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This procedure decomposes the spatial relationships among sampling sites into components, the eigenvectors or principal coordinate axes, which reflect variation at specific spatial scales.
This procedure decomposes the problem of inferring a network of size p into p different feature selection problems, where the goal is to identify the regulators of one of the genes of the network.
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This procedure decomposed the raw time-series activity data into a "seasonal" component indicating rhythms, and a "trend" component indicative of changes in overall activity.
In this procedure, the full mechanism was decomposed in two sub-mechanisms, Kinetic Informative Detachable (KID) mechanism and Feeding Mechanism (FEM) linked by a special bridging reaction via a single gateway substance which belongs to FEM.
Each C-node subgraph can be recursively decomposed using this procedure until we are at the level of individual nodes (Figure 2C).
This procedure does not require additional step to decompose surfactant into carbon.
This procedure uses the Restricted Maximum Likelihood Method (REML) to decompose variances and allows incorporation of fixed and random effects.
This procedure explains the cyclic patterns of data by spectral analysis; a complex time series with cyclic components is decomposed into sine and cosine terms describing the seasonal changes and a linear term to identify the trend.
Jason Bell watches this procedure.
In this procedure, the scale duality technique is conducted using the GP analog, which can allow material to exist as particles via a lumping process and allows them to decompose into atoms at crack tips and interfaces.
Repeat this procedure.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com