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
Theory of non-Markovian dynamics in Fluorescence spectra
(2014-12) Kumar, Abhishek; Erlingsson, Sigurður Ingi; Manolescu, Andrei
Robust quantum coherence is an important prerequisite for any system that may be used to perform quantum information processing tasks. For systems that can be probed optically, the fluorescence spectrum may provide indirect evidence of coherence times when supplemented with an appropriate model of the decay process. A common approach is to assume a Markovian system, resulting in exponential decay of correlation functions and Lorentzian features in the associated spectrum. For physical systems with strongly history-dependent (non-Markovian) dynamics, there is currently limited systematic theoretical approach to establish the associated spectrum. In this thesis, we present a detailed theoretical method to obtain the fluorescence spectrum for a general system undergoing non-Markovian dynamics. This procedure can be used to systematically account for features in fluorescence spectra due to genuine non-Markovian dynamics. We apply this method to study the non-Markovian behaviour in the side peak of the Mollow-triplet and absorption spectrum of coherent population trapping systems.
Applications of latent variable methods in primary aluminium production
(2025) Manolescu, Petre; Sævarsdóttir, Guðrún Arnbjörg; Duchesne, Carl; Department of Engineering
The aluminum industry is currently facing challenges on multiple fronts. One of them is the raw material quality and their impact on process stability. In modern aluminum smelter plants, there are increasing environmental and cost efficiency demands while raw material quality decreases. The digitalization of the manufacturing industry, dubbed Industry 4.0, opens up new avenues of exploration to meet the demands of the 21st century aluminum production. In this research, big data analytics methods were employed to determine the stability of a potline. An anomaly detection tool was built using principal component analysis. The prototype showed that drifts in process operations can be correctly identified earlier than with existing univariate statistical process control tools. The multivariate prototype was later implemented in production at Alcoa Fjardaal. Using the principal component analysis model, a ranking system was developed as the prioritization method of the problematic reduction cells. The variables’ contributions plots were used to help identify the issues in the problem cells. The model performance was assessed with the help of operators attending to the pots on the shop floor. A comparison with existing plant tools showed the potential of advanced notice of the onset of process anomalies. Through an anode tracking system, smelter data was collected to estimate the net carbon consumption (NCC) of individual anodes using latent variable methods. The properties of individual anodes were thus linked to their corresponding reduction cell performance. A multivariate statistical analysis of the linked carbon and pot operation databases, was compared to the existing net carbon consumption formula developed by R&D Carbon. The comparison demonstrated that the prediction for net carbon consumption, for individual anodes, significantly increased using the anode tracking system and latent variable methods. The anode tracking data was split into several blocks determined by manufacturing steps sequence. The impact of each block onto the net carbon consumption was determined using the sequential multi-block partial least squares algorithm. This methodology helped establish a link between green anode properties and the net carbon consumption of individual anodes.
Technological Change in Wealth Management : The development of a conceptual CRM model for the HNWI client interaction under the consideration of AI
(2025-03) Zimmermann, Fabian; Durst, Susanne; Department of Business and Economics
The constantly developing landscape of financial technology significantly shapes the financial sector and how cooperation and individuals interact with each other in a financial setting. While the developments incrementally unfold, the magnitude of their impact is difficult to comprehend and not addressed in wealth management. Many of today’s management models, which are used in academia and business alike, fall short of incorporating the latest developments and are on the brink of becoming outdated or obsolete in the eye of artificial developments. One particular field of interest within financial services, is the wealth management area. The development in this specific field is of particular interest, as it is a human-driven field of finance in which the human aspect (relationship and trust) plays a crucial role in the client interaction. Several models exist which conceptually address the client relationship management, to grasp their complexity. These commonly used models become increasingly impacted and jeopardized by artificial intelligence which impacts the composition of their factors leading to incomplete and outdated models. This doctoral study addresses the issue by analyzing existing models, extracting the factors which hold their validity despite technological developments and incorporating new factors. Thus, proposing a contemporary and conceptual client relationship model (CRM) model enhanced by AI factors. Thereby, this research broadens the understanding of the current developments in the wealth management sector and identifies key areas of development. It offers a conceptual and contemporary CRM model factoring in AI considerations to enable practitioners and academics alike to identify areas of developments and foster a better understanding of the interconnection between the factors in the client interaction. Thereby it provides considerations on AI and uncovers the increasing interconnectivity of CRM factor models.
Stability of EEG-Biometrics : The role of artifacts in within vs. cross session authentication
(2026) Höller, Yvonne; Bathke, Arne C.; Uhl, Andreas; Faculty of Psychology
Artifacts in the electroencephalogram (EEG) have not been investigated systematically regarding their effect on EEG biometric performance. Since artifacts vary over time, they can be expected to lower EEG biometric performance in cross session cross-validation. However, because of their presumed uniqueness, they might increase within session cross-validation performance of an EEG biometric system. We examined the EEG biometric performance of 15 features within and across 6 sessions recorded over 3 days, including 3 different cognitive conditions, before and after artifact exclusion in a sample of up to 90 individuals. Our results revealed significant effects of artifact exclusion depending on the cross-validation scenario and the feature. Overall, within session cross-validation was not systematically affected differently by artifact exclusion as compared to cross session cross-validation, but depending on the feature and the type of cognitive task employed, artifact exclusion led to better or worse results, with some frequency-dependent features performing better when artifacts were excluded. Our results emphasize the overestimation of biometric performance in within session cross-validation (with equal error rates (EERs) down to 0) as compared to cross session cross-validation (with EERs for the same feature above 30) and the low reliability of results obtained when evaluating EEG biometric performance in small samples. We recommend that future developments of EEG biometrics must test their robustness against artifacts in the data.
Stratification and mixed layer depth around Iceland : Characterization and inter-annual variability
(2026-05-29) Ruiz-Angulo, Angel; Portela, Esther; de Marez, Charly; Macrander, Andreas; Ólafsdóttir, Sólveig Rósa; Meunier, Thomas; Jónsson, Steingrímur; Pérez-Hernández, M. Dolores; Faculty of Natural Resource Sciences
The ocean around Iceland is a key region where major water masses and currents interact, influencing the global ocean circulation. Here, we analyze 29 years (1990–2019) of quarterly hydrographic section data collected around Iceland. The hydrographic properties around Iceland show important spatial variability. Based on temperature, salinity, and stratification structure, we classified the Icelandic waters in three distinct regions: the south, the north, and northeast regions. The warm and salty Atlantic Waters that dominate the south show the deepest winter mixed layers (∼ 500 m) while the north and northeast show shallower depths (∼ 100 m). Based on the decomposition of total stratification into temperature and salinity contributions, we find that the subsurface stratification is mainly controlled by temperature in the south and by salinity in the northwest, while in the north, the North Icelandic Irminger Current and East Icelandic Current alternate seasonally, shifting the region between temperature-dominated and salinity-dominated stratification. The interannual variability of the mixed layer and of its thermohaline properties is also large around Iceland. Mixed layer waters were generally colder in the 1990's, then warmed until approximately 2015, and became colder again from 2015 to 2018. In the northeast, a multidecadal mixed layer warming trend emerges from the interannual variability as the Atlantic Water progresses northeastward, which is responsible for transforming locally the upper stratification from salinity-dominated into temperature-dominated. This is associated with the “Atlantification” of the Arctic. Within the mixed layer south of Iceland, density has continuously decreased since the mid 1990's. Elsewhere, we observe density-compensated changes in mixed layer temperature and salinity, without clear long trends. This study provides an unprecedented and detailed description of the seasonal to multi-decadal variability of the mixed layer depth and stratification around Iceland, showing links between this regional variability and changing North Atlantic under global warming.
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