Tau neutrino search with Cherenkov telescopes

Cherenkov telescopes could have the capability of detecting high energy tau neutrinos by searching for very inclined showers. If a tau lepton, produced by a tau neutrino, escapes from the Earth crust, it will decay and initiate an air shower which can be detected by a fluorescence/Cherenkov telescope. Here we present a detailed Monte Carlo simulation of event rates induced by tau neutrinos in the energy range from 1 PeV to 1 EeV. Topographic conditions are taken into account for a set of example locations. As expected, we find a neutrino sensitivity which depends on the shape of the energy spectrum from astrophysical sources. We compare our findings with the sensitivity of the dedicated IceCube neutrino telescope under different conditions. We also find that a difference of several factors can be observed depending on the topographic conditions of the sites sampled.Comment: Proceedings of 33nd International Cosmic Ray Conference - 2013 - Rio de Janeiro, Brazi

John Cage, in pace con il mondo e con l'assurdo. Percorso e influenza sul suolo italiano

Prendendo in analisi il periodo artistico e storico del secondo Novecento, questo scritto si propone di analizzare, nel contesto di una trasformazione allargata dal campo musicale a quello delle arti tutte, il protagonista di questa rivoluzione: John Cage, padre dell’alea e dell’indeterminazione in musica, filosofo buddista e conoscitore di funghi

Sentinel of the extraordinary: the IceCube alert system for neutrino flares

The IceCube Neutrino Observatory has the invaluable capability of continuously monitoring the whole sky. This has affirmed the role of IceCube as a sentinel, providing real-time alerts to the astrophysical community on the detection of high-energy neutrinos and neutrino flares from a variety of astrophysical sources. As a response to the IceCube alerts, different observatories can join forces in the multi-messenger observation of transient events and the characterisation of their astrophysical sources. The 2017 breakthrough identification of blazar TXS 0506+056 as the source of high-energy neutrinos and UHE gamma rays was proof of this strategy. The Gamma-ray Follow-Up (GFU) is the IceCube program for identifying high-energy muon neutrino single events, as well as outstanding neutrino flares from relevant sources and the whole wide universe. While the identification of single high-energy neutrinos is shared on public alert distribution networks, partner Imaging Air Cherenkov Telescopes are sent low-latency alerts following the detection of neutrino flares, for which they have dedicated follow-up programs. I will present an overview of the GFU platform together with new results from the analysis of recorded neutrino flares, after a dozen years of GFU operation and hundreds of alerts being sent.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions. 11 pages and 6 figure

Towards a user-centered framework to support proactive Building Operation and Maintenance: preliminary results of a communication platform between users and stakeholders

none3noUsers’ needs and behaviors can alter the building efficiency, thus leading to significant efforts to support Building Operation & Maintenance (O&M) tasks. This work develops the preliminary concepts of a framework for O&M including users’monitoring and engagement strategies. In the context of a complex university building, we developed and tested a users-stakeholders communication platform including a web-based application to report and check failures and damages to building’s components and devices.openBernardini, Gabriele; Di Giuseppe, Elisa; D’Orazio, MarcoBernardini, Gabriele; Di Giuseppe, Elisa; D’Orazio, Marc

COVID-19 impact on end-user's maintenance requests. A text mining approach

COVID-19 pandemic changed our way of working, limiting the usual physical attendance of working spaces. Despite the drastic reduction in the number of daily users due to the pandemic restrictions, working buildings were often kept open to provide services to internal and external users. Pandemic obliged to change operation and maintenance (O&M) plans, due to the increase of ventilation requirements and the reduction of other types of services, with a strong impact on cost and management. Now the pandemic is reducing its effects and is time to question the future asset of buildings’ O&M plans, based on the pandemic lesson. Data collected by Computerized Maintenance Management Systems (CMMS) during COVID-19 then become an important source of understanding the future management of working places. End-users’ maintenance requests are usually expressed by natural language, then a text mining approach can be a useful tool to discover hidden knowledge from unstructured data stored in CMMS. This study applies text mining methods, including sentiment analysis, to the field of building maintenance, with the scope to evaluate how COVID-19 changed some aspects of the facility management process, including users’ perception

The hunt for cosmic neutrino sources with IceCube

IceCube is a cubic-kilometer neutrino telescope under construction at the geographic South Pole. Once completed it will comprise 4800 optical sensors deployed on 80 vertical strings at depths in the ice between 1450 and 2450 meters. Part of the array is already operational and data was recorded in the configurations with 9 (year 2006/2007), 22 (year 2007/2008) and 40-strings (year 2008/2009) respectively. Here we report preliminary results on the search for point-like neutrino sources using data collected with the first 22 strings (IC-22).Comment: 10 pages, 3 figures, prepared for the Scineghe08 Conference, Padova/Italy (2008

Retroperitoneal and Retrograde Total Laparoscopic Hysterectomy – Technique with Three- and Five-millimeter Trocars

In this chapter, we describe total laparoscopic hysterectomy (TLH) using retroperitoneal and retrograde technique: it combines the retroperitoneal coagulation of the uterine artery and the retrograde approach to the pelvic organs, as in oncological surgeries. We report our experience in applying this modified TLH with 3-mm instruments and without uterine manipulator, in order to demonstrate its safety and feasibility

MAGIC gamma-ray telescopes hunting for neutrinos and their sources

The discovery of an astrophysical flux of high-energy neutrinos by the IceCube Collaboration marks a major breakthrough in the ongoing search for the origin of cosmic rays. Presumably, the neutrinos, together with gamma rays, result from pion decay, following hadronic interactions of protons accelerated in astrophysical objects to ultra-relativistic energies. So far, the neutrino sky map shows no significant indication of astrophysical sources. Here, we report first results from follow-up observations, of sky regions where IceCube has detected muon tracks from energetic neutrinos, using the MAGIC telescopes which are sensitive to gamma rays at TeV energies. Furthermore, we show that MAGIC has the potential to distinguish air showers induced by tau neutrinos from the background of hadronic showers in the PeV-EeV energy range, employing a novel analysis method to the data obtained with high-zenith angle observations.Peer Reviewe

Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user’s maintenance requests

Preventive conservation of cultural heritage can avoid or minimize future damage, deterioration, loss and consequently, any invasive intervention. Recently, Machine Learning methods were proposed to support preventive conservation and maintenance plans, based on their ability to predict the future state of the built heritage by collected data. Several data sources were used, such as structural data and images depicting the evolution of the deterioration state, but till now textual information, exchanged by people living or working in historical buildings to require maintenance interventions, was not used to support conservation programmes. This work proposes a method to support preventive conservation programs based on the analysis of data collected into CMMS (computer maintenance management software). In a Cultural Heritage building in Italy, hosting a University Campus, data about end-user’s maintenance requests collected for 34 months were analysed, and LSTM neural networks were trained to predict the category of each request. Results show a prediction accuracy of 96.6%, thus demonstrating the potentialities of this approach in dynamically adapting the maintenance program to emerging issues