Synthesis and characterization of conducting copolymers from aniline and o‐anisidine in HCl solutions

Conducting poly(aniline‐co‐o‐anisidine) (PAS) films with different ratios of aniline units in the polymer chain were prepared by oxidative polymerization of different molar ratios of aniline and o‐anisidine in 1 M HCl using cyclic voltammetry. Due to the much higher reactivity of o‐anisidine, the structure and properties of PASs were found to be dominated by the o‐anisidine units. The polymerization of poly‐o‐anisidine and PASs followed zero‐order kinetics with respect to formation of the polymer (film thickness) and the autocatalytic polymerization of aniline was completely inhibited. In contrast to polyaniline, a decrease in the polymerization temperature was found to increase the amount of copolymer formed and its redox charge. The presence of aniline units in PASs led to a pronounced increase in the molecular weight and conductivity, and a decrease in the solubility in organic solvents. Repetitive charging/discharging cycles showed that PASs resist degradation more than polyaniline. Copyright © 2003 Society of Chemical Industry     

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.     

Effect of electric field on the vapor-phase growth of carbon nanostructures

A mathematical model of dc gas discharge plasma has been developed in order to determine the electric field strength at a substrate surface during plasmachemical deposition of carbon nanostructures. A numerical solution of the model equations has been obtained using the experimentally determined boundary conditions and model parameters. A comparison of the solution to experiment confirms the adequacy of the proposed mathematical model, which provides the electric field profiles and the electron and ion density distributions near the substrate surface. Estimations show that, for carbon nanostructures with a characteristic size of about 30 nm, the electric field strength at the surface is sufficiently high to provide for their directional growth along the field.     

Effet de la pression sur la transition ruisselant-pulsé dans les réacteurs catalytiques à lit fixe arrosé

The effect of pressure on transition displacement between trickle and pulsed regimes (TPR: trickle-pulsed transition) in catalytic trickle bed reactors (CTBR) is not properly predicted by existing theoretical models and empirical correlations. Based on high pressure TPR data available to date, a modified Charpentier diagram is proposed to quantify directly the effect of pressure in non-foaming systems.     

The influence of chloride-ion concentration on the Pitting of steel in simulated concrete environment

The influence of chloride-ion concentration on the pitting behaviour of steel in saturated Ca(OH)₂ solution was studied using the D.C. polarization technique. Results seem to indicate a threshold chloride level for the on-set of severe pitting around 3000 ppm, the pitting potential E_p exhibits dramatic drop and large hysteresis loops develop. The results provide insight into the pitting performance of steel in concrete and highlight the poor repassivation of this metal/environment system.     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

Optimisation of solar photovoltaic (PV) parameters using meta-heuristics

Microsystem Technologies - This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves...     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.