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The paper presents a cascade model of a physically supported neural network designed to predict pressure drop in three-phase flow (oil, gas, water) in a pipe section with various angles of inclination. To overcome the constraints of existing empirical correlations and computation-intensive numerical modeling methods, we propose an architecture that decomposes the problem into three sequential physically interpretable subtasks: regression prediction of the fluid hold-up coefficient, fluid flow regime classification, and pressure gradient evaluation. Each subtask is solved by a separate fully connected neural network, the output of which is passed to the next model in the cascade. Training and testing of the proposed architecture was performed on an extensive synthetic dataset (8 · 10⁷ records) generated using a semi-empirical model. Verification is performed on independent experimental data. A comparative analysis with a single fully connected (non-cascade) neural network is made, and the sensitivity of the models is examined using Sobol and Borgonovo methods. The cascade model demonstrates superior accuracy and ensures high interpretability of results by providing intermediate physical parameters (fluid hold-up coefficient, flow regime). The developed model has low computational complexity, which allows it to be used in real-time systems and digital twins of hydraulic systems in the oil and gas industry.

A possible conformational change, which accompanies electron tranport in Rb. sphaeroides photosynthetic reaction center (RC), was studied using quantum-chemical approach. A kinetic model which takes into account two conformational states of RC is proposed. The model quantitatively describes experimental temperature dependencies of recombination reaction rate P⁺Q_A^- → PQ_A. Quantum-chemical modeling of primary quinone (Q_A) binding site permits one to propose a minor shift of Q_A as a conformational change of interest. The shift is accompanied by break of a hydrogen bond between 4–C=O group of Q_A and histidine M²¹⁹, and formation of a new hydrogen bond between Q_A and hydroxyl group of threonine M²²². Characteristics of this conformational change were obtained from quantum-chemical calculations and match parameters of kinetic model in qualitative fashion.

Mechanical unfolding of two identical in structure but differ in their amino acid sequences immunoglobulinbinding domains of proteins L and G under the action of external forces have been investigating  using the method of molecular dynamics with explicit model of solvent. Mechanical characteristics of these proteins have been calculated. It has been shown that in the way of the mechanical unfolding of both proteins appear intermediate states. Calculations revealed three significantly different ways of mechanical unfolding of proteins L and G.

We have investigated dynamics of synthetic genetic oscillators — repressilators — coupled through autoinducer diffusion. The model of the system with phase-repulsive coupling structure is under consideration. We have examined emergence of periodic regimes, stable inhomogeneous steady states depending on the main systems’ parameters: coupling strength and maximal transcription rate. It has been shown that autoinducer production module added to the isolated repressilator cause the limit cycle to disappear through infinite period bifurcation for sufficiently large transcription rate. We have found hysteresis of limit cycle and stable steady state the size of which is determined by ratio between mRNA and protein lifetimes. Two coupled oscillators system demonstrates stable anti-phase oscillations which can become a chaotic regime through invariant torus emergence or via Feigenbaum scenario.

The approach to optimization of integral estimation of bio-systems state is presented. The approach is included the procedures of decreasing of variability of integral estimation based on statistical modeling of experimental data set and optimization the quantity of a state characteristics on a base of their relative contribution to the integral estimation using parallel calculation.

We consider the hierarchical model of financial crashes introduced by A. Johansen and D. Sornette which reproduces the log-periodic power law behavior of the price before the critical point. In order to build the generalization of this model we introduce the dependence of an influence exponent on an ultrametric distance between agents. Much attention is being paid to a problem of critical point universality which is investigated by comparison of probability density functions of the crash times corresponding to systems with various total numbers of agents.

We consider two possible computational methods of the interpretation of experimental data obtained by means of the magnetic force microscopy. These methods of macrospin distribution simulation and reconstruction can be used for research of magnetization reversal processes of nanodots in ordered 2D arrays of nanodots. New approaches to the development of high-performance superscale algorithms for parallel executing on a supercomputer clusters for solving direct and inverse task of the modeling of magnetic states, types of ordering, reversal processes of nanosystems with a collective behavior are proposed. The simulation results are consistent with experimental results.

The dynamics of coupled elements’ ensembles are investigated in the context of description of spatio-temporal processes in the myocardium. Basic element is map-based model constructed by simplification and reduction of Luo-Rudy model. In particular, capabilities of the model in replication of different regimes of cardiac activity are shown, including excitable and oscillatory regimes. The dynamics of 1D and 2D lattices of coupled oscillatory elements with a random distribution of individual frequencies are considered. Effects of cluster synchronization and transition to global synchronization by increasing of coupling strength are discussed. Impulse propagation in the chain of excitable cells has been observed. Analysis of 2D lattice of excitable elements with target and spiral waves have been made. The characteristics of the spiral wave has been analyzed in depending on the individual parameters of the map and coupling strength between elements of the lattice. A study of mixed ensembles consisting of excitable and oscillatory elements with a gradient changing of the properties have been made, including the task for description of normal and pathological activity of the sinoatrial node.

We study the centrally symmetric mathematical model of electrodiffusion. This model describes in particular the transport of the Li⁺ ions in certain electrochemical current sources. We demonstrate that the steady state solution of the considered model exists and is unique if the boundary values of the ion concentration and electric potential are given. This solution also proves to be the stable attractor of the time-dependent solutions with different initial value distributions.

This paper presents a model mixture of probability distributions of signal and noise. Typically, when analyzing the data under conditions of uncertainty it is necessary to use nonparametric tests. However, such an analysis of nonstationary data in the presence of uncertainty on the mean of the distribution and its parameters may be ineffective. The model involves the implementation of a case of a priori non-parametric uncertainty in the processing of the signal at a time when the separation of signal and noise are related to different general population, is feasible.