Estimation of elastic and viscous properties of the left ventricle based on annulus plane harmonic behavior

Assessment of left ventricular (LV) function with an emphasis on contractility has been a challenge in cardiac mechanics during the recent decades. The LV function is usually described by the LV pressurevolume (P-V) diagram. The standard P-V diagrams are easy to interpret but difficult to obtain and require invasive instrumentation for measuring the corresponding volume and pressure data. In the present study, we introduce a technique that can estimate the viscoelastic properties of the LV based on harmonic behavior of the ventricular chamber and it can be applied non-invasively as well. The estimation technique is based on modeling the actual long axis displacement of the mitral annulus plane toward the cardiac base as a linear damped oscillator with time-varying coefficients. The time-varying parameters of the model were estimated by a standard Recursive Linear Least Squares (RLLS) technique. LV stiffness at end-systole and end diastole was in the range of 61.86-136.00 dyne/g.cm and 1.25-21.02 dyne/g.cm, respectively. The only input used in this model was the long axis displacement of the annulus plane, which can also be obtained non-invasively using tissue Doppler or MR imaging

Apparent suppression of turbulent magnetic dynamo action by a dc magnetic field

Numerical studies of the effect of a dc magnetic field on dynamo action (development of magnetic fields with large spatial scales), due to helically-driven magnetohydrodynamic turbulence, are reported. The apparent effect of the dc magnetic field is to suppress the dynamo action, above a relatively low threshold. However, the possibility that the suppression results from an improper combination of rectangular triply spatially-periodic boundary conditions and a uniform dc magnetic field is addressed: heretofore a common and convenient computational convention in turbulence investigations. Physical reasons for the observed suppression are suggested. Other geometries and boundary conditions are offered for which the dynamo action is expected not to be suppressed by the presence of a dc magnetic field component.Comment: To appear in Physics of Plasma

Correlation between magnetic interactions and domain structure in A1 FePt ferromagnetic thin films

We have investigated the relationship between the domain structure and the magnetic interactions in a series of FePt ferromagnetic thin films of varying thickness. As-made films grow in the magnetically soft and chemically disordered A1 phase that may have two distinct domain structures. Above a critical thickness $d_{cr}\sim 30$ nm the presence of an out of plane anisotropy induces the formation of stripes, while for $d<d_{cr}$ planar domains occur. Magnetic interactions have been characterized using the well known DCD-IRM remanence protocols, $\delta M$ plots, and magnetic viscosity measurements. We have observed a strong correlation between the domain configuration and the sign of the magnetic interactions. Planar domains are associated with positive exchange-like interactions, while stripe domains have a strong negative dipolar-like contribution. In this last case we have found a close correlation between the interaction parameter and the surface dipolar energy of the stripe domain structure. Using time dependent magnetic viscosity measurements, we have also estimated an average activation volume for magnetic reversal, $\langle V_{ac}\rangle \sim 1.37\times 10^{4}$ nm$^{3},$ which is approximately independent of the film thickness or the stripe period.Comment: 25 pages, 11 figure

Speech recognition through physical reservoir computing with neuromorphic nanowire networks

The hardware implementation of the reservoir computing paradigm represents a key aspect for taking into advantage of neuromorphic data processing. In this context, self-organised nanonetworks represent a versatile and scalable computational substrate for multiple tasks by exploiting the emerging collective behaviour of the system arising from complexity. The emerging behaviour allows spatio-temporal processing of multiple input signals and relies on the nonlinear interaction in between a multitude of nanoscale memristive elements. By means of a physics-based grid-graph modeling, we report on the implementation of reservoir computing for a speech recognition task in a memristive nanonetwork based on nanowires (NWs) acting as a physical reservoir. Besides analysing the pre-processing step for the transduction of the audio samples in electrical stimuli to be applied to the physical reservoir, we analyse the effect of the network size and the adoption of virtual nodes on computing performances. Results show that memristive nanonetworks allow in materia implementation of reservoir computing for the realisation of brain-inspired neuromorphic systems with reduced training cost

Memristive devices based on single ZnO nanowires-from material synthesis to neuromorphic functionalities

Memristive and resistive switching devices are considered promising building blocks for the realization of artificial neural networks and neuromorphic systems. Besides conventional top-down memristive devices based on thin films, resistive switching devices based on nanowires (NWs) have attracted great attention, not only for the possibility of going beyond current scaling limitations of the top-down approach, but also as model systems for the localization and investigation of the physical mechanism of switching. This work reports on the fabrication of memristive devices based on ZnO NWs, from NW synthesis to single NW-based memristive cell fabrication and characterization. The bottom-up synthesis of ZnO NWs was performed by low-pressure chemical vapor deposition according to a self-seeding vapor-solid (VS) mechanism on a Pt substrate over large scale (∼cm2), without the requirement of previous seed deposition. The grown ZnO NWs are single crystalline with wurtzite crystal structure and are vertically aligned respect to the growth substrate. Single NWs were then contacted by means of asymmetric contacts, with an electrochemically active and an electrochemically inert electrode, to form NW-based electrochemical metallization memory cells that show reproducible resistive switching behaviour and neuromorphic functionalities including short-term synaptic plasticity and paired pulse facilitation. Besides representing building blocks for NW-based memristive and neuromorphic systems, these single crystalline devices can be exploited as model systems to study physicochemical processing underlaying memristive functionalities thanks to the high localization of switching events on the ZnO crystalline surface

Magnetic field dependence of antiferromagnetic resonance in NiO

We report on measurements of magnetic field and temperature dependence of antiferromagnetic resonances in the prototypical antiferromagnet NiO. The frequencies of the magnetic resonances in the vicinity of 1 THz have been determined in the time-domain via time-resolved Faraday measurements after selective excitation by narrow-band superradiant terahertz (THz) pulses at temperatures down to 3 K and in magnetic fields up to 10 T. The measurements reveal two antiferromagnetic resonance modes, which can be distinguished by their characteristic magnetic field dependencies. The nature of the two modes is discussed by comparison to an eight-sublattice antiferromagnetic model, which includes superexchange between the next-nearest-neighbor Ni spins, magnetic dipolar interactions, cubic magneto-crystalline anisotropy, and Zeeman interaction with the external magnetic field. Our study indicates that a two-sublattice model is insufficient for the description of spin dynamics in NiO, while the magnetic-dipolar interactions and magneto-crystalline anisotropy play important roles