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A viable alternative can be represented by simulating the network and the energy harvester to predict the behavior of motes possibly using real-world traces.
Figures 4 and 5 show a qualitative similarity between the marginal probability density functions obtained by simulating the network and those obtained by solving the Fokker-Planck equation corresponding to the mean-field equations.
These fractions can only be determined by simulating the network, although the values Φ ( V ) = { { φ k m : ∑ φ k m ≤ 1, v m ∈ V ( v k ) } : v k ∈ V } will change when a schedule is used that is optimised using those values in the temporal reuse-aware rate region C e = ∑ k ∈ K v k ∈ V α k ∑ v m ∈ V ( v k ) φ k m v m α k ≥ 0, ∑ k α k = 1, k ∈ K (7).
Continuous gene expression data is generated by simulating the network dynamics with non-linear ODEs (see Methods).
Given an ergodic Markov chain, a Monte Carlo simulation method has been proposed: the probability of being in state x in the long run can be estimated empirically by simulating the network for a sufficiently long time and by counting the percentage of time the chain spends in that state regardless of the starting state [ 8].
Afterwards, as a final step, the algorithm determines which DEPCs are not necessary to be directly perturbed (see Figure 3c) by simulating the network response (according to the model assumed to compute attractors) under perturbation of the minimal combination of genes but the gene belonging to specific DEPCs one at a time.
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Que and co go on to study how this might work in practice by simulating the networks associated with influential individuals, such as Trump, Elon Musk, Emma Watson, and Justin Timberlake.
By simulating the resistance network, we have acquired the variation trend of the equivalent total resistance when the total layer number is odd or even and found that the simulation results are basically consistent with the experimental results.
The method that we consider, called a mass-spring system, synthesizes sound by simulating the vibrations of a network of interconnected masses, springs, and dampers.
The clusters identified by the MCL are defined by simulating the stochastic flow within the networks [ 16].
By simulating the presence of glycols in the network, the dependence of crack induction time on glycol fraction is effectively demonstrated.
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by simulating the networks
by simulating the experiment
by simulating the swimming
by simulating the world
by simulating the maize
by simulating the distribution
by simulating the transport
by simulating the column
by simulating the evolution
by simulating the comparison
by simulating the learning
by simulating the process
by simulating the surgery
by simulating the model
by simulating the fluid
by simulating the growth
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com