5,803 research outputs found

    Nanocharacterisation of precipitates in austenite high manganese steels with advanced techniques: HRSTEM and DualEELS mapping

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    To achieve optimal mechanical properties in high manganese steels, the precipitation of nanoprecipitates of vanadium and niobium carbides is under investigation. It is shown that under controlled heat treatments between 850°C and 950°C following hot deformation, few-nanometre precipitates of either carbide can be produced in test steels with suitable contents of vanadium or niobium. The structure and chemistry of these precipitates are examined in detail with a spatial resolution down to better than 1 nm using a newly commissioned scanning transmission electron microscope. In particular, it is shown that the nucleation of vanadium carbide precipitates often occurs at pre-existing titanium carbide precipitates which formed from titanium impurities in the bulk steel. This work will also highlight the links between the nanocharacterisation and changes in the bulk properties on annealing

    Impacts of Remotely Sensed Land Use Data on Watershed Hydrologic Change Assessment

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    Urbanization affects the stream system of a watershed. Increased urbanization alters the land cover and surface characteristics, the stream channel characteristics, and pollutant load of a stream system by increasing the amount of impervious surface. Once rural, forest, or wetland areas are changed to streets, highways, parking lots, sidewalks, and building rooftops. This results in large volumes of runoff being generated for an intense storm over a relatively short time period. As a result, sensitive ecosystems are likely to be damaged by increased urbanization. Projecting the impact of land use changes on a watershed scale often requires the use of remote sensing data to derive the required inputs on land cover and the related amount of impervious surface. Such forecasts are then used to devise alternative land use and stormwater control policies. One critical question that arises then is the impact of land use/land cover (LULC) mapping error on the resulting hydrologic model projections. In this research, we developed a methodology to assess those impacts. The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was used to estimate the peak hydrograph for a baseline land use condition and then used to estimate the impact of LULC mapping accuracy levels on those forecasts. The Big Darby Creek Watershed located near Columbus, Ohio, which is experiencing increased urbanization, was selected to map LULC, calibrate a hydrologic model, and assess the hydrologic change due to LULC mapping error. The resulting analysis showed that modest changes in land cover classification did not produce significant impacts on the hydrologic modeling results in rural basins. However, the hydrologic changes are noticeable in urbanizing watersheds. The framework developed in this paper can be used for future modeling efforts to understand the hydrological impact of LULC change in a watershed at a large scale

    The effect of fission-energy Xe ion irradiation on the structural integrity and dissolution of the CeO2_2 matrix

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    © 2016 The Authors.This work considers the effect of fission fragment damage on the structural integrity and dissolution of the CeO₂ matrix in water, as a simulant for the UO₂ matrix of spent nuclear fuel. For this purpose, thin films of CeO₂ on Si substrates were produced and irradiated by 92 MeV 129Xe23+ ions to a fluence of 4.8 × 1015 ions/cm2 to simulate fission damage that occurs within nuclear fuels along with bulk CeO₂ samples. The irradiated and unirradiated samples were characterised and a static batch dissolution experiment was conducted to study the effect of the induced irradiation damage on dissolution of the CeO₂ matrix. Complex restructuring took place in the irradiated films and the irradiated samples showed an increase in the amount of dissolved cerium, as compared to the corresponding unirradiated samples. Secondary phases were also observed on the surface of the irradiated CeO₂ films after the dissolution experiment.The irradiation experiment was performed at the Grand AccĂ©lĂ©rateur National d’Ions Lourds (GANIL) Caen, France, and supported by the French Network EMIR. The support in planning and execution of the experiment by the CIMAP-CIRIL and the GANIL staff, especially, I. Monnet, C. Grygiel, T. Madi and F. Durantel is much appreciated. Thanks are given to I. Buisman and M. Walker from the Department of Earth Sciences, University of Cambridge for help in conducting electron probe microanalysis and polishing the samples, respectively. A.J. Popel acknowledges funding from the UK EPSRC (grant EP/I036400/1 and EP/L018616/1) and Radioactive Waste Management Ltd (formerly the Radioactive Waste Management Directorate of the UK Nuclear Decommissioning Authority, contract NPO004411A-EPS02)

    Parental Support, Sibling Influences and Family Dynamics across the Development of Canadian Interuniversity Student-Athletes

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    A vast body of research focuses on the role of parents in athlete development; however, little is known about developmental influences of siblings. In general, family dynamics (i.e., patterns of relating or interacting among family members) have yet to be investigated in youth sport contexts. This study examines how family dynamics and the individual roles of parents and siblings influence the development of Canadian interuniversity student-athletes over time. Participants included four male and six female student-athletes. Each participant took part in a qualitative retrospective timeline interview. All data was subjected to a thematic analysis. Results indicate that siblings and parents play separate yet intricately connected roles in athlete development throughout childhood and adolescence. Overall, participants described a cohesive family unit built on shared values and joint participation in sport activities. They described stable and dynamic forms of support from their parents over time, and positive and negative sibling influences. These findings offer valuable insight into the dynamic nature of parent and sibling relationships with athletes in youth sport and beyond, as well as how these relationships operate in the broader family environment to optimize (and, at times, hinder) athletic development

    Phytochemical Screening and Antimicrobial Analysis of Fadogia andersonii Robyn Plant Extract

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    Medicinal plants extracts are now generally considered as effective medicines that play a major role in modern pharmacy. The plant Fadogia andersonii belonging to the Family Rubiaceae, which is used in ethno-medicine was studied. Preliminary phytochemical analyses of the whole plant revealed the presence of the following metabolites: Saponins, terpenes, steroids, flavonoids, tannins, alkaloids, cardiac glycosides and carbohydrates. Anthraquinones was found to be absent. Antimicrobial screening of the methanol plant\u2019s extract carried out (in vitro) on Staphylococcus aureus , Escherichia coli , Salmonella typhi , Pseudomonas aeruginosa , Bacillus cereus , Klebsiella pneumonia, Streptococcus pneumoniae , Streptococcus pyogenes , Candida albican and Aspergillus flavus showed that the extract has activity on the tested microorganisms. However, it showed no inhibitory effect against Escherichia coli. The extract was found to inhibit the growth of S. aureus, B. cereus, S. pyogenes and C. albican at 25mg/ml with a corresponding MBC at 50mg/ml. S. typhi and S. pneumonia were inhibited at 50mg/ml with a corresponding MBC at 100mg/ml. It also inhibited the growth of P. aeruginosa, K. pneumonia and A. flavus at 100mg/ml with a corresponding MBC at 200mg/ml. The observed antimicrobial effects were believed to be due to the presence of active principles which were detected in the phytochemical screening

    Large-scale Nonlinear Variable Selection via Kernel Random Features

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    We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201
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