8,611 research outputs found
1+1-dimensional p-wave superconductors from intersecting D-branes
In this work we explore 1+1 dimensional p-wave superconductors using the
probe D-brane construction. Specifically, we choose three intersecting D-brane
models: D1/D5, D2/D4 and D3/D3 systems. According to the dilaton running
behavior, we denote the former two systems as nonconformal models and the last
system as conformal. We find that all three models are qualitatively similar in
describing superconducting condensate as well as some basic features (such as
the gap formation and DC superconductivity) of superconducting conductivity.
There also exist some differences among the three models as far as the AC
conductivity is concerned. Specifically, for D3/D3 model there is no peak at
nonzero frequency for the imaginary part of the conductivity, which is present
in the nonconformal models; their asymptotic behaviors are different-for D1/D5
the real part of the AC conductivity approaches one at large frequency limit,
for D2/D4 it slowly goes to a certain nonzero constant smaller than one and for
D3/D3 it goes to zero. We find the profile of the AC conductivity for the D1/D5
system is very similar to that of higher dimensional p-wave superconductors.Comment: v2: matched with the published versio
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
There is a growing need for distributed graph processing systems that are
capable of gracefully scaling to very large graph datasets. Unfortunately, this
challenge has not been easily met due to the intense memory pressure imposed by
process-centric, message passing designs that many graph processing systems
follow. Pregelix is a new open source distributed graph processing system that
is based on an iterative dataflow design that is better tuned to handle both
in-memory and out-of-core workloads. As such, Pregelix offers improved
performance characteristics and scaling properties over current open source
systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up
to 35x speedup compared to distributed GraphLab), and makes more effective use
of available machine resources to support Big(ger) Graph Analytics
Thermodynamic conditions during growth determine the magnetic anisotropy in epitaxial thin-films of LaSrMnO
The suitability of a particular material for use in magnetic devices is
determined by the process of magnetization reversal/relaxation, which in turn
depends on the magnetic anisotropy. Therefore, designing new ways to control
magnetic anisotropy in technologically important materials is highly desirable.
Here we show that magnetic anisotropy of epitaxial thin-films of half-metallic
ferromagnet LaSrMnO (LSMO) is determined by the proximity
to thermodynamic equilibrium conditions during growth. We performed a series of
X-ray diffraction and ferromagnetic resonance (FMR) experiments in two
different sets of samples: the first corresponds to LSMO thin-films deposited
under tensile strain on (001) SrTiO by Pulsed Laser Deposition (PLD; far
from thermodynamic equilibrium); the second were deposited by a slow Chemical
Solution Deposition (CSD) method, under quasi-equilibrium conditions. Thin
films prepared by PLD show a in-plane cubic anisotropy with an overimposed
uniaxial term. A large anisotropy constant perpendicular to the film plane was
also observed in these films. However, the uniaxial anisotropy is completely
suppressed in the CSD films. The out of plane anisotropy is also reduced,
resulting in a much stronger in plane cubic anisotropy in the chemically
synthesized films. This change is due to a different rotation pattern of
MnO octahedra to accomodate epitaxial strain, which depends not only on
the amount of tensile stress imposed by the STO substrate, but also on the
growth conditions. Our results demonstrate that the nature and magnitude of the
magnetic anisotropy in LSMO can be tuned by the thermodynamic parameters during
thin-film deposition.Comment: 6 pages, 8 Figure
Astronomy in the Cloud: Using MapReduce for Image Coaddition
In the coming decade, astronomical surveys of the sky will generate tens of
terabytes of images and detect hundreds of millions of sources every night. The
study of these sources will involve computation challenges such as anomaly
detection and classification, and moving object tracking. Since such studies
benefit from the highest quality data, methods such as image coaddition
(stacking) will be a critical preprocessing step prior to scientific
investigation. With a requirement that these images be analyzed on a nightly
basis to identify moving sources or transient objects, these data streams
present many computational challenges. Given the quantity of data involved, the
computational load of these problems can only be addressed by distributing the
workload over a large number of nodes. However, the high data throughput
demanded by these applications may present scalability challenges for certain
storage architectures. One scalable data-processing method that has emerged in
recent years is MapReduce, and in this paper we focus on its popular
open-source implementation called Hadoop. In the Hadoop framework, the data is
partitioned among storage attached directly to worker nodes, and the processing
workload is scheduled in parallel on the nodes that contain the required input
data. A further motivation for using Hadoop is that it allows us to exploit
cloud computing resources, e.g., Amazon's EC2. We report on our experience
implementing a scalable image-processing pipeline for the SDSS imaging database
using Hadoop. This multi-terabyte imaging dataset provides a good testbed for
algorithm development since its scope and structure approximate future surveys.
First, we describe MapReduce and how we adapted image coaddition to the
MapReduce framework. Then we describe a number of optimizations to our basic
approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table
Specification analysis in regime-switching continuous-time diffusion models for market volatility
We examine model specification in regime-switching continuous-time diffusions for modeling S&P 500 Volatility Index (VIX). Our investigation is carried out under two nonlinear diffusion frameworks, the NLDCEV and the CIRCEV frameworks, and our focus is on the nonlinearity in regime-dependent drift and diffusion terms, the switching components, and the endogeneity in regime changes. While we find strong evidence of regime-switching effects, models with a switching diffusion term capture the VIX dynamics considerably better than models with only a switching drift, confirming the presence and importance of volatility regimes. Strong evidence of nonlinear endogeneity in regime changes is also detected. Meanwhile, we find significant nonlinearity in the regime-dependent diffusion specification, suggesting that the nonlinearity in the VIX dynamics cannot be accounted for by regime-switching effects alone. Finally, we find that models based on the CIRCEV specification are significantly closer to the true data generating process of VIX than models based on the NLDCEV specification uniformly across all regime-switching specifications
Assessment of household energy utilized for cooking in Ikeja, Lagos state, Nigeria
Household cooking energy accounts for a major part of the total energy consumed in Nigeria. Factors affecting the choice of Household energy utilized for cooking and the type preferred in Ikeja area of Lagos state were investigated in this study. Data were obtained through oral interview and administration of structured questionnaire on 250 randomly sampled households in the study area. MATLAB was used to conduct descriptive statistics, inferential statistics and percentage difference between used energy and preference energy. The study revealed that kerosene and Gas (LPG) were mostly used for daily cooking (48.60%) and (36.30%) respectively. Only a small proportion use Charcoal, firewood and electricity for their daily cooking, the percentage being 7.10%, 5.7% and 2.4% for charcoal, firewood and electricity respectively. However preference rating of household energy was highest in Gas followed by electricity, kerosene, charcoal and firewood respectively. Chi-test, linear-by-linear relationship test, likelihood ratio test revealed that level of income, level of education and type of employment affects the choice of fuel used for cooking and the type preferred. http://dx.doi.org/10.4314/njt.v35i4.1
Polymeric routes to silicon carbide and silicon oxycarbide CMC
An overview of two approaches to the formation of ceramic composite matrices from polymeric precursors is presented. Copolymerization of alkyl- and alkenylsilanes (RSiH3) represents a new precursor system for the production of Beta-SiC on pyrolysis, with copolymer composition controlling polymer structure, char yield, and ceramic stoichiometry and morphology. Polysilsesquioxanes which are synthesized readily and can be handled in air serve as precursors to Si-C-O ceramics. Copolymers of phenyl and methyl silsesquioxanes display rheological properties favorable for composite fabrication; these can be tailored by control of pH, water/methoxy ratio and copolymer composition. Composites obtained from these utilize a carbon coated, eight harness satin weave Nicalon cloth reinforcement. The material exhibits nonlinear stress-strain behavior in tension
Keratin 6a marks mammary bipotential progenitor cells that can give rise to a unique tumor model resembling human normal-like breast cancer.
Progenitor cells are considered an important cell of origin of human malignancies. However, there has not been any single gene that can define mammary bipotential progenitor cells, and as such it has not been possible to use genetic methods to introduce oncogenic alterations into these cells in vivo to study tumorigenesis from them. Keratin 6a is expressed in a subset of mammary luminal epithelial cells and body cells of terminal end buds. By generating transgenic mice using the Keratin 6a (K6a) gene promoter to express tumor virus A (tva), which encodes the receptor for avian leukosis virus subgroup A (ALV/A), we provide direct evidence that K6a(+) cells are bipotential progenitor cells, and the first demonstration of a non-basal location for some biopotential progenitor cells. These K6a(+) cells were readily induced to form mammary tumors by intraductal injection of RCAS (an ALV/A-derived vector) carrying the gene encoding the polyoma middle T antigen. Tumors in this K6a-tva line were papillary and resembled the normal breast-like subtype of human breast cancer. This is the first model of this subtype of human tumors and thus may be useful for preclinical testing of targeted therapy for patients with normal-like breast cancer. These observations also provide direct in vivo evidence for the hypothesis that the cell of origin affects mammary tumor phenotypes
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