Horizontal Branch Stars: The Interplay between Observations and Theory, and Insights into the Formation of the Galaxy

We review HB stars in a broad astrophysical context, including both variable and non-variable stars. A reassessment of the Oosterhoff dichotomy is presented, which provides unprecedented detail regarding its origin and systematics. We show that the Oosterhoff dichotomy and the distribution of globular clusters (GCs) in the HB morphology-metallicity plane both exclude, with high statistical significance, the possibility that the Galactic halo may have formed from the accretion of dwarf galaxies resembling present-day Milky Way satellites such as Fornax, Sagittarius, and the LMC. A rediscussion of the second-parameter problem is presented. A technique is proposed to estimate the HB types of extragalactic GCs on the basis of integrated far-UV photometry. The relationship between the absolute V magnitude of the HB at the RR Lyrae level and metallicity, as obtained on the basis of trigonometric parallax measurements for the star RR Lyrae, is also revisited, giving a distance modulus to the LMC of (m-M)_0 = 18.44+/-0.11. RR Lyrae period change rates are studied. Finally, the conductive opacities used in evolutionary calculations of low-mass stars are investigated. [ABRIDGED]Comment: 56 pages, 22 figures. Invited review, to appear in Astrophysics and Space Scienc

Angiopoietin-1 promotes functional neovascularization that relieves ischemia by improving regional reperfusion in a swine chronic myocardial ischemia model

10.1007/s11373-006-9082-xJournal of Biomedical Science134579-59

Applications of back propagation neural networks in predicting nutrient effluent concentrations from permeable pavements

Artificial intelligence techniques such as neural networks are modelling tools that can be applied to analyse urban runoff water quality issues. Artificial neural networks are frequently used to model various highly variable and non-linear physical phenomena in the water and environmental engineering fields. The application of neural networks for analysing the performance of permeable pavements is timely and novel. Artificial neural networks are a promising tool for environmental process assessment and modelling. Feed-forward neural networks are the most widely adopted methodology for the prediction and forecasting of water quality and quantity variables. This paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton and Bayesian Regularization algorithms. The back-propagation neural network models incorporating these algorithms performed classification and regression tasks without knowledge of the underlying physical processes occurring throughout the pavement system. The neural networks were statistically assessed for their goodness of prediction of outflow water quality with respect to ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root square mean error, mean absolute relative error and the coefficient of correlation for the prediction versus measured dataset. These performance indices compared the measured and estimated water quality parameters. The neural network models were functions of the readily available water quality parameters. Three neural network models were assessed for their efficiency in accurately simulating effluent water quality parameters from various experimental pavement systems. The models predicted all key parameters with high correlation coefficients and low minimum statistical errors. The back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with regard to these two sustainable technologies. Using an input layer consisting of pH, temperature, electric conductivity, turbidity, total dissolved solids, dissolved oxygen, and redox potential, the back-propagation feed forward neural network models performed optimally as nutrient predictors with regard to sustainable (urban) drainage systems such as permeable pavements. The results showed that for varied effluent concentrations of nutrients, applications of artificial neural networks computed a convincing outcome

Non-isotropic emitter orientation and its implications for efficiency analysis of organic light-emitting diodes

The efficiency of organic light-emitting diodes (OLEDs) is limited since only a small fraction of the consumed electrical power is converted into visible light that is finally extracted to air. Most of the efficiency loss is caused by suboptimal radiative quantum efficiency (RQE) of the emitting guest-host system and by dissipating a huge part of the radiated energy to optical modes such as surface plasmons or waveguided modes, which cannot easily be extracted by common outcoupling structures. In order to increase the external quantum efficiency (EQE) of OLEDs new approaches are needed. Recent studies show that the EQE can be enhanced considerably by horizontally oriented emitters, a feature that is well known for fluorescent emitters and has lately been demonstrated in phosphorescent state-of-the-art OLEDs. By means of optical simulations we investigated the influence of non-isotropic emitter orientation on the effective RQE and the outcoupling factor. We show that in order to achieve a consistent efficiency analysis it is indispensable to account for possible deviations from isotropy. Ignoring these orientation effects leads to significant misinterpretation of the RQE and other factors, which determine the external quantum efficiency of a device. Furthermore, we demonstrate the huge potential for efficiency enhancement of mainly parallel dipole emitter orientation in both fluorescent and phosphorescent OLEDs

Comprehensive efficiency analysis of organic light-emitting diodes featuring emitter orientation and triplet-to-singlet up-conversion

We present a method to achieve a consistent, comprehensive efficiency analysis of fluorescent organic light-emitting diodes (OLEDs) showing non-isotropic emitter orientation and triplet-to-singlet up-conversion. Combining photoluminescence lifetime and external quantum efficiency measurements on OLEDs with varying cavity length allows for an independent determination of the radiative emitter efficiency under optical as well as electrical excitation. The difference clearly shows a significant enhancement of the singlet exciton fraction to more than 25% under electrical operation. Furthermore, the presented method does not require detailed information about the emitting system and is generally applicable for a comprehensive efficiency analysis of bottom-emitting OLEDs

Towards a qAOP framework for predictive toxicology - Linking data to decisions

International audienceThe adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area