Methodological approaches to the condition assessment of reinforced concrete architectural heritage

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Modal Identification of Structures with Interacting Diaphragms

System identification proves in general to be very efficient in the extraction of modal parameters of a structure under ambient vibrations. However, great difficulties can arise in the case of structures composed of many connected bodies, whose mutual interaction may lead to a multitude of coupled modes. In the present work, a methodology to approach the identification of interconnected diaphragmatic structures, exploiting a simplified analytical model, is proposed. Specifically, a parametric analysis has been carried out on a numerical basis on the simplified model, i.e., a multiple spring–mass model. The results were then exploited to aid the identification of a significant case study, represented by the Pavilion V, designed by Riccardo Morandi as a hypogeum hall of the Turin Exhibition Center. The structure is indeed composed of three blocks separated by expansion joints, whose characteristics are unknown. As the main result, light was shed on the contribution of the stiffness of the joints to the global dynamic behavior of structures composed of interacting diaphragms, and, in particular, on the effectiveness of the joints of Pavilion V

Genome-based retrospective analysis of a Providencia stuartii outbreak in Rome, Italy. Broad spectrum IncC plasmids spread the NDM carbapenemase within the hospital

Providencia stuartii is a member of the Morganellaceae family, notorious for its intrinsic resistance to several antibiotics, including last-resort drugs such as colistin and tigecycline. Between February and March 2022, a four-patient outbreak sustained by P. stuartii occurred in a hospital in Rome. Phenotypic analyses defined these strains as eXtensively Drug-Resistant (XDR). Wholegenome sequencing was performed on the representative P. stuartii strains and resulted in fully closed genomes and plasmids. The genomes were highly related phylogenetically and encoded various virulence factors, including fimbrial clusters. The XDR phenotype was primarily driven by the presence of the (NDM)-N-bla- 1 metallo- beta-lactamase alongside the rmtC 16S rRNA methyltransferase, conferring resistance to most beta-lactams and every aminoglycoside, respectively. These genes were found on an IncC plasmid that was highly related to an NDM-IncC plasmid retrieved from a ST15 Klebsiella pneumoniae strain circulating in the same hospital two years earlier. Given its ability to acquire resistance plasmids and its intrinsic resistance mechanisms, P. stuartii is a formidable pathogen. The emergence of XDR P. stuartii strains poses a significant public health threat. It is essential to monitor the spread of these strains and develop new strategies for their control and treatment

Nursing students' interprofessional educational experiences in the clinical context: findings from an Italian cross-sectional study

To explore nursing students' interprofessional educational (IPE) experiences during their most recent clinical rotation and to explore the factors supporting IPE experiences

Nursing students' involvement in shift-to-shift handovers: Findings from a national study

Background Effective performance of clinical handovers should be one of the priorities of nursing education to promote efficient communication skills and ensure patient safety. However, to date, no studies have explored to what extent nursing students are involved in handovers. Objective To explore nursing students' handover involvement during their clinical rotations and associated factors. Method This was a secondary analysis of a large national cross-sectional study that involved 9607 undergraduate nursing students in 27 universities across 95 three-year Italian baccalaureate nursing programs. The involvement in the clinical handovers was the end point (from 0, never, to 3, always). A path analysis was performed to identify variables directly and indirectly affecting students' handover involvement. Results Handover involvement was reported as \u2018only a little\u2019, \u2018to some extent\u2019, and \u2018always\u2019 by 1739 (18.1%), 2939 (30.6%), and 4180 (43.5%) students, respectively; only 749 (7.8%) of students reported never being involved. At the path analysis explaining the 19.1% of variance of nursing students' involvement, some variables emerged that directly increased the likelihood of being involved in handovers. These were being female (\u3b2\u202f=\u202f0.115, p\u202f<\u202f0.001); having children (\u3b2\u202f=\u202f0.107, p\u202f=\u202f0.011); being a 3rd-year student (\u3b2\u202f=\u202f0.142, p\u202f<\u202f0.001) and being a 2nd-year student as compared to a 1st-year student (\u3b2\u202f=\u202f0.050, p\u202f=\u202f0.036); and having a longer clinical rotation (\u3b2\u202f=\u202f0.015, p\u202f<\u202f0.001) in units with high \u2018quality of the learning environment\u2019 (\u3b2\u202f=\u202f0.279, p\u202f<\u202f0.001). Moreover, students who were supervised by the nurse teacher (\u3b2\u202f=\u202f 120.279, p\u202f<\u202f0.001), or by a nurse on a daily basis (\u3b2\u202f=\u202f 120.253, p\u202f=\u202f0.004), or by the staff (\u3b2\u202f=\u202f 120.190, p\u202f<\u202f0.001) reported being less involved in handovers as compared to those students supervised by a clinical nurse. Variables with indirect effects also emerged (model of student's supervision adopted at the unit level, and number of previous clinical rotations attended by students). Moreover, handover involvement explained 11.5% of students self-reported degree of competences learned during the clinical experience. Conclusions Limiting students' opportunity to be involved in handover can prevent the development of communication skills and the professional socialization processes. Strategies at different levels are needed to promote handover among undergraduate nursing students

Nursing students' interprofessional educational experiences in the clinical context: findings from an Italian cross-sectional study

To explore nursing students' interprofessional educational (IPE) experiences during their most recent clinical rotation and to explore the factors supporting IPE experiences

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

The GAPS Programme with HARPS-N at TNG. XV. A substellar companion around a K giant star identified with quasi-simultaneous HARPS-N and GIANO measurements

Context. Identification of planetary companions of giant stars is made difficult because of the astrophysical noise, that may produce radial velocity variations similar to those induced by a companion. On the other hand any stellar signal is wavelength dependent, while signals due to a companion are achromatic. Aims: Our goal is to determine the origin of the Doppler periodic variations observed in the thick disk K giant star TYC 4282-605-1 by HARPS-N at the Telescopio Nazionale Galileo (TNG) and verify if they can be due to the presence of a substellar companion. Methods: Several methods have been used to exclude the stellar origin of the observed signal including a detailed analysis of activity indicators and bisector and the analysis of the photometric light curve. Finally, we have conducted an observational campaign to monitor the near-infrared (NIR) radial velocity with GIANO at the TNG in order to verify whether the NIR amplitude variations are comparable with those observed in the visible. Results: Both optical and NIR radial velocities show consistent variations with a period at 101 days and similar amplitude, pointing to the presence of a companion orbiting the target. The main orbital properties obtained for our giant star with a derived mass of M = 0.97 ± 0.03M☉ are M_Psini = 10.78 ± 0.12M_J; P = 101.54 ± 0.05 days; e = 0.28 ± 0.01 and a = 0.422 ± 0.009 AU. The chemical analysis shows a significant enrichment in the abundance of Na I, Mg I, Al I, and Si I while the rest of the analyzed elements are consistent with the solar value demonstrating that the chemical composition corresponds with an old K giant (age = 10.1 Gyr) belonging to local thick disk. Conclusions: We conclude that the substellar companion hypothesis for this K giant is the best explanation for the observed periodic radial velocity variation. This study also shows the high potential of multi-wavelength radial velocity observations for the validation of planet candidates. Based on observations collected at the Italian Telescopio Nazionale Galileo (TNG), operated on the island of La Palma by the Fundación Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica) at the Spanish Observatorio del Roque de Los Muchachos of the Instituto de Astrofísica de Canarias, in the frame of the programme Global Architecture of Planetary Systems (GAPS)