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This article considers the task of multiclass classification of coniferous trees with varying degrees of damage by insect pests on images obtained using unmanned aerial vehicles (UAVs). We propose the use of convolutional neural networks (CNNs) for the classification of fir trees Abies sibirica and Siberian pine trees Pinus sibirica in unmanned aerial vehicles (UAV) imagery. In our approach, we develop three CNN models based on the classical U-Net architecture, designed for pixel-wise classification of images (semantic segmentation). The first model, Mo-U-Net, incorporates several changes to the classical U-Net model. The second and third models, MSC-U-Net and MSC-Res-U-Net, respectively, form ensembles of three Mo-U-Net models, each varying in depth and input image sizes. Additionally, the MSC-Res-U-Net model includes the integration of residual blocks. To validate our approach, we have created two datasets of UAV images depicting trees affected by pests, specifically Abies sibirica and Pinus sibirica, and trained the proposed three CNN models utilizing mIoULoss and Focal Loss as loss functions. Subsequent evaluation focused on the effectiveness of each trained model in classifying damaged trees. The results obtained indicate that when mIoULoss served as the loss function, the proposed models fell short of practical applicability in the forestry industry, failing to achieve classification accuracy above the threshold value of 0.5 for individual classes of both tree species according to the IoU metric. However, under Focal Loss, the MSC-Res-U-Net and Mo-U-Net models, in contrast to the third proposed model MSC-U-Net, exhibited high classification accuracy (surpassing the threshold value of 0.5) for all classes of Abies sibirica and Pinus sibirica trees. Thus, these results underscore the practical significance of the MSC-Res-U-Net and Mo-U-Net models for forestry professionals, enabling accurate classification and early detection of pest outbreaks in coniferous trees.

A Large Language Model (LLM) is an advanced artificial intelligence algorithm that utilizes deep learning methodologies and extensive datasets to process, understand, and generate humanlike text. These models are capable of performing various tasks, such as summarization, content creation, translation, and predictive text generation, making them highly versatile in applications involving natural language understanding. Generative AI, often associated with LLMs, specifically focuses on creating new content, particularly text, by leveraging the capabilities of these models. Developers can harness LLMs to automate complex processes, such as extracting relevant information from system requirement documents and translating them into a structured database schema. This capability has the potential to streamline the database design phase, saving significant time and effort while ensuring that the resulting schema aligns closely with the given requirements. By integrating LLM technology with Natural Language Processing (NLP) techniques, the efficiency and accuracy of generating database schemas based on textual requirement specifications can be significantly enhanced. The proposed tool will utilize these capabilities to read system requirement specifications, which may be provided as text descriptions or as Entity-Relationship Diagrams (ERDs). It will then analyze the input and automatically generate a relational database schema in the form of SQL commands. This innovation eliminates much of the manual effort involved in database design, reduces human errors, and accelerates development timelines. The aim of this work is to provide a tool can be invaluable for software developers, database architects, and organizations aiming to optimize their workflow and align technical deliverables with business requirements seamlessly.

The purpose of the study is to simulate the dynamics of traffic flows on city road networks as well as to systematize the current state of affairs in this area. The introduction states that the development of intelligent transportation systems as an integral part of modern transportation technologies is coming to the fore. The core of these systems contain adequate mathematical models that allow to simulate traffic as close to reality as possible. The necessity of using supercomputers due to the large amount of calculations is also noted, therefore, the creation of special parallel algorithms is needed. The beginning of the article is devoted to the up-to-date classification of traffic flow models and characterization of each class, including their distinctive features and relevant examples with links. Further, the main focus of the article is shifted towards the development of macroscopic and microscopic models, created by the authors, and determination of the place of these models in the aforementioned classification. The macroscopic model is based on the continuum approach and uses the ideology of quasi-gasdynamic systems of equations. Its advantages are indicated in comparison with existing models of this class. The model is presented both in one-dimensional and two-dimensional versions. The both versions feature the ability to study multi-lane traffic. In the two-dimensional version it is made possible by introduction of the concept of “lateral” velocity, i. e., the speed of changing lanes. The latter version allows for carrying out calculations in the computational domain which corresponds to the actual geometry of the road. The section also presents the test results of modeling vehicle dynamics on a road fragment with the local widening and on a road fragment with traffic lights, including several variants of traffic light regimes. In the first case, the calculations allow to draw interesting conclusions about the impact of a road widening on a road capacity as a whole, and in the second case — to select the optimal regime configuration to obtain the “green wave” effect. The microscopic model is based on the cellular automata theory and the single-lane Nagel – Schreckenberg model and is generalized for the multi-lane case by the authors of the article. The model implements various behavioral strategies of drivers. Test computations for the real transport network section in Moscow city center are presented. To achieve an adequate representation of vehicles moving through the network according to road traffic regulations the authors implemented special algorithms adapted for parallel computing. Test calculations were performed on the K-100 supercomputer installed in the Centre of Collective Usage of KIAM RAS.

The model of the process of using machinery and equipment is considered, which takes into account the probabilistic nature of the process of their operation and sale. It takes into account the possibility of random hidden failures, after which the condition of the machine deteriorates abruptly, as well as the randomly arising need for premature (before the end of its service life) sale of the machine, which requires, generally speaking, random time. The model is focused on assessing the market value and service life of machines in accordance with International Valuation Standards. Strictly speaking, the market value of a used machine depends on its technical condition, but in practice, appraisers only take into account its age, since generally accepted measures of the technical condition of machines do not yet exist. As a result, the market value of a used machine is assumed to be equal to the average market value of similar machines of the corresponding age. For these purposes, appraisers use coefficients that reflect the influence of the age of machines on their market value. Such coefficients are not always justified and do not take into account either the degradation of the machine or the probabilistic nature of the process of its use. The proposed model is based on the anticipation of benefits principle. In it, we characterize the state of the machine by the intensity of the benefits it brings. The machine is subjected to a complex Poisson failure process, and after failure its condition abruptly worsens and may even reach its limit. Situations also arise that preclude further use of the machine by its owner. In such situations, the owner puts the machine up for sale before the end of its service life (prematurely), and the sale requires a random timing. The model allows us to take into account the influence of such situations and construct an analytical relationship linking the market value of a machine with its condition, and calculate the average coefficients of change in the market value of machines with age. At the same time, it is also possible to take into account the influence of inflation and the scrap cost of the machine. We have found that the rate of prematurely sales has a significant impact on the cost of new and used machines. The model also allows us to take into account the influence of inflation and the scrap value of the machine. We have found that the rate of premature sales has a significant impact on the service life and market value of new and used machines. At the same time, the dependence of the market value of machines on age is largely determined by the coefficient of variation of the service life of the machines. The results obtained allow us to obtain more reasonable estimates of the market value of machines, including for the purposes of the system of national accounts.

This paper is dedicated to the analysis of medical and biological data obtained through locomotor training and testing of astronauts conducted both on Earth and during spaceflight. These experiments can be described as the astronaut’s movement on a treadmill according to a predefined regimen in various speed modes. During these modes, not only the speed is recorded but also a range of parameters, including heart rate, ground reaction force, and others, are collected. In order to analyze the dynamics of the astronaut’s condition over an extended period, it is necessary to perform a qualitative segmentation of their movement modes to independently assess the target metrics. This task becomes particularly relevant in the development of an autonomous life support system for astronauts that operates without direct supervision from Earth. The segmentation of target data is complicated by the presence of various anomalies, such as deviations from the predefined regimen, arbitrary and varying duration of mode transitions, hardware failures, and other factors. The paper includes a detailed review of several contemporary retrospective (offline) nonparametric methods for detecting multiple changepoints, which refer to sudden changes in the properties of the observed time series occurring at unknown moments. Special attention is given to algorithms and statistical measures that determine the homogeneity of the data and methods for detecting change points. The paper considers approaches based on dynamic programming and sliding window methods. The second part of the paper focuses on the numerical modeling of these methods using characteristic examples of experimental data, including both “simple” and “complex” speed profiles of movement. The analysis conducted allowed us to identify the preferred methods, which will be further evaluated on the complete dataset. Preference is given to methods that ensure the closeness of the markup to a reference one, potentially allow the detection of both boundaries of transient processes, as well as are robust relative to internal parameters.

The paper considers the problem of transverse impact on a thin preloaded fiber. The commonly accepted theory of transverse impact on a thin fiber is based on the classical works of Rakhmatulin and Smith. The simple relations obtained from the Rakhmatulin – Smith theory are widely used in engineering practice. However, there are numerous evidences that experimental results may differ significantly from estimations based on these relations. A brief overview of the factors that cause the differences is given in this article.

This paper focuses on the shear wave velocity, as it is the only feature that can be directly observed and measured using high-speed cameras or similar methods. The influence of the fiber preload on the wave speed is considered. This factor is important, since it inevitably arises in the experimental results. The reliable fastening and precise positioning of the fiber during the experiments requires its preload. This work shows that the preload significantly affects the shear wave velocity in the impacted fiber.

Numerical calculations were performed for Kevlar 29 and Spectra 1000 yarns. Shear wave velocities are obtained for different levels of initial tension. A direct comparison of numerical results and analytical estimations with experimental data is presented. The speed of the transverse wave in free and preloaded fibers differed by a factor of two for the setup parameters considered. This fact demonstrates that measurements based on high-speed imaging and analysis of the observed shear waves should take into account the preload of the fibers.

This paper proposes a formula for a quick estimation of the shear wave velocity in preloaded fibers. The formula is obtained from the basic relations of the Rakhmatulin – Smith theory under the assumption of a large initial deformation of the fiber. The formula can give significantly better results than the classical approximation, this fact is demonstrated using the data for preloaded Kevlar 29 and Spectra 1000. The paper also shows that direct numerical calculation has better corresponding with the experimental data than any of the considered analytical estimations.

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.

The work focuses on mathematical modeling of light influence mechanisms on macromolecular composition of microalgae batch culture. It is shown that even with a single limiting factor, the growth of microalgae is associated with a significant change in the biochemical composition of the biomass in any part of the batch curve. The well-known qualitative models of microalgae are based on concepts of enzymatic kinetics and do not take into account the possible change of the limiting factor during batch culture growth. Such models do not allow describing the dynamics of the relative content of biochemical components of cells. We proposed an alternative approach which is based on generally accepted two-stage photoautotrophic growth of microalgae. Microalgae biomass can be considered as the sum of two macromolecular components — structural and reserve. At the first stage, during photosynthesis a reserve part of biomass is formed, from which the biosynthesis of cell structures occurs at the second stage. Model also assumes the proportionality of all biomass structural components which greatly simplifies mathematical calculations and experimental data fitting. The proposed mathematical model is represented by a system of two differential equations describing the synthesis of reserve biomass compounds at the expense of light and biosynthesis of structural components from reserve ones. The model takes into account that a part of the reserve compounds is spent on replenishing the pool of macroergs. The rates of synthesis of structural and reserve forms of biomass are given by linear splines. Such approach allows us to mathematically describe the change in the limiting factor with an increase in the biomass of the enrichment culture of microalgae. It is shown that under light limitation conditions the batch curve must be divided into several areas: unlimited growth, low cell concentration and optically dense culture. The analytical solutions of the basic system of equations describing the dynamics of macromolecular biomass content made it possible to determine species-specific coefficients for various light conditions. The model was verified on the experimental data of biomass growth and dynamics of chlorophyll $a$ content of the red marine microalgae Pоrphуridium purpurеum batch culture.

Laminar-turbulent transition is the subject of an active research related to improvement of economic efficiency of air vehicles, because in the turbulent boundary layer drag increases, which leads to higher fuel consumption. One of the directions of such research is the search for efficient methods, that can be used to find the position of the transition in space. Using this information about laminar-turbulent transition location when designing an aircraft, engineers can predict its performance and profitability at the initial stages of the project. Traditionally, $e^N$ method is applied to find the coordinates of a laminar-turbulent transition. It is a well known approach in industry. However, despite its widespread use, this method has a number of significant drawbacks, since it relies on parallel flow assumption, which limits the scenarios for its application, and also requires computationally expensive calculations in a wide range of frequencies and wave numbers. Alternatively, flow analysis can be done by using Dynamic Mode Decomposition, which allows one to analyze flow disturbances using flow data directly. Since Dynamic Mode Decomposition is a dimensionality reduction method, the number of computations can be dramatically reduced. Furthermore, usage of Dynamic Mode Decomposition expands the applicability of the whole method, due to the absence of assumptions about the parallel flow in its derivation.

The presented study proposes an approach to finding the location of a laminar-turbulent transition using the Dynamic Mode Decomposition method. The essence of this approach is to divide the boundary layer region into sets of subregions, for each of which the transition point is independently calculated, using Dynamic Mode Decomposition for flow analysis, after which the results are averaged to produce the final result. This approach is validated by laminar-turbulent transition predictions of subsonic and supersonic flows over a 2D flat plate with zero pressure gradient. The results demonstrate the fundamental applicability and high accuracy of the described method in a wide range of conditions. The study focuses on comparison with the $e^N$ method and proves the advantages of the proposed approach. It is shown that usage of Dynamic Mode Decomposition leads to significantly faster execution due to less intensive computations, while the accuracy is comparable to the such of the solution obtained with the $e^N$ method. This indicates the prospects for using the described approach in a real world applications.

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.