First-principles thermodynamic modeling of lanthanum chromate perovskites

Tendencies toward local atomic ordering in (A,A′)(B,B′)O_(3−δ) mixed composition perovskites are modeled to explore their influence on thermodynamic, transport, and electronic properties. In particular, dopants and defects within lanthanum chromate perovskites are studied under various simulated redox environments. (La_(1−x),Sr_x)(Cr_(1−y),Fe_y)O_(3−δ) (LSCF) and (La_(1−x),Sr_x)(Cr_(1−y),Ru_y)O_(3−δ) (LSCR) are modeled using a cluster expansion statistical thermodynamics method built upon a density functional theory database of structural energies. The cluster expansions are utilized in lattice Monte Carlo simulations to compute the ordering of Sr and Fe(Ru) dopant and oxygen vacancies (Vac). Reduction processes are modeled via the introduction of oxygen vacancies, effectively forcing excess electronic charge onto remaining atoms. LSCR shows increasingly extended Ru-Vac associates and short-range Ru-Ru and Ru-Vac interactions upon reduction; LSCF shows long-range Fe-Fe and Fe-Vac interaction ordering, inhibiting mobility. First principles density functional calculations suggest that Ru-Vac associates significantly decrease the activation energy of Ru-Cr swaps in reduced LSCR. These results are discussed in view of experimentally observed extrusion of metallic Ru from LSCR nanoparticles under reducing conditions at elevated temperature

Reconstructing the linear power spectrum of cosmological mass fluctuations

We describe an attempt to reconstruct the initial conditions for the formation of cosmological large-scale structure. The power spectrum of the primordial fluctuations is affected by bias, nonlinear evolution and redshift-space distortions, but we show how these effects can be corrected for analytically. Using eight independent datasets, we obtain excellent agreement in the estimated linear power spectra given the following conditions. First, the relative bias factors for Abell clusters, radio galaxies, optical galaxies and IRAS galaxies must be in the ratios 4.5:1.9:1.3:1. Second, the data require redshift-space distortion: \Omega^{0.6}/b_{\ss I} = 1.0 \pm 0.2. Third, low values of $\Omega$ and bias are disfavoured. The shape of the spectrum is extremely well described by a CDM transfer function with an apparent value of the fitting parameter $\Omega h =0.25$. Tilted models predict too little power at 100 Mpc wavelengths.Comment: Edinburgh Astronomy Preprint 26/93. Accepted for publication in Monthly Notices of the RAS. 13 pages of LaTeX, plus 10 PostScript figures. You will need the mn.sty style file (from babbage: get mn.sty). The figure .ps files are in the usual self-unpacking unix scrip

Quantifying angular clustering in wide-area radio surveys

We quantify the angular clustering of radio galaxies in the NVSS and FIRST radio surveys using the two-point correlation function and the moments of counts-in-cells - both important points of comparison with theory. These investigations consistently demonstrate that the slope of the correlation function for radio galaxies agrees with that for optically-selected galaxies, gamma = 1.8. We describe how to disentangle the imprint of galaxy clustering from the two observational problems: resolution of radio galaxies into multiple components and gradients in source surface density induced by difficulties in processing "snapshot" radio observations (significant in both surveys below 15 mJy). This study disagrees in some respects with previous analyses of the angular clustering of radio galaxies.Comment: 26 pages, 13 figures, accepted for publication in MNRA

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris \ufb02ows are among the most hazardous phenomena in mountain areas. To cope with debris \ufb02ow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris \ufb02ow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris \ufb02ow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris \ufb02ow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full- waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the in\ufb02uence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris \ufb02ows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris \ufb02ow routing modeling reveals that the choice of the interpolation algorithm does not signi\ufb01cantly affect the model outcomes

Multiple Quantum Phases in Graphene with Enhanced Spin-Orbit Coupling: From the Quantum Spin Hall Regime to the Spin Hall Effect and a Robust Metallic State

We report an intriguing transition from the quantum spin Hall phase to the spin Hall effect upon segregation of thallium adatoms adsorbed onto a graphene surface. Landauer-B\"uttiker and Kubo-Greenwood simulations are used to access both edge and bulk transport physics in disordered thallium-functionalized graphene systems of realistic sizes. Our findings not only quantify the detrimental effects of adatom clustering in the formation of the topological state, but also provide evidence for the emergence of spin accumulation at opposite sample edges driven by spin-dependent scattering induced by thallium islands, which eventually results in a minimum bulk conductivity $\sim 4e^{2}/h$, insensitive to localization effects

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts