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The main limitation in engineering in vitro tissues is the lack of a sufficient blood vessel system — the vascularization.
Current microfluidic chip-based tissue culture systems lack a capillary endothelial vessel system, which would enable perfusion with blood.
The control system is designed based on the passivity of every vessel system due to the nonlinear property.
It is part of the Ship Escort Response Vessel System that was created after the Exxon Valdez ran aground in 1989 and spilled nearly 11 million gallons of crude oil.
When cells traveling in the bloodstream find their ZIP code sequence is recognized by the cells lining a particular region of the blood vessel system, they can probably squeeze through the blood vessel wall and into the surrounding tissue.
The Ship Escort Response Vessel System provides two escort tugboats for each tanker traveling through the sound after leaving the Valdez Marine Terminal with crude from the North Slope.
In this paper, our main goal is to solve optimal control problem by using reinforcement learning (RL) algorithm for marine surface vessel system with known dynamic.
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We analyze theoretically the new iterative procedures on a model problem, which represents a simplified blood-vessel system.
One of the big issues in medical tissue engineering is how to create a type of blood-vessel system so that you can transport your feed, and also oxygen to all nooks and crannies of the tissue.
The design and fabrication of this In-Vessel system has to satisfy the demands of high power RF as well as ultra high vacuum (UHV) compatibility.
Secondly the description of the current design is given both for the in-vessel system and for the ex-vessel one.
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