DEVELOPING AN INTELLIGENT DIGITAL MOCK UP TO INTEGRATE DESIGN AND MANUFACTURING DISCIPLINES

This paper presents an approach to develop an intelligent digital mock-up (DMU) through integration of design and manufacturing disciplines to enable a better understanding of assembly related issues during design evolution. The intelligent DMU will contain tolerance information related to manufacturing capabilities so it can be used as a source for assembly simulations of realistic models to support the manufacturing decision making process within the design domain related to tolerance build ups. A literature review of the contributing research areas is presented, from which identification of the need for an intelligent DMU has been developed. The proposed methodology including the applications of cellular modelling and potential features of the intelligent DMU are presented and explained. Finally a conclusion examines the work to date and the future work to achieve an intelligent DMU.

A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs

Three-dimensional reconstruction from volumetric medical images (e.g. CT, MRI) is a well-established technology used in patient-specific modelling. However, there are many cases where only 2D (planar) images may be available, e.g. if radiation dose must be limited or if retrospective data is being used from periods when 3D data was not available. This study aims to address such cases by proposing an automated method to create 3D surface models from planar radiographs. The method consists of (i) contour extraction from the radiograph using an Active Contour (Snake) algorithm, (ii) selection of a closest matching 3D model from a library of generic models, and (iii) warping the selected generic model to improve correlation with the extracted contour.

This method proved to be fully automated, rapid and robust on a given set of radiographs. Measured mean surface distance error values were low when comparing models reconstructed from matching pairs of CT scans and planar X-rays (2.57–3.74 mm) and within ranges of similar studies. Benefits of the method are that it requires a single radiographic image to perform the surface reconstruction task and it is fully automated. Mechanical simulations of loaded bone with different levels of reconstruction accuracy showed that an error in predicted strain fields grows proportionally to the error level in geometric precision. In conclusion, models generated by the proposed technique are deemed acceptable to perform realistic patient-specific simulations when 3D data sources are unavailable.

Reducing the future threat from (liver) fluke: realistic prospect or quixotic fantasy?

The liver fluke remains an economically significant parasite of livestock and is emerging as an important zoonotic infection of humans. The incidence of the disease has increased in the last few years, as a possible consequence of changes to the World's climate. Future predictions suggest that this trend is likely to continue. Allied to the changing pattern of disease, reports of resistance to triclabendazole (TCBZ) have appeared in the literature, although they do not all represent genuine cases of resistance. Nevertheless, any reports of resistance are a concern, because triclabendazole is the only drug that has high activity against the migratory and damaging juvenile stages of infection. How to deal with the twin problems (of increasing incidence and drug resistance) is the overall theme of the session on “Trematodes: Fasciola hepatica epidemiology and control” and of this review to introduce the session.

Greater knowledge of fluke epidemiology and population genetics will highlight those regions where surveillance is most required and indicate how quickly resistant populations of fluke may arise. Models of disease risk are becoming increasingly sophisticated and precise, with more refined data analysis programmes and Geographic Information Systems (GIS) data. Recent improvements have been made in our understanding of the action of triclabendazole and the ways in which flukes have become resistant to it. While microtubules are the most likely target for drug action, tubulin mutations do not seem to be involved in the resistance mechanism. Rather, upregulation of drug uptake and metabolism processes appear to be more important and the data relating to them will be discussed. The information may help in the design of new treatment strategies or pinpoint potential molecular markers for monitoring fluke populations. Advances in the identification of novel targets for drugs and vaccines will be made by the various “-omics” technologies that are now being applied to Fasciola. A major area of concern in the current control of fasciolosis is the lack of reliable tests for the diagnosis of drug (TCBZ) resistance. This has led to inaccurate reports of resistance, which is hindering successful disease management, as farmers may be encouraged to switch to less effective drugs. Progress with the development of a number of new diagnostic tests will be reviewed.

Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.

On the nudging terms at open boundaries in regional ocean models

For open boundary conditions (OBCs) in regional models, a nudging term added to radiative and/or advective conditions during the wave or flow propagation outward from the model domain of interest is widely used, to prevent the predicted boundary values from evolving to become quite different from the external data, especially for a long-term integration. However, nudging time scales are basically unknown, leading to many empirical selections. In this paper, a method for objectively estimating nudging time scales during outward propagation is proposed, by using internal model dynamics near the boundary. We tested this method and other several commonly used OBCs for cases of both an idealized model domain and a realistic configuration, and model results demonstrated that the proposed method improves the model solutions. Many similarities are found between the nudging and mixing time scales, in magnitude, spatial and temporal variations, since the nudging mainly replaces the effect of the mixing terms in this study. However, the mixing time scale is not an intrinsic property of the nudging term because in other studies the nudging term might replace terms other than the mixing terms and, thus, should reflect other characteristic time scales.

A Lagrangian approach for modeling road collisions using second-order models of traffic flow

In [M. Herty, A. Klein, S. Moutari, V. Schleper, and G. Steinaur, IMA J. Appl. Math., 78(5), 1087–1108, 2013] and [M. Herty and V. Schleper, ZAMM J. Appl. Math. Mech., 91, 763–776, 2011], a macroscopic approach, derived from fluid-dynamics models, has been introduced to infer traffic conditions prone to road traffic collisions along highways’ sections. In these studies, the governing equations are coupled within an Eulerian framework, which assumes fixed interfaces between the models. A coupling in Lagrangian coordinates would enable us to get rid of this (not very realistic) assumption. In this paper, we investigate the well-posedness and the suitability of the coupling of the governing equations within the Lagrangian framework. Further, we illustrate some features of the proposed approach through some numerical simulations.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.