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The most abundant biopolymer accessible on earth is lignocellulose. The hydrolysis of lignocelluloses is completed through enzyme groups such as ligninases, hemicelluloses, and cellulases as independently and together called lignocellulolytic enzymes. This review is focused on dealing with enhanced production of cellulose using modern techniques like strain improvement, consolidated bioprocessing, metabolic engineering, and recombinant technology, and defines the technology to boost the enzyme activity and its yield. Furthermore, the restrains linked with the production of cellulolytic enzyme and the direction of upcoming research to offer a complete strategy to improve cellulase yield with novel properties for industrial application at cost‐effective levels are mentioned.Practical applicationsCellulase is a complex enzyme consisting of an endoglucanase, exoglucanase, and β‐D‐glucosidase which acts in combination to release a small unit of glucose. Cellulase has vast potential in the industrial sector especially in the food and beverage as well as paper and pulp industries. The parameters such as strain improvement and heterologous gene expression are involved to enhance the cellulase production. This review discusses the cost‐effective production of cellulolytic enzyme and also emphasizes the limitations and future prospects of this enzyme production on a large scale.

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The scientific fields’ deals with day to day problems, like economic planning and engineering design, mostly are disconnected, high dimensional, multimodal and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using conventional process based on gradient. The natural phenomenon motivates and animal group behavior characteristics, the investigators have anticipated many efficient natural heuristic algorithms for high-dimensional complex optimization problems in real-world. This manuscript deals with one of the meta-heuristic algorithm, Whale Swarm Optimization algorithm (WSO) and compares the performance with RMS prop optimization techniques.

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The multilocational studies were conducted to evaluate 21 hybrids of tomato in Pratapgarh(Uttar Pradesh), Varanasi (Uttar Pradesh) and Burdwan (West Bengal), India during August–February of 2015–16 and 2016–17. Data on four quality traits viz., total soluble solids, titrable acidity, carotene content and lycopene content were estimated. Combined analysis of variance (ANOVA) revealed significant effects of locations, genotypes, genotype-location interaction and parents vs hybrids-location interaction for all the traits. The significance of combining ability effects (general combining ability and specific combining ability) and their interaction with location revealed environmental influences on combining ability effects for all the traits. The Present study indicated higher estimates of to , lower estimates of heritability and GCA/SCA ratio for quality traitsandthereby implied preponderance of non-additive gene action in determination of quality traits studied. Desirable parental lines were identified for different traits like EC 620438 and BS 24-2 for TSS, BS 24-2 and Superbug for titrable acidity, Columbia, EC 620438 and Superbug for carotene content, and H 86 and EC 620541 for lycopene content. Similarly, desirable specific combiners for different traits were identified. The hybrid combination EC 620438×BS 24-2 appeared good specific combiner for TSS, carotene and lycopene content. The parental lines/hybrids identified in present study will be useful in identifying hybrids adapted to a range of environments.

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