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International projects
| MEDUSSE – Seasonal-to-decadal climate predictability in the Mediterranean: process understanding and services | |
| Sezónna až dekádová predpovedateľnosť klímy v Stredomorí: pochopenie procesov a implementácie | |
| Program: | COST |
| Duration: | 8.10.2024 – 7.10.2028 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | Climate forecasting has enormous potential influence in different socio-economic sectors, such as agriculture, health, water management, and energy. Actionable climate information is particularly relevant at seasonal-to-decadal timescales, where predictability is linked to slow fluctuations of the system such as those in the ocean, sea-ice and land-surface, thus bridging weather/sub-seasonal predictions (mainly relying on atmospheric initial condition) with future projections (mainly based on atmospheric radiative forcing). Seasonal-to-decadal climate forecasting has progressed considerably in recent years, but prediction skill over the Mediterranean is still limited. Better understanding the drivers of regional climate anomalies as well as exploring untapped sources of predictability constitute a much-needed and timely effort.Climate variability and change pose significant challenges to society worldwide. As a result, there is a growing demand to develop improved climate information products and outlooks to help decision making and sustainable development. This is particularly critical in the Mediterranean, a region sensible to natural hazards (e.g. droughts, floods) and vulnerable to climate stress (i.e. global warming). Such an improvement can only be achieved by coordinating efforts of research groups with different expertise and trans-disciplinary. In this Action, both the scientific challenge and societal challenge will be addressed by establishing a network of experts on climate variability, predictability, prediction and application. The Action will provide support to increase awareness and capability, and guidance to suitably evolve climate knowledge into services. Specific objectives include cross-cutting training and collaboration, empowering national hydro-meteorological agencies, and fostering a continuous communication between climate researchers and stakeholders. |
| DYNALIFE – Information, Coding, and Biological Function: the Dynamics of Life | |
| Informácia, kódovanie a biologická funkcia: Dynamika života | |
| Program: | COST |
| Duration: | 19.9.2022 – 18.9.2026 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | In the mid-twentieth century two new scientific disciplines emerged forcefully: molecular biology and information-communication theory. At the beginning cross-fertilisation was so deep that the term genetic code was universally accepted for describing the meaning of triplets of mRNA (codons) as amino acids.However, today, such synergy has not take advantage of the vertiginous advances in the two disciplines and presents more challenges than answers. These challenges are not only of great theoretical relevance but also represent unavoidable milestones for next generation biology: from personalized genetic therapy and diagnosis, to artificial life, to the production of biologically active proteins. Moreover, the matter is intimately connected to a paradigm shift needed in theoretical biology, pioneered long time ago in Europe, and that requires combined contributions from disciplines well outside the biological realm. The use of information as a conceptual metaphor needs to be turned into quantitative and predictive models that can be tested empirically and integrated in a unified view. The successful achievement of these tasks requires a wide multidisciplinary approach, and Europe is uniquely placed to construct a world leading network to address such an endeavour. The aim of this Action is to connect involved research groups throughout Europe into a strong network that promotes innovative and high-impact multi and inter-disciplinary research and, at the same time, to develop a strong dissemination activity aimed at breaking the communication barriers between disciplines, at forming young researchers, and at bringing the field closer to a broad general audience. |
| DONUT – European Doctoral Network for Neural Prostheses and Brain Research | |
| Európska doktorandská sieť pre neurálne protézy a výskum mozgu | |
| Program: | Horizont Európa |
| Duration: | 1.1.2024 – 31.12.2027 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | DONUT, European Doctoral Network for Neural Prostheses and Brain Research has the mission to provide a multidisciplinary and inter-sectoral network for young talented researchers. The ambition of the project is to serve as a springboard for the expansion of EU partners into the fast-developing Brain-Computer Interface (BCI) technology and connected scientific disciplines. The DN will leverage the complementary expertise of 7 academic beneficiaries and 8 associated partners from 8 EU countries, to guide its 10 doctoral candidates (DCs) to address and solve deep problems in brain research, development of different BCI applications and systems with the latest technological advancements.The proposed DN will integrate existing research in BCI systems to make it more user-friendly, suitable for different types of potential end-users and for modern medical diagnostics. The DN would also provide excellent opportunities for career development of young researchers under the umbrella of German doctorate graduate school PK NRW (Graduate School for Applied Research in North Rhine-Westphalia, with over 180 participating professors), regularly offering specialised trainings and courses including Scientific Research Writing, Academic Presentation Skills, etc. Early scientific independence is one of key goals of training programmes.It is the ambition of DONUT to build a strong and lasting network not only between the DCs but also between the participating beneficiaries and associated partners. DONUT researchers will benefit of a dense network of contacts with scientists acquired during network-wide training events, to improve their career prospects in the European and worldwide innovation sector, having the opportunity to become scientists employable in both the industrial and academic sectors. The participation of 7 industrial participants in research and training programmes will guarantee extensive inter-sectoral experience for the trainees and maximise the impact. Project Partners:Rhine-Waal University of Applied Sciences (HSRW), GermanyRadboud University (RU), NetherlandsKatholieke Universiteit Leuven (“KU Leuven”) (KUL), BelgiumUniversidad Miguel Hernández de Elche (UMH), SpainAarhus University (AU), DenmarkKauno technologijos universitetas (KTU), LithuaniaInstitute of Measurement Science, Slovak Academy of Sciences (IMSAV), Slovakia |
National projects
| MRCartilage – Automatic data evaluation tool from the longitudinal quantitative MRI studies of articular cartilage | |
| Automatický softvérový nástroj na výhodnocovanie kvantitatívnych MRI štúdií artikulárných chrupaviek v čase | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Dr. Szomolányi Pavol, (PhD.) |
| Annotation: | The aim of the project is to design a comprehensive tool for automatic evaluation of human articular cartilage data from quantitative MRI. Data obtained from the Osteoarthritis Initiative database, and measured at Institute of Measurement Science and Medical University of Vienna will be segmented using an automated segmentation tool based on convolutional neural networks. The annotated data will then be registered on quantitative MRI data that will be available from the database (T2 and T1rho mapping, gagCEST, sodium MR) using automated or semiautomated tools developed within this project. The data obtained will be evaluated at multiple time points according to MR measurements that will be available. In addition to quantitative MR data, this will include volumetric data, cartilage thickness, and texture analysis of quantitative maps. Patient evaluation will be based on risk factor groups (transverse ligament rupture, meniscus rupture and menisectomy). The expected number of patients is approximately 4000 divided into individual groups in the ratio 40/30/30. The output of the project will be a compiled version of an automatic cartilage evaluation tool that will be available in a public source (such as website of Institute of Measurement). |
| Causal analysis of measured signals and time series | |
| Kauzálna analýza nameraných signálov a časových radov | |
| Program: | VEGA |
| Duration: | 1.1.2022 – 31.12.2025 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | The project is focused on the causal analysis of measured time series and signals. It builds on the previous results of the team, concerning the generalization of the Granger test and the design of new tests in the reconstructed state spaces. The aim of the project is the development of new methods for bivariate and multidimensional causal analysis. We will see the investigated time series and signals as one-dimensional manifestations of complex systems or subsystems. We will also extend the detection of causality to multivariate cases – dynamic networks with nodes characterized by time series. Such complex networks are common in the real world. Biomedical applications are among the best known. Brain activity, determined by multichannel electroencephalographic signals, is a crucial example. We want to help show that causality research is currently at a stage that allows for ambitious goals in the study of effective connectivity (i.e., directed interactions, not structural or functional links) in the brain. |
| MATHMER – Advanced mathematical and statistical methods for measurement and metrology | |
| Pokročilé matematické a štatistické metódy pre meranie a metrológiu | |
| Program: | APVV |
| Duration: | 1.7.2022 – 31.12.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | Mathematical models and statistical methods for analysing measurement data, including the correct determination of measurement uncertainty, are key to expressing the reliability of measurements, which is a prerequisite for progress in science, industry, health, the environment and society in general. The aim of the project is to build on traditional metrological approaches and develop new alternative mathematical and statistical methods for modelling and analysing measurement data for technical and biomedical applications. The originality of the project lies in the application of modern mathematical methods for modelling and detecting dependence and causality, as well as statistical models, methods and algorithms for determining measurement uncertainty using advanced probabilistic and computational methods based on the use of the characteristic function approach (CFA). In contrast to traditional approximation and simulation methods, the proposed methods allow working with complex and at the same time accurate probabilistic measurement models and analytical methods. Particular emphasis is placed on stochastic methods for combining information from different independent sources, on modelling dependence and causality in dynamic processes, on accurate methods for determining the probability distribution of values that can be reasonably attributed to the measured quantity based on a combination of measurement results and expert knowledge, and on the development of methods for comparative calibration, including the probabilistic representation of measurement results with a calibrated instrument. An important part of the project is the development of advanced numerical methods and efficient algorithms for calculating complex probability distributions by combining and inverting characteristic functions. These methods are widely applicable in various fields of measurement and metrology. In this project they are applied to the calibration of temperature and pressure sensors. |
| cBCI-VR – Collaborative BCI post-stroke neurorehabilitation using a patient-therapist interactive VR environment | |
| Pacient-terapeut kolaboratívna BCI-VR neurorehabilitácia po cievnej mozgovej príhode | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2024 – 31.8.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | A growing body of evidence suggests that integrated brain-computer interface (BCI) technologies and virtual reality (VR) environments provide a flexible platform for a range of neurorehabilitation therapies, including significant motor recovery and cognitive-behavioral therapy following stroke. When a subject is immersed in such an environment, their perceptual level of social interaction is often impaired due to a suboptimal interface quality that lacks the social aspect of human interactions. The project proposes a user-friendly intelligent BCI system with a suitable VR environment in which both patient and therapist interact through their person-specific avatar representations. On the one hand, the patient voluntarily and at his/her own pace controls his/her activity in the environment and interacts with the therapist through a BCI-driven mental imagery process. On the other hand, the therapist\’s unrestricted motor and communication skills allow for full control of the environment. Thus, the VR environment can be flexibly modified by the therapist, allowing for the creation and selection of different occupational therapy scenarios according to the patient\’s recovery needs, mental states, and immediate reactions. |
| TInVR – Trustworthy human–robot and therapist–patient interaction in virtual reality | |
| Dôveryhodná interakcia človek–robot a terapeut–pacient vo virtuálnej realite | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | We aim to study specific forms of social interaction using state-of-the-art technology – virtual reality (VR) which is motivated by its known benefits. The project has two main parts, human–robot interaction (HRI) and therapist–patient interaction (TPI). The interactions are enabled using head-mounted displays and controllers allowing the human to act in VR. We propose two research avenues going beyond the state-of-the-art in respective contexts. In HRI, we will develop scenarios allowing the humanoid robot to learn, understand and imitate human motor actions using flexible feedback. Next, we develop scenarios for testing and validating human trust in robot behavior based on multimodal signals. We will also investigate physical interaction with a humanoid robot NICO. In TPI with stroke patients, we develop a series of VR-based occupational therapy procedures for motor and cognitive impairment neurorehabilitation using an active and passive brain-computer interface, and we will validate these procedures. We expect observations from HRI experiments to be exploited in TPI. The proposed project is highly multidisciplinary, combining knowledge and research methods from psychology, social cognition, robotics, machine learning and neuroscience. We expect to identify features and mechanisms leading to trustworthy processes with a human in the loop, as a precondition of success, be it a collaborative task or treatment in VR. |
| Smart deep brain stimulation as a treatment strategy in treatment-resistant depression | |
| Inteligentná hĺbková mozgová stimulácia ako inovatívna stratégia pre liečbu mozgových porúch | |
| Program: | VEGA |
| Duration: | 1.1.2022 – 31.12.2025 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | Impaired connectivity between different brain areas underlines the pathophysiology of multiple brain disorders. It is possible that impaired connectivity between the prefrontal cortex and ventral pallidum is involved in depression. Smart deep brain simulation, combining real-time detection of the neuronal activity in the prefrontal cortex with the stimulation of the ventral tegmental area might be thus effective in depression. We aim to examine the cortico-tegmental connectivity and to test the antidepressant-like effectiveness of the smart deep brain stimulation in an animal model of depression. |
| EDABSS – EEG data analysis by blind source separation methods | |
| Analýza EEG signálu pomocou metód hľadania skrytých zdrojov | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2024 – 31.8.2026 |
| Project leader: | Mgr. Rošťáková Zuzana, PhD. |
| Annotation: | Blind source separation (BSS) approaches are unsupervised machine learning methods focused on the detection of hidden, directly unobservable (latent) structure of real-world data. They play a crucial role in image processing, medical imaging, and music. The proposed project focuses mainly on human electroencephalogram (EEG), for which BSS is beneficial when detecting the narrowband brain oscillations representing brain processes either in health or disease. Two-dimensional BSS methods like principal or independent component analysis are easily applicable and understandable for a broader medical and neurophysiological community. However, the estimated latent component properties are usually incompatible with the real electrophysiological signal character. Consequently, they miss their neurophysiological interpretation. Tensor decomposition is a complex but more flexible mathematical procedure that allows adapting the model structure and constraints to the solution to mimic real-world signal characteristics. The proposed project focuses on tensor decomposition as a tool for i) EEG preprocessing, artefact detection and removal, ii) EEG latent structure analysis using a nonnegative tensor decomposition with block structure allowing to model various relationships between latent components, and iii) post-decomposition analysis of latent component dynamic properties. Obtaining comprehensive information about EEG latent structure and developing novel, user-friendly algorithms is crucial for better understanding brain processes and new methods for treating neurophysiological diseases and disorders. |
| ECMeNaM – Efficient computation methods for nanoscale material characterization | |
| Efektívne výpočtové metódy pre charakterizáciu materiálov v nano mierke | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The aim of the project is to design and implement effective calculation methods for evaluating the results of measuring the mechanical properties of materials at the nanoscale using instrumented indentation methods (IIT) and atomic force microscopy (AFM). Both of these methods are able to provide highly localized information on the mechanical properties of the material, such as Young\’s modulus of elasticity (both methods), hardness (IIT method), or point-to-surface adhesion (AFM method). The principle is the analysis of the recording of the position of the measuring tip and the force interaction between the tip and the sample surface. The determination of the resulting values on the basis of data recorded by the instrument in both of these methods is based on non-trivial mathematical-statistical methods and calculation procedures working with data subjected to relatively high uncertainty or random noise, where it is also necessary to quantify the uncertainty of the measurement result. Both of these methods work with data of a similar nature, but each has certain specifics. The results obtained for IIT can thus serve as a reference for AFM. The project partners are the Czech Metrology Ins titute (CMI is the national metrology institute of the Czech Republic with top infrastructure in the field), the Institute of Measurement Science SAS (IMS SAS), and the Mathematical Institute SAS (MI SAS), which are academic institutions with extensive experience in basic research and applications of mathematics statistics in the field of measurement and metrology. This combination of partners brings a natural synergy and a combination of the necessary competencies for this |
| Theoretical properties and applications of special families of probability distributions | |
| Teoretické vlastnosti a aplikácie špeciálnych tried rozdelení pravdepodobnosti | |
| Program: | VEGA |
| Duration: | 1.1.2024 – 31.12.2027 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | In the project, problems related to probability distributions and their applications in mathematical modeling will be studied. We will analyze some classes of distributions (distributions generated by partial summations, the Schröter family) and study properties of distributions belonging to these classes. Issues related to calibration regression models will be addressed. New methods for solving multivariate statistical problems will be developed. These methods will be based on the calculation of exact probability distributions using the inverse transformation of the characteristic function of the distribution of the output variable. Entropy, another property of probability distributions, plays an important role in detecting causality in time series. The primary area of application is theuse of the distribution of test statistics in hypothesis testing. The new results obtained during the solution of the project will also be applied to mathematical modeling in metrology, linguistics and actuarial mathematics. |