Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

The effects of pre-weaning undernutrition on the expression levels of free radical deactivating enzymes in the mouse brain

A mild degree of undernutrition brought about by restricting the amount of food in the diet is known to alter the life span of an animal. It has been hypothesised that this may be related to the effects of undernutrition on an animals anti-oxidant defense system. We have therefore, used real-time PCR (rt-PCR) techniques to determine the levels of mRNA expression for manganese superoxide dismutase (MnSOD), copper/zinc superoxide dismutase (Cu/ZnSOD), glutathione peroxidase 1 (GPx 1) and catalase in the brains of Quackenbush mice undernourished from conception until 21-post-natal days of age. It was found that 21- and 61-day-old undernourished mice had a deficit in the expression of Cu/ZnSOD in both the cerebellum and forebrain regions compared to age-matched controls. The expression of MnSOD was found to be greater in the cerebellum, but not the forebrain region, of 21-day-old undernourished mice. There were no significant differences in the expression of GPx 1 and catalase between control and undernourished or previously undernourished mice. Our results confirm that undernutrition during the early life of a mouse may disrupt some of the enzymes involved in the anti-oxidant defense systems.

Stress corrosion cracking and hydrogen embrittlement of an Al-Zn-Mg-Cu alloy

The age hardening, stress corrosion cracking (SCC) and hydrogen embrittlement (HE) of an Al-Zn-Mg-Cu 7175 alloy were investigated experimentally. There were two peak-aged states during ageing. For ageing at 413 K, the strength of the second peak-aged state was slightly higher than that of the first one, whereas the SCC susceptibility was lower, indicating that it is possible to heat treat 7175 to high strength and simultaneously to have high SCC resistance. The SCC susceptibility increased with increasing Mg segregation at the grain boundaries. Hydrogen embrittlement (HE) increased with increased hydrogen charging and decreased with increasing ageing time for the same hydrogen charging conditions. Computer simulations were carried out of (a) the Mg grain boundary segregation using the embedded atom method and (b) the effect of Mg and H segregation on the grain boundary strength using a quasi-chemical approach. The simulations showed that (a) Mg grain boundary segregation in Al-Zn-Mg-Cu alloys is spontaneous, (b) Mg segregation decreases the grain boundary strength, and (c) H embrittles the grain boundary more seriously than does Mg. Therefore, the SCC mechanism of Al-Zn-Mg Cu alloys is attributed to the combination of HE and Mg segregation induced grain boundary embrittlement. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Precipitation induced densification in a sintered Al-Zn-Mg-Cu alloy

It is demonstrated that slow cooling to 200 degrees C from a high sintering temperature (620 degrees C) reduces porosity in an Al-8Zn-2.5Mg-1Cu powder compact when compared to isothermal sintering at the higher temperature for a longer time. The reduction in porosity is attributed to shrinkage associated with removal of solute from the aluminium solid solution and heterogeneous precipitation of the eta phase (MgZn2), particularly onto pore surfaces. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.