coexistence of the complete community both through a cascade of period-doubling bifurcations and through a Neimark – Sacker bifurcation leading to the emergence of quasi-periodic oscillations. Although quite simple, the model proposed in this work demonstrates dynamics of comunity similar to that natural systems and experiments observe: with a lag of predator oscillations relative to the prey by about a quarter of the period, long-period antiphase cycles of predator and prey, as well as hidden cycles in which the prey density remains almost constant, and the predator density fluctuates, demonstrating the influence fast evolution exhibits that masks the trophic interaction. At the same time, the variation of intra-population parameters of phytoplankton or zooplankton can lead to pronounced changes the community experiences in the dynamic mode: sharp transitions from regular to quasi-periodic dynamics and further to exact cycles with a small period or even stationary dynamics. Quasi-periodic dynamics can arise at sufficiently small phytoplankton growth rates corresponding to stable or regular community dynamics. The change of the dynamic mode in this area (the transition from stable dynamics to quasi-periodic and vice versa) can occur due to the variation of initial conditions or external influence that changes the current abundances of components and shifts the system to the basin of attraction of another dynamic mode.