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In this paper, a simple home-made Forcespinning system was designed and successfully applied for the production of different metal oxide ultrafine tubes such as SnOx and ZnO. The morphology, purity, crystallinity and porosity of the forcespun samples were investigated by SEM, XRD, and EDS, TGA, and BET analysis. The samples were deposited unto a microsensing platform and their gas-sensing properties against different hazardous vapors such as ethanol, acetone and benzene were studied through preliminary tests at different detection temperatures. The results revealed different sensing behaviors of the prepared sensors towards the different vapors. Interestingly, the gas sensor based on SnO x exhibited a higher and faster sensor response towards ethanol at 180°C as operating temperature. On the other hand, ZnO presented a higher response to acetone at 250°C. The ability of the prepared sensors to discriminate between the different vapors was explored.

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The aim of the present study is to investigate and explore the capability of the multilayer perceptron neural network to classify seismic signals recorded by the local seismic network of Agadir (M orocco). The problem is divided into two main steps, the feature extraction step and classification step. In the former, relevant discriminant features are extracted from the seismic signal based on the time and frequency domains. These are selected based on the analysts' experience. In the latter step, a process of trial an error was carried out to find the best neural network architecture. Classification results on a data set of 343 seis mic signals have demonstrated that the accuracy of the proposed classier can achieve more than 94%.