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- 저자 : Federico Lisi (검색결과 2 건)
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Amato Santoro,Federico Alvino,Giovanni Antonelli,Maria Caputo,Margherita Padeletti,Matteo Lisi,Sergio Mondillo 한국심초음파학회 2014 Journal of Cardiovascular Imaging (J Cardiovasc Im Vol.22 No.4
Background: Intensive training induces two morphological myocardial typologies of athlete’s heart. Endurance training (ET)induces eccentric remodeling, bradycardia and better diastolic filling. Strength training (ST) determines concentric chamberremodelling maintaining a normal heart rate (HR). Aim of the study was to compare ET and ST athletes’ heart using speckletracking echocardiography (STE). Methods: 33 professional ET, 36 ST athletes, and 17 healthy controls (CT) were enrolled. All subjects underwent standardtransthoracic echocardiography at rest and STE. Results: In ET group, HR was lower than ST group and CT group (p < 0.001; p < 0.01). ET group had higher E/A ratio thanST group and CT group (p < 0.01; p < 0.001). The left ventricular apical circumferential strain in ET group was lower than STgroup and CT group (-21.6 ± 4.1% vs. -26.8 ± 7.7%, p < 0.05; vs. -27.8 ± 5.6%, p < 0.01). ET group had lower left ventriculartwist (LVT) and untwisting (UTW) than ST group (6.2 ± 0.1° vs. 12.0 ± 0.1°, p < 0.01; -67.3 ± 22.9°/s vs. -122.5 ± 52.8°/s, p< 0.01) and CT group (10.0 ± 0.1°, p < 0.01; -103.3 ± 29.3°/s, p < 0.01). The univariate analysis showed significant correlationbetween E/A ratio and HR (r = -0.54; p < 0.001), LVT (r = -0.45; p < 0.01), UTW (r = 0.24; p < 0.05). At the multivariateanalysis only HR was confirmed as independent predictor of diastolic function in all groups (Beta -0.52; p < 0.001). Conclusion: In ET there was a better global systolic and diastolic functional reserve at rest observed with strain analysis and itmaybe depended on autonomic modulation.
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Multi-Agent Quality of Experience Control
Francesco Delli Priscoli,Alessandro Di Giorgio,Federico Lisi,Salvatore Monaco,Antonio Pietrabissa,Vincenzo Suraci,Lorenzo Ricciardi Celsi 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.2
In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalitiesis to track the personalized desired QoE level of the applications. The paper proposes to perform such a taskby dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), thisselection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The papershows that such an approach offers the opportunity to cope with some practical implementation problems: in particular,it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactoryperformance results even in the presence of several hundreds of Agents.
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