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
How different life-history strategies respond to changing environments : A multi-decadal study of groundfish communities
(2025-12) Sólmundsson, Jón; Sigurðsson, Ólafur; Jónsdóttir, Ingibjörg G.; Jónsson, Steingrímur; Faculty of Natural Resource Sciences
In recent decades, climate variability has led to significant shifts in the distribution and prevalence of various fish species in the North Atlantic. These changes are well documented for several groundfish species within the zoogeographical transition waters surrounding Iceland, coinciding with variations in seawater temperature. This study analyses groundfish assemblage structure through the frameworks of life-history theory and biogeography. Utilizing annual bottom trawl survey data from 1987 to 2024, groundfish species in the continental shelf waters around Iceland were categorized into three primary life-history strategies: periodic, opportunistic, and equilibrium, based on their life-history traits. Over the study period, these strategies demonstrated distinct responses to environmental changes, influenced by the biogeography of the species. Increased water temperatures were accompanied by an influx of Atlantic and Boreal species, while species richness for Arctic fish remained similar or decreased. Boreal periodic species emerged as the most prominent in Icelandic waters, maintaining long-term stability in abundance and distribution. However, environmental conditions in recent decades have favoured southerly warmer-water (Atlantic) species across life-history strategies, while most Arctic species have experienced declining abundance trends. Opportunistic strategists exhibited the most pronounced temporal changes during the warming period.
Cross-cultural validation of the profile of mood scale : evaluation of the psychometric properties of short screening versions
(2025) Schmalbach, Ileana; Schmalbach, Bjarne; Aghababa, Alireza; Brand, Ralf; Chang, Yu Kai; Çiftçi, Muhammet Cihat; Elsangedy, Hassan; Fernández Gavira, Jesús; Huang, Zhijian; Kristjánsdóttir, Hafrún; Mallia, Luca; Nosrat, Sanaz; Pesce, Caterina; Rafnsson, Daði; Medina Rebollo, Daniel; Timme, Sinika; Brähler, Elmar; Petrowski, Katja; Department of Sport Science; Department of Psychology
The Profile of Mood States (POMS) is one of the most widely applied scales for measuring mood. Considering the advantages of short scales and increased international research, the aim of the present study was to evaluate cross-culturally the psychometric properties of a short 16-item version of the POMS. Data were collected from 15,693 participants across 10 different countries worldwide. Initially, we identified the original versions of the POMS in various languages. Subsequently, we selected 16 items based on the previously validated short form (POMS-16) for analysis. Psychometric properties of the POMS were then evaluated in samples from each studied population for each language version. Confirmatory factor analysis was conducted to assess its invariance across age groups and gender, alongside reliability estimation. Most language versions of the POMS-16 showed a good fit with the four-factor model, except for the Chinese (traditional) and Turkish versions. Reliability was generally high, except for the Vigor subscale in a small subset of languages. Regarding measurement invariance, the majority of language versions were invariant across gender and age groups, except for the Farsi language version across gender, and the Chinese, Farsi, Finnish, and Turkish versions across age. These findings enhance the cross-cultural applicability of the POMS-16, contributing to its utility in diverse populations and thus enhancing the comparability of the results. In addition, we introduced the first versions of the POMS in Farsi, Finnish, and Icelandic.
AI-based estimator for computational discovery and synthesis of customized microwave absorbing materials
(2023-09-07) Yadav, Ravi; Panwar, Ravi; Department of Engineering
This article investigates the viability of a deep neural network (DNN) for the computational discovery and synthesis of efficient microwave-absorbing materials and structures. A DNN is trained to tackle specific objectives in a constrained environment by utilizing the conventional forward and reverse approaches. In the forward approach, the DNN predicts various topologies of the absorbers and it is found to be effective in determining the stacking sequence of microwave-absorbing materials and their associated thicknesses. Designing a microwave absorber is observed to be exceptionally cumbersome utilizing a DNN if the material database increases unexpectedly. Following that, the solution is offered by addressing the reverse approach, in which a DNN is utilized to forecast the electromagnetic (EM) properties based on user-defined specifications. It is a convincing and simple method of designing thin and wideband customized absorbers. DNN prediction is authenticated by fabricating two distinct absorbers based on the frequency-dependent EM properties. Furthermore, the synthesized model is tested and validated with the response of the EM mixing model and microwave measurements. The suggested DNN strategy can effectively fix the issues in designing thin and broadband absorbers.
An infrastructure for robotic applications as cloud computing services
(2014) Mouradian, Carla; Errounda, Fatima Zahra; Belqasmi, Fatna; Glitho, Roch; Department of Computer Science
Robotic applications are becoming ubiquitous. They are widely used in several areas (e.g., healthcare, disaster management, and manufacturing). However, their provisioning still faces several challenges such as cost and resource usage efficiency. Cloud computing is an emerging paradigm that may aid in tackling these challenges. It has three main facets: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). This paper focuses on the IaaS aspects of robotic applications as cloud computing services. It proposes an architecture that enables cost efficiency through virtualization and dynamic task delegation to robots, including robots that might belong to other clouds. Overlays and RESTful Web services are used as cornerstones. A prototype is built using LEGO Mindstorms NXT as the robotic platform, and JXTA as the overlay middleware. Related work is reviewed, the functional entities and interfaces of the architecture are described, and the prototype architecture is presented along with the implemented scenario.
Continuous Location Statistics Sharing Algorithm with Local Differential Privacy
(Institute of Electrical and Electronics Engineers Inc., 2018-07-02) Zahra, Fatima; Liu, Yan; Abe, Naoki; Liu, Huan; Pu, Calton; Hu, Xiaohua; Ahmed, Nesreen; Qiao, Mu; Song, Yang; Kossmann, Donald; Liu, Bing; Lee, Kisung; Tang, Jiliang; He, Jingrui; Saltz, Jeffrey; Department of Computer Science
Continuous sharing of location statistics produces valuable knowledge to understand important phenomena, such as popular places or pattern behaviors. Most importantly, data should be shared without jeopardizing users' privacy. Differential privacy becomes de-facto technique for private statistical data release. Much work focuses on the centralized setting where users send their original data to a trusted server. Then the server adds controlled noises to generate differentially private statistics. This centralized approach is vulnerable to attacks where an adversary may access the true data by attacking the trusted server. Local differential privacy neutralizes this type of attacks by allowing each user to obfuscate their data before it reaches the server for statistical analysis. In this paper, we propose an algorithm to share location statistics that leverages local differential privacy combined with w-event privacy. Our solution guarantees the user's privacy when continuously releasing statistics over infinite streams. Experimental evaluation on real-life data shows our solution with strong privacy guarantee.
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