Large-area homogeneous periodic surface structures generated on the surface sputtered boron carbide thin films by femtosecond laser processing

Amorphous and crystalline sputtered boron carbide thin films have a very high hardness even surpassing that of bulk crystalline boron carbide (≈41 GPa). However, magnetron sputtered B-C films have high friction coefficients (C.o.F) which limit their industrial application. Nanopatterning of materials surfaces has been proposed as a solution to decrease the C.o.F. The contact area of the nanopatterned surfaces is decreased due to the nanometre size of the asperities which results in a significant reduction of adhesion and friction. In the present work, the surface of amorphous and polycrystalline B-C thin films deposited by magnetron sputtering was nanopatterned using infrared femtosecond laser radiation. Successive parallel laser tracks 10 μm apart were overlapped in order to obtain a processed area of about 3 mm2. Sinusoidal-like undulations with the same spatial period as the laser tracks were formed on the surface of the amorphous boron carbide films after laser processing. The undulations amplitude increases with increasing laser fluence. The formation of undulations with a 10 μm period was also observed on the surface of the crystalline boron carbide film processed with a pulse energy of 72 μJ. The amplitude of the undulations is about 10 times higher than in the amorphous films processed at the same pulse energy due to the higher roughness of the films and consequent increase in laser radiation absorption. LIPSS formation on the surface of the films was achieved for the three B-C films under study. However, LIPSS are formed under different circumstances. Processing of the amorphous films at low fluence (72 μJ) results in LIPSS formation only on localized spots on the film surface. LIPSS formation was also observed on the top of the undulations formed after laser processing with 78 μJ of the amorphous film deposited at 800 °C. Finally, large-area homogeneous LIPSS coverage of the boron carbide crystalline films surface was achieved within a large range of laser fluences although holes are also formed at higher laser fluences.

Sparse matrix multiplication on a reconfigurable many-core architecture

Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.

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.