Sentence examples for substantial optimization from inspiring English sources

The numerical optimization algorithm in HyPhy can distribute multiple-dataset optimization across multiple nodes of a computer cluster, resulting in substantial optimization time reductions.

However, the methods used thus far require substantial optimization steps and have various limitations in domain identification.

Coupling cChIP-seq with more complex library amplification schemes may prove useful for lower cell amounts, but would likely require substantial optimization of the amplification.

In particular, it is clear from our experience that there are technical challenges in adapting sequencing tools to zebrafish that require substantial optimization and development.

Although substantial optimization of the fabrication process by the user laboratory is required, mechanically spotted arrays can be fabricated "in house", are highly flexible in their design and content, and can be manufactured in reasonably large numbers providing a marked cost savings over commercial array platforms.

However, to use this approach, one has to determine the number of states as well as the process to define a domain based on the states of CpG sites, both of which cannot be rationally deduced and would require substantial optimization through trials and errors.

Therefore, after evaluating the potential and the failure of the IM and IC methods, we have introduced substantial optimizations and demonstrated the clinical relevance of the quantitative IM technique in association with IC.

Significant progress has recently been made to increase the power output of systems designed to convert organic wastes to electricity, but substantial additional optimization will be required for large-scale electricity production.

This means that substantial further optimization is needed before AGOs have reached the stage where they could enter into clinical trials.

A substantial number of optimization experiments were conducted, generating a corresponding number of diverse neural architectures, which revealed several unexpected statistics, including the relative commonality of nodes combining inner-product and Gaussian functions.

It shows that the predicted results by ANN are in substantial agreement with optimization results by NSGA-II with a good accuracy and the designers could firstly determine the numerical value of the cycle net power output or the exergy efficiency as needed, and then the key cycle parameters could be retrieved by the query system fast and accurately.