Scale-invariant moving finite elements for nonlinear partial differential equations in two dimensions

A scale-invariant moving finite element method is proposed for the adaptive solution of nonlinear partial differential equations. The mesh movement is based on a finite element discretisation of a scale-invariant conservation principle incorporating a monitor function, while the time discretisation of the resulting system of ordinary differential equations is carried out using a scale-invariant time-stepping which yields uniform local accuracy in time. The accuracy and reliability of the algorithm are successfully tested against exact self-similar solutions where available, and otherwise against a state-of-the-art h-refinement scheme for solutions of a two-dimensional porous medium equation problem with a moving boundary. The monitor functions used are the dependent variable and a monitor related to the surface area of the solution manifold. (c) 2005 IMACS. Published by Elsevier B.V. All rights reserved.

Linear and nonlinear microrheology of dense colloidal suspensions

The length and time scales accessible to optical tweezers make them an ideal tool for the examination of colloidal systems. Embedded high-refractive-index tracer particles in an index-matched hard sphere suspension provide 'handles' within the system to investigate the mechanical behaviour. Passive observations of the motion of a single probe particle give information about the linear response behaviour of the system, which can be linked to the macroscopic frequency-dependent viscous and elastic moduli of the suspension. Separate 'dragging' experiments allow observation of a sample's nonlinear response to an applied stress on a particle-by particle basis. Optical force measurements have given new data about the dynamics of phase transitions and particle interactions; an example in this study is the transition from liquid-like to solid-like behaviour, and the emergence of a yield stress and other effects attributable to nearest-neighbour caging effects. The forces needed to break such cages and the frequency of these cage breaking events are investigated in detail for systems close to the glass transition.

A tungsten complex containing a highly delocalized metal-ligand system

Nucleophilic attack of (triphenylphosphonio) cyclopentadienide on the dichlorodiazomethane-tungsten complex trans[ BrW(dppe)(2)(N2CCl2)]PF6 [dppe is 1,2-bis(diphenylphosphino) ethane] results in C-C bond formation and affords the title compound, trans-[W(C24H18ClN2P)Br(C26H24P2)(2)]PF6 center dot 0.6CH(2)Cl(2). This complex, bis[1,2- bis(diphenylphosphino)ethane] bromido{chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethanediido} tungsten hexafluorophosphate dichloromethane 0.6-solvate, contains the previously unknown ligand chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethane. Evidence from bond lengths and torsion angles indicates significant through-ligand delocalization of electron density from tungsten to the nominally cationic phosphorus(V) centre. This structural analysis clearly demonstrates that the tungsten-dinitrogen unit is a powerful pi-electron donor with the ability to transfer electron density from the metal to a distant acceptor centre through an extended conjugated ligand system. As a consequence, complexes of this type could have potential applications as nonlinear optical materials and molecular semiconductors.

Electrochemical characterisation and anodic stripping voltammetry at mesoporous platinum rotating disc electrodes

Using the technique of liquid crystal templating a rotating disc electrode (RDE) was modified with a high surface area mesoporous platinum film. The surface area of the electrode was characterised by acid voltammetry, and found to be very high (ca. 86 cm(2)). Acid characterisation of the electrode produced distorted voltammograms was interpreted as being due to the extremely large surface area which produced a combination of effects such as localised pH change within the pore environment and also ohmic drop effects. Acid voltammetry in the presence of two different types of surfactant, namely Tween 20 and Triton X-100, suggested antifouling properties associated with the mesoporous deposit. Further analysis of the modified electrode using a redox couple in solution showed typical RDE behaviour although extra capacitive currents were observed due to the large surface area of the electrode. The phenomenon of underpotential deposition was exploited for the purpose of anodic stripping voltammetry and results were compared with data collected for microelectrodes. Underpotential deposition of metal ions at the mesoporous RDE was found to be similar to that at conventional platinum electrodes and mesoporous microelectrodes although the rate of surface coverage was found to be slower at a mesoporous RDE. It was found that a mesoporous RDE forms a suitable system for quantification of silver ions in solution.

Towards dynamical system models of language-related brain potentials

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

Wiener-System subspace identification for mobile wireless mm-wave networks

We discuss the feasibility of wireless terahertz communications links deployed in a metropolitan area and model the large-scale fading of such channels. The model takes into account reception through direct line of sight, ground and wall reflection, as well as diffraction around a corner. The movement of the receiver is modeled by an autonomous dynamic linear system in state space, whereas the geometric relations involved in the attenuation and multipath propagation of the electric field are described by a static nonlinear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a single-output Wiener model from time-domain measurements of the field intensity when the receiver motion is simulated using a constant angular speed and an exponentially decaying radius. The identification procedure is validated by using the model to perform q-step ahead predictions. The sensitivity of the algorithm to small-scale fading, detector noise, and atmospheric changes are discussed. The performance of the algorithm is tested in the diffraction zone assuming a range of emitter frequencies (2, 38, 60, 100, 140, and 400 GHz). Extensions of the simulation results to situations where a more complicated trajectory describes the motion of the receiver are also implemented, providing information on the performance of the algorithm under a worst case scenario. Finally, a sensitivity analysis to model parameters for the identified Wiener system is proposed.

Chemical and aerosol characterisation of the troposphere over West Africa during the monsoon period as part of AMMA

During June, July and August 2006 five aircraft took part in a campaign over West Africa to observe the aerosol content and chemical composition of the troposphere and lower stratosphere as part of the African Monsoon Multidisciplinary Analysis (AMMA) project. These are the first such measurements in this region during the monsoon period. In addition to providing an overview of the tropospheric composition, this paper provides a description of the measurement strategy (flights performed, instrumental payloads, wing-tip to wing-tip comparisons) and points to some of the important findings discussed in more detail in other papers in this special issue. The ozone data exhibits an "S" shaped vertical profile which appears to result from significant losses in the lower troposphere due to rapid deposition to forested areas and photochemical destruction in the moist monsoon air, and convective uplift of ozone-poor air to the upper troposphere. This profile is disturbed, particularly in the south of the region, by the intrusions in the lower and middle troposphere of air from the southern hemisphere impacted by biomass burning. Comparisons with longer term data sets suggest the impact of these intrusions on West Africa in 2006 was greater than in other recent wet seasons. There is evidence for net photochemical production of ozone in these biomass burning plumes as well as in urban plumes, in particular that from Lagos, convective outflow in the upper troposphere and in boundary layer air affected by nitrogen oxide emissions from recently wetted soils. This latter effect, along with enhanced deposition to the forested areas, contributes to a latitudinal gradient of ozone in the lower troposphere. Biogenic volatile organic compounds are also important in defining the composition both for the boundary layer and upper tropospheric convective outflow. Mineral dust was found to be the most abundant and ubiquitous aerosol type in the atmosphere over Western Africa. Data collected within AMMA indicate that injection of dust to altitudes favourable for long-range transport (i.e. in the upper Sahelian planetary boundary layer) can occur behind the leading edge of mesoscale convective system (MCS) cold-pools. Research within AMMA also provides the first estimates of secondary organic aerosols across the West African Sahel and have shown that organic mass loadings vary between 0 and 2 μg m−3 with a median concentration of 1.07 μg m−3. The vertical distribution of nucleation mode particle concentrations reveals that significant and fairly strong particle formation events did occur for a considerable fraction of measurement time above 8 km (and only there). Very low concentrations were observed in general in the fresh outflow of active MCSs, likely as the result of efficient wet removal of aerosol particles due to heavy precipitation inside the convective cells of the MCSs. This wet removal initially affects all particle size ranges as clearly shown by all measurements in the vicinity of MCSs.

A hyperstable neural network for the modelling and control of nonlinear systems

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Purification and functional characterisation of Rhinocerase, a novel serine protease from the venom of Bitis gabonica rhinoceros

Background: Serine proteases are a major component of viper venoms and are thought to disrupt several distinct elements of the blood coagulation system of envenomed victims. A detailed understanding of the functions of these enzymes is important both for acquiring a fuller understanding of the pathology of envenoming and because these venom proteins have shown potential in treating blood coagulation disorders. Methodology/Principal Findings: In this study a novel, highly abundant serine protease, which we have named rhinocerase, has been isolated and characterised from the venom of Bitis gabonica rhinoceros using liquid phase isoelectric focusing and gel filtration. Like many viper venom serine proteases, this enzyme is glycosylated; the estimated molecular mass of the native enzyme is approximately 36kDa, which reduces to 31kDa after deglycosylation. The partial amino acid sequence shows similarity to other viper venom serine proteases, but is clearly distinct from the sequence of the only other sequenced serine protease from Bitis gabonica. Other viper venom serine proteases have been shown to exert distinct biological effects, and our preliminary functional characterization of rhinocerase suggest it to be multifunctional. It is capable of degrading α and β chains of fibrinogen, dissolving plasma clots and of hydrolysing a kallikrein substrate. Conclusions/Significance: A novel multifunctional viper venom serine protease has been isolated and characterised. The activities of the enzyme are consistent with the known in vivo effects of Bitis gabonica envenoming, including bleeding disorders, clotting disorders and hypotension. This study will form the basis for future research to understand the mechanisms of serine protease action, and examine the potential for rhinocerase to be used clinically to reduce the risk of human haemostatic disorders such as heart attacks and strokes.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.