Diffuse optical light in galaxy clusters. I. Abell 3888

We are undertaking a program to measure the characteristics of the intracluster light ( ICL; total flux, profile, color, and substructure) in a sample of 10 galaxy clusters with a range of cluster mass, morphology, and redshift. We present here the methods and results for the first cluster in that sample, A3888. We have identified an ICL component in A3888 in V and r that contains 13% +/- 5% of the total cluster light and extends to 700 h(70)(-1) kpc (similar to 0.3r(200)) from the center of the cluster. The ICL color in our smallest radial bin is V - r 0.3 +/- 0.1, similar to the central cluster elliptical galaxies. The ICL is redder than the galaxies at 400 h(70)(-1) kpc < r < 700 h(70)(-1) kpc, although the uncertainty in any one radial bin is high. Based on a comparison of V - r color with simple stellar models, the ICL contains a component that formed more than 7 Gyr ago ( at z less than 1) with a high-metallicity ( 1.0 Z(circle dot) < Z(ICL) less than or similar to 2.5 Z(circle dot)) and a more centralized component that contains stars formed within the past 5 Gyr ( at z similar to 1). The profile of the ICL can be roughly fitted by a shallow exponential in the outer regions and a steeper exponential in the central region. We also find a concentration of diffuse light around a small group of galaxies 1.4 h(70)(-1) Mpc from the center of the cluster. In addition, we find three low surface brightness features near the cluster center that are blue ( V - r 0.0) and contain a total flux of 0.1M*. Based on these observations and X-ray and galaxy morphology, we suggest that this cluster is entering a phase of significant merging of galaxy groups in the core, whereupon we expect the ICL fraction to grow significantly with the formation of a cD galaxy, as well as the infall of groups.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Population dynamics and gene flow of Helicoverpa armigera (Lepidoptera : Noctuidae) on cotton and grain crops in the Murrumbidgee Valley, Australia

The population dynamics of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) in the Murrumbidgee Valley, Australia, has been characterized using five highly variable microsatellite loci. In the 2001-2002 growing season, there were very high levels of migration into the Murrumbidgee Valley with no detectable genetic structuring, consistent with previous analyses on a national scale. By contrast, there was significant genetic structuring over the 2002-2003 growing season, with three distinct genetic types detected. The first type corresponded to the first two generations and was derived from local individuals emerging from diapause and their progeny. The second genetic type corresponded to generation 3 and resulted from substantial immigration into the region. There was another genetic shift in generation 4, which accounts for the third genetic type of the season. This genetic shift occurred despite low levels of immigration. During the third generation of the 2002-2003 growing season, different population dynamics was characterized for H. armigera on maize, Zea mays L., and cotton Gossipium hirsutum L. Populations on cotton tended to cycle independently with very little immigration from outside the region or from maize within the region. Maize acted as a major sink for immigrants from cotton and from outside the region. If resistance were to develop on cotton under these circumstances, susceptible individuals from maize or from other regions would not dilute this resistance. In addition, resistance is likely to be transferred to maize and be perpetuated until diapause, from where it may reemerge next season. If low levels of immigration were to occur on transgenic cotton, this may undermine the effectiveness of refugia, especially noncotton refugia.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision-making raised by the US Environmental Protection Agency (EPA)'s science-policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision-making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision-theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision-making involving risk assessment. The substitution of decision-theoretic frameworks for default assumption-driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk-aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low 'hormetic' doses of some agents). In many cases, the administrative burden of applying decision-analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision-theoretic principles that transcend local differences among jurisdictions.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

Classifying an opponent's behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.