Archivio della ricerca della Scuola Superiore Sant'Anna
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Trattamento dei dati sanitari per finalità di ricerca medica: esenzione dal consenso. L’orientamento tracciato dall’Autorità e le prospettive alla luce del novellato art. 110 GDPR
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I diritti fondamentali delle donne migranti: uno sguardo alla condizione di vulnerabilità femminile
No full textArtificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis
No full textMuscle Synergy Analysis of Healthy Subjects Using a Soft Elbow Exosuit During Load-Carrying Tasks
No full textSoft assistive wearable robotics, or soft exosuits, have shown great potential in enhancing human motor function while preserving the user's natural movement. They are increasingly used to reduce fatigue and the risk of injury during physically demanding tasks and everyday activities. A deeper understanding of how assistive forces from soft exoskeletons influence muscle coordination during various tasks is crucial for developing optimized control and rehabilitation strategies for these devices. This study investigates muscle synergies in healthy participants carrying loads, as often performed in daily activities, over an extended period, both with and without the assistance of a soft elbow exosuit. Synergies similarity was analyzed by comparing the extracted patterns in both conditions, as well as their composition in terms of muscle contributions. The results show that, on average, in the with-exosuit condition fewer synergies are required to explain the performed movements. This suggests the idea that the assistive device 'aggregates' motor primitives while reducing muscle activation. Furthermore, we found that synergies dominated by the wrist flexor, biceps, and infraspinatus were not altered by the exosuit assistance. However, the deltoid-dominated synergy observed during load carrying without the exosuit was altered by the exosuit assistance, shifting to a triceps-dominated synergy
Assessing the Value of Ecosystem Services in Decentralized Sanitation Systems: A Case Study in a Vulnerable Mountain Area
No full textBRIDGING THE GAP: HOW GHG ACCOUNTING TOOLS CAN SUPPORT BEER COMPANIES IN THE IMPLEMENTATION OF CIRCULAR ECONOMY STRATEGIES
No full textHeterogeneous Multi-robot Systems Cooperative Exploration of Unknown Environment
No full textThis paper introduces a novel method for the coordinated control of heterogeneous multi-robot systems composed of a UGV and a UAV and focuses on cooperative exploration in unknown environments. The proposed approach utilizes a numerical optimization solver to manage the non-linear dynamics and constraints of both agents. We introduce a weighting parameter to adapt the team behavior either prioritizing proximity between the agents or independently pursuing the exploration. The proposed solution has been extensively tested in a simulation environment and the experimental results demonstrate the method’s computational efficiency, adaptability, and suitability for real-time deployment in autonomous exploration missions. This approach advances multi-robot coordination by providing a flexible and robust framework for heterogeneous agent collaboration in complex, unstructured environments
Approximate regular equivalence by partition refinement
No full textRegular equivalence aims to identify nodes that have links to nodes that are themselves equivalent, and is considered to capture key relational properties in networks. Exact equivalences are notoriously difficult to emerge in real-world networks because of the rather stringent criteria required. This has motivated the development of approximate approaches, which, however, do not scale well to large networks. In this paper, we present a new method to compute approximate regular equivalences for weighted networks based on a partition refinement algorithm. This is parameterized by a tolerance that determines the extent to which two nodes may be deemed equivalent. We also show an asymptotic result for networks with power-law distribution that analytically provides a partition of approximately equivalent nodes. Using a number of benchmark networks, we show that our method outperforms the state of the art in terms of precision and running time. When the asymptotic partition is used to initialize the partition refinement algorithm for real-world networks, it avoids the problem of aggressive clustering that affects binary networks