Variability in concentrations of potentially toxic elements in urban parks from six European cities

Use of a harmonised sampling regime has allowed comparison of concentrations of copper, chromium, nickel, lead and zinc in six urban parks located in different European cities differing markedly in their climate and industrial history. Wide concentrations ranges were found for copper, lead and zinc at most sites, but for chromium and nickel a wide range was only seen in the Italian park, where levels were also considerably greater than in other soils. As might be expected, the soils from older cities with a legacy of heavy manufacturing industry (Glasgow, Torino) were richest in potentially toxic elements (PTEs); soils from Ljubljana, Sevilla and Uppsala had intermediate metal contents, and soils from the most recently established park, in the least industrialised city (Aveiro), displayed lowest concentrations. When principal component analysis was applied to the data, associations were revealed between pH and organic carbon content; and between all five PTEs. When pH and organic carbon content were excluded from the PCA, a distinction became clear between copper, lead and zinc (the "urban" metals) on the one hand, and chromium and nickel on the other. Similar results were obtained for the surface (0-10 cm depth) and sub-surface (10-20 cm depth) samples. Comparisons with target or limit concentrations were limited by the existence of different legislation in different countries and the fact that few guidelines deal specifically with public-access urban soils intended for recreational use

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Prediction and Simulator Verification of Roll/Lateral Adverse Aeroservoelastic Rotorcraft–Pilot Couplings

The involuntary interaction of a pilot with an aircraft can be described as pilot-assisted oscillations. Such phenomena are usually only addressed late in the design process when they manifest themselves during ground/flight testing. Methods to be able to predict such phenomena as early as possible are therefore useful. This work describes a technique to predict the adverse aeroservoelastic rotorcraft–pilot couplings, specifically between a rotorcraft’s roll motion and the resultant involuntary pilot lateral cyclic motion. By coupling linear vehicle aeroservoelastic models and experimentally identified pilot biodynamic models, pilot-assisted oscillations and no-pilot-assisted oscillation conditions have been numerically predicted for a soft-in-plane hingeless helicopter with a lightly damped regressive lead–lag mode that strongly interacts with the roll modeat a frequency within the biodynamic band of the pilots. These predictions have then been verified using real-time flight-simulation experiments. The absence of any similar adverse couplings experienced while using only rigid-body models in the flight simulator verified that the observed phenomena were indeed aeroelastic in nature. The excellent agreement between the numerical predictions and the observed experimental results indicates that the techniques developed in this paper can be used to highlight the proneness of new or existing designs to pilot-assisted oscillation

Aspects of radiative K^+_e3 decays

We re-investigate the radiative charged kaon decay K+- --> pi0 e+- nu_e gamma in chiral perturbation theory, merging the chiral expansion with Low's theorem. We thoroughly analyze the precision of the predicted branching ratio relative to the non-radiative decay channel. Structure dependent terms and their impact on differential decay distributions are investigated in detail, and the possibility to see effects of the chiral anomaly in this decay channel is emphasized.Comment: 15 pages, 6 figure

Quantitative predictions on auxin-induced polar distribution of PIN proteins during vein formation in leaves

The dynamic patterning of the plant hormone auxin and its efflux facilitator the PIN protein are the key regulator for the spatial and temporal organization of plant development. In particular auxin induces the polar localization of its own efflux facilitator. Due to this positive feedback auxin flow is directed and patterns of auxin and PIN arise. During the earliest stage of vein initiation in leaves auxin accumulates in a single cell in a rim of epidermal cells from which it flows into the ground meristem tissue of the leaf blade. There the localized auxin supply yields the successive polarization of PIN distribution along a strand of cells. We model the auxin and PIN dynamics within cells with a minimal canalization model. Solving the model analytically we uncover an excitable polarization front that triggers a polar distribution of PIN proteins in cells. As polarization fronts may extend to opposing directions from their initiation site we suggest a possible resolution to the puzzling occurrence of bipolar cells, such we offer an explanation for the development of closed, looped veins. Employing non-linear analysis we identify the role of the contributing microscopic processes during polarization. Furthermore, we deduce quantitative predictions on polarization fronts establishing a route to determine the up to now largely unknown kinetic rates of auxin and PIN dynamics.Comment: 9 pages, 4 figures, supplemental information included, accepted for publication in Eur. Phys. J.

Identifying nonlinear wave interactions in plasmas using two-point measurements: a case study of Short Large Amplitude Magnetic Structures (SLAMS)

A framework is described for estimating Linear growth rates and spectral energy transfers in turbulent wave-fields using two-point measurements. This approach, which is based on Volterra series, is applied to dual satellite data gathered in the vicinity of the Earth's bow shock, where Short Large Amplitude Magnetic Structures (SLAMS) supposedly play a leading role. The analysis attests the dynamic evolution of the SLAMS and reveals an energy cascade toward high-frequency waves.Comment: 26 pages, 13 figure

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Rate of convergence of truncated stochastic approximation procedures with moving bounds

The paper is concerned with stochastic approximation procedures having three main characteristics: truncations with random moving bounds, a matrix valued random step-size sequence, and a dynamically changing random regression function. We study convergence and rate of convergence. Main results are supplemented with corollaries to establish various sets of sufficient conditions, with the main emphases on the parametric statistical estimation. The theory is illustrated by examples and special cases.Comment: 30 page

Determination of set-membership identifiability sets

International audienceThis paper concerns the concept of set-membership identifiability introduced in \cite{jauberthie}. Given a model, a set-membership identifiable set is a connected set in the parameter domain of the model such that its corresponding trajectories are distinct to trajectories arising from its complementary. For obtaining the so-called set-membership identifiable sets, we propose an algorithm based on interval analysis tools. The proposed algorithm is decomposed into three parts namely {\it mincing}, {\it evaluating} and {\it regularization} (\cite{jaulin2}). The latter step has been modified in order to obtain guaranteed set-membership identifiable sets. Our algorithm will be tested on two examples