Numerical simulation of magnetite segregation in a dense medium cyclone

Numerical simulations of turbulent driven flow in a dense medium cyclone with magnetite medium have been conducted using Fluent. The predicted air core shape and diameter were found to be close to the experimental results measured by gamma ray tomography. It is possible that the Large eddy simulation (LES) turbulence model with Mixture multi-phase model can be used to predict the air/slurry interface accurately although the LES may need a finer grid. Multi-phase simulations (air/water/medium) are showing appropriate medium segregation effects but are over-predicting the level of segregation compared to that measured by gamma-ray tomography in particular with over prediction of medium concentrations near the wall. Further, investigated the accurate prediction of axial segregation of magnetite using the LES turbulence model together with the multi-phase mixture model and viscosity corrections according to the feed particle loading factor. Addition of lift forces and viscosity correction improved the predictions especially near the wall. Predicted density profiles are very close to gamma ray tomography data showing a clear density drop near the wall. The effect of size distribution of the magnetite has been fully studied. It is interesting to note that the ultra-fine magnetite sizes (i.e. 2 and 7 mu m) are distributed uniformly throughout the cyclone. As the size of magnetite increases, more segregation of magnetite occurs close to the wall. The cut-density (d(50)) of the magnetite segregation is 32 gm, which is expected with superfine magnetite feed size distribution. At higher feed densities the agreement between the [Dungilson, 1999; Wood, J.C., 1990. A performance model for coal-washing dense medium cyclones, Ph.D. Thesis, JKMRC, University of Queensland] correlations and the CFD are reasonably good, but the overflow density is lower than the model predictions. It is believed that the excessive underflow volumetric flow rates are responsible for under prediction of the overflow density. (c) 2006 Elsevier Ltd. All rights reserved.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Why Scotland needs more than just a new migration policy

The Scottish Executive has adopted a policy to combat Scotland's declining population by encouraging inward migration. Using a multi-state population model this paper presents nine long-term population scenarios for Scotland using three net international migration levels and three fertility paths. The results show inward migration can slow population decline but makes little difference to population ageing. Without a higher fertility rate Scotland's population will become demographically unsustainable. Our simulations show that a higher fertility rate substantially reduces the future ageing.

Mechanical alloying of MoSi2 with ternary alloying elements. Part 2 - computer simulation

A computer model of the mechanical alloying process has been developed to simulate phase formation during the mechanical alloying of Mo and Si elemental powders with a ternary addition of Al, Mg, Ti or Zr. Using the Arhennius equation, the model balances the formation rates of the competing reactions that are observed during milling. These reactions include the formation of tetragonal C11(b) MOSi2 (t-MoSi2) by combustion, the formation of the hexagonal C40 MoSi2 polymorph (h-MoSi2), the transformation of the tetragonal to the hexagonal form, and the recovery of t-MoSi2 from h-MoSi2 and deformed t-MoSi2. The addition of the ternary additions changes the free energy of formation of the associated MoSi2 alloys, i.e. Mo(Si, Al)(2), Mo(Mg, Al)(2), (Mo, Ti)Si-2 (Mo, Zr)Si-2 and (Mo, Fe)Si-2, respectively. Variation of the energy of formation alone is sufficient for the simulation to accurately model the observed phase formation. (C) 2003 Elsevier B.V. All rights reserved.

Opposite effects of androgen receptor CAG repeat length on increased risk of left-handedness in males and females

Prenatal exposure to testosterone has been hypothesised to effect lateralization by influencing cell death in the foetal brain. Testosterone binds to the X chromosome linked androgen receptor, which contains a polymorphic polyglutamine CAG repeat, the length of which is positively correlated with testosterone levels in males, and negatively correlated in females. To determine whether the length of the androgen receptor mediates the effects of testosterone on laterality, we examined the association between the number of CAG repeats in the androgen receptor gene and handedness for writing. Association was tested by adding regression terms for the length of the androgen receptor alleles to a multi-factorial-threshold model of liability to left-handedness. In females we found the risk of left-handedness was greater in those with a greater number of repeats (p=0.04), this finding was replicated in a second independent sample of female twins (p=0.014). The length of the androgen receptor explained 6% of the total variance and 24% of the genetic variance in females. In males the risk of left-handedness was greater in those with fewer repeats (p=0.02), with variation in receptor length explaining 10% of the total variance and 24% of the genetic variance. Thus, consistent with Witelson's theory of testosterone action, in all three samples the likelihood of left handedness increased in those individuals with variants of the androgen receptor associated with lower testosterone levels.

Solution of the dual reflection equation for A(n-1)((1)) solid-on-solid model

We obtain a diagonal solution of the dual reflection equation for the elliptic A(n-1)((1)) solid-on-solid model. The isomorphism between the solutions of the reflection equation and its dual is studied. (C) 2004 American Institute of Physics.

Mixed model formulations for multi-environment trials

When studying genotype X environment interaction in multi-environment trials, plant breeders and geneticists often consider one of the effects, environments or genotypes, to be fixed and the other to be random. However, there are two main formulations for variance component estimation for the mixed model situation, referred to as the unconstrained-parameters (UP) and constrained-parameters (CP) formulations. These formulations give different estimates of genetic correlation and heritability as well as different tests of significance for the random effects factor. The definition of main effects and interactions and the consequences of such definitions should be clearly understood, and the selected formulation should be consistent for both fixed and random effects. A discussion of the practical outcomes of using the two formulations in the analysis of balanced data from multi-environment trials is presented. It is recommended that the CP formulation be used because of the meaning of its parameters and the corresponding variance components. When managed (fixed) environments are considered, users will have more confidence in prediction for them but will not be overconfident in prediction in the target (random) environments. Genetic gain (predicted response to selection in the target environments from the managed environments) is independent of formulation.