Hybrid Programming Research Articles

Principal component analysis for yield and its attributing characters of pearl millet (Pennisetum glaucum [L.] R.Br.)

An experiment was conducted at Agricultural Research Station, Navgaon (Alwar) during kharif season of 2019 to study the genetic divergence among 31 genotypes of pearl millet based on quantitative data of grain yield and its nine component traits using hierarchical cluster and principal component analysis (PCA). Principal Component Analysis (PCA) indicated that three components with eigen values more than one accounted for about 73.35% of the total variation among nine quantitative characters responsible for seed yield in pearl millet genotypes. The principal components PC1, PC2 and PC3 contributed about 37.44%, 22.63% and 13.28%, respectively to the total variation. The first principal component exhibited high positive loading for grain yield, stover yield, plant height, spike length, spike thickness and 1000-grain weight which contributed more to the diversity. The second principal component showed high loading for days to 50% flowering, days to maturity and 1000-grain weight. Cluster analysis grouped the genotypes into five clusters indicated that grain yield, stover yield, 1000-grain weight and days to maturity contributed maximum towards genetic divergence. The grouping patterns of genotypes in principal component analysis and cluster analysis were almost in agreement with each other with minor deviations. The maximum inter cluster distance between genotypes of cluster V and III with cluster II, indicate that genotypes included in these clusters have high heterotic response and produce better seggregants of used in Pearl millet hybridization programme.

Runko: Modern multiphysics toolbox for plasma simulations

runko is a new open-source plasma simulation framework implemented in C++ and Python. It is designed to function as an easy-to-extend general toolbox for simulating astrophysical plasmas with different theoretical and numerical models. Computationally intensive low-level kernels are written in modern C++ taking advantage of polymorphic classes, multiple inheritance, and template metaprogramming. High-level functionality is operated with Python scripts. The hybrid program design ensures good code performance together with ease of use. The framework has a modular object-oriented design that allows the user to easily add new numerical algorithms to the system. The code can be run on various computing platforms ranging from laptops (shared-memory systems) to massively parallel supercomputer architectures (distributed-memory systems). The framework supports heterogeneous multiphysics simulations in which different physical solvers can be combined and run simultaneously. Here, we showcase the framework’s relativistic particle-in-cell (PIC) module by presenting (i) 1D simulations of relativistic Weibel instability, (ii) 2D simulations of relativistic kinetic turbulence in a suddenly stirred magnetically-dominated pair plasma, and (iii) 3D simulations of collisionless shocks in an unmagnetized medium.

Parallelization of Array Method with Hybrid Programming: OpenMP and MPI

For parallelization of applications with high processing times and large amounts of storage in High Performance Computing (HPC) systems, shared memory programming and distributed memory programming have been used; a parallel application is represented by Parallel Task Graphs (PTGs) using Directed Acyclic Graphs (DAGs). For the execution of PTGs in HPC systems, a scheduler is executed in two phases: scheduling and allocation; the execution of the scheduler is considered an NP-complete combinatorial problem and requires large amounts of storage and long processing times. Array Method (AM) is a scheduler to execute the task schedule in a set of clusters; this method was programmed sequentially, analyzed and tested using real and synthetic application workloads in previous work. Analyzing the proposed designs of this method in this research work, the parallelization of the method is extended using hybrid OpenMP and MPI programming in a server farm and using a set of geographically distributed clusters; at the same time, a novel method for searching free resources in clusters using Lévy random walks is proposed. Synthetic and real workloads have been experimented with to evaluate the performance of the new parallel schedule and compare it to the sequential schedule. The metrics of makespan, waiting time, quality of assignments and search for free resources were evaluated; the results obtained and described in the experiments section show a better performance with the new version of the parallel algorithm compared to the sequential version. By using the parallel approach with hybrid programming applied to the extraction of characteristics of the PTGs, applied to the search for geographically distributed resources with Lévy random walks and applied to the metaheuristic used, the results of the metrics are improved. The makespan is decreased even when the loads increase, the times of the tasks in the waiting queue are decreased, the quality of assignments in the clusters is improved by causing the tasks with their subtasks to be assigned in the same clusters or in cluster neighbors and, finally, the searches for free resources are executed in different geographically distributed clusters, not sequentially.

Genetic Diversity Analysis for Yield and Morpho-physiological Traits in Elite Rice (Oryza sativa L.) Genotypes under Semi-dry System Through D2 Statistics

Genetic divergence is a useful tool for choosing the parents to employ in a hybridization program. The current study was conducted at Agricultural College, Aswaraopet, Telangana, to evaluate genetic diversity among 30 rice genotypes during Kharif 2021 using D2 analysis under a semi-dry system. Based on the analysis, thirty genotypes were divided into ten clusters based on D2 values. Cluster II was found largest with twelve genotypes followed by cluster I with ten genotypes and clusters III, IV, V, VI, VII, VIII, IX, and X had one genotype each, indicating that there was a sizable amount of variation among the genotypes. Cluster II (284.13) had the highest intra-cluster D2 value, followed by cluster I. (242.63) while the highest inter-cluster distance was found between Cluster VIII and IV (1503.42) followed by cluster VI and IV (1316.83), cluster VI and III (1316.15), cluster IX and IV (1303.55), cluster VIII and X (1261.09), cluster VIII and IX (1132.95), V and III (1113.91), and cluster VII and V (1101.7) suggesting that crosses involving lines from these clusters would produce wider and more desirable recombination. The average inter-cluster distance for Cluster VIII and VI was the lowest (231.01), followed by Cluster IX and X (394.3) and Cluster III and IV (397.72). The genotypes of these clusters have the most gene complexes, as shown by the shortest inter-cluster distance. The findings revealed that root biomass contributed the most to genetic divergence (24.59 percent), ranking first followed by total biomass (21.60%), root length (17.70%) and grain yield/plant (14.02%), culm strength (8.04%), plant height (5.74%), 1000 grain weight (4.36 %). The traits viz., root biomass, total biomass, root length, grain yield/plant, and culm strength contributed 85.95% of the total deviation and need to be exploited to develop varieties suitable for the semi-dry system in rice. The genotypes BPT 5204, WGL 697, RNR 21278, RNR 28361, WGL 915, PR 126, and JGL 28545 were to be better for further hybridization programs to develop varieties for the semi-dry system.

Evaluation of The Instructor-Student Interaction in Online Classes: Instructors’ Perspective

Due to the COVID-19 pandemic, the learning process shifted from face-to-face learning to online learning as schools and universities were closed almost worldwide. After the pandemic restrictions, it seems that many educational institutions continue to offer online courses as an alternative to face-to-face classes, or they have begun to implement hybrid programs in which the students have both face-to-face and online courses. With the shift from face-to-face to online learning, the learning process has been affected negatively from different perspectives with varying degrees; however, the interaction between instructors and students has become more challenging to pursue at expected levels. Students tend to avoid interacting for many different reasons in the online learning process, or the instructors have some obstacles to a sound interaction with their students. By exploring the experiences and perceptions of the English language instructors, this descriptive study aimed to examine the perceptions of instructors regarding teacher-student interaction in online classes, the challenges faced by the practitioners, and their suggestions for course designers, policymakers and other related stakeholders. The researchers employed a qualitative research approach. The participants were 17 instructors working at the school of foreign languages at a state university. Semi-structured interviews were conducted to collect the qualitative data. The data was analysed by following the content analysis procedures. The findings reported insights into the interaction issues in online classes; the instructors had negative perceptions of the interaction in online classes due to the challenges they faced. In the light of the qualitative data, the present research offers valuable suggestions for decision-makers and practitioners, such as taking the participation of the learners into consideration in assessments and providing in-service training programs for EFL instructors.

Principle Component Analysis and Diversity Studies in Sunflower Lines (Helianthus annuus)

Background: Sunflower crop being a highly cross pollinated with high yield potential, can be grown in all seasons with its suitability in variety of inter and series cropping systems due to its photo insensitive nature and can be adapted to a variety of environmental conditions also. So keeping in mind of its potential and future use, research was conducted with available lines to study PCA and its diversity that might be used in hybridization programmes. Methods: The 45 lines of sunflower were grown in a randomised block design with three replications at the Research Farm, Department of Genetics and Plant Breeding, College of Agriculture, Vijayapur during summer 2018-19 for study on principal component analysis and diversity analysis. Result: At the genotypic stage, the analysis of variance showed highly significant variations among the genotypes for all of the characters. Whereas Divergence analysis using principal component analysis and hierarchical cluster analysis has been shown to be effective in determining which genetically distant parents should be used for hybridization. Divergence in genotypes were divided into nine major groups or clusters and out of nine components, four components had more than one eigen value (Table-2). The first principal component explained 33.40 per cent of total variation, while the second, third, fourth, fifth, sixth, seventh, eighth and ninth principal components explained 23.30, 14.10, 11.19, 6.30, 4.90, 3.60, 1.90 and 0.60 per cent, respectively.

Embracing brain and behaviour: Designing programs of complementary neurophysiological and behavioural studies

AbstractNeuroIS—the methods and knowledge of neuroscience applied to the information systems (IS) domain—has become an established research field within the IS discipline. A key advantage of NeuroIS is its ability to provide insights into human cognition beyond those obtained using behavioural techniques alone. Nevertheless, in neuroscience, there is renewed interest in examining behaviour together with neurophysiological methods to better inform our understanding of neural processes. In this research opinion article, we argue that in the field of NeuroIS, there is an opportunity for hybrid programs of study that combine neurophysiological and behavioural methods in a complementary manner. We outline four strategies for designing complementary neurophysiological and behavioural experiments in a research program: (1) observe the relationship between neural processes and behavioural change; (2) combine neurophysiological and behavioural methods to enhance internal, external, and ecological validity; (3) extend, rather than replicate, experiments based on theory; and (4) use neurophysiological and behavioural experiments together to evaluate IT artefact design. By applying these strategies, researchers can more effectively design programs using complementary neurophysiological and behavioural methods, which, in turn, can help to provide richer insights into the phenomena under study as well as accelerate the advancement of IS knowledge.

Genetic Diversity and Correlation Coefficient Studies in Muskmelon (Cucumis melo L.)

Background: Genetic diversity provides opportunity for plant breeders to develop new and improved cultivars with desirable characteristics, which include both farmer-preferred traits (yield potential and large seed, etc.) and breeder preferred traits (pest and disease resistance and photosensitivity, etc.). Yield being a complex quantitative character, direct selection for yield may not result in successful improvement. Information on character association and direct and indirect effects of component traits on yield would greatly help in formulating the selection criteria and using them effectively in crop improvement programme. Therefore present study was aimed to evaluate genetic diversity in 25 muskmelon genotypes. Methods: 25 muskmelon genotypes were evaluated at Dr. Y.S.R Horticultural University, College of Horticulture, Anantharajupeta, during 2017-2018 in randomized block design with three replications. Genetic diversity among genotypes was estimated using Mahalanobis D2 statistics. Result: Cluster analysis revealed distinctive clustering pattern and grouped the genotypes in to seven clusters. cluster II was the largest comprising of eight genotypes, while cluster III, IV, VI, VII consist of only one genotype. Cluster VII and IV were found to be considerably different from the rest of the clusters. Genotypes falling in these clusters which are genetically more diverse, can be exploited in a hybridization programmes. Genetic variability in terms of correlation analysis were studied for yield and yield attributing traits. Fruit yield had a positive correlation with fruit volume, fruit diameter, number of fruits per plant, fruit weight and flesh thickness. While it had a negative correlation with days to appearance of first pistillate flower and days to first fruit harvest.

Genetic diversity using principal component analysis and hierarchical cluster analysis in rice

A set of 100 germplasm lines with four checks viz., BPT-5204, PSB-68, Siri1253 and MGD-101 were evaluated in augmented block design during Kharif 2020. The observations were documented for 5 quantitative traits viz., days to 50% flowering, panicle length, number of panicles per square meter, 1000 grain weight and grain yield by principal component analysis and cluster analysis to determine the relationship and genetic divergence among the individuals. The cumulative variance of 55.60% was explained by 1st two principal components (PC1 and PC2) with eigen values greater than 1. Component 1 with variance of 32.10% had contribution from days to 50% flowering, panicle length, panicles per square meter and grain yield while principal component 2 accounting 23.50% total variability has contribution from days to 50% flowering and panicles per square meter. The remaining variability of 17.68%, 16.10% and 10.60% was consolidated in PC3, PC4 and PC5. Results from cluster analysis grouped 100 germplasm lines into four clusters with minimum individuals constituted in cluster 1 and maximum individuals were found in cluster 4. The lines in cluster 1 (2.62) showed maximum divergence followed by cluster 3 (2.23). The maximum inter cluster Euclidean distance was observed between clusters 2 and cluster 3 followed by cluster 1 and cluster 2 giving a scope for selection of parents for hybridization programme from these clusters to realize high genetic variation and novel combinations for yield increment.

Unravelling the G × E interactions using AMMI biplot for phenology and agro-morphological traits in linseed (Linum usitatissimum L.)

In the present study, additive main effects and multiplicative interactions (AMMI) biplot analysis was used to identify stable genotypes for days to flowering, maturity, plant height and number of branches per plant to dissect GEI in linseed. Trials were conducted in randomized complete block design with two replications over three consecutive years(2016-17, 2017-18 and 2018-19). ANOVA analysis revealed genotype and G×E interaction effects contributed significant sumofsquare for days to flower (97.31% and 0.56%); days to maturity (89.27% and 8.90%); plant height (98.29% and 0.70%) and number of branches per plant (86.40% and 2.41%respectively). The dissection of GE interaction for all the traits was mostly explained by the first and second principal component axis (IPCA1 and IPCA2). The SSI statistic fully sync with the results of the AMMI1 biplot analysis for all the traits of top ranked genotypes across the environments. Results of genotypes stability in AMMI1 and AMMI2 biplot analyses were shown differential response with some exceptions that indicates the different sets of genes were responsible for the cumulative expression of traits under study. In the present study environments imposed variable effects with few exceptions towards the genotype stability in both AMMI1 and AMMI2 analysis models for all the traits studied. Hence, the desirable genotypes identified in linseed for phenology and agro-morphological traits could be utilized in hybridization program and varietal recommendation under semi-arid conditions.

Characterization of diverse sesame (Sesamum indicum L.) panel through morphological and molecular marker analysis

Sesame is one of the most important and ancient oilseeds crop with high and good quality oils content. Present Investigation was carried out for the assessment of the genetic diversity using 33 sesame genotypes through morphologically and 21 SSR markers. In the morphological diversity analysis, nine clusters were observed with 33 genotypes using all phenological, yield and its contributing attributes. Among the 9 clusters, cluster II had maximum number of genotypes (14) followed by cluster III (6), cluster IV (5), cluster I (3), cluster V, VI, VII, VIII and IX had one genotype to each cluster. The highest inter-cluster distance was observed between cluster VI and I (386.70), which indicates maximum diversity between the genotypes of these clusters, could be further used in crossing programme. However, SSR markers grouped 33 genotypes into six clusters at 0.51 dissimilarity index. In which cluster V had maximum (7) genotypes and cluster VI had minimum (3) genotypes. The results of genetic diversity tallied with the morphological diversity, such as BRT-04 are the farthest from BRT-08, BRT-09 and BRT-10, however BRT-08, BRT-09 and BRT-10 are in the same cluster in both morphological and molecular diversity analysis. The UPGMA dendrogramwas constructed using Jaccard's dissimilarity coefficients based on SSR markers score on thirty-three genotypes. Polymorphism information content (PIC) value varied between 0.15 and 0.36 with an average of 0.27. The genotypes from the distinct clusters can be utilized for hybridization programme to recover heterotic pools.

Salinity Stress Tolerance in Potato Cultivars: Evidence from Physiological and Biochemical Traits.

Salinity stress is a major constraint to sustainable crop production due to its adverse impact on crop growth, physiology, and productivity. As potato is the fourth most important staple food crop, enhancing its productivity is necessary to ensure food security for the ever-increasing population. Identification and cultivation of salt-tolerant potato genotypes are imperative mitigating strategies to cope with stress conditions. For this purpose, fifty-three varieties of potato were screened under control and salt stress conditions for growth and yield-related traits during 2020. Salt stress caused a mean reduction of 14.49%, 8.88%, and 38.75% in plant height, stem numbers, and tuber yield, respectively in comparison to control. Based on percent yield reduction, the genotypes were classified as salt-tolerant (seven genotypes), moderately tolerant (thirty-seven genotypes), and salt-sensitive genotypes (nine genotypes). Seven salt-tolerant and nine salt-sensitive genotypes were further evaluated to study their responses to salinity on targeted physiological, biochemical, and ionic traits during 2021. Salt stress significantly reduced the relative water content (RWC), membrane stability index (MSI), photosynthesis rate (Pn), transpiration rate (E), stomatal conductance, and K+/Na+ ratio in all the sixteen genotypes; however, this reduction was more pronounced in salt-sensitive genotypes compared to salt-tolerant ones. The better performance of salt-tolerant genotypes under salt stress was due to the strong antioxidant defense system as evidenced by greater activity of super oxide dismutase (SOD), peroxidase (POX), catalase (CAT), and ascorbate peroxidase (APX) and better osmotic adjustment (accumulation of proline). The stepwise regression approach identified plant height, stem numbers, relative water content, proline content, H2O2, POX, tuber K+/Na+, and membrane stability index as predominant traits for tuber yield, suggesting their significant role in alleviating salt stress. The identified salt-tolerant genotypes could be used in hybridization programs for the development of new high-yielding and salt-tolerant breeding lines. Further, these genotypes can be used to understand the genetic and molecular mechanism of salt tolerance in potato.

The Impact of Instructional Modality on the Relationship of Psychological Sense of Community to Thriving among Graduate Students

McMillan and Chavis (1986) defined a psychological sense of community (PSC) as feelings of influence, membership, integration, and shared emotional connection. The importance of PSC to graduate student success has been established, but to date, no one has examined the extent to which PSC predicts student thriving when data is disaggregated by instructional modality. The purpose of this study was to determine whether PSC contributes to student thriving to a greater extent for online learners than for those in face-to-face or hybrid programs. Data from the 2018 Graduate Thriving QuotientTM survey administration were analyzed. Analyses of variance determined there were no statistically significant differences in levels of thriving or PSC between the modalities. Multiple hierarchical regression analysis confirmed that PSC accounted for a larger percentage of variance in Thriving Quotient scores among online learners (20.6%) than for either hybrid learners (12.9% of variance) or face-to-face learners (7.5%). This finding suggests that when the proportion of the degree program that is conducted online increases, PSC becomes even more important to students’ thriving.

Determination of table olive characteristics of new olive varieties obtained by crossbreeding of Gemlik and Memecik variety

Objective: The objective of this study was to investigate the physical and chemical characteristics of 10 hybrid type table olives obtained as a result of a national level hybridization program in the Olive Research Institute. Material and Methods: The samples selected from Memecik x Gemlik and Gemlik x Memecik combinations, which came to the pre-elimination stage in the project, were evaluated in terms of table olives characteristics. These products were obtained in four olive seasons. Olives were subjected to some physical and chemical tests and analyzes. The main olive processing techniques (green split olive, natural turning black olives and Spanish style green olives) were applied to test the suitability of the cultivar candidates to processing techniques. Results: The olive variety candidates, GM 41, GM 39, MG 11 and MG 5 attracted more attention than the main varieties of the cross breeding project, “Memecik” and “Gemlik”, in terms of some table olive characteristics like high flesh/pit ratio and the number of olive fruits per kilogram. Especially, The variety, GM 41 was found to be significantly important candidate for the natural black olive processing because of having high number of olives per kilogram. Conclusion: As a result of this study, it was concluded thata the hybrid variety candidates, MG5 and MG13 were found to be promising in terms of table olive characteristics and registered as HAYAT and ARSEL, respectively.

Genetic studies to determine the selection criteria for advancing BC1F3 families of Azucena /CB 87R IN rice for restorer development

The aim of this study was to figure out genetic variability, correlation and path co-coefficients for seven traits in 42 F3 families of the cross Azucena/CB 87R*1 aimed for developing new restorer lines with indica tropical japonica background for their utilization in a hybrid program. The population exhibited high PCV  and GCV  for the traits viz., the number of productive tillers per plant, panicle weight and single plant yield. For the same traits, high heritability combined with high GAM was also deduced.  The traits panicle weight, the number of productive tillers per plant, panicle length, flag leaf length and flag leaf width showed positive and significant associations with single plant yield. Though the traits panicle weight and the number of productive tillers were negatively correlated, they both exerted a high direct effect on single plant yield. Keywords: Rice, indica-tropical japonica restorers, Variability, Heritability, Correlation, Path analysis

Genetic studies on green fodder yield and quality traits in fodder maize (Zea mays L.)

An experiment was conducted to evaluate the genetic divergence in 93 inbreds of fodder maize through genetic variability, D2 and principal component analysis. Observations were recorded on ten biometrical traits and a quality trait. The variability analysis revealed higher GCV and PCV for green fodder yield per plant and higher heritability coupled with high genetic advance for the traits viz., plant height, leaf length and green fodder yield per plant. Hence, the variability study indicated that the direct selection for the above traits would be effective. Diversity analysis sorted the 93 inbreds into eleven clusters. In the case of inter cluster distance, the maximum distance was obtained between clusters VIII and X followed by clusters VI and VIII and VIII and XI indicating that these cluster pairs have a more genetic divergence and the inbreds of these clusters could be utilized in a fodder maize hybridization programme. The D2analysis suggests that the inbreds viz., GETM30, GETM84, GETM12, and GETM81 were distinct and diversified and classed as promising inbreds based on the cluster mean and divergence. These genotypes could be utilized in a fodder maize hybridization programme to obtain heterotic fodder maize hybrids apart from getting the desired segregants in later generations. According to the principle component (PC) analysis, out of 11 PC groups, four PCs recorded more than one Eigenvalue, which contributes 70.30% of the total variability. However, the variability for green fodder yield and its attributing traits were higher in PC1, PC2, PC3 and PC4 and hence, the inbreds that recorded higher PC scores in these components could be utilized in the further hybridization programme to enhance the biomass yield in fodder maize. Keywords: Fodder maize inbreds, variability, diversity, PCA

Fractional order modelling and robust multi-model intelligent controllers’ synthesis for ACP1000 nuclear power plant

In this research work, a third generation Pressurized Water Reactor (PWR) type Nuclear Power Plant (NPP) of 1100 MWe rating called Advanced Chinese PWR (ACP1000) NPP is adopted. The research and development work is mainly encompassed of addition of third steam generator loop, hybrid programming platforms, fractional order modular modelling and intelligent controllers. The ACP1000 NPP is comprised of primary loop, secondary loop and balance of plant (BOP). The ACP1000 nonlinear dynamics is precisely modelled with greater efficiency using fractional order differential equations. The entire plant is modelled into eighteen modules in a fractional order framework in Visual Basic (VB) environment. The eighteen modules are governed by nine controllers. Controllers are configured in a fashion of an adaptive neuro-fuzzy inference system (ANFIS). An innovative multi input single output (MISO) ANFIS virtual instrument (VI) is designed for training and configuring controllers in LabVIEW. The developed MISO ANFIS VI in combination is implemented for multi-input multi output (MIMO) ANFIS controllers in parallel computing framework. The variable transfer between fractional order multi- modular framework in VB (FO- MMF-VB) and robust intelligent ANFIS controllers in LabVIEW (RI-ANFIS-LV) is established in client server configuration and system exec VI for calling external code in VB. This hybrid programming framework of Visual Basic and LabVIEW is actually a development of LabVIEW Visual Basic Integration (LabVb) Toolkit used to evaluate the closed loop transient performance of ACP1000 nuclear power plant. Various parameters are simulated for dynamic transient simulations under load manoeuvring from 100% power to 50% power. The results are analysed and validated against benchmark design data and Preliminary Safety Analysis Report (PSAR). All the results are within the protection systems design limits under normal operating conditions.

Phenotypic diversity for fruit quality traits and bioactive compounds in red-fleshed guava: Insights from multivariate analyses and machine learning algorithms

Precise characterization of genetic resources is critically important for developing high yielding and nutri-rich guava cultivars. This study aimed at delineating phenotypic diversity for fruit yield, quality and bioactive compounds in the newly developed red-fleshed guava selections and hybrids. Analysis of Variance (ANOVA) and the associated effect-size estimates revealed a high degree of phenotypic diversity for most of tree growth and fruit quality traits, minerals and bioactive compounds in the guava accessions analyzed. Principal Component Analysis efficiently discerned key trends in data with first and second Principal Components representing nutritional and bioactive compounds, and fruit yield attributes, respectively. Grouping of guava accessions into separate k-means clusters evinced a fairly high genetic divergence for the traits examined. Correlation and network analyses revealed that total antioxidant activity of fruits was strongly and positively regulated by total phenols (TP), β-carotene (β-car) and flavonoids (Flav). Gradient Boosting and Random Forest regression models also confirmed an overarching influence of TP, β-car and Flav in determining the total antioxidant activity. Our findings are expected to assist the guava breeders in selecting suitable parental combinations for future hybridization programs. The promising guava accessions identified by us meet the fresh consumption and processing standards, and may thus be considered for commercial planting.

Evaluation of yield contributing characters and cluster analysis of soybean genotypes

A morphological divergence study among the twenty genotypes based on nine yield and yield contributing characters through the D2 statistic indicated the presence of substantial diversity by forming clusters with a wide range of inter-cluster distances. The soybean genotypes under investigation were divided into five clusters. Cluster I had the most genotypes, with 10, followed by clusters III and V, each with five and three genotypes. The relative divergence indicates how much each cluster varies from the others. Cluster I and Cluster III have the most significant order of divergence, followed by Cluster III and Cluster IV. The results revealed that the parents in these clusters are genetically heterogeneous. It's possible that a hybridization program obtained a significant heterotic response. Clusters I and II found the minimum inter-cluster distances, indicating limited genetic diversity. Cluster III had the maximum seed yield per plant cluster value. Individual performance was highest for the genotypes BINAsoybean-3, BINAsoybean-2, and Shohag for the trait seed yield per plant.

Assessment of genetic variability in cotton (Gossypium hirsutum L.) genotypes

The selection of high-yielding cotton lines is critical for the evaluation of phenotypic characteristics. For Cotton Breeders, it is always in preference to select desirable genotypes, based on the diversified characters from the available germplasm. The selected phenotypic diversity can be useful for aiding information in parental selection, adherence to this, a set of 12 advanced cotton lines were evaluated for genetic diversity at Cotton Research Institute, Tandojam during Kharif season in the year of 2020. The experiment was laid out in Randomized Complete Block Design (RCBD) with three replications while seven agronomical traits, were included in the experiment. The mean square of genotypes, was differed significantly (P<0.05) for all studied traits, representing that genetic diversity exists in these cotton germplasms and can be used in further utilization. Considering the agronomic performance, the genotype of B-2 was on the top in average values including plant height (174.40 cm), bolls plant-1 (42.067), seed cotton yield plant-1(139.34 g), GOT% (37.800%), and staple length (28.00 mm), hence indicating its valuable breeding resources for future cotton breeding. A Genetic Distance of 92.683 was found between B5 and B2 genotypes, which is high enough, and revealing that this pair may be used in a hybridization program for vigorous hybrid production and better selection in subsequent generations. The Variance Percentages for the first, second, and third principal components were 42.70, 23.10, and 17.20, respectively. The first three components contributed 83.00 percent of the variation for genotypes, which is high enough for the cotton crop improvements. The cotton genotypes, were divided into three categories, based on phenotypic data. The number of groups obtained, might be beneficial in generating cotton genotypes with a variety of characteristics and diversifying the Cotton Gene Pool. Keywords: cotton, genetic variations, morphological traits, seed yield.