Providing 'best practice' placements begins with mentoring skills : overview of the Program of Experience in the Palliative Approach (PEPA) quality improvement initiatives for mentors

Objectives PEPA is funded by the Department of Health and Ageing and aims to further improve the skill and confidence of the generalist workforce to work with people with palliative care needs. Recent quality improvement initiatives to promote transfer of learning into practice include appointment of a clinical educator, implementation of an online module for mentors and delivery of a mentoring workshop (collaborating with NSAP and PCC4U). This paper presents an overview of outcomes from these quality improvement initiatives. Methods PEPA host sites are selected based on their specialist palliative care level. Host site managers are surveyed six-monthly and participants are surveyed pre and three months post-placement to collect open and fixed response data on their experience of the program. Participants in the mentoring workshop (n=39) were asked to respond to a survey regarding the workshop outcomes. Results The percentage of placement participants who strongly agreed they ‘have the ability to implement the interventions required for people who have a life-limiting illness’ increased from 35% in 2011 (n=34) to 51% in 2012 (n=91) post-placement. Responses from mentor workshop participants indicated that 76% of respondents (n=25) agreed that they were able to identify principles for mentoring in the context of palliative care. In 2012, 61% of host site managers (n=54) strongly agreed that PEPA supports clinician working with people with a life-limiting illness. Conclusion Strategies to build the capabilities of palliative care professionals to mentor and support the learning experience of PEPA participants are critical to ongoing improvements of the program.

A time-based quantitative approach for selecting lean strategies for manufacturing organisations

Lean strategies have been developed to eliminate or reduce waste and thus improve operational efficiency in a manufacturing environment. However, in practice, manufacturers encounter difficulties to select appropriate lean strategies within their resource constraints and to quantitatively evaluate the perceived value of manufacturing waste reduction. This paper presents a methodology developed to quantitatively evaluate the contribution of lean strategies selected to reduce manufacturing wastes within the manufacturers’ resource (time) constraints. A mathematical model has been developed for evaluating the perceived value of lean strategies to manufacturing waste reduction and a step-by-step methodology is provided for selecting appropriate lean strategies to improve the manufacturing performance within their resource constraints. A computer program is developed in MATLAB for finding the optimum solution. With the help of a case study, the proposed methodology and developed model has been validated. A ‘lean strategy-wastes’ correlation matrix has been proposed to establish the relationship between the manufacturing wastes and lean strategies. Using the correlation matrix and applying the proposed methodology and developed mathematical model, authors came out with optimised perceived value of reduction of a manufacturer's wastes by implementing appropriate lean strategies within a manufacturer's resources constraints. Results also demonstrate that the perceived value of reduction of manufacturing wastes can significantly be changed based on policies and product strategy taken by a manufacturer. The proposed methodology can also be used in dynamic situations by changing the input in the programme developed in MATLAB. By identifying appropriate lean strategies for specific manufacturing wastes, a manufacturer can better prioritise implementation efforts and resources to maximise the success of implementing lean strategies in their organisation.

A critical analysis of business process management education and alignment with industry demand : an Australian perspective

Business Process Management (BPM) is accepted globally as an organizational approach to enhance productivity and drive cost efficiencies. Studies confirm a shortage of BPM skilled professionals with limited opportunities to develop the required BPM expertise. This study investigates this gap starting from a critical analysis of BPM courses offered by Australian universities and training institutions. These courses were analyzed and mapped against a leading BPM capability framework to determine how well current BPM education and training offerings in Australia address the core capabilities required by BPM professionals globally. To determine the BPM skill-sets sought by industry, online recruitment advertisements were collated, analyzed, and mapped against this BPM capability framework. The outcomes provide a detailed overview on the alignment of available BPM education/training and industry demand. These insights are useful for BPM professionals and their employers to build awareness of the BPM capabilities required for a BPM mature organization. Universities and other training institutions will benefit from these results by understanding where demand is, where the gaps are, and what other BPM education providers are supplying. This structured comparison method could continue to provide a common ground for future discussion across university-industry boundaries and continuous alignment of their respective practices.

Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms

In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.

Appetite, satiety, and food reward in obese individuals : a behavioral phenotype approach

Why do some people remain lean while others are susceptible to obesity, and why do obese individuals vary in their successes in losing weight? Despite physiological processes that promote satiety and satiation, some individuals are more susceptible to overeating. While the phenomena of susceptibility to weight gain, resistance to treatment or weight loss, and individual variability are not novel, they have yet to be exploited and systematically examined to better understand how to characterise phenotypes of obesity. The identification and characterisation of distinct phenotypes not only highlight the heterogeneous nature of obesity but may also help to inform the development of more tailored strategies for the treatment and prevention of obesity. This review examines the evidence for different susceptible phenotypes of obesity that are characterised by risk factors associated with the hedonic and homeostatic systems of appetite control.

Editorial : Global 21st century professionals : developing capability to work with Indigenous and other traditionally-oriented peoples

International agreement on the framework for protecting the rights of Indigenous populations within nation states has occurred alongside unprecedented levels of globalisation of other previously nation-based activities such as economic and social provision and planning. As the idea of the postcolonial democratic state emerges, this collection undertakes an international and comparative examination of the role of higher education in educating globally aware professionals who are able to work effectively and in cultural safety with Indigenous Peoples...

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

A coordinated voltage control approach for coordination of OLTC, voltage regulator and DG to regulate voltage in a distribution feeder

Integration of small-scale electricity generators, known as Distributed Generation (DG), into the distribution networks has become increasingly popular at the present. This tendency together with the falling price of synchronous-type generator has potential to give the DG a better chance in participating in the voltage regulation process together with other devices already available in the system. The voltage control issue turns out to be a very challenging problem for the distribution engineers since existing control coordination schemes would need to be reconsidered to take into account the DG operation. In this paper, we propose a control coordination technique, which is able to utilize the ability of the DG as a voltage regulator, and at the same time minimizes interaction with other active devices, such as On-load Tap Changing Transformer (OLTC) and voltage regulator. The technique has been developed based on the concept of control zone, Line Drop Compensation (LDC), as well as the choice of controller's parameters. Simulations carried out on an Australian system show that the technique is suitable and flexible for any system with multiple regulating devices including DG.

Sampling designs on stream networks using the pseudo-Bayesian approach

Monitoring stream networks through time provides important ecological information. The sampling design problem is to choose locations where measurements are taken so as to maximise information gathered about physicochemical and biological variables on the stream network. This paper uses a pseudo-Bayesian approach, averaging a utility function over a prior distribution, in finding a design which maximizes the average utility. We use models for correlations of observations on the stream network that are based on stream network distances and described by moving average error models. Utility functions used reflect the needs of the experimenter, such as prediction of location values or estimation of parameters. We propose an algorithmic approach to design with the mean utility of a design estimated using Monte Carlo techniques and an exchange algorithm to search for optimal sampling designs. In particular we focus on the problem of finding an optimal design from a set of fixed designs and finding an optimal subset of a given set of sampling locations. As there are many different variables to measure, such as chemical, physical and biological measurements at each location, designs are derived from models based on different types of response variables: continuous, counts and proportions. We apply the methodology to a synthetic example and the Lake Eacham stream network on the Atherton Tablelands in Queensland, Australia. We show that the optimal designs depend very much on the choice of utility function, varying from space filling to clustered designs and mixtures of these, but given the utility function, designs are relatively robust to the type of response variable.

A therapeutic approach to assessing legal capacity in Australia

Australia lacks a satisfactory, national paradigm for assessing legal capacity in the context of testamentary, enduring power of attorney and advance care directive documents. Capacity assessments are currently conducted on an ad hoc basis by legal and/or medical professionals. The reliability of the assessment process is subject to the skill set and mutual understanding of the legal and/or medical professional conducting the assessment. There is a growth in the prevalence of diseases such as dementia. Such diseases impact upon cognition which increasingly necessitates collaboration between the legal and medical professions when assessing the effect of mentally disabling conditions upon legal capacity. Miscommunication and lack of understanding between legal and medical professionals involved could impede the development of a satisfactory paradigm. This article will discuss legal capacity assessment in Australia and how to strengthen the relationship between legal and medical professionals involved in capacity assessments. The development of a national paradigm would promote consistency and transparency of process, helping to improve the professional relationship and maximising the principles of autonomy, participation and dignity.

Too many cooks? Co-creating and co-teaching studio courses in the creative media context : a career-focussed approach

This Case Study relates to the creation and implementation of career‐focussed courses in Creative Media for film, television, animation, broadcast and web contexts. The paper examines the advantages and disadvantages of co‐teaching, and how different professional and academic backgrounds and disciplines can productively inform curriculum design and delivery in the academic/professional context. The authors, as co‐creators and co‐lecturers, have developed a number of courses which represent current working models for intermediate to advanced level academic/professional study, and attract students from across the creative disciplines; including theatre, media, visual arts and music. These courses are structured to develop in students a wide range of aesthetic and technical skills, as well as their ability to apply those skills professionally within and across the creative media industries. Issues regarding the balance between academic rigour, practical hands‐on skill development, assessment, logistics, resources, teamwork and other issues, are examined in the paper.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.