Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

SuperWASP-North extrasolar planet candidates: candidates from fields 17h

We have performed photometric observations of nearly seven million stars with 8 <V <15 with the SuperWASP-North instrument from La Palma between 2004 May to September. Fields in the right ascension range 17-18h, yielding over 185000 stars with sufficient quality data, have been searched for transits using a modified box least-squares (BLS) algorithm. We find a total of 58 initial transiting candidates which have high signal-to-noise ratio in the BLS, show multiple transit-like dips and have passed visual inspection. Analysis of the blending and the inferred planetary radii for these candidates leave, a total of seven transiting planet candidates which pass all the tests plus four which pass the majority. We discuss the derived parameters for these candidates and their properties and comment on the implications for future transit searches.

Fast model based feature matching technique applied to airport lighting

The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

The Oscillations of Tau Pegasi

We present extensive spectroscopic time series observations of the multiperiodic, rapidly rotating, delta Scuti star tau Pegasi. Information about the oscillations is contained within the patterns of line-profile variation of the star's blended absorption-line spectrum. We introduce the new technique of Doppler deconvolution with which to extract these patterns by modeling the intrinsic stellar spectrum and the broadening functions for each spectrum in the time series. Frequencies and modes of oscillation are identified from the variations using the technique of Fourier-Doppler imaging and a two-dimensional least-squares cleaning algorithm. We find a rich mode spectrum with degrees up to l = 20 and with frequencies below about 35 cycles day-1. Those modes with the largest amplitudes have frequencies that lie within a narrow band. We conclude that the observed spectrum can be explained if the modes of tau Peg propagate in the prograde direction with l ~= |m| and with frequencies that are about equal in the corotating frame of the star. We discuss the implications of these results for the prospect of delta Scuti seismology.

Development of the kinetic model of platinum catalyzed ammonia oxidation in a microreactor

The ammonia oxidation reaction on supported polycrystalline platinum catalyst was investigated in an aluminum-based microreactor. An extensive set of reactions was included in the chemical reactor modeling to facilitate the construction of a kinetic model capable of satisfactory predictions for a wide range of conditions (NH3 partial pressure, 0.01-0.12 atm; O-2 partial pressure, 0.10-0.88 atm; temperature, 523-673 K; contact time, 0.3-0.7 ms). The elementary surface reactions used in developing the mechanism were chosen based on the literature data concerning ammonia oxidation on a Pt catalyst. Parameter estimates for the kinetic model were obtained using multi-response least squares regression analysis using the isothermal plug-flow reactor approximation. To evaluate the model, the behavior of a microstructured reactor was simulated by means of a complete Navier-Stokes model accounting for the reactions on the catalyst surface and the effect of temperature on the physico-chemical properties of the reacting mixture. In this way, the effect of the catalytic wall temperature non-uniformity and the effect of a boundary layer on the ammonia conversion and selectivity were examined. After further optimization of appropriate kinetic parameters, the calculated selectivities and product yields agree very well with the values actually measured in the microreactor. (C) 2002 Elsevier Science B.V. All rights reserved.

Chemometric techniques in multivariate statistical modelling of process plant

The techniques of principal component analysis (PCA) and partial least squares (PLS) are introduced from the point of view of providing a multivariate statistical method for modelling process plants. The advantages and limitations of PCA and PLS are discussed from the perspective of the type of data and problems that might be encountered in this application area. These concepts are exemplified by two case studies dealing first with data from a continuous stirred tank reactor (CSTR) simulation and second a literature source describing a low-density polyethylene (LDPE) reactor simulation.

Prenatal and postnatal inflammation in relation to cortisol levels in preterm infants at 18 months corrected age

Objective

To examine whether early inflammation is related to cortisol levels at 18 months corrected age (CA) in children born very preterm.

Study Design

Infants born ≤ 32 weeks gestational age were recruited in the NICU, and placental histopathology, MRI, and chart review were obtained. At 18 months CA developmental assessment and collection of 3 salivary cortisol samples were carried out. Generalized least squares was used to analyze data from 85 infants providing 222 cortisol samples.

Results

Infants exposed to chorioamnionitis with funisitis had a significantly different pattern of cortisol across the samples compared to infants with chorioamnionitis alone or no prenatal inflammation (F[4,139] = 7.3996, P <.0001). Postnatal infections, necrotizing enterocolitis and chronic lung disease were not significantly associated with the cortisol pattern at 18 months CA.

Conclusion

In children born very preterm, prenatal inflammatory stress may contribute to altered programming of the HPA axis.

Keywords: preterm, chorioamnionitis, funisitis, premature infants, hypothalamic-pituitary-adrenal axis, infection, cortisol, stress

Metabolic signatures of human Alzheimer's disease (AD): H-1 NMR analysis of the polar metabolome of post-mortem brain tissue

Brain tissue from so-called Alzheimer's disease (AD) mouse models has previously been examined using H-1 NMR-metabolomics, but comparable information concerning human AD is negligible. Since no animal model recapitulates all the features of human AD we undertook the first H-1 NMR-metabolomics investigation of human AD brain tissue. Human post-mortem tissue from 15 AD subjects and 15 age-matched controls was prepared for analysis through a series of lyophilised, milling, extraction and randomisation steps and samples were analysed using H-1 NMR. Using partial least squares discriminant analysis, a model was built using data obtained from brain extracts. Analysis of brain extracts led to the elucidation of 24 metabolites. Significant elevations in brain alanine (15.4 %) and taurine (18.9 %) were observed in AD patients (p ≤ 0.05). Pathway topology analysis implicated either dysregulation of taurine and hypotaurine metabolism or alanine, aspartate and glutamate metabolism. Furthermore, screening of metabolites for AD biomarkers demonstrated that individual metabolites weakly discriminated cases of AD [receiver operating characteristic (ROC) AUC <0.67; p < 0.05]. However, paired metabolites ratios (e.g. alanine/carnitine) were more powerful discriminating tools (ROC AUC = 0.76; p < 0.01). This study further demonstrates the potential of metabolomics for elucidating the underlying biochemistry and to help identify AD in patients attending the memory clinic

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

The effect of AMS δ13C values on 14C ages measured on a 0.5MV NEC compact accelerator

Many AMS systems can measure 14C, 13C and 12C simultaneously thus providing δ13C values which can be used for fractionation normalization without the need for offline 13C /12C measurements on isotope ratio mass spectrometers (IRMS). However AMS δ13C values on our 0.5MV NEC Compact Accelerator often differ from IRMS values on the same material by 4-5‰ or more. It has been postulated that the AMS δ13C values account for the potential graphitization and machine induced fractionation, in addition to natural fractionation, but how much does this affect the 14C ages or F14C? We present an analysis of F14C as a linear least squares fit with AMS δ13C results for several of our secondary standards. While there are samples for which there is an obvious correlation between AMS δ13C and F14C, as quantified with the calculated probability of no correlation, we find that the trend lies within one standard deviation of the variance on our F14C measurements. Our laboratory produces both zinc and hydrogen reduced graphite, and we present our results for each type. Additionally, we show the variance on our AMS δ13C measurements of our secondary standards.

Multiloop robust H∞ control design based on the dynamic PLS approach to chemical processes

In this paper, a multiloop robust control strategy is proposed based on H∞ control and a partial least squares (PLS) model (H∞_PLS) for multivariable chemical processes. It is developed especially for multivariable systems in ill-conditioned plants and non-square systems. The advantage of PLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variable model rather than in the original space of the highly dimensional variables. Without conventional decouplers, the dynamic PLS framework automatically decomposes the MIMO process into multiple single-loop systems in the PLS subspace so that the controller design can be simplified. Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace. The feasibility and the effectiveness of the proposed approach are illustrated by the simulation results of a distillation column and a mixing tank process. Comparisons between H∞_PLS control and conventional individual control (either H∞ control or PLS control only) are also made

Metabolomics reveals novel biomarkers of illegal 5-nitromimidazole treatment in pigs. Further evidence of drug toxicity uncovered

The aim of the study was to investigate the potential of a metabolomics platform to distinguish between pigs treated with ronidazole, dimetridazole and metronidazole and non-medicated animals (controls), at two withdrawal periods (day 0 and 5). Livers from each animal were biochemically profiled using UHPLC–QTof-MS in ESI+ mode of acquisition. Several Orthogonal Partial Least Squares-Discriminant Analysis models were generated from the acquired mass spectrometry data. The models classified the two groups control and treated animals. A total of 42 ions of interest explained the variation in ESI+. It was possible to find the identity of 3 of the ions and to positively classify 4 of the ionic features, which can be used as potential biomarkers of illicit 5-nitroimidazole abuse. Further evidence of the toxic mechanisms of 5-nitroimidazole drugs has been revealed, which may be of substantial importance as metronidazole is widely used in human medicine.