Unexpected features of branched flow through high-mobility two-dimensional electron gases

GaAs-based two-dimensional electron gases (2DEGs) show a wealth of remarkable electronic states, and serve as the basis for fast transistors, research on electrons in nanostructures, and prototypes of quantum-computing schemes. All these uses depend on the extremely low levels of disorder in GaAs 2DEGs, with low-temperature mean free paths ranging from microns to hundreds of microns. Here we study how disorder affects the spatial structure of electron transport by imaging electron flow in three different GaAs/AlGaAs 2DEGs, whose mobilities range over an order of magnitude. As expected, electrons flow along narrow branches that we find remain straight over a distance roughly proportional to the mean free path. We also observe two unanticipated phenomena in high-mobility samples. In our highest-mobility sample we observe an almost complete absence of sharp impurity or defect scattering, indicated by the complete suppression of quantum coherent interference fringes. Also, branched flow through the chaotic potential of a high-mobility sample remains stable to significant changes to the initial conditions of injected electrons.Comment: 22 pages, 4 figures, 1 tabl

Complementary hydro-mechanical coupled finite/discrete element and microseismic modelling to predict hydraulic fracture propagation in tight shale reservoirs

This paper presents a novel approach to predict the propagation of hydraulic fractures in tight shale reservoirs. Many hydraulic fracture modelling schemes assume that the fracture direction is pre-seeded in the problem domain discretization. This is a severe limitation as the reservoir often contains large numbers of pre-existing fractures that strongly influence the direction of the propagating fracture. To circumvent these shortcomings a new fracture modelling treatment is proposed where the introduction of discrete fracture surfaces is based on new and dynamically updated geometrical entities rather than the topology of the underlying spatial discretization. Hydraulic fracturing is an inherently coupled engineering problem with interactions between fluid flow and fracturing when the stress state of the reservoir rock attains a failure criterion. This work follows a staggered hydro-mechanical coupled finite/discrete element approach to capture the key interplay between fluid pressure and fracture growth. In field practice the fracture growth is hidden from the design engineer and microseismicity is often used to infer hydraulic fracture lengths and directions. Microsesimic output can also be computed from changes of the effective stress in the geomechanical model and compared against field microseismicity. A number of hydraulic fracture numerical examples are presented to illustrate the new technology

Discovery of Candidate H2_2O Disk Masers in AGN and Estimations of Centripetal Accelerations

Based on spectroscopic signatures, about one-third of known H$_2$O maser sources in active galactic nuclei (AGN) are believed to arise in highly inclined accretion disks around central engines. These "disk maser candidates" are of interest primarily because angular structure and rotation curves can be resolved with interferometers, enabling dynamical study. We identify five new disk maser candidates in studies with the Green Bank Telescope, bringing the total number published to 30. We discovered two (NGC1320, NGC17) in a survey of 40 inclined active galaxies (v_{sys}< 20000 kms^{-1}). The remaining three disk maser candidates were identified in monitoring of known sources: NGC449, NGC2979, NGC3735. We also confirm a previously marginal case in UGC4203. For the disk maser candidates reported here, inferred rotation speeds are 130-500 kms^{-1}. Monitoring of three more rapidly rotating candidate disks (CG211, NGC6264, VV340A) has enabled measurement of likely orbital centripetal acceleration, and estimation of central masses (2-7x10^7 M_\odot) and mean disk radii (0.2-0.4pc). Accelerations may ultimately permit estimation of distances when combined with interferometer data. This is notable because the three AGN are relatively distant (10000<v_{sys}<15000 kms^{-1}). As signposts of highly inclined geometries at galactocentric radii of \sim0.1-1pc, disk masers also provide robust orientation references that allow analysis of (mis)alignment between AGN and surrounding galactic stellar disks, even without interferometric mapping. We find no preference among published disk maser candidates to lie in high-inclination galaxies, providing independent support for conclusions that central engines and galactic plane orientations are not correlated. (ABRIDGED)Comment: 7 figures, accepted for publication in ApJ, Dec. 10, 200

Application of machine learning techniques to tuberculosis drug resistance analysis

MOTIVATION: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. RESULTS: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers. AVAILABILITY AND IMPLEMENTATION: The source code can be found at http://www.robots.ox.ac.uk/ davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Application of machine learning techniques to tuberculosis drug resistance analysis

Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P &lt; 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers. Availability and implementation The source code can be found at http://www.robots.ox.ac.uk/ davidc/code.php Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

The Relation between Morphology and Dynamics of Poor Groups of Galaxies

We investigate the relation between the projected morphology (b/a) and the velocity dispersion (sigma_v) of groups of galaxies using two recently compiled group catalogs, one based on the 2MASS redshift survey and the other on the SDSS Data Release 5 galaxy catalog. We find that the sigma_v of groups is strongly correlated with the group projected b/a and size, with elongated and larger groups having a lower sigma_v. Such a correlation could be attributed to the dynamical evolution of groups, with groups in the initial stages of formation, having small sigma_v, a large size and an elongated shape that reflects the anisotropic accretion of galaxies along filamentary structures. The same sort of correlations, however, could also be reproduced in prolate-like groups, if the net galaxy motion is preferentially along the group elongation, since then the groups oriented close to the line of sight will appear more spherical, will have a small projected size and large sigma_v, while groups oriented close to the sky-plane will appear larger in projection, more elongated, and will have smaller sigma_v. We perform tests that relate only to the dynamical evolution of groups (eg., calculating the fraction of early type galaxies in groups) and indeed we find a strong positive (negative) correlation between the group sigma_v (projected major axis) with the fraction of early type galaxies. We conclude that (a) the observed dependencies of the group sigma_v on the group projected size and b/a, should be attributed mostly to the dynamical state of groups and (b) groups in the local universe do not constitute a family of objects in dynamical equilibrium, but rather a family of cosmic structures that are presently at various stages of their virialization process.Comment: ApJ accepted, 8 pages, 8 figure

Reservoir stress path and induced seismic anisotropy: Results from linking coupled fluid-flow/geomechanical simulation with seismic modelling

We present a workflow linking coupled fluid-flow and geomechanical simulation with seismic modelling to predict seismic anisotropy induced by nonhydrostatic stress changes. We generate seismic models from coupled simulations to examine the relationship between reservoir geometry, stress path and seismic anisotropy. The results indicate that geometry influences the evolution of stress, which leads to stress-induced seismic anisotropy. Although stress anisotropy is high for the small reservoir, the effect of stress arching and the ability of the side-burden to support the excess load limit the overall change in effective stress and hence seismic anisotropy. For the extensive reservoir, stress anisotropy and induced seismic anisotropy are high. The extensive and elongate reservoirs experience significant compaction, where the inefficiency of the developed stress arching in the side-burden cannot support the excess load. The elongate reservoir displays significant stress asymmetry, with seismic anisotropy developing predominantly along the long-edge of the reservoir. We show that the link between stress path parameters and seismic anisotropy is complex, where the anisotropic symmetry is controlled not only by model geometry but also the nonlinear rock physics model used. Nevertheless, a workflow has been developed to model seismic anisotropy induced by non-hydrostatic stress changes, allowing field observations of anisotropy to be linked with geomechanical models

Multi-decadal trends in large-bodied fish populations in the New South Wales Murray-Darling Basin, Australia

Context: Native fish populations in Australia's Murray-Darling Basin (MDB) have experienced severe declines since European settlement. Information on their status is needed to guide management and recovery. Aims: To quantify trends in MDB fish populations in New South Wales (NSW) from 1994 to 2022. Methods: Relative abundance, biomass, and size structure were examined using generalised additive mixed models at NSW MDB and river catchment (valley) scales for five native species (Murray cod, Maccullochella peelii, golden perch, Macquaria ambigua, silver perch, Bidyanus bidyanus, Macquarie perch, Macquaria australasica, freshwater catfish, Tandanus tandanus) and one alien species (common carp, Cyprinus carpio). Key results: There was strong inter-annual variation in relative abundance, biomass and population structure for all species. At the Basin scale, relative abundance of Murray cod, golden perch and common carp increased across the time series, with no clear trends for silver perch, Macquarie perch or freshwater catfish. Patterns in relative abundance, biomass, and population structure were variable among valleys for most species. Conclusions and implications: Although native fish populations in the MDB remain degraded and face escalating threats, recent increases in the abundance of some native species are an encouraging sign that integrated restoration efforts can improve the outlook for native fish

The Structure of 2MASS Galaxy Clusters

We use a sample of galaxies from the Two Micron All Sky Survey (2MASS) Extended Source Catalog to refine a matched filter method of finding galaxy clusters that takes into account each galaxy's position, magnitude, and redshift if available. The matched filter postulates a radial density profile, luminosity function, and line-of-sight velocity distribution for cluster galaxies. We use this method to search for clusters in the galaxy catalog, which is complete to an extinction-corrected K-band magnitude of 13.25 and has spectroscopic redshifts for roughly 40% of the galaxies, including nearly all brighter than K = 11.25. We then use a stacking analysis to determine the average luminosity function, radial distribution, and velocity distribution of cluster galaxies in several richness classes, and use the results to update the parameters of the matched filter before repeating the cluster search. We also investigate the correlations between a cluster's richness and its velocity dispersion and core radius, using these relations to refine priors that are applied during the cluster search process. After the second cluster search iteration, we repeat the stacking analysis. We find a cluster galaxy luminosity function that fits a Schechter form, with parameters M_K* - 5 log h = -23.64\pm0.04 and \alpha = -1.07\pm0.03. We can achieve a slightly better fit to our luminosity function by adding a Gaussian component on the bright end to represent the brightest cluster galaxy (BCG) population. The radial number density profile of galaxies closely matches a projected Navarro-Frenk-White (NFW) profile at intermediate radii, with deviations at small radii due to well-known cluster centering issues and outside the virial radius due to correlated structure. The velocity distributions are Gaussian in shape, with velocity dispersions that correlate strongly with richness.Comment: 13 pages, 10 figures, 3 tables. Updated to match version published in Ap