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In other words, if we let α = d 1 + d, for a given value of d, we expect the average length of paths taken by such a random walk to be equivalent to d, thus we call d the depth of the random walk.
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Longer reads, greater sequencing depth, the random nature of single molecule sequencing errors and its cost and assembly performance suggests this technology will be increasingly used to produce finished microbial genomes (Koren et al., 2013).
We obtain 240 semantic predications for depth 4 and 4,679 semantic predications for depth 6 by the random walk filtering.
The standard deviation of the random depths of the craters for each subreflector was chosen to be the roughness percentage w.r.t the operating wavelength.
The statistical means of the random depths of the craters were assumed to be identical to the baseline smooth values, as if, the walls were smooth.
The spatial correlation of the random depths of the subreflectors are assumed to follow an exponential model with parameter η for walls-1,2,3.
The combination of the longer reads, depth of coverage and random nature of sequencing errors facilitates de novo assemblies for microbial isolates [ 15, 20, 21].
For the most commonly used mobility model in the simulation of ad hoc networks, Bettstetter et al. [45] presented an in-depth analysis on the node distribution of the random waypoint (RWP) mobility model.
The third random field governs the depth of the infiltration front.
However, the very depth of genetic characterization afforded by the diallel, as well as legitimate aspects of the random parents commonplace described above, suggests a more reflective approach.
For these simulations, we also introduced random variability in the soma depth of the neurons.
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