A complete X-ray sample of the high latitude sky from HEAO-1 A-2: log N lo S and luminosity functions

An experiment was performed in which a complete X-ray survey of the 8.2 steradians of the sky at galactic latitudes where the absolute value of b is 20 deg down to a limiting sensitivity of 3.1 x ten to the minus 11th power ergs/sq cm sec in the 2-10 keV band. Of the 85 detected sources 17 were identified with galactic objects, 61 were identified with extragalactic objects, and 7 remain unidentified. The log N - log S relation for the non-galactic objects is well fit by the Euclidean relationship. The X-ray spectra of these objects were used to construct log N - log S in physical units. The complete sample of identified sources was used to construct X-ray luminosity functions, using the absolute maximum likelihood method, for clusters galaxies and active galactic nuclei

Comparison of the COBE FIRAS and DIRBE Calibrations

We compare the independent FIRAS and DIRBE observations from the COBE in the wavelength range 100-300 microns. This cross calibration provides checks of both data sets. The results show that the data sets are consistent within the estimated gain and offset uncertainties of the two instruments. They show the possibility of improving the gain and offset determination of DIRBE at 140 and 240 microns.Comment: Accepted for publication in the Astrophysical Journal 11 pages, plus 3 figures in separate postscript files. Figure 3 has three part

Measuring Molecular, Neutral Atomic, and Warm Ionized Galactic Gas Through X-Ray Absorption

We study the column densities of neutral atomic, molecular, and warm ionized Galactic gas through their continuous absorption of extragalactic X-ray spectra at |b| > 25 degrees. For N(H,21cm) < 5x10^20 cm^-2 there is an extremely tight relationship between N(H,21cm) and the X-ray absorption column, N(xray), with a mean ratio along 26 lines of sight of N(xray)/N(H,21cm) = 0.972 +- 0.022. This is significantly less than the anticpated ratio of 1.23, which would occur if He were half He I and half He II in the warm ionized component. We suggest that the ionized component out of the plane is highly ionized, with He being mainly He II and He III. In the limiting case that H is entirely HI, we place an upper limit on the He abundance in the ISM of He/H <= 0.103. At column densities N(xray) > 5x10^20 cm^-2, which occurs at our lower latitudes, the X-ray absorption column N(xray) is nearly double N(H,21cm). This excess column cannot be due to the warm ionized component, even if He were entirely He I, so it must be due to a molecular component. This result implies that for lines of sight out of the plane with |b| ~ 30 degrees, molecular gas is common and with a column density comprable to N(H,21cm). This work bears upon the far infrared background, since a warm ionized component, anticorrelated with N(H,21cm), might produce such a background. Not only is such an anticorrelation absent, but if the dust is destroyed in the warm ionized gas, the far infrared background may be slightly larger than that deduced by Puget et al. (1996).Comment: 1 AASTeX file, 14 PostScript figure files which are linked within the TeX fil

Are state laws granting pharmacists authority to vaccinate associated with HPV vaccination rates among adolescents?

We explored whether state laws allowing pharmacists to administer human papillomavirus (HPV) vaccinations to adolescents are associated with a higher likelihood of HPV vaccine uptake

Neither dust nor black carbon causing apparent albedo decline in Greenland\u27s dry snow zone: Implications for MODIS C5 surface reflectance

Remote sensing observations suggest Greenland ice sheet (GrIS) albedo has declined since 2001, even in the dry snow zone. We seek to explain the apparent dry snow albedo decline. We analyze samples representing 2012–2014 snowfall across NW Greenland for black carbon and dust light-absorbing impurities (LAI) and model their impacts on snow albedo. Albedo reductions due to LAI are small, averaging 0.003, with episodic enhancements resulting in reductions of 0.01–0.02. No significant increase in black carbon or dust concentrations relative to recent decades is found. Enhanced deposition of LAI is not, therefore, causing significant dry snow albedo reduction or driving melt events. Analysis of Collection 5 Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data indicates that the decline and spectral shift in dry snow albedo contains important contributions from uncorrected Terra sensor degradation. Though discrepancies are mostly below the stated accuracy of MODIS products, they will require revisiting some prior conclusions with C6 data

Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed

A contingency analysis of LeActiveMath's learner model

We analyse how a learner modelling engine that uses belief functions for evidence and belief representation, called xLM, reacts to different input information about the learner in terms of changes in the state of its beliefs and the decisions that it derives from them. The paper covers xLM induction of evidence with different strengths from the qualitative and quantitative properties of the input, the amount of indirect evidence derived from direct evidence, and differences in beliefs and decisions that result from interpreting different sequences of events simulating learners evolving in different directions. The results here presented substantiate our vision of xLM is a proof of existence for a generic and potentially comprehensive learner modelling subsystem that explicitly represents uncertainty, conflict and ignorance in beliefs. These are key properties of learner modelling engines in the bizarre world of open Web-based learning environments that rely on the content+metadata paradigm

Partner selection in green supply chains using PSO – a practical approach

Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected pre-requisite in the partner selection process. This paper proposes a three-stage model that combines Dempster-Shafer belief acceptability theory and particle swarm optimization technique for the first time in this application. This enables optimization of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer

Parallel classification and feature selection in microarray data using SPRINT

The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop‐in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method