http://espace.library.uq.edu.au/ The University of Queensland en Fez http://blogs.law.harvard.edu/tech/rss http://espace.library.uq.edu.au/view/UQ:106945 2007-08-24T18:32:16Z Harding, A. S. http://espace.library.uq.edu.au/view/UQ:105878 2007-08-24T17:52:01Z Fry, B. http://espace.library.uq.edu.au/view/UQ:107422 2007-08-24T18:50:50Z O'Brien, K. J. http://espace.library.uq.edu.au/view/UQ:107214 2007-08-24T18:41:44Z Gibbons, S. http://espace.library.uq.edu.au/view/UQ:105990 2007-08-24T17:56:02Z Maskhao, P. http://espace.library.uq.edu.au/view/UQ:106990 2007-08-24T18:34:19Z Porter, M. http://espace.library.uq.edu.au/view/UQ:107170 2007-08-24T18:40:15Z Akbar, D. H. http://espace.library.uq.edu.au/view/UQ:106128 2007-08-24T18:02:22Z White, J. http://espace.library.uq.edu.au/view/UQ:106753 2007-08-24T18:24:28Z Meyer, E. http://espace.library.uq.edu.au/view/UQ:106281 2007-08-24T18:07:45Z Morgan, A. T. http://espace.library.uq.edu.au/view/UQ:106265 2007-08-24T18:07:12Z Doherty, S. http://espace.library.uq.edu.au/view/UQ:107396 2007-08-24T18:49:52Z Sills, D. J. http://espace.library.uq.edu.au/view/UQ:105720 2007-08-24T17:46:00Z Andrewartha, R. http://espace.library.uq.edu.au/view/UQ:107508 2007-08-24T18:53:41Z Holcombe, T. http://espace.library.uq.edu.au/view/UQ:106282 2007-08-24T18:07:46Z Weir, K. http://espace.library.uq.edu.au/view/UQ:106797 2007-08-24T18:25:55Z Rutley, E. B. http://espace.library.uq.edu.au/view/UQ:107291 2007-08-24T18:44:58Z Martinez-Garcia, A. N. http://espace.library.uq.edu.au/view/UQ:9094 The complex co-evolutionary systems approach (CCeSA) provides a well-suited framework for analysing agricultural systems, serving as a bridge between biophysical and socioeconomic sciences, allowing for the explanation of phenomena, and for the use of metaphors for thinking and action. By studying agricultural systems as self-generated, hierarchical, complex co-evolutionary farming systems (CCeFSs), one can investigate the interconnections between the elements that constitute CCeFSs, along with the relationships between CCeFSs and other systems, as a fundamental step to understanding sustainability as an emergent property of the system. CCeFSs are defined as human activity systems emerging from the purposes, gestalt, mental models, history and weltanschauung of the farm manager, and from his dynamic co-evolution with the environment while managing the resources at his hand to achieve his own multiple, conflicting, dynamic, semi-structured and constrained purposes. A sustainable CCeFS is described as one that exhibits both enough fitness to achieve its multiple, dynamic, constrained, semi-structured, and often incommensurable and conflicting purposes while performing above threshold values for failure, and enough flexibility to dynamically co-evolve with its changing biophysical and socioeconomic environment for a given future period. Fitness and flexibility are essential features of sustainable CCeFSs because they describe the systems' dynamic capacity to explore and exploit its dynamic phase space while co-evolving with it. This implies that a sustainable CCeFS is conceived as a set of dynamic, co-evolutionary processes, contrasting with the standard view of sustainability as an equilibrium or steady state. Achieving sustainable CCeFSs is a semi-structured, constrained, multi-objective, and dynamic optimisation management problem with an intractable search phase space, that can be solved within the CCeSA with the help of a multi-objective co-evolutionary optimisation tool. Carnico-ICSPEA2, a Co-Evolutionary Navigator (CoEvoNav) used as a CCeSA's tool for harnessing the complexity of the CCeFS of interest and its environment towards sustainability, is introduced. The software was designed by its end-user - the farm manager and author of this thesis - as an aid for the analysis and optimisation of the "San Francisco" ranch, a beef cattle enterprise running on temperate pastures and fodder crops in the central plateau of Mexico. By combining a non-linear simulator and a multi-objective evolutionary algorithm with a deterministic and stochastic framework, the CoEvoNav imitates the co-evolutionary pattern of the CCeFS of interest. As such, the software was used by the farm manager to "navigate" through his CCeFS's co-evolutionary phase space towards achieving sustainability at farm level. The ultimate goal was to enhance the farm manager's decision-making process and co-evolutionary skills, through an increased understanding of his system, the co-evolutionary process between his mental models, the CCeFS, and the CoEvoNav, and the continuous discovery of new, improved sets of heuristics. An overview of the methodological, theoretical and philosophical framework of the thesis is introduced. Also, a survey of the Mexican economy, its agricultural sector, and a statistical review of the Mexican beef industry are presented. Concepts such as modern agriculture, the reductionist approach to agricultural research, models, the system's environment, sustainability, conventional and sustainable agriculture, complexity, evolution, simulators, and multi-objective optimization tools are extensively reviewed. Issues concerning the impossibility of predicting the long-term, detailed future behaviour of CCeFSs, along with the use of simulators as decision support tools in the quest for sustainable CCeFSs, are discussed. The rationale behind the simulator used for this study, along with that of the multi-objective evolutionary tools used as the makeup of Carnico-ICSPEA2, are explained. A description of the "San Francisco" ranch, its key on-farm sustainability indicators in the form of objective functions, constraints, and decision variables, and the semi-structured, multi-objective, dynamic, constrained management problem posed by the farm manager's planned introduction of a herd of bulls for fattening as a way to increase the fitness of his CCeFS via a better management of the system's feed surpluses and the acquisition of a new pick-up truck are described as a case study. The tested scenario and the experimental design for the simulations are presented as well. Results from using the CoEvoNav as the farm manager's extended phenotype to solve his multi-objective optimisation problem are described, along with the implications for the management and sustainability of the CCeFS. Finally, the approach and tools developed are evaluated, and the progress made in relation to methodological, theoretical, philosophical and conceptual notions is reviewed along with some future topics for research. 2005-10-18T00:00:00Z Martinez-Garcia, Alejandro N. http://espace.library.uq.edu.au/view/UQ:106836 2007-08-24T18:27:17Z Abdul Wahab, A. http://espace.library.uq.edu.au/view/UQ:106796 2007-08-24T18:25:53Z Wong, P. http://espace.library.uq.edu.au/view/UQ:105579 2007-08-24T17:41:11Z Finnerty, J. J. http://espace.library.uq.edu.au/view/UQ:106296 2007-08-24T18:08:19Z Clark, F. http://espace.library.uq.edu.au/view/UQ:105737 2007-08-24T17:46:53Z Morgan, B. http://espace.library.uq.edu.au/view/UQ:106283 2007-08-24T18:07:49Z Docking, K. M. http://espace.library.uq.edu.au/view/UQ:107565 2007-08-24T18:55:39Z Jiang, W. W. http://espace.library.uq.edu.au/view/UQ:105792 2007-08-24T17:48:51Z Tsurutani, C. http://espace.library.uq.edu.au/view/UQ:107399 2007-08-24T18:49:58Z Holland, B. W. http://espace.library.uq.edu.au/view/UQ:106532 2007-08-24T18:16:51Z Scott, F. http://espace.library.uq.edu.au/view/UQ:106723 2007-08-24T18:23:22Z Steen, J. T. http://espace.library.uq.edu.au/view/UQ:106376 2007-08-24T18:11:07Z Hatherall, W. http://espace.library.uq.edu.au/view/UQ:107118 2007-08-24T18:38:32Z Wickramasinghe, D. W. http://espace.library.uq.edu.au/view/UQ:105632 2007-08-24T17:42:56Z Austen, S. http://espace.library.uq.edu.au/view/UQ:106069 2007-08-24T17:59:24Z Bailey, M. http://espace.library.uq.edu.au/view/UQ:106774 2007-08-24T18:25:09Z Frentiu, F. http://espace.library.uq.edu.au/view/UQ:10609 In this thesis I consider the general problem of how to make the best possible phase measurements using feedback. Both the optimum input state and optimum feedback are considered for both single-mode dyne measurements and two-mode interferometric measurements. I derive the optimum input states under general dyne measurements when the mean photon number is fixed, both for general states and squeezed states. I propose a new feedback scheme that introduces far less phase uncertainty than mark II feedback, and is very close to the theoretical limit. I also derive results for the phase variance when there is a time delay in the feedback loop, showing that there is a lower limit to the introduced phase variance, and this is approached quite accurately under some conditions. I derive the optimum input states for interferometry, showing that the phase uncertainty scales as 1/N for all the common measures of uncertainty. This is contrasted with the |j0> state, which does not scale as 1/N for all measures of phase uncertainty. I introduce an adaptive feedback scheme that is very close to optimum, and can give scaling very close to 1/N for the uncertainty. Lastly I consider the case of continuous measurements, for both the dyne and interferometric cases. 2004-03-31T00:00:00Z Berry, Dominic William http://espace.library.uq.edu.au/view/UQ:107437 2007-08-24T18:51:20Z Lian, A. B. http://espace.library.uq.edu.au/view/UQ:171255 Abstract Screen modelling has traditionally been based on rudimentary empirical ‘factor’ methods, or semi-empirical data-fitting techniques. Both of these methods have significant limitations in practice, and industrial screen optimization remains somewhat of a ‘black art’. This thesis introduces the concept of dimensional analysis and scale model similitude to the problem of modelling vibrating screens. This leads to a new method of modelling industrial screens. A small-scale screen can be built at a more convenient scale in the laboratory, and used to predict the performance of a large industrial scale machine. Verification of the scaling theory is based on three separate methods: 1. Firstly, the scaling theory is developed by analysing particle-level forces acting in a dry granular system. It is shown that scale-up of granular systems can be achieved using Froude scaling: that is, if the geometry and operating variables in an experiment are scaled in a pre-defined manner, the behaviour of the particles in the full-scale can be predicted from the behaviour of the particles at the smaller scale. 2. Secondly, the scaling rules are applied to a number of idealized granular systems using numerical simulations via the discrete element method (DEM). The granular systems modelled include inclined vibrating feeders and inclined vibrating screens. It is found that simulations performed at different scales yield almost identical dimensionless responses when the geometry and operating conditions are scaled according to Froude scaling rules. 3. Thirdly, the scaling rules are applied to modelling physical screening data. A dataset obtained from a larger pilot-scale screen in a thesis by R. De Pretto (1992) is reproduced at a smaller scale in this thesis using a purpose-built laboratory-scale screen. The throughput, efficiency curves and cut size are shown to be predictable at all feed rates, despite the former dataset being based on a screen with a feed sample size of around 5000 kg per test and the latter dataset obtained using a feed sample size of less than 30 kg per test. The thesis also touches on modelling the screening efficiency curves. A fully dimensionless version of the proven and familiar Whiten screen model is proposed. Scale-up and modelling of industrial screens M. M. Hilden viii Finally, some of the further possibilities of this theory are discussed briefly in a section on further work; these include further applications of the Screening Physical Model and the application of Froude scaling to the modelling of other granular systems. 2009-03-23T10:52:09Z Hilden, M. http://espace.library.uq.edu.au/view/UQ:107377 2007-08-24T18:49:02Z Bodnaruk, K. P. http://espace.library.uq.edu.au/view/UQ:105820 2007-08-24T17:50:06Z Stewart, T. J. http://espace.library.uq.edu.au/view/UQ:106732 2007-08-24T18:23:41Z Martins, N. http://espace.library.uq.edu.au/view/UQ:106114 2007-08-24T18:01:54Z Alexander, R. http://espace.library.uq.edu.au/view/UQ:106457 2007-08-24T18:13:46Z Gwynne, M. http://espace.library.uq.edu.au/view/UQ:105501 2007-08-24T17:37:48Z Snape-Jenkinson, C. J. http://espace.library.uq.edu.au/view/UQ:105953 2007-08-24T17:54:39Z Kibbler, H. http://espace.library.uq.edu.au/view/UQ:107366 2007-08-24T18:48:41Z Higgins, C. E. http://espace.library.uq.edu.au/view/UQ:107257 2007-08-24T18:43:28Z Onederra, I. A. http://espace.library.uq.edu.au/view/UQ:106832 2007-08-24T18:27:09Z Henricksen, K. M. http://espace.library.uq.edu.au/view/UQ:106328 2007-08-24T18:09:28Z Eadie, B. A. http://espace.library.uq.edu.au/view/UQ:106273 2007-08-24T18:07:28Z Lo, M. http://espace.library.uq.edu.au/view/UQ:107461 2007-08-24T18:52:08Z McLaughlin, D. P.