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Under the traditional dispatching mode, we choose to adjust thermal power output to deal with the randomicity of wind farm.
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For the in-transit mode, we chose the number of MPI processes in two different ways.
In the design of the gradient mode, we chose a mobile phase composition of 75% methanol in the first 5 minutes of the run to allow sufficient peak resolution of the amide and the ester peaks, and subsequently a gradient with increasing methanol content up to 95% was set over 10 minutes followed by holding for another ten minutes (Table 1).
As a second alternative growth mode, we chose "hit-and-stick".
The mode that we choose depends on the distance d between the sending node and the receiving node.
In this experiment, we evaluate the performance of the proposed method in identification mode, in which we choose the first image in each class as gallery sample and other 11 ones as probe samples.
As all mode switches involve transitions from or to a proactive mode, between proactive modes or changes within a proactive mode, we have chosen to manage these mode switches through the propagation of special proactive update entries.
Though algorithms have been developed to identify viable pathways using elementary mode analysis [20, 21], we choose to focus specifically on graph-based pathfinding to examine how parameters and heuristics can be used to efficiently guide the search in large-scale metabolic networks.
To be able to investigate fracture process regions preceding cracks of mode I, II and III we choose as underlying continuum model the polar and micropolar, respectively, continuum with dislocation motion on the microlevel.
We choose different modes to evaluate the efficiency contribution of different strategies in LDABoost.
Lastly, we choose two modes from the top of the candidate list to be tested during the full RDO process.
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