162 resultados para Term Securities Lending Facility (TSLF)


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Tagging animals is frequently employed in ecological studies to monitor individual behaviour, for example postrelease survival and dispersal of captive-bred animals used in conservation programmes. While the majority of studies focus on the efficacy of tags in facilitating the relocation and identification of individuals, few assess the direct effects of tagging in biasing animal behaviour. We used an experimental approach with a control to differentiate the effects of handling and tagging captive-bred juvenile freshwater pearl mussels, Margaritifera margaritifera, prior to release into the wild. Marking individuals with passive integrated transponder (PIT) tags significantly decreased their burrowing rate and, therefore, increased the time taken to burrow into the substrate. This effect was contributed to, in part, by the detrimental impacts of handling, which also significantly affected activity, burrowing ability and the time taken for each individual to emerge and start probing the substrate. Disturbance during handling and tagging may lead to indirect mortality after release by increasing the risk of predation or dislodgement during flooding, thereby potentially compromising any conservation strategy contingent on population supplementation or reintroduction. This is the first study to demonstrate that handling and PIT tagging has a detrimental impact on invertebrate behaviour. Moreover, our results provide useful information that will inform freshwater bivalve conservation strategies.

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Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

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Following automation of lighthouses around the coastline of Ireland, reports of accelerated deterioration of interior granite stonework have increased significantly with an associated deterioration in the historic structure and rise in related maintenance costs. Decay of granite stone- work primarily occurs through granular disintegration with the effective grusification of granite surfaces. A decay gradient exists within the towers whereby the condition of granite in the lower levels is much worse than elsewhere. The lower tower levels are also regions with highest rela- tive humidity values and greatest salt concentrations. Data indicate that post-automation decay may have been trig- gered by a change in micro-environmental conditions within the towers associated with increased episodes of condensation on stone surfaces. This in turn appears to have facilitated deposition and accumulation of hygro- scopic salts (e.g. NaCl) giving rise to widespread evidence of deliquescence in the lower tower levels. Evidence indicates that the main factors contributing to accelerated deterioration of interior granite stonework are changes in micro-environmental conditions, salt weathering, chemical weathering through the corrosive effect of strongly alkaline conditions on alumino-silicate minerals within the granite and finally, the mica-rich characteristics of the granite itself which increases its structural and chemical susceptibility to subaerial weathering processes by creating points of weakness within the granite. This case study demonstrates how seemingly minor changes in micro-environmental conditions can unintentionally trigger the rapid and extensive deterioration of a previously stable rock type and threaten the long-term future of nationally iconic opera- tional historic structures.

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Wear particle accumulation is one of the main contributors to osteolysis and implant failure in hip replacements. Altered kinematics produce significant differences in wear rates of hip replacements in simulator studies due to varying degrees of multidirectional motion. Gait analysis data from 153 hip-replacement patients 10-years post-operation were used to model two- and three-dimensional wear paths for each patient. Wear paths were quantified in two dimensions using aspect ratios and in three dimensions using the surface areas of the wear paths, with wear-path surface area correlating poorly with aspect ratio. The average aspect ratio of the patients wear paths was 3.97 (standard deviation ¼ 1.38), ranging from 2.13 to 10.86. Sixty percent of patients displayed aspect ratios between 2.50 and 3.99. However, 13% of patients displayed wear paths with aspect ratios 45.5, which indicates reduced multidirectional motion. The majority of total hip replacement (THR) patients display gait kinematics which produce multidirectional wear paths, but a significant minority display more linear paths.