51 research outputs found

    Dynamical response of Mediterranean precipitation to greenhouse gases and aerosols

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    Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic Oscillation–Arctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901–2010, roughly one-third (31±17%) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol–cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and the economy in the Mediterranean region

    Models and applications for risk assessment and prediction of Asian soybean rust epidemics.

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    Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed

    Promoting physical activity in the United States : challenge to physical education

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    The status of adult physical activity participation was examined to determine the status quo of involvement and to suggest strategies for encouraging adults to increase their physical activity. Data from seven selected surveys, 1961- 1982, provided the information for the secondary analyses. National and regional data were studied from several perspectives, i.e., geographically, demographically, with respect to lifestyle, and from a leisure point of view

    1917-07-12 Paintsville Herald

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    Paintsville Herald published on July 12, 1917

    Array-Aided Multifrequency GNSS Ionospheric Sensing: Estimability and Precision Analysis

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    The dual-frequency Global Positioning System has proven to be an effective means of measuring the Earth's ionosphere and its total electron content (TEC). With the advent of multifrequency signals from more Global Navigation Satellite Systems (GNSSs), the opportunity arises to construct many more ionosphere-sensing combinations of GNSS data. With such diversity, various estimable ionospheric delays with differing interpretations (and of different precision) can be formed. How such estimable ionospheric delays should be interpreted, and the extent to which they contribute to the precision with which the unbiased TEC can be estimated, are the topics of this paper. Based on multifrequency GNSS code-only, phase-only, and phase-and-code data, we derive the closed-form solutions of different types of ionospheric observables that each can serve as input of an externally provided ionospheric model for TEC determination. Within such a general least-squares framework, we generalize the widely used phase-to-code levelling technique to its multifrequency version. We also show that only certain specific linear combinations of the observables contribute to the TEC solutions. As a further improvement of the multifrequency GNSS-derived TEC solution, we propose and study the usage of an array of GNSS antennas. Analytical solutions, supported by numerical examples, of this array-based concept are presented, together with a discussion on its relevance for TEC determination. This concerns the roles of time averaging and time differencing, of integer ambiguity resolution, and of the number of frequencies and number of array antennas in determining TEC

    The George-Anne

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    A decade of Semantic Web research through the lenses of a mixed methods approach

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    The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well

    The Sidney Review Wed, February 9, 1977

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