This research examines Al2O3-Graphene-CNT/water ternary hybrid nanofluid flow over a wedge surface. The suction/injection, thermal radiation, magnetic field, heat sink/source, and convection effects are taken into consideration. The governing equations illustrating for wedge surface is solved using the MATLAB software inbuilt “bvp4c solver”. The Nusselt number, temperature profile, and velocity are all computed numerically. The velocity field and temperature field are plotted to demonstrate how physical factors affect them as well as the flow of fluid. Using the aforementioned computational methods, the dataset has been quantitatively prepared for the Nusselt number. Artificial intelligence based computational techniques namely fuzzy particle swarm optimization and artificial neural network are used to precisely predict the Nusselt number values. FPSO and ANN algorithms are trained with the numerical data from the simulation. The Nusselt number rises with the increment of Rd. For each case under analysis, the ANN algorithm’s regression (R) and mean squared error (MSE) values vary from 0.999483948 to 0.999999995 and 0.0000000153658–0.000115601. For the FPSO algorithm, the corresponding ranges for the regression (R) and mean squared error (MSE) are 0.992017238–0.999956746 and 0.000524116–0.002054421 respectively, for all assessed cases.
This work presents an analysis of steady, incompressible, two-dimensional flow of a non-Newtonian ternary hybrid nanofluid across a cylindrical surface, incorporating variable viscosity, variable thermal conductivity, and thermal radiation effects. The ternary hybrid nanofluid is composed of engine oil taken as base fluid, formulated using a combination of three different types of nanoparticles (Al2O3-Graphene-CNT) and modeled, using the Maxwell fluid model. Applying similarity transformations, the governing PDEs are converted into ODEs and numerically solved via MATLAB's bvp4c solver. To further enhance predictive accuracy, an artificial neural network and fuzzy particle swarm optimization are incorporated to estimate the Nusselt number, thereby analyzing heat transfer performance. Results reveal that as the radiation parameterRdrises from 2.1 to 4.0, the heat transfer rate improves from 93.69 % (for suction parameterS =- 0.2) to 147.66 % (for heat source/sink parameter Qh = 1.0 and thermal conductivity parameter epsilon = 1.0), indicating a substantial enhancement in thermal performance.
Glutamine is the first amino acid synthesized during nitrogen assimilation and is essential for protein synthesis, amino acid biosynthesis, and various regulatory processes in plants. In rice (Oryza sativa), glutamine not only supports embryogenesis and shoot organogenesis but also plays a pivotal role in modulating stress and defense responses. This review synthesizes findings from over 120 studies conducted across 33 countries, highlighting recent advances in glutamine signaling, sensing, and its role in plant defense mechanisms. In rice, glutamine is a key regulator of protein biosynthesis and nitrogen metabolism, significantly impacting plant growth and stress adaptation under challenging environmental conditions. It initiates long-distance signaling pathways that enhance resistance to biotic stresses, such as pathogen infections, and tolerance to abiotic stresses, including salinity, metal toxicity, water scarcity, and heat stress. Transcriptomic analyses have further elucidated glutamine’s involvement in activating stress-related defense genes and biochemical pathways critical for maintaining plant health. Moreover, glutamine homeostasis and its interaction with the GABA pathway regulate carbon and nitrogen (C/N) metabolism in plant cells, optimizing energy use for gene expression and defense responses. Exogenous glutamine and GABA applications have been shown to mitigate stress effects and enhance pathogen resistance. Additionally, glutamine influences light- and nutrient-dependent synthesis of phytochemicals, contributes to redox balance, and upregulates key defense mechanisms against both biotic and abiotic stressors. Collectively, these findings underscore the multifaceted and central role of glutamine in sustaining rice growth and resilience under adverse conditions.
Tuberose (Agave amica L.) is an important ornamental crop cultivated widely for its fragrant flowers, landscaping, and essential oil extraction. However, its genetic base is narrow due to vegetative propagation. Mutation breeding using chemical mutagens has emerged as a potential strategy to induce variability and develop new cultivars. The present investigation was conducted using bulbs of cv. Prajwal treated with different doses of Ethyl Methane Sulfonate (EMS), Methyl Methane Sulfonate (MMS), and Diethyl Sulfate (DES). The treatments were laid out in Randomized Block Design (RBD) with three replications. Observations were recorded on vegetative, flowering, and bulb yield traits. Results indicated that lower concentrations of mutagens significantly improved parameters such as spike length, number of florets, and bulb yield, whereas higher concentrations caused inhibitory effects. EMS proved to be the most effective mutagen in inducing useful variability. The study demonstrates the potential of chemical mutagenesis in broadening the genetic base of tuberose and suggests its application in breeding programs aimed at developing commercially viable cultivars.
The study was conducted over two years to investigate genetic variability, heritability and genetic advance as a percentage of the mean among 40 genotypes of bitter gourd (Momordia charantia L.). The genotypes were cultivated at the Horticulture Research Centre, SVPUAT, Modipuram (Meerut), during the Zaid seasons of 2023 and 2024. The experiment followed a randomized block design (RBD) with three replications and a spacing of 1 m × 0.75 m. A total of sixteen quantative traits were recorded and analyzed to identify genetic factors contributing to enhanced yield potential. Analysis of variance (ANOVA) revealed that mean squares due to genotypes were significant for all the traits studied. The pooled phenotypic coefficient of variation (PCV) exhibited an average increase of about 5 % over the genotypic coefficient of variation (GCV), with mean values of 11.06 % and 10.07 %, respectively. In 2023, the highest genotypic and phenotypic coefficients of variation were observed for primary branches per vine (23.24 and 23.57, respectively), whereas in 2024, the maximum values were recorded for total fruit yield per vine (41.93 and 42.22 respectively). Similarly, heritability (broad sense, h2bs) and genetic advance (GA) were highest for primary branches per vine in 2023 (97.28 % and 47.22 %, respectively, while in 2024, total fruit yield per vine recorded the maximum heritability and genetic advance (98.67 % and 85.82 %, respectively). Overall, traits exhibiting high PCV and GCV values corresponded with high heritability and genetic advance estimates for the same characters, indicating the predominance of additive gene action and the potential for improvement through simple selection.
