Journal Issue: Научная визуализация
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Volume
2026-18
Number
1
Issue Date
Journal Title
Научная визуализация
Journal ISSN
2079-3537
Том журнала
Том журнала
Научная визуализация
Научная визуализация (2026-18)
Статьи
Публикация
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Pressure-Gradient Method for the Visualization of a Wave Attractor
(НИЯУ МИФИ, 2026) Elistratov, S.
A wave attractor, a phenomenon of self-focusing of internal/inertial waves on a closed trajectory, has recently been widely studied from different viewpoints. How-ever, there is a lack of investigations concerning its visualization. Peculiar set-ups lately studied show that conventional methods need some improvement. Herewith, in gas dynamics, the Schlieren method, based on the density gradient, is widely used. Concerning incompressible flows, it is inapplicable; however, pressure can be considered instead density. In this work, a pressure gradient is used as a way to visualize an attractor.
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Using Sperm Imaging with Laser Interference Microscopy for Comprehensive Assessment of the Functional State of Cells during Cryopreservation and under the Action of Molecular Hydrogen
(НИЯУ МИФИ, 2026) Deryugina, A. V.; Ivaschenko, M. N. ; Ignatiev, P. S. ; Metelin, V. B.
Significant advances have been made in sperm cryopreservation but the search for effective sperm cryopreservation technologies is a pressing issue in modern biology and medicine. The most effective cryopreservation leaves 50-60% of viable cells. The paper discusses the use of molecular hydrogen (H2) as a new approach to enhancing sperm protection during freezing and thawing. H2 is a universal antioxidant and limits damage to biomolecules. Visual registration of spermatozoa under the action of H2 was performed using modern microscopy techniques. Laser interference microscopy was used in the work. Laser interference microscopy records the cell surface architectonics depending on the modulation of the optical density of cellular structures. This visualization option provides information on the metabolic level and expands the possibilities for interpreting experimental results. Sample preparation, dyes, and fixatives are not used in interference visualization. The paper presents an analysis of phase images of spermatozoa during cryopreservation and using H2 as a cryoprotector. Verification of the method for analyzing phase characteristics of spermatozoa as an indicator of the metabolic state of cells was performed by analyzing clinical and laboratory parameters of spermatozoa. The phase height of spermatozoa during cryopreservation decreased, the intensity of energy processes decreased, and the oxidative potential of cells increased. A direct correlation was shown between the phase height of spermatozoa and the concentration of ATP, and an inverse correlation was found from the concentration of malondialdehyde (MDA). The use of H2 determined an increase in the phase height of spermatozoa, an increase in energy metabolism, and a decrease in cell oxidation. Changes in the metabolic activity of spermatozoa under the action of H2 were combined with an improvement in sperm fertility. Thus, phase interference microscopy allows for a qualitative and quantitative assessment of the physiological state of spermatozoa. It is an objective method of vital analysis of complex metabolic activity of cells. It can be used for express diagnostics of their functional state.
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Automated Diabetic Retinopathy Diagnosis and Classification Using Deep Learning with Capsule Network Layers and Stochastic Ensemble Approach
(НИЯУ МИФИ, 2026) Abini, M. A.; Priya, S Sridevi Sathya
Diabetic retinopathy (DR) remains one of the most common vision-related complications of diabetes and requires timely, accurate diagnosis to prevent severe outcomes. Conventional diagnostic approaches rely on the expertise of ophthalmologists, who manually examine retinal images for lesions—a process that can be time-consuming and prone to fatigue-related errors. To address these limitations, this work proposes a fully automated framework for DR detection and stage classification that leverages recent advances in deep learning. The study focuses on the five recognized stages of DR, ranging from the earliest form, non-proliferative diabetic retinopathy (NPDR), through to the advanced proliferative stage (PDR). The method integrates two powerful pre-trained convolutional neural networks, ResNetV2 and MobileNet, with capsule network layers to enhance feature representation. A stochastic ensemble strategy is applied to further strengthen the robustness of predictions. Experimental evaluation on the Kaggle APTOS 2019 dataset demonstrates a test accuracy of 99.81%, outperforming comparable methods in the literature. Performance was assessed using standard metrics such as precision, recall, F1-score, and the ROC curve. Beyond classification accuracy, the approach also offers improved interpretability through capsule-based visualization techniques and ensemble-driven lesion localization, enabling better identification of retinal abnormalities across different DR stages.
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Visual Display of Tropical Cyclone Structure Zone Hazard Assessment Based on Almost Periodic Analysis
(НИЯУ МИФИ, 2026) Paramonov, A. A; Kalach, A. V.
A visual hazard assessment of tropical cyclone structure zones based on almost periodic anal-ysis is proposed. We consider a visualization toolkit that allows us to customize and display both tropical cyclone zones whose radii are multiples of the found characteristic near-periods and shading of the interzonal space taking into account the degree of hazard relative to the cyclone center.
As the main tools we used the visualization modules of the matplotib library Circle and Wedge, which allow us to customize the identified structural zones according to their hazard de-gree. This development can be useful for emergency-rescue services as an operational diagnostic tool to support decision-making in emergencies caused by natural hazards.
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Enhanced Crack Width and Depth Measurement through Binary Image Processing and Geometric Analysis
(НИЯУ МИФИ, 2026) Bodke, Kavita ; Bhirud, Sunil; Sangle, Keshav Kashinath
This paper presents a novel, image-based approach for automatically quantifying structural crack width and depth in concrete using binary image processing techniques. Concrete cracks are critical indicators of potential structural failure, and traditional manual inspection methods are often time-consuming, unsafe, and prone to inaccuracies. The proposed method automates crack detection by converting RGB images of concrete surfaces into binary images, isolating the cracks, and measuring their width using the Euclidean distance formula. The depth of the cracks is then estimated using trigonometric relationships based on the measured crack width and viewing angles (30°, 45°, and 60°). This lightweight, cost-effective approach provides a practical alternative to more complex machine learning-based detection methods, making it ideal for real-time infrastructure health monitoring. The results highlight the effectiveness of this technique in accurately measuring crack width and depth across multiple angles, providing critical data for infrastructure health monitoring.