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The present work focuses on the study of the water absorption phenomenon through the pith of Raffia vinifera along the stem. The water absorption kinetics was studied experimentally by the gravimetric method with the discontinuous control of the sampling mass at temperature of 30 ∘ C. The samples of 70 mm × 8 mm × 4 mm were taken from twelve sampling zones of the stem of Raffia vinifera. The result shows that the percentage of water absorption of the pith of Raffia vinifera increases from the periphery to the center in the radial position and from the base to the leaves in the longitudinal position. Fick's second law was adopted for the study of the water diffusion. Eleven models were tested for the modelling of the water absorption kinetics and the model of Sikame Tagne (2014) is the optimal model. The diffusion coefficients of two stages were determined by the solution of the Fick equation in the twelve sampling zones described by Sikame Tagne et al. (2014). The diffusion coefficients decreased from the center to the periphery in the radial position and from the base to the leaves in the longitudinal position.

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This work was aimed to apply the artificial neural network (ANNs) for predicting indoor air temperature in modern building, seven hours in advance in humid region, using as inputs only the outdoor air temperature and the last six hourly values of indoor air temperature. The building experiment is built with cement hallow block in the town of Douala in Cameroon, and the experimentation was carried out for six months. Experimental data were used to determine the optimal ANN structure with Levenberg-Marquardt algorithm by using Matlab software. The optimal structure was the multilayer perceptron (MLP) with seven input variables, thirty hidden neurons and one neuron in the output layer. The activation functions were respectively the hyperbolic tangent in the hidden layer and the linear function in the output layer. Moreover, the indoor air temperature results simulated by using the developed ANN model were strongly correlated with the experimental data. These results testified that ANN can be valuable tool for hourly indoor air temperature prediction in particular and others indoor air parameters of building, such as relative humidity, cooling loads.