Twenty-nine genotypes of boro rice (Oryza sativa L.) were grown in a randomized block design with three replications in plots of 4m x 1m with a crop geometry of 20 cm x 20 cm between November-April, in Regional Agricultural Research Station, Nagaon, India. Quantitative data were collected on five randomly selected plants of each genotype per replication for yield/plant, and six other yield components, namely plant height, panicles/plant, panicle length, effective grains/panicle, 100 grain weight and harvest index. Mean values of the characters for each genotype were used for analysis of variance and covariance to obtain information on genotypic and phenotypic correlation along with coheritability between two characters. Path analyses were carried out to estimate the direct and indirect effects of boro rice's yield components. The objective of the study was to identify the characters that mostly influence the yield for increasing boro rice productivity through breeding program. Correlation analysis revealed significant positive genotypic correlation of yield/plant with plant height (0.21), panicles/plant (0.53), panicle length (0.53), effective grains/panicle (0.57) and harvest index (0.86). Path analysis based on genotypic correlation coefficients elucidated high positive direct effect of harvest index (0.8631), panicle length (0.2560) and 100 grain weight (0.1632) on yield/plant with a residual effect of 0.33. Plant height and panicles/plant recorded high positive indirect effect on yield/plant via harvest index whereas effective grains/panicle on yield/plant via harvest index and panicle length. Results of the present study suggested that five component characters, namely harvest index, effective grains/plant, panicle length, panicles/plant and plant height influenced the yield of boro rice. A genotype with higher magnitude of these component characters could be either selected from the existing genotypes or evolved by breeding program for genetic improvement of yield in boro rice.

No abstract

A model is presented for shifting the manual intensive manufacturing process of complex biomedical devices towards more lean and efficient production process via application of concepts of cyber physical systems in combination with big data and analytics in a closed loop manner. The concept model is capable of handling high product volumes and variety, has ability for self-adaptation and correction in various operating conditions, and offers real-time quality control. The approach acknowledges the challenge of these industries operating in a strict regulated environment and the higher standards of built-in quality required by developing a closed loop process, proposed to be built in accordance to the requirements of regulatory bodies and current Industry 4.0 practices. The proposed model illustrates that modern manufacturing methodologies and concepts can be integrated and adopted in such highly regulated manufacturing environments and that the model can be deployed to different production scenarios.

A technique is presented for shifting the manufacturing quality control of complex biomechanical catheters away from destructive testing of finished parts. This technique uses a more efficient real-time in-process monitoring through the application of machine vision inspection of patient critical quality parameters. The approach acknowledges the challenge of this industry operating in a strict regulated environment. The higher standards of built-in quality are achieved by developing automated inspection solutions that are more accurate and repeatable. Machine vision system and associated inspection job tools are developed and used to detect defects at crucial stages of manufacturing. The vision system is then tested for its robustness using a statistical approach to ensure its measurement capability is within the allowable process range and tolerances. The integrated solution developed is proven to be robust and highly precise in maintaining the manufacturing process stable. It enabled the manufacturing process to move away from a destructive double sampling plan with a standard LTPD of 5% to an otherwise real-time 100% non-destructive verification of units. This technique provides an alternative to otherwise cost-inefficient quality control inspections utilized in regulated manufacturing environment. It gives confidence to these conservative industries to move towards adopting digital manufacturing and Industry 4.0 practices.