Comprehensive Assessment of Seawater Quality on an Improved Attribute Recognition Model

The attribute recognition model （ARM） has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model （IARM） is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.     

The 21-cm forest

We examine the prospects for studying the pre-reionization intergalactic medium (IGM) through the so-called 21-cm forest in spectra of bright high-redshift radio sources. We first compute the evolution of the mean optical depth τ for models that include X-ray heating of the IGM gas, Wouthuysen–Field coupling, and reionization. Under most circumstances, the spin temperature T _S grows large well before reionization begins in earnest; this occurs so long as the X-ray luminosity of high-redshift starbursts (per unit star formation rate) is comparable to that in nearby galaxies. As a result,  τ≲ 10⁻³  throughout most of reionization, and background sources must sit well beyond the reionization surface in order to experience absorption that is measurable by square-kilometre class telescopes. H  ii regions produce relatively large 'transmission gaps' and may therefore still be observable during the early stages of reionization. Absorption from sheets and filaments in the cosmic web fades once T _S becomes large and should be rare during reionization. Minihaloes can produce strong (albeit narrow) absorption features. Measuring their abundance would yield useful limits on the strength of feedback processes in the IGM as well as their effect on reionization.     

Voids in a ΛCDM universe

We study the formation and evolution of voids in the dark matter distribution using various simulations of the popular Λ cold dark matter cosmogony. We identify voids by requiring them to be regions of space with a mean overdensity of −0.8 or less – roughly the equivalent of using a spherical overdensity group finder for haloes. Each of the simulations contains thousands of voids. The distribution of void sizes in the different simulations shows good agreement when differences in particle and grid resolution are accounted for. Voids very clearly correspond to minima in the smoothed initial density field. Apart from a very weak dependence on the mass resolution, the rescaled mass profiles of voids in the different simulations agree remarkably well. We find a universal void mass profile of the form  ρ(< r )/ρ( r _eff) ∝ exp[( r / r _eff)^α]  , where r _eff is the effective radius of a void and  α∼ 2  . The mass function of haloes in voids is steeper than that of haloes that populate denser regions. In addition, the abundances of void haloes seem to evolve somewhat more strongly between redshifts ∼1 and 0 than the global abundances of haloes.     

On an analytical framework for voids: their abundances, density profiles and local mass functions

We present a general analytical procedure for computing the number density of voids with radius above a given value within the context of gravitational formation of the large-scale structure of the Universe out of Gaussian initial conditions. To this end, we develop an accurate (under generally satisfied conditions) extension of the unconditional mass function to constrained environments, which allows us both to obtain the number density of collapsed objects of certain mass at any distance from the centre of the void, and to derive the number density of voids defined by collapsed objects. We have made detailed calculations for the spherically averaged mass density and halo number density profiles for particular voids. We also present a formal expression for the number density of voids defined by galaxies of a given type and luminosity. This expression contains the probability for a collapsed object of certain mass to host a galaxy of that type and luminosity (i.e. the conditional luminosity function) as a function of the environmental density. We propose a procedure to infer this function, which may provide useful clues as to the galaxy formation process, from the observed void densities.     

A theory of pressure sensor performance in snow

A theory of pressure sensor response in snow is derived and used to examine the sources of measurement errors in snow water equivalent (SWE) pressure sensors. Measurement errors in SWE are caused by differences in the compressibility of the pressure sensor and the adjacent snow layer, which produces a shear stress along the perimeter of the sensor. When the temperature at the base of the snow cover equals 0 °C, differences in the snowmelt rate between the snow–SWE sensor interface and the adjacent snow–soil interface may also produce a shear stress along the sensor's perimeter. This shear stress perturbs the pressure field over the sensor, producing SWE measurement errors. Snow creep acts to reduce shear stresses along the SWE sensor's perimeter at a rate that is inversely proportional to the snow viscosity. For sustained periods of differential snowmelt, a difference in the mass of snow over the sensor compared with the surrounding soil will develop, producing additional permanent errors in SWE measurements. The theory indicates that SWE pressure sensor performance can be improved by designing a sensor with a high Young's modulus (low compressibility), low aspect ratio, large diameter and thermal properties that match those of the surrounding soil. Simulations of SWE pressure sensor errors using the theory are in close agreement with observed errors and may provide a means to correct historical SWE measurements for use in hydrological hindcast or climate studies. Published in 2003 by John Wiley & Sons, Ltd.