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RePast is a popular agent toolkit with proven capabilities to fulfil the modelling challenges of large multi-agent systems (MASs).
Good agreements of resulting simulations have been successfully provided at all abstraction levels proving capability and facility of using VHDL-AMS for coupled distributed physical models.
Because of their proven capabilities in pattern recognition and feature extraction, artificial neural networks (ANNs) have been extensively employed by researchers both in the time domain (Vafaei et al. 2011, 2013) and the frequency domain (Vafaei et al. 2015; Vafaei and Alih 2017) damage identification methods.
Our proposed system has three proven capabilities: first, evaluating the viability of intelligent well completion application; second, optimizing the operation of on/off valves based on a control strategy; and third, determining an optimal configuration for the operation of valves.
The test aims to prove the capability of the SI algorithms to solve any type of FPPs.
These results not only reveal a positive correlation between exosome secretion and metastatic potential but also prove the capability of this method to test cancer metastasis, proposing the possible application of this method for clinical use.
Taking the opportunity to prove the capability of the business as usual team provided everyone -- both inside and outside the team -- with even more confidence in what we could do.
All results prove the capability of clutter suppression and improving SNR based on the new method.
In order to prove the capability of proposed hybrid algorithm, twofold experiment is conducted.
The comparisons against the published data prove the capability of the present DFIB model.
The experimental results prove the capability of our system to adapt to the user's writing style.
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