Contacts
Intranet
SK
Anna Krakovská
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. |
National projects
| 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: | SRDA |
| 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. |