112 resultados para Application efficiency


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Capacity reduction programmes, in the form of buybacks or decommissioning, have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programmes is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet, which tends to increase the effective fishing power of the remaining fleet. In this paper, the effects of a buyback programme on average technical efficiency in Australia’s Northern Prawn Fishery are examined using a multi-output production function approach with an explicit inefficiency model. As expected, the results indicate that average efficiency of the remaining vessels was generally greater than that of the removed vessels. Further, there was some evidence of an increase in average scale efficiency in the fleet as the remaining vessels were closer, on average, to the optimal scale. Key factors affecting technical efficiency included company structure and the number of vessels fishing. In regard to fleet size, our model suggests positive externalities associated with more boats fishing at any point in time (due to information sharing and reduced search costs), but also negative externalities due to crowding, with the latter effect dominating the former. Hence, the buyback resulted in a net increase in the individual efficiency of the remaining vessels due to reduced crowding, as well as raising average efficiency through removal of less efficient vessels.

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Currently, open circuit Bayer refineries pump seawater directly into their operations to neutralise the caustic fraction of the Bayer residue. The resulting supernatant has a reduced pH and is pumped back to the marine environment. This investigation has assessed modified seawater sources generated from different ion filtration processes to compare their relative capacities to neutralise bauxite residues. An assessment of the chemical stability of the neutralisation products, neutralisation efficiency, discharge water quality, bauxite residue composition, and associated economic benefits have been considered to determine the most preferable seawater filtration process based on implementation costs, savings to operations and environmental benefits. The mechanism of neutralisation for each technology was determined to be predominately due to the formation of Bayer hydrotalcite and calcium carbonate, however variations in neutralisation capacity and efficiencies have been observed. The neutralisation efficiency of each feed source has been found to be dependent on the concentration of magnesium, aluminium, calcium and carbonate. These studies have revealed that multiple neutralisation steps occur throughout the process. Environmental, economic and social advantages and disadvantages of the different filtration technologies have been explored to determine the most sustainable method for the neutralisation of bauxite residues. The relative degree of “green” associated with nanofiltered seawater and reverse osmosis filtered seawater are discussed.

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Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

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This thesis investigates how Open Government Data (OGD) concepts and practices might be implemented in the State of Qatar to achieve more transparent, effective and accountable government. The thesis concludes with recommendations as to how Qatar, as a developing country, might enhance the accessibility and usability of its OGD and implement successful and sustainable OGD systems and practices.

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Internationally, the delivery of vocational education and training is being challenged by increasing skills shortages in certain industries and/or rapidly changing skill requirements. To respond to this challenge, rigid and centralised state bureaucracies are increasingly adopting partnerships between schools and industry as a strategy to encourage school-to-work transition programmes to address the local labour market demand. Drawing on experiences in Australia, this paper reports on a case study of government led partnerships between schools and industry. The Queensland Gateway to industry schools initiative currently involves over 120 schools. The study investigated how two commonly used partnership principles were understood by the Gateway to industry partners. Twelve school–industry partnerships from four industry sectors were analysed in terms of the principles of ‘efficiency’ and ‘effectiveness’ derived from the public–private partnership literature. The study found that some evidence of partnership activities associated with efficiency and effectiveness may be assigned to Gateway schools projects. However, little evidence was found that the above underlying principles were addressed systematically. Some of these partnerships were tenuously facilitated by individuals who had limited infrastructure or strategic support. Implications are that industry–school partnership stakeholders would benefit from applying partnership principles regarding implementation and management to ensure the sustainability of partnerships.

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This article integrates the material/energy flow analysis into a production frontier framework to quantify resource efficiency (RE). The emergy content of natural resources instead of their mass content is used to construct aggregate inputs. Using the production frontier approach, aggregate inputs will be optimised relative to given output quantities to derive RE measures. This framework is superior to existing RE indicators currently used in the literature. Using the exergy/emergy content in constructing aggregate material or energy flows overcomes a criticism that mass content cannot be used to capture different quality of differing types of resources. Derived RE measures are both ‘qualitative’ and ‘quantitative’, whereas existing RE indicators are only qualitative. An empirical examination into the RE of 116 economies was undertaken to illustrate the practical applicability of the new framework. The results showed that economies, on average, could reduce the consumption of resources by more than 30% without any reduction in per capita gross domestic product (GDP). This calculation occurred after adjustments for differences in the purchasing power of national currencies. The existence of high variations in RE across economies was found to be positively correlated with participation of people in labour force, population density, urbanisation, and GDP growth over the past five years. The results also showed that economies of a higher income group achieved higher RE, and those economies that are more dependent on imports and primary industries would have lower RE performance.

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This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

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In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

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This thesis studied cadmium sulfide and cadmium selenide quantum dots and their performance as light absorbers in quantum dot-sensitised solar cells. This research has made contributions to the understanding of size dependent photodegradation, passivation and particle growth mechanism of cadmium sulfide quantum dots using SILAR method and the role of ZnSe shell coatings on solar cell performance improvement.

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Society is increasingly calling for professionals across government, industry, business and civil society to be able to problem-solve issues related to climate change and sustainable development as part of their work. In particular there is an emerging realisation of the fundamental need to swiftly reduce the growing demand for energy across society, and to then meet the demand with low emissions options. A key ingredient to addressing such issues is equipping professionals with emerging knowledge and skills to address energy challenges in all aspects of their work. The Council of Australian Governments has recognised this need, signing the National Partnership Agreement on Energy Efficiency in July 2009, which included a commitment to assist business and industry obtain the knowledge, skills and capacity to pursue cost-effective energy efficiency opportunities.2 Engineering will play a critical part among the professions, with Engineers Australia acknowledging that, ‘The need to make changes in the way energy is used and supplied throughout the world represents the greatest challenge to engineers in moving toward sustainability.’

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This report presents the findings of an investigation of energy efficiency resources for undergraduate engineering education, undertaken by web-based research, conversations with educators, and a university survey. The investigation draws on the results of a number of previous investigations undertaken by the research team for NFEE related to energy efficiency education and presents the following findings and recommendations, as explained in greater detail in the body of the report. The findings suggest that even though certain EE concepts and principles have been identified by lecturers as being important there is little to no coverage of a number of these concepts in some programs/courses. Similarly, many topics relating to the most important EE workforce skills and significant shortages as identified in industry research, do not rate highly in terms of both perceived importance by lecturers, or coverage within existing courses. Overall, these findings suggest that despite growing awareness of the importance of EE in both industry and academia, the current depth and breadth of EE content in courses does not reflect this. It confirms that efforts in these areas can be better supported.

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The Energy Efficiency (EE) Graduate Attributes Project focuses on engineering as a priority profession that has a significant role to play in addressing energy demand and supply issues in Australia. Specifically, this project aims to support embedding EE knowledge and skills throughout the engineering undergraduate curriculum, to help build capacity within the Australian workforce across major sectors of the economy, from mining, manufacturing and industrial applications to design, construction, maintenance and retrofitting built environments. The resultant report is intended to assist in future consultation with key groups such as Engineers Australia (EA), the Australian Council of Engineering Deans (ACED) and the eight EA colleges, to support systemic curriculum renewal and promote the design and development of high quality EE engineering education resources. The project is based on a whole-of-program outcomes-based approach to curriculum renewal, creating a transparent framework for integrating EE. This comprises collaborative consideration by academics and professional engineers who have experience in teaching and practising EE, to identify what students should learn to be equipped with relevant competencies by the time they graduate.

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Embedding metallic nanoparticles in organic solar cells can enhance the photoabsorption through light trapping processes. This paper investigates how gold islands obtained by annealing 1–5 nm thick Au layers affect the photoabsorption. Using finite-difference time-domain simulations, the cell efficiency for various island geometries and thicknesses are analyzed and the properties of the islands for maximal photocurrent are discussed. It is shown that a careful choice of size and concentration of gold islands could contribute to enhance the power conversion efficiencies when compared to standard organic solar cell devices. The conclusions are then compared to experimental data for thermally annealed gold islands in bulk heterojunction solar cells. The results of this paper will contribute to the optimization of plasmonic organic solar cell systems and will pave the way for the development of highly efficient organic solar cell devices.

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Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.

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Background The requirement for dual screening of titles and abstracts to select papers to examine in full text can create a huge workload, not least when the topic is complex and a broad search strategy is required, resulting in a large number of results. An automated system to reduce this burden, while still assuring high accuracy, has the potential to provide huge efficiency savings within the review process. Objectives To undertake a direct comparison of manual screening with a semi‐automated process (priority screening) using a machine classifier. The research is being carried out as part of the current update of a population‐level public health review. Methods Authors have hand selected studies for the review update, in duplicate, using the standard Cochrane Handbook methodology. A retrospective analysis, simulating a quasi‐‘active learning’ process (whereby a classifier is repeatedly trained based on ‘manually’ labelled data) will be completed, using different starting parameters. Tests will be carried out to see how far different training sets, and the size of the training set, affect the classification performance; i.e. what percentage of papers would need to be manually screened to locate 100% of those papers included as a result of the traditional manual method. Results From a search retrieval set of 9555 papers, authors excluded 9494 papers at title/abstract and 52 at full text, leaving 9 papers for inclusion in the review update. The ability of the machine classifier to reduce the percentage of papers that need to be manually screened to identify all the included studies, under different training conditions, will be reported. Conclusions The findings of this study will be presented along with an estimate of any efficiency gains for the author team if the screening process can be semi‐automated using text mining methodology, along with a discussion of the implications for text mining in screening papers within complex health reviews.