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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Generative artificial intelligence (GenAI) use and dependence : an approach from behavioral economics
(2025) Robayo-Pinzon, Oscar; Rojas-Berrio, Sandra; Camargo, Jorge E.; Foxall, Gordon R.; Department of Business and Economics
Objective: This study aims to explore the perceived dependence on Generative Artificial Intelligence (GenAI) tools among young adults and examine the relative reinforcing value of AI chatbots use compared to monetary rewards, applying a behavioral economics approach. Participants/methods: A total of 420 university students from Bogotá, Colombia, participated in an online survey. The study employed a Multiple Choice Procedure (MCP) to assess the relative reinforcement between different durations of GenAI use (1, 2, and 4 weeks) and monetary rewards, which varied in amount and delay. Additionally, an adapted AI Dependence Scale evaluated levels of dependence on AI tools. Data analysis included repeated measures ANOVA to examine the effects of reward magnitude and delay on choices, and correlations to assess the relationship between perceived dependence and reinforcement values. Results: Participants reported low average dependence on AI tools (mean AI Dependence Scale score = 65.6), with no significant gender differences. MCP findings indicated significant differences in crossover points across varying durations or delays for AI chatbots use, suggesting a higher relative value of use for the option to use AI chatbots immediately. The average reinforcement value for AI use versus monetary rewards did significantly vary with reward magnitude. On the other hand, significant differences were found in the levels of perceived dependence on AI, according to the average daily time of AI tool use. Conclusion: The results suggest that young adults exhibit low perceived dependence on GenAI tools but show differential reinforcement values based on usage duration or delay conditions. This behavioral economics approach provides novel insights into decision-making patterns related to AI chatbots use, emphasizing the need for further research to understand the psychological and social factors influencing dependence on AI technologies.
Firm size as a moderator of stakeholder pressure and circular economy practices : Implications for economic and sustainability performance in SMEs
(2025) Ahmadov, Tarlan; Durst, Susanne; Gerstlberger, Wolfgang; Nguyen, Quang M.; Department of Business and Economics
PURPOSE: This study examines the interplay between stakeholder pressure (internal and external), circular economy (CE) practices, firm size, and their impact on the sustainability and economic performance of Small and Medium sized Enterprises. This research underscores firm size as a key moderator in the relationship between stakeholder pressures and CE adoption, aiming to provide a comprehensive understanding of this dynamic in SMEs. METHODOLOGY: Based on a cross-sectional survey of 124 SMEs in Estonia, Latvia, and Lithuania, with respondents primarily being owners and managers of firms, a three-step approach tested the proposed model for CE practices. First, Confirmatory Factor Analysis (CFA) was used to ensure that the observed variables represented latent constructs. Second, Ordinary Least Squares (OLS) and Weighted Least Squares (WLS) regression methods were used to control for factors influencing CE adoption. Finally, the interaction terms assessed the moderating role of firm size. FINDINGS: The research shows that firm size moderates these effects, with external stakeholder pressure significantly influencing CE adoption more than internal pressure. These finding underscores how firm size shapes SMEs’ responses to stakeholder pressure when adopting CE practices. IMPLICATIONS: This study provides empirical evidence that stakeholder pressure significantly influences SMEs in the Baltic States to adopt CE practices, thus impacting economic and sustainability performance. Smaller firms can enhance CE practices by strategically managing stakeholders, whereas larger SMEs should align with external stakeholder expectations for more effective CE initiatives, leading to improved organizational performance. ORIGINALITY AND VALUE: This study demonstrates how stakeholder pressures drive CE practices and impact organizational sustainability and economic performance. Firm size plays a crucial role as a moderator amplifying the influence of external stakeholder pressure on CE practices.
Proteomic characterization of Lysinibacillus reveals early-stage PET biodegradation potential
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Collaborative GenAI : Humanized Interaction Fields for Knowledge Creation
(Academic Conferences and Publishing International Limited, 2025) Böhm, Karsten; Durst, Susanne; Kianto, Aino; Toth, Ilona; Department of Business and Economics
Generative AI (GenAI) is increasingly becoming part of our habits, both in our professional and private lives. The use of this is a way to shape, change and influence knowledge creation and utilisation, and thus a very interesting phenomenon for the field of Knowledge Management (KM). In a previous work, the authors of this paper focused on the bidirectional effects of KM processes due to the interaction between humans and machines using natural language as a medium. The result of this work was the generative and responsive artificial intelligence (GRAI) model, which not only generates content on demand, but also adapts and modifies knowledge-related interactions. This research focusses on the concept of interaction fields and investigates the collaborative nature of those interaction fields to develop the conceptual model even further. This is achieved by relating to the characteristics of collaborative robotics (COBOTs) as an established form of human-machine interaction and the main differences of human-machine interactions to derive the concept of Humanized Interaction Fields (HIF) that describe relevant aspects of interaction in the field of Human-centered AI (HCAI). The research contributes to the understanding for the co-creation of knowledge between human and machine.
AI Evaluations of Sarcoma From Magnetic Resonance Imaging
(2025-11-18) Gunnarsson, Arnar Evgení; Gargiulo, Paolo; Department of Engineering
Background: Sarcomas are a rare and heterogeneous group of malignant tu- mors, making early detection and diagnosis a high priority. Diagnosis tradition- ally relies on expert interpretation of radiological imaging and histopathological biopsies, processes that are time-consuming, subjective, prone to inter-observer variability and are invasive. Methods: This research investigates the potential of artificial intelligence (AI), specifically radiomics and machine learning (ML), to support sarcoma diagnosis, characterization and grading based on MRI scans. Quantitative features were ex- tracted from multiple image transforms, including original, wavelet, Laplacian of Gaussian, square, square root, logarithm, exponential, gradient, and local binary pattern transforms. From these images, first-order statistics and texture descrip- tors (GLCM, GLSZM, GLRLM, NGTDM) were computed. A diverse set of ML models were evaluated, including Random Forest, Logistic Regression, SGDClas- sifier, Ridge Classifier, LightGBM, XGBoost, and CatBoost. Models were trained on three primary tasks: (1) binary classification of healthy vs. sarcoma tissue, (2) sarcoma grading based on the FNCLCC system, and (3) differentiation between bone and soft tissue sarcomas. In addition, a habitat generation framework was introduced, clustering radiomic segments into biologically meaningful subregions to enhance grade and sub-group classification. Model performance was assessed using AUC-ROC, accuracy, and macro- and micro-averaged F1-score, precision, and recall. Results: For binary classification of healthy vs. sarcoma tissue, models per- formed strongly, achieving AUC-ROC scores ranging from 0.829 to 0.999 depend- ing on data preprocessing, with consistently high metric values overall. This demon- strates that ML methods are well suited to distinguishing healthy from patholog- ical tissue. Grade classification achieved AUC-ROC scores ranging from 0.618 to 0.690 using radiomics and between 0.520-0.591 using habitats, with metrics gen- erally above baseline for three-class classification. While performance remains modest, results indicate meaningful correlation between radiomic inputs and FN- CLCC grade. Sub-group classification of bone vs. soft tissue sarcomas achieved AUC-ROC scores of 0.750-0.863 using radiomics and 0.585-0.707 using habitats, indicating that ML methods can effectively aid in sarcoma sub-type differentia- tion. Conclusions: The findings demonstrate that combining multiple image trans- forms with radiomics for diagnostic, characterization and grading classification feasible and effective. Advanced ML models substantially improve performance across diagnostic and prognostic tasks. Furthermore, the habitat generation ap- proach offers additional biological interpretability and can enhance grade and sub- group prediction. These results highlight the promise of AI-driven radiomics pipelines to support clinical decision making in sarcoma diagnosis and hopefully earlier management and extended survival. Keywords: Artificial Intelligence, Machine Learning, Sarcoma, Diagnostics, Sarcoma Grading, Radiomics, Habitats.
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