Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available

Resonant Soft X-ray Reflectivity in the Study of Magnetic Properties of Low-Dimensional Systems

In this review, the technique of resonant soft X-ray reflectivity in the study of magnetic low-dimensional systems is discussed. This technique is particularly appealing in the study of magnetization at buried interfaces and to discriminate single elemental contributions to magnetism, even when this is ascribed to few atoms. The major fields of application are described, including magnetic proximity effects, thin films of transition metals and related oxides, and exchange-bias systems. The fundamental theoretical background leading to dichroism effects in reflectivity is also briefly outlined

Spin control using chiral templated nickel

This Letter reports an original spin valve device that is based on a chiral templated nickel material. Chirality in Ni is induced by exploiting co-electrodeposition of an organic chiral template. In this specific case, the chiral templating is enantiopure tartaric acid (TA). Facile electrodeposition (co-deposition) in ambient conditions produces a nickel chiral-templated material. Z-shaped magnetoresistance curves, switching sign as a function of TA handedness, prove the peculiar ferromagnetic character induced by the presence of a chiral compound. Synchrotron measurements using circular polarized light, x-ray natural circular dichroism, confirm the chirality of the Ni in the TA/Ni composite. Density functional theory calculation proves the existence of a strong electronic delocalization involving the tartaric acid and Ni. The significant finding of this Letter is that chiral templated Ni paves the way for future spin valve, which will be able to control the spin without an external magnetic field (as indeed foreseen within the chiral induced spin selectivity-effect framework)

VAMPnets for deep learning of molecular kinetics

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models

GaAs(110) surface electronic structure by metastable deexcitation spectroscopy

Metastable deexcitation spectroscopy was applied to study the surface valence electronic structure of clean cleaved GaAs(110). Metastable deexcitation spectroscopy was flanked by angle-resolved photoemission. An effective surface density of states was derived from the experimental spectrum through deconvolution. Two groups of states were observed in the 0-4 and 5-8 eV range of binding energy, respectively. These features were ascribed to emission from surface states. A plane-by-plane tight-binding density-of-states calculation was performed. More quantitative insights were obtained by comparing experimental and theoretical results. The most prominent feature of the first group of states of deconvolution was assigned to surface state A(5). Contributions from states A(4), A(3), A(1)', and A(2)' were also observed. The doublet of the second group of features was identified with C-2 and C-1. Relative amplitudes of effective surface density of states were related to surface charge density

Deep learning Markov and Koopman models with physical constraints

The long-timescale behavior of complex dynamical systems can be described by linear Markov or Koopman models in a suitable latent space. Recent variational approaches allow the latent space representation and the linear dynamical model to be optimized via unsupervised machine learning methods. Incorporation of physical constraints such as time-reversibility or stochasticity into the dynamical model has been established for a linear, but not for arbitrarily nonlinear (deep learning) representations of the latent space. Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints. We prove that the model is an universal approximator for reversible Markov processes and that it can be optimized with either maximum likelihood or the variational approach of Markov processes (VAMP). We demonstrate that the model performs equally well for equilibrium and systematically better for biased data compared to existing approaches, thus providing a tool to study the long-timescale processes of dynamical systems

Structural transition in Fe ultrathin epitaxial films grown on Ni(111)

A structural study of Fe ultrathin epitaxial films, grown at room temperature on Ni(111), has been performed in the 1.5-18 ML coverage range by angle-scanned photoelectron diffraction. Both backscattering and forward-scattering energy regimes have been employed, in order to enhance the structural sensitivity at lower and higher film thicknesses, respectively. Modeling of the experimental data has been performed with multiple scattering calculations. We found indications that Fe atoms in the first layer occupy fcc hollow sites and stack with a pseudomorphic fcc structure up to 2 ML. Concerning the growth mode at these early stages, data suggest that a good substrate wetting and a sharp Fe/Ni interface take place. Between 3 and 6 ML, transition to a bcc(110) phase develops. By quantitative R-factor analysis, we found that Nishiyama-Wassermann (NW) in-plane orientation of the bcc(110) cell ((bcc)parallel to(fcc)) is favored over the Kurdjumov-Sachs ((bcc)parallel to(fcc)) orientation. The best-fit vertical interlayer distance between bcc(110) planes is d(NW)=2.11 Angstrom (+3.9% expansion) at 6 ML and relaxes to d(NW)=2.05 Angstrom (+1.0%) at 18 ML, in agreement with the angular shift observed for the forward-focusing features. In the same coverage range, the angle between bcc(110) surface basis vectors changes from 67.7degrees to 69.0degrees, corresponding to -1.7% and -1.0% contractions of the surface cell area, respectively

Magnetism and interlayer coupling in fcc Fe/Co films

The magnetism of epitaxial fee Fe films deposited on Co(100) and sandwiched between two Co(100) films was investigated by x-ray magnetic circular dichroism. The dependence of the Fe magnetism on the film thickness is complex and qualitatively similar on Co(100) and in fee Co/Fe/Co(100) trilayers. The fee Fe film magnetization presents a pronounced oscillation, suggesting a partial antiferromagnetic ordering in the 5-10 monolayer thickness range. The fee Fe films mediate an oscillatory, indirect coupling in Co/Fe/Co(100) structures that alternates in correspondence with the changes of the Fe magnetization

Stability and optimal control for some classes of tritrophic systems

The objective of this paper is to study an optimal resource management problem for some classes of tritrophic systems composed by autotrophic resources (plants), bottom level consumers (herbivores) and top level consumers (humans). The first class of systems we discuss are linear chains, in which biomass flows from plants to herbivores, and from herbivores to humans. In the second class of systems humans are omnivorous and hence compete with herbivores for plant resources. Finally, in the third class of systems humans are omnivorous, but the plant resources are partitioned so that humans and herbivores do not complete for the same ones. The three trophic chains are expressed as Lotka-Volterra models, which seems to be a suitable choice in contexts where there is a shortage of food for the consumers. Our model parameters are taken from the literature on agro-pastoral systems in Sub-Saharan Africa

Influence of size, shape and core\u2013shell interface on surface plasmon resonance in Ag and Ag@MgO nanoparticle films deposited on Si/SiOx

Ag and Ag@MgO core-shell nanoparticles (NPs) with a diameter of d = 3-10 nm were obtained by physical synthesis methods and deposited on Si with its native ultrathin oxide layer SiOx (Si/SiOx). Scanning electron microscopy and transmission electron microscopy (TEM) images of bare Ag NPs revealed the presence of small NP aggregates caused by diffusion on the surface and agglomeration. Atomic resolution TEM gave evidence of the presence of crystalline multidomains in the NPs, which were due to aggregation and multitwinning occurring during NP growth in the nanocluster source. Co-deposition of Ag NPs and Mg atoms in an oxygen atmosphere gave rise to formation of a MgO shell matrix surrounding the Ag NPs. The behaviour of the surface plasmon resonance (SPR) excitation in surface differential reflectivity (SDR) spectra with p-polarised light was investigated for bare Ag and Ag@MgO NPs. It was shown that the presence of MgO around the Ag NPs caused a red shift of the plasmon excitation, and served preserve its existence after prolonged (five months) exposure to air, realizing the possibility of technological applications in plasmonic devices. The Ag NP and Ag@MgO NP film features in the SDR spectra could be reproduced by classical electrodynamics simulations by treating the NP-containing layer as an effective Maxwell Garnett medium. The simulations gave results in agreement with the experiments when accounting for the experimentally observed aggregation