Spiky ice and penitente tilting

Indexación: Scopus.Under certain conditions, at high altitude, the surface of snow develops spike-like structures known as penitentes. This is a rather counterintuitive phenomenon, which is a consequence of surface sublimation at a given point as a result of the incidence of light scattered by the surrounding region. Following the existing literature, we model the time evolution of the phenomenon described above as a 1D diffusion equation with a non-local source term, as it represents the light coming from all the line of sight defined for a point of the curve. For small initial perturbations in the surface, the system undergoes a thermodynamic instability which triggers the formation of spikes. For sunlight coming in at a given angle, numerical simulations account for a feature observed in the real system: penitentes get tilted in the direction of the sunlight. © Published under licence by IOP Publishing Ltd.We thank R. Rojas and R. Soto for interesting discussions. P.G. was financially supported by Facultad de Ciencias Exactas, UNAB, to attend SOCHIFI 2016 Meeting.https://iopscience.iop.org/article/10.1088/1742-6596/1043/1/01200

The Supernumerary Robotic 3rd Thumb for Skilled Music Tasks

Wearable robotics bring the opportunity to augment human capability and performance, be it through prosthetics, exoskeletons, or supernumerary robotic limbs. The latter concept allows enhancing human performance and assisting them in daily tasks. An important research question is, however, whether the use of such devices can lead to their eventual cognitive embodiment, allowing the user to adapt to them and use them seamlessly as any other limb of their own. This paper describes the creation of a platform to investigate this. Our supernumerary robotic 3rd thumb was created to augment piano playing, allowing a pianist to press piano keys beyond their natural hand-span; thus leading to functional augmentation of their skills and the technical feasibility to play with 11 fingers. The robotic finger employs sensors, motors, and a human interfacing algorithm to control its movement in real-time. A proof of concept validation experiment has been conducted to show the effectiveness of the robotic finger in playing musical pieces on a grand piano, showing that naive users were able to use it for 11 finger play within a few hours

Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields

Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure

Genetic-Background Modulation of Core and Variable Autistic-Like Symptoms in Fmr1 Knock-Out Mice

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Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Computational and experimental analysis of fusion bonding in thermoplastic composites: Influence of process parameters

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Stochastic modeling of mesoscopic elasticity random field

International audienceIn the homogenization setting, the effective properties of a heterogeneous material can be retrieved from the solution of the so-called corrector problem. In some cases of practical interest, obtaining such a solution remains a challenging computational task requiring an extremely fine discretization of microstructural features. In this context, Bignonnet et al. recently proposed a framework where smooth mesoscopic elasticity random fields are defined through a filtering procedure. In this work, we investigate the capabilities of information-theoretic random field models to accurately represent such mesoscopic elasticity fields. The aim is to substantially reduce the homogenization cost through the use of coarser discretizations while solving mesoscale corrector problems. The analysis is performed on a simple but non-trivial model microstructure. First of all, we recall the theoretical background related to the filtering and multiscale frameworks, and subsequently characterize some statistical properties of the filtered stiffness field. Based on these properties, we further introduce a random field model and address its calibration through statistical estimators and the maximum likelihood principle. Finally, the validation of the model is discussed by comparing some quantities of interest that are obtained either from numerical experiments on the underlying random microstructure or from model-based simulations. It is shown that for the case under study, the information-theoretic model can be calibrated with a limited set of realizations and still allows for accurate predictions of the effective properties

Multiscale modelling of the composite reinforced foam core of a 3D sandwich structure

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