Study on synthetic and bio-based phenol-formaldehyde adhesives for the wood-based industry

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Experimental comparison of fibre and grating-based dispersion compensation schemes for 40 channel 10Gb/s DWDM systems

We report an experimental comparison between broadband fibre Bragg gratings (FBGs) and conventional dispersion compensating fibre (DCF) for a 40 x 10Gb/s DWDM system over 525km. A performanceoptimised configuration using FBG compensators is presented.

Photo-sketch synthesis and recognition based on subspace learning

This paper aims to reducing difference between sketches and photos by synthesizing sketches from photos, and vice versa, and then performing sketch-sketch/photo-photo recognition with subspace learning based methods. Pseudo-sketch/pseudo-photo patches are synthesized with embedded hidden Markov model. Because these patches are assembled by averaging their overlapping area in most of the local strategy based methods, which leads to blurring effect to the resulted pseudo-sketch/pseudo-photo, we integrate the patches with image quilting. Experiments are carried out to demonstrate that the proposed method is effective to produce pseudo-sketch/pseudo-photo with high quality and achieve promising recognition results. © 2009.

Influence of La-doping on phase transformation and photocatalytic properties of ZnTiO3 nanoparticles synthesized via modified sol-gel method

Non-doped and La-doped ZnTiO3 nanoparticles were successfully synthesized via a modified sol–gel method. The synthesized nanoparticles were structurally characterized by PXRD, UV-vis DRS, FT-IR, SEM-EDS, TEM, Raman and photoluminescence spectroscopy. The results show that doping of La into the framework of ZnTiO3 has a strong influence on the physico-chemical properties of the synthesized nanoparticles. XRD results clearly show that the non-doped ZnTiO3 exhibits a hexagonal phase at 800 °C, whereas the La-doped ZnTiO3 exhibits a cubic phase under similar experimental conditions. In spite of the fact that it has a large ionic radius, the La is efficiently involved in the evolution process by blocking the crystal growth and the cubic to hexagonal transformation in ZnTiO3. Interestingly the absorption edge of the La-doped ZnTiO3 nanoparticles shifted from the UV region to the visible region. The photocatalytic activity of the La-doped ZnTiO3 nanoparticles was evaluated for the degradation of Rhodamine B under sunlight irradiation. The optimum photocatalytic activity was obtained for 2 atom% La-doped ZnTiO3, which is much higher than that of the non-doped ZnTiO3 as well as commercial N-TiO2. A possible mechanism for the degradation of Rhodamine B over La-doped ZnTiO3 was also discussed by trapping experiments. More importantly, the reusability of these nanoparticles is high. Hence La-doped ZnTiO3 nanoparticles can be used as efficient photocatalysts for environmental applications.

Self-assembled SnO2 micro- and nanosphere-based gas sensor thick films from an alkoxide-derived high purity aqueous colloid precursor

Tin oxide is considered to be one of the most promising semiconductor oxide materials for use as a gas sensor. However, a simple route for the controllable build-up of nanostructured, sufficiently pure and hierarchical SnO2 structures for gas sensor applications is still a challenge. In the current work, an aqueous SnO2 nanoparticulate precursor sol, which is free of organic contaminants and sorbed ions and is fully stable over time, was prepared in a highly reproducible manner from an alkoxide Sn(OR)4 just by mixing it with a large excess of pure neutral water. The precursor is formed as a separate liquid phase. The structure and purity of the precursor is revealed using XRD, SAXS, EXAFS, HRTEM imaging, FTIR, and XRF analysis. An unconventional approach for the estimation of the particle size based on the quantification of the Sn-Sn contacts in the structure was developed using EXAFS spectroscopy and verified using HRTEM. To construct sensors with a hierarchical 3D structure, we employed an unusual emulsification technique not involving any additives or surfactants, using simply the extraction of the liquid phase, water, with the help of dry butanol under ambient conditions. The originally generated crystalline but yet highly reactive nanoparticles form relatively uniform spheres through self-assembly and solidify instantly. The spheres floating in butanol were left to deposit on the surface of quartz plates bearing sputtered gold electrodes, producing ready-for-use gas sensors in the form of ca. 50 μm thick sphere-based-films. The films were dried for 24 h and calcined at 300°C in air before use. The gas sensitivity of the structures was tested in the temperature range of 150-400°C. The materials showed a very quickly emerging and reversible (20-30 times) increase in electrical conductivity as a response to exposure to air containing 100 ppm of H2 or CO and short (10 s) recovery times when the gas flow was stopped.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

Experimental comparison of artificial neural network and volterra based nonlinear equalization for CO-OFDM

A novel artificial neural network (ANN)-based nonlinear equalizer (NLE) of low complexity is demonstrated for 40-Gb/s CO-OFDM at 2000 km, revealing ∼1.5 dB enhancement in Q-factor compared to inverse Volterra-series transfer function based NLE.

Large volume metrology process model:measurability analysis with integration of metrology classification model and feature-based selection model

Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators. © Springer-Verlag Berlin Heidelberg 2010.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Two-B or not two-B? Design patterns for hybrid metaheuristics

Real world search problems, characterised by nonlinearity, noise and multidimensionality, are often best solved by hybrid algorithms. Techniques embodying different necessary features are triggered at specific iterations, in response to the current state of the problem space. In the existing literature, this alternation is managed either statically (through pre-programmed policies) or dynamically, at the cost of high coupling with algorithm inner representation. We extract two design patterns for hybrid metaheuristic search algorithms, the All-Seeing Eye and the Commentator patterns, which we argue should be replaced by the more flexible and loosely coupled Simple Black Box (Two-B) and Utility-based Black Box (Three-B) patterns that we propose here. We recommend the Two-B pattern for purely fitness based hybridisations and the Three-B pattern for more generic search quality evaluation based hybridisations.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

A study of business user based information systems development, and modelling success factors

This thesis describes research into business user involvement in the information systems application building process. The main interest of this research is in establishing and testing techniques to quantify the relationships between identified success factors and the outcome effectiveness of 'business user development' (BUD). The availability of a mechanism to measure the levels of the success factors, and quantifiably relate them to outcome effectiveness, is important in that it provides an organisation with the capability to predict and monitor effects on BUD outcome effectiveness. This is particularly important in an era where BUD levels have risen dramatically, user centred information systems development benefits are recognised as significant, and awareness of the risks of uncontrolled BUD activity is becoming more widespread. This research targets the measurement and prediction of BUD success factors and implementation effectiveness for particular business users. A questionnaire instrument and analysis technique has been tested and developed which constitutes a tool for predicting and monitoring BUD outcome effectiveness, and is based on the BUDES (Business User Development Effectiveness and Scope) research model - which is introduced and described in this thesis. The questionnaire instrument is designed for completion by 'business users' - the target community being more explicitly defined as 'people who primarily have a business role within an organisation'. The instrument, named BUD ESP (Business User Development Effectiveness and Scope Predictor), can readily be used with survey participants, and has been shown to give meaningful and representative results.