Avaliação do concreto de cimento portland com resíduo da produção de scheelita em substituição ao agregado miúdo

From the 70`s, with the publication of the Manifesto for Environment UN Conference, held in Stockholm, in Sweden (1972), defend and improve the environment became part of our daily lives. Thus, several studies have emerged in several segments in order to reuse the waste. Some examples of waste incorporated in portland cement concrete are: rice husk ash, bagasse ash of cane sugar, powder-stone, microsilica, tire rubber, among others. This research used the residue of the mining industry Scheelite, to evaluate the incorporation of the residue composition of Portland cement concrete, replacing the natural sand. The percentage of residue were incorporated from 0% to 100%, with a variation of 10%, 11 being produced concrete mix in the ratio 1:2:3:0.60, by mass. We evaluated the following characteristics of concrete: slump test, compressive strength, tensile strength by diametral compression, water absorption, porosity and density, based on the ABNT, through tests performed in the Laboratory of Civil Construction, UFRN. The trace with the addition of 60% scheelite residue was obtained which better performance. Therefore, the use of the waste from the production of Scheelite is feasible due to the durability parameters (water absorption and porosity), sustainability, and the good results of the resistance of the concrete

Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth