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The charging rate of the capacitor by using the TENG has been optimized by changing the inductance and the switching on/off time.
To obtain these figures in performance, the design has been optimized by changing its geometrical parameters and the material choice.
Method has been optimized by changing the pH of analyte solution, solid phase dosage, volume of eluents, flow rate of sample solution and volume of the sample solutions.
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Furthermore, the local TBR was optimized by changing the material proportion for each Pn level (1 5 MW/m2).
The network topology was optimized by changing number of neurons in hidden layers.
The membrane electrodes were optimized by changing types and amounts of ionic sites and plasticizers.
Different transfer functions e.g. TANSIG, LOGSIG and PURELIN were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers.
However, these properties could be optimized by changing the crosslinking density (either azobenzene or side-chain LC concentration) in the polymeric network.
An objective function based on relative production rate is optimized by changing systematically the process parameters such as temperature, space time and catalyst activity.
Two illustrative examples are used in which rivet strength is optimized by changing rivet design and riveting direction from the original manufacturing parameters.
The power distribution in the core was optimized by changing the uranium enrichment to maintain the fuel temperature at less than the limit (1600 °C).
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