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In this paper, we consider two variants of the concept of sharp minimum for mathematical programming problems with quasiconvex objective function and inequality constraints. It investigated the problem of describing a variant of a simple subgradient method with switching along productive and non-productive steps, for which, on a class of problems with Lipschitz functions, it would be possible to guarantee convergence with the rate of geometric progression to the set of exact solutions or its vicinity. It is important that to implement the proposed method there is no need to know the sharp minimum parameter, which is usually difficult to estimate in practice. To overcome this problem, the authors propose to use a step adjustment procedure similar to that previously proposed by B. T. Polyak. However, in this case, in comparison with the class of problems without constraints, it arises the problem of knowing the exact minimal value of the objective function. The paper describes the conditions for the inexactness of this information, which make it possible to preserve convergence with the rate of geometric progression in the vicinity of the set of minimum points of the problem. Two analogs of the concept of a sharp minimum for problems with inequality constraints are considered. In the first one, the problem of approximation to the exact solution arises only to a pre-selected level of accuracy, for this, it is considered the case when the minimal value of the objective function is unknown; instead, it is given some approximation of this value. We describe conditions on the inexact minimal value of the objective function, under which convergence to the vicinity of the desired set of points with a rate of geometric progression is still preserved. The second considered variant of the sharp minimum does not depend on the desired accuracy of the problem. For this, we propose a slightly different way of checking whether the step is productive, which allows us to guarantee the convergence of the method to the exact solution with the rate of geometric progression in the case of exact information. Convergence estimates are proved under conditions of weak convexity of the constraints and some restrictions on the choice of the initial point, and a corollary is formulated for the convex case when the need for an additional assumption on the choice of the initial point disappears. For both approaches, it has been proven that the distance from the current point to the set of solutions decreases with increasing number of iterations. This, in particular, makes it possible to limit the requirements for the properties of the used functions (Lipschitz-continuous, sharp minimum) only for a bounded set. Some computational experiments are performed, including for the truss topology design problem.

In this paper, the turbulent Taylor – Couette flow is investigated using two-dimensional modeling based on the averaged Navier – Stokes (RANS) equations and a new two-fluid approach to turbulence at Reynolds numbers in the range from 1000 to 8000. The flow due to a rotating internal and stationary external cylinders. The case of ratio of cylinder diameters 1:2 is considered. It is known that the emerging circular flow is characterized by anisotropic turbulence and mathematical modeling of such flows is a difficult task. To describe such flows, either direct modeling methods are used, which require large computational costs, or rather laborious Reynolds stress methods, or linear RANS models with special corrections for rotation, which are able to describe anisotropic turbulence. In order to compare different approaches to turbulence modeling, the paper presents the numerical results of linear RANS models SARC, SST-RC, Reynolds stress method SSG/LRR-RSM-w2012, DNS direct turbulence modeling, as well as a new two-fluid model. It is shown that the recently developed twofluid model adequately describes the considered flow. In addition, the two-fluid model is easy to implement numerically and has good convergence.

The article deals with the nonlinear boundary-value problem of hydrogen permeability corresponding to the following experiment. A membrane made of the target structural material heated to a sufficiently high temperature serves as the partition in the vacuum chamber. Degassing is performed in advance. A constant pressure of gaseous (molecular) hydrogen is built up at the inlet side. The penetrating flux is determined by mass-spectrometry in the vacuum maintained at the outlet side.

A linear model of dependence on concentration is adopted for the coefficient of dissolved atomic hydrogen diffusion in the bulk. The temperature dependence conforms to the Arrhenius law. The surface processes of dissolution and sorptiondesorption are taken into account in the form of nonlinear dynamic boundary conditions (differential equations for the dynamics of surface concentrations of atomic hydrogen). The characteristic mathematical feature of the boundary-value problem is that concentration time derivatives are included both in the diffusion equation and in the boundary conditions with quadratic nonlinearity. In terms of the general theory of functional differential equations, this leads to the so-called neutral type equations and requires a more complex mathematical apparatus. An iterative computational algorithm of second-(higher- )order accuracy is suggested for solving the corresponding nonlinear boundary-value problem based on explicit-implicit difference schemes. To avoid solving the nonlinear system of equations at every time step, we apply the explicit component of difference scheme to slower sub-processes.

The results of numerical modeling are presented to confirm the fitness of the model to experimental data. The degrees of impact of variations in hydrogen permeability parameters (“derivatives”) on the penetrating flux and the concentration distribution of H atoms through the sample thickness are determined. This knowledge is important, in particular, when designing protective structures against hydrogen embrittlement or membrane technologies for producing high-purity hydrogen. The computational algorithm enables using the model in the analysis of extreme regimes for structural materials (pressure drops, high temperatures, unsteady heating), identifying the limiting factors under specific operating conditions, and saving on costly experiments (especially in deuterium-tritium investigations).

Although human skills are heavily involved in the Requirements Engineering process, in particular, in requirements elicitation, analysis and specification, still methodology and formalism play a determining role in providing clarity and enabling analysis. In this paper, we propose a method for deriving formal specifications, which are applicable to dependable software systems. First, we clarify what the method itself is. Computer science has a proliferation of languages and methods, but the difference between the two is not always clear. This is a conceptual contribution. Furthermore, we propose the idea of Layered Fault Tolerant Specification (LFTS). The principle consists in layering specifications in (at least) two different layers: one for normal behaviors and others (if more than one) for abnormal behaviors. Abnormal behaviors are described in terms of an Error Injector (EI), which represent a model of the expected erroneous interference coming from the environment. This structure has been inspired by the notion of an idealized Fault Tolerant component, but the combination of LFTS and EI using rely guarantee thinking to describe interference is our second contribution. The overall result is the definition of a method for the specification of systems that do not run in isolation but in the real, physical world. We propose an approach that is pragmatic to its target audience: techniques must scale and be usable by non-experts, if they are to make it into an industrial setting. This article is making tentative steps, but the recent trends in Software Engineering such as Microservices, smart and software-defined buildings, M2M micropayments and Devops are relevant fields continue the investigation concerning dependability and rely guarantee thinking.

The advent of laser scanning technologies has revolutionized forestry. Their use made it possible to switch from studying woodlands using manual measurements to computer analysis of stereo point images called point clouds.

Automatic calculation of some tree parameters (such as trunk diameter) using a point cloud requires the removal of foliage points. To perform this operation, a preliminary segmentation of the stereo image into the “foliage” and “trunk” classes is required. The solution to this problem often involves the use of machine learning methods.

One of the most popular classifiers used for segmentation of stereo images of trees is a random forest. This classifier is quite demanding on the amount of memory. At the same time, the size of the machine learning model can be critical if it needs to be sent by wire, which is required, for example, when performing distributed learning. In this paper, the goal is to find a classifier that would be less demanding in terms of memory, but at the same time would have comparable segmentation accuracy. The search is performed among classifiers such as logistic regression, naive Bayes classifier, and decision tree. In addition, a method for segmentation refinement performed by a decision tree using logistic regression is being investigated.

The experiments were conducted on data from the collection of the University of Heidelberg. The collection contains hand-marked stereo images of trees of various species, both coniferous and deciduous, typical of the forests of Central Europe.

It has been shown that classification using a decision tree, adjusted using logistic regression, is able to produce a result that is only slightly inferior to the result of a random forest in accuracy, while spending less time and RAM. The difference in balanced accuracy is no more than one percent on all the clouds considered, while the total size and inference time of the decision tree and logistic regression classifiers is an order of magnitude smaller than of the random forest classifier.

The article presents a technology for building clinical decision support systems (CDSS) based on service containers using Docker and a web interface that runs directly in the browser without installing specialized software on workstation of a clinician. A modular architecture is proposed in which each application module is packaged as an independent service container combining a lightweight web server, a user interface, and computational components for medical image processing. Communication between the browser and the server side is implemented via a persistent bidirectional WebSocket connection with binary message serialization (MessagePack), which provides low latency and efficient transfer of large data. For local storage of images and analysis of results, browser facilities (IndexedDB with the Dexie.js wrapper) are used to speed up repeated data access. Three-dimensional visualization and basic operations with DICOM data are implemented with Three.js and AMI.js: this toolchain supports the integration of interactive elements arising from the task context (annotations, landmarks, markers, 3D models) into volumetric medical images.

Server components and functional modules are assembled as a set of interacting containers managed by Docker. The paper discusses the choice of base images, approaches to minimizing containers down to runtime-only executables without external utilities, and the organization of multi-stage builds with a dedicated build container. It describes a hub service that launches application containers on user request, performs request proxying, manages sessions, and switches a container from shared to exclusive mode at the start of computations. Examples of application modules are provided (fractional flow reserve estimation, quantitative flow ratio computation, aortic valve closure modeling), along with the integration of a React-based interface with a three-dimensional scene, a versioning policy, automated reproducibility checks, and the deployment procedure on the target platform.

It is demonstrated that containerization ensures portability and reproducibility of the software environment, dependency isolation and scalability, while the browser-based interface provides accessibility, reduced infrastructure requirements, and interactive real-time visualization of medical data. Technical limitations are noted (dependence on versions of visualization libraries and data formats) together with practical mitigation measures.

Reliable plant disease diagnosis requires not only high classification accuracy, but also stable generalization and interpretable decision-making. Although attention mechanisms are useful to these deep learning models, the performance also depends on where and how they are integrated into the network architecture. This study presents a design-optimized YOLO11m-based classification framework that systematically investigates the impact of Convolutional Block Attention Module (CBAM) injection at different architectural levels for plant disease diagnosis. We perform a comparative modelcontrolled analysis of three model architectures: (i) the baseline YOLO11m-Cls architecture lacking attention, (ii) only adding the backbone block along with CBAM and (iii) a hybrid architecture that includes reduced backbone attention combined with CBAM added at classification head. All models are trained and tested under the same experimental settings using a largescale dataset with around 90 000 images of 38 types of plant diseases. Experimental results clearly show that the uniform injection of CBAM into the backbone reduces stability but causes generalization to worsen with a higher validation loss and significantly lower Top-1 accuracy (≈ 90.5%), while the hybrid attention design balances stability and discrimination, with Top-1 accuracy up to 99.71%, Top-5 accuracy up to 99.99% and near-baseline validation behavior respectively Grad-CAMbased interpretability analysis also demonstrates that the hybrid model generates enhanced and biologically interpretable activation maps, which are more disease-specific with less distraction from background. Notably, the aim of our work is not for achieving superior performance over all classifiers but instead only to provide design-level evidence on how placing attention modulates rigidity and interpretability in YOLO-based classification models. The results provide practical architectural considerations for building dependable and interpretable AI systems in agriculture.

It was carried out a theoretical analysis of the energy migration rate in the process of non-photochemical quenching of fluorescence pigment-protein complex that performed by means of orange carotenoid-protein in the phycobilisomes of cyanobacteria. It is shown that the observed rate of energy transfer can not be interpreted in the framework of inductive-resonant mechanism of energy migration (Förster’s theory). On the contrary, according to the calculations the implementation of the exciton mechanism is fully consistent with the experimentally observed high quenching rate. An essential feature of the implementation of the exciton mechanism is to comply with a number of structural and functional conditions that require fine-tuning of the molecular system in the interaction of donor and acceptor molecules both each other and with the local molecular environment.

High-purity hydrogen is required for clean energy and a variety of chemical technology processes. A considerable part of hydrogen is to be obtained by methane conversion. Different alloys, which may be wellsuited for use in gas-separation plants, were investigated by measuring specific hydrogen permeability. One had to estimate the parameters of diffusion and sorption to numerically model the different scenarios and experimental conditions of the material usage (including extreme ones), and identify the limiting factors. This paper presents a nonlinear model of hydrogen permeability in accordance with the specifics of the experiment, the numerical method for solving the boundary-value problem, and the results of parametric identification for the alloy V₈₅Ni₁₅.

Numerical investigation of mixing non-isothermal streams of sodium coolant in a T-branch is carried out in the FlowVision CFD software. This study is aimed at argumentation of applicability of different approaches to prediction of oscillating behavior of the flow in the mixing zone and simulation of temperature pulsations. The following approaches are considered: URANS (Unsteady Reynolds Averaged Navier Stokers), LES (Large Eddy Simulation) and quasi-DNS (Direct Numerical Simulation). One of the main tasks of the work is detection of the advantages and drawbacks of the aforementioned approaches.

Numerical investigation of temperature pulsations, arising in the liquid and T-branch walls from the mixing of non-isothermal streams of sodium coolant was carried out within a mathematical model assuming that the flow is turbulent, the fluid density does not depend on pressure, and that heat exchange proceeds between the coolant and T-branch walls. Model LMS designed for modeling turbulent heat transfer was used in the calculations within URANS approach. The model allows calculation of the Prandtl number distribution over the computational domain.

Preliminary study was dedicated to estimation of the influence of computational grid on the development of oscillating flow and character of temperature pulsation within the aforementioned approaches. The study resulted in formulation of criteria for grid generation for each approach.

Then, calculations of three flow regimes have been carried out. The regimes differ by the ratios of the sodium mass flow rates and temperatures at the T-branch inlets. Each regime was calculated with use of the URANS, LES and quasi-DNS approaches.

At the final stage of the work analytical comparison of numerical and experimental data was performed. Advantages and drawbacks of each approach to simulation of mixing non-isothermal streams of sodium coolant in the T-branch are revealed and formulated.

It is shown that the URANS approach predicts the mean temperature distribution with a reasonable accuracy. It requires essentially less computational and time resources compared to the LES and DNS approaches. The drawback of this approach is that it does not reproduce pulsations of velocity, pressure and temperature.

The LES and DNS approaches also predict the mean temperature with a reasonable accuracy. They provide oscillating solutions. The obtained amplitudes of the temperature pulsations exceed the experimental ones. The spectral power densities in the check points inside the sodium flow agree well with the experimental data. However, the expenses of the computational and time resources essentially exceed those for the URANS approach in the performed numerical experiments: 350 times for LES and 1500 times for ·DNS.