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.
Characteristics of resistive switching of memristive elements based on zirconium dioxide doped with yttrium ZrO2(Y) (yttrium content 12
Graphene oxide quantum dots (GOQDs) represent a promising class of carbon nanomaterials with exceptional optical, electronic, and biocompatible properties. This study presents a novel, environmentally friendly synthesis of GOQDs using pulsed underwater discharge plasma, enabling the production of highly functionalized quantum dots without additional chemical precursors. The synthesized GOQDs were comprehensively characterized using a suite of physicochemical techniques, including X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), UV-visible absorption, photoluminescence spectroscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Structural analyses confirmed the formation of nanocrystalline, defect-rich particles with an average size of 2-8 nm, featuring a high density of oxygen-containing functional groups predominantly located at edges and defect sites. Optical studies revealed an absorption band at approximately 240 nm, a calculated optical band gap of 2.4 eV, and green-yellow photoluminescence centered at 565 nm, attributed to the synergistic effects of defect-state emission and quantum confinement. Thermal analysis identified distinct, reproducible heat capacity peaks at approximately 41.5 degrees C, 55.3 degrees C, and 59.6 degrees C, which correlate with in situ Raman spectral evolution and are interpreted as configurational relaxations and defect annealing within the metastable carbon lattice. Furthermore, the magnetocaloric effect (MCE) and entropy changes (Delta S) were investigated under external magnetic fields ranging from 0.2 to 1.0 T. The observed maxima in MCE and Delta S curves coincide with the thermal anomalies in heat capacity, demonstrating a strong coupling between structural rearrangements and magnetic entropy.
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.
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.
