Understanding coastal urban and peri‐urban indigenous people’s vulnerability and adaptive capacity to climate change

This brief presentation will commence to address a number of emerging questions that are underpinned by the current NCCARF research project: Understanding Urban and Peri‐urban Indigenous People’s vulnerability and adaptive capacity to Climate Change. In terms of climate change adaptation barriers, options and priorities, what are the major considerations for indigenous communities residing in coastal peri-urban and urban places that may differ from those of their non-indigenous counterparts and possibly from Indigenous communities living away from the coasts? It will speculate on whether these considerations can be addressed through existing planning and management frameworks.

Indigenous climate change adaptation perspectives : understanding urban and peri‐urban indigenous people’s vulnerability and adaptive capacity to climate change

The National Climate change Adaptation Research Plan: Indigenous Communities (2011) highlighted that research on Indigenous communities and climate change, including the variables of impacts, vulnerability and adaptive capacity and adaptation has been limited. While most research has focused on identifying the biophysical impacts of climate change, a minority of studies have considered the Indigenous knowledge and peoples whom continue to reside in Australia and care for; ‘country’;. The report concluded that “there is a need for research that expands knowledge about these and other dimensions of Indigenous adaptation to climate change.“ This paper reviews work in progress on a NCCARF funded research project that is seeking to investigate select coastal urban and per-urban Indigenous community vulnerability to, and capacity for climate change adaptation. Working collaboratively with Indigenous communities resident in Adelaide, Heywood/Portland, Mornington Peninsula, Stradbroke Island and Brisbane, it seeks to explore and articulate strategies that enhance Indigenous capacity to climate change including possible protocols, frameworks, processes and procedures that may lead directly to a more informed appreciation of what is transpiring around Australia’s coastal per-urban regions for their Indigenous communities who still hold strong bonds and responsibilities to their ‘country’.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

An adaptive fuzzy min-max conflict-resolving classifier

This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.

Information systems development projects as complex adaptive systems

This research considers information systems development (ISD) projects as complex adaptive systems. We investigate the question whether complex adaptive systems (CAS) theory is relevant as a theoretical foundation for understanding ISD, and if so, which kind of understanding can be achieved by utilizing the theory? We introduce key concepts of CAS theory such as interaction, emergence, interconnected autonomous agents, selforganization, co-evolution, poise at the edge of chaos, time pacing, and poise at the edge of time to analyse and understand ISD in practice. We demonstrate the strength of such a CAS approach through an empirical case study presentation and analysis. While our work contributes to a complexity theory of ISD, the case examination also provides practical advice derived from this perspective to successfully cope with complexity in ISD in an adaptive manner.

Adaptive patch size determination for patch-based image completion

Patch-based image completion proceeds by iteratively filling the target (unknown) region by the best matching patches in the source image. In most existing such algorithms, the size of the patches is either fixed and specified by a default number or simply chosen to be inversely proportional to the spatial frequency. However, it is noted that the patch size affects how well the filled patch captures the local characteristics of the source image and thus the final completion accuracy. Thus in this paper we propose a new method to compute appropriate patch sizes for image completion to improve its performance. In particular, we formulate the patch size determination as an optimization problem that minimizes an objective function involving image gradients and distinct and homogenous features. Experimental results show that our method can provide a significant enhancement to patch-based image completion algorithms.

An adaptive algorithm for finding frequent sets in landmark windows

We consider a CPU constrained environment for finding approximation of frequent sets in data streams using the landmark window. Our algorithm can detect overload situations, i.e., breaching the CPU capacity, and sheds data in the stream to “keep up”. This is done within a controlled error threshold by exploiting the Chernoff-bound. Empirical evaluation of the algorithm confirms the feasibility.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Adaptive autobiographical memory in younger and older adults : the indirect association of integrative and instrumental reminiscence with depressive symptoms

Despite the established effectiveness of reminiscence-based interventions for depression, little research exists into the pathways through which specific reminiscence functions are related to depressive symptoms. Drawing on theory of the mechanisms of change in cognitive-reminiscence therapy, the current study tests the hypothesised indirect associations of adaptive integrative and instrumental reminiscence functions with depressive symptoms and whether these relationships might differ among younger and older adults. Questionnaires were completed by a large community sample of the Australian population. Multiple mediation models were tested in two groups: younger adults (n=730, M age=52.24, SD=9.84) and older adults (n=725, M age= 73.59, SD=6.29). Results were consistent across age groups, indicating that there was direct relationship between these reminiscence functions and depressive symptoms, but that integrative reminiscence is indirectly associated with depressive symptoms through meaning in life, self-esteem, and optimism, and that instrumental reminiscence is indirectly associated with depressive symptoms through primary control and self-efficacy. This study provides support for the relationships between constructs underlying the proposed mechanisms of change in cognitive-reminiscence therapy for the treatment of depression, and suggests these relationships are similar for younger and older adults.