arXiv subjects

Branislava Lalic

Publications and source records attributed to Branislava Lalic.

6 recordsLinked to original sources

arXiv2026

CA20108 COST Action: A Methodology for Developing FAIR Micrometeorological Networks

This article reports the outcomes of the FAIRNESS COST Action (CA20108), a coordinated European initiative aimed at advancing micrometeorological data toward compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The article presents three core achievements: (i) a structured inventory of urban and rural micrometeorological networks across Europe; (ii) the design and deployment of the FAIR Micrometeorological Portal, providing a digital infrastructure for data discovery, access, and standardized metadata description; and (iii) methodological guidance for quality control, gap detection, and gap filling tailored to the specific characteristics of micrometeorological time series. By providing both technical infrastructure and community-driven standards, the FAIRNESS outputs advance micrometeorological data from isolated datasets into coherent, reusable resources. Beyond technical developments, the FAIRNESS systematically addressed gaps in knowledge and skills within the micrometeorological community. A key outcome is the beginner-oriented book Micrometeorological Measurements - An Introduction for Beginners, which provides structured guidance on measurement design, instrumentation, data management, and quality assurance. In parallel, FAIRNESS implemented a comprehensive capacity-building programme, including summer schools, workshops, and short-term scientific missions, targeting both domain-specific competencies and transferable skills such as FAIR data stewardship, interdisciplinary collaboration, and practical problem solving. Together, these efforts contribute to strengthening the long-term usability of micrometeorological data and fostering a more integrated, FAIR-oriented research culture within the European meteorological community.

cs.DL↗

arXiv2025

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2025

Redefining Influenza Transmission Seasonality Using the Novel Seasonality Index

The impact of climate conditions on influenza epidemiology has mostly been studied by addressing a singular aspect of transmission and a climate variable correlating to it. As climate change unfolds at an unprecedented rate, we urgently need new multidisciplinary approaches that can embrace complexity of disease transmission in the fast-changing environment and help us better understand the implications for health. In this study, we have implemented a novel seasonality index to capture a vast network of climate, infectious, and socio-behavioural mechanisms influencing a seasonal influenza epidemic. We hypothesize that intricate, region-specific behavioural patterns are cross regulating the influenza spreading and dynamics of epidemics with changes in meteorological conditions within a specific season. To better understand the phenomena, we analysed weekly surveillance data from temperate European countries and redefined seasonal transitions using the seasonality index. This approach allowed us to characterize influenza seasonality more accurately in relation to specific atmospheric conditions. Key findings include: i) a strong correlation between influenza infection rates and the seasonality index across different climate zones and social groups, and ii) a high linear correlation between winter duration, determined by the seasonality index, and the time scale of low-frequency peaks in the infection rates power spectral density.

q-bio.QM↗

arXiv2025

Seasonal Changes -- Time for Paradigm Shift

Season and their transitions play a critical role in sharpening ecosystems and human activities, yet traditional classifications, meteorological and astronomical, fail to capture the complexities of biosphere-atmosphere interactions. Conventional definitions often overlook the interplay between climate variables, biosphere processes, and seasonal anticipation, particularly as global climate change disrupts traditional patterns. This study addresses the limitations of current seasonal classification by proposing a framework based on phenological markers such as NDVI, EVI, LAI, fPAR, and the Bowen ratio, using plants as a nature-based sensor of seasonal transitions. Indicators derived from satellite data and ground observations provide robust foundations for defining seasonal boundaries. The normalized daily temperature range (DTRT), validated in crop and orchard regions, is hypothesized as a reliable seasonality index to capture transitions. We demonstrated the alignment of this index with phenological markers across boreal, temperate, and deciduous forests. Analyzing trends, extreme values and inflection points in the seasonality index time series, we established a methodology to identify seasonal onset, duration, and transitions. This universal, scalable classification aligns with current knowledge and perception of seasonal shifts and captures site-specific timing. Findings reveal shifts in the Euro-Mediterranean region, with winters shortening, summers extending, and transitions becoming more pronounced. Effects include the Gulf Stream s influence on milder transitions, urban heat islands accelerating seasonal shifts, and large inland lakes moderating durations. This underscores the importance of understanding seasonal transitions to enable climate change adaptive strategies in agriculture, forestry, urban planning, medicine, trade, marketing, and tourism.

physics.ao-ph↗

arXiv2024

Modelling Mosquito Population Dynamics using PINN-derived Empirical Parameters

Vector-borne diseases continue to pose a significant health threat globally with more than 3 billion people at risk each year. Despite some limitations, mechanistic dynamic models are a popular approach to representing biological processes using ordinary differential equations where the parameters describe the different development and survival rates. Recent advances in population modelling have seen the combination of these mechanistic models with machine learning. One approach is physics-informed neural networks (PINNs) whereby the machine learning framework embeds physical, biological, or chemical laws into neural networks trained on observed or measured data. This enables forward simulations, predicting system behaviour from given parameters and inputs, and inverse modelling, improving parameterisation of existing parameters and estimating unknown or latent variables. In this paper, we focus on improving the parameterisation of biological processes in mechanistic models using PINNs to determine inverse parameters. In comparing mechanistic and PINN models, our experiments offer important insights into the strengths and weaknesses of both approaches but demonstrated that the PINN approach generally outperforms the dynamic model. For a deeper understanding of the performance of PINN models, a final validation was used to investigate how modifications to PINN architectures affect the performance of the framework. By varying only a single component at a time and keeping all other factors constant, we are able to observe the effect of each change.

physics.bio-ph↗

arXiv2023

Constructing a Searchable Knowledge Repository for FAIR Climate Data

The development of a knowledge repository for climate science data is a multidisciplinary effort between the domain experts (climate scientists), data engineers whos skills include design and building a knowledge repository, and machine learning researchers who provide expertise on data preparation tasks such as gap filling and advise on different machine learning models that can exploit this data. One of the main goals of the CA20108 cost action is to develop a knowledge portal that is fully compliant with the FAIR principles for scientific data management. In the first year, a bespoke knowledge portal was developed to capture metadata for FAIR datasets. Its purpose was to provide detailed metadata descriptions for shareable \micro data using the WMO standard. While storing Network, Site and Sensor metadata locally, the system passes the actual data to Zenodo, receives back the DOI and thus, creates a permanent link between the Knowledge Portal and the storage platform Zenodo. While the user searches the Knowledge portal (metadata), results provide both detailed descriptions and links to data on the Zenodo platform.

cs.DL↗