"cohesiveness for" is a valid phrase that can be used in written English.
It is used to describe the quality of being cohesive or united, often in regard to a group or team. Here is an example: "The cohesiveness for our sports team was evident in the way we worked together on the field." In this sentence, "cohesiveness for" is used to describe the sense of unity among the members of the sports team. It could also be used in a similar way in other contexts, such as describing the cohesiveness for a project group or a family.
Opened in the last decade or so amid the latest wave of Soviet immigration, the clubs have become islands of reassuring cohesiveness for New York's changing Russian community.
(Wilson 2000, pp. 16 18) A group of humans cannot have cohesiveness, for instance, unless there is some preference for other humans.
On the other hand, the PIE approach reveals significant physical cohesiveness for HMPV-induced genes.
The stickiness and cohesiveness for drug loaded films were generally similar with no significant differences.
This strict empirical value represents the P-value for the physical cohesiveness for the test set of genes.
In contrast, PIE indicates no physical cohesiveness for genes that were upregulated or downregulated after Chlamydia infection of human lung epithelial cells.
The same trend was observed for stickiness and cohesiveness values for paracetamol loaded CMC : CAR : GLY and amoxicillin loaded SA : CMC : GLY films, while the work of adhesion showed a significant difference (P = 0.016) between these two films.
It is now easy to see that by trying for smallest k first, and checking all k-clique communities on that level for cohesiveness, any cohesive k-clique community is at the same time maximally cohesive.
That is, PA quantifies the cohesiveness of foraging for individuals within a pack.
The comparison between the blank films showed similar stickiness and work of adhesion for both formulations, whilst the cohesiveness values for blank CAR : CMC : GLY film were higher but the difference was not significant (P = 0.127).
Figure 6a shows the performance of the clustering algorithms applied on the SSS network, with and without scoring by cohesiveness weighting, for predicting yeast small complexes.
