Lead zirconate titanate, with Zr/Ti ratio of 53/47 was prepared by the polymeric precursor method. It was investigated the barium (II) modification at 0.0, 0.2, 0.4 and 0.6 mol% in substitution to the lead (II) cation in A site of perovskite structure. The powder samples were characterized by XRD and the diffraction patterns were used to Rietveld refinement. The percentages of tetragonal and rhombohedral phases and a systematic study of the effect of barium (II) on the morphology and the dielectric properties of PZT were carried out. The results showed that the tetragonal phase is favored and the ceramic density is improved with the barium (II) insertion. The Curie temperature (Tc) is increased besides the slight reduction of dielectric constant (Kc).
Although machine learning algorithms have been successful when applied to several tasks, the selection of the most suitable for a given dataset is not straightforward. The recommendation of machine learning algorithms can be automated through the use of meta-learning, but this requires efficient methods for the characterizations of datasets, i.e. meta-features extraction. In this work we propose to accelerate the extraction of clustering-based meta-features on GPUs, taking advantage of the optimized libraries and API from the RAPIDS framework. We parallelized a well-known meta-feature extraction tool (MFE) via RAPIDS to accelerate the clustering meta-features extraction process. Our experiment shows that significantly less time is required to complete the extraction, up to 10x faster than the MFE implementation. These results are promising and suggest greater feasibility for large-scale experiments involving meta-learning.
Low crystalline PZT powder samples were successfully synthesized using polymeric precursor method and slow decomposition steps. The polymeric resin precursor was thermal treated in a muffle type oven varying the temperature from 250 °C to 700 °C and the time from 3 to 24 hours in order to investigate the order/disorder mechanism toward the amorphous powders. Powder samples with low crystalline phases were obtained at lower temperatures and long time of thermal treatment, demonstrating a kinetic dependence for organic removal and a thermodynamic barrier for crystallization processes. Through XRD and FTIR spectroscopy characterizations the long time thermal treated samples showed to be composed of the solid solution of metal oxides in absent of organic matter, originating broad XRD peaks profiles and no carbonaceous bands in FTIR spectra. A Photoluminescence characterization showed that the peak emission is higher for disordered and homogeneous phases, which only can be reached through the long time of thermal treatment.
O Aedes aegypti é o vetor de um dos problemas de saúde pública mais difíceis de serem combatidos no mundo tropical: a proliferação da epidemia de dengue. Como não há vacina ou tratamento específico, e a erradicação do seu vetor se tornou praticamente impossível, a melhor maneira de se evitar a doença é realizando o controle do mosquito Aedes aegypti. Com isso, este trabalho apresenta uma metodologia computacional para a segmentação de ovos nas palhetas ovitrampas que visa auxiliar especialistas nessa contagem. Para o desenvolvimento deste trabalho realizamos testes com as redes neurais convolucional desenvolvida para segmentação: U-Net, Segnet e uma rede pré treinada. Em seguida, realizamos uma etapa de pós-processamento baseada em morfologia matemática. Os resultados alcançados foram promissores, e a rede U-Net foi a que apresentou melhor desempenho, com Acurácia de 98,65% na segmentação dos ovos e erro médio quadrado de 4,25% na contagem.
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