Nonlinear frequency response curves estimation and stability analysis of randomly excited systems in the subspace framework

In this paper, the periodic solutions of nonlinear mechanical systems are studied starting from the nonlinear state-space model estimated using the nonlinear subspace identification (NSI) technique. In its standard form, NSI needs the input-output data from a nonlinear structure undergoing broadband excitation and requires the prior knowledge of the locations and kind of nonlinearities to be estimated. The method allows the estimation of the nonlinear features of the system and the indirect study of its periodic solutions using a single broadband excitation, without the need of feedback control loops. To this end, the nonlinear frequency response curves of the system are estimated merging the harmonic balance method with the NSI technique and using a continuation approach. Then, a monodromy-based stability analysis is developed in the nonlinear state-space framework to study the stability of the periodic solutions of the system and to track its bifurcations. The method is validated considering conservative nonlinearities on two numerical examples and one experimental application, the latter comprising a double-well oscillator with period-doubling phenomena. The effects of noise and nonlinear modeling errors are also evaluated

Identification of a Duffing Oscillator under Different Types of Excitation

In many engineering applications the dynamics may significantly be affected by nonlinear effects, which must be accounted for in order to accurately understand and robustly model the dynamics. From a practical point of view, it is very important to solve theinverse problemrelated to system identification and output prediction. In this paper the recently developed Nonlinear Subspace Identification (NSI) method is presented and applied to an oscillator described by the Duffing equation, with different types of excitation including random forces, which are demonstrated to be very suitable for the identification process. The estimates of system parameters are excellent and, as a consequence, the behaviour of the system, including the jump phenomena, is reconstructed to a high level of fidelity. In addition, the possible memory limitations affecting the method are overcome by the development of a novel algorithm, based on a specific computation of the QR factorisation

An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach

An Illustration of New Methods in Machine Condition Monitoring, Part II: Adaptive outlier detection

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damagesensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-ofthe-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The second paper in the pair will deal with novelty detection. Although there has been considerable progress in the use of outlier analysis for novelty detection, most of the papers produced so far have suffered from the fact that simple algorithms break down if multiple outliers are present or if damage is already present in a training set. The objective of the current paper is to illustrate the use of phase-space thresholding; an algorithm which has the ability to detect multiple outliers inclusively in a data set

Urticaria in an infant with SARS-CoV-2 positivity

Last months have been marked by the global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 19 (COVID-19) pandemic

A review of the literature of surgical and nonsurgical treatments of invasive squamous cells carcinoma

Cutaneous squamous cell carcinoma (cSCC) is an increasing public health problem. It is a primary malignant skin tumor with Malpighian differentiation and together with basal cell carcinoma is classified among nonmelanoma skin cancers (NMSCs). cSCC usually occurs on photoexposed areas, such as the head, the neck, and the extremities, and its incidence increases with age. Invasive forms of this skin tumor tend to be more aggressive showing a higher metastatic potential, usually regarding regional lymph nodes. Treatment options for invasive cSCCs include both surgical and nonsurgical options. The therapeutic choice depends on several factors, such as anatomic location, risk factors for tumor recurrence, age, and health status of the patient. This review aims to provide an overview of the current evidence on therapeutic surgical and nonsurgical management of invasive cSCC

IL-17 and its role in inflammatory, autoimmune, and oncological skin diseases. State of art

Recent data support the theory of the involvement of IL-17 in the pathogenesis of several chronic inflammatory skin diseases (psoriasis, atopic dermatitis, acne, hidradenitis suppurativa) and autoimmune skin diseases (alopecia areata, vitiligo, bullous diseases). Even if the role of IL-17 in inflammatory and autoimmune diseases has been reported extensively, its role in tumor is still controversial. Some reports show that Th17 cells eradicate tumors, while others reveal that they promote the initiation and early growth of tumors. Herein, we review the role of IL-17 in the involvement of some common dermatologic diseases: psoriasis, atopic dermatitis, hidradenitis suppurativa, acne, vitiligo, melanoma, and nonmelanoma skin cancers