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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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.
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Continuous Location Statistics Sharing Algorithm with Local Differential Privacy
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(Institute of Electrical and Electronics Engineers Inc., 2019-05) Errounda, Fatima Zahra; Liu, Yan; Department of Computer Science
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