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Piezoelectric energy harvesters (PEHs) are a reduced, but fundamental, source of power for embedded, remote, and no-grid connected electrical systems. Some key limits, such as low power density, poor conversion efficiency, high internal impedance, and AC output, can be partially overcome by matching their internal electrical impedance to that of the applied resistance load. However, the applied resistance load can vary significantly in time, since it depends on the vibration frequency and the working temperature. Hence, a real-time tracking of the applied impedance load should be done to always harvest the maximum energy from the PEH. This paper faces the above problem by presenting an active control able to track and follow in time the optimal working point of a PEH. It exploits a non-conventional AC–DC converter, which integrates a single-stage DC–DC Zeta converter and a full-bridge active rectifier, controlled by a dedicated algorithm based on pulse-width modulation (PWM) with maximum power point tracking (MPPT). A prototype of the proposed converter, based on discrete components, was created and experimentally tested by applying a sudden variation of the resistance load, aimed to emulate a change in the excitation frequency from 30 to 70 Hz and a change in the operating temperature from 25 to 50 °C. Results showed the effectiveness of the proposed approach, which allowed to match the optimal load after 0.38 s for a ΔR of 47 kΩ and after 0.15 s for a ΔR of 18 kΩ.

Objective: To verify whether platelet responsiveness to leptin is associated with metabolic syndrome risk factors. Design: Cross-sectional study. Subjects: We studied 169 consecutive patients, mean age ¼ 43.679.9 years, with overweight (N ¼ 57) or obesity (N ¼ 112). Measurements: Cluster analysis was used to generate three clusters based on platelet responsiveness to increasing doses of leptin. Profiles of metabolic syndrome risk factors of the three clusters were compared by discriminant analysis. Results: Platelet responsiveness to leptin was absent in cluster 1, whereas cluster 3 had the greatest platelet aggregation response to leptin pre-incubation. Plasma leptin levels significantly decreased from cluster 1 to cluster 3 in both gender. Patients in cluster 2 had an intermediate profile of leptin responsiveness. Highest body mass index (BMI) values were more frequent in non-responders, whereas the prevalence of high waist circumference, as well as hypertriglyceridemia and hypertension, increased with increasing responsiveness to leptin from cluster 1 to cluster 3. Pattern of metabolic syndrome risk factors qualified as group specific in 69.0% of the cluster 1, 54.9% of the cluster 2 and 55.8% of the cluster 3. Circulating leptin, waist circumference, plasma triglycerides and BMI defined distinctive patterns of metabolic syndrome risk factors in the clusters. Conclusions: In overweight and obese outpatients, metabolic syndrome risk factors parallel to some extent platelet responsiveness to leptin. Such a correlation involves plasma leptin levels, waist circumference, plasma triglycerides and BMI, and may contribute to the excess risk of cardiovascular events in overweight and obese patients.

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