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.
Nýlega bætt við
Environmental Sustainability of Hydropower and Its Adaptation to Climate Change
(Springer Science and Business Media Deutschland GmbH, 2026) Patro, Epari Ritesh; Balouchi, Behnam; Gosselin, Marie Pierre; Aparicio, Maria Ubierna; Finger, David Christian; Department of Engineering
Despite the ongoing technological innovation and the rapidly increasing penetration of new renewable energy sources, the hydropower sector managed to remain a central actor for power production worldwide while establishing itself as a strong support to compensate the intermittency induced by most of these new energy sources. However, the sustainability of hydropower is increasingly challenged by climate-induced hydrological shifts, sediment regime changes, and complex socio-environmental trade-offs. This chapter argues that sustainable hydropower in the 21st century demands a holistic, multi-stakeholder approach that integrates climate adaptation, sediment management, ecological integrity, and social equity. Drawing on case studies and modelling examples, the physical foundations, e.g. climate, hydrology, sediment dynamics, and their interconnections with ecosystems and society are highlighted. As a renewable and affordable, usually low-carbon source of energy, hydropower can significantly contribute to the fulfilment of the UN Sustainable Development Goals. However, any hydropower plant also potentially induces important ecological, social, and economic impacts at the local scale, calling for a sustainable strategy in building and management, but also in existing hydropower infrastructures.
Alpha rhythm and Alzheimer's disease : Has Hans Berger's dream come true?
(2025-04) Babiloni, Claudio; Arakaki, Xianghong; Baez, Sandra; Barry, Robert J.; Benussi, Alberto; Blinowska, Katarzyna; Bonanni, Laura; Borroni, Barbara; Bayard, Jorge Bosch; Bruno, Giuseppe; Cacciotti, Alessia; Carducci, Filippo; Carino, John; Carpi, Matteo; Conte, Antonella; Cruzat, Josephine; D'Antonio, Fabrizia; Della Penna, Stefania; Del Percio, Claudio; De Sanctis, Pierfilippo; Escudero, Javier; Fabbrini, Giovanni; Farina, Francesca R.; Fraga, Francisco J.; Fuhr, Peter; Gschwandtner, Ute; Güntekin, Bahar; Guo, Yi; Hajos, Mihaly; Hallett, Mark; Hampel, Harald; Hanoğlu, Lutfu; Haraldsen, Ira; Hassan, Mahmoud; Hatlestad-Hall, Christoffer; Horváth, András Attila; Ibanez, Agustin; Infarinato, Francesco; Jaramillo-Jimenez, Alberto; Jeong, Jaeseung; Jiang, Yang; Kamiński, Maciej; Koch, Giacomo; Kumar, Sanjeev; Leodori, Giorgio; Li, Gang; Lizio, Roberta; Lopez, Susanna; Ferri, Raffaele; Maestú, Fernando; Marra, Camillo; Marzetti, Laura; McGeown, William; Miraglia, Francesca; Moguilner, Sebastian; Moretti, Davide V.; Mushtaq, Faisal; Noce, Giuseppe; Nucci, Lorenzo; Ochoa, John; Onorati, Paolo; Padovani, Alessandro; Pappalettera, Chiara; Parra, Mario Alfredo; Pardini, Matteo; Pascual-Marqui, Roberto; Paulus, Walter; Pizzella, Vittorio; Prado, Pavel; Rauchs, Géraldine; Ritter, Petra; Salvatore, Marco; Santamaria-García, Hernando; Schirner, Michael; Soricelli, Andrea; Taylor, John Paul; Tankisi, Hatice; Tecchio, Franca; Teipel, Stefan; Kodamullil, Alpha Tom; Triggiani, Antonio Ivano; Valdes-Sosa, Mitchell; Valdes-Sosa, Pedro; Vecchio, Fabrizio; Vossel, Keith; Yao, Dezhong; Yener, Görsev; Ziemann, Ulf; Kamondi, Anita
In this “centenary” paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8–12 Hz) in 1924, his foresight of substantial clinical applications in patients with “senile dementia,” and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients’ quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.
Circular Economy Practices in Manufacturing SMEs : Exploration of Stakeholder Pressure, Managerial Perception, and the Mediating Role of Circular Economy Orientation
(2025-06-30) Ahmadov, Tarlan; Durst, Susanne; Nguyen, Quang M.; Foli, Samuel; Gerstlberger, Wolfgang; Department of Business and Economics
This study delves into the dynamics of Circular Economy (CE) practices in Small and Medium-sized Enterprises (SMEs), acknowledging their essential contribution to promoting sustainability. As we explore the various influences on SMEs' adoption of CE, we closely examine the distinct impacts of internal and external stakeholder pressure. Additionally, we highlight the role of positive managerial perceptions and introduce a fresh perspective by framing CE orientation as a mediating force. Employing a survey methodology, our data collection spanned three phases, resulting in 196 responses from the Estonian SMEs. The results challenge the assumptions of uniform stakeholder pressures, unveiling nuanced effects on CE practices. Significantly, a heightened CE orientation emerges as a driving factor in enhancing organisational responsiveness to external stakeholder pressure. This study advances our understanding of the intricate relationships between stakeholder dynamics, managerial perceptions, and CE practices, providing valuable insights essential for SMEs to navigate the path towards sustainable practices. This study presents both theoretical and practical contributions and suggests avenues for future research to further explore the multifaceted nature of the relationships uncovered in this study.
Attention-based dynamic multilayer graph neural networks for loan default prediction
(2025-03-01) Zandi, Sahab; Korangi, Kamesh; Óskarsdóttir, María; Mues, Christophe; Bravo, Cristián; Department of Computer Science
Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods.
Time–frequency ridge characterisation of sleep stage transitions : Towards improving electroencephalogram annotations using an advanced visualisation technique
(2025-03-01) McCausland, Christopher; Biglarbeigi, Pardis; Bond, Raymond; Yadollahikhales, Golnaz; Kennedy, Alan; Islind, Anna Sigridur; Arnardóttir, Erna Sif; Finlay, Dewar; Department of Computer Science; Department of Engineering
Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages. This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.
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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