Investigating the process of teaching and learning principles through practice : a case study on collegiate action by media practitioners and teachers, designing a skills-based learning program

This paper in the journalism education field reports on the construction of a new subject as part of a postgraduate coursework degree. The subject, or unit1 will offer both Journalism students and other students an introductory experience of creating media, using common ‘new media’ tools, with exercises that will model the learning of communication principles through practice. It has been named ‘Fundamental Media Skills for the Workplace’. The conceptualisation and teaching of it will be characteristic of the Journalism academic discipline that uses the ‘inside perspective’—understanding mass media by observing from within. Proposers for the unit within the Journalism discipline have sought to extend the common teaching approach, based on training to produce start-ready recruits for media jobs, backed by a study of contexts, e.g. journalistic ethics, or media audiences. In this proposal, students would then examine the process to elicit additional knowledge about their learning. The paper draws on literature of journalism and its pedagogy, and on communication generally. It also documents a ‘community of practice’ exercise conducted among practitioners as teachers for the subject, developing exercises and models of media work. A preliminary conclusion from that exercise is that it has taken a step towards enhancing skills-based learning for media work, as a portal to more generalised knowledge.

Fragment-based version management for repositories of business process models

As organizations reach higher levels of Business Process Management maturity, they tend to accumulate large collections of process models. These repositories may contain thousands of activities and be managed by different stakeholders with varying skills and responsibilities. However, while being of great value, these repositories induce high management costs. Thus, it becomes essential to keep track of the various model versions as they may mutually overlap, supersede one another and evolve over time. We propose an innovative versioning model, and associated storage structure, specifically designed to maximize sharing across process models and process model versions, reduce conflicts in concurrent edits and automatically handle controlled change propagation. The focal point of this technique is to version single process model fragments, rather than entire process models. Indeed empirical evidence shows that real-life process model repositories have numerous duplicate fragments. Experiments on two industrial datasets confirm the usefulness of our technique.

In situ preparation and protein delivery of silicate/alginate composite microspheres with core-shell structure

It is predicted that with increased life expectancy in the developed world, there will be a greater demand for synthetic materials to repair or regenerate lost, injured or diseased bone (Hench & Thompson 2010). There are still few synthetic materials having true bone inductivity, which limits their application for bone regeneration, especially in large-size bone defects. To solve this problem, growth factors, such as bone morphogenetic proteins (BMPs), have been incorporated into synthetic materials in order to stimulate de novo bone formation in the center of large-size bone defects. The greatest obstacle with this approach is that the rapid diffusion of the protein from the carrier material, leading to a precipitous loss of bioactivity; the result is often insufficient local induction or failure of bone regeneration (Wei et al. 2007). It is critical that the protein is loaded in the carrier material in conditions which maintains its bioactivity (van de Manakker et al. 2009). For this reason, the efficient loading and controlled release of a protein from a synthetic material has remained a significant challenge. The use of microspheres as protein/drug carriers has received considerable attention in recent years (Lee et al. 2010; Pareta & Edirisinghe 2006; Wu & Zreiqat 2010). Compared to macroporous block scaffolds, the chief advantage of microspheres is their superior protein-delivery properties and ability to fill bone defects with irregular and complex shapes and sizes. Upon implantation, the microspheres are easily conformed to the irregular implant site, and the interstices between the particles provide space for both tissue and vascular ingrowth, which are important for effective and functional bone regeneration (Hsu et al. 1999). Alginates are natural polysaccharides and their production does not have the implicit risk of contamination with allo or xeno-proteins or viruses (Xie et al. 2010). Because alginate is generally cytocompatible, it has been used extensively in medicine, including cell therapy and tissue engineering applications (Tampieri et al. 2005; Xie et al. 2010; Xu et al. 2007). Calcium cross-linked alginate hydrogel is considered a promising material as a delivery matrix for drugs and proteins, since its gel microspheres form readily in aqueous solutions at room temperature, eliminating the need for harsh organic solvents, thereby maintaining the bioactivity of proteins in the process of loading into the microspheres (Jay & Saltzman 2009; Kikuchi et al. 1999). In addition, calcium cross-linked alginate hydrogel is degradable under physiological conditions (Kibat PG et al. 1990; Park K et al. 1993), which makes alginate stand out as an attractive candidate material for the protein carrier and bone regeneration (Hosoya et al. 2004; Matsuno et al. 2008; Turco et al. 2009). However, the major disadvantages of alginate microspheres is their low loading efficiency and also rapid release of proteins due to the mesh-like networks of the gel (Halder et al. 2005). Previous studies have shown that a core-shell structure in drug/protein carriers can overcome the issues of limited loading efficiencies and rapid release of drug or protein (Chang et al. 2010; Molvinger et al. 2004; Soppimath et al. 2007). We therefore hypothesized that introducing a core-shell structure into the alginate microspheres could solve the shortcomings of the pure alginate. Calcium silicate (CS) has been tested as a biodegradable biomaterial for bone tissue regeneration. CS is capable of inducing bone-like apatite formation in simulated body fluid (SBF) and its apatite-formation rate in SBF is faster than that of Bioglass® and A-W glass-ceramics (De Aza et al. 2000; Siriphannon et al. 2002). Titanium alloys plasma-spray coated with CS have excellent in vivo bioactivity (Xue et al. 2005) and porous CS scaffolds have enhanced in vivo bone formation ability compared to porous β-tricalcium phosphate ceramics (Xu et al. 2008). In light of the many advantages of this material, we decided to prepare CS/alginate composite microspheres by combining a CS shell with an alginate core to improve their protein delivery and mineralization for potential protein delivery and bone repair applications

Translating competency statements into desired outcomes : a representational model for conceptualising the competency acquisition process

The need to develop effective and efficient training programs has been recognised by all sectors engaged in training. In responding to the above need, focus has been directed to developing good competency statements and performance indicators to measure the outcomes. Very little has been done to understand how the competency statements get translated into good performance. To conceptualise this translation process, a representational model based on an information processing paradigm is proposed and discussed. It is argued that learners’ prior knowledge and the effectiveness of the instructional material are two variables that have significant bearing on how effectively the competency knowledge is translated into outcomes. To contextualise the model examples from apprentice training are used.

Deliberation and collaboration in the policy process : a Web 2.0 approach

This paper is a summary of a PhD thesis proposal. It will explore how the Web 2.0 platform could be applied to enable and facilitate the large-scale participation, deliberation and collaboration of both governmental and non-governmental actors in an ICT supported policy process. The paper will introduce a new democratic theory and a Web 2.0 based e-democracy platform, and demonstrate how different actors would use the platform to develop and justify policy issues.

Exploring the impact of entrepreneur optimism on the new venture process

By integrating two theoretical foundations of entrepreneurship research, behaviour and process, this conceptual paper proposes a new model to examine the behaviour of the entrepreneur across the new venture development process. Existing macro level research on the new venture creation process recognises the entrepreneur as a central agent in the process yet generally avoids, at each stage of the process, an examination of the micro level psychological experiences of the individual entrepreneur. Similarly, behavioural research examining entrepreneur individual differences has failed to systematically explore the emotion and behaviour of the entrepreneur across the cycle of the new venture creation process. We propose a conceptual framework to integrate the new venture creation process of opportunity discovery, evaluation and exploitation, with the psychological capital (efficacy, hope, resilience and optimism) of the individual entrepreneur. Propositions for future research to facilitate deeper insight into the impact of entrepreneur behaviour on the new venture creation process and ultimately the success or failure of the new venture are provided.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Online environmental information systems

In today’s information society, electronic tools, such as computer networks for the rapid transfer of data and composite databases for information storage and management, are critical in ensuring effective environmental management. In particular environmental policies and programs for federal, state, and local governments need a large volume of up-to-date information on the quality of water, air, and soil in order to conserve and protect natural resources and to carry out meteorology. In line with this, the utilization of information and communication technologies (ICTs) is crucial to preserve and improve the quality of life. In handling tasks in the field of environmental protection a range of environmental and technical information is often required for a complex and mutual decision making in a multidisciplinary team environment. In this regard e-government provides a foundation of the transformative ICT initiative which can lead to better environmental governance, better services, and increased public participation in environmental decision- making process.

Optimization of container process at seaport terminals

Seaport container terminals are an important part of the logistics systems in international trades. This paper investigates the relationship between quay cranes, yard machines and container storage locations in a multi-berth and multi-ship environment. The aims are to develop a model for improving the operation efficiency of the seaports and to develop an analytical tool for yard operation planning. Due to the fact that the container transfer times are sequence-dependent and with the large number of variables involve, the proposed model cannot be solved in a reasonable time interval for realistically sized problems. For this reason, List Scheduling and Tabu Search algorithms have been developed to solve this formidable and NP-hard scheduling problem. Numerical implementations have been analysed and promising results have been achieved.

Using virtual worlds for collaborative business process modeling

Purpose Process modeling is a complex organizational task that requires many iterations and communication between the business analysts and the domain specialists. The challenge of process modeling is exacerbated, when the process of modeling has to be performed in a cross-organizational, distributed environment. In this paper we suggest a 3D environment for collaborative process modeling, using Virtual World technology. Design/methodology/approach We suggest a new collaborative process modeling approach based on Virtual World technology. We describe the design of an innovative prototype collaborative process modeling approach, implemented as a 3D BPMN modeling environment in Second Life. We use a case study to evaluate the suggested approach. Findings Based on our case study application, we show that our approach increases user empowerment and adds significantly to the collaboration and consensual development of process models even when the relevant stakeholders are geographically dispersed. Research limitations implications – We present design work and a case study. More research is needed to more thoroughly evaluate the presented approach in a variety of real-life process modeling settings. Practical implications Our research outcomes as design artifacts are directly available and applicable by business process management professionals and can be used by business, system and process analysts in real-world practice. Originality/value Our research is the first reported attempt to develop a process modeling approach on the basis of virtual world technology. We describe a novel and innovative 3D BPMN modeling environment in Second Life.

Business process simulation in virtual worlds

This is an invited presentation made as a short preview of the virtual environment research work being undertaken at QUT in the Business Process Management (BPM) research group, known as BPMVE. Three projects are covered, spatial process visualisation, with applications to airport check-in processes, collaborative process modelling using a virtual world BPMN editing tool and business process simulation in virtual worlds using Open Simulator and the YAWL workflow system. In addition, the relationship of this work to Organisational Psychology is briefly explored. Full Video/Audio is available at: http://www.youtube.com/user/BPMVE#p/u/1/rp506c3pPms

Single-crystal Raman spectroscopy of natural schafarzikite FeSb2O4 from Pernek, Slovak Republic

The single crystal Raman spectra of natural mineral schafarzikite FeSb2O4 from the Pernek locality of the Slovak Republic are presented for the first time. Raman spectra of natural mineral apuanite Fe2+Fe43+Sb4O12S, originating from the Apuan Alps in Italy, as well as spectra of synthetic ZnSb2O4 and arsenite mineral trippkeite CuAs2O4 are also presented for the first time. The spectra of the antimonite minerals are characterized by a strong band in the region 660 – 680 cm-1 with shoulders on either side, and a band of medium intensity near 300 cm-1. The spectrum of the arsenite mineral is characterized by a medium band near 780 cm-1 with a shoulder on the high wavenumber side and a strong band at 370 cm-1. Assignments are proposed based on the spectral comparison between the compounds, symmetry modes of the bands and prior literature. The single crystal spectra of schafarzikite showed good mode separation, allowing bands to be assigned a symmetry species of A1g, B1g, B2g or Eg.

Data and process requirements for product recall coordination

When an organisation becomes aware that one of its products may pose a safety risk to customers, it must take appropriate action as soon as possible or it can be held liable. The ability to automatically trace potentially dangerous goods through the supply chain would thus help organisations fulfill their legal obligations in a timely and effective manner. Furthermore, product recall legislation requires manufacturers to separately notify various government agencies, the health department and the public about recall incidents. This duplication of effort and paperwork can introduce errors and data inconsistencies. In this paper, we examine traceability and notification requirements in the product recall domain from two perspectives: the activities carried out during the manufacturing and recall processes and the data collected during the enactment of these processes. We then propose a workflow-based coordination framework to support these data and process requirements.

Does sea-level rise have an impact on saltwater intrusion?

Climate change effects are expected to substantially raise the average sea level. It is widely assumed that this raise will have a severe adverse impact on saltwater intrusion processes in coastal aquifers. In this study we hypothesize that a natural mechanism, identified as the “lifting process” has the potential to mitigate or in some cases completely reverse the adverse intrusion effects induced by sea-level rise. A detailed numerical study using the MODFLOW-family computer code SEAWAT, was completed to test this hypothesis and to understand the effects of this lifting process in both confined and unconfined systems. Our conceptual simulation results show that if the ambient recharge remains constant, the sea-level rise will have no long-term impact (i.e., it will not affect the steady-state salt wedge) on confined aquifers. Our transient confined flow simulations show a self-reversal mechanism where the wedge which will initially intrude into the formation due to the sea-level rise would be naturally driven back to the original position. In unconfined systems, the lifting process would have a lesser influence due to changes in the value of effective transmissivity. A detailed sensitivity analysis was also completed to understand the sensitivity of this self-reversal effect to various aquifer parameters.