538 research outputs found

    Localization of Electrical Insulation Failures in Superconducting Collared Coils by Analysis of the Distortion of a Pulsed Magnetic Field

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    The localization of possible electrical faults in superconducting accelerator magnets may, in most cases, be a complex, expensive and time-consuming process. In particular, inter-turn short circuits and failures of the ground insulation are well detectable when the magnet is collared, but often disappear after disassembly for repair due to the release of the pre-stress in the coils. The fault localization method presented in this paper is based on the measurement and analysis of the magnetic field generated inside the magnet aperture by a high voltage pulse. The presence of the fault modifies the distribution of the current in the coils and produces a distortion of the magnetic field. The described method aims at locating both the longitudinal and azimuthal position of the fault-affected area. The test method, the transient case FEM models and the implemented experimental set-up are presented and discussed for the LHC dipole models

    Towards Intelligent Databases

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    This article is a presentation of the objectives and techniques of deductive databases. The deductive approach to databases aims at extending with intensional definitions other database paradigms that describe applications extensionaUy. We first show how constructive specifications can be expressed with deduction rules, and how normative conditions can be defined using integrity constraints. We outline the principles of bottom-up and top-down query answering procedures and present the techniques used for integrity checking. We then argue that it is often desirable to manage with a database system not only database applications, but also specifications of system components. We present such meta-level specifications and discuss their advantages over conventional approaches

    Passive tracer in a flow corresponding to a two dimensional stochastic Navier Stokes equations

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    In this paper we prove the law of large numbers and central limit theorem for trajectories of a particle carried by a two dimensional Eulerian velocity field. The field is given by a solution of a stochastic Navier--Stokes system with a non-degenerate noise. The spectral gap property, with respect to Wasserstein metric, for such a system has been shown in [9]. In the present paper we show that a similar property holds for the environment process corresponding to the Lagrangian observations of the velocity. In consequence we conclude the law of large numbers and the central limit theorem for the tracer. The proof of the central limit theorem relies on the martingale approximation of the trajectory process

    The benefits of inositol-stabilized arginine silicate as a workout ingredient

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    Background The purpose of this study was to examine the benefits of inositol-stabilized arginine silicate (ASI; Nitrosigine) as a workout ingredient in healthy adults. ASI has been previously shown to significantly enhance blood levels of arginine up to six hours post-dose and increase nitric oxide levels. To investigate reports of enhanced energy, increased muscle pump and stamina during workouts, and faster muscle recovery post-workout, ASI (1,500mg/ day) was tested in a double-blind placebo-controlled crossover-design (DBPC-X) study using the POMS vigor-activity and fatigue-inertia sub-scores, blood flow measurements, leg circumference measurements, and biomarkers of muscle recovery (creatine kinase (CK) and lactate dehydrogenase (LDH)) as outcome measures

    Sepsis biomarkers and diagnostic tools with a focus on machine learning.

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    Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers

    Cut Points and Diffusions in Random Environment

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    In this article we investigate the asymptotic behavior of a new class of multi-dimensional diffusions in random environment. We introduce cut times in the spirit of the work done by Bolthausen, Sznitman and Zeitouni, see [4], in the discrete setting providing a decoupling effect in the process. This allows us to take advantage of an ergodic structure to derive a strong law of large numbers with possibly vanishing limiting velocity and a central limit theorem under the quenched measure.Comment: 44 pages; accepted for publication in "Journal of Theoretical Probability

    Muons tomography applied to geosciences and volcanology

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    Imaging the inner part of large geological targets is an important issue in geosciences with various applications. Dif- ferent approaches already exist (e.g. gravimetry, electrical tomography) that give access to a wide range of informations but with identified limitations or drawbacks (e.g. intrinsic ambiguity of the inverse problem, time consuming deployment of sensors over large distances). Here we present an alternative and complementary tomography method based on the measurement of the cosmic muons flux attenuation through the geological structures. We detail the basics of this muon tomography with a special emphasis on the photo-active detectors.Comment: Invited talk at the 6th conference on New Developments In Photodetection (NDIP'11), Lyon-France, July 4-8, 2011; Nuclear Instruments and Methods in Physics Research Section A, 201

    A Brownian particle in a microscopic periodic potential

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    We study a model for a massive test particle in a microscopic periodic potential and interacting with a reservoir of light particles. In the regime considered, the fluctuations in the test particle's momentum resulting from collisions typically outweigh the shifts in momentum generated by the periodic force, and so the force is effectively a perturbative contribution. The mathematical starting point is an idealized reduced dynamics for the test particle given by a linear Boltzmann equation. In the limit that the mass ratio of a single reservoir particle to the test particle tends to zero, we show that there is convergence to the Ornstein-Uhlenbeck process under the standard normalizations for the test particle variables. Our analysis is primarily directed towards bounding the perturbative effect of the periodic potential on the particle's momentum.Comment: 60 pages. We reorganized the article and made a few simplifications of the conten
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