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
Endometriosis is not associated with or linked to the GALT gene
(2001) Stefansson, Hreinn; Einarsdottir, Arna; Geirsson, Reynir T.; Jonsdottir, Kristin; Sverrisdottir, Gudlaug; Gudnadottir, Vala G.; Gunnarsdottir, Steinunn; Manolescu, Andrei; Gulcher, Jeffrey; Stefansson, Kari; Department of Engineering
Objective: To investigate a possible association between the carrier frequency of the N314D mutation in the galactose-1-phosphate uridyl transferase (GALT) gene and endometriosis and linkage to the short arm of chromosome 9, where the GALT gene resides. Design: Association and linkage study. Setting: Population material collected for case and family studies in endometriosis. Patient(s): Women diagnosed with endometriosis by laparotomy or laparoscopy.Intervention(s): Association with the GALT gene investigated by genotyping 85 affected women and 213 unrelated control women and a scan for linkage to chromosome 9 in 205 women from 64 families with endometriosis. Main Outcome Measure(s): Multipoint parametric lod scores and frequency of alleles.Result(s): There was no significant difference in allele frequency for the N314D polymorphism in patients compared with control subjects. No evidence for linkage was found to chromosome 9p, where the GALT gene resides. Conclusion(s): The experiments reported herein provide no evidence supporting involvement of the GALT locus in the development of endometriosis.
Area-based conservation in Iceland: Status and the way forward to the 30x30 area cover goal
(University of Iceland, Environment and Natural Resources, 2026-09) Siltanen, Jukka Kalervo; Pétursson, Jón Geir; Faculty of Life and Environmental Sciences (UI); Líf- og umhverfisvísindadeild (HÍ); School of Engineering and Natural Sciences (UI); Verkfræði- og náttúruvísindasvið (HÍ)
The Kunming–Montreal Global Biodiversity Framework (KM GBF) Target 3 (“30x30”) commits countries to achieve 30% protection of their territories by the year 2030 and that target is now in the new National Iceland Biodiversity Policy, recently published. This report evaluates Iceland’s status and progress towards the KM-GBF Target 3 on terrestrial and inland water areas, and provides strategic recommendations to expand and improve area based conservation following the National Iceland Biodiversity Policy. Importantly, this target is not only about the 30% as quantity, but more about its quality, hence issues of governance and management and ensuring it covers representative nature. To achieve Target 3 countries can employ two key strategies of area-based conservation, formally protected areas (PAs) and a more recent strategy called other effective conservation measures (OECMs).
Global and regional models for identification of cooling technology in thermal power generation for water demand estimations in water-energy nexus studies
(2022-12-20) Lohrmann, Alena; Lohrmann, Christoph; Luukka, Pasi; Department of Computer Science
Water-energy nexus studies aim to connect the process of power generation with the corresponding water demand. The lack of information on the currently installed cooling systems at individual power plants is a challenge for the assessment of the water use on the power plant-level, which complicates decision-making in water management, especially in water-stressed regions. In this study, we investigate the spatial and temporal trends in cooling technology installations globally. Based on that, we propose a machine learning model for cooling technology identification on a regional and global level, which uses a combination of feature selection and classification algorithms. The global model demonstrates an average test set accuracy of 85.42%, which corresponds to only a minor underestimation of the actual global water footprint of 1.78% when the cooling technology and water footprint of individual power plant units is unknown. Apart from that, a special emphasis was placed on regions characterized by high and extremely high water stress, where mistakes in water policy planning and water management may lead to an unsustainable water use or even to an overexploitation of water resources. In these regions, the calculated test set accuracy was 80.83%, which is significantly larger than the average accuracy of a majority class model. The results and the method proposed in this study enable cooling system identification in individual power units using information available from other sources, such as the water stress score or seasonal freshwater availability in the region.
Accuracy and predictive power of sell-side target prices for global clean energy companies
(2021-11-01) Lohrmann, Christoph; Lohrmann, Alena; Department of Computer Science
Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.
Nonspecificity, strife and total uncertainty in supervised feature selection
(2022-03) Lohrmann, Christoph; Luukka, Pasi; Department of Computer Science
In this paper we propose three novel feature ranking methods for supervised feature selection in the context of classification which are based on possibility theory. All three methods – nonspecificity, strife and total uncertainty – are tested on eight artificial data sets and ten medical real-world data sets and benchmarked against ReliefF, the Fisher score, the fuzzy entropy and similarity (FES), the Fuzzy similarity and entropy (FSAE) filter, symmetrical uncertainty as well as using no feature selection. The feature ranking methods were applied following two approaches: (1) using a fixed threshold for the number of highest-ranking features selected and (2) using a hybrid feature selection approach with a classifier (k-nearest neighbor classifier, decision tree, similarity classifier, SVM) to select the optimal number of features to select. The results indicate that strife and the Fisher score are the two feature ranking methods that for both approaches are on average ranked the highest in terms of the test set accuracy on the real-world data sets. Besides that, for the hybrid approach, strife uses most of the time a considerably smaller number of features than nonspecificity and total uncertainty. In terms of stability, which was measured with the adjusted stability measure (ASM), the Fisher score and strife were among the most stable feature ranking methods in this study. Additionally, strife's feature subsets were diverse compared to those of the remaining feature selection methods, making it a good candidate to be included in a feature selection ensemble.
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