Geodesic motions versus hydrodynamic flows in a gravitating perfect fluid: Dynamical equivalence and consequences

Stimulated by the methods applied for the observational determination of masses in the central regions of the AGNs, we examine the conditions under which, in the interior of a gravitating perfect fluid source, the geodesic motions and the general relativistic hydrodynamic flows are dynamically equivalent to each other. Dynamical equivalence rests on the functional similarity between the corresponding (covariantly expressed) differential equations of motion and is obtained by conformal transformations. In this case, the spaces of the solutions of these two kinds of motion are isomorphic. In other words, given a solution to the problem "hydrodynamic flow in a perfect fluid", one can always construct a solution formally equivalent to the problem "geodesic motion of a fluid element" and vice versa. Accordingly, we show that, the observationally determined nuclear mass of the AGNs is being overestimated with respect to the real, physical one. We evaluate the corresponding mass-excess and show that it is not always negligible with respect to the mass ofthe central dark object, while, under circumstances, can be even larger than the rest-mass of the circumnuclear gas involved.Comment: LaTeX file, 22 page

Novel Techniques for Constraining Neutron-Capture Rates Relevant for r-Process Heavy-Element Nucleosynthesis

The rapid-neutron capture process ($r$ process) is identified as the producer of about 50\% of elements heavier than iron. This process requires an astrophysical environment with an extremely high neutron flux over a short amount of time ($\sim$ seconds), creating very neutron-rich nuclei that are subsequently transformed to stable nuclei via $\beta^-$ decay. One key ingredient to large-scale $r$-process reaction networks is radiative neutron-capture ($n,\gamma$) rates, for which there exist virtually no data for extremely neutron-rich nuclei involved in the $r$ process. Due to the current status of nuclear-reaction theory and our poor understanding of basic nuclear properties such as level densities and average $\gamma$-decay strengths, theoretically estimated ($n,\gamma$) rates may vary by orders of magnitude and represent a major source of uncertainty in any nuclear-reaction network calculation of $r$-process abundances. In this review, we discuss new approaches to provide information on neutron-capture cross sections and reaction rates relevant to the $r$ process. In particular, we focus on indirect, experimental techniques to measure radiative neutron-capture rates. While direct measurements are not available at present, but could possibly be realized in the future, the indirect approaches present a first step towards constraining neutron-capture rates of importance to the $r$ process.Comment: 62 pages, 24 figures, accepted for publication in Progress in Particle and Nuclear Physic

Multi-scale analysis of the energy performance of supermarkets

The retail sector accounts for more than 3% of the total electricity consumption in the UK and approximately 1% of total UK CO2 emissions. The overarching aim of this project was to understand the energy consumption of the Tesco estate (the market leader), identify best practice, and find ways to identify opportunities for energy reduction. The literature review of this work covered the topic of energy consumption in the retail sector, and reviewed benchmarks for this type of buildings from the UK, Europe and the US. Related data analysis techniques used in the industry or presented in the literature were also reviewed. This revealed that there are many different analysis and forecasting techniques available, and that they fall into two different categories: techniques that require past energy consumption data in order to calculate the future consumption, such as statistical regression, and techniques that are able to estimate the energy consumption of buildings, based on the specific building’s characteristics, such as thermal simulation models. These are usually used for new buildings, but they could also be used in benchmarking exercises, in order to achieve best practice guides. Gaps in the industry knowledge were identified, and it was suggested that better analytical tools would enable the industry to create more accurate energy budgets for the year ahead leading to better operating margins. Benchmarks for the organisation’s buildings were calculated. Retail buildings in the Tesco estate were found to have electrical intensity values between 230 kWh/m2 and 2000 kWh/m2 per year. Still the average electrical intensity of these buildings in 2010-11 was found to be less than the calculated UK average of the 2006-07 period. The effect of weather on gas and electricity consumption was investigated, and was found to be significant (p<0.001). There was an effect related to the day-of-the-week, but this was found to be more related to the sales volume on those days. Sales volume was a proxy that was used to represent the number of customers walking through the stores. The built date of the building was also considered to be an interesting factor, as the building regulations changed significantly throughout the years and the sponsor did not usually carry out any fabric work when refurbishing the stores. User behaviour was also identified as an important factor that needed to be investigated further, relating to both how the staff perceives and manages the energy consumption in their work environment, as well as how the customers use the refrigeration equipment. Following a statistical analysis, significant factors were determined and used to create multiple linear regression models for electricity and gas demands in hypermarkets. Significant factors included the sales floor area of the store, the stock composition, and a factor representing the thermo-physical characteristics of the envelope. Two of the key findings are the statistical significance of operational usage factors, represented by volume of sales, on annual electricity demand and the absence of any statistically significant operational or weather related factors on annual gas demand. The results suggest that by knowing as little as four characteristics of a food retail store (size of sales area, sales volume, product mix, year of construction) one can confidently calculate its annual electricity demands (R2=0.75, p<0.001). Similarly by knowing the size of the sales area, product mix, ceiling height and number of floors, one can calculate the annual gas demands (R2=0.5, p<0.001). Using the models created, along with the actual energy consumption of stores, stores that are not as energy efficient as expected can be isolated and investigated further in order to understand the reason for poor energy performance. Refrigeration data from 10 stores were investigated, including data such as the electricity consumption of the pack, outside air temperature, discharge and suction pressure, as well as percentage of refrigerant gas in the receiver. Data mining methods (regression and Fourier transforms) were employed to remove known operational patterns (e.g. defrost cycles) and seasonal variations. Events that have had an effect on the electricity consumption of the system were highlighted and faults that had been identified by the existing methodology were filtered out. The resulting dataset was then analysed further to understand the events that increase the electricity demand of the systems in order to create an automatic identification method. The cases analysed demonstrated that the method presented could form part of a more advanced automatic fault detection solution; potential faults were difficult to identify in the original electricity dataset. However, treating the data with the method designed as part of this work has made it simpler to identify potential faults, and isolate probable causes. It was also shown that by monitoring the suction pressure of the packs, alongside the compressor run-times, one could identify further opportunities for electricity consumption reduction

An empirical study of electricity and gas demand drivers in large food retail buildings of a national organisation

AbstractFood retail buildings account for a measurable proportion of a country's energy consumption and resultant carbon emissions so energy-operating costs are key business considerations. Increased understanding of end-use energy demands in this sector can enable development of effective benchmarking systems to underpin energy management tools. This could aid identification and evaluation of interventions to reduce operational energy demand. Whilst there are a number of theoretical and semi-empirical benchmarking and thermal modelling tools that can be used for food retail building stocks, these do not readily account for the variance of technical and non-technical factors that can influence end-use demands.This paper discusses the various drivers of energy end-uses of typical UK food retail stores. It reports on an empirical study of one organisation's hypermarket stock to evaluate the influence of various factors on annual store electricity and gas demands. Multiple regression models are discussed in the context of the development and application of a methodology for estimating annual energy end-use demand in food retail buildings. The established models account for 75% of the variation in electricity demand, 50% of the variation in gas demand in stores without CHP and 77% of the variation in gas demand in stores with CHP

Phylogenetic analysis of strains of Orf virus isolated from two outbreaks of the disease in sheep in Greece

<p>Abstract</p> <p>Background</p> <p>Although orf is endemic around the world, there are few descriptions of <it>Orf virus </it>strains and comparisons of these strains. We report the sequence and phylogenetic analysis of the partial B2L gene of <it>Orf virus </it>from two outbreaks of the disease in Greece. The first was an outbreak of genital form of the disease in a flock imported from France, whilst the second was an outbreak of the disease in the udder skin of ewes and around the mouth of lambs in an indigenous flock.</p> <p>Results</p> <p>Phylogenetic analysis was performed on a part (498 bp) of the B2L gene of 35 <it>Parapoxvirus </it>isolates, including the two <it>Orf virus </it>isolates recovered from each of the two outbreaks in the present study. This analysis revealed that the maximum nucleotide and amino-acid variation amongst <it>Orf virus </it>strains worldwide (n = 33) was 8.1% and 9.6%, respectively. The homology of the nucleotide and amino-acid sequences between the two Greek isolates was 99.0% and 98.8%, respectively. The two Greek isolates clustered only with <it>Orf virus </it>strains.</p> <p>Conclusions</p> <p>We suggest that there can be differences between strains based on their geographical origin. However, differences in the origin of strains or in the clinical presentation of the disease may not be associated with their pathogenicity. More work is required to determine if differing clinical presentations are linked to viral strain differences or if other factors, e.g., flock immunity, method of exposure or genetic susceptibility, are more important to determine the clinical presentation of the infection.</p

To be or not to be in the EU: the international economic effects of Brexit uncertainty

This paper evaluates the impact of Brexit-related uncertainty on the economies of the UK, EU, and the US. We propose a measure of Brexit uncertainty that has not been employed before in the literature. We first construct a binary variable by selecting Brexit-related events. We subsequently employ the Qual VAR model of Dueker [2005. “Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of US Recessions.” Journal of Business & Economic Statistics 23: 96–104] to transform this variable to a continuous latent variable that captures uncertainty on important economic and financial variables. Next, this latent variable enters a structural Factor-Augmented Vector AutoRegression model combined with 452 macro and financial variables for the sample countries. Overall, our results indicate that the prolonged period of uncertainty, had a positive effect on the economies of major EU countries and negative effects for the UK economy. Additionally, the UK is the most important net sender of uncertainty spillovers in the EU, while Germany and France are among the most important net receivers of uncertainty shocks

Integrating children's perspectives in policy-making to combat poverty and social exclusion experienced by single-parent families: a transnational comparative approach

This is the final report of a research project that addressed social exclusion and poverty as it relates to single parent families and their children in particular. The rising numbers of single parent families and children throughout the EU and the increased likelihood that these families will live in poverty and experience many different forms of social exclusion in their daily lives brings in sharp focus the need to address the issue as an urgent one in our efforts to eradicate poverty and social exclusion. The focus on the children of single parent families seeks to rectify a long-standing problem in our knowledge and understanding of single parent families and the social problems they face, namely, the fact that little, if anything, is known about how these children experience and understand their lives as members of these families. The research set out to contribute to policy development and the transnational exchange of best practice by adding a much-neglected dimension on single parent families. The project used a cross-national comparative qualitative research design and methods (Mangen 1999) which involved all partners in the design of each research phase including the analysis; partners were England, Cyprus and Greece