Fermion confinement induced by geometry

We consider a five-dimensional model in which fermions are confined in a hypersurface due to an interaction with a purely geometric field. Inspired by the Rubakov-Shaposhnikov field-theoretical model, in which massless fermions can be localized in a domain wall through the interaction of a scalar field, we show that particle confinement may also take place if we endow the five-dimensional bulk with a Weyl integrable geometric structure, or if we assume the existence of a torsion field acting in the bulk. In this picture, the kind of interaction considered in the Rubakov-Shaposhnikov model is replaced by an interaction of fermions with a geometric field, namely a Weyl scalar field or a torsion field. We show that in both cases the confinement is independent of the energy and mass of the fermionic particle. We generalize these results to the case in which the bulk is an arbitrary n-dimensional curved space.     

Neural Network Based Algorithm for Dynamic System Optimization

A class of artificial neural networks with a two‐layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standard dynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.     

A comparison of finite elements for nonlinear beams: the absolute nodal coordinate and geometrically exact formulations

Two of the most popular finite element formulations for solving nonlinear beams are the absolute nodal coordinate and the geometrically exact approaches. Both can be applied to problems with very large deformations and strains, but they differ substantially at the continuous and the discrete levels. In addition, implementation and run-time computational costs also vary significantly. In the current work, we summarize the main features of the two formulations, highlighting their differences and similarities, and perform numerical benchmarks to assess their accuracy and robustness. The article concludes with recommendations for the choice of one formulation over the other.     

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.     

Computing the homology of groups: The geometric way

In this paper, we present several algorithms related with the computation of the homology of groups, from a geometric perspective (that is to say, carrying out the calculations by means of simplicial sets and using techniques of Algebraic Topology). More concretely, we have developed some algorithms which, making use of the effective homology   method, construct the homology groups of Eilenberg–MacLane spaces K(G,1)$K(G,1)$ for different groups G$G$, allowing one in particular to determine the homology groups of G$G$.     

Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is Magnetic Resonance Spectroscopy. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this paper, a Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies. This procedure does not alleviate the high dimensionality of the data by itself. For this reason, dimensionality reduction was subsequently implemented using Moving Window with Variance Analysis for feature selection or Principal Component Analysis for feature extraction. The combined method yielded very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks. In most cases, the classification accuracy improved on previously reported results.     

CrAlN coatings deposited by cathodic arc evaporation at different substrate bias

CrAlN is a good candidate as an alternative to conventional CrN coatings especially for high temperature oxidation-resistance applications. Different CrAlN coatings were deposited on hardened steel substrates by cathodic arc evaporation (CAE) from chromium-aluminum targets in a reactive nitrogen atmosphere at negative substrate bias between − 50 and − 400 V. The negative substrate bias has important effects on the deposition growth rate and crystalline structure. All our coatings presented hardness higher than conventional CrN coatings. The friction coefficient against alumina and tungsten carbide balls was around 0.6. The sliding wear coefficient of the CrAlN coatings was very low while an important wear was observed in the balls before a measurable wear were produced in the coatings. This effect was more pronounced as the negative substrate bias was increased.     

Multiwavelength laser action of Nd:YAlO3 single crystals grown by the laser heated pedestal growth method

We describe the growth of high quality transparent fibers of pure and Nd-doped YAlO₃ from unreacted pedestals (green rods) by the laser heated pedestal growth technique. The X-ray diffraction analysis shows that the fibers are monocrystalline, with a distorted Pnma perovskite structure and grow along 0 1 0 direction (b axis). In this work, multi-wavelength laser action is demonstrated for these fibers in the two main laser channels of Nd³⁺ ions. In the ⁴F_3/2 → ⁴I_11/2 laser transition, low threshold multi-wavelength laser emission is obtained at wavelengths of 1065, 1073 and 1081 nm. Additionally, for the first time in LHPG grown YAlO₃ fibers, laser action in the ⁴F_3/2 → ⁴I_13/2 transition is demonstrated at a wavelength of 1341 nm.     

Neurotrophic control of ovarian development

Substantial evidence now exists indicating that the neurotrophins, a family of growth factors required for the survival, development, and differentiation of various neuronal populations of the nervous system, are also important for the development of nonneuronal tissues. Such a function was first suggested by studies showing the presence of high-affinity neurotrophin receptors in a variety of nonneuronal tissues including those of the cardiovascular, endocrine, immune, and reproductive systems. Within the latter, the gonads appear to be a preferential site of neurotrophin action as suggested by the presence in the mammalian ovary of at least four of the five known neurotrophins and all of the neurotrophin receptors thus far identified. While the various functions that the neurotrophins may have in the ovary are still being elucidated, it is now clear that in addition to recruiting the ovarian innervation, they play a direct role in the regulation of two different maturational periods that are critical for the acquisition of female reproductive function: early follicular development and ovulation. Neurotrophins facilitate the development of newly formed follicles by promoting the initial differentiation and the subsequent growth of primordial follicles. These actions appear to be related to the ability of neurotrophins to sustain the proliferation of both mesenchymal and granulosa cells, and to induce the synthesis of follicle stimulating hormone (FSH) receptors. At the time of the first ovulation, neurotrophins contribute to the ovulatory cascade by increasing prostaglandin E(2) release, reducing gap junction communication, and inducing cell proliferation within the thecal compartment of preovulatory follicles.