An interdisciplinary roundtable discussion entitled "Challenges and Prospects of Legal Regulation of Medicine and Medical Technologies" was held at NSU.

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A roundtable discussion entitled "Challenges and Prospects of Legal Regulation of Medicine and Medical Technologies" was held at Novosibirsk State University. It served as a discussion platform for professionals from various fields—lawyers, physicians, biologists, scientists, and practitioners. Participants discussed issues of transplantation, obtaining informed voluntary consent for medical interventions, cloning, surrogacy and genetic donation, cryopreservation, and artificial intelligence in medicine. The topics covered law, medical aspects, and ethics. The event was organized by Institute of Medicine and Medical Technologies (IMMT) of NSU, curators of the Digital Medicine and Digital Jurisprudence programs.

The roundtable was attended by representatives of leading research and educational centers from Novosibirsk, Moscow, Nizhny Novgorod, Irkutsk, and Belgorod, including the V.M. Lebedev Russian State University of Justice, the Volga Region Medical University of the Ministry of Health of the Russian Federation, the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, and the Novosibirsk Region Bar Association "Legal Defense in Medicine."

Yulia Samoylova, Director of the NSU Institute of Medical and Technical Medicine and Professor, noted that dialogue between representatives of medicine and law is extremely important. The issues discussed cover a wide range of modern medical problems and touch upon the implementation of advanced technologies. Yulia Samoylova expressed confidence that such dialogue will help reach joint solutions and expressed hope that similar events will be organized and continued in the future.

Ekaterina Mayer, MD, professor at the Institute of Medical and Technical Medicine (IMMT) of Novosibirsk State University, delivered a brilliant presentation on "Current Issues in the Legal Regulation of Medical Practice." Her presentation generated great interest among the participants. She highlighted issues of medical education, the important role of law in the medical profession, and cited numerous practical examples.

Larisa Tatarenko, Privolzhsky Medical University of the Ministry of Health of the Russian Federation, Nizhny Novgorod, highlighted the problematic aspects of genetic material donation. Elena Shevchuk, East Siberian Branch of the V.M. Lebedev Russian State University of Justice, Irkutsk, discussed in more detail the specifics of judicial practice regarding compensation for damage to health during the provision of medical services.

A powerful presentation by Eduard Chuiko, CEO of M-Genomics and a junior researcher at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, focused on the challenges of modern transplantation. Students asked numerous questions and actively participated in the discussion, examining various aspects of bioethics and cloning.

Overall, the roundtable generated great interest among participants and provided a lively discussion platform for current issues in bioethics and biolaw. Further interdisciplinary events on similar topics are planned for the future.

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The NSU team took first place in the prestigious international competition in computational linguistics SemEval-2026.

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A team of researchers from Novosibirsk State University won first place in the international scientific competition SemEval-2026 Task 8 "MTRAGEval: Evaluating Multi-Turn RAG Conversations." The team was led by NSU Associate Professor of Industrial Engineering and a research fellow at the Laboratory of Applied Digital Technologies. Faculty of Mechanics and Mathematics of NSUAssociate Professor Ivan Bondarenko. The results of the competition will be presented at the world's largest conference on computational linguistics, ACL, which will be held in the summer of 2026.

The competition was hosted by IBM and consisted of three tracks. The NSU team participated in Task B—a task of generating answers to user questions based on provided reference documents and the history of a multi-step dialogue. Of the 26 participating teams, the NSU team took first place, achieving a quality metric of 0.7827 (conditioned harmonic mean), significantly exceeding the organizers' best baseline result (0.6390) by 14.4 percentage points.

SemEval (Semantic Evaluation) is an annual international workshop on methods and algorithms for computational semantics, held for over 20 years. This event hosts competitions in various areas of computational linguistics. This year, SemEval presented 13 challenging research problems to participants. One of the most interesting and significant problems was Task 8, which assessed the performance of RAG (Retrieval-Augmented Generation) systems in multi-step dialogues. Such RAG systems address a key issue with modern large language models: their limited worldview and the difficulty of adapting them to specialized subject areas. The "knowledge" of a large language model is limited to the training set and does not include relevant or domain-specific information. RAG integrates language models with external knowledge bases, enabling them to find and use relevant information when generating responses.

"Our team proposed three key approaches that secured our victory in the competition. The first involved iteratively improving the system prompt using an LLM agent. We developed a multi-agent system in which a large Gemini neural network analyzes the model's performance and suggests improvements to the system prompt. This process is repeated iteratively until a plateau is reached. The second approach involved using in-context learning, in which the model learns to perform a task based on several examples of correct solutions to the problem provided in the input context. For each problem category, the researchers selected the most typical examples using the medoid method in a metric embedding space. These examples were added to the prompt to demonstrate the correct behavior of the model. This approach consistently demonstrated the best results," explained Ivan Bondarenko.

The researchers created several network algorithms using both approaches and evaluated their advantages before deciding to combine them. Among a variety of ensemble methods, they chose a method using a judge neural network that would select the best ensemble response in each case. The team combined seven disparate language models (Gemini-3-Pro-Preview, GLM-4.6, Llama-3.3-70B-Instruct, Qwen3-235B-A22B-Instruct, Claude 4.5 Haiku, Qwen2.5-32B-Instruct, and their own model, Meno-Lite-0.1) and used GPT-4o-mini to select the best response in each case. The diversity of models and approaches provided an additional boost in quality.

"The Meno-Lite-0.1 model, our team's own development based on Qwen2.5-7B-Instruct, deserves special attention. This compact model with 7 billion parameters was specifically retrained for use in RAG pipelines on a corpus of Russian- and English-language educational data. Despite its small size, Meno-Lite demonstrated performance comparable to significantly larger models and contributed to the ensemble's final result," explained Ivan Bondarenko.

The NSU team that participated in the competition included current and former NSU students and staff: Mikhail Kulakov, a master's student in the machine learning program implemented jointly with the School of Data Analysis and the Faculty of Mathematics and Mechanics of NSU; Ivan Chernov, a fourth-year student at the NSU Institute of Intelligent Robotics; Mikhail Komarov, a graduate of the NSU Institute of Intelligent Robotics and chief engineer of the RAGU open source project; Oleg Sedukhin, a graduate of the NSU Faculty of Information Technology; and Roman Derunets, a graduate of the NSU Institute of Intelligent Robotics and a participant in the Meno project.

A scientific paper describing their proposed solution has been submitted for peer review and will be presented at the ACL (Association for Computational Linguistics) conference, the world's largest scientific forum on computational linguistics. Ivan Bondarenko emphasized that the results obtained are already being used in the development of the university's internal project, Meno, an intelligent system based on RAG technologies. The methods developed by the team members can be used to improve the quality of dialog systems that work with external knowledge bases, including corporate and educational applications.

Material prepared by: Elena Panfilo, NSU press service

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

Dreams of "smart machines," the defeat of expert systems, and the triumph of transformers

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Sergey Ospichev, PhD in Physics and Mathematics, Deputy Director of the Mathematics Center in Akademgorodok, and Acting Head of the Department of Computer Science and ICT at the Specialized Scientific Center of Novosibirsk State University, discussed how artificial intelligence evolved from the fantasies of the past about thinking machines to today's large-scale language models. His lecture, "Artificial Intelligence: Origins and Evolution," was held as part of "Darwin Week"—a popular science marathon traditionally held at Novosibirsk State University in February. This year, the event was held for the first time on the new NSU campus.

From Golem to "Rent a Human"

Sergey Ospichev began his lecture with a quote from the film "Blade Runner," which, in his opinion, describes AI very well: "I don't think, I calculate, but the difference is already becoming unclear." He cited the definition of AI given by Chinese researcher YX Zhong back in 2006 in her article "A Cognitive Approach and AI Research": "Artificial intelligence is a branch of modern science and technology aimed, on the one hand, at exploring the secrets of the human mind and bestowing upon machines the advantages of human intelligence, and on the other, at enabling machines to perform functions as intelligently as they are capable of…"

Sergei Ospichev cited the earliest example of artificial intelligence, which existed, however, only as a fantasy of a "non-living" yet powerful assistant to humans. This was a clay giant, brought to life through Kabbalistic rituals. It was activated and deactivated by a magic word written on a scroll and placed in the idol's mouth. Upon receiving an order, it independently decided how to carry it out. It operated according to a predetermined program, a kind of machine operating from instructions. Back then, in the 17th century, humans gave orders to an artificial intelligence, albeit a primitive and fictitious one, but recently this has begun to change.

"A portal called 'Rent a Human' has appeared online, where neural networks can select a human to perform various tasks they couldn't do on their own: for example, photographing objects, delivering goods or receiving packages, or emotionally evaluating certain events or phenomena. While this platform is still experimental, a trend is emerging: AI is now beginning to manage people. Whether this is a good thing or not is still unknown, but this is the world we live in," said Sergey Ospichev.

First ancestors

Sergey Ospichev proposed examining the evolution of AI from the early 20th century. He discussed the ups and downs of this challenging path and analyzed the important milestones in this process.

The first to embark on this path was the German researcher David Hilbert (1862-1943), one of the most renowned mathematicians of the last century. The telegraph and railways became symbols of that time, and the prevailing mood was optimism and faith in science. Hilbert proposed the creation of a unified formal language of mathematics, based on simple arithmetic. This language was to presuppose the algorithmic decidability of all science. Why was this so necessary? With the advent of the telegraph, the world changed. Science instantly became international, and scientific knowledge became instantaneous. Scientists from different countries now had the opportunity to actively communicate with each other, exchange news, and organize international conferences, congresses, forums, and symposia. Therefore, mathematicians urgently needed a unified formal language understandable to all scientists.

An arithmometer is a desktop mechanical machine designed to accurately perform four arithmetic operations: addition, subtraction, multiplication, and division.

"At the beginning of the last century, many believed that science would solve all problems, and that a good adding machine would enable one to perform any calculation and achieve great achievements in mathematics, physics, and other sciences. David Hilbert was no exception, proposing to formalize mathematics. However, the Austrian logician, mathematician, and philosopher of mathematics, Kurt Gödel (1906-1978), entered the picture with his incompleteness theorem, according to which any algorithmically decidable theory that extends arithmetic is incomplete. He argued that it is impossible to formalize mathematics based on arithmetic and using algorithmic methods. An 'artificial' mathematician cannot replace living intelligence. For us scientists, on the one hand, this is very sad, because we will never see an automated mathematician, but on the other, it is wonderful, because we will always have work to do," explained Sergei Ospichev.

A Turing machine is an abstract computing machine, a mathematical model of computation, proposed by the eminent British mathematician Alan Turing (1912–1954) in 1936 to formalize the concept of an algorithm. It is considered the foundation of computability theory and is used to formally define which problems can be solved using algorithms.

A key discovery during this early period of AI was the Turing machine. This scientist shifted discussions of algorithms from philosophy to engineering. During World War II, the idea of Turing's abstract machine was combined with the idea of breaking the German Enigma encryption machine, which was then actively used to transmit secret messages. Ultimately, Alan Turing developed the Bombe, a code-breaking machine that earned him a place in history as the Enigma breaker and the founder of AI.

"The Turing machine became the ancestor of modern computers, but its creator also formulated the Entscheidungsproblem (decidability problem), proving that not all computations can be performed by computers—there are algorithms that cannot be written in any programming language. This poses a complex problem: on the one hand, an engineering approach is used, creating complex adding machines and computing machines, while on the other, scientists are well aware that not all problems can be solved with these tools. I like to call this 'computability schizophrenia,'" said Sergei Ospichev.

At the start

The term "artificial intelligence" emerged in 1956 at a Dartmouth seminar. This seminar is considered the beginning of AI development. A surprising situation arose here: not a single paper was published following the seminar, yet many of its participants became widely recognized as the "founding fathers" of AI. Important events in the background: the Cold War and the start of the space race. There was talk in the scientific community that computing power would not be sufficient to launch satellites into space.

Humanity has already invented computers and confidently uses them. The era of microchips has not yet arrived. "Smart machines" are still weak and gigantic in size—one of the fastest computers occupies 280 square meters and weighs 25 tons. It is only suitable for simple arithmetic calculations. A new method of calculation must be adopted, accelerated, and optimized. At a Dartmouth seminar, American mathematician John McCarthy (1927–) coined the term "artificial intelligence." He would later invent the Lisp programming language, become the founder of functional programming, and receive the Turing Award for his enormous contribution to artificial intelligence research.

Under the ban

Another crucial link in the evolution of AI was the invention of American psychologist and neurophysiologist Frank Rosenblatt (1928-1971) of Cornell University (USA). He designed and built the first numerical computer, the Mark I, which could recognize some handwritten letters of the English alphabet. Crucially, the computer learned all this on its own. The Mark I became the first neural network built in hardware. Naturally, the invention was a resounding success, spurring the need to study perceptrons and create increasingly complex neural networks.

The Rosenblatt perceptron (1957–1960) is one of the first artificial neural network models, simulating the brain's perception process. It consists of sensory (S), associative (A), and reactive (R) elements, operating as a linear binary classifier with a threshold activation function. It is based on learning with weight correction.

However, the euphoria was short-lived. A few years later, the book "Perceptrons" by MIT AI scientist Marvin Minsky (1927-2016) and mathematician Seymour Papert (1928-2016) was published. In it, the authors argued that "…increasing the size of a perceptron does not improve its ability to solve complex problems." Thus, Minsky was likely trying to attract attention (and funding) to his work, but the result was unexpected: interest in neural networks waned, funding for research ceased, the term "AI" itself was banned, and Minsky earned the nickname "Neural Network Killer." Thus, due to the rivalry between the two organizations, AI development stalled for decades.

Too complicated!

Sergey Ospichev surprised the audience when he said that the first multilayer neural networks appeared in the 1970s. Since neural networks were tacitly banned and even mentioning them was discouraged, let alone pursuing research in this area, the expert system relied on logical rules.

Logical programming languages are becoming increasingly popular. This isn't surprising: since, as Marvin Minsky wrote in his book, we can't train a system because it doesn't work, we have to write all the rules ourselves. The first very complex expert systems are emerging. One of them, MYCIN, is a medical expert system initially created at Stanford and designed to diagnose infectious diseases (meningitis, sepsis) and recommend antibiotics. It used a rule base (about 600) and backward inference, demonstrating accuracy on par with expert doctors and even higher. True, it was only 2.6% higher, but still. By comparison, it suggested acceptable therapy in 65% of cases, while doctors did so in 62.5% of cases. This system raised the first questions about AI ethics, but it never found application due to the complexity of data entry, as the patient had to answer approximately 200 questions before the system could make a treatment decision. At best, data entry took half an hour or more, said Sergei Ospichev.

Generation V

The 1980s were marked by a technological boom in Japan and the advent of microprocessors. Japan was dominating the computing market. The flow of data was growing, and computing power to process it was becoming insufficient.

The advent of microprocessors changed the world of computers—they became smaller and more powerful. They now weighed 5 kg instead of 28 tons. True, they were expensive, and not everyone could afford a personal computer at home, but it was a major step forward.

Seeking to maintain technological leadership, in 1982 the Japanese government initiated a massive 11-year program with funding of 50 billion yen ($500 million). Other countries later joined the race. A breakthrough in applied AI was expected, but the bets were placed on technologies that were already obsolete at the outset: supercomputers with hardware capable of distributed computing. The term "AI" remains taboo: instead, scientific papers use terms such as "data processing," "automated image analysis," "automated approach to formula processing," and so on. Imperative languages began to flourish, while logical ones began to lose ground.

Dark blue thaw

In the 1990s, personal computers became ubiquitous, and the World Wide Web grew exponentially. A new certainty arose: machines were smarter than humans! Confirmation of this appeared in 1997 and was widely publicized. A sensation: the IBM supercomputer Deep Blue defeated world champion Garry Kasparov for the first time in a six-game classical match, with a score of 3.5–2.5. This historic event marked the first victory of artificial intelligence over a reigning champion, marking a new era in chess and the development of AI technologies.

"Of course, this was very important for AI companies—it was a wonderful opportunity for them to emerge from the shadows and develop AI openly: publish articles about their research in journals, open departments at universities, implement their developments, and apply for funding. True, there were theories that this victory was the result of a coding error that caused the computer to make an unconventional move that determined the outcome of the game. But on the other hand, Deep Blue opened up AI to society, and people realized that AI was possible, that it was something big, important, and that it would change our lives. By today's standards, Deep Blue was a very weak computer, with very little artificial intelligence, and it didn't yet have thinking, but rather computation, but it was certainly one of the most important steps in modern AI," shared Sergey Ospichev.

Video cards – a second life

Multilayer neural networks were further developed by developments not originally intended for serious tasks—gaming video cards. They made it possible to overcome the insufficient computing power of the computers of the time for the necessary calculations.

The market was oversaturated with video cards—they were being produced in far greater numbers than gamers of the time needed, and they were much more expensive than they could afford. Furthermore, these video cards were much more powerful than the games of the time. Then, technology was developed that allowed them to be used for computing. Nvidia, the company that manufactured them, began donating these video cards to various universities for free, so that scientists could try them out in solving their own problems. In 2012, Ilya Sutskever, Geoffrey Hinton, and Alex Krizhevsky, the developers of the AlexNet convolutional neural network, also received them. By combining two video cards and obtaining 6 GB of video memory, they were able to win a major image processing competition. In creating their neural network, they outperformed classic machine learning algorithms developed 5-7 years earlier, demonstrating the superiority of the GPU—a specialized electronic chip for parallel data processing, graphics rendering, and acceleration of complex calculations. They succeeded in setting off a chain reaction that led to the popularity of deep learning today. Neural networks were rehabilitated," said Sergey Ospichev.

Three Horsemen of AI

Today, the development of neural networks is driven by three AI horsemen: arXiv, the largest free open archive (repository) of electronic preprints of scientific articles, transformers, and a chatbot based on the Generative Pretrained Transformer (GPT).

ArXiv is a preprint database containing 2.5 million articles, over 30,000 downloads per month, and 200 AI articles per day.

"Machine learning science is advancing very rapidly, and decisions to publish articles in scientific journals are made over a fairly long period of time—a year or two. Within two years, an article in machine learning will have disappeared from the world of machine learning—it will have lost its relevance and novelty. On this resource, you can immediately post your article so that colleagues can read it, discuss it, start using it, and share recommendations without waiting for official publication. Articles appear here instantly, making ArXiv one of the main hubs of machine learning today," explained Sergey Ospichev.

The second "horseman of AI" is Transformers—the next generation of neural networks, a kind of bridge between AlexNet and modern GPT systems. They enable deep learning for text processing. Next to them is the "third horseman," ChatGPT—a chatbot based on a generative pre-trained Transformer, which already receives billions of queries per year. GPT allows us to quickly and efficiently process texts, translate them from one language to another, search for data, generate sentences from them, and so on. It appeared in 2020, and its "successors" were subsequently developed, which are now our constant assistants.

What a twist!

And yet, no matter how tempting it may be to embrace AI, one cannot trust it completely. Whatever it does must be verified by natural intelligence. For example, after his lecture, Sergey Ospichev admitted that several opening quotes were generated by an AI neural network. The phrase in question was not found in the film "Blade Runner." And the photo of the Chinese researcher who outlined her vision of AI in a scientific paper cited in the lecture was also generated by the DeepSeek neural network.

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Scientists from Novosibirsk State University and Volgograd State Technical University have patented a new polymer material for the oil and gas industry.

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Researchers from NSU and a team of scientists from Volgograd State Technical University (VolSTU), led by Doctor of Technical Sciences and Professor Viktor Kablov, have developed a water-swelling elastomer composition for the manufacture of sealing elements for packer equipment as part of the Competence Center's program "Technologies for Modeling and Developing New Functional Materials with Predetermined Properties" (CNFM) at Novosibirsk State University, which is being implemented with financial support from the NTI Foundation.

Packer equipment is a downhole device (seal) that, when installed, seals the annular space between the casing (or openhole wall) and the wellbore assembly, separating intervals—wellbore sections by depth—that are considered separate wellbore operating zones. This prevents interlayer crossflows, isolates individual inflow and injection zones, and ensures wellbore operation according to a specified pattern, withstanding pressure fluctuations and exposure to aggressive environments.

"Ordinary rubber doesn't swell in water, but we were faced with the challenge of creating packer rubbers that could be effectively used as a seal in oil and gas wells under high pressure. The presence of salt in the drilling fluid complicated the creation of such a material. Our development involves introducing swelling polymers into the material, which expand very well when exposed to liquid, but don't readily integrate with rubber. We needed to find modifying additives to overcome this incompatibility," explained Viktor Kablov.

The water-swelling elastomer composition is based on nitrile butadiene rubber and includes sulfur as a vulcanizing agent, Altax as a mercaptan vulcanization accelerator, and zinc oxide and stearin as vulcanization activators. Carbon black is used as a filler, along with sodium carboxymethyl cellulose as a water-swelling agent and a polymer modifying material that improves component compatibility.

"The key part of our development was selecting a durable base. The matrix was based on rubber, into which we introduced particles of water-swelling polymers capable of absorbing water or aqueous solutions. The particles expand in volume, increasing the volume and contact pressure of the sealing element, which is critical for sealing. To increase the speed and uniformity of penetration of the aqueous phase into the material, fibers are added to the composition, forming capillary channels for mass transfer," explained Viktor Kablov.

When selecting components and determining their proportions, the scientists used several neural networks. One of them, Deep Seek, generated an optimal prediction for the composition of the material with the specified properties and a number of useful recommendations for improving its properties. Next, they applied a program for modeling the behavior of composite materials, previously developed as part of the project "Computer-aided materials science of multicomponent nanostructured elastomeric materials with specified properties for extreme operating conditions."

"This program—a digital assistant for elastomer developers—is part of the Center of Excellence's program, 'Technologies for Modeling and Developing New Functional Materials with Predetermined Properties,' implemented at Novosibirsk State University and supported by the NTI Foundation. Together with the Center of Excellence, we have created a distributed research and technology center equipped not only with a wide range of testing equipment available at NSU, Volgograd State Technical University, and its branch, the Volga Polytechnic Institute, but also with technological equipment enabling the production of pilot batches of materials and products. To handle complex software, we have created a powerful computing cluster that enables the use of software products with artificial intelligence modules, including remote collaboration with our colleagues in other cities," explained Viktor Kablov.

The new polymer material has successfully passed laboratory testing in various operating environments simulating drilling fluids and on model seals. Our industrial partner, Intov-Elast LLC, one of the leading manufacturers of packer devices in Russia, has expressed interest in the development. Currently, rig testing of packer devices using the rubbers developed by the scientists is underway at the partner's and its customers' testing facilities.

Material prepared by: Elena Panfilo, NSU press service

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

NSU has developed an AI service for creating audio versions of scientific books.

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Novosibirsk State University has launched a service for automatically creating audio versions of books from the digitized collection of the University's Scientific Library. The project is based on developments by the NSU Research Center for Artificial Intelligence (AI Center) and is currently undergoing testing. Following the successful completion of the pilot project, the technology is planned to be rolled out to other libraries.

At this stage, we are talking about converting books from the university press and materials posted in the electronic library into audio format, with the permission of the copyright holders—a total of about seven thousand titles.

The audio is generated by a neural network: the text is extracted from a PDF, pre-processed, and then an audio version is created. "In the future, we plan to convert all books available in the NSU e-library into audio format. Currently, this number is around 7,000," said Evgeny Pavlovsky, a leading researcher at the NSU Center for Artificial Intelligence and a PhD candidate in physics and mathematics. According to him, the service is not intended to completely replace traditional reading, but is being created as an alternative form of access to text.

"We don't create a voiceover that completely replicates the original. It's an additional way to work with the book. For mass use, it's important that the solution isn't resource-intensive: one book takes about half an hour of processor time, and we're talking about a 16-core processor, even without a graphics card," he explained.

The service is based on the Kappa framework, developed at the NSU AI Center. It is designed for managing datasets and artificial intelligence models, testing them, and evaluating them before implementing them in workflows. The framework allows for checking the correctness of models and reducing the risk of errors or so-called AI "hallucinations." In the new project, Kappa is used to prepare training data for voiceover and collect feedback on the quality of the results.

The first hundred books have already been read in pilot mode, and the team is now awaiting feedback from the library and users. Here's one of them. examples of the service's operationNSU emphasizes that the project is being considered a technological test. Once the technology itself and the mechanisms for interacting with the library have been refined, the service may be offered to other universities and public libraries through a partner platform or in other formats. According to the developers, in terms of computing resources, audio recording of the entire collection is possible within a month, but organizational preparation and verification of the audio recording results may take up to a year.

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NSU is developing a method for comprehensive predictive diagnostics of age-related muscle failure.

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Scientists Institute of Medicine and Medical Technologies NSU's Institute of Medical and Technical Medicine (IMMT) is developing a comprehensive diagnostic method for the neuromuscular system, which will ultimately aid in the prevention and correction of age-related muscle weakness (sarcopenia). The work is being conducted as part of the project "Comprehensive Modulation of Neuroplasticity Reserves in Sarcopenia Correction," which is part of the strategic technology project "Center for the Integration of Biomedicine and Pharmaceutics," supported by the Priority 2030 program.

The term "sarcopenia" comes from the Greek words sarcos (meat, flesh) and penia (deficiency). It refers to a progressive and systemic loss of muscle mass, strength, and function. It has now been established that it is not simply age-related muscle atrophy, a consequence of aging, but a clinically confirmed condition that can be diagnosed and treated. Sarcopenia can be caused not only by aging but also by other factors, including lifestyle, work habits, diet, and acute and chronic illnesses.

The project, implemented by NSU scientists, is based on the development and scientific substantiation of a neurocentric model for the prevention and correction of sarcopenia, based on the complex modulation of the central nervous system's neuroplasticity reserves, that is, mechanisms influencing the nervous system's ability to change structure and function in response to new experiences, learning, and changes in the environment.

NSU, with its proposed neurocentric model, is a pioneer not only in Russia but also in the post-Soviet space. Most research teams in our country and worldwide continue to develop strategies aimed directly at muscle tissue and its metabolism. The prevailing model views sarcopenia primarily as a localized muscle tissue problem, focusing on the study of mitochondrial dysfunction, cellular aging, chronic inflammation, and insulin resistance.

The dominant model recognizes the role of the nervous system as a factor in pathogenesis, such as the decline in motor neurons and the deterioration of neuromuscular transmission with age. However, placing neuroplasticity at the center of research attention and therapeutic intervention is a fundamentally new approach, which constitutes its main scientific novelty and potential advantage.

"The uniqueness of the approach proposed at NSU lies in the fact that we view sarcopenia not only as a degenerative process in muscle tissue caused by diseases, conditions, or age, but also as a result of dysfunction of the central neuromuscular control mechanisms. From this perspective, the most promising approach is to study neuroplasticity, as it underlies the restoration and maintenance of motor function. Targeted correction of nervous system plasticity, for example, through cognitive-motor training, neurostimulation, or pharmacological interventions, can become a key element in developing effective strategies to combat sarcopenia," explained Daria Podchinenova, PhD, Deputy Director for Research at the NSU Institute of Medical and Medical Technologies.

In 2025, the first stage of the project resulted in the creation and patenting of a unique, Siberian-first structured database of key clinical and paraclinical markers of sarcopenia and body composition (the ratio of various body components—fat, lean mass, muscle, water, etc.), as well as a database of neuroimaging maps (brain images obtained using various imaging methods). The uniqueness of the assembled database lies in the fundamental expansion of the diagnostic research field. The database is not limited to standard sarcopenia indicators (muscle mass, grip strength, gait speed), but was developed for the comprehensive study of sarcopenia as a systemic process. Functional diagnostics, biochemistry, and cellular immunology data are integrated within a single platform. The database is intended to form the basis for a personalized approach to the diagnosis and management of sarcopenia. In total, it contains data from nearly 3,000 patients.

Based on the collected information, NSU scientists developed a comprehensive diagnostic algorithm for assessing the neuromuscular system and key methodological recommendations for the diagnosis and application of neuroplasticity modulation methods in sarcopenia prevention and correction programs for implementation in healthcare institutions and gerontology centers.

Also, in parallel, the necessary potential elements of the diagnostic complex are being developed – key neuromarkers (BDNF, galanin, beta-amyloid, tau protein, myokines: meteorin-like protein, irisin, myostatin, FGF-21, IGF-1 metabolites, insulin), neuroimaging markers and predictors, such as the volume of gray matter in the motor areas, the thickness of the precentral gyrus, indicating a decrease in neuroplasticity and associated with the risk of development and progression of sarcopenia.

"Thanks to projects like these, NSU is developing a new strategic direction within the personalized ("7P") medicine paradigm. A longitudinal observation system has been launched at the NSU Medical Center, collecting data from the same patients over a long period of time, and unique databases have been created. Students, residents, and young scientists from the NSU Institute of Medicine and Medical Technologies are participating in this work," said Maria Matveeva, MD, Associate Professor of the Department of Clinical Health Modeling and Personalized Medicine at the NSU Institute of Medical and Medical Technologies and the project manager.

The overall project is designed to last five years, and by 2030, the plan is to develop a method for comprehensive diagnostic assessment of the neuromuscular system, including MRI protocols, biomarker panels, and approaches to modulating neuroplasticity in comprehensive sarcopenia prevention and correction programs. These are planned to be tested at partner clinical centers—with whom collaboration agreements have already been concluded—in order to identify specific, most effective methods for modulating neuroplasticity.

Active longevity is a priority for Russian healthcare, so identifying and promptly addressing the factors that can limit age-related physical activity, reduce life expectancy, and impair quality of life is crucial. Furthermore, the guidelines being developed for diagnosing and modulating neuroplasticity will help reduce rehabilitation costs through early prevention of sarcopenia and associated diseases.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

How the Feminine Ideal Evolved in Literature: From Helen of Troy to the Present Day

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

The topic of one of the lectures at the "Darwin Week" popular science marathon at NSU, held for the first time on the new campus, was "The Evolution of the Ideal: From Helen of Troy to the Present Day." Sergey Vasiliev, a lecturer at the Center for Continuous Education at the NSU Institute of Humanities, served as the expert.

Despite the sunny weather and Saturday, the auditorium was packed—interest in the humanities portion of the science marathon was no less than in the natural sciences.

At the beginning of the lecture, Sergei Vasiliev outlined the framework of the conversation:

We'll discuss only the ideal in the Western literary tradition. The world is diverse, and Eastern cultures had different ideas, but today we'll focus specifically on the Western part. We'll discuss how the ideal is embodied in literary texts, without touching on painting and sculpture. Even with these limitations, the topic remains vast—this will be just a brief introduction.

The starting point was the ancient world and the image of Helen of Troy in Homer's Iliad. However, as the lecturer emphasized, for the ancient Greeks, Helen was not the moral ideal of a woman:

"She was considered aesthetically beautiful, but the ideal woman for the ancient Greeks included fidelity to her husband, motherhood, and devotion. In this sense, Penelope from the Odyssey was closer to the ideal, waiting ten years for her husband's return, deceiving suitors and unraveling at night the shroud woven during the day."

The lecturer moved from antiquity to the Middle Ages and the phenomenon of courtly love, when the ideal of female beauty became unattainable.

"An ideal is something we strive for but never fully achieve. Courtly love is admiration for an unattainable woman, elevating her to the status of almost unattainable perfection," explained Sergei Vasiliev.

A separate section of the lecture was devoted to the 19th century—a time when ideals became increasingly contradictory. Sergei Vasiliev contrasted the culture of decadence with the Victorian tradition:

Decadence is a sense of the decline of an era. It is characterized by an admiration for what was previously considered aesthetically unappealing. The ugly begins to become beautiful precisely because it is ugly. Let's recall Baudelaire and his poem "Carrion," where a decomposing corpse is described as an aesthetic object.

Victorian culture, on the contrary, sought to aestheticize reality, despite the social contradictions of the industrial era.

"Victorians see a complex, often cruel world, but they strive to find and celebrate beauty—even in tragic images. It's an attempt to maintain an ideal in a rapidly changing world," noted Sergei Vasiliev.

The First World War, according to the lecturer, became a cultural turning point:

— From a cultural point of view, the 20th century begins in 1914. The tragedy of war destroys the idea of a single, utopian ideal.

In the era of modernism, it is no longer possible to talk about one ideal:

Modernists have different practices, different aesthetics, different notions of beauty. Symbolists extol the unattainable image of "eternal femininity." Acmeists appeal to concrete, earthly people. Futurists are more interested in machines and the urban world than in the traditional feminine image.

In Soviet literature of the 1930s, the canon of socialist realism was formed – with a clearly defined image of the working woman, the communist, the mother.

After World War II, the era of postmodernism began, where, according to the lecturer, the very conversation about the ideal became ironic:

Postmodernism asserts that everything has already happened. We're dealing with a cultural game, with quotations and parodies. You can turn to a classic image—but only to reinterpret it or even ridicule it.

At the end of his lecture, Sergei Vasiliev noted that the situation with the modern feminine ideal remains open:

It seems the era of postmodernism is drawing to a close, but it still can't seem to end. Is there a single ideal today? Probably not. The ideal was "finished" in Western culture at the beginning of the 20th century, and since then we've lived in a world of multiple versions and interpretations.

This Darwin Week lecture served not only as a historical and literary review, but also as a reason to reflect on how cultural eras shape our understanding of beauty and why the ideal always says more about the time than about the object of admiration itself.

After the presentation, the audience asked the lecturer questions; the most interesting ones were awarded gifts from the university.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

Unexpected turns of technological progress

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

Vladimir Surdin's lecture, delivered as part of the "Darwin Week" popular science marathon at Novosibirsk State University, generated enormous interest—500 people gathered in the conference hall, and another 500 watched the broadcast in three auditoriums on the new NSU campus.

Vladimir Surdin is a renowned astronomer, author of over 100 scientific papers and dozens of textbooks. He is also an educator, the author of numerous popular science books and videos, and a laureate of the Russian Academy of Sciences Prize, the Belyaev Prize, and numerous other awards. The vast majority of his works are related to space in one way or another, and his "Darwinian lecture" on the evolution of the space industry, which until recently seemed stagnant, is now experiencing a return to active development.

Vladimir Surdin began his speech by noting that predicting the future of technological progress is extremely difficult. Looking at the luxury limousines of the 1960s and 1970s, who could have imagined that half a century later, "small" cars would become so popular. They could fit, besides the driver, only a briefcase or backpack. But they're also very fuel-efficient and easier to park on city streets.

Or take air travel: in the 1930s, it seemed like airships were the future, but they were quickly and confidently replaced by airplanes, and today we are witnessing the dawn of unmanned drones, which are completely different from the aviation we are used to.

"Such unexpected turns are common in the evolution of technology. And cosmonautics is no exception," Vladimir Surdin emphasized.

Until the late 1980s, it seemed that orbital stations would be the main route for the development of near-Earth spaceflight. In 1987, Spaceflight magazine published a forecast that by the year 2000, there would be approximately fifty people in orbit at any one time, the vast majority of whom would be Soviet cosmonauts. In reality, in the first decades of this century, this number was typically ten times smaller, and this was due not to the collapse of the USSR, but to the computerization and automation of many work processes, which eliminated the need for large crews.

In the 1980s, the second mainstay of technology was considered to be reusable shuttles, capable of sending both the crews of those stations and tons of payload into space. But even here, advances in electronics played a role: modern satellites are much lighter, with popular CubeSats weighing grams rather than tons, and powerful rockets aren't required to launch them into orbit. As a result, the American shuttle program was shelved for many years, and the Soviet Buran fell victim to the economic crisis, having completed only one test flight.

Looking a little beyond low-Earth orbit, it's clear that the most popular destination is the Moon, the closest large space object to us. Both humans and manned spacecraft have landed there numerous times.

"You could say we know the visible hemisphere of the Moon quite extensively. But not deeply, literally—we haven't drilled deeper than one and a half meters," Vladimir Surdin emphasized.

For decades, humanity had only photographed the far side of the Moon, and even then only sparingly. But the Chinese were the first to land there. And the result was quite unexpected. In 2013, China succeeded in sending its rover to the visible side for the first time, traveling just 100 meters. Just six years later, in 2019, their Chang'e-4 spacecraft successfully landed on the far side of our satellite, where a new version of the rover operated for several years. According to Vladimir Surdin, Chinese space exploration has been experiencing a boom in recent years, with the industry's pace of development comparable to that of the Soviet Union during its heyday.

He also recalled that during the American Apollo program, when several expeditions landed on the surface of the Moon, they brought back to Earth about 400 kg of lunar soil.

"And then a far-sighted decision was made: half the samples were distributed to leading scientific laboratories around the world for study. The other half were frozen. Half a century passed, research methods had advanced significantly, new opportunities had opened up, but there were no new expeditions to collect lunar soil. Then those reserves came in handy: they were thawed, handed over to scientists again, and in recent years a whole wave of interesting discoveries related to lunar soil has emerged," the scientist noted.

Humanity is currently setting itself a new goal: to create a manned base on the lunar surface. A number of countries, including our own, have similar projects in place. But achieving this requires solving a whole host of complex problems. These problems extend beyond the construction and operation of the base itself. Astronauts and cargo must first be delivered there, and the era of large rockets, as mentioned earlier, is over. Today, they must be redesigned.

Moreover, while engineers in a number of countries have already more or less completed the construction of heavy launch vehicles, the descent modules that will deliver the crew from lunar orbit to the surface and become their home for several weeks are still only in the design stage for all countries participating in the new "moon race."

At the same time, efforts are being focused on the development and construction of a lunar orbital base. It is seen not only as a staging post for colonizing the moon itself, but also as a convenient testing ground for various situations and nuances that may arise on longer expeditions, primarily to Mars. Until 2022, Russia also participated in this project, responsible for the station's docking ports, through which supply ships would be attached. After cooperation with us was severed, this task was assigned to engineers from the UAE. Time will tell how successfully the Arab engineering school will cope with it.

As mentioned earlier, Roscosmos also has its own separate project for a manned expedition to the Moon. However, as Surdin noted, while we've managed to maintain a strong position in orbital spaceflight, the gap is widening in more distant areas.

"For a long time, we haven't implemented projects involving launching spacecraft beyond Earth orbit. Many specialists who previously participated in such projects have retired, and they've been replaced by young people who simply don't have the experience yet. We saw the consequences of this with the Luna-25 accident. Essentially, we must now relearn from our mistakes and rebuild our competencies, and this will inevitably take time," the scientist recalled.

This is due to the constant postponements of the planned stages of Roscosmos's "lunar program."

Under these circumstances, he believes, perhaps we shouldn't engage in this race so directly. Instead of spreading resources thin on a global manned base project, it's better to focus on a narrow area and achieve results that will secure our space program a worthy position in the exploration and development of our planet's natural satellite.

Another option is to explore lunar caves. There's a popular view among experts that it's better to locate a lunar base not on the surface, but underground. This is primarily due to the high levels of cosmic radiation (on the lunar surface, it's approximately 200-300 times higher than on Earth). Moreover, such caves have recently been discovered, but only the entrances are known; no one has explored them. This is an interesting challenge for Russian cosmonautics, which has extensive experience in creating unmanned exploration vehicles, primarily lunar rovers.

Our space industry has already proven that it shouldn't be discounted even in the most challenging circumstances. As Surdin recalled, who would have believed in the 1950s that the Soviet Union, just beginning to recover from the brutal devastation of war, would become the first country to launch first a satellite and then a man into space? This, too, was a striking example of the unexpected turns in the evolution of technological progress.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

NSU scientists have developed a new multi-step approach to compensating for nonlinear distortions in fiber-optic communication lines.

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

A new multi-step approach for compensating for nonlinear distortions in fiber-optic communication lines based on digital backpropagation, which utilizes a sophisticated model of nonlinear effects using perturbation theory, has been developed by scientists from Novosibirsk State University. This approach was developed as part of a comprehensive research project aimed at overcoming the influence of nonlinear physical effects and random noise on optical sensors and fiber-optic communication lines. In this large-scale project, NSU scientists, in collaboration with colleagues from Ulyanovsk State University, combined photonics and machine learning, enabling the development of new methods for analyzing, optimizing, and controlling nonlinear processes, leveraging both the high-speed signal processing in optical systems and the ability of machine learning to discover and exploit hidden information. The project "Machine Learning for Applied Problems of Nonlinear Photonics," led by former NSU Rector, RAS Academician, and Doctor of Physical and Mathematical Sciences Mikhail Fedoruk, received grant support from the Russian Science Foundation last year after winning an interdisciplinary competition in the category "Conducting Fundamental Scientific Research and Exploratory Scientific Research on the Instructions (Directives) of the President of the Russian Federation."

Union of Mathematicians and Physicists

Particular attention is currently being paid to the development of "smart" lasers that can adapt to external conditions and dynamically control their parameters in real time. Such systems ensure more precise and reliable operation of optical fiber systems in challenging environments, opening up new possibilities for their application in telecommunications, industrial automation, sensors, medicine, and security systems. The project is relevant in light of the current challenges facing the telecommunications and laser industries. Optical communication lines form the foundation of digital infrastructure and support all areas related to digital technologies. Increasing volumes of transmitted data require the development of new technologies, which necessitates considering nonlinear and noise effects. The operation of many fiber laser systems is also associated with nonlinear effects, such as the Kerr effect, Raman scattering, and Brillouin scattering. On the one hand, these effects can be useful, for example, for mode locking in pulsed lasers, but on the other, they can lead to instability and destabilize the system. Machine learning methods, particularly new neural network algorithms of various architectures, can play a key role in managing this nonlinearity, said Mikhail Fedoruk.

The project aims to address the scientific challenge associated with the complexity of analyzing, modeling, and managing nonlinear and random processes in photonics, a key area of modern science and included in the list of cross-cutting technologies of the NTI Platform. Using machine learning methods to study nonlinear effects and their manifestations in the presence of random processes opens up new opportunities for improving the performance of photonic systems and creating new solutions.

"The 'Machine Learning for Applied Problems of Nonlinear Photonics' project is interdisciplinary. It was launched at the initiative of NSU and brought together the efforts of two research groups—applied mathematicians and laser physicists. The first group is affiliated with NSU, and the project's main contributors—Oleg Sidelnikov, Anastasia Bednyakova, and Alexey Redyuk—graduated from NSU's Faculty of Mathematics and Mechanics and defended their theses and then their PhD dissertations under my supervision. The second group represents Ulyanovsk State University. It is led by Andrey Fotiadi, a recognized expert in nonlinear fiber optics, fiber lasers, and sensors and a PhD candidate in physics and mathematics. Both groups are focused on modeling in two main areas. The first focuses on machine learning methods in lasers and nonlinear photonic systems, while the second focuses on machine learning methods and nonlinear technologies in optical communication lines," said Mikhail Fedoruk.

Interdisciplinary approach

Universal methods for analyzing nonlinear systems still lack a single approach, but solving many applied problems in this field requires new approaches, making the importance of this research increasingly relevant. Improving the performance of existing systems and developing new engineering concepts requires understanding and correctly interpreting nonlinear effects and their interaction with random noise. Using machine learning methods to study nonlinear effects and their manifestations in the presence of random processes opens up new opportunities for improving the performance of photonic systems and creating new solutions.

"Machine learning algorithms can adapt to changing conditions and efficiently process large volumes of data, extracting hidden dependencies and enabling real-time system performance improvement. This interdisciplinary approach, combining photonics and machine learning, enables the development of new methods for analyzing, optimizing, and controlling nonlinear processes, leveraging both the high-speed signal processing in optical systems and the ability of machine learning to discover and exploit hidden information," explained Mykhailo Fedoruk.

Smart photonics

The project team combines the expertise of two research groups, which is essential for the successful implementation of the project, namely, the development of machine learning algorithms and the creation of smart photonic devices using them. As part of this collaboration, a group of laser physicists with expertise in developing modern nonlinear and microwave photonic devices will develop functional devices and control tools. These devices will serve as testbeds for new original mathematical algorithms being developed by a group of NSU applied mathematicians with experience in modeling physical systems and applying machine learning methods to their optimization and control. The synergy between these two areas will enable not only the development of new mathematical apparatus for machine learning but also the creation of new photonic devices for real-world applications, offering fundamentally new consumer characteristics over existing analogs.

The project partner's contribution will consist of conducting preliminary laser testing experiments with the aim of transferring the results necessary for the development and testing of machine learning algorithms to the NSU team, as well as developing and implementing additional electronic control systems for the lasers' operating mode, which are necessary for the joint operation of photonic devices with trained algorithms.

Machine learning

As part of the project, the NSU team, led by Mikhail Fedoruk, will conduct a wide range of theoretical and numerical studies, which will be divided into two main research areas: "Machine Learning Methods in Lasers and Nonlinear Photonic Systems" and "Machine Learning Methods and Nonlinear Technologies in Optical Communication Lines."

The first area of focus will involve the development of neural network algorithms based on recurrent neural network architectures, transformer architectures, and reinforcement learning algorithms for implementing optoelectronic feedback in fiber laser systems. To stabilize radiation generation and control its key frequency-temporal and spectral characteristics, optoelectronic feedback based on machine learning algorithms will be implemented in fiber laser configurations. Further analysis of laser radiation using the nonlinear Fourier transform (NFT) is planned. The second area of focus involves the development of a compensation scheme for dispersion and nonlinear effects based on deep convolutional neural networks. This will be followed by the integration of approaches based on digital backpropagation and signal processing algorithms based on perturbation theory to compensate for nonlinear distortions.

Neural network algorithms

During the first year of the project, the scientists achieved important results in both of its main areas. In developing machine learning methods for lasers and nonlinear photonic systems, they conducted a range of theoretical, numerical, and experimental studies aimed at creating neural network control algorithms for a single-frequency fiber laser with an external ring resonator. They developed and implemented models based on long-short-term memory (LSTM) and transformer neural network architectures, which allow predicting the control voltage of a thermo-optical phase shifter based on a photodetector signal, simulating the behavior of a classic PID controller.

"We continued exploring new applications of NFT for analyzing optical fields in dissipative media. We considered the House-Ginzburg-Landau equation (HGLE) as an important example used for modeling laser resonators. As a result, we investigated the dependence of the generation mode type on the HGLE parameters—saturation energy and saturation power. We identified the ranges of parameter values in which HGLE solitons are close to those of the nonlinear Schrödinger equation, and demonstrated that in this case, the dynamics of a field obeying the HGLE can be described with high accuracy using only a discrete spectrum. For single-pulse modes, we described in detail the stages of generating a single-soliton solution from noise, and demonstrated the relationship between these stages and qualitative changes in the discrete spectrum parameters," explained Mikhail Fedoruk.

Neural network

Equally effective were the studies conducted within the "Machine Learning Methods and Nonlinear Technologies in Optical Communication Lines" program. A deep, complex-valued convolutional neural network was developed for modeling the propagation of optical signals in a wavelength-division multiplexed fiber communication line.

"The architecture of this network simulates the method of splitting into physical processes and is based on coupled nonlinear Schrödinger equations. We also studied the impact of key neural network model parameters on modeling accuracy, including the width of convolutional and nonlinear filters, as well as the number of layers per fiber span. We developed and tested an effective approach to network training based on pre-optimization of convolutional filters to compensate for chromatic dispersion. The obtained results demonstrate high accuracy in modeling signal propagation over long communication lines and confirm the applicability of the proposed architecture to the analysis and optimization of fiber-optic systems with wavelength division multiplexing," explained Mikhail Fedoruk.

Prospects

The scientist emphasized that the practical application of the obtained results will improve the efficiency of fiber-optic communication lines, which forms the basis for the development of high-speed data transmission infrastructure, which is strategically important for the connectivity of the Russian Federation. The continuous implementation of new telecommunications and laser technologies, including the use of machine learning methods proposed in the project, facilitates the development of strategic areas such as the transition to advanced digital and intelligent manufacturing technologies, the creation of systems for processing large volumes of data, machine learning, and artificial intelligence. The project's results can find practical application in several strategically important sectors of the real economy. Solving the problem of transmitting growing volumes of information directly impacts the development of new government digital services, the advancement of science and new technologies, as well as many other areas of industry, business, and everyday life.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

The Lost World of the Jehol Biota: Feathered Dinosaurs, Toothed Birds, and Four-Winged Microraptors

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

Associate Professor of the Department of Historical Geology and Paleontology Faculty of Geology and Geophysics Igor Kosenko, a candidate of geological and mineralogical sciences from Novosibirsk State University, spoke about the formation of modern ecosystems and the unique biota of Jehol, which forever changed scientists' understanding of dinosaurs and other prehistoric creatures that inhabited our planet 120 million years ago. His lecture, held as part of Darwin Week—a popular science marathon traditionally held by NSU in February—explored the world at the end of the Mesozoic Era, the Cretaceous Period. This year, the event was held for the first time on the new NSU campus.

The origin of life on the planet

Terrestrial ecosystems of the Cretaceous were very different from those of today. However, the origins of modern terrestrial ecosystems date back to the first half of the Cretaceous. Modern terrestrial ecosystems support a wide variety of plants, from mosses and lichens to giant sequoias and angiosperms. Currently, angiosperms predominate, although gymnosperms also thrive. Among animals, higher vertebrates, namely mammals and birds, are of great importance. And among freshwater vertebrates, bony fishes predominate. But this was not always the case.

Our planet formed approximately 4.5 billion years ago. Life also emerged on it relatively quickly, by the standards of its history. The first ecosystems were composed of cyanobacteria, which appeared approximately 3.8 billion years ago. Today, their remains are stromatolites—the fossilized remains of cyanobacterial mats. These primitive organisms, whose cells lacked a nucleus, possessed the ability to photosynthesize, releasing oxygen. Hundreds of millions of years later, the accumulation of oxygen in the Earth's atmosphere eventually killed off all organisms adapted to anoxic conditions, but it gave rise to new inhabitants of the planet, who formed ecosystems from the first multicellular marine organisms, known as the "Ediacaran biota." This occurred at the end of the Proterozoic Era, approximately 630 million years ago. These creatures did not yet have a mineral skeleton, so only rare traces of them have survived to this day in a handful of locations with special conditions. They are found on every continent. Such sites have also been discovered in our country—in the White Sea and in Eastern Siberia. All the fossil remains that scientists have discovered are quite diverse, representing the imprints of soft-bodied organisms. It is believed that most of this biota left no descendants, although some organisms are considered the ancestors of certain modern groups of organisms, such as arthropods.

Then, approximately 538 million years ago, the Cambrian explosion occurred, when the soft-bodied Ediacaran biota was suddenly replaced by a multitude of organisms with mineral skeletons: mollusks, echinoderms, brachiopods, and chordates. The world changed. Suddenly, the fossil record became filled with numerous fossils with mineral skeletons. Admittedly, the first organisms were quite primitive. For example, the earliest chordates looked like lancelets and led a bottom-dwelling lifestyle. They are our most distant Cambrian ancestors. Then, ecosystems gradually began to become more complex.

During the Ordovician period—460-443 million years ago—corals, an important group of organisms, emerged. They became the primary reef builders, leading to a rapid expansion of biodiversity. All these events in the evolution of the biosphere were linked to global geological events, including the constant drift of continents, the opening and closing of oceans, and fluctuations in sea levels. The evolution of the biota was largely a response to the geological evolution of our planet.

Land development

Then, in the Silurian—443-420 million years ago—a crucial event for terrestrial ecosystems occurred: the appearance of the first land plants, the rhyniophytes. They presumably evolved from algae and inhabited coastal areas of bodies of water. Although they did not yet venture far from the coast, they were nonetheless the first plants to colonize land.

Many important events related to the colonization of land by animals occurred during the Devonian period. Approximately 400 million years ago, animals related to arachnids began to emerge onto land, and the first amphibians, descended from lobe-finned fishes, appeared.

"The Carboniferous period, which lasted from approximately 359 to 299 million years ago, was critical for coal accumulation. Terrestrial ecosystems began to occupy all of Earth's surface, and numerous forests of giant ferns, club mosses, and horsetails emerged, reaching gigantic sizes—growing as large as modern trees. The emergence of numerous plants increased atmospheric oxygen levels, leading to the growth of arthropods, which eventually reached gigantic proportions," said Igor Kosenko.

The lowlands were filled with forests of giant tree-like horsetails, ferns, and club mosses. Two-meter-long centipedes called Arthropleura inhabited the land, and giant dragonflies called Meganeura soared through the air. Overall, the Carboniferous period was characterized by an increase in the diversity of terrestrial vertebrates. The first representatives of the group of higher vertebrates, the synapsids, appeared, becoming the ancestors of mammals. The appearance of diopsids led to the emergence of reptiles, and ultimately, the formation of birds. Initially, these animals were not very large.

New conditions

During the Permian period of the Paleozoic Era—from 299 to 252 million years ago—continental drift resulted in the formation of the single supercontinent Pangea. As a result, a humid climate gave way to an arid one, and organisms adapted to the new conditions began to thrive. Gymnosperms dominated the plant kingdom, while amphibians were supplanted by reptiles, which became highly diversified. Remarkably, they developed an important advanced trait: the egg, protected from the external environment by a shell, enabling the embryo to survive in arid conditions. Furthermore, reptiles abandoned intermediate stages of development (such as tadpoles in frogs), enabling them to more successfully colonize arid landscapes. The group of higher vertebrates—the synapsids—distinguished themselves from another group, the diapsids, by their skull structure. Incidentally, humans are also synapsids.

"The Paleozoic era ended with the Permian-Triassic extinction, the largest in Earth's history. Compared to that, the giant meteorite impact that occurred 66 million years ago, which wiped out most of the dinosaurs, was relatively minor. The Permian-Triassic extinction was associated with catastrophic volcanic eruptions in what is now Siberia approximately 252 million years ago. The scale of this event was such that 57% of organism families, 83% of genera, 81% of marine species, and 70% of terrestrial species became extinct. Modern scientists estimate its duration at approximately 60,000 years," explained Igor Kosenko.

After the disaster

Then began a new frontier in the evolution of our planet's biosphere—the Mesozoic Era. In its first period, the Triassic, the Earth's ecosystems gradually began to recover from a catastrophic extinction. New marine ecosystems formed, where the dominant groups of the Paleozoic (such as brachiopods) were replaced by new groups of organisms—bivalves, which were more sophisticated and better adapted to changing environmental conditions. Reptiles dominated among terrestrial vertebrates, while relatively advanced groups appeared among conifers, such as pines, araucarias, and cypresses. Ferns continued to evolve. While the Permian was the age of the mammal-like synapsids, the Triassic saw an order of magnitude increase in the diversity of diapsids. At the end of the Triassic, the first dinosaurs appeared. Moreover, two main groups—the saurischians and the ornithischians—appeared almost simultaneously. At first they were very modest in size, but later they developed into real giants.

The Triassic period also saw the first successful attempt by reptiles to master the air, and flying animals emerged. The first representatives of these animals were slightly larger than a modern house mouse. They continued their development in the Jurassic, followed by the Cretaceous, which scientists gained a detailed understanding of thanks to the discovery of the unique Jehol Fauna.

The first birds

The Jehol Biota is a complex of fossil organisms dating back to the Cretaceous period, 133–120 million years old. They are preserved in continental deposits in northeastern China. It was here that scientists discovered unique finds—feathered dinosaurs, birds, mammals, the first flowering plants, and other exquisitely preserved fossils.

"The study of the Jehol biota sheds light on the origins of modern ecosystems. Detailed paleontological research has allowed us not only to reconstruct what the East Asian world looked like 133-120 million years ago. Representatives of the Jehol biota were first discovered in Liaoning Province. We now know that many dinosaurs were covered in feathers. We know what these dinosaurs ate. And thanks to modern paleontological methods, we've even been able to reconstruct dinosaur coloration. This uniquely preserved fossil site has allowed scientists to completely revise their understanding of Mesozoic terrestrial ecosystems. We have a better understanding of the diversity of the Early Cretaceous world," explained Igor Kosenko.

The first discovery was made in the mid-1990s. The footprints of a small, bipedal predatory dinosaur, Sinosauropteryx, were discovered. Feathers can be discerned along the contours of this creature's tail. It was this footprint, the first feathered dinosaur known to science, that sparked researchers' interest in the Jehol Biota. Numerous similar discoveries were subsequently made. The same deposits later yielded footprints of various Cretaceous birds, which perfectly preserved not only their plumage and skeleton, but also their stomach contents. Now paleontologists could not only determine what the animal looked like but also what (or what) it ate.

The remains of ancient mammals, which had already occupied various ecological niches by that time, also deserved special attention. These included arboreal and marine animals, as well as predators. Thanks to these finds, scientists learned that these predators preyed on dinosaurs.

Typical representatives of the fauna

Igor Kosenko introduced the audience to key representatives of the Jehol Biota. The most striking of these was Sinosauropteryx, a bipedal dinosaur with short upper limbs. Using modern microscopic and chemical analysis techniques, its coloration was reconstructed. It turned out that Sinosauropteryx's body was reddish, with white stripes on its tail. Its abdomen was noticeably lighter than its back, and its head was two-toned, reminiscent of the mask of a modern raccoon. This feathered dinosaur fed on small vertebrates, insects, and reptiles—bones of the latter were found in the stomach cavities of Sinosauropteryx.

"The name 'Jehol Fauna' was first proposed by the American paleontologist Amadeus Grabau back in 1923. The term 'Jehol Biota' was codified in 1962 by the Chinese scientist J.-W. Gu. Scientists noted that three organisms are very common in Mesozoic rocks in northeastern China: the bivalve freshwater crustacean conchostracans, the larvae of dipteran mayflies, and the bony fish Lycoptera. The state of preservation of these specimens astounds scientists—every scale on the fish, every leg on the larvae, and even the eggs inside the conchostracans, which died 125 million years ago, can be seen in exquisite detail," explained Igor Kosenko.

Another typical representative of the Jehol biota is Psittacosaurus. Interestingly, the skeletal remains of these primitive horned dinosaurs are found in abundance in today's Kuzbass region. These animals were widespread in Siberia and East Asia—in Mongolia, China, and even Thailand.

Two quite different groups of animals inhabited the air: birds and feathered dinosaurs. Among the birds of the Jehol Biota, Confuciusornis, which lived in northeastern China, is notable. It was named after the Chinese philosopher Confucius. Confuciusornis differs from primitive birds in that it lacks teeth in its beak. Scientists are now discovering beautifully preserved birds. A pair of Confuciusornis was discovered, one with a luxuriously long tail, the other without such a tail ornament. Researchers have concluded that these birds, like modern birds, exhibited sexual dimorphism, and that millions of years ago, a male and female were frozen in stone.

"For their time, Confuciusornis were quite advanced birds, as, for example, Archaeopteryx had a toothed beak and a long tail consisting of numerous vertebrae. Pterosaurs were another group of animals that mastered the air. They varied greatly in size and diet. Interestingly, some pterosaur remains have also been found to have hair-like coverings, suggesting that pterosaurs weren't scaly, naked reptiles like snakes, lizards, or turtles. Like mammals and birds, they also had hair, which likely served to maintain body temperature," noted Igor Kosenko.

A very common animal in the Jehol Biota was the small, feathered flying dinosaur Microraptor. Remarkably, it had four wings, not just two! This was another attempt by vertebrates to colonize the air. Admittedly, it wasn't entirely successful—Microraptor survived for several million years before becoming extinct. Its appearance has also been reconstructed from perfectly preserved imprints, and melanosomes in fossilized feathers have revealed that its plumage was black.

Some discoveries have shed light on the behavior of ancient animals—impressions in stone have provided scientists with information that was impossible to extract from even the best-preserved skeletal remains, much less individual bones. They discovered clusters of Psittacosaurus juveniles, including one adult, and concluded that these dinosaurs were herd animals, with "nannies" watching over their young. The fact that ancient mammals hunted dinosaurs was also established by paleo-discoveries from the Jehol Biota. One such hunter was Repenomamus, and its prey were the same Psittacosaurus, which, incidentally, was herbivorous. How did the scientists reach this conclusion? They found the skeletal remains of Psittacosaurus juveniles in the stomach cavities of this predator. But there was another unique find—the skeletons of a Repenomamus and a Psittacosaurus, locked in a deadly fight that proved fatal for both the predator and its prey, said Igor Kosenko.

General interest

As the scientist mentioned, Chinese people show a keen interest in paleontology. This is common among both scientists and laypeople. As soon as an interesting discovery is made, massive research begins. Years of research are conducted, and large-scale excavations begin. Paleontological discoveries are popularized, sensationalized, and reported on in the press and news feeds. Every significant discovery becomes a sensation.

"Excavations are conducted over large areas, so the number of finds increases. Enormous museums are being built at excavation sites, which are highly sought after by both local residents and numerous tourists. Despite the admission fee, the number of visitors is high. They are interesting for both children and adults. A striking example is the museum in Chaoyang (Liaoning Province), where some of the first organisms of the Jehol biota were discovered. Surrounding the museum are sculptures of key vertebrates of this biota—dinosaurs and other ancient animals. Visitors have the opportunity to tour the excavations and see the rock layers in which the paleontological finds were made, as well as the finds themselves, which are displayed under glass," added Igor Kosenko.

These places are so rich in paleontological finds that imprints of prehistoric fish and various invertebrates are abundantly displayed in numerous souvenir shops. Chinese residents eagerly buy and collect them. Tourists also rarely leave empty-handed, as such souvenirs are inexpensive.

Promising Transbaikalia

Igor Kosenko described the joint work of scientists from the Institute of Petroleum Geology and Geophysics (IPGG) SB RAS with Chinese paleontologists in both China and Transbaikal, Russia. It turns out that these areas share a similar geological history during the Cretaceous, which explains the similar biota. Studying excavation sites in northeastern China and comparing them with those in Transbaikal, the scientists noticed clear similarities.

For several years, researchers from the Mesozoic and Cenozoic Paleontology and Stratigraphy Laboratory at the IPGG SB RAS have been studying the continental Mesozoic of Transbaikalia and participating in work at the Turga section (also known as the Middendorf outcrop). During the Cretaceous, freshwater lakes were present here; today, much of the outcrop consists of grassy slopes. Nevertheless, many interesting finds have been made here, including the remains of fish, conchostracans, and insects of the same species that make up the Jehol biota. In terms of preservation, these are comparable to specimens discovered in northeastern China: the fish have all their scales intact, and the crustaceans have microscopic eggs.

Together with our Chinese colleagues, we studied this section and obtained zircon grains of volcanic origin. This means that their age is the same as the age of the layer in which they were discovered. So, we were fortunate to be able to date our paleontological finds. Dating using the radioactive decay method showed that they are 124 million years old. This means that the Transbaikal and Chinese sites of prehistoric flora and fauna are contemporaneous. This means that Transbaikal, along with northeastern China, was the center of origin of the Jihol biota. Therefore, it is quite possible that it could be a treasure trove of feathered dinosaurs. In this regard, we continue to study the Mesozoic continental sections of Transbaikal, attempting to make new discoveries, reconstruct the habitats of fossil animals, and compare them with classic sites. We can already say that in some sections of Transbaikalia, the diversity of fish exceeds what we observe in classical sections of China. In 2024, during a joint expedition to Transbaikalia, our colleague from China discovered a chain of fossilized dinosaur footprints. This means that we still have many interesting discoveries ahead, shedding light on our planet's distant past, concluded Igor Kosenko.

Material prepared by: Elena Panfilo, NSU press service

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