Generalized azimuthal shear deformations in compressible isotropic elastic materials

In this article we study the azimuthal shear deformations in a compressible Isotropic elastic material. This class of deformations involves an azimuthal displacement as a function of the radial and axial coordinates. The equilibrium equations are formulated in terms of the Cauchy-Green strain tensors, which form an overdetermined system of partial differential equations for which solutions do not exist in general. By means of a Legendre transformation, necessary and sufficient conditions for the material to support this deformation are obtained explicitly, in the sense that every solution to the azimuthal equilibrium equation will satisfy the remaining two equations. Additionally, we show how these conditions are sufficient to support all currently known deformations that locally reduce to simple shear. These conditions are then expressed both in terms of the invariants of the Cauchy-Green strain and stretch tensors. Several classes of strain energy functions for which this deformation can be supported are studied. For certain boundary conditions, exact solutions to the equilibrium equations are obtained. © 2005 Society for Industrial and Applied Mathematics.

A matrix converter based Inductive Power Transfer system

Typical Inductive Power Transfer (IPT) systems employ two power conversion stages to generate a high frequency current from low frequency utility supply. This paper proposes a matrix converter based IPT system that facilitates the generation of high frequency current through a single power conversion stage. The proposed matrix converter topology transforms a 3-phase low frequency voltage system to a high frequency single phase voltage which in turn powers a series compensated IPT system. A comprehensive mathematical model is developed to investigate the behavior of the proposed IPT topology. Theoretical results are presented in comparison to simulations, which are performed in Matlab/ Simulink, to demonstrate the applicability of the proposed concept and the validity of the developed model.

Solar evaporator-collectors : analyses and applications

Taking into consideration of growing energy needs and concern for environmental degradation, clean and inexhaustible energy source, such as solar energy, is receiving greater attention for various applications. The use of solar energy system reduces pollution, waste and has little or no harmful effects on the environment. It is appreciated that this source of energy can be complementary rather than being competitive to conventional energy sources. In order to collect and harness energy from the sun, a solar collector is essential. A solar collector is basically a heat exchanger that transforms solar radiant energy into heat or thermal energy. Improvement of performance is essential for commercial acceptance of their use in such applications. Many studies have been undertaken on the enhancement of thermal performance of solar collectors, using diverse materials of various shapes, dimensions and layouts. In the literature, various collector designs have been proposed and tested with the objective of meeting these requirements [1-8]. Omer et al. [1] found the efficiency of a solar collector of about 70% in a solar assisted heat pump system. Traditional solar collectors are single phase collectors, in which the working fluid is either air or water. Different modifications are suggested and applied to improve the heat transfer between the absorber and working fluid in a collector. These modifications include the use of absorber with fins attached [2,3], corrugated absorber [4,5], matrix type absorber [6], V-groove solar air collector [7]. Karim et al. [8] approached a review of design and construction of three types (flat, vee-grooved, and finned) of air collectors. Two-phase collectors, on the other hand, have significant potential for continuous operation round the clock, when used in conjunction with a compressor, as found in a solar assisted heat-pump cycle.

Australian consumer attitudes and decision making on renewable energy technology and its impact on the transformation of the energy sector

This paper critically examines research on consumer attitudes and behavior towards solar photovoltaic (PV) and renewable energy technology in Australia. The uptake of renewable energy technology by residential consumers in Australia in the past decade has transformed the electricity supply and demand paradigm. Thus, this paper reviews Australian research on consumer behavior, understanding and choices in order to identify gaps in knowledge. As the role of the consumer transforms there is a critical need to understand the ways consumers may respond to future energy policies to mitigate unforeseen negative social and economic consequence of programs designed to achieve positive environmental outcomes.

Fourier active appearance models

Gaining invariance to camera and illumination variations has been a well investigated topic in Active Appearance Model (AAM) fitting literature. The major problem lies in the inability of the appearance parameters of the AAM to generalize to unseen conditions. An attractive approach for gaining invariance is to fit an AAM to a multiple filter response (e.g. Gabor) representation of the input image. Naively applying this concept with a traditional AAM is computationally prohibitive, especially as the number of filter responses increase. In this paper, we present a computationally efficient AAM fitting algorithm based on the Lucas-Kanade (LK) algorithm posed in the Fourier domain that affords invariance to both expression and illumination. We refer to this as a Fourier AAM (FAAM), and show that this method gives substantial improvement in person specific AAM fitting performance over traditional AAM fitting methods.

Lighting invariant urban street classification

In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

Structure analysis on the nanoscale : closed WS2 nanoboxes through a cascade of topo- and epitactic processes

Closed WS2 nanoboxes were formed by topotactic sulfidization of a WO3/WO3 center dot 1/3H(2)O intergrowth precursor. Automated diffraction tomography was used to elucidate the growth mechanism of these unconventional hollow structures. By partial conversion and structural analysis of the products, each of them representing a snapshot of the reaction at a given point in time, the overall reaction can be broken down into a cascade of individual steps and each of them identified with a basic mechanism. During the initial step of sulfidization WO3 center dot 1/3H(2)O transforms into hexagonal WO3 whose surface allows for the epitaxial induction of WS2. The initially formed platelets of WS2 exhibit a preferred orientation with respect to the nanorod surface. In the final step individual layers of WS2 coalesce to form closed shells. In essence, a cascade of several topotactic reactions leads to epitactic induction and formation of closed rectangular hollow boxes made up from hexagonal layers.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

A new approach to estimating trends in chlamydia incidence

Objectives Directly measuring disease incidence in a population is difficult and not feasible to do routinely. We describe the development and application of a new method of estimating at a population level the number of incident genital chlamydia infections, and the corresponding incidence rates, by age and sex using routine surveillance data. Methods A Bayesian statistical approach was developed to calibrate the parameters of a decision-pathway tree against national data on numbers of notifications and tests conducted (2001-2013). Independent beta probability density functions were adopted for priors on the time-independent parameters; the shape parameters of these beta distributions were chosen to match prior estimates sourced from peer-reviewed literature or expert opinion. To best facilitate the calibration, multivariate Gaussian priors on (the logistic transforms of) the time-dependent parameters were adopted, using the Matérn covariance function to favour changes over consecutive years and across adjacent age cohorts. The model outcomes were validated by comparing them with other independent empirical epidemiological measures i.e. prevalence and incidence as reported by other studies. Results Model-based estimates suggest that the total number of people acquiring chlamydia per year in Australia has increased by ~120% over 12 years. Nationally, an estimated 356,000 people acquired chlamydia in 2013, which is 4.3 times the number of reported diagnoses. This corresponded to a chlamydia annual incidence estimate of 1.54% in 2013, increased from 0.81% in 2001 (~90% increase). Conclusions We developed a statistical method which uses routine surveillance (notifications and testing) data to produce estimates of the extent and trends in chlamydia incidence.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Non-market use and non-use values for preserving ecosystem services over time : a choice experiment application to coral reef ecosystems in New Caledonia

Non-use values (i.e. economic values assigned by individuals to ecosystem goods and services unrelated to current or future uses) provide one of the most compelling incentives for the preservation of ecosystems and biodiversity. Assessing the non-use values of non-users is relatively straightforward using stated preference methods, but the standard approaches for estimating non-use values of users (stated decomposition) have substantial shortcomings which undermine the robustness of their results. In this paper, we propose a pragmatic interpretation of non-use values to derive estimates that capture their main dimensions, based on the identification of a willingness to pay for ecosystem protection beyond one's expected life. We empirically test our approach using a choice experiment conducted on coral reef ecosystem protection in two coastal areas in New Caledonia with different institutional, cultural, environmental and socio-economic contexts. We compute individual willingness to pay estimates, and derive individual non-use value estimates using our interpretation. We find that, a minima, estimates of non-use values may comprise between 25 and 40% of the mean willingness to pay for ecosystem preservation, less than has been found in most studies.

Searching for semantic person queries using channel representations

It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to relate and easy to understand. However such queries are not easily utilised within intelligent surveillance systems as they are difficult to transform into a representation that can be searched for automatically in large camera networks. In this paper we propose a novel approach that transforms such a semantic query into an avatar that is searchable within a video stream, and demonstrate state-of-the-art performance for locating a subject in video based on a description.

The neural correlates of justified and unjustified killing: An fMRI study

Despite moral prohibitions on hurting other humans, some social contexts allow for harmful actions such as the killing of others. One example is warfare, where killing enemy soldiers is seen as morally justified. Yet, the neural underpinnings distinguishing between justified and unjustified killing are largely unknown. To improve understanding of the neural processes involved in justified and unjustified killing, participants had to imagine being the perpetrator whilst watching “first-person perspective” animated videos where they shot enemy soldiers (‘justified violence’) and innocent civilians (‘unjustified violence’). When participants imagined themselves shooting civilians compared to soldiers, greater activation was found in the lateral orbitofrontal cortex (OFC). Regression analysis revealed that the more guilt participants felt about shooting civilians, the greater the response in the lateral OFC. Effective connectivity analyses further revealed an increased coupling between lateral OFC and the tempoparietal junction (TPJ) when shooting civilians. The results show that the neural mechanisms typically implicated with harming others, such as the OFC, become less active when the violence against a particular group is seen as justified. This study therefore provides unique insight into how normal individuals can become aggressors in specific situations.

Static load bearing exercises during rehabilitation of individuals with transfemoral amputation fitted with osseointegrated implant: Load compliance

Osseointegration has been introduced in the orthopaedic surgery in the 1990’s in Gothenburg (Sweden). To date, there are two frequently used commercially available human implants: the OPRA (Integrum, Sweden) and ILP (Orthodynamics, Germany) systems. The rehabilitation program with both systems include some form of static load bearing exercises. These latter involved following a load progression that is monitored by the bathroom scale, providing only the load applied on the vertical axis. The loading data could be analysed through different biomechanical variables. For instance, the load compliance, corresponding to the difference between the load recommended (LR) and the load actually applied on the implant, will be presented here.

Load bearing exercises and functional outcome of individuals with transfemoral amputation fitted with OPRA fixation

Individuals with lower limb amputation fitted with an OPRA osseointegrated fixation are facing an extensive rehabilitation program including static load bearing exercises (LBE). The application of a suitable amount of stress stimulates osseointegration and prepares the bone to tolerate the forces and moments that will be incurred during activities of daily living (ADL. At present, the monitoring is typically carried out using a normal bathroom weighing scale. This scale provides information only on the magnitude of the vertical component of the applied force. The moment around the long axis of the fixation when the femur is perpendicular to the ground is not assessed and neither are the components of force and moment generated on the other two axes.