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Conventional methods use only one or two geological parameters (i.e. structure or curvature, which is the second derivative of the structure) to characterize a naturally fractured reservoir.
According to Peter Gleick of the Pacific Institute in Oakland, California, in the 1930s it took 200 tonnes of water to make a tonne of steel in America; now it takes only 20 tonnes of water, and the best Korean methods use only 3-4 tonnes.
Because these methods use only a color image, it is unclear which reason explains the modeling failure in the situation.
Image-based methods use only the spatial information of single frames for video content analysis and are generally fast.
Ligand-based methods use only information obtained from the ligands for predicting activity, dependent on their similarity or dissimilarity to previously known active ligands.
Most of these methods use only one criterion at once and need an entire genome-scale PPI dataset to assess the reliability of each PPI pair.
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The classification method produced comparable results to spectrometric band-based methods using only grayscale images, but was slower and had lower classification resolution.
Limitations of previous analyses include inadequate statistical methods using only bivariate correlations or poorly specified multiple regression models.
Methods using only image processing rely, similar to grammar-based approaches, on prior knowledge and assumptions about the facades' appearance.
Hilbert and Bernays (1939) suggest that the methods used only provides a consistency proof for first-order arithmetic with open induction.
We show that using such a "polydesign" framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design.
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