Research training and national innovation systems in Australia, Finland and the United States : a policy and systems study supported by 30 case studies of research students in the fields of geospatial science, wireless communication, biosciences, and materials science and engineering

Reforms to the national research and research training system by the Commonwealth Government of Australia sought to effectively connect research conducted in universities to Australia's national innovation system. Research training has a key role in ensuring an adequate supply of highly skilled people for the national innovation system. During their studies, research students produce and disseminate a massive amount of new knowledge. Prior to this study, there was no research that examined the contribution of research training to Australia's national innovation system despite the existence of policy initiatives aiming to enhance this contribution. Given Australia's below average (but improving) innovation performance compared to other OECD countries, the inclusion of Finland and the United States provided further insights into the key research question. This study examined three obvious ways that research training contributes to the national innovation systems in the three countries: the international mobility and migration of research students and graduates, knowledge production and distribution by research students, and the impact of research training as advanced human capital formation on economic growth. Findings have informed the concept of a research training culture of innovation that aims to enhance the contribution of research training to Australia's national innovation system. Key features include internationally competitive research and research training environments; research training programs that equip students with economically-relevant knowledge and the capabilities required by employers operating in knowledge-based economies; attractive research careers in different sectors; a national commitment to R&D as indicated by high levels of gross and business R&D expenditure; high private and social rates of return from research training; and the horizontal coordination of key organisations that create policy for, and/or invest in research training.

Materials Science and Engineering Laboratory.

"December 1993"--P. [2] of cover.

Frontiers in Materials Science and Technology

This special issue aims to provide up-to-date knowledge and the latest scientific concepts and technological developments in the processing, characterization, testing, mechanics, modeling and applications of a broad range of advanced materials. The many contributors, from Denmark, Germany, UK, Iran, Saudi Arabia, Malaysia, Japan, the People’s Republic of China, Singapore, Taiwan, USA, New Zealand and Australia, present a wide range of topics including: nanomaterials, thin films and coatings, metals and alloys, composite materials, materials processing and characterization, biomaterials and biomechanics, and computational materials science and simulation. The work will therefore be of great interest to a broad spectrum of researchers and technologists.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

Microneedle arrays as transdermal and intradermal drug delivery systems: Materials science, manufacture and commercial development

Transdermal drug delivery offers a number of advantages for the patient, due not only its non-invasive and convenient nature, but also factors such as avoidance of first pass metabolism and prevention of gastrointestinal degradation. It has been demonstrated that microneedle arrays can increase the number of compounds amenable to transdermal delivery by penetrating the skin's protective barrier, the stratum corneum, and creating a pathway for drug permeation to the dermal tissue below. Microneedles have been extensively investigated in recent decades for drug and vaccine delivery as well as minimally invasive patient monitoring/diagnosis. This review focuses on a range of critically important aspects of microneedle technology, namely their material composition, manufacturing techniques, methods of evaluation and commercial translation to the clinic for patient benefit and economic return. Microneedle research and development is finally now at the stage where commercialisation is a realistic possibility. However, progress is still required in the areas of scaled-up manufacture and regulatory approval.

Engineering of 3D self-directed quantum dot ordering in multilayer InGaAs/GaAs nanostructures by means of flux gas composition

Lateral ordering of InGaAs quantum dots on the GaAs (001) surface has been achieved in earlier reports, resembling an anisotropic pattern. In this work, we present a method of breaking the anisotropy of ordered quantum dots (QDs) by changing the growth environment. We show experimentally that using As(2) molecules instead of As(4) as a background flux is efficient in controlling the diffusion of distant Ga adatoms to make it possible to produce isotropic ordering of InGaAs QDs over GaAs (001). The control of the lateral ordering of QDs under As(2) flux has enabled us to improve their optical properties. Our results are consistent with reported experimental and theoretical data for structure and diffusion on the GaAs surface.

A Simple Visual Demonstration of Phase Diagram Concepts for an Introductory Materials Science Course Using Colored Solvents

A simple and effective demonstration to help students comprehend phase diagrams and understand phase equilibria and transformations is created using common chemical solvents available in the laboratory. Common misconceptions surrounding phase diagram operations, such as components versus phases, reversibility of phase transformations, and the lever rule are addressed. Three different binary liquid mixtures of varying compatibility create contrastive phase equilibrium cases, where colorful dyes selectively dissolved in each of corresponding phases allow for quick and unambiguous perceptions of solubility limit and phase transformations. Direct feedback and test scores from a group of students show evidence of the effectiveness of the visual and active teaching tool.