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International Rice Research Institute (IRRI) has launched a flagship project-4 consisting of 58 rice genotypes ‘Antenna Panel’ (a panel of diverse genotypes having various beneficial genes introgressed) from the Global Rice Array-IV (GRA-IV). Exploring diversity provides an opportunity for plant breeders to develop resilient crops and analysis of diversity is important for any crop improvement programme. Mahalanobis’s distance matrix thus obtained was further subjected to clustering by the UPGMA hierarchical agglomerative clustering method to decipher the degree of genetic divergence in the ‘Antenna Panel’ genotypes of IRRI’s Global Rice Array-IV. On the basis of D2 values, rice genotypes were grouped into eleven clusters. Cluster I was the largest and contained the maximum number of genotypes. The inter-cluster distance ranged from 5.8 between cluster I and cluster VIII to 18.8 between cluster V and cluster X. The highest inter cluster distance was recorded between clusters V and X (18.80) followed by clusters II and V (17.78). The intra cluster distance was found to be maximum in cluster II (5.20) followed by cluster V (5.01), and cluster I (4.25). The character days to 50% flowering (33.1%) showed a maximum contribution followed by plant height (30.2%), grain yield (10.3%), the number of grains per panicle (10.00%), panicle length (7.3 %), the number of tillers per plant (4.8%) and 1000-grain weight (4.3%). If a superior genotype with an ample amount of genetic divergence and well adapted in India’s Indo-Gangetic Plains can be selected, from these global databases then it can be of great help to break the stagnation of yield by improving the genetic potential of the existing rice varieties through hybridization and exploitation of heterosis. In this context, if hybridization is attempted between the genotypes included in the cluster V (SUPA, ZANTON::IRGC 31248-1 and URAIBOOL::IRGC 52785-1) and cluster X (Hokkai 188, M202) greater heterosis can be expected.

The prime objective of an intelligent system would have been to be able to interpret and communicate reliably between natural languages. Neural Machine Translation (NMT) has demonstrated a possible solution to the machine translation problem with its recent growth. Despite NMT's high data requirements, systems using different NMT models for low-resource languages have recently shown some astounding outcomes. Given the dearth of digitally accessible text datasets, Nepali is one of these low resource languages. In our study, we explore a bidirectional neural machine translation (NMT) system for the low-resource English-Nepali language pair. A parallel text corpus with over 20,500 sentences and the open-source OpenNMT toolkit are used in the construction of the system. Our NMT system's efficacy is evaluated using the automated evaluation metrics, BLEU and METEOR. For the English to Nepali, the system scored 19.83, 53.32 BLEU and METEOR, and for the Nepali to English, that is 21.94 and 57.52. [ABSTRACT FROM AUTHOR]

Involving intracellular delay into a mathematical model and investigating the delayed systems by incorporating optimal control is of great importance to study the cell‐to‐cell interactions of the disease leprosy. Keeping this in mind, we have proposed two different variants of delay‐induced mathematical models with time delay in the process of proliferation of Mycobacterium leprae bacteria from the infected cells and a similar delay to indicate the time‐lag both in the proliferation of M. leprae bacteria and the infection of healthy cells after getting attached with the bacterium. In this research article, we have performed a comparative study between these two delayed systems equipped with optimal control therapeutic approach to determine which one acts better to unravel the complexities of the transmission and dissemination of leprosy into a human body as far as scheduling a perfect drug dose regime depending on this analysis remains our main priority. Our investigations suggest that adopting optimal control strategy consisting of combined drug therapy eliminates the oscillatory behavior of the delayed systems completely. Existence of optimal control solutions are demonstrated in detail. To achieve the optimal control profiles of the drug therapies and to obtain the optimality systems, Pontryagin's Minimum principle with delay in state are employed for our controlled systems. Furthermore, the analytical as well as the numerical outcomes obtained in this research article indicate that the delayed bacterial proliferation and M. leprae‐induced infection model equipped with optimal control policy performs more realistically and accurately in the form of a safe and cost‐effective double‐drug therapeutic regimen. All the mathematical results are verified numerically and the numerical results are compared with some recent clinical data in our article as well. [ABSTRACT FROM AUTHOR]