Great adaptations : China's creative clusters and the new social contract

The transformation of China's urban landscape has witnessed a boom in cultural adaptation, namely the adaptation of a Western idea, the creative cluster. This chapter examines the formatting of hundreds of creative clusters-art centres, animation bases, cultural zones, and incubators. The cluster has important implications for how we understand China going forward into the second decade of the 21st century. The cluster phenomenon has resulted in to a substantive remaking of the social contract, between officials, entrepreneurs, local residents, academics-and most significantly cultural producers. However, these processes of adaption are mostly driven by real estate developers working in partnership with local government officials. Cut and paste design is the fast road to completion. In this sense, the description 'creative' may well be redundant.

More than experiential learning or volunteering : a case study of community service learning within the Australian context

Community service learning is the integration of experiential learning and community service into coursework such that community needs are met and students gain both professional skills and a sense of civic responsibility. A critical component is student reflection. This paper provides an example of the application of community service learning within an undergraduate health unit at the Queensland University of Technology. Based on survey data from 36 program participants, it demonstrates the impact of CSL on student outcomes. Results show that students benefited by developing autonomy through real world experiences, through increased self-assurance and achievement of personal growth, through gaining new insights into the operations of community service organisations and through moving towards becoming responsible citizens. Students expect their CSL experience to have long-lasting impact on their lives, with two-thirds of participants noting that they would like to continue volunteering as part of their future development.

Identifying with an avatar : a multidisciplinary perspective

Avatars perform a complex range of inter-related functions. They not only allow us to express a digital identity, they facilitate the expression of physical motility and, through non-verbal expression, help to mediate social interaction in networked environments. When well designed, they can contribute to a sense of “presence” (a sense of being there) and a sense of “co-presence” (a sense of being there with others) in digital space. Because of this complexity, the study of avatars can be enriched by theoretical insights from a range of disciplines. This paper considers avatars from the perspectives of critical theory, visual communication, and art theory (on portraiture) to help elucidate the role of avatars as an expression of identity. It goes on to argue that identification with an avatar is also produced through their expression of motility and discusses the benefits of film theory for explaining this process. Conceding the limits of this approach, the paper draws on philosophies of body image, Human Computer Interaction (HCI) theory on embodied interaction, and fields as diverse as dance to explain the sense of identification, immersion, presence and co-presence that avatars can produce.

Ourplace : the convergence of locative media and online participatory culture

The trans-locative potential of the Internet has driven the design of many online applications. Online communities largely cluster around topics of interest, which take precedence over participants’ geographical locations. The site of production is often disregarded when creative content appears online. However, for some, a sense of place is a defining aspect of creativity. Yet environments that focus on the display and sharing of regionally situated content have, so far, been largely overlooked. Recent developments in geo-technologies have precipitated the emergence of a new field of interactive media. Entitled locative media, it emphasizes the geographical context of media. This paper argues that we might combine practices of locative media (experiential mapping and geo-spatial annotation) with aspects of online participatory culture (uploading, file-sharing and search categorization) to produce online applications that support geographically ‘located’ communities. It discusses the design considerations and possibilities of this convergence,making reference to an example, OurPlace 3G to 3D, which has to date been developed as a prototype.1 It goes on to discuss the benefits and potential uses of such convergent applications, including the co-production of spatial- emporal narratives of place.

The effects of sensing and seizing of market opportunities and reconfiguring activities on the organisational resource base

This study has important implications for marketing theory and practice. In an era of turbulent market environments, the organisational ability to sense and seize market opportunities and to reconfigure the resource base accordingly, has significant effects on performance. This paper uses a dynamic capability framework to explain more explicitly the intricacies of the relationship between sensing and seizing of market opportunities and reconfiguring the resource base (i.e. dynamic capabilities) and the resource base. We investigate how the attributes of dynamic capability deployment, timing, frequency and speed, influence the resource base. We test the proposed framework using survey data from 228 large organisations. Findings show that the timing and frequency of dynamic capability deployment have significant effects on the resource base.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.