2,322 research outputs found

    The Time of Flight System of the AMS-02 Space Experiment

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    The Time-of-Flight (TOF) system of the AMS detector gives the fast trigger to the read out electronics and measures velocity, direction and charge of the crossing particles. The new version of the detector (called AMS-02) will be installed on the International Space Station on March 2004. The fringing field of the AMS-02 superconducting magnet is 1.0Ă·2.51.0\div2.5 kG where the photomultiplers (PM) are installed. In order to be able to operate with this residual field, a new type of PM was chosen and the mechanical design was constrained by requiring to minimize the angle between the magnetic field vector and the PM axis. Due to strong field and to the curved light guides, the time resolution will be 150Ă·180150\div180 ps, while the new electronics will allow for a better charge measurement.Comment: 5 pages, 4 figures. Proc. of 7th Int. Conf. on Adv. Tech. and Part. Phys., 15-19 October 2001,Como (Italy

    The AMS-02 Time of Flight System. Final Design

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    The AMS-02 detector is a superconducting magnetic spectrometer that will operate on the International Space Station. The time of flight (TOF) system of AMS-02 is composed by four scintillator planes with 8, 8, 10, 8 counters each, read at both ends by a total of 144 phototubes. This paper describes the new design, the expected performances, and shows preliminary results of the ion beam test carried on at CERN on October 2002.Comment: 4 pages, 6 EPS figures. Proc. of the 28th ICRC (2003

    UNIMIB@NEEL-IT: Named Entity Recognition and Linking of Italian Tweets

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    Questo articolo descrive il sistema proposto dal gruppo UNIMIB per il task di Named Entity Recognition and Linking applicato a tweet in lingua italiana (NEEL-IT). Il sistema, che rappresenta un approccio iniziale al problema, \ue8 costituito da tre passaggi fondamentali: (1) Named Entity Recognition tramite l\u2019utilizzo di Conditional Random Fields, (2) Named Entity Linking considerando sia approcci supervisionati sia modelli di linguaggio basati su reti neurali, e (3) NIL clustering tramite un approccio basato su grafi.This paper describes the framework proposed by the UNIMIB Team for the task of Named Entity Recognition and Linking of Italian Tweets (NEEL-IT). The proposed pipeline, which represents an entry level system, is composed of three main steps: (1) Named Entity Recognition using Conditional Random Fields, (2) Named Entity Linking by considering both Supervised and Neural-Network Language models, and (3) NIL clustering byusing a graph-based approach

    The accuracy of NIRS in predicting chemical composition and fibre digestibility of hay-based total mixed rations

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    The aim of this study was to develop near-infrared spectroscopy (NIRS) prediction models for the estimation of chemical components and the fibre undegradable fractions (uNDF) of hay-based total mixed rations (TMR). A total of 205 TMR samples were used for the study. All the chemical components were measured using standard AOAC reference methods and expressed as percentages of dry matter (DM). Prediction models were developed using both cross- and independent validation and different mathematical treatments applied on spectral data. The best spectral treatment was chosen based on the method which simultaneously achieved the lowest root mean square error and the highest explained variance in cross-validation. The coefficient of determination in external validation (R2P) was the greatest for starch prediction model (R2P = 0.84), followed by acid detergent fibre (ADF; R2P = 0.79), and amylase-treated ash-corrected NDF with addition of sodium sulphite (aNDFom) and crude protein prediction models (CP; R2P = 0.73). The concordance correlation coefficient (CCC) in validation ranged from 0.66 (ash prediction model) to 0.92 (starch prediction model), indicating substantial to accurate models’ predictive ability. This study indicated that NIRS can be a screening method for the prediction of CP, Starch, aNDFom, ADF, acid detergent lignin (ADL), uNDF and Ash. The use of TMR utilised in various herds provided high variability for the NIRS calibration dataset, implying that the developed NIRS pre-diction models could be applicable to TMR collected from herds located in the Parmigiano Reggiano cheese production area.Highlights NIRS can be successfully employed to determine quickly and at cost-effective different compositional and digestibility traits in hay-based TMR. TMR analysis predicted by NIRS can support nutritionists in the formulation of diets containing a proper nutrient profile to sustain physiological, metabolic, and immunological processes. The use of NIR technology for TMR analysis can allow frequent monitoring of rations and increasingly timely corrections, maximising cows’ diet utilisation and conversion of the ingested feed

    The TOF counters of the AMS-02 experiment: space qualification tests and beam test results

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    The scintillator counters of the TOF system of AMS-02 is beeing constructed to match the needs of the AMS-02 experiment that is armed by a high aperture superconducting dipole magnet. The goals of the TOF-02 hodoscopes actually are: to give the fast trigger to the all sub-detectors of AMS-02; to measure the particle velocity ensuring a 1 Ă— 10 9 albedo rejection; to measure the absolute charge by particle energy loss, up to at least Z = 20 . In spring of 2005 all the TOF counter planes will be assembled and the space qualification tests will be performed. A description of the first test results and of the TOF performances will be given
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