A parametric mechanical model based on a Lagrangian formulation is here proposed to predict the dynamic response of roller batteries during the vehicles transit across the so-called compression towers in ropeways transportation systems. The model describes the dynamic interaction between the ropeway substructures starting from the modes and frequencies of the system to the forced dynamic response caused by the vehicles transit. The analytical model is corroborated and validated via an extensive experimental campaign devoted to the dynamic characterization of the roller battery system. The data acquired on site via a custom-design sensor network allowed to identify the frequencies and damping ratios by employing the Frequency Domain Decomposition (FDD) method. The high fidelity modeling and the system identification procedure are discussed.
In the era of the Internet of Things (IoT), drug developers can potentially access a wealth of real-world, participant-generated data that enable better insights and streamlined clinical trial processes. Protection of confidential data is of primary interest when it comes to health data, as medical condition influences daily, professional, and social life. Current approaches in digital trials entail that private user data are provisioned to the trial investigator that is considered a trusted party. The aim of this paper is to present the technical requirements and the research challenges to secure the flow and control of personal data and to protect the interests of all the involved parties during the first phases of a clinical trial, namely the characterization of the potential patients and their possible recruitment. The proposed architecture will let the individuals keep their data private during these phases while providing a useful sketch of their data to the investigator. Proof-of-concept implementations are evaluated in terms of performances achieved in real-world environments.
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