All issues

A numerical method is proposed that approximates the equations of the dynamics of a weakly compressible viscous flow in the presence of a polymer component of the flow. The behavior of the flow under the influence of a static external periodic force in a periodic square cell is investigated. The methodology is based on a hybrid approach. The hydrodynamics of the flow is described by a system of Navier – Stokes equations and is numerically approximated by the linearized Godunov method. The polymer field is described by a system of equations for the vector of stretching of polymer molecules $\bf R$, which is numerically approximated by the Kurganov – Tedmor method. The choice of model relationships in the development of a numerical methodology and the selection of modeling parameters made it possible to qualitatively model and study the regime of elastic turbulence at low Reynolds $Re \sim 10^{-1}$. The polymer solution flow dynamics equations differ from the Newtonian fluid dynamics equations by the presence on the right side of the terms describing the forces acting on the polymer component part. The proportionality coefficient $A$ for these terms characterizes the backward influence degree of the polymers number on the flow. The article examines in detail how the flow and its characteristics change depending on the given coefficient. It is shown that with its growth, the flow becomes more chaotic. The flow energy spectra and the spectra of the polymers stretching field are constructed for different values of $A$. In the spectra, an inertial sub-range of the energy cascade is traced for the flow velocity with an indicator $k \sim −4$, for the cascade of polymer molecules stretches with an indicator $−1.6$.

A 2D numerical model of the immersion of a cold oceanic plate into the thickness of the Earth’s upper mantle has been developed, where the stage of the initial immersion of the plate is preceded by the establishment of a regime of thermogravitational convection of the mantle substance. The model approximation of the mantle is a two-dimensional image of an incompressible Newtonian quasi-liquid in a Cartesian coordinate system, where, due to the high viscosity of the medium, the equations of mantle convection are accepted in the Stokes approximation. It is assumed that seawater that has leaked here enters the first horizons of the mantle together with the plate. With depth, the increase in pressure and temperature leads to certain losses of its light fractions and fluids, losses of water and gases of water-containing minerals of the plate, restructuring of their crystal lattice and, as a consequence, phase transformations. These losses cause an increase in the plate density and an uneven distribution of stresses along the plate (the initial sections of the plate are denser), which subsequently, together with the effect of mantle currents on the plate, causes its fragmentation. The state of mantle convection is considered when the plate and its individual fragments have descended to the bottom of the upper mantle. Computational schemes for solving the model equations have been developed. Mantle convection calculations are performed in terms of the Stokes approximation for vorticity and the stream function, and SPH is used to calculate the state and subsidence of the plate. A number of computational experiments have been performed. It is shown that fragmentation of the plate occurs due to the effect of mantle convection on the plate and the development of inhomogeneous stress fields along the plate. Following the equations of the model, the time of the final stage of subduction is estimated, i.e. the time of the entire oceanic plate reaching the bottom of the upper mantle. In geodynamics, this process is determined by the collision of plates that immediately follows subduction and is usually considered as the final stage of the Wilson cycle (i. e., the cycle of development of folded belts).

This paper investigates neural network models and polynomial models based on Chebyshev polynomials for interference compensation. It is shown that the neural network model provides compensation for parasitic interference without the need for parameter tuning, unlike the polynomial model, which requires the selection of optimal delays. The L-BFGS method is applied to both architectures, achieving a compensation level comparable to the LS solution for the polynomial model, with an NMSE result of −23.59 dB and requiring fewer than 2000 iterations, confirming its high efficiency. Additionally, due to the strong generalization ability of neural network architectures, the first-order method for neural networks demonstrates faster convergence compared to the polynomial model. In 20 000 iterations, the neural network model achieves a 0.44 dB improvement in compensation level compared to the polynomial model. In contrast, the polynomial model can only achieve high compensation levels with optimal first-order method parameter tuning, highlighting one of the key advantages of neural network models.

This study investigates the influence of a time-periodic (modulation) magnetic field upon the development of ferroconvection in a densely packed medium saturated with couple stress ferromagnetic fluid. The Darcy model is used to describe the flow in porous medium. The research is important from practical and theoretical point of view. A time-periodic magnetic field is essential in circumscribing channels where the effect of gravity is less or nonexistent to generate circulation. There are numerous engineering uses for this in the manufacturing of magnetic field sensors, charged particle electrode materials, modulators, magnetic resonators, and optical devices. The resulting physical eigenvalue problem is dealt with by using isothermal boundary conditions and the regular perturbation technique with a small time-periodic amplitude. The onset criteria were defined on the supposition that the exchange of stability principle holds. The shift in the thermal Rayleigh number is dependent on the associated parameters: magnetic parameter, Vadasz number, couple stress parameter, porosity, and frequency of the time-periodic function. The results in this case indicate that the onset of ferroconvection can be enhanced or reduced by appropriate changes in the governing parameters.

The paper proposes and investigates a mathematical model of a distributed computing system of parallel interacting processes competing for the use of a limited number of copies of a structured software resource. In cases of unlimited and limited parallelism by the number of processors of a multiprocessor system, the problems of determining operational and exact values of the execution time of heterogeneous and identically distributed competing processes in a synchronous mode are solved, which ensures a linear order of execution of blocks of a structured software resource within each of the processes without delays. The obtained results can be used in a comparative analysis of mathematical relationships for calculating the implementation time of a set of parallel distributed interacting competing processes, a mathematical study of the efficiency and optimality of the organization of distributed computing, solving problems of constructing an optimal layout of blocks of an identically distributed system, finding the optimal number of processors that provide the directive execution time of given volumes of computations. The proposed models and methods open up new prospects for solving problems of optimal distribution of limited computing resources, synchronization of a set of interacting competing processes, minimization of system costs when executing parallel distributed processes.

The influence of solar activity on various processes on Earth has long been the subject of close study, which resulted in the appearance of the term “space weather”. The most striking manifestation of solar activity are the so-called “solar flares”, which are explosive releases of energy in the solar atmosphere, resulting in a flow of photons and charged particles reaching the Earth with a slight delay. After two or three days, a plasma flow reaches the Earth. Thus, a solar flare is an event stretched out in time for several days. The impact of solar flares on human health and the technosphere is a popular subject for discussion and scientific research. This article provides a quantitative assessment of the trigger effect of solar flares on the release of energy as a result of seismic events. The article provides an estimate in the form of a “percentage” of the released seismic energy of the trigger effect of solar flares on the release of seismic energy worldwide and in 8 areas of the Pacific Fire Ring. The initial data are a time series of solar flares from July 31, 1996 to the end of 2024. The time points of the greatest local extremes of solar flare intensity and released seismic energy were studied in successive time intervals of 1 day. For each pair of time sequences in sliding time windows, the “lead measures” of each time sequence relative to the other were estimated using a parametric model of the intensity of interacting point processes. The difference between the “direct” lead measure of the time points of local extremes of solar flare intensity relative to the moments of maximum released seismic energy and the “reverse” lead measure was calculated. The average value of the difference in lead measures provides an estimate of the share of the intensity of seismic events for which solar flares are a trigger.

The Industrial Internet of Things (IIoT) networks plays a significant role in enhancing industrial automation systems by connecting industrial devices for real time data monitoring and predictive maintenance. However, this connectivity introduces new vulnerabilities which demand the development of advanced intrusion detection systems. The nuclear facilities are considered one of the closest examples of critical infrastructures that suffer from high vulnerability through the connectivity of IIoT networks. This paper develops a robust intrusion detection approach using machine and ensemble learning algorithms specifically determined for IIoT networks. This approach can achieve optimal performance with low time complexity suitable for real-time IIoT networks. For each algorithm, Grid Search is determined to fine-tune the hyperparameters for optimizing the performance while ensuring time computational efficiency. The proposed approach is investigated on recent IIoT intrusion detection datasets, WUSTL-IIOT-2021 and Edge-IIoT-2022 to cover a wider range of attacks with high precision and minimum false alarms. The study provides the effectiveness of ten machine and ensemble learning models on selected features of the datasets. Synthetic Minority Over-sampling Technique (SMOTE)-based multi-class balancing is used to manipulate dataset imbalances. The ensemble voting classifier is used to combine the best models with the best hyperparameters for raising their advantages to improve the performance with the least time complexity. The machine and ensemble learning algorithms are evaluated based on accuracy, precision, recall, F1 Score, and time complexity. This evaluation can discriminate the most suitable candidates for further optimization. The proposed approach is called the XCL approach that is based on Extreme Gradient Boosting (XGBoost), CatBoost (Categorical Boosting), and Light Gradient- Boosting Machine (LightGBM). It achieves high accuracy, lower false positive rate, and efficient time complexity. The results refer to the importance of ensemble strategies, algorithm selection, and hyperparameter optimization in enhancing the performance to detect the different intrusions across the IIoT datasets over the other models. The developed approach produced a higher accuracy of 99.99% on the WUSTL-IIOT-2021 dataset and 100% on the Edge-IIoTset dataset. Our experimental evaluations have been extended to the CIC-IDS-2017 dataset. These additional evaluations not only highlight the applicability of the XCL approach on a wide spectrum of intrusion detection scenarios but also confirm its scalability and effectiveness in real-world complex network environments.

The article presents a systematic investigation of the capabilities of the lattice Boltzmann method (LBM) for modeling the propagation of acoustic waves. The study considers the problem of wave propagation from a point harmonic source in an unbounded domain, both in a quiescent medium (Mach number $M=0$) and in the presence of a uniform mean flow ($M=0.2$). Both scenarios admit analytical solutions within the framework of linear acoustics, allowing for a quantitative assessment of the accuracy of the numerical method.

The numerical implementation employs the two-dimensional D2Q9 velocity model and the Bhatnagar – Gross – Krook (BGK) collision operator. The oscillatory source is modeled using Gou’s scheme, while spurious high-order moment noise generated by the source is suppressed via a regularization procedure applied to the distribution functions. To minimize wave reflections from the boundaries of the computational domain, a hybrid approach is used, combining characteristic boundary conditions based on Riemann invariants with perfectly matched layers (PML) featuring a parabolic damping profile.

A detailed analysis is conducted to assess the influence of computational parameters on the accuracy of the method. The dependence of the error on the PML thickness ($L_{\text{PML}}^{}$) and the maximum damping coefficient ($\sigma_{\max}^{}$), the dimensionless source amplitude ($Q'_0$), and the grid resolution is thoroughly examined. The results demonstrate that the LBM is suitable for simulating acoustic wave propagation and exhibits second-order accuracy. It is shown that achieving high accuracy (relative pressure error below $1\,\%$) requires a spatial resolution of at least $20$ grid points per wavelength ($\lambda$). The minimal effective PML parameters ensuring negligible boundary reflections are identified as $\sigma_{\max}^{}\geqslant 0.02$ and $L_{\text{PML}}^{} \geqslant 2\lambda$. Additionally, it is shown that for source amplitudes $Q_0' \geqslant 0.1$, nonlinear effects become significant compared to other sources of error.

This paper presents an experimental study of the application of automatic annotation of text data in the question – answer format (QA pairs) under conditions of limited computing resources and data protection requirements. Unlike traditional approaches based on rigid rules or the use of external APIs, we propose using small language models with a small number of parameters that can function locally without a GPU on standard CPU systems. Two models were selected for testing — Gemma-3-4b and Qwen-2.5-3b (quantized 4-bit versions) — and a corpus of documents with a clear structure and a formally rigorous style of presentation was used as source material. An automatic annotation system was developed that implements the full cycle of QA dataset generation: automatic division of the source document into logically connected fragments, formation of “question – answer” pairs using the Gemma-3-4b model, preliminary verification of their correctness using Qwen-2.5-3b based on evidence span from the context and expert quality assessment. The results are exported in JSONL format. Performance evaluation covers the entire QA pair generation system, including fragment processing by the local language model, text preprocessing and postprocessing modules. Performance is measured by the time it takes to generate a single QA pair, the total throughput of the system, RAM usage, and CPU load, which allows for an objective assessment of the computational efficiency of the proposed approach when running on a CPU. An experiment on an extended sample of 12 documents showed that automatic annotation demonstrates stable performance when processing different types of documents, while manual annotation is characterized by significantly higher time costs and high variability. Depending on the type of document, the acceleration of annotation compared to the manual process ranges from 8 to 14 times. Quality analysis showed that most of the generated QA pairs have high semantic consistency with the original context, with only a limited proportion of data requiring expert correction or exception. Although full manual validation of the corpus (the “gold standard”) was not performed as part of this work, the combination of automatic evaluation and selective expert review allows us to consider the resulting quality level acceptable for preliminary automated annotation tasks. Overall, the results confirm the practical applicability of small language models for building autonomous and reproducible automatic text annotation systems under limited computational resources and provide a basis for further research in the field of effective training corpus preparation for natural language processing tasks.

The semiclassical approximation method is applied for solution construction of the Fokker–Planck equation with quadratic nonlocal nonlinearity and various coefficients in models of asset returns estimation. Analitical expressions determining nonlinear evolution operator are obtained in semiclasical approximation.