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For an attractive NNN atomic interaction, e.g., on Ag(110), Ni(110), and Cu(110) surfaces, the structure type in the LES group is various, and when the cluster size increases the structure type with fewer rows will be gradually excluded from the LES group and replaced by the new one with more rows.
In essence, the Calinski-Harabasz index is higher when the cluster centers are further away from each other (i.e. the clusters are better delineated from each other), and when the cluster radii are smaller (i.e. the clusters are tighter).
From Figure 9, there is a big difference between clusters 5 to 7, and when the cluster number was over 16, the performance of the classifier became stable.
This occurs when the intraclass correlation coefficient is non-zero, and when the cluster has been used in the randomisation process (e.g. stratified blocks within centre) or when patients are assigned to clusters after randomisation with different probabilities (e.g. a surgery trial in which surgeons treat patients in only one arm).
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It is overestimated when the within-cluster effect is smaller than the contextual effect and when the within-cluster effect is ten times as large (Table 3).
At intra-annual scale, Cox regression and monthly frequency methods are used to examine whether and when the flood clustering exists, respectively.
The age effect on both seeking ADC and edentulism was significant when adjusted for other explanatory variables and when the potential clustering effect of birth cohorts was considered.
The period effect on both seeking ADC and edentulism was also significant when adjusted for other explanatory variables and when the potential clustering effect of birth cohorts was considered.
As Figs. 4 and 5 showed, the difference of the frequencies of the attributes being selected as centers was smaller and smaller when the cluster number (k) increased.
We assume the simulation is event-based, and when the network works, cluster controllers receive packet-in messages with M/M/1/m queuing theory.
For the antibodies dataset, MUSI still fails to provide meaningful results and places all the sequences into one cluster, but the quality of the results obtained by the Gibbs sampling tool is competitive to Hammock and when the number of clusters reported by Hammock (74) is used as initial number of clusters for the Gibbs sampling tool, the KLD of the result is even slightly higher.
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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