A matrix iteration for dynamic network summaries

We propose a new algorithm for summarizing properties of large-scale time-evolving networks. This type of data, recording connections that come and go over time, is being generated in many modern applications, including telecommunications and on-line human social behavior. The algorithm computes a dynamic measure of how well pairs of nodes can communicate by taking account of routes through the network that respect the arrow of time. We take the conventional approach of downweighting for length (messages become corrupted as they are passed along) and add the novel feature of downweighting for age (messages go out of date). This allows us to generalize widely used Katz-style centrality measures that have proved popular in network science to the case of dynamic networks sampled at non-uniform points in time. We illustrate the new approach on synthetic and real data.

Measuring the consumer benefits of improving farm animal welfare to inform welfare labelling

Policy makers in the European Union are envisioning the introduction of a community farm animal welfare label which would allow consumers to align their consumption habits with their farm animal welfare preferences. For welfare labelling to be viable the market for livestock products produced to higher welfare standards has to be sufficiently segmented with consumers having sufficiently distinct and behaviourally consistent preferences. The present study investigates consumers’ preferences for meat produced to different welfare standards using a hypothetical welfare score. Data is obtained from a contingent valuation study carried out in Britain. The ordered probit model was estimated using Bayesian inference to obtain mean willingness to pay. We find decreasing marginal WTP as animal welfare levels increase and that people’s preferences for different levels of farm animal welfare are sufficiently differentiated making the introduction of a labelling scheme in the form of a certified rating system appear feasible.

A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent alpha. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for alpha may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.

Effect of triclosan or a phenolic farm disinfectant on the selection of antibiotic-resistant Salmonella enterica

Objective: To determine the effect of growth of five strains of Salmonella enterica and their isogenic multiply antibiotic-resistant (MAR) derivatives with a phenolic farm disinfectant or triclosan (biocides) upon the frequency of mutation to resistance to antibiotics or cyclohexane. Methods: Strains were grown in broth with or without the biocides and then spread on to agar containing ampicillin, ciprofloxacin or tetracycline each at 4x MIC or agar overlaid with cyclohexane. Incubation was for 24 and 48 h and the frequency of mutation to resistance was calculated for strains with and without prior growth with the biocides. MICs were determined and the presence of mutations in the acrR and marR regions was determined by sequencing and the presence of mutations in gyrA by light-cycler analysis, for a selection of the mutants that arose. Results: The mean frequency of mutation to antibiotic or cyclohexane resistance was increased similar to10- to 100-fold by prior growth with the phenolic disinfectant or triclosan. The increases were statistically significant for all antibiotics and cyclohexane following exposure to the phenolic disinfectant (P less than or equal to 0.013), and for ampicillin and cyclohexane following exposure to triclosan (P less than or equal to 0.009). Mutants inhibited by >1 mg/L ciprofloxacin arose only from strains that were MAR. Reduced susceptibility to ciprofloxacin (at 4x MIC for parent strains) alone was associated with mutations in gyrA. MAR mutants did not contain mutations in the acrR or marR region. Conclusions: These data renew fears that the use of biocides may lead to an increased selective pressure towards antibiotic resistance.

Farm disinfectants select for cyclohexane resistance, a marker of multiple antibiotic resistance, in Escherichia coli

Aims: The aim of this study was to determine if three classes of farm disinfectants were able to select for ciprofloxacin or cyclohexane tolerant [ indicative of a multiple antibiotic resistance ( MAR) phenotype] Escherichia coli and if cyclohexane-tolerant E. coli could be isolated from farms. Methods and Results: Chicken slurry containing ca 1 : 99 ratio ciprofloxacin resistant : susceptible E. coli ( 10 different resistant strains examined) was treated for 24 h with each of the disinfectants and examined for survival of resistant : susceptible strains. Ciprofloxacin-sensitive ( n = 5) and - resistant ( n = 5) E. coli were grown with sublethal concentrations of the disinfectants and then plated to agar containing ciprofloxacin or overlaid with cyclohexane. Escherichia coli ( n = 389) isolated from farms were tested for cyclohexane tolerance. Minimum inhibitory concentrations ( MIC) were determined against representative isolates and mutants. The disinfectants did not select for the ciprofloxacin-resistant E. coli in poultry slurry but following growth with each of the three disinfectants, higher numbers ( Pless than or equal to 0(.)023) of cyclohexane-tolerant E. coli were isolated and these had a MAR phenotype. Of the 389 farm E. coli tested, only one was cyclohexane tolerant. Conclusions: It is possible that in a farm environment, E. coli could be exposed to similar concentrations of the disinfectants that are selected for MAR type organisms under these laboratory conditions. Significance and Impact of the Study: Data from this study suggest that cyclohexane-resistant E. coli are not common on farms, but in view of the ease of isolating them in the laboratory with farm disinfectants, further investigations on farms are warranted.

Commonly used farm disinfectants can select for mutant Salmonella enterica serovar Typhimurium with decreased susceptibility to biocides and antibiotics without compromising virulence

Objectives: To determine if one passage of Salmonella enterica serovar Typhimurium in the presence of farm disinfectants selected for mutants with decreased susceptibility to disinfectants and/or antibiotics. Methods: Eight Salmonella Typhimurium strains including field isolates and laboratory mutants were exposed to either a tar oil phenol (PFD) disinfectant, an oxidizing compound disinfectant (OXC), an aldehyde based disinfectant (ABD) or a dairy sterilizer disinfectant (based on quaternary ammonium biocide) in agar. The susceptibility of mutants obtained after disinfectant exposure to antibiotics and disinfectants was determined as was the accumulation of norfloxacin. The proteome of SL1344 after exposure to PFD and OXC was analysed using two-dimensional liquid chromatography mass spectrometry. Results: Strains with either acrB or tolC inactivated were more susceptible to most disinfectants than other strains. The majority (3/5) of mutants recovered after disinfectant exposure required statistically significantly longer exposure times to disinfectants than their parent strains to generate a 5 log kill. Small decreases in antibiotic susceptibility were observed but no mutants were multiply antibiotic-resistant (MAR). Notably exposure to ABD decreased susceptibility to ciprofloxacin in some strains. Mutants with increased disinfectant tolerance were able to survive and persist in chicks as well as in parent strains. Analysis of proteomes revealed significantly increased expression of the AcrAB-TolC efflux system after PFD exposure. Conclusions: Data presented demonstrate that efflux pumps are required for intrinsic resistance to some disinfectants and that exposure to disinfectants can induce expression of the AcrAB-TolC efflux system, but that single exposure was insufficient to select for MAR strains.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

An experiment in the Assimilation of Data in Dynamical Analysis

A system for continuous data assimilation is presented and discussed. To simulate the dynamical development a channel version of a balanced barotropic model is used and geopotential (height) data are assimilated into the models computations as data become available. In the first experiment the updating is performed every 24th, 12th and 6th hours with a given network. The stations are distributed at random in 4 groups in order to simulate 4 areas with different density of stations. Optimum interpolation is performed for the difference between the forecast and the valid observations. The RMS-error of the analyses is reduced in time, and the error being smaller the more frequent the updating is performed. The updating every 6th hour yields an error in the analysis less than the RMS-error of the observation. In a second experiment the updating is performed by data from a moving satellite with a side-scan capability of about 15°. If the satellite data are analysed at every time step before they are introduced into the system the error of the analysis is reduced to a value below the RMS-error of the observation already after 24 hours and yields as a whole a better result than updating from a fixed network. If the satellite data are introduced without any modification the error of the analysis is reduced much slower and it takes about 4 days to reach a comparable result to the one where the data have been analysed.

Problems in four-dimensional data assimilation

With the introduction of new observing systems based on asynoptic observations, the analysis problem has changed in character. In the near future we may expect that a considerable part of meteorological observations will be unevenly distributed in four dimensions, i.e. three dimensions in space and one in time. The term analysis, or objective analysis in meteorology, means the process of interpolating observed meteorological observations from unevenly distributed locations to a network of regularly spaced grid points. Necessitated by the requirement of numerical weather prediction models to solve the governing finite difference equations on such a grid lattice, the objective analysis is a three-dimensional (or mostly two-dimensional) interpolation technique. As a consequence of the structure of the conventional synoptic network with separated data-sparse and data-dense areas, four-dimensional analysis has in fact been intensively used for many years. Weather services have thus based their analysis not only on synoptic data at the time of the analysis and climatology, but also on the fields predicted from the previous observation hour and valid at the time of the analysis. The inclusion of the time dimension in objective analysis will be called four-dimensional data assimilation. From one point of view it seems possible to apply the conventional technique on the new data sources by simply reducing the time interval in the analysis-forecasting cycle. This could in fact be justified also for the conventional observations. We have a fairly good coverage of surface observations 8 times a day and several upper air stations are making radiosonde and radiowind observations 4 times a day. If we have a 3-hour step in the analysis-forecasting cycle instead of 12 hours, which is applied most often, we may without any difficulties treat all observations as synoptic. No observation would thus be more than 90 minutes off time and the observations even during strong transient motion would fall within a horizontal mesh of 500 km * 500 km.

Modelling the economics of farm-based anaerobic digestion in a UK whole-farm context

Anaerobic digestion (AD) technologies convert organic wastes and crops into methane-rich biogas for heating, electricity generation and vehicle fuel. Farm-based AD has proliferated in some EU countries, driven by favourable policies promoting sustainable energy generation and GHG mitigation. Despite increased state support there are still few AD plants on UK farms leading to a lack of normative data on viability of AD in the whole-farm context. Farmers and lenders are therefore reluctant to fund AD projects and policy makers are hampered in their attempts to design policies that adequately support the industry. Existing AD studies and modelling tools do not adequately capture the farm context within which AD interacts. This paper demonstrates a whole-farm, optimisation modelling approach to assess the viability of AD in a more holistic way, accounting for such issues as: AD scale, synergies and conflicts with other farm enterprises, choice of feedstocks, digestate use and impact on farm Net Margin. This modelling approach demonstrates, for example, that: AD is complementary to dairy enterprises, but competes with arable enterprises for farm resources. Reduced nutrient purchases significantly improve Net Margin on arable farms, but AD scale is constrained by the capacity of farmland to absorb nutrients in AD digestate.

Sequence analysis of a CTX-M-1 IncI1 plasmid found in Salmonella enterica 4,5,12,i:-, Escherichia coli and Klebsiella pneumoniae on a single UK pig farm

OBJECTIVES: In 2009, CTX-M Enterobacteriaceae and Salmonella isolates were recovered from a UK pig farm, prompting studies into the dissemination of the resistance and to establish any relationships between the isolates. METHODS: PFGE was used to elucidate clonal relationships between isolates whilst plasmid profiling, restriction analysis, sequencing and PCR were used to characterize the CTX-M-harbouring plasmids. RESULTS: Escherichia coli, Klebsiella pneumoniae and Salmonella 4,5,12:i:- and Bovismorbificans resistant to cefotaxime (n = 65) were recovered and 63 were shown by PCR to harbour a group 1 CTX-M gene. The harbouring hosts were diverse, but the group 1 CTX-M plasmids were common. Three sequenced CTX-M plasmids from E. coli, K. pneumoniae and Salmonella enterica serotype 4,5,12:i:- were identical except for seven mutations and highly similar to IncI1 plasmid ColIb-P9. Two antimicrobial resistance regions were identified: one inserted upstream of yacABC harbouring ISCR2 transposases, sul2 and floR; and the other inserted within shfB of the pilV shufflon harbouring the ISEcp1 transposase followed by blaCTX-M-1. CONCLUSIONS: These data suggest that an ST108 IncI1 plasmid encoding a blaCTX-M-1 gene had disseminated across multiple genera on this farm, an example of horizontal gene transfer of the blaCTX-M-1 gene.

Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

A peak reduction scheduling algorithm for storage devices on the low voltage network

Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology.

RMetS special interest group meeting: high resolution data assimilation

Data assimilation (DA) systems are evolving to meet the demands of convection-permitting models in the field of weather forecasting. On 19 April 2013 a special interest group meeting of the Royal Meteorological Society brought together UK researchers looking at different aspects of the data assimilation problem at high resolution, from theory to applications, and researchers creating our future high resolution observational networks. The meeting was chaired by Dr Sarah Dance of the University of Reading and Dr Cristina Charlton-Perez from the MetOffice@Reading. The purpose of the meeting was to help define the current state of high resolution data assimilation in the UK. The workshop assembled three main types of scientists: observational network specialists, operational numerical weather prediction researchers and those developing the fundamental mathematical theory behind data assimilation and the underlying models. These three working areas are intrinsically linked; therefore, a holistic view must be taken when discussing the potential to make advances in high resolution data assimilation.