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A good correspondence between the presented method and the finite element method is observed.
Results showed very good agreement between the presented method and finite elements and demonstrated the usefulness of the method in how to use standard test data for a general application.
Statistical comparison between the presented method and a reported spectrophotometric one was carried out on pure and pharmaceutical formulation and revealed no significant difference.
As expected, the level of agreement between the presented method and DESeq correlates with the number of timepoints where DESeq identified genes to be differentially expressed.
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Comparisons between FE results show that the presented method has good performances in predicting the failure load of considered frames.
Hence, besides a more accurate estimation of the risk gradient between HbA1c and diabetic complications the presented method can be of importance in the clinic for prognosis and pathogenesis understanding which HbA1c values in time relate to any developed diabetic complications.
In this way, we clarified the differences between conventional methods and our presented method; we confirmed the advantage of the presented method in identifying the optimal global allocation of defensive resources.
Time and diversity estimates differ between data sets thus the presented methods allow comparisons of the diversity of CRISPR loci sampled from different populations.
However, there are 28 planned comparisons within a single learning technique between the 8 presented methods, each among the measures of both precision and accuracy.
Comparison was made between the present method and the finite line source model with superposition.
Comparisons between the present method and alternative approaches available in the literature are given.
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