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The issues identified in the data can be allocated to the following clusters: i) Assessment practices — how the service was assessed as to whether it was meeting the intent of the quality indicator/standard.
At first as in Fig. 2a, we use Algorithm 1 to select the first cluster head and include all SNs in its range in the cluster, source nodes included in the cluster would not be included in any other following clusters.
The following clusters were identified by adding the sampling interval to the previous number.
They permitted us to exclude the following clusters: cluster 1, 3 and 8 (growth phase regulated) and cluster 9 (regulated by an undetermined factor) (Supplemental Table S1).
The following clusters were characteristic of particular locations; Cluster 1 was predominantly subjects from The Netherlands and Framingham and characterized by a high intake of sugar and sugar products, legumes/nuts/seeds, and a moderate intake of the other food groups.
From the items of the dimension "Patient's IONB self-management" the following clusters were produced: cluster 1 = continent (n = 68); cluster 2 = difficulty compressing/decompressing bladder (n = 48); cluster 3 = all problems (those of cluster 2 and difficulty waking-up at night) (n = 43).
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The following cluster algorithms are available: Figure 6 Wizard Step 4: Cluster Dataset - advanced view.
We then apply the following cluster merging algorithm to handle this overclustering problem.
From the result in proposition 2, we suggest the following clustering operation to maximize the spectral efficiency for V-MIMO transmission.
Maternal blood was assayed to determine granulocyte and monocyte phenotype by using monoclonal antibodies, which included the following cluster of differentiation (CD) markers: CD11b, CD14, CD15, CD16, CD18, CD49d, CD62L, CD64, CD66b, and HLA-DR.
This was accomplished by use of the following clustering procedure.
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CEO of Professional Science Editing for Scientists @ prosciediting.com