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BRIGHT METAL-POOR STARS FROM THE HAMBURG/ESO SURVEY. II. A CHEMODYNAMICAL ANALYSIS
Beers, Timothy C.,Placco, Vinicius M.,Carollo, Daniela,Rossi, Silvia,Lee, Young Sun,Frebel, Anna,Norris, John E.,Dietz, Sarah,Masseron, Thomas American Astronomical Society 2017 The Astrophysical journal Vol.835 No.1
<P>We obtain estimates of stellar atmospheric parameters for a previously published sample of 1777 relatively bright (9 < B < 14) metal-poor candidates from the Hamburg/ESO Survey. The original Frebel et al. analysis of these stars was able to derive estimates of [Fe/H] and [C/Fe] only for a subset of the sample, due to limitations in the methodology then available. A new spectroscopic analysis pipeline has been used to obtain estimates of T-eff, log g, [Fe/H], and [C/Fe] for almost the entire data set. This sample is very local-about 90% of the stars are located within 0.5 kpc of the Sun. We consider the chemodynamical properties of these stars in concert with a similarly local sample of stars from a recent analysis of the Bidelman and MacConnell 'weak metal' candidates by Beers et al. We use this combined sample to identify possible members of the halo stream of stars suggested by Helmi et al. and Chiba & Beers, as well as stars that may be associated with stripped debris from the putative parent dwarf of the globular cluster Omega Centauri, suggested to exist by previous authors. We identify a clear increase in the cumulative frequency of carbon-enhanced metal-poor (CEMP) stars with declining metallicity, as well as an increase in the fraction of CEMP stars with distance from the Galactic plane, consistent with previous results. We also identify a relatively large number of CEMP stars with kinematics consistent with the metal-weak thick-disk population, with possible implications for its origin.</P>
Beer Singh,Amit Saxena,Avanish Kumar Srivastava,Devendra Kumar Dubey,Arvind Kumar Gupta 한국탄소학회 2007 Carbon Letters Vol.8 No.4
Samples of active carbon of 1150 m2/g surface area were impregnated with ammoniacal salts of copper, chromium and silver, with and without triethylenediamine. The samples of impregnated carbon were aged at 50℃, with and without 90% RH (relative humidity), for a little more than one year and chemically evaluated periodically. Initially copper (II) and chromium (VI) reduced very fast in the samples in humid atmosphere to the extent of 30% and 60% respectively in four months. These values were found to be unaffected by the presence of triethylenediamine (TEDA) indicating that the chemical did not retard the reduction process of chromium (VI) and copper (II). However, in the absence of humidity the reduction of the impregnants was significantly less (10-12%, w/w) in four months. It was quite evident; therefore, that the moisture was mainly responsible for the reduction of chromium (VI) and copper (II) species in impregnated carbons. The prolonged ageing of the samples with and without triethylenediamme after four months with and without humid atmosphere showed that the extent of reduction of chromium (VI) was very low, i.e. 5-10% and of copper (II) was 2-25%. Silver is not reduced due to carbon, as it remained unchanged in concentration on storage. The impregnated carbon samples (100 g) without triethylenediamine, which were aged at room temperature for 5 years in absence of humidity and unaged when evaluated against cyanogen chloride (CNCl) at a concentration of 4 mg/L and airflow rate of 30 lpm showed a high degree of protection (80- 110 minutes).
A neural network approach for simulating stationary stochastic processes
Beer, Michael,Spanos, Pol D. Techno-Press 2009 Structural Engineering and Mechanics, An Int'l Jou Vol.32 No.1
In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the "pattern" of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feed-forward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.
Singh, Beer,Saxena, Amit,Srivastava, Avanish Kumar,Dubey, Devendra Kumar,Gupta, Arvind Kumar Korean Carbon Society 2007 Carbon Letters Vol.8 No.4
Samples of active carbon of $1150\;m^2/g$ surface area were impregnated with ammoniacal salts of copper, chromium and silver, with and without triethylenediamine. The samples of impregnated carbon were aged at $50^{\circ}C$, with and without 90% RH (relative humidity), for a little more than one year and chemically evaluated periodically. Initially copper (II) and chromium (VI) reduced very fast in the samples in humid atmosphere to the extent of 30% and 60% respectively in four months. These values were found to be unaffected by the presence of triethylenediamine (TEDA) indicating that the chemical did not retard the reduction process of chromium (VI) and copper (II). However, in the absence of humidity the reduction of the impregnants was significantly less (10-12%, w/w) in four months. It was quite evident; therefore, that the moisture was mainly responsible for the reduction of chromium (VI) and copper (II) species in impregnated carbons. The prolonged ageing of the samples with and without triethylenediamme after four months with and without humid atmosphere showed that the extent of reduction of chromium (VI) was very low, i.e. 5-10% and of copper (II) was 2-25%. Silver is not reduced due to carbon, as it remained unchanged in concentration on storage. The impregnated carbon samples (100 g) without triethylenediamine, which were aged at room temperature for 5 years in absence of humidity and unaged when evaluated against cyanogen chloride (CNCl) at a concentration of 4 mg/L and airflow rate of 30 lpm showed a high degree of protection (80- 110 minutes).
A neural network approach for simulating stationary stochastic processes
Michael Beer,Pol D. Spanos 국제구조공학회 2009 Structural Engineering and Mechanics, An Int'l Jou Vol.32 No.1
In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the “pattern” of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feedforward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.