The evolution of QUT’s Student Success Program : 20,000 students later

“The Student Success Program (SSP) is a monitoring and early intervention program in operation at QUT designed to identify and support those students deemed to be at risk of disengaging for their learning and their institution” (Nelson, Quinn, Marrington & Clarke, 2011, p. 83). This report reflects on the development of the program since its inception in 2007. In acknowledging similar initiatives within the sector that monitor student learning engagement, the Nuts & Bolts session allows for identification and discussion of the critical success factors for these intervention and support programs.

Numerical solution of stress intensity factors of multiple cracks in great number with eigen COD boundary integral equations

Based on the eigen crack opening displacement (COD) boundary integral equations, a newly developed computational approach is proposed for the analysis of multiple crack problems. The eigen COD particularly refers to a crack in an infinite domain under fictitious traction acting on the crack surface. With the concept of eigen COD, the multiple cracks in great number can be solved by using the conventional displacement discontinuity boundary integral equations in an iterative fashion with a small size of system matrix. The interactions among cracks are dealt with by two parts according to the distances of cracks to the current crack. The strong effects of cracks in adjacent group are treated with the aid of the local Eshelby matrix derived from the traction BIEs in discrete form. While the relatively week effects of cracks in far-field group are treated in the iteration procedures. Numerical examples are provided for the stress intensity factors of multiple cracks, up to several thousands in number, with the proposed approach. By comparing with the analytical solutions in the literature as well as solutions of the dual boundary integral equations, the effectiveness and the efficiencies of the proposed approach are verified.

Stable strong order 1.0 schemes for solving stochastic ordinary differential equations

The Balanced method was introduced as a class of quasi-implicit methods, based upon the Euler-Maruyama scheme, for solving stiff stochastic differential equations. We extend the Balanced method to introduce a class of stable strong order 1. 0 numerical schemes for solving stochastic ordinary differential equations. We derive convergence results for this class of numerical schemes. We illustrate the asymptotic stability of this class of schemes is illustrated and is compared with contemporary schemes of strong order 1. 0. We present some evidence on parametric selection with respect to minimising the error convergence terms. Furthermore we provide a convergence result for general Balanced style schemes of higher orders.

Adaptive evolution of color vision as seen through the eyes of butterflies

Butterflies and primates are interesting for comparative color vision studies, because both have evolved middle- (M) and long-wavelength- (L) sensitive photopigments with overlapping absorbance spectrum maxima (lambda(max) values). Although positive selection is important for the maintenance of spectral variation within the primate pigments, it remains an open question whether it contributes similarly to the diversification of butterfly pigments. To examine this issue, we performed epimicrospectrophotometry on the eyes of five Limenitis butterfly species and found a 31-nm range of variation in the lambda(max) values of the L-sensitive photopigments (514-545 nm). We cloned partial Limenitis L opsin gene sequences and found a significant excess of replacement substitutions relative to polymorphisms among species. Mapping of these L photopigment lambda(max) values onto a phylogeny revealed two instances within Lepidoptera of convergently evolved L photopigment lineages whose lambda(max) values were blue-shifted. A codon-based maximum-likelihood analysis indicated that, associated with the two blue spectral shifts, four amino acid sites (Ile17Met, Ala64Ser, Asn70Ser, and Ser137Ala) have evolved substitutions in parallel and exhibit significant d(N)/d(S) >1. Homology modeling of the full-length Limenitis arthemis astyanax L opsin placed all four substitutions within the chromophore-binding pocket. Strikingly, the Ser137Ala substitution is in the same position as a site that in primates is responsible for a 5- to 7-nm blue spectral shift. Our data show that some of the same amino acid sites are under positive selection in the photopigments of both butterflies and primates, spanning an evolutionary distance >500 million years.

Experimental observations of rapid maize streak virus evolution reveal a strand-specific nucleotide substitution bias

Background. Recent reports have indicated that single-stranded DNA (ssDNA) viruses in the taxonomic families Geminiviridae, Parvoviridae and Anellovirus may be evolving at rates of ∼10-4 substitutions per site per year (subs/site/year). These evolution rates are similar to those of RNA viruses and are surprisingly high given that ssDNA virus replication involves host DNA polymerases with fidelities approximately 10 000 times greater than those of error-prone viral RNA polymerases. Although high ssDNA virus evolution rates were first suggested in evolution experiments involving the geminivirus maize streak virus (MSV), the evolution rate of this virus has never been accurately measured. Also, questions regarding both the mechanistic basis and adaptive value of high geminivirus mutation rates remain unanswered. Results. We determined the short-term evolution rate of MSV using full genome analysis of virus populations initiated from cloned genomes. Three wild type viruses and three defective artificial chimaeric viruses were maintained in planta for up to five years and displayed evolution rates of between 7.4 × 10-4 and 7.9 × 10-4 subs/site/year. Conclusion. These MSV evolution rates are within the ranges observed for other ssDNA viruses and RNA viruses. Although no obvious evidence of positive selection was detected, the uneven distribution of mutations within the defective virus genomes suggests that some of the changes may have been adaptive. We also observed inter-strand nucleotide substitution imbalances that are consistent with a recent proposal that high mutation rates in geminiviruses (and possibly ssDNA viruses in general) may be due to mutagenic processes acting specifically on ssDNA molecules. © 2008 Walt et al; licensee BioMed Central Ltd.

Recombination hotspots and host susceptibility modulate the adaptive value of recombination during maize streak virus evolution

Background Maize streak virus -strain A (MSV-A; Genus Mastrevirus, Family Geminiviridae), the maize-adapted strain of MSV that causes maize streak disease throughout sub-Saharan Africa, probably arose between 100 and 200 years ago via homologous recombination between two MSV strains adapted to wild grasses. MSV recombination experiments and analyses of natural MSV recombination patterns have revealed that this recombination event entailed the exchange of the movement protein - coat protein gene cassette, bounded by the two genomic regions most prone to recombination in mastrevirus genomes; the first surrounding the virion-strand origin of replication, and the second around the interface between the coat protein gene and the short intergenic region. Therefore, aside from the likely adaptive advantages presented by a modular exchange of this cassette, these specific breakpoints may have been largely predetermined by the underlying mechanisms of mastrevirus recombination. To investigate this hypothesis, we constructed artificial, low-fitness, reciprocal chimaeric MSV genomes using alternating genomic segments from two MSV strains; a grass-adapted MSV-B, and a maize-adapted MSV-A. Between them, each pair of reciprocal chimaeric genomes represented all of the genetic material required to reconstruct - via recombination - the highly maize-adapted MSV-A genotype, MSV-MatA. We then co-infected a selection of differentially MSV-resistant maize genotypes with pairs of reciprocal chimaeras to determine the efficiency with which recombination would give rise to high-fitness progeny genomes resembling MSV-MatA. Results Recombinants resembling MSV-MatA invariably arose in all of our experiments. However, the accuracy and efficiency with which the MSV-MatA genotype was recovered across all replicates of each experiment depended on the MSV susceptibility of the maize genotypes used and the precise positions - in relation to known recombination hotspots - of the breakpoints required to re-create MSV-MatA. Although the MSV-sensitive maize genotype gave rise to the greatest variety of recombinants, the measured fitness of each of these recombinants correlated with their similarity to MSV-MatA. Conclusions The mechanistic predispositions of different MSV genomic regions to recombination can strongly influence the accessibility of high-fitness MSV recombinants. The frequency with which the fittest recombinant MSV genomes arise also correlates directly with the escalating selection pressures imposed by increasingly MSV-resistant maize hosts.

Comparing measures of fat-free mass in overweight older adults using three different bioelectrical impedance devices and three prediction equations

Objectives: To compare measures of fat-free mass (FFM) by three different bioelectrical impedance analysis (BIA) devices and to assess the agreement between three different equations validated in older adult and/or overweight populations. Design: Cross-sectional study. Setting: Orthopaedics ward of Brisbane public hospital, Australia. Participants: Twenty-two overweight, older Australians (72 yr ± 6.4, BMI 34 kg/m2 ± 5.5) with knee osteoarthritis. Measurements: Body composition was measured using three BIA devices: Tanita 300-GS (foot-to-foot), Impedimed DF50 (hand-to-foot) and Impedimed SFB7 (bioelectrical impedance spectroscopy (BIS)). Three equations for predicting FFM were selected based on their ability to be applied to an older adult and/ or overweight population. Impedance values were extracted from the hand-to-foot BIA device and included in the equations to estimate FFM. Results: The mean FFM measured by BIS (57.6 kg ± 9.1) differed significantly from those measured by foot-to-foot (54.6 kg ± 8.7) and hand-to-foot BIA (53.2 kg ± 10.5) (P < 0.001). The mean ± SD FFM predicted by three equations using raw data from hand-to-foot BIA were 54.7 kg ± 8.9, 54.7 kg ± 7.9 and 52.9 kg ± 11.05 respectively. These results did not differ from the FFM predicted by the hand-to-foot device (F = 2.66, P = 0.118). Conclusions: Our results suggest that foot-to-foot and hand-to-foot BIA may be used interchangeably in overweight older adults at the group level but due to the large limits of agreement may lead to unacceptable error in individuals. There was no difference between the three prediction equations however these results should be confirmed within a larger sample and against a reference standard.

Front interactions in a three-component system

The three-component reaction-diffusion system introduced in [C. P. Schenk et al., Phys. Rev. Lett., 78 (1997), pp. 3781–3784] has become a paradigm model in pattern formation. It exhibits a rich variety of dynamics of fronts, pulses, and spots. The front and pulse interactions range in type from weak, in which the localized structures interact only through their exponentially small tails, to strong interactions, in which they annihilate or collide and in which all components are far from equilibrium in the domains between the localized structures. Intermediate to these two extremes sits the semistrong interaction regime, in which the activator component of the front is near equilibrium in the intervals between adjacent fronts but both inhibitor components are far from equilibrium there, and hence their concentration profiles drive the front evolution. In this paper, we focus on dynamically evolving N-front solutions in the semistrong regime. The primary result is use of a renormalization group method to rigorously derive the system of N coupled ODEs that governs the positions of the fronts. The operators associated with the linearization about the N-front solutions have N small eigenvalues, and the N-front solutions may be decomposed into a component in the space spanned by the associated eigenfunctions and a component projected onto the complement of this space. This decomposition is carried out iteratively at a sequence of times. The former projections yield the ODEs for the front positions, while the latter projections are associated with remainders that we show stay small in a suitable norm during each iteration of the renormalization group method. Our results also help extend the application of the renormalization group method from the weak interaction regime for which it was initially developed to the semistrong interaction regime. The second set of results that we present is a detailed analysis of this system of ODEs, providing a classification of the possible front interactions in the cases of $N=1,2,3,4$, as well as how front solutions interact with the stationary pulse solutions studied earlier in [A. Doelman, P. van Heijster, and T. J. Kaper, J. Dynam. Differential Equations, 21 (2009), pp. 73–115; P. van Heijster, A. Doelman, and T. J. Kaper, Phys. D, 237 (2008), pp. 3335–3368]. Moreover, we present some results on the general case of N-front interactions.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Cometary evolution : Clues from chondritic interplanetary dust particles

Cometary and interplanetary dust particles (IDP) are compared, and the mineralogical evolution of comet nuclei is discussed. Chondritic IDP have properties consistent with properties expected for cometary dust. The complex and varied mineralogy of these particles may indicate mineral alteration processes that occur in comet nuclei. Depending on the thermal budget of a comet, the upper few meters of nucleus material may maintain temperatures within regimes of hydrocryogenic (200 to 237K) and low-temperature aqueous (274 to 400K) alteration. Thus, layer silicates, carbonates, and sulfates may be important components of cometary dust and, correspondingly are common constituents of chondritic IDPs. Alteration of comet starting materials may be a common occurrence, and depends on the specific physical and chemical properties of each individual comet.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.