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
Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990–2023 : a systematic analysis for the Global Burden of Disease Study 2023
(2026-10) E.GakidouEmmanuelaGBD 2023 Second-hand smoke Collaborators; Department of Psychology
Background Second-hand smoke (SHS) exposure remains a major source of morbidity and mortality among non-smokers. This study presents the first dedicated Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) publication to systematically quantify SHS prevalence and evaluate its effect as a global risk factor across a broader range of outcomes. Methods Using the GBD 2023 comparative risk assessment framework, we estimated SHS exposure prevalence and attributable disease burden across 204 countries and territories from 1990 to 2023, stratified by age and sex. Exposure estimates were derived by synthesising population-based surveys and household composition data through spatiotemporal Gaussian process regression. Relative risks for nine health outcomes, now including asthma, were estimated using the Burden of Proof methodology, and applied to calculate attributable deaths and disability-adjusted life-years (DALYs). Findings In 2023, an estimated 2·71 billion (95% UI 2·44–3·02) people worldwide were exposed to SHS, including 767 million (687–858) children aged 0–14 years. Age-standardised prevalence was highest in southeast Asia, east Asia, and Oceania (48·4% [44·0–53·4]), with female individuals in some countries experiencing nearly 1·8 times the exposure prevalence of male individuals. Despite a 22·2% (10·6–32·4) decline in global age-standardised prevalence since 1990, this progress has not been sufficient to reduce the absolute number of exposed individuals, which has remained stable globally and has risen sharply in sub-Saharan Africa (98·5% [62·0–144·4] increase) and North Africa and the Middle East (72·1% [47·8–101·8] increase). In 2023, SHS exposure accounted for 1·66 million (1·33–2·07) deaths and 44·8 million (35·8–54·3) DALYs globally. Ischaemic heart disease was the leading contributor to SHS-attributable DALYs (12·0 million [9·31–15·2]) overall, while lower respiratory infections predominated among children (5·16 million [3·48–7·12]). Interpretation We estimated that SHS remains a substantial driver of global health loss, particularly through cardiovascular and paediatric respiratory diseases. Persistent geographical disparities and growing absolute numbers of exposed individuals in several regions underscore the urgent need for accelerated implementation and enforcement of comprehensive tobacco control measures, particularly to protect women and children in both public and domestic environments. Funding Bloomberg Philanthropies and the Gates Foundation.
Automated deep learning-based arousal detection agrees with ten expert scorers in unseen data
(2026-10) Pitkänen, Henna; Huttunen, Riku; Tashakori, Masoumeh; Rissanen, Marika; Rusanen, Matias; Gretarsdottir, Heidur; Arnardottir, Erna Sif; Paunio, Tiina; Kulkas, Antti; Nikkonen, Sami; Leppänen, Timo; Korkalainen, Henri; Department of Engineering
Study Objectives: Automatic arousal scoring is essential for the cost-effective analysis of polysomnograms. Many previous automatic methods have been tested in homogeneous, single-center datasets, and all against one scorer, on event-by-event basis, or in 30-s segments. We developed a deep learning-based arousal scoring model and tested it at 1-s resolution in an independent dataset scored by ten expert scorers from seven different sleep centers. Materials and Methods: A fully convolutional neural network model was developed using 1847 openly available polysomnograms from the Multi-Ethnic Study of Atherosclerosis (MESA) and tested in 205 hold-out polysomnograms from the MESA cohort, and in an independent Sleep Revolution (SR) cohort of 50 polysomnograms, each scored by ten scorers. Model performance was evaluated on an event-by-event basis and in 1-s segments. In the SR dataset, the performance was tested against each scorer individually and against their majority agreement. Results: In the MESA test set, the model achieved an event-by-event F1-score of 0.77, a 1-s segment κ-value of 0.67, and an arousal index (ArI) intraclass correlation coefficient (ICC) of 0.83. In the multi-scorer SR dataset, the model reached a median event-by-event F1-score, a 1-s κ-value, and an ArI ICC of 0.61, 0.50, and 0.66, respectively, exceeding the corresponding median values of the scorers among themselves (0.57, 0.46, and 0.59, respectively). Conclusions: The model achieved higher median arousal scoring agreement with ten manual scorers than the scorers did among themselves in an independent dataset. This suggests that the model generalizes well and can reliably be used in different sleep recordings.
From trauma to tolerance : Psychological predictors of gambling harm among male UK Armed Forces Veterans
(2026-09) Ekenna, Adanma; Dekel, Dana; Engward, Hilary; Godier-McBard, Lauren; Kay, Chris; Kersey, Thomas; Fossey, Matt; Dymond, Simon; Department of Psychology
Introduction: Veterans face disproportionately high risks of gambling-related harm, influenced by trauma exposure, emotional dysregulation, and maladaptive coping. However, little is known about how these interconnected psychological traits contribute to gambling harm severity in Veterans. Methods: A cross-sectional survey of 184 UK Armed Forces Veterans assessed socio-demographic and military characteristics, gambling engagement, posttraumatic stress disorder (PTSD), adverse childhood experiences (ACEs), impulsivity, intolerance of uncertainty (IU), and psychological flexibility (PsyFlex). Associations with gambling harm severity, measured using the Problem Gambling Severity Index (PGSI), were examined through chi-squared tests, correlations, and stepwise logistic regression. Results: Of the sample, 25% scored ≥1 on the PGSI, with 3.3% meeting the threshold for problem gambling. Gambling harm severity was significantly associated with PTSD re-experiencing and avoidance, household dysfunction ACE, negative and positive urgency, inhibitory IU, and low PsyFlex. Regression analysis identified PTSD re-experiencing (OR 2.16), PTSD avoidance (OR 0.27), inhibitory IU (OR 7.96), and prospective IU (OR 0.12) as significant predictors. Discussion: The findings underscore the multi-faceted psychological vulnerabilities underlying gambling harm in Veterans, thus supporting the need for trauma-informed, personalized interventions. Targeted screening for IU, impulsivity, and PsyFlex could improve prevention and treatment efforts.
Correction to : Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery (Nature Energy, (2019), 4, 12, (1040-1048), 10.1038/s41560-019-0501-4)
(2025-03) Lohrmann, Alena; Farfan, Javier; Caldera, Upeksha; Lohrmann, Christoph; Breyer, Christian; Department of Computer Science
Correction to: Nature Energyhttps://doi.org/10.1038/s41560-019-0501-4, published online 25 November 2019.
Troubled waters : Estimating the role of the power sector in future water scarcity crises
(2023-11-01) Lohrmann, Alena; Farfan, Javier; Lohrmann, Christoph; Kölbel, Julian Fritz; Pettersson, Frank; Department of Computer Science
One of the effects of climate change is on freshwater availability. The widespread drought in the summer of 2022 impeded access to freshwater, putting into question the reliability of the current and future energy generation and evoking concerns of competition of different industries for water. In response to climate change, energy transition scenarios represent pathways to a more sustainable energy system, but often overlook the water footprint of the energy sector. Therefore, this study uses machine learning for the identification of thermal power plants’ cooling systems to estimate the water footprint of the current and future energy system using six energy transition scenarios. It is built on published data on thermal power plants announced globally, with a total capacity of 3277 GW, which are planned to be installed between 2020 and 2050. The results demonstrate that the water consumption of the global power sector may increase by up to 50% until 2050, compared to the 2020 level. The findings also emphasize that every new thermal power plant installed in the future will be associated with a higher average water demand per unit of generated electricity. Hence, the rising stress on water systems becomes another argument supporting the transition towards renewables.
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