It is now well known that the diffusion coefficient (D) measured in a laboratory in low earth orbit (LEO) is less than the corresponding value measured in a terrestrial laboratory. However, all LEO laboratories are subject to transient accelerations (g-jitter) superimposed on the steady reduced gravity environment of the space platform. In measurements of the diffusion coefficients for dilute binary alloys of Pb-(Ag, Au,Sb), Sb-(Ga,In), Bi-(Ag,Au,Sb), Sn-(Au,Sb), Al-(Fe, Ni,Si), and In-Sb in which g-jitter was suppressed, it was found that D proportional to T (temperature) if g-jitter was suppressed, rather than D proportional to T(2) as observed by earlier workers with g-jitter present. Furthermore, when a forced g-jitter was applied to a diffusion couple, the value measured for D increased. The significance of these results is reviewed in the light of recent work in which ab initio molecular dynamics simulations predicted a D proportional to T relationship.

Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.

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The Massey University Bachelor of Engineering (Honours) programme is based on a series of Projectbased learning (PBL) courses. It is observed historically that students have difficulty initiating project work, planning projects and deciding what decisions and information should be their focus. "Rapid Learning Cycles", developed by K. Radeka, combines New Product Development models "Lean Product Development" and "Agile Scrum" into a framework that uses regular cycles for project execution. The process identifies Key Decisions to be made in a project and determines the Knowledge Gaps to be closed within cycles. This allows team members to make these high impact/ high unknown decisions with a better understanding of the alternatives, maximising the value of the time spent learning. The approach has the potential to be used where student projects are short (one or two semesters), allowing students to make better-rationalized decisions and complete projects with a disciplined framework.

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