A New Technique for Studying Vapour–liquid Equilibria of Multi-Component Systems

A Fourier transform infrared gas-phase method is described herein and capable of deriving the vapour pressure of each pure component of a poorly volatile mixture and determining the relative vapour phase composition for each system. The performance of the present method has been validated using two standards (naphthalene and ferrocene), and a Raoult’s plot surface of a ternary system is reported as proof-of-principle. This technique is ideal for studying solutions comprising two, three, or more organic compounds dissolved in ionic liquids as they have no measurable vapour pressures.

Serum Amyloid P-Component Prevents Cardiac Remodeling in Hypertensive Heart Disease

The potential for serum amyloid P-component (SAP) to prevent cardiac remodeling and identify worsening diastolic dysfunction (DD) was investigated. The anti-fibrotic potential of SAP was tested in an animal model of hypertensive heart disease (spontaneously hypertensive rats treated with SAP [SHR - SAP] × 12 weeks). Biomarker analysis included a prospective study of 60 patients with asymptomatic progressive DD. Compared with vehicle-treated Wistar-Kyoto rats (WKY-V), the vehicle-treated SHRs (SHR-V) exhibited significant increases in left ventricular mass, perivascular collagen, cardiomyocyte size, and macrophage infiltration. SAP administration was associated with significantly lower left ventricular mass (p < 0.01), perivascular collagen (p < 0.01), and cardiomyocyte size (p < 0.01). Macrophage infiltration was significantly attenuated in the SHR-SAP group. Biomarker analysis showed significant decreases in SAP concentration over time in patients with progressive DD (p < 0.05). Our results indicate that SAP prevents cardiac remodeling by inhibiting recruitment of pro-fibrotic macrophages and that depleted SAP levels identify patients with advancing DD suggesting a role for SAP therapy.

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.

Learning to read with a critical eye: cultivating discerning readers of media reports with a science component.

It is important for young people to be able to read science-related media reports with discernment. ‘Getting Newswise’ was a research project designed to enable science and English teachers, working collaboratively, to equip pupils through the curriculum with critical reading skills appropriate for science news. Phase one of the study found that science and English teachers respond differently to science news articles and eight categories of critical response were identified. These findings informed phase two, in which classroom activities were devised whereby pupils examined, evaluated and responded to science-related news reports. Science-English collaboration had positive outcomes for pupil understanding

Genetic analyses reveal high levels of seed and pollen flow in hawthorn (Crataegus monogyna Jacq.), a key component of hedgerows

Hedgerows represent important components of agri-environment landscapes that are increasingly coming under threat from climate change, emergent diseases, invasive species and land use change. Given that population genetic data can be used to inform best-practice management strategies for woodland and hedgerow tree species, we carried out a study on hawthorn (Crataegus monogyna Jacq.), a key component of hedgerows, on a regional basis using a combination of nuclear and chloroplast microsatellite markers. We found that levels of genetic diversity were high and comparable to, or slightly higher than, other tree species from the same region. Levels of population differentiation for both sets of markers, however, were extremely low, suggesting extensive gene flow via both seed and pollen. These findings suggest that a holistic approach to woodland management, one which does not necessarily rely on the concept of “seed zones” previously suggested, but which also takes into account populations with high and/or rare chloroplast (i.e. seed-specific) genetic variation, might be the best approach to restocking and replanting.

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.