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First, we build a multi-agent-based distributed WSN (DWSN) model and energy consumption model.
However, it depends only on a probability model and energy efficiency could not be maximized.
Theoretical algorithms were derived based on the Component method, kinematic hardening model, and energy conservation theory.
In this section, we present the network model and energy model and introduce briefly the Laplacian matrix and spectral classification.
The method developed in this study consists of a stop angle model, airflow model, and energy cost calculation.
The integration of bi-arc surface model and energy method is proposed to design a developed blank.
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The two-fluid model and energy-minimization multi-scale (EMMS -based drag modEMMS -basedbinedrag simodelsons.
The enhanced filtered drag model and energy-minimization multi-scale (EMMS) drag models were found to achieve superior predictions in all fluidization regimes, while the other drag models were only capable of predicting certain fluidization regimes.
Afterwards, the coordinates of 2OG and Fe(II) were added to the Jmjd6 model and energies were minimised using DeepView.
We examine separately how network models and energy models affect our algorithm performance.
The causal structure captured in the respective models seems to be the key difference between climate models and energy models.
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