What is the new paradigm in product quality?

The current product quality paradigm is founded upon a customer-focused product development process, in which the functionality and behaviour of a product are designed to fulfil the needs of customers, and technological innovation is used to expand the capability and enhance the performance of the product. However, this view of product quality does not reflect the current practices of today's leading manufacturers, who now offer "total solutions" based upon an integrated package of products and services with well defined characteristics tailored to individual needs. Concepts such as globalisation, mass customisation, product branding, e-commerce, and sustainability suggest that a new product quality paradigm is evolving. This paper will discuss our current understanding of product quality issues and outline our vision of the new quality paradigm for product developers

The value of simulation and immersive virtual reality environments to design decision making in new product development

In response to the need to develop more sophisticated, higher quality products product developers are more and more expecting engineering simulations to provide them with the data, information, and knowledge required to make design decisions. Engineering simulations provide insight into the behaviour of virtual product designs and have the capacity to be executed many thousands of times to provide a comprehensive coverage of the solution space being explored. However, engineering simulations are only representative of a reduced set of product properties and are bound by the constraints imposed by the fidelity of the underlying physics of the simulation tools being used. In this paper, we will briefly explore a number of different types of engineering simulation that the Virtual Engineering Centre has been involved in creating and consider the value that the data, information and knowledge that each creates to the decision making process. We will also explore how different visualisation methods being used at the Virtual Engineering Centre support decision making by individuals and groups of people in a design review context. Specifically, we will discuss the use of immersive virtual reality in reviewing simulation results and highlight the limitations of using this technology in group decision making using a case study taken from our work with Bentley Motors. We will conclude that whilst virtual technologies provide opportunities to generate data and information early in the product development process, the ever increasing demands for accuracy and fidelity in representation in mature product sectors outstrips the capability of the technology to fully support decision making in a complete virtual world

Galaxy And Mass Assembly (GAMA): curation and reanalysis of 16.6k redshifts in the G10/COSMOS region

We discuss the construction of the Galaxy And Mass Assembly (GAMA) 10h region (G10) using publicly available data in the Cosmic Evolution Survey region (COSMOS) in order to extend the GAMA survey to z ∼ 1 in a single deg2 field. In order to obtain the maximum number of high precision spectroscopic redshifts we re-reduce all archival zCOSMOS-bright data and use the GAMA automatic cross-correlation redshift fitting code autoz. We use all available redshift information (autoz, zCOSMOS-bright 10k, PRIMUS, VVDS, SDSS and photometric redshifts) to calculate robust best-fitting redshifts for all galaxies and visually inspect all 1D and 2D spectra to obtain 16 583 robust redshifts in the full COSMOS region. We then define the G10 region to be the central ∼1 deg2 of COSMOS, which has relatively high spectroscopic completeness, and encompasses the CHILES VLA region. We define a combined r < 23.0 mag and i < 22.0 mag G10 sample (selected to have the highest bijective overlap) with which to perform future analysis, containing 9861 sources with reliable high-precision VLT-VIMOS spectra. All tables, spectra and imaging are available at http://ict.icrar.org/cutout/G10

Galaxy And Mass Assembly (GAMA): growing up in a bad neighbourhood - how do low-mass galaxies become passive?

Both theoretical predictions and observations of the very nearby Universe suggest that low-mass galaxies (log$_{10}$[M$_{*}$/M$_{\odot}$]<9.5) are likely to remain star-forming unless they are affected by their local environment. To test this premise, we compare and contrast the local environment of both passive and star-forming galaxies as a function of stellar mass, using the Galaxy and Mass Assembly survey. We find that passive fractions are higher in both interacting pair and group galaxies than the field at all stellar masses, and that this effect is most apparent in the lowest mass galaxies. We also find that essentially all passive log$_{10}$[M$_{*}$/M$_{\odot}$]<8.5 galaxies are found in pair/group environments, suggesting that local interactions with a more massive neighbour cause them to cease forming new stars. We find that the effects of immediate environment (local galaxy-galaxy interactions) in forming passive systems increases with decreasing stellar mass, and highlight that this is potentially due to increasing interaction timescales giving sufficient time for the galaxy to become passive via starvation. We then present a simplistic model to test this premise, and show that given our speculative assumptions, it is consistent with our observed results.Comment: 20 pages, 12 figures, Accepted to MNRA

ProFit : Bayesian profile fitting of galaxy images

We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level c++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit, respectively). R ProFit is also available pre-built from cran; however, this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two-dimensional integration for a useful number of profiles, including Sérsic, Core-Sérsic, broken-exponential, Ferrer, Moffat, empirical King, point-source, and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and galfit. libprofit is both faster and more accurate than galfit at integrating the ubiquitous Sérsic profile for the most common values of the Sérsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fitting parameters from fitting images from different sources (SDSS versus KiDS) than from using different codes (ProFit versus galfit). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS, and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST.Publisher PDFPeer reviewe

Peer mentorship and positive effects on student mentor and mentee retention and academic success

This study examined how the introduction of peer mentorship in an undergraduate health and social welfare programme at a large northern university affected student learning. Using an ethnographic case study approach, the study draws upon data collected from a small group of mentors and their mentees over a period of one academic year using interviews, reflective journals, assessment and course evaluation data. Analysis of the data collected identified a number of key findings: peer mentorship improves assessment performance for both mentee and mentor; reduces stress and anxiety, enhances participation and engagement in the academic community, and adds value to student outcomes