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Laura Iannetti

BETA CAE Italy Srl

A dynamic finite element modell to determine in-situ biomechanics of a running shoe

Other Authors

Serafim Chaztimoisiadis, BETA CAE System | Evagelos Karatsis, BETA CAE System | Alexander Tsouknidas, Department of Mechanical Engineering, University of Western Macedonia | Maria Papagiannaki, School of Physical Education & Sport Science, Aristotle University of Thessaloniki | Dimitrios Sagris, Department of Mechanical Engineering, Technical Educational Institute of Central Macedonia | Stergios Maropoulos, Department of Mechanical Engineering, Technical Educational Institute of Western Macedonia


As a periodic motion, running generates transient forces that reach up to 2.5 times the athlete’s body mass. The transition of these loads to the runner’s lower extremities, is mitigated only by apt footwear. This investigation introduces a dynamic Finite Element (FE) model of a running shoe, considering time varying plantar pressure distributions and boundary conditions. For this purpose, a commercial running shoe was scanned by means of a micro CT device and its gel based midsole, reverse-engineered at a 200μm accuracy. The obtained model was used to suggest improvements of material allocation within the midsole system. Both, altered positioning and varying gel volume led to different midsole responses that could be tuned more efficiently to the specific strike pattern. The shock absorbing capacity of technical midsole systems, is critical both to athletes and patients in need of prescribed therapeutic footwear. Advanced modeling techniques used in this study provide an effective alternative to conventional experimentation, both in the conceptual design and optimization of modern footwear.

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Stefano Porziani

University of Roma "Tor Vergata"

Hemo-Elastic study of ascending thoracic aorta aneurysms through RBF mesh morphing

Other Authors

Emiliano Costa, RINA Consulting S.p.A. | Marco E. Biancolini, University of Roma "Tor Vergata" | Katia Capellini, BioCardioLab, Fondazione CNR-Regione Toscana "G. Monasterio", Massa | Simona Celi, BioCardioLab, Fondazione CNR-Regione Toscana "G. Monasterio", Massa


The present paper aims at describing an approach to perform parametric fluid-structure interaction (FSI) analyses of healthy subjects with ascending thoracic aorta aneurysms (aTAA). Such numerical studies are performed through ANSYS® Workbench™ and foresee the use of both computational structural mechanics (CSM) and computational fluid-dynamics (CFD) software to handle the FSI task according to the two-way approach, and of a mesh morphing technique implemented in the RBF Morph™ ACT™ Extension, based on the radial basis functions (RBF) mathematical framework, to apply the shape variations in the respect of aneurysm shape growth. Both 3D surface models of healthy subjects and mean boundary conditions data for performing CFD simulations are obtained from 3D phase contrast magnetic resonance imaging (PC-MRI) acquisition, whilst the material properties of the aortic wall are modelled adopting an isotropic and hyperelastic model.

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stefano vassanelli

University of Padova

Global biohybrid network of biological and artificial neurons: a perspective on future IoT biomedical systems


The rapid development of implantable sensors and actuators for recording and intervention on human physiological parameters will lead to IoT based biomedical systems. This perspective implies, however, computational challenges, including the capability to process very heterogeneous and noisy real-world inputs in real time, reliably and with a very low power consumption. The brain has evolved throughout evolution to best perform this difficult task by implementing a sophisticated parallel computing approach based on networks of spiking neurons where nanoscale synaptic links take over part of the processing load. We show how, for the first time, a hybrid network of biological and artificial neurons communicating through nanoelectronic synaptic links can be established over internet connection. The technology paves the way to investigation and development of novel brain-inspired computing paradigms supporting IoT and autonomous systems and to advanced neuroprosthetic devices.

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