Her Behind Him

Poetry by Tim Brenna

Generating the Benefits of Competition: Challenges and Opportunities in Opening Electricity Markets

The move from regulation to competition in different parts of the economy is one of the great success stories of the past 30 years. And more competition in the electricity sector could offer lower consumer prices and improved stability of supply. So why has market deregulation in electricity been difficult to achieve?public services, electricity sector competition, market deregulation

Putting a Floor on Energy Savings: Comparing State Energy Efficiency Resource Standards

Energy efficiency resource standards (EERS) refer to policies that require utilities and other covered entities to achieve quantitative goals for reducing energy use by a certain year. EERS policies generally apply to electricity and natural gas sales and electricity peak demand, though they also cover other energy sources in Europe. Our study aggregates information about the requirements of existing EERS policies for electricity sales in the United States. We convert quantitative goals into comparable terms to compare the nominal stringency of EERS programs across states. EERS programs also differ in their nonquantitative requirements, including flexibility measures, measurement and verification programs, and penalties and positive incentives. We compare the U.S. policies to similar policies in the European Union and discuss important policy issues, including exogenous changes in fuel prices and issues with utility management of energy efficiency programs.energy efficiency, electricity, energy efficiency resource standards, state regulation

Action research in collaborative improvement

There is an increasing need to apply and transfer continuous improvement (CI) to inter-organisational processes. As such collaborative improvement (CoI) is emerging as a new concept within managerial literature and practice. This paper begins with a discussion on the logic and value of applying action research (AR) in empirical research in the field of CI and CoI to contribute to both theory and practice. It introduces the theory and characteristics of AR and describes the implementation of an AR process in an inter-organisational setting through the adoption of an AR model. Finally, it discusses the generation of theory through AR and concludes that AR is relevant and valid in research on CI and CoI as it contributes both to concerns of practitioners and the body of knowledge

Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability

Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20 % just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools

Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability

Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20 % just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools

Economic Analysis of Improving Cold Tolerance in Rice in Australia

The occurrence of low night temperatures during reproductive development is one of the factors most limiting rice yields in southern Australia. Yield losses due to cold temperature are the result of incomplete pollen formation and subsequent floret sterility. Researchers have found that in 75% of years, rice farmers suffer losses between 0.5 and 2.5 t/ha. Research is being undertaken to identify overseas rice varieties, that are cold tolerant under the local weather conditions and by using those genotypes as parent material, develop cold tolerance varieties of rice. A yield simulation model was used to measure reduction in losses due to cold at different minimum threshold temperatures, while the SAMBOY Rice model was used to measure the costs and returns of a breeding program for cold tolerance. The results of the economic analysis reveal that new cold tolerant varieties would lead to significant increase in financial benefits through reduction in losses due to cold, and an increase in yield from the better use on nitrogen by the cold tolerant varieties. The returns to investment on the research project are estimated to be high

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic.

BACKGROUND: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. METHODS: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemic-free world' and 'world including a pandemic' are of interest. RESULTS: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. CONCLUSIONS: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest

Role of Lactobacilli in Flavour Development of Cheddar Cheese.

End of Project ReportCheddar cheese is a complex microbial ecosystem. The internal cheese environment, in particular of hard and semi-hard cheeses, is not conducive to the growth of many microorganisms. At the beginning of ripening the dominant microorganisms are the starter bacteria which are present at high levels (~109/g). However, during ripening, non-starter lactic acid bacteria (NSLAB) grow from relatively low levels (<103/g) at the beginning of ripening, to 108/g within 6 - 8 weeks. Other bacteria, e.g. enterococci and staphylococci, may also be present but in much lower numbers. In a previous study of mature and extra mature Cheddar cheeses from different manufacturers (see End of Project Report No. 1), it was found that the NSLAB population was dominated by strains of Lb. paracasei. However, their contribution to cheese flavour and their source(s) are still unclear, nor is it known if the NSLAB flora is unique to each plant. Hence, understanding the growth of this group of organisms in cheese is a key to defining their role in flavour development. The biochemistry of flavour development in cheese is poorly understood. For most cheese varieties, including Cheddar, proteolysis, which results in the accumulation of free amino acids, is of vital importance for flavour development. Increasing evidence suggests that the main contribution of amino acids is as substrates for the development of more complex flavour and aroma compounds. The manner by which such compounds are generated in cheese is currently the focus of much research. Starter bacteria have been shown to contain a range of enzymes capable of facilitating the conversion of amino acids to potential flavour compounds. However, the potential of lactobacilli (NSLAB) to produce similar enzymes has only recently been investigated. Hence, although, it is generally accepted that the cheese starter flora is the primary defining influence on flavour development, the contribution of NSLAB is also considered significant. The objectives of these studies were: - to develop a greater understanding of the behaviour of NSLAB in cheese, and - to identify suitable strains, and other cheese bacteria, to be used as starter adjuncts for flavour improvement.Department of Agriculture, Food and the Marin