• Energy Research
• Restricted close
• Embargo close
• GB close
• FI close

We have observed and reported previously that very small precipitates with sizes of the order of 2–10nm can form in silicon from slow diffusing transition metals. These nano-precipitates can act as exceptionally powerful recombination centers. In this paper we present experimental results on the recombination dynamics and show that the behavior is not well described by previous work on larger precipitates or by the conventional Shockley-Read-Hall model widely used for recombination associated with point defects. The basic reason for this is that the Fermi sea of the metal particle system allows a huge degree of freedom for charge exchange and many size dependent effects on the capture rates. These defects, which are Coulomb attractive for the minority carrier can stably capture and bind several minority carriers with large cross section. However majority carrier capture is into a repulsive centre, but the potentials associated with sub 10 nm particles are sufficiently sharp that the strength of tunneling process hugely enhances the majority carrier capture rate. In the limit of ultra-small (but still metallic) systems Coulomb blockade effects will further modify majority carrier capture. All cross sections are net charge state dependent but self-consistent three dimensional solutions to the Poisson and Schrodinger equations can successfully model capture and recombination physics. The measured and calculated trends clearly show that such precipitates may be lifetime controlling in multi-crystalline Si.

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

Plant communities, through species richness and composition, strongly influence soil microorganisms and the ecosystem processes they drive. To test the effects of other plant community attributes, such as the identity of dominant plant species, evenness, and spatial arrangement, we set up a model mesocosm experiment that manipulated these three attributes in a full factorial design, using three grassland plant species (Anthoxanthum odoratum, Plantago lanceolata, and Lotus corniculatus). The impact of the three community attributes on the soil microbial community structure and functioning was evaluated after two growing seasons by ester-linked phospholipid fatty-acids analysis, substrate-induced respiration, basal respiration, and nitrogen mineralization and nitrification rates. Our results suggested that the dominant species identity had the most prevalent influence of the three community attributes, with significant effects on most of the measured aspects of microbial biomass, composition and functioning. Evenness had no effects on microbial community structure, but independently influenced basal respiration. Its effects on nitrogen cycling depended on the identity of the dominant plant species, indicating that interactions among species and their effects on functioning can vary with their relative abundance. Systems with an aggregated spatial arrangement had a different microbial community composition and a higher microbial biomass compared to those with a random spatial arrangement, but rarely differed in their functioning. Overall, it appears that dominant species identity was the main driver of soil microorganisms and functioning in these model grassland communities, but that other plant community attributes such as evenness and spatial arrangement can also be important.

Abstract The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle, is to improve building and system performances in terms of economics, comfort, environmental impact and durability. Several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in particular with respect to the cross-disciplinary role of data. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in modelling strategies. Visualization and data analysis techniques are successfully used in a wide variety of applications, both in theoretical and applied domains, but questions remains about their robustness, efficiency and applicability to the problems introduced before. The paper aims to analyze critically these topics by means of case studies, showing a possible path to create a multi-scale methodology able to synthesize all the relevant aspects.

AbstractIntroductionFoetal alcohol spectrum disorders (FASD) are a set of preventable conditions where the foetus is exposed to alcohol in utero and as a result suffers adverse consequences. To develop a public health strategy related to FASD, it is important to first establish what is known by the public about this condition. This study aimed to assess the current level of knowledge about FASD in the UK general population.MethodsA mixed methodology study was conducted using a 17‐item questionnaire and focus group sessions. Four focus groups were held with an average of 10 people in each group. Semi‐structured questions and thematic analysis of interviews alongside quantitative analysis of the questionnaire data was completed. The research was approved by an National Health service (NHS) research ethical committee.ResultsA total of 674 people responded to the questionnaire and a majority (86.7%) had heard about FASD, with most receiving their information from the media (26.2%) or from their work (27.7%). Four broad themes emerged. Overall these were: a general lack of knowledge about the subject; information about the subject needed to be personally relevant; there was a need for further education; and there was a lack of clarity in the current guidance on alcohol use in pregnancy.Discussion and conclusionsCurrently there appears to be a superficial level of knowledge about FASD in the UK general public. More detailed work in subgroups, such as young women, to identify their specific needs may be necessary before targeted public health and educational interventions can be developed to meet the needs of the general public.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

The increasing effects of climate change and extreme weather events on the operation of power systems highlight the need for developing effective techniques and models for evaluating the challenges that power systems may have to deal with in the future for maintaining high levels of resilience. Within this context, the aim of the `Resilient Electricity Networks for Great Britain (RESNET)' project is to develop and demonstrate a comprehensive approach to analyse the effects of climate-related changes on the resilience of the Great Britain's electricity system, and to develop tools for quantifying the value of adaptation measures that would enhance its resilience. This paper presents an overview of the key research activities and findings of RESNET project, specifically addressing possible climate change implications related to demand and supply scenarios, component ratings, and impact of extreme weather on system reliability. The final aim is to provide insights on various climate and weather-related challenges that the Great Britain's electricity system might face in the future.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.