Magnetodielectric effect in composites of nanodimensional glass and CuO nanoparticles

Nanocomposites comprising CuO particles of average diameter 21 nm coated with 5 nm silica glass containing iron ions were synthesized by a chemical route. An ion exchange reaction at the nanoglass/CuO interface produced iron-doped CuO with copper ion vacancies within the nanoparticles. Room temperature ferromagnetic-like behavior was observed in the nanocomposites. This was ascribed to uncompensated spins contributed by Fe ions with associated copper ion vacancies. A rather high value of magnetodielectric parameter in the range 16–26% depending on the measuring frequency was exhibited by these nanocomposites at a magnetic field of 10 KOe. This was caused by a magnetoresistance of 33% in the iron doped CuO nanoparticles. The experimental results were fitted to the Maxwell–Wagner Capacitor model developed by Catalan. These materials will be suited for magnetic sensor applications.     

Yb-doped yttria–alumino–silicate nano-particles based optical fibers: Fabrication and characterization

An efficient method to fabricate transparent glass ceramic fibers containing in-situ grown Yb³⁺ doped oxide nano-particles based on yttria–alumino–silicate glass is presented. These large-mode area Yb³⁺ doped fibers having a core diameter around 25.0 μm were drawn by a proper control over the involved process parameters; by this, the size of nano-particles was maintained within 5–10 nm. The main spectroscopic and laser properties of the fabricated fibers along with the nano-structuration results are reported. These results reveal that the developed method offers new scopes for the contemporary Yb³⁺ fiber based devices.     

Space–time inhomogeneity, anisotropy and gravitational collapse

We investigate the evolution of non-adiabatic collapse of a shear-free spherically symmetric stellar configuration with anisotropic stresses accompanied with radial heat flux. The collapse begins from a curvature singularity with infinite mass and size on an inhomogeneous space–time background. The collapse is found to proceed without formation of an even horizon to singularity when the collapsing configuration radiates all its mass energy. The impact of inhomogeneity on various parameters of the collapsing stellar configuration is examined in some specific space–time backgrounds.     

A synthesis technique of single square loop frequency selective surface at terahertz frequency

In this paper, we have explored and extended the use of frequency selective surface towards the terahertz regime of the electromagnetic spectrum where interesting applications such as imaging, sensing and communication exist. We have discussed a synthesis technique to design the single square loop frequency selective surface (SSLFSS) at 150 and 300 GHz which have found suitable application in the fast analysis and fabrication of the frequency selective surface. Moreover, the analytical results have been supported by the CST Microwave Studio and Ansoft HFSS commercial simulators. We have discussed the angular insensitivity of the SSLFSS at 150 GHz as well as 300 GHz. However, the specific problems arise at terahertz frequencies as compared to the radio and microwave frequencies are the ohmic losses. The proposed analysis has been extended from 100 GHz to 350 GHz to discuss the ohmic and dielectric losses. We have also discussed the other important issues which are very much significant in the terahertz regime of the spectrum such as skin depth and surface roughness.     

Half-metallic behavior of Co2YZ (Y = V,Cr; Z = Al,Ga) under pressure: a DFT study

The structural, electronic and magnetic properties of Co-based Heusler compounds Co₂YZ (Y = V, Cr; Z = Al, Ga) under pressure are studied using first principles density functional theory. The calculations are performed within generalized gradient approximation. The total magnetic moment decreases slightly on compression. Under application of external pressure, the valence band and conduction band are shifted downward which leads to the modification of electronic structure. There exists an indirect band gap along Г–X for all the alloys studied. Co₂CrAl shows half-metallic nature up to 85 GPa. After this pressure transition from true half-metallic behavior to nearly half-metallic behavior is observed and at 90 GPa it shows metallic behavior. Co₂CrGa shows nearly half-metallic behavior at ambient pressure, but true half-metallic behavior is observed as pressure is increased to 100 GPa. For Co₂VGa, true half-metallic to nearly half-metallic transition is observed at 40 GPa and around 100 GPa, Co₂VGa shows metallic behavior. For Co₂VAl, true half-metallic behavior is not observed at ambient as well as higher pressures. The half metal-to-metal transition in Co₂VAl and Co₂CrAl is accompanied by quenching of magnetic moment.     

The Graphical Horseshoe Estimator for Inverse Covariance Matrices

We develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data using the horseshoe prior. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators, such as the graphical lasso and the graphical smoothly clipped absolute deviation. The most prominent benefit is that when the true inverse covariance matrix is sparse, the graphical horseshoe provides estimates with small information divergence from the sampling model. The posterior mean under the graphical horseshoe prior can also be almost unbiased under certain conditions. In addition to these theoretical results, we also provide a full Gibbs sampler for implementing our estimator. MATLAB code is available for download from github at http://github.com/liyf1988/GHS. The graphical horseshoe estimator compares favorably to existing techniques in simulations and in a human gene network data analysis. Supplementary materials for this article are available online.     

Chance constrained uncertain classification via robust optimization

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out to be intractable. The key novelty is in employing Bernstein bounding schemes to relax the CCP as a convex second order cone program whose solution is guaranteed to satisfy the probabilistic constraint. Prior to this work, only the Chebyshev based relaxations were exploited in learning algorithms. Bernstein bounds employ richer partial information and hence can be far less conservative than Chebyshev bounds. Due to this efficient modeling of uncertainty, the resulting classifiers achieve higher classification margins and hence better generalization. Methodologies for classifying uncertain test data points and error measures for evaluating classifiers robust to uncertain data are discussed. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle data uncertainty and outperform state-of-the-art in many cases.