Mathematical modelling of intramembranous bone formation during fracture healing

During fracture healing, many complex and cryptic interactions occur between cells and bio-chemical molecules to bring about repair of damaged bone. In this thesis two mathematical models were developed, concerning the cellular differentiation of osteoblasts (bone forming cells) and the mineralisation of new bone tissue, allowing new insights into these processes. These models were mathematically analysed and simulated numerically, yielding results consistent with experimental data and highlighting the underlying pattern formation structure in these aspects of fracture healing.

The potential for new understandings of normal and abnormal cognition by integration of neuroimaging and behavioral data : Not an exercise in carrying coals to Newcastle

Discovering the means to prevent and cure schizophrenia is a vision that motivates many scientists. But in order to achieve this goal, we need to understand its neurobiological basis. The emergent metadiscipline of cognitive neuroscience fields an impressive array of tools that can be marshaled towards achieving this goal, including powerful new methods of imaging the brain (both structural and functional) as well as assessments of perceptual and cognitive capacities based on psychophysical procedures, experimental tasks and models developed by cognitive science. We believe that the integration of data from this array of tools offers the greatest possibilities and potential for advancing understanding of the neural basis of not only normal cognition but also the cognitive impairments that are fundamental to schizophrenia. Since sufficient expertise in the application of these tools and methods rarely reside in a single individual, or even a single laboratory, collaboration is a key element in this endeavor. Here, we review some of the products of our integrative efforts in collaboration with our colleagues on the East Coast of Australia and Pacific Rim. This research focuses on the neural basis of executive function deficits and impairments in early auditory processing in patients using various combinations of performance indices (from perceptual and cognitive paradigms), ERPs, fMRI and sMRI. In each case, integration of two or more sources of information provides more information than any one source alone by revealing new insights into structure-function relationships. Furthermore, the addition of other imaging methodologies (such as DTI) and approaches (such as computational models of cognition) offers new horizons in human brain imaging research and in understanding human behavior.

Policing in Rural Australia: The country cop as law enforcer and local resident

The strategies and techniques that police officers employ are adaptations to the types of communities they serve and the law enforcement system of which they are part. Observations of policing in rural and urban areas of Australia indicate that, despite being part of a single state police service, officers develop working philosophies that are systematically adapted to the locations they serve. Bayley (1989) has observed that while crimes are policed in the city, people are policed in the country. Rural police officers often adopt a community-based model of policing in which officers become integrated into a community and establish compatible community relations. While this model can produce successful results, with integration into informal social networks providing police increased opportunities to solve crime, rural police regularly find themselves occupying competing roles of law enforcer and local resident. This chapter will outline how the organisation and structure of rural communities impacts upon policing, noting distinct issues associated with police work in rural settings. Before examining current aspects of rural policing, a brief discussion of the historical and cultural context of rural policing is provided.

Patient group directions : the application and integration of knowledge in advancing nursing practice

Background: The introduction of Patient Group Directions (PGD) has changed significantly the way in which nurses can now administer prescription only medicines as a one-off for patients requiring this level of service. PGD’s are a written authority to administer drugs to patients that are not identified at the time of treatment. Aim: The aim of this project was to develop a PGD for use within an Outreach team to administer colloid boluses to patients presenting with hypovolemia. Method: Using a case exemplar this paper will discuss the development of a PGD using aspects of transitional change theory to highlight the potential barriers that were encountered. Implications for Practice: The implications for this PGD are wide reaching. First it now enables members from the nursing Outreach team to administer colloid fluid boluses to a prescribed patient cohort without the need for prescription. Second, it ensures the deteriorating patient has interventions initiated in a timely and appropriate manner to reduce inadvertent admission to high care areas. Last, it will improve inter-professional team-working and communication so much so that collaborative patient care reduces health costs and identifies earlier those patients requiring substantially greater nursing and medical input. Conclusion: The experience of developing a working PGD for fluid administration has meant that the Outreach team is able to respond to patients in a more effective way. In addition, it is the experience of developing this PGD that has enabled the team to contemplate other PGD’s in the execution of Outreach work.

Building energy simulation and parallel computing : opportunities and challenges

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Stefan problems for melting nanoscaled particles

Under certain conditions, the mathematical models governing the melting of nano-sized particles predict unphysical results, which suggests these models are incomplete. This thesis studies the addition of different physical effects to these models, using analytic and numerical techniques to obtain realistic and meaningful results. In particular, the mathematical "blow-up" of solutions to ill-posed Stefan problems is examined, and the regularisation of this blow-up via kinetic undercooling. Other effects such as surface tension, density change and size-dependent latent heat of fusion are also analysed.

An efficient forward-backward algorithm to MSDEPP including batteries and voltage control devices

Electric distribution networks are now in the era of transition from passive to active distribution networks with the integration of energy storage devices. Optimal usage of batteries and voltage control devices along with other upgrades in network needs a distribution expansion planning (DEP) considering inter-temporal dependencies of stages. This paper presents an efficient approach for solving multi-stage distribution expansion planning problems (MSDEPP) based on a forward-backward approach considering energy storage devices such as batteries and voltage control devices such as voltage regulators and capacitors. The proposed algorithm is compared with three other techniques including full dynamic, forward fill-in, backward pull-out from the point of view of their precision and their computational efficiency. The simulation results for the IEEE 13 bus network show the proposed pseudo-dynamic forward-backward approach presents good efficiency in precision and time of optimization.

Bacterial adherence and biofilm formation on medical implants : a review

Biofilms are a complex group of microbial cells that adhere to the exopolysaccharide matrix present on the surface of medical devices. Biofilm-associated infections in the medical devices pose a serious problem to the public health and adversely affect the function of the device. Medical implants used in oral and orthopedic surgery are fabricated using alloys such as stainless steel and titanium. The biological behavior, such as osseointegration and its antibacterial activity, essentially depends on both the chemical composition and the morphology of the surface of the device. Surface treatment of medical implants by various physical and chemical techniques are attempted in order to improve their surface properties so as to facilitate bio-integration and prevent bacterial adhesion. The potential source of infection of the surrounding tissue and antimicrobial strategies are from bacteria adherent to or in a biofilm on the implant which should prevent both biofilm formation and tissue colonization. This article provides an overview of bacterial biofilm formation and methods adopted for the inhibition of bacterial adhesion on medical implants

Dynamics of industry architecture and firm’s capability development : role of knowledge integration within product innovation

Aligned with the decline of Marshalian view of industry as constituting homogeneous set of firms, the new perspective is emerging by concentrating more on dynamics of sectors as the building block of industrial changes. Based on new assumptions, much of the action in terms of strategy, technology, and knowledge development does not happen either among firms within a stable industry, or through the growth or decline of certain sectors compared to others. Instead, the action happens in terms of the definition, redefinition, drawing, and redrawing of the very nature of these sectors. Technology does not progress and develop within a sector; rather it shapes (and is shaped by) the encompassing architecture of multiple sectors.

Models of protein linear molecular motors for dynamic nanodevices

Protein molecular motors are natural nano-machines that convert the chemical energy from the hydrolysis of adenosine triphosphate into mechanical work. These efficient machines are central to many biological processes, including cellular motion, muscle contraction and cell division. The remarkable energetic efficiency of the protein molecular motors coupled with their nano-scale has prompted an increasing number of studies focusing on their integration in hybrid micro- and nanodevices, in particular using linear molecular motors. The translation of these tentative devices into technologically and economically feasible ones requires an engineering, design-orientated approach based on a structured formalism, preferably mathematical. This contribution reviews the present state of the art in the modelling of protein linear molecular motors, as relevant to the future design-orientated development of hybrid dynamic nanodevices. © 2009 The Royal Society of Chemistry.