Regional aviation and economic growth : cointegration and causality analysis in Australia

Air transport is a critical link to regional, rural and remote communities in Australia. Air services provide important economic and social benefits but very little research has been done on assessing the value of regional aviation. This research provides the first empirical evidence that there is short and long run causality between regional aviation and economic growth. The authors analysed 88 regional airports in Australia over a period of 1985–86 to 2010–11 to determine the catalytic impacts of regional air transport on regional economic growth. The analysis was conducted using annual data related to total airport passenger movements – for the level of airport activity, and real aggregate taxable income – to represent economic growth. A significant bi-directional relationship was established: airports have an impact on regional economic growth and the economy directly impacts regional air transport. The economic significance of regional air transport confirms the importance of the airport as infrastructure for regional councils and the need for them to maintain and develop local airports. Funding should be targeted at airports directly to support regional development.

Equilibrium and column studies of iron exchange with strong acid cation resin

The exchange of iron species from iron (III) chloride solutions with a strong acid cation resin has been investigated in relation to a variety of water and wastewater applications. A detailed equilibrium isotherm analysis was conducted wherein models such as Langmuir Vageler, Competitive Langmuir, Freundlich, Temkin, Dubinin Astakhov, Sips and Brouers-Sotolongo were applied to the experimental data. An important conclusion was that both the bottle-point method and solution normality used to generate the ion exchange equilibrium information influenced which sorption model fitted the isotherm profiles optimally. Invariably, the calculated value for the maximum loading of iron on strong acid cation resin was substantially higher than the value of 47.1 g/kg of resin which would occur if one Fe3+ ion exchanged for three “H+” sites on the resin surface. Consequently, it was suggested that above pH 1, various iron complexes sorbed to the resin in a manner which required less than 3 sites per iron moiety. Column trials suggested that the iron loading was 86.6 g/kg of resin when 1342 mg/L Fe (III) ions in water were flowed at 31.7 bed volumes per hour. Regeneration with 5 to 10 % HCl solutions reclaimed approximately 90 % of exchange sites.

The rise of low-cost sensing for managing air pollution in cities

Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

Applying design-led innovation in a not-for-profit aged care provider to create shared value

This study examines the role of Design-Led Innovation in creating shared value; sustainable competitive advantage for an organisation and social value for the communities in which it operates. A case study analysed an undertaking by a not-for-profit aged care organisation to create a sustainable competitive advantage in the market by reinventing the experience of ageing and defining an innovative future business model. This paper reflects on the role of Design-Led Innovation in facilitating this change agenda and explores the particular relevance of the associated techniques in a not-for-profit, human services context. It was found that the Design-Led Innovation approach was effective in achieving the goal of defining a way for the organisation to create shared value.

The 'gourmet' sausage factory: Keeping it human

Whilst tertiary institutions continue to invest heavily in the technological aspects of online Teaching & Learning (T&L), there does not appear to have been a commensurate investment in the “human” aspects of the use of the technology. Despite the broad recognition that teaching and learning materials need to be adapted for and to the onscreen medium, little attention appears to have been paid thus far to the actual people who are delivering it – who equally need to “adapt themselves” to that medium, in order to maximise the benefit of the technology by maximising the human communication skills of those using the online medium – as distinct from the technical skills required to drive and deliver the bits and bytes. The REdelivery Initiative was a direct response to that notion. This paper details – by way of a narrative of one of the workshop participants – that part of the process involving the professional development of academics specifically in and specifically for the digital, online, T&L context, in order to both illuminate and maximise the potential and opportunities afforded by the technology.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

Optimising ketocarotenoid production in potato tubers : effect of genetic background, transgene combinations and environment

Astaxanthin is a high value carotenoid produced by some bacteria, a few green algae, several fungi but only a limited number of plants from the genus Adonis. Astaxanthin has been industrially exploited as a feed supplement in poultry farming and aquaculture. Consumption of ketocarotenoids, most notably astaxanthin, is also increasingly associated with a wide range of health benefits,as demonstrated in numerous clinical studies. Currently astaxanthin is produced commercially by chemical synthesis or from algal production systems. Several studies have used a metabolic engineering approach to produce astaxanthin in transgenic plants. Previous attempts to produce transgenic potato tubers biofortified with astaxanthin have met with limited success. In this study we have investigated approaches to optimising tuber astaxanthin content. It is demonstrated that the selection of appropriate parental genotype for transgenic approaches and stacking carotenoid biosynthetic pathway genes with the cauliflower Or gene result in enhanced astaxanthin content, to give six-fold higher tuber astaxanthin content than has been achieved previously. Additionally we demonstrate the effects of growth environment on tuber carotenoid content in both wild type and astaxanthin-producing transgenic lines and describe the associated transcriptome and metabolome restructuring.

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150µm and >150µm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150µm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150µm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Fast and accurate business process drift detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

Log delta analysis: Interpretable differencing of Business process event logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.

A framework for understanding and generating integrated solutions for residential peak energy demand

Supplying peak energy demand in a cost effective, reliable manner is a critical focus for utilities internationally. Successfully addressing peak energy concerns requires understanding of all the factors that affect electricity demand especially at peak times. This paper is based on past attempts of proposing models designed to aid our understanding of the influences on residential peak energy demand in a systematic and comprehensive way. Our model has been developed through a group model building process as a systems framework of the problem situation to model the complexity within and between systems and indicate how changes in one element might flow on to others. It is comprised of themes (social, technical and change management options) networked together in a way that captures their influence and association with each other and also their influence, association and impact on appliance usage and residential peak energy demand. The real value of the model is in creating awareness, understanding and insight into the complexity of residential peak energy demand and in working with this complexity to identify and integrate the social, technical and change management option themes and their impact on appliance usage and residential energy demand at peak times.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Investigation of the effects of the fatty acid profile on fuel properties using a Multi-Criteria Decision Analysis

The structural features of fatty acids in biodiesel, including degree of unsaturation, percentage of saturated fatty acids and average chain length, influence important fuel properties such as cetane number, iodine value, density, kinematic viscosity, higher heating value and oxidation stability. The composition of fatty acid esters within the fuel should therefore be in the correct ratio to ensure fuel properties are within international biodiesel standards such as ASTM 6751 or EN 14214. This study scrutinises the influence of fatty acid composition and individual fatty acids on fuel properties. Fuel properties were estimated based on published equations, and measured according to standard procedure ASTM D6751 and EN 14214 to confirm the influences of the fatty acid profile. Based on fatty acid profile-derived calculations, the cetane number of the microalgal biodiesel was estimated to be 11.6, but measured 46.5, which emphasises the uncertainty of the method used for cetane number calculation. Multi-criteria decision analysis (MCDA), PROMETHEE-GAIA, was used to determine the influence of individual fatty acids on fuel properties in the GAIA plane. Polyunsaturated fatty acids increased the iodine value and had a negative influence on cetane number. Kinematic viscosity was negatively influenced by some long chain polyunsaturated fatty acids such as C20:5 and C22:6 and some of the more common saturated fatty acids C14:0 and C18:0. The positive impact of average chain length on higher heating value was also confirmed in the GAIA plane

Corporate-cooperative management of fisheries: A potential alternative governance structure for low value small fisheries?

While the economic and environmental benefits of fisheries management are well accepted, the costs of effective management in low value fisheries, including the research necessary to underpin such management, may be considerable relative to the total economic benefits they may generate. Co-management is often seen as a panacea in low value fisheries. Increasing fisher participation increases legitimacy of management decision in the absence of detailed scientific input. However, where only a small number of operators exist, the potential benefits of co-management are negated by the high transaction cost to the individual fishers engaging in the management process. From an economic perspective, sole ownership has been identified as the management structure which can best achieve biological and economic sustainability. Moving low value fisheries with a small number of participants to a corporate-cooperative management model may come close to achieving these sole ownership benefits, with lower transaction costs. In this paper we look at the applicability of different management models with industry involvement to low value fisheries with a small number of participants. We provide an illustration as to how a fishery could be transitioned to a corporate-cooperative management model that captures the key benefits of sole management at a low cost and is consistent with societal objectives.