Design and Performance of the Matching Beamline Between the Bnl Ebis and an Rfq.

A part of a new EBIS-based heavy ion preinjector, the low energy beam transport (LEBT) section between the high current EBIS and the RFQ is a challenging design, because it must serve many functions. In addition to the requirement to provide an efficient matching between the EBIS and the RFQ, this line must serve as a fast ''switchyard'', allowing singly charged ions from external sources to be transported into the EBIS trap region, and extracted, highly charged ions to be deflected to off-axis diagnostics (time-of-flight or emittance). The space charge of the 5-10 mA extracted heavy ion beam is a major consideration in the design, and the space charge force varies for different ion beams having Q/m from 1-0.16. The line includes electrostatic lenses, spherical and parallel-plate deflectors, magnetic solenoid, and diagnostics for measuring current, charge state distributions, emittance, and profile. A prototype of this beamline has been built, and results of tests are presented

Trialling water treatment residuals in the remediation of former mine site soils: investigating improvements achieved for plants, earthworms and soil solution.

During clarification processes of raw water a vast amount of by-product known as drinking water treatment residuals (WTRs) are produced, being principally composed of hydroxides of the Al or Fe salts added during water treatment plus the impurities they remove. Aluminium-based (Al-WTR) and iron-based (Fe-WTR) materials were applied at 10% w/w to degraded, bare (un-vegetated) soils from a restored coal mining site in central England (pH <3.9) to study their potential amelioration effects on earthworm mortality, biomass yield of seedling plants and element concentrations in plant tissues, earthworm tissues and in soil solutions. A separate treatment with agricultural lime was also conducted for comparison to evaluate whether any observed improvements were attributable to the liming capacity of the WTRs. After completion of the trials all samples were subjected to a wet-dry cycle and the experiments were repeated (i.e. simulating longer-term effects in the field). Both types of WTRs significantly increased biomass of plants and, in some treatments, survival of earthworms was also enhanced compared to non-amended soils. Excess plant tissue element concentrations and element concentrations in soil solutions were reduced in amended soils. The implications are that adding WTRs to mining impacted soils is a potentially viable, sustainable and low cost remediation method that could be used globally to improve the soil condition. This article is protected by copyright. All rights reserved

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features