Category: Success-Stories

Challenge

Modern factories and hundreds of kilometres of pipelines that cross our landscape are literally packed with sensors. These continuously generate thousands of data points about pressure, temperature, and vibrations. The problem is that the volume and complexity of this information are so enormous that ordinary computers cannot process it efficiently. As a result, important warning signals of an impending failure often go unnoticed until it is too late.

Solution

A team of experts from the Faculty of Natural Sciences and Informatics at Constantine the Philosopher University in Nitra set out to solve this problem using artificial intelligence (AI). Their goal was to develop intelligent models that function like an experienced inspection technician—except they can monitor thousands of parameters simultaneously and in real time.

To train this “digital brain,” they needed enormous computing power. They found it in the supercomputer LUMI, one of the most powerful systems in the world. On this digital giant, they tested thousands of different scenarios and combinations to find the most accurate way to detect even the smallest leak in a pipeline.

Impact

The use of a supercomputer produced results that would be impossible to achieve on a standard office PC:

• Incredible Speed: What would take a conventional computer months or even years was completed by the supercomputer in just a few weeks thanks to its “raw power.”
• Accuracy Above All: Scientists are aiming for more than 92% accuracy in detecting failures directly in the field, seeking to outperform even traditional physics-based calculations.
• A Safety Net for Nature: Early detection of oil or other substance leaks means that technicians can reach the incident before it has time to contaminate the soil or water.
• A Slovak Solution for Europe: The developed methods are already helping private companies and other researchers in Central Europe modernize their operations.

Looking Ahead

This success is not the end of the journey. The Nitra-based team plans to further refine their models and deploy them in real-world operations as intelligent applications. In the future, these tools will monitor not only industrial pipelines but also production efficiency—making industry greener, safer, and more competitive.

The greatest benefit for the researchers was the immense power of the system, which meant they encountered no technical limits. Combined with fast technical support, this allowed them to fully focus on what truly matters—making industry smarter.

Challenge

When millions of people play mobile games, a data chaos emerges. Most players simply “wander” through the game world, while only a small percentage do something important—such as purchasing an upgrade or angrily quitting the game. For ordinary computers, finding these key moments in a sea of routine activity is an almost unsolvable problem, because important events make up only a tiny fraction of the massive dataset. It is like trying to find one specific face in a blurred crowd at a stadium without a proper pair of binoculars.

Solution

The team from Nitra enlisted the help of the supercomputer Leonardo. It is equipped with thousands of graphics cards capable of “thinking” many times faster than a regular laptop. The researchers worked with a large organic dataset of about 9 GB and used advanced technologies that function as simulators of behavior.

Thanks to this computing power, gaming experts were able to incorporate feedback from specialists almost immediately. Instead of researchers spending weeks struggling with analysis and searching for the right groups of players, the supercomputer delivered precise answers within just a few days.

Impact

The use of supercomputing infrastructure has produced results that will benefit not only players, but the entire digital industry:

• An End to Guesswork: Researchers developed a precise methodological approach to dealing with extremely imbalanced data, which can also be applied to detecting fraud in banking or in medicine.
• Games That Understand You: A prototype model was developed that can predict a player’s needs already in the early stages of the game, allowing the experience to be tailored to each individual.
• Game-Changing Speed: What would previously have taken weeks was completed by the Slovak team in just a few days thanks to HPC.
• A Universal Guide: Although the research was conducted on a specific game, the developed approach is a “hack” that any programmer in the world can use for any gaming platform.

Looking Ahead

The team from the Constantine the Philosopher University in Nitra, specifically the Faculty of Natural Sciences, now plans to validate the acquired insights in real-world operations. In doing so, Slovak researchers have demonstrated that research from Slovakia can achieve global relevance—provided it has access to the right tools to overcome digital barriers.

Challenge: Millions of Microlocations in the Digital World

Planning a city according to people’s needs means understanding space. The concept of the “15-minute city” suggests that everything essential should be accessible within walking distance. However, for artificial intelligence (AI) to provide meaningful guidance to cities, it must first process vast amounts of data about every street, sidewalk, and existing service.

The Slovak team worked with data on the scale of hundreds of gigabytes, including map data from OpenStreetMap, digital elevation maps, and databases containing thousands of public amenities. The challenge was to transform these datasets into complex mathematical matrices of walking distances. 

In the initial phase of the project, we needed to identify weaknesses in our process through rapid iterations and quickly reach results, understand them, and then adjust the input parameters again. Even at this stage, we had to process substantial volumes of data in which the neural network could detect significant patterns. On a regular computer, this would have taken weeks. Without extreme computing power, it would not have been possible to achieve the first meaningful results in such a short time,” says Róbert Pollák, head of the NextUseAI research team. 

Solution: The Power of the MeluXina Supercomputer

The breakthrough came thanks to access to the European supercomputer MeluXina in Luxembourg, specifically to the part dedicated to artificial intelligence (the AI Factory). Here, Slovak experts were given access to powerful graphics accelerators capable of processing thousands of operations simultaneously.

In this environment, the team built and tested advanced neural networks. These networks were trained to recognize relationships between buildings, services, and their surroundings across different cities. Supercomputing enabled us to experiment with different configurations and quickly fix errors, which would not have been possible under normal conditions. Thanks to this, we were able to rapidly generate a method for producing spatial recommendations for the two largest Slovak cities—Bratislava and Košice. The models can learn from the spatial structure of one or multiple cities and provide recommendations for another city,” adds Timotej Kendereš, a data analyst at CIKE responsible for working with the MeluXina supercomputer.

Results: Data in the Service of People

The result is not just a dry table of numbers. The AI model aims to propose concrete functions for underused urban spaces to city planners and strategists in order to improve public amenities and walkable accessibility within neighborhoods. NextUseAI will then evaluate spatial recommendations in the context of residents’ needs and the city’s strategic priorities, accompanied by a clear explanatory rationale. 

Although the results are currently still in the experimental phase and serve to calibrate the entire system, they have already demonstrated an important insight: artificial intelligence can detect patterns and connections that may escape human observation. For example, the system can identify “blind spots” in a city—areas where a particular service is missing—and suggest placing it in a nearby underused building.

Impact and Future Potential

NextUseAI does not end in the laboratory. Its ambition is to become a practical tool that helps city leaders make decisions based on data rather than intuition.

For residents, this could mean in the future:

• More efficient local governance: Public funds will be invested in buildings with a clear purpose and tangible benefits.
• Less time spent in cars: Services will be located where people actually live.
• A more attractive environment: Abandoned buildings will get a new chance instead of falling into decay.

The use of European supercomputers is thus bringing a technological leap to Slovak urban planning. It shows that AI does not have to remain an abstract concept, but can become a useful ally that helps us build cities where people can live better and healthier lives.

The research was carried out by an international team of experts in artificial intelligence and industrial analytics from both academia and the private sector. The project involved the company Prounion a.s. in cooperation with Constantine the Philosopher University in Nitra, as well as additional academic partners from the Czech Republic and Hungary.

Challenge

Modern production lines generate enormous volumes of data—from machine states and operating speeds to temperatures and production counts. Despite this, key operational decisions are still often made based on experience and intuition.

The researchers focused on a real production line for filled pasta products, where the product passes through a fixed sequence of machines—from raw material preparation, through forming and filling, to thermal processing and packaging. They identified two decisions with a critical impact on production efficiency:

• Early warning: Is it possible to predict whether the packaging machine will stop within the next 10 minutes?
• In-shift planning: Can it be reliably determined during the working day whether packaging will still take place later the same day?

Answering these questions required working with large volumes of time-series data while strictly respecting real production conditions—models were allowed to use only the information that is genuinely available at a given moment to an operator or shift supervisor.

Solution

The research team first unified data from all machines into a single time axis and processed it to accurately reflect the real operation of the production line. They then developed machine-learning models that worked exclusively with information available at the given moment—exactly as an operator or shift manager would have it in practice.

A key milestone of the project was access to high-performance computing resources. NSCC Slovakia facilitated access for the research team to the European EuroHPCsupercomputing infrastructure, specifically to the Karolina supercomputer in the Czech Republic. This made it possible to rapidly experiment with different models, test them on real production days, and validate their behavior under conditions close to real industrial practice.

The supercomputer thus became not just a technical tool, but a key driver of innovation, enabling the transition from theoretical analytics to decisions that can be used in real operations.

Results

The model focused on early warning of packaging machine stoppages achieved very high accuracy. It was able to reliably identify situations in which a stoppage was likely within the next 10 minutes, while keeping the number of false alarms to a minimum. This means the alerts are trustworthy and do not overwhelm operators with unnecessary warnings.

The second model, designed for in-shift planning, was able with high reliability to determine whether packaging would still take place later the same day. Managers thus gained a practical basis for decisions related to staffing, work planning, and efficient use of time.

Both approaches share a common principle: they do not predict abstract numbers, but instead answer concrete questions that production teams face every day.

Impact and future potential

This success story shows that artificial intelligence in industry does not have to be a futuristic experiment. When analytics is focused on real operational decisions and supported by the right infrastructure, it can become a quiet and reliable assistant to production.

The solution is easily extendable to other production lines and sectors. Looking ahead, additional data—such as product types, planned maintenance, or shift schedules—can be integrated, allowing models to be even more precisely tailored to the specific needs of companies.

The key message is clear:
When data, artificial intelligence, and supercomputers are aligned with real industrial needs, the result is solutions with immediate practical value.

A Slovak research team from the University of Žilina has brought together medicine, artificial intelligence, and European supercomputers in a joint project with a clear goal: to improve the accuracy of breast cancer detection and support doctors in the interpretation of mammographic images.

Challenge

Mammography generates enormous volumes of imaging data. A single project may work with hundreds of thousands of images at extremely high resolution. The Slovak team from the University of Žilina worked with more than 434,000 mammograms, representing data on the scale of several terabytes.

At the same time, the team decided to use a foundation model—a massive neural network with nearly a billion parameters, originally developed for general image analysis. Such a model has enormous potential, but it also places extreme demands on computing power, memory, and data processing speed.

It quickly became clear that standard research infrastructure was simply not sufficient for such a volume of computations. Without a supercomputer, the project could not have continued.

Solution

The breakthrough came when the project gained access to the AI Factory VEGA in Slovenia, which is part of the European EuroHPC initiative. For the first time, Slovak medical AI research was able to work on infrastructure with a level of performance it had never had access to before.

On this platform, state-of-the-art NVIDIA H100 graphics accelerators, designed specifically for artificial intelligence, were available. The researchers built a complete technological pipeline there, from processing mammographic images to training the model itself.

First, the data had to be cleaned, optimized, and prepared so it could be loaded efficiently during computation. Then the process of adapting the large AI model began, as it “learned” to understand the subtle details of mammography. This was not a one-off computation—it was an incremental process in which the model improved step by step.

The supercomputer thus became not only a powerful tool but a key partner in research. It made it possible to do what was previously virtually impossible: to train a massive medical AI model at once using an enormous volume of data.

Results

Researchers have shown that artificial intelligence can learn from mammographic images in a way that gradually enables it to distinguish between healthy tissue and changes that may signal a problem. In other words, the system began to learn how to “look” at images in a manner similar to a physician—searching for subtle details and small deviations that can be very difficult for the human eye to notice.

This progress is particularly important because it represents the first step toward enabling artificial intelligence to flag changes that a human might not notice at first glance. It is not about replacing the physician, but about providing a supporting tool that can help clinicians make decisions with greater confidence, especially in borderline and ambiguous cases.

Impact and future potential

If this research continues to be further developed, artificial intelligence could become a silent assistant in preventive screening. It can speed up the evaluation of imaging data, reduce the risk of overlooking subtle changes, and help detect disease at a stage when it is still highly treatable.

Pre ženy to v praxi znamená väčšiu šancu na včasné odhalenie rakoviny a tým aj vyššiu nádej na úplné uzdravenie. Pri negatívnych nálezoch môžu dostať ženy nezávislý a objektívny doplnkový názor a tým si znížia neistotu po skríningu.  Hoci je pred vedcami ešte ďalšia práca, už dnes je jasné, že smer, ktorým sa výskum uberá, má veľký zmysel. Cieľ je jednoduchý, ale silný. Využiť moderné technológie tak, aby pomáhali chrániť zdravie a životy žien. 

The foundation of fair and sustainable decision-making is participation — involving people in the processes that shape the land and environment they live in. However, if such processes are not well designed, they can lead to distrust, conflicts, and short-sighted solutions. 

The research team from the Slovak University of Agriculture in Nitra therefore sought a way to capture, analyse, and connect these diverse perspectives. Their goal was to understand soil as a form of social and cultural capital — a space that brings together economic, environmental, and human values. To achieve this, they needed to process extensive datasets reflecting public discussions, attitudes, and values related to land and soil across the European context. 

Solution

To better understand how different stakeholders perceive soil and its value, the team combined data analytics with participatory approaches. During the testing phase, they processed extensive textual data, expert documents, media outputs, and public statements that reflect societal attitudes toward soil and the landscape. 

The team applied text mining methods to process the data, enabling the identification of recurring themes, linguistic patterns, and emotional attitudes related to land use. This approach opens the door to new insights, allowing researchers to derive from data how opinions are formed, where tensions arise, and what values people associate with the landscapes they inhabit. 

The goal of the research is not merely to collect information, but to transform it into actionable insights that help build consensus among the public, experts, and policymakers. 

Use of HPC Infrastructure 

The analysis of such extensive textual data required computational power beyond the capabilities of standard workstations. Therefore, the research team used the computing infrastructure provided by NSCC Slovakia to carry out the data processing. 

In the testing phase, the computations were performed on a supercomputer using 128 core*h in an R environment, enabling parallel processing of large datasets within a short time. This approach significantly reduced the analysis time while allowing the application of complex methodological frameworks typical for social and environmental data — such as modelling relationships between actors, tracking the occurrence of key concepts, and visualizing linguistic patterns. 

Thanks to HPC computing, it was possible to: 

• process extensive text files from various sources without capacity limitations, 

• generate clear and structured data outputs that would take several times longer to produce on standard computers, 

• test the potential of the supercomputer for social science and interdisciplinary research that connects human behaviour, data, and spatial relationships. 

Results

The test computations confirmed that the use of high-performance computing infrastructure enables efficient processing and analysis of extensive textual data originating from various social, environmental, and cultural sources. By applying text mining methods, the team was able to gain insights into key themes and the relationships between different stakeholders involved in land-use decision-making. 

The analysis revealed significant differences in how various groups perceive soil and the landscape — whether in terms of economic, ecological, or value-based priorities. These insights help identify areas where misunderstandings and conflicts arise, while also highlighting shared values that can serve as a foundation for constructive dialogue. 

The research confirmed that the use of HPC infrastructure significantly improves data processing efficiency and enables complex analyses to be carried out in a timeframe that would be unfeasible with standard computing resources. This established a reliable foundation for the main phase of the project, in which the results of the testing stage will be expanded with new data sources and methodological approaches. 

The obtained results represent the first step toward developing a tool capable of linking quantitative data with social contexts — thereby contributing to a deeper understanding of the relationship between people, the landscape, and decisions regarding its use. 

Impact and future: 

The project confirmed that a high-performance computing environment provides significant benefits for social science and environmental research dealing with complex, unstructured data. The combination of social research and computational analytics has created a new approach that can be used to gain a deeper understanding of the relationship between humans, the landscape, and societal change. 

From a methodological perspective, the project serves as a model example of how HPC can support interdisciplinary research that integrates environmental data, text corpora, legislation, and public discourse. Such an approach holds great potential within European initiatives focused on sustainable land management and landscape planning. 

The results thus create a transferable framework that can be applied in both European and national projects — ranging from public policy research and participatory planning to the assessment of the social impacts of environmental decisions. 

Data today can tell stories that we could not have captured just a few years ago. The research team harnessed the computational power of a supercomputer to analyse vast textual datasets in order to better understand how society perceives soil, landscape, and their value. The project demonstrates that the future of soil is hidden in data — and that high-performance computing can support not only scientists but also communities striving to find balance between development and sustainability. 

There’s no reason to be afraid.

You can think of a supercomputer as an extremely powerful machine with thousands of “brains” working together. It’s not sitting in your office or glowing under your desk — it’s housed in a specialized data center, and you control it conveniently through a web browser.

You simply prepare your task and submit it to the system. While the supercomputer gets to work, you can relax and enjoy a cup of coffee. Within minutes or hours, you’ll receive results that would take your laptop weeks to compute — or that it might not be able to handle at all.

Who can it help?

- students processing large amounts of data
- scientists testing new artificial intelligence algorithms
- meteorologists working on weather forecasting
- designers and engineers running simulations and developing new solutions
- doctors and biologists analyzing genomes or medical data
- small innovative companies without their own computing infrastructure

And many other fields are waiting for someone brave enough to explore them.

Why is it important?

We need a new impulse for innovation. We have smart people, bold ideas, and now also a tool that saves time, money, and opens the path to world-class results. The supercomputer is here to accelerate scientific progress and drive economic growth.

The first webinar coming soon

The authors of the guide are preparing a practical webinar designed for complete beginners. We’ll show that access to supercomputing is truly within everyone’s reach — for anyone unafraid to explore new possibilities. The goal is to spark curiosity and break down the barriers between technology and its users.

User Guide

Challenge: Recognizing whether tissue is infected with bacteria is not always straightforward. In the early stages of infection, the differences between healthy and damaged cells often cannot be detected even under a microscope. Although traditional biochemical tests can confirm the presence of bacteria, they are usually time-consuming and require sample collection.

Solution: To identify subtle differences between healthy and infected tissue, the researchers decided to combine experimental measurements with advanced data processing. Raman spectra obtained from different depths and regions of the tissue contained an enormous amount of information that could not be reliably evaluated using conventional visual methods.

The scientists therefore sought to verify whether this method could reliably distinguish healthy tissue from tissue infected with Staphylococcus aureus one of the most common causes of skin and mucous membrane inflammations. At the same time, the researchers focused on monitoring the effectiveness of photodynamic therapy—an experimental treatment based on carbon quantum dots that, when exposed to blue visible light, destroy bacteria without harming healthy cells.

Use of HPC Infrastructure

The team employed a mathematical analysis based on the Euclidean cosine of the squares of the first differentiated values, which enables the comparison of similarities between spectra after their transformation. This method eliminates background interference, highlights chemical changes in the tissue structure, and allows precise identification of differences caused by the presence of bacteria or the effects of treatment.

The computational power of a supercomputer was used to process the extensive datasets. Thanks to parallel data processing, it was possible to rapidly analyze hundreds of measurements from different tissue layers and visualize their similarities in a clear results matrix. Such an approach would be virtually impossible through manual evaluation.

The solution was developed through close collaboration among experts from multiple disciplines—biology, physics, materials research, and computational science. Reconstructed skin tissues were provided by the SK-NETVAL laboratories at the Institute of Experimental Pharmacology and Toxicology of the Centre of Experimental Medicine, Slovak Academy of Sciences (SAS), which also performed the exposure to the tested substances. The photodynamic treatment was applied by the team from the Polymer Institute of SAS, and the Raman data were collected at the Institute of Physics of SAS in cooperation with the Centre for Advanced Material Application.

Results

The analysis of the spectral data revealed significant chemical differences between healthy and infected tissue that can be detected using Raman microscopy. Samples infected with Staphylococcus aureus showed distinct spectral characteristics at all analyzed depths.

The results were particularly interesting in the samples that underwent photodynamic treatment. After the application of carbon quantum dots and subsequent activation with blue light, the chemical spectra closely approached those of healthy tissue. This suggests that the treatment effectively suppresses bacterial infection without damaging the cells themselves.

The applied algorithm proved to be a reliable and fast tool for comparing spectral data. Thanks to its implementation in the HPC environment, it was possible to automatically process large volumes of measurements and evaluate the results objectively, without any subjective interference from the researcher.

Impact and Future Potential

The project has brought new insights into the potential use of light and data analysis in medical diagnostics. It has demonstrated that combining Raman microscopy with computational methods enables not only the identification of bacterial infection in tissue but also the monitoring of treatment effectiveness in real time.

In the future, this approach could be applied in the development of new antibacterial therapies or in preclinical drug testing, where it is essential to quickly and accurately assess structural changes in tissue without invasive procedures. The research team also plans to extend the methodology to other types of bacteria and tissues and to leverage the computing power of the supercomputer for testing advanced artificial intelligence algorithms that could further automate the analysis.

The project is proof that the integration of biomedicine, physics, materials research, and computational science opens new possibilities for disease diagnosis and treatment.
Slovak research teams are thus not only demonstrating their scientific excellence but also contributing to pushing the boundaries of modern medicine.

Challenge: Hydrogen is increasingly seen as the “fuel of the future” — carbon-free, clean, and applicable across industry, energy, and transportation. However, to make it truly accessible, it must be produced more cheaply and efficiently. Traditionally, expensive metals such as platinum have been used for this purpose, but they are not suitable for large-scale deployment. Scientists are therefore searching for new materials capable of catalyzing (accelerating) the reaction through which hydrogen is released from water.

Solution: A team of researchers from the Institute of Chemistry at Pavol Jozef Šafárik University in Košice and the Institute of Materials Research of the Slovak Academy of Sciences focused on molybdenum phosphide (MoP) — an inexpensive and readily available material with the potential to replace costly metals. They studied how MoP performs in different environments — from acidic to alkaline — and why it is able to maintain its efficiency.

The laboratory alone was not enough. The reactions on the catalyst’s surface are extremely fast and occur at the atomic level. To truly understand them, it was necessary to combine experimental research with computational simulations on a supercomputer.

Use of HPC Infrastructure

Collaboration with NSCC Slovakia and the use of a supercomputer enabled the researchers to create computer models of the catalyst and simulate what happens when hydrogen atoms bind to its surface. 

Thanks to HPC, the team was able to:

• uncover the reaction mechanism — how hydrogen atoms behave on the surface of MoP
• verify the stability of the material in different environments
• predict potential improvements to the catalyst even before it is produced in the laboratory.

Impact

The outcome is significant for several reasons:

• MoP is cheaper and more accessible than platinum, which could reduce the cost of hydrogen production.
• The material works across a wide range of environments, meaning it could be deployed in various types of electrolyzers worldwide.
• The combination of experiments and HPC simulations saves both time and costs — allowing researchers to identify the best solutions much faster.

This research shows that HPC is not just for physicists or computer scientists — it can also play a crucial role in advancing green energy. Thanks to the computational power of the supercomputer, Slovak researchers contributed to global knowledge on eco-friendly hydrogen production and paved the way for new technologies that can directly impact energy independence and sustainability.

A study of the mechanism of the hydrogen evolution reaction catalysed by molybdenum phosphide in different media – ScienceDirect

Challenge: The brown bear (Ursus arctos) is one of the most iconic yet also controversial species in our nature. In Slovakia, its population is relatively stable, but monitoring its movement and behavior is crucial both for nature conservation and for human safety. Traditional methods such as visual observation or tracking are time-consuming and often inaccurate. Modern camera traps can capture thousands of images from the forest, but evaluating them manually is practically impossible.

Solution: A team of researchers from the Faculty of Natural Sciences and Informatics at UKF in Nitra developed an artificial intelligence system aimed at automatically recognizing whether a bear is present in an image or not. They used convolutional neural networks (CNNs) – the same principle applied, konvolučné neurónové siete (CNN) for example, in facial recognition on mobile phones.

For training the model, they collected:

• 4 974 images with a bear
• 656 images without a bear (other animals or empty forest)

The data were provided by the Slovak Hunting Chamber, the National Zoological Garden in Bojnice, and the State Nature Conservancy of the Slovak Republic.

Use of HPC infrastructure: Training artificial intelligence on such data is extremely computationally demanding. It requires repeated processing of thousands of high-resolution images (512×512 px), parameter tuning, and testing of different model architectures.

A regular computer would need weeks for this process. Thanks to the supercomputer and NSCC Slovakia, it was possible to:

• train the models within a few hours to days
• compare multiple approaches (ResNet, MobileNet, YOLOv8/v10)
• analyze the weak points of the model and visualize what it had “learned"

HPC enabled researchers to experiment quickly and efficiently – allowing them to move from a basic model to a methodology applicable in the future.

Results

• The model learned to recognize the basic features of the bear and achieved high accuracy during training (>90%).
• In real conditions (night shots, noise, camera movement), however, the accuracy is still insufficient for deployment in the field.

Impact and future: Although the results are not yet perfect, the research shows that artificial intelligence has great potential in nature conservation. In the future, automatic analysis of camera traps could:

• help monitor the population size and movement of bears
• reduce the risk of conflicts with humans
• save researchers hundreds of hours of manual work

The next step is to expand the dataset and use synthetic data – computer-generated images that will enrich the training database. Here too, the supercomputer will be crucial, since generating and processing such data is again highly demanding.

Thanks to the supercomputer, Slovak researchers managed to build the first step towards a system that could, in the future, facilitate the monitoring of the brown bear – a species that is part of Slovakia’s natural environment and cultural heritage.