In light water reactor fuel, gaseous fission products segregate to grain boundaries, resulting in the nucleation and growth of large intergranular fission gas bubbles. The segregation rate is controlled by diffusion of fission gas atoms through the grains and interaction with the boundaries. Based on the mechanisms established from earlier density functional theory (DFT) and empirical potential calculations, diffusion models for xenon (Xe), uranium (U) vacancies and U interstitials in UO 2 have been derived for both intrinsic (no irradiation) and irradiation conditions. Segregation of Xe to grain boundaries is described by combining the bulk diffusion model with a model for the interaction between Xe atoms and three different grain boundaries in UO 2 (Σ5 tilt, Σ5 twist and a high angle random boundary), as derived from atomistic calculations. The present model does not attempt to capture nucleation or growth of fission gas bubbles at the grain boundaries. The point defect and Xe diffusion and segregation models are implemented in the MARMOT phase field code, which is used to calculate effective Xe and U diffusivities as well as to simulate Xe redistribution for a few simple microstructures.

Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Soft computing.La publicació final està disponible a Springer a través de http://dx.doi.org/10.1007/s00500-016-2399-0 This is a copy of the author 's final draft version of an article published in the journal Soft computing.The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2399-0 Article publicat / Published article:Arguedas, M. [et al.] (2016) A model for providing emotion awareness and feedback using fuzzy logic in online learning. " Soft computing". Doi: 10.1007/s00500-016-2399-0 Abstract: Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students' learning performance. Section/Category: Methodologies & Application Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation A MODEL FOR PROVIDING EMOTION AWARENESS AND FEEDBACK USING FUZZY LOGIC IN ONLINE LEARNINGAbstract Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advan...

 This project contributes to the NEAMS vision by: • Providing atomic-level insight of fuel behavior and performance (UO 2 and clad) to meso-and/or engineering-scale models, thus enabling a sciencebased modeling approach. Highlights  This project contributes to the NEAMS vision by: • Developing atomistic methodology for simulating phenomena critical for meso-and/or engineering-scale model development, thus enabling a science-based modeling approach.

USA andersson @Ianl.gov RECEIVED DATE CORRESPONDING AUTHOR FOOTNOTEDespite the chemical and structural simplicity of MgB2' at 39 K this compound has the highest known Tc of any binary compound. Electron doping by substituting Al for Mg leads to decreasing Tc and the observed concentration dependent rate of decrease has been proposed to arise from the non-ideal character of MgB2-AIB2 solid solutions, which derives from the existence of an ordered M~.sAlo.sB2 compound. Heterogeneous nano-scale structure patterns in solid solutions have emerged as an important concept for complex materials, ranging from actinide alloys and oxides to high-temperature cuprate superconductors and mallganite-based materials exhibiting colossal magnetoresistivity. In this work we investigate the formation of structural heterogeneities in Mg 1 . ,AI.B2' which take the form of nano-scale AI-AI and AI-Mg domains of different geometry and size, using molecular statics/dynamics simulations and in particular we study the corresponding signatures in diffraction experiments. In order to undertake this task we first derive appropriate 1 Mg-AI-B semi-empirical potentials within the Modified Embedded Atom Method formalism.These potentials are also applied to explore the equilibrium Mg 1 _ x Al x B 2 phase diagram for 0 < x < 0.5. Additionally, density functional theory calculations were utilized to study the influence of heterogeneities on the electronic structure and charge distribution in Mg 1 _ x AI.B 2 • KEYWORDS

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