Influence of external magnetic fields on growth of alloy nanoclusters

Kinetic Monte Carlo simulations are performed to study the influence of external magnetic fields on the growth of magnetic fcc binary alloy nanoclusters with perpendicular magnetic anisotropy. The underlying kinetic model is designed to describe essential structural and magnetic properties of CoPt_3-type clusters grown on a weakly interacting substrate through molecular beam epitaxy. The results suggest that perpendicular magnetic anisotropy can be enhanced when the field is applied during growth. For equilibrium bulk systems a significant shift of the onset temperature for L1_2 ordering is found, in agreement with predictions from Landau theory. Stronger field induced effects can be expected for magnetic fcc-alloys undergoing L1_0 ordering.Comment: 10 pages, 3 figure

Time-Dependent Density Functional Theory for Driven Lattice Gas Systems with Interactions

We present a new method to describe the kinetics of driven lattice gases with particle-particle interactions beyond hard-core exclusions. The method is based on the time-dependent density functional theory for lattice systems and allows one to set up closed evolution equations for mean site occupation numbers in a systematic manner. Application of the method to a totally asymmetric site exclusion process with nearest-neighbor interactions yields predictions for the current-density relation in the bulk, the phase diagram of non-equilibrium steady states and the time evolution of density profiles that are in good agreement with results from kinetic Monte Carlo simulations.Comment: 11 pages, 3 figure

GIS and Genetic Diversity- Case Studies in Stylosanthes

We present a new technique for mapping the potential occurrence of wild germplasm based in climate data and show its application to six important Stylosanthes species. The method can be used to develop hypotheses as to the distribution for purposes of collection and/or in situ conservation. It can also be used to investigate genetic diversity with a species. We present some first results based in isozyme data from S. guianensis

A Survey on Continuous Time Computations

We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature

The Modular Group, Operator Ordering, and Time in (2+1)-Dimensional Gravity

A choice of time-slicing in classical general relativity permits the construction of time-dependent wave functions in the ``frozen time'' Chern-Simons formulation of $(2+1)$-dimensional quantum gravity. Because of operator ordering ambiguities, however, these wave functions are not unique. It is shown that when space has the topology of a torus, suitable operator orderings give rise to wave functions that transform under the modular group as automorphic functions of arbitrary weights, with dynamics determined by the corresponding Maass Laplacians on moduli space.Comment: 8 pages, LaTe

Reconstruction from Radon projections and orthogonal expansion on a ball

The relation between Radon transform and orthogonal expansions of a function on the unit ball in \RR^d is exploited. A compact formula for the partial sums of the expansion is given in terms of the Radon transform, which leads to algorithms for image reconstruction from Radon data. The relation between orthogonal expansion and the singular value decomposition of the Radon transform is also exploited.Comment: 15 page

Large Diffeomorphisms in (2+1)-Quantum Gravity on the Torus

The issue of how to deal with the modular transformations -- large diffeomorphisms -- in (2+1)-quantum gravity on the torus is discussed. I study the Chern-Simons/connection representation and show that the behavior of the modular transformations on the reduced configuration space is so bad that it is possible to rule out all finite dimensional unitary representations of the modular group on the Hilbert space of $L^2$-functions on the reduced configuration space. Furthermore, by assuming piecewise continuity for a dense subset of the vectors in any Hilbert space based on the space of complex valued functions on the reduced configuration space, it is shown that finite dimensional representations are excluded no matter what inner-product we define in this vector space. A brief discussion of the loop- and ADM-representations is also included.Comment: The proof for the nonexistence of the one- and two-dimensional representations of PSL(2,Z) in the relevant Hilbert space, has been extended to cover all finite dimensional unitary representations. The notation is slightly improved and a few references are added

Estrogen treatment following severe burn injury reduces brain inflammation and apoptotic signaling

<p>Abstract</p> <p>Background</p> <p>Patients with severe burn injury experience a rapid elevation in multiple circulating pro-inflammatory cytokines, with the levels correlating with both injury severity and outcome. Accumulations of these cytokines in animal models have been observed in remote organs, however data are lacking regarding early brain cytokine levels following burn injury, and the effects of estradiol on these levels. Using an experimental animal model, we studied the acute effects of a full-thickness third degree burn on brain levels of TNF-α, IL-1β, and IL-6 and the protective effects of acute estrogen treatment on these levels. Additionally, the acute administration of estrogen on regulation of inflammatory and apoptotic events in the brain following severe burn injury were studied through measuring the levels of phospho-ERK, phospho-Akt, active caspase-3, and PARP cleavage in the placebo and estrogen treated groups.</p> <p>Methods</p> <p>In this study, 149 adult Sprague-Dawley male rats received 3rd degree 40% total body surface area (TBSA) burns. Fifteen minutes following burn injury, the animals received a subcutaneous injection of either placebo (n = 72) or 17 beta-estradiol (n = 72). Brains were harvested at 0.5, 1, 2, 4, 6, 8, 12, 18, and 24 hours after injury from the control (n = 5), placebo (n = 8/time point), and estrogen treated animals (n = 8/time point). The brain cytokine levels were measured using the ELISA method. In addition, we assessed the levels of phosphorylated-ERK, phosphorylated-Akt, active caspase-3, and the levels of cleaved PARP at the 24 hour time-point using Western blot analysis.</p> <p>Results</p> <p>In burned rats, 17 beta-estradiol significantly decreased the levels of brain tissue TNF-α (~25%), IL-1β (~60%), and IL-6 (~90%) when compared to the placebo group. In addition, we determined that in the estrogen-treated rats there was an increase in the levels of phospho-ERK (<it>p </it>< 0.01) and Akt (<it>p </it>< 0.05) at the 24 hour time-point, and that 17 beta-estradiol blocked the activation of caspase-3 (<it>p </it>< 0.01) and subsequent cleavage of PARP (<it>p </it>< 0.05).</p> <p>Conclusion</p> <p>Following severe burn injury, estrogens decrease both brain inflammation and the activation of apoptosis, represented by an increase in the levels of phospho-Akt and inhibition of caspase-3 activation and PARP cleavage. Results from these studies will help further our understanding of how estrogens protect the brain following burn injury, and may provide a novel, safe, and effective clinical treatment to combat remote secondary burn injury in the brain and to preserve cognition.</p

Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation