1. AI Adoption in European EnterprisesMaja Ćukušić, 2015, published scientific conference contribution Abstract: This paper examines the data on AI adoption in European companies by analysing how different indicators of digital readiness, technological infrastructure and organisational capacity contribute to the successful integration of AI. The study analyses data from 31 European countries and uses fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify three different pathways to AI adoption: digital maturity (a combination of high digital intensity, internet access and ICT security), technology-based readiness (based on in-house data analytics, cloud computing and ICT specialists) and capability-based empowerment (through ICT training alongside ICT staff and ICT security). The results show that multiple configurations can lead to effective AI adoption, underlining the principle of equivalence. The findings underscore the important role of ICT security across all pathways and suggest that workforce development is able to compensate for infrastructural gaps. Some practical guidance for businesses and policy makers are also provided.
Keywords: AI adoption, digital transformation, fsQCA, ICT security, data analytics, cloud computing, ICT skills training, European enterprises
Published in RUP: 04.03.2026; Views: 69; Downloads: 3
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2. Implementation of the Science on a Sphere Visualization System as a Web ApplicationJurij Anžič, Ciril Bohak, 2025, independent scientific component part or a chapter in a monograph Abstract: SOS is a visualization system originally developed by the U.S. National Oceanic and Atmospheric Administration National Oceanic and Atmospheric Administration (NOAA) to project dynamic global datasets onto a spherical display, enabling audiences to explore complex Earth system processes in an intuitive and immersive way. While highly efective in museums, science centers, and classrooms, the traditional Science on a Sphere (SoS) hardware installation requires dedicated infrastructure, limiting its accessibility and scalability. This paper presents the design and implementation of a web-based application that reproduces the core functionality of the Science on a Sphere (SoS) system in a browser environment. The application utilizes modern web technologies to render real-time spherical visualizations of global datasets. Users can load, manipulate, and interact with datasets such as atmospheric phenomena, ocean currents, or planetary imagery without the need for specialized hardware. The system also supports interactive controls for rotating, zooming, and overlaying multiple data layers, extending the pedagogical potential of Science on a Sphere (SoS) by enabling personal exploration on laptops, tablets, and mobile devices. By transitioning Science on a Sphere (SoS) from a physical installation to a lightweight, browser-based platform, the proposed solution broadens access to scientific visualizations, promotes remote and classroom learning, and ensures that Science on a Sphere (SoS) content can be integrated into modern online education ecosystems.
Keywords: Science on a Sphere (SOS), Web-based visualization, WebGL, WebGPU, Geospatial data, Educational technology
Published in RUP: 29.01.2026; Views: 242; Downloads: 10
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3. Biodiversity genomics research practices require harmonising to meet stakeholder needs in conservationElena Bužan, Christian de Guttry, Chiara Bortoluzzi, Nathaniel R. Street, Kay Lucek, Anna Rosling, Lino Ometto, Alice Mouton, Luísa S. Marins, María José Ruiz-López, José Melo-Ferreira, Elisabet Ottosson, Camila J. Mazzoni, Robert M. Waterhouse, 2025, original scientific article Abstract: Biodiversity resilience relies on genetic diversity, which sustains the evolutionary potential of organisms in dynamic ecosystems. Genomics is a powerful tool for accurately estimating genetic diversity across genomes of species and populations. However, integration of genomic data into conservation efforts faces challenges due to the heterogeneity of approaches employed. Establishing common sets of standards for genomic data production and analysis is essential to consistently interpret results and clearly communicate outcomes to stakeholders. While the European Reference Genome Atlas (ERGA) community has contributed significantly to the standardisation of reference genome methodologies in synergy with other initiatives, there is now an urgent need to extend these principles to downstream analyses. ERGA aims to build on its experience to help establish harmonised approaches in applied biodiversity genomics research, aligned with ongoing efforts to define standardised metrics for measuring and reporting genetic diversity. Establishing consensus on best practices for genome-wide data generation methods and applications will substantially increase accuracy, interpretability, and comparability, together with enhanced stakeholder capacities. By identifying key opportunities and challenges, as well as conducting preliminary stakeholder mapping and examining case studies, the goal is to build an inclusive framework that ensures the relevance and widespread adoption of these best practices: fostering trust and confidence in genomics research practices to meet stakeholder needs in biodiversity conservation. We call upon the broader research community to join efforts in establishing these approaches, recognising the importance of participation of end-users, to foster the integration of genomic data into the toolkit for measuring and reporting genetic diversity.
Keywords: best practices, biodiversity genomics, genome-wide genetic diversity, stakeholder engagement, standardisation, whole genome resequencing data
Published in RUP: 16.01.2026; Views: 258; Downloads: 2
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4. Plants in danger : floral and other plant traits as drivers of vulnerability in Mediterranean countriesAmparo Lázaro, Anastasia Stefanaki, Martha Charitonidou, Joana Cursach, Maria Panitsa, Ioannis Bazos, Rosa Ranalli, Mauro Fois, Marta Galloni, Gianluigi Bacchetta, Živa Fišer, 2026, original scientific article Abstract: Plants with complex floral morphologies are adapted to be pollinated by restricted pollinator assemblages and may suffer pollinator limitation. Understanding how floral traits and other variables relate to plant vulnerability can provide a powerful tool for predicting the conservation status and prioritizing the assessment of plants with scarce field data. Using circa 3000 records of rare and threatened (sensu IUCN) entomophilous plant taxa from seven Mediterranean countries, we evaluated how six floral traits and other eight intrinsic and extrinsic variables were related to plant vulnerability (less vs. more threatened plants). Besides, we analyzed 29 experts' opinions regarding the floral traits most related to floral complexity. Floral shape, reproductive unit, and flowering duration were good vulnerability indicators. Taxa with lip- and flag-shaped flowers were the most threatened, which agrees with the opinion of experts who considered lip- and flag-shaped flowers to have more complex morphologies. Also, plants with cylindrical inflorescences or solitary flowers were more threatened than those with flat-spherical inflorescences; and longer flowering durations reduced the probability of being threatened. Regarding extrinsic variables, coastal and freshwater habitats, i.e. habitats heavily impacted by human activities, had the highest percentage of highly threatened taxa. Yet, plant vulnerability decreased with maximum elevation and total distribution range. These results may serve as a basis for managers and practitioners when field data are scarce or unavailable, so that, depending on their traits, species could be provisionally listed in Red Lists as deserving priority for assessment to ascertain conservation status and actions.
Keywords: floral complexity, floral shape, flowering duration, functional reproductive unit, plant conservation, red data books, threatened Mediterranean vascular flora
Published in RUP: 02.12.2025; Views: 372; Downloads: 4
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5. Is open source the future of AI? : a data-driven approachDomen Vake, Bogdan Šinik, Jernej Vičič, Aleksandar Tošić, 2025, original scientific article Abstract: Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means to enhance trust and transparency. Yet, open-sourcing comes with its own challenges, such as risks of illicit applications, limited financial incentives, and intellectual property concerns. Positioned between these extremes are hybrid approaches—including partially open models and licensing restrictions—that aim to balance openness with control. In this paper, we adopt a data-driven approach to examine the open-source development of LLMs. By analyzing contributions in model improvements, modifications, and methodologies, we assess how community efforts impact model performance. Our findings indicate that the open-source community can significantly enhance models, demonstrating that community-driven modifications can yield efficiency gains without compromising performance. Moreover, our analysis reveals distinct trends in community growth and highlights which architectures benefit disproportionately from open-source engagement. These insights provide an empirical foundation to inform balanced discussions among industry experts and policymakers on the future direction of AI development.
Keywords: large language models, artificial intelligence, open source, data science, HuggingFace
Published in RUP: 25.09.2025; Views: 681; Downloads: 4
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6. Decentrilized message application running on blockchain technologies : final project paperEkaterina Bochvaroska, 2025, undergraduate thesis Keywords: distributed messaging system, digital communication, blockchain, trans actions, consensus protocols, decentralization, distributed trust, data integrity
Published in RUP: 05.08.2025; Views: 609; Downloads: 21
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8. Testing life-cycle assessment data quality with Benford’s law reveals geographic variationBogdan Šinik, Aleksandar Tošić, 2025, original scientific article Abstract: Life-Cycle Assessment (LCA) is a methodology that is used extensively for evaluating the environmental impacts of products and processes throughout their lifetime. The method is highly dependent on the quality and accuracy of the underlying data. Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. In this study, we perform Benford’s law conformity tests (first digit) on all numerical data in ecoinvent, focusing on individual compartments (air, water, soil, and natural resources) and environmental elementary flows (carbon, toxic substances, greenhouse gases, and heavy metals), and discrepancies across continents are examined. Life Cycle Inventory data met the requirements of Benford’s law and generally exhibited high conformity. Substantial differences in conformity were observed between Africa and Europe. Individual processes and measurements were inspected to further isolate potential sources of the non-conformity. The statistical significance of the results was increased using open-source databases available on OpenLCA Nexus, including WorldSteel, OzLCI2019, ELCD, NEEDS, BioenergieDat, and Exiobase. Finally, the Environmental Performance Index (EPI) was used, and a strong correlation between continental Benford conformity results and corresponding EPI scores was observed. The findings suggest that discrepancies in conformity across continents reflect differences in data transparency and reporting practices. European datasets generally show higher conformity, likely owing to the use of more standardized methodologies. In contrast, data from regions with limited infrastructure or less established LCA practices tend to show lower conformity. Benford’s Law offers a simple and computationally efficient alternative to conventional data quality assessments without requiring additional metadata or probabilistic modeling. Its application can support the detection of systemic biases and improve the reliability of LCA-based indicators such as environmental product declarations.
Keywords: anomaly detection, Benford’s law, data integrity
Published in RUP: 12.06.2025; Views: 2405; Downloads: 35
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9. Estimation of task-related dynamic brain connectivity via data inflation and classification model explainabilityPeter Rogelj, 2025, original scientific article Abstract: Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.
Keywords: EEG, functional connectivity, data inflation, classification, explainability, saliency maps
Published in RUP: 04.06.2025; Views: 1949; Downloads: 17
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