Hydraulic binders

Hydraulic binders play a vital role in the economic and social development because they are essential components of concrete, the most widely used construction material. Nowadays, Portland cement is the most predominantly used hydraulic binder due to its properties and widespread availability. Cement manufacture consumes large amount of non-renewable raw materials and energy, and it is a carbon-intensive process. Many efforts are, therefore, being undertaken towards the developing “greener” hydraulic binders. Concomitantly, binders must also correspond to market demand in terms of performance and aesthetic as well as fulfill mandatory regulations. In order to pursue these goals, different approaches have been followed including the improvement of the cement manufacturing process, production of blended cements, and testing innovative hydraulic binders with a different chemistry. This chapter presents a brief history of hydraulic binder’s discovery and use as well as the environmental and economic context of cement industry. It, then, describes the chemistry and properties of currently most used hydraulic binders—common cements and hydraulic limes—and that of the more promising binders for future applications, namely special Portland cements, aluminous cements, calcium sulfoaluminate cements, and alkali-activated cements.

Processing and characterization of 3D dense chitosan pieces, for orthopedic applications, by adding plasticizers

In this work, plasticizer agents were incorporated in a chitosan based formulation, as a strategy to improve the fragile structure of chitosan based-materials. Three different plasticizers: ethylene glycol, glycerol and sorbitol, were blended with chitosan to prepare 3D dense chitosan specimens. The properties of the obtained structures were assessed for mechanical, microstructural, physical and biocompatibility behavior. The results obtained revealed that from the different specimens prepared, the blend of chitosan with glycerol has superior mechanical properties and good biological behavior, making this chitosan based formulation a good candidate to improve robust chitosan structures for the construction of bioabsorbable orthopedic implants.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.