SUMMARYIn this paper we solve the synthesis problem of ÿnding a completely passive electric circuit analog to a vibrating beam. The synthesis problem is of interest when one wants to suppress beam mechanical vibrations by using distributed piezoelectric transduction. Indeed, an e ective electromechanical energy transduction is guaranteed when the electric circuit (interconnecting the transducers' terminals) is resonant at all mechanical resonance frequencies and is able to mimic all the mechanical modal shapes. The designed electric circuit behaves as an electric controller of mechanical vibrations (i.e. an electric vibration damper) once suitably endowed with a set of resistors. Because of its completely passive nature, it does not require external power units and stands as an economical means of controlling vibrations.

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.

The outbreak of the COVID-19 pandemic has led to a disruption of surgical care. The aim of this multi-centric, retrospective study was to evaluate the impact of the pandemic on surgical activity for thyroid disease among the Italian Units of Endocrine Surgery. Three phases of the pandemic were identified based on the epidemiological situation and the public measures adopted from the Italian Government (1st phase: from 9th March to 3rd May 2020; 2nd phase: from 4th May to 14th June; 3rd phase: from 15th June to 31st). The patients operated upon during these phases were compared to those who underwent surgery during the same period of the previous year. Overall, 3892 patients from 28 Italian endocrine surgical units were included in the study, 1478 (38%) operated upon during COVID-19 pandemic, and 2414 (62%) during the corresponding period of 2019. The decrease in the number of operations was by 64.8%, 44.7% and 5.1% during the three phases of COVID-19 pandemic, compared to 2019, respectively. During the first and the second phases, the surgical activity was dedicated mainly to oncological patients. No differences in post-operative complications were noted between the two periods. Oncological activity for thyroid cancer was adequately maintained during the COVID-19 pandemic.

A high automation degree is one of the most important features of data driven modeling tools and it should be taken into consideration in classification systems design. In this regard, constructive training algorithms are essential to improve the automation degree of a modeling system. Among neuro-fuzzy classifiers, Simpson's (1992) min-max networks have the advantage of being trained in a constructive way. The use of the hyperbox, as a frame on which different membership functions can be tailored, makes the min-max model a flexible tool. However, the original training algorithm evidences some serious drawbacks, together with a low automation degree. In order to overcome these inconveniences, in this paper two new learning algorithms for fuzzy min-max neural classifiers are proposed: the adaptive resolution classifier (ARC) and its pruning version (PARC). ARC/PARC generates a regularized min-max network by a succession of hyperbox cuts. The generalization capability of ARC/PARC technique mostly depends on the adopted cutting strategy. By using a recursive cutting procedure (R-ARC and R-PARC) it is possible to obtain better results. ARC, PARC, R-ARC, and R-PARC are characterized by a high automation degree and allow to achieve networks with a remarkable generalization capability. Their performances are evaluated through a set of toy problems and real data benchmarks. The paper also proposes a suitable index that can be used for the sensitivity analysis of the classification systems under consideration.

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