Scientists Rank Russian Regions by Climate Risk Levels
Researchers from HSE University and the Russian Academy of Sciences have assessed the levels of climate risks across Russian regions. Using five key climate risks—heatwaves, water stress, wildfires, extreme precipitation, and permafrost degradation—the scientists ranked the country’s regions according to their need for adaptation to climate change. Krasnoyarsk Krai, Irkutsk Region, and Sverdlovsk Region rank among the highest for four of the five climate risks considered. The study has been published in Science of the Total Environment.
Climate change can shift the geographical distribution of natural hazards—such as wildfires, extreme precipitation, and melting glaciers—which in turn impacts regional economies and the well-being of the population. An accurate assessment of climate risks is essential for effective adaptation to climate change.
A team of researchers from HSE University and the Russian Academy of Science's Obukhov Institute of Atmospheric Physics and Institute of Geography has proposed a new approach to climate risk assessment. The scientists identified five key climate risks—heatwaves, water stress, wildfires, extreme precipitation, and permafrost degradation—and, using various indicators, assessed the current and future danger each phenomenon poses to the region, as well as the region’s exposure and vulnerability to these risks.
'For each risk, we created a ranking of Russian regions and pinpointed the areas where that risk is most severe. For example, the risk of heatwaves is highest in the regions of the middle belt and the southern part of European Russia. The impact of water stress on agriculture is most pronounced in the south of European Russia, the Volga region, and the southern Urals. Central Siberian regions face the highest risk of wildfires. Extreme precipitation is particularly common in the southern Far East and in some parts of European Russia, while the risk of permafrost degradation is characteristic of Russia’s northeastern regions,' explains Alexander Chernokulsky, Associate Professor at the HSE Faculty of Geography and Geoinformation Technologies, Deputy Director of the Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences, and primary author of the study.
The scientists identified nine regions that rank in the top 25% for three climate risks: Amur, Arkhangelsk, Leningrad, and Moscow Regions; Zabaikalsky, Krasnodar, and Khabarovsk Krais; and the Republics of Bashkortostan and Komi. Three regions—Krasnoyarsk Krai, Irkutsk Region, and Sverdlovsk Region—ranked in the top 25% for four climate risks simultaneously.
When compiling the ranking, the scientists also considered various indicators of climate hazards within each risk.
'For example, population size was considered when estimating the impact of heat and cold waves. The distribution of forests was taken into account when studying wildfire impacts, and the extent of agricultural land was used to assess drought-related risks. Many of Russia’s regions are comparable in size to large countries and are climatically diverse and unevenly developed. Therefore, it is important to assess climate risks primarily in the areas where people live, forests grow, and fields are cultivated,' says Alexander Sheludkov, Associate Professor at the Faculty of Geography and Geoinformation Technologies of HSE University.
The resulting ranking can be used to inform the development of regional climate adaptation plans and to guide the allocation of funding for adaptation measures.
'Ideally, the regions most affected by climate change should be prioritised when planning adaptation measures. Our study's findings can help identify the most significant combination of risks for a given area, and which risk component—hazard, exposure, or vulnerability—contributes most to the overall risk. This makes it possible to use our approach to refine existing climate adaptation plans and to select the most effective adaptation measures and strategies,' says Igor Makarov, Head of the Laboratory for Economics of Climate Change at HSE University and co-author of the study.
See also:
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.
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.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.
‘The Peak of Stupidity’ and ‘The Valley of Despair’: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.