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To implement an object-oriented model in given target hardware, it is imperative to derive a set of tasks from the designed objects.
Therefore, the need for flexible synthesis routes to afford carefully diversified libraries of catalysts and to screen them in given target reactions is one of the most important issues to be addressed in the future.
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They are extremely useful for defining limits to system or device behavior, for enabling identification of areas in which inefficiencies occur, and in giving targets for design.
In 22 of 45 areas, all residents living in the given target area were regarded as study subjects.
Indeed, a stronger correlation of both quantities might be expected since in any single investigation, tracer uptake at a given time point in any given target region (the liver included) is proportional to the overall scale of the AIF and, consequently, to its value at the chosen time point.
Thereby we can estimate how many nodes are required in a given target area, and also calculate coverage rate of the target area in advance to the real deployment of mobile sensors.
In such a network of self-deployable mobile sensors, it is difficult to evaluate the effectiveness of mobile sensor network deployment in a given target area because we cannot predict the coverage rate for the target area.
The mobile sensor network, established in a given target area where terrain status is unknown, is required to maximize sensing coverage with mobile sensors and maintain the connectivity as much as a network administrator requires.
When self-deployable mobile sensors are deployed in a given target area to be required for monitoring, sensing, and detecting; however, it is difficult to predict how many sensors are needed in the target area and how much connectivity the sensor network have, which prevents guaranteeing the effectiveness of network deployment.
The first is the correlation of the predicted and observed expression profiles in a given target cluster (P).
"Coverage" displays the percentage of positions in a given target having 10 or more overlapping sequence reads, or 20 or more overlapping sequence reads.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com