989 resultados para Astronautics in earth sciences


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Mode of access: Internet.

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Mode of access: Internet.

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Vol. 1-56 edited by Robert Jameson; vol. 57, by Laurence Jameson; New series, by Thomas Anderson, Sir William Jardine and John Hutton Balfour, and vol. 3-19, for America, by Henry D. Rogers.

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Students in a physical sciences course were introduced to cooperative learning at the University of Queensland, Gatton Campus. Groups of four to five students worked together in tutorial and practical sessions. Mid-term and practical examinations were abolished and 40% of total marks were allocated to the cooperative learning activities. A peer- and self-assessment system was successfully adapted to account for individual performance in cooperative learning group assignments. The results suggest that cooperative learning was very well received by students, and they expressed willingness to join cooperative learning groups in other courses. In addition, cooperative learning offered many benefits to students in terms of graduate attributes such as teamwork, communication, lifelong learning and problem-solving.

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The developments of models in Earth Sciences, e.g. for earthquake prediction and for the simulation of mantel convection, are fare from being finalized. Therefore there is a need for a modelling environment that allows scientist to implement and test new models in an easy but flexible way. After been verified, the models should be easy to apply within its scope, typically by setting input parameters through a GUI or web services. It should be possible to link certain parameters to external data sources, such as databases and other simulation codes. Moreover, as typically large-scale meshes have to be used to achieve appropriate resolutions, the computational efficiency of the underlying numerical methods is important. Conceptional this leads to a software system with three major layers: the application layer, the mathematical layer, and the numerical algorithm layer. The latter is implemented as a C/C++ library to solve a basic, computational intensive linear problem, such as a linear partial differential equation. The mathematical layer allows the model developer to define his model and to implement high level solution algorithms (e.g. Newton-Raphson scheme, Crank-Nicholson scheme) or choose these algorithms form an algorithm library. The kernels of the model are generic, typically linear, solvers provided through the numerical algorithm layer. Finally, to provide an easy-to-use application environment, a web interface is (semi-automatically) built to edit the XML input file for the modelling code. In the talk, we will discuss the advantages and disadvantages of this concept in more details. We will also present the modelling environment escript which is a prototype implementation toward such a software system in Python (see www.python.org). Key components of escript are the Data class and the PDE class. Objects of the Data class allow generating, holding, accessing, and manipulating data, in such a way that the actual, in the particular context best, representation is transparent to the user. They are also the key to establish connections with external data sources. PDE class objects are describing (linear) partial differential equation objects to be solved by a numerical library. The current implementation of escript has been linked to the finite element code Finley to solve general linear partial differential equations. We will give a few simple examples which will illustrate the usage escript. Moreover, we show the usage of escript together with Finley for the modelling of interacting fault systems and for the simulation of mantel convection.

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Data quality is a difficult notion to define precisely, and different communities have different views and understandings of the subject. This causes confusion, a lack of harmonization of data across communities and omission of vital quality information. For some existing data infrastructures, data quality standards cannot address the problem adequately and cannot full all user needs or cover all concepts of data quality. In this paper we discuss some philosophical issues on data quality. We identify actual user needs on data quality, review existing standards and specification on data quality, and propose an integrated model for data quality in the eld of Earth observation. We also propose a practical mechanism for applying the integrated quality information model to large number of datasets through metadata inheritance. While our data quality management approach is in the domain of Earth observation, we believe the ideas and methodologies for data quality management can be applied to wider domains and disciplines to facilitate quality-enabled scientific research.

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The development of new, health supporting food of high quality and the optimization of food technological processes today require the application of statistical methods of experimental design. The principles and steps of statistical planning and evaluation of experiments will be explained. By example of the development of a gluten-free rusk (zwieback), which is enriched by roughage compounds the application of a simplex-centroid mixture design will be shown. The results will be illustrated by different graphics.