Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. However, for complex bioassays, finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task. In this study, we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips. The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets. The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer, and the learner fetches the experiences and updates the routing paths. The proposed algorithm was applied to benchmark suites I and III as two different test benches, and it achieved significant improvements over state-of-the-art techniques.

Conventional biomedical analysers are replaced by digital microfluidic biochips and they are adequate to integrate different biomedical functions, essential for diverse bioassay operations. From the last decade, microfluidic biochips are getting plenty of acceptances in the field of miscellaneous healthcare sectors like DNA analysis, drug discovery and clinical diagnosis. These devices are also bearing a vital role in the area of safety critical applications such as food safety testing, air quality monitoring etc. As these devices are used in safety critical applications, clinical diagnosis and real-time biomolecular assay operations, these must have properties like precision, reliability and robustness. To accept it for discriminating purposes, the microfluidic device must endorse its preciseness and strength by following sublime testing strategy. Here, an optimized droplet traversal technique is proposed to investigate the multiple defective electrodes of a digital microfluidic biochip by embedding boundary cum row traversal and KNIGHT traversal procedure (based on the famous Knight Tour Problem). The proposed approach also enumerates the traversal time for a fault-free biochip. In addition to identifying the faulty electrodes, a Module Sequencing Graph based reconfiguration technique is proposed here to reinstate the device for normal bioassay operation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Field experiment was conducted during November 2009 to March 2010, to investigate growth and yield attributes of advanced tomato mutants (TM-110 and TM-219) along with three cultivars (BARI tomato-3, BARI tomato-14 and BINA tomato-5) following randomized complete block design with three replications. There were significant genotypic differences in respect of morpho-physiological characters (plant height, branch and leaf area, total dry mass, absolute growth rate and relative growth rate), biochemical parameter (nitrate reductase), reproductive characters (number of effective and non-effective flower cluster, number of flowers and reproductive efficiency), phenological characters (days to flowering start, days to first harvest and harvesting duration), yield attributes and fruit yield. In general high yielding genotypes showed superior performance in plant height, branch number, leaf area, total dry mass production, absolute growth rate, nitrate reductase activity and fruit size compared to low yielding ones. Relative growth rate,chlorophyll, photosynthesis, reproductive characters and fruit number had no relationship with fruit yield in tomato. The variety BARI tomato-14 showed the highest fruit yield (77.7 t ha-1) due to its superiority in respect of morpho-physiological, biochemical and larger fruit sizes. In contrast, the mutant TM-110 and the variety BINA tomato-5 produced the lower fruit yield (average 51.2 t ha -1) due to poor performance in growth characters and smaller size of fruits. However, TM-110 matured 4-15 days earlier than the other mutant/varieties. Further, harvesting duration was higher in high yielding genotypes than low yielding ones. The highest harvesting duration was recorded in BARI tomato-14 (28 days) followed by BARI tomato-3 (26 days). This information may be implicated in future plant breeding programme.