Measures of nutrient processes as indicators of stream ecosystem health

To better understand how freshwater ecosystems respond to changes in catchment land-use, it is important to develop measures of ecological health that include aspects of both ecosystem structure and function. This study investigated measures of nutrient processes as potential indicators of stream ecosystem health across a land-use gradient from relatively undisturbed to highly modified. A total of seven indicators (potential denitrification; an index of denitrification potential relative to sediment organic matter; benthic algal growth on artificial substrates amended with (a) N only, (b) P only, and (c) N and P; and δ15N of aquatic plants and benthic sediment) were measured at 53 streams in southeast Queensland, Australia. The indicators were evaluated by their response to a defined gradient of agricultural land-use disturbance as well as practical aspects of using the indicators as part of a monitoring program. Regression models based on descriptors of the disturbance gradient explained a large proportion of the variation in six of the seven indicators. Denitrification index, algal growth in N amended substrate, and δ15N of aquatic plants demonstrated the best regression. However, the δ15N value of benthic sediment was found to be the best indicator overall for incorporation into a monitoring program, as samples were relatively easy to collect and process, and were successfully collected at more than 90% of the study sites.

The stabilising role of the costovertebral joints and spinous processes on thoracic spine stability

Introduction The importance of in vitro biomechanical testing in today’s understanding of spinal pathology and treatment modalities cannot be stressed enough. Different studies have used differing levels of dissection of their spinal segments for their testing protocols[1, 2]. The aim of this study was to assess the impact of removing the costovertebral joints and partial resection of the spinous process sequentially, on the stiffness of the immature thoracic bovine spinal segment. Materials and Methods Thoracic spines from 6-8 week old calves were used. Each spine was dissected and divided into motion segments with 5cm of attached rib on each side and full spinous processes including levels T4-T11 (n=28). They were potted in polymethylemethacrylate. An Instron Biaxial materials testing machine with a custom made jig was used for testing. The segments were tested in flexion/extension, lateral bending and axial rotation at 37⁰C and 100% humidity, using moment control to a maximum 1.75 Nm with a loading rate of 0.3 Nm per second. They were first tested intact for ten load cycles with data collected from the tenth cycle. Progressive dissection was performed by removing first the attached ribs, followed by the spinous process at its base. Biomechanical testing was carried out after each level of dissection using the same protocol. Statistical analysis of the data was performed using repeated measures ANOVA. Results In combined flexion/extension there was a significant reduction in stiffness of 16% (p=0.002). This was mainly after resection of the ribs (14%, p=0.024) and mainly occurred in flexion where stiffness reduced by 22% (p=0.021). In extension, stiffness dropped by 13% (p=0.133). However there was no further significant change in stiffness on resection of the spinous process (<1%) (p=1.00). In lateral bending there was a significant decrease in stiffness of 13% (p<0.001). This comprised a drop of 11% on resection of the ribs (p=0.009) and a further 8% on resection of the spinous process (p=0.014). There was no difference between left and right bending. In axial rotation there was no significant change in stiffness after each stage of dissection (p=0.253). There was no difference between left and right rotation. Conclusion The costovertebral joints play a significant role in providing stability to the bovine thoracic spine in both flexion/extension and lateral bending, whereas the spinous processes play a minor role. Both elements have little effect on axial rotation stability.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Compressed sensing using hidden Markov models with application to vision based aircraft tracking

This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Enhanced stochastic mobility prediction with multi-output Gaussian processes

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

Event interval analysis: Why do processes take time?

Through the application of process mining, valuable evidence-based insights can be obtained about business processes in organisations. As a result the field has seen an increased uptake in recent years as evidenced by success stories and increased tool support. However, despite this impact, current performance analysis capabilities remain somewhat limited in the context of information-poor event logs. For example, natural daily and weekly patterns are not considered. In this paper a new framework for analysing event logs is defined which is based on the concept of event gap. The framework allows for a systematic approach to sophisticated performance-related analysis of event logs containing varying degrees of information. The paper formalises a range of event gap types and then presents an implementation as well as an evaluation of the proposed approach.

Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Processes of transformation : art into pop and back again

This research project was a case study for managing and innovating an interdisciplinary practice: specifically across music, performance and contemporary art. Key works included painting/sound/video installation, experimental performance, electronic pop music, music video and electronic pop music performance. An idiosyncratic and transformative use of colour emerged as an underlying theme and strategy for cohesion. The project offers strategies for the challenges of interdisciplinary practice specifically addressing the limitations related to institutionalised value systems, aesthetic traditions and disciplinary languages.

Pathways to peace : a phenomenological exploration of the processes of healing and posttraumatic growth following childhood sexual assault

This thesis explored pathways to healing in men and women who experienced traumatic sexual abuse in childhood and considered themselves to be in a place of wellness. The thesis synthesises current knowledge in this area and has produced a number of models with direct implications for clinical practice. This unique work has also contributed to advancing theoretical understanding of healing following sexual assault.