Machine Learning Applied to the Blind Identification of Multiple Delays in Distributed Systems

This paper focuses on the application of the Least-Square Support Vector Machine (LS-SVM) regression for the modeling of frequency responses of complex interconnect structures. The goal is to obtain a delayed-rational model (DRM) for the structure accounting for multiple time-delays generated by wave propagation and reflections along the channel. A novel approach for the time-delays estimation based on the LS-SVM regression is introduced. The delays are estimated using the dual space formulation of the LS-SVM with an ad-hoc kernel that considers a possible delay interval. The results highlight the lower order of DRMs obtained using the delays identified through this method when comparing to the vector fitting approach by applying it to a high-speed cable link

Bayesian Optimization of Hyperparameters in Kernel-Based Delay Rational Models

This paper presents an automatic procedure for the optimization of the hyperparameters of a delay rational model approximating the frequency-domain behavior of high-speed interconnects. The proposed model is built via a kernel-based regression, such as the Least-Square Support Vector Machine (LS-SVM), by considering an ad-hoc kernel with two hyperparameters related to the propagation delays introduced by the system. Such hyperparameters, along with the Tikhonov regularizer used by the LS-SVM regression, are carefully tuned via an automatic approach based on a k-fold cross-validation and Bayesian optimization. The feasibility of the effectiveness of the proposed modeling approach are investigated on a high-speed link

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Defining Kawasaki disease and pediatric inflammatory multisystem syndrome-temporally associated to SARS-CoV-2 infection during SARS-CoV-2 epidemic in Italy: results from a national, multicenter survey

Background: There is mounting evidence on the existence of a Pediatric Inflammatory Multisystem Syndrome-temporally associated to SARS-CoV-2 infection (PIMS-TS), sharing similarities with Kawasaki Disease (KD). The main outcome of the study were to better characterize the clinical features and the treatment response of PIMS-TS and to explore its relationship with KD determining whether KD and PIMS are two distinct entities. Methods: The Rheumatology Study Group of the Italian Pediatric Society launched a survey to enroll patients diagnosed with KD (Kawasaki Disease Group - KDG) or KD-like (Kawacovid Group - KCG) disease between February 1st 2020, and May 31st 2020. Demographic, clinical, laboratory data, treatment information, and patients' outcome were collected in an online anonymized database (RedCAP®). Relationship between clinical presentation and SARS-CoV-2 infection was also taken into account. Moreover, clinical characteristics of KDG during SARS-CoV-2 epidemic (KDG-CoV2) were compared to Kawasaki Disease patients (KDG-Historical) seen in three different Italian tertiary pediatric hospitals (Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste; AOU Meyer, Florence; IRCCS Istituto Giannina Gaslini, Genoa) from January 1st 2000 to December 31st 2019. Chi square test or exact Fisher test and non-parametric Wilcoxon Mann-Whitney test were used to study differences between two groups. Results: One-hundred-forty-nine cases were enrolled, (96 KDG and 53 KCG). KCG children were significantly older and presented more frequently from gastrointestinal and respiratory involvement. Cardiac involvement was more common in KCG, with 60,4% of patients with myocarditis. 37,8% of patients among KCG presented hypotension/non-cardiogenic shock. Coronary artery abnormalities (CAA) were more common in the KDG. The risk of ICU admission were higher in KCG. Lymphopenia, higher CRP levels, elevated ferritin and troponin-T characterized KCG. KDG received more frequently immunoglobulins (IVIG) and acetylsalicylic acid (ASA) (81,3% vs 66%; p = 0.04 and 71,9% vs 43,4%; p = 0.001 respectively) as KCG more often received glucocorticoids (56,6% vs 14,6%; p < 0.0001). SARS-CoV-2 assay more often resulted positive in KCG than in KDG (75,5% vs 20%; p < 0.0001). Short-term follow data showed minor complications. Comparing KDG with a KD-Historical Italian cohort (598 patients), no statistical difference was found in terms of clinical manifestations and laboratory data. Conclusion: Our study suggests that SARS-CoV-2 infection might determine two distinct inflammatory diseases in children: KD and PIMS-TS. Older age at onset and clinical peculiarities like the occurrence of myocarditis characterize this multi-inflammatory syndrome. Our patients had an optimal response to treatments and a good outcome, with few complications and no deaths

Machine Learning-Based Uncertainty Quantification of Passive Intermodulation in Aluminum Contact

This paper deals with the development of a surrogate model for the uncertainty quantification and the stochastic analysis of passive intermodulation (PIM) in an Aluminum-Aluminum contact based on the least-squares support vector machine (LS-SVM) regression. Starting from a small set of training pairs collecting the configuration of the un-certain parameters and the corresponding PIM level, the LS-SVM allows to build a closed-form approximation of such non-linear relationship. Such model, can be suitably used within a Monte Carlo (MC) scenario in order to accelerate the simulation process and provide all the statistical quantities of interest. The results show a considerable speed-up on the computational time compared to a plain MC simulation, while achieving an accurate approximation of the PIM probability density function

Multiple Delay Identification in Long Interconnects via LS-SVM Regression

This work presents a novel approach for the accurate estimation of multiple time-delays from the frequency response of a distributed system. The proposed approach is based on a powerful and flexible machine learning technique, namely, the least-square support vector machine (LS-SVM). The LS-SVM regression is used to construct a metamodel of the transfer function describing a generic linear time-invariant system in a delayed-rational form. Specifically, after some manipulation the LS-SVM model precisely identifies the dominant propagation delays of the original system. The essential steps and critical criteria for the delay identification procedure are carefully discussed throughout the paper. Once the system delays have been identified, the rational part of the metamodel expansion is then obtained by means of a progressive application of the conventional vector fitting algorithm. Numerical examples are presented to illustrate the feasibility and performance of the proposed technique and to compare its performances with what is provided by state-of-the-art techniques. The results clearly highlight the capability of the proposed approach to identify the dominant delays in distributed systems, thus allowing to construct compact delayed rational models

Voltage inverter with push-pull topology to inject energy into electrical systems with modulation SPWM

This paper presents a proposal for a voltage inverter topology based on push-pull converters, switched at high frequency to inject energy into the grid from a source of DC power. A system using two reverse voltage static converters provides the power grid; energy in the form of alternating current, that can work in conjunction with the provision of utility power. Aiming at the possible use of renewable energy sources, that can be stored in the form of voltage continuous, such as wind, solar, hydroelectric and others. The functioning of topology is presented, such as the power and control circuits, as well as sizing components, theoretical and practical results achieved with the assembly of a prototype 100W of power and switching in 40khz, which after filtering provides the frequency of 60Hz, which is compatible with the Brazilian electrical system