FSUE State Scientific Research Institute of Aviation Systems or GosNIIAS for short (Russian: ГосНИИАС) is a State Research Centre of the Russian Federation in operations research and aviation weapons systems development. Founded by the decree of the Council of Ministers of the USSR on 26 February 1946 from a number of laboratories of the Flight Research Institute. The new institute was named NII-2. In March 1994 the institute was re-named with the its current name (GosNIIAS).Initially, the institute was located in the buildings of the former Sergievo-Elizabethan Asylum.
We present a framework that reconstructs time-resolved galactic cosmic-ray (GCR) proton and helium energy spectra from the global neutron monitor network, providing data about GCR flux without direct satellite observations. Two methods are utilized and compared: a calibrated yield function plus force-field scheme and artificial neural networks trained on multi-station neutron monitor count rates coupled with heliophysical indices. The reconstructed spectral time series reproduce both large-scale solar-cycle modulation and short-term disturbances and extend to periods lacking daily spacecraft data, including 2006-2011 (consistent with PAMELA) and 2019-2022 (consistent with AMS-02 Bartels rotation averages). Artificial neural networks deliver excellent performance across energies, with markedly lower mean absolute percentage error and χ^2/dof near unity. A thorough validation confirms robustness and establishes neutron monitors as an effective real-time GCR spectrometer that can be utilized for various purposes.
In this paper, we propose a new single-photo 3D reconstruction model DiffuseVoxels focused on 3D inpainting of destroyed parts of a building. We use frustum-voxel model 3D reconstruction pipeline as a starting point for our research. Our main contribution is an iterative estimation of destroyed parts from a Gaussian noise inspired by diffusion models. Our input is twofold. Firstly, we mask the destroyed region in the input 2D image with a Gaussian noise. Secondly, we remove the noise through many iterations to improve the 3D reconstruction. The resulting model is represented as a semantic frustum voxel model, where each voxel represents the class of the reconstructed scene. Unlike classical voxel models, where each unit represents a cube, frustum voxel models divides the scene space into trapezium shaped units. Such approach allows us to keep the direct contour correspondence between the input 2D image, input 3D feature maps, and the output 3D frustum voxel model.
Mathematical representations for creating so-called ‘‘virtual’’ pilots are under consideration. The approach in use is based on sequential transitions from the business processes, which plainly formalize subject activities but lack the capacity to apply meaningful mathematical tools, to the Markovian processes, which plainly formalize crew activities and enable the use of convenient and efficient mathematical methods, yet require substantial volumes of empirical data for their identification, and, finally, to the quantum representations, which plainly formalize crew activities and, when employing the quantum likelihood method, need practically acceptable volume of empirical data for the model identification, while enabling the application of novel relevant mathematical tools. The approach in use makes it possible to solve three topical applied problems: to implement simulation basing on small sample of empirical data for predicting behavior, to solve the classification problem for diagnostic purposes, and to plan a certain activity for adaptive training to improve professional skills. The activity model in use combines two types of qubit systems, viz.: the cluster system representing steps of a subject activity and the branch systems representing mistakes arising during the implementation of the given activity steps. The quantum cluster system is connected to the branch systems with the aid of entangling by collapse, and vice versa.
The article considers theoretical and methodological foundations of military-technical cooperation of the Russian Federation with foreign countries, analyzes the current state of the global market of armaments and military equipment and the dynamics of its development in 2021–2024. Special attention is devoted to the economic aspects of military-technical cooperation of the Russian Federation in modern conditions. The authors illustrate that its further development and increase in Russia's export earnings can be achieved through planning and implementation of a set of measures, including the growth of direct supplies of military products, the development of alternative financial and economic trade mechanisms, the use of digital assets and cryptocurrencies, the comprehensive expansion of participation of allied countries in integration associations and a series of others, including the expansion of cooperation in providing services for the standardization of armaments and military equipment.
