espace
Curtin's institutional repository, espace, is an open access digital collection of Curtin research publications and higher degree by research theses. espace facilitates worldwide discoverability of Curtin research outputs, via search engines such as Google and Google Scholar, and services like OAIster and Trove.
For instructions on how Curtin Researchers can deposit their research outputs into espace, refer to the Library’s Guide to espace.
Recently Added
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(2025)Engineering educational institutions consistently emphasise incorporating practical learning skills in their curriculum to enhance students' professional knowledge as well as cognitive knowledge. One of the teaching ...
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(2025)Two main risk factors for Alzheimer’s Disease are the APOE4 gene and a diet high in saturated fat. Additionally, the protein amyloid-beta is a primary pathological characteristic of Alzheimer’s Disease. This thesis ...
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(2025)Traditional teaching methods in Indonesia remain entrenched, with a strong emphasis on content knowledge, often at the expense of fostering students’ skills. This study examined the impact of a STEM-Problem-Based Learning ...
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(2024)Decarbonisation of the organic chemical industry necessitates the transition from thermochemical processes to electro-organic manufacturing, which is more compatible with renewables. However, the transition is difficult ...
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(2024)The halophile was identified as Marinobacter sp that could degrade about 70% ibuprofen (IBU) at a concentration of 50 mg L-1 and above 30% bisphenol A (BPA) at a concentration of 20 mg L-1 within five weeks at 8% NaCl. ...
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(2024)Drawing on case studies from the Marvel, DC and Star Wars franchises and interviews with leading entertainment industry practitioners, this research interrogates the various approaches to commercial transmedia storytelling ...
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(2024)Peripheral Metabolism of Lipoprotein-amyloid Beta as a Risk Factor for Diabetes-induced Alzheimer's Disease and the Therapeutic Effects of Probucol and its Analogue
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(2025)This research investigated the applicability of bagging and boosting ensemble machine learning algorithms in predicting petrophysical properties, namely porosity, permeability and water saturation which is a vital aspect ...
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(2022)The Bayesian learning model developed in this research addressed a need for technology by which complex industry datasets may be synthesised and represents a fundamentally novel approach to this problem than those previously ...