All issues

Modeling banking systems using a network approach has received growing attention in recent years. One of the notable models is that developed by Iori et al, who proposed a banking system model for analyzing systemic risks in interbank networks. The model is built based on the simple dynamics of several bank balance sheet variables such as deposit, equity, loan, liquid asset, and interbank lending (or borrowing) in the form of difference equations. Each bank faces random shocks in deposits and loans. The balance sheet is updated at the beginning or end of each period. In the model, banks are grouped into either potential lenders or borrowers. The potential borrowers are those that have lack of liquidity and the potential lenders are those which have excess liquids after dividend payment and channeling new investment. The borrowers and the lenders are connected through the interbank market. Those borrowers have some percentage of linkage to random potential lenders for borrowing funds to maintain their safety net of the liquidity. If the demand for borrowing funds can meet the supply of excess liquids, then the borrower bank survives. If not, they are deemed to be in default and will be removed from the banking system. However, in their paper, most part of the interbank borrowing-lending mechanism is described qualitatively rather than by detailed mathematical or computational analysis. Therefore, in this paper, we enhance the mathematical parts of borrowing-lending in the interbank market and present an algorithm for simulating the model. We also perform some simulations to analyze the effects of the model’s parameters on banking stability using the number of surviving banks as the measure. We apply this technique to analyze the effects of a macroprudential policy called loan-to-deposit ratio based reserve requirement for banking stability.

When determining therapeutic absorbed doses in the process of radioiodine therapy, the method of individual dosimetric planning is increasingly used in Russian medicine. However, for the successful implementation of this method, it is necessary to have appropriate software that allows modeling the pharmacokinetics of radioiodine in the patient’s body and calculate the necessary therapeutic activity of a radiopharmaceutical drug to achieve the planned therapeutic absorbed dose in the thyroid gland.

Purpose of the work: development of a software package for pharmacokinetic modeling and calculation of individual absorbed doses in radioiodine therapy based on a five-chamber model of radioiodine kinetics using two mathematical optimization methods. The work is based on the principles and methods of RFLP pharmacokinetics (chamber modeling). To find the minimum of the residual functional in identifying the values of the transport constants of the model, the Hook – Jeeves method and the simulated annealing method were used. Calculation of dosimetric characteristics and administered therapeutic activity is based on the method of calculating absorbed doses using the functions of radioiodine activity in the chambers found during modeling. To identify the parameters of the model, the results of radiometry of the thyroid gland and urine of patients with radioiodine introduced into the body were used.

A software package for modeling the kinetics of radioiodine during its oral intake has been developed. For patients with diffuse toxic goiter, the transport constants of the model were identified and individual pharmacokinetic and dosimetric characteristics (elimination half-lives, maximum thyroid activity and time to reach it, absorbed doses to critical organs and tissues, administered therapeutic activity) were calculated. The activity-time relationships for all cameras in the model are obtained and analyzed. A comparative analysis of the calculated pharmacokinetic and dosimetric characteristics calculated using two mathematical optimization methods was performed. Evaluation completed the stunning-effect and its contribution to the errors in calculating absorbed doses. From a comparative analysis of the pharmacokinetic and dosimetric characteristics calculated in the framework of two optimization methods, it follows that the use of a more complex mathematical method for simulating annealing in a software package does not lead to significant changes in the values of the characteristics compared to the simple Hook – Jeeves method. Errors in calculating absorbed doses in the framework of these mathematical optimization methods do not exceed the spread of absorbed dose values from the stunning-effect.

We found a predominance of α-rhythm patterns in the left hemisphere in healthy people compared to people with COVID-19 history. Moreover, we observe a significant decrease in the left hemisphere contribution to the speech center area in people who have undergone COVID-19 when performing speech tasks.

Our findings show that the signal in healthy subjects is more spatially localized and synchronized between hemispheres when performing tasks compared to people who recovered from COVID-19. We also observed a decrease in low frequencies in both hemispheres after COVID-19.

EEG-patterns of COVID-19 are detectable in an unusual frequency domain. What is usually considered noise in electroencephalographic (EEG) data carries information that can be used to determine whether or not a person has had COVID-19. These patterns can be interpreted as signs of hemispheric desynchronization, premature brain ageing, and more significant brain strain when performing simple tasks compared to people who did not have COVID-19.

In our work, we have shown the applicability of neural networks in helping to detect the long-term effects of COVID-19 on EEG-data. Furthermore, our data following other studies supported the hypothesis of the severity of the long-term effects of COVID-19 detected on the EEG-data of EEG-based BCI. The presented findings of functional activity of the brain– computer interface make it possible to use machine learning methods on simple, non-invasive brain–computer interfaces to detect post-COVID syndrome and develop progress in neurorehabilitation.

Numerical simulation of hull flow, marine propellers and other basic problems of ship hydrodynamics using Cartesian adaptive locally-refined grids is advantageous with respect to numerical setup and makes an express analysis very convenient. However, when more accurate viscous phenomena are needed, they condition some problems including a sharp increase of cell number due to high levels of main grid adaptation needed to resolve boundary layers and time step decrease in simulations with a free surface due to decrease of transit time in adapted cells. To avoid those disadvantages, additional boundary layer grids are suggested for resolution of boundary layers. The boundary layer grids are one-dimensional adaptations of main grid layers nearest to a wall, which are built along a normal direction. The boundary layer grids are additional (or chimerical), their volumes are not subtracted from main grid volumes. Governing equations of flow are integrated in both grids simultaneously, and the solutions are merged according to a special algorithm. In simulations of ship hull flow boundary layer grids are able to provide sufficient conditions for low-Reynolds turbulence models and significantly improve flow structure in continues boundary layers along smooth surfaces. When there are flow separations or other complex phenomena on a hull surface, it can be subdivided into regions, and the boundary layer grids should be applied to the regions with simple flow only. This still provides a drastic decrease of computational efforts. In simulations of marine propellers, the boundary layer grids are able to provide refuse of wall functions on blade surfaces, what leads to significantly more accurate hydrodynamic forces. Altering number and configuration of boundary grid layers, it is possible to vary a boundary layer resolution without change of a main grid. This makes the boundary layer grids a suitable tool to investigate scale effects in both problems considered.

A simple model was developed to describe the dependence of net CO₂ exchange in a sphagnum peat bog as a function of incoming solar radiation, air temperature, and soil moisture. It was parameterized using the field measurement data from two neighboring sites in an undisturbed peat bog (the pine mire with shrub and sphagnum and the shrub-sphagnum mire with rare pine) in Moscow Region. Measurements were conducted during the second part of the growing season, when the groundwater level was below 30 cm. It was shown that is a key parameter influencing the photosynthesis and respiration rates of a sphagnum moss and peat soil. The developed model allows to explain from 71 % to 74 % of the variation of CO₂ exchange in the peat bog.

A large number of methods have been developed to solve the Navier – Stokes equations in the case of incompressible flows, the most popular of which are methods with velocity correction by the SIMPLE algorithm and its analogue — the method of splitting by physical variables. These methods, developed more than 40 years ago, were used to solve rather simple problems — simulating both stationary flows and non-stationary flows, in which the boundaries of the calculation domain were stationary. At present, the problems of computational fluid dynamics have become significantly more complicated. CFD problems are involving the motion of bodies in the computational domain, the motion of contact boundaries, cavitation and tasks with dynamic local adaptation of the computational mesh. In this case the computational mesh changes resulting in violation of the velocity divergence condition on it. Since divergent velocities are used not only for Navier – Stokes equations, but also for all other equations of the mathematical model of fluid motion — turbulence, mass transfer and energy conservation models, violation of this condition leads to numerical errors and, often, to undivergence of the computational algorithm.

This article presents an implicit method of splitting by physical variables that uses divergent velocities from a given time step to solve the incompressible Navier – Stokes equations. The method is developed to simulate flows in the case of movable and contact boundaries treated in the Euler paradigm. The method allows to perform computations with the integration step exceeding the explicit time step by orders of magnitude (Courant – Friedrichs – Levy number $CFL\gg1$). This article presents a variant of the method for incompressible flows. A variant of the method that allows to calculate the motion of liquid and gas at any Mach numbers will be published shortly. The method for fully compressible flows is implemented in the software package FlowVision.

Numerical simulating classical fluid flow around circular cylinder at low Reynolds numbers ($50 < Re < 140$), when laminar flow is unsteady and the Karman vortex street is formed, are presented in the article. Good agreement of calculations with the experimental data published in the classical works of Van Dyke and Taneda is demonstrated.

This paper is dedicated to the analysis of medical and biological data obtained through locomotor training and testing of astronauts conducted both on Earth and during spaceflight. These experiments can be described as the astronaut’s movement on a treadmill according to a predefined regimen in various speed modes. During these modes, not only the speed is recorded but also a range of parameters, including heart rate, ground reaction force, and others, are collected. In order to analyze the dynamics of the astronaut’s condition over an extended period, it is necessary to perform a qualitative segmentation of their movement modes to independently assess the target metrics. This task becomes particularly relevant in the development of an autonomous life support system for astronauts that operates without direct supervision from Earth. The segmentation of target data is complicated by the presence of various anomalies, such as deviations from the predefined regimen, arbitrary and varying duration of mode transitions, hardware failures, and other factors. The paper includes a detailed review of several contemporary retrospective (offline) nonparametric methods for detecting multiple changepoints, which refer to sudden changes in the properties of the observed time series occurring at unknown moments. Special attention is given to algorithms and statistical measures that determine the homogeneity of the data and methods for detecting change points. The paper considers approaches based on dynamic programming and sliding window methods. The second part of the paper focuses on the numerical modeling of these methods using characteristic examples of experimental data, including both “simple” and “complex” speed profiles of movement. The analysis conducted allowed us to identify the preferred methods, which will be further evaluated on the complete dataset. Preference is given to methods that ensure the closeness of the markup to a reference one, potentially allow the detection of both boundaries of transient processes, as well as are robust relative to internal parameters.

Large Language Models (LLMs) are transforming software engineering by bridging the gap between natural language and programming languages. These models have revolutionized communication within development teams and the Software Development Life Cycle (SDLC) by enabling developers to interact with code using natural language, thereby improving workflow efficiency. This survey examines the impact of LLMs across various stages of the SDLC, including requirement gathering, system design, coding, debugging, testing, and documentation. LLMs have proven to be particularly useful in automating repetitive tasks such as code generation, refactoring, and bug detection, thus reducing manual effort and accelerating the development process. The integration of LLMs into the development process offers several advantages, including the automation of error correction, enhanced collaboration, and the ability to generate high-quality, functional code based on natural language input. Additionally, LLMs assist developers in understanding and implementing complex software requirements and design patterns. This paper also discusses the evolution of LLMs from simple code completion tools to sophisticated models capable of performing high-level software engineering tasks. However, despite their benefits, there are challenges associated with LLM adoption, such as issues related to model accuracy, interpretability, and potential biases. These limitations must be addressed to ensure the reliable deployment of LLMs in production environments. The paper concludes by identifying key areas for future research, including improving the adaptability of LLMs to specific software domains, enhancing their contextual understanding, and refining their capabilities to generate semantically accurate and efficient code. This survey provides valuable insights into the evolving role of LLMs in software engineering, offering a foundation for further exploration and practical implementation.

A simple statistical model is developed for the dynamics of the mean global annual temperature. The model combines the logarithmic effect of carbon dioxide concentration increase and the input by climatic cycles. Model parameters are determined from data of instrumental observations for 1850–2010. The model confirms the presence of climatic cycles with the period of 10.5 and 68.8 years in the global temperature dynamics. The trajectories of the global temperature changes for the XXI century are obtained under the scenarios of carbon dioxide concentration changes from the 5^th IPCC Assessment Report. The comparison revealed that the global temperature trajectories from the Report are 0.9–1.8 °C above those obtained in the model.

The paper considers the problem of transverse impact on a thin preloaded fiber. The commonly accepted theory of transverse impact on a thin fiber is based on the classical works of Rakhmatulin and Smith. The simple relations obtained from the Rakhmatulin – Smith theory are widely used in engineering practice. However, there are numerous evidences that experimental results may differ significantly from estimations based on these relations. A brief overview of the factors that cause the differences is given in this article.

This paper focuses on the shear wave velocity, as it is the only feature that can be directly observed and measured using high-speed cameras or similar methods. The influence of the fiber preload on the wave speed is considered. This factor is important, since it inevitably arises in the experimental results. The reliable fastening and precise positioning of the fiber during the experiments requires its preload. This work shows that the preload significantly affects the shear wave velocity in the impacted fiber.

Numerical calculations were performed for Kevlar 29 and Spectra 1000 yarns. Shear wave velocities are obtained for different levels of initial tension. A direct comparison of numerical results and analytical estimations with experimental data is presented. The speed of the transverse wave in free and preloaded fibers differed by a factor of two for the setup parameters considered. This fact demonstrates that measurements based on high-speed imaging and analysis of the observed shear waves should take into account the preload of the fibers.

This paper proposes a formula for a quick estimation of the shear wave velocity in preloaded fibers. The formula is obtained from the basic relations of the Rakhmatulin – Smith theory under the assumption of a large initial deformation of the fiber. The formula can give significantly better results than the classical approximation, this fact is demonstrated using the data for preloaded Kevlar 29 and Spectra 1000. The paper also shows that direct numerical calculation has better corresponding with the experimental data than any of the considered analytical estimations.