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Luca Di Angelo, Francesco Duronio, Angelo De Vita and Andrea Di Mascio
In this paper, an efficient and robust Cartesian Mesh Generation with Local Refinement for an Immersed Boundary Approach is proposed, whose key feature is the capability of high Reynolds number simulations by the use of wall function models, bypassing th...
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Luca Di Angelo, Francesco Gherardini, Paolo Di Stefano and Francesco Leali
A systematic method to design new tower-like structures with an enhanced visual impact quality is proposed. The method is applied to the design of electricity pylons, which are very critical structures due to their visual impact on landscape and on citiz...
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Stefano Zaffagnini, Angelo Boffa, Luca Andriolo, Davide Reale, Maurizio Busacca, Alessandro Di Martino and Giuseppe Filardo
Different surgical procedures have been proposed over the past few years to treat cartilage lesions. The aim of this study was to compare mosaicplasty and matrix-assisted autologous chondrocyte transplantation (MACT) at long-term follow-up. Forty-three p...
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Danilo Avola, Luigi Cinque, Angelo Di Mambro, Anxhelo Diko, Alessio Fagioli, Gian Luca Foresti, Marco Raoul Marini, Alessio Mecca and Daniele Pannone
In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of ...
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Alberto Bacci, Angelo Bosotti, Simone Di Mitri, Illya Drebot, Luigi Faillace, Paolo Michelato, Laura Monaco, Michele Opromolla, Rocco Paparella, Vittoria Petrillo, Marcello Rossetti Conti, Andrea Renato Rossi, Luca Serafini and Daniele Sertore
We present a study of an innovative scheme to generate high repetition rate (MHz-class) GeV electron beams by adopting a two-pass two-way acceleration in a super-conducting Linac operated in Continuous Wave (CW) mode. The beam is accelerated twice in the...
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Federico Cabitza, Andrea Campagner, Domenico Albano, Alberto Aliprandi, Alberto Bruno, Vito Chianca, Angelo Corazza, Francesco Di Pietto, Angelo Gambino, Salvatore Gitto, Carmelo Messina, Davide Orlandi, Luigi Pedone, Marcello Zappia and Luca Maria Sconfienza
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, t...
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