Numerical simulations of dwarf galaxies in the Fornax Cluster

I have carried out simulations of the evolution of dwarf galaxies falling into a Fornax-like Cluster using the Moving Box technique. I am interested in following the journey of the galaxies into the cluster and characterizing their size, star formation rate, gas and dark matter content, stellar dynamics, and evolution, depending on the orbit and the initial mass at the time of orbital injection. Some of the galaxies are effectively transformed into Ultra Diffuse Galaxies (UDG) while some others are allowed to be briefly identified as “jellyfish". Serendipitously, I realized that these simulations produce galaxies whose morphology is similar to a particular galaxy in the Fornax Cluster: NGC1427A. I identified that gaseous and stellar tails of this galaxy may be explainable given that they are subject to different environmental effects (ram-pressure stripping and tidal forces). I was also able to provide some falsifiable predictions on the position of the galaxy with respect to the center of the Cluster and its projected orbital direction. Finally, I have contributed to the development of a technique to study low dimensional-manifolds in the simulations. In particular, I concentrated on the analysis of gaseous tails of simulated jellyfish galaxies with the aim to investigate regions of recent star formation and mixing between the galactic gaseous material and the hot gas of the cluster

A versatile facility for the calibration of X-ray polarimeters with polarized and unpolarized controlled beams

We devised and built a versatile facility for the calibration of the next generation X-ray polarimeters with unpolarized and polarized radiation. The former is produced at 5.9 keV by means of a Fe55 radioactive source or by X-ray tubes, while the latter is obtained by Bragg diffraction at nearly 45 degrees. Crystals tuned with the emission lines of X-ray tubes with molybdenum, rhodium, calcium and titanium anodes are employed for the efficient production of highly polarized photons at 2.29, 2.69, 3.69 and 4.51 keV respectively. Moreover the continuum emission is exploited for the production of polarized photons at 1.65 keV and 2.04 keV and at energies corresponding to the higher orders of diffraction. The photons are collimated by means of interchangeable capillary plates and diaphragms, allowing a trade-off between collimation and high fluxes. The direction of the beam is accurately arranged by means of high precision motorized stages, controlled via computer so that long and automatic measurements can be done. Selecting the direction of polarization and the incidence point we can map the response of imaging devices to both polarized and unpolarized radiation. Changing the inclination of the beam we can study the systematic effects due to the focusing of grazing incidence optics and the feasibility of instruments with large field of view.Comment: 12 pages, 11 figure

A CNN approach for audio classification in construction sites

Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers. The proposed architecture works on the mel-spectrogram representation of the input audio frames and it demonstrates its effectiveness in environmental sound classification (ESC) achieving a high accuracy. In summary, our contribution shows that techniques employed for general ESC can be also successfully adapted to a more specific environmental sound classification task, such as event recognition in construction sites

ASAP – A sub-sampling approach for preserving topological structures modeled with geodesic topographic mapping

Topological data analysis tools enjoy increasing popularity in a wide range of applications, such as Computer graphics, Image analysis, Machine learning, and Astronomy for extracting information. However, due to computational complexity, processing large numbers of samples of higher dimensionality quickly becomes infeasible. This contribution is twofold: We present an efficient novel sub-sampling strategy inspired by Coulomb's law to decrease the number of data points in d-dimensional point clouds while preserving its homology. The method is not only capable of reducing the memory and computation time needed for the construction of different types of simplicial complexes but also preserves the size of the voids in d-dimensions, which is crucial e.g. for astronomical applications. Furthermore, we propose a technique to construct a probabilistic description of the border of significant cycles and cavities inside the point cloud. We demonstrate and empirically compare the strategy in several synthetic scenarios and an astronomical particle simulation of a dwarf galaxy for the detection of superbubbles (supernova signatures). (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

A multi-domain ontology on healthy ageing for the characterization of older adults status and behaviour

Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users’ profile

ASTRI SST-2M archive system: a prototype for the Cherenkov Telescope Array

The ASTRI project of the Italian National Institute for Astrophysics (INAF) is developing, in the framework of the Cherenkov Telescope Array (CTA), an end-to-end prototype system based on a dual-mirror small-sized Cherenkov telescope. Data preservation and accessibility are guaranteed by means of the ASTRI Archive System (AAS) that is responsible for both the on-site and off-site archiving of all data produced by the different sub- systems of the so-called ASTRI SST-2M prototype. Science, calibration, and Monte Carlo data together with the dedicated Instrument Response Functions (IRFs) (and corresponding metadata) will be properly stored and organized in different branches of the archive. A dedicated technical data archive (TECH archive) will store the engineering and auxiliary data and will be organized under a parallel database system. Through the use of a physical system archive and a few logical user archives that reflect the different archive use-cases, the AAS has been designed to be independent from any specific data model and storage technology. A dedicated framework to access, browse and download the telescope data has been identified within the proposal handling utility that stores and arranges the information of the observational proposals. The development of the whole archive system follows the requirements of the CTA data archive and is currently carried out by the INAF-OAR & ASI-Science Data Center (ASDC) team. The AAS is fully adaptable and ready for the ASTRI mini-array that, formed of at least nine ASTRI SST-2M telescopes, is proposed to be installed at the CTA southern site

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes

The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This paper presents the initial results of an ongoing project whose focus is the application of Artificial Intelligence and Machine Learning techniques for conceptualizing, cleaning, and analyzing such an important and valuable dataset, with the goal of providing predictive insights to better support diabetologists in their diagnostic and therapeutic choices.Comment: The work has been presented at the conference Ital-IA 2022 (https://www.ital-ia2022.it/

The NESTORE e-Coach: Designing a Multi-Domain Pathway to Well-Being in Older Age

This article describes the coaching strategies of the NESTORE e-coach, a virtual coach for promoting healthier lifestyles in older age. The novelty of the NESTORE project is the definition of a multi-domain personalized pathway where the e-coach accompanies the user throughout different structured and non-structured coaching activities and recommendations. The article also presents the design process of the coaching strategies, carried out including older adults from four European countries and experts from the different health domains, and the results of the tests carried out with 60 older adults in Italy, Spain and The Netherlands