Improved pretreatments applied to the sugarcane bagasse and release of lignin and hemicellulose from the cellulose-enriched fractions by sulfuric acid hydrolysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Peat humic substances enriched with nutrients for agricultural applications: competion between nutrients and non-essential metals present in tropical soils

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The stigma and prejudice of leprosy: influence on the human condition

Introduction: To analyze the knowledge, feelings and perceptions involving patients affected by leprosy, as a better understanding of these factors may be useful to decrease the stigma and prejudice associated with the condition. Methods: The study cohort consisted of 94 patients who underwent treatment for leprosy at the Health Units in the City of Cuiaba, Mato Grosso (MT), Brazil. The study questionnaire included items to collect information on socio-demographic data, knowledge about the disease, stigma, prejudice, self-esteem and quality of life of leprosy patients. Bivariate analyses were used to assess the data based on the chi-square test with a 5% significance threshold. Results: The results revealed that the study population consisted predominantly of males (55.3%) with an income between 1 and 3 times the minimum wage (67%). The survey respondents reported that the most significant difficulties related to the treatment were the side effects (44.7%) and the duration of the treatment (28.7%). A total of 72.3% of the subjects were knowledgeable about the disease, of whom 26.6% had the leprosy reaction. Stigma and prejudice were cited by 93.6% of the participants. Based on the responses, 40.4% of patients reported being depressed and sad, and 69.1% of the subjects encountered problems at work after being diagnosed. A total of 45.7% of the patients rated their quality of life between bad and very bad. Conclusions: Our results suggest that leprosy causes suffering in patients beyond pain and discomfort and greatly influences social participation.

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

The effect of filter cakes enriched with soluble phosphorus used as a fertilizer on the sugarcane ratoons

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Body condition score of Nellore beef cows: a heritable measure to improve the selection of reproductive and maternal traits

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effects of age condition on the distribution and integrity of inorganic fillers in dental resin composites

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Cultivo de Eucalyptus urograndis em atmosfera enriquecida com CO2: mudanças no proteoma cloroplastidial

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)