Wind Farm energy evaluation and modeling from wind data

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The failure of the Italian constitutional reform signals a crisis of representation in politics

The reasons for the rejection of the Italian constitutional reform seem to be social-cultural rather than political. Marco Scalvini and Monica Fabris identify a new class of disadvantaged and disenchanted voters who feel that they have lost both the political influence and the social protections that existed pre-austerity

Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control, and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a novel online distributed control law. Finally, numerical examples involving a cubic formation following a straight path in the 3D space are provided to validate the proposed control strategies.Comment: 28 pages, 2 figures, submitted to the European Journal of Control on June 23rd, 2023 (version 1

Adaptive Consensus-based Regulation of Open-Channel Networks

This paper deals with water management over open-channel networks subject to water height imbalance. Specifically, it is devised a fully distributed adaptive consensus-based algorithm within the discrete-time domain capable of (i) providing a suitable tracking reference that stabilizes the water increments over the underlying network at a common level; (ii) coping with general flow constraints related to each channel of the considered system. This iterative procedure is derived by solving a guidance problem that guarantees to steer the regulated network - represented as a closed-loop system - while satisfying requirements (i) and (ii), provided that a suitable design for the local feedback law controlling each channel flow is already available. The proposed solution converges exponentially fast towards the average consensus without violating the imposed constraints over time. In addition, numerical results are reported to support the theoretical findings, and the performance of the developed algorithm is discussed in the context of a realistic scenario.Comment: 13 pages, 5 figures, submitted to IEEE Access (version 1

A Proximal Point Approach for Distributed System State Estimation

System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in the group, which is supposed to access only relative measurements w.r.t. some neighbors state. Exploiting the standard least-squares paradigm, the system state estimation task is faced in this work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the emergence of interesting connections between the structural properties of the stochastic matrices describing the system dynamics and the convergence behavior toward the optimal estimate. A deep analysis of such relations is provided, jointly with a further discussion on the penalty parameter that characterizes the Proximal Point approach.Comment: 6 pages, 2 figures, 1 table, manuscript n 3555, \c{opyright} 2020 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Standardizing the role of endomyocardial biopsy in current clinical practice worldwide

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Uncertainty-aware data-driven predictive control in a stochastic setting

Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant. However, DDPC is built upon input/output trajectories. Therefore, the finite sample effect of stochastic data, due to, e.g., measurement noise, may have a detrimental impact on closed-loop performance. Exploiting a formal statistical analysis of the prediction error, in this paper we propose the first systematic approach to deal with uncertainty due to finite sample effects. To this end, we introduce two regularization strategies for which, differently from existing regularization-based DDPC techniques, we propose a tuning rationale allowing us to select the regularization hyper-parameters before closing the loop and without additional experiments. Simulation results confirm the potential of the proposed strategy when closing the loop.Comment: 6 pages, 1 figure, this work has been submitted and accepted for publication at the IFAC World Congress 2023, Yokohama, Japa

Risk factors and outcome among a large patient cohort with community-acquired acute hepatitis C in Italy

BACKGROUND: The epidemiology of acute hepatitis C has changed during the past decade in Western countries. Acute HCV infection has a high rate of chronicity, but it is unclear when patients with acute infection should be treated. METHODS: To evaluate current sources of hepatitis C virus (HCV) transmission in Italy and to assess the rate of and factors associated with chronic infection, we enrolled 214 consecutive patients with newly acquired hepatitis C during 1999-2004. The patients were from 12 health care centers throughout the country, and they were followed up for a mean (+/- SD) period of 14+/-15.8 months. Biochemical liver tests were performed, and HCV RNA levels were monitored. RESULTS: A total of 146 patients (68%) had symptomatic disease. The most common risk factors for acquiring hepatitis C that were reported were intravenous drug use and medical procedures. The proportion of subjects with spontaneous resolution of infection was 36%. The average timespan from disease onset to HCV RNA clearance was 71 days (range, 27-173 days). In fact, 58 (80%) of 73 patients with self-limiting hepatitis experienced HCV RNA clearance within 3 months of disease onset. Multiple logistic regression analyses showed that none of the variables considered (including asymptomatic disease) were associated with increased risk of developing chronic hepatitis C. CONCLUSIONS: These findings underscore the importance of medical procedures as risk factors in the current spread of HCV infection in Italy. Because nearly all patients with acute, self-limiting hepatitis C - both symptomatic and asymptomatic - have spontaneous viral clearance within 3 months of disease onset, it seems reasonable to start treatment after this time period ends to avoid costly and useless treatment

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed

Endomyocardial biopsy in the clinical context: current indications and challenging scenarios

Endomyocardial biopsy (EMB) is an invasive procedure originally developed for the monitoring of heart transplant rejection. Over the year, this procedure has gained a fundamental complementary role in the diagnostic work-up of several cardiac disorders, including cardiomyopathies, myocarditis, drug-related cardiotoxicity, amyloidosis, other infiltrative and storage disorders, and cardiac tumours. Major advances in EMB equipment and techniques for histological analysis have significantly improved diagnostic accuracy of EMB. In recent years, advanced imaging modalities such as echocardiography with three-dimensional and myocardial strain analysis, cardiac magnetic resonance and bone scintigraphy have transformed the non-invasive approach to diagnosis and prognostic stratification of several cardiac diseases. Therefore, it emerges the need to re-define the current role of EMB for diagnostic work-up and management of cardiovascular diseases. The aim of this review is to summarize current knowledge on EMB in light of the most recent evidences and to discuss current indications, including challenging scenarios encountered in clinical practice