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
Attribute Conditioning is insensitive to cue competition and is not predicted by the Big Five Personality Traits
(2026-05) Quigley, Martyn; Dymond, Simon; Kiely, Katie; Bradley, Alex; Haselgrove, Mark; Department of Psychology
When a neutral stimulus is paired with a stimulus denoting an attribute, the neutral stimulus inherits that attribute (i.e., Attribute Conditioning; AC). The current experiments examined whether this effect is sensitive to cue competition, specifically blocking (Experiment 1, n = 245) and overshadowing (Experiment 2, n = 213), and whether personality traits can predict this effect (n = 458). Participants were shown cartoon images of people (CSs) paired with healthy or unhealthy foods (USs) and completed the Big Five Inventory. An AC effect was evident—people paired with healthy foods were rated healthier than people paired with unhealthy foods. However, there was no evidence of cue competition or personality traits impacting the AC effect, although females displayed a stronger AC effect than males. These findings indicate that AC is a robust phenomenon of relevance to social learning processes but is insensitive to factors that influence other forms of conditioning.
Returns on rights: A 28-year investment analysis of Iceland's ITQ quotas
(2025-11-13) Gunnlaugsson, Stefán Bjarni; Faculty of Business Administration
The present study analyses the return and risk profile of Permanent Quota Shares (PQS) in Iceland's Individual Transferable Quota (ITQ) fisheries management system between 1992 and 2019. The hypothetical "armchair fisherman" is defined as an investor who does not partake in fishing activities but holds PQS and leases out the associated catch entitlements. This construct was devised to assess the financial performance of quota ownership as a passive investment. PQS represent tradable rights to a fixed share of the Total Allowable Catch (TAC), with the potential to generate returns through both capital gains and annual lease income. The objective of this research is to examine PQS as investment instruments by comparing their performance to major domestic and international asset classes. The analysis uses a 28-year dataset covering Iceland’s five most valuable demersal species –cod, haddock, saithe, redfish and Greenland halibut – to apply standard financial metrics, including average return, standard deviation and the Sharpe ratio. The results demonstrate that an investor adopting an armchair fishing strategy would have achieved an average annual return of 21.6%, with a Sharpe ratio of 0.72. This significantly outperforms equities, bonds, and other benchmarks. The analysis employs a 28-year dataset encompassing Iceland's five most valuable demersal species – cod, haddock, saithe, redfish and Greenland halibut – to implement standard financial metrics, including average return, standard deviation and the Sharpe ratio. The findings indicate that an investor who had adopted a passive investment approach would have attained an average annual return of 21.6%, with a Sharpe ratio of 0.72. This investment strategy has been shown to significantly outperform traditional benchmarks such as equities and bonds. The findings demonstrate the strong financial performance of PQS and emphasise the importance of governance structures in shaping the distribution of economic rents in rights-based fisheries systems.
Atlas and Updated Rules for the Scoring of Cyclic Alternating Pattern (CAP) in Human Sleep. A Consensus Report by a Taskforce of the European Sleep Research Society
(2026-08) Parrino, Liborio; Gretarsdottir, Heidur; Thomas, Robert; Rosenzweig, Ivana; Bruni, Oliviero; Senel, Gulcin Benbir; Mendonça, Fábio; Ravelo García, Antonio G.; Arnardóttir, Erna Sif; Ferri, Raffaele; Department of Engineering
The cyclic alternating pattern is a hallmark of the dynamic organisation of non-rapid eye movement sleep, reflecting the brain's oscillatory regulation of arousal and sleep stability. Since the original publication of the rules for scoring the cyclic alternating pattern in 2001, major advances in sleep neurophysiology, signal analysis and international sleep staging standards have necessitated a comprehensive revision. This consensus report by a Taskforce of the European Sleep Research Society presents the updated 2025 Atlas and Rules for the scoring of the cyclic alternating pattern in human sleep. The new guidelines preserve the foundational framework of the cyclic alternating pattern while introducing several critical innovations: (i) expanded definitions and a broader catalogue of Phase A electroencephalographic patterns; (ii) refined onset and offset criteria based on amplitude and frequency shifts, with strict inter-lead concordance requirements; (iii) standardised metrics for the quantification and reporting of the cyclic alternating pattern; (iv) formal guidance for scoring the cyclic alternating pattern in paediatric populations and optional scoring in rapid eye movement sleep; and (v) a technical framework for computerised detection and analysis. These updates align scoring with contemporary sleep staging standards, enhancing precision, reproducibility and applicability across research and clinical contexts.
Assessing the relevance of biosignal-controlled robotic rehabilitation technologies : a systematic review
(2026-09) Santoriello, Vittorio; Cesarelli, Giuseppe; Ficuciello, Fanny; Giugliano, Carmine; Angelone, Francesca; Russo, Michela; Ponsiglione, Alfonso Maria; Romano, Maria; Department of Engineering
Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.
Residual deep neural network and response features for inverse modeling of microwave passives
(2026-07) Koziel, Slawomir; Sahu, Kaustab C.; Pietrenko-Dabrowska, Anna; Department of Engineering
Electromagnetic (EM) solvers are widely used in microwave design for their accuracy, but are computationally expensive. Surrogate models offer a remedy, yet face challenges such as the curse of dimensionality. This work introduces a novel inverse modeling approach using deep learning and carefully selected response features to predict circuit geometry from desired operating parameters such as center frequency and power split ratio. Data samples for model training are collected through a performance-driven sequential process. Setting the model at the feature level implicitly reduces dimensionality, as the feature set is smaller than the geometry parameter set. This allows reliable regressors to be built with far fewer training samples, greatly lowering computational cost. Additional savings are achieved through explicit dimensionality reduction, carried out using global sensitivity analysis. To set up an inverse surrogate, we employ a customized residual deep residual neural network (ResNet), which is particularly well-suited for capturing dependencies between features and geometry parameters. This is due to ResNet’s resilience to a certain degree of parametric redundancy in the circuit structure. The inverse model produces parameter vectors sufficiently close to the optimal ones, thus only minor refinement is necessary. Also, our model is shown to be more accurate than state-of-the-art forward and inverse metamodels. The proposed ResNet embedded in the inverse modeling regime and combined with dimensionality-reduced sequential sampling are the main artificial intelligence-related contributions of this study. Engineering applications encapsulate the successful employment of our framework for rapid global circuit optimization, as exemplified using three microwave passives.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- 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