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
An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis
(2025-08) Correra, Simona; Gunnarsson, Arnar Evgení; Recenti, Marco; Mercaldo, Francesco; Nardone, Vittoria; Santone, Antonella; Jónsson, Halldór; Gargiulo, Paolo; Department of Engineering
Objective: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. Methods: A total of 186 MRI scans from 86 patients diagnosed with bone and soft tissue sarcoma were manually segmented to isolate tumor regions and corresponding healthy tissue. From these segmentations, 851 handcrafted radiomic features were extracted, including wavelet-transformed descriptors. A Random Forest classifier was trained to distinguish between tumor and healthy tissue, with hyperparameter tuning performed through nested cross-validation. To ensure transparency and interpretability, model behavior was explored through Feature Importance analysis and Local Interpretable Model-agnostic Explanations (LIME). Results: The model achieved an F1-score of 0.742, with an accuracy of 0.724 on the test set. LIME analysis revealed that texture and wavelet-based features were the most influential in driving the model’s predictions. Conclusions: By enabling accurate and interpretable classification of sarcomas in MRI, the proposed method provides a non-invasive approach to tumor classification, supporting an earlier, more personalized and precision-driven diagnosis. This study highlights the potential of explainable AI to assist in more secure clinical decision-making.
An AI-Enhanced Multiband Terahertz Metamaterial Biosensor for Intelligent Leukaemia Detection
(2026-01-01) Hamza, Musa N.; Alibakhshikenari, Mohammad; Virdee, Bal; Lavadiya, Sunil; Din, Iftikhar ud; Sanches, Bruno; Koziel, Slawomir; Naqvi, Syeda Iffat; Panda, Abinash; Farmani, Ali; Mezache, Zinelabiddine; Zakeri, Hassan; Naser-Moghadasi, Mohammad; Saber, Takfarinas; Department of Engineering
This study presents an artificial intelligence (AI)-augmented micron-scale terahertz (THz) metamaterial biosensor for early-stage leukaemia diagnosis. The proposed biosensor employs a tri-negative (ε, μ, n) perfect absorber structure with an optimised multiband spectral response, enabling high-Q narrowband resonances across the 0.5–2 THz frequency range. These engineered electromagnetic characteristics enhance field confinement and analyte interaction, enabling the detection of refractive index variations as small as ∼0.014 RIU between healthy and leukaemia-affected blood samples. Spectral and field analyses reveal distinct resonance shifts, absorption variations and altered electric and magnetic field distributions in the presence of cancerous samples. Quantitative performance evaluation demonstrates excellent sensing characteristics, including a Q-factor of 268.34, a figure of merit (FOM) of 56722.80 RIU−1, and Euclidean sensitivity of 355.859564 THz RIU−1. These values indicate improved performance compared with previously reported THz biosensors for cancer detection. AI integration enables automated classification of healthy and cancerous samples using S-parameter processing and full-spectrum similarity metrics, achieving classification accuracy exceeding 95%. Comparative analysis with state-of-the-art biosensors demonstrates leukaemia-specific dielectric targeting, early-stage detection capability and an AI-assisted analytical framework for enhanced spectral interpretation. The results highlight the potential of the proposed platform as a noninvasive and label-free approach for blood cancer diagnostics, combining high spectral resolution, full-spectrum analysis and automated decision-making within a unified sensing framework.
A Low-Profile Hybrid Decoupling Method for Ultra-Compact Planar Antenna Arrays
(2026) Taheri, Sina Hasibi; Lalbakhsh, Ali; Zahur, Nabeel; Koziel, Slawomir; Szczepanski, Stanislaw; Zareanborji, Amirhassan; Department of Engineering
This paper presents a novel and compact decoupling methodology for ultra-compact planar antenna arrays with center-to-center spacing smaller than 0.3 λ. In such densely packed configurations, strong multidirectional mutual coupling significantly degrades the array performance. To address this challenge, a comprehensive decoupling strategy is proposed for a 2 × 2 array operating in the 5.8-6 GHz band. The approach integrates three coordinated mechanisms: I-shaped metallic strips for coupling-path cancellation, a dumbbell-shaped defected ground structure (DGS) for ground-current suppression, and dielectric perturbation for reducing substrate-mediated coupling. The proposed structure achieves effective two-dimensional decoupling while maintaining a low-profile and easy-to-fabricate configuration. By performing a systematic parametric study and Genetic Algorithm (GA) the structure is optimized in the multidimensional design space. Both simulated and measured results demonstrate that the proposed method successfully reduces the transmission coefficients between array elements to below -20 dB across the target frequency band, satisfying the critical design requirement. The achieved isolation enhancement does not degrade the impedance matching, and the measured radiation patterns further demonstrate that the proposed decoupling structure improves the realized gain by 2.85 dB and 4.34 dB at the lower and upper edges of the operating frequency band, respectively.
Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
(2025-05) Forni, Riccardo; Maruotto, Ida; Zanuccoli, Anna; Nicoletti, Riccardo; Trimigno, Luca; Corbellino, Matteo; Travé-Huarte, Sònia; Giannaccare, Giuseppe; Gargiulo, Paolo; Department of Engineering
Objective: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. Methods: This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18–85) who suffered from dry eye and responded to the Ocular Surface Disease Index questionnaire for scoring ocular discomfort symptoms and infrared meibography for capturing imaging of meibomian glands. By leveraging pixel brightness variations, the algorithm provides real-time detection and classification of long, medium, and short meibomian glands, offering a quantitative assessment of gland atrophy. Results: A novel parameter, namely “atrophy index”, a quantitative measure of gland degeneration, is introduced. Atrophy index is the first instrumental measurement to assess single- and multiple-gland morphology. Conclusions: This tool provides a robust, scalable metric for integrating quantitative meibography into clinical practice, making it suitable for real-time screening and advancing the management of dry eyes owing to meibomian gland dysfunction.
Adapting in times of crisis : how social media marketing of gambling changed in response to major shifts in the gambling landscape
(2026) Houghton, Scott; Boy, Frederic; Bradley, Alexander; James, Richard J.E.; Wardle, Heather; Dymond, Simon; Department of Psychology
Gambling marketing on social media in countries like Great Britain (GB) is relatively well understood. Little is known, however, about how such marketing is impacted by major changes to the gambling landscape, like the COVID-19 pandemic. Here, we assessed changes in the frequency, sentiment, and content of gambling marketing on Twitter by Great Britain (GB) gambling operators and affiliates. We analysed n = 353,134 tweets from 10 operators and affiliates posted between January 2020 and July 2022. Using machine learning, we categorised tweets based on content and tracked how social media use by operators and affiliates changed during the pandemic. Findings revealed decreases in the frequency of tweets posted during the first national lockdown, particularly for affiliates, and a greater proportion of sports content related tweets, compared to direct advertising, as the pandemic continued. Postings by affiliates tended to include more positive sentiments. Our findings highlight the speed at which gambling operators and affiliates adapted their social media marketing campaigns to large structural changes like the COVID-19 lockdowns.
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