No description available.

Food group contributions to energy, carbohydrate, protein, total fat, saturated fat, cholesterol, fiber, calcium, iron, folate, zinc, vitamins A and C were evaluated for Canadian adults aged 18--65 years. Twenty four hour recall data from the 1997--98 Food Habits of Canadians survey were used. Mean nutrient intakes exceeded the RNI for all age-gender groups except, calcium for older women. Mean iron (women 18--49 years) and zinc (men and women 50--65 years) intakes were borderline. The differences in food group contribution to nutrient intake among smokers and non-smokers indicated that smokers generally obtained nutrients from foods higher in energy and fat and lower in other nutrients. Important food sources for individuals meeting the RNI for calcium were fluid milk and cheese. Important sources of folate were citrus fruit juices, breads, and lettuce/cabbages/greens as were cereals and beef/veal for iron. Zinc sources were primarily other beef cuts or ground beef.

Mobile phones have evolved from being devices just to make phone calls to become smartphones with added capabilities like surfing the network. Wireless communication has played a very important role in the evolution of smartphones. The work in this thesis aims to study the potential to reduce the energy consumption of the 3G communications by using a hybrid architecture. An idea first presented in the paper by Vergara and Nadjm-Tehrani [1]. This architecture consists of a group of nodes that communicate using WiFi or Bluetooth to forward their traffic using one node's 3G interface. In this thesis the named energy sharing scheme is implemented on Android mobile devices and experiments have been performed using a number of realistic traces to assess achievable gains and the energy footprint of the scheme itself. Even though communication technologies, screen features, multimedia capabilities, or processing power have been taken to the highest level, phones' batteries have not improved at the same speed. Nowadays battery lifetime has become a major issue with respect to cellular communication. With 3G communications Internet connection anytime and anywhere is provided to the terminals but this technology is optimized for peak performance whereas in underutilization it wastes a lot of energy. This makes it a big black hole from power consumption point of view when transmitting small amounts of data.

The continuously variable transmission (CVT), which has been in use in some of the vehicles in the market today, presents the possibility of decoupling the engine speed and the vehicle speed. By this way, it is now possible to operate the engine at its maximum efficient or performance point and fix it at that operating point without losing from the vehicle speed. Instead of using gears, which are the main transmission elements of conventional transmission, CVT uses two pulleys and a belt. By changing the pulley diameters, a continuously variable transmission ratio is obtained. Besides all its advantages, it has some big drawbacks like low efficiency, torque transmission ability and limited speed range. With developing technology, however, new solutions are developed to eliminate these drawbacks. In this study simulation models for the performance and fuel consumption of different types and arrangements of continuously variable transmission (CVT) systems are developed. Vehicles, which are equipped with two different arrangements of CVT and an automatic transmission, are modelled by using Matlab&amp / #8217 / s simulation toolbox Simulink. By defining the required operating points for better acceleration performance and fuel consumption, and operating the engine at these points, performance optimization is satisfied. These transmissions are compared with each other according to their &amp / #8216 / 0-100 kph&amp / #8217 / acceleration performances, maximum speeds, required time to travel 1000 m. and fuel consumptions for European driving cycles ECE and EUDC. These comparisons show that CVT systems are superior to automatic transmission, according to their acceleration and fuel consumption performances. CVTs also provide smoother driving, while they can eliminate jerks at gear shifting points.

This thesis presents a new typology of television advertising that re-orientates existing research into advertising effectiveness and more accurately reflects new directions in communication theory. The typology provides a consumer-centric approach to analysing television advertisements and a different conceptualisation of the advertising response process. Conventional research into advertising effectiveness has examined almost every aspect of the advertising mix to identify what makes an advertisement effective. The research is based on a number of assumptions. For example, mass communication is seen as a linear process with the advertiser at one end of a communication continuum and the consumer at the other. The function of advertising, in this reception paradigm, is to inform and then to influence the consumer and measures of its success include accuracy of recall and recognition. This process of persuasion comprises a number of hierarchical steps that should lead to purchase or to a positive propensity to purchase. The power of persuasion is related to the level of involvement between the advertised product and the potential customer and with the appropriateness of the advertised message and its execution. For example, elements such as music, humour and the use of celebrities have been studied to assess their persuasive powers and to understand their communication effects. This thesis takes a different approach to understanding how advertising works and makes a number of different assumptions. According to this thesis, before it is possible to study the effects of advertising, it is necessary to find out how people respond to it. This introduces the three key concepts that underpin this thesis. These are reader-response theory, personal construct theory and uses and gratifications theory. Reader-response theory suggests that the meaning and significance of any form of communication is co-created at the point of engagement. The meaning of a television advertisement, for example, is located, not in the advertisement itself, as in conventional research, but in the interaction between the advertisement and the viewer. The meanings that result in this process of negotiation are as much a reflection of personal, social and cultural experience as they are a response to particular executional and message strategies. To understand how consumers make sense of these communication texts it is necessary to study them at the point of reception. The second key concept, personal construct theory, proposes that the way individuals make sense of their experiences and understand the world is determined by the personal constructs that they hold. Identifying these constructs will enable researchers to understand the meanings that consumers attach to communication messages and to focus more fundamentally on the psychological basis of the response process than on its individual components. Studying advertising effectiveness in the context of personal construct theory places the consumer at the centre of the response process and focuses attention on how meaning is negotiated. This has a number of important implications for practioners both in relation to the construction of television advertisements and in understanding consumers� responses to them. For example, practioners need to recognise the importance of producing television advertisements that address their audience as readers of media texts rather than merely as consumers of media products. This re-conceptualising of the audience is clearly articulated in uses and gratifications theory, the third key concept in this study. Uses and gratifications theory, suggests that it is as important to understand what consumers do with advertising as it is to study what advertising does to consumers. This is in contrast to the emphasis on persuasion strategies in conventional advertising research. Reader-response theory, personal construct theory and uses and gratifications theory suggest a more dynamic relationship between an advertisement and a consumer than is recognised by conventional research. These theories are encapsulated in a new typology of television advertising presented in this thesis.

Thesis (PhD)--University of South Australia, 2002

No description available.

This thesis offers one step in a direction that will help consumers make better choices in response to a growing demand for a more sustainable living (Grant 2008, Pollan 2008). In a world of seismic economic, environmental and social change the need for a more sustainable way of behaving is rapidly becoming a priority for mere survival (Porritt 2006). Indeed, it has been suggested that the collapse of economic growth in 2008 has primarily been the result of a dependence on outmoded models of consumption (Hamilton 2003, James 2008). The first section of the thesis documents as a narrative the shift from a label design, which is the result of a research paper, to the launch of a food brand within a university community, which is the commercial outcome of research. The second section of the thesis is the study that examines a nascent label design consisting of a list of ingredients as semiotic triggers that inform the consumer about the product at the point of purchase. The methodology is drawn from mediated discourse analysis (Scollon 2005, Norris & Jones 2005) and multimodal discourse analysis where each mode is viewed as a system of representation with rules and regularities attached to it (Kress & van Leeuwen 2006). I focus on the nascent shift in modal values of packaging design within the site of engagement of a supermarket. The site of engagement is where mediated actions at moments in time and space occur (Norris & Jones 2005). These mediated actions are the focus of attention of the relevant participants (Scollon 2005), and operate at different levels of attention (Norris 2004). The third section contains the appendices.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.