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
Antibiotic resistance genes, antibiotic residues, and microplastics in influent and effluent wastewater from treatment plants in Norway, Iceland, and Finland
(2025-11-15) Tiwari, Ananda; Jaén-Gil, Adrián; Karavaeva, Anastasia; Gomiero, Alessio; Ásmundsdóttir, Ásta Margrét; Silva, Maria João; Salmivirta, Elisa; Tran, Tam T.; Sarekoski, Anniina; Cook, Jeremy; Lood, Rolf; Pitkänen, Tarja; Krolicka, Adriana; Faculty of Natural Resource Sciences
Monitoring antimicrobial resistance genes (ARGs) in wastewater influents (pre-treatment) and effluents (post-treatment) provides insights into community-level circulation, potential amplification during treatment, and risks associated with gene release into surface waters. Pollutants such as antibiotic residues and microplastics (MPs) may influence ARG dynamics, highlighting the need to assess their dynamics across wastewater environments. In this study, we analyzed ARGs and bacterial communities using Oxford Nanopore (ONP) metagenomics and qPCR in wastewater samples from Mekjarvik (Norway), Reykjavik (Iceland), and Mariehamn (Åland, Finland). Antibiotic residues were quantified via high-performance liquid chromatography (HPLC), and MPs were characterized using micro-fourier transform infrared spectroscopy (μ-FTIR) in Mekjarvik and Reykjavik. Metagenomic analysis identified 193 unique ARGs, with the highest average (±SD) in Reykjavik (66.3 ± 4.1), followed by Mekjarvik (61.3 ± 14.1) and Mariehamn (18.0 ± 2.2). ONP sequencing revealed that many ARGs were plasmid-associated, co-occurring with metal stress genes. Common plasmids were Col440I, IncQ2, and ColRNAI. Mercury-related genes dominated metal stress genes (64.9 %), followed by multimetal (23.7 %) and copper (6.4 %). Of 45 antibiotics screened, only sulfamethoxazole and sulfapyridine were consistently detected. Polyethylene (∼60 %) was the dominant MP type; Reykjavik influent had the highest MP load (8200 MP/m3). While treatment reduced ARGs, antibiotic residues, and larger MPs, it was less effective against fine particles and key ARGs, including carbapenemase- and ESBL-associated genes. Clinically relevant ARGs and potential pathogens (e.g., Acinetobacter baumannii, Pseudomonas aeruginosa) persisted in effluents, highlighting risks to downstream ecosystems. These findings underscore the need for regular monitoring of both influents and effluents to assess treatment performance and safeguard environmental health.
Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities
(2025-01) Prisco, Giuseppe; Pirozzi, Maria Agnese; Santone, Antonella; Cesarelli, Mario; Esposito, Fabrizio; Gargiulo, Paolo; Amato, Francesco; Donisi, Leandro; Department of Engineering
Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks. This study aimed to evaluate the potential of machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at the lumbar region, to classify risk levels associated with load lifting based on the Revised NIOSH Lifting Equation. Methods: To compute postural sway parameters, the IMU captured acceleration data in both anteroposterior and mediolateral directions, aligning closely with the body’s center of mass. Eight participants undertook two scenarios, each involving twenty consecutive lifting tasks. Eight machine learning classifiers were tested utilizing two validation strategies, with the Gradient Boost Tree algorithm achieving the highest accuracy and an Area under the ROC Curve of 91.2% and 94.5%, respectively. Additionally, feature importance analysis was conducted to identify the most influential sway parameters and directions. Results: The results indicate that the combination of sway metrics and the Gradient Boost model offers a feasible approach for predicting biomechanical risks in load lifting. Conclusions: Further studies with a broader participant pool and varied lifting conditions could enhance the applicability of this method in occupational ergonomics.
Collaborative Registered Replication of Griskevicius et al. (2010) : Can Pro-environmental Behavior Be Promoted by Priming Status Motivation?
(2025-09-15) Lazarevic, Ljiljana B.; Wagge, Jordan R.; Balci, Busra Bahar; Buchanan, Erin M.; Greene, Nathaniel R.; Folwarczny, Michał; Lazić, Aleksandra; Want, Stephen C.; Lee, Seungyeon; Raddatz, Megan C.; Hehman, Eric; Gentile, Adriana; Petrović, Marija B.; De Luca, Paul; Kelly, Andrew J.; Talbot, Karine; Tobia, Jessica; Chalik, Lisa; Tsoi, Lily; Florence, Joey H.; Weissgerber, Sophia C.; Schouler, Niklas; Buron, Laurianne; Christopherson, Cody D.; Richter, Johanna; Senftner, Karina; Pazda, Adam D.; Allen, Peter J.; Kingston, Francesca; Sigurdsson, Valdimar; Grahe, Jon; Department of Business and Economics
The present study presents the results of a collaborative registered replication of Griskevicius et al. (2010, Experiment 1). As part of the Collaborative Replication and Education Project, 24 student groups from six countries (N = 3,774) investigated whether pro-environmental behavior can be promoted by priming status motives (desires for social status and prestige). This large, multi-site replication showed no evidence to support the hypothesis that hypothetical pro-environmental behavior can be stimulated by having participants read a story designed to prime status motives. We performed several exploratory analyses to investigate whether extension variables (i.e., equating “green” choices with prosocial behavior, political beliefs, sampling methods, location, duration of data collection, and gender) moderated the hypothesized effect of status motives on pro-environmental choices, but these analyses produced null results. One limitation of the study is that most data collection sites did not include a manipulation check, and the one site that did found a much weaker effect (d = 0.32) than the extremely large effect originally reported (d = 3.69). As a result, it remains unclear whether the null result reflects a failure of this specific priming method or a challenge to the underlying theory.
Climate Vulnerability and Firms’ Default Risk : The Moderating Role of Country-Level Corruption
(2026-01-01) García-Gómez, Conrado Diego; Demir, Ender; Díez-Esteban, José María; Lizarzaburu Bolaños, Edmundo; Department of Business and Economics
This paper examines the relationship between a country’s climate vulnerability and corporate default risk, utilizing a sample of 2,483 firms across 33 European countries. We find that higher country-level climate vulnerability (as measured by the ND-Gain index) is associated with an increased corporate default risk, as measured by the z-score. In addition, we identify that country-level corruption exacerbates the negative impact of climate vulnerability on corporate financial stability. Even firms with strong financial positions face heightened default risks, highlighting the pervasive threat of climate change. Corruption exacerbates this risk by undermining environmental governance, distorting resource allocation, and weakening climate adaptation strategies. Our results remain robust when considering alternative measures of climate vulnerability and default risk, varying model specifications, and addressing endogeneity using instrumental variables. This study emphasizes the critical interplay between climate vulnerability, governance, and corporate resilience, offering insights for policymakers and practitioners alike.
Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling
(2025-12) Tabbal, Judie; Ebadi, Aida; Mheich, Ahmad; Kabbara, Aya; Güntekin, Bahar; Yener, Görsev; Paban, Veronique; Gschwandtner, Ute; Fuhr, Peter; Verin, Marc; Babiloni, Claudio; Allouch, Sahar; Hassan, Mahmoud
Neurodegenerative diseases like Parkinson’s (PD) and Alzheimer’s (AD) exhibit considerable heterogeneity of functional brain features within patients, complicating diagnosis and treatment. Here, we use electroencephalography (EEG) and normative modeling to investigate neurophysiological mechanisms underpinning this heterogeneity. Resting-state EEG data from 14 clinical units included healthy adults (n = 499) and patients with PD (n = 237) and AD (n = 197), aged over 40. Spectral and source connectivity analyses provided features for normative modeling, revealing significant, frequency-dependent EEG deviations with high heterogeneity in PD and AD. Around 30% of patients exhibited spectral deviations, while ~80% showed functional source connectivity deviations. Notably, the spatial overlap of deviant features did not exceed 60% for spectral and 25% for connectivity analysis. Furthermore, patient-specific deviations correlated with clinical measures, with greater deviations linked to worse UPDRS for PD (⍴ = 0.24, p = 0.025) and MMSE for AD (⍴ = −0.26, p = 0.01). These results suggest that EEG deviations could enrich individualized clinical assessment in Precision Neurology.
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