Self-induced balloon motions and their effects on wind data

Self-induced balloon motions and effects on wind measurement

Impact of Vegetative Treatment Systems on Multiple Measures of Antibiotic Resistance in Agricultural Wastewater

Wastewater is an important vector of antibiotic resistant bacteria and antibiotic resistance genes (ARB/G). While there is broad agreement that ARB/G from agricultural (ag) wastewaters can be transported through the environment and may contribute to untreatable infectious disease in humans and animals, there remain large knowledge gaps surrounding applied details on the types and amounts of ARB/G associated with different agricultural wastewater treatment options and different ag production systems. This study evaluates a vegetative treatment system (VTS) built to treat the wastewater from a beef cattle feedlot. Samples were collected for three years, and plated on multiple media types to enumerate tetracycline and cefotaxime-resistant bacteria. Enterobacteriaceae isolates (n = 822) were characterized for carriage of tetracycline resistance genes, and E. coli isolates (n = 673) were phenotyped to determine multi-drug resistance (MDR) profiles. Tetracycline resistance in feedlot runoff wastewater was 2-to-3 orders of magnitude higher compared to rainfall runoff from the VTS fields, indicating efficacy of the VTA for reducing ARB over time following wastewater application. Clear differences in MDR profiles were observed based on the specific media on which a sample was plated. This result highlights the importance of method, especially in the context of isolate-based surveillance and monitoring of ARB in agricultural wastewaters

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

The Solar Neighborhood VIII: Discovery of New High Proper Motion Nearby Stars Using the SuperCOSMOS Sky Survey

Five new objects with proper motions between 1.0 arcsec/yr and 2.6 arcsec/yr have been discovered via a new RECONS search for high proper motion stars utilizing the SuperCOSMOS Sky Survey. The first portion of the search, discussed here, is centered on the south celestial pole and covers declinations -90 degrees to -57.5 degrees. Photographic photometry from SuperCOSMOS and JHKs near-infrared photometry from 2MASS for stars nearer than 10 pc are combined to provide a suite of new M_Ks-color relations useful for estimating distances to main sequence stars. These relations are then used to derive distances to the new proper motion objects as well as previously known stars with mu >= 1.0 arcsec/yr (many of which have no trigonometric parallaxes) recovered during this phase of the survey. Four of the five new stars have red dwarf colors, while one is a nearby white dwarf. Two of the red dwarfs are likely to be within the RECONS 10 pc sample, and the white dwarf probably lies between 15 and 25 pc. Among the 23 known stars recovered during the search, there are three additional candidates for the RECONS sample that have no trigonometric parallaxes.Comment: 17 pages, 5 figures; accepted for publication in Astronomy Journa

The 3D soft X-ray cluster-AGN cross-correlation function in the ROSAT NEP survey

X-ray surveys facilitate investigations of the environment of AGNs. Deep Chandra observations revealed that the AGNs source surface density rises near clusters of galaxies. The natural extension of these works is the measurement of spatial clustering of AGNs around clusters and the investigation of relative biasing between active galactic nuclei and galaxies near clusters.The major aims of this work are to obtain a measurement of the correlation length of AGNs around clusters and a measure of the averaged clustering properties of a complete sample of AGNs in dense environments. We present the first measurement of the soft X-ray cluster-AGN cross-correlation function in redshift space using the data of the ROSAT-NEP survey. The survey covers 9x9 deg^2 around the North Ecliptic Pole where 442 X-ray sources were detected and almost completely spectroscopically identified. We detected a >3sigma significant clustering signal on scales s<50 h70^-1 Mpc. We performed a classical maximum-likelihood power-law fit to the data and obtained a correlation length s_0=8.7+1.2-0.3 h_70-1 Mpc and a slope gamma=1.7$^+0.2_-0.7 (1sigma errors). This is a strong evidence that AGNs are good tracers of the large scale structure of the Universe. Our data were compared to the results obtained by cross-correlating X-ray clusters and galaxies. We observe, with a large uncertainty, that the bias factor of AGN is similar to that of galaxies.Comment: 4 pages, 2 figure, proceedings of the Conference "At the edge of the Universe", Sintra Portugal, October 2006. To be published on the Astronomical Society of the Pacific Conference Series (ASPCS

Computing NodeTrix Representations of Clustered Graphs

NodeTrix representations are a popular way to visualize clustered graphs; they represent clusters as adjacency matrices and inter-cluster edges as curves connecting the matrix boundaries. We study the complexity of constructing NodeTrix representations focusing on planarity testing problems, and we show several NP-completeness results and some polynomial-time algorithms. Building on such algorithms we develop a JavaScript library for NodeTrix representations aimed at reducing the crossings between edges incident to the same matrix.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016