The reliability, accuracy and minimal detectable difference of a multi-segment kinematic model of the foot-shoe complex

Kinematic models are commonly used to quantify foot and ankle kinematics, yet no marker sets or models have been proven reliable or accurate when wearing shoes. Further, the minimal detectable difference of a developed model is often not reported. We present a kinematic model that is reliable, accurate and sensitive to describe the kinematics of the foot–shoe complex and lower leg during walking gait. In order to achieve this, a new marker set was established, consisting of 25 markers applied on the shoe and skin surface, which informed a four segment kinematic model of the foot–shoe complex and lower leg. Three independent experiments were conducted to determine the reliability, accuracy and minimal detectable difference of the marker set and model. Inter-rater reliability of marker placement on the shoe was proven to be good to excellent (ICC = 0.75–0.98) indicating that markers could be applied reliably between raters. Intra-rater reliability was better for the experienced rater (ICC = 0.68–0.99) than the inexperienced rater (ICC = 0.38–0.97). The accuracy of marker placement along each axis was <6.7 mm for all markers studied. Minimal detectable difference (MDD90) thresholds were defined for each joint; tibiocalcaneal joint – MDD90 = 2.17–9.36°, tarsometatarsal joint – MDD90 = 1.03–9.29° and the metatarsophalangeal joint – MDD90 = 1.75–9.12°. These thresholds proposed are specific for the description of shod motion, and can be used in future research designed at comparing between different footwear.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

A context sensitive model for querying linked scientific data

This thesis provides a query model suitable for context sensitive access to a wide range of distributed linked datasets which are available to scientists using the Internet. The model is designed based on scientific research standards which require scientists to provide replicable methods in their publications. Although there are query models available that provide limited replicability, they do not contextualise the process whereby different scientists select dataset locations based on their trust and physical location. In different contexts, scientists need to perform different data cleaning actions, independent of the overall query, and the model was designed to accommodate this function. The query model was implemented as a prototype web application and its features were verified through its use as the engine behind a major scientific data access site, Bio2RDF.org. The prototype showed that it was possible to have context sensitive behaviour for each of the three mirrors of Bio2RDF.org using a single set of configuration settings. The prototype provided executable query provenance that could be attached to scientific publications to fulfil replicability requirements. The model was designed to make it simple to independently interpret and execute the query provenance documents using context specific profiles, without modifying the original provenance documents. Experiments using the prototype as the data access tool in workflow management systems confirmed that the design of the model made it possible to replicate results in different contexts with minimal additions, and no deletions, to query provenance documents.

Selling James Bond : product placement in the James Bond films

The character of James Bond for many people is intrinsically linked in their minds with particular brands – Aston Martin, Bollinger, Omega, Smirnoff vodka, and so on. This direct association between character and brand highlights the intrinsic role of product placement in the film industry, and in the James Bond films in particular. Selling James Bond: Product Placement in the James Bond Films provides a comprehensive overview of the history of product placement in the James Bond series – charting the progression of the practice and drawing direct correlations to significant cultural and historical events that impacted upon the number and types of products incorporated into the series. While primarily a financial arrangement, it is also important that the practice of product placement be examined and understood in relation to these cultural contexts, an area of research so far largely ignored by academic study. Through extensive content analysis of the official James Bond film series, as well as utilising directors’ commentary and industry reports, this book illustrates the strong impact specific cultural and historical events have had on the practice of product placement in the series. In doing so, it provides an exciting and in-depth “behind the scenes” look at the James Bond film series, and its complicated and sometimes contentious history of product placement. In the process, it charts the gradual emergence of product placement from the more traditional background shot to becoming so embedded in the actual film narrative that they have become simply yet another method for filmmakers to produce cultural meaning.

Standardisation of digital human models

Digital human models (DHM) have evolved as useful tools for ergonomic workplace design and product development, and found in various industries and education. DHM systems which dominate the market were developed for specific purposes and differ significantly, which is not only reflected in non-compatible results of DHM simulations, but also provoking misunderstanding of how DHM simulations relate to real world problems. While DHM developers are restricted by uncertainty about the user need and lack of model data related standards, users are confined to one specific product and cannot exchange results, or upgrade to another DHM system, as their previous results would be rendered worthless. Furthermore, origin and validity of anthropometric and biomechanical data is not transparent to the user. The lack of standardisation in DHM systems has become a major roadblock in further system development, affecting all stakeholders in the DHM industry. Evidently, a framework for standardising digital human models is necessary to overcome current obstructions.

An estimation of crash risks using a log-linear model and the induced exposure technique

Prevention and safety promotion programmes. Traditionally, in-depth investigations of crash risks are conducted using exposure controlled study or case-control methodology. However, these studies need either observational data for control cases or exogenous exposure data like vehicle-kilometres travel, entry flow or product of conflicting flow for a particular traffic location, or a traffic site. These data are not readily available and often require extensive data collection effort on a system-wide basis. Aim: The objective of this research is to propose an alternative methodology to investigate crash risks of a road user group in different circumstances using readily available traffic police crash data. Methods: This study employs a combination of a log-linear model and the quasi-induced exposure technique to estimate crash risks of a road user group. While the log-linear model reveals the significant interactions and thus the prevalence of crashes of a road user group under various sets of traffic, environmental and roadway factors, the quasi-induced exposure technique estimates relative exposure of that road user in the same set of explanatory variables. Therefore, the combination of these two techniques provides relative measures of crash risks under various influences of roadway, environmental and traffic conditions. The proposed methodology has been illustrated using Brisbane motorcycle crash data of five years. Results: Interpretations of results on different combination of interactive factors show that the poor conspicuity of motorcycles is a predominant cause of motorcycle crashes. Inability of other drivers to correctly judge the speed and distance of an oncoming motorcyclist is also evident in right-of-way violation motorcycle crashes at intersections. Discussion and Conclusions: The combination of a log-linear model and the induced exposure technique is a promising methodology and can be applied to better estimate crash risks of other road users. This study also highlights the importance of considering interaction effects to better understand hazardous situations. A further study on the comparison between the proposed methodology and case-control method would be useful.

A ‘service logic’ rationale for business model innovation

Successful firms use business model innovation to rethink the way they do business and transform industries. However, current research on business model innovation is lacking theoretical underpinnings and is in need of new insights. This objective of this paper is to advance our understanding of both the business model concept and business model innovation based on service logic as foundation for customer value and value creation. We present and discuss a rationale for business models based on ‘service logic’ with service as a value-supporting process and compared it with a business model based on ‘goods logic’ with goods as value-supporting resources. The implications for each of the business model dimensions: customer, value proposition, organizational architecture and revenue model, are described and discussed in detail.

An improved model of tumour-immune system interactions

The immune system plays an important role in defending the body against tumours and other threats. Currently, mechanisms involved in immune system interactions with tumour cells are not fully understood. Here we develop a mathematical tool that can be used in aiding to address this shortfall in understanding. This paper de- scribes a hybrid cellular automata model of the interaction between a growing tumour and cells of the innate and specific immune system including the effects of chemokines that builds on previous models of tumour-immune system interactions. In particular, the model is focused on the response of immune cells to tumour cells and how the dynamics of the tumour cells change due to the immune system of the host. We present results and predictions of in silico experiments including simulations of Kaplan-Meier survival-like curves.

Stigmergy in Web 2.0 : a model for site dynamics

Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes.

Thinking Critically About Rural Gender Relations: Toward a Rural Masculinity/Male Peer Support Model of Separation/Divorce Sexual Assault

After decades of neglect, a growing number of scholars have turned their attention to issues of crime and criminal justice in the rural context. Despite this improvement, rural crime research is underdeveloped theoretically, and is little informed by critical criminological perspectives. In this article, we introduce the broad tenets of a multi-level theory that links social and economic change to the reinforcement of rural patriarchy and male peer support, and in turn, how they are linked to separation/divorce sexual assault. We begin by addressing a series of misconceptions about what is rural, rural homogeneity and commonly held presumptions about the relationship of rurality, collective efficacy (and related concepts) and crime. We conclude by recommending more focused research, both qualitative and quantitative, to uncover specific link between the rural transformation and violence against women.

Is the unigram relevance model term independent? Classifying term dependencies in query expansion

This paper develops a framework for classifying term dependencies in query expansion with respect to the role terms play in structural linguistic associations. The framework is used to classify and compare the query expansion terms produced by the unigram and positional relevance models. As the unigram relevance model does not explicitly model term dependencies in its estimation process it is often thought to ignore dependencies that exist between words in natural language. The framework presented in this paper is underpinned by two types of linguistic association, namely syntagmatic and paradigmatic associations. It was found that syntagmatic associations were a more prevalent form of linguistic association used in query expansion. Paradoxically, it was the unigram model that exhibited this association more than the positional relevance model. This surprising finding has two potential implications for information retrieval models: (1) if linguistic associations underpin query expansion, then a probabilistic term dependence assumption based on position is inadequate for capturing them; (2) the unigram relevance model captures more term dependency information than its underlying theoretical model suggests, so its normative position as a baseline that ignores term dependencies should perhaps be reviewed.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

Application of a human bone engineering platform to an in vitro and in vivo breast cancer metastasis model

Breast cancer in its advanced stage has a high predilection to the skeleton. Currently, treatment options of breast cancer-related bone metastasis are restricted to only palliative therapeutic modalities. This is due to the fact that mechanisms regarding the breast cancer celI-bone colonisation as well as the interactions of breast cancer cells with the bone microenvironment are not fully understood, yet. This might be explained through a lack of appropriate in vitro and in vivo models that are currently addressing the above mentioned issue. Hence the hypothesis that the translation of a bone tissue engineering platform could lead to improved and more physiological in vitro and in vivo model systems in order to investigate breast cancer related bone colonisation was embraced in this PhD thesis. Therefore the first objective was to develop an in vitro model system that mimics human mineralised bone matrix to the highest possible extent to examine the specific biological question, how the human bone matrix influences breast cancer cell behaviour. Thus, primary human osteoblasts were isolated from human bone and cultured under osteogenic conditions. Upon ammonium hydroxide treatment, a cell-free intact mineralised human bone matrix was left behind. Analyses revealed a similar protein and mineral composition of the decellularised osteoblast matrix to human bone. Seeding of a panel of breast cancer cells onto the bone mimicking matrix as well as reference substrates like standard tissue culture plastic and collagen coated tissue culture plastic revealed substrate specific differences of cellular behaviour. Analyses of attachment, alignment, migration, proliferation, invasion, as well as downstream signalling pathways showed that these cellular properties were influenced through the osteoblast matrix. The second objective of this PhD project was the development of a human ectopic bone model in NOD/SCID mice using medical grade polycaprolactone tricalcium phosphate (mPCL-TCP) scaffold. Human osteoblasts and mesenchymal stem cells were seeded onto an mPCL-TCP scaffold, fabricated using a fused deposition modelling technique. After subcutaneous implantation in conjunction with the bone morphogenetic protein 7, limited bone formation was observed due to the mechanical properties of the applied scaffold and restricted integration into the soft tissue of flank of NOD/SCID mice. Thus, a different scaffold fabrication technique was chosen using the same polymer. Electrospun tubular scaffolds were seeded with human osteoblasts, as they showed previously the highest amount of bone formation and implanted into the flanks of NOD/SCID mice. Ectopic bone formation with sufficient vascularisation could be observed. After implantation of breast cancer cells using a polyethylene glycol hydrogel in close proximity to the newly formed bone, macroscopic communication between the newly formed bone and the tumour could be observed. Taken together, this PhD project showed that bone tissue engineering platforms could be used to develop an in vitro and in vivo model system to study cancer cell colonisation in the bone microenvironment.

A computer model to simulate scoliosis surgery

Use of patient-specific computer models as a pre-operative planning tool permits predictions of the likely deformity correction and allows a more detailed investigation of the biomechanical influence of different surgical procedures on the scoliotic spinal anatomy. In this paper, patient-specific computer models are used of adolescent idiopathic scoliosis patients who underwent a single rod anterior procedure at the Mater Children’s Hospital in Brisbane, to predict deformity correction and to investigate the change in biomechanics of the scoliotic spine due to surgical compressive forces applied during implant placement.