Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Experimental investigation on lateral impact response of concrete-filled double-skin tube columns using horizontal-impact-testing system

This paper presents an experimental investigation on the lateral impact performance of axially loaded concrete-filled double-skin tube (CFDST) columns. These columns have desirable structural and constructional properties and have been used as columns in building, legs of off shore platforms and as bridge piers. Since they could be vulnerable to impact from passing vessels or vehicles, it is necessary to understand their behaviour under lateral impact loads. With this in mind, an experimental method employing an innovative instrumented horizontal impact testing system (HITS) was developed to apply lateral impact loads whilst the column maintained a static axial pre-loading to examine the failure mechanism and key response parameters of the column. These included the time histories of impact force, reaction forces, global lateral deflection and permanent local buckling profile. Eight full scale columns were tested for key parameters including the axial load level and impact location. Based on the test data, the failure mode, peak impact force, impact duration, peak reaction forces, reaction force duration, column maximum and residual global deflections and column local buckling length, depth and width under varying conditions are analysed and discussed. It is evident that the innovative HITS can successfully test structural columns under the combination of axial pre-loading and impact loading. The findings on the lateral impact response of the CFDST columns can serve as a benchmark reference for their future analysis and design.

Green Falcon IV: An Unmanned Aerial System and an integrated Wireless Sensor Network for remote sensing tasks

This report summarises the development of an Unmanned Aerial System and an integrated Wireless Sensor Network (WSN), suitable for the real world application in remote sensing tasks. Several aspects are discussed and analysed to provide improvements in flight duration, performance and mobility of the UAV, while ensuring the accuracy and range of data from the wireless sensor system.

Paradigmatic considerations for creative practice in Creative Industries research: The case of Australia's Indie 100

This paper begins with the assertion that research grounded in creative practice constitutes a new paradigm. We argue both for and against the idea. We argue against the idea in terms of applying it to the idealised ‘lone artist’ engaged in the production of their art, whose focus of research is a self-reflection upon the art they produce, and whose art is also the findings of the research. Our position is that such an approach cannot be considered as anything other than a form of auto-phenomenography, that such efforts are part of qualitative research, and they are thus trivial in paradigmatic terms. However, we argue in the positive for understanding the artistic event – by which we mean any mass ecology of artistic practice – as being paradigmatically new in terms of research potentials and demands. Our exemplar for that argument is a practice-led, large-scale annual event called Indie 100 which has run for five years and has demonstrated a distinct paradigmatic ‘settling in’ over its duration while clearly pushing paradigmatic boundaries for research into creative practice.

Craving mediates stress in predicting lapse during alcohol dependence treatment

Background Stress, craving, and depressed mood have all been implicated in alcohol use treatment lapses. Few studies have examined all 3 factors. Progress has been limited because of difficulties with craving assessment. The Alcohol Craving Experience Questionnaire (ACE) is a new measure of alcohol craving. It is both psychometrically sound and conceptually rigorous. This prospective study examines a stress–treatment response model that incorporates mediation by craving and moderation by depressed mood and pharmacotherapy. Methods Five hundred and thirty-nine consecutively treated alcohol-dependent patients voluntarily participated in an abstinence-based 12-week cognitive-behavioral therapy (CBT) program at a hospital alcohol and drug outpatient clinic. Measures of stress, craving, depressed mood, and alcohol dependence severity were administered prior to treatment. Treatment lapse and treatment dropout were assessed over the 12-week program duration. Results Patients reporting greater stress experienced stronger and more frequent cravings. Stronger alcohol craving predicted lapse, after controlling for dependence severity, stress, depression, and pharmacotherapy. Alcohol craving mediated stress to predict lapse. Depressed mood and anticraving medication were not significant moderators. Conclusions Among treatment seeking, alcohol-dependent patients, craving mediated the relationship between stress and lapse. The effect was not moderated by depressed mood or anticraving medication.

The Pandolf load carriage equation is a poor predictor of metabolic rate while wearing explosive ordnance disposal protective clothing

This investigation aimed to quantify metabolic rate when wearing an explosive ordnance disposal (EOD) ensemble (~33kg) during standing and locomotion; and determine whether the Pandolf load carriage equation accurately predicts metabolic rate when wearing an EOD ensemble during standing and locomotion. Ten males completed 8 trials with metabolic rate measured through indirect calorimetry. Walking in EOD at 2.5, 4.0 and 5.5km·h−1 was significantly (p < 0.05) greater than matched trials without the EOD ensemble by 49% (127W), 65% (213W) and 78% (345W), respectively. Mean bias (95% limits of agreement) between predicted and measured metabolism during standing, 2.5, 4 and 5.5km·h−1 were 47W (19 to 75W); −111W (−172 to −49W); −122W (−189 to −54W) and −158W (−245 to −72W), respectively. The Pandolf equation significantly underestimated measured metabolic rate during locomotion. These findings have practical implications for EOD technicians during training and operation and should be considered when developing maximum workload duration models and work-rest schedules.