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July 2, 2026
Researchers Discover How Spelling Errors Slow Down Reading in Russian
Psycholinguists from the Centre for Language and Brain at HSE University–St Petersburg have shown that words that are frequently misspelled are processed more slowly by readers, even when presented with the correct spelling. The researchers confirmed this effect for the first time using Russian-language materials and found that response speed is most strongly linked to how confidently individuals can distinguish the correct spelling of a word from an incorrect one. The study has been published in The Mental Lexicon.
July 2, 2026
HSE Develops App for Assessing Phonological Processing in Children
Researchers at the HSE Centre for Language and Brain have developed a new digital tool for assessing children's phonological processing skills—the ZARYA (Sound Analysis of the Russian Language) test battery. It is the first standardised application in Russia designed to provide a fast and reliable assessment of children's ability to distinguish speech sounds, retain them in working memory, and perform phonemic analysis. The app runs on Android tablets and smartphones and is available for download from RuStore. Details of the test validation have been published in the Journal of Speech, Language, and Hearing Research.
July 1, 2026
Scientists Discover Why Europium 'Misbehaves'
Europium is a rare-earth metal responsible for the pure red glow in displays and other luminescent materials. For a long time, however, it refused to emit light when surrounded by certain organic molecules known as acylpyrazolone ligands. Chemists have now uncovered the reason: in europium complexes with these ligands, a 'black window' appears—a charge-transfer state in which the energy absorbed by the ligand is dissipated as heat rather than emitted as light. Understanding this mechanism opens the way to designing more efficient red-emitting materials for displays, fluorescent thermometers, and chemical sensors. The results have been published in Dalton Transactions.

 

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Межстрановой опыт прогнозирования макроэкономических и кредитных кризисов и его применение для России

Экономическая политика. 2020. Т. 15. № 5. С. 130–159.
Mamonov M. E., Pestova A., Pankova V., Akhmetov R.

This paper provides a joint analysis of business and credit cycles with a focus on unobservable factors affecting both cycles, at the cross-country level. Using quarterly data for 19 developed countries and Russia for the period from 1994 to 2018, we build a system of two dynamic probit models, which includes a cross-correlation between the errors of the equations governing the probability of a recession and the probability of credit crisis. The results show that, first, our system allows us to correctly predict 91% of episodes of joint realization of macroeconomic and credit crises and 89% of non-crisis periods in the training sample, and 92% and 95% respectively in the testing sample. Second, switching from two independent regression models to a system of correlated equations significantly (by 16 percentage points) increases the share of correctly predicted crisis episodes while only slightly (by 7 percentage points) reducing the proportion of correctly predicted non-crisis episodes. Third, our system can predict an approaching crisis earlier, by 1–4 quarters, in comparison with similar single models. Our results complement the literature on forecasting recessions and credit crises. Fourth, it is revealed that the models which have been constructed on developed countries allow one to predict crisis events for Russia. The model we have constructed correctly predicts 100% of joint crisis episodes and 92% of joint non-crisis episodes in the training sample as well as 86% of joint crisis and 90% of joint non-crisis episodes in the testing sample for Russia.

Research target: Economics and Management
Priority areas: economics
Language: Russian
Full text
DOI
Keywords: бизнес-циклыbusiness cyclescross-country analysisКризисыкредитные циклыcredit cyclesbivariate dynamic probitcrisis predictionin-sample forecastingout-of-sample forecastingсистема динамических пробит-моделейпрогнозирование поворотных точеквневыборочный прогноз
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