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September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
September 7, 2026
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
September 7, 2026
‘Speech, Facial Expressions, and Gestures Cannot Lie
Would you like to know whether a speaker’s trembling voice or an accidental gesture can give them away? At HSE University in Nizhny Novgorod, researchers are developing an algorithm that analyses speech, facial expressions, and gestures, and determines whether information is truthful with 92% accuracy. The project has applications ranging from forensic examination and bank recruitment to fundamental research. Anna Khomenko, head of the research group and Senior Research Fellow at the Centre for Language and Brain at the HSE Faculty of Humanities in Nizhny Novgorod, explains how students and researchers are working together to create a corpus of video recordings, train a classifier, and prepare to introduce computer vision technology.

 

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Selective area epitaxy of gallium phosphide-based nanostructures on microsphere lithography-patterned Si wafers for visible light optoelectronics

Materials Research Bulletin. 2025. Vol. 182. Article 113126.
Dvoretckaia L. N., Fedorov V. V., Pavlov A., Sergey D. Komarov, Eduard I. Moiseev, Miniv D. V., Kaveev A. K., Smirnov A. G., Kirilenko D. A., Mozharov A. M., Mukhin I. S.

In this study, we present the selective area plasma-assisted molecular beam epitaxial growth of GaP-based nanoheterostructures (nanostubs), incorporating direct bandgap GaAsP or GaPN segments, on patterned SiO2/ Si(001) wafers. A microsphere optical lithography and anisotropic Si wet-etching techniques were employed for wafer-scale surface patterning through SiO2 growth mask, allowing to obtain either planar or pyramidal pit nucleation site morphologies. X-ray diffraction reciprocal space mapping and Raman microspectroscopy studies confirm compositional homogeneity of the nanostub arrays. The dilute nitride nanostubs display the narrowest and most intense visible red photoluminescence response at room temperature, which is an order of magnitude higher compared to the GaAsP ones. The formation of the nitrogen sub-band in GaPN alloy was confirmed in the framework of density functional theory, providing insights for interpreting the experimental results. These findings demonstrate the feasibility of the proposed approach for fabricating the ordered arrays of nanoscale visible light emitters on silicon.

Research target: Physics Nanotechnologies
Language: English
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Keywords: DFTIII-V on Sinanoheterostructuresselective area growthdilute nitride semiconductorsнаногетероструктурынизкоразмерные нитридные полупроводникиизбирательный ростIII-V на Siтеория функционала плотности
Publication based on the results of:
Study of disk microlasers and photonic integrated circuits for optical switching and sensing elements (2025)
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