A comparative study of the effects of metal contamination on collembola in the field and in the laboratory

We examined the species diversity and abundance of Collembola at 32 sampling points along a gradient of metal contamination in a rough grassland site ( Wolverhampton, England), formerly used for the disposal of metal-rich smelting waste. Differences in the concentrations of Cd, Cu, Pb and Zn between the least and most contaminated part of the 35 metre transect were more than one order of magnitude. A gradient of Zn concentrations from 597 to 9080 mug g(-1) dry soil was found. A comparison between field concentrations of the four metals and previous studies on their relative toxicities to Collembola, suggested that Zn is likely to be responsible for any ecotoxicological effects on springtails at this site. Euedaphic ( soil dwelling) Collembola were extracted by placing soil cores into Tullgren funnels and epedaphic ( surface dwelling) species were sampled using pitfall traps. There was no obvious relationship between the total abundance, or a range of commonly used diversity indices, and Zn levels in soils. However, individual species showed considerable differences in abundance. Metal "tolerant'' (e.g., Ceratophysella denticulata) and metal "sensitive'' (e.g., Cryptopygus thermophilus) species could be identified. Epedaphic species appeared to be influenced less by metal contamination than euedaphic species. This difference is probably due to the higher mobility and lower contact with the soil pore water of epedaphic springtails in comparison to euedaphic Collembola. In an experiment exposing the standard test springtail, Folsomia candida, to soils from all 32 sampling points, adult survival and reproduction showed small but significant negative relationships with total Zn concentrations. Nevertheless, juveniles were still produced from eggs laid by females in the most contaminated soils with 9080 mug g(-1) Zn. Folsomia candida is much more sensitive to equivalent concentrations of Zn in the standard OECD soil. Thus, care should be taken in extrapolating the results of laboratory toxicity tests on metals in OECD soil to field soils, in which, the biological availability of contaminants is likely to be lower. Our studies have shown the importance of ecotoxicological effects at the species level. Although there may be no differences in overall abundance, sensitive species that are numerous in contaminated sites, and which may play important roles in decomposition("keystone species'') can be greatly reduced in numbers by pollution.

Comparative characterisation by atomic force microscopy and ellipsometry of soft and solid thin films

Ellipsometry and atomic force microscopy (AFM) were used to study the film thickness and the surface roughness of both 'soft' and solid thin films. 'Soft' polymer thin films of polystyrene and poly(styrene-ethylene/butylene-styrene) block copolymer were prepared by spin-coating onto planar silicon wafers. Ellipsometric parameters were fitted by the Cauchy approach using a two-layer model with planar boundaries between the layers. The smooth surfaces of the prepared polymer films were confirmed by AFM. There is good agreement between AFM and ellipsometry in the 80-130 nm thickness range. Semiconductor surfaces (Si) obtained by anisotropic chemical etching were investigated as an example of a randomly rough surface. To define roughness parameters by ellipsometry, the top rough layers were treated as thin films according to the Bruggeman effective medium approximation (BEMA). Surface roughness values measured by AFM and ellipsometry show the same tendency of increasing roughness with increased etching time, although AFM results depend on the used window size. The combined use of both methods appears to offer the most comprehensive route to quantitative surface roughness characterisation of solid films. Copyright (c) 2007 John Wiley & Sons, Ltd.

Function of the hydration layer around an antifreeze protein revealed by atomistic molecular dynamics simulations

Atomistic molecular dynamics simulations are used to investigate the mechanism by which the antifreeze protein from the spruce budworm, Choristoneura fumiferana, binds to ice. Comparison of structural and dynamic properties of the water around the three faces of the triangular prism-shaped protein in aqueous solution reveals that at low temperature the water structure is ordered and the dynamics slowed down around the ice-binding face of the protein, with a disordering effect observed around the other two faces. These results suggest a dual role for the solvation water around the protein. The preconfigured solvation shell around the ice-binding face is involved in the initial recognition and binding of the antifreeze protein to ice by lowering the barrier for binding and consolidation of the protein:ice interaction surface. Thus, the antifreeze protein can bind to the molecularly rough ice surface by becoming actively involved in the formation of its own binding site. Also, the disruption of water structure around the rest of the protein helps prevent the adsorbed protein becoming covered by further ice growth.

MTR: the multi-tasking rover - a new concept in rover design

In this paper we present the novel concepts incorporated in a planetary surface exploration rover design that is currently under development. The Multitasking Rover (MTR) aims to demonstrate functionality that will cover many of the current and future needs such as rough-terrain mobility, modularity and upgradeability. The rover system has enhanced mobility characteristics. It operates in conjunction with Science Packs (SPs) and Tool Packs (TPs)-modules attached to the main frame of the rover, which are either special tools or science instruments and alter the operation capabilities of the system.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Novel concepts for a planetary surface exploration rover

Purpose - This paper aims to address some of the needs of present and upcoming rover designs, and introduces novel concepts incorporated in a planetary surface exploration rover design that is currently under development. Design/methodology/approach - The Multitasking Rover (MTR) is a highly re-configurable system that aims to demonstrate functionality that will cover many of the current and future needs such as rough-terrain mobility, modularity and upgradeability. It comprises a surface mobility platform which is highly re-configurable, which offers centre of mass re-allocation and rough terrain stability, and also a set of science/tool packs - individual subsystems encapsulated in packs which the rover picks up, transports and deploys. Findings - Early testing of the suspension system suggests exceptional performance characteristics. Originality/value - Principles employed in the design of the MTR can be used in future rover systems to reduce associated mission costs and at the same time provide multiples the functionality.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A forward-constrained regression algorithm for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

A 'Kimberley protest': diamond mining, export sanctions, and poverty in Awatia, Ghana

The temporary suspension of diamond exports in Ghana in 2006 and 2007 is arguably the most significant move to address mounting criticisms of the Kimberley Process Certification Scheme (KPCS), an international initiative aimed at stemming the flow of rough diamonds used to finance wars. The ban, which took effect in November 2006, was much praised, particularly in civil society circles, where it continues to be seen as a genuine effort to prevent the smuggling of ‘conflict diamonds’. At the time, Ghana was accused of harbouring stones originating from rebel-held territories in neighbouring Côte d’Ivoire. No evidence was found in support of the case that it was a repository for ‘conflict diamonds’, however, and exports resumed early in March 2007. This article examines the context for the accusations of Ghana’s implication in the smuggling of illicit diamonds, and draws on recent fieldwork to explain how the suspension has affected Akwatia, the country’s main diamondiferous area. The actions taken raise important questions about how suspected violators – particularly smaller diamond-producing nations – of the KPCS should be handled, and underscore how global compacts can have a host of negative repercussions at the village level.

Perspex machine

We introduce the perspex machine which unifies projective geometry and the Turing machine, resulting in a supra-Turing machine. Specifically, we show that a Universal Register Machine (URM) can be implemented as a conditional series of whole numbered projective transformations. This leads naturally to a suggestion that it might be possible to construct a perspex machine as a series of pin-holes and stops. A rough calculation shows that an ultraviolet perspex machine might operate up to the petahertz range of operations per second. Surprisingly, we find that perspex space is irreversible in time, which might make it a candidate for an anisotropic spacetime geometry in physical theories. We make a bold hypothesis that the apparent irreversibility of physical time is due to the random nature of quantum events, but suggest that a sum over histories might be achieved by sampling fluctuations in the direction of time flow. We propose an experiment, based on the Casimir apparatus, that should measure fluctuations of time flow with respect to time duration- if such fluctuations exist.

Another Proof of the Preceding Theory

Another Proof of the Preceding Theory was produced as part of a residency run by Artists in Archeology in conjunction with the Stonehenge Riverside project. The film explores the relationship between science, work and ritual, imagining archaeology as a future cult. As two robed disciples stray off from the dig, they are drawn to the drone of the stones and proceed to play the henge like a gigantic Theremin. Just as a Theremin is played with the hand interfering in an electric circuit and producing sound without contact, so the stones respond to the choreographed bodily proximity. Finally, one of the two continues alone to the avenue at Avebury, where the magnetic pull of the stones reaches its climax. Shot on VHS, the film features a score by Zuzushi Monkey, with percussion and theremin sounds mirroring the action. The performers are mostly artists and archeologists from the art and archaeology teams. The archeologists were encouraged to perform their normal work in the robes, in an attempt to explore the meeting points of science and ritual and interrogate our relationship to an ultimately unknowable prehistoric past where activities we do not understand are relegated to the realm of religion. Stonehenge has unique acoustic properties, it’s large sarsen stones are finely worked on the inside, left rough on the outside, intensifying sound waves within the inner horseshoe, but since their real use, having been built over centuries, remains ambiguous, the film proposes that our attempts to decode them may themselves become encoded in their cumulative meaning for future researchers.

Generalised Dirichelt-to-Neumann map in time dependent domains

We study the heat, linear Schrodinger and linear KdV equations in the domain l(t) < x < ∞, 0 < t < T, with prescribed initial and boundary conditions and with l(t) a given differentiable function. For the first two equations, we show that the unknown Neumann or Dirichlet boundary value can be computed as the solution of a linear Volterra integral equation with an explicit weakly singular kernel. This integral equation can be derived from the formal Fourier integral representation of the solution. For the linear KdV equation we show that the two unknown boundary values can be computed as the solution of a system of linear Volterra integral equations with explicit weakly singular kernels. The derivation in this case makes crucial use of analyticity and certain invariance properties in the complex spectral plane. The above Volterra equations are shown to admit a unique solution.