What is the Hidden Depolarization Mechanism in Low Luminosity AGN?

Millimeter wavelength polarimetry of accreting black hole systems can provide a tomographic probe of the accretion flow on a wide range of linear scales. We searched for linear polarization in two low luminosity active galactic nuclei (LLAGN), M81 and M84, using the Combined Array for Millimeter Astronomy (CARMA) and the Submillimeter Array (SMA). We find upper limits of $\sim 1 - 2\%$ averaging over the full bandwidth and with a rotation measure (RM) synthesis technique. These low polarization fractions, along with similar low values for LLAGN M87 and 3C84, suggest that LLAGN have qualitatively different polarization properties than radio-loud sources and Sgr A*. If the sources are intrinsically polarized and then depolarized by Faraday rotation then we place lower limits on the RM of a few times $10^7\, {\rm rad\, m^{-2}}$ for the full bandwidth case and $\sim 10^9\, {\rm rad\, m^{-2}}$ for the RM synthesis analysis. These limits are inconsistent with or marginally consistent with expected accretion flow properties. Alternatively, the sources may be depolarized by cold electrons within a few Schwarzschild radii from the black hole, as suggested by numerical models.Comment: Accepted for publication in ApJ

Non-clasical Nucleation in Supercooled Nickel

The dynamics of homogeneous nucleation and growth of crystalline nickel from the super-cooled melt is examined during rapid quenching using molecular dynamics and a modified embedded atom method potential. The character of the critical nuclei of the crystallization transition is examined using common neighbor analysis and visualization. At nucleation the saddle point droplet consists of randomly stacked planar structures with an in plane triangular order. These results are consistent with previous theoretical results that predict that the nucleation process in some metals is non-classical due to the presence of long-range forces and a spinodal.Comment: 4 pages, 5 figure

A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)

Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under consideration based on processing time to handle complicated data in controlled amount of time. The proposed MMFM’s performance is assessed with real-time weather prediction dataset where the data is acquired from sensor (observed) and image data. MMFM is used to conduct semantic analytics and similarity-based classification on this collection. The processing time based on records and samples are investigated for the various data sizes, instances, and entries. It is found that the proposed MMFM gets 70 seconds of processing time for 2GB data and 0.99 seconds while handling 5000 records for various classification instances

Ab Initio Green-Kubo Approach for the Thermal Conductivity of Solids

We herein present a first-principles formulation of the Green-Kubo method that allows the accurate assessment of the non-radiative thermal conductivity of solid semiconductors and insulators in equilibrium ab initio molecular dynamics calculations. Using the virial for the nuclei, we propose a unique ab initio definition of the heat flux. Accurate size- and time convergence are achieved within moderate computational effort by a robust, symptotically exact extrapolation scheme. We demonstrate the capabilities of the technique by investigating the thermal conductivity of extreme high and low heat conducting materials, namely diamond Si and tetragonal ZrO2