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BACKGROUND Progress in genome sequencing is proceeding at an exponential pace, and several new algal genomes are becoming available every year. One of the challenges facing the community is the association of protein sequences encoded in the genomes with biological function. While most genome assembly projects generate annotations for predicted protein(More)
This study uses more than a decade's worth of data across Arizona to characterize the spatiotemporal distribution, frequency, and source of extreme aerosol events, defined as when the concentration of a species on a particular day exceeds that of the average plus two standard deviations for that given month. Depending on which of eight sites studied,(More)
Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of(More)
Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people's behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain,(More)
Dynamic risk assessment refers to a risk management framework where frequent updates of risk evaluation information are used to evaluate risk exposure, as close as possible to real-time. In this paper, we present the incident object description exchange format extended model for dynamic risk assessment. This format attempts to overcome the absence of(More)
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