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Statistical reliability was obtained among three raters for this method, supporting its reproducibility not only across raters, but within different software packages.
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This method supports a designer in determining the location of split-lines by using the geometric model of the product.
This method supports collaboration of users by allowing individual designers to view each other's designs during the evolutionary process and share their designs via case injection.
Therefore, this method supported route optimization without any additional signaling because the LMA receives the state information of route optimization from the PIG.
This method supports the developer to sketch a suitable modular software-architecture in regards to the hardware-architecture and the specific requirements of real-time and dependability.
In addition, this method supports estimation of the nonlinear behaviour of the piled raft foundation by considering the nonlinear behaviour of the piles.
When learning the structure of Bayesian network from event streams, this method supports calculating score metric incrementally when new data is arrived or edges in the network are changed.
This method supports evidence for synergy, competition or antagonism for selected compounds as it is reflected by the FIC index (FICI).
The results obtained by this method supported those of the diffusion chamber method.
This method supports browsing very large data sets at all resolution scales with minimal memory requirements.
This method, supported by feedback loops considering in-class and out-of-class learning experiences, helped students to cope with the material taught (Koopman et al., 2008).
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