Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive

The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics and use this information for the completion of morphosyntactic patterns. The semantic properties considered are telicity (also combined with definiteness) and agentivity. Both act at the interface between semantics and morphosyntax: they are semantically determined and syntactically encoded. The tasks were submitted to both the computational model and a group of Italian native speakers. The comparison between the two groups of data allows us to investigate to what extent neural language models capture significant aspects of human semantic competence.Comment: 7 pages, in Italian languag

The Efficiency of Waste Sector in Italy: An Application by Data Envelopment Analysis

With growing environmental legislation and mounting popular concern for the need to pursuing a sustainable growth, there has been an increasing recognition in developed nations of the importance of waste reduction, recycling and reuse maximization. This empirical study investigates both ecological and economic performances of urban waste systems in 78 major Italian towns for the years 2015 and 2016. To this purpose the study employs the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. More specifically, in the context of environmental performance we implement two output-oriented DEA models in order to consider both constant and variable returns to scale. In addition, we include an undesirable output – the total amount of waste collected – in the two models considered. The results show that there is variability among the municipalities analysed: Northern and Central major towns show higher efficiency scores than Southern and Islands ones

Reconstruction of the evolution phases of a landslide by using multi‑layer back‑analysis methods

Back analysis is the most common method to study landslide movements after the event, and it allows us to understand how a landslide evolved along the slope. This paper presents the back-analysis of the Pomarico landslide (Basilicata, Italy) that occurred on January 25th, 2019, on the southwestern slope of the Pomarico hill. The landslide, of rotational clayey retrogressive type—planar sliding, evolved in different phases until it caused a paroxysmal movement in the early afternoon on January 29th, 2019. The landslide caused the collapse of a bulkhead (built at the end of the twentieth century) and of some buildings along the village’s main road. In this paper, a multi-layer back-analysis study is presented, based on the limit equilibrium model (LEM), applying the solution proposed by Morgenstern and Price in Geotechnique 15(1):79–93zh, (1965) and implemented in the freeware software SSAP 2010. The analysis allowed the reconstruction of the entire landslide evolution, using geotechnical parameters obtained from both laboratory and in situ tests, and data from the literature. The application of multilayer back-analysis made it possible to avoid the homogenisation of the layers, modelling the event according to the real conditions present on the slope. The use of the SSAP software made it possible to curb the problem related to the theoretical limitation of the shape of the rupture surfaces, by evaluating independently the friction angle locally and by discarding all those surfaces, which, due to this problem, presented a non-reliable factor of safety (FS) value. The modelling revealed a slope that is highly unstable as the height of the water table changes. The FS calculated under water table conditions close to ground level was less than 1 (FS = 0.98), simulating the first landslide movement (November 2018). The subsequent model reconstructed the critical surface responsible for the January 2019 movement and calculated the FS present on the slope (FS = 1.01). Eventually, the paroxysmal event on January 29th, 2019, was modelled, returning an FS of 0.83, and a sliding surface that sets below the bulkhead, causing its failure. Furthermore, the modelling of the slope in the presence of adequate retaining structures demonstrated the (non-) effectiveness of the retaining wall system represented by the bulkhead. The proposed method of analysis suggests further applications in similar complex multi-layer soil-structure interaction scenarios

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study

BACKGROUND:Falls are a common adverse event in both elderly inpatients and patients admitted to rehabilitation units. The Hendrich Fall Risk Model II (HIIFRM) has been already tested in all hospital wards with high fall rates, with the exception of the rehabilitation setting. This study's aim is to address the feasibility and predictive performances of HIIFRM in a hospital rehabilitation department. METHODS: A 6 months prospective study in a Italian rehabilitation department with patients from orthopaedic, pulmonary, and neurological rehabilitation wards. All admitted patients were enrolled and assessed within 24 h of admission by means of the HIIFRM. The occurrence of falls was checked and recorded daily. HIIFRM feasibility was assessed as the percentage of successful administrations at admission. HIIFRM predictive performance was determined in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC), best cutoff, sensitivity, specificity, positive and negative predictive values, along with their asymptotic 95% confidence intervals (95% CI). RESULTS: One hundred ninety-one patents were admitted. HIIFRM was feasible in 147 cases (77%), 11 of which suffered a fall (7.5%). Failures in administration were mainly due to bedridden patients (e.g. minimally conscious state, vegetative state). AUC was 0.779(0.685-0.873). The original HIIFRM cutoff of 5 led to a sensitivity of 100% with a mere specificity of 49%(40-57%), thus suggesting using higher cutoffs. Moreover, the median score for non-fallers at rehabilitation units was higher than that reported in literature for geriatric non fallers. The best trade-off between sensitivity and specificity was obtained by using a cutoff of 8. This lead to sensitivity\u2009=\u200973%(46-99%), specificity\u2009=\u200972%(65-80%), positive predictive value\u2009=\u200917% and negative predictive value\u2009=\u200997%. These results support the use of the HIIFRM as a predictive tool. CONCLUSIONS: The HIIFRM showed satisfactory feasibility and predictive performances in rehabilitation wards. Based on both available literature and these results, the prediction of falls among all hospital wards, with high risk of falling, could be achieved by means of a unique tool and two different cutoffs: a standard cutoff of 5 in geriatric wards and an adjusted higher cutoff in rehabilitation units, with predictive performances similar to those of the best-preforming pathology specific tools for fall-risk assessmen

Intertidal Mediterranean coralline algae habitat is expecting a shift towards a reduced growth and a simplified associated fauna under climate change

Coralline algae represent the most important bioconstructors in the Mediterranean Sea and are currently impaired by the effects of climate change (CC), particularly by global warming and ocean acidification (OA). We studied the effects of these two drivers on Ellisolandia elongate, an intertidal coralline algae that is known to host a rich biodiversity of associated fauna. We cultured turfs of E. elongate in experimental conditions of increased temperature and OA (using the values of the IPCC scenario RCP- 8.5 expected for 2100: actual mean temperature +3 degrees C and pH = 7.78), and estimated alteration of algal linear growth and community structure, focusing especially on peracarid crustaceans and annelids. Our findings revealed a decrease in linear growth, yet with no significant changes on structural integrity, and a simplification of associated community, in particular for peracarids. Our study contributes to understand community-level response to CC drivers, highlighting the vulnerability of the fauna associated to an important Mediterranean marine habitat

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.Comment: 20 pages, 13 figure

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients