Sentence examples for controlling and validation of from inspiring English sources

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One of the advantages of the in vivo assay used in this study is the controlling and validation of the specific carotenoid substrates.

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Once the control and validation of the model is confirmed, it is then used for generating synthetic series of various lengths of any desired duration.

The design process and its milestones for NZEB are different from conventional projects and require identification, contracting, control, and validation of the performance targets.

On the other hand, microsphere suspension array-based protocols for bacterial detection and molecular typing, for example, require the user to couple probes to beads, purify, and provide quality control and validation of the coupling and final suspension for each probe/bead set prior to use [4], [33].

Urine standards, both certified and in-house, are used for quality control and validation of data.

As a control and validation of the SEC−MALS technique, the individual proteins and ssDNA−protein complexes were evaluated.

While the standardisation, quality control and validation of surveillance methods is important, efforts are also needed to stimulate the debate and development of simple procedures for using data and clearly defined surveillance responsibilities.

These features provide valuable information for data quality control and validation of error-prone EST sequences, help identify data abnormalities including EST chimeras, and facilitate detection of new potential functional motifs embedded in large EST datasets.

Thus, the trimmed dbEST sequences present some obstacles for data quality control and validation of error-prone EST sequences, as well as data mining of sequence features with potential biological meanings whose detection relies on the terminus information in ESTs.

Such previously ignored features provide valuable insight for data quality control and validation of error-prone ESTs, as well as the ability to identify novel functional motifs embedded in large EST datasets.

Additional contributions include a comprehensive evaluation of several soft computing optimization approaches, thorough analysis of the optimized controller, its comparison to baseline control strategies, and validation of its operation with real energy availability forecasts.

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