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These results show that there can be significant discrepancies in the accuracy of prediction of Time Zero, which in turn could have significant implications for the diagnosis and timely inception of treatment in severe sepsis.
Prediction of time series uses a sequence of historical values to develop a model for forecasting future values [46].
In this paper, we address problems of description and prediction of time series by developing architectures of granular time series.
Combining cell growth and fermentation kinetic model then permits the prediction of time profiles for the production of bioproducts, such as ethanol, during lignocellulosic hydrolysate fermentation.
The fourth prediction of time lags between plant and insect radiations follows up on the idea that nucleotide change correlates with time.
Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series.
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Nevertheless, as shown above, the predictive power demonstrates that DFBA-based methods are useful for an accurate prediction of time-resolved metabolite concentrations and flux rates of metabolic networks for which kinetic parameters cannot be obtained but solving at least the NLP problems are possible.
Both the prediction of the disease trajectory, as well as prediction of time-of-death, are difficult, and have been reported as potential barriers to timely access to hospice palliative care [ 42, 43].
In this paper, a new method based on a simplified coefficient for the prediction of time-dependent deflections is presented.
The prediction of time-varying damage due to corrosion is important for assessing the residual life of a structure and making decisions on maintenance/repair.
This correlation enables prediction of time-dependent carbonation depths of concretes based on the environmental exposure, mix design and oxygen permeability of the concrete.
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