940 resultados para Data base management.
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Microzooplankton (the 20 to 200 µm size class of zooplankton) is recognised as an important part of marine pelagic ecosystems. In terms of biomass and abundance heterotrophic dinoflagellates are one of the important groups of organism in microzooplankton. However, their rates - grazing and growth - , feeding behaviour and prey preferences are poorly known and understood. A set of data was assembled in order to derive a better understanding of heterotrophic dinoflagellates rates, in response to parameters such as prey concentration, prey type (size and species), temperature and their own size. With these objectives, literature was searched for laboratory experiments with information on one or more of these parameters effect studied. The criteria for selection and inclusion in the database included: (i) controlled laboratory experiment with a known dinoflagellate feeding on a known prey; (ii) presence of ancillary information about experimental conditions, used organisms - cell volume, cell dimensions, and carbon content. Rates and ancillary information were measured in units that meet the experimenter need, creating a need to harmonize the data units after collection. In addition different units can link to different mechanisms (carbon to nutritive quality of the prey, volume to size limits). As a result, grazing rates are thus available as pg C dinoflagellate-1 h-1, µm3 dinoflagellate-1 h-1 and prey cell dinoflagellate-1 h-1; clearance rate was calculated if not given and growth rate is expressed as the growth rate per day.
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I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.
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Microzooplankton (the 20 to 200 µm size class of zooplankton) is recognised as an important part of marine pelagic ecosystems. In terms of biomass and abundance pelagic ciliates are one of the important groups of organism in microzooplankton. However, their rates - grazing and growth - , feeding behaviour and prey preferences are poorly known and understood. A set of data was assembled in order to derive a better understanding of pelagic ciliates rates, in response to parameters such as prey concentration, prey type (size and species), temperature and their own size. With these objectives, literature was searched for laboratory experiments with information on one or more of these parameters effect studied. The criteria for selection and inclusion in the database included: (i) controlled laboratory experiment with a known ciliates feeding on a known prey; (ii) presence of ancillary information about experimental conditions, used organisms - cell volume, cell dimensions, and carbon content. Rates and ancillary information were measured in units that meet the experimenter need, creating a need to harmonize the data units after collection. In addition different units can link to different mechanisms (carbon to nutritive quality of the prey, volume to size limits). As a result, grazing rates are thus available as pg C/(ciliate*h), µm**3/(ciliate*h) and prey cell/(ciliate*h); clearance rate was calculated if not given and growth rate is expressed as the growth rate per day.
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In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.
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Personal data about users (customers) is a key component for enterprises and large organizations. Its correct analysis and processing can produce relevant knowledge to achieve different business goals. For example, the monetisation of this data has become a valuable asset for many companies, such as Google, Facebook or Twitter, that obtain huge profits mainly from targeted advertising.
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Aims: Familial hypercholesterolemia (FH) is an autosomal dominant disorder with increased cardiovascular risk, caused by mutations in LDLR, APOB and PCSK 9 genes. Although it is described that over 1700 variants have been found, none of the existing databases are completely updated. The aim of this work is to construct a FH database in order to provide a unique source of verified information about variants associated with FH for a more accurate genetic diagnosis.
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Mode of access: Internet.
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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.
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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.
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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.
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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.
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Transportation Systems Center, Cambridge, Mass.
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National Highway Traffic Safety Administration, Washington, D.C.
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National Highway Traffic Safety Administration, Office of Vehicle Safety Research, Washington, D.C.