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In this paper, we propose a novel decentralized approach enabling us to consider the heterogeneous characteristics of sensor nodes.
To accomplish this task, a new superstructure is proposed that follows the energy and mass streams from sources to sinks, enabling us to consider heat exchange between streams in two separate stages of the HENS before and after mixers.
Unlike the classical constant time-bin sampling (reported in Fig. 1B for comparison), conditional sampling is robust relative to the time-warp, enabling us to consider patterns of different duration and equal spatial distribution just like a same pattern (see red circled samples in Fig. 1A).
The addition of these premises allowed us to consider disease transmission that can occur between premises that are in close spatial proximity, enabling us to consider how likely infection is to jump from the network of premises for which we have movement data to another network of premises for which no movement data were available for analysis.
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The model used enables us to consider the instability pattern for an arbitrary k y.
A general root branching density in a space of finite dimension was used and enabled us to consider various morphological properties.
Although we are not able to quantify the level of the score S that enables us to consider an interaction reliable, the score S can be used as a complementary measure to evaluate the reliability of a predicted interaction.
It is just that devolution, as is the case in Scotland and Wales, enables us to consider our own context.
This design also enables us to consider multiple theories' interactions in assessing individual behaviors.
Next, we establish a version of Theorem 4.2 which enable us to consider locally Lipschitz perturbations for equation (1.2).
This enables us to consider the not-diffracted part, by adding an additional order with efficiency η nd =1−η i, i=T,R.
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