Influence of occupants’ behaviour on energy and carbon emission reduction in a higher education building in the UK

This article focuses on one of the case studies in the Carbon Brainprint research project funded by the Higher Education Funding Council for England (Chatterton, J., D. Parsons, J. Nicholls, P. Longhurst, M. Bernon, A. Palmer, F. Brennan, et al. 2015. “Carbon Brainprint – An Estimate of the Intellectual Contribution of Research Institutions to Reducing Greenhouse Gas Emissions.” Process Safety and Environmental Protection 96: 74–81). The UK total CO2e emissions in 2010 amounted to 582MtCO2e. It is estimated that non-domestic buildings and domestic buildings were responsible for 18% (106MtCO2e) and 28% (165MtCO2e) of these emissions, respectively. A case study method was used to investigative the opportunity of using occupants’ awareness and behavioural interventions to reduce energy use and carbon emissions in a non-domestic building of a higher education institution. An action research approach, informed by the theory of planned behaviour, was argued for this case study. It has demonstrated 20% savings in lighting, office equipment and catering energy use, largely through user awareness and behaviour change. If this level of saving were to be reflected throughout the non-domestic building stock it would represent an annual reduction in the order of 7MtCO2e in the UK. These figures relate specifically to non-domestic buildings. However, some of the techniques involved are directly transferable to domestic buildings, with the potential for further emission reductions

Cloning Hubble Deep Fields I: A Model-Independent Measurement of Galaxy Evolution

We present a model-independent method of quantifying galaxy evolution in high-resolution images, which we apply to the Hubble Deep Field (HDF). Our procedure is to k-correct all pixels belonging to the images of a complete set of bright galaxies and then to replicate each galaxy image to higher redshift by the product of its space density, 1/V_{max}, and the cosmological volume. The set of bright galaxies is itself selected from the HDF, because presently the HDF provides the highest quality UV images of a redshift-complete sample of galaxies (31 galaxies with I<21.9, \bar{z}=0.5, and for which V/V_{max} is spread fairly). These galaxies are bright enough to permit accurate pixel-by-pixel k-corrections into the restframe UV (\sim 2000 A). We match the shot noise, spatial sampling and PSF smoothing of the HDF data, resulting in entirely empirical and parameter-free ``no-evolution'' deep fields of galaxies for direct comparison with the HDF. In addition, the overcounting rate and the level of incompleteness can be accurately quantified by this procedure. We obtain the following results. Faint HDF galaxies (I>24) are much smaller, more numerous, and less regular than our ``no-evolution'' extrapolation, for any interesting geometry. A higher proportion of HDF galaxies ``dropout'' in both U and B, indicating that some galaxies were brighter at higher redshifts than our ``cloned'' z\sim0.5 population.Comment: 51 pages, 23 figures, replacement includes figures not previously include

Understanding Infrared Galaxy Populations: the SWIRE Legacy Survey

We discuss spectral energy distributions, photometric redshifts, redshift distributions, luminosity functions, source-counts and the far infrared to optical luminosity ratio for sources in the SWIRE Legacy Survey. The spectral energy distributions of selected SWIRE sources are modelled in terms of a simple set of galaxy and quasar templates in the optical and near infrared, and with a set of dust emission templates (cirrus, M82 starburst, Arp 220 starburst, and AGN dust torus) in the mid infrared. The optical data, together with the IRAC 3.6 and 4.5 mu data, have been used to determine photometric redshifts. For galaxies with known spectroscopic redshifts there is a notable improvement in the photometric redshift when the IRAC data are used, with a reduction in the rms scatter from 10% in (1+z) to 5%. While further spectroscopic data are needed to confirm this result, the prospect of determining good photometric redshifts for the 2 million extragalactic objects in SWIRE is excellent. The distribution of the different infrared sed types in the L{ir}/L{opt} versus L{ir} plane, where L{ir} and L{opt} are the infrared and optical bolometric luminosities, is discussed. Source-counts at 24, 70 and 160 mu are discussed, and luminosity functions at 3.6 and 24 mu are presented.Comment: 8 pages, 14 figures, to appear in proceedings of 'Spitzer IR Diagnostics Conference, Nov 14-16, 2005

Supporting the implementation of genomic selection in a guide dogs' population using simulation

For service dog populations, applying genomic selection would enable more efficient breeding for complex traits such as health, welfare, and trainability. However, the transition from phenotypic to genomic selection requires genomic information. Different data collection scenarios can be envisioned based on the number of individuals, the number of markers, and the genotyping technology. The aim of this study was to identify the optimal scenario for data collection to implement genomic selection and investigate complex trait architecture with whole genome sequence (WGS) information. To do so, we simulated the UK Guide Dogs' population based on their pedigree and existing high-coverage WGS data with AlphaSimR, and then phased and imputed with AlphaPeel for various scenarios. The existing pedigree was extended with additional generations to evaluate scenarios' outcomes in the future. The scenarios considered were composed of diverse genotyping densities and sequencing coverages for the puppies. All scenarios were compared using individual imputation accuracy against the true simulated WGS. Low-pass sequencing scenarios (0.5 to 2X depth) achieved accuracy of 0.986 to 0.998. SNP array genotyping (25K to 710K markers) was inferior, with an accuracy of 0.560 to 0.732. For the UK Guide Dogs, the simulation revealed low-pass sequencing as the best strategy for obtaining WGS information for downstream use in genomic selection and analysis of complex traits

Evaluating genotyping strategies for a small managed population with simulation

Background: Collecting genomic information is crucial to advance breeding for complex traits such as health, welfare, and behaviour in domesticated populations. For that purpose, different data collection scenarios can be envisioned based on the number of individuals, the number of markers, and the genotyping technology. This study developed a simulation framework, based on a service dog population, aiming to identify an optimal and cost-effective genotyping strategy that would support the implementation of genomic selection,investigation of the genetic architecture of traits of interest, and track loci of interest.Methods: We simulated a population based on the existing pedigree, using the gene drop method in AlphaSimR. The existing pedigree was extended with additional progeny generations to evaluate the outcomes of different genotyping strategies in the future. We generated genotype data based on existing high-coverage whole-genome sequences (WGS) for the current breeding dogs and evaluated different scenarios for genotyping the progeny. The genotyping options considered SNP arrays of various densities and WGS callsets produced from different sequencing depths. We then phased and imputed the genotype data to high-coverage WGS using AlphaPeel.Results: All scenarios were compared based on individual imputation accuracy against the simulated true whole-genome genotype. Averaged over five generations of simulated progeny, low-pass sequencing (0.5 to 2X depth) achieved accuracies of 0.998 to 0.999. The accuracy of SNP array genotyping (25K to 710K markers) was lower, with means of 0.911 to 0.938.Conclusions: Our simulation was tailored to identify the most cost-effective and efficient strategy for downstream use in genomic selection and genetic research into traits and loci of interest. Low-pass sequencing outperformed SNP array genotyping in imputation accuracy of whole-genome genotypes as expected. Additionally, low-pass sequencing technology was the most affordable genotyping approach currently available for dogs. Thus, it appears to be the optimal choice for balancing the goals of regimented breeding programmes such as those that produce service dogs. This simulation framework could also be adapted to addressother objectives for breeding organisations working with small population

Supporting the implementation of genomic selection in a guide dogs' population using simulation

For service dog populations, applying genomic selection would enable more efficient breeding for complex traits such as health, welfare, and trainability. However, the transition from phenotypic to genomic selection requires genomic information. Different data collection scenarios can be envisioned based on the number of individuals, the number of markers, and the genotyping technology. The aim of this study was to identify the optimal scenario for data collection to implement genomic selection and investigate complex trait architecture with whole genome sequence (WGS) information. To do so, we simulated the UK Guide Dogs' population based on their pedigree and existing high-coverage WGS data with AlphaSimR, and then phased and imputed with AlphaPeel for various scenarios. The existing pedigree was extended with additional generations to evaluate scenarios' outcomes in the future. The scenarios considered were composed of diverse genotyping densities and sequencing coverages for the puppies. All scenarios were compared using individual imputation accuracy against the true simulated WGS. Low-pass sequencing scenarios (0.5 to 2X depth) achieved accuracy of 0.986 to 0.998. SNP array genotyping (25K to 710K markers) was inferior, with an accuracy of 0.560 to 0.732. For the UK Guide Dogs, the simulation revealed low-pass sequencing as the best strategy for obtaining WGS information for downstream use in genomic selection and analysis of complex traits

The Economic Impact of the Value Chain of a Marcellus Shale Well

The Economic Impact of the Value Chain of a Marcellus Shale Well Site examines the direct economic impact of a Marcellus Shale well located in Southwestern Pennsylvania. This study seeks to fill a critical information gap on the impact of gas drilling and extraction from Marcellus Shale deposits deep underground: an assessment of the economic impacts – emphasizing the direct economic impact, rather than just focusing on the perceived benefits and impacts affecting the region. Our analysis is based on extensive field research, including a site visit and interviews with industry participants. It is further cross-validated by examining similar costs for development of Marcellus Wells by a vertically-integrated exploration and production firm

Prospectus, September 2, 1974

FACULTY, STUDENTS CONQUER REGISTRATION; Enrollment Procedures Eased By Mail; Dr. Staerkel Welcomes Student Body; Lonnie & The Lugnutz Top Activities; All-College Cookout And Activity Day Schedule; Meeting Set For Prospectus Jobs; Campus Artist Needed For Cartoon Series; StuGo Prexy, Karen Coleman, Offers Welcome; Adjusting To College Life; Parking Regulations; Convocations Plans Activities; Debaters Defend Championships; Music Director Ernie Hoffman Looking For Interested Students; Athletic Season Review; Fast Freddy\u27s Football Forecast; Kirby Wins Babe Ruth Scholarship; Don Grothe New Links Coach; Classified Ads; Here Are Your P/C Student Officers; SIMS Lecture Sept. 10; P/C Bridge Club Opens Season In Sunday Session; Prospectus Staff Salary Increase; M*A*S*H Headlines Film Season; StuGo Works Through Summer; Theatre Troupe To Hold Auditionshttps://spark.parkland.edu/prospectus_1974/1011/thumbnail.jp