Stellar Surface Magneto-Convection as a Source of Astrophysical Noise. I. Multi-component Parameterisation of Absorption Line Profiles

We outline our techniques to characterise photospheric granulation as an astrophysical noise source. A four component parameterisation of granulation is developed that can be used to reconstruct stellar line asymmetries and radial velocity shifts due to photospheric convective motions. The four components are made up of absorption line profiles calculated for granules, magnetic intergranular lanes, non-magnetic intergranular lanes, and magnetic bright points at disc centre. These components are constructed by averaging Fe I $6302 \mathrm{\AA}$ magnetically sensitive absorption line profiles output from detailed radiative transport calculations of the solar photosphere. Each of the four categories adopted are based on magnetic field and continuum intensity limits determined from examining three-dimensional magnetohydrodynamic simulations with an average magnetic flux of $200 \mathrm{G}$. Using these four component line profiles we accurately reconstruct granulation profiles, produced from modelling 12 x 12 Mm$^2$ areas on the solar surface, to within $\sim \pm$ 20 cm s$^{-1}$ on a $\sim$ 100 m s$^{-1}$ granulation signal. We have also successfully reconstructed granulation profiles from a $50 \mathrm{G}$ simulation using the parameterised line profiles from the $200 \mathrm{G}$ average magnetic field simulation. This test demonstrates applicability of the characterisation to a range of magnetic stellar activity levels.

Low frequency localized wavepackets in dusty plasmas with opposite charge polarity dust components

The reductive perturbation technique is employed to investigate the modulational instability of dust-acoustic (DA) waves propagating in a four-component dusty plasma. The dusty plasma consists of both positive- and negative-charge dust grains, characterized by a different mass, temperature and density, in addition to a background of Maxwellian electrons and ions. Relying on a multi-fluid plasma model and employing a multiple scales technique, a nonlinear Schrodinger type equation (NLSE) is obtained for the electric potential amplitude perturbation. The occurrence of localized electrostatic wavepackets is shown, in the form of oscillating structures whose modulated envelope is modelled as a soliton (or multi-soliton) solution of the NLSE. The DA wave characteristics, as well as the associated stability thresholds, are studied analytically and numerically. The relevance of these theoretical results with dusty plasmas observed in cosmic and laboratory environments is analysed in detail, by considering realistic multi-component plasma configurations observed in the polar mesosphere, as well as in laboratory experiments.

Acoustic solitary waves in dusty and/or multi-ion plasmas with cold, adiabatic, and hot constituents

Large nonlinear acoustic waves are discussed in a four-component plasma, made up of two superhot isothermal species, and two species with lower thermal velocities, being, respectively, adiabatic and cold. First a model is considered in which the isothermal species are electrons and ions, while the cooler species are positive and/or negative dust. Using a Sagdeev pseudopotential formalism, large dust-acoustic structures have been studied in a systematic way, to delimit the compositional parameter space in which they can be found, without restrictions on the charges and masses of the dust species and their charge signs. Solitary waves can only occur for nonlinear structure velocities smaller than the adiabatic dust thermal velocity, leading to a novel dust-acoustic-like mode based on the interplay between the two dust species. If the cold and adiabatic dust are oppositely charged, only solitary waves exist, having the polarity of the cold dust, their parameter range being limited by infinite compression of the cold dust. However, when the charges of the cold and adiabatic species have the same sign, solitary structures are limited for increasing Mach numbers successively by infinite cold dust compression, by encountering the adiabatic dust sonic point, and by the occurrence of double layers. The latter have, for smaller Mach numbers, the same polarity as the charged dust, but switch at the high Mach number end to the opposite polarity. Typical Sagdeev pseudopotentials and solitary wave profiles have been presented. Finally, the analysis has nowhere used the assumption that the dust would be much more massive than the ions and hence, one or both dust species can easily be replaced by positive and/or negative ions and the conclusions will apply to that plasma model equally well. This would cover a number of different scenarios, such as, for example, very hot electrons and ions, together with a mix of adiabatic ions and dust (of either polarity) or a very hot electron-positron mix, together with a two-ion mix or together with adiabatic ions and cold dust (both of either charge sign), to name but some of the possible plasma compositions.

Convergence of products from Povarov and von Miller reactions: approaches to helquinoline

Yttrium triflate or triflic acid catalysed Povarov reaction of methyl anthranilate with ethyl vinyl ether, both as aldehyde surrogate and as alkene, gave the desired 2-methyl-4-ethoxytetrahydroquinoline diastereoisomers as the major products along with four component coupling von Miller adducts. A proton NMR-study, using yttrium triflate as catalyst, revealed that the cis-diastereoisomers were the initial major products in both the Povarov and von Miller reactions but that these isomerised to the trans-diastereoisomers under the reaction conditions. Two distinct pathways for forming von Miller adducts were uncovered with the initial Povarov products being converted to von Miller adducts under the reaction conditions. Replacement of the 4-ethoxy with a 4-methoxy group under acidic conditions gave predominantly the trans-diastereoisomer, which was subsequently converted to a cis/trans mixture of the tetrahydroquinoline antibiotic helquinoline. It was also possible to convert the von Miller products to Povarov products under acidic conditions

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.

Colour Doppler ultrasound of the ocular circulation in patients with systemic lupus erythematosus identifies altered microcirculatory haemodynamics

We assessed whether quantitative analysis of Doppler flow velocity waveforms is able to identify subclinical microvascular abnormalities in SLE and whether eigenvector analysis can detect changes not detectable using the resistive index (RI). Fifty-four SLE patients with no conventional cardiovascular risk factors, major organ involvement or retinopathy were compared to 32 controls. Flow velocity waveforms were obtained from the ophthalmic artery (OA), central retinal artery (CRA) and common carotid artery (CA). The waveforms were analysed using eigenvector decomposition and compared between groups at each arterial site. The RI was also determined. The RI was comparable between groups. In the OA and CRA, there were significant differences in the lower frequency sinusoidal components (P <0.05 for each component). No differences were apparent in the CA between groups. Eigenvector analysis of Doppler flow waveforms, recorded in proximity of the terminal vascular bed, identified altered ocular microvascular haemodynamics in SLE. Altered waveform structure could not be identified by changes in RI, the traditional measure of downstream vascular resistance. This analytical approach to waveform analysis is more sensitive in detecting preclinical microvascular abnormalities in SLE. It may hold potential as a useful tool for assessing disease activity, response to treatment, and predicting future vascular complications.

Human figure segmentation using independent component analysis

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.

Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

A bimetallic catalyst on a dual component support for low temperature total methane oxidation

Palladium, platinum bimetallic catalysts supported on η-Al₂O₃, ZSM-5(23) and ZSM-5(80), with and without the addition of TiO₂, were prepared and used for low temperature total methane oxidation (TMO). The catalysts were tested under reaction temperatures of 200-500 °C with a GHSV of 100,000 mL g^-1 h^-1. It was found that all four components, palladium, platinum, an acidic support and oxygen carrier were needed to achieve a highly active and stable catalyst. The optimum support being 17.5% TiO₂ on ZSM-5(80) where the T_10% was observed at only 200 °C. On addition of platinum, longer time on stream experiments showed no decrease in the catalyst activity over 50 h at 250 °C.