Topology of amorphous tetrahedral semiconductors on intermediate lengthscales

Using the recently-proposed ``activation-relaxation technique'' for optimizing complex structures, we develop a structural model appropriate to a-GaAs which is almost free of odd-membered rings, i.e., wrong bonds, and possesses an almost perfect coordination of four. The model is found to be superior to structures obtained from much more computer-intensive tight-binding or quantum molecular-dynamics simulations. For the elemental system a-Si, where wrong bonds do not exist, the cost in elastic energy for removing odd-membered rings is such that the traditional continuous-random network is appropriate. Our study thus provides, for the first time, direct information on the nature of intermediate-range topology in amorphous tetrahedral semiconductors.Comment: 4 pages, Latex and 2 postscript figure

Structural, electronic, and dynamical properties of amorphous gallium arsenide: a comparison between two topological models

We present a detailed study of the effect of local chemical ordering on the structural, electronic, and dynamical properties of amorphous gallium arsenide. Using the recently-proposed ``activation-relaxation technique'' and empirical potentials, we have constructed two 216-atom tetrahedral continuous random networks with different topological properties, which were further relaxed using tight-binding molecular dynamics. The first network corresponds to the traditional, amorphous, Polk-type, network, randomly decorated with Ga and As atoms. The second is an amorphous structure with a minimum of wrong (homopolar) bonds, and therefore a minimum of odd-membered atomic rings, and thus corresponds to the Connell-Temkin model. By comparing the structural, electronic, and dynamical properties of these two models, we show that the Connell-Temkin network is energetically favored over Polk, but that most properties are little affected by the differences in topology. We conclude that most indirect experimental evidence for the presence (or absence) of wrong bonds is much weaker than previously believed and that only direct structural measurements, i.e., of such quantities as partial radial distribution functions, can provide quantitative information on these defects in a-GaAs.Comment: 10 pages, 7 ps figures with eps

Guidance on the Selection of Appropriate Indicators for Quantification of Antimicrobial Usage in Humans and Animals

An increasing variety of indicators of antimicrobial usage has become available in human and veterinary medicine, with no consensus on the most appropriate indicators to be used. The objective of this review is therefore to provide guidance on the selection of indicators, intended for those aiming to quantify antimicrobial usage based on sales, deliveries or reimbursement data. Depending on the study objective, different requirements apply to antimicrobial usage quantification in terms of resolution, comprehensiveness, stability over time, ability to assess exposure and comparability. If the aim is to monitor antimicrobial usage trends, it is crucial to use a robust quantification system that allows stability over time in terms of required data and provided output; to compare usage between different species or countries, comparability must be ensured between the different populations. If data are used for benchmarking, the system comprehensiveness is particularly crucial, while data collected to study the association between usage and resistance should express the exposure level and duration as a measurement of the exerted selection pressure. Antimicrobial usage is generally described as the number of technical units consumed normalized by the population at risk of being treated in a defined period. The technical units vary from number of packages to number of individuals treated daily by adding different levels of complexity such as daily dose or weight at treatment. These technical units are then related to a description of the population at risk, based either on biomass or number of individuals. Conventions and assumptions are needed for all of these calculation steps. However, there is a clear lack of standardization, resulting in poor transparency and comparability. By combining study requirements with available approaches to quantify antimicrobial usage, we provide suggestions on the most appropriate indicators and data sources to be used for a given study objective