Developing a spatial decision support system for sustainable built environments in Abu Dhabi

 The research aims at developing a set of sustainability indicators for the challenging Abu Dhabi built environment and examine the possible use of GIS. The research has illustrated the real potential of the sustainability indicators for managing built environment sustainability performance and provides a clear perspective on how the proposed indicators can be used to develop a DSS to assess and improve Abu Dhabi’s sustainability.

A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing

In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.

An intelligent decision support system for leukaemia diagnosis using microscopic blood images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

An early-stage design decision-support tool for selecting building assemblies to minimise a building's life cycle energy demand

The significant effects of the building industry on the natural environment are well documented and improving the environmental performance of buildings is an on-going challenge. This is particularly the case for projects with restrictive budgets and timelines and because many existing environmental assessment tools are designed to be used too late in the design process. The use of tools during the early design stages may assist in achieving greater improvements in a building’s environmental performance. However, user-friendly tools with the ability to comprehensively compare environmental information between various building assemblies and materials, which can be easily adopted during the early design stages of a project, are not readily available. This paper presents the progress to date in developing a tool which supports building designers in identifying and selecting preferred building assemblies with the aim of minimising a building’s life cycle energy demand. The tool is based on comprehensive energy performance data for a broad range of building assemblies across all Australian climate zones. Allowing for adjustments to a set of pre-defined and user-defined assemblies the designer is able to see how assemblies perform in relation to each other. This provides valuable information to support decision-making relating to minimising the life cycle energy demand of buildings.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.

The impact of Porter's strategy types on the role of market research and customer relationship management

Purpose – This paper seeks to investigate the influence of Porter’s strategy types on the use of customer relationship management (CRM) techniques and traditional market research, against theoretical and empirical evidence that differences in strategy types may result in variation in favoured marketing information sources and procedure.

Design/methodology/approach – Depth interviews generated a series of scale items, which were combined with others derived from the literature in a questionnaire measuring strategy types, the roles of market research, and the characteristics of CRM systems. Responses were obtained from 240 senior marketing managers in Australia, and applied to the testing of five research propositions.

Findings – ANOVA found no differences in CRM usage among the strategy types. Variation was widespread, however, in four roles of traditional market research: enhancing strategic decision making, increasing usability of existing data, presenting plans to senior management, and achieving productivity and political outcomes.

Research limitations/implications – Future researchers using the Porter strategic types should separate “marketing differentiators” from “product differentiators” because they function and compete differently.

Practical implications – All organisations can benefit from CRM systems, but “marketing differentiators” exhibit a relatively higher usage of traditional market research. This is likely to be because they compete by creating softer product differences, while others do so on harder characteristics such as price or product functionality.

Originality/value – This is the first study to use the Porter types to explain differences between the roles and uses of market research and CRM within organisations.

Combination of decisions from a multiple neural network classifier system

In this paper, a study of the effectiveness of a multiple classifier system (MCS) in a medical diagnostic task is described. A hybrid network, based on the integration of a fuzzy ARTMAP and the probabilistic neural network, is employed as the basis of the MCS. Outputs from multiple networks are combined using some decision combination method to reach a final prediction. By using a real medical database, a set of experiments has been conducted to evaluate the performance of the MSC with different network configurations. The experimental results reveal the potential of the MCS as a useful decision support tool in the medical field.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

What are the perceived learning needs of Australian general practice registrars for quality prescribing?

BACKGROUND: Little is known about the perceived learning needs of Australian general practice (GP) registrars in relation to the quality use of medicines (QUM) or the difficulties experienced when learning to prescribe. This study aimed to address this gap. METHODS: GP registrars' perceived learning needs were investigated through an online national survey, interviews and focus groups. Medical educators' perceptions were canvassed in semi-structured interviews in order to gain a broader perspective of the registrars' needs. Qualitative data analysis was informed by a systematic framework method involving a number of stages. Survey data were analysed descriptively. RESULTS: The two most commonly attended QUM educational activities took place in the workplace and through regional training providers. Outside of these structured educational activities, registrars learned to prescribe mainly through social and situated means. Difficulties encountered by GP registrars included the transition from hospital prescribing to prescribing in the GP context, judging how well they were prescribing and identifying appropriate and efficient sources of information at the point of care. CONCLUSIONS: GP registrars learn to prescribe primarily and opportunistically in the workplace. Despite many resources being expended on the provision of guidelines, decision-support systems and training, GP registrars expressed difficulties related to QUM. Ways of easing the transition into GP and of managing the information 'overload' related to medicines (and prescribing) in an evidence-guided, efficient and timely manner are needed. GP registrars should be provided with explicit feedback about the process and outcomes of prescribing decisions, including the use of audits, in order to improve their ability to judge their own prescribing.

Integrating knowledge systems in local coastal management; the case of an estuary entrance management support system (EEMSS) in Australia

The management of social, economic, and ecological assets in coastal zones is fundamental to the maintenance and sustainability of coastal resources. A significant issue in this discussion is the role of governance structures. In Australia the governance of the coastal zone includes a range of institutional authorities, processes, and procedures that set the context for decision making about coastal management. As well as the formal institutional arrangements there is also a maze of other interests such as development commissions, NGOs, Indigenous Native Title holders and other stakeholders including recreational interests. A major issue for governance arrangements is the considerable gap that often exists between how those interests interpret and develop their positions especially when the knowledge is derived from different systems – scientific, managerial, lay and indigenous. This paper will explore the development of an Estuary Entrance Management Support System (EEMSS) in south west Victoria Australia. The EEMSS is a decision support tool to assist estuary managers in determining whether to artificially open a river mouth. A significant part of the process adopted was community participation which involved a ‘steps’ approach to engage local community groups and landholders. It is the process of engaging different knowledge systems in a meaningful conversation that has led to a system that now gains support from all of the stakeholders in the management of different estuaries. The paper will discuss the processes that surround the EEMSS and outline some lessons that arise in context of the ‘project state’.

How information technology can support family law and mediation

In Australia, before a divorcing couple can have their case heard by the Family Court, they must undertake mediation. Thus it is useful to develop information technology tools to support negotiation and mediation in family law. Most negotiation support systems focus upon integrative bargaining. In doing so, they tend to ignore issues of fairness. In Australian Family Law, the interests of the children, as opposed to those of their parents/guardians, are paramount. We investigate the use of providing BATNAs and integrative bargaining in providing family mediation decision support. The discussion is highlighted with examples taken from the domain of Australian Family Law

Future integrated factories: a system of systems engineering perspective

The manufacturing sector has gone through tremendous change in the last decade. We have witnessed the transformation from stand alone, manual processes to smart and integrated systems, from hand written reports to interactive computer-based dashboards. Future integrated factories will operate as a system of systems through intelligent machines, human factors integration, and integrated supply chains. To effectively operate and manage these emerging enterprises, a systems science approach is required. Modelling and simulation is recognised as a key enabling technology, with application from stakeholder engagement and knowledge elicitation to operational decision support through self-tuning and self-assembling simulations. Our research has led to the introduction of effective modelling and simulation methods and tools to enable real time planning, dynamic risk analysis and effective visualisation for production processes, resources and systems. This paper discusses industrial applicable concepts for real-time simulation and decision support, and the implications to future integrated factories, or factories of the future, are explored through relevant case studies from aerospace manufacturing to mining and materials processing enterprises.

The influence of patient complexity and nurses` experience on haemodynamic decision-making following cardiac surgery

Critical care nurses’ haemodynamic decision-making in the immediate postoperative cardiac surgical context is complex. To optimise patient outcomes, nurses of varying levels of experience are required to make complex decisions rapidly and accurately. In a dynamic clinical context such as critical care, the quality of such decision-making is likely to vary considerably. The aim of this study was to describe variability of nurses’ haemodynamic decision-making in the 2-hour period after cardiac surgery as a function of interplay between decision complexity, nurses’ levels of experience, and the support provided. A descriptive study based on naturalistic decision-making was used. Data were collected using continuous non-participant observation of clinical practice for a 2-hour period and follow-up interview. Purposive sampling was used to recruit 38 nurses for inclusion in the study. The quality of nurses’ decision-making was influenced by interplay between the complexity of patients’ haemodynamic presentations, nurses’ levels of cardiac surgical intensive care experience, and the form of decision support provided by nursing colleagues. Two factors specifically influenced decision-making quality: nurses’ utilisation of evidence for practice and the experience levels of both nurses and their colleagues. The findings have implications for staff resourcing decisions and postoperative patient management, and may be used to inform nurses’ professional development and education.

Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.