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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.

Coconut plantation plays a vital role in the economy and source of living for millions of farmers around the world, especially in tropical regions. The rugose spiraling whitefly is a highly destructive pest causing severe damage to coconut trees and significantly reducing their productivity. The aim of this paper is to develop and analyze a mathematical model that captures the dynamics of whitefly and to highlight the benefits of using biological control to mitigate the impact of pest damaging coconut palms. To be more realistic, a stage-structured model with maturation delay and lag in the implementation of the control measures has been considered in the model. We identify the equilibrium points of the system and perform a stability analysis to assess the system behavior. The numerical simulation of the proposed system is also reported. The findings reveal that introducing the population of parasitoids can effectively reduce the rugose spiraling whitefly population presenting a promising strategy for mitigating the pest’s impact.

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.

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.

The paper outlines the approaches to mathematical modeling correlation adaptometry techniques widely used in biology and medicine. The analysis is based on models employed in descriptions of structured biological systems. It is assumed that the distribution density of the biological population numbers satisfies the equation of Kolmogorov-Fokker-Planck. Using this technique evaluated the effectiveness of treatment of patients with obesity. All patients depending on the obesity degree and the comorbidity nature were divided into three groups. Shows a decrease in weight of the correlation graph computed from the measured in the patients of the indicators that characterizes the effectiveness of the treatment for all studied groups. This technique was also used to assess the intensity of the training loads in academic rowing three age groups. It was shown that with the highest voltage worked with athletes for youth group. Also, using the technique of correlation adaptometry evaluated the effectiveness of the treatment of hormone replacement therapy in women. All the patients depending on the assigned drug were divided into four groups. In the standard analysis of the dynamics of mean values of indicators, it was shown that in the course of the treatment were observed normalization of the averages for all groups of patients. However, using the technique of correlation adaptometry it was found that during the first six months the weight of the correlation graph was decreasing and during the second six months the weight increased for all study groups. This indicates the excessive length of the annual course of hormone replacement therapy and the practicality of transition to a semiannual rate.

Modern power transportation systems are the complex engineering systems. Such systems include both point facilities (power producers, consumers, transformer substations, etc.) and the distributed elements (f.e. power lines). Such structures are presented in the form of the graphs with different types of nodes under creating the mathematical models. It is necessary to solve the system of partial differential equations of the hyperbolic type to study the dynamic effects in such systems.

An approach similar to one already applied in modeling similar problems earlier used in the work. New variant of the splitting method was used proposed by the authors. Unlike most known works, the splitting is not carried out according to physical processes (energy transport without dissipation, separately dissipative processes). We used splitting to the transport equations with the drain and the exchange between Reimann’s invariants. This splitting makes possible to construct the hybrid schemes for Riemann invariants with a high order of approximation and minimal dissipation error. An example of constructing such a hybrid differential scheme is described for a single-phase power line. The difference scheme proposed is based on the analysis of the properties of the schemes in the space of insufficient coefficients.

Examples of the model problem numerical solutions using the proposed splitting and the difference scheme are given. The results of the numerical calculations shows that the difference scheme allows to reproduce the arising regions of large gradients. It is shown that the difference schemes also allow detecting resonances in such the systems.

Epidemic models are widely used to mimic social activity, such as spreading of rumors or panic. Simultaneously, models of excitable media are traditionally used to simulate the propagation of activity. Spreading of activity in the virtual community was simulated within two models: the SIRS epidemic model and the Wiener – Rosenblut model of the excitable media. We used network versions of these models. The network was assumed to be heterogeneous, namely, each element of the network has an individual set of characteristics, which corresponds to different psychological types of community members. The structure of a virtual network relies on an appropriate scale-free network. Modeling was carried out on scale-free networks with various values of the average degree of vertices. Additionally, a special case was considered, namely, a complete graph corresponding to a close professional group, when each member of the group interacts with each. Participants in a virtual community can be in one of three states: 1) potential readiness to accept certain information; 2) active interest to this information; 3) complete indifference to this information. These states correspond to the conditions that are usually used in epidemic models: 1) susceptible to infection, 2) infected, 3) refractory (immune or death due to disease). A comparison of the two models showed their similarity both at the level of main assumptions and at the level of possible modes. Distribution of activity over the network is similar to the spread of infectious diseases. It is shown that activity in virtual networks may experience fluctuations or decay.

The article analyzes empirical data on the long-term demographic and economic dynamics of the countries of the world for the period from the beginning of the 19th century to the present. Population and GDP of a number of countries of the world for the period 1500–2016 were selected as indicators characterizing the long-term demographic and economic dynamics of the countries of the world. Countries were chosen in such a way that they included representatives with different levels of development (developed and developing countries), as well as countries from different regions of the world (North America, South America, Europe, Asia, Africa). A specially developed mathematical model was used for modeling and data processing. The presented model is an autonomous system of differential equations that describes the processes of socio-economic modernization, including the process of transition from an agrarian society to an industrial and post-industrial one. The model contains the idea that the process of modernization begins with the emergence of an innovative sector in a traditional society, developing on the basis of new technologies. The population is gradually moving from the traditional sector to the innovation sector. Modernization is completed when most of the population moves to the innovation sector.

Statistical methods of data processing and Big Data methods, including hierarchical clustering were used. Using the developed algorithm based on the random descent method, the parameters of the model were identified and verified on the basis of empirical series, and the model was tested using statistical data reflecting the changes observed in developed and developing countries during the period of modernization taking place over the past centuries. Testing the model has demonstrated its high quality — the deviations of the calculated curves from statistical data are usually small and occur during periods of wars and economic crises. Thus, the analysis of statistical data on the long-term demographic and economic dynamics of the countries of the world made it possible to determine general patterns and formalize them in the form of a mathematical model. The model will be used to forecast demographic and economic dynamics in different countries of the world.

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.

The theoretical study of consensus makes it possible to analyze the various situations that social groups that make decisions in this way have to face in real life, abstracting from the specific characteristics of the groups. It is relevant for practice to study the dynamics of a social group consisting of loyal experts who, in the process of seeking consensus, yield to each other. In this case, psychological “traps” such as false consensus or groupthink are possible, which can sometimes lead to managerial decisions with dire consequences.

The article builds a mathematical consensus model for a group of loyal experts based on modeling using regular Markov chains. Analysis of the model showed that with an increase in the loyalty (decrease in authoritarianism) of group members, the time to reach consensus increases exponentially (the number of agreements increases), which is apparently due to the lack of desire among experts to take part of the responsibility for the decision being made. An increase in the size of such a group leads (ceteris paribus):

– to reduce the number of approvals to consensus in the conditions of striving for absolute loyalty of members, i. e. each additional loyal member adds less and less “strength” to the group;

– to a logarithmic increase in the number of approvals in the context of an increase in the average authoritarianism of members. It is shown that in a small group (two people), the time for reaching consensus can increase by more than 10 times compared to a group of 5 or more members), in the group there is a transfer of responsibility for making decisions.

It is proved that in the case of a group of two absolutely loyal members, consensus is unattainable.

A reasonable conclusion is made that consensus in a group of loyal experts is a special (special) case of consensus, since the dependence of the time until consensus is reached on the authoritarianism of experts and their number in the group is described by different curves than in the case of a regular group of experts.