Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

Detailed analytical investigation of magnetic field line random walk in turbulent plasmas: II. Isotropic turbulence

The random walk of magnetic field lines in the presence of magnetic turbulence in plasmas is investigated from first principles. An isotropic model is employed for the magnetic turbulence spectrum. An analytical investigation of the asymptotic behavior of the field-line mean-square displacement is carried out. in terms of the position variable z. It is shown that varies as similar to z ln z for large distance z. This result corresponds to a superdiffusive behavior of field line wandering. This investigation complements previous work, which relied on a two-component model for the turbulence spectrum. Contrary to that model, quasilinear theory appears to provide an adequate description of the field line random walk for isotropic turbulence.

Nonlinear electrostatic excitations of charged dust in degenerate ultra-dense quantum dusty plasmas

The linear and nonlinear properties of low-frequency electrostatic excitations of charged dust particles (or defects) in a dense collisionless, unmagnetized Thomas-Fermi plasma are investigated. A fully ionized three-component model plasma consisting of electrons, ions, and negatively charged massive dust grains is considered. Electrons and ions are assumed to be in a degenerate quantum state, obeying the Thomas-Fermi density distribution, whereas the inertial dust component is described by a set of classical fluid equations. Considering large-amplitude stationary profile travelling-waves in a moving reference frame, the fluid evolution equations are reduced to a pseudo-energy-balance equation, involving a Sagdeev-type potential function. The analysis describes the dynamics of supersonic dust-acoustic solitary waves in Thomas-Fermi plasmas, and provides exact predictions for their dynamical characteristics, whose dependence on relevant parameters (namely, the ion-to-electron Fermi temperature ratio, and the dust concentration) is investigated. An alternative route is also adopted, by assuming weakly varying small-amplitude disturbances off equilibrium, and then adopting a multiscale perturbation technique to derive a Korteweg–de Vries equation for the electrostatic potential, and finally solving in terms for electric potential pulses (electrostatic solitons). A critical comparison between the two methods reveals that they agree exactly in the small-amplitude, weakly superacoustic limit. The dust concentration (Havnes) parameter h = Zd0nd0/ne0 affects the propagation characteristics by modifying the phase speed, as well as the electron/ion Fermi temperatures. Our results aim at elucidating the characteristics of electrostatic excitations in dust-contaminated dense plasmas, e.g., in metallic electronic devices, and also arguably in supernova environments, where charged dust defects may occur in the quantum plasma regime.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Controlled release of a model antibacterial drug from a novel self-lubricating silicone biomaterial

Abstract There is considerable interest in developing medical devices that provide controlled delivery of biologically active agents, for example, to reduce the incidence of device-related infection. Silicone elastomers are one of the commonest biomaterials used in medical device production. However, they have a relatively high coefficient of friction and the resulting lack of lubricity can cause pain and tissue damage on device insertion and removal. Novel silicone cross-linking agents have recently been reported that produce inherently ‘self-lubricating’ silicone elastomers with very low coefficients of friction. In this study, the model antibacterial drug metronidazole has been incorporated into these self-lubricating silicone elastomers to produce a novel bioactive biomaterial. The in vitro release characteristics of the bioactive component were evaluated as a function of cross-linker composition and drug loading. Although conventional matrix-type release kinetics were observed for metronidazole from the silicone systems, it was also observed that increasing the concentration of the cross-linking agent responsible for the lubricious character (tetra(oleyloxy)silane) relative to that of the standard non-lubricious cross-linking agent (tetrapropoxysilane) produced an increase in the metronidazole flux rate by up to 65% for a specified drug loading. The results highlight the potential for developing lubricious silicone medical devices with enhanced drug release characteristics.

The Validation of a Two-Dimensional Transient Catalyst Model for Direct Injection Two-Stroke Applications

This paper describes the detailed validation of a computer model designed to simulate the transient light-off in a two-stroke oxidation catalyst. A plug flow reactor is employed to provide measurements of temperature and gas concentration at various radial and axial locations inside the catalyst. These measurements are recorded at discrete intervals during a transient light-off in which the inlet temperature is increased from ambient to 300oC at rates of up to 6oC/sec. The catalyst formulation used in the flow reactor, and its associated test procedures, are then simulated by the computer and a comparison made between experimental readings and model predictions. The design of the computer model to which this validation exercise relates is described in detail in a separate technical paper. The first section of the paper investigates the warm-up characteristics of the substrate and examines the validity of the heat transfer predictions between the wall and the gas in the absence of chemical reactions. The predictions from a typical single-component CO transient light-off test are discussed in the second section and are compared with experimental data. In particular the effect of the temperature ramp on the light-off curve and reaction zone development is examined. An analysis of the C3H6 conversion is given in the third section while the final section examines the accuracy of the light-off curves which are produced when both CO and C3H6 are present in the feed gas. The analysis shows that the heat and mass transfer calculations provided reliable predictions of the warm-up behaviour and post light-off gas concentration profiles. The self-inhibition and cross-inhibition terms in the global rate expressions were also found to be reasonably reliable although the surface reaction rates required calibration with experimental data.

Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems

Coloured effluents from textile industries are a problem in many rivers and waterways. Prediction of adsorption capacities of dyes by adsorbents is important in design considerations. The sorption of three basic dyes, namely Basic Blue 3, Basic Yellow 21 and Basic Red 22, onto peat is reported. Equilibrium sorption isotherms have been measured for the three single component systems. Equilibrium was achieved after twenty-one days. The experimental isotherm data were analysed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth isotherm equations. A detailed error analysis has been undertaken to investigate the effect of using different error criteria for the determination of the single component isotherm parameters and hence obtain the best isotherm and isotherm parameters which describe the adsorption process. The linear transform model provided the highest R2 regression coefficient with the Redlich-Peterson model. The Redlich-Peterson model also yielded the best fit to experimental data for all three dyes using the non-linear error functions. An extended Langmuir model has been used to predict the isotherm data for the binary systems using the single component data. The correlation between theoretical and experimental data had only limited success due to competitive and interactive effects between the dyes and the dye-surface interactions.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Further assessment of the complement component 2 and factor B region associated with age-related macular degeneration

PURPOSE. Polymorphic variation in genes involved in regulation of the complement system has been implicated as a major cause of genetic risk, in addition to the LOC387715/HTRA1 locus and other environmental influences. Previous studies have identified polymorphisms in the complement component 2 (CC2) and factor B (CFB) genes, as potential functional variants associated with AMD, in particular CFB R32Q and CC2 rs547154, both of which share strong linkage disequilibrium (LD). METHODS. Data derived from the HapMap Project were used to select 18 haplotype-tagging SNPs across the extended CC2/ CFB region for genotyping, to measure the strength of LD in 318 patients with neovascular AMD and 243 age-matched control subjects to identify additional potential functional variants in addition to those originally reported. RESULTS. Strong LD was measured across this region as far as the superkiller viralicidic activity 2-like gene (SKIV2L). Nine SNPs were identified to be significantly associated with the genetic effect observed at this locus. Of these, a nonsynonymous coding variant SKIV2L R151Q (rs438999; OR, 0.48; 95% confidence interval [CI], 0.31- 0.74; P < 0.001), was in strong LD with CFB R32Q, rs641153 (r2 = 0.95) and may exert a functional effect. When assessed within a logistic regression model measuring the effects of genetic variation at the CFH and LOC387715/HTRA1 loci and smoking, the effect remained significant (OR, 0.38; 95% CI, 0.22- 0.65; P < 0.001). Additional variation identified within this region may also confer a weaker but independent effect and implicate additional genes within the pathogenesis of AMD. CONCLUSIONS. Because of the high level of LD within the extended CC2/CFB region, variation within SKIV2L may exert a functional effect in AMD. Copyright © Association for Research in Vision and Ophthalmology.

The component masses of the cataclysmic variable V347 Puppis

We present time-resolved spectroscopy and photometry of the double-lined eclipsing cataclysmic variable V347 Pup ( = LB 1800). There is evidence of irradiation on the inner hemisphere of the secondary star, which we correct for using a model to give a secondary-star radial velocity of K R = 198 +/- 5 km s(-1). The rotational velocity of the secondary star in V347 Pup is found to be v sin i = 131 +/- 5 km s(-1) and the system inclination is i = 84degrees.0 +/- 2degrees.3. From these parameters we obtain masses of M-1 = 0.63 +/- 0.04 M for the white dwarf primary and M-2 = 0.52 +/- 0.06 M for the M0.5V secondary star, giving a mass ratio of q = 0.83 +/- 0.05. On the basis of the component masses, and the spectral type and radius of the secondary star in V347 Pup, we find tentative evidence for an evolved companion. V347 Pup shows many of the characteristics of the SW Sex stars, exhibiting single-peaked emission lines, high-velocity S-wave components and phase-offsets in the radial velocity curve. We find spiral arms in the accretion disc of V347 Pup and measure the disc radius to be close to the maximum allowed in a pressureless disc.

Willingness to use functional breads. Applying the Health Belief Model across four European countries

The present study focused on the role of the Health Belief Model (HBM) in predicting willingness to use functional breads, across four European countries: UK (N = 552), Italy (N = 504), Germany (N = 525) and Finland (N = 513). The behavioural evaluation components of the HBM (the perceived benefits and barriers conceptualized respectively as perceived healthiness and pleasantness) and the health motivation component were good predictors of willingness to use functional breads whereas threat perception components (perceived susceptibility and perceived anticipated severity) failed as predictors. This result was common in all four countries and across products. The role of 'cue to action' was marginal. On the whole the HBM fit was similar across the countries and products in terms of significant predictors (the perceived benefits, barriers and health motivation) with the exception of self-efficacy which was significant only in Finland. Young consumers seemed more interested in the functional bread with a health claim promoting health rather than in reducing risk of disease, whereas the opposite was true for older people. However, functional staple foods, such as bread in this European study, are still perceived as common foods rather than as a means of avoiding diseases. Consumers seek these foods for their healthiness (the perceived benefits) as they expect them to be healthier than regular foods and for the pleasantness (the perceived barriers) as they do not expect any change in the sensory characteristics due to the addition of the functional ingredients. The importance of health motivation in willingness to use products with health claims implies that there is an opening for developing better models for explaining health-promoting food choices that take into account both food and health-related factors without making a reference to disease-related outcome. (C) 2008 Elsevier Ltd. All rights reserved.

A photospheric bright point model

Aims. A magneto-hydrostatic model is constructed with spectropolarimetric properties close to those of solar photospheric magnetic bright points.
Methods. Results of solar radiative magneto-convection simulations are used to produce the spatial structure of the vertical component of the magnetic field. The horizontal component of magnetic field is reconstructed using the self-similarity condition, while the magneto-hydrostatic equilibrium condition is applied to the standard photospheric model with the magnetic field embedded. Partial ionisation processes are found to be necessary for reconstructing the correct temperature structure of the model.
Results. The structures obtained are in good agreement with observational data. By combining the realistic structure of the magnetic field with the temperature structure of the quiet solar photosphere, the continuum formation level above the equipartition layer can be found. Preliminary results are shown of wave propagation through this magnetic structure. The observational consequences of the oscillations are examined in continuum intensity and in the Fe I 6302 angstrom magnetically sensitive line.

Complement Component 3: an assessment of association with AMD and analysis of gene-gene and gene-environment interactions in a Northern Irish cohort

Purpose: A non-synonymous single nucleotide polymorphism ( SNP) in complement component 3 has been shown to increase the risk of age-related macular degeneration (AMD). We assess its effect on AMD risk in a Northern Irish sample, test for gene-gene and gene-environment interaction, and review a risk prediction model.