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The presence of a contact boundary between water and transformer oil greatly reduces the electrical strength of the oil phase. The presence of an electric field leads to varying degrees of polarization at the interface and the appearance of a force acting on a liquid with a higher dielectric constant (water) in the direction of a liquid with a lower dielectric constant (oil). This leads to the contact surface instability development. Instability as a result of its development leads to a stream of water being drawn into oil volume and a violation of the insulating gap. In this work, we experimentally and numerically study electrohydrodynamic instability at the phase boundary between electrically weakly conductive water and transformer oil in a highly inhomogeneous electric field directed perpendicular to the contact boundary. The results of a full-scale and numerical experiment of studying of the electrohydrodynamic instability development in a strong electric field at the interface between water and transformer oil are presented. The system consists of a spherical electrode with a radius of 3.5 mm, placed in water with a conductivity of 5 $\mu S/cm$, and a thin blade electrode 0.1 mm thick, placed in transformer oil of the GK brand. The contact boundary passes at the same distance from the nearest points of the electrodes, equal to 3 mm. The work shows that at a certain electric field strength, the cone-shaped structure of water grows towards the electrode immersed in transformer oil. A numerical correspondence was obtained for both the shape of the resulting water structure (cone) during the entire growth time and the size measured from its top to the level of the initial contact boundary of phase separation. The dynamics of this structure growth has been studied. Both in numerical calculations and in experiment, it was found that the size of the resulting cone along the electrode connection line depends linearly on time.

This paper describes the design of an aquatic robot moving on the surface of a fluid and driven by two internal moving masses. The body of the aquatic robot in cross section has the shape of a symmetrical airfoil with a sharp edge. In this prototype, two internal masses move in circles and are rotated by a single DC motor and a gear mechanism that transmits torque from the motor to each mass. Angular velocities of moving masses are used as a control action, and the developed kinematic scheme for transmitting rotation from the motor to the moving masses allows the rotation of two masses with equal angular velocities in magnitude, but with a different direction of rotation. It is also possible to install additional tail fins of various shapes and sizes on the body of this robot. Also in the work for this object, the equations of motion are presented, written in the form of Kirchhoff equations for the motion of a solid body in an ideal fluid, which are supplemented by terms of viscous resistance. A mathematical description of the additional forces acting on the flexible tail fin is presented. Experimental studies on the influence of various tail fins on the speed of motion in the fluid were carried out with the developed prototype of the robot. In this work, tail fins of the same shape and size were installed on the robot, while having different stiffness. The experiments were carried out in a pool with water, over which a camera was installed, on which video recordings of all the experiments were obtained. Next processing of the video recordings made it possible to obtain the object’s movements coordinates, as well as its linear and angular velocities. The paper shows the difference in the velocities developed by the robot when moving without a tail fin, as well as with tail fins having different stiffness. The comparison of the velocities developed by the robot, obtained in experimental studies, with the results of mathematical modeling of the system is given.

While working on activity recognition using wearable sensors for healthcare applications, the main issue arises in the classification of activities. When we attempt to classify activities like walking, sitting, or running from accelerometer and gyroscope data, the signals often overlap and noise complicates the classification process. The existing methods do not have solid mathematical foundations to handle this issue. We started with the standard magnitude approach where one can compute $m =  \sqrt{a^2_1 + a^2_2 + a^2_3}$ from the accelerometer readings, but this approach failed because different activities ended up in overlapping regions. We therefore developed a different approach. Instead of collapsing the 6-dimensional sensor data into simple magnitudes, we keep all six dimensions and treat each activity as a rectangular box in this 6D space. We define these boxes using simple interval constraints. For example, walking occurs when the $x$-axis accelerometer reading is between $2$ and $4$, the $y$-axis reading is between $9$ and $10$, and so on. The key breakthrough is what we call a separability index $s = \frac{d_{\min}^{}}{\sigma}$ that determines how accurately the classification will work. Here dmin represents how far apart the activity boxes are, and $\sigma$ represents the amount of noise present. From this simple idea, we derive a mathematical formula $P(\text{error}) \leqslant (n-1)\exp\left(-\frac{s^2}8\right)$  that predicts the error rate even before initiating the experiment. We tested this on the standard UCI-HAR and WISDM datasets and achieved $86.1 %$ accuracy. The theoretical predictions matched the actual results within $3 %$. This approach outperforms the traditional magnitude methods by $30.6 %$ and explains why certain activities overlap with each other.

Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and strongly correlated trajectories that reduce the statistical efficiency of return estimation. These well-known limitations often lead to slow convergence and unstable learning dynamics, particularly in settings where only a small fraction of collected trajectories is actually informative for policy improvement. A key challenge is therefore to identify a compact yet diverse subset of episodes that contributes most to the accuracy of value estimates while preserving sufficient exploration of the environment. To address this challenge, we reformulate episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using quantum-inspired sampling techniques. Our method, MC+ QUBO, inserts a combinatorial filtering step into the standard MC policy-evaluation pipeline: given a batch of trajectories, it selects a subset that maximizes cumulative reward and encourages broad state-space coverage. This selection procedure is expressed as a QUBO model, where linear terms favor high-return episodes, quadratic terms penalize redundancy between trajectories, and additional coupling terms can be used to enforce coverage-related constraints or promote structural diversity. Within this framework, we investigate two black-box QUBO solvers: Simulated Quantum Annealing (SQA), which emulates tunneling-based exploration of the search landscape, and Simulated Bifurcation (SB), a dynamical-systems-based iterative optimization method. Both solvers demonstrate the ability to efficiently navigate the combinatorial structure of the trajectory-selection problem and to handle batch sizes that are otherwise computationally expensive for exhaustive or deterministic search. Experiments in a finite-horizon GridWorld environment show that MC+QUBO consistently outperforms vanilla MC in convergence speed, stability of return estimates, and final policy quality. These results highlight the promise of quantum-inspired optimization as a practical decision-making subroutine within reinforcement-learning algorithms, offering a scalable way to improve sample efficiency without modifying the underlying learning paradigm.

This paper describes a method for generating a synthetic database of stenoses, consisting of 1620 entries. Each entry represents the results of a numerical experiment simulating the three-dimensional flow of a viscous incompressible fluid through a tube with a variable cross-section: pressure drop, mean flow rate, cross-sectionally averaged inlet blood flow velocity, maximum stenosis severity, stenosis length, stenosis asymmetry, tube radius, and Reynolds number. The database was validated by comparison with other models (with elastic walls) and bench experiments, showing a deviation in pressure drops of no more than 4%. The synthetic stenosis database was used to train a physics-informed neural network for the rapid estimation of pressure drop based on four key input parameters: Reynolds number, stenosis length, stenosis severity, and stenosis asymmetry coefficient. The physics-informed aspect was achieved by introducing penalties into the loss function for the absence of a positive pressure drop and for the lack of monotonicity of the pressure drop with respect to the input parameters. The physics-informed neural network demonstrated higher accuracy on hemodynamically significant stenoses when tested on a validation set and on new stenoses not represented in the database. The mean relative error for stenoses with a length of 8 healthy vessel radii was 6% for the physics-informed network and 13% for a classical neural network. The errors for short stenoses with a length of 4 radii were nearly identical: 9.5% for the physics-informed network and 10% for the classical neural network. The developed method for the functional assessment of the hemodynamic significance of stenoses can be used both as a standalone tool for clinical stenosis evaluation and as a component of network blood flow models. The approach becomes most relevant when modeling multi-vessel disease, which is predominant in clinical practice. The key advantage of the method lies in the physical correctness of the results and accuracy comparable to classical modeling, but with significantly lower computational costs.

Main theoretical considerations of dynamical changes of forest vegetation are discussed. It is shown that vegetation dynamics (succession) and soil dynamics are linked, and common dynamics is a result of biological turnover of nutrition elements. Main modelling approaches are examined and unsolved problems are formulated. An example of computer experiment on comparison of forest growth at stationary and global warming scenario is considered.

Статья представляет собой пример компьютерного моделирования в области инженерной механики. Численным методом находятся поля скорости в гидроциклоне, которые недоступны прямому измерению. Рассматривается численное моделирование трехмерной гидродинамики на основе k-ε RNG модели турбулентности в гидроциклоне со встроенным инжектором, содержащим 5 тангенциально направленных сопла. Показано, что направление движения инжектируемой жидкости зависит от расхода жидкости через инжектор. Расчеты показывают в соответствии с экспериментами, что зависимость сплит-параметра от расхода инжектируемой жидкости имеет немонотонный характер, связанный с отношением мощности основного потока и инжектируемой жидкости.

Modification of the invariant imbedding method to describe the interaction of 3D electromagnetic field with “woodpile” photonic crystal of finite thickness is considered in this paper. This modification allows solving a problem of evanescent modes resonant amplification during numerical calculations for the first layer of photonic crystal. The mathematical model created in this work gives good agreement with physical experiment results.

The possibility to study temperature fluctuations using URANS approach is studied. The results of numerical simulation of mixing processes for triple-jet sodium at different temperatures are presented. The processes were simulated using FlowVision software system and LMS model for turbulent heat transfer. The analysis and experiment data are compared. Validated was the possibility to determine the energy-carrying frequencies of temperature fluctuations using URANS approach and LMS model when mixing triple-jet sodium at different temperatures.

Based on CFD computations we provide the analysis of the possibilities for using modern hybrid distributed computational environments for large complex system simulation. We argue that only multilevel approach supported by new mathematical models of transport properties, dynamical representation of the problem with transport and internal processes separated, and modern paradigm of programming, taking into account specific properties of heterogeneous system, will make it possible to scale the problem effectively.