Author: Halyna Hyryavets

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.

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.