A Possible Hidden Population of Spherical Planetary Nebulae

We argue that relative to non-spherical planetary nebulae (PNs), spherical PNs are about an order of magnitude less likely to be detected, at distances of several kiloparsecs. Noting the structure similarity of halos around non-spherical PNs to that of observed spherical PNs, we assume that most unobserved spherical PNs are also similar in structure to the spherical halos around non-spherical PNs. The fraction of non-spherical PNs with detected spherical halos around them, taken from a recent study, leads us to the claim of a large (relative to that of non-spherical PNs) hidden population of spherical PNs in the visible band. Building a toy model for the luminosity evolution of PNs, we show that the claimed detection fraction of spherical PNs based on halos around non-spherical PNs, is compatible with observational sensitivities. We use this result to update earlier studies on the different PN shaping routes in the binary model. We estimate that ~30% of all PNs are spherical, namely, their progenitors did not interact with any binary companion. This fraction is to be compared with the ~3% fraction of observed spherical PNs among all observed PNs. From all PNs, ~15% owe their moderate elliptical shape to the interaction of their progenitors with planets, while \~55% of all PNs owe their elliptical or bipolar shapes to the interaction of their progenitors with stellar companions.Comment: AJ, in pres

Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation

© 2019 Elsevier Ltd Uncertainty propagation through the simulation models is critical for computational mechanics engineering to provide robust and reliable design in the presence of polymorphic uncertainty. This set of companion papers present a general framework, termed as non-intrusive imprecise stochastic simulation, for uncertainty propagation under the background of imprecise probability. This framework is composed of a set of methods developed for meeting different goals. In this paper, the performance estimation is concerned. The local extended Monte Carlo simulation (EMCS) is firstly reviewed, and then the global EMCS is devised to improve the global performance. Secondly, the cut-HDMR (High-Dimensional Model Representation) is introduced for decomposing the probabilistic response functions, and the local EMCS method is used for estimating the cut-HDMR component functions. Thirdly, the RS (Random Sampling)-HDMR is introduced to decompose the probabilistic response functions, and the global EMCS is applied for estimating the RS-HDMR component functions. The statistical errors of all estimators are derived, and the truncation errors are estimated by two global sensitivity indices, which can also be used for identifying the influential HDMR components. In the companion paper, the reliability and rare event analysis are treated. The effectiveness of the proposed methods are demonstrated by numerical and engineering examples

Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis

© 2019 Elsevier Ltd Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stochastic simulation framework developed in the companion paper, this work aims at developing efficient methods to estimate the failure probability functions subjected to rare failure events with the hybrid uncertainties being characterized by imprecise probability models. The imprecise stochastic simulation methods are firstly improved by the active learning procedure so as to reduce the computational costs. For the more challenging rare failure events, two extended subset simulation based sampling methods are proposed to provide better performances in both local and global parameter spaces. The computational costs of both methods are the same with the classical subset simulation method. These two methods are also combined with the active learning procedure so as to further substantially reduce the computational costs. The estimation errors of all the methods are analyzed based on sensitivity indices and statistical properties of the developed estimators. All these new developments enrich the imprecise stochastic simulation framework. The feasibility and efficiency of the proposed methods are demonstrated with numerical and engineering test examples

Phylogeny of the Quambalariaceae fam. nov., including important Eucalyptus pathogens in South Africa and Australia

The genus Quambalaria consists of plant-pathogenic fungi causing disease on leaves and shoots of species of Eucalyptus and its close relative, Corymbia. The phylogenetic relationship of Quambalaria spp., previously classified in genera such as Sporothrix and Ramularia, has never been addressed. It has, however, been suggested that they belong to the basidiomycete orders Exobasidiales or Ustilaginales. The aim of this study was thus to consider the ordinal relationships of Q. eucalypti and Q. pitereka using ribosomal LSU sequences. Sequence data from the ITS nrDNA were used to determine the phylogenetic relationship of the two Quambalaria species together with Fugomyces (= Cerinosterus) cyanescens. In addition to sequence data, the ultrastructure of the septal pores of the species in question was compared. From the LSU sequence data it was concluded that Quambalaria spp. and F. cyanescens form a monophyletic clade in the Microstromatales, an order of the Ustilaginomycetes. Sequences from the ITS region confirmed that Q. pitereka and Q. eucalypti are distinct species. The ex-type isolate of F. cyanescens, together with another isolate from Eucalyptus in Australia, constitute a third species of Quambalaria, Q. cyanescens (de Hoog & G.A. de Vries) Z.W. de Beer, Begerow & R. Bauer comb. nov. Transmission electron-microscopic studies of the septal pores confirm that all three Quambalaria spp. have dolipores with swollen lips, which differ from other members of the Microstromatales (i.e. the Microstromataceae and Volvocisporiaceae) that have simple pores with more or less rounded pore lips. Based on their unique ultrastructural features and the monophyly of the three Quambalaria spp. in the Microstromatales, a new family, Quambalariaceae Z.W. de Beer, Begerow & R. Bauer fam. nov., is described

Condensation of microturbulence-generated shear flows into global modes

In full flux-surface computer studies of tokamak edge turbulence, a spectrum of shear flows is found to control the turbulence level and not just the conventional (0,0)-mode flows. Flux tube domains too small for the large poloidal scale lengths of the continuous spectrum tend to overestimate the flows, and thus underestimate the transport. It is shown analytically and numerically that under certain conditions dominant (0,0)-mode flows independent of the domain size develop, essentially through Bose-Einstein condensation of the shear flows.Comment: 5 pages, 4 figure

Quantitative analysis of powder mixtures by raman spectrometry : the influence of particle size and its correction

Particle size distribution and compactness have significant confounding effects on Raman signals of powder mixtures, which cannot be effectively modeled or corrected by traditional multivariate linear calibration methods such as partial least-squares (PLS), and therefore greatly deteriorate the predictive abilities of Raman calibration models for powder mixtures. The ability to obtain directly quantitative information from Raman signals of powder mixtures with varying particle size distribution and compactness is, therefore, of considerable interest In this study, an advanced quantitative Raman calibration model was developed to explicitly account for the confounding effects of particle size distribution and compactness on Raman signals of powder mixtures. Under the theoretical guidance of the proposed Raman calibration model, an advanced dual calibration strategy was adopted to separate the Raman contributions caused by the changes in mass fractions of the constituents in powder mixtures from those induced by the variations in the physical properties of samples, and hence achieve accurate quantitative determination for powder mixture samples. The proposed Raman calibration model was applied to the quantitative analysis of backscatter Raman measurements of a proof-of-concept model system of powder mixtures consisting of barium nitrate and potassium chromate. The average relative prediction error of prediction obtained by the proposed Raman calibration model was less than one-third of the corresponding value of the best performing PLS model for mass fractions of barium nitrate in powder mixtures with variations in particle size distribution, as well as compactness

Evolution and Yields of Extremely Metal Poor Intermediate Mass Stars

Intermediate mass stellar evolution tracks from the main sequence to the tip of the AGB for five initial masses (2 to 6Msun) and metallicity Z=0.0001 have been computed. The detailed 1D structure and evolution models include exponential overshooting, mass loss and a detailed nucleosynthesis network with updated nuclear reaction rates. The network includes a two-particle heavy neutron sink for approximating neutron density in the He-shell flash. It is shown how the neutron-capture nucleosynthesis is important in models of very low metallicity for the formation of light neutron-heavy species, like sodium or the heavy neon and magnesium isotopes. The models have high resolution, as required for modeling the third dredge-up. All sequences have been followed from the pre-main sequence to the end of the AGB when all envelope mass is lost. Detailed structural and chemical model properties as well as yields are presented. This set of stellar models is based on standard assumptions and updated input physics. It can be confronted with observations of extremely-metal poor stars and may be used to assess the role of AGB stars in the origin of abundance anomalies of some Globular Cluster members of correspondingly low metallicity.Comment: 40 pages, 11 figures, to appear in ApJS, including 5 electronic table

Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks

Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of fifteen fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R2 values varying between 0.9965 – 0.9971 and 0.9974 – 0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely 0.25 h-1 for surface contaminant fungi, 0.45 h-1 for blue stain fungi and between 0.30 and 0.40 h-1 for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.Les biopesticides à base de bactéries endophytes naturelles pour lutter contre les maladies des plantes constituent une alternative écologique aux traitements chimiques. Les espèces de Bacillus produisent une grande variété de métabolites biologiquement actifs tels que les lipopeptides ituriniques. Cette étude porte sur la production de biopesticides par des bactéries endophytes naturelles isolées du Quercus suber L. Des réseaux neuronaux artificiels ont été utilisés pour maximiser le pourcentage d’inhibition provoquée par l’activité antifongique des composés bioactifs produits par Bacillus amyloliquefaciens. Les composés actifs, produits en culture liquide, ont inhibé la croissance de 15 champignons et avaient un spectre d’activé antifongique plus large contre les contaminants fongiques de surface, les champignons de bleuissement et les champignons phytopathogènes. Un réseau neuronal 19-7-6-1 a été choisi pour prédire le pourcentage d’inhibition produit par les composés bioactifs antifongiques. Une bonne concordance entre les valeurs observées et prédites a été obtenue; les valeurs de R2 variaient de 0,9965 a` 0,9971 et de 0,9974 a` 0,9989 pour les bases d’apprentissage et de test. Le réseau neuronal 19-7-6-1 a été utilisé pour établir les taux de dilution qui maximisent la production des composés bioactifs antifongiques, nommément 0,25 h−1 pour les contaminants fongiques de surface, 0,45 h−1 pour les champignons de bleuissement et entre 0,30 et 0,40 h−1 pour les champignons phytopathogènes. Les réseaux neuronaux artificiels ont un potentiel élevé pour modéliser et optimiser ces processus biologiques

False Panama disorder on banana

Musa disease fact sheet on false panama disorder : symptoms, incidence, causes and recommendation