A smart tool for the diagnosis of Parkinsonian syndrome using wireless watches

This work is licensed under a Creative Commons Attribution 3.0 License.Early detection and diagnosis of Parkinson disease will provide a good chance for patients to take early actions and prevent its further development. In this paper, a smart tool for the diagnosis of Parkinsonian syndromes is designed and developed using low-cost Texas Instruments eZ430-Chronos wireless watches. With this smart tool, Parkinson Bradykinesia is detected based on the cycle of a human gait, with the watch worn on the foot, and Parkinson Tremor shaking is detected and differed by frequency 0 to 8 Hz on the arm in real-time with a developed statistical diagnosis chart. It can be used in small clinics as well as home environment due to its low-cost and easy-use property

Hall conductance of a pinned vortex lattice in a high magnetic field

We calculate the quasiparticle contribution to the zero temperature Hall conductance of two-dimensional extreme type-II superconductors in a high magnetic field, using the Landau basis. As one enters the superconducting phase the Hall conductance is renormalized to smaller values, with respect to the normal state result, until a quantum level-crossing transition is reached. At high values of the order parameter, where the quasiparticles are bound to the vortex cores, the Hall conductance is expected to tend to zero due to a theorem of Thouless.Comment: To appear in Journ. Phys. : Cond. Matte

Andreev experiments on superconductor/ferromagnet point contacts

Andreev reflection is a smart tool to investigate the spin polarisation P of the current through point contacts between a superconductor and a ferromagnet. We compare different models to extract P from experimental data and investigate the dependence of P on different contact parameters.Comment: 14 pages, 5 figures, accepted for publication in Fizika Nizkikh Temperatu

Motion by Stopping: Rectifying Brownian Motion of Non-spherical Particles

We show that Brownian motion is spatially not symmetric for mesoscopic particles embedded in a fluid if the particle is not in thermal equilibrium and its shape is not spherical. In view of applications on molecular motors in biological cells, we sustain non-equilibrium by stopping a non-spherical particle at periodic sites along a filament. Molecular dynamics simulations in a Lennard-Jones fluid demonstrate that directed motion is possible without a ratchet potential or temperature gradients if the asymmetric non-equilibrium relaxation process is hindered by external stopping. Analytic calculations in the ideal gas limit show that motion even against a fluid drift is possible and that the direction of motion can be controlled by the shape of the particle, which is completely characterized by tensorial Minkowski functionals.Comment: 11 pages, 5 figure

Age - Not Marbling - Indicates Tenderness

Test panels and shearing machine measurements agree: Tenderness of steak is related to animal\u27s age. Marbling appears unrelated to tenderness, flavor or juiciness. More research on beef tenderness planned

Spectroscopic Evidence for Multiple Order Parameter Components in the Heavy Fermion Superconductor CeCoIn_5

Point-contact spectroscopy was performed on single crystals of the heavy-fermion superconductor CeCoIn_5 between 150 mK and 2.5 K. A pulsed measurement technique ensured minimal Joule heating over a wide voltage range. The spectra show Andreev-reflection characteristics with multiple structures which depend on junction impedance. Spectral analysis using the generalized Blonder-Tinkham-Klapwijk formalism for d-wave pairing revealed two coexisting order parameter components, with amplitudes Delta_1 = 0.95 +/- 0.15 meV and Delta_2 = 2.4 +/- 0.3 meV, which evolve differently with temperature. Our observations indicate a highly unconventional pairing mechanism, possibly involving multiple bands.Comment: 4 pages, 3 figure

Non-Life Insurance Pricing: Multi Agents Model

We use the maximum entropy principle for pricing the non-life insurance and recover the B\"{u}hlmann results for the economic premium principle. The concept of economic equilibrium is revised in this respect.Comment: 6 pages, revtex

A Damping of the de Haas-van Alphen Oscillations in the superconducting state

Deploying a recently developed semiclassical theory of quasiparticles in the superconducting state we study the de Haas-van Alphen effect. We find that the oscillations have the same frequency as in the normal state but their amplitude is reduced. We find an analytic formulae for this damping which is due to tunnelling between semiclassical quasiparticle orbits comprising both particle-like and hole-like segments. The quantitative predictions of the theory are consistent with the available data.Comment: 7 pages, 5 figure

Thermal Conductivity Anisotropy in Superconducting UPt3UPt_3

Recent thermal conductivity measurements on $UPt_3$ single crystals by Lussier et al. indicate the existence of a strong b--c anisotropy in the superconducting state. We calculate the thermal conductivity in various unconventional candidate states appropriate for the $UPt_3$ ``B phase" and compare with experiment, specifically the $E_{2u}$ and $E_{1g}$ $(1,i)$ states predicted in some Ginzburg-Landau analyses of the phase diagram. For the simplest realizations of these states over spherical or ellipsoidal Fermi surfaces, the normalized $E_{2u}$ conductivity is found, surprisingly, to be completely isotropic. We discuss the effects of inelastic scattering and realistic Fermi surface anisotropy, and deduce constraints on the symmetry class of the $UPt_3$ ground state.Comment: 4 postscript pages, UFL102

Grain size analysis in permanent magnets from Kerr microscopy images using machine learning techniques

Understanding the relationships between composition, structure, processing and properties helps in the development of improved materials for known applications as well as for new applications. Materials scientists, chemists and physicists have researched these relationships for many years, until the recent past, by characterizing the bulk properties of functional materials and describing them with theoretical models. Magnets are widly used in electric vehicles (EV), hybrid electric vehicles (HEV), data storage, power generation and transmission, sensors etc. The search for novel magnetic phases requires an efficient quantitative microstructure analysis of microstructural information like phases, grain distribution and micromagnetic structural information like domain patterns, and correlating the information with intrinsic magnetic parameters of magnet samples. The information out of micromagnetic domains helps in obtaining the optimized microstructures in magnets that have good intrinsic magnetic properties. This paper is aimed at introducing the use of a traditional machine learning (ML) model with a higher dimensional feature set and a deep learning (DL) model to classify various regions in sintered NdFeB magnets based on Kerr-microscopy images. The obtained results are compared against reference data, which is generated manually by subject experts. Additionally, the results were compared against the approach for grain analysis, which is based on the electron backscatter diffraction (EBSD) technique. Further, the challenges faced by the traditional machine learning model for classifying microstructures in Kerr micrographs are discussed