Detailed analysis of data from heat pumps installed via the Renewable Heat Premium Payment Scheme

The RHPP policy provided subsidies for private householders, Registered social landlords and communities to install renewable heat measures in residential properties. Eligible measures included air and ground-source heat pumps, biomass boilers and solar thermal. Around 18,000 heat pumps were installed via this scheme. DECC funded a detailed monitoring campaign, which covered 700 heat pumps (around 4% of the total). The aim of this monitoring campaign was to assess the efficiencies of the heat pumps and to estimate the carbon and bill savings and amount of renewable heat generated. Data was collected from 31/10/2013 to 31/03/2015. This report represents the analysis of this data and represents the most complete and reliable data in-situ residential heat pump performance in the UK to date

Enhanced EnHub : dynamic simulation of housing stock energy systems

In the UK, heating systems are the most prominent contributor to residential energy demand, with about 80% of the share. Their representation has thus been at the core of all UK-focussed Housing Stock Energy Models (HSEMs). However, these HSEMs estimate heating demand based on monthly or annual energy balances, with correspondingly approximate representations of heating systems and practices (incl. energy conversion, distribution and spatiotemporal control). This paper describes an extension to the dynamic HSEM: EnHub, to rigorously simulate space heating and hot-water components (i.e. heaters, boilers, pumps, radiators, end-point registers, thermostats, taps). Baseline simulations estimate the English housing stock's energy use as 35.9 mtoe. Alternative scenarios in which heating systems are substituted across the board to district heating or ground-source heat pumps predict a reduction in demand to 30 and 18 mtoe respectively; the latter potentially being zero-carbon if the power sector is successfully decarbonised

Nuclear factor-kappa B localization and function within intrauterine tissues from term and preterm labor and cultured fetal membranes

Abstract Background The objective of this study was to quantify the nuclear localization and DNA binding activity of p65, the major transactivating nuclear factor-kappa B (NF-kappaB) subunit, in full-thickness fetal membranes (FM) and myometrium in the absence or presence of term or preterm labor. Methods Paired full-thickness FM and myometrial samples were collected from women in the following cohorts: preterm no labor (PNL, N = 22), spontaneous preterm labor (PTL, N = 21), term no labor (TNL, N = 23), and spontaneous term labor (STL, N = 21). NF-kappaB p65 localization was assessed by immunohistochemistry, and DNA binding activity was evaluated using an enzyme-linked immunosorbent assay (ELISA)-based method. Results Nuclear p65 labeling was rare in amnion and chorion, irrespective of clinical context. In decidua, nuclear p65 labeling was greater in the STL group relative to the TNL cohort, but there were no differences among the TNL, PTL, and PNL cohorts. In myometrium, diffuse p65 nuclear labeling was significantly associated with both term and preterm labor. There were no significant differences in ELISA-based p65 binding activity in amnion, choriodecidual, and myometrial specimens in the absence or presence of term labor. However, parallel experiments using cultured term fetal membranes demonstrated high levels of p65-like binding even the absence of cytokine stimulation, suggesting that this assay may be of limited value when applied to tissue specimens. Conclusions These results suggest that the decidua is an important site of NF-kappaB regulation in fetal membranes, and that mechanisms other than cytoplasmic sequestration may limit NF-kappaB activation prior to term

Effective one-dimensionality of AC hopping conduction in the extreme disorder limit

It is argued that in the limit of extreme disorder AC hopping is dominated by "percolation paths". Modelling a percolation path as a one-dimensional path with a sharp jump rate cut-off leads to an expression for the universal AC conductivity, that fits computer simulations in two and three dimensions better than the effective medium approximation.Comment: 6 postscript figure

Robust averaging protects decisions from noise in neural computations

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making

Transgressing the moral economy: Wheelerism and management of the nationalised coal industry in Scotland

This article illuminates the links between managerial style and political economy in post-1945 Britain, and explores the origins of the 1984–1985 miners' strike, by examining in longer historical context the abrasive attitudes and policies of Albert Wheeler, Scottish Area Director of the National Coal Board (NCB). Wheeler built on an earlier emphasis on production and economic criteria, and his micro-management reflected pre-existing centralising tendencies in the industries. But he was innovative in one crucial aspect, transgressing the moral economy of the Scottish coalfield, which emphasised the value of economic security and changes by joint industrial agreement

Benefit of temporal fine structure to speech perception in noise measured with controlled temporal envelopes

This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. This article appeared in EAVES, J.M., SUMMERFIELD, A.Q. and KITTERICK, P.T., 2011. Benefit of temporal fine structure to speech perception in noise measured with controlled temporal envelopes. Journal of the Acoustical Society of America, 130(1), pp. 501-507. and may be found at http://dx.doi.org/10.1121/1.3592237Previous studies have assessed the importance of temporal fine structure (TFS) for speech perception in noise by comparing the performance of normal-hearing listeners in two conditions. In one condition, the stimuli have useful information in both their temporal envelopes and their TFS. In the other condition, stimuli are vocoded and contain useful information only in their temporal envelopes. However, these studies have confounded differences in TFS with differences in the temporal envelope. The present study manipulated the analytic signal of stimuli to preserve the temporal envelope between conditions with different TFS. The inclusion of informative TFS improved speech reception thresholds for sentences presented in steady and modulated noise, demonstrating that there are significant benefits of including informative TFS even when the temporal envelope is controlled. It is likely that the results of previous studies largely reflect the benefits of TFS, rather than uncontrolled effects of changes in the temporal envelope

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data