Satellite interferometric data for seismic damage assessment

Radar satellites allow the collection of data on large areas without direct access to structures. Thereby, they appear very attractive for Structural Health Monitoring (SHM) purposes. Data collected by satellites can be processed to obtain temporal histories of displacements through which the health state of a monitored system can be potentially identified. However, anomalies in the time histories of displacements are not necessarily due to damage. Environmental phenomena, such as variations in atmospheric temperature, and rain, can modify the behavior of structures without compromising their safety. The impact of these phenomena on the structural response can hinder the identification of anomalies or lead to false alarms if such alterations are misinterpreted as damage. Furthermore, if the monitored system is a historical structure, uncertainties on the structural behavior are inevitably increased during aging. The purpose of this article is to discuss the possibility of identifying damage due to seismic actions considering the impact of variations of environmental factors on the time histories of the displacements retrieved by satellite data. The structural health condition of a historical structure located in the city of Rome (Italy) hit by the October 2016 Central Italy earthquakes is investigated based on interferometric satellite data. The satellite data are acquired by COSMO-SkyMed (CSM) of the Italian Space Agency between 2010 and 2019 and are processed by CNR IREA

Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center

Objectives: To evaluate accuracy and inter-observer variability using Vesical Imaging-Reporting and Data System (VI-RADS) for discrimination between non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Methods: Between September 2017 and July 2018, 78 patients referred for suspected bladder cancer underwent multiparametric MRI of the bladder (mpMRI) prior to transurethral resection of bladder tumor (TURBT). All mpMRI were reviewed by two radiologists, who scored each lesion according to VI-RADS. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each VI-RADS cutoff. Receiver operating characteristics curves were used to evaluate the performance of mpMRI. The Ƙ statistics was used to estimate inter-reader agreement. Results: Seventy-five patients were included in the final analysis, 53 with NMIBC and 22 with MIBC. Sensitivity and specificity were 91% and 89% for reader 1 and 82% and 85% for reader 2 respectively when the cutoff VI-RADS > 2 was used to define MIBC. At the same cutoff, PPV and NPV were 77% and 96% for reader 1 and 69% and 92% for reader 2. When the cutoff VI-RADS > 3 was used, sensitivity and specificity were 82% and 94% for reader 1 and 77% and 89% for reader 2. Corresponding PPV and NPV were 86% and 93% for reader 1 and 74% and 91% for reader 2. Area under curve was 0.926 and 0.873 for reader 1 and 2 respectively. Inter-reader agreement was good for the overall score (Ƙ = 0.731). Conclusions: VI-RADS is accurate in differentiating MIBC from NMIBC. Inter-reader agreement is overall good. Key Points: • Traditionally, the local staging of bladder cancer relies on transurethral resection of bladder tumor. • However, transurethral resection of bladder tumor carries a significant risk of understaging a cancer; therefore, more accurate, faster, and non-invasive staging techniques are needed to improve outcomes. • Multiparametric MRI has proved to be the best imaging modality for local staging; therefore, its use in suitable patients has the potential to expedite radical treatment when necessary and non-invasive diagnosis in patients with poor fitness

The KIMORE dataset: KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation

The paper proposes a free dataset, available at the following link1, named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, Depth videos and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject's performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise

An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept

none8This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject's performance and able to discriminate between the pathological and healthy condition.Capecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, FedericaCapecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, Federic

Crilin: A Semi-Homogeneous Calorimeter for a Future Muon Collider

Calorimeters, as other detectors, have to face the increasing performance demands of the new energy frontier experiments. For a future Muon Collider the main challenge is given by the Beam Induced Background that may pose limitations to the physics performance. However, it is possible to reduce the BIB impact by exploiting some of its characteristics by ensuring high granularity, excellent timing, longitudinal segmentation and good energy resolution. The proposed design, the Crilin calorimeter, is an alternative semi-homogeneous ECAL barrel for the Muon Collider based on Lead Fluoride Crystals (PbF2) with a surface-mount UV-extended Silicon Photomultipliers (SiPMs) readout with an optimized design for a future Muon Collider

Insulin-resistance HCV infection-related affects vascular stiffness in normotensives

Background and Aims. Arterial stiffness evaluated as pulse wave velocity, is an early marker of vascular damage and an independent predictor for cardiovascular events. We investigated if the insulin resistance/hyperinsulinemia chronic hepatitis C virus infection-related could influence arterial stiffness. Methods. We enrolled 260 outpatients matched for age, body mass index, gender, ethnicity: 52 with never-treated uncomplicated chronic hepatitis C virus infection (HCV+), 104 never-treated hypertensives (HT) and 104 healthy subjects (NT). Pulse wave velocity was evaluated by a validated system employing high-fidelity applanation tonometry. We also measured: fasting plasma glucose and insulin, total, LDL- and HDL-cholesterol, triglyceride, creatinine, e-GFR-EPI, HOMA, quantitative HCV-RNA. Results. HCV+ patients with respect to NT had an increased pulse wave velocity (7.9 ± 2.1 vs 6.4 ± 2.1 m/s; P < 0.0001), similar to that observed in HT group (8.8 ± 3.2 m/s). HCV+ patients, in comparison with NT, had higher triglyceride, creatinine, fasting insulin and HOMA (3.2 ± 1.3 vs 2.5 ± 1.0; P < 0.0001). At linear regression analysis, the correlation between pulse wave velocity and HOMA was similar in HT (r = 0.380, P < 0.0001) and HCV+ (r = 0.369, P = 0.004) groups. At multiple regression analysis, HOMA resulted the major determinant of pulse wave velocity in all groups, explaining respectively 11.8%, 14.4% and 13.6% of its variation in NT, HT and HCV+. At correlational analysis hepatitis C virus-RNA and HOMA demonstrated a strong and linear relationship between them, explaining the 72.4% of their variation (P = 0.022). Conclusions. We demonstrated a significant and direct correlation between HOMA and pulse wave velocity in HCV+ patients, similar to that observed in hypertensive

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Research on database and information technologies has been rapidly evolving over the last couple of years. This evolution was lead by three major forces: Big Data, AI and Connected World that open the door to innovative research directions and challenges, yet exploiting four main areas: (i) computational and storage resource modeling and organization; (ii) new programming models, (iii) processing power and (iv) new applications that emerge related to health, environment, education, Cultural Heritage, Banking, etc. The 24th East-European Conference on Advances in Databases and Information Systems (ADBIS 2020), the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020) and the 16th Workshop on Business Intelligence and Big Data (EDA 2020), held during August 25–27, 2020, at Lyon, France, and associated satellite events aimed at covering some emerging issues related to database and information system research in these areas. The aim of this paper is to present such events, their motivations, and topics of interest, as well as briefly outline the papers selected for presentations. The selected papers will then be included in the remainder of this volume

Mu2e Crystal Calorimeter Readout Electronics: Design and Characterisation

The Mu2e experiment at Fermi National Accelerator Laboratory will search for the charged-lepton flavour-violating neutrinoless conversion of negative muons into electrons in the Coulomb field of an Al nucleus. The conversion electron with a monoenergetic 104.967 MeV signature will be identified by a complementary measurement carried out by a high-resolution tracker and an electromagnetic calorimeter, improving by four orders of magnitude the current single-event sensitivity. The calorimeter—composed of 1348 pure CsI crystals arranged in two annular disks—has a high granularity, 10% energy resolution and 500 ps timing resolution for 100 MeV electrons. The readout, based on large-area UV-extended SiPMs, features a fully custom readout chain, from the analogue front-end electronics to the digitisation boards. The readout electronics design was validated for operation in vacuum and under magnetic fields. An extensive radiation hardness certification campaign certified the FEE design for doses up to 100 krad and 1012 n1MeVeq/cm2 and for single-event effects. A final vertical slice test on the final readout chain was carried out with cosmic rays on a large-scale calorimeter prototype

The Mu2e Crystal Calorimeter: An Overview

The Mu2e experiment at Fermilab will search for the standard model-forbidden, charged lepton flavour-violating conversion of a negative muon into an electron in the field of an aluminium nucleus. The distinctive signal signature is represented by a mono-energetic electron with an energy near the muon's rest mass. The experiment aims to improve the current single-event sensitivity by four orders of magnitude by means of a high-intensity pulsed muon beam and a high-precision tracking system. The electromagnetic calorimeter complements the tracker by providing high rejection power in muon to electron identification and a seed for track reconstruction while working in vacuum in presence of a 1 T axial magnetic field and in a harsh radiation environment. For 100 MeV electrons, the calorimeter should achieve: (a) a time resolution better than 0.5 ns, (b) an energy resolution <10%, and (c) a position resolution of 1 cm. The calorimeter design consists of two disks, each loaded with 674 undoped CsI crystals read out by two large-area arrays of UV-extended SiPMs and custom analogue and digital electronics. We describe here the status of construction for all calorimeter components and the performance measurements conducted on the large-sized prototype with electron beams and minimum ionizing particles at a cosmic ray test stand. A discussion of the calorimeter's engineering aspects and the on-going assembly is also reported

Data-driven clustering of combined Functional Motor Disorders based on the Italian registry

Functional Motor Disorders (FMDs) represent nosological entities with no clear phenotypic characterization, especially in patients with multiple (combined FMDs) motor manifestations. A data-driven approach using cluster analysis of clinical data has been proposed as an analytic method to obtain non-hierarchical unbiased classifications. The study aimed to identify clinical subtypes of combined FMDs using a data-driven approach to overcome possible limits related to "a priori" classifications and clinical overlapping