Investigating the hydrodynamic performance of a gross pollutant trap

Field studies show that the internal screens in a gross pollutant trap (GPT) are often clogged with organic matter, due to infrequent cleaning. The hydrodynamic performance of a GPT with fully blocked screens was comprehensively investigated under a typical range of onsite operating conditions. Using an acoustic Doppler velocimeter (ADV), velocity profiles across three critical sections of the GPT were measured and integrated to examine the net fluid flow at each section. The data revealed that when the screens are fully blocked, the flow structure within the GPT radically changes. Consequently, the capture/retention performance of the device rapidly deteriorates. Good agreement was achieved between the experimental and the previous 2D computational fluid dynamics (CFD) velocity profiles for the lower GPT inlet flow conditions.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

The relationship between substrate metabolism, exercise and appetite control : does glycogen availability influence the motivation to eat, energy intake or food choice?

The way in which metabolic fuels are utilised can alter the expression of behaviour in the interests of regulating energy balance and fuel availability. This is consistent with the notion that the regulation of appetite is a psychobiological process, in which physiological mediators act as drivers of behaviour. The glycogenostatic theory suggests that glycogen availability is central in eliciting negative feedback signals to restore energy homeostasis. Due to its limited storage capacity, carbohydrate availability is tightly regulated and its restoration is a high metabolic priority following depletion. It has been proposed that such depletion may act as a biological cue to stimulate compensatory energy intake in an effort to restore availability. Due to the increased energy demand, aerobic exercise may act as a biological cue to trigger compensatory eating as a result of perturbations to muscle and liver glycogen stores. However, studies manipulating glycogen availability over short-term periods (1-3 days) using exercise, diet or both have often produced equivocal findings. There is limited but growing evidence to suggest that carbohydrate balance is involved in the short-term regulation of food intake, with a negative carbohydrate balance having been shown to predict greater ad libitum feeding. Furthermore, a negative carbohydrate balance has been shown to be predictive of weight gain. However, further research is needed to support these findings as the current research in this area is limited. In addition, the specific neural or hormonal signal through which carbohydrate availability could regulate energy intake is at present unknown. Identification of this signal or pathway is imperative if a casual relationship is to be established. Without this, the possibility remains that the associations found between carbohydrate balance and food intake are incidental.

Public participation, values and interests in the procurement of infrastructure projects in Australia : a review and future research direction

In many countries, the main providers for major infrastructure projects are government or public agencies. Public infrastructure projects includes economic and social infrastructure such as transportation, education and health facilities. Most decision-making models for delivery of public infrastructure projects are heavily weighted towards financial/economic factors. In Australia, public participation is an essential instrument in the procurement of infrastructure and development within Australia. This study reviews the public participation, values and interests in the procurement of infrastructure projects in Australia, and identifies the research direction in this research area in order to improve the decision-making models that capture stakeholder social, economical and environmental concerns in infrastructure projects.

Music is a Wordless Knowing of Others : Resilience in Virtual Ensembles

Music making affects relationships with self and others by generating a sense of belonging to a culture or ideology (Bamford, 2006; Barovick, 2001; Dillon & Stewart, 2006; Fiske, 2000; Hallam, 2001). Whilst studies from arts education research present compelling examples of these relationships, others argue that they do not present sufficiently validated evidence of a causal link between music making experiences and cognitive or social change (Winner & Cooper, 2000; Winner & Hetland, 2000a, 2000b, 2001). I have suggested elsewhere that this disconnection between compelling evidence and observations of the effects of music making are in part due to the lack of rigor in research and the incapacity of many methods to capture these experiences in meaningful ways (Dillon, 2006). Part of the answer to these questions about rigor and causality lay in the creative use of new media technologies that capture the results of relationships in music artefacts. Crucially, it is the effective management of these artefacts within computer systems that allows researchers and practitioners to collect, organize, analyse and then theorise such music making experiences.

Wavelet based approaches and high resolution methods for complex process models

Many industrial processes and systems can be modelled mathematically by a set of Partial Differential Equations (PDEs). Finding a solution to such a PDF model is essential for system design, simulation, and process control purpose. However, major difficulties appear when solving PDEs with singularity. Traditional numerical methods, such as finite difference, finite element, and polynomial based orthogonal collocation, not only have limitations to fully capture the process dynamics but also demand enormous computation power due to the large number of elements or mesh points for accommodation of sharp variations. To tackle this challenging problem, wavelet based approaches and high resolution methods have been recently developed with successful applications to a fixedbed adsorption column model. Our investigation has shown that recent advances in wavelet based approaches and high resolution methods have the potential to be adopted for solving more complicated dynamic system models. This chapter will highlight the successful applications of these new methods in solving complex models of simulated-moving-bed (SMB) chromatographic processes. A SMB process is a distributed parameter system and can be mathematically described by a set of partial/ordinary differential equations and algebraic equations. These equations are highly coupled; experience wave propagations with steep front, and require significant numerical effort to solve. To demonstrate the numerical computing power of the wavelet based approaches and high resolution methods, a single column chromatographic process modelled by a Transport-Dispersive-Equilibrium linear model is investigated first. Numerical solutions from the upwind-1 finite difference, wavelet-collocation, and high resolution methods are evaluated by quantitative comparisons with the analytical solution for a range of Peclet numbers. After that, the advantages of the wavelet based approaches and high resolution methods are further demonstrated through applications to a dynamic SMB model for an enantiomers separation process. This research has revealed that for a PDE system with a low Peclet number, all existing numerical methods work well, but the upwind finite difference method consumes the most time for the same degree of accuracy of the numerical solution. The high resolution method provides an accurate numerical solution for a PDE system with a medium Peclet number. The wavelet collocation method is capable of catching up steep changes in the solution, and thus can be used for solving PDE models with high singularity. For the complex SMB system models under consideration, both the wavelet based approaches and high resolution methods are good candidates in terms of computation demand and prediction accuracy on the steep front. The high resolution methods have shown better stability in achieving steady state in the specific case studied in this Chapter.

Development of a composite line source emission model for traffic interrupted microenvironments and its application in particle number emissions at a bus station

A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

“Entrepreneurial management” as a strategic choice in firm behavior : linking it with performance

Establishing the core principals of “entrepreneurial management” within an organization describes a certain strategic choice that affects a company in six dimensions, according to Stevenson (1983). Our aim is to empirically measure entrepreneurial management (it’s existence and degree) and to link this measured strategic choice (for or against) entrepreneurial management with firm performance. Our argument here is that companies that follow core principals of entrepreneurial management should outperform other more administrative firms in certain measures of strategic performance. This paper builds on an empirical investigation published by Brown, Davidson & Wiklund (2001), who have developed and tested a reliable measurement instrument for Stevenson’s definition of “entrepreneurial management” (Stevenson 1983, Stevenson & Jarillo 1990). In the first part of our paper we aim to replicate and to some extent improve this study. In the second part we link the measured degree of “entrepreneurial management” with firm performance. To our knowledge, even so Stevenson’s definition of entrepreneurial management is commonly acknowledged and Brown et al. (2001) developed a reliable instrument to empirically capture this behavioral approach to management, the construct of entrepreneurial management never before has been linked to firm performance in an empirical study. Since most papers on corporate entrepreneurship and firm performance are based on Covin & Slevin’s (1991) or Miller’s (1983) concept of entrepreneurial orientation, we contribute to the literature on corporate entrepreneurship in a novel way, given the fact that the entrepreneurial management dimensions measured in our study can theoretically and empirically be clearly distinguished from the construct of entrepreneurial orientation as defined by Covin & Selvin (1991).

The feasibility of vibration analysis as a technique to detect osseointegration of transfemoral implants

One of the main causes of above knee or transfemoral amputation (TFA) in the developed world is trauma to the limb. The number of people undergoing TFA due to limb trauma, particularly due to war injuries, has been increasing. Typically the trauma amputee population, including war-related amputees, are otherwise healthy, active and desire to return to employment and their usual lifestyle. Consequently there is a growing need to restore long-term mobility and limb function to this population. Traditionally transfemoral amputees are provided with an artificial or prosthetic leg that consists of a fabricated socket, knee joint mechanism and a prosthetic foot. Amputees have reported several problems related to the socket of their prosthetic limb. These include pain in the residual limb, poor socket fit, discomfort and poor mobility. Removing the socket from the prosthetic limb could eliminate or reduce these problems. A solution to this is the direct attachment of the prosthesis to the residual bone (femur) inside the residual limb. This technique has been used on a small population of transfemoral amputees since 1990. A threaded titanium implant is screwed in to the shaft of the femur and a second component connects between the implant and the prosthesis. A period of time is required to allow the implant to become fully attached to the bone, called osseointegration (OI), and be able to withstand applied load; then the prosthesis can be attached. The advantages of transfemoral osseointegration (TFOI) over conventional prosthetic sockets include better hip mobility, sitting comfort and prosthetic retention and fewer skin problems on the residual limb. However, due to the length of time required for OI to progress and to complete the rehabilitation exercises, it can take up to twelve months after implant insertion for an amputee to be able to load bear and to walk unaided. The long rehabilitation time is a significant disadvantage of TFOI and may be impeding the wider adoption of the technique. There is a need for a non-invasive method of assessing the degree of osseointegration between the bone and the implant. If such a method was capable of determining the progression of TFOI and assessing when the implant was able to withstand physiological load it could reduce the overall rehabilitation time. Vibration analysis has been suggested as a potential technique: it is a non destructive method of assessing the dynamic properties of a structure. Changes in the physical properties of a structure can be identified from changes in its dynamic properties. Consequently vibration analysis, both experimental and computational, has been used to assess bone fracture healing, prosthetic hip loosening and dental implant OI with varying degrees of success. More recently experimental vibration analysis has been used in TFOI. However further work is needed to assess the potential of the technique and fully characterise the femur-implant system. The overall aim of this study was to develop physical and computational models of the TFOI femur-implant system and use these models to investigate the feasibility of vibration analysis to detect the process of OI. Femur-implant physical models were developed and manufactured using synthetic materials to represent four key stages of OI development (identified from a physiological model), simulated using different interface conditions between the implant and femur. Experimental vibration analysis (modal analysis) was then conducted using the physical models. The femur-implant models, representing stage one to stage four of OI development, were excited and the modal parameters obtained over the range 0-5kHz. The results indicated the technique had limited capability in distinguishing between different interface conditions. The fundamental bending mode did not alter with interfacial changes. However higher modes were able to track chronological changes in interface condition by the change in natural frequency, although no one modal parameter could uniquely distinguish between each interface condition. The importance of the model boundary condition (how the model is constrained) was the key finding; variations in the boundary condition altered the modal parameters obtained. Therefore the boundary conditions need to be held constant between tests in order for the detected modal parameter changes to be attributed to interface condition changes. A three dimensional Finite Element (FE) model of the femur-implant model was then developed and used to explore the sensitivity of the modal parameters to more subtle interfacial and boundary condition changes. The FE model was created using the synthetic femur geometry and an approximation of the implant geometry. The natural frequencies of the FE model were found to match the experimental frequencies within 20% and the FE and experimental mode shapes were similar. Therefore the FE model was shown to successfully capture the dynamic response of the physical system. As was found with the experimental modal analysis, the fundamental bending mode of the FE model did not alter due to changes in interface elastic modulus. Axial and torsional modes were identified by the FE model that were not detected experimentally; the torsional mode exhibited the largest frequency change due to interfacial changes (103% between the lower and upper limits of the interface modulus range). Therefore the FE model provided additional information on the dynamic response of the system and was complementary to the experimental model. The small changes in natural frequency over a large range of interface region elastic moduli indicated the method may only be able to distinguish between early and late OI progression. The boundary conditions applied to the FE model influenced the modal parameters to a far greater extent than the interface condition variations. Therefore the FE model, as well as the experimental modal analysis, indicated that the boundary conditions need to be held constant between tests in order for the detected changes in modal parameters to be attributed to interface condition changes alone. The results of this study suggest that in a clinical setting it is unlikely that the in vivo boundary conditions of the amputated femur could be adequately controlled or replicated over time and consequently it is unlikely that any longitudinal change in frequency detected by the modal analysis technique could be attributed exclusively to changes at the femur-implant interface. Therefore further development of the modal analysis technique would require significant consideration of the clinical boundary conditions and investigation of modes other than the bending modes.

Visualizing digital media interactions : providing feedback on jam2jam AV performances

Instrumental music performance is a well-established case of real-time interaction with technology and, when extended to ensembles, of interaction with others. However, these interactions are fleeting and the opportunities to reflect on action is limited, even though audio and video recording has recently provided important opportunities in this regard. In this paper we report on research to further extend these reflective opportunities through the capture and visualization of gestural data collected during collaborative virtual performances; specifically using the digital media instrument Jam2jam AV and the specifically-developed visualization software Jam2jam AV Visualize. We discusses how such visualization may assist performance development and understanding. The discussion engages with issues of representation, authenticity of virtual experiences, intersubjectivity and wordless collaboration, and creativity support. Two usage scenarios are described showing that collaborative intent is evident in the data visualizations more clearly than in audio-visual recordings alone, indicating that the visualization of performance gestures can be an efficient way of identifying deliberate and co-operative performance behaviours.

The Silk Road project

The Silk Road Project was a practice-based research project investigating the potential of motion capture technology to inform perceptions of embodiment in dance performance. The project created a multi-disciplinary collaborative performance event using dance performance and real-time motion capture at Deakin University’s Deakin Motion Lab. Performances at Deakin University, December 2007.

Boards of directors' contribution to strategy : a literature review and research agenda

Research Question/Issue: Over the last four decades, research on the relationship between boards of directors and strategy has proliferated. Yet to date there is little theoretical and empirical agreement regarding the question of how boards of directors contribute to strategy. This review assesses the extant literature by highlighting emerging trends and identifying several avenues for future research. Research Findings/Results: Using a content-analysis of 150 articles published in 23 management journals up to 2007, we describe and analyze how research on boards of directors and strategy has evolved over time. We illustrate how topics, theories, settings, and sources of data interact and influence insights about board–strategy relationships during three specific periods. Theoretical Implications: Our study illustrates that research on boards of directors and strategy evolved from normative and structural approaches to behavioral and cognitive approaches. Our results encourage future studies to examine the impact of institutional and context-specific factors on the (expected) contribution of boards to strategy, and to apply alternative methods to fully capture the impact of board processes and dynamics on strategy making. Practical Implications: The increasing interest in boards of directors’ contribution to strategy echoes a movement towards more strategic involvement of boards of directors. However, best governance practices and the emphasis on board independence and control may hinder the board contribution to the strategic decision making. Our study invites investors and policy-makers to consider the requirements for an effective strategic task when they nominate board members and develop new regulations.

Experimental modelling of coupled water flow and associated movements in swelling soils

Modelling of water flow and associated deformation in unsaturated reactive soils (shrinking/swelling soils) is important in many applications. The current paper presents a method to capture soil swelling deformation during water infiltration using Particle Image Velocimetry (PIV). The model soil material used is a commercially available bentonite. A swelling chamber was setup to determine the water content profile and extent of soil swelling. The test was run for 61 days, and during this time period, the soil underwent on average across its width swelling of about 26% of the height of the soil column. PIV analysis was able to determine the amount of swelling that occurred within the entire face of the soil box that was used for observations. The swelling was most apparent in the top layers with strains in most cases over 100%.

Clone Detection in Repositories of Business Process Models

Business process model repositories capture precious knowledge about an organization or a business domain. In many cases, these repositories contain hundreds or even thousands of models and they represent several man-years of effort. Over time, process model repositories tend to accumulate duplicate fragments, as new process models are created by copying and merging fragments from other models. This calls for methods to detect duplicate fragments in process models that can be refactored as separate subprocesses in order to increase readability and maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.