The synthesis and spectroscopic characterisation of hydrotalcite formed from aluminate solutions

Raman spectroscopy has been used to characterise nine hydrotalcites prepared from aluminate and magnesium solutions (magnesium chloride and seawater). The aluminate hydrotalcites are proposed to have the following formula Mg6Al2(OH)16(CO32-).xH2O, Mg6Al2(OH)16(CO32-,SO42-).xH2O, and Mg6Al2(OH)16(SO42-).xH2O. The synthesis of these hydrotalcites using seawater results in the intercalation of sulfate anions into the hydrotalcite interlayer. The spectra have been used to assess the molecular assembly of the cations and anions in the hydrotalcite structures. The spectra have been conveniently subdivided into spectral features based upon the carbonate anion, the hydroxyl units and water units. This investigation has shown the ideal conditions to form hydrotalcite from aluminate solutions is at pH 14 using magnesium chloride. Changes in synthesis conditions resulted in the formation of impurity products aragonite, thenardite, and gypsum.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Engineering sustainable solutions program : critical literacies for engineers portfolio

Purpose While a number of universities in Australia have embraced concepts such as project/problem‐based learning and design of innovative learning environments for engineering education, there has been a lack of national guidance on including sustainability as a “critical literacy” into all engineering streams. This paper was presented at the 2004 International Conference on Engineering Education in Sustainable Development (EESD) in Barcelona, Spain, outlining a current initiative that is seeking to address the “critical literacy” dilemma. Design/methodology/approach The paper presents the positive steps taken by Australia's peak engineering body, the Institution of Engineers Australia (EA), in considering accreditation requirements for university engineering courses and its responsibility to ensure the inclusion of sustainability education material. It then describes a current initiative called the “Engineering Sustainable Solutions Program – Critical Literacies for Engineers Portfolio” (ESSP‐CL), which is being developed by The Natural Edge Project (TNEP) in partnership with EA and Unesco. Findings Content for the module was gathered from around the world, drawing on research from the publication The Natural Advantage of Nations: Business Opportunities, Innovation, and Governance in the Twenty‐first Century. Parts of the first draft of the ESSP‐CL have been trialled at Griffith University, Queensland, Australia with first year environmental engineering students, in May 2004. Further trials are now proceeding with a number of other universities and organisations nationally and internationally. Practical implications It is intended that ESSP‐CL will be a valuable resource to universities, professional development activities or other education facilities nationally and internationally. Originality/value This paper fulfils an identified information/resources need.