Calibrating the parameters: changing hearts and minds about open access monographs

The advent of open access (OA) publishing presents welcome new opportunities for reducing the barriers of cost and time to the dissemination of research work in UK universities. However, it does present some challenges to the traditional model of monograph publication in the humanities and social sciences. In common with many other academic institutions, the University of Sussex is developing policies that will permit it to embrace OA publication. This paper describes how, in doing this, Sussex is addressing the challenges associated with OA to ensure that the careers of doctoral students, academics and researchers are not affected adversely by the change in the publishing landscape for monographs both in the UK and internationall

For me or not for me? - that is the question : a study of mature students' decision making and higher education

The views expressed in this report are the authors ' and do not necessarily reflect those of the Department for Education an

Heavy Hybrids from NRQCD

We present results for the exotic $0^{+-}$ and $1^{-+}$ $b\bar{b}g$ hybrids at $\beta=6.0$. A leading order NRQCD action is used to generate the heavy quark propagators. The optimisation of operators is discussed and we compare our results to the predictions of recent potential calculations.Comment: 3 pages, four figures, Latex, uses espcrc2.sty. Talk presented at LATTICE97, Edinburg

The uptake and utilisation of amino acids by mammals

Imperial Users onl

Welfare Reform and Immigrant Participation in the Supplemental Security Income Program

We examine the effect of the 1996 welfare reform legislation on participation in the Supplemental Security Income (SSI) program by immigrants. Although none of the immigrants on the SSI rolls before welfare reform lost eligibility, the potential exists for future impacts on the SSI caseload and the well-being of recent immigrants. We use microdata files from the Social Security Administration’s Continuous Work History Sample matched to administrative data on SSI participation for the period 1993 to 1999. We estimate simple models of SSI participation and compare our results to the existing literature. We then estimate a series of difference-in-differences models of SSI participation. These models compare SSI participation by immigrants relative to nativeborn individuals, and among affected immigrants relative to unaffected immigrants and native-born individuals, before and after welfare reform. Descriptive results indicate that the percentage of immigrants and natives receiving SSI decreased after welfare reform, but by a larger percentage for natives than for immigrants. The probability of SSI participation decreased after welfare reform for immigrants who were affected by the legislation relative to immigrants who were unaffected. The difference-in-differences estimate is positive for immigrants relative to otherwise similar natives, but the estimated effect among affected immigrants is about half as large as the effect for unaffected immigrants. When the sample is limited to low earners as a proxy for the SSI means test, the results are qualitatively unchanged but quantitatively much stronger. Authors’ Acknowledgements We are grateful to Ulyses Balderas for assisting with the collection of some data used here. A previous version of this paper was presented at the 2004 Western Regional Science Association Annual Meeting, February 25-28, 2004, Maui, HI.

Compressive and Noncompressive Power Spectral Density Estimation from Periodic Nonuniform Samples

This paper presents a novel power spectral density estimation technique for band-limited, wide-sense stationary signals from sub-Nyquist sampled data. The technique employs multi-coset sampling and incorporates the advantages of compressed sensing (CS) when the power spectrum is sparse, but applies to sparse and nonsparse power spectra alike. The estimates are consistent piecewise constant approximations whose resolutions (width of the piecewise constant segments) are controlled by the periodicity of the multi-coset sampling. We show that compressive estimates exhibit better tradeoffs among the estimator's resolution, system complexity, and average sampling rate compared to their noncompressive counterparts. For suitable sampling patterns, noncompressive estimates are obtained as least squares solutions. Because of the non-negativity of power spectra, compressive estimates can be computed by seeking non-negative least squares solutions (provided appropriate sampling patterns exist) instead of using standard CS recovery algorithms. This flexibility suggests a reduction in computational overhead for systems estimating both sparse and nonsparse power spectra because one algorithm can be used to compute both compressive and noncompressive estimates.Comment: 26 pages, single spaced, 9 figure

Designing High Thermal Conductive Materials Using Artificial Evolution

There is a growing need for efficient and effective methods of heat dissipation. One driving force for this need is computer processors. As the processor grows faster and more powerful, it requires more electricity to perform tasks which results in high amounts of heat generated. Designing materials with high thermal-conductivity can enable heat dissipation to allow faster and more powerful computers. Creating such materials is often a trial-and-error process by which several material composites are tested for desirable thermal conductivity. In this research, we employed the use of a genetic algorithm, which mimics the process of evolution through natural selection, as an alternative to exhaustive trial-and-error approaches to help design a graphene based template material with high thermal conductivity. The algorithm creates a population of randomly generated configurations, then uses an open source physics (molecular dynamics) simulator, LAMMPS, linked with High-Performance Computing to run a molecular dynamics simulation for each composite to derive a fitness, or score for the material. The highest scoring materials undergo crossover to create offspring for the next generation. Over time, these algorithms have the potential to find a composite with desirable conductivity through this pseudo-evolution process