Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

The effects of content presentation format and user characteristics on novice developers’ understanding of process models

Process models are used by information professionals to convey semantics about the business operations in a real world domain intended to be supported by an information system. The understandability of these models is vital to them being used for information systems development. In this paper, we examine two factors that we predict will influence the understanding of a business process that novice developers obtain from a corresponding process model: the content presentation form chosen to articulate the business domain, and the user characteristics of the novice developers working with the model. Our experimental study provides evidence that novice developers obtain similar levels of understanding when confronted with an unfamiliar or a familiar process model. However, previous modeling experience, the use of English as a second language, and previous work experience in BPM are important influencing factors of model understanding. Our findings suggest that education and research in process modeling should increase the focus on human factors and how they relate to content and content presentation formats for different modeling tasks. We discuss implications for practice and research.

Person-independent facial expression detection using constrained local models

In automatic facial expression detection, very accurate registration is desired which can be achieved via a deformable model approach where a dense mesh of 60-70 points on the face is used, such as an active appearance model (AAM). However, for applications where manually labeling frames is prohibitive, AAMs do not work well as they do not generalize well to unseen subjects. As such, a more coarse approach is taken for person-independent facial expression detection, where just a couple of key features (such as face and eyes) are tracked using a Viola-Jones type approach. The tracked image is normally post-processed to encode for shift and illumination invariance using a linear bank of filters. Recently, it was shown that this preprocessing step is of no benefit when close to ideal registration has been obtained. In this paper, we present a system based on the Constrained Local Model (CLM) which is a generic or person-independent face alignment algorithm which gains high accuracy. We show these results against the LBP feature extraction on the CK+ and GEMEP datasets.

Design Led Innovation : exploring the synthesis of needs, technologies and business models

The term Design is used to describe a wide range of activities. Like the term innovation, it is often used to describe both an activity and an outcome. Many products and services are often described as being designed, as they describe a conscious process of linking form and function. Alternatively, the many and varied processes of design are often used to describe a cost centre of an organisation to demonstrate a particular competency. However design is often not used to describe the ‘value’ it provides to an organisation and more importantly the ‘value’ it provides to both existing and future customers. Design Led Innovation bridges this gap. Design Led Innovation is a process of creating a sustainable competitive advantage, by radically changing the customer value proposition. A conceptual model has been developed to assist organisations apply and embed design in a company’s vision, strategy, culture, leadership and development processes.

Distinguishing forced versus discretionary replacements in new product diffusion models

In many product categories of durable goods such as TV, PC, and DVD player, the largest component of sales is generated by consumers replacing existing units. Aggregate sales models proposed by diffusion of innovation researchers for the replacement component of sales have incorporated several different replacement distributions such as Rayleigh, Weibull, Truncated Normal and Gamma. Although these alternative replacement distributions have been tested using both time series sales data and individual-level actuarial “life-tables” of replacement ages, there is no census on which distributions are more appropriate to model replacement behaviour. In the current study we are motivated to develop a new “modified gamma” distribution by two reasons. First we recognise that replacements have two fundamentally different drivers – those forced by failure and early, discretionary replacements. The replacement distribution for each of these drivers is expected to be quite different. Second, we observed a poor fit of other distributions to out empirical data. We conducted a survey of 8,077 households to empirically examine models of replacement sales for six electronic consumer durables – TVs, VCRs, DVD players, digital cameras, personal and notebook computers. This data allows us to construct individual-level “life-tables” for replacement ages. We demonstrate the new modified gamma model fits the empirical data better than existing models for all six products using both a primary and a hold-out sample.

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.

An analytical method to solve a general class of nonlinear reactive transport models

Three recent papers published in Chemical Engineering Journal studied the solution of a model of diffusion and nonlinear reaction using three different methods. Two of these studies obtained series solutions using specialized mathematical methods, known as the Adomian decomposition method and the homotopy analysis method. Subsequently it was shown that the solution of the same particular model could be written in terms of a transcendental function called Gauss’ hypergeometric function. These three previous approaches focused on one particular reactive transport model. This particular model ignored advective transport and considered one specific reaction term only. Here we generalize these previous approaches and develop an exact analytical solution for a general class of steady state reactive transport models that incorporate (i) combined advective and diffusive transport, and (ii) any sufficiently differentiable reaction term R(C). The new solution is a convergent Maclaurin series. The Maclaurin series solution can be derived without any specialized mathematical methods nor does it necessarily involve the computation of any transcendental function. Applying the Maclaurin series solution to certain case studies shows that the previously published solutions are particular cases of the more general solution outlined here. We also demonstrate the accuracy of the Maclaurin series solution by comparing with numerical solutions for particular cases.

Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. 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.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

Automated error correction of business process models

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Configurable multi-perspective business process models

A configurable process model provides a consolidated view of a family of business processes. It promotes the reuse of proven practices by providing analysts with a generic modelling artifact from which to derive individual process models. Unfortunately, the scope of existing notations for configurable process modelling is restricted, thus hindering their applicability. Specifically, these notations focus on capturing tasks and control-flow dependencies, neglecting equally important ingredients of business processes such as data and resources. This research fills this gap by proposing a configurable process modelling notation incorporating features for capturing resources, data and physical objects involved in the performance of tasks. The proposal has been implemented in a toolset that assists analysts during the configuration phase and guarantees the correctness of the resulting process models. The approach has been validated by means of a case study from the film industry.

In vitro and in vivo examination of the cell surface glycoprotein CDCP1

A number of reports have demonstrated the importance of the CUB domaincontaining protein 1 (CDCP1) in facilitating cancer progression in animal models and the potential of this protein as a prognostic marker in several malignancies. CDCP1 facilitates metastasis formation in animal models by negatively regulating anoikis, a type of apoptosis triggered by the loss of attachment signalling from cell-cell contacts or cell-extra cellular matrix (ECM) contacts. Due to the important role CDCP1 plays in cancer progression in model systems, it is considered a potential drug target to prevent the metastatic spread of cancers. CDCP1 is a highly glycosylated 836 amino acid cell surface protein. It has structural features potentially facilitating protein-protein interactions including 14 N-glycosylation sites, three CUB-like domains, 20 cysteine residues likely to be involved in disulfide bond formation and five intracellular tyrosine residues. CDCP1 interacts with a variety of proteins including Src family kinases (SFKs) and protein kinase C ä (PKCä). Efforts to understand the mechanisms regulating these interactions have largely focussed on three CDCP1 tyrosine residues Y734, Y743 and Y762. CDCP1-Y734 is the site where SFKs phosphorylate and bind to CDCP1 and mediate subsequent phosphorylation of CDCP1-Y743 and -Y762 which leads to binding of PKCä at CDCP1-Y762. The resulting trimeric protein complex of SFK•CDCP1•PKCä has been proposed to mediate an anti-apoptotic cell phenotype in vitro, and to promote metastasis in vivo. The effect of mutation of the three tyrosines on interactions of CDCP1 with SFKs and PKCä and the consequences on cell phenotype in vitro and in vivo have not been examined. CDCP1 has a predicted molecular weight of ~90 kDa but is usually detected as a protein which migrates at ~135 kDa by Western blot analysis due to its high degree of glycosylation. A low molecular weight form of CDCP1 (LMWCDCP1) of ~70 kDa has been found in a variety of cancer cell lines. The mechanisms leading to the generation of LMW-CDCP1 in vivo are not well understood but an involvement of proteases in this process has been proposed. Serine proteases including plasmin and trypsin are able to proteolytically process CDCP1. In addition, the recombinant protease domain of the serine protease matriptase is also able to cleave the recombinant extracellular portion of CDCP1. Whether matriptase is able to proteolytically process CDCP1 on the cell surface has not been examined. Importantly, proteolytic processing of CDCP1 by trypsin leads to phosphorylation of its cell surface-retained portion which suggests that this event leads to initiation of an intracellular signalling cascade. This project aimed to further examine the biology of CDCP1 with a main of focus on exploring the roles played by CDCP1 tyrosine residues. To achieve this HeLa cells stably expressing CDCP1 or the CDCP1 tyrosine mutants Y734F, Y743F and Y762F were generated. These cell lines were used to examine: • The roles of the tyrosine residues Y734, Y743 and Y762 in mediating interactions of CDCP1 with binding proteins and to examine the effect of the stable expression on HeLa cell morphology. • The ability of the serine protease matriptase to proteolytically process cell surface CDCP1 and to examine the consequences of this event on HeLa cell phenotype and cell signalling in vitro. • The importance of these residues in processes associated with cancer progression in vitro including adhesion, proliferation and migration. • The role of these residues on metastatic phenotype in vivo and the ability of a function-blocking anti-CDCP1 antibody to inhibit metastasis in the chicken embryo chorioallantoic membrane (CAM) assay. Interestingly, biochemical experiments carried out in this study revealed that mutation of certain CDCP1 tyrosine residues impacts on interactions of this protein with binding proteins. For example, binding of SFKs as well as PKCä to CDCP1 was markedly decreased in HeLa-CDCP1-Y734F cells, and binding of PKCä was also reduced in HeLa-CDCP1-Y762F cells. In contrast, HeLa-CDCP1-Y743F cells did not display altered interactions with CDCP1 binding proteins. Importantly, observed differences in interactions of CDCP1 with binding partners impacted on basal phosphorylation of CDCP1. It was found that HeLa-CDCP1, HeLa-CDCP1-Y743F and -Y762F displayed strong basal levels of CDCP1 phosphorylation. In contrast, HeLa-CDCP1-Y734F cells did not display CDCP1 phosphorylation but exhibited constitutive phosphorylation of focal adhesion kinase (FAK) at tyrosine 861. Significantly, subsequent investigations to examine this observation suggested that CDCP1-Y734 and FAK-Y861 are competitive substrates for SFK-mediated phosphorylation. It appeared that SFK-mediated phosphorylation of CDCP1- Y734 and FAK-Y861 is an equilibrium which shifts depending on the level of CDCP1 expression in HeLa cells. This suggests that the level of CDCP1 expression may act as a regulatory mechanism allowing cells to switch from a FAK-Y861 mediated pathway to a CDCP1-Y734 mediated pathway. This is the first time that a link between SFKs, CDCP1 and FAK has been demonstrated. One of the most interesting observations from this work was that CDCP1 altered HeLa cell morphology causing an elongated and fibroblastic-like appearance. Importantly, this morphological change depended on CDCP1- Y734. In addition, it was observed that this change in cell morphology was accompanied by increased phosphorylation of SFK-Y416. This suggests that interactions of SFKs with CDCP1-Y734 increases SFK activity since SFKY416 is critical in regulating kinase activity of these proteins. The essential role of SFKs in mediating CDCP1-induced HeLa cell morphological changes was demonstrated using the SFK-selective inhibitor SU6656. This inhibitor caused reversion of HeLa-CDCP1 cell morphology to an epithelial appearance characteristic of HeLa-vector cells. Significantly, in vitro studies revealed that certain CDCP1-mediated cell phenotypes are mediated by cellular pathways dependent on CDCP1 tyrosine residues whereas others are independent of these sites. For example, CDCP1 expression caused a marked increase in HeLa cell motility that was independent of CDCP1 tyrosine residues. In contrast, CDCP1- induced decrease in HeLa cell proliferation was most prominent in HeLa- CDCP1-Y762F cells, potentially indicating a role for this site in regulating proliferation in HeLa cells. Another cellular event which was identified to require phosphorylation of a particular CDCP1 tyrosine residue is adhesion to fibronectin. It was observed that the CDCP1-mediated strong decrease in adhesion to fibronectin is mostly restored in HeLa-CDCP1-Y743F cells. This suggests a possible role for CDCP1-Y743 in causing a CDCP1-mediated decrease in adhesion. Data from in vivo experiments indicated that HeLa-CDCP1-Y734F cells are more metastic than HeLa-CDCP1 cells in vivo. This indicates that interaction of CDCP1 with SFKs and PKCä may not be required for CDCP1-mediated metastasis formation of HeLa cells in vivo. The metastatic phenotype of these cells may be caused by signalling involving FAK since HeLa-CDCP1- Y734F cells are the only CDCP1 expressing cells displaying constitutive phosphorylation of FAK-Y861. HeLa-CDCP1-Y762F cells displayed a very low metastatic ability which suggests that this CDCP1 tyrosine residue is important in mediating a pro-metastatic phenotype in HeLa cells. More detailed exploration of cellular events occurring downstream of CDCP1-Y734 and -Y762 may provide important insights into the mechanisms altering the metastatic ability of CDCP1 expressing HeLa cells. Complementing the in vivo studies, anti-CDCP1 antibodies were employed to assess whether these antibodies are able to inhibit metastasis of CDCP1 and CDCP1 tyrosine mutants expressing HeLa cells. It was found that HeLa- CDCP1-Y734F cells were the only cell line which was markedly reduced in the ability to metastasise. In contrast, the ability of HeLa-CDCP1, HeLa- CDCP1-Y743F and -Y762F cells to metastasise in vivo was not inhibited. These data suggest a possible role of interactions of CDCP1 with SFKs, occurring at CDCP1-Y734, in preventing an anti-metastatic effect of anti- CDCP1 antibodies in vivo. The proposal that SFKs may play a role in regulating anti-metastatic effects of anti-CDCP1 antibodies was supported by another experiment where differences between HeLa-CDCP1 cells and CDCP1 expressing HeLa cells (HeLa-CDCP1-S) from collaborators at the Scripps Research Institute were examined. It was found that HeLa-CDCP1-S cells express different SFKs than CDCP1 expressing HeLa cells generated for this study. This is important since HeLa-CDCP1-S cells can be inhibited in their metastatic ability using anti-CDCP1 antibodies in vivo. Importantly, these data suggest that further examinations of the roles of SFKs in facilitating anti-metastatic effects of anti-CDCP1 antibodies may give insights into how CDCP1 can be blocked to prevent metastasis in vivo. This project also explored the ability of the serine protease matriptase to proteolytically process cell surface localised CDCP1 because it is unknown whether matriptase can cleave cell surface CDCP1 as it has been reported for other proteases such as trypsin and plasmin. Furthermore, the consequences of matriptase-mediated proteolysis on cell phenotype in vitro and cell signalling were examined since recent reports suggested that proteolysis of CDCP1 leads to its phosphorylation and may initiate cell signalling and consequently alter cell phenotype. It was found that matriptase is able to proteolytically process cell surface CDCP1 at low nanomolar concentrations which suggests that cleavage of CDCP1 by matriptase may facilitate the generation of LWM-CDCP1 in vivo. To examine whether matriptase-mediated proteolysis induced cell signalling anti-phospho Erk 1/2 Western blot analysis was performed as this pathway has previously been examined to study signalling in response to proteolytic processing of cell surface proteins. It was found that matriptase-mediated proteolysis in CDCP1 expressing HeLa cells initiated intracellular signalling via Erk 1/2. Interestingly, this increase in phosphorylation of Erk 1/2 was also observed in HeLa-vector cells. This suggested that initiation of cell signalling via Erk 1/2 phosphorylation as a result of matriptase-mediated proteolysis occurs by pathways independent of CDCP1. Subsequent investigations measuring the flux of free calcium ions and by using a protease-activated receptor 2 (PAR2) agonist peptide confirmed this hypothesis. These data suggested that matriptase-mediated proteolysis results in cell signalling via a pathway induced by the activation of PAR2 rather than by CDCP1. This indicates that induction of cell signalling in HeLa cells as a consequence of matriptase-mediated proteolysis occurs via signalling pathways which do not involve phosphorylation of Erk 1/2. Consequently, it appears that future attempts should focus on the examination of cellular pathways other than Erk 1/2 to elucidate cell signalling initiated by matriptase-mediated proteolytic processing of CDCP1. The data presented in this thesis has explored in vitro and in vivo aspects of the biology of CDCP1. The observations summarised above will permit the design of future studies to more precisely determine the role of CDCP1 and its binding partners in processes relevant to cancer progression. This may contribute to further defining CDCP1 as a target for cancer treatment.

Corrected mean-field models for spatially-dependent advection-diffusion-reaction phenomena

In the exclusion-process literature, mean-field models are often derived by assuming that the occupancy status of lattice sites is independent. Although this assumption is questionable, it is the foundation of many mean-field models. In this work we develop methods to relax the independence assumption for a range of discrete exclusion process-based mechanisms motivated by applications from cell biology. Previous investigations that focussed on relaxing the independence assumption have been limited to studying initially-uniform populations and ignored any spatial variations. By ignoring spatial variations these previous studies were greatly simplified due to translational invariance of the lattice. These previous corrected mean-field models could not be applied to many important problems in cell biology such as invasion waves of cells that are characterised by moving fronts. Here we propose generalised methods that relax the independence assumption for spatially inhomogeneous problems, leading to corrected mean-field descriptions of a range of exclusion process-based models that incorporate (i) unbiased motility, (ii) biased motility, and (iii) unbiased motility with agent birth and death processes. The corrected mean-field models derived here are applicable to spatially variable processes including invasion wave type problems. We show that there can be large deviations between simulation data and traditional mean-field models based on invoking the independence assumption. Furthermore, we show that the corrected mean-field models give an improved match to the simulation data in all cases considered.

Towards a classification of criterion referenced assessment models in mathematics courses : student and academic perspectives

We present the findings of a study into the implementation of explicitly criterion- referenced assessment in undergraduate courses in mathematics. We discuss students' concepts of criterion referencing and also the various interpretations that this concept has among mathematics educators. Our primary goal was to move towards a classification of criterion referencing models in quantitative courses. A secondary goal was to investigate whether explicitly presenting assessment criteria to students was useful to them and guided them in responding to assessment tasks. The data and feedback from students indicates that while students found the criteria easy to understand and useful in informing them as to how they would be graded, it did not alter the way the actually approached the assessment activity.