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Currently, a significant development has been observed in the direction of distributed computing theory, where computational tasks are solved collectively by resource-constrained devices. In practice, this scenario is implemented when processing data in Internet of Things systems, with the aim of reducing system latency and network infrastructure load, as data is processed on edge network computing devices. However, the rapid growth and widespread adoption of IoT systems raise questions about the need to develop methods for reducing the resource intensity of computations. The resource constraints of computing devices pose the following issues regarding the distribution of computational resources: firstly, the necessity to account for the transit cost between different devices solving various tasks; secondly, the necessity to consider the resource cost associated directly with the process of distributing computational resources, which is particularly relevant for groups of autonomous devices such as drones or robots. An analysis of modern publications available in open access demonstrated the absence of proposed models or methods for distributing computational resources that would simultaneously take into account all these factors, making the creation of a new mathematical model for organizing distributed computing in IoT systems and its solution methods topical. This article proposes a novel mathematical model for distributing computational resources along with heuristic optimization methods, providing an integrated approach to implementing distributed computing in IoT systems. A scenario is considered where there exists a leader device within a group that makes decisions concerning the allocation of computational resources, including its own, for distributed task resolution involving information exchanges. It is also assumed that no prior knowledge exists regarding which device will assume the role of leader or the migration paths of computational tasks across devices. Experimental results have shown the effectiveness of using the proposed models and heuristics: achieving up to a 52% reduction in resource costs for solving computational problems while accounting for data transit costs, saving up to 73% of resources through supplementary criteria optimizing task distribution based on minimizing fragment migrations and distances, and decreasing the resource cost of resolving the computational resource distribution problem by up to 28 times with reductions in distribution quality up to 10%.

A mathematical model for the evolution of microbial populations during prolonged cultivation in a chemostat has been constructed. This model generalizes the sequence of the well-known mathematical models of the evolution, in which such factors of the genetic variability were taken into account as chromosomal mutations, mutations in plasmid genes, the horizontal gene transfer, the plasmid loss due to cellular division and others. Liapunov’s function for the generic model of evolution is constructed. The existence proof of bounded, positive invariant and globally attracting set in the state space of the generic mathematical model for the evolution is presented because of the application of La-Salle’s theorem. The analytic description of this set is given. Numerical methods for estimate of the number of limit sets, its location and following investigation in the mathematical models for evolution are discussed.

This article is dedicated to the construction of the approximate mathematical model of the nonlinear elasticity theory for plane strain state. The third order effects method applied to symbolic computing. There three boundary value problems for the first, the second and the third order effects has been obtained within this method, which gets ability to use well-elaborated methods of the linear elasticity theory for the solution of specific problems. This method can be applied for analytical solving of plane problems of nonlinear elasticity theory of stress concentration around holes in mathematical package Maple. Considered example of the triangular hole. The influence of external loads on the stress concentration factor.

A vector-matrix approach to the theoretical design of metabolic pathways converting chemical compounds, viz., preset substrates, into desirable products is described. It is a mathematical basis for computer–aided generation of alternative biochemical reaction sets executing the given substrate–product conversion. The pathways are retrieved from the used database of biochemical reactions and utilize the reaction stoichiometry and restrictions based on the irreversibility of a part of them. Particular attention is paid to the analysis of restriction interrelations. It is shown that the number of restrictions can be notably reduced due to the existence of families of parallel restricting planes in the space of reaction flows. Coinciding planes of contradirectional restrictions result in the existence of fixed reaction flow values. The problem of exclusion of so called futile cycles is also considered. Utilization of these factors allows essential lowering of the problem complexity and necessary computational resources. An example of alternative biochemical pathway computation for conversion of glucose and glycerol into succinic acid is given. It is found that for a preset “substrate–product” pair many pathways have the same high-energy bond balance.

Traditional geological search methods going to be ineffective. The exploration depth of kimberlite bodies and ore deposits has increased significantly. The only direct exploration method is to drill a system of wells to the depths that provide access to the enclosing rocks. Due to the high cost of drilling, the role of inter-well survey methods has increased. They allows to increase the mean well spacing without significantly reducing the kimberlite or ore body missing probability. The method of inter-well radio wave survey is effective to search for high contrast conductivity objects. The physics of the method based on the dependence of the electromagnetic wave propagation on the propagation medium conductivity. The source and receiver of electromagnetic radiation is an electric dipole, they are placed in adjacent wells. The distance between the source and receiver is known. Therefore we could estimate the medium absorption coefficient by the rate of radio wave amplitude decrease. Low electrical resistance rocks corresponds to high absorption of radio waves. The inter-well measurement data allows to estimate an effective electrical resistance (or conductivity) of the rock. Typically, the source and receiver are immersed in adjacent wells synchronously. The value of the of the electric field amplitude measured at the receiver site allows to estimate the average value of the attenuation coefficient on the line connecting the source and receiver. The measurements are taken during stops, approximately every 5 m. The distance between stops is much less than the distance between adjacent wells. This leads to significant spatial anisotropy in the measured data distribution. Drill grid covers a large area, and our point is to build a three-dimensional model of the distribution of the electrical properties of the inter-well space throughout the whole area. The anisotropy of spatial distribution makes hard to the use of standard geostatistics approach. To build a three-dimensional model of attenuation coefficient, we used one of machine learning theory methods, the method of nearest neighbors. In this method, the value of the absorption coefficient at a given point is calculated by $k$ nearest measurements. The number $k$ should be determined from additional reasons. The spatial distribution anisotropy effect can be reduced by changing the spatial scale in the horizontal direction. The scale factor $\lambda$ is one yet external parameter of the problem. To select the parameters $k$ and $\lambda$ values we used the determination coefficient. To demonstrate the absorption coefficient three-dimensional image construction we apply the procedure to the inter-well radio wave survey data. The data was obtained at one of the sites in Yakutia.

In this work we have developed a new efficient program for the numerical simulation of 3D global chemical transport on an adaptive finite-difference grid which allows us to concentrate grid points in the regions where flow variables sharply change and coarsen the grid in the regions of their smooth behavior, which significantly minimizes the grid size. We represent the adaptive grid with a combination of several dynamic (tree, linked list) and static (array) data structures. The dynamic data structures are used for a grid reconstruction, and the calculations of the flow variables are based on the static data structures. The introduction of the static data structures allows us to speed up the program by a factor of 2 in comparison with the conventional approach to the grid representation with only dynamic data structures.

We wrote and tested our program on a computer with 6 CPU cores. Using the computer microarchitecture simulator gem5, we estimated the scalability property of the program on a significantly greater number of cores (up to 32), using several models of a computer system with the design “computational cores – cache – main memory”. It has been shown that the microarchitecture of a computer system has a significant impact on the scalability property, i.e. the same program demonstrates different efficiency on different computer microarchitectures. For example, we have a speedup of 14.2 on a processor with 32 cores and 2 cache levels, but we have a speedup of 22.2 on a processor with 32 cores and 3 cache levels. The execution time of a program on a computer model in gem5 is 104–105 times greater than the execution time of the same program on a real computer and equals 1.5 hours for the most complex model.

Also in this work we describe how to configure gem5 and how to perform simulations with gem5 in the most optimal way.

Image denoising is one of the fundamental problems in digital image processing. This problem usually refers to the reconstruction of an image from an observed image degraded by noise. There are many factors that cause this degradation such as transceiver equipment, or environmental influences, etc. In order to obtain higher quality images, many methods have been proposed for image denoising problem. Most image denoising method are based on total variation (TV) regularization to develop efficient algorithms for solving the related optimization problem. TV-based models have become a standard technique in image restoration with the ability to preserve image sharpness.

In this paper, we focus on Poisson noise usually appearing in photon-counting devices. We propose an effective regularization model based on combination of first-order and fractional-order total variation for image reconstruction corrupted by Poisson noise. The proposed model allows us to eliminate noise while edge preserving. An efficient alternating minimization algorithm is employed to solve the optimization problem. Finally, provided numerical results show that our proposed model can preserve more details and get higher image visual quality than recent state-of-the-art methods.

The development of a high-temperature gas-cooled reactor (HTGR) constituting a part of nuclear power-and-process station and intended for large-scale hydrogen production is now in progress in the Russian Federation. One of the key objectives in development of the high-temperature gas-cooled reactor is the computational justification of the accepted design.

The article gives the procedure for the computational analysis of thermal and physical characteristics of the high-temperature gas-cooled reactor. The procedure is based on the use of the state-of-the-art codes for personal computer (PC).

The objective of thermal and physical analysis of the reactor as a whole and of the core in particular was achieved in three stages. The idea of the first stage is to justify the neutron physical characteristics of the block-type core during burn-up with the use of the MCU-HTR code based on the Monte Carlo method. The second and the third stages are intended to study the coolant flow and the temperature condition of the reactor and the core in 3D with the required degree of detailing using the FlowVision and the ANSYS codes.

For the purpose of carrying out the analytical studies the computational models of the reactor flow path and the fuel assembly column were developed.

As per the results of the computational modeling the design of the support columns and the neutron physical characteristics of the fuel assembly were optimized. This results in the reduction of the total hydraulic resistance of the reactor and decrease of the maximum temperature of the fuel elements.

The dependency of the maximum fuel temperature on the value of the power peaking factors determined by the arrangement of the absorber rods and of the compacts of burnable absorber in the fuel assembly is demonstrated.

We present a novel model for the dynamical trap of the stimulus – response type that mimics human control over dynamic systems when the bounded capacity of human cognition is a crucial factor. Our focus lies on scenarios where the subject modulates a control variable in response to a certain stimulus. In this context, the bounded capacity of human cognition manifests in the uncertainty of stimulus perception and the subsequent actions of the subject. The model suggests that when the stimulus intensity falls below the (blurred) threshold of stimulus perception, the subject suspends the control and maintains the control variable near zero with accuracy determined by the control uncertainty. As the stimulus intensity grows above the perception uncertainty and becomes accessible to human cognition, the subject activates control. Consequently, the system dynamics can be conceptualized as an alternating sequence of passive and active modes of control with probabilistic transitions between them. Moreover, these transitions are expected to display hysteresis due to decision-making inertia.

Generally, the passive and active modes of human control are governed by different mechanisms, posing challenges in developing efficient algorithms for their description and numerical simulation. The proposed model overcomes this problem by introducing the dynamical trap of the stimulus-response type, which has a complex structure. The dynamical trap region includes two subregions: the stagnation region and the hysteresis region. The model is based on the formalism of stochastic differential equations, capturing both probabilistic transitions between control suspension and activation as well as the internal dynamics of these modes within a unified framework. It reproduces the expected properties in control suspension and activation, probabilistic transitions between them, and hysteresis near the perception threshold. Additionally, in a limiting case, the model demonstrates the capability of mimicking a similar subject’s behavior when (1) the active mode represents an open-loop implementation of locally planned actions and (2) the control activation occurs only when the stimulus intensity grows substantially and the risk of the subject losing the control over the system dynamics becomes essential.

The work is devoted to the description of the author’s formal statements of the clustering problem for a given number of clusters, algorithms for their solution, as well as the results of using this toolkit in medicine.

The solution of the formulated problems by exact algorithms of implementations of even relatively low dimensions before proving optimality is impossible in a finite time due to their belonging to the NP class.

In this regard, we have proposed a hybrid algorithm that combines the advantages of precise methods based on clustering in paired distances at the initial stage with the speed of methods for solving simplified problems of splitting by cluster centers at the final stage. In the development of this direction, a sequential hybrid clustering algorithm using random search in the paradigm of swarm intelligence has been developed. The article describes it and presents the results of calculations of applied clustering problems.

To determine the effectiveness of the developed tools for optimal clustering of multidimensional objects according to a variety of heterogeneous indicators, a number of computational experiments were performed using data sets including socio-demographic, clinical anamnestic, electroencephalographic and psychometric data on the cognitive status of patients of the cardiology clinic. An experimental proof of the effectiveness of using local search algorithms in the paradigm of swarm intelligence within the framework of a hybrid algorithm for solving optimal clustering problems has been obtained.

The results of the calculations indicate the actual resolution of the main problem of using the discrete optimization apparatus — limiting the available dimensions of task implementations. We have shown that this problem is eliminated while maintaining an acceptable proximity of the clustering results to the optimal ones. The applied significance of the obtained clustering results is also due to the fact that the developed optimal clustering toolkit is supplemented by an assessment of the stability of the formed clusters, which allows for known factors (the presence of stenosis or older age) to additionally identify those patients whose cognitive resources are insufficient to overcome the influence of surgical anesthesia, as a result of which there is a unidirectional effect of postoperative deterioration of complex visual-motor reaction, attention and memory. This effect indicates the possibility of differentiating the classification of patients using the proposed tools.