[Reprints of addresses]

Made-up set; title supplied.

Food marketing information related to television in New York City /

"Some implications of exploratory study III - Television ... for other marketing information for consumers programs": 2 leaves inserted.

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties. (c) 2005 Elsevier Ltd. All rights reserved.

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

Trans-Planckian problem in the healthy extension of Horava-Lifshitz gravity

Planck scale physics may influence the evolution of cosmological fluctuations in the early stages of cosmological evolution. Because of the quasiexponential redshifting, which occurs during an inflationary period, the physical wavelengths of comoving scales that correspond to the present large-scale structure of the Universe were smaller than the Planck length in the early stages of the inflationary period. This trans-Planckian effect was studied before using toy models. The Horava-Lifshitz (HL) theory offers the chance to study this problem in a candidate UV complete theory of gravity. In this paper we study the evolution of cosmological perturbations according to HL gravity assuming that matter gives rise to an inflationary background. As is usually done in inflationary cosmology, we assume that the fluctuations originate in their minimum energy state. In the trans-Planckian region the fluctuations obey a nonlinear dispersion relation of Corley-Jacobson type. In the "healthy extension" of HL gravity there is an extra degree of freedom which plays an important role in the UV region but decouples in the IR, and which influences the cosmological perturbations. We find that in spite of these important changes compared to the usual description, the overall scale invariance of the power spectrum of cosmological perturbations is recovered. However, we obtain oscillations in the spectrum as a function of wave number with a relative amplitude of order unity and with an effective frequency which scales nonlinearly with wave number. Taking the usual inflationary parameters we find that the frequency of the oscillations is so large as to render the effect difficult to observe.

State asset management reform in Indonesia : a wicked problem

Efficient state asset management is crucial for government departments that rely on the operations of their state assets in order to fulfil their public functions, which include public service provision and others. These assets may be expensive, extensive and or, complex, and can have a major impact on the ability of governments to perform its function over extended periods. Various governments around the world have increasingly recognised the importance of an efficient state asset management laws, policies, and practices; exemplified by the surge in state asset management reform. This phenomenon is evident in Indonesia, in particular through the establishment of the Directorate General of State Assets in 2006, who was appointed as the ultimate state asset manager (of Republic of Indonesia) and the proprietor of state asset management reform. The Directorate General of State Assets too has pledged its adherence to good governance principles within its state asset management laws and policies reform. However the degree that good governance principles are conceptualised is unknown, resulting in questions of how and to what extent is good governance principles evident within Indonesia's reformed state asset management laws and policies. This study seeks to understand the level of which good governance principles are conceptualised and understood within reformed state asset management policies in Indonesia (as a case study), and identify the variables that play a role in the implementation of said reform. Although good governance improvements has been a central tenet in Indonesian government agenda, and state asset management reform has propelled in priority due to found neglect and unfavourable audit results; there is ambiguity in regards to the extent that good governance is conceptualised within the reform, how and whether this relationship is understood by state asset managers (i.e government officials), and what (and how) other variables play a supporting and/or impeding role in the reform. Using empirical data involving a sample of four Indonesian regional governments and 70 interviews; discrepancy in which good governance principles are conceptualised, the level it is conceptualised, at which stage of state asset management practice it is conceptualised, and the level it is understood by state asset managers (i.e government officials) was found. Human resource capacity and capability, the notion of 'needing more time', low legality, infancy of reform, and dysfunctional sense of stewardship are identified as specific impeding variables to state asset management reform; whilst decentralisation and regional autonomy regime, political history, and culture play a consistent undercurrent key role in good governance related reforms within Indonesia. This study offers insights to Indonesian policy makers interested in ensuring the conceptualisation and full implementation of good governance in all areas of governing, particularly within state asset management practices. Most importantly, this study identifies an asymmetry in good governance understanding, perspective, and assumptions between policy maker (i.e high level government officials) and policy implementers (i.e low level government officials); to be taken into account for future policy evolvements and/or writing. As such, this study suggests the need for a modified perspective and approach to good governance conceptualisation and implementation strategies, one that acknowledges and incorporates a nation's unique characteristics and no longer denies the double-edged sword of simplified assumptions of governance.

Queensland diabetic foot hospitalisations (2005-10) : in what state is our foot hospital problem?

Background Diabetes foot complications are a leading cause of overall avoidable hospital admissions. Since 2006, the Queensland Diabetes Clinical Network has implemented programs aimed at reducing diabetes-related hospitalisation. The aim of this retrospective observational study was to determine the incidence of diabetes foot-related hospital admissions in Queensland from 2005 to 2010. Methods Data on all primary diabetes foot-related admissions in Queensland from 2005-2010 was obtained using diabetes foot-related ICD-10-AM (hospital discharge) codes. Queensland diabetes foot-related admission incidences were calculated using general population data from the Australian Bureau of Statistics. Furthermore, diabetes foot-related sub-group admissions were analysed. Chi-squared tests were used to assess changes in admissions over time. Results Overall, 24,917 diabetes foot-related admissions occurred, resulting in the use of 260,085 bed days or 1.4% of all available Queensland hospital bed days (18,352,152). The primary reasons for these admissions were foot ulcers (49.8%), cellulitis (20.7%), peripheral vascular disease (17.8%) and osteomyelitis (3.8%). The diabetes foot-related admission incidence among the general population (per 100,000) reduced by 22% (103.0 in 2005, to 80.7 in 2010, p < 0.001); bed days decreased by 18% (1,099 to 904, p < 0.001). Conclusion Diabetes foot complications appear to be the primary reason for 1.4 out of every 100 hospital beds used in Queensland. There has been a significant reduction in the incidence of diabetes foot-related admissions in Queensland between 2005 and 2010. This decrease has coincided with a corresponding decrease in amputations and the implementation of several diabetes foot clinical programs throughout Queensland.

Stagnancy in Indonesia’s reformed state asset management policies and practices : a wicked problem?

The policies and regulations governing the practice of state asset management have emerged as an urgent question among many countries worldwide for there is heightened awareness of the complex and crucial role that state assets play in public service provision. Indonesia is an example of such country, introducing a ‘big-bang’ reform in state asset management laws, policies, regulations, and technical guidelines. Indonesia exemplified its enthusiasm in reforming state asset management policies and practices through the establishment of the Directorate General of State Assets in 2006. The Directorate General of State Assets have stressed the new direction that it is taking state asset management laws and policies through the introduction of Republic of Indonesia Law Number 38 Year 2008, which is an amended regulation overruling Republic of Indonesia Law Number 6 Year 2006 on Central/Regional Government State Asset Management. Law number 38/2008 aims to further exemplify good governance principles and puts forward a ‘the highest and best use of assets’ principle in state asset management. The purpose of this study is to explore and analyze specific contributing influences to state asset management practices, answering the question why innovative state asset management policy implementation is stagnant. The methodology of this study is that of qualitative case study approach, utilizing empirical data sample of four Indonesian regional governments. Through a thematic analytical approach this study provides an in-depth analysis of each influencing factors to state asset management reform. Such analysis suggests the potential of an ‘excuse rhetoric’; whereby the influencing factors identified are a smoke-screen, or are myths that public policy makers and implementers believe in, as a means to ex-plain stagnant implementation of innovative state asset management practice. Thus this study offers deeper insights of the intricate web that influences state as-set management innovative policies to state asset management policy makers; to be taken into consideration in future policy writing.

AG15 (Sustainable Agricultural State-level Investment Program) natural resource management extension in irrigated cotton and grain

Natural Resource Management project developing reources and supporting best practice management for irrigated cotton and grain growers in Queensland.

An extension of Mangler transformation to a 3-D problem

Considering the linearized boundary layer equations for three-dimensional disturbances, a Mangler type transformation is used to reduce this case to an equivalent two-dimensional one.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Enhancement of fish production in Borno State with extension services

Borno State possesses great potentials for fish production both from inland fisheries and aquaculture. The socio-economic and environmental production factors are suitable for fish production. If the potential of the State were well harnessed, it would be playing significant roles in achieving self-sufficiency in fish production in Nigeria. But the situation at the moment is that its fisheries potentials are not being optimally utilized. While the inland waters of Lake Chad are currently being recklessly exploited, aquaculture development is given little or no attention. It is evident that there is a missing link between research results and the potential end users. Because information in fish production variables is a pre-requisite for fisheries development, the gap that exists between two poles must be bridged, fisheries Extension provides this important link between research result and the end users of research findings. The paper examines the importance of extension services as the key to unlock fish production information that are usually consigned to the pages of academic journals and research publications