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
Robust statistical feature extraction and deep learning surrogates for highly accurate guided wave-based determination of global corrosion parameters in metallic plates
(2027-01) Koziel, Slawomir; Zima, Beata; Pietrenko-Dabrowska, Anna; Department of Engineering
Accurate evaluation of corrosion in metal plates is essential across many industries, particularly in shipbuilding. Among available techniques, non-destructive testing (NDT) is especially valuable, as it enables the assessment of material degradation without compromising structural integrity. Within this domain, guided ultrasonic waves have attracted increasing attention in recent years due to their potential for estimating statistical corrosion parameters, such as mean thickness loss and its standard deviation. However, practical application of these methods remains challenging because of the complex interaction between wave propagation and spatially varying thickness profiles. This study presents an advanced methodology for precise estimation of corrosion parameters using deep learning-based inverse models. The corrosion morphology is modeled as a stochastic field, while guided wave responses, obtained through numerical simulations and recorded by a sensor network, are postprocessed to extract statistical descriptors. In particular, the mean vector and covariance matrix of sensor signals, including wave packet location, width, and amplitude, are computed. To ensure robustness, feature scaling and elliptic envelope-based covariance estimation are employed, effectively mitigating the influence of outliers, which are especially pronounced at higher levels of thickness loss and surface roughness. The inverse model establishes a mapping between extracted feature vectors and the corresponding corrosion parameters, enabling their direct prediction. This framework addresses the intrinsic non-uniqueness of the problem, where identical statistical descriptors may arise from infinitely many realizations of the stochastic corrosion field. Extensive numerical investigations demonstrate that reliable feature extraction, particularly robust handling of outliers, combined with the ability of deep neural networks to capture nonlinear relationships, is critical for accurate parameter estimation. Prediction performance is further improved by aggregating responses from multiple independent realizations of the stochastic corrosion field. Overall, the results demonstrate that the proposed framework, combining robust statistical feature extraction, covariance-based feature representation and nonlinear inverse modeling, provides improved prediction accuracy compared with the previously developed feature-based linear regression model and the conventional time-of-flight approach under the considered numerical simulation conditions.
Design-ready farfield pattern modeling of broadband antennas using multi-branch attention-based recurrent convolutional decoder neural network
(2027-01) Koziel, Slawomir; Sahu, Kaustab C.; Pietrenko-Dabrowska, Anna; Department of Engineering
Behavioral modeling is becoming increasingly important in antenna development, particularly in accelerating simulation-based design processes such as optimization. However, the construction of accurate data-driven models remains a challenging task, typically limited to electrical responses such as reflection coefficients or integral performance metrics (e.g., gain). In contrast, the behavioral modeling of radiation patterns—although crucial from a design perspective—has been relatively scarce in the existing literature. This research introduces a new surrogate modeling framework for broadband antennas that leverages customized deep learning techniques. The proposed approach enables accurate and comprehensive modeling of both electrical responses (e.g., reflection coefficient, gain) and far-field radiation patterns across a broad range of frequencies, elevation/azimuth angles, and geometric parameters. As a result, the developed surrogates are highly versatile and applicable for addressing a wide spectrum of design and optimization tasks. For accurate representation of radiation patterns, a hybrid deep learning architecture is developed that integrates multi-branch recurrent processing with cross-attention–based feature fusion. Six independent RNN branches (each combining FC, LSTM, and GRU layers) extract complementary representations of the geometric input space, which are subsequently fused through a cascade of attention modules. The fused embedding is decoded using a transposed-convolutional refinement network to reconstruct the full radiation pattern. Extensive validation studies, performed using two ultra-wideband antenna designs, confirm the excellent predictive accuracy and robustness of the proposed surrogates. The results also demonstrate their advantages over multiple benchmark methods, including conventional regression models and various deep learning schemes. Furthermore, the practical utility of the framework is demonstrated through real-world design tasks, including impedance matching enhancement, size reduction, omnidirectional radiation, and gain stability. Overall, the presented methodology represents a significant advancement toward comprehensive multi-response antenna modeling and substantially extends the capabilities of behavioral metamodeling in contemporary antenna design practice.
Optimizing SiGe–SiO2 Visible–Short‐Wave Infrared Photoresponse by Modulating Interplay Between Strain and Defects Through Annealing
(2024-08) Sultan, Muhammad Taha; Stavarache, Ionel; Manolescu, Andrei; Arnalds, Unnar Bjarni; Teodorescu, Valentin Serban; Svavarsson, Halldor Gudfinnur; Ingvarsson, Snorri; Ciurea, Magdalena Lidia; Verkfræðideild
ENLIGHT : A consensus checklist for reporting laboratory-based studies on the non-visual effects of light in humans
(2023-12) ENLIGHT Consortium; Department of Psychology
Background: There is no consensus on reporting light characteristics in studies investigating non-visual responses to light. This project aimed to develop a reporting checklist for laboratory-based investigations on the impact of light on non-visual physiology. Methods: A four-step modified Delphi process (three questionnaire-based feedback rounds and one face-to-face group discussion) involving international experts was conducted to reach consensus on the items to be included in the checklist. Following the consensus process, the resulting checklist was tested in a pilot phase with independent experts. Findings: An initial list of 61 items related to reporting light-based interventions was condensed to a final checklist containing 25 items, based upon consensus among experts (final n = 60). Nine items were deemed necessary to report regardless of research question or context. A description of each item is provided in the accompanying Explanation and Elaboration (E&E) document. The independent pilot testing phase led to minor textual clarifications in the checklist and E&E document. Interpretation: The ENLIGHT Checklist is the first consensus-based checklist for documenting and reporting ocular light-based interventions for human studies. The implementation of the checklist will enhance the impact of light-based research by ensuring comprehensive documentation, enhancing reproducibility, and enabling data aggregation across studies. Funding: Network of European Institutes for Advanced Study (NETIAS) Constructive Advanced Thinking (CAT) programme; Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust, 204686/Z/16/Z); Netherlands Organisation for Health Research and Development VENI fellowship (2020–09150161910128); U.S. Department of Defense Grant (W81XWH-16-1-0223); National University of Singapore (NUHSRO/2022/038/Startup/08); and National Research Foundation Singapore (NRF2022-THE004-0002).
Neurocognitive function and health-related quality of life in a nationwide cohort of long-term childhood brain tumor survivors
(2023-04-01) Helligsoe, Anne Sophie L.; Henriksen, Louise T.; Kenborg, Line; Lassen-Ramshad, Yasmin; Wu, Lisa M.; Winther, Jeanette F.; Hasle, Henrik; Amidi, Ali; Department of Psychology
Background. Childhood brain tumor survivors are at high risk of late effects, especially neurocognitive impairment. Limited data are available examining neurocognitive function and associations with quality of life (QoL) in childhood brain tumor survivors. Our aim was to examine neurocognitive function in childhood brain tumor survivors, and associations with QoL and symptom burden. Methods. Five–year survivors of brain tumors over the age of 15 were identified in the Danish Childhood Cancer Registry (n = 423). Eligible and consenting participants completed neuropsychological tests and questionnaires assessing QoL, insomnia, fatigue, anxiety, and depression. Survivors treated with radiation (n = 59) were statistically compared with survivors not treated with radiation (n = 102). Results. In total, 170 survivors participated (40.2% participation rate). Sixty-six percent of the survivors who completed neurocognitive tests (n = 161) exhibited overall neurocognitive impairment. Survivors treated with radiation, especially whole-brain irradiation, exhibited poorer neurocognitive outcomes than survivors not treated with radiation. Neurocognitive outcomes for survivors treated with surgery were below normative expectations. Furthermore, a number of survivors experienced significant fatigue (40%), anxiety (23%), insomnia (13%), and/or depression (6%). Survivors treated with radiation reported lower quality of life (QoL) and higher symptom burden scores than survivors not treated with radiation; particularly in physical functioning, and social functioning with symptoms of fatigue. Neurocognitive impairment was not associated with QoL or symptom burden. Conclusions. In this study, a majority of the childhood brain tumor survivors experienced neurocognitive impairment, reduced QoL, and high symptom burden. Although not associated with each other, it is apparent that childhood brain tumor survivors experience not only neurocognitive dysfunction but may also experience QoL impairments and significant symptom burden.
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