Dynamic binary translation using run-time feedbacks

Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC® platforms.

Modelling predicts positive and negative interactions between three Australian tropical tree species in monoculture and binary mixture

Computer modelling promises to. be an important tool for analysing and predicting interactions between trees within mixed species forest plantations. This study explored the use of an individual-based mechanistic model as a predictive tool for designing mixed species plantations of Australian tropical trees. The 'spatially explicit individually based-forest simulator' (SeXI-FS) modelling system was used to describe the spatial interaction of individual tree crowns within a binary mixed-species experiment. The three-dimensional model was developed and verified with field data from three forest tree species grown in tropical Australia. The model predicted the interactions within monocultures and binary mixtures of Flindersia brayleyana, Eucalyptus pellita and Elaeocarpus grandis, accounting for an average of 42% of the growth variation exhibited by species in different treatments. The model requires only structural dimensions and shade tolerance as species parameters. By modelling interactions in existing tree mixtures, the model predicted both increases and reductions in the growth of mixtures (up to +/- 50% of stem volume at 7 years) compared to monocultures. This modelling approach may be useful for designing mixed tree plantations. (c) 2006 Published by Elsevier B.V.

Simulation of binary mixture adsorption of methane and CO2 at supercritical conditions in carbons

Knowledge of the adsorption behavior of coal-bed gases, mainly under supercritical high-pressure conditions, is important for optimum design of production processes to recover coal-bed methane and to sequester CO2 in coal-beds. Here, we compare the two most rigorous adsorption methods based on the statistical mechanics approach, which are Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, for single and binary mixtures of methane and carbon dioxide in slit-shaped pores ranging from around 0.75 to 7.5 nm in width, for pressure up to 300 bar, and temperature range of 308-348 K, as a preliminary study for the CO2 sequestration problem. For single component adsorption, the isotherms generated by DFT, especially for CO2, do not match well with GCMC calculation, and simulation is subsequently pursued here to investigate the binary mixture adsorption. For binary adsorption, upon increase of pressure, the selectivity of carbon dioxide relative to methane in a binary mixture initially increases to a maximum value, and subsequently drops before attaining a constant value at pressures higher than 300 bar. While the selectivity increases with temperature in the initial pressure-sensitive region, the constant high-pressure value is also temperature independent. Optimum selectivity at any temperature is attained at a pressure of 90-100 bar at low bulk mole fraction of CO2, decreasing to approximately 35 bar at high bulk mole fractions. (c) 2005 American Institute of Chemical Engineers.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Efficient spatial clustering algorithm using binary tree

In this paper we present an efficient k-Means clustering algorithm for two dimensional data. The proposed algorithm re-organizes dataset into a form of nested binary tree*. Data items are compared at each node with only two nearest means with respect to each dimension and assigned to the one that has the closer mean. The main intuition of our research is as follows: We build the nested binary tree. Then we scan the data in raster order by in-order traversal of the tree. Lastly we compare data item at each node to the only two nearest means to assign the value to the intendant cluster. In this way we are able to save the computational cost significantly by reducing the number of comparisons with means and also by the least use to Euclidian distance formula. Our results showed that our method can perform clustering operation much faster than the classical ones. © Springer-Verlag Berlin Heidelberg 2005

Functionalisation of nanoparticles as electrolytes in fuel cells

Inorganic metal oxide materials are generally poor proton conductors as conductivities are lower than 10-5-10-6 S.cm-1. However, by functionalising Silica, Zirconia or Titania, proton conduction increases by up to 5 orders of magnitude. Hence, functionalised nanomaterials are becoming very competitive against conventional electrolyte materials such as Nafion. In this work, sol-gel processes are employed to produce silica phosphate, zirconia phosphate and titania phosphate functionalised nanoparticles. Furthermore, conductivities at hydrate conditions are investigated, and nanoparticle formation and functionalisation effects on proton conductivity are discussed. Results show conductivities up to 10-1 S.cm-1 (95% RH). Proton conduction increases with the functionalisation content, however heat treatment of nanoparticles locks the functionality in the crystal phase, thus inhibiting proton conduction. Controlling the mesopore phase allows for high proton conduction at hydrated conditions, clearly indicating facilitated ion transport through the pore channels.

Nanocomposite Nafion-Silica membranes for direct methanol fuel cells

Commercially available proton exchange membranes such as Nafion do not meet the requirements for high power density direct methanol fuel cells, partly due to their high methanol permeability. The aim of this work is to develop a new class of high-proton conductivity membranes, with thermal and mechanical stability similar to Nafion and reduced methanol permeability. Nanocomposite membranes were produced by the in-situ sol-gel synthesis of silicon dioxide particles in preformed Nafion membranes. Microstructural modification of Nafion membranes with silica nanoparticles was shown in this work to reduce methanol crossover from 7.48x10-6 cm2s^-1 for pure Nafion® to 2.86 x10-6 cm2s^-1 for nanocomposite nafion membranes (Methanol 50% (v/v) solution, 75 degrees C). Best results were achieved with a silica composition of 2.6% (w/w). We propose that silica inhibits the conduction of methanol through Nafion by blocking sites necessary for methanol diffusion through the polymer electrolyte membrane. Effects of surface chemistry, nanoparticle formation and interactions with Nafion matrix are further addressed.