4 research outputs found
Length-weight relationships and condition factor of Asian sheat catfish, Wallago attu (Bloch & Schneider, 1801) inhabiting different rivers of India
No full textWallago attu is a freshwater catfish and has been classified as ‘Vulnerable’ in Red Data List of threatened species by IUCN. Length-weight relationship (LWR) of wild populations helps in understanding the pattern of fish growth that could be useful in fisheries management. The current work was carried out to the LWR and condition factor (K) of W. attu collected from five rivers of India (Ganga, Yamuna, Hooghly, Gomti and Pampa). A total of 261 fish specimens were sampled for LWR study. The value of b for the population of river Gomti (t=1.0312), Ganga (t=1.4109) and Yamuna (t=0.3365) was not significantly different from isometric growth (b=3) in Pauly’s t-test whereas the populations of Hooghly (t=10.3609) and river Pampa (t=3.4593) were significantly different (p<0.001) indicating positive allometric growth in the fish of rivers Hooghly and Pampa. Linear plot of log a over b resulted in straight line showed strong relationship. Low K value (K<1) indicated the poor health conditions of the fish in all five rivers. This study would provide information regarding the status of LWRs of W. attu in five rivers of India that could serve as a baseline data for future sustainable management and conservation of this vulnerable fish
Prediction of the Viscosity of Iron-Cuo/Water-Ethylene Glycol Non-Newtonian Hybrid Nanofluids Using Different Machine Learning Algorithms
No full textViscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number; thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R2 and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R2 = 0.992) and lowest error metrics. At the other end, is the Extreme Learning Machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Author
Marine Stock Enhancement in India: Current Status and Future Prospects
No full textIndia is a 12 mega-diversity nation known for its biodiversity richness. The geographic territory of India is an integral part of Central Indian Ocean Region consisting of three distinct marine ecosystem zones such as the Arabian Sea, Bay of Bengal and Indian Ocean. India is endowed with an exclusive economic zone of 2.02 million km2, coastline of over 8000 km and a variety of coastal ecosystems. The estimated number of marine fish species known from India constitutes 2443 species distributed in 230 families. According to the IUCN extant (2014), 50 species are threatened (6 of them critically endangered, 7 endangered and 37 vulnerable), while 45 are near-threatened. Marine fish diversity is in ever-increasing danger with depletion of resources. Overdependence on fish has led to overfishing resulting in the dwindling of diversity and abundance of stocks. Central Marine Fisheries Research Institute has initiated marine stock assessment practices in India and its present report in 2016 recorded a total of 709 species which is lower than 730 species recorded in 2015 in the landings showing an alarming situation on the exploited marine fishery resources of India. This situation demands restorative measures such as restocking, stock enhancement and sea ranching
