Lobachevsky State University of Nizhny Novgorod - National Research University (UNN), also known as Lobachevsky University, is a university in Nizhny Novgorod, Russia. UNN was established in 1916 as the People's University of Russia.[citation needed] In 1932, the city of Nizhny Novgorod was renamed to Gorky; accordingly, UNN was renamed to State University of Gorky. In 1956 it was again renamed to Lobachevsky State University of Gorky after the Russian mathematician Nikolai Lobachevsky. In 1990, it took its current name, Lobachevsky State University of Nizhny Novgorod.[citation needed]UNN is one of the classical research universities in Russia.[citation needed] UNN provides education to over 30,000 undergraduate & graduate students and 1000 postgraduates. The University staff comprises over 1200 Candidates of Sciences (PhDs) and over 450 Doctors of Sciences.[citation needed]UNN is involved in cooperation with foreign partners.[citation needed] It works in connection with various Russian and foreign companies (Intel, Microsoft, NVIDIA, Yazaki, Cisco Systems, Sberbank).[citation needed] The University is also a member of the Association of Russian Leading Universities and was a member until 2022 of the European University Association (EUA).
We study experimentally the minimum polynomial method for direction finding of a useful source using an antenna array under the influence of active interference with unknown number of sources. To implement this method, we propose a digital signal processing algorithm, which provides estimates the number of active interference sources and ensures their suppression. The implementation of the algorithm does not require direct inversion of the correlation matrix, thus reducing its computational complexity. The experimental results demonstrated high efficiency of the minimum polynomial method in the context of direction finding accuracy and high performance of the digital signal processing algorithm in terms of noise suppression.
The study investigates the ion beam mixing of multilayered CeO₂/Pd films for potential applications in electrocatalysis. The research explores the effects of irradiating the films with He⁺, Ne⁺, and Ar⁺ ions at varying fluences, using both Monte-Carlo simulations (TRIDYN code) and experimental techniques such as SEM, SIMS, and X-ray diffraction. Theoretical effective diffusion parameter for ballistic mixing was calculated using TRIDYN. Experimental observations showed films recrystallization for light ions and at low fluences. Ar+ irradiation at the highest fluence (5·1016 cm− 2) completely disrupted the layered structure, creating a porous, highly mixed film with potential catalytic benefits due to its developed surface.
Mathematical models are considered for the design of laser input–output ports enabling efficient optical mode transfer between optical fibers and waveguide channels of a photonic integrated circuit via a diffraction grating. Optimal geometric parameters of diffraction gratings are determined to minimize optical losses when using laser radiation at the wavelength of 1550 nm. The calculations were performed for “diffraction grating”–“planar optical waveguide” systems formed within a titanium dioxide cladding layer with high refractive index contrast on the surface of thin-film lithium niobate, considering varying thicknesses of the TiO2 and LiNbO3 layers.
Introduction: Early diagnosis of acute ischemic stroke (AIS) is vital to improve prognosis and reduce mortality. While diffusion-weighted MRI is the gold standard, non-contrast CT (NCCT) is more accessible and enables faster assessment. The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) scoring system facilitates standardized evaluation of ischemic lesions on NCCT. Deep learning (DL) holds promise for improving early AIS diagnosis by augmenting NCCT diagnostic accuracy. Objective: To evaluate the diagnostic performance of DL models in predicting the ASPECTS score and diagnosing AIS using NCCT images. Methods: This study followed PRISMA guidelines and was registered in PROSPERO (CRD420251000160). PubMed, Embase, and Scopus screened from inception to 19 May 2025. Studies included that diagnosed AIS using NCCT, with confirmation by a ground truth defined as DWI, CTA, CTP. The ground truth was used as a comparator to evaluate the performance of the DL model. The primary outcomes were HSROC curves summarizing the diagnostic performance of DL models in predicting ASPECTS scores and diagnosing AIS. As secondary outcomes, pooled sensitivity, specificity, and AUC values were estimated for ASPECTS prediction and AIS diagnosis using DL models. Results: Out of 265 screened studies, 20 met the inclusion criteria, of which 14 were eligible for meta-analysis. A total of five studies involving 3,799 patients contributed to the analysis of ASPECTS score prediction, while nine studies with 11,297 patients were included in the evaluation of AIS diagnosis prediction. The summary point of the HSROC curve corresponding to ASPECTS score prediction revealed a sensitivity of 86.7% (95% CI: 81.1–90.8), and specificity of 86.9% (95% CI: 72.7–94.3, Figure 1). In terms of diagnosing AIS, the HSROC summary point indicated a sensitivity and specificity of 80.9% (95% CI: 67.8–89.5) and 89.9% (95% CI: 81.1–94.9), respectively (Figure 1). Figures 2 and 3 present the pairwise meta-analysis results of ASPECTS prediction and AIS diagnosis, reporting sensitivity, specificity, accuracy, and pooled AUCs of 0.85 (95% CI: 0.79–0.91) and 0.85 (95% CI: 0.78–0.93), respectively. Conclusion: Deep learning models demonstrate high diagnostic accuracy in predicting ASPECTS scores and detecting acute ischemic stroke on non-contrast CT within 24 hours of onset. These findings suggest that DL may improve early AIS diagnosis, though further validation is required for clinical use.
Gallium oxide (Ga2O3) is one of the most promising wide-bandgap semiconductors for the development of next-generation electronic and optoelectronic devices. Doping of Ga2O3 with various impurities via ion implantation is widely employed in the fabrication of such devices. This requires a comprehensive investigation of the influence of implantation conditions on defect formation, the behavior of defects and impurities before and after annealing, and the resulting properties of the ion-doped layers. Boron, as a dopant, is of particular interest due to its minimal atomic mass and smallest ionic radius among the elements isovalent with gallium. In this study, the cathodoluminescence spectra of β-Ga2O3Fe single crystals with a surface orientation of (2¯01), subjected to boron ion implantation (both as-implanted and after subsequent annealing), have been studied. Known luminescence lines associated with native defects have been identified, along with certain features specific to boron, such as a pronounced separation of the green emission line and a significant reduction of the UV line at higher irradiation fluences. The discussion of these observations is conducted in the context of previously established data.
