Analytical Elastic Stiffness Model for 3D Woven Orthogonal Interlock Composites

This research presents the development of an analytical model to predict the elastic stiffness performance of orthogonal interlock bound 3D woven composites as a consequence of altering the weaving parameters and constituent material types. The present approach formulates expressions at the micro level with the aim of calculating more representative volume fractions of a group of elements to the layer. The rationale in representing the volume fractions within the unit cell more accurately was to improve the elastic stiffness predictions compared to existing analytical modelling approaches. The models developed in this work show good agreement between experimental data and improvement on existing predicted values by models published in literature.

Discovery of novel Agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening

The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) similarity, three-dimensional (3D) pharmacophore searches, and docking studies by using the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compounds followed by extraction of structural neighbors of functionally confirmed hits resulted in identification of 15 compounds active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

The four I-model for scaffolding the professional development of experienced teachers in the use of virtual learning environments for classroom teaching.

Models of professional development for teachers have been criticized for not being embedded in the context in which teachers are familiar, namely their own classrooms. This paper discusses an adapted-Continuous Practice Improvement model, which qualitative findings indicate was effective in facilitating the transfer of creative and innovative teaching approaches from the expert or Resident Teacher’s school to the novice or Visiting Teachers’ classrooms over the duration of the project. The cultural shift needed to embed and extend the use of online teaching across the school was achieved through the positive support and commitment of the principals in the Visiting Teachers’ schools, combined with the success of the professional development activities offered by the Visiting Teachers to their school-based colleagues.

FROM LINE-DRAWINGS TO IMPRESSIONS OF 3D OBJECTS - DEVELOPING A MODEL TO ACCOUNT FOR THE SHAPES THAT PEOPLE SEE

The interpretations people attach to line drawings reflect shape-related processes in human vision. Their divergences from expectations embodied in related machine vision traditions are summarized, and used to suggest how human vision decomposes the task of interpretation. A model called IO implements this idea. It first identifies geometrically regular, local fragments. Initial decisions fix edge orientations, and this information constrains decisions about other properties. Relations between fragments are explored, beginning with weak consistency checks and moving to fuller ones. IO's output captures multiple distinctive characteristics of human performance, and it suggests steady progress towards understanding shape-related visual processes is possible.

Modelling 3D camera movement for vibration characterisation and multiple object identification with application to lighting assessment

Utilising cameras as a means to survey the surrounding environment is becoming increasingly popular in a number of different research areas and applications. Central to using camera sensors as input to a vision system, is the need to be able to manipulate and process the information captured in these images. One such application, is the use of cameras to monitor the quality of airport landing lighting at aerodromes where a camera is placed inside an aircraft and used to record images of the lighting pattern during the landing phase of a flight. The images are processed to determine a performance metric. This requires the development of custom software for the localisation and identification of luminaires within the image data. However, because of the necessity to keep airport operations functioning as efficiently as possible, it is difficult to collect enough image data to develop, test and validate any developed software. In this paper, we present a technique to model a virtual landing lighting pattern. A mathematical model is postulated which represents the glide path of the aircraft including random deviations from the expected path. A morphological method has been developed to localise and track the luminaires under different operating conditions. © 2011 IEEE.

Single crystal CVD diamond growth strategy by the use of a 3D geometrical model: Growth on (113) oriented substrates

The quality of single crystal diamond obtained by microwave CVD processes has been drastically improved in the last 5 years thanks to surface pretreatment of the substrates [A. Tallaire, J. Achard, F. Silva, R.S. Sussmann, A. Gicquel, E. Rzepka, Physica Status Solidi (A) 201, 2419-2424 (2004); G. Bogdan, M. Nesladek, J. D'Haen, J. Maes, V.V. Moshchalkov, K. Haenen, M. D'Olieslaeger, Physica Status Solidi (A) 202, 2066-2072 (2005); M. Yamamoto, T. Teraji, T. Ito, Journal of Crystal Growth 285, 130-136 (2005)]. Additionally, recent results have unambiguously shown the occurrence of (110) faces on crystal edges and (113) faces on crystal corners [F. Silva, J. Achard, X. Bonnin, A. Michau, A. Tallaire, O. Brinza, A. Gicquel, Physica Status Solidi (A) 203, 3049-3055 (2006)]. We have developed a 3D geometrical growth model to account for the final crystal morphology. The basic parameters of this growth model are the relative displacement speeds of (111), (110) and (113) faces normalized to that of the (100) faces, respectively alpha, beta, and gamma. This model predicts both the final equilibrium shape of the crystal (i.e. after infinite growth time) and the crystal morphology as a function of alpha, beta, gamma, and deposition time.

An optimized operating point, deduced from the model, has been validated experimentally by measuring the growth rate in (100), (111), (110), and (113) orientations. Furthermore, the evolution of alpha, beta, gamma as a function of methane concentration in the gas discharge has been established. From these results, crystal growth strategies can be proposed in order, for example, to enlarge the deposition area. In particular, we will show, using the growth model, that the only possibility to significantly increase the deposition area is, for our growth conditions, to use a (113) oriented substrate. A comparison between the grown crystal and the model results will be discussed and characterizations of the grown film (Photoluminescence spectroscopy, EPR, SEM) will be presented. (C) 2008 Elsevier B.V. All rights reserved.

3D deflagration simulations leaving bound remnants:A model for 2002cx-like Type Ia supernovae

2002cx-like supernovae are a sub-class of sub-luminous Type Ia supernovae (SNe). Their light curves and spectra are characterized by distinct features that indicate strong mixing of the explosion ejecta. Pure turbulent deflagrations have been shown to produce such mixed ejecta. Here, we present hydrodynamics, nucleosynthesis and radiative-transfer calculations for a 3D full-star deflagration of a Chandrasekhar-mass white dwarf. Our model is able to reproduce the characteristic observational features of SN 2005hk (a prototypical 2002cx-like supernova), not only in the optical, but also in the near-infrared. For that purpose we present, for the first time, five near-infrared spectra of SN 2005hk from -0.2 to 26.6 d with respect to B-band maximum. Since our model burns only small parts of the initial white dwarf, it fails to completely unbind the white dwarf and leaves behind a bound remnant of ~1.03Mconsisting mainly of unburned carbon and oxygen, but also enriched by some amount of intermediate-mass and iron-group elements from the explosion products that fall back on the remnant.We discuss possibilities for detecting this bound remnant and how it might influence the late-time observables of 2002cx-like SNe. © 2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.