Sorting is an important operation in droplet microfluidics. In this paper, we propose a novel, active technique for sorting of drops. The technique involves sending of drops to a one-dimensional (1D) microfluidic network at calculated time intervals. We make use of the hydrodynamic interactions to sort the drops. With the help of a simple model from the literature for the movement of drops in 1D microchannel networks, we present computational results that corroborate the use of the design of inlet spacing for active sorting of drops. Using genetic algorithm, we identified inlet spacings that are insensitive to potential experimental errors. The identified spacings can be used to completely sort a large number of drops. We also observed that for a given set of drops, identifying inlet spacing is a one-time process as this spacing would result in the same sorting efficacy for any other operating flow rate.
The ability to manipulate drops is essential for integrating multiple processes in droplet microfluidics‐based lab‐on‐a‐chip devices. Examples of such droplet manipulation operations include sorting, sequencing, synchronization, and so forth. Microfluidic networks are promising platforms for performing these functionalities. This work explores the design of entry times of drops, a set of operating parameters, in a microfluidic network to achieve a desired functionality. Here, we specifically focus on sorting of drops at the exit of the network. For the first time, droplet microfluidic networks are perceived as hybrid dynamical systems. This viewpoint allows us to develop a methodology for designing the entry times of drops in microfluidic networks to achieve desired functionalities through the notion of constraint satisfaction. The solution to this constraint satisfaction problem provides entry times that result in the desired functionality. The proposed method allows one to establish the existence/nonexistence of entry time solutions for a desired functionality.
Data management is one of the challenging issues in grid computing and its environments. Because grid computing systems and its applications deals with huge amount of data sets, due to the heterogeneous grid resources that belongs to different organizations and various locations with many access policies. Here To achieve the promising potentials of tremendous distributed resources, useful and capable Scheduling Algorithms are important. Task Scheduling is the mapping of tasks to a selected group of resources which may be distributed in different administrative domains. In this the Parallel Processing of the distributed systems will works using the grid scheduling algorithms. Genetic Algorithm which is a type of scheduling algorithm used for task scheduling to the various resources are working as parallel in the distributed systems.Basically, a Grid scheduler receives applications from Grid users, selects sufficient resources for these applications according to acquired information from the Grid Information Service module, and in conclusion generates application to resource mappings based on assured objective functions and predicted resource performance. Information about the status of available resources is very important for a Grid scheduler to make a proper scheduling, particularly when the heterogeneous and self motivated nature of the Grid is taken into account .The function of the Grid information service is to provide such information to Grid schedulers.
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