Work integrated learning for life : encouraging agentic engagement

In a rapidly changing world where new work patterns impact on our health, relationships and social fabric, it is critical that we reconsider the role universities could or should play in helping students prepare for the complexities of the 21st century. Efforts to respond to economic imperatives such as the skills shortage have seen a rush to embed work integrated and career development learning in the curriculum as well as a strengthening of the discourse that the university’s role is primarily to produce industry ready or ‘oven ready and self basting’ graduates (Atkins, 1999). This narrow focus on ‘giving industry what industry wants’ (Patrick, Peach & Pocknee, 2009) ignores the importance of helping students develop the types of skills and dispositions they will need. To enable students to thrive not just survive socially and economically in a radically unknowable world, where knowledge becomes obsolete, we need to be ready to develop new futures (Barnett, 2004). This paper considers the concept of ‘work’, the role it plays in our lives, and our aspirations to build sustainable, socially connected communities. We revisit the assumptions underlying the employability argument (Atkins, 1999) in the light of changing notions of work (Hagel, Seely Brown & Davison, 2010), and the need for higher education to contribute to a better and more sustainable society (Pocock, 2003). Specifically we present initiatives developed from work integrated learning (WIL) programs in the United Kingdom and Australia, where WIL programs are framed within the broader context of real world and life-wide curriculum (Jackson, 2010), and where transferable skills and elements of work-related learning programs prepare students for less certain job futures. Such approaches encourage students to take an agentic role (Billett & Pavlova, 2005) in selecting their work possibilities to develop resilience and capabilities to deal with new and challenging situations, assisting students to become who they want to be not just what they want to be. The theoretical and operational implications and challenges of shaping real world and life-wide curriculum will be investigated in more depth in the next phase of this research.

Application of electrical resistivity method to detect deep cracks in unsaturated residual soil slope

Crack is a significant influential factor in soil slope that could leads to rainfall-induced slope instability. Existence of cracks at soil surface will decrease the shear strength and increase the hydraulic conductivity of soil slope. Although previous research has shown the effect of surface-cracks in soil stability, the influence of deep-cracks on soil stability is still unknown. The limited availability of deep crack data due to the difficulty of effective investigate methods could be one of the obstacles. Current technology in electrical resistivity can be used to detect deep-cracks in soil. This paper discusses deep cracks in unsaturated residual soil slopes in Indonesia using electrical resistivity method. The field investigation such as bore hole and SPT tests was carried out at multiple locations in the area where the electrical resistivity testing have been conducted. Subsequently, the results from bore-hole and SPT test were used to verify the results of the electrical resistivity test. This study demonstrates the benefits and limitations of the electrical resistivity in detecting deep-cracks in a residual soil slopes.

Learning domain ontology for tag recommendation

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging and represent those in a form of ontology, but the application of the learned ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

The rademacher complexity of coregularized kernel classes

In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the co-regularized least squares (CoRLS) algorithm, in which the views are reproducing kernel Hilbert spaces (RKHS's), and the disagreement penalty is the average squared difference in predictions. The final predictor is the pointwise average of the predictors from each view. We call the set of predictors that can result from this procedure the co-regularized hypothesis class. Our main result is a tight bound on the Rademacher complexity of the co-regularized hypothesis class in terms of the kernel matrices of each RKHS. We find that the co-regularization reduces the Rademacher complexity by an amount that depends on the distance between the two views, as measured by a data dependent metric. We then use standard techniques to bound the gap between training error and test error for the CoRLS algorithm. Experimentally, we find that the amount of reduction in complexity introduced by co regularization correlates with the amount of improvement that co-regularization gives in the CoRLS algorithm.

Variable Speed Limits : conceptual design for Queensland practice

Variable Speed Limits (VSL) is an Intelligent Transportation Systems (ITS) control tool which can enhance traffic safety and which has the potential to contribute to traffic efficiency. Queensland's motorways experience a large volume of commuter traffic in peak periods, leading to heavy recurrent congestion and a high frequency of incidents. Consequently, Queensland's Department of Transport and Main Roads have considered deploying VSL to improve safety and efficiency. This paper identifies three types of VSL and three applicable conditions for activating VSL on for Queensland motorways: high flow, queuing and adverse weather. The design objectives and methodology for each condition are analysed, and micro-simulation results are presented to demonstrate the effectiveness of VSL.

Successes and stresses : A case study on relations between international higher degree students in Australia and their universities.

The paper examines the situation of postgraduate international students studying in Australia, mostly at doctoral level; a group widely seen as sought-after by Australian universities and employers, though also exposed to difficulties in aspects like learning culture, language and temporary employment. The investigation follows a novel path, as an exercise in practice-led research on issues involved in Higher Degree supervision. It is in fact an exercise within an advanced program of professional development for HD research supervisors. It begins by deploying a journalistic method, to obtain and present information. This has entailed the publishing of two feature articles about the lives of scholars for Subtropic, a campus based online magazine in Brisbane, www.subtropic.com.au. The next step is a review of a set of supervisions, citing issues raised in individual cases. Parallels can be seen between the two information-getting and analytical processes, with scope for contradictions. An exegetical statement deals with supervisory issues that have been exposed, and implications for learning, with recommendations for developing the quality of the experience of these students.

Evaluating effects of eco-driving at traffic intersections based on traffic micro-simulation

Eco-driving is an initiative driving behavior which aims to reduce fuel consumption and emissions from automobiles. Recently, it has attracted increasing interests and has been adopted by many drivers in Australia. Although many of the studies have revealed considerable benefits in terms of fuel consumption and emissions after utilising eco-driving, most of the literature investigated eco-driving effects on individual driver but not traffic flow. The driving behavior of eco-drivers will potentially affect other drivers and thereby affects the entire traffic flow. To comprehensively assess and understand how effectively eco-driving can perform, therefore, measurement on traffic flow is necessary. In this paper, we proposed and demonstrated an evaluation method based on a microscopic traffic simulator (Aimsun). We focus on one particular eco-driving style which involves moderate and smooth acceleration. We evaluated both traffic performance (travel time) and environmental performance (fuel consumption and CO2 emission) at traffic intersection level in a simple simulation model. The before-and-after comparisons indicated potentially negative impacts when using eco-driving, which highlighted the necessity to carefully evaluate and improve eco-driving before wide promotion and implementation.

Exploring the parameter space of a rabbit ventricular action potential model to investigate the effect of variation on action potential and calcium transients

Computational models for cardiomyocyte action potentials (AP) often make use of a large parameter set. This parameter set can contain some elements that are fitted to experimental data independently of any other element, some elements that are derived concurrently with other elements to match experimental data, and some elements that are derived purely from phenomenological fitting to produce the desired AP output. Furthermore, models can make use of several different data sets, not always derived for the same conditions or even the same species. It is consequently uncertain whether the parameter set for a given model is physiologically accurate. Furthermore, it is only recently that the possibility of degeneracy in parameter values in producing a given simulation output has started to be addressed. In this study, we examine the effects of varying two parameters (the L-type calcium current (I(CaL)) and the delayed rectifier potassium current (I(Ks))) in a computational model of a rabbit ventricular cardiomyocyte AP on both the membrane potential (V(m)) and calcium (Ca(2+)) transient. It will subsequently be determined if there is degeneracy in this model to these parameter values, which will have important implications on the stability of these models to cell-to-cell parameter variation, and also whether the current methodology for generating parameter values is flawed. The accuracy of AP duration (APD) as an indicator of AP shape will also be assessed.

Novel corrosion sensor based on carbon nanotube composites for structural health monitoring

Bridges are important infrastructures of all nations and are required for transportation of goods as well as human. A catastrophic failure can result in loss of lives and enormous financial hardship to the nation. Hence, there is an urgent need to monitor our infrastructures to prolong their life span, at the same time catering for heavier and faster moving traffics. Although various kinds of sensors are now available to monitor the health of the structures due to corrosion, they do not provide permanent and long term measurements. This paper investigates the fabrication of Carbon Nanotube (CNT) based composite sensors for structural health monitoring. The CNTs, a key material in nanotechnology has aroused great interest in the research community due to their remarkable mechanical, electrochemical, piezoresistive and other physical properties. Multi-wall CNT (MWCNT)/Nafion composite sensors were fabricated to evaluate their electrical properties when subjected to chemical solutions, to simulate a chemical reaction due to corrosion and real life corrosion experimental tests. The electrical resistance of the sensor electrode was dramatically changed due to corrosion. The novel sensor is expected to effectively detect corrosion in structures based on the measurement of electrical impedances of the CNT composite.

Phenomenological modeling of cell-to-cell and beat-to-beat variability in isolated Guinea Pig ventricular myocytes

Experimental action potential (AP) recordings in isolated ventricular myoctes display significant temporal beat-to-beat variability in morphology and duration. Furthermore, significant cell-to-cell differences in AP also exist even for isolated cells originating from the same region of the same heart. However, current mathematical models of ventricular AP fail to replicate the temporal and cell-to-cell variability in AP observed experimentally. In this study, we propose a novel mathematical framework for the development of phenomenological AP models capable of capturing cell-to-cell and temporal variabilty in cardiac APs. A novel stochastic phenomenological model of the AP is developed, based on the deterministic Bueno-Orovio/Fentonmodel. Experimental recordings of AP are fit to the model to produce AP models of individual cells from the apex and the base of the guinea-pig ventricles. Our results show that the phenomenological model is able to capture the considerable differences in AP recorded from isolated cells originating from the location. We demonstrate the closeness of fit to the available experimental data which may be achieved using a phenomenological model, and also demonstrate the ability of the stochastic form of the model to capture the observed beat-to-beat variablity in action potential duration.

Connecting online with external students : do we really know how to?

Students are often time poor and find it difficult to manage their time in relation to study and other external factors including work. Online study is no exception to this and in many cases where the student is studying in an online only environment, they are also working in full time employment. Higher Education institutions are now offering an abundance of courses online to attract more under-graduate and post-graduate students. It is in this sense that there is an ever-increasing need to understand the student of today and find ways to connect with them and support them in their studies. This paper will report on a small-scale case study of an undergraduate online-only group of first year education students and their associated online experiences in developing a sense of community whilst interacting with a learning management system and its associated tools. Further the paper will explore the mis-conceptions that are widely held by course designers and lecturers involved with online courses.

Issues and concerns of microscopic calibration process at different network levels : case study of Pacific Motorway

Calibration process in micro-simulation is an extremely complicated phenomenon. The difficulties are more prevalent if the process encompasses fitting aggregate and disaggregate parameters e.g. travel time and headway. The current practice in calibration is more at aggregate level, for example travel time comparison. Such practices are popular to assess network performance. Though these applications are significant there is another stream of micro-simulated calibration, at disaggregate level. This study will focus on such microcalibration exercise-key to better comprehend motorway traffic risk level, management of variable speed limit (VSL) and ramp metering (RM) techniques. Selected section of Pacific Motorway in Brisbane will be used as a case study. The discussion will primarily incorporate the critical issues encountered during parameter adjustment exercise (e.g. vehicular, driving behaviour) with reference to key traffic performance indicators like speed, lane distribution and headway; at specific motorway points. The endeavour is to highlight the utility and implications of such disaggregate level simulation for improved traffic prediction studies. The aspects of calibrating for points in comparison to that for whole of the network will also be briefly addressed to examine the critical issues such as the suitability of local calibration at global scale. The paper will be of interest to transport professionals in Australia/New Zealand where micro-simulation in particular at point level, is still comparatively a less explored territory in motorway management.

Arterial traffic congestion analysis using Bluetooth duration data

The aim of this study is to assess the potential use of Bluetooth data for traffic monitoring of arterial road networks. Bluetooth data provides the direct measurement of travel time between pairs of scanners, and intensive research has been reported on this topic. Bluetooth data includes “Duration” data, which represents the time spent by Bluetooth devices to pass through the detection range of Bluetooth scanners. If the scanners are located at signalised intersections, this Duration can be related to intersection performance, and hence represents valuable information for traffic monitoring. However the use of Duration has been ignored in previous analyses. In this study, the Duration data as well as travel time data is analysed to capture the traffic condition of a main arterial route in Brisbane. The data consists of one week of Bluetooth data provided by Brisbane City Council. As well, micro simulation analysis is conducted to further investigate the properties of Duration. The results reveal characteristics of Duration, and address future research needs to utilise this valuable data source.

Arterial travel time estimation : revisiting the classical procedure

Travel time is an important network performance measure and it quantifies congestion in a manner easily understood by all transport users. In urban networks, travel time estimation is challenging due to number of reasons such as, fluctuations in traffic flow due to traffic signals, significant flow to/from mid link sinks/sources, etc. The classical analytical procedure utilizes cumulative plots at upstream and downstream locations for estimating travel time between the two locations. In this paper, we discuss about the issues and challenges with classical analytical procedure such as its vulnerability to non conservation of flow between the two locations. The complexity with respect to exit movement specific travel time is discussed. Recently, we have developed a methodology utilising classical procedure to estimate average travel time and its statistic on urban links (Bhaskar, Chung et al. 2010). Where, detector, signal and probe vehicle data is fused. In this paper we extend the methodology for route travel time estimation and test its performance using simulation. The originality is defining cumulative plots for each exit turning movement utilising historical database which is self updated after each estimation. The performance is also compared with a method solely based on probe (Probe-only). The performance of the proposed methodology has been found insensitive to different route flow, with average accuracy of more than 94% given a probe per estimation interval which is more than 5% increment in accuracy with respect to Probe-only method.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.