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

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 $1.0\div2.5$ 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 $150\div180$ 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 CDF-II silicon tracking system

The CDFII silicon tracking system, SVX, for Run II of the Fermilab Tevatron has up to 8 cylindrical layers with average radii spanning from {approx} (1.5 to 28.7) cm, and lengths ranging from {approx} (90 to 200) cm for a total active-area of {approx} 6 m{sup 2} and {approx} 7.2 x 10{sup 5} readout channels. SVX will improve the CDFII acceptance and efficiency for both B and high-Pt physics dependent upon b-tagging. Along with the description of the SVX we report some alignment survey data from the SVX assembly phase and the actual status of the alignment as it results from the offline data analysis. The problems encountered are also reviewed

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

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

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

Towards a Semantic Document Management System for Public Administration

This work has two objectives: to summarize the experiences carried out over the past four years by the National Interuniversity Consortium for Informatics (CINI) in the Datalake project funded by the CRUI in collaboration with the Directorate General of Automated Information Systems (DGSIA) of the Ministry of Justice, in synergy with other related projects of the Ministry; and to demonstrate how the experiences, Proof of Concepts, and functional specifications produced can serve as a repository of functionalities for a “semantic document management system for PA,” which aims to evolve the information systems of PAs into platforms where unstructured data can be exploited and integrated with structured data to enhance and add value to the digital services provided by the PA, and where governance processes can be conducted using all knowledge expressed in documents and other forms of unstructured data. The judicial organization, proceedings, processes, user needs, functional structure of the Datalake, and implementation architecture are described, aiming towards a design and production pathway directed at all PAs

Simulation of the Fine Mesh Photomultiplers in the Tof of AMS-02

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The Future of Sustainable Data Preparation

Data preparation has an important role in data analysis, and it is time and resource-consuming, both in terms of human and computational resources. The "Discount quality for responsible data science" project aims to focus on data-quality-based data preparation, analyzing the main characteristics of related tasks, and proposing methods for improving the sustainability of the data preparation tasks, considering also new emerging techniques based on generative AI. The paper discusses the main challenges that emerged in the initial research work in the project, as well as possible strategies for developing more sustainable data preparation frameworks