The impact of divergent historical and cultural factors on convergence in global communication practice

Communication practice is increasingly converging around globally consistent approaches and techniques shaped by both globalisation and globalising communications technologies. However, this paper argues, national and regional practice histories and cultural characteristics have shaped, and continue to shape, practice in individual markets. The paper analyses the extent of that these divergent histories and cultures have shaped the structure and practices of the public relations industry in Australia and other countries. The paper challenges the common assumptions about public relations development and industry practice having developed from a predominantly US-based model progressively disseminated globally. It traces the history of public relations in Australia, counterpointing its distinctive origins, to the US-origin thesis. It also examines the impact of demography and diverse national culture on industry shape and practice, comparing the Australian industry to that of other industries around the world. It uses mini-case studies of campaigns in specific countries to assess the extent to which they are culturally bound by historical and cultural differences and the extent to which they are capable of being transferred or adapted to individual markets. For instance, assumptions about globally consistent brand identities are contradicted by McDonald’s’ branding practices in markets such as Canada and Japan. The paper also discusses how emerging market PR industries are being shaped by distinctive and divergent cultures and development paths and may create new structural and practice models as the emerging economies becoming dominant internationally. The authors suggest that history and cultural diversity continue, and will continue to, shape national and regional practices.

Generative experimentation and social simulation : exploring gaming for model vVerification and validation

The agent-based modelling paradigm has been actively applied to address social normative issues such as values, cognition, morality and behaviours. The abstraction of human and human-like social processes and mechanisms result in misalignment of computational model with existing verification and validation techniques and expose significant challenges. We argue that human sources represent a sound approach for verification and validation of agent-based social simulation models. We propose a novel conceptual gaming framework that extracts required information from relevant sources as part of game play

A framework for mobile elearning (mLearning) with analytical decision model

Mobile eLearning (mLearning) can create a revolution in eLearning with the popularity of smart mobile devices and Application. However, contents are the king to make this revolution happen. Moreover, for an effective mLearning system, analytical aspects such as, quality of contents, quality of results, performance of learners, needs to be addressed. This paper presents a framework for personal mLearning. In this paper, we have used graph-based model called bipartite graph for content authentication and identification of the quality of results. Furthermore, we have used statistical estimation process for trustworthiness of weights in the bipartite graph using confidence interval and hypothesis test as analytical decision model tool.

A decentralized cloud firewall framework with resources provisioning cost optimization

Cloud computing is becoming popular as the next infrastructure of computing platform. Despite the promising model and hype surrounding, security has become the major concern that people hesitate to transfer their applications to clouds. Concretely, cloud platform is under numerous attacks. As a result, it is definitely expected to establish a firewall to protect cloud from these attacks. However, setting up a centralized firewall for a whole cloud data center is infeasible from both performance and financial aspects. In this paper, we propose a decentralized cloud firewall framework for individual cloud customers. We investigate how to dynamically allocate resources to optimize resources provisioning cost, while satisfying QoS requirement specified by individual customers simultaneously. Moreover, we establish novel queuing theory based model M/Geo/1 and M/Geo/m for quantitative system analysis, where the service times follow a geometric distribution. By employing Z-transform and embedded Markov chain techniques, we obtain a closed-form expression of mean packet response time. Through extensive simulations and experiments, we conclude that an M/Geo/1 model reflects the cloud firewall real system much better than a traditional M/M/1 model. Our numerical results also indicate that we are able to set up cloud firewall with affordable cost to cloud customers.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

A new evidence-based approach to weight management in primary care: the Counterweight Programme

Background/Aims Obesity has become a global epidemic, and a major preventable cause of morbidity and mortality. Management strategies and treatment protocols are however poorly developed and evaluated. The aim of the Counterweight Programme is to develop an evidence-based model for the management of obesity in primary care.

Methods The Counterweight Programme is based on the theoretical model of Evidence-Based Quality Assessment aimed at improving the management of obese adults (18–75 years) in primary care. The model consists of four phases: (1) practice audit and needs assessment, (2) practice support and training, (3) practice nurse-led patient intervention, and (4) evaluation. Patient intervention consisted of screening and treatment pathways incorporating evidence-based approaches, including patient-centred goal setting, prescribed eating plans, a group programme, physical activity and behavioural approaches, anti-obesity medication and weight maintenance strategies. Weight Management Advisers who are specialist obesity dietitians facilitated programme implementation. Eighty practices were recruited of which 18 practices were randomized to act as controls and receive deferred intervention 2 years after the initial audit.

Results By February 2004, 58 of the 62 (93.5%) intervention practices had been trained to run the intervention programme, 47 (75.8%) practices were active in implementing the model and 1256 patients had been recruited (74% female, 26% male, mean age 50.6 years, SD 14). At baseline, 75% of patients had at one or more co-morbidity, and the mean body mass index (BMI) was 36.9 kg/m2 (SD 5.4). Of the 1256 patients recruited, 91% received one of the core lifestyle interventions in the first 12 months. For all patients followed up at 12 months, 34% achieved a clinical meaningful weight loss of 5% or more. A total of 51% of patients were classed as compliant in that they attended the required level of appointments in 3, 6, and 12 months. For fully compliant patients, weight loss improved with 43% achieving a weight loss of 5% or more at 12 months.

Conclusion The Counterweight Programme is an evidence-based weight management model which is feasible to implement in primary care.

Predicting climate warming effects on green turtle hatchling viability and dispersal performance

Ectotherms are taxa considered highly sensitive to rapid climate warming. This is because body temperature profoundly governs their performance, fitness and life history. Yet, while several modelling approaches currently predict thermal effects on some aspects of life history and demography, they do not consider how temperature simultaneously affects developmental success and offspring phenotypic performance, two additional key attributes that are needed to comprehensively understand species responses to climate warming. Here, we developed a stepwise, individual-level modelling approach linking biophysical and developmental models with empirically derived performance functions to predict the effects of temperature-induced changes to offspring viability, phenotype and performance, using green sea turtle hatchlings as an ectotherm model. Climate warming is expected to particularly threaten sea turtles, as their life-history traits may preclude them from rapid adaptation. Under conservative and extreme warming, our model predicted large effects on performance attributes key to dispersal, as well as a reduction in offspring viability. Forecast sand temperatures produced smaller, weaker hatchlings, which were up to 40% slower than at present, albeit with increased energy stores. Conversely, increases in sea surface temperatures aided swimming performance. Our exploratory study points to the need for further development of integrative individual-based modelling frameworks to better understand the complex outcomes of climate change for ectotherm species. Such advances could better serve ecologists to highlight the most vulnerable species and populations, encouraging prioritization of conservation effort to the most threatened systems.

The use of traits to interpret responses to large scale - edge effects: a study of epigaeic beetle assemblages across a Eucalyptus forest and pine plantation edge

Context: Edge effects due to habitat loss and fragmentation have pervasive impacts on many natural ecosystems worldwide. Objective: We aimed to explore whether, in tandem with the resource-based model of edge effects, species feeding-guild and flight-capacity can help explain species responses to an edge. Methods: We used a two-sided edge gradient that extended from 1000 m into native Eucalyptus forest to 316 m into an exotic pine plantation. We used generalised additive models to examine the continuous responses of beetle species, feeding-guild species richness and flight-capable group species richness to the edge gradient and environmental covariates. Results: Phytophagous species richness was directly related to variation in vegetation along the edge gradient. There were more flight-capable species in Eucalyptus forest and more flightless species in exotic pine plantation. Many individual species exhibited multiple-peaked edge-profiles. Conclusions: The resource based model for edge effects can be used in tandem with traits such as feeding-guild and flight-capacity to understand drivers of large scale edge responses. Some trait-groups can show generalisable responses that can be linked with drivers such as vegetation richness and habitat structure. Many trait-group responses, however, are less generalisable and not explained by easily measured habitat variables. Difficulties in linking traits with resources along the edge could be due to unmeasured variation and indirect effects. Some species’ responses reached the limits of the edge gradient demonstrating the need to examine edge effects at large scales, such as kilometres.

Amalgamation of health services in South-West Victoria: reinvention or survival?

In Australia 'the hospital' has long been considered the cornerstone of small, rural health services. However, this premise has been altered significantly by the introduction of casemix loading and diagnostic-related groups that promote a rationalised output-based model of management. In the light of these changes, many rural health services have struggled to reinvent themselves by establishing a range of service models such as Multi-purpose Service (MPS) and Health Streams, while maintaining traditional models (i.e. bush nursing centres, nursing homes and aged-care facilities). These changes are about survival. This paper analyses one such case in south-west Victoria, the Macarthur and District Community Outreach Service, and compares the outcomes with other similar Victorian rural health research projects. Particular attention is paid to the nature of the health services, the management of change and the proposed health outcomes for the local rural communities. In conclusion, it is argued that this study adds to the body of knowledge surrounding the construction of models of community health and development programming, These models impact upon future rural and remote area initiatives throughout Australia.

Indirect discrimination and the worker-carer: It's just not working

Although initially Australia saw some high-profile successes in antidiscrimination cases for workers with family responsibilities, recent trends in appellate decisions such as Schou suggest that indirect discrimination concepts do not succeed for workers with family responsibilities. The limitations of an individual complaint model to address systemic disadvantage such as that experienced by ‘workercarers’ is simply in too much tension with entrenched expectations surrounding the contract of employment and the ‘ideal’ or ‘unencumbered’ worker. A re-imagining of the employment relationship and the role of the employer will be necessary to achieve substantive equality for workers with family responsibilities.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Considering the receiver in knowledge sharing: when the receiver seems ready the sharer appears

The knowledge needs and knowledge-related behaviours of receivers are among the most crucial, yet of ten overlooked, aspects of successful knowledge-sharing. This research examines how sharers consider receivers' knowledge needs and knowledge-related behaviours when choosing whether to share their knowledge and which channels to use for the transmission of that knowledge. A new theory of knowledge sharing - Receiver Theory - is introduced, and a receiver-based model of knowledge sharing is developed from existing literature. Two exploratory case studies are conducted using the model as a guiding framework. A key finding shows that perceived receiver knowledge needs and behaviours are Important motivators and inhibitors in sharer choices in intra-organisational knowledge sharing. This finding was suggested for both personalised and codified knowledge sharing strategies. The study suggests that for companies to realise more effective knowledge sharing, they should develop better ways to connect potential sharers with receivers' real knowledge needs. The study also suggests that sharing on a need-to know basis impedes change In organisational power structures and prevents the integration of isolated pockets of knowledge that may yield new value.

Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s⁻¹. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s⁻¹, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.