Multi-scale stacked sequential learning

Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring labels exhibit some kind of relationship. The paper main contribution is two-fold: first, we generalize the stacked sequential learning, highlighting the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions. We tested the method on two tasks: text lines classification and image pixel classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning as well as state-of-the-art conditional random fields.     

Homogenization-based multiscale crack modelling: From micro-diffusive damage to macro-cracks

The existence of a representative volume element (RVE) for a class of quasi-brittle materials having a random heterogeneous microstructure in tensile, shear and mixed mode loading is demonstrated by deriving traction–separation relations, which are objective with respect to RVE size. A computational homogenization based multiscale crack modelling framework, implemented in an FE² setting, for quasi-brittle solids with complex random microstructure is presented. The objectivity of the macroscopic response to the micro-sample size is shown by numerical simulations. Therefore, a homogenization scheme, which is objective with respect to macroscopic discretization and microscopic sample size, is devised. Numerical examples including a comparison with direct numerical simulation are given to demonstrate the performance of the proposed method.     

A dynamic variational multiscale method for viscoelasticity using linear tetrahedral elements

In this article, we develop a dynamic version of the variational multiscale (D‐VMS) stabilization for nearly/fully incompressible solid dynamics simulations of viscoelastic materials. The constitutive models considered here are based on Prony series expansions, which are rather common in the practice of finite element simulations, especially in industrial/commercial applications. Our method is based on a mixed formulation, in which the momentum equation is complemented by a pressure equation in rate form. The unknown pressure, displacement, and velocity are approximated with piecewise linear, continuous finite element functions. To prevent spurious oscillations, the pressure equation is augmented with a stabilization operator specifically designed for viscoelastic problems, in that it depends on the viscoelastic dissipation. We demonstrate the robustness, stability, and accuracy properties of the proposed method with extensive numerical tests in the case of linear and finite deformations.     

A Systematic Study of Restorative Crown-Materials Combinations for Dental Implants: Characterization of Mechanical Properties under Dynamic Loads

This study aimed to find the optimum mechanical characteristics of the restorative materials for the manufacture of implant crowns subjected to impact loading when different combinations of materials are used for the inner and outer crown. Several combinations of external–internal crown restorative materials were analyzed. The dynamic stresses at eight different zones of a dental implant subjected to an impact load and the influence of several mechanical properties, such as the Young’s modulus, Poisson’s ratio, density, and initial velocity, were analyzed and compared. A detailed 3D model was created, including the crown, the retention screw, the implant, and a mandible section. The model was then built by importing the 3D geometries from CAD software. The whole 3D model was carefully created in order to guarantee a finite element mesh that produced results adjusted to physical reality. Then, we conducted a numerical simulation using the finite element method (FEM). The results of the FEM analysis allowed for evaluating the effect that different combinations of restorative materials and mechanical properties had on the stress distribution in various regions of the implant. The choice of restorative material is a factor to be considered in order to preserve the integrity of osseointegration. Restorative materials transfer more or less stress to the dental implant and surrounding bone, depending on their stiffness. Therefore, an inadequate Young’s modulus of the rehabilitation material can affect the survival of the implant over time. Eight interactive graphics were provided on a web-based surface platform to help clinical dentists, researchers, and manufacturers to select the best restorative materials combination for the crown.     

Personalization and user verification in wearable systems using biometric walking patterns

In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.     

Superconducting Proximity Effects in Ferromagnet/Superconductor Heterostructures

We consider in this paper superconducting proximity effects in clean Ferromagnet/Ferromagnet/Superconductor (F1/F2/S) heterostructures. We make use of a numerical self-consistent solution of the microscopic Bogoliubov?Cde Gennes equations to investigate the superconducting transition temperatures as a function of the angle ?? between the magnetizations in the ferromagnetic layers. Unlike in F1/S/F2 trilayers, we find that the superconducting transition temperature is a nonmonotonic function of ??.     

Power generation efficiency analysis of offshore wind farms connected to a SLPC (single large power converter) operated with variable frequencies considering wake effects

This paper deals with the power generation efficiency analysis of a proposed offshore wind farm topology, consisting of a SLPC (single large power converter) that simultaneously controls a group of generators. This common converter can operate at a VF (variable frequency) or at a CF (constant frequency). The results are compared with the conventional onshore wind farm scheme, where individual power converters are connected to each turbine, guaranteeing maximum power generation for the entire wind farm. A methodology to analyze different wind speed and direction scenarios, and to compute the optimal electrical frequency for each one, is presented and applied to different case studies depending on the wind farm size. In order to obtain more realistic values of wind speeds, the wake effect amongst wind turbines is considered. A wake model considering single, partial and multiple wakes inside a wind farm and taking into account different wind directions, is presented. Both wind farm topologies are analyzed by means of simulations, taking into account both wind speed variability in wind farms and the number of wind turbines. The possible resulting benefits of simplifying the MPCs (multiple power converters) of each turbine, namely saving costs, reducing losses and maintenance and increasing the reliability of the system, are analyzed, focusing on the total power extraction. The SLPC-VF scheme is also compared with a CF scheme SLPC-CF, and it is shown that a significant power increase of more than 33% can be obtained with SLPC-VF.     

A Fittingness Factor-Based Spectrum Management Framework for Cognitive Radio Networks

In order to increase cognitive radios (CRs) operation efficiency, there has been an increasing interest in strengthening awareness level about spectrum utilisation. In this respect, this paper proposes to exploit the fittingness factor concept to capture the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. First, a new knowledge management functional architecture for optimizing spectrum management has been constructed. It integrates a set of advanced statistics capturing the influence of the dynamic radio environment on the fittingness factor. Then, a knowledge manager (KM) exploiting these statistics to monitor time-varying suitability of spectrum resources has been proposed to support the spectrum selection (SS) decision-making process. In particular, a new Fittingness Factor-based strategy combining two SS and spectrum mobility (SM) functionalities has been proposed, following either a greedy or a proactive approach. Results have shown that, with a proper fittingness factor function, the greedy approach efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains in terms of the user dissatisfaction probability. The proactive approach has been shown to maintain the introduced performance gain while minimizing the signalling requirements in terms of spectrum handover rate.     

Development and validation of a high-performance liquid chromatography-mass spectrometry assay for determination of amphetamine, methamphetamine, and methylenedioxy derivatives in meconium

A procedure based on liquid chromatography-mass spectrometry (LC-MS) is described for determination of amphetamine, methamphetamine, and methylendioxy derivatives in meconium, using 3,4-methylendioxypropylamphetamine as internal standard. The analytes were initially extracted from the matrix by 17 mM methanolic HCl. Subsequently, a solid-phase extraction with Bondelut Certify columns was applied. Chromatography was performed on a C(18) reversed-phase column using a linear gradient of 10 mM ammonium bicarbonate, pH 9.0-methanol as a mobile phase. Analytes were determined in LC-MS single ion monitoring mode with an atmospheric pressure ionization-electrospray interface. The method was validated in the range 0.005-1.00 microg/g using 1 g of meconium per assay. Mean recoveries ranged between 61.1 and 87.2% for different analytes. The quantification limits were 0.005 microg/g meconium for amphetamine, methamphetamine, and 4-hydroxy-3-methoxymethamphetamine and 0.004 microg/g meconium for 3,4-methylenedioxyamphetamine, 3,4-methylenedioxymethamphetamine, 3,4-methylenedioxyethylamphetamine, and N-methyl-1-(3,4-methylenedioxyphenyl)-2-butanamine. The method was applied to analysis of meconium in newborns to assess eventual fetal exposure to amphetamine derivatives.