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Migration has a significant impact on the shaping of the demographic structure of the territories population, the state of regional and local labour markets. As a rule, rapid change in the working-age population of any territory due to migration processes results in an imbalance in supply and demand on labour markets and a change in the demographic structure of the population. Migration is also to a large extent a reflection of socio-economic processes taking place in the society. Hence, the issues related to the study of migration factors, the direction, intensity and structure of migration flows, and the prediction of their magnitude are becoming topical issues these days.

Mathematical tools are often used to analyze, predict migration processes and assess their consequences, allowing for essentially accurate modelling of migration processes for different territories on the basis of the available statistical data. In recent years, quite a number of scientific papers on modelling internal and external migration flows using mathematical methods have appeared both in Russia and in foreign countries in recent years. Consequently, there has been a need to systematize the currently most commonly used methods and tools applied in migration modelling to form a coherent picture of the main trends and research directions in this field.

The presented review considers the main approaches to migration modelling and the main components of migration modelling methodology, i. e. stages, methods, models and model classification. Their comparative analysis was also conducted and general recommendations on the choice of mathematical tools for modelling were developed. The review contains two sections: migration modelling methods and migration models. The first section describes the main methods used in the model development process — econometric, cellular automata, system-dynamic, probabilistic, balance, optimization and cluster analysis. Based on the analysis of modern domestic and foreign publications on migration, the most common classes of models — regression, agent-based, simulation, optimization, probabilistic, balance, dynamic and combined — were identified and described. The features, advantages and disadvantages of different types of migration process models were considered.

The paper is devoted to the secondary use of spectrum in telecommunication networks. It is emphasized that one of the solutions to this problem is the use of cognitive radio technologies and dynamic spectrum access for the successful functioning of which a large amount of information is required, including the parameters of base stations and network subscribers. Storage and processing of information should be carried out using a radio environment map, which is a spatio-temporal database of all activity in the network and allows you to determine the frequencies available for use at a given time. The paper presents a two-level model for forming a map of the radio environment of a cellular communication system LTE, in which the local and global levels are highlighted, which is described by the following parameters: a set of frequencies, signal attenuation, signal propagation map, grid step, current time count. The key objects of the model are the base station and the subscriber unit. The main parameters of the base station include: name, identifier, cell coordinates, range number, radiation power, numbers of connected subscriber devices, dedicated resource blocks. For subscriber devices, the following parameters are used: name, identifier, location, current coordinates of the device cell, base station identifier, frequency range, numbers of resource blocks for communication with the station, radiation power, data transmission status, list of numbers of the nearest stations, schedules movement and communication sessions of devices. An algorithm for the implementation of the model is presented, taking into account the scenarios of movement and communication sessions of subscriber devices. A method for calculating a map of the radio environment at a point on a coordinate grid, taking into account losses during the propagation of radio signals from emitting devices, is presented. The software implementation of the model is performed using the MatLab package. The approaches are described that allow to increase the speed of its work. In the simulation, the choice of parameters was carried out taking into account the data of the existing communication systems and the economy of computing resources. The experimental results of the algorithm for the formation of a radio environment map are demonstrated, confirming the correctness of the developed model.

In Russian medicine two radiopharmaceuticals are currently used for radionuclide therapy of bone metastases: ⁸⁹Sr-chloride and ¹⁵³Sm-oxabifor. The first one has many side effects, so its use is limited. The second one is available only in clinics, its transportation to which does not take much time. Currently, the third radiopharmaceutical ¹⁸⁸Re-solerene is undergoing clinical trials. Due to the generator method of obtaining ¹⁸⁸Re, this radiopharmaceutical should become available for use in many regions of our country. Therefore, there is a need for a comparative analysis of the characteristics of these radiopharmaceuticals, including on the basis of mathematical modeling.

The article discusses the features of mathematical modeling the kinetics of osteotropic radiopharmaceutical drugs in the human body with bone metastases. Based on the four-compartment model, a complex of modeling and calculation of pharmacokinetic and dosimetric characteristics of radiopharmaceuticals for radionuclide therapy of bone metastases was developed and tested. Using clinical data, the transport constants of the model were identified and the individual characteristics of Russian radiopharmaceuticals labeled ⁸⁹Sr, 153Sm and ¹⁸⁸Re were calculated (effective half-lives, maximum activity in the compartments and the times of their achievement, absorbed doses to bone tissue and metastases, endosteal bone layer, red bone marrow, blood, kidneys and bladder). The time activity dependencies for all compartments of the model are obtained and analyzed. A comparative analysis of the pharmacokinetics and dosimetry of three radiopharmaceuticals (⁸⁹Sr-chloride, ¹⁵³Sm-oxabiphore, ¹⁸⁸Re-solerene) was carried out.

From a comparative analysis of the pharmacokinetic and dosimetric characteristics of these radiopharmaceutical drugs, it follows that the best of them for widespread use in many regions of our country should be ¹⁸⁸Re-solerene, taking into account the generator method of obtaining ¹⁸⁸Re in a hospital.

The article deals with the nonlinear boundary-value problem of hydrogen permeability corresponding to the following experiment. A membrane made of the target structural material heated to a sufficiently high temperature serves as the partition in the vacuum chamber. Degassing is performed in advance. A constant pressure of gaseous (molecular) hydrogen is built up at the inlet side. The penetrating flux is determined by mass-spectrometry in the vacuum maintained at the outlet side.

A linear model of dependence on concentration is adopted for the coefficient of dissolved atomic hydrogen diffusion in the bulk. The temperature dependence conforms to the Arrhenius law. The surface processes of dissolution and sorptiondesorption are taken into account in the form of nonlinear dynamic boundary conditions (differential equations for the dynamics of surface concentrations of atomic hydrogen). The characteristic mathematical feature of the boundary-value problem is that concentration time derivatives are included both in the diffusion equation and in the boundary conditions with quadratic nonlinearity. In terms of the general theory of functional differential equations, this leads to the so-called neutral type equations and requires a more complex mathematical apparatus. An iterative computational algorithm of second-(higher- )order accuracy is suggested for solving the corresponding nonlinear boundary-value problem based on explicit-implicit difference schemes. To avoid solving the nonlinear system of equations at every time step, we apply the explicit component of difference scheme to slower sub-processes.

The results of numerical modeling are presented to confirm the fitness of the model to experimental data. The degrees of impact of variations in hydrogen permeability parameters (“derivatives”) on the penetrating flux and the concentration distribution of H atoms through the sample thickness are determined. This knowledge is important, in particular, when designing protective structures against hydrogen embrittlement or membrane technologies for producing high-purity hydrogen. The computational algorithm enables using the model in the analysis of extreme regimes for structural materials (pressure drops, high temperatures, unsteady heating), identifying the limiting factors under specific operating conditions, and saving on costly experiments (especially in deuterium-tritium investigations).

The advent of laser scanning technologies has revolutionized forestry. Their use made it possible to switch from studying woodlands using manual measurements to computer analysis of stereo point images called point clouds.

Automatic calculation of some tree parameters (such as trunk diameter) using a point cloud requires the removal of foliage points. To perform this operation, a preliminary segmentation of the stereo image into the “foliage” and “trunk” classes is required. The solution to this problem often involves the use of machine learning methods.

One of the most popular classifiers used for segmentation of stereo images of trees is a random forest. This classifier is quite demanding on the amount of memory. At the same time, the size of the machine learning model can be critical if it needs to be sent by wire, which is required, for example, when performing distributed learning. In this paper, the goal is to find a classifier that would be less demanding in terms of memory, but at the same time would have comparable segmentation accuracy. The search is performed among classifiers such as logistic regression, naive Bayes classifier, and decision tree. In addition, a method for segmentation refinement performed by a decision tree using logistic regression is being investigated.

The experiments were conducted on data from the collection of the University of Heidelberg. The collection contains hand-marked stereo images of trees of various species, both coniferous and deciduous, typical of the forests of Central Europe.

It has been shown that classification using a decision tree, adjusted using logistic regression, is able to produce a result that is only slightly inferior to the result of a random forest in accuracy, while spending less time and RAM. The difference in balanced accuracy is no more than one percent on all the clouds considered, while the total size and inference time of the decision tree and logistic regression classifiers is an order of magnitude smaller than of the random forest classifier.

An analytical solution is obtained for seismic wave fields in a spherically symmetric Earth. In the case of an arbitrary layered medium, the solution, which includes Bessel functions, is constructed by means of a differential sweep method. Asymptotic of Bessel functions is used for stable calculation of wave fields. It is shown that the classical asymptotic in the case of a sphere of large (in wavelengths) dimensions gives an error in the solution. The new asymptotic is used for efficient calculation of a solution without errors with high detail. A program has been created that makes it possible to carry out calculations for high-frequency (1 hertz and higher) teleseismic wave fields in a discrete (layered) sphere of planetary dimensions. Calculations can be carried even out on personal computers with OpenMP parallelization.

In the works of Burmin (2019) proposed a spherically symmetric model of the Earth. It is characterized by the fact that in it the outer core has a viscosity and, therefore, an effective shear modulus other than zero. For this model of the Earth, a highly detailed calculation was carried out with a carrier frequency of 1 hertz. As a result of the analytical calculation, it was found that highfrequency oscillations of small amplitude, the so-called “precursors”, appear ahead of the PKP waves. An analytical calculation showed that the theoretical seismograms for this model of the Earth are in many respects similar to the experimental data. This confirms the correctness of the ideas underlying its construction.

The article presents a technology for building clinical decision support systems (CDSS) based on service containers using Docker and a web interface that runs directly in the browser without installing specialized software on workstation of a clinician. A modular architecture is proposed in which each application module is packaged as an independent service container combining a lightweight web server, a user interface, and computational components for medical image processing. Communication between the browser and the server side is implemented via a persistent bidirectional WebSocket connection with binary message serialization (MessagePack), which provides low latency and efficient transfer of large data. For local storage of images and analysis of results, browser facilities (IndexedDB with the Dexie.js wrapper) are used to speed up repeated data access. Three-dimensional visualization and basic operations with DICOM data are implemented with Three.js and AMI.js: this toolchain supports the integration of interactive elements arising from the task context (annotations, landmarks, markers, 3D models) into volumetric medical images.

Server components and functional modules are assembled as a set of interacting containers managed by Docker. The paper discusses the choice of base images, approaches to minimizing containers down to runtime-only executables without external utilities, and the organization of multi-stage builds with a dedicated build container. It describes a hub service that launches application containers on user request, performs request proxying, manages sessions, and switches a container from shared to exclusive mode at the start of computations. Examples of application modules are provided (fractional flow reserve estimation, quantitative flow ratio computation, aortic valve closure modeling), along with the integration of a React-based interface with a three-dimensional scene, a versioning policy, automated reproducibility checks, and the deployment procedure on the target platform.

It is demonstrated that containerization ensures portability and reproducibility of the software environment, dependency isolation and scalability, while the browser-based interface provides accessibility, reduced infrastructure requirements, and interactive real-time visualization of medical data. Technical limitations are noted (dependence on versions of visualization libraries and data formats) together with practical mitigation measures.

Aberrant alternative splicing of the CD44 gene drives colorectal cancer progression and facilitates the emergence of cancer stem cells. Although biomedical research recognizes this transmembrane glycoprotein as a major catalyst of malignancy, deciphering its multi-isoform regulatory networks remains a complex analytical challenge. To address this knowledge gap, this study presents a machine learning framework designed to decode these biological mechanisms. The author constructed a neural network regressor based on beta regression to model bounded isoform proportions. This computational architecture jointly estimates both the mean and the precision parameters of the underlying probability distribution. Furthermore, the system employs elastic net regularization to perform quantitative feature selection from highdimensional molecular expression data.

The investigation evaluates the proposed framework using gene expression profiles from colorectal cancer patients. The primary objective involves identifying specific ribonucleic acid-binding proteins acting as regulatory splicing factors. The experimental design contrasts two distinct mathematical modeling strategies. The first configuration incorporates an independent ”one-vs-all” approach that treats each transcript variant as an isolated regression target. The second formulation utilizes a structured ”isoform tree” method that directly mirrors hierarchical exon inclusion relationships. Validation experiments on synthetically generated datasets confirmed the mathematical integrity of the network. The model recovered true distribution parameters with precision and exhibited no systematic bias. Comprehensive empirical comparisons subsequently demonstrated that the independent ”one-vs-all” layout consistently outperforms the hierarchical tree configuration in predictive stability and accuracy.

The computational analysis maps the regulatory landscape of the CD44 gene. The framework validates several established splicing factors while uncovering new candidate proteins, including ACO1, NUDT21, and AGO2. Based on these statistical associations, the paper introduces a biological hypothesis. This concept functionally connects intracellular iron metabolism via the ACO1 protein with the shifting balance of CD44 variants. These discoveries provide deeper insights into oncogenic splicing regulation. Ultimately, they highlight molecular targets for future therapeutic interventions aimed at suppressing the cancer stem cell phenotype.

In present work we used the methods of molecular dynamics simulations and quantum chemistry to study the concept, according to which aspartic and glutamic residues play a key role in initiation of helix formation in oligopeptides. It has been shown, that the first turn of the alpha-helix can be organized from various amino acid sequences with Asp and Glu residues on the N-terminus. Thermodynamic properties of such a process were analyzed. The obtained results do not interfere with known experimental and statistical data and they substantially elaborate present views on the processes of early peptide folding stages.

In the paper we construct the model of phase change. Process of hydriding / dehydriding is taken as an example. A single powder particle is considered under the assumption about its symmetry. A ball, a cylinder, and a flat plate are examples of such symmetrical shapes. The model desribes both the "shrinking core"(when the skin of the new phase appears on the surface of the particle) and the "nucleation and growth"(when the skin does not appear till complete vanishing of the old phase) scenarios. The model is the non-classical boundary-value problem with the free boundary and nonlinear Neumann boundary condition. The symmetry assumptions allow to reduce the problem to the single spatial variable. The model was tested on the series of experimental data. We show that the particle shape’s influence on the kinetics is insignificant. We also show that a set of particles of different shapes with size distribution can be approxomated by the single particle of the "average" size and of a simple shape; this justifies using single particle approximation and simple shapes in mathematical models.