Sentence examples for nominal interaction from inspiring English sources

The nominal interaction rate of about 1 GHz at the design luminosity of 1034cm−2s−1 must be reduced online by about seven order of magnitude to an event rate of O(100) Hz going to mass storage, and consisting of several different streams, one of them entirely dedicated to the calibration.

We observed that the inverse association of XRCC1 399Gln on SCC risk was significantly stronger among the individuals with a family history of skin cancer (P for trend, 0.001) than women without family history of skin cancer (P for trend, 0.71; P for ordinal interaction, 0.01; P for nominal interaction, 0.02) (Table 3).

Unlike the standard robust approaches to the DC, this technique allows the inclusion of the nominal interactions into the nominal model; thus the conservativeness of the robust stability conditions is relaxed.

We also observed that the variant at CLU has a nominal significant interaction with time.

Third, a nominal treatment interaction was observed such that, compared with patients without DM, patients with DM had a significantly greater relative reduction in hospitalization for unstable angina or coronary revascularizations.

And while there was nominal evidence for interaction with the rs4925 SNP in GSTO-1 and arsenic exposure, we not find evidence for interaction with selected SNPs in MTHFR and arsenic (Table 5).

Overall, we observed nominal evidence of interaction between the WHR-GRS and the diet score, such that the GRS effect was stronger in those with higher versus lower diet scores.

Twelve pairs of the SNPs displayed nominal evidence of interaction at the 5% level, but none were significant after applying a Bonferroni correction for the 105 likelihood ratio tests undertaken.

Significant SNP-SNP interactions (nominal P <0.01) were detected by using the interactive tree-based method.

In this paper we have evaluated the gene expression programming (GEP) methodology for modeling the effect of different variables (continuous and nominal) and their interactions on the properties of direct compression formulations.

This chapter presents the fundamentals of different artificial intelligence methods, artificial neural networks (ANN), genetic algorithms and fuzzy logic, as useful tools to model the effect of different variables (continuous and nominal) and their interactions on the properties of pharmaceutical formulations.