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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Unsupervised machine learning in sleep research : a scoping review
(2025-11-01) Biedebach, Luka; Ferreira-Santos, Daniela; Stefanos, Marie Ange; Lindhagen, Alva; Pires, Gabriel Natan; Arnardóttir, Erna Sif; Islind, Anna Sigridur; Department of Engineering; Department of Computer Science
Study Objectives Unsupervised machine learning—an approach that identifies patterns and structures within data without relying on labels—has demonstrated remarkable success in various domains of sleep research. This underscores the broader utility of machine learning, suggesting that its capabilities extend beyond current applications and warrant further exploration for novel insights in sleep studies, focusing specifically on unsupervised machine learning. Methods This paper outlines a scoping review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews. A comprehensive search covering various search terms focusing on the intersection between unsupervised machine learning and sleep led to 3960 publications. After screening all titles and abstracts with two independent reviewers, ultimately, 356 publications were included in the full-text review. The data extracted from the full texts included information about the machine learning methods and types of sleep data, as well as the study population. Results There has been a steep increase in the number of publications in this research area in the past 10 years. Clustering is the most commonly used method, but other methods are gaining popularity. Apart from classical polysomnography, data from wearable devices, nearables, video, audio, and medical imaging techniques have been used as input to unsupervised machine learning. The broad search allowed us to explore various applications within sleep research, ranging from the general population to populations with various sleep disorders. Conclusion The review mapped existing research on unsupervised learning in sleep research, identified gaps in the literature, and derived directions for future research.
Correlates of sedentary behaviour in adults with intellectual disabilities—A systematic review
(2018-10-17) Oppewal, Alyt; Hilgenkamp, Thessa I.M.; Elinder, Liselotte Schäfer; Freiberger, Ellen; Rintala, Pauli; Guerra-Balic, Myriam; Giné-Garriga, Maria; Cuesta-Vargas, Antonio; Oviedo, Guillermo R.; Sansano-Nadal, Oriol; Izquierdo-Gómez, Rocio; Einarsson, Ingi; Teittinen, Antti; Melville, Craig A.; Department of Sport Science
Individuals with intellectual disabilities (ID) are at high risk for high levels of sedentary behaviour. To inform the development of programmes to reduce sedentary behaviour, insight into the correlates is needed. Therefore, the aim of this study is to review the evidence on correlates of sedentary behaviour in adults with ID. We performed a systematic literature search in Ovid Medline, Ovid Embase, Web of Science and Google Scholar up to 19 January 2018, resulting in nine included studies that were published from 2011 to 2018. Correlates were categorized according to the ecological model. Studies predominantly focused on individual level correlates. Of those correlates studied in more than one study, having epilepsy was associated with less sedentary behaviour and inconsistent results were found for sex, genetic syndromes, weight status, physical health, mobility, level of ID, and mental health. Of the few interpersonal and environmental factors studied, only living arrangements were studied in more than one study, with inconsistent results. To date, we.
A Review of Secondary Aluminum Production and Its Byproducts
(2021-09) Padamata, Sai Krishna; Yasinskiy, Andrey; Polyakov, Peter; Department of Engineering
Secondary aluminum production is required for the conservation of the environment. It can significantly reduce greenhouse gas emissions and energy consumption and reduce the consumption of alumina, a source of primary aluminum. Secondary aluminum production requires sorting processes for the metal scrap before starting the refining process. Salt slags generated from both primary and secondary aluminum production need to be recycled/treated as they are considered hazardous byproducts. This review paper discusses the methods used for sorting and refining aluminum waste and managing and utilizing slag cakes/slag from recycling techniques.
A model invalidation-based approach for elucidating biological signalling pathways, applied to the chemotaxis pathway in R. sphaeroides
(2009-10-31) Roberts, Mark; August, Elias; Hamadeh, Abdullah; Maini, Philip; McSharry, Patrick McSharry; Armitage, Judith; Papachristodoulou, Antonis; Department of Engineering
Using the electrodermal activity signal and machine learning for diagnosing sleep
(2023) Piccini, Jacopo; August, Elias; Óskarsdóttir, María; Arnardóttir, Erna Sif; Department of Engineering; Department of Computer Science
Introduction: The use of the electrodermal activity (EDA) signal for health diagnostics is becoming increasingly popular. The increase is due to advances in computational methods such as machine learning (ML) and the availability of wearable devices capable of better measuring EDA signals. One field where work on EDA has significantly increased is sleep research, as changes in EDA are related to different aspects of sleep and sleep health such as sleep stages and sleep-disordered breathing; for example, obstructive sleep apnoea (OSA). Methods: In this work, we used supervised machine learning, particularly the extreme gradient boosting (XGBoost) algorithm, to develop models for detecting sleep stages and OSA. We considered clinical knowledge of EDA during particular sleep stages and OSA occurrences, complementing a standard statistical feature set with EDA-specific variables. Results: We obtained an average macro F1-score of 57.5% and 66.6%, depending on whether we considered five or four sleep stages, respectively. When detecting OSA, regardless of the severity, the model reached an accuracy of 83.7% or 78.4%, depending on the measure used to classify the participant's sleep health status. Conclusion: The research work presented here provides further evidence that, in the future, most sleep health diagnostics might well do without complete polysomnography (PSG) studies, as wearables can detect well the EDA signal.
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