449 research outputs found

    Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network

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    In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the short-term x(t+6)x(t+6). The performance prediction was evaluated and compared with another studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in order to obtain a new estimator of the predictions, which also allowed us to compute uncertainties of predictions for noisy Mackey--Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level (σN\sigma_{N}) from 0.01 to 0.1.Comment: 11 pages, 8 figure

    Implantació i gestió d'un sistema de Prevenció de Riscos Laborals a un Centre d'Ensenyament Secundari

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    Les direccions de les empreses han de tenir una política de prevenció de riscos laborals, amb un conjunt de directrius i objectius generals sobre aquest tema per transmetre a la resta de mandos i treballadors per donar sensació, real, de que tant al lloc on es treballa com els processos de treball, màquines, etc, es troben i estan fets amb les condicions de seguretat adequades. En la política s’establiran els criteris a seguir en matèria de prevenció de riscos, i que dins de les finalitats i finalitat de l’empresa contribuiran a la consecució d’uns objectius fixats, aquests objectius seran mesurables (si és possible) i s’han de poder aconseguir. És un pas molt important per demostrar un compromís sincer en matèria de seguretat, i a la vegada aconseguir els dels empleats.3r Premi Santa Bàrbara 2005Award-winnin

    Understanding the factors influencing yield strength on Mg alloys

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    Taking the Hall–Petch relationship as a starting point, the factors contributing towards Mg alloy strengthening are analysed, and their relative importance quantified. Solid-solution strengthening is modelled employing a power-law approach. The effects of various processing schedules are reviewed, showing that these play a relatively minor role. Grain refinement effects are described employing thermodynamic and kinetic formulations via the interdependence theory approach. The effects of rare earths are examined, showing that their major contribution is towards grain size control, an effect often in conflict with solid-solution strengthening. A computational approach is proposed, successfully modelling 104 grades reported in the literature. The approach may aid in tailoring and designing Mg alloys for yield strength.The authors wish to acknowledge nancial support from the Accelerated Metallurgy Project, which is co-funded by the European Commission in the 7th Framework Programme (Contract NMP4-LA-2011-263206), by the European Space Agency and by the individual partner organisations.This is the original submitted version of the manuscript. It does not include changes arising from peer-review or editing. The final published version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S1359645414003279#

    L\'evy-areas of Ornstein-Uhlenbeck processes in Hilbert-spaces

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    In this paper we investigate the existence and some useful properties of the L\'evy areas of Ornstein-Uhlenbeck processes associated to Hilbert-space-valued fractional Brownian-motions with Hurst parameter H∈(1/3,1/2]H\in (1/3,1/2]. We prove that this stochastic area has a H\"older-continuous version with sufficiently large H\"older-exponent and that can be approximated by smooth areas. In addition, we prove the stationarity of this area.Comment: 18 page

    Random attractors for degenerate stochastic partial differential equations

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    We prove the existence of random attractors for a large class of degenerate stochastic partial differential equations (SPDE) perturbed by joint additive Wiener noise and real, linear multiplicative Brownian noise, assuming only the standard assumptions of the variational approach to SPDE with compact embeddings in the associated Gelfand triple. This allows spatially much rougher noise than in known results. The approach is based on a construction of strictly stationary solutions to related strongly monotone SPDE. Applications include stochastic generalized porous media equations, stochastic generalized degenerate p-Laplace equations and stochastic reaction diffusion equations. For perturbed, degenerate p-Laplace equations we prove that the deterministic, infinite dimensional attractor collapses to a single random point if enough noise is added.Comment: 34 pages; The final publication is available at http://link.springer.com/article/10.1007%2Fs10884-013-9294-

    Las moléculas de adhesión y la respuesta inmune

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    Adhesion molecules (AM) are involved in antigenic recognition and cell migration. These molecules determine the contact among several groups of cells and the relationship between some cells and the extracellular matrix. In addition, they act as direct activation signals upon various immunocompetent cells. According to their structure and functional characteristics they have been grouped in: lnmunoglobulin superfamily, integrins (very late antigens, leukointegrins and cytoadhesins) and selectins (E-selectins, P-selectins y L-selectins). Their mechanisms of action have Seen largel y studied in regard to the leukocyte migration process, which include: selectin-mediated margination; endothelial adhesion and chemotaxis involving integrins and in a less extent by the immunoglobulins superfamily members. The possibility exists that manipulation of important steps of these processes could be used in the future as therapeutic tools and basic research.Las moléculas de adhesión (MA) participan en el reconocimiento antigénico y en la migración celular. Determinan el contacto entre varios grupos celulares y la relación entre algunas células y la matriz extracelular, actuando además como señales que contribuyen directamente a la activación celular. Con base en su estructura y características funcionales se pueden agrupar como: superfamilia de las inmunoglobulinas, las integrinas (que a su vez se subdividen en tres grupos: antígenos muy tardíos, integrinas leucocitarias y citoadhesinas) y las selectinas (E-selectinas, P-selectinas y L-selectinas). Su mecanismo de acción se ha estudiado en detalle en los procesos de migración leucocitaria que comprende: marginación por medio de selectinas, adhesión al endotelio y quimiotaxis a través de integrinas y, en menor grado, por miembros de la superfamilia de la inmunoglobulinas. La manipulación de los procesos en los cuales intervienen estas moléculas, permitiría que en un futuro sean utilizados como terapia antiinflamatoria en medicina y biologí

    The Atacama Cosmology Telescope: CO(J = 3 - 2) mapping and lens modeling of an ACT-selected dusty star-forming galaxy

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    We report Northern Extended Millimeter Array (NOEMA) CO(J=3−2J = 3 - 2) observations of the dusty star-forming galaxy ACT-S\,J020941+001557 at z=2.5528z = 2.5528, which was detected as an unresolved source in the Atacama Cosmology Telescope (ACT) equatorial survey. Our spatially resolved spectral line data support the derivation of a gravitational lens model from 37 independent velocity channel maps using a pixel-based algorithm, from which we infer a velocity-dependent magnification factor μ≈7−22\mu \approx 7-22 with a luminosity-weighted mean \left\approx 13. The resulting source-plane reconstruction is consistent with a rotating disk, although other scenarios cannot be ruled out by our data. After correction for lensing, we derive a line luminosity LCO(3−2)′=(5.53±0.69)×1010  K km s−1 pc2L^{\prime}_{\rm CO(3-2)}= (5.53\pm 0.69) \times 10^{10}\,{\rm \,K\,km\,s^{-1}\,pc^{2}}, a cold gas mass Mgas=(3.86±0.33)×1010 M⊙M_{{\rm gas}}= (3.86 \pm 0.33) \times 10^{10}\,M_{\odot}, a dynamical mass Mdyn sin2 i=3.9−1.5+1.8×1010 M⊙M_{\rm dyn}\,{\rm sin}^2\,i = 3.9^{+1.8}_{-1.5} \times 10^{10}\,M_{\odot}, and a gas mass fraction fgas csc2 i=1.0−0.4+0.8f_{\rm gas}\,{\rm csc}^2\,i = 1.0^{+0.8}_{-0.4}. The line brightness temperature ratio of r3,1≈1.6r_{3,1}\approx 1.6 relative to a Green Bank Telescope CO(J=1−0J=1-0) detection may be elevated by a combination of external heating of molecular clouds, differential lensing, and/or pointing errors.Comment: 8 pages, 5 figures, accepted to Ap

    TSSK3, a novel target for male contraception, is required for spermiogenesis

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    We have previously shown that members of the family of testis-specific serine/threonine kinases (TSSKs) are post-meiotically expressed in testicular germ cells and in mature sperm in mammals. The restricted post-meiotic expression of TSSKs as well as the importance of phosphorylation in signaling processes strongly suggest that TSSKs have an important role in germ cell differentiation and/or sperm function. This prediction has been supported by the reported sterile phenotype of the TSSK6 knock-out (KO) mice and of the double TSSK1/TSSK2 KO. The aim of this study was to develop KO mouse models of TSSK3 and to validate this kinase as a target for the development of a male contraceptive. We used CRISPR/Cas9 technology to generate the TSSK3 KO allele on B6D2F1 background mice. Male heterozygous pups were used to establish three independent TSSK3 KO lines. After natural mating of TSSK3 KO males, females that presented a plug (indicative of mating) were monitored for the following 24 days and no pregnancies or pups were found. Sperm numbers were drastically reduced in all three KO lines and, remarkably, round spermatids were detected in the cauda epididymis of KO mice. From the small population of sperm recovered, severe morphology defects were detected. Our results indicate an essential role of TSSK3 in spermiogenesis and support this kinase as a suitable candidate for the development of novel nonhormonal male contraceptives.Fil: Nayyab, Saman. University of Massachussets; Estados UnidosFil: Gervasi, María G.. University of Massachussets; Estados UnidosFil: Tourzani, Darya A.. University of Massachussets; Estados UnidosFil: Caraballo, Diego Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Jha, Kula N.. No especifíca;Fil: Teves, Maria E.. University of Virginia; Estados UnidosFil: Cui, Wei. University of Massachussets; Estados UnidosFil: Georg, Gunda I.. University of Minnesota; Estados UnidosFil: Visconti, Pablo E.. University of Massachussets; Estados UnidosFil: Salicioni, Ana M.. University of Massachussets; Estados Unido
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