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
Associations between strength and power and throwing performance in high-level male and female javelin throwers
(2026-05-18) Köykkä, Miika; Ihalainen, Johanna K.; Saari, Anssi; Waller, Benjamin; Cronin, Neil J.
Javelin throwing presents some unique characteristics compared to other overarm throwing sports and imposes considerable physical demands on the throwers. As these demands have not been adequately quantified in the literature, this study investigated the associations between strength, power, and javelin throwing performance. Thirteen male (age: 24 ± 3 years, personal best: 77.0 ± 5.7 m, javelin throwing as the primary sport: 8 ± 3 years) and fourteen female (24 ± 5 years, 55.6 ± 5.7 m, 8 ± 4 years) javelin throwers were tested for javelin throwing performance, unilateral and bilateral vertical jumps, standing overhead medicine ball throw, and isometric maximal strength in various upper and lower body exercises. In males, throwing performance was associated moderately to strongly with bilateral and unilateral drop and depth jump measures, and with forearm supination strength of the throwing arm (R = .56 to .72, p < .05). In females, throwing performance was associated moderately to strongly with ankle plantarflexion and hip abduction strength in each leg, as well as with grip and shoulder internal rotation strength of the throwing arm (R = .55 to .73, p < .05). The results show clear sex-specific differences in important strength and power measures for javelin throwing. In males, better throwers possess greater lower body power whereas better female throwers display greater upper and lower body maximal strength.
A Randomized Controlled Trial of an iCBT Program to Reduce Infertility-Related Stress : A Novel Digital Approach
(2026-06-23) Haflidadottir, Sigurbirna; Helgadottir, Fjola Dogg; Sighvatsson, Magnus Blondahl
The global burden of infertility and its associated psychological distress is substantial, yet many individuals face barriers to accessing psychological support. A scalable, low-cost online intervention presents a promising solution. A randomized controlled trial (RCT) was conducted (experimental vs. waitlist control group) to evaluate the efficacy of a fully automated cognitive behavioral therapy (CBT) program in reducing infertility-related stress among women attempting to conceive. Data were analyzed using linear mixed models on an intention-to-treat basis. The study enrolled 63 Icelandic women experiencing infertility-related stress. The intervention, Overcome Fertility Stress, is an online program designed to identify and modify maladaptive behaviors and thought patterns contributing to infertility-related distress. It delivers personalized treatment through prewritten text, audio, and video responses based on user input. Infertility-related stress was measured using the 46-item Fertility Problem Inventory (FPI), which assesses a global stress score and five domains of infertility-related stress. Participants in the experimental group demonstrated a significant reduction in infertility-related stress (mean reduction score = 22.85, p < 0.05), whereas the control group showed no significant changes (p > 0.05). The within-group effect was large, but controlled between-group effects and confidence intervals should be prioritized when interpreting treatment benefit. These findings provide preliminary controlled evidence that a fully automated CBT program may reduce infertility-related stress, although the underpowered sample and low adherence require cautious interpretation. This RCT, alongside prior feasibility studies, suggests that Overcome Fertility Stress may be a promising low-intensity intervention for infertility-related stress. No significant effects were observed on secondary outcomes measuring depression, anxiety, stress, or worry, suggesting the program is specific to infertility-related stress rather than a broader intervention for emotional disorder symptoms. Larger, adequately powered trials with stronger engagement strategies are needed. Future research should focus on expanding access and integrating digital psychological support into standard fertility care.
An Icelandic pangenome reference
(2026-08-19) Holley, Guillaume; Eggertsson, Hannes P.; Kristmundsdottir, Snaedis; Beyter, Doruk; Skuladottir, Astros Th; Moore, Kristjan H.S.; Olason, Pall I.; Gylfason, Arnaldur; Magnusson, Olafur T.; Oddsson, Asmundur; Stefansson, Hreinn; Helgason, Agnar; Masson, Gisli; Sulem, Patrick; Gudbjartsson, Daniel F.; Stefansson, Kari; Halldorsson, Bjarni V.; Department of Engineering
Reference bias is an issue that affects most genomic studies analysing short reads mapped to a reference genome1,2. It can be mitigated by mapping to multiple haplotypes represented in a pangenome3, 4–5. Here we introduce two new methods to address reference bias: Emblask for pangenome construction and Weaver for mapping to pangenomes at scale. Emblask is a hybrid long- and short-read haplotype-resolved dual assembly pipeline for parent–offspring trio data. Using Emblask, we assembled 698 Icelandic haplotypes and added them to the Human Pangenome Reference Consortium (HPRC) pangenome4 to construct an Icelandic pangenome reference (HPRC-ICE) including 51.41 million small variants. We mapped the short reads of 57,630 Icelanders to HPRC-ICE with Weaver and called 98.96 million variants, representing a 6.17% increase over mapping to a linear reference. We uncovered new variants in low-mappability regions, including a pathogenic single nucleotide polymorphism (SNP) in GBA1 that associates with early onset Parkinson’s disease and a missense SNP in CBS that is pathogenic for homocystinuria. We replicated the GBA1 association in the UK Biobank6 with a targeted remapping of 429,193 British and Irish participants.
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.
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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