National Center for Frame Analysis

After its successful campaign of measurements beyond the Polar Arctic Circle, the PolarquEEEst experiment measured the cosmic charged particle rate at sea level in a latitude interval between 35^{circ } N and 82^{circ } N. In this paper, these measurements are described and the corresponding results are discussed.

ABSTRACT Health and medical care are safety-critical environments. The development of a safe and reliable healthcare software system is one of the most important processes which may affect the accuracy of clinical decision making and have an impact on determining diagnoses of and treatment for diseases. The aim of this study is to assess if software quality models, such as McCall, can have an impact on healthcare software quality for the purpose of reducing software failures. We have identified the main criticalities of healthcare software systems based on existing literature, software quality models and programming language standards; we have related these criticalities to software characteristics; we have analysed the relationship between the main criticalities of healthcare software systems and their software characteristics. The following criticalities - the risk of changing hard-coded software and the importance of developing maintainable software – must be addressed at the start of the software development process.

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

A search for highly electrically charged objects (HECOs) and magnetic monopoles is presented using 2.2 hbox {fb}{^{-1}} of p-p collision data taken at a centre of mass energy (E_{CM}) of 8 TeV by the MoEDAL detector during LHC’s Run-1. The data were collected using MoEDAL’s prototype Nuclear Track Detectord array and the Trapping Detector array. The results are interpreted in terms of Drell–Yan pair production of stable HECO and monopole pairs with three spin hypotheses (0, 1/2 and 1). The search provides constraints on the direct production of magnetic monopoles carrying one to four Dirac magnetic charges and with mass limits ranging from 590 GeV/c^{2} to 1 TeV/c^{2}. Additionally, mass limits are placed on HECOs with charge in the range 10e to 180e, where e is the charge of an electron, for masses between 30 GeV/c^{2} and 1 TeV/c^{2}.

While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

The existence of independent, yet time correlated, Extensive Air Showers (EAS) has been discussed over the past years, with emphasis on possible physical mechanisms that could justify their observation. The detector network of the Extreme Energy Events (EEE) Collaboration, with its approximately 60 cosmic ray telescopes deployed over the Italian territory, has the potential to search for such events, employing different analysis strategies. In this paper, we have analyzed a set of EEE data, corresponding to an approximately five month observation period, searching for multi-coincidence events among several far telescopes, within a time window of 1 ms. Events with up to 12 coincident telescopes have been observed. Results were compared to expectations from a random distribution of events and discussed with reference to the relativistic dust grain hypothesis.

The goal of the PolarquEEEst experiment was to measure the cosmic charged particle rate at latitudes greater than 66^{circ } N, where no systematic and accurate measurements at sea level have ever been performed. A latitude range well above the Arctic Circle was explored on board of a sailboat, up to the unprecedented northernmost value of 82^{circ }07^{prime } N. In this paper a description of the experimental set-up is reported, then the procedures for calibration and data analysis are described in detail. The results show that the rate measured in this latitude range stays constant within a novel accuracy of pm 1%.

A simulation tool based on GEMC framework to describe the MRPC telescope of the Extreme Energy Events (EEE) Project is presented. The EEE experiment is mainly devoted to the study of the secondary cosmic muons by using MRPC telescope distributed in high schools and research centres in Italy and at CERN. This takes into account the muon interactions with EEE telescopes and the structures surrounding the experimental apparata; it consists of a dedicated event generator producing realistic muon distribution and a detailed geometry description of the detector. Microscopic behaviour of MRPCs has been included to produce experimental-like data. A method to estimate the chamber efficiency directly from data has been implemented and tested by comparing the experimental and simulated polar angle distribution of muons.

The INFN Tier-1 datacentre provides computing resources to several HEP and Astrophysics experiments. These are organized in Virtual Organizations submitting jobs to our computing facilities through Computing Elements, acting as Grid interfaces to the Local Resource Manager. We are phasing-out our current LRMS (IBM/Platform LSF 9.1.3) and CEs (CREAM) set to adopt HTCondor as a replacement for LSF and HTCondor-CE in place of CREAM. A small instance has been set up to practice with the cluster management and evaluate the feasibility of our migration plans to a new LRMS and CE set. A second cluster instance has been setup to work on production. A number of management tools have been adapted or rewritten in order to integrate the new system with the existing infrastructure. Two different accounting solution for the HTCondor-CE have been implemented, and the more reliable one have been adopted. A python tool has been written to disentangle the management of HTCondor machines from our puppet instance, and to enable a quicker configuration of the cluster nodes. The monitoring tools tied to the old system are being adapted to also work on the new one. Finally, the most relevant setup steps have been documented in a public wiki page and a support mailing has been created to help other INFN sites willing to migrate their LRMS and CE to HTCondor. This document reports about our experience with HTCondor-CE on top of HTCondor and the integration of this system into our infrastructure.