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A 150-days study was conducted on the continuous exposure of magnetized water at 0.00, 0.10, 0.15 and 0.20 Tesla (T) on quadruplicate treatments of Jade Perch Scortum barcoo juveniles in a recirculating system. Each replicate consisted of 18 fish with an average weight of 7.52 g over all treatments. The feeding efficiencies, growth, whole-body proximate plasma parameters, survival were measured and liver histopathology was observed. Our results show no significant improvement in water quality parameters, specific growth rate and body weight gain in this study. However, the best feed conversion ratio was recorded in fish exposed to 0.10, 0.15 and 0.20 T, which are significantly better than the control 0.00 T. Though survival was unaffected but plasma biochemistry and liver histopathology were affected even within the treatment. No liver, plasma or blood abnormalities were detected in fish exposed to 0.10 T and the control 0.00 T. But, one out of four livers from fish in the 0.15 T treatment showed a localized inflammatory response. These changes increased in the liver of fish exposed to 0.20 T, high AST and necrosis in this group is evidence of the liver cells damaged or been at risk. A significantly higher crude protein and lipid were noticed in the exposed fish compared with the control, 0.15 T had the highest crude protein and lipid while the control has the lowest. Based on the overall findings, on growth performance and looking at other factors like absence of any physiological disorder 0.10 T can be used as an effective and affordable technique improve the feeding efficiencies of Jade Perch. K E Y W O R D Scell count, Jade Perch, magnetic device, magnetic field, plasma biochemistry, water quality | INTRODUC TI ONMagnets have been used to improve various water quality parameters in tilapia culture and have been recognized as a new way to enhance some water quality parameters such as mineral solubility by reducing

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The performance of selected leaf meals of high dietary fibre in the feed of a tropical commercial carp, hybrid lemon fin barb (Barbonymus gonionotus ♀ × Hypsibarbus wetmorei ♂) was evaluated in a 56‐day feeding trial. The tropical carp juveniles (9.43 ± 0.05 g) were randomly stocked in 60‐L aquaria at 15 fish per aquarium. Five isonitrogenous and isocaloric diets (30% crude protein, 17 kJ/g gross energy) containing no leaf meal and 10% napier grass (Pennisetum purpureum), alfalfa (Medicago sativa), water spinach (Ipomea aquatica) and Gliricidia sepium leaf meals were formulated and tested. Three aquaria were randomly assigned to each experimental diet. The results showed that fish‐fed diets containing leaf meals showed superior growth performance and body composition which was better than those fed control diet. The leaf meal‐treated groups also had higher values of protein, lipid, energy retention and production of digestive enzymes amylase. Conclusively, the results indicated that leaf meal fibre provided better performance showing the inherent prebiotic effect of the utilization of these leaf meal in hybrid lemon fin barb .

Abstract-the application of machine learning models such as support vector machine (SVM) and artificial neural networks (ANN) in predicting reservoir properties has been effective in the recent years when compared with the traditional empirical methods. Despite that the machine learning models suffer a lot in the faces of uncertain data which is common characteristics of well log dataset. The reason for uncertainty in well log dataset includes a missing scale, data interpretation and measurement error problems. Feature Selection aimed at selecting feature subset that is relevant to the predicting property. In this paper a feature selection based on mutual information criterion is proposed, the strong point of this method relies on the choice of threshold based on statistically sound criterion for the typical greedy feedforward method of feature selection. Experimental results indicate that the proposed method is capable of improving the performance of the machine learning models in terms of prediction accuracy and reduction in training time.