Cooperative Research Centre

About: Cooperative Research Centre is a based out in . It is known for research contribution in the topics: Population & Sea ice. The organization has 7633 authors who have published 8607 publications receiving 429721 citations.

Algorithmic prediction in policing: assumptions, evaluation, and accountability

Abstract: The goal of predictive policing is to forecast where and when crimes will take place in the future. The idea has captured the imagination of law enforcement agencies around the world. Many agencies...

Facile synthesis of comb, star, and graft polymers via reversible addition–fragmentation chain transfer (RAFT) polymerization

Abstract: Reversible addition-fragmentation chain transfer (RAFT) polymerization has been shown to be a facile means of synthesizing comb, star, and graft polymers of styrene. The precursors required for these reactions were synthesized readily from RAFT-prepared poly(vinylbenzyl chloride) and poly(styrene-co-vinylbenzyl chloride), which gave intrinsically well-defined star and comb precursors. Substitution of the chlorine atom in the vinylbenzyl chloride moiety with a dithiobenzoate group proceeded readily, with a minor detriment to the molecular weight distribution. The kinetics of the reaction were consistent with a living polymerization mechanism, except that for highly crowded systems, there were deviations from linearity early in the reaction due to steric hindrance and late in the reaction due to chain entanglement and autoacceleration. A crosslinked polymer-supported RAFT agent was also prepared, and this was used in the preparation of graft polymers with pendant polystyrene chains. (C) 2002 Wiley Periodicals, Inc.

Uncertainty associated with model predictions of surface and crown fire rates of spread

Abstract: The degree of accuracy in model predictions of rate of spread in wildland fires is dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data. On the basis of a compilation of 49 fire spread model evaluation datasets involving 1278 observations in seven different fuel type groups, the limits on the predictability of current operational models are examined. Only 3% of the predictions (i.e. 35 out of 1278) were considered to be exact predictions according to the criteria used in this study. Mean percent error varied between 20 and 310% and was homogeneous across fuel type groups. Slightly more than half of the evaluation datasets had mean errors between 51 and 75%. Under-prediction bias was prevalent in 75% of the 49 datasets analysed. A case is made for suggesting that a ?35% error interval (i.e. approximately one standard deviation) would constitute a reasonable standard for model performance in predicting a wildland fire's forward or heading rate of spread. We also found that empirical-based fire behaviour models developed from a solid foundation of field observations and well accepted functional forms adequately predicted rates of fire spread far outside of the bounds of the original dataset used in their development. We examined error statistics of operational wildland fire spread models.We compiled 49 fire spread model evaluation datasets involving 1278 observations.Mean percent error varied between 20 and 310% and was homogeneous across fuel type groups.The analysis suggests that a ?35% error interval is a reasonable standard for model adequacy.

Profiling bacterial survival through a water treatment process and subsequent distribution system

Abstract: Aims: To profile fractions of active bacteria and of bacteria culturable with routine heterotrophic plate count (HPC) methods through a typical water treatment process and subsequent distribution system. In doing so, investigate how water treatment affects both bacterial abundance and diversity, and reveal the identities of active bacteria not detected by traditional HPC culture. Methods and Results: Profiling active fractions was performed by flow cytometric cell sorting of either membrane-intact (BacLightTM kit) or enzymatically active (carboxyfluorescein diacetate, CFDA) bacteria, followed by eubacterial 16S rDNA-directed PCR and denaturing gradient gel electrophoresis (DGGE). Water treatment significantly reduced active bacterial numbers detected by the BacLightTM kit and CFDA assay by 2·89 and 2·81 log respectively. Bacterial diversity was also reduced from >20 DGGE bands in the active fractions of reservoir water to only two bands in the active fractions of finished water. These two bands represented Stenotrophomonas maltophila, initially culturable by HPC, and a Burkholderia-related species. Both species maintained measurable traits of physiological activity in distribution system bulk water but were undetected by HPC. Conclusions: Flow cytometric cell sorting with PCR-DGGE, to assess water treatment efficacy, identified active bacteria from a variety of major phylogenetic groups undetected by routine HPC. Following treatment S. maltophila and a Burkholderia-related species retained activity and entered distribution undetected by HPC. Significance and Impact of the Study: Methods used here demonstrate how water treatment operators can better monitor water treatment plant efficacy and assess distribution system instability by the detection and identification of active bacteria recalcitrant to routine HPC culture.