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Background and Objectives: Adhesion of microorganisms on facemask surfaces is a major problem that produces contam- ination of the mask wearer either by inhalation or by direct contact. Generally, physicochemical properties of the material and the microorganism are responsible for this adhesion and are also reported to influence the filtration efficiency of face- masks. However, theses surface proprieties and their effect on particles attachment on facemask materials remain poorly documented. The purpose of this study was to investigate the physicochemical properties of seven facemasks and evaluate the influence of these characteristics on the adhesion of Sataphylococcus aureus. Materials and Methods: Physicochemical properties is done by contact angle method and scanning electron microscopy while theoretical adhesion of S. aureus is done according to XDLVO approach. Results: The obtained results showed that all masks have a hydrophobic character. The electron donor and electron acceptor parameters change depending on each mask. Chemical analysis demonstrates the presence of two chemical elements (car- bon and oxygen). Predictive adhesion demonstrate that S. aureus has an attractive behavior towards the masks used but the potential of adhesion is not the same. Conclusion: Such information is valuable to understand attachment of biological particles and to contribute in the inhibition of this attachment.

Classification is a crucial stage in the processing of satellite images that influence considerably the quality of the result. A variety of methods is proposed in the literature for the purposes of image classification. They present many differences in their basic principles, thus in the quality of the results obtained. Therefore, a study of different classification methods seems to be essential. The classification of satellite images with conventional methods can be done in several ways using different algorithms. These algorithms can be divided into two main categories: supervised and non-supervised. Decision tree on the contrary is a machine learning tool. It is a plain model characterized by the simplicity of understanding and interpretation. This work aims firstly, to classify a high resolution Quickbird satellite image of an urban area by the decision tree method and compare it with the conventional classification algorithms in order to evaluate its efficiency. The methodology consists of two main stages: classification and evaluation of results. The second is based on the calculation of a number of statistical indices derived from the confusion matrix: the statistical parameter “kappa’ and the overall coefficient of precision.