Subject-specific finite element modelling of the human hand complex : muscle-driven simulations and experimental validation

This paper aims to develop and validate a subject-specific framework for modelling the human hand. This was achieved by combining medical image-based finite element modelling, individualized muscle force and kinematic measurements. Firstly, a subject-specific human hand finite element (FE) model was developed. The geometries of the phalanges, carpal bones, wrist bones, ligaments, tendons, subcutaneous tissue and skin were all included. The material properties were derived from in-vivo and in-vitro experiment results available in the literature. The boundary and loading conditions were defined based on the kinematic data and muscle forces of a specific subject captured from the in-vivo grasping tests. The predicted contact pressure and contact area were in good agreement with the in-vivo test results of the same subject, with the relative errors for the contact pressures all being below 20%. Finally, sensitivity analysis was performed to investigate the effects of important modelling parameters on the predictions. The results showed that contact pressure and area were sensitive to the material properties and muscle forces. This FE human hand model can be used to make a detailed and quantitative evaluation into biomechanical and neurophysiological aspects of human hand contact during daily perception and manipulation. The findings can be applied to the design of the bionic hands or neuro-prosthetics in the future

Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework

Mesoscopic Resistance Fluctuations in Cobalt Nanoparticles

We present measurements of mesoscopic resistance fluctuations in cobalt nanoparticles and study how the fluctuations with bias voltage, bias fingerprints, respond to magnetization reversal processes. Bias fingerprints rearrange when domains are nucleated or annihilated. The domain-wall causes an electron wavefunction phase-shift of $\approx 5\pi$. The phase-shift is not caused by the Aharonov-Bohm effect; we explain how it arises from the mistracking effect, where electron spins lag in orientation with respect to the moments inside the domain-wall. Dephasing time in Co at $0.03K$ is short, $\tau_\phi\sim ps$, which we attribute to the strong magnetocrystalline anisotropy.Comment: 5 pages 3 figs colou

Morphological evolution of a 3D CME cloud reconstructed from three viewpoints

The propagation properties of coronal mass ejections (CMEs) are crucial to predict its geomagnetic effect. A newly developed three dimensional (3D) mask fitting reconstruction method using coronagraph images from three viewpoints has been described and applied to the CME ejected on August 7, 2010. The CME's 3D localisation, real shape and morphological evolution are presented. Due to its interaction with the ambient solar wind, the morphology of this CME changed significantly in the early phase of evolution. Two hours after its initiation, it was expanding almost self-similarly. CME's 3D localisation is quite helpful to link remote sensing observations to in situ measurements. The investigated CME was propagating to Venus with its flank just touching STEREO B. Its corresponding ICME in the interplanetary space shows a possible signature of a magnetic cloud with a preceding shock in VEX observations, while from STEREO B only a shock is observed. We have calculated three principle axes for the reconstructed 3D CME cloud. The orientation of the major axis is in general consistent with the orientation of a filament (polarity inversion line) observed by SDO/AIA and SDO/HMI. The flux rope axis derived by the MVA analysis from VEX indicates a radial-directed axis orientation. It might be that locally only the leg of the flux rope passed through VEX. The height and speed profiles from the Sun to Venus are obtained. We find that the CME speed possibly had been adjusted to the speed of the ambient solar wind flow after leaving COR2 field of view and before arriving Venus. A southward deflection of the CME from the source region is found from the trajectory of the CME geometric center. We attribute it to the influence of the coronal hole where the fast solar wind emanated from.Comment: ApJ, accepte