LiDAR mapping of tidal marshes for ecogeomorphological modelling in the TIDE project

The European research project TIDE (Tidal Inlets Dynamics and Environment) is developing and validating coupled models describing the morphological, biological and ecological evolution of tidal environments. The interactions between the physical and biological processes occurring in these regions requires that the system be studied as a whole rather than as separate parts. Extensive use of remote sensing including LiDAR is being made to provide validation data for the modelling. This paper describes the different uses of LiDAR within the project and their relevance to the TIDE science objectives. LiDAR data have been acquired from three different environments, the Venice Lagoon in Italy, Morecambe Bay in England, and the Eden estuary in Scotland. LiDAR accuracy at each site has been evaluated using ground reference data acquired with differential GPS. A semi-automatic technique has been developed to extract tidal channel networks from LiDAR data either used alone or fused with aerial photography. While the resulting networks may require some correction, the procedure does allow network extraction over large areas using objective criteria and reduces fieldwork requirements. The networks extracted may subsequently be used in geomorphological analyses, for example to describe the drainage patterns induced by networks and to examine the rate of change of networks. Estimation of the heights of the low and sparse vegetation on marshes is being investigated by analysis of the statistical distribution of the measured LiDAR heights. Species having different mean heights may be separated using the first-order moments of the height distribution.

Raman selection rules for vibration-rotation transitions in symmetric top molecules

Symmetry restrictions on Raman selection rules can be obtained, quite generally, by considering a Raman allowed transition as the result of two successive dipole allowed transitions, and imposing the usual symmetry restrictions on the dipole transitions. This leads to the same results as the more familiar polarizability theory, but the vibration-rotation selection rules are easier to obtain by this argument. The selection rules for symmetric top molecules involving the (+l) and (-l) components of a degenerate vibrational level with first-order Coriolis splitting are derived in this paper. It is shown that these selection rules depend on the order of the highest-fold symmetry axis Cn, being different for molecules with n=3, n=4, or n ≧ 5; moreover the selection rules are different again for molecules belonging to the point groups Dnd with n even, and Sm with 1/2m even, for which the highest-fold symmetry axes Cn and Sm are related by m=2n. Finally it is shown that an apparent anomaly between the observed Raman and infra-red vibration-rotation spectra of the allene molecule is resolved when the correct selection rules are used, and a value for the A rotational constant of allene is derived without making use of the zeta sum rule.

Bis[1,1 '-N,N '-(2-picolyl)aminomethyl]ferrocene as a redox sensor for transition metal ions

The compound bis[1,1'-N,N'-(2-picolyl) aminomethyl] ferrocene, L-1, was synthesized. The protonation constants of this ligand and the stability constants of its complexes with Ni2+, Cu2+, Zn2+, Cd2+ and Pb2+ were determined in aqueous solution by potentiometric methods at 25degreesC and at ionic strength 0.10 mol dm(-3) in KNO3. The compound L-1 forms only 1:1 (M:L) complexes with Pb2+ and Cd2+ while with Ni2+ and Cu2+ species of 2:1 ratio were also found. The complexing behaviour of L-1 is regulated by the constraint imposed by the ferrocene in its backbone, leading to lower values of stability constants for complexes of the divalent first row transition metals when compared with related ligands. However, the differences in stability are smaller for the larger metal ions. The structure of the copper complex with L-1 was determined by single-crystal X-ray diffraction and shows that a species of 2:2 ratio is formed. The two copper centres display distorted octahedral geometries and are linked through the two L1 bridges at a long distance of 8.781(10) Angstrom. The electrochemical behaviour of L-1 was studied in the presence of Ni2+, Cu2+, Zn2+, Cd2+ and Pb2+, showing that upon complexation the ferrocene-ferrocenium half-wave potential shifts anodically in relation to that of the free ligand. The maximum electrochemical shift (DeltaE(1/2)) of 268 mV was found in the presence of Pb2+, followed by Cu2+ (218 mV), Ni2+ (152 mV), Zn2+ (111 mV) and Cd2+ (110 mV). Moreover, L-1 is able to electrochemically and selectively sense Cu2+ in the presence of a large excess of the other transition metal cations studied.

Higher oxides of chlorine: absorption cross-sections of Cl2O6 and Cl2O4, the decomposition of Cl2O6, and the reactions of OClO with O and O-3

The absorption cross-sections of Cl2O6 and Cl2O4 have been obtained using a fast flow reactor with a diode array spectrometer (DAS) detection system. The absorption cross-sections at the wavelengths of maximum absorption (lambda(max)) determined in this study are those of Cl2O6: (1.47 +/- 0.15) x 10(-17) cm(2) molecule(-1), at lambda(max) = 276 nm and T = 298 K; and Cl2O4: (9.0 +/- 2.0) x 10(-19) cm(2) molecule(-1), at lambda(max) = 234 nm and T = 298 K. Errors quoted are two standard deviations together with estimates of the systematic error. The shapes of the absorption spectra were obtained over the wavelength range 200-450 nm for Cl2O6 and 200-350 nm for Cl2O4, and were normalized to the absolute cross-sections obtained at lambda(max) for each oxide, and are presented at 1 nm intervals. These data are discussed in relation to previous measurements. The reaction of O with OCIO has been investigated with the objective of observing transient spectroscopic absorptions. A transient absorption was seen, and the possibility is explored of identifying the species with the elusive sym-ClO3 or ClO4, both of which have been characterized in matrices, but not in the gas-phase. The photolysis of OCIO was also re-examined, with emphasis being placed on the products of reaction. UV absorptions attributable to one of the isomers of the ClO dimer, chloryl chloride (ClClO2) were observed; some Cl2O4 was also found at long photolysis times, when much of the ClClO2 had itself been photolysed. We suggest that reports of Cl2O6 formation in previous studies could be a consequence of a mistaken identification. At low temperatures, the photolysis of OCIO leads to the formation of Cl2O3 as a result of the addition of the ClO primary product to OCIO. ClClO2 also appears to be one product of the reaction between O-3 and OCIO, especially when the reaction occurs under explosive conditions. We studied the kinetics of the non-explosive process using a stopped-flow technique, and suggest a value for the room-temperature rate coefficient of (4.6 +/- 0.9) x 10(-19) cm(3) molecule(-1) s(-1) (limit quoted is 2sigma random errors). The photochemical and thermal decomposition of Cl2O6 is described in this paper. For photolysis at k = 254 nm, the removal of Cl2O6 is not accompanied by the build up of any other strong absorber. The implications of the results are either that the photolysis of Cl2O6 produces Cl-2 directly, or that the initial photofragments are converted rapidly to Cl-2. In the thermal decomposition of Cl2O6, Cl2O4 was shown to be a product of reaction, although not necessarily the major one. The kinetics of decomposition were investigated using the stopped-flow technique. At relatively high [OCIO] present in the system, the decay kinetics obeyed a first-order law, with a limiting first-order rate coefficient of 0.002 s(-1). (C) 2004 Elsevier B.V. All rights reserved.

Mechanisms underlying agonist efficacy

Agonist efficacy is a measure of how well an agonist can stimulate a response system linked to a receptor. Efficacy can be assessed in functional assays and various parameters (E-max, K-A/EC50, E-max center dot K-A/EC50) determined. The E-max center dot K-A/EC50 parameter provides a good estimate of efficacy across the full range of efficacy. A convenient assay for the efficacy of agonists for some receptors is provided by the [S-35]GTP[S] (guanosine 5'-[gamma-[S-35]thio]triphosphate)-binding assay. in this assay, the normal GTP-binding event in GPCR (G-protein-coupled receptor) activation is replaced by the binding of the non-hydrolysable analogue [S-35]GTP[S]. This assay may be used to profile ligands for their efficacy, and an example here is the D-2 dopamine receptor where an efficacy scale has been set up using this assay. The mechanisms underlying the assay have been probed. The time course of [S-35]GTP[S] binding follows a pseudo-first-order reaction with [S-35]GTP[S] binding reaching equilibrium after approx. 3 h. The [S-35]GTP[S]-binding event is the rate-deter mining step in the assay. Agonists regulate the maximal level of [S-35]GTP[S] bound, rather than the rate constant for binding. The [S-35]GTP[S]-binding assay therefore determines agonist efficacy on the basis of the amount of [S-35]GTP[S] bound rather than the rate of binding.

Planning rigid body motions using elastic curves

This paper tackles the problem of computing smooth, optimal trajectories on the Euclidean group of motions SE(3). The problem is formulated as an optimal control problem where the cost function to be minimized is equal to the integral of the classical curvature squared. This problem is analogous to the elastic problem from differential geometry and thus the resulting rigid body motions will trace elastic curves. An application of the Maximum Principle to this optimal control problem shifts the emphasis to the language of symplectic geometry and to the associated Hamiltonian formalism. This results in a system of first order differential equations that yield coordinate free necessary conditions for optimality for these curves. From these necessary conditions we identify an integrable case and these particular set of curves are solved analytically. These analytic solutions provide interpolating curves between an initial given position and orientation and a desired position and orientation that would be useful in motion planning for systems such as robotic manipulators and autonomous-oriented vehicles.

Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Disentangling multi-level systems: averaging, correlations and memory

We consider two weakly coupled systems and adopt a perturbative approach based on the Ruelle response theory to study their interaction. We propose a systematic way of parameterizing the effect of the coupling as a function of only the variables of a system of interest. Our focus is on describing the impacts of the coupling on the long term statistics rather than on the finite-time behavior. By direct calculation, we find that, at first order, the coupling can be surrogated by adding a deterministic perturbation to the autonomous dynamics of the system of interest. At second order, there are additionally two separate and very different contributions. One is a term taking into account the second-order contributions of the fluctuations in the coupling, which can be parameterized as a stochastic forcing with given spectral properties. The other one is a memory term, coupling the system of interest to its previous history, through the correlations of the second system. If these correlations are known, this effect can be implemented as a perturbation with memory on the single system. In order to treat this case, we present an extension to Ruelle's response theory able to deal with integral operators. We discuss our results in the context of other methods previously proposed for disentangling the dynamics of two coupled systems. We emphasize that our results do not rely on assuming a time scale separation, and, if such a separation exists, can be used equally well to study the statistics of the slow variables and that of the fast variables. By recursively applying the technique proposed here, we can treat the general case of multi-level systems.

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory

The role of dust in climate changes today, at the last glacial maximum and in the future.

Natural mineral aerosol (dust) is an active component of the climate system and plays multiple roles in mediating physical and biogeochemical exchanges between the atmosphere, land surface and ocean. Changes in the amount of dust in the atmosphere are caused both by changes in climate (precipitation, wind strength, regional moisture balance) and changes in the extent of dust sources caused by either anthropogenic or climatically induced changes in vegetation cover. Models of the global dust cycle take into account the physical controls on dust deflation from prescribed source areas (based largely on soil wetness and vegetation cover thresholds), dust transport within the atmospheric column, and dust deposition through sedimentation and scavenging by precipitation. These models successfully reproduce the first-order spatial and temporal patterns in atmospheric dust loading under modern conditions. Atmospheric dust loading was as much as an order-of-magnitude larger than today during the last glacial maximum (LGM). While the observed increase in emissions from northern Africa can be explained solely in terms of climate changes (colder, drier and windier glacial climates), increased emissions from other regions appear to have been largely a response to climatically induced changes in vegetation cover and hence in the extent of dust source areas. Model experiments suggest that the increased dust loading in tropical regions had an effect on radiative forcing comparable to that of low glacial CO2 levels. Changes in land-use are already increasing the dust loading of the atmosphere. However, simulations show that anthropogenically forced climate changes substantially reduce the extent and productivity of natural dust sources. Positive feedbacks initiated by a reduction of dust emissions from natural source areas on both radiative forcing and atmospheric CO2 could substantially mitigate the impacts of land-use changes, and need to be considered in climate change assessments.

How well can we simulate past climates? Evaluating the models using global palaeoenvironmental datasets.

Global syntheses of palaeoenvironmental data are required to test climate models under conditions different from the present. Data sets for this purpose contain data from spatially extensive networks of sites. The data are either directly comparable to model output or readily interpretable in terms of modelled climate variables. Data sets must contain sufficient documentation to distinguish between raw (primary) and interpreted (secondary, tertiary) data, to evaluate the assumptions involved in interpretation of the data, to exercise quality control, and to select data appropriate for specific goals. Four data bases for the Late Quaternary, documenting changes in lake levels since 30 kyr BP (the Global Lake Status Data Base), vegetation distribution at 18 kyr and 6 kyr BP (BIOME 6000), aeolian accumulation rates during the last glacial-interglacial cycle (DIRTMAP), and tropical terrestrial climates at the Last Glacial Maximum (the LGM Tropical Terrestrial Data Synthesis) are summarised. Each has been used to evaluate simulations of Last Glacial Maximum (LGM: 21 calendar kyr BP) and/or mid-Holocene (6 cal. kyr BP) environments. Comparisons have demonstrated that changes in radiative forcing and orography due to orbital and ice-sheet variations explain the first-order, broad-scale (in space and time) features of global climate change since the LGM. However, atmospheric models forced by 6 cal. kyr BP orbital changes with unchanged surface conditions fail to capture quantitative aspects of the observed climate, including the greatly increased magnitude and northward shift of the African monsoon during the early to mid-Holocene. Similarly, comparisons with palaeoenvironmental datasets show that atmospheric models have underestimated the magnitude of cooling and drying of much of the land surface at the LGM. The inclusion of feedbacks due to changes in ocean- and land-surface conditions at both times, and atmospheric dust loading at the LGM, appears to be required in order to produce a better simulation of these past climates. The development of Earth system models incorporating the dynamic interactions among ocean, atmosphere, and vegetation is therefore mandated by Quaternary science results as well as climatological principles. For greatest scientific benefit, this development must be paralleled by continued advances in palaeodata analysis and synthesis, which in turn will help to define questions that call for new focused data collection efforts.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.