Sentence examples for strength path from inspiring English sources

Furthermore, the migraineurs showed a dysregulated brain topology, characterized by increased strength, path, and clustering coefficient.

Fig. 5 Diffrences in network parameters (strength, path length, clustering coefficient) between the migraineurs and controls.

Next, network measures such as network strength, path length, efficiency, clustering and energetic cost were calculated.

In this study, we focused on the network parameters of connection strength, path length, and clustering coefficient [13].

Post-hoc comparison was conducted with two-tailed Student t-test for assessing the network parameters (strength, path length and clustering coefficient) between the migraine and control groups.

There were no significant correlations between migraine clinical characteristics (age, headache history, attack frequency, duration, pain intensity and time from the last attack) and the network parameters (strength, path length and clustering coefficient) (p > 0.05).

Comparison of these two cases demonstrates a spectrum of rock avalanche behavior, corresponding to high- and low-strength path materials.

To statistically compare the strength of path representation in the replays between selected regions, path representation strength was defined as the sum total of the posterior probability.

But in a fact sheet issued last week, the atmospheric administration noted that hurricanes span 200 to 300 miles wide, much larger than the current size of the spill, and doubted that the oil could have much effect on the strength or path of a storm.

p_{ij} = frac{{left[ {tau_{ij} } right]^{alpha } left[ {eta_{ij} } right]^{beta } }}{{sumlimits_{x in allowed} {left[ {tau_{ij} } right]^{alpha } left[ {eta_{ij} } right]^{beta } } }} (6 where p ij is the probability value to select the next city; α is the pheromone trail constant; β is the guide investigation constant; τ ij is the pheromone strength of path between city i to j.

Technically, sample size for an SEM varies depending on many factors, including fit index, model size, distribution of the variables, amount of missing data, reliability of the variables, and strength of path parameters (Fan et al. 1999; Muthen and Muthen 2002; Fritz and MacKinnon 2007; Iacobucci 2010).