606 research outputs found
Motion detection technology as a tool for cardiopulmonary resuscitation (CPR) quality improvement
No full textThe most popular method of training in basic life support and AED remains instructor-led training courses. Recent reviews provide good evidence to support alternative methods of training including lay instructors, self-directed learning (web, video, poster) and CPR feedback/prompt devices
Interfacial instability of two rotating viscous immiscible fluids in a cylinder
No full textA complete original study of the linear temporal instability analysis of two viscous and immiscible fluids enclosed in a rigid cylinder rotating about its axis and separated by a cylindrical interface is performed for the case of higher density fluid located in the annulus. The results of the present contribution fill the lack of an overall assessment of the system behavior due to the increase of both the analytical difficulties and the number of the governing parameters when the several physical effects are all included. The analysis is carried out numerically by discretizing the equations of the evolution of disturbances separately in the two phases formulated in a rotating reference frame. Normal mode analysis leads to a generalized eigenvalue problem which is solved by means of a Chebyshev collocation spectral method. The investigation of the preferred modes of instability is carried out over wide ranges of the parameters space. The behavior of the system is physically discussed and is compared to inviscid asymptotic limits and to viscous approximate solutions of the previous literature
Piccoli Ior. Le Casse diocesane da Porta Pia alla Seconda guerra mondiale
No full textLa questione del rapporto tra Chiesa ed economia in Italia interessa da tempo la ricerca storica a livello sia nazionale che internazionale, tuttavia non era stata sinora svolta un’indagine sugli aspetti finanziari delle diocesi. Si tratta di una lacuna di non poco conto considerate le ricchezze possedute dalla Chiesa cattolica italiana, la sua rilevante influenza culturale e politica, nonché il suo radicamento storico e territoriale. Grazie a nuove scoperte documentarie emerse presso alcuni archivi vaticani, il libro recupera dall’oblio della storia un interessante soggetto attivo in ambito finanziario a livello diocesano: la Cassa diocesana – anche detta Promotoria o Cassa ecclesiastica –, mediante la quale venivano raccolti e amministrati capitali destinati a opere di carità e religione. Riccardo Semeraro ricostruisce la storia tra gli anni postunitari e la Seconda guerra mondiale di questi «endowment funds» diocesani, analizzandone la struttura, il funzionamento e la gestione finanziaria volta al perseguimento dei fini stabiliti dai legati pii. Fra i temi affrontati, emergono le strategie anticonfisca degli enti ecclesiastici, il dialogo tra Vaticano e diocesi e il rapporto tra Chiesa e capitalismo
Student profiles to improve searching in e-learning systems
No full textEuropean countries have accumulated an enormous quantity of information in Digital Libraries (DLs). Offering seamless universal access to those collections will have a formidable impact on citizens' activities. Students could use information in DLs for improving their curricula, but it is difficult to find the exact chunk of material that solves a specific problem. A possible solution is to develop technologies that learn user preferences for customising information search. This paper focuses on a system based on Machine Learning techniques, the Profile Extractor, which automatically builds student models. An experimental session has been performed, evaluating the accuracy of the system
Riduzione o mantenimento in schiavitù o in servitù
No full textSi tratta di un'analisi del delitto di riduzione o manteninento in schiavitù o servitù, svolta tenendo in considerazione la tipicità del reato ed i rapporti intercorrenti con il delitto di tratta di persone
Riduzione o mantenimento in schiavitù o in servitù
No full textSi tratta di un'analisi del delitto di riduzione o manteninento in schiavitù o servitù, svolta tenendo in considerazione la tipicità del reato ed i rapporti intercorrenti con il delitto di tratta di persone
«Per il rispetto alle somme Chiavi»: storia dello IOR prima dello IOR
No full textLa rilevanza economica della Chiesa rappresenta certamente per la storiografia italiana un dato acquisito della modernità ma, allo stesso tempo, un interrogativo, sia descrittivo che interpretativo, rispetto alle trasformazioni avvenute nella fase successiva. Per tali ragioni, la questione complessiva del rapporto tra Chiesa cattolica ed economia nell’Italia dell’età contemporanea è stata ripetutamente indicata dalla comunità scientifica nazionale degli storici economici come uno dei percorsi in prospettiva più fecondi ed innovativi della disciplina specialistica della Storia economica. All’interno della storiografia sul tema sopracitato, un nucleo tematico di grande interesse è certamente quello relativo alla gestione delle finanze vaticane. In merito al percorso che conduceva l’Amministrazione Pontificia per le Opere di Religione attraverso diverse trasformazioni, la ricostruzione storica è oggi ancora estremamente lacunosa: poco o nulla si conosce in merito agli specifici servizi da essa esercitati, i capitali amministrati e, soprattutto, le ragioni che imposero un progressivo ripensamento del suo funzionamento. Obiettivo di questo studio è quello di offrire un primo contributo in questa direzione ricostruendo il contesto, le problematiche e le riflessioni interne alla Santa Sede che, a inizio anni Trenta, spinsero verso l’introduzione di uno statuto e altri documenti cruciali per il funzionamento della citata Amministrazione
Graph Neural Networks for fluid mechanics: data-assimilation and optimization
No full textThis PhD thesis investigates the application of Graph Neural Networks (GNNs) in the field of Computational Fluid Dynamics (CFD), with a focus on data-assimilation and optimization. The work is structured into three main parts: data-assimilation for Reynolds-Averaged Navier-Stokes (RANS) equations based on GNN models; data-assimilation augmented by GNN and adjoint-based enforced physical constraint; fluid systems optimization by ML techniques.
In the first part, the thesis explores the potential of GNNs to bypass traditional closure models, which often require manual calibration and are prone to inaccuracies. By leveraging high-fidelity simulation data, GNNs are trained to directly learn the unresolved flow quantities, offering a more flexible framework for the RANS closure problem. This approach eliminates the need for manually tuned closure models, providing a generalized and data-driven alternative. Moreover, in this first part, a comprehensive study of the impact of data quantity on GNN performance is conducted, designing an Active Learning strategy to select the most informative data among those available.
Building on these results, the second part of the thesis addresses a critical challenge often faced by ML models: the lack of guaranteed physical consistency in their predictions. To ensure that the GNNs not only minimize errors but also produce physically valid results, this part integrates physical constraints directly into the GNN training process. By embedding key fluid mechanics principles into the machine learning framework, the model produces predictions that are both reliable and consistent with the underlying physical laws, enhancing its applicability to real-world problems.
In the third part, the thesis demonstrates the application of GNNs to optimize fluid dynamics systems, with a particular focus on wind turbine design. Here, GNNs are employed as surrogate models, enabling rapid predictions of various design configurations without the need for performing a full CFD simulation at each iteration. This approach significantly accelerates the design process and demonstrates the potential of ML-driven optimization in CFD workflows, allowing for more efficient exploration of design spaces and faster convergence toward optimal solutions.
On the methodology side, the thesis introduces a custom GNN architecture specifically tailored for CFD applications. Unlike traditional neural networks, GNNs are inherently capable of handling unstructured mesh data, which is common in fluid mechanics problems involving irregular geometries and complex flow domains. To this end, the thesis presents a two-fold interface between Finite Element Method (FEM) solvers and the GNN architecture. This interface transforms FEM vector fields into numerical tensors that can be efficiently processed by the neural network, allowing data exchange between the simulation environment and the learning model.Cette thèse de doctorat explore l'application des réseaux de neurones en graphes (GNN) dans le domaine de la dynamique des fluides numérique (CFD), avec un accent particulier sur l'assimilation de données et l'optimisation. Le travail est structuré en trois parties principales: assimilation de données pour les équations de Navier-Stokes moyennées à la Reynolds (RANS) basée sur des modèles GNN; assimilation de données augmentée par les GNN avec des contraintes physiques imposées par la méthode adjointe; optimisation des systèmes fluides par des techniques d'apprentissage automatique (ML).
Dans la première partie, la thèse examine le potentiel des GNN pour contourner les modèles de fermeture traditionnels, qui nécessitent souvent une calibration manuelle et sont sujets à des inexactitudes. En exploitant des données de simulation à haute fidélité, les GNN sont entraînés à apprendre directement les quantités non résolues de l'écoulement, offrant ainsi un cadre plus flexible pour le problème de fermeture des équations RANS. Cette approche élimine le besoin de modèles de fermeture calibrés manuellement, fournissant une alternative généralisée et basée sur les données. De plus, dans cette première partie, une étude approfondie de l'impact de la quantité de données sur les performances des GNN est réalisée, avec la conception d'une stratégie d'Active Learning pour sélectionner les données les plus informatives parmi celles disponibles.
Sur la base de ces résultats, la deuxième partie de la thèse aborde un défi critique souvent rencontré par les modèles d'apprentissage automatique: l'absence de garantie de cohérence physique dans leurs prédictions. Afin de garantir que les GNN non seulement minimisent les erreurs, mais produisent également des résultats physiquement valides, cette partie intègre des contraintes physiques directement dans le processus d'entraînement des GNN. En incorporant les principes clés de la mécanique des fluides dans le cadre de l'apprentissage automatique, le modèle produit des prédictions à la fois fiables et cohérentes avec les lois physiques sous-jacentes, améliorant ainsi son applicabilité aux problèmes réels.
Dans la troisième partie, la thèse démontre l'application des GNN pour optimiser les systèmes de dynamique des fluides, avec un accent particulier sur la conception des éoliennes. Ici, les GNN sont utilisés comme modèles de substitution, permettant des prédictions rapides de diverses configurations de conception sans avoir besoin de réaliser une simulation CFD complète à chaque itération. Cette approche accélère considérablement le processus de conception et montre le potentiel de l'optimisation basée sur l'apprentissage automatique dans le cadre de la CFD, permettant une exploration plus efficace des espaces de conception et une convergence plus rapide vers des solutions optimales.
Sur le plan méthodologique, la thèse introduit une architecture GNN sur mesure spécifiquement adaptée aux applications CFD. Contrairement aux réseaux de neurones traditionnels, les GNN sont intrinsèquement capables de gérer des données de maillage non structurées, ce qui est courant dans les problèmes de mécanique des fluides impliquant des géométries irrégulières et des domaines d'écoulement complexes. À cette fin, la thèse présente une interface en deux parties entre les solveurs de la méthode des éléments finis (FEM) et l'architecture GNN. Cette interface transforme les champs vectoriels FEM en tenseurs numériques pouvant être traités efficacement par le réseau neuronal, permettant ainsi l'échange de données entre l'environnement de simulation et le modèle d'apprentissage
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