Geothermometry, ore geochemistry and fluid flow in the Esperanza Vein, Huachocolpa District (Huancavelica, Peru)

The Esperanza Zn-Pb-Ag vein, owned by Compañía de Minas Buenaventura S.A.A., lies over 4000 to 4650 masl in the Western Cordillera of the Peruvian Central Andes. The Esperanza low sulphidation epithermal vein trends ~E-W along 1500 m; it dips to the South and can be followed to 350 m depth. As other veins of the district, like Teresita and Bienaventurada, it is hosted by intermediate to felsic volcanics (andesitic to dacitic compositions) of the Huachocolpa Group (Middle Miocene to Upper Pliocene). The mineralisation occurs mostly as open space filling related to fracture development during the Quechua III deformational event. Main ore minerals are sphalerite, galena, tetrahedrite, pyrite, chalcopyrite and Ag and Pb sulfosalts; quartz, barite and calcite are the main gangue minerals. Current production grades are ~5% Zn, ~8Oz/t Ag, ~3% Pb; usually very low Cu (mean ~0.04%).

A Prediction Model to Diabetes using Artificial Metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.