Reactant Minimization for Multi-Target Sample Preparation on Digital Microfluidic Biochips Using Network Flow Models

2019 ◽  
Author(s):  
Kang-Yi Fan ◽  
Shigeru Yamashita ◽  
Juinn-Dar Huang
Keyword(s):  
Sample Preparation ◽  
Network Flow ◽  
Flow Models ◽  
Target Sample ◽  
Microfluidic Biochips ◽  
Network Flow Models ◽  
Digital Microfluidic

Optimization of Multi-Target Sample Preparation On-Demand With Digital Microfluidic Biochips

2019 ◽  
Vol 38 (2) ◽  
pp. 253-266 ◽  
Author(s):  
Sudip Poddar ◽  
Sukanta Bhattacharjee ◽  
Subhas C. Nandy ◽  
Krishnendu Chakrabarty ◽  
Bhargab B. Bhattacharya
Keyword(s):  
Sample Preparation ◽  
On Demand ◽  
Target Sample ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

scholarly journals Optimum Flows in Directed Bipartite Dynamic Network. The Static Approach

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Combinatorial Optimization ◽  
Network Flow ◽  
Dynamic Network ◽  
Time Varying ◽  
Flow Models ◽  
Dynamic Network Flow ◽  
Static Approach ◽  
Network Flow Models ◽  
Discrete Values ◽  
Time Expanded Network

The theory of flows is one of the most important parts of Combinatorial Optimization and it has various applications. In this paper we study optimum (maximum or minimum) flows in directed bipartite dynamic network and is an extension of article [9]. In practical situations, it is easy to see many time-varying optimum problems. In these instances, to account properly for the evolution of the underlying system overtime, we need to use dynamic network flow models. When the time is considered as a variable discrete values, these problems can be solved by constructing an equivalent, static time expanded network. This is a static approach.

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