Solving Functional Constraints by Variable Substitution

Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ CSP-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other non-functional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints

A H2 PEM fuel cell and high energy dense battery hybrid energy source for an urban electric vehicle

Electric vehicles are set to play a prominent role in addressing the energy and environmental impact of an increasing road transport population by offering a more energy efficient and less polluting drive-train alternative to conventional internal combustion engine (ICE) vehicles. Given the energy (and hence range) and performance limitations of electro-chemical battery storage systems, hybrid systems combining energy and power dense storage technologies have been proposed for vehicle applications. The paper discusses the application of a hydrogen fuel cell as a range extender for an urban electric vehicle for which the primary energy source is provided by a high energy dense battery. A review of fuel cell systems and automotive drive-train application issues are discussed, together with an overview of the battery technology. The prototype fuel cell and battery component simulation models are presented and their performance as a combined energy/power source assessed for typical urban and sub-urban driving scenario

Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores

Modern business applications and scientific databases call for inherently dynamic data storage environments. Such environments are characterized by two challenging features: (a) they have little idle system time to devote on physical design; and (b) there is little, if any, a priori workload knowledge, while the query and data workload keeps changing dynamically. In such environments, traditional approaches to index building and maintenance cannot apply. Database cracking has been proposed as a solution that allows on-the-fly physical data reorganization, as a collateral effect of query processing. Cracking aims to continuously and automatically adapt indexes to the workload at hand, without human intervention. Indexes are built incrementally, adaptively, and on demand. Nevertheless, as we show, existing adaptive indexing methods fail to deliver workload-robustness; they perform much better with random workloads than with others. This frailty derives from the inelasticity with which these approaches interpret each query as a hint on how data should be stored. Current cracking schemes blindly reorganize the data within each query's range, even if that results into successive expensive operations with minimal indexing benefit. In this paper, we introduce stochastic cracking, a significantly more resilient approach to adaptive indexing. Stochastic cracking also uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision-making. Thereby, we bring adaptive indexing forward to a mature formulation that confers the workload-robustness previous approaches lacked. Our extensive experimental study verifies that stochastic cracking maintains the desired properties of original database cracking while at the same time it performs well with diverse realistic workloads.Comment: VLDB201

An econometric analysis of Australian domestic tourism demand

In 2007, the total spending by domestic visitors was AUD 43 billion, which was 1.5 times higher than the aggregate expenditure by international tourists in Australia. Moreover, domestic visitors consumed 73.7% of the Australian produced tourism goods and services whereas international tourists consumed 26.3%. Hence, this shows that domestic tourism is an important sector for the overall tourism industry in Australia. This present research determines the factors that influence domestic tourism demand in Australia and examines how changes in the economic environment in Australia could influence this demand. The main aim of this research is to achieve sustainability of domestic tourism businesses in Australia. In Chapters Two and Three, a review of the tourism demand literature is conducted. Most of the empirical papers argued that household income and travel prices are the main demand determinants. However, the literature has largely neglected other possible indicators, namely consumers‟ perceptions of the future economy, household debt and working hours, which may play an important role in influencing domestic tourism demand in Australia. The PhD thesis is divided into three parts. For the initial phase, a preliminary study is conducted using Johansen‟s cointegration analysis to examine the short- and long-run coefficients for the determinants of Australian domestic tourism demand. In the next section of this thesis, an alternative approach using panel data analysis to estimate the income and price elasticities of the demand is applied, as a panel data framework provides more information from the data and more degrees of freedom. In the final section, this thesis also investigates whether other factors (such as the consumer sentiment index, and measures of household debt and working hours) influence Australians‟ demand for domestic trips. This study reveals several distinct findings. First, the income elasticity for domestic visitors of friends and relatives (VFR) and interstate trips is negative, implying that Australian households will not choose to travel domestically when there is an increase in household income. In contrast, the study finds that the income variables are positively vi correlated with domestic business tourism demand, indicating that the demand is strongly responsive to changes in Australia‟s economic conditions. Second, an increase in the current prices of domestic travel can cause the demand for domestic trips to fall in the next one or two quarters ahead. Third, the coefficients for lagged dependent variables are negative, indicating perhaps, that trips are made on a periodic basis. Finally, to a certain extent, the consumer sentiment index, household debt and working hours have significant influences on domestic tourism demand. The current econometric analysis has significant implications for practitioners. A better understanding of income and travel cost impacts on Australian households‟ demand allows tourism companies to develop price strategies more effectively. Moreover, tourism researchers can use these indicators (such as measures of consumers‟ confidence about their future economy, household debt and working hours) to investigate how changes in these factors may have an impact on individual decisions to travel

Modelling and risk analysis of the western rock lobster (Panulirus cygnus) fishery of Western Australia

The predictive power for short-term forecasting of selected biomass dynamic models was examined using the standardised catch and effort data from the 1944/45 to 1990/91 season of the western rock lobster. Risk analysis of the fishery based on the predicted fishing efforts with the Deriso-Schnute delay-difference model indicates a high probability of recruitment failure. Some hypothetical management strategies of reducing fishing effort were evaluated by taking into consideration the total catch and biological risk to the fishery

Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge

This viewgraph presentation reviews Structural Health Monitoring Analysis for the Orbiter Wing Leading Edge. The Wing Leading Edge Impact Detection System (WLE IDS) and the Impact Analysis Process are also described to monitor WLE debris threats. The contents include: 1) Risk Management via SHM; 2) Hardware Overview; 3) Instrumentation; 4) Sensor Configuration; 5) Debris Hazard Monitoring; 6) Ascent Response Summary; 7) Response Signal; 8) Distribution of Flight Indications; 9) Probabilistic Risk Analysis (PRA); 10) Model Correlation; 11) Impact Tests; 12) Wing Leading Edge Modeling; 13) Ascent Debris PRA Results; and 14) MM/OD PRA Results