A travelling wave model of ripple formation on ion bombarded surfaces

NOTICE: this is the author’s version of a work that was accepted for publication in Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol 303, May 2013, DOI:10.1016/j.nimb.2012.11.031We present a mathematical model describing surface modification resulting from atomic motion after ion bombardment. The model considers only the defect production and recovery process induced by the local atom rearrangement and is essentially independent of surface topography changes formed by both sputtering and surface diffusion. A stable analytic, travelling wave solution is presented for a specific incident angle, which agrees with experimental observation excellently. © 2013 Elsevier B.V. All rights reserved

On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

AbstractNowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing

Lineability in subsets of measure and function spaces

We show, among other results, that if lambda denotes the Lebesgue measure on the Borel sets in [0, 1] and X is an infinite dimensional Banach space, then the set of measures whose range is neither closed nor convex is lineable in ca(lambda, X). We also show that, in certain situations, we have lineability of the set of X-valued and non-sigma-finite measures with relatively compact range. The lineability of sets of the type L-p(I)\L-q (I) is studied and some open questions are proposed. Some classical techniques together with the converse of the Lyapunov Convexity Theorem are used