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The comparative results of several benchmark circuits in maximum cases and compared the outputs of the many previous works without a substantial load on the hardware.
The two solvers are used for same cases and compared to corresponding experimental results.
Results have also been obtained for various cases and compared with those of other investigators.
Furthermore, Fig. 10 illustrates the normalized throughput for each of the test cases and compared among each algorithm.
The positioning of the intercooler was studied in 10 different cases and compared with the base case 0 without intercooling.
We reviewed our experience with this technology in 100 consecutive cases and compared the outcomes to published results.
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In the next subsection, we evaluate the performance of MAX-FAIR, MAX, and HDR methods under all four cases and compare their corresponding achieved throughput and fairness.
In this section, we run Monte Carlo simulations on the proposed TD-CRHAN scheme and the three conventional cases and compare the results with the numerical results.
Finally, we demonstrate the utility of our new code with typical use cases and compare with the popular packages computem and MULTEM [21, 33, 34].
In this section, we derive performance bounds for various cases and compare the performance of different schemes operating in the CoMP scenario.
We further test the applicability of the method with two widely referenced machine-learning cases and compare the results with that of several popular statistical clustering methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com