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Публикация

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THERMAL DESORPTION OF HYDROGEN FROM TITANIUM FILMS CO-DEPOSITED BY MAGNETRON SPUTTERING

(НИЯУ МИФИ, 2025) Samarov, V.; Chernyatev, Yu.; Tarasyuk, G.; Pisarev, А.; Тарасюк, Григорий Михайлович; Писарев, Александр Александрович

Metal hydrides are promising materials for hydrogen storage for hydrogen energy applications. This is primarily due to their safety in comparison with storage in gaseous or liquid states. Powders are mainly used as a basis for storage. However, a lot of research has been done with metal films, which allows modeling the hydrogenation and dehydrogenation processes under more defined conditions than those in working with powders. Titanium is one of the materials that has been considered in works on hydrogen accumulation and desorption. It can absorb hydrogen up to a ratio of H/Ti=2. Its disadvantage is a too high temperature of the hydrides decomposition. Most work has been done with powders, though films promise a smaller decomposition temperature. Films are usually saturated in the hydrogen gas, and there are no works on the co-deposition of hydrogen-titanium films. The present work presents first data on the dehydrogenation kinetics of thin titanium films deposited in the hydrogen gas of low pressure.

Публикация

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Swarm Intelligence: A Quantitative Analysis of Research Publications and Trends

(НИЯУ МИФИ, 2026) Joseph, Jeena; Thomas, Binu; Masalsky, L. S.; Logunova, O. S.; Govind, Sabeen

The research investigates the scholarly landscape of swarm intelligence by conducting a comprehensive bibliometric analysis of data sourced from the Scopus database. The search was performed with the help of keywords "swarm intelligence" on the date of 25 January 2024, and eventually, it was found that a total of 1374 articles with diversified sources up to 800 were identified and have been dated between 1996 to 2024. Related dimensions to be studied include annual scientific production, collaboration of publication and bibliographic coupling, leading authors, main sources and affiliations, and keyword co-occurrence. Visualizations used here include line graphs, bubble charts, network diagrams, and three-field plots to present key findings. One can observe from the results that the number of publications increases linearly, although a sharp increase is significantly noticed in 2024. Prolific and influential authors or sources in this area are identified. Moreover, keyword co-occurrence analysis brings out the central concepts or thematic areas cutting across the articles on research in swarm intelligence. A publication collaboration study, such as bibliographic coupling analysis, helps to unveil the international research linked network and the extent to which papers are interlinked. Overall, the understanding that this research has provided me is the tendency, dynamics, and change that has happened in the swarm intelligence research.

Публикация

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Neural Network-Based Dynamic Grasping of Moving Objects with Robotic Manipulators

(НИЯУ МИФИ, 2026) Cao, Yin; Boguslavsky, A. A.

We explore the use of the open‐source library Stable Baselines3 to implement reinforcement learning via deep neural networks for controlling a manipulator of grasping moving objects along a conveyor belt. Unlike static object grasping, this task requires accounting for dynamic factors, significantly complicating the process. We provide a detailed description of the physical-kinematic modeling of the manipulator in PyBullet .and the integration of both the manipulator’s and the moving objects’ parameters into the neural network for training. The results of this study demonstrate that the decision-making capabilities and autonomous behavior provided by reinforcement learning algorithms can be successfully applied to complex tasks, such as dynamic object grasping.

Публикация

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GSDMM Clustering Results Visualization Technique for Short Texts

(НИЯУ МИФИ, 2026) Chigarev, B. N.

The aim of the study is to propose a technique for visualizing the results of short text clustering using the Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM) algorithm, in order to facilitate the analysis of the results and the selection of the hyperparameters of this algorithm and dictionary. GSDMM is selected as the most popular short text clustering algorithm on GITHUB. The algorithm implemented by Ryan Walker on Rust was used. The program Scimago Graphica was used to create bar charts. 16486 bibliometric records on the topic “Visualization” exported from the Scopus database on November 12, 2024 served as the source of short texts. Only Author keywords are used as short texts in this paper. A technique for visualizing the results of short text clustering using the GSDMM algorithm is proposed, which is based on comparing the occurrence of keywords in a given cluster and in each of the other clusters. It is shown that the cluster topics obtained using the GSDMM algorithm can be compared with the results of author keyword clustering performed using the VOSviewer program. The obtained results can be interpreted as a certain stability of cluster themes obtained by essentially different methods. The author suggests to expand the study by creating a thematic dictionary of abbreviations, analyzing the influence of the dictionary on the clustering results of the GSDMM algorithm, and extending the method of visualizing the clustering results to other short texts such as titles and abstracts.

Публикация

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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.