Multivariate prediction models were developed to predict optimal cup stem orientations for the chosen design parameters of 12 hip implants.
Although these optimization approaches can accurately predict optimal growth and production fluxes in some cases [ 47], other experimental settings produce inaccurate predictions [ 48].
Among seven variables, UMSE time, liquid solid ratio, and UMSE power were the statistically significant variables and these three factors were further optimized by Box Behnken design to predict optimal extraction conditions and find out operability ranges with maximum extraction yields.
To predict optimal biomass growth under aerobic conditions, the network was optimized through the biomass reaction by flux balance analysis using the "Single Objective optimizer" tool for the wild type strain.
The functionality of MetaNET has been validated in following case studies: To predict optimal biomass growth under aerobic conditions, the network was optimized through the biomass reaction by flux balance analysis using the "Single Objective optimizer" tool for the wild type strain.
Subsequently this method was improved to permit wave forecasters to predict optimal routes for mariners.
However, failure strength alone does not predict optimal in vivo reactivity.
Equations were derived to predict optimal resistance load, maximum damping ratio and the shift of the resonance frequency.
Finally, a procedure to quantify these trends and predict optimal design results in new locations is proposed.
A control strategy was developed to predict optimal ground source heat pump water flow rates under part load operation.
These can potentially also be used to predict optimal pathways in a metabolic network, involving the minimum number of bond changes.
