A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling

Parallel nonlinear models using radial kernels on local mesh support have been designed and implemented for application to real-world problems. Although this recently developed approach reduces the memory requirements compared with other methodologies suggested over the last few years, its computational cost makes parallelisation necessary, especially for big datasets with many instances or attributes. In this work, several strategies for the parallelisation of this methodology are proposed and compared. The MPI communication protocol and the OpenMP application programming interface are used to implement the algorithm. The performance of this methodology is compared with various machine learning methods, with particular consideration of techniques using radial basis functions (RBF). Different methods are applied to model the daily maximum air temperature from real meteorological data collected from the Agroclimatic Station Network of the Phytosanitary Alert and Information Network of Andalusia, an autonomous community of southern Spain. The obtained goodness-of-fit measures illustrate the effectiveness of this nonlinear methodology, and its training process is shown to be simpler than those of other powerful machine learning methods.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds), and by the University of Alicante

Giant magnetic anisotropy at nanoscale: overcoming the superparamagnetic limit

It has been recently observed for palladium and gold nanoparticles, that the magnetic moment at constant applied field does not change with temperature over the range comprised between 5 and 300 K. These samples with size smaller than 2.5 nm exhibit remanence up to room temperature. The permanent magnetism for so small samples up to so high temperatures has been explained as due to blocking of local magnetic moment by giant magnetic anisotropies. In this report we show, by analysing the anisotropy of thiol capped gold films, that the orbital momentum induced at the surface conduction electrons is crucial to understand the observed giant anisotropy. The orbital motion is driven by localised charge and/or spin through spin orbit interaction, that reaches extremely high values at the surfaces. The induced orbital moment gives rise to an effective field of the order of 103 T that is responsible of the giant anisotropy.Comment: 15 pages, 2 figures, submitted to PR

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

A methodology based on the Galerkin formulation of the finite element method has been analyzed for predicting the compressive strength of the lightweight aggregate concrete using ultrasonic pulse velocity. Due to both the memory requirements and the computational cost of this technique, its parallelization becomes necessary for solving this problem. For this purpose a mixed MPI/OpenMP parallel algorithm has been designed and different approaches and data distributions analyzed. On the other hand, this Galerkin methodology has been compared with multiple linear regression models, regression trees and artificial neural networks. Based on different measures of goodness of fit, the effectiveness of the Galerkin methodology, compared with these statistical techniques for data mining, is shown.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds)

Bacterial viruses enable their host to acquire antibiotic resistance genes from neighbouring cells

Prophages are quiescent viruses located in the chromosomes of bacteria. In the human pathogen, Staphylococcus aureus, prophages are omnipresent and are believed to be responsible for the spread of some antibiotic resistance genes. Here we demonstrate that release of phages from a subpopulation of S. aureus cells enables the intact, prophage-containing population to acquire beneficial genes from competing, phage-susceptible strains present in the same environment. Phage infection kills competitor cells and bits of their DNA are occasionally captured in viral transducing particles. Return of such particles to the prophagecontaining population can drive the transfer of genes encoding potentially useful traits such as antibiotic resistance. This process, which can be viewed as ‘auto-transduction’, allows S. aureus to efficiently acquire antibiotic resistance both in vitro and in an in vivo virulence model (wax moth larvae) and enables it to proliferate under strong antibiotic selection pressure. Our results may help to explain the rapid exchange of antibiotic resistance genes observed in S. aureus

Germanium-on-silicon platforms for nonlinear photonics in the mid-infrared

We review our progress in the characterization of the nonlinear transmission properties of low loss germanium-on-silicon waveguides. Simple pump-probe experiments are employed to demonstrate their use for all-optical control

Surface plasmon resonance of capped Au nanoparticles

In this Rapid Communication we show the relationship between surface plasmon resonance damping and the intensity of surface bonding for capped Au nanoparticles, (NPs). Up to now the influence of capping has been included as a phenomenological modification of the scattering constant. It is indicated here that the effective NP size is the parameter mainly affected by surface bonding. Experimental results in different Au-thiol NPs are shown to be in excellent agreement with the expression we propose for damping. Moreover, according to our model the resonance profile gives a deep insight of the interface bonding strength