Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
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Ni–Fe-based alloy as oxygen evolving anode for sustainable aluminum production
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Achieving global net-zero carbon targets by 2050 requires the decarbonization of metal production. Molten salt electrolysis, combined with the rapidly expanding renewable energy sector, provides a transformative and sustainable alternative to conventional metallurgical processes and reduces greenhouse gas emissions. Today, aluminum is produced by electrolysis in molten fluoride melts, using consumable carbon anodes for their feasibility, low cost, good conductivity, and efficient reaction kinetics. However, achieving carbon-free aluminum production requires the development of a cost-effective, non-consumable, and efficient oxygen evolving anode (OEA)—a critical challenge that remains unsolved. Here, we demonstrate an earth-abundant and easily processable Ni–Fe-based anode, capable of forming a protective NiFe2O4 oxide, both ex-situ and in-situ, that enhances stability and catalytic activity for OEA. The principal approach to evaluating OEA alloy suitability combines analyzing oxide thermodynamics, investigating oxidation kinetics, and performing extended electrolysis in fluoride melts to elucidate the mechanism of protective oxide formation. By optimizing the Ni/Fe ratio in alloy, we demonstrate reduced alloy corrosion, high purity aluminum production, and efficient oxygen evolution. This method lays the framework for a durable and active Ni–Fe-based OEA, thereby advancing carbon-free sustainable aluminum production.
Nanopore sequencing identifies parent-of-origin specific age-associated methylation changes at imprinted loci in the human genome
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Aging is accompanied by widespread DNA methylation changes, yet their full genomic scope and parent-of-origin dynamics remain poorly understood. Here, we apply nanopore long-read sequencing to 7,284 whole blood samples enabling methylation measurements of 17,959,684 high-quality CpG units. Over 20% of the measured high quality CpG units undergo age-associated changes, predominantly hypomethylation. From these data, we construct a methylation aging clock from 1,373 high-quality CpG units, with median absolute prediction error of 2.43 years. Importantly, phasing the methylation to parental haplotypes enables systematic analysis of age effects in parent-of-origin specific context, uncovering 702 high-quality CpG units with parent-of-origin specific age-association, most of which are located at imprinted genomic regions. At the DIRAS3 locus, we detect age-dependent hypermethylation on the active paternal allele, indicative of attenuation of parent-of-origin specific methylation with age. Together, these findings establish nanopore sequencing as a powerful tool for mapping both genome-wide and parent-of-origin specific signatures of methylation aging and provide evidence that methylation patterns at imprinted loci become progressively altered with age.
One Size Still Does Not Fit All : Configuration Pathways of Environmental, Social, and Governance and Board Diversities for Higher Firm Performances Across One- and Two-Tier Systems
(2026-02) Zhu, Baoying; Brozkova, Dominika; Ahmadov, Tarlan; Durst, Susanne; Prokop, Viktor; Department of Business and Economics
There is an ongoing, albeit uncertain, debate among stakeholders regarding the benefits of environmental, social, and governance (ESG) reporting, reflected in mixed empirical findings on its impact on firm performance. In addition to this reporting, diversity (gender and cultural) on boards, which is strongly promoted at the EU level, has gained increasing importance. However, prior research offers inconsistent results and leaves significant shortcomings regarding how the interaction of this diversity with the ESG pillars affects firm performance across governance structures. This study employs a fuzzy-set qualitative comparative analysis and Refinitiv Eikon data of 554 European Union companies from 2023 to examine configuration pathways that combine gender and cultural diversity with ESG pillars, thus influencing firm performance across different governance forms. Drawing on Upper Echelons and Contingency Theories—enhanced by insights from Resource Dependence and Social Identity Theory lenses—we show that configurations combining gender and cultural diversity with the social pillar of ESG are linked to higher performance within one-tier firms. In two-tier firms, this higher performance is driven by environmental and social ESG pillars combined with cultural diversity. Recognizing that there is no single governance structure in the ESG and diversity context, we offer practitioners guidance on aligning ESG and diversity strategies with their governance architectures, developing governance-sensitive policies, increasing transparency in diversity integration, promoting ESG competency training, and providing targeted incentives.
Knowledge management in the age of generative artificial intelligence – from SECI to GRAI
(2026-02-03) Böhm, Karsten; Durst, Susanne; Department of Business and Economics
Purpose – Generative Artificial Intelligence (GenAI) models are now able not only to recognize complex patterns from large amounts of input data but also to display them in context. This fact invites a critical analysis of the SECI model and its further applicability as an analytical framework for knowledge generation and transfer in organizations. This conceptual paper aims to take the SECI model with the individual SECI phases and analyze how GenAI changes the assumptions and descriptions of the original SECI framework. More specifically, the aim is to propose a revised SECI framework. Design/methodology/approach – This paper aims to contribute to theory development of theories present in the literature. More specifically, it seeks to make a conceptual contribution that draws on one of the four types of conceptual contributions proposed by Deborah J. MacInnis, namely, envisioning, and is based on previous literature and the authors’ thoughts and experiences to propose a revised SECI framework called GRAI, which stands for Generative Receptive Artificial Intelligence. Findings – A better understanding of the further applicability of the SECI framework that arises with the introduction and application of GenAI models is not only relevant to the existing knowledge management (KM) theory but also to organizations. The proposed revised perspective of the SECI model, summarized in the GRAI framework, reflects the use of GenAI technologies in the corporate environment and thus allows the necessary stimulation of a discussion on how KM in general, and knowledge generation, in particular, will be affected and augmented by AI. Originality/value – To the authors’ knowledge, this paper is the first to systematically and comprehensively examine the established SECI framework and its wider applicability in terms of the potential impact of GenAI models on KM practices in organizations. The proposed GRAI framework is seen as a relevant contribution to the further development of KM theory.
It’s Not Easy : Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store
(2026-02) Rosenzweig, Ben; Dalla Valle, Valentino; Apruzzese, Giovanni; Fass, Aurore; Department of Computer Science
Google Chrome is the most popular Web browser. Users can customize it with extensions that enhance their browsing experience. The most well-known marketplace of such extensions is the Chrome Web Store (CWS). Developers can upload their extensions on the CWS, but such extensions are made available to users only after a vetting process carried out by Google itself. Unfortunately, some malicious extensions bypass such checks, putting the security and privacy of downstream browser extension users at risk. In this article, we carry out a comprehensive real-world security analysis of malicious extensions in the CWS. Specifically, we scrutinize the extent to which automated mechanisms reliant on supervised machine learning (ML) can be used to detect malicious extensions on the CWS. To this end, we first collect 7,140 malicious extensions published in 2017–2023 and which have been flagged as malicious by Google. We combine this dataset with 63,598 benign extensions published or updated on the CWS before 2023, and we develop three supervised-ML-based classifiers—leveraging both original features as well as techniques inspired by prior work. We show that, in a “lab setting”, our classifiers work well (e.g., 98% accuracy). Then, we collect a new, and more recent, set of 35,462 extensions from the CWS, published or last updated in 2023, with unknown ground truth. We were eventually able to identify 68 malicious extensions that bypassed the vetting process of the CWS. However, our classifiers also reported over 1k likely malicious extensions which may overestimate their true number. Based on this finding (further supported with other experiments and realistic analyses), we elucidate, for the first time, a strong concept drift effect on browser extensions. We also provide factual evidence that commercial detectors (e.g., VirusTotal) work poorly to detect known malicious extensions. Altogether, our results highlight the fact that detecting malicious browser extensions is a fundamentally hard problem which has not (yet) received an adequate degree of attention. This requires additional work both by the research community and by Google itself—potentially by revising their approaches. In the meantime, we informed Google of our discoveries, and we released our artifacts.
Flokkar í Opnum vísindum
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- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts