1,193 research outputs found

    Reactor

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    Yellowwood Poetry Contest Prize Winner. Contest Judge: Sandra Beasle

    The Bispectrum of IRAS Galaxies

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    We compute the bispectrum for the galaxy distribution in the IRAS QDOT, 2Jy, and 1.2Jy redshift catalogs for wavenumbers 0.05<k<0.2 h/Mpc and compare the results with predictions from gravitational instability in perturbation theory. Taking into account redshift space distortions, nonlinear evolution, the survey selection function, and discreteness and finite volume effects, all three catalogs show evidence for the dependence of the bispectrum on configuration shape predicted by gravitational instability. Assuming Gaussian initial conditions and local biasing parametrized by linear and non-linear bias parameters b_1 and b_2, a likelihood analysis yields 1/b_1 = 1.32^{+0.36}_{-0.58}, 1.15^{+0.39}_{-0.39} and b_2/b_1^2=-0.57^{+0.45}_{-0.30}, -0.50^{+0.31}_{-0.51}, for the for the 2Jy and 1.2Jy samples, respectively. This implies that IRAS galaxies trace dark matter increasingly weakly as the density contrast increases, consistent with their being under-represented in clusters. In a model with chi^2 non-Gaussian initial conditions, the bispectrum displays an amplitude and scale dependence different than that found in the Gaussian case; if IRAS galaxies do not have bias b_1> 1 at large scales, \chi^2 non-Gaussian initial conditions are ruled out at the 95% confidence level. The IRAS data do not distinguish between Lagrangian or Eulerian local bias.Comment: 30 pages, 11 figure

    Emerging technologies for monitoring behavioural and psychological symptoms of dementia

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    (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Behavioural and psychological symptoms of dementia (BPSD) are complex array of symptoms that have devastating impact on patients, carers and their loved ones. In this paper we argue that with the combined use of pervasive computing and big data, we could make significant progress in the diagnosis of the causes of BPSD, monitoring response to treatment and helping in the prevention of these symptoms. We review the available technologies, such as Cloud computing and context aware systems, and how they could help in managing and hopefully preventing the Behavioural and Psychological Symptoms of Dementia.Peer ReviewedPostprint (author's final draft

    Power Spectrum of Velocity Fluctuations in the Universe

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    We investigate the power spectrum of velocity fluctuations in the universe, V2(k)V^2(k), starting from four different measures of velocity: (1) the power spectrum of velocity fluctuations from peculiar velocities of galaxies; (2) the rms peculiar velocity of galaxy clusters; (3) the power spectrum of velocity fluctuations from the power spectrum of density fluctuations in the galaxy distribution; (4) and the bulk velocity from peculiar velocities of galaxies. We show that measures (1) and (2) are not consistent with each other and either the power spectrum from peculiar velocities of galaxies is overestimated or the rms cluster peculiar velocity is underestimated. The amplitude of velocity fluctuations derived from the galaxy distribution (measure 3) depends on the parameter β\beta. We estimate the parameter β\beta on the basis of measures (2) and (4). The power spectrum of velocity fluctuations from the galaxy distribution in the Stromlo-APM redshift survey is consistent with the observed rms cluster velocity and with the observed large-scale bulk flow when the parameter β\beta is in the range 0.4-0.5. In this case the value of the function V(k)V(k) at wavelength λ=120h1\lambda=120h^{-1}Mpc is 350\sim 350 km s1^{-1} and the rms amplitude of the bulk flow at the radius r=60h1r=60h^{-1} Mpc is 340\sim 340 km s1^{-1}. The velocity dispersion of galaxy systems originates mostly from the large-scale velocity fluctuations with wavelengths λ>100h1\lambda >100h^{-1} Mpc.Comment: Astrophysical Journal, Vol. 493, in press: 23 pages, uses AAS Latex, and 14 separate postscript figure

    A modal model for diffraction gratings

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    A description of an algorithm for a rather general modal grating calculation is presented. Arbitrary profiles, depth, and permittivity are allowed. Gratings built up from sub-gratings are allowed, as are coatings on the sidewalls of lines, and arbitrary complex structure. Conical angles and good conductors are supported

    The Power Spectrum of the PSC Redshift Survey

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    We measure the redshift-space power spectrum P(k) for the recently completed IRAS Point Source Catalogue (PSC) redshift survey, which contains 14500 galaxies over 84% of the sky with 60 micron flux >= 0.6 Jansky. Comparison with simulations shows that our estimated errors on P(k) are realistic, and that systematic errors due to the finite survey volume are small for wavenumbers k >~ 0.03 h Mpc^-1. At large scales our power spectrum is intermediate between those of the earlier QDOT and 1.2 Jansky surveys, but with considerably smaller error bars; it falls slightly more steeply to smaller scales. We have fitted families of CDM-like models using the Peacock-Dodds formula for non-linear evolution; the results are somewhat sensitive to the assumed small-scale velocity dispersion \sigma_V. Assuming a realistic \sigma_V \approx 300 km/s yields a shape parameter \Gamma ~ 0.25 and normalisation b \sigma_8 ~ 0.75; if \sigma_V is as high as 600 km/s then \Gamma = 0.5 is only marginally excluded. There is little evidence for any `preferred scale' in the power spectrum or non-Gaussian behaviour in the distribution of large-scale power.Comment: Latex, uses mn.sty, 14 pages including 11 Postscript figures. Accepted by MNRA

    Steps toward the power spectrum of matter. I.The mean spectrum of galaxies

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    We calculate the mean power spectrum of galaxies using published power spectra of galaxies and clusters of galaxies. On small scales we use the power spectrum derived from the 2-dimensional distribution of APM galaxies, on large scales we use power spectra derived from 3-dimensional data for galaxy and cluster samples. Spectra are reduced to real space and to the amplitude of the power spectrum of APM galaxies. Available data indicate the presence of two different populations in the nearby Universe. Clusters of galaxies sample a relatively large region in the Universe where rich, medium and poor superclusters are well represented. Their mean power spectrum has a spike on scale 120 h^{-1}Mpc, followed by an approximate power-law spectrum of index n = -1.9 towards small scales. The power spectrum found from LCRS and IRAS 1.2 Jy surveys is flatter around the maximum, which may represent regions of the Universe with medium-rich and poor superclusters.Comment: LaTex (sty files added), 35 pages, 5 PostScript figures and Table with mean power spectrum embedded, Astrophysical Journal (accepted

    CIRCULAR DICHROISM OF LIGHT-HARVESTING COMPLEXES FROM PURPLE PHOTOSYNTHETIC BACTERIA

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    The CD spectra of a range of antenna complexes from several different species of purple photosynthetic bacteria were recorded in the wavelength range of 190 to 930 nm. Analysis of the far UV CD (190 to 250 nm) showed that in each case except for the B800-850 from Chr. vinosum the secondary structure of the light-harvesting complexes contains a large amount of α-helix (50%) and very little 0-pleated sheet. This confirms the predictions of the group of Zuber of a high a-helical content based upon consideration of the primary structures of several antenna apoproteins. The CD spectra from the carotenoids and the bacteriochlorophylls show considerable variations depending upon the type of antenna complex. The different amplitude ratios in the CD spectrum for the bacteriochlorophyll Qy, Qx and Soret bands indicate not only different degrees of exciton coupling, but also a strong and variable hyperchromism (Scherz and Parson, 1984a, b)

    Chemical characterisation and source identification of atmospheric aerosols in the Snowy Mountains, south-eastern Australia

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    Characterisation of atmospheric aerosols is of major importance for: climate, the hydrological cycle, human health and policymaking, biogeochemical and palaeo-climatological studies. In this study, the chemical composition and source apportionment of PM2.5 (particulate matter with aerodynamic diameters less than 2.5 μm) at Yarrangobilly, in the Snowy Mountains, SE Australia are examined and quantified. A new aerosol monitoring network was deployed in June 2013 and aerosol samples collected during the period July 2013 to July 2017 were analysed for 22 trace elements and black carbon by ion beam analysis techniques. Positive matrix factorisation and back trajectory analysis and trajectory clustering methods were employed for source apportionment and to isolate source areas and air mass travel pathways, respectively. This study identified the mean atmospheric PM2.5 mass concentration for the study period was (3.3 ± 2.5) μg m−3. It is shown that automobile (44.9 ± 0.8)%, secondary sulfate (21.4 ± 0.9)%, smoke (12.3 ± 0.6)%, soil (11.3 ± 0.5)% and aged sea salt (10.1 ± 0.4)% were the five PM2.5 source types, each with its own distinctive trends. The automobile and smoke sources were ascribed to a significant local influence from the road network and bushfire and hazard reduction burns, respectively. Long-range transport are the dominant sources for secondary sulfate from coal-fired power stations, windblown soil from the inland saline regions of the Lake Eyre and Murray-Darling Basins, and aged sea salt from the Southern Ocean to the remote alpine study site. The impact of recent climate change was recognised, as elevated smoke and windblown soil events correlated with drought and El Niño periods. Finally, the overall implications including potential aerosol derived proxies for interpreting palaeo-archives are discussed. To our knowledge, this is the first long-term detailed temporal and spatial characterisation of PM2.5 aerosols for the region and provides a crucial dataset for a range of multidisciplinary research. Crown Copyright © 2018 Published by Elsevier B.V
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