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We call our method using this definition MLRA-RD (Relaying time Duration).
The proposed crowdsourcing method using this platform has potential to scale up data collection for expanding the existing CML.
Then, the proposed method using this modified model is employed to predict the service life of tunnel lining structures.
One problem in the method using this function is that this can be applied successfully only to detailed surveys with close track lines and/or many crossovers.
We chose this particular forest policy as the conservation intervention in our study and tested our method using this landscape as a template.
The comparison of the neural network method with the multiple linear regression method, using this data set, revealed that for complex nonlinear problems such as the correlation of the CN with the hydrocarbon type characterization, the neural network approach could provide a better model.
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Our hook-calibration method uses this model together with the positional-dependent nearest-neighbour affinity correction.
One major limitation of methods using this approach is that they are protein- and position- independent.
We focus on the first case study and illustrate our methods using this data set.
Such a structure is less commonly used, but some integration methods use this structure [12, 14].
Pure coalescence-based methods use this complete information (MSVAR, Beaumont 1999).
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CEO of Professional Science Editing for Scientists @ prosciediting.com