102 resultados para Chlorate reduction


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The feasibility of halving greenhousegasemissions from hotels by 2030 has been studied as part of the Carbon Vision Buildings Programme. The aim of that programme was to study ways of reducing emissions from the existing stock because it will be responsible for the majority of building emissions over the next few decades. The work was carried out using detailed computer simulation using the ESP-r tool. Two hotels were studied, one older and converted and the other newer and purpose-built, with the aim of representing the most common UKhotel types. The effects were studied of interventions expected to be available in 2030 including fabric improvements, HVAC changes, lighting and appliance improvements and renewable energy generation. The main finding was that it is technically feasible to reduce emissions by 50% without compromising guest comfort. Ranking of the interventions was problematical for several reasons including interdependence and the impacts on boiler sizing of large reductions in the heating load

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The issue of diversification in direct real estate investment portfolios has been widely studied in academic and practitioner literature. Most work, however, has been done using either partially aggregated data or data for small samples of individual properties. This paper reports results from tests of both risk reduction and diversification that use the records of 10,000+ UK properties tracked by Investment Property Databank. It provides, for the first time, robust estimates of the diversification gains attainable given the returns, risks and cross‐correlations across the individual properties available to fund managers. The results quantify the number of assets and amount of money needed to construct both ‘balanced’ and ‘specialist’ property portfolios by direct investment. Target numbers will vary according to the objectives of investors and the degree to which tracking error is tolerated. The top‐level results are consistent with previous work, showing that a large measure of risk reduction can be achieved with portfolios of 30–50 properties, but full diversification of specific risk can only be achieved in very large portfolios. However, the paper extends previous work by demonstrating on a single, large dataset the implications of different methods of calculating risk reduction, and also by showing more disaggregated results relevant to the construction of specialist, sector‐focussed funds.

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Olive oil, an important component of the Mediterranean diet, is rich in polyphenols and is known to possess positive health effects relative to other dietary fats. In addition, the leaves of the olive plant (Olea europaea) contain similar phenolics (oleuropein, luteolin-7-glucoside, apigenin-7-glucoside, verbascoside and hydroxytyrosol) to those of olives and olive oil, although at higher concentrations. For example, the most abundant is the secoiridoid, oleuropein, representing 1–14% of olive leaf weight vs. 0.005–0.12% in olive oil. Although currently considered a waste product of the olive oil industry, recent research has suggested beneficial effects of phenolic-rich olive leaf extracts (OLE) in modifying cardiovascular risk biomarkers such as blood pressure, hyperglycaemia, oxidative stress and inflammation, as well as improving vascular function and lipid profiles. Despite this, data regarding the biological actions of OLE has mostly derived from animal, in vitro and ex vivo studies, with limited evidence deriving from human trials. Although the absorption and metabolism of olive oil phenolics has been investigated, less is known about the bioavailability of phenolics from OLE, limiting the interpretation of existing in vitro and ex vivo data. The current review will begin by describing the phenolic composition of olive leaves in comparison with that of the better studied olive oil. It will then review the effects of OLE on cardiovascular risk factors, covering both animal and human studies and will end by considering potential mechanisms of action

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This article explores the contribution that artisanal and small-scale mining (ASM) makes to poverty reduction in Tanzania, based on data on gold and diamond mining in Mwanza Region. The evidence suggests that people working in mining or related services are less likely to be in poverty than those with other occupations. However, the picture is complex; while mining income can help reduce poverty and provide a buffer from livelihood shocks, peoples inability to obtain a formal mineral claim, or to effectively exploit their claims, contributes to insecurity. This is reinforced by a context in which ASM is peripheral to large-scale mining interests, is only gradually being addressed within national poverty reduction policies, and is segregated from district-level planning.

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In Sub-Saharan Africa (SSA) the technological advances of the Green Revolution (GR) have not been very successful. However, the efforts being made to re-introduce the revolution call for more socio-economic research into the adoption and the effects of the new technologies. The paper discusses an investigation on the effects of GR technology adoption on poverty among households in Ghana. Maximum likelihood estimation of a poverty model within the framework of Heckman's two stage method of correcting for sample selection was employed. Technology adoption was found to have positive effects in reducing poverty. Other factors that reduce poverty include education, credit, durable assets, living in the forest belt and in the south of the country. Technology adoption itself was also facilitated by education, credit, non-farm income and household labour supply as well as living in urban centres. Inarguably, technology adoption can be taken seriously by increasing the levels of complementary inputs such as credit, extension services and infrastructure. Above all, the fundamental problems of illiteracy, inequality and lack of effective markets must be addressed through increasing the levels of formal and non-formal education, equitable distribution of the 'national cake' and a more pragmatic management of the ongoing Structural Adjustment Programme.

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It is known that the empirical orthogonal function method is unable to detect possible nonlinear structure in climate data. Here, isometric feature mapping (Isomap), as a tool for nonlinear dimensionality reduction, is applied to 1958–2001 ERA-40 sea-level pressure anomalies to study nonlinearity of the Asian summer monsoon intraseasonal variability. Using the leading two Isomap time series, the probability density function is shown to be bimodal. A two-dimensional bivariate Gaussian mixture model is then applied to identify the monsoon phases, the obtained regimes representing enhanced and suppressed phases, respectively. The relationship with the large-scale seasonal mean monsoon indicates that the frequency of monsoon regime occurrence is significantly perturbed in agreement with conceptual ideas, with preference for enhanced convection on intraseasonal time scales during large-scale strong monsoons. Trend analysis suggests a shift in concentration of monsoon convection, with less emphasis on South Asia and more on the East China Sea.

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Delayed ettringite formation (DEF) is a chemical reaction with proven damaging effects on hydrated concrete. Ettringite crystals can cause cracks and their widening due to pressure on cracked walls caused by the positive volume difference in the reaction. Concrete may show improvements in strength at early ages but further growth of cracks causes widening and spreading through the concrete structure. In this study, finely dispersed crystallization nuclei achieved by adding air-entraining agent (AEA) and short vibration of specimens is presented as the main prerequisite for reducing DEF-induced deterioration of hydrated concrete. The study presents the method and mechanism for obtaining the required nucleation. Controlling long-term DEF by providing AEA-induced crystallisation nuclei, prevented excessive and rapid initial strength improvements, and resulted in a slight increase of compressive strength of fine grained concrete with only marginally lower density.

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Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.

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Collectively small and medium sized enterprises (SMEs) are significant energy users although many are unregulated by existing policies due to their low carbon emissions. Carbon reduction is often not a priority but smart grids may create a new opportunity. A smart grid will give electricity suppliers a picture of real-time energy flows and the opportunity for consumers to receive financial incentives for engaging in demand side management. As well as creating incentives for local carbon reduction, engaging SMEs with smart grids has potential for contributing to wider grid decarbonisation. Modelling of buildings, business activities and technology solutions is needed to identify opportunities for carbon reduction. The diversity of the SME sector complicates strategy development. SMEs are active in almost every business area and occupy the full range of property types. This paper reviews previous modelling work, exposing valuable data on floor space and energy consumption associated with different business activities. Limitations are seen with the age of this data and an inability to distinguish SME energy use. By modelling SME energy use, electrical loads are identified which could be shifted on demand, in a smart network. Initial analysis of consumption, not constrained by existing policies, identifies heating and cooling in retail and commercial offices as having potential for demand response. Hot water in hotel and catering and retail sectors may also be significant because of the energy storage potential. Areas to consider for energy efficiency schemes are also indicated.