Innovative low temperature plasma approach for deposition of alumina films

Alumina films were deposited from a new plasma method using aluminum acetylacetonate (AAA) powder as precursor. The AAA was sputtered in argon and oxygen plasma mixtures. It was investigated the effect of the oxygen proportion (O2%) on the properties of the coatings. Deposition rate was derived from the layer height measured by profilometry. The elemental composition and molecular structure of the films were determined by Rutherford backscattering and infrared spectroscopies, respectively. Grazing incidence X-ray diffraction was used to investigate the microstructure of the films while hardness was determined by nanoindentation technique. Inspections on the surface morphology and on the film composition were conducted associating scanning electron microscopy and energy dispersive spectroscopy. Incorporation of oxygen affects the plasma kinetics and consequently the properties of the coatings. As moderated concentrations of oxygen ( 25%) are incorporated, the structure become rich in metallic aluminum with carbon rising at low proportions. The deposited layer is not homogeneous in thickness once the chemical composition of the precursor is changed by the action of the reactive oxygen plasma. Oxygen ablation on the film surface also contributes to the lack of homogeneity of the structure, especially as high oxygen proportions are imposed. Hardness data (0.5-2.0 GPa) corroborated the idea of an amorphous structure. Based on the results presented here it was possible to identify the oxygen concentration in the plasma atmosphere which mostly removed organics while preserving the stoichiometric alumina precipitation, subject of great relevance as one considers the reduction in the energy necessary for the creation of fully oxide coatings.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Estadual Paulista Laboratório de Plasmas TecnológicosUniversidade Federal de São Paulo (UNIFESP) Departamento de Ciências Exatas e da TerraUniversidade de São Paulo Departamento de Física NuclearUniversidade de São Paulo Departamento de Física AplicadaUNIFESP, Depto. de Ciências Exatas e da TerraSciEL

Soil Class Prediction By Data Mining In An Area Of The Sedimentary Sao Francisco Basin

The objective of this work was to evaluate different strategies for the prediction of soil class distribution on digital soil maps of areas without reference data, in the sedimentary basin of San Francisco, in the north of the state of Minas Gerais, Brazil. The strategies included: taxonomic generalization, training by field observations, training set expansion, and the use of different data mining algorithms. Four matrices were developed, differentiated by the volume of data for machine learning and by soil taxonomic levels to be predicted. The performance of the machine learning algorithms - Random Forest, J48, and MLP-, associated with discretization, class balancing, variable selection, and expansion of the training set was evaluated. Class balancing, variable discretization by equal frequencies, and the Random Forest algorithm showed the best performances. The representativeness extension of field observations, that assumes a larger training area, brought no predictive gain. Soil taxonomic generalization to the suborder level reduces the fragmentation of mapped polygons and improves the accuracy of digital soil maps. When generated by training on in situ soil observations at the mapping area, digital soil maps are as accurate as those trained on preexistent maps.5191396140

Soil Class Prediction By Data Mining In An Area Of The Sedimentary São Francisco Basin

O objetivo deste trabalho foi avaliar diferentes estratégias para a predição da distribuição de classes de solo em mapas pedológicos digitais de áreas sem dados de referência, na bacia sedimentar do São Francisco, no Norte de Minas Gerais. As estratégias incluíram: o detalhamento da legenda, o treinamento por observações em campo, a ampliação do conjunto de treinamento e o uso de diferentes algoritmos de mineração de dados. Foram elaboradas quatro matrizes, diferenciadas pelo volume de dados, para o aprendizado dos algoritmos, e pelo nível taxonômico das classes de solo a serem preditas. Avaliou-se o desempenho dos algoritmos de aprendizado de máquina - Random Forest, J48 e MLP -, associados a procedimentos de discretização, balanceamento de classes, seleção de variáveis e expansão do conjunto de treinamento. O balanceamento de classes, a discretização de variáveis por frequências iguais e o algoritmo Random Forest apresentaram os melhores desempenhos. A extensão da representatividade das observações em campo, que presume uma área de treinamento mais ampla, não trouxe ganho preditivo. A generalização taxonômica para subordem diminui a fragmentação dos polígonos mapeados e aumenta a acurácia dos mapas pedológicos digitais. Quando são produzidos após treinamento por observações de solo in situ, na área de mapeamento, os mapas pedológicos digitais têm valores de acurácia equivalentes aos dos treinados em mapas preexistentes.5191396140

Soil class prediction by data mining in an area of the sedimentary São Francisco basin

O objetivo deste trabalho foi avaliar diferentes estratégias para a predição da distribuição de classes de solo em mapas pedológicos digitais de áreas sem dados de referência, na bacia sedimentar do São Francisco, no Norte de Minas Gerais. As estratégias incluíram: o detalhamento da legenda, o treinamento por observações em campo, a ampliação do conjunto de treinamento e o uso de diferentes algoritmos de mineração de dados. Foram elaboradas quatro matrizes, diferenciadas pelo volume de dados, para o aprendizado dos algoritmos, e pelo nível taxonômico das classes de solo a serem preditas. Avaliou-se o desempenho dos algoritmos de aprendizado de máquina – Random Forest, J48 e MLP –, associados a procedimentos de discretização, balanceamento de classes, seleção de variáveis e expansão do conjunto de treinamento. O balanceamento de classes, a discretização de variáveis por frequências iguais e o algoritmo Random Forest apresentaram os melhores desempenhos. A extensão da representatividade das observações em campo, que presume uma área de treinamento mais ampla, não trouxe ganho preditivo. A generalização taxonômica para subordem diminui a fragmentação dos polígonos mapeados e aumenta a acurácia dos mapas pedológicos digitais. Quando são produzidos após treinamento por observações de solo in situ, na área de mapeamento, os mapas pedológicos digitais têm valores de acurácia equivalentes aos dos treinados em mapas preexistentes.The objective of this work was to evaluate different strategies for the prediction of soil class distribution on digital soil maps of areas without reference data, in the sedimentary basin of San Francisco, in the north of the state of Minas Gerais, Brazil. The strategies included: taxonomic generalization, training by field observations, training set expansion, and the use of different data mining algorithms. Four matrices were developed, differentiated by the volume of data for machine learning and by soil taxonomic levels to be predicted. The performance of the machine learning algorithms – Random Forest, J48, and MLP –, associated with discretization, class balancing, variable selection, and expansion of the training set was evaluated. Class balancing, variable discretization by equal frequencies, and the Random Forest algorithm showed the best performances. The representativeness extension of field observations, that assumes a larger training area, brought no predictive gain. Soil taxonomic generalization to the suborder level reduces the fragmentation of mapped polygons and improves the accuracy of digital soil maps. When generated by training on in situ soil observations at the mapping area, digital soil maps are as accurate as those trained on preexistent maps

Correlation between presence of Leishmania RNA virus 1 and clinical characteristics of nasal mucosal leishmaniosis

AbstractIntroductionMucosal leishmaniosis (ML) is a severe clinical form of leishmaniosis. Complex factors related to the parasite and the host are attributed to the development of mucosal lesions. Leishmania RNA virus 1 (LRV1) can disrupt immune response, and may be the main determinant of severity of the disease; it should be investigated.ObjectiveTo study the existence of clinical differences between patients with ML with endosymbiosis by LRV1 and. those without it.MethodsA cross-sectional cohort study with clinical evaluation, polymerase chain reaction (PCR) detection of Leishmania, species classification, and search of LRV1 was performed. Only patients with confirmed diagnosis of ML by positive PCR and with nasal mucosa injuries were included in this analysis.ResultsOut of 37 patients, 30 (81.1%) were diagnosed with Leishmania braziliensis, five (13.5%) with Leishmania guyanensis, and two (5.4%) with mixed infection of L. braziliensis and L. guyanensis. LVR1 virus was present in 26 (70.3%) of the cases.ConclusionCorrelation between clinical phenotype and presence of LRV1 was not observed, although the frequency of the virus is two-fold higher in mucosal lesions than that found in the literature on skin lesions in the same geographical area

Caracterização das propriedades do compósito Zircônia-Espinélio

Cerâmicas do tipo espinélio têm propriedades excelentes, sustentadoras da sua aplicação industrial: alto ponto de fusão (2135 oC), grande estabilidade em baixas e altas temperaturas, baixa condutividade térmica, alta resistência aos ácidos, dentre outras. Por isso, o espinélio é largamente utilizado como material refratário em fornos industriais e para uso como material de resistência à corrosão em cadinhos. Nesta pesquisa, foram preparados materiais cerâmicos compostos de Zircônia e Espinélio em diferentes proporções. Para esses materiais foram empregadas as seguintes caracterizações: microscopia óptica, microscopias eletrônicas de varredura (MEV) e dureza por microindentação Vickers. Foram realizados com diferentes proporções de zircônia e espinélio, para verificar a influência da concentração de zircônia na dureza do material. O composto cerâmico com maior percentual de zircônia apresentou a maior dureza, de 300 HV

De novo colorectal cancer after liver and kidney transplantation–Microenvironment disturbance

Colorectal cancer (CRC) is a major health burden and may arise as a complication of solid organ transplantation. Our study aimed to assess the incidence of the CRC in kidney and liver transplanted patients at a tertiary and reference center and to describe their clinical and pathological features. Twelve patients, 10 men and two women, with a mean age of 60 years, composed our cohort, ten of them submitted to CRC resection. Transplanted organ was liver in five patients and kidney in seven. Regarding overall survival, patients submitted to renal transplantation were all deceased 5 years after CRC diagnosis, while those subjected to hepatic transplantation had a survival of 60% at the fifth year. Pathology examination showed seven patients with advanced disease (stage III/IV) and high amount of necrosis. Tumor microenvironment was disturbed, with low inflammatory infiltrate, absence of natural killer cells and no PD-L1 expression. CRC exhibited microsatellite instability in 40%, with expression of cancer stem cell markers (CD133, CD44 and ALDH1), as well as P53 (50%) and KRAS mutations (41.7%). CRC cancer after kidney and hepatic transplantation is a rare, but aggressive and deadly event. Regular follow-up should be instituted in these patients

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good