Institution
Australian National University
Education•Canberra, Australian Capital Territory, Australia•
About: Australian National University is a education organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Population & Galaxy. The organization has 34419 authors who have published 109261 publications receiving 4315448 citations. The organization is also known as: The Australian National University & ANU.
Topics: Population, Galaxy, Stars, Zircon, Politics
Papers published on a yearly basis
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Durham University1, University of Nottingham2, University of Cambridge3, Johns Hopkins University4, Australian National University5, Liverpool John Moores University6, University of New South Wales7, University of Oxford8, University of St Andrews9, California Institute of Technology10, University of Leeds11, University of Edinburgh12
TL;DR: In this paper, the dependence of galaxy clustering on luminosity and spectral type using the 2dF Galaxy Redshift Survey (2dFGRS) was investigated using the principal-component analysis of Madgwick et al.
Abstract: We investigate the dependence of galaxy clustering on luminosity and spectral type using the 2dF Galaxy Redshift Survey (2dFGRS). Spectral types are assigned using the principal-component analysis of Madgwick et al. We divide the sample into two broad spectral classes: galaxies with strong emission lines ('late types') and more quiescent galaxies ('early types'). We measure the clustering in real space, free from any distortion of the clustering pattern owing to peculiar velocities, for a series of volume-limited samples. The projected correlation functions of both spectral types are well described by a power law for transverse separations in the range 2<(σ/h-1 Mpc)<15, with a marginally steeper slope for early types than late types. Both early and late types have approximately the same dependence of clustering strength on luminosity, with the clustering amplitude increasing by a factor of 2.5 between L* and 4L*. At all luminosities, however, the correlation function amplitude for the early types is 50 per cent higher than that of the late types. These results support the view that luminosity, and not type, is the dominant factor in determining how the clustering strength of the whole galaxy population varies with luminosity.
481 citations
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TL;DR: It is demonstrated that phase-perfect nanowires, of arbitrary diameter, can be achieved simply by tailoring basic growth parameters: temperature and V/III ratio, and this ability to tune crystal structure between twin-free zinc blende and stacking-fault-free wurtzite will enhance the performance of nanowire devices.
Abstract: Controlling the crystallographic phase purity of III-V nanowires is notoriously difficult, yet this is essential for future nanowire devices. Reported methods for controlling nanowire phase require dopant addition, or a restricted choice of nanowire diameter, and only rarely yield a pure phase. Here we demonstrate that phase-perfect nanowires, of arbitrary diameter, can be achieved simply by tailoring basic growth parameters: temperature and V/III ratio. Phase purity is achieved without sacrificing important specifications of diameter and dopant levels. Pure zinc blende nanowires, free of twin defects, were achieved using a low growth temperature coupled with a high V/III ratio. Conversely, a high growth temperature coupled with a low V/III ratio produced pure wurtzite nanowires free of stacking faults. We present a comprehensive nucleation model to explain the formation of these markedly different crystal phases under these growth conditions. Critical to achieving phase purity are changes in surface energy of the nanowire side facets, which in turn are controlled by the basic growth parameters of temperature and V/III ratio. This ability to tune crystal structure between twin-free zinc blende and stacking-fault-free wurtzite not only will enhance the performance of nanowire devices but also opens new possibilities for engineering nanowire devices, without restrictions on nanowire diameters or doping.
481 citations
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TL;DR: In this paper, the authors consider several statistical models for the analysis of the abundance of a rare species and show how to obtain standard errors for the parameter estimates, and also estimate the mean abundance of animals at a site.
481 citations
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University of New South Wales1, Met Office2, University of Washington3, University of Leeds4, University of Edinburgh5, University of California, Berkeley6, Columbia University7, Goddard Institute for Space Studies8, National Oceanography Centre9, Australian National University10, University of Tokyo11, Université Paris-Saclay12, Breakthrough Institute13, Utrecht University14, Stockholm University15, Scripps Institution of Oceanography16, University of Illinois at Urbana–Champaign17, Max Planck Society18
TL;DR: Evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S, is assessed, using a Bayesian approach to produce a probability density function for S given all the evidence, and promising avenues for further narrowing the range are identified.
Abstract: We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.
480 citations
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TL;DR: The possibility that history of depression is a risk factor for dementia needs to be taken seriously and explanations of the association need to be further researched.
Abstract: Objective: This review updates an earlier meta-analysis of the data on history of depression as a risk factor for dementia. It also considers the available evidence on the hypotheses proposed to ex...
480 citations
Authors
Showing all 34925 results
Name | H-index | Papers | Citations |
---|---|---|---|
Cyrus Cooper | 204 | 1869 | 206782 |
Nicholas G. Martin | 192 | 1770 | 161952 |
David R. Williams | 178 | 2034 | 138789 |
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Anton M. Koekemoer | 168 | 1127 | 106796 |
Robert G. Webster | 158 | 843 | 90776 |
Ashok Kumar | 151 | 5654 | 164086 |
Andrew White | 149 | 1494 | 113874 |
Bernhard Schölkopf | 148 | 1092 | 149492 |
Paul Mitchell | 146 | 1378 | 95659 |
Liming Dai | 141 | 781 | 82937 |
Thomas J. Smith | 140 | 1775 | 113919 |
Michael J. Keating | 140 | 1169 | 76353 |
Joss Bland-Hawthorn | 136 | 1114 | 77593 |
Harold A. Mooney | 135 | 450 | 100404 |