Archivio Istituzionale della Ricerca- Università del Salento
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Endoscopic techniques for the diagnosis of pancreatic cystic lesions
No full textPancreatic cysts are mostly incidental findings on computed tomography or magnetic resonance imaging scans, with few patients presenting with abdominal pain or other symptoms. The accurate diagnosis of cysts is important as management depends on the type (neoplastic or non-neoplastic). Cross-sectional imaging is fast being replaced with endoscopic ultrasound (EUS) and various techniques based on that such as EUS-guided fine needle aspiration, EUS-guided needle confocal laser endomicroscopy, EUS-through-the-needle biopsy, and contrast-enhanced EUS. Clinical studies have reported varying diagnostic and adverse event rates with these modalities. In addition, American, European, and Kyoto guidelines for the diagnosis and management of pancreatic cysts have provided different recommendations. In this editorial, we elaborate on the clinical guidelines, recent studies, and comparison of different endoscopic methods for the diagnosis of pancreatic cysts
Regioni e mare territoriale dopo la sentenza n. 16 del 2024 della Corte costituzionale
Full text linkLa sentenza n. 16 del 2024 della Corte costituzionale, nel confermare l’esercizio della competenza regionale in materia di pesca sulle acque costiere, ravvisa l’illegittimità del riferimento a un «mare territoriale regionale», sostituendovi quello allo «spazio marittimo prospiciente». Rispetto alle pronunce precedenti, la sentenza afferma con maggior vigore la capacità di esercizio legislativo regionale sulla parte marittima del territorio; la precisazione lessicale, al contempo, sottintende la persistenza di una non completa equiparazione giurisprudenziale fra terraferma e mare territoriale quali parti del territorio dello Stato-ordinamento, rispetto a quanto invece sembrerebbe prospettare il dato teorico
All-polymer syntactic foams: Linking large strain cyclic experiments to Quasilinear Viscoelastic modelling for materials characterisation
Full text linkThe time-dependent behaviour of polymeric composites is critical in a broad range of applications, including those in marine, aerospace, and automotive environments. In the present study, we assess the validity of the quasi-linear viscoelastic (QLV) model to fit the stress–strain behaviour of all-polymer syntactic foams under large cyclic compressional strain in a novel experimental configuration. These syntactic foams were manufactured by adding hollow polymer microspheres of various sizes and wall thicknesses into a polyurethane matrix. These materials are known for their relatively large initial stiffness, and strong recoverability after large strains. In the QLV model, several strain energy functions (SEFs) were employed, including neo-Hookean, Ogden type I, and type II. The bulk and shear moduli are presented in the form of a Prony series. By estimating these experimental data using optimisation, the natural viscoelastic material properties and coefficients associated with the SEF were determined. The influence of the microsphere filling fraction was also explored. We show that at the strain rate considered here of 0.013 s−1, the compressible QLV model coupled with the Ogden-I SEF is capable of providing an excellent fit to experimental data. Critically, this fit can be achieved over a range of cycles via model optimisation to the first cyclic response only
Predicting traffic volumes on road infrastructures in the context of multi-risk assessment frameworks
Full text linkIn multi-risk assessment frameworks involving road infrastructures, measures of exposure to
natural hazards include traffic volumes. However, traffic counts are usually collected through
traffic counter/radar stations which only cover a small part of the road network. In this study,
country-wide Annual Average Daily Traffic (AADT) prediction models based on Italian data were
developed to provide direct risk exposure measures both in terms of traffic volumes (continuous
variable) and traffic volume discrete classes, using province-/municipality-related geographic,
socio-economic and road-related variables as predictors. To ease transferability and applicability
of the models, only publicly available predictors were selected. Traditional statistical techniques
(generalized linear models for predicting traffic values and ordered logistic models for traffic
classes) and Machine Learning (ML) approaches (XGBoost for both regression and classification
problems) were used. Both the direct estimation of traffic volumes and the classification into
traffic ranges provided satisfactory results in terms of goodness-of-fit and predictive accuracy
metrics. Results show that population, occupation, tourism, density, number of lanes, urban
environment, complex intersections and ring roads were generally related to an increase in traffic
volumes. Distance from large cities and accessibility metrics are inversely related to traffic
instead. The application of the XGBoost ML approach proved to be more accurate than traditional
approaches only for heavy vehicles. It was discussed how the obtained models can be used as
input modules for overall multi-risk assessment frameworks involving road infrastructures
Del Prete, Antonella, Schino, Anna Lisa & Totaro, Pina, éd., The Philosophers and the Bible. The debate on Sacred Scripture in Early Modern Thought, Leida, Boston, Brill, 2022, 303 p.
No full textImpact of Endoscopic Ultrasound-guided biliary drainage on the management of difficult biliary cannulation in patients with distal malignant biliary obstruction
No full textBackground: Biliary drainage (BD) in patients with distal malignant biliary obstruction (DMBO) implies a higher risk of difficult biliary cannulation (DBC) during endoscopic retrograde cholangiopancreatography (ERCP). After standard cannulation failure, the endoscopist may proceed with advanced cannulation techniques and/or, with endoscopic ultrasound-guided biliary drainage (EUS-BD). Materials: This was a retrospective study of consecutive patients with DMBO and dilated common bile duct (CBD, > 12mm) that underwent ERCP for endoscopic BD in four European centers. The rates of DBC, technical and, clinical success, and procedure-related adverse events (AEs) were assessed. The predictive factors for AEs were also investigated through regression analysis. EUS-BD approach was considered as first option after standard cannulation failure or as final option after advanced cannulation failure. Results: A total of 1016 patients with DMBO were included in the study, with 524(51.6%) matching the definition of DBC. Clinical success was achieved in 956 cases (94.1%). One-hundred-sixty-seven patients (16,4%) experienced procedural-related AEs. Subjects with DBC showed a higher risk for AEs (p = 0.003), however, patients undergoing "early" EUS-BD showed a risk of AEs comparable to those managed with standard cannulation (p = 0.3776). The attempt of any advanced cannulation technique was independently associated with the occurrence of AEs (p = 0.001). Conclusions: The risk of AEs is higher in patients with DMBO, and DBC, this appears to be mainly related to the advanced cannulation techniques. In patients with dilated CBD (>12mm) "early" EUS-BD may minimize the risk of adverse events
Mean ergodic and related properties of generalized Cesàro operators in BK-sequence spaces
No full textRecent results concerning the linear dynamics and mean ergodicity of compact operators in Banach spaces, together with additional new results, are employed to investigate various spectral properties of generalized Cesàro operators acting in large classes of classical BK-sequence spaces. Of particular interest is to determine the eigenvalues and the corresponding eigenvectors of such operators and to decide whether (or not) the operators are power bounded, mean ergodic and supercyclic
ALL ROAD LEAD TO MOVEMENT? PHYSICAL LITERACY AND ACTIVE BREAK IN PRIMARY SCHOOL – SCUOLA ATTIVA PROJECT
Full text linkIn recent years Physical Literacy has received increasing interest as an educational process aimed at promoting health in children. According to numerous researches physical activity is considered an unavoidable area of educational intervention for health’s promotion. In this article we explain the Active Break project (Scuola Attiva Kids -Sport e Salute). The twofold aim of the Active Breaks was to increase physical activity levels, and to integrate motor experiences into curricular teachin
An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors
Full text linkThe aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for a consultation. Without historical insights into common behaviors and potential anomalies detected with unobtrusive techniques (e.g., non-wearable devices), timely and informed medical interventions become challenging. To address this, we propose an edge-based Internet of Things (IoT) framework that enables real-time monitoring and anomaly detection using non-wearable sensors to assist doctors and caregivers in assessing the health of elderly patients. By processing data locally, the system minimizes privacy concerns and ensures immediate data availability, allowing healthcare professionals to detect unusual behavioral patterns early. The system employs advanced machine learning (ML) models to identify deviations that may indicate potential health risks. A prototype of our system has been developed to test its feasibility and demonstrate, through the application of two of the most frequently used ML models, i.e., isolation forest and Long Short-Term Memory (LSTM) networks, that it can provide scalability, efficiency, and reliability in the context of elderly care. Further, the provided dashboard enables caregivers and healthcare professionals to access real-time alerts and longitudinal trends, facilitating proactive interventions. The proposed approach improves healthcare responsiveness by providing instant insights into patient behavior, facilitating more accurate diagnoses and interventions. This study lays the groundwork for future advancements in the field and offers valuable insights for the research community to harness the full potential of combining edge computing, artificial intelligence (AI), and the IoT in elderly care
