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The pushover curve of each prototype is developed and the seismic response factors are determined accordingly.
Existing methods do not allow performing the high-precision metal deposition without using photomasks, while making a photomask for each prototype is a long and expensive process.
After learning, adapted weight vectors of neural units (the SOM prototypes) produce a Voronoi tessellation of the data space, where each prototype is the geometric center of its Voronoi polyhedron (receptive field) where it is the BMU for the data samples.
Each prototype is referred to by its number (numbers in red diamonds) and has a functional signature represented in form of a sequence profile.
Prototype step Here, we note that the choice of prototype parameters for each prototype is independent of the choice of other prototypes.
During training, two prototypes are computed and the cost of computing each prototype is O mN), where N is the number of features and m is the number of training samples which belong to a given class.
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Each prototype was developed and evolved on the previous design, encompassing performance improvements and fabrication enhancements.
Cutoffs for each prototype were established using Youden indices to maximise sensitivity and specificity.
Scores for the query to each EV genomic region of each prototype are calculated and displayed in the results table.
Since the MLVs were obtained by artificial egg- and cell-adapted processes, the isolation year and location for each prototype was used as a reference.
Each SOM prototype is considered as a singleton cluster and two clusters that are the most similar according to a predefined (dis)similarity criterion are merged iteratively until a predetermined number of clusters is obtained.
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CEO of Professional Science Editing for Scientists @ prosciediting.com