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Background Monitoring the transcutaneous permeation of exogenous molecules using conventional techniques generally requires long pre-analytical preparation or labelling of samples. However, Raman spectroscopy is a label-free and non-destructive method which provides spatial distribution of tracked actives in skin. The aim of our study was to prove the interest of Raman imaging coupled with multivariate curve resolution alternating least square (MCR-ALS) analysis in monitoring retinol penetration into frozen and living human skin. Materials and methods After topical treatment of skin samples by free or encapsulated retinol, thin cross sections were analysed by Raman imaging (up to 100 µm depth). Mann-Whitney test was used to identify retinol spectroscopic markers in skin. MCR-ALS was used to estimate retinol contribution in Raman spectral images. Heat maps were constructed to compare the distribution of free and encapsulated retinol in skin models. Results We identified the bands at 1158, 1196 and 1591 cm-1 as specific features for monitoring retinol in skin. Moreover, our MCR-ALS results showed an improvement of retinol penetration (up to 30 µm depth) with the encapsulated form as well as storage reservoir formation in stratum corneum, for each skin model. Finally, greater retinol penetration into living skin was observed. Conclusion This study shows a proof of concept for the evaluation of retinol penetration in skin using Raman imaging coupled with MCR-ALS. This concept needs to be validated on more subjects to include inter-individual variability but also other factors affecting skin permeation (age, sex, pH, etc). Our study can be extended to other actives.

The objective of this paper is the realization of a desired frequency–amplitude dependence of a specific vibration mode of a nonlinear system. To this end, we introduce an additional nonlinearity with an a priori arbitrary mathematical form in the considered system and synthesize its restoring force through optimization. To greatly facilitate the optimization process, the restoring force is optimized piece by piece from low to large motion amplitudes. The suppression of a modal interaction and the enforcement of isochronicity are considered to demonstrate the proposed procedure.

Jointed structures are ubiquitous constituents of engineering systems; however, their dynamic properties (e.g., natural frequencies and damping ratios) are challenging to identify correctly due to the complex, nonlinear nature of interfaces. This research seeks to extend the efficacy of traditional experimental methods for linear system identification (such as impact testing, shaker ringdown testing, random excitation, and force or amplitude-control stepped sine testing) on nonlinear jointed systems, e.g., the half Brake–Reußbeam, by augmenting them with full-field data collected by high-speed videography. The full-field response is acquired using high-speed cameras combined with Digital Image Correlation (DIC), which enables studying the spatial–temporal dynamic characteristics of the system. As this is a video-based experiment, additional constraints are attached to the beam at the node points to remove the rigid body motion, which ensures that the beam is in the view of the camera during the entire test. The use of a video-based method introduces new sources of experimental error, such as noise from the high-speed camera's fan and electrical noise, and so the measurement accuracy of DIC is validated using accelerometer data. After validating the DIC data, the measurements are recorded for several types of excitation, including hammer testing, shaker ringdown testing, fixed sine testing, and stepped sine testing. Using the DIC data to augment standard nonlinear system identification techniques, modal coupling and the mode shapes’ evolution are investigated. The suitability of videography methods for nonlinear system identification of nonlinear beams is explored for the first time in this paper, and recommendations for techniques to facilitate this process are made. This article focuses on developing an accurate data collection methodology as well as recommendations for nonlinear testing with DIC, which paves the way for video-based investigation of nonlinear system identification. In Part-II (Jin et al., 2021) of this work, the same data set is used for a rigorous assessment of nonlinear system identification with full-field DIC data. ISSN:0888-3270 ISSN:1096-1216

This paper deals with the nonlinear system identification of structures exhibiting distributed nonlinearities, which has become of great interest recently, due to the continuous interest to improve the performance of structures. This brings the need for designing lighter and more flexible structural elements, which are usually characterized by moderate and large deformation, resulting in a distributed nonlinear behavior. In this framework, system identification remains a particularly challenging problem, especially when experimental measurements are considered. This work proposes a method to perform such a task, based on a convenient model order reduction of the considered structure, followed by a nonlinear system identification algorithm. The methodology is validated on a very thin beam undergoing large-amplitude oscillations, firstly using numerical data and then considering an experimental test bench. On the experimental side, the nonlinearity is first characterized using just the measured data, in order to acquire information that would help the identification process. Eventually, nonlinear system identification is performed in the reduced-order domain. An ad-hoc version of the nonlinear subspace identification (NSI) algorithm is used, but the presented methodology can also be applied with other nonlinear identification tools. Results confirm the goodness of the identification strategy in obtaining a reliable model which takes into account the distributed nonlinear behavior.

UNSTRUCTURED There is an unprecedented demand for infodemic management due to rapidly evolving information about the novel COVID-19 pandemic. This viewpoint paper details the evolution of a Canadian digital information tool, Chloe for COVID-19, based on incremental leveraging of artificial intelligence techniques. By providing an accessible summary of Chloe’s development, we show how proactive cooperation between health, technology, and corporate sectors can lead to a rapidly scalable, safe, and secure virtual chatbot to assist public health efforts in keeping Canadians informed. We then highlight Chloe’s strengths, the challenges we faced during the development process, and future directions for the role of chatbots in infodemic management. The information presented here may guide future collaborative efforts in health technology in order to enhance access to accurate and timely health information to the public.

In the present article, we follow up our recent work on the experimental assessment of two data-driven nonlinear system identification methodologies. The first methodology constructs a single nonlinear-mode model from periodic vibration data obtained under phase-controlled harmonic excitation. The second methodology constructs a state-space model with polynomial nonlinear terms from vibration data obtained under uncontrolled broadband random excitation. The conclusions drawn from our previous work (experimental) were limited by uncertainties inherent to the specimen, instrumentation, and signal processing. To avoid these uncertainties in the present work, we pursued a completely numerical approach based on synthetic measurement data obtained from simulated experiments. Three benchmarks are considered, which feature geometric, unilateral contact, and dry friction nonlinearity, respectively. As in our previous work, we assessed the prediction accuracy of the identified models with a focus on the regime near a particular resonance. This way, we confirmed our findings on the strengths and weaknesses of the two methodologies and derive several new findings: First, the state-space method struggles even for polynomial nonlinearities if the training data is chaotic. Second, the polynomial state-space models can reach high accuracy only in a rather limited range of vibration levels for systems with non-polynomial nonlinearities. Such cases demonstrate the sensitivity to training data inherent in the method, as model errors are inevitable here. Third, although the excitation does not perfectly isolate the nonlinear mode (exciter-structure interaction, uncontrolled higher harmonics, local instead of distributed excitation), the modal properties are identified with high accuracy., Vibration, 3 (3)

Mechanical joints have a significant influence on the dynamic response of assembled structures. Due to friction, wear, and non-idealized boundary conditions, joints introduce significant nonlinearity into the dynamics of assembled structures. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this research, the response analysis for a beam with a bolted lap joint is studied with the help of several available identification techniques. The experimental setup and data capture are described in Part I of this work, providing high spatial resolution data for a variety of excitation methods. The nonlinear identification of the data is the focus of this paper, aiming to perform nonlinear modal analysis and to localize the nonlinear characteristics of the structure with a series of different approaches.

The dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. The focus of this paper is to compare nonlinear system identification methods applied to different measurement techniques and to exploit the use of high spatial resolution data. ISSN:0888-3270 ISSN:1096-1216

Hysteresis is a nonlinear effect that shows up in a wide variety of engineering and scientific fields. The identification of hysteretic systems from input-output data is an important but challenging question, which has been studied by using both tailored parametric white-box identification methods as by using black-box identification methods. The white-box modeling approach is by far the most common in identifying hysteretic systems, and has the advantage of resulting into an interpretable model, but it requires to be adjusted to a specific hysteresis model. A black-box approach can be used more universally, but results in models containing many parameters that cannot easily be interpreted. In the current paper, we propose a two-step identification procedure that combines the best of the two approaches. We employ the Bouc-Wen hysteretic model to generate data that is used for identification. The system is identified using a black-box polynomial nonlinear state-space identification procedure. We reduce the number of parameters in this model by applying a polynomial decoupling method that results in a more parsimonious representation. We compare the full black-box model with the decoupled model and show that the proposed method results in a comparable performance, while significantly reducing the number of parameters.

The need of lightweight design in structural engineering is steadily growing due to economic and ecological reasons. This usually causes the structure to exhibit moderate to large displacements and rotations, resulting in a distributed nonlinear behavior. However, the characterization of geometrical nonlinearities is challenging and sensitive to structural boundaries and loading. It is commonly performed with numerical simulations, utilizing particularly finite element formulations. This study comprises simulations of a clamped-clamped beam with moderate to large amplitude oscillations. Four different (commercial and noncommercial) numerical approaches are considered: three finite element representations and one assumed-modes approach. A first comparison is conducted when the system is under a sine sweep excitation over one single mode. Subsequently, a modified model featuring nonlinear internal resonance is considered, to disclose differences in the modeling of the nonlinearity when coupling between modes occurs. The results show some expected features for geometrical nonlinearities in all methods, but also some important differences, especially when the modal interaction is activated.

The industrial partners of the Foundation for an industrial safety culture (FonCSI) agree on the importance of considering human and organizational factors (HOF) for safety. Nevertheless, many questions remain regarding how to address this issue in industrial organizations. In this short chapter, HOF experts of companies supporting FonCSI and representing various industrial sectors (energy, transports, oil & gas) expose their viewpoint on HOF goals, strategies, approaches, methods and tools.

Industrial robot identification is usually based on the inverse dynamic identification model (IDIM) that comes from Newton's laws and has the advantage of being linear with respect to the parameters. Building the IDIM from the measurement signals allows the use of linear regression techniques like the least-squares (LS) or the instrumental variable (IV) for instance. Nonetheless, this involves a careful preprocessing to deal with sensor noise. An alternative in system identification is to consider an output error approach where the model's parameters are iteratively tuned in order to match the simulated model's output and the measured system's output. This paper proposes an extensive comparison of three different output error approaches in the context of robot identification. One of the main outcomes of this work is to show that choosing the input torque as target identification signal instead of the output position may lead to a gain in robustness versus modeling errors and noise and in computational time. Theoretical developments are illustrated on a six degrees-of-freedom rigid robot.

The progress accomplished during the past decade in nonlinear system identification in structural dynamics is considerable. The objective of the present paper is to consolidate this progress by challenging the community through a new benchmark structure exhibiting complex nonlinear dynamics. The proposed structure consists of two offset cantilevered beams connected by a highly flexible element. For increasing forcing amplitudes, the system sequentially features linear behaviour, localised nonlinearity associated with the buckling of the connecting element, and distributed nonlinearity resulting from large elastic deformations across the structure. A finite element-based code with time integration capabilities is made available at https://sem.org/nonlinear-systems-imac-focus-group/ . This code permits the numerical simulation of the benchmark dynamics in response to arbitrary excitation signals.

Nonlinear system identification has become of great interest during the last decades. However, a common and shared framework is not present yet, and the identification may be challenging, especially when real engineering structures are considered with strong nonlinearities. Subspace methods have proved to be effective when dealing with local nonlinearities, both in time domain (TNSI method) and in frequency domain (FNSI method). This study reports an improvement for both methods, as a first attempt to account for distributed nonlinearities, which is still an open question in the research community. A numerical beam under moderately large oscillations that exhibits geometric nonlinearity is considered. The object of the identification process is to exploit its behavior through the correct identification of the parameters that define the nonlinearity. Results show a high level of confidence between the two methods, and suggest that a more complete analysis of distributed nonlinear phenomena can be conducted based on these approaches.

This paper presents a Bayesian model updating methodology for dynamical systems with geometric nonlinearities based on their nonlinear normal modes (NNMs) extracted from broadband vibration data. Model parameters are calibrated by minimizing selected metrics between identified and model-predicted NNMs. In the first approach, a deterministic formulation is adopted, and parameters are updated by minimizing a nonlinear least-squares objective function. A probabilistic approach based on Bayesian inference is next investigated, where a Transitional Markov Chain Monte Carlo is implemented to sample the joint posterior probability distribution of the nonlinear model parameters. Bayesian model calibration has the advantage to quantify parameter uncertainty and to provide an estimation of model evidence for model class selection. The two formulations are evaluated when applied to a numerical cantilever beam with geometrical nonlinearity. The NNMs of the beam are derived from simulated broadband data through nonlinear subspace identification and numerical continuation. Accuracy of model updating results is studied with respect to the level of measurement noise, the number of available datasets, and modeling errors.

Two approaches for experimental identification of the nonlinear dynamical characteristics of jointed structures are investigated, (a) Nonlinear Modal Testing, (b) State-Space Model Identification. Both require only minimal a priori knowledge of the specimen. For method (a), the definition of nonlinear modes as periodic motions is used, in its generalized formulation recently proposed for nonconservative systems. The theoretically required negative damping compensating the frictional dissipation is experimentally realized by properly controlled excitation. This permits the extraction of modal frequencies, damping ratios and vibrational deflection shapes as a function of the vibration level. For method (b), a state-space model with multivariate polynomial nonlinear terms is identified from the vibration response to a properly designed excitation signal. Both methods are applied to a structure with bolted joints. The quality of the extracted modal and state-space models, respectively, is assessed by comparing model-based predictions of the forced vibration response to reference measurements.

The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the nonlinear basis functions using a limited number of measured output variables. This representation assumes that the observed nonlinearities are localised in physical space, which is a generic case in mechanics. A two-step identification procedure is derived for the grey-box model parameters, integrating nonlinear subspace initialisation and weighted least-squares optimisation. The complete procedure is applied to an electrical circuit mimicking the behaviour of a single–input, single–output (SISO) nonlinear mechanical system and to a single–input, multiple–output (SIMO) geometrically nonlinear beam structure.