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In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training data are generated from a parametric and stochastic finite element model. The learning activity is performed by a support vector machine with Bayesian optimization of the hyperparameters, in which a penalty matrix is introduced to minimize the probability of missed alarms. Then, the estimated structural health conditions are used to update an exponential degradation model with random coefficients suitable for providing a prediction of the remaining useful life of the simulated corroded column. The results obtained show the potentiality of the proposed framework and its possible future extension for different types of damage and structural types.

Lower Urinary Tract Symptoms (LUTs) in men are usually associated to benign prostatic hyperplasia (BPH), a non-malignant prostate enlargement. Unfortunately, BPH etiology is still unclear. Recent works highlighted a relevant inflammation role in BPH onset and development. Consequently, to complement the 5-α reductase (and α-adrenergic receptor agonists-based therapy, an anti-inflammatory therapy should be devised. To reduce potential adverse effects of multi-drug treatment, plant extract-based therapies are becoming increasingly common. Serenoa repens, the main phytotherapic treatment for BPH, is not sufficient to front the multi-faceted etiology of BPH. In response to this, a novel, multiple phytotherapic agents-based formulation, LENILUTS®, was developed. In the present work, we compared, using an in vitro approach, the prostatic safety and efficacy of LENILUTS® with a commercial formulation, based only on Serenoa repens, and a 5αR inhibitor, Dutasteride. Furthermore, preliminary in vitro experiments to investigate the active principles, bioaccessibility and bioavailability of LENILUTS® were performed. Our results showed a better prostatic safety and therapeutic efficacy of LENILUTS® compared to the commercial formulation and Dutasteride, with increased anti-inflammatory, and pro-apoptotic activity, and a stronger inhibitory effect on the release of the key enzyme 5αR and Prostatic-Specific Antigen (PSA). The limited bioaccessibility and bioavailability of the active principles of LENILUTS® were highlighted. Considering the results obtained, the LENILUTS® formulation is more promising for BPH and LUTs therapy compared to formulations based on Serenoa repens only, but further efforts should be made to improve the bioaccessibility and bioavailability of the active principles.

Nowadays energy storage is strongly needed to allow grid safety and stability due to the wide penetration of renewable plants. Mainly economic and technological issues impede a relevant integration of conventional storage devices in the energy system. In this scenario, the hybridization of different storage technologies can be a techno-economic solution useful to overcome these issues and promote their diffusion. Hybridization allows multi-operation modes of the Energy Storage System (ESS), merging the positive features of base-technologies and extending their application ranges. This paper provides a dynamic analysis of a hybrid energy storage system (H-ESS) consisting of a flywheel and a battery pack coupled to a photovoltaic generation plant and a residential load up to 20 kW. A dynamic model of the overall micro-grid (MG) was developed implementing the H-ESS preliminary sizing and a suitable management algorithm. The instantaneous behavior of each component was evaluated. A brief summary of the MG performance at different weather and load conditions was provided together with a characterization of the impact of power fluctuations on the battery current and on the power exchange with the grid.