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In Cameroon, wild strains of Clarias gariepinus (Burchell, 1822) are often used for artificial reproduction in hatchery facilities, but there is insufficient information on their reproductive capacity and progeny performance. The reproductive capacity of two strains of C. gariepinus from the Mezam and Mungo Rivers and the performance of their F1 larvae obtained following hybridization were assessed. Reproduction was carried out with four crosses as follows: Mezam (♂) x Mezam (♀) (T1), Mezam (♂) x Mungo (♀) (T2), Mungo (♂) x Mezam (♀) (T3) and Mungo (♂) x Mungo (♀) (T4). Thereafter, three days old larvae obtained following reproduction were subjected to a 30 day experimental trial with standard feeds. The performance of larvae obtained from T4 was not assessed since larvae obtained were too few for the experiment. Although the broodstock of the different strains did not differ significantly in size, Mezam stock had significantly (p = 0.00) bigger eggs when compared to Mungo stock. Fecundity of 52480 was recorded for Mezam stock compared to 32985 for Mungo stock. Fertilization and hatchability rates differed significantly (p < 0.05) among the crosses, with the highest (82.28 and 81.31%, respectively) in T1, followed by T3 (65.96 and 61.28%, respectively), and the least (38.61 and 2.81%, respectively) in T4. The best larval survival was recorded in T1 (37.17%) and the least in T2 (15.67%), meanwhile T3 recorded best growth after 30 days. In terms of fertilization and hatchability, T1 was more suitable for artificial reproduction, but T3 is more encouraged for aquaculture based on its reproductive capacity and larval performance.

This paper presents the load-dependent power loss in a wind turbine gearbox under real-time operating wind speed for three different oil formulations. The gear power loss was determined using mathematical models from the values of gear loss factors and specific film thickness experimentally determined by other researchers. The bearing power loss was determined using the new SKF calibrated model. Wind data from Bafoussam, a town in Cameroon was used to validate the model. A back propagation neural network with different numbers of hidden neurons was designed for power loss modeling and prediction. The achieved results reveal that the load-dependent power loss in a wind turbine gearbox is greatly influenced by wind speed and oil type. Finally, it is shown that the predictive performance of the neural network is also influenced by the number of neurons in the hidden layer.