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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.

For the future installation of a wind farm in Cameroon, the wind energy potentials of three of Cameroon’s coastal cities (Kribi, Douala and Limbe) are assessed using NASA average monthly wind data for 31 years (1983–2013) and compared through Weibull statistics. The Weibull parameters are estimated by the method of maximum likelihood, the mean power densities, the maximum energy carrying wind speeds and the most probable wind speeds are also calculated and compared over these three cities. Finally, the cumulative wind speed distributions over the wet and dry seasons are also analyzed. The results show that the shape and scale parameters for Kribi, Douala and Limbe are 2.9 and 2.8, 3.9 and 1.8 and 3.08 and 2.58, respectively. The mean power densities through Weibull analysis for Kribi, Douala and Limbe are 33.7 W/m2, 8.0 W/m2 and 25.42 W/m2, respectively. Kribi’s most probable wind speed and maximum energy carrying wind speed was found to be 2.42 m/s and 3.35 m/s, 2.27 m/s and 3.03 m/s for Limbe and 1.67 m/s and 2.0 m/s for Douala, respectively. Analysis of the wind speed and hence power distribution over the wet and dry seasons shows that in the wet season, August is the windiest month for Douala and Limbe while September is the windiest month for Kribi while in the dry season, March is the windiest month for Douala and Limbe while February is the windiest month for Kribi. In terms of mean power density, most probable wind speed and wind speed carrying maximum energy, Kribi shows to be the best site for the installation of a wind farm. Generally, the wind speeds at all three locations seem quite low, average wind speeds of all the three studied locations fall below 4.0m/s which is far below the cut-in wind speed of many modern wind turbines. However we recommend the use of low cut-in speed wind turbines like the Savonius for stand alone low energy needs

In this paper, temperature extremes are forecast by employing the block maxima method of the Generalized extreme value(GEV) distribution to analyse temperature data from the Cameroon Development Corporation (C.D.C). By considering two sets of data (Raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data while in the simulated data, the return values show an increasing trend but with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend but with an upper bound. This clearly shows that temperatures in the tropics even-though show a sign of increasing in the future, there is a maximum temperature at which there is no exceedence. The results of this paper are very vital in Agricultural and Environmental research., {"references":["Myriam Charras-Garrido, Pascal Lezaud. Extreme Value Analysis : an\nIntroduction. Journal de la Societe Francaise de Statistique et Societe\nMathematique de France, 2013, 154 (2), pp 66-97. (hal-00917995).","S. Eduardo. Martins et al, Generalized maximum-likelihood generalised\nextreme-value quantile estimators for hydrologic data, 2000.","Hosking et al Estimation of the Generalized Extreme value distribution\nby the method of the probability-weighted moments, 1985 .","Husna Hasan, N. Salam Modelling extreme temperature in Malaysia using\ngeneralised extreme value distribution, 2012.","Meehl, George A, Tebaldi, Claudia, More Intense, More Frequent, and\nLonger Lasting Heat Waves in the 21st Century. Science 305 (5686):\n9947. Bibcode:2004Sci...305..994M. doi:10.1126/science.1098704.\nPMID 15310900, (13 August 2004).","Sheng Ngong Estimation of hot and cold spells with extreme value theory,\n2012.","Notes on probability distributions in Easy-fit pro.","H. W. Van Den Brink, Estimating return periods of extreme evens from\nECMWF seasonal forecast ensembles, 2004."]}

In this paper, to model a real life wind turbine, a probabilistic approach is proposed to model the dynamics of the blade elements of a small axial wind turbine under extreme stochastic wind speeds conditions. It was found that the power and the torque probability density functions even-dough decreases at these extreme wind speeds but are not infinite. Moreover, we also fund that it is possible to stabilize the power coefficient (stabilizing the output power)above rated wind speeds by turning some control parameters. This method helps to explain the effect of turbulence on the quality and quantity of the harness power and aerodynamic torque., {"references":["Boubekeur Bourhezzar, Houria Siguerdidjane, Nonlinear Controle of\nVariable Speed Wind Turbines for Power Regulation, Proceedings of the\n2005 IEEE Conference on Control Applications Toronto, Canada, August\n28-31, 2005.","Bianchi, F.D.; de Battista, H.; Mantz, R.j., Wind\nTurbine Control Systems, 2007,xx, 208p., Hardcover\n(http://www.springer.com/978-1-84628-492-2).","Oliver Probst, Jaime Martnez, Jorge Elizondo and Oswaldo Monroy\n(2011). Small Wind Turbine Technology,Wind Turbines, Dr. Ibrahim\nAl-Bahadly (Ed.), ISBN: 978-953-307-221-0, InTech, Available from:\nhttp://www.intechopen.com/books/wind-turbines/small-wind-turbine-technology","C. Nichita, M.El Mokadem and B. Dakyo, Wind Turbine Simulation\nProcedures, Wind Engineering Volume 30, No. 3, 2006, pp 187-200.","Arnab Sarkar, Sunita Singh, Debojyoti Mitra Wind climate modeling\nusing Weibull and extreme value distribution , International Journal of\nEngineering, Science and Technology, Vol. 3, No. 5, 2011, pp. 100-106.","Janos Galambos, James Lechner, Emil Simiu Extreme value theory and\nApplications,1993."]}

This chapter explores the different ways in which solar radiation (SR) can be quantified for use in photovoltaic applications. Some solar radiation models that incorporate different combinations of parameters are presented. The parameters mostly used include the clearness index (Kt), the sunshine fraction (SF), cloud cover (CC) and air mass (m). Some of the models are linear while others are nonlinear. These models will be developed for the estimation of the direct (Hb) and diffuse (Hd) components of global solar radiation (H) on both the horizontal and tilted surfaces. Models to determine the optimal tilt and azimuthal angles for solar photovoltaic (PV) collectors in terms of geographical parameters are equally presented. The applicable, statistical evaluation models that ascertain the validity of the SR mathematical models are also highlighted.