All issues

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 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.

Modern GPUs are massively-parallel processors, offering substantial amount of computational power in energy-efficient package. We discuss the benefits of utilizing this computing power for modeling problems in bio-informatics, such as molecular dynamics, quantum chemistry and sequence analysis.

Pattern formation described by the scalar Fisher–Kolmogorov–Petrovsky–Piscounov equation with nonlocal competition loses and convection linear on coordinates is considered numerically. Initial function localized around a point is shown to transform in a function localized around a ring with symmetrically sited local maxima. The ring radius and number of maxima depend on convection.

Mathematical model of non-stationary ventilation processes of coal mining is presented. Numerical simulation of dead-end excavation ventilation processes using local ventilation fan and occurred non-stationary aerodynamic processes by reversing main ventilation fan in modeling of coal mining are presented in this study Poisson on circle and other areas.