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However, the various coordination approaches handle the scenarios with very different results.
All these approaches handle packet scheduling and resource allocation in the MAC layer, while PHY layer performs beamforming and achieves multiuser diversity gain.
The provided approaches handle both major goals of dynamic management in data centers, maximizing power efficiency and minimizing SLA violations, considering the inherent trade-off between these goals.
Depending on the data models and architectural styles existing approaches handle transactions at three different levels such as middleware, data store, and client side.
Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.
The agent based modeling approaches handle entity heterogeneity and physical space.
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The design optimisation approach handles uncertainty using statistical formulations.
This approach handles non-linear landforms, and remains relatively computationally efficient.
Each approach handles the open quantum system along the transport direction in a different manner.
The approach handles uncertainties associated both with design variables and fitness functions.
When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans.
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