Detection of an artificial target with low radar cross-section in presence of backscatter

This paper presents an approach to improve the detection of an artificial target with low radar cross-section in presence of clutter. The target proposed in the paper modulates the phase response of the circularly polarized incident signal by means of rotation. The same physical phenomenon can be used to steer the modulated response in a non-specular direction. The bi-static measurements of the response of the target have demonstrated good agreement with theoretical prediction as well as with full-wave simulation.

Changes in modal parameters of a steel truss bridge due to artificial damage

This study investigated how damage changes the modal parameters of a real bridge by means of a field experiment which was conducted on a real steel truss bridge consecutively subjected to four artificial damage scenarios. In the experiment, both the forced and free vibrations of the bridge were recorded, the former for identifying higher modes available exclusively and the latter for lower modes with higher resolution. Results show that modal parameters are little affected by damage causing low stress redistribution. Modal frequencies decrease as damage causing high stress redistribution is applied; such a change can be observed if the damage is at the non-nodal point of the corresponding mode shape. Mode shapes are distorted due to asymmetric damage; they show an amplification in the damaged side as damage is applied at the non-nodal point. Torsion modes become more dominant as damage is applied either asymmetrically or on an element against large design loads. © 2013 Taylor & Francis Group, London.

Artificial receptors for the extraction of nucleoside metabolite 7-methylguanosine from aqueous media made by molecular imprinting

A series of imprinted polymers targeting nucleoside metabolites, prepared using a template analogue approach, are presented. These were prepared following selection of the optimum functional monomer by solution association studies using 1H-NMR titrations whereby methacrylic acid was shown to be the strongest receptor with and affinity constant of 621 ± 51 L mol-1 vs. 110 ± 16 L mol-1 for acrylamide. The best performing polymers were prepared using methanol as porogenic co-solvent and although average binding site affinities were marginally reduced, 2.3×104 L mol-1 vs. 2.7×104 L mol-1 measured for a polymer prepared in acetonitrile, these polymers contained the highest number of binding sites, 5.27 μmol g-1¬¬ vs. 1.64 μmol g-1, while they also exhibited enhanced selectivity for methylated guanosine derivatives. When applied as sorbents in the extraction of nucleoside derivative cancer biomarkers from synthetic urine samples, significant sample clean-up and recoveries of up to 90% for 7-methylguanosine were achieved.

METAMORPHOSE VI – the Virtual Institute for artificial electromagnetic materials and metamaterials: Origin, mission, and activities

In this paper, we outline the background, mission, and activities of the Virtual Institute for Artificial Electromagnetic Materials and Metamaterials (METAMORPHOSE VI). This international association, founded in the framework of the FP-6 Network of Excellence METAMORPHOSE, aims at promoting and developing research, training, and dissemination activities in the emerging and highly dynamic field of advanced electromagnetic materials and metamaterials at both European and International levels. More than 300 researchers are currently associated with the METAMORPHOSE VI which networks them together in a learnt society. After a brief description of the association and its mission, we present an overview of the activities developed by the METAMORPHOSE VI, with a particular emphasis on the coordination of the European Doctoral Program on Metamaterials (EUPROMETA) and the organization of the International Congress on Advanced Electromagnetic Materials in Microwaves and Optics – metamaterials congress.

THE ARTIFICIAL URINARY SPHINCTER - A NEW SOLUTION FOR INCONTINENT PATIENTS

Treatment of urinary incontinence with the artificial urinary sphincter has been available in centres such as London and Liverpool for a number of years. This service is now available in the department of urology of the Belfast City Hospital. Twelve patients have had successful implantation of an artificial urinary sphincter for urinary incontinence, and ten are now fully continent. One patient with Wegener's granulomatosis developed active disease in his urethra which has precluded activation of the device. One patient has had the device removed because of erosion into the urethra.

Chemistry and Mechanism of Alizarin Red S and Methylene Blue Biosorption onto Olive Stone: Equilibrium and Kinetic

The biosorption process of anionic dye Alizarin Red S (ARS) and cationic dye methylene blue (MB) as a function of solution pH, initial concentration and contact time onto olive stone (OS) biomass has been investigated. The main objectives of the current study are to: (i) study the chemistry and the mechanism of ARS and MB biosorption onto olive stone and the type of OS–ARS, MB interactions occurring, (ii) study the biosorption equilibrium and kinetic experimental data required for the design and operation of column reactors. Equilibrium biosorption isotherms and kinetics were also examined. Experimental equilibrium data were fitted to four different isotherms by non-linear regression method, however, the biosorption experimental data for ARS and MB dyes were well interpreted by the Temkin and Langmuir isotherms, respectively. The maximum monolayer adsorption capacity for ARS and MB dyes were 109.0 and 102.6 mg/g, respectively. The kinetic data of the two dyes could be better described by the pseudo second-order model. The data showed that olive stone can be effectively used for removing dyes from wastewater.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

An Integrated Toolkit for the Conservation of Stone-Built Heritage

Rapid in situ diagnosis of damage is a key issue in the preservation of stone-built cultural heritage. This is evident in the increasing number of congresses, workshops and publications dealing with this issue. With this increased activity has come, however, the realisation that for many culturally significant artefacts it is not possible either to remove samples for analysis or to affix surface markers for measurement. It is for this reason that there has been a growth of interest in non-destructive and minimally invasive techniques for characterising internal and external stone condition. With this interest has come the realisation that no single technique can adequately encompass the wide variety of parameters to be assessed or provide the range of information required to identify appropriate conservation. In this paper we describe a strategy to address these problems through the development of an integrated `tool kit' of measurement and analytical techniques aimed specifically at linking object-specific research to appropriate intervention. The strategy is based initially upon the acquisition of accurate three-dimensional models of stone-built heritage at different scales using a combination of millimetre accurate LiDAR and sub-millimetre accurate Object Scanning that can be exported into a GIS or directly into CAD. These are currently used to overlay information on stone characteristics obtained through a combination of Ground Penetrating Radar, Surface Permeametry, Colorimetry and X-ray Fluorescence, but the possibility exists for adding to this array of techniques as appropriate. In addition to the integrated three-dimensional data array provided by superimposition upon Digital Terrain Models, there is the capability of accurate re-measurement to show patterns of surface loss and changes in material condition over time. Thus it is possible to both record and base-line condition and to identify areas that require either preventive maintenance or more significant pre-emptive intervention. In pursuit of these goals the authors are developing, through a UK Government supported collaboration between University Researchers and Conservation Architects, commercially viable protocols for damage diagnosis, condition monitoring and eventually mechanisms for prioritizing repairs to stone-built heritage. The understanding is, however, that such strategies are not age-constrained and can ultimately be applied to structures of any age.