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An ANN and a data acquisition board with designed signal conditioning unit are used for data optimization and to collect experimental data, respectively.
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Analysis of data for data optimization was done using the neural network method.
Security issues are strictly connected with the data used for model optimization, which can impose some security constrains if information employed is sensible.
The data are used for the optimization of the logistics chain (often just up to the company gate).
In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization.
For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process.
Data used for optimization were HIV prevalence; the number of new diagnoses from targeted testing, routine testing in high prevalence settings, and routine testing in acute care; and the proportion of diagnoses in the acute phase of infection for each of targeted testing, routine testing in high prevalence settings, and routine testing in acute care.
The experimental data have been used for the optimization by using the proposed methodology.
Moreover, it is also parameterized: the user should indicate which fraction of data should be used for the optimization to speed up the search.
Note that as with all pre-processing steps, the resulting data is only used for the optimization itself.
Our independent assessment of the parameters with data not used for cost function optimization allowed us to further restrict the minima and reach a compromise between cost function optimality and biological plausibility.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com