A Context-Aware Recommendation System with a Crowding Forecaster

Recommendation systems (RSs) are increasing their popularity in recent years. Many big IT companies like Google, Amazon and Netflix, have a RS at the core of their business. In this paper, we propose a modular platform for enhancing a RS for the tourism domain with a crowding forecaster, which is able to produce an estimation about the current and future occupation of different Points of Interest (PoIs) by taking into consideration also contextual aspects. The main advantage of the proposed system is its modularity and the ability to be easily tailored to different application domains. Moreover, the use of standard and pluggable components allows the system to be integrated in different application scenarios

The First Billion Years Project:Finding Infant Globular Clusters at z=6

We explored a suite of high-resolution cosmological simulations from the First Billion Years Project (FiBY) at $z \geq 6$. All substructures within the simulations have been identified with the SUBFIND algorithm. From our analysis, two distinct groups of objects emerge. We hypothesise that the substructures in the first group, which appear to have a high baryon fraction ($f_{\rm b} \geq 0.95$), are possible infant GC candidates. Objects belonging to the second group have a high stellar fraction ($f_{\rm star} \geq 0.95$) and show a potential resemblance to infant ultra-faint dwarf galaxies. The high baryon fraction objects identified in this study are characterised by a stellar content similar to the one observed in present-day GCs, but they still contain a high gas fraction ($f_{\rm gas} \sim 0.95$) and a relatively low amount of dark matter. They are compact, dense systems. Their sizes are consistent with recent estimates based on the first observations of possible proto-GCs at high redshifts. These types of infant GC candidates appear to be more massive and more abundant in massive host galaxies, indicating that the assembly of galaxies via mergers may play an important role in building several GC-host scaling relations. Specifically, we express the relation between the mass of the most massive infant GC and its host stellar mass as $\log(M_{\rm cl}) = (0.31\pm0.15)\log(M_{\rm *,gal} + (4.17\pm1.06)$. We also report a new relation between the most massive infant GC and the parent specific star formation rate of the form $\log(M_{\rm cl}) = (0.85\pm0.30)\log(sSFR) + \alpha$ that describes the data at both low and high redshift. Finally, we assess the present-day GC mass (GC number) -- halo mass relation offers a satisfactory description of the behaviour of our infant GC candidates at high redshift, suggesting that such a relation may be set at formation.Comment: 17 pages, 11 figures, accepted by A&

SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers

The research aimed to investigate the incidence of SARS-CoV-2 breakthrough infections and their determinants in a large European cohort of more than 60,000 health workers

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

RICERCHE STORICO BIBLICHE

STUDI DEI MODELLI DI INTERVENTO DIVINO NELLA STORIA E RASSEGNA SUI DIVERSI SETTORI BIBLICI, SIA NARRATIVI CHE POETICI, PER ILLUSTRARE IL PROCESSO DI DEFINIZIONE DELLA AZIONE DIVINA INTRASTORICA

ICARE: An Intuitive Context-Aware Recommender with Explanations

The chapter presents a framework, called Intuitive Context-Aware Recommender with Explanations (ICARE), that can provide contextual recommendations, together with their explanations, useful to achieve a specific and predefined goal. We apply ICARE in the healthcare scenario to infer personalized recommendations related to the activities (fitness and rest periods) a specific user should follow or avoid in order to obtain a high value for the sleep quality score, also on the base of their current context and the physical activities performed during the past days. We leverage data mining techniques to extract frequent and context-aware sequential rules that can be used both to provide positive and negative recommendations and to explain them

The Synergies of Context and Data Aging in Recommendations

In this paper, we investigate the synergies of data aging and contextual information in data mining techniques used to infer frequent, up-to-date, and contextual user behaviours that enable making recommendations on actions to take or avoid in order to fulfill a specific positive goal. We conduct experiments in two different domains: wearable devices and smart TVs