Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

How should pathogen transmission be modelled?

Host-pathogen models are essential for designing strategies for managing disease threats to humans, wild animals and domestic animals. The behaviour of these models is greatly affected by the way in which transmission between infected and susceptible hosts is modelled. Since host-pathogen models were first developed at the beginning of the 20th century, the 'mass action' assumption has almost always been used for transmission. Recently, however, it has been suggested that mass action has often been modelled wrongly. Alternative models of transmission are beginning to appear, as are empirical tests of transmission dynamics.

Eutectic solidification mode in sodium modified Al-7 mass %Si-3.5 mass %Cu-0.2 mass %Mg casting alloys

The influence of sodium (Na) on nucleation and growth of the Al-Si eutectic in a commercial hypoeutectic Al-Si-Cu-Mg foundry alloy has been investigated. The microstructural evolution during eutectic solidification was studied by a quenching technique. By comparing the orientation of the aluminium in the eutectic to that of the surrounding primary aluminium dendrites by EBSD, the eutectic solidification mode could be determined. The results show that the eutectic solidification starts near the mould wall and evolves with front growth opposite the thermal gradient on a macro-scale, and on a micro-scale with independent heterogeneous nucleation of eutectic grains in interdendritic spaces. Na-modified alloys therefore behave significantly differently from those modified by other elemental additions.

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

Mass balance characterisation of Al-7Si-Mg alloy microstructures as a function of solution treatment time

Mass balance calculations were performed to model the effect of solution treatment time on A356 and A357 alloy microstructures. Image analysis and electron probe microanalysis were used to characterise microstructures and confirm model predictions. In as-cast microstructures, up to 8 times more Mg is tied up in the pi-phase than in Mg2Si. The dissolution of pi is accompanied by a corresponding increase in the amount of beta-phase. This causes the rate of pi dissolution to be limited by the rate of beta formation. It is predicted that solution treatments of the order of tens of minutes at 540degreesC produce near-maximum T6 yield strengths, and that Mg contents in excess of 0.52 wt% have no advantage.

Immune responses to ISCOM (R) formulations in animal and primate models

ISCOMs(R) are typically 40 nm cage-like structures comprising antigen, saponin, cholesterol and phospholipid. ISCOMs(R) have been shown to induce antibody responses and activate T helper cells and cyrolytic T lymphocytes in a number of animal species, including non-human primates. Recent clinical studies have demonstrated that ISCOMs(R) are also able to induce antibody and cellular immune responses in humans. This review describes the current understanding of the ability of ISCOMs(R) to induce immune responses and the mechanisms underlying this property. Recent progress in the characterisation and manufacture of ISCOMs(R) will also be discussed. (C) 2001 Elsevier Science Ltd. All rights reserved.

Hemoglobin-degrading, aspartic proteases of blood-feeding parasites - Substrate specificity revealed by homology models

Blood-feeding parasites, including schistosomes, hookworms, and malaria parasites, employ aspartic proteases to make initial or early cleavages in ingested host hemoglobin. To better understand the substrate affinity of these aspartic proteases, sequences were aligned with and/or three-dimensional, molecular models were constructed of the cathepsin D-like aspartic proteases of schistosomes and hookworms and of plasmepsins of Plasmodium falciparum and Plasmodium vivax, using the structure of human cathepsin D bound to the inhibitor pepstatin as the template. The catalytic subsites S5 through S4' were determined for the modeled parasite proteases. Subsequently, the crystal structure of mouse renin complexed with the nonapeptidyl inhibitor t-butyl-CO-His-Pro-Phe-His-Leu [CHOHCH2]Leu-Tyr-Tyr-Ser-NH2 (CH-66) was used to build homology models of the hemoglobin-degrading peptidases docked with a series of octapeptide substrates. The modeled octapeptides included representative sites in hemoglobin known to be cleaved by both Schistosoma japonicum cathepsin D and human cathepsin D, as well as sites cleaved by one but not the other of these enzymes. The peptidase-octapeptide substrate models revealed that differences in cleavage sites were generally attributable to the influence of a single amino acid change among the P5 to P4' residues that would either enhance or diminish the enzymatic affinity. The difference in cleavage sites appeared to be more profound than might be expected from sequence differences in the enzymes and hemoglobins. The findings support the notion that selective inhibitors of the hemoglobin-degrading peptidases of blood-feeding parasites at large could be developed as novel anti-parasitic agents.

The plausibility of long-wavelength stress correlation or stress magnitude as a mechanism for precursory seismicity: Results from two simple elastic models

Observations of accelerating seismic activity prior to large earthquakes in natural fault systems have raised hopes for intermediate-term eartquake forecasting. If this phenomena does exist, then what causes it to occur? Recent theoretical work suggests that the accelerating seismic release sequence is a symptom of increasing long-wavelength stress correlation in the fault region. A more traditional explanation, based on Reid's elastic rebound theory, argues that an accelerating sequence of seismic energy release could be a consequence of increasing stress in a fault system whose stress moment release is dominated by large events. Both of these theories are examined using two discrete models of seismicity: a Burridge-Knopoff block-slider model and an elastic continuum based model. Both models display an accelerating release of seismic energy prior to large simulated earthquakes. In both models there is a correlation between the rate of seismic energy release with the total root-mean-squared stress and the level of long-wavelength stress correlation. Furthermore, both models exhibit a systematic increase in the number of large events at high stress and high long-wavelength stress correlation levels. These results suggest that either explanation is plausible for the accelerating moment release in the models examined. A statistical model based on the Burridge-Knopoff block-slider is constructed which indicates that stress alone is sufficient to produce accelerating release of seismic energy with time prior to a large earthquake.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.