Creative Work Beyond the Creative Industries : Innovation, Employment and Education

Creative workers are employed in sectors outside the Creative Industries often in greater numbers than within. This is the first book to explore the phenomena of the embedded creative and creative services through a range of sectors, disciplines, and perspectives. Despite the emergence of these creative workers, very little is known about their work life, and why companies seek to employ them. This book asks: how does creative work actually ‘embed’ into a service or product supply chain? What are creative services? What work are embedded creatives doing? Which industries are they working in? This collection explores these questions in relation to innovation, employment and education, using various methods and theoretical approaches, in order to examine the value of the embedded creative and creative services and to discover the implications of education and training for these creative workers. This book will be of interest to practitioners, policy makers and industry leaders in the Creative Industries, in particular digital media, application development, design, journalism, media and communication. It will also appeal to academics and scholars of innovation, Cultural Studies, business management and Labour Studies.

Distance education and the application of academagogy : a case study

The education sector has dramatically changed in the past half decade. In a time of globalisation of education and tightening budgets, various paradigm shifts and challenges have rapidly changed learning and teaching. These include: meeting student expectation for more engaging, more interactive learning experiences, the increased focus to deliver content online, and the complexities of fast-changing technologies. Rising to these challenges and responding to them is a complex and multi-faceted task. This paper discusses educational theories and issues and explores current educational practices in the context of teaching undergraduate students via distance education in the university context. A case study applies a framework drawn from engineering education using the learner-centric concept of academagogy. Results showed that academagogy actively empowers students to build effective learning, and engages facilitators in meaningful teaching and delivery methods.

The impact of organizational boundaries on (re)building capabilities

The literature concerning firm boundaries has focussed extensively on the rationale for different boundary choices and the economic efficiencies that such choices can make. There is also an acknowledged position that a firm’s boundary choices may impact the ability of a firm to maintain and even build new capabilities, though such choices may not be optimal from an economic efficiency perspective. It is in this context that we seek to investigate how firms make this potential trade-off in respect of their boundary choices and how these choices are implemented across a wide range of activities. Using qualitative data from three public sector construction oriented organizations, we observe that neither pure make nor buy decisions assisted significantly in capability building. Dual modes – where firms make and buy the same product or service simultaneously – provided firms with some opportunities to manage this paradox, but the most successful decisions seemed to occur in respect of using intermediate governance modes such as alliances. We also observed that the boundary choice was just one dimension of the capability building process and firms pursuing the same boundary choice decisions often had quite divergent outcomes on the basis of their boundary management and the ability of knowledge to move across firm boundaries.

Adhesive characteristics of low dimensional carbon nanomaterial on actin

The biosafety of carbon nanomaterial needs to be critically evaluated with both experimental and theoretical validations before extensive biomedical applications. In this letter, we present an analysis of the binding ability of two dimensional monolayer carbon nanomaterial on actin by molecular simulation to understand their adhesive characteristics on F-actin cytoskeleton. The modelling results indicate that the positively charged carbon nanomaterial has higher binding stability on actin. Compared to crystalline graphene, graphene oxide shows higher binding influence on actin when carrying positive surface charge. This theoretical investigation provides insights into the sensitivity of actin-related cellular activities on carbon nanomaterial.

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.

The effect of nitroxides on swarming motility and biofilms, multicellular behaviors in Pseudomonas aeruginosa

The ability of NO to induce biofilm dispersion has been well established. Here we investigated the effect of nitroxides (sterically hindered nitric oxide analogues) on biofilm formation and swarming motility in Pseudomonas aeruginosa. A transposon mutant unable to produce nitric oxide endogenously (nirS) was deficient in swarming motility relative to wild type and the complemented strain. Moreover, expression of the nirS gene was up-regulated by 9.65-fold in wild type swarming cells when compared to planktonic cells. Wild type swarming levels were substantially restored upon exogenous addition of nitroxide containing compounds, consistent with the hypothesis that NO is necessary for swarming motility. Here, we showed that nitroxides not only mimicked the dispersal activity of NO, but also prevented biofilms from forming in flow cell chambers. In addition, a nirS transposon mutant was deficient in biofilm formation relative to wild type and the complemented strain, thus implicating NO in the formation of biofilms. Intriguingly despite its stand alone action in inhibiting biofilm formation and promoting dispersal, a nitroxide partially restored the ability of a nirS mutant to form biofilms.

A pilot study on understanding the journey of advanced prostate cancer patients

Objective: To understand the journey of advanced prostate cancer patients for supporting development of an innovative patient journey browser. Background: Prostate cancer is one of the common cancers in Australia. Due to the chronic nature of the disease, it is important to have effective disease management strategy and care model. Multi-disciplinary care is a well-proven approach for chronic disease management. The Multi-disciplinary team (MDT) can function more effectively if all the required information is available for the clinical decision support. The development of innovative technology relies on an accurate understanding of the advanced prostate cancer patient’s journey over a prolonged period. This need arises from the fact that advanced prostate cancer patients may follow various treatment paths and change their care providers. As a result of this, it is difficult to understand the actual sources of patient’s clinical records and their treatment patterns. The aim of the research is to understand variable sources of clinical records, treatment patterns, alternative therapies, over the counter (OTC) medications of advanced prostate cancer patients. This study provides better and holistic understanding of advanced prostate cancer journey. Methods: The study was conducted through an on-line survey developed to seek and analyse the responses from the participants. The on-line questionnaire was carefully developed through consultations with the clinical researchers at the Australian Prostate Cancer Research Centre-Queensland, prostate cancer support group representatives and health informaticians at the Australian e-Health Research Centre. The non-identifying questionnaire was distributed to the patients through prostate cancer support groups in Queensland, Australia. The pilot study was carried out between August 2010 and December 2010. Results: The research made important observations about the advanced prostate cancer journey. It showed that General Practitioner (GP) was the common source of patient’s clinical records (41%) followed by Urologist (14%) and other clinicians (14%). The data analysis also showed that selenium was the common complementary supplement (55%) used by the patients and about 48% patients did not use any OTC drugs. The most common OTC used by the patients was Paracetamol (about 45%). Conclusion: The results have provided a foundation to the architecture of the proposed technology solution. The outcomes of this study are incorporated in design of the proposed patient journey browser system. A basic version of the system is currently being used at the advanced prostate cancer MDT meetings.

The distribution of absorptive capacity among construction supply chain participants

The absorptive capacity of organisations is one of the key drivers of innovation performance in any industry. This research seeks to refine our understanding of the relationship between absorptive capacity and innovation performance, with a focus on characterising the absorptive capacity of the different participant groups within the Australian road industry supply chain. One of the largest and most comprehensive surveys ever undertaken of innovation in road construction was completed in 2011 by the Queensland University of Technology (QUT), based on the Australian road industry. The survey of over 200 construction industry participants covered four sectors, comprising suppliers (manufacturers and distributors), consultants (engineering consultants), contractors (head and subcontractors) and clients (state government road agencies). The survey measured the absorptive capacity and innovation activity exhibited by organisations within each of these participant groups, using the perceived importance of addressing innovation obstacles as a proxy for innovation activity. One of the key findings of the survey is about the impact of participant competency on product innovation activity. The survey found that the absorptive capacity of industry participants had a significant and positive relationship with innovation activity. Regarding the distribution of absorptive capacity, the results indicate that suppliers are more likely to have high levels of absorptive capacity than the other participant groups, with 32% of suppliers showing high absorptive capacity, ahead of contractors (18%), consultants (11%), and clients (7%). These results support the findings of previous studies in the literature and suggest the importance of policies to enhance organisational learning, particularly in relation to openness to new product ideas.

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters : theory and application to microfilaments

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Inhibition of homogenous formation of calcium carbonate by poly (acrylic acid). The effect of molar mass and end-group functionality

The ability of poly(acrylic acid) (PAA) with different end groups and molar masses prepared by Atom Transfer Radical Polymerization (ATRP) to inhibit the formation of calcium carbonate scale at low and elevated temperatures was investigated. Inhibition of CaCO3 deposition was affected by the hydrophobicity of the end groups of PAA, with the greatest inhibition seen for PAA with hydrophobic end groups of moderate size (6–10 carbons). The morphologies of CaCO3 crystals were significantly distorted in the presence of these PAAs. The smallest morphological change was in the presence of PAA with long hydrophobic end groups (16 carbons) and the relative inhibition observed for all species were in the same order at 30 °C and 100 °C. As well as distorting morphologies, the scale inhibitors appeared to stabilize the less thermodynamically favorable polymorph, vaterite, to a degree proportional to their ability to inhibit precipitation.

Numerical modelling of load bearing light gauge steel frame wall systems exposed to realistic design fires

Fire safety has become an important part in structural design due to the ever increasing loss of properties and lives during fires. Conventionally the fire rating of load bearing wall systems made of Light gauge Steel Frames (LSF) is determined using fire tests based on the standard time-temperature curve in ISO834 [1]. However, modern commercial and residential buildings make use of thermoplastic materials, which mean considerably high fuel loads. Hence a detailed fire research study into the fire performance of LSF walls was undertaken using realistic design fire curves developed based on Eurocode parametric [2] and Barnett’s BFD [3] curves using both full scale fire tests and numerical studies. It included LSF walls without cavity insulation, and the recently developed externally insulated composite panel system. This paper presents the details of finite element models developed to simulate the full scale fire tests of LSF wall panels under realistic design fires. Finite element models of LSF walls exposed to realistic design fires were developed, and analysed under both transient and steady state fire conditions using the measured stud time-temperature curves. Transient state analyses were performed to simulate fire test conditions while steady state analyses were performed to obtain the load ratio versus time and failure temperature curves of LSF walls. Details of the developed finite element models and the results including the axial deformation and lateral deflection versus time curves, and the stud failure modes and times are presented in this paper. Comparison with fire test results demonstrate the ability of developed finite element models to predict the performance and fire resistance ratings of LSF walls under realistic design fires.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Challenges of knowledge integration in small and medium enterprises

This study attempts to develop a better understanding of the challenges of knowledge integration (KI) within the innovation process in Small and Medium Enterprises (SMEs). Using several case studies, this study investigates how knowledge integration may be managed within the context of innovation in SMEs. The research places particular focus on identifying the challenges of knowledge integration in SMEs in relation to three aspects of knowledge integration activities, namely knowledge identification, knowledge acquisition, and knowledge sharing. Four distinct tasks emerged in the knowledge integration process, namely team building capability, capturing tacit knowledge, role of knowledge management (KM) systems, and technological systemic integration. The paper suggests that managing knowledge integration in SMEs can be best managed by focusing on these four tasks, which in turn will lead to innovation.