This study presents a density functional theory (DFT) investigation of the electronic response of graphene doped with various atoms (B, N, Al, Si, S, Ga) under biaxial strain. The calculations were performed using the PBE exchange–correlation functional within the generalized gradient approximation (GGA), as implemented in the DMol³ code. The Fermi energy was used as the primary indicator to evaluate strain sensitivity across a deformation range from −0.05 to +0.05. The results reveal a strong dependence of the electronic response on the type of dopant. Ga- and Al-doped graphene systems exhibit the most pronounced Fermi level shifts, up to 0.6 eV, indicating high sensitivity to mechanical strain. In contrast, B- and N-doped graphene show more moderate but stable and linear changes, which may be advantageous for predictable sensor behavior. These findings highlight the critical role of dopant selection in engineering strain-responsive graphene materials and support a design framework for their integration into high-performance flexible electronics and sensing applications.

The study aims to analyze the existing computer vision techniques for commercial drone detection to identify their advantages, disadvantages, and determine the best approaches in different application scenarios. The research methodology used synthesis methods to explore and propose combinations of techniques based on an analysis of the methodology and results of other works in the literature. It employed algorithms and sensor data analysis to assess the effectiveness of detection methods, and deduction to formulate hypotheses and conclusions based on data and theories. The main research results include the development of computer vision methods for detecting commercial drones, identifying their visual detectability at different altitudes, analyzing different object detection methods, and evaluating the applicability of these methods for commercial applications. In addition, the study identified the advantages and disadvantages of applying computer vision to commercial drone detection and offered recommendations for further research and practical implementation. The practical value of this study is to improve the detection systems of commercial drones, thereby enhancing the safety and efficiency of their use.

The analysis presented in this concise review is directed towards evaluating the contemporary trends and practical implementations of the ELU-4 electronic linear accelerator across a broad spectrum of scientific and technological domains. The ELU-4, which is an electron linear accelerator, represents a key technological achievement in the field of accelerator technology. Various aspects of ELU-4 utilization are explored, spanning from its applications in medicine and ecology to its relevance in space exploration. Current data presented in scientific articles and reports from the last ten years are researched and analysed. This succinct review not only underscores the conventional applications of this accelerator but also underscores its potential in nascent fields of science and technology. The incorporation of ELU-4 into diverse research and engineering endeavors presents notable prospects for innovation and the advancement of scientific knowledge frontiers.

This study presents a satellite-based approach to monitoring snow cover dynamics in Kazakhstan using long-term MODIS observations. The methodology enables the extraction of key snow season metrics, including first snowfall date (FSD), snowfall duration (SDC), and last snowfall date (LSD). The approach was applied to representative regions of southeastern Kazakhstan, including the Tien Shan, Kolsai Lakes, Bartogay Reservoir, and Lake Balkhash. A spatiotemporal analysis of snow cover variability for 2001–2024 was conducted, and relationships with global climate indices (ENSO and AO) were identified. The results demonstrate significant regional differences in snow cover stability and highlight the sensitivity of low-lying regions to climate variability.

Currently, in the Republic of Kazakhstan, within the framework of the strategic directions of the space industry, fundamental and applied scientific space research is being developed, and practical problems are being solved using space technology. In these studies, special attention is paid to the reliability of on-board equipment under the complex influence of outer space factors. In the development and design of such work as docking and assembly of elements of orbital stations, remote control of automatic interplanetary stations and many others, mathematical and computer modeling is used, which is a highly effective and relatively low-cost method. The purpose of this work is to study the dependence of the energy losses of relativistic ions of cosmic radiation when particles pass through graphene nanostructures in order to predict their radiation resistance when exposed to cosmic radiation particles. The simulation of the trajectories and energy losses of relativistic ions during passing through graphite crystals were performed. Numerical values ​​of the critical channeling angles in a graphite crystal are obtained. It is shown that ion energy losses do not depend on the point of entry of the ion into the crystal and increase sharply with decreasing energy in the energy range less than 1000 MeV/nucleon.