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Department of Health and Environmental Sciences
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1.Interpretation of complexometric titration data: An intercomparison of methods for estimating models of trace metal complexation by natural organic ligands

Author:Pižeta,I.;Sander,S. G.;Hudson,R. J.M.;Omanović,D.;Baars,O.;Barbeau,K. A.;Buck,K. N.;Bundy,R. M.;Carrasco,G.;Croot,P. L.;Garnier,C.;Gerringa,L. J.A.;Gledhill,M.;Hirose,K.;Kondo,Y.;Laglera,L. M.;Nuester,J.;Rijkenberg,M. J.A.;Takeda,S.;Twining,B. S.;Wells,M.

Source:Marine Chemistry,2015,Vol.173

Abstract:With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model their most common type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they were familiar with to model synthetic titration data that are typical of those obtained by applying state-of-the-art electrochemical methods - anodic stripping voltammetry (ASV) and competitive ligand equilibration-adsorptive cathodic stripping voltammetry (CLE-ACSV) - to the analysis of natural waters. Herein, we compare their estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations ([M ]), and evaluate the influence of the various methods and assumptions used on these results.The ASV-type titrations were designed to test each participant's ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameters. For the four samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate 'measurements' of ambient [M ]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one's parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean-squared relative error between the fitted observations and the simulated curves should be comparable to the expected precision of the analytical method and upon visual inspection the distribution of residuals should not be skewed. f f

2.Bioaccumulation of rare earth elements in paddy soils in and surrounding the REEs Mine

Author:LIU Panpan;CHEN Zheng;SUN Guoxin;LI Hong

Source:Acta Scientiae Circumstantiae,2016,Vol.36

Abstract:© 2016, Science Press. All right reserved. The increasing production of rare earth elements (REEs) in southern China may greatly impact the local ecosystem and agriculture. Therefore, the movement of rare earth elements (REEs) from paddy soils in the REEs mines and surrounding areas to different rice tissues were studied. Results showed that REEs concentrations in mining paddy soils was in a range between 193.82 mg·kg-1 and 965.28 mg·kg-1, with a mean value of 332.55 mg·kg-1. It was about 3 times higher than the background REEs in China. REEs have similar physicochemical properties during their migration and accumulation process in the soil-rice system. The REEs levels in different rice tissues were in the order of root > leaf > grain. The iron plaque on the rice roots surface does not show significant barrier effects on the accumulation of REEs. The mobility and bioavailability characteristics of REEs in rice-soil system are closely related to the genotypes of rice and physicochemical properties of soil.

3.Surveillance video summarisation by jointly applying moving object detection and tracking


Source:International Journal of Computational Vision and Robotics,2014,Vol.4

Abstract:With the growth of massive storage of surveillance video data, it has become imperative to design efficient tools for video content browsing and management. This paper describes an integrative approach for surveillance video summarisation that jointly apply moving object detection and tracking. In the proposed scheme, moving objects are first detected and tracked. The static summarisation is generated to contain some key frames which provide details of the moving objects. The main advantages of our approach include the preservation of important information and economic computational cost. The high performance background modelling with Gaussian mixture model, together with the multi-scale morphological processing, brings together a highly accurate moving object detection tool. The proposed matching criterions for Kalman filtering enhances the tracking accuracy. We experimented with highway surveillance videos and outdoor surveillance videos, demonstrating satisfactory performances. Copyright © 2014 Inderscience Enterprises Ltd.
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